{
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  {
   "cell_type": "code",
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   "source": [
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from bokeh.io import output_notebook, show, save\n",
    "from bokeh.models import Range1d, Circle, ColumnDataSource, MultiLine\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.plotting import from_networkx\n",
    "from bokeh.palettes import Blues8, Reds8, Purples8, Oranges8, Viridis8, Spectral8\n",
    "from bokeh.transform import linear_cmap\n",
    "from pathlib import Path"
   ]
  },
  {
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   "execution_count": 16,
   "metadata": {},
   "outputs": [
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   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
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   "source": [
    "edges_path = Path(r'C:\\Users\\KSpicer\\Documents\\GitHub\\solo_projects\\weird_fiction_visualizations\\datasets\\fake_data_gephi_file.csv')\n",
    "edges_df = pd.read_csv(edges_path)\n",
    "edges_df = edges_df.dropna()\n",
    "edges_df['target'] = edges_df['target'].astype('int')\n",
    "\n",
    "G = nx.from_pandas_edgelist(edges_df, source='source', target='target', edge_attr='Semester and Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1225"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Weird Fiction Discussion Board Visualization'\n",
    "\n",
    "HOVER_TOOLTIPS = [('Poster', '@index')]\n",
    "\n",
    "plot = figure(tooltips=HOVER_TOOLTIPS,\n",
    "    tools='pan, wheel_zoom, save, reset', active_scroll='wheel_zoom',\n",
    "    x_range = Range1d(-50, 50), y_range = Range1d(-50, 50), title=title)\n",
    "\n",
    "network_graph = from_networkx(G, nx.kamada_kawai_layout, scale=50, center=(0,0))\n",
    "\n",
    "network_graph.node_renderer.glyph = Circle(size=15, fill_color='skyblue')\n",
    "\n",
    "network_graph.edge_renderer.glyph = MultiLine(line_alpha=0.5, line_width=1)\n",
    "\n",
    "plot.renderers.append(network_graph)\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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     "data": {
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Application\",\"version\":\"2.4.3\"}};\n  const render_items = [{\"docid\":\"3dbd01dc-ae89-477a-aaee-4e30b36a71a0\",\"root_ids\":[\"3222\"],\"roots\":{\"3222\":\"2f4100a7-dcf2-4559-be6d-f383562dd3d4\"}}];\n  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "3222"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "degrees = dict(nx.degree(G))\n",
    "nx.set_node_attributes(G, name='degree', values=degrees)\n",
    "number_to_adjust_by = 5\n",
    "adjusted_node_size = dict([(node, degree+number_to_adjust_by) for node, degree in nx.degree(G)])\n",
    "nx.set_node_attributes(G, name='adjusted_node_size', values=adjusted_node_size)\n",
    "\n",
    "size_by_this_attribute = 'adjusted_node_size'\n",
    "color_by_this_attribute = 'adjusted_node_size'\n",
    "\n",
    "color_palette = Oranges8\n",
    "\n",
    "HOVER_TOOLTIPS_1 = [('Poster', '@index'), ('Degree', '@degree')]\n",
    "plot_1 = figure(tooltips=HOVER_TOOLTIPS_1,\n",
    "    tools='pan, wheel_zoom, save, reset', active_scroll='wheel_zoom',\n",
    "    x_range = Range1d(-50, 50), y_range = Range1d(-50, 50), title=title)\n",
    "\n",
    "network_graph = from_networkx(G, nx.circular_layout, scale=50, center=(0,0))\n",
    "minimum_value_color = min(network_graph.node_renderer.data_source.data[color_by_this_attribute])\n",
    "maximum_value_color = max(network_graph.node_renderer.data_source.data[color_by_this_attribute])\n",
    "network_graph.node_renderer.glyph = Circle(size=size_by_this_attribute, fill_color=linear_cmap(color_by_this_attribute, color_palette, minimum_value_color, maximum_value_color))\n",
    "\n",
    "network_graph.edge_renderer.glyph = MultiLine(line_alpha=0.5, line_width=1)\n",
    "\n",
    "plot_1.renderers.append(network_graph)\n",
    "\n",
    "show(plot_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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