Appendix - 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",
    "# *Decision Based Models*\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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    "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.SIRModel as sir\n",
    "\n",
    "model = sir.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}"
      ]
     },
     "execution_count": 5,
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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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    "<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': {600: 1, 807: 1},\n",
       "  'node_count': {0: 985, 1: 14, 2: 1},\n",
       "  'status_delta': {0: -2, 1: 2, 2: 0}},\n",
       " {'iteration': 5,\n",
       "  'status': {},\n",
       "  'node_count': {0: 985, 1: 14, 2: 1},\n",
       "  'status_delta': {0: 0, 1: 0, 2: 0}},\n",
       " {'iteration': 6,\n",
       "  'status': {},\n",
       "  'node_count': {0: 985, 1: 14, 2: 1},\n",
       "  'status_delta': {0: 0, 1: 0, 2: 0}},\n",
       " {'iteration': 7,\n",
       "  'status': {},\n",
       "  'node_count': {0: 985, 1: 14, 2: 1},\n",
       "  'status_delta': {0: 0, 1: 0, 2: 0}},\n",
       " {'iteration': 8,\n",
       "  'status': {549: 1},\n",
       "  'node_count': {0: 984, 1: 15, 2: 1},\n",
       "  'status_delta': {0: -1, 1: 1, 2: 0}},\n",
       " {'iteration': 9,\n",
       "  'status': {714: 1},\n",
       "  'node_count': {0: 983, 1: 16, 2: 1},\n",
       "  'status_delta': {0: -1, 1: 1, 2: 0}},\n",
       " {'iteration': 10,\n",
       "  'status': {158: 1, 535: 1, 547: 1, 776: 1, 795: 1},\n",
       "  'node_count': {0: 978, 1: 21, 2: 1},\n",
       "  'status_delta': {0: -5, 1: 5, 2: 0}},\n",
       " {'iteration': 11,\n",
       "  'status': {494: 2, 553: 1, 907: 1},\n",
       "  'node_count': {0: 976, 1: 22, 2: 2},\n",
       "  'status_delta': {0: -2, 1: 1, 2: 1}},\n",
       " {'iteration': 12,\n",
       "  'status': {},\n",
       "  'node_count': {0: 976, 1: 22, 2: 2},\n",
       "  'status_delta': {0: 0, 1: 0, 2: 0}},\n",
       " {'iteration': 13,\n",
       "  'status': {138: 1, 153: 1},\n",
       "  'node_count': {0: 974, 1: 24, 2: 2},\n",
       "  'status_delta': {0: -2, 1: 2, 2: 0}},\n",
       " {'iteration': 14,\n",
       "  'status': {404: 1},\n",
       "  'node_count': {0: 973, 1: 25, 2: 2},\n",
       "  'status_delta': {0: -1, 1: 1, 2: 0}},\n",
       " {'iteration': 15,\n",
       "  'status': {206: 1, 614: 1},\n",
       "  'node_count': {0: 971, 1: 27, 2: 2},\n",
       "  'status_delta': {0: -2, 1: 2, 2: 0}},\n",
       " {'iteration': 16,\n",
       "  'status': {},\n",
       "  'node_count': {0: 971, 1: 27, 2: 2},\n",
       "  'status_delta': {0: 0, 1: 0, 2: 0}},\n",
       " {'iteration': 17,\n",
       "  'status': {393: 1, 557: 1, 635: 1},\n",
       "  'node_count': {0: 968, 1: 30, 2: 2},\n",
       "  'status_delta': {0: -3, 1: 3, 2: 0}},\n",
       " {'iteration': 18,\n",
       "  'status': {340: 1, 371: 1, 404: 2, 709: 1},\n",
       "  'node_count': {0: 965, 1: 32, 2: 3},\n",
       "  'status_delta': {0: -3, 1: 2, 2: 1}},\n",
       " {'iteration': 19,\n",
       "  'status': {385: 1, 835: 1},\n",
       "  'node_count': {0: 963, 1: 34, 2: 3},\n",
       "  'status_delta': {0: -2, 1: 2, 2: 0}},\n",
       " {'iteration': 20,\n",
       "  'status': {253: 1, 541: 1, 848: 1},\n",
       "  'node_count': {0: 960, 1: 37, 2: 3},\n",
       "  'status_delta': {0: -3, 1: 3, 2: 0}},\n",
       " {'iteration': 21,\n",
       "  'status': {371: 2},\n",
       "  'node_count': {0: 960, 1: 36, 2: 4},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 22,\n",
       "  'status': {117: 1, 457: 1},\n",
       "  'node_count': {0: 958, 1: 38, 2: 4},\n",
       "  'status_delta': {0: -2, 1: 2, 2: 0}},\n",
       " {'iteration': 23,\n",
       "  'status': {217: 1, 429: 1},\n",
       "  'node_count': {0: 956, 1: 40, 2: 4},\n",
       "  'status_delta': {0: -2, 1: 2, 2: 0}},\n",
       " {'iteration': 24,\n",
       "  'status': {582: 1},\n",
       "  'node_count': {0: 955, 1: 41, 2: 4},\n",
       "  'status_delta': {0: -1, 1: 1, 2: 0}},\n",
       " {'iteration': 25,\n",
       "  'status': {126: 1, 345: 1, 477: 1, 665: 1, 732: 1, 932: 1, 998: 1},\n",
       "  'node_count': {0: 948, 1: 48, 2: 4},\n",
       "  'status_delta': {0: -7, 1: 7, 2: 0}},\n",
       " {'iteration': 26,\n",
       "  'status': {113: 1, 201: 1, 634: 1, 888: 1, 910: 1, 995: 1, 998: 2},\n",
       "  'node_count': {0: 942, 1: 53, 2: 5},\n",
       "  'status_delta': {0: -6, 1: 5, 2: 1}},\n",
       " {'iteration': 27,\n",
       "  'status': {26: 1,\n",
       "   283: 1,\n",
       "   346: 1,\n",
       "   581: 1,\n",
       "   652: 1,\n",
       "   761: 1,\n",
       "   790: 1,\n",
       "   802: 1,\n",
       "   896: 1,\n",
       "   944: 1},\n",
       "  'node_count': {0: 932, 1: 63, 2: 5},\n",
       "  'status_delta': {0: -10, 1: 10, 2: 0}},\n",
       " {'iteration': 28,\n",
       "  'status': {24: 1, 41: 1, 44: 1, 534: 1, 539: 1, 566: 1, 682: 1},\n",
       "  'node_count': {0: 925, 1: 70, 2: 5},\n",
       "  'status_delta': {0: -7, 1: 7, 2: 0}},\n",
       " {'iteration': 29,\n",
       "  'status': {29: 1, 330: 1, 394: 1, 526: 1, 649: 1, 909: 1, 918: 1},\n",
       "  'node_count': {0: 918, 1: 77, 2: 5},\n",
       "  'status_delta': {0: -7, 1: 7, 2: 0}},\n",
       " {'iteration': 30,\n",
       "  'status': {94: 1, 138: 2, 283: 2, 497: 1, 505: 1, 639: 1, 857: 1, 926: 1},\n",
       "  'node_count': {0: 912, 1: 81, 2: 7},\n",
       "  'status_delta': {0: -6, 1: 4, 2: 2}},\n",
       " {'iteration': 31,\n",
       "  'status': {142: 2,\n",
       "   188: 1,\n",
       "   284: 1,\n",
       "   309: 1,\n",
       "   377: 1,\n",
       "   541: 2,\n",
       "   621: 1,\n",
       "   681: 1,\n",
       "   715: 1,\n",
       "   801: 1,\n",
       "   952: 1},\n",
       "  'node_count': {0: 903, 1: 88, 2: 9},\n",
       "  'status_delta': {0: -9, 1: 7, 2: 2}},\n",
       " {'iteration': 32,\n",
       "  'status': {165: 1, 328: 1, 515: 1, 549: 2, 593: 1, 744: 1, 808: 1, 961: 1},\n",
       "  'node_count': {0: 896, 1: 94, 2: 10},\n",
       "  'status_delta': {0: -7, 1: 6, 2: 1}},\n",
       " {'iteration': 33,\n",
       "  'status': {199: 1, 509: 1, 615: 1, 659: 1},\n",
       "  'node_count': {0: 892, 1: 98, 2: 10},\n",
       "  'status_delta': {0: -4, 1: 4, 2: 0}},\n",
       " {'iteration': 34,\n",
       "  'status': {112: 1, 163: 1, 301: 1, 411: 1, 436: 1, 536: 1, 612: 1, 663: 1},\n",
       "  'node_count': {0: 884, 1: 106, 2: 10},\n",
       "  'status_delta': {0: -8, 1: 8, 2: 0}},\n",
       " {'iteration': 35,\n",
       "  'status': {4: 1,\n",
       "   65: 1,\n",
       "   81: 1,\n",
       "   89: 1,\n",
       "   107: 1,\n",
       "   125: 1,\n",
       "   153: 2,\n",
       "   307: 1,\n",
       "   347: 1,\n",
       "   391: 1,\n",
       "   619: 1,\n",
       "   679: 1,\n",
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       "   850: 1,\n",
       "   917: 1,\n",
       "   956: 1},\n",
       "  'node_count': {0: 869, 1: 120, 2: 11},\n",
       "  'status_delta': {0: -15, 1: 14, 2: 1}},\n",
       " {'iteration': 36,\n",
       "  'status': {299: 1,\n",
       "   311: 1,\n",
       "   313: 1,\n",
       "   331: 1,\n",
       "   375: 1,\n",
       "   516: 1,\n",
       "   638: 1,\n",
       "   764: 1,\n",
       "   834: 1},\n",
       "  'node_count': {0: 860, 1: 129, 2: 11},\n",
       "  'status_delta': {0: -9, 1: 9, 2: 0}},\n",
       " {'iteration': 37,\n",
       "  'status': {20: 1,\n",
       "   114: 1,\n",
       "   207: 1,\n",
       "   240: 1,\n",
       "   337: 1,\n",
       "   348: 2,\n",
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       "   556: 1,\n",
       "   587: 1,\n",
       "   677: 1,\n",
       "   725: 1,\n",
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       "   836: 1,\n",
       "   903: 1,\n",
       "   995: 2},\n",
       "  'node_count': {0: 847, 1: 140, 2: 13},\n",
       "  'status_delta': {0: -13, 1: 11, 2: 2}},\n",
       " {'iteration': 38,\n",
       "  'status': {58: 1,\n",
       "   208: 1,\n",
       "   265: 1,\n",
       "   332: 1,\n",
       "   335: 1,\n",
       "   353: 2,\n",
       "   389: 1,\n",
       "   629: 1,\n",
       "   866: 1},\n",
       "  'node_count': {0: 839, 1: 147, 2: 14},\n",
       "  'status_delta': {0: -8, 1: 7, 2: 1}},\n",
       " {'iteration': 39,\n",
       "  'status': {201: 2,\n",
       "   268: 1,\n",
       "   503: 1,\n",
       "   562: 1,\n",
       "   650: 1,\n",
       "   703: 1,\n",
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       "   807: 2,\n",
       "   828: 1,\n",
       "   978: 1,\n",
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       "  'node_count': {0: 830, 1: 154, 2: 16},\n",
       "  'status_delta': {0: -9, 1: 7, 2: 2}},\n",
       " {'iteration': 40,\n",
       "  'status': {101: 1,\n",
       "   176: 1,\n",
       "   266: 1,\n",
       "   268: 2,\n",
       "   337: 2,\n",
       "   341: 1,\n",
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       "   557: 2,\n",
       "   607: 1,\n",
       "   678: 1,\n",
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       "  'node_count': {0: 822, 1: 158, 2: 20},\n",
       "  'status_delta': {0: -8, 1: 4, 2: 4}},\n",
       " {'iteration': 41,\n",
       "  'status': {68: 1,\n",
       "   100: 1,\n",
       "   109: 1,\n",
       "   136: 1,\n",
       "   207: 2,\n",
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       "   687: 1,\n",
       "   785: 1,\n",
       "   848: 2,\n",
       "   916: 1},\n",
       "  'node_count': {0: 813, 1: 165, 2: 22},\n",
       "  'status_delta': {0: -9, 1: 7, 2: 2}},\n",
       " {'iteration': 42,\n",
       "  'status': {254: 1, 329: 1, 576: 1, 599: 1, 853: 1, 912: 1, 948: 1},\n",
       "  'node_count': {0: 806, 1: 172, 2: 22},\n",
       "  'status_delta': {0: -7, 1: 7, 2: 0}},\n",
       " {'iteration': 43,\n",
       "  'status': {40: 1,\n",
       "   82: 1,\n",
       "   112: 2,\n",
       "   467: 1,\n",
       "   628: 1,\n",
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       "  'node_count': {0: 796, 1: 180, 2: 24},\n",
       "  'status_delta': {0: -10, 1: 8, 2: 2}},\n",
       " {'iteration': 44,\n",
       "  'status': {31: 1,\n",
       "   40: 2,\n",
       "   217: 2,\n",
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       "   332: 2,\n",
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       "  'node_count': {0: 784, 1: 187, 2: 29},\n",
       "  'status_delta': {0: -12, 1: 7, 2: 5}},\n",
       " {'iteration': 45,\n",
       "  'status': {51: 1,\n",
       "   155: 1,\n",
       "   242: 1,\n",
       "   327: 1,\n",
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       "   376: 1,\n",
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       "  'node_count': {0: 772, 1: 196, 2: 32},\n",
       "  'status_delta': {0: -12, 1: 9, 2: 3}},\n",
       " {'iteration': 46,\n",
       "  'status': {66: 1,\n",
       "   202: 1,\n",
       "   229: 1,\n",
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       "  'node_count': {0: 757, 1: 210, 2: 33},\n",
       "  'status_delta': {0: -15, 1: 14, 2: 1}},\n",
       " {'iteration': 47,\n",
       "  'status': {30: 1,\n",
       "   91: 1,\n",
       "   101: 2,\n",
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       "  'node_count': {0: 743, 1: 221, 2: 36},\n",
       "  'status_delta': {0: -14, 1: 11, 2: 3}},\n",
       " {'iteration': 48,\n",
       "  'status': {46: 1,\n",
       "   151: 1,\n",
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       "  'node_count': {0: 724, 1: 237, 2: 39},\n",
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       " {'iteration': 49,\n",
       "  'status': {17: 1,\n",
       "   76: 1,\n",
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       "  'node_count': {0: 708, 1: 249, 2: 43},\n",
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       " {'iteration': 50,\n",
       "  'status': {67: 1,\n",
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       " {'iteration': 51,\n",
       "  'status': {63: 1,\n",
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       "  'node_count': {0: 678, 1: 271, 2: 51},\n",
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       " {'iteration': 52,\n",
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       " {'iteration': 53,\n",
       "  'status': {63: 2,\n",
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       " {'iteration': 54,\n",
       "  'status': {73: 1,\n",
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       " {'iteration': 55,\n",
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       " {'iteration': 56,\n",
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       " {'iteration': 57,\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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       " {'iteration': 72,\n",
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       " {'iteration': 73,\n",
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       " {'iteration': 74,\n",
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       " {'iteration': 75,\n",
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       " {'iteration': 76,\n",
