MarkII.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e7c982a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dash\n",
    "from dash import dash_table\n",
    "from dash import html\n",
    "from dash import dcc\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from bisect import bisect_left, bisect_right\n",
    "from jupyter_dash import JupyterDash\n",
    "import dash_bootstrap_components as dbc\n",
    "#from dash.dependencies import Input, Output\n",
    "import cufflinks as cf\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import copy\n",
    "import CropNBalFunctions as cnbf\n",
    "import CropNBalUICompenents as uic\n",
    "#from dash_extensions.enrich import Output, DashProxy, Input, MultiplexerTransform\n",
    "from dash.exceptions import PreventUpdate\n",
    "import os\n",
    "from dash import Dash, dcc, html, Input, Output, State, MATCH, ALL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d1e1eaf",
   "metadata": {},
   "source": [
    "## General components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c5a29b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "CropCoefficients, EndUseCatagoriesDropdown, metFiles, MetDropDown, MonthIndexs = uic.Generalcomponents()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec295acb",
   "metadata": {},
   "source": [
    "## Core crop model components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ff89461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Temperature accumulation')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Scallers = cnbf.Scallers \n",
    "StagePropns = cnbf.StagePropns\n",
    "\n",
    "# Graphs scallers over thermal time series\n",
    "Graph = plt.figure()\n",
    "plt.plot(Scallers.loc[:,'scaller'])\n",
    "plt.plot(Scallers.loc[:,'cover'])\n",
    "plt.plot(Scallers.loc[:,'rootDepth'])\n",
    "T_mat = 100\n",
    "for p in StagePropns.index:\n",
    "    plt.plot(StagePropns.loc[p,'PrpnTt']*T_mat,StagePropns.loc[p,'PrpnMaxDM'],'o',color='k')\n",
    "    plt.text(StagePropns.loc[p,'PrpnTt']*T_mat+3,StagePropns.loc[p,'PrpnMaxDM'],p,verticalalignment='top')\n",
    "    plt.plot([StagePropns.loc[p,'PrpnTt']*T_mat]*2,[0,Scallers.loc[round(StagePropns.loc[p,'PrpnTt'] * T_mat),'max']],'--',color='k',lw=1)\n",
    "plt.ylabel('Relative DM accumulation')\n",
    "plt.xlabel('Temperature accumulation')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8e856b2",
   "metadata": {},
   "source": [
    "## Graph constructors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59329a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "def AddTimeLines(fig,ypos,start):\n",
    "    weighting = [.99,.5,.05]\n",
    "    align = ['left','center','right']\n",
    "    pos = 0\n",
    "    for p in uic.Positions:\n",
    "        c = pd.read_pickle(p+'Config.pkl')\n",
    "        fig.add_trace(go.Scatter(x=[c['EstablishDate']]*2,y=[0,ypos*1.1],line = {'color':'grey','dash':'dash'},mode='lines',showlegend=False))\n",
    "        fig.add_trace(go.Scatter(x=[c['HarvestDate']]*2,y=[0,ypos*1.1],line = {'color':'grey','dash':'dash'},mode='lines',showlegend=False))\n",
    "        if pos != 0:\n",
    "            fig.add_annotation(dict(ax= c['HarvestDate'], y= ypos, xref=\"x\", yref=\"y\",text=\"\",showarrow=True,\n",
    "                                x= c['EstablishDate'], ay= ypos, axref = \"x\", ayref='y', arrowhead = 3, arrowwidth=1.5, arrowcolor='grey',))\n",
    "            fig.add_annotation(dict(x= c['HarvestDate'], y= ypos, xref=\"x\", yref=\"y\",text=\"\",showarrow=True,\n",
    "                                ax= c['EstablishDate'], ay= ypos, axref = \"x\", ayref='y', arrowhead = 3, arrowwidth=1.5, arrowcolor='grey',))\n",
    "        else:\n",
    "            fig.add_annotation(dict(ax= start, y= ypos, xref=\"x\", yref=\"y\",text=\"\",showarrow=True,\n",
    "                                x= c['HarvestDate'], ay= ypos, axref = \"x\", ayref='y', arrowhead = 3, arrowwidth=1.5, arrowcolor='grey',))\n",
    "            \n",
    "        middate = c['EstablishDate'].astype(dt.datetime) + (c['HarvestDate'].astype(dt.datetime)-c['EstablishDate'].astype(dt.datetime))*weighting[pos]\n",
    "        fig.add_trace(go.Scatter(x=[middate], y = [ypos], text = p[:-1] + ' Crop <br>'+ c['Crop'], mode=\"lines+text\",showlegend=False,textposition='middle '+align[pos]))\n",
    "        pos +=1\n",
    "    return fig\n",
    "\n",
    "def CropNGraph(NBalance,start,end):\n",
    "    cols = ['brown','orange','red','blue','green']\n",
    "    fig = go.Figure()\n",
    "    hdates = []\n",
    "    pdates = []\n",
    "    for c in uic.Positions:\n",
    "        config = pd.read_pickle(c+\"Config.pkl\")\n",
    "        hdates.append(config['HarvestDate'])\n",
    "        pdates.append(config['HarvestDate'].astype(dt.datetime)+dt.timedelta(days=7))\n",
    "    base = [0,0,0]\n",
    "    pos=0\n",
    "    for c in ['Root','Stover','FieldLoss','DressingLoss','SaleableProduct']:\n",
    "        data = NBalance.loc[hdates,c]\n",
    "        fig.add_trace(go.Bar(x =  pdates, y = data, base = base, offsetgroup=0, name=c, text=c,width=86400000*14,marker={'color':cols[pos]}))\n",
    "        base = np.add(base,data)\n",
    "        pos+=1\n",
    "    data = [0]* len(NBalance.index)\n",
    "    for c in ['Root','Stover','FieldLoss','DressingLoss','SaleableProduct']:\n",
    "        data = np.add(data,NBalance.loc[:,c])\n",
    "    data = data.where(data > 0, np.nan)\n",
    "    fig.add_trace(go.Scatter(x=NBalance.index,y=data,name='Crop N',line = {'color':'green',},connectgaps=False))\n",
    "    ypos = data.max()*1.1\n",
    "    for p in ['P','C']:\n",
    "        colpos = 0\n",
    "        data = [0] * len(NBalance.index)\n",
    "        for r in ['resRoot', 'resStover', 'resFeildLoss']:\n",
    "            data = np.add(data,NBalance.loc[:,p+r])\n",
    "            fig.add_trace(go.Scatter(x=NBalance.index+ dt.timedelta(days=14),y=data.where(data > 0.1, np.nan),name=r,line = {'color':cols[colpos],'dash':'dot'},connectgaps=False,showlegend=False))\n",
    "            colpos+=1\n",
    "    fig = AddTimeLines(fig,ypos,start)\n",
    "    fig.update_layout(title_text=\"Crop N\", title_font_size = 30, title_x = 0.5, title_xanchor = 'center')\n",
    "    fig.update_yaxes(title_text=\"Nitrogen (kg/ha)\", title_font_size = 20, range=[0,ypos*1.1])\n",
    "    fig.update_xaxes(title_text=None,range= [start,end])\n",
    "    fig.update_layout(legend_traceorder=\"reversed\")\n",
    "    return fig\n",
    "\n",
    "def CropWaterGraph(cropWater):\n",
    "    NData = cropWater.reset_index()\n",
    "    fig = px.line(data_frame=NData,x='Date',y='Values',color='Component',color_discrete_sequence=['brown','orange','red','green'],\n",
    "                 )#range_x = [c['EstablishDate']-dt.timedelta(days=7),c['HarvestDate']+dt.timedelta(days=7)])\n",
    "    fig.update_layout(title_text=\"Cover and Root Depth\", title_font_size = 30, title_x = 0.5, title_xanchor = 'center')\n",
    "    fig.update_yaxes(title_text=\"Cover (m2/m2) and depth (m)\", title_font_size = 20)\n",
    "    fig.update_xaxes(title_text=None)\n",
    "    return fig\n",
    "\n",
    "def NInputsGraph(NBalance,start,end):\n",
    "    fig = go.Figure()\n",
    "    fig.add_trace(go.Scatter(x=NBalance.index,y=NBalance.loc[:,'SOMNmineraliation'].cumsum(),name='SOM mineralisation',line = {'color':'black'},connectgaps=False))\n",
    "    fig.add_trace(go.Scatter(x=NBalance.index,y=NBalance.loc[:,'ResidueMineralisation'].cumsum(),name='Residues mineralisation',line = {'color':'orange'},connectgaps=False))\n",
    "    ypos = NBalance.loc[:,['SOMNmineraliation','ResidueMineralisation']].cumsum().max().max()\n",
    "    fig = AddTimeLines(fig,ypos,start)\n",
    "    fig.update_layout(title_text=\"N Inputs\", title_font_size = 30, title_x = 0.5, title_xanchor = 'center')\n",
    "    fig.update_yaxes(title_text=\"Nitrogen (kg/ha)\", title_font_size = 20,range=[0,ypos*1.1])\n",
    "    fig.update_xaxes(title_text=None,range=[start,end])\n",
    "    \n",
    "    return fig\n",
    "\n",
    "def SoilNGraph(NBalance,start,end,trigger,Fertiliser):\n",
    "    CurrentConfig = pd.read_pickle(\"Current_Config.pkl\")\n",
    "    FieldConfig = pd.read_pickle(\"Field_Config.pkl\")\n",
    "    fig = go.Figure()\n",
    "    fig.add_trace(go.Scatter(x=[FieldConfig['MinNDate']], y=[FieldConfig['MinN']], mode=\"markers\", name='Measured Soil N',line = {'color':'darkblue'}, marker_symbol = 'x',marker_size=15))\n",
    "    fig.add_trace(go.Scatter(x=NBalance.index,y=NBalance.loc[:,'SoilMineralN'],name='Estimated Soil N',line = {'color':'brown'},connectgaps=False))\n",
    "    ypos = NBalance.loc[:,'SoilMineralN'].max()*1.3\n",
    "    fig = AddTimeLines(fig,ypos,start)\n",
    "    runningmeanCU = []\n",
    "    last7days = []\n",
    "    for u in NBalance.CropUptake:\n",
    "        if len(last7days) >= 14:\n",
    "            del last7days[0]\n",
    "        last7days.append(u)\n",
    "        runningmeanCU.append(np.mean(last7days)*10)\n",
    "    fig.add_trace(go.Scatter(x=[CurrentConfig['EstablishDate'],CurrentConfig['HarvestDate'],end],y=[trigger]*2,name='Fertiliser Trigger',line = {'color':'magenta'}, mode=\"lines\",connectgaps=False))\n",
    "    eN = CurrentConfig['EstablishN'] \n",
    "    if eN>0:\n",
    "        edate = CurrentConfig['EstablishDate'] \n",
    "        dbfedate = (edate.astype(dt.datetime) - dt.timedelta(days=1)).strftime('%Y-%m-%d')\n",
    "        fig.add_trace(go.Bar(x = [edate], y = [eN], base = NBalance.loc[dbfedate,'SoilMineralN'],\n",
    "                             width=86400000*3, name = 'Planting N',marker={'color':'purple'}))\n",
    "    for f in Fertiliser.index:\n",
    "        n = f.strftime('%d-%b') + ' ' + str(int(Fertiliser[f])) + ' kg/ha'\n",
    "        fig.add_trace(go.Bar(x =  [f], y = [Fertiliser[f]], base = trigger ,width=86400000*3, name = n,marker={'color':'crimson'}))\n",
    "    fig.update_xaxes(range= [start,end],title_text=None)\n",
    "    fig.update_yaxes(title_text=\"Nitrogen (kg/ha)\", title_font_size = 20,range = [0,ypos*1.1])\n",
    "    fig.update_layout(title_text=\"SoilN\", title_font_size = 30, title_x = 0.5, title_xanchor = 'center')\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "51bd5a5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test = CurrentConfig['EstablishDate'].astype(dt.datetime) - dt.timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9cf25bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5cd322c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBalance.loc[test.strftime('%Y-%m-%d'),'SoilMineralN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "883f3e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CropNGraph(NBalance,start,end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b15e4467",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SoilNGraph(NBalance,start,end,30,Fertiliser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "edb522fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NInputsGraph(NBalance,start,end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7495e258",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# runningmeanCU = []\n",
