SLC_WSM_Performance_Analyzer_SIv2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn. metrics import accuracy_score\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "import joblib\n",
    "from pathlib import Path\n",
    "import pickle\n",
    "from datetime import date\n",
    "from patsy import dmatrices\n",
    "import statsmodels.api as sm\n",
    "from collections import defaultdict\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "#import calender\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "\n",
    "import jenkspy\n",
    "Writer = animation.writers['pillow']\n",
    "writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)\n",
    "af_to_104m3 = 0.123348"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DailyMean( maindf, month, yrs, days, monthnumber, inputyr):\n",
    "\n",
    "    Daylist = defaultdict(list)\n",
    "    DayFrame= defaultdict(list)\n",
    "    timecol = ['Year', 'Month' , 'Day']\n",
    "\n",
    "    for i in days:\n",
    "        Daylist[month+ str(i)]= []\n",
    "        DayFrame[month + str(i)] = pd.DataFrame(yrs, columns=['Year'])\n",
    "    \n",
    "\n",
    "    for i in yrs:\n",
    "        for j in days:\n",
    "            Daylist[month+str(j)].append(maindf.loc[str(i)+'-'+ monthnumber +'-'+str(j)]['SLCDPU_DC_Water_Use'])\n",
    "            DayFrame[month+str(j)]['Day']=j\n",
    "            DayFrame[month+str(j)]['Month'] = int(monthnumber)\n",
    "\n",
    "    for i in DayFrame:\n",
    "        DayFrame[i]['Daily_DC_Delivery'] = Daylist[i]\n",
    "        \n",
    "    for i in DayFrame:\n",
    "        DayFrame[i]['Hist_Mean_DC_Delivery']= np.mean(DayFrame[i]['Daily_DC_Delivery'])\n",
    "        del DayFrame[i]['Daily_DC_Delivery']\n",
    "        \n",
    "        ##put into year of choice\n",
    "        DayFrame[i]['Year']=inputyr\n",
    "        #create the date for input into figure DF\n",
    "        DayFrame[i].insert(loc=0, column='Date', value=pd.to_datetime(DayFrame[i][['Year', 'Month', 'Day']]))\n",
    "        DayFrame[i] = DayFrame[i].drop(columns = timecol)\n",
    "        DayFrame[i]=DayFrame[i].set_index('Date')\n",
    "        DayFrame[i]=DayFrame[i].iloc[0]\n",
    "        DayFrame[i] = pd.DataFrame(DayFrame[i]).T\n",
    "        \n",
    "        \n",
    "    return DayFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make a function to do all of the below for me so evaluate scenarios much quicker/easier\n",
    "\n",
    "\n",
    "def ForecastDataPrep(DFin, DF0cons, Outyear, DellDF, simulation):\n",
    "    af_to_104m3 = 0.123348\n",
    "    \n",
    "    #set the index to time\n",
    "    DFin=DFin.set_index('Time')\n",
    "    DellDF=DellDF.set_index('Time')\n",
    "    \n",
    "    #Use historical data, prior to WY2021\n",
    "    Histyrs=DF0cons.copy()\n",
    "    Histyrs=Histyrs.set_index('Time')\n",
    "    Histyrs = Histyrs[:\"2020-10-31\"]\n",
    "    \n",
    "    #add info to Dell reservoir deadpool\n",
    "    DellDF['Mtn_Deadpool'] = 800\n",
    "    DellDF['Little_Deadpool'] = 1000\n",
    "    \n",
    "    #Select time of importance 2021, 2022\n",
    "    DFin = DFin[DFin.index.year.isin([Outyear])].copy()\n",
    "    Dell = DellDF[DellDF.index.year.isin([Outyear])].copy()\n",
    "    \n",
    "    #remove WY months not in WY\n",
    "    #DFin = DFin[~DFin.index.month.isin([10,11,12])]\n",
    "    \n",
    "    #remove months that are not if interst in historical dataset\n",
    "    Histyrs = Histyrs[~Histyrs.index.month.isin([1,2,3,11,12])]\n",
    "    \n",
    "    #convert daily outputs (AF) to 104m3\n",
    "    for i in DFin:       \n",
    "        DFin[i]=DFin[i]*af_to_104m3\n",
    "        Histyrs[i]=Histyrs[i]*af_to_104m3\n",
    "    '''\n",
    "    Using the historical daily DC water usage, Find the mean daily DC usage and add it to the \n",
    "    Main DF to compare 2021 and 2022 water usage.\n",
    "    '''\n",
    "    yrs = np.arange(2001,2021,1)\n",
    "    Aprdays = np.arange(1,31,1)\n",
    "    Maydays = np.arange(1,32,1)\n",
    "    Jundays = np.arange(1,31,1)\n",
    "    Juldays = np.arange(1,32,1)\n",
    "    Augdays = np.arange(1,32,1)\n",
    "    Sepdays = np.arange(1,31,1)\n",
    "    Octdays = np.arange(1,32,1)\n",
    "\n",
    "    #Set up DF for mean daily DC water usage for WY 2021\n",
    "    Apr = DailyMean(Histyrs,'Apr', yrs, Aprdays, '04', Outyear)\n",
    "    May = DailyMean(Histyrs,'May', yrs, Maydays, '05', Outyear)\n",
    "    Jun = DailyMean(Histyrs,'Jun', yrs, Jundays, '06', Outyear)\n",
    "    Jul = DailyMean(Histyrs,'Jul', yrs, Juldays, '07', Outyear)\n",
    "    Aug = DailyMean(Histyrs,'Aug', yrs, Augdays, '08', Outyear)\n",
    "    Sep = DailyMean(Histyrs,'Sep', yrs, Sepdays, '09', Outyear)\n",
    "    Oct = DailyMean(Histyrs,'Oct', yrs, Octdays, '10', Outyear)\n",
    "\n",
    "    DC_Mean = pd.DataFrame()\n",
    "    for i in Apr:\n",
    "        DC_Mean = DC_Mean.append(Apr[i])\n",
    "    for i in May:\n",
    "        DC_Mean = DC_Mean.append(May[i])\n",
    "    for i in Jun:\n",
    "        DC_Mean = DC_Mean.append(Jun[i])\n",
    "    for i in Jul:\n",
    "        DC_Mean = DC_Mean.append(Jul[i])\n",
    "    for i in Aug:\n",
    "        DC_Mean = DC_Mean.append(Aug[i])\n",
    "    for i in Sep:\n",
    "        DC_Mean = DC_Mean.append(Sep[i])\n",
    "    for i in Oct:\n",
    "        DC_Mean = DC_Mean.append(Oct[i])\n",
    "    \n",
    "    #create an empty column for mean delivery\n",
    "    DFin['Hist_Mean_DC_Delivery'] = 0\n",
    "\n",
    "    #Update the Output2021 with historical period daily DC usage\n",
    "    DFin.update(DC_Mean)\n",
    "    \n",
    "    DFin['Extra_DC'] = DFin['SLCDPU_DC_Water_Use']-DFin['Hist_Mean_DC_Delivery']\n",
    "\n",
    "    #Make negative values zero for simplicity\n",
    "    DFin.loc[DFin['Extra_DC'] < 0, ['Extra_DC']] = 0\n",
    "    \n",
    "    #del DFin['Unnamed: 6']\n",
    "    #create a DF for monthly sum of Outputs\n",
    "    DFinMonthly =DFin.copy()\n",
    "    HistyrsMonthly = Histyrs.copy()\n",
    "\n",
    "    #Take the monthly sum\n",
    "    DFinMonthly = DFinMonthly.resample('M').sum()\n",
    "    HistyrsMonthly = HistyrsMonthly.resample('M').sum()\n",
    "\n",
    "\n",
    "    #convert this one to acre feet, no need it all units are m3\n",
    "   # for i in DFinMonthly:\n",
    "    #    DFinMonthly[i] = DFinMonthly[i]*(1/0.2713280731353764)\n",
    "        \n",
    "    #for i in HistyrsMonthly:\n",
    "     #   HistyrsMonthly[i] = HistyrsMonthly[i]*(1/0.2713280731353764)\n",
    "\n",
    "    \n",
    "    return DFin, DFinMonthly, Dell\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Sup_Dem_Figure2(DayDF, MonthDF, Dell, yr, IndoorCons, OutdoorCons, FigPath, simulation, modeltype):\n",
    "    \n",
    "    #need to convert MGD to m3x10^4\n",
    "    #DayDF = DayDF*0.454609\n",
    "   \n",
    "    #need to convert Acre-Feet to 10^4m3\n",
    "    #MonthDF = MonthDF*0.123348\n",
    "    \n",
    "    MonthDF['Month'] = MonthDF.index\n",
    "    MonthDF['Month'] = pd.to_datetime(MonthDF['Month'], format='%m').dt.month_name().str.slice(stop=3)\n",
    "    MonthDF.index = MonthDF['Month']\n",
    "    del MonthDF['Month']\n",
    "    \n",
    "    simulation.split()[0]\n",
    "    simulation = simulation.split()\n",
    "    if simulation[0] == 'Surplus':\n",
    "        replacement = 'Wet'\n",
    "    if simulation[0] == 'Drought':\n",
    "        replacement = 'Dry'\n",
    "    if simulation[0] == 'Average':\n",
    "        replacement = 'Average'\n",
    "\n",
    "    simulation[0] = replacement\n",
    "    str1 = \" \" \n",
    "\n",
    "    simulation = str1.join(simulation)\n",
    "    \n",
    "    labelsize = 15\n",
    "    linewidth = 2\n",
    " \n",
    "    #Reduce width to increase space between bars\n",
    "    width = 1\n",
    "    widthM = .95\n",
    "\n",
    "    fig, ax = plt.subplots(2,1, constrained_layout=True)\n",
    "    fig.set_size_inches(7,9)\n",
    "    DayDF['ExtraDC'] = DayDF.SLCDPU_DC_Water_Use-DayDF.Hist_Mean_DC_Delivery\n",
    "    \n",
    "    #Daily Figure\n",
    "    ax[0].bar(DayDF.index, DayDF.SLCDPU_Prod_Demands, width=width, \n",
    "        color='blue', label='Sim: '+ simulation)\n",
    "    ax[0].bar(DayDF.index, DayDF.SLCDPU_DC_Water_Use, width=width, \n",
    "        color='r', label=  'Modeled Deer Creek Request')\n",
    "    ax[0].plot(DayDF.index, DayDF.Hist_Mean_DC_Delivery, linewidth = linewidth,\n",
    "        color='purple', label='Hist. Mean Deer Creek Request')\n",
    "    ax[0].set_ylim(0,105)\n",
    "  #  ax[0].set_xlim(737772.25+60, 738173.75-60)\n",
    "    ax[0].annotate('A.', xy=(DayDF.index[308], 110), size = labelsize+2)\n",
    "    ax[0].legend(loc = 'upper left', fontsize = labelsize)\n",
    "    ax[0].set_ylabel('Daily System Demand ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[0].tick_params(axis = 'both', labelsize = labelsize-4 )\n",
    "   \n",
    "\n",
    "    #Monthly Figure\n",
    "   # df['Month'] = pd.to_datetime(df['Month'], format='%m').dt.month_name().str.slice(stop=3)\n",
    "\n",
    "    ax[1].bar(MonthDF.index, MonthDF.SLCDPU_Prod_Demands, width=widthM, \n",
    "        color='blue', label=modeltype)\n",
    "    ax[1].bar(MonthDF.index, MonthDF.SLCDPU_DC_Water_Use, width=widthM, \n",
    "        color='red', label='Deer Creek Reservoir Usage')\n",
    "    ax[1].hlines(MonthDF.Hist_Mean_DC_Delivery.iloc[4], 3.5, 4.5, linewidth = linewidth, linestyle='-', color='purple')\n",
    "    ax[1].hlines(MonthDF.Hist_Mean_DC_Delivery.iloc[5], 4.5, 5.5, linewidth = linewidth, linestyle='-', color='purple')\n",
    "    ax[1].hlines(MonthDF.Hist_Mean_DC_Delivery.iloc[6], 5.5, 6.5, linewidth = linewidth, linestyle='-', color='purple')\n",
    "    ax[1].hlines(MonthDF.Hist_Mean_DC_Delivery.iloc[7], 6.5, 7.5, linewidth = linewidth, linestyle='-', color='purple')\n",
    "    ax[1].hlines(MonthDF.Hist_Mean_DC_Delivery.iloc[8], 7.5, 8.5, linewidth = linewidth, linestyle='-', color='purple')\n",
    "    ax[1].hlines(MonthDF.Hist_Mean_DC_Delivery.iloc[9], 8.5, 9.5, linewidth = linewidth, linestyle='-', color='purple')\n",
    "    ax[1].set_ylim(0,2400)\n",
    "  #  ax[1].set_xlim(1.5, 10.5)\n",
    "   # x_min, x_max = ax[1].get_xlim()\n",
    "    ax[1].annotate('B.', xy=(10.1, 2800),size = labelsize+2)\n",
    "    ax[1].set_ylabel('Monthly System Demands ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[1].set_xlabel('Time', size = labelsize)\n",
    "    ax[1].tick_params(axis = 'both', labelsize = labelsize-2)\n",
    "    #Save Figure\n",
    "    #fig.savefig(FigPath+str(yr)+simulation+'.pdf')\n",
    "    fig.savefig(FigPath+str(yr)+simulation+'.png', dpi = 300)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Sup_Dem_Figure_Err_CSD(Day, Month, DayL, MonthL, DayH, MonthH,DayO, MonthO,\n",
    "                       yr, IndoorCons, OutdoorCons, FigPath, simulation, modeltype):\n",
    "    \n",
    "    #need to convert MGD to m3x10^4\n",
    "    #DayDF = DayDF*0.454609\n",
    "   \n",
    "    #need to convert Acre-Feet to 10^4m3\n",
    "    #MonthDF = MonthDF*0.123348\n",
    "    \n",
    "    #Copy dfs \n",
    "    DayDF = Day.copy()\n",
    "    MonthDF = Month.copy()\n",
    "    \n",
    "    DayDFL = DayL.copy()\n",
    "    MonthDFL = MonthL.copy()\n",
    "    \n",
    "    DayDFH = DayH.copy()\n",
    "    MonthDFH = MonthH.copy()\n",
    "    \n",
    "    DayDFO = DayO.copy()\n",
    "    MonthDFO = MonthO.copy()\n",
    "    \n",
    "    #Set up a daily DF for DC water use\n",
    "    PredD = pd.DataFrame()\n",
    "    PredD['Low'] = DayDFL['SLCDPU_DC_Water_Use']\n",
    "    PredD['Mean'] = DayDF['SLCDPU_DC_Water_Use']\n",
    "    PredD['High'] = DayDFH['SLCDPU_DC_Water_Use']\n",
    "    PredD['Obs'] = DayDFO['SLCDPU_DC_Water_Use']\n",
    "    \n",
    "    #Define Error bars for visualization\n",
    "    PredD['UErr'] = PredD['High'] - PredD['Mean']\n",
    "    PredD['LErr'] = PredD['Mean'] - PredD['Low']\n",
    "\n",
    "    asymmetric_errorD = [PredD['LErr'], PredD['UErr'] ]\n",
    "    \n",
    "    #Set up a Monthly DF for DC water use\n",
    "    PredM = pd.DataFrame()\n",
    "    PredM['Low'] = MonthDFL['SLCDPU_DC_Water_Use']\n",
    "    PredM['Mean'] = MonthDF['SLCDPU_DC_Water_Use']\n",
    "    PredM['High'] = MonthDFH['SLCDPU_DC_Water_Use']\n",
    "    PredM['Obs'] = MonthDFO['SLCDPU_DC_Water_Use']\n",
    "    \n",
    "    #Define Error bars for visualization\n",
    "    PredM['UErr'] = PredM['High'] - PredM['Mean']\n",
    "    PredM['LErr'] = PredM['Mean'] - PredM['Low']\n",
    "    PredM['LErr'] = [0 if x < 0 else x for x in PredM['LErr']]\n",
    "    asymmetric_errorM = [PredM['LErr'], PredM['UErr'] ]\n",
    "    \n",
    "     #Set up a Monthly DF for Demand water use\n",
    "    Pred_DM = pd.DataFrame()\n",
    "    Pred_DM['Low'] = MonthDFL['SLCDPU_Prod_Demands']\n",
    "    Pred_DM['Mean'] = MonthDF['SLCDPU_Prod_Demands']\n",
    "    Pred_DM['High'] = MonthDFH['SLCDPU_Prod_Demands']\n",
    "    Pred_DM['Obs'] = MonthDFO['SLCDPU_Prod_Demands']\n",
    "    \n",
    "\n",
    "    #Define Error bars for visualization\n",
    "    Pred_DM['UErr'] = Pred_DM['High'] - Pred_DM['Mean']\n",
    "    Pred_DM['LErr'] = Pred_DM['Mean'] - Pred_DM['Low']\n",
    "\n",
    "    asymmetric_errorDemM = [Pred_DM['LErr'], Pred_DM['UErr'] ]\n",
    "\n",
    "    \n",
    "    MonthDF['Month'] = MonthDF.index\n",
    "    MonthDF['Month'] = pd.to_datetime(MonthDF['Month'], format='%m').dt.month_name().str.slice(stop=3)\n",
    "    MonthDF.index = MonthDF['Month']\n",
    "    del MonthDF['Month']\n",
    "    \n",
    "   \n",
    "    MonthDFO.index = MonthDF.index\n",
    "    \n",
    "    \n",
    "    \n",
    "    simulation.split()[0]\n",
    "    simulation = simulation.split()\n",
    "    if simulation[0] == 'Surplus':\n",
    "        replacement = 'Wet'\n",
    "    if simulation[0] == 'Drought':\n",
    "        replacement = 'Dry'\n",
    "    if simulation[0] == 'Average':\n",
    "        replacement = 'Average'\n",
    "\n",
    "    simulation[0] = replacement\n",
    "    str1 = \" \" \n",
    "\n",
    "    simulation = str1.join(simulation)\n",
    "    \n",
    "    labelsize = 12\n",
    "    linewidth = 2\n",
    " \n",
    "    #Reduce width to increase space between bars\n",
    "    width = 1\n",
    "    widthM = .95\n",
    "\n",
    "    fig, ax = plt.subplots(3,2, constrained_layout=True, gridspec_kw={'width_ratios': [3, 1]})\n",
    "    fig.set_size_inches(11,7)\n",
    "    DayDF['ExtraDC'] = DayDF.SLCDPU_DC_Water_Use-DayDF.Hist_Mean_DC_Delivery\n",
    "    \n",
    "    #Daily Figure\n",
    "    #Demand Variability\n",
    "    ax[0,0].plot(DayDF.index, DayDF.SLCDPU_Prod_Demands, linewidth = linewidth, \n",
    "        color='blue', label='Sim: '+ simulation)\n",
    "    ax[0,0].fill_between(DayDF.index, DayDFL.SLCDPU_Prod_Demands, DayDFH.SLCDPU_Prod_Demands, \n",
    "        color='steelblue')\n",
    "    ax[0,0].plot(DayDFO.index, DayDFO.SLCDPU_Prod_Demands, linewidth = linewidth, \n",
    "        color='slategrey', label = 'Observed Demands')\n",
    "    #Deer Creek Variability  \n",
    "    ax[0,0].plot(DayDF.index, DayDF.SLCDPU_DC_Water_Use, linewidth = linewidth,\n",
    "        color='r', label=  'Modeled Deer Creek Request')\n",
    "    ax[0,0].fill_between(DayDF.index, DayDFL.SLCDPU_DC_Water_Use, DayDFH.SLCDPU_DC_Water_Use, \n",
    "        color='coral')\n",
    "    #Observed Deer Creek use\n",
    "    ax[0,0].plot(DayDFO.index, DayDFO.SLCDPU_DC_Water_Use, color='maroon',\n",
    "                    label=  'Observed Deer Creek Request', linewidth = linewidth-1)\n",
    "    \n",
    "    ax[0,0].plot(DayDF.index, DayDF.Hist_Mean_DC_Delivery, linewidth = linewidth,\n",
    "        color='purple', label='Hist. Mean Deer Creek Request')\n",
    "    ax[0,0].set_ylim(0,105)\n",
    "  #  ax[0].set_xlim(737772.25+60, 738173.75-60)\n",
    "    ax[0,0].annotate('A.', xy=(DayDF.index[15], 92), size = labelsize+2)\n",
    "    ax[0,0].set_ylabel('Daily ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[0,0].tick_params(axis = 'both', labelsize = labelsize-4 )\n",
    "    ax[0,0].margins(0)\n",
    "    \n",
    "    \n",
    "    #Observed vs predicted Deer Dreek Daily use\n",
    "    ax[0,1].errorbar(PredD.Obs, PredD.Mean, yerr = asymmetric_errorD, fmt='.k', ecolor = 'coral', mec = 'red') \n",
    "    ax[0,1].plot([0, 30], [0, 30], ls=\"--\", c=\".3\")\n",
    "    ax[0,1].set_ylabel('Predicted ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[0,1].set_xticks(np.arange(0, 31, 10))\n",
    "    ax[0,1].set_yticks(np.arange(0, 45, 10))\n",
    "    ax[0,1].set_ylim(0,45)\n",
    "    ax[0,1].annotate('B.', xy=(1, 40),size = labelsize+2)\n",
    "    ax[0,1].margins(0)\n",
    "    \n",
    "   \n",
    "\n",
    "    #Monthly Demands\n",
    "    ax[1,0].bar(MonthDF.index, MonthDF.SLCDPU_Prod_Demands,align = 'edge', width=-widthM/2, \n",
    "        color='blue', label=modeltype, yerr = asymmetric_errorDemM, capsize = 4)\n",
    "    ax[1,0].bar(MonthDFO.index, MonthDFO.SLCDPU_Prod_Demands, align = 'edge', width=widthM/2, \n",
    "        color='slategrey', label='Observed Demands')\n",
    "    ax[1,0].set_ylabel('Monthly ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[1,0].set_xlabel('Time', size = labelsize)\n",
    "    ax[1,0].tick_params(axis = 'both', labelsize = labelsize-2)\n",
    "    ax[1,0].set_ylim(0,2500)\n",
    "    ax[1,0].annotate('C.', xy=(MonthDF.index[0], 2200),size = labelsize+2)\n",
    "    ax[1,0].margins(0)\n",
    "    \n",
    "    \n",
    "    #Observed vs predicted Monthly Demand use\n",
    "    ax[1,1].errorbar(Pred_DM.Obs, Pred_DM.Mean, yerr = asymmetric_errorDemM, fmt='.k', ecolor = 'steelblue', mec = 'blue') \n",
    "    ax[1,1].plot([0, 2500], [0, 2500], ls=\"--\", c=\".3\")\n",
