scoping_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CCU - literature overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load some libraries for reading the data from the database and plotting \n",
    "\n",
    "import django\n",
    "import sys, os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sys.path.append('/home/galm/software/django/tmv/BasicBrowser/')\n",
    "os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"BasicBrowser.settings\")\n",
    "django.setup()\n",
    "\n",
    "from scoping.models import *\n",
    "\n",
    "from django.db.models import Count, Sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12838"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = Project.objects.get(pk=106)\n",
    "dos = DocOwnership.objects.filter(query__project=p,relevant__in=[1,2,3])\n",
    "dos.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>finish</th>\n",
       "      <th>relevant</th>\n",
       "      <th>user__username</th>\n",
       "      <th>cumsum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-05 14:11:32.553465+00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>vicl</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-11-05 14:13:33.473189+00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>vicl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-05 14:14:30.634022+00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>vicl</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-05 14:15:48.387171+00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>vicl</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-05 14:22:05.033141+00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>vicl</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            finish  relevant user__username  cumsum\n",
       "0 2018-11-05 14:11:32.553465+00:00         1           vicl       0\n",
       "1 2018-11-05 14:13:33.473189+00:00         2           vicl       1\n",
       "2 2018-11-05 14:14:30.634022+00:00         2           vicl       2\n",
       "3 2018-11-05 14:15:48.387171+00:00         2           vicl       3\n",
       "4 2018-11-05 14:22:05.033141+00:00         1           vicl       4"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.DataFrame.from_dict(list(dos.order_by('finish').values(\n",
    "    'finish','user__username','relevant'\n",
    ")))\n",
    "\n",
    "ratings['cumsum']=ratings.index\n",
    "ratings.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f107aaa2860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots( figsize=(8,8))\n",
    "\n",
    "times = [d.to_pydatetime() for d in ratings['finish']]\n",
    "\n",
    "ax.fill_between(times,0,ratings['cumsum'],color=\"#f7f7f7\")\n",
    "\n",
    "markers = ('o','v','^','1','s','P','+','x','D')\n",
    "cols = [\"None\",\"green\",\"red\",\"orange\"]\n",
    "cols = ['#fbb4ae','#b3cde3','#ccebc5','#decbe4','#fed9a6','#ffffcc','#e5d8bd','#fddaec']\n",
    "for i, (name, group) in enumerate(ratings.groupby('user__username')):\n",
    "    times = [d.to_pydatetime() for d in group['finish']]\n",
    "    ax.bar(\n",
    "        times,\n",
    "        group['cumsum'],\n",
    "        label=name,\n",
    "        width=0.2,\n",
    "        color=cols[i]\n",
    "        #s=15, \n",
    "        #marker=markers[i],\n",
    "    )\n",
    "    #ax.bar(times, group['cumsum'])\n",
    "    \n",
    "plt.setp(ax.get_xticklabels(), rotation=30)\n",
    "\n",
    "plt.legend(facecolor=\"#f7f7f7\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f107c3e5c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many documents are relevant or not\n",
    "rel = list(Doc.objects.filter(docproject__project=p).values('docproject__relevant').annotate(\n",
    "    n = Count('pk') # Count the pk - primary keys - of documents with each value\n",
    ").order_by('-n'))\n",
    "\n",
    "for r in rel: # get the full name of relevance\n",
    "    r['relevant'] = DocProject.Relevance[r['docproject__relevant']][1]\n",
    "\n",
    "# Put this into a dataframe\n",
    "rel_df = pd.DataFrame.from_dict(rel) # we have to wrap the queryset in a list, so pandas can read it properly\n",
    "rel_df.tail() # what do the last few rows look like?\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.bar(rel_df.index,rel_df['n'])\n",
    "ax.set_xticks(rel_df.index)\n",
    "ax.set_xticklabels(rel_df['relevant'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f1067af1ef0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f107c185be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "docs = Doc.objects.filter(docproject__project=p,docproject__relevant=1)\n",
    "# how many documents in each year of each relevance category?\n",
    "by_year = list(docs.values('PY').annotate(\n",
    "    n = Count('pk') # Count the pk - primary keys - of documents in each value of PY (year)\n",
    ").order_by('PY'))\n",
    "\n",
    "# Put this into a dataframe\n",
    "by_year_df = pd.DataFrame.from_dict(list(by_year)) # we have to wrap the queryset in a list, so pandas can read it properly\n",
    "\n",
    "plt.plot(by_year_df['PY'],by_year_df['n'],label=name)\n",
    "# Add some labels\n",
    "plt.xlabel('Publication year')\n",
    "plt.ylabel('Number of documents')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PY</th>\n",
       "      <th>ar</th>\n",
       "      <th>ar_name</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>2010 - 2014</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>2015 - 2019</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>2015 - 2019</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>2015 - 2019</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>2015 - 2019</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PY  ar      ar_name    n\n",
       "29  2014   5  2010 - 2014  153\n",
       "30  2015   6  2015 - 2019  248\n",
       "31  2016   6  2015 - 2019  311\n",
       "32  2017   6  2015 - 2019  322\n",
       "33  2018   6  2015 - 2019  325"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils.utils import ars\n",
    "\n",
    "syear = 1985\n",
