{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Sustainable Energy Transitions</h1>\n",
    "<div>A project by <a href=\"http://www.ssgouridis.org\" target=\"_blank\" >Sgouris Sgouridis</a> and <a href=\"http://www.csaladen.es\" target=\"_blank\" >Dénes Csala</a> at <a  href=\"http://www.masdar.ac.ae\" target=\"_blank\">Masdar Institute of Science and Technology</a></div>\n",
    "<h2><br>Plotting notebook for social media images</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This workbook will guide you through the mining, processing, formatting and saving the input data for the <a target=\"_blank\" href=\"http://set.csaladen.es/set2.html\">SET 2.0</a> model.\n",
    "\n",
    "<p>This is document has been created using  <a target=\"_blank\" href=\"http://ipython.org/\">IPython Notebook</a> in the <a target=\"_blank\" href=\"https://store.continuum.io/cshop/anaconda/\">Anaconda</a> distribution and it can be edited and run in <i>active</i> mode by clicking download in top right corner of this page. The code is partitioned into sections, called <i>cells</i>. When you are using this workbook in <i>active</i> mode, double-click on a cell to edit it and then run using <code>Ctrl + Enter</code>. Hitting <code>Shift + Enter</code> runs the code and steps into the next cell, while <code>Alt + Enter</code> runs the code and adds a new, empty cell. If you are running this notebook on a presonal computer, you will need a machine with at least <i>1GB</i> of memory (<i>2GB</i> recommended) and a processor of <i>1GHz</i>.\n",
    "<h2>Data</h2>\n",
    "<p>The major data sources for this work are the <a href=\"http://set.csaladen.es/data/results/index.html\" target=\"_blank\">outputs</a> generated by the SET 2.0 model.\n",
    "</p>\n",
    "<h2>Processing</h2>\n",
    "<p>The data processing has been done using the <a target=\"_blank\" href=\"https://store.continuum.io/cshop/anaconda/\">Anaconda</a> distribution of <a target=\"_blank\" href=\"https://www.python.org/\">Python 2.7</a> using the <i>IPython Notebook</i> editor. If you have not generated your own data, please download <a href=\"http://set.csaladen.es/data/results/index.html\" target=\"_blank\">all</a> results files, and run this <i>Iptyhon</i> notebook in the same folder. Then the data is loaded into a <a target=\"_blank\" href=\"http://pandas.pydata.org/\">pandas</a> dataframe, which represents the backbone of the data analysis. Numerical processing is done with <a target=\"_blank\" href=\"http://www.numpy.org/\">NumPy</a> and for plotting we use <a target=\"_blank\" href=\"http://matplotlib.org/\">matplotlib</a>. Please make sure you have all of these compoents set up in your <i>Python</i> installation. The workbook generates <i>.jpg</i> files to be inlcuded on the <a target=\"_blank\" href=\"http://set.csaladen.es/\">main</a> data presentation site.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Code</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd, numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.font_manager as font_manager\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mycolors=['#dd1c77','#df65b0','#980043']\n",
    "abc='abcdefghijklmnopqrstuvwxyz' #for plot labeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up save path on your local computer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path=\"E:\\Skydrive\\GitHub\\set\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Cross-sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#for plot styling - not necessary\n",
