Untitled.ipynb

{
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.20.2\n",
      "4.5.3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"f99c0d73-5ef8-42c1-a851-c11f64b8e38a\">Loading BokehJS ...</span>\n",
       "    </div>"
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
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       "\n",
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       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
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       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
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       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
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       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
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       "      // store reference to server id on output_area\n",
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       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
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       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
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       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"f99c0d73-5ef8-42c1-a851-c11f64b8e38a\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"f99c0d73-5ef8-42c1-a851-c11f64b8e38a\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'f99c0d73-5ef8-42c1-a851-c11f64b8e38a' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"f99c0d73-5ef8-42c1-a851-c11f64b8e38a\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"f99c0d73-5ef8-42c1-a851-c11f64b8e38a\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"f99c0d73-5ef8-42c1-a851-c11f64b8e38a\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'f99c0d73-5ef8-42c1-a851-c11f64b8e38a' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"f99c0d73-5ef8-42c1-a851-c11f64b8e38a\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===========================================================\n",
    "# Visualizing the March Madness Basketball Turnament Results with datashader\n",
    "\n",
    "# import \n",
    "\n",
    "from dask.distributed import Client, progress\n",
    "import dask.dataframe as dd\n",
    "from dask.distributed import progress\n",
    "\n",
    "import distributed\n",
    "print(distributed.__version__)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "import tornado\n",
    "print(tornado.version)\n",
    "\n",
    "import datashader as ds\n",
    "import datashader.glyphs\n",
    "import datashader.transfer_functions as tf\n",
    "\n",
    "from bokeh.plotting import figure, output_notebook, show\n",
    "from IPython.core.display import HTML, display\n",
    "from bokeh.models import BoxZoomTool\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "from datashader.bokeh_ext import InteractiveImage\n",
    "from datashader.colors import Hot\n",
    "#from datashader.colors import Cold\n",
    "from colorcet import fire\n",
    "\n",
    "from datashader.utils import export_image\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Round</th>\n",
       "      <th>Region_Number</th>\n",
       "      <th>Region_Name</th>\n",
       "      <th>Seed1</th>\n",
       "      <th>Score1</th>\n",
       "      <th>Team1</th>\n",
       "      <th>Team2</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Seed2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>St Johns</td>\n",
       "      <td>Southern</td>\n",
       "      <td>59</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "      <td>VCU</td>\n",
       "      <td>Marshall</td>\n",
       "      <td>65</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>NC State</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>56</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>San Diego St</td>\n",
       "      <td>80</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>5</td>\n",
       "      <td>58</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>65</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Round  Region_Number Region_Name  Seed1  Score1       Team1  \\\n",
       "0  1985      1              1        West      1      83    St Johns   \n",
       "1  1985      1              1        West      2      81         VCU   \n",
       "2  1985      1              1        West      3      65    NC State   \n",
       "3  1985      1              1        West      4      85        UNLV   \n",
       "4  1985      1              1        West      5      58  Washington   \n",
       "\n",
       "          Team2  Score2  Seed2  \n",
       "0      Southern      59     16  \n",
       "1      Marshall      65     15  \n",
       "2        Nevada      56     14  \n",
       "3  San Diego St      80     13  \n",
       "4      Kentucky      65     12  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#====================================================\n",
    "# read in the turnament data\n",
    "\n",
    "# source: https://data.world/michaelaroy/ncaa-tournament-results/workspace/file?filename=Big_Dance_CSV.csv\n",
    "df = dd.read_csv('/home/andrewrs/Desktop/github/march_madness/Big_Dance_CSV.csv', header = 0, sep= ',')  \n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#======================================================================\n",
    "# build the vertices map for the lines  \n",
    "#\n",
    "\n",
    "# centered on zero zero, build by 5s and 10s middle is 20 i guess\n",
    "# this makes 1 quarter of the bracket. \n",
    "\n",
    "# possible x's: \n",
    "x = [-60, -50, -40, -30, -20, -10]\n",
    "\n",
    "# back up 5 for the national champ space \n",
    "x = [it-5 for it in x]\n",
    "\n",
    "# possible y's 1st column\n",
    "y1 = [-80, -70, -60, -50, -40, -30, -20, -10, 10, 20, 30, 40, 50, 60, 70, 80]\n",
    "\n",
    "# possible y's second column\n",
    "y2 = [-75, -55, -35, -15, 15, 35, 55, 75]\n",
    "\n",
    "# possible y's second column\n",
    "y3 = [-65, -25, 25, 65]\n",
    "\n",
    "# possible y's for\n",
    "y4 = [-45, 45]\n",
    "\n",
    "y5 = [0]\n",
    "\n",
    "# build the xy corrdinates\n",
    "\n",
    "x_cord = [np.repeat(x[0], len(y1)).tolist(), \\\n",
    "          np.repeat(x[1], len(y1)).tolist(), \\\n",
    "          np.repeat(x[1], len(y2)).tolist(), \\\n",
    "          np.repeat(x[2], len(y2)).tolist(), \\\n",
    "          np.repeat(x[2], len(y3)).tolist(), \\\n",
    "          np.repeat(x[3], len(y3)).tolist(), \\\n",
    "          np.repeat(x[3], len(y4)).tolist(), \\\n",
    "          np.repeat(x[4], len(y4)).tolist(), \\\n",
    "          np.repeat(x[4], len(y5)).tolist(), \\\n",
    "          np.repeat(x[5], len(y5)).tolist()]\n",
    "\n",
    "y_cord = [y1, y1, y2, y2, y3, y3, y4, y4, y5, y5]\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=======================================================\n",
    "# unnest the points\n",
    "y = [item for sublist in y_cord for item in sublist]\n",
    "x = [item for sublist in x_cord for item in sublist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "output_type": "display_data"
    }
   ],
   "source": [
    "#======================================================\n",
    "# plot the bracket points with bokeh\n",
    "\n",
    "from bokeh.plotting import figure, output_file, show\n",
    "\n",
    "p = figure(plot_width=400, plot_height=400)\n",
    "\n",
    "# make the glyph\n",
    "p.circle(x, y, size = 4, color = 'navy', alpha = 1)\n",
    "\n",
    "# show the frame \n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#===================================================================\n",
    "# now trasform to make 4 of them \n",
    "x_top_left = x\n",
    "y_top_left = [it+100 for it in y]\n",
    "\n",
    "# reflection \n",
    "x_top_right = [it*-1 for it in x]\n",
    "y_top_right = y_top_left\n",
    "\n",
    "x_bottom_left = x\n",
    "y_bottom_left = [it-100 for it in y]\n",
    "\n",
    "# reflection \n",
    "x_bottom_right = [it*-1 for it in x]\n",
    "y_bottom_right = y_bottom_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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{\"b3771f9e-ea47-4e45-abdb-33d8f6df91e4\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"2373eda4-5876-4327-98e6-3cd373186821\",\"type\":\"BasicTicker\"},{\"attributes\":{\"plot\":null,\"text\":\"\"},\"id\":\"983eabb8-0ef1-45b1-b7cc-518a2c41160e\",\"type\":\"Title\"},{\"attributes\":{\"callback\":null},\"id\":\"d6ece714-0bc2-4125-9d92-856491aa6d3c\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"7439ae23-a2a7-4726-8ffb-18638bb85fad\",\"type\":\"SaveTool\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"y\"],\"data\":{\"x\":[-65,-65,-65,-65,-65,-65,-65,-65,-65,-65,-65,-65,-65,-65,-65,-65,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-55,-45,-45,-45,-45,-45,-45,-45,-45,-45,-45,-45,-45,-35,-35,-35,-35,-35,-35,-25,-25,-25,-15],\"y\":[20,30,40,50,60,70,80,90,110,120,130,140,150,160,170,180,20,30,40,50,60,70,80,90,110,120,130,140,150,160,170,180,25,45,65,85,115,135,155,175,25,45,65,85,115,135,155,175,35,75,125,165,35,75,125,165,55,145,55,145,100,100]}},\"id\":\"3c76e882-522d-4d47-ac8a-f34ae208bd04\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"e4573b50-b40b-4e9d-87b1-992cd795a8c2\",\"type\":\"PanTool\"},{\"id\":\"7788cfec-e362-4ddb-8aad-8b11b7785d36\",\"type\":\"WheelZoomTool\"},{\"id\":\"699f8ebd-aa44-4af1-803c-422f06dc4abd\",\"type\":\"BoxZoomTool\"},{\"id\":\"7439ae23-a2a7-4726-8ffb-18638bb85fad\",\"type\":\"SaveTool\"},{\"id\":\"51df7750-69dd-4ded-adc9-5e901c547f87\",\"type\":\"ResetTool\"},{\"id\":\"e9dee7e2-c153-4427-8bcc-0bd038268629\",\"type\":\"HelpTool\"}]},\"id\":\"721b2c8d-b829-46a0-9422-f3e14d00ecd9\",\"type\":\"Toolbar\"},{\"attributes\":{\"formatter\":{\"id\":\"2a9c4f4c-cfd8-4c75-9214-e6b7dc60d507\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"4e5040e1-fc38-4a55-b663-3231224ed88a\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"07f5417d-ea75-4c7b-b5a7-48e6522b84d7\",\"type\":\"BasicTicker\"}},\"id\":\"8d0a998e-1344-4252-a362-dbe97c5cfefe\",\"type\":\"LinearAxis\"},{\"attributes\":{\"formatter\":{\"id\":\"87189a3f-8ec0-4e7b-b14f-bb27acc81519\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"4e5040e1-fc38-4a55-b663-3231224ed88a\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"2373eda4-5876-4327-98e6-3cd373186821\",\"type\":\"BasicTicker\"}},\"id\":\"524dc31c-deae-412c-a144-e85b5084e074\",\"type\":\"LinearAxis\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"4e5040e1-fc38-4a55-b663-3231224ed88a\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"2373eda4-5876-4327-98e6-3cd373186821\",\"type\":\"BasicTicker\"}},\"id\":\"28bc5cef-e070-4437-b1bb-f03a19220726\",\"type\":\"Grid\"},{\"attributes\":{\"plot\":{\"id\":\"4e5040e1-fc38-4a55-b663-3231224ed88a\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"07f5417d-ea75-4c7b-b5a7-48e6522b84d7\",\"type\":\"BasicTicker\"}},\"id\":\"5ada87b7-033d-4715-be2f-9459fa20ca75\",\"type\":\"Grid\"},{\"attributes\":{\"fill_color\":{\"value\":\"navy\"},\"line_color\":{\"value\":\"navy\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1580d7a2-e4e8-4349-84c1-2acf92035fca\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"b1057015-4dc8-4e4b-b482-80590d0ac85c\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"87189a3f-8ec0-4e7b-b14f-bb27acc81519\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null},\"id\":\"0811bcb2-5886-4010-86cc-6ab071f253d9\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"cfd3a56e-1dc9-4e19-a440-220cb3cbb893\",\"type\":\"LinearScale\"},{\"attributes