NDlib.ipynb

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    "<span>\n",
    "<img src=\"http://ndlib.readthedocs.io/en/latest/_static/ndlogo2.png\" width=\"260px\" align=\"right\"/>\n",
    "</span>\n",
    "<span>\n",
    "<b>Author:</b> <a href=\"http://about.giuliorossetti.net\">Giulio Rossetti</a><br/>\n",
    "<b>Python version:</b>  3.6<br/>\n",
    "<b>NDlib version:</b>  4.0.1<br/>\n",
    "<b>Last update:</b> 25/09/2018\n",
    "</span>"
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    "<a id='top'></a>\n",
    "# *Intro to NDlib: Network Diffusion library*\n",
    "\n",
    "``NDlib`` is a python library designed to provide support to the analysis of diffusive phenomena occurring on top of complex network structures.\n",
    "\n",
    "In this notebook are introduced some of the main features of the library and an overview of its functionalities.\n",
    "\n",
    "**Note:** this notebook is purposely not 100% comprehensive, it only discusses the basic things you need to get started."
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    "## Table of Contents\n",
    "\n",
    "1. [Installing NDlib](#install)\n",
    "2. [Simulation Workflow](#workflow)\n",
    "    1. [Graph Creation](#graph)\n",
    "    2. [Model Selection and Configuration](#model)\n",
    "    3. [Simulation Execution](#simulation)\n",
    "    4. [Results Visualisation](#visual)\n",
    "3. [Available models](#models)\n",
    "    1. [Epidemics](#epidemics)\n",
    "    2. [Opinion Dynamics](#opinion)\n",
    "4. [Advanced Model Configurations](#advanced)\n",
    "    1. [Node Attributes](#nodes)\n",
    "    2. [Edge Attributes](#edges)\n",
    "    3. [Infection Seeds Selection](#seeds)\n",
    "        1. [Model Stability](#stability)\n",
    "        2. [Stability Visualisation](#stability_vis)\n",
    "5. [Comparing Diffusion models](#comparing)\n",
    "6. [Diffusion on Dynamic Networks](#dynamic)\n",
    "    1. [DynetX: a library for dynamic network modeling](#dynetx)\n",
    "        1. [Snapshot Graphs](#snapshots)\n",
    "        2. [Interaction Networks](#interactions)\n",
    "    2. [Available models](#models2)\n",
    "    3. [Example: SIR](#dynsir)\n",
    "7. [Custom Model Definition](#custom)\n",
    "    1. [Compartments](#compartments)\n",
    "        1. [Node compartments](#nc)\n",
    "        2. [Edge compartments](#ec)\n",
    "        3. [Time compartments](#tc)\n",
    "    2. [Compartments Composition](#composition)\n",
    "        1. [Cascading Composition](#cascading)\n",
    "        2. [Conditional Composition](#conditional)\n",
    "    3. [Example: SIR](#sir)\n",
    "8. [NDQL: Network Diffusion Query Language](#ndql)\n",
    "    1. [Syntax](#syntax)\n",
    "    2. [Command line tools](#cmd)\n",
    "    3. [Example: SIR](#sir2)\n",
    "9. [Conclusions](#conclusion)"
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   "source": [
    "<a id='install'></a>\n",
    "## 1. Installing NDlib ([to top](#top))"
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   "source": [
    "As a first step, we need to make sure that ``NDlib`` is installed and working.\n",
    "\n",
    "The library is available for both python 2.7 and 3.x, and its stable version can be installed using ``pip``:"
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   "source": [
    "    pip install ndlib"
   ]
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   "source": [
    "On the project [GitHub](https://github.com/GiulioRossetti/ndlib) are also available the nightly builds that can be installed as follows:"
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   "source": [
    "    pip install git+https://github.com/GiulioRossetti/ndlib.git > /dev/null"
   ]
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   "source": [
    "In order to check if ``ndlib`` has been correctly installed just try to import it"
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   "source": [
    "import ndlib"
   ]
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    "<a id='workflow'></a>\n",
    "## 2. Simulation Workflow ([to top](#top))\n",
    "\n",
    "``Ndlib`` breaks the simulation of diffusive phenomena into a standard workflow:\n",
    "- Network Creation\n",
    "- Diffusion model Selection and Configuration\n",
    "- Simulation execution\n",
    "- Results visualisation\n",
    "\n",
    "In this section we will observe how to templating such workflow describing a simple *SIR* simulation.\n",
    "\n",
    "<img src=\"img/sir.png\"/>"
   ]
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   "source": [
    "<a id=\"graph\"></a>\n",
    "### 2.A Graph object creation ([to top](#top))"
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    "As a first step we need to define the network topology that will be used as playground to study diffusive phenomena.\n",
    "\n",
    "``NDlib`` leverage [``networkx``](https://networkx.github.io) data structure to provide support for both directed and undirected graphs.\n",
    "\n",
    "In this example, to perform our simulation, we instantiate a Erdos-Renyi graph as follows:"
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   "source": [
    "import networkx as nx\n",
    "\n",
    "g = nx.erdos_renyi_graph(1000, 0.1)"
   ]
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    "<a id=\"model\"></a>\n",
    "### 2.B Model Selection and Configuration ([to top](#top))\n",
    "\n",
    "After having defined the graph, we can select the diffusion model to simulate. In our example we import the SIR model and instantiate it on our graph."
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   "source": [
    "import ndlib.models.epidemics as ep\n",
    "\n",
    "model = ep.SIRModel(g)"
   ]
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    "Every diffusion model has its own parameter, ``NDlib`` offers a common interface to specify them: ``ModelConfig``.\n",
    "``ModelConfig`` takes care of validating model parameters.\n",
    "\n",
    "Indeed, every model has its own parameters: model specific parameter list and definitions are available on the project [documentation site](http://ndlib.readthedocs.io).\n",
    "\n",
    "In order to get a description of the required parameters just access the ``parameter`` field"
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      "{\n",
      "  \"model\": {\n",
      "    \"beta\": {\n",
      "      \"descr\": \"Infection rate\",\n",
      "      \"range\": [\n",
      "        0,\n",
      "        1\n",
      "      ],\n",
      "      \"optional\": false\n",
      "    },\n",
      "    \"gamma\": {\n",
      "      \"descr\": \"Recovery rate\",\n",
      "      \"range\": [\n",
      "        0,\n",
      "        1\n",
      "      ],\n",
      "      \"optional\": false\n",
      "    }\n",
      "  },\n",
      "  \"nodes\": {},\n",
      "  \"edges\": {}\n",
      "}\n"
     ]
    }
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   "source": [
    "import json\n",
    "print(json.dumps(model.parameters, indent=2))"
   ]
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    "Similarly, to obtain a list of the statuses implemented in the selected model just access the ``available_statuses`` field"
   ]
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     "data": {
      "text/plain": [
       "{'Susceptible': 0, 'Infected': 1, 'Removed': 2}"
      ]
     },
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    "model.available_statuses"
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   "source": [
    "import ndlib.models.ModelConfig as mc\n",
    "\n",
    "cfg = mc.Configuration()\n",
    "cfg.add_model_parameter('beta', 0.001) # infection rate\n",
    "cfg.add_model_parameter('gamma', 0.01) # recovery rate"
   ]
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   "source": [
    "``ModelConfig`` also allows to describe the initial condition of the simulation. It makes possible, for instance, to specify the initial percentage of infected nodes in the network."
   ]
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   "source": [
    "cfg.add_model_parameter(\"percentage_infected\", 0.01)\n",
    "model.set_initial_status(cfg)"
   ]
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   "source": [
    "<a id=\"simulation\"></a>\n",
    "### 2.C Simulation Execution ([to top](#top))\n",
    "\n",
    "Once described the network, the model and the initial conditions it is possible to perform the simulation.\n",
    "\n",
    "``NDlib`` models diffusive phenomena as **discrete-time**, **agent-based** processes: during every iteration all nodes are evaluated and, their statuses are updated accordingly to the model rules.\n",
    "\n",
    "Iterations can be required (incrementally) by using two methods:\n",
    "- ``iteration()``\n",
    "- ``iteration_bunch(nbunch, node_status=False)``\n",
    "\n",
    "The former computes a single iteration step, the latter executes ``nbunch`` iterations. \n",
    "\n",
    "The ``node_status`` parameter allows to return the individual node status at each iteration."
   ]
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       "  'status': {66: 1},\n",
       "  'node_count': {0: 986, 1: 13, 2: 1},\n",
       "  'status_delta': {0: -1, 1: 1, 2: 0}},\n",
       " {'iteration': 5,\n",
       "  'status': {471: 1, 612: 1, 730: 1},\n",
       "  'node_count': {0: 983, 1: 16, 2: 1},\n",
       "  'status_delta': {0: -3, 1: 3, 2: 0}},\n",
       " {'iteration': 6,\n",
       "  'status': {},\n",
       "  'node_count': {0: 983, 1: 16, 2: 1},\n",
       "  'status_delta': {0: 0, 1: 0, 2: 0}},\n",
       " {'iteration': 7,\n",
       "  'status': {69: 2, 275: 1},\n",
       "  'node_count': {0: 982, 1: 16, 2: 2},\n",
       "  'status_delta': {0: -1, 1: 0, 2: 1}},\n",
       " {'iteration': 8,\n",
       "  'status': {27: 1, 118: 1, 198: 1, 802: 1},\n",
       "  'node_count': {0: 978, 1: 20, 2: 2},\n",
       "  'status_delta': {0: -4, 1: 4, 2: 0}},\n",
       " {'iteration': 9,\n",
       "  'status': {837: 1, 975: 1},\n",
       "  'node_count': {0: 976, 1: 22, 2: 2},\n",
       "  'status_delta': {0: -2, 1: 2, 2: 0}},\n",
       " {'iteration': 10,\n",
       "  'status': {60: 1, 197: 1, 269: 1, 374: 1, 534: 1, 586: 1, 622: 1},\n",
       "  'node_count': {0: 969, 1: 29, 2: 2},\n",
       "  'status_delta': {0: -7, 1: 7, 2: 0}},\n",
       " {'iteration': 11,\n",
       "  'status': {10: 1, 132: 1, 144: 1, 159: 1, 351: 1},\n",
       "  'node_count': {0: 964, 1: 34, 2: 2},\n",
       "  'status_delta': {0: -5, 1: 5, 2: 0}},\n",
       " {'iteration': 12,\n",
       "  'status': {90: 1, 168: 1, 251: 1, 600: 1},\n",
       "  'node_count': {0: 960, 1: 38, 2: 2},\n",
       "  'status_delta': {0: -4, 1: 4, 2: 0}},\n",
       " {'iteration': 13,\n",
       "  'status': {680: 1, 797: 1, 961: 1, 972: 1},\n",
       "  'node_count': {0: 956, 1: 42, 2: 2},\n",
       "  'status_delta': {0: -4, 1: 4, 2: 0}},\n",
       " {'iteration': 14,\n",
       "  'status': {955: 1},\n",
       "  'node_count': {0: 955, 1: 43, 2: 2},\n",
       "  'status_delta': {0: -1, 1: 1, 2: 0}},\n",
       " {'iteration': 15,\n",
       "  'status': {132: 2, 358: 1, 404: 1, 472: 1, 973: 1, 995: 1},\n",
       "  'node_count': {0: 950, 1: 47, 2: 3},\n",
       "  'status_delta': {0: -5, 1: 4, 2: 1}},\n",
       " {'iteration': 16,\n",
       "  'status': {11: 1,\n",
       "   15: 1,\n",
       "   26: 1,\n",
       "   186: 1,\n",
       "   190: 1,\n",
       "   233: 1,\n",
       "   262: 1,\n",
       "   526: 1,\n",
       "   586: 2,\n",
       "   634: 1,\n",
       "   653: 1,\n",
       "   864: 1},\n",
       "  'node_count': {0: 939, 1: 57, 2: 4},\n",
       "  'status_delta': {0: -11, 1: 10, 2: 1}},\n",
       " {'iteration': 17,\n",
       "  'status': {7: 1, 49: 1, 78: 1, 115: 1, 168: 2, 177: 1, 327: 1, 574: 1},\n",
       "  'node_count': {0: 932, 1: 63, 2: 5},\n",
       "  'status_delta': {0: -7, 1: 6, 2: 1}},\n",
       " {'iteration': 18,\n",
       "  'status': {24: 1, 93: 1, 587: 1, 792: 1, 842: 1},\n",
       "  'node_count': {0: 927, 1: 68, 2: 5},\n",
       "  'status_delta': {0: -5, 1: 5, 2: 0}},\n",
       " {'iteration': 19,\n",
       "  'status': {88: 1, 380: 1, 600: 2, 973: 2},\n",
       "  'node_count': {0: 925, 1: 68, 2: 7},\n",
       "  'status_delta': {0: -2, 1: 0, 2: 2}},\n",
       " {'iteration': 20,\n",
       "  'status': {162: 1, 427: 1, 581: 1, 672: 1, 718: 1, 721: 1, 989: 1},\n",
       "  'node_count': {0: 918, 1: 75, 2: 7},\n",
       "  'status_delta': {0: -7, 1: 7, 2: 0}},\n",
       " {'iteration': 21,\n",
       "  'status': {189: 1, 641: 1, 732: 1, 827: 1, 927: 1, 964: 1},\n",
       "  'node_count': {0: 912, 1: 81, 2: 7},\n",
       "  'status_delta': {0: -6, 1: 6, 2: 0}},\n",
       " {'iteration': 22,\n",
       "  'status': {16: 1,\n",
       "   120: 1,\n",
       "   200: 1,\n",
       "   449: 1,\n",
       "   482: 1,\n",
       "   484: 1,\n",
       "   721: 2,\n",
       "   726: 1,\n",
       "   741: 1,\n",
       "   892: 2},\n",
       "  'node_count': {0: 904, 1: 87, 2: 9},\n",
       "  'status_delta': {0: -8, 1: 6, 2: 2}},\n",
       " {'iteration': 23,\n",
       "  'status': {22: 1,\n",
       "   32: 1,\n",
       "   317: 1,\n",
       "   351: 2,\n",
       "   402: 1,\n",
       "   504: 1,\n",
       "   542: 1,\n",
       "   729: 1,\n",
       "   821: 1,\n",
       "   907: 1,\n",
       "   938: 1,\n",
       "   939: 1,\n",
       "   948: 1},\n",
       "  'node_count': {0: 892, 1: 98, 2: 10},\n",
       "  'status_delta': {0: -12, 1: 11, 2: 1}},\n",
       " {'iteration': 24,\n",
       "  'status': {28: 1,\n",
       "   130: 1,\n",
       "   219: 1,\n",
       "   347: 1,\n",
       "   418: 1,\n",
       "   441: 1,\n",
       "   626: 1,\n",
       "   642: 1,\n",
       "   715: 1,\n",
       "   726: 2,\n",
       "   890: 1},\n",
       "  'node_count': {0: 882, 1: 107, 2: 11},\n",
       "  'status_delta': {0: -10, 1: 9, 2: 1}},\n",
       " {'iteration': 25,\n",
       "  'status': {49: 2,\n",
       "   94: 1,\n",
       "   199: 1,\n",
       "   444: 1,\n",
       "   509: 1,\n",
       "   518: 1,\n",
       "   573: 1,\n",
       "   613: 1,\n",
       "   690: 1,\n",
       "   918: 1,\n",
       "   932: 1,\n",
       "   956: 1},\n",
       "  'node_count': {0: 871, 1: 117, 2: 12},\n",
       "  'status_delta': {0: -11, 1: 10, 2: 1}},\n",
       " {'iteration': 26,\n",
       "  'status': {153: 1,\n",
       "   215: 1,\n",
       "   239: 1,\n",
       "   386: 1,\n",
       "   399: 1,\n",
       "   594: 1,\n",
       "   626: 2,\n",
       "   802: 2,\n",
       "   917: 1,\n",
       "   920: 1,\n",
       "   932: 2},\n",
       "  'node_count': {0: 863, 1: 122, 2: 15},\n",
       "  'status_delta': {0: -8, 1: 5, 2: 3}},\n",
       " {'iteration': 27,\n",
       "  'status': {12: 1,\n",
       "   43: 1,\n",
       "   106: 1,\n",
       "   154: 1,\n",
       "   249: 1,\n",
       "   447: 1,\n",
       "   579: 1,\n",
       "   584: 1,\n",
       "   648: 1,\n",
       "   838: 1,\n",
       "   858: 1,\n",
       "   916: 1},\n",
       "  'node_count': {0: 851, 1: 134, 2: 15},\n",
       "  'status_delta': {0: -12, 1: 12, 2: 0}},\n",
       " {'iteration': 28,\n",
       "  'status': {4: 1,\n",
       "   61: 1,\n",
       "   89: 1,\n",
       "   187: 1,\n",
       "   308: 1,\n",
       "   575: 1,\n",
       "   613: 2,\n",
       "   680: 2,\n",
       "   905: 1},\n",
       "  'node_count': {0: 844, 1: 139, 2: 17},\n",
       "  'status_delta': {0: -7, 1: 5, 2: 2}},\n",
       " {'iteration': 29,\n",
       "  'status': {207: 1,\n",
       "   237: 1,\n",
       "   254: 1,\n",
       "   292: 1,\n",
       "   519: 1,\n",
       "   530: 1,\n",
       "   550: 1,\n",
       "   561: 1,\n",
       "   588: 1,\n",
       "   691: 1,\n",
       "   705: 1,\n",
       "   738: 1,\n",
       "   784: 1,\n",
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       "  'node_count': {0: 830, 1: 153, 2: 17},\n",
       "  'status_delta': {0: -14, 1: 14, 2: 0}},\n",
       " {'iteration': 30,\n",
       "  'status': {42: 1,\n",
       "   51: 1,\n",
       "   171: 1,\n",
       "   178: 1,\n",
       "   203: 1,\n",
       "   213: 1,\n",
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       "   347: 2,\n",
       "   513: 1,\n",
       "   601: 1,\n",
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       "   700: 1,\n",
       "   710: 1,\n",
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       "   734: 1,\n",
       "   764: 1,\n",
       "   810: 1,\n",
       "   867: 1},\n",
       "  'node_count': {0: 813, 1: 169, 2: 18},\n",
       "  'status_delta': {0: -17, 1: 16, 2: 1}},\n",
       " {'iteration': 31,\n",
       "  'status': {11: 2,\n",
       "   73: 1,\n",
       "   92: 1,\n",
       "   103: 1,\n",
       "   148: 1,\n",
       "   175: 1,\n",
       "   508: 1,\n",
       "   535: 1,\n",
       "   546: 1,\n",
       "   620: 1,\n",
       "   724: 1,\n",
       "   845: 1,\n",
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       "   985: 1},\n",
       "  'node_count': {0: 800, 1: 181, 2: 19},\n",
       "  'status_delta': {0: -13, 1: 12, 2: 1}},\n",
       " {'iteration': 32,\n",
       "  'status': {193: 1,\n",
       "   326: 1,\n",
       "   350: 1,\n",
       "   422: 1,\n",
       "   498: 1,\n",
       "   558: 1,\n",
       "   562: 1,\n",
       "   717: 1,\n",
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       "   844: 1,\n",
       "   903: 1,\n",
       "   915: 1},\n",
       "  'node_count': {0: 786, 1: 195, 2: 19},\n",
       "  'status_delta': {0: -14, 1: 14, 2: 0}},\n",
       " {'iteration': 33,\n",
       "  'status': {58: 1,\n",
       "   125: 1,\n",
       "   137: 1,\n",
       "   203: 2,\n",
       "   220: 1,\n",
       "   343: 1,\n",
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       "   817: 1,\n",
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       "  'node_count': {0: 771, 1: 209, 2: 20},\n",
       "  'status_delta': {0: -15, 1: 14, 2: 1}},\n",
       " {'iteration': 34,\n",
       "  'status': {17: 1,\n",
       "   114: 1,\n",
       "   125: 2,\n",
       "   278: 1,\n",
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       "  'node_count': {0: 759, 1: 219, 2: 22},\n",
       "  'status_delta': {0: -12, 1: 10, 2: 2}},\n",
       " {'iteration': 35,\n",
       "  'status': {131: 1,\n",
       "   143: 1,\n",
       "   166: 1,\n",
       "   204: 1,\n",
       "   228: 1,\n",
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       "  'node_count': {0: 738, 1: 238, 2: 24},\n",
       "  'status_delta': {0: -21, 1: 19, 2: 2}},\n",
       " {'iteration': 36,\n",
       "  'status': {79: 1,\n",
       "   95: 1,\n",
       "   97: 1,\n",
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       "   199: 2,\n",
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       "  'node_count': {0: 721, 1: 246, 2: 33},\n",
       "  'status_delta': {0: -17, 1: 8, 2: 9}},\n",
       " {'iteration': 37,\n",
       "  'status': {3: 1,\n",
       "   257: 1,\n",
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       "  'node_count': {0: 696, 1: 271, 2: 33},\n",
       "  'status_delta': {0: -25, 1: 25, 2: 0}},\n",
       " {'iteration': 38,\n",
       "  'status': {25: 1,\n",
       "   73: 2,\n",
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       "  'node_count': {0: 680, 1: 283, 2: 37},\n",
       "  'status_delta': {0: -16, 1: 12, 2: 4}},\n",
       " {'iteration': 39,\n",
       "  'status': {176: 1,\n",
       "   307: 1,\n",
       "   318: 1,\n",
       "   338: 1,\n",
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       "   445: 1,\n",
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       "  'node_count': {0: 665, 1: 296, 2: 39},\n",
       "  'status_delta': {0: -15, 1: 13, 2: 2}},\n",
       " {'iteration': 40,\n",
       "  'status': {9: 1,\n",
       "   81: 1,\n",
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       "  'node_count': {0: 643, 1: 316, 2: 41},\n",
       "  'status_delta': {0: -22, 1: 20, 2: 2}},\n",
       " {'iteration': 41,\n",
       "  'status': {64: 1,\n",
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       "  'node_count': {0: 628, 1: 329, 2: 43},\n",
       "  'status_delta': {0: -15, 1: 13, 2: 2}},\n",
       " {'iteration': 42,\n",
       "  'status': {44: 1,\n",
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       "  'node_count': {0: 605, 1: 351, 2: 44},\n",
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       " {'iteration': 43,\n",
       "  'status': {5: 1,\n",
       "   19: 1,\n",
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       "  'node_count': {0: 578, 1: 375, 2: 47},\n",
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       " {'iteration': 44,\n",
       "  'status': {0: 1,\n",
       "   20: 1,\n",
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       " {'iteration': 45,\n",
       "  'status': {46: 1,\n",
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       " {'iteration': 46,\n",
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       " {'iteration': 47,\n",
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       " {'iteration': 48,\n",
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       " {'iteration': 49,\n",
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       " {'iteration': 50,\n",
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       " {'iteration': 51,\n",
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       " {'iteration': 52,\n",
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       " {'iteration': 53,\n",
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       " {'iteration': 54,\n",
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       " {'iteration': 55,\n",
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       " {'iteration': 57,\n",
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       " {'iteration': 58,\n",
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       " {'iteration': 59,\n",
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       " {'iteration': 60,\n",
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       " {'iteration': 61,\n",
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       " {'iteration': 62,\n",
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       " {'iteration': 63,\n",
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       " {'iteration': 64,\n",
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       " {'iteration': 65,\n",
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       " {'iteration': 66,\n",
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       " {'iteration': 67,\n",
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       " {'iteration': 68,\n",
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       " {'iteration': 69,\n",
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       " {'iteration': 70,\n",
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       " {'iteration': 71,\n",
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       "   149: 1,\n",
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       " {'iteration': 72,\n",
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       " {'iteration': 73,\n",
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       "  'status_delta': {0: -3, 1: -1, 2: 4}},\n",
       " {'iteration': 74,\n",
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       "   159: 2,\n",
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       "  'node_count': {0: 100, 1: 669, 2: 231},\n",
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       " {'iteration': 75,\n",
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       "   247: 1,\n",
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       " {'iteration': 76,\n",
       "  'status': {2: 1,\n",
       "   35: 2,\n",
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       " {'iteration': 77,\n",
       "  'status': {119: 2,\n",
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       "   164: 1,\n",
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       "  'node_count': {0: 84, 1: 658, 2: 258},\n",
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       " {'iteration': 78,\n",
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       "  'node_count': {0: 80, 1: 660, 2: 260},\n",
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       " {'iteration': 79,\n",
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       " {'iteration': 80,\n",
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       "   176: 2,\n",
