diff --git a/MLP_Monthly_Demand.ipynb b/MLP_Monthly_Demand.ipynb
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@@ -0,0 +1,2195 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn. metrics import accuracy_score\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.metrics import mean_absolute_percentage_error\n",
+    "\n",
+    "from sklearn.utils import resample\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.model_selection import LeaveOneOut\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "import seaborn as sns; sns.set()\n",
+    "import joblib\n",
+    "from sklearn.linear_model import LassoCV\n",
+    "from sklearn.datasets import make_regression\n",
+    "from pathlib import Path\n",
+    "import copy\n",
+    "\n",
+    "#Trying out recusive feature elimination to compare with step wise regression\n",
+    "from sklearn.feature_selection import RFE\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "import sklearn\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import layers\n",
+    "from tensorflow.keras.models import load_model\n",
+    "\n",
+    "from progressbar import ProgressBar\n",
+    "\n",
+    "\n",
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import datetime\n",
+    "import calendar\n",
+    "\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "#custom functions\n",
+    "def NSC(y_pred,y_true):\n",
+    "    \"\"\"\n",
+    "    Nash-Sutcliffe Coefficient\n",
+    "    \"\"\"\n",
+    "    return 1 - sum((y_pred-y_true)**2)/sum((y_true-np.mean(y_true))**2)\n",
+    "\n",
+    "def corr(data1, data2):\n",
+    "    \"data1 & data2 should be numpy arrays.\"\n",
+    "    mean1 = data1.mean() \n",
+    "    mean2 = data2.mean()\n",
+    "    std1 = data1.std()\n",
+    "    std2 = data2.std()\n",
+    "\n",
+    "#     corr = ((data1-mean1)*(data2-mean2)).mean()/(std1*std2)\n",
+    "    corr = ((data1*data2).mean()-mean1*mean2)/(std1*std2)\n",
+    "    return corr\n",
+    "\n",
+    "\n",
+    "def mean_absolute_percentage_error(y_true, y_pred): \n",
+    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
+    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#This is the final dataset to make predictions on\n",
+    "p = Path('C:/Users/Ryan/Box/Dissertation/Paper1/Data/Processed_Training_Data')\n",
+    "#dir_data = 'C:/Users/Ryan/Box/Dissertation/Paper1/Data/Model_Input_Data/Monthly/Input/FinalVars'\n",
+    "\n",
+    "slc = {i.stem[0:3] : pd.read_excel(i) for i in p.glob('**/*.xlsx')}\n",
+    "\n",
+    "#rename the gpcd column\n",
+    "for i in slc:\n",
+    "    slc[i].rename(columns={i+'_gpcd': 'Obs_gpcd'}, inplace=True)\n",
+    "    slc[i]=slc[i].set_index('Year')\n",
+    "snow=pd.read_excel('C:/Users/Ryan/Box/Dissertation/Paper1/Data/Alta_LCC_snow.xlsx')\n",
+    "snow=snow.set_index('Year')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create training and testing data, use most recent low, average, and high water years\n",
+    "slc_train=copy.deepcopy(slc)\n",
+    "slc_test=copy.deepcopy(slc)\n",
+    "\n",
+    "#2008 is a high year\n",
+    "#2011 and 2017 are average years\n",
+    "#2014 and 2016 are below average years\n",
+    "#2015 is a very  low year\n",
+    "\n",
+    "IN_WY_Months = ['Jan' , 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug','Sep', 'Oct']\n",
+    "Prior_YR_WY_Months = ['Nov', 'Dec']\n",
+    "testWYyrs = [2008,2015,2017]\n",
+    "#testPrioWYyrs = [2007,2014,2016]\n",
+    "\n",
+    "\n",
+    "for i in slc:\n",
+    "    #Select the training/testing dataframes\n",
+    "    slc_train[i]=slc_train[i][~slc_train[i].index.isin(testWYyrs)]\n",
+    "    slc_test[i]=slc_test[i][slc_test[i].index.isin(testWYyrs)]\n",
+    "   \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create training and testing data\n",
+    "#slc_train=copy.deepcopy(slc)\n",
+    "#slc_test=copy.deepcopy(slc)\n",
+    "\n",
+    "#Train_yrs= 26\n",
+    "#\n",
+    "\n",
+    "#for i in slc:\n",
+    "    #Select the training/testing dataframes\n",
+    " #   slc_train[i]=slc_train[i][:Train_yrs]\n",
+    "  #  slc_test[i]=slc_test[i][Train_yrs:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    \n",
+    "\n",
+    "#Determine the indoor mean to subtract from outdoor\n",
+    "I_mean_train=(slc_train['Jan']['Obs_gpcd']+\n",
+    "              slc_train['Feb']['Obs_gpcd']+\n",
+    "              slc_train['Mar']['Obs_gpcd']+\n",
+    "              slc_train['Nov']['Obs_gpcd']+\n",
+    "             slc_train['Dec']['Obs_gpcd'])/5\n",
+    "I_mean_test=(slc_test['Jan']['Obs_gpcd']+\n",
+    "             slc_test['Feb']['Obs_gpcd']+\n",
+    "             slc_test['Mar']['Obs_gpcd']+\n",
+    "             slc_test['Nov']['Obs_gpcd']+\n",
+    "             slc_test['Dec']['Obs_gpcd'])/5\n",
+    "#This uses total demand for irrigation seasons\n",
+    "#I_mean_train=np.zeros(23)\n",
+    "#I_mean_test=np.zeros(10)\n",
+    "\n",
+    "for i in slc_train:\n",
+    "    slc_train[i]['Iave']=I_mean_train\n",
+    "    #for now include testing years\n",
+    "    slc_test[i]['Iave']=I_mean_test\n",
+    "\n",
+    "IrrSeason= ['Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct']\n",
+    "Indoor=['Jan', 'Feb', 'Mar', 'Nov', 'Dec']\n",
+    "colrem=['Iave', 'Obs_gpcd']\n",
+    "#set the target gpcd to indoor for indoor and total-indoor for outdoor\n",
+    "#change to indoor to separate outdoor demands from total\n",
+    "for i in Indoor:\n",
+    "    slc_train[i]['Target_gpcd']=slc_train[i]['Obs_gpcd']\n",
+    "    slc_train[i]= slc_train[i].drop(columns=colrem)\n",
+    "    #for now include testing years\n",
+    "    slc_test[i]['Target_gpcd']=slc_test[i]['Obs_gpcd']\n",
+    "    slc_test[i]= slc_test[i].drop(columns=colrem)\n",
+    "    \n",
+    "\n",
+    "#Establish outdoor conservation thresholds\n",
+    "O_cons = 0.25\n",
+    "time = 20\n",
+    "\n",
+    "\n",
+    "for i in IrrSeason:\n",
+    "    slc_train[i]['Target_gpcd']=slc_train[i]['Obs_gpcd']-slc_train[i]['Iave']\n",
+    "    slc_train[i].loc[slc_train[i]['Target_gpcd'] < 0, 'Target_gpcd'] = 0\n",
+    "    \n",
+    "    #add in snow info\n",
+    "    slc_train[i]=pd.concat([slc_train[i], snow], axis=1, join=\"inner\")\n",
+    "    \n",
+    "    slc_train[i]= slc_train[i].drop(columns=colrem)\n",
+    "    #for now include testing years\n",
+    "    slc_test[i]=pd.concat([slc_test[i], snow], axis=1, join=\"inner\")\n",
+    "    slc_test[i]['Target_gpcd']=slc_test[i]['Obs_gpcd']-slc_test[i]['Iave']\n",
+    "    slc_test[i].loc[slc_test[i]['Target_gpcd'] < 0, 'Target_gpcd'] = 0\n",
+    "    \n",
+    "    #create monthly historical mean and conservation trends\n",
+    "    Out_mean = np.mean(slc_train[i]['Target_gpcd'].loc[2000:])\n",
+    "    goal = (1-O_cons)*Out_mean\n",
+    "    O_cons_rate = (Out_mean -goal)/time\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    slc_train[i]['cons_goal'] = Out_mean- ((slc_train[i].index-2000)*O_cons_rate)\n",
+    "    slc_train[i].loc[ slc_train[i].index <2000, ['cons_goal']] = Out_mean\n",
+    "    \n",
+    "    t=slc_train[i]['Target_gpcd'].copy()\n",
+    "    c=slc_train[i]['cons_goal'].copy()\n",
+    "    slc_train[i] = slc_train[i].drop(columns=['Target_gpcd', 'cons_goal'])\n",
+    "    slc_train[i]['Target_gpcd'] = t\n",
+    "    slc_train[i]['cons_goal'] = c\n",
+    "    \n",
+    "    slc_test[i]['cons_goal'] = Out_mean - ((slc_test[i].index-2000)*O_cons_rate) \n",
+    "    \n",
+    " \n",
+    "    \n",
+    "    \n",
+    "    slc_test[i]= slc_test[i].drop(columns=colrem)\n",
+    "\n",
+    "    \n",
+    "#Determine the historical indoor mean to apply conservation strategies too\n",
+    "Indmean = np.mean(slc_train['Jan']['Target_gpcd'].loc[2000:]+\n",
+    "                  slc_train['Feb']['Target_gpcd'].loc[2000:]+\n",
+    "                  slc_train['Mar']['Target_gpcd'].loc[2000:]+\n",
+    "                  slc_train['Nov']['Target_gpcd'].loc[2000:]+\n",
+    "                 slc_train['Dec']['Target_gpcd'].loc[2000:])/5\n",
+    "'''Per UDWR 2001, Utah's Water Resources: Planning for the future, the govenor ordered a\n",
+    "25% reduction in water use by 2025\n",
+    "'''\n",
+    "cons = 0.28\n",
+    "\n",
+    "goal = (1-cons)*Indmean\n",
+    "\n",
+    "time = 20\n",
+    "\n",
+    "cons_rate = (Indmean -goal)/time\n",
+    "\n",
+    "#print(cons_rate , goal)\n",
+    "\n",
+    "#create feature called cons_goal!\n",
+    "for i in Indoor:\n",
+    "    slc_test[i]['cons_goal'] = Indmean-((slc_test[i].index-2000)*cons_rate) \n",
+    "    slc_train[i]['cons_goal'] = Indmean-((slc_train[i].index-2000)*cons_rate) \n",
+    "    \n",
+    "    slc_train[i].loc[ slc_train[i].index <2000, ['cons_goal']] = Indmean\n",
+    "    \n",
+    "    \n",
+    "Cons_mean_test=(slc_test['Jan']['cons_goal']+\n",
+    "                slc_test['Feb']['cons_goal']+\n",
+    "                slc_test['Mar']['cons_goal']+\n",
+    "                slc_test['Nov']['cons_goal']+\n",
+    "                slc_test['Dec']['cons_goal'])/5\n",
+    "\n",
+    "#split training and testing data into features and targets\n",
+    "slc_train_target=copy.deepcopy(slc_train)\n",
+    "slc_train_features=copy.deepcopy(slc_train)\n",
+    "\n",
+    "slc_test_target=copy.deepcopy(slc_test)\n",
+    "slc_test_features=copy.deepcopy(slc_test)\n",
+    "\n",
+    "\n",
+    "target=['Target_gpcd','Housing']\n",
+    "for i in slc_train_target:\n",
+    "    slc_train_target[i]=slc_train_target[i]['Target_gpcd']\n",
+    "    #for now include testing years\n",
+    "    slc_test_target[i]=slc_test_target[i]['Target_gpcd']\n",
+    "\n",
+    "\n",
+    "    slc_train_features[i]= slc_train_features[i].drop(columns=target)\n",
+    "    #for now include testing years\n",
+    "    slc_test_features[i]= slc_test_features[i].drop(columns=target)\n",
+    "    \n",
+    "#need to remove year from the list to run plots below\n",
+    "for i in slc_train:\n",
+    "    slc_train[i]=slc_train[i].drop(columns=['Housing',  'Population', 'PopulationDensity'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Use features from MLR \n",
+    "\n",
+    "Apr = ['Val_Apr_Monthly_mean_Day_temp_C', 'Val_Apr_Monthly_precip_mm', 'LitCotCrk_AcFt_WR_Apr',\n",
+    "       'LitCotCrk_AcFt_WR_Mar', 'Target_gpcd']\n",
+    "May = ['Val_May_Monthly_precip_mm', 'Val_May_Monthly_mean_Day_temp_C', 'Target_gpcd']\n",
+    "Jun = ['Val_Jun_Monthly_precip_mm', 'Val_Apr_Monthly_mean_Day_temp_C', 'BigCotCrk_AcFt_WR_Jun', \n",
+    "       'Val_May_Monthly_mean_Day_temp_C', 'CityCrk_AcFt_WR_May', 'BigCotCrk_AcFt_WR_May', \n",
+    "       'Val_May_Monthly_precip_mm', 'Target_gpcd']\n",
+    "Jul = ['UrbPopulationDensity', 'Val_Jul_Monthly_mean_Day_temp_C', 'Val_Apr_Monthly_mean_Day_temp_C',\n",
+    "       'Val_May_Monthly_mean_Day_temp_C', 'Target_gpcd']\n",
+    "Aug = ['Val_Aug_Monthly_precip_mm', 'UrbPopulationDensity', 'LitCotCrk_AcFt_WR_Apr',\n",
+    "       'Val_Apr_Monthly_mean_Day_temp_C', 'Val_Aug_Monthly_mean_Day_temp_C', 'LitCotCrk_AcFt_WR_Mar', \n",
+    "       'Val_Apr_Monthly_precip_mm', 'Val_May_Monthly_mean_Day_temp_C', 'Val_Jul_Monthly_mean_Day_temp_C', \n",
+    "       'Val_Jun_Monthly_mean_Day_temp_C', 'Val_Jun_Monthly_precip_mm', 'BigCotCrk_AcFt_WR_May',\n",
+    "       'LitCotCrk_AcFt_WR_May', 'Target_gpcd']\n",
+    "Sep = ['Val_Sep_Monthly_precip_mm', 'Val_Sep_Monthly_mean_Day_temp_C', 'Val_Aug_Monthly_mean_Day_temp_C', \n",
+    "       'Val_May_Monthly_mean_Day_temp_C', 'Val_Apr_Monthly_mean_Day_temp_C', 'Val_Jun_Monthly_mean_Day_temp_C',\n",
+    "       'Val_Jul_Monthly_mean_Day_temp_C', 'Target_gpcd']\n",
+    "Oct = ['cons_goal','Target_gpcd']\n",
+    "\n",
+    "\n",
+    "#select these features for training and testing\n",
+    "slc_train['Apr'] = slc_train['Apr'][Apr]\n",
+    "slc_test['Apr'] = slc_test['Apr'][Apr]\n",
+    "\n",
+    "slc_train['May'] = slc_train['May'][May]\n",
+    "slc_test['May'] = slc_test['May'][May]\n",
+    "\n",
+    "slc_train['Jun'] = slc_train['Jun'][Jun]\n",
+    "slc_test['Jun'] = slc_test['Jun'][Jun]\n",
+    "\n",
+    "slc_train['Jul'] = slc_train['Jul'][Jul]\n",
+    "slc_test['Jul'] = slc_test['Jul'][Jul]\n",
+    "\n",
+    "slc_train['Aug'] = slc_train['Aug'][Aug]\n",
+    "slc_test['Aug'] = slc_test['Aug'][Aug]\n",
+    "\n",
+    "slc_train['Sep'] = slc_train['Sep'][Sep]\n",
+    "slc_test['Sep'] = slc_test['Sep'][Sep]\n",
+    "\n",
+    "slc_train['Oct'] = slc_train['Oct'][Oct]\n",
+    "slc_test['Oct'] = slc_test['Oct'][Oct]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_plots(PerfDF, month):\n",
+    "    \n",
+    "    plotmin = PerfDF[['y_pred', 'y_test']].min().min()\n",
+    "    plotmax = PerfDF[['y_pred', 'y_test']].max().max()\n",
+    "    \n",
+    "    plotmin_tot = PerfDF[['y_pred_tot', 'y_test_tot']].min().min()\n",
+    "    plotmax_tot = PerfDF[['y_pred_tot', 'y_test_tot']].max().max()\n",
+    "    \n",
+    "    \n",
+    "    fig, ax = plt.subplots(3,2, constrained_layout=True)\n",
+    "    fig.set_size_inches(9,10)\n",
+    "\n",
+    "    ax[0,0].scatter(PerfDF['y_test'], PerfDF['y_pred'],color='blue', alpha=0.5)\n",
+    "    ax[0,0].set_ylabel('Predicted')\n",
+    "    ax[0,0].set_xlabel('Observed')\n",
+    "    ax[0,0].set_ylim(plotmin-5,plotmax+5)\n",
+    "    ax[0,0].set_xlim(plotmin-5,plotmax+5)\n",
+    "    ax[0,0].set_title('Outdoor Model Performance')\n",
+    "    ax[0,0].plot([plotmin,plotmax],[plotmin,plotmax], color='red', linestyle='--' )\n",
+    "    ax[0,0].spines['bottom'].set_color('black')\n",
+    "    ax[0,0].spines['left'].set_color('black')\n",
+    "    \n",
+    "    ax[0,1].scatter(PerfDF['y_test_tot'], PerfDF['y_pred_tot'],color='blue', alpha=0.5)\n",
+    "    ax[0,1].set_ylabel('Predicted')\n",
+    "    ax[0,1].set_xlabel('Observed')\n",
+    "    ax[0,1].set_ylim(plotmin_tot-5,plotmax_tot+5)\n",
+    "    ax[0,1].set_xlim(plotmin_tot-5,plotmax_tot+5)\n",
+    "    ax[0,1].set_title('Indoor and Outdoor \\n Model Performance')\n",
+    "    ax[0,1].plot([plotmin_tot,plotmax_tot],[plotmin_tot,plotmax_tot], color='red', linestyle='--' )\n",
+    "    ax[0,1].spines['bottom'].set_color('black')\n",
+    "    ax[0,1].spines['left'].set_color('black')\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    gs = ax[1, 1].get_gridspec()\n",
+    "    # remove the underlying axes\n",
+    "    ax[1,0].remove()\n",
+    "    ax[1,1].remove()\n",
+    "\n",
+    "    PerfDF['Error'] = (PerfDF['y_pred']-PerfDF['y_test'])\n",
+    "    axbig1 = fig.add_subplot(gs[1, :])\n",
+    "    axbig1.set_title(month+' Outdoor demand Timeline Evaluation')\n",
+    "    axbig1.axhline(y = 0 , color = 'black')\n",
+    "    #axbig1.bar(PerfDF.index, PerfDF['y_pred'], color='orange', label='Predicted')\n",
+    "    Error1 = axbig1.bar(PerfDF.index, PerfDF['Error'],color='blue', label='Prediction Error')\n",
+    "    axbig1.set_xlabel('Year')\n",
+    "    axbig1.set_ylabel('Error (GPCD)')\n",
+    "    axbig1.spines['bottom'].set_color('black')\n",
+    "    axbig1.spines['left'].set_color('black')\n",
+    "    gradientbars_sliced(Error1)\n",
+    "    \n",
+    "    \n",
+    "    gs2 = ax[2, 1].get_gridspec()\n",
+    "    # remove the underlying axes\n",
+    "    ax[2,0].remove()\n",
+    "    ax[2,1].remove()\n",
+    "\n",
+    "\n",
+    "    #create error value\n",
+    "    PerfDF['Error_tot'] = (PerfDF['y_pred_tot']-PerfDF['y_test_tot'])\n",
+    "    \n",
+    "    axbig2 = fig.add_subplot(gs2[2, :])\n",
+    "    axbig2.set_title(month+' Total Demand Error Timeline Evaluation')\n",
+    "    Error2 = axbig2.bar(PerfDF.index, PerfDF['Error_tot'], color='blue', label='Predicted')\n",
+    "    axbig2.axhline(y = 0 , color = 'black')\n",
+    "    #axbig2.bar(PerfDF.index, PerfDF['y_test_tot'],color='blue', label='Observed')\n",
+    "    axbig2.set_xlabel('Year')\n",
+    "    axbig2.set_ylabel('Error (GPCD)')\n",
+    "    axbig2.spines['bottom'].set_color('black')\n",
+    "    axbig2.spines['left'].set_color('black')\n",
+    "    gradientbars_sliced(Error2)\n",
+    "    \n",
+    "    fig.suptitle(month+ ' Evaluation', size = 16)\n",
+    "    fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/MLP' + month + '_demand.pdf')    \n",
+    "    \n",
+    "    O_r2 = sklearn.metrics.r2_score(PerfDF['y_test'],PerfDF['y_pred'])\n",
+    "    O_rmse= sklearn.metrics.mean_squared_error(PerfDF['y_test'],PerfDF['y_pred'], squared = False)\n",
+    "    O_mae= sklearn.metrics.mean_absolute_error(PerfDF['y_test'],PerfDF['y_pred'])\n",
+    "    O_mape= sklearn.metrics.mean_absolute_percentage_error(PerfDF['y_test'],PerfDF['y_pred'])\n",
+    "    \n",
+    "    T_r2 = sklearn.metrics.r2_score(PerfDF['y_test_tot'],PerfDF['y_pred_tot'])\n",
+    "    T_rmse= sklearn.metrics.mean_squared_error(PerfDF['y_test_tot'],PerfDF['y_pred_tot'], squared = False)\n",
+    "    T_mae= sklearn.metrics.mean_absolute_error(PerfDF['y_test_tot'],PerfDF['y_pred_tot'])\n",
+    "    T_mape= sklearn.metrics.mean_absolute_percentage_error(PerfDF['y_test_tot'],PerfDF['y_pred_tot'])\n",
+    "    \n",
+    "    print('The outdoor Demand prediction RMSE is ', O_rmse)\n",
+    "    print('The outdoor Demand prediction R2 is ', O_r2)\n",
+    "    \n",
+    "    print('The Total Demand prediction RMSE is ', T_rmse)\n",
+    "    print('The Total Demand prediction R2 is ', T_r2)\n",
+    "    print('The Total Demand prediction MAE is ', T_mae)\n",
+    "    print('The Total Demand prediction MAPE is ', T_mape, '%')     \n",
+    "    \n",
+    "  \n",
+    "    #set DF up so that all months can be easily combined, basically year-month index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Indoor_Demand_Model(df, month):\n",
+    "    #Make matching indoor demand model\n",
+    "    datetime_object = datetime.datetime.strptime(month, \"%b\")\n",
+    "    df[month]['month'] = datetime_object.month\n",
+    "    \n",
+    "    df[month]['Year']=df[month].index\n",
+    "    \n",
+    "     #set up dates so all months can be combined and sorted\n",
+    "    day=[]\n",
+    "    for index, row in df[month].iterrows():\n",
+    "        day.append(calendar.monthrange(int(row['Year']), int(row['month']))[1])\n",
+    "    \n",
+    "    df[month]['Day']=day\n",
+    "    \n",
+    "  \n",
+    "    df[month]['Date'] = pd.to_datetime(df[month][['Year', 'month', 'Day']])\n",
+    "    \n",
+    "    #PerfDF=PerfDF.set_index('Date')\n",
+    "    df[month]=df[month].drop(columns=['month', 'Day'])\n",
+    "    df[month]=df[month].reset_index(drop=True)\n",
+    "   \n",
+    "    \n",
+    "    colrem = slc_test[i].columns\n",
+    "    df[month]['y_pred'] =  df[month]['cons_goal']\n",
+    "    df[month]['y_test'] =  df[month]['Target_gpcd']\n",
+    "    df[month]['y_pred_tot'] =  df[month]['cons_goal']\n",
+    "    df[month]['y_test_tot'] =  df[month]['Target_gpcd']\n",
+    "       \n",
+    "    df[month] = df[month].set_index('Date')\n",
+    "    \n",
+    "    df[month] = df[month].drop(columns=colrem)\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "The MLP model needs to take in normalized numpy arrays. The MLP Water Demand Model needs to:\n",
+    "1) take in all of the data (training and testing)\n",
+    "2) normalize that months training and testing inputs (0-1)\n",
+    "3) train and save the model, '_ReluDem_{val_loss:.8f}.h5'\n",
+    "4) predict on testing dataset\n",
+    "5) convert to original units\n",
+    "6) Deliver prediction datastat (with orginial testing data) to send to figure generator.\n",
+    "'''\n",
+    "\n",
+    "def MLP_OutdoorDemandModel(df_train, df_test,month, checkpointfilename):\n",
+    "    \n",
+    "    traindf = df_train.copy()\n",
+    "    testdf = df_test.copy()\n",
+    "    \n",
+    "    #find the min of input data\n",
+    "    dfmin = traindf.append(testdf).copy()\n",
+    "    dfmin = list(dfmin.min())\n",
+    "\n",
+    "    #normalize process A\n",
+    "    traindf = traindf-dfmin\n",
+    "    testdf = testdf-dfmin\n",
+    "      \n",
+    "    #find the min of input data\n",
+    "    dfmax = traindf.append(testdf).copy()\n",
+    "    dfmax = list(dfmax.max())\n",
+    "\n",
+    "    #normalize process A\n",
+    "    traindf = traindf/dfmax\n",
+    "    testdf = testdf/dfmax\n",
+    "    \n",
+    "    #separate into training and testing features and targets\n",
+    "    features_tr = traindf.drop(columns =['Target_gpcd']).copy()\n",
+    "    features_val = testdf.drop(columns =['Target_gpcd']).copy()\n",
+    "    \n",
+    "    target_tr = traindf['Target_gpcd'].copy()\n",
+    "    target_val = testdf['Target_gpcd'].copy()\n",
+    "    \n",
+    "    #convert to numpy array for model\n",
+    "    features_tr = np.array(features_tr.to_numpy())\n",
+    "    features_val = np.array(features_val.to_numpy())\n",
+    "    \n",
+    "    target_tr_ = np.array(target_tr.to_numpy())\n",
+    "    target_val = np.array(target_val.to_numpy())\n",
+    "    \n",
+    "    #we want a y_test for later on too\n",
+    "    y_test = (target_val.copy()*dfmax[-1])+dfmin[-1]\n",
+    "   \n",
+    "    \n",
+    "    \n",
+    "    #Get the numpy shape for the model\n",
+    "    shape = features_tr.shape\n",
+    "\n",
+    "    \n",
+    "    #now input the model\n",
+    "    \n",
+    "    #start_time = time.time()\n",
+    "\n",
+    "    checkpoint_filepath = 'MLP_Models/'+str(month)+ '/'\n",
+    "    checkpoint_filename = checkpoint_filepath+checkpointfilename\n",
+    "    callback = keras.callbacks.ModelCheckpoint(\n",
+    "        filepath=checkpoint_filename,\n",
+    "        monitor='val_loss',\n",
+    "        mode='min',\n",
+    "        save_best_only=True)\n",
+    "\n",
+    "\n",
+    "    input_1 = layers.Input(shape=(shape[1],))\n",
+    "    x = layers.Dense(128, activation=\"relu\")(input_1)\n",
+    "    x = layers.Dense(128, activation=\"relu\")(x)\n",
+    "    x = layers.Dense(64, activation=\"relu\")(x)\n",
+    "    x = layers.Dense(64, activation=\"relu\")(x)\n",
+    "    x = layers.Dense(32, activation=\"relu\")(x)\n",
+    "    x = layers.Dense(32, activation=\"relu\")(x)\n",
+    "    x = layers.Dense(16, activation=\"relu\")(x)\n",
+    "    x = layers.Dense(16, activation=\"relu\")(x)\n",
+    "    x = layers.Dense(8, activation=\"relu\")(x)\n",
+    "    x = layers.Dense(8, activation=\"relu\")(x)\n",
+    "    x = layers.Dense(1)(x)\n",
+    "\n",
+    "    model = keras.Model(inputs=input_1,outputs=x)\n",
+    "    model.compile(loss='mse', optimizer=keras.optimizers.Adam(1e-4), metrics=['mae'])\n",
+    "    #print(model.summary())\n",
+    "    print(month, \" is being trained and calibrated.\")\n",
+    "    #training time for 50 epochs took 3601seconds, 60 mins, 500 epochs\n",
+    "    model = model.fit(features_tr, target_tr, epochs=500, batch_size=1,\n",
+    "                        validation_data=(features_val,target_val),shuffle=True,\n",
+    "                      callbacks=[callback], verbose=0)\n",
+    "\n",
+    "    #load the model with highest performance\n",
+    "    bestmodel = [f for f in listdir(checkpoint_filepath) if isfile(join(checkpoint_filepath, f))]\n",
+    "    bestmodel.sort()\n",
+    "    bestmodel = checkpoint_filepath+bestmodel[0]\n",
+    "    model=load_model(bestmodel)\n",
+    "    \n",
+    "    #Make predictions on the validation data\n",
+    "    y_pred = model.predict(features_val)\n",
+    "    #make sure no negative values\n",
+    "   # y_pred = [0 if x < 0 else x for x in y_pred]\n",
+    "    \n",
+    "    #convert predictions to gpcd and place into OG dataframe\n",
+    "    y_pred = (y_pred*dfmax[-1])+dfmin[-1]\n",