\":{\"data_source\":{\"id\":\"3c76e882-522d-4d47-ac8a-f34ae208bd04\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1580d7a2-e4e8-4349-84c1-2acf92035fca\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"65be440c-72fa-486b-a714-66f08a454d59\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"dbcec93e-f457-4b0c-8377-2b211f1dd190\",\"type\":\"CDSView\"}},\"id\":\"0786cb6a-c9bf-4351-8998-ef3338719c96\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"e4573b50-b40b-4e9d-87b1-992cd795a8c2\",\"type\":\"PanTool\"},{\"attributes\":{\"source\":{\"id\":\"3c76e882-522d-4d47-ac8a-f34ae208bd04\",\"type\":\"ColumnDataSource\"}},\"id\":\"dbcec93e-f457-4b0c-8377-2b211f1dd190\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"7788cfec-e362-4ddb-8aad-8b11b7785d36\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"51df7750-69dd-4ded-adc9-5e901c547f87\",\"type\":\"ResetTool\"},{\"attributes\":{\"below\":[{\"id\":\"8d0a998e-1344-4252-a362-dbe97c5cfefe\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"524dc31c-deae-412c-a144-e85b5084e074\",\"type\":\"LinearAxis\"}],\"plot_height\":400,\"plot_width\":400,\"renderers\":[{\"id\":\"8d0a998e-1344-4252-a362-dbe97c5cfefe\",\"type\":\"LinearAxis\"},{\"id\":\"5ada87b7-033d-4715-be2f-9459fa20ca75\",\"type\":\"Grid\"},{\"id\":\"524dc31c-deae-412c-a144-e85b5084e074\",\"type\":\"LinearAxis\"},{\"id\":\"28bc5cef-e070-4437-b1bb-f03a19220726\",\"type\":\"Grid\"},{\"id\":\"c6e7e207-e42c-4e27-bad3-d1bbe84c0e4b\",\"type\":\"BoxAnnotation\"},{\"id\":\"0786cb6a-c9bf-4351-8998-ef3338719c96\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"983eabb8-0ef1-45b1-b7cc-518a2c41160e\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"721b2c8d-b829-46a0-9422-f3e14d00ecd9\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"d6ece714-0bc2-4125-9d92-856491aa6d3c\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"cfd3a56e-1dc9-4e19-a440-220cb3cbb893\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"0811bcb2-5886-4010-86cc-6ab071f253d9\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"b1057015-4dc8-4e4b-b482-80590d0ac85c\",\"type\":\"LinearScale\"}},\"id\":\"4e5040e1-fc38-4a55-b663-3231224ed88a\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"07f5417d-ea75-4c7b-b5a7-48e6522b84d7\",\"type\":\"BasicTicker\"},{\"attributes\":{\"overlay\":{\"id\":\"c6e7e207-e42c-4e27-bad3-d1bbe84c0e4b\",\"type\":\"BoxAnnotation\"}},\"id\":\"699f8ebd-aa44-4af1-803c-422f06dc4abd\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"e9dee7e2-c153-4427-8bcc-0bd038268629\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"2a9c4f4c-cfd8-4c75-9214-e6b7dc60d507\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"c6e7e207-e42c-4e27-bad3-d1bbe84c0e4b\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"65be440c-72fa-486b-a714-66f08a454d59\",\"type\":\"Circle\"}],\"root_ids\":[\"4e5040e1-fc38-4a55-b663-3231224ed88a\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.13\"}};\n",
       "  var render_items = [{\"docid\":\"b3771f9e-ea47-4e45-abdb-33d8f6df91e4\",\"elementid\":\"54919212-737e-41ba-8739-81bafe5dfe56\",\"modelid\":\"4e5040e1-fc38-4a55-b663-3231224ed88a\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "4e5040e1-fc38-4a55-b663-3231224ed88a"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===============================================\n",
    "# look at top left\n",
    "p = figure(plot_width=400, plot_height=400)\n",
    "\n",
    "# make the glyph\n",
    "p.circle(x_top_left, y_top_left, size = 4, color = 'navy', alpha = 1)\n",
    "\n",
    "# show the frame \n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"d15019e8-029d-4429-a8b4-273aba6ec899\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"930264f3-07b9-4b8b-bca7-1c4abe089ba0\":{\"roots\":{\"references\":[{\"attributes\":{\"source\":{\"id\":\"1e6b6656-9328-41fc-a0de-4e29d380bcf7\",\"type\":\"ColumnDataSource\"}},\"id\":\"99c74d0c-1154-411b-8cfa-80adb81f68ea\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"fee999bc-ae2a-417b-afb9-6c7267166fec\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"b39dc158-2dd7-48c1-a3e0-f58392a99fe1\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"67b2343c-7aec-46dc-a9b1-a12dbb6cc678\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"4041f823-527a-47ab-a895-c6de420b9bd7\",\"type\":\"BasicTicker\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"18a35e29-2dff-4f64-9c9c-0d8f6c3b4a65\",\"type\":\"Circle\"},{\"attributes\":{\"fill_color\":{\"value\":\"navy\"},\"line_color\":{\"value\":\"navy\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"763daf10-22b5-472d-85e7-0aa04e0e4119\",\"type\":\"Circle\"},{\"attributes\":{\"below\":[{\"id\":\"fc90e9ad-538e-4715-a980-dbebdca48e6c\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"5b806650-b001-494c-a50e-3a11423a6a20\",\"type\":\"LinearAxis\"}],\"plot_height\":400,\"plot_width\":400,\"renderers\":[{\"id\":\"fc90e9ad-538e-4715-a980-dbebdca48e6c\",\"type\":\"LinearAxis\"},{\"id\":\"5a8e47af-e72f-45c0-a4b1-26f04da5c845\",\"type\":\"Grid\"},{\"id\":\"5b806650-b001-494c-a50e-3a11423a6a20\",\"type\":\"LinearAxis\"},{\"id\":\"e7ca38f3-0462-4367-a59a-8eabad60edf8\",\"type\":\"Grid\"},{\"id\":\"d6060cf8-af2f-472c-bd2c-cf632423174e\",\"type\":\"BoxAnnotation\"},{\"id\":\"1a1402c6-ed40-4ce3-9e39-af746d61ddcb\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"d274ee68-bd2e-405a-97b1-5fbc45b6a67e\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"b62e25bf-4cda-46ed-aaf0-f535c944c160\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"c3d516d7-aa72-4f0e-9f81-419cac0c85cb\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"fee999bc-ae2a-417b-afb9-6c7267166fec\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"637dfece-58bc-4adb-ac0c-17e73070ef9c\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"b39dc158-2dd7-48c1-a3e0-f58392a99fe1\",\"type\":\"LinearScale\"}},\"id\":\"d366f30e-5bff-478c-a6e6-74b3cce32f3d\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"8f431e18-cbad-44fa-aefd-15b05a94385d\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"formatter\":{\"id\":\"8f431e18-cbad-44fa-aefd-15b05a94385d\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"d366f30e-5bff-478c-a6e6-74b3cce32f3d\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"67b2343c-7aec-46dc-a9b1-a12dbb6cc678\",\"type\":\"BasicTicker\"}},\"id\":\"5b806650-b001-494c-a50e-3a11423a6a20\",\"type\":\"LinearAxis\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"y\"],\"data\":{\"x\":[65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,45,45,45,45,45,45,45,45,45,45,45,45,35,35,35,35,35,35,25,25,25,15],\"y\":[20,30,40,50,60,70,80,90,110,120,130,140,150,160,170,180,20,30,40,50,60,70,80,90,110,120,130,140,150,160,170,180,25,45,65,85,115,135,155,175,25,45,65,85,115,135,155,175,35,75,125,165,35,75,125,165,55,145,55,145,100,100]}},\"id\":\"1e6b6656-9328-41fc-a0de-4e29d380bcf7\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"overlay\":{\"id\":\"d6060cf8-af2f-472c-bd2c-cf632423174e\",\"type\":\"BoxAnnotation\"}},\"id\":\"301a435a-474b-46c0-aa25-8c0380bf64bb\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"formatter\":{\"id\":\"ac60cdfe-0b68-4657-adab-8efc06dde57e\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"d366f30e-5bff-478c-a6e6-74b3cce32f3d\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"4041f823-527a-47ab-a895-c6de420b9bd7\",\"type\":\"BasicTicker\"}},\"id\":\"fc90e9ad-538e-4715-a980-dbebdca48e6c\",\"type\":\"LinearAxis\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"48de0f96-0ae5-477c-bea7-418f40b4f835\",\"type\":\"PanTool\"},{\"id\":\"0171fa0c-f894-4904-8c16-a40080c4448e\",\"type\":\"WheelZoomTool\"},{\"id\":\"301a435a-474b-46c0-aa25-8c0380bf64bb\",\"type\":\"BoxZoomTool\"},{\"id\":\"614bdceb-1f3b-436e-bb3b-a43e2fddeda0\",\"type\":\"SaveTool\"},{\"id\":\"080bd63a-6faf-402f-945b-269481745f05\",\"type\":\"ResetTool\"},{\"id\":\"c4576a8d-4f62-4478-bde1-014a82ed4835\",\"type\":\"HelpTool\"}]},\"id\":\"b62e25bf-4cda-46ed-aaf0-f535c944c160\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"080bd63a-6faf-402f-945b-269481745f05\",\"type\":\"ResetTool\"},{\"attributes\":{\"callback\":null},\"id\":\"637dfece-58bc-4adb-ac0c-17e73070ef9c\",\"type\":\"DataRange1d\"},{\"attributes\":{\"plot\":null,\"text\":\"\"},\"id\":\"d274ee68-bd2e-405a-97b1-5fbc45b6a67e\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"48de0f96-0ae5-477c-bea7-418f40b4f835\",\"type\":\"PanTool\"},{\"attributes\":{\"plot\":{\"id\":\"d366f30e-5bff-478c-a6e6-74b3cce32f3d\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"4041f823-527a-47ab-a895-c6de420b9bd7\",\"type\":\"BasicTicker\"}},\"id\":\"5a8e47af-e72f-45c0-a4b1-26f04da5c845\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null},\"id\":\"c3d516d7-aa72-4f0e-9f81-419cac0c85cb\",\"type\":\"DataRange1d\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"d366f30e-5bff-478c-a6e6-74b3cce32f3d\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"67b2343c-7aec-46dc-a9b1-a12dbb6cc678\",\"type\":\"BasicTicker\"}},\"id\":\"e7ca38f3-0462-4367-a59a-8eabad60edf8\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"1e6b6656-9328-41fc-a0de-4e29d380bcf7\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"763daf10-22b5-472d-85e7-0aa04e0e4119\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"18a35e29-2dff-4f64-9c9c-0d8f6c3b4a65\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"99c74d0c-1154-411b-8cfa-80adb81f68ea\",\"type\":\"CDSView\"}},\"id\":\"1a1402c6-ed40-4ce3-9e39-af746d61ddcb\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"0171fa0c-f894-4904-8c16-a40080c4448e\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"614bdceb-1f3b-436e-bb3b-a43e2fddeda0\",\"type\":\"SaveTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"d6060cf8-af2f-472c-bd2c-cf632423174e\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"c4576a8d-4f62-4478-bde1-014a82ed4835\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"ac60cdfe-0b68-4657-adab-8efc06dde57e\",\"type\":\"BasicTickFormatter\"}],\"root_ids\":[\"d366f30e-5bff-478c-a6e6-74b3cce32f3d\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.13\"}};\n",
       "  var render_items = [{\"docid\":\"930264f3-07b9-4b8b-bca7-1c4abe089ba0\",\"elementid\":\"d15019e8-029d-4429-a8b4-273aba6ec899\",\"modelid\":\"d366f30e-5bff-478c-a6e6-74b3cce32f3d\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "d366f30e-5bff-478c-a6e6-74b3cce32f3d"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===============================================\n",
    "# look at top right \n",
    "p = figure(plot_width=400, plot_height=400)\n",
    "\n",
    "# make the glyph\n",
    "p.circle(x_top_right, y_top_right, size = 4, color = 'navy', alpha = 1)\n",
    "\n",
    "# show the frame \n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"962405bc-93e3-447f-a3ea-2b09c3857f82\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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      "application/javascript": [
       "(function(root) {\n",
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Application\",\"version\":\"0.12.13\"}};\n",
       "  var render_items = [{\"docid\":\"81a5a4b3-1579-4f70-93a6-7df7323dc60e\",\"elementid\":\"962405bc-93e3-447f-a3ea-2b09c3857f82\",\"modelid\":\"0fa6da92-5af5-4b0f-9bd9-058123530b0f\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "0fa6da92-5af5-4b0f-9bd9-058123530b0f"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===============================================\n",
    "# look at bottom left\n",
    "p = figure(plot_width=400, plot_height=400)\n",
    "\n",
    "# make the glyph\n",
    "p.circle(x_bottom_left, y_bottom_left, size = 4, color = 'navy', alpha = 1)\n",
    "\n",