    "ybin = 5\n",
    "\n",
    "bypy = docs.filter(\n",
    "       PY__in=range(syear,2019)\n",
    ").values('PY').annotate(\n",
    "    n = Count('UT', distinct=True)\n",
    ").order_by('PY')\n",
    "\n",
    "bypy = []\n",
    "for py in range(syear,2019):\n",
    "    pn = (py-syear)//ybin\n",
    "    pyd = {\"PY\": py}\n",
    "    pyd['ar'] = pn\n",
    "    pyd['ar_name'] = f\"{syear+ybin*pn} - {syear+ybin*(pn+1)-1}\"\n",
    "    pyd['n'] = docs.filter(PY=py).count()\n",
    "    bypy.append(pyd)\n",
    "\n",
    "\n",
    "bypy = pd.DataFrame.from_dict(list(bypy))\n",
    "\n",
    "bypy.head()\n",
    "\n",
    "bypy.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PY</th>\n",
       "      <th>Technology</th>\n",
       "      <th>ar</th>\n",
       "      <th>ar_name</th>\n",
       "      <th>n</th>\n",
       "      <th>ysum</th>\n",
       "      <th>share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>0</td>\n",
       "      <td>1985 - 1989</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>0</td>\n",
       "      <td>1985 - 1989</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1987</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>0</td>\n",
       "      <td>1985 - 1989</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1988</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>0</td>\n",
       "      <td>1985 - 1989</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1989</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>0</td>\n",
       "      <td>1985 - 1989</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PY          Technology  ar      ar_name  n  ysum  share\n",
       "0  1985  Biofuel from algae   0  1985 - 1989  0     3    0.0\n",
       "1  1986  Biofuel from algae   0  1985 - 1989  0     3    0.0\n",
       "2  1987  Biofuel from algae   0  1985 - 1989  0     0    NaN\n",
       "3  1988  Biofuel from algae   0  1985 - 1989  0     0    NaN\n",
       "4  1989  Biofuel from algae   0  1985 - 1989  0     1    0.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_dict = []\n",
    "\n",
    "ts = Category.objects.filter(project=p,level=1)\n",
    "\n",
    "#allreldocs = Doc.objects.filter(query=q)\n",
    "pys = list(range(syear,2019))\n",
    "for t in ts:\n",
    "    if t.level==2:\n",
    "        continue\n",
    "    for py in pys:           \n",
    "        ydocs = docs.filter(PY=py)\n",
    "        if t.name==\"Other mineralisation products\":\n",
    "            mins = [t] + list(Category.objects.filter(parent_category=t))\n",
    "            trdocs = set(list(ydocs.filter(\n",
    "                query__category__in=mins,docownership__relevant=1,docownership__query__category__in=mins\n",
    "            ).values_list('UT',flat=True)))\n",
    "            ttdocs = set(list(ydocs.filter(\n",
    "                category__in=mins\n",
    "            ).values_list('UT',flat=True)))\n",
    "        else:          \n",
    "            trdocs = set(list(ydocs.filter(\n",
    "                query__category=t,docownership__relevant=1,docownership__query__category=t\n",
    "            ).values_list('UT',flat=True)))\n",
    "            ttdocs = set(list(ydocs.filter(\n",
    "                category=t\n",
    "            ).values_list('UT',flat=True)))\n",
    "            \n",
    "        tdocs = Doc.objects.filter(\n",
    "            UT__in=trdocs.union(ttdocs)\n",
    "        )\n",
    "        d = {\n",
    "            'Technology': t.name,\n",
    "            'PY': py,\n",
    "            'n': tdocs.count()\n",
    "        }\n",
    "        doc_dict.append(d)\n",
    "        \n",
    "for py in doc_dict:\n",
    "    pn = (py['PY']-syear)//ybin\n",
    "    py['ar'] = pn\n",
    "    py['ar_name'] = f\"{syear+ybin*pn} - {syear+ybin*(pn+1)-1}\"\n",
    "\n",
    "\n",
    "        \n",
    "df = pd.DataFrame.from_dict(doc_dict)\n",
    "\n",
    "techorder = list(df.groupby('Technology')['n'].sum().sort_values(ascending=False).index)\n",
    "\n",
    "df['Technology'] = pd.Categorical(\n",
    "    df['Technology'],\n",
    "    techorder\n",
    ")\n",
    "\n",
    "df['ysum'] = df['n'].groupby(df['PY']).transform('sum')\n",
    "\n",
    "df['share'] = df['n'] / df['ysum'] * 100\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ar_name</th>\n",
       "      <th>Technology</th>\n",
       "      <th>n</th>\n",
       "      <th>ysum</th>\n",
       "      <th>share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985 - 1989</td>\n",
       "      <td>Other mineralisation products</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985 - 1989</td>\n",
       "      <td>Cement/concrete production</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>42.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985 - 1989</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985 - 1989</td>\n",
       "      <td>EOR</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>57.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985 - 1989</td>\n",
       "      <td>Polymers and bioplastics</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ar_name                     Technology  n  ysum      share\n",
       "0  1985 - 1989  Other mineralisation products  0     7   0.000000\n",
       "1  1985 - 1989     Cement/concrete production  3     7  42.857143\n",
       "2  1985 - 1989             Biofuel from algae  0     7   0.000000\n",
       "3  1985 - 1989                            EOR  4     7  57.142857\n",
       "4  1985 - 1989       Polymers and bioplastics  0     7   0.000000"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ardf = df[['Technology','ar_name','n']].groupby(['ar_name','Technology']).sum()\n",
    "\n",
    "ardf = ardf.reset_index()\n",
    "\n",
    "ardf['ysum'] = ardf['n'].groupby(ardf['ar_name']).transform('sum')\n",
    "\n",
    "ardf['share'] = ardf['n'] / ardf['ysum'] * 100\n",
    "\n",
    "ardf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1068e68be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmin = -1#0#1.5\n",
    "\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [12,10]\n",
