ParameterListBuilder1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xml.etree.ElementTree as ET\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Read in the core parameters taken from Overseer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: '.\\\\CropCoeff.xlsx'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-dc9d873f457b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#ReadData\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mCoreParams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.\\CropCoeff.xlsx'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msheetname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'CoreParams'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;31m#Fix names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mCoreParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Crop'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCoreParams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Crop'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\" (\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"_\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCoreParams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mCoreParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Crop'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCoreParams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Crop'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"(\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"_\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCoreParams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\io\\excel.pyc\u001b[0m in \u001b[0;36mread_excel\u001b[1;34m(io, sheetname, **kwds)\u001b[0m\n\u001b[0;32m    149\u001b[0m     \u001b[0mengine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'engine'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msheetname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msheetname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\io\\excel.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, io, **kwds)\u001b[0m\n\u001b[0;32m    186\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 188\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    189\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'xlrd'\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBook\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\xlrd\\__init__.pyc\u001b[0m in \u001b[0;36mopen_workbook\u001b[1;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[0;32m    392\u001b[0m         \u001b[0mpeek\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile_contents\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    393\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 394\u001b[1;33m         \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    395\u001b[0m         \u001b[0mpeek\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    396\u001b[0m         \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: '.\\\\CropCoeff.xlsx'"
     ]
    }
   ],
   "source": [
    "#ReadData\n",
    "CoreParams = pd.read_excel('.\\CropCoeff.xlsx', sheetname='CoreParams')\n",
    "#Fix names\n",
    "CoreParams['Crop'] = [str(CoreParams.ix[X,'Crop']).replace(\" (\",\"_\") for X in range(CoreParams.index.size)]\n",
    "CoreParams['Crop'] = [str(CoreParams.ix[X,'Crop']).replace(\"(\",\"_\") for X in range(CoreParams.index.size)]\n",
    "CoreParams['Crop'] = [str(CoreParams.ix[X,'Crop']).replace(\")\",\"\") for X in range(CoreParams.index.size)]\n",
    "CoreParams['Crop'] = [str(CoreParams.ix[X,'Crop']).replace(\"/\",\"_\") for X in range(CoreParams.index.size)]\n",
    "CoreParams['Crop'] = [str(CoreParams.ix[X,'Crop']).replace(\" \",\"\") for X in range(CoreParams.index.size)]\n",
    "#SetIndex\n",
    "CoreParams.set_index('Crop', inplace=True,drop=True)\n",
    "#some useful lists\n",
    "SCUMParamList = ['[Root].MaximumRootDepth.Value',\n",
    "                '[Root].KLModifier.XYPairs.Y' ,\n",
    "                '[Stover].CoverFunction.Expanding.SigCoverFunction.Xo.Value' ,\n",
    "                '[Stover].CoverFunction.Expanding.SigCoverFunction.b.Value',\n",
    "                '[Stover].CoverFunction.Expanding.SigCoverFunction.Ymax.Value' ,\n",
    "                '[Stover].XoBiomass.Value',\n",
    "                '[Stover].bBiomass.Value' ,\n",
    "                '[Product].ExpectedYield.Value',\n",
