emiliom/TWAPvsNEWS2basins.ipynb

## TWAPvsNEWS2basins.ipynb
{
 "metadata": {
  "name": "TWAP (TFDD) vs NEWS2 (STN30) basins"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "Assessing the results of overlaying TWAP (TFDD) and NEWS2 (STN30) basins"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Updated 2014-May-6, with the new, final TFDD basins dataset. 9/22/2013. Some exploration of basin match-up results, indicators of overlay \"reliability\". The [IPython notebook code is in my github gist.](https://gist.github.com/emiliom/6665041) **Emilio Mayorga.**"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Setup, to pull the relevant data from my database (held in a PostgreSQL/PostGIS RDBMS)\n%cd /usr/mayorgadat/workmain/RIGHT NOW/GlobalNEWS/TWAP_GEF_Sybil/data\n%run twap_pgglobriv_init.py",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "/usr/mayorgadat/workmain/RIGHT NOW/GlobalNEWS/TWAP_GEF_Sybil/data\n"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# All the assessment data are in the pg view v_tfdd_news_assess. Read into pandas Dataframe\nsql_str = \"select * from newstwap.v_tfdd_news_assess\"\ntf_basqaqc = pdsql.read_frame(sql_str, conn)\ntf_basqaqc",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nInt64Index: 286 entries, 0 to 285\nData columns (total 10 columns):\ntwap_bid              286  non-null values\ntwap_bcode            286  non-null values\ntwap_basinname        286  non-null values\ntwap_basinarea_km2    286  non-null values\nperc_twap_intrsct     286  non-null values\nnews_basinid          283  non-null values\nnews_basinname        283  non-null values\nnews_basinarea_km2    283  non-null values\nnews_perc_intrsct     286  non-null values\nnews_intrsct_cnt      286  non-null values\ndtypes: float64(4), int64(3), object(3)\n</pre>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 286 entries, 0 to 285\nData columns (total 10 columns):\ntwap_bid              286  non-null values\ntwap_bcode            286  non-null values\ntwap_basinname        286  non-null values\ntwap_basinarea_km2    286  non-null values\nperc_twap_intrsct     286  non-null values\nnews_basinid          283  non-null values\nnews_basinname        283  non-null values\nnews_basinarea_km2    283  non-null values\nnews_perc_intrsct     286  non-null values\nnews_intrsct_cnt      286  non-null values\ndtypes: float64(4), int64(3), object(3)"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Sample DataFrame content\ntf_basqaqc.head()",
     "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>twap_bid</th>\n      <th>twap_bcode</th>\n      <th>twap_basinname</th>\n      <th>twap_basinarea_km2</th>\n      <th>perc_twap_intrsct</th>\n      <th>news_basinid</th>\n      <th>news_basinname</th>\n      <th>news_basinarea_km2</th>\n      <th>news_perc_intrsct</th>\n      <th>news_intrsct_cnt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 15</td>\n      <td> AKPA</td>\n      <td>    Akpa</td>\n      <td>    2434</td>\n      <td> 100.000000</td>\n      <td>  953</td>\n      <td>  GHAASBasin953</td>\n      <td>   12303.400390</td>\n      <td> 95.843155</td>\n      <td>  2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 16</td>\n      <td> ALSK</td>\n      <td>   Alsek</td>\n      <td>   28236</td>\n      <td> 100.000000</td>\n      <td>  517</td>\n      <td>          Alsek</td>\n      <td>   25856.500000</td>\n      <td> 76.925883</td>\n      <td>  7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 17</td>\n      <td> AMCR</td>\n      <td> Amacuro</td>\n      <td>    3719</td>\n      <td>  99.237863</td>\n      <td> 2865</td>\n      <td> GHAASBasin2865</td>\n      <td>    3055.699951</td>\n      <td> 70.154287</td>\n      <td>  3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 18</td>\n      <td> AMUR</td>\n      <td>    Amur</td>\n      <td> 2092710</td>\n      <td>  99.989908</td>\n      <td>   12</td>\n      <td>           Amur</td>\n      <td> 1752600.000000</td>\n      <td> 82.723235</td>\n      <td> 34</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 19</td>\n      <td> AMZN</td>\n      <td>  Amazon</td>\n      <td> 5888270</td>\n      <td>  99.968977</td>\n      <td>    1</td>\n      <td>         Amazon</td>\n      <td> 5846870.000000</td>\n      <td> 97.185582</td>\n      <td> 36</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": "   twap_bid twap_bcode twap_basinname  twap_basinarea_km2  perc_twap_intrsct  \\\n0        15       AKPA           Akpa                2434         100.000000   \n1        16       ALSK          Alsek               28236         100.000000   \n2        17       AMCR        Amacuro                3719          99.237863   \n3        18       AMUR           Amur             2092710          99.989908   \n4        19       AMZN         Amazon             5888270          99.968977   \n\n   news_basinid  news_basinname  news_basinarea_km2  news_perc_intrsct  \\\n0           953   GHAASBasin953        12303.400390          95.843155   \n1           517           Alsek        25856.500000          76.925883   \n2          2865  GHAASBasin2865         3055.699951          70.154287   \n3            12            Amur      1752600.000000          82.723235   \n4             1          Amazon      5846870.000000          97.185582   \n\n   news_intrsct_cnt  \n0                 2  \n1                 7  \n2                 3  \n3                34  \n4                36  "
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Distribution of TFDD basin areas (x-axis is log10 of basin area in km2)\ntf_basqaqc['twap_basinarea_km2_log10'] = np.log10(tf_basqaqc['twap_basinarea_km2'])\ntf_basqaqc['twap_basinarea_km2_log10'].hist(label='TFDD basin area, log10(km2))');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x465d390>"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Mean and median TFDD basin area (km2)\nprint \"%.1f, %.1f\" % (tf_basqaqc['twap_basinarea_km2'].mean(), tf_basqaqc['twap_basinarea_km2'].median())",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "216959.5, 22185.0\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": "Assessment of TFDD vs NEWS2 basins starts here"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Distribution of % of TFDD basin that's overlaid by NEWS basins\ntf_basqaqc['perc_twap_intrsct'].hist();",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x45dddd0>"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Distribution of % of TFDD basin overlaid by the \"dominant\" STN30/NEWS basin\ntf_basqaqc['news_perc_intrsct'].hist();",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x48cac10>"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Distribution of ratio (log10) of dominant NEWS basin area over TFDD basin area\n# A large ratio corresponds to a \"small\" TFDD basin whose dominant overlaid NEWS basin is much larger\ntf_basqaqc['basarea_ratio'] = np.log10(tf_basqaqc['news_basinarea_km2'] / tf_basqaqc['twap_basinarea_km2'])\ntf_basqaqc['basarea_ratio'].hist();",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x48c9690>"
      }
     ],
     "prompt_number": 8
    }
   ],
   "metadata": {}
  }
 ]
}