    "# last7days = []\n",
    "# for u in NBalance.CropUptake:\n",
    "#     # if len(last7days) >= 14:\n",
    "#         del last7days[0]\n",
    "#     last7days.append(u)\n",
    "#     runningmeanCU.append(np.mean(last7days))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9fdff82a",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# PreviousConfig = pd.read_pickle(\"Previous_Config.pkl\")\n",
    "# CurrentConfig = pd.read_pickle(\"Current_Config.pkl\")\n",
    "# FollowingConfig = pd.read_pickle(\"Following_Config.pkl\")\n",
    "# FieldConfig = pd.read_pickle(\"Field_Config.pkl\")\n",
    "# Tt = CalculateMedianTt(PreviousConfig[\"EstablishDate\"].astype(dt.datetime),FollowingConfig[\"HarvestDate\"].astype(dt.datetime),metFiles[FieldConfig[\"Location\"]])\n",
    "# NBalance = MakeNBalanceFrame(Tt.index)\n",
    "# NBalance = CalculateCropOutputs(Tt,CropCoefficients,NBalance)\n",
    "# NBalance = CalculateSOMMineralisation(Tt, NBalance)\n",
    "# NBalance = CalculateResidueMineralisation(Tt,NBalance)\n",
    "# NBalance = CalculateSoilMineralN(NBalance)\n",
    "# NBalance = CalculateFertiliserApplications(NBalance,30,0.8,3)\n",
    "# Fertiliser = NBalance.FertiliserN.where(NBalance.FertiliserN>0,np.nan).dropna()\n",
    "# start = PreviousConfig['HarvestDate'].astype(dt.datetime)-dt.timedelta(30)\n",
    "# end = FollowingConfig['HarvestDate'].astype(dt.datetime)+dt.timedelta(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee1c6709",
   "metadata": {},
   "outputs": [],
   "source": [
    "ConfigFiles = []\n",
    "mydir = 'C:\\GitHubRepos\\SVS'\n",
    "for File in os.listdir(mydir):\n",
    "    if File.endswith('.pkl'):\n",
    "        if ('_SavedConfig' in File):\n",
    "            ConfigFiles.append(File.replace('_SavedConfig.pkl',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "57c0bbd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeNBalanceFrame(index):\n",
    "    columns = ['Root', 'Stover', 'FieldLoss', 'DressingLoss','SaleableProduct', 'TotalCrop', 'Cover','RootDepth', 'SOMNmineraliation', 'PresRoot', 'PresStover', 'PresFeildLoss', 'CresRoot', 'CresStover', 'CresFeildLoss', 'ResidueMineralisation', 'LeachingLoss', 'GasiousLoss', 'SoilMineralN','UnMeasuredSoilN','FertiliserN']\n",
    "    return pd.DataFrame(index = index, columns = columns, data= 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a349b6df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Empty arrays to hold example data\n",
    "BiomassScaller = []\n",
    "CoverScaller = []\n",
    "RootDepthScaller = []\n",
    "#Example parameters\n",
    "Xo_Biomass = 50\n",
    "b_Biomass = Xo_Biomass*0.2\n",
    "A_cov = 1\n",
    "T_mat = Xo_Biomass*2\n",
    "T_sen = T_mat-30\n",
    "Xo_cov = T_mat * 0.25\n",
    "b_cov = Xo_cov * 0.3\n",
    "#Example thermal time series\n",
    "Tts = range(150)\n",
    "#Calculate biomass, cover and root depth callers at each Tt in the series\n",
    "for tt in Tts:\n",
    "    BiomassScaller.append(1/(1+np.exp(-((tt-Xo_Biomass)/(b_Biomass)))))\n",
    "    cover = 0\n",
    "    if tt < T_sen:\n",
    "        cover = A_cov * 1/(1+np.exp(-((tt-Xo_cov)/b_cov)))\n",
    "        rd = tt/T_sen\n",
    "    else:\n",
    "        if tt < T_mat:\n",
    "            cover = A_cov * (1-(tt-T_sen)/(T_mat-T_sen))\n",
    "            rd = 1.0\n",
    "    CoverScaller.append(cover)\n",
    "    RootDepthScaller.append(rd)\n",
    "\n",
    "#PackScallers into a data frame    \n",
    "Scallers = pd.DataFrame(index=Tts,data=BiomassScaller,columns=['scaller'])\n",
    "Scallers.loc[:,'cover'] = CoverScaller\n",
    "Scallers.loc[:,'rootDepth'] = RootDepthScaller\n",
    "Scallers.loc[:,'max'] = Scallers.max(axis=1)\n",
    "\n",
    "#Set up data frame with the assumed proportion of MAX DM accumulated at each reproductive stage\n",
    "PrpnMaxDM = [0.0066,0.015,0.5,0.75,0.86,0.95,0.9933,0.9995]\n",
    "StagePropns = pd.DataFrame(index = uic.Methods, data = PrpnMaxDM,columns=['PrpnMaxDM']) \n",
    "#Calculate the proportion of thermal time that each stage will accumulate at and graph\n",
    "for p in StagePropns.index:\n",
    "    TTatProp = bisect_left(Scallers.scaller,StagePropns.loc[p,'PrpnMaxDM'])\n",
    "    StagePropns.loc[p,'PrpnTt'] = TTatProp/T_mat\n",
    "\n",
    "def CalculateMedianTt(Start, End, Met):\n",
    "    ## Calculate median thermaltime for location\n",
    "    duration = (End-Start+dt.timedelta(days=1)).days\n",
    "    FirstYear = int(Met.Year[0])\n",
    "    years = [x for x in Met.Year.drop_duplicates().values[1:-1]]\n",
    "    day = Start.day\n",
    "    month = Start.month\n",
    "    FirstDate = dt.date(FirstYear,month,day)\n",
    "    TT = pd.DataFrame(columns = years,index = range(1,duration+1))\n",
    "    for y in years:\n",
    "        start = str(int(y)) + '-' + str(Start.month) + '-' + str(Start.day)\n",
    "        try:\n",
    "            TT.loc[:,y] = Met.loc[start:,'tt'].values[:duration]\n",
    "        except:\n",
    "            do = 'nothing'\n",
    "    TTmed = TT.median(axis=1)\n",
    "    TTmed.index = pd.date_range(start=Start,periods=duration,freq='D',name='Date')\n",
    "    TTmed.name = 'Tt'\n",
    "    return TTmed\n",
    "\n",
    "def calcDelta(Integral):\n",
    "    prior = Integral[0]\n",
    "    delta = []\n",
    "    for i in Integral:\n",
    "        delta.append(i-prior)\n",
    "        prior = i\n",
    "    return delta\n",
    "\n",
    "def CalculateCropOutputs(AllTt, CropCoefficients, NBalance):\n",
    "    NBalance.loc[:,'CropUptake'] = 0\n",
    "    for pos in  uic.Positions:\n",
    "        Config = pd.read_pickle(pos+'Config.pkl')\n",
    "        CropFilter = (CropCoefficients.loc[:,'EndUse'] == Config[\"EndUse\"])&(CropCoefficients.loc[:,'Group'] == Config[\"Group\"])\\\n",
    "                     &(CropCoefficients.loc[:,'Colloquial Name'] == Config[\"Crop\"])&(CropCoefficients.loc[:,'Type'] == Config[\"Type\"])\n",
    "        Params = pd.Series(index=CropCoefficients.loc[CropFilter,uic.CropParams].columns, data = CropCoefficients.loc[CropFilter,uic.CropParams].values[0])\n",
    "        ## Calculate model parameters \n",
    "        \n",
    "        Tt = AllTt[Config['EstablishDate']:Config['HarvestDate']].cumsum()\n",
    "        Tt_Harv = Tt[Config['HarvestDate']] \n",
    "        Tt_estab = Tt_Harv * (StagePropns.loc[Config['EstablishStage'],'PrpnTt']/StagePropns.loc[Config['HarvestStage'],'PrpnTt'])\n",
    "        CropTt = Tt+Tt_estab #Create array of Tt accumulations during crop duration.\n",
    "        Xo_Biomass = (Tt_Harv + Tt_estab) *.45 * (1/StagePropns.loc[Config[\"HarvestStage\"],'PrpnTt'])\n",
    "        b_Biomass = Xo_Biomass * .25\n",
    "        T_mat = Xo_Biomass * 2.2222\n",
    "        T_maxRD = StagePropns.loc[\"EarlyReproductive\",'PrpnTt']*T_mat\n",
    "        T_sen = StagePropns.loc[\"MidReproductive\",'PrpnTt']*T_mat\n",
    "        Xo_cov = Xo_Biomass * 0.4 / Params['rCover']\n",
    "        b_cov = Xo_cov * 0.2\n",
    "        a_harvestIndex = Params['Typical HI'] - Params['HI Range']\n",
    "        b_harvestIndex = Params['HI Range']/Params['Typical Yield']\n",
    "        # Calculate fitted patterns\n",
    "        BiomassScaller = []\n",
    "        CoverScaller = []\n",
    "        RootDepthScaller = []\n",
    "        for tt in CropTt:\n",
    "            BiomassScaller.append(1/(1+np.exp(-((tt-Xo_Biomass)/(b_Biomass)))))\n",
    "            if tt < T_maxRD:\n",
    "                rd = tt/T_maxRD\n",
    "            else:\n",
    "                if tt < T_mat:\n",
    "                    rd = 1.0\n",
    "            RootDepthScaller.append(rd)\n",
    "            if tt < T_sen:\n",
    "                cover = 1/(1+np.exp(-((tt-Xo_cov)/b_cov)))\n",
    "            else:\n",
    "                if tt < T_mat:\n",
    "                    cover = (1-(tt-T_sen)/(T_mat-T_sen))\n",
    "            CoverScaller.append(cover)\n",
    "        #Crop Failure.  If yield is very low of field loss is very high assume complete crop failure.  Uptake equation are too sensitive saleable yields close to zero and field losses close to 100\n",
    "        if ((Config[\"SaleableYield\"] * Config['Units']) <(Params['Typical Yield']*0.05)) or (Config[\"FieldLoss\"]>95):\n",
    "            Config[\"SaleableYield\"] = Params['Typical Yield']\n",
    "            Config[\"FieldLoss\"] = 100\n",
    "            FreshTotalProductWt = Params['Typical Yield'] * (1/(1-Params['Typical Dressing Loss %']/100))\n",
    "        else:\n",
    "            FreshTotalProductWt = Config[\"SaleableYield\"] * (1/(1-Config[\"FieldLoss\"]/100)) * (1/(1-Config[\"DressingLoss\"]/100))\n",
    "        DryTotalProductWt = FreshTotalProductWt * Config['Units'] * (1-Config[\"MoistureContent\"]/100)\n",
    "        DryFieldLossWt = DryTotalProductWt * Config[\"FieldLoss\"]/100\n",
    "        FieldLossN = DryFieldLossWt * Params['Product [N]']/100\n",
    "        DryDressingLossWt = DryTotalProductWt * Config[\"DressingLoss\"]/100\n",
    "        DressingLossN = DryDressingLossWt * Params['Product [N]']/100\n",
    "        DrySaleableProductWt = DryTotalProductWt - DryFieldLossWt - DryDressingLossWt\n",
    "        SaleableProductN = DrySaleableProductWt * Params['Product [N]']/100\n",
    "        HI = a_harvestIndex + FreshTotalProductWt * b_harvestIndex\n",
    "        DryStoverWt = DryTotalProductWt * 1/HI - DryTotalProductWt \n",
    "        StoverN = DryStoverWt * Params['Stover [N]']/100\n",
    "        DryRootWt = (DryStoverWt+DryTotalProductWt) * Params['P Root']\n",
    "        RootN = DryRootWt * Params['Root [N]']/100\n",
    "        CropN = RootN + StoverN + FieldLossN + DressingLossN + SaleableProductN\n",
    "        dates = Tt[Config['EstablishDate']:Config['HarvestDate']].index\n",
    "        StageCorrection = 1/(StagePropns.loc[Config[\"HarvestStage\"],'PrpnMaxDM'])\n",
    "        NBalance.loc[Tt[dates].index,'Root']  = np.multiply(np.multiply(BiomassScaller , RootN),  StageCorrection)\n",
    "        NBalance.loc[Tt[dates].index,'Stover'] = np.multiply(np.multiply(BiomassScaller , StoverN), StageCorrection)\n",
    "        NBalance.loc[Tt[dates].index,'SaleableProduct'] = np.multiply(np.multiply(BiomassScaller , SaleableProductN), StageCorrection)\n",
    "        NBalance.loc[Tt[dates].index,'FieldLoss'] = np.multiply(np.multiply(BiomassScaller , FieldLossN), StageCorrection)\n",
    "        NBalance.loc[Tt[dates].index,'DressingLoss'] = np.multiply(np.multiply(BiomassScaller , DressingLossN), StageCorrection)\n",
    "        NBalance.loc[Tt[dates].index,'TotalCrop'] = np.multiply(np.multiply(BiomassScaller , CropN),StageCorrection)\n",
    "        NBalance.loc[Tt[dates].index,'CropUptake'] = calcDelta(NBalance.loc[Tt[dates].index,'TotalCrop'])\n",
    "        NBalance.loc[Tt[dates].index,'Cover'] = np.multiply(CoverScaller, Params[\"A cover\"])\n",
    "        NBalance.loc[Tt[dates].index,'RootDepth'] = np.multiply(RootDepthScaller, Params[\"Max RD\"])\n",
    "        # if len(c[\"DefoliationDates\"])>0:\n",
    "        #     CropN.sort_index(inplace=True)\n",
