docs/notebooks/demo_Network_Graph_classes.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/Yquetzal/tnetwork/blob/master/demo_Network_Graph_classes.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dynamic Network Classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rXGlQyXR_Y_S"
   },
   "source": [
    "## Table of Contents\n",
    "\n",
    "1. [Creating a simple graph](#creating)\n",
    "  *   [Using a snapshot representation](#sn)\n",
    "  *  [Using an interval graph representation](#ig) \n",
    "  *  [Using a Link Stream graph representation](#ls) \n",
    "2. [Visualization](#vizu)\n",
    "3. [Conversion between graph types](#conversion)\n",
    "4. [Aggregation/Slicing](#aggregation)\n",
    "  *   [Slicing](#aggregation)\n",
    "  *   [Cumulated graphs](#aggregation)\n",
    "  *   [Resampling](#aggregation)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If tnerwork library is not installed, you need to install it, for instance using the following command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%capture #avoid printing output\n",
    "#!pip install --upgrade git+https://github.com/Yquetzal/tnetwork.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-03-12T09:00:47.110Z",
     "iopub.status.busy": "2020-03-12T09:00:47.101Z",
     "iopub.status.idle": "2020-03-12T09:00:47.801Z",
     "shell.execute_reply": "2020-03-12T09:00:47.925Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import tnetwork as tn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O56VJX-03n2K"
   },
   "source": [
    "<a id='creating'></a>\n",
    "## Creating simple dynamic graphs and accessing their properties\n",
    "We will represent a graph with similar properties using snapshots and interval graphs\n",
    "<a id='sn'></a>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dQ4WmmdmBZhz"
   },
   "source": [
    "### Using a snapshot representation\n",
    "`DynGraphSN` is the class used to represent dynamic networks with snapshots (SN). The time at which each snapshot occurs is represented by an integer, which can be numbers in a sequence (1,2,3, etc.) or POSIX timestamps. A Frequency parameter allows to specify the time between each snapshot. By default, its value is 1. It is useful when there are missing snaphsots, e.g., like in SocioPatterns data, a snapshot every 20s, but many snapshots are empty.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "viLCeEeVgFga"
   },
   "outputs": [],
   "source": [
    "dg_sn = tn.DynGraphSN(frequency=1)\n",
    "dg_sn.add_node_presence(\"a\",1) #add node a in snapshot 1\n",
    "dg_sn.add_nodes_presence_from([\"a\",\"b\",\"c\"],[2,3,4,5]) #add nodes a,b,c in snapshots 2 to 5\n",
    "dg_sn.add_nodes_presence_from(\"d\",[1,2,4,5]) #add node d in snapshots 1, 2, 4 and 5\n",
    "\n",
    "dg_sn.add_interaction(\"a\",\"b\",2) #link a and b in snapshot 2\n",
    "dg_sn.add_interaction(\"a\",\"d\",2) #link a and d in snapshot 2\n",
    "dg_sn.add_interactions_from((\"b\",\"d\"),[4,5]) #link b and d in snapshots 4 and 5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Em7-bNvL38pL"
   },
   "source": [
    "<a id='ig'></a>\n",
    "\n",
    "### Using an interval graph representation.\n",
    "`DynGraphIG` is the class used to represent dynamic networks with Interval Graphs (IG). Nodes and edges are present during time intervals, that are closed on the left and open on the right, e.g., (0,10) corresponds to the interval [0,10[, e.g., the node or edge exist from time 0 (included) to time 10 (excluded).\n",
    "\n",
    "\n",
    "Note the similarity between the functions used for snapshots\n",
    "\n",
    "Both graphs are equivalent if the snapshots of `dg_sn` have a duration of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tONhImcRgrEk"
   },
   "outputs": [],
   "source": [
    "dg_ig = tn.DynGraphIG()\n",
    "\n",
    "dg_ig.add_node_presence(\"a\",(1,2)) #add node a from time 1 to 2 (not included, time duration =2-1 = 1)\n",
    "dg_ig.add_nodes_presence_from([\"a\",\"b\",\"c\"],(2,6)) # add nodes a,b,c from 2 to 6\n",
    "dg_ig.add_nodes_presence_from(\"d\",[(1,3),(4,6)]) #add node d from 1 to 3 and from 4 to 6\n",
    "\n",
    "dg_ig.add_interaction(\"a\",\"b\",(2,3)) # link nodes a and b from 2 to 3 \n",
    "dg_ig.add_interaction(\"a\",\"d\",(2,3)) # link nodes a and d from 2 to 3 \n",
    "dg_ig.add_interactions_from((\"b\",\"d\"),(4,6)) # link nodes b and d from 4 to 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='ls'></a>\n",
    "\n",
    "### Using a Link Stream representation\n",
    "`DynGraphLS` is the class used to represent dynamic networks with Link Streams (LS). In a link stream, interactions are ponctual (no duration), but time is continuous. Nodes duration can be represented as intervals, or simply ignored.\n",
    "Note that if time is discrete, a link stream can represent data equivalent to a snapshot sequence: each edge of each snapshot is represented as an interaction at the corresponding time in the link stream. Discrete time can be handled using the `frequency` parameter of a link stream. In this example, we create a link stream equivalent to the one represented with other types.\n",
    "\n",
    "Note the similarity between the functions used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('b', 'd')\n"
     ]
    }
   ],
   "source": [
    "dg_ls = tn.DynGraphLS(frequency=1)\n",
    "\n",
    "dg_ls.add_node_presence(\"a\",(1,2)) #add node a from time 1 to 2 (not included, time duration =2-1 = 1)\n",
    "dg_ls.add_nodes_presence_from([\"a\",\"b\",\"c\"],(2,6)) # add nodes a,b,c from 2 to 6\n",
    "dg_ls.add_nodes_presence_from(\"d\",[(1,3),(4,6)]) #add node d from 1 to 3 and from 4 to 6\n",
    "\n",
    "\n",
    "dg_ls.add_interaction(\"a\",\"b\",2) #link a and b at time 2\n",
    "dg_ls.add_interaction(\"a\",\"d\",2) #link b and d at time 2\n",
    "dg_ls.add_interactions_from((\"b\",\"d\"),[4,5]) #link b and d at times 4 and 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cTPffJ614gr1"
   },
   "source": [
    "### Accessing functions\n",
    "Using accessing functions, we can check that both graphs are very similar\n",
    "(Note that intervals are coded using the tnetwork.Intervals class, and are printed as \\[start,end\\[. Therefore, 2 snapshots of duration 1 at times 1 and 2 code a situation similar to an interval \\[1,3["
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "CVaBJQdaxTPI",
    "outputId": "9bc7f7d7-debe-4ef1-cb3f-9a8e8848e973"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('a', 'b'), ('a', 'd')]\n",
      "[('a', 'b'), ('a', 'd')]\n",
      "[('a', 'b'), ('a', 'd')]\n",
      "[('b', 'd')]\n",
      "[('b', 'd')]\n",
      "[('b', 'd')]\n"
     ]
    }
   ],
   "source": [
    "print(dg_sn.graph_at_time(2).edges)\n",
    "print(dg_ig.graph_at_time(2).edges)\n",
    "print(dg_ls.graph_at_time(2).edges)\n",
    "print(dg_sn.graph_at_time(4).edges)\n",
    "print(dg_ig.graph_at_time(4).edges)\n",
    "print(dg_ls.graph_at_time(4).edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "KnreFdJailIu",
    "outputId": "a2c84c94-42bb-4938-86b1-2646400b6690"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': [1, 2, 3, 4, 5], 'd': [1, 2, 4, 5], 'b': [2, 3, 4, 5], 'c': [2, 3, 4, 5]}\n",
      "{'a': [1,6[ , 'b': [2,6[ , 'c': [2,6[ , 'd': [1,3[ [4,6[ }\n",
      "{'a': [1,6[ , 'b': [2,6[ , 'c': [2,6[ , 'd': [1,3[ [4,6[ }\n"
     ]
    }
   ],
   "source": [
    "print(dg_sn.node_presence())\n",
    "print(dg_ig.node_presence())\n",
    "print(dg_ls.node_presence())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "diRsLh9n5cxs"
   },
   "source": [
    "<a id='vizu'></a>\n",
    "\n",
    "## Visualization\n",
    "We can use a basic visualization to compare nodes presence of both representation. \n",
    "\n",
    "See the notebook on visualization to see more possibilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1005
    },
    "colab_type": "code",
    "id": "wBgAA-DshLGt",
    "outputId": "68d3b911-17f5-4ac7-daf2-cdb995fd5791"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/numpy/core/numeric.py:2327: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return bool(asarray(a1 == a2).all())\n"
     ]
    },
    {
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    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = tn.plot_longitudinal(dg_sn,height=200)\n",
    "plot = tn.plot_longitudinal(dg_ig,height=200)\n",
    "plot = tn.plot_longitudinal(dg_ls,height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to plot the graph at any given time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = tn.plot_as_graph(dg_sn,ts=2,auto_show=True,width=300,height=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = tn.plot_as_graph(dg_ig,ts=[1.5,2.5,3.3],auto_show=True,width=200,height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mri1TMSp6Cuf"
