{
 "metadata": {
  "name": "TOU_pricing_my_usage"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#%reset\n",
      "import pandas as pd\n",
      "import TOU_pricing\n",
      "import numpy as np\n",
      "#reload(TOU_pricing)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gray_light = '#bbbbbb'\n",
      "gray_med = '#737373'\n",
      "red_orange = '#ff3700'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_demand_new = pd.read_csv('raw_data/new_apt_demand_thru_july2014.csv',index_col='timestamp',parse_dates=True)\n",
      "my_demand_old = pd.read_csv('raw_data/old_apt_demand.csv',index_col='timestamp',parse_dates=True)\n",
      "\n",
      "green_button_sample = pd.read_csv('raw_data/Green_Button_Sample_Inland_SingleFamily.csv',index_col='timestamp',parse_dates=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_demand = my_demand_old.append(my_demand_new)\n",
      "\n",
      "my_demand_cost = TOU_pricing.BGE_elec_cost(my_demand)\n",
      "green_button_sample_cost = TOU_pricing.BGE_elec_cost(green_button_sample)\n",
      "\n",
      "my_demand_cost['BGE-R_cost'] = my_demand_cost['USAGE'] * my_demand_cost['BGE-R_cost_perkWh']\n",
      "my_demand_cost['BGE-RL_cost'] = my_demand_cost['USAGE'] * my_demand_cost['BGE-RL_cost_perkWh']\n",
      "my_demand_cost['BGE-EV_cost'] = my_demand_cost['USAGE'] * my_demand_cost['BGE-EV_cost_perkWh']\n",
      "\n",
      "green_button_sample_cost['BGE-R_cost'] = green_button_sample_cost['USAGE'] * green_button_sample_cost['BGE-R_cost_perkWh']\n",
      "green_button_sample_cost['BGE-RL_cost'] = green_button_sample_cost['USAGE'] * green_button_sample_cost['BGE-RL_cost_perkWh']\n",
      "green_button_sample_cost['BGE-EV_cost'] = green_button_sample_cost['USAGE'] * green_button_sample_cost['BGE-EV_cost_perkWh']\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "month = 'jul-2014'\n",
      "\n",
      "print 'Total usage:', round(sum(my_demand_cost['USAGE'].ix[month]),2),'kWh \\n'\n",
      "print 'R total: $', round(sum(my_demand_cost['USAGE'].ix[month] * my_demand_cost['BGE-R_cost_perkWh'].ix[month]),2)\n",
      "print 'RL total: $', round(sum(my_demand_cost['USAGE'].ix[month] * my_demand_cost['BGE-RL_cost_perkWh'].ix[month]),2)\n",
      "print 'EV total: $', round(sum(my_demand_cost['USAGE'].ix[month] * my_demand_cost['BGE-EV_cost_perkWh'].ix[month]),2)\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Total usage: 671.6 kWh \n",
        "\n",
        "R total: $ 57.87\n",
        "RL total: $ 54.38\n",
        "EV total: $ 47.31\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "jul_elec_bill = 93.49\n",
      "print round(sum(my_demand_cost['USAGE'].ix['jul-2014'] * my_demand_cost['BGE-R_cost_perkWh'].ix['jul-2014']),2)/jul_elec_bill"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.618996684137\n"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "my_monthly_diffs = pd.DataFrame(np.zeros((7,3)),columns=['R-RL','R-EV','USAGE'],index=[1,2,3,4,5,6,7])\n",