+    "    shape = y_pred.shape\n",
+    "    y_pred = y_pred.reshape(shape[0])\n",
+    "    #measure the modeling performance of the MLP model\n",
+    "    O_r2_test = sklearn.metrics.r2_score(y_test, y_pred)\n",
+    "    O_rmse_test = sklearn.metrics.mean_squared_error(y_test, y_pred, squared = False)\n",
+    "    print('Outdoor R2 is ', O_r2_test)\n",
+    "    print('Outdoor RMSE is ', O_rmse_test)\n",
+    "        \n",
+    "    #put df in same format as MLR model\n",
+    "    PerfDF=pd.DataFrame(list(zip(y_pred, y_test)), columns=['y_pred', 'y_test'])\n",
+    "\n",
+    "#Add indoor demands\n",
+    "    Indoor=['Jan', 'Feb', 'Mar', 'Nov', 'Dec']\n",
+    "    if month in Indoor:\n",
+    "        PerfDF['y_test']=PerfDF['y_test']\n",
+    "        PerfDF['y_pred']=PerfDF['y_pred']\n",
+    "    else:\n",
+    "        PerfDF['y_test_tot']=PerfDF['y_test']+list(I_mean_test)\n",
+    "        PerfDF['y_pred_tot']=PerfDF['y_pred']+list(Cons_mean_test)\n",
+    "\n",
+    "    T_r2 = sklearn.metrics.r2_score(PerfDF['y_test_tot'], PerfDF['y_pred_tot'])\n",
+    "    T_rmse= sklearn.metrics.mean_squared_error(PerfDF['y_test_tot'], PerfDF['y_pred_tot'], \n",
+    "                                               squared = False)\n",
+    "  \n",
+    "\n",
+    "    PerfDF['Year'] = list(slc_test['Jul'].index)\n",
+    "    PerfDF=PerfDF.set_index('Year')\n",
+    "   \n",
+    "\n",
+    "    datetime_object = datetime.datetime.strptime(month, \"%b\")\n",
+    "    PerfDF['month'] = datetime_object.month\n",
+    "    PerfDF['Year']=PerfDF.index\n",
+    "   \n",
+    "    \n",
+    "    #set up dates so all months can be combined and sorted\n",
+    "    day=[]\n",
+    "    for index, row in PerfDF.iterrows():\n",
+    "        day.append(calendar.monthrange(int(row['Year']), int(row['month']))[1])\n",
+    "    \n",
+    "    PerfDF['Day']=day\n",
+    "  \n",
+    "    PerfDF['Date'] = pd.to_datetime(PerfDF[['Year', 'month', 'Day']])\n",
+    "    \n",
+    "    #PerfDF=PerfDF.set_index('Date')\n",
+    "    PerfDF=PerfDF.drop(columns=['Year', 'month', 'Day'])\n",
+    "    PerfDF=PerfDF.reset_index()\n",
+    "    \n",
+    "    model_plots(PerfDF, month)\n",
+    "    \n",
+    "    \n",
+    "    return PerfDF\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#PerfDF = MLP_OutdoorDemandModel(slc_train['Apr'], slc_test['Apr'],'Apr', 'ReluDem_{val_loss:.8f}.h5')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams[\"axes.grid\"] = False\n",
+    "plt.rcParams[\"axes.facecolor\"] ='white'\n",
+    "#plt.rcParams[\"axes.edgecolor\"]['bottom'] = 'black'\n",
+    "\n",
+    "def gradientbars_sliced(bars):\n",
+    "    ax = bars[0].axes\n",
+    "    xmin, xmax = ax.get_xlim()\n",
+    "    ymin, ymax = ax.get_ylim()\n",
+    "    for bar in bars:\n",
+    "        bar.set_zorder(1)\n",
+    "        bar.set_facecolor(\"none\")\n",
+    "        x, y = bar.get_xy()\n",
+    "        w, h = bar.get_width(), bar.get_height()\n",
+    "        grad = np.linspace(y, y + h, 256).reshape(256, 1)\n",
+    "        ax.imshow(grad, extent=[x, x + w, y, y + h], aspect=\"auto\", zorder=0, origin='lower',\n",
+    "                  vmin= - max(np.abs(ymin), ymax), vmax=max(np.abs(ymin), ymax), cmap='Spectral')\n",
+    "    ax.axis([xmin, xmax, ymin, ymax])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Make a function to put all of the predictions together\n",
+    "def Demand_Forecast(prediction_dictionary, pdict, df, pred, test, units, plotname, model):\n",
+    "    FinalDF=pd.DataFrame()\n",
+    "    if pdict is True:\n",
+    "        print('yes')\n",
+    "        for i in prediction_dictionary:\n",
+    "            FinalDF=FinalDF.append(prediction_dictionary[i])\n",
+    "\n",
+    "        FinalDF=FinalDF.sort_index()\n",
+    "    else:\n",
+    "        print('no')\n",
+    "        FinalDF = df\n",
+    "        \n",
+    "    months = np.arange(1,6,1)\n",
+    "    Low = list()\n",
+    "    Ave=list()\n",
+    "    High = list()\n",
+    "    for i in months:\n",
+    "        Low.append('Low'+str(i))\n",
+    "        Ave.append('Ave'+str(i))\n",
+    "        High.append('High'+str(i))\n",
+    "        \n",
+    "    Low.append('Drought')\n",
+    "    Ave.append('Average')\n",
+    "    High.append('Surplus')\n",
+    "    \n",
+    "    months2 = np.arange(7,13,1)\n",
+    "    for i in months2:\n",
+    "        Low.append('Low'+str(i))\n",
+    "        Ave.append('Ave'+str(i))\n",
+    "        High.append('High'+str(i))\n",
+    "    \n",
+    "    Conditions = High+Low+Ave\n",
+    "\n",
+    "    #adjust date range to improve figure\n",
+    "    #FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n",
+    "    FinalDF['Date'] = Conditions\n",
+    "    \n",
+    "    FinalDF.index = FinalDF['Date']\n",
+    "    del FinalDF['Date']\n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    plotmin_tot = FinalDF[[pred, test]].min().min()\n",
+    "    plotmax_tot = FinalDF[[pred, test]].max().max()\n",
+    "\n",
+    "    fig, ax = plt.subplots(1,5, constrained_layout=True)\n",
+    "    fig.set_size_inches(12,4)\n",
+    "\n",
+    "    gs2 = ax[0].get_gridspec()\n",
+    "    # remove the underlying axes\n",
+    "    ax[0].remove()\n",
+    "    ax[1].remove()\n",
+    "    ax[2].remove()\n",
+    "    axbig = fig.add_subplot(gs2[:3])\n",
+    "    #axbig.set_title('Total demand Timeline Evaluation')\n",
+    "    axbig.plot(FinalDF[pred], color='orange', label= model+'Predicted')\n",
+    "    axbig.plot(FinalDF[test],color='blue', label='Observed')\n",
+    "    axbig.set_xlabel('Supply Conditions')\n",
+    "    axbig.set_ylim(plotmin_tot-.9,plotmax_tot*1.2)\n",
+    "    axbig.set_ylabel('Demand ('+ units+')')\n",
+    "    axbig.legend(loc = 'upper left')\n",
+    "    axbig.set_facecolor(\"white\")\n",
+    "    axbig.spines['bottom'].set_color('black')\n",
+    "    axbig.spines['left'].set_color('black')\n",
+    "    \n",
+    "    xticks = axbig.xaxis.get_major_ticks()\n",
+    "    for i,tick in enumerate(xticks):\n",
+    "        if i%12 != 5:\n",
+    "            tick.label1.set_visible(False)\n",
+    "\n",
+    "    ax[3].remove()\n",
+    "    ax[4].remove()\n",
+    "\n",
+    "\n",
+    "    axbig2 = fig.add_subplot(gs2[3:])\n",
+    "    axbig2.scatter(FinalDF[test], FinalDF[pred],color='blue', alpha=0.5)\n",
+    "    axbig2.set_ylabel('Predicted (' + units+')' )\n",
+    "    axbig2.set_xlabel('Observed (' + units+')')\n",
+    "    axbig2.set_ylim(plotmin_tot*.95,plotmax_tot*1.2)\n",
+    "    axbig2.set_xlim(plotmin_tot*.95,plotmax_tot*1.2)\n",
+    "   # axbig2.set_title('Indoor and Outdoor \\n Model Performance')\n",
+    "    axbig2.plot([plotmin_tot,plotmax_tot],[plotmin_tot,plotmax_tot], color='red', linestyle='--' )\n",
+    "    #axbig2.set_xticks(np.arange(plotmin_tot, plotmax_tot, 100).round())\n",
+    "    #axbig2.set_yticks(np.arange(plotmin_tot, plotmax_tot, 100).round())\n",
+    "    axbig2.set_facecolor(\"white\")\n",
+    "    axbig2.spines['bottom'].set_color('black')\n",
+    "    axbig2.spines['left'].set_color('black')\n",
+    "    #ax[2].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "   \n",
+    "    fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/MLP' +str(plotname)+'.pdf')\n",
+    "    r2 = sklearn.metrics.r2_score(FinalDF[test], FinalDF[pred])\n",
+    "    MAE= sklearn.metrics.mean_absolute_error(FinalDF[test], FinalDF[pred])\n",
+    "    RMSE= sklearn.metrics.mean_squared_error(FinalDF[test], FinalDF[pred], squared = False)\n",
+    "    MAPE=np.mean(np.abs((FinalDF[test]- FinalDF[pred])/FinalDF[test])*100)\n",
+    "\n",
+    "    print('Total R2 is ', r2)\n",
+    "    print('Total MAE is ', MAE)\n",
+    "    print('Total RMSE is ', RMSE)\n",
+    "    print('Total MAPE is ', MAPE)\n",
+    "    \n",
+    "    FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n",
+    "    FinalDF.index = FinalDF['Date']\n",
+    "    del FinalDF['Date']\n",
+    "    \n",
+    "    return FinalDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0% |                                                                        |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Apr  is being trained and calibrated.\n",
+      "WARNING:tensorflow:Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback.\n",
+      "Outdoor R2 is  0.5937303166522887\n",
+      "Outdoor RMSE is  7.136751982304181\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 14% |##########                                                              |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  7.136751982304181\n",
+      "The outdoor Demand prediction R2 is  0.5937303166522887\n",
+      "The Total Demand prediction RMSE is  12.898387879850468\n",
+      "The Total Demand prediction R2 is  0.36591391256584893\n",
+      "The Total Demand prediction MAE is  9.725359704707211\n",
+      "The Total Demand prediction MAPE is  0.06394925048253002 %\n",
+      "May  is being trained and calibrated.\n",
+      "WARNING:tensorflow:Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback.\n",
+      "Outdoor R2 is  0.6421361019957679\n",
+      "Outdoor RMSE is  18.340410522587963\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 28% |####################                                                    |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  18.340410522587963\n",
+      "The outdoor Demand prediction R2 is  0.6421361019957679\n",
+      "The Total Demand prediction RMSE is  24.469164452473105\n",
+      "The Total Demand prediction R2 is  0.6954449078313416\n",
+      "The Total Demand prediction MAE is  18.59359277450169\n",
+      "The Total Demand prediction MAPE is  0.07331031030203554 %\n",
+      "Jun  is being trained and calibrated.\n",
+      "WARNING:tensorflow:Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback.\n",
+      "Outdoor R2 is  0.7037698465716781\n",
+      "Outdoor RMSE is  15.453846755583172\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 42% |##############################                                          |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  15.453846755583172\n",
+      "The outdoor Demand prediction R2 is  0.7037698465716781\n",
+      "The Total Demand prediction RMSE is  26.6924380684419\n",
+      "The Total Demand prediction R2 is  0.15153665791766124\n",
+      "The Total Demand prediction MAE is  26.008916095753232\n",
+      "The Total Demand prediction MAPE is  0.0646337216287104 %\n",
+      "Jul  is being trained and calibrated.\n",
+      "WARNING:tensorflow:Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback.\n",
+      "Outdoor R2 is  0.08881442090794056\n",
+      "Outdoor RMSE is  34.8770180795522\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 57% |#########################################                               |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  34.8770180795522\n",
+      "The outdoor Demand prediction R2 is  0.08881442090794056\n",
+      "The Total Demand prediction RMSE is  45.558475531944254\n",
+      "The Total Demand prediction R2 is  0.0656137934324208\n",
+      "The Total Demand prediction MAE is  44.88932443910257\n",
+      "The Total Demand prediction MAPE is  0.09522422195710811 %\n",
+      "Aug  is being trained and calibrated.\n",
+      "WARNING:tensorflow:Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback.\n",
+      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001C7C700BF70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
+      "Outdoor R2 is  0.20881026074967468\n",
+      "Outdoor RMSE is  39.07940262776053\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 71% |###################################################                     |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  39.07940262776053\n",
+      "The outdoor Demand prediction R2 is  0.20881026074967468\n",
+      "The Total Demand prediction RMSE is  42.35673206586278\n",
+      "The Total Demand prediction R2 is  0.46790554579600674\n",
+      "The Total Demand prediction MAE is  42.07823195500299\n",
+      "The Total Demand prediction MAPE is  0.10671687922495991 %\n",
+      "Sep  is being trained and calibrated.\n",
+      "WARNING:tensorflow:Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback.\n",
+      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001C7CD102820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
+      "Outdoor R2 is  0.7621385786438251\n",
+      "Outdoor RMSE is  13.459084829054108\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 85% |#############################################################           |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  13.459084829054108\n",
+      "The outdoor Demand prediction R2 is  0.7621385786438251\n",
+      "The Total Demand prediction RMSE is  27.359354265706965\n",
+      "The Total Demand prediction R2 is  0.4232520472114184\n",
+      "The Total Demand prediction MAE is  18.087291746794886\n",
+      "The Total Demand prediction MAPE is  0.0519614094361071 %\n",
+      "Oct  is being trained and calibrated.\n",
+      "WARNING:tensorflow:Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback.\n",
+      "WARNING:tensorflow:7 out of the last 7 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001C7CD102790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
+      "Outdoor R2 is  0.05795157194571121\n",
+      "Outdoor RMSE is  16.842878791418663\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% |########################################################################|\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  16.842878791418663\n",
+      "The outdoor Demand prediction R2 is  0.05795157194571121\n",
+      "The Total Demand prediction RMSE is  16.75399148592276\n",
+      "The Total Demand prediction R2 is  0.4302415344651209\n",
+      "The Total Demand prediction MAE is  14.724250275722406\n",
+      "The Total Demand prediction MAPE is  0.07507922567012597 %\n",
+      "yes\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "<ipython-input-11-de83e5ce486e>:92: UserWarning: constrained_layout not applied.  At least one axes collapsed to zero width or height.\n",
+      "  fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/MLP' +str(plotname)+'.pdf')\n",
+      "<ipython-input-11-de83e5ce486e>:103: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total R2 is  0.9590024871295585\n",
+      "Total MAE is  20.72183314673465\n",
+      "Total RMSE is  25.88379917103648\n",
+      "Total MAPE is  6.265183895913638\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Ryan\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:132: UserWarning: constrained_layout not applied.  At least one axes collapsed to zero width or height.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Separate indoor and outdoor prediction months\n",
+    "Outdoor_Months=['Apr', 'May' , 'Jun', 'Jul', 'Aug', 'Sep', 'Oct']\n",
+    "IndoorMonths = ['Jan', 'Feb', 'Mar', 'Nov', 'Dec']\n",
+    "\n",
+    "\n",
+    "\n",
+    "slc_val=copy.deepcopy(slc_test)\n",
+    "# calibrate and predict with the outdoor model\n",
+    "pbar = ProgressBar()\n",
+    "for i in pbar(Outdoor_Months):\n",
+    "    \n",
+    "    PerfDF  = MLP_OutdoorDemandModel(slc_train[i],slc_test[i], i, 'ReluDem_{val_loss:.8f}.h5')\n",
+    "    \n",
+    "    colrem = slc_test[i].columns\n",
+    "    slc_val[i] = slc_val[i].reset_index(drop=True)\n",
+    "    slc_val[i] = pd.concat([slc_val[i], PerfDF], axis=1, join=\"inner\")\n",
+    "    slc_val[i] = slc_val[i].set_index('Date')\n",
+    "    slc_val[i] = slc_val[i].drop(columns=colrem)\n",
+    "\n",
+    "#Calibrate and predict with the indoor model\n",
+    "for i in IndoorMonths:\n",
+    "    slc_val = Indoor_Demand_Model(slc_val, i)\n",
+    "\n",
+    "\n",
+    "#Put it all together and display the results\n",
+    "#Demand_Forecast(slc_val)\n",
+    "df = pd.DataFrame()\n",
+    "Pred_Obs = Demand_Forecast(slc_val, True, df, 'y_pred_tot', 'y_test_tot', 'GPCD', 'Seasonal_term_pred_gpcd', 'SLC-WDM')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#save predictions\n",
+    "np.save('MLP_Models/Slc_Pred.npy', slc_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slc_val = np.load('MLP_Models/Slc_Pred.npy', allow_pickle =True).item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "yes\n",
+      "Total R2 is  0.9590024871295585\n",
+      "Total MAE is  20.72183314673465\n",
+      "Total RMSE is  25.88379917103648\n",
+      "Total MAPE is  8.958201208413598\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-16-a83d24a1b2ca>:104: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Put it all together and display the results\n",
+    "#Demand_Forecast(slc_val)\n",
+    "df = pd.DataFrame()\n",
+    "Pred_Obs = Demand_Forecast(slc_val, True, df, 'y_pred_tot', 'y_test_tot', 'GPCD', '_Seasonal_term_pred_gpcd', 'MLP ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas.tseries.offsets import MonthEnd\n",
+    "cols = ['y_test_tot', 'y_pred_tot']\n",
+    "monthorder = ['Jan', 'Feb' , 'Mar', 'Apr', 'May', 'Jun' , 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+    "Pred_Obs = Pred_Obs[cols]\n",
+    "\n",
+    "#input population into DF to calculate total water demands.\n",
+    "Population = []\n",
+    "UR_gpcd = []\n",
+    "slc_pred = copy.deepcopy(slc)\n",
+    "for i in monthorder:\n",
+    "    slc_train[i] = copy.deepcopy(slc[i].loc[:2010])\n",
+    "    slc_pred[i] = slc_pred[i].loc[2015:]\n",
+    "    slc_pred[i]['UR_gpcd'] = np.mean(slc_train[i]['Obs_gpcd'])\n",
+    "    Population.append(np.round(slc_pred[i]['Population'],0))\n",
+    "    \n",
+    "UR=pd.DataFrame()\n",
+    "for i in slc_pred:\n",
+    "    slc_pred[i]= pd.DataFrame(slc_pred[i]['UR_gpcd'])\n",
+    "    slc_pred[i]=slc_pred[i].reset_index()\n",
+    "    slc_pred[i]['M'] = datetime.datetime.strptime(i, \"%b\").month\n",
+    "    slc_pred[i]['D'] = 1\n",
+    "    slc_pred[i]['Date'] = pd.to_datetime(slc_pred[i].Year*10000+slc_pred[i].M*100+slc_pred[i].D,format='%Y%m%d')+MonthEnd(1)\n",
+    "    slc_pred[i].index = slc_pred[i].Date\n",
+    "    slc_pred[i]=slc_pred[i].drop(columns = ['M', 'D', 'Date', 'Year'])\n",
+    "    UR = UR.append(slc_pred[i])\n",
+    "\n",
+    "UR=UR.sort_index()\n",
+    "Population = np.sort(np.array(Population).reshape(36,))\n",
+    "\n",
+    "#place in to prediction df\n",
+    "Pred_Obs['Population'] = Population\n",
+    "Pred_Obs['UR_gpcd'] = UR['UR_gpcd']\n",
+    "\n",
+    "\n",
+    "\n",
+    "#Now we can form some acre-feet predictions.\n",
+    "gpcd=['y_test_tot','y_pred_tot','UR_gpcd']\n",
+    "for i in gpcd:\n",
+    "    Pred_Obs[i+str('_AF')] = Pred_Obs[i]*Pred_Obs['Population']*9.33454e-5\n",
+    "\n",
+    "    \n",
+    "remcol=['y_test_tot','y_pred_tot','UR_gpcd', 'Population']\n",
+    "Ann_Eval = Pred_Obs.drop(columns = remcol).copy()\n",
+    "Ann_Eval = Ann_Eval.resample('Y').sum()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Demand_Forecast' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-cd7082b84100>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m Pred_AF = Demand_Forecast(slc_val, False, Pred_Obs, \n\u001b[0m\u001b[0;32m      2\u001b[0m             'y_pred_tot_AF', 'y_test_tot_AF', 'Acre-Feet', 'MLP_Seasonal_pred_AF', 'MLP')\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'Demand_Forecast' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "Pred_AF = Demand_Forecast(slc_val, False, Pred_Obs, \n",
+    "            'y_pred_tot_AF', 'y_test_tot_AF', 'Acre-Feet', 'MLP_Seasonal_pred_AF', 'MLP')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no\n",
+      "Total R2 is  0.6283371213177069\n",
+      "Total MAE is  63.03597863247863\n",
+      "Total RMSE is  77.93344228795345\n",
+      "Total MAPE is  30.78863740465362\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-12-a010c38583fc>:104: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Pred_UR_GPCD = Demand_Forecast(slc_val, False, Pred_Obs, 'UR_gpcd',\n",
+    "                'y_test_tot', 'gpcd', 'UR_Seasonal_pred__gpcd', ' ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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>y_test_tot_AF</th>\n",
+       "      <th>y_pred_tot_AF</th>\n",
+       "      <th>StationaryError</th>\n",
+       "      <th>MLPError</th>\n",
+       "      <th>StationaryErrorAF</th>\n",
+       "      <th>MLPErrorAF</th>\n",
+       "      <th>S_Perror</th>\n",
+       "      <th>MLP_Perror</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-12-31</th>\n",
+       "      <td>100550.276769</td>\n",
+       "      <td>88885.303742</td>\n",
+       "      <td>139.858017</td>\n",
+       "      <td>-155.307626</td>\n",
+       "      <td>12207.162705</td>\n",
+       "      <td>-11664.973027</td>\n",
+       "      <td>12.140357</td>\n",
+       "      <td>-11.601135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-31</th>\n",
+       "      <td>81646.774954</td>\n",
+       "      <td>80032.105575</td>\n",
+       "      <td>512.522236</td>\n",
+       "      <td>-18.067616</td>\n",
+       "      <td>32430.298904</td>\n",
+       "      <td>-1614.669379</td>\n",
+       "      <td>39.720245</td>\n",
+       "      <td>-1.977628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-31</th>\n",
+       "      <td>86505.457871</td>\n",
+       "      <td>83166.319007</td>\n",
+       "      <td>455.384862</td>\n",
+       "      <td>-52.171378</td>\n",
+       "      <td>28891.579705</td>\n",
+       "      <td>-3339.138863</td>\n",
+       "      <td>33.398563</td>\n",
+       "      <td>-3.860033</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            y_test_tot_AF  y_pred_tot_AF  StationaryError    MLPError  \\\n",
+       "Date                                                                    \n",
+       "2015-12-31  100550.276769   88885.303742       139.858017 -155.307626   \n",
+       "2016-12-31   81646.774954   80032.105575       512.522236  -18.067616   \n",
+       "2017-12-31   86505.457871   83166.319007       455.384862  -52.171378   \n",
+       "\n",
+       "            StationaryErrorAF    MLPErrorAF   S_Perror  MLP_Perror  \n",
+       "Date                                                                \n",
+       "2015-12-31       12207.162705 -11664.973027  12.140357  -11.601135  \n",
+       "2016-12-31       32430.298904  -1614.669379  39.720245   -1.977628  \n",
+       "2017-12-31       28891.579705  -3339.138863  33.398563   -3.860033  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Pred_UR_GPCD['StationaryError'] = ((Pred_UR_GPCD['UR_gpcd']-Pred_UR_GPCD['y_test_tot'])/Pred_UR_GPCD['y_test_tot'])*100\n",
+    "Pred_UR_GPCD['MLPError'] = ((Pred_UR_GPCD['y_pred_tot']-Pred_UR_GPCD['y_test_tot'])/Pred_UR_GPCD['y_test_tot'])*100\n",
+    "Pred_UR_GPCD['StationaryErrorAF'] = Pred_UR_GPCD['UR_gpcd_AF']-Pred_UR_GPCD['y_test_tot_AF']\n",
+    "Pred_UR_GPCD['MLPErrorAF'] = Pred_UR_GPCD['y_pred_tot_AF']-Pred_UR_GPCD['y_test_tot_AF']\n",
+    "Evalcol = ['y_test_tot_AF', 'y_pred_tot_AF', 'StationaryError','MLPError', 'StationaryErrorAF', 'MLPErrorAF']\n",
+    "Eval = Pred_UR_GPCD[Evalcol]\n",
+    "\n",
+    "Annual = Eval.resample('Y').sum()\n",
+    "Annual['S_Perror'] = Annual['StationaryErrorAF']/Annual['y_test_tot_AF']*100\n",
+    "Annual['MLP_Perror'] = Annual['MLPErrorAF']/Annual['y_test_tot_AF']*100\n",
+    "Annual"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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>y_test_tot_AF</th>\n",
+       "      <th>y_pred_tot_AF</th>\n",