    "    ax[1,1].set_ylabel('Predicted ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[1,1].set_xticks(np.arange(0, 2501, 500))\n",
    "    ax[1,1].set_yticks(np.arange(0, 2501, 500))\n",
    "    ax[1,1].set_ylim(0,2500)\n",
    "    ax[1,1].annotate('D.', xy=(40, 2200),size = labelsize+2)\n",
    "    ax[1,1].margins(0)\n",
    "   \n",
    "    \n",
    "    #Monthly DC\n",
    "    ax[2,0].bar(MonthDF.index, MonthDF.SLCDPU_DC_Water_Use,align = 'edge', width=-widthM/2, \n",
    "        color='red', label=modeltype, yerr = asymmetric_errorM, capsize = 4)\n",
    "    ax[2,0].bar(MonthDFO.index, MonthDFO.SLCDPU_DC_Water_Use, align = 'edge', width=widthM/2, \n",
    "        color='maroon', label='Observed Demands')\n",
    "    ax[2,0].annotate('B.', xy=(10.1, 2800),size = labelsize+2)\n",
    "    ax[2,0].set_ylabel('Monthly ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[2,0].set_xlabel('Time', size = labelsize)\n",
    "    ax[2,0].tick_params(axis = 'both', labelsize = labelsize-2)\n",
    "    ax[2,0].set_ylim(0,1300)\n",
    "    ax[2,0].annotate('E.', xy=(MonthDF.index[0], 1150),size = labelsize+2)\n",
    "    ax[2,0].margins(0)\n",
    "    \n",
    "    \n",
    "     #Observed vs predicted Deer Creek use\n",
    "    ax[2,1].errorbar(PredM.Obs, PredM.Mean, yerr = asymmetric_errorM, fmt='.k', ecolor = 'coral', mec = 'red') \n",
    "    ax[2,1].plot([0, 1300], [0, 1300], ls=\"--\", c=\".3\")\n",
    "    ax[2,1].set_ylabel('Predicted ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[2,1].set_xlabel('Observed ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[2,1].set_xticks(np.arange(0, 1301, 250))\n",
    "    ax[2,1].set_yticks(np.arange(0, 1301, 250))\n",
    "    ax[2,1].set_ylim(0,1300)\n",
    "    ax[2,1].annotate('F.', xy=(20, 1150),size = labelsize+2)\n",
    "    ax[2,1].margins(0)\n",
    "    \n",
    "    #Save Figure\n",
    "    #fig.savefig(FigPath+str(yr)+simulation+'.pdf')\n",
    "    plt.subplots_adjust(right=1)\n",
    "    handles, labels = ax[0,0].get_legend_handles_labels()\n",
    "    lgd = ax[0,0].legend(handles, labels, loc = 'upper left', fontsize = labelsize, ncol = 2, bbox_to_anchor=(.1, 1.7))\n",
    "    text = ax[0,0].text(-0.0,1.05, \" \", transform=ax[0,0].transAxes)\n",
    "    \n",
    "    fig.savefig(FigPath+str(yr)+simulation+'.pdf',bbox_extra_artists=(lgd,text), bbox_inches='tight', dpi = 300)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Sup_Dem_Figure_Err_Trad(Day, Month, DayO, MonthO, yr, IndoorCons, OutdoorCons, FigPath, simulation, modeltype):\n",
    "    \n",
    "    #need to convert MGD to m3x10^4\n",
    "    #DayDF = DayDF*0.454609\n",
    "   \n",
    "    #need to convert Acre-Feet to 10^4m3\n",
    "    #MonthDF = MonthDF*0.123348\n",
    "    \n",
    "    #Copy dfs \n",
    "    DayDF = Day.copy()\n",
    "    MonthDF = Month.copy()\n",
    "    \n",
    "    DayDFO = DayO.copy()\n",
    "    MonthDFO = MonthO.copy()\n",
    "    \n",
    "    #Set up a daily DF for DC water use\n",
    "    PredD = pd.DataFrame()\n",
    "    PredD['Low'] = DayDF['SLCDPU_DC_Water_Use']*.9\n",
    "    PredD['Mean'] = DayDF['SLCDPU_DC_Water_Use']\n",
    "    PredD['High'] = DayDF['SLCDPU_DC_Water_Use']*1.1\n",
    "    PredD['Obs'] = DayDFO['SLCDPU_DC_Water_Use']\n",
    "    PredD.index = DayDF.index\n",
    "    \n",
    "    PredDD = pd.DataFrame()\n",
    "    PredDD['Low'] = DayDF['SLCDPU_Prod_Demands']*.9\n",
    "    PredDD['Mean'] = DayDF['SLCDPU_Prod_Demands']\n",
    "    PredDD['High'] = DayDF['SLCDPU_Prod_Demands']*1.1\n",
    "    PredDD['Obs'] = DayDF['SLCDPU_Prod_Demands']\n",
    "    PredDD.index = DayDF.index\n",
    "    \n",
    "    #Define Error bars for visualization\n",
    "    PredD['UErr'] = PredD['High'] - PredD['Mean']\n",
    "    PredD['LErr'] = PredD['Mean'] - PredD['Low']\n",
    "\n",
    "    asymmetric_errorD = [PredD['LErr'], PredD['UErr'] ]\n",
    "    \n",
    "    #Set up a Monthly DF for DC water use\n",
    "    PredM = pd.DataFrame()\n",
    "    PredM['Low'] = MonthDF['SLCDPU_DC_Water_Use']*.9\n",
    "    PredM['Mean'] = MonthDF['SLCDPU_DC_Water_Use']\n",
    "    PredM['High'] = MonthDF['SLCDPU_DC_Water_Use']*1.1\n",
    "    PredM['Obs'] = MonthDFO['SLCDPU_DC_Water_Use']\n",
    "    \n",
    "    #Define Error bars for visualization\n",
    "    PredM['UErr'] = PredM['High'] - PredM['Mean']\n",
    "    PredM['LErr'] = PredM['Mean'] - PredM['Low']\n",
    "    PredM['LErr'] = [0 if x < 0 else x for x in PredM['LErr']]\n",
    "    asymmetric_errorM = [PredM['LErr'], PredM['UErr'] ]\n",
    "    \n",
    "     #Set up a Monthly DF for Demand water use\n",
    "    Pred_DM = pd.DataFrame()\n",
    "    Pred_DM['Low'] = MonthDF['SLCDPU_Prod_Demands']*.9\n",
    "    Pred_DM['Mean'] = MonthDF['SLCDPU_Prod_Demands']\n",
    "    Pred_DM['High'] = MonthDF['SLCDPU_Prod_Demands']*1.1\n",
    "    Pred_DM['Obs'] = MonthDFO['SLCDPU_Prod_Demands']\n",
    "    \n",
    "\n",
    "    #Define Error bars for visualization\n",
    "    Pred_DM['UErr'] = Pred_DM['High'] - Pred_DM['Mean']\n",
    "    Pred_DM['LErr'] = Pred_DM['Mean'] - Pred_DM['Low']\n",
    "\n",
    "    asymmetric_errorDemM = [Pred_DM['LErr'], Pred_DM['UErr'] ]\n",
    "\n",
    "    \n",
    "    MonthDF['Month'] = MonthDF.index\n",
    "    MonthDF['Month'] = pd.to_datetime(MonthDF['Month'], format='%m').dt.month_name().str.slice(stop=3)\n",
    "    MonthDF.index = MonthDF['Month']\n",
    "    del MonthDF['Month']\n",
    "    \n",
    "   \n",
    "    MonthDFO.index = MonthDF.index\n",
    "    \n",
    "    \n",
    "    \n",
    "    simulation.split()[0]\n",
    "    simulation = simulation.split()\n",
    "    if simulation[0] == 'Surplus':\n",
    "        replacement = 'Wet'\n",
    "    if simulation[0] == 'Drought':\n",
    "        replacement = 'Dry'\n",
    "    if simulation[0] == 'Average':\n",
    "        replacement = 'Average'\n",
    "\n",
    "    simulation[0] = replacement\n",
    "    str1 = \" \" \n",
    "\n",
    "    simulation = str1.join(simulation)\n",
    "    \n",
    "    labelsize = 12\n",
    "    linewidth = 2\n",
    " \n",
    "    #Reduce width to increase space between bars\n",
    "    width = 1\n",
    "    widthM = .95\n",
    "\n",
    "    fig, ax = plt.subplots(3,2, constrained_layout=True, gridspec_kw={'width_ratios': [3, 1]})\n",
    "    fig.set_size_inches(11,7)\n",
    "    DayDF['ExtraDC'] = DayDF.SLCDPU_DC_Water_Use-DayDF.Hist_Mean_DC_Delivery\n",
    "    \n",
    "    #Daily Figure\n",
    "    #Demand Variability\n",
    "    ax[0,0].plot(DayDF.index, DayDF.SLCDPU_Prod_Demands, linewidth = linewidth, \n",
    "        color='blue', label='Sim: '+ simulation)\n",
    "    ax[0,0].fill_between(PredDD.index, PredDD.Low, PredDD.High, \n",
    "        color='steelblue')\n",
    "    ax[0,0].plot(DayDFO.index, DayDFO.SLCDPU_Prod_Demands, linewidth = linewidth, \n",
    "        color='slategrey', label = 'Observed Demands')\n",
    "    #Deer Creek Variability  \n",
    "    ax[0,0].plot(DayDF.index, DayDF.SLCDPU_DC_Water_Use, linewidth = linewidth,\n",
    "        color='r', label=  'Modeled Deer Creek Request')\n",
    "    ax[0,0].fill_between(PredD.index, PredD.Low, PredD.High, \n",
    "        color='coral')\n",
    "    #Observed Deer Creek use\n",
    "    ax[0,0].plot(DayDFO.index, DayDFO.SLCDPU_DC_Water_Use, color='maroon',\n",
    "                    label=  'Observed Deer Creek Request', linewidth = linewidth-1)\n",
    "    \n",
    "    ax[0,0].plot(DayDF.index, DayDF.Hist_Mean_DC_Delivery, linewidth = linewidth,\n",
    "        color='purple', label='Hist. Mean Deer Creek Request')\n",
    "    ax[0,0].set_ylim(0,105)\n",
    "  #  ax[0].set_xlim(737772.25+60, 738173.75-60)\n",
    "    ax[0,0].annotate('A.', xy=(DayDF.index[15], 92), size = labelsize+2)\n",
    "    ax[0,0].set_ylabel('Daily ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[0,0].tick_params(axis = 'both', labelsize = labelsize-4 )\n",
    "    ax[0,0].margins(0)\n",
    "    \n",
    "    \n",
    "    #Observed vs predicted Deer Dreek Daily use\n",
    "    ax[0,1].errorbar(PredD.Obs, PredD.Mean, yerr = asymmetric_errorD, fmt='.k', ecolor = 'coral', mec = 'red') \n",
    "    ax[0,1].plot([0, 30], [0, 30], ls=\"--\", c=\".3\")\n",
    "    ax[0,1].set_ylabel('Predicted ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[0,1].set_xticks(np.arange(0, 31, 10))\n",
    "    ax[0,1].set_yticks(np.arange(0, 45, 10))\n",
    "    ax[0,1].set_ylim(0,45)\n",
    "    ax[0,1].annotate('B.', xy=(1, 40),size = labelsize+2)\n",
    "    ax[0,1].margins(0)\n",
    "     \n",
    "    \n",
    "   \n",
    "\n",
    "    #Monthly Demands\n",
    "    ax[1,0].bar(MonthDF.index, MonthDF.SLCDPU_Prod_Demands,align = 'edge', width=-widthM/2, \n",
    "        color='blue', label=modeltype, yerr = asymmetric_errorDemM, capsize = 4)\n",
    "    ax[1,0].bar(MonthDFO.index, MonthDFO.SLCDPU_Prod_Demands, align = 'edge', width=widthM/2, \n",
    "        color='slategrey', label='Observed Demands')\n",
    "    ax[1,0].set_ylabel('Monthly ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[1,0].set_xlabel('Time', size = labelsize)\n",
    "    ax[1,0].tick_params(axis = 'both', labelsize = labelsize-2)\n",
    "    ax[1,0].set_ylim(0,2500)\n",
    "    ax[1,0].annotate('C.', xy=(MonthDF.index[0], 2000),size = labelsize+2)\n",
    "    ax[1,0].margins(0)\n",
    "    \n",
    "    \n",
    "    #Observed vs predicted Monthly Demand use\n",
    "    ax[1,1].errorbar(Pred_DM.Obs, Pred_DM.Mean, yerr = asymmetric_errorDemM, fmt='.k', ecolor = 'steelblue', mec = 'blue') \n",
    "    ax[1,1].plot([0, 2500], [0, 2500], ls=\"--\", c=\".3\")\n",
    "    ax[1,1].set_ylabel('Predicted ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[1,1].set_xticks(np.arange(0, 2501, 500))\n",
    "    ax[1,1].set_yticks(np.arange(0, 2501, 500))\n",
    "    ax[1,1].set_ylim(0,2500)\n",
    "    ax[1,1].annotate('D.', xy=(40, 2200),size = labelsize+2)\n",
    "    ax[1,1].margins(0)\n",
    "   \n",
    "    \n",
    "    #Monthly DC\n",
    "    ax[2,0].bar(MonthDF.index, MonthDF.SLCDPU_DC_Water_Use,align = 'edge', width=-widthM/2, \n",
    "        color='red', label=modeltype, yerr = asymmetric_errorM, capsize = 4)\n",
    "    ax[2,0].bar(MonthDFO.index, MonthDFO.SLCDPU_DC_Water_Use, align = 'edge', width=widthM/2, \n",
    "        color='maroon', label='Observed Demands')\n",
    "    ax[2,0].annotate('B.', xy=(10.1, 2800),size = labelsize+2)\n",
    "    ax[2,0].set_ylabel('Monthly ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[2,0].set_xlabel('Time', size = labelsize)\n",
    "    ax[2,0].tick_params(axis = 'both', labelsize = labelsize-2)\n",
    "    ax[2,0].set_ylim(0,1300)\n",
    "    ax[2,0].annotate('E.', xy=(MonthDF.index[0], 1150),size = labelsize+2)\n",
    "    ax[2,0].margins(0)\n",
    "\n",
    "    \n",
    "    \n",
    "     #Observed vs predicted Deer Creek use\n",
    "    ax[2,1].errorbar(PredM.Obs, PredM.Mean, yerr = asymmetric_errorM, fmt='.k', ecolor = 'coral', mec = 'red') \n",
    "    ax[2,1].plot([0, 1300], [0, 1300], ls=\"--\", c=\".3\")\n",
    "    ax[2,1].set_ylabel('Predicted ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[2,1].set_xlabel('Observed ($\\mathregular{x10^{4}m^{3}}$)', size = labelsize)\n",
    "    ax[2,1].set_xticks(np.arange(0, 1301, 250))\n",
    "    ax[2,1].set_yticks(np.arange(0, 1301, 250))\n",
    "    ax[2,1].set_ylim(0,1300)\n",
    "    ax[2,1].margins(0)\n",
    "    ax[2,1].annotate('F.', xy=(20, 1150),size = labelsize+2)\n",
    "    \n",
    "    #Save Figure\n",
    "    #fig.savefig(FigPath+str(yr)+simulation+'.pdf')\n",
    "    plt.subplots_adjust(right=1)\n",
    "    handles, labels = ax[0,0].get_legend_handles_labels()\n",
    "    lgd = ax[0,0].legend(handles, labels, loc = 'upper left', fontsize = labelsize, ncol = 2, bbox_to_anchor=(.1, 1.7))\n",
    "    text = ax[0,0].text(-0.0,1.05, \" \", transform=ax[0,0].transAxes)\n",
    "    \n",
    "    fig.savefig(FigPath+str(yr)+simulation+'.png',bbox_extra_artists=(lgd,text), bbox_inches='tight', dpi = 300)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we need to calculate the RRV metrics\n",
    "def RRV(df2021, clim, dem, maxseverity, timestep, y):\n",
    "    df = df2021.copy()\n",
    "    df['Clim'] = clim\n",
    "    df['Demand'] = dem\n",
    "\n",
    "\n",
    "    #period of interest is from April to October\n",
    "    if timestep == 'Daily':\n",
    "        df = df.loc[str(y)+'-04-01':str(y)+'-10-31']\n",
    "        \n",
    "    if timestep =='Monthly':\n",
    "       # df = df.loc['Apr':'Oct']\n",
    "        df = df.loc[str(y)+'-04-01':str(y)+'-10-31']\n",
    "    \n",
    "    #length of study period\n",
    "    T = len(df)\n",
    "    #make sure ExtraDC is never less than 0\n",
    "    for i in np.arange(1,T,1):\n",
    "        if df['Extra_DC'].iloc[i] < 0:\n",
    "                df['Extra_DC'].iloc[i] = 0\n",
    "\n",
    "    '''\n",
    "    Reliability\n",
    "    Reliability = sum of timesteps Zt/T\n",
    "    Zt is 0 if DC water exceeds (U) the historical average and 1 if it does not (S)\n",
    "    '''\n",
    "\n",
    "    df['Zt'] = 1\n",
    "    for i in np.arange(0,T,1):\n",
    "        if df['Extra_DC'].iloc[i] > 1:\n",
    "                df['Zt'].iloc[i] = 0\n",
    "        if df['Extra_DC'].iloc[i] < 1:\n",
    "                df['Extra_DC'].iloc[i] = 0\n",
    "\n",
    "    Rel = df['Zt'].sum()/T\n",
    "\n",
    "    '''\n",
    "    Resilience\n",
    "    Resilience = sum of timesteps Wt/(T-sum(Zt))\n",
    "    Wt is 1 if Xt is U and Xt+1 is S\n",
    "    '''\n",
    "\n",
    "    df['Wt']=0\n",
    "    for i in np.arange(1,T,1):\n",
    "        if df['Zt'].iloc[i-1] == 0 and df['Zt'].iloc[i] == 1:\n",
    "            df['Wt'].iloc[i] = 1\n",
    "    #To get in days do 1/Res        \n",
    "    Res = 1/((1+df['Wt'].iloc[0:T-1].sum())/(T+1-df['Zt'].sum()))\n",
    "\n",
    "    '''\n",
    "    Vulnerability\n",
    "    We use Exposure and severity to determine Vulnerability\n",
    "    Exposure DCwater requests > hist ave, WRI_s) is an index from 0-1, WRI_s =1- WR_s/WR_h\n",
    "    Severity is the amount of ExtraDC water, and then normalized based on the\n",
    "    largest value to provide values from 0-1\n",
    "    '''\n",
    "    #Exposure\n",
    "    df['WRI_s'] = df['SLCDPU_DC_Water_Use']/(df['Hist_Mean_DC_Delivery']+1)\n",
    "    for i in np.arange(0,T,1):\n",
    "        if df['WRI_s'].iloc[i] > 1:\n",
    "            df['WRI_s'].iloc[i] = 1\n",
    "    #This is average exposure\n",
    "    Exp = df['WRI_s'].sum()/T\n",
    "\n",
    "    #Severity\n",
    "    Max_Severity = df['Extra_DC'].max()\n",
    "    #if MaxSeverity == 0:\n",
    "     #   MaxSeverity = 1\n",
    "    #This is the maximum found for all simulations    \n",
    "    MaxSeverity = maxseverity\n",
    "    df['Severity'] = df['Extra_DC']/MaxSeverity\n",
    "    #This is average severity\n",
    "    Sev = df['Severity'].sum()/(T+1-df['Zt'].sum())\n",
    "    MaxSevSI = df['Severity'].max()*MaxSeverity\n",
    "    MaxSevNorm = df['Severity'].max()\n",
    "\n",
    "\n",
    "    df['Vulnerability'] = (0.5*df['WRI_s'])+(0.5*df['Severity'])\n",
    "    #Vulerability = Exposure +Severity\n",
    "    Vul = (0.5*Exp) + (0.5*Sev)\n",
    "    print(df.columns)\n",
    "\n",
    "    return Rel, Res, Vul, df, Max_Severity, MaxSevSI, MaxSevNorm\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RRVanalysis(RRV_Assessment, Period):\n",
    "    \n",
    "    RRV_Breaks_Data = RRV_Assessment[21:].copy()\n",
    "\n",
    "\n",
    "    #Find the natural breaks in the RRV\n",
    "    #The eval data set has values greater than the historical and are identified as Nan in the \n",
    "    #eval dataframe. These values will be marked as extreme\n",
    "    Avebreaks = jenkspy.jenks_breaks(RRV_Breaks_Data['Vulnerability'], nb_class=4)\n",
    "    print(Avebreaks)\n",
    "    RRV_Assessment['Jenks_VCatAve'] = pd.cut(RRV_Assessment['Vulnerability'],\n",
    "                            bins=Avebreaks,\n",
    "                            labels=['low', 'medium', 'high', 'very_high'],\n",
    "                                        include_lowest=True)\n",
    "    Peakbreaks = jenkspy.jenks_breaks(RRV_Breaks_Data['Maximum_Severity'], nb_class=4)\n",
    "    print(Peakbreaks)\n",
    "    RRV_Assessment['Jenks_VCatPeak'] = pd.cut(RRV_Assessment['Maximum_Severity'],\n",
    "                            bins=Peakbreaks,\n",
    "                            labels=[ 'low', 'medium', 'high','very_high' ],\n",
    "                                        include_lowest=True)\n",
    "    \n",
    "    HistMeanReliability = RRV_Assessment_M['Reliability'][21:].mean()\n",
    "    HistMeanResilience = RRV_Assessment_M['Resilience'][21:].mean()\n",
    "    HistMeanVulnerability = RRV_Assessment_M['Vulnerability'][21:].mean()\n",
    "    HistMeanMaximum_Severity = RRV_Assessment_M['Maximum_Severity'][21:].mean()\n",
    "    print('Historical Reliability, Resilience, Vulnerability, Peak Seveirty',HistMeanReliability,\n",
    "          HistMeanResilience,HistMeanVulnerability,HistMeanMaximum_Severity)\n",
    "    \n",
    "    \n",
    "    \n",
    "    #Seems like percent difference may be a good metric \n",
    "    RRV_Assessment['Perc_Rel_Hist'] = (((RRV_Assessment['Reliability']-HistMeanReliability)/HistMeanReliability)*100)\n",
    "    RRV_Assessment['Perc_Res_Hist'] = (((HistMeanResilience-RRV_Assessment['Resilience'])/HistMeanResilience)*100)\n",
    "    RRV_Assessment['Perc_Vul_Hist'] = (((RRV_Assessment['Vulnerability']-HistMeanVulnerability)/HistMeanVulnerability)*100)\n",
    "    RRV_Assessment['Perc_MSev_Hist'] = (((RRV_Assessment['Maximum_Severity']-HistMeanMaximum_Severity)/HistMeanMaximum_Severity)*100)\n",
    "\n",
    "    \n",
    "    RRV_Assessment.replace(\"MLRD\", \n",
    "               \"DD\", \n",
    "               inplace=True)\n",
    "  #  RRV_Assessment[RRV_Assessment['Climate']=='Drought'] = 'Dry'\n",