    "                '[Root].RootProportion.Value' ,\n",
    "                '[Product].HarvestIndex.Value' ,\n",
    "                '[Product].DryMatterContent.Value' ,\n",
    "                '[Phenology].CanopyExpanding.Target.Value' ,\n",
    "                '[Phenology].YieldIncreasing.Target.Value',\n",
    "                '[Phenology].Senescing.Target.Value',\n",
    "                '[Stover].NConc.Value',\n",
    "                '[Product].MinimumNConc.Value',\n",
    "                '[Product].MaximumNConc.Value',\n",
    "                '[Root].MinimumNConc.Value',\n",
    "                '[Root].MaximumNConc.Value',\n",
    "                '[Nodule].NFixationOption']\n",
    "CropList = CoreParams.index.tolist()\n",
    "CropList.sort()\n",
    "SCUMParamList.sort()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Set up working dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Step1 = CoreParams.ix[:,~(CoreParams.columns == 'TT_Sen')].copy()\n",
    "SCUMParams = Step1.ix[:,~(Step1.columns == 'TT_Mat')].copy()\n",
    "SCUMParams['[Nodule].NFixationOption'] = [str(SCUMParams.ix[X,'[Nodule].NFixationOption']).replace(\"'\",\"\") for X in range(SCUMParams.index.size)]\n",
    "AncillaryParams = pd.DataFrame(index=CoreParams.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Do some simple calculations first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Calculate the inflection point ot the DM sigmoid as 50% of the crops duration\n",
    "SCUMParams['[Stover].XoBiomass.Value'] = np.round(CoreParams.TT_Mat*0.5,0)\n",
    "#The curvature parameter of the DM sigmoid is 1/5 of the inflection point\n",
    "SCUMParams['[Stover].bBiomass.Value'] = np.round(SCUMParams['[Stover].XoBiomass.Value']*0.2,0)\n",
    "#The duration of the Phase from the start to end of senescence is the total tt accumulated to mat less that to sen\n",
    "SCUMParams['[Phenology].Senescing.Target.Value'] = CoreParams['TT_Mat'] - CoreParams['TT_Sen']                                                       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Then calculate some additional values that will be needed for other parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#The total amount of biomass if the yield of the crop reached the expected value\n",
    "AncillaryParams['YMax'] = (SCUMParams['[Product].ExpectedYield.Value']* \\\n",
    "                          SCUMParams['[Product].DryMatterContent.Value'])/  \\\n",
    "                          (1-SCUMParams['[Root].RootProportion.Value'])/ \\\n",
    "                          SCUMParams['[Product].HarvestIndex.Value']\n",
    "\n",
    "#The amount of DM at maturity will be slightly less than YMax because the sigmoid won't have reached its asymtote by then\n",
    "AncillaryParams['DMatMatur'] = AncillaryParams['YMax'] * \\\n",
    "                               1/(1+np.exp(-(CoreParams['TT_Mat']-\\\n",
    "                                             SCUMParams['[Stover].XoBiomass.Value'])/\\\n",
    "                                           SCUMParams['[Stover].bBiomass.Value']))\n",
    "#Root DM at this point is a function of root Proportion\n",
    "AncillaryParams['RootDM'] =  AncillaryParams['DMatMatur'] * SCUMParams['[Root].RootProportion.Value']\n",
    "#Product DM at this point is a function of harvest index\n",
    "AncillaryParams['ProductDM'] = (AncillaryParams['DMatMatur'] - \\\n",
    "                                AncillaryParams['RootDM']) * \\\n",
    "                                SCUMParams['[Product].HarvestIndex.Value']\n",
    "#Stover DM is the residual\n",
    "AncillaryParams['StoverDM'] = AncillaryParams['DMatMatur'] - \\\n",
    "                              AncillaryParams['RootDM'] - \\\n",
    "                              AncillaryParams['ProductDM']\n",
    "#We are assuming 20% of the stover biomass is produced during the reproductive phase\n",
    "AncillaryParams['StoverInRepro'] = AncillaryParams['StoverDM'] * 0.2\n",
    "#So the amount of biomass at the start of the reproductive phase will be 80% of the total stover \n",
    "#adjusted to account for root proportion\n",
    "AncillaryParams['DMatStartRepro'] = (AncillaryParams['StoverDM'] - \\\n",
    "                                    AncillaryParams['StoverInRepro']) * \\\n",
    "                                    (1+SCUMParams['[Root].RootProportion.Value'])\n",