    "plt.style.use('fivethirtyeight')\n",
    "#plt.style.use('classic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fpath = 'Comfortaa.ttf'\n",
    "prop = font_manager.FontProperties(fname=fpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fuels=[u'hydro',u'nuclear',u'biofuels',u'other',u'biomass',u'coal',u'oil',u'gas',u'geoth.',u'wind',u'PV',u'CSP']\n",
    "#fuelcolors=['#5e4fa2','#3288bd','#66c2a5','#abdda4','#9e0142','#d53e4f','#f46d43','#78c679','#e6f598','#fee08b','#fdae61']\n",
    "#fuelcolors=['#41b6c4','#1d91c0','#225ea8','#253494','#5e4fa2','#7a0177','#ae017e','#dd3497','#a6d96a','#d9ef8b','#fee08b','#fdae61']\n",
    "fuelcolors=[np.array([40,40,80])/256.0,\n",
    "            np.array([85,20,52])/256.0,\n",
    "            np.array([131,13,98])/256.0,\n",
    "            np.array([213,9,98])/256.0,\n",
    "            np.array([0,64,16])/256.0,\n",
    "            np.array([112,133,16])/256.0,\n",
    "            np.array([251,212,31])/256.0,\n",
    "            np.array([254,159,28])/256.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:7: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=9,center=False).mean()\n",
      "C:\\Anaconda\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:18: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=9,center=False).mean()\n"
     ]
    }
   ],
   "source": [
    "#redefine smooth window\n",
    "moving_window=9\n",
    "ee=[0,20,20,20,20,20,14,10,7]\n",
    "dd=[0,1000,1500,2000,2500,3000,3000,3000,3000]\n",
    "for year in {1,2,3,4,5,6,7,8}:\n",
    "    df=pd.read_excel('twitter/x'+str(year)+'.xlsx',sheetname='net')\n",
    "    df=pd.rolling_mean(df,moving_window)\n",
    "    sets={}\n",
    "    for n in df.columns:\n",
    "        sets[n]=[[],[]]\n",
    "        x=df[n].index\n",
    "        m=df[n].as_matrix()\n",
    "        for i in range(len(x)):\n",
    "            sets[n][0].append(x[i])\n",
    "            sets[n][1].append(m[i])\n",
    "\n",
    "    df=pd.read_excel('twitter/x'+str(year)+'.xlsx',sheetname='gross')\n",
    "    df=pd.rolling_mean(df,moving_window)\n",
    "    sets2={}\n",
    "    for n in df.columns:\n",
    "        sets2[n]=[[],[]]\n",
    "        x=df[n].index\n",
    "        m=df[n].as_matrix()\n",
    "        for i in range(len(x)):\n",
    "            sets2[n][0].append(x[i])\n",
    "            sets2[n][1].append(m[i])\n",
    "    sets2.pop('net');\n",
    "\n",
    "    x=sets[u'CSP'][0]\n",
    "    y=[0 for i in range(len(x))]\n",
    "    net=pd.DataFrame([0 for i in range(len(x))])\n",
    "    gross=pd.DataFrame([0 for i in range(len(x))])\n",
    "    labels=[]\n",
    "\n",
    "    fig = plt.figure(figsize=(8,5))                                                               \n",
    "    ax=[]\n",
    "    ax.append(plt.subplot2grid((2,2), (0,0), rowspan=2, colspan=2, axisbg=None, axisbelow=True))\n",
    "    fig.tight_layout()  \n",
    "    fig.subplots_adjust(wspace=0.35)\n",
    "\n",
    "    for i in range(len(fuels[5:])):\n",
    "        fuel=fuels[::-1][i]\n",
    "        if fuel==u'geoth.':fuel='geo'\n",
    "        gross+=pd.DataFrame(sets2[fuel][1])\n",
    "        y=np.vstack((sets2[fuel][1],y)) \n",
    "\n",
    "    g=pd.DataFrame(range(151))\n",
    "    g[0]=0\n",