    "# show the frame \n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"10d5f957-be0d-4278-883b-82d74a851951\"></div>\n",
       "</div>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"a841801a-2eba-4c0f-8137-883ccf68f7b5\":{\"roots\":{\"references\":[{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"448d8204-b6ae-4293-a67d-81a96ca44e1b\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"135e9211-6de2-464a-947b-c000f9c659a1\",\"type\":\"BasicTicker\"}},\"id\":\"c04a7b34-1091-481b-af42-7354149392df\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"b05d0275-2da9-437e-930c-b3db42dd9874\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1ec91a3a-7407-4056-92f2-f7a3bbfb1f4a\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"a82d91dc-def9-4d4f-a103-ea7e65567ad5\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"e5894bec-8559-4306-95af-46220b11304e\",\"type\":\"CDSView\"}},\"id\":\"e6bc5311-e1a8-46fe-84af-7f3c4b214b55\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"3d4929da-b132-4117-b28d-b6bca3bd9cb4\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"overlay\":{\"id\":\"ee149545-3f28-4cd8-a55d-b92035f0e0f2\",\"type\":\"BoxAnnotation\"}},\"id\":\"fa362aeb-8a3d-4dc0-b8f4-81b96a6f7227\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"404ce327-ca8e-4e20-a151-db96bf2e3cf6\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"46b78a49-38ce-45fa-b7d9-fea7617a9deb\",\"type\":\"ResetTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"a82d91dc-def9-4d4f-a103-ea7e65567ad5\",\"type\":\"Circle\"},{\"attributes\":{\"formatter\":{\"id\":\"2ffdd1e4-f0be-4bd3-865e-e259b393e696\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"448d8204-b6ae-4293-a67d-81a96ca44e1b\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"135e9211-6de2-464a-947b-c000f9c659a1\",\"type\":\"BasicTicker\"}},\"id\":\"a679ecfc-0d96-4003-9e3e-11de829a1794\",\"type\":\"LinearAxis\"},{\"attributes\":{\"source\":{\"id\":\"b05d0275-2da9-437e-930c-b3db42dd9874\",\"type\":\"ColumnDataSource\"}},\"id\":\"e5894bec-8559-4306-95af-46220b11304e\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"b4c3cd26-eb49-424b-ba79-3e806f9f7c7d\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"y\"],\"data\":{\"x\":[65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,45,45,45,45,45,45,45,45,45,45,45,45,35,35,35,35,35,35,25,25,25,15],\"y\":[-180,-170,-160,-150,-140,-130,-120,-110,-90,-80,-70,-60,-50,-40,-30,-20,-180,-170,-160,-150,-140,-130,-120,-110,-90,-80,-70,-60,-50,-40,-30,-20,-175,-155,-135,-115,-85,-65,-45,-25,-175,-155,-135,-115,-85,-65,-45,-25,-165,-125,-75,-35,-165,-125,-75,-35,-145,-55,-145,-55,-100,-100]}},\"id\":\"b05d0275-2da9-437e-930c-b3db42dd9874\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_color\":{\"value\":\"navy\"},\"line_color\":{\"value\":\"navy\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1ec91a3a-7407-4056-92f2-f7a3bbfb1f4a\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"a4bb6544-ef0d-4ecd-8d3a-78ecfcdb5e46\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"71ef43c3-51e4-498c-9eb5-7c9b7b103cbe\",\"type\":\"PanTool\"},{\"attributes\":{\"below\":[{\"id\":\"6668b7d7-c4fb-4c1b-9055-06897d802896\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"a679ecfc-0d96-4003-9e3e-11de829a1794\",\"type\":\"LinearAxis\"}],\"plot_height\":400,\"plot_width\":400,\"renderers\":[{\"id\":\"6668b7d7-c4fb-4c1b-9055-06897d802896\",\"type\":\"LinearAxis\"},{\"id\":\"86d43250-6304-4c90-9836-0c9892ed5860\",\"type\":\"Grid\"},{\"id\":\"a679ecfc-0d96-4003-9e3e-11de829a1794\",\"type\":\"LinearAxis\"},{\"id\":\"c04a7b34-1091-481b-af42-7354149392df\",\"type\":\"Grid\"},{\"id\":\"ee149545-3f28-4cd8-a55d-b92035f0e0f2\",\"type\":\"BoxAnnotation\"},{\"id\":\"e6bc5311-e1a8-46fe-84af-7f3c4b214b55\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"d1d2aee4-b68f-49f8-afeb-f56dea97b440\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"83db1d71-e5a8-4bdd-81c0-4ff2c27e1f7a\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"66b102ce-ec23-414a-bd1b-ca582585a145\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"8de18544-7358-4d6f-a40a-63ed33f7d7ff\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"7bc85e8b-59c3-48f6-80b7-f86d48e7a4a6\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"b4c3cd26-eb49-424b-ba79-3e806f9f7c7d\",\"type\":\"LinearScale\"}},\"id\":\"448d8204-b6ae-4293-a67d-81a96ca44e1b\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"plot\":{\"id\":\"448d8204-b6ae-4293-a67d-81a96ca44e1b\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"9cb9324e-a92f-4668-b5a5-65ba6286872a\",\"type\":\"BasicTicker\"}},\"id\":\"86d43250-6304-4c90-9836-0c9892ed5860\",\"type\":\"Grid\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"ee149545-3f28-4cd8-a55d-b92035f0e0f2\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"9cb9324e-a92f-4668-b5a5-65ba6286872a\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"2ffdd1e4-f0be-4bd3-865e-e259b393e696\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"formatter\":{\"id\":\"8d6cc4e9-2887-4268-b55b-8e093b29edc6\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"448d8204-b6ae-4293-a67d-81a96ca44e1b\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"9cb9324e-a92f-4668-b5a5-65ba6286872a\",\"type\":\"BasicTicker\"}},\"id\":\"6668b7d7-c4fb-4c1b-9055-06897d802896\",\"type\":\"LinearAxis\"},{\"attributes\":{\"callback\":null},\"id\":\"66b102ce-ec23-414a-bd1b-ca582585a145\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"135e9211-6de2-464a-947b-c000f9c659a1\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"8d6cc4e9-2887-4268-b55b-8e093b29edc6\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null},\"id\":\"7bc85e8b-59c3-48f6-80b7-f86d48e7a4a6\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"8de18544-7358-4d6f-a40a-63ed33f7d7ff\",\"type\":\"LinearScale\"},{\"attributes\":{\"plot\":null,\"text\":\"\"},\"id\":\"d1d2aee4-b68f-49f8-afeb-f56dea97b440\",\"type\":\"Title\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"71ef43c3-51e4-498c-9eb5-7c9b7b103cbe\",\"type\":\"PanTool\"},{\"id\":\"3d4929da-b132-4117-b28d-b6bca3bd9cb4\",\"type\":\"WheelZoomTool\"},{\"id\":\"fa362aeb-8a3d-4dc0-b8f4-81b96a6f7227\",\"type\":\"BoxZoomTool\"},{\"id\":\"a4bb6544-ef0d-4ecd-8d3a-78ecfcdb5e46\",\"type\":\"SaveTool\"},{\"id\":\"46b78a49-38ce-45fa-b7d9-fea7617a9deb\",\"type\":\"ResetTool\"},{\"id\":\"404ce327-ca8e-4e20-a151-db96bf2e3cf6\",\"type\":\"HelpTool\"}]},\"id\":\"83db1d71-e5a8-4bdd-81c0-4ff2c27e1f7a\",\"type\":\"Toolbar\"}],\"root_ids\":[\"448d8204-b6ae-4293-a67d-81a96ca44e1b\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.13\"}};\n",
       "  var render_items = [{\"docid\":\"a841801a-2eba-4c0f-8137-883ccf68f7b5\",\"elementid\":\"10d5f957-be0d-4278-883b-82d74a851951\",\"modelid\":\"448d8204-b6ae-4293-a67d-81a96ca44e1b\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "448d8204-b6ae-4293-a67d-81a96ca44e1b"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===============================================\n",
    "# look at bottom right\n",
    "p = figure(plot_width=400, plot_height=400)\n",
    "\n",
    "# make the glyph\n",
    "p.circle(x_bottom_right, y_bottom_right, size = 4, color = 'navy', alpha = 1)\n",
    "\n",
    "# show the frame \n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#==============================================================\n",
    "# put it all together and get the winner location \n",
    "\n",
    "x_all = x_top_left + x_top_right + x_bottom_right + x_bottom_left\n",
    "y_all = y_top_left + y_top_right + y_bottom_right + y_bottom_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"38cb58c9-6386-4bad-a07b-007080c587bf\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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       "  var render_items = [{\"docid\":\"7fbaf944-9ceb-43c5-9966-108cf9b25514\",\"elementid\":\"38cb58c9-6386-4bad-a07b-007080c587bf\",\"modelid\":\"a39d70d5-5cf5-46e0-b31c-cbc4d9053ea2\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "a39d70d5-5cf5-46e0-b31c-cbc4d9053ea2"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#=============================================================\n",
    "# plot all \n",
    "\n",
    "#===============================================\n",
    "# look at bottom right\n",
    "p = figure(plot_width=400, plot_height=400)\n",
    "\n",
    "# make the glyph\n",
    "p.circle(x_all, y_all, size = 4, color = 'navy', alpha = 1)\n",
    "\n",
    "# show the frame \n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#================================================================\n",
    "# now add one spot on the 1 and three position regions for the semi-final winner\n",
    "\n",
    "# region 1 vs region 4 \n",
    "x_all.append(-15)\n",
    "x_all.append(-5)\n",
    "y_all.append(0)\n",
    "y_all.append(0)\n",
    "\n",
    "\n",
    "# region 2 vs region 3 \n",
    "x_all.append(15)\n",
    "x_all.append(5)\n",
    "y_all.append(0)\n",
    "y_all.append(0)\n",
    "\n",
    "# national champ \n",
    "# region 2 vs region 3 \n",
    "x_all.append(-5)\n",
    "x_all.append(5)\n",
    "y_all.append(50)\n",
    "y_all.append(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"1b38d7d1-c9bd-4761-a816-64e0c3815b09\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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       "  var render_items = [{\"docid\":\"bf4897ca-4846-4b23-be74-66c0ee70d3f0\",\"elementid\":\"1b38d7d1-c9bd-4761-a816-64e0c3815b09\",\"modelid\":\"2fd48d73-bdf2-4df2-ae9c-a800014a5dd5\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "2fd48d73-bdf2-4df2-ae9c-a800014a5dd5"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#=============================================================\n",
    "# plot all with semi and national champ spots \n",
    "\n",
    "#===============================================\n",
    "# look at bottom right\n",
    "p = figure(plot_width=400, plot_height=400)\n",
    "\n",
    "# make the glyph\n",
    "p.circle(x_all, y_all, size = 4, color = 'navy', alpha = 1)\n",
    "\n",
    "# show the frame \n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"74598ac0-137c-4642-801a-2ddf8c1444b7\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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       "  var render_items = [{\"docid\":\"bf545b73-3d80-428f-b664-6de1d756b49a\",\"elementid\":\"74598ac0-137c-4642-801a-2ddf8c1444b7\",\"modelid\":\"ad1bcc86-31ab-40b2-ba52-d183b4932f60\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "ad1bcc86-31ab-40b2-ba52-d183b4932f60"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#=============================================================\n",
    "# scale out the x axis to make it look more like a regular bracket\n",
    "\n",
    "x_all = [it*8 for it in x_all]\n",
    "\n",
    "y_all = [it*2 for it in y_all]\n",
    "#=============================================================\n",
    "# plot all with semi and national champ spots \n",
    "\n",
    "#===============================================\n",
    "# look at bottom right\n",
    "p = figure(plot_width=800, plot_height=600)\n",
    "\n",
    "# make the glyph\n",
    "p.circle(x_all, y_all, size = 4, color = 'navy', alpha = 1)\n",
    "\n",