    "        #     for dd in Config[\"DefoliationDates\"]:\n",
    "        #         StoverNtoRemove = (CropN.loc[('+ Stover',dd),'Values'].values[0] - CropN.loc[('Root',dd),'Values'].values[0]) * 0.7\n",
    "        #         TotalNtoRemove = StoverNtoRemove\n",
    "        #         if Params['Yield type'] == 'Standing DM':\n",
    "        #             StoverNtoRemove=0\n",
    "        #             TotalNtoRemove = (CropN.loc[('TotalCrop',dd),'Values'].values[0] - CropN.loc[('Root',dd),'Values'].values[0]) * 0.7\n",
    "        #         DefCovFact = 0.3\n",
    "        #         for d in Tt[dates][dd:].index:\n",
    "        #             CropN.loc[('+ Stover',d),'Values'] = CropN.loc[('+ Stover',d),'Values'] - StoverNtoRemove \n",
    "        #             CropN.loc[('TotalCrop',d),'Values'] = CropN.loc[('TotalCrop',d),'Values'] - TotalNtoRemove\n",
    "        #             CropWater.loc[('Cover',d),'Values'] = CropWater.loc[('Cover',d),'Values'] * DefCovFact\n",
    "        #             DefCovFact = min(1.0,DefCovFact + Tt[d] * 0.00001)\n",
    "    return NBalance\n",
    "\n",
    "def CalculateResidueMineralisation(Tt, NBalance):\n",
    "    PreviousConfig = pd.read_pickle(\"Previous_Config.pkl\")\n",
    "    CurrentConfig = pd.read_pickle(\"Current_Config.pkl\")\n",
    "    FollowingConfig = pd.read_pickle(\"Following_Config.pkl\")\n",
    "    for d in Tt.index[:-1]:\n",
    "        if d == PreviousConfig['HarvestDate']:\n",
    "            NBalance.loc[d,'PresRoot'] = NBalance.loc[d,'Root']\n",
    "            NBalance.loc[d,'PresStover'] = NBalance.loc[d,'Stover'] * PreviousConfig[\"ResidueTreatment\"]\n",
    "            NBalance.loc[d,'PresFeildLoss'] = NBalance.loc[d,'FieldLoss'] \n",
    "        if d == CurrentConfig['HarvestDate']:\n",
    "            NBalance.loc[d,'CresRoot'] = NBalance.loc[d,'Root']\n",
    "            NBalance.loc[d,'CresStover'] = NBalance.loc[d,'Stover'] * CurrentConfig[\"ResidueTreatment\"]\n",
    "            NBalance.loc[d,'CresFeildLoss'] = NBalance.loc[d,'FieldLoss']\n",
    "        \n",
    "        for pool in ['PresRoot', 'PresStover', 'PresFeildLoss', 'CresRoot', 'CresStover', 'CresFeildLoss']:\n",
    "            tomorrow = d + dt.timedelta(1)\n",
    "            mineralisation = NBalance.loc[d,pool] * 0.001 * Tt[d]\n",
    "            NBalance.loc[d,'ResidueMineralisation'] += mineralisation\n",
    "            NBalance.loc[tomorrow,pool] = NBalance.loc[d,pool] - mineralisation\n",
    "    return NBalance  \n",
    "\n",
    "def CalculateSOMMineralisation(Tt, NBalance):\n",
    "    PreviousConfig = pd.read_pickle(\"Previous_Config.pkl\")\n",
    "    FieldConfig = pd.read_pickle(\"Field_Config.pkl\")\n",
    "    for d in Tt[PreviousConfig['HarvestDate']:].index:\n",
    "        NBalance.loc[d,'SOMNmineraliation'] =  FieldConfig['HWEON'] * Tt[d] * 0.005\n",
    "    return NBalance\n",
    "\n",
    "def CalculateSoilMineralN(NBalance):\n",
    "    PreviousConfig = pd.read_pickle(\"Previous_Config.pkl\")\n",
    "    CurrentConfig = pd.read_pickle(\"Current_Config.pkl\")\n",
    "    FollowingConfig = pd.read_pickle(\"Following_Config.pkl\")\n",
    "    FieldConfig = pd.read_pickle('Field_Config.pkl')\n",
    "    \n",
    "    dbfh = np.datetime64(PreviousConfig['HarvestDate'].astype(dt.datetime)  - dt.timedelta(1))\n",
    "    NBalance.loc[dbfh,'UnMeasuredSoilN'] = 100\n",
    "    for d in NBalance.loc[PreviousConfig['HarvestDate']:,:].index:\n",
    "        yesterday = d - dt.timedelta(1)\n",
    "        NBalance.loc[d, 'UnMeasuredSoilN'] = NBalance.loc[yesterday, 'UnMeasuredSoilN'] -\\\n",
    "                                             NBalance.loc[d,'CropUptake'] +\\\n",
    "                                             NBalance.loc[d,['SOMNmineraliation','ResidueMineralisation']].sum()\n",
    "    Adjustment = NBalance.loc[FieldConfig['MinNDate'], 'UnMeasuredSoilN'] - FieldConfig['MinN']\n",
    "    NBalance.loc[:,'SoilMineralN'] = NBalance.loc[:,'UnMeasuredSoilN'] - Adjustment\n",
    "    return NBalance\n",
    "    \n",
    "def CalculateFertiliserApplications(NBalance,trigger,efficiency,splits):    \n",
    "    CurrentConfig = pd.read_pickle(\"Current_Config.pkl\")\n",
    "    FieldConfig = pd.read_pickle('Field_Config.pkl')\n",
    "    duration = (CurrentConfig['HarvestDate'].astype(dt.date)-CurrentConfig['EstablishDate'].astype(dt.date)).days\n",
    "    # Apply Planting N\n",
    "    NBalance.loc[CurrentConfig['EstablishDate']:,'SoilMineralN'] = NBalance.loc[CurrentConfig['EstablishDate']:,'SoilMineralN'] + (CurrentConfig[\"EstablishN\"] * efficiency)\n",
    "    # Calculate further N requirements\n",
    "    InitialSoilN = NBalance.loc[CurrentConfig['EstablishDate'],'SoilMineralN']\n",
    "    #print('InitialSoilN ' + str(InitialSoilN))\n",
    "    InCropMineralisation =  NBalance.loc[CurrentConfig['EstablishDate']:CurrentConfig['HarvestDate'],['SOMNmineraliation','ResidueMineralisation']].sum().sum()\n",
    "    #print('InCropMineralisation ' + str(InCropMineralisation))\n",
    "    CropN = NBalance.loc[CurrentConfig['HarvestDate'],'TotalCrop'] - NBalance.loc[CurrentConfig['EstablishDate'],'TotalCrop']\n",
    "    #print('CropN ' +str(CropN))\n",
    "    #print('Trigger ' +str(trigger))\n",
    "    NFertReq = (CropN + trigger) -  InitialSoilN - InCropMineralisation\n",
    "    NFertReq = NFertReq * 1/efficiency\n",
    "    #print(NFertReq)\n",
    "    NAppn = np.ceil(NFertReq/splits)\n",
    "    FertApplied = 0\n",
    "    FertAppNo = 0\n",
    "    ffd = (CurrentConfig['HarvestDate'].astype(dt.datetime) - dt.timedelta(days=5)).strftime('%Y-%m-%d')\n",
    "    if splits>0:\n",
    "        for d in NBalance[CurrentConfig['EstablishDate']:ffd].index:\n",
    "            yesterday = d - dt.timedelta(days=1)\n",
    "            if (NBalance.loc[d,'SoilMineralN'] < trigger) and (FertApplied < NFertReq):\n",
    "                NBalance.loc[d:,'SoilMineralN'] = NBalance.loc[yesterday:,'SoilMineralN'] + (NAppn * efficiency)\n",
    "                NBalance.loc[d,'FertiliserN'] = NAppn\n",
    "    NBalance.loc[:FieldConfig['MinNDate'],'SoilMineralN'] = np.nan\n",
    "    return NBalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8933dcc8",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "Positions = ['Previous_','Current_','Following_']\n",
    "Actions = [\"EstablishDate\", \"HarvestDate\"]\n",
    "\n",
    "CropParams = ['EndUse', 'Group','Colloquial Name', 'Type', 'Family', 'Genus', 'Specific epithet', 'Sub species',\n",
    "       'Typical Establish Stage', 'Typical Establish month', 'Typical Harvest Stage',\n",
    "       'Typical Harvest month', 'Typical Yield', 'Typical Yield Units',\n",
    "       'Yield type', 'Typical HI', 'HI Range',\n",
    "       'Moisture %', 'Typical Dressing Loss %', 'Typical Field Loss %', 'P Root', 'Max RD', 'A cover', 'rCover', 'k_ME',\n",
    "       'Nfixation', 'Root [N]', 'Stover [N]', 'Product [N]','Product [P]', 'Product [K]', 'Product [S]',\n",
    "       'Product [Ca]', 'Product [Mg]', 'Product [Na]', 'Product [Cl]',\n",
    "       'Stover [P]', 'Stover [K]', 'Stover [S]', 'Stover [Ca]', 'Stover [Mg]','Stover [Na]', 'Stover [Cl]']\n",
    "\n",
    "CropConfigs = [\"EndUse\",\"Group\",\"Crop\",\"Type\",\"SaleableYield\",\"Units\",\"FieldLoss\",\"DressingLoss\",\n",
    "               \"MoistureContent\",\"EstablishDate\",\"EstablishStage\",\"HarvestDate\",\"HarvestStage\",\n",
    "               \"ResidueTreatment\",\"EstablishN\",\"DefoliationDates\"]\n",
    "\n",
    "Units = pd.DataFrame(index = ['t/ha','kg/ha'],data=[1000,1],columns=['toKG/ha'])\n",
    "UnitsDropDown = [{'label':i,'value':Units.loc[i,'toKG/ha']} for i in Units.index]\n",
    "\n",
    "Methods = ['Seed','Seedling','Vegetative','EarlyReproductive','MidReproductive','LateReproductive','Maturity','Late']\n",
    "EstablishStageDropdown = [{'label':i,'value':i} for i in Methods[:2]]\n",
    "HarvestStageDropdown = [{'label':i,'value':i} for i in Methods[2:]]\n",
    "\n",
    "ResTreats = pd.DataFrame(index = [\"Baled\",\"Burnt\",\"Grazed\",\"Incorporated\",\"Retained\"],\n",
    "                                 data = [0.2,0.1,0.6,0.7,1.0], columns = ['propn'])\n",
    "ResidueTreatDropdown = [{'label':i,'value':ResTreats.loc[i,'propn']} for i in ResTreats.index]\n",
    "\n",
    "def splitprops(prop_ID,propExt):\n",
    "    prop_ID = prop_ID.replace(propExt,'')\n",
    "    return prop_ID.split('_')[0]+'_', prop_ID.split('_')[1]\n",
    "\n",
    "def Generalcomponents():\n",
    "    # Read in Crop coefficients table and filter out nasty ones\n",
    "    CropCoefficients = pd.read_excel('C:\\\\GitHubRepos\\\\Overseer-testing\\\\CropCoefficients\\\\CropCoefficientTable.xlsx',skiprows=2, engine='openpyxl')\n",
    "\n",
    "    # Make lists of EndUse options\n",
    "    EndUseCatagories = CropCoefficients.loc[:,'EndUse'].drop_duplicates().dropna().values\n",
    "    EndUseCatagories.sort()\n",
    "\n",
    "    # Make some drop down lists\n",
    "    EndUseCatagoriesDropdown = [{'label':i,'value':i} for i in EndUseCatagories]\n",
    "    CropDropDown = [{'label':i,'value':i} for i in CropCoefficients.index]\n",
    "\n",
    "\n",
    "\n",
    "    # Read in weather data files\n",
    "    LincolnMet = pd.read_csv('C:\\GitHubRepos\\Weather\\Broadfields\\LincolnClean.met',delimiter = '\\t')\n",
    "    LincolnMet.name = 'Lincoln'\n",
    "    GoreMet = pd.read_csv('C:\\GitHubRepos\\Weather\\OtherLocations\\GoreClean.met',delimiter = '\\t')\n",
    "    GoreMet.name = 'Gore'\n",
    "    WhatatuMet = pd.read_csv('C:\\GitHubRepos\\Weather\\OtherLocations\\WhatatuClean.met',delimiter = '\\t')\n",
    "    WhatatuMet.name = 'Napier'\n",
    "    PukekoheMet = pd.read_csv('C:\\GitHubRepos\\Weather\\OtherLocations\\PukekoheClean.met',delimiter = '\\t')\n",
    "    PukekoheMet.name = 'Pukekohe'\n",
    "\n",
    "    #Load met files into dictionary \n",
    "    metFiles ={'Pukekohe':PukekoheMet,'Whatatu':WhatatuMet,'Lincoln':LincolnMet,'Gore':GoreMet}\n",
    "\n",
    "    ## Function to calculate thermal time from temperature\n",
    "    def tt(x,b):\n",
    "        return max(0,x-b)\n",
    "\n",
    "    # Calculate thermal time for each met file and get date in correct format\n",
    "    for f in metFiles.keys():\n",
    "        metFiles[f].loc[:,'Date'] = pd.to_datetime(metFiles[f].loc[:,'Date'])\n",
    "        metFiles[f].loc[:,'tt'] = [tt(x,0) for x in metFiles[f].Temp]\n",
    "        metFiles[f].set_index('Date',inplace=True)\n",
    "\n",
    "    # Make a drop down list with met file options\n",
    "    MetDropDown = [{'label':i,'value':i} for i in metFiles.keys()]\n",
    "\n",
    "    MonthIndexs = pd.DataFrame(index = range(1,13),columns=['Name'],data=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'])\n",
    "    \n",
    "    return CropCoefficients, EndUseCatagoriesDropdown, metFiles, MetDropDown, MonthIndexs\n",
    "\n",
    "def validateConfigs():\n",
    "    NotSet = 0\n",
    "    for pos in Positions+['field_']:\n",
    "        Config=pd.read_pickle(pos+\"Config.pkl\")\n",
    "        NotSet += Config.isnull().sum()\n",
    "    if NotSet > 0: \n",