+       "      <th>StationaryError</th>\n",
+       "      <th>MLPError</th>\n",
+       "      <th>StationaryErrorAF</th>\n",
+       "      <th>MLPErrorAF</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-01-31</th>\n",
+       "      <td>4709.274282</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>7.342278</td>\n",
+       "      <td>-14.631378</td>\n",
+       "      <td>345.767994</td>\n",
+       "      <td>-689.031718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-02-28</th>\n",
+       "      <td>4888.568674</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>4.579880</td>\n",
+       "      <td>-17.762379</td>\n",
+       "      <td>223.890584</td>\n",
+       "      <td>-868.326110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-03-31</th>\n",
+       "      <td>4493.545735</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>16.816118</td>\n",
+       "      <td>-10.532955</td>\n",
+       "      <td>755.639941</td>\n",
+       "      <td>-473.303171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-04-30</th>\n",
+       "      <td>5114.524352</td>\n",
+       "      <td>4424.462629</td>\n",
+       "      <td>24.996996</td>\n",
+       "      <td>-13.492197</td>\n",
+       "      <td>1278.477435</td>\n",
+       "      <td>-690.061723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-05-31</th>\n",
+       "      <td>8583.758986</td>\n",
+       "      <td>7329.383576</td>\n",
+       "      <td>13.335806</td>\n",
+       "      <td>-14.613358</td>\n",
+       "      <td>1144.713444</td>\n",
+       "      <td>-1254.375410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-06-30</th>\n",
+       "      <td>12890.020382</td>\n",
+       "      <td>12029.729591</td>\n",
+       "      <td>14.946044</td>\n",
+       "      <td>-6.674084</td>\n",
+       "      <td>1926.548117</td>\n",
+       "      <td>-860.290792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-07-31</th>\n",
+       "      <td>16379.389681</td>\n",
+       "      <td>15065.430252</td>\n",
+       "      <td>12.453508</td>\n",
+       "      <td>-8.022029</td>\n",
+       "      <td>2039.808677</td>\n",
+       "      <td>-1313.959429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-08-31</th>\n",
+       "      <td>15197.197243</td>\n",
+       "      <td>13680.049464</td>\n",
+       "      <td>13.006844</td>\n",
+       "      <td>-9.983076</td>\n",
+       "      <td>1976.675810</td>\n",
+       "      <td>-1517.147779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-30</th>\n",
+       "      <td>11502.006931</td>\n",
+       "      <td>9997.498678</td>\n",
+       "      <td>10.339270</td>\n",
+       "      <td>-13.080398</td>\n",
+       "      <td>1189.223590</td>\n",
+       "      <td>-1504.508254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-10-31</th>\n",
+       "      <td>6784.423105</td>\n",
+       "      <td>6257.536733</td>\n",
+       "      <td>13.992224</td>\n",
+       "      <td>-7.766119</td>\n",
+       "      <td>949.291652</td>\n",
+       "      <td>-526.886372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-11-30</th>\n",
+       "      <td>5299.251908</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>-0.312916</td>\n",
+       "      <td>-24.135659</td>\n",
+       "      <td>-16.582211</td>\n",
+       "      <td>-1279.009344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-12-31</th>\n",
+       "      <td>4708.315488</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>8.361965</td>\n",
+       "      <td>-14.613994</td>\n",
+       "      <td>393.707671</td>\n",
+       "      <td>-688.072925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-31</th>\n",
+       "      <td>3747.489710</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>36.470100</td>\n",
+       "      <td>-3.444037</td>\n",
+       "      <td>1366.713253</td>\n",
+       "      <td>-129.064919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-02-29</th>\n",
+       "      <td>3545.403337</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>45.887264</td>\n",
+       "      <td>2.059609</td>\n",
+       "      <td>1626.888577</td>\n",
+       "      <td>73.021455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-03-31</th>\n",
+       "      <td>3241.788769</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>63.817536</td>\n",
+       "      <td>11.618154</td>\n",
+       "      <td>2068.829713</td>\n",
+       "      <td>376.636022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-04-30</th>\n",
+       "      <td>4525.764752</td>\n",
+       "      <td>4694.943492</td>\n",
+       "      <td>42.911118</td>\n",
+       "      <td>3.738125</td>\n",
+       "      <td>1942.056267</td>\n",
+       "      <td>169.178740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-05-31</th>\n",
+       "      <td>5202.834938</td>\n",
+       "      <td>5185.090028</td>\n",
+       "      <td>89.172399</td>\n",
+       "      <td>-0.341062</td>\n",
+       "      <td>4639.492745</td>\n",
+       "      <td>-17.744910</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-06-30</th>\n",
+       "      <td>11629.504953</td>\n",
+       "      <td>11039.544603</td>\n",
+       "      <td>28.896039</td>\n",
+       "      <td>-5.072962</td>\n",
+       "      <td>3360.466324</td>\n",
+       "      <td>-589.960350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-07-31</th>\n",
+       "      <td>13113.950619</td>\n",
+       "      <td>14337.084069</td>\n",
+       "      <td>42.098780</td>\n",
+       "      <td>9.326964</td>\n",
+       "      <td>5520.813183</td>\n",
+       "      <td>1223.133450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-08-31</th>\n",
+       "      <td>12143.936026</td>\n",
+       "      <td>10751.914416</td>\n",
+       "      <td>43.074404</td>\n",
+       "      <td>-11.462689</td>\n",
+       "      <td>5230.928053</td>\n",
+       "      <td>-1392.021609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-09-30</th>\n",
+       "      <td>10495.557894</td>\n",
+       "      <td>10423.453507</td>\n",
+       "      <td>22.335182</td>\n",
+       "      <td>-0.686999</td>\n",
+       "      <td>2344.201933</td>\n",
+       "      <td>-72.104388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-10-31</th>\n",
+       "      <td>6268.234299</td>\n",
+       "      <td>5507.951502</td>\n",
+       "      <td>24.823425</td>\n",
+       "      <td>-12.129138</td>\n",
+       "      <td>1555.990459</td>\n",
+       "      <td>-760.282797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-11-30</th>\n",
+       "      <td>4167.182691</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>28.251982</td>\n",
+       "      <td>-13.168559</td>\n",
+       "      <td>1177.311686</td>\n",
+       "      <td>-548.757899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-31</th>\n",
+       "      <td>3565.126967</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>44.784007</td>\n",
+       "      <td>1.494977</td>\n",
+       "      <td>1596.606712</td>\n",
+       "      <td>53.297825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-01-31</th>\n",
+       "      <td>4299.786860</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>20.317090</td>\n",
+       "      <td>-17.889861</td>\n",
+       "      <td>873.591554</td>\n",
+       "      <td>-769.225876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-02-28</th>\n",
+       "      <td>4049.243978</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>29.212750</td>\n",
+       "      <td>-12.809379</td>\n",
+       "      <td>1182.895524</td>\n",
+       "      <td>-518.682994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-03-31</th>\n",
+       "      <td>3543.906179</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>51.586028</td>\n",
+       "      <td>-0.376567</td>\n",
+       "      <td>1828.160440</td>\n",
+       "      <td>-13.345195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-04-30</th>\n",
+       "      <td>3936.532388</td>\n",
+       "      <td>3859.594721</td>\n",
+       "      <td>66.203610</td>\n",
+       "      <td>-1.954453</td>\n",
+       "      <td>2606.126535</td>\n",
+       "      <td>-76.937667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-05-31</th>\n",
+       "      <td>7427.320860</td>\n",
+       "      <td>7950.105689</td>\n",
+       "      <td>34.048489</td>\n",
+       "      <td>7.038673</td>\n",
+       "      <td>2528.890489</td>\n",
+       "      <td>522.784829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-30</th>\n",
+       "      <td>14063.436455</td>\n",
+       "      <td>12988.558077</td>\n",
+       "      <td>7.821565</td>\n",
+       "      <td>-7.643071</td>\n",
+       "      <td>1099.980875</td>\n",
+       "      <td>-1074.878377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-07-31</th>\n",
+       "      <td>16247.673230</td>\n",
+       "      <td>14424.964831</td>\n",
+       "      <td>16.018969</td>\n",
+       "      <td>-11.218273</td>\n",
+       "      <td>2602.709812</td>\n",
+       "      <td>-1822.708400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-08-31</th>\n",
+       "      <td>11051.688592</td>\n",
+       "      <td>12219.774566</td>\n",
+       "      <td>59.033664</td>\n",
+       "      <td>10.569299</td>\n",
+       "      <td>6524.216672</td>\n",
+       "      <td>1168.085974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-09-30</th>\n",
+       "      <td>8903.103479</td>\n",
+       "      <td>8740.975650</td>\n",
+       "      <td>45.885378</td>\n",
+       "      <td>-1.821026</td>\n",
+       "      <td>4085.222721</td>\n",
+       "      <td>-162.127830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-10-31</th>\n",
+       "      <td>5193.040924</td>\n",
+       "      <td>5329.540553</td>\n",
+       "      <td>52.410841</td>\n",
+       "      <td>2.628511</td>\n",
+       "      <td>2721.716423</td>\n",
+       "      <td>136.499629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-11-30</th>\n",
+       "      <td>3925.935013</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>37.708201</td>\n",
+       "      <td>-10.070825</td>\n",
+       "      <td>1480.399472</td>\n",
+       "      <td>-395.374029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-31</th>\n",
+       "      <td>3863.789912</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>35.138277</td>\n",
+       "      <td>-8.624406</td>\n",
+       "      <td>1357.669187</td>\n",
+       "      <td>-333.228928</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            y_test_tot_AF  y_pred_tot_AF  StationaryError   MLPError  \\\n",
+       "Date                                                                   \n",
+       "2015-01-31    4709.274282    4020.242564         7.342278 -14.631378   \n",
+       "2015-02-28    4888.568674    4020.242564         4.579880 -17.762379   \n",
+       "2015-03-31    4493.545735    4020.242564        16.816118 -10.532955   \n",
+       "2015-04-30    5114.524352    4424.462629        24.996996 -13.492197   \n",
+       "2015-05-31    8583.758986    7329.383576        13.335806 -14.613358   \n",
+       "2015-06-30   12890.020382   12029.729591        14.946044  -6.674084   \n",
+       "2015-07-31   16379.389681   15065.430252        12.453508  -8.022029   \n",
+       "2015-08-31   15197.197243   13680.049464        13.006844  -9.983076   \n",
+       "2015-09-30   11502.006931    9997.498678        10.339270 -13.080398   \n",
+       "2015-10-31    6784.423105    6257.536733        13.992224  -7.766119   \n",
+       "2015-11-30    5299.251908    4020.242564        -0.312916 -24.135659   \n",
+       "2015-12-31    4708.315488    4020.242564         8.361965 -14.613994   \n",
+       "2016-01-31    3747.489710    3618.424792        36.470100  -3.444037   \n",
+       "2016-02-29    3545.403337    3618.424792        45.887264   2.059609   \n",
+       "2016-03-31    3241.788769    3618.424792        63.817536  11.618154   \n",
+       "2016-04-30    4525.764752    4694.943492        42.911118   3.738125   \n",
+       "2016-05-31    5202.834938    5185.090028        89.172399  -0.341062   \n",
+       "2016-06-30   11629.504953   11039.544603        28.896039  -5.072962   \n",
+       "2016-07-31   13113.950619   14337.084069        42.098780   9.326964   \n",
+       "2016-08-31   12143.936026   10751.914416        43.074404 -11.462689   \n",
+       "2016-09-30   10495.557894   10423.453507        22.335182  -0.686999   \n",
+       "2016-10-31    6268.234299    5507.951502        24.823425 -12.129138   \n",
+       "2016-11-30    4167.182691    3618.424792        28.251982 -13.168559   \n",
+       "2016-12-31    3565.126967    3618.424792        44.784007   1.494977   \n",
+       "2017-01-31    4299.786860    3530.560984        20.317090 -17.889861   \n",
+       "2017-02-28    4049.243978    3530.560984        29.212750 -12.809379   \n",
+       "2017-03-31    3543.906179    3530.560984        51.586028  -0.376567   \n",
+       "2017-04-30    3936.532388    3859.594721        66.203610  -1.954453   \n",
+       "2017-05-31    7427.320860    7950.105689        34.048489   7.038673   \n",
+       "2017-06-30   14063.436455   12988.558077         7.821565  -7.643071   \n",
+       "2017-07-31   16247.673230   14424.964831        16.018969 -11.218273   \n",
+       "2017-08-31   11051.688592   12219.774566        59.033664  10.569299   \n",
+       "2017-09-30    8903.103479    8740.975650        45.885378  -1.821026   \n",
+       "2017-10-31    5193.040924    5329.540553        52.410841   2.628511   \n",
+       "2017-11-30    3925.935013    3530.560984        37.708201 -10.070825   \n",
+       "2017-12-31    3863.789912    3530.560984        35.138277  -8.624406   \n",
+       "\n",
+       "            StationaryErrorAF   MLPErrorAF  \n",
+       "Date                                        \n",
+       "2015-01-31         345.767994  -689.031718  \n",
+       "2015-02-28         223.890584  -868.326110  \n",
+       "2015-03-31         755.639941  -473.303171  \n",
+       "2015-04-30        1278.477435  -690.061723  \n",
+       "2015-05-31        1144.713444 -1254.375410  \n",
+       "2015-06-30        1926.548117  -860.290792  \n",
+       "2015-07-31        2039.808677 -1313.959429  \n",
+       "2015-08-31        1976.675810 -1517.147779  \n",
+       "2015-09-30        1189.223590 -1504.508254  \n",
+       "2015-10-31         949.291652  -526.886372  \n",
+       "2015-11-30         -16.582211 -1279.009344  \n",
+       "2015-12-31         393.707671  -688.072925  \n",
+       "2016-01-31        1366.713253  -129.064919  \n",
+       "2016-02-29        1626.888577    73.021455  \n",
+       "2016-03-31        2068.829713   376.636022  \n",
+       "2016-04-30        1942.056267   169.178740  \n",
+       "2016-05-31        4639.492745   -17.744910  \n",
+       "2016-06-30        3360.466324  -589.960350  \n",
+       "2016-07-31        5520.813183  1223.133450  \n",
+       "2016-08-31        5230.928053 -1392.021609  \n",
+       "2016-09-30        2344.201933   -72.104388  \n",
+       "2016-10-31        1555.990459  -760.282797  \n",
+       "2016-11-30        1177.311686  -548.757899  \n",
+       "2016-12-31        1596.606712    53.297825  \n",
+       "2017-01-31         873.591554  -769.225876  \n",
+       "2017-02-28        1182.895524  -518.682994  \n",
+       "2017-03-31        1828.160440   -13.345195  \n",
+       "2017-04-30        2606.126535   -76.937667  \n",
+       "2017-05-31        2528.890489   522.784829  \n",
+       "2017-06-30        1099.980875 -1074.878377  \n",
+       "2017-07-31        2602.709812 -1822.708400  \n",
+       "2017-08-31        6524.216672  1168.085974  \n",
+       "2017-09-30        4085.222721  -162.127830  \n",
+       "2017-10-31        2721.716423   136.499629  \n",
+       "2017-11-30        1480.399472  -395.374029  \n",
+       "2017-12-31        1357.669187  -333.228928  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Eval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no\n",
+      "Total R2 is  0.6235333383547133\n",
+      "Total MAE is  2043.3946037944281\n",
+      "Total RMSE is  2529.7467407378826\n",
+      "Total MAPE is  30.788637404653628\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-12-a010c38583fc>:104: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Pred_UR_AF = Demand_Forecast(slc_val, False, Pred_Obs, 'UR_gpcd_AF', \n",
+    "                             'y_test_tot_AF', 'Acre-Feet', 'UR_Seasonal_pred_UR_AF', ' ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''I am putting a hard code on the limits here to make both figure have same color scale'''\n",
+    "\n",
+    "plt.rcParams[\"axes.grid\"] = False\n",
+    "plt.rcParams[\"axes.facecolor\"] ='white'\n",
+    "#plt.rcParams[\"axes.edgecolor\"]['bottom'] = 'black'\n",
+    "\n",
+    "def gradientbars_sliced(bars, ymin, ymax):\n",
+    "    ax = bars[0].axes\n",
+    "    xmin, xmax = ax.get_xlim()\n",
+    " #   ymin, ymax = ax.get_ylim()\n",
+    "    for bar in bars:\n",
+    "        bar.set_zorder(1)\n",
+    "        bar.set_facecolor(\"none\")\n",
+    "        x, y = bar.get_xy()\n",
+    "        w, h = bar.get_width(), bar.get_height()\n",
+    "        grad = np.linspace(y, y + h, 256).reshape(256, 1)\n",
+    "        ax.imshow(grad, extent=[x, x + w, y, y + h], aspect=\"auto\", zorder=0, origin='lower',\n",
+    "                  vmin= - max(np.abs(ymin), ymax), vmax=max(np.abs(ymin), ymax), cmap='Spectral_r')\n",
+    "    ax.axis([xmin, xmax, ymin, ymax])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x792 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Reduce width to increase space between bars\n",
+    "width = .9\n",
+    "widthM = 1\n",
+    "titlesize = 14\n",
+    "\n",
+    "Ann_Eval['Supply'] = ['Surplus', 'Drought', 'Average']\n",
+    "Ann_Eval.index = Ann_Eval['Supply']\n",
+    "\n",
+    "DayDF = Ann_Eval\n",
+    "\n",
+    "fig, ax = plt.subplots(3,1, constrained_layout=True)\n",
+    "fig.set_size_inches(9,11)\n",
+    "DayDF['Error_SLC_WDM'] = DayDF.y_pred_tot_AF-DayDF.y_test_tot_AF\n",
+    "DayDF['Error_UR'] = DayDF.UR_gpcd_AF-DayDF.y_test_tot_AF\n",
+    "\n",
+    "#save this df to load into MLR file for comparision figure\n",
+    "DayDF.to_excel('MLP_Models/MLP_Ann_Comp.xlsx')\n",
+    "\n",
+    "\n",
+    "#Annual Figure\n",
+    "\n",
+    "ax[0].bar(DayDF.Supply, DayDF.UR_gpcd_AF, width=width, \n",
+    "    color='red', label='Base')\n",
+    "ax[0].bar(DayDF.Supply, DayDF.y_pred_tot_AF, width=width, \n",
+    "    color='orange', label='MLP')\n",
+    "ax[0].bar(DayDF.Supply, DayDF.y_test_tot_AF, width=width, \n",
+    "    color='blue', label='Observed')\n",
+    "ax[0].set_ylim(0,max(max(Ann_Eval.y_test_tot_AF), max(Ann_Eval.y_pred_tot_AF),max(Ann_Eval.UR_gpcd_AF))*1.2)\n",
+    "#ax.annotate('Max Extra DC use: '+str(np.round(np.max(DayDF.ExtraDC),1))+'MGD', \n",
+    " #              xy=(DayDF.index[269], 150))\n",
+    "#ax.annotate('Max Daily DC use: '+str(np.round(np.max(DayDF.SLCDPU_DC_Water_Use),1))+'MGD', \n",
+    " #              xy=(DayDF.index[269], 130))\n",
+    "ax[0].legend(loc = 'upper left')\n",
+    "ax[0].set_ylabel('Acre-Feet')\n",
+    "ax[0].set_title(' Comparison of Seasonal Forecasts vs Observed', size = titlesize)\n",
+    "ax[0].spines['bottom'].set_color('black')\n",
+    "ax[0].spines['left'].set_color('black')\n",
+    "\n",
+    "\n",
+    "Error_UR = ax[1].bar(DayDF.index, DayDF.Error_UR, width=width, \n",
+    "    color='red', label='Base')\n",
+    "ax[1].set_ylabel('Acre-Feet')\n",
+    "ax[1].set_title('Base: Difference From The Observed', size = titlesize)\n",
+    "gradientbars_sliced(Error_UR, min(min(DayDF.Error_UR), min(DayDF.Error_SLC_WDM)),\n",
+    "                    max(max(DayDF.Error_UR), max(DayDF.Error_SLC_WDM)))\n",
+    "ax[1].spines['bottom'].set_color('black')\n",
+    "ax[1].spines['left'].set_color('black')\n",
+    "\n",
+    "\n",
+    "\n",
+    "Error_SLC_WDM = ax[2].bar(DayDF.Supply, DayDF.Error_SLC_WDM, width=width, \n",
+    "    color='red', label='Base')\n",
+    "ax[2].set_ylabel('Acre-Feet')\n",
+    "ax[2].set_title('MLP: Difference From The Observed', size = titlesize)\n",
+    "gradientbars_sliced(Error_SLC_WDM, min(min(DayDF.Error_UR), min(DayDF.Error_SLC_WDM)),\n",
+    "                    max(max(DayDF.Error_UR), max(DayDF.Error_SLC_WDM)))\n",
+    "ax[2].spines['bottom'].set_color('black')\n",
+    "ax[2].spines['left'].set_color('black')\n",
+    "\n",
+    "fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/SLC_WDM_MLPvsUR.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "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>y_test_tot_AF</th>\n",
+       "      <th>y_pred_tot_AF</th>\n",
+       "      <th>UR_gpcd_AF</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Error_SLC_WDM</th>\n",
+       "      <th>Error_UR</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Supply</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Surplus</th>\n",
+       "      <td>100550.276769</td>\n",
+       "      <td>88885.303742</td>\n",
+       "      <td>112757.439473</td>\n",
+       "      <td>Surplus</td>\n",
+       "      <td>-11664.973027</td>\n",
+       "      <td>12207.162705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Drought</th>\n",
+       "      <td>81646.774954</td>\n",
+       "      <td>80032.105575</td>\n",
+       "      <td>114077.073858</td>\n",
+       "      <td>Drought</td>\n",
+       "      <td>-1614.669379</td>\n",
+       "      <td>32430.298904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Average</th>\n",
+       "      <td>86505.457871</td>\n",
+       "      <td>83166.319007</td>\n",
+       "      <td>115397.037575</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>-3339.138863</td>\n",
+       "      <td>28891.579705</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         y_test_tot_AF  y_pred_tot_AF     UR_gpcd_AF   Supply  Error_SLC_WDM  \\\n",
+       "Supply                                                                         \n",
+       "Surplus  100550.276769   88885.303742  112757.439473  Surplus  -11664.973027   \n",
+       "Drought   81646.774954   80032.105575  114077.073858  Drought   -1614.669379   \n",
+       "Average   86505.457871   83166.319007  115397.037575  Average   -3339.138863   \n",
+       "\n",
+       "             Error_UR  \n",
+       "Supply                 \n",
+       "Surplus  12207.162705  \n",
+       "Drought  32430.298904  \n",
+       "Average  28891.579705  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/RFR_Monthly_Demand.ipynb b/RFR_Monthly_Demand.ipynb
new file mode 100644
index 0000000..5c725cb
--- /dev/null
+++ b/RFR_Monthly_Demand.ipynb
@@ -0,0 +1,2249 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_absolute_percentage_error\n",
+    "\n",
+    "from sklearn. metrics import accuracy_score\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.utils import resample\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.model_selection import LeaveOneOut\n",
+    "from sklearn import preprocessing\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "import seaborn as sns; sns.set()\n",
+    "import joblib\n",
+    "from sklearn.linear_model import LassoCV\n",
+    "from sklearn.datasets import make_regression\n",
+    "from pathlib import Path\n",
+    "import copy\n",
+    "\n",
+    "#Trying out recusive feature elimination to compare with step wise regression\n",