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       " {'iteration': 77,\n",
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       " {'iteration': 79,\n",
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       " {'iteration': 80,\n",
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       " {'iteration': 81,\n",
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       " {'iteration': 82,\n",
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       " {'iteration': 83,\n",
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       " {'iteration': 84,\n",
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       " {'iteration': 85,\n",
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       "   260: 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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       "  'node_count': {0: 101, 1: 652, 2: 247},\n",
       "  'status_delta': {0: -4, 1: 1, 2: 3}},\n",
       " {'iteration': 89,\n",
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       " {'iteration': 90,\n",
       "  'status': {56: 1,\n",
       "   77: 1,\n",
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       "  'node_count': {0: 91, 1: 653, 2: 256},\n",
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       " {'iteration': 91,\n",
       "  'status': {323: 1,\n",
       "   489: 2,\n",
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       "  'node_count': {0: 85, 1: 652, 2: 263},\n",
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       " {'iteration': 92,\n",
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       "  'node_count': {0: 83, 1: 651, 2: 266},\n",
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       " {'iteration': 93,\n",
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       "   210: 2,\n",
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       " {'iteration': 94,\n",
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       "   72: 2,\n",
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       " {'iteration': 95,\n",
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       " {'iteration': 96,\n",
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       "   156: 1,\n",
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       " {'iteration': 97,\n",
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       " {'iteration': 98,\n",
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       "   152: 2,\n",
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       " {'iteration': 99,\n",
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       "   49: 2,\n",
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       " {'iteration': 100,\n",
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       "   155: 2,\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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       "   272: 2,\n",
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       " {'iteration': 104,\n",
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       " {'iteration': 105,\n",
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       "   277: 1,\n",
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       " {'iteration': 106,\n",
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       " {'iteration': 107,\n",
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       " {'iteration': 108,\n",
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       " {'iteration': 109,\n",
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       "  'node_count': {0: 35, 1: 589, 2: 376},\n",
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       " {'iteration': 110,\n",
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       "  'node_count': {0: 34, 1: 583, 2: 383},\n",
       "  'status_delta': {0: -1, 1: -6, 2: 7}},\n",
       " {'iteration': 111,\n",
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       "  'node_count': {0: 33, 1: 581, 2: 386},\n",
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       " {'iteration': 112,\n",
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       "  'node_count': {0: 31, 1: 581, 2: 388},\n",
       "  'status_delta': {0: -2, 1: 0, 2: 2}},\n",
       " {'iteration': 113,\n",
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       " {'iteration': 114,\n",
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       "  'node_count': {0: 27, 1: 579, 2: 394},\n",
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       " {'iteration': 115,\n",
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       "  'status_delta': {0: -1, 1: -5, 2: 6}},\n",
       " {'iteration': 116,\n",
       "  'status': {149: 2,\n",
       "   190: 2,\n",
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       "   379: 2,\n",
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       "   616: 2,\n",
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       " {'iteration': 117,\n",
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       "  'node_count': {0: 24, 1: 558, 2: 418},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 118,\n",
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       "   67: 2,\n",
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       "  'node_count': {0: 24, 1: 548, 2: 428},\n",
       "  'status_delta': {0: 0, 1: -10, 2: 10}},\n",
       " {'iteration': 119,\n",
       "  'status': {408: 2, 473: 2, 492: 2, 592: 2, 646: 2, 740: 2},\n",
       "  'node_count': {0: 24, 1: 542, 2: 434},\n",
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       " {'iteration': 120,\n",
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       " {'iteration': 121,\n",
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       "  'node_count': {0: 23, 1: 531, 2: 446},\n",
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       " {'iteration': 122,\n",
       "  'status': {51: 2, 647: 2, 737: 2, 769: 2, 788: 2},\n",
       "  'node_count': {0: 23, 1: 526, 2: 451},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 123,\n",
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       "  'status_delta': {0: -2, 1: -4, 2: 6}},\n",
       " {'iteration': 124,\n",
       "  'status': {354: 2, 808: 2, 888: 2},\n",
       "  'node_count': {0: 21, 1: 519, 2: 460},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 125,\n",
       "  'status': {45: 2, 251: 2, 307: 2, 364: 2, 810: 2, 867: 2},\n",
       "  'node_count': {0: 21, 1: 513, 2: 466},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 126,\n",
       "  'status': {110: 2, 259: 2, 637: 2, 771: 2, 864: 1},\n",
       "  'node_count': {0: 20, 1: 510, 2: 470},\n",
       "  'status_delta': {0: -1, 1: -3, 2: 4}},\n",
       " {'iteration': 127,\n",
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       "  'node_count': {0: 18, 1: 506, 2: 476},\n",
       "  'status_delta': {0: -2, 1: -4, 2: 6}},\n",
       " {'iteration': 128,\n",
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       "  'node_count': {0: 14, 1: 506, 2: 480},\n",
       "  'status_delta': {0: -4, 1: 0, 2: 4}},\n",
       " {'iteration': 129,\n",
       "  'status': {69: 2, 108: 2, 124: 2, 326: 2},\n",
       "  'node_count': {0: 14, 1: 502, 2: 484},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 130,\n",
       "  'status': {87: 2, 104: 2, 139: 2, 684: 2, 704: 2},\n",
       "  'node_count': {0: 14, 1: 497, 2: 489},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 131,\n",
       "  'status': {247: 1,\n",
       "   287: 2,\n",
       "   300: 2,\n",
       "   439: 2,\n",
       "   469: 2,\n",
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       "   813: 2},\n",
       "  'node_count': {0: 12, 1: 491, 2: 497},\n",
       "  'status_delta': {0: -2, 1: -6, 2: 8}},\n",
       " {'iteration': 132,\n",
       "  'status': {396: 2, 598: 2, 822: 1, 940: 2, 949: 2, 972: 2},\n",
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       " {'iteration': 133,\n",
       "  'status': {258: 2, 544: 2},\n",
       "  'node_count': {0: 11, 1: 485, 2: 504},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 134,\n",
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       "  'node_count': {0: 11, 1: 480, 2: 509},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 135,\n",
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       "  'status_delta': {0: -1, 1: -3, 2: 4}},\n",
       " {'iteration': 136,\n",
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       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 137,\n",
       "  'status': {50: 2,\n",
       "   275: 2,\n",
       "   444: 2,\n",
       "   504: 2,\n",
       "   522: 2,\n",
       "   545: 2,\n",
       "   703: 2,\n",
       "   750: 2,\n",
       "   997: 2},\n",
       "  'node_count': {0: 10, 1: 463, 2: 527},\n",
       "  'status_delta': {0: 0, 1: -9, 2: 9}},\n",
       " {'iteration': 138,\n",
       "  'status': {59: 2, 779: 2, 785: 2},\n",
       "  'node_count': {0: 10, 1: 460, 2: 530},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 139,\n",
       "  'status': {13: 2, 223: 2, 318: 2, 732: 2, 752: 2, 780: 2},\n",
       "  'node_count': {0: 10, 1: 454, 2: 536},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 140,\n",
       "  'status': {100: 2, 270: 2, 482: 2, 645: 2, 662: 2, 800: 2, 989: 2},\n",
       "  'node_count': {0: 10, 1: 447, 2: 543},\n",
       "  'status_delta': {0: 0, 1: -7, 2: 7}},\n",
       " {'iteration': 141,\n",
       "  'status': {278: 2, 619: 2, 866: 2, 953: 2},\n",
       "  'node_count': {0: 10, 1: 443, 2: 547},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 142,\n",
       "  'status': {564: 2, 614: 2, 968: 2},\n",
       "  'node_count': {0: 10, 1: 440, 2: 550},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 143,\n",
       "  'status': {86: 2, 488: 1, 783: 1, 839: 2, 879: 2},\n",
       "  'node_count': {0: 8, 1: 439, 2: 553},\n",
       "  'status_delta': {0: -2, 1: -1, 2: 3}},\n",
       " {'iteration': 144,\n",
       "  'status': {41: 2, 54: 2, 66: 2, 422: 2, 563: 2, 579: 2},\n",
       "  'node_count': {0: 8, 1: 433, 2: 559},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 145,\n",
       "  'status': {46: 2, 57: 2, 222: 2, 248: 2, 495: 2, 527: 2},\n",
       "  'node_count': {0: 8, 1: 427, 2: 565},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 146,\n",
       "  'status': {102: 2, 231: 2, 347: 2, 548: 2, 573: 2, 814: 2},\n",
       "  'node_count': {0: 8, 1: 421, 2: 571},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 147,\n",
       "  'status': {479: 2, 500: 2, 958: 1, 975: 2},\n",
       "  'node_count': {0: 7, 1: 419, 2: 574},\n",
       "  'status_delta': {0: -1, 1: -2, 2: 3}},\n",
       " {'iteration': 148,\n",
       "  'status': {194: 2, 316: 2, 367: 2, 533: 2, 546: 2, 735: 2, 895: 2},\n",
       "  'node_count': {0: 7, 1: 412, 2: 581},\n",
       "  'status_delta': {0: 0, 1: -7, 2: 7}},\n",
       " {'iteration': 149,\n",
       "  'status': {213: 2, 273: 2},\n",
       "  'node_count': {0: 7, 1: 410, 2: 583},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 150,\n",
       "  'status': {233: 2, 253: 2, 801: 2, 898: 2},\n",
       "  'node_count': {0: 7, 1: 406, 2: 587},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 151,\n",
       "  'status': {38: 2, 234: 2, 252: 2, 430: 2, 738: 2, 905: 2},\n",
       "  'node_count': {0: 7, 1: 400, 2: 593},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 152,\n",
       "  'status': {309: 2, 957: 2},\n",
       "  'node_count': {0: 7, 1: 398, 2: 595},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 153,\n",
       "  'status': {505: 2, 532: 2, 640: 2, 857: 2, 873: 2},\n",
       "  'node_count': {0: 7, 1: 393, 2: 600},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 154,\n",
       "  'status': {47: 2, 296: 2, 403: 2, 878: 2, 976: 2},\n",
       "  'node_count': {0: 7, 1: 388, 2: 605},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 155,\n",
       "  'status': {343: 2, 828: 2},\n",
       "  'node_count': {0: 7, 1: 386, 2: 607},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 156,\n",
       "  'status': {21: 2, 75: 2, 228: 1, 325: 2, 328: 2, 979: 1},\n",
       "  'node_count': {0: 5, 1: 384, 2: 611},\n",
       "  'status_delta': {0: -2, 1: -2, 2: 4}},\n",
       " {'iteration': 157,\n",
       "  'status': {58: 2, 119: 1, 405: 2, 609: 2, 622: 2},\n",
       "  'node_count': {0: 4, 1: 381, 2: 615},\n",
       "  'status_delta': {0: -1, 1: -3, 2: 4}},\n",
       " {'iteration': 158,\n",
       "  'status': {835: 2, 958: 2},\n",
       "  'node_count': {0: 4, 1: 379, 2: 617},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 159,\n",
       "  'status': {462: 2, 535: 2, 922: 2},\n",
       "  'node_count': {0: 4, 1: 376, 2: 620},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 160,\n",
       "  'status': {53: 2, 177: 2, 246: 2, 570: 2, 689: 2},\n",
       "  'node_count': {0: 4, 1: 371, 2: 625},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 161,\n",
       "  'status': {150: 2, 412: 1},\n",
       "  'node_count': {0: 3, 1: 371, 2: 626},\n",
       "  'status_delta': {0: -1, 1: 0, 2: 1}},\n",
       " {'iteration': 162,\n",
       "  'status': {97: 2, 237: 2},\n",
       "  'node_count': {0: 3, 1: 369, 2: 628},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 163,\n",
       "  'status': {9: 2},\n",
       "  'node_count': {0: 3, 1: 368, 2: 629},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 164,\n",