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       " {'iteration': 81,\n",
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       "   222: 1,\n",
       "   281: 1,\n",
       "   293: 1,\n",
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       "   320: 1,\n",
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       "  'node_count': {0: 67, 1: 658, 2: 275},\n",
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       " {'iteration': 82,\n",
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       " {'iteration': 83,\n",
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       "   51: 2,\n",
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       " {'iteration': 84,\n",
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       " {'iteration': 85,\n",
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       "   76: 2,\n",
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       " {'iteration': 86,\n",
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       " {'iteration': 87,\n",
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       " {'iteration': 88,\n",
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       " {'iteration': 89,\n",
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       " {'iteration': 90,\n",
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       " {'iteration': 91,\n",
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       " {'iteration': 92,\n",
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       " {'iteration': 93,\n",
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       " {'iteration': 94,\n",
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       " {'iteration': 95,\n",
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       " {'iteration': 96,\n",
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       " {'iteration': 97,\n",
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       " {'iteration': 98,\n",
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       " {'iteration': 99,\n",
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       " {'iteration': 100,\n",
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       " {'iteration': 101,\n",
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       " {'iteration': 102,\n",
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       " {'iteration': 103,\n",
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       " {'iteration': 104,\n",
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       " {'iteration': 106,\n",
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       " {'iteration': 107,\n",
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       " {'iteration': 109,\n",
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       " {'iteration': 110,\n",
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       " {'iteration': 111,\n",
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       " {'iteration': 112,\n",
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       " {'iteration': 113,\n",
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       " {'iteration': 114,\n",
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       " {'iteration': 115,\n",
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       " {'iteration': 116,\n",
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       "  'node_count': {0: 5, 1: 522, 2: 473},\n",
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       " {'iteration': 117,\n",
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       "  'node_count': {0: 5, 1: 518, 2: 477},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 118,\n",
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       " {'iteration': 119,\n",
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       " {'iteration': 120,\n",
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       " {'iteration': 121,\n",
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       " {'iteration': 122,\n",
       "  'status': {370: 2,\n",
       "   385: 2,\n",
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       " {'iteration': 123,\n",
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       " {'iteration': 124,\n",
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       " {'iteration': 125,\n",
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       " {'iteration': 126,\n",
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       " {'iteration': 127,\n",
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       " {'iteration': 128,\n",
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       " {'iteration': 129,\n",
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       " {'iteration': 130,\n",
       "  'status': {4: 2,\n",
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       "  'node_count': {0: 3, 1: 450, 2: 547},\n",
       "  'status_delta': {0: 0, 1: -11, 2: 11}},\n",
       " {'iteration': 131,\n",
       "  'status': {477: 2, 678: 2, 762: 2, 848: 2, 896: 2},\n",
       "  'node_count': {0: 3, 1: 445, 2: 552},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 132,\n",
       "  'status': {205: 2, 327: 2},\n",
       "  'node_count': {0: 3, 1: 443, 2: 554},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 133,\n",
       "  'status': {295: 2, 686: 2, 777: 2},\n",
       "  'node_count': {0: 3, 1: 440, 2: 557},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 134,\n",
       "  'status': {116: 2, 428: 2},\n",
       "  'node_count': {0: 3, 1: 438, 2: 559},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 135,\n",
       "  'status': {72: 2, 581: 2, 681: 2, 847: 2, 942: 2},\n",
       "  'node_count': {0: 3, 1: 433, 2: 564},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 136,\n",
       "  'status': {36: 2, 124: 2, 243: 2, 342: 2, 674: 2},\n",
       "  'node_count': {0: 3, 1: 428, 2: 569},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 137,\n",
       "  'status': {96: 2, 595: 2, 745: 2, 836: 2},\n",
       "  'node_count': {0: 3, 1: 424, 2: 573},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 138,\n",
       "  'status': {19: 2, 48: 2, 111: 2, 269: 2, 352: 1, 811: 2, 907: 2, 938: 2},\n",
       "  'node_count': {0: 2, 1: 418, 2: 580},\n",
       "  'status_delta': {0: -1, 1: -6, 2: 7}},\n",
       " {'iteration': 139,\n",
       "  'status': {27: 2, 110: 2, 307: 2, 431: 2, 550: 2},\n",
       "  'node_count': {0: 2, 1: 413, 2: 585},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 140,\n",
       "  'status': {339: 2, 372: 2, 730: 2},\n",
       "  'node_count': {0: 2, 1: 410, 2: 588},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 141,\n",
       "  'status': {8: 2, 639: 2, 832: 2},\n",
       "  'node_count': {0: 2, 1: 407, 2: 591},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 142,\n",
       "  'status': {54: 2, 213: 2, 536: 2, 553: 2, 754: 2, 975: 2},\n",
       "  'node_count': {0: 2, 1: 401, 2: 597},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 143,\n",
       "  'status': {360: 2, 388: 2, 706: 2, 840: 2},\n",
       "  'node_count': {0: 2, 1: 397, 2: 601},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 144,\n",
       "  'status': {},\n",
       "  'node_count': {0: 2, 1: 397, 2: 601},\n",
       "  'status_delta': {0: 0, 1: 0, 2: 0}},\n",
       " {'iteration': 145,\n",
       "  'status': {793: 2},\n",
       "  'node_count': {0: 2, 1: 396, 2: 602},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 146,\n",
       "  'status': {251: 2, 585: 2, 635: 2, 998: 2},\n",
       "  'node_count': {0: 2, 1: 392, 2: 606},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 147,\n",
       "  'status': {276: 2, 320: 2, 380: 2, 522: 2, 667: 2, 669: 2, 889: 2},\n",
       "  'node_count': {0: 2, 1: 385, 2: 613},\n",
       "  'status_delta': {0: 0, 1: -7, 2: 7}},\n",
       " {'iteration': 148,\n",
       "  'status': {62: 2,\n",
       "   63: 2,\n",
       "   100: 2,\n",
       "   215: 2,\n",
       "   463: 2,\n",
       "   532: 2,\n",
       "   608: 2,\n",
       "   843: 2,\n",
       "   957: 2},\n",
       "  'node_count': {0: 2, 1: 376, 2: 622},\n",
       "  'status_delta': {0: 0, 1: -9, 2: 9}},\n",
       " {'iteration': 149,\n",
       "  'status': {173: 2, 545: 2, 668: 2, 683: 2},\n",
       "  'node_count': {0: 2, 1: 372, 2: 626},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 150,\n",
       "  'status': {24: 2, 139: 2, 167: 2, 187: 2, 501: 2},\n",
       "  'node_count': {0: 2, 1: 367, 2: 631},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 151,\n",
       "  'status': {417: 2, 948: 2, 959: 2},\n",
       "  'node_count': {0: 2, 1: 364, 2: 634},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 152,\n",
       "  'status': {12: 2, 42: 2, 201: 2, 315: 2, 345: 2, 565: 2},\n",
       "  'node_count': {0: 2, 1: 358, 2: 640},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 153,\n",
       "  'status': {2: 2, 262: 2, 496: 2, 627: 2, 696: 2, 756: 2, 868: 2, 965: 2},\n",
       "  'node_count': {0: 2, 1: 350, 2: 648},\n",
       "  'status_delta': {0: 0, 1: -8, 2: 8}},\n",
       " {'iteration': 154,\n",
       "  'status': {},\n",
       "  'node_count': {0: 2, 1: 350, 2: 648},\n",
       "  'status_delta': {0: 0, 1: 0, 2: 0}},\n",
       " {'iteration': 155,\n",
       "  'status': {711: 1, 755: 2},\n",
       "  'node_count': {0: 1, 1: 350, 2: 649},\n",
       "  'status_delta': {0: -1, 1: 0, 2: 1}},\n",
       " {'iteration': 156,\n",
       "  'status': {82: 2, 752: 2, 914: 2, 926: 2},\n",
       "  'node_count': {0: 1, 1: 346, 2: 653},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 157,\n",
       "  'status': {214: 2, 375: 2, 412: 2, 414: 2, 841: 2, 861: 2},\n",
       "  'node_count': {0: 1, 1: 340, 2: 659},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 158,\n",
       "  'status': {657: 2, 689: 2, 731: 2},\n",
       "  'node_count': {0: 1, 1: 337, 2: 662},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 159,\n",
       "  'status': {6: 2, 169: 2, 174: 2, 606: 2, 807: 2},\n",
       "  'node_count': {0: 1, 1: 332, 2: 667},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 160,\n",
       "  'status': {155: 2},\n",
       "  'node_count': {0: 1, 1: 331, 2: 668},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 161,\n",
       "  'status': {515: 2, 539: 2, 592: 2},\n",
       "  'node_count': {0: 1, 1: 328, 2: 671},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 162,\n",
       "  'status': {},\n",
       "  'node_count': {0: 1, 1: 328, 2: 671},\n",
       "  'status_delta': {0: 0, 1: 0, 2: 0}},\n",
       " {'iteration': 163,\n",
       "  'status': {739: 2, 899: 2, 911: 2},\n",
       "  'node_count': {0: 1, 1: 325, 2: 674},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 164,\n",
       "  'status': {547: 2, 794: 2, 830: 2},\n",
       "  'node_count': {0: 1, 1: 322, 2: 677},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 165,\n",
       "  'status': {599: 2, 977: 2},\n",
       "  'node_count': {0: 1, 1: 320, 2: 679},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 166,\n",
       "  'status': {177: 2, 582: 2, 943: 2},\n",
       "  'node_count': {0: 1, 1: 317, 2: 682},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 167,\n",
       "  'status': {25: 2},\n",
       "  'node_count': {0: 1, 1: 316, 2: 683},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 168,\n",
       "  'status': {258: 2, 526: 2},\n",
       "  'node_count': {0: 1, 1: 314, 2: 685},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 169,\n",
       "  'status': {3: 2, 394: 2, 786: 2, 795: 2},\n",
       "  'node_count': {0: 1, 1: 310, 2: 689},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 170,\n",
       "  'status': {211: 2, 234: 2},\n",
       "  'node_count': {0: 1, 1: 308, 2: 691},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 171,\n",
       "  'status': {184: 2, 188: 2, 366: 2, 416: 2, 833: 2, 882: 2},\n",
       "  'node_count': {0: 1, 1: 302, 2: 697},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 172,\n",
       "  'status': {478: 2, 758: 2},\n",
       "  'node_count': {0: 1, 1: 300, 2: 699},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 173,\n",
       "  'status': {294: 2, 461: 2, 601: 2, 870: 2},\n",
       "  'node_count': {0: 1, 1: 296, 2: 703},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 174,\n",
       "  'status': {504: 2, 564: 2},\n",
       "  'node_count': {0: 1, 1: 294, 2: 705},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 175,\n",
       "  'status': {34: 2, 218: 2, 233: 2, 483: 2, 769: 2, 855: 2},\n",
       "  'node_count': {0: 1, 1: 288, 2: 711},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 176,\n",
       "  'status': {134: 2, 408: 2, 823: 2, 856: 2, 949: 2},\n",
       "  'node_count': {0: 1, 1: 283, 2: 716},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 177,\n",
       "  'status': {154: 2, 456: 2, 472: 2, 566: 2, 875: 2},\n",
       "  'node_count': {0: 1, 1: 278, 2: 721},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 178,\n",
       "  'status': {149: 2, 652: 2},\n",
       "  'node_count': {0: 1, 1: 276, 2: 723},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 179,\n",
       "  'status': {5: 2, 109: 2, 503: 2, 514: 2},\n",
       "  'node_count': {0: 1, 1: 272, 2: 727},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 180,\n",
       "  'status': {114: 2, 122: 2, 749: 2, 800: 2, 846: 2},\n",
       "  'node_count': {0: 1, 1: 267, 2: 732},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 181,\n",
       "  'status': {740: 2, 776: 2},\n",
       "  'node_count': {0: 1, 1: 265, 2: 734},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 182,\n",
       "  'status': {367: 2, 743: 2, 753: 2, 850: 2},\n",
       "  'node_count': {0: 1, 1: 261, 2: 738},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 183,\n",
       "  'status': {469: 2, 757: 2},\n",
       "  'node_count': {0: 1, 1: 259, 2: 740},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 184,\n",
       "  'status': {348: 2, 557: 2, 879: 2},\n",
       "  'node_count': {0: 1, 1: 256, 2: 743},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 185,\n",
       "  'status': {282: 2, 819: 2},\n",
       "  'node_count': {0: 1, 1: 254, 2: 745},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 186,\n",
       "  'status': {47: 2, 495: 2, 530: 2, 531: 2, 897: 2},\n",
       "  'node_count': {0: 1, 1: 249, 2: 750},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 187,\n",
       "  'status': {390: 2, 466: 2, 560: 2, 568: 2, 628: 2, 787: 2, 946: 2},\n",
       "  'node_count': {0: 1, 1: 242, 2: 757},\n",
       "  'status_delta': {0: 0, 1: -7, 2: 7}},\n",
       " {'iteration': 188,\n",
       "  'status': {145: 2, 404: 2, 913: 2},\n",
       "  'node_count': {0: 1, 1: 239, 2: 760},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 189,\n",
       "  'status': {29: 2, 578: 2},\n",
       "  'node_count': {0: 1, 1: 237, 2: 762},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 190,\n",
       "  'status': {500: 2},\n",
       "  'node_count': {0: 1, 1: 236, 2: 763},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 191,\n",
       "  'status': {474: 2},\n",
       "  'node_count': {0: 1, 1: 235, 2: 764},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 192,\n",
       "  'status': {87: 2, 467: 2},\n",
       "  'node_count': {0: 1, 1: 233, 2: 766},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 193,\n",
       "  'status': {},\n",
       "  'node_count': {0: 1, 1: 233, 2: 766},\n",
       "  'status_delta': {0: 0, 1: 0, 2: 0}},\n",
       " {'iteration': 194,\n",
       "  'status': {298: 2, 890: 2},\n",
       "  'node_count': {0: 1, 1: 231, 2: 768},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 195,\n",
       "  'status': {77: 2, 894: 2},\n",
       "  'node_count': {0: 1, 1: 229, 2: 770},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 196,\n",
       "  'status': {18: 2, 90: 2, 403: 2},\n",
       "  'node_count': {0: 1, 1: 226, 2: 773},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 197,\n",
       "  'status': {340: 2},\n",
       "  'node_count': {0: 1, 1: 225, 2: 774},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 198,\n",
       "  'status': {663: 2},\n",
       "  'node_count': {0: 1, 1: 224, 2: 775},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 199,\n",
       "  'status': {13: 2, 673: 2},\n",
       "  'node_count': {0: 1, 1: 222, 2: 777},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iterations = model.iteration_bunch(200, node_status=True)\n",
    "iterations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "9428e785-c5ce-4df3-992c-5863acef8aa6"
    }
   },
   "source": [
    "To abstract from iterations details it is possible to transform them into diffusion **trends** using the ``build_trends(iterations)`` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "nbpresent": {
     "id": "0fe368c9-aee0-4f5f-a37a-a44557b21901"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'trends': {'node_count': {0: [990,\n",
       "     989,\n",
       "     989,\n",
       "     987,\n",
       "     986,\n",
       "     983,\n",
       "     983,\n",
       "     982,\n",
       "     978,\n",
       "     976,\n",
       "     969,\n",
       "     964,\n",
       "     960,\n",
       "     956,\n",
       "     955,\n",
       "     950,\n",
       "     939,\n",
       "     932,\n",
       "     927,\n",
       "     925,\n",
       "     918,\n",
       "     912,\n",
       "     904,\n",
       "     892,\n",
       "     882,\n",
       "     871,\n",
       "     863,\n",
       "     851,\n",
       "     844,\n",
       "     830,\n",
       "     813,\n",
       "     800,\n",
       "     786,\n",
       "     771,\n",
       "     759,\n",
       "     738,\n",
       "     721,\n",
       "     696,\n",
       "     680,\n",
       "     665,\n",
       "     643,\n",
       "     628,\n",
       "     605,\n",
       "     578,\n",
       "     555,\n",
       "     538,\n",
       "     517,\n",
       "     496,\n",
       "     480,\n",
       "     451,\n",
       "     429,\n",
       "     412,\n",
       "     394,\n",
       "     373,\n",
       "     354,\n",
       "     339,\n",
       "     321,\n",
       "     300,\n",
       "     284,\n",
       "     270,\n",
       "     263,\n",
       "     249,\n",
       "     234,\n",
       "     215,\n",
       "     198,\n",
       "     184,\n",
       "     167,\n",
       "     153,\n",
       "     140,\n",
       "     130,\n",
       "     117,\n",
       "     109,\n",
       "     105,\n",
       "     102,\n",
       "     100,\n",
       "     97,\n",
       "     93,\n",
       "     84,\n",
       "     80,\n",
       "     76,\n",
       "     71,\n",
       "     67,\n",
       "     63,\n",
       "     57,\n",
       "     53,\n",
       "     51,\n",
       "     49,\n",
       "     40,\n",
       "     39,\n",
       "     36,\n",
       "     33,\n",
       "     31,\n",
       "     28,\n",
       "     27,\n",
       "     24,\n",
       "     24,\n",
       "     23,\n",
       "     21,\n",
       "     21,\n",
       "     20,\n",
       "     17,\n",
       "     14,\n",
       "     13,\n",
       "     12,\n",
       "     12,\n",
       "     12,\n",
       "     12,\n",
       "     11,\n",
       "     11,\n",
       "     11,\n",
       "     9,\n",
       "     8,\n",
       "     8,\n",
       "     6,\n",
       "     5,\n",
       "     5,\n",
       "     5,\n",
       "     5,\n",
       "     4,\n",
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       "     3,\n",
       "     3,\n",
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       "     3,\n",
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       "     3,\n",
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       "     3,\n",
       "     3,\n",
       "     3,\n",
       "     3,\n",
       "     3,\n",
       "     3,\n",
       "     3,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     1,\n",
       "     1,\n",
       "     1,\n",
       "     1,\n",
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       "     2,\n",
       "     9,\n",
       "     0,\n",
       "     4,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     1,\n",
       "     3,\n",
       "     3,\n",
       "     4,\n",
       "     3,\n",
       "     7,\n",
       "     3,\n",
       "     6,\n",
       "     5,\n",
       "     5,\n",
       "     8,\n",
       "     7,\n",
       "     3,\n",
       "     7,\n",
       "     5,\n",
       "     5,\n",
       "     10,\n",
       "     3,\n",
       "     6,\n",
       "     4,\n",
       "     3,\n",
       "     5,\n",
       "     9,\n",
       "     8,\n",
       "     7,\n",
       "     6,\n",
       "     8,\n",
       "     18,\n",
       "     4,\n",
       "     4,\n",
       "     4,\n",
       "     4,\n",
       "     10,\n",
       "     6,\n",
       "     12,\n",
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       "     2,\n",
       "     2,\n",
       "     5,\n",
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       "     3,\n",
       "     8,\n",
       "     13,\n",
       "     9,\n",
       "     4,\n",
       "     4,\n",
       "     12,\n",
       "     11,\n",
       "     6,\n",
       "     8,\n",
       "     5,\n",
       "     5,\n",
       "     6,\n",
       "     3,\n",
       "     3,\n",
       "     6,\n",
       "     4,\n",
       "     9,\n",
       "     6,\n",
       "     6,\n",
       "     4,\n",
       "     6,\n",
       "     2,\n",
       "     6,\n",
       "     5,\n",
       "     6,\n",
       "     4,\n",
       "     5,\n",
       "     5,\n",
       "     7,\n",
       "     5,\n",
       "     2,\n",
       "     3,\n",
       "     4,\n",
       "     3,\n",
       "     4,\n",
       "     0,\n",
       "     5,\n",
       "     6,\n",
       "     7,\n",
       "     10,\n",
       "     2,\n",
       "     3,\n",
       "     6,\n",
       "     4,\n",
       "     5,\n",
       "     6,\n",
       "     5,\n",
       "     11,\n",
       "     5,\n",
       "     2,\n",
       "     3,\n",
       "     2,\n",
       "     5,\n",
       "     5,\n",
       "     4,\n",
       "     7,\n",
       "     5,\n",
       "     3,\n",
       "     3,\n",
       "     6,\n",
       "     4,\n",
       "     0,\n",
       "     1,\n",
       "     4,\n",
       "     7,\n",
       "     9,\n",
       "     4,\n",
       "     5,\n",
       "     3,\n",
       "     6,\n",
       "     8,\n",
       "     0,\n",
       "     1,\n",
       "     4,\n",
       "     6,\n",
       "     3,\n",
       "     5,\n",
       "     1,\n",
       "     3,\n",
       "     0,\n",
       "     3,\n",
       "     3,\n",
       "     2,\n",
       "     3,\n",
       "     1,\n",
       "     2,\n",
       "     4,\n",
       "     2,\n",
       "     6,\n",
       "     2,\n",
       "     4,\n",
       "     2,\n",
       "     6,\n",
       "     5,\n",
       "     5,\n",
       "     2,\n",
       "     4,\n",
       "     5,\n",
       "     2,\n",
       "     4,\n",
       "     2,\n",
       "     3,\n",
       "     2,\n",
       "     5,\n",
       "     7,\n",
       "     3,\n",
       "     2,\n",
       "     1,\n",
       "     1,\n",
       "     2,\n",
       "     0,\n",
       "     2,\n",
       "     2,\n",
       "     3,\n",
       "     1,\n",
       "     1,\n",
       "     2]}}}]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trends = model.build_trends(iterations)\n",
    "trends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "3f73d8fc-52c2-4d61-becb-40cb61b95e01"
    }
   },
   "source": [
    "<a id=\"top\"></a>\n",
    "###  2.D Results Visualisation ([to top](#top))\n",
    "\n",
    "Finally, ``NDlib`` allows to inspect the behavior of the simulated model using standard plots such as the ``DiffusionTrend`` and ``DiffusionPrevalence`` ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "nbpresent": {
     "id": "e99ca466-636e-41c6-92df-5c47e9e8f284"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from ndlib.viz.mpl.DiffusionTrend import DiffusionTrend\n",
    "viz = DiffusionTrend(model, trends)\n",
    "viz.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "nbpresent": {
     "id": "1329a916-f0ee-426f-a543-c4e93c038fc5"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ndlib.viz.mpl.DiffusionPrevalence import DiffusionPrevalence\n",
    "viz = DiffusionPrevalence(model, trends)\n",
    "viz.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ccea099f-8005-4323-a95c-d62f233c22f8"
    }
   },
