Bokeh_nfhp example (super simple).ipynb

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   "source": [
    "# Python 3.5\n",
    "# import packages\n",
    "\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import OrderedDict \n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.models import Range1d\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_execution_time': 0.181,\n",
       " 'auth_check': {'auth_level': None, 'session': None, 'success': True},\n",
       " 'features': [{'properties': {'high_km': '7269.47',\n",
       "    'low_km': '64399.396',\n",
       "    'moderate_km': '18433.776',\n",
       "    'not_scored_km': '22864.678',\n",
       "    'place_name': 'Colorado',\n",
       "    'scored_km': '145694.138000001',\n",
       "    'source_id': 'gu_state:783534a7-82aa-4a24-9eb4-3a6549f1b295',\n",
       "    'veryhigh_km': '1358.96',\n",
       "    'verylow_km': '54232.5360000004'},\n",
       "   'type': 'Feature'}],\n",
       " 'forGrid': [{'dataIndex': 'source_id',\n",
       "   'header': 'source_id',\n",
       "   'type': 'character varying',\n",
       "   'typeObj': None},\n",
       "  {'dataIndex': 'place_name',\n",
       "   'header': 'place_name',\n",
       "   'type': 'character varying',\n",
       "   'typeObj': None},\n",
       "  {'dataIndex': 'scored_km',\n",
       "   'header': 'scored_km',\n",
       "   'type': 'double precision',\n",
       "   'typeObj': None},\n",
       "  {'dataIndex': 'not_scored_km',\n",
       "   'header': 'not_scored_km',\n",
       "   'type': 'double precision',\n",
       "   'typeObj': None},\n",
       "  {'dataIndex': 'verylow_km',\n",
       "   'header': 'verylow_km',\n",
       "   'type': 'double precision',\n",
       "   'typeObj': None},\n",
       "  {'dataIndex': 'low_km',\n",
       "   'header': 'low_km',\n",
       "   'type': 'double precision',\n",
       "   'typeObj': None},\n",
       "  {'dataIndex': 'moderate_km',\n",
       "   'header': 'moderate_km',\n",
       "   'type': 'double precision',\n",
       "   'typeObj': None},\n",
       "  {'dataIndex': 'high_km',\n",
       "   'header': 'high_km',\n",
       "   'type': 'double precision',\n",
       "   'typeObj': None},\n",
       "  {'dataIndex': 'veryhigh_km',\n",
       "   'header': 'veryhigh_km',\n",
       "   'type': 'double precision',\n",
       "   'typeObj': None}],\n",
       " 'forStore': [{'name': 'source_id', 'type': 'character varying'},\n",
       "  {'name': 'place_name', 'type': 'character varying'},\n",
       "  {'name': 'scored_km', 'type': 'double precision'},\n",
       "  {'name': 'not_scored_km', 'type': 'double precision'},\n",
       "  {'name': 'verylow_km', 'type': 'double precision'},\n",
       "  {'name': 'low_km', 'type': 'double precision'},\n",
       "  {'name': 'moderate_km', 'type': 'double precision'},\n",
       "  {'name': 'high_km', 'type': 'double precision'},\n",
       "  {'name': 'veryhigh_km', 'type': 'double precision'}],\n",
       " 'success': True,\n",
       " 'type': 'FeatureCollection'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BCB retrieve Data - DataDistillery.org\n",
    "\n",
    "hciUrl = \"https://beta-gc2.datadistillery.org/api/v1/sql/bcb?q=select source_id, place_name, scored_km, not_scored_km, verylow_km, low_km, moderate_km, high_km, veryhigh_km from nfhp.hci2015_summaries_mp where source_id='gu_state:783534a7-82aa-4a24-9eb4-3a6549f1b295'\"\n",
    "hciRequest = requests.get(url = hciUrl, verify=True).json()\n",
    "data = hciRequest['features'][0]['properties']\n",
    "hciRequest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>veryLow</th>\n",
       "      <td>37.223554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>44.201776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moderate</th>\n",
       "      <td>12.652380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>4.989542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veryHigh</th>\n",
       "      <td>0.932749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "            Ranking\n",
       "veryLow   37.223554\n",
       "low       44.201776\n",
       "moderate  12.652380\n",
       "high       4.989542\n",
       "veryHigh   0.932749"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to dataframe\n",
    "summary_dict = OrderedDict([('Ranking',0)])\n",
    "summary_dict['Ranking'] = OrderedDict([ ('veryLow',((float(data['verylow_km']))/(float(data['scored_km'])))*100),\n",
    "('low',((float(data['low_km']))/(float(data['scored_km'])))*100),\n",
    "('moderate',((float(data['moderate_km']))/(float(data['scored_km'])))*100),\n",
    "('high',((float(data['high_km']))/(float(data['scored_km'])))*100),\n",
    "('veryHigh',((float(data['veryhigh_km']))/(float(data['scored_km'])))*100)])\n",
    "df_data = pd.DataFrame(summary_dict, index=summary_dict['Ranking'].keys()) \n",
    "display(df_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "veryLow 37.2235539085 1\n",
      "low 44.2017756404 2\n",
      "moderate 12.6523800155 3\n",
      "high 4.98954185789 4\n",
      "veryHigh 0.932748577709 5\n"
     ]
    },
    {
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       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
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       "     \"<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",
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       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
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       "}(window));"
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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(\"39ccfd3f-1a57-466c-9a60-cfb40028f279\");\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(\"39ccfd3f-1a57-466c-9a60-cfb40028f279\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '39ccfd3f-1a57-466c-9a60-cfb40028f279' 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(\"39ccfd3f-1a57-466c-9a60-cfb40028f279\")).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));"
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Application\",\"version\":\"0.12.13\"}};\n",
       "  var render_items = [{\"docid\":\"36b59d84-5919-43c5-a275-fa87f753d132\",\"elementid\":\"9644439d-7039-474e-9526-1a60b74d2722\",\"modelid\":\"caf37b93-0b22-41be-82e3-101debd0c197\"}];\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": "caf37b93-0b22-41be-82e3-101debd0c197"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bokeh plot\n",
    "\n",
    "series = df_data.loc[:,'Ranking']\n",
    "\n",
    "p = figure(width=800, height=600, y_range=series.index.tolist())\n",
    "p.background_fill_color = \"white\"\n",
    "p.grid.grid_line_alpha=1.0\n",
    "p.grid.grid_line_color = \"gray\"\n",
    "p.xaxis.axis_label = 'Percentage [%]'\n",
    "p.xaxis.axis_label_text_font_size = '14pt'\n",
    "p.xaxis.major_label_text_font_size = '14pt'\n",
    "p.yaxis.major_label_text_font_size = '14pt'\n",
    "p.yaxis.axis_label = 'NFHP Ranking'\n",
    "p.yaxis.axis_label_text_font_size = '14pt'\n",
    "\n",
    "# bar colors\n",
    "bcolors = ['violet', 'cyan', '#a1dab4','orange', 'red']\n",
    "\n",
    "# plot order\n",
    "order = ['veryLow','low','moderate','high','veryHigh']\n",
    "\n",
    "j = 1\n",
    "for k in order: #[::-1]:\n",
    "    v = series[k]\n",
    "    print (k,v,j)\n",
    "    p.rect(x=v/2, y=j, width=abs(v), height=0.8,color=bcolors[j-1],\n",
    "    width_units=\"data\", height_units=\"data\")\n",
    "    j += 1\n",
    "    \n",
    "output_notebook()\n",
    "show(p)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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