       "  'status': {271: 2, 279: 2, 515: 2},\n",
       "  'node_count': {0: 3, 1: 365, 2: 632},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 165,\n",
       "  'status': {136: 2, 236: 2, 483: 2, 720: 2, 822: 2, 999: 2},\n",
       "  'node_count': {0: 3, 1: 359, 2: 638},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 166,\n",
       "  'status': {78: 2, 164: 2, 179: 2, 340: 2, 368: 2, 674: 2},\n",
       "  'node_count': {0: 3, 1: 353, 2: 644},\n",
       "  'status_delta': {0: 0, 1: -6, 2: 6}},\n",
       " {'iteration': 167,\n",
       "  'status': {897: 2},\n",
       "  'node_count': {0: 3, 1: 352, 2: 645},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 168,\n",
       "  'status': {35: 2, 411: 2, 706: 2, 886: 2},\n",
       "  'node_count': {0: 3, 1: 348, 2: 649},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 169,\n",
       "  'status': {85: 2, 90: 2, 206: 2, 447: 2, 454: 2, 562: 2, 821: 2},\n",
       "  'node_count': {0: 3, 1: 341, 2: 656},\n",
       "  'status_delta': {0: 0, 1: -7, 2: 7}},\n",
       " {'iteration': 170,\n",
       "  'status': {793: 2, 947: 2},\n",
       "  'node_count': {0: 3, 1: 339, 2: 658},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 171,\n",
       "  'status': {299: 2, 635: 2, 643: 2, 664: 2, 783: 2},\n",
       "  'node_count': {0: 3, 1: 334, 2: 663},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 172,\n",
       "  'status': {665: 2},\n",
       "  'node_count': {0: 3, 1: 333, 2: 664},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 173,\n",
       "  'status': {121: 1, 633: 2, 816: 2},\n",
       "  'node_count': {0: 2, 1: 332, 2: 666},\n",
       "  'status_delta': {0: -1, 1: -1, 2: 2}},\n",
       " {'iteration': 174,\n",
       "  'status': {230: 2, 717: 2, 852: 2},\n",
       "  'node_count': {0: 2, 1: 329, 2: 669},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 175,\n",
       "  'status': {212: 2, 555: 2, 688: 2},\n",
       "  'node_count': {0: 2, 1: 326, 2: 672},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 176,\n",
       "  'status': {632: 2, 712: 2, 884: 2},\n",
       "  'node_count': {0: 2, 1: 323, 2: 675},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 177,\n",
       "  'status': {144: 2},\n",
       "  'node_count': {0: 2, 1: 322, 2: 676},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 178,\n",
       "  'status': {61: 2, 170: 2},\n",
       "  'node_count': {0: 2, 1: 320, 2: 678},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 179,\n",
       "  'status': {174: 2, 200: 2, 815: 2, 929: 2, 969: 2},\n",
       "  'node_count': {0: 2, 1: 315, 2: 683},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 180,\n",
       "  'status': {185: 2, 550: 2},\n",
       "  'node_count': {0: 2, 1: 313, 2: 685},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 181,\n",
       "  'status': {116: 2, 187: 2, 536: 2, 692: 2},\n",
       "  'node_count': {0: 2, 1: 309, 2: 689},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 182,\n",
       "  'status': {94: 2, 806: 2, 858: 2, 962: 2, 977: 2},\n",
       "  'node_count': {0: 2, 1: 304, 2: 694},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 183,\n",
       "  'status': {12: 2, 304: 2, 597: 2, 693: 2},\n",
       "  'node_count': {0: 2, 1: 300, 2: 698},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 184,\n",
       "  'status': {6: 2, 220: 2, 464: 2, 918: 2},\n",
       "  'node_count': {0: 2, 1: 296, 2: 702},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 185,\n",
       "  'status': {241: 2, 419: 2, 826: 2, 941: 2},\n",
       "  'node_count': {0: 2, 1: 292, 2: 706},\n",
       "  'status_delta': {0: 0, 1: -4, 2: 4}},\n",
       " {'iteration': 186,\n",
       "  'status': {498: 2, 559: 2, 784: 2},\n",
       "  'node_count': {0: 2, 1: 289, 2: 709},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 187,\n",
       "  'status': {267: 2},\n",
       "  'node_count': {0: 2, 1: 288, 2: 710},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 188,\n",
       "  'status': {219: 2, 398: 2},\n",
       "  'node_count': {0: 2, 1: 286, 2: 712},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 189,\n",
       "  'status': {374: 2, 499: 2},\n",
       "  'node_count': {0: 2, 1: 284, 2: 714},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 190,\n",
       "  'status': {208: 2, 429: 2, 625: 2, 657: 2, 967: 2},\n",
       "  'node_count': {0: 2, 1: 279, 2: 719},\n",
       "  'status_delta': {0: 0, 1: -5, 2: 5}},\n",
       " {'iteration': 191,\n",
       "  'status': {133: 2, 437: 2, 567: 2},\n",
       "  'node_count': {0: 2, 1: 276, 2: 722},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}},\n",
       " {'iteration': 192,\n",
       "  'status': {103: 2},\n",
       "  'node_count': {0: 2, 1: 275, 2: 723},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 193,\n",
       "  'status': {},\n",
       "  'node_count': {0: 2, 1: 275, 2: 723},\n",
       "  'status_delta': {0: 0, 1: 0, 2: 0}},\n",
       " {'iteration': 194,\n",
       "  'status': {274: 2, 765: 2},\n",
       "  'node_count': {0: 2, 1: 273, 2: 725},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 195,\n",
       "  'status': {381: 2, 795: 2},\n",
       "  'node_count': {0: 2, 1: 271, 2: 727},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 196,\n",
       "  'status': {129: 2, 870: 2},\n",
       "  'node_count': {0: 2, 1: 269, 2: 729},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 197,\n",
       "  'status': {44: 2, 84: 2},\n",
       "  'node_count': {0: 2, 1: 267, 2: 731},\n",
       "  'status_delta': {0: 0, 1: -2, 2: 2}},\n",
       " {'iteration': 198,\n",
       "  'status': {349: 2},\n",
       "  'node_count': {0: 2, 1: 266, 2: 732},\n",
       "  'status_delta': {0: 0, 1: -1, 2: 1}},\n",
       " {'iteration': 199,\n",
       "  'status': {31: 2, 523: 2, 578: 2},\n",
       "  'node_count': {0: 2, 1: 263, 2: 735},\n",
       "  'status_delta': {0: 0, 1: -3, 2: 3}}]"
      ]
     },
     "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",
       "     988,\n",
       "     987,\n",
       "     985,\n",
       "     985,\n",
       "     985,\n",
       "     985,\n",
       "     984,\n",
       "     983,\n",
       "     978,\n",
       "     976,\n",
       "     976,\n",
       "     974,\n",
       "     973,\n",
       "     971,\n",
       "     971,\n",
       "     968,\n",
       "     965,\n",
       "     963,\n",
       "     960,\n",
       "     960,\n",
       "     958,\n",
       "     956,\n",
       "     955,\n",
       "     948,\n",
       "     942,\n",
       "     932,\n",
       "     925,\n",
       "     918,\n",
       "     912,\n",
       "     903,\n",
       "     896,\n",
       "     892,\n",
       "     884,\n",
       "     869,\n",
       "     860,\n",
       "     847,\n",
       "     839,\n",
       "     830,\n",
       "     822,\n",
       "     813,\n",
       "     806,\n",
       "     796,\n",
       "     784,\n",
       "     772,\n",
       "     757,\n",
       "     743,\n",
       "     724,\n",
       "     708,\n",
       "     689,\n",
       "     678,\n",
       "     659,\n",
       "     645,\n",
       "     628,\n",
       "     611,\n",
       "     597,\n",
       "     584,\n",
       "     565,\n",
       "     548,\n",
       "     536,\n",
       "     509,\n",
       "     491,\n",
       "     474,\n",
       "     448,\n",
       "     422,\n",
       "     400,\n",
       "     379,\n",
       "     361,\n",
       "     345,\n",
       "     323,\n",
       "     309,\n",
       "     301,\n",
       "     279,\n",
       "     256,\n",
       "     245,\n",
       "     227,\n",
       "     206,\n",
       "     191,\n",
       "     180,\n",
       "     166,\n",
       "     152,\n",
       "     137,\n",
       "     128,\n",
       "     122,\n",
       "     120,\n",
       "     110,\n",
       "     105,\n",
       "     101,\n",
       "     96,\n",
       "     91,\n",
       "     85,\n",
       "     83,\n",
       "     79,\n",
       "     76,\n",
       "     74,\n",
       "     70,\n",
       "     66,\n",
       "     62,\n",
       "     59,\n",
       "     55,\n",
       "     50,\n",
       "     49,\n",
       "     46,\n",
       "     45,\n",
       "     40,\n",
       "     37,\n",
       "     37,\n",
       "     35,\n",
       "     35,\n",
       "     34,\n",
       "     33,\n",
       "     31,\n",
       "     28,\n",
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       "     26,\n",
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       "     18,\n",
       "     14,\n",
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       "     12,\n",
       "     11,\n",
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       "     11,\n",
       "     10,\n",
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       "     10,\n",
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       "     10,\n",
       "     10,\n",
       "     10,\n",
       "     10,\n",
       "     8,\n",
       "     8,\n",
       "     8,\n",
       "     8,\n",
       "     7,\n",
       "     7,\n",
       "     7,\n",
       "     7,\n",
       "     7,\n",
       "     7,\n",
       "     7,\n",
       "     7,\n",
       "     7,\n",
       "     5,\n",
       "     4,\n",
       "     4,\n",
       "     4,\n",
       "     4,\n",
       "     3,\n",
       "     3,\n",
       "     3,\n",
       "     3,\n",
       "     3,\n",
       "     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",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2],\n",
       "    1: [10,\n",
       "     10,\n",
       "     11,\n",
       "     12,\n",
       "     14,\n",
       "     14,\n",
       "     14,\n",
       "     14,\n",
       "     15,\n",
       "     16,\n",
       "     21,\n",
       "     22,\n",
       "     22,\n",
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       "     25,\n",
       "     27,\n",
       "     27,\n",
       "     30,\n",
       "     32,\n",
       "     34,\n",
       "     37,\n",
       "     36,\n",
       "     38,\n",
       "     40,\n",
       "     41,\n",
       "     48,\n",
       "     53,\n",
       "     63,\n",
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       "     94,\n",
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       "     106,\n",
       "     120,\n",
       "     129,\n",
       "     140,\n",
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       "     154,\n",
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       "     9,\n",
       "     6,\n",
       "     9,\n",
       "     4,\n",
       "     2,\n",
       "     6,\n",
       "     6,\n",
       "     7,\n",
       "     3,\n",
       "     2,\n",
       "     3,\n",
       "     3,\n",
       "     6,\n",
       "     12,\n",
       "     6,\n",
       "     10,\n",
       "     6,\n",
       "     6,\n",
       "     6,\n",
       "     5,\n",
       "     6,\n",
       "     3,\n",
       "     6,\n",
       "     4,\n",
       "     6,\n",
       "     4,\n",
       "     4,\n",
       "     5,\n",
       "     8,\n",
       "     5,\n",
       "     2,\n",
       "     5,\n",
       "     4,\n",
       "     5,\n",
       "     9,\n",
       "     3,\n",
       "     6,\n",
       "     7,\n",
       "     4,\n",
       "     3,\n",
       "     3,\n",
       "     6,\n",
       "     6,\n",
       "     6,\n",
       "     3,\n",
       "     7,\n",
       "     2,\n",
       "     4,\n",
       "     6,\n",
       "     2,\n",
       "     5,\n",
       "     5,\n",
       "     2,\n",
       "     4,\n",
       "     4,\n",
       "     2,\n",
       "     3,\n",
       "     5,\n",
       "     1,\n",
       "     2,\n",
       "     1,\n",
       "     3,\n",
       "     6,\n",
       "     6,\n",
       "     1,\n",
       "     4,\n",
       "     7,\n",
       "     2,\n",
       "     5,\n",
       "     1,\n",
       "     2,\n",
       "     3,\n",
       "     3,\n",
       "     3,\n",
       "     1,\n",
       "     2,\n",
       "     5,\n",
       "     2,\n",
       "     4,\n",
       "     5,\n",
       "     4,\n",
       "     4,\n",
       "     4,\n",
       "     3,\n",
       "     1,\n",
       "     2,\n",
       "     2,\n",
       "     5,\n",
       "     3,\n",
       "     1,\n",
       "     0,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     2,\n",
       "     1,\n",
       "     3]}}}]"
      ]
     },
     "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": {},