    "# show the frame \n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"3b2e912f-3324-4eac-bfb4-9c8b62e2214a\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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,440,440,440,440,440,440,440,440,440,440,440,440,440,440,440,440,440,440,440,440,360,360,360,360,360,360,360,360,360,360,360,360,280,280,280,280,280,280,200,200,200,120,-520,-520,-520,-520,-520,-520,-520,-520,-520,-520,-520,-520,-520,-520,-520,-520,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-440,-360,-360,-360,-360,-360,-360,-360,-360,-360,-360,-360,-360,-280,-280,-280,-280,-280,-280,-200,-200,-200,-120,-120,-40,120,40,-40,40],\"y\":[40,60,80,100,120,140,160,180,220,240,260,280,300,320,340,360,40,60,80,100,120,140,160,180,220,240,260,280,300,320,340,360,50,90,130,170,230,270,310,350,50,90,130,170,230,270,310,350,70,150,250,330,70,150,250,330,110,290,110,290,200,200,40,60,80,100,120,140,160,180,220,240,260,280,300,320,340,360,40,60,80,100,120,140,160,180,220,240,260,280,300,320,340,360,50,90,130,170,230,270,310,350,50,90,130,170,230,270,310,350,70,150,250,330,70,150,250,330,110,290,110,290,200,200,-360,-340,-320,-300,-280,-260,-240,-220,-180,-160,-140,-120,-100,-80,-60,-40,-360,-340,-320,-300,-280,-260,-240,-220,-180,-160,-140,-120,-100,-80,-60,-40,-350,-310,-270,-230,-170,-130,-90,-50,-350,-310,-270,-230,-170,-130,-90,-50,-330,-250,-150,-70,-330,-250,-150,-70,-290,-110,-290,-110,-200,-200,-360,-340,-320,-300,-280,-260,-240,-220,-180,-160,-140,-120,-100,-80,-60,-40,-360,-340,-320,-300,-280,-260,-240,-220,-180,-160,-140,-120,-100,-80,-60,-40,-350,-310,-270,-230,-170,-130,-90,-50,-350,-310,-270,-230,-170,-130,-90,-50,-330,-250,-150,-70,-330,-250,-150,-70,-290,-110,-290,-110,-200,-200,0,0,0,0,100,100]}},\"id\":\"524d3520-ddd6-4124-a1f8-849186094f73\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"754221de-a902-474b-9599-92ddf3cf9f9b\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"plot\":{\"id\":\"a2c57497-3633-4817-9336-1bd8fa76c9d2\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"a4a404e4-9d05-4d68-a3c7-e0debe703cce\",\"type\":\"BasicTicker\"}},\"id\":\"30c1196b-5057-46b2-933d-41d2f3851a9e\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"7c2f8ccd-516b-4e38-b84d-3372f67ae725\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null},\"id\":\"4f4064ee-b96a-4698-a877-db84849c1955\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"c8388901-7a14-4d63-8cbb-c2c52c88091f\",\"type\":\"BasicTicker\"},{\"attributes\":{\"plot\":null,\"text\":\"\"},\"id\":\"4f49c193-4fb0-4719-b5be-1b61ec907b1c\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"a4a404e4-9d05-4d68-a3c7-e0debe703cce\",\"type\":\"BasicTicker\"}],\"root_ids\":[\"a2c57497-3633-4817-9336-1bd8fa76c9d2\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.13\"}};\n",
       "  var render_items = [{\"docid\":\"99cae8ef-c501-47ca-9b11-d6e04377f159\",\"elementid\":\"3b2e912f-3324-4eac-bfb4-9c8b62e2214a\",\"modelid\":\"a2c57497-3633-4817-9336-1bd8fa76c9d2\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "a2c57497-3633-4817-9336-1bd8fa76c9d2"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#===============================================\n",
    "# look at bottom right\n",
    "from bokeh.models import HoverTool\n",
    "\n",
    "hover = HoverTool(tooltips=[\n",
    "    (\"x\", \"$x\"),\n",
    "    (\"y\", \"$y\"),\n",
    "])\n",
    "\n",
    "p = figure(plot_width=800, plot_height=600, tools = [hover])\n",
    "\n",
    "# make the glyph\n",
    "p.circle(x_all, y_all, size = 5, color = 'navy', alpha = 1)\n",
    "\n",
    "# show the frame \n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#==========================================================================\n",
    "# define th 64 unique positional paths to the championship \n",
    "\n",
    "# they will be defined by x_RegionNumber_Seed \n",
    "\n",
    "# they should all be length 14 \n",
    "# there is a bunch of structure repeating by each sub-octave \n",
    "# this can be coded out but, I can't think of it right now. \n",
    "# x's are the same for each \n",
    "\n",
    "# defines the path for each seed in region 1 to win the whole turnament\n",
    "x1_1 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_1 = [360, 360, 350, 350, 330, 330, 290, 290, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_16 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_16 = [340, 340, 350, 350, 330, 330, 290, 290, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_8 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_8 = [320, 320, 310, 310, 330, 330, 290, 290, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_9 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_9 = [300, 300, 310, 310, 330, 330, 290, 290, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_5 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_5 = [280, 280, 270, 270, 250, 250, 290, 290, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_12 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_12 = [260, 260, 270, 270, 250, 250, 290, 290, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_4 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_4 = [240, 240, 230, 230, 250, 250, 290, 290, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_13 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_13 = [220, 220, 230, 230, 250, 250, 290, 290, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_6 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_6 = [180, 180, 170, 170, 150, 150, 110, 110, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_11 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_11 = [160, 160, 170, 170, 150, 150, 110, 110, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_3 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_3 = [140, 140, 130, 130, 150, 150, 110, 110, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_14 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_14 = [120, 120, 130, 130, 150, 150, 110, 110, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_7 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_7 = [100, 100, 90, 90, 70, 70, 110, 110, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_10 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_10 = [80, 80, 90, 90, 70, 70, 110, 110, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_2 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_2 = [60, 60, 50, 50, 70, 70, 110, 110, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "x1_15 = [-520, -440, -440, -360, -360, -280, -280, -200, -200, -120, -120, -40, -40, 40]\n",
    "y1_15 = [40, 40, 50, 50, 70, 70, 110, 110, 200, 200, 0, 0, 50, 50]\n",
    "\n",
    "# make a numpy array of the x and ys from region 1 \n",
    "\n",
    "r1_x_list = [x1_1, x1_2, x1_3, x1_4, x1_5, x1_6, x1_7, x1_8, x1_9, x1_10, x1_11, x1_12, x1_13, x1_14, x1_15, x1_16]\n",
    "r1_y_list = [y1_1, y1_2, y1_3, y1_4, y1_5, y1_6, y1_7, y1_8, y1_9, y1_10, y1_11, y1_12, y1_13, y1_14, y1_15, y1_16]\n",
    "\n",
    "r1_x = np.zeros([16, 14])\n",
    "for i in range(0, 16):\n",
    "    r1_x[i,:] = r1_x_list[i]\n",
    "    \n",
    "r1_y = np.zeros([16, 14])\n",
    "for i in range(0, 16):\n",
    "    r1_y[i,:] = r1_y_list[i]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#======================================================\n",
    "# calcualte the rest of the region paths same a before, national champ is unchanged in each\n",
    "# y and x expantion factors \n",
    "r1_y = r1_y * 1.75\n",
    "r1_x = r1_x * 2\n",
    "\n",
    "r4_y = np.copy(r1_y)\n",
    "r4_y[:,0:10] = r4_y[:,0:10] - 400 * 1.75\n",
    "r4_x = np.copy(r1_x)\n",
    "\n",
    "r2_y = np.copy(r1_y)\n",
    "r2_x = np.copy(r1_x) * -1\n",
    "\n",
    "r3_y = np.copy(r2_y)\n",
    "r3_y[:,0:10] = r2_y[:,0:10] - 400 * 1.75\n",
    "r3_x = np.copy(r2_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#======================================================\n",
    "# stack the regions up\n",
    "# !! here, I've run into a problem. I'm not sure what the region numbers mean. \n",
    "#  I've researched it a bit, but I'm having trouble figuring out what the region numbers mean in the \n",
    "#  dataset, and how regions are used in general in the ncaa bracket process. lining them up clockwise from 1 to 4,\n",
    "#  I found that the exhibited idential features to this article: \n",
    "#      https://www.foxsports.com/college-basketball/story/ncaa-tournament-bracket-history-which-seeds-regions-brackets-have-most-winners-2017-teams-can-win-031517\n",
    "# But, in a functionally different order. In mine top right is 1, and 1 plays region 4, which sits below it. \n",
    "# On the top right is region 2 and three sits below it. Comparing my results with the article, It looks like my \n",
    "# Region 1 is the articles region 1, but, the others are different. In this relationship:\n",
    "\n",
    "#  mine,  theirs \n",
    "#  1   ,  1\n",
    "#  2   ,  4\n",
    "#  3   ,  2\n",
    "#  4,  ,  3 \n",
    "\n",
    "# Meaning, in the dataset I'm using region 1 plays region 2, and region 3 plays region 4. My assumption was\n",
    "#  the regions were like seeds, where 1 would play 4 and 2 would play 3. \n",
    "\n",
    "# So, I don't know how to organize these for sure. Here, I have it put so it corresponds to the fox sports article\n",
    "\n",
    "x_lines = np.vstack([r1_x, r4_x, r2_x, r3_x])\n",
    "y_lines = np.vstack([r1_y, r4_y, r2_y, r3_y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "jitter_x = 4\n",
    "jitter_y = 4\n",
    "\n",
    "import math\n",
    "\n",
    "x_big = []\n",
    "y_big = []\n",
    "for i in range(0, 64):\n",
    "    for it in x_lines[i,:].tolist():\n",
    "        x_big.append(it+float(np.random.uniform(-jitter_x, jitter_x, 1)))\n",
    "    x_big.append(math.nan)\n",
    "    for it in y_lines[i,:].tolist():\n",
    "        y_big.append(it+float(np.random.uniform(-jitter_y, jitter_y, 1)))\n",
    "    y_big.append(math.nan)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#==========================================\n",
    "# artificially more lines \n",
    "jitter_x = 3\n",
    "jitter_y = 3\n",
    "import math\n",
    "x_big = []\n",
    "y_big = []\n",
    "\n",
    "for j in range(0, 1000):\n",
    "    for i in range(0, 64):\n",
    "        for it in x_lines[i,:].tolist():\n",
    "            x_big.append(it+float(np.random.standard_t(10, 1)))\n",
    "        x_big.append(math.nan)\n",
    "        for it in y_lines[i,:].tolist():\n",
    "            y_big.append(it+float(np.random.standard_t(10, 1)))\n",
    "        y_big.append(math.nan)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "both = pd.DataFrame(x_big)\n",
    "both['y'] = y_big\n",
    "both.columns = ['x', 'y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(both['x'][14])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#==================================================================================\n",
    "# some color palletes that dont start with black \n",
    "# heres the hot pallete \n",
    "Hot_orig = [\"black\", \"maroon\", \"darkred\", \"red\", \"orangered\", \"darkorange\", \"orange\", \"gold\", \"yellow\", \"white\"]\n",
    "\n",
    "# palletes I got from a graphic design blog. \n",
    "# https://blog.graphiq.com/finding-the-right-color-palettes-for-data-visualizations-fcd4e707a283\n",
    "# droppered them off a few pictures in that webpage \n",
    "\n",
    "cool = ['#000000', '#11174b', '#162065', '#1c2b7f', '#24448e', '#2d5e9e', '#3577ae', '#3d91be',  '#46aace', '#62bed2', '#8acdce', '#b3ddcc', '#dcecc9', '#ffffff']\n",
    "\n",
    "neon = ['#000000', '#2d0f41', '#3d1459', '#4d1a70', '#5e1f88', '#742796', '#973490', '#b8428c',  '#db5087', '#e96a8d', '#ee8b97', '#f3aca2', '#f9cdac', '#ffffff']\n",
    "\n",
    "hot = ['#000000', '#4c3430', '#753c2c', '#9d4429', '#c74e29', '#e35b2c', '#e77235', '#eb8a40',  '#f0a04b', '#f5b857', '#f9d063', '#fde86e', '#fded86', '#ffffff']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from colorcet import fire\n",
