Cal-Heatmap Visualization.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Create a Cal-Heatmap from the clustering data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import matplotlib\n",
      "\n",
      "import json\n",
      "import time\n",
      "from datetime import datetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#from IPython.core.display import Javascript, display\n",
      "from IPython.display import Javascript"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "from IPython.core.display import Image \n",
      "def css_styling():\n",
      "    styles = open(\"./styles/custom.css\", \"r\").read()\n",
      "    return HTML(styles)\n",
      "css_styling()"
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     "language": "python",
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        "        margin-right:auto;\n",
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        "        line-height: 145%;\n",
        "        font-size: 130%;\n",
        "        width:800px;\n",
        "        margin-left:auto;\n",
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        "    .prompt{\n",
        "        display: None;\n",
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        "    .text_cell_render h5 {\n",
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        "        font-size: 22pt;\n",
        "        color: #4057A1;\n",
        "        font-style: italic;\n",
        "        margin-bottom: .5em;\n",
        "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "    }\n",
        "    \n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
        "        }  \n",
        "</style>\n",
        "<script>\n",
        "    MathJax.Hub.Config({\n",
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        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
        "                }\n",
        "        });\n",
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        "<IPython.core.display.HTML at 0x1080a4f50>"
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     "prompt_number": 5
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = json.load(open(\"./styles/bmh_matplotlibrc.json\"))\n",
      "matplotlib.rcParams.update(s)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv(\"./MotifProfiles.csv\", parse_dates=True, index_col='Date')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def timestampcombine_parse(date,time):\n",
      "    time = datetime.strptime(time, \"%H:%M:%S\").time()\n",
      "    pydatetime = datetime.combine(date, time)\n",
      "    return pydatetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df.drop(['SAXstring'], axis=1)\n",
      "df = df.T.unstack().reset_index()\n",
      "df['timestampstring'] = map(timestampcombine_parse, df.Date, df.level_1)\n",
      "df.index = df.timestampstring"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df.resample(\"D\", how='sum')\n",
      "# df = df.drop(['Date','level_1','timestampstring'],axis=1)\n",
      "df.columns = [\"CP_TotalChiller_kW\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.melt(df.reset_index(), id_vars='timestampstring')\n",
      "df['epochtime'] = df.timestampstring.map(lambda x: int(time.mktime(x.timetuple())))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestampstring</th>\n",
        "      <th>variable</th>\n",
        "      <th>value</th>\n",
        "      <th>epochtime</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2012-07-12</td>\n",
        "      <td> CP_TotalChiller_kW</td>\n",
        "      <td>  9478.841936</td>\n",
        "      <td> 1342044000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2012-07-13</td>\n",
        "      <td> CP_TotalChiller_kW</td>\n",
        "      <td> 13162.299988</td>\n",
        "      <td> 1342130400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2012-07-14</td>\n",
        "      <td> CP_TotalChiller_kW</td>\n",
        "      <td>  1848.891999</td>\n",
        "      <td> 1342216800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2012-07-15</td>\n",
        "      <td> CP_TotalChiller_kW</td>\n",
        "      <td>  1124.272000</td>\n",
        "      <td> 1342303200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2012-07-16</td>\n",
        "      <td> CP_TotalChiller_kW</td>\n",
        "      <td> 12771.150001</td>\n",
        "      <td> 1342389600</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
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       "prompt_number": 12,
       "text": [
        "  timestampstring            variable         value   epochtime\n",
        "0      2012-07-12  CP_TotalChiller_kW   9478.841936  1342044000\n",
        "1      2012-07-13  CP_TotalChiller_kW  13162.299988  1342130400\n",
        "2      2012-07-14  CP_TotalChiller_kW   1848.891999  1342216800\n",
        "3      2012-07-15  CP_TotalChiller_kW   1124.272000  1342303200\n",
        "4      2012-07-16  CP_TotalChiller_kW  12771.150001  1342389600"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.index = df.epochtime\n",
      "df_forjson = pd.DataFrame(df['value'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_forjson.value.to_json()"
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     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://cmiller8.github.io/20140903Harvard/steinhausencalendar.html width=900 height=900></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://cmiller8.github.io/20140903Harvard/steinhausencalendar.html width=900 height=900></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<IPython.core.display.HTML at 0x10dbe60d0>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}