    "  #  RRV_Assessment[RRV_Assessment['Climate']=='Surplus'] = 'Wet'\n",
    "\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    return RRV_Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RRVplotsErr(RRV_Assessment, Period, ErrL, ErrH):\n",
    "\n",
    "#Plot the info\n",
    "    Plotdf = RRV_Assessment.copy()\n",
    "    Plotdf = Plotdf[:21]\n",
    "    columns = ['Demand', 'Reliability', 'Resilience', 'Vulnerability', 'Maximum_Severity']\n",
    "    Plotdf = Plotdf[columns]\n",
    "    #Plotdf.index = Plotdf['Demand']\n",
    "    width = .5\n",
    "    colors = ['blue', 'red', 'green']\n",
    "    TickRange = np.arange(0,9,1)\n",
    "    Ticklabels = ['OBSD' ,'TD', 'CSD-WDM','OBSD' ,'TD', 'CSD-WDM','OBSD' ,'TD', 'CSD-WDM' ]\n",
    "    Ticklabelsize = 9\n",
    "    Labelsize = 11\n",
    "    \n",
    "    \n",
    "    #Define the error bars\n",
    "    asymmetric_errorRel = [ErrL['Reliability'], ErrH['Reliability']]\n",
    "    asymmetric_errorRes = [ErrL['Resilience'], ErrH['Resilience']]\n",
    "    asymmetric_errorVul = [ErrL['Vulnerability'], ErrH['Vulnerability']]\n",
    "    asymmetric_errorSev = [ErrL['Maximum_Severity'], ErrH['Maximum_Severity']]\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "    fig, ax = plt.subplots(4,1)\n",
    "    fig.set_size_inches(8,8)\n",
    "\n",
    "    #Daily Figure\n",
    "    #Reliability\n",
    "    ax[0].bar(Plotdf.index, Plotdf.Reliability, width=width, \n",
    "        color=colors, label=modeltype, yerr = asymmetric_errorRel, capsize = 10)\n",
    "    ax[0].set_ylim(0,1.2)\n",
    "    #ax[0].set_xlim(737772.25+60, 738173.75-60)\n",
    "    #ax[0].legend(loc = 'upper left', fontsize = 13)\n",
    "    ax[0].set_ylabel('Reliability', size = Labelsize)\n",
    "    ax[0].tick_params(axis = 'both', labelsize = Ticklabelsize )\n",
    "    ax[0].xaxis.set_visible(False)\n",
    "    ax[0].axvline(2.5, color='black', linestyle='-')\n",
    "    ax[0].axvline(5.5, color='black', linestyle='-')\n",
    "\n",
    "\n",
    "    #Resilience\n",
    "    ax[1].bar(Plotdf.index, Plotdf.Resilience, width=width, \n",
    "        color=colors, label=modeltype, yerr = asymmetric_errorRes, capsize = 10)\n",
    "    #ax[1].set_ylim(0,1)\n",
    "    #ax[0].set_xlim(737772.25+60, 738173.75-60)\n",
    "    #ax[0].legend(loc = 'upper left', fontsize = 13)\n",
    "    ax[1].set_ylabel('Resilience', size = Labelsize)\n",
    "    ax[1].tick_params(axis = 'both', labelsize = Ticklabelsize )\n",
    "    ax[1].xaxis.set_visible(False)\n",
    "    ax[1].axvline(2.5, color='black', linestyle='-')\n",
    "    ax[1].axvline(5.5, color='black', linestyle='-')\n",
    "\n",
    "\n",
    "    \n",
    "    #Vulnerability\n",
    "    ax[2].bar(Plotdf.index, Plotdf.Vulnerability, width=width, \n",
    "        color=colors, label=modeltype, yerr = asymmetric_errorVul, capsize = 10)\n",
    "    ax[2].set_ylim(0,1)\n",
    "    #ax[0].set_xlim(737772.25+60, 738173.75-60)\n",
    "    #ax[0].legend(loc = 'upper left', fontsize = 13)\n",
    "    ax[2].set_ylabel('Vulnerability', size = Labelsize)\n",
    "    ax[2].tick_params(axis = 'both', labelsize = Ticklabelsize )\n",
    "    ax[2].xaxis.set_visible(False)\n",
    "    ax[2].axvline(2.5, color='black', linestyle='-')\n",
    "    ax[2].axvline(5.5, color='black', linestyle='-')\n",
    "    \n",
    "    \n",
    "    #Severity\n",
    "    ax[3].bar(Plotdf.index, Plotdf.Maximum_Severity, width=width, \n",
    "        color=colors, label=modeltype, yerr = asymmetric_errorSev, capsize = 10)\n",
    "    ax[3].set_ylim(0,1.1*max(Plotdf.Maximum_Severity))\n",
    "    #ax[0].set_xlim(737772.25+60, 738173.75-60)\n",
    "    #ax[0].legend(loc = 'upper left', fontsize = 13)\n",
    "    ax[3].set_ylabel('Severity', size = Labelsize)\n",
    "    ax[3].tick_params(axis = 'both', labelsize = Ticklabelsize )\n",
    "    ax[3].set_xticks(TickRange)\n",
    "    ax[3].set_xticklabels(Ticklabels)\n",
    "    ax[3].axvline(2.5, color='black', linestyle='-')\n",
    "    ax[3].axvline(5.5, color='black', linestyle='-')\n",
    "\n",
    "    plt.figtext(0.25, 0.065, \"Dry\", ha=\"center\",fontsize = Labelsize)\n",
    "    plt.figtext(0.5, 0.065, \"Average\", ha=\"center\",fontsize = Labelsize)\n",
    "    plt.figtext(0.75, 0.065, \"Wet\", ha=\"center\",fontsize = Labelsize)\n",
    "    fig.savefig(FigPath+Period+'RRV.png', dpi = 300)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "'''\n",
    "This section determine the bounds of error/ uncertainty in the RRV estimates\n",
    "\n",
    "'''\n",
    "\n",
    "#Determine bounds of errors in TD\n",
    "\n",
    "def RRV_UncertainDF(RRV_Assessment_processed):\n",
    "\n",
    "    RRV_Assessment_processed['col'] = RRV_Assessment_processed['Climate'].astype(str) + '_'+ RRV_Assessment_processed['Demand'].astype(str)\n",
    "    RRV_Assessment_processed.index = RRV_Assessment_processed['col']\n",
    "    TD = RRV_Assessment_processed[:3].copy()\n",
    "    TD = TD.append(RRV_Assessment_processed[7:10].copy())\n",
    "    TD = TD.append(RRV_Assessment_processed[14:17].copy())\n",
    "    TD=TD.T[2:8]\n",
    "#we actual want this to be zero\n",
    "#    TD['Drought_ErrL'] = abs(TD['Drought_TD_Lo']-TD['Drought_TD'])\n",
    " #   TD['Drought_ErrH'] = abs(TD['Drought_TD_Hi']-TD['Drought_TD'])\n",
    "\n",
    "  #  TD['Average_ErrL'] = abs(TD['Average_TD_Lo']-TD['Average_TD'])\n",
    "   # TD['Average_ErrH'] = abs(TD['Average_TD_Hi']-TD['Average_TD'])\n",
    "\n",
    "    #TD['Surplus_ErrL'] = abs(TD['Surplus_TD_Lo']-TD['Surplus_TD'])\n",
    "    #TD['Surplus_ErrH'] = abs(TD['Surplus_TD_Hi']-TD['Surplus_TD'])\n",
    "    \n",
    "    TD['Drought_ErrL'] = abs(TD['Drought_TD']-TD['Drought_TD'])\n",
    "    TD['Drought_ErrH'] = abs(TD['Drought_TD']-TD['Drought_TD'])\n",
    "\n",
    "    TD['Average_ErrL'] = abs(TD['Average_TD']-TD['Average_TD'])\n",
    "    TD['Average_ErrH'] = abs(TD['Average_TD']-TD['Average_TD'])\n",
    "\n",
    "    TD['Surplus_ErrL'] = abs(TD['Surplus_TD']-TD['Surplus_TD'])\n",
    "    TD['Surplus_ErrH'] = abs(TD['Surplus_TD']-TD['Surplus_TD'])\n",
    "\n",
    "    ErrCols = ['Drought_ErrL','Drought_ErrH','Average_ErrL','Average_ErrH','Surplus_ErrL','Surplus_ErrH']\n",
    "\n",
    "    TDErr = TD[ErrCols]\n",
    "    TDErr =TDErr.drop(labels = ['MaxSevSI', 'MaxSeverity'], axis = 0)\n",
    "\n",
    "\n",
    "    #Determine bounds of errors in CSD_WDM\n",
    "    CSD = RRV_Assessment_processed[4:7].copy()\n",
    "    CSD  = CSD.append(RRV_Assessment_processed[11:14].copy())\n",
    "    CSD  = CSD.append(RRV_Assessment_processed[18:21].copy())\n",
    "    CSD =CSD.T[2:8]\n",
    "\n",
    "    CSD['Drought_ErrL'] = abs(CSD['Drought_CSD_WDM_Lo']-CSD['Drought_CSD_WDM'])\n",
    "    CSD['Drought_ErrH'] = abs(CSD['Drought_CSD_WDM_Hi']-CSD['Drought_CSD_WDM'])\n",
    "\n",
    "    CSD['Average_ErrL'] = abs(CSD['Average_CSD_WDM_Lo']-CSD['Average_CSD_WDM'])\n",
    "    CSD['Average_ErrH'] = abs(CSD['Average_CSD_WDM_Hi']-CSD['Average_CSD_WDM'])\n",
    "\n",
    "    CSD['Surplus_ErrL'] = abs(CSD['Surplus_CSD_WDM_Lo']-CSD['Surplus_CSD_WDM'])\n",
    "    CSD['Surplus_ErrH'] = abs(CSD['Surplus_CSD_WDM_Hi']-CSD['Surplus_CSD_WDM'])\n",
    "\n",
    "    ErrCols = ['Drought_ErrL','Drought_ErrH','Average_ErrL','Average_ErrH','Surplus_ErrL','Surplus_ErrH']\n",
    "\n",
    "    CSDErr = CSD[ErrCols]\n",
    "    CSDErr =CSDErr.drop(labels = ['MaxSevSI', 'MaxSeverity'], axis = 0)\n",
    "\n",
    "\n",
    "    ObsErr = CSDErr.copy()\n",
    "    for i in ObsErr.columns:\n",
    "        ObsErr[i] = 0\n",
    "\n",
    "    RRV_Error = ObsErr.copy()\n",
    "    RRV_Error = RRV_Error.append(TDErr)\n",
    "    RRV_Error = RRV_Error.append(CSDErr)\n",
    "\n",
    "    #Set up labels to later concatenate\n",
    "    LErrcols = ['Drought_ErrL','Average_ErrL',\t'Surplus_ErrL']\n",
    "    LErrcols_TD = [x+\"_TD\" for x in LErrcols]\n",
    "    LErrcols_CSD = [x+\"_CSD\" for x in LErrcols]\n",
    "\n",
    "    HErrcols = ['Drought_ErrH',\t'Average_ErrH',\t'Surplus_ErrH']\n",
    "    HErrcols_TD = [x+\"_TD\" for x in HErrcols]\n",
    "    HErrcols_CSD = [x+\"_CSD\" for x in HErrcols]\n",
    "\n",
    "\n",
    "    #Index out low and high columns\n",
    "    RRV_ErrorL = RRV_Error[LErrcols]\n",
    "    RRV_ErrorH = RRV_Error[HErrcols]\n",
    "\n",
    "    #Index out rows\n",
    "    #Low\n",
    "    RRV_ErrorL_O = RRV_ErrorL[0:4]\n",
    "    RRV_ErrorL_TD = RRV_ErrorL[4:8]\n",
    "    RRV_ErrorL_TD.columns = LErrcols_TD\n",
    "    RRV_ErrorL_CSD = RRV_ErrorL[8:12]\n",
    "    RRV_ErrorL_CSD.columns = LErrcols_CSD\n",
    "\n",
    "    #High\n",
    "    RRV_ErrorH_O = RRV_ErrorH[0:4]\n",
    "    RRV_ErrorH_TD = RRV_ErrorH[4:8]\n",
    "    RRV_ErrorH_TD.columns = HErrcols_TD\n",
    "    RRV_ErrorH_CSD = RRV_ErrorH[8:12]\n",
    "    RRV_ErrorH_CSD.columns = HErrcols_CSD\n",
    "\n",
    "\n",
    "    #Concatenate back into DF\n",
    "    #Low\n",
    "    RRV_ErrorL = pd.concat([RRV_ErrorL_O, RRV_ErrorL_TD,RRV_ErrorL_CSD], axis = 1)\n",
    "    #High\n",
    "    RRV_ErrorH = pd.concat([RRV_ErrorH_O, RRV_ErrorH_TD,RRV_ErrorH_CSD], axis = 1)\n",
    "\n",
    "    #Traverse \n",
    "    RRV_ErrorL = RRV_ErrorL.T\n",
    "    RRV_ErrorH = RRV_ErrorH.T\n",
    "\n",
    "    #reorder index\n",
    "    ReIndexL = ['Drought_ErrL', 'Drought_ErrL_TD','Drought_ErrL_CSD',\n",
    "               'Average_ErrL','Average_ErrL_TD', 'Average_ErrL_CSD',\n",
    "               'Surplus_ErrL', 'Surplus_ErrL_TD', 'Surplus_ErrL_CSD']\n",
    "\n",
    "    ReIndexH = ['Drought_ErrH', 'Drought_ErrH_TD','Drought_ErrH_CSD',\n",
    "               'Average_ErrH','Average_ErrH_TD', 'Average_ErrH_CSD',\n",
    "               'Surplus_ErrH', 'Surplus_ErrH_TD', 'Surplus_ErrH_CSD']\n",
    "\n",
    "    RRV_ErrorL = RRV_ErrorL.reindex(ReIndexL)\n",
    "    RRV_ErrorH = RRV_ErrorH.reindex(ReIndexH)\n",
    "    \n",
    "    return RRV_ErrorL, RRV_ErrorH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Average_S_CSD_WDM_Dell', 'Average_S_CSD_WDM_Dell_Hi', 'Average_S_CSD_WDM_Dell_Lo', 'Average_S_CSD_WDM_Ind0_Out0', 'Average_S_CSD_WDM_Ind0_Out0_Hi', 'Average_S_CSD_WDM_Ind0_Out0_Lo', 'Average_S_OBSD_Dell', 'Average_S_OBSD_Ind0_Out0', 'Average_S_TD_Dell', 'Average_S_TD_Dell_Hi', 'Average_S_TD_Dell_Lo', 'Average_S_TD_Ind0_Out0', 'Average_S_TD_Ind0_Out0_Hi', 'Average_S_TD_Ind0_Out0_Lo', 'Drought_S_CSD_WDM_Dell', 'Drought_S_CSD_WDM_Dell_Hi', 'Drought_S_CSD_WDM_Dell_Lo', 'Drought_S_CSD_WDM_Ind0_Out0', 'Drought_S_CSD_WDM_Ind0_Out0_Hi', 'Drought_S_CSD_WDM_Ind0_Out0_Lo', 'Drought_S_OBSD_Dell', 'Drought_S_OBSD_Ind0_Out0', 'Drought_S_TD_Dell', 'Drought_S_TD_Dell_Hi', 'Drought_S_TD_Dell_Lo', 'Drought_S_TD_Ind0_Out0', 'Drought_S_TD_Ind0_Out0_Hi', 'Drought_S_TD_Ind0_Out0_Lo', 'Surplus_S_CSD_WDM_Dell', 'Surplus_S_CSD_WDM_Dell_Hi', 'Surplus_S_CSD_WDM_Dell_Lo', 'Surplus_S_CSD_WDM_Ind0_Out0', 'Surplus_S_CSD_WDM_Ind0_Out0_Hi', 'Surplus_S_CSD_WDM_Ind0_Out0_Lo', 'Surplus_S_OBSD_Dell', 'Surplus_S_OBSD_Ind0_Out0', 'Surplus_S_TD_Dell', 'Surplus_S_TD_Dell_Hi', 'Surplus_S_TD_Dell_Lo', 'Surplus_S_TD_Ind0_Out0', 'Surplus_S_TD_Ind0_Out0_Hi', 'Surplus_S_TD_Ind0_Out0_Lo'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set path to load  data\n",
    "#This is to my infused drought to  2021\n",
    "GSDataPath = Path('C:/Users/rjohnson18/Box/Dissertation/GoldSim_Model/Exported_Results/Forecasting/DroughtSurplusAverage_Data')\n",
    "FigPath = 'C:/Users/rjohnson18/Box/Dissertation/Paper2/Figures/'\n",
    "\n",
    "\n",
    "#load historical streamflows from BCC, LCC, CC, PC\n",
    "GSResults = {i.stem[0:30] : pd.read_excel(i, skiprows=3) for i in GSDataPath.glob('**/*.xlsx')}\n",
    "\n",
    "for i in GSResults:\n",
    "    if 'SLCDPU_SW_Used' in GSResults[i].columns:\n",
    "        del GSResults[i]['SLCDPU_SW_Used']\n",
    "    \n",
    "GSResults.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Determine the maximum historical system severity\n",
    "Clim_Scenario = 'Average Supply and OBS Demands.'\n",
    "\n",
    "Daily2021, Monthly2021, Dell2021 = ForecastDataPrep(GSResults['Average_S_OBSD_Ind0_Out0'], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults['Average_S_OBSD_Dell'], Clim_Scenario)\n",
    "#Get the historical mean DC deliverity values for one year\n",
    "Hist_Mean_DC = list(Daily2021['Hist_Mean_DC_Delivery'].copy())\n",
    "\n",
    "#Get the reference perid simulation results\n",
    "Hist = GSResults['Average_S_OBSD_Ind0_Out0'].copy()\n",
    "Hist.index = Hist['Time']\n",
    "del Hist['Time']\n",
    "#Convert GS output from AF to M3\n",
    "Hist = Hist*af_to_104m3\n",
    "\n",
    "#Select the first 20 years\n",
    "Hist = Hist.loc['2000-10-01':'2020-12-31']\n",
    "\n",
    "#Make the data to input into long term TS\n",
    "Oct_Dec_Hist_Mean_DC = Hist_Mean_DC[268:]\n",
    "Hist_Mean_DC = Hist_Mean_DC*20\n",
    "Hist_Mean_DC = Oct_Dec_Hist_Mean_DC+Hist_Mean_DC\n",
    "Hist['Hist_Mean_DC_Delivery'] = Hist_Mean_DC\n",
    "Hist['Extra_DC'] = Hist['SLCDPU_DC_Water_Use']-Hist['Hist_Mean_DC_Delivery']\n",
    "\n",
    "for i in np.arange(0,len(Hist),1):\n",
    "    if Hist['Extra_DC'].iloc[i] <1:\n",
    "            Hist['Extra_DC'].iloc[i] = 0\n",
    "Historical_Max_Daily_DC = max(Hist['Extra_DC'])\n",
    "\n",
    "#Resample to monthly resolution and convert back to AF\n",
    "HistMonthly = Hist.resample('M').sum()\n",
    "Historical_Max_Monthly_DC = max(HistMonthly['Extra_DC'])\n",
    "HistMonthly = HistMonthly.groupby(HistMonthly.index.month).mean()\n",
    "MaveStreamT = HistMonthly['SLCDPU_Streamflow']\n",
    "MaveDemandT = HistMonthly['SLCDPU_Prod_Demands']\n",
    "MaveDC = HistMonthly['SLCDPU_DC_Water_Use']\n",
    "\n",
    "#Determine average seasonal demands, streamflow, and deer creek use\n",
    "HistM = Hist.resample('M').sum()\n",
    "HistM['Time'] = HistM.index\n",
    "RemMonths = [4,5,6,7,8,9,10]\n",
    "HistMApr_Oct = pd.DataFrame(columns = HistM.columns)\n",
    "for i in np.arange(0,len(HistM),1):\n",
    "    if HistM['Time'].iloc[i].month in RemMonths:\n",
    "        HistMApr_Oct = HistMApr_Oct.append(HistM.iloc[i])\n",
    "HistAnn = HistMApr_Oct.resample('Y').sum()\n",
    "HistAnnmean = HistAnn.groupby(HistAnn.index.month).mean()\n",
    "SeasAveDemand = HistAnnmean['SLCDPU_Prod_Demands'].values[0]\n",
    "SeasAveStream = HistAnnmean['SLCDPU_Streamflow'].values[0]\n",
    "SeasAveDC = HistAnnmean['Hist_Mean_DC_Delivery'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    " \n",
    "'''\n",
    "Determine RRV for all scenarios\n",
    "'''\n",
    "\n",
    "RRV_Assessment_D =pd.DataFrame(columns =['Climate', 'Demand', 'Reliability',\n",
    "    'Resilience', 'Vulnerability', 'MaxSeverity', 'MaxSevSI', 'Maximum_Severity'])\n",
    "\n",
    "RRV_Data_D =pd.DataFrame(columns = ['SLCDPU_Prod_Demands', 'SLCDPU_Shortages', 'SLCDPU_GW',\n",
    "       'SLCDPU_Streamflow', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery',\n",
    "       'Extra_DC', 'ExtraDC', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'])\n",
    "\n",
    "RRV_Assessment_M =pd.DataFrame(columns =['Climate', 'Demand', 'Reliability',\n",
    "    'Resilience', 'Vulnerability',  'MaxSeverity','MaxSevSI', 'Maximum_Severity'])\n",
    "\n",
    "RRV_Data_M = pd.DataFrame(columns = ['SLCDPU_Prod_Demands', 'SLCDPU_Shortages', 'SLCDPU_GW',\n",
    "       'SLCDPU_Streamflow', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery',\n",
    "       'Extra_DC', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RJOHNS~1\\AppData\\Local\\Temp/ipykernel_28896/4067824212.py:189: UserWarning: This figure was using constrained_layout, but that is incompatible with subplots_adjust and/or tight_layout; disabling constrained_layout.\n",
      "  plt.subplots_adjust(right=1)\n",
      "C:\\Users\\RJOHNS~1\\AppData\\Local\\Temp/ipykernel_28896/4067824212.py:189: UserWarning: This figure was using constrained_layout, but that is incompatible with subplots_adjust and/or tight_layout; disabling constrained_layout.\n",
      "  plt.subplots_adjust(right=1)\n",
      "C:\\Users\\RJOHNS~1\\AppData\\Local\\Temp/ipykernel_28896/4067824212.py:189: UserWarning: This figure was using constrained_layout, but that is incompatible with subplots_adjust and/or tight_layout; disabling constrained_layout.\n",
      "  plt.subplots_adjust(right=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "This line omits the errors in traditional demands\n",
    "'''\n",
    "\n",
    "\n",
    "sup = ['Drought', 'Average', 'Surplus']\n",
    "Demands = ['TD']# 'CSD_WDM']#,'TD', 'CSD_WDM'] #'TD'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Indcons = 0\n",
    "Outcons = 0\n",
    "maxseverity = 1\n",
    "\n",
    "for i in sup:\n",
    "    for d in Demands:\n",
    "        \n",
    "        SysSim = i + '_S_'+d+'_Ind0_Out0'\n",
    "        SysSimO = i + '_S_OBSD_Ind0_Out0'\n",
    "        \n",
    "        SysSimL = SysSim+'_Lo'\n",
    "        SysSimH = SysSim+'_Hi'\n",
    "        \n",
    "        DemandL = d+'_Lo'\n",
    "        Demand = d\n",
    "        DemandH = d+'_Hi'\n",
    "        DemandO = 'Obs'\n",
    "        \n",
    "        DellSim = i + '_S_' + d + '_Dell'\n",
    "        DellSimO = i + '_S_OBSD_Dell'\n",
    "        DellSimL = DellSim +'_Lo'\n",
    "        DellSimH = DellSim + '_Hi'\n",
    "        modeltype = d\n",
    "        Clim_Scenario = i +' Supply and '+ d +' Demands'\n",
    "\n",
    "         #This does the mean\n",
    "        Daily2021, Monthly2021, Dell2021 = ForecastDataPrep(GSResults[SysSim], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSim], Clim_Scenario)\n",
    "         #This does the Observed\n",
    "        Daily2021_O, Monthly2021_O, Dell2021_O = ForecastDataPrep(GSResults[SysSimO], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSimO], Clim_Scenario)\n",
    "        \n",
    "        \n",
    "        \n",