    "short_fire = fire[:]\n",
    "\n",
    "short_fire = [short_fire[i] for i in [42, 84, 126, 168, 210, 252]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================================================\n",
    "# define the create image function\n",
    "def create_image(df, x_range, y_range, w, h, cmap):\n",
    "    cvs = ds.Canvas(plot_width=w, plot_height=h, x_range = x_range, y_range = y_range)\n",
    "    agg = cvs.line(df, 'x', 'y', ds.count(), )\n",
    "    return tf.shade(agg, cmap = cmap, how='eq_hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=======================================================\n",
    "# build the raw bracket image\n",
    "test_img = create_image(both, x_range = (-650, 650), y_range = (-450, 450), w=1000, h=800, cmap=short_fire)\n",
    "test_img = tf.dynspread(test_img, threshold = .1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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'/>"
      ],
      "text/plain": [
       "<xarray.Image (y: 800, x: 1000)>\n",
       "array([[4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       ...,\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080]], dtype=uint32)\n",
       "Coordinates:\n",
       "  * y        (y) float64 -449.4 -448.3 -447.2 -446.1 -444.9 -443.8 -442.7 ...\n",
       "  * x        (x) float64 -649.3 -648.0 -646.8 -645.4 -644.1 -642.8 -641.5 ..."
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=================================\n",
    "# export the image \n",
    "export_image(tf.set_background(test_img, 'black'),'pics/bracket_test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [],
   "source": [
    "#======================================================\n",
    "# now the data has to be used to dictate the lines which are drawn. \n",
    "# 1) a list of x and y's for lines origniating at the seed positions and terminating at the team \n",
    "#       in that positions loss \n",
    "# 2) increase the number of identical lines drawn by a multiple for each win  \n",
    "# 3) apply jitter, which will make longer winning lines thicker and brighter after using data shader\n",
    "# 4) Convert to pandas dataframe \n",
    "# 5) plot. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Round</th>\n",
       "      <th>Region_Number</th>\n",
       "      <th>Region_Name</th>\n",
       "      <th>Seed1</th>\n",
       "      <th>Score1</th>\n",
       "      <th>Team1</th>\n",
       "      <th>Team2</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Seed2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>St Johns</td>\n",
       "      <td>Southern</td>\n",
       "      <td>59</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "      <td>VCU</td>\n",
       "      <td>Marshall</td>\n",
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       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>NC State</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>56</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985</td>\n",
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       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>San Diego St</td>\n",
       "      <td>80</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>5</td>\n",
       "      <td>58</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>65</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Round  Region_Number Region_Name  Seed1  Score1       Team1  \\\n",
       "0  1985      1              1        West      1      83    St Johns   \n",
       "1  1985      1              1        West      2      81         VCU   \n",
       "2  1985      1              1        West      3      65    NC State   \n",
       "3  1985      1              1        West      4      85        UNLV   \n",
       "4  1985      1              1        West      5      58  Washington   \n",
       "\n",
       "          Team2  Score2  Seed2  \n",
       "0      Southern      59     16  \n",
       "1      Marshall      65     15  \n",
       "2        Nevada      56     14  \n",
       "3  San Diego St      80     13  \n",
       "4      Kentucky      65     12  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#===========================================================\n",
    "# reading in the data \n",
    "#====================================================\n",
    "# read in the turnament data\n",
    "\n",
    "# source: https://data.world/michaelaroy/ncaa-tournament-results/workspace/file?filename=Big_Dance_CSV.csv\n",
    "df = dd.read_csv('/home/andrewrs/Desktop/github/march_madness/Big_Dance_CSV.csv', header = 0, sep= ',')  \n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Round</th>\n",
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       "      <th>Region_Name</th>\n",
       "      <th>Seed1</th>\n",
       "      <th>Score1</th>\n",
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       "      <th>Team2</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Seed2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>83</td>\n",
       "      <td>St Johns</td>\n",
       "      <td>Southern</td>\n",
       "      <td>59</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "      <td>VCU</td>\n",
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       "      <td>15</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>NC State</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>56</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>San Diego St</td>\n",
       "      <td>80</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>5</td>\n",
       "      <td>58</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>65</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Round  Region_Number Region_Name  Seed1  Score1       Team1  \\\n",
       "0  1985      1              1        West      1      83    St Johns   \n",
       "1  1985      1              1        West      2      81         VCU   \n",
       "2  1985      1              1        West      3      65    NC State   \n",
       "3  1985      1              1        West      4      85        UNLV   \n",
       "4  1985      1              1        West      5      58  Washington   \n",
       "\n",
       "          Team2  Score2  Seed2  \n",
       "0      Southern      59     16  \n",
       "1      Marshall      65     15  \n",
       "2        Nevada      56     14  \n",
       "3  San Diego St      80     13  \n",
       "4      Kentucky      65     12  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#==================================================\n",
    "# make a map for the first year\n",
    "df_1985 = df.loc[(df['Year'] == 1985),:]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#========================================================\n",
    "# each team must be matched to their region_seed position\n",
    "\n",
    "region = [1,2,3,4] * 16\n",
    "seed = np.repeat(np.linspace(1, 16, 16), 4)\n",
    "\n",
    "lookup = pd.DataFrame(region)\n",
    "lookup[1] = seed\n",
    "lookup.columns = ['region', 'seed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
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       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
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       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
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       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
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       "      <th>35</th>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>37</th>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>3</td>\n",
       "      <td>14.0</td>\n",
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       "      <th>55</th>\n",
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       "      <th>56</th>\n",
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       "      <th>57</th>\n",
       "      <td>2</td>\n",
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       "      <th>58</th>\n",
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       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>4</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region  seed\n",
       "0        1   1.0\n",
       "1        2   1.0\n",
       "2        3   1.0\n",
       "3        4   1.0\n",
       "4        1   2.0\n",
       "5        2   2.0\n",
       "6        3   2.0\n",
       "7        4   2.0\n",
       "8        1   3.0\n",
       "9        2   3.0\n",
       "10       3   3.0\n",
       "11       4   3.0\n",
       "12       1   4.0\n",
       "13       2   4.0\n",
       "14       3   4.0\n",
       "15       4   4.0\n",
       "16       1   5.0\n",
       "17       2   5.0\n",
       "18       3   5.0\n",
       "19       4   5.0\n",
       "20       1   6.0\n",
       "21       2   6.0\n",
       "22       3   6.0\n",
       "23       4   6.0\n",
       "24       1   7.0\n",
       "25       2   7.0\n",
       "26       3   7.0\n",
       "27       4   7.0\n",
       "28       1   8.0\n",
       "29       2   8.0\n",
       "..     ...   ...\n",
       "34       3   9.0\n",
       "35       4   9.0\n",
       "36       1  10.0\n",
       "37       2  10.0\n",
       "38       3  10.0\n",
       "39       4  10.0\n",
       "40       1  11.0\n",
       "41       2  11.0\n",
       "42       3  11.0\n",
       "43       4  11.0\n",
       "44       1  12.0\n",
       "45       2  12.0\n",
       "46       3  12.0\n",
       "47       4  12.0\n",
       "48       1  13.0\n",
       "49       2  13.0\n",
       "50       3  13.0\n",
       "51       4  13.0\n",
       "52       1  14.0\n",
       "53       2  14.0\n",
       "54       3  14.0\n",
       "55       4  14.0\n",
       "56       1  15.0\n",
       "57       2  15.0\n",
       "58       3  15.0\n",
       "59       4  15.0\n",
       "60       1  16.0\n",
       "61       2  16.0\n",
       "62       3  16.0\n",
       "63       4  16.0\n",
       "\n",
       "[64 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "#==========================================================================================\n",
    "# match team to region seed position\n",
    "\n",
    "a_data = pd.read_csv('/home/andrewrs/Desktop/github/march_madness/Big_Dance_CSV.csv')\n",
    "b_data = b_data.loc[(b_data['Year'] == 1985),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Round</th>\n",
       "      <th>Region_Number</th>\n",
       "      <th>Region_Name</th>\n",
       "      <th>Seed1</th>\n",
       "      <th>Score1</th>\n",
       "      <th>Team1</th>\n",
       "      <th>Team2</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Seed2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>St Johns</td>\n",
       "      <td>Southern</td>\n",
       "      <td>59</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "      <td>VCU</td>\n",
       "      <td>Marshall</td>\n",
       "      <td>65</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>NC State</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>56</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>San Diego St</td>\n",
       "      <td>80</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "      <td>5</td>\n",
       "      <td>58</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>65</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Round  Region_Number Region_Name  Seed1  Score1       Team1  \\\n",
       "0  1985      1              1        West      1      83    St Johns   \n",
       "1  1985      1              1        West      2      81         VCU   \n",