+    "from sklearn.feature_selection import RFE\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "import sklearn\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import layers\n",
+    "from tensorflow.keras.models import load_model\n",
+    "\n",
+    "from progressbar import ProgressBar\n",
+    "\n",
+    "\n",
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import datetime\n",
+    "import calendar\n",
+    "\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "#custom functions\n",
+    "def NSC(y_pred,y_true):\n",
+    "    \"\"\"\n",
+    "    Nash-Sutcliffe Coefficient\n",
+    "    \"\"\"\n",
+    "    return 1 - sum((y_pred-y_true)**2)/sum((y_true-np.mean(y_true))**2)\n",
+    "\n",
+    "def corr(data1, data2):\n",
+    "    \"data1 & data2 should be numpy arrays.\"\n",
+    "    mean1 = data1.mean() \n",
+    "    mean2 = data2.mean()\n",
+    "    std1 = data1.std()\n",
+    "    std2 = data2.std()\n",
+    "\n",
+    "#     corr = ((data1-mean1)*(data2-mean2)).mean()/(std1*std2)\n",
+    "    corr = ((data1*data2).mean()-mean1*mean2)/(std1*std2)\n",
+    "    return corr\n",
+    "\n",
+    "\n",
+    "def mean_absolute_percentage_error(y_true, y_pred): \n",
+    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
+    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#This is the final dataset to make predictions on\n",
+    "p = Path('C:/Users/Ryan/Box/Dissertation/Paper1/Data/Processed_Training_Data')\n",
+    "#dir_data = 'C:/Users/Ryan/Box/Dissertation/Paper1/Data/Model_Input_Data/Monthly/Input/FinalVars'\n",
+    "\n",
+    "slc = {i.stem[0:3] : pd.read_excel(i) for i in p.glob('**/*.xlsx')}\n",
+    "\n",
+    "#rename the gpcd column\n",
+    "for i in slc:\n",
+    "    slc[i].rename(columns={i+'_gpcd': 'Obs_gpcd'}, inplace=True)\n",
+    "    slc[i]=slc[i].set_index('Year')\n",
+    "snow=pd.read_excel('C:/Users/Ryan/Box/Dissertation/Paper1/Data/Alta_LCC_snow.xlsx')\n",
+    "snow=snow.set_index('Year')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create training and testing data, use most recent low, average, and high water years\n",
+    "slc_train=copy.deepcopy(slc)\n",
+    "slc_test=copy.deepcopy(slc)\n",
+    "\n",
+    "#2008 is a high year\n",
+    "#2011 and 2017 are average years\n",
+    "#2014 and 2016 are below average years\n",
+    "#2015 is a very  low year\n",
+    "\n",
+    "IN_WY_Months = ['Jan' , 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug','Sep', 'Oct']\n",
+    "Prior_YR_WY_Months = ['Nov', 'Dec']\n",
+    "testWYyrs = [2008,2015,2017]\n",
+    "#testPrioWYyrs = [2007,2014,2016]\n",
+    "\n",
+    "\n",
+    "for i in slc:\n",
+    "    #Select the training/testing dataframes\n",
+    "    slc_train[i]=slc_train[i][~slc_train[i].index.isin(testWYyrs)]\n",
+    "    slc_test[i]=slc_test[i][slc_test[i].index.isin(testWYyrs)]\n",
+    "   \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create training and testing data\n",
+    "#slc_train=copy.deepcopy(slc)\n",
+    "#slc_test=copy.deepcopy(slc)\n",
+    "\n",
+    "#Train_yrs= 26\n",
+    "#\n",
+    "\n",
+    "#for i in slc:\n",
+    "    #Select the training/testing dataframes\n",
+    " #   slc_train[i]=slc_train[i][:Train_yrs]\n",
+    "  #  slc_test[i]=slc_test[i][Train_yrs:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    \n",
+    "\n",
+    "#Determine the indoor mean to subtract from outdoor\n",
+    "I_mean_train=(slc_train['Jan']['Obs_gpcd']+\n",
+    "              slc_train['Feb']['Obs_gpcd']+\n",
+    "              slc_train['Mar']['Obs_gpcd']+\n",
+    "              slc_train['Nov']['Obs_gpcd']+\n",
+    "             slc_train['Dec']['Obs_gpcd'])/5\n",
+    "I_mean_test=(slc_test['Jan']['Obs_gpcd']+\n",
+    "             slc_test['Feb']['Obs_gpcd']+\n",
+    "             slc_test['Mar']['Obs_gpcd']+\n",
+    "             slc_test['Nov']['Obs_gpcd']+\n",
+    "             slc_test['Dec']['Obs_gpcd'])/5\n",
+    "#This uses total demand for irrigation seasons\n",
+    "#I_mean_train=np.zeros(23)\n",
+    "#I_mean_test=np.zeros(10)\n",
+    "\n",
+    "for i in slc_train:\n",
+    "    slc_train[i]['Iave']=I_mean_train\n",
+    "    #for now include testing years\n",
+    "    slc_test[i]['Iave']=I_mean_test\n",
+    "\n",
+    "IrrSeason= ['Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct']\n",
+    "Indoor=['Jan', 'Feb', 'Mar', 'Nov', 'Dec']\n",
+    "colrem=['Iave', 'Obs_gpcd']\n",
+    "#set the target gpcd to indoor for indoor and total-indoor for outdoor\n",
+    "#change to indoor to separate outdoor demands from total\n",
+    "for i in Indoor:\n",
+    "    slc_train[i]['Target_gpcd']=slc_train[i]['Obs_gpcd']\n",
+    "    slc_train[i]= slc_train[i].drop(columns=colrem)\n",
+    "    #for now include testing years\n",
+    "    slc_test[i]['Target_gpcd']=slc_test[i]['Obs_gpcd']\n",
+    "    slc_test[i]= slc_test[i].drop(columns=colrem)\n",
+    "    \n",
+    "\n",
+    "#Establish outdoor conservation thresholds\n",
+    "O_cons = 0.25\n",
+    "time = 20\n",
+    "\n",
+    "\n",
+    "for i in IrrSeason:\n",
+    "    slc_train[i]['Target_gpcd']=slc_train[i]['Obs_gpcd']-slc_train[i]['Iave']\n",
+    "    slc_train[i].loc[slc_train[i]['Target_gpcd'] < 0, 'Target_gpcd'] = 0\n",
+    "    \n",
+    "    #add in snow info\n",
+    "    slc_train[i]=pd.concat([slc_train[i], snow], axis=1, join=\"inner\")\n",
+    "    \n",
+    "    slc_train[i]= slc_train[i].drop(columns=colrem)\n",
+    "    #for now include testing years\n",
+    "    slc_test[i]=pd.concat([slc_test[i], snow], axis=1, join=\"inner\")\n",
+    "    slc_test[i]['Target_gpcd']=slc_test[i]['Obs_gpcd']-slc_test[i]['Iave']\n",
+    "    slc_test[i].loc[slc_test[i]['Target_gpcd'] < 0, 'Target_gpcd'] = 0\n",
+    "    \n",
+    "    #create monthly historical mean and conservation trends\n",
+    "    Out_mean = np.mean(slc_train[i]['Target_gpcd'].loc[2000:])\n",
+    "    goal = (1-O_cons)*Out_mean\n",
+    "    O_cons_rate = (Out_mean -goal)/time\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    slc_train[i]['cons_goal'] = Out_mean- ((slc_train[i].index-2000)*O_cons_rate)\n",
+    "    slc_train[i].loc[ slc_train[i].index <2000, ['cons_goal']] = Out_mean\n",
+    "    \n",
+    "    t=slc_train[i]['Target_gpcd'].copy()\n",
+    "    c=slc_train[i]['cons_goal'].copy()\n",
+    "    slc_train[i] = slc_train[i].drop(columns=['Target_gpcd', 'cons_goal'])\n",
+    "    slc_train[i]['Target_gpcd'] = t\n",
+    "    slc_train[i]['cons_goal'] = c\n",
+    "    \n",
+    "    slc_test[i]['cons_goal'] = Out_mean - ((slc_test[i].index-2000)*O_cons_rate) \n",
+    "    \n",
+    " \n",
+    "    \n",
+    "    \n",
+    "    slc_test[i]= slc_test[i].drop(columns=colrem)\n",
+    "\n",
+    "    \n",
+    "#Determine the historical indoor mean to apply conservation strategies too\n",
+    "Indmean = np.mean(slc_train['Jan']['Target_gpcd'].loc[2000:]+\n",
+    "                  slc_train['Feb']['Target_gpcd'].loc[2000:]+\n",
+    "                  slc_train['Mar']['Target_gpcd'].loc[2000:]+\n",
+    "                  slc_train['Nov']['Target_gpcd'].loc[2000:]+\n",
+    "                 slc_train['Dec']['Target_gpcd'].loc[2000:])/5\n",
+    "'''Per UDWR 2001, Utah's Water Resources: Planning for the future, the govenor ordered a\n",
+    "25% reduction in water use by 2025\n",
+    "'''\n",
+    "cons = 0.28\n",
+    "\n",
+    "goal = (1-cons)*Indmean\n",
+    "\n",
+    "time = 20\n",
+    "\n",
+    "cons_rate = (Indmean -goal)/time\n",
+    "\n",
+    "#print(cons_rate , goal)\n",
+    "\n",
+    "#create feature called cons_goal!\n",
+    "for i in Indoor:\n",
+    "    slc_test[i]['cons_goal'] = Indmean-((slc_test[i].index-2000)*cons_rate) \n",
+    "    slc_train[i]['cons_goal'] = Indmean-((slc_train[i].index-2000)*cons_rate) \n",
+    "    \n",
+    "    slc_train[i].loc[ slc_train[i].index <2000, ['cons_goal']] = Indmean\n",
+    "    \n",
+    "    \n",
+    "Cons_mean_test=(slc_test['Jan']['cons_goal']+\n",
+    "                slc_test['Feb']['cons_goal']+\n",
+    "                slc_test['Mar']['cons_goal']+\n",
+    "                slc_test['Nov']['cons_goal']+\n",
+    "                slc_test['Dec']['cons_goal'])/5\n",
+    "\n",
+    "#split training and testing data into features and targets\n",
+    "slc_train_target=copy.deepcopy(slc_train)\n",
+    "slc_train_features=copy.deepcopy(slc_train)\n",
+    "\n",
+    "slc_test_target=copy.deepcopy(slc_test)\n",
+    "slc_test_features=copy.deepcopy(slc_test)\n",
+    "\n",
+    "\n",
+    "target=['Target_gpcd','Housing']\n",
+    "for i in slc_train_target:\n",
+    "    slc_train_target[i]=slc_train_target[i]['Target_gpcd']\n",
+    "    #for now include testing years\n",
+    "    slc_test_target[i]=slc_test_target[i]['Target_gpcd']\n",
+    "\n",
+    "\n",
+    "    slc_train_features[i]= slc_train_features[i].drop(columns=target)\n",
+    "    #for now include testing years\n",
+    "    slc_test_features[i]= slc_test_features[i].drop(columns=target)\n",
+    "    \n",
+    "#need to remove year from the list to run plots below\n",
+    "for i in slc_train:\n",
+    "    slc_train[i]=slc_train[i].drop(columns=['Housing',  'Population', 'PopulationDensity'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Use features from MLR \n",
+    "\n",
+    "Apr = ['Val_Apr_Monthly_mean_Day_temp_C', 'Val_Apr_Monthly_precip_mm', 'LitCotCrk_AcFt_WR_Apr',\n",
+    "       'LitCotCrk_AcFt_WR_Mar', 'Target_gpcd']\n",
+    "May = ['Val_May_Monthly_precip_mm', 'Val_May_Monthly_mean_Day_temp_C', 'Target_gpcd']\n",
+    "Jun = ['Val_Jun_Monthly_precip_mm', 'Val_Apr_Monthly_mean_Day_temp_C', 'BigCotCrk_AcFt_WR_Jun', \n",
+    "       'Val_May_Monthly_mean_Day_temp_C', 'CityCrk_AcFt_WR_May', 'BigCotCrk_AcFt_WR_May', \n",
+    "       'Val_May_Monthly_precip_mm', 'Target_gpcd']\n",
+    "Jul = ['UrbPopulationDensity', 'Val_Jul_Monthly_mean_Day_temp_C', 'Val_Apr_Monthly_mean_Day_temp_C',\n",
+    "       'Val_May_Monthly_mean_Day_temp_C', 'Target_gpcd']\n",
+    "Aug = ['Val_Aug_Monthly_precip_mm', 'UrbPopulationDensity', 'LitCotCrk_AcFt_WR_Apr',\n",
+    "       'Val_Apr_Monthly_mean_Day_temp_C', 'Val_Aug_Monthly_mean_Day_temp_C', 'LitCotCrk_AcFt_WR_Mar', \n",
+    "       'Val_Apr_Monthly_precip_mm', 'Val_May_Monthly_mean_Day_temp_C', 'Val_Jul_Monthly_mean_Day_temp_C', \n",
+    "       'Val_Jun_Monthly_mean_Day_temp_C', 'Val_Jun_Monthly_precip_mm', 'BigCotCrk_AcFt_WR_May',\n",
+    "       'LitCotCrk_AcFt_WR_May', 'Target_gpcd']\n",
+    "Sep = ['Val_Sep_Monthly_precip_mm', 'Val_Sep_Monthly_mean_Day_temp_C', 'Val_Aug_Monthly_mean_Day_temp_C', \n",
+    "       'Val_May_Monthly_mean_Day_temp_C', 'Val_Apr_Monthly_mean_Day_temp_C', 'Val_Jun_Monthly_mean_Day_temp_C',\n",
+    "       'Val_Jul_Monthly_mean_Day_temp_C', 'Target_gpcd']\n",
+    "Oct = ['cons_goal','Target_gpcd']\n",
+    "\n",
+    "\n",
+    "#select these features for training and testing\n",
+    "slc_train['Apr'] = slc_train['Apr'][Apr]\n",
+    "slc_test['Apr'] = slc_test['Apr'][Apr]\n",
+    "\n",
+    "slc_train['May'] = slc_train['May'][May]\n",
+    "slc_test['May'] = slc_test['May'][May]\n",
+    "\n",
+    "slc_train['Jun'] = slc_train['Jun'][Jun]\n",
+    "slc_test['Jun'] = slc_test['Jun'][Jun]\n",
+    "\n",
+    "slc_train['Jul'] = slc_train['Jul'][Jul]\n",
+    "slc_test['Jul'] = slc_test['Jul'][Jul]\n",
+    "\n",
+    "slc_train['Aug'] = slc_train['Aug'][Aug]\n",
+    "slc_test['Aug'] = slc_test['Aug'][Aug]\n",
+    "\n",
+    "slc_train['Sep'] = slc_train['Sep'][Sep]\n",
+    "slc_test['Sep'] = slc_test['Sep'][Sep]\n",
+    "\n",
+    "slc_train['Oct'] = slc_train['Oct'][Oct]\n",
+    "slc_test['Oct'] = slc_test['Oct'][Oct]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_plots(PerfDF, month):\n",
+    "    plt.rcParams[\"axes.grid\"] = False\n",
+    "    plt.rcParams[\"axes.facecolor\"] ='white'\n",
+    "    plotmin = PerfDF[['y_pred', 'y_test']].min().min()\n",
+    "    plotmax = PerfDF[['y_pred', 'y_test']].max().max()\n",
+    "    \n",
+    "    plotmin_tot = PerfDF[['y_pred_tot', 'y_test_tot']].min().min()\n",
+    "    plotmax_tot = PerfDF[['y_pred_tot', 'y_test_tot']].max().max()\n",
+    "    \n",
+    "    \n",
+    "    fig, ax = plt.subplots(3,2, constrained_layout=True)\n",
+    "    fig.set_size_inches(9,10)\n",
+    "\n",
+    "    ax[0,0].scatter(PerfDF['y_test'], PerfDF['y_pred'],color='blue', alpha=0.5)\n",
+    "    ax[0,0].set_ylabel('Predicted')\n",
+    "    ax[0,0].set_xlabel('Observed')\n",
+    "    ax[0,0].set_ylim(plotmin-5,plotmax+5)\n",
+    "    ax[0,0].set_xlim(plotmin-5,plotmax+5)\n",
+    "    ax[0,0].set_title('Outdoor Model Performance')\n",
+    "    ax[0,0].plot([plotmin,plotmax],[plotmin,plotmax], color='red', linestyle='--' )\n",
+    "    ax[0,0].spines['bottom'].set_color('black')\n",
+    "    ax[0,0].spines['left'].set_color('black')\n",
+    "    \n",
+    "    ax[0,1].scatter(PerfDF['y_test_tot'], PerfDF['y_pred_tot'],color='blue', alpha=0.5)\n",
+    "    ax[0,1].set_ylabel('Predicted')\n",
+    "    ax[0,1].set_xlabel('Observed')\n",
+    "    ax[0,1].set_ylim(plotmin_tot-5,plotmax_tot+5)\n",
+    "    ax[0,1].set_xlim(plotmin_tot-5,plotmax_tot+5)\n",
+    "    ax[0,1].set_title('Indoor and Outdoor \\n Model Performance')\n",
+    "    ax[0,1].plot([plotmin_tot,plotmax_tot],[plotmin_tot,plotmax_tot], color='red', linestyle='--' )\n",
+    "    ax[0,1].spines['bottom'].set_color('black')\n",
+    "    ax[0,1].spines['left'].set_color('black')\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    gs = ax[1, 1].get_gridspec()\n",
+    "    # remove the underlying axes\n",
+    "    ax[1,0].remove()\n",
+    "    ax[1,1].remove()\n",
+    "\n",
+    "    PerfDF['Error'] = (PerfDF['y_pred']-PerfDF['y_test'])\n",
+    "    axbig1 = fig.add_subplot(gs[1, :])\n",
+    "    axbig1.set_title(month+' Outdoor demand Timeline Evaluation')\n",
+    "    axbig1.axhline(y = 0 , color = 'black')\n",
+    "    #axbig1.bar(PerfDF.index, PerfDF['y_pred'], color='orange', label='Predicted')\n",
+    "    Error1 = axbig1.bar(PerfDF.index, PerfDF['Error'],color='blue', label='Prediction Error')\n",
+    "    axbig1.set_xlabel('Year')\n",
+    "    axbig1.set_ylabel('Error (GPCD)')\n",
+    "    axbig1.spines['bottom'].set_color('black')\n",
+    "    axbig1.spines['left'].set_color('black')\n",
+    "    gradientbars_sliced(Error1)\n",
+    "    \n",
+    "    \n",
+    "    gs2 = ax[2, 1].get_gridspec()\n",
+    "    # remove the underlying axes\n",
+    "    ax[2,0].remove()\n",
+    "    ax[2,1].remove()\n",
+    "\n",
+    "\n",
+    "    #create error value\n",
+    "    PerfDF['Error_tot'] = (PerfDF['y_pred_tot']-PerfDF['y_test_tot'])\n",
+    "    \n",
+    "    axbig2 = fig.add_subplot(gs2[2, :])\n",
+    "    axbig2.set_title(month+' Total Demand Error Timeline Evaluation')\n",
+    "    Error2 = axbig2.bar(PerfDF.index, PerfDF['Error_tot'], color='blue', label='Predicted')\n",
+    "    axbig2.axhline(y = 0 , color = 'black')\n",
+    "    #axbig2.bar(PerfDF.index, PerfDF['y_test_tot'],color='blue', label='Observed')\n",
+    "    axbig2.set_xlabel('Year')\n",
+    "    axbig2.set_ylabel('Error (GPCD)')\n",
+    "    axbig2.spines['bottom'].set_color('black')\n",
+    "    axbig2.spines['left'].set_color('black')\n",
+    "    gradientbars_sliced(Error2)\n",
+    "    \n",
+    "    fig.suptitle(month+ ' Evaluation', size = 16)\n",
+    "    fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/RFR' + month + '_demand.pdf')    \n",
+    "    \n",
+    "    O_r2 = sklearn.metrics.r2_score(PerfDF['y_test'],PerfDF['y_pred'])\n",
+    "    O_rmse= sklearn.metrics.mean_squared_error(PerfDF['y_test'],PerfDF['y_pred'], squared = False)\n",
+    "    O_mae= sklearn.metrics.mean_absolute_error(PerfDF['y_test'],PerfDF['y_pred'])\n",
+    "    O_mape= sklearn.metrics.mean_absolute_percentage_error(PerfDF['y_test'],PerfDF['y_pred'])\n",
+    "    \n",
+    "    T_r2 = sklearn.metrics.r2_score(PerfDF['y_test_tot'],PerfDF['y_pred_tot'])\n",
+    "    T_rmse= sklearn.metrics.mean_squared_error(PerfDF['y_test_tot'],PerfDF['y_pred_tot'], squared = False)\n",
+    "    T_mae= sklearn.metrics.mean_absolute_error(PerfDF['y_test_tot'],PerfDF['y_pred_tot'])\n",
+    "    T_mape= sklearn.metrics.mean_absolute_percentage_error(PerfDF['y_test_tot'],PerfDF['y_pred_tot'])\n",
+    "    \n",
+    "    print('The outdoor Demand prediction RMSE is ', O_rmse)\n",
+    "    print('The outdoor Demand prediction R2 is ', O_r2)\n",
+    "    \n",
+    "    print('The Total Demand prediction RMSE is ', T_rmse)\n",
+    "    print('The Total Demand prediction R2 is ', T_r2)\n",
+    "    print('The Total Demand prediction MAE is ', T_mae)\n",
+    "    print('The Total Demand prediction MAPE is ', T_mape, '%')     \n",
+    "  \n",
+    "    #set DF up so that all months can be easily combined, basically year-month index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Indoor_Demand_Model(df, month):\n",
+    "    #Make matching indoor demand model\n",
+    "    datetime_object = datetime.datetime.strptime(month, \"%b\")\n",
+    "    df[month]['month'] = datetime_object.month\n",
+    "    \n",
+    "    df[month]['Year']=df[month].index\n",
+    "    \n",
+    "     #set up dates so all months can be combined and sorted\n",
+    "    day=[]\n",
+    "    for index, row in df[month].iterrows():\n",
+    "        day.append(calendar.monthrange(int(row['Year']), int(row['month']))[1])\n",
+    "    \n",
+    "    df[month]['Day']=day\n",
+    "    \n",
+    "  \n",
+    "    df[month]['Date'] = pd.to_datetime(df[month][['Year', 'month', 'Day']])\n",
+    "    \n",
+    "    #PerfDF=PerfDF.set_index('Date')\n",
+    "    df[month]=df[month].drop(columns=['month', 'Day'])\n",
+    "    df[month]=df[month].reset_index(drop=True)\n",
+    "   \n",
+    "    \n",
+    "    colrem = slc_test[i].columns\n",
+    "    df[month]['y_pred'] =  df[month]['cons_goal']\n",
+    "    df[month]['y_test'] =  df[month]['Target_gpcd']\n",
+    "    df[month]['y_pred_tot'] =  df[month]['cons_goal']\n",
+    "    df[month]['y_test_tot'] =  df[month]['Target_gpcd']\n",
+    "       \n",
+    "    df[month] = df[month].set_index('Date')\n",
+    "    \n",
+    "    df[month] = df[month].drop(columns=colrem)\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#make a model function to determine best parameters\n",
+    "#Xt, yt are training features, training targets\n",
+    "def fit_model(X_train, y_train):\n",
+    "  \n",
+    "  #model dictionary and hyper-params to search over\n",
+    "    model = {}\n",
+    "    model['RandomForestRegressor'] = [RandomForestRegressor(), {'n_estimators':np.arange(5,75,5),\n",
+    "                                                                'criterion' : ['mse'],\n",
+    "                                                                'max_depth':np.arange(2,40,2),\n",
+    "                                                                'max_features': [0.2, 0.4, 0.5, 0.6, 0.8],\n",
+    "                                                                'bootstrap':['False'],\n",
+    "                                                                'oob_score':['True'],\n",
+    "                                                                'random_state':[42],\n",
+    "                                                                'n_jobs': [-1]\n",
+    "                                                                   }] \n",
+    "  #Loop over models, hyper-params, and tune with cross validation    \n",
+    "    pbar = ProgressBar()\n",
+    "    for i in pbar(model.keys()):\n",
+    "        clf = GridSearchCV(model[i][0], model[i][1], n_jobs = -1, cv = 5)\n",
+    "        GS = clf.fit(X_train,y_train)\n",
+    "        model[i].append(GS)\n",
+    "        clf_b = model[i][2].best_estimator_\n",
+    "    print(clf_b)\n",
+    "    return clf_b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "The MLP model needs to take in normalized numpy arrays. The MLP Water Demand Model needs to:\n",
+    "1) take in all of the data (training and testing)\n",
+    "2) normalize that months training and testing inputs (0-1)\n",
+    "3) train and save the model, RFR_Models/\n",
+    "4) predict on testing dataset\n",
+    "5) convert to original units\n",
+    "6) Deliver prediction datastat (with orginial testing data) to send to figure generator.\n",
+    "'''\n",
+    "\n",
+    "def RFR_OutdoorDemandModel(df_train, df_test,month, checkpointfilename):\n",
+    "    print(month, \" Random Forest model training and calibration\")\n",
+    "    \n",
+    "    traindf = df_train.copy()\n",
+    "    testdf = df_test.copy()\n",
+    "    \n",
+    "    #find the min of input data\n",
+    "    dfmin = traindf.append(testdf).copy()\n",
+    "    dfmin = list(dfmin.min())\n",
+    "\n",
+    "    #normalize process A\n",
+    "    traindf = traindf-dfmin\n",
+    "    testdf = testdf-dfmin\n",
+    "      \n",
+    "    #find the min of input data\n",
+    "    dfmax = traindf.append(testdf).copy()\n",
+    "    dfmax = list(dfmax.max())\n",
+    "\n",
+    "    #normalize process A\n",
+    "    traindf = traindf/dfmax\n",
+    "    testdf = testdf/dfmax\n",
+    "    \n",
+    "    #separate into training and testing features and targets\n",
+    "    features_tr = traindf.drop(columns =['Target_gpcd']).copy()\n",
+    "    features_val = testdf.drop(columns =['Target_gpcd']).copy()\n",
+    "    \n",
+    "    target_tr = traindf['Target_gpcd'].copy()\n",
+    "    target_val = testdf['Target_gpcd'].copy()\n",
+    "    \n",
+    "    #convert to numpy array for model\n",
+    "    features_tr = np.array(features_tr.to_numpy())\n",
+    "    features_val = np.array(features_val.to_numpy())\n",
+    "    \n",
+    "    target_tr_ = np.array(target_tr.to_numpy())\n",
+    "    target_val = np.array(target_val.to_numpy())\n",
+    "    \n",
+    "    #we want a y_test for later on too\n",
+    "    y_test = (target_val.copy()*dfmax[-1])+dfmin[-1]\n",
+    "   \n",
+    "    \n",
+    "    \n",
+    "    #Get the numpy shape for the model\n",
+    "    shape = features_tr.shape\n",
+    "\n",
+    "    \n",
+    "    #now input the model\n",
+    "    model = fit_model(features_tr, target_tr)\n",
+    "    joblib.dump(model, checkpointfilename + '/'+ month + '/RFR_Demand_model.pkl') \n",
+    "    \n",
+    "    #Make predictions on the validation data\n",
+    "    y_pred = model.predict(features_val)\n",
+    "    #make sure no negative values\n",
+    "   # y_pred = [0 if x < 0 else x for x in y_pred]\n",
+    "    \n",
+    "    #convert predictions to gpcd and place into OG dataframe\n",
+    "    y_pred = (y_pred*dfmax[-1])+dfmin[-1]\n",
+    "    shape = y_pred.shape\n",
+    "    y_pred = y_pred.reshape(shape[0])\n",
+    "    #measure the modeling performance of the MLP model\n",
+    "    O_r2_test = sklearn.metrics.r2_score(y_test, y_pred)\n",
+    "    O_rmse_test = sklearn.metrics.mean_squared_error(y_test, y_pred, squared = False)\n",
+    "    print('Outdoor R2 is ', O_r2_test)\n",
+    "    print('Outdoor RMSE is ', O_rmse_test)\n",
+    "        \n",
+    "    #put df in same format as MLR model\n",
+    "    PerfDF=pd.DataFrame(list(zip(y_pred, y_test)), columns=['y_pred', 'y_test'])\n",
+    "\n",
+    "#Add indoor demands\n",
+    "    Indoor=['Jan', 'Feb', 'Mar', 'Nov', 'Dec']\n",
+    "    if month in Indoor:\n",
+    "        PerfDF['y_test']=PerfDF['y_test']\n",
+    "        PerfDF['y_pred']=PerfDF['y_pred']\n",
+    "    else:\n",
+    "        PerfDF['y_test_tot']=PerfDF['y_test']+list(I_mean_test)\n",
+    "        PerfDF['y_pred_tot']=PerfDF['y_pred']+list(Cons_mean_test)\n",
+    "\n",
+    "    T_r2 = sklearn.metrics.r2_score(PerfDF['y_test_tot'], PerfDF['y_pred_tot'])\n",
