domains.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Why Domain Knowledge is Crucial\n",
    "\n",
    "## Paul Hobson (@pmhobson)\n",
    "### Geosyntec Consultants\n",
    "\n",
    "## Github: https://github.com/Geosyntec/wqio\n",
    "\n",
    "## Reference - Helsel and Hirsch (USGS) http://pubs.usgs.gov/twri/twri4a3/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "from scipy import stats\n",
    "\n",
    "import wqio\n",
    "\n",
    "seaborn.set(style='ticks', context='talk')\n",
    "np.random.seed(0)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def probabiity_plot(raw, ros, **kwargs):\n",
    "    ax = plt.gca()\n",
    "    color = kwargs.pop('color')\n",
    "    _ = kwargs.pop('marker', None)\n",
    "    qraw, xraw = stats.probplot(raw, fit=False)\n",
    "    praw  = stats.norm.cdf(qraw) * 100\n",
    "    \n",
    "    qros, xros = stats.probplot(ros, fit=False)\n",
    "    pros = stats.norm.cdf(qros) * 100\n",
    "    \n",
    "    ax.plot(praw, xraw, mfc=color, mec='white', linestyle='None',\n",
    "            marker='o', mew=0.75, alpha=0.75, label='Original', **kwargs)\n",
    "    ax.plot(pros, xros, mec=color, mfc='none', linestyle='None',\n",
    "            marker='s', mew=0.75, alpha=0.75, label='Imputed', **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Load some water quality data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>res</th>\n",
       "      <th>qual</th>\n",
       "      <th>parameter</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.00</td>\n",
       "      <td>=</td>\n",
       "      <td>Cu</td>\n",
       "      <td>mg/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.20</td>\n",
       "      <td>=</td>\n",
       "      <td>Pb</td>\n",
       "      <td>mg/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.62</td>\n",
       "      <td>=</td>\n",
       "      <td>Pb</td>\n",
       "      <td>mg/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.00</td>\n",
       "      <td>ND</td>\n",
       "      <td>Cu</td>\n",
       "      <td>mg/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.00</td>\n",
       "      <td>ND</td>\n",
       "      <td>Pb</td>\n",
       "      <td>mg/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.50</td>\n",
       "      <td>ND</td>\n",
       "      <td>Pb</td>\n",
       "      <td>mg/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.57</td>\n",
       "      <td>=</td>\n",
       "      <td>Pb</td>\n",
       "      <td>mg/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.66</td>\n",
       "      <td>=</td>\n",
       "      <td>Pb</td>\n",
       "      <td>mg/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.75</td>\n",