    "\n",
    "both_axes = True\n",
    "\n",
    "if both_axes:\n",
    "    fig, axs = plt.subplots(2,1)\n",
    "    ax = axs[0]\n",
    "    ax2 = axs[1]\n",
    "else:\n",
    "    fig, ax = plt.subplots(1,1)\n",
    "    \n",
    "pdf = df.pivot(\n",
    "    index='PY',\n",
    "    columns='Technology',\n",
    "    values='n'\n",
    ")#.fillna(0)\n",
    "\n",
    "\n",
    "pdf.plot.bar(\n",
    "    stacked=True, \n",
    "    ax=ax,\n",
    "    legend=False,    \n",
    "    lw=0.1,\n",
    "    edgecolor='grey'#,\n",
    "    #align='center'\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "\n",
    "ylabs = [1985,1990,1995,2000,2005,2010,2015]\n",
    "yts = [x-syear for x in ylabs]\n",
    "\n",
    "\n",
    "ax.xaxis.set_ticks(yts)\n",
    "ax.xaxis.set_ticklabels(ylabs)\n",
    "\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(0)\n",
    "\n",
    "plt.legend(handles[::-1], labels[::-1],title='Technology',loc=\"center left\",bbox_to_anchor=(1, 1))\n",
    "\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Number of Publications on Technology')\n",
    "\n",
    "ax.grid(True)\n",
    "\n",
    "ax.set_xlim(xmax=2018-syear-xmin)\n",
    "ax.set_xlim(xmin=xmin)\n",
    "ax.set_ylim(ymax=700)\n",
    "\n",
    "if both_axes:\n",
    "    mids = []\n",
    "\n",
    "    ticklabels = []\n",
    "    for name, group in bypy.groupby('ar_name'):\n",
    "        ticklabels.append(name)\n",
    "        count = \"[{}]\".format(group['n'].sum())\n",
    "        miny = group['PY'].min() - syear \n",
    "        maxy = group['PY'].max() - syear \n",
    "        width = maxy-miny + 0.6\n",
    "        bwidth = 1.8\n",
    "        mid = group['PY'].median() - syear \n",
    "        mids.append(mid)\n",
    "        maxn = group['n'].max() *1.3 + 60\n",
    "        #ax.text(mid,maxn,count,ha=\"center\")\n",
    "        ar = group['ar'].max()\n",
    "        ax.annotate(\n",
    "            count,\n",
    "            xy=(mid,maxn),\n",
    "            xytext=(mid,maxn+50),\n",
    "            ha=\"center\",\n",
    "            arrowprops=dict(arrowstyle='-[, widthB={}, lengthB=1.5'.format(width), lw=2.0)\n",
    "        )\n",
    "        art = ardf[ardf['ar_name']==name]\n",
    "        b = 0\n",
    "        h = 0\n",
    "        for t in techorder:\n",
    "            v = art[art['Technology']==t]\n",
    "            if not v.empty:\n",
    "                v = float(v['share'])\n",
    "                ax2.bar(\n",
    "                    mid,v,bottom=b,align=\"center\",\n",
    "                    width=bwidth,color=handles[h].patches[0]._facecolor\n",
    "                )        \n",
    "                if v > 10:\n",
    "                    ax2.text(mid+bwidth*0.5+0.1,b+v*0.5,\"{}%\".format(round(v)),va=\"center\")\n",
    "                b+=v\n",
    "            h+=1\n",
    "\n",
    "    ax = ax2\n",
    "\n",
    "    for tick in ax.get_xticklabels():\n",
    "        tick.set_rotation(0)\n",
    "\n",
    "\n",
    "    #legend = plt.legend(position=\"right\")#handles[::-1], labels[::-1],title='Technology')\n",
    "    #legend.remove()\n",
    "\n",
    "    ax.xaxis.set_ticks(mids)\n",
    "    ax.xaxis.set_ticklabels(ticklabels)\n",
    "\n",
    "    ax.set_xlabel('Year')\n",
    "    ax.set_ylabel('Share of Publications on Technology')\n",
    "\n",
    "    ax.set_xlim(xmax=2018-syear-xmin)\n",
    "    ax.set_xlim(xmin=xmin)\n",
    "\n",
    "plt.savefig('plots/all_ars.png',bbox_inches='tight') \n",
    "plt.savefig('plots/all_ars.eps',bbox_inches='tight') \n",
    "plt.savefig('plots/all_ars.svg',bbox_inches='tight') \n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ar_name</th>\n",
       "      <th>Technology</th>\n",
       "      <th>n</th>\n",
       "      <th>ysum</th>\n",
       "      <th>share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Other mineralisation products</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Cement/concrete production</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>EOR</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Polymers and bioplastics</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Methane</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Desalination</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Synthesis gas</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>EGR/CBM</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Formic acid</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>general CCU</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Acetic acid</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Urea</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>DME</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>C fiber and graphene</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>OME</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Other chemicals</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1986 - 1990</td>\n",
       "      <td>Other products</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1991 - 1995</td>\n",
       "      <td>Other mineralisation products</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>4.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1991 - 1995</td>\n",
       "      <td>Cement/concrete production</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>23.809524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1991 - 1995</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>9.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1991 - 1995</td>\n",
       "      <td>EOR</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>42.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1991 - 1995</td>\n",
       "      <td>Polymers and bioplastics</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1991 - 1995</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>4.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1991 - 1995</td>\n",
       "      <td>Methane</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1991 - 1995</td>\n",