    "    for i in range(len(fuels)):\n",
    "        fuel=fuels[::-1][i]\n",
    "        gross+=pd.DataFrame(sets[fuel][1])\n",
    "        net+=pd.DataFrame(sets[fuel][1])\n",
    "        if fuel not in {u'hydro',u'nuclear',u'biofuels',u'other',u'biomass'}:\n",
    "            y=np.vstack((sets[fuel][1],y)) \n",
    "        else:\n",
    "            g+=pd.DataFrame(sets[fuel][1])\n",
    "    y=np.vstack((g[0],y))\n",
    "\n",
    "    sp=ax[0].stackplot(x,y,alpha=0.9,colors=fuelcolors+fuelcolors[1:])\n",
    "    proxy = [mpl.patches.Rectangle((0,0), 0,0, facecolor=(fuelcolors+fuelcolors[1:])[j-1]) for j,pol in enumerate(sp)][::-1]\n",
    "    #ax[0].legend(proxy[7:], (labels[7:]),loc=2,framealpha=0,fontsize=11) \n",
    "\n",
    "    ax2=ax[0].twinx()\n",
    "    ax3=ax[0].twinx()\n",
    "    ax2.plot([0],[0],linewidth=2,linestyle='-',color='k',label='Net')\n",
    "    ax3.plot([0],[0],linewidth=2,linestyle='--',color='w',label='    ')\n",
    "    ax2.plot(x,net,linewidth=2,linestyle='--',color='w')\n",
    "    ax2.plot([1958,2100],[2000,2001],lw=0.5,color=\"grey\")\n",
    "\n",
    "    yl=350000\n",
    "    ax[0].set_ylim(0,yl)\n",
    "    ax2.set_ylim(0,yl)\n",
    "    ax[0].set_xlim(1950,2100)\n",
    "    ax[0].set_yticklabels([0,50,100,150,200,250,300],fontproperties=prop,size=12)\n",
    "    ax[0].set_xticklabels([1940,1960,1980,2000,2020,2040,2060,2080,2100],fontproperties=prop,size=12)\n",
    "    #ax[0].set_xlabel('Year',size=12)\n",
    "    ax[0].set_ylabel('Primary Energy Demand [1000 TWh / year]',fontproperties=prop,size=14,labelpad=10)\n",
    "    ax2.set_yticklabels([])\n",
    "    ax3.set_yticklabels([])\n",
    "    ax3.set_yticks([])\n",
    "    #ax2.legend(ncol=2,loc=2,framealpha=0,fontsize=12)\n",
    "    #ax3.legend(ncol=2,loc=2,framealpha=0,fontsize=12)\n",
    "    ax2.text(1.045, 0.5, str(dd[year])+' W / person',\n",
    "        horizontalalignment='right',fontproperties=prop,\n",
    "        verticalalignment='center',rotation=90,\n",
    "        transform=ax2.transAxes,size=14,color='k')\n",
    "\n",
    "    ax[0].text(0.5, 1.02, r\"Fossil phase-out: 2020-2075   |   Emissions cap: 990 GtCO$_2$\",\n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='bottom',fontproperties=prop,\n",
    "        transform=ax[0].transAxes,size=14,color='k')\n",
    "    ax[0].text(0.5, 1.11, r\"EROEI = \"+str(ee[year])+\" in year 2014   |   Demand = \"+str(dd[year])+\" W / person in year 2100\",\n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='bottom',fontproperties=prop,\n",
    "        transform=ax[0].transAxes,size=14,color='k')\n",
    "\n",
    "    ax[0].plot([2014,2014.001],[160000,300000],color='grey',ls='--',lw=1)\n",
    "    ax[0].text(0.43, 0.96, r\"historical     projection\",\n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='top',fontproperties=prop,\n",
    "        transform=ax[0].transAxes,size=10,color='grey')\n",
    "\n",
    "    ax[0].grid('off')\n",
    "    ax2.grid('off')\n",
    "    ax3.grid('off')\n",
    "    #ax[0].set_axis_bgcolor('w')\n",
    "    #ax2.set_axis_bgcolor('w')\n",
    "    #ax3.set_axis_bgcolor('w')\n",
    "    \n",
    "    plt.suptitle('Detailed Sustainable Energy Transition Path',fontproperties=prop,size=17,y=1.15)\n",
    "    #plt.savefig(path+'/twitter/cut'+str(year)+'.png',bbox_inches = 'tight', facecolor=\"w\", pad_inches = 0.1, dpi=150)\n",