    "#Then we can invert the DM sigmoid to work out how much Tt has passed when this amout of biomass has accumulated\n",
    "AncillaryParams['TtatStartRepro'] = np.log(1/(AncillaryParams['DMatStartRepro']/\\\n",
    "                                          AncillaryParams['DMatMatur'])-1) * \\\n",
    "                                        SCUMParams['[Stover].bBiomass.Value'] * -1 + \\\n",
    "                                        SCUMParams['[Stover].XoBiomass.Value']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Calculate remaining Phenology parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#The tt duration of the canopy expanding (vegetative) phase\n",
    "SCUMParams['[Phenology].CanopyExpanding.Target.Value'] = np.round(AncillaryParams['TtatStartRepro'],0)\n",
    "#The tt duration of the yield increasing phase (growing reproductive DM and canopy not senescing)\n",
    "#is what is left of thermal time to start of senescence after the vegetative phase is taken out\n",
    "SCUMParams['[Phenology].YieldIncreasing.Target.Value'] = np.round(CoreParams['TT_Sen'] - SCUMParams['[Phenology].CanopyExpanding.Target.Value'],0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Generate arays of kl modifier values, adjusted for root depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def StringMaker(DepthInts,RootDepth):\n",
    "    YArray = ''\n",
    "    for Depth in DepthInts:\n",
    "        if Depth > 0:\n",
    "            YArray += ','\n",
    "        if Depth < 305:\n",
    "            klFact = 1\n",
    "        elif Depth < RootDepth:\n",
    "            klFact = 1*np.exp(-.002*(1500/(RootDepth-300)*(Depth-300)))\n",
    "        else:\n",
    "            klFact = 0\n",
    "        klFact = np.round(klFact,3)\n",
    "        YArray += str(klFact)\n",
    "    return YArray\n",
    "\n",
    "DepthInts = range(0,1499,50)\n",
    "SCUMParams['[Root].KLModifier.XYPairs.Y'] = \\\n",
    "    [StringMaker(DepthInts,SCUMParams.ix[X,'[Root].MaximumRootDepth.Value']) \\\n",
    "     for X in range(SCUMParams.index.size)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Build XML and write to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Crops = ET.Element('Crops')\n",
    "for Crop in CropList:\n",
    "    CropElement = ET.SubElement(Crops, 'Cultivar')\n",
    "    NameTag = ET.SubElement(CropElement, 'Name')\n",
    "    NameTag.text = str(Crop)\n",
    "    for Param in SCUMParamList:\n",
    "        Command = ET.SubElement(CropElement,'Command')\n",
    "        Command.text = str(Param + ' = ' + str(SCUMParams.ix[Crop,Param]))\n",
    "\n",
    "ET.ElementTree(Crops).write('.\\Crop.xml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Graph kl patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xce0fc88>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nYmqpVoWRzm4HAAAAcHmGaZop3zSMxyW1Mk3ztRvfn5cUYZpm99vGDJJkNU2z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LnZJUoV+Qtnp7v8un6k3M5RiH9JhgZCiHSTkgKFCkFg4JMfM4tDhw9Qf5Mn5XyrcGnvKdU54jVN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\nUTusso14wwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc8d80f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def DoubleMaker(Crop, DepthInts,RootDepth):\n",
    "    YArray = ''\n",
    "    for Depth in DepthInts:\n",
    "        if Depth < 305:\n",
    "            klFact = 1\n",
    "        elif Depth < RootDepth:\n",
    "            klFact = 1*np.exp(-.002*(1500/(RootDepth-300)*(Depth-300)))\n",
    "        else:\n",
    "            klFact = 0\n",
    "        klFact = np.round(klFact,3)\n",
    "        KLMods.ix[Crop,Depth] = klFact\n",
    "    return YArray\n",
    "\n",
    "KLMods = pd.DataFrame(index = SCUMParams.index, columns = DepthInts)\n",
    "\n",
    "for Crop in CropList:\n",
    "    DoubleMaker(Crop, DepthInts, SCUMParams.ix[Crop,'[Root].MaximumRootDepth.Value'])\n",
    "    \n",
    "KLMods.transpose().plot(legend = False, figsize = (18,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Crop\n",
       "Barley_Spring                1,1,1,1,1,1,1,0.819,0.67,0.549,0.449,0.368,0.3...\n",
       "Maize_Short                  1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Maize_Med                    1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Maize_Long                   1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Oats_Spring                  1,1,1,1,1,1,1,0.819,0.67,0.549,0.449,0.368,0.3...\n",