    "     #Plot the information\n",
    "        Sup_Dem_Figure_Err_CSD(Daily2021, Monthly2021, Daily2021, Monthly2021,\n",
    "                   Daily2021, Monthly2021, Daily2021_O, Monthly2021_O, \n",
    "                   2021,Indcons, Outcons, FigPath, Clim_Scenario, modeltype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RJOHNS~1\\AppData\\Local\\Temp/ipykernel_28896/4067824212.py:189: UserWarning: This figure was using constrained_layout, but that is incompatible with subplots_adjust and/or tight_layout; disabling constrained_layout.\n",
      "  plt.subplots_adjust(right=1)\n",
      "C:\\Users\\RJOHNS~1\\AppData\\Local\\Temp/ipykernel_28896/4067824212.py:189: UserWarning: This figure was using constrained_layout, but that is incompatible with subplots_adjust and/or tight_layout; disabling constrained_layout.\n",
      "  plt.subplots_adjust(right=1)\n",
      "C:\\Users\\RJOHNS~1\\AppData\\Local\\Temp/ipykernel_28896/4067824212.py:189: UserWarning: This figure was using constrained_layout, but that is incompatible with subplots_adjust and/or tight_layout; disabling constrained_layout.\n",
      "  plt.subplots_adjust(right=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "This section adds limits to illustrate the range of uncertainty in systems analysis due to demand uncertainty\n",
    "\n",
    "'''\n",
    "\n",
    "\n",
    "sup = ['Drought', 'Average', 'Surplus']\n",
    "Demands = ['CSD_WDM']#, 'TD']#,'TD', 'CSD_WDM'] #'TD'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Indcons = 0\n",
    "Outcons = 0\n",
    "maxseverity = 1\n",
    "\n",
    "for i in sup:\n",
    "    for d in Demands:\n",
    "        \n",
    "        SysSim = i + '_S_'+d+'_Ind0_Out0'\n",
    "        SysSimO = i + '_S_OBSD_Ind0_Out0'\n",
    "        \n",
    "        SysSimL = SysSim+'_Lo'\n",
    "        SysSimH = SysSim+'_Hi'\n",
    "        \n",
    "        DemandL = d+'_Lo'\n",
    "        Demand = d\n",
    "        DemandH = d+'_Hi'\n",
    "        DemandO = 'Obs'\n",
    "        \n",
    "        DellSim = i + '_S_' + d + '_Dell'\n",
    "        DellSimO = i + '_S_OBSD_Dell'\n",
    "        DellSimL = DellSim +'_Lo'\n",
    "        DellSimH = DellSim + '_Hi'\n",
    "        modeltype = d\n",
    "        Clim_Scenario = i +' Supply and '+ d +' Demands_Unc'\n",
    "\n",
    "        #This does the low range\n",
    "        Daily2021_L, Monthly2021_L, Dell2021_L = ForecastDataPrep(GSResults[SysSimL], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSimL], Clim_Scenario)\n",
    "        \n",
    "        #This does the mean\n",
    "        Daily2021, Monthly2021, Dell2021 = ForecastDataPrep(GSResults[SysSim], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSim], Clim_Scenario)\n",
    "        \n",
    "        #This does the High range\n",
    "        Daily2021_H, Monthly2021_H, Dell2021_H = ForecastDataPrep(GSResults[SysSimH], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSimH], Clim_Scenario)\n",
    "        \n",
    "        #This does the Observed\n",
    "        Daily2021_O, Monthly2021_O, Dell2021_O = ForecastDataPrep(GSResults[SysSimO], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSimO], Clim_Scenario)\n",
    "        \n",
    "        \n",
    "        \n",
    "     #Plot the information\n",
    "        Sup_Dem_Figure_Err_CSD(Daily2021, Monthly2021, Daily2021_L, Monthly2021_L,\n",
    "                   Daily2021_H, Monthly2021_H, Daily2021_O, Monthly2021_O, \n",
    "                   2021,Indcons, Outcons, FigPath, Clim_Scenario, modeltype)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjohnson18\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1732: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_block(indexer, value, name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Drought Supply and CSD_WDM Demands\n",
      "Reliability:  0.7616822429906542 Resilience:  17.333333333333332 Vulnerability:  0.32744893339296033\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Drought Supply and CSD_WDM Demands\n",
      "Reliability:  0.48130841121495327 Resilience:  28.0 Vulnerability:  0.4376280226342143\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Drought Supply and CSD_WDM Demands\n",
      "Reliability:  0.4252336448598131 Resilience:  62.0 Vulnerability:  0.5746569159352134\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Drought Supply and CSD_WDM Demands\n",
      "Reliability:  0.48130841121495327 Resilience:  56.0 Vulnerability:  0.43594989880152996\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Drought Supply and CSD_WDM Demands\n",
      "Reliability:  0.2857142857142857 Resilience:  6.0 Vulnerability:  0.36370940953793895\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Drought Supply and CSD_WDM Demands\n",
      "Reliability:  0.2857142857142857 Resilience:  6.0 Vulnerability:  0.4796299672765914\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Drought Supply and CSD_WDM Demands\n",
      "Reliability:  0.2857142857142857 Resilience:  6.0 Vulnerability:  0.594323718458343\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Drought Supply and CSD_WDM Demands\n",
      "Reliability:  0.2857142857142857 Resilience:  6.0 Vulnerability:  0.48591451238112227\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Drought Supply and TD Demands\n",
      "Reliability:  0.5186915887850467 Resilience:  52.0 Vulnerability:  0.4671219230034452\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Drought Supply and TD Demands\n",
      "Reliability:  0.40654205607476634 Resilience:  32.0 Vulnerability:  0.6158859218714229\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Drought Supply and TD Demands\n",
      "Reliability:  0.22897196261682243 Resilience:  33.2 Vulnerability:  0.7060187011238583\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Drought Supply and TD Demands\n",
      "Reliability:  0.48130841121495327 Resilience:  56.0 Vulnerability:  0.43594989880152996\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Drought Supply and TD Demands\n",
      "Reliability:  0.2857142857142857 Resilience:  3.0 Vulnerability:  0.5446755209596106\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Drought Supply and TD Demands\n",
      "Reliability:  0.0 Resilience:  8.0 Vulnerability:  0.6788500395928283\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Drought Supply and TD Demands\n",
      "Reliability:  0.0 Resilience:  8.0 Vulnerability:  0.7432400522247073\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Drought Supply and TD Demands\n",
      "Reliability:  0.2857142857142857 Resilience:  6.0 Vulnerability:  0.48591451238112227\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Average Supply and CSD_WDM Demands\n",
      "Reliability:  0.8691588785046729 Resilience:  14.5 Vulnerability:  0.22346822162927477\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Average Supply and CSD_WDM Demands\n",
      "Reliability:  0.8037383177570093 Resilience:  14.333333333333334 Vulnerability:  0.39172805756721746\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Average Supply and CSD_WDM Demands\n",
      "Reliability:  0.5654205607476636 Resilience:  31.333333333333336 Vulnerability:  0.47534164449000316\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Average Supply and CSD_WDM Demands\n",
      "Reliability:  0.7757009345794392 Resilience:  24.500000000000004 Vulnerability:  0.3763420257807302\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Average Supply and CSD_WDM Demands\n",
      "Reliability:  0.7142857142857143 Resilience:  1.5 Vulnerability:  0.1916728975270144\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Average Supply and CSD_WDM Demands\n",
      "Reliability:  0.5714285714285714 Resilience:  2.0 Vulnerability:  0.35272279516701077\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Average Supply and CSD_WDM Demands\n",
      "Reliability:  0.42857142857142855 Resilience:  5.0 Vulnerability:  0.5119661761983707\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Average Supply and CSD_WDM Demands\n",
      "Reliability:  0.5714285714285714 Resilience:  2.0 Vulnerability:  0.3389445675591389\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Average Supply and TD Demands\n",
      "Reliability:  0.7289719626168224 Resilience:  29.5 Vulnerability:  0.31291863023630484\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Average Supply and TD Demands\n",
      "Reliability:  0.6074766355140186 Resilience:  17.0 Vulnerability:  0.4750514048091226\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Average Supply and TD Demands\n",
      "Reliability:  0.37850467289719625 Resilience:  33.5 Vulnerability:  0.590174376377419\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Average Supply and TD Demands\n",
      "Reliability:  0.7757009345794392 Resilience:  24.500000000000004 Vulnerability:  0.3763420257807302\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Average Supply and TD Demands\n",
      "Reliability:  0.5714285714285714 Resilience:  2.0 Vulnerability:  0.3317459373520213\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Average Supply and TD Demands\n",
      "Reliability:  0.42857142857142855 Resilience:  1.6666666666666667 Vulnerability:  0.5171066207662158\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Average Supply and TD Demands\n",
      "Reliability:  0.0 Resilience:  8.0 Vulnerability:  0.6383118477905592\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Average Supply and TD Demands\n",
      "Reliability:  0.5714285714285714 Resilience:  2.0 Vulnerability:  0.3389445675591389\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Surplus Supply and CSD_WDM Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.00371455502103514\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Surplus Supply and CSD_WDM Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.03820125733739681\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Surplus Supply and CSD_WDM Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.11787200510731669\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Surplus Supply and CSD_WDM Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.00876169318282463\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Surplus Supply and CSD_WDM Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.01304762109920212\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Surplus Supply and CSD_WDM Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.059643683549564126\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Surplus Supply and CSD_WDM Demands\n",
      "Reliability:  0.8571428571428571 Resilience:  1.0 Vulnerability:  0.17342595712588632\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Surplus Supply and CSD_WDM Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.03737438111424002\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Surplus Supply and TD Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.004042402290750448\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Surplus Supply and TD Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.051216322654921674\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Surplus Supply and TD Demands\n",
      "Reliability:  0.8271028037383178 Resilience:  19.0 Vulnerability:  0.2570768495468575\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Surplus Supply and TD Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.00876169318282463\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Surplus Supply and TD Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.025507481649607443\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Surplus Supply and TD Demands\n",
      "Reliability:  0.8571428571428571 Resilience:  1.0 Vulnerability:  0.13714714394864452\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Surplus Supply and TD Demands\n",
      "Reliability:  0.5714285714285714 Resilience:  2.0 Vulnerability:  0.31646838458639437\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Surplus Supply and TD Demands\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.03737438111424002\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "This section adds limits to illustrate the range of uncertainty in systems analysis due to demand uncertainty\n",
    "\n",
    "'''\n",
    "\n",
    "\n",
    "sup = ['Drought', 'Average', 'Surplus']\n",
    "Demands = ['CSD_WDM']#, 'TD']#,'TD', 'CSD_WDM'] #'TD'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Indcons = 0\n",
    "Outcons = 0\n",
    "maxseverity = 1\n",
    "\n",
    "for i in sup:\n",
    "    for d in Demands:\n",
    "        \n",
    "        SysSim = i + '_S_'+d+'_Ind0_Out0'\n",
    "        SysSimO = i + '_S_OBSD_Ind0_Out0'\n",
    "        \n",
    "        SysSimL = SysSim+'_Lo'\n",
    "        SysSimH = SysSim+'_Hi'\n",
    "        \n",
    "        DemandL = d+'_Lo'\n",
    "        Demand = d\n",
    "        DemandH = d+'_Hi'\n",
    "        DemandO = 'Obs'\n",
    "        \n",
    "        DellSim = i + '_S_' + d + '_Dell'\n",
    "        DellSimO = i + '_S_OBSD_Dell'\n",
    "        DellSimL = DellSim +'_Lo'\n",
    "        DellSimH = DellSim + '_Hi'\n",
    "        modeltype = d\n",
    "        Clim_Scenario = i +' Supply and '+ d +' Demands'\n",
    "\n",
    "        #This does the low range\n",
    "        Daily2021_L, Monthly2021_L, Dell2021_L = ForecastDataPrep(GSResults[SysSimL], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSimL], Clim_Scenario)\n",
    "        \n",
    "        #This does the mean\n",
    "        Daily2021, Monthly2021, Dell2021 = ForecastDataPrep(GSResults[SysSim], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSim], Clim_Scenario)\n",
    "        \n",
    "        #This does the High range\n",
    "        Daily2021_H, Monthly2021_H, Dell2021_H = ForecastDataPrep(GSResults[SysSimH], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSimH], Clim_Scenario)\n",
    "        \n",
    "        #This does the Observed\n",
    "        Daily2021_O, Monthly2021_O, Dell2021_O = ForecastDataPrep(GSResults[SysSimO], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSimO], Clim_Scenario)\n",
    "        \n",
    "        \n",
    "        \n",
    "     #Plot the information\n",
    " #       Sup_Dem_Figure_Err_CSD(Daily2021, Monthly2021, Daily2021_L, Monthly2021_L,\n",
    "  #                 Daily2021_H, Monthly2021_H, Daily2021_O, Monthly2021_O, \n",
    "   #                2021,Indcons, Outcons, FigPath, Clim_Scenario, modeltype)\n",
    "        \n",
    "    #Determine RRV\n",
    "    #Low\n",
    "        Rel_D, Res_D, Vul_D, Daily2021RRV,Max_Severity,MaxSevSI, MaxSevNorm = RRV(Daily2021_L, i, DemandL, Historical_Max_Daily_DC, 'Daily', 2021)\n",
    "        print('For Daily scenario ', Clim_Scenario)\n",
    "        print('Reliability: ', Rel_D, 'Resilience: ', Res_D, 'Vulnerability: ', Vul_D)\n",
    "        RRVass_D = list([i, DemandL, Rel_D, Res_D, Vul_D, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "        RRV_Assessment_D = RRV_Assessment_D.reset_index(drop=True)\n",
    "        RRV_Assessment_D.loc[len(RRV_Assessment_D.index)] = RRVass_D\n",
    "        RRV_Data_D = RRV_Data_D.append(Daily2021RRV)\n",
    "    \n",
    "    #Mean\n",
    "        Rel_D, Res_D, Vul_D, Daily2021RRV,Max_Severity,MaxSevSI, MaxSevNorm = RRV(Daily2021, i, Demand, Historical_Max_Daily_DC, 'Daily', 2021)\n",
    "        print('For Daily scenario ', Clim_Scenario)\n",
    "        print('Reliability: ', Rel_D, 'Resilience: ', Res_D, 'Vulnerability: ', Vul_D)\n",
    "        RRVass_D = list([i, Demand, Rel_D, Res_D, Vul_D, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "        RRV_Assessment_D = RRV_Assessment_D.reset_index(drop=True)\n",
    "        RRV_Assessment_D.loc[len(RRV_Assessment_D.index)] = RRVass_D\n",
    "        RRV_Data_D = RRV_Data_D.append(Daily2021RRV)\n",
    "       \n",
    "    #High\n",
    "        Rel_D, Res_D, Vul_D, Daily2021RRV,Max_Severity,MaxSevSI, MaxSevNorm = RRV(Daily2021_H, i, DemandH, Historical_Max_Daily_DC, 'Daily', 2021)\n",
    "        print('For Daily scenario ', Clim_Scenario)\n",
    "        print('Reliability: ', Rel_D, 'Resilience: ', Res_D, 'Vulnerability: ', Vul_D)\n",
    "        RRVass_D = list([i, DemandH, Rel_D, Res_D, Vul_D, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "        RRV_Assessment_D = RRV_Assessment_D.reset_index(drop=True)\n",
    "        RRV_Assessment_D.loc[len(RRV_Assessment_D.index)] = RRVass_D\n",
    "        RRV_Data_D = RRV_Data_D.append(Daily2021RRV)\n",
    "       \n",
    "    \n",
    "    #Obs\n",
    "        Rel_D, Res_D, Vul_D, Daily2021RRV,Max_Severity,MaxSevSI, MaxSevNorm = RRV(Daily2021_O, i, DemandO, Historical_Max_Daily_DC, 'Daily', 2021)\n",
    "        print('For Daily scenario ', Clim_Scenario)\n",
    "        print('Reliability: ', Rel_D, 'Resilience: ', Res_D, 'Vulnerability: ', Vul_D)\n",
    "        RRVass_D = list([i, DemandO, Rel_D, Res_D, Vul_D, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "        RRV_Assessment_D = RRV_Assessment_D.reset_index(drop=True)\n",
    "        RRV_Assessment_D.loc[len(RRV_Assessment_D.index)] = RRVass_D\n",
    "        RRV_Data_D = RRV_Data_D.append(Daily2021RRV)\n",
    "        \n",
    "    #Monthly RRV Low\n",
    "        Rel_M, Res_M, Vul_M, Monthly2021RRV, Max_Severity,MaxSevSI, MaxSevNorm = RRV(Monthly2021_L, i, DemandL, Historical_Max_Monthly_DC, 'Monthly', 2021)\n",
    "        print('For Monthly scenario ', Clim_Scenario)\n",
    "        print('Reliability: ', Rel_M, 'Resilience: ', Res_M, 'Vulnerability: ', Vul_M)\n",
    "        RRVass_M = list([i, DemandL, Rel_M, Res_M, Vul_M, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "        RRV_Assessment_M = RRV_Assessment_M.reset_index(drop=True)\n",
    "        RRV_Assessment_M.loc[len(RRV_Assessment_M.index)] = RRVass_M\n",
    "        Monthly2021RRV['AveT_Demands'] = np.array(MaveDemandT[3:10])\n",
    "        Monthly2021RRV['AveT_Stream'] = np.array(MaveStreamT[3:10])\n",
