Query_the_RPC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the rock property catalog\n",
    "\n",
    "This notebook accompanies a blog post — [The Rock Property Catalog again](http://www.agilegeoscience.com/blog/2015/10/5/the-rock-property-catalog-again)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is very hacky. It will change. Stay tuned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "class RPC(object):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def _query_ssw(self, filters, properties, options):\n",
    "        \n",
    "        base_url = \"http://www.subsurfwiki.org/api.php\"\n",
    "        q = \"action=ask&query=[[RPC:%2B]]\"\n",
    "        q += ''.join(filters) if filters else ''\n",
    "        q += '|%3F' + '|%3F'.join(properties) if properties else ''\n",
    "        q += '|' + '|'.join(options) if options else ''\n",
    "        q += '&format=json'\n",
    "        \n",
    "        return requests.get(base_url, params=q)\n",
    "    \n",
    "    def _get_formats(self, response):\n",
    "        formats = {}\n",
    "        for item in response.json()['query']['printrequests']:\n",
    "            if item[u'mode'] == 1:\n",
    "                formats[item[u'label']] = item[u'typeid'].lstrip('_')\n",
    "        return formats\n",
    "    \n",
    "    def _build_dataframe(self, response):\n",
    "        \"\"\"\n",
    "        Takes the response of a query and returns a pandas\n",
    "        dataframe containing the results.\n",
    "        \"\"\"\n",
    "        try:\n",
    "            s = list(response.json()['query']['results'].keys())\n",
    "        except Exception as e:\n",
    "            raise e\n",
    "        samples = [i[4:] for i in s]\n",
    "        df = pd.DataFrame(samples)\n",
    "\n",
    "        # We'll need to know the formats of the columns.\n",
    "        formats = self._get_formats(response)\n",
    "        properties = formats.keys()\n",
    "\n",
    "        # Now traverse the JSON and build the DataFrame.\n",
    "        for prop in properties:\n",
    "            temp = []\n",
    "            for row in list(s):\n",
    "                p = response.json()['query']['results'][row]['printouts']\n",
    "                if p[prop]:\n",
    "                    if formats[prop] == 'qty':   # Quantity, number + unit\n",
    "                        temp.append(p[prop][0]['value'])\n",
    "                    elif formats[prop] == 'wpg':  # Wiki page\n",
    "                        temp.append(p[prop][0]['fulltext'])\n",
    "                    else:                         # Anything else: num, txt, tem, etc.\n",
    "                        temp.append(p[prop][0])\n",
    "                else:\n",
    "                    temp.append(None)\n",
    "            df[prop] = temp\n",
    "        \n",
    "        df = df.set_index(0)\n",
    "        df.index.name = None\n",
    "\n",
    "        return df\n",
    "    \n",
    "    def query(self, filters=None, properties=None, options=None):\n",
    "        r = self._query_ssw(filters, properties, options)\n",
    "        if r.status_code == 200:\n",
    "            return self._build_dataframe(r)\n",
    "        else:\n",
    "            print(\"Something went wrong.\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delta</th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Vp</th>\n",
       "      <th>Epsilon</th>\n",
       "      <th>Vs</th>\n",
       "      <th>Citation</th>\n",
       "      <th>Rho</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100015</th>\n",
       "      <td>0.6263</td>\n",
       "      <td>Shale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.955311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sondergeld, 2011</td>\n",
       "      <td>None</td>\n",
       "      <td>Haynesville Shale 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100072</th>\n",
       "      <td>0.1180</td>\n",
       "      <td>Shale</td>\n",
       "      <td>3774.891853</td>\n",
       "      <td>0.245000</td>\n",
       "      <td>2508.933067</td>\n",
       "      <td>Wang, 2002</td>\n",
       "      <td>None</td>\n",
       "      <td>Hard shale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100010</th>\n",
       "      <td>0.7319</td>\n",
       "      <td>Shale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.586081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sondergeld, 2011</td>\n",
       "      <td>None</td>\n",
       "      <td>Haynesville Shale 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100051</th>\n",
       "      <td>0.0320</td>\n",
       "      <td>Shale</td>\n",
       "      <td>2464.704037</td>\n",
       "      <td>0.258000</td>\n",
       "      <td>1470.468283</td>\n",
       "      <td>Wang, 2002</td>\n",
       "      <td>None</td>\n",
       "      <td>Shale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100077</th>\n",
       "      <td>0.0950</td>\n",
       "      <td>Shale</td>\n",
       "      <td>3792.983055</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>2276.750803</td>\n",
       "      <td>Wang, 2002</td>\n",
       "      <td>None</td>\n",
       "      <td>Hard shale</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Delta Lithology           Vp   Epsilon           Vs  \\\n",
       "100015  0.6263     Shale          NaN  0.955311          NaN   \n",
       "100072  0.1180     Shale  3774.891853  0.245000  2508.933067   \n",
       "100010  0.7319     Shale          NaN  0.586081          NaN   \n",
       "100051  0.0320     Shale  2464.704037  0.258000  1470.468283   \n",
       "100077  0.0950     Shale  3792.983055  0.240000  2276.750803   \n",
       "\n",
       "                Citation   Rho          Description  \n",
       "100015  Sondergeld, 2011  None  Haynesville Shale 2  \n",
       "100072        Wang, 2002  None           Hard shale  \n",
       "100010  Sondergeld, 2011  None  Haynesville Shale 1  \n",
       "100051        Wang, 2002  None                Shale  \n",
       "100077        Wang, 2002  None           Hard shale  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpc = RPC()\n",
    "\n",
    "filters = [\"[[lithology::Shale||Sandstone||Limestone]][[Delta::%2B]]\"]\n",
    "properties = ['Citation', 'Description', 'Lithology', 'Vp', 'Vs', 'Rho', 'Delta', 'Epsilon']\n",
    "options = [\"limit=100\"]\n",
    "\n",
    "df = rpc.query(filters, properties, options)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10e3c6828>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Pf5r0Mh5quVTdZpnDm4YuAK7PbStmkptsNstzz70AvAnMA4by7jGNbLIcFKwuFRNQRtPe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\nzv8H/3jjGe9rangAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1092c3780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.Vs, df.Vp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "liths = df.groupby('Lithology')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EskqBR0REskqBR0REskozF4hIQdGiboVPgUdECsaPbqni9zU/pEffj9jSC/56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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e374eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,6))\n",
    "ax = plt.subplot(111)\n",
    "for name, group in liths:\n",
    "    ax.plot(group.Vp, group.Vs, marker='o', linestyle='', ms=6, label=name)\n",
    "ax.legend(loc=2)\n",
    "\n",
    "title = '{0}\\ rocks\\ from\\ the\\ RPC'.format(len(df))\n",
    "plt.title('$\\mathrm{' + title + '}$', size=18)\n",
    "plt.xlabel('$V_\\mathrm{P}\\ [m/s]$', size=16)\n",
    "plt.ylabel('$V_\\mathrm{S}\\ [m/s]$', size=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr />\n",
    "<p style=\"color:gray;\">© Agile Geoscience 2015. This notebook is licensed under the terms of <a href=\"https://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0</a>. Please use it!</p>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}