       "      <td>Desalination</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1991 - 1995</td>\n",
       "      <td>Synthesis gas</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1991 - 1995</td>\n",
       "      <td>EGR/CBM</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1991 - 1995</td>\n",
       "      <td>Formic acid</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2011 - 2015</td>\n",
       "      <td>Synthesis gas</td>\n",
       "      <td>13</td>\n",
       "      <td>906</td>\n",
       "      <td>1.434879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2011 - 2015</td>\n",
       "      <td>EGR/CBM</td>\n",
       "      <td>15</td>\n",
       "      <td>906</td>\n",
       "      <td>1.655629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2011 - 2015</td>\n",
       "      <td>Formic acid</td>\n",
       "      <td>13</td>\n",
       "      <td>906</td>\n",
       "      <td>1.434879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2011 - 2015</td>\n",
       "      <td>general CCU</td>\n",
       "      <td>9</td>\n",
       "      <td>906</td>\n",
       "      <td>0.993377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2011 - 2015</td>\n",
       "      <td>Acetic acid</td>\n",
       "      <td>8</td>\n",
       "      <td>906</td>\n",
       "      <td>0.883002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2011 - 2015</td>\n",
       "      <td>Urea</td>\n",
       "      <td>5</td>\n",
       "      <td>906</td>\n",
       "      <td>0.551876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2011 - 2015</td>\n",
       "      <td>DME</td>\n",
       "      <td>5</td>\n",
       "      <td>906</td>\n",
       "      <td>0.551876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2011 - 2015</td>\n",
       "      <td>C fiber and graphene</td>\n",
       "      <td>4</td>\n",
       "      <td>906</td>\n",
       "      <td>0.441501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2011 - 2015</td>\n",
       "      <td>OME</td>\n",
       "      <td>3</td>\n",
       "      <td>906</td>\n",
       "      <td>0.331126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2011 - 2015</td>\n",
       "      <td>Other chemicals</td>\n",
       "      <td>4</td>\n",
       "      <td>906</td>\n",
       "      <td>0.441501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2011 - 2015</td>\n",
       "      <td>Other products</td>\n",
       "      <td>2</td>\n",
       "      <td>906</td>\n",
       "      <td>0.220751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Other mineralisation products</td>\n",
       "      <td>337</td>\n",
       "      <td>1251</td>\n",
       "      <td>26.938449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Cement/concrete production</td>\n",
       "      <td>236</td>\n",
       "      <td>1251</td>\n",
       "      <td>18.864908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>183</td>\n",
       "      <td>1251</td>\n",
       "      <td>14.628297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>EOR</td>\n",
       "      <td>107</td>\n",
       "      <td>1251</td>\n",
       "      <td>8.553157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Polymers and bioplastics</td>\n",
       "      <td>114</td>\n",
       "      <td>1251</td>\n",
       "      <td>9.112710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>61</td>\n",
       "      <td>1251</td>\n",
       "      <td>4.876099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Methane</td>\n",
       "      <td>51</td>\n",
       "      <td>1251</td>\n",
       "      <td>4.076739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Desalination</td>\n",
       "      <td>24</td>\n",
       "      <td>1251</td>\n",
       "      <td>1.918465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Synthesis gas</td>\n",
       "      <td>25</td>\n",
       "      <td>1251</td>\n",
       "      <td>1.998401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>EGR/CBM</td>\n",
       "      <td>15</td>\n",
       "      <td>1251</td>\n",
       "      <td>1.199041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Formic acid</td>\n",
       "      <td>18</td>\n",
       "      <td>1251</td>\n",
       "      <td>1.438849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>general CCU</td>\n",
       "      <td>18</td>\n",
       "      <td>1251</td>\n",
       "      <td>1.438849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Acetic acid</td>\n",
       "      <td>16</td>\n",
       "      <td>1251</td>\n",
       "      <td>1.278977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Urea</td>\n",
       "      <td>10</td>\n",
       "      <td>1251</td>\n",
       "      <td>0.799361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>DME</td>\n",
       "      <td>9</td>\n",
       "      <td>1251</td>\n",
       "      <td>0.719424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>C fiber and graphene</td>\n",
       "      <td>12</td>\n",
       "      <td>1251</td>\n",
       "      <td>0.959233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>OME</td>\n",
       "      <td>9</td>\n",
       "      <td>1251</td>\n",
       "      <td>0.719424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Other chemicals</td>\n",
       "      <td>4</td>\n",
       "      <td>1251</td>\n",
       "      <td>0.319744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2016 - 2020</td>\n",
       "      <td>Other products</td>\n",
       "      <td>2</td>\n",
       "      <td>1251</td>\n",
       "      <td>0.159872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ar_name                     Technology    n  ysum      share\n",
       "0    1986 - 1990  Other mineralisation products    0     4   0.000000\n",
       "1    1986 - 1990     Cement/concrete production    1     4  25.000000\n",
       "2    1986 - 1990             Biofuel from algae    0     4   0.000000\n",
       "3    1986 - 1990                            EOR    3     4  75.000000\n",
       "4    1986 - 1990       Polymers and bioplastics    0     4   0.000000\n",