    "        Monthly2021RRV['Perc_Demand'] = ((Monthly2021RRV['SLCDPU_Prod_Demands']-Monthly2021RRV['AveT_Demands'])/Monthly2021RRV['AveT_Demands'])*100\n",
    "        Monthly2021RRV['Perc_Stream'] = ((Monthly2021RRV['SLCDPU_Streamflow']-Monthly2021RRV['AveT_Stream'])/Monthly2021RRV['AveT_Stream'])*100\n",
    "        RRV_Data_M = RRV_Data_M.append(Monthly2021RRV)\n",
    "        \n",
    "    #Monthly RRV Mean\n",
    "        Rel_M, Res_M, Vul_M, Monthly2021RRV, Max_Severity,MaxSevSI, MaxSevNorm = RRV(Monthly2021, i, Demand, Historical_Max_Monthly_DC, 'Monthly', 2021)\n",
    "        print('For Monthly scenario ', Clim_Scenario)\n",
    "        print('Reliability: ', Rel_M, 'Resilience: ', Res_M, 'Vulnerability: ', Vul_M)\n",
    "        RRVass_M = list([i, Demand, Rel_M, Res_M, Vul_M, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "        RRV_Assessment_M = RRV_Assessment_M.reset_index(drop=True)\n",
    "        RRV_Assessment_M.loc[len(RRV_Assessment_M.index)] = RRVass_M\n",
    "        Monthly2021RRV['AveT_Demands'] = np.array(MaveDemandT[3:10])\n",
    "        Monthly2021RRV['AveT_Stream'] = np.array(MaveStreamT[3:10])\n",
    "        Monthly2021RRV['Perc_Demand'] = ((Monthly2021RRV['SLCDPU_Prod_Demands']-Monthly2021RRV['AveT_Demands'])/Monthly2021RRV['AveT_Demands'])*100\n",
    "        Monthly2021RRV['Perc_Stream'] = ((Monthly2021RRV['SLCDPU_Streamflow']-Monthly2021RRV['AveT_Stream'])/Monthly2021RRV['AveT_Stream'])*100\n",
    "        RRV_Data_M = RRV_Data_M.append(Monthly2021RRV)\n",
    "        \n",
    "    #Monthly RRV High\n",
    "        Rel_M, Res_M, Vul_M, Monthly2021RRV, Max_Severity,MaxSevSI, MaxSevNorm = RRV(Monthly2021_H, i, DemandH, Historical_Max_Monthly_DC, 'Monthly', 2021)\n",
    "        print('For Monthly scenario ', Clim_Scenario)\n",
    "        print('Reliability: ', Rel_M, 'Resilience: ', Res_M, 'Vulnerability: ', Vul_M)\n",
    "        RRVass_M = list([i, DemandH, Rel_M, Res_M, Vul_M, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "        RRV_Assessment_M = RRV_Assessment_M.reset_index(drop=True)\n",
    "        RRV_Assessment_M.loc[len(RRV_Assessment_M.index)] = RRVass_M\n",
    "        Monthly2021RRV['AveT_Demands'] = np.array(MaveDemandT[3:10])\n",
    "        Monthly2021RRV['AveT_Stream'] = np.array(MaveStreamT[3:10])\n",
    "        Monthly2021RRV['Perc_Demand'] = ((Monthly2021RRV['SLCDPU_Prod_Demands']-Monthly2021RRV['AveT_Demands'])/Monthly2021RRV['AveT_Demands'])*100\n",
    "        Monthly2021RRV['Perc_Stream'] = ((Monthly2021RRV['SLCDPU_Streamflow']-Monthly2021RRV['AveT_Stream'])/Monthly2021RRV['AveT_Stream'])*100\n",
    "        RRV_Data_M = RRV_Data_M.append(Monthly2021RRV)\n",
    "        \n",
    "    #Monthly RRV Obs\n",
    "        Rel_M, Res_M, Vul_M, Monthly2021RRV, Max_Severity,MaxSevSI, MaxSevNorm = RRV(Monthly2021_O, i, DemandO, Historical_Max_Monthly_DC, 'Monthly', 2021)\n",
    "        print('For Monthly scenario ', Clim_Scenario)\n",
    "        print('Reliability: ', Rel_M, 'Resilience: ', Res_M, 'Vulnerability: ', Vul_M)\n",
    "        RRVass_M = list([i, DemandO, Rel_M, Res_M, Vul_M, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "        RRV_Assessment_M = RRV_Assessment_M.reset_index(drop=True)\n",
    "        RRV_Assessment_M.loc[len(RRV_Assessment_M.index)] = RRVass_M\n",
    "        Monthly2021RRV['AveT_Demands'] = np.array(MaveDemandT[3:10])\n",
    "        Monthly2021RRV['AveT_Stream'] = np.array(MaveStreamT[3:10])\n",
    "        Monthly2021RRV['Perc_Demand'] = ((Monthly2021RRV['SLCDPU_Prod_Demands']-Monthly2021RRV['AveT_Demands'])/Monthly2021RRV['AveT_Demands'])*100\n",
    "        Monthly2021RRV['Perc_Stream'] = ((Monthly2021RRV['SLCDPU_Streamflow']-Monthly2021RRV['AveT_Stream'])/Monthly2021RRV['AveT_Stream'])*100\n",
    "        RRV_Data_M = RRV_Data_M.append(Monthly2021RRV)\n",
    "        \n",
    "        \n",
    "RRV_Assessment_D = RRV_Assessment_D.drop_duplicates()\n",
    "RRV_Assessment_M = RRV_Assessment_M.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.6074766355140186 Resilience:  42.5 Vulnerability:  0.3540292966762114\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.42857142857142855 Resilience:  5.0 Vulnerability:  0.39853556357413344\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.7570093457943925 Resilience:  17.666666666666668 Vulnerability:  0.2490012806123898\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.7142857142857143 Resilience:  1.5 Vulnerability:  0.2553748440593089\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.7570093457943925 Resilience:  8.833333333333334 Vulnerability:  0.306354792592737\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.42857142857142855 Resilience:  2.5 Vulnerability:  0.3416437702015835\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.616822429906542 Resilience:  27.666666666666668 Vulnerability:  0.3003560007546465\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.42857142857142855 Resilience:  5.0 Vulnerability:  0.35676095870750674\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.024293758945569833\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.03317811869777296\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.07423685811964738\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.09549131094511536\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.6355140186915887 Resilience:  11.285714285714286 Vulnerability:  0.45973318872781965\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.42857142857142855 Resilience:  5.0 Vulnerability:  0.4417046379279297\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.07597861357876862\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.10186985082399916\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.9065420560747663 Resilience:  5.25 Vulnerability:  0.18461673273259266\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.7142857142857143 Resilience:  3.0 Vulnerability:  0.17895847159826417\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.8691588785046729 Resilience:  9.666666666666666 Vulnerability:  0.17626138354917023\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.7142857142857143 Resilience:  3.0 Vulnerability:  0.1826132544114741\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.0014026440479695714\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.004377852558287953\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.514018691588785 Resilience:  35.0 Vulnerability:  0.3968397200176736\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.2857142857142857 Resilience:  6.0 Vulnerability:  0.46830444676955246\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.6915887850467289 Resilience:  13.4 Vulnerability:  0.3693503133472828\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.5714285714285714 Resilience:  4.0 Vulnerability:  0.3685815199453192\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.06741557159302704\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.1240662515305654\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.5327102803738317 Resilience:  20.2 Vulnerability:  0.3759931588961612\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.2857142857142857 Resilience:  6.0 Vulnerability:  0.434493225479941\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.8037383177570093 Resilience:  4.777777777777778 Vulnerability:  0.2422416846818659\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.42857142857142855 Resilience:  2.5 Vulnerability:  0.3901899669620371\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.8037383177570093 Resilience:  10.75 Vulnerability:  0.31202471279291555\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.5714285714285714 Resilience:  2.0 Vulnerability:  0.2867999583472992\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.5233644859813084 Resilience:  25.750000000000004 Vulnerability:  0.5985406320599944\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.2857142857142857 Resilience:  3.0 Vulnerability:  0.650981189307284\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.03807884622272456\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  1.0 Resilience:  1.0 Vulnerability:  0.063942931505177\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity', 'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Daily scenario  Historical\n",
      "Reliability:  0.5373831775700935 Resilience:  33.333333333333336 Vulnerability:  0.4311720659669085\n",
      "Index(['SLCDPU_Prod_Demands', 'SLCDPU_Streamflow', 'SLCDPU_Shortages',\n",
      "       'SLCDPU_GW', 'SLCDPU_DC_Water_Use', 'Hist_Mean_DC_Delivery', 'Extra_DC',\n",
      "       'Time', 'Clim', 'Demand', 'Zt', 'Wt', 'WRI_s', 'Severity',\n",
      "       'Vulnerability'],\n",
      "      dtype='object')\n",
      "For Monthly scenario  Historical\n",
      "Reliability:  0.2857142857142857 Resilience:  6.0 Vulnerability:  0.4890147230600157\n"
     ]
    }
   ],
   "source": [
    "#need to add to the historical RRV assessments to determine breaks\n",
    "years =  [2001,2002,2003,2004, 2005, 2006, 2007,2008, 2009, 2010,\n",
    "          2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,2020]\n",
    "RRV_Assessment_D = RRV_Assessment_D.reset_index(drop=True)\n",
    "RRV_Assessment_M = RRV_Assessment_M.reset_index(drop=True)\n",
    "\n",
    "Clim_Scenario = 'Historical'\n",
    "modeltype = 'Obs'\n",
    "for y in years:\n",
    "    HistDay = Hist.loc[str(y)+'-01-01':str(y)+'-12-31'].copy()\n",
    "    HistMonth = HistM.loc[str(y)+'-01-01':str(y)+'-12-31'].copy()\n",
    "    #HistMonth.index =  pd.to_datetime(HistMonth.index, format='%m').month_name().str.slice(stop=3)\n",
    "    Rel_D, Res_D, Vul_D, DailyRRV,Max_Severity,MaxSevSI, MaxSevNorm = RRV(HistDay, 'Hist', 'Obs', Historical_Max_Daily_DC, 'Daily',y)\n",
    "    print('For Daily scenario ', Clim_Scenario)\n",
    "    print('Reliability: ', Rel_D, 'Resilience: ', Res_D, 'Vulnerability: ', Vul_D)\n",
    "    RRVass_D = list(['Hist', 'Obs', Rel_D, Res_D, Vul_D, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "    RRV_Assessment_D = RRV_Assessment_D.reset_index(drop=True)\n",
    "    RRV_Assessment_D.loc[len(RRV_Assessment_D.index)] = RRVass_D\n",
    "    RRV_Data_D = RRV_Data_D.append(DailyRRV)\n",
    "\n",
    "    Rel_M, Res_M, Vul_M, MonthlyRRV, Max_Severity,MaxSevSI, MaxSevNorm = RRV(HistMonth, 'Hist', 'Obs', Historical_Max_Monthly_DC, 'Monthly',y)\n",
    "    print('For Monthly scenario ', Clim_Scenario)\n",
    "    print('Reliability: ', Rel_M, 'Resilience: ', Res_M, 'Vulnerability: ', Vul_M)\n",
    "    RRVass_M = list(['Hist', 'Obs', Rel_M, Res_M, Vul_M, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "    RRV_Assessment_M = RRV_Assessment_M.reset_index(drop=True)\n",
    "    RRV_Assessment_M.loc[len(RRV_Assessment_M.index)] = RRVass_M\n",
    "    RRV_Data_M = RRV_Data_M.append(MonthlyRRV)\n",
    "    RRV_Data_M['AveT_Demands'] = MaveDemandT\n",
    "    RRV_Data_M['AveT_Stream'] = MaveStreamT\n",
    "    RRV_Data_M['Perc_Demand'] = ((RRV_Data_M['SLCDPU_Prod_Demands']-RRV_Data_M['AveT_Demands'])/RRV_Data_M['AveT_Demands'])*100\n",
    "    RRV_Data_M['Perc_Stream'] = ((RRV_Data_M['SLCDPU_Streamflow']-RRV_Data_M['AveT_Stream'])/RRV_Data_M['AveT_Stream'])*100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['Drought_Obs', 'Drought_TD', 'Drought_CSD_WDM', 'Average_Obs',\\n       'Average_TD', 'Average_CSD_WDM', 'Surplus_Obs', 'Surplus_TD',\\n       'Surplus_CSD_WDM'],\\n      dtype='object')] are in the [index]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\RJOHNS~1\\AppData\\Local\\Temp/ipykernel_28896/2596042491.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m            'Surplus_Obs', 'Surplus_TD', 'Surplus_CSD_WDM']\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mRRV_AssD\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRRV_Assessment_D\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkeyrows\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mRRV_AssM\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRRV_Assessment_M\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkeyrows\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    929\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 931\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    932\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    933\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1151\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot index with multidimensional key\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1153\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1154\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1155\u001b[0m             \u001b[1;31m# nested tuple slicing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1091\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1092\u001b[0m         \u001b[1;31m# A collection of keys\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1093\u001b[1;33m         \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1094\u001b[0m         return self.obj._reindex_with_indexers(\n\u001b[0;32m   1095\u001b[0m             \u001b[1;33m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1314\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1315\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis)\u001b[0m\n\u001b[0;32m   1372\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1373\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1374\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1375\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['Drought_Obs', 'Drought_TD', 'Drought_CSD_WDM', 'Average_Obs',\\n       'Average_TD', 'Average_CSD_WDM', 'Surplus_Obs', 'Surplus_TD',\\n       'Surplus_CSD_WDM'],\\n      dtype='object')] are in the [index]\""
     ]
    }
   ],
   "source": [
    "keyrows = ['Drought_Obs','Drought_TD',  'Drought_CSD_WDM', \n",
    "           'Average_Obs', 'Average_TD', 'Average_CSD_WDM',\n",
    "           'Surplus_Obs', 'Surplus_TD', 'Surplus_CSD_WDM']\n",
    "\n",
    "RRV_AssD = RRV_Assessment_D.loc[keyrows]\n",
    "RRV_AssM = RRV_Assessment_M.loc[keyrows]\n",
    "\n",
    "RRV_ErrorLD, RRV_ErrorHD = RRV_UncertainDF(RRV_Assessment_D)\n",
    "RRV_ErrorLM, RRV_ErrorHM = RRV_UncertainDF(RRV_Assessment_M)\n",
    "\n",
    "RRVplotsErr(RRV_AssD, 'Day', RRV_ErrorLD, RRV_ErrorHD)\n",
    "RRVplotsErr(RRV_AssM, 'Month', RRV_ErrorLM, RRV_ErrorHM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SLCDPU_Prod_Demands</th>\n",
       "      <th>SLCDPU_Shortages</th>\n",
       "      <th>SLCDPU_GW</th>\n",
       "      <th>SLCDPU_Streamflow</th>\n",
       "      <th>SLCDPU_DC_Water_Use</th>\n",
       "      <th>Hist_Mean_DC_Delivery</th>\n",
       "      <th>Extra_DC</th>\n",
       "      <th>ExtraDC</th>\n",
       "      <th>Zt</th>\n",
       "      <th>Wt</th>\n",
       "      <th>WRI_s</th>\n",
       "      <th>Severity</th>\n",
       "      <th>Vulnerability</th>\n",
       "      <th>Clim</th>\n",
       "      <th>Demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>17.059028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.675241</td>\n",
       "      <td>9.919646</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.007323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003662</td>\n",
       "      <td>Drought</td>\n",
       "      <td>Obs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-02</th>\n",
       "      <td>17.182376</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.203171</td>\n",
       "      <td>9.672950</td>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.002569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.007320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003660</td>\n",
       "      <td>Drought</td>\n",
       "      <td>Obs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-03</th>\n",
       "      <td>17.996473</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.478589</td>\n",
       "      <td>9.327576</td>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.007323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003662</td>\n",
       "      <td>Drought</td>\n",
       "      <td>Obs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-04</th>\n",
       "      <td>17.725108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.096210</td>\n",
       "      <td>9.500263</td>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.002569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003660</td>\n",
       "      <td>Drought</td>\n",
       "      <td>Obs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>18.082817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.113479</td>\n",
       "      <td>9.524933</td>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003662</td>\n",
       "      <td>Drought</td>\n",
       "      <td>Obs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-27</th>\n",
       "      <td>16.923346</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.411629</td>\n",
       "      <td>9.642113</td>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.004404</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003654</td>\n",
       "      <td>Drought</td>\n",
       "      <td>Obs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-28</th>\n",
       "      <td>16.688984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.903435</td>\n",
       "      <td>10.451276</td>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.004404</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003654</td>\n",
       "      <td>Drought</td>\n",
       "      <td>Obs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-29</th>\n",
       "      <td>16.528632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.719999</td>\n",
       "      <td>9.833303</td>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.004404</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003654</td>\n",
       "      <td>Drought</td>\n",