    "        return [html.Button(\"Update NBalance\",disabled=True,\n",
    "                           style={'width':'100%','height':'150%',\"font-size\":\"150%\"},\n",
    "                           id=\"RefreshButton\")]\n",
    "    else: \n",
    "        return [html.Button(\"Update NBalance\",disabled=False,\n",
    "                           style={'width':'100%','height':'150%',\"font-size\":\"150%\"},\n",
    "                           id=\"RefreshButton\")]\n",
    "    \n",
    "def updateConfig(keys,values,ConfigAddress):\n",
    "    Config = pd.read_pickle(ConfigAddress)\n",
    "    its = range(len(keys))\n",
    "    for i in its:\n",
    "        Config[keys[i]] = values[i]\n",
    "    Config.to_pickle(ConfigAddress)\n",
    "\n",
    "def UpdateCropOptions(pos, inputDF, outputDF, CropCoefficients, EndUseCatagoriesDropdown):\n",
    "    c = pd.read_pickle(pos+\"Config.pkl\")\n",
    "    PopulateDefaults = False\n",
    "    DropDownMembers = pd.Series(index = ['Group','Crop','Type'],dtype=object)\n",
    "    DropDownOptions = pd.Series(index = ['EndUse','Group','Crop','Type'],dtype=object)\n",
    "    \n",
    "    #Set up values series\n",
    "    Values = pd.Series(index = ['EndUse','Group','Crop','Type'],data=[None]*4)\n",
    "    Values['EndUse'] = inputDF['EndUse']\n",
    "    if (Values['EndUse']!=None):\n",
    "        Values['Group'] = inputDF['Group']\n",
    "        if (Values['Group']!= None):\n",
    "            Values['Crop'] = inputDF['Crop']\n",
    "            if (Values['Crop'] != None):\n",
    "                Values['Type'] = inputDF['Type']\n",
    "    # Default drop down configs\n",
    "    outputDF['EndUse'] = dcc.Dropdown(value = Values['EndUse'], options = EndUseCatagoriesDropdown,placeholder='Pick End use',\n",
    "                                      style = adStyle,\n",
    "                                      id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":\"EndUse\"})\n",
    "    outputDF['Group'] = dcc.Dropdown(options = [], disabled = True, placeholder='', \n",
    "                                     style = ddStyle,\n",
    "                                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":\"Group\"})\n",
    "    outputDF['Crop'] = dcc.Dropdown(options = [], disabled = True, placeholder='', \n",
    "                                    style = ddStyle,\n",
    "                                    id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":\"Crop\"})\n",
    "    outputDF['Type'] = dcc.Dropdown(options = [], disabled = True, placeholder='', \n",
    "                                    style = ddStyle,\n",
    "                                    id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":\"Type\"})\n",
    "    \n",
    "    # Set drop down configs based on selected values\n",
    "    if Values['EndUse'] != None:\n",
    "        Values, DropDownOptions = checkGroupOptions(Values, DropDownOptions, CropCoefficients,pos) \n",
    "        outputDF['Group'] = dcc.Dropdown(options = DropDownOptions['Group'],placeholder = 'Pick Group', value = Values['Group'],\n",
    "                                         style = adStyle,\n",
    "                                         id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":\"Group\"})\n",
    "        if Values['Group'] != None:\n",
    "            Values, DropDownOptions = checkCropOptions(Values,DropDownOptions,CropCoefficients,pos)\n",
    "            outputDF['Crop'] = dcc.Dropdown(options = DropDownOptions['Crop'],placeholder = 'Pick Crop', value = Values['Crop'], \n",
    "                                            style = adStyle,\n",
    "                                            id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":\"Crop\"})        \n",
    "            if Values['Crop'] != None:\n",
    "                Values, DropDownOptions = checkTypeOptions(Values,DropDownOptions,CropCoefficients,pos)\n",
    "                outputDF['Type'] = dcc.Dropdown(options = DropDownOptions['Type'],placeholder = 'Pick Type', value = Values['Type'], \n",
    "                                                style = adStyle,\n",
    "                                                id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":\"Type\"})\n",
    "        \n",
    "    #Enable Crop data fields and populate with default values if all crop selection catagories are made\n",
    "    PopulateDefaults = (Values[\"EndUse\"]!=None) & (Values[\"Group\"]!=None) & (Values[\"Crop\"]!=None) & (Values[\"Type\"]!=None)\n",
    "    if (PopulateDefaults == True):\n",
    "        CropFilter = (CropCoefficients.loc[:,'EndUse'] == Values[\"EndUse\"])&(CropCoefficients.loc[:,'Group'] == Values[\"Group\"])\\\n",
    "                     &(CropCoefficients.loc[:,'Colloquial Name'] == Values[\"Crop\"])&(CropCoefficients.loc[:,'Type'] == Values[\"Type\"])\n",
    "        Params = pd.Series(index=CropCoefficients.loc[CropFilter,CropParams].columns, data = CropCoefficients.loc[CropFilter,CropParams].values[0])\n",
    "        outputDF['SaleableYield'] = dcc.Input(type=\"number\",disabled = False, value = Params[\"Typical Yield\"],min=0,\n",
    "                                              style = aiStyle,\n",
    "                                              id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\": \"SaleableYield\"})\n",
    "        outputDF['Units'] = dcc.Dropdown(options = UnitsDropDown, disabled = False, value = Units.loc[Params[\"Typical Yield Units\"],\"toKG/ha\"], \n",
    "                                         style = adStyle,\n",
    "                                         id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"Units\"})\n",
    "        outputDF['Product Type'] = html.Div(Params['Yield type'] + \" yield\", \n",
    "                                           style = textStyle,\n",
    "                                           id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"displaytext\",\"id\":\"ProductType\"})\n",
    "        outputDF['FieldLoss'] = dcc.Input(type=\"number\",disabled = False, value = Params[\"Typical Field Loss %\"],min=0,\n",
    "                                          style = aiStyle,\n",
    "                                          id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\": \"FieldLoss\"})\n",
    "        outputDF['DressingLoss'] = dcc.Input(type=\"number\",disabled = False, value = Params[\"Typical Dressing Loss %\"],min=0,\n",
    "                                             style = aiStyle,\n",
    "                                             id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\": \"DressingLoss\"})\n",
    "        outputDF['MoistureContent'] = dcc.Input(type=\"number\",disabled = False, value = (round(Params[\"Moisture %\"],0)),min=0,\n",
    "                                                style = aiStyle,\n",
    "                                                id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\": \"MoistureContent\"})\n",
    "        outputDF['EstablishStage'] = dcc.Dropdown(options = EstablishStageDropdown, disabled = False, value =Params[\"Typical Establish Stage\"], \n",
    "                                                  style = adStyle,\n",
    "                                                  id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"EstablishStage\"})\n",
    "        outputDF['HarvestStage'] = dcc.Dropdown(options = HarvestStageDropdown, disabled = False, value =Params[\"Typical Harvest Stage\"], \n",
    "                                                style = adStyle,\n",
    "                                                id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"HarvestStage\"})\n",
    "        outputDF[\"ResidueTreatment\"] = dcc.Dropdown(options = ResidueTreatDropdown, disabled = False, value = 1.0, \n",
    "                                                    style = adStyle,\n",
    "                                                    id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"ResidueTreatment\"})\n",
    "        outputDF[\"EstablishN\"] = dcc.Input(type=\"number\",disabled = False, value = 0, min=0, \n",
    "                                                    style = aiStyle,\n",
    "                                                    id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"EstablishN\"})\n",
    "        updateConfig([\"SaleableYield\",\"Units\",\"FieldLoss\",\"DressingLoss\",\"MoistureContent\",\n",
    "                      \"EstablishStage\",\"HarvestStage\",\"ResidueTreatment\",\"EstablishN\"],\n",
    "                     [Params[\"Typical Yield\"],Units.loc[Params[\"Typical Yield Units\"],\"toKG/ha\"],Params[\"Typical Field Loss %\"],\n",
    "                      Params[\"Typical Dressing Loss %\"],Params[\"Moisture %\"],Params[\"Typical Establish Stage\"],Params[\"Typical Harvest Stage\"],1.0,0.0],\n",
    "                     pos+\"Config.pkl\")\n",
    "        updateConfig([\"EndUse\",\"Group\",\"Crop\",\"Type\"],Values.values,pos+\"Config.pkl\")\n",
    "    else:\n",
    "        outputDF['SaleableYield'] = dcc.Input(type=\"number\",disabled = True, placeholder='',min=0,\n",
    "                                              style = diStyle,\n",
    "                                              id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\": \"SaleableYield\"})\n",
    "        outputDF['Units'] = dcc.Dropdown(options = [], disabled = True, placeholder='', \n",
    "                                         style = ddStyle,\n",
    "                                         id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"Units\"})\n",
    "        outputDF['Product Type'] = html.Div('',\n",
    "                                           style=dict(display='flex', justifyContent='left'),\n",
    "                                           id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"displaytext\",\"id\":\"ProductType\"})\n",
    "        outputDF['FieldLoss'] = dcc.Input(type=\"number\",disabled = True, placeholder='',min=0,\n",
    "                                          style = diStyle,\n",
    "                                          id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\": \"FieldLoss\"})\n",
    "        outputDF['DressingLoss'] = dcc.Input(type=\"number\",disabled = True, placeholder='',min=0,\n",
    "                                             style = diStyle,\n",
    "                                             id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\": \"DressingLoss\"})\n",
    "        outputDF['MoistureContent'] = dcc.Input(type=\"number\",disabled = True, placeholder='',min=0,\n",
    "                                                style = diStyle,\n",
    "                                                id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\": \"MoistureContent\"})\n",
    "        outputDF['EstablishStage'] = dcc.Dropdown(options = [], disabled = True, placeholder='', \n",
    "                                                  style = ddStyle,\n",