+    "    T_rmse= sklearn.metrics.mean_squared_error(PerfDF['y_test_tot'], PerfDF['y_pred_tot'], \n",
+    "                                               squared = False)\n",
+    "\n",
+    "    PerfDF['Year'] = list(slc_test['Jul'].index)\n",
+    "    PerfDF=PerfDF.set_index('Year')\n",
+    "   \n",
+    "\n",
+    "    datetime_object = datetime.datetime.strptime(month, \"%b\")\n",
+    "    PerfDF['month'] = datetime_object.month\n",
+    "    PerfDF['Year']=PerfDF.index\n",
+    "   \n",
+    "    \n",
+    "    #set up dates so all months can be combined and sorted\n",
+    "    day=[]\n",
+    "    for index, row in PerfDF.iterrows():\n",
+    "        day.append(calendar.monthrange(int(row['Year']), int(row['month']))[1])\n",
+    "    \n",
+    "    PerfDF['Day']=day\n",
+    "  \n",
+    "    PerfDF['Date'] = pd.to_datetime(PerfDF[['Year', 'month', 'Day']])\n",
+    "    \n",
+    "    #PerfDF=PerfDF.set_index('Date')\n",
+    "    PerfDF=PerfDF.drop(columns=['Year', 'month', 'Day'])\n",
+    "    PerfDF=PerfDF.reset_index()\n",
+    "    \n",
+    "    model_plots(PerfDF, month)\n",
+    "    \n",
+    "    \n",
+    "    return PerfDF\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#PerfDF = RFR_OutdoorDemandModel(slc_train['M'], slc_test['Apr'],'Apr', 'RFR_Models/')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#plt.rcParams[\"axes.edgecolor\"]['bottom'] = 'black'\n",
+    "\n",
+    "def gradientbars_sliced(bars):\n",
+    "    ax = bars[0].axes\n",
+    "    xmin, xmax = ax.get_xlim()\n",
+    "    ymin, ymax = ax.get_ylim()\n",
+    "    for bar in bars:\n",
+    "        bar.set_zorder(1)\n",
+    "        bar.set_facecolor(\"none\")\n",
+    "        x, y = bar.get_xy()\n",
+    "        w, h = bar.get_width(), bar.get_height()\n",
+    "        grad = np.linspace(y, y + h, 256).reshape(256, 1)\n",
+    "        ax.imshow(grad, extent=[x, x + w, y, y + h], aspect=\"auto\", zorder=0, origin='lower',\n",
+    "                  vmin= - max(np.abs(ymin), ymax), vmax=max(np.abs(ymin), ymax), cmap='Spectral')\n",
+    "    ax.axis([xmin, xmax, ymin, ymax])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Make a function to put all of the predictions together\n",
+    "def Demand_Forecast(prediction_dictionary, pdict, df, pred, test, units, plotname, model):\n",
+    "    FinalDF=pd.DataFrame()\n",
+    "    if pdict is True:\n",
+    "        print('yes')\n",
+    "        for i in prediction_dictionary:\n",
+    "            FinalDF=FinalDF.append(prediction_dictionary[i])\n",
+    "\n",
+    "        FinalDF=FinalDF.sort_index()\n",
+    "    else:\n",
+    "        print('no')\n",
+    "        FinalDF = df\n",
+    "        \n",
+    "    months = np.arange(1,6,1)\n",
+    "    Low = list()\n",
+    "    Ave=list()\n",
+    "    High = list()\n",
+    "    for i in months:\n",
+    "        Low.append('Low'+str(i))\n",
+    "        Ave.append('Ave'+str(i))\n",
+    "        High.append('High'+str(i))\n",
+    "        \n",
+    "    Low.append('Drought')\n",
+    "    Ave.append('Average')\n",
+    "    High.append('Surplus')\n",
+    "    \n",
+    "    months2 = np.arange(7,13,1)\n",
+    "    for i in months2:\n",
+    "        Low.append('Low'+str(i))\n",
+    "        Ave.append('Ave'+str(i))\n",
+    "        High.append('High'+str(i))\n",
+    "    \n",
+    "    Conditions = High+Low+Ave\n",
+    "\n",
+    "    #adjust date range to improve figure\n",
+    "    #FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n",
+    "    FinalDF['Date'] = Conditions\n",
+    "    \n",
+    "    FinalDF.index = FinalDF['Date']\n",
+    "    del FinalDF['Date']\n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    plotmin_tot = FinalDF[[pred, test]].min().min()\n",
+    "    plotmax_tot = FinalDF[[pred, test]].max().max()\n",
+    "\n",
+    "    fig, ax = plt.subplots(1,5, constrained_layout=True)\n",
+    "    fig.set_size_inches(12,4)\n",
+    "\n",
+    "    gs2 = ax[0].get_gridspec()\n",
+    "    # remove the underlying axes\n",
+    "    ax[0].remove()\n",
+    "    ax[1].remove()\n",
+    "    ax[2].remove()\n",
+    "    axbig = fig.add_subplot(gs2[:3])\n",
+    "   # axbig.set_title('Total demand Timeline Evaluation')\n",
+    "    axbig.plot(FinalDF[pred], color='orange', label=model +' Predicted')\n",
+    "    axbig.plot(FinalDF[test],color='blue', label='Observed')\n",
+    "    axbig.set_xlabel('Supply Conditions')\n",
+    "    axbig.set_ylim(plotmin_tot-.9,plotmax_tot*1.2)\n",
+    "    axbig.set_ylabel(units)\n",
+    "    axbig.legend(loc = 'upper left', facecolor='white', framealpha=1)\n",
+    "    axbig.set_facecolor(\"white\")\n",
+    "    axbig.spines['bottom'].set_color('black')\n",
+    "    axbig.spines['left'].set_color('black')\n",
+    "    \n",
+    "    xticks = axbig.xaxis.get_major_ticks()\n",
+    "    for i,tick in enumerate(xticks):\n",
+    "        if i%12 != 5:\n",
+    "            tick.label1.set_visible(False)\n",
+    "\n",
+    "    ax[3].remove()\n",
+    "    ax[4].remove()\n",
+    "\n",
+    "\n",
+    "    axbig2 = fig.add_subplot(gs2[3:])\n",
+    "    axbig2.scatter(FinalDF[test], FinalDF[pred],color='blue', alpha=0.5)\n",
+    "    axbig2.set_ylabel('Predicted')\n",
+    "    axbig2.set_xlabel( 'Observed')\n",
+    "    axbig2.set_ylim(plotmin_tot*.95,plotmax_tot*1.2)\n",
+    "    axbig2.set_xlim(plotmin_tot*.95,plotmax_tot*1.2)\n",
+    "    #axbig2.set_title('Indoor and Outdoor \\n Model Performance')\n",
+    "    axbig2.plot([plotmin_tot,plotmax_tot],[plotmin_tot,plotmax_tot], color='red', linestyle='--' )\n",
+    "    #axbig2.set_xticks(np.arange(plotmin_tot, plotmax_tot, 100).round())\n",
+    "    #axbig2.set_yticks(np.arange(plotmin_tot, plotmax_tot, 100).round())\n",
+    "    axbig2.set_facecolor(\"white\")\n",
+    "    axbig2.spines['bottom'].set_color('black')\n",
+    "    axbig2.spines['left'].set_color('black')\n",
+    "    #ax[2].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "    fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/' +str(plotname)+'.pdf')\n",
+    "    r2 = sklearn.metrics.r2_score(FinalDF[test], FinalDF[pred])\n",
+    "    MAE= sklearn.metrics.mean_absolute_error(FinalDF[test], FinalDF[pred])\n",
+    "    RMSE= sklearn.metrics.mean_squared_error(FinalDF[test], FinalDF[pred], squared = False)\n",
+    "    MAPE=np.mean(np.abs((FinalDF[test]- FinalDF[pred])/FinalDF[test])*100)\n",
+    "\n",
+    "    print('Total R2 is ', r2)\n",
+    "    print('Total MAE is ', MAE)\n",
+    "    print('Total RMSE is ', RMSE)\n",
+    "    print('Total MAPE is ', MAPE)\n",
+    "    \n",
+    "    FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n",
+    "    FinalDF.index = FinalDF['Date']\n",
+    "    del FinalDF['Date']\n",
+    "    \n",
+    "    return FinalDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0% |                                                                        |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Apr  Random Forest model training and calibration\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% |########################################################################|\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RandomForestRegressor(bootstrap='False', max_depth=8, max_features=0.2,\n",
+      "                      n_estimators=60, n_jobs=-1, oob_score='True',\n",
+      "                      random_state=42)\n",
+      "Outdoor R2 is  -4.784893338029128\n",
+      "Outdoor RMSE is  26.930292746421916\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0% |                                                                        |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  26.930292746421916\n",
+      "The outdoor Demand prediction R2 is  -4.784893338029128\n",
+      "The Total Demand prediction RMSE is  16.618970531515703\n",
+      "The Total Demand prediction R2 is  -0.05265387641000907\n",
+      "The Total Demand prediction MAE is  15.092009529914506\n",
+      "The Total Demand prediction MAPE is  0.10649790004688718 %\n",
+      "May  Random Forest model training and calibration\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% |########################################################################|\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RandomForestRegressor(bootstrap='False', max_depth=2, max_features=0.2,\n",
+      "                      n_estimators=10, n_jobs=-1, oob_score='True',\n",
+      "                      random_state=42)\n",
+      "Outdoor R2 is  -1.1831466291918025\n",
+      "Outdoor RMSE is  45.29930732739214\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0% |                                                                        |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  45.29930732739214\n",
+      "The outdoor Demand prediction R2 is  -1.1831466291918025\n",
+      "The Total Demand prediction RMSE is  32.56903457403079\n",
+      "The Total Demand prediction R2 is  0.4604429690601838\n",
+      "The Total Demand prediction MAE is  29.42617513782513\n",
+      "The Total Demand prediction MAPE is  0.13780347130873585 %\n",
+      "Jun  Random Forest model training and calibration\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% |########################################################################|\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RandomForestRegressor(bootstrap='False', max_depth=12, max_features=0.8,\n",
+      "                      n_estimators=65, n_jobs=-1, oob_score='True',\n",
+      "                      random_state=42)\n",
+      "Outdoor R2 is  -0.8935814228663828\n",
+      "Outdoor RMSE is  39.071848808186445\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0% |                                                                        |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  39.071848808186445\n",
+      "The outdoor Demand prediction R2 is  -0.8935814228663828\n",
+      "The Total Demand prediction RMSE is  31.083456779938523\n",
+      "The Total Demand prediction R2 is  -0.15057575134453471\n",
+      "The Total Demand prediction MAE is  23.47741538461537\n",
+      "The Total Demand prediction MAPE is  0.0631426770931016 %\n",
+      "Jul  Random Forest model training and calibration\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% |########################################################################|\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RandomForestRegressor(bootstrap='False', max_depth=2, max_features=0.4,\n",
+      "                      n_estimators=10, n_jobs=-1, oob_score='True',\n",
+      "                      random_state=42)\n",
+      "Outdoor R2 is  0.027954152753366657\n",
+      "Outdoor RMSE is  36.0229522332086\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0% |                                                                        |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  36.0229522332086\n",
+      "The outdoor Demand prediction R2 is  0.027954152753366657\n",
+      "The Total Demand prediction RMSE is  40.5401773802947\n",
+      "The Total Demand prediction R2 is  0.2601233165218487\n",
+      "The Total Demand prediction MAE is  36.77841286492378\n",
+      "The Total Demand prediction MAPE is  0.08072793132550753 %\n",
+      "Aug  Random Forest model training and calibration\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% |########################################################################|\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RandomForestRegressor(bootstrap='False', max_depth=2, max_features=0.4,\n",
+      "                      n_estimators=15, n_jobs=-1, oob_score='True',\n",
+      "                      random_state=42)\n",
+      "Outdoor R2 is  -1.9943928828660233\n",
+      "Outdoor RMSE is  76.0259724939203\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0% |                                                                        |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  76.0259724939203\n",
+      "The outdoor Demand prediction R2 is  -1.9943928828660233\n",
+      "The Total Demand prediction RMSE is  67.34085853757178\n",
+      "The Total Demand prediction R2 is  -0.3449345146461422\n",
+      "The Total Demand prediction MAE is  54.64760139235301\n",
+      "The Total Demand prediction MAPE is  0.15452599803434766 %\n",
+      "Sep  Random Forest model training and calibration\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% |########################################################################|\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RandomForestRegressor(bootstrap='False', max_depth=4, max_features=0.6,\n",
+      "                      n_estimators=20, n_jobs=-1, oob_score='True',\n",
+      "                      random_state=42)\n",
+      "Outdoor R2 is  0.25615199300610714\n",
+      "Outdoor RMSE is  23.801023184780178\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0% |                                                                        |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  23.801023184780178\n",
+      "The outdoor Demand prediction R2 is  0.25615199300610714\n",
+      "The Total Demand prediction RMSE is  16.340060141129065\n",
+      "The Total Demand prediction R2 is  0.7942775551083546\n",
+      "The Total Demand prediction MAE is  13.205604544159542\n",
+      "The Total Demand prediction MAPE is  0.04479004224424165 %\n",
+      "Oct  Random Forest model training and calibration\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Ryan\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "100% |########################################################################|\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RandomForestRegressor(bootstrap='False', max_depth=2, max_features=0.2,\n",
+      "                      n_estimators=5, n_jobs=-1, oob_score='True',\n",
+      "                      random_state=42)\n",
+      "Outdoor R2 is  -0.6883881924824697\n",
+      "Outdoor RMSE is  22.548406822294364\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% |########################################################################|\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  22.548406822294364\n",
+      "The outdoor Demand prediction R2 is  -0.6883881924824697\n",
+      "The Total Demand prediction RMSE is  28.679174434013966\n",
+      "The Total Demand prediction R2 is  -0.6695041264232156\n",
+      "The Total Demand prediction MAE is  25.051604358974355\n",
+      "The Total Demand prediction MAPE is  0.13036006289594138 %\n",
+      "yes\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "<ipython-input-14-01d5912c7083>:92: UserWarning: constrained_layout not applied.  At least one axes collapsed to zero width or height.\n",
+      "  fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/RFR' +str(plotname)+'.pdf')\n",
+      "<ipython-input-14-01d5912c7083>:103: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total R2 is  0.942712866403471\n",
+      "Total MAE is  22.686154498499704\n",
+      "Total RMSE is  30.59692714279257\n",
+      "Total MAPE is  0.4345247666218068\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Ryan\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:132: UserWarning: constrained_layout not applied.  At least one axes collapsed to zero width or height.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Separate indoor and outdoor prediction months\n",
+    "Outdoor_Months=['Apr', 'May' , 'Jun', 'Jul', 'Aug', 'Sep', 'Oct']\n",
+    "IndoorMonths = ['Jan', 'Feb', 'Mar', 'Nov', 'Dec']\n",
+    "\n",
+    "\n",
+    "\n",
+    "slc_val=copy.deepcopy(slc_test)\n",
+    "# calibrate and predict with the outdoor model\n",
+    "pbar = ProgressBar()\n",
+    "for i in pbar(Outdoor_Months):\n",
+    "    \n",
+    "    PerfDF  = RFR_OutdoorDemandModel(slc_train[i],slc_test[i], i, 'RFR_Models')\n",
+    "    \n",
+    "    colrem = slc_test[i].columns\n",
+    "    slc_val[i] = slc_val[i].reset_index(drop=True)\n",
+    "    slc_val[i] = pd.concat([slc_val[i], PerfDF], axis=1, join=\"inner\")\n",
+    "    slc_val[i] = slc_val[i].set_index('Date')\n",
+    "    slc_val[i] = slc_val[i].drop(columns=colrem)\n",
+    "\n",
+    "#Calibrate and predict with the indoor model\n",
+    "for i in IndoorMonths:\n",
+    "    slc_val = Indoor_Demand_Model(slc_val, i)\n",
+    "\n",
+    "\n",
+    "#Put it all together and display the results\n",
+    "#Demand_Forecast(slc_val)\n",
+    "df = pd.DataFrame()\n",
+    "Pred_Obs = Demand_Forecast(slc_val, True, df, 'y_pred_tot', 'y_test_tot', 'GPCD', 'Long_term_pred_gpcd', 'SLC-WDM')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#save predictions\n",
+    "np.save('RFR_Models/Slc_Pred.npy', slc_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#save predictions\n",
+    "slc_val = np.load('RFR_Models/Slc_Pred.npy', allow_pickle = True).item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "yes\n",
+      "Total R2 is  0.942712866403471\n",
+      "Total MAE is  22.686154498499704\n",
+      "Total RMSE is  30.59692714279257\n",
+      "Total MAPE is  10.516310077140144\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-30-895b8f9ae21d>:103: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Put it all together and display the results\n",
+    "#Demand_Forecast(slc_val)\n",
+    "df = pd.DataFrame()\n",
+    "Pred_Obs = Demand_Forecast(slc_val, True, df, 'y_pred_tot', 'y_test_tot', 'GPCD', 'RFR_Seasonal_pred_gpcd', 'RFR')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas.tseries.offsets import MonthEnd\n",
+    "cols = ['y_test_tot', 'y_pred_tot']\n",
+    "monthorder = ['Jan', 'Feb' , 'Mar', 'Apr', 'May', 'Jun' , 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+    "Pred_Obs = Pred_Obs[cols]\n",
+    "\n",
+    "#input population into DF to calculate total water demands.\n",
+    "Population = []\n",
+    "UR_gpcd = []\n",
+    "slc_pred=copy.deepcopy(slc)\n",
+    "for i in monthorder:\n",
+    "    slc_train[i] = copy.deepcopy(slc[i].loc[:2010])\n",
+    "    slc_pred[i] = slc_pred[i].loc[2015:]\n",
+    "    slc_pred[i]['UR_gpcd'] = np.mean(slc_train[i]['Obs_gpcd'])\n",
+    "    Population.append(np.round(slc_pred[i]['Population'],0))\n",
+    "    \n",
+    "UR=pd.DataFrame()\n",
+    "for i in slc_pred:\n",
+    "    slc_pred[i]= pd.DataFrame(slc_pred[i]['UR_gpcd'])\n",
+    "    slc_pred[i]=slc_pred[i].reset_index()\n",
+    "    slc_pred[i]['M'] = datetime.datetime.strptime(i, \"%b\").month\n",
+    "    slc_pred[i]['D'] = 1\n",
+    "    slc_pred[i]['Date'] = pd.to_datetime(slc_pred[i].Year*10000+slc_pred[i].M*100+slc_pred[i].D,format='%Y%m%d')+MonthEnd(1)\n",
+    "    slc_pred[i].index = slc_pred[i].Date\n",
+    "    slc_pred[i]=slc_pred[i].drop(columns = ['M', 'D', 'Date', 'Year'])\n",
+    "    UR = UR.append(slc_pred[i])\n",
+    "\n",
+    "UR=UR.sort_index()\n",
+    "Population = np.sort(np.array(Population).reshape(36,))\n",
+    "\n",
+    "#place in to prediction df\n",
+    "Pred_Obs['Population'] = Population\n",
+    "Pred_Obs['UR_gpcd'] = UR['UR_gpcd']\n",
+    "\n",
+    "\n",
+    "\n",
+    "#Now we can form some acre-feet predictions.\n",
+    "gpcd=['y_test_tot','y_pred_tot','UR_gpcd']\n",
+    "for i in gpcd:\n",
+    "    Pred_Obs[i+str('_AF')] = Pred_Obs[i]*Pred_Obs['Population']*9.33454e-5\n",
+    "\n",
+    "    \n",
+    "remcol=['y_test_tot','y_pred_tot','UR_gpcd', 'Population']\n",
+    "Ann_Eval = Pred_Obs.drop(columns = remcol).copy()\n",
+    "Ann_Eval = Ann_Eval.resample('Y').sum()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no\n",
+      "Total R2 is  0.9419716482520051\n",
+      "Total MAE is  734.3629178694044\n",
+      "Total RMSE is  993.1935741775599\n",
+      "Total MAPE is  10.516310077140147\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-12-359c24012985>:103: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Pred_AF = Demand_Forecast(slc_val, False, Pred_Obs, \n",
+    "            'y_pred_tot_AF', 'y_test_tot_AF', 'Acre-Feet', 'RFR_Seasonal_term_pred_AF', 'RFR')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no\n",
+      "Total R2 is  0.6283371213177069\n",
+      "Total MAE is  63.03597863247863\n",
+      "Total RMSE is  77.93344228795345\n",
+      "Total MAPE is  30.78863740465362\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-12-359c24012985>:103: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Pred_UR_GPCD = Demand_Forecast(slc_val, False, Pred_Obs, 'UR_gpcd',\n",
+    "                'y_test_tot', 'gpcd', 'Seasonal_pred_UR_gpcd', 'Unit-Rate')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>y_test_tot_AF</th>\n",
+       "      <th>y_pred_tot_AF</th>\n",
+       "      <th>StationaryError</th>\n",
+       "      <th>RFRError</th>\n",
+       "      <th>StationaryErrorAF</th>\n",
+       "      <th>RFRErrorAF</th>\n",
+       "      <th>S_Perror</th>\n",
+       "      <th>RFR_Perror</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-12-31</th>\n",
+       "      <td>100550.276769</td>\n",
+       "      <td>96041.403290</td>\n",
+       "      <td>139.858017</td>\n",
+       "      <td>-87.788392</td>\n",
+       "      <td>12207.162705</td>\n",
+       "      <td>-4508.873479</td>\n",
+       "      <td>12.140357</td>\n",
+       "      <td>-4.484198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-31</th>\n",
+       "      <td>81646.774954</td>\n",
+       "      <td>87444.477996</td>\n",
+       "      <td>512.522236</td>\n",
+       "      <td>62.752961</td>\n",
+       "      <td>32430.298904</td>\n",
+       "      <td>5797.703042</td>\n",
+       "      <td>39.720245</td>\n",
+       "      <td>7.100958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-31</th>\n",
+       "      <td>86505.457871</td>\n",
+       "      <td>89205.005195</td>\n",
+       "      <td>455.384862</td>\n",
+       "      <td>9.392539</td>\n",
+       "      <td>28891.579705</td>\n",
+       "      <td>2699.547324</td>\n",
+       "      <td>33.398563</td>\n",
+       "      <td>3.120667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            y_test_tot_AF  y_pred_tot_AF  StationaryError   RFRError  \\\n",
+       "Date                                                                   \n",
+       "2015-12-31  100550.276769   96041.403290       139.858017 -87.788392   \n",
+       "2016-12-31   81646.774954   87444.477996       512.522236  62.752961   \n",
+       "2017-12-31   86505.457871   89205.005195       455.384862   9.392539   \n",
+       "\n",
+       "            StationaryErrorAF   RFRErrorAF   S_Perror  RFR_Perror  \n",
+       "Date                                                               \n",
+       "2015-12-31       12207.162705 -4508.873479  12.140357   -4.484198  \n",
+       "2016-12-31       32430.298904  5797.703042  39.720245    7.100958  \n",
+       "2017-12-31       28891.579705  2699.547324  33.398563    3.120667  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Pred_UR_GPCD['StationaryError'] = ((Pred_UR_GPCD['UR_gpcd']-Pred_UR_GPCD['y_test_tot'])/Pred_UR_GPCD['y_test_tot'])*100\n",
+    "Pred_UR_GPCD['RFRError'] = ((Pred_UR_GPCD['y_pred_tot']-Pred_UR_GPCD['y_test_tot'])/Pred_UR_GPCD['y_test_tot'])*100\n",
+    "Pred_UR_GPCD['StationaryErrorAF'] = Pred_UR_GPCD['UR_gpcd_AF']-Pred_UR_GPCD['y_test_tot_AF']\n",
+    "Pred_UR_GPCD['RFRErrorAF'] = Pred_UR_GPCD['y_pred_tot_AF']-Pred_UR_GPCD['y_test_tot_AF']\n",
+    "Evalcol = ['y_test_tot_AF', 'y_pred_tot_AF', 'StationaryError','RFRError', 'StationaryErrorAF', 'RFRErrorAF']\n",
+    "Eval = Pred_UR_GPCD[Evalcol]\n",