   },
   "source": [
    "<a id='conversion'></a>\n",
    "\n",
    "## Conversion between snapshots and interval graphs\n",
    "We convert the snapshot representation into an interval graph representation, using a snapshot lenght of 1. \n",
    "\n",
    "We check that both graphs are now similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "wGlIom-PhRZn",
    "outputId": "dd47a8d8-3641-4529-9ad0-9c8e7e9dc3fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': [1,6[ , 'd': [1,3[ [4,6[ , 'b': [2,6[ , 'c': [2,6[ }\n",
      "{'a': [1,6[ , 'b': [2,6[ , 'c': [2,6[ , 'd': [1,3[ [4,6[ }\n",
      "{frozenset({'b', 'a'}): [(2, 3)], frozenset({'d', 'a'}): [(2, 3)], frozenset({'d', 'b'}): [(4, 6)]}\n",
      "{frozenset({'b', 'a'}): [(2, 3)], frozenset({'d', 'a'}): [(2, 3)], frozenset({'b', 'd'}): [(4, 6)]}\n"
     ]
    }
   ],
   "source": [
    "converted_to_IG = dg_sn.to_DynGraphIG()\n",
    "print(converted_to_IG.node_presence())\n",
    "print(dg_ig.node_presence())\n",
    "print(converted_to_IG.edge_presence())\n",
    "print(dg_ig.edge_presence())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TxL5hGKX67-L"
   },
   "source": [
    "Reciprocally, we transform the interval graph into a snapshot representation and check the similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "hyj4TUdbyjGx",
    "outputId": "72819307-2ec4-4c36-c34a-b3132f234dd8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': [1, 2, 3, 4, 5], 'd': [1, 2, 4, 5], 'b': [2, 3, 4, 5], 'c': [2, 3, 4, 5]}\n",
      "{'a': [1, 2, 3, 4, 5], 'd': [1, 2, 4, 5], 'b': [2, 3, 4, 5], 'c': [2, 3, 4, 5]}\n",
      "{frozenset({'b', 'a'}): [2], frozenset({'d', 'a'}): [2], frozenset({'b', 'd'}): [4, 5]}\n",
      "{frozenset({'b', 'a'}): [2], frozenset({'d', 'a'}): [2], frozenset({'b', 'd'}): [4, 5]}\n"
     ]
    }
   ],
   "source": [
    "converted_to_SN = dg_ig.to_DynGraphSN(slices=1)\n",
    "print(converted_to_SN.node_presence())\n",
    "print(dg_sn.node_presence())\n",
    "print(converted_to_SN.edge_presence())\n",
    "print(dg_sn.edge_presence())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': [1,6[ , 'd': [1,3[ [4,6[ , 'b': [2,6[ , 'c': [2,6[ }\n",
      "{'a': [1, 2, 3, 4, 5], 'd': [1, 2, 4, 5], 'b': [2, 3, 4, 5], 'c': [2, 3, 4, 5]}\n",
      "{frozenset({'b', 'a'}): SortedSet([2]), frozenset({'d', 'a'}): SortedSet([2]), frozenset({'d', 'b'}): SortedSet([4, 5])}\n",
      "{frozenset({'b', 'a'}): [2], frozenset({'d', 'a'}): [2], frozenset({'b', 'd'}): [4, 5]}\n"
     ]
    }
   ],
   "source": [
    "converted_to_LS = dg_sn.to_DynGraphLS()\n",
    "print(converted_to_LS.node_presence())\n",
    "print(dg_sn.node_presence())\n",
    "print(converted_to_LS.edge_presence())\n",
    "print(dg_sn.edge_presence())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l6geADSO7LDb"
   },
   "source": [
    "<a id='aggregation'></a>\n",
    "\n",
    "## Aggregation/Slicing\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Slicing'></a>\n",
    "\n",
    "### Slicing\n",
    "One can conserve only a chosen period using the slice function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MkvzCpm1Ob3ff+XVJxn9G5qGTneEzGd8Ntrro97yAev1ekucm+bGMrzV5b5LvJHmU/ev/12tq/Ucyvsh98/qDvH9dnOSZk/f1mMnjuzI+nWTu6q/X0s5dA+u1tHPXkHpNbbd0c9c2NbwqU3d/L2oO63kD7QTLSza9yXdu2u6FGV87MUpybZLnb3q+krwh48R8R8YXdj9i0f9hc/gPn7leSb50gm1eP3n+vhnfvfW1yc7xpSTvSPKARb/fBdXrrUm+PNm3vpLkg0keZ//aul6TdWdN2p27xWse5P3rssn7GU3e3xXTNTB39dVrmeeugfVa2rlrSL0m65Zy7tqmhlflnqFyIXNYTV4IAAAG89nfAAB0EyoBAOgmVAIA0E2oBACgm1AJAEA3oRIAgG5CJQAA3YRKAAC6CZUAAHQTKgEA6CZUAgDQ7f8A7xb5ODPW71cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sliced_SN = dg_sn.slice(2,4) #Keep only the snapshots from 2 to 4\n",
    "sliced_IG = dg_ig.slice(1.5,3.5) #keep only what happens between 1.5 and 3.5 in the interval graph\n",
    "\n",
    "plot = tn.plot_longitudinal(sliced_SN,height=200)\n",
    "plot = tn.plot_longitudinal(sliced_IG,height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating cumulated graphs\n",
    "It can be useful to create cumulated weighted graphs to summarize the presence of nodes and edges over a period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "%matplotlib inline\n",
    "g_cumulated = dg_sn.cumulated_graph()\n",
    "\n",
    "#Similarly for interval graphs: \n",
    "#g_cumulated = dg_ig.cumulated_graph()\n",
    "\n",
    "#Draw with node size and edge width propotional to weights in the cumulated graph\n",
    "nx.draw_networkx(g_cumulated,node_size=[g_cumulated.nodes[n]['weight']*100 for n in g_cumulated.nodes], width = [g_cumulated[u][v]['weight'] for u,v in g_cumulated.edges])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphs can also be cumulated only over a specific period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_cumulated = dg_sn.cumulated_graph([1,2]) # create a static graph cumulating snapshots \n",
    "g_cumulated = dg_ig.cumulated_graph((1,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling\n",
    "Sometimes, it is useful to study dynamic network with a lesser temporal granularity than the original data.\n",
    "\n",
    "Several functions can be used to aggregate dynamic graphs, thus yielding snapshots covering larger periods.\n",
    "\n",
    "To exemplify this usage, we use a dataset from the sociopatterns project (http://www.sociopatterns.org) that can be loaded in a single command in the chosen format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BE7L9tBA1FNH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph will be loaded as:  <class 'tnetwork.dyn_graph.dyn_graph_sn.DynGraphSN'>\n"
     ]
    }
   ],
   "source": [
    "sociopatterns = tn.graph_socioPatterns2012(tn.DynGraphSN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dKO13kfV8YO7"
   },
   "source": [
    "For this original network loaded as a snapshot representation, we print the number of snapshots and the first and last dates (the dataset covers 9 days, including a week-end with no activity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "zlzT1S2c2zKb",
    "outputId": "05cf3f25-7c4a-40bc-cb56-fc083548eefa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# snapshots: 11273\n",
      "first date: 2012-11-19 05:36:20  laste date: 2012-11-27 16:14:40\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "all_times = sociopatterns.snapshots_timesteps()\n",
    "print(\"# snapshots:\",len(all_times))\n",
    "print(\"first date:\",datetime.utcfromtimestamp(all_times[0]),\" laste date:\",datetime.utcfromtimestamp(all_times[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Be careful, the plot takes a few seconds to draw. \n",
    "to_plot_SN = tn.plot_longitudinal(sociopatterns,height=500,sn_duration=20,to_datetime=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-tiybse78sQk"
   },
   "source": [
    "We then aggregate on fixed time periods using the `aggregate_time_period` function. Although there are several ways to call this function, the simplest one is using a string such as \"day\", \"hour\", \"month\", etc.\n",
    "Note how the beginning of the first snapshot is now on midnight of the day on which the first observation was made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZGX7M3g_2u3w"
   },
   "outputs": [],
   "source": [
    "sociopatterns_Day = sociopatterns.aggregate_time_period(\"day\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "jatmSRA71Vwe",
    "outputId": "7fe144a9-3d4e-4973-bea7-3ba16dd4678e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# snapshots: 7\n",
      "first date: 2012-11-19 00:00:00  laste date: 2012-11-27 00:00:00\n"
     ]
    }
   ],
   "source": [
    "\n",
    "all_times = sociopatterns_Day.snapshots_timesteps()\n",
    "print(\"# snapshots:\",len(all_times))\n",
    "print(\"first date:\",datetime.utcfromtimestamp(all_times[0]),\" laste date:\",datetime.utcfromtimestamp(all_times[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "to_plot_SN = tn.plot_longitudinal(sociopatterns_Day,height=800,to_datetime=True,sn_duration=24*60*60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "esS6ElU49Q1u"
   },
   "source": [
    "Another way to aggregate is to use sliding windows. In this example, we use non-overlapping windows of one hour, but it is possible to have other parameters, such as overlapping windows.\n",
    "Note how, this time, the first snapshot starts exactly at the time of the first observation in the original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zUqkDgDr3DNO"
   },
   "outputs": [],
   "source": [
    "sociopatterns_hour_window = sociopatterns.aggregate_sliding_window(bin_size=60*60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "MQzD1UkB3OMR",