    "                                                  id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"EstablishStage\"})\n",
    "        outputDF['HarvestStage'] = dcc.Dropdown(options = [], disabled = True, placeholder='', \n",
    "                                                style = ddStyle,\n",
    "                                                id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"HarvestStage\"})\n",
    "        outputDF[\"ResidueTreatment\"] = dcc.Dropdown(options = [], disabled = True, placeholder='', \n",
    "                                                    style = ddStyle,\n",
    "                                                    id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"ResidueTreatment\"})\n",
    "        outputDF['EstablishN'] = dcc.Input(type=\"number\",disabled = True, placeholder='',min=0,\n",
    "                                             style = diStyle,\n",
    "                                             id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\": \"EstablishN\"})\n",
    "        updateConfig([\"SaleableYield\",\"Units\",\"FieldLoss\",\"DressingLoss\",\"MoistureContent\",\n",
    "                      \"EstablishStage\",\"HarvestStage\",\"ResidueTreatment\",\"EstablishN\"],\n",
    "                     [0.0,1.0,0.0,0.0,0.0,\"Seed\",\"EarlyReproductive\",1.0,0.0],\n",
    "                     pos+\"Config.pkl\")\n",
    "        updateConfig([\"EndUse\",\"Group\",\"Crop\",\"Type\"],[None,None,None,None],pos+\"Config.pkl\")\n",
    "    return list(outputDF[0:4]), list(outputDF[4:14])\n",
    "\n",
    "def checkGroupOptions(Values,DropDownOptions,CropCoefficients,pos):\n",
    "    GroupSelections = CropCoefficients.loc[CropCoefficients.loc[:,'EndUse'] == Values['EndUse'],\"Group\"].drop_duplicates().dropna().values\n",
    "    GroupSelections.sort()\n",
    "    if len(GroupSelections)<=1:\n",
    "        DropDownOptions['Group'] = [{'label':\"No Groups for \" +Values['EndUse']+\" EndUse\",'value': GroupSelections[0]}]\n",
    "        Values['Group'] = GroupSelections[0]\n",
    "        Values, DropDownOptions = checkCropOptions(Values,DropDownOptions,CropCoefficients,pos)\n",
    "    else:\n",
    "        DropDownOptions['Group'] = [{'label':i,'value':i} for i in GroupSelections]\n",
    "        DropDownOptions['Crop'] = []\n",
    "        DropDownOptions['Type'] = []\n",
    "    return Values, DropDownOptions\n",
    "\n",
    "def checkCropOptions(Values,DropDownOptions,CropCoefficients,pos):\n",
    "    GroupSelections = CropCoefficients.loc[CropCoefficients.loc[:,'EndUse'] == Values['EndUse'],\"Group\"].drop_duplicates().dropna().values\n",
    "    GroupSelections.sort()\n",
    "    DropDownOptions['Group'] = [{'label':i,'value':i} for i in GroupSelections] \n",
    "    CropSelections = CropCoefficients.loc[(CropCoefficients.loc[:,'EndUse'] == Values['EndUse'])&(CropCoefficients.loc[:,'Group'] == Values['Group']),\"Colloquial Name\"].drop_duplicates().dropna().values\n",
    "    CropSelections.sort()\n",
    "    if len(CropSelections) <= 1:\n",
    "            DropDownOptions['Crop'] = [{'label':CropSelections[0]+\" is the only \" + Values['EndUse']+\" crop\",'value': CropSelections[0]}]\n",
    "            Values['Crop'] = CropSelections[0]\n",
    "            Values, DropDownOptions = checkTypeOptions(Values, DropDownOptions,CropCoefficients,pos)\n",
    "    else:\n",
    "        DropDownOptions['Crop'] = [{'label':i,'value':i} for i in CropSelections]\n",
    "        DropDownOptions['Type'] = []\n",
    "    return Values, DropDownOptions\n",
    "\n",
    "def checkTypeOptions(Values,DropDownOptions,CropCoefficients,pos):\n",
    "    GroupSelections = CropCoefficients.loc[CropCoefficients.loc[:,'EndUse'] == Values['EndUse'],\"Group\"].drop_duplicates().dropna().values\n",
    "    GroupSelections.sort()\n",
    "    DropDownOptions['Group'] = [{'label':i,'value':i} for i in GroupSelections] \n",
    "    CropSelections = CropCoefficients.loc[(CropCoefficients.loc[:,'EndUse'] == Values['EndUse'])&(CropCoefficients.loc[:,'Group'] == Values['Group']),\"Colloquial Name\"].drop_duplicates().dropna().values\n",
    "    CropSelections.sort()\n",
    "    DropDownOptions['Crop'] = [{'label':i,'value':i} for i in CropSelections]\n",
    "    TypeSelections = CropCoefficients.loc[(CropCoefficients.loc[:,'EndUse'] == Values['EndUse'])&(CropCoefficients.loc[:,'Group'] == Values['Group'])&(CropCoefficients.loc[:,'Colloquial Name'] == Values['Crop']),\"Type\"].drop_duplicates().dropna().values\n",
    "    if len(TypeSelections) <= 1:\n",
    "            DropDownOptions['Type'] = [{'label':Values['Crop']+\" has no Type options\",'value': TypeSelections[0]}]\n",
    "            Values['Type'] = TypeSelections[0]\n",
    "    else:\n",
    "        DropDownOptions['Type'] = [{'label':i,'value':i} for i in TypeSelections]\n",
    "    return Values, DropDownOptions\n",
    "\n",
    "def SetDatePicker(pos,act,PHtext,minDate,selDate,isEnabled):\n",
    "    if isEnabled:\n",
    "        DateP = dcc.DatePickerSingle(id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Event\",\"RetType\":\"date\",\"id\":act}, date = selDate, placeholder = PHtext,\n",
    "                                          min_date_allowed=minDate, max_date_allowed=dt.date(2025, 12, 31), \n",
    "                                          initial_visible_month = selDate, display_format='D-MMM-YYYY')     \n",
    "    else:\n",
    "        DateP = dcc.DatePickerSingle(id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Event\",\"RetType\":\"date\",\"id\":act}, placeholder = PHtext, disabled = True)\n",
    "    return DateP\n",
    "\n",
    "def UpdateDatePickerOptions(datedf):\n",
    "    for d in datedf.index:\n",
    "        pos = d[0]\n",
    "        act = d[1]\n",
    "        updateConfig([act],[np.datetime64(datedf.loc[d,'date'])],pos+\"Config.pkl\")\n",
    "    \n",
    "    posc=0\n",
    "    for pos in Positions:\n",
    "        for act in Actions:\n",
    "            if (pos == \"Previous_\") and (act == \"EstablishDate\"):\n",
    "                minDate = dt.date(2020,1,1)\n",
    "                isEnabled = datedf.loc[(pos,act),'date']!=None\n",
    "                selDate = datedf.loc[(pos,act),'date']#.astype(dt.datetime)\n",
    "                PHtext = 'Select Establish Date'\n",
    "            else:\n",
    "                isEnabled = datedf.iloc[posc-1,0]!=None\n",
    "                if isEnabled:\n",
    "                    minDate = datedf.iloc[posc-1,0]#.astype(dt.datetime)\n",
    "                else:\n",
    "                    minDate = dt.date(2020,1,1)\n",
    "                if datedf.iloc[posc,0]==None:\n",
    "                    selDate = None\n",
    "                else:    \n",
    "                    selDate = datedf.iloc[posc,0]#.astype(dt.datetime)\n",
    "                if act == 'HarvestDate':\n",
    "                    if isEnabled:\n",
    "                        PHtext = 'Select Harvest Date'\n",
    "                    else:\n",
    "                        PHtext = 'Set Prior Crop dates first'\n",
    "                if act == 'EstablishDate':\n",
    "                    if isEnabled:\n",
    "                        PHtext = 'Select Planting Date'\n",
    "                    else:\n",
    "                        PHtext = 'Set Prior Crop dates first'\n",
    "            globals()[pos+act] = SetDatePicker(pos,act,PHtext,minDate,selDate,isEnabled)\n",
    "            posc +=1\n",
    "    \n",
    "    return Previous_EstablishDate, Previous_HarvestDate, Current_EstablishDate, Current_HarvestDate, Following_EstablishDate, Following_HarvestDate\n",
    "\n",
    "def makeDataSeries(names,values):\n",
    "    Names = []\n",
    "    for n1 in range(len(names)):\n",
    "        for n2 in range(len(names[n1])):\n",
    "            Names.append(names[n1][n2]['id']['id'])\n",
    "    df = pd.Series(index=Names,data=values)\n",
    "    return df\n",
    "\n",
    "def makeDateDataDF(names,dates):\n",
    "    p=0\n",
    "    df = pd.DataFrame(index = range(0,6), columns=['pos','act','date'])\n",
    "    for n1 in range(len(names)):\n",
    "        for n2 in range(len(names[n1])):\n",
    "            df.loc[p,'pos'] = names[n1][n2]['id']['pos']\n",
    "            df.loc[p,'act'] = names[n1][n2]['id']['id']\n",
    "            df.loc[p,'date'] = dates[p]\n",
    "            p+=1\n",
    "    df.set_index(['pos','act'],inplace=True)\n",
    "    return df\n",
    "\n",
    "ddStyle = style={\"height\":\"95%\",\"font-size\":\"95%\",'color':'#3a3f44','background-color':'#3a3f44','border': '#3a3f44'}\n",
    "adStyle = style={\"height\":\"95%\",\"font-size\":\"95%\"}\n",
    "diStyle = style={\"width\": \"95%\",\"height\":\"95%\",\"font-size\":\"95%\",'color':'#3a3f44','background-color':'#3a3f44'}\n",
    "aiStyle = style={\"width\": \"95%\",\"height\":\"95%\",\"font-size\":\"95%\"}\n",
    "dpStyle = style={\"height\":\"95%\",'color':'#3a3f44','background-color':'#3a3f44'}\n",
    "headingStyle = {\"width\":\"95%\",\"height\":\"100%\",\"font-size\":\"150%\", \"justifyContent\":'left'}\n",
    "textStyle = {\"width\":\"95%\",\"height\":\"95%\",\"font-size\":\"95%\",\"justifyContent\":'left'}\n",
    "colStyle = {\"height\":\"95%\"}\n",
    "hrStyle = {'height':'8%'}\n",
    "drStyle = {'height':'10%'}\n",
    "trStyle = {'height':'5%'}\n",
    "brStyle = {'height':'1%'}\n",
    "\n",
    "def CropInputs(pos,EndUseCatagoriesDropdown,disableDates,EDatePHtext,HDatePHtext):\n",
    "    return dbc.Container([\n",
    "    ## Crop Type informaiton\n",
    "    dbc.Row([html.B(pos[:-1]+ \" crop information\",\n",
    "                     style=headingStyle,\n",
    "                     id = {\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Title\",\"RetType\":\"displaytext\",\"id\":\"PositionTitle\"})],\n",
    "           style=hrStyle),\n",
    "    dbc.Row([dbc.Col([html.B('End use',\n",
    "                            style = textStyle)],\n",
    "                     width=3, align='center',style = colStyle),\n",
    "             dbc.Col([html.B('Group', \n",
    "                             style = textStyle)],\n",
    "                     width=3, align='center',style = colStyle),\n",
    "             dbc.Col([html.B('Crop', \n",
    "                             style = textStyle)], \n",
    "                     width=3, align='center',style = colStyle),\n",
    "             dbc.Col([html.B('Type', \n",
    "                             style = textStyle)], \n",
    "                     width=3, align='center',style = colStyle)],\n",
    "           style=trStyle), \n",
    "    dbc.Row([dbc.Col([dcc.Dropdown(options = EndUseCatagoriesDropdown, placeholder=' Pick end use',\n",
    "                                   style=adStyle,\n",
    "                                   id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":\"EndUse\"})],\n",
    "                     width=True ,align='center', style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"children\",\"id\":\"EndUse\"}),\n",
    "             dbc.Col([dcc.Dropdown(options = [], placeholder='' ,\n",