       "      <td>ND</td>\n",
       "      <td>Pb</td>\n",
       "      <td>mg/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.86</td>\n",
       "      <td>=</td>\n",
       "      <td>Pb</td>\n",
       "      <td>mg/L</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    res qual parameter units\n",
       "0  2.00    =        Cu  mg/L\n",
       "1  4.20    =        Pb  mg/L\n",
       "2  4.62    =        Pb  mg/L\n",
       "3  5.00   ND        Cu  mg/L\n",
       "4  5.00   ND        Pb  mg/L\n",
       "5  5.50   ND        Pb  mg/L\n",
       "6  5.57    =        Pb  mg/L\n",
       "7  5.66    =        Pb  mg/L\n",
       "8  5.75   ND        Pb  mg/L\n",
       "9  5.86    =        Pb  mg/L"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = (\n",
    "    wqio.testing\n",
    "        .getTestROSData()\n",
    "        .assign(parameter=np.random.choice(['Cu', 'Pb'], size=35))\n",
    "        .assign(units='mg/L')\n",
    ")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What's wrong with this?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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s2lvN27uXsHjf8qhZCloab92RLRw+lckD0z/HoO79nY4TVtEwCqoUqFu+tQaC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\n0SAiIiJB+f8BtdsUOFMwNRcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10954f2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "seaborn.violinplot(data=df, x='parameter', y='res')\n",
    "seaborn.despine(trim=True, offset=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1095ab208>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v5DH9eeC2MdqOWlzhbHEB645sZk3aZs6XFDodx1HDew1hurmBMf1G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\nQVHRICIiIkFR0SAiIiJB+f8BQLEJ6fkfb8wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1095ab588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "seaborn.violinplot(data=df, x='parameter', y='res')\n",
    "seaborn.despine(trim=True, offset=10)\n",
    "ax.axhline(y=0, color='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Negative Concentrations make no sense. \n",
    "## Most environmental quantities are lognormal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GDOjMmo8P8Pbs9Qk/xTs9zcfoIRdw7Zg+tG6RfIukxpusjEyu6Xcll5tLmGcX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\nSofbh3q+iIhETlxMTAA2A70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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10978e908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['logres'] = np.log10(df['res'])\n",
    "seaborn.violinplot(data=df, x='parameter', y='logres')\n",