+    "\n",
+    "Annual = Eval.resample('Y').sum()\n",
+    "Annual['S_Perror'] = Annual['StationaryErrorAF']/Annual['y_test_tot_AF']*100\n",
+    "Annual['RFR_Perror'] = Annual['RFRErrorAF']/Annual['y_test_tot_AF']*100\n",
+    "Annual"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>y_test_tot_AF</th>\n",
+       "      <th>y_pred_tot_AF</th>\n",
+       "      <th>StationaryError</th>\n",
+       "      <th>RFRError</th>\n",
+       "      <th>StationaryErrorAF</th>\n",
+       "      <th>RFRErrorAF</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-01-31</th>\n",
+       "      <td>4709.274282</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>7.342278</td>\n",
+       "      <td>-14.631378</td>\n",
+       "      <td>345.767994</td>\n",
+       "      <td>-689.031718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-02-28</th>\n",
+       "      <td>4888.568674</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>4.579880</td>\n",
+       "      <td>-17.762379</td>\n",
+       "      <td>223.890584</td>\n",
+       "      <td>-868.326110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-03-31</th>\n",
+       "      <td>4493.545735</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>16.816118</td>\n",
+       "      <td>-10.532955</td>\n",
+       "      <td>755.639941</td>\n",
+       "      <td>-473.303171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-04-30</th>\n",
+       "      <td>5114.524352</td>\n",
+       "      <td>5557.657356</td>\n",
+       "      <td>24.996996</td>\n",
+       "      <td>8.664208</td>\n",
+       "      <td>1278.477435</td>\n",
+       "      <td>443.133003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-05-31</th>\n",
+       "      <td>8583.758986</td>\n",
+       "      <td>9176.888274</td>\n",
+       "      <td>13.335806</td>\n",
+       "      <td>6.909901</td>\n",
+       "      <td>1144.713444</td>\n",
+       "      <td>593.129288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-06-30</th>\n",
+       "      <td>12890.020382</td>\n",
+       "      <td>13307.081413</td>\n",
+       "      <td>14.946044</td>\n",
+       "      <td>3.235534</td>\n",
+       "      <td>1926.548117</td>\n",
+       "      <td>417.061030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-07-31</th>\n",
+       "      <td>16379.389681</td>\n",
+       "      <td>15968.633837</td>\n",
+       "      <td>12.453508</td>\n",
+       "      <td>-2.507760</td>\n",
+       "      <td>2039.808677</td>\n",
+       "      <td>-410.755844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-08-31</th>\n",
+       "      <td>15197.197243</td>\n",
+       "      <td>15385.678067</td>\n",
+       "      <td>13.006844</td>\n",
+       "      <td>1.240234</td>\n",
+       "      <td>1976.675810</td>\n",
+       "      <td>188.480824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-30</th>\n",
+       "      <td>11502.006931</td>\n",
+       "      <td>11167.055626</td>\n",
+       "      <td>10.339270</td>\n",
+       "      <td>-2.912112</td>\n",
+       "      <td>1189.223590</td>\n",
+       "      <td>-334.951305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-10-31</th>\n",
+       "      <td>6784.423105</td>\n",
+       "      <td>5377.195898</td>\n",
+       "      <td>13.992224</td>\n",
+       "      <td>-20.742032</td>\n",
+       "      <td>949.291652</td>\n",
+       "      <td>-1407.227207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-11-30</th>\n",
+       "      <td>5299.251908</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>-0.312916</td>\n",
+       "      <td>-24.135659</td>\n",
+       "      <td>-16.582211</td>\n",
+       "      <td>-1279.009344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-12-31</th>\n",
+       "      <td>4708.315488</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>8.361965</td>\n",
+       "      <td>-14.613994</td>\n",
+       "      <td>393.707671</td>\n",
+       "      <td>-688.072925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-31</th>\n",
+       "      <td>3747.489710</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>36.470100</td>\n",
+       "      <td>-3.444037</td>\n",
+       "      <td>1366.713253</td>\n",
+       "      <td>-129.064919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-02-29</th>\n",
+       "      <td>3545.403337</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>45.887264</td>\n",
+       "      <td>2.059609</td>\n",
+       "      <td>1626.888577</td>\n",
+       "      <td>73.021455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-03-31</th>\n",
+       "      <td>3241.788769</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>63.817536</td>\n",
+       "      <td>11.618154</td>\n",
+       "      <td>2068.829713</td>\n",
+       "      <td>376.636022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-04-30</th>\n",
+       "      <td>4525.764752</td>\n",
+       "      <td>5306.992783</td>\n",
+       "      <td>42.911118</td>\n",
+       "      <td>17.261791</td>\n",
+       "      <td>1942.056267</td>\n",
+       "      <td>781.228031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-05-31</th>\n",
+       "      <td>5202.834938</td>\n",
+       "      <td>5868.527367</td>\n",
+       "      <td>89.172399</td>\n",
+       "      <td>12.794802</td>\n",
+       "      <td>4639.492745</td>\n",
+       "      <td>665.692429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-06-30</th>\n",
+       "      <td>11629.504953</td>\n",
+       "      <td>13309.224754</td>\n",
+       "      <td>28.896039</td>\n",
+       "      <td>14.443605</td>\n",
+       "      <td>3360.466324</td>\n",
+       "      <td>1679.719800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-07-31</th>\n",
+       "      <td>13113.950619</td>\n",
+       "      <td>14604.791665</td>\n",
+       "      <td>42.098780</td>\n",
+       "      <td>11.368359</td>\n",
+       "      <td>5520.813183</td>\n",
+       "      <td>1490.841046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-08-31</th>\n",
+       "      <td>12143.936026</td>\n",
+       "      <td>13947.523364</td>\n",
+       "      <td>43.074404</td>\n",
+       "      <td>14.851753</td>\n",
+       "      <td>5230.928053</td>\n",
+       "      <td>1803.587338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-09-30</th>\n",
+       "      <td>10495.557894</td>\n",
+       "      <td>10397.886259</td>\n",
+       "      <td>22.335182</td>\n",
+       "      <td>-0.930600</td>\n",
+       "      <td>2344.201933</td>\n",
+       "      <td>-97.671636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-10-31</th>\n",
+       "      <td>6268.234299</td>\n",
+       "      <td>5917.407847</td>\n",
+       "      <td>24.823425</td>\n",
+       "      <td>-5.596894</td>\n",
+       "      <td>1555.990459</td>\n",
+       "      <td>-350.826452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-11-30</th>\n",
+       "      <td>4167.182691</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>28.251982</td>\n",
+       "      <td>-13.168559</td>\n",
+       "      <td>1177.311686</td>\n",
+       "      <td>-548.757899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-31</th>\n",
+       "      <td>3565.126967</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>44.784007</td>\n",
+       "      <td>1.494977</td>\n",
+       "      <td>1596.606712</td>\n",
+       "      <td>53.297825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-01-31</th>\n",
+       "      <td>4299.786860</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>20.317090</td>\n",
+       "      <td>-17.889861</td>\n",
+       "      <td>873.591554</td>\n",
+       "      <td>-769.225876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-02-28</th>\n",
+       "      <td>4049.243978</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>29.212750</td>\n",
+       "      <td>-12.809379</td>\n",
+       "      <td>1182.895524</td>\n",
+       "      <td>-518.682994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-03-31</th>\n",
+       "      <td>3543.906179</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>51.586028</td>\n",
+       "      <td>-0.376567</td>\n",
+       "      <td>1828.160440</td>\n",
+       "      <td>-13.345195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-04-30</th>\n",
+       "      <td>3936.532388</td>\n",
+       "      <td>3699.420400</td>\n",
+       "      <td>66.203610</td>\n",
+       "      <td>-6.023372</td>\n",
+       "      <td>2606.126535</td>\n",
+       "      <td>-237.111989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-05-31</th>\n",
+       "      <td>7427.320860</td>\n",
+       "      <td>9034.321100</td>\n",
+       "      <td>34.048489</td>\n",
+       "      <td>21.636338</td>\n",
+       "      <td>2528.890489</td>\n",
+       "      <td>1607.000240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-30</th>\n",
+       "      <td>14063.436455</td>\n",
+       "      <td>14241.150965</td>\n",
+       "      <td>7.821565</td>\n",
+       "      <td>1.263663</td>\n",
+       "      <td>1099.980875</td>\n",
+       "      <td>177.714510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-07-31</th>\n",
+       "      <td>16247.673230</td>\n",
+       "      <td>14567.296693</td>\n",
+       "      <td>16.018969</td>\n",
+       "      <td>-10.342260</td>\n",
+       "      <td>2602.709812</td>\n",
+       "      <td>-1680.376538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-08-31</th>\n",
+       "      <td>11051.688592</td>\n",
+       "      <td>14396.571957</td>\n",
+       "      <td>59.033664</td>\n",
+       "      <td>30.265813</td>\n",
+       "      <td>6524.216672</td>\n",
+       "      <td>3344.883366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-09-30</th>\n",
+       "      <td>8903.103479</td>\n",
+       "      <td>9757.294033</td>\n",
+       "      <td>45.885378</td>\n",
+       "      <td>9.594301</td>\n",
+       "      <td>4085.222721</td>\n",
+       "      <td>854.190554</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-10-31</th>\n",
+       "      <td>5193.040924</td>\n",
+       "      <td>5856.145127</td>\n",
+       "      <td>52.410841</td>\n",
+       "      <td>12.769093</td>\n",
+       "      <td>2721.716423</td>\n",
+       "      <td>663.104203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-11-30</th>\n",
+       "      <td>3925.935013</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>37.708201</td>\n",
+       "      <td>-10.070825</td>\n",
+       "      <td>1480.399472</td>\n",
+       "      <td>-395.374029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-31</th>\n",
+       "      <td>3863.789912</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>35.138277</td>\n",
+       "      <td>-8.624406</td>\n",
+       "      <td>1357.669187</td>\n",
+       "      <td>-333.228928</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            y_test_tot_AF  y_pred_tot_AF  StationaryError   RFRError  \\\n",
+       "Date                                                                   \n",
+       "2015-01-31    4709.274282    4020.242564         7.342278 -14.631378   \n",
+       "2015-02-28    4888.568674    4020.242564         4.579880 -17.762379   \n",
+       "2015-03-31    4493.545735    4020.242564        16.816118 -10.532955   \n",
+       "2015-04-30    5114.524352    5557.657356        24.996996   8.664208   \n",
+       "2015-05-31    8583.758986    9176.888274        13.335806   6.909901   \n",
+       "2015-06-30   12890.020382   13307.081413        14.946044   3.235534   \n",
+       "2015-07-31   16379.389681   15968.633837        12.453508  -2.507760   \n",
+       "2015-08-31   15197.197243   15385.678067        13.006844   1.240234   \n",
+       "2015-09-30   11502.006931   11167.055626        10.339270  -2.912112   \n",
+       "2015-10-31    6784.423105    5377.195898        13.992224 -20.742032   \n",
+       "2015-11-30    5299.251908    4020.242564        -0.312916 -24.135659   \n",
+       "2015-12-31    4708.315488    4020.242564         8.361965 -14.613994   \n",
+       "2016-01-31    3747.489710    3618.424792        36.470100  -3.444037   \n",
+       "2016-02-29    3545.403337    3618.424792        45.887264   2.059609   \n",
+       "2016-03-31    3241.788769    3618.424792        63.817536  11.618154   \n",
+       "2016-04-30    4525.764752    5306.992783        42.911118  17.261791   \n",
+       "2016-05-31    5202.834938    5868.527367        89.172399  12.794802   \n",
+       "2016-06-30   11629.504953   13309.224754        28.896039  14.443605   \n",
+       "2016-07-31   13113.950619   14604.791665        42.098780  11.368359   \n",
+       "2016-08-31   12143.936026   13947.523364        43.074404  14.851753   \n",
+       "2016-09-30   10495.557894   10397.886259        22.335182  -0.930600   \n",
+       "2016-10-31    6268.234299    5917.407847        24.823425  -5.596894   \n",
+       "2016-11-30    4167.182691    3618.424792        28.251982 -13.168559   \n",
+       "2016-12-31    3565.126967    3618.424792        44.784007   1.494977   \n",
+       "2017-01-31    4299.786860    3530.560984        20.317090 -17.889861   \n",
+       "2017-02-28    4049.243978    3530.560984        29.212750 -12.809379   \n",
+       "2017-03-31    3543.906179    3530.560984        51.586028  -0.376567   \n",
+       "2017-04-30    3936.532388    3699.420400        66.203610  -6.023372   \n",
+       "2017-05-31    7427.320860    9034.321100        34.048489  21.636338   \n",
+       "2017-06-30   14063.436455   14241.150965         7.821565   1.263663   \n",
+       "2017-07-31   16247.673230   14567.296693        16.018969 -10.342260   \n",
+       "2017-08-31   11051.688592   14396.571957        59.033664  30.265813   \n",
+       "2017-09-30    8903.103479    9757.294033        45.885378   9.594301   \n",
+       "2017-10-31    5193.040924    5856.145127        52.410841  12.769093   \n",
+       "2017-11-30    3925.935013    3530.560984        37.708201 -10.070825   \n",
+       "2017-12-31    3863.789912    3530.560984        35.138277  -8.624406   \n",
+       "\n",
+       "            StationaryErrorAF   RFRErrorAF  \n",
+       "Date                                        \n",
+       "2015-01-31         345.767994  -689.031718  \n",
+       "2015-02-28         223.890584  -868.326110  \n",
+       "2015-03-31         755.639941  -473.303171  \n",
+       "2015-04-30        1278.477435   443.133003  \n",
+       "2015-05-31        1144.713444   593.129288  \n",
+       "2015-06-30        1926.548117   417.061030  \n",
+       "2015-07-31        2039.808677  -410.755844  \n",
+       "2015-08-31        1976.675810   188.480824  \n",
+       "2015-09-30        1189.223590  -334.951305  \n",
+       "2015-10-31         949.291652 -1407.227207  \n",
+       "2015-11-30         -16.582211 -1279.009344  \n",
+       "2015-12-31         393.707671  -688.072925  \n",
+       "2016-01-31        1366.713253  -129.064919  \n",
+       "2016-02-29        1626.888577    73.021455  \n",
+       "2016-03-31        2068.829713   376.636022  \n",
+       "2016-04-30        1942.056267   781.228031  \n",
+       "2016-05-31        4639.492745   665.692429  \n",
+       "2016-06-30        3360.466324  1679.719800  \n",
+       "2016-07-31        5520.813183  1490.841046  \n",
+       "2016-08-31        5230.928053  1803.587338  \n",
+       "2016-09-30        2344.201933   -97.671636  \n",
+       "2016-10-31        1555.990459  -350.826452  \n",
+       "2016-11-30        1177.311686  -548.757899  \n",
+       "2016-12-31        1596.606712    53.297825  \n",
+       "2017-01-31         873.591554  -769.225876  \n",
+       "2017-02-28        1182.895524  -518.682994  \n",
+       "2017-03-31        1828.160440   -13.345195  \n",
+       "2017-04-30        2606.126535  -237.111989  \n",
+       "2017-05-31        2528.890489  1607.000240  \n",
+       "2017-06-30        1099.980875   177.714510  \n",
+       "2017-07-31        2602.709812 -1680.376538  \n",
+       "2017-08-31        6524.216672  3344.883366  \n",
+       "2017-09-30        4085.222721   854.190554  \n",
+       "2017-10-31        2721.716423   663.104203  \n",
+       "2017-11-30        1480.399472  -395.374029  \n",
+       "2017-12-31        1357.669187  -333.228928  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Eval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-14-01d5912c7083>:92: UserWarning: constrained_layout not applied.  At least one axes collapsed to zero width or height.\n",
+      "  fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/RFR' +str(plotname)+'.pdf')\n",
+      "<ipython-input-14-01d5912c7083>:103: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n",
+      "C:\\Users\\Ryan\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:132: UserWarning: constrained_layout not applied.  At least one axes collapsed to zero width or height.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total R2 is  0.6235333383547133\n",
+      "Total MAE is  2043.3946037944281\n",
+      "Total RMSE is  2529.7467407378826\n",
+      "Total MAPE is  30.771253177933406\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Pred_UR_AF = Demand_Forecast(slc_val, False, Pred_Obs, 'UR_gpcd_AF', \n",
+    "                             'y_test_tot_AF', 'Acre-Feet', 'Seasonal_pred_UR_AF', 'Unit-Rate')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''I am putting a hard code on the limits here to make both figure have same color scale'''\n",
+    "\n",
+    "plt.rcParams[\"axes.grid\"] = False\n",
+    "plt.rcParams[\"axes.facecolor\"] ='white'\n",
+    "#plt.rcParams[\"axes.edgecolor\"]['bottom'] = 'black'\n",
+    "\n",
+    "def gradientbars_sliced(bars, ymin, ymax):\n",
+    "    ax = bars[0].axes\n",
+    "    xmin, xmax = ax.get_xlim()\n",
+    " #   ymin, ymax = ax.get_ylim()\n",
+    "    for bar in bars:\n",
+    "        bar.set_zorder(1)\n",
+    "        bar.set_facecolor(\"none\")\n",
+    "        x, y = bar.get_xy()\n",
+    "        w, h = bar.get_width(), bar.get_height()\n",
+    "        grad = np.linspace(y, y + h, 256).reshape(256, 1)\n",
+    "        ax.imshow(grad, extent=[x, x + w, y, y + h], aspect=\"auto\", zorder=0, origin='lower',\n",
+    "                  vmin= - max(np.abs(ymin), ymax), vmax=max(np.abs(ymin), ymax), cmap='Spectral_r')\n",
+    "    ax.axis([xmin, xmax, ymin, ymax])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "can only concatenate str (not \"int\") to str",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py\u001b[0m in \u001b[0;36m_na_arithmetic_op\u001b[1;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[0;32m    141\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 142\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\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    143\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\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\\computation\\expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[1;34m(op, a, b, use_numexpr)\u001b[0m\n\u001b[0;32m    234\u001b[0m             \u001b[1;31m# error: \"None\" not callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 235\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[misc]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    236\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\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\\computation\\expressions.py\u001b[0m in \u001b[0;36m_evaluate_numexpr\u001b[1;34m(op, op_str, a, b)\u001b[0m\n\u001b[0;32m    119\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\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    121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[1;34m(op, op_str, a, b)\u001b[0m\n\u001b[0;32m     68\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"ignore\"\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---> 69\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\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     70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-51-2f27d412f5b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     22\u001b[0m ax[0].bar(DayDF.Supply, DayDF.UR_gpcd_AF, width=width, \n\u001b[0;32m     23\u001b[0m     color='red', label='Base')\n\u001b[1;32m---> 24\u001b[1;33m ax[0].bar(DayDF.Supply+1, DayDF.y_pred_tot_AF, width=width, \n\u001b[0m\u001b[0;32m     25\u001b[0m     color='orange', label='RFR')\n\u001b[0;32m     26\u001b[0m ax[0].bar(DayDF.Supply, DayDF.y_test_tot_AF, width=width, \n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\common.py\u001b[0m in \u001b[0;36mnew_method\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m     63\u001b[0m         \u001b[0mother\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\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     66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\arraylike.py\u001b[0m in \u001b[0;36m__add__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m     87\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"__add__\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__add__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\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---> 89\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_arith_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moperator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\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     90\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"__radd__\"\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\\series.py\u001b[0m in \u001b[0;36m_arith_method\u001b[1;34m(self, other, op)\u001b[0m\n\u001b[0;32m   4993\u001b[0m         \u001b[0mlvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4994\u001b[0m         \u001b[0mrvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4995\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marithmetic_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\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   4996\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4997\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_construct_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mres_name\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\\ops\\array_ops.py\u001b[0m in \u001b[0;36marithmetic_op\u001b[1;34m(left, right, op)\u001b[0m\n\u001b[0;32m    187\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    188\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"ignore\"\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--> 189\u001b[1;33m             \u001b[0mres_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_na_arithmetic_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\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    190\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    191\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mres_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py\u001b[0m in \u001b[0;36m_na_arithmetic_op\u001b[1;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[0;32m    147\u001b[0m             \u001b[1;31m#  will handle complex numbers incorrectly, see GH#32047\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    148\u001b[0m             \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 149\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_masked_arith_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\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    150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    151\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mis_cmp\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNotImplemented\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\\ops\\array_ops.py\u001b[0m in \u001b[0;36m_masked_arith_op\u001b[1;34m(x, y, op)\u001b[0m\n\u001b[0;32m    109\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\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[0;32m    110\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"ignore\"\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--> 111\u001b[1;33m                 \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxrav\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\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    112\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m     \u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaybe_upcast_putmask\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x792 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Reduce width to increase space between bars\n",