    "                                   style=ddStyle,\n",
    "                                   id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":\"Group\"})], \n",
    "                      width=3 ,align='center',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"children\",\"id\":\"Group\"}),\n",
    "             dbc.Col([dcc.Dropdown(options = [], placeholder=' ',\n",
    "                                   style=ddStyle,\n",
    "                                   id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":\"Crop\"})], \n",
    "                     width=3 ,align='center',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"children\",\"id\":\"Crop\"}),\n",
    "             dbc.Col([dcc.Dropdown(options = [], placeholder='',\n",
    "                                   style=ddStyle,\n",
    "                                   id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":\"Type\"})], \n",
    "                     width=3 ,align='center',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"children\",\"id\":\"Type\"})],\n",
    "           style=drStyle),\n",
    "    dbc.Row([],style=brStyle),\n",
    "    ## Crop Harvest Information\n",
    "    dbc.Row([dbc.Col([html.B(pos[:-1]+ ' harvest information',\n",
    "                               style=headingStyle)],\n",
    "                     width=12, align='left',style = colStyle)],\n",
    "            style=hrStyle),\n",
    "    dbc.Row([dbc.Col([dcc.Input(type=\"number\",placeholder = \"\",min=0,disabled=True,\n",
    "                                style=diStyle,\n",
    "                                id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\": \"SaleableYield\"})],\n",
    "                      width=2, align='center',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"children\",\"id\":\"SaleableYield\"}),\n",
    "             dbc.Col([dcc.Dropdown(options = [], placeholder = \"\",disabled=True,\n",
    "                                   style=ddStyle,\n",
    "                                   id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"Units\"})], \n",
    "                     width=2, align='center',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"children\",\"id\":\"Units\"}),\n",
    "             dbc.Col(html.Div('',\n",
    "                              style=dict(display='flex', justifyContent='left'),\n",
    "                              id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"displaytext\",\"id\":\"ProductType\"}),\n",
    "                     width=3,align='right',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"children\",\"id\":\"Product Type\"}),\n",
    "             dbc.Col([html.Div('at',\n",
    "                               style=dict(display='flex', justifyContent='center'))],\n",
    "                     width = 1, align='center', style = colStyle),\n",
    "             dbc.Col([dcc.Input(type=\"number\",min=0,max=96,disabled=True,\n",
    "                                style=diStyle,\n",
    "                                id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"MoistureContent\"})],\n",
    "                     width=2, align='center', style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"children\",\"id\":\"MoistureContent\"}),\n",
    "             dbc.Col([html.Div('% Moisture',\n",
    "                               style=dict(display='flex', justifyContent='left'))], \n",
    "                     width=2, align='left',style = colStyle)],\n",
    "           style=drStyle), \n",
    "    dbc.Row([],style=brStyle),\n",
    "    \n",
    "    dbc.Row([dbc.Col([dcc.Input(type=\"number\",min=0,max=100,disabled=True,\n",
    "                                style=diStyle,\n",
    "                                id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"FieldLoss\"})],\n",
    "                     width=2, align='right',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"children\",\"id\":\"FieldLoss\"}),\n",
    "             dbc.Col([html.Div('Field Loss (%)',\n",
    "                               style=dict(display='flex', justifyContent='left'))],\n",
    "                     width=2, align='left',style = colStyle),\n",
    "             dbc.Col([dcc.Input(type=\"number\",min=0,max=100,disabled=True,\n",
    "                                style=diStyle,\n",
    "                                id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"DressingLoss\"})],\n",
    "                      width=2, align='right',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"children\",\"id\":\"DressingLoss\"}),\n",
    "            dbc.Col([html.Div('Dressing loss (%)',\n",
    "                               style=dict(display='flex', justifyContent='left'))], \n",
    "                     width=2, align='left',style = colStyle)],\n",
    "           style=drStyle), \n",
    "    dbc.Row([],style=brStyle),\n",
    "    \n",
    "\n",
    "    # Crop Management Information    \n",
    "    dbc.Row([dbc.Col([html.B(pos[:-1]+ ' management information',\n",
    "                               style=headingStyle)],\n",
    "                     width=12, align='left',style = colStyle)],\n",
    "            style=hrStyle),\n",
    "    dbc.Row([dbc.Col([html.Div('Planting Date',\n",
    "                               style = textStyle)], \n",
    "                     width=4, align='center',style = colStyle),\n",
    "             dbc.Col([html.Div('Planting method',\n",
    "                               style = textStyle)], \n",
    "                     width=4, align='center',style = colStyle),\n",
    "             dbc.Col([html.Div('Planting Nitrogen', \n",
    "                               style = textStyle)],\n",
    "                     width=4, align='center',style = colStyle)],\n",
    "             style=trStyle),    \n",
    "    dbc.Row([dbc.Col([dcc.DatePickerSingle(min_date_allowed=dt.date(2020, 1, 1),\n",
    "                                           max_date_allowed=dt.date(2025, 12, 31), initial_visible_month=dt.date(2021, 5, 15),\n",
    "                                           placeholder = EDatePHtext, display_format='D-MMM-YYYY', disabled = disableDates,\n",
    "                                           style = dpStyle,\n",
    "                                           id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Event\",\"RetType\":\"date\",\"id\":\"EstablishDate\"})], \n",
    "                     width=4, align='center',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Event\",\"RetType\":\"children\",\"id\":\"EstablishDate\"}),\n",
    "             dbc.Col([dcc.Dropdown(options =[], placeholder='',disabled=True,\n",
    "                                   style=ddStyle,\n",
    "                                   id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"EstablishStage\"})],\n",
    "                     width=4, align='center',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"children\",\"id\":\"EstablishStage\"}),\n",
    "             dbc.Col([dcc.Input(type=\"number\",min=0,max=400,disabled=True,\n",
    "                                style=diStyle,\n",
    "                                id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"EstablishN\"})],\n",
    "                     width=2, align='center',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"children\",\"id\":\"EstablishN\"}),\n",
    "            dbc.Col([html.Div('kg/ha',\n",
    "                               style=dict(display='flex', justifyContent='left'))],\n",
    "                    width=2, align='left',style = colStyle)],\n",
    "            style=drStyle), \n",
    "    dbc.Row([dbc.Col([html.Div('Harvest Date',\n",
    "                               style = textStyle)], \n",
    "                     width=4, align='center',style = colStyle),\n",
    "            dbc.Col([html.Div('Harvest Stage',\n",
    "                               style = textStyle)],\n",
    "                     width=4, align='center',style = colStyle),\n",
    "            dbc.Col([html.Div('Residue Treatment',\n",
    "                               style = textStyle)],\n",
    "                     width=4, align='center',style = colStyle)],\n",
    "            style=trStyle),\n",
    "    dbc.Row([dbc.Col([dcc.DatePickerSingle(min_date_allowed=dt.date(2020, 1, 1),\n",
    "                                           max_date_allowed=dt.date(2025, 12, 31), initial_visible_month=dt.date(2021, 5, 15),\n",
    "                                           placeholder = HDatePHtext,display_format='D-MMM-YYYY',disabled=True,\n",
    "                                           style = dpStyle,\n",
    "                                           id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Event\",\"RetType\":\"date\",\"id\":\"HarvestDate\"})], \n",
    "                     width=4, align='center',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"Event\",\"RetType\":\"children\",\"id\":\"HarvestDate\"}), \n",
    "             dbc.Col([dcc.Dropdown(options = [],placeholder='',disabled=True,\n",
    "                                   style=ddStyle,\n",
    "                                   id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"HarvestStage\"})], \n",
    "                     width=4, align='center',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"children\",\"id\":\"HarvestStage\"}),\n",
    "             dbc.Col([dcc.Dropdown(options =[], placeholder='',disabled=True,\n",
    "                                   style=ddStyle,\n",
    "                                   id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":\"ResidueTreatment\"})],\n",
    "                     width=4, align='center',style = colStyle,\n",
    "                     id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"children\",\"id\":\"ResidueTreatment\"})],\n",
    "            style=drStyle), \n",
    "    dbc.Row([dbc.Col([html.Div('Defoliatoin Dates')], \n",
    "                     width=3, align='center',style = colStyle),\n",
    "            dbc.Col([dcc.Checklist(options=[],\n",
    "                                   id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"defoliation\",\"RetType\":\"value\",\"id\":\"DefoliationDates\"})],\n",
    "                    width=9, align='left',style = colStyle,\n",
    "                    id={\"pos\":pos,\"Group\":\"Crop\",\"subGroup\":\"defoliation\",\"RetType\":\"children\",\"id\":\"DefoliationDates\"})],\n",
    "            style=drStyle)],\n",
    "    style={\"height\": \"100%\"},fluid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c7a189",
   "metadata": {},
   "source": [
    "## App layout and callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3e64fe50",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8050/\n"
     ]
    }
   ],
   "source": [
    "# Empty the config files\n",
    "FieldConfigs = ['FieldNameInput','Location','HWEON','MinN','MinNDate','FertSplits']\n",
    "FieldConfig = pd.Series(index = FieldConfigs, data = [\"\"]+[None]*5)\n",
    "FieldConfig.to_pickle(\"Field_Config.pkl\")\n",
    "\n",
    "PreviousConfig = pd.Series(index = uic.CropConfigs,data = [None]*15+[[]])\n",