    "outputId": "4ef6e456-33bf-4a00-fdc9-c8da2c96b704"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# snapshots: 86\n",
      "first date: 2012-11-19 05:36:20  laste date: 2012-11-27 15:36:20\n"
     ]
    }
   ],
   "source": [
    "all_times = sociopatterns_hour_window.snapshots_timesteps()\n",
    "print(\"# snapshots:\",len(all_times))\n",
    "print(\"first date:\",datetime.utcfromtimestamp(all_times[0]),\" laste date:\",datetime.utcfromtimestamp(all_times[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t0T7/7FJFs7fPO8hpBUBOrvrWjH6st3+eSnaGqK1SyaynjzisvmTPJ5ckaJ5/dqmilvbNbpsjQE4rAHI8VTY5gt4+dqQPP7RSieR1yQ5htB2wPVo7cG2Mvh1f208k9Y5npeY7L1Uqid6Ptkjagv4FQB1SX4Ex9CeSNY5Wiq+Hrz6sUglRevs49bqWSNvQGvvsr9nrF0bejrfuJ+/v7yF3RlNweYCS71xbqSR6P9oiaUvt+NuCnExwRrixo5GOm2HsaPO0SykoW759+5a8V066z9JXD61UYrUNGmkbunY7WFIke1Y7SNo1k5A1l1sqUbDXViqJ3o+2SNpS215I94Czw42dks+Zbexoc/81/vR2u9HDw8Pv+//69Sury2ztq+eZRQGYiBkc50xAugeAOmYcO5CdETJCtTzSNnStLMhaQHz9NZJ6z/4az1j3k0h9oBaJzbQSPlFlalK5TLX2Qf8CoIzl2GnNqYtEixTf0ZJyS+2W47Isj0T08vLyQo+Pj79frzmCjkMl/KGSt7c3+vnz5x/v39std41XcvleJftEbK8V2kMTMx5Oquk7+/dbHOoC4GxYjB1tTt0MY69lAWzhl15fX+np6YmI6Ol2u71y17nIIdTKzkTffpaouufamLIbd41Xcv2kRvXee3ut6FE8PZrDrek7W1rHHwBnxWLsaHPqZkArxSd5bw8OySHkjqCnjlLPukNoufuwt9tZjvjnZEFK0iqz0LIDNjNn8iUAHAl2zs+D+YJQcwQ9d3pGg/dt5ZbKB5oj6KX3ecL66H5OWmUWWmRVZrKHxsdomc1WALTQo2qPZm6aSU4sAuYLwhHyH963lVsqH2yPuJeOoEc63m9xdP/z589E9HEx4L0/1NJTViUSPX3MbLYCoIUeVXs0c9pMcmIREFt7WZYHItou1T/ZPw5oYT3e/+vXr9+adZ4Xh63P5bVdR3KWUDkAYB7gq/KMSvvRLL//SUT/Kl2UCuf0xvu2cmmLPPf8mhNZ7+/vtCzLlCHDEXJG3pBUIYkqI6OhZ3tmsxUALfSWPTlaViUCI9N+NAvC/yGi/938/yci+v7hhgZbvFwokMP7tnJpizz3/Jot+bu7u2lDhrkQsyb0HBlJFZKtyj3RnPbY94WWgyRIkgeAJzfWLMZL7/tHZOQcLl5J3W63KxH9rp3Vc+U+YyeYsU1HUyNHc0bOYI8WqaqZJKwA6E1v2RPIPfnhkEolve8RaVtZG9JrUTmvuYcHziocXBIvXSvRcKz/PqNtiGTirtghBKAfWt/Mja+ZpLFa2p66JrdG6G23Q2Ktz8/PdLlcstU3au5DFK8zaU/XSsNjs4RQNZVu9tdEauceTa4oBzemotuGyMY+EmawFQA9qPXNkuujjjtt1RWi8jym+Sxrux1SqeRyudDDw4N4O5iLl2vv4xXtF6hVOY84sFZq8yei50n2fP7otiE6rg0z2AqAHljktpWqlkXDuuqK1j7WdjNbEEqqSFhV1oiy3dxSdxbhLltmDze3EGU8AQCOJze39/osIviiEZgsCDVVJCRw0jWRZFVqq0q04NEOWrj8iVL+XEuVEw9265nnqZU18mCPPUflwUbLtwWgJyXfKfHNW97f3+lyuSTvE8UXbdFWXam9H2dna8k9kwWh+bYlI68RSValtqqE9WdGg8ux5CqVSCqz9FDbt4bLFS0p+UvQyhp5sMeeWvtIJKwiVfgB4Egkflcrjcb5+LWYQunzPWFRdSXlf6RzoLXknksBvyO2poFfpJPyWpllFrbtXpbFvTM8ml72ma0fAeCdmX54WWgx7v3PKPuYLAitq5Nwq+xcKNmbQ5fIy1jbzaMdrEjZStreaGr41qkEWlkjb/bYo7GPZHd15nEDQAuS6kiaeSw31lp8vAckFcm412vnMWtfbbIgPKpSSC6U7A2JvEzqubkQ17qtnAuVebSDFam2SdsbTQ3fYudLGgbVyiB54KgUFQDOjqQ6kmb85K5t8fEeqK1IVjuPhdUhPCs1X1auGscMkjujOJsaviYM6n0BCAAAEejtS3vf32RBeFSIiTvN5D3EtWc9Wp9qD9fGfRWK1HVHqZlboXnO1HesqcwRxSZENv15tY33ttZgPd5L4yZS3wHAEut0m9nSVzg0bUn56lG+aKkNvyzL8khELy8vL/T4+PjHIidXheT5+ZmI+KoKNXg/mr5Fk/+ktVUkFfgaiQFpH5PI+3i0yZaUbmLt2PLe1hqk9pFUScqNG+712ewJAIdEw1Vbvo37nJnGWmrxJpFAI7L3Ra+vr/T09ERE9HS73V6568xCxtIvLaVB1Eqk05iaZ9XaKpIKfI3cibSPSeR9PNpkS02Im+sv3ttag9Q+kupGo6sDAOAZyVjTVtNKEdFP50jNV7USaOEqlYCPINQEAAAAgAiYLwhHSHx4zDPgtr81R/St8jM82qclX0Tbx2bJTakdWxHbWoPke9b0hbPbE4AjmcVP57D24eb51FY5hFuscg4i1/C9Xq/0/fv3P177+vUrEdGH1/eSMkRt+RlRdiZbnlNbmziKTUpox1bkttagrR/uIZEbAPA3ZxhrLT681j6H5xByXC4X1Ze6vX5bjWD7elTZEE0Flre3t2Sn4ewTEe1g5wZSj8+KBtdftq9Hd7DaH5GcTXL2SfmWyDYDoIaWBYn2Gm5sRie38Fv9jHYtE0J2ZovkpFDLSZsozrmk8C55neOMpyI1p7NnaG8KiwomUW1jXb2FI6p9ALCCm8OJynOLxfyfe28UJGscrl0jT1ubLwglJ4WsT9p4pOevnNEnkUagacMM7U1h0a6otjnquaPaBwArNKd9axQdzuDLa9c43L8dZYd/HPIpAAAAAADALYecMpZU2UhdL7m3V3o+6+iTSCPQtKF0bdTEZYvvMWpfOOq5o9oHACtqTuLnqm9JTvprnsUDpRzL0honVR3JQxUy81PG1pU41koDkSbuFauT1EedRPIO1PDb+lT0viCtVKMlso8BoAfSKhtcbuH+mv24klYa8uiba+2w9zPaXMrU/d1WKlmxrsSxVhqIiES9Xfs3d3/J69GBGn5bn4pOr34d2ccA0ANplQ2J70xdI6005NE319ph72cscilPV6lklWqZ6dd7i4beTLs/s+5kWqDdCYT92kF/BOAYZhtrGkm5te2a9xyF+YJQU4lDcu1WksXj9rEW7XH0WY/ojwrdpvrc+/u7qx2iWomVSN9/C9Z5Re/v77QsS+hUAgB6U1tJZO9fOd8fpVJJ6pkk0nHLslT79qPOB5gvCDVyK1ppFo/bx1q0x9FnPaI/KnSb6nPehFBr7RDp+2/h/v6evn379mEH9devXx8c8+fPn4ko77Dv7u7CpxIA0Jv9uNvu7K2vp8bg3r9yY+3h4YG9vye2dki1d8tahWxty/V6zd67VLWst318zYSTkQr7SbeJW7aVtSHpI5G066wHJ1pDCev7ZipvuKWlUo3V50axFQAtSHwwVwkodS+iucaOtB339/eq6NN6vfbcgBWHyM5YXFtz/Uhqt4a58JXVe0eGwUo2aWn7llwbvYaMLSpxSMIWUavZ1NpHYpP39/fkATcuxOPdVgC0YF0VaJ/2xfngiGkbpTWJVnJn9BrHfEHIhXP2f3PboLPs/NSGm7jwldV7R4bBSp/d0nbp53gNGY+uxOE9PNrz+e7u7thwWCrE491WALTQs3/fbjfWB0dM28itd1JrFu31R9NlJtTKg1jIiQAAQAujnTEAIB5SCZ3a649k/NbIpNRu/ZYUzls/c+SW9FHb5asNpQryo7fpj3wG7nNSNvPEiMo/3L956C8A9AJVtsq05BWPeq8ELAg7wW0Nl6osbP9NUslF+rlE4yf77bNxdqi125btdRIFeQ+UbLOq3KdSKrjridJ9J/VZe5t5WxS22Gf7t7bKSe5kJQAzIk37qh1T0WnJKx71XilYEHakdWtYUsllyzqAPW9J5zpvj+evVdIfQc42q8q9NKWC6zspG3PXeKPFPi3VELAABGdDWxFJijaH2aMvasl1HPVeKV0WhNoau2eVGTkjnlXawTmZURYDADCWiHJV5gtCTeFnq3CexxAXR09ZHg/yKTkksjPc89fmknjPq0yhzVvTXC+RQfBmjz0t0kGp985WDQmAI9FUJ5shh1DiM3tUYznCV5svCDXbmlbbnR63lTmsZHkkivDekMjOcGjzWohk0kYef7lp89YkFQS4+0TMkWuRDipdF8mXAOCB0pjaV+sg4v1SBF8k8Zk9qrEc4at9ryAmxUqW52y05LVEs6F2oHPXS+7j0ekCAOYgVa2jxV95oOU5R71XgtmCsEbWw2q70+O2cm8ihvq8q7SDGFiHXWruAwD4G4ypj0Scn4mIltoQybIsj0T08vLyQn/99deHePkqA1E6VJKKtUslJLb3OSMRk1Y911kGcbDS8kIfBKAdjKmPeJqfX19f6enpiYjo6Xa7vXLXmewQphaVqwzElpRBcu+1PO4+IxEHWrTQLfCJVdgFfRCAdjCmPhJxfu6WQ7hKOZR2+WrkRzytvKVopXW0sjxR7NDCmeyglW6SvPcMtEhbnc1WAGixGl8t/s0zLfO89v5uD5WUpBxq7sf9yjhCrduaktwKh1aWx7sdWtDYMLodtNJN27ZGHB9WaOzGcRZbAaDFanylrpf6N8+0zPNTVSqxljvJ3e8ItW5rap9P+z7vdmhB07bodmiRboo4PqywaPtZbAWAlp7jK1KlEo7e83zYSiWt5MLNqHARm1HJxxGSnnNVXFr6/YyVOKKHlgCIAqpL9cWTfzZZEFofp9aGm71X6OhZZcPic46itKWe2/7WqOHv+0PL5x5F6Rm5MbFv6xkqcXChE66CiQbvYwiAI5FUl9LAXf/+/p6svx5pPNY+q9Q/t1RokmKyINwqaKcqaPTGe4WOXlU2vO50cZS2t3P/nvqOP3/+TEQfF0v7a1s+9yhqn2Hf1jNU4uBCJ7UVTFKVFAAAbdWlNNff3d2FrJq0RTvPp9ZK2jnQPF3P6kaRvrgR9Kiy4XlX9AjQ54AFqUoKAIDjie7TW+Z5D5hvrY3Y4o20rUxkl/8U7eh+i6K9Rvl9/3oEJX2rtIIIbW2lpS/UXgPAGbH2J72rlo1AKzWz/p2q6ra+lor+HVH9ZGis9fn5mYiIfv782fR6JKyOjrdIk4xiu6X+9vam+h65cIIk0Tm3le9lwVyyzQx93xOpSkoAgD+R+M6UD05VG+PSoCKPwVqpGQ7Ov69zeG+7mS8INTlKqSTSmtcj5UVZHR1vkSYZSa4Dl55TWukm9VoEJf2cbaR9P0K+ZCsWfT9VSQkA8JGS75RWG+PuGZmj/On6Ob3tNnSHkNvd0b7uidL2cU5SRFOpJIItrPEaCj+C0vcNaQgdnqQeAJiVM/vsiJgvCDXH0LnTyNrXvcjO1G4fc+3SqsDnjvR7sM8W7RF6LkR+RF7F0Wgq/7y/v9OyLOI+Et02RHzf4WQrUrk6s0nxADAKzgefoWqSVnanlqP8tvmCcIQEjBfZGevtY+39ckf6vaE9Qs+FCR8eHqbJR1nRfF93d3dJ26xSKh7zJVvh+g6XY3OGMDoAo+DG3fV6/XDtbGONk0Pb+96c1EzueqJj/bbZSgEhK3A0qVD729vbVIvDEqXxdrlcprfBljO1FYAj0J6iXX1wzjdJ7hndf299ryRCt71+WZYhi2eTBaH1SRstXsJg1s/Bhbs4Iqm9a0O9mjDqloghCuu0i4g2yKFNNyjZ0+P4AMAD1nO7NsXFu+/SVoaSXE80TinEZEFYWsnWbKESfZzsvIfBJErl0i1jabhrSyS1d+1z1oa9I4YorEP8EW2QQ5tukAvreB0fAHjA2ndwKS5Hfb412spQtZWkjrLDIcllayUAqVo356BT9/FGTVUR63ZFmeCiPCeYD1QnAQCAP2leEF6vV/rHP/6RvWYNyaTyBlIh0ZKaeeSj7LlQqdQ+mnvPglUljwj0SD3YM+sYsrieKLZ9ALDC2hdp5rMen2+NtpJLbeWXMKeMf/z4QZ8+fSKifDUFq1yEWY+yl+yzV37XVvqIjqSSR04ZPzq56j3afhF9DGnTDbTXR7cPAFZo/W7J/8w2Z1lUwUpVTRqV9mUaMraqJKKNo3vPM9hSW6mkpPwuvfVy6w4AACAASURBVE9kSpU8vKcTSOGU/1PU9Aurajkj0TpIzfUz2AcAK0b63QjjTlMFK1fVhbvnkZguCK0kZyJXKuGALA8AAAAAvGK6IMxVU+B2ODT38V6phKMUDpbYZ59DoJXemIkZq5Ns0bRPkqNSc82ZgX0A+Eitb7H+zMh49y1L7ZbssiyPRPTyn//8h/7666+sJtzXr1/p4eHhj10yiYaclPX+Xrler/T9+3f23/f2ISrnIqTu6d0Olsye9M+1T9Juq2vODOwDwEe0vkUiZD2rMDXHCN/y+vpKT09PRERPt9vtlbtOvEO4LMsDEW1XG5+I/o6ZlxqUqigBPqLJRUix2nnWgbQF7Wt776z2s1pIw1cBALRI/c86v//69et3iT+tv+qBxuP9k4j+lfqH0pan5W5gzeePpvR8NaFerUI6iA9Ov+bh7ENUVv4vpXXAzuDsSPyPdWWTSONO63+0r3urVPI/RPS/m///RETfif48eq0JB1sUgY6wG1ayT81ORK3iOYgLTr/m0dhHcvJa8+8AzI5kfFmPk0jjrsX/1L5ujXglcrvdrkR0Xf9/v+tVsyirqWASVVrE+6IVAAAAAOfFPElGE761qmASCatTRrWK5yAu3k+ojcb6dLbm3wGYHZwyztPif2pft6b5lPHLyws9Pj7+fj0VR+eqLHDxco5I+QQ5rBJGNaeSwRzg9GueHodKYGcA/ganjPNo/U+Lv9JgfspYiqbKglUFk2hYdfLWU8kgHtEc5NFw9tGevMZ4AuAjLeNI+3dEtP6nxV/1IJSuQkq+JsqvCMmvpihtAf2R/HI8cz+CfQA4BkQl5Ght5c1fHZJDaBUX504vew8la47ie28L6I9GvoBj5n4E+wBwDJC6kqO1lcd1gfmCcCuxQvTn6lbyek52hsN7KFnzfN7bAvpjIS8zcz+CfQA4BkhdydHayuO6wGxByG19vr29FRW6t9dfLpdpfn1sS/VZ3IdortBXy/Y6DgzkiZxewZEbT9oxNqN9APDG6MMUvZA+ZzQ/Y7Ig1G59EuVDO9++fVOFk2sqffSmVrF935ZZKyhYbK/PWoUiVYUm9VqOqOkVHKXvUFsNaTb7ANCDFqkrq8od3sYj166UXXJ+xuMax2RBaL31ebvd6OHhQVz9xGPN0dot3n1bZq2gYLG9PmsVilR/5vr458+fiUi+IPLWVim1z30W+wDQg1wKWImelTtGwrVLs2bZX0+UT5c7ao3jbyW1wdsvAwC8gTGSB/YBoA2MITlaW3mTujJZEGq2PlMVSLhrclVLaj//KGqfSav8vtomWh6GNhRxpioUqefhxoD22b21VYrVeOr1OQCAP+lZuWMkuXatc2/LmmVkRSqTBeF2W/nt7e1DRZItuX/TXDMTz8/PdLlcqhZvW1tFysPQhiJqTq9z742S4LtFOya46kBR0fgYLS3jDwCgg/PlrYcvR1PKc5b6mZYwfStmIeORjjRKngHH5XJht4st8jM92oeobXtdex9vW/M5LL5zq+pAnqjxMZL25sYfAKCOXA5haixHkbipfU6Nn0GlkhOz/jLCDgXQwv2q1vzalkjyREhDSBF91wGA0fT2D1bybN5JzfOnrFTSmyh5BhzbE0X78K72aDr3OnZA4lDbdySv7+8tkeQh8pWGoJHgkZwuxvgAIE2Lf+DkszSyah7HZq0s2H6eJ/JXWal7pRJu1Wv1t9fdCs4OkuPotffhjqZ7lOUBPLV9h+Pz5890f3+fHCu1kjwjQzmp/qyVlyndDwDQ5h8k8lml+3scmxpZMA6vlZW6W3tbeWS70l+WJdlI7nru79lIKZtv/56JHoXAJSGNCD8oLLm/v59izPQKLUWrJgBAb6RjrTZl5SxhYg6v7TZfZUgqUESrHFFDbaWSmh2Olff39+RhAo8h9Z6FwPf3JCpXxvHU32rbypELu9RK8hzdp6wrlUje661fAHAEGv+jTVl5f3+nZVnE958pZLxF669CVSrZIjmBE61yRA0j2nB3dzf0yLqGnoXAte/z1t+snye3uyyV5Bndp0Z8R976BQBH0LPf393dJe/PpXx4jIzVhowlaS2j7eDP2qAZjwtA4BeJJA/6FACgF2fwL1p5tBFgQQgORZs7cvZck15ElZEBAICIRJjDDpGdiV5KrIYRbfCYb7GllJuilUSRIPkevPU36+eR2NV7vpw2R8cCb/0CgCPo2e9nL12XQ5I3ONoOS22+wLIsj0T08vLyQo+Pj3/8m2T34QynPkunYlMyIqtESOkUbeq9X79+db0gvF6v9P379z9ey0mipK5PvXfGU8bbnVFrqZmUXSP3nTPJWQFwBCl/2TJfEcmErKNELkrPX/LbKbv1tMPr6ys9PT0RET3dbrdX7rouIePZSolZsJXT4SR3rIgyqAAAAPhjXxxBW4o1JR3H3X8mpO1apcC8rX2QQ9gJLixHlJdAaZWdSR3p9xIOTG17t1RpkdjqrLIzWrt6DM1sKfWdHnjrFwAcTa3cU83YiZLKInnOlrS4kXbAgrATIwp1c0f6vchnbCVOUlvquSottaHTM8nOcJIFpeo3EXaRLfqCFm/9AoCj6Sn3JXmPxzEoeU6pnFft/XuBBSE4FO3Cw/tCxRMaW0W0a8RnBgCck4hpcVgQdmJEWI67/9vb2+9/Hz2prvmN6zNtydmn1naRThnnbCOBe1/q+4+YZ9pqHy1rv4hoKwBasPJFmp2xKKksqWdKtbf2wMhIO2BB2AkuLFfSInp+fqbL5VI8tfX29kY/f/4UPcv2upE5GdYl2ThWG3KVNjyeJrWwDdcf9t8/0cecSo+5Olt69p3UmNuO12i2AqAFzVjbj511XuJ80QxjZzu3l9q7Rep7R6b0YEHYkdSXWMoFuFwuH04fcX9r7y29phctJQu1p9z2dvK+fX/U98J9jsdcnS09ny815nKf691WALRQ42ulPlXr472OtZoFmsb3jlo0d1kQWukQRg/VtDy/RMMQACvW/hRxnLWytt3jzjEAAKz0XhOZLwglIZbSlnTUsNYWzg6ligucdIwE7/lykrZzvzQ1lSq8V2xJcZgSPfM5OZkaD/S0T046I0peEwBWaHytZRWyiGNN83ycXaXz1RHpK+YLQsm2b23o0Ov2cQrODusOBAcnHcORqkjhNV9O0nbNv3EyK6XP8UitrMpe8T5XTSDVRyTyPx4o2SdXMYG7niivZXi73ejh4SGcRA8ALZT8Z64KUovcyuxyWJxdpfPVESH1eDMn+INV8Xz/2kq0nTIN3p2Flpr2SBTvS30kCqXJJJd7q7lXy7UAzE7Kn+z/fUU7/0QcaxGfmcN8QSjZ9q3dVva+fbyFs0PpGL+2jdFtIv13TTghkk1SaJ5fMrbOEKbp0UbvdgCgBy1++sz0Xtcc4auX2i3HZVkeiejl5eWFHh8f//g3HCr5m9TzX69X+v79+x/XpbbgS4dKtveMhOR7l7x3pn6SouX719ojov2s2tjSHwGYFYyLOnqva2rf+/r6Sk9PT0RET7fb7ZW7rsuCEPCkFoRfv36dOrQLAAAAgDFIF4TDZGdaro9MbttXuysU3W5H7vKc6Rdvi12j97WW6MTs/QKAGiTjxSraE50WX+rBDw+RnWm5Pjq5CiZSqZmZZXm0/YToox1qrolitxIWdt2/lyhGX7OQvMq9F4CzUVsh6IxzfosvTb0ufW942ZmW62egpoKJ5NpIdjuqn3iv3mKNhV0l/+7RZhaSV7XXAjAjtWPgjHN+T196lB8+VHYmtSWaqriRqhwQMbSj2WrXVB4pXet5O359NotKKzNXa9EeoLG0awRqfUnNOPM2hgDwzuzVfyznWE8++zDZGc3Ws1SUd8XjdnPtVrsEzj5clRMv9inZRFupRNJPIsqLaMLjknBDzq4RJaB6+hLuvV7GEABHoqlasmXm6j+cf5b40pTsnMRHvb+/0+VySd7TEvMFIZcjd71erT/qNx63m0c8E1flxIt9Ss+hrVTCEal6SwrNdyj5bnO2k1YW8FRBYER/9jKGADgS66pPM1T/4fzzvl0pX1rrR+7u7g6p5IJKJWA6zly9pQaJbSI5bACAb3IHTiIuFFMh8h70tsdhp4xrt54lSItDH0nP9uY+84ht5VqsK5VYXOuRVN/JFUYvEd0ee0aMrdlsCIAE67GWG0dRTh/Xpi9Jwsq5zzxijXPYKWOLVTNXkL7niryW2mdaw53bJPh9e3N28Fwg3LLwecRDRlJSfUdbGD1XgD462vQBorzD5q6Z2YYASLCax4jKvtlzutOWWpvsw8qpud36M7X4W0llOINTXsOd0uLhNf8+EqvC59v3etsd9kCpAP1ZkIwF7hrYEIA6JPPYWfE8Px92yrjXvS3vb0ntM+3fp7GnRztYYlXL2HueSup7TJ1Oy70+c1+wTh84ow0BkGA1j+1J+eAop497zu3Wn6nlsFPGROmTnm9vb/Tz588/7vH8/EyXyyXEiUcOaYiztKUutadXO1ihlWKJrJK//c7X8bEfIyvc62fj+fmZiOrssX3P6ntmH08AWFCaq1PkfDkYS5eQcS4Es5LbSr5cLtnt5iiOWhLilGypS+w5O1ZSLFHyVFq/W49tsiLVttRhKu7aHKvvAQCUx09prpbeM3pFpJr3eaya1GVBeJbQXi0txa3PwKzfO/DP7BUWAOiBxGefrZpSRA6TnSFKh/Y4iQ3PFTda0Nhnf030tkvQqMDX5FJGyVNZ6ZXDExnuO0zlAnJSTBy5CgtnGH8AbJFIhUnScErVhSLlxSOHUIF2O5iT2IgS2tNS264Z2i5BqgJfm0vpWZYnxfZ5OQkirbxDdLjvMNV3UlJMXC3xnATEWcYfAFskUmGpKmSS8GipopRXP9brfIAHKbVDZGewRdyHs4WY9784V0e0LIsqhyWabUoL3DPKO2i/Q0gWAVDHduwsy6L6cZQLE6++a+vLe1f66MHlcvltI8637OerlP/i7nMk5pbXqHjnKmtw4Z/oDrw2ZLm/prQFHzXEJbFPhJPCPeDSK6KPCSta7FOqyAA7g7OjqULGpX1t4cLN+/t78+va+cdKIeMIzBeEmtV9rrIGF/6JjlaWZ3/NSulXWtQQlySkO2s6QQlNBZMz0mKf0nWwMzg7mipkXNrXPkycCjeXPnM02vkn0qlqF17O2y+A3rTK8szO2foDAACcAVT/8c0hlUo41rBwajcsV60hSr6clbyMVNV9i8fTWUSytpckDKKdFLbirO2W0mKfqOMJgB60VhIpvb7en6sSVLrPSLR+RlN5am8fomMPmyy1W5TLsjwS0cvLyws9Pj7+fj0VL9+rmaeqk2xZ4+jbTiO53hNW8jK5fIVoh0ok+SJE5byKs+YQEkGjsUSLfaKNJwB6UJPzptEelqwR9vfxRq2mcq4yW84+HNJ57/X1lZ6enoiInm632yt33SGyM1o18/Ue0s4wOu6ewkpeJnefaCHm2rxHjU1mx6uD9EKLfaKNJwB6UJPzpqmmZbFGGE2LysGefXWkkRVMDpWd2Wp+Sd8HZfM2sKMUk5ZqP2fY6dLaxyJVAQCQRjPuUvN5tApBs/qKobIzuXuUjqxviZRnoM2ZaMmL8hZalUh7cK9vf0HNnkvXIlMQUcZBi9Y+qdel741uKwC0cDIy3LVcVTGi/LjbEqlCUMu8Kpm7SvNk6X4tDJWd+fz5MxF97AylI+sRfkVo5HRSiu2l+0jwFlqtlfbYvx6t2oiWFpmCWeWItljLOHiVgABgBBr5pt5VxTyOwZb2SuaulK2PWvuIV2/LsjwQ0TbA/8n0STacLUScOorPhbi4a2ZbFKXYtzdKvgkYx9l8CQDANzXz9FEVqTQ7hP8kon+VLtJsd3LbxNrXI20rS6opWJ3G9RZaLX12TVjC2/duQYu8wxnkUzQhLUm6ijRVAYAzYCkv0+NZRtN7Xh05b2sWhP9DRP+7+f9PRPT9ww2Z7U4ifS6hlEjbypLteKvTuN5Cq9vn+fXrVzJVIEXvsIQ3pNVstAXTR3//VlhXbJGmKgBwBmr8j7b6luRvr/6q97w6ct4We7zb7XYlot91ZjQrVo9f6lnwZntvz+MVjYxD7hrscAEAtGj9j7b6luRvz/Sex0bNk4dUKimdrO3xmaMZEfbzaAcOi7DE2q80FV6iY9WmyLaxDqnU9i8AzkaL7NUsO4REbbJXnqWuDomJcFVGUurkuWomkdTMuW1fSZK7JOwXPVm+JSyx9o1t/zhDNROrNkW3jWZs5XxM6b25/gXA2ZD4DU2VDQkex52F7JVXqatDKpVwpNTJc1vGM6iZS3PhSmG/GXLqrMISROeoZmLVphls01IBQTqeNP8OwOxI/Ib1OPE47ixkr7xKXbnImva2beqBM1Sb4EB/aOPMfUdCTf+KVkkBAOCXUoRv1Bx4SKWS3LWcnIhEniUiVrIzs9pHEpaQtN2b5I4F6Dt5au3D2WRLJMkrAHpQ63dbP9ObX9JUIXt/f6fL5fLhdc6fLMsyNKVnaKWSnJyItbSEF6xkZ2a1j2Q7XtJ2b5I7FqDv5Km1D2cTCaNDPAAchdbvpqTFtBJ0Hv2SpgrZ3d2dyCbbSmXX6/XDfY7yM/6sDYAR0ReAAAAQjZzfncUna9tRsomXXdBDZGc4ODmakgxE5DweSSjzDLIzHLX2maHtJdB38ozoOzPYDQAJ0rGz5r+l5nfteIk0vqz8T5RKJeZwcjTa6yPl8UhCmdJqE7OFRInq7DNL20tYtXtW+1m36/n5mYg++p1VymYWuwFghUR2hpvfUlJzs+Hd9w6VnbG6R7Q8HqtqE546kiVa+5yJUrul8imz2q/GPpzNUsng6+teQjwAHIWF7EzK/+TGUqS5vUZSruU+PUAOYUck6uQSlfbo6udbWiRRzlSlo1btP7pg+QhgMwD+JIKPjMDqW6LYcGgOIYdGuma93tsvdo2aOccs6ucrEkkU7jnPVKXDWu1/S6ScnB6k2s+deOQkI85uQzA3nI+0yGHOzdXR5bBKvkU6z0yVQ5jLf+N2NPYOmTtqzh1Z93g03WLbdxb185WWahBnqtJh8TyrlEHkA1g9KElAbNlLRhDBhmB+OB/58PCgyu/WzO3cv3mc2zlKbZf69ZE+p4u1ufwAaYk6cG7OGq5Y220RwlylDM42zqRpFBq7nKX/AVAiF8FJbQKdDStfMcrnDP/WNKrfudCOt4nPYttXKxHiXVJEEk7gKtdYbaN7DUtYh4m9fOdHoi06XyvRA8DMaH2txnfl7jOTnFjUtgxfEGpUvzk8/hrJbftKQuoSeRnt66ORhBNawhUSvIYlUu1OhX0lf3v6zo9EW3R+36dgQwD0IcvSXL2vxGH1uZ6J2pbxMyHNG5Lh2iUJqWvvI3ndAy3P5rldPUiFfZGCYYtU/gKAM2HpazWVOGby8RHb0mVBaJEDptle9boVq5UO0UrTRNzRqFGxX1+3kNnxupWfeoZUZZ6WfhSlj0hItUvz3Xr4zgGYAaRezIP5gjCCrMcR1OaEaaVp9u/1bOeUTVJVH1Jb7dr8MM92SLENMayK/S2q/TPZZk+uL6SIGr4BIALSylrAP4dUKrGSB7G49ihqn6mlLR7tsCX1fKmqDykHos0P0zyDF7tZOk7vEkQt1PQFTEoA9AOpF3MwLIewFBJF9QAA6tGMnyhh5RZZnihtBCAi2gpUEatsHUlLRa8WDqlUsn/NWmLDg2zIntq8iZZ8C++5Gi35e1b5YV5lZ7ZYfI+cRJNkLHoMK5d8BidVlZMy8tZGACKirUA1e/pPKy0VvVrpXqkktaK1Dlt5kA3Zo63YQpSWjknJs0StQtGSyyWV8Snd06vszJaavpOr/JOTffAcQt+Sk+XJSVXlpIwAAO1oK1BZpf/MSktFr1a6z4Rvb28fJjaEg9OkbJUichUKyQKQCxvsf2Ver9ff10S0xUgsq6KMZh03raHk6Cf4AbBCEtKVzufrv880/2tVQKKoQBxyyrg33kJ+RH3t4LG9VkhCmC1hTq+yM1us+86+wDpR/hS7N3sQlQvHc6RSBNbXU6FkjjOGrsA50YR0JUjGaS7lw9tcZ+GfvVZQOuSUMcfnz5+J6GOH0b7uLeRH1Hlb12F7rZCEE1pCgBEkSHr2He7e0moCoyhVueHgxgoXSuY4Y+gKnJOjUiykKR/esLCF1wpKQ609UwUOEAf0o49oqgmMAt8bAPMQweeMYpSMzyGnjLXXcq+ntpS1n3kUPZ/JY3tr0FabqKlyIv1cT4zoO6tNPdpjj8Y+Vj5jljEHQImj0mr2PidCOg+RzTMdUZWr6rlqtz+XZXkkopeXlxd6fHz8/TpXjYKIPlReWPNyUo3UxOm95vekEk/XKhQcnK1SFT0ik8sDlPaHGptEklmpTVrWJDOn+qNHe+wp2SfVrlR/waESAP5Ek0O4jimNL8r5HO8/1ld6HirpIcvz+vpKT09PRERPt9vtlbvukBzCy+WSvVZamULzmR6o2fblbJWq6BGZXJ6KtD/U2CSKBAnXd1r+5u6/x6M99kjss6dUFUd6HwBmRuMj1zHV4ou29/e6ANzT4p+5+6yMlOVxkbEZ5VcBAAAAAMCMmC8IuUoQqZ2vZVnY7VGLXESPcDIYKzXVNyKizRexyi+JkqdyFLPaY9Z2AdAbq6pQFvc/I73tn/1s6xzC6/VK379//+Par1+/0sPDQ3InUHr9LPk9qfamJD/OsGuqbaOVTc5gWw2z2mPWdgHQm96HGjA281jb/7Acwuv1StfrtfuXerlcft9/WZYQeU5SSsfvtxVMzrZo3JKrVKJpewQ7tTgESWH0/X1mzJlr+Z7PNrYA2GIl/SbZ1OEqdM067iS+ZZT0XvOC8MePH/T6+veC89u3b2zImCswr72eKHYBbK692wlZUtyaKLYdWk77RjkpXEvLKbMz9J3ezN6/ADgCq4oeM407777FNIfwdrsllcVzBea113OfGwWuvVtqi1tHskPLad8oJ4Vraen7Z+g7vZm9fwFwBD0rekTFu2/5x+gHAAAAAAAAYzHdIdSehOn9ukckJ4hqi1uXVM490XLSbPZTai2nzFr7TnQskrFn718AHEHPih4R0FbiKr33CJpPGf/nP/+hx8dHkfPt/XoUeh0MyKmce7RRy/cYvQ+UOPpQyQz2s1T4n9E+AByNVUWPaGgrcUnfW8thp4z3KuVE+hMyVq9HQfL8kionrSrno2n5HqP3gRItfb+270THMu94RvsAcDRWFT2ioa3EJX1vbw6tVCLZuWi53htaHcWW66PYZEvLzvCMvyq3zLo77oVfv34REWwLgAb45nY8r2sOWxBK5DC08hmjjZdDc+SeC2tpr+cqvHjMw9CG91Kvc3jvGyV6FDc/A5ykU4r//Oc/v/+GbQEoo/FLHGcZU9w8LFnXSKTpenHYglArh1Ern+EFzfNp25ILgz08PNC3b9/c73T0lBXy3jdKzCq51BuJpFMK2BaAMhahzLOMqfv7++Q8vBZT4MhJ8R2BWaWSUpLoGqLhWP9de73XBY+GUlu1RLeHFTOFACXjQXOQxFuoQkuqLbXjyHr8AXAWtGNnP88TxfQ/ElKVtaTrmlGYVippYRvC0V7vcRtaE6bVtp0Lgx21rWwBty1+uVw+vL4sC729vYnvzW3Ne+wnKVK24foIV9VniySsE8U2RLp0DG6sbMnZNsp4AqA3Gr/EwV0fyf9o0fir0b7o0EMlvfC4Db3fMt7uYmgH0R5u+/iobWULuG1xbqs99R1//vyZ7u/vk782U1vzHvtJCs33yFX12SJpdxTbEOmetWVMRBpPAPSm53iI5H+0WLQtTMgY8EjkP8BHpL8U7+/vk7JHAAAAANAxxYLQ0ynaUp6WxbNy4VNPdiiROw3dovAuub93NM8puVYSbl+viZDbY22fHu8FIDKpeUyTtqMl2ljTyIJFqtjSvCD88uULPT4+/tFpfv78+cc1z8/PREQfXufgrn9+fqbL5eJ20pIcKbdAakfPcKHhnLSBxf0jwKUbpMbW/vrUNZL+wl3jMbeHs8/+7+13Lrneq18B4Eg0OW8c2jk/EjWyYBp/PhKzSiW5sF3qoEAO7vrL5eI6RDhaKidaHkZrpZWZq01I0w2k6ve1eO1TkgoI2uu9+hUAjsRizGvnfK9+JkXNHNWaPjZlpRIpo49egxicScIA2DGTHBEAHsEc/idWMlm9fZf5gpDL3dLkH4w+el1LKc4vkcBowbt9JNRWm9jiMcxpgSQv0jrXJFpuT4nIckQAHIHFmK+RUosyd2ny0yXVSSRw8mLWvst8QaiRDdHiXQZi2/aUvAz3/J8/fyYi/SDa490+EixU2iOFHzRI8iKl+XVnzaMbWTgegAhoJNO0cxd3faS5K+eHa6qT7OXTuLXDEb6ry7cw2ySioabtZ7YX0CHpK9p8OeTRAQC2SHPetHPXLHMd147a+d/L2YjupesiHr22QLut3Oszo2EhF5OTr4kCJ1/Usss3a+m6kuzDFqnc0Zl2TQHYI5VP0UrR9J4Dj0Ljn0s22kt+pa4/SmrukNJ1uaPX0r+jOWLttjKScNtYJYlK8jUR+pBFzsmWM5SuK8k+1NyTI5KtANCikf7yJptyBNb+WSsLtp/rLDkkcF86ej1r+EqzrWyRCzBDLlRtnsQqSdR6Hw9YP+cZStdJpYks+kUkWwGgpafvrJUQ88ToZ93PdZYcsiBMyYOcIdkdISjgAcnu83pN1P7ItXHve1LXYXcegDxWY6Q0TqP6n1k4ZEHYeno2hfewjTYEZZELEOnoPgcnO1MSOpXIr0TJU7GWJ5KMv+013sdWyj5cG7Vtl35+9HEGAIdmfGmRjNOI/udIes5jcc567xi9bVtC83y3240eHh6ykjUSIh3d5+BkZ3I5qBL5lUi/PEd/j97H1mj7jP58AHoyun9H9D9W0nEcqzRN73kMnm1CIp+uTZFKObhcLn+cLF21nrj2vr29TXPCFgAAgB96zyGrNI1keT/kEgAAIABJREFUrmsh7ILQe/hP83ycCrmWo9TMe6IJV+ROlqZe5/Bmn9F9e/Tnlxj9fKM/H4CejO7foz+/xIh0pDCVSr58+UKPj4+qgyG1IdGjtk0t0Ki9cyrkHDm198ina4l04QqrE2ve7CORaOKqBmwV77n35v6ebWxJbSJ9bwT7ANBCi0RcaW7n5q7IczsnKVez3hk9t4tn32VZHohom0n9iYh+K2wfISOzbptGQar23nJfMCcSiabUe2rGYqQxtaKppCC1ieS9AJyBXhJxOSm2SONMIilX057Rc7tmh/CfRPQv7h8tlbs5vG8l7+ml9s5df5SaeU80z2qleu/RPqXxlFOzP4O8U8o+LTaRvtezTQAYTcmXzlKpREPPOc3abot0y5HZIfz+8vJCf/31V1X+2/PzMxHJ1c695Xrl0FRT6EFPNfPeaPUbJWXMIi16rJXwW/A45kbbx6NNAPCCpETmbAcfS/SY0zR2e319paenJyKip9vtxpaWE+8Q3m63KxFd1//frkxr49glbbnEM1R9zghG5/L1VDPvjTZcqt2+924XT/3c07OsjH6m0Z8PgGckYdPZF4B7esxpPQh7yhjkiaz8brXjd7ZfoQAA0BOtT5VcH9FPt9jBc7TKZEFYG8delkWVPxepQgB3NL02f1JLJOX3LRah9pwcTQQ7jFbC3+Ixt2f0M43+fABGwPlmzqdKrtfe0wMWduAY3XaTBWHNMfV1NZwKv+SOXkeBO5rOtXcrb9FydD/1eqQQl0WoPXoBdY0SvoXUjOdfrCm0sjNE7RUEIsliANADrW+WXD86taoGCztYXNsDsxWW9pi69F6RkbZDK28xKr/AmoihglGU5BqWZSk6k211l21f2753e41nNLIzVp8XJToBwEhWv76mLYE4DN9y06h+zxCqkbS35eh+KiTtMdTObbunwqXv7++qA0hcyNWjHVJwNuCu1VS5mSFkw9EzTWMG3wNAC5K5qxQe3fvgEVU/WuH8Mze3WMjOHMXwBaFU9XuWHaRce7lruDCeNCTtMdTObbunnvXu7k5d+SWFRzuk4GzAXdsSkogYsuHQpmkQ5UPJCBMD8F8kc1fJd+z9mOSe3tD4ZyJ5Sp2HtruYIWcJgUqRtAtH9z/Sq/ILmAdNmobkGvQzAP5LjznnDPNYFAm0LgvCliPWElHLKPSQTyldH2ULviZVQFr5JXrKgaaajbZN++obuQodnsecRri1tjoQKpWAs6KpPLb9uzSuZhhTUebYGsSVSj68cVkeiejl5eWFHh8ff7/ecsS69N5IuU095VNy19/f34c5rJF6To3dSu2NYocU67O/vb1lK/nsbVC6XovHMaftI6nXW/BoEwCssKgENHMVMqJ4c4t5pRIp1vlMtfceTU/5lNL13jvnSuo5NXYrtTeKHVJIn733d+5xzNX0kd6fD8AsWPTvmauQEcWeW3IMzSFcj6VvDwiA/wJ75Nn3H6J5dggBACAqmLt4PNd6Nl8QamLpWrHYKLIhRDYSGDPbh0MjucLZJ3qlki2l8SSRcej5+SPQjC2tZFHt5wMwCxb9Wzt3nWVMSdLiiMbNXeYLQo08iJYosiFEfSuVcESyD4f2SH+K6JVKtmz7kURip6ZqkGcZhBSasbWXLOKqmUirvXi1CQBWWFcCKo2vM42p2rS4o+auLisIyIN85O3t7TD19iih0ijPORqtXSQSB1x1kijjtcUmqX/bV3uJaBMArOhRCQhjyj+HbSlxYT/tPaJ0JM1Jrdad0xWuaoXHUKm2UomG6JVKOLQK+RwzVSfZIrEPd02UcQPAKGp983Z+O/uYaqlCdgSHLQgtwpmRQqI9t3i5bXquaoXHUKm2UomG6JVKOCxsQzRXdZItEvtw18xqEwCs6JnOcxZaqpAdQewZ8qSc+RcWAAAAEBXPVciaF4TX65Wu12uzgrmESCeRej6rtmqFR7v1rDASyQ4cKWmC1PeequJSMxajVRCQ2kdTmaX0XiLfNgHACu34kpIaUxHH14gqZEfQvCD88eMHvb6ywtdZODXz5+dnulwuITtKDan2bgfh3j459fcoxcK55+QO3WyvT9lktWHpPhHQVgqwqCzA9SmPOT+a9koqJXDXRLIJAFZYjy/t9d7H18gqZL0ZGjLm9MEulws9PDyEPgCgyZVItbem7bNXKsm1a7Wh5D7eaan2Y41Hm41+ptGfD0BPRvfv0Z9fYmQVst64zCEsVaA4A+tWcuSdLgAAAADEYOiCsKYCRZRFoSZfbX9tbQgwuqwKkSy30Ooar2ies0cljtpnOQoLCavWz48+zgDgGD2+PPqcLVbSaBq/fZTPMV0QSlTL//jwiY+sSytGpHY+a9sZXVaFSJYDaXWNVzSVAiSVOCSkqgl4tZmmn0uqAHF+KyfvBMCsaMcXkXzs5O5zf3/v1udssZJG0/r5IzD9FO0XiXDonyBMLEfS17w7Fk+s1Tqw8wUAOJrV/5yFVFqcB0yfhCswz6HdyYgUqtGEfbUnjjgi2Ydj1ioaGjR9h6uyoSVS39GEZyQ+hruGez2SrQDQognZascOR6QxZREy9mqf5gXhly9f6PHxMVtg3gpPK+kSI06KRrIPR+TTwVZo2stV2dASqe+MftbRnw9ATyxSULREGlNWFaNaP7PL57TeAGEmAAAAYB7OFJEB/8V82dnzhJD300dbWk4ZH/GZHkipvUtPB2uV4iMkK6+g7+QZ/ayjPx+A3qx+1KLCmIRIY2qEgkWqIlWPOW2pDTcty/JIRC8vLy/0+Pj4x7+VyrGkKk1smaVSCVf+Z992LocwV8Fk/3c0++RyBUudvlYpPlIuYo8SSLP0HSL52JJWQ8pVv4luKwA0WFQ+0hLJNxOVNyRKa5wtnI/aws1vUru9vr7S09MTEdHT7XZjS8t1CUxzxZuloeUZKpUQyYpYE/E5Y6UKJpHtU1uRpPRe7fu8oh1Dkutn6TtE8rHVUg1pFj8EgIYRfjKSbybi56iaRa1Ej5Czj7XdwmRyzhAKrKVltyiifUq/vlKyPGeR6jlyJ9BbP7L0AWt/8dAuADwA2TMwZEFYOqK9P2Jd2sKOtN2sObKulRRp3VY+Ei4PozYcLDkJF0naIIVFKEfaF7zJ/0h8ANenUnlQ2/6Sey8AZ2BEmHhLdN+8x7raVG6NYGm3IQvC0hHq/b+XtkUjbTdrjqxrJUWO2la2gKskcr1eP1yrff5ZK0xYfI/Se3iT/5H4gIeHh2SfankvAGegNEZyFUn2FY5Kf4+sxHEU1tWmcmsES7p8C6WwlnRLGlvYc9NrwsVEfl5qvvtt+HimXQoArCiNq8vl8vualrxlb2kqLUjznFPXj8J8QWix9awNlUYK7WhCU9p2Wd1nJBahO24LPpIdUlg8vzTE4C2EWvrs3L+XUlT24WMPjhmAI5GML0nqhWTsaNOFZhqPXMrY1if3XCOUMF8QWlVNSN0ntT0d7VcEFyolItHrkgMDkUNfUvtotuD394kKF4bg/m4JzXizX67tpWfThFU8plcA0BvJ+LJK3eqZLuQdScpYzRrB7PlM79aZtQB29LCO9sh6iwRJRLR20N4nMlp5GavP8oD2+wcAyDlyfHnzLd6wlLXREGpBOAuSKhs9XgdAQ8R+ZPXMZ5a5AqCGNRf3DNJolmgksHrbzWXpuqOOWI9AI6ti+fqsA85brpsnWmwTMZ+He+ZSDuEWSf6ydzsA0ANNLi5Hbo46gy9P2VCah3mETzZfEGqOW7MPddAR6xFo5Dx6vz4D3nLdPNFiG2+yMxK4Z9b4DYnUk3c7ANCD1Dji5Gg4cnPUGaSfSr4o51uO8MmHlq5rJbUl7a3TWEnurPeyeiYif7ayYpY2ScKU2pDBLLbZ0yt0AokrAOTjy9q/ePZXR6aRjAqpH7blZqHczf0K8RLCqZXc4bbiufbmQuql+3ixFfgTSSUOouPSATyHbzShYU24mOgcFW8AyMGNL00lII6o0mhW1dJK/ignvyORrGnlsAVhSYn7j4cSKHdv8RLCqX0ObSi8JaTuxVbgT2rDlL2+T8+heE1oWDImclUYUq/PkLoCAAc3vmorAa1ycZGl0azSSLiw+9Y+nPxOrX/TcKhn037p3jsJADNzlvE3SuIBgGjUjIlVLq71PrOSss8ohvzU1VYdaKlScCS1z3FkRRIvtmplNlmQ2j4+y/epwVrJnwt5webgjGjTRUrjYR1fUX0zkd0aRGLbqSqV9GAbvnp7e6OfP3+OfqQkkkoS3PNz79W2t6WiQxSs8jk8IfnecODhb1qq/aTG0/b/n5+f6XK5hA5vAdCCNl2kNHdtx1dE30x07Lx6ukolNfH4XMM95cVpq4oQ/ff5paezS/lks1d0mFUWpPS9RZSC6UVLtZ8cl8sF4S1welrSu2rmrghYzKtSHz5VpZLWI9M5xfNIuySpsGbP548mL9O70spZ1fAldo2+c9yr76TG0Fn7EQB7SrJq279zc92sY6pFds6DfzZfEErUtEtxb6nIpfR+I9BI0OSOjmukNCLJy2grtmj6T+7ovmeblJDIDmjsur8mim1a+o6m2sIZK/8AwFErq7aFqwQ0w5iqtQ83X205yj7mC0LJlqhFNZPUUXZPaLbGc0fHa4+ae9+ab6nYIsnn4I7uR0bSF2rDypFs09J3NDINZ6z8AwCHRb/nKgHNMKY0bZBIzdTeu4Vhh0pa4/GejmqDY+C21C+Xi8sfBVas7U6FG/bpFblrAABAAioBHcPb2xvr20dgviBsqXCgCf16DBNvsWpLrT0j2id3tF67pe650oaGUrslO+qzVd9okWWwGJcR+xEAUjSVMrTkqm9F8T8cGvto0+KOso/5grClwoFEtkV7z1FYtUV6BD26fUpH67Vb6p4rbWg4LFQQqPpGiyxDzbicoR8BIEVTCYir5qMlkv/h6NmGo+zT5VNaHGaNbItXrNoiOYIe3T6S163uD+LT0ne04xL9CIA0GBtz4W5ZPutxdC2SI+i9ZVt6UPtsNWr4nu0g5ajw5CxhUMl3LpHOiNpfAGhlRKWMGfxPzzas9+49p7laEM4oFVKDJF+OKC2Hwb3uwYbW3+/z8zMRUVINn8ivHTSUqvSslTVKmmC5v2dZ/Ej6l0YaImJ/AaAXXKpG6kDE1jefBWk6SulvrjrZEesjVwvCWY+ja6mtxOFdJqPl+01dd7lcxNdqPssbuQG/VtbgQp+Sv2dB0r80fSBqfwGghdw4SvkijW/WfGZEJOkoNT75qDnNbEGoUTAnSof2vBy97gHCVECDtMoNKmucw38AcAbO5ru8YbIgrFXollRQmCG3QBumklTiWHPmtry/vyd/mXmxobUkUVQ7lND0l7NX1ijZai/XMJO0FQA90PppC4mvWatL1TAih3PFZEFYu23Jvc97FRIt2jDVw8NDUVImdc+7uzvXciuWkkSR7VDCejzNEo5JUWrbXq5hJmkrAHqg9Z010mhnqS5VQ4usViuucghXzlyFZF95Yo9E2dzzRCZ5NquwgWc79IDrE6k+Ff2wiTZMzKVscKzjbH+9Z5sA0ANuLHB/c2wrSi3LEnqxp00Bs1IE6e17TBaEtduWZ6kEoGlPi8DnDGrvXNiAKB0KnaUiyZba5+f6Tkuf8hiy0YTU39/faVmWqpQWDo82AcCK2hSwLTVKGFF8uTYFjEhnB+71I3yOyYKw5rj1mSoBbO3z69evZlV3jhnU3jUnkVPh9Rn6j6a/WFUK4PD4Kz71TJwd7u7uzNvg0SYAWGHRv2uUMKL4cgulAq9KIeIVxLIsD0S03X76tP137XFr7r2zcoY2jmJG22pyKwHsAMAMYByPRbOl9E8i+levBzkLR6iZRyGVP6EJG6wnjGfM8VptkzpFvaX3d+6xT408hdfrngB4waJ/c35rhjQxC6UCq9et0SwI/4eI/nfz/5+I6Lvt48yPlZr5/u9oC6FcrqAUTgU/eo5XyjYtyv+11Uy89ilNxQQJnG1TdvNqEwCsaJmj1iob27G0jqPWceqFGqUC7anhUaFz8YLwdrtdiej3ufBIK3pvWKmZRz5Aos0VbL13JCyqsuzfW1PNxDPSiglS+3Cv7+0GwBlomaP2rONoZYaKZBL7cNe3vN6b5lMI1+uVrtdrl92t6KrlkqPmVjuEM+xilGRTzkyLbTSyM1H6jrSSS+tnbO9PFMc+ALRQO0flqilpKy55hrOPZC3jeT5vXhD++PGDXl9fmx9EUoQ+UihQI5/SA++2Su0wW8mmRN+9fn9///Bai2209vPed7SyM7XXcHbzbh8AWrCQndmi9VHex1fJPpK1jJZQsjMWSIrQR9pWHv383m1lIcWzVrSZbQdntHyQ976jeT6JLbX29m4fAFoY3b9Hf36J0vNJ1jLWn2mFmwWhZFsZzEXrwm2taIMcL8ChCakDAEAr0cLfW9wsCCU7RJEqcXDSGCUZkZ6f7xXrSjfRGd2u0Z9fQhIGXukRUvduHwBa0IyvHngfXyX77MPfFu05au3TvCD88uULPT4+/rGzJ3Ww2ioLo0NpGjhpDE2VBS1rCDXaL5PWSjez0SOcLql4EqXvHOUHZk1JACCHxfjSzmnR/Q/X3n0Fllp/fpTPa/6UNWRXs3r1/sW3cnS1iTWEGpGWSjczcnQ4PXLf6QVSEgCoQ+u/ovufUnujrHXMZWc0IVHtVqr3reQ9rZU4tESzT2+iyxYR1X+n+youpXEZqerLUf18/ZwZ+hEAUnpWKildH2F8tVRK8p4e5UZ2ZjYsKnFwtFStOAvRZYtWJOH0tTrAFm3fiFT1pdYmHLnxNEs/AqAH+2o+qUolErbXzzy+tr5L46OOYmhS3swVKHpW4mipWnEWRsv+WKJVxbfEq80sbaIdT15tAoAFmv7do5qP9/HVOrfXLHZPJzsD5Mwuk4EQHQAAxGH12bPPTVqiVTsauiDUyrDMKjvz/v7O7lKk4E4pRbIPh1WIrmeupjd6tiuqzTTPLc3/qbk3ANHQyM68v7/TsiymlU28z2Mp+9RUO9L4kTCyM1skR833x8utqw54QSM7c3d3ZyI1Esk+HFahXs7+M1Ij2zO7tI/UJts2cv3lLP0IACLdPHJ3d2cezvQ+j6Wer6ba0VaOhoiyEmFhZGe2SBxl9OPlGmqO3gMZkuLiM9pTEk6/XC6/X5dI+Mwq7aPNM+T6y4z9CAAwnq1vWZZleP6k6YJQsgXacgx7hlCNJJRZ207vW+0SuO34bbu0xcVnIXdyHSdhAQAWjJ6Tvc/zFulI2jktTMh4rVTChVhKIaia0E5kJKHM2vCx9612CZLteG1x8VnoeXIdAACI2tMtSukoqTktUqWS3BxeGwIu+eswIePUkXMuVMMxUlZjBNLQOgAAAHA0LekWNeko0VLJJOklkdqzYl6phIj/FcHlfc2a4C5pb8k+mlPYkdTeOSxC6t5DDhKkfYHrH6nKI7OOrRYfo31vRFsBcCSlcZTyWdF8tsb/5Py2tpJUb/+z1IaWlmV5JKKXf//73/Tp06fstVyek5ZIeVGlnIAtVvbZ3zOKrfZIDk5IDpVERdN3rIjUXzS5lBK0741kKwCOpNZ3RRpT1v5HS42tXl9f6enpiYjo6Xa7saXlDglMW+UzRcqL0jxrj3ZFstUebUg94tZ8jhHfXaT+Yl2FZuaKSQAcSe3YiDSmRlfB6vlZhywIoV4OAAAAxEBbLaq1Usn6vhkiPJE5ZEFYI7KcIpKsilaFXFOpxPrzgS9GfHeR+ot1FRrteyPZCgAt2mpRFiku2zWC9/CxpgrZUZ9vxeE6Jevx8tyRbI5Isirc0X3uCLpltQn8yopNr8ojs/QXrexD7m/te6PZCgAt2pCodQjTe/hYU4Vsy37tQ+TPVx++wlqPl0fZ6Wuhh/zOGSpMALu+MGt/0co+WElnAABAzaIstfbx5qsPXxBqj1tviRaqSeVhpNqQkgjhjrJLXo+OVlJkpran0La7RXoFAHBucikZ0jnN+vO9USMRp5nnR/nnQxaEz8/PRET08+dP+vnz5xEfOZzc0fQ9nE1yZcm416NP7LVH+mdoewptmb4zyD4AAI5HM6dxbNcC+9cvl0uIH6caHytZ+3ia3w5ZEFodmPCeW7DF4mi6tixZJPtw1Npthran0JbpO4PsAwCgHz1LZHJrgcvlEiY1Q9NmydrH0/wW55TGCYFcj5yUbMGsIfUUrbIP0aiVxSBCuBwAAFIcsiC0OpIdXXZGawfu9PX7+zv7ehT7cKTaxrV3y162gGiOkHopn+b9/Z2WZWmWfYiQt7NiLYsRsV8A0AuNrJPWb8wwd2naLJnzJfPbUfZpXhB++fKFHh8fiwnrmi3Pz58/E9HHBVFk2ZmcHbRSPJwdItmHI9UGbbtmCqlv+xEnWaQdW3vpg2i7ZNayGBH7BQC9kMo6lSSbOH+VItLcJZUFk659JG0/yj7Nn7Ieo7Y8Jh1pcsohbcd6HH1ZllNNTmcK6bZgaZfZZZ9a+hT6IwB/09L3L5fL9GNnVomqw5bl2sod3OvRDc6FRDVhP66yyQxhP84+moNJM9gnhYVtotuAiA9pcX2q1Obce2ef2ACQolGA4MYdF0KdYW7nKIWEJT78KL992IKwFPr646Em2Fbm4EKiXCiZKB06z23rR4AL+3H20faf6PZJIbEN0fx6g9x3e71eP1x7u93o4eGBtU/pvQCAv9GePt6Pu1wIdYa5naPUtpIPn06HcGWGyehISvY6mz217T2TfWYNYXC09IVZbQKAN87kg1vx4KOGLMslIZya93lmzU9KbZdz7eJOJ62vR97xaTnJVtt/IiFVwk+p33urj9kDa/vUvDei3QCQIK1IUvLZ0nEazWdrqmnVVDA5xQ7himR7dCY9tdrqEZyy+fb1qHlOXNhP8r172V7vhaa/WFX+idSPjrKP5L2R7AaABG1Fkpwvb5XD8khtNS0OT35mWOC+tD1qUenDCz1lL6LahCgdTpB+7x6213sx4juN1I88PaunZwHAgppKJVJfrvlMr4xYm0xfqQQSDwAAAAAAPhiyIJRIPGjyFbzTM+ctqk04ZvreaxnR3kg29vSsnp4FAAusKpVoK3pEYcQcdZR9ltqtyGVZHono5eXlhR4fH1XvvV6v9P379z9e+/r164fQ30y7iJJaqtw1HpJNj2Sm770WbV/AoZJ+9pnJbgBIkByaaKkhHn0caQ6VtP5tYZ/X11d6enoiInq63W6v3HVuxH/WwwRc49/e3kIvEva7n6W8OO6arQr8r1+/fuuntQzaKGidi2QR7hWJjIzV39HZjgmujZIqQJL7zGQ3ADi0/lHim7fjK3pVLs4+Pfz2kQxZEKaUu7dCw5ITO1FP90nC5aXTWTl1eO71KLayOME1c/8BsjFUup4D/QKAND19M8adD4YsCEvK3ZJfDlF/XUhOKNWeSta+7hGLE1wz9x+g7yOznnYE4Eh6+maMOx/8Y/QDAAAAAACAsXTZISzlblmcuvV6KqmUSyFRaS+1jVM+n6HCi8UJrsj9p4TXpOUjSX13ObX/UqWA0n2i2QeAHvT0zVH9cS9GnQMwXxBK8t+2yuZvb29m1RZGY6XMXrJPzl6cavzsPD8/0+Vy+aO9s1Uz6an8Hz2HR+JDnp+fi9dy/xbdPgC0kptbOF/L+d0zzlFStDnSlpgvCKX5b7nGRc0Bq32m0olj7X0iD67aPJXL5fLhZNZs1Ux69nmP44mj9lkvl8vhnwnATLScrpXcB4yt0ta8ILxer3S9Xn//EpipBvHRpMKBsCcAAAAAetO8IPzx4we9vrI6hx+Q5MtFzQGrfab393dalqU5HOjRJlpm6g/W9GxjJPvVPisqAgEAvDOyWldzpZJ///vf9OnTp+y1nz9//n2YJLVVrFX99pxzUEr6//Xr1x+ai0R/V2khog/VWzhWe0axiZaZ+oM1OFTyNy1VfVARCADgGetDJa4qldzf39PDw8MflTU45yvBc9USSbURi894eHhg7x+lUonVc3L9IYodUkieXVtZw4MSvhXaigBW7wXgDGhL181aoq4Hnjc2DlkQLsuirhZApFM/H23IPZr2piq3lK5PhZgjqcBrVO/PpoavtU2ENgEAYtDbN5/ZX7VUIQtxyvjLly/0+PhY/FWw7gxK6FGZ4mg0z6TZHV2v15xEimIfq+ePZIcU0b9bAEBcRvjms2Bdhcya5gXhw8PD7/+2rwEAAAAAgBgcVstYc0qmR2WKoxnR3kgq8JqTVGdTw6+xjee8FABAHI7wzWfzV2t7S1WTRs9dhy0I9yrnpRBzSn9vrUYRtQOVnj9lH66Si1Q13qt9uOfndBe11W0iV2zRfrfS6kAAAFCixv9oOJu/0laYGjl3HbYgJNKpmadi5ms1iggh6Zrnl1bWKFUkiTKYUs+Zy1+RtmuGii2a77Y2LwUAAFJY+h/ttbP5qxr7jJq7zCuVEMm2gCV6fQCAP7GS0okesumhxwhNQgD+ppeMzOzzeqt/Xu0zyuccUqlkvwWs3UJdiZIDRmSnNj5StXwEKQme9/f3P3ZMSzI9s9qHk4Mo2WNvv+ghm1r/YYV3+wDQglYiTiObti/KkCKq/671z1s4+4SRnZGw3zLVbKGWqpx4xSoPIHIuXA0pCZ79a6lrovYTDVw4vSRbtP/36CHm0c83+vMB6Ik2xKmVTUsxg/+u9c+19+7BIQvC/Tbo7NvGJVrCXdx9ogykXs+8Vm+ZBamdJGMJ4w8A0AMrXzKb/16J5msPWRBKtokl740UqtGovWsZrWZeC2eT7TPXhoz310RGE3qQjC3t+PNuS21lnx6f79k+ALRgHQKWfmb0MVXrn6X3PsI+XSuV/Pr1S2yQz58/E1HegJFCNRq1d4t7W96/FxKb1IaMLbblvdAz9CDBuy25lAEiOwes/XwAZmFE/55hTPVsw2G+X3rhsiwPRLRdon4isqtU4nlnCwDgG/gPAABoQ7Ps/CcR/UtyoVSVe4vk2pTKudfcuZ6PKo4UAAAgAElEQVSngzVVKzzZR2KT1mskbfdqn9y4OfLknfdTfqNP3nu3DwAtjOjfM4ypnm04yj6LNMzI7BB+f3l5ocfHx98vWkhCPD8/ExF9qEYRLXcutfAo2Ydr+1rlZLuAkd7fk30sFmya/EyJ5JEH+0ikYIjk+aepqjgz6exxfaS3PmEU+wDQwpE6nzONqZLdUlW29r46dU3rHPX6+kpPT09ERE+3243VCRTvEN5utysRXdf/51asFnlsl8tFdW+vuXM1qu5c29cqJzX392QfSacuXaNpo0TyyIN9rKVgUlVxJH9HQVJJQdv2mewDQAu9x9GsY0pTjW1FUoFtKtkZAAAAAIAzsu4cpmRoPMmCmS8ILWLdpRy5Hp95FKVnbW3j6PyqI9C0sSZHcQS1/cLqegAAAPaU0oEkyghHyc6Icwg/vHFZHonoZZ9DSNSWf1DKkcu9HgVJHdmWNka3j4SWvuHVPtp+cYacHAAAiMz1eqXv37833ePr169NC0LzHEINLfkH3H0kr0eBsw93Tcv9Z6Wlb3i1j7ZfnCEnBwAAwDG4ziH0upPTinZ3S7JzJLn/TGhtAs7BGfo+AKOwGl9nGqc90+iscbsg9CoP0opWMiX1+v6aCLIqlkgkWmZqL5Bxhr4PwCisxhfGqUxqZgRuF4Re5UFaaZFMkVwzq922WEu0gDk4Q98HYBRW4+ts4zTVNonUTOkePeiyILQQjR159NqKVHs17ZJcu14Tcdt91kNDPWlJK5jpQIp2bFkddAPgDEh98F4yJff3GcaR1C95kprZYr4g1IREtRx19NoCTcWW9/f35OuS4+jba759++ZWVmVPS7URzl4rHttrQe3Y0o4/7+Eb7dhalqXZ93i3CQBWcH4m5Xclc5TkPpHmdg6NX9La7Sj7mC8Ie24Hr780IqBps1W7brcbPTw80Ldv39zvsLWEzlP2+vz5M93f37ttrwW1Y0s7/ryHb7Rjy6I93m0CgBWcn7GYp7j7RJrbOXr6iKPsc8ineNkO9Yq1fWZdEOW4v78P/wtzizZkU7oXAADUYuVD4It8c0ilEu32KEekbWVN2BL24V9LvT5ryGHFOmTjNTxRi2ZsWaUPzJqGAMAei9AwB3cf7z5HQimVqfXeIUPG9/f3v0OWv379ynakNcy3TazMXR9pW3lrByL+4EzNQPv8+TMRfRxcke2z3QUrhbxnDTms9AzZSPBuS8nYIuL7FA6VAMAzYvx79zkSerYhdMhY6jzXMF/0XwYcvapKzDI5zVqJBvRHO7asqicBAMCsNC8Ir9crXa/X5K/rt7e37HvXf9deH/0X+5ofVmovhzS0Ohs5u6XaHlW+ZvRJ8Qj9yELaKvd3pP4CgCUjxn8En1OiZxvCVCr58eMHvb6ytZKJ6G9VbiL6oMStVebeXh9VBiKVH7ZXLSf6c5HsQcF8NDm7pSbvyGr40nC6tI9w429/DWdLb/SUttrf07stALCGS8lI+RnOt+Re3891EXyOBEkqi8RXj7TPIYHpy+Vifs+oMhBS1fJcyKqlsklUcnaTXh/JPpJwujSsKRl/OVt646jvNlJ/AcASqZ/hfEvudaSJ8Yy2T/xMTgAAAACAg5BUhkpdX5LdSVV+mW6HsEf8O2rOgTY/rEWeZSZ62i06pXZJ2h3JNkd9t5FsAkBvLOaiGcZUqSLJPtXEooLJUekrS21YZFmWRyJ6+c9//kOPj4+i5GwkfP+N9rADav7+jZXdZqSllnFE2+BQCQDHo52LZvTB1+uVvn//zv77169f/wj5lq6XsL+nltfXV3p6eiIierrdbuyhj+47hG9vb0kHveVyufzuKMuysPliM3SmPZx9uL+567evRxx4kol8WZbfg+LXr190vV5/vx6tvSVaFjZbJONvVrY+Y+tMOR/DMdNCGgAJLQs5yRw18zhaw74R23XIKWMJklOCM5z602wftxDJVhqbcP1Esk0fxSZH9ZEtUWxDpDtl3BK+OYtPAmCLdnylXtcSaRyVKpJsw77fvn0zCZOHrVRSi+QX+wyn/o5qQyRbaZ5VesI68injEc8ZxTZEuu9W0i80n1NzDQCRaBlflp/plVSUhasedrvd6OHhQVy9TfOZPXCzIAQAAAAAiEZpdzPK7qebBaGkase69Ro5UfWoU1aRTnNpnpW7dl/FJvIpY1QKyNNy2tGir9XeD4AIjFC2iDSOauzTqzqZNd1PGdeqnHNq3ZFzw4hklUo4W0mvibZIJvpzwEjalbt+7Q+RfzjUHiqR9J39e6PZhij9o1DqG6QHmGY8nQ2AhJbTxGc40a+xjyZvmVsTta5xDjtlvKpq1xSJr1Ezj5wbRiSvVNJyTURynT3Vrtz1q42jOZktEsV7zTjTVMKJQOq7lfoGbTWBmuowAERGUimJe73VX0VAYx/N+oRbEx21xnETMp6RWt28lJp55KPsHC07eFLld+vPPYraX+iz2qMV7c7GrHYAoAWJr8CYisthC0Iu7p6KqeeOWEfJDePCV6kj6+/v77QsS3ZbeX+UnbtPlF9eWvts21Xags/ZIULKgbXsQ3R7SNGMLUjKAKBD4iu0VTzOQEmmRnLtdLIz9/f3v49eE/3310JqKzR3xJq7jze48FWqbXd3d2o5DO4+UdDap/Te3PWSz/WEtexDdHtI0YwtSMoAoEMyjkpj5oxjSjMvc9dOLzuzqpbnQlwRQ1ktoTvNZ0iegSiO3SRobZsKXfT8Xlqp6Tue22ONZShKYrfRheYBOJKW8dXimzGm/svouf2wBaHmpI02zONlG1oSytS8zsEJW+bs5sE+W7QpBKWQeuv1o0PttX1HInI6awrGlly6QSpRW2K30YXmATgKyfjifIXV3D7rmNL409Fz+2ELQs1WsTbM42UbujaUabUd3BIeOxptCoFF/8ldP5qWMHiJWVMw9v/OhYy3baytFKB5FgCiIRlf24obRP/1FWtNeQmR5igrLPzPUXYbPxOC0+Jx4XFGzvA9nKGNAPQG46iOKHYbespYe21vtfRWSs8xSu3di332pPIhLEKYva/vQW3fsbh3BFrHVmulAM2zABAN6fiS+mzt50QbU7W5fLXtPMpuQ04Zp6op5K4n+tPoXkNc2jbWwqmZ75+ByJd9tuSkVSzY28G7Sn5t38n1hZnIfZ/bigApSjlOEhtGrgIEQAnp+LLw2VHmKI4WqS7pvMTNUb3tdmjIWPrwpUoTnjuPpIKG9HWOkpq5Z/us9MwRTdkhgkp+Td/h+oLkvdEoVQypzbGR2DByFSAAJPQaX6nrI8xRHK120FZ14d7bgy4LQqtqClA8BwAAAEAEtDWdJdVejlzvmC8INVUWOCSyId6PqWty4axy3iLlYYywTxSsbeNBVucIavNPra4BYGZG5Hd7xEJ+Z4uk2stR6x3zBaFmO/Xz589E9FF7RyIb4j0MVpMDqc15i5yHcYR9otJim5SsgQdZnSOQ2k0ynmbrUwC0UjO+ZhxHnB008jtbJNVejlrvDJ0pZugcOXrkQK4VXoj+HGwR0dpn+/qyLL/tcLlcputLLbYBHynlR0mvAeAMcCFLif+JkK/dyqz+1nw1YRHuOmMIp3a7eYv3MLoVI7fUvROl8kgP0C8AaAfjqA4rSRmu4tIRi2vzBWFrKPCsIRyLLWHvYXQrzqZ0ryG6pEML6BcAtINxVEerpMwKV3HpCLp8ilW4CyEcAPScZQEIAACeaJGU8UDzgvB6vdL1ehWthlv+jrjTITmCXqP2zrFWYYhoqy2lI/fSsOioo/vWSOSXtHIHM465M4fLAbAiNWbWuQVzex7NnM9dn6qmdJQfW2q3gpdleSSil3//+9/06dMn26diiJTHoMkJXNvFdY6aKhSRbLVFmr9SWuzNkgdT6kdaSSct0Ww2y48AAEayLfV4RBWkaH4mhXbOJ5L77Vb7vL6+0tPTExHR0+12e+WuC3VENVIeg+ZZS5U1en++J6T5K6XBMUsezGj5pWg2iz6pAOCBo8dRND+TombO73V9LaEWhLOT2iGUVnVJ3YvI9y5Jy27O7FVstFV9zoa272DnEAA58D/nJNSCMFLFBU3MX1KZRctWnNjjdjwX0pUcuZeEUSPnk2lCDyl7WeLRZtp0gFnSBwA4AgsJNC0e/YwWTRu07Q0jO/Plyxd6fHwsJo+mKihw5CqYRIE7gs5VkihtCXM2kVzjcTueC+lKjtxLwqgPDw9h5VdS7WsZE58/f6b7+/tpkr216QCzpA8AcASc/6nxIZH9jBap7ExJWm9ktanmT3l4ePj93/a11N9SZugce46opjGj3VqYyR4tbbm/vxeP0Sg78ACA46j1IWfzM9qKLd6k9Q7bcrPYTo20rcyFqWpDmZLd1ff3d7pcLlX3P5qWijal9nhsrwaLaj/aayKhHUOR0wcAOBqMl7GMtH+z7MzLyws9Pj6K3tOi0RNtW/l6vdL379//eO3r16/08PDAtmubyCsNr2vu742SHfavc++dMfygtc3s9tiDQyUA9APjZSzW9ncpO9OynToT2ootVvf3RosdvG21W4OqPnm0fTzKmADAAxgvYxllf1QqMSZ3XH99rbQjOrPUTAtn2gWzULyX/h3Rfj0qs5zBbgBIwPiqI3rFqOYF4Y8fP+j1ld2BNMW7VETpuD4XBraqNuFdaqYFidTMLO3tqXifu08U+42QxeCIZDcAJGB81dHTbkfZIY6OC/mXiqh9vh7t8m4rLaMrdhxJT8X73vc5Ak/P6ulZALDAU5/29Cwlej4rKpWcCKjBt5MKx0cPPwAAAAArvQ/7hFoQeq9UUns0vOZEcYnZZAJK7ZHYMEr4oafife/7HIGnZ/X0LABY0Lv6kYZI46vns3LVzKzntKGVSiTVN/54WOeVSiRK5dqKLZw6PGfP+/v7KXfDNJVfOKKEH6wU76V/R+svUvsg6R0APRbzrHZuT8110cbX1i+1zPOaambWc9rQSiWRvmwpWmmd0r1ytuWun5UzSatYKd7PWimgpb2osABAX2pkofZzXURq1jSe2j60Uol2izXS9jFR+gj629ub+P1re6X3iWYfDo34co09I6MV7Y72K7uVM7cdAAss/OSZ5vaUn2lJ+7GuVKXBZQz2+fmZiIh+/vw5+EnqKR1Bf35+psvl8sfCJtVeTxIAR8CV/COSS6vsbUs0x+KAs822XZJrZuXMbQfACm1KBjd3cffhro+CxM9s255q7zpHpealvd241KDQh0o08e9UPd7c9R4pPevlciluE2vbG8k+HBZ5EhLbRkRimyPyTLxy5rYDYIk2JWPPOu6kaT6RxqnUz+QWa+scxdG7mhmHeEG4LMsDEW1b8IlIXqkkV7lD+/pMOz+t1UkAOCPS0PCMPgMAMB8eKnFpdgj/SUT/2r/YUqmEO4Wjfd1jWKgU21+WpRgOXpZFlSPnXZZHwhlzTaVIbGNhP+9wIZuUXEYknwFARLQ+J7qP0j5/yi/t52ovlbg0C8L/IaL/3fz/JyL6bvs4dXjcbs7lYawr/uv1+uF9e+kYTdu8y/JIkOZPnFEKJGcbzTXR4UI2mv7v0WcAEBGtz4nuo7TPn/JL+9e8VOISe9Db7XYlot8rmEgreg9cLpc/Tster9dsmPjt7U0dSl6v9TzAJKE+7bNvbav9LK9IiqSvfaT1mmi2SaFNuShVtoEOITgzEv/T4nMk10ccX5HnHCKnp4y1eFyc1p6Wbalasn2vx5BYyylQ7ZZ65BOnI06WR7ENUToEox03VtWBItkNAAmelC08ji/N3M6lsuxDxqU1zFHpYCaVSv76668u5df2RFIz15x41Kq6137+aFpOgWq31COfOB3xnFFsQ+QrNSKS3QCQ4KlPe3qWFc3cwqWy7F8rVTk5yueZVCo5akHmSdHbEo8LWgAAAAAcg4d1gMmy86iQrcfQMMdItfFe92yl5XSZ5NS21WeNZsRzRrENka9n9fQsAFjgqU97epYVi7k9VYVsjXaOnLuW2i3ZZVkeiejl5eWFHh8fm5JQZ03glpRgK5Vmq/nbs61akm61Ok2RE3x7j6co/SWFpAoQUb7SkfSaUjUhjzlOALSgySFMVYWqqUgSqbpULj9dM+fX3KeW19dXenp6IiJ6ut1urE6gWWC6d7H5iGjVxq1s6JmWji1Vvbf4rNH0Hk9R+ksKSRWgEtJrrKsJAeAdTZ9OjZEa3xKpulQuh1Az59fcpzeHZmdLdj0kO2lRsNohbLlmVru17HZFsE+PvnMWUPUHgGOJ4FMjMcqehy0INdvQuSPcUTqa9mh66nWra2a1G0euvRHkaHr0HU/tayUl47BFclq/dI/1mpI0hMccJwBakIyN7bXLsiT9kmZsRBpHVn6Au8/IOeqwBaFmGzp3hDsK2qPpNfezvL8XLORictdHkKMZ0XcikZJg4KSbuNclMg45aQgi7ISAOdGMr7u7O9ZfPTw8TFldysoPcPdJVTBzV6kEjAVhMD0IY/DMZhttvq715wAwM7X9XpILfTa49B4PHPYkFtvHs24rS9olDYOlkuUj2Y1TddfeQxPG8GYf676zvyZC2DyH1j5vb28fXufGiuSeAMwM54O5uYUbX7Mu+npX3Bo5Rx22INxvj0q2jyOHZ3LbyqXXU0rlW3Lb99HDWhJVd8k9pGEMj/Zp6TtE5XBMhLB5Dq19Um1LjZXIYSwArOB