       "5    1986 - 1990                       Methanol    0     4   0.000000\n",
       "6    1986 - 1990                        Methane    0     4   0.000000\n",
       "7    1986 - 1990                   Desalination    0     4   0.000000\n",
       "8    1986 - 1990                  Synthesis gas    0     4   0.000000\n",
       "9    1986 - 1990                        EGR/CBM    0     4   0.000000\n",
       "10   1986 - 1990                    Formic acid    0     4   0.000000\n",
       "11   1986 - 1990                    general CCU    0     4   0.000000\n",
       "12   1986 - 1990                    Acetic acid    0     4   0.000000\n",
       "13   1986 - 1990                           Urea    0     4   0.000000\n",
       "14   1986 - 1990                            DME    0     4   0.000000\n",
       "15   1986 - 1990           C fiber and graphene    0     4   0.000000\n",
       "16   1986 - 1990                            OME    0     4   0.000000\n",
       "17   1986 - 1990                Other chemicals    0     4   0.000000\n",
       "18   1986 - 1990                 Other products    0     4   0.000000\n",
       "19   1991 - 1995  Other mineralisation products    1    21   4.761905\n",
       "20   1991 - 1995     Cement/concrete production    5    21  23.809524\n",
       "21   1991 - 1995             Biofuel from algae    2    21   9.523810\n",
       "22   1991 - 1995                            EOR    9    21  42.857143\n",
       "23   1991 - 1995       Polymers and bioplastics    0    21   0.000000\n",
       "24   1991 - 1995                       Methanol    1    21   4.761905\n",
       "25   1991 - 1995                        Methane    0    21   0.000000\n",
       "26   1991 - 1995                   Desalination    0    21   0.000000\n",
       "27   1991 - 1995                  Synthesis gas    0    21   0.000000\n",
       "28   1991 - 1995                        EGR/CBM    0    21   0.000000\n",
       "29   1991 - 1995                    Formic acid    0    21   0.000000\n",
       "..           ...                            ...  ...   ...        ...\n",
       "103  2011 - 2015                  Synthesis gas   13   906   1.434879\n",
       "104  2011 - 2015                        EGR/CBM   15   906   1.655629\n",
       "105  2011 - 2015                    Formic acid   13   906   1.434879\n",
       "106  2011 - 2015                    general CCU    9   906   0.993377\n",
       "107  2011 - 2015                    Acetic acid    8   906   0.883002\n",
       "108  2011 - 2015                           Urea    5   906   0.551876\n",
       "109  2011 - 2015                            DME    5   906   0.551876\n",
       "110  2011 - 2015           C fiber and graphene    4   906   0.441501\n",
       "111  2011 - 2015                            OME    3   906   0.331126\n",
       "112  2011 - 2015                Other chemicals    4   906   0.441501\n",
       "113  2011 - 2015                 Other products    2   906   0.220751\n",
       "114  2016 - 2020  Other mineralisation products  337  1251  26.938449\n",
       "115  2016 - 2020     Cement/concrete production  236  1251  18.864908\n",
       "116  2016 - 2020             Biofuel from algae  183  1251  14.628297\n",
       "117  2016 - 2020                            EOR  107  1251   8.553157\n",
       "118  2016 - 2020       Polymers and bioplastics  114  1251   9.112710\n",
       "119  2016 - 2020                       Methanol   61  1251   4.876099\n",
       "120  2016 - 2020                        Methane   51  1251   4.076739\n",
       "121  2016 - 2020                   Desalination   24  1251   1.918465\n",
       "122  2016 - 2020                  Synthesis gas   25  1251   1.998401\n",
       "123  2016 - 2020                        EGR/CBM   15  1251   1.199041\n",
       "124  2016 - 2020                    Formic acid   18  1251   1.438849\n",
       "125  2016 - 2020                    general CCU   18  1251   1.438849\n",
       "126  2016 - 2020                    Acetic acid   16  1251   1.278977\n",
       "127  2016 - 2020                           Urea   10  1251   0.799361\n",
       "128  2016 - 2020                            DME    9  1251   0.719424\n",
       "129  2016 - 2020           C fiber and graphene   12  1251   0.959233\n",
       "130  2016 - 2020                            OME    9  1251   0.719424\n",
       "131  2016 - 2020                Other chemicals    4  1251   0.319744\n",
       "132  2016 - 2020                 Other products    2  1251   0.159872\n",
       "\n",
       "[133 rows x 5 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "ardf"
   ]
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  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PY</th>\n",
       "      <th>Technology</th>\n",
       "      <th>ar</th>\n",
       "      <th>ar_name</th>\n",
       "      <th>n</th>\n",
       "      <th>ysum</th>\n",
       "      <th>share</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1990</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>1</td>\n",
       "      <td>1990 - 1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1991</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>1</td>\n",
       "      <td>1990 - 1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1992</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>1</td>\n",
       "      <td>1990 - 1994</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1993</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>1</td>\n",
       "      <td>1990 - 1994</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>15.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1994</td>\n",
       "      <td>Biofuel from algae</td>\n",
       "      <td>1</td>\n",
       "      <td>1990 - 1994</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PY          Technology  ar      ar_name  n  ysum      share\n",
       "5  1990  Biofuel from algae   1  1990 - 1994  0     0        NaN\n",
       "6  1991  Biofuel from algae   1  1990 - 1994  0     0        NaN\n",