   "source": [
    "The proposed visualisation are realised using the [``matplotlib``](https://matplotlib.org) python library. They are fully customizable and all the metadata they visualize is gathered by the ``model`` object.\n",
    "\n",
    "To obtain web-oriented versions of such plots ``NDlib`` exposes a second visualisation endpoint built on top of [``bokeh``](https://bokeh.pydata.org/en/latest/): the plotting facilities its defines are collected within the sub-package ``ndlib.viz.bokeh`` and follow the same rationale of their ``matplotlib`` counterpart."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "a9bd10e4-0421-4a26-a23f-89a7c4a6d40c"
    }
   },
   "source": [
    "<a id=\"models\"></a>\n",
    "## 3. Available models ([to top](#top))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "000e4199-f50b-4ea2-bbee-504ec2241ceb"
    }
   },
   "source": [
    "The analysis of diffusive phenomena that unfold on top of complex networks is a task able to attract growing interests from multiple fields of research.\n",
    "\n",
    "In order to provide a succinct framing of such complex and extensively studied problem it is possible to split the related literature into two broad, related, sub-classes: **Epidemics** and **Opinion Dynamics**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "7f7f36d3-bba7-41ee-aef9-178fffde0c92"
    }
   },
   "source": [
    "<a id=\"epidemics\"></a>\n",
    "### 3.A Epidemics ([to top](#top))\n",
    "\n",
    "When we talk about epidemics, we think about contagious diseases caused by biological pathogens, like influenza, measles, chickenpox and sexually transmitted viruses that spread from person to person. \n",
    "\n",
    "Several elements determine the patterns by which epidemics spread through groups of people: the properties carried by the pathogen (its contagiousness, the length of its infectious period and its severity), the structure of the network as well as the mobility patterns of the people involved. \n",
    "\n",
    "In ``NDlib`` are implemented the following 12 Epidemic models:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "14d28c55-bb33-4479-9fbc-3b988509315b"
    }
   },
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/SIm.html'>SI</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/SIS.html'>SIS</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/SIR.html'>SIR</a></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/SEIR.html'>SEIR</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/SEIS.html'>SEIS</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/SWIR.html'>SWIR</a></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/Threshold.html'>Threshold</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/GeneralisedThreshold.html'>Generalised Threshold</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/KThreshold.html'>Kertesz Threshold</a></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/Profile.html'>Profile</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/ProfileThreshold.html'>Profile-Threshold</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/epidemics/IndependentCascades.html'>Independent Cascades</a></td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "bdcdf439-7e48-45c9-9a31-f545cd7082a7"
    }
   },
   "source": [
    "<a id=\"opinions\"></a>\n",
    "### 3.B Opinion Dynamics ([to top](#top))\n",
    "\n",
    "A different field related with modelling social behaviour is that of opinion dynamics.\n",
    "\n",
    "Recent years have witnessed the introduction of a wide range of models that attempt to explain how opinions form in a population, taking into account various social theories (e.g. bounded confidence or social impact).\n",
    "\n",
    "These models have a lot in common with those seen in epidemics and spreading. In general, individuals are modelled as agents with a state and connected by a social network.\n",
    "\n",
    "The social links can be represented by a complete graph (*mean field* models) or by more realistic complex networks, similar to epidemics and spreading.\n",
    "\n",
    "The state is typically represented by variables, that can be *discrete* (similar to the case of spreading), but also *continuous*, representing for instance a probability to choose one option or another. The state of individuals changes in time, based on a set of update rules, mainly through interaction with the neighbours.\n",
    "\n",
    "While in many spreading and epidemics models this change is irreversible (susceptible to infected), in opinion dynamics the state can oscillate freely between the possible values, simulating thus how opinions change in reality.\n",
    "\n",
    "In ``NDlib`` are implemented the following 6 Opinion Dynamics models:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f13e6790-2a8f-4245-9116-687e0fca0d78"
    }
   },
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/opinion/Voter.html'>Voter</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/opinion/QVoter.html'>Q-Voter</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/opinion/MajorityRule.html'>Majority Rule</a></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/opinion/Snajzd.html'>Sznajd</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/opinion/COD.html'>Cognitive Opinion Dynamics</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/opinion/AlgorithmicBias.html'>Algorithmic Bias</a></td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c9690abd-6b82-431b-847c-1c13ea64ef82"
    }
   },
   "source": [
    "<a id=\"advanced\"></a>\n",
    "## 4 Advanced Model Configurations ([to top](#top))\n",
    "\n",
    "As already discussed, the ``ModelConfig`` object is the common interface ``NDlib`` use to set up simulation experiments.\n",
    "\n",
    "``ModelConfig`` allows to specify four categories of experiment configurations:\n",
    "- **Model** configuration (as already discussed)\n",
    "- **Node** Configuration\n",
    "- **Edge** Configuration\n",
    "- Simulation **Initial Conditions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "bd8f5f38-85d0-4670-ad44-7f72d9911956"
    }
   },
   "source": [
    "<a id=\"nodes\"></a>\n",
    "### 4.A Node Attributes ([to top](#top))\n",
    "\n",
    "Node configuration involves the instantiation of both the *mandatory* and *optional* parameters attached to individual nodes.\n",
    "\n",
    "Let's consider as an example the ``Threshold`` model. \n",
    "\n",
    "In such model a *susceptible* node in order to become *infected* needs that at least $\\tau\\%$ of its neighbors are already *infected*. \n",
    "\n",
    "We can assign a value of $\\tau\\%$ to every node as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "nbpresent": {
     "id": "efc80df0-5287-4721-80ef-6251b5c4f516"
    }
   },
   "outputs": [],
   "source": [
    "model = ep.ThresholdModel(g)\n",
    "\n",
    "config = mc.Configuration()\n",
    "config.add_model_parameter('percentage_infected', 0.1)\n",
    "\n",
    "threshold = 0.25\n",
    "for i in g.nodes():\n",
    "    config.add_node_configuration(\"threshold\", i, threshold) # node attribute setting\n",
    "\n",
    "model.set_initial_status(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c03c6a66-b052-4807-86bc-ac9c5a58ef9d"
    }
   },
   "source": [
    "<a id=\"edges\"></a>\n",
    "### 4.B Edge Attributes ([to top](#top)) \n",
    "\n",
    "Edge configuration involves the instantiation of both the *mandatory* and *optional* parameters attached to individual edges.\n",
    "\n",
    "Let's consider as an example the ``IndependentCascades`` model. \n",
    "\n",
    "In such model a *susceptible* node become *infected* with probability $p$, where $p$ is a value attached to the link connecting the *susceptible* node to an *infected* neighbor.\n",
    "\n",
    "We can assign a value of $p$ to every edge as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "nbpresent": {
     "id": "9bf3f682-408b-4dc5-92ad-37522958f0e6"
    }
   },
   "outputs": [],
   "source": [
    "model = ep.IndependentCascadesModel(g)\n",
    "\n",
    "config = mc.Configuration()\n",
    "config.add_model_parameter('percentage_infected', 0.1)\n",
    "\n",
    "threshold = 0.1\n",
    "for e in g.edges():\n",
    "    config.add_edge_configuration(\"threshold\", e, threshold) # edge attribute setting\n",
    "\n",
    "model.set_initial_status(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "696e1baf-ecd9-4023-bc9e-97b47457152f"
    }
   },
   "source": [
    "<a id=\"seeds\"></a>\n",
    "### 4.C Infection Seeds Selection ([to top](#top))\n",
    "\n",
    "Status configuration allows to specify explicitly the status of a set of nodes at the beginning of the simulation.\n",
    "\n",
    "So far we have assumed that a random sample of nodes (10% in our examples) were initially infected: in order to cover more specific simulation scenarios we can also explicitly *specify* the nodes belonging to each *status* at the beginning of the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "nbpresent": {
     "id": "7e7040d6-62ab-4455-891a-b622852735aa"
    }
   },
   "outputs": [],
   "source": [
    "import ndlib.models.ModelConfig as mc\n",
    "\n",
    "# Model Configuration\n",
    "config = mc.Configuration()\n",
    "\n",
    "infected_nodes = [0, 1, 2, 3, 4, 5]\n",
    "config.add_model_initial_configuration(\"Infected\", infected_nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c2adc5fc-0abe-4c82-9710-ed2fdeb498b4"
    }
   },
   "source": [
    "**NB:** Explicit status specification takes priority over the percentage specification expressed via model definition (e.g. percentage_infected)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c6ecf5d8-7628-44dd-8378-8d88ebda4f5b"
    }
   },
   "source": [
    "<a id=\"stability\"></a>\n",
    "#### 4.C.a Model Stability ([to top](#top))\n",
    "\n",
    "Indeed, different initial conditions can affect the overall unfolding of the diffusive process.\n",
    "\n",
    "In order to analyse the stability of a model w.r.t. the initial seeds ``NDlib`` implements a ``multi_runs`` facility.\n",
    "\n",
    "``multi_runs`` allows the parallel execution of multiple instances of a given model starting from different initial infection conditions.\n",
    "\n",
    "We can instantiate ``multi_runs`` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "nbpresent": {
     "id": "29974cb8-670b-41ad-b971-a6724156d22c"
    }
   },
   "outputs": [],
   "source": [
    "from ndlib.utils import multi_runs\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "model = ep.SIRModel(g)\n",
    "config = mc.Configuration()\n",
    "config.add_model_parameter('beta', 0.001)\n",
    "config.add_model_parameter('gamma', 0.01)\n",
    "config.add_model_parameter(\"percentage_infected\", 0.05)\n",
    "model.set_initial_status(config)\n",
    "\n",
    "trends = multi_runs(model, execution_number=10, iteration_number=100, nprocesses=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "7ffbd058-8531-45bd-a881-03eb62ed2bd0"
    }
   },
   "source": [
    "In our example the initial seeds for each instance of the model were specified by the ``percentage_infected`` model parameter.\n",
    "\n",
    "Indeed we can also explicitly parametrize the seed sets as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "nbpresent": {
     "id": "bf34e091-0643-41c2-96da-31e16396f6a8"
    }
   },
   "outputs": [],
   "source": [
    "model = ep.SIRModel(g)\n",
    "\n",
    "config = mc.Configuration()\n",
    "config.add_model_parameter('beta', 0.001)\n",
    "config.add_model_parameter('gamma', 0.01)\n",
    "model.set_initial_status(config)\n",
    "\n",
    "infection_sets = [(1, 2, 3, 4, 5), (3, 23, 22, 54, 2), (98, 2, 12, 26, 3), (4, 6, 9) ]\n",
    "trends1 = multi_runs(model, execution_number=4, iteration_number=100, infection_sets=infection_sets, nprocesses=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "36a0a7ae-1fcc-46d0-814e-b80b2a7c5487"
    }
   },
   "source": [
    "<a id=\"stability_vis\"></a>\n",
    "#### 4.C.b Stability Visualisation ([to top](#top))\n",
    "\n",
    "Model stability can be easily analised by using the visualisation facilities offered by the library: both ``DiffusionTrend`` and ``DiffusionPrevalence`` plots allows to plot mean trends along with their point-wise variation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "nbpresent": {
     "id": "34df8d46-7ebe-4a2f-8f5e-d6f1872fa631"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = DiffusionTrend(model, trends)\n",
    "viz.plot(percentile=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "nbpresent": {
     "id": "94fa1df2-f659-4788-baca-ec3a37661630"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = DiffusionPrevalence(model, trends)\n",
    "viz.plot(percentile=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e152a844-f26c-4151-9aaf-276a41d5170d"
    }
   },
   "source": [
    "<a id=\"comparing\"></a>\n",
    "## 5. Comparing Diffusion models ([to top](#top))\n",
    "\n",
    "A common goal for which diffusion simulations are executed is to perform comparison among different models (or different instantiations of a same model).\n",
    "\n",
    "To address such demands ``NDlib`` provides visual comparison plots.\n",
    "\n",
    "To show how they work, as a first step we execute a second model (in this exampe an **SI** model) over our original graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "nbpresent": {
     "id": "83c9c8d8-c485-4b3e-a5c4-32fec616e3d2"
    }
   },
   "outputs": [],
   "source": [
    "model1 = ep.SIModel(g)\n",
    "cfg = mc.Configuration()\n",
    "cfg.add_model_parameter('beta', 0.001)\n",
    "cfg.add_model_parameter(\"percentage_infected\", 0.01)\n",
    "model1.set_initial_status(cfg)\n",
    "\n",
    "trends1 = multi_runs(model1, execution_number=10, iteration_number=100, nprocesses=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "eb42ef55-d83f-415e-ab46-34429f6b5253"
    }
   },
   "source": [
    "Then, we can compare them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "nbpresent": {
     "id": "8a64a937-c48d-4fbd-a344-3eba502fa205"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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a8vhCRs2atEuEZhkx0xTfW4B6LvLJE0K8FtUYUAAHgP2lU+8XQtw7xxg1Gs1MSPRC10G1udbpUcfMHDz9ObX+tP4OWHNbebxtQSGtWmfovU6aZcRMBWp36fGpi46/t/T4oJTyNVLKO4A/QQnW/5xDfBqNZibE2qHnMPgqy+IE8MI/K2ujSDNc/77ycWlDJgq1m8EbXvx4NZrLMFOBqgVyUsroRcfvAiTwhTHHvlx6vGGWsWk0mpmQ7IP+48r8dWSvE8C5p6HtSXXstt8tC5eUygC2aj1Url6amDWayzBTgQoBxbEHhBBrgAagW0p5YuS4lDIOxFCiptFoFhLLhIGTpTWnMWm6VD+88KB6vve9UFESIikhPaB6OlWvX/x4NZppMFOBGgJCQoixZT53lh5/Osl4F5CaTWAajWYGJLvByo9P69kmPPNFKGag5UbYcGf5XCYK4WbtTq5Z1sxUoF4qPf4OgBDCB3wIld57bOxAIUQDEAB65hijRqO5HGZeGb76KsYfP/odVWbuq4SbPlAWokIKPGGo26LdyTXLmpn+6/xHVOHDHwohXgVOA9cAw8C3Lxo74td/ZE4RajSayzPcrlJ2Y1N7AyfhldJH8paPllu5SxtZyFKo3ox9iY+/ZcsFDlijmR4zqimVUv6XEOIvgQeAraXDQ8CvSSmTFw3/9dLjY2g0moWhkIHh86owYoRUP/Kpz4FtU9j88+Qi27AyBfJFm8xwP8PuepJ2ASEG8LoMAh4nhgOyeZtM0cS0JD63QXXATWXAjdMhSOdNYpkiybxJyOOkJugm4HXhdxk4HDpFqFkYZrzpQUr5R0KIB1HVeQnggJQyNnaMEMKF2iv1feDR+QhUo7layJsWyZyJy3DgcTpwG45Li8DQWXA6saSDdM4kOdxL5U8/hTM7QDq8gXO192L3q++OTruIBwfOmvXUON0AFC2bTN5ESnAagrDXhUMIipbNQDJPVyyLAAQCj0vFksyZDKTyKrEvwOsy8LkMAh6DoMeJx2ngdjpwGQ6cDjEudtOyKVoSiRw9L/QamOYSzGpXnpTyAnDhMueLwN/ONiiN5mqlYNq80hEnkS9iCCUNEpVycwgQQuAxHIR9LiqsGP6hduIiwmB/DPJxNh76NK7sAIXIOoauv5+Qq9wqw5mJk63ZiVESJwCXoYTkYi51fORcwFP+01G0bPJFm1TOpMvOjh4fSRQ6BLgNBwXLxpZqjWDkvEOAx2ngKgmayyHwuw0iPjd+j3HJGDRXB3rbuEazTDAtm2M9CbKmRW1wYoNAKZVUWbYknkiS7zmI6QrgMIoERYFVR/8Gd7aXQqiZ3r33Y48RJ0chgemrwfLVXDaGkddwzGBWo8QMfBiTnrelxLYlPrdzwn2llJi2xLIkRdPEltCfzCNlGhsIeZzjRCrocdIQ8Y4TSM3KZVb/l4UQa1GVfHcCLYBXSukcc74C5ccngb8qzag0Gs0lsG3Jyd4k8UyBqoBn0jFCCATgcEjC6dMIvwe71Eyw+uhXcSc7KAYa6L3+AWx3cMzNTRyWRapmA+m8STpvkS6Y5IoW2YJFKm/SGcvSHs3QPpShYNnUhTzUh700hL04HALblli2xGkIKnxuKgMuqvxuqgJuKvxujMusQzmEwGFMfl4IgcsQuCbRNiklBcumYNqjx7qzWdqH0lQFPayu8hPxuXSKcAUzY4ESQrwV+CrgZ/xsfRQpZUwI8XrgNuAY8J9zjFOjWVHkTYtM3sK0JUXTZjhTYCCVp/oS4jQWV7ITIzeM6VezIU/sLOHO/Uhh0Lfrt7A8kdGx8bzND04keLzXTzx/dNrx9cRy9MRy0xorBER8LhoiXrY2hNnWFGZdTQDnHNNzQgg8zvHK5XUZSCnJ5C0OdQzjczlZXeWnJuSZ8+tplh8zEighxBbg64AXVXL+deD/B6onGf5PwGuBN6EFSqNRqblske5YlsFUHkFpHQaB4RBU+d1T3QJHIYEndhrTN9LfyaL62MMAxNfcTTG4iqIl6Uja7G8v8uT5AkXbhXRIEIKAW1Xt+d0GPrcTn8uB3+2kIeyltdpPa5Ufv9ugL5GnN55lIJnHlmA4VIxFy2YoXSCWKTKUKTCcLhDLFoll1M+JniTfebkLt9NBxOdSRR5OB4ZDYNmSYmkdqiboprnST3Olj/qwl5DHSdDrxOcyppwRCSEIeJwEPE7ypsXx3iTOgRRNER81QQ9Br/OyMzrNlcNMZ1D3o8Tp81LKjwEIIaxLjB0pL9defJqrnlTe5GhnnJxp4XUaVPndM05NCTOHb+AVLHcIhJpZhNqfxB2/QMyo5LOpuzn1ZJrOpM1IVkxIyXWrq3nTdS2sqw1Me22ptUqJ1XQwLZtYtsj5aJpj3QmO9SToieUYSOYveU3XcJbDHfEJxw1DUBfy0FLpZ1Wlj9YqP+trg0R8rknuogosPEEDy5b0xHN0DGdwOgS1pRRl2OvSZfBXMDMVqDtQX/o+M9VAKWWfECKNWqPSaK5a4pkihzuH8TiNaaXwJsW28A4dx5Y20aKbswMm3YPDvPPct4nZNp/kbTyTcQA2QkBT0MHWcJ47r1lLY/P0jWBH1n2cDse0ZyFOw0FN0ENN0MPe1Wpml8qbpPMmBdMmb9qYtrqnq7QW1ZfI0zmcoXNYzSZTeZNU3iRftMvpxXPl16gJuVlfG2RLQ5gdq8LUhcYXkRgOMSpili2Jpgp0x3K4DAfNlT4q/W4MQ2AINRN0O3U68EpgpgLVACSllH3THJ8HglOO0mhWKAPJHK92J0b3B00Hy5YkskVi2QLDmSIDyRwDfT30xlJ0ZJzEcmkAfo9v4SbDz9hOrGYPv1LvYnOVQWtYECoOUQw0kq9qHb2vlHLCrM2yJbmiRd60StuaBEGPk2S+iGVJpJC4HAaGEDgcSgikLFXmlUrGDYfAWUoBjtw/6HESvEyl3erqADesndi5N29adMdyo+J1Pprm3GCawWSBweQQB9qGAKgPe9ixKsKOVRG2NITwu8uvZTgEIa+LEGp21zGU4UI0rd6fUKYbIa+LlkofFX63FqtlzEwFKg2EhRCGlPJSqT0AhBAhoALon21wGs2VSt606BjKciGaptLvnvZ+nidP9vONF9rJF8uVa0gLYZtIYQCSgEtwR6CNn08ewGG4WH3Le/hEJFAaK3FmoyVxUkaw2YJFqmBiiNG9taMYDgeVfhetfj9Bn5OA21kSIUm2aKmKv7xJwVIzoaJl4xAo0SrNsHKmRa5ok89bSAlGaY1oNnuYPE6DtTUB1tYERo9ZtqQ7luVUX5JXexIc607Ql8jTl+jn8eP9OByC9bUBtjeF2doQZn1dcPS1nYaDiknW9rIFi2M9CQTQEPHSUuUfJ3Ka5cFM/4+8CtwC7AFemGLs/0B5/f1sFnFpNFckpmXTm8hxbkB9Y68Jeqa17mNaNv96oJ0nT6jvc2Gfkwq/m0qfQb09QGPES1PYSVPQQa3Ppvm5b+N2CWLrfw5npGH0PmPFKW9Jkrk8IY+T3a0VRHwuVTVYcnPwOJVTxWRrYUII/G4nfreT2tD00pK2LUnmTaKpPL2JHMmc2l0yIopjxVGWfvE4lQvF5dKJhkPQUuWnpcrPHVvrsWxJ20CKo91xXu1OcHYgxek+9fMI3TgNwca6IOtqg6yrDbC+JkhlYLxI+dwGPreBLSUDyQI98RyNJaFyjxFWIcToBmnN4jNTgfo2cCvwKSHEPVJKe7JBQoidwF+h/k1+fW4hajRXBrmixZHOGJmCRcTrmnbZczxb5EtPnOZ0XwqnIXj3a9Zw20bVRs2VuIA7bmP5ymsuoQtP4E62Y3qria37+dHjRnaYpLuWIc8ayBTxuQy2N4WVSJYEQO05WpiUlqO0DhTxuVhbEyBv2thS7Z+ypXKNGKkGtGxJKmcSTReIpvKYthwVL1uC0yFwGmrNym2MF1HDIdhYH2JjfYi37oJMweR4T5LjPQmO9yboHMqWfi/bg0b8LlZX+1lbHWBDXZBtjWGchgOHUDHLklB1x7LjvlDIMa/pcggCXhcVPhcBjxO30zEasyi5ZehS9/llpgL1j6gW7m8AHhdC/O3IPUqitBq4B3g34EP1iPrWfAWr0SxXCqbN0c44RVNOuxBiKF3gR8d6efJEP7miTUXAxUdfv5H1taVlW9vEnbyA5Sm3YncUElSd/g8Aolt+GWl4sCxJJl/AkS/iaNzAlsoIYZ9rSVNWQgi8k+2+HYPf7aQurIRXzepsiqYq0sgWTDIFlWIcyhQAlTr0uowJsz6/28me1ZXsWV0JKME/O5CibSBN26B6jGeKHMnEOVKqHAx4DK5fU8VN66rZVB8aU2QxebXgiBtGJm8ynC5gy/GO7yMzRK/LIOxzEvEqEZssXs30EfKi/9BTXqD6PD0K7OWiDbpjhwHPA2+WUg7OKcJ5Yu/evfLgwYNLHYZmBWJaNq90xUnnTSK+qfcynRtM8djxfp49G8UutbbYsSrCb9y2dtx6iTPVg3f4ZHnPE1Bz9CFCnT8hW72D7t33kyyYGELQ5EoRad2Bt3bt/L/BJUYZ2loMZwpE0wXSebNsyYSaEToNVZk3WTpVSkl/Ms/5aJrzg2kOd8bpGi57BgY8BjtWRbimuYLtjWEq/LN3pyiWnC/yps1IUtPhgCq/m5qgm6DXhd899V6vlYIQ4mdSyr2zvn6mAlV6USdqlvTrwPXAyKfKAg4CDwMPSSnN2QY232iB0iwEI/55w+lLWxSBKpo40DbE4yf6OT+oqvCEgOvXVHHvzgbW1lxU7Gpb+HsPIA0P0lDf6j3DJ2k68L+RwuDcTX9O3FVLS5WfWq/AsAuw+jVgrPyFftuW5Exl05QrWqRLdk3JXBFbSkIe15QVkx1DGZ4/F+WFc0P0J8bv1/K5DZoqvDRFfGxrCnNdS8WcZqMjlZI5U9WVOR0OGiKqLD/kda3oTcVLIlAXBWAAVaiCiOh8iJIQ4m7gi4AB/LOU8q8mGfNLwJ+ivqYcllL+8uXuqQVKM59IKYmm8pzpT5G3bKr8k4uTlJLn26J888UOYhlVNOD3GNy2oYY7ttZTH55oCgvgzPTjjb6K6VMmLUZumFXPfgKjEKe/9efoX/821tcGCXmdkBqAxmshVL8wb/YKoWDaRFN52ocyZAoWI3/3R/7CeZ0G3kkKMnoTOY50xjjSGedMf4psYXyBsmEItjaEuLalgrXVAVqq/FOmLy+HadmkCxambWM4BPVhL/UhL2Gfc8XNrJZcoOabkuCdQhnRdgIvAu+UUh4bM2YjqmDj9VLKYSFEnZTysuXsWqA080Uqb3K2P8VQOk/Q47rkH6v2oQxfe/48p3pTALRW+7lrWz03rq2+/N4bKfH3voAUAun0gl2k8cBf4ImdIRnZwuCND7CmLqyqzQoZcBjQcmO5pftVjpSSVN7EsiUjf94KpqUKMtKF0Y7BPpcSrHFFEbJkRxXPcSGa5lBHjJO9Scb9mRTQEPbSEFFmurUhD80VPjbUBWdcJGHZknTepGjbuJ2OklC58LkNvM4rv+hirgK1HPMBNwBnpJRtAEKIbwK/gDKdHeE3gC9LKYcBphInjWa+6IvnONaTwOsyqJmkJUamYPLCuSGeb4tyvDcJEkJeJ7+0t4VbN9ZMq+TcyA7iKKYpeGsoFC1qj30Vd+wMlq8a977fY2OkoqxF+RS03qDFaQxCqI26F1Mf8Smj2YJFKlekP1UYV/DgdKiGjBGfiwq/m22NYe7Z0UgyV+RQR4wTvUkuRNN0xXL0xtXPWDwuB9ubwlzTXMGOpsi0yvMNhyBccsAoWvaoXZN6H0oImyp8k76fq4FLCpQQ4l3z9SJSyq/OYPgqoGPM753AjReN2QQghHgGlQb8UynlDy6+kRDiPuA+gPr6evbv3z+DMDSa8RRKjfkMhyAnYGwb6Zwp+e45k5f6LazS5gvDATc1GLxhtcBnXaD7xCV7fI4ipEQWUkgMhIixauhZKjufxHI4ObrmN0h3FaGrVw2WFggH9Bye/zd7FWGXnDEKtiRZKosHVenlEAIErAXW1gA1YNrQl4HBrGQoJ4nmJB0JSV/G5sWzOV48q74vV/sEGyocrI04qPUJanwCzyXajlyK07bkVMmsd8R092r6KnK5GdTDXLpKb6bMRKCmgxMECESpAAAgAElEQVTYCOwDmoGnhBA7L249L6V8EHgQVIpv37598xyG5mrAtiVnBpJ0Deeom6T30am+JA8+1cZAUoJhsKM5zC3rq9mzunJGi+uWZVPsfInqiJOmhkY8dgbxyKPgMuDmD3L9+ou+p6UGoOV68FXOx9vUlDAtm2TOZLC04di0JGGva1xads0k10VTeY50xXmlM86xngSxgsXBATg4MDJCUuF30hjx0hjx0VjhZVWFj/W1wSnXtDIFk2xRrY1V+l00hL1UBT0rvuPw5T49T3FpgboOGGk60wF0lZ43ASPmXzFgNl/tuhhvMNs85v4jdAIHSo0QzwkhTqEE68VZvJ5Gc0mklJzqT9ITy1EdGO9Ablo2jx7u5tHD3UgJq6v9/Obr1tNU4Zvx62QKJtZQO5t8Garrm1XG7pXvQzELDTth/evHX1BIg68CvBVzfIeai3EaDioDbioDbtbWBBgolagnckU8TtWeZLLKu+qgh9s313H75josW3JuMM2r3XHOR9P0xnP0JfOjbUnGbiJ2OJQ34aa6IFsbw2xpCONzjxesEVcPKSW5os2x3gROh4M11QEaIt4VK1SXFCgp5b7JjgshPgu8DngI+Asp5bmLzq8B/gC1TnRQSnn/DGN6EdhY6trbBbwDuLhC7xHgncD/EULUoFJ+bTN8HY1mSs5H03THctRcJE6HO2N8/UA7ffEcCLj3mkbetmvVjBe1i5ZNPFcgJHLs9PXijzSo3FIxCye+qwbt+MWJFxYy0LxNrz0tME7DQWOF6lkVy5aMe5Nl5wvl0K4cL8b+vzccgg11QTbUlbcPWLYcnZX1xHL0xJUZ7oVohnMDac4NpPnhq32j3oLXNFdw28YaKsfsjRNCjNo0mZZN20CK89E0zZU+KnzuUYeLlcJMGxb+KqrV+6ellH8w2Rgp5Xng/UKIKPCAEOJlKeW/Tfc1pJSmEOLDwA9R60v/IqV8VQjxSZTgPVo6d5cQ4hhq79X9UsroTN6LRjMVPbEsbQNpaoKeUXHqS+T4twPtHOpQ2eT6iJf3vGYNWxvDl7vVBEzLJp4r4jQEW2v91CXO4nCHwFH6SJ55DPJJqNkE9TvHX1zMgDeiU3uLiMMhqAqoFvcb6yTpgmonotqKWKQLJoVsEafhIOiZfIY1WlIe9nJtc/l4tmDRNpjiZG+So91xzg2mR70Fv/NyJ7tbK3n9ljq2NobHFdk4DQdVAQ+mZdM1nONCNIMEQh4nzRW+FdFleEZl5kKI51AOEtVSysQUY8NAFHhBSnnLnKKcB3SZuWYmRFN5DnfEqPS7Rz/kRzpjfPnJM+SKNl6Xg7fsWsWdW+un/UfALrUqz1kWLoegtcpPY4UP1+AJSHaDv9SY2irCIx+A7DDs+wNovqhKNzUAq/ZAYLJG1pqlQEpllNufyNETz2HZEp/LmFaH4IsZ8RZ89uwgL7UPY5eKbnxug80NIbY0hNjRFKHlEg0lc0W1cdlpCJorfTSEfRNShovFYpeZbwMSU4kTgJQyIYRIANtnFZlGs0RkCxavdseJ+Mri9NixPv71wAWkhD1rKnnXTasnbeMwGZYtSeSKWFJSH/KyJRIi4it1ek30QKwDgrXlC9r2K3GqWK2EaCzFLHiC4J/YS0mzdAghCHtdhL0u1lQHiGeLdMWyRFMFhGDczGfEU/BSqbix3oLDmQI/OTnAT88MMpDMc6g9xqH2GNBBa7Wf2zbWcPO66nFl6N7S/i7LlnQOZbkQzZT2avmvuM3AM51BxVANCJum2nskhKgDelANDpd8JVfPoDTTwbYlr3TFSeVMwj4Xli35+oELPH5c/XN/83VNvHXXqmm30EiUWk60VPlpqvCNr9bKp6D9OZWuK9kZYZvw6Ech1Qe3/g6suXX8TVMDsGo3BGrm5f1qFpZc0SKeKVC05bhjsUyRVL5sumMI5SnodjouWfAwkMxzojfBid4kL7cPk86rqj7DEGxvCrO7tZJdLRUTvjhJKUnnldWS322wqtSoMbAInoCLPYN6CVUg8RmUF9/l+AxquVerguaKoTueZSiTpybgJW9afOXJsxzqiGEYgvfdspZbNlxeGIqWTTpvYkmJ0+FgdXWAxgrvRG84y1T7l1y+sjgBnHlCiVOoAVpvHn9NPqmEya9Te1cKXpeBNzJ5Vadp2WSKFgXTJpM3SRdM4llz9EuN2xhfMVgb8lAbquW2jbUULZuX24d5+vQgr3Qpl/YjHXEeFrCxLsjrt9Rx/ZoqXKVWJUGvkyBO8qZF20AaW6bwOB3UhbxUBtz43cac7JsWipnOoO4GvocqP38C+DTwjJQyWzrvRfWL+n3gjtJl90gpfzSfQc8GPYPSTEUqb/LiuSEq/W4yBZPPP3aKs/1pAh6D337DJjbVhy55babUHsLtdNBU4aU66CHodo72YZrAwCmIXRg/E0r1wX//Lpg5uPV3Yc2YpVtpQzoKq28Gz6Xj0Fz5jKwhDaULqmLQspFAwO2cVETiWeV08dKFYY52xzEt9Tc97HNy++Y6rl9bRXOFb8JsqWjZZEqegKDEtCnipSbkmbdWLYvuxSeEuB/VjHAEG4iXnkdQprEj7VEekFJ+drbBzSdaoDSXw7IlL18YxiqlQ/76Ryfpi+eoCrq5/67Nk+5tsqUkmTMpWjYVfhetVX4q/e5Li9II2Rh0vKCKHEQpnSNt+PH/gv5jauZ028fGl5BnhiDSDLWb5/Fda5Y7I9ZMyVyRzuEsiZyJz2VcMj2XK1o83xblx8f76BwqtxQJ+5xsbQyzvSnCjqYw1cGJNkxFyyaVN7FtSdjvornCR2XAPac9VkvVbuNW4M9QTg4X/1camV39qZTymdkGNt9ogdJcCsuWnOxNMJDMky5YfPaHJ4llirRU+fjYXZvH7UMZGZ8otXZojHhZVekn6JnmN07bgvbn1afGNaYK6/j/hZ89rDbevunz4B1Ttm6bKr23+hZwTq8ZomblIaUkkTVpH0oTTRVAqH1YHqdjQlNEKSWn+lL85FQ/r3YnRp30R2is8LK9KcLm+hAb64MT/o1nC6p03uGA+rAyxQ16nDMuW19SN3MhRCWwCxgpQRoAXh4xcV1OaIHSTIZlS070JBhI5RlOF/nsj06SLVhsbQzx0Ts2jkt12CWna5C0Vqkd/DPO20fPwdCZ8am9eCd892NKiCYtKx+Eui1Q0YJGA2UBSWSLxLJFEtlSKxeXc0JJuZSSnniOY90JjnbHOd6TIFe0x42pCrrZWBdkc32IzQ0hmip8OITAlsptvWDZCAEhr4uagJuIz03AY0wpWEvqZl4Soifmcg+NZqkYEaf+VI6e4Rx/+8QZCqbN7tUVfHDfhtHUhpSSRCmV11rlp7nKN2VDvEnJJ5U4jd1ga5vw7JfU47rbJ4qTVVCzpnDTHN6pZqUx4iZRU0rVFUybWKZAx3CWaCoPorxmJYSgqcJHU4WPN2yrVw4UJRum0/0p2gbSDKUKHEgNcaBtCICA18me1kpet6mW9bUBhBBIKcmbNuejGWyZRggIe12l4g3P7D4TU7Ac221oNAtOpmBypj/FULpA+2CWv9t/BsuS3Lqxhvfesna0ckpKSTSTpy7oZW1tYPaLx7YN/ceV2DjGfJCP/zdEz6jKvL3vmXhdLgF128Zfo9FchNvpoC7spS7sJVuwGE4X6I6XxcrjVJuGDYeyZNpUHxot+rFsSXcsy+n+JCd6k5zsTRLLFHnq1ABPnRqgscLLjWurqAt5qQ66qQ54qA66EUCuaHOmP8XZ/hSt1X4aI755rQactUAJIfYBvwTsZnyK7yXg21LK/XMNTqOZb/KmRcdQlo6hDC7DQddwWZzeuL2ed97QOprLl1ISTRdorPCxuT40tz0jyR7IxSAwZkNuohuOfFM9v+kD4A6Mv8Y2lfVR8OrulKuZGWp25aOp0ke2VGAxVGrWqCr8JJLx61ctVX5aqvy8fks9Ukq6YlmeOTPIT88M0hPL8cjL3eNeI+Ax2FgSuZ2rIqyq8NFR2hS8rTFM3SU6Rc+U2VTx1QBfB94wcuiiISM3/DHwq1LKwTlFOE/oNSjNcDrP0a4EEoj4XBzvSfA3Pz6FaUnu3F7Pr4wRJ4DBVJ6GiJfN9aGpK/MuRzEH7c+CO1je8yRt+NGfwMAJWLcPXvORiddlolC1AarWzP61NZoSIym6fNEmb1mkcibxbLG0rqrcLrxOA4/LMboR3bRsjnTGOd2fZDBVIJrOM5DMk8ia4+69e3UFb9/TQsjrpDbkYWNpdraoa1BCCDdKeK5BCdNzqDWoztKQZuD1wM2olu0/EkLcJKUszDZAjWY+KJg2x3qSozYwp/qSfPHx05iW5PYttRPFKZ2jLjQP4gQqhYcYvyH31A+VOPkqYM+7J15jK5cAwo1ze22NpoQoWSypFJyLutJ2OtOySect4tkCw5mCqlC11TzL4zS4rrWC3avL66ZSSgZSeU73pTjRm+D5tiFeuhDj5fYYN6+r5l03r563mGea4vswcC0wBLxTSvnjScb8iRDiLuAbpbEfAj4/pyg1mjnSNpjCsiUej4P9J/v52vMXMEtrTu+6ec2oONlSEk3naQh72dwQnrs4ZYYg3jXeay/VDy9/TT2//r7JN97mEhBZrcvKNQuO03AQ8TuI+F20VgdGZ1qpvMlAUs2YbClHN7eCcmbfuSrCntWVvHXXKh493MNPTvXz07ODdMez/OcHXjMvNkozFaj/UYrxvkuIEwBSyh+V2q3/O6qfkxYozZIRTeXpGs4S8jh58Kk2nj2rOrPcvqWWX7tpzWg6w7IlQ5k8a6oDrKkOzF2cLBP6joE3NH7T7c8eBjMPra+B1hsnXielWn+KrJrb62s0s2DsTKsm6GFTvRzdwDuCaUvi2QKJnAkIfv7aRu7aVs+3Xuzg9Vtq583jb6YCtRnIAd+ZxtjvlMZumWlQGs18UTBtjvcmsGzJn/33MXpiOdxOB+9+zZpxvnpFS5Xpbm0M0ziLjriTEu9U7uPBMXueeg5DxwE1M9r77smvy8WVOLknb6eg0SwmhkMQ8bkmHK8Nqdl90bJpj2a4MJTmV25spbV6/v7dzlSgXEBRTqOyQkppCyGKs3gNjWZesG3Jmf4kRdPmwafa6InlaKrw8uHbN7KqsixCedMimTO5tqViUguYWVHMQvQ0+McY+dsmvPiQer7z7ZObvpp59Vi1fn7i0GgWGJfhYH1dkLqwh1N9yUmbNc6WmYpHO7BJCLFbSvnS5QYKIfYAIeDkbIPTaGaLadmc7E3Sn8zzg6O9tA2kqQq6+aN7txH0lv/Z54pqR/51LREqA/O43hM9q/YuOcZ8xE58DxJdyql8y5smXiOlmj017QLX/JTpajSLRcjrYldLJbP3JprITF0Av4eq3ntICFF7qUFCiHrgIdR61XdnH55GM3PypsXhzhgDqTxtA2l+fKwfwxB8aN+GScVpV0vl/IpTNqYKI7xjZk/ZYXjl2+r5nveMr+gbOybSAsG6+YtFo1lEHA6xpDOoTwO/jiozPyGE+CdgP9AFeIFW4HZUryg/qtrvM/MUq0YzJdmCEifLlpiW5KGftgHwjutb2FAXHB0XzxaRSHa1Vk6aX581UsLASfAExhdGvPx1lfZbtWeinRGocw4X1Gycv1g0miucGQmUlLJfCHEv8AjQANxf+rkYgeqm+5apOu9qNPNFpmDycnsMhxBICV947BS5os31a6u4c6tyYxip1FPVSaH5b9KW6lOOEWNnQQMnoe1Jle7bM4mdkbRVd93mvZPPrDSaq5QZFzBIKV8QQmwDPgK8DdhBOVVoA0eB/wD+TkoZm69ANZrLMSJOhhAULJtP/+DEaFHEe29R+5xGGsFtrg/TVOGd/3bXlqnEyBspH7MteOFB9XzbmyffeJsegqp14K+a33g0miucWVXYlYTnU8CnhBAuYOSTNSSlLF76So1m/knnTV7uGMYpHBRMm7/8wQn64jmaK338/t1b8LudpeaCRfasriLiX6BZSqpPuY+P7eV0+kcwfF6119jxtonXFFLgDULV2oWJSaO5gplzCXhJkPrmIRaNZsbkihaHOmK4HA5yRZu//P5x+hN5Wqp8PHD3FkJeJUbDmQJrawMLJ06WqSyNxopTLgaH/k093/NecF5UmWebUMjB6pu0W7lGMwmz7+Wr0SwxpmVztCuOQK0tfeaHJ+hP5Fld7efj92wdFadMwSTodtJaFbj8DefCyOzJGNOZ9KV/hWJGlY233DDxmswQ1G2e3OpIo9FcfgYlhGidjxeRUrbPx300mhFsW3KqL0k6b+JzOfn0D9Wa00hab6QFu2VLMgWLvWsq57X8dRyTzZ76j5ULI/a+b3xFH6j9ToFaVVau0WgmZaoU37l5eA05jdfRaGbE+Wia3kSOCp+bz/34FOcG0tSGPPzeGzePihOo1N762sDobGpBSPaOX3sqZOCZL6nn2986sTDCKqriibqtE4VLo9GMMpVw6E+PZtnRF89xPpom4nXx9/vPcrw7Qdjn5P43bqbSX06xRdN5akJumisX0NPOMmHo7PjKvYMPQbpfVebt/P8mXpMdhoZrwDVPnn8azQplKoGaza7BeuAPgHvRAqeZZxK5Isd6EgTdTr6y/ywvt8fwuQ3uf+MW6sd08Yym89QGPWxpnIeWGZcj3jl+9nThWWjbr9aibvmt8VZHANm46pAbali4mDSaFcJlBUpKeXa6NxJCRIDfR+2PCqDE6QjwJ3MJUKMZIVe0eKUzjsfp4O9/cpbDHXH8HoPff+NmWqvULGmkTXt92MPmhvDCrTuBWkcaPFk2fc1E4cA/qOe73wWR5vHjrQIgoXaLTu1pNNNgzmtDQogA8NvAx4AISphOAn8qpfzWXO+v0YAqdjjekyBftHn42fMc7YoT8Dp54I2bWV1drs6Lpgvz06Z9yoBM6D2q2rg7DGVx9NxXoJBWVXub7p54TSYGq7QRrEYzXWYtUEIID6pb7gNADUqYzgOfBL4qpbTnI0CNRkrJ2YEk0VSerz3fztGuOCGvkwfu3kJLVXl9KZrOq5nTQosTwFCbKiEPlHo9dTwPPYfAHYCbPzRJ1V5C2R9pI1iNZtrMWKCEEAbwG8AfA40oYeoG/jfwz9pJQjPfdAxlOB9N880XOnmlM07Q6+Tj92wZV/wQTeepDrrnp037VGSGYOhcuRGhmVddcgGu+2XwVY4fL201ZpU2gtVoZsK0BUoo47J3AZ8A1qCEaQDlcP4VKWVuIQLUXN30xXOc6EvyHwe7ONQew+8xeODu8eIUyxSo8LvZutBrTqBmQj1HwBcGUdrn/up3ID0IlWtgw50Tr8nGoHI1eIITz2k0mksyLYESQrwd+DNUy3cBxIDPAV+QUqYXLjzN1cxQKs8r3TEeeamLg+eH8bnHF0QADKXzhH0utjeFcRoLbIySHoTuQ+D2lW2LUn1KoACu/58TLYtsU6X7KtcsbGwazQpkKieJN6HWlK5FCVMK+Fvgr6WU8YUPT3O1EssUONwZ4/8e6uH5tiE8Lge/d9dm1taUZyHRdJ6qgJttjYsgTrEO5Q7hqxhvZ3TwYSVCa25TG28vJhuD2s3gnMeGiBrNVcJUM6hHYbSD7/eAv0Sl9epLXXOnhZTy1EyCEkLcDXwRMFDrWn91iXFvQ7X2uF5KeXAmr6FZnpiWzYWoWnP6wdFenj49iNMQ/M4bNo02HFzUUnKARA/0vQqB6vH7mjpegM4X1Gxq97smeTM5dS68amHj02hWKDMpkrin9DNTZmR1VCrC+DJwJ9AJvCiEeFRKeeyicSHgt4ADs4hJswxJ5Ioc706QLVo8fXqQx4+rVu0fvWMjWxvVRthswSKZL9JS5WND7SJU61lF1ePJXzlenJI98GzJzujad0zs5WSbar2q+XrtVK7RzJLp5EXEHH9mmnu5ATgjpWyTUhaAbwK/MMm4T6EKNHRxxgqgP5HjZ+eHkVLyk5MDPHqoG4cDPvi69VzbXEHRshlM5RFCsmd1JZvqF6FaDyDWDtIcn9Yz8/CTv1Zl5s03wJY3jb9G2qoJYf1O3YRQo5kDU81slqKL2iqgY8zvncCNYwcIIXYDLVLK7wohJms5PzLuPuA+gPr6evbv3z//0WrmTNGyyRVthIBvnTN5rtvCIeAX17toyLbRcawNy5Z4XQZpQ3DowiIFJm218dZhAL2lY5IN5x6mdvAcOW8dRyrfjnVsbDs0qWZPTg/0nAJmlN3WaDRjmMrqaLH+FEwbIYQD+Bvg3VONlVI+CDwIsHfvXrlv374FjU0zM9QG3DTt0TSNXhcPPXOOF/qH8HicfGjfBnavrsSWksF0nmtXRagJLbIDQ88RyArwVpSPnfw+xA+C14fnnj/itoqLOtKkByG0Guq3aTsjjWaOLMc2GF3A2CY5zaVjI4SAHcB+tTWLBuBRIcSbdaHElcXZgTTtQ2kiPhdffvIshzpieFwOfvsNm9hWWnOKpvOsqwksvjhlhlQbjRGnCICew3DwX9Tzmz8IF4tTIaOcJOq0155GMx8sx466LwIbhRBrhRBu4B2oakIApJRxKWWNlHKNlHIN8DygxekKozeWpX0oTaXfzf959jyHOmIEvE7+4J4to+IUyxaoDXlYvZCdcCfDtmDghBKbEaGJdcBTn1Vpv21vgTW3jr9GSsinoG6bLorQaOaJZSdQUkoT+DDwQ+A48G0p5atCiE8KId68tNFp5oN4psjx3iSVPjf/frCTZ89E8bgcfOzOTaP7nDIFE5dDsHmxiiHGEj2r1p7cpQ3BuRg8+ReqKKL1Jtj1KxOvycbUjMpXMfGcRqOZFcsxxYeU8nuofVdjj33iEmP3LUZMmvkhW7B4pStG0OPkR8f6+MHRXhwOwUdev5H1tUqcTMsebdPudi7yd6j0IAyfK6f2rALs/7RqQFi9AV7z0bLF0QhWUdWrVq9b3Fg1mhXOsptBaVYu2YLF0a4YhnDw4vkhvvWiKta877a17FylOtJKKRnOFNjaEFrYNu2TUcypFhreSFmEXnwIBk8pwdr38ckdIbLDULNFu0VoNPPMspxBaVYe8WyRVzqVOJ3qT/Ivz5wD4JdvbOXm9eVChKFMgeYqHw0Vi9wO3baVlRGyLDTnnoIzj6kNuq/7+ESXcoBCSlX56Q65Gs28o2dQmgWnL57j5fZhPE6DzuEMX37yDLYNb76uiTduL/9hT+VMgl4n62tDix/kUBtkBstrSPHOcnfc698HVZNsCbQKatZVvx0c+qOk0cw3+lOlWTCklJwbTHG0O07E66IvkeMLj5/GtCS3b6nlF3eVPepyRQtT2mxvjCy8t97FDJ1XhREjrdvNPDz9OfW45tbJW2hIWxVGNF6r22hoNAuETvFpFgTblpwZSNI5nKMm6OFwR4x/fKqNbMHi+rVV/NpNayjtY8O0bFJ5k92tlfjci1yiHetQXnuB6vHrTrF2CDXCje+ffE9Tegiq1usOuRrNAjIvAiWEqALeAKwuHToPPC6lHJqP+2uuLEzL5mRvkv5knkqfi0de7uK/DnUDsGdNJe9/7brRWZItJUOZAjuaIkT8i1wUkeiFvmMQqCrvXTr/NJx9HAwXvPZ+cPknXpeNK0Gr0lV7Gs1CMmeBEkL8Iar9uwdVbAvKwTwvhPiUlPIv5/oamisHy5Yc7U4QyxTwuw0+/9hpjnbFEQLevqeZe3c2js6cVNuMPOtqA9RHFtkpIp+E3ldK4lT6GCR74cCD6vme96guuBdj5tWjXnfSaBacOQmUEOKjwJ8DfcDXgTOAD+VI/lbgz4UQSSnl3801UM2VwbnBNLFMgSq/my8+rsQp6HXywX3r2d4UGTd2KFOgMeJjTfUSOEX0HQW3tyxOtgnPfEFtxm25ETbeNfE6aasWGqv26JJyjWYRmOsM6sPAOeAGKWV07AkhxD3Ad4GPAFqgrgIGkjkuRNPUBD1872gvL7fH8LkNPvGmbdSHx8+Qouk8dSEPm+pDozOqRWPonLIlGuuzd/ibMHhaFUrc9IHJ150yw6p1e6B60ULVaK5mLpujEEJ8odQY8FK0Aj+5WJwApJTfB7KU16U0K5hMweRYd4JKv5tTvUn+/aDahPv+1667pDgtSjfci8kOq5LysX2aOg/Cq48oUbr1d8AzyT/5QgZcPr3upNEsIlMl0T8KHBdCvOMS5y8ArxNChC8+IYS4C5Xua59biJrljmnZHOtK4HYapPImX95/Binh565pZFfr+M2tSypOZgF6XgFvqFyxN3BClZQj4Zp3QN3WidfZlvLma9gJhi581WgWi6kE6o1ABvi6EOIxIcTmi87/Haqp4StCiL8SQrxfCPFbQoivAo+giiW+PO9Ra5YNti051ZckVTDxuQz+fv9ZElmTrY0h3ra7eXSclJLB1BKKk21D36tgF8FZmtHF2pUJrFWADXfAjrdNcp0JmSjUbgbvhO9hGo1mAZmqYeGPhRA7gI+Xfg4LIf4G+JSUMiul/JIQogL4I+D3UYIEqpqvAHxSSvnFhQtfs5RIqfY69SfzVAc8fOvFdk72Jqnwu/jAvg2jIiRLpeQNES+b60OL704uJQyeVE4RI+tO6QF4/FNqZtR8A9xw38R1J9tUfaHqtk7s/TQN4vE4g4ODFAqFeXgTGs3ywO12U1NTQyQSmXrwHJkyXyGlLACfFEL8K/AllFC9Uwjx21LK/5JSfkoI8RXgTsrrTReAx6SUgwsVuOb/tXfe8VFVef9/f5NJJr03QoDQEQxFmlhoYttVUdR1izRddXVVVmXXdW3oPu7iz7qPru5iwb6PXdzHBo8IKqAUAVdqMLQAgfQ6k2nn98e56QUISWZIzvv1mtfM3HvuPedeLvPJOed7Pl//s6ewUi/EjQzlu73FfPyfPIKC4KbJA4gN12uadCi5ix5xYQxK8YM4gXaKKN4HUcn6u6calj8EDkt8zvpdXTRfDV6XDoroMQJiehx3lU6nk8OHD5ORkUF4eHjnB4IYDB2AUgqHw7eP3qsAACAASURBVEFubi52u52wsI5dHnLMA+pKqRzgpyIyA3gSeE9EPgFuUUrtBv6ng9poCEAOFDvYXVBJYqSdI+XVLPoqB4ArR/dicFpdkEFRlYv0uDAG+kucyg5ZbuSJdT2k716G0v0Qkw6T72oaMu51axujjDENI/2Og/z8fJKTk4mIaGahr8FwkiIiREREkJSURH5+Pr169Tr6QSfAca80VEq9BwwBHkP3mn4QkXut7LeGbkBRRTU78spIiLDj9Sme/mIXDpeX0/rEceGpdeav1R4vIcFB9EuO8o84OUvrLca1nCL2r4Wdn+ke01m36ay59fF5tTilj2qzOIHuQUVFGY8+Q9ckOjoap9PZ4fW0aSm8UqpKKfUH4DRgPfAAWqjOb8/GGQKPKpdHm7+GhxIksHjVbvYVVpESY+e6s/s1GMoqc7gZnBpFSLAfHBc8Lji4GeyRdcN3VYWwxorZGXV105BxpeoCIk7QY8/j8WCzmYg/Q9fEZrPh8Xg6vJ5j+uUQkQEicp2I3CUi14vIAACl1Bal1CRgDhADfCwib4tIRmvnM5ycuL0+thwoJSQ4mFBbEEu3HmbVrkJCbUHcMnUgEaF1P8hlDjdJ0XYSo/zguFCT20l567z0lA9WP6XzN/UYCUN+2vS4ygIdDNGGgIjmMPNOhq5KZz3bR1uoKyLyNLAd+AfwEPAssF1EasPHlVKvAIOBf6ItjraJyO9FpJOtqQ0dhVKKnXnlONw+ouw2thws5X/W6SVu153dl94JdXMtXp/C7fMxICXKPz/SJft0ivaa3E4A/3lHD/fZY+CMm5umba8s0L2mpMHNu0gYDIZO52g9qPnATUAl8IT1+QmgAviNiPyhpqBSqlQpdRNwOlrQHgY2icikjmi4ofPw+RTZR8o5UuEkPiKUI2XO2qSDF43owbi+Da1/Shwu+iVFNuhRdRpVRTooIryRU8T3bwECZ97aNDNuRQFEJkPqqcYA1mAIII72v/HX6LVNFyql5iul/qGUmg/8BL3W6deND1BKrUebxd4MZADL27fJhs7E4/Wx7VAZB4qdJEbYqXJ5+Nvn2VRWexnRK7bBYlyAimoPUaE2esb7IXrN69aLccOi64Iiyg7Cqr8BCkb+Qgc/1KAUVOTrMHLjEmFoI1988QWnn3460dHaV/Kll17yd5PazOTJk8nMzPR3M2o5mkD1ASqVUqvrb7S+V6C9+JqgNM+gh/1ea4+GGjqfao+X73NLyK+oJilKR+z99/Jd5BY76BEXxm8m9Seo3nBYtceLx+tjWE8/ZMUFbQLrra5zinBXwcqH6xzKh82oK6uUXqwb1wtShtYJmuG4ycnJ4frrr2fIkCFEREQQHx/PKaecwuzZs/niiy8alM3MzOTUU09tsG3OnDmISO0rODiYlJQULr74Yr7++usOa/eePXsQEW6++eY2n6O4uJgZM2ZQWVnJY489xquvvsrEiRPbsZV1bNq0iQULFrBnz54OOX8gcrQ/GY8APUVkkFJqZ81Gy/IoCsht7WCl1BFg9gm30tCp+HzalmhXfgVKQWKkHaUUL67azbaDZcSE27jj3MENhvC8PkWZw83IXnGdnxUXtAls8Z46p3GfF1Y9BaW5EJthzTtJw/Ix6ZA8xMw5nQDr169n0qRJhISEMGvWLIYNG4bD4SA7O5ulS5cSHR3NlClTjulczz77LFFRUbhcLrZs2cKiRYv49NNP+fzzzzvsR/9EWbduHSUlJbzwwgvMmDHj6AecAJs2beKBBx4IuF5OR3I0gXoTuAP4VEQeRqfW6EedrdFbHds8Q2dSI0w5BZU43B6i7SHYbVps3t94oDZi7/ZzB5McXRedp5SiqNLFoLRoEvwRtef1QN4WsEfp4AelYP1iyF2ro/gm3dkwM66rSveyjDidMA888ABVVVVs2rSJESNGNNmfl5d3zOe64oorSEqqW3s2adIkpk+fziOPPBKwAlVzfQkJCUcpaWgLRxviuxdYCmQCzwCfoM1fM4HP0Zl0DV0ApXQgxA8HyrAFCUmRYdhtwSil+HDzQZZsOogI/HbKAPomNVzcWuJw0yMujJ5x4f5pfNFu8Dh1OgyALe/Dzk/0+qdJd+qeUg0+jxaoHsN1WnfDCZGdnU1iYmKz4gSQlpbW7PZj4ZxzzqmtozMREebMmcOaNWuYNGkSkZGRJCYm8utf/5qKioracpmZmcyerQeIpkyZUjtEWUN1dTV/+ctfGDZsGGFhYcTFxXHxxRezcePGJnUqpXjuuecYP348UVFRREVFkZWVxX333QfAggULmDt3bpO65syZ06b6iouLue6660hKSiIyMpLJkyezYcOGdrl/7cnRzGKdwAUici5wDpAIFALLlVJLO6F9hk5iX2EVB4qdJEWF1v4n83h9LF69h6+zC0BgzhmZjOwV1+A4l8dHUBD0T/ZTSHnFESjOqXN9+PEL2PQ6OmJvHqTVm+9QCiqLdEBEczmfDMdN//792bFjB++99167D3H9+OOPgH96J5s2beKiiy5i7ty5/PKXv2TFihW88MILBAUFsWjRIgCefPJJPvnkExYtWsSf/vQnTjmlLlWL2+3mggsuYPXq1cycOZObb76Z0tJSnnvuOc4880y+/PJLxowZU1t+5syZvP7664wfP567776buLg4tm/fzjvvvMODDz7IjBkzOHToUJO6+vfvf9z1ud1uzj//fNatW8fMmTM5/fTT2bRpE9OmTSMxMbCScR5T2JJSahmwrIPbYvATh0ud7CqoIDHSXisyldUenlqezbZD5YTYhBsnDWB0n4bh2UopSp0uhveMJdTmh/BsZxkc2qzXO0mQXuf0zbN635i50OeMhuUdxTooIja96bk6iXEP/Z/f6m6NtXdPa9Nx99xzD8uWLePyyy9n4MCBnHXWWYwdO5bJkyc3+ME+FoqKigBwuVxs3bqVO+64A4Crr766TW07Eb7//nvWrFnD+PHjAbjhhhsoKytj8eLFPP7440RFRXHppZdSUlLCokWLOPfcc5k8eXLt8U8//TQrVqzg008/5fzz6wx2brrpJk499VTmz5/PihUrAHjrrbd4/fXXufrqq3n55ZcJqrfUwefzATB8+HAmTJjQbF3HW9/ixYtZt24d9913Hw888EBt2aFDh3LbbbfRp0/g5Jg1iz66OaVVbrYeKiM+PLQ28m7z/hLu+/AHth0qJzYihLt/ckoTcQIoc7pJiwkjKbpjHY2bxe2Eg5sgNAKCQ8FZAque1O4RQ6c3dYqomXdKGtj5be3CTJgwgQ0bNjB79mxKS0tZvHgxN910E0OHDmXixInk5OQc87kGDx5McnIyPXv25Nxzz2Xv3r088sgj3HTTTR14Bc0zYcKEWnGqYerUqXg8nmOKonvttdcYMmQIo0ePpqCgoPblcrk499xz+frrr3E4HAC8/vrrADz66KMNxAlo8r096vvggw8IDg6u/QOghhtvvJGYmMDKeWYWfnRjSqpcbN5fQpTdRkhwEEWVLl77di8b9hQD0Dsxgt+dM7BZuyK314dS0C/ZD4aoPq/uLeGFkGhtY7TqKW3ymnIKjPxlo/Ienfep9+l+n3dqa08lkMnKyqpd+7N3715WrlzJ888/z1dffcX06dPZsGEDoaFH95J+9913iYmJoby8nA8++IDXXnvtmA1JXS5XbQ+shpq5nLbQr1+/Jttqhr8KCwuPevy2bdtwOBwkJye3WKagoIBevXqRnZ1Njx49SE1NbVNbj7e+nJwcevTo0USM7HY7/fr1o7i4uM3taG+MQHVTDpc62XqojOgwG3ZbMKt2FfDymj1Uu33YQ4K4bFRPzhua1ux6JqUUJVVuhvaIJizEDyHlBdm6x1Qz77T1Qzi0Sc8rnXVb09xOVUU6Ys9kxO1w+vTpw6xZs5g5cyZnn302q1atYu3atZx11llHPXbixIm1UXyXXXYZ4eHh3HvvvYwePZoLL7yw1WNXr17dJJz9/vvvZ8GCBW26juDglp9rpVSL++qXycrK4vHHH2+xTGticrx0dn2dhRGoboZSitxiBzuPlBMfrv+qfWn1Hr7YfgSA0X3i+dX43i2avNZkx02PDyM11g9DexVHoHhvXfLB/B2w6Q39ecLNENFoktdZom2M2skA1nBsiAjjx49n1apVHDhwoE3n+Otf/8qbb77J7bffznnnndeqaIwYMYJlyxpOkzfXC+osBg4cSH5+PlOnTj3qMN2gQYNYsmQJhw8fbrUX1VoQ0vHU169fP5YuXUpZWVmDXlR1dTU5OTnExzcdzvcXZg6qm7G/qIrsw+UkRtgpc7j5y8fb+GL7EYKDhblnZnLL1AGtOpAXVrpIjdHZcTs9as9VCXnfQ0ScXr9UcQS+fFTPOw25SCcYrI+7CpRopwiz3qlDWLZsWbNpFxwOB0uX6kDfoUOHtunc8fHx3HrrrWzfvp1//etfRy07bdq0Bi9/CtSsWbPIy8trsUdz+PDh2s+/+tWvAPjDH/5QGxRRQ/3eWs1wZeOhzOOtb/r06Xi9Xh577LEGZZ599lnKyspau6xOx/SguhFHypxk5+tovf1FVTy2bAdlDg+JUaHcMnUAfZNaH68vrKwmNcbO4FQ/ZMf1eSHvBx0QERyq55s+f7AubftpMxuW91SDywG9xkGIH3p63YTbbruNwsJCLrnkErKysoiIiGD//v288cYb7Ny5k1mzZpGVldXm88+bN48nnniCP//5z/ziF79otRcVSMybN49ly5bx+9//nuXLlzN16lRiYmLYt28fn3/+OWFhYbU2UFdeeSVXXXUVr7zyCtnZ2VxyySXEx8ezc+dOPvvsM3744QcAxo4dS1BQEA899BDFxcVERkbSt29fxo8ff1z1zZ07l0WLFvHggw+ye/duJkyYwMaNG3n77bfp379/p+R5OlaMQHUTSh1uthzU0Xrb88r42+fZVLt9nJIew82TBxAV1vKj4PUpiqtcpETbGZwW0/nipBQU7NJh5VFJuie1/EEoPwTxmTpte/15J59HZ9PtOcbMO3Uwjz/+OEuWLOHrr7/m3XffpaSkhNjYWIYPH86dd97ZYCFpW0hISOC3v/0tCxcu5LXXXqtdGBvohISE8NFHH/HMM8/w6quvcv/99wOQnp7OuHHjmlzHG2+8wdlnn80LL7zAgw8+SHBwMH379uXKK6+sLdO7d29efPFFHn74YW688UbcbjezZ89m/Pjxx1VfaGhorZh98MEHvPvuu4wdO5Zly5Yxf/78gPL6k2OZ8OtsROQC4G9AMPC8Umpho/23o53UPUA+cI1Sam9r5xwzZoxav359B7U4sHG4vGzYW4TdFszG/SU891UOXq9ifL8Erj+7H7ZWMt463V7KnW4GpESRER/hn9TthT9C4S4dFOF1655T/naI7gHn/xeE1Vs87PNCZaFejOvH9U7btm077nVABsPJxLE84yKyQSk1ptVCrRBwPSgryeHfgXPRZrTrRORDpdTWesU2AmOUUlUiciPw/4CrOr+1gU+Vy8P3uaUEIXy+/QhvrdsPwHnDUvnFuN4N3MgbU+pwISKM7pNAbISfwrMLd+veU1SSDif/6nEtThGJcM59DcWpJmV70iC/ipPBYGgfAk6g0LmkdimlcgBE5H+A6UCtQCml6nv4fwN0/lLzk4Ciimp+OFiKiPD2+lxtWQT8bGwvfnJqWotBDh6vjxKHm+ToUAamRtcaxnY6hbt18sGoJEBg7fNwYD2ERmpxikppWN5RBDE9ISHTH601GAztTCAKVE9gf73vucD4FsoCXIs2sW2CiFwPXA+Qmppaa/XRHXB5fVS7fVR74bXtbnaX+rAFCVcNtpFlO8CB7c2H/vp8oFCE2YIpCBYKOtensw5vtQ50CLIBR8g4+DG9cj/DF2Rja+8bKM+1QW49p2zlBQRCC2D7Sj81uo7Y2FjKy8v93QyDocNwOp0d/psaiAJ1zIjI1cAYoNm08kqpRcAi0HNQjf2ruiJlTjc5RyooqnJTVeXmuZU/kl+pSIwJ4XfTBjVxIq+hyuWhyuUlLiKEwWnR/knXDnoO6cg2KDsAkT21x96Py+HwRxASDBPnM7p3o79XPE5wV0Pv8XWO5n5m27ZtREcbQ1pD1yUsLIxRo0YdveAJEIgCdQDoVe97hrWtASIyDbgbmKSUqu6ktgUsDpeXPYWVHCp1Em4L4vv9Jby+dh9er6JPYgS/mzaIhMimdjPlTjdOj4+EiBAGpUYTFxHiH1dy0D2mvP9oU9fIZL12aeenemgPYOy1WoQaHOMEZwX0GhMw4mQwGNqHQBSodcBAEemLFqafAw3M1URkFPBP4AIra2+3ptTh5vv9JYgIESFBvLxmL9/m6MV8U4ek8MvxvQlpJlKvxOEiym4jKyOW6DA/50byVEPuevC6dLSeUvDDu3UuEaOuhsGN7G6cZbpcr7Ha0dxgMHQpAk6glFIeEbkZ+AwdZv6iUmqLiDwIrFdKfQg8gk45/7b11/4+pdQlfmu0HymtcrNxfzERIcH850AZr327l9IqN/aQIK45sy+n92s+v0uZ0014aDDD0v2UKqM+Xo9Om+F1a6FRCja+CluXAALjrodB59WVV0r764XF6HBy03MyGLokASdQAEqpj4GPG227r97nrmcJ3QZKq9xsyi2h2u3jlTV72bSvBIABKVH8+uy+9Iht/oe73OkmNFjI8lcep/r4fHD4B6guh4gE3ZNa8zTsXQ0SrJMOZp7Z8JjKQojpoQ1ggwPyETYYDO2A+d99EqKUIr+8mi0HS9lysIx/rd2Pw+UlPDSYK0dnMGVISovrm8ocboKDhayMOP+Fj9eglF7TVJmvh/WqCmHFQijK0bmbJs6H9EaTsJWFOrw8ZSgcY64cg8FwcmIE6iSjotpD9uFyDhQ7WLL5YG3uppG945gzIZP4ZgIhoM6FPDrMxrD0WP+kyaiPzwcFO6B0vw6IKMiGlQ/rAImoVJj8x6YO5FXFeggwdZgRJ4OhG2AE6iTB61PsLqhkX1El+4scvLx6DyXWXNPV4/tw9sCkVhfeFle5yEgIp39ydLM5njoVrxsOb9Fu5JHJejHu5w/o4b3UYXD2/KYees4yCA2HtOFmWM9g6CaY/+knAS6Pj22HyiisqGbtniLe2ZCLzwcDU6O4/ux+pMQ079bt8foor/aggKE9YkiLC4BgArdTB0S4KnROp6IcWP5fWpwyz4Yzbm6acNBZCmKDHqPAdvTMrAaDoWtgxkkCnCqXh437iskvd/Lm+lzeWqfF6afDe3DXhac0K07VHi8FFdVUujxkJkYwvm9CYIhTZQHs+wY8Dh0QUbJPG7+6q3Q69ubEqaoYbHad68mkzTB0MFVVVdx666307t2b4OBgMjMz/d2kNrNixQpEhJdeesnfTWkzpgcVwBRWVLP1YBm7Cyp5Y+0+jpRVExYSxHUT+zGmT0KT8jUeemEhQQxLjyExyu7/4TzQvaOCbCg7qIfubHY9rLfyYR29l35aC6naC8EeCz1GmJ5TAJOTk8PChQv58ssv2bdvH3a7nbS0NMaNG8ecOXMapGLPzMwkKiqqNsdRezJnzhxefvll8vPza1PHHy8PP/wwTz31FPPnz2f48OEd6gby5JNPEhcXd8IpSboyRqACEJfHR05BBbsOV/B/2w7z5U5t8pqREM4tUwaS1ijVutenKHe6USgGpkbRIzY8MIQJdNRd3vc6Yi8ySS/E3fASbP9fvS0tS0fr1RcnT7Ue1otKhdRTzZxTALN+/XomTZpESEgIs2bNYtiwYTgcDrKzs1m6dCnR0dENBCrQWbZsGVlZWTzyyCMdXteTTz5JZmamEahWMP/zA4zCimq2Hipj7e4iPvr+ECVVOiz84uE9uHh4eoPcTdUeL+VOD8FBQs+4cDISwv0fOl6fsjzI2wxhsbrXdHgLfPMMlOdpG6Oh02H4VXof6ESDVSX6e4+ROpzcpGoPaB544AGqqqrYtGkTI0aMaLI/Ly+vmaMCl7y8PHr37n30goZOwcxBBQgV1R6+zy3h7Q25PPLZDl7/Zh8lVW76JUfy4CXDuGxURq04eX2KgkonXp/ilLRoJvRPpH9KVGCJU8k+HQwRHq9TtG9dAv93vxanuD5wwUI4bVadOLmqdBr35EHQ5wyITjXidBKQnZ1NYmJis+IEkJaW1sktasiCBQsQEXbs2MGf/vQnMjIysNvtjBgxgo8/rvMCeOmllxARdu/ezcqVKxERRIQFCxbUllm/fj2XXXYZSUlJ2O12Bg8ezEMPPdRsivRdu3Yxd+5cMjIyCA0NJT09nenTp7NhwwYARIS9e/c2qEtEGmSzPZ76lixZwqhRowgLC6NXr17ce++9uN3u9ruRfsL0oPxMtcfLvsIq1vxYyCc/5LEjT6doiIsM4YrTMjijf1KD4bqKag/VHi8DU6JJjw33T4bb1vB5oXiPlQE3QX9f9TfY85XeP+wyGPHzhkN6zlIgSAdK2I0D+MlE//792bFjB++99x4zZszwd3NaZPbs2YSEhDB//nxcLhdPPvkkl156KTt37iQzM5OJEyfy6quvctttt5GUlMTdd98NwPDhwwH46KOPmDFjBgMGDOCOO+4gISGBNWvWcN9997Fp0ybefvvt2rrWr1/POeecg9vt5tprr+XUU0+lqKiIlStXsnr1akaPHt1sXQDJycnHXd/777/P5ZdfTmZmJvfddx82m43Fixfz0Ucfdcat7VCMQPmJGjeIL7Pz+d/Nh9icW4IghIcGc9HwHpw3NK2BDZHXpyiuchETbmN4RgKR9gD7p1NKR+nlb9cO45FJep3Tl49owbLZ4YxboPeEhsdVFUFolA6E6A5Reo8O9ncLmmf+jjYdds8997Bs2TIuv/xyBg4cyFlnncXYsWOZPHlyQKW8T0pK4t///nftWsEpU6Ywbtw4/vnPf/LXv/6Vfv360a9fP+655x5SU1O5+uq6HKhOp5Nrr72W8ePHs3z5cmw2/X/vhhtuYMSIEdx+++2sWLGCyZMno5Rizpw5VFdXs3bt2lqBA7jrrrvw+XwAXH311c3Wdbz1eb1e5s2bR0JCAmvXrq0NDrnhhhsa1H2yYob4/IDT7eXrXfnc9+EP/OXj7XyfW0pIcBAXZqXx6BUjuGh4egNxqnJ5KK5y0T85klG94gNPnKor4OB3cHCjDmiITIK9a+Dj32txik7TQ3r1xclTrQUsIgl6ju4e4tQFmTBhAhs2bGD27NmUlpayePFibrrpJoYOHcrEiRPJycnxdxMBmDdvXoOF7GPHjiUqKors7KNn5Fy2bBmHDx9m7ty5lJSUUFBQUPv6yU9+AsDSpUsB2LRpE1u2bGHu3LnNCkTQMTigHE99GzZsYP/+/cydO7dB5GJsbCy/+c1vjlpXoBNgv3RdG6fby49HKnj1m70s334Ej1cRFAQTByVz6cieTWyKfEpRVFVNVKiNMZnx/k+J0RifT1sV5e+EELteeOtxwppn4MfPdZnep8PpN+peEughP0exnpdKH1WX96m70MaeSiCTlZVVu9amZl7l+eef56uvvqqddwkNPb5lAkVFRbhcrgbbTmQ+q1+/fk22JSYmUlhYeNRjt23bBsA111zTYpnDhw8D1AreiSTyO576av4AGDJkSJMyQ4cObXMbAgUjUJ2Aw+Xlq+x8Ptx8kA17i3G6vQjCyN5xXDWmF+nNLKItc7hx+3z0SYikT2Jk4ISN11BdAUe26sCGiHjtPL53NWx8DSoOQ3AIjJ4LA8+rEyC3Q697ShygffaCA0xwDSdMnz59mDVrFjNnzuTss89m1apVrF27lrPOOuu4zjNjxgxWrlzZYJtSqs3tCg5uPoDoWM5ZU+aRRx5h5MiRzZZJT09vc9v8XV8gYwSqA6lwunlnQy5vb8jlQLEDERCEASlR/Gx0L4b0iGlyjMPlpcLlISU6lL5JUYE3nOd16wi9wpy6XtOR7fDdy3rxLWjxOfN3EN+n7jhnCRAEvcab5ILdABFh/PjxrFq1igMHmiTEPiqPPfYYxcXFHdCy42fgwIEAREZGMm1a65l+Bg0aBOihvqPRknfm8dRX0zPcvn17k31bt249ahsCnQD79Tv5UUpR4nDz780H+de3+zhU5kSA6DAbZ/RPYuKgZHonRDQ5Rkfn+Yi22zitdxxxEQHmnODz6Z5R/g5QHt1rqjgC3zwL+7/VZcLi9LqmAVProvSU0o4QYXFWckEz19SVWLZsGVOmTKmdyK/B4XDUzpO0Zahp9OjR7dK+9uD8888nJSWFhQsXctVVV5GQ0NDFxeFw4PF4iI6OZsSIEQwbNowXX3yR3/72twwbNqxBWaVUrTBFRUVRVFR0QvWNHj2ajIwMFi9ezJ133lk7D1VWVsY//vGP9rwNfsEIVDvhdHsprKjm35sP8uHmQxwsdRAEJEXauWRkOmcNSGqSdt3p9lLp0msaUqLt9IyPICbM1uJfVn7B59P5mgp3gasSwmO14euGxbBzKSgvBNth6CX6FVJPfH0eHaUXlwlJAyAogNZpGdqF2267jcLCQi655BKysrKIiIhg//79vPHGG+zcuZNZs2aRlZXl72aeEJGRkbzyyitceumlDB48mGuuuYYBAwZQUlLC9u3bee+993j//feZPHkyIsLixYs555xzGDduXG2YeUlJCStXruSCCy7glltuAeD000/nhRde4N577+WUU04hKCiIiy+++LjqCw4O5oknnuBnP/sZ48aN47rrrsNms/Hiiy+SmJjIvn37/Hz3TgwjUCdAqcNNcaWLgyUO1u4p4rMteeSVOhGEhIhQLh6RzqRByQ2EyeGyREkgLjyEQSnRJESF+j8/U2N8Pqg8oj30PA69PikiAXYtg42va4NXBPqfo9c1RTTyBnQ79DxVahbEdo/x8u7I448/zpIlS/j666959913KSkpITY2luHDh3PnnXd2GRuf888/n3Xr1rFw4UJee+018vPziY+Pp3///tx+++0NIvbGjh3L3CRxdwAAEzFJREFUunXr+POf/8xbb73FP/7xD5KSkhg3bhxnnlmXHfqhhx6iqKiIv//975SUlKCUYvfu3URGRh5XfVdccQXvvPMODz74IAsWLCAlJYU5c+YwceJEzjvvvE69T+2NnMjE48nEmDFj1Pr169vlXA6Xlx/zy9mcW8p3e4vZsLeYMofuCcVFhnBxVjqTBtcJU03aC59PERMRQkZcOLERIYHl/FAfZ6meV3KW1NkUFeyEtc/p9Big8zKNntNwnqn+8QpIH9lt55u2bdsWUOuADIb25liecRHZoJQa09Y6TA/qOPB4feQWVfHxD3n837bD7Cmoqt2XGmPnvKFpDYTJ6fZSUe0hJFjITIwgKdpORGgA33K3A4r26NDx0AiISIQDG2Dnp9q2CPS2MXOh1+kNw8N9Xqgu08N6EcmQMgRCAiDFh8FgOGkJ4F/LwMHrU+SVOHh/8wE++U8eB0scta4P4/smcPbAZPonR9bOHem5JTeRobbASnvRHErpnlJJrmXiGqRDwX/8HHK+0O4QoEPCB/8Uhl8JtnqBDu4q7aOHaI+92HQIjfTLpRgMhq6FEahWcHt97Cus5PW1+1ixPZ/iKheCEBsewk+yejBlcEqDuSO310epw01EaDAjMuKJiwgJrICH+niqoSIfSvbqIbmSvZD3AxxYr4MiaohOg4HnQ/8pdT55Po9Owa58eggwLUv3rMy6JoPB0I4YgWqEx+tjV34Fy7cdYd3eIv6TW4rXpxCElOgwLhiWxsRBybVWRC6PD4fbi8fnwxYkDEmLJjUmLPBMXMFycSjRQ3jlh3T6i0ObtUWRu264kvA46DkW+kzQ4iNWkIfPA45SPbQX3087jodGNF+XwWAwnCBGoNBrE3YdqeDDzQf5fNsRPYRn6YsgZPWMY9opKQxKicbp9VJR7YZqHQcQGRpMelwYcRGhRIfZmoSS+x2fF1wVUH4EynL1HFPuWtj/jeUibhHTE3qN06/EAXWiBDrJoLNMr21KGgjRPUyGW4PB0OF0S4EqrKgmp6CS3QWV5ORX8M2PRewtqkQp3TkIDw3mlLQYhvWMYURGHNFhNqpcHmwhwinJ0dhDggkJDiLUFhSYguQs1T2lqgL9uTRX95IOboSyeqv6YzOg32Tt7hDTKBRcKfBWg7NcR/Eln6J7TGYYz2AwdBLdRqCqXF6eXp7Nql2FZB8px6d0z6kmyj46zMaYzARG946jf3IUXgVunxeUEGILYlRaPLHhATqn5HFpISo/pG2ICnbo99JcKN4Ljnqr1UMjoc+Z0H+q1VOyrsdTrYf5fD7AUurQKOgxXBu6mkW2x0191wCDoSvRWcuTuo1A7S2s5KXVe/D4FLYgIT02jB5x4aTHhtEzPpz+yVG1PaKwkCDiwkOJCrMRFhJMVCD54Smlh9y8Lr0QtmQvHPgOjmzTuZiKd+vghfrYo3UvqfcESDtVb/M4tau48umxyrBo7fgQFqvtiGzhcAypAQzNY7PZ8Hg8hISYHqeh6+HxeJrYW3UEAfTL27GEBAcx7ZRUxvSJ57Q+OnVFaHAQITYhJDgIW5AE5l+7Suk5pKoiHQZeVaB7R8V7tCgVZOuhuBokCJIGQfJgSOgPif10ziWPEzxuPfRnC9OBEGHxYI/UPSUzdNeuhIWFUVFRQXx8vL+bYjC0O+Xl5YSFdbyvZrcRqAEpUSy8/CTJMOnz6UWv5Ychdz3kb4WiH7UwlR9Gd3nqEdNTOzukj4CUoXoYr0bY3E7tQB6dru2IQqOMYWsnkJyczL59+7Db7YSHhwfmHz8Gw3GilMLhcFBQUEDv3r07vL5uI1ABi9cD7ko9j+RxQtFuvUD20GYrfboDqPfjJsEQ1wvi+0LqUJ0qPTxB96JqzuF2AkrPHaWeCuHx3SspYAAQFhZGamoqeXl5VFdXH/0Ag+EkwW63k5qaanpQXRavF4p2wb5vIe97ncaiMl+/qooaiklkMiQP0a+kQTrXkgRpQXI7rXQWRRAWA1GpdT2k0EhjNeRnYmNjiY2N9XczDIaTFiNQHY3Pp/MmHdigXRoOboTCbJ26AhquNwI9P5SWpY1W00fp3o/XpXtHKB2tJzad9iK6p34PjYZg809pMBi6FuZX7UTwuHRod9lB/e4o1t51lflQfhBKD+qUFfVdGhDdQ7JbUXNxvSG2p+79RKXq4AWvS69nUugeUmSy5SoepvMt2exmyM5gMHR5jEA1xuvRkXLlebrn4yi2gg2qtIlqeZ5e7Fp20DJSVVpE6r/XYolRsF2npUgcoOeO4no3nBdSuigiEBSiAxrC47SImeg6g8HQTek+AlVVBGv+rsOsq8u04LidOgjBVamHzpylWoTqC06DNUX1xKfGdiIsVgcpRCToz/ZoPQ9kj9YGqhFJen9ImPatC4m0/Ovq9YCCgvV8kVl7ZDAYDLUEpECJyAXA34Bg4Hml1MJG++3AK8BooBC4Sim1p9WTlh2Erx5DC0/Nxkbh2jWiY48Ge4wWnLBYLR4h1nqhmB46rDu6J8Sk1q0hstl1hJ00irgzgmMwGAxtIuAESkSCgb8D5wK5wDoR+VAptbVesWuBYqXUABH5OfAwcFWrJw6LgaHTdUBBeJwWnBrHhNCIul6QPUYboQYFWwITDMGhZqjNYDAYOpmAEyhgHLBLKZUDICL/A0wH6gvUdGCB9fkd4GkREdWaQVRcb7joiQ5psMFgMBjan0AUqJ7A/nrfc4HxLZVRSnlEpBRIBArqFxKR64Hrra/VIvJDh7T45CaJRvfNAJj70hLmvjSPuS/NM/hEDg5EgWo3lFKLgEUAIrJeKTXGz00KOMx9aR5zX5rH3JfmMfeleURk/YkcH4gz+AeAXvW+Z1jbmi0jIjYgFh0sYTAYDIYuQiAK1DpgoIj0FZFQ4OfAh43KfAjMtj5fASxvdf7JYDAYDCcdATfEZ80p3Qx8hg4zf1EptUVEHgTWK6U+BF4AXhWRXUARWsSOxqIOa/TJjbkvzWPuS/OY+9I85r40zwndFzEdD4PBYDAEIoE4xGcwGAwGgxEog8FgMAQm3UKgROQCEdkhIrtE5I/+bo+/EJFeIvKFiGwVkS0iMs/aniAiy0Qk23rvdnnKRSRYRDaKyP9a3/uKyLfWM/OmFbDT7RCROBF5R0S2i8g2EZlgnhcQkdus/0M/iMi/RCSsOz4zIvKiiBypv8a0pedDNP9t3Z/vReS0o52/ywtUPeukC4GhwC9EZKh/W+U3PMAdSqmhwOnAb6178Ufgc6XUQOBz63t3Yx6wrd73h4EnlFIDgGK0vVZ35G/Ap0qpIcAI9D3q1s+LiPQEbgXGKKVORQdz1Viudbdn5iXggkbbWno+LgQGWq/rgWePdvIuL1DUs05SSrmAGuukbodS6pBS6jvrczn6x6Yn+n68bBV7GbjUPy30DyKSAfwUeN76LsBUtI0WdMN7AiAiscBEdNQsSimXUqqEbv68WNiAcGsdZgRwiG74zCilvkRHUtenpedjOvCK0nwDxIlIj9bO3x0EqjnrpJ5+akvAICKZwCjgWyBVKXXI2pUHpPqpWf7iSeAPQE1ulUSgRCnlsb5312emL5APLLaGP58XkUi6+fOilDoAPArsQwtTKbAB88zU0NLzcdy/xd1BoAyNEJEo4F3gd0qpsvr7rAXP3WbtgYhcBBxRSm3wd1sCEBtwGvCsUmoUUEmj4bzu9rwAWHMq09ECng5E0nSYy8CJPx/dQaCOxTqp2yAiIWhxel0p9Z61+XBNV9t6P+Kv9vmBM4FLRGQPevh3KnreJc4avoHu+8zkArlKqW+t7++gBas7Py8A04DdSql8pZQbeA/9HJlnRtPS83Hcv8XdQaCOxTqpW2DNrbwAbFNKPV5vV33rqNnAks5um79QSt2llMpQSmWin43lSqlfAV+gbbSgm92TGpRSecB+EalxpD4Hnfam2z4vFvuA00Ukwvo/VXNfuv0zY9HS8/EhMMuK5jsdKK03FNgs3cJJQkR+gp5nqLFOesjPTfILInIW8BXwH+rmW/6Enod6C+gN7AV+ppRqPPHZ5RGRycB8pdRFItIP3aNKADYCVyulqv3ZPn8gIiPRwSOhQA4wF/2Hbbd+XkTkAXSSVA/6+fg1ej6lWz0zIvIvYDI63chh4H7gA5p5Piwxfxo9HFoFzFVKtep23i0EymAwGAwnH91hiM9gMBgMJyFGoAwGg8EQkBiBMhgMBkNAYgTKYDAYDAGJESiDwWAwBCRGoAyGLoCITBYRZS04Nhi6BEagDF0CEXnJ+oFe0Wj7pSKywFrjdFIiInOsaxjp77YYDJ2J7ehFDIaTmkupW9W+wo/tOBHmAJOAPcCmFspUATvovvY6hi6IESiDoQuglFoLDPF3OwyG9sQM8RkMBoMhIDECZeiS1AQNUDe8d781R1X7auaYIBGZaaWpzhcRl4gctNJ3j2+hngXW+V6yjr9ZRNaKSIm1faRVzi4iV4rIKyKyWUQKRMQpIntF5HURGd3MuedY7ZxkbVrc6Br2NL7e1oIkRGSKiLwnInnWteWJyPsiMrWVY2rqyhSR3iLynIjkiki1iOwWkUdFJKaFY0NFZJ6IrLbuh1tEDlvX/3cRmdBSvQYDmCE+Q9fFhTavjAXC0LmMKloqLCLR6LQJ06xNCigHegA/A64QkXlKqadbOoV1/HTAax1bn3PRBpo15y6x3nsDvwR+JiLXKKVerXeMw7qGBCAEKLO21ZDf0vU0c33/Bdxdr/5SIAU9R3epiCxUSt3VyilGAC9abSlH/3GbCdwBTBKRM6zUEzX12YCl1IlrTZ2JVr3Drc9rjvUaDN0P04MydEmUUquVUmnAm9amR5VSafVfjQ55BS1O3wHnAxFKqVj0D/I9aNH5m4ic2UKVM9AuzTcBMUqpeHQm0RxrfwXw3+gU6lFKqQSlVDjQB+20bwMWiUjvetfwptXO1dameY2uYeyx3AsR+Tl14vQ0kGK1Lxl4ytr+RxG5upXTvIQO0MhSSsUAUcC1QDUwBriuUflfosWpCpiJvp/xgN265puBzcfSfkM3RillXuZ10r/QP6AKWNHC9gWtHDvNKrMdiG2hzB+tMv/baPsCa7sCrj+B9r9gneP+ZvatsPbNaeX4yVaZPY22C5Bt7ftXC8e+Ye3fDQQ12ldzbT8A9maOfcrav7zR9mes7c/6+9kwr5P3ZXpQBkPdPNVzSqnSFsq8br1PEZHgZvYXoofA2sq/rfeWemhtZSQwwPr8Xy2UecB6zwTGtVDmcdV8bqMPrPdTG20vs957HEMbDYZmMQJlMMAZ1vs9VuBAkxc6MzNABHrupDHrlVKe1ioRkQQRudcKGigUEU+9gI33rWLp7XJFdZxmvecrpbY0V0ApVX/91GnNlaHu+htTc1x8o+2fWO/TReRDEZkhIs3dN4OhRUyQhMFQ91d+3DGWj2hmW6sBCyIyFFiOnpeqoRwd9KDQGWvjgchjbMOxkmy9H20Bby46I2xyC/sbB33U4LTeG/yWKKVWish9wH3AxdYLEdkOfAT8UymVfZQ2Gbo5pgdlMNT9P7hMKSXH8NrTzDm8R6ljMVqcvkMHU0QrpWKUUqlKB0JcaZWT9rigZgjroPO2iFLqz8Ag4C7gM/Sw3xB05N9WEZnV2W0ynFwYgTIYdCg36JDvdseKzBuHFrFLlFKfKaUah7ynNj2yXajp2fU6SrmMRuXbBaXUbqXUQqXUBeiIyCnAl+ge1zMiktKe9Rm6FkagDF0dn/XeWs+kZi3OhR3Uhtoff6VUS0Nt01rYDsd2DS3xnfUeKSLNBkCIyCD08F798u2OUsqrlFoBXAS40cOZYzqqPsPJjxEoQ1enJpqstfmll6z380XkgtZOJiKNgwGOhZrIwNTmegwikoVeN9QSx3INLbEJ2GV9/lMLZRZY73uAtW2oowkiEtrKbhd1Q6L29qjP0DUxAmXo6tRErl0gIs2GPCulPkW7QAjwvoj8XkRqgwWs6LtLReRD4PE2tGEbOghBgDdFZIB13hARmQEsoxWXi3rXMENEYo+nYqWUQi80Bh1R91RNNJ2IJIrIfwO/sPbfo5TyNXeeNvCKiCwWkfMtlw6sOjOBl9FzYg7gq3aqz9AFMQJl6Oq8DxShJ+tzReSQiOxpxrNuFnpNTxjw/4DDIlIsImXoNU7vY0WiHS/Wj/6t6KG6yUC2dd4K4F20G8PvWjnFq+hex1lAgYgcsK7h62Os/03gIevrzcARESkCjgC3WNsXKqVeb+74NhKGThPyKVBq3ctK9GLgq9A9qBuUUgXtWKehi2EEytClsX4Ap6B7SPnoMOo+1qt+uUql1GXo+ZH3gIPocPIQ9BDZW8Bc6n7Qj7cd7wNT0b2lcuu8e4FHgVHoHlZLx25He/l9ih4uTLPan9HSMc2c4x7gHGAJUIC2KioEPgSmqdZ9+NrCH4E/WG3OQYfRBwM/oiMaT1MNfQcNhiaIHgEwGAwGgyGwMD0og8FgMAQkRqAMBoPBEJAYgTIYDAZDQGIEymAwGAwBiREog8FgMAQkRqAMBoPBEJAYgTIYDAZDQGIEymAwGAwBiREog8FgMAQkRqAMBoPBEJD8f3XAJOi18HmSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ndlib.viz.mpl.TrendComparison import DiffusionTrendComparison\n",
    "viz = DiffusionTrendComparison([model, model1], [trends, trends1], statuses=['Infected'])\n",
    "viz.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "nbpresent": {