    "    plt.savefig(path+'/twitter/x'+str(year)+'.png',bbox_inches = 'tight', pad_inches = 0.1, dpi=150)\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:7: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=9,center=False).mean()\n",
      "C:\\Anaconda\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:18: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=9,center=False).mean()\n"
     ]
    },
    {
     "data": {
      "image/png": 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YyNfXF3Nzc7Zt28bYsWPJz8+nY8eODBo0qMRK1P3791FRUcHKykrpfuzZs0dpemVvOdWu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\n/gN//BP2BE39ZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb389b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#redefine smooth window\n",
    "moving_window=9\n",
    "ee=[0,20,20,20,20,20,14,10,7]\n",
    "dd=[0,1000,1500,2000,2500,3000,3000,3000,3000]\n",
    "for year in {3}:\n",
    "    df=pd.read_excel('twitter/x'+str(year)+'.xlsx',sheetname='net')\n",
    "    df=pd.rolling_mean(df,moving_window)\n",
    "    sets={}\n",
    "    for n in df.columns:\n",
    "        sets[n]=[[],[]]\n",
    "        x=df[n].index\n",
    "        m=df[n].as_matrix()\n",
    "        for i in range(len(x)):\n",
    "            sets[n][0].append(x[i])\n",
    "            sets[n][1].append(m[i])\n",
    "\n",
    "    df=pd.read_excel('twitter/x'+str(year)+'.xlsx',sheetname='gross')\n",
    "    df=pd.rolling_mean(df,moving_window)\n",
    "    sets2={}\n",
    "    for n in df.columns:\n",
    "        sets2[n]=[[],[]]\n",
    "        x=df[n].index\n",
    "        m=df[n].as_matrix()\n",
    "        for i in range(len(x)):\n",
    "            sets2[n][0].append(x[i])\n",
    "            sets2[n][1].append(m[i])\n",
    "    sets2.pop('net');\n",
    "\n",
    "    x=sets[u'CSP'][0]\n",
    "    y=[0 for i in range(len(x))]\n",
    "    net=pd.DataFrame([0 for i in range(len(x))])\n",
    "    gross=pd.DataFrame([0 for i in range(len(x))])\n",
    "    labels=[]\n",
    "\n",
    "    fig = plt.figure(figsize=(8,5))                                                               \n",
    "    ax=[]\n",
    "    ax.append(plt.subplot2grid((2,2), (0,0), rowspan=2, colspan=2, axisbg=None, axisbelow=True))\n",
    "    fig.tight_layout()  \n",
    "    fig.subplots_adjust(wspace=0.35)\n",
    "\n",
    "    for i in range(len(fuels[5:])):\n",
    "        fuel=fuels[::-1][i]\n",
    "        if fuel==u'geoth.':fuel='geo'\n",
    "        gross+=pd.DataFrame(sets2[fuel][1])\n",
    "        y=np.vstack((sets2[fuel][1],y)) \n",
    "\n",
    "    g=pd.DataFrame(range(151))\n",
    "    g[0]=0\n",
    "    for i in range(len(fuels)):\n",
    "        fuel=fuels[::-1][i]\n",
    "        gross+=pd.DataFrame(sets[fuel][1])\n",
    "        net+=pd.DataFrame(sets[fuel][1])\n",
    "        if fuel not in {u'hydro',u'nuclear',u'biofuels',u'other',u'biomass'}:\n",
    "            y=np.vstack((sets[fuel][1],y)) \n",
    "        else:\n",
    "            g+=pd.DataFrame(sets[fuel][1])\n",
    "    y=np.vstack((g[0],y))\n",
    "\n",
    "    sp=ax[0].stackplot(x,y,alpha=0.9,colors=fuelcolors+fuelcolors[1:])\n",
    "    proxy = [mpl.patches.Rectangle((0,0), 0,0, facecolor=(fuelcolors+fuelcolors[1:])[j-1]) for j,pol in enumerate(sp)][::-1]\n",
    "    #ax[0].legend(proxy[7:], (labels[7:]),loc=2,framealpha=0,fontsize=11) \n",
    "\n",
    "    ax2=ax[0].twinx()\n",