       "      <td>Obs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-30</th>\n",
       "      <td>16.417619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.441233</td>\n",
       "      <td>10.135505</td>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.004404</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003654</td>\n",
       "      <td>Drought</td>\n",
       "      <td>Obs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-31</th>\n",
       "      <td>16.355945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.484404</td>\n",
       "      <td>10.183611</td>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.005504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003650</td>\n",
       "      <td>Drought</td>\n",
       "      <td>Obs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>642 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            SLCDPU_Prod_Demands  SLCDPU_Shortages  SLCDPU_GW  \\\n",
       "2021-04-01            17.059028               0.0   5.675241   \n",
       "2021-04-02            17.182376               0.0   6.203171   \n",
       "2021-04-03            17.996473               0.0   7.478589   \n",
       "2021-04-04            17.725108               0.0   7.096210   \n",
       "2021-04-05            18.082817               0.0   7.113479   \n",
       "...                         ...               ...        ...   \n",
       "2021-10-27            16.923346               0.0   6.411629   \n",
       "2021-10-28            16.688984               0.0   5.903435   \n",
       "2021-10-29            16.528632               0.0   6.719999   \n",
       "2021-10-30            16.417619               0.0   6.441233   \n",
       "2021-10-31            16.355945               0.0   6.484404   \n",
       "\n",
       "            SLCDPU_Streamflow  SLCDPU_DC_Water_Use  Hist_Mean_DC_Delivery  \\\n",
       "2021-04-01           9.919646             0.007339               0.002202   \n",
       "2021-04-02           9.672950             0.007339               0.002569   \n",
       "2021-04-03           9.327576             0.007339               0.002202   \n",
       "2021-04-04           9.500263             0.007339               0.002569   \n",
       "2021-04-05           9.524933             0.007339               0.002202   \n",
       "...                       ...                  ...                    ...   \n",
       "2021-10-27           9.642113             0.007339               0.004404   \n",
       "2021-10-28          10.451276             0.007339               0.004404   \n",
       "2021-10-29           9.833303             0.007339               0.004404   \n",
       "2021-10-30          10.135505             0.007339               0.004404   \n",
       "2021-10-31          10.183611             0.007339               0.005504   \n",
       "\n",
       "            Extra_DC ExtraDC Zt Wt     WRI_s  Severity  Vulnerability  \\\n",
       "2021-04-01       0.0     NaN  1  0  0.007323       0.0       0.003662   \n",
       "2021-04-02       0.0     NaN  1  0  0.007320       0.0       0.003660   \n",
       "2021-04-03       0.0     NaN  1  0  0.007323       0.0       0.003662   \n",
       "2021-04-04       0.0     NaN  1  0  0.007320       0.0       0.003660   \n",
       "2021-04-05       0.0     NaN  1  0  0.007323       0.0       0.003662   \n",
       "...              ...     ... .. ..       ...       ...            ...   \n",
       "2021-10-27       0.0     NaN  1  0  0.007307       0.0       0.003654   \n",
       "2021-10-28       0.0     NaN  1  0  0.007307       0.0       0.003654   \n",
       "2021-10-29       0.0     NaN  1  0  0.007307       0.0       0.003654   \n",
       "2021-10-30       0.0     NaN  1  0  0.007307       0.0       0.003654   \n",
       "2021-10-31       0.0     NaN  1  0  0.007299       0.0       0.003650   \n",
       "\n",
       "               Clim Demand  \n",
       "2021-04-01  Drought    Obs  \n",
       "2021-04-02  Drought    Obs  \n",
       "2021-04-03  Drought    Obs  \n",
       "2021-04-04  Drought    Obs  \n",
       "2021-04-05  Drought    Obs  \n",
       "...             ...    ...  \n",
       "2021-10-27  Drought    Obs  \n",
       "2021-10-28  Drought    Obs  \n",
       "2021-10-29  Drought    Obs  \n",
       "2021-10-30  Drought    Obs  \n",
       "2021-10-31  Drought    Obs  \n",
       "\n",
       "[642 rows x 15 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RRV_Data_D.loc[(RRV_Data_D['Clim']== 'Drought') & (RRV_Data_D['Demand']=='Obs')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = 'CSD_WDM_Hi'\n",
    "s = 'Drought'\n",
    "\n",
    "x = RRV_Data_D.loc[(RRV_Data_D['Clim']== s) & (RRV_Data_D['Demand']==d)]\n",
    "y = RRV_Data_M.loc[(RRV_Data_M['Clim']== s) & (RRV_Data_M['Demand']==d)]\n",
    "\n",
    "x = x[~x.index.duplicated(keep='first')]\n",
    "y = y[~y.index.duplicated(keep='first')]\n",
    "\n",
    "#x.loc['2021-06-15':'2021-07-30']\n",
    "#number of days of extra DC water\n",
    "DCextraD = len(x.loc[x['Extra_DC']> 5])\n",
    "DCextraM = len(y.loc[y['Extra_DC']> 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>SLCDPU_Prod_Demands</th>\n",
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       "      <th>AveT_Demands</th>\n",
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       "      <th>Time</th>\n",
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       "      <th>2021-06-30</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "    <tr>\n",
       "      <th>2021-07-31</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
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       "      <td>CSD_WDM_Hi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>1628.946023</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>331.152376</td>\n",
       "      <td>221.536708</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "    <tr>\n",
       "      <th>2021-10-31</th>\n",
       "      <td>796.211340</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>306.109031</td>\n",
       "      <td>384.754482</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "            SLCDPU_Prod_Demands  SLCDPU_Shortages   SLCDPU_GW  \\\n",
       "2021-06-30          1679.469364           0.03212  327.180570   \n",
       "2021-07-31          1937.427036           0.00000  342.190788   \n",
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       "\n",
       "            SLCDPU_Streamflow  SLCDPU_DC_Water_Use  Hist_Mean_DC_Delivery  \\\n",
       "2021-06-30        1333.663246           152.461890              15.654484   \n",
       "2021-07-31         584.003441           755.901214             294.770257   \n",
       "2021-08-31         340.724180           972.635984             532.207794   \n",
       "2021-09-30         221.536708           942.822773             293.628533   \n",
       "2021-10-31         384.754482            88.622578              17.575408   \n",
       "\n",
       "              Extra_DC Zt Wt  WRI_s  Severity  Vulnerability     Clim  \\\n",
       "2021-06-30  137.142002  0  0    1.0  0.210230       0.605115  Drought   \n",
       "2021-07-31  461.130957  0  0    1.0  0.706884       0.853442  Drought   \n",
       "2021-08-31  440.428191  0  0    1.0  0.675148       0.837574  Drought   \n",
       "2021-09-30  649.194239  0  0    1.0  0.995172       0.997586  Drought   \n",
       "2021-10-31   71.047170  0  0    1.0  0.108911       0.554455  Drought   \n",
       "\n",
       "                Demand  AveT_Demands  AveT_Stream  Perc_Demand  Perc_Stream  \\\n",
       "2021-06-30  CSD_WDM_Hi           NaN          NaN          NaN          NaN   \n",
       "2021-07-31  CSD_WDM_Hi           NaN          NaN          NaN          NaN   \n",
       "2021-08-31  CSD_WDM_Hi           NaN          NaN          NaN          NaN   \n",
       "2021-09-30  CSD_WDM_Hi           NaN          NaN          NaN          NaN   \n",
       "2021-10-31  CSD_WDM_Hi           NaN          NaN          NaN          NaN   \n",
       "\n",
       "           Time  \n",
       "2021-06-30  NaT  \n",
       "2021-07-31  NaT  \n",
       "2021-08-31  NaT  \n",
       "2021-09-30  NaT  \n",
       "2021-10-31  NaT  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.loc[y['Extra_DC']> 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>1679.469364</td>\n",
       "      <td>0.03212</td>\n",
       "      <td>327.180570</td>\n",
       "      <td>1333.663246</td>\n",
       "      <td>152.461890</td>\n",
       "      <td>15.654484</td>\n",
       "      <td>137.142002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.210230</td>\n",
       "      <td>0.605115</td>\n",
       "      <td>Drought</td>\n",
       "      <td>CSD_WDM_Hi</td>\n",
       "      <td>1459.747721</td>\n",
       "      <td>3204.590291</td>\n",
       "      <td>15.052028</td>\n",
       "      <td>-58.382722</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-31</th>\n",
       "      <td>1937.427036</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>342.190788</td>\n",
       "      <td>584.003441</td>\n",
       "      <td>755.901214</td>\n",
       "      <td>294.770257</td>\n",
       "      <td>461.130957</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.706884</td>\n",
       "      <td>0.853442</td>\n",
       "      <td>Drought</td>\n",
       "      <td>CSD_WDM_Hi</td>\n",
       "      <td>1839.911808</td>\n",
       "      <td>1616.449267</td>\n",
       "      <td>5.299995</td>\n",
       "      <td>-63.871217</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>1829.300179</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>342.190788</td>\n",
       "      <td>340.724180</td>\n",
       "      <td>972.635984</td>\n",
       "      <td>532.207794</td>\n",
       "      <td>440.428191</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.675148</td>\n",
       "      <td>0.837574</td>\n",
       "      <td>Drought</td>\n",
       "      <td>CSD_WDM_Hi</td>\n",
       "      <td>1722.337728</td>\n",
       "      <td>659.257501</td>\n",
       "      <td>6.210306</td>\n",
       "      <td>-48.316981</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>1628.946023</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>331.152376</td>\n",
       "      <td>221.536708</td>\n",
       "      <td>942.822773</td>\n",
       "      <td>293.628533</td>\n",
       "      <td>649.194239</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995172</td>\n",
       "      <td>0.997586</td>\n",
       "      <td>Drought</td>\n",
       "      <td>CSD_WDM_Hi</td>\n",
       "      <td>1238.755594</td>\n",
       "      <td>452.485918</td>\n",
       "      <td>31.498581</td>\n",
       "      <td>-51.040088</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-31</th>\n",
       "      <td>796.211340</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>306.109031</td>\n",
       "      <td>384.754482</td>\n",
       "      <td>88.622578</td>\n",
       "      <td>17.575408</td>\n",
       "      <td>71.047170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.108911</td>\n",
       "      <td>0.554455</td>\n",
       "      <td>Drought</td>\n",
       "      <td>CSD_WDM_Hi</td>\n",
       "      <td>630.213126</td>\n",
       "      <td>401.770104</td>\n",
       "      <td>26.340012</td>\n",
       "      <td>-4.235164</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SLCDPU_Prod_Demands  SLCDPU_Shortages   SLCDPU_GW  \\\n",
       "2021-06-30          1679.469364           0.03212  327.180570   \n",
       "2021-07-31          1937.427036           0.00000  342.190788   \n",
       "2021-08-31          1829.300179           0.00000  342.190788   \n",
       "2021-09-30          1628.946023           0.00000  331.152376   \n",
       "2021-10-31           796.211340           0.00000  306.109031   \n",
       "2021-06-30          1679.469364           0.03212  327.180570   \n",
       "2021-07-31          1937.427036           0.00000  342.190788   \n",
       "2021-08-31          1829.300179           0.00000  342.190788   \n",
       "2021-09-30          1628.946023           0.00000  331.152376   \n",
       "2021-10-31           796.211340           0.00000  306.109031   \n",
       "\n",
       "            SLCDPU_Streamflow  SLCDPU_DC_Water_Use  Hist_Mean_DC_Delivery  \\\n",
       "2021-06-30        1333.663246           152.461890              15.654484   \n",
       "2021-07-31         584.003441           755.901214             294.770257   \n",
       "2021-08-31         340.724180           972.635984             532.207794   \n",
       "2021-09-30         221.536708           942.822773             293.628533   \n",
       "2021-10-31         384.754482            88.622578              17.575408   \n",
       "2021-06-30        1333.663246           152.461890              15.654484   \n",
       "2021-07-31         584.003441           755.901214             294.770257   \n",
       "2021-08-31         340.724180           972.635984             532.207794   \n",
       "2021-09-30         221.536708           942.822773             293.628533   \n",
       "2021-10-31         384.754482            88.622578              17.575408   \n",
       "\n",
       "              Extra_DC Zt Wt  WRI_s  Severity  Vulnerability     Clim  \\\n",
       "2021-06-30  137.142002  0  0    1.0  0.210230       0.605115  Drought   \n",
       "2021-07-31  461.130957  0  0    1.0  0.706884       0.853442  Drought   \n",
       "2021-08-31  440.428191  0  0    1.0  0.675148       0.837574  Drought   \n",
       "2021-09-30  649.194239  0  0    1.0  0.995172       0.997586  Drought   \n",
       "2021-10-31   71.047170  0  0    1.0  0.108911       0.554455  Drought   \n",
       "2021-06-30  137.142002  0  0    1.0  0.210230       0.605115  Drought   \n",
       "2021-07-31  461.130957  0  0    1.0  0.706884       0.853442  Drought   \n",
       "2021-08-31  440.428191  0  0    1.0  0.675148       0.837574  Drought   \n",
       "2021-09-30  649.194239  0  0    1.0  0.995172       0.997586  Drought   \n",
       "2021-10-31   71.047170  0  0    1.0  0.108911       0.554455  Drought   \n",
       "\n",
       "                Demand  AveT_Demands  AveT_Stream  Perc_Demand  Perc_Stream  \\\n",
       "2021-06-30  CSD_WDM_Hi           NaN          NaN          NaN          NaN   \n",
       "2021-07-31  CSD_WDM_Hi           NaN          NaN          NaN          NaN   \n",
       "2021-08-31  CSD_WDM_Hi           NaN          NaN          NaN          NaN   \n",
       "2021-09-30  CSD_WDM_Hi           NaN          NaN          NaN          NaN   \n",
       "2021-10-31  CSD_WDM_Hi           NaN          NaN          NaN          NaN   \n",
       "2021-06-30  CSD_WDM_Hi   1459.747721  3204.590291    15.052028   -58.382722   \n",
       "2021-07-31  CSD_WDM_Hi   1839.911808  1616.449267     5.299995   -63.871217   \n",
       "2021-08-31  CSD_WDM_Hi   1722.337728   659.257501     6.210306   -48.316981   \n",
       "2021-09-30  CSD_WDM_Hi   1238.755594   452.485918    31.498581   -51.040088   \n",
       "2021-10-31  CSD_WDM_Hi    630.213126   401.770104    26.340012    -4.235164   \n",
       "\n",
       "           Time  \n",
       "2021-06-30  NaT  \n",
       "2021-07-31  NaT  \n",
       "2021-08-31  NaT  \n",
       "2021-09-30  NaT  \n",
       "2021-10-31  NaT  \n",
       "2021-06-30  NaT  \n",
       "2021-07-31  NaT  \n",
       "2021-08-31  NaT  \n",
       "2021-09-30  NaT  \n",
       "2021-10-31  NaT  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.loc[y['Extra_DC']> 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "MaveStreamT = HistMonthly['SLCDPU_Streamflow']\n",
    "MaveDemandT = HistMonthly['SLCDPU_Prod_Demands']\n",
    "MaveDC = HistMonthly['SLCDPU_DC_Water_Use']\n",
    "MaveExtraDC = HistMonthly['Extra_DC']\n",
    "SeasonExtraDC = MaveExtraDC.sum()\n",
    "\n",
    "NonRRVassessment =pd.DataFrame(columns =['Climate', 'Demand','DCmaxD','DCmaxM', 'maxDemD','maxDemM','maxDemMMonth', 'DCextraD', 'DCextraM', 'DCmaxDate', 'DCAAmax', 'DCAAmaxDate',\n",
    "                        'DCmaxM','DCmaxMonth','DCAAmaxM','DCAAmaxMonth', 'SeasonD', 'SeasonDR', \n",
    "                        'SeasonDC','SeasonDCextra', 'SeasonStrm', \n",
    "                        'SeasonStrmRatio'])\n",
    "sup = ['Drought']\n",
    "Demands = ['Obs', 'TD', 'CSD_WDM_Lo', 'CSD_WDM', 'CSD_WDM_Hi']\n",
    "\n",
    "for s in sup:\n",
    "    for d in Demands:    \n",
    "        x = RRV_Data_D.loc[(RRV_Data_D['Clim']== s) & (RRV_Data_D['Demand']==d)]\n",
    "        y = RRV_Data_M.loc[(RRV_Data_M['Clim']== s) & (RRV_Data_M['Demand']==d)]\n",
    "        x = x[~x.index.duplicated(keep='first')]\n",
    "        y = y[~y.index.duplicated(keep='first')]\n",
    "        #x.loc['2021-06-15':'2021-07-30']\n",
    "        #number of days of extra DC water\n",
    "        DCextraD = len(x.loc[x['Extra_DC']> 0])\n",
    "        DCextraM = len(y.loc[y['Extra_DC']> 100])\n",
    "        #max slcdpu demand\n",
    "        maxDemD = x['SLCDPU_Prod_Demands'].max()\n",
    "        \n",
    "        #Max Day\n",
    "        DCmaxD = x['SLCDPU_DC_Water_Use'].max()\n",
    "        DCmaxDate = x.loc[x['SLCDPU_DC_Water_Use']==DCmaxD].index\n",
    "        DCmaxDate = DCmaxDate.format('%Y-%m-%d')[1]\n",
    "        \n",
    "        #DC above average max\n",
    "        DCAAmax = x['Extra_DC'].max()\n",
    "        DCAAmaxM = y['Extra_DC'].max()\n",
    "        DCAAmaxDate = x.loc[x['Extra_DC']==DCAAmax].index\n",
    "        DCAAmaxDate = DCAAmaxDate.format('%Y-%m-%d')[1]\n",
    "        DCAAmax = DCAAmax\n",
    "        \n",
    "        #peak month\n",
    "        maxDemM = y['SLCDPU_Prod_Demands'].max()\n",
    "        DCmaxM = y['SLCDPU_DC_Water_Use'].max()\n",
    "        maxDemMMonth = y.loc[y['SLCDPU_Prod_Demands']==maxDemM].index\n",
    "        maxDemMMonth = maxDemMMonth.format('%Y-%m-%d')[1]\n",
    "        \n",