       "2  1985      1              1        West      3      65    NC State   \n",
       "3  1985      1              1        West      4      85        UNLV   \n",
       "4  1985      1              1        West      5      58  Washington   \n",
       "\n",
       "          Team2  Score2  Seed2  \n",
       "0      Southern      59     16  \n",
       "1      Marshall      65     15  \n",
       "2        Nevada      56     14  \n",
       "3  San Diego St      80     13  \n",
       "4      Kentucky      65     12  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#==============================================\n",
    "# get 3 column dataframe region number, seed, team name, for the year \n",
    "# no need for joins and things here yet I think\n",
    "\n",
    "\n",
    "region_number = b_data['Region_Number'].append(b_data['Region_Number'])\n",
    "\n",
    "seed = b_data['Seed1'].append(b_data['Seed2'])\n",
    "\n",
    "team = b_data['Team1'].append(b_data['Team2'])\n",
    "\n",
    "lookup = pd.DataFrame(region_number)\n",
    "lookup[1] = seed\n",
    "lookup[2] = team\n",
    "lookup.columns = ['region', 'seed', 'team']\n",
    "\n",
    "# needs assignment\n",
    "lookup = lookup.drop_duplicates(subset = ['region', 'seed'], keep='first')\n",
    "\n",
    "len(lookup)\n",
    "\n",
    "#lookup.merge(b_data, left_on = ['region', 'seed'], right_on = ['Region_Number','Seed1' ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================================================\n",
    "# Now team names correspond to region-positions, which correspond to \n",
    "#     unique geometric paths to the championship defined eariler\n",
    "# now I need the length of each team path defined through \"win-steps\"\n",
    "# I'll try this by searching for the number of times the team is listed, which will work until the championship\n",
    "# one minus this number is the number of point pairs, or win steps to light up. \n",
    "\n",
    "all_teams = lookup['team']\n",
    "\n",
    "wins = []\n",
    "\n",
    "for team in all_teams:\n",
    "\n",
    "    sub = b_data.loc[(b_data['Team1'] == team),:]\n",
    "    sub2 = b_data.loc[(b_data['Team2'] == team),:]\n",
    "\n",
    "    # check both sides \n",
    "    max_round1 = sub['Round'].max()\n",
    "    max_round2 = sub2['Round'].max()\n",
    "    \n",
    "    if math.isnan(max_round1):\n",
    "        max_round1 = 0\n",
    "    if math.isnan(max_round2):\n",
    "        max_round2 = 0\n",
    "\n",
    "    # get the total max\n",
    "    max_round = np.max([max_round1, max_round2])\n",
    "\n",
    "    if max_round == 6:\n",
    "        champ_game = b_data.loc[(b_data['Round'] == 6),:]\n",
    "\n",
    "        if champ_game['Team1'].item() == team:\n",
    "            if champ_game['Score1'].item() > champ_game['Score2'].item():\n",
    "                max_round = 7\n",
    "            elif champ_game['Score2'].item() > champ_game['Score1'].item():\n",
    "                max_round = 6\n",
    "        if champ_game['Team2'].item() == team:\n",
    "            if champ_game['Score2'].item() > champ_game['Score1'].item():\n",
    "                max_round = 7\n",
    "            elif champ_game['Score1'].item() > champ_game['Score2'].item():\n",
    "                max_round = 6 \n",
    "    else:\n",
    "        max_round = max_round\n",
    "    number_of_wins = max_round - 1\n",
    "    wins.append(number_of_wins)\n",
    "\n",
    "lookup['wins'] = wins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>seed</th>\n",
       "      <th>team</th>\n",
       "      <th>wins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>St Johns</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>VCU</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NC State</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>UNLV</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Tulsa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Georgia Tech</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Loyola Illinois</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>SMU</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>Temple</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Duke</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Ohio St</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Louisiana Tech</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Texas Tech</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>UAB</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>USC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>LSU</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>Purdue</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>Marshall</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>San Diego St</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>UTEP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>Lehigh</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>Mercer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>Iona</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>Old Dominion</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>Wichita St</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>DePaul</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>North Carolina A&amp;T</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>Pepperdine</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>Iowa St</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>Boston College</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Michigan St</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>Illinois St</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>Fairleigh Dickinson</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>Middle Tennessee St</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>Miami Ohio</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>Auburn</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>Oregon St</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>Dayton</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region  seed                 team  wins\n",
       "0        1     1             St Johns     4\n",
       "1        1     2                  VCU     1\n",
       "2        1     3             NC State     3\n",
       "3        1     4                 UNLV     1\n",
       "4        1     5           Washington     0\n",
       "5        1     6                Tulsa     0\n",
       "6        1     7              Alabama     2\n",
       "7        1     8                 Iowa     0\n",
       "8        2     1           Georgetown     5\n",
       "9        2     2         Georgia Tech     3\n",
       "10       2     3             Illinois     2\n",
       "11       2     4      Loyola Illinois     2\n",
       "12       2     5                  SMU     1\n",
       "13       2     6              Georgia     1\n",
       "14       2     7             Syracuse     1\n",
       "15       2     8               Temple     1\n",
       "16       3     1             Oklahoma     3\n",
       "17       3     2              Memphis     4\n",
       "18       3     3                 Duke     1\n",
       "19       3     4              Ohio St     1\n",
       "20       3     5       Louisiana Tech     2\n",
       "21       3     6           Texas Tech     0\n",
       "22       3     7                  UAB     1\n",
       "23       3     8                  USC     0\n",
       "24       4     1             Michigan     1\n",
       "25       4     2       North Carolina     3\n",
       "26       4     3               Kansas     1\n",
       "27       4     4                  LSU     0\n",
       "28       4     5             Maryland     2\n",
       "29       4     6               Purdue     0\n",
       "..     ...   ...                  ...   ...\n",
       "1        1    15             Marshall     0\n",
       "2        1    14               Nevada     0\n",
       "3        1    13         San Diego St     0\n",
       "4        1    12             Kentucky     2\n",
       "5        1    11                 UTEP     1\n",
       "6        1    10              Arizona     0\n",
       "7        1     9             Arkansas     1\n",
       "8        2    16               Lehigh     0\n",
       "9        2    15               Mercer     0\n",
       "10       2    14         Northeastern     0\n",
       "11       2    13                 Iona     0\n",
       "12       2    12         Old Dominion     0\n",
       "13       2    11           Wichita St     0\n",
       "14       2    10               DePaul     0\n",
       "15       2     9        Virginia Tech     0\n",
       "16       3    16   North Carolina A&T     0\n",
       "17       3    15         Pennsylvania     0\n",
       "18       3    14           Pepperdine     0\n",
       "19       3    13              Iowa St     0\n",
       "20       3    12           Pittsburgh     0\n",
       "21       3    11       Boston College     2\n",
       "22       3    10          Michigan St     0\n",
       "23       3     9          Illinois St     1\n",
       "24       4    16  Fairleigh Dickinson     0\n",
       "25       4    15  Middle Tennessee St     0\n",
       "26       4    14                 Ohio     0\n",
       "28       4    12           Miami Ohio     0\n",
       "29       4    11               Auburn     2\n",
       "30       4    10            Oregon St     0\n",
       "31       4     9               Dayton     0\n",
       "\n",
       "[64 rows x 4 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#==========================================\n",
    "# define line points dependent on number of wins\n",
    "# length has to include the connector, so they will all be odd, except the last \n",
    "def length_index(wins):\n",
    "    ll = 2 + (wins*2) + 1\n",
    "    if ll > 14:\n",
    "        ll = 14\n",
    "    return ll\n",
    "\n",
    "# defines xy index based on the way the x and y line geometeries were desiged and stored\n",
    "def xy_index(region, seed):\n",
    "    xy_index = (((region - 1)*16) + seed) - 1\n",
    "    return xy_index\n",
    "\n",
    "# repeating lines: scale true paths linearly\n",
    "def repeater_true(wins, multi = 100, base = 1):\n",
    "    if wins == 0:\n",
    "        lr = base    \n",
    "    else:\n",
    "        lr = (wins+1)*multi\n",
    "    return lr\n",
    "\n",
    "# repeating lines\n",
    "def repeater_highlight(wins, multi = 1000, base = 2):\n",
    "    if wins == 0:\n",
    "        lr = base\n",
    "    elif wins == 5:\n",
    "        lr = wins*(multi**2)\n",
    "    elif wins == 6:\n",
    "        lr = wins*(multi**3)    \n",
    "    else:\n",
    "        lr = wins*multi\n",
    "    return lr\n",
    "\n",
    "\n",
    "def jitter_val(jitter = 5, df = 10):\n",
    "    jv = float(np.random.standard_t(10, 1))\n",
    "    if jv > jitter:\n",
    "        jv = jitter\n",
    "    if jv < -jitter:\n",
    "        jv = -jitter\n",
    "    return jv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'lookup' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-602a98b346f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# for loops for filling in all the lines based on the data. This is still just one year\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m#     needs to be built into a for loop including each year.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mregion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlookup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'region'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlookup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'seed'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlookup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'wins'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m     \u001b[0;31m# get length and line position indecies\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mli\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlength_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'lookup' is not defined"