     "id": "b0908a6c-0be3-448a-bd69-813efb85538f"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5LgVKSukC5wghaoBfAHOP4bUPAA+AKha7fPnyMdmj5nVMvIMtO/ZSGfGLuXqOX8Q1p2is5yiBSRd2la56Phgtns9uBQeMYBgsm5ABobABgYhK8oV8YfMcCITVTXNMxONxPvWpT/Hoo49y8OBBpk2bxp49e070tobF2rVrufjii1m9ejU33HDDqM8fiUQ499xzR33eXMadiy8XKWUX8BSwFKgRQqQFdyowvDaeGs1I8RzyShFBscut6PxJqOKyJefzx2aql3vZwrGFIeZw3CP5du3axU033cTcuXOJRqPU1tYyb948Vq5cmelBlKa5uZmzzjprTPZxww03IISgra1t2HN86Utf4r/+679473vfy0MPPcT9998/ijvM5/777+ehhx4as/lPBcadBSWEaARsKWWXEKIMuBwVIPEU8C7gf4GVwGMnbpeaUxYrDk4SonX9j3FSFLnzCgXKLTh/yi1TVCg4nqOmyxUocs+2CgXq+NXkW79+PRdddBHBYJDrr7+eM888k0Qiwfbt23niiSeorKzk4osvPi57GQ3WrFnDggULuO+++8Z8rfvvv5/m5uYxsW5OFcadQAGTgO/551AG8BMp5a+FEJuB/xVCfA54CXjwRG5Sc4oS74BEx8ACZSfyNUOIfMHwXN+lF8wd5P/MTfBNP+RXijAKBao/ERLZEPQxPoe6++67icfjbNy4kYULFxY9f+TIkTFdf7Q5cuQI06dPP9Hb0PiMOxeflPJlKeW5UsqzpZRnSSnv8R/fJaU8X0p5mpTy3VLK1Ineq+YUJNkJye6BxzjJ/N9FjvtOSvV8iWCIzPN5v3vZ1xa6+AbjOISab9++nfr6+pLiBDBx4sQx38NArFq1CiEEW7du5TOf+QxTp04lHA6zcOFCfvvb32bGPfTQQwgh2L17N08//TRCCIQQrFq1KjNm/fr1vOMd76ChoYFwOMwZZ5zBvffeW7JF+o4dO7jxxhuZOnUqoVCIyZMnc/XVV7NhwwYAhBDs3bs3by0hRN5517Gs99hjj3HuuecSiUSYNm0ad955J7ZtF40bb4xHC0qjOXEkupSF5Dqqu20pnCSQmyfuWztS+q013P673koPKAimACVomTBzzy8iMZgAjb1AzZ49m61bt/LII4+wYsWKMV9vuKxcuZJgMMgnP/lJLMvi/vvv55prrmHbtm00NzezbNkyfvCDH3DrrbfS0NDA7bffDsDZZ58NwG9+8xtWrFjBaaedxic+8Qnq6up44YUXuOuuu9i4cSM//elPM2utX7+eSy+9FNu2+eAHP8hZZ51FR0cHTz/9NM8//zyLFi0quRZAY2PjMa/3i1/8gne+8500Nzdz1113EQgEWL16Nb/5zW+Ox0c7pmiB0miGimNlrR8nCWZFP+NSFAkUqIi7/1xA/+ZP2r0n8h/zHPWYYWaTgI0AJd2BA841AJ/cOrRxBdxxxx2sWbOGd77zncyZM4cLL7yQ8847j+XLlw/aDvx40tDQwK9+9SuE7/K8+OKLOf/88/nWt77FF77wBWbNmsWsWbO44447aGpq4tprr828NplM8sEPfpAlS5bwxz/+kUBAXTY//OEPs3DhQm677TbWrl3L8uXLkVJyww03kEqlePHFFzMCB/DpT38az7eGr7322pJrHet6rutyyy23UFdXx4svvkhDQ0NmbO7a45Vx5+LTaE4Ydjx73+nHg5y2kkoJQ3+PZyj1XH9W0MlRKWLp0qVs2LCBlStX0t3dzerVq7n55puZP38+y5YtY9euXSd6iwDccsstGXECOO+886ioqGD79u2DvnbNmjW0tLRw44030tXVRVtbW+b2tre9DYAnnngCgI0bN7Jp0yZuvPHGkgJhGINfco9lvQ0bNrB//35uvPHGjDgBVFdX84//+I+DrnWyoy0ojWaopPoAoXTEjgP1xWPSIeaFWpPu1/Txjf3Pnw4ZD5VnH4t3QPcBCFdAeSN0H1QVKKqnghmiX8HrLxdK+gm+oWj/52DHyIIFCzLh0ulzle985zs888wzmXOXUOjYSmN2dHRgWVbeYyM5z5o1a1bRY/X19bS3tw/62i1btgDwgQ98oN8xLS0tABnBG0l+0LGsl/4CMHducSro/Pnzh72HkwUtUBrNUEl2QsC/0CZ7So9x7exPJ6mSaQGEWWKwVALkJKGiKceFl0PmDMr0o/J8UfFcMAewxvorsZSuoO7aY5LIO2PGDK6//nquu+463vzmN/Pcc8/x4osvcuGFFx7TPCtWrODpp5/Oe0yOIOjDNEt9/kObMz3mvvvu45xzzik5ZvLkycPe24le72RGC5RGM1QSXRAsU+KQ6kegPFsFUCS7oecQ1M2kpJXjWtDXkq0EkeqBMj90XXr5QgTZKhS5gRJ589lgpjMvID9PKod06LmTUhbYGIWhCyFYsmQJzz33HAcPHnvO/Je//GU6OzvHYGfHzpw5cwAoLy/nsssuG3Ds6aefDihX32CIfj77Y1kvbRm+9tprRc9t3rx50D2c7OgzKI1mKKQDJIyAurBbsdKVHzwX4u1+hJ3MClAuqR7ltnOtrOBYsezzud/qcy0oKC1Qdgy690HnPmXlpV2FJQvG+jlUuXOPgDVr1pQMe04kEplzkuG4mhYtWsRll12WdztRXHHFFUyYMIEvfvGLdHR0FD2fSCTo7e0FYOHChZx55pl897vfZdOmTUVjcy22ioqKkvMdy3qLFi1i6tSprF69Oq+CRk9PD9/85jeP/c2eZGgLSqMZCrkBEkKgxCcFRln+ONeGeCsE/PMSJwVmjivNsyHWqu6HKqC8XgmLa2UFo5RADWRB2X7elXT9ROIuKKtV8xcm63rpHCpD7c0IjMiKuvXWW2lvb+eqq65iwYIFRKNR9u/fz49+9CO2bdvG9ddfz4IFC4Y9/8lAeXk53//+97nmmms444wz+MAHPsBpp51GV1cXr732Go888gi/+MUvWL58OUIIVq9ezaWXXsr555+fCTPv6uri6aef5q1vfSv/9E//BMAFF1zAgw8+yJ133sm8efMwDIMrr7zymNYzTZOvfvWrvOc97+H888/nQx/6EIFAgO9+97vU19ezb9++E/zpjQwtUBrNUEgHSOTiJJXLr/CxeAdUpX9PQbjgeVCvq2jK3rfjyhIKlpPXF6rQgjJKCFR6TLhKCZ2TVFZcMEpRKHq6CoUwlFh6bv/5XEPgK1/5Co899hjPPvssP//5z+nq6qK6upqzzz6bf/3Xfz1lyvhcccUVrFu3ji9+8Yv88Ic/pLW1ldraWmbPns1tt92WF7F33nnnsW7dOj772c/yk5/8hG9+85s0NDRw/vnn86Y3vSkz7t5776Wjo4Ovf/3rdHV1IaVk9+7dlJeXH9N673rXu/jZz37GPffcw6pVq5gwYQI33HADy5Yt4y1vectx/ZxGGzGSg8fxzuLFi+X69etP9DY044Ejr0CyS1klALE2aDoLqibljzv6GrzwX2xpegfzmptUkERVTueXeJs6nyqrzZ45pXqUVRWMQsUEVdIoGMnO51pQM12JSqoXEu0QqYGoH1bcc0AJYdVkCJRB1x4lPFWT1bjcKuqpvqwFJV01Z27UoEYzRLZs2TJorpsQYoOUcvFw19BnUBrNUEh0ZSPyQJ1DlSp55KSUBZX7e27OUtqCyp0r3WLDTqifbkpFCSZ7CipJ+Mm6kvzzLzftBvQtoXRRf8/LXzvT4NC3qISp5tct4jUnKVqgNJrByA2QSBMIlY7kS7vXMshs6Dkym+CbG+JtBPxzKpldxzB9y8c/QxK+Wy7t6ssUipXZ++n9GTlj8jwk6fuDVFrXaE4StEBpNADtu/wOtSWwSzxuhiAVK66H5ySV+y89BrKRfGlxMoLFeVEh34qy4mRKFKUtm3RghDByxMcXFS/XesqxjKBYoEq68wdoSa/RnGC0QGlOXTxPhXMPRqoP2rbmh3oXPl+y55KTH0YuJVh96qxKkBWdtDBlrKcIRaTPgawYGUtH5uRApZN0Mx14fVFJW2e51l1axDwvX3xKCVS/4egazYlHC5TmuHKwM04sNfL8myHhJKBjV8m6ebbrsa/dF6SuvWpMfwKV7IRgqaoLIr+1hmurXlGgrJh0eLmbFqh0BF+JucyQL0Rudr8ZCyodYi4GsKByekvljskTn34sKH0GpTlJ0QKlGXUOdiY40pUoetxxPXa3xUjax+mCaMVVwEKqt+iphO3S2pdS1lH3QZWP1F+fp0R3fi5TBpkvfp4Nfb57TxjZcybHInO+BGquoiRZ4YeYk3UpyoIqEuSeQRUIVG6oeJ6LL9eCKmEpaQtKcxIzJgIlhGgQQrxVCHG1EGKA1qOaU5HWviS722N4Xv439o6YRWfcxnaP0wUx2a0uwPHigqCW4xFLuXgdeyAQVKJRQshwHWWJmcHi54yA7/7z8ZzsWsJACjPbfj2TiCvUXNJvm5FL2iWY7PFzlPzPKWNBUWBByWNz8XluiaRckTOfRjM0jld60rAESghxgRDiR0KIfy3x3LXALuA3wCPAPiHE+0e2Tc14wfMkPQmHpO3Rlcjv6LmvM05Z0CRpH6eLYbxdtWbvO1r0VMJycZJ9ON0HIFytRMNJFLu7nAT9Vgw3CyL5XEflOQFBbBJJKxsokS4um7aq0sVfcwlGVR6TdKH3iBIpyLGgcqL5wO+2WxBinjs+bYGl18kNMS/kdZwPqTl2EokEwWCJL22jzHAtqGuB9wJ5cbZCiNOA7wIVgAOkgCjwkBDirBHsUzNOiNsunieJhkz2tGfPdPpSDr0Jh/Jw4Pi4+DwXrF7lNnOSRRF6sZRDsHc/LgWlfgrPqwrzmHIJhPzE2S5o3QqH/woJVeB0grWPg4cOEXdN9W3T8q2zQFiJgWGWsFoEVDap8yTXyrocc8+gKBQoX8RyLbz0eK+EQPVX1kgLlGYISCmJx+McPHiQCRMmjPl6w61xkq6d/6uCxz/sz/k0cCVgAd8H3gPcAnxomOtpxgnxlAMCoqEArX1JepI2VZEgR7qTBAxBwBCknJFZUJ0xi02HsudFkaDJG6bXYhg5F187nl+HLtWbdaEB8Vgf0cRhnODMnJn9oIeccaT68isx5GIE1LwH1qkzoEiViuADqqIhiFgcOtSJnUqQ+S4YSYDRpl7rOaXn9lwldGnRiCSy1SWkVFXQPRfKUkocAcpdMr2qpMyGukeTvtXmv7f+1ksHaWg0gxAMBmlqaqKqqmrwwSNkuAI1EXCBwjr6f4v6uvlvUso+AN8N+B7gouFuUjN+6IxbhP3eO2HT5FBXgmijyeHuBJWRIJ6UpEZoQXXELEBQGVF/vp0xi6TjEg3l/DnbOUEagbAqJVTZlHnI7dpLwAzgFBoOuVF5oASnZICET2Gpo7Q7sWICVWFBVagd1tyWff4d31I/m86CllehvIFiBBw5CE9+VrnpLr0Lak+HGeeqgIvv/T9o3wFLPwovfB2i9fA3/66srgr/W+0f/p8S6bd9BZqXKQHa938qGKSQRDfUzID64qZ+Gs2JZLguvjqgV+aclPnBEHNRbr9n0o9LKfcCcWDqCPapGSd0xCzCQfVnVRkJcLgryeHuBK4nMQ2BaQiSzsgEqjNuEQkaGEJgCGU1pArPtRKdWbdXsEwJlP/naqcShHoPIiM1WLl7MYPFjQiT3dkmhYPhpNR4I6Bq7QFUT8meD5XVKjEBCFeqkkT9hXhPXAAX/QvMuwrqZmVzp4xAtkBtx271s7xRWWNmOOuijFSrn6keJVyl2n7kvm+rr//nNZoTxHAFKgZUCyFy/+emLaQXZHGIh4WyuDSnMCnHJWl7BE31ZyWEEqS97XHKfevGEAJPgusNfOYhpcQq4Qp0XI+Y5RAys3+6BoKYVRC2He/Iv6h7TibPye48AAhMw8h3N/rli6SU9CRt2rr7aOvu5WCPjTPIfoFsG43yhuw5kRGA2hnqfv2crMsxEIZobckcrQxTF8Oilep+ul6fYWTD0Tv3qJ9pq6myCZL+WVdaoOw+1Y7DtfuNj8AIlM4Bc219NqU5oQxXoDaj/tzfmfPYDSj33trcgUKICqAaODzMtTTjhITlYhRcBKsiQRxPEglmzzcEDBpq3hGz2HG0dP6SOlrKLhQKGPQkcyIGXV+MzJzvT0L41oSN277lGB74AAAgAElEQVQbO1SFUXgeZgTBitGTdNiwp5Mt+1s41JXkYGeCxFDckmn3XnnB4XG96pDKhLl+oIKprJayWj9KcBA8J7+tR7hS/UwLVHkjKsBikhJZ184KlBVXrj4rptZN9hSXbjKD2TO7XFo2QfvOwfen0YwRwxWon6CuMw8IIb4uhHgEFRThAD8uGPtGf+z2Ye9SMy7oSdhFbaxNQ1Bfnn+GIxncgmqPWbT0JHEKhCxeogpFKGDQk8h5PH0Bzt1LIKIEpPcItm2DMAkYBrbjqYv2kZdVkIDnYKWSCAENIUllJIhhCKyhCFQsff7UmP/42e+GRTfA6W9V4pEWm3BlcYCgFYeegu9ynptfHilcqcQk/T4rJgBSzVs3U7kZMwIVy/aakh786uPw2D9B597sfMJQz7k5Iu+5KmS+Yyd0Hxr8vWs0Y8BwBeq/gT8B5cA/Atf4j9/jnznl8neo/4Z/HOZamnFCR9wiEhg8EkwAjtu/QEkpae1N4UkVnp6/hk24YI2gaZC03ayY2XGKrvzBMpUX1bGLmFFOwD8PSzkePPtV+MPdcFQ5BpKJGAHDwLB68YCwmyBmjcCCitTAvCuVW89zsu66TEPBHOyYyoXKqzQh8sPIwwXRU+n1jKCyooQBYb9vlRVTLj4rrlyQqV4V+LHmTmgr+M6Ye05l+YVwo3XQ8kp+CxGN5jgxLIGSUtrApcBK4JvAl4DlUsp7c8cJIYJAGfBLikPSNacQ6QTddIDEQEjA9vp38SVsF9vxCAdMOuP5h/tdcYtIoHgNAVl3XaITAmEcT7K7LaY8V+mKDk6KhGsSMA2EADPegjzysnpd6zZAEo/HCJoCw+6hcfNqznjuFoxdawf/ENJnUBUD5Id4drYwbDCi3JBpMXItMCMqMrAwaCFXoNLWUZporR8mbqhxtTPB8N2bqd5sHlh6f0IoAXrybuXGS3+CeQLV59f+C6j1Dm/sv1ahRjNGDLvUkZTSlVL+QEr5USnlp6WUfyoxxpZSvk9K+Q4p5baRbVVzMqPOhqSKqhsEgShy3eXSm7D9XCqTlp5sEEHKcUk5HgGz+M9WQjYBONEJRgBv7b8Teem7WeEKV0G0lqTtEPAPy+qOPJc1Yjr3gDBI9PURNA3MZDeR9i0YnsuETd9Gbvj+wIVVcy0oKVU1iEJcJz/PKlKTDZRI9kBdsxI4N9eCkvnFYMuqybO8IjX5Z1RVk7MWVKpbjZWOyp8COONt0HyhCsX/4+egax9FdQVjbdmqF4GwEqrWrf2/d41mDNDFYjWjQizl9FdvoQjTECSs/gWqtc+iLGgq153lZoQnYbn9BqKZQqgq6a6t+jTF2jAP/Jm6g38k0ecn9QbCuASwXIlpCpAutUeeze67ax/SDGEnuwhKi2DsCIZn4QXCqq7e5sfg6X8v3R8KcnKgGpWl5DolwrtFfl5VtF5ZOOmqEhVNvgtP5Act5FlQNdn7ZbXK0skVqGDEbwcv/WoU/qeWPtuqngpv/DhMPV/tb9//qUTjtIUkpXKH5p57hSrUY25++SqNZiwZkUAJxQohxDeEEL8WQjxZ8Hy5EGKZEOLNI9um5mSnK2FnEnQHQ539lLZEXE/SEbMyUX9SSHr8mn69SacoCCNNKGDQnXSUeAgBnXvw/EISySOvZcbZrocAXjxk07PvFYKpTrxoPSCg5yCWBwGrD8NNEYqpXlLJuvnsPvs2vFA5HFwPf/lB8QacpIoSTOdAubayYuxk8di886RyZQyleqBqirJWzIBKqLX92oBmML/KQzSn/nI6ByqQY5WBEklJToV2Ab2+QFVOUvM1v0n93r5DuQTTAmUn/CoXuRXScypyaDTHiWELlBBiDvAy8FNUiaO3AcsLhiWBB4G1QogL0Rw3EpZbMo9orMhN0E3jSckvXjrAQ8/v5ld/PcTzO9vY3xEnMECybl/KwctxFZYFArT1pTJrlAzC8BzKnF6stj3qHEkInPZdSCQGAu/o1owxYrseR2IeX34xSdumtQCkmi9VOUSeg9NzBMNJYNhxgr1KoFJVM4jVzqd76WfUJHueLW6X0ZfOgWpU512urayOIgtK5oe/B8uVxeI4Kqk3TcVEJbaeo4ImcgnnnEGl3YGhgjEVE9XPpO/iQ0KPH41XNVn9rD9N/WzbrgTL9gWqv7MmM5g9x9JojgPDrWZeC/wBOBN4BbiLgsKxoM6pgG9QnDOlGWMOdMbpig9QPWAUsV2PpO1mEnTT/PKvh3j0pUM89VorP9twgG89vYs7Hn2VHUf7iis/+HTHLQwEwk1hJjspC5m09Vk4rkdPwi4OwpCSspa/UNm2kUDnDmw7BWW1eO1+lQUBke4dGUG0PY/9vZIKYiy0NgKCvikXqlI/gOzcB0Ag2U4wpip5WVUzCJqC7ug0Nc6Ow6GN+ftIh5iX+yHmnuNXkyhh8eUKVCCkXHKRqvzgh/R9zynuwFuWMy6dA1VYjqmiKRsMEYwo0Uz1qLXTVS4qmlTIeqpH1fSz4ln3nlmiekaoXJ2r6eRdzXFiuBbUJ4BpwOPAYinl54D+Mg5/6f984zDXykMIMU0I8ZQQYrMQYpMQ4hb/8TohxBohxHb/Z+1orDde6U05+cmrY0ip6uSbDnXzi5cOgoCrz5nM3yyYyKQadaE91J3o18XX2mtRFjIx7Bih7l1+5QlJW5+VZ1mlEW4Kw4njROuxw7VYhn+h7tydkYZoz27iSSXWScvjaExyCeswceirO5N4oCZT7UF27MEAjFQ3ob5cgTLUGdcM/8943wv5G0+7zzIRfMIXgpyLeVpsjIL/duWNKn8pl5DfesNO5J8vQb4FlQ4xNwvKaoaiyoIDVb8v3ek3HYYOSsDSVlTHTrVX11IBEoVrgnL5uZYui6Q5bgxXoK5G/c/7hJRywP7dUsodqFJHpw1zrUIcf935wAXAR4UQ84FPAU9KKecAT/q/v26Jp5yiHKKxorA6eUfM4r/X7gQJ15wzmRVvmMrfnTedC2aqOnTdCbtkuSPL8ehJ2USCJsJJYSbbEU4SQwhaehKlG5a7yTwbxXIkJDoRqR68QBQnXEvAjRNvVZZR0nZoiUuu4HkADjW+Gcv1MhaU6N6LaRoEE0cxrZiaI1JPwBQkHQ93mi9Q+1/MBgxID7avUfcbz/B3In2RKM+6+XKTdHNpOL10aHrVJGXdBAvOlwKhrNuvopEityGo39P5UsnunPOniQVr+1Uu2neon6k+JYqlGjQCIFRxWY3mODBcgZoJJKWUm4c4vheoHOZaeUgpD0sp/+Lf7wW2AFNQovk9f9j3yCYPv+6wXQ/bk/QmnUE7X3qeHHF/pr6kQ8C3ChzX4+tP7aAv6TB/chVXLcyeq9RE1UWvK24jEEXljvpSTkZsDCeG4VqYVg/RkElX3CZYaHkAhp3MCJcphDp7a9ulcq2qZpCqOQ2BwPXPoRK2R7BnH6exnz6i7Ks4V53V+RaU2b2fQCBEqGevKkJb1ZwJEBCAFW2C2mbl5jv8V7Xwvv+D7gOqBt/MZeox4bvdymqzgRKek82BynsT/fw3jNap4IVAgfvOCGTDyCsmqq+KRrD/McnubARfZUH19dxzKISytAbKFAhFoVdXltAcH4bbbkMCQwrZEkIEgCpKnFGNFCFEM3Au8GegSUqZrhFzBGjq5zU3ATcBNDU1sXbt2tHe1gnHkxD3i6euPRgY8HpjOR6O32BwuCRsF09Cr4A/7HXYesihKiy4enIvh7duyIxzOlxcx+bQkSP07mnn+UOBvNp9KcfDcT0ShsBwEgivHBnbhheIYPv5Tz0Fb8ZwUiAFdHUjJRztjNHc8gozPEmL0URS1DLb+zNeyxae2nQeCctlQd8zSAlrOI9YqwV2ivZOgyWeiUh0sL+tjymth6lxPVpEE7sOK4vB9SRdPUmmhxYw3d5F60tr2NE1mYWbHiZqe+yquZSWLW2ovCMPDvxJiZKdACMd8JAEs2XoH64loOXlbBNCn9rGFUSsNg7vB7wkHHo2/3VSMicVpcH22L59LzU9O2m0PXb2lHN0UzY/K2BXcZ7t4R3Zyos7+5DmdhU5aJTI4VITq+d3PsXASqbRjJzhCtRu4EwhxCwp5a5Bxl4KBFGWzqjhF6H9OfD/pJQ9ueHHUkophChpOkgpHwAeAFi8eLFcvnz5aG7rpKAzZvHygS4k8IYZtVRFSrtruuM2G/Z2EDQEb5zdQKhEhYah8NyONspDAboTNs+u+ytmwODjb5nL3En5JXnc9hjm1k1YgTIqm0/nnGm1VEeze/vzrnaCpkHQNIge/jPSiGDYCWJT3pA9NykgemQdUoSRZgjPU8I86ehRTEMQnTSHYMVkAkcepcHaT9m0WjYd6uYS1iEEPMGbuLS8jAl1goVTazD3zcRp3cbp5T1UtbUSMA2iU85g1iR15tOXdJhYHWFS8xXw2K+YGt/E1PKdYB+B6kbmLXsH88ygCjYIlcPkc1Ty7f4XVdh4rA0mnVNcq28gOvcqt1yuFSUl7HAgWscZng2eBzOW5r/Oc6H9h9BrcFajgHg3BA3mz5/L/LranPkmwu6JEDvKRRN6VYRfKFocmJFLrA0mL+inl5VGM3oM18X3G9TXp1sHGiSEKAfuQ1lcjw1zrVLzBlHi9LCU8hH/4RYhxCT/+UnA0dFab7yRG4DQn/vOdj02He5WxVAprnk3VCzHw3Y9TEPwsw37sR3JeTPrisQJoCaqzkk641ZRuSPb9UikIwGlxHASSCME0sOw+zmUlx7CjiN995ZhgCslRuceBAKragZW5QykMCmLH6K7qwvj8F+oIM4OprGLaXQlpaoN6Hm41dMBCPXuJ9yjSkpaVTMyywVMoT6nykmqR5OdgD9/Qz155juy5zaelQ1QyKu3JwY42+mH2hnFLj7hzyNdFWJeMqDBzCb0Jruzbrlog3L35VbEaPDdfN37/SrwAzRoBHW+1fe6/e+lOY4MV6C+DHQCNwshPieEyGvTKYSoFEK8G1gPnAUcQoWbjxihTKUHgS1Syq/kPPVLVG1A/J+jJojjjb6UOhMKGAZ9ydLCs7O1D8dVbTBMw6AjNkBfogFIi+HuthjP7WjHNAXvWVS6N2VlJIBhCGIpF8eVqpK4TzynSoTw/KACIUAIzGTpQ3nhJBHktHUHlWCbaEEaJlbFFKQZxKpqRgBG53YmHn0OgDUoi6M7pcTD8SR2pRKoSMcWAsl2pBHELs8GFYRMU0XyQTaaz1Fh7Zx2aXZjrpNtiWEGcgIlSgQzDBdTiXdRK45covXK2uo5oIQnEFGCGanJr4aRPofq3AtlNflV4EsRivpt549fnp3m9clwi8W2oYISeoBPo858GgGEEB0o8fpf4AygA7hGSjlalSbfBFwHXCKE2Ojf3gZ8EbhcCLEduMz//XVJLKUskXBhnySf1t4kh7oS1JSpb/NRP9eokN6kTUt3iUoIOSRtDykl//OiipJ7y/wmJlSVdg8ZQmTW7Es5JHNyoWI5QipcG+lfI71AGYF46fMQw00WRfZF+g4AErticqYSQrLmNBBQ1bKeKbFNOATYW3MBAF1JiURVV09WqICOaKvKcbIqp+ed/RiGL2SuhOk5LrUzVxQIj0CaIXa3+pZfWV02UGLUBCqorCDPLo7yS5N2JaZr6FVOUoIWqQI75wtJbiRfeAixTOkGkKlRP1bWaPIYSbHYZ4GFwP+guuUaKLdfjX/fRfWGWiSl3NDfPMNZV0oppJRnSynP8W+/lVK2SykvlVLOkVJeJqV83fYHiFsOQVMQNEtbUHvb41SGg5myQUHTIGW7Re7A/R1xDnQO3FAvlnLYfLiXrUd6KY8EuGrh5AHHpyP5YiknzxXZlbQybTSEl+3kKgMRTDuGcIstPMOKFX3br0sdxECQqpwOUmImO0nVKAuh/sgzeBKeYyHTGtW5UldKZorX9pVNRQBCqn2lctx7uaRcV50Lzb4Ems7Mt57UrrFFkPaYpULpy/yCsCJQnK80XAJhZUFJ2f95UTppOF2eqGqSErSKCfnxDXWz1OfYtXfgDr+5mMHivlUazSgzolp8Usp9UsprgVpgGfBe4H3AJUCdX8m8sD+UZgxxPZmp+G0aAsfLb52etF16k05eh1ugqPdS0nZp6U3RZ9kDVh7vilv8cqM631hxzhSioYEvwJWRABJVXy+33FFnzM7sSYlRzhVUSowSyaGm1Y0sOC8J9e0Doc6OhGdh2jGS1SoJVgjllXqcNzK/Qa3VnZKYQkUQxmQZTiRb587qR6Ayn+fSj8Ll9xSfEQGOCPoRkp6ycLwS5YhGghHMniP1J3qVk8hLFK6cBBjKSgpXq/qBoASueroSvM7dQ1s/XAk9B3XxWM2YMirVzKWUCd+y+amU8sdSyrVSSp1ufgKwHK8o+DdXCHr7OZOKBEza+7Lfntv6Ugj8yjcD5Ent60hwtDdJedhk+Rn9R6fZrkdbX8oXKOhJZl18SdvNBFoAGHYcmVMc1QuECCTai+Y0rB4q962hbssPlWUAhHuUq9GqnI5wLbxgBV6wEidco8LQqeEVMZfTa9X8XUkPw0BZkI6LXTktM3+qsliggoagOz7ARdmvFmF7BilHnbURjKqghf5cccMhEPEroIviHKg0FQWZFmnBCkSgaqKKNkyTyYfaoYSvY1dOr6gSCEPNFS/+d9FoRgvdbuMUo7BArJTk1b1r7UsWWU8AZSGT1l4LKSVSSvZ3xDPh6aXarKfX2tcRQyCYXl9esk8TqKi9uOUwf1IVMxsqQKrzLcvJttHIxXDiJLxAJsnYC0QJJI7m14DzHAKJduq2/5zqvU8wccNXEU6CYO9+tTdfoNxgOcKzSNWchislT3ABTZUBokEIm5BywfYEcdvFlRLLFygpDOxKFewhnGRm7WgoQFufRW9/UY+uBaFybM9TeV2eVBZOuDIb2XcMHO1Jli76GwipKD45QOBFpCo/arByEiqS0K/HJ3PmTUfybXoEfroSfvvPsOYu2P1M/5sLRcGvXZjB86BjtyqvpNGMkEEd4kKI6aO1mJRy3+CjNCMh5bp5gQNB06A3adNYGcb1JG29Vsm8KNNQ5zAJW1VBT9ouFeEgkYBJR9xiYnWxeyrpuBzsUmdUM+pKWwdJ2yUaNjl7Sg2hgMGEKuUO6/LLHTmuR2/SJmm7rN16lK0tvezY301rQnLlHIu/PzMMholwLcxUF25ElVg0nCThrmwPzLL2V5n8f/dgeBZOpB4vVEEgkcIN1xCyeumc8y7a7QZ+eugyFlQYCCGoiRi0xDz6LEFVSF2s0wJll09CmiGQkkCiHTdSixeMquKzQYP97XHmTqwqLgLh2hBtxLI9LMfLlnMqq+s/2m4AjvamKAuZxTlqRkCJkxggdD1d7ijRqX6vaPCtOUOJpRnMttVonOv/g/kRk2W16nV/eQimLMpvspgmGFU5Uam+bNWKjl3QukVVpJh0Tn6bEI3mGBnKie0QndKDIoe4nmYExFNupuwQQMg0Mm69dCsL0+gnjFiobrbtMYtIQP1TRYImnTEbKWVRL6aU7XGoS51jTK0tffFN2C6zGsszF9gJlUqgOv1yR44n6Yzb/OjP+3n1oLo4Cs9DYvD0Ppv3zQ9hCIEbjBLq2kmiaREIgXCTlHWoPk+9U5dT1vrXbHHXyuk4nuSrL5mU16T4x2bwKibzu8p3EsdicqXaS3VY0BKDbtujPKiEJNF4DomGBfRNUlF6wk3hhaoRbjLjoosETboTNu2xFI2VBedPfoh53O9dlTm/q5k+rIt1zHJKR3MbAeWKC1X0HxZuBpXlluhUezfD2Sg9IVSZpL4jqnJ6zXRY/mkVJDFhngrsePx2aNsGL/8YFt9Yeg1hqJyocIVqOdKxUyX7xjvUaxvnDh62rtH0w1BcfGKUbtqdeByIpVQEX5pQICtQnTFrwJbskYDJ4e4kR3tTlIfVxdQ0BLbjFRWEBeWmO+RbUNP7saA8Kakqy37Db6jwLai4hURi+2009rarLIT3LJ7MfUtdJkQF3SnJjk61rgxGMa0ezJQSMcPqJdKpwqd7pl/K4SW3Y5epM7BU9Uye2mvx56OCJ3f08op/THKoV801pUL9KdZE1GfRlVRuTQOBF4xyZPE/qxYcgOEkcCI1FNYlqQgFONCZUIVmCwmESdkeIdPIfm6haMlgioFwPUnK9nBL1VMUhp8DNUDFByOYFaSqyX6gRk4YeXljfpDD1MWqiWG0Ts1//oeUuGz9rcqRKkWkCrr3qUjBlleU2AlD5WB17VPJvxrNMBlUNKSURqkbqr9TN7AZ+AAwG4j4t1nAjcCrQBewwn+NZoxRApX9qFUkn3I3tfQkKR8gyq4sZNLld6/Ns5aESqQtpK03RVtfCsMQTK4ptqDS7TEqctZs8nOkVMFYZdV1Jyx6kw5lIZO3z61hZqXk3dWbqaaXvxzJnvV4wSjth3bw+d9u4dcvvIxMdOMGK7Eqp+FEJ3D4gjtpn3stbVMv4+dbLaSf+fDwjgDSdTjUp8Riclqgwuo9dqckjuMQKKHdQno45ROLHjf9LwFpF2cWCYEwCdslHDD6bSsyFGy3wE2YS9rFV9jMMG+Mka0mUTkJXDe/WG2kyu9l2E9B4bpZMOet6qxq3XdKj0u34Di0Ud1Pi7AQSuiObsk2cywk1p4fqKHRFDDchoUXoBJxnwfeIKV8SEq5W0pp+bc9UsrvAYuAF4AfCyGWjN62NaXwPEncdgmUcOF1JSwSljtgvT1DCAwhis6oAoaRabuey9YWlV8zuSZS1KwQ1PlTbXkII2c/tdEgpqGqjicdl56EwyE/GXhKTRmG5xDu2sb72/6Lf+J/2VAgUD/fHGPrkW7iB16m15Y8kTiDX+9UVdvdcA09zW/hd/tDdCU9ptcEqY4G2dktePFgksOFApVjQTXQTTl9dCY9bn68j59sSYHnIo0AbrgGzwxlIgXTVIQDtPdZKnE3FzNM0nYJB8x+GzMOBVWV3sMt5eMzTP8sqUR19LxNNilhqZ6iRCOQE1BhBiFaC84AuW7n/J06xzq6WZV12vY4HNyQH70XKlefTWGSrxFQrsJDL2XPwdLE2mD/CzoKUDMgw7VqPoM6T7pZStlvuI6U0gY+iioW+5lhrqUZIpbroSr/FAtUW29qSMWna6OhIrEJBwzaCypNqAi+uIrgqy3t3kvYLo0V+RFmQdOgOl1NIuHSl7I52qPC26fWliE8i1DfQQKGYBGvsb/HoTWuLtBdSY/nj5oY0uHt0S0IBM85c/nBqym+89cUrieJ25JHt1sg4b3nTFCJw0Lw3VddbA/qygRlQfVB1ITV++yKW4hoLYZ0+etRl/aE5LHtFl19cZyyCSAM3Eidqpyei/95Wq5vJXkOmCFcYeJ4kmBAkBogh2wwHFfi+FZUEUZA3QZy8QHMvwoWvAvmXIEqtVTgZqyeCqkBrJhQBbzhenV/x5Pw4gPw1Ofh0ZuzibrBaLZLbyFmSFlqB9ZnAzASnXDwJWX9JbsG3r/mdc1wBeoCoGsoSbhSyj0oN9/SQYZqRojleiVFKGAYdMVtykqElw+FcMCgN2XnuZqSjsth3/KZ2s/5E0BlgTVmGsI/k5L0pmxiKZeWnmyghXASmKlOhIBqI0EzhzNW1B/32jie4Pz6FAvkNqrDgnPPOYeQCX/YY/PfLyX55XaLPksyr1ayYFoty09vpLEiRLevLWnrCVSQBEBP0sWqmokbrmFvh9qL48Fvdjo4ZarMpBuuKVnNAsB2/M/FtVWIueshEJhCYI2g15YqpitKn3MJU138ByudVDkF5r5NWTfCKD4HizaoMHhvgGLBsy+GS+6As/8OZl+qRM1z4LVfD+2NBMLKyjr4F+htURZYpEIFVqSrXGg0JRiuQFUAFUKIQb6+gT+mAhjEF6EZKZbjIUv0nQ2ZBt1Ju2T+01BIW2TpHlMA7b0pDnb2HyDhepKAYRT1mRJCUFseRKLaVyQshyO+QE2uKcOwY5mk3KAJC9jOhiMOridZs0e52N494SDCs7ErJvOG5gl8amkZkYDg2f0Ov9imLL33n+5BoIyAabBi4cSMcOcKVNrF12kJ3EgdVtUM9nZnBWXNAYNeqc54vGB5SQPUECJ7zuTaEKrAdtW/g2mUFhcpJZ2xwfOEkpZL0DBUsm/RwoYKWzcGCYwNlanIQtdSlk6hdW0GoHbm4F1yJ58LZ78blt4Mb75NPbbzqaELTLBMuRQPblD3AxElrlYsv7K6RpPDcAVqG8rF95EhjP2IP3bbYAM1IyOecgiU6JsUCRrURUMDRvANhXQl7wOdcXa29WUsn1IClbBcGipCJd2NdX7bjd6UQ8AUGaGbWhvFcOIEEm2AqtpwFjvY3ObyzAGHjoRkcoXBWWwHIUg0nAXAmQ0Bbn9jGeW+6+7cJoMz6oMqjwlYcloT0yrURX5KZY5AhQUCj047BIaJE6pmd6+qkDCzGpKu4A/blQvKC5RRyjwNGIJE+pzJtSBSnREUIUQm1yuXpO2xp33w2slJxyMUMLBLBUkABMsHt6DMkNp3qTOiNJUTyTRZHAo1M2Di2eCmYMcf8p9r2aTceaUIlat6gHkVNYRqW6LRlGC4AvUg6n/rvwsh7hRCFP3lCyEqhBC3A19CxQp9Z/jb1AyFPsspGawghBi0Rt5ghP2E3SNdCba29OJ56kJbVRaguixIb9LOay+fcl3qK0qHVadDzXuTDqYwSNoelRE1j2HFCCSVBWUIWGTuxPEk33tFudeumBUk2q5K8KQFCuD0OpNVby7j7aeF+PACgZdTtcEMBPn4uUHeOtNk2bSsy7E6LEBCt6UiDltjNnHXpCYkuf50BykMntjUoiwkI6CsKDff8gkYgkTKVRd3ISBan9fKPp3rlYvlesQtd8Aah0AmqMXub1zjGYOfQaWTeB2rf4EKRqBqyrFVJ593pfr52m+z7sEjL8Mf7oa1X1RBEEPFGbhivub1y3AF6muo/ksmsArVLPB5IcTP/NvzqIaB96Csp6V9FRIAACAASURBVMeA/x6F/WoGIO632RgLyoIm7b0Wmw/3UFcWyiToTq+LZsKh2/osvLRISRXlVorGyjAS6IolOHRYJddOrS1T5YuS7Qjp4oaqcEOVNIpuJtFG3JZEAoLlE1OEuncjhUmydm7evNOrTK47K0xD0MYNV+c9N62hig/Oh2gwJ0fMS1AeMvCkIJZy2NcRB2HQXClZUOsysz5Kb9LhmW3qYutEalXCbg4B0yDpuMhkL1ROhkCIpO1i+pajRBYJlO16JCy3OPqvgITtEjINnFJBEpAfkdcfhjrvQ3oD1wKsnqpcgUNl8jnqNYkO2PsC9ByCp+9T5ZeQSqyGghCQGq1OPJpTjeH2g5KoPKg7gT5U7tMFwAr/doH/WC9wB/BuKftLttCMBlJK4pZymY0FpiFwpaQmGiJgGuzrUBeVab5ANVSGaW6I0hFLYTke4aBBWaj0mdeEijBI6Onu4uihPYDHlNoowrMz1pMdnUCy5nSCBixgBwDLpgWo73oVgSRZOwfZT5sJ4bl4wfy6d26wqijIwXQSVJX7eVkJm/0dcUAwrb4CIQzedraqxfe7TYfpTth4oWqMgurdQvg9ouyUSoYFEraXV5fQLRCihOViuW7p4AcfKSWW4w7s4hsKuWWQBkoUjlSpSDxriGIhDJj7t+r+5l+oyD47nrXSjrwytHkCYR3Jp+mXkfSDcqWU9wKTUKL0OeBb/u1z/mOTpZSfl1LqU9AxpjelAglGes40EPXl4YyFtr9DnRtMq42Scjyqy4LMaqxgdmMFrX3J4hJAOTRVRRDSpSueYk8igvAcplaHaOnszriGnLIJJOvOwBSCpcEdmAZc0Ryges9vAYg3nZeZz0x2+N/cFRKJNPPFywuVFxSbtZFGgOpyFQShqlmocOtpEyeQqmpmUXM9k2oitPVa/OvPX+b3OxOU0hTDS+EYZaqKApCwnbxcNKcgjylhu3iS/l13KNFzpXJzDuYKHBAzqMREMngli9qZQxcogJkXKUHq3Au9h6G2GS6+XT135JX+E4BzCYR140NNv4y4Np6UMg486t80J4hDXQlC5vALczqux7ef3c2u1j7u/Nv5eeWJSrGvQ13Mp9dF8aSk3HfnTa8vJ2gaVET6/9OaEHVBunRYJvt6BUhJc7gHU0Yx4kqg7OgE5cITcEn5LqLnRjk99TLhnr24oSp6p14EgLDjIEIEEh04ZQ2qTh8Cr+Bi7JmRbIiD9Agku0g2nEVVmYpC64rbmffUVFfLETNAvSG47fLT+f4Le3nlQDc/WHeYP0UDfGSRw8za7PsL2H0kK+cS9b8cJP0yR2qtYiGKWw5h0yxqEJmL4/p1MIQAoaIi+62hOBiBiDrnGSygoqxWuQGd1NDKMgXCKr/q1Z+phNyLP6OK4pbVqFyn7gNQM23gOYyA2ptr91/0VvO6RZcfOgVIOS5HupMDisJAeFLy4LO7+b+d7RztSfHU1qMDjo9bDq29KUxTMKlaXfgjweyf0qSasqL8p+xiLrW92wiZkHRgf6+HFAaz5EHKrM6Mi8+JNmJVTcMzI5QljzI73EPNDvUdqGvm32Yi9EwnQbJ2DnbFFALJTvAcVfWhoEeSNNW5l6pO3kGq5jScaFOmBf2h7gTtfRbBgGpLn7Z6JlRG+MTlp3PLpXNoqAyzr8/g7ufivNbui8v/Z++9wyw7rzLf37fDyXUqh865W2qFVkutbMmyLScMOICNMTYeLgPMwHCBy3DxHYJhwINhMIxnCNfM9TBw4XrA48E2xja2JStYObe6W93q3F1dXTmcvON3/1j7pDrnVFUHyS253uepp8I5Z59dVXt/61trvet9dYiBQcmWQVWtNY5fV/OwTKNl0LbkBiRjZovNSCMay39a017uaKWwk0uLylZhGJJFXchs0jXvgeveD2/5LdHfUwqGr5PHVlrmg1WixCra4pIDVMTW+4BS6pNKqc9GH5+MfnbhBjiruGBM5aW3cjHlPa01f/P4aR49PlOTJLr/yOSSC2LVBn5dTzLa1SsS1gqzt/lRbDdHTyRGG4TQn1TY8SRpZxLbmQUkg0KZVHp3AND78t8Tz52U7Gnjm+VYYYA2TIJEH07PDoJ4N1Z5mjCWbX1fw0RbKazyFF56DV6XuMh0R5T3qpL6ht4UgdaYDT0kpRQ3burl9957PbduTFHxNZ94tMT+SR/DKxB0raHoy+/jBTpywZC/palUk9BuGDkcxy2DotuZlOAHIdV/gYI6+eRiUA1QK0FmSILMSinndhL2fLA5UxqpBqgVEiUAvNUAtYpWXHSAUoJ/B5wDPgf8CvAvoo9fiX52Tin1MdVuGGYVHXF8stBe3qYNwlBzpsFc8ELxv547x30vTWKain/7tp2MdCeYL3o8e2au42uqpbANvUm8QJOOm016e0siN4qR7KG7oYK0MWvimUkM0yIWBSg/OQQ6pNK7C4CusUcAWNjyfTWbd9PN4WY2RLp0JpX+3QRWhqBdgAKCeDdBvBend0ctm8hGWeep6XrJEqmqsZjXE7MM/tVdm3nTWo3nh/zBY0WeHnPRXWspRIrxftjsaGwYMs9UhRupTFiRHmEneEFY23BoLjGDslKQ6EAxb3luDLLrobJMX8jJdw5ia66XzxMHOytUhD7MHJP00LBWFSVW0RaXkkH9d+B3gC7AQYRj/z76eDT6WRfwiei5q1gBtNZM5Z0m1YalMF/2cP3woujlz56e48vPj2EY8HP3bOeatd3ce/UQAN96aaLj605OSyN980Aa1w+X7VfV4FXAK2HG4jUVB4ANWYMgDLHjSUwvjzZstDKxS1NU+nbVnhfYGXIb3lz7XukAP1W3NddmnPOZazgf9rTNOKasEWa6rm5SX+hZdO4boqFjy1Btg4JK9vKRt9/GvbvX4CmbPz6YpmKmKXny/6rJHkWwDAPXaw5QGo0VWXGEHQJPyQ1qPScF7S03Voqu4Vb796VQteboBB0KmaJTEEsPyvCvVxIDw3Z4/nPwtV+Fkw9KUFxl8q2iDS5Wzfx9wEeib38PGNFa36W1/tHo4y5gBPhk9JwPK6Xee+mn+/qH44cUHL/m4bQczswUL0pjz/ED/uYJkVL8kX0buWmT9FDu3D5A3DY4fD7P2bn2IqLHpwoAbB3I4AZBLQtZ/k1ll2wqo6aDB7C+yyAIIetH5b3kICr00YaJ070VreT4C1u+r0YtV16JIN6LXjTbE1oJhvuyzBTdpgxoulihK5vFVVYTOaFa4qui6gxsW0bL/FIVhhnjx+7YxuaBDG6gOD1bxg+kdOeFYZPYlGkonKD+fp7fOMRLR6p5xW9Wpe8UyFYEO3FhXlSJLMS7JcC0g1cSQsVSQWwkyqLa9aFCH47fJ1+ffkzIG6tMvlW0wcVmUD+NVB5+XWv9a1rrlqtLa53TWv87ZFZKRa95XSMMNZO5S6ulu0GIH4bMlZbXais6PvNl76JUIv5p/3lmCi4b+pK8dXd9d52KWdy5bQCA+19qJUuUXJ/z8xVMU7GpXxbzpG3JoGZb69fGE54CK45pQG8cbuFF1jDFxqxBiCblim+QnxqUMpmyQBksbHkH5b7d5DbeWzuU6Zdxoz5SIzSwdTDD+t5ELUjNFB2GuxJcs7ab69f1UHD8GnW7uzGDUtJXUwq64taSZTWlFNsGpa9zarqIUvK/c70Qo6HIZxpKtPmiYOk0PK7pTDWvuCGPn5jhD79xBKeTJ9RFwA9CDo0to7sH0Le5M+Xcqwil3EqKvFM7jCxBlBh7vl7SG98vGVngidpFFYG3/PW0itc9LjZA3QQEwKdX8NxPR8/dd5Hv9ZpByQs4NVNs6V1cCBxP9NcWyt6yxxlfqNQUC4qOv+Ky4ESuwldeFKuEH799cwt9+S1Rme+R49MtxzwxJYvWpr5UrawYVy6MH4D5JdxTtYbipDTVgTvKD/A7/Bkf5zOs65Il2y5PoQAvMYRGsiEVuMzt/ADjt3ysPphbJUfEe5reompnH7cMtg92MdKdYDxXYaQ7wVUjWUxD0Z2yuXqki7myqF5k4laN3DbcFce2DJKWScI2O2ZQVWwZEP3jasnT8QJKnt/y99Sa2rGKi4apO6lJlL2Ab740wYujC5yaKV5aia8BXqAjG/lljpfqFyZkpywp0S3EiEqb3pFbhOFrASWGhYttSk48WP86cKVXhar7UnllOP04TB9d6a+1itcpLjZAdQH5aAZqSWiti0Aues3rGmU3IFfx29qjr/wYPjHTJAw1lSXM7vwgZGyhTFfCpuT6fOx/7ec3v3RwydkaqLP2gkDzhh0D7Bxu/bes701x9ZouHC/kO8eaNdWq5b1tgxlxzDUU8bAiq/D0kc59Cbcgu2LDgumj3DzxeTmOGiNdGgXAKk0BCicxACi85CBGmx266eXr5IjGt/BD0nELpRSGodg53MXejb3sGu5qInGM9CTZ3J9mruQ22H/IDJcXhCTjFknbXDZr2dwvAerUTAkDRcULcLywRc1DUSc5lNwAyzCinyvcNo67YahxvIDJiJ1ZcoKm0uClwA1CKl64bPDFMIVyXlmUbfmOiL7aSek1LVbPd/IStKy4ZFmhD1NHGk6gAKNPAgq23iM/O/eMHMeryPHPPQsEMHdq6U3PKl73uNgANQn0KKXWLvdEpdQ6oAfo4Pv8+sFC2cXxgmWDxFLIV3xsU6FhyYxotujWhjefPDlLriyzSV96YWzJ4z97Zp79owskYyYf2Nd5iPLNV0kW9ejxZsfTaga1bVAIEl0JC+UWxLYhlpaSTjtNt2rgcvLw8B8SUwGBmSJhQWbsMTRgFCdFmi3eSxhLE8a6UO2sGMKQcFH2BBKgGvthpqEY7Iq3VVTf2JeqCR1Uy3wb+1L4gSYdM7Eto611SSPW9iSxTMVU3sENQgpOQNmrB6AqGkt5ZS+oBTDLUG2p5l4YMlN0axJJJW953b6VwgtCHC9sUbdoi+wa2Xg0svXcomgOAsRSkklVe1VhIEGme71kQ1U23+F/qqtKnH5UgtbIdbDjbfKzc8/KxqU8K+W/wJPjpvvEybe46rr7vYqLDVAPRZ//aAUU8j+KPj9wke/1msF8ySNmGZcUoAqOKJJbhkGu0mqzXsWZuRIpWxbjh47WY//XD4wzNt/evkBrzf96TrKVH7pxXXP/ZRH2bOghZhmcnCoyU3Bqr68RJAYzOH4o9PbyrKgVxFJSnmnH3CpMyHMe+TQUp/F7t1Hc92+Im4r0+ccwAaMwLsQBK0toZ9FmAt3m6lLoyP6iGV4Y0tVBoHYxLNMgHTPxgpChSJZp22AaPwxJxUxsY/lbwzTqfbixhTKFii9CsW0o90Go2wzxKkpua6DwA12bbQMiYdmlA8p8yeV8h/97IxxPdACXzaBAsqDFlHMdik18FT0bwY0CVHkO+ndIYAsC2PkOUaY49zQc+pI8p1re2/pG6N8u81mFCbmG5s9KhpWMhH4NSwLV+efAKSx/vqt43eFiA9QfIhvD9wMPKKXeoZSq0amUUv1KqR9WSj0F/DAQAp+65LO9ghGEmoLjk45ZK2bgLUa1rGdFfZS5UvsAVXB8CmWfZMzk3FyZ45NFErbBHdv6CUPNXz9+um3/6shEntHZMtmkxZt2DS15LnHL5Pr1slA8fVpmoqYKDvmKTyZhMdQVx68GhNJ8nSWW6oW5k1BoSJgDH0qzcOLbMPYcxDLkbv4Fcj1X4cd7scoz9BSOC4kCcG1RMg+tBGrxrxH6hGa8o1Bs4gIII9mkjeOH/Nitm/i5N21n95osOvrdLVO1VK/aoVrmG50tU3Q9/A6SRH6ohbHXMMRrmwZlp00GFYRMFeoBquT6kbtuZ+TKHrMrMEEsuQFBSHsTxHboWS8ZDUiGpEyINZSFk33y2S1KBt27MVJN1zL0e+cvyOPP/y0cvx+mDovt/IZbpYy4dq88PnFQNjipvub3t+LyMXFgZdp+q3hd4WLVzJ8Hfha5hd8A/BOQU0rNKKUKSAnw7xAyhQZ+LnrN6xZlLwAtw5y58sUFKKEca5SSAJUv+237IBMLldoi+PAxWdRv29rPh27dSDpu8tJYjqdOzba87r6IlfemXUNNatuNaAxs+zbLYvH0aTnW8YbyXnWRTSgHtF/vBylDtNjGnpcdNYCbB7QsUAC3/gxm1yAhBsU1twGagbPiK6STPYSmjbbELTa0EmK2F8HwKwSJRYtYBIXY068U2YSN64f0pWPcsqWvHjgsA8tUyyoDQZ0ocXqmSKilrwQwV3JZKHvReSk8PxSViYbXWoYSq45FC68XNLNBS27QMl+1GAsVn3ybYLcYJTfANtXKBWjjXZAekMzGK0nQacwurRhkRqRXNXyNXAd2UhiYYQDr98Hu90jm9difyms23lYjy7DuRvl87lkJcO0Qi6zhV6no33O4FDXzvwDupl66M4BeIEXdevR+4K7oua9rlN0AlNirl1z/oph8TgMpQimF1loCXwMayRF+EPJIRGK4a8cgXQmbH75J+kp/++SZJqWCuaLL06fnMAyWzJ7GFiq11+1Z341pKl6eKDBfcpvKeyA7jwQuLU6zZkyUC849I+Wh0pwMYs6fkTLfhlswDenxFNbeDkBqMtq/pIdRmprYaxDLNhEljMBtYe+BMPiMKLCvFKm4Sbs0yTYVtmGsJIFqIkqAKKmXXJ9f/+IBfvsfD9aYhY4ftqiDVB13F/eXHD9gKl//ncXccOmzKVQ8Kl6wLLGj7AXEraWFalvQuxncstDA2w389myAoWtqau5i3NhbZ+/d8KMwtLv+/K1vrH+9di/C9ju0tLOuaYv47Cq+p3BJWnxa60e11m8BBoB7gR+NPu4FBrTW92qtH7v007zysVB2sQ2jtugsxeTTWnNyqtCySDhB0LwoKlrkcBrJEftHF8iVfdb2JNg2KAvlG3cOsnkgzXzR47OPnKwpKnz7yCRhqLlpYy+96faq1lprErZRU0VIxSyuW9sNGp49M8eJBgZfEGpipoHtFpp31FVYcdkln3sG8mMweVh+vmYPGBaWIbmG27WJSmpNPVvJDKFRNTmjIJZtcrHVaEK7daft+iHphNWWENEJCdukMbhqrVHRJsMwVEc1iUas7UliW0KUKDo+CvjOsWkKFZ+Zgst8yWsIUEFLPFS0zkKV3YDJfD2DKjpLl/j8IMTxRWLJacMKbPz9HD8gbhkXxjRN9kp2owMZ4m15vEfmphqR6qvTxg0L3vBLkol1b4go6BHiXTCwQ4gTS4nLxrtk1m5Vs+97CpdFzVxrPae1vl9r/XfRx/1a685ibq9DzJc84jVFb92UDS3GxEKFw+M55hb1DAoVv4kBFjMN5hsGdsNQN5EjHozIEXfvHKyLkxqKn75rK8mYyVMnZ/nic+fwg5AHXpbn3nt1Z8kbL9AkFmUg+zZLQ/zxE7OcmVoANNsG0zh+IPTs8oxkRe1gp2TnW8nVF591NwES0zSAUswP3UI1UKjMEIGVlFIhoG3xcSpUfAI/BAxCq9UZ1gtWTpCoIm4ZGKouxOqHmqRl1suXK6Cam4YS/T7g/HwFpeBbDQPO4zmZVat4QVRea73lFgeo+bJXKw8CTYPF7VDV+tOw5HVXFbI1DdWkD7gslIK+rRJ0VqpIEe9qDsapPvjB/wLv+sOW8YCmMl/HczDkPAqdJbha4BZbafKreE3hNWm3oZT6b0qpSaXUgYaf9SmlvqmUOhp97l3qGJcTVYJErGHxqXjt+wFlN+DIRJ5sIlabc6kiX2k+RsI2m4LYqZkiubJHMmYyX3LZPzqPYcAdkfJDFet6k/zsPdtQCr70/BifeegECyWPdb1Jdo10Hkdz/ZC+TJykbdbKUXs39GIYcGQ8h+8HrMkYpGIWrh/SHTck+Cy1aMXSkO6vB6ioKW4qA6VDlF9hbui22tONVB9BQxM+jIJfoDWFUhHfzrQucEj/bsWSSxGUUqQTVu139YKQVEOQi9vmiujY1TLfVMFhdK7CxEJ9lz++UMEyRU2i5AYtM1KaVrmjs1G5MB39PkXHx49YgO3gRJm4YnkBWhB9QOdCmaaZ4bqNxkpgp2lJF81Ykw5iDdGmhdOPQGkJSnk8K+aIK1WYKEx11gJcxWsCr8kAhYjPvmPRzz4G3Ke13gHcF33/qqBKkGhkZ+XaMPnCUHN4PIdtGnQlLObLbtPOuOTKDFQVtmlEMzAh4/NlTs0U6U9LMHjo6BRhCHvW97Sli1+/vocfu1WkgJ48KSSHe68eWrIE5kZZyHA2UZvPySQsrhrJUl1sdnU5EAYEWpMxfSn7qGUuo4mDEDjSy4hYWqahMN0cprOAmxyAwZ0AqOwIRrK79nfRZlwU02Mm6zIwQ3vbCPGkunDJp56IyQeSYaQabOoTlrEiiaEqUeLUdJFvHZIdfndK/icTuQqGUlS8kFI0QtAIyzBagsqZOSGjbI/KtkU3IAx1x3MpOj6mUsRMY0mihBdIGdA01JJD4G1hGKLpt1JYsUgKqfOoRA29W2DtjULCePRPOqukm7b4RpVbCUBtUZqRINVuLm8VrwmsKEAppU5cho/jl+uktdYPAYuv0ncDfxV9/VfAey7X+y2HKkGiCrvDQjE6V2K+5NGVsCVQaCnfQL2P0I5dN75Q4dB4jt5kDEMpio7PV18cB2jS0VuMe68erg3cJmNmS6bVCk0yZtKXiTWpgd+8uVcovgp2ZAPMilRvU6oNQaIdqqWbaikHMAGlfbz0Gmwvj7rrV+De34LujViJTH1ORxmUVYouO2Sky6avb4jZorP4HdCahhLrytGVqJsTBtEMVBUrkTsCUXUHOHQ+x/Oj85im4t17ZJh1PGJc+mHYIgALrcO6fhAykXNQKNb1JEnGTNAIAaJDBpWv+MQsg5hlkF9idk6U1KmdzyUJ0K4E6f6VGREqBbf/rJQFx/fDka92fm4sLQoTyyEMI4V0vcr+ew1jpVvOzZfhvV7pIYZhrfX56OtxoO3KrZT6aSLh2uHhYR544IFlDyxDlrSUZ6pw/BA/CCk3LD5BqMmdqP95Qy0ZkqkUo6r+nCfPysJSfby8aAELQs0Lp8SMcDx66OunfAoln209Bt0LRxldosx+T7eGdSFr05rpY88t+3s+NwrKtCg4PuXIbnzE1YQRgSNuwNljh/DMJI+fDSBwiXtSaXXi7QPg3mOPk/BCDlQ2kz8ogRUdUPJSKLeE8hRzp1xgEKbKVNQJAq1q3AvteszkHY4pIHaCkqcpRDJLjef++PiFZ1Ch1pTcgHJEiDh4zuRwdFw/1FS8gOIyXldhqFGhRy4qx14/ZJItnibwXU6PTzH6Uq6W/Sw+ltay05o8Yta+P3euTOCHxArnsUOfgq+ZPHGQR2debkt9LzoBkW8kQah54FT7v4MbCJOw+rs+eN5ayfbi4hH6wswzOojOomnc4PSt+SC7jn4G/cR/54X8WsqpDkI1oQeH/inqSxmiYqIWlX11KAPECjjz1IWpua/iisFK7+ifusjjvwd410W+9qKhtdZKtYx4Vh/7C+AvAPbt26fvueeeZY93YqpA0Q24bl1328efjmaO4g2usrNFh1u39kdMMTEhPDdfprfB3sELQtwg5Lat/cwVpafUl26+kbxAlKyrx5kvuTzx1H5My+Aj9+xm/ZCUvETZ3GVNd7LFWfej1yz7K6K1Zq7kcVf8KMbQVRzJWcwUXLFu15oPzD7ChGNzx7YkFKbw1u3lutiYLEBf/jUp4b3xV+uDl1XkzsNzs5DOctMtt9X7R4Upngm249hZBhYOsLPfElJFeZZT3bcyOu/USpeFqdPsjZ8lkcjA1jdSdgOePDlDNmFHvkoBCsVNmy+87ehFVP3+dJyZosO+zX1koj7UXNHlhdH5WlkVJKCFoW7JdLeePMjxSVmI333Hbjb0JTH3P8NCoFiz6yYWyi4a6E/H2T86T3fSZlN/OupferxhxyB+EHJgLMfCwQOYVplrdl/N8/kz5KaLMLidm/dtqJ1bFX4geonVc5wtuty6ta92vTTi8PkccyWPTNxituhw85a+i1LCXzGcPJx5QjKpdihOSUROD0YGku8A8wQcv49bJ/8G3v577QOL1hKkAk8ytFgGNt7a/JzCJJx/QRiGXgU2v2F5y/tVXHFY0dWptf7shRxUKfVW4HepK5hXgD+/sFO7YEwopdZorc8rpdYgw8KXBfmKT67iRTTk1gyn4Pj0pVqp244fkrBFTufcfLnF9dY2DfIVn7IbdKQH26ZB41rz5RfGcP2QvRt72B4FJy+Q8tFQNkHFCy5q0fECTcbyMSoLUJxhsGsT56NmvwocPrjdJ0gKxbiCSX8wIaWTuTMyxAnwwCeFTryxTnpg7Bn5vPaGenDyypDowqQHp+Rj9W8G92XpMdgpEjELPxCKstYabaeIhZ4obCPlyp3DXbw0nmMwk8D1Qwa7Lm6HbJsGccuo9bwaSSrtMuaFshfJIzX3Yzb3pzk+WWTzQKo2yNyXjjFbdJkuODL4C0zmK3zqmy/Tk7T54x+5ISq3Cf375Yk8c0WH6YJkYsPZRC0gldz2Q9tVenkdwiBtF6BKXnOJ8XLp+3WEnapaE7cGh8AFMwGZQZmPqwapfT8BkweFDPGtj8umJ7lo46GUEC7MmJT8CtMyc9UYzMpzcj0ZlgQxtwjxFdrer+KKwWUlSSil7lRKPQB8HbgZ8IHPANu11r98Od+rDb4MfDT6+qPAly7XgQuOj+/rtrMj1UHaxYFLA5WotzCTd2qzS4uh0eQrXiQSu/S/YzJfEbq4gh+6ab28XmvmSi6712QZ6Uq0DPauFG4Q0mW4sqAUxulKWOjo+IZfbloEHStDxp2SX/J8dcB2SEo6D38KTjwgu9vAg9FqgLqx4c0K0LuVmG3ieCHx7IBQ1d0ixLvl7xC9oeOHJFNpDDteC1Agi3dvKkah4gvF/AIZfI3IJoQoYShaSCqL/2Naa7oSNsVFPca7dwyyvi/JB/ZtqF0Lw90SxMajQK+BoxMF0DKWULXpUMCR8Twzb8PkdQAAIABJREFUBRfbNCm7AcmYSTZhkYl+r4ITtO0ZVbzm2TnN0gzS6jWmuUQb+ZXAMIV5F7T2DKnkhLo+sAuy6+rsPTspQSk9IHYbX/vV5Zl4SrWq6BcmpffpleXx8qpj72sRlyVAKaVuUkp9DRGRvRvR3vtrYJfW+l9rrZeW2L7w9/sc8BiwSyk1qpT6ScS9961KqaPIoPAnlzrGSuH6obCfDJnoX4xOtF7bEKKE1prTs6WOC2jCMmXI05UA9dnvnOAX/sdzfPG5czUCBQgbrGqTccfWfjb0yuzNTMlhU1+KoWyCbMq+6E6f64dkVUV2pX4FO6jQl7IpewGGW0A3BmBl1BUbqgHqjn8D171fav+P/hf43AflY3w/oOqlv8CT90gPEDMN3DAgHrOhd5Po9SWy2FY9MDh+SE9Xpj4sGqFqp1HxhTyQvAgGXxXZpE3R9Uk0zECBkAlapAA1bB5ItWwENg+k+cR7ruOatfUy8JpsFKByFeKmSdI2OTZZFz3dPyqLZohmpuDSl4rVgtma7gRKqeYMqg1JouQGTSXd6nW3GGGo8YKwyUZ+xXJHl4LUQKtCRBjIGVRlk4aulgyqGqR6NsI7fx8Gr5Kf/fOvw8vf6OxNZcWa56MCTxTUH/sTePK/StArjL8iv94qXllcUgFaKbUb+B2k16SQ5fHzwG9qrY8s9dpLgdb6Rzs89JbL/V7V0pupFLmyR98iFYbZokPcbC2nxCyhmufKPiU3YCDTvgSVjJnMFN1oYBQePjqN1vAPz53jqwfOc8e2fs7Mlmr9DdNUvGfvOkAGe7MJmy2R9FDKNjFN1TFbWxqapL8gmYxXhsoCw9keDo/n6XNmCSNlhzCUv0Wsa1B2qQujUsoZ3CVabLE07P/7ZqfVLXfXFQgqC/JcwyRumZhKya4+MVILQo1K4n4Ykk3GIL1Teg0NSMcttg2mOTpVuCgGXxWZuIXjh4xkm28Hy1AYilpp1wtCkrbJYCZOV9KqZTqdMBJlUBO5Si0TOjZVD1AvjC7w3r3r6UvFa0FmPNLgG4mCm2UoNJqi017GKF/xmuSdYpbRtLGpwg3Cmk4gEFHfL151f8XIDMLs8aikF907lRz0bJLAApJpDV8Lp78jQciwINEjrM4nPiMiw09+Bg78T9j9g7D93ubhcDsl12IYSsBzi9J/Ajj1Hbjhx4BStDnqrOC/iisPFxWglFLbgN8CPohkYQr4CvAbWusXLtvZXQGozoskbJP5sgvUd/FhKLYImXjrRS8BymN0vkTCal7EqllZOm5hKCWNdw2HxnJoDZv6U3QlLA6cy/Htw6IAEbcNbtrYy727hxmOFq+KH3Ddhr5aMDIMRX8mxkLJb2mmLwcdauI6F9X7NRQm6BkcQocas7JAkJDh2ep5KwWMR9nTyHX1Acyrf0A+2r+JlFsiPbe4ZWCbhvR9LFsCVyyNpeqZi0KCOLH2ArHrokzyQjT4FiNhS6BcHGyUUsQsoZrbpsLxQvq7Yiil2NyX4sWx3JIBqvp/qvbyKl7A2dkShiEB4uRUkYWy1zTHNr4g2UY1uFX7YCU3aNHyA8gtGu62TYNCG6q5F4RN/laWcYFyRxeLeBesuUHKbel+Yd2FPnSva36eaUHPZglmVUVz04bbfw7W7oEX/6dshp7+S/n6qu+HXe+UDZFhyjyemxc9wMIUTL8sx9ABvPx12Pl2CYydCBuruCJxQauYUmo98JtIj8dC1o/7gF/TWj95+U/vu4+iI/JDcctgvuwRhnV6c8H1O9orGEoRhprJnEN/OsaxyQLPnJ7lyESeUzMlFPBbP3gNG3pTGErUpau2FvfsGuTNVw1zcrrIU6dm2dCbZO/G3qbGd8HxGcjEWgLRQDrOZM65oACltcYMHWK2lgXETkJpmoQJg8kQZ8rFSsp7e0FIfyba+Y5FAWrtDSt7o8qCaLFFzeyqPXut79MtfTVLa4yIMq2UagnwjTANxcb+DirYK0TcMkjY4g+1GAnbwPM1tgluENATBZO+tChuiLZd+/OrBplqVnRyuojWYoyYTdq8OLrA/tF57toxWHtNNZiNdCcIQl3rBZbaeEIFEQ0+3UCKMQ2FF2hcPyTWELQXEyJMQ110v/KCkRmUzcfUERn27RoWa42W5w3BzNFmUoVSsPku2HQnjD4NB74AM8fghc/BoS/CjrfDte+T67Y0JwHq1MOyGUp0C5Hn6D/DjrcKa3A1QL2msNJB3SGl1H8CjgI/CdjAo8CbtNZvfb0GJ4BcxSNmVkVgm9XFF0oe1W6J1prHT8zw8kS+9ngQ6Z6dmC7yia8e4qsvjnN8skgQaPxA842DUjfPJmxipsGBczLQtHejsJa2DKT5wL4N3L5toIWVVfYCNva1LsxdiQsvYfihJq0q9YtBGbJIODnWpcFpsHoItBaWYKO455oVBCit5TXZ+s7ZMgwSMbOFsl3NXMpuQCZhNc07vRIwDEVvOkasTaCJN6hJaE1NCskwFFsH0m3LaVUMpGMYhmK24OL6Ya3/tH0ow57Ia2v/oiG2ajBb053EC0IZS4hm5BYPDVe8oO0ck1KtorGLhWpNQ7XNyF4x9G4S1XMnL+W9doilhBzhtZmbUgZsuAXe8Um49+OStXtlCVKP/5lkUvkx+SedeVT+CDvfLkK0blGCW/58Z2ULt9Os1iq+m1hpXeQE8PNAHHgWeKfW+i6t9YOv2JldIahO6UPVSqF+40/kKpFlA3zrpQn+/IHj/OE3jtQYXr1Jm+6kzTcOTRCGcMOGHv6Pt+3k4z8g1gOPnZgWmRpDcXSqgB9otg6mm2al2qHk+nQnbbLJ1iwpYUvZ7EIa4I4f0q3KUmapwjChNEdWlTBNsyZ/plAkbENKKF5Jsp7MkCw8S8EtCNOvgeprmqqjwGvSNim6Pr2pV6dnsL43RbrNuSQsq6aDZxiKVMNGoT8TJxWTmaLqR6O4r2UaNfr7RL5S6z9tG8xw/XqxDHnx3EItAAahrukzDnXFcf2w9vuLJ1Tz/9Txw7acGI1ukTISHUCDrx8Y57Hj05Hc0auUQYEEjMFdoiaSaD9PCED3xqUVy5WCkeulP/X2T0hp+ewTkp27RVGPOL9fnjt8rWRYIOoUoQ8Th1q1/GZPSa+qcNkmU1ZxmbDSAFXNxzWwHvhLpdTYBX6ce2V+hVcOXhA2MZ9ihslCWRagiheQr/jELZP9o/P87RNnAFGTvu+wXOhWNOf01KlZlIIP37aJPet72DqY4dp1WTxf1+zanzsjjK6bNi0/bFpyA7YMpFuo7SDZx2BX/ILKN54f0qVzzVprdgoK45iVOfp6uim6vvSgIzsKxiJVijU3SAPcyS+9C/UqsotuQMo22dDXptSD9J0cP2iZHXul0JeONZXE6uchGZTjh2STzdmcaSj2berlzu0D3Ll9gBs39bYQGWpMvoUKxxsyqOFsgpHuBGU34OikBPczsyWCQNOXjsn8XBjWyDXtLDeKjt8ylA1gG2YLDb7sBiyUXT735Bn+8tFTGEoy51dc7qgRhgnZtUsPzCZ7pffUibHXiMGrYOPtkjUd/YasTqPPQnFSrt/0IAxeLVlZ/rxIJBXGm2nrC6NSekx0S8m6uIRY7SpedVxIZzkSU2EYGLnIj9cUFu8wE7bJfGTDXrV1H50r8affPobWsGeD7Ay/cXC8Vj554MgkQaC5YUNP0zDpWyLbi/temsQPQp47I/2nGzcuHaBkENdcMrPoT8daFLIJfZRfxnAWMMszTQtAGPokw6JYcVdhxSXglOfpzYr/UxNBorH/5FUgPSwZVTv4FcmcEs1Gg4ahOpYkE5aBZRhLkhBeDdimGCuWvaDtMLZhKCzTwDINMnGLuG009YqGs/I33T+6QL7ik0lYDEXXQbXM98LoAqNzJf74W9LYv2qNEFIUdTZf0fVbTAvzjtdEkKifs2qhmpe9gLF5yUwcL6zZeSxnJf+qwzCELLF4rqkTdr1TPh/7FpgmnHpIvl8TDYYnsrDtXvnZ4a9IsJo9DgtjkBuH8YOQjmxEElkYe3Z1ZuoKwko76T/xip7FFYCKF1DxAnoaFqHFLCfbVMyWPPwgZCpfwQtC/tO3jlLxQm7Z0se/vmcbv/2PBzk1XeLho1Pcs2uI+49INrXYh2nP+h76MzGm8g5feHaUohMw3J1gTXd7xWitNX6oyTse167tXlKVfHGpynDmSU7tR0Uq0Sr0cbMbcXpFQdwKytgGHXa2mlTcJh0zyVV8NmVSUkaZPSE73aHdUMnLbna60jrRD/L4mj0XJDWTsE2SMWNJgsSrAcuQLmMYDeguBaUUQ10Jzi9U6E5K4KgSJZ6K5LC2D2Zq/7s9G3r454MTPH58hgdfnqJY8bl6bZaP3r65dsz+jPSxXD+k5DYHnVzJb5v1xdow+SRA1eeRpgsufWm7KeMLQhn67jQS8aqhHVmiEwZ2ysDv7Ak49xyMPoXM3d0gfauBHSKDdOiL0jOdPyNl6YkDgJag9MRnZAh8zwflZ+eegfU3tzdnbITWMtNlXtK0ziqWwEqljv5q+We9tlFwfE5MFrl5S29tAaky+Kqo/zxguuDyjUMTTOUdtgym+am7tmIoxbuuW8Offvs4XzswTiZhMV/0GOlOsHtt/WLXWthXb75qiM8/PVpTJr9pY09L4Cm5PmUvQCnR+lvbk6R/mQUkYZs1T6c4LsnpA4RWAh3NjuTKLvbUCYgPolK9KL+CnWqTTMfSNdvu4Wyc2ZIrBImqOvnQNRKMVF4ypL4tYt3dGKB8R+Zd0sspqTfDNg360vFXnCCxHBrljqr9xqXQn4kxOlfPJKtU8+pAd1WeCmDncBdx22A2Epnds6Gbn3/zDmzTEAt7Q5GONgdFx69l71DfULXrm1mmQankMlNw6M/E8SM9xzOz9fOaylfoS9tN7L5c2ePQWI5bt/Z1ZCa+KohF5bnKfGu/SutWz6jNd0uAOvJVyJ2L+l1XyWtT/fJ56z1CN3/pH+GOn4dkN6DgzGNw/H45zqY7hcihQwl0624SLb9OmDkuVYa1ey7jL7+KRrxW/aAuOypewHSx0uRkmqs0+zNVMZmv4AZBzWfpJ+7YXNvJ3rSpj6FsnKm8w18+cgoQH6bqvNNC2WOm6KC12K83LoDtyntlL+Dadd3cvWOQ27b2c9VIdkVDuEPZOGXXIzF7GI2uBSc0aBTDQ0MweYDphSJdYR6znSinnazdoNlkjJ6UTcJCbnIQzT0dipK0nZKdrzLr5cPQl+b1yPVtTQaXQjpudexPvZqwTNHKi1vGihbtTNwCVXfpXdOdbHp8W0OAsk2jRpa4eUsf/3sUnCCysI9bmIaKmIOKguPXekb5ir+k00lPMsaBcwsUHB8v0CLq3RigCg6aZjWJmaJDvuIxk3dbD/hqY2Cn9JQWq1AUp4VIsfkN8rHxNlh/kwxxz52UfmjPRrl2UwNy3fVsEao6SsgQ5TkZGlYGvPj5+rEPf0U+2ykJkqNPi7pJO+TOS4AqzUrQXMUrgosOUEqp9ymlfvxynsx3E2U3wDbMpjJIrtxa449bovxwbKJIoeKzpidRs/wGaZx/33VraseM2wZ3bh/AC0JmCg6DXTFu2dLPtqEMMdvgtq0yl5FNWmwdbFZKqDqodiftJUt67dCXimPlTmFW5gjj9V2oG4Rk4hbDfT3sHoxxTWqOEbsoBImpI3DsvraGcbap2DqQIX7+aSmTpPplV+pVJIgpJSW/3k3SP9ChNJyHr60PXl4AYpbR1ojx1YZtGJhKLcusrMKKMr9q/7InZdc2L0rB1oHm0YAfv20Tv3jvDv71G7c10e1dXxyCLUPV5rOKTl3uaLrgLBkwY5ZBwjZ5cXSeguNTcn1mCvXAM5V3QNcDlNYydD6QiXNmrtTRvfdVQywlZTqnUKeGl2ZlEzSwQwKQnZRB4MwQbHlj/bVr90ZzUFHVomtYMvj1N8um6eV/lp+feRRyY/Xe6MkH6xbxVkKqAqNP11UqqijPSbkw3Qfabw2iq7hsuJQM6lPAf7tcJ/LdRskN6E7aTOYdKpGLrRe0GgjGLSnJvBDpqN2+tb8leLxh+0DNUfXObQOkYuKvtGO4i10jWdJxqzZP833XrqErYfHW3cMtmZHjh3Qn7WYR2eLMiiyvM8E86fxpvERzVuY0UJetTD9D3ihD8UBuxG/8hsyUHPhC22OmbAUv/r18c817I4fTcpOIK9m1cn7FGekNLFYMeI3BMBRx22yRuFoKQ5lYjUVpKFUjSmzoS7XMs2WTNns39rb8771Q3I0NQ/T4qiMOQcS8my44JNsoljciFbMINRyfKjA6J4to9VKdKriRVYlcS2UvwPFCUjGLiheQK18BLrTJHsm+S3Oy6bGTIqdlLFq2Mmtg0x3UUsrq4HhVu9FOQNca2PZm+f7lrwtx58X/Kd/f8KMiZhx4ovlXhRWXUuDY80K+qGZMY89DoitST1HtyUG+s/zoxSqWxaWW+Fq29Uqp80qpK+DqvjCUG+zWpwtORxkY2zRIxyyePysBqpoBLX7Oh2/dxM6RDO+6XrKpMNRNi1x1cVnbk+BPPnQjP7indSEvuX6N8QXIRT/2rEzSLwW3hDn5IqlsX9OQLUjpqaoLhzKkNHL6UfjOH4ksDMALf1efJWnE6FNig5Dshe2R7KGmuZlsJ6F7rexq+7cvfZ6vEXQn6xuKlSCbjDUNxVaZeNsGV273oIBEpBBRJWcUHV8MFr2AIFiZ3mI2YeP6ARM56SXuHBaG4HTewVSKSnSd58te7W6OmQZjC1dIVpAdkYwJhJnXTksv2SMZ0vXvl0A1sFMyoMaydfd66N0smyYnDw/9oVDM04PSe1ofOQO9/PXmYV4zJkoYVSffc89I2bBaMjfM9qy/wsTy9+kqlsVKlSQeVkr9B6XUu5RSS3QN6y+5xPN6VRGGmkpkt55N2JydLdWa2lprzs6WmizQj07mcbyQrYPpWhN8MW7Z0sevfd9uBjJxHF+o4Y0NbdNQdMXtVjp4A7SG7sbSUiUnP5w7CQsdxsrCQBhKyqQ3m8FpOH5V6DVpNSy2Jx+E5/5Kjnv9B0WRHA3f+ePmZrTWsD+q11/zvrrwJxrsRYoWA7sifb7XR4tz21DmgqSjkjGTZMys0c33buzFNBW3bb2wUmdVX7A7GsguRhlUruRdkPdebypeGwDeu1Fu35mifF8tRU7k6xlZOm4xkat09Ch71dG3RXpN7eSRQIKHGYNrfwju+mUJMMlFf+tEt2yeror8U6tzfNe8T8p+a28UGa7KvPSpFsOwpFSdHpCyYhV2on2fqjAhPlXBa26vfkVhpSvIncDHEM+laaXUi8AQgFKqgy/zawdeWDd9s02DihcykStjKsVDR6f59S8e4L/cf7RGyX3suCzc7bKndig6Pmt7ky0/703bLRP/VQShxjJVsz5cYUJuiFSfBKF2N8b0UamjJ6SU2LiTd/yA7pRVjxtTh+Gp/0e+vvGjsgO97v1SVnFy8PAfSfnCd+DskxIYkz317ClwwUrWVamrMK0LJkVcybgYRttwNlEblr1z+wCf/fF9XDUimWaopUTXSckh1BpDqYYAVVWTkGHpyULlgu1FqgSJ7YMZetI2YSgyXq4f4gchcyW3Vn40lOieXxFkCZC65FKW7UpB10jdONN3WvueSknQWrOnHrxS/bD9zfL8RLeopIOQgFbagzPj4OYiC5EIgSdZlRK5sFVcPFYaoNYhyuV/BhwArgaSSKZ0Vil1Qin1V0qpnwJWXqy/QuAFukkyJmGZzBY9YpbBYyckGD17ep7PfucEBcdn/7kFlJIsaSXQ0LaH0Z208cMQwyuSnHyO+Oxh7NxprNIUZcdnsCte72+FoYhd2qnIjqBbvJgKU5JZeRXIj8P86VpPqCqC6kdlPicI6Ek2nMeRr8vnq39QbAxAAssbfkHKeFOH4e8+Av/jQ/DQH8jju99bXyy8ygXTx79X0JuONfk3NfYpHa9qsCiBavF8k+uHpBNW7TU9qRgaGW9w/ID5kidyUyuEHzk6o0TSaTAaU5gpulS8gKIToDVNqhSZuH1lkCVWivQgBNUgodq756b6JJBc90Py/Q0fqveR0gNCK0/0yD30tf9TlNPPPN55AB0k8Gnd/Jxq78mwO7MAV7EirOgq11qf11r/vdb657XWNwADwHlk7X0cWAt8BPi/gV4ApdQXlFK/qJTaqy6UgvYqYyrv8F8fPlETa03HRQfOCzRHxnMYhrCiHjk2w+997SWCQHP1miy9KbH0btRfWwzHD0jHzLY27MmYCRqs4hiGM49ZmSWWP01y6nnC8nTzvJOblxunKtNixWX3dv4FyW5OPSxfJ3ubhhv703HKUalGaVU/DycvMyCo+jR+FYkeuPtXpI9k2vWP/m2iCl1F4F4UQ+97AZm4hUK1XeDdIKQvHWPf5j72bOjBNFRk5SLwgrBJo7A3MqIsOj65iieSLkvcUmdnS5yeqctOjS1UCALNcFecZMysKZrMFl38UDNdlH5UI2KWQcULlhTDvaIQzyIzFKFsm+025cB4BlCw8x3wgb8WFqrW8rpUv1zjN31UgtbsCaGdP/Qf4cu/UBdG7gS3IUAVZ+QYsZRsGhdjYVR6uatYFhc1Aq21nldKudHXdyqlEsCtwF3A/wUkgPdGHxrIKaUe0Vp//+U57cuLbx0a5+C5HLNFj99/33UopVjfk+KRY9OEIVy9Nsv3X7eGP/rWy4zOSvP49q390pdSGj+sm9otRtHxm2ZfGpG0TUwCrPx5gkSPzBAB2igRz52lK7Gj/uTcODz4B2Kf/Yb/Q5rBsVT7urwO5cYIA7rcgOlCEd9aX8uoADj+7aj2vlcC0WIM7oL3/Pnyf7x2C8EqMA1FOm7iBZqY1Xxd+GFIJsqQ+tIx0vFunjw5ixeE2KZIJWUbHJir2XfRFXaduURvbyJX4be/cpBQw++99zqGs4lasKraklQzqKm8gwImFiptB35BWJ9dbR+5wmBaEmTKsxKs2pWY7VRkq6rrDL/AlZ5SvKtu7bHhFhFDnnxJhGjnTsO3fht2v1sYf8aiv5UZE+p5V6QWU5iQ+9KMSXXDLdXv0zAQNiBaiBuvo1L4K4GVkiR+WSl1i1Kq7V9Ta13RWj+otf5doFp03Qf8MvBFwAPe2e61VwKOThZAyY16eLxODX0m0sfbt7GXa9d187Nv3IZSYFuKfZt7KVR8RrIJBjKxtnbwIP2EThRlpRR9qoDnu7XgBOCoBFlyxP0G8dWXvyrzFlrDI5+uN3nb4anPwpd/Hr7yi6S/8ctc9dSvs/aJ36EnHu3mtYZj35SvGzOiC4EOI++o1QDVCdmk3dbSQkGTC3PcMtk13NWURSUaekw90fVTcn0Kjt+RXq615i8fPYXna4JA83dPnwXq/afqvF41g5oqCFHC9cPmUYYIplKUXisZFEgfqrIgqvntYJgSvPwGtXS/Ij0pw4RYl2wArYT0Ya//EXjnH8D1H5B/2qEvwtc+JvdeY2ZsJyQwgtyjfrmBRERzH+r8C/Dof4a5M6vlvxVgpYXs/wg8Biwopb6plPo4jdaybaC1flZr/cda6x/SWg8B11ziub5iODldrJEkvh1p57l+2ODPJMynfZv7+PV37ebfvfNqUjELLwwZ6U6yvjdVK6M1ouIFdCXstuW9KvqCCSqqmUBR8UKymQwsyAKDV4aTD8kOL7tOMp8H/6P0iBZj9GmhyhoWdK9Hda9H22kSC8cZOvK38pzJQzKgmOyVuvvFwK/I618nTL1XApmY1YGlqVos6ge74gx2xSMlk+bHqyK1JVfkjdrp7wE8dHSal8ZypBMWMcvgmVNzHB7PcTYKUJuqAaqWQclCrVX7PlPMMmqiyItRdoMrrz+V6JYgs5SGXnqoOUAFXuQijWRgjY+B3EfX/wi89XfltXMn4f7flR7V6UcjLb4YOEU5lpOXz4e/KixYO1Ev82kND/6+SIUd/kc51iqWxEpXl18EvgAsAG8BPo70oVBKPaWU+pRS6geVUh1pbVrrNqvpdx9ho0aZgqdPz5GveBwcW8DxQjb1p5p6QduHMmwdzOAFIXHLIJuw6E6K4eBix9Oi67O2u5W9V4NbIhPkMCtzqMCp/TjQmkx3jwQRryLKy+f3ywne+3HY+ibZ6d3/CSlDVFGeh8f+VL6+4UPwA5+GH/g05Xt+C0yb2Kn7RXfsaJQ9bXuLBLvc+QuXa/EqzQO6q2iBaPc1/111VBZerFCilGLHUBdBGGKoZgv7ngZPqE7Z01xJrDQAPnLbJr7vOjEP+NyTZzhdzaD6mzOo6YJLzDTJxNordtim0aKKXsXLE/mOj33XEEtDZqRevmuHRLaZcQf18luiuzMtfOgq+P5Pwd4Py/NmT8DDn4LHozK4QogShUnpXT39Wal02CkJVGEAUy/LLKFSkknlxlqHeaeOwokH6x/nnm1vslieg+nX/5zVSkkS/1lr/QGt9TpgO6JuXrUCvQn4JeAfgEnq9PMPKaU2XP5Tvrw4N1/G8UJ6UjGuX99NEGi+c2yaZ6v2Fx38mfIVj/W9KZRSGIZiQ2+SfIOCdMn1SVgGA11L0GMLk8RnDrHrqV9j3SO/hl0Yo1DxySZtUnakFZY7Dy99SUpqI9dKULjtX8lciFeCb/4G7P87CTSP/7mUE4avhat/oPY2qeFtFK//CckSn/yLOjli+1tE7DLVf+E+ODoU5tQqOqIdPd0LNCm7vUtwwjbZOdxFNtUsbZW0xcYDTdtSnNaav37sFGU3YM+Gbm7b0sc7r11DT8rm1HSJkhPQlbBqdvU9qRimqVgoediWalG3qMI2DRwvbPGM8oOQ2aLDQqnNwvndxprrlqakN5akq2VqK9pEdpqzanztNe+V3uzN/1IypxPfhvFo81jJiX1HtXw+cVA2kDqUQPTMZ+sbwdCX4NM4zzh/VgJfLCVBNpaWQLTYZNEtSplx/lRrsH2d4YLrM1rrE5G6eXV8egT4EeBPgEb5gf8XOKWMSKGrAAAgAElEQVSUOqmU+mul1E9e8tm+Ajg8LvXhjX0p3rRLatffPjxZNxBsI+CqtdDSG/2dhrIJQi2PuX5IxQu4dn1Px3IMYQhzp4m9/BUhHRUnWfPYb5OZPcDm/pRUzhJZuQiPfbPewAUpO7zhl2D3e2SDvv/vped07mm5ie74ebnxItimom/PO6PMy4vIETcIOUJrYeclu1fuweMWpW6/3A39PQ7JgpqZfF4Qkkl0boyPdCe4eqS5RGUainRUJm7HqvvmSxM8e3qehG3w0ds3o5QEnR++aX3tORv6UrWgZxqK/qivNb3srJNuUVWp+CFeoGs9rCsKyxGG7YT0mEK/Pv9ULVNbSblv2mhRNsGKC/P12oiu/tRnxYsqPwYHPi9BIxYRo178vBxz7rSU/ZSCHW+Tx0afEEaf78oGcfKQ6PsZ0RyhYQpLttFk0XdFasm05d59nc9ZXXIDQWs9qbX+vNb6F7TWe5EsCuATwCNIAPsw8BeX+l6vBF6ekOG+Db1J9qzvoSdtM5FzyFdkDml9mwHbshfQG7meVpGwTYZiLurUQ+izT7InPUcmzIvYZfXDjerUWsvE+uiTMH+GMNHLwuBelFdi24H/RPzY1+Q5hiXlgcnD8vXGW+snYVhw40ek5Jfsq9tV3/oznWeTbvkp6IlcbXe+o/7zeJcoP+igZq+xJNxSizvuKlphNDD5qhCx3s4iuEqplk2NZaia1cfiAPWV/WP87eNS2vvwbZuaytF3bh+olfU29TdvJhYTJRbjiZMz/PE3X6biBS1Ej4oXYJsGubLXUtZ+TSDZF5EZKqJ4XoVhSMBayT0AwurrGpEgc+w+8aM697RkVm/997JZHH9RHj/8FRkk7t0kVHYrLkP1hQkRXz7/vEgpffs/iGhzI6omi/OjMqDvOxIADVN0Cl/HeMWctrTWvwmglIoDtyEU9CsOxyIb7g19KUxD8cYdg3zp+TEAbmzjz6QCB6aPsn7X9c0HCkPWB6d52ddsHIzRUxmF0TM0qz5FC5WSnXVV3t/d+W4OpW9nT98/kz32RXj6v0m6f8u/lF2VQujgdloarunBOj115Dr4/j8S/bxUn1gQdIIVh7f9jsxTDe2WXWSjZtnIHiFZWA0LqJUQiZja7+nL7i3ZvvS5imZk4hZzJa8WdEKtSV2gS7ARZVAaXQtQWmu+8Ow5/vGFMVDwL+7YzF07mkuuhlL89F1b+fILY7x5VzOzrUqUmM63LsZ+EPI3j58mV/a5dl2WGzb00E39mihUfGzTwA9DChWf3gsQ0r0ikOqTbAfVSqhI9knVwm7TO9ZahuUTWbkvTFtKfff/rggsJ3rkmLvfLYor294Eh/8JDv6DVCeUATvfKa/deDuceEC0NRPdEjAf+bQEzcnD8KaPCZMQ5HVV9RjDrG9A7ZRY2fdvbT5PpyBsxte4UDNcWgb1BPDQck/SWjsNFPQrDrUA1Ss7zDfuHKxVCVr6T6GHPbmfTOEMPfmjzXXh3Dm6wgLbN6xhqLenrtuV7m/4GJCPZA9MHxaWXrIHc9fb2NCfoevWj8AbflGyo2PfhPv+vVzEROU9Jy87p8VpfbxLgtm171v+F46lJTiB3BSNgSbdD+v2Qu/W+ofvNDObKjmx5F6d31gRsgkbb1EGEl9GhbwduqK5qELFR2vN/3jqLP/4whiGAT9z99ZaeRokeJ2ZKxKEmg19KX7uTdsZijQj/SDk7FxxyQzq2TNzNTXz6YLbQoZYiGxoLMOoafq9plAjUejWQJTo7uwWUJyRjKuR2LB2L2y4Ve6TwgSkemH72wBDrOatpPSo5k9JQNkS7dO3RvYgpx+R9eDxP60zY6sEqNGn6+9jWPX1ozQrj7lF6UMvtvuYOyW9r3bkitcYLjqD0lp/sMNDV7RqRCNCDecXyhiGqlmt92fivH/fBqbyFXYMNYwohj7J6YMUijmG12zGLE3LMN/QVVLymjqMke6jd/EQX1so2VUB7H4PqVSSzdUKzOa7hIn04CelyQqy41q/T4LD8DUwefDy/AHaaZYtHtpNdsPZpyBlyU0SBvWBxFUsi2TMpFFIS9PM0Fspqnp8Bcfni8+f4+sHxjFNxc/es419m5r/h16gSdsWJddvsakvugHpmFXzt5pqk0Hdf2Sq9vVUwaHo1BvxWmtyFY+uuI1lKqbzLts7jB1dsYilpRdkWHVV8irsJIuZl4AwZNN9MHydBJXQrw/s7vsJIS0ELlz7fiCUjd7COcmijnxVnrf9LfX3G7pGyEmFSbj/d6TMl10L7/h9eP7/g5e/JoP5O+6tz1Q5eQk8hQn5vmcT3POxuhUJwPkX4Qv/mzB0h6+B7JrL+Zd71fFKlPj2Aa+J7bXjB8Q1rOlOYBkKQg8Mm3ddt+ifGnrEZ4+gKnME8R76MzEwBqTxGUuJy6dpS/rfKGES75JsZXHjduxZmSZPdNcbpo0Y2CEDgg98UpqjG28DtEi1ZNdKmc8tLk2nXRHU8sdI9koZ8fx+iCVlB7dKjlgxGvuUQaixDaMtE285VAPUfYcnmViooBRtgxPIDF8mYbUlVPhhSE/KrkkpLc6gJnIVXhqrZ+iTuUrTcRxf7ONNQ2GiyJU9ym4gsl0XiKLjYxqdWYSvGKoDu2a89d60k6CsOsMP5F4zbGHHWjHo3SJWGulozCI9CHf/W5g9KZYga/fKvZ+NPKiO3y9Z0c63N5/Dlrtlozp9VILQ3f9W7q2bf1LK7oe+WDdXXHyOKCnVjz0na0LXsJQg7/ttWYcO/oOU+7vetTxx5ArGZQ9QWuvRy33MVwqOFxLTmvW9SfzcONbki2SScUIrSWgmUKGL4ZVQOgQU8yrLhtIhYhMpuQjTffU5pOKUyKHoRbTPTXfA7f+m3ueZPlqfndj97s6U2FQ/vO13hRK+9kapK49cJxdb72YYe2bp4FKeb2UjtejmtbHKaIfsGiklTByqlwdXsSI0MvmWY/AthZ6UjUbUTgB+8g1b2gYnEHX+tZlUS2lO5LhgJJtkNCVlocUZ1APRoPqNm3p49vQ8EzkHxw9qMkyOFzZ3VZUmX/EuOEBprXlpPEdP0mb70HdBTCk12N5bSimpGpSmkWKQkmCy/ub6vdo1LPex1vXFv2rXMXR1vUcUzwqJ4i2/EVUeoo1vJQ+E4gJcraTc+jOSEVUWJDDu/bAcK9dAQ7fi4nXVs1FmpB7/Mwlia2+UjOz4/ULSUNFm+7m/kSCV7OCQFAZC4OjecMUO3L9iJInXAhw/IANs6k8Tzp/GSGTIEyelQAVlUCZBvAuUiXKLrNv/5wzOPCPX7e0/J7ujVJ8Eg6rh3+BVkhlpDRMvyrR5YQLe+KsSzB77E6kND13TzKRrBysugpahD5WgzjhK9oKZkJKC2aZBXVmQ4DV4Vf1nEwekxl0tMfhO5KOzwkugb6u8dpUccUEwDEXSNvBDGT/oTS8xo7MEelORCaKCD926sYUQ0QitNd0pm+6STcULahlK2RM1+2zSIhM3idsGZTeg6Pik4xZ+EPLw0WkA3nXdWo5NFsiVfRaKXk0Oqew1B72EZTFddGo9rpUiV/FZiLKvzf3pFufqVxw9GzpnFj2bhPBgJ+UerNrLV2EnJfCUZ+ski9KMBI7uhtFPpSTbcov1oOVXZONo2tKvuuknJCBtvUdKdVZChuD9SmSiuK/9OW59owSn3JiozKy7ER74D/LYVd8vmdfZJyRotetNay1kjLkTku1doaLP39MBquKFoGFjymdo/GEGrrqTw3mFp02sBiZbYvYl+p//c2LuPMqOSYB5/M8k+1h/k2hrlecl6Nz7m/Xa9PwZeOD3pJz3lV+SCxXEd+aWn2oVnex4onm5+KvBxDCgb7PQURdTyt0SYAgDyG5YNPq2wviB5gCVuYBeklKvC1bQdwPZpM1cyavZuF8MbtzUy/reJHfvHOTt14ws82wpmw13xTk2WWgKUJv7UyQsE9MwGMjEOTdXZirvkI5bPHNmjnzFZ31fkm2DadZ0J8mV80wUKjh+SDougSXWoCOYtM3/v703j5Pkqu58vycicq/M2reupbvVm3qT1K3WziAhCRBgDMaAYVgMzzbvzQwG+8PAwzY24jNmBsYM2GDMMwMYMJhlAIFYLCS0IAkhtbbW1vtaXd21r1mVe8Z9f9zIrMyqzOrq6uq17vfzyU92RkZG3IiKjhPn3N85h5F4BrdVVUw+rkbvWIKQY5PO6V5UzdHTM3BnzHwPZgVR03zUdWoFHWipd7hRezezjV6kCRBtlJSrjVDXtfrB8vgTuhamE9ARCqV0v6p8Bnp26vtDJS8P9HdXvl1Xs9j7M21kxo7q+evt79bf7/6Jjtasf40Oz5fS/yI8+EkdsoyuuGAN1IXp150jCh1Drzv2ZVYc+Bahez/M+sQzxDNZnYaUmaJx9zdo3/k/cNJjSNM6+J3P6aZ+Smmv6ZHPajFDsG5GgVegrltPerZs0sZJRE+oXvf/LNw4KaU9s9mTnQXjUhrGy2f0hb7iqnLjBNr7Ekt7Y4V1L9CL8lIjGnSKSr7gaTYaLNAaC/JfX72B12yZf9JbNzvUocVar49U2VhCPixLiIV8xSLG//zwIb71+DF+8YK+4b5ifQsiwoo6fQ0NTWZIej2rJpPZsjwt2xLySjGdWXjZo2Qmz1A8TU3AIey3OTF2gbSXPx2CdXpOODGiDUzrlsrKVtunq5anJ7X6rvlyHYXwR/R8VWpCq/DSCS2s8IV0BKb9Cl1FYr5KEd3X6wfP1AQ88f/pe8GO9+h7y5bf10Zr5AC8+KPy3/U+DT/6Y52HefRRXYkmM11xF6fNnp/OLd90BixrDyrvKq51DrDyxN1YtgXZBJGnvsim9hsYDK1mxdEfY2WnyGMxtfZ3abzhnTN//MwU7Pt3/RQkois7hBs8F77kthCI6mTa/fdqr6cwh5McL7/4LEfHvmeTmdJPZ7Pnm5wAxDr1RGnR2LnQvq1ysUzb0QZzvEeHFhBTifwcEfY7uJ6pmF0kdqE4lixIHpvNu4T9upVHxG/rfKW83nvQmelL1hDRpb12901ycjzFyXE9t+VzhBvXau+hNRYEdMWIQrv56XRe96cqwRLh8PA0SimSmTyCcPWq+qpikJPjSWxLENH9yUam0osWWpw3CuG7ob36gXB2V+lSYu060TbWqf8PFgg3kG/ZzOTRZ6lfe502TAWirZBZD8P7qj/MWpauufnA32ppfMsG6LxW34PcrC5y++RX4JG/g+E92ojmszqqk0vqebjpQR0q3PS75TmU+Zyen3ILUnXRxzHfvPdz34X7/kbPnb3jhwufPpiHS85AicgdwD+glYRfUUp9qtq6Fi4fcb+K7YBc8Vatxnnqq0T6fstK9zEUkGveTHrbe4m2rNYXRGpClxvZ8iYdKz78IGx7l66TlxgtT2x1c7rjbU0zXP7amR0nx/XFWL9qZtnIQa9Ve8lFmktrT6dpfeUDaFpbLvm27PLfzybWrisoz9fUzbDkBHyWZ1xkTpHYhWJbUkn8PIdMzi3mOIkILdEAg5NpFBQ9ItBe3Y6V9dy8vplDQ9Ps749zeHiKbd31RSMW9NkoYDCeIp7Mkc7lUcztexYL+kik8ji2EHRsxlMZ4qlcxTYzWa+7byzoI57KEg1qj24onir2q7poqGn1Kqif4v9RIKbD/7EVZSFApRSH0jH67E1cF2hkTpCzYRWE66oXch47CrUrtSc28CJc8yfefHVcPxh3XacrXIwe1oKJIqJFH9veAc//H93s9MH/Du/6iU7SV0p7V0P7ZkKMlg/iHdB1TWVh196fw68+rg1g901LYpzgEjNQXr+qLwKvBHqBJ0XkbqXU7krrt6hRmlUW1bgGtr5ZP6m0bEIe+wJ2cgy2vRNn5Y0ECxdVOq7/UN3bdAz36j/U5YaCtdroBKJa3Vf4oyqlKxiPH9XGT0R7RLZ/RrJawHeF7oxbaG7m5rTB6thRuX01eBOtpxGm80dmNXVb1hHec0bAsXGVIuK3T2uephTHshbkQWVmNTtsqgnodu/o7soFdH6WHtum9hib2ud63StiQa31mUwzndGCBoDfHBzm4OAU77iuG8e2sC2hpmSffstmZCpd0UANTaZxleK7T/Zw70sD/Nnt69jSUUvveJLO+vCiz895wbIWlnIhoo3NLE6MJ+kdSyK+KPFUbq7cXmR+UZK/RldHv/H9Onzv90KO7VfpKErPE3Drx2aaLw7u0XX91r5Sp7dE23QVmq++Uk9TPPuvsPZWePh/eblbswxj8wbY+lb9QF5qgA4/BL/4sDZOV74dbv7wqc/JArmkDBRwLXBQKXUYQES+C7wBqGigokyD3YDz8g/NuNGxdrjjv5dLSEEbFgV0btceUsd2ncBqOdqQ+IL6wiid1BTROU1uTpdWCUT1H7HrurkhAV9QhwqOP6E9oeQEtG469WTt6VK/Sof56i875aqGpaFQ7LUmtPj/bgv1oACCJf3HCobDknIjEnBsAo4O/1VS0LlK0Rj143hVz6fTecamMyil+NfHj5HM5FnTUsPL1s6t+xj22wzG06xtqSnztlxXcWx0mj19k9z7kk423XlklG3d9UymskymstSFL7KySYtkZCrN/oEpGsIBktk8w1PpsuLTC8Lxz9wznIA2To3rZ6IqHdu02GLFNi3MKJCa1Ou3bNKG5raPw88/BL/+H7pAQD6jDV6sY+YeOD2sPar7P6GLU6+51ZOz52HXt3RYcePrdQ3CJcy7utQMVAdwvORzL7oVfREReR/wPoAr23081/x7uMdcb9VqKMB7Wup5omSxqz0eAXwKDj9WfRPZHLiDWvl3/Mnq67kC2UHtZfUdAg7NM65FkvHBwBGwepZ+24aKpLJ5xi1hcN/ivdapdI7kKTyMvKt45qRTdo9IZvMI8MjJ8if0VNZl1FUVHWml9KvByTKQUBzd9wKqz+bAuMtUQldA/9mT+1mVPVp5HHnFg8cdSoebdxVHx7N87YUcea+A7q4j/RxvHkUp4bc92pC6SuEqCNgWjn3+Paps3sVV2mgrpUOflf4MhQTmU+Eq3Y7HEqFPAAUjSjGwb5G3Y1dBdkjfM04eA0qKBbgC2X5mCvwU7mUuHH7UW1TPxuhW6saewxWb4cbrOdH2SlLBmRIhdi5B29DDrBh4EKdvN/TtLtkmDDTfxOGaN8Ajjy7uGKpwqRmoU6KU+jJeZfWNK1vU1W/6MyILkf4GaipPEGamvdbn8zQmBD3pmJmqnjRXSnIMArVnLwSXntLjNfX0zhlD8RR+xy5WhFgMD+8fpDbkx6ryhOp6ZYhetrapzHMZmkxhWVJW6RxgYCLJnv54WeivgO5L5rDi5D6Gj08Qr+kiurqR48+exHZ0Mm9fEtJNl7OmeW4IemQ6zYbWKO11M/8vnjk2xt/d9QJ5SXPThkZ2n5xgMpnDXrGF9togE8ksjjVjlOLpLFs76+aM+1yRdxW7+yYYmkxT43NwbCGZzdNWG2R9a3lycSbnsvPICBvaoqeUzB8cjNM/kS67Fkam02xfWU8suMjrIzWhH34rzf0kRsvr8lW4l7nXb+fIQ99ktGYdGy7fQlfFcdwC2Y+Q2HMP432HaK8NahMVaaH76vfQXW0q4gy41AzUCaC0SWKnt6wikeZuIk1nmNuz0HJDtrMw4wRnPxn2LFxIhvlZijwfv2OTdxVWFa8ik3OJBp05IobmKkm04XkezLJ5XRKpLRbgORTDUxnS2TzPHtftHTa2R9nTF+f+PQMVDVTIp8N8BQOVybn800MHGZxMsaIuxHtvXMXXfnOEJw6P8tLJCVbUheaE92xLeL53gu3d9dSGF2/YF4PrKvb1T3qht5nz57Mt+iaSrG6KlKkUR6bSTKVzHBtNzPu3VkoxMJkmMkuxaIsOpS7aQM0njlrAPPWkCtGz+g+oCTi8MOGyo76+YsNNgPFN7+b52gli65qoWWRe30K51GbJnwTWichqEfEDbwPuPs9jMhiWBL9jkXerz0Slc+5p3eC0HJ2yhooFXBQhv0N3g34AG55Kc2Isydh0lrqwj/fetBoEHj8yymRybtXskM9mPJkh5/WLOjAQ56mjYzi2xZ/euo6gz2bzCn1Tfamk9l8pPtsiGnTYdXyM8USGTM6tONalRinFwaE4A5NpmiLlxsa2BKVgtKSGoVKKYyMJmmuCxJPZss7as0lkdNmo2fN+QZ/N4OT5qwx/YjxJ0LG1clMp9vXH53RSLjCe0nORE4lTNbs8cy4pA6WUygHvB34J7AG+r5RaotLfBsP5JWBbuPPcoHOuWyaEOBW2JdT4fWSqNB30OxaXNUW01HwyxdGRBADbu+tpjQW5qrOOfF7x6/1Dc34rom/khUKzD+8fQgQ2tEVZ4XlVBeXg3v54meE9Pprgrmd7dTFnL3fr2Z5xHjs0zCMHhnjq6Oi8RqAS2bzL7pOT7O6b4OjwNIOTqTmNGAv0jiXoHUsWuw7PJuJ36BlNFI3lRDJLMpvH71j4bJuT49UTjycS2YpqzKDPZjKdLRYPOBWuq5iuUAx4MaSyea+aiPaYakN+hqfSHB9NVFxfP6T4GaxQCX+puaQMFIBS6hdKqfVKqTVKqU+e7/EYDEuF37GqtioqcLqVwesjPl3yq9L+bIs1LTWgYCCe5smjo8BMn7TbN+pJ9Af2Dlb07BzLYnQ6QzyV5aW+SQRhQ8ncTXM0QEssQDKT5+iIrmSQdxVfePAgP372JD9/vq94TE01ARojAWpDfvKuYtfx8QXfoJVSHBycKuZz9Y4leb53gt6xyjfgE+Mp6kL+OaHSAkGfzVQ6VyzGe3w0UTzv0aBD/0SqqqEZiKcIVakmIui5v4Uwkcyyt39pKjYMe95g6fE2hAMcG03M+bumstoDjPhtJs5BR+VLzkAZDJcqfscif4oQV7DKvEE1akM+crOsnqu0Gs3vWDRE/NSFfeTzipPjKUJ+m41t2shs7qilNRZgdDrDd3b28PjhEQ4OTpHwyh4V5OYDk2mODGkDtGGWuKDgRRXCfI8cGCpWbL/npX4mZoUPLa/6hN+2eK53vJibNR/9Eyn6JpI0hP2E/Q61XpmngcnUnJBhMpMvtrSfD59t0z+RIpnJMzyVKc4pFQQslfpsZXIuE8kswSrVRPy2xcj0wsJmI9PpYgWOM8F1FcdHE3NCw7al1ZSzDWZhfwVjFl+gQV0sxkAZDBcJgXnmoPKuwmdbZXXyFkLY78wJOWVyblHZ6nesskrlV3TWFudPLBFu36Rzbu7bPcCXHjrEf/vZbv7fHz5PMpMvtuc4PDhF32QK2xYumyWo2LRCG6jdfZNk8y4/3qU1TXURH+msy8+eP1l93Ep4vnecVLb6TXoylWXfQJyGcKDMQ/DZFumcy/SsG/z4AudVokGHvokkvWMJXYaqZNvRoI/jo4k5cziFcGc1zyzsd3TVj1M8hCilGIqnsSxhMnlm80CTqSyprFvRINsijM06H+OJDHc9e4InjozgsyxGz3JHZWOgDIaLBMe2yrrzlpLJuYtSVAV9FpYlZYYvl1fFbfkdi9aSBNKrV5YrTG/d0MJ7blrFqze3smNVPbGQw2QyxzM9Wu2nUOwfioOCy5oicwzoxjZtoA4MxLnnxX7GprN0NoT401esRaG4f+8gIxXa0oNOQs7lFb89PMLTR8c4OjzN6HSGiUSWiUSW8USGl05MEPY5FfOTLBHGZnksA/EU4QUU9LW8ObYT40miQR+Hh6b47eERQBu/VM5lfJb3NzyVnrfUlW0JOXeu0ZxNMpsnndWV8QdOcx4o76ri+ZlIZDk+lqjqdYf9DgOTqbJlTxwZ5eH9Q3z10SOAViSeTeHKpSYzNxguWZx5kkAT2Rxttadfy05EqAv7SaRzxRp8hTkG0GGnttogCoVjW2ztKJczO7bFKzbMJHQ+sHeAbzx2jMcPj3DT2ibqw35Ojumb3PrWKH0TSZpqAsUn9ljIR3djmJ6RBD96VifLv3l7J7GQj+3d9TzbM86Pd53gj15WufJJLORDKUUm73J8NEFeqTKP0GdbhALVbsA2/RNJuhp0uaJMzmU8kaVhgdUsYkEfiUyeAwNx/u7efeTyiqaIn3WtUSI+h739k2zvri8q4wYndUjuOzt7CPpsGmsCNNX42dgWI+blRPlsiyPDU2xur61a9imezIJopeRYIlO1GkglTo4n2dcfx+elKiioerx+x2JqOlvsKaaUKs57pbMujx4c4brVDSSz+eK1s9QYD8pguEiIeO0pZivY4qksNQGnLCn2dKgP63BagYLEHLQBK8wTbeuqO+WN6JpVDViW8OLJCSaTWSwRDgzqm9rqxgi1YR9jiUyZx1bYvuvC2pYatnbU4rMt7tjShmXBIweG6ZuorowTEQKOTV3YT2MkQEPJKzqP7D7g2Eyn88UQYeG8FkJwqWyez/1qP//+Yl/F3/tsHeL63K/2k/MqYxS8qJDfRpTw3PFx0rk8U+kcubzLD5/p5TcHR7h/zyDff/I4//TgIf7qxy8U59piQR/D8QyHh6aqjnsgnibks+coJRfCwGTKm4PT56cxEqgacgRtwAppBKmsfggo1NW/b88AeaUqphksFcZAGQwXCT7b4orOOiyhqGBLZfO4KLZ4N/XFEA345oQOS0NxG9tj/NVrN/HHVbyYsm0FfWzpiOG6sPPoKOlcniPDCUSgoz7EitoQ61pqGPXq+sHMPBTAW67uJJnN01TjpzUW5D+sa0Yp+OqjR5ZMVl2GUMznGYqnCZQ0Y3z0wDC7esb50TMnKs5znRxP8nf37ieVdVnXqufWdh4ZLeZ+1QQdcq7ihd4JRqYyjCezPHVsFMsS3npNF6/a3EpHfYjJZI5vPT5Tnqgx4qdnNFGxT1Yurxs8hjzVoGNZRRXeqUhltaE8HaVnwLGL209kchz3lI9Bn8XoVIa9fZNnVW5uDJTBcBER9Nlc0VVHznWJp7JMpXNc0Vl32vLyUgqVzUspnSuJBBxaYoGyfk25eeTF11Q6kRQAACAASURBVK7SlQsePzzCoaFpXFfR1RAm6NOJt