     "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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QXWff2sI6anxFRI6IyOMi8h/t9mER+ZKIPGP/D23kdVNcWLQ6RoRWkxdF6r3neyAiRKmGL0WKSxadEtQxwBeRlye2vR1Vxnwt2VBEssAAMHFOPVyKAPgpY8x1KDn+iIhcD7wf+AdjzNXAP9jvKS5SGNNMUk5SCiN1nsj6HkKaSDZFiksZnRLU36B2pY+JyPeLyI+hnn0Af9HS9mbARx0nNgzGmDFjzHfs5wXgCLAXeAvwCdvsE8BbN/K6KS4sWoNznQ3K/c9lvFTFlyLFJY5Mh+1/C/gB4GrgT+02AT5nbU5JOMeJr3GeICKHgBcD9wE7jTFjoCQmIjuWOea9wHsBdu7cyd13332+upfiHBCRoeIPNuxQU6PzZKlR8/qoS57JqETgdSMm4OzR2U3ta4oUKc4PpNM0MiJyAPgAGuc0D/wV8JvGmFqiTRZ4HHWq+D7r1behEJFe4KvArxljPisis8aYwcT+GWPMinaoW2+91dx///0b3bUUG4BiJeToeLHhpbdnuMCuoTwnzpaZnK+xd6TA5HyNIDTcfLjVLJoiRYqtABF5wBhz63qP71SCwhhzAvg3q7SpA9est1OrwRLgZ4D/Y4z5rN08ISK7rfS0Gzhzvq6f4vyj1YsvStigAHIZsTYoVQeKyNrPbQzTi3UKWZ+egr+BvU6RIsVG4qJLFis6E30EOGKM+e3Ers8D77Kf3wV87kL3LcXGYYkXX6O6rn7P+B6+760rJ18tMEzMVDlxttXpNEWKFFsJHUtQDiKyE7gTDd7tNsb88kZ1ahW8Angn8KiIPGS3/TzwG8CnROQ9qGPG91+g/qQ4D2j14nPeeo6Msr7g2+VVLTBkOhCEakGk3oAb1dkUKVKcF3RMUDYw93dQNV/y+F9OtBkEjqIxU4eNMaPn2M8GjDHfQB0z2uF1G3WdFJuLpV58us2p+jK+4Hv6GARhhDqMrg31ILKSWEpRKVJsZXSazTyDOkW8F6gBXwaqre2MMbPAH9jz/9Nz72aKyw3JTEcijqBUkhJRcvIsQXWa7qgeGqJEMtoUKVJsTXRqg3oPqtZ7CrjRGPMGYG6Ztp+y/9+8vq6luJzhzErO+cFgCC2peDaLRCxBdUhQgbHJaNNiiClSbGV0SlDvRN/rHzXGPL9K24fR1EhpTagUHcNYBvHEpjQyVooCPEta/rolqKhxjTQTRYoUWxedEtQNKOncvVpDY0wIzALDnXcrxaWCuWKdYqXzooKu3IbnqTu5sz8ZS1pAw0kiCDtLyJcktGQuv4VywHw5rS+VIsVWQacEVQAqlnzWgh6g0uE1UlwiCMKIU9MVjp8pdXysE2wcGRmDVfGZJglKBMIOJKgoMk2E5tzXjTGcmqpwbKKUqv1SpNgi6JSgxoCetVTJFZGXooS2miowxSWKIDREkdp8OoVBpSVHQg0Vn6HhHOF7giB0ouGrh83OEc4rMIhUQgtD01QcMUWKFJuHTgnqbvt/xUwSIuIBv46aDL7UebdSXExoLcnuEFlHhCixf63SiWvmJ1R8LnbJSVWeI68OCKUeRE3tnTAVhKbhmNHJ+VKkSHH+0ClB/Td0zvkFEfm+dg1E5DrUFf27UFf03z2nHqbY0jDG8PzZCifOLtXkamkMW/k2MEwv1HnmdIlqfXWbUZQgKERUxWcZryFB2aKF9cDw7FiRM3NLIh6WoJ4gItdHUIJyfU0lqBQptgY6IihjzOPAjwO9wF+IyHPAEICI/LmIPAE8BrwBJbL32dx9KS5RRAZK1YCphdoSZwXn1AA66RerAYsVbbsaHFk4J4nIxkBFxjQ5SYgI9TCiVA0Zm66uKqFpkG7SSSImKMdLndi0UqRIcf7QcS4+Y8wH0VIao8BhIIdmdvgnwAvs51HgrcaYTyx3nhSXBoKETafSIhmFURxsG4RRQ522Fq++WMWngbrqxafbMk02qLgfYWSo1FaWzpwNyjlauHOGUexyngpQKVJsDawrF58x5nMi8gU0aPflwG6U7CaAb6GVbVN/3csAQRg1JvRKLaS3ED9SSduTIxBoH7dUrUdU6iH9XRnEqvQgJqOGig/w/WYvviTminW68sunPXIxUNmMUK2bhhdf0nmi08DfFClSnB+sO1msMSZCUx19eeO6k+Jig3rq6YRerbVKUM0qvgZBBdGSEhkTs1Vmi3X2betiuDfbKLbhSMgQE1TGU8FfRJPG1gJhuDfD1EKduXLIrlX6C1DIeVTrsbovDJeq/VKkSLG5uOjKbaTYWggSJFQJltqgGu0SRBa2xCK5/UGosUgmkSfP8wRPBGNMg1wydlklIuzb1sW+kTw7B/NkfKFaD1e0QzXKdViScyq+epvYqBQpUmwuUoJKcU4Iwth206oaS36tB81phYrVFmnLxNLVbDFoEIknsb3IkYgjF4DuvM/2gTz5rEc+6xGEZkUvwYbq0Hc2qOYyHvp55XtOkSLFhcGyKj4RObpB1zDGmCs36Fwp1olyLeTkVIWdg3n6u9at2QWgVA05NV1h12C+iZRaCSopQdWCqClAtlQJGOzJJtrG+05NV+ixdiRPQBopjVypjaV9EhF6ChmKlZDZUsDOrMfzZytkfWHvSKHRzklXjqBcdopk38PoHBjqxL1w+kF4yb+GXM/6z5MiRYoVbVCHVjnWsHxdpuS+VF+yBVCshJQqIRMz1XMmqGIlpFgJGZ+pksvE9qAlElSCoFozOJQS9iqXZ09EyGWEaj2iK2cJyvPIZzwWiAkv67cX/AtZ3V6qhNR7DaVKQDUw7BnOW8cL7UMyE3oUqcTU7Hq+zoEBmHwazhyBM0/CvlvO4UQpUqRYaaZ67TLbr0QDdgvAnwFfBU6hhLQbeA3wz9EcfD+FFi5MsckIrZPCRgSh1kPNxlALokbQbMYT6qEhDCN8SyDJSwVhhKDk4IlWwXWIbGJYEchl1HmhZu1ZnsBIX46phRr10NWCat+vjK/2qiDUlEWRUfKr1SPyOb+RgFb7EbuvJz0RXe2pdSOwwcLV+XM4SYoUKWAFgjLGfLV1m4jsRes8nQW+2xjzXJtDPy4ivwL8NZruKF1GbgE46WYjglBjbzxDxrdSTcajHoaUahFRFNLXnWm0E1Epxfcgl9HSGU1OCVEcm9RQvdl+egJdeY/Bniwzi3W1SXntGSrj2+SxkaGeiGsq1ZSgIgMY7Y8nmscvMtbRw/UVObcSHKENQg7SHMkpUpwrOnWS+K/ANuAHlyEnAIwxR9F8fTvtMSk2GWEbZ4D1wpFdZFRiEhG6cvoonZysMDpZYXqh3lDJeQn1Wi7jkfWFMDTUrZTUKKPhJZwXkpkkRNg+kEMkrgHVDhlPpbPQJn11POOcJoxR53UnPWHd110WCRHRbecyRE6CqqcElSLFuaJTY8RdwKIx5p7VGhpj7hGRReC719WzFBuKJEG1xiB1iibHiMiQ8aRhi6oFEWFkmFmsxyQjWkTMZSLP+EKpGlKuhmQznmacMAbf85oCcyHOu9eT9zm8s3vF2k8Z30M8Jb+kd2GDoGw7QawERcNBwhgluMiYRgxWxzAmlaBSpNhAdCpBbWeNpCY6A/r2mBSbjKDFo269MKbZjhWEBt8TspagHBEmvfYcGbo8ennrzOBczUMTZynPNOxXltyIzzHYk2Vbf37ZvvmeJpANI0MtjK/v7tcYrJMEjQwUxsSu8tmMnJsNKqzHB6cElSLFOWM99aAKIvK9a2j7PUCXPSbFJsIY02R7Wks28eWQtNc4+L5mdPASUlk9UNJpTUeU9b2GtFWqaTasWMUnDTtSLEGtvW8iQiajFytX43x/TuJzLuYNG5S9jiNcR5zrVvGFVXC1PKP6Ok+yRXDqAfj2/4ZacbN7kuIyRqcE9VnUW++jIvLq5RqJyCuBj6Jalc+sv3spNgIuE7hD9RwkqKS9xk8kbe3O+2QzQl9XhlzGs3n4YscFh4yvROB5Qq1ubVmJUu6ZBNGJyLIOEcvBqQhd4lqRmIBcfSpB+6RZ0mPyzmc9RIRovSq+oBYTVHiRE9TEEzD+qP6lSLFJ6NQG9cvAW1BX86+IyL2om/lpu38P8GrgDvT9fwb4lY3paor1IpkTD5pdvDuFq5uUy8Q1mpyK7/r9vRhjePp0iVqgEovnedaOZLNA+Jrxwfek4cmnWc+1lHsmIXGtx0rmYqScg0bGk0afjQGMQawzhaszlZSg1C61zsEJq7GKL7rIcyXXS/q/eHZz+5HiskZHBGWMmReRVwF/CLweJaKXtTRz88qXgHcZY9KAkE1G2BIkW69HVOuasLWQWz7zdzs4CapgpaRaQCJYVz3ushkPqiFRBF5WmtR0zosv4wnlWmRjkFx596QEZZZkKl8LXKZzd7+5jEe9GlqbWNKLzzpJENvNclmv4aa+LgRJFd8lQlCVuc3tR4rLGh2nFDDGjANvtGq8twMvIXaEOAt8B/j0Wjz9UlwYBInKtiJQCw3PnylTDyOu39/bkUefq5vke0LO8yhWwgZBOWQTruKeqOOCQ8YWGSzkPMq1kFItjLOU+54lKBp97RTu2k6yy2U9itWQSj1q3KfaoABbLt5dv2Dvw6zbSaIGkVPxXcQEFYWxm3xtcXP7kuKyxrmU2/gG8I0N7MuaISIfBd4MnDHG3Gi3DaOZLQ4Bx4F/ZoyZ2Yz+bTUoqcTVacvV0Gb+jqgk0gqtBS5lUcYXdgzk8DwY6m1+jLIJwvJEGq7iyX0NR4lK1NCK+b5zdPCgHiHrUPI1ynMYmrwLK7WIQs6zkpXElXobLu4JBw1Ynyt+UI3Z7WJ2kqiXY0kwjedKsYm4WLOZfxyNyUri/WihxKuBf7DfUxDboHJ+rMJynm2rVaBthYtDymWEQs5n/7auhmu4g5NiQL3w3O5kmiLnMVeuJiQoqwt0jg7rkaCSTha+L42+VOpJt3caThLONuUCgl1Q8bpqQoX1S0PFVy/H/XeBxylSbALOiaBEZIeI3CUi77R/d4nIjo3q3HIwxnwNmG7Z/BbAlZj/BPDW892PzUAYGY6fKTO9sPYVepiI83GTtyOFzgkqttcsh+R1Ml4sQXlenKYol1HniWoQFw10gpfLJrEeL4mkitBJUCJQswTlshi7vICg3n1+o49ufOwJT9wLD/4fCAOq9YjjZ8oslJchn0vFSaJejFWVQRlqJXjgE3D6oc3tV4rLDutS8Vn7068Cr1pm/9eAX7jAdqidxpgxAGPM2HJEKSLvBd4LsHPnTu6+++4L18MNQChZqv4AEgV0RbNrOqbm9VKXApPhImGmtymJ69nRIk9RXvP1K/4wIR4zo9P4tCe3iAwVfxADTI0ugJelJgU8EzBzfNa28an4Q2BCm2nCb5zT9deL6kwe7cxIb/AoZ4ZVDRmWGJWAqtePF1V5noCa14Mfljj1dImyP4JTJHpRlTPPLTTub3Z0Go+IfaOfJ1ObZWw8y0LPlQSSx4uqFMzCkmsPTT/EwIz2N/QDRi+yZ8uhu3iCHRNjYCICP2L6S3/OjolvUH3iMcb2r+2ZS5FiI9AxQYnI+4DfQ6UvAQJgyu4esed8DXC3iPwHY8yHN6ivGwJjzB8AfwBw6623mjvvvHNzO9QhZot1nj9TJjJw8+G+puDY5TA6WebsXI29IwXmigGLFV3di0BPIcM1e9ZWt8gYw1OnipSqITcdun7Zshf1IOKpU0VqQcS+kS4Qw8nJCoWcz/X7exvnOnKySLUe0Z1XZ4vr919PIedxZq66pP1a4c5bqYWM9OcY6ctydLxE1vfYPpBjdLJMf3eWK3d18/TpIovlAE+EnoLP1Xt6ODpeYrZY55o9L6A378HX7oP5gD1X7eTZwjVUahG5rMeNB/qWXvzpKjxpk/f7Wa68yJ6tBk4+AA8/qhKh58NN1wKPgOdx7cV6TykuSnSk4hORFwMfRFMY3QO8Cegzxuw2xuwG+lDb0D22zQftMRcCEyKy2/ZzN3DmAl33gsLljTPGrFk959Ry2UycLTzre43SFGuFq5vkebIsOYGq2Zy7dyYTOy4k87yKCIWs11QsMGuXSxnPa0pH1AlEYrtTLqFqNEENmRvVOCjb1vXHeSXq8ZBbOEFQWYTaQiPgdnZ2nrqNH1t2zJy9xsuoim+jSscvnoVSq0bbYmECKjaSo1aC6WPnft16KVZRRiEUz6hPHJSeAAAgAElEQVSHYr28ed6JYQBTz13c3pEpOkanNqifssd8CrjTGPMlY0zDimqMqRpj/g6VoP4cJamf3KjOroLPA++yn98FfO4CXfeCIpkEtVhZ28vq7ClZ3yNrUwH1dfl4XmcEVbd1kzLeysyhhQe9xjV9T0tbtGYid3asMNIgXd85SWScw8Kau9YER8KFjKcVeUXoGr+XwiOfID/9RCMuK+ml5wg1Wxpn5LlPk3nisxoDFNaIDJSLC0RGvRCjyLRPWusSxXoZmzh2Azz5yrPwwMfhW/9TCSKJyrzu+84f6vdn/x4e+JiS1LmgXlJnD1fKeHY03re4Seu+09+BB/8ITnxzc66fYlPQKUG9BrUz/4Qxy0eL2H0/btveue7eLQMR+RPgW8C1InJSRN4D/AbwBhF5BniD/X7JIZmotVRdmwTVCETNeGzvz7G9P8ee4XyjNIVZ44q7XNVChSs5SDjsHMwx0p+jO+fR15Vhx2CO7QO5pjZ5S2JBaJqCeXsLPjsHc2zrb26/VuwYyDPSn6OvO9MIyM2Uz2Kqi3RNPtqQqpIE6KSuTHVGiWbq2ZigEKgVyfqa0gmWyWfYkKCs2/5G5LEbvQ/K05rR4dkvN+9bnIDqAkw9o5LFwpgS2tknzu2aLkjXy8TXcVjYpNSaxbNQmoKJxzfn+ik2BZ3aoLYDs84ZYSUYY06LyCznIZu5MeZfLrPrdRt9ra2GpMRTWUPS1yhRTj3rg+f57N/eBbjqtUGj4uxqKNVCgtA0SquvhN5Cht5C/HjtGS4saZPPqppRA3oTruki7B5a2n6t6Cn49BT0Hg2RWkrrRQyQK41hErn+HJzK0q8XCUyE1MuqWgtrREbw6iX1CrREVq6F9BRaXp/QEpTYsTzXjOaVeRh7OCa6Y1+Bq18PGZvRvThpY68MzJ2Esg37K5+jI0PNElQmr2SdlASTZHUh4TJapFniLyt0SlDzwKCI9BhjVlweikgP0A+kwbIbCEdQIjQK/q0E52KedPF2cOq+8goEtVAOmF6os3s4T8lmCB/sWXd8dxNyGU2DFIWsydmjI0QRPP3XeLk+JPNivNoiGPDri5jaDLC7WYKyY+EFRcTYEvBnj0AUEBkPqZc0rspKfdVaBMe+DmeeAAT23qLJYgH8rP53kkg7VBfgyBfh4Mth6CA892WYfEbPtf822PNiGP22ks3wYega1gzjR++Ga96k5yhNxqQ49mCsYjzX9ESu3z3btZ/JoOONzs138gFYOA3Xfu/KqetTgros0amK7zuoXenH1tD2P9q2D3TaqRTtoQ4FcdLVeri6ei6IXJ67pQTgpIblnC0iYxifqTK1UOPYRJkgiMj4Qm+hs/x9yyGX8Rp2pw6Tlq+Os0fg1AN4j38WMSFeUG7kKM9OHgGanTByjqDquu4ySMP2EhnBC5olqGo9gtF7VeU08Rg89pmYLPLWw2+lyfTMk5op/NFPqW3pxH3xuZ6w5tPZ5zXV0J5b4cDL1CZ0+sH4HMXJ+PP4Y3HWh1ZbVadwx/fusveRUF1WNtDN3Bg4/jV45ksws4LdzBglymRfUlwW6HRa+ANUYfIrIvKrIjLQ2kBEdovIb6OZz409JsUGwJXN8BIecKt58tUDlaDaOTY0TbZtMFcMGqU5ipWAIDLks37HJTCWg+dJQ124oRKUMfD8N6E4iZiIXHkCopDIWEeIqaeWXNNJRn5QbJzCTYZRpgsvKJORSIOQPSGoV1T15fnQNaRE4giiYF+L2goSVG1RA2IXxmH2hAbEFgYg1xsf51R7I4ehe8TuW4zvsTQVn694NlFu/hwIypj4+L6d8XY/C34OqhuYm69egpq91tRzy7erxt6UqQR1eaGjmcYY81ngk/a4nwPGReReEfmMiHxRRB4FjqHSkwf8oTHmLza605cbwshQsfYfY9TjrJDTn25qoUa5FhfnK9fCpjQ9Dc87vw1B2SwLpWrIYiVoSGPVesR8KWBqoUatHjUm77XanzpBLhtnmVgXKvOx7cXh7FM68btrLI6CiYjEx3gZ/KJWh3H85Mp8AHh1nYC1NIeOa5jVOLF8OG/d8yGqLKpjQq4HBvfriYKKSjnLSVC1IhQtqdQWY0+/578J1aKq+nK9elxY1wlcRFVt+X4liFpRj6vMxYSU7db/ThW3kmqxNB1LI+0QVNTFPJNT4nWOEr07wM9vbAHDYkJFOTe6fLvKXNzO2dwcFsY7l6qiUKXjdZdOPg+YO5VKh22wnmnh3cDPAwtAHngp8Da0gu4NQM7uez/wng3p5WWOs3M1nh3TAFKXlse5cZ+Zq/H0KQ1MLVZCjk2UGJ2MV9D1QJ0k2hOUxgmVayHPjZWYXqzbVEoljp8pNTKVX7W7u5Fvb7B7Y+xPDvlzkaCMUdfje38/tv+A2mqKZxuTa3bhJJiIatduomw3fm0eKvNxzj6bhw8SKj7fehBmCtTzI9rX2kyjcnBUXVRCyHSpdANKDE7SgKUE9dhn4FsfVFJNTvRnn9RzDV8JBUtuc6csURTUWSGTg0K/Tq7lWZWeggr0bINsV3yuTMEme22j+i3Pwv0fgfs/uvyY1krxdXM9CYLaqX1IpkE6V5Qm4zFKSoOtqM7HElQUxhLemSPwjx+BJ/+qs+uevB/u/xic/MfO+3w+MHtCwwWe+Pxm92TLoWOCMorfQIsT/hM05dGH7d+v2m17jDG/tZIreoq1o1qPqAURZ+ZqjczbQ71ZhnqzZHyPMDLMFQPKtZBaYJhaqFOuaoyUKwrYTvLpyfts68+Sz+o5phfqVOuRlms3UMipu3dXzufg9gIjfVn6NshBwmGoJ8tIf45t/dnOD65a6WlhHCaf1m3GxJ5m+24DILt4EkxIkO0h6Nqpgbozx2MJyg1NUMMLaxjxiDJWKsn3EmRUgsrWZuIg5OoiUVCDXDd0b9O29XIzQSUzgdeK6mlXmlJ1VlKKqZdV8tr9QiUFgNnjOiknycepDhfGmgmqJ+Eo2zWkGdXbSUkn7lUJauq5ZkJPol5SEsjk9dqOoHp2QGFQx7e4Apl0guJUPEYr2bYqc839LU1b+9U39Lcef7iz6y5OKDmOPbh62wuB+dN6/5NPbnZPthzOpdxGCfhL+5fiPMLFMdWDCM/TjOC5jMfhnd2cnasxOlmmVFNpx6n3Tk5VuXpPppH9IN+GoESEvSNdDPaEPDumqYmqdU3e2pX3m1IgDfRkGehZB4msgmzG4+D2rtUbtkNpKlaLjD8Cu260tp2yTrAjV8HovfjVGerGI/B6iLI5pAzMHMfrfgGQcCCpLSImxGS7Cbq2QWUak+0h8HvIAIX6jAYh+x5BUCQIAnKZLiUJL6tSkJ+LvfiSEtTcyXgyXhyPJahMXu+hZ8Tamfri9lGgUpODI6jFM0okQVVJw8toJbZ8r/ZlYUwn4OSxlXkdI3fdhXEYOrB0TF0WiUy/qg6TEpSTchbHoW8DckKXJgGj16guKgll2sS+VeZ0bF0dlcqsZvlwatzKnKrr1qondh6B5+pMslFw4QJbpT9bCBu7HE5xXpAMzo0i0+SR52KJWh0dFsoBlVpI3bqlr1TzKS7BbqgFEWFkmkpmdISFCTj+dbj6DfGEuhJK0/DsP8CVr9XJtRMUp9S5AGDmuN02GU/cXUPg5xAixBjCXA9htl8lqPmTyD6d8wpzz8DsMdj1QjABYaYfUxiGCkS5XgLpJovBr2i6oUxGiOpFwiCAXJdKUNkCVOtKVO0IavZE7OCwMB5LTQP7NSjYecw5CWr+lJWguuNzOMJZnIhtMgMHIHTOGYNqw3JjM3yF/h7Pfsna6hJSyuLECgRVtxJUC0HNn9TPT34RTt2vn7ddC4deAdNH4dR34Nrv0WOf/hsYPAC7blKPxcmn4Zq7mgnIeSF2Dyvpzo3CyJVL++RSOWW6tH+VWVWLOpf3KNTj+3fFx5SmtQ/J32DwAFz1epW8oZkQaiVtv/cWtQVuNMIAjnxe++7n4Ko3xE4ojvidanajQy4uYqQEtcVhjFlSgjxZwVbJBSspKUmN9OWYWqgxMVsjjCI8kYYbdTuoRCbUKpF1lliZ0FbE8/eoKsnLwA1rqHgy+m21BVTn4bYOTZalyViCKp7VScCpvvp26oTu5xDRSSrK9BPkR3SSLM8quYc1Bka/BDUNdPVNgMl0UyzsJQLCnl2EtR7NSGEJJusLYVAkNMREmO1StdpyNqjZE3F+u4UxnVSzBZX6pp+D7SrNkbcEs3hGVXVNKr5BQKzHnp1chw8rWYkPA3tjgivbSW/8UQ32DWzi18ED2pfF2ImkCZV57We2W4mmZ5sSYO9O6N9r+z8eSy+nH1IifPKvlGi7hvT7yfvh2Nfgrt/Q2K0zT6gq8uAdely9rITtZZTkFs/oIqMtQVmJp3+3qifnTinxZvI6bmMPq5t6kqDOPKH3nrT1nX4I9t4Wqz+TBDX+iNouZ5+HV/5E+7E5F8yd0Gu4nIrGwEveab0xLVFHoY5Jvk0i4ssUKxKUrVx7rjDGmNRZYp0IbcXXJJIElc0Ivu9RqYX4npZMH+rNMLNYZ7ZYx/c1QexKruEiQi7rQyWkbNMn9awn1skYXQWHNVVRrQVzozqRrxQHsxzcCjzbpZPN1LNWgqqo9FYYsBJUGRDCXD/1wjBeJg+VOfq6MlzvP40vdsIaf5SMlyHfXWBy+GYm9l1L/+AQ4TOP6/jZCc0Twasvqtt61wj4GbXRLJ5RgvKyKh05Z4J6OV7texntY75XSeDwnbD7RZZ8UIIRL3YKyCUmq3y/kuvcqLYrDED/Hl1xv+GX1LHheVvkumQ9GyuzSk77boPDr9ZjZ08sH3BbmtR+92zX8972HlVNZrIqXfTtie1FYw/DiW/BfR+y6rdIg40LA0oMQUXzArpJeeyhmKCKk6rSKwxAnyWWds+M81gEGDyoBDV7Qu+pa0ilnbGHrRfgHc3PRq0Ie18C+16qeQqnntU4M5dwNuntODsaLzDOB4pTKqX5OX0/HClVF5ptlcWplKASWE2CejdxjbdO4Y4zXKLefMYYqvWIfNbrvDz4GuGq4YLGDUWRabIneSLks0pQQWTI+h7d+Qy+L2qzkuYS7Mshn0jcKiJ051qOcd5dSbtGK0rTcZxMLREvY4xO3j3b4jx1YN2u7UTpVFBdgyt3tHGu7fFLvvMGXbGPPxJnUxjYpyvsfD+CTnBBvp8o26eecgunoDxL9vQ/QtVOgCaCqE5/T5bTnnCm2k0h8ggzPYjn60Q5cxyfHfj1omabsB58pnuE0Ai+l0P8rEo0zu177qROhs4rrjKnk2S+T+0m3cPx/eX6dJJ0kmFynyXchgQweCBWB7nfJdcLSDypu/+7btLJvDpv+zDffnydA8SgVf9l8nFqJREY2KN/oBLbxOPNbv6VmXiRAPDM38bS3vzpuJ2TdPt36xh62fhZCOvav54RfUbCmvah16rEFk7rPfTvUvWqeEtzBLpnY9dNsPN6XQBNPQtnHo+fkbBuJcusSjgQP7e1kp5TREk5u0rqrXpFJdnl1NrufgcP6Pg46S2pBXDf+3bq/uRvvxZUF/S5yyXUwrWS9qtrqLNzVexzkjzXJmA1gvpjwKzSph32AK9dx3EXFaYX64zPVNkzXGCo9/ysvMLIEBmVmkSgGhny2WYyTCZd7c6rxNSd85gLIoLI0LUGe1I+E1fazWU8/NZyGo/8qapVXvnjzWqnJGafj1UqSdXK1LPwyJ/B3lvh2rsS7UebV4/jj8HhV67c0bGHVJ207SoN8szkdGV/+iGNf3Krz6HD+r/Q35jDw9wgiCA925WgjnxBJ9ee7UoK86fBGArdfQz2ZJlZrDMxW6We6UF8S1D3fZj8jpdRD4q6cLB2s6I/SLmaIxt2MejnlHicSm/qOY1z2nGtjiEoeWXbvPy5Hp0wWYGgQCcwp3JrOr5X78VJB46gnI2rEU/VJuA2CnU8ROLxWwmFAbU/PfXX+kyIF7vAO5w5EucmrMzpJJrvUxtbUFHJsWebEpDr63Nf0SwdN/0z/X3Dmo6LG5uorn0tDCq5ZQrN9rUoiiXIwUN2HK19c/KZuD+gZOH5cXB0YMuKPPzHMPO8/oYjV8JtP7TyWDz+WbXDveI/tpeAHGFuu1rv0xFU0o4Kush79NOq7nzVT8Uq29UQ1m34gMArbKKfKIQHP6nE/7J/v3bCC6oajuBl4I7/sKk2sRUJyhjzrzo5mYiMoPFPbyOWoL6z7t5tcVRq6vU2vVg7bwQV2HRG2YzHzsE8c6V6k4oP4mBXiNV/PYUMc6Vg2TRHrchZR4kwMkslrqAa2x3GH9Ncce0wN5pYgS7GBt+5k/rijT/cTFCuvVPRnVmFoMIAjt+jBv7FCZW2CoMwcjX0bof5MUB0Au6zq/zCAIIh8nKYTB4MSK+drCYe0+te/UadLO0KX7qH2T6QY7ZY1/yDmT5q+14BCw/CwjjZsX/EC32M+I2VaWXkhSwMncQbeiGDvlPxBTohjz+qk+qeW7RchMv60I7o872qMnRwMVagq/hcT0wAu1649HgnpdXLSv5BRb87BxRHcu0Iqmwzued6oWsNDi6gasPStDp7nPiWEo+TZsRTqdSEOk7lGX1+dt6gkpeJ1AbXNaxjUZrU33juhBLHI3+qNqbqgrbLdiuZOMl04IAlt4JO+u55c2rNXK9KYW4cnceklxhf5wmaXFDNnFApvbqgfZx4YmXnhXrF2vXOwNmnYd8tzfuNiaXD7dfpYspJwU6CcjXESlP6HBcndWGzu81v3A7FyXhxUFlQLcHEY/rOlqbUQeOWd6/tXItnoDynWfRL0/EYbgI2JC2AiPSJyAeA59D6T93AEeDtxphlZrOLHy7GqJOaSp0iTOTSG+rNcmhH9xJ1Yj7jNYJOXTmI7rwfa3/WkP0hn/HwvLjQXxOKk7FaZOKx9icwJq4blOuNA0pBX5AoWJomZ9auUPfdphPU3KmVOznxaPPqvDyrBJXJwaFX276e0ck1YxcMhUEEiLLddo4RPBc3VC/rxHXVG3SCdegeoSfvM9AdLzrCK14Lr3k/9O+x2SYigkx3g0zCfD+zV7yV+vA1KsE4O9LofToxDx1Ue0hSBZRrs9LO9VoJyiIZ4wSxCjTfB0OHlh6f74slKBfgmiS9fH8czNsacOtUc6upWZv62wMv/gG44tWabQKsLSen9jHQyfeQXXhMPBYnwR06BHteon3r3RFnZXfST2lavf9E4Ko3NsdlAQwf0m3dw3Zyt7Yup05LkmyPlbRA27paV6UpfQ7rpXjb2SM6bl2DGqqQPHc7zJ2MJaJ2zicupZOfUweSTJe+T0E1tqMOW4l19kT8rnWSOT4Z9Dx7XKXI578Vvy+nH1p7lns3fgBjj6y9D+cB50RQItIlIj+Lpjf6RTR7+VHgncBNNjXSJYtVK6xuAFTFZ1aUgvJZrxEC0mMJqivvNbI/rIWgMr7QO/80/ae+Qr5VGCwlAipnT7Q/QXlGV7GZfPyyOU8v95K4ND2j39YMEE69cuV3KdE4FVArTj+k7Y/erefafXNsF3GT6d5bYhtFITHBWhVfaANvBXTV7dvj99wM2bymK3ISTc92RITtA7kGyecznqrthg4jgAQ1wkysfnGOlgZ0IhJPpZTTD+r/Ay/XiTafsOG1U7l4fqwiyuSXqnjcvfXtbB/349Rg9XKjnlXTOTxPJ+7kqt7BTXKFDggqCSftVeaUiIev0O+922H7tUoQk0/FY3LwFbFU4o4df6TZJlOcVClqcJ/aQxzRZnKx2tJJh9NH42Na7yPpgg8xmZam4zRLO6wn5eTTsaONO/fMMVUPPvTH+iwe/0Z8LpfU1127FS6lU9eA9t95apamEqq/a3VhsTgRk0MnxSGTdr/ZUfViXDyjtsmDr1RJ8L4Pad8f/CN47LPLx10lye7M5tbfWhdBiUhWRH4UJaNfB4aBk8B7gRcYY/6PWWsVvIsUUaJU+fkkqEb+vRV+qVzGI5/1yWW8Ro6+rO/RW/DxPaErv7pHngBDY1+l9/TX6Z9/unln0pBbnm62Gzm4ukX9e+KJd3FCVTbOiB4FOnkdvVtJav409O3W9s69+NjXl57btZ8+pm1v/hc6uYGucEHVX1d+l352RAXQtxvxs4R57ZOIbiPfp9LJ1Vbl2LtLbTr5PlU5oYUTB3uyWpHXOY0M7kcwYEJCP7YhuSrHxhhLUL5KjOVZJb/9t2vDpATVtYzqxE1g7VSAzu40dEX7YzN5vYcoUDuGU9kl4fow3+JYUJxUyaF7nSodN5mbSCfj7dfq98GDqnLt3aE2nrlRlZ723bb02PHHbJjALm2TyauEC81xWYXBmKycF+ApWzihZCX+rsQCQESJ0mHQxjrNHNXnNtcDO27UbfOn9BkvDMa2q9kT+hyeuFefxUc+FS+m5kZjr8t2GTFaid/9vrOjsR115CprXwticiivILUtucZULBHPjartrzSp6tSrvkvJff609n3023ovz3yp/bmKU/obwuYVqLToKA5KRDzgB1FpaT86r02gJPVhY8wy+VMuPbgs4aBSThhGSx0LNgAuBiqzgiee5wlX7uomjKIm9d+B7V3Ug4jCWmKayjMM+CV6CwGZ6Ufh0IvifcUptSM4F9mzT8KexP56WSeH2iLsvatZRVGejr+DvjhBVSeEm94eTxT7blOVxNG7NZjSBXTWirF77gv/uU2o2gPX/V+w/2XNGbcPvEzz2SVVVIP7qd3xEyzM6BiIoBPEK23BZ+ed5Xnw0h9WUrHqQRHh4PYudg2FdOXsqzKwHy+bR2pWxWfh3mdj0EnU/Q7VBbjmu2NppzAQ22aW0+07QmnnRLH/dpVMViKRwf264j95v459voWgnBTXqo5yq/n1Bqp2b4ufkcIg7LwR7vx5/T38DNz+PrWrmFB/p6QE2G09PBfH9RyD++HF79Tf3xGLn4vVdEnpaM9L4Jm/V9VcaTqWYpwnYvIaDsOH9ZmdO6ULgb5d0D2kUkxYB0Ld1jMCiEpnYS2OJZs+pmS69yWx44tblLTCpXRKhhKAOg+FyZCILNSJya6TxLxJya00FUt0+25X8n/1z6jTh7GxVo99Fo59VQPqWxdC7jnI9erzW5rRsdkErHlGFZF3AE+i5TMOoIUI3w9cYYz5vcuJnCDOEu5Qqa8uRRmjmRpahct6m20OjXLtq5Bfxhfy2WYi8j1ZGzkBzJ5A6iUynlGVRRLugd1xnf4ft3rpMNAX/NjXVTLq36uSTX4AEA0WLU42S1xnn7L2gSFVy7mV89AhNQgHFY3onzulK8LSVKwe2X9brJrxfHUzTtrjRDQFT0u6HOnd3iAiSbplt7oEZ7uaV9ko+TfICVRC7NL7ay9BEUtQoK7YB18eH++cFESaJ8wkcitIUJ6npOyvsLZ09rTFCe1Q63XcfTsVX72sq/lWz7dO4RwWAPr26j3274oXAfleVanufclSJ4zksU76yXU3/x4iMWknFyFdg+pNaIwm5HUq5VYbnXvWsl0qZftZXSjUivo96SXpxqHbehi6VFU92zWuCtSe5uxP2S69v3ak4lI6DezT7872OH00znqSTMzrkHRkce/a7Kj+zZ2MiSwMYskt262qvVqp2U7ZPQx7Xwz7boUr7tTFXFjX+LAkgqp1MfdV+nL32QlK00qGyUrM68SqEpSIvBWt7XQDKjHNA78D/LYxZoW8/Zc2gtA0lbWo1sNVg1vnSwGnpitsH8ixvV9tIMVKwImzFQZ6Mm3Lojv1YXaFTBAbgmQqHqfqcavJypyu+g+8TCUgR2BP/X9qHwprutK68rv0wS4MKElUF5v12QBTz9ggyxY7h4h6hI09rC/N6H36Mg0etElR27hUrxGeSIPHznkUPR9v6BAy+RBBth9jNG6sYYNyBOWkg323N0sKhcGYoHLLuOvnV5Cg1gJnT2sUHmzJm1cYUAItntVFwHc+qeofZ6/qNP6mcV470Vbn1YGhE3QNK0G5Cb5V+nFwcTmtLvYH7lC70PijSvDZrqX37SS8XK+NQcsCdox23RS74FPU533wAA0pO6jo+zF8SMc3k1dV4PQx3b7tKn1v3Ji6eL+krc9pC5wEVZpUo2VhME7O65Dvj222IvquuYwgDjuuVwcVp6Uo9Cv5zZ/SPg3sXT4/4aFX2WwfX9X0VK6/pSl7rgFVu4/epwR1+FXtz9MOYw+rt+2Bl639mGWw4rJcRL4NfAa4Ef0lfxM4ZIz5pcuZnKBZxQdaNn01FKshlVrEqamq9c4znJ2vUamHzCy2X23EEtR5JihnKO4e1lXlWS3q13DDLfSrnjzfa2M3qqquKU3pC7z7hUow0OzKXJxUvbqzC1UX9AVop6IavkLztblS46P3tTd4dwhPYmLaiJAOueI11IavoTxyU+P3cYuVCKPSzRWvVUmh9cXuHtYXd/eLWk8bY+dN6la999b1dbB3V7P9pXd38/7hwzpJzp7QSX1hXMto5Hq01Px6B8nz4Jo3qvp3YBmCWQ6ZXLPHYjsPRVAiGrlS+5lE9zDc8DYlpVyPjn3rfQwf1jHddZO2cVJotgtGrlHSyliSyPdB3sZeuWcvCmD4KrWnFQZVUjj9oD7Pu16UyPaeULeVZ6zauBBLtvlelZa06JhKViKxc0zOJv11nrCLZ+OA6GxXnFbr1APaxmkpCoOxxFkvx04k7TC4H7ZdozbBZBaXZCaWAbvQSQZYrwVzo+pN661Re7MCVpOgbkU53gDfBg4C/6vDrAnGGPMD6+ve1kUtjDBG44dq9YjaGgiqZqvTRpHhzGyVvu4MpUqIMe0dLZJ5+NaSDWLdKM/qXyavNo6n/lpXortujB/Ynu26f/CgvpjP32PVCL1w539ukRLsSrQ6H6tbdlwXx9lA+wlMROOkrnkTfOO3VU0w+bS+yH3nIEF5YicrszEZPwb2UbrhHUSlgCA0ZPy4/FJjzXLoFfrXChG47s0rn79nBF763vX3z/N0gp8/pZNE0mkEdPI5eAc8/bfw1E5YZdAAACAASURBVF/pZHb4VXDT96//mg57b9G/9aBnm2Zlbyf9OOy4LlY1t2L/S/VvOfhZeNG/0M/GxJ6cfbvisISuQc1U4SR8VzBy5riO5a6blNiGDsXxeAP79LpTz+gxixOxbXT2hEpCfTvja+R647gniFWqTrXrbFKgTgpTz+j7ue1qDbgFuO/DSlozx2MtRddgszp3983LjwXoPZx5Ql3JnbNRKUF2fbtUFT9/Og6wXg1BTZ1vRDSDxzliLU4S7o1+Tcv3tcIAFxVBzRbrBKFhpC+77IQWWBfznpxPEBhqQewwcWa2ymBvlq6cz4wtAjjSl6WasFNNzNUoVsNGSXXnaFGsRsyVVJrqyfuNEu/tCg424fg39EEST1Vjy61AHU4+oAGPB16mL1G9pBPZ8JWq+nCrKufB517YwQO6anzuK6r+G9izVI3git2VrA1KJE5H5AhqJRWQiPZ/5vnYi6hTlVHL6RoS1LrP0gzfPhduYdFkg9oKGNwPz6O/Q6Y1bgCVRI59XX/3fJ86cmw2nFTdNXj+sxc4iWVhrJnAHTEkJXZnu+reFjsLDO5XCT+sxWpcl25q+qhKEXteHAej77ohPp/L9uGCqBu2Kav660oQ1NknVZsR1VUqdxg8oAQ1ZmthRYHmg3R9zfXou7wSBvfrfDH1rNqtjn/dZlMJdUw83y50TqtDyME7NL7q2b9v7604sF9JLSirBN+6MFoHViOoT5zzFS4y1IKIsekq5VpId95vBL62wgXp9nT5LFQCQjtRzRbrTC7UmSkGXLmrm/EZPZcWBYzwPaG/W5O5zpcCfE/I5zxK1ZBqYJiYrbJQ1pXVWVyMk6y88i9Nw9GvxoF944/B635xeRG7VoJn/k5fzr7dKi1VF3TFM7BPVRsuO/jCRLOKbvCAElhrCp0kRPS8syf0Gt3D+vA6A3SyyN9ycBJWdcG+xPtXbr8CmmxQGzTvuaBmVwoltkFtEYYauUptWMuNc/ewBs8+83eqkmv19NsMDB5U29hKqqmNRN8uzSSfzNbgJDdHGhA/e4OJZ3DkKpvtfXuc/cSRyvP3qHQ29nC8bXtCmmiks0LJ2C0gnIqzd7e+g6ApnxC9XlJyHLDkcuaJ2HY1dKhRYobB/Ss70rhz5HqVnJ7+2zizinixROXufeJRJajZ47bwZZt4L1CHoGpRbXIbgNVSHf3ghlzlIsLUfK1BPrPFeluCMsZQD1Vd1FfIcEZqBJEeU6qG1IOIOnDibLmh1pucrxFFqqo7sL2LwZ4MQagBtdMLmlKnWA2ph1oeo7fgM18OqAXRktRGSzA3qivhvl2AKCmcuj+OvVnSPhH5/sDH9GXJ5OAKGy8xcEA99aaeiW1TzqNnYJ9OZi62qV26HYAb/4kGWNZLqsLIdumLWZ7W1elqTDF4QCfYeil2ulgnNtoGBXFcmktFteUkqN4d8KqfZsVUmtfcpb/rOZD/hmLbVfCa/9Q+w8b5wAverJ6hSXXz4deorao/QVA7b4BX/3Sz3bR/D7z6P1mHGDtHuBCCoKp/1Xld5GVyzeSS64kDzZOS2sGXq/NH/14lBBFVt4kX23cdBg/E5FIY0KS7O2/Qvrzqp+Pzr4RCv17r7BGV+kwI132fPjvbro6vkynEWV5mR60TzOFmG+nUc3D6O7GWxDmEnCPOo2HjIkG9AkGNWhBRrITMWbsC0JBkkggjQ7EaYiKD70Eh5+ETElbLRJGhVI1tUQvloGFDmi0GjUKAPhFDuTrbB3L0FjKqvjOGxdkZS2LC/u1d+J4059ILau2DZGdPqCF28EBs3zjyRRXHk6gu2HQyCY89Fzey43p9yCH2oHruy0pkhf74Jc52xftzPfFKqxXZLs1JdvhVMLRfXzanJlyLw0PP9maVzzlAJHYvlw1S8nkJFZ8xseTk6CCKDJVaSLW+fAjBSgjC9jZNY8yy+5agd9sSt/km+BmdaFZbaV9I9O/RPHIXAtmCldoSz4SfsemIEgsiEUsILVk9enc0P5sNL0BiqaY0qRJhMht60iEi6W7v+TByhWY2Sbq8j1yhtq8kCv1xKqnKnNa5ckTZv2vtcUuuaGVpSueAq1+vEqUbk77d2pfyjNrSXDmd7dfru+3+bnq7qvWcCn81+9cacXkTlIng2x+m8q3/l2dPzXNsokSlHlHI+mR8oVJrnlyiyHB0osTzZ8rUQ0PG8xBg+Nhn2XHkD5ibnCAIIzK+15C83Lmcw0M248ETfwn3/I9GKqBcxqN34l66H/hfZKeOkM145LMeI30q+nue6ErsgY9pupJWkpodBQzsuEH/hq/Uh3bsobjNxONwz++p3cEVz3NE4/lw1ZvitoP7ddvZp5TIenc125nccb2rxOO0wqk71vLyOEN/8rh1QkQaKrmNk6Ccis/GwyVUfMboc/LsWImnTxc5OdVmUbEC5op1nj5dZGZxaWjh9GKdp08VWSyfe4xJig2GCyEAuP5tze9JKxzZuaTGS86ViJc79Or2bdz5+3ZpbNN6MGAXj36ueQ5wcA4hoBWT509r+9YktvneWNXZPRxn9zhHXHIEJSJ3ichTIvKsiLx/xca1ImbxDOWJ55DxRzAoWewbyVPI+YSRoZLwzpsp1qnWIuu5pbYkpo/SXRrFr8wx/8jfEEaGQs5j70iB7rzPvu35RjkMgN76BJx5StVwj3wagGxUpnfyAUy1SM/xvyHr6Wy3YzBPf3eWwe6MqtwWxlQUf+7L8T00PPCsGsHz4Arrz/LEX6rEFIVa3XRxHI58TtUC4sGL3qEeVwduh8GEl5zTTYMt69DyEu29RdPYLKdCXA57b9GXat8a4yMOvlxXs/vvWL3tKnBC6MbZoPR/EKp6Lykj1cOIWhBRDzUd1uR8nUotbHuedijXIiq1iKmFpSRUrkVU6hHTi0ul+xSbDBdCsM++Uze8TRd77Qhm3236LiQzsiTRs109A/fdtrw0sv+lqoq76vXrl4K3X6uquoN3NM8BSbgchUc+p/NB11CsbUniwMs1q8fuF23Yi7aFZPtzh4j4wP8E3oDmBvxHEfm8MeaJtgfUS9TKRYIoy8Dpuxl50R3kbabUhUrIYjlgrlinK+cTRYbphTq1IGLXUF6Dao2BB++huz5DSTLkph7H7HktXb076OvK8IJ9OsnPF9S+BNA7cV+cY2vqGZgfIzd+hEq9hAEytXkKM4/CrjvIZTyu2t2tBHPkW3FG5aNfgateZyPcR2MPPKdG2HWTqm6mj6mhVrw48WRYV3G+Z5sSzy3vWjou+V61NTWK3bWslrqH4Y4f6fTn0fiVV//02tsP7LUpic4dXkPFtzFwElQYYlV88b56oGo/z4OdA3lOT1c4NV3hyl1rq+3jHC/CaKlq0KmfXaLiFFsIrSEEI1eqnaoddt2ofyud6/q3rHy93h3w8h/tvJ9JZPJaen4l7LpZF61zoxDNafxUO+S64dZ3n1t/Wru3oWfbfLwUeNYYcxRARP4UeAvQlqDK1TrTuQOEfkhf5ST5x/+koRvuLweMjS1yLIw4mfEwqLi5rTfPzlIBxkSJY3YUP5slO3IT08ceIn/kz9hWuQImYueKgXLAxHiRrqxHPjuqqsV9t6pB8f6PkfWyhEGV0cIL2F8+Qt9zn4cokSetXlY7Uddg7MBw/0dVjF48q2o4l5gTVD13+NVKUI/8mRJRaUrbTD2nBtzVXEAHD6paMN8bZ6W+iOEkno2qfOyJ4IkQGc0okqSLINTvnggjfVnOzteYL4VUayH5NaSeWikJsdsWrmLXiiLDfcemOTDSzd7BLk5MlTg1W+alh4cb5FqphUwt1sHQUCm3js8jJ2cZnY6zXmd84eVXjtCbz3DfsWmmFmv4Hrzk4BA7+gocGZvn6NmihrTt6uPK7bF34PRinSiKeHRsjlI1JJfxeMVVI3TbVFKlWsA9z05RCyIKWY9XXr0NX4R7nptisbI2iXGkN8fth4cp18PGuRx2DRS45eAQM8Ua9x2bbrsAWA6+J9x+eJihHlXh1cOIbzwzSSkhGQ90ZbluZz8RhodOzjZde724akcv1+7q49RsmUdGZ+mgyxuKQXMj26ZPAHXGM9tYfGT1JLLX71mh+vYacakR1F5gNPH9JNCkhxKR96JZ17lm3zbuDa9nu7f4/7P35nF21GW+//tbVWfv7tNrOp09gewJoGwBAQOo6Fy3wQF1EAQ3GEeHccOr/hR11BkQlblwGVScUTDcex1UGJdBRNNsokAAgdAhe6fT6U7vffqstX1/f3yr6pzTeycdsng+r1cn3edU1fdbdep8n3qe5/N8Huan2hh8rjXYLmMLLDeBBEozAX1uDhktD9cMJtfRLeuoSTuQ2kd4cBul3TEyloYp4+Rdm/ZonuHqFaQGGpk/YKL17kQC+0Ir2RJ5Da/L76WQPYA4+IsxJ9fbsAHHrmJubxp6ny57r9OIYLUW5y9ch/nDGoZ5EDoPYhnVdFatpDndQyzTQZcWo1Cy/WjEsn0096ZJVTUz8OijE253vKCgJ7EJ0etk2LdtgjYDM4AjIhS0ajQ3z74dBQpaMjBSA/tHMPVqhGvRv2cYU6vCElF6OvJE5DhioqOQ1+tw0BHSoXd3sZ26BPJ6Ay4C3S1wYPvEYi4HMy5PHLBJGHDJ0jAP7bVIFVy2NRssrVVG0tRqsEU4mHfMGUSjeG8XHMl/77ECVquP51/QWVyj8+QBO2CvPvO8xjnzQvxqjxXU9j2kCf5qWZiwLpAI8noDwwWXP+zpCozDKy8brGtUy9DzPTa7h5zA+La9ZBAPCZ7vdXBGE34mQdfiMN0Zycv9dpnaC0D74jBt/Q7dGXfMe1PhxRd1zmxREZZXBhy29ttlRk7XNM5d1kLWdHm+o3vKh4jpQBOCtywN83inTargIidjZR5BhDV4h5vAkCa/bI+Qbf/zlPt0Nh2+eTnRDNR4j8dln6iU8nsowVuWLF0qt2uLufCCZdTmT/E05tTmf+7K8eK+LLURgwVJg7wtaevNowuXD57VRMgvnNVCLFl4Ns8fNHmkUINIdXLZuhpakkUW0HMHcvx5XwbLcdnwurksWXiWYuGkz1ReimvxbNcCjFyYlxPv5uzlKbBHiU4aMZYs3KBqJgbPUQoL/qlFaliyoITF4yN/BnS9CHYWGpazvH4p2OfD4F6WNCyfWKfLx8B5yoNKTFGzdBxgX2+OvpRJfbVq+ni4SOdtdnVlVc6yMcqeg9nAu5nfEKVnqICua6xZWIVpu2zbn8FxJWsXVU1aNiClZPuBDJm8gxCC05aeEng1jit5pTNN3nSJhDTWLpqY7balfYCX7QOYtsua167gWbMdOVKg0zC46oLVaAK2H8iSydtEwxp502Vu3cllepA7e9I0FzrQNcGZS+pwXMnmV3oZlpCrrSJeGGFpQ4K9/VlMXbDitGVsMfdhOZLauMH+gRxaSzMb1zSTNx12dWdp789Sl9EwNI2saWPXRNm4cQWpvMULT+ylysizfn6SF/YP0aNrLGqIk3DSrJ+fpKV2rFZlKXb0pNnTmyG2oInGvE0Vg6ydl2R+XZT9gzlePpBijxYh1gS1CYvzljdOS0Isazo8vqMPMxpi48bV6vN8Yg8JmeG0RXXMqQ7TnzF5sWOYYRLoUY1kk85rFtVSlzj0TtvbD6bZ25dhjx6jbq5DwpGcs6z+qHRgtyzIZ5YjrDQXJecSDU89iVVza7j2MMc90QzUflQbEB8LgImFpCJVDOUttvWkWdWyBBJLCBsa8bDBjtR+9sQGuHDVHM5dOxcpJQf+tI+tncM8Kps5Y0E9NVEjWDw6B4fplnVQXcez4Ub+6uQWTMclYujsGOqgPa6ehHvmLKcl6lFQq5qgaiNSSoa72gGHXiuCs/jcIAxjOS6h0UrmdYun1xIhmhzbQt2IlIcDJ8NhqDcca/BzUNokjR9nAl0INKEUJFwpy3JQxRCf+jtsaDTWhOgeLNDZn2d+Q5SQPn7xtSuLuSef+emriPiUdlDb+IxR9Z4qAhdCMUb7RswgxLT5lR5ylvKMMgWbp/b0c1JTdbB/S12EPQdzDKYt4lENXRNURQz2D2bJmDar59Zw4SoVEpYIft92kD19GSKGzqWnL+D+5zrZ1ZNmS/sgmYLNkoYEb1jTzJ2P7OKxHX2cv6IRyxNXNm0X6cLSOQk6BrP0ZUwcV7Jl7yDDOZNF9XHec9ZCDF3wbPsgnYM5kvEwl54+n2ho8uWqqTrKnr4MOw4WvdSL18yhJRkja9ocGNpJ30gBXResbK7hkrXTY5q5rmRXb5quoTzDWZMdPWmGMiaN1RH++jXzCBuKUDWYtsjnXTRNsiAZ552vmQeIce85VTvHpI1IV7fU8N1HdnNgKI8rJRuWNXDR6uayY6hc5+Hd064ry0oxRsOyXXZ1Z8nGo0ATVVGDFfPHz6dOdayZ4kRj8T0NLBdCLBVChIH3AP810cZVEXXDP/B8J999ZBfffWQXt/1uB7t603QPqzDQunmK4iyEir0j4OGXD3Jn605+16aIB1JK9g9mg+Pu7s3w+M4+/m3zLvb0ZehOFWnG7f1j5fjTBTsIixRsm30DGe84ae7YvJNn2wfH7FPBzOA7i9ps1UF5+n6uVOVm5TkoN2gv76OhOoyhawx6NPH23vHDjKVGCCiTx1KMQfX7tu4R7np8D3nLoWMgy22/38mf9w8jpWRvT45UxgnO9KXOFJmCE+RP/uv5A/yfP7azqydDNKQRjxhIJH/Y1c/3Ht3NHZt38vTeATqHcjiOZGljcTE6a0k98YiOZbusmltNY1WEBXXKI32xcxjTdmmpi7KgLsbaeTWYtsOTO/uVgZJKw1ITGvPqosyrjeI4Li8fSPFSp8pLnb20HiEE55zUgK4J8pbDunk1UxongAV1MarCBtIW6Ggk42Hm1iivKx42OOckv9BW8PoVk9SHjYKmCRbVJ4Jz3NI+yHDOYv2CJGFDRS10TXDagjok6uHhpKYq+lIWr3RmyI3D4OweMtl+IDNhnspxJamMy/rmOuZWxWlMRLl4dXneOJWz2daZHrccYbowbZftBzJ0DRQm3GYgrchhkZCmym8sZ9zavpzpsKMrw8GhiY81U5xQBkpKaQMfA34DtAE/kVJO2LM4GtI5Y0k9VVED25UUbJehrMU9T7aTsxySsVBZWGFpY4LzTm4iHjYYylo8u08ZjsGsRbpge/VPBj0jBZ7bN8TBVJ67n9xL3ireoJ2DYxemgYxJxrQoOA4522FrZwopJU/s7KN7OM9Te2bQWbOCcVETN4hFdGrih6+wDEpJQgj1xCilLLNQtkOZBwWKhNBSFyaka5i2y2DaHvdLXmqEgLIFTBk+r4g8b9Pel6HtQIpt3SP0pQv8YWcfjivJFRxGcjaxUKhsv4tWzQnud9OG9oEMLpKwIchaDrbjksnbDGUtfvlCF70jBTRNlCW7k/EQb17Xwvy6OG/wFswFdSoikC0oIsO6eUnvga4RIeCR7b3kTQfX64emazCnOhoYtl+9cIDhnMXC+jinLar1jhnnwpVzWNKY4KJVEwjHjkIyFmJuMoYQAsuWzKmOlD0knLGkjlUtNbx2YS2LGmYW5p3vnePvtvUwmDWpT0R4/fJyI7egPkZzTZTGqggNiQgDaYuc6Yy7YOcKDrmCw9AEXQwKngD13NoYVRGDkxurqYqWG+lU1iZvunT05WecT/Mx4qnVdA8VJiyFyOQdbEfSlAwTCWnYjhzXsPYOm+RMl55hc9Ykvw4rxCeEmAvMAxJMwuCVUr5qWXYp5a+BX093+3edXpQ0sRyXO1t3cWAoh+NKljQkym5wIQT/45QWLlzVxP/63Y6AOt45mCNrOsyrjRHSBbt60gxk1FNN3lRf/GhIx3RcekfGPu30Z0xGCpYKQwnY05dhZ0868LwK9vRraCoYH1VRg9ULZk9vThNKk8KV6qc0eW2rF8bkCpqSEZqSEXZ2ZUllLXKmO0ZKy3bLi8MLJXV4tquMl+24OK4SEX65O4Vlu0rFxHQoWJK87WK7kmQkTDQiGEibGLrG6pYazlhSj5SS/3q2m/a+LC/sH2TV/CrSBRvLkayamwTh8sL+YYayFvWJMLXxcpmpM5fUc+aSYjuPBXUxEhGDTMGmJhZifq1azBc3xFkzL8nWzmFePpCiPhahYLvoQmNOdYSQrnQmh72i4zevm1v2fbt4TTMXM33BUSEEzVVRdmezCAQtyfKcVTxs8P5zl0z7eKWYXxsjEtLJmw550+GCFU1jGoRKCcvnVCGUMEzw2WXzYxdzv5SgVHmm7H2vvq4uHuKspfWYtgqR+uE8KYuqNbYj6R8xgx5zM0G24AS50/FKIWxHkrdUPrQ2EcK0JJm8w1DGprm2eP450yGdt3FdiYs692k3S50EM/aghBCaEOJTQohdQCcqrNYKbJ7g5/cTHOqYQ0jXOPdkFQawHZd5EyRlYyGdeNjAtB3yls3+wSzZgs2c6ggL69WTWcFyWN1S4x1LsnJuNRFDC76MAC91DrOtO8VAWuULljVVURXROThS4NEdfQx4rnt+Gq08Knh1IYQK/UgpefnAMKkSWSzHVWwrbYI4fM9InlTOIlNCnd5xcIStB4an8KDUIlVwFANNINjdmwkehjIF25PsUgtFPKyzIKnux8aqCAkvpG3aknnJGCD584EhMgWbvrR6GJpTFeXckxsRQlCwHJqrJycmgPJcmqrV4ljqtfihOgS0HUhheYY1Yug0JMLMr4uR8Bax+XVxTltQS++weVj07GSsaExXNZeTSKSU9KVMct6DZbbgMDBijXkg6BkulDUjtR0JrqClOkZNJExdIszrVzaqY5V4HcXmompZdVyl11nwHiB8uFIG4tKl0RXTVmM7Xq7OlRA2BPGIjpSy7B4rWC6O4wbXumvADFRM+lJm2YPNRJBSkvOMnBCCVNahsz9PX6roAeVMB9dVEYCwoRGPqHPrS5kcGMgHP10DBUy72M5mKDs7heQzMlBCCA14ALgZWAoMozwniTJWBe9vAWSBfZTTvo95rJ9fy9xkFF0TrJs/Po9fCEEypsInPSMFuobVl3vl3GqWNCRAwML6OJefuYA5NVF0XePckxqIhHRG8hauKzmYyvPQ1m7uebI9yEudPCfB/Lo4juOy8+AIIV0jFtYrHtQxCCEEhibImDZbO1Ps6BkJPCbbK94djyQ5nLN4vmOQl7tHSHsL5XDW4tcvdnPvn/YxnFX3h588H22ggIBwoAlBtmAHi1zecsgUbDKmTdayiRg6yYgyHPNKvIm86RAxNOYmo7iu5Bd/PsBgToXz6uJhFtXHg3t/adPUxcVCiCBP5YftfCxrTLCyuRpXQneqgEQSDenoukZVxGBBXRwhBGcsrmM4Z9M9VODADKWhSq9PIqwT0jWiIZ36RLnnly2ocNu+PhVm939P54vfr74Rk+7BAv0lkY6hjEX/iEVdLEIiFGLVnBpyBUn3UIHd3dlgMfc/n2TcUG2kojrJuDHGuPjeEajP0sfAiEX3YIHeYTP4jA2jKJs2nCkeI1twsF1JIqJTHTOwHZeRnEMqa9M9WGDfBDnOUhQsNyDKzK0NI6XqprCvNxdEh3wPKxpSN3PC0w4tWC7dg4XgZyij6unmJNW6mJolAzXTEN81wP8AuoDLpZRPCCFcoEdKucgzYOcBX0M1O7xRSnn3rMz0VULY0Lj63KV0DedYWD/xl7Mmpi5d51COVN4KvqTxsM4HXreU+kSYaMjgmtct4WAqz6KGBPXxEEMZk750gT/u7mfQW4w6vLzUovo4a1qSPN+QIGfZLG5I8KsXDjCYtYLW4hUcO/C/qI4LSGVUfC8HxmdoDWctcrZDCOgZLrB0Tpyn9g4wlDNxXcneviy10TDRsE7OdCgtQSoW8LpenzD1+kiJJzaQNslZLsP5Aovq4zTXRPnbsxepBycPKlwEJ8+p5qWDQ/y5Y4jqqEFLVQSBEij+69cs4NQFtaxqmV6x5QUrmphfG+OkOeXfGSEEy5ureC6Tons4hwRihh7cz+94zXzOGsqxormazoE8lu2SOcQce9Z0COs66+bXKC9kVKGzacsyD9U3AoNpi2rv+2zZSsoslbNpSirjXrBcLNtl5dwq6uNhGqvCSlXG81IGRiwaasJB2K6hOkQybhAxNIayNkMZi+GsTa1HOXdKiDC+sdKEwHLU2CM5Rf0HiHoGShMiMBqgQoO2I4lVKdblSM4mnbcx9OJxplozsgUVCo6GdZprI4RDGr3DJtmCQzrnUBU1yBZU7rA6qoxkJKSxrDnO8DgGKB7RiYU1+kcs8ubsRH1maqDeh/KWPiOlfGL0m1JKF3hUCHEh8EvgLiHEdinlHw9/qq8ekvEQyfjk9QvVniTS7p4Mlu1SHTWCEMryktBCbbwYw2+oirC7N8OW9kF29aSDG6hgOYQMjeaaKIau8fqVxeTr79oO4romedMhFjnRqgKOb4QMDdNRFD6nxED5+ajxDFQqb6nPO6TTNZxnKGvS1pUKEtQHhnLUzg0TC2vkMinM3AhtKfWe5bjoLjQiSTYqpqDrjeWrWjjDHSwMSZoXucSMQbTMEFpesH+kOBfHlQhHEtIE71giKdgSsAjrKbR0mrZtSiVDA7bPoN23BuxJjX29xnE5q0nl1gTKaLW1tZUtnq8MKeOgS7AFtLXNnL/luIqWX+Mt/t0dPfRp5eeNlyNpa9OC8YaykOlT4/nXOJOFtsESCr+rHkiaNYmbhmwGdAAJ+7Nw0NCwbRcd2LurWELgSonuSIaz0OaN4Yf4dG+ebW3qevvj5LKQF6C70JdXxxKOauHz8kixD5nuQiovkIDuSPpySq1f8wzly95xJ4LtSKQrKWiCV4aLtXa6I+nNwUCXpuYE9BYEfV1qm3A4TGNjI8nkWBFnKSVhw+tvZx1+5GemK56v+f7zUa+XZcOklI4Q4hMoiaFPA39zaNM7dlHjhfh296nwXENi6n5FfsjhiZ19CKGeYFtqo7Ru6yEZCwU1LaWI1ByGVAAAIABJREFUeTIwQzmrYqCOMYR0EeQKQI4xSOOF+FJ5i6xlUx0OM5gxeWrXICF06hMRsqYdeEMhYaPbw8ybNx9XD6EJQTxsqFoiT4xW1wQFxwUpSUQM0gWbqnAI23XJ2w4NiYgKE+miLKHvh3b8guGekTwSSEbVOGFDC/IoM4GUqrO0lJJISFO0clv1N0vlVGdpBIR1jZqYUXa/u66kYBXVHWJhHcspHstf8C1b5bHCIa1s8ZUeQ9B21OfgjHPepq08If/4/nhCCGKex5K3XK+Wp5j78V+LhXWl5uE9TIR0TRkbVy3Kfv1bKfFFSqm6IgCxsDoP23GD6wQE1ztvOkHeShPKWMTCOpqmHmJtrwedf9b+MV2PkOF3jvbr6EK6Rjg0/ucopfS8f+nRx4t5M/9YYUNTXqJQ18vfL5fLsX//fiKRCNFoeY5SCEEiqpMtOGUhyUPFTO/CKmBYSlka4MwDY0rapZTbgBRw7qFP79iF+oKpHEDBcgKDNRlWzq2mvkpRjaujITauamTDsgaWNCVYPmd8hlncuzGGK+0VjjkEHpQHtdgVhWONcSxUyusR5khJxnS8sJZkw5IGkvGQanbpuOTTAzQ1NmGEo9iuJGepZpbC85RALSCxkOa1dNEQCK/9hwoZ+cXAo1WC/IVR0wQhQ92LYV0LanoOVe/NdZWhcTy2oU/B94uIZeDtjW3sOFoRXh2jeKzS7XxPqXx/Nb4QwitYHjuGHHWc0uvhut77svia9Ofk1bQJoeYe0lVYLWSIsiJqGEtl9vfzC3OL85AlXhbFsYKxi/sCQRG2Nx3lNWnl24yqdphUaslxvSJf73oF8y25Vr7GZOlzlxCCeDxOY2Mjvb294x47HlEthuxDvZFKMNNH8oNAixBC88J5oDqTLxBCzJNSBvEALx8VA6amAR2HqI6GCOkatqOeehqrpqZ4tiRjfOaSVWNev/aCkybcJxaqGKhjFSEv3u/DHmUJxnGISeX9z1EG1PCaWIi5NVEW1cXp7C/QnzGpo0BjUzMFj/kmUE/JRkQLFjpdCGoS6r7Lmbb31K0WKUPzFmrPLPjh5NKF0F/YamIhamIhj74uDqmGRUoZGEdQi5taeNX7mhDeYi89I1u+vzIQxTnabolqhlP0Tv3XXJcgbqOMoAqt6pqn3OCIMap15b3dGGMQdb18H3Uu6rVSdYRwqLhia97rvic2XkRNEwLH+7yVoS4n0UhvnLK5eq/4Yxq6hjEBa1tQrMkTlBgyWbymo6+Dn/sK6VrZ++o8Sz8/iSbG3sjV1dX09/ePO5+6qhD1VWOFhw8FM/Wg2lG3RWmDoGe9//961LZvBUIoo3bCoSZqEC5ZgRZ6hXyzDd+1Li5s00fHQJaXOodne0oVeDC8EJ8P03GDjr2DWTMgv5TCD+H5qg6OK2mpiWLZMM+rH+oYyJLJm1hSLeSGpgXhJKuEYl4a4io+YauFx/AWHk0rSiYBJcZpbG9hreSJfqbwPZjSv0sNQrAYe+OUvqe8C/W34Xk/imiiXvM9gdFeRtnY3iIf0ku0QkrOo3RftY/n8XhGxZFyjGF23ZLtJjhvf0EP/p5gG3+epfNS11sU27WMmu90RU/8kKB/aE34XmrRyNiOCm9aXp8y30MLGWLCY5V6pKNhGAa2PX4ITxPjy3gdCmZqoH7r/f/Gktc2oS7lvwghPiOEeKMQ4pPAj1DXa6ws9wmAqohB1PNuhBBB/dNsw/egRqbZbqAUD77Uzf97uoPBzKFLoVQwMfynTB+mo+L1Octhd2+Gn27pYCBdKNvebxtxcrMK6cYjOvNqo1iOy9waVd6QylvkLAfTy8FEDS0g4OQsx1tcyj0Av/0HqMUu5uUe/Nd8R89fCEufzn34OQy13fStlL8A+qFF36OQFBc43ZufAE/DsDiGv0ALIdB1v0ql6C1JOXY+pX86XihR13wJqmLtS7A95S/4eUO19pYYibLQmwzGnohsIIQ6N/86jLeZb8SC8w1INMV5jrJPar9xRxwfYtQc/IJen7Vo2SpH5+fhXCm9h4Gxo5SGEgXja/29WozimRqonwGDKKo5AFLK+4D7UWoS/wI8CHwTSAK7gC/NykyPMQghgrxTImJMKwd1KPBzUDM1UDnTIV2wsB13XHmlCg4fGdMuawNhOkr/rms4T8F2MR2X/95a7Os1UrCxHJdYWGfDsjrOWV7HJeuaCIc0lWjXdVbNrWZJU4KIoXk/OrGQTkhTeQ/Xy81MtBD6i1zEiwdpmp+HKvdCxt9/bL5kOijN/4SNEuPiGcJwSFP9phJhauMhFeYq8ep8Y+Y/+ftT841b0ROgmCOi1MCVi6aWnlqZERz1up+D8b2NYl7GD9sV9x8nyhWgNNU41i8t95SCa0P59favhSjxPmZiA8q8OCEC4+OLCrtShRdDuvLGQ4Y2xnsqnoM/z/Hvs1cTM8pBebp24/VeuAzVY+lvUAriwyhv6xYp5QmrdOobpeQRMk6AV9QoZkzZHMiagQDtwZE86xhLCa3g8JDKW1ilBsqS5DWHwayJRH25t3am6E8XSMZCjORVb6VExEDXNc5cpuSC9vbkyOQdBtMWtbEwq+fVMHQwRU0spJhmgPDyBQXb96BEkMgHgjyV60pCelFB21/w/QV9KrszOl8yFXzvqTT/I7wwI97irwmB5pEJQmiBVJNlS3StGIYS3krvRSoDT8AnRfj5G39c3+PxF3w9WNiLYULfCwgMTYmn4Y+leQu5b5Q1TeB6YTB/PH2Sldr3Gif1oCj/DETJtfGvtzpWMRQ4Ey+ldA6ad91U2YPKK6qHB21ayudlx5ol9f9DxayIxUopHSnlv0kpL5ZSrpRSniWl/MKJbJxA5aHgyBqoeFjH0ERgbKaLgXRR7qTvMNSOK5gYw7miCr2Ukpxl0Z0qYNoutbEQF6xoQkrJ7b/fyW2/38mL+4cxHZdEuPy5MGIUwzFCCGqr1PuBcZFF4oNPBy4+5ZZ7MH7+yUdpHsp1iyGriRa/MfmScWDabiD+Wsw9iSDhXmSVjb9g697iGbD0gsLmcg8i8KgEY1h74OWNIFjwy72I4vUp/X/0dETJGL7RKrIAx7LpxkNpHmpanukoDwqKIc/S1zQBV1999ZjParzXSr1OPxxpeCzORNTg2g9/YNrGpnguyhM7mjih1MxfbSxvrqY6FmJF8+yJkI5GLKxjaNqM9fj6M4VAIqnCADwyGMlbmLYS0pSoGpqsqWjkc2qibFjWQFONyi/1pPI8uqM38KBK4deqOJ7348vKlBoXP4SVjIXQvadhUAWbfggnrCvac3yUSKe/YNlucaGeaN0ZnS8ZDZ9G7rgS2y5lz1HitRUPPl7uRogiPdu0lZHZu2cPf3fdtaxatYq6ZBULWpp4zanr+cA11/BIa+u4eSifGu7n5MoX7XJPyfdeRjP/1TUu5r38axD01qJoOCeCv5Br2vi9n0qvQxDKKxnrx/f8iNv/163B34bmFUrPwDj4c/a9J1B/+9d5soLd6RzraGFGIT5P1qhLSjl/mtvvARZKKU/ICtOljQk+/1erj+gYsZCuCjJnqMc3kDGDxShTmB1drArKkcrbOFKJsqbyjvKmvOeI6ohBbTzMJ9+4AttxufOR3XQOZnEcSfWotgmRkBYUl0bD5bRfzYt3SS+HEAnptNTGgoJK2ymqMyTCOtXjePNBHqrEo5lovRJ+vmSCcy7tV2W7/vFEWVPN0mNPtMD54UDXlTy7ZQuXvPEiQqEQV111FWvXriWXy7F9+3Z+8YtfEE9U8fqNF+KbGlFyTSbzjKDoCRYNs6C0cbqfb9IEOLLogYQNQXgGq1bI0JgsjuLPx3HLc4CaEGy6527a29v5+49fjyYUpdxvf/X973+fO++8c8rxhRBEQmOvtf8gM7N81vjHOho4FMMx05kfG2d6nCIWVkVvWVM1prPd8gZyPnb3plX7cU+s01e41nVRMVBHCKmche26VEcMUjmTguOiS3W7l3oxhqeS/59Pq6aWTdXlqiMRLzfguHJM+w3fuPgGyocKkxW79xr6ZE/vxVBh8bXJE+T+Cu64xbCX6xUY+3/7pILRYxdDXmIST00VEhdcyTe+/k9ks1mee+45TjvttLLtbr/9dvbu61TGyFVEA10DV4ogvFmavxp9Hq4rsexiCxMtoBCOCid653OkFquiZ1o+v9Efw2hPLRQKEQoduRTCsY4jHeKLABUp7sNAWNcI6yqx/LNn9/PjJ9vJW+UGp2ckz/3PdXLPk+1IKSnYDiN5C00TVEcMsqZTJvdfweFDSkkq7xmoqIFEhfx8QzJaKmj9/CQttaqZXkuyvGbO0NUTuxBijAakb1zGq0dRtU7+Yj/xV3l0HmrSnMqohL5puxQsdwzLLWxowQI/WqLLZ635854Ivhe1c+dOGhoaxhgnAE3TmD9flV3u2bOHqliIr//TV4M5+vf11/7pKwgh2Lt3r3fO0Lm/g498+IOctGwpyeo4SxbN4/zzXseme+72TxW88/z3H3yfjRecS1NDkqqqKtavX8+XvlROQC4UCnzjG99g7dq1RKNRamtredvb3sZzzz1Xtl1raytCCH74wx9y2223sWLFCqoScU5dt4Y7/vdtZbm5pUuX8thjj7JvXztVntyZEILW1lZg/HyTj97eXq666ioaGhpIJBJcfPHFY+YyGR5++GHe9KY3UVtbSzQa5ZRTTpmWt/Zq4oiF3oRqZjgH6DlSY/wlQAgR5CwGs4r40DNSYFF98aN7as8AQzkLy3YZzJiYjlJyTsZC1MZDDGUthnKqC2gFs4OM17hO0zQWN0V5el8fEqgORTB0bYyHEtI1/vbsRezqSZcpi4P6jBc1xcjknaDfTul74ZA2LrFB1wQRQ1MU7SlyBaqAVQZ9qiYkSfj/lNC6/VBaUBPkNRqMhHyPpvxYpe9NBiHU/E8+aRn/vf0Vfvazn3HppZeOMyc1oeAZy8uRWI6L65mZ0acjXYe3vfUtHOjs5Nprr+PkFSsYGU6xdeuLPPHE47z3iisDC3XllVeyadMmzjrrbD73+c9TX1fHtm3buO+++/jqV78KgGVZvPnNb+YPf/gDV155JR/72McYHh7m+9//Pq973et49NFHOeOMM8rmcNttt9Hd3c21115LdXU19977f/jMpz7B8NAQX/nyjQDceuut/M//+Tn6+vu46eZvBQ8qq1dPnTp485vfTH19PV/+8pfp7u7m9ttv54ILLuDJJ59k3bp1k+77ve99j+uuu44NGzbwhS98gUQiwW9/+1v+7u/+jl27dvHNb35zyvFfDUxqoIQQFwAbR71cJYSYrLZJALXAm73fx6ieVzAz+AXBPivvYCrPIq8VSF+6wI6DI4EI5r6BLCFDo2A5zKmOUhNVT+QHhwsVAzWLGEgrGn8yZlBfHUEzBNm8reRjNG1codjGqsiEklixsB6ohoyGXpLE/85vt8/WKUwI1SJehYcdLxym6149kqt+H22AP/HGFaPmPL3gjKYJvvjFL/Lwww/zrne9i+XLl3Peeedx5plnsnHjRlavXj02DAae1psIDPdoc9vW1sb2V17hpptu4oYbbih7z7SU3qEAfvKTn7Bp0ybe97738aMf/QitZN5uSQnB7bffTmtrKw8++CCXXHJJ8PpHP/pR1q1bx6c//enA6/Gxfft22traWLBAde3++7//e8477zz+5Z+/zrUf+RALFizgne98J9/5zq3kcjne+7dXjAnxTobFixfz05/+NHjYuPTSSznzzDP59Kc/zYMPPjjhfl1dXfzDP/wD73nPe7j33nvLzuX666/n29/+Ntdddx0nnTSxBNurhanuoguBLwM3ej+gCnJvnOTnS8D1wEpUUe9XZnvSf2kYzcrqHTHZ25fh9t/v4J4n2xnKWsFN2jGYo99bPGtiIao9A9WTnlmTnYGMyQ8e38PWAxWppPGgWJIuyViIiKGhe4wvXz381aq0PxIYTdH2f5+IkHC4OOecc9iyZQvvf//7GR4e5j/+4z/46Ec/ypo1azj//PPZs2e3yt9QnICmFSnQYxl8BK0gNm/eTE9PeRAn2FTApk2bALjlllvKjBNQ9vePf/xjVq1axemnn05fX1/wY5omb3zjG3n88cfJ5coL4q+44orAOIFqU/GJT3wC27b5xS/GCuzM9Ja54YYbys779NNP541vfCMPP/ww6XR6wv3uu+8+CoUCH/zgB8vOpa+vj7e97W24rsvvfve7mU3mCGGqEN/zKMkiH+9HqZf/ZJJ9XJSK+Vbg51LK8RUFK5g2Tp5TRVtXioV1cdq6UvSlC+zsSXNgKI+UknjY4MwltTyxq4/u4bzXplnSkowGNNO+kZl1Kd3WlaJjIEs6b7F2XqXIdzRUuFUJvfosPFC1OYamTUgOOFyM9lSOBPx2HKpWSYX1DF0Eag6xUUzD2cD69ev54Q9/CEB7ezuPPPIId911F4899hjvuvSveewPTwXb+iMbusBxx+95tHjxYr7whS/wz//8z7S0tHDaaadx8cUXc9lll/Ha08/AlyjasWMHLS0tNDc3Tzq/trY2crkcTU1NE27T19fHwoULg7/HC9OtWbMGgN27dxfPx3OQZ0IFn+z4Dz30EO3t7axdu3bc/dra2gB4wxveMOGxDx48NiRUJzVQUsoHUC3eARBCvB/VbuOaIz2xCoo4ZUEtpyyoZVu3ai2eztsMGCZSSt5x2nw2nNRAx0CWLe2DDGZNcpaqzVnTUkN3Ko8QMJSdWS3U/qEcBcthuKKSNC76veLnlmSUcEnOyVdyOBE8qFKpn1Ih2CN9bosXL+aqq67iyiuv5Pzzz+eJJ57gmaefYv78BWXbaZog5oXExhMu/drXvsYHPvABfvWrX/HYY49x11138c1vfpMbbriBm266CZi8aLkUUkrWr1/Pt7/97Qm3GW28xjvuRPVlAlVucLiYjoaiv83dd99NS0vLuNssW7bssOcyG5gpSeJCoCJLcJTgt/jImg79Ho18Yb1ihNUnwkQMjcGMSZ2nedacjJIxbUK6RrowlkwppWRHT5rmmijJWIj+dIHBrMnihgTdw8rjypkOOdMOGicer8gUbPb2Z1jZXD1uY8iZwqfxL6qPY+haUG8iUTp4R7m+8bBQnLoc89uraXeFEJx99tk88cQTdB04wLr1pwAwODhWoKbUIynFsmXL+PjHP87HP/5x8vk8l1xyCTfffDOf+tSnmDNnDitXruSBBx7g4MGDk3pRy5cvp7e3l4suumhMKHAivPzyy2Ne872XUgNwqAa/ra2NDRs2jHlN13UWL1484X7Lly8HoLGxcVIv6ljAjL6pUspHpJRPHqnJVDA5/BYfQ1mTnGkHbeJB5al8bcB0waapWiXjq6Mh1YLZHPuE2Z3K81/PH+DuJ/diOS4/f66Te/+0jyd29pEv0f7bfwKIzT61Z4AHnuuclZxaznTIFGwMXaMlqa6/r/4gpaprmo6O3bGKMTU7otge40ic1W9/+9txPaBcLsdDDz0EwOo1q6murqZ57lx+//vfl3kKu3fv5v777y/bd3h4GMsqjxpEo9EgLOYbuSuuuAJQ+Rx3VD+v0jGuuuoquru7J/SgxguJbdq0if379wd/m6bJd77zHXRd561vfWvwelVVFYODg9Pyfkpx8803l+3z7LPP8vDDD3PxxRdTVTWxus3ll19OJBLhxhtvHJM3A3XtCoWZ5ayPFI7vx+K/MPgtPoZzVpCgD7TZhKCxKkLHQBbLdoOC3XhYdVv121SXYjBjkTFt+tMO9z/XSX/GJGc6/GZrd1mvq30DWZY3j2mafFwhlbcYydt0DGQ5dWHdYR2rP1PAdNT1D3mq4b56uEQSCx3nBgqfNVg0SuUqDrOLT3ziE/T39/P2t7+d9evXE4/H6ejo4N5772X79u1cddVVrF+/HtuRXPd3H+UrN36Jt7zlLbzzne/kwIED3Hnnnaxbt46nn346OObmzZv5yEc+wrve9S5WrlxJVVUVW7Zs4a677uLss89m5cqVAFx22WW8+93v5u6772bHjh28/e1vp66uju3bt/Ob3/yGl156CYDrr7+e3/72t3zmM5/h97//PRdddBE1NTXs27eP3/3ud0SjUTZv3lx2XitWrODss8/muuuu82jm9/L000/zxS9+sSxXtWHDBn75y1/ysY99jHPPPRdd17nooouYM2fOpNetvb2dSy65hLe//e10dXVx++23E4vFpqSIL1iwgH/7t3/jQx/6EKtXr+bKK69k8eLF9Pb28uKLL3L//ffz8ssvs2TJkpl8jEcEExooIcT4PvPMIaWUR5+veALAb/FxMJUnY9o0VZfX09QnigoFa1tqAAKGWcGrpi9dYLKmje01Cnq2fTAYwy/QbKqO0DtS4MDQzAgWxyJynoEuODPTNBwPAxlVj1ZfW6SMR4wSDyqkj9tN97hBiQ0KinoDzbvZN1Df/va3eeCBB3j88cf56U9/ytDQEMlkklNOOYXPfvazXH311SilL8mnP30D2fQI99xzD62traxZs4Yf/OAHbNmypcxAnXrqqVx66aW0trayadMmHMdh0aJFfP7zn+dTn/pU2fj33nsv559/Pj/4wQ/46le/iq7rLF26lMsuuyzYJhQK8atf/Yo77riDe+65hxtvVKTmefPmcdZZZ/H+979/zHl9/OMfJ5VKcdttt7Fv3z4WLVrErbfeyvXXX1+23T/+4z+ye/du7rvvPu68805c12Xz5s1TGqgHH3yQT37yk4EntGHDBr75zW9yyimnTHnNr7nmGlasWMEtt9zCd7/7XYaGhmhsbGTlypX80z/9E3Pnzp3yGK8GxIRJO6W7NxuQUsrpk/tfRZxxxhnymWeeOdrTmBHu27KfLXsHAHjtojouO7P4JPbsvkH+8+kOkrEQn33LqmAx+d+bd7J/IMuX3raG7QfTPN8xxLtOX8Azewf4zUvFfkU1sRDnL2/kVy90AfDWU+fx4EvdNFVH+IeLl7+KZzn7+PfH97Dj4AinLqzlPWctOqxjbX6lh4e2dnPawjre7V3/nz+3n6d2D9BcFeeCk5tY0BhjTjI8xZEmRltb27SKNY8EXCkpmMUGhJonqQQqkT8bObyZwvKa7emaIDpBvdixgtbWVi688EL+4z/+g6uvvvpoT+eIYTr3qBBii5TyjEk3mgSThfguPNSDHikIIS5D1WWtBs6SUj5T8t7ngA+ipJX+QUr5m6MyySOMmhKh0ZbaaNl7yxoT1MbDLGtKlD3p+k/3I3mb7QdH2NaV4oWOIbKeV7GkMU7HYJ7TFtZy5tJ6th5IYTuS1XOraX2l54RQQ895OTXLOXzJp5G8DRIaSzT1wrrfXVlJHSUix68LVeojFWuMvHDfUWIn+pqER1tdu4JXFxMaKCnlI6/mRKaJl4BLge+WviiEWAO8B1gLzAMeFkKskFKecDqAfuEtMKbNfG08zGffsmrMPr4SRSpvBwt193Ae2/OeT1tUx7Wvbwi2/8gFy/C7sMbCOr1ej6OwcXwuurajnr5h/L5CM4Wfz6stUQ4PQnxCcsqS6uOcZl4M6/nafP7rR8s+6Jo4IvVXFRzbOK5WHCllm5TylXHeegfwf6WUBSnlHmAncNarO7tXBzUxw+vzojEvGZ16B4oGaiRvBbmYwaxJ1lM5b4iXh6JKFZ6bPGmezsHsrMz/aCBnOUFrdsc9/Mi1z4isjRWvm1/DEgvrJ8QiGrTlQJSokx89D+poj13B0cFhsfiEEM3AQiAupXx0dqZ0SJgP/LHk7/3eayccauNhQoZGImwEDLKp4FOg04WiB5UuOIHKREPVxLkSX79vb3+WpU1HrjHjkUTOcrA9z2k2PKicp4lY2knZl6Ma3S33+IVyobQgxDd5V9kKiti4ceOMKeMVjI9D+jYJId4NfAEVUgOvI3XJ+7XAf6Lu8r+WUo7M4NgPA+NRSL7gKVuMu9s4r417hwghPgJ8BKC5uXmMwOOxDikl82yXpAatrV3T2mdHj01q0ObPzx9kz5DEdl3MISW26UrJ80/1T/h02jXkkOqz+NOz3YjuQ0/6H030ZF0691u4UtKT02ht7TjkY7lSsm2nhe1KXnimP1CQKDiSRTjUjvTR2rrnsOecTCYZGZn212bWoYTMBY5VIiEuJfaxUR5TwTGAfD5/xNfPGRsoIcS/AJ9BGYUCEGKUgZBSDgkhuoG/Bd4ObJru8aWUh1LavB/lyflYAByY4PjfA74HisW3cePGQxju6GKm7BV9Ry89L3QRb65mfnWBQU8FYW5VGNN2ufDCNRPuuyaVp/ORXTiaYOPGibc7lrGtO0Wb3UHeckjWxti48dAZiZmCzXPWLkbyNhddOL7W2Wygra2N6urju/asghMb0WiU17zmNUd0jBnloIQQbwJuQInBXg5UAb0TbP4jPA/qcCY4TfwX8B4hREQIsRRYDjw1xT5/MfBzUAMZMwh1AZi2G7w3ERqrItTHw6TzNkPZ6atc/Wl3P//5TEdQZ3U0kTWLIT63JPTSO1Lgx39sZ1//2Pxax0CWe/7YzsFUeQ2YHy6MHqeEkQoqOJ4w02/Zx1De/2eklPdNwZJ70tv2tYc6udEQQvy1EGI/cA7wKyHEbwCklFtRCusvAw8Cf38iMvgOFVFDR9eE6gBbYjBypkNsCgOlaYJFDYot2NaVmvaYWw+keLZ9kM6hoy+TlDeLJIlSA93en2FXT5rNr4ztqflK9wivdKd4dHv581fOdLAdN1COqKCCCo4cZmqgzvb+v3fSrQApZQYYZvx80iFBSvlzKeUCKWVEStkspbyk5L2vSylPklKulFL+92yNeSIg6knvWLZbRhJwXDmlgQKYX6sEabd1TT8nUvBo3TNVUT8SyJpOoCtXev6WI8nbTlAPVopU3sJxZCCa60MxAuWsKE9XUEEFk2Om37JaICWlnC7nuPKYeQwgGtIDbTh7FIttoi6upZhfFyMa0tk/lOOlzmFS+cmNji+VBJAZR6QUP4rsAAAgAElEQVR2Kkgp2duXCSjxh4tcifBtqYGyXRckmPbYcUbyat6jz9UPF1Y8qAoqOPKYqYEaAGqEEPGpNvRyQdVA91TbVnBkUdpQz3UliYgR0FpqSqjSE2FOdZRkPES2YPOTZzr4f09NzoKzHBmE1Pxaq5lg/2COnz67nwe3To+lOBV8Q6drYpQHpeboe3s+pJSkPPWMTMEJtvOPZTsyoO5XUEEFRw4z/Zb5xIO3TrqVgq/I+NgMx6hgllHqQQE0JyOEPD21uvjUBkrXBG89ZR5Lm6qwbJf2/gzuJPVEBdvB8WJq44XPpsJg1iSVs+gYmJ38le9BhXRtTIgPCLw9H1mzaJSklBwoyaPlLBsp5TGvB1dBBScCZmqg7kI9e39DCDFuRywhhC6E+P+Aj6JIEnce3hQrOFz4JAkfLTWxoJ1GbXx6tU0nz6niIxcsY2lTAseV9GcmLogp2G5gwA4lxJcu2NiunJWiWigayZBRbqDsCQzUSN7GLPGaSg1lzlSvl2oiVnD0cd9993HqqacSi8UQQhx39Y2lWLJkCcdj+cuRwEwbFv4CRZBYBjwrhPgBkAAQQnxMCHEHsBf4irfLv1UaHB59hHRR1t9pfl2MkEeTnkxFYjw0eMoS+wYmTkMWbBdfk7VgTW5kDqby/OSZDnpK6NzZgoM7SwbKdSUF2/egRFkOzvbCkJbjlnmEqbxVJirbOVQ8V1/maDqh0Qqmh9bWVoQQ3HLLLYe0//bt23nve99LMpnk9ttv55577jliSvCtra18+ctfZmho6Igcv4JyHMpj4NWo2qePA9d4r0ngX73fBeAC3wY+e5jzq2AWIIQoI0M0VUdoqYniOJK6GS609Z5B6xzMcfoEXaULlhMs+OYkdVBSSjZv62HrgWGklLz7TNUGw/e63FmQi8nbinUXNnRCmoaUEteVaJoo85wyBZtq71qkclZQI2a5Lv3pYv2X32m4JloxUMcKWltbsW2bW2+9lde+dtaqWiYc6ytf+QpXX301tbW1R3SsCg7BQEkpbeATQoj/DbwfVZPUgvLGDqLqn34kpdw2mxOt4PBQWpBbGw9xxYbFmLYzbT0/Hw2JMAjoTk0c4jOdIp19skLdfQNZOody2I5kuISOnik43r6Hb6B81l00pAVeY952iIeNMm8qlbcCAzWSV40cF9ZX0ZPKl7Ub8cOF08ndVfDqoLtb8bDq6+uP8kwqmG0cMhVJSrlTSvlFKeUbpJRrpZSrpZQbpZSfqxinYw9RzxAZuhKaVe0LZu5A1yfCRA2dVM6iYDtjlBYACpYbkCRMr5Pv/sFsmccipeRPuwcC76QwypsBgmMcDnKmg+O4qrOwl4fzQ36lxtOnlUORWr6sMU40pDOSt3mle4ThrEU+EIo9PnUJjwfs3bsXIQRf/vKX+eUvf8mZZ55JNBqlpaWFz3zmM9h28bMSQgTdbZcuXYoQoqxV+fDwMJ/97Gc5+eSTiUQiNDU18d73vpfdu8c2DDdNk5tvvpnTTjuNeDxOMpnkjDPO4Pbbbwfg6quv5itf+UrZWP48D2W8jo4OLr/8cpLJJDU1NbztbW9j165ds3EJTxhUMr1/IfALSyPG4fXUaUhEiIQ0hnMWDzx/gN09aa5+3VLmlrT+KJQUBJuOy67eDPc/18n6BUkuWavqtvvSJh2DWaRUDf78hd91ZeClTMYUnC7yvjRRSMfwDJTp5cVK80wjJXR4n2LeUhtj/2CO/nSBTX9qpyYaQhOKbDGd+rEKDg+//vWvueOOO7juuuv4wAc+wAMPPMAtt9xCXV0dn//85wG45557+NnPfsbPf/5zvvOd79DY2EhVlVLdHx4e5txzz2Xfvn184AMfYO3atXR1dXHHHXdw9tln88wzz7B4sYpTm6bJJZdcQmtrK29605t43/veRzQa5cUXX+RnP/sZH/vYx7j22mtJpVJlYwFBi/WZjDc0NMQFF1xAR0cH1113HWvWrOGRRx7hwgsvJJc7+uorxwoOVc18GbAUqEflnwaB3V4vpgqOQfghvsMtMI2FdWpjYYazGdr7MwznLF45mBploIo5KMtxGciYDOVMtnYOBwaqd6RA1rSpS4RxSogMSutuFpsLWspYhnQtMFCBB1XSGyrteVBSSlLe7801UV6/cg55y6U3XaA/XSARMY5qkW5bR/qojT0ZVi+c/VYsW7duZevWrYFHdN1117F+/Xpuu+22wEC9733vY+fOnfz85z/nne98Z5n39KUvfYndu3fzxz/+kVNPPTV4/eqrr2b9+vXceOON/PCHPwTg1ltvpbW1lc997nN84xvfKJuH690n55xzDqeccsq4Y810vJtvvpm9e/fy7//+71xzjUrlf/SjH+Uf//Ef+dd//VcqUJh2iE8I0SyE+KYQohPYATwE/F/g/3m/7xRC7BdC/IsQounITLeCQ4VvoGajwNRn/g144bnRIrIFq7jwm7YkY9o4jmQ4Zwd9cgYyJnnLpbk6iqEL8iXKE37obTZCfH5NVljXMDwmo2mP9aDSngdVsF0KloOha9TFQixtTHDdxpN471kegaNgB91zKziyGG0EhBBceOGFdHd3k05PbqillGzatIkLLriA+fPn09fXF/wkEgk2bNjAQw89FGy/adMm6urq+NKXvjTmWJo29ec90/Huv/9+mpubueqqq8qO89nPVnhlpZiWByWEuBjV3ynJ+L2XfMxDteL4kBDib6SUrYc9wwpmBb769mwsrqOp6UPZ8lonP5+k6wLLcQPvxLQd0nnFluvPFHBdSUttlKGcieO4mLajlBt85XG3yLg7VJheuDFkaEG78oLtIKUsU4jw816pvIXlqW3oJdT8k5oSrGyp5pWukSkV4I8kjoSncqxi2bJlY15raGgAoL+/PwjljYfe3l76+/t56KGHaGoa/3m51PDs2LGD0047jWh0el2qD3e83bt3c+aZZ6Lr5fdSS0tLhR1YgikNlBDiVOBXQBhwgF8A/w28iArtAdQB64G3AG9Dhf7+Wwhxhqc0XsFRRlN1BF0XQYfcw8Gi+jgIqI9HSOXNMZp5puPVHWkaeccJ+k+BYu6tnZ9kwHttQV2Mdq/dRSpvkSnYZey9gu0eVr6nYCu9vWhIw48Y+kbL9+aEKKpNpPM2lu2OqXMSQvD6FXPY3p1WTMYKjjhGL96lmKpjrf/+G97whml7JYeTm53N8SrdeIuYjgf1A5Rx2gH8jZTyxQm2+yPwfSHEKcB9wMnAv1NUQK/gKGJZUxU3XLJqVkJ8JzVV8YW/Wk3PSIF7nmwvE2OFYojPZ831lxiojsEcK+dWM5yzEEKwqD7Os2FV9DiY9Q1UaYjwcA2UmlsiYgSG1HRcbFcGBksvqYkKaOnjeJpLGxN84a9WY+iV3ufHOpqamqitrSWVSvGGN0zdA3XFihW0tbVRKBSIRCZ+iJvIqMx0vGXLlrF9+3YcxykzxF1dXQwPD0+5/18KJl2thBAbUP2cUsBbJjFOAaSUL6A8qRHgDCFExUAdI6iJhQjPQoJfCEFVNERNLKTyR56BGsyYmLYbhPj8hbxULbxrKMdg1sJyXKoiBlXRUNDyI5WzVL7KlUEg2TqEhod5ywnyYv5cEmEjmE/BdjEdNygE1jUtMFB5j6QxEREiETWIHMUQXwXTg6ZpXHHFFTz11FPcd999427T01PsA3bFFVcwODjI1772tTHblXo0flhxYGDgsMZ7xzvewcGDB7n77rvLtrnpppumOLO/LEzlQfndcL8npRxL5J8AUspdQojvoQRj3wn86RDnV8ExjETYIKRrpPM2Pak8m/60j5PnVAVeSzIWYjhrBQWxIUPR0wcyJgXLpc7TAYyHfQNlB0W6IV3Dst3gWDPBb7Z2s/3gCB+5YFngzcXDGumCMlCW42I75R6Ub8hyloPjVPo9nQj4+te/zhNPPMHll1/O5ZdfzoYNGwiHw7S3t/PrX/+a008/PWDVXX/99fziF7/ga1/7Gk8//TRvetObiEajbN26lVdeeYWHH34YgA0bNgCKzHDFFVcQjUZZt24d69atm9F4N9xwA/feey8f/vCH2bJlC2vXrqW1tZUnn3wyoK9XMLWBOh1FI//pIRz7P1EG6sxD2LeC4wDRkEbE0Bh0XPZ6lPMX9g8HQqq18RD7+lWYztA1oiGd4ZxFf6ZA3nZIenkeXxl8JG8FNVCGJrAY2wpjKliOS3t/loG0yc6edGDg4hEDI6Pqm0zHxfb093RNjAnxAVRXxGCPeySTSZ544gm+9a1v8ZOf/IQHHngAwzBYsGAB5513Hh/60IeCbcPhMA899BDf+ta3uPfee/n85z9PNBpl+fLlAQ0c4HWvex033XQTd955Jx/+8IexbZsbb7yRdevWzWi8uro6HnvsMT75yU9y9913I6Vk48aNbN68mYsvvvhVvU7HMsRkCTkhxF5gIRCVUs6oNaoQIgTkgf1SyglU244uzjjjDPnMM88c7Wkc17jrsd3s6klz2qJant83hBCCpuowfWmTc09q4PEdfYDqKaVr0DWc5/TFdWxpH+SCFU28ZX0Lf9rdz/3PdbJ2XpKC47Lz4AiN1RH6Rgpcfe4SVrbUTHs+HQNZ/s9T+xjMmLxp7Vx29abZ1ZPm7y86mR09aR56qZsNJzVwxpJ6fvjEHkAQ0gVZ0+HLb1/LA8938sdd/fzVKS2cv/zoVUu0tbUdMcHTCiqYDUznHhVCbJFSnnGoY0wVx0gC6ZkaJwBvnxHvGBWcoKiKKE9jn8fEk1JSsF3CnsfkIxbWaaxWFN5dfRkAFtTHgvc0TZCznIDu7VPZC06xaHfzK0pYdjJ0DGaDY4zkrcADq44YRSUJW3lQUqrwou9BSVlUsaiIwVZQwdHHVAaqGsgcxvFzwF9O4cZfIBKegSpl8mVNVehaSjSIhnTm1SoDNZQxCRu6oqujjIShCQYyJrbjEgnpgeHzSRLP7hvk6T0D/PjJ9knn0zmYC8J1w54quRCCRMTA8OpQLMdVLTa8At5oSEdKSd50AqZfstJOo4IKjjqmCrTPRqa4wsk9gZGIKCNUqspg2S7VkVAZ0SAW1rlgeRNLGhKk8hYNiXAguBoN6Ri6YChrkogYzKkudvw1veaHT+8dKKunGg+249I9XBSvHckr5QpDFxi6plh8QqmkWx5JQtdEoHKeKtiBoa2tGKgKKjjqqGSCKzgsJDxFdL92SdeE139JEDE0NE3gupJkzEDTBEsaE2OOEQ8b6JoGOGRMm5Vzq0u8Hcm27hEyJWrjUspx61G6U/mA8h4yNEbyNpogaNZoaAJNqPkpFp8yXn5t2GDWxLQdNE1USBIVVHAMYDoeUrMQwjmUH6D5SJ9ABUcXiYgRFOTWJcJBjVBI1wiXtLiom6S1fKxEaRwJy5urylpj/Hn/UKA8AeVaf6U4MJQnY9rUxsMYmmCkYONIGXhjhqbkjhwpsVwV4tM1EbQi6UnlcVypWnPoFZp5BRUcbUznWygO46eCExyJiBGIsC6oiwfeSNhQOSjd83TqJ5EHioa0wIiEDI2VzdWBwbIcyYjXnynpNQnMWPa4x+lPFyhYLvPrYhi6huM1Tgw8KF3177EdiWW7uK6SY/JljToGctiuPKpq5RVUUEERU8Uxrpni/Qr+wpEIq/xRwYL5tVHylsNA2iSka2VNAkcLzJZCCBEU6zZVRYiFDXS9WFTrkx78wt9cwYGxkcJAUunkOVUcGFI9dVwpgxyTCvGp12xPi0/XRGA82weyuFJSHakYqAoqOBYwqYGSUv7o1ZpIBccnEhGDkGeEFtXHGcpZ7DhIYKA0rxA2OQVt29fbm1OjmH7F3k0ulqNyTtURdYzsOB6U68qARLF8ThV/2Knqr5QHpY5l6KpZo+0WlczDhkZ9IkzY0BjJWURDesWDqqCCYwSVQHsFh4VYSCcZCxEL68yvi9GSVLVNdfEQVVGDqKGRjIWnzOnMqY6gaYLVc6sBApJEtmAHZAafFZgxx8ofpbyap3jYoD4RDujvljPagyqSJEC1H6kvyZ3ZrluROaqggmMExxVVSQjxTVQ7DxPYBVwjpRzy3vsc8EFUS5B/kFL+5qhN9C8Imia4YsNiMgWbsKFz+uI65tVGaaqKEDF0PnjesqD9xmS4aFUz6xfUMs/rzOtTwvOWopn7pAtgTHsPUOG9gu1QEwshhAg8MndMDkp5VaUeVE3UoCpiMJKzKjmoCio4hjDjR0UhxKVCiKum3vKI4LfAOinlKcB24HPenNYA/3975x4mVXUl+t+qfkIDTTfQDQgI8jaGAMFnAMFww0PkFfGKg0BILqJCdODqyJAg8w06EJ1kohlhnPhqFB98gQFyo+K9goPEVysY0ZYA0iiRRzfPpoF+rvvH2VVd1VQ1r+6uQ/f6fd/5zqld+5y99q5TZ52199pr3w58BxgBPCUi9pSpJ5qnJtHWWU4JAaFDRtOQRZLeNIk2zc++CFxyYoDLWjYJuY8nOGvndFlFyBMvpQYFdbi4lJLyytAE2yZhUSyCii0hzIIKLoqYkuR1+7Vu5pZYUGiWareOYfiBC+nL+Fe8dZ7qHVVdr6rBAYj3gQ7ueCzwiqqWqOpuYCdwTTxkNGqHxIBn7ZwuD1pQUrMFdaKUsvJKWqY5BRW2hlTQASMpECAArovPs6CCSi/cyzA41mUYRny50C6+M1zIRWQf0EZV66vbcDrwqju+DE9hBdnr0s5ARGYAMwCys7PZuHFjHYpoXCh7jldw9EA5xQlQXgmtUoW843s5fqiM3RUH2HjkrxH53/+mjOMnKzidcpCNR3ew82A5x4947zIFCQfZePivnCxTvv1bGUkBZduRAMeLK9iTV8DprxP4+kgFxws9d/b9OwvYuD++VlR6ejpFRUVxlcG4NGjRogV33HEHy5Ytq9dyT58+XefPzxqViYhsAjYBm4HNwfGemk65WIFE5P8CbaN8NV9V17g884Fy4KUayo0apl1VnwaeBi+a+ZAhQy5WZKMO2L6/iK8+2ENZhTdxNqtFKt+/PIO9n+ylZft0hlxfFSBfVfl84y5KDp9k6OAeZLdIJXlXIQe2fgvAlX07cE3nTE6VVvB5xQ6KSyvomt2MU3uP0ad/Z3q3a0HHghPs/XM+ZeWVXHPNFXTNim8Iyby8PJo3bx5XGeqLjRs3MnTo0Ii0tLQ0evTowZ133sns2bNJTLykhsvrnaSkpHq/X1JTU+nXr1+dlnG2X/0HblNARSQPyAIQkfaq+m1tC6SqNa6XLCJTgdHAD7VqrZC9eMuCBOkA1LpsRv2RmOCNF6lWujEor4svEJAzVtktKinndFkFqUkJZDX3xpKaJiciIqgqaa6LLyEgVW7mbm5VcGJxq7RkUhK9RRJbNLGHYTyYNGkSo0aNQlXZv38/OTk5zJkzh7y8PJ5++ul4i2fEgbP9Ey8DBoVtV1E1bvWNiOzBs7DeBWLPxKwlRGQE8A/Ajap6MuyrtcAKEfk10B7oDnxY1/IYdUdwDArcZNtAgOSEAAE5U0HtPHiCkrIKWjZNDjlZNHEBaMvKNRQZPSnBTdSt1NAqv8ExqBapSWQ0TaasQs86Z8uoG/r378/kyZNDn++55x569erF73//ex555BHatInf+lxGfKjRSUJV96nqa6o6W1X7Aq2BfXgW1Qd4yuBOYBmQASAifxCR+0Wkn0SL6Hlx/A5vCZC3RGSriCxzcn4OvAZ8AbwB3Kuq579WuOEbgh534Dk1BC2ohIBERE4vr6jk4/wjFJ0up2fbqm65JkkJoSgWQQUl4kU1V9VQUNlU13UUCAh/d+3l/OQHnUlOMi8+P5CWlsZ1112HqrJr166I73Jzcxk/fjytW7cmJSWFnj178sgjj1BeHjmJe8iQIXTu3Jn8/HzGjx9Py5YtycjIYNq0aZw4cYLKykoeffRRunTpQmpqKv3792fz5s1nyFJcXMy8efPo2rUrKSkptG3blilTprBnT9XyL3l5eYgIc+bMiVqfSZMmkZycTEFBQSht37593H333XTq1Ink5GTat2/PjBkzOHjw4Bnnf/7554wYMYK0tDQyMzOZPHly1HwNifPqy1DVoyJS6o5vEJFU4Fo862oekAqMd5sCRSLyrqqOrg1hVbVbDd89AjxSG+UY8ScxEAhZUCgku+XlA0KEBbXt2+McLi4hIy2ZoT2zQulNkqsC0AYn7YKbX0XVUvLhk3LTmyaF4v0Z/iComDIzM0Npf/rTnxg/fjzdunVj7ty5ZGZm8t5777FgwQK2bt3KypUrI65RXFzMTTfdxODBg1m8eDEfffQRzz77LKdPn6ZVq1Z88MEHzJ49m7KyMh5//HFuueUW9uzZExrTKS8vZ/jw4WzevJlbb72VuXPnsmPHDpYuXcr69evJzc2lQ4cO9O7dm6uvvpoVK1bw2GOPkZBQ9aJz/Phx1qxZw8iRI0OW4Ndff831119PaWkpP/3pT+natSs7d+5k6dKlbNiwgdzcXNLTvfVed+/ezaBBgygpKWHWrFl07NiRdevWMWLEiDpt/3hzNieJuXhdeB9Hs0hU9TTwDvCOiNyLp6AGADcCA/EU18jaFtpo+ATHi4I0SUoIs6CqFNQne45w9GQZA7u3Cc29gioFlVBtZd+g0qp0S3akJl5iUSM2/Eu8JYjO0HkXfYmTJ09SWFgYGoNatmwZW7Zs4eqrr6ZHjx6A5zk2ffp0rr32Wt5+++2Q88Rdd93F9773PebMmcPGjRsJd34qLCzkwQcf5IEHHgBg5syZHDlyhNdee43+/fvz3nvvkZTkvZj07t2bsWPHsmLFCu666y4AnnvuOTZv3swDDzzAr371q9B1hw0bxujRo5k3bx7Lly8HYOrUqcyaNYs333yTUaNGhfKuXLmSU6dOMXXq1FBaUClu2bKFDh06hNInTpzIddddx29+8xsWLlwIwPz58zly5Ahvv/12yKHk3nvvZcKECWzZsuWi296vnO3f+RjwHnBMRN4SkYeJGqazClX9RFV/o6o/VtUsvMmzhnFeBMeLgjRJTgwbg/K6+ErKKzh2qpSEgHBTr8jxibTkBPp0aEmvtpGeTcFQTKdKK0hwCszwBw8//DBt2rQhKyuLPn368NRTTzFhwgTWrl0byvPWW29x4MABfvKTn3D06FEKCwtDW1AhrF+/PuK6CQkJzJ49OyJt0KBBqCozZ84MKadgOsCOHTtCaatXryYQCDBvXqQSvvnmm+nbty9r1qyhstJ7aQp24+Xk5ETkzcnJITMzk9Gjvc6kY8eO8cc//pExY8aQmpoaUY/OnTvTrVu3UD0qKytZt24dAwYMiPB2FBEefPDB82jhS4+zdfH9PVWefD90G3gefR/iWVfv4LmhR0VVv6wFOY1GxpkWVNX6UqVuom7R6XLKKpS0lESaJEfeyiLCyO+2O+O613TJ5KP8w5wqrYi66KHvqQVLxa/MmDGDiRMnUlZWxmeffcaSJUvYu3cvqalVkUjy8vIAmD59eszrHDhwIOJzu3btIq4BkJGRAUCXLl2iph86dCiUtnv3btq3bx/6LpzvfOc7bN26lcLCQrKyssjMzOTmm29mzZo1HDt2jPT0dPLz89m0aRP33HMPycmeL9n27duprKzkmWee4ZlnnolajyuuuAKAgwcPcuLECXr16nVGniuvvDJmOzQEzhbN/LfAbwFE5Aq8Lrt/A1rgdeUNAO4PP0dEJgHvquo3dSGw0ThIdFEfgqSlJHpu5mFefMdPlVFWUUmz84j8kN0ilStap7F9fxFVsxQMP9C9e3eGDfNmmYwcOZKBAwcycOBAZs6cySuvvAIQ+s0ee+wx+vbtG/U67du3j/gcPhZUnVjfhd8b53ufTJ06ldWrV7Ny5Up+9rOfsXz5clSVKVOqIsQFrzl58uSIbr9wmjRpEpH3knyhukjO2UlCVb8CvhKRhXgKqi1VY02DgT4u64sAIvI1zsJS1eivCIYRg+oWVNPkRBIDQlKCF0uvolI5frqc0vJKmrU4P6+7m3pls6vgRI2r/Brx54YbbuDOO+8kJyeHn//859xwww10794d8Dz8gsqsrunatStvvPEGR48epWXLlhHfffHFF7Ro0YLWrVuH0kaNGkWbNm3IyckJKahevXpxzTVV0de6deuGiFBaWnrWemRlZdGsWbOQ9Vi9/IbMBXfAq+pBVV2pqvepaj8g6O/4KF6XX1tgMi5qg2GcD8HFBYM0SfaCuia7SOPFJeUUnS6jtKIywkvvXOjUqin3DunGj7/f4eyZjbjyy1/+koSEBBYsWADA8OHDycrKYvHixRw+fPiM/KdOnar1EFHjxo2jsrKSxYsXR6S//vrrbNmyhTFjxhAIVD1Kk5KSmDRpEu+++y4rVqxgx44dZ1hJrVq1YtSoUaxatYr333+f6qhqyB09ISGB0aNHk5uby4YNGyLyhDttNERqfcq8qv4SQESSgevxugUN47zwFjqs+tM3c2NMwYCxJ0srOH6qHNRbe+p8aduySe0IatQp3bp14/bbb+ell15i06ZNDBo0iJycHMaNG0fPnj2ZPn063bp14+jRo3z55ZesWrWK1atXU5shzKZNm8YLL7zAkiVLyM/PZ/DgwezcuZOnnnqK7OxsHn300TPOmTp1Kk888QR33303gUAgYgJykKVLlzJw4EAGDx7MlClT6NevH5WVlXz11VesWbOGKVOmhLz4Fi1axOuvv87o0aOZPXs2HTp0YN26dRFzqhoiF6KgPgDyz5ZJVUtxLugXUIZhhOYsQdXyGUEFVVxazvHTXnDXc1nOw7h0mT9/Pi+//DILFixgw4YNDB8+nI8++ojFixfz4osvUlBQQEZGBl27dmXOnDn06dPn7Bc9D5KSknjzzTdZtGgRr776KqtWraJly5ZMnDiRRYsW0bFjxzPO6d+/P1dddRXbtm1j2LBhEW7kQTp27MjHH3/MkiVLWLNmDS+++CKpqal07NiRW265hdtuuy2Ut2vXrmzatIm5c+fy5JNPkp/aLOkAAA24SURBVJKSwsiRI1m+fDnZ2dm1Wl8/IbU1UCwi+/GimV8y0/AHDBigubm58RbDiMF/vLOL/MJiEhMC/PO4qwB47aNv2PL1Ee64thPvfXWI3QXFzLqpO5dlNCyLKC8vj969e8dbDMOIybncoyLysaoOuNAyarOLbwBwySgnw/8EQxUlhVlSQQvqREk5xSUVBAJCm2bm7GAYDZFaU1Cqure2rmUYUBX1ISlsMm1QQRUUlVBeWUnT5ASLnWcYDRSbRm/4lmCUhwgFlVCloMrKK0lLtqUxDKOhYgrK8C2JNXTxHSoupdRFkTAMo2Fi/27Dt9TUxXfkZCmChBYjNAyj4WEWlOFbgm7miYFIC0oEUG+iYkuLBmEYDRZTUIZvCU7UDbegOmY0pVWzFJISAjRPTaJXu+axTr/ksViBhl+pr3vTuvgM3xKti69N8xTm/qhnvESqNxITEykvL49YCsIw/EJ5eXloLa66xCwow7eE5kElNr4ozqmpqZw4cSLeYhhGVIqKis5YwqQuMAVl+JamzgGieWrjM/TbtGlDQUEBJ0+etK4+wzeoamjl4+DS9XVJ4/vnG5cMAy7PJCkQoHcDHmeKRWpqKtnZ2ezfv5+SkpJ4i2MYIVJSUsjOzq4XC8oUlOFbmiQncF3XVvEWI26kp6eTnp4ebzEMI25YF59hGIbhS0xBGYZhGL7EFJRhGIbhS0xBGYZhGL7EFJRhGIbhS0xBGYZhGL7EFJRhGIbhS6Qxz1IXkSJge7zlOEdaA4XxFuIcuZRkhUtLXpO1bjBZ64aeqnrBM+0b+0Td7ao6IN5CnAsikmuy1g2Xkrwma91gstYNIpJ7MedbF59hGIbhS0xBGYZhGL6ksSuop+MtwHlgstYdl5K8JmvdYLLWDRcla6N2kjAMwzD8S2O3oAzDMAyfYgrKMAzD8CWNVkGJyAgR2S4iO0XkoXjLE46IdBSRDSKSJyKfi8h9Ln2hiPxNRLa6bVS8ZQUQkXwR+czJlOvSMkXkLRHZ4fYZPpCzZ1jbbRWR4yJyv1/aVUSeFZGDIrItLC1qO4rHE+7+/YuI9PeBrI+JyJdOntUi0tKldxaRU2Htu6w+Za1B3pi/u4jMc227XUSG+0DWV8PkzBeRrS49bm1bw3Oq9u5ZVW10G5AA7AKuAJKBT4Er4y1XmHztgP7uuDnwV+BKYCHwv+MtXxR584HW1dJ+BTzkjh8ClsRbzij3wH7gcr+0KzAY6A9sO1s7AqOA1wEBrgM+8IGsPwIS3fGSMFk7h+fzUdtG/d3df+1TIAXo4p4VCfGUtdr3/wosiHfb1vCcqrV7trFaUNcAO1X1K1UtBV4BxsZZphCquk9VP3HHRUAecFl8pTpvxgIvuOMXgHFxlCUaPwR2qeqeeAsSRFX/GzhcLTlWO44FctTjfaCliLSrH0mjy6qq61W13H18H+hQX/KcjRhtG4uxwCuqWqKqu4GdeM+MeqEmWUVEgNuAl+tLnljU8JyqtXu2sSqoy4Bvwj7vxacKQEQ6A/2AD1zSLGceP+uHbjOHAutF5GMRmeHSslV1H3g3MpAVN+miczuRf3I/tivEbke/38PT8d6Wg3QRkS0i8o6IDIqXUFGI9rv7uW0HAQdUdUdYWtzbttpzqtbu2caqoCRKmu/87UWkGfAH4H5VPQ4sBboCfYF9eKa+H/iBqvYHRgL3isjgeAtUEyKSDIwBVrokv7ZrTfj2HhaR+UA58JJL2gd0UtV+wBxghYi0iJd8YcT63X3btsAkIl+s4t62UZ5TMbNGSauxXRurgtoLdAz73AH4Nk6yREVEkvB+9JdUdRWAqh5Q1QpVrQT+k3rsdqgJVf3W7Q8Cq/HkOhA0393+YPwkPIORwCeqegD8266OWO3oy3tYRKYCo4G/Uzfw4LrKDrnjj/HGdHrET0qPGn53v7ZtIjABeDWYFu+2jfacohbv2caqoD4CuotIF/c2fTuwNs4yhXD9zM8Aear667D08P7a8cC26ufWNyKSJiLNg8d4A+Xb8Npzqss2FVgTHwmjEvEW6sd2DSNWO64FpjjPqOuAY8FulXghIiOAfwDGqOrJsPQ2IpLgjq8AugNfxUfKKmr43dcCt4tIioh0wZP3w/qWLwrDgC9VdW8wIZ5tG+s5RW3es/Hw/vDDhudR8le8N4758ZanmmwD8UzfvwBb3TYKWA585tLXAu18IOsVeB5PnwKfB9sSaAX8P2CH22fGW1YnV1PgEJAeluaLdsVTmvuAMry3zZ/Gake87pJ/d/fvZ8AAH8i6E2+MIXjPLnN5f+zujU+BT4BbfNK2MX93YL5r2+3AyHjL6tKfB2ZWyxu3tq3hOVVr96yFOjIMwzB8SWPt4jMMwzB8jikowzAMw5eYgjIMwzB8iSkowzAMw5eYgjIMwzB8iSkow2hgiMgQEVERyY+3LIZxMZiCMhocIvK8e0BvrJY+zi2xMCQ+kl08IjLN1aFvvGUxjLomMd4CGEY9Mo6qGe4b4yjHxTANuBFviZOtMfKcxJtg+rf6Eckw6gZTUIbRwFDVD4Fe8ZbDMC4W6+IzDMMwfIkpKKPBE3QaoKp772E3RhXaopwTEJE73ZLVBSJSKiLfirf09rUxylnorve8O3+WiHwoIkddel+XL1lEbhaR/xSRT0WkUEROi8geEXlJRL4f5drTnJw3uqTnqtUhv3p9a3KSEJGhIrJKRPa7uu0Xb5n2m2o4J1hWZxHp5OTfKyIlIrJbRB6PtdSDq/N9IvJn1x5lInLA1f/fReT6WOUajRfr4jMaA6XAASAdSAWKgROxMrvo7KvwokeDFxCzCG+J69uAW0XkPlX9XaxLuPPHAhXu3HB+BKwL+3zSldEJuAO4TUSmq+rysDynXB0ygSTguEsLUhCrPlHqtwgvGGqwbsfwFpUbB4wTkcWqOq+GS3wPeNbJUoT3otsZmAvcKCI3qGpZWHmJwHqqlGuwzFau3D7u+L1zrYPRODALymjwqOqfVbUtVevoPK6qbcO3aqfk4CmnvwA3A2mqmg5kAP+Itxjfb0XkBzGKnACMAO4BWqhqBpBN1TIIJ4Dn8Jadb62qaaraBLgc+De8F8enRaRTWB1edXL+2SXdV60OV59LW4jI7VQpp98BWU6+NsCTLv0hEZlcw2Wex3PQ+K6qtgCa4UUHLwEGAP+rWv478JTTSeBOoKkrM8XVeRZeNG7DiKS+w97bZltdb3gPUAU2xkhfWMO5w1ye3cRYIgR40OX5Y7X0hS5dgRkXIf8z7hoPR/luo/tuWg3nD3F58qulC94SCAq8HOPcFcFzgUC174J12wakRDn3Sff929XSn3LpS+N9b9h2aW1mQRlGJMFxqudV9XCMPCvcfmhwsbhqHMLrArtQgt1/sSy0C6Uv0M0dL4qR55/c/nJiryz8a1UtiZL+X25/VbX04DLg7TCM88AUlGFEcoPb/71zHDhjA3JdnqZ4YyfVyVXV8poKEZFMEfmlcxo4JCLlYQ4bq1229rVSoyr6u32Bqn4eLYOqhs+f6h8tD96K1NEInpdRLf11tx8rImtFZIKIRGs3w4jAnCQMI5LgW366285G0yhpNTosiMiVwNt441JBivCcHhRIxnvIp51D+edDG7c/2wTevcBlYfmrU93pI8hpt494rqjqOyKyAFgA3OI2RORL4P8A/6GqO84ik9EIMQvKMCIJ/ifGqqqcw5Yf5RoVZynjOTzl9AmeM0VzVW2hqtnqOUJMdPmkNioUhZQ6um5MVPWfgR7APOBNvG6/Xnief1+IyJT6lsnwP6agDCOSA25/ZV1c3HnmXYOnxMao6puqWt3lPfvMM2uFoGXXqcZc0KFa/lpBVXer6mJVHYHnoj4U+G88i+spEcmqzfKMSx9TUEZjotLta7JMgnNxflxHMoQe/qoaq6ttWIx0OLc6xOITt08TkagOECLSA697Lzx/raOqFaq6ERgNlOF1Zw6oq/KMSxNTUEZjIuhN1rKGPM+7/YCzdTuJSHVngHPhmNtnR7MYROS7ePOGYnEudYjFVmCnO/7HGHkWun0+8OEFlHEGIpJcw9elVHWJ1nvXo+FvTEEZjYmg59oIEYnq8qyqb+BFgQB4VkT+KTyviGSIyFgRWQP8+gJkyMNzQhDgVRHp5q6bJCITgLeoIcpFWB0miMi5OHGEUFUFfuE+jhWRJ4PedCLSSkSeACa573+hqpXRrnMB5IjIcyIy3EXpwJXZGXgBL7rHKWBTLZVnNBBMQRmNidXAYbzB+r0isk9E8qPErJuCN6cnAc/z7FsXP+6YO/+/gDEXIoB76P8cr6tuCLBDRI7jKaU/4EVjuL+GSyzHszoGAoUi8jdXh3fPsfxXgUfcx1nAQRE5DBwEZrv0xar60nlVrGZS8ZYJeQM4JiJHRKQYbzL0/8SzoO5S1cJaLNNoAJiCMhoN7gE4FM9CKsBzo77cbeH5ilV1PN74yCo8t+wmeO7fO/Em6t6KF8roQuRYDdyEZy0V4cXW2wM8DvTDs7Binfsl8D9wD3ugrZO/Q6xzolzjF3hhltYAhXihig4Ba4FhWnMcvgvhIbzoG2/ghXtKxlP+u/A8GvtrZNxBwwBAPKvfMAzDMPyFWVCGYRiGLzEFZRiGYfgSU1CGYRiGLzEFZRiGYfgSU1CGYRiGLzEFZRiGYfgSU1CGYRiGLzEFZRiGYfgSU1CGYRiGLzEFZRiGYfiS/w8vOrCk6r/GDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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"