      "my_monthly_diffs['R-RL'] = my_demand_cost['BGE-R_cost'].groupby(my_demand_cost.index.month).sum()-my_demand_cost['BGE-RL_cost'].groupby(my_demand_cost.index.month).sum()\n",
      "my_monthly_diffs['R-EV'] = my_demand_cost['BGE-R_cost'].groupby(my_demand_cost.index.month).sum()-my_demand_cost['BGE-EV_cost'].groupby(my_demand_cost.index.month).sum()\n",
      "my_monthly_diffs['USAGE'] = my_demand_cost['USAGE'].groupby(my_demand_cost.index.month).sum()\n",
      "my_monthly_diffs.index = ['Jan','Feb','Mar','Apr','May','Jun','Jul']\n",
      "my_monthly_diffs = my_monthly_diffs.drop(['May'])\n",
      "my_monthly_diffs[:10]"
     ],
     "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>R-RL</th>\n",
        "      <th>R-EV</th>\n",
        "      <th>USAGE</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Jan</th>\n",
        "      <td> 1.216777</td>\n",
        "      <td>  1.456228</td>\n",
        "      <td>  787.34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Feb</th>\n",
        "      <td> 1.812211</td>\n",
        "      <td>  2.080608</td>\n",
        "      <td> 1086.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mar</th>\n",
        "      <td> 0.972739</td>\n",
        "      <td>  1.036178</td>\n",
        "      <td>  641.49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Apr</th>\n",
        "      <td> 0.047755</td>\n",
        "      <td> -0.440503</td>\n",
        "      <td>  195.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jun</th>\n",
        "      <td> 2.626015</td>\n",
        "      <td>  8.097076</td>\n",
        "      <td>  636.66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jul</th>\n",
        "      <td> 3.481570</td>\n",
        "      <td> 10.552970</td>\n",
        "      <td>  671.60</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "         R-RL       R-EV    USAGE\n",
        "Jan  1.216777   1.456228   787.34\n",
        "Feb  1.812211   2.080608  1086.16\n",
        "Mar  0.972739   1.036178   641.49\n",
        "Apr  0.047755  -0.440503   195.86\n",
        "Jun  2.626015   8.097076   636.66\n",
        "Jul  3.481570  10.552970   671.60"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sum(my_monthly_diffs['R-RL'])\n",
      "print sum(my_monthly_diffs['R-EV'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10.1570672\n",
        "22.7825565\n"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "width = .4\n",
      "ind = np.arange(len(my_monthly_diffs['R-EV']))+1\n",
      "fig, ax = plt.subplots(figsize=[4,4])\n",
      "ax.barh(ind,my_monthly_diffs['R-EV'],width,color=red_orange,label='EV')\n",
      "ax.barh(ind+width,my_monthly_diffs['R-RL'],width,color=gray_light,label='RL')\n",
      "ax.set_yticks(ind+width)\n",
      "ax.set_yticklabels(my_monthly_diffs.index)\n",
      "xlim([-2,12])\n",