10NYVbUBxlJ6BvbxrYYG9qivOLyZi5vj5HJu7TGgtRH/NyxuZVYyOHAwBSf+Nlu+idSVfe7EPKu4sjwFDuPjJDLu4R9Dn2TKVxXMTyVLh6jUopf7dUFbTM5l+d6x8u2M5nM8ulf7mU6leOKzlo+esfltNcFiady7O6bSTyOBX0kM3kOD0/x20MjuC5cu7qB121t5x3XreTPb19PwGex88gozxzT83YiQkMkwL7+ybKEYIDpdN6rY629mIjfZii+sHmgxXg6IZ/NyFQG11X0T6YYiqexbeFN2zsBeHDfUDHMeDYwBspguMgI+x2u6q5HAVs6Yosvj+PhdyyCPtsrijojMZ/Zn12WIOwqxfB0hpEKCq7pdI6u+hCOLRwYmOKxg8OATtBVeKEvEda1RKkPB4indIfev3ztRt5z4+ridmqCDi01foI+m4+/fjOdDSEGJlLc+dOXeOnkxGkdn1KKh/cP8T9/uZf/9O2nufPu3XzxwUP84sU+gj6L8USW0USGXEmx1919k/SNzxjDnUdGy7Z5/94BxqezrGmJ8P5b1+LYFtdfpjsPFMJ8BerCfhzL4lHvXNyxeaaHW3M0wO97N/tv/PZoUaJvW0JtyM+LJyfKPMfRRBq7JCTneErJxALk5oPx9GmnIRQ6Jk9lcrzYq897R12Im9c3Ew7YHB6a5sjQ9CmFHYvFGCiD4SKkJuBw3eqGJanxB7rDbjrraoHELDXgbOVdPJWjoy5ITdApeypP5/Jk8nnWtUXZ4oXtHvFuymubIwQcq1jfzbaENc0R0rOqOaSyeaIBHwHHJhbSLeubagL89es2sa27jmQmz2fu3VdUCZ6KbN7lK48e4auPHuGlE5Oksy51EW3Qf3NwxpCcGEuWFeG9f48uiHvzhmYAdh2fkbPn8i4P7tPVM958dVfxmG7wDNTTx8bmVKl48ugoyUye9W01rG4ql9rfvrGVNS0RxhNZvvvkTDMGv3e+XjgxUUz8HZxMzy1uLbpCxXzk8i6j05mqXYv39E3y2fv28fMX+hidpWy0EEa99iACrGoME/TZ3OqJYx7aN0Qqc3byoYyBMhguUhaq3FoIdSEf6XyebE7NkavPNlDZfJ6uhjBbVtSC6OTNvKsny7d21NJeGypWmyjEDrsbI9TPUotFgz7qw76i1wB6Wy0xLWsP+XXPqmzeJeiz+cBt67hjSxuuC1988GBZKK0SE8ksn75nL48eGMbnCH944yr+/m1X8dm3XEUspCs+HBtJEHBshqbSxeMemUrzTM+YDmVt62Bdaw25vK5eAfB0zxgTiSwr6oLFpGWA1liQ1c0R0lmXXcdnDGjeVcX+V6/e3DZnnLYl/NFNl2Hbwq/3DfHT504WQ2Zhv0Mu77KnL850OlfML8vm3aLQJOxzGJyaP/Q5lc7hKlWxEv6zPWN85t59PHd8gu8/eZw///4uPvXve9jbr89vOGDTP5GmZywBCCu9zse3b2zFsoTnesc5Mjw97/4XizFQBoOhWNm8VGJewG9bWKJDe6lsnmjIRzTo0/NhnbUksjndXqMtRn0kQE3AYWNbrPi03lYbJOBY1FeoSL6yMVIWnnJRZZXLm6OB4veWCG+7povbNraQyyv+/lf7OVRB7TadzvHQvkE+8dOXODAwRX3Ex1+/bhO3Xt5CfdiPbQnXrp4Jx4X9Nn7bKopMHtg7iFKwY2U9dWE/167Wc2qFMN99u7WxuX1ja3EuSClF3lVFL6o0zPfs8TGG4roh4bauyp0KOupDxVDfD57u5S9//CIvnNAhtdqQn7FEmv0DcZQoBuMp3v9vz/C13xwBKIYpB+MpJhJZEpncnCTh4SkdGjw4OFXmIT1xZITPP3CQXF5x45pGrllVj2MJe/rifO6+/aSyuh5iMpvnhCeQWNUYIe8q6iN+rvDSDp45Xj5Ht1SYPCiDwaBly0iZxLyAeOWFcnnFdCZX1h4+GvSxZUWMqXSejnotc7ctobU2yLbuOh47OFLsnVSpvUdtyEfIb5PJudiWYItQU7L/xkiA3hI1m4jwzutXMp3J8/ihEf7Xvft41eY2LNENFo+NJNjVO15siHhZc4QP3LZujvd2ZWct9+3u5/HDI7x1R1fx+2ze5SGv+O0rN7aSdxVrm2tA4LnecfYPxDkwMEXIb3Pt6gZGplOAIAI5V3FVVy3/thOe651gMJ7ipZOT/OIFLVN/1ebWeRsavm5rO90NYb71+DH6J1J85pf7uGNLG2+/tpvGcIDhqQw1AR+/3N1PKuvym4PD/P72ThoifoKOze6Tk8UcsEjAYWtnLQHHLrb4eLZnjK8/ptWCTVE/KxsiPNMzhlLw2ivaeevVnYgIiUyOT9+zj6PD0zzTM8aNa5rI5V0GJtOIQHPUz/B0GkeEpqgOwx4ZmkIpNa9kfTEYA2UwGLAtIRpyGI6nK5ZLqgk4DE9lcCo0PmyKBmkqL7FHc42f2ze24rctXre1HfESS2djWcKqhjB7+uM6eTXqL0tQLVRnL735WSL8yctWk8rk2XV8nLuemdXyTWDjihg3rWnk+ssa58jvlVK01wapD/sZT2TZPxBno2d0Hz88wlQqR3djmLUtNUylc4QDNutba9jfP8WXHtIdrm9a24SroLM+TFdDGL9tcWhoioGJNBvbY+w5OcmH/8/zM+cjGuA/rG2e708AwNaOWj75xi38cvcAP3i6l3t39/OaLW3Uhf3FlvBPeUV7ldJV4t+4rYNIwCmbm5pIZtnVM86VXXXkXEU25/Lbw/p3ti0MxzMMx7Un9ftXd/D6K1YUz2/Y7/DydU0cHZ7mNweHuXFNE9MZrR7sqA9hibCyQR/3aCLDfbsH6B1Lkc65Z6QmrYQxUAaDAdAVBUanMxXL8UQCDgcGp7iio3ZBbc1jIX+xb1Q6l0eQqnNmTdEAzpAuMrumubwahs+2qAvriuulE/yObfH+W9fy631DjJXUo6sL+dixqmGOx1TKdDpPUzTA9pX1PLBngMcPj7CxPUb/RIp/29kDaO9JRMjkXUI+m6tX1rO/fyY8dtvlLWTdPLUhf1EkEQ049LpJXr6uiT0nJxHROWQ3rmnk6pX1VQUKs3E8o75/IM6unnEePzzKHVv03NXAZIrjo0mvjxf8ev8Qr79yxZy/SW3IRzyVZVfPGE3RINOZHPsH4ti28Pm3bWN0OsP+/jh1YX2+ZnPdZY18e2cPL52cZCyR4ZhXbX5lY5isVwor6LO5qqtOt66fSJLOGgNlMBjOErGQj4jfruhBhf02NQGb1tqFqQZDfpugo6XrqazLirrqv3Nsi676MAcHp4olf0ppqQmyfzA+5wbvs61isdrTIZnLs6kxxs3rm7h/zwA7j47ypu2dfPZX+0mk81zVXcdNa5uK6wd9Nld21vGdncdBweaOGCvqQoxMp8sqkwe9wrs3XNZIQ0QnGs9nKE/Fy9Y2satnnN8cGi4aqKe8XKlrVzdwZHiawck0z/eOs61bz23t7Z/kCw8c5LVb23nd1nam0jkOD02xrz+OUrC5PUZNwKEm4NDtlXiqRE3A4aquOp4+OsZvD43Q79XkW9UYQaEIeIaoqyGsK2okMoxOp5e887ERSRgMBkAboZYqsvWQ36a7MXJaT8gFgUPOdamtYHhKaasN0t0Qqrj9gix8vm7CCyWVzRMLOsSCDtu762mvDZFI57nzpy8xMJGiqyHEf7p5jdc5VxWPI+izi9L5V22aUeKVjjfos1DoebLL22JVjdPIdJrh6cqqu6lUrqhqvKqrjpDfpmckUazgUAjvXbuqgVtKZN6gK41/8cGDTKVy/GTXCabTOWoCDi3RILuOa8FFJW+pGjet0Ub6N4eGyzwomFF2+myLlY1hBGHPEvWnKsUYKIPBAOib7ZqWmorfhf0Oa5orf1eNxho/OddFvN+fat/r22IVvwv7Hda1RBmdPvPK2VOZrL6hingFaesAGJ3KUBv28ee3ry8anUzeJRrQikVXKd738jV89DWXc1VXXbG9Sen8lt+2in2UqjGRzFIf9tMaDc5JdI6nsti23m+hJ9X1l2mD8tjBYUam0hwemsbnCFs6annZ2iZsW8u8h+JpvvTrQ0wmtXFLZ13u36tzuVLZPLv7JrEsise7EK7srCUSsOkdTRa7Ka9siBSPtUDhutg/YAyUwWC4SCjkFVmWVG3St1A66kN0N4QX3NCvEtm8i9+2aYhosUHE77BjZT0i4NjCB29bVyYASWVd6iM+Ao6FoOd1CmKKdC4/JxwpIkSDTtVW8qlsHkSxoS3K+tYo0ZJE5+l0Dkvgis46ruyqYzqTI53LF72Yxw6NFMN7hdJWtSEfV3fXoxR8+p697O2LUxv28b6X65qJ977UTybn8uzxcVxXsaEtNm/x3NmUVsdAQWttkIDPmlNpZF1rDQIcGZ6eI28/U4yBMhgMZwXH1rlPdeHq7dNPh8uaa2iK+hfcVHA28ZT2ngqCAssS1rdH+cBt6/jr122a4yEWQpNB39xahZmc7sc0m5pAZQOVdxXxVJatK7RxcWyLzV6i81giQ851uaJLfxcL+tjWVU88lWNlY5iWWIDxRJafeM0crynpyXWLV+liKK4l4P/55jXcuKaRVU1h4qkcDx8Y4kkvLLhjZeUcrPm4cc3MXNyqxjBZr35hKWuaaxDR1ThmVwY5U4yBMhgMZ43WaHDB/ZVOhWXpuZ2gzzqtFhOgvSdLZM4cW1MkQHdjiFVNFXppKT33ZltSrFVYIK9UUQJfSizkI+vOvUmPJdOsbakpExEUEp2DPouruuvLwqC1YR9XdNYynswWk3+n03lsW7iyayZMt7E9RqtXeePNV3dyeXuMvKu4faMWj/zihT5e9BJ+ZzebzOZdBuMp3T6+wmtgMsWa5khRGLOyMUI2r4jM6q91WXMEEaF/MjVvd+PFYFR8BoPhrNG2yB5V1fDZFls66njq2Gixkd6pyLuKsUSGKztr5ygUo0EfouZ6d4WiuYXcrVjIYTKRK5tzqrTvgGMze2u5vEvAsYuNEWfv/5pVDRU9zMaaAM3RANu76/nJrpMAbFkRKzNklggfvG09PaPTXOcZsvFklg2tUVpiAQYn9TzX2paaMtGGUvqcbO2opSFS+QHiyaOj5FzFW3d08rPn+7h+dUPFWo0rakMEfRaTySz9k0nqq2xvMRgPymAwXFSE/DZXdNQVmwDOh1KK0USa9a1RmiooFEN+e453BFpkEAv5ioYjFvTNCd0FKsjxC0q+UqYzeVqi1RsDzhf+7KoPUxN0WN+mw4/XVFDhddSHuGFNE5YIeVfh2EJ7XbCs0/GOVeXe00gizcqGMC2xII5tVXzVh/2kcy47VjZw5+s301gTwFVqjtzfsoRVjRFEYG/f0goljIEyGAwXHbVhH5vaY4wmMvOq5kYTGTrrQ3TWV/fkSuv9FUjl8mW1A8N+B9czPdojsio2iAw4OiRYKonPuS5Ns6pvLBQ9B2bxnhtW8a4bVpbNCVViMpWly6tusb27joaIH9uWMsMWT2WJBX2sPoUqsy7sK1ZRL6VSnpyevxMOLLGSz4T4DAbDRUlrbZBENsfR4YSuxYcOeblKIehKC/URP2uao/N6KQ0RfzHPqIBSqkzxVqpCzORdauZRw0WDDtmci23ZXokm5lSIXyiWJaxsiLA/Fy/OK1XDVQpXKdpqgwR9Ns3RAB961XpcRdFApnN58kqxecWpK4JUqp0oULHSyNoWreTrGUuSL+mrdaYYA2UwGC5aVjVGaI0FyeRcMnmXdNbFbwt+x8bnWIR89ilvljVBB8cS0rl8sWwRUBbKKl2eybm011a/dcaCPk6Opwj6dBXw+rD/jFqjNEUDHBiMV22XUWAqlSsaJ9AtTsaSWZo8WX027xJP5biqq3ZBc3ehWepFpRRI5dDm+tYoCJwcT5LK5uf2rFokxkAZDIaLlkKl9TMRCmrhRS3P9oxjhQRLdN3A0pu4bQlhb64q56qK3kWBmqBDzlPyJbN5VjVWLym0EPyORXtdiMHJ9LwVOTJ5l44SUUpdyEfIpyvFW6IrTWztrKU+srBwo8+2iHjH7LMtcq4i5NgVvdE1LREE6JtILamBMnNQBoNh2VMX9rO1I8Z4IsNUOkddBUNQE9I5TiKVFXwFZn8XC525qq29NjhHyFFKIpOjPuwrC0vq8GCYiWSW0WSGje2x0+7AXOd1WgYqKvgKNNfoPmCJTL6sPcqZYgyUwWAwoNuGbF5Ry1Q6V7GOXm2Jki9YIcxVIOBYKEWxE/BCq5jPRzTooy7sq5qzNJ3J0d04N5erKRog5LdY3xKlfRGS/0KnZUBXMa+Q+wXaky3kki1lySMT4jMYDAaP1togV1t11ATmelBhv0PGzRPxO/POKQUcG58txFM5uhuXLg9s84raiqo60AaikhDDZ1tsX1lfNod2OpQa12zenbem4prmGp7vHadnNFF1ndPFGCiDwWAooVoYLOCzyOe1COJU1AQdBiZSNIQXJy+vhN+xKkq8T8VijRNoo6x7T2k14nz7X9uipea9Y0tnoEyIz2AwGBZA0LEJOBbRKmGuUmJBHwGfXTUkdrFgW0LE75DNaz1fJQVfgfWtOq9qKeegLu6zZzAYDOcIyxKiIWdBCrVo0EdrLLhk+UDnk7qwj4EJXTKpUg5Uga0ddXzk1RvYtCK6ZPu+qDwoEXmLiLwkIq6I7Jj13V+IyEER2Scirz5fYzQYDJcuHXWhBRmo+rCP1ZUK0F6E1Ib8JLM5gj4bax6DG/LbbO6ILWnb94vNg3oReBPwz6ULRWQT8DZgM7AC+JWIrFdKLW1pXYPBsKxpq12Y6EHXszvLgzlHhP02OVctyDBH/DM5YEvBReVBKaX2KKX2VfjqDcB3lVJppdQR4CBw7bkdncFgMFx6BH02QccmsgC5fDToIHPquS+ei82DqkYH8HjJ515v2RxE5H3A+wBaW1t56KGHzvrgDAaD4WImkclz+IRFjz2/8cm7ipyr6N+3NL7PBWegRORXQFuFr/5KKfWTM92+UurLwJcBduzYoW655ZYz3aTBYDBc0vRPJIkEnFO2jM/lXVI5d9HFcWdzwRkopdTti/jZCaCr5HOnt8xgMBgMZ8jpzL3VnEFh3NlcVHNQ83A38DYRCYjIamAdsPM8j8lgMBgMZ8BFZaBE5PdEpBe4Afi5iPwSQCn1EvB9YDdwD/BfjILPYDAYLm4uuBDffCil7gLuqvLdJ4FPntsRGQwGg+FscVF5UAaDwWBYPhgDZTAYDIYLEmOgDAaDwXBBYgyUwWAwGC5IjIEyGAwGwwWJKKXO9xjOGyISByrV9lvONAHD53sQFxjmnMzFnJO5mHMylw1KqUX337ioZOZngX1KqR2nXm35ICJPmXNSjjknczHnZC7mnMxFRJ46k9+bEJ/BYDAYLkiMgTIYDAbDBclyN1BfPt8DuAAx52Qu5pzMxZyTuZhzMpczOifLWiRhMBgMhguX5e5BGQwGg+ECxRgog8FgMFyQLFsDJSJ3iMg+ETkoIh893+M5H4hIl4g8KCK7ReQlEfmgt7xBRO4TkQPee/35Huu5RERsEXlWRH7mfV4tIk9418r3RMR/vsd4rhGROhH5gYjsFZE9InKDuU7kz73/Ny+KyHdEJLjcrhUR+ZqIDIrIiyXLKl4Xovm8d26eF5Htp9r+sjRQImIDXwReA2wC3i4im87vqM4LOeBDSqlNwPXAf/HOw0eB+5VS64D7vc/LiQ8Ce0o+fxr4nFJqLTAG/NF5GdX55R+Ae5RSlwNXos/Psr1ORKQD+ACwQym1BbCBt7H8rpWvA3fMWlbtungNupnsOuB9wJdOtfFlaaCAa4GDSqnDSqkM8F3gDed5TOccpVSfUuoZ799x9E2nA30uvuGt9g3gjednhOceEekEXgd8xfsswK3AD7xVltX5ABCRWuDlwFcBlFIZpdQ4y/g68XCAkIg4QBjoY5ldK0qph4HRWYurXRdvAL6pNI8DdSLSPt/2l6uB6gCOl3zu9ZYtW0RkFbANeAJoVUr1eV/1A63naVjng78HPgK43udGYFwplfM+L8drZTUwBPyLF/r8iohEWMbXiVLqBPAZoAdtmCaApzHXClS/Lk77vrtcDZShBBGpAX4I/JlSarL0O6XzEJZFLoKI/A4wqJR6+nyP5QLDAbYDX1JKbQOmmRXOW07XCYA3r/IGtPFeAUSYG+pa9pzpdbFcDdQJoKvkc6e3bNkhIj60cfq2UupH3uKBguvtvQ+er/GdY24CfldEjqLDvrei517qvDAOLM9rpRfoVUo94X3+AdpgLdfrBOB24IhSakgplQV+hL5+lvu1AtWvi9O+7y5XA/UksM5T3PjRk5t3n+cxnXO8+ZWvAnuUUp8t+epu4A+9f/8h8JNzPbbzgVLqL5RSnUqpVehr4gGl1DuAB4E3e6stm/NRQCnVDxwXkQ3eotuA3SzT68SjB7heRMLe/6PCOVnW14pHtevibuDdnprvemCiJBRYkWVbSUJEXoueb7CBrymlPnmeh3TOEZGXAY8ALzAz5/KX6Hmo7wPdwDHgrUqp2ROhlzQicgvwX5VSvyMil6E9qgbgWeCdSqn0+RzfuUZErkILR/zAYeC96AfcZXudiMgngD9Aq2GfBf4YPaeybK4VEfkOcAu61cgA8HHgx1S4LjxD/o/oUGgCeK9Sat5q58vWQBkMBoPhwma5hvgMBoPBcIFjDJTBYDAYLkiMgTIYDAbDBYkxUAaDwWC4IDEGymAwGAwXJMZAGQyXGCJyi4goL+HYYLhoMQbKcMkhIl/3btAPzVr+RhG508txuigRkfd4x3DV+R6LwXC2cU69isFwyfBGZjLcHzqP4zgT3gPcDBwFdlVZJwHsY3mW2TFcQhgDZTBcYiildgKXn+9xGAxnignxGQwGg+GCxBgowyVPQTTATHjv494cVfFV4TeWiLzLa1k9JCIZETnptfG+rsp+7vS293Xv9+8XkZ0iMu4tv8pbLyAibxGRb4rIcyIyLCIpETkmIt8WkasrbPs93jhv9hb9y6xjODr7eOcTSYjIK0TkRyLS7x1bv4jcJSK3zvObwr5WiUi3iPxvEekVkbSIHBGRz4hIrMpv/SLyQRF5zDsfWREZ8I7/iyJyQ7X9GpYvJsRnWA5k0IUsa4Egup/RVLWVRSSKbp9wu7dIAXGgHXgr8GYR+aBS6h+rbcL7/RuAvPfbUl6JLqZZ2Pa4994N/EfgrSLyfyml/rXkN0nvGBoAHzDpLSswVO14Khzf3wJ/VbL/CaAFPUf3RhH5lFLqL+bZxJXA17yxxNEPuquADwE3i8iNXguKwv4c4F5mjGthn43efq/w/v3bhR6DYXlgPCjDJY9S6jGlVBvwPW/RZ5RSbaWvWT/5Jto4PQO8GggrpWrRN+SPoY3OP4jITVV2+SZ0xeb/DMSUUvXorqKHve+ngM+j26jXKKUalFIhYCW6wr4DfFlEukuO4XveOB/zFn1w1jFcs5BzISJvY8Y4/SPQ4o2vGfiCt/yjIvLOeTbzdbRAY6tSKgbUAH8EpIEdwJ/MWv8/oo1TAngX+nzWAwHvmN8PPLeQ8RuWGUop8zKvS+qFvoEq4KEqy++c57e3e+vsBWqrrPNRb52fzVp+p7dcAe87g/F/1dvGxyt895D33Xvm+f0t3jpHZy0X4ID33Xeq/PbfvO+PANas7wrH9iIQqPDbL3jfPzBr+T95y790vq8N87q4XsaDMhjKKcxT/W+l1ESVdb7tvb9CROwK34+gQ2CL5afeezUPbbFcBaz1/v23Vdb5hPe+Cri2yjqfVZV7HP3Ye98ya/mk996+gDEaDEWMgTIYyrnRe/+YJxyY80J3ZAYIo+dOZvOUUio3305EpEFE/toTDYyISK5EsHGXt9qKJTmiGbZ770NKqZcqraCUKs2f2l5pHWaOfzaF39XPWv7v3vsbRORuEXmTiFQ6bwZDGUYkYTCUU3jKr1vg+uEKy+YVLIjIJuAB9LxUgTha9KDQXWvrgcgCx7BQmr33UyXw9qI7wzZX+X626KNAynsvu68opX4tIn8D/A3weu+FiOwFfg78s1LqwCnGZFiGGA/KYCin8H/i95RSsoDX0QrbyJ9iH/+CNk7PoMUUUaVUTCnVqrQQ4i3eerIUB1SB4FnablWUUv8NWA/8BfBLdNjvcrTyb7eIvPtcj8lw4WMMlMFQzoD33j3vWovEU+ZdizZiv6uU+qVSarbkvXXuL5eEgmfXdYr1OmetvyQopY4opT6llLoDrYh8BfAw2uP6JxFpWcr9GS5+jIEyLCdc730+z6SQi/OaszSG4s1fKVUt1HZ7leWwsGOoxjPee0REKgogRGQ9OrxXuv6So5TKK6UeAn4HyKLDmTvO1v4MFyfGQBmWEwU12XzzS1/33l8tInfMtzERmS0GWAgFZWBrJY9BRLai84aqsZBjqMYu4KD377+sss6d3vtRYOci9jEHEfHP83WGmZBoYCn2Z7h0MAbKsJwoKNfuEJGKkmel1D3oKhAC3CUiHxaRoljAU9+9UUTuBj67iDHsQYsQBPieiKz1tusTkTcB9zFPlYuSY3iTiNSezo6VUgqdaAxaUfeFgppORBpF5PPA273vP6aUcittZxF8U0T+RURe7VXpwNvnKuAb6DmxJPDIEu3PcIlgDJRhOXEXMIqerO8VkT4ROVqhZt270Tk9QeB/AgMiMiYik+gcp7vwlGini3fT/wA6VHcLcMDb7hTwQ3Q1hj+bZxP/ivY6XgYMi8gJ7xgeXeD+vwd80vv4fmBQREaBQeBPveWfUkp9u9LvF0kQ3SbkHmDCO5fT6GTgP0B7UP+3Ump4CfdpuAQwBsqwbPBugK9Ae0hDaBn1Su9Vut60Uur30PMjPwJOouXkPnSI7PvAe5m5oZ/uOO4CbkV7S3Fvu8eAzwDb0B5Wtd/uRdfyuwcdLmzzxt9Z7TcVtvEx4Dbgyo1G1wAAAI5JREFUJ8AwulTRCHA3cLuavw7fYvgo8BFvzIfRMnobOIRWNG5X5XUHDQYARHv9BoPBYDBcWBgPymAwGAwXJMZAGQwGg+GCxBgog8FgMFyQGANlMBgMhgsSY6AMBoPBcEFiDJTBYDAYLkiMgTIYDAbDBYkxUAaDwWC4IDEGymAwGAwXJMZAGQwGg+GC5P8HUtRBe9z1S3IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ndlib.viz.mpl.PrevalenceComparison import DiffusionPrevalenceComparison\n",
    "viz = DiffusionPrevalenceComparison([model, model1], [trends, trends1], statuses=['Infected'])\n",
    "viz.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "72eae37f-eb5f-4c68-b553-98cd57ade290"
    }
   },
   "source": [
    "The method parameter ``statuses`` takes as input a list of the models statuses trends we want to compare.\n",
    "So, for instance, if we are interested in comparing both the trends for *Infected* and *Susceptible* nodes we can do something like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "nbpresent": {
     "id": "4b067c6d-aae0-45f3-949b-3e16019becc0"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ndlib.viz.mpl.TrendComparison import DiffusionTrendComparison\n",
    "viz = DiffusionTrendComparison([model, model1], [trends, trends1], statuses=['Infected', 'Susceptible'])\n",
    "viz.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5444515e-f990-42c9-8d47-228e2dd6228d"
    }
   },
   "source": [
    "<a id=\"dynamic\"></a>\n",
    "## 6. Diffusion on Dynamic Networks ([to top](#top))\n",
    "\n",
    "So far we assumed that a *static* network topology. In real world scenario it is likely to observe nodes (as well as edges) that appear and desapear as time goes by, deeply affecting network structure and connectivity.\n",
    "\n",
    "Indeed, topological transformations have huge implications on how diffusive phenomena unfold. \n",
    "\n",
    "``NDlib`` leverages [``DyNetx``](http://dynetx.readthedocs.io/en/latest/) to model time-evolving graphs. In the following we briefly introduce some [``DyNetx``](http://dynetx.readthedocs.io/en/latest/) primitives that allows to build and analyse dynamic networks.\n",
    "\n",
    "A dynamic network is a topology having timestamps attached to edges (and/or nodes). As an example:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/rete.png\" width=\"50%\" align=\"center\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "273f25ce-7799-4ac2-9f53-cd7ff3f6ba0f"
    }
   },
   "source": [
    "<a id=\"dynetx\"></a>\n",
    "### 6.A DyNetX: a library for dynamic network modeling ([to top](#top))\n",
    "\n",
    "[``DyNetx``](http://dynetx.readthedocs.io/en/latest/) is a Python software package that extends [``networkx``](https://networkx.github.io) with dynamic network models and algorithms.\n",
    "\n",
    "We developed [``DyNetx``](http://dynetx.readthedocs.io/en/latest/) as a support library for ``NDlib``. It provides a generic implementation of dynamic network topology that can be used to model directed/undirected\n",
    "- [Snapshot Graphs](#snapshots)\n",
    "- [Interaction Networks](#interactions)\n",
    "\n",
    "In [``DyNetx``](http://dynetx.readthedocs.io/en/latest/) a generic dynamic graph can be built using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "nbpresent": {
     "id": "b1623c00-7e81-47a7-aa4b-adb1d3d8b619"
    }
   },
   "outputs": [],
   "source": [
    "import dynetx as dn\n",
    "\n",
    "g = dn.DynGraph() # empty dynamic graph\n",
    "\n",
    "g.add_interaction(u=1, v=2, t=0, e=2) # adding the edge (1,2) at t=0 that vanishes at time e=2\n",
    "g.add_interactions_from([(1, 4), (2, 5), (3, 1)], t=1) # adding some edges at time t=1\n",
    "g.add_interactions_from([(2, 6), (3, 2)], t=2) # adding some edges at time t=2\n",
    "g.add_interactions_from([(1, 5)], t=3) # adding some edges at time t=3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "246a6874-119c-4ca3-9f0a-3d9192b81f85"
    }
   },
   "source": [
    "<a id=\"snapshots\"></a>\n",
    "#### 6.A.a Snapshot Graphs ([to top](#top))\n",
    "\n",
    "Often, network history is partitioned into a series of snap- shots, each one of them corresponding either to the state of the network at a time $t$ or to the aggregation of observed interactions during a period. Formally,\n",
    "\n",
    "> A ``Snapshot Graph`` $G_t$ is defined by a temporally ordered set $⟨G_1, G_2\\dots G_t⟩$ of static graphs where each snapshot $G_i = (V_i, E_i)$ is univocally identified by the sets of nodes $V_i$ and edges $E_i$.\n",
    "\n",
    "Network snapshots can be effectively used, for instance, to model a phenomenon that generates network perturbations (almost) at regular intervals. In this scenario, context-dependent temporal windows are used to partition the network history into consecutive snapshots: time-bounded observations describing a precise, static, discretization of the network life.\n",
    "\n",
    "Considering our dynamic network example we can identify the following snapshot graphs:\n",
    "\n",
    "<img src=\"img/ex1.png\" width=\"35%\" align=\"left\"/><img src=\"img/ex2.png\" width=\"25%\" align=\"left\"/><img src=\"img/ex3.png\" width=\"35%\" align=\"left\"/>\n",
    "\n",
    "[``DyNetx``](http://dynetx.readthedocs.io/en/latest/) allows to (among the other things):\n",
    "- List the snapshots of the loaded graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "nbpresent": {
     "id": "32c90ef7-d369-41cf-874e-ed1ee42e0eb6"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.temporal_snapshots_ids()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d1c5b478-a181-41f9-a187-c50a4ab4a6c2"
    }
   },
   "source": [
    "- Access a specific snapshot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "nbpresent": {
     "id": "4a3c3856-ce1a-410b-bf8d-9ed46f4e0024"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([(1, 2), (1, 4), (1, 3), (2, 5)])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g1 = g.time_slice(1)\n",
    "g1.edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "860905f3-ca6d-449a-b48b-f8b3b956f262"
    }
   },
   "source": [
    "Moreover, snapshot graphs can also be read from/wite to file. For additional details refer to the official [documentation](http://dynetx.readthedocs.io/en/latest/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f27a5517-1f54-483f-926e-ec1c78e301b8"
    }
   },
   "source": [
    "<a id=\"interactions\"></a>\n",
    "#### 6.A.b Interaction networks ([to top](#top))\n",
    "\n",
    "An ``Interaction network`` models a dynamic structure in which both nodes and edges may appear and disappear as time goes by. Usually, ``Intercation network`` are used in absence of a clear aggregation time scale, or when make sense to analyse a dynamic networok as a continuos stream of edges. Formally,\n",
    "\n",
    "> An ``interaction network`` is a graph $G = (V, E, T)$ where: $V$ is a set of triplets of the form $(v, t_s, t_e)$, with $v$ a vertex of the graph and $t_s$, $t_e \\in T$ are respectively the birth and death timestamps of the corresponding vertex (with $t_s \\leq t_e$); $E$ is a set of quadruplets $(u, v, t_s, t_e)$, with $u, v \\in V$ are vertices of the graph and $t_s,t_e \\in T$ are respectively the birth and death timestamps of the corresponding edge (with $t_s \\leq t_e$).\n",
    "\n",
    "Considering our dynamic network example we can identify the following interaction stream:\n",
    "\n",
    "<img src=\"img/ex4.png\"  />\n",
    "\n",
    "[``DyNetx``](http://dynetx.readthedocs.io/en/latest/) allows to to obtain the edge stream of a given dynamic graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "nbpresent": {
     "id": "6723d9a3-cf64-4878-a76e-623ee03854ab"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, '+', 0)\n",
      "(1, 4, '+', 1)\n",
      "(2, 5, '+', 1)\n",
      "(3, 1, '+', 1)\n",
      "(1, 2, '-', 2)\n",
      "(2, 6, '+', 2)\n",
      "(3, 2, '+', 2)\n",
      "(1, 5, '+', 3)\n"
     ]
    }
   ],
   "source": [
    "for i in g.stream_interactions():\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "37371b56-1291-4287-bfb8-fcf022dcce42"
    }
   },
   "source": [
    "In the former representation: \n",
    "- the first two values identify the nodes involved in an edge\n",
    "- the third value identify the edge operation ('+' apparence, '-' vanishing)\n",
    "- the last value identify the timestamp\n",
    "\n",
    "Also ``interaction networks`` can be read from/wite to file. For additional details refer to the official [documentation](http://dynetx.readthedocs.io/en/latest/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "a3eab819-81a5-43f9-9e24-c5c0c5095f0d"
    }
   },
   "source": [
    "<a id=\"models2\"></a>\n",
    "### 6.B Available models ([to top](#top))\n",
    "\n",
    "As we have discussed, network topology may evolve as time goes by.\n",
    "\n",
    "In order to automatically leverage network dynamics ``NDlib`` enables the definition of diffusion models that work on ``Snapshot Graphs`` as well as on ``Interaction Networks``.\n",
    "\n",
    "In particular, so far, ``NDlib`` implements dynamic network versions of the following epidemic models:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "04e8b432-d50e-4e74-b43f-10d35a0a660d"
    }
   },
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/dynamics/dSI.html'>SI</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/dynamics/dSIS.html'>SIS</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/dynamics/dSIR.html'>SIR</a></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/dynamics/dProfile.html'>Profile</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/dynamics/dProfileThreshold.html'>Threshold</a></td>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/reference/models/dynamics/dKThreshold.html'>Kertesz Threshold</a></td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f81d52f6-72f1-4e02-895c-294f91a1910d"
    }
   },
   "source": [
    "<a id=\"dynsir\"></a>\n",
    "### 6.C Example: SIR ([to top](#top))\n",
    "\n",
    "Let's instantiate a revised SIR model on a dynamic netework, first on ``snapshot graphs``..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "nbpresent": {
     "id": "61d9a95d-694c-4a51-a5c5-e8493c7a5eb6"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ndlib.models.dynamic as dm\n",
    "\n",
    "# Dynamic Network topology\n",
    "dg = dn.DynGraph()\n",
    "\n",
    "# Naive synthetic dynamic graph \n",
    "# At each timestep t a new graph having the same set of node ids is created\n",
    "for t in range(0, 30):\n",
    "    g = nx.erdos_renyi_graph(200, 0.01)\n",
    "    dg.add_interactions_from(g.edges(), t)\n",
    "\n",
    "# Model selection\n",
    "model = dm.DynSIModel(dg)\n",
    "\n",
    "# Model Configuration\n",
    "config = mc.Configuration()\n",
    "config.add_model_parameter('beta', 0.01)\n",
    "config.add_model_parameter(\"percentage_infected\", 0.1)\n",
    "model.set_initial_status(config)\n",
    "\n",
    "# Simulate snapshot based execution\n",
    "iterations = model.execute_snapshots()\n",
    "trends = model.build_trends(iterations)\n",
    "\n",
    "viz = DiffusionTrend(model, trends)\n",
    "viz.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "a2b6da6d-a6b0-4c36-914b-c8a794ae8025"
    }
   },
   "source": [
    "then in ``interaction networks``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "nbpresent": {
     "id": "4e669671-ea40-4b39-8c23-6161e95689c7"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = dm.DynSIModel(dg)\n",
    "\n",
    "# Model Configuration\n",
    "config = mc.Configuration()\n",
    "config.add_model_parameter('beta', 0.01)\n",
    "config.add_model_parameter(\"percentage_infected\", 0.1)\n",
    "model.set_initial_status(config)\n",
    "\n",
    "# Simulation interaction graph based execution\n",
    "iterations = model.execute_iterations()\n",
    "trends = model.build_trends(iterations)\n",
    "\n",
    "viz = DiffusionTrend(model, trends)\n",
    "viz.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e4c37b2f-5ea8-499f-ac22-fa56132234a2"
    }
   },
   "source": [
    "We can easily observe that the model we adopt to describe network dynamics (``snapshot graphs`` or ``interaction networks``) deeply affects the unfolding of a same diffusive process over a given evolving graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c4386bf3-1d9d-49be-89d7-4e9297a549ab"
    }
   },
   "source": [
    "<a id=\"custom\"></a>\n",
    "## 7. Custom Model Definition ([to top](#top))\n",
    "\n",
    "``NDlib`` comes with a handy syntax for compositional (custom) model definition to support its users in designing novel diffusion models.\n",
    "\n",
    "At a higher level of abstraction a generic diffusion process can be described by two components:\n",
    "\n",
    "1. The nodes' ``statuses`` it exposes, and\n",
    "2. the ``transition rules`` that regulate status changes.\n",
    "\n",
    "We recall that all models of ``NDlib`` assume an agent-based, discrete-time, simulation engine. \n",
    "\n",
    "During each iteration all the nodes in the network are asked to \n",
    "1. evaluate their current status and to \n",
    "2. (eventually) apply a matching transition rule. \n",
    "\n",
    "A generic ``transition rule`` can be expressed with something like:"
   ]
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    "> **if** ``actual_node_status`` **and** ``condition`` **then** ``new_node_status``"
   ]
  },
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    "The ``condition`` can be easily decomposed into the evaluation of atomic operations that we will call ``compartments``. The evaluation of a compartment can return either ``True`` (condition satisfied) or ``False`` (condition not satisfied).\n",
    "\n",
    "A simple ``condition`` is composed by a single ``compartment``.\n",
    "\n",
    "Indeed, several ``compartments`` can be described, each one of them capturing an atomic operation."