+    "width = .2\n",
+    "widthM = 1\n",
+    "titlesize = 14\n",
+    "\n",
+    "Ann_Eval['Supply'] = ['Surplus', 'Drought', 'Average']\n",
+    "Ann_Eval.index = Ann_Eval['Supply']\n",
+    "\n",
+    "DayDF = Ann_Eval\n",
+    "\n",
+    "fig, ax = plt.subplots(3,1, constrained_layout=True)\n",
+    "fig.set_size_inches(9,11)\n",
+    "DayDF['Error_SLC_WDM'] = DayDF.y_pred_tot_AF-DayDF.y_test_tot_AF\n",
+    "DayDF['Error_UR'] = DayDF.UR_gpcd_AF-DayDF.y_test_tot_AF\n",
+    "\n",
+    "#save this df to load into MLR file for comparision figure\n",
+    "DayDF.to_excel('RFR_Models/RFR_Ann_Comp.xlsx')\n",
+    "\n",
+    "\n",
+    "#Annual Figure\n",
+    "\n",
+    "ax[0].bar(DayDF.Supply, DayDF.UR_gpcd_AF, width=width, \n",
+    "    color='red', label='Base')\n",
+    "ax[0].bar(DayDF.Supply+1, DayDF.y_pred_tot_AF, width=width, \n",
+    "    color='orange', label='RFR')\n",
+    "ax[0].bar(DayDF.Supply, DayDF.y_test_tot_AF, width=width, \n",
+    "    color='blue', label='Observed')\n",
+    "ax[0].set_ylim(0,max(max(Ann_Eval.y_test_tot_AF), max(Ann_Eval.y_pred_tot_AF),max(Ann_Eval.UR_gpcd_AF))*1.2)\n",
+    "#ax.annotate('Max Extra DC use: '+str(np.round(np.max(DayDF.ExtraDC),1))+'MGD', \n",
+    " #              xy=(DayDF.index[269], 150))\n",
+    "#ax.annotate('Max Daily DC use: '+str(np.round(np.max(DayDF.SLCDPU_DC_Water_Use),1))+'MGD', \n",
+    " #              xy=(DayDF.index[269], 130))\n",
+    "ax[0].legend(loc = 'upper left')\n",
+    "ax[0].set_ylabel('Acre-Feet')\n",
+    "ax[0].set_title(' Comparison of Seasonal Forecasts vs Observed', size = titlesize)\n",
+    "ax[0].spines['bottom'].set_color('black')\n",
+    "ax[0].spines['left'].set_color('black')\n",
+    "\n",
+    "\n",
+    "Error_UR = ax[1].bar(DayDF.index, DayDF.Error_UR, width=width, \n",
+    "    color='red', label='Base')\n",
+    "ax[1].set_ylabel('Acre-Feet')\n",
+    "ax[1].set_title('Base: Difference From The Observed', size = titlesize)\n",
+    "gradientbars_sliced(Error_UR, min(min(DayDF.Error_UR), min(DayDF.Error_SLC_WDM)),\n",
+    "                    max(max(DayDF.Error_UR), max(DayDF.Error_SLC_WDM)))\n",
+    "ax[1].spines['bottom'].set_color('black')\n",
+    "ax[1].spines['left'].set_color('black')\n",
+    "\n",
+    "\n",
+    "\n",
+    "Error_SLC_WDM = ax[2].bar(DayDF.Supply, DayDF.Error_SLC_WDM, width=width, \n",
+    "    color='red', label='Base')\n",
+    "ax[2].set_ylabel('Acre-Feet')\n",
+    "ax[2].set_title('RFR: Difference From The Observed', size = titlesize)\n",
+    "gradientbars_sliced(Error_SLC_WDM, min(min(DayDF.Error_UR), min(DayDF.Error_SLC_WDM)),\n",
+    "                    max(max(DayDF.Error_UR), max(DayDF.Error_SLC_WDM)))\n",
+    "ax[2].spines['bottom'].set_color('black')\n",
+    "ax[2].spines['left'].set_color('black')\n",
+    "\n",
+    "fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/MLR_MLP_RFR_Base.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/SLC_WDM.ipynb b/SLC_WDM.ipynb
new file mode 100644
index 0000000..8fa0243
--- /dev/null
+++ b/SLC_WDM.ipynb
@@ -0,0 +1,3035 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn. metrics import accuracy_score\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn.metrics import mean_absolute_percentage_error\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.utils import resample\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.model_selection import LeaveOneOut\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "import seaborn as sns; sns.set()\n",
+    "import joblib\n",
+    "from sklearn.linear_model import LassoCV\n",
+    "from sklearn.datasets import make_regression\n",
+    "from pathlib import Path\n",
+    "import copy\n",
+    "\n",
+    "#Trying out recusive feature elimination to compare with step wise regression\n",
+    "from sklearn.feature_selection import RFE\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "import sklearn\n",
+    "\n",
+    "from progressbar import ProgressBar\n",
+    "\n",
+    "\n",
+    "\n",
+    "import datetime\n",
+    "import calendar\n",
+    "\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "#custom functions\n",
+    "def NSC(y_pred,y_true):\n",
+    "    \"\"\"\n",
+    "    Nash-Sutcliffe Coefficient\n",
+    "    \"\"\"\n",
+    "    return 1 - sum((y_pred-y_true)**2)/sum((y_true-np.mean(y_true))**2)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#This is the final dataset to make predictions on\n",
+    "p = Path('C:/Users/Ryan/Box/Dissertation/Paper1/Data/Processed_Training_Data')\n",
+    "#dir_data = 'C:/Users/Ryan/Box/Dissertation/Paper1/Data/Model_Input_Data/Monthly/Input/FinalVars'\n",
+    "\n",
+    "slc = {i.stem[0:3] : pd.read_excel(i) for i in p.glob('**/*.xlsx')}\n",
+    "\n",
+    "#rename the gpcd column\n",
+    "for i in slc:\n",
+    "    slc[i].rename(columns={i+'_gpcd': 'Obs_gpcd'}, inplace=True)\n",
+    "    slc[i]=slc[i].set_index('Year')\n",
+    "snow=pd.read_excel('C:/Users/Ryan/Box/Dissertation/Paper1/Data/Alta_LCC_snow.xlsx')\n",
+    "snow=snow.set_index('Year')\n",
+    "#need to remove certain features\n",
+    "'''\n",
+    "lots of colinearity between mountain and valley, since we are focused on the valley we will use valley conditions.\n",
+    "meantemp and maxtemps days > are good predictors, however, they will be difficult to predict into the future.\n",
+    "Mill creek and city creek show high colinarity. Since city creek is a source we will remove mill creek.\n",
+    "Removing red butte and emigration creeks as they are not supply streams and show co-linearity\n",
+    "!!! removing the aggregated Seven canyons streamflow. There appears to be a unique climate signial that very well helps\n",
+    "demand forecasting by including the supply streamflows!!!!\n",
+    "Precipitation days is removed as they will be difficult to predict with certainity in the future.\n",
+    "Days above max, mean is removed as its colinear with mean temp and is difficult to predict into future.\n",
+    "Percent are selected, sqmi is basicaly the same\n",
+    "Removed reshousingdensity at it is highly coorelated wiht respopulationdensity\n",
+    "removed Urban_Area_Perc as it has high colinearity with res pop dens\n",
+    "Residential_Area_Perc removed as colinear wiht res pop dens)\n",
+    "'''\n",
+    "colrem= ['Dem_AF', 'seven', 'meantemp_days', 'maxtemp_days', 'mean_max', 'mill', 'precip_days', \n",
+    "         'Days_abovemax','Days_abovemean', 'red' , 'emig', 'sqmi','max_Days_WO',\n",
+    "         'mtn','ResHouseDensity', 'Urban_Area_Perc','Residential_Area_Perc', 'IrrPopulationDensity',\n",
+    "         'Irrigated_Area_Perc','CityCrk_AcFt_WR_Mar', 'LitCotCrk_AcFt_WR_Jun']#, 'AcFt', 'WO', 'days', 'days', 'above' , 'Perc']\n",
+    "\n",
+    "\n",
+    "for i in slc:\n",
+    "    for j in colrem:\n",
+    "        slc[i]=slc[i].loc[:,~slc[i].columns.str.contains(j , case=False)] \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create training and testing data, use most recent low, average, and high water years\n",
+    "slc_train=copy.deepcopy(slc)\n",
+    "slc_test=copy.deepcopy(slc)\n",
+    "\n",
+    "#2008 is a high year\n",
+    "#2011 and 2017 are average years\n",
+    "#2014 and 2016 are below average years\n",
+    "#2015 is a very  low year\n",
+    "\n",
+    "IN_WY_Months = ['Jan' , 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug','Sep', 'Oct']\n",
+    "Prior_YR_WY_Months = ['Nov', 'Dec']\n",
+    "testWYyrs = [2008,2015,2017]\n",
+    "#testPrioWYyrs = [2007,2014,2016]\n",
+    "\n",
+    "\n",
+    "for i in slc:\n",
+    "    #Select the training/testing dataframes\n",
+    "    slc_train[i]=slc_train[i][~slc_train[i].index.isin(testWYyrs)]\n",
+    "    slc_test[i]=slc_test[i][slc_test[i].index.isin(testWYyrs)]\n",
+    "   \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    \n",
+    "\n",
+    "#Determine the indoor mean to subtract from outdoor\n",
+    "I_mean_train=(slc_train['Jan']['Obs_gpcd']+\n",
+    "              slc_train['Feb']['Obs_gpcd']+\n",
+    "              slc_train['Mar']['Obs_gpcd']+\n",
+    "              slc_train['Nov']['Obs_gpcd']+\n",
+    "             slc_train['Dec']['Obs_gpcd'])/5\n",
+    "I_mean_test=(slc_test['Jan']['Obs_gpcd']+\n",
+    "             slc_test['Feb']['Obs_gpcd']+\n",
+    "             slc_test['Mar']['Obs_gpcd']+\n",
+    "             slc_test['Nov']['Obs_gpcd']+\n",
+    "             slc_test['Dec']['Obs_gpcd'])/5\n",
+    "#This uses total demand for irrigation seasons\n",
+    "#I_mean_train=np.zeros(23)\n",
+    "#I_mean_test=np.zeros(10)\n",
+    "\n",
+    "for i in slc_train:\n",
+    "    slc_train[i]['Iave']=I_mean_train\n",
+    "    #for now include testing years\n",
+    "    slc_test[i]['Iave']=I_mean_test\n",
+    "\n",
+    "IrrSeason= ['Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct']\n",
+    "Indoor=['Jan', 'Feb', 'Mar', 'Nov', 'Dec']\n",
+    "colrem=['Iave', 'Obs_gpcd']\n",
+    "#set the target gpcd to indoor for indoor and total-indoor for outdoor\n",
+    "#change to indoor to separate outdoor demands from total\n",
+    "for i in Indoor:\n",
+    "    slc_train[i]['Target_gpcd']=slc_train[i]['Obs_gpcd']\n",
+    "    slc_train[i]= slc_train[i].drop(columns=colrem)\n",
+    "    #for now include testing years\n",
+    "    slc_test[i]['Target_gpcd']=slc_test[i]['Obs_gpcd']\n",
+    "    slc_test[i]= slc_test[i].drop(columns=colrem)\n",
+    "    \n",
+    "\n",
+    "#Establish outdoor conservation thresholds\n",
+    "O_cons = 0.25\n",
+    "time = 20\n",
+    "\n",
+    "\n",
+    "for i in IrrSeason:\n",
+    "    slc_train[i]['Target_gpcd']=slc_train[i]['Obs_gpcd']-slc_train[i]['Iave']\n",
+    "    slc_train[i].loc[slc_train[i]['Target_gpcd'] < 0, 'Target_gpcd'] = 0\n",
+    "    \n",
+    "    #add in snow info\n",
+    "    slc_train[i]=pd.concat([slc_train[i], snow], axis=1, join=\"inner\")\n",
+    "    \n",
+    "    slc_train[i]= slc_train[i].drop(columns=colrem)\n",
+    "    #for now include testing years\n",
+    "    slc_test[i]=pd.concat([slc_test[i], snow], axis=1, join=\"inner\")\n",
+    "    slc_test[i]['Target_gpcd']=slc_test[i]['Obs_gpcd']-slc_test[i]['Iave']\n",
+    "    slc_test[i].loc[slc_test[i]['Target_gpcd'] < 0, 'Target_gpcd'] = 0\n",
+    "    \n",
+    "    #create monthly historical mean and conservation trends\n",
+    "    Out_mean = np.mean(slc_train[i]['Target_gpcd'].loc[2000:])\n",
+    "    goal = (1-O_cons)*Out_mean\n",
+    "    O_cons_rate = (Out_mean -goal)/time\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    slc_train[i]['cons_goal'] = Out_mean- ((slc_train[i].index-2000)*O_cons_rate)\n",
+    "    slc_train[i].loc[ slc_train[i].index <2000, ['cons_goal']] = Out_mean\n",
+    "    \n",
+    "    t=slc_train[i]['Target_gpcd'].copy()\n",
+    "    c=slc_train[i]['cons_goal'].copy()\n",
+    "    slc_train[i] = slc_train[i].drop(columns=['Target_gpcd', 'cons_goal'])\n",
+    "    slc_train[i]['Target_gpcd'] = t\n",
+    "    slc_train[i]['cons_goal'] = c\n",
+    "    \n",
+    "    slc_test[i]['cons_goal'] = Out_mean - ((slc_test[i].index-2000)*O_cons_rate) \n",
+    "    \n",
+    " \n",
+    "    \n",
+    "    \n",
+    "    slc_test[i]= slc_test[i].drop(columns=colrem)\n",
+    "\n",
+    "    \n",
+    "#Determine the historical indoor mean to apply conservation strategies too\n",
+    "Indmean = np.mean(slc_train['Jan']['Target_gpcd'].loc[2000:]+\n",
+    "                  slc_train['Feb']['Target_gpcd'].loc[2000:]+\n",
+    "                  slc_train['Mar']['Target_gpcd'].loc[2000:]+\n",
+    "                  slc_train['Nov']['Target_gpcd'].loc[2000:]+\n",
+    "                 slc_train['Dec']['Target_gpcd'].loc[2000:])/5\n",
+    "'''Per UDWR 2001, Utah's Water Resources: Planning for the future, the govenor ordered a\n",
+    "25% reduction in water use by 2025\n",
+    "'''\n",
+    "cons = 0.28\n",
+    "\n",
+    "goal = (1-cons)*Indmean\n",
+    "\n",
+    "time = 20\n",
+    "\n",
+    "cons_rate = (Indmean -goal)/time\n",
+    "\n",
+    "#print(cons_rate , goal)\n",
+    "\n",
+    "#create feature called cons_goal!\n",
+    "for i in Indoor:\n",
+    "    slc_test[i]['cons_goal'] = Indmean-((slc_test[i].index-2000)*cons_rate) \n",
+    "    slc_train[i]['cons_goal'] = Indmean-((slc_train[i].index-2000)*cons_rate) \n",
+    "    \n",
+    "    slc_train[i].loc[ slc_train[i].index <2000, ['cons_goal']] = Indmean\n",
+    "    \n",
+    "    \n",
+    "Cons_mean_test=(slc_test['Jan']['cons_goal']+\n",
+    "                slc_test['Feb']['cons_goal']+\n",
+    "                slc_test['Mar']['cons_goal']+\n",
+    "                slc_test['Nov']['cons_goal']+\n",
+    "                slc_test['Dec']['cons_goal'])/5\n",
+    "\n",
+    "#split training and testing data into features and targets\n",
+    "slc_train_target=copy.deepcopy(slc_train)\n",
+    "slc_train_features=copy.deepcopy(slc_train)\n",
+    "\n",
+    "slc_test_target=copy.deepcopy(slc_test)\n",
+    "slc_test_features=copy.deepcopy(slc_test)\n",
+    "\n",
+    "\n",
+    "target=['Target_gpcd','Housing']\n",
+    "for i in slc_train_target:\n",
+    "    slc_train_target[i]=slc_train_target[i]['Target_gpcd']\n",
+    "    #for now include testing years\n",
+    "    slc_test_target[i]=slc_test_target[i]['Target_gpcd']\n",
+    "\n",
+    "\n",
+    "    slc_train_features[i]= slc_train_features[i].drop(columns=target)\n",
+    "    #for now include testing years\n",
+    "    slc_test_features[i]= slc_test_features[i].drop(columns=target)\n",
+    "    \n",
+    "#need to remove year from the list to run plots below\n",
+    "for i in slc_train:\n",
+    "    slc_train[i]=slc_train[i].drop(columns=['Housing',  'Population', 'PopulationDensity'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "We want to see the trends for the training data,# and will want to have them modeled into the future based\n",
+    "on consevation goals\n",
+    "\n",
+    "From plots, There appears to be very little trends in training data. The last 10 years do show decreasing trends\n",
+    "likely due to conservation goals estabished in 2005\n",
+    "\n",
+    "\n",
+    "Thinking of a stepwise process:\n",
+    "1) identify any trends (training and testing): check, no trends in training. however in 2000, Utah established a \n",
+    "   conservation goal. To reduce the gpcd water use by 25% by 2025. Evaluating water use from 2005-2017 shows a decline\n",
+    "   in water use.\n",
+    "2) Determine conservation goals (annual reduction /yr, slope), over 25yrs a 75 gpcd reduction is requested (3gpcd/yr), check\n",
+    "3) Separately model indoor and outdoor demands per year. check \n",
+    "4) Training Outdoor demand (Apr-Oct) will be that month's total demand minus that years Jan-Mar mean indoor demand\n",
+    "5) Final model = modeled indoor demand - conservation reduction + Outdoor demand (if Apr-Oct).\n",
+    "'''\n",
+    "\n",
+    "def Outdoor_Demand_Model(TrainDF, month, X_train_features, y_train_target, X_test_features, y_test_target,\n",
+    "                          snowfeatures, conservation, cor_threshold, colinearity_thresh, cv_splits,\n",
+    "                         model_type, scoring ):\n",
+    "  \n",
+    "\n",
+    "#subset these features out of main DF and put into cute heatmap plot\n",
+    "    \n",
+    "    DFcor = copy.deepcopy(TrainDF[month])\n",
+    "    \n",
+    "    #if snowfeatures is True:\n",
+    "     #   print('LCC Snowfeatures are being used')\n",
+    "    if snowfeatures is False:\n",
+    "        snow=['Nov_snow_in','Dec_snow_in', 'Jan_snow_in','Feb_snow_in', \n",
+    "              'Mar_snow_in','Apr_snow_in', 'Total_snow_in', 'Snow_shortage']\n",
+    "        DFcor=DFcor.drop(columns=snow)\n",
+    "\n",
+    "    \n",
+    "    cor=DFcor.copy()\n",
+    "    if conservation is False:\n",
+    "        del cor['cons_goal']\n",
+    "        cor = cor.corr()\n",
+    "        cor =cor.iloc[:,-1:]\n",
+    "    if conservation is True:\n",
+    "        cor = cor.corr()\n",
+    "        cor =cor.iloc[:,-2:]\n",
+    "        del cor['cons_goal']\n",
+    "        \n",
+    "    cor['Target_gpcd']=np.abs(cor['Target_gpcd'])\n",
+    "    cor=cor.sort_values(by=['Target_gpcd'], ascending=False)\n",
+    "    cor=cor.dropna()\n",
+    "\n",
+    "#Selecting highly correlated features\n",
+    "    relevant_features = cor[cor['Target_gpcd']>cor_threshold]\n",
+    "    CorFeat = list(relevant_features.index)\n",
+    "\n",
+    "    CorDF= DFcor[CorFeat]\n",
+    "    cor = np.abs(CorDF.corr())\n",
+    "    cor = cor.mask(np.tril(np.ones(cor.shape)).astype(np.bool))\n",
+    "    #remove colinearity\n",
+    "    cor = cor[cor.columns[cor.max() < colinearity_thresh]]\n",
+    "    CorFeat=cor.columns\n",
+    "    cor = cor.T\n",
+    "    cor = cor[CorFeat]\n",
+    "    \n",
+    "    #print('Remaining features are', CorFeat)\n",
+    "\n",
+    "    \n",
+    "   #Set up training and testing data \n",
+    "    X_train = X_train_features[month][CorFeat].copy()\n",
+    "#X_train = slc_train_features['Jul'][JulF]\n",
+    "    y_train = y_train_target[month].copy()\n",
+    "\n",
+    "    X_test = X_test_features[month][CorFeat].copy()\n",
+    "#X_test = slc_test_features['Jul'][JulF]\n",
+    "    y_test = y_test_target[month].copy()\n",
+    "\n",
+    "    # step-1: create a cross-validation scheme\n",
+    "    folds = KFold(n_splits = cv_splits, shuffle = True, random_state = 42)\n",
+    "\n",
+    "# step-2: specify range of hyperparameters to tune\n",
+    "    if len(CorFeat) > 1 :\n",
+    "        hyper_params = [{'n_features_to_select': list(range(1, len(CorFeat)))}]\n",
+    "        \n",
+    "\n",
+    "# step-3: perform grid search\n",
+    "# 3.1 specify model, key to set intercept to false\n",
+    "        trainmodel = model_type\n",
+    "        trainmodel.fit(X_train, y_train)\n",
+    "        rfe = RFE(trainmodel)             \n",
+    "\n",
+    "# 3.2 call GridSearchCV()\n",
+    "        model_cv = GridSearchCV(estimator = rfe, \n",
+    "                        param_grid = hyper_params, \n",
+    "                        scoring= scoring, \n",
+    "                        cv = folds, \n",
+    "                        verbose = 0,\n",
+    "                        return_train_score=True)      \n",
+    "\n",
+    "# fit the model\n",
+    "        model_cv.fit(X_train, y_train)\n",
+    "\n",
+    "# create a KFold object with 5 splits \n",
+    "        folds = KFold(n_splits = cv_splits, shuffle = True, random_state = 42)\n",
+    "        scores = cross_val_score(trainmodel, X_train, y_train, scoring=scoring, cv=folds)\n",
+    "       # print('CV scores = ', scores) \n",
+    "\n",
+    "# cv results\n",
+    "        cv_results = pd.DataFrame(model_cv.cv_results_)\n",
+    "\n",
+    "    \n",
+    "     #code to select features for final model, tell how many features\n",
+    "        N_feat=cv_results.loc[cv_results['mean_test_score'].idxmax()]\n",
+    "        N_feat=N_feat['param_n_features_to_select']\n",
+    "        #print('Number of features to select is ', N_feat)\n",
+    "    # intermediate model\n",
+    "        n_features_optimal = N_feat\n",
+    "\n",
+    "        Int_model = model_type\n",
+    "        Int_model.fit(X_train, y_train)\n",
+    "\n",
+    "        rfe = RFE(Int_model, n_features_to_select=n_features_optimal)             \n",
+    "        rfe = rfe.fit(X_train, y_train)\n",
+    "\n",
+    "#make the final model with rfe features\n",
+    "\n",
+    "# tuples of (feature name, whether selected, ranking)\n",
+    "# note that the 'rank' is > 1 for non-selected features\n",
+    "\n",
+    "        Features =list(zip(X_train.columns,rfe.support_,rfe.ranking_))\n",
+    "        FeaturesDF=pd.DataFrame(Features, columns=['Feature', 'Important', 'Score'])\n",
+    "        FeaturesDF = FeaturesDF[FeaturesDF.Score<=1]\n",
+    "        RFE_features = list(FeaturesDF['Feature'])\n",
+    "       # print('The final features are ', RFE_features)\n",
+    "        \n",
+    "        #select only RFE features for model training/validation\n",
+    "        X_train = X_train[RFE_features]\n",
+    "        X_test = X_test[RFE_features]\n",
+    "\n",
+    "    \n",
+    "    #plot of selected features to make sure not colinear\n",
+    "        CorDF= X_train.copy()\n",
+    "        CorDF['Target_gpcd']=slc_train[month]['Target_gpcd']\n",
+    "\n",
+    "        Final_model = model_type\n",
+    "        Final_model.fit(X_train, y_train)\n",
+    "        \n",
+    "   \n",
+    "        \n",
+    "    else:\n",
+    "          \n",
+    "       #Set up training and testing data to have a random non-correlated feature then\n",
+    "        X_train = X_train_features[month]['HousingDensity'].copy()\n",
+    "        X_test = X_test_features[month]['HousingDensity'].copy()\n",
+    "        cv_results = 0\n",
+    "        cor = 0\n",
+    "        \n",
+    "        len1 = len(X_train)\n",
+    "        len2 = len(X_test)\n",
+    "        \n",
+    "        X_train = np.array(X_train).reshape(len1, 1)\n",
+    "        X_test = np.array(X_test).reshape(len2, 1)\n",
+    "        \n",
+    "        Final_model = model_type\n",
+    "        Final_model.fit(X_train, y_train)\n",
+    "        \n",
+    "    # Get training data model performance to tune hyperparameters\n",
+    "    yt_pred = Final_model.predict(X_train)\n",
+    "    \n",
+    "    yt_pred = [0 if x < 0 else x for x in yt_pred]\n",
+    "    O_r2_train = sklearn.metrics.r2_score(y_train, yt_pred)\n",
+    "    O_rmse_train = sklearn.metrics.mean_squared_error(y_train, yt_pred, squared = False)\n",
+    "   \n",
+    "# predict X_test\n",
+    "    y_pred = Final_model.predict(X_test)\n",
+    "    \n",
+    "    y_pred = [0 if x < 0 else x for x in y_pred]\n",
+    "    O_r2_test = sklearn.metrics.r2_score(y_test, y_pred)\n",
+    "    O_rmse_test = sklearn.metrics.mean_squared_error(y_test, y_pred, squared = False)\n",
+    "    \n",
+    " \n",
+    "\n",
+    "#plot the predictions\n",
+    "    PerfDF=pd.DataFrame(list(zip(y_pred, y_test)), columns=['y_pred', 'y_test'])\n",
+    "\n",
+    "#Add indoor demands\n",
+    "    Indoor=['Jan', 'Feb', 'Mar', 'Nov', 'Dec']\n",
+    "    if month in Indoor:\n",
+    "        PerfDF['y_test']=PerfDF['y_test']\n",
+    "        PerfDF['y_pred']=PerfDF['y_pred']\n",
+    "    else:\n",
+    "        PerfDF['y_test_tot']=PerfDF['y_test']+list(I_mean_test)\n",
+    "        PerfDF['y_pred_tot']=PerfDF['y_pred']+list(Cons_mean_test)\n",
+    "\n",
+    "    T_r2 = sklearn.metrics.r2_score(PerfDF['y_test_tot'], PerfDF['y_pred_tot'])\n",