       "7  1992  Biofuel from algae   1  1990 - 1994  0     3   0.000000\n",
       "8  1993  Biofuel from algae   1  1990 - 1994  2    13  15.384615\n",
       "9  1994  Biofuel from algae   1  1990 - 1994  0     2   0.000000"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['ar']==1)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Technology</th>\n",
       "      <th>Cement/concrete production</th>\n",
       "      <th>Other mineralisation products</th>\n",
       "      <th>EOR</th>\n",
       "      <th>Methane</th>\n",
       "      <th>Biofuel from algae</th>\n",
       "      <th>Synthesis gas</th>\n",
       "      <th>OME</th>\n",
       "      <th>Methanol</th>\n",
       "      <th>Polymers and bioplastics</th>\n",
       "      <th>Desalination</th>\n",
       "      <th>EGR/CBM</th>\n",
       "      <th>Acetic acid</th>\n",
       "      <th>Formic acid</th>\n",
       "      <th>C fiber and graphene</th>\n",
       "      <th>Urea</th>\n",
       "      <th>DME</th>\n",
       "      <th>Other chemicals</th>\n",
       "      <th>Other products</th>\n",
       "      <th>general CCU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PY</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>29</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>40</td>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>36</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>54</td>\n",
       "      <td>73</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>66</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>72</td>\n",
       "      <td>105</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>42</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>75</td>\n",
       "      <td>111</td>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>62</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>89</td>\n",
       "      <td>121</td>\n",
       "      <td>35</td>\n",
       "      <td>17</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Technology  Cement/concrete production  Other mineralisation products  EOR  \\\n",
       "PY                                                                           \n",
       "1985                                 2                              0    1   \n",
       "1986                                 1                              0    2   \n",
       "1987                                 0                              0    0   \n",
       "1988                                 0                              0    0   \n",
       "1989                                 0                              0    1   \n",
       "1990                                 0                              0    0   \n",
       "1991                                 0                              0    0   \n",
       "1992                                 0                              0    2   \n",
       "1993                                 3                              0    6   \n",
       "1994                                 0                              0    1   \n",
       "1995                                 2                              1    0   \n",
       "1996                                 4                              0    3   \n",
       "1997                                 5                              0    1   \n",
       "1998                                 2                              2    3   \n",
       "1999                                 6                              0    0   \n",
       "2000                                 1                              1    1   \n",
       "2001                                 5                              2    2   \n",
       "2002                                 4                              4    2   \n",
       "2003                                 9                              4    1   \n",
       "2004                                 7                              5    3   \n",
       "2005                                 4                              7    5   \n",
       "2006                                 7                              6    3   \n",
       "2007                                11                             11    5   \n",
       "2008                                10                             11    5   \n",
       "2009                                14                             17    5   \n",
       "2010                                17                             20    5   \n",
       "2011                                13                             32    8   \n",
       "2012                                29                             40    8   \n",
       "2013                                40                             49   16   \n",
       "2014                                36                             60   12   \n",
       "2015                                54                             73   31   \n",
       "2016                                72                            105   38   \n",
       "2017                                75                            111   34   \n",
       "2018                                89                            121   35   \n",
       "\n",
       "Technology  Methane  Biofuel from algae  Synthesis gas  OME  Methanol  \\\n",
       "PY                                                                      \n",
       "1985              0                   0              0    0         0   \n",
       "1986              0                   0              0    0         0   \n",
       "1987              0                   0              0    0         0   \n",
       "1988              0                   0              0    0         0   \n",
       "1989              0                   0              0    0         0   \n",
       "1990              0                   0              0    0         0   \n",
       "1991              0                   0              0    0         0   \n",
       "1992              0                   0              0    0         0   \n",
