{
 "metadata": {
  "name": "poking_eyeball_rps"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# import some modules using standard naming conventions\n",
      "from pylab import *\n",
      "import pandas as pd\n",
      "\n",
      "# find the .csv files and read them into pandas data sets\n",
      "import glob\n",
      "flist = glob.glob('/usgs/data1/csherwood/poking_eyeball/*.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def Phi(mm):\n",
      "    return -1.44296504*log(mm)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def my_read(csv):\n",
      "    phi = float(csv[-6:-4])/10. # extract phi size from CSV file name\n",
      "    df = pd.read_csv(csv)     # create Pandas DataFrame from each CSV file\n",
      "    mg = df.geom_mean.mean()  # mean of geometric means\n",
      "    m  = df.arith_mean       \n",
      "    ma = m.mean()             # mean of arithmetic means\n",
      "    sa = m.std()              # std of arithmetic means\n",
      "    return phi,ma,sa,mg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# list of stats for each file\n",
      "stats = [my_read(f) for f in flist]\n",
      "# convert to array\n",
      "stats = array(stats)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create a data frame from the stats array\n",
      "df_stats = pd.DataFrame(stats,columns=['Phi','Ma','Sa','Mg'])\n",
      "df_stats"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Phi</th>\n",
        "      <th>Ma</th>\n",
        "      <th>Sa</th>\n",
        "      <th>Mg</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 0.0</td>\n",
        "      <td> 0.557322</td>\n",
        "      <td> 0.067476</td>\n",
        "      <td> 0.468722</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 0.5</td>\n",
        "      <td> 0.543682</td>\n",
        "      <td> 0.134539</td>\n",
        "      <td> 0.430470</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 1.0</td>\n",
        "      <td> 0.487743</td>\n",
        "      <td> 0.082410</td>\n",
        "      <td> 0.381277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 1.5</td>\n",
        "      <td> 0.373238</td>\n",
        "      <td> 0.048243</td>\n",
        "      <td> 0.267245</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 2.0</td>\n",
        "      <td> 0.326144</td>\n",
        "      <td> 0.031638</td>\n",
        "      <td> 0.209683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.301667</td>\n",
        "      <td> 0.034709</td>\n",
        "      <td> 0.199168</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>6</strong></td>\n",
        "      <td> 3.0</td>\n",
        "      <td> 0.235943</td>\n",
        "      <td> 0.032090</td>\n",
        "      <td> 0.134345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>7</strong></td>\n",
        "      <td> 3.5</td>\n",
        "      <td> 0.238555</td>\n",
        "      <td> 0.015709</td>\n",
        "      <td> 0.135005</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>8</strong></td>\n",
        "      <td> 4.0</td>\n",
        "      <td> 0.204032</td>\n",
        "      <td> 0.015977</td>\n",
        "      <td> 0.106308</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "   Phi        Ma        Sa        Mg\n",
        "0  0.0  0.557322  0.067476  0.468722\n",
        "1  0.5  0.543682  0.134539  0.430470\n",
        "2  1.0  0.487743  0.082410  0.381277\n",
        "3  1.5  0.373238  0.048243  0.267245\n",
        "4  2.0  0.326144  0.031638  0.209683\n",
        "5  2.5  0.301667  0.034709  0.199168\n",
        "6  3.0  0.235943  0.032090  0.134345\n",
        "7  3.5  0.238555  0.015709  0.135005\n",
        "8  4.0  0.204032  0.015977  0.106308"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# list of full DataFrames\n",
      "df_list = [pd.read_csv(f) for f in flist]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1st column of stats array is the phi size\n",
      "phi = stats[:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# make a dictionary of phi_size: full data_frame\n",
      "d = dict(zip(phi, df_list))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(phi,phi,'-k')\n",
      "plot(phi-0.5,phi,'--k') # one sieve larger\n",
      "for p,df in d.iteritems():\n",
      "    plot(p*ones_like(df.geom_mean),Phi(df.geom_mean),'o',color='0.8')\n",
      "plot(phi,Phi(df_stats.Mg),'or')\n",
      "xlim([-.5,4.5])\n",
      "xlabel('Sieve size (phi)')\n",
      "ylabel('DGS geometric mean (phi)')\n",
      "title('MOF Poking Eyeball Tests, May 3, 2013')\n",
      "grid()\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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kPEVFRYxejywrvuzh4YFNmzahoqKCNv5MXR94HdgNDQ1FZGQk/SufievX19cj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\nslKnuDLJy8sjM3YJBB2kxTN8dZ3WZtkIBAKYmZk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8/PhR+fQhRHNkqqcQKkpKSsLd3Z2F\nCxdy8OBB8vPz2b59+y+NdeLECYqKimSqp/gpcuYvhIrmz5/P8ePHgcazln7FkSNHWLx48Z9KTfzH\nyZm/EEJokJz5CyGEBknxF0IIDZLiL4QQGiTFXwghNEiKvxBCaJAUfyGE0KD/AUmTbdQ4J8sJAAAA\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3e98590>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}