    "    ax3=ax[0].twinx()\n",
    "    ax2.plot([0],[0],linewidth=2,linestyle='-',color='k',label='Net')\n",
    "    ax3.plot([0],[0],linewidth=2,linestyle='--',color='w',label='    ')\n",
    "    ax2.plot(x,net,linewidth=2,linestyle='--',color='w')\n",
    "    ax2.plot([1958,2100],[2000,2001],lw=0.5,color=\"grey\")\n",
    "\n",
    "    yl=240000\n",
    "    ax[0].set_ylim(0,yl)\n",
    "    ax2.set_ylim(0,yl)\n",
    "    ax[0].set_xlim(1950,2100)\n",
    "    ax[0].set_yticklabels([0,50,100,150,200,250],fontproperties=prop,size=12)\n",
    "    ax[0].set_xticklabels([1940,1960,1980,2000,2020,2040,2060,2080,2100],fontproperties=prop,size=12)\n",
    "    #ax[0].set_xlabel('Year',size=12)\n",
    "    ax[0].set_ylabel('Primary Energy Demand [1000 TWh / year]',fontproperties=prop,size=14,labelpad=10)\n",
    "    ax2.set_yticklabels([])\n",
    "    ax3.set_yticklabels([])\n",
    "    ax3.set_yticks([])\n",
    "    #ax2.legend(ncol=2,loc=2,framealpha=0,fontsize=12)\n",
    "    #ax3.legend(ncol=2,loc=2,framealpha=0,fontsize=12)\n",
    "    ax2.text(1.045, 0.5, str(dd[year])+' W / person',\n",
    "        horizontalalignment='right',fontproperties=prop,\n",
    "        verticalalignment='center',rotation=90,\n",
    "        transform=ax2.transAxes,size=14,color='k')\n",
    "\n",
    "    ax[0].text(0.5, 1.02, r\"Fossil phase-out: 2020-2075   |   Emissions cap: 990 GtCO$_2$\",\n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='bottom',fontproperties=prop,\n",
    "        transform=ax[0].transAxes,size=14,color='k')\n",
    "    ax[0].text(0.5, 1.11, r\"EROEI = \"+str(ee[year])+\" in year 2014   |   Demand = \"+str(dd[year])+\" W / person in year 2100\",\n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='bottom',fontproperties=prop,\n",
    "        transform=ax[0].transAxes,size=14,color='k')\n",
    "\n",
    "    ax[0].plot([2014,2014.001],[160000,230000],color='grey',ls='--',lw=1)\n",
    "    ax[0].text(0.43, 0.96, r\"historical     projection\",\n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='top',fontproperties=prop,\n",
    "        transform=ax[0].transAxes,size=10,color='grey')\n",
    "\n",
    "    ax[0].grid('off')\n",
    "    ax2.grid('off')\n",
    "    ax3.grid('off')\n",
    "    #ax[0].set_axis_bgcolor('w')\n",
    "    #ax2.set_axis_bgcolor('w')\n",
    "    #ax3.set_axis_bgcolor('w')\n",
    "    \n",
    "    plt.suptitle('Detailed Sustainable Energy Transition Path',fontproperties=prop,size=17,y=1.15)\n",
    "    #plt.savefig(path+'/twitter/cut'+str(year)+'.png',bbox_inches = 'tight', facecolor=\"w\", pad_inches = 0.1, dpi=150)\n",
    "    plt.savefig(path+'/twitter/x.png',bbox_inches = 'tight', pad_inches = 0.1, dpi=150)\n",
    "    #plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><br><p>We would like to express our gratitude to all of the developers of the libraries used and especially to the affiliates of <i>EIA, BP and World Bank</i> for their great database and openly accesible data. The data manipulation algorithms are open sourced and freely reproducible under an <a href=\"http://opensource.org/licenses/MIT\" target=\"_blank\">MIT license</a>.</p>\n",
    "<br>\n",
    "<p><a href=\"http://www.csaladen.es\" target=\"_blank\">Dénes Csala</a>&nbsp;&nbsp;|&nbsp;&nbsp;2016</p>"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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