emiliom · December 15, 2013 18:20
diff --git a/NEWS_trajectories.ipynb b/NEWS_trajectories.ipynb
 {
 "metadata": {
  "name": "NEWS_trajectories"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Accessing NEWS scenarios raw csv input and output files. Amazon basin example.\n",
      "Plot trajectories (1970-2050) for chosen variables and scenario. Emilio Mayorga, 12/15/2013"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# file path location of input nd output csv files\n",
      "pth = \"/usr/mayorgadat/gnews_n_drv/newsmodel/MAscenarios/NEWSruns\"\n",
      "subdirs = {'NEWSOutput': 'run5/output', \n",
      "           'disch_fqrem_reservret_ng': 'run4/inputs/preproctbls'}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Scenario information and utilities to access the input-output csv files and read the data into Pandas data frames.\n",
      "Scenario codes used in file and variable names."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scenyr = {'c7':1970, 'c0':2000, 'g3':2030, 'g5':2050}\n",
      "scen = scenyr.keys()\n",
      "\n",
      "def scen_varname_dict(varlst, sc):\n",
      "    out_dct = {}\n",
      "    for var in varlst:\n",
      "        out_dct.update({sc+var: var})\n",
      "    return out_dct"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For one data file, specifiy the desired variables as a list. Read all years (for 2030 & 2050, currently it's hardwired to read only the GO scenario). Get back a Pandas data frame with data for all basins, all years, for the selected variables (plus basinid), and with a couple of additional, useful variables created (year and scenario code). The second function extracts a data frame with data only from the requested basin id."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def create_df(csvbasename, varlst):\n",
      "    tmpls = []\n",
      "    for sc in scen:\n",
      "        col_scen_var_name = scen_varname_dict(varlst, sc)\n",
      "        colnames_sel = ['basinid'] + col_scen_var_name.keys()\n",
      "        fname = \"%s0_%s.csv\" % (sc, csvbasename)\n",
      "        basout = pd.read_csv(os.path.join(pth, subdirs[csvbasename], fname))\n",
      "        basout = basout[colnames_sel]\n",
      "        basout.rename(columns=col_scen_var_name, inplace=True)\n",
      "        basout['year'] = scenyr[sc]\n",
      "        basout['scenario'] = sc[0]\n",
      "        tmpls.append(basout)\n",
      "    return pd.concat(tmpls)\n",
      "\n",
      "def basinsubset(basall, basinid):\n",
      "    basdata = basall[basall.basinid == basinid]\n",
      "    basdata.sort_index(by='year', ascending=True, inplace=True)\n",
      "    return basdata"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### For the Amazon basin (basinid 1), examine trajectories in N & P nutrient form yields and reservoir retention factors.\n",
      "First pull the data for all basins, then for the Amazon only, then plot and list as data tables these trajectories for the Amazon."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "basoutputall_npyld = create_df('NEWSOutput', ['DINyld','DONyld','PNyld', 'DIPyld','DOPyld','PPyld'])\n",
      "basoutputall_npyld['TNyld'] = basoutputall_npyld.DINyld + basoutputall_npyld.DONyld + basoutputall_npyld.PNyld\n",
      "basoutputall_npyld['TPyld'] = basoutputall_npyld.DIPyld + basoutputall_npyld.DOPyld + basoutputall_npyld.PPyld\n",
      "\n",
      "basinputall_dam = create_df('disch_fqrem_reservret_ng', ['Ddin0to1', 'Ddip0to1', 'Dsed0to1'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "basoutput_1_npyld = basinsubset(basoutputall_npyld, 1)\n",
      "basinput_1_dam = basinsubset(basinputall_dam, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = figure(figsize=(18,5), dpi=100)\n",
      "\n",
      "ax1 = fig.add_subplot(131)\n",
      "bastmp = basoutput_1_npyld\n",
      "ax1.plot(bastmp.year, bastmp.DINyld)\n",
      "ax1.plot(bastmp.year, bastmp.DONyld)\n",
      "ax1.plot(bastmp.year, bastmp.PNyld)\n",
      "ax1.legend(['DIN', 'DON', 'PN'])\n",
      "ax1.set_ylabel('N form yield (kg N/km2/yr)')\n",
      "\n",
      "ax2 = fig.add_subplot(132)\n",
      "bastmp = basoutput_1_npyld\n",
      "ax2.plot(bastmp.year, bastmp.DIPyld)\n",
      "ax2.plot(bastmp.year, bastmp.DOPyld)\n",
      "ax2.plot(bastmp.year, bastmp.PPyld)\n",
      "ax2.legend(['DIP', 'DOP', 'PP'])\n",
      "ax2.set_ylabel('P form yield (kg P/km2/yr)')\n",
      "\n",
      "ax3 = fig.add_subplot(133)\n",
      "bastmp = basinput_1_dam\n",
      "ax3.plot(bastmp.year, bastmp.Ddin0to1)\n",
      "ax3.plot(bastmp.year, bastmp.Ddip0to1)\n",
      "ax3.plot(bastmp.year, bastmp.Dsed0to1)\n",
      "ax3.legend(['Ddin', 'Ddip', 'Dsed'])\n",