    "        DCmaxMonth = y.loc[y['SLCDPU_DC_Water_Use']==DCmaxM].index\n",
    "        DCmaxMonth = DCmaxMonth.format('%Y-%m-%d')[1]\n",
    "        \n",
    "        #DC above aveage month\n",
    "        DCAAmaxM = y['Extra_DC'].max()\n",
    "        DCAAmaxMonth = y.loc[y['Extra_DC']==DCAAmaxM].index\n",
    "        DCAAmaxMonth = DCAAmaxMonth.format('%Y-%m-%d')[1]\n",
    "        DCAAmaxM =DCAAmaxM\n",
    "        \n",
    "        \n",
    "        #Find the total season demands\n",
    "        SeasonD = y['SLCDPU_Prod_Demands'].sum()\n",
    "        SeasonD_Perc = ((SeasonD-SeasAveDemand)/SeasAveDemand)*100\n",
    "        SeasonDR =SeasonD/SeasAveDemand\n",
    "        \n",
    "        SeasonDC = y['SLCDPU_DC_Water_Use'].sum()\n",
    "        SeasonDC_Perc = ((SeasonDC-SeasAveDC)/SeasAveDC)*100\n",
    "        SeasonDC = SeasonDC\n",
    "        \n",
    "        SeasonDCextra = y['Extra_DC'].sum()\n",
    "        SeasonDCextra_Perc = ((SeasonDCextra-SeasonExtraDC)/SeasonExtraDC)*100\n",
    "        SeasonDCextra = SeasonDCextra\n",
    "        \n",
    "        SeasonStrm = y['SLCDPU_Streamflow'].sum()\n",
    "        SeasonStrm_Perc = ((SeasonStrm-SeasAveStream)/SeasAveStream)*100\n",
    "        SeasonStrmRatio = ((SeasonStrm)/SeasAveStream)\n",
    "        \n",
    "        \n",
    "        SimStats =list([s, d,DCmaxD,DCmaxM, maxDemD,maxDemM,maxDemMMonth, DCextraD, DCextraM, DCmaxDate, DCAAmax, DCAAmaxDate,DCmaxM, DCmaxMonth,\n",
    "                         DCAAmaxM,DCAAmaxMonth, SeasonD,SeasonDR,\n",
    "                        SeasonDC,SeasonDCextra, SeasonStrm, SeasonStrmRatio])\n",
    "        \n",
    "        #print(SimStats)\n",
    "        NonRRVassessment.loc[len(NonRRVassessment)] = SimStats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Climate</th>\n",
       "      <th>Demand</th>\n",
       "      <th>DCmaxD</th>\n",
       "      <th>DCmaxM</th>\n",
       "      <th>maxDemD</th>\n",
       "      <th>maxDemM</th>\n",
       "      <th>maxDemMMonth</th>\n",
       "      <th>DCextraD</th>\n",
       "      <th>DCextraM</th>\n",
       "      <th>DCmaxDate</th>\n",
       "      <th>...</th>\n",
       "      <th>DCmaxM</th>\n",
       "      <th>DCmaxMonth</th>\n",
       "      <th>DCAAmaxM</th>\n",
       "      <th>DCAAmaxMonth</th>\n",
       "      <th>SeasonD</th>\n",
       "      <th>SeasonDR</th>\n",
       "      <th>SeasonDC</th>\n",
       "      <th>SeasonDCextra</th>\n",
       "      <th>SeasonStrm</th>\n",
       "      <th>SeasonStrmRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Drought</td>\n",
       "      <td>Obs</td>\n",
       "      <td>26.889864</td>\n",
       "      <td>754.347029</td>\n",
       "      <td>57.245807</td>\n",
       "      <td>1749.160984</td>\n",
       "      <td>2021-07-31</td>\n",
       "      <td>111</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-08-26</td>\n",
       "      <td>...</td>\n",
       "      <td>754.347029</td>\n",
       "      <td>2021-08-31</td>\n",
       "      <td>356.637453</td>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>8260.442873</td>\n",
       "      <td>1.026502</td>\n",
       "      <td>2077.568545</td>\n",
       "      <td>924.189095</td>\n",
       "      <td>4962.704489</td>\n",
       "      <td>0.526174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Drought</td>\n",
       "      <td>TD</td>\n",
       "      <td>40.433474</td>\n",
       "      <td>1127.548738</td>\n",
       "      <td>72.577963</td>\n",
       "      <td>2248.683379</td>\n",
       "      <td>2021-07-31</td>\n",
       "      <td>127</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-08-03</td>\n",
       "      <td>...</td>\n",
       "      <td>1127.548738</td>\n",
       "      <td>2021-07-31</td>\n",
       "      <td>832.778481</td>\n",
       "      <td>2021-07-31</td>\n",
       "      <td>10566.174702</td>\n",
       "      <td>1.313029</td>\n",
       "      <td>3042.509877</td>\n",
       "      <td>1888.429227</td>\n",
       "      <td>5753.458914</td>\n",
       "      <td>0.610015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Drought</td>\n",
       "      <td>CSD_WDM_Lo</td>\n",
       "      <td>19.390306</td>\n",
       "      <td>514.313054</td>\n",
       "      <td>51.226424</td>\n",
       "      <td>1425.594510</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-09-12</td>\n",
       "      <td>...</td>\n",
       "      <td>514.313054</td>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>220.684521</td>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>7183.614833</td>\n",
       "      <td>0.892688</td>\n",
       "      <td>1194.398025</td>\n",
       "      <td>293.299750</td>\n",
       "      <td>5753.458914</td>\n",
       "      <td>0.610015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Drought</td>\n",
       "      <td>CSD_WDM</td>\n",
       "      <td>26.902199</td>\n",
       "      <td>721.302100</td>\n",
       "      <td>56.086336</td>\n",
       "      <td>1689.053503</td>\n",
       "      <td>2021-07-31</td>\n",
       "      <td>111</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-09-11</td>\n",
       "      <td>...</td>\n",
       "      <td>721.302100</td>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>427.673566</td>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>8271.803224</td>\n",
       "      <td>1.027914</td>\n",
       "      <td>2026.220081</td>\n",
       "      <td>878.167552</td>\n",
       "      <td>4962.704489</td>\n",
       "      <td>0.526174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drought</td>\n",
       "      <td>CSD_WDM_Hi</td>\n",
       "      <td>35.536559</td>\n",
       "      <td>972.635984</td>\n",
       "      <td>63.326863</td>\n",
       "      <td>1937.427036</td>\n",
       "      <td>2021-07-31</td>\n",
       "      <td>123</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-09-06</td>\n",
       "      <td>...</td>\n",
       "      <td>972.635984</td>\n",
       "      <td>2021-08-31</td>\n",
       "      <td>649.194239</td>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>9409.158127</td>\n",
       "      <td>1.169250</td>\n",
       "      <td>2912.682975</td>\n",
       "      <td>1758.942558</td>\n",
       "      <td>5753.458914</td>\n",
       "      <td>0.610015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Climate      Demand     DCmaxD       DCmaxM    maxDemD      maxDemM  \\\n",
       "0  Drought         Obs  26.889864   754.347029  57.245807  1749.160984   \n",
       "1  Drought          TD  40.433474  1127.548738  72.577963  2248.683379   \n",
       "2  Drought  CSD_WDM_Lo  19.390306   514.313054  51.226424  1425.594510   \n",
       "3  Drought     CSD_WDM  26.902199   721.302100  56.086336  1689.053503   \n",
       "4  Drought  CSD_WDM_Hi  35.536559   972.635984  63.326863  1937.427036   \n",
       "\n",
       "  maxDemMMonth DCextraD DCextraM   DCmaxDate  ...       DCmaxM  DCmaxMonth  \\\n",
       "0   2021-07-31      111        3  2021-08-26  ...   754.347029  2021-08-31   \n",
       "1   2021-07-31      127        4  2021-08-03  ...  1127.548738  2021-07-31   \n",
       "2   2021-06-30       51        1  2021-09-12  ...   514.313054  2021-09-30   \n",
       "3   2021-07-31      111        3  2021-09-11  ...   721.302100  2021-09-30   \n",
       "4   2021-07-31      123        4  2021-09-06  ...   972.635984  2021-08-31   \n",
       "\n",
       "     DCAAmaxM DCAAmaxMonth       SeasonD  SeasonDR     SeasonDC  \\\n",
       "0  356.637453   2021-09-30   8260.442873  1.026502  2077.568545   \n",
       "1  832.778481   2021-07-31  10566.174702  1.313029  3042.509877   \n",
       "2  220.684521   2021-09-30   7183.614833  0.892688  1194.398025   \n",
       "3  427.673566   2021-09-30   8271.803224  1.027914  2026.220081   \n",
       "4  649.194239   2021-09-30   9409.158127  1.169250  2912.682975   \n",
       "\n",
       "   SeasonDCextra   SeasonStrm  SeasonStrmRatio  \n",
       "0     924.189095  4962.704489         0.526174  \n",
       "1    1888.429227  5753.458914         0.610015  \n",
       "2     293.299750  5753.458914         0.610015  \n",
       "3     878.167552  4962.704489         0.526174  \n",
       "4    1758.942558  5753.458914         0.610015  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NonRRVassessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Climate', 'Demand', 'DCmaxD', 'DCmaxM', 'maxDemD', 'maxDemM',\n",
       "       'maxDemMMonth', 'DCextraD', 'DCextraM', 'DCmaxDate', 'DCAAmax',\n",
       "       'DCAAmaxDate', 'DCmaxM', 'DCmaxMonth', 'DCAAmaxM', 'DCAAmaxMonth',\n",
       "       'MaxPercHist_DM', 'MaxPercHist_StrmM', 'SeasonD', 'SeasonDR',\n",
       "       'SeasonD_Perc', 'SeasonDC', 'SeasonDC_Perc', 'SeasonDCextra',\n",
       "       'SeasonDCextra_Perc', 'SeasonStrm', 'SeasonStrm_Perc',\n",
       "       'SeasonStrmRatio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NonRRVassessment.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2021-09-30\n",
       "1    2021-09-30\n",
       "2    2021-08-31\n",
       "Name: DCmaxMonth, dtype: object"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns =['Climate', 'Demand', 'DCmaxD', 'maxDemD','maxDemM', 'DCextraD', 'DCextraM', 'DCmaxDate', 'DCAAmax', 'DCAAmaxDate',\n",
    "                #        'DCmaxM','DCmaxMonth','DCAAmaxM','DCAAmaxMonth']\n",
    "NonRRVassessment['DCmaxMonth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Sensitivity(df1, df2):\n",
    "    df = pd.concat([df1, df2], axis=1)\n",
    "    df = df.loc[:,~df.columns.duplicated()]\n",
    "    DemVarDrought = df[df['Climate'] == 'Drought'].copy()\n",
    "    DemVarAve = df[df['Climate'] == 'Average'].copy()\n",
    "    DemVarSurplus = df[df['Climate'] == 'Surplus'].copy()\n",
    "    \n",
    "    SupVarObs = df[df['Demand'] == 'OBSD'].copy()\n",
    "    SupVarTrad = df[df['Demand'] == 'TD'].copy()\n",
    "    SupVarCSD = df[df['Demand'] == 'CSD_WDM'].copy()\n",
    "    \n",
    "    Demands = [DemVarDrought, DemVarAve, DemVarSurplus]\n",
    "    Supply = [SupVarObs, SupVarTrad, SupVarCSD]\n",
    "    \n",
    "    RelRange_D = []\n",
    "    ResRange_D = []\n",
    "    AVRange_D = []\n",
    "    PSRange_D = []\n",
    "    \n",
    "    #for Demand\n",
    "    for i in Demands:\n",
    "        Rel = (max(i['Reliability'])-min(i['Reliability']))/(max(i['SeasonDR'])-min(i['SeasonDR']))\n",
    "        Res = (max(i['Resilience'])-min(i['Resilience']))/(max(i['SeasonDR'])-min(i['SeasonDR']))\n",
    "        AV = (max(i['Vulnerability'])-min(i['Vulnerability']))/(max(i['SeasonDR'])-min(i['SeasonDR']))\n",
    "        PS = (max(i['Maximum_Severity'])-min(i['Maximum_Severity']))/(max(i['SeasonDR'])-min(i['SeasonDR']))\n",
    "        RelRange_D.append(Rel)\n",
    "        ResRange_D.append(Res)\n",
    "        AVRange_D.append(AV)\n",
    "        PSRange_D.append(PS)\n",
    "        \n",
    "        \n",
    "    RelRange_S = []\n",
    "    ResRange_S = []\n",
    "    AVRange_S = []\n",
    "    PSRange_S = []\n",
    "    \n",
    "    #for Supply\n",
    "    for i in Supply:\n",
    "        Rel = (max(i['Reliability'])-min(i['Reliability']))/(max(i['SeasonStrmRatio'])-min(i['SeasonStrmRatio']))\n",
    "        Res = (max(i['Resilience'])-min(i['Resilience']))/(max(i['SeasonStrmRatio'])-min(i['SeasonStrmRatio']))\n",
    "        AV = (max(i['Vulnerability'])-min(i['Vulnerability']))/(max(i['SeasonStrmRatio'])-min(i['SeasonStrmRatio']))\n",
    "        PS = (max(i['Maximum_Severity'])-min(i['Maximum_Severity']))/(max(i['SeasonStrmRatio'])-min(i['SeasonStrmRatio']))\n",
    "        RelRange_S.append(Rel)\n",
    "        ResRange_S.append(Res)\n",
    "        AVRange_S.append(AV)\n",
    "        PSRange_S.append(PS)\n",
    "    \n",
    "    DemandSensitivity = [max(RelRange_D), max(ResRange_D), max(AVRange_D), max(PSRange_D)]\n",
    "    SupplySensitivity = [ max(RelRange_S),max(ResRange_S), max(AVRange_S),max(PSRange_S)] \n",
    "    \n",
    "    SensitivityDF = pd.DataFrame(columns=['Metric', 'Demand', 'Supply'])\n",
    "    SensitivityDF['Metric'] = ['Reliability', 'Resilience', 'Vulnerability', 'Severity']\n",
    "    SensitivityDF['Demand'] = DemandSensitivity\n",
    "    SensitivityDF['Supply'] = SupplySensitivity\n",
    "    return SensitivityDF\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "SensitivityDay = Sensitivity(RRV_Assessment_D[:9], NonRRVassessment)\n",
    "SensitivityMonth = Sensitivity(RRV_Assessment_M[:9], NonRRVassessment)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metric</th>\n",
       "      <th>Demand</th>\n",
       "      <th>Supply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Reliability</td>\n",
       "      <td>0.771606</td>\n",
       "      <td>0.374830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resilience</td>\n",
       "      <td>97.722058</td>\n",
       "      <td>34.738149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vulnerability</td>\n",
       "      <td>0.627990</td>\n",
       "      <td>0.356647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Severity</td>\n",
       "      <td>2.321531</td>\n",
       "      <td>0.749413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Metric     Demand     Supply\n",
       "0    Reliability   0.771606   0.374830\n",
       "1     Resilience  97.722058  34.738149\n",
       "2  Vulnerability   0.627990   0.356647\n",
       "3       Severity   2.321531   0.749413"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SensitivityDay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metric</th>\n",
       "      <th>Demand</th>\n",
       "      <th>Supply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Reliability</td>\n",
       "      <td>0.997164</td>\n",
       "      <td>0.541374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resilience</td>\n",
       "      <td>6.980147</td>\n",
       "      <td>4.421219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vulnerability</td>\n",
       "      <td>0.700447</td>\n",
       "      <td>0.342141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Severity</td>\n",
       "      <td>2.547381</td>\n",
       "      <td>0.800178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Metric    Demand    Supply\n",
       "0    Reliability  0.997164  0.541374\n",
       "1     Resilience  6.980147  4.421219\n",
       "2  Vulnerability  0.700447  0.342141\n",
       "3       Severity  2.547381  0.800178"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SensitivityMonth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1153.7840257297516"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(MaveDC[3:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9431.673248799998"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SeasAveStream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "OBS_Drought_Sim = GSResults['Drought_S_OBSD_Ind0_Out0']\n",
    "FD_Drought_Sim = GSResults['Drought_S_FD_Ind0_Out0']\n",
    "MLRD_Drought_Sim = GSResults['Drought_S_MLRD_Ind0_Out0']\n",
    "\n",
    "OBS_Average_Sim = GSResults['Average_S_OBSD_Ind0_Out0']\n",
    "FD_Average_Sim = GSResults['Average_S_FD_Ind0_Out0']\n",
    "MLRD_Average_Sim = GSResults['Average_S_MLRD_Ind0_Out0']\n",
    "\n",
    "OBS_Surplus_Sim = GSResults['Surplus_S_OBSD_Ind0_Out0']\n",
    "FD_Surplus_Sim = GSResults['Surplus_S_FD_Ind0_Out0']\n",
    "MLRD_Surplus_Sim = GSResults['Surplus_S_MLRD_Ind0_Out0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>SLCDPU_Prod_Demands</th>\n",
       "      <th>SLCDPU_Streamflow</th>\n",
       "      <th>SLCDPU_Shortages</th>\n",
       "      <th>SLCDPU_GW</th>\n",
       "      <th>SLCDPU_DC_Water_Use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7607</th>\n",
       "      <td>2021-07-30</td>\n",
       "      <td>490.9</td>\n",
       "      <td>95.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.49</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time  SLCDPU_Prod_Demands  SLCDPU_Streamflow  SLCDPU_Shortages  \\\n",
       "7607 2021-07-30                490.9              95.62               0.0   \n",
       "\n",
       "      SLCDPU_GW  SLCDPU_DC_Water_Use  \n",
       "7607      89.49                180.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OBS_Average_Sim[OBS_Average_Sim['Time']== '2021-07-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>SLCDPU_Prod_Demands</th>\n",
       "      <th>SLCDPU_Streamflow</th>\n",
       "      <th>SLCDPU_Shortages</th>\n",
       "      <th>SLCDPU_GW</th>\n",
       "      <th>SLCDPU_DC_Water_Use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7607</th>\n",
       "      <td>2021-07-30</td>\n",
       "      <td>502.2</td>\n",
       "      <td>95.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.49</td>\n",
       "      <td>191.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time  SLCDPU_Prod_Demands  SLCDPU_Streamflow  SLCDPU_Shortages  \\\n",
       "7607 2021-07-30                502.2              95.62               0.0   \n",
       "\n",
       "      SLCDPU_GW  SLCDPU_DC_Water_Use  \n",
       "7607      89.49                191.3  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLRD_Average_Sim[MLRD_Average_Sim['Time']== '2021-07-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>SLCDPU_Prod_Demands</th>\n",
       "      <th>SLCDPU_Streamflow</th>\n",
       "      <th>SLCDPU_Shortages</th>\n",
       "      <th>SLCDPU_GW</th>\n",
       "      <th>SLCDPU_DC_Water_Use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7607</th>\n",
       "      <td>2021-07-30</td>\n",
       "      <td>588.4</td>\n",
       "      <td>95.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.49</td>\n",
       "      <td>276.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time  SLCDPU_Prod_Demands  SLCDPU_Streamflow  SLCDPU_Shortages  \\\n",
       "7607 2021-07-30                588.4              95.62               0.0   \n",