       "1993              0                   2              0    0         1   \n",
       "1994              0                   0              0    0         0   \n",
       "1995              0                   0              0    0         0   \n",
       "1996              0                   0              0    0         1   \n",
       "1997              0                   3              1    0         2   \n",
       "1998              0                   0              0    0         1   \n",
       "1999              0                   0              0    0         0   \n",
       "2000              0                   0              0    0         1   \n",
       "2001              0                   0              0    0         1   \n",
       "2002              0                   1              0    1         2   \n",
       "2003              0                   2              0    0         1   \n",
       "2004              0                   0              0    0         1   \n",
       "2005              0                   3              2    0         0   \n",
       "2006              0                   1              0    0         0   \n",
       "2007              1                   0              0    0         2   \n",
       "2008              1                   5              0    0         1   \n",
       "2009              1                  11              0    1         1   \n",
       "2010              1                  14              2    0         5   \n",
       "2011              1                  17              4    0         7   \n",
       "2012              4                  18              2    0         6   \n",
       "2013              3                  25              1    0         5   \n",
       "2014              6                  29              0    1        12   \n",
       "2015             12                  66              6    2        19   \n",
       "2016             18                  55              3    3        17   \n",
       "2017             16                  62              9    5        17   \n",
       "2018             17                  66             13    1        27   \n",
       "\n",
       "Technology  Polymers and bioplastics  Desalination  EGR/CBM  Acetic acid  \\\n",
       "PY                                                                         \n",
       "1985                               0             0        0            0   \n",
       "1986                               0             0        0            0   \n",
       "1987                               0             0        0            0   \n",
       "1988                               0             0        0            0   \n",
       "1989                               0             0        0            0   \n",
       "1990                               0             0        0            0   \n",
       "1991                               0             0        0            0   \n",
       "1992                               0             0        0            1   \n",
       "1993                               0             0        0            1   \n",
       "1994                               0             0        0            1   \n",
       "1995                               0             0        0            0   \n",
       "1996                               0             0        0            0   \n",
       "1997                               1             0        0            0   \n",
       "1998                               0             0        0            1   \n",
       "1999                               1             0        0            1   \n",
       "2000                               0             0        0            0   \n",
       "2001                               1             0        0            0   \n",
       "2002                               0             0        0            0   \n",
       "2003                               0             0        0            0   \n",
       "2004                               1             0        1            0   \n",
       "2005                               1             2        1            0   \n",
       "2006                               0             0        1            0   \n",
       "2007                               1             0        4            0   \n",
       "2008                               0             0        0            0   \n",
       "2009                               1             2        0            0   \n",
       "2010                               2             1        1            0   \n",
       "2011                               2             4        2            0   \n",
       "2012                              15             4        2            1   \n",
       "2013                              14             3        3            5   \n",
       "2014                              13             7        5            0   \n",
       "2015                              27             5        3            2   \n",
       "2016                              42             7        5            1   \n",
       "2017                              44             9        5            6   \n",
       "2018                              28             8        5            9   \n",
       "\n",
       "Technology  Formic acid  C fiber and graphene  Urea  DME  Other chemicals  \\\n",
       "PY                                                                          \n",
       "1985                  0                     0     0    0                0   \n",
       "1986                  0                     0     0    0                0   \n",
       "1987                  0                     0     0    0                0   \n",
       "1988                  0                     0     0    0                0   \n",
       "1989                  0                     0     0    0                0   \n",
       "1990                  0                     0     0    0                0   \n",
       "1991                  0                     0     0    0                0   \n",