      "ylim([min(ind)-width,max(ind)+3*width])\n",
      "title('Cost Savings on BGE Electricity Bill, My Usage \\n Compared to Residential \"R\" Plan')\n",
      "xlabel('Cost Saved (USD)')\n",
      "legend(loc='lower right')\n",
      "subplots_adjust(top=.85)\n",
      "fig.savefig('plots/My_Cost_Savings_TOU_Pricing.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Mnj0b06ZNQ1BQEGbNmsXt/Pfu3YOnpycMDQ0xdepUrFmzhuuhfPzxx4iMjISRkRE2btxY\noV4DAwOsXbsWrVu3hrW1NaKiorB3714YGhqiWbNm+Prrr7Fq1Sq0b98eCQkJFe5BMjExQa9evRAX\nFwd/f3+5aW/ulOXfl7/sKRaLcfr0aezYsQMuLi5wcnKCWCzmDtWioqJga2uLVq1aYe/evdi2bZvC\nANOmTRv89NNPCA8PR79+/WBvby+3zIraU1nP6MCBAxCJRDAwMMC0adMwefJkjBw5klvu69evQyQS\nYe7cuTA2NoabmxvMzc2xevVqrozMzMwK9xmV9a4UKbvyWfby8/NT2NajR4/i1atXeO+992BqaopF\nixbh5cuXAIBp06Zh+vTpWLJkCYyNjbFo0SIwxiAQCLBs2TJMnjwZEokE8fHxCtfBm5+dOHECOjo6\n6NWrFzp27AipVFphvjfLLgtYFhYWcHJyglAohJubm9LlFggEiIuLg0gkgr6+PgICAvDee+9h2rRp\ncvMo+275aQ8fPlRa15vz9u/fH2KxWOE0oLT39OeffyrtPZVZv349evTogcDAQAwYMADGxsbYvXs3\nN13ZNjxs2DB4eHjAwcEBPj4+8Pf3l2vDihUr4Orqil69eiEkJASTJk2CgYEBN72y7UAZAavrvnsD\ncuPGDbi5uSE7O5uX92mQtzNp0iSYm5tj1apVdV7Xw4cPMXr0aIWHqTWxZ88ebN++Xe7eMU0aOXIk\nXF1dK9y0+VYqPb1N2PHjx1lRURG7ffs28/PzY35+fppuElGBe/fuMWNj4yqvIvFRUVERc3BwYMeP\nH9dYG27dusWuXbvGnj9/zvbu3csMDQ3Zn3/+WasyG93YtLd1/PhxmJmZwcvLC7a2tnVy5zFRr2XL\nlqF3795YtWoVWrVqpenmvJWoqCi8++67cHJyqtF5K1UpKCiAn58fjI2NceDAAezZswe2tra1KpMO\n0wghvEA9I0IIL1AwqidUOQZKXapK5VF+nJ+qDR48GOHh4dWaty7bUVONMf1IgwhGe/bsgYuLC4yM\njGBubo5Ro0bJDYRsDKqTkqUyQqEQ+vr6MDAwgJubG9auXVvrNpVP5aGIqkaPlx8YW+bUqVNK70x+\nm3ZMnDgRYWFh2L17N5fGo+yHQSQSQSwWw9PTEz/88IPc99q0aYMHDx5w31fW7rJ0MJaWlpg0aRKu\nXbtWrTY3RPU+GC1ZsgSLFi3CkCFDcOPGDVy9ehVeXl6V3jPDB7VNifGmqlKyVEdycjLy8/OxceNG\nrFu3TmWXoeuz8vcrvSkvL48bqjRjxgykp6cr/b6yssvSwSQlJUEgEGDhwoWqa3w9U6+DUWpqKkJC\nQhASEoKlS5fC1NQU77zzDqZMmYJly5YBKE1jsnXrVtjZ2cHLy0tuqERZmorg4GCYmZmhW7du+PPP\nP/Hzzz/Dzs4O3bp1k0vmNXHiRMyaNQsjRoyAiYkJFi5cKJd24tNPP4WlpSVatWqFjz76SO5XLjg4\nGAEBAQgKCoKpqSnCwsIgk8mwY8cO9OjRA25ubvjpp5+4gMIYw48//ggbGxs4ODhUej+JspQsiYmJ\nmDhxIqysrLBs2TJkZmZWa72Wtaf8ukpOTsbHH38MS0tLzJ07V27H27FjB1xdXWFoaAhra2uuh/Zm\nj6UsHYuVlRW++eabCvX+8ssv8PX1RadOnbBp0yZulHdZT+Tw4cPo3Lkz7O3tuZHrp0+fxrp167gb\nQcvGS5VPIHb//n14eHjgnXfegb29PdavX1/1CPJyKgtIQqEQgwYNQvv27XHq1KkK31H2PUD+Tntj\nY2NMmTIFv/32G168eFFh3upsWzNmzICJiQlGjRqFmzdvVnv5eKO29xto0o8//siaNm3KcnNzlc6z\nfPly1r9/f3b79m127tw5ZmVlxaKjoxljpSOltbW12Zo1a1h2djb76KOPWNu2bdmECRNYZmYm27Vr\nF2vbti1XVmBgINPT02NhYWEsIyODjR49mo0ePZqbvnfvXpadnc1ycnLY/PnzWe/evblpK1asYE2b\nNmWbN29mMpmMyWQyNnv2bDZ69GiWmprKrl69ymxtbdmvv/7KGGPsxIkTrG3btuzChQvs2rVrrGfP\nnkwoFCocpc5YxSwIRUVFTF9fn4WGhrInT56wmTNnsn79+ildTwKBgN27d4+VlJSw2NhYpq+vz93H\n8u+//zKJRMKOHj3K8vLy2Nq1a1mvXr0YY4xlZWUxc3NzdufOHcYYYw8ePGD3799njDEWHBzMjZ7P\nyclhurq63LobO3Ysa9q0KdfmY8eOMXt7exYXF8cyMzPZqFGj2OLFixlj/xsZ7+/vz9LT01lUVBRr\n1qwZl5UhODiYjR8/Xm55yo9Mv3fvHjt79ix7+fIlu3btGnNycmKhoaFK111VytpTXFzMXr58yY4d\nO8a0tLTY1atXq10GY/LZBf755x82ceJE5uPjwxirOOK/qm1LW1ubbdu2jWVnZ7MpU6Zw5dYn9ToY\nrV+/ntnZ2VU6T9euXVlUVBT3fsmSJWzmzJmMsdJ/ePk0FLGxsUwgELDk5GTGGGOvXr1iurq6LC0t\njTFWGozKpxS5ffu2XFqF8vLz85menh7LyspijJVuMOUD2+vXr5mVlRVLT0/nPtu0aRObNm0aY4yx\noKAgtmzZMm7ajh07KqTCKO/NHerw4cPM1dWVe19UVMR0dXW59rxJIBAwAwMDpqurywQCAVu7di03\nbdu2bVz6EcYYKy4uZi1btmT//PMP+/fff5mxsTGLjIxkL1++lCuz/M5WVTqWMWPGsH379nHTk5KS\nmI2NDWPsfzv/lStXuOmdOnVip0+frlBPmfLB6E2hoaFsyJAhStddVcraIxaLWfPmzZlAIGA//vhj\ntb9fpnw6mPbt27Pp06ezmzdvMsYqTz+iaNsqvx/ExcUxExOTt26PptXrwzQLCwvcunVLaQrQgoIC\nJCcnK01xAgA2NjbceLKyG+Ds7OwAlI4aNzIy4tJ5CAQCdO3alftux44d8erVK65LvHv3bnh7e6NF\nixawtLSETCbDn3/+yc1ffnT1rVu3kJ6eDnt7ey7NwooVK7jc4pcvX5ZLK1I+XUN1XLx4US6tg66u\nLjp06IC4uDil30lKSkJubi6+//57rF+/Ho8ePQJQmhJi3759XDvfeecdFBUVcVkTw8PDsWnTJpia\nmmLWrFnIysqqUHZ8fLzcunszHcvZs2cRFBTE1dG/f3+kpaXhyZMn3Dxvk2alvMLCQnz66afo3r07\nDA0NMXv2bC7dTG08ffoUWVlZWLZsGRYtWgSZTPbWZZSlg7l79y6+/fZbWFtbK5yvqm2r/Lo1MTHB\nP//8U6+uvAL1/JyRi4sLBAKBXIrZ8kQiEezt7ZWmOHlb7L+j5Mvcvn0bTZs2RefOnfH3339jzpw5\nWLx4MR48eID09HTo6OjInVQuS2cBlI5sNjc3R0pKCpdmIS8vjyu/R48eSlM0KPJmSpbevXvLZUUo\nKirC3bt30atXr0rLadq0Kf7v//4PAwcOxNKlSwGUpoSYMGGCXEqIwsJCfPDBBwCAQYMG4ezZs0hJ\nSUFqaiq+/PLLCuW+mY7l/v37culYPDw8EBoaKldHUVERWrZsWWl7gdIfDVbJyfvvvvsOt2/fxsGD\nB5Gbm4tNmzapbEfV19dHcHAw3n33XYXpkytT3YsO1dm2GoJ6HYzatGmD+fPnY8GCBVi7di0yMzPx\n9OlT7Nq1C2vWrAFQmr5jw4YNuHPnDqRSKSIiIrgUJzWRlJSEffv2ITMzE6tWrcLAgQMhFAq5NAwm\nJiYoKCjA4sWLFZ6ILCMUCuHv74+FCxfi5s2beP36Ne7fv8+dqB48eDAiIiLw+++/Izk5ucrEdG+m\nZPH09MSNGzewc+dOPHnyBEuXLkX37t2rTK1aZunSpdi3bx8ePnwIf39/HD58GEePHkVRURGKiopw\n8uRJFBYW4s6dO9xJV21tbTRr1gwikahCeWXpWMrW3cqVK+XSsYwfP14uvUhWVlalebTeXPaUlBSl\n6zszMxMSiQQtW7bEH3/8USGHdG0JBAIsWbIEX3/9NZf6uDqqG0zedtuqr+p1MAJK8/esXbsWx48f\nR5cuXWBvb49ff/2VS3GxYMECDBs2DCNGjMDnn3+OjRs3ol+/fgAU319S1aXYqVOn4tChQ3BycoKZ\nmRk3Vs3JyQnTpk2Dh4cH+vbtC1tbW7nHxCiqKzg4GP3790dQUBCMjIwwcuRIPH78GEBpMAoODsbU\nqVMxYcIEBAUFVdq2N1Oy6Onp4bfffkNMTAy6d+8OHR0d7Nu3r9JlK8/W1hYeHh7YuHEjxGIxoqKi\nEB0djY4dO6JDhw5cnuYXL15g0aJFaNGiBbp16waxWMzl934zHUtUVBR27doFV1dX9OjRA+bm5lx9\ngwYNwqpVq/Dtt9+iRYsWcHV15dJ9VPV/6devHzp27Ig2bdqgW7duFabPnj0bMpkMrVu3xty5czFt\n2rRa39/05ve9vb1hYmKC7du3v1UZ1Uk/UpNtqz5mlaCxaW/hww8/lMsLRAhRnXrfM1InituE1B0K\nRm+Brw+/I6QhoMM0QggvKH/8ghpRb4MQzeFLf4Q3h2nsv+N0NPFasWIF1d8I66b6+RGEyvAmGBFC\nGjcKRoQQXqBghNJ0E1R/46ub6ucXXlxNU0ViMELI2+PTvkc9I0IIL1AwIoTwAgUjQggvUDAihPAC\nBSNCCC9QMCKE8AIFI0IIL/BioKymSSQS5ObmaroZdUIsFiMnJ0fTzSCkSnTT43/rP3PmjMbqr0ue\nnp68uamN8I+m973y6DCNEMILFIwIIbxAwYgQwgsUjAghvEDBiBDCCxSMCCG8QMGIEMILKg9G+vr6\nlU53d3eXeyY8IYQAdRCMqnrsED0IkRCiSJ0cpsXExMDHx4d7P2PGDISFhdVFVYSQBkItY9Oq0xsK\nDg7m/nZ3d1drovImTbTg6emptvrUSUtLi3qi9ZzEQITsvHyVlCWVSiGVSlVSlqrxZqBs+WCkbsXF\nJWA9NVZ9HSvRdANILQniC1RW1ps/9CtXrlRZ2bVVJ4dpzZs3x4sXL7j3T58+rYtqCCENSJ0EIwcH\nB6SkpKCwsBAZGRn49ddf66IaQkgDotLDNJlMBrFYjGbNmmHBggVwcXGBmZkZvLy8VFkNIaQBUmk+\no+joaGzbtg0RERFv1wge5DNquOeMSH0niEed7R+a3vfKU1nPaOvWrTh06BDWrFmjqiIJIY0IZXoE\n9YwIvzWWnhGNTSOE8AIFI0IIL1AwIoTwAgUjQggvUDAihPACb8amaZLEQKTS8T+EqJLEQKTpJqgF\nXdonpBHj075Hh2mEEF6gYEQI4QUKRoQQXqBgRAjhBbqaBkAikSA3N1fTzaiSWCxGTk6OpptBSJ2g\nq2n/rf/MmTMaq7+6PD09eXPlgzQMmt73yqPDNEIIL1AwIoTwAgUjQggvUDAihPACBSNCCC9QMCKE\n8AIFI0IIL9Q6GOnr66uiHYSQRq7WwUggEKiiHYSQRk4lh2kxMTHw8fHh3s+YMQNhYWEAACsrK3zx\nxRewt7fHkCFDkJqaqooqCSENTJ2cMxIIBFyPSSAQQCaTITk5Ga6urggPD6+LKgkh9ZxaBspOmDAB\nAODh4YFVq1YpnCc4OJj7293dHe7u7mpoWakmTbTg6emptvpqSktLiw6L64jEQITsvHxNN6POSaVS\nSKVSTTdDIZUEo+bNm+PFixfc+6dPn8pNl0gkAICmTZvi+fPnCssoH4zUrbi4pJ48UbZE0w1osBpL\nDvQ3f+hXrlypuca8QSWHaQ4ODkhJSUFhYSEyMjLw66+/qqJYQkgjUquekUwmg1gsRrNmzbBgwQK4\nuLjAzMwMXl5eCucvfy6JEELKq1U+o+joaGzbtg0RERG1awQP8hnVj8M0UlcE8eBNXh910vS+V16N\ne0Zbt27FoUOHsGbNGlW2hxDSSFGmR1DPiFDPiA9obBohhBcoGBFCeIGCESGEFygYEUJ4gYIRIYQX\nKBgRQniBniiL0kGSjWVsElFMYiDSdBMaPbrPiJBGjE/7Hh2mEUJ4gYIRIYQXKBgRQniBghEhhBco\nGKE0E2VZNkpCiGbQ1TT873FLPFgVhKiVpve98qhnRAjhBQpGhBBeoGBECOEFCkaEEF6gYEQI4QUK\nRoQQXqDI1WCNAAAW80lEQVRgRAjhhRoHo6NHj0IoFOL27duqbA8hpJGqcTCKiIjAkCFD3voBjq9f\nv65plYSQBqxGwaiwsBDx8fH49ttvceDAAQCAVCqFh4cHhg0bBltbW3z99dfc/Pr6+li2bBkcHBxw\n6dIl1bScENKg1CjT47FjxzBw4EBYWlqiRYsWSExMBADExMTg8uXL6NChAwYOHAg3Nzc4Ozvj2bNn\naNGiBa5evarSxhNCGo4aBaOIiAjMnj0bADBy5EjukK1Lly5wdnYGAIwYMQKnT5+Gs7MzhEIhJk6c\nWGmZwcHB3N/u7u5wd3evSdOUMjI0QE6+4tSyTZpoQV+f0o6Shk8qlUIqlWq6GQq99UDZ7OxsWFhY\noEWLFhAIBCgpKYFAIEBYWBhmzpyJ5ORkAEBISAhevnyJxYsXQyQSoaBAeY5pdQzWq+wR1o310caE\n1OuBsj///DMmTJiAtLQ0pKamIj09HW3atMH58+dx48YNJCUlIT8/H0ePHsXAgQPros2EkAborYPR\n/v37MXz4cLnP/Pz8sH//fri7u2PlypXo1asXRo4cCScnJwD/S9FBCCHKqCyfUUxMDEJCQnDixIm3\nbwQdphGiEfX6MK0y1AMihNRUo8n0SD0jQipqsD0jQgipKQpGhBBeoGBECOEFCkaEEF6gYEQI4YUa\njU2rjyQGIgjiFQ9JkRjQuDRCNK3RXNonhFTEp32PDtMIIbxAwYgQwgsUjAghvEDBiBDCCxSMCCG8\nQMEIgEQigUAgqNFLIpFouvmENAh0af+/9Z85c6ZG3/X09OTNpVFC3pam973yqGdECOEFCkaEEF6g\nYEQI4QUKRoQQXqBgRAjhBQpGhBBeoGBECOEFCkaEEF5462AkFAoxfvx47n1xcTFatGgBHx8flTaM\nENK4vHUw0tPTw40bN/D8+XMAwJkzZ2Bubv5WD3AsLi5+22oJIQ1cjdLODh48GCdPnoSfnx8iIiIQ\nEBCACxcuAAAuX76MWbNmQSaTwd7eHitXroSVlRV2796NkydPIicnB4wxnDt3Tq7M4OBg7m93d3e4\nu7vXeKHeVpMmWvD09KzRd7W0tGBkaIDsvHwVt4oQ1ZNKpZBKpZpuhkJvPTZNJBLh4sWLWLVqFfbu\n3QsXFxf85z//QUhICE6cOIGCggLo6upCS0sLBw8eRFJSEtatW4fdu3djzpw5SExMhJWVlXwjeDA2\nTdnTZqv1fXoiLamnNL3vlVejnpGdnR3S0tIQEREBb29vuWkymQxLlixBTEwMGGNo0qQJ1q1bBwDw\n8PCoEIgIIQSoxdU0X19fzJs3DwEBAXKRdcuWLTA2NkZCQgL27NmDnJwcbpqpqWntWksIabBq/Kii\nSZMmQSKRoEuXLnLHoBkZGejTpw8AIDQ0tNYNJIQ0Dm/dMyq7amZmZoYZM2Zwn5V9/sknn+CHH35A\nt27dYGFhwX1efh5CCHkTJVcDncAmjZem973y6A5sQggvUDAihPACBSNCCC9QMCKE8AIFI0IIL1Aw\nIoTwQo1vemxIJAYiCOILavV9Qkjt0H1GhDRifNr36DCNEMILFIwIIbxAwYgQwgsUjAghvEDBCIBE\nIuGyCqjrJZFINL3YhPAKXU37b/1nzpxRa52enp68uYpBGi9N73vlUc+IEMILFIwIIbxAwYgQwgsU\njAghvEDBiBDCCxSMCCG8QMGIEMILFIwIIbxQo2CkpaUFR0dH7pWenq50Xnd3d1y5cqXGDSSENA41\nSq6mq6uLpKSkas1LD24khFSHyg7Tbt++jaCgIPTs2RPTp0/H06dPuWmHDh2CnZ0dhg4dilu3bqmq\nSkJIA1KjnpFMJoOjoyMAoG3btjh06BDmz5+P7777DhYWFtiyZQu2b9+OhQsXgjGG+/fv48qVKzh6\n9Cjmz5+PEydOVCgzODiY+9vd3R3u7u41WqCaaNJEC56enmqrDyg91C3fa5QYiJCdl6/WNpDGRyqV\nQiqVaroZCtVooKxIJEJBwf9yRj958gTW1tZo3bo1AKCkpARWVlY4fvw4+vfvj5kzZ2L48OEoKSmB\npaUl0tLS0LRp0