     ]
    }
   ],
   "source": [
    "#=============================================================\n",
    "# create win paths for each team in each year uniquely by applying a partial region seed position path \n",
    "# Also, multiplicative increase in number of lines drawn to represent win path based on how many wins\n",
    "# This is what the heatmap will be aggregating later \n",
    "#       (experiment with the number of lines increase and the jitter hyperparameters and jitter disribution)\n",
    "\n",
    "\n",
    "jitter_x = 4\n",
    "jitter_y = 4\n",
    "\n",
    "import math\n",
    "\n",
    "# Must length (number of points) depends on number of wins\n",
    "x_big = []\n",
    "y_big = []\n",
    "\n",
    "# for loops for filling in all the lines based on the data. This is still just one year\n",
    "#     needs to be built into a for loop including each year. \n",
    "for region, seed, wins, in zip(lookup['region'], lookup['seed'], lookup['wins']):\n",
    "    # get length and line position indecies\n",
    "    li = length_index(wins)\n",
    "    xyi = xy_index(region, seed)\n",
    "    rep_lines = line_repeater(wins)\n",
    "    \n",
    "    for i in range(0, rep_lines):\n",
    "        for it in x_lines[xyi,0:li].tolist():\n",
    "            x_big.append(it + float(np.random.standard_t(10, 1)))\n",
    "        x_big.append(math.nan)\n",
    "    \n",
    "    for i in range(0, rep_lines):\n",
    "        for it in y_lines[xyi,:li].tolist():\n",
    "            y_big.append(it + float(np.random.standard_t(10, 1)))\n",
    "        y_big.append(math.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "both = pd.DataFrame(x_big)\n",
    "both['y'] = y_big\n",
    "both.columns = ['x', 'y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>x</th>\n",
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       "      <td>327.047539</td>\n",
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       "      <td>0.426124</td>\n",
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       "      <th>11</th>\n",
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       "      <td>329.260787</td>\n",
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       "      <td>-278.126647</td>\n",
       "      <td>289.605656</td>\n",
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       "      <th>19</th>\n",
       "      <td>-198.691141</td>\n",
       "      <td>291.391673</td>\n",
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       "      <td>201.188120</td>\n",
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       "      <td>200.558695</td>\n",
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       "      <td>-1.015997</td>\n",
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       "      <th>23</th>\n",
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       "      <th>26</th>\n",
       "      <td>-439.539377</td>\n",
       "      <td>349.663639</td>\n",
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       "      <th>27</th>\n",
       "      <td>-359.070072</td>\n",
       "      <td>350.909315</td>\n",
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       "      <th>28</th>\n",
       "      <td>-360.309688</td>\n",
       "      <td>331.878764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-280.663913</td>\n",
       "      <td>333.101402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600226</th>\n",
       "      <td>-441.569823</td>\n",
       "      <td>-230.791723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600227</th>\n",
       "      <td>-360.311323</td>\n",
       "      <td>-230.712596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600228</th>\n",
       "      <td>-359.926698</td>\n",
       "      <td>-249.041264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600229</th>\n",
       "      <td>-278.667142</td>\n",
       "      <td>-251.349748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600230</th>\n",
       "      <td>-281.245495</td>\n",
       "      <td>-289.791105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600231</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600232</th>\n",
       "      <td>-521.343658</td>\n",
       "      <td>-237.654131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600233</th>\n",
       "      <td>-438.175296</td>\n",
       "      <td>-240.248526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600234</th>\n",
       "      <td>-438.997045</td>\n",
       "      <td>-229.052725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600235</th>\n",
       "      <td>-360.243429</td>\n",
       "      <td>-230.512058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600236</th>\n",
       "      <td>-359.393038</td>\n",
       "      <td>-250.601486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600237</th>\n",
       "      <td>-277.788180</td>\n",
       "      <td>-250.369853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600238</th>\n",
       "      <td>-280.006464</td>\n",
       "      <td>-289.838211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600239</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600240</th>\n",
       "      <td>-519.850556</td>\n",
       "      <td>-321.338542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600241</th>\n",
       "      <td>-440.797497</td>\n",
       "      <td>-320.405361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600242</th>\n",
       "      <td>-439.826249</td>\n",
       "      <td>-310.201933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600243</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600244</th>\n",
       "      <td>-522.065194</td>\n",
       "      <td>-319.724425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600245</th>\n",
       "      <td>-439.698524</td>\n",
       "      <td>-318.924134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600246</th>\n",
       "      <td>-439.941634</td>\n",
       "      <td>-310.695630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600247</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600248</th>\n",
       "      <td>-520.954386</td>\n",
       "      <td>-100.082208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600249</th>\n",
       "      <td>-441.148363</td>\n",
       "      <td>-99.820635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600250</th>\n",
       "      <td>-440.603245</td>\n",
       "      <td>-89.454086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600251</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600252</th>\n",
       "      <td>-520.158916</td>\n",
       "      <td>-98.712423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600253</th>\n",
       "      <td>-439.916636</td>\n",
       "      <td>-99.267046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600254</th>\n",
       "      <td>-440.550332</td>\n",
       "      <td>-90.251604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600255</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600256 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 x           y\n",
       "0      -518.721653  359.512515\n",
       "1      -438.303831  359.879452\n",
       "2      -440.330083  348.317224\n",
       "3      -360.056039  348.926266\n",
       "4      -358.280885  328.299650\n",
       "5      -280.583813  327.047539\n",
       "6      -279.075665  289.453399\n",
       "7      -199.948818  291.546821\n",
       "8      -197.754174  202.979365\n",
       "9      -119.948988  199.338766\n",
       "10     -121.979548    0.426124\n",
       "11             NaN         NaN\n",
       "12     -520.056169  359.251052\n",
       "13     -438.999204  359.927651\n",
       "14     -439.814940  348.056906\n",
       "15     -359.438796  350.355826\n",
       "16     -360.323731  330.034540\n",
       "17     -281.222405  329.260787\n",
       "18     -278.126647  289.605656\n",
       "19     -198.691141  291.391673\n",
       "20     -200.610370  201.188120\n",
       "21     -121.228533  200.558695\n",
       "22     -118.976187   -1.015997\n",
       "23             NaN         NaN\n",
       "24     -518.610216  359.259496\n",
       "25     -439.282761  360.148278\n",
       "26     -439.539377  349.663639\n",
       "27     -359.070072  350.909315\n",
       "28     -360.309688  331.878764\n",
       "29     -280.663913  333.101402\n",
       "...            ...         ...\n",
       "600226 -441.569823 -230.791723\n",
       "600227 -360.311323 -230.712596\n",
       "600228 -359.926698 -249.041264\n",
       "600229 -278.667142 -251.349748\n",
       "600230 -281.245495 -289.791105\n",
       "600231         NaN         NaN\n",
       "600232 -521.343658 -237.654131\n",
       "600233 -438.175296 -240.248526\n",
       "600234 -438.997045 -229.052725\n",
       "600235 -360.243429 -230.512058\n",
       "600236 -359.393038 -250.601486\n",
       "600237 -277.788180 -250.369853\n",
       "600238 -280.006464 -289.838211\n",
       "600239         NaN         NaN\n",
       "600240 -519.850556 -321.338542\n",
       "600241 -440.797497 -320.405361\n",
       "600242 -439.826249 -310.201933\n",
       "600243         NaN         NaN\n",
       "600244 -522.065194 -319.724425\n",
       "600245 -439.698524 -318.924134\n",
       "600246 -439.941634 -310.695630\n",
       "600247         NaN         NaN\n",
       "600248 -520.954386 -100.082208\n",
       "600249 -441.148363  -99.820635\n",
       "600250 -440.603245  -89.454086\n",
       "600251         NaN         NaN\n",
       "600252 -520.158916  -98.712423\n",
       "600253 -439.916636  -99.267046\n",
       "600254 -440.550332  -90.251604\n",
       "600255         NaN         NaN\n",
       "\n",
       "[600256 rows x 2 columns]"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================================================\n",
    "# define the create image function\n",
    "def create_image(df, x_range, y_range, w, h, cmap):\n",
    "    cvs = ds.Canvas(plot_width=w, plot_height=h, x_range = x_range, y_range = y_range)\n",
    "    agg = cvs.line(df, 'x', 'y', ds.count(), )\n",
    "    return tf.shade(agg, cmap = cmap, how='eq_hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=======================================================\n",
    "# build the raw bracket image\n",
    "test_img = create_image(both, x_range = (-650, 650), y_range = (-450, 450), w=1000, h=800, cmap = fire)\n",
    "test_img = tf.dynspread(test_img, threshold = .1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
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'/>"
      ],
      "text/plain": [
       "<xarray.Image (y: 800, x: 1000)>\n",
       "array([[4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       ...,\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080]], dtype=uint32)\n",
       "Coordinates:\n",
       "  * y        (y) float64 -449.4 -448.3 -447.2 -446.1 -444.9 -443.8 -442.7 ...\n",
       "  * x        (x) float64 -649.3 -648.0 -646.8 -645.4 -644.1 -642.8 -641.5 ..."