       "Oats_Autumn                  1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Wheat_Spring                 1,1,1,1,1,1,1,0.819,0.67,0.549,0.449,0.368,0.3...\n",
       "Wheat_Autumn                 1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Broccoli_winter_spring       1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Broccoli_summer              1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "BrusselSprouts               1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Cabbage_winter_spring        1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Cabbage_summer               1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Cauliflower_winter_spring    1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Cauliflower_summer           1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Lettuce                      1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Spinach                      1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Green_Beans                  1,1,1,1,1,1,1,0.741,0.549,0.407,0.301,0.223,0....\n",
       "Dried_Beans                  1,1,1,1,1,1,1,0.741,0.549,0.407,0.301,0.223,0....\n",
       "Lentils                      1,1,1,1,1,1,1,0.741,0.549,0.407,0.301,0.223,0....\n",
       "Green_Peas                   1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Dried_Peas                   1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Kumara                       1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Potatoes_Short               1,1,1,1,1,1,1,0.819,0.67,0.549,0.449,0.368,0.3...\n",
       "Potatoes_Medium              1,1,1,1,1,1,1,0.819,0.67,0.549,0.449,0.368,0.3...\n",
       "Potatoes_Long                1,1,1,1,1,1,1,0.819,0.67,0.549,0.449,0.368,0.3...\n",
       "Beets                        1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Carrots                      1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Parsnips                     1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Clover_1styear               1,1,1,1,1,1,1,0.819,0.67,0.549,0.449,0.368,0.3...\n",
       "Ryegrass_1styear             1,1,1,1,1,1,1,0.741,0.549,0.407,0.301,0.223,0....\n",
       "Clover_2ndyear               1,1,1,1,1,1,1,0.819,0.67,0.549,0.449,0.368,0.3...\n",
       "Ryegrass_2ndyear             1,1,1,1,1,1,1,0.741,0.549,0.407,0.301,0.223,0....\n",
       "Sweetcorn                    1,1,1,1,1,1,1,0.819,0.67,0.549,0.449,0.368,0.3...\n",
       "Squash                       1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Onions                       1,1,1,1,1,1,1,0.223,0,0,0,0,0,0,0,0,0,0,0,0,0,...\n",
       "Tomato                       1,1,1,1,1,1,1,0.497,0.247,0.122,0,0,0,0,0,0,0,...\n",
       "Oats&Rye                     1,1,1,1,1,1,1,0.819,0.67,0.549,0.449,0.368,0.3...\n",
       "Brassica                     1,1,1,1,1,1,1,0.741,0.549,0.407,0.301,0.223,0....\n",
       "Mustard                      1,1,1,1,1,1,1,0.741,0.549,0.407,0.301,0.223,0....\n",
       "Lupins                       1,1,1,1,1,1,1,0.741,0.549,0.407,0.301,0.223,0....\n",
       "Phacelia                     1,1,1,1,1,1,1,0.741,0.549,0.407,0.301,0.223,0....\n",
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       "Barley_SpringForage          1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Triticalie_Spring            1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Triticalie_Autumn            1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "RyeCorn_Spring               1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "RyeCorn_Autumn               1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Oats_SpringForage            1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Oats_AutumnForage            1,1,1,1,1,1,1,0.905,0.819,0.741,0.67,0.607,0.5...\n",
       "Kale                         1,1,1,1,1,1,1,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,...\n",
       "Rape                         1,1,1,1,1,1,1,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,...\n",
       "Name: [Root].KLModifier.XYPairs.Y, dtype: object"
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     "execution_count": 20,
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     "output_type": "execute_result"
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