    "seaborn.despine(trim=True, offset=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's use Regression on Order Statistics (ROS) to impute censored values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter</th>\n",
       "      <th>final_data</th>\n",
       "      <th>res</th>\n",
       "      <th>qual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cu</td>\n",
       "      <td>3.111178</td>\n",
       "      <td>5.00</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cu</td>\n",
       "      <td>6.295104</td>\n",
       "      <td>9.50</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cu</td>\n",
       "      <td>6.295104</td>\n",
       "      <td>11.00</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pb</td>\n",
       "      <td>3.915496</td>\n",
       "      <td>5.00</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Pb</td>\n",
       "      <td>3.915496</td>\n",
       "      <td>5.50</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pb</td>\n",
       "      <td>4.655012</td>\n",
       "      <td>5.75</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pb</td>\n",
       "      <td>5.553616</td>\n",
       "      <td>9.50</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   parameter  final_data    res qual\n",
       "0         Cu    3.111178   5.00   ND\n",
       "1         Cu    6.295104   9.50   ND\n",
       "2         Cu    6.295104  11.00   ND\n",
       "15        Pb    3.915496   5.00   ND\n",
       "16        Pb    3.915496   5.50   ND\n",
       "17        Pb    4.655012   5.75   ND\n",
       "18        Pb    5.553616   9.50   ND"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = (\n",
    "    df.groupby(by=['parameter'])\n",
    "      .apply(lambda g: wqio.ros.MR(g).data)\n",
    "      .reset_index()\n",
    "      .drop('level_1', axis=1)\n",
    ")\n",
    "df2.query(\"qual == 'ND'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9xVsIpSY34KaLr+GGvqNcTsWuXwgZY9oDU4Au+MMnGf9+QgeA4dbamP7JS/QQ\nqrB191HGfbycvQdPuC4l5EZe3pXvXd+fJo3TXJcSt8q95czfupSP10yL6f2G4imELu80gNsG3kym\nw5tRA+o9O+4F4BAwwlqbC2CMaQG8B/wVuLW+FYp73Tu25ImHR/PZgs18OHN9XGwj0aZFE35y22C6\nd0yMnVtdSk5KZmSPoQzuMpAZG+cyPWsuRaVacNeFPhf04LuX3kTHFu1dl1KrYHtCp4Eh1tr1VY73\nAxZYa5uFqb6IUE/oTHsPHuelCUvYdyh2e0XXDO7GHTcOIC1Vs91cyCvOZ2bWfGZtmk9haeyM2vu8\nvq/1hGJp3bi+7Xryzf7X0bVVJ9elVFXvntApqr8HKB2I/bfLcoYObc/nfx+8jtc+XMaStbtdl3NO\n0hokc/93r+DKSzq6LiWhZaQ15jsDbuC63sOZaxczM2teTOxH5EnycN617b/8PNp58HBpx37c2HcU\nnVrUtMxn9Aq2J/QiMBi4y1q7MXDsYvxL9qy31v4ojDWGnXpCNfP5fHwyZyMfzFhfe+Mo0DSjIb+9\nZwSdLnQ+Bi5VlJSVsHj7CmZsmsehk0dclxPzUpMbMLTbIK7tNZw2zaJ+oZp694QeASYC640xFRva\nNQI+BX5ev9q+Yoy5BHgF6AVsA35irV1WTbspwEigYl6xz1obvysMOuTxePj2qL5kpKcx/uMVrss5\nq+bNGvHoj6+mbUv9KESj1JRURvQYwlVmMOuzNzEzaz6bDm51XVbMOa9RU0b2GMqIHkNo0jDDdTn1\nVmMIBe4JquxBoAHwbeAEMA///UKtAl/XizGmIf6bX/+I/8bX/wNMMsZ0rmZl7v7AUGvt6vqeV4Jz\nzeDuFBaV8v606FwUIyM9lf++b5QCKAYkeZLo374P/dv3YV/uAWZtms/Snaso1TJAZ9WpRXuu6TWc\nQR37O131OtTO9i8JdqlcH/4p2/U1Aii31r4S+Hq8MeYX+O9N+qCikTGmFf7gywrBOeUc3Dy8F9mH\nT7JodXTdT+TxwEN3DqVd65ieH5OQ2je/gHuG3s6tA29m/talzNm8iNyC2J0ME2rJniQGduzPNb2u\nokvLi+JyZY+zhVDniFXh1wPYVOWYDRyv7BL8i6lOCczO2wr8KhaXC4o1Ho+He74ziG17jnI4J3ou\nMN88vDd9u7V1XYbUQ5OGGdx08TWM7jOS1Xs3MGvTfLYe3um6LGeaNMxguLmSkT2Gcn56fL+5qjGE\ngrhAH2rhmDWgAAAR2ElEQVSN8a9LV1kBZ87KSwOWAL8BtgP3AtOMMT2stYdrO4kxJhPIrHK4XZ0q\nTkANU1O459uDeOr1Oa5LAaBV8wy+c3Uf12VIiCQnJXNZx/5c1rE/u4/tY9am+SzbtZoyb2IsK9W+\neTuu7fUNLu90KakpsXuT7LmIpoHFfPyTHSpLp8oWEtbaScCkSodeNsb8J/7hvPeDOM9DwGP1qDPh\n9e3elt5d25C1/ZDrUvjutRdr1es41bFFe+7/xg+4deAY5tpFzNm8iNPF8blxc//2fbiu93B6tOka\nl0NuZxNNv72b8U9+qMwA737tgDG34Z8N90Glww2BQoLzAv6VHiprB0THW/sY8c0RvZ2HUIvzG3Nl\n/4uc1iDhd156U759yQ3c2PdqluxYybSNczh86qjrsuotJSmFod0GcX3vEbEwxTpsoimE5gBpxpgH\n8U/T/iH+CQgzqrRLA/5sjNmIfzju5/hDaGYwJ7HW5gA5lY8ZY0rqV3ri6d21Na0zm3A4p7q9DiNj\nxKAuJCVpIdJEkZqSynBzJd/odgWr927gsw2z2XVsr+uyzlmj1EZc3WMYV/caRrNGms0ZNSFkrS0x\nxowGXgaexH+f0BhrbaExZmygzQPW2reNMW3xb7SXCawERltrg+0JSQh4PB6u6NeBT+e4m6Q4uF9H\nZ+cWd5KSkhjYsR+XXnQxWQcsk9bNZOvhHa7LqlVGWmOu6z2cUT2HkZ5a9cpD4jqn/YTilVZMqJut\ne47y+5eC6oCGXNuWTXn21zc7ObdEF5/Px+aD2/hozVS2H4mu2wcAGqemM7rvSEb1HJbIu9HWe8UE\nkTN0bZ9Jo4YNKCyK/E2Gfbq1ifg5JTp5PB56XdCdnm27sS57E/9eOYkDJ9xPmmmQ3IBre13FDX1H\n0TituqU3BRRCUg9JSUl0v6gF62zkt3fu2SlxL+RK9TweD/3b96Zvux7M37qUj1ZPJc/RbLorOg/g\n1kvHkJlxvpPzxxKFkNRLtw4tnYRQt4taRPycEhsq9jUa1OkSPlw5hXlbl0Ts3G2bteauK2+jR5uu\nETtnrFMISb106VD1vt/wO69JQ5o30/CGnF1GWmN+NOR7DO5yKa8veo+jp3Nqf1IdeTwebup7DTf3\nuzZhbjINFc1vlXrpfGHkQ6jzhZkJd0Of1J1p05X//eZvGNp1UFhev0VGcx694WfccumNCqA6UAhJ\nvTRpnEar5pFdTt5F70tiW6MGDblv2J3cPeT7pCSFYr1lv77tevL7Mb+Kxp1MY4ZCSOqta4RDoUt7\nXQ+Surmq+2B+fd1PaZxa/+HcUT2G8Yurf0xGWuMQVJa4FEJSb90uahmxc3k8/qnhInVl2nThd6Mf\npEk9wuOmi6/hB1fcohU7QkDfQak30ylyIdSh7fmkN0qN2PkkPrVv3o5fXfefNExJO+fnXtvrKm4Z\ncKOuS4aIQkjqrUOb88lIj0ww9OrSOiLnkfh3UeaFPDD8rnN6Tv/2ffj+oG8pgEJIIST1lpTkoXfX\nyKxg0FcrJUgI9Wvfmxv6jgqqbfP087h/2J0kefRnM5T03ZSQuKRH+PcFTG2QrJ6QhNy3+o+mTdPa\nV+D44eBbtfxOGCiEJCQu6dmOpKTwDlFc3L2tNrCTkEtNacD3B33zrG36tutJ//a9I1RRYtFvtIRE\nk8Zp9OrSmo3bwrdw5OUXdwjba0ti69++D//3xl+Qm3/8jMeSk5LpdUF3XQcKE4WQhMyQSzqGLYTS\nUlO4tHf7sLy2CEDXVh2Bjo6rSDwajpOQuaxPB9IahO5u9K+/dnsapuo9k0i8UQhJyKQ3bMDl/S4K\ny2uPuKxLWF5XRNxSCElIjbq8W8hfs12rpvTorP2DROKRQkhCqmuHTDpeENqNvK4erIvCIvFKISQh\n5fF4uG6oCdnrNUpLYdilnUP2eiISXRRCEnKD+3WkaUbDkLzW8Mu6kt5Qe7SIxCuFkIRcaoNkrhlc\n/2tDHo+Ha4d0D0FFIhKtFEISFlcP7k6DlPr9eF3W50JaZzYJUUUiEo0UQhIWzTIaMnRA/a7l3PCN\nniGqRkSilUJIwuaGYT3q/NyuHVrQPYKb5YmIGwohCZt2rZvRz7St03PrE2AiEjsUQhJW1w899zBp\n3iydgX20TpxIIlAISVj17db