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=================================\n",
    "# export the image \n",
    "export_image(tf.set_background(test_img, 'black'),'pics/1985')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Round</th>\n",
       "      <th>Region_Number</th>\n",
       "      <th>Region_Name</th>\n",
       "      <th>Seed1</th>\n",
       "      <th>Score1</th>\n",
       "      <th>Team1</th>\n",
       "      <th>Team2</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Seed2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1985</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Championship</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>Villanova</td>\n",
       "      <td>66</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Round  Region_Number   Region_Name  Seed1  Score1       Team1  \\\n",
       "62  1985      6              1  Championship      1      64  Georgetown   \n",
       "\n",
       "        Team2  Score2  Seed2  \n",
       "62  Villanova      66      8  "
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#======================================================\n",
    "# double check \n",
    "# after a bit of goofing, we see the 8 seed in region 4 won the turnament in 1985, paying against the 1 seed \n",
    "#    from region 2. Does the data table reflect this?   \n",
    "\n",
    "# Yes! Villanova, an 8 seed beat Georgetown, a 1 seed\n",
    "champ_game = b_data.loc[(b_data['Round'] == 6),:]\n",
    "champ_game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#============================================================================\n",
    "# now loop the whole thing for every year and build the table of lines. \n",
    "\n",
    "# re-read all data\n",
    "a_data = pd.read_csv('/home/andrewrs/Desktop/github/march_madness/Big_Dance_CSV.csv')\n",
    "\n",
    "\n",
    "# hyper parameters for the plot \n",
    "jitter_x = 10\n",
    "jitter_y = 10\n",
    "deg_free = 1\n",
    "multi = 1\n",
    "base = 1\n",
    "\n",
    "# Must length (number of points) depends on number of wins\n",
    "x_big = []\n",
    "y_big = []\n",
    "\n",
    "years = [it for it in a_data['Year'].drop_duplicates()]\n",
    "\n",
    "for year in years:\n",
    "    # get one turnament at a time\n",
    "    b_data = a_data.loc[(a_data['Year'] == year),:]\n",
    "    \n",
    "    # build year-specific lookup table \n",
    "    region_number = b_data['Region_Number'].append(b_data['Region_Number'])\n",
    "    seed = b_data['Seed1'].append(b_data['Seed2'])\n",
    "    team = b_data['Team1'].append(b_data['Team2'])\n",
    "    lookup = pd.DataFrame(region_number)\n",
    "    lookup[1] = seed\n",
    "    lookup[2] = team\n",
    "    lookup.columns = ['region', 'seed', 'team']\n",
    "    lookup = lookup.drop_duplicates(subset = ['region', 'seed'], keep='first')\n",
    "    \n",
    "    # add number of wins column to lookup table\n",
    "    all_teams = lookup['team']\n",
    "    wins = []\n",
    "    for team in all_teams:\n",
    "        sub = b_data.loc[(b_data['Team1'] == team),:]\n",
    "        sub2 = b_data.loc[(b_data['Team2'] == team),:]\n",
    "        max_round1 = sub['Round'].max()\n",
    "        max_round2 = sub2['Round'].max()\n",
    "        if math.isnan(max_round1):\n",
    "            max_round1 = 0\n",
    "        if math.isnan(max_round2):\n",
    "            max_round2 = 0\n",
    "        max_round = np.max([max_round1, max_round2])\n",
    "        if max_round == 6:\n",
    "            champ_game = b_data.loc[(b_data['Round'] == 6),:]\n",
    "            if champ_game['Team1'].item() == team:\n",
    "                if champ_game['Score1'].item() > champ_game['Score2'].item():\n",
    "                    max_round = 7\n",
    "                elif champ_game['Score2'].item() > champ_game['Score1'].item():\n",
    "                    max_round = 6\n",
    "            if champ_game['Team2'].item() == team:\n",
    "                if champ_game['Score2'].item() > champ_game['Score1'].item():\n",
    "                    max_round = 7\n",
    "                elif champ_game['Score1'].item() > champ_game['Score2'].item():\n",
    "                    max_round = 6 \n",
    "        else:\n",
    "            max_round = max_round\n",
    "        number_of_wins = max_round - 1\n",
    "        wins.append(number_of_wins)\n",
    "    lookup['wins'] = wins\n",
    "    \n",
    "    # Write out the lines \n",
    "    for region, seed, wins, in zip(lookup['region'], lookup['seed'], lookup['wins']):\n",
    "        li = length_index(wins)\n",
    "        xyi = xy_index(region, seed)\n",
    "        rep_lines = repeater_true(wins, multi = multi, base = base)\n",
    "        for i in range(0, rep_lines):\n",
    "            for it in x_lines[xyi,0:li].tolist():\n",
    "                x_big.append(it + jitter_val(jitter = jitter_x, df = deg_free))\n",
    "            x_big.append(math.nan)\n",
    "        for i in range(0, rep_lines):\n",
    "            for it in y_lines[xyi,:li].tolist():\n",
    "                y_big.append(it + jitter_val(jitter = jitter_y, df = deg_free))\n",
    "            y_big.append(math.nan)\n",
    "\n",
    "# make pandas df for datashader plot of all years \n",
    "both = pd.DataFrame(x_big)\n",
    "both['y'] = y_big\n",
    "both.columns = ['x', 'y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================================================\n",
    "# define the create image function\n",
    "def create_image(df, x_range, y_range, w, h, cmap):\n",
    "    cvs = ds.Canvas(plot_width=w, plot_height=h, x_range = x_range, y_range = y_range)\n",
    "    agg = cvs.line(df, 'x', 'y', ds.count(), )\n",
    "    return tf.shade(agg, cmap = cmap, how='eq_hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=======================================================\n",
    "# build the raw bracket image\n",
    "#fire[26:len(fire)]\n",
    "\n",
    "\n",
    "test_img = create_image(both, x_range = (-1200, 1200), y_range = (-700, 700), w=1400, h=800, cmap = fire)\n",
    "test_img = tf.dynspread(test_img, threshold = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
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'/>"
      ],
      "text/plain": [
       "<xarray.Image (y: 800, x: 1400)>\n",
       "array([[4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       ...,\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080]], dtype=uint32)\n",
       "Coordinates:\n",
       "  * y        (y) float64 -699.1 -697.4 -695.6 -693.9 -692.1 -690.4 -688.6 ...\n",
       "  * x        (x) float64 -1.199e+03 -1.197e+03 -1.196e+03 -1.194e+03 ..."
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=================================\n",
    "# export the image \n",
    "export_image(tf.set_background(test_img, 'black'),'pics/1985-2017_extra_true_lines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=========================================================================\n",
    "# with tweaks, the bracket can be drawn in many ways\n",
    "# here it is with no repeats, all line are \"true paths\" that an actual team took.\n",
    "\n",
    "#============================================================================\n",
    "# now loop the whole thing for every year and build the table of lines. \n",
    "\n",
    "# re-read all data\n",
    "a_data = pd.read_csv('/home/andrewrs/Desktop/github/march_madness/Big_Dance_CSV.csv')\n",
    "\n",
    "\n",
    "# hyper parameters for the plot \n",
    "jitter_x = 0\n",
    "jitter_y = 0\n",
    "deg_free = 10\n",
    "multi = 1\n",
    "base = 1\n",
    "\n",
    "# Must length (number of points) depends on number of wins\n",
    "x_big = []\n",
    "y_big = []\n",
    "\n",
    "years = [it for it in a_data['Year'].drop_duplicates()]\n",
    "\n",
    "for year in years:\n",
    "    # get one turnament at a time\n",
    "    b_data = a_data.loc[(a_data['Year'] == year),:]\n",
    "    \n",
    "    # build year-specific lookup table \n",
    "    region_number = b_data['Region_Number'].append(b_data['Region_Number'])\n",
    "    seed = b_data['Seed1'].append(b_data['Seed2'])\n",
    "    team = b_data['Team1'].append(b_data['Team2'])\n",
    "    lookup = pd.DataFrame(region_number)\n",
    "    lookup[1] = seed\n",
    "    lookup[2] = team\n",
    "    lookup.columns = ['region', 'seed', 'team']\n",
    "    lookup = lookup.drop_duplicates(subset = ['region', 'seed'], keep='first')\n",
    "    \n",
    "    # add number of wins column to lookup table\n",
    "    all_teams = lookup['team']\n",
    "    wins = []\n",
    "    for team in all_teams:\n",
    "        sub = b_data.loc[(b_data['Team1'] == team),:]\n",
    "        sub2 = b_data.loc[(b_data['Team2'] == team),:]\n",
    "        max_round1 = sub['Round'].max()\n",
    "        max_round2 = sub2['Round'].max()\n",
    "        if math.isnan(max_round1):\n",
    "            max_round1 = 0\n",
    "        if math.isnan(max_round2):\n",
    "            max_round2 = 0\n",
    "        max_round = np.max([max_round1, max_round2])\n",
    "        if max_round == 6:\n",
    "            champ_game = b_data.loc[(b_data['Round'] == 6),:]\n",
    "            if champ_game['Team1'].item() == team:\n",
    "                if champ_game['Score1'].item() > champ_game['Score2'].item():\n",
    "                    max_round = 7\n",
    "                elif champ_game['Score2'].item() > champ_game['Score1'].item():\n",
    "                    max_round = 6\n",
    "            if champ_game['Team2'].item() == team:\n",
    "                if champ_game['Score2'].item() > champ_game['Score1'].item():\n",
    "                    max_round = 7\n",
    "                elif champ_game['Score1'].item() > champ_game['Score2'].item():\n",
    "                    max_round = 6 \n",
    "        else:\n",
    "            max_round = max_round\n",
    "        number_of_wins = max_round - 1\n",
    "        wins.append(number_of_wins)\n",
    "    lookup['wins'] = wins\n",
    "    \n",
    "    # Write out the lines \n",
    "    for region, seed, wins, in zip(lookup['region'], lookup['seed'], lookup['wins']):\n",
    "        li = length_index(wins)\n",
    "        xyi = xy_index(region, seed)\n",
    "        rep_lines = line_repeater(wins, multi = multi, base = base)\n",
    "        for i in range(0, rep_lines):\n",
    "            for it in x_lines[xyi,0:li].tolist():\n",
    "                x_big.append(it + jitter_val(jitter = jitter_x, df = deg_free))\n",
    "            x_big.append(math.nan)\n",
    "        for i in range(0, rep_lines):\n",
    "            for it in y_lines[xyi,:li].tolist():\n",
    "                y_big.append(it + jitter_val(jitter = jitter_y, df = deg_free))\n",
    "            y_big.append(math.nan)\n",
    "\n",
    "# make pandas df for datashader plot of all years \n",
    "both = pd.DataFrame(x_big)\n",
    "both['y'] = y_big\n",
    "both.columns = ['x', 'y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================================================\n",
    "# define the create image function\n",
    "def create_image(df, x_range, y_range, w, h, cmap):\n",
    "    cvs = ds.Canvas(plot_width=w, plot_height=h, x_range = x_range, y_range = y_range)\n",
    "    agg = cvs.line(df, 'x', 'y', ds.count(), )\n",
    "    return tf.shade(agg, cmap = cmap, how='eq_hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
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'/>"
      ],
      "text/plain": [
       "<xarray.Image (y: 800, x: 1400)>\n",
       "array([[4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       ...,\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080],\n",
       "       [4278190080, 4278190080, 4278190080, ..., 4278190080, 4278190080,\n",
       "        4278190080]], dtype=uint32)\n",
       "Coordinates:\n",
       "  * y        (y) float64 -699.1 -697.4 -695.6 -693.9 -692.1 -690.4 -688.6 ...\n",
       "  * x        (x) float64 -1.199e+03 -1.197e+03 -1.196e+03 -1.194e+03 ..."
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=======================================================\n",
    "# build the raw bracket image\n",
    "test_img = create_image(both, x_range = (-1200, 1200), y_range = (-700, 700), w=1400, h=800, cmap = fire[10:len(fire)])\n",
    "test_img = tf.dynspread(test_img, threshold = .8)\n",
    "\n",
    "# export\n",
    "export_image(tf.set_background(test_img, 'black'),'pics/1985-2017_true_paths_hist')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}