    "PreviousConfig.to_pickle(\"Previous_Config.pkl\")\n",
    "\n",
    "CurrentConfig = pd.Series(index = uic.CropConfigs,data = [None]*15+[[]])\n",
    "CurrentConfig.to_pickle(\"Current_Config.pkl\")\n",
    "\n",
    "FollowingConfig = pd.Series(index = uic.CropConfigs,data = [None]*15+[[]])\n",
    "FollowingConfig.to_pickle(\"Following_Config.pkl\")\n",
    "\n",
    "import ast\n",
    "\n",
    "app = JupyterDash(external_stylesheets=[dbc.themes.SLATE])\n",
    "\n",
    "\n",
    "#Planting and Harvest date callback\n",
    "@app.callback(Output({\"pos\":ALL,\"Group\":\"Crop\",\"subGroup\":\"Event\",\"RetType\":\"children\",\"id\":ALL},'children'), \n",
    "              Input({\"pos\":ALL,\"Group\":\"Crop\",\"subGroup\":\"Event\",\"RetType\":\"date\",\"id\":ALL},'date'), prevent_initial_call=True)\n",
    "def StateCrop(dates):\n",
    "    datedf = uic.makeDateDataDF(dash.callback_context.inputs_list,dates)\n",
    "    return uic.UpdateDatePickerOptions(datedf)\n",
    "\n",
    "# Crop type information callback\n",
    "@app.callback(Output({\"pos\":MATCH,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"children\",\"id\":ALL},\"children\"),\n",
    "              Output({\"pos\":MATCH,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"children\",\"id\":ALL},\"children\"),\n",
    "              Input({\"pos\":MATCH,\"Group\":\"Crop\",\"subGroup\":\"Catagory\",\"RetType\":\"value\",\"id\":ALL},\"value\"),\n",
    "              prevent_initial_call=True)\n",
    "def ChangeCrop(values):\n",
    "    if not any(values):\n",
    "        raise PreventUpdate\n",
    "    inputDF = uic.makeDataSeries(dash.callback_context.inputs_list,values)\n",
    "    outputDF = uic.makeDataSeries(dash.callback_context.outputs_list,[\"\"]*14)\n",
    "    pos = dash.callback_context.outputs_list[0][0]['id']['pos']\n",
    "    return UpdateCropOptions(pos,inputDF,outputDF,CropCoefficients,EndUseCatagoriesDropdown)\n",
    "\n",
    "# Defoliation callback\n",
    "@app.callback(Output({\"pos\":MATCH,\"Group\":\"Crop\",\"subGroup\":\"defoliation\",\"RetType\":\"children\",\"id\":\"DefoliationDates\"},\"children\"),\n",
    "              Input({\"pos\":MATCH,\"Group\":\"Crop\",\"subGroup\":\"Event\",\"RetType\":\"date\",\"id\":ALL},'date'), \n",
    "              prevent_initial_call=True)\n",
    "def DefoliationOptions(dates):\n",
    "    datedf = pd.DataFrame.from_dict(dash.callback_context.inputs,orient='index',columns=['date'])\n",
    "    datedf.index = [x.replace(\".date\",\"\") for x in datedf.index]\n",
    "    pos = dash.callback_context.outputs_list['id']['pos']\n",
    "    DefCheckMonths = []\n",
    "    if (datedf.iloc[0,0] != None) and (datedf.iloc[1,0] != None):\n",
    "        cropMonths = pd.date_range(dt.datetime.strptime(str(datedf.iloc[0,0]),'%Y-%m-%d'),\n",
    "                                   dt.datetime.strptime(str(datedf.iloc[1,0]),'%Y-%m-%d'),freq='MS')\n",
    "        DefCheckMonths = [{'label':MonthIndexs.loc[i.month,'Name'],'value':i} for i in cropMonths]    \n",
    "    return [dcc.Checklist(id={\"pos\":\"Previous_\",\"Group\":\"Crop\",\"subGroup\":\"defoliation\",\"RetType\":\"value\",\"id\":\"DefoliationDates\"}, options = DefCheckMonths, value=[])]\n",
    "\n",
    "# Crop yield information callback\n",
    "@app.callback(Output({\"pos\":MATCH,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":MATCH},'value'),\n",
    "              Input({\"pos\":MATCH,\"Group\":\"Crop\",\"subGroup\":\"data\",\"RetType\":\"value\",\"id\":MATCH},'value'), prevent_initial_call=True)\n",
    "def setInputValue(value):\n",
    "    pos = dash.callback_context.inputs_list[0]['id']['pos']\n",
    "    outp = dash.callback_context.inputs_list[0]['id']['id']\n",
    "    uic.updateConfig([outp],[value],pos+\"Config.pkl\")\n",
    "    return value\n",
    "\n",
    "# Activate Load buttons\n",
    "@app.callback(Output({\"Group\":\"Field\",\"subGroup\":\"FileButton\",\"RetType\":\"children\",\"id\":'LoadButton'},\"children\"),\n",
    "              Input({\"Group\":\"Field\",\"subGroup\":\"Place\",\"RetType\":\"value\",\"id\":\"FieldNamePicker\"},'value'),\n",
    "              prevent_initial_call=True)\n",
    "def FieldSet(selection):\n",
    "    if (selection == None): \n",
    "        raise PreventUpdate\n",
    "    else:\n",
    "        loadbutton = html.Button(\"Load Config\",id=\"LoadButton\",style = {'width':'100%','height':'110%'})\n",
    "    return loadbutton\n",
    "\n",
    "# Activate Save button\n",
    "@app.callback(Output({\"Group\":\"Field\",\"subGroup\":\"FileButton\",\"RetType\":\"children\",\"id\":\"SaveButton\"},\"children\"),\n",
    "              Input({\"Group\":\"Field\",\"subGroup\":\"Place\",\"RetType\":\"value\",\"id\":\"FieldNameInput\"},'value'),\n",
    "              prevent_initial_call=True)\n",
    "def FieldSet(inputval):\n",
    "    if (inputval  == None):\n",
    "        raise PreventUpdate\n",
    "    else:\n",
    "        savebutton = html.Button(\"Save Config\",id=\"SaveButton\",style = {'width':'100%','height':'110%'})\n",
    "    return savebutton\n",
    "\n",
    "# Config load callback\n",
    "@app.callback(Output({\"Group\":\"UI\",\"id\":ALL},\"children\"),\n",
    "              Output(\"LLtext\",\"children\"),\n",
    "              Input(\"LoadButton\",\"n_clicks\"),\n",
    "              Input({\"Group\":\"Field\",\"subGroup\":\"Place\",\"RetType\":\"value\",\"id\":\"FieldNamePicker\"},'value'),\n",
    "              Input({\"Group\":\"Field\",\"subGroup\":\"Place\",\"RetType\":\"value\",\"id\":\"FieldNameInput\"},'value'),\n",
    "              prevent_initial_call=True)\n",
    "def loadConfig(n_clicks,selection,inputval):\n",
    "    if n_clicks is None:\n",
    "        raise PreventUpdate\n",
    "    else:\n",
    "        if selection != None:\n",
    "            FieldName = selection\n",
    "        else:\n",
    "            FieldName = inputval\n",
    "        config = pd.read_pickle(FieldName+\"_SavedConfig.pkl\")\n",
    "        time = dt.datetime.now()\n",
    "        config[\"PreviousVal\"].to_pickle(\"Previous_Config.pkl\")\n",
    "        config[\"CurrentVal\"].to_pickle(\"Current_Config.pkl\")\n",
    "        config[\"FollowingVal\"].to_pickle(\"Following_Config.pkl\")\n",
    "        config[\"FieldVal\"].to_pickle(\"Field_Config.pkl\")\n",
    "    return (config[\"PreviousUI\"], config[\"CurrentUI\"], config[\"FollowingUI\"], config[\"FieldUI\"]), FieldName+\" loaded at \"+ str(time)\n",
    "\n",
    "# Config Save callback\n",
    "@app.callback(Output(\"LStext\",'children'), \n",
    "              Input(\"SaveButton\",\"n_clicks\"), \n",
    "              Input({\"Group\":\"UI\",\"id\":ALL},\"children\"),\n",
    "              Input({\"Group\":\"Field\",\"subGroup\":\"Place\",\"RetType\":\"value\",\"id\":\"FieldNamePicker\"},'value'),\n",
    "              Input({\"Group\":\"Field\",\"subGroup\":\"Place\",\"RetType\":\"value\",\"id\":\"FieldNameInput\"},'value'),\n",
    "              prevent_initial_call=True)\n",
    "def SaveConfig(n_clicks,UIs,selection,inputval):\n",
    "    if n_clicks is None:\n",
    "        raise PreventUpdate\n",
    "    else:\n",
    "        if selection != None:\n",
    "            FieldName = selection\n",
    "        else:\n",
    "            FieldName = inputval\n",
    "        ConfigDF = pd.Series(index=[\"FieldUI\",\"PreviousUI\",\"CurrentUI\",\"FollowingUI\",\"FieldVal\",\"PreviousVal\",\"CurrentVal\",\"FollowingVal\"],dtype=object)\n",
    "        ConfigDF[\"PreviousUI\"] = UIs[0]\n",
    "        ConfigDF[\"CurrentUI\"] = UIs[1]\n",
    "        ConfigDF[\"FollowingUI\"] = UIs[2]\n",
    "        ConfigDF[\"PreviousUI\"] = UIs[0]\n",
    "        ConfigDF[\"FieldUI\"] = UIs[3]\n",
    "        ConfigDF[\"PreviousVal\"] = pd.read_pickle(\"Previous_Config.pkl\")\n",
    "        ConfigDF[\"CurrentVal\"] = pd.read_pickle(\"Current_Config.pkl\")\n",
    "        ConfigDF[\"FollowingVal\"] = pd.read_pickle(\"Following_Config.pkl\")\n",
    "        ConfigDF[\"FieldVal\"] = pd.read_pickle(\"Field_Config.pkl\")\n",
    "        ConfigDF.to_pickle(FieldName+\"_SavedConfig.pkl\")\n",
    "        time = dt.datetime.now()\n",
    "        return \"Last Save \" + str(time)\n",
    "\n",
    "# Field data callbacks\n",
    "@app.callback(Output({\"Group\":\"Field\",\"subGroup\":ALL,\"RetType\":\"value\",\"id\":ALL},'value'),\n",
    "              Output({\"Group\":\"Field\",\"subGroup\":ALL,\"RetType\":\"date\",\"id\":ALL},'date'),\n",
    "              Input({\"Group\":\"Field\",\"subGroup\":ALL,\"RetType\":\"value\",\"id\":ALL},'value'),\n",
    "              Input({\"Group\":\"Field\",\"subGroup\":ALL,\"RetType\":\"date\",\"id\":ALL},'date'),\n",
    "              prevent_initial_call=True)\n",
    "def setInputValue(values,date):\n",
    "    inputDF = uic.makeDataSeries(dash.callback_context.inputs_list[:-1],values)\n",
    "    for v in inputDF.index:\n",
    "        if inputDF[v] != None:\n",
    "            uic.updateConfig([v],[inputDF[v]],\"Field_Config.pkl\")\n",
    "    uic.updateConfig(['MinNDate'],[np.datetime64(date[0])],\"Field_Config.pkl\")    \n",
    "    return values,date\n",
    "\n",
    "#Validate config callback to activate \"Update NBalance\" button for running the model\n",
    "@app.callback(Output(\"RefreshButtonRow\",'children'),\n",
    "              Input({\"pos\":ALL,\"Group\":ALL,\"subGroup\":ALL,\"RetType\":\"value\",\"id\":ALL},'value'), \n",
    "              Input({\"pos\":ALL,\"Group\":ALL,\"subGroup\":ALL,\"RetType\":\"date\",\"id\":ALL},'date'),\n",
    "              Input({\"Group\":ALL,\"subGroup\":ALL,\"RetType\":\"value\",\"id\":ALL},'value'), \n",
    "              prevent_initial_call=True)\n",
    "def checkConfigAndEnableUpdate(values,dates,field):\n",
    "    return uic.validateConfigs()\n",
    "\n",
    "@app.callback(\n",
    "    Output('CropUptakeGraph','figure'),\n",
    "    Output('MineralNGraph','figure'),\n",
    "    Output('NInputsGraph','figure'),\n",
    "    Input('RefreshButton','n_clicks'), prevent_initial_call=True)\n",
    "def RefreshGraphs(n_clicks):\n",
    "    if n_clicks is None:\n",
    "        raise PreventUpdate\n",
    "    else:\n",
    "        PreviousConfig = pd.read_pickle(\"Previous_Config.pkl\")\n",
    "        CurrentConfig = pd.read_pickle(\"Current_Config.pkl\")\n",
    "        FollowingConfig = pd.read_pickle(\"Following_Config.pkl\")\n",
    "        FieldConfig = pd.read_pickle(\"Field_Config.pkl\")\n",
    "        start = PreviousConfig['HarvestDate'].astype(dt.datetime)-dt.timedelta(30)\n",
    "        end = FollowingConfig['HarvestDate'].astype(dt.datetime)+dt.timedelta(30)\n",
    "        Tt = CalculateMedianTt(PreviousConfig[\"EstablishDate\"].astype(dt.datetime),FollowingConfig[\"HarvestDate\"].astype(dt.datetime),metFiles[FieldConfig[\"Location\"]])\n",
    "        NBalance = MakeNBalanceFrame(Tt.index)\n",
    "        NBalance = CalculateCropOutputs(Tt,CropCoefficients,NBalance)\n",
    "        NBalance = CalculateSOMMineralisation(Tt,NBalance)\n",
    "        NBalance = CalculateResidueMineralisation(Tt,NBalance)\n",