8MDfuuPE1K70rbo2cow791rTbx9EdcovsTM19tfc5KxHsYyVZNCtWIeKz2Q2AFiL4zhmA7MyEQrq17U1twW+ZIaRO1E+lnbNfNPuksMj9ixA2L6GR5ZG2d7a8SgBq0PqHGfyJhp4Vt9a5a5SknEvZGQnec56s2zsbOWmVVqTq+NGwyv2LnlZQI8tTe89IdgHAAq1/iO5PtFi3d1s1iZu7jpKUcyk7M+J+1vR8vrPJy1i1K5J9Uljm/kV22BZ9Z/969LxKACxpSb04A7XtTfkUSdWko+b8eTM/T4zn0Nf6bBoZHUjuAKK6vpO7F9F/QzPoYwCUQQUlezz5HpeyMyPuZ03P58sd9efkVjwMvlIYnWuXRHJHQnQpBE4OInKbpJT6DidNxL0ulXE6g20BkIAKSu2k1gUtlZWsfZQ72ZlZDpVYtjfVdu6ov+fQV+o5Pn/+/Dvxn2sXR4/qFJ6xkOSJSqlfcHZosc9ZbAuABFRQame7LkjJy2nnLmsf1cXjSU7XXi6X34u6ZVl+X799PbqyuURmR/v3bNzf35u3z/uPBQnasL/VCf5oP7yOAGEvAOxZQ6UzjSNt6HzPaDuYLwg1p2uPOjkzI1o1eQ9IQp6aqiTcVnt0WR4uNKMphm6Fl7FY+u60lUokcCkYW7zYB4DecP6HS18qsfXfM4wjTUidIxcaPmJuN18QaraBZzgtOwqtmrwHJCFPTWH1/TXb0DNR3Eo32iorqWLoPZ9lBNKqItJKChK4FIwtXuwDQG96ViqZYRxZpGvlQsNHzO1IkpmQKAsfKZL2pELPs9mBaM42SZFUFTmzfQDoDcbXWHrb32xBuMbINeEZ7tqSWncUJHldXLtK703ZzntIVKLwXqsC773tEnJjKDcmep9o94gmZ7I2ZCx5n1f7AKDFsrKWdszNMI5SbdDaYXSVLZMFYW0OE6fKnVPrjrIo1OZSbts1a5UTz+Hs0ZS+85KCfSvPz890uVxC/Ag7anzMWvEGgD3WYwpj52+0dhhtN5MF4VHx/0h5Bi25lLXtjGCf0gKjNg8jQttzjP7OL5cLPTw8hDjR7um79vQsANQyuh+P/nwLRlQnsyZUDuGMx9SJ5m3XkewrTxCdw56eVO4BAOBsWFZQyn0GUf85zWRBeFR8O9Ix9Vr5lG/fvqneu2XWPAxJu7gTyN77yUrtd3fGKi6146MHkewGAMfouSPqODoqfeWotY/JglBblSOl0K3F+xZzrYL47XZjpVfu7++nFxHO5RnmFN45vPeTlVoFey0zVHHRPKuV3SyeBQCvWPsf7fVRx1Ht/KKtsmXxmRLMvgVtVQ6Qpnbb+e3trfpE8ygkp9q4ds1I7rux+t48ff8AAD/AN+RJzVezpewMmV0ttqdHb3GXqA1rcb8MrHY4vIRQj9pq995PUtSGzWvvbXn/I7CoZmNFJLsBICE1vrTjSHu995Cx9XzVMs/39DlDFoTaEPP+b287XSm87mR5CaH2fI5U1ZJISMLmRG3jJrL8j/XYyoVvzpCmAcCWEXOX1/lyZfS8edScNuxb0IaYPf96AL5IVS2JBjforcYNFjL/JWfrvRRP9H4FAIjHUXNa84Lwer3S9Xpt2q1oqejhFa+hJC/PdcbqGiPYjq1oY4jD+vsdXR0AAE+M6Pfex9ro5wtTqeTHjx/0+vpa9d61ykJtRQ/PbMN+b29vHxTI95UhJNcQ6cOEkRfVJSJV1xhBKu8l0hjiaEk5SY2z7f+vfQr9CID/8vz8TET5ShrS+So1Br2j9TmW8/mRvmho4F4blx8dx9eS+xIllSFS17T87QmL7zJSdY0R1FZ9iUCvlJO1TwFwVlI+4nK5FN+nma8kn+mNViWV2vn8SIYuCGc7sr0np2C+r6yRs0VL2G/GkOFKqjrJ7LuF3Pd5BkkECaX0k5JNUDUIgDrO5I9Lfjg357eky/Wez4cuCGc7mr6ldExd0vb393dalqU67Oc5ZGhRbUJiQy/ttYD7PonkaReRxpAWC2mISNWQAOhByjdL/LXUH/eU1TqCWj+89y2l6yX3t/ZRzQvCL1++0OPjo2kVkhmqKaS2wGvU21vCfp5Dhlw1FqI69XYOL+21wOL7jDSGtGhssZeUSfmtmfoOAFJSPoLzG1rffLvd6OHhIbTslcYPc/axSpez9lHNs8MaE7fcdYjUOTTM2i4rJFIrAFiQkpQBAOio9c1n8enR2mkuO8NJOGjg7rG+HuEXhcW2eGtVCc9b86nn6CH/4aW9FmhsxrFeHz23J5VLo/mu974kZ9uI9gGgFs28ofWvKf8TzRdZ2Efrt4+qMHWI7IzkyPoW7rrt697ze1qrTVhUlcg9g0espAjOJB2itRl3vffxtCWXw7OH8z17X5Iikr8BIAISf3WGsVYz1x0xnx+SUCQ5sq4lQn5PS7UJyX1anmE0mu9P+13PKh3Ss89HGE8rmhweie+RtD2SfQBoQTO+eowL72NthH3W+/Sezw9ZEJ5R/oKo7Ug5BKj/i7b/zCAdAhkZHZxtYDMA2jlyfEX037P4n0MWhK2njlN4zw3TSGBoj6BL7+l5MGlkZ7T9J7p0iIV8ipZIcjSpvsP1Eam8Uwnv/gYAK6zHlxbv/nuEfY7yz6E0KFapiAi/HHqGREfd05Kj5E+82yHFiGeOJEdj/aw5SY0o/gYAKzz5Ao/+e4R9jvpMP9/8iYm2rQzATJTG39vbWzKVAwtFAMBMhFoQet9K3tIzJCr9fM8hQItKJRIihvpGPLP3/rLFuu+0hHu8+yEAtHjymZ6eZWVm/xxqQbjF41bylp5bvBJ1eE/b/iksnm9fbYJojh2cvbxArpqGtrrLDFWAjqpyI8G7HwJAi4X/4ciN070v9+q/t/axqMy2ZbR/jjMLgN94HCQjSFWbiLLLVYKTJspd1/J6dM7WXgB60up/tNdHqxzUy6+M9ldhF4Qet5K39Hw+yb29V1mwsE/qHlqpH6+k2pFSt9cq4feoBnM0mootFpWTtM8CwEysvkjjfzhmqEK2xXr8l/x573mseUH45csXenx8/GPSaq04oa1sMitaO2yv915lgQtLpPqP1A5aqR9vNlmxkJ2RVPuZqaKLpL0cXP/KvT6L3QDIIfFFnC/f/r369RmqkG2RtJ37m1srbe9Zspu1rZoXhOs2r+VWr1V1gZFYPB9nB21VBq+2koYlpO0aLfVjxVHtiFrRxfq74/pX7vWIdgNAS2mspSpotKbwePbNKSRtl9pEW5HE2lbNC8Lr9UrX69W0mgJkWP5Gq34ezW7Sahyl9p65kkdLmyNWBDiSUr+D3QD4yNavzzpGJG0spS/l5rpRc1rzgvDHjx/0+vpq8Sy/kZza8Z67YyGNoZXD4F73aCtNWLSHCrxHm6wcJVkUScZpi7XszPv7e3I3UNLvItkNAC0lP7n/95Rf//btm2rMevbNRHwbt36gNu1H68+tbRXqUEmkygGaY+I9pDG826pnWCC6HE2p77T0F+69kcI0PSqV1EpJRLIbAFpyOXIpP5oaD7fbjZWKiiI1s4VrY+maFo6yVagF4Xo0fTZ6DIBZbSVhZjkaorb+4t3ZAgB8weXI/fr1i67XKxHVLU6iSc2M5ChbhVoQzlpNoUfVDu/b7j0rlUTqJylKtmn5bpdlSUo/RLKZdd9ZlqU6xON9nAHQAy5smhoP3BiJOnYkbTxKjsaaUAvC6NUUtNdqVN2jbbtbVSohil11I0UuvLL9bqUhzv17U+GMSDbTViqRjJV1p6PmvQCcDS5s+vDw8EeIOeWv9q9HYx9GT7WlNgVldBg9zixwQjSq7lF2dyyJ6lBqSKUASNt/hvSBHmMF4wwAPWeoGiRpS017R4fRQy0Ij1LrtkCzxautNrFezx1r92qTLb0qlVjdeyS5Shyp71l7ElAT1vFITViqJBMhtXm0cQZADSXJlNTcdKbxopl7LdYCR3HIgrCl4saWo9S6LdBU4thfn1In31dHKOU8ebRJCW3FiDOR6/spctU0JCEPz+SeP/W6RCZCUx0gdx8AoqPJp+WqY3HMMF6s515PlY8OWRBKKo+0XO9V+kFaiUOiTr6vjiBVkPdK6vm0FSO4NnpvewmLSiWlahqjHU8rmrCURCYid88c0fsaAHs0fXqWuVqDdu4tXe+p8pF4QbgsywMRbZ/6k/S9WrXtM1acAADIKYW0tn/nKgJsf5Wv94T/AQCcEc0O4T+J6F81H6IV0B2t1t2T2iPr2mPt3m1iIU9wphxC7bXRbZCjViJmy77aCBFV3TOSXA8AEnrmvM3glyxytjX/fiSLdAuX2SH8/poyNRMAACAASURBVO9//5s+fRJvFprhvRJHDm0dRG2txCg24dpo9XpkJDtgs9uA43q90vfv383u9/XrVyKiqnt+/foVC0IwHS3+B4dK/M3Vr6+v9PT0RET0dLvd2FrD4h3C2//f3plHTXKV9/l5NasOGg0IrAFGArGIgDAgQwDnsNlsthODAbPYQBY2OxyMiU1i7BAwXgCbEzDGJlJOwr6I1SwJ5AiCGHFEwAlEAsxqMGAkQAqbRjLSNzPSmz+qeqamp+/tut3Vfe/t+j3n1Pn6q66ufu+v71Z3eV/3LeCos67cvdpSXWmk/vCHDx+eW5C61/TJQKnXl0BIh9Tra0z7PHbs2HE0HWZ2NH19NBhD5SyEWJ7ph8pJ+9ptZ/vUPyH61M21PNgOFb2ltLq6KrczXXJ3SGfRZ/cRLDY1tcx9StzZlTLtt4xuJaZ9HqFdsbB83pm+Z23awPCRSo4cOZK8OH5CifWQEKn02Yk/xFKN1PvXVEetst5elw5VdQhLnyZe187f1PuUuLNriJ20q/5sLvruil3F99TA0FFVtm/fPjeyQO4IAkKskj51ztD1RZ/711RHrdL+delQVYew1GliIUT9xDp3uSMICCHEqqmqQ1j69My6dhNtws6udXlvLzHt81hXJJFJZIHaRrpWFTh+sn5pVhSGGvOREH0ZwrPFst85pghKQ9x7FSzdIdy/fz8nn3xy1Ev5WCJNhKKTwPFRE/oS0y32XTVMa6VGcpnFxMN7bWmfRygSR2re6WozS9fu/zWt1Umhjw4we/1PSREEhFgli0YvikWXSq2ba4+glMI8fardVLJr1665C7IXXbA9TQ3rCeZFJxnCC/ysyCbd76plWis1kss0Ew/vNaZ9Hn0jboSYpU2MGsrWhKF1WDTaixCbxLxOR2p0qUXq5po7gClrCOfpk6veqWrKeFZ0gZJZdgv9vBGh3L6NlmWW/bGoEqHztaQ3hVrcL2wCY8xfQsQYqu1K9VtYS1kbqu0qza/sIB3CefPbQ7mJmI4uUHLGCW1BT9EiFLHlyJEjmNmgAbbXTYoLg5AONeWHFIbIO9MRNGrylj+PFFundZil4djylxAxUuqfUJ0UKlN93LCUXtaGbru6hPSpyu3MPJcNQ7uJgPKnuELDx7O0OP3004H+Ifu2b9++Nhc3q2LVLgxqZoi8M31tn/WttRBKS5+6Z9G6aJPylxAxUuqf1PLUpxyVXtZWaV/o3tW4ndna2mJra+s4z+ZjZjLUmzJ8XFNjXCrzhuBrYMi8E9JjVvSBWghNM3Ujucz7bMrGnOnPT39vbflLiBjLlpG+3yHSWdfylaU7hFdccQUHDzah8VKHlZehxKDy84aSY1PAKfSJrFD6FODQeSI2BF9Doz103qldj2n6RAEK1T3zllf0YdP0FKLLvPJlZjPdMaXW431mwUpvu1ZpX5/6fJV1zqBzuUMNK/eZQl3FNPSyLDqsm5qW6cgKUN/IxRBT530offphwrryTi16TNNniUSo7pn32WXyXa16CtFlVj6ejgw265pl2uFao/+kLFmZTuOsa7r00XOVdU55vSo0hdqXvm5bakG/u8iB8p0QJ7LqyGA1R//p2/bWlsZBO4SzhpRj51Pv06XEYeVFbUrVZzqyAoSdWpb69LUur/Ql5pNZrCvvTK4vNV+EmKdPSIc+uqZqmHp/IUpnVj6eritmlZNlyk6tkZJgdtsb0yd2TZc+9ckq6xxbdPjRzE4Frr700kvZs2fPsFYFqCFyQDf81aois/TZuh/7bAnahVwbQP90bVqkkljeWXW0n1LyRYxFytYyZaWP5jXoJkQf1tF2haipHKW4nUklVF8t2/c5ePAge/fuBdjr7gdD1xU5ZRyihsgB68jUy6whKGXNU4pX9xCbFqkklneGivYTopR8EWORsrVMuvpoXoNuQvQhZ4espnKUw+3Muvo+VXUIFTlApFC7N/wuctewGNJNCLEprDqCSVUdwpoiB6x0nn+Je5ey5mkI90Qx10N93JSUmn9SomkMRSn5og8pti6jW+71PELkIEeeLtGNXIhVu52ZNTNhZsFlVkO2Y1V1CLuUPsQccwuzzOvuU0Hq/UsaGUtxT9Q3GkeXmiO5pLhy6OMyZZZ7h1LzRR9S3D70YZ77i5rdOwmRSqpbFTix/lkk+lYtDF3/dJnlUm5Sz2xtbZ1w/dDtWD2/QoWEtqYv83qZ+9eKGt0wfbSZ5fqg9nwxpMulea4hNs29kxDzSHGrknJ+U1h1nZBLv2o7hDVP1fRxF9MdfVj1uoEcpLidST0/770+7+ckxTZNax5j0XSORR8h+pLqVmWa1DJVswuaCUPUI7Pu0d39vYrv7LJ0h3D//v2ceuqpx2Wavi4zUs9vAilb1kNb0Ete/9aX0LA4kHy+z/1rmurru9xA05pCiKFJaaNS2+hQ2979v9b2LXWZWB/3PrN+i1W631u6QziZaokNm4bcN6Se71LyGrAYKXaHrq017dOkTjekZv6ap/r6LAfoc/2YWLRcbEp5EmIIhigPMfcp6/j+XCy7jGs67bO0WKULmqU7hFtbW2xtbR23sHKakOuH1PO1seqp3tD9a3G30sf+TZ86F4sxK48vW2/UHPlHiBrYlLY9REqbHNNicv269Vq6Q3jFFVdw8GDQ8TUQ3mmUer5L6dvUQ1vEU1yrmNnMdQNHjhzBzBaK9FHKcHxKpJJNnjoX6QwdKSBUnvqgPCg2jVT3Xyn36NO217qmd4joW33qolXqU+2mktK3qYciccyyO+b2YtZ9tm/fvnCkj1KG41Ps3/Spc5FGyu/ex/1FqDwNbYsQNTBE25p6j0kbWPOo+xDRt0J10br0KbtXNRImbi/M7GhmOHz4cJYhYyE2iVobFyHGxKQNFLNZlz7VdghLnzJOdauSMg0W82a+iF05WLXbGbG5pPzuobIyfb+Q+4whbRGiBoaaMk6Jv74J5SilTUu5xyL3WRRbdMrDzE4Frr7qqqvYvXv3whECYLaX8+4U6iwP4GeccUbRHUJI2xyxtbXF5Zdf3uu+k7RrU0k5aRHrJbRIO1RPzCsTs8rfpkV4EaIPKW1RiHnlblPLUWqbvC7fwwcPHmTv3r0Ae909uOljELczixobcy8yz5VNDazai3uf+5es4ardzojNJTWPL1ImNjHCixDrJNV9Vu2ktsl93Iitk0Hczpx00kkLfTY0TTM5H/OQXsMQc4obi5Qpq2l9pl/X8sSV6uZjTK5AVqHNJmmWGklhXtqHmO4RYhMYIt/PK3e110WbWj8P4nZmz549C3025KV7EyKUDO0ao0sffUp3h7FKfUpP+zxWqU2ImjQbOpLCxDWEEGIYlmnDS6+LNrl+rnZTSenuHnLbl/v757FK+0pP+zxy2F+TZkPbKrdGQhwjd77P/f3z2OT6udoOoRBCCCHEWFj1xspqO4Slu50ZYuv+MpS+/mmV+pSe9nnkyDs1aTa0PrldPQhRErnzfe7vn0cO+0Ku6YaeSs7aIewTRSBE6ZFKVmnfJrjDCEVsgfluiKCMBbirIiXvpJahWFScWhhCn1me/88880y5NRKjZ+fOnceVhZhbpxCxchc6X0ukkhR9UtqueW3a1tbWCbYMPZVcdq9KzCTFHcbYfPdNIrzAONK76elbFYqMIMT6medqblPZsWPH0bR309mNTlYCWTuEi4wMTih9WLmEKdF1DDEvyix9QvlhmXxSSnpTSI3EkUJIy5p0SklzKL3TS05KLitCrJMhdtHGyt2iUbZKIUWfrg7T9UnKfc4888yZ9d7QS+eKHCHchGHlIaaMl53eGyLY9qpY15R/KelNoTslMW+aZigda9JpiDRP36PksiLEOlllvt++ffsJU641tOddFtVn+nMp93H3mfXe0O1okR3CsQ4rT7MpEVtEOjVVkEIIkYLqtzIpskMYitpR07DyELbOu8c8b+klR3hZW7Du9ntqiO/cN/pGl6F0LCVf9GHISAp9IpXUkHeEGIpV1gWzIpjUVo4W1adPBLZ5nx3KlhBFdgg3IVLJqsnhLb0W9u3bx44dO45WNPO0KmGt2Dwb9+3bByxfNiba1FwhpxDSrft/LFJJDXlHiFoI1V81laPulPfhw4d718mh62bVyd1O45VXXnncZ6fbtyEpskMYoqY1PUPYGrvHUOsYcrFKO3bs2HHcNPu87ypBk3k2zFqI3edzs+5T+zKElDSHdOtzvz7fU0LeEWJINjkSx1AM2RGbVSfH6ufp9m1IquoQ1sBkeunQoUOD3AvqHl4XZTG2PDVEORRiDAzZdi3y3VBPXbTqdr7r23CdVNUhLD06ydDTuDEXIYuuHShlrdgq3fJM55N5aS5Bkz42zlpHIrczcfq4LFrGFUYJeUeIZcm9BCnmnqU01tXOh1hlnbOWDuEyEUm6lB6dZN6wd6r39tj37Nq1K+qapPSIFKmRSmad73vvkGd5KEeTPjbOyl9yOzPM/WKuMErPO0Isy7rK/3S7NKvtKr0uSrFvqDZtXS73yu5hiZnMGmIWcbqFqNRR5q6NXQ/2k+grs6YPNCW6emrIO0LUwMSVWmkROkpnXS731tKbWHZkcELpU8bzprKGisTR5/rSpwZXGamk9Hwyj0U94S9DTZoNvdzgyJEjmJkilYjRssolPNPfM6usTV9Tcl2UMmVbW5tW1fBS6aNhpdsH5QzHr1KrGn6HGDl+o5o0W8WUsSKViDGzrvIfKms5bFmU0PKeeVGllmFtv89avkUIIYQQYgPY1GUkVXUIp6MLlEYNOw5LsXGVdpSSxkXJYX9Nmg1taygKwKyoCtpUIjaRdUeOWvaa3HSjF8WiHQ3FujSpqkM4HV2gtMp4ngfzUNSEaU/loc/OuyZ2PZTVgIWG3fukvXv99OuS0jgksbwz6/z0NSl5p3RSIgX00Sf0Xh+NSqyHhBiCddYbq4y+MTSz1njHoh1NWKSuhvW2aVV1CLuUur4n9sOFoib0iR6RGmGihogUfYfd53lyLzmNizArb8fyzjz65IVSy1OIvhVkH32WoTbdhJhFqM4Zug0JlZdVRt8YmkXXGw9VV6+SajuEpRLzYL6Mi5BpVzPz7lWD5/dZw+59r08dIQx9tiR9VpV3NpGcURWEEKJLn3oo5Zpc7VK1HcISt6bPcxcS2oE0nZYUlyx9vqvEaa3QsPustPdxVTB9n2565/0uJeizaN4JRTDp0idySw3rdiakuOXpo88y1KSbECH61AlDuKYJ3aPE9jzEou1z6jU52qViIpUsG42iBBadPppOy9BpK3FaKzTsPivtfVwVxO4977Ml6JPq/b7rtT7VjUMsKkcNzEpvLDJPnwhC86InlB75R4hl6FMnpESXChFq20psz0MMYWupEVvq+RVGwNinwULpXlSPTdczFsGky6zINmZWzRN5Cjt27EjupE2iAPTRY5H7C1EDi+RrlYXVEaq3qw9dN9RwapcSp2oWtSl1SnSR+5fW+K8yUomZJU8r5mYI7/ep15cwVb4I8/LOdLrmTXWlLtmoVTchlmGIpSah60uog/syhK2lRhsrcoQwFhB6HQGeF6U77J7itbzPlGifIebY/UtjFcPucOwJamtrK+n63OTwfl/CVPkizMs70+mad33qko1adRNiGWLTyrPqrunXoetLqYP7klJXp06p97l+lfVPeT0FwsPQ6wrwvAyrytgp01pjIVWT0jXcVO/3QojNINY2T+jjEqymDuAs+tbViyxfyUmRHcJ5kQNqeKJIGVbuswtyOu0p9y9xOH4Im2L3qH0n7YRN8H4/FJM1ofPKS2pZmbzf9/616SbEUIRchQ3lQqyGtj3EKqfUl7lnClk7hCHP3X0iB9S4jic1vd3rp9Pe57tq8fw+oY8n977UvpN2Qmh6YtnXtemRsiY0tayE7p87aoAQJRGL0DHrfKic1OAGbBFWNaW+cZtKQiwTRaD0dTwp0SZChK7v6xW95GnHIfSZp0ONlcos+kzHpL6ujUXLe5/PzYueULNuQgxFSoSOWLmrwQ3YoqxiSn2d9U/vDqGZ7QK6lu0BuPbaaxf+8oMHDwJwzTXXLPTZkivqra2tE9KVmt7Q9X3uMxZ9Sk6jGI5Z+aUPfcvKrGuUv4Q4RkqdHSs788qyyt3wTH6neVjf3riZvQj4/cVNEkIIIYQQmTjD3a8IvZnSIZweIQQ4DfjB4rYVyx7gcuAMIH1YYvORPnGkTxzpE0baxJE+caRPnDHrswf4tkc6fb2njN19C5h27tZvHLIyOrt4rnH3jUzjMkifONInjvQJI23iSJ840ifOyPWZm96T1mGFEEIIIYQoF3UIhRBCCCFGjjqEs9kC/oATp8hFg/SJI33iSJ8w0iaO9IkjfeJInwi9N5UIIYQQQojNRCOEQgghhBAjRx1CIYQQQoiRow6hEEIIIcTIUYdQCCGEEGLkqEO4JqzjEVMIMSwqX3HMTHW9ECtiU8rXRiSiBmLhYoQQy6HyFcfdbzSz3pGpxoSZ3TK3DaViZg8ws1vktqNUzOwPzOxcd78xty1DoApixZjZfYFzAAe+6u6XZDapKMxsr7tf3b42NezHY2Z3Bu4EXA98092/nNmkojCzBwI/BdwM+Ki7X5zZpKIws+cC93H3J7j7ETPb5u435LarFMzsxcC5ZvZodz+U256SMLN/CzwG+EMz+whwQ/tgoXoaMLPnAC8AzjazZwA/rl0X+SFcIWb2aOA/Ad8HDDgTeAvwvBHGUTwBM3sY8HvAi939I+25kzblaWtZzOwRwMuA3TT553PAC9390qyGFYKZ/RLwKppg9QZsA/7FpNM85oarnULfC3yTJqj9/wYe7e7fUaewwcweA7wd+EV3vzC3PSVhZv8UeDfwK+7+vvac8k2LmT0WuAB4E/BzwGPd/RN5rVoeTRmviHYa4tXAH9OMYPw88Azg0cCFZra/vW6Uv4GZnUVTmO4I/J6ZPQmOTm2NUpMuZnY28EbgPODewNOAWwEPy2lXKZjZnYDXAy8FHgT8Fs0o4TYz2wXNNPJY85I3/Ai4BPgT4Ergr83s/u5+g5mdaWY3y2tlPszsHJqH82e7+4Vmtt/MftbMntI+iI0WM9sNPAV4qbu/z8x+0sxeDnzQzC5o/9+W2cxsmNlPAm8GnubuTwUuA15hZj+R17LlGWVluSb2AD+gmcY67O7fcve3A/cHdgEXmdlpYxwNa0cvfhv4RPv3KuBZZvbvQJ3CtrJ9IfBed3+Vu3+vHUF9N/AYM9uR18K8tPq8AniHu5/v7kfc/a+BH9GMGF5kZpeY2b7JFFdWg/PycZqlQc8DPgW838yeD3wYuO0Yy1m7lvIjwGfd/XwzuxXwPuCVwH8EXm5mF5rZnpx25sLdr6d5uPr7tpPzAeAM4MvA7YCLgQfD+DZztZ3ljwOvdvc3tqdfS6PPvdtrqi1T1RpeCf8IOHfyTzuF9VXgcTSN18vMbOfYClU7jfcB4OPu/i6adRiXAb9sZi83s5tNOsojrZRvSvMwcTEcV8F8BjgLOGVy4Ug7hzuBt9FM2QBgZufRNFb/DTifZgr5wvaha3TTxp0881XgvjSN+a/TjGy8CDjs7peNcbOJux+hySN3N7OX0kwbfwF4PE2d/e+B04GXjK18mdlJZraTJtbvbYCHAJe0a1B/091/Gngv8Jr2gWtsZet+wCvc/bmTE+7+buDTwCvN7OZVD/K4u44BD9p1me3rVwCXAj/XOXdS+/c/AF8CTslt85r1Oanzenvn9Wk0Qcc/AbwBuEN7XAXcpavrGA7g4cCeqTyzn6aBv3X7/+nAO2nWpo5Cn0k6aUbgt7Wv7w58FrhX57rHAd8BzsptcyZ9JnlmN/CxSVmjeaj4IvBd4K257c2lT/v6V4HDNGtz901d9+dt/XyT3DZn0uYJbfn5X8Br23O72r/nAN8A7p7b5kw6bZt+DdwT+DzwnOlrajo0Qjgw3uaGlnfTVLzPaReh4seeHi4GrgNGtaXfZzw9tRtJfgD8Ec26ntvQrL/8KHCZu39xSteNZTJa7O4fcvdr2lHliWZbNKNjJ7fXfYimof/WWPSZpNPdr6HZuY+7fxZ4qLt/ujMydhXwdZqNJqOho8+N7UjPYWAH8Atm9pb29S8BzwEeYWa/m83YDHTLibtfQDPN9zKaGZvJcgRoZiz+nmakeRRM1SEHgL8C7gyc3b6/1b53NXAQOHWd9uWmUzffMBlV92ObbC6lGSX8N2Z2E6908412GQ+Emd2HZrfRbYGvuftL2/MPoJkS3UWz0+8lwL72783c/WfzWLxepvT5qrv/SXve3JvF/35smvjJNKOEH3f3B2Yzeo3M06d9fXPgb4FH0kz/nevud8tk8lqJ6LPdG3cqx+0oNrN3ALvd/ZF5LF4vIX3a955Hs+nmEPDP3P1z7cabu7n7p7IYvGbm6HPC7lkzew9wnbs/cb2Wrp9I2TqH5sHhacD/AZ5JM4DxazSzFPfPY/F66dt2mdlpNKPxB4DndjrQ1aAO4QCY2T2BD9JMd+4Gbk8zuvWs9mnidsC/pslUd6cZWr4ReICPwP1MQJ+PAL/RHTHsFKy/AB4LnO3u1+aweZ301ae99gBNpbwPuIPyzwn63JZmo9JjgTu3I4kbzTx9zOyuwOuAF7n7B/NZmoeAPhfR1M/T+ecsmvzzGOAum55/erRdt6AZUX46zXr4r9G4UfvFTdcGktouoxlN/q80S50eXOUoYe4569oPmgX+XwFe0v5/Mk3n7zLglp3rdgA3p3EbclfgtNy2F6DPvhnXn03jO+3c3LaXpg/NJrAP0Sw1OCe37QXqc3vg92lGM+6a2/aS9AF25ra1ZH3a9+5IM3PzN2PIPwlt12Tg6FyaB9E9uW0vQJ8T2q72mp3AGbltX/TQGsLl+efAt2k2kODu1wHvohnFuTscndY67O7fd/cPu/vnvVkzNwZi+twDjndd4O5/C9zD3S9bv6lZ6KVPZy3hW4GHufsXMtm7bnrnH3f/O+C/A09w989nsXb99NFnu7dROGp2ibEgKfnnq8D7aRx4jyH/9Gm7tnnb0wE+4+5X+ghGBluS2i4z2+Xuh9z98gy2DsKo3A2siK8DnwSugaM+rn7Y/v8T0Lg5aBea/kM2K/PRRx+f0ufqHIZmoq8+pwDXuvvrM9mZi7767HH3a9z909kszUNS/eM1u8RYjNT888lslq6fPtrc0Mk7Y1tfltR2eYVrBqcZ29PiKvgkzZDyVjuKc8SbtQPfpHEHMnFm+RUz+5c5Dc1Esj4jq3j66vNlM3tKTkMz0VefL5nZv8poZy5U/8RR/gmjvBNndPpohHBJvAkPNXntnd2OV9Gst4DG/cyV7v6GHDbmRPrESdTndTlszEmiPq/PYGJWVL7iKP+EUd6JM0Z91CFckq67lJYdNO4drqPxF/ci4D60TxRjQ/rEkT5xpE8c6RNH+oSRNnHGqI86hEvix3znPd7d3zFZvE3j5f7lwI+Be3sTH3J0SJ840ieO9IkjfeJInzDSJs4Y9dEawiWY7NizxpHy28zs3M7bX6RZfPpod/9iDvtyI33iSJ840ieO9IkjfcJImzhj1UeOqZekzSgfB57p7m/snL8FjTf3z2YzrgCkTxzpE0f6xJE+caRPGGkTZ4z6qEPYAzP7NZqQNRd1zk3C1jybZs3pX+azMC/SJ470iSN94kifONInjLSJI32ORx3CObRDxm8E/oHmSeHNmU0qCukTR/rEkT5xpE8c6RNG2sSRPieiTSURzOwMmkDef0gTx/ANZnYH4KXufqizDX2USJ840ieO9IkjfeJInzDSJo70mY06hHFuShPU+iJ3/5iZfQU4D7i9mf2Wd8LPmdnuTdpt1BPpE0f6xJE+caRPHOkTRtrEkT6z8AICKpd8AHec+v9ngCuBi4Hbted+GXghsDu3vdKnrEP6SB/pI32kTXmH9DnxkNuZAJNt594EPO8GQD8A3A84FbjQzH4DeCfwHR/LUwTSZx7SJ470iSN94kifMNImjvQJo00lU5jZKe5+beT9yQ6kU4D3AA8B/szdn7s2IzMifeJInzjSJ470iSN9wkibONJnPhoh7GBNcPMXm9ltQ9f4sR70TuCWwHvGkmGkTxzpE0f6xJE+caRPGGkTR/r0JPecdSkHcH/gCHAj8F7grrQjqDOuNeDZNP6LstsuffIf0kf6SB/pI23KO6RP/0NTxoCZnQy8miYczQXAu4Dv0WSMT7j7kRmfOQf4lrtfs05bcyB94kifONInjvSJI33CSJs40icNTRk3bKfZWXSJu38SuCNwHc2C0ke1mQoAM7s1gLt/YUQZRvrEkT5xpE8c6RNH+oSRNnGkTwq5hyhLOYBTZ5x7M3A98BxgD80i0w/Tbkkf0yF9pI/0kT7Sp7xD2kifoQ45pm5x94OT12a23d2PuPuTzexLwCuAewKPBc5z96/nsjMX0ieO9IkjfeJInzjSJ4y0iSN9+qM1hC2TLeed/7d7u77AzJ4G/Bfgre7+5Fw25kT6xJE+caRPHOkTR/qEkTZxpE9/1CGkcVTp7je2r+/g7l9rXxtwE+BtwH53/6mMZmZD+sSRPnGkTxzpE0f6hJE2caRPGqPeVGItnQzzOuCZZrYdjvolujNwJvDQfJbmQfrEkT5xpE8c6RNH+oSRNnGkz2KMboTQzHbR+BracnefPEGY2fOB3wQe6u6f61y/E9jlI9l1JH3iSJ840ieO9IkjfcJImzjSZ3lGtanEzF4M/BOaTHOxmf25u//QzG5Fs6j06d0MA+Duh4BD67d2/UifONInjvSJI33iSJ8w0iaO9BmG0YwQmtnbgDvRrBm4E3A34JXufkH7ZHGau38np405kT5xpE8c6RNH+sSRPmGkTRzpMxyj6BCa2TOA3wUe4u7faM+9CTgH+Gl3P5zRvOxInzjSJ470iSN94kifMNImjvQZlo3fVGJmpwL3At4CfMvMdrRv/SWwCzh5xmc2XpcJ0ieO9IkjfeJInzjSRiWs8gAABcRJREFUJ4y0iSN9hmcM4mwBXwI+5u43dJ4Y/h9wansAYGYP7u5MGgnSJ470iSN94kifONInjLSJI30GZuM7hO6+BZzv7v9z6q3rgJsC+wHM7KnA24FbrdfCvEifONInjvSJI33iSJ8w0iaO9Bmejd5lbGYGnOTu1894+7vtcaOZ3ZvGW/mvuPu312ljTqRPHOkTR/rEkT5xpE8YaRNH+qyGjRwhNLMXmNmDvOGGWesGvNlN8w3gZ4APAn/k7u9cr6V5kD5xpE8c6RNH+sSRPmGkTRzps2LcfaMO4PnAjcDngSd1zm+bum478LH22tfktlv6lHFIH+kjfaSPtCnvkD6rPzZqytjM7gs8BvhTYC/wAjM7y91f7M3TxNGg1u5+xMwuAr7p7k/LaPbakD5xpE8c6RNH+sSRPmGkTRzpsx42qkMI7AD+DngNjQfy7wNPNLPbAM9290PtELN78yjxSuBgNmvXj/SJI33iSJ840ieO9AkjbeJInzWwcY6pzewMd7+8fX068ETgycC3gWe6+xVm9jvAV9z9vRlNzYL0iSN94kifONInjvQJI23iSJ/Vs3Edwglmtq0dSj6ZJpbhU4FtwEXAC4FHuPsHctqYE+kTR/rEkT5xpE8c6RNG2sSRPquj+g6hmT0SuDXN9Pe7gR9445/oaMZpXz8ceAFwP5oh5ldnMnmtSJ840ieO9IkjfeJInzDSJo70yYAXsLNl0YMmRM03gf8LfA/4MvA7wFmda7a1f28DXA38WW67pU8Zh/SRPtJH+kib8g7pk0n33AYsbDj8PE2ImrsBewAD/oJmS/p5wF071+6miXf40dx2S58yDukjfaSP9JE25R3SJ6P2uQ1Y2PBm3cBngD1T53+dJr7hq4CTO+fvldtm6VPOIX2kj/SRPtKmvEP65DtqdjvzA5p4hacB15jZbne/3t3/s5n9iOap4VPAGwHc/dP5TM2C9IkjfeJInzjSJ470CSNt4kifTFS7qcTMbkqTKb7u7g9rz+3yY4tO30TzhPGojGZmQ/rEkT5xpE8c6RNH+oSRNnGkTz6qjWXs7j8CHgecbWafMLObTjJMy+U0wa23mZnlsTIf0ieO9IkjfeJInzjSJ4y0iSN98lFNh9DMnmRmf2pm7zezh7QZ4W+Ap9N4Mf+SmT3KzM41s3sAvwpc5u43eK3DoAlInzjSJ470iSN94kifMNImjvQpiJwLGPsewG8D3wDeCXwUOAz8QvveScBdgFcDPwauoNmifkFuu6VPGYf0kT7SR/pIm/IO6VPWUfwaQjN7IPA+4PHu/uH23PnAPYEHuvv1nWvvAtwcOOjun81h77qRPnGkTxzpE0f6xJE+YaRNHOlTHkXvMjazXTSxCl8LXGTHvJP/D+DhwHYzM3f39u8Xc9q7bqRPHOkTR/rEkT5xpE8YaRNH+pRJ0WsIvVlIejHwfW/WC9zQvnUZjbPK3X5siPPWZnZKDjtzIX3iSJ840ieO9IkjfcJImzjSp0yK7hACuPtb3P0lU6d/SOOh/M4AZvaPgQ8C+9dsXnakTxzpE0f6xJE+caRPGGkTR/qUR/EdwmnMbBtwPXAQ2GlmtwDeD1zi7l/OalwBSJ840ieO9IkjfeJInzDSJo70yU91HUIAdz8EfBc4Hfgr4NPu/qy8VpWD9IkjfeJInzjSJ470CSNt4kifvFTXIeysNfgh8FbgFHd/REaTikL6xJE+caRPHOkTR/qEkTZxpE9+qusQdjhA47PoQZntKJUDSJ8YB5A+MQ4gfWIcQPrEOID0CXEAaRPjANInC8X7IYxhZnvd/ercdpSK9IkjfeJInzjSJ470CSNt4kifPFTdIRRCCCGEEMtT85SxEEIIIYQYAHUIhRBCCCFGjjqEQgghhBAjRx1CIYQQQoiRow6hEEIIIcTIUYdQCCGEEGLkqEMohBBCCDFy1CEUQgghhBg56hAKIYQQQowcdQiFEEIIIUbO/wdvcVab7ZEtOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot =tn.plot_longitudinal(sociopatterns_hour_window,height=800,to_datetime=True,sn_duration=60*60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Network - Graph classes.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "nteract": {
   "version": "0.22.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}