2nCcXXH1FN1KS9aMpkgj0my5hlZTk4epzmK6dnORhxCCtEyeSKBRC\nEnbDLu0UdM/m0t4X0qxJozBXJCLRQiEkYde0cUMG9Apu+++rBqoXJJJIFEISEUMHdKq1TZPGafTt\nXrfZdCISmxRCEhH9zAU0qmUNuEF9O2hCgkiC0W+8RESDlGQu6XHBWdsM0rRskYSjEJKIGdDzwhof\na5iaQk9tXCeScFJcF1CZMeYS4BWgF7AN+Im1dlk17W4HngBaAXOBe621RyJZq5y7vt3b4vGAz3fm\nY727tSElJTnyRYmIU1HTEzLGNAQmA28AzYC/A5OMMY2rtLsYGAt8D2gBHALGR7ZaqYsmjdPo0Lb6\nXVd7d2kd4WpEJBpETQgBI4Bya+0r1tpya+144DBwQ5V2dwKfWGtXWGuLgN8C1xtjtNplDDAdq//f\n1L2G4yIS36IphHoAm6ocs4HjlZnK7ay1uUBu4LhEua4dWpxxrEFKco09JBGJb9F0TagxUFDlWAGQ\nXsd21TLGZAKZVQ4Hdyel1FvHds3PONah7Xmami2SoKIphPKBquu1pAOnqxyrLnDSgbwgz/MQ8Ng5\nVych0bZFE1KSkygr9355rEPb8xxWJCIuRdPbz82cOaT2taG36toZY1oAzQPHg/FC4PmVP0bWoV6p\ng+TkpDM2qzOdNDVbJFF5fNXNl3XAGJMK7ASexj9N+4fAk0Ana21hpXb9gPnAjcAq/KHSxlp7cz3O\n3RHYFTjX7lqaR8c3LIblFRSzacdhvD4fTdLT6Nm5NUlJHtdliUj41PgLHjU9IWttCTAauB3IAX4K\njLHWFhpjxhpjxgbarQPuB8bhnz3XBrjbTdVSFxnpaQzq24ErLr6I3l3bKIBEEljU9IRcUk9IRCSs\nor8nJCIiiUchJCIiziiERETEGYWQiIg4oxASERFnFEIiIuKMQkhERJxRCImIiDMKIRERcUYhJCIi\nziiERETEGYWQiIg4oxASERFnFEIiIuKMQkhERJxRCImIiDMKIRERcUYhJCIiziiERETEGYWQiIg4\noxASERFnFEIiIuKMQkhERJxRCImIiDMKIRERcUYhJCIiziiERETEGYWQiIg4oxASERFnFEIiIuKM\nQkhERJxRCImIiDMKIRERcUYhJCIizqS4LqCCMebnwK+AJsAk4D+stQXVtGsBHAHyKx1+21r7nxEp\nVEREQiYqQsgYcxP+ABqOP2AmAM8AP62m+SXARmvtxRErUEREwiJahuN+CLxurd1urT0F/A/wQ2OM\np5q2lwDrIlqdiIiERcR6QsaYZPxDbVV5AQNMrHRsK5ABtAOyq7S/BOhojNkMNAOmAr+01p4MedEi\nIhJWkewJjQByq/lYBzQGKl//qfg8vZrXOQHMAa4A+uMPqpfDU7KIiIRTxHpC1trZ1BB6xph1QKNK\nhyrCJ6+a13mgynMfBRYGW4cxJhPIrHK4XbDPFxGR0ImKiQnAZqBHpa8NcMJae6Byo8A1oqeBf1hr\n9wQONwJKzuFcDwGP1aPW6q5TiYhIHURLCL0DvGyMmYj/GtD/Au9WbWSt9RljBgJPGWPuA5oCTwHj\nz+FcLwDvVTmWDKRx5vUnEREJI4/P53NdAwDGmIeA/wLOA6YA91triwKPnQaut9YuNsa0AV7Ef43J\nhz9QfmmtLXVTuYiI1FXUhJCIiCSeaLlPSEREEpBCSEREnFEIiYiIMwohERFxRiEkIiLORMt9QpJg\njDEpwIWu6xCpRba1tsx1EfFMISSu9AHWuC5CpBaXAGtdFxHPFELiSnHgvyOBXS4LEalGJ/wLJRfX\n1lDqRyEkrpQH/rvfWrvbZSEiVRljUgOflp+1odSbJiaIiIgzCiEREXFGISQiIs4ohMSVHOAPgf+K\nRBv9fEaIVtEWERFn1BMSERFnFEIiIuKMQkhERJxRCImIiDMKIRERcUYhJCIiziiERETEGS1gKhFh\njLke+DXQD/AAK4BHrbWrnBYmCcsY4wUKAS/gC3wsBX5prc0yxvwI+J61drS7KuOfekISdsaY+4Hx\nwLNAa+ACYCYwxxjTy2VtkvAus9Y2sdY2BTKBDcA0Y4z+NkaIVkyQsDLGpAMHgduttVOrPPYksAp4\nCPjAWvtS4PiDwC3W2hGRrlcSR6An1Mdau6nSsd74g6gFMAa4D8gGrgf2AT+x1i52UG7cUtpLuA3B\nP+w7veoD1tpHrLUT+WooRCTSPBWfGGPOBx4GNlhrcwOHrwQmA83x9+SnBNpJiOiakIRbC+C4tdbr\nuhCRaiwJ9IjAv4vqF8AtlR5fbK19N/D5m8aYnwE3Au9EsMa4phCScDsENDfGJFtrv7ZLpTGmGZDv\npiwRAAZXHo6rxt4qX2cDbcJYT8LRcJyE21KgBLihmsfGAa/j30I5rdLxzAjUJRKMC6p83RHYHfky\n4pd6QhJW1toiY8x/A68aY+7FPyuuEfALYBT+MfcHgeuNMf8A2gE/wH8RWMS1YcaY24CPgR8D5wNT\nz/4UORfqCUnYWWvHAv8FPAYcAXYBg4DhgaGQp/G/IToM/Bt4002lkmBqmwzjwz+h5h4gF7gdGG2t\nLQh3YYlEU7RFRMQZ9YRERMQZhZCIiDijEBIREWcUQiIi4oxCSEREnFEIiYiIMwohERFxRiEkIgAY\nYzKMMXe7rkMSi0JIRCr8Ev/SNCIRoxASkQqe2puIhJaW7REJkcC+NPcAj+Bffflz4MfW2sOBxy8H\n/gQMxP8GcBXwgLV2ozFmOPA+8C5wL/CGtfaXxphf4++ddABOAR8CD1lry4wxvwd64N9u4D/wb4vx\nOyAH+BvQCvgEuLtiG43AcFtFfVnAf1trPzfG/Aj/quYAPmttsjGmAfAk8EP8q5wvAh621u6q9O/9\nI/ATYJe19orQfTclUagnJBJaT+Af1roS/4rLHwMYY5oA04DFQB9gKJCMf7fOCq2AC4FLgH8YY36A\nP1R+CnQFHgDuAm6t9JxvBV6nP/6Aehl4NNDmTuA2Apu0GWNuAP4M/AboC7yFf6fQPvgD8FlgNdC2\n0r9lOPBt4Ar827TPNcZU3nbjNuAq/Ntgi5wzbeUgElpPWmsnAwR6F9uNMf3wb+73BPCctdYH7DbG\njAN+X+X5T1fqabQH7rLWzgw8ti+ws2evSu1PA7+y1vqMMS/j3xbjcWvtGmCNMWYt0DPQ9nfAn6y1\nHwe+ftEYcyXwM2vt/caYfKDUWnvEGNMIeAgYZq1dGajnJ8Ae4Lv4e2wAr1prt9Tj+yUJTiEkEloL\nKz6x1u40xuQCfay164wx44GHA6FkgAHA8SrP31np+fOMMQONMY/jH3bri79HtLBS+z2BUAMoqPoa\nQCFfbRjYC7jMGPNYpcdT8W9pXVWXwPPmGWMqj9k3BLpXV69IXSiEREKrrMrXyUC5MeYCYCWwHv8e\nNW/j76H8T5X2hRWfBK7fvIh/99nJ+PdjGlulfWk1NXhrqC0Z+BX+YcEKHqC4mrYVfxuG499Lp3L7\nE9XVK1IXCiGR0BoIbAIwxnQHmgHr8G+IVmCtvb6ioTFmNGefkfZf+If3ngi0T8HfE1pcx9o2Ax2t\ntV/2XowxT+AfKnyBr2/yth1/oLauNByXAkwAXgLm1bEGka9RCImE1uPGmGz8w2wvAbOstZsDQ3Bt\njTHXARa4Hri/ltfaD4wyxnyIf2jsEaA5/iGxYFUOuT8D7xljLDAXuAH/JIUbA4+fBtoYYzpZa3cF\ntlt/0RhTin833EeAEfivO4mEhGbHiYTWG8Br+HsK2/lqJlvFtuXv4e8ZDQNGA02NMZ0CbareL/Ez\n/Nds1uCfar0MeAr/taSK9lWfU+PX1tpPAq/5G/zTs38C/LDSxIeJ+If3NhhjWgXafYx/Ft0aoBNw\nbcWUc5FQ0H1CIiESuG9muLV2getaRGKFekIiIuKMQkhERJzRcJyIiDijnpCIiDijEBIREWcUQiIi\n4oxCSEREnFEIiYiIMwohERFx5v8DItV6eM5U9VIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109806da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['logres'] = np.log10(df2['final_data'])\n",
    "seaborn.violinplot(data=df2, x='parameter', y='logres')\n",
    "seaborn.despine(trim=True, offset=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Basic Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>parameter</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cu</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Pb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>final_data</th>\n",
       "      <th>res</th>\n",
       "      <th>final_data</th>\n",
       "      <th>res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.402092</td>\n",
       "      <td>10.055333</td>\n",
       "      <td>9.783981</td>\n",
       "      <td>10.169500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.489774</td>\n",
       "      <td>5.200170</td>\n",
       "      <td>5.979342</td>\n",
       "      <td>5.716263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.915496</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.537552</td>\n",
       "      <td>7.145000</td>\n",
       "      <td>5.328965</td>\n",
       "      <td>5.637500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.710000</td>\n",
       "      <td>7.075000</td>\n",