       "1992                  0                     0     0    0                0   \n",
       "1993                  0                     0     0    0                0   \n",
       "1994                  0                     0     0    0                0   \n",
       "1995                  0                     0     0    0                0   \n",
       "1996                  0                     0     0    0                0   \n",
       "1997                  0                     0     0    0                0   \n",
       "1998                  1                     0     1    1                0   \n",
       "1999                  0                     0     0    0                0   \n",
       "2000                  0                     0     1    0                0   \n",
       "2001                  0                     0     0    0                0   \n",
       "2002                  1                     0     0    0                0   \n",
       "2003                  0                     0     1    0                0   \n",
       "2004                  1                     0     0    0                0   \n",
       "2005                  0                     0     1    1                0   \n",
       "2006                  1                     1     0    0                0   \n",
       "2007                  0                     0     1    0                0   \n",
       "2008                  0                     0     0    0                0   \n",
       "2009                  0                     0     0    0                0   \n",
       "2010                  0                     0     2    1                0   \n",
       "2011                  0                     0     0    2                0   \n",
       "2012                  2                     0     1    0                2   \n",
       "2013                  3                     1     1    1                1   \n",
       "2014                  3                     1     0    1                0   \n",
       "2015                  5                     2     3    1                1   \n",
       "2016                  5                     6     2    2                0   \n",
       "2017                  6                     2     6    3                2   \n",
       "2018                  7                     4     2    4                2   \n",
       "\n",
       "Technology  Other products  general CCU  \n",
       "PY                                       \n",
       "1985                     0            0  \n",
       "1986                     0            0  \n",
       "1987                     0            0  \n",
       "1988                     0            0  \n",
       "1989                     0            0  \n",
       "1990                     0            0  \n",
       "1991                     0            0  \n",
       "1992                     0            0  \n",
       "1993                     0            0  \n",
       "1994                     0            0  \n",
       "1995                     0            0  \n",
       "1996                     0            0  \n",
       "1997                     0            0  \n",
       "1998                     0            0  \n",
       "1999                     0            0  \n",
       "2000                     0            0  \n",
       "2001                     0            0  \n",
       "2002                     0            0  \n",
       "2003                     0            1  \n",
       "2004                     0            1  \n",
       "2005                     0            0  \n",
       "2006                     0            0  \n",
       "2007                     0            1  \n",
       "2008                     0            2  \n",
       "2009                     0            1  \n",
       "2010                     1            0  \n",
       "2011                     0            0  \n",
       "2012                     0            0  \n",
       "2013                     2            3  \n",
       "2014                     0            4  \n",
       "2015                     0            2  \n",
       "2016                     0            5  \n",
       "2017                     1            6  \n",
       "2018                     1            7  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f106901c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = 0\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "scs = []\n",
    "for t in Category.objects.filter(project=p,level=2).order_by('-ndocs'):\n",
    "    trdocs = set(list(ydocs.filter(\n",
    "        query__category=t,docownership__relevant=1,docownership__query__category=t\n",
    "    ).values_list('UT',flat=True)))\n",
    "    ttdocs = set(list(ydocs.filter(\n",
    "        category=t\n",
    "    ).values_list('UT',flat=True)))\n",
    "    tdocs = Doc.objects.filter(\n",
    "        UT__in=trdocs.union(ttdocs)\n",
    "    )    \n",
    "    scs.append({\n",
    "        'title': t.name,\n",
    "        'n': tdocs.count()\n",
    "    })\n",
    "\n",
    "sdf = pd.DataFrame.from_dict(scs).sort_values('n',ascending=False)\n",
    "\n",
    "for i, row in sdf.iterrows():  \n",
    "    ax.bar(1,row['n'],bottom=b, label=row['title'])\n",
    "    t = \"\\n\".join(row['title'].split())\n",
    "    #ax.text(1.45, b+row['n']/2, row['title'].replace(' chem','\\nchem'),va=\"top\")\n",
    "    b+=row['n']\n",
    "    \n",
    "plt.legend()\n",
    "\n",
    "ax.xaxis.set_ticks([1])\n",
    "ax.xaxis.set_ticklabels([\"Other mineralisation products\"])\n",
    "    \n",
    "\n",
    "plt.savefig('plots/other_mineralisation.png',bbox_inches='tight') \n",
    "plt.savefig('plots/other_mineralisation.eps',bbox_inches='tight') \n",
    "plt.savefig('plots/other_mineralisation.svg',bbox_inches='tight') \n",
    "\n",
    "plt.show()\n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tmv",
   "language": "python",
   "name": "tmv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}