       "\n",
       "      SLCDPU_GW  SLCDPU_DC_Water_Use  \n",
       "7607      89.49                276.5  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FD_Average_Sim[FD_Average_Sim['Time']== '2021-07-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(MonthlySum2021.SLCDPU_DC_Water_Use.sum()-MonthlySum2021.Hist_Mean_DC_Delivery.sum())/MonthlySum2021.Hist_Mean_DC_Delivery.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Output2021['PercentAboveDC'] =(Output2021['Extra_DC']/Output2021['Hist_Mean_DC_Delivery'])*100\n",
    "Output2021.replace([np.inf, -np.inf], 0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Output2021.loc['2021-06-1':'2021-07-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ExtraDays = Output2021[Output2021['Extra_DC']>1 ] \n",
    "len(ExtraDays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ExtraDays = Output2021[Output2021['Extra_DC']>30 ] \n",
    "ExtraDays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ExtraDays = Output2021[Output2021['SLCDPU_DC_Water_Use']>140 ] \n",
    "ExtraDays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ExtraDays = Output2021[Output2021['SLCDPU_Prod_Demands']>140 ] \n",
    "ExtraDays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ExtraDays = Output2021[Output2021['Hist_Mean_DC_Delivery']>10] \n",
    "ExtraDays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Percent change from scenario obs\n",
    "    RRV_Assessment['Perc_Rel_Scen'] = 0\n",
    "    RRV_Assessment['Perc_Rel_Scen'].iloc[0] = ((RRV_Assessment['Reliability'].iloc[0]/RRV_Assessment['Reliability'].iloc[0])*100)-100\n",
    "    RRV_Assessment['Perc_Rel_Scen'].iloc[1] = ((RRV_Assessment['Reliability'].iloc[1]/RRV_Assessment['Reliability'].iloc[0])*100)-100\n",
    "    RRV_Assessment['Perc_Rel_Scen'].iloc[2] = ((RRV_Assessment['Reliability'].iloc[2]/RRV_Assessment['Reliability'].iloc[0])*100)-100\n",
    "\n",
    "    RRV_Assessment['Perc_Rel_Scen'].iloc[3] = ((RRV_Assessment['Reliability'].iloc[3]/RRV_Assessment['Reliability'].iloc[3])*100)-100\n",
    "    RRV_Assessment['Perc_Rel_Scen'].iloc[4] = ((RRV_Assessment['Reliability'].iloc[4]/RRV_Assessment['Reliability'].iloc[3])*100)-100\n",
    "    RRV_Assessment['Perc_Rel_Scen'].iloc[5] = ((RRV_Assessment['Reliability'].iloc[5]/RRV_Assessment['Reliability'].iloc[3])*100)-100\n",
    "\n",
    "    RRV_Assessment['Perc_Rel_Scen'].iloc[6] = ((RRV_Assessment['Reliability'].iloc[6]/RRV_Assessment['Reliability'].iloc[6])*100)-100\n",
    "    RRV_Assessment['Perc_Rel_Scen'].iloc[7] = ((RRV_Assessment['Reliability'].iloc[7]/RRV_Assessment['Reliability'].iloc[6])*100)-100\n",
    "    RRV_Assessment['Perc_Rel_Scen'].iloc[8] = ((RRV_Assessment['Reliability'].iloc[8]/RRV_Assessment['Reliability'].iloc[6])*100)-100\n",
    "\n",
    "\n",
    "    RRV_Assessment['Perc_Res_Scen'] = 0\n",
    "    RRV_Assessment['Perc_Res_Scen'].iloc[0] = ((RRV_Assessment['Resilience'].iloc[0]/RRV_Assessment['Resilience'].iloc[0])*100)-100\n",
    "    RRV_Assessment['Perc_Res_Scen'].iloc[1] = ((RRV_Assessment['Resilience'].iloc[1]/RRV_Assessment['Resilience'].iloc[0])*100)-100\n",
    "    RRV_Assessment['Perc_Res_Scen'].iloc[2] = ((RRV_Assessment['Resilience'].iloc[2]/RRV_Assessment['Resilience'].iloc[0])*100)-100\n",
    "\n",
    "    RRV_Assessment['Perc_Res_Scen'].iloc[3] = ((RRV_Assessment['Resilience'].iloc[3]/RRV_Assessment['Resilience'].iloc[3])*100)-100\n",
    "    RRV_Assessment['Perc_Res_Scen'].iloc[4] = ((RRV_Assessment['Resilience'].iloc[4]/RRV_Assessment['Resilience'].iloc[3])*100)-100\n",
    "    RRV_Assessment['Perc_Res_Scen'].iloc[5] = ((RRV_Assessment['Resilience'].iloc[5]/RRV_Assessment['Resilience'].iloc[3])*100)-100\n",
    "\n",
    "    RRV_Assessment['Perc_Res_Scen'].iloc[6] = ((RRV_Assessment['Resilience'].iloc[6]/RRV_Assessment['Resilience'].iloc[6])*100)-100\n",
    "    RRV_Assessment['Perc_Res_Scen'].iloc[7] = ((RRV_Assessment['Resilience'].iloc[7]/RRV_Assessment['Resilience'].iloc[6])*100)-100\n",
    "    RRV_Assessment['Perc_Res_Scen'].iloc[8] = ((RRV_Assessment['Resilience'].iloc[8]/RRV_Assessment['Resilience'].iloc[6])*100)-100\n",
    "\n",
    "\n",
    "    RRV_Assessment['Perc_Vul_Scen'] = 0\n",
    "    RRV_Assessment['Perc_Vul_Scen'].iloc[0] = ((RRV_Assessment['Vulnerability'].iloc[0]/RRV_Assessment['Vulnerability'].iloc[0])*100)-100\n",
    "    RRV_Assessment['Perc_Vul_Scen'].iloc[1] = ((RRV_Assessment['Vulnerability'].iloc[1]/RRV_Assessment['Vulnerability'].iloc[0])*100)-100\n",
    "    RRV_Assessment['Perc_Vul_Scen'].iloc[2] = ((RRV_Assessment['Vulnerability'].iloc[2]/RRV_Assessment['Vulnerability'].iloc[0])*100)-100\n",
    "\n",
    "    RRV_Assessment['Perc_Vul_Scen'].iloc[3] = ((RRV_Assessment['Vulnerability'].iloc[3]/RRV_Assessment['Vulnerability'].iloc[3])*100)-100\n",
    "    RRV_Assessment['Perc_Vul_Scen'].iloc[4] = ((RRV_Assessment['Vulnerability'].iloc[4]/RRV_Assessment['Vulnerability'].iloc[3])*100)-100\n",
    "    RRV_Assessment['Perc_Vul_Scen'].iloc[5] = ((RRV_Assessment['Vulnerability'].iloc[5]/RRV_Assessment['Vulnerability'].iloc[3])*100)-100\n",
    "\n",
    "    RRV_Assessment['Perc_Vul_Scen'].iloc[6] = ((RRV_Assessment['Vulnerability'].iloc[6]/RRV_Assessment['Vulnerability'].iloc[6])*100)-100\n",
    "    RRV_Assessment['Perc_Vul_Scen'].iloc[7] = ((RRV_Assessment['Vulnerability'].iloc[7]/RRV_Assessment['Vulnerability'].iloc[6])*100)-100\n",
    "    RRV_Assessment['Perc_Vul_Scen'].iloc[8] = ((RRV_Assessment['Vulnerability'].iloc[8]/RRV_Assessment['Vulnerability'].iloc[6])*100)-100\n",
    "\n",
    "    \n",
    "    RRV_Assessment['Perc_MSev_Scen'] = 0\n",
    "    RRV_Assessment['Perc_MSev_Scen'].iloc[0] = ((RRV_Assessment['Maximum_Severity'].iloc[0]/RRV_Assessment['Maximum_Severity'].iloc[0])*100)-100\n",
    "    RRV_Assessment['Perc_MSev_Scen'].iloc[1] = ((RRV_Assessment['Maximum_Severity'].iloc[1]/RRV_Assessment['Maximum_Severity'].iloc[0])*100)-100\n",
    "    RRV_Assessment['Perc_MSev_Scen'].iloc[2] = ((RRV_Assessment['Maximum_Severity'].iloc[2]/RRV_Assessment['Maximum_Severity'].iloc[0])*100)-100\n",
    "\n",
    "    RRV_Assessment['Perc_MSev_Scen'].iloc[3] = ((RRV_Assessment['Maximum_Severity'].iloc[3]/RRV_Assessment['Maximum_Severity'].iloc[3])*100)-100\n",
    "    RRV_Assessment['Perc_MSev_Scen'].iloc[4] = ((RRV_Assessment['Maximum_Severity'].iloc[4]/RRV_Assessment['Maximum_Severity'].iloc[3])*100)-100\n",
    "    RRV_Assessment['Perc_MSev_Scen'].iloc[5] = ((RRV_Assessment['Maximum_Severity'].iloc[5]/RRV_Assessment['Maximum_Severity'].iloc[3])*100)-100\n",
    "\n",
    "    RRV_Assessment['Perc_MSev_Scen'].iloc[6] = ((RRV_Assessment['Maximum_Severity'].iloc[6]/RRV_Assessment['Maximum_Severity'].iloc[6])*100)-100\n",
    "    RRV_Assessment['Perc_MSev_Scen'].iloc[7] = ((RRV_Assessment['Maximum_Severity'].iloc[7]/RRV_Assessment['Maximum_Severity'].iloc[6])*100)-100\n",
    "    RRV_Assessment['Perc_MSev_Scen'].iloc[8] = ((RRV_Assessment['Maximum_Severity'].iloc[8]/RRV_Assessment['Maximum_Severity'].iloc[6])*100)-100\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#need to add to the historical RRV assessments to determine breaks\n",
    "years =  [2001,2002,2003,2004, 2005, 2006, 2007,2008, 2009, 2010,\n",
    "          2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,2020]\n",
    "Clim_Scenario = 'Historical'\n",
    "modeltype = 'Obs'\n",
    "for y in years:\n",
    "    Daily, Monthly, Dell = ForecastDataPrep(GSResults['Average_S_OBSD_Ind0_Out0'], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                            y, GSResults['Average_S_OBSD_Dell'], Clim_Scenario)\n",
    "\n",
    "    #Sup_Dem_Figure(DayDF, MonthDF, Dell, yr, IndoorCons, OutdoorCons, FigPath, simulation):\n",
    "    #Sup_Dem_Figure(Output2021, MonthlySum2021, Dell2021, 2021,Indcons, Outcons, FigPath, Clim_Scenario, modeltype)\n",
    "    # Sup_Dem_Figure2(Daily, Monthly, Dell, y,0, 0, FigPath, Clim_Scenario, modeltype)\n",
    "\n",
    "\n",
    "    Rel_D, Res_D, Vul_D, DailyRRV,Max_Severity,MaxSevSI, MaxSevNorm = RRV(Daily, 'Hist', 'OBSD', Historical_Max_Daily_DC, 'Daily',y)\n",
    "    print('For Daily scenario ', Clim_Scenario)\n",
    "    print('Reliability: ', Rel_D, 'Resilience: ', Res_D, 'Vulnerability: ', Vul_D)\n",
    "    RRVass_D = list(['Hist', 'OBSD', Rel_D, Res_D, Vul_D, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "    RRV_Assessment_D.loc[len(RRV_Assessment_D)] = RRVass_D\n",
    "    RRV_Data_D = RRV_Data_D.append(DailyRRV)\n",
    "\n",
    "    Rel_M, Res_M, Vul_M, MonthlyRRV, Max_Severity,MaxSevSI, MaxSevNorm = RRV(Monthly, 'Hist', 'OBSD', Historical_Max_Monthly_DC, 'Monthly',y)\n",
    "    print('For Monthly scenario ', Clim_Scenario)\n",
    "    print('Reliability: ', Rel_M, 'Resilience: ', Res_M, 'Vulnerability: ', Vul_M)\n",
    "    RRVass_M = list(['Hist', 'OBSD', Rel_M, Res_M, Vul_M, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "    RRV_Assessment_M.loc[len(RRV_Assessment_M)] = RRVass_M\n",
    "    #RRV_Data_M = RRV_Data_M.append(MonthlyRRV)\n",
    "    RRV_Data_M['AveT_Demands'] = MaveDemandT\n",
    "    RRV_Data_M['AveT_Stream'] = MaveStreamT\n",
    "    RRV_Data_M['Perc_Demand'] = ((RRV_Data_M['SLCDPU_Prod_Demands']-RRV_Data_M['AveT_Demands'])/RRV_Data_M['AveT_Demands'])*100\n",
    "    RRV_Data_M['Perc_Stream'] = ((RRV_Data_M['SLCDPU_Streamflow']-RRV_Data_M['AveT_Stream'])/RRV_Data_M['AveT_Stream'])*100\n",
    "    \n",
    "#print(RRV_Assessment_D)\n",
    "#del RRV_Assessment_D['MaxSeverity']\n",
    "#del RRV_Assessment_M['MaxSeverity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "This section illustrates traditional demand use errors (blanketly stated at 10%)\n",
    "\n",
    "'''\n",
    "\n",
    "\n",
    "sup = ['Surplus', 'Drought', 'Average']\n",
    "Demands = ['TD']\n",
    "\n",
    "\n",
    "Indcons = 0\n",
    "Outcons = 0\n",
    "maxseverity = 1\n",
    "\n",
    "for i in sup:\n",
    "    for d in Demands:\n",
    "        \n",
    "        SysSim = i + '_S_'+d+'_Ind0_Out0'\n",
    "        SysSimO = i + '_S_OBSD_Ind0_Out0'\n",
    "        \n",
    "        SysSimL = SysSim+'_Lo'\n",
    "        SysSimH = SysSim+'_Hi'\n",
    "        \n",
    "        DemandL = d+'_Lo'\n",
    "        Demand = d\n",
    "        DemandH = d+'_Hi'\n",
    "        DemandO = 'Obs'\n",
    "        \n",
    "        DellSim = i + '_S_' + d + '_Dell'\n",
    "        DellSimO = i + '_S_OBSD_Dell'\n",
    "        DellSimL = DellSim +'_Lo'\n",
    "        DellSimH = DellSim + '_Hi'\n",
    "        modeltype = d\n",
    "        Clim_Scenario = i +' Supply and '+ d +' Demands'\n",
    "\n",
    "        #This does the mean\n",
    "        Daily2021, Monthly2021, Dell2021 = ForecastDataPrep(GSResults[SysSim], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSim], Clim_Scenario)\n",
    " \n",
    "        \n",
    "        #This does the Observed\n",
    "        Daily2021_O, Monthly2021_O, Dell2021_O = ForecastDataPrep(GSResults[SysSimO], GSResults['Average_S_OBSD_Ind0_Out0'],\n",
    "                                                                2021, GSResults[DellSimO], Clim_Scenario)\n",
    "        \n",
    "       # Sup_Dem_Figure_Err_Trad(Daily2021, Monthly2021,Daily2021_O, Monthly2021_O, \n",
    "        #           2021,Indcons, Outcons, FigPath, Clim_Scenario, modeltype)\n",
    "        \n",
    "        \n",
    "    #Low\n",
    "        Rel_D, Res_D, Vul_D, Daily2021RRV,Max_Severity,MaxSevSI, MaxSevNorm = RRV(Daily2021_L, i, DemandL, Historical_Max_Daily_DC, 'Daily', 2021)\n",
    "        print('For Daily scenario ', Clim_Scenario)\n",
    "        print('Reliability: ', Rel_D, 'Resilience: ', Res_D, 'Vulnerability: ', Vul_D)\n",
    "        RRVass_D = list([i, DemandL, Rel_D, Res_D, Vul_D, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "        RRV_Assessment_D.loc[len(RRV_Assessment_D)] = RRVass_D\n",
    "        RRV_Data_D = RRV_Data_D.append(Daily2021RRV)\n",
    "    \n",
    "    #Mean\n",
    "        Rel_D, Res_D, Vul_D, Daily2021RRV,Max_Severity,MaxSevSI, MaxSevNorm = RRV(Daily2021, i, Demand, Historical_Max_Daily_DC, 'Daily', 2021)\n",
    "        print('For Daily scenario ', Clim_Scenario)\n",
    "        print('Reliability: ', Rel_D, 'Resilience: ', Res_D, 'Vulnerability: ', Vul_D)\n",
    "        RRVass_D = list([i, Demand, Rel_D, Res_D, Vul_D, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "        RRV_Assessment_D.loc[len(RRV_Assessment_D)] = RRVass_D\n",
    "        RRV_Data_D = RRV_Data_D.append(Daily2021RRV)\n",
    "       \n",
    "    #High\n",
    "        Rel_D, Res_D, Vul_D, Daily2021RRV,Max_Severity,MaxSevSI, MaxSevNorm = RRV(Daily2021_H, i, DemandH, Historical_Max_Daily_DC, 'Daily', 2021)\n",
    "        print('For Daily scenario ', Clim_Scenario)\n",
    "        print('Reliability: ', Rel_D, 'Resilience: ', Res_D, 'Vulnerability: ', Vul_D)\n",
    "        RRVass_D = list([i, DemandH, Rel_D, Res_D, Vul_D, Max_Severity,MaxSevSI, MaxSevNorm])\n",
    "        RRV_Assessment_D.loc[len(RRV_Assessment_D)] = RRVass_D\n",
    "        RRV_Data_D = RRV_Data_D.append(Daily2021RRV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MaveStreamT = HistMonthly['SLCDPU_Streamflow']\n",
    "MaveDemandT = HistMonthly['SLCDPU_Prod_Demands']\n",
    "MaveDC = HistMonthly['SLCDPU_DC_Water_Use']\n",
    "MaveExtraDC = HistMonthly['Extra_DC']\n",
    "SeasonExtraDC = MaveExtraDC.sum()\n",
    "\n",
    "NonRRVassessment =pd.DataFrame(columns =['Climate', 'Demand','DCmaxD','DCmaxM', 'maxDemD','maxDemM','maxDemMMonth', 'DCextraD', 'DCextraM', 'DCmaxDate', 'DCAAmax', 'DCAAmaxDate',\n",
    "                        'DCmaxM','DCmaxMonth','DCAAmaxM','DCAAmaxMonth','MaxPercHist_DM', 'MaxPercHist_StrmM', 'SeasonD', 'SeasonDR', \n",
    "                        'SeasonD_Perc','SeasonDC', 'SeasonDC_Perc','SeasonDCextra', 'SeasonDCextra_Perc', 'SeasonStrm', \n",
    "                                         'SeasonStrm_Perc', 'SeasonStrmRatio'])\n",
    "sup = ['Drought']\n",
    "Demands = ['CSD_WDM', 'TD']\n",
    "\n",
    "for s in sup:\n",
    "    for d in Demands:    \n",
    "        x = RRV_Data_D.loc[(RRV_Data_D['Clim']== s) & (RRV_Data_D['Demand']==d)]\n",
    "        y = RRV_Data_M.loc[(RRV_Data_M['Clim']== s) & (RRV_Data_M['Demand']==d)]\n",
    "        #x.loc['2021-06-15':'2021-07-30']\n",
    "        #number of days of extra DC water\n",
    "        DCextraD = len(x.loc[x['Extra_DC']> 5])\n",
    "        DCextraM = len(y.loc[y['Extra_DC']> 5])\n",
    "        #max slcdpu demand\n",
    "        maxDemD = x['SLCDPU_Prod_Demands'].max()\n",
    "        \n",
    "        #Max Day\n",
    "        DCmaxD = x['SLCDPU_DC_Water_Use'].max()\n",
    "        DCmaxDate = x.loc[x['SLCDPU_DC_Water_Use']==DCmaxD].index\n",
    "        DCmaxDate = DCmaxDate.format('%Y-%m-%d')[1]\n",
    "        \n",
    "        #DC above average max\n",
    "        DCAAmax = x['Extra_DC'].max()\n",
    "        DCAAmaxM = y['Extra_DC'].max()\n",
    "        DCAAmaxDate = x.loc[x['Extra_DC']==DCAAmax].index\n",
    "        DCAAmaxDate = DCAAmaxDate.format('%Y-%m-%d')[1]\n",
    "        DCAAmax = DCAAmax\n",
    "        \n",
    "        #peak month\n",
    "        maxDemM = y['SLCDPU_Prod_Demands'].max()\n",
    "        DCmaxM = y['SLCDPU_DC_Water_Use'].max()\n",
    "        maxDemMMonth = y.loc[y['SLCDPU_Prod_Demands']==maxDemM].index\n",
    "        maxDemMMonth = maxDemMMonth.format('%Y-%m-%d')[1]\n",
    "        \n",
    "        DCmaxMonth = y.loc[y['SLCDPU_DC_Water_Use']==DCmaxM].index\n",
    "        DCmaxMonth = DCmaxMonth.format('%Y-%m-%d')[1]\n",
    "        \n",
    "        #DC above aveage month\n",
    "        DCAAmaxM = y['Extra_DC'].max()\n",
    "        DCAAmaxMonth = y.loc[y['Extra_DC']==DCAAmaxM].index\n",
    "        DCAAmaxMonth = DCAAmaxMonth.format('%Y-%m-%d')[1]\n",
    "        DCAAmaxM =DCAAmaxM\n",
    "        \n",
    "        #Find the max percentage of historical monthly demand\n",
    "        y['Hist_Mean_Demand_M']= MaveDemandT[3:10].values\n",
    "        y['PercHist_DM'] = ((y['SLCDPU_Prod_Demands']-y['Hist_Mean_Demand_M'])/y['Hist_Mean_Demand_M'])*100\n",
    "        MaxPercHist_DM = max(y['PercHist_DM'])\n",
    "        \n",
    "        #Find the max percentage of historical monthly streamflow\n",
    "        y['Hist_Mean_Strm_M']= MaveStreamT[3:10].values\n",
    "        y['PercHist_StrmM'] = ((y['SLCDPU_Streamflow']-y['Hist_Mean_Strm_M'])/y['Hist_Mean_Strm_M'])*100\n",
    "        MaxPercHist_StrmM = max(y['PercHist_StrmM'])\n",
    "        \n",
    "        #Find the total season demands\n",
    "        SeasonD = y['SLCDPU_Prod_Demands'].sum()\n",
    "        SeasonD_Perc = ((SeasonD-SeasAveDemand)/SeasAveDemand)*100\n",
    "        SeasonDR =SeasonD/SeasAveDemand\n",
    "        \n",
    "        SeasonDC = y['SLCDPU_DC_Water_Use'].sum()\n",
    "        SeasonDC_Perc = ((SeasonDC-SeasAveDC)/SeasAveDC)*100\n",
    "        SeasonDC = SeasonDC\n",
    "        \n",
    "        SeasonDCextra = y['Extra_DC'].sum()\n",
    "        SeasonDCextra_Perc = ((SeasonDCextra-SeasonExtraDC)/SeasonExtraDC)*100\n",
    "        SeasonDCextra = SeasonDCextra\n",
    "        \n",
    "        SeasonStrm = y['SLCDPU_Streamflow'].sum()\n",
    "        SeasonStrm_Perc = ((SeasonStrm-SeasAveStream)/SeasAveStream)*100\n",
    "        SeasonStrmRatio = ((SeasonStrm)/SeasAveStream)\n",
    "        \n",
    "        \n",
    "        SimStats =list([s, d,DCmaxD,DCmaxM, maxDemD,maxDemM,maxDemMMonth, DCextraD, DCextraM, DCmaxDate, DCAAmax, DCAAmaxDate,DCmaxM, DCmaxMonth,\n",
    "                         DCAAmaxM,DCAAmaxMonth,MaxPercHist_DM, MaxPercHist_StrmM, SeasonD,SeasonDR, SeasonD_Perc,\n",
    "                        SeasonDC, SeasonDC_Perc,SeasonDCextra, SeasonDCextra_Perc, SeasonStrm, SeasonStrm_Perc, SeasonStrmRatio])\n",
    "        \n",
    "        #print(SimStats)\n",
    "        NonRRVassessment.loc[len(NonRRVassessment)] = SimStats"
   ]
  }
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