      "ax3.set_ylabel('Reservoir Retention Factor (0-1)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x495fd90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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sX74cgHfeeYdVq1Zha2vLsmXLCAkJMfg8S26rsEB37yqrmdxbxCj8OjFRmaS4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TAAAX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\nE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      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "basoutput_1_npyld"
     ],
     "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>DINyld</th>\n",
        "      <th>DIPyld</th>\n",
        "      <th>DONyld</th>\n",
        "      <th>DOPyld</th>\n",
        "      <th>PNyld</th>\n",
        "      <th>PPyld</th>\n",
        "      <th>basinid</th>\n",
        "      <th>scenario</th>\n",
        "      <th>year</th>\n",
        "      <th>TNyld</th>\n",
        "      <th>TPyld</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 114.465294</td>\n",
        "      <td> 14.808620</td>\n",
        "      <td> 257.047516</td>\n",
        "      <td> 13.802054</td>\n",
        "      <td> 298.917570</td>\n",
        "      <td> 127.649034</td>\n",
        "      <td> 1</td>\n",
        "      <td> c</td>\n",
        "      <td> 1970</td>\n",
        "      <td> 670.430379</td>\n",
        "      <td> 156.259709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 122.558266</td>\n",
        "      <td> 16.179745</td>\n",
        "      <td> 243.116318</td>\n",
        "      <td> 13.134938</td>\n",
        "      <td> 281.660123</td>\n",
        "      <td> 120.280974</td>\n",
        "      <td> 1</td>\n",
        "      <td> c</td>\n",
        "      <td> 2000</td>\n",
        "      <td> 647.334706</td>\n",
        "      <td> 149.595657</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 142.632462</td>\n",
        "      <td> 18.145676</td>\n",
        "      <td> 236.836594</td>\n",
        "      <td> 12.887953</td>\n",
        "      <td> 265.506498</td>\n",
        "      <td> 113.584564</td>\n",
        "      <td> 1</td>\n",
        "      <td> g</td>\n",
        "      <td> 2030</td>\n",
        "      <td> 644.975553</td>\n",
        "      <td> 144.618192</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 139.427444</td>\n",
        "      <td> 17.684406</td>\n",
        "      <td> 230.408676</td>\n",
        "      <td> 12.530482</td>\n",
        "      <td> 256.198258</td>\n",
        "      <td> 109.800094</td>\n",
        "      <td> 1</td>\n",
        "      <td> g</td>\n",
        "      <td> 2050</td>\n",
        "      <td> 626.034379</td>\n",
        "      <td> 140.014983</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "       DINyld     DIPyld      DONyld     DOPyld       PNyld       PPyld  \\\n",
        "0  114.465294  14.808620  257.047516  13.802054  298.917570  127.649034   \n",
        "0  122.558266  16.179745  243.116318  13.134938  281.660123  120.280974   \n",
        "0  142.632462  18.145676  236.836594  12.887953  265.506498  113.584564   \n",
        "0  139.427444  17.684406  230.408676  12.530482  256.198258  109.800094   \n",
        "\n",
        "   basinid scenario  year       TNyld       TPyld  \n",
        "0        1        c  1970  670.430379  156.259709  \n",
        "0        1        c  2000  647.334706  149.595657  \n",
        "0        1        g  2030  644.975553  144.618192  \n",
        "0        1        g  2050  626.034379  140.014983  "
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "basinput_1_dam"
     ],
     "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>Ddin0to1</th>\n",
        "      <th>Ddip0to1</th>\n",
        "      <th>Dsed0to1</th>\n",
        "      <th>basinid</th>\n",
        "      <th>scenario</th>\n",
        "      <th>year</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.00014</td>\n",
        "      <td> 0.00026</td>\n",
        "      <td> 0.00030</td>\n",
        "      <td> 1</td>\n",
        "      <td> c</td>\n",
        "      <td> 1970</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.00014</td>\n",
        "      <td> 0.00028</td>\n",
        "      <td> 0.00032</td>\n",
        "      <td> 1</td>\n",
        "      <td> c</td>\n",
        "      <td> 2000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.01175</td>\n",
        "      <td> 0.05264</td>\n",
        "      <td> 0.04961</td>\n",
        "      <td> 1</td>\n",
        "      <td> g</td>\n",
        "      <td> 2030</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.01655</td>\n",
        "      <td> 0.07415</td>\n",
        "      <td> 0.07644</td>\n",
        "      <td> 1</td>\n",
        "      <td> g</td>\n",
        "      <td> 2050</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "   Ddin0to1  Ddip0to1  Dsed0to1  basinid scenario  year\n",
        "0   0.00014   0.00026   0.00030        1        c  1970\n",
        "0   0.00014   0.00028   0.00032        1        c  2000\n",
        "0   0.01175   0.05264   0.04961        1        g  2030\n",
        "0   0.01655   0.07415   0.07644        1        g  2050"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
 }