       "      <td>8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.120000</td>\n",
       "      <td>11.125000</td>\n",
       "      <td>12.880000</td>\n",
       "      <td>12.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.180000</td>\n",
       "      <td>20.180000</td>\n",
       "      <td>22.970000</td>\n",
       "      <td>22.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "parameter         Cu                    Pb           \n",
       "          final_data        res final_data        res\n",
       "count      15.000000  15.000000  20.000000  20.000000\n",
       "mean        9.402092  10.055333   9.783981  10.169500\n",
       "std         5.489774   5.200170   5.979342   5.716263\n",
       "min         2.000000   2.000000   3.915496   4.200000\n",
       "25%         6.537552   7.145000   5.328965   5.637500\n",
       "50%         7.500000   8.710000   7.075000   8.500000\n",
       "75%        10.120000  11.125000  12.880000  12.880000\n",
       "max        20.180000  20.180000  22.970000  22.970000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = (\n",
    "    df2.drop('logres', axis=1)\n",
    "       .groupby(by='parameter')\n",
    "       .describe()\n",
    "       .unstack(level='parameter')\n",
    ")\n",
    "summary.columns = summary.columns.swaplevel(0, 1)\n",
    "summary.sort(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Probability Plots are likely better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x109998748>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0FwfqHoskSVNBI4Zi+v0uHeil7/zmDxvZXkS8YIT9F4y2DfwqIl49wnvfBbyr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10997b198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg = seaborn.FacetGrid(data=df2, col='parameter', size=4, aspect=2)\n",
    "fg.map(probabiity_plot, 'res', 'final_data')\n",
    "fg.set_axis_labels(y_var='Concentration', x_var='Probability')\n",
    "fg.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Benefit of `wqio`: legit probability scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1099869e8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TT7VTN80XJDVKRJwREec3O45yVPMT71eM3l5UktQA8+a2l3WHeN7csRPesSxe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1099810b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg = seaborn.FacetGrid(data=df2, col='parameter', size=4, aspect=2)\n",
    "fg.map(probabiity_plot, 'res', 'final_data')\n",
    "fg.set_axis_labels(x_var='Concentration', y_var='Z-score')\n",
    "fg.set(xscale='prob', xlim=(0.5, 99.5))\n",
    "fg.add_legend()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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
}