   ]
  },
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     "id": "4ca11e96-1eb1-4123-a705-41012ffcf906"
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    "A custom model, having three statuses (``Susceptible``, ``Infected``, ``Recovered``), can be instantiated as follows:"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "import ndlib.models.CompositeModel as gc\n",
    "import ndlib.models.compartments.NodeStochastic as ns\n",
    "\n",
    "# Composite Model instantiation\n",
    "model = gc.CompositeModel(g)\n",
    "model.add_status(\"Susceptible\")\n",
    "model.add_status(\"Infected\")\n",
    "model.add_status(\"Recovered\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4c198280-5dfc-4bf1-816a-19150d308637"
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   "source": [
    "<a id=\"compartments\"></a>\n",
    "### 7.A Compartments ([to top](#top))\n",
    "\n",
    "> *Which are the atomic conditions that can be used to define complex transition rules?*\n",
    "\n",
    "To answer such question we identified three families of ``compartments`` (and some operations to combine them)."
   ]
  },
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    "<a id=\"nc\"></a>\n",
    "#### 7.A.a Node Compartments ([to top](#top))\n",
    "In this class fall all those compartments that evaluate conditions tied to node status/features. They model stochastic events as well as deterministic ones.\n",
    "\n",
    "<table>\n",
    "    <tr><td><b>Name</b></td><td><b>Use case</b></td><tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/custom/compartments/NodeStochastic.html'>Node Stochastic</a></td>\n",
    "        <td>Consider a rule that requires a <b>probability</b> $\\beta$ to be satisfied. </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/custom/compartments/NodeCategoricalAttribute.html'>Node Categorical Attribute</a></td>\n",
    "        <td>Consider a rule that requires a specific value of a <b>categorical</b> node attribute to be satisfied (e.g. “Sex”=”male”).</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/custom/compartments/NodeNumericalAttribute.html'>Node Numerical Attribute</a></td>\n",
    "        <td>Consider a rule that requires a specific value of a <b>numerical</b> node attribute to be satisfied (e.g. “Age” >= 18).</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/custom/compartments/NodeThreshold.html'>Node Threshold</a></td>\n",
    "        <td>Consider a rule that requires that, at least, a <b>percentage</b> $\\beta$ of Infected neighbors for a node to be satisfied.</td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "Let's add to our model a rule employing a node stochastic compartment:"
   ]
  },
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   "source": [
    "import ndlib.models.compartments.NodeStochastic as ns\n",
    "\n",
    "# Compartment description\n",
    "c1 = ns.NodeStochastic(0.02, triggering_status=\"Infected\")\n",
    "\n",
    "# Rule definition\n",
    "model.add_rule(\"Susceptible\", \"Infected\", c1)"
   ]
  },
  {
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   "source": [
    "The **Susceptble -> Infected** rule defined works as follows:\n",
    "- if a node $n$ is *susceptible*, and\n",
    "- if $n$ has at least an *infected* neighbor (``triggering_status``)\n",
    "- then, with probability $0.02$, $n$ status will shift to *infected*"
   ]
  },
  {
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   "source": [
    "<a id=\"ec\"></a>\n",
    "#### 7.A.b Edge Compartments ([to top](#top))\n",
    "In this class fall all those compartments that evaluate conditions tied to edge features. They model stochastic events as well as deterministic ones.\n",
    "\n",
    "\n",
    "<table>\n",
    "    <tr><td><b>Name</b></td><td><b>Use case</b></td><tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/custom/compartments/EdgeStochastic.html'>Edge Stochastic</a></td>\n",
    "        <td>Consider a rule tha requires a direct link among an infected node and a susceptible one and that is subject to a <b>probability</b> $\\beta$ tied to such edge.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/custom/compartments/EdgeCategoricalAttribute.html'>Edge Categorical Attribute</a></td>\n",
    "        <td>Consider a rule tha requires a link among an infected node and a susceptible one that has a specific <b>categorical</b> value (e.g. “type”=”co-worker”)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/custom/compartments/EdgeNumericalAttribute.html'>Edge Numerical Attribute</a></td>\n",
    "        <td>Consider a rule tha requires a link among an infected node and a susceptible one that has a specific <b>numerical</b> value (e.g. “weight”<3)</td>\n",
    "    </tr>  \n",
    "</table>\n",
    "\n",
    "Let's add to our model a rule employing an edge stochastic compartment:"
   ]
  },
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   "execution_count": 31,
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   "source": [
    "import ndlib.models.compartments.EdgeStochastic as es\n",
    "\n",
    "c2 = es.EdgeStochastic(0.02, triggering_status=\"Recovered\")\n",
    "\n",
    "# Rule definition\n",
    "model.add_rule(\"Infected\", \"Recovered\", c2)"
   ]
  },
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    "The **Infected -> Recovered** rule defined works as follows:\n",
    "- if a node $n$ is *infected*, and\n",
    "- let $\\Gamma$ be the set containing the *recovered* (``triggering_status``) neighbors of $n$\n",
    "    - then for each node $v\\in \\Gamma$ with probability $0.02$, $n$ status will shift to *recovered*"
   ]
  },
  {
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   "source": [
    "<a id=\"tc\"></a>\n",
    "#### 7.A.c Time Compartments ([to top](#top))\n",
    "In this class fall all those compartments that evaluate conditions tied to temporal execution. They can be used to model, for instance, lagged events as well as triggered transitions.\n",
    "\n",
    "<table>\n",
    "    <tr><td><b>Name</b></td><td><b>Use case</b></td><tr>\n",
    "    <tr>\n",
    "        <td><a href='http://ndlib.readthedocs.io/en/latest/custom/compartments/CountDown.html'>Count Down</a></td>\n",
    "        <td>Consider a rule that has an <b>incubation</b> period of $t$ iterations.</td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "\n",
    "Let's add to our model a rule employing a count down compartment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
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    "nbpresent": {
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   "source": [
    "import ndlib.models.compartments.CountDown as cd\n",
    "\n",
    "c3 = cd.CountDown(\"incubation\", iterations=10)\n",
    "\n",
    "# Rule definition\n",
    "model.add_rule(\"Recovered\", \"Susceptible\", c3)"
   ]
  },
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    "The **Recovered -> Susceptible** rule defined works as follows:\n",
    "- if a node $n$ is *recovered* a count down named ``incubation`` is instantiated\n",
    "- during each iteration ``incubation`` is decremented\n",
    "- when ``incubation=0`` $n$ shifts to *susceptible*"
   ]
  },
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   "source": [
    "<a id=\"composition\"></a>\n",
    "### 7.B Compartments Composition ([to top](#top))\n",
    "\n",
    "Compartment can be chained in multiple ways so to describe complex transition rules. \n",
    "\n",
    "In particular, a transition rule can be seen as a tree whose nodes are compartments and edges connections among them.\n",
    "\n",
    "As an example consider the following picture describing the structure of a composite **Susceptible->Infected** transition rule."
   ]
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   "source": [
    "<img src=\"img/rule.png\"/>"
   ]
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    "- The initial node status is evaluated at the root of the tree (the master compartment)\n",
    "    - If the operation described by such compartment is satisfied the conditions of (one of) its child compartments is evaluated\n",
    "    - If a path from the root to one leaf of the tree is completely satisfied the transition rule applies and the node change its status.\n",
    "\n",
    "Compartments can be combined following two criteria:\n",
    "\n",
    "- Cascading Composition\n",
    "- Conditional Composition\n",
    "\n",
    "A ``transition rule`` can be defined by employing all possible combinations of cascading and conditional compartment composition."
   ]
  },
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    "<a id=\"cascading\"></a>\n",
    "### 7.B.a Cascading Composition ([to top](#top))\n",
    "\n",
    "Since each compartment identifies an atomic condition it is natural to imagine rules described as chains of compartments.\n",
    "\n",
    "A compartment chain identify and ordered set of conditions that needs to be satisfied to allow status transition (it allows describing an **AND** logic).\n",
    "\n",
    "To implement such behaviour each compartment exposes a parameter (named ``composed``) that allows to specify the subsequent compartment to evaluate in case it condition is satisfied.\n",
    "\n",
    "#### Example\n",
    "\n",
    "In the following scenario the **Susceptible->Infected** rule is implemented using three NodeStochastic compartments chained as follows:\n",
    "\n",
    "$$C_1 \\rightarrow C_2 \\rightarrow C_3$$\n",
    "\n",
    "- If the node $n$ is *Susceptible*\n",
    " - $C_1$: if at least a neighbor of the actual node is *Infected*, with probability $0.5$ evaluate compartment $C_2$\n",
    " - $C_2$: with probability $0.4$ evaluate compartment $C_3$\n",
    " - $C_3$: with probability $0.2$ allow the transition to the *Infected* state"
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Network generation\n",
    "g = nx.erdos_renyi_graph(1000, 0.1)\n",
    "\n",
    "# Composite Model instantiation\n",
    "model = gc.CompositeModel(g)\n",
    "\n",
    "# Model statuses\n",
    "model.add_status(\"Susceptible\")\n",
    "model.add_status(\"Infected\")\n",
    "\n",
    "# Compartment definition and chain construction\n",
    "c3 = ns.NodeStochastic(0.2)\n",
    "c2 = ns.NodeStochastic(0.4, composed=c3)\n",
    "c1 = ns.NodeStochastic(0.5, \"Infected\", composed=c2)\n",
    "\n",
    "# Rule definition\n",
    "model.add_rule(\"Susceptible\", \"Infected\", c1)\n",
    "\n",
    "# Model initial status configuration\n",
    "config = mc.Configuration()\n",
    "config.add_model_parameter('percentage_infected', 0.1)\n",
    "\n",
    "# Simulation execution\n",
    "model.set_initial_status(config)\n",
    "iterations = model.iteration_bunch(100)\n",
    "\n",
    "trends = model.build_trends(iterations)\n",
    "viz = DiffusionTrend(model, trends)\n",
    "viz.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "778456fc-636a-4c72-bd1e-498b2faf46e1"
    }
   },
   "source": [
    "<a id=\"conditional\"></a>\n",
    "### 7.B.a Conditional Composition ([to top](#top))\n",
    "\n",
    "Conditional compartment composition allows to describe rules as trees.\n",
    "\n",
    "A compartment tree identify and ordered and disjoint set of conditions that needs to be satisfied to allow status transition (it allows describing an **OR** logic).\n",
    "\n",
    "``ConditionalComposition`` compartment allows to describe branching pattern as follows:\n",
    "\n",
    "$$if\\ C_i then\\ C_j else\\ C_z$$\n",
    "\n",
    "``ConditionalComposition`` evaluate the guard compartment ($C_i$) and, depending from the result it gets (True or False) move to the evaluation of one of its two child compartments ($C_j$ and $C_z$).\n",
    "\n",
    "#### Example\n",
    "\n",
    "In the following scenario the **Susceptible->Infected** rule is implemented using three NodeStochastic compartments combined as follows:\n",
    "\n",
    "- If the node $n$ is *Susceptible*\n",
    "    - $C_1$: if at least a neighbor of the actual node is *Infected*, with probability $0.5$ evaluate compartment $C_2$ else evaluate compartment $C_3$\n",
    "        - $C_2$: with probability $0.2$ allow the transition to the *Infected* state\n",
    "        - $C_3$: with probability $0.1$ allow the transition to the *Infected* state\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "nbpresent": {
     "id": "edd9cad1-5539-41b9-90ef-392c49102118"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ndlib.models.compartments.ConditionalComposition as cif\n",
    "\n",
    "# Network generation\n",
    "g = nx.erdos_renyi_graph(1000, 0.1)\n",
    "\n",
    "# Composite Model instantiation\n",
    "model = gc.CompositeModel(g)\n",
    "\n",
    "# Model statuses\n",
    "model.add_status(\"Susceptible\")\n",
    "model.add_status(\"Infected\")\n",
    "\n",
    "# Compartment definition\n",
    "c1 = ns.NodeStochastic(0.5, \"Infected\")\n",
    "c2 = ns.NodeStochastic(0.2)\n",
    "c3 = ns.NodeStochastic(0.1)\n",
    "\n",
    "# Conditional Composition\n",
    "cc = cif.ConditionalComposition(c1, c2, c3)\n",
    "\n",
    "# Rule definition\n",
    "model.add_rule(\"Susceptible\", \"Infected\", cc)\n",
    "\n",
    "# Model initial status configuration\n",
    "config = mc.Configuration()\n",
    "config.add_model_parameter('percentage_infected', 0.1)\n",
    "\n",
    "# Simulation execution\n",
    "model.set_initial_status(config)\n",
    "iterations = model.iteration_bunch(30)\n",
    "trends = model.build_trends(iterations)\n",
    "\n",
    "viz = DiffusionTrend(model, trends)\n",
    "viz.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "168b9b9a-7b3d-4fa5-8307-8ea107a5ef1a"
    }
   },
   "source": [
    "<a id=\"sir\"></a>\n",
    "### 7.C Example: SIR ([to top](#top)) \n",
    "\n",
    "Let's describe, and simulate, a **SIR** model using compartments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "nbpresent": {
     "id": "a04b59c6-a262-4554-922e-4443dd86ec0a"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ndlib.models.CompositeModel import CompositeModel\n",
    "from ndlib.models.compartments.NodeStochastic import NodeStochastic\n",
    "\n",
    "# Network definition\n",
    "g1 = nx.erdos_renyi_graph(n=1000, p=0.1)\n",
    "\n",
    "# Model definition\n",
    "SIR = CompositeModel(g1)\n",
    "SIR.add_status('Susceptible')\n",
    "SIR.add_status('Infected')\n",
    "SIR.add_status('Removed')\n",
    "\n",
    "# Compartments\n",
    "c1 = NodeStochastic(triggering_status='Infected', rate=0.001, probability=1)\n",
    "c2 = NodeStochastic(rate=0.01, probability=1)\n",
    "\n",
    "# Rules\n",
    "SIR.add_rule('Susceptible', 'Infected', c1)\n",
    "SIR.add_rule('Infected', 'Removed', c2)\n",
    "\n",
    "# Configuration\n",
    "config = mc.Configuration()\n",
    "config.add_model_parameter('percentage_infected', 0.1)\n",
    "SIR.set_initial_status(config)\n",
    "\n",
    "# Simulation\n",
    "iterations = SIR.iteration_bunch(300, node_status=False)\n",
    "trends = SIR.build_trends(iterations)\n",
    "viz = DiffusionTrend(SIR, trends)\n",
    "viz.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d8cf489f-1a62-48ef-a44c-ea6a9181365e"
    }
   },
   "source": [
    "<a id=\"ndql\"></a>\n",
    "## 8. NDQL: Network Diffusion Query Language ([to top](#top))\n",
    "\n",
    "``NDlib`` aims to reach an heterogeneous audience composed by technicians as well as analysts. \n",
    "\n",
    "In order to abstract from the its programming interface we designed a query language to describe diffusion simulations, ``NDQL``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4f3a90ce-4815-4a1d-bbeb-3c7ad2a2818d"
    }
   },
   "source": [
    "<a id=\"syntax\"></a>\n",
    "### 8.A Syntax ([to top](#top)) \n",
    "\n",
    "An ``NDQL`` script is composed of a minimum set of directives:\n",
    "\n",
    "1. Network specification:\n",
    "    - CREATE_NETWORK (-), LOAD_NETWORK (-)\n",
    "2. Model definition:\n",
    "    - MODEL, STATUS, COMPARTMENT (+), IF-THEN-ELSE (+), RULE,\n",
    "3. Model initialization and simulation execution\n",
    "    - INITIALIZE, EXECUTE\n",
    "\n",
    "Directives marked with (+) are optional while the ones marked with (-) are mutually exclusive w.r.t. their class.\n",
    "\n",
    "The complete language directive specification is the following:\n",
    "\n",
    "> **MODEL** model_name \n",
    "> \n",
    "> **STATUS** status_name\n",
    "> \n",
    "> **COMPARTMENT** compartment_name <br/>\n",
    "> **TYPE** compartment_type <br/>\n",
    "> **COMPOSE** compartment_name <br/>\n",
    "> [**PARAM** param_name numeric]+ <br/>\n",
    "> [**TRIGGER** status_name]\n",
    "> \n",
    "> **IF** compartment_name_1 **THEN** compartment_name_2 **ELSE** compartment_name_3 **AS** rule_name\n",
    "> \n",
    "> **RULE** rule_name <br/>\n",
    "> **FROM** status_name <br/>\n",
    "> **TO** status_name <br/>\n",
    "> **USING** compartment_name\n",
    "> \n",
    "> **INITIALIZE** <br/>\n",
    "> [**SET** status_name ratio]+\n",
    "> \n",
    "> **CREATE_NETWORK** network_name <br/>\n",
    "> **TYPE** network_type <br/>\n",
    "> [**PARAM** param_name numeric]+\n",
    ">\n",
    "> **LOAD_NETWORK** network_name **FROM** network_file\n",
    ">\n",
    "> **EXECUTE** model_name **ON** network_name **FOR** iterations\n",
    "\n",
    "The **CREATE_NETWORK** directive can take as network_type any [``networkx``](https://networkx.github.io) graph generator name (param_name are inherited from generator function parameters)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "33e400c8-4679-4979-84e8-d977b20549de"
    }
   },
   "source": [
    "<a id=\"cmd\"></a>\n",
    "### 8.B Command line tools ([to top](#top)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "9c13b623-e83f-448b-b105-dbd744ee3888"
    }
   },
   "source": [
    "``NDlib`` installs two command line tools: \n",
    "- NDQL_translate \n",
    "- NDQL_execute\n",
    "\n",
    "The former command allows to translate a generic, well-formed, ``NDQL`` script into an equivalent Python one. <br/>\n",
    "It can be executed as\n",
    "\n",
    "> ``NDQL_translate`` query_file python_file\n",
    "\n",
    "where *query_file* identifies the target ``NDQL`` script and *python_file* specifies the desired name for the resulting Python script.\n",
    "\n",
    "The latter command allows to directly execute a generic, well-formed, ``NDQL`` script. <br/>\n",
    "It can be executed as\n",
    "\n",
    "> ``NDQL_execute`` query_file result_file\n",
    "\n",
    "where *query_file* identifies the target ``NDQL`` script and *result_file* specifies the desired name for the execution results. Execution results are saved as JSON files with the following syntax:\n",
    "\n",
    "[{\"trends\":<br/>\n",
    "   {\"node_count\": {\"0\": [270, 179, 15, 0, 0], \"1\": [30, 116, 273, 256, 239], \"2\": [0, 5, 12, 44, 61]},<br/>\n",
    "    \"status_delta\": {\"0\": [0, -91, -164, -15, 0], \"1\": [0, 86, 157, -17, -17], \"2\": [0, 5, 7, 32, 17]}},<br/>\n",
    "    \"Statuses\": {\"1\": \"Infected\", \"2\": \"Removed\", \"0\": \"Susceptible\"}<br/>\n",
    " }]\n",
    " \n",
    "where \n",
    "- ``node_count`` describes the trends built on the number of nodes per status \n",
    "- ``status_delta`` describe the trends built on the fluctuations of number of nodes per status \n",
    "- ``Statuses`` provides a map from numerical id to status name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "9366817a-e254-446a-acc3-459f57325ae8"
    }
   },
   "source": [
    "<a id=\"sir2\"></a>\n",
    "### 8.C Example: SIR ([to top](#top)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5eb813d1-a0f4-48ce-ac73-4bf89f11f2c0"
    }
   },
   "source": [
    "Let's describe a **SIR** model using ``NDQL``.\n",
    "\n",
    "#### Network creation\n",
    "\n",
    "> **CREATE_NETWORK** g1<br/>\n",
    "> **TYPE** erdos_renyi_graph<br/>\n",
    "> **PARAM** n 300<br/>\n",
    "> **PARAM** p 0.1\n",
    "\n",
    "#### Model definition \n",
    "\n",
    "> **MODEL** SIR\n",
    "> \n",
    "> **STATUS** Susceptible <br/>\n",
    "> **STATUS** Infected <br/>\n",
    "> **STATUS** Removed <br/>\n",
    "> \n",
    "> **COMPARTMENT** c1 <br/>\n",
    "> **TYPE** NodeStochastic<br/>\n",
    "> **PARAM** rate 0.1<br/>\n",
    "> **TRIGGER** Infected\n",
    "> \n",
    "> **COMPARTMENT** c2<br/>\n",
    "> **TYPE** NodeStochastic<br/>\n",
    "> **PARAM** rate 0.1\n",
    "> \n",
    "> **RULE**<br/>\n",
    "> **FROM** Susceptible<br/>\n",
    "> **TO** Infected<br/>\n",
    "> **USING** c1\n",
    "> \n",
    "> **RULE**<br/>\n",
    "> **FROM** Infected<br/>\n",
    "> **TO** Removed<br/>\n",
    "> **USING** c2\n",
    "\n",
    "#### Simulation initialization and execution\n",
    "\n",
    "> **INITIALIZE**<br/>\n",
    "> **SET** Infected 0.1<br/>\n",
    "> \n",
    "> **EXECUTE** SIR **ON** g1 **FOR** 100\n",
    "\n",
    "The complete query can be find in ``data/ndql_sir.txt``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "dd77cdb3-9443-49c2-be1f-5d9bf890b3ba"
    }
   },
   "source": [
    "In order to translate our query into the corresponding python script we can run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "nbpresent": {
     "id": "88a0915e-37d2-4e28-953f-94f0e604e81a"
    }
   },
   "outputs": [],
   "source": [
    "!NDQL_translate data/ndql_sir.txt data/sir_g.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "nbpresent": {
     "id": "cb4b21c8-c884-4c34-a071-2adcc0f9c583"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{\"iteration\": 0, \"status\": {}, \"node_count\": {\"0\": 270, \"1\": 30, \"2\": 0}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 1, \"status\": {}, \"node_count\": {\"0\": 178, \"1\": 120, \"2\": 2}, \"status_delta\": {\"0\": -92, \"1\": 90, \"2\": 2}}, {\"iteration\": 2, \"status\": {}, \"node_count\": {\"0\": 17, \"1\": 262, \"2\": 21}, \"status_delta\": {\"0\": -161, \"1\": 142, \"2\": 19}}, {\"iteration\": 3, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 239, \"2\": 61}, \"status_delta\": {\"0\": -17, \"1\": -23, \"2\": 40}}, {\"iteration\": 4, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 217, \"2\": 83}, \"status_delta\": {\"0\": 0, \"1\": -22, \"2\": 22}}, {\"iteration\": 5, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 198, \"2\": 102}, \"status_delta\": {\"0\": 0, \"1\": -19, \"2\": 19}}, {\"iteration\": 6, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 181, \"2\": 119}, \"status_delta\": {\"0\": 0, \"1\": -17, \"2\": 17}}, {\"iteration\": 7, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 164, \"2\": 136}, \"status_delta\": {\"0\": 0, \"1\": -17, \"2\": 17}}, {\"iteration\": 8, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 151, \"2\": 149}, \"status_delta\": {\"0\": 0, \"1\": -13, \"2\": 13}}, {\"iteration\": 9, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 136, \"2\": 164}, \"status_delta\": {\"0\": 0, \"1\": -15, \"2\": 15}}, {\"iteration\": 10, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 119, \"2\": 181}, \"status_delta\": {\"0\": 0, \"1\": -17, \"2\": 17}}, {\"iteration\": 11, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 111, \"2\": 189}, \"status_delta\": {\"0\": 0, \"1\": -8, \"2\": 8}}, {\"iteration\": 12, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 102, \"2\": 198}, \"status_delta\": {\"0\": 0, \"1\": -9, \"2\": 9}}, {\"iteration\": 13, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 88, \"2\": 212}, \"status_delta\": {\"0\": 0, \"1\": -14, \"2\": 14}}, {\"iteration\": 14, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 78, \"2\": 222}, \"status_delta\": {\"0\": 0, \"1\": -10, \"2\": 10}}, {\"iteration\": 15, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 70, \"2\": 230}, \"status_delta\": {\"0\": 0, \"1\": -8, \"2\": 8}}, {\"iteration\": 16, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 64, \"2\": 236}, \"status_delta\": {\"0\": 0, \"1\": -6, \"2\": 6}}, {\"iteration\": 17, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 59, \"2\": 241}, \"status_delta\": {\"0\": 0, \"1\": -5, \"2\": 5}}, {\"iteration\": 18, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 56, \"2\": 244}, \"status_delta\": {\"0\": 0, \"1\": -3, \"2\": 3}}, {\"iteration\": 19, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 49, \"2\": 251}, \"status_delta\": {\"0\": 0, \"1\": -7, \"2\": 7}}, {\"iteration\": 20, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 46, \"2\": 254}, \"status_delta\": {\"0\": 0, \"1\": -3, \"2\": 3}}, {\"iteration\": 21, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 44, \"2\": 256}, \"status_delta\": {\"0\": 0, \"1\": -2, \"2\": 2}}, {\"iteration\": 22, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 37, \"2\": 263}, \"status_delta\": {\"0\": 0, \"1\": -7, \"2\": 7}}, {\"iteration\": 23, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 32, \"2\": 268}, \"status_delta\": {\"0\": 0, \"1\": -5, \"2\": 5}}, {\"iteration\": 24, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 30, \"2\": 270}, \"status_delta\": {\"0\": 0, \"1\": -2, \"2\": 2}}, {\"iteration\": 25, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 25, \"2\": 275}, \"status_delta\": {\"0\": 0, \"1\": -5, \"2\": 5}}, {\"iteration\": 26, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 25, \"2\": 275}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 27, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 24, \"2\": 276}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 28, \"status\": {}, 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\"1\": 10, \"2\": 290}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 36, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 9, \"2\": 291}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 37, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 9, \"2\": 291}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 38, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 7, \"2\": 293}, \"status_delta\": {\"0\": 0, \"1\": -2, \"2\": 2}}, {\"iteration\": 39, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 7, \"2\": 293}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 40, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 7, \"2\": 293}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 41, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 6, \"2\": 294}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 42, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 5, \"2\": 295}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 43, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 4, \"2\": 296}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 44, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 4, \"2\": 296}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 45, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 4, \"2\": 296}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 46, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 3, \"2\": 297}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 47, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 3, \"2\": 297}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 48, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 3, \"2\": 297}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 49, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 3, \"2\": 297}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 50, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 3, \"2\": 297}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 51, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 2, \"2\": 298}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 52, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 2, \"2\": 298}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 53, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 2, \"2\": 298}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 54, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 2, \"2\": 298}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 55, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 2, \"2\": 298}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 56, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 2, \"2\": 298}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 57, 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\"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 72, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 1, \"2\": 299}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 73, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 74, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 75, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 76, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 77, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 78, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 79, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 80, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 81, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 82, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 83, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 84, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 85, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 86, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 87, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 88, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 89, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 90, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 91, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 92, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 93, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 94, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 95, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 96, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 97, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 98, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 99, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}]\n"
     ]
    }
   ],
   "source": [
    "!python data/sir_g.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4b481601-e190-4359-a541-809eacddb7ec"
    }
   },
   "source": [
    "The translated script is then"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "nbpresent": {
     "id": "a3a08442-2cf5-452a-a366-92c0240fd0d5"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import networkx as nx\n",
      "import numpy as np\n",
      "import json\n",
      "from ndlib.models.ModelConfig import Configuration\n",
      "from ndlib.models.CompositeModel import CompositeModel\n",
      "from ndlib.models.compartments.NodeStochastic import NodeStochastic\n",
      "\n",
      "g1 = nx.erdos_renyi_graph(n=300, p=0.1)\n",
      "SIR = CompositeModel(g1)\n",
      "SIR.add_status('Susceptible')\n",
      "SIR.add_status('Infected')\n",
      "SIR.add_status('Removed')\n",
      "c1 = NodeStochastic(triggering_status='Infected', rate=0.1, probability=1, name=\"None\")\n",
      "c2 = NodeStochastic(rate=0.1, probability=1, name=\"None\")\n",
      "SIR.add_rule('Susceptible', 'Infected', c1)\n",
      "SIR.add_rule('Infected', 'Removed', c2)\n",
      "config = Configuration()\n",
      "config.add_model_parameter('percentage_infected', 0.1)\n",
      "SIR.set_initial_status(config)\n",
      "iterations = SIR.iteration_bunch(100, node_status=False)\n",
      "res = json.dumps(iterations)\n",
      "print(res)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(\"data/sir_g.py\") as f:\n",
    "    l = f.read()\n",
    "    print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "bd098043-0eb5-4cff-8615-2ff87712f004"
    }
   },
   "source": [
    "In order to execute the query we can run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "nbpresent": {
     "id": "6e76a5e5-a340-46c2-801b-0101dbaaab2f"
    }
   },
   "outputs": [],
   "source": [
    "!NDQL_execute data/ndql_sir.txt data/res.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "3736aa2c-e373-4dc9-b66b-ad6acbaaee65"
    }
   },
   "source": [
    "As result we get"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "nbpresent": {
     "id": "3e028c60-fc4e-49d9-8c4c-1d80de6b5550"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{\"trends\": {\"node_count\": {\"0\": [270, 191, 19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"1\": [30, 106, 266, 258, 233, 207, 182, 166, 153, 139, 120, 109, 96, 92, 83, 74, 65, 55, 48, 44, 37, 34, 29, 26, 21, 18, 16, 16, 14, 11, 10, 10, 8, 6, 6, 6, 6, 5, 5, 5, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"2\": [0, 3, 15, 42, 67, 93, 118, 134, 147, 161, 180, 191, 204, 208, 217, 226, 235, 245, 252, 256, 263, 266, 271, 274, 279, 282, 284, 284, 286, 289, 290, 290, 292, 294, 294, 294, 294, 295, 295, 295, 297, 298, 298, 298, 298, 298, 298, 298, 298, 298, 299, 299, 299, 299, 299, 299, 299, 299, 299, 299, 299, 299, 299, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300]}, \"status_delta\": {\"0\": [0, -79, -172, -19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"1\": [0, 76, 160, -8, -25, -26, -25, -16, -13, -14, -19, -11, -13, -4, -9, -9, -9, -10, -7, -4, -7, -3, -5, -3, -5, -3, -2, 0, -2, -3, -1, 0, -2, -2, 0, 0, 0, -1, 0, 0, -2, -1, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"2\": [0, 3, 12, 27, 25, 26, 25, 16, 13, 14, 19, 11, 13, 4, 9, 9, 9, 10, 7, 4, 7, 3, 5, 3, 5, 3, 2, 0, 2, 3, 1, 0, 2, 2, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}, \"Statuses\": {\"0\": \"Susceptible\", \"1\": \"Infected\", \"2\": \"Removed\"}}]\n"
     ]
    }
   ],
   "source": [
    "with open(\"data/res.json\") as f:\n",
    "    l = f.read()\n",
    "    print(l)"
   ]
  },
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   "source": [
    "<a id=\"conclusion\"></a>\n",
    "## 9. Conclusions ([to top](#top))\n"
   ]
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    "In this notebook we introduced the basic facilities offered by ``NDlib``.\n",
    "\n",
    "For any issue, suggestion, bug report feel free to contact us on the official [GitHub repository](https://github.com/GiulioRossetti/ndlib) of the project.\n",
    "\n",
    "Moreover, give also a look to [``NDlib-REST``](https://github.com/GiulioRossetti/ndlib-rest) and [``NDlib-viz``](https://github.com/rinziv/NDLib-Viz) the experiment server and web-based visual interface built on top of our library."
   ]
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