/81ggcDZWvzeGuVtIEekU00QNP7XnkqSchfUlICIyMjpeeRyi8snUMihCiiknNG\npqamaNOmDQ4dOgTGGF69eoWUlBQApYHo4MGDePnyJY4cOQInJye5XhEhhAA1DEaKejdbtmxBdHQ0\nHBwc4OjoiLi4OG7etm3bwtnZGeHh4diwYUPtWkwIaZAouRronBFpvDS975VHd2ATQniBghEhhBco\nGBFCeIGCESGEFygYEUJ4gYIRIYQXVHIHNiH1lZGREXJycjTdjDonkUiQnZ2t6WZUiu4zAmBkaICc\n/IKqZ6xDNFBWMzS97amLsuXk0/JTMCKNWmPZ9upDMKJzRoQQXqBgRAjhBQpGhBBeoGBECOEFCkYo\nvewpEAhU8pJIJJpeHFJLRoYGKtseFL2MDA2q1Q4rKyvo6upCJBJxL09PT+jr66OoqKjC/I6Ojtiy\nZYuqV4fa0H1GAHJzc3HmzBmVlKXuXNpE9XLyC+o0pYwgvnq3kQgEAkRGRsLDw0Puc2tra/z8888I\nDAzkPrt+/Tpu3ryJgIAAlbZVnahnREg9ExgYiD179sh9tmfPHnh7e9frnjkFI0J4TNE9QOPGjcP5\n8+fx8OFDAMDr168REREh11OqjygYEcJTjDEMGzYMEomEe+3YsQMWFhZwd3dHeHg4AODcuXN48eIF\nvL29Ndzi2qFgRAhPCQQCHDt2DDk5Odxr8uTJAEoP1cqCUXh4OAICAqClpaXJ5tYaBSNC6qHhw4fj\n4cOHiI6OxpEjR+r9IRpAwYgQXlM2bkxPTw8ffPABPvzwQ1hZWcHJyUnNLVM9urRPyBskBqJqX36v\nafnV5ePjI3f45eXlhUOHDgEoPVTbvXs31q9fr/I2akKtg5G+vj4KCwtV0RZCeIEvqVxSU1Mrnd6v\nXz+8fv1aTa2pe7U+TKPHVRNCVEEl54yKiorw3nvvwcnJCYMHD0ZMTAwAIC0tDTY2Npg+fTpsbGzw\n8ccf49WrV6qokhDSwKjknJGOjg6OHj0KkUiE9PR0BAYGIjo6GgBw69YtfPPNN/jmm28wePBgxMXF\noW/fvhXKCA4O5v52d3eHu7u7KppWLU2aaKlsGIeWlpZcb5EyOBI+kUqlkEqlmm6GQrXO9CgSiVBQ\nUIA1a9bg1KlTKCoqwv3795GRkYGcnBz06dMHf//9NwBg3bp1EAqFWLhwoXwjGvDjremx1fym6W1P\nXepDpkeV9IxiYmJw4cIFREVFQU9PDy1btkReXh4AQCwWc/Npa2vTyW5CiEIqOWf08OFDmJmZQSQS\nYf/+/bx/CgEhhH9qFYxkMhnEYjGGDRuG3NxcdO7cGbGxsbCxseHmefNqG119I4QoUqtzRtHR0di2\nbRsiIiJq1wg6Z0Q0RNPbnro06HNGW7duxaFDh7BmzRpVtocQ0kjRc9NAPaPGTNG2J5FIkJubW2d1\nisXiaj3F1srKCk+ePIGWlhY6deoELy8vLF++HM2bN8fEiRNhYWGB1atXV6vOBt0zIqShUmUaYkWq\ne09b+bSz169fh4+PD2xsbDBu3Dgun3ZDQqP2CakHbG1tMWDAAJw4cYL7jC89GlWhYEQIj5UFnKtX\nr+L06dNwc3PTcIvqDh2mEcJTZWlngdLxn1OnTsUnn3yi4VbVHeoZEcJTZWln8/LycPToUYSHh+Pq\n1auabladoZ4R6jaZ1tsk0iJEEaFQCF9fX8ycORNz587FuXPnADS8G4gpGIE/ybQIqcy8efPQunVr\nxMfHAwCKi4vx/PlzbrpQKIS2trammldrdJ8RadT4fJ9RmzZtsGPHDrknyk6bNg2PHj2CWCxGWFiY\n3Pxubm44f/68wrLqw31GFIxIo9ZYtr36EIzoBDYhhBcoGBFCeIGCESGEF+hqGmnUJBJJg7tErohE\nItF0E6pEJ7AJacT4tO/RYRqg8aclNOb6G/Oy86F+PqFgBM1vEI25/sa87Hyon08oGBFCeIGCESGE\nF3hzApsQohk8CAEAeHJpny8rgxCiOXSYRgjhBQpGhBBeoGBECOEF3gSj+fPno3PnznBycsKsWbMg\nk8nUUu/58+fRuXNndOjQAd98841a6izz999/o3///ujSpQvc3d3x448/qrV+ACgpKYGjoyN8fHzU\nXndRURECAwPRsWNH2NjY4NKlS2qtPzQ0FL169YKzszNmzZpV5/VNmjQJrVq1gp2dHfdZQUEBhg4d\nCktLSwwbNgyFhYVqrV9T+51CjCd+/fVXVlJSwkpKStiUKVPY9u3b1VKvg4MDi4mJYWlpaaxTp04s\nKytLLfUyxtijR49YUlISY4yxrKws1qZNG5afn6+2+hlj7KuvvmJjxoxhPj4+aq2XMcbmzp3Lli5d\nymQyGXv16hXLzc1VW91Pnz5lVlZWrLCwkJWUlLBBgwax06dP12md58+fZ4mJiczW1pb7bP369WzG\njBns+fPnbPr06WzDhg1qrV9T+50ivOkZeXp6QigUQigUYsCAAYiJianzOvPy8gAAffv2RevWreHl\n5cWl9FQHExMTODg4AADeeecddOnSBQkJCWqr/+HDhzh16hSmTJmikSuaZ8+exeLFi9G8eXM0adIE\nhoaGaqtbR0cHjDHk5eVBJpPh2bNndT6YtE+fPhXquHz5MiZPnoxmzZph0qRJdbr9KapfE/udMrwJ\nRuWFhoaq5bDhjz/+gLW1NfdeE4cKZe7du4cbN26gR48eaqtz9uzZ2LBhA4RC9W8GDx8+xPPnzxEU\nFISePXti/fr1cvmc65qOjg62bt0KKysrmJiYoHfv3mpd92XKb4PW1ta4fPmy2ttQRl37nTJq3Qo9\nPT1hZ2dX4VX+KZmrVq2CSCTCyJEj1dk0jSooKIC/vz82bdoEPT09tdQZGRmJli1bwtHRUSO9oufP\nn+POnTvw8/ODVCrFjRs3cPDgQbXVn5WVhaCgIKSkpCAtLQ1xcXE4efKk2uovo4l1rwgv9juNHSAq\nsGvXLtarVy8mk8nUUl9ubi5zcHDg3s+YMYNFRkaqpe4yL1++ZJ6enmzTpk1qrXfRokXM3NycWVlZ\nMRMTE6arq8vGjx+v1jZYW1tzf586dYqNHj1abXVHRkYyf39/7v2WLVvYggUL6rze1NRUuXM2I0aM\nYImJiYwxxhISEpifn59a62dM/fudMrw5TDt9+jQ2bNiA48ePo3nz5mqps+wcxfnz55GWloYzZ86g\nZ8+eaqkbKP1VnDx5MmxtbdVyNae8tWvX4u+//0Zqair2798PDw8P7NmzR61t6NChA+Lj4/H69Wuc\nPHkS77//vtrq7tOnDxISEpCdnY0XL17gl19+gZeXl9rqL9OzZ0/s3LkTMpkMO3fuhIuLi1rr18R+\np5RGQ2E57du3Z5aWlszBwYE5ODiwoKAgtdQrlUqZtbU1a9euHfv666/VUmeZCxcuMIFAwLp27cot\n9y+//KLWNjBWug40cTXt9u3brGfPnqxr165s7ty5rLCwUK3179q1i/Xt25d169aNLV26lJWUlNRp\nfaNHj2ampqZMW1ubmZubs507d7L8/Hzm6+vLLCws2NChQ1lBQUGd19+0aVNmbm7OduzYobH9ThFe\nDJQlhBDeHKYRQho3CkaEEF6gYEQI4QUKRoQQXqBgxFM5OTn48MMP0bZtW3Tp0gXe3t64e/fuW5ez\ndu1apdMOHz4MLy8v2Nvbw87ODsePH69Nk6ukr6+v8POSkhK4ubmBMQapVFrhLuCJEyfi0KFDAEoT\n2Pv6+qJr167o0qULtm3bBgAIDg6Gubk5HBwc0L59e4wbNw5paWlcGUOHDsWjR4/qZsGISlAw4qnJ\nkyfDxMQE8fHxuHHjBpYtW4bMzMy3LmfdunUKP3/+/Dk+++wz/Pjjj0hOTkZ8fDzs7e1r2+xKKUsv\nfPz4cbi7uyudLhAIuGmzZs3Chg0bcO3aNSQlJcHd3Z2bZ86cObh69SpiYmJgb2+PPn36oKioCAAw\nbtw4fP/996pfKKIyFIx4qLCwEImJiVi3bh1atGgBAHBxcUG/fv0AAD/88AN69eoFb29v7lE3RUVF\nGD58OBwdHWFnZ4fY2Fh89tlnkMlkcHR0xPjx4+XqePToEYRCITdwUldXF1ZWVgBKxyj16NEDzs7O\nWLBgAV6+fIm8vDxuell9lpaWKCkpQUZGBubPnw9XV1cEBgYiNTUVAJCZmYnJkyfD2tq60h5aaGgo\nxowZU+V6kclk+Oeff2BmZgYA0NbWRseOHbnpZXepmJmZYcGCBTAyMsK+ffsAAL6+vhpJ0UKqj4IR\nD506dQp9+vRROC05ORlhYWH45ZdfsHHjRkydOhUAcPDgQdja2iIpKQnJyclwcHDAF198AR0dHSQl\nJSE8PFyunDZt2qBHjx549913MWXKFCQlJXHT/Pz8cPnyZSQkJODZs2eIjo6GoaEhHBwcuOAXGRmJ\ngQMHQktLC8uXL8fo0aMRFxcHf39/fPnllwCAL774AtbW1rh582alg2CTk5PRqVOnKteLjo4OJk2a\nhNatW2PMmDFVPnPMx8cHt2/fBgA0a9YMOjo6ePz4cZX1EM2gYMRDlT0tJTIyEh988AEMDQ3RqVMn\ndOzYEfHx8XBwcMBPP/2E5cuXIy0tTen5mfL27NkDqVSK1q1bY+jQodxhzF9//YVx48bB1tYWp06d\nQlRUFADA398fBw4cAADs378f/v7+ePXqFZeGxNHREUuWLOEyH0RFRWHSpEkQCASYNGmSwjbk5+dD\nS0sLWlpaAAChUFhh8ChjjFsnn3/+OZKSkuDs7IyPP/4Yn332mdLlKykpkVuX7dq144IT4R8KRjw0\naNAgXLhwQeE0Rc9GFwgEcHR0RHx8PExNTeHr64vIyMhq1dW5c2csW7YMe/bs4Q5p5s2bh8DAQNy4\ncQOffvopcnJyAJT2NE6fPo2cnBwkJibCw8MDr1+/hlAoxKVLl5CUlMS9ylR1g/+by2NjY1PhRH1K\nSgqX9wkALC0tMXfuXJw+fZprsyKRkZFyKWIYYxpJl0Kqh/4zPKSvrw8nJycsXboUWVlZAErz3pw/\nfx5DhgzBkSNHkJeXhzt37uDu3bvo0aMH0tPToa+vj6CgIIwdOxbJyckAgBYtWuDZs2cV6nj06BES\nExMBlO6kFy9eRK9evQCUnuvp0KEDcnJyEBERIdeu7t27Y+bMmfDx8YFAIECzZs0wePBgbN26FSUl\nJWCMcXUPHDgQYWFheP36NXbv3q1wWUUiEUpKSlBcXAygNMlcq1atuEOwhIQEFBcXo02bNigqKpI7\nNIuNjeXaXD6gZWRkICQkBHl5eXLnov766y+5c0yEZzQyIo5U6enTpywwMJBZWVmxLl26sCFDhrB7\n9+4xxhj74YcfmKurKxs0aBCTSqWMMcbCwsKYra0tc3JyYiNGjGBPnz5ljDG2ceNG5uTkxMaNGydX\n/oMHD5iHhweztrZmzs7O7P/+7/9YZmYmY4yx/fv3sy5durDu3buzzz77jH344Yfc937++WcmFArZ\n+fPnuc8yMjLYwoULmYODA7OxsWGrV69mjDGWmZnJJk2axDp16sTWrl3LRCKRwmUdOHAgu379Ovc+\nOTmZTZ48mXXt2pUFBgayhIQExhhjBQUFbPDgwaxTp07M0dGRjR8/nt28eZMxxlhwcDAzMzNjXbt2\nZW3btmVjx45laWlpXJkvXrxg7dq1q8F/gqgLDZQlGnfkyBEkJCTg888/r7M6Dh48iBs3bmDlypV1\nVgepHQpGRONev36NPn36IDY2ts4edV52gt7U1LROyie1R8GIEMILdAKbEMILFIwIIbxAwYgQwgsU\njAghvEDBiBDCCxSMCCG88P8To1qpI10DKgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gb_monthly_diffs = pd.DataFrame(np.zeros((12,3)),columns=['R-RL','R-EV','USAGE'],index=[1,2,3,4,5,6,7,8,9,10,11,12])\n",