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   },
   "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"
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   },
   "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"
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   },
   "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": [
    "import ndlib.models.epidemics.ThresholdModel as th\n",
    "\n",
    "model = th.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": [
    "import ndlib.models.epidemics.IndependentCascadesModel as ids\n",
    "\n",
    "model = ids.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 = sir.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 = sir.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": {},
     "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": {},
     "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": [
    "import ndlib.models.epidemics.SIModel as si\n",
    "model1 = si.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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": {},
     "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": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ndlib.models.dynamic.DynSIModel as si\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 = si.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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = si.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:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "560c2366-88b6-4dfa-b545-ead65a7c8744"
    }
   },
   "source": [
    "> **if** ``actual_node_status`` **and** ``condition`` **then** ``new_node_status``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e53c17d4-1b13-4161-b96f-1dbb7c79476e"
    }
   },
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4ca11e96-1eb1-4123-a705-41012ffcf906"
    }
   },
   "source": [
    "A custom model, having three statuses (``Susceptible``, ``Infected``, ``Recovered``), can be instantiated as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "nbpresent": {
     "id": "418542af-5735-4c68-9b84-aabe78496ff9"
    }
   },
   "outputs": [],
   "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"
    }
   },
   "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)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e0c2ef51-f35d-4c53-96dd-0ff4acd914f5"
    }
   },
   "source": [
    "<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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "nbpresent": {
     "id": "25732824-6459-41e9-a222-b7acf1aecbe5"
    }
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c6d15e7b-8171-4ce9-bd79-096f46827a52"
    }
   },
   "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*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "11070a54-9acc-4fee-af16-2998e2432de4"
    }
   },
   "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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "nbpresent": {
     "id": "a5b4ec77-7feb-4f22-b163-b00a3c6ca255"
    }
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "03512ce9-3ee3-4984-b103-476707831f36"
    }
   },
   "source": [
    "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*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "57b1e919-113f-4c4f-a95c-88eed24f9dbb"
    }
   },
   "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,
   "metadata": {
    "nbpresent": {
     "id": "f6954013-a642-4949-9573-5cd86a86afb2"
    }
   },
   "outputs": [],
   "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\", c1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "aa70551b-8f19-4c6f-8e90-7e3b135a489d"
    }
   },
   "source": [
    "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*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "bad9d5cc-c2f5-4d24-8ecb-91f15563a1b3"
    }
   },
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5f9418d4-220f-4f32-aeca-202c3b9bb0ba"
    }
   },
   "source": [
    "<img src=\"img/rule.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f2ca9d5b-0b54-48e4-962e-b39a764c7d25"
    }
   },
   "source": [
    "- 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c16e2c4d-83bc-4958-a7e9-92a87011e28c"
    }
   },
   "source": [
    "<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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "nbpresent": {
     "id": "3e1de5f8-a6ff-4a45-9a61-4931005f1e6b"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": {},
     "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": {},
     "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\": 198, \"1\": 101, \"2\": 1}, \"status_delta\": {\"0\": -72, \"1\": 71, \"2\": 1}}, {\"iteration\": 2, \"status\": {}, \"node_count\": {\"0\": 32, \"1\": 257, \"2\": 11}, \"status_delta\": {\"0\": -166, \"1\": 156, \"2\": 10}}, {\"iteration\": 3, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 276, \"2\": 24}, \"status_delta\": {\"0\": -32, \"1\": 19, \"2\": 13}}, {\"iteration\": 4, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 247, \"2\": 53}, \"status_delta\": {\"0\": 0, \"1\": -29, \"2\": 29}}, {\"iteration\": 5, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 224, \"2\": 76}, \"status_delta\": {\"0\": 0, \"1\": -23, \"2\": 23}}, {\"iteration\": 6, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 201, \"2\": 99}, \"status_delta\": {\"0\": 0, \"1\": -23, \"2\": 23}}, {\"iteration\": 7, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 186, \"2\": 114}, \"status_delta\": {\"0\": 0, \"1\": -15, \"2\": 15}}, {\"iteration\": 8, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 173, \"2\": 127}, \"status_delta\": {\"0\": 0, \"1\": -13, \"2\": 13}}, {\"iteration\": 9, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 158, \"2\": 142}, \"status_delta\": {\"0\": 0, \"1\": -15, \"2\": 15}}, {\"iteration\": 10, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 141, \"2\": 159}, \"status_delta\": {\"0\": 0, \"1\": -17, \"2\": 17}}, {\"iteration\": 11, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 132, \"2\": 168}, \"status_delta\": {\"0\": 0, \"1\": -9, \"2\": 9}}, {\"iteration\": 12, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 121, \"2\": 179}, \"status_delta\": {\"0\": 0, \"1\": -11, \"2\": 11}}, {\"iteration\": 13, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 112, \"2\": 188}, \"status_delta\": {\"0\": 0, \"1\": -9, \"2\": 9}}, {\"iteration\": 14, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 100, \"2\": 200}, \"status_delta\": {\"0\": 0, \"1\": -12, \"2\": 12}}, {\"iteration\": 15, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 87, \"2\": 213}, \"status_delta\": {\"0\": 0, \"1\": -13, \"2\": 13}}, {\"iteration\": 16, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 78, \"2\": 222}, \"status_delta\": {\"0\": 0, \"1\": -9, \"2\": 9}}, {\"iteration\": 17, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 70, \"2\": 230}, \"status_delta\": {\"0\": 0, \"1\": -8, \"2\": 8}}, {\"iteration\": 18, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 64, \"2\": 236}, \"status_delta\": {\"0\": 0, \"1\": -6, \"2\": 6}}, {\"iteration\": 19, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 58, \"2\": 242}, \"status_delta\": {\"0\": 0, \"1\": -6, \"2\": 6}}, {\"iteration\": 20, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 51, \"2\": 249}, \"status_delta\": {\"0\": 0, \"1\": -7, \"2\": 7}}, {\"iteration\": 21, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 47, \"2\": 253}, \"status_delta\": {\"0\": 0, \"1\": -4, \"2\": 4}}, {\"iteration\": 22, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 44, \"2\": 256}, \"status_delta\": {\"0\": 0, \"1\": -3, \"2\": 3}}, {\"iteration\": 23, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 39, \"2\": 261}, \"status_delta\": {\"0\": 0, \"1\": -5, \"2\": 5}}, {\"iteration\": 24, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 38, \"2\": 262}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 25, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 35, \"2\": 265}, \"status_delta\": {\"0\": 0, \"1\": -3, \"2\": 3}}, {\"iteration\": 26, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 34, \"2\": 266}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 27, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 30, \"2\": 270}, \"status_delta\": {\"0\": 0, \"1\": -4, \"2\": 4}}, {\"iteration\": 28, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 28, \"2\": 272}, \"status_delta\": {\"0\": 0, \"1\": -2, \"2\": 2}}, {\"iteration\": 29, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 24, \"2\": 276}, \"status_delta\": {\"0\": 0, \"1\": -4, \"2\": 4}}, {\"iteration\": 30, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 19, \"2\": 281}, \"status_delta\": {\"0\": 0, \"1\": -5, \"2\": 5}}, {\"iteration\": 31, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 19, \"2\": 281}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 32, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 17, \"2\": 283}, \"status_delta\": {\"0\": 0, \"1\": -2, \"2\": 2}}, {\"iteration\": 33, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 15, \"2\": 285}, \"status_delta\": {\"0\": 0, \"1\": -2, \"2\": 2}}, {\"iteration\": 34, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 12, \"2\": 288}, \"status_delta\": {\"0\": 0, \"1\": -3, \"2\": 3}}, {\"iteration\": 35, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 12, \"2\": 288}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 36, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 10, \"2\": 290}, \"status_delta\": {\"0\": 0, \"1\": -2, \"2\": 2}}, {\"iteration\": 37, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 8, \"2\": 292}, \"status_delta\": {\"0\": 0, \"1\": -2, \"2\": 2}}, {\"iteration\": 38, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 7, \"2\": 293}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 39, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 6, \"2\": 294}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 40, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 6, \"2\": 294}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 41, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 6, \"2\": 294}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 42, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 6, \"2\": 294}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 43, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 4, \"2\": 296}, \"status_delta\": {\"0\": 0, \"1\": -2, \"2\": 2}}, {\"iteration\": 44, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 3, \"2\": 297}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 45, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 3, \"2\": 297}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 46, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 3, \"2\": 297}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"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\": 1, \"2\": 299}, \"status_delta\": {\"0\": 0, \"1\": -2, \"2\": 2}}, {\"iteration\": 50, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 1, \"2\": 299}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 51, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 1, \"2\": 299}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 52, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 1, \"2\": 299}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 53, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 1, \"2\": 299}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 54, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 1, \"2\": 299}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 55, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": -1, \"2\": 1}}, {\"iteration\": 56, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 57, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 58, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 59, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 60, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 61, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 62, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 63, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 64, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 65, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 66, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 67, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 68, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 69, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 70, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 71, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 72, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"iteration\": 73, \"status\": {}, \"node_count\": {\"0\": 0, \"1\": 0, \"2\": 300}, \"status_delta\": {\"0\": 0, \"1\": 0, \"2\": 0}}, {\"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": [
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     ]
    }
   ],
   "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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