    "        NBalance = CalculateSoilMineralN(NBalance)\n",
    "        trigger = 30\n",
    "        efficiency = 0.8\n",
    "        splits = FieldConfig['FertSplits']\n",
    "        NBalance = CalculateFertiliserApplications(NBalance,trigger,efficiency,splits)\n",
    "        Fertiliser = NBalance.FertiliserN.where(NBalance.FertiliserN>0,np.nan).dropna()\n",
    "        return CropNGraph(NBalance,start,end), SoilNGraph(NBalance,start,end,trigger,Fertiliser), NInputsGraph(NBalance,start,end)\n",
    "\n",
    "SavedConfigFiles = []\n",
    "mydir = 'C:\\GitHubRepos\\SVS'\n",
    "for File in os.listdir(mydir):\n",
    "    if File.endswith('.pkl'):\n",
    "        if ('_SavedConfig' in File):\n",
    "            SavedConfigFiles.append(File.replace('_SavedConfig.pkl',''))\n",
    "            \n",
    "app.layout = html.Div([\n",
    "                dbc.Row([\n",
    "                    dbc.Col([dbc.Row(dbc.Card(CropInputs('Previous_',EndUseCatagoriesDropdown,False,'Select Planting Date',''),\n",
    "                                             style={\"height\": \"100%\"}),\n",
    "                                     style={\"height\": \"45vh\"},\n",
    "                                     id={\"Group\":\"UI\",\"id\":\"PreviousConfigUI\"}),\n",
    "                             dbc.Row(dbc.Card(CropInputs('Current_',EndUseCatagoriesDropdown,True, '',''),\n",
    "                                             style={\"height\": \"100%\"}),\n",
    "                                     style={\"height\": \"45vh\"},\n",
    "                                     id={\"Group\":\"UI\",\"id\":\"CurrentConfigUI\"}),\n",
    "                             dbc.Row(dbc.Card(CropInputs('Following_',EndUseCatagoriesDropdown,True, '',''),\n",
    "                                             style={\"height\": \"100%\"}),\n",
    "                                     style={\"height\": \"45vh\"},\n",
    "                                     id={\"Group\":\"UI\",\"id\":\"FollowingConfigUI\"})\n",
    "                            ],\n",
    "                           width = 4),\n",
    "                    dbc.Col([dbc.Row(html.H1(\"Field Name\",\n",
    "                                             style=headingStyle,\n",
    "                                             id=\"FNtext\")),\n",
    "                             dbc.Row(dcc.Dropdown(options = SavedConfigFiles, placeholder='Select saved field',\n",
    "                                                  style = adStyle,\n",
    "                                                  id={\"Group\":\"Field\",\"subGroup\":\"Place\",\"RetType\":\"value\",\"id\":\"FieldNamePicker\"})),\n",
    "                             dbc.Row([],style=brStyle),\n",
    "                             dbc.Row([dbc.Col(width=6),\n",
    "                                      dbc.Col(html.Button(\"Load Config\", disabled=True, \n",
    "                                                 style = {'width':'100%','height':'110%'},\n",
    "                                                 id=\"LoadButton\"),\n",
    "                                              width = 6,\n",
    "                                              id={\"Group\":\"Field\",\"subGroup\":\"FileButton\",\"RetType\":\"children\",\"id\":\"LoadButton\"})\n",
    "                                      ]),\n",
    "                             dbc.Row(html.Div(\"Last Load\",\n",
    "                                              style=dict(display='flex', justifyContent='right'),\n",
    "                                              id=\"LLtext\")),\n",
    "                             dbc.Row(dbc.Col(dcc.Input(type=\"text\",debounce=True, placeholder='or type new field name',min=0,\n",
    "                                                        style = {'width':'100%'},\n",
    "                                              id={\"Group\":\"Field\",\"subGroup\":\"Place\",\"RetType\":\"value\",\"id\":\"FieldNameInput\"}),\n",
    "                                              width = 12)),\n",
    "                             dbc.Row([],style=brStyle),\n",
    "                             dbc.Row([dbc.Col(width=6),\n",
    "                                      dbc.Col(html.Button(\"Save Config\",disabled=True,\n",
    "                                                          style = {'width':'100%','height':'110%'},\n",
    "                                                          id=\"SaveButton\"),\n",
    "                                              width = 6,\n",
    "                                              id={\"Group\":\"Field\",\"subGroup\":\"FileButton\",\"RetType\":\"children\",\"id\":\"SaveButton\"})]),\n",
    "                             dbc.Row(html.Div(\"Last Save\",\n",
    "                                              style=dict(display='flex', justifyContent='right'), \n",
    "                                              id=\"LStext\")),\n",
    "                             dbc.Row([],style=brStyle),\n",
    "                             dbc.Row(html.H1(\"Field Location\",\n",
    "                                            style=headingStyle)),\n",
    "                             dbc.Row(dcc.Dropdown(options = MetDropDown, placeholder='Select closest location',\n",
    "                                                  id={\"Group\":\"Field\",\"subGroup\":\"Place\",\"RetType\":\"value\",\"id\":\"Location\"})),\n",
    "                             dbc.Row([],style=brStyle),\n",
    "                             dbc.Row(html.H1(\"Soil Test Values\",\n",
    "                                            style=headingStyle,)),\n",
    "                             dbc.Row([dbc.Col(html.Div(\"HWEON test\"\n",
    "                                              ,style=dict(display='flex', justifyContent='right'), \n",
    "                                              id=\"HWEONtext\"),\n",
    "                                              width = 6),\n",
    "                                     dbc.Col(dcc.Input(type=\"number\", placeholder='Enter value',min=0,\n",
    "                                                       style= {'width':'100%'},\n",
    "                                               id={\"Group\":\"Field\",\"subGroup\":\"Soil\",\"RetType\":\"value\",\"id\":\"HWEON\"}),\n",
    "                                            width = 5)]),\n",
    "                             dbc.Row([],style=brStyle),\n",
    "                             dbc.Row([dbc.Col(html.Div(\"Mineral N test date\", \n",
    "                                              style=dict(display='flex', justifyContent='right'),\n",
    "                                              id=\"MinDatetext\"),\n",
    "                                              width=6),\n",
    "                                     dbc.Col([dcc.DatePickerSingle(id={\"Group\":\"Field\",\"subGroup\":\"Soil\",\"RetType\":\"date\",\"id\":\"MinNDate\"}, min_date_allowed=dt.date(2020, 1, 1),\n",
    "                                                    max_date_allowed=dt.date(2025, 12, 31), initial_visible_month=dt.date(2021, 5, 15),\n",
    "                                                    placeholder = 'Select date',display_format='D-MMM-YYYY')],\n",
    "                                             style = {'width':'10%'},\n",
    "                                             id={\"Group\":\"Field\",\"subGroup\":\"Soil\",\"RetType\":\"children\",\"id\":\"MinNDate\"}, \n",
    "                                             width=5, align='center')]), \n",
    "                             dbc.Row([],style=brStyle),\n",
    "                             dbc.Row([dbc.Col(html.Div(\"Mineral N test\"\n",
    "                                              ,style=dict(display='flex', justifyContent='right', \n",
    "                                              id=\"MinNtext\")),\n",
    "                                             width = 6),\n",
    "                                     dbc.Col(dcc.Input(type=\"number\", placeholder='Enter value',min=0,\n",
    "                                                       style= {'width':'100%'},\n",
    "                                                       id={\"Group\":\"Field\",\"subGroup\":\"Soil\",\"RetType\":\"value\",\"id\":\"MinN\"}),\n",
    "                                            width = 5)]),\n",
    "                             dbc.Row([],style=brStyle),\n",
    "                             dbc.Row(html.H1(\"Fertiliser info\",\n",
    "                                            style=headingStyle,)),\n",
    "                             dbc.Row([dbc.Col(html.Div(\"Fertiliser Splits\"\n",
    "                                              ,style=dict(display='flex', justifyContent='right', \n",
    "                                              id=\"SplitsText\")),\n",
    "                                             width = 6),\n",
    "                                     dbc.Col(dcc.Input(type=\"number\", placeholder='Enter value',min=0,\n",
    "                                                       style= {'width':'100%'},\n",
    "                                                       id={\"Group\":\"Field\",\"subGroup\":\"Soil\",\"RetType\":\"value\",\"id\":\"FertSplits\"}),\n",
    "                                            width = 5)]),\n",
    "                             dbc.Row([],style=brStyle),\n",
    "                             dbc.Row([],style=brStyle),\n",
    "                             dbc.Row([dbc.Col(width =2),\n",
    "                                      dbc.Col([html.Button(\"Update NBalance\",disabled=True,\n",
    "                                                         style={'width':'100%','height':'150%',\"font-size\":\"150%\"},\n",
    "                                                         id=\"RefreshButton\")],\n",
    "                                            width = 10,\n",
    "                                            id=\"RefreshButtonRow\")],\n",
    "                                     style={\"width\":\"90%\"}, align='center', justify=\"centre\"),\n",
    "                             dbc.Row(html.H1(\"\"))\n",
    "                            ],\n",
    "                            width = 2, \n",
    "                            id={\"Group\":\"UI\",\"id\":\"FieldUI\"}),\n",
    "                    dbc.Col([dcc.Loading(id='CropGraphLoad',children = dbc.Row(dbc.Card(dcc.Graph(id='CropUptakeGraph')))),\n",
    "                             dcc.Loading(id='SoilGraphLoad',children = dbc.Row(dbc.Card(dcc.Graph(id='MineralNGraph')))),\n",
    "                             dcc.Loading(id='InputsGraphLoad',children = dbc.Row(dbc.Card(dcc.Graph(id='NInputsGraph'))))\n",
    "                            ],\n",
    "                           width = 6)\n",
    "                        ], \n",
    "                )\n",
    "                     ])\n",
    "# Run app and display result inline in the notebook\n",
    "app.run_server(mode='external')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57e27e95",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": []
  }
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