      "gb_monthly_diffs['R-RL'] = green_button_sample_cost['BGE-R_cost'].groupby(green_button_sample_cost.index.month).sum()-green_button_sample_cost['BGE-RL_cost'].groupby(green_button_sample_cost.index.month).sum()\n",
      "gb_monthly_diffs['R-EV'] = green_button_sample_cost['BGE-R_cost'].groupby(green_button_sample_cost.index.month).sum()-green_button_sample_cost['BGE-EV_cost'].groupby(green_button_sample_cost.index.month).sum()\n",
      "gb_monthly_diffs['USAGE'] = green_button_sample_cost['USAGE'].groupby(green_button_sample_cost.index.month).sum()\n",
      "gb_monthly_diffs.index = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "width = .4\n",
      "ind = np.arange(len(gb_monthly_diffs['R-EV']))+1\n",
      "fig, ax = plt.subplots(figsize=[4,8])\n",
      "ax.barh(ind,gb_monthly_diffs['R-EV'],width,color=red_orange,label='EV')\n",
      "ax.barh(ind+width,gb_monthly_diffs['R-RL'],width,color=gray_light,label='RL')\n",
      "ax.set_yticks(ind+width)\n",
      "ax.set_yticklabels(gb_monthly_diffs.index)\n",
      "xlim([-2,12])\n",
      "ylim([min(ind)-width,max(ind)+3*width])\n",
      "title('Cost Savings on BGE Electricity Bill \\n Green Button Sample \\n Compared to Residential \"R\" Plan')\n",
      "xlabel('Cost Saved (USD)')\n",
      "legend(loc='lower right')\n",
      "subplots_adjust(top=.9)\n",
      "fig.savefig('plots/GB_Cost_Savings_TOU_Pricing.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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W9vfWwcEBDx8+1NsZstcuKFq1agWFQoEff/wxz+VKpRLu7u655kd49913i1Wf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\nRK5CYII=\n"
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'Annual Usage:', round(sum(gb_monthly_diffs['USAGE']),1),'kWh'\n",
      "print 'Annual Cost Savings: $',round(sum(gb_monthly_diffs['R-RL']),2)\n",
      "print 'Annual Cost Savings: $',round(sum(gb_monthly_diffs['R-EV']),2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Annual Usage: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "8343.3 kWh\n",
        "Annual Cost Savings: $ 27.23\n",
        "Annual Cost Savings: $ 60.25\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "\n",
      "#plot2 = gb_monthly_diffs['USAGE'].plot(marker='o')\n",
      "plot1 = my_monthly_diffs['USAGE'].plot(marker='o')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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5eXmALvxhYWHWx4WEhJCXl0dCQsIVr5uSkkJwcDAAgYGBREREEB8fD9i+4X1h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\nbT+oWbMmN998s7tDdbsaNWp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      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}