+    "    T_rmse= sklearn.metrics.mean_squared_error(PerfDF['y_test_tot'], PerfDF['y_pred_tot'], \n",
+    "                                               squared = False)\n",
+    "\n",
+    "    #print('Total R2 is ', T_r2)\n",
+    "    #print('Total rmse is ', T_rmse)\n",
+    "  \n",
+    "\n",
+    "    PerfDF['Year'] = list(slc_test['Jul'].index)\n",
+    "    PerfDF=PerfDF.set_index('Year')\n",
+    "   \n",
+    "\n",
+    "    datetime_object = datetime.datetime.strptime(month, \"%b\")\n",
+    "    PerfDF['month'] = datetime_object.month\n",
+    "    PerfDF['Year']=PerfDF.index\n",
+    "   \n",
+    "    \n",
+    "    #set up dates so all months can be combined and sorted\n",
+    "    day=[]\n",
+    "    for index, row in PerfDF.iterrows():\n",
+    "        day.append(calendar.monthrange(int(row['Year']), int(row['month']))[1])\n",
+    "    \n",
+    "    PerfDF['Day']=day\n",
+    "  \n",
+    "    PerfDF['Date'] = pd.to_datetime(PerfDF[['Year', 'month', 'Day']])\n",
+    "    \n",
+    "    #PerfDF=PerfDF.set_index('Date')\n",
+    "    PerfDF=PerfDF.drop(columns=['Year', 'month', 'Day'])\n",
+    "    PerfDF=PerfDF.reset_index()\n",
+    "    \n",
+    "    params = [snowfeatures, conservation, cor_threshold, colinearity_thresh]\n",
+    "    \n",
+    "    return X_test, PerfDF, O_rmse_train, O_r2_train ,O_rmse_test, O_r2_test , params, cv_results, cor , Final_model.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Indoor_Demand_Model(df, month):\n",
+    "    #Make matching indoor demand model\n",
+    "    datetime_object = datetime.datetime.strptime(month, \"%b\")\n",
+    "    df[month]['month'] = datetime_object.month\n",
+    "    \n",
+    "    df[month]['Year']=df[month].index\n",
+    "    \n",
+    "     #set up dates so all months can be combined and sorted\n",
+    "    day=[]\n",
+    "    for index, row in df[month].iterrows():\n",
+    "        day.append(calendar.monthrange(int(row['Year']), int(row['month']))[1])\n",
+    "    \n",
+    "    df[month]['Day']=day\n",
+    "    \n",
+    "  \n",
+    "    df[month]['Date'] = pd.to_datetime(df[month][['Year', 'month', 'Day']])\n",
+    "    \n",
+    "    #PerfDF=PerfDF.set_index('Date')\n",
+    "    df[month]=df[month].drop(columns=['month', 'Day'])\n",
+    "    df[month]=df[month].reset_index(drop=True)\n",
+    "   \n",
+    "    \n",
+    "    colrem = slc_test[i].columns\n",
+    "    df[month]['y_pred'] =  df[month]['cons_goal']\n",
+    "    df[month]['y_test'] =  df[month]['Target_gpcd']\n",
+    "    df[month]['y_pred_tot'] =  df[month]['cons_goal']\n",
+    "    df[month]['y_test_tot'] =  df[month]['Target_gpcd']\n",
+    "       \n",
+    "    df[month] = df[month].set_index('Date')\n",
+    "    \n",
+    "    df[month] = df[month].drop(columns=colrem)\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams[\"axes.grid\"] = False\n",
+    "plt.rcParams[\"axes.facecolor\"] ='white'\n",
+    "#plt.rcParams[\"axes.edgecolor\"]['bottom'] = 'black'\n",
+    "\n",
+    "def gradientbars_sliced(bars):\n",
+    "    ax = bars[0].axes\n",
+    "    xmin, xmax = ax.get_xlim()\n",
+    "    ymin, ymax = ax.get_ylim()\n",
+    "    for bar in bars:\n",
+    "        bar.set_zorder(1)\n",
+    "        bar.set_facecolor(\"none\")\n",
+    "        x, y = bar.get_xy()\n",
+    "        w, h = bar.get_width(), bar.get_height()\n",
+    "        grad = np.linspace(y, y + h, 256).reshape(256, 1)\n",
+    "        ax.imshow(grad, extent=[x, x + w, y, y + h], aspect=\"auto\", zorder=0, origin='lower',\n",
+    "                  vmin= - max(np.abs(ymin), ymax), vmax=max(np.abs(ymin), ymax), cmap='Spectral')\n",
+    "    ax.axis([xmin, xmax, ymin, ymax])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_plots(PerfDF, cv_results,cor, X_test_RFE, coef, scoring, month):\n",
+    "    \n",
+    "    plotmin = PerfDF[['y_pred', 'y_test']].min().min()\n",
+    "    plotmax = PerfDF[['y_pred', 'y_test']].max().max()\n",
+    "    \n",
+    "    plotmin_tot = PerfDF[['y_pred_tot', 'y_test_tot']].min().min()\n",
+    "    plotmax_tot = PerfDF[['y_pred_tot', 'y_test_tot']].max().max()\n",
+    "    \n",
+    "    # plotting cv results\n",
+    "    plt.figure(figsize=(12,10))\n",
+    "    sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
+    "    plt.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/' + month + '_corMatrix.pdf')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    fig, ax = plt.subplots(3,3, constrained_layout=True)\n",
+    "    fig.set_size_inches(9,10)\n",
+    "\n",
+    "\n",
+    "    ax[0,0].plot(cv_results[\"param_n_features_to_select\"], cv_results[\"mean_test_score\"])\n",
+    "    ax[0,0].plot(cv_results[\"param_n_features_to_select\"], cv_results[\"mean_train_score\"])\n",
+    "    ax[0,0].set_xlabel('number of features')\n",
+    "    ax[0,0].set_ylabel(scoring)\n",
+    "    ax[0,0].set_title(\"Optimal Number of Features\")\n",
+    "    ax[0,0].legend(['test score', 'train score'], loc='upper left')\n",
+    "    ax[0,0].spines['bottom'].set_color('black')\n",
+    "    ax[0,0].spines['left'].set_color('black')\n",
+    "\n",
+    "    ax[0,1].scatter(PerfDF['y_test'], PerfDF['y_pred'],color='blue', alpha=0.5)\n",
+    "    ax[0,1].set_ylabel('Predicted')\n",
+    "    ax[0,1].set_xlabel('Observed')\n",
+    "    ax[0,1].set_ylim(plotmin-5,plotmax+5)\n",
+    "    ax[0,1].set_xlim(plotmin-5,plotmax+5)\n",
+    "    ax[0,1].set_title('Outdoor Model Performance')\n",
+    "    ax[0,1].plot([plotmin,plotmax],[plotmin,plotmax], color='red', linestyle='--' )\n",
+    "    ax[0,1].spines['bottom'].set_color('black')\n",
+    "    ax[0,1].spines['left'].set_color('black')\n",
+    "    \n",
+    "    ax[0,2].scatter(PerfDF['y_test_tot'], PerfDF['y_pred_tot'],color='blue', alpha=0.5)\n",
+    "    ax[0,2].set_ylabel('Predicted')\n",
+    "    ax[0,2].set_xlabel('Observed')\n",
+    "    ax[0,2].set_ylim(plotmin_tot-5,plotmax_tot+5)\n",
+    "    ax[0,2].set_xlim(plotmin_tot-5,plotmax_tot+5)\n",
+    "    ax[0,2].set_title('Indoor and Outdoor \\n Model Performance')\n",
+    "    ax[0,2].plot([plotmin_tot,plotmax_tot],[plotmin_tot,plotmax_tot], color='red', linestyle='--' )\n",
+    "    ax[0,2].spines['bottom'].set_color('black')\n",
+    "    ax[0,2].spines['left'].set_color('black')\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    gs = ax[1, 1].get_gridspec()\n",
+    "    # remove the underlying axes\n",
+    "    ax[1,0].remove()\n",
+    "    ax[1,1].remove()\n",
+    "    ax[1,2].remove()\n",
+    "\n",
+    "    PerfDF['Error'] = (PerfDF['y_pred']-PerfDF['y_test'])\n",
+    "    axbig1 = fig.add_subplot(gs[1, :])\n",
+    "    axbig1.set_title(month+' Outdoor demand Timeline Evaluation')\n",
+    "    axbig1.axhline(y = 0 , color = 'black')\n",
+    "    #axbig1.bar(PerfDF.index, PerfDF['y_pred'], color='orange', label='Predicted')\n",
+    "    Error1 = axbig1.bar(PerfDF.index, PerfDF['Error'],color='blue', label='Prediction Error')\n",
+    "    axbig1.set_xlabel('Year')\n",
+    "    axbig1.set_ylabel('Error (GPCD)')\n",
+    "    axbig1.spines['bottom'].set_color('black')\n",
+    "    axbig1.spines['left'].set_color('black')\n",
+    "    gradientbars_sliced(Error1)\n",
+    "    \n",
+    "    \n",
+    "    gs2 = ax[2, 1].get_gridspec()\n",
+    "    # remove the underlying axes\n",
+    "    ax[2,0].remove()\n",
+    "    ax[2,1].remove()\n",
+    "    ax[2,2].remove()\n",
+    "\n",
+    "    #create error value\n",
+    "    PerfDF['Error_tot'] = (PerfDF['y_pred_tot']-PerfDF['y_test_tot'])\n",
+    "    \n",
+    "    axbig2 = fig.add_subplot(gs2[2, :])\n",
+    "    axbig2.set_title(month+' Total Demand Error Timeline Evaluation')\n",
+    "    Error2 = axbig2.bar(PerfDF.index, PerfDF['Error_tot'], color='blue', label='Predicted')\n",
+    "    axbig2.axhline(y = 0 , color = 'black')\n",
+    "    #axbig2.bar(PerfDF.index, PerfDF['y_test_tot'],color='blue', label='Observed')\n",
+    "    axbig2.set_xlabel('Year')\n",
+    "    axbig2.set_ylabel('Error (GPCD)')\n",
+    "    axbig2.spines['bottom'].set_color('black')\n",
+    "    axbig2.spines['left'].set_color('black')\n",
+    "    gradientbars_sliced(Error2)\n",
+    "    \n",
+    "    fig.suptitle(month+ ' Evaluation', size = 16)\n",
+    "    fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/' + month + '_demand.pdf')    \n",
+    "    \n",
+    "    O_r2 = sklearn.metrics.r2_score(PerfDF['y_test'],PerfDF['y_pred'])\n",
+    "    O_rmse= sklearn.metrics.mean_squared_error(PerfDF['y_test'],PerfDF['y_pred'], squared = False)\n",
+    "    O_mae= sklearn.metrics.mean_absolute_error(PerfDF['y_test'],PerfDF['y_pred'])\n",
+    "    O_mape= sklearn.metrics.mean_absolute_percentage_error(PerfDF['y_test'],PerfDF['y_pred'])\n",
+    "    \n",
+    "    T_r2 = sklearn.metrics.r2_score(PerfDF['y_test_tot'],PerfDF['y_pred_tot'])\n",
+    "    T_rmse= sklearn.metrics.mean_squared_error(PerfDF['y_test_tot'],PerfDF['y_pred_tot'], squared = False)\n",
+    "    T_mae= sklearn.metrics.mean_absolute_error(PerfDF['y_test_tot'],PerfDF['y_pred_tot'])\n",
+    "    T_mape= sklearn.metrics.mean_absolute_percentage_error(PerfDF['y_test_tot'],PerfDF['y_pred_tot'])\n",
+    "    \n",
+    "    print('The outdoor Demand prediction RMSE is ', O_rmse)\n",
+    "    print('The outdoor Demand prediction R2 is ', O_r2)\n",
+    "    \n",
+    "    print('The Total Demand prediction RMSE is ', T_rmse)\n",
+    "    print('The Total Demand prediction R2 is ', T_r2)\n",
+    "    print('The Total Demand prediction MAE is ', T_mae)\n",
+    "    print('The Total Demand prediction MAPE is ', T_mape, '%')                                                       \n",
+    "    \n",
+    "    print('The final set of features for ' + month + ' are', list(X_test_RFE.columns))\n",
+    "    print('The coefficients for each feature are', coef)\n",
+    "    #set DF up so that all months can be easily combined, basically year-month index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#make an optimization function\n",
+    "#put in your parameter dictionary, month of interest, and scoring method (RMSE or R2)\n",
+    "def Demand_Optimization(Param_dict, month, scoring):\n",
+    "    print('The automated algorithm automatically optimizes the respective model by looping over input parameters within')\n",
+    "    print('the training data. In addiiton, the algorithm checks for colinearity between features, removing the one with')\n",
+    "    print('less correlation to the target.')\n",
+    "    param_list = []\n",
+    "    performance_list=[]\n",
+    "    for i in Param_dict['snowfeatures']:\n",
+    "   #     print('Snowfeatures is ' + str(i))\n",
+    "        for j in Param_dict['conservation']:\n",
+    "    #        print('Conservation is ' + str(j))\n",
+    "            for k in Param_dict['cor_threshold']:\n",
+    "     #           print('Correlation threshold: ', k)\n",
+    "                #pbar = ProgressBar()\n",
+    "                for l in Param_dict['colinearity_thresh']:\n",
+    "      #              print('Colinearity threshold: ', l)\n",
+    "                    X_test_RFE, PerfDF, O_rmse_train,O_r2_train, O_rmse_test, O_r2_test, params, cv_results, cor, coef = Outdoor_Demand_Model(slc_train, month, \n",
+    "                            slc_train_features, slc_train_target, slc_test_features,slc_test_target, \n",
+    "                            snowfeatures= i, conservation = j, cor_threshold = k, colinearity_thresh = l, cv_splits = 5,\n",
+    "                            model_type = linear_model.Ridge(fit_intercept = False, alpha=1), \n",
+    "                            scoring = 'neg_root_mean_squared_error')\n",
+    "                    param_list.append(params)\n",
+    "                    if scoring =='R2':\n",
+    "                        performance_list.append(O_r2_test)\n",
+    "                    if scoring =='RMSE':\n",
+    "                        performance_list.append(O_rmse_test)\n",
+    "                        \n",
+    "      \n",
+    "                    \n",
+    "    #take model performances and put into DF so they can be joined and sorted                \n",
+    "    ParamDF = pd.DataFrame(param_list, columns =list(Param_dict.keys()))\n",
+    "    PerfDF = pd.DataFrame(performance_list, columns =[scoring])     \n",
+    "    ParamEval = pd.concat([ParamDF, PerfDF], axis=1)  \n",
+    "    \n",
+    "    if scoring =='R2':\n",
+    "        ParamEval = ParamEval.sort_values(by=[scoring], ascending = False)\n",
+    "    else:\n",
+    "        ParamEval = ParamEval.sort_values(by=[scoring])\n",
+    "    \n",
+    "    #select the first row of parameters as this is the one that shows the greatest performance\n",
+    "    ParamEval=ParamEval.head(1)\n",
+    "    \n",
+    "    X_test_RFE, PerfDF, O_rmse_train,O_r2_train,O_rmse_test, O_r2_test, params, cv_results, cor, coef = Outdoor_Demand_Model(slc_train, month, \n",
+    "                            slc_train_features, slc_train_target, slc_test_features,slc_test_target, \n",
+    "                            snowfeatures= list(ParamEval['snowfeatures'])[0] , \n",
+    "                            conservation = list(ParamEval['conservation'])[0],\n",
+    "                            cor_threshold = list(ParamEval['cor_threshold'])[0],\n",
+    "                            colinearity_thresh = list(ParamEval['colinearity_thresh'])[0],\n",
+    "                            cv_splits = 5, model_type = linear_model.Ridge(fit_intercept = False, alpha=1), \n",
+    "                            scoring = 'neg_root_mean_squared_error')\n",
+    "    model_plots(PerfDF, cv_results, cor, X_test_RFE, coef,  scoring, month)\n",
+    "    print('The best training parameters are below with their scoring method: ', scoring)\n",
+    "    print(ParamEval)\n",
+    "    return  PerfDF, cv_results, cor, X_test_RFE, coef"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#Make a function to put all of the predictions together\n",
+    "def Demand_Forecast(prediction_dictionary, pdict, df, pred, test, units, plotname, model):\n",
+    "    FinalDF=pd.DataFrame()\n",
+    "    if pdict is True:\n",
+    "        print('yes')\n",
+    "        for i in prediction_dictionary:\n",
+    "            FinalDF=FinalDF.append(prediction_dictionary[i])\n",
+    "\n",
+    "        FinalDF=FinalDF.sort_index()\n",
+    "    else:\n",
+    "        print('no')\n",
+    "        FinalDF = df\n",
+    "        \n",
+    "    months = np.arange(1,6,1)\n",
+    "    Low = list()\n",
+    "    Ave=list()\n",
+    "    High = list()\n",
+    "    for i in months:\n",
+    "        Low.append('Low'+str(i))\n",
+    "        Ave.append('Ave'+str(i))\n",
+    "        High.append('High'+str(i))\n",
+    "        \n",
+    "    Low.append('Drought')\n",
+    "    Ave.append('Average')\n",
+    "    High.append('Surplus')\n",
+    "    \n",
+    "    months2 = np.arange(7,13,1)\n",
+    "    for i in months2:\n",
+    "        Low.append('Low'+str(i))\n",
+    "        Ave.append('Ave'+str(i))\n",
+    "        High.append('High'+str(i))\n",
+    "    \n",
+    "    Conditions = High+Low+Ave\n",
+    "\n",
+    "    #adjust date range to improve figure\n",
+    "    #FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n",
+    "    FinalDF['Date'] = Conditions\n",
+    "    \n",
+    "    FinalDF.index = FinalDF['Date']\n",
+    "    del FinalDF['Date']\n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    plotmin_tot = FinalDF[[pred, test]].min().min()\n",
+    "    plotmax_tot = FinalDF[[pred, test]].max().max()\n",
+    "\n",
+    "    fig, ax = plt.subplots(1,5, constrained_layout=True)\n",
+    "    fig.set_size_inches(12,4)\n",
+    "\n",
+    "    gs2 = ax[0].get_gridspec()\n",
+    "    # remove the underlying axes\n",
+    "    ax[0].remove()\n",
+    "    ax[1].remove()\n",
+    "    ax[2].remove()\n",
+    "    axbig = fig.add_subplot(gs2[:3])\n",
+    "    #axbig.set_title('Total demand Timeline Evaluation')\n",
+    "    axbig.plot(FinalDF[pred], color='orange', label= model)\n",
+    "    axbig.plot(FinalDF[test],color='blue', label='Observed')\n",
+    "    axbig.set_xlabel('Supply Conditions')\n",
+    "    axbig.set_ylim(plotmin_tot-.9,plotmax_tot*1.2)\n",
+    "    axbig.set_ylabel('Demand ('+ units+')')\n",
+    "    axbig.legend(loc = 'upper left')\n",
+    "    axbig.set_facecolor(\"white\")\n",
+    "    axbig.spines['bottom'].set_color('black')\n",
+    "    axbig.spines['left'].set_color('black')\n",
+    "    \n",
+    "    xticks = axbig.xaxis.get_major_ticks()\n",
+    "    for i,tick in enumerate(xticks):\n",
+    "        if i%12 != 5:\n",
+    "            tick.label1.set_visible(False)\n",
+    "\n",
+    "    ax[3].remove()\n",
+    "    ax[4].remove()\n",
+    "\n",
+    "\n",
+    "    axbig2 = fig.add_subplot(gs2[3:])\n",
+    "    axbig2.scatter(FinalDF[test], FinalDF[pred],color='blue', alpha=0.5)\n",
+    "    axbig2.set_ylabel('Predicted (' + units+')' )\n",
+    "    axbig2.set_xlabel('Observed (' + units+')')\n",
+    "    axbig2.set_ylim(plotmin_tot*.95,plotmax_tot*1.2)\n",
+    "    axbig2.set_xlim(plotmin_tot*.95,plotmax_tot*1.2)\n",
+    "   # axbig2.set_title('Indoor and Outdoor \\n Model Performance')\n",
+    "    axbig2.plot([plotmin_tot,plotmax_tot],[plotmin_tot,plotmax_tot], color='red', linestyle='--' )\n",
+    "    #axbig2.set_xticks(np.arange(plotmin_tot, plotmax_tot, 100).round())\n",
+    "    #axbig2.set_yticks(np.arange(plotmin_tot, plotmax_tot, 100).round())\n",
+    "    axbig2.set_facecolor(\"white\")\n",
+    "    axbig2.spines['bottom'].set_color('black')\n",
+    "    axbig2.spines['left'].set_color('black')\n",
+    "    #ax[2].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "    fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/' +str(plotname)+'.pdf')\n",
+    "    r2 = sklearn.metrics.r2_score(FinalDF[test], FinalDF[pred])\n",
+    "    MAE= sklearn.metrics.mean_absolute_error(FinalDF[test], FinalDF[pred])\n",
+    "    RMSE= sklearn.metrics.mean_squared_error(FinalDF[test], FinalDF[pred], squared = False)\n",
+    "    MAPE=np.mean(np.abs((FinalDF[test]- FinalDF[pred])/FinalDF[test])*100)\n",
+    "\n",
+    "    print('Total R2 is ', r2)\n",
+    "    print('Total MAE is ', MAE)\n",
+    "    print('Total RMSE is ', RMSE)\n",
+    "    print('Total MAPE is ', MAPE)\n",
+    "    \n",
+    "    FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n",
+    "    FinalDF.index = FinalDF['Date']\n",
+    "    del FinalDF['Date']\n",
+    "    \n",
+    "    return FinalDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0% |                                                                        |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The model is automatically selecting features and calibrating the  Apr outdoor demand model.\n",
+      "The automated algorithm automatically optimizes the respective model by looping over input parameters within\n",
+      "the training data. In addiiton, the algorithm checks for colinearity between features, removing the one with\n",
+      "less correlation to the target.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 14% |##########                                                              |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  10.091412513931665\n",
+      "The outdoor Demand prediction R2 is  0.1876987417608621\n",
+      "The Total Demand prediction RMSE is  13.268172887056254\n",
+      "The Total Demand prediction R2 is  0.32903540800937014\n",
+      "The Total Demand prediction MAE is  13.216378923217809\n",
+      "The Total Demand prediction MAPE is  0.09463600480450761 %\n",
+      "The final set of features for Apr are ['Val_Apr_Monthly_mean_Day_temp_C', 'Val_Apr_Monthly_precip_mm', 'LitCotCrk_AcFt_WR_Apr', 'LitCotCrk_AcFt_WR_Mar']\n",
+      "The coefficients for each feature are [ 5.63580354 -0.30790015  0.0073984  -0.01200673]\n",
+      "The best training parameters are below with their scoring method:  RMSE\n",
+      "     snowfeatures  conservation  cor_threshold  colinearity_thresh       RMSE\n",
+      "285         False         False           0.25                 0.8  10.091413\n",
+      "The model is automatically selecting features and calibrating the  May outdoor demand model.\n",
+      "The automated algorithm automatically optimizes the respective model by looping over input parameters within\n",
+      "the training data. In addiiton, the algorithm checks for colinearity between features, removing the one with\n",
+      "less correlation to the target.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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FiFgf2Jmim13ducD+mbk6cCBwaUT02ho0UZIkSZLUThOBSS3Wz6gtPwaMb1peFXiiaXlXYDXgDorUaHRE3AzsBLwrMy8HyMxfRMQZwMrAUz1VqtHZ2dnTNkl98w9IkiQNtrbPzf3HNcYM2T3Ohn97pF/nW07m8FuK7nazgN9RpES3t9h3DDAlM8eUydHfgc9k5s0RsTlwXma+vbfj2fVOkiRJ0iIvMx8HDgNuBKYCF2Tm7RExOSLG9fK+TopU6cSIuAf4DkUXvV6ZKEkLxz8gSZI02NqeKN2z5tAlShs82r9EaaiZKEmSJElSjZM5SJIkSapoNBbJkGdImShJkiRJUo2JkiRJkqQKAyUTJUmSJEnqxkRJkiRJUoVjlEyUJEmSJKkbEyVJkiRJFQZKJkqSJEmS1I2JkiRJkqSKDiMlEyVJkiRJqjNRkiRJklRhoGSiJEmSJEnd2FCSJEmSpBq73kmSJEmq8IGzJkqSJEmS1I2JkiRJkqSKhnGKiZIkSZIk1ZkoSZIkSapwjJKJkiRJkiR1Y6IkSZIkqcJAyURJkiRJkroxUZIkSZJU4RglEyVJkiRJ6sZESZIkSVKFgZKJkiRJkiR1Y6IkSZIkqaLDSMlESZIkSZLqTJQkSZIkVRgomShJkiRJUjc2lCRJkiSpxq53kiRJkip84KwNJWmhHNhYvt1VkAbFGZ0z210FSZIWKTaUJEmSJFUYKDlGSZIkSZK6MVGSJEmSVGGiZKIkSZIkSd2YKEmSJEmqaHQYKZkoSZIkSVKNiZIkSZKkCscomShJkiRJUjcmSpIkSZIqOoyUTJQkSZIkqc5ESZIkSVKFgZKJkiRJkiR1Y6IkSZIkqaJhpGSiJEmSJEl1NpQkSZIkqcaud5IkSZIq7HlnoiRJkiRJ3ZgoSZIkSapwMgcTJUmSJEnqxkRJkiRJUoWBkg0lSZIkSYuJiNgdOBxYCpiYmaf1sN/2wKmZuVa5vBpwFjAaeAHYIzMf6e1Ydr2TJEmSVNFoNIbs1V8RsTpwHLAFMBbYPyLWbbHfW4ATgebCzwOuzMyNyp9P6Ot4NpQkSZIkLQ62AW7IzOmZOQu4BNilxX5nAcd0LUTEysCGwA/LVedQpFK9suudJEmSpIrGEMYpEbEisGKLTTMyc0bT8mhgWtPyNGCzWlkHA3cBtzatfgfwN+CkiBgP/AP4Ul/1MlGSJEmS1E4TgIdbvCbU9usAOpuWG8D8roWIWB/YGfhG7X3DgY0o0qhNgcuBc/uqlImSJEmSpIohfo7SRGBSi/UzasuPAeObllcFnmha3hVYDbgDWBoYHRE3A3sDz2XmVeV+FwCn9FUpG0qSJEmS2qbsXjejH7teDxwdEasAsyjSo/2byjkKOAogIsYAUzJzfLn8WERsm5nXAB8H7uzrYHa9kyRJklTV0Ri6Vz9l5uPAYcCNwFTggsy8PSImR8S4Pt6+E3BoRNwHfBnYp6/jNTo7O/vaR1IPDmws7x+QlghndM5sdxUkSa9o++Nen91qwyG7x1lhyh/bfr6t2PVOkiRJUtXQjlFaJNn1TpIkSZJqTJQkSZIkVQzxrHeLJBMlSZIkSaqxoSRJkiRJNXa9kyRJklQ1gGm7l1QmSpIkSZJUY6IkSZIkqcrJHEyUJEmSJKnOREmSJElSRcMxSiZKkiRJklRnoiRJkiSpyjFKJkqSJEmSVGeiJEmSJKnCMUomSpIkSZLUjYmSJEmSpCrHKJkoSZIkSVKdiZIkSZKkKscomShJkiRJUp2JkiRJkqSKhmOUTJQkSZIkqc6GkiRJkiTV2PVOkiRJUpWTOZgoSZIkSVKdiZIkSZKkKidzMFGSJEmSpDoTJUmSJEkVDeMUEyVJkiRJqjNRkiRJklTlGCUTJUmSJEmqM1GSJEmSVNHwOUomSpIkSZJUZ6IkSZIkqcoxSiZKkiRJklRnoiRJkiSpyjFKJkqSJEmSVGeiJEmSJKmi4RglEyVJkiRJqrOhJEmSJEk1dr2TJEmSVOVkDiZKkiRJklRnoiRJkiSpyskcTJQkSZIkqc5ESZIkSVKF04ObKEmSJElSNyZKkiRJkqqc9c5ESZIkSZLqTJQkSZIkVThGyURJkiRJkroxUZIkSZJU5RglEyVJkiRJqjNRkiRJklTlGCUTJUmSJEmqM1GSJEmSVNFYRMcoRcTuwOHAUsDEzDyth/22B07NzLVq6zcCbs3MN/R1LBMlSZIkSYu8iFgdOA7YAhgL7B8R67bY7y3AiUCjtn4E8H1g6f4cz0RJkiRJUttExIrAii02zcjMGU3L2wA3ZOb08n2XALsAx9bedxZwDHB8bf1JwERg8/7Uy0RJkiRJUlWjMXQvmAA83OI1oVar0cC0puVpwFubd4iIg4G7gFtr63cARmTmJf29BCZKktqm0WjwmdNP5q0bvoe5L7/MefsdxFN/fWjB9q0P+Xc233cvnn/qaQB+esCXefLBh9jzzO+zaryT+fPmce7n/42nH3q4XacgSZIW3kRgUov1M2rLHUBn03IDmN+1EBHrAzsDW9PUgIqIVSnGNW0zkEr12lCKiN9SDIK6qGndSOBvQGTm07X9JwFTMnNSL2U+ArycmdG0bjhFi/DqzNx7ICfQR/0H/VgRsRmwc2YeGhF7A1vVy4mIrYCjM3OrV1v3dirrfxXwIMUHcBlgMnBEZj4/iMf5GLBOZp48WGUO8Ph7AQdRDAbsAM7KzFPaUZfXqw13/BhLLbMM3/mXbVjrvZuyy0nH8YMdP7Ng+xobj2XSXgfwt7umvvKeT2wPwHe3+DDrbLkFu578rcp7JEnSIBjCyRzK7nUz+rHrY8D4puVVgSealncFVgPuoBiHNDoibgbOBd4E3BRRNAsiYiowPjOf6+lgfSVKZwN7ABc1rduJom/g063f0i8jIuI9mXlvubw11dbhYBrsY60LvGXhq7XIu6OroRcRS1F8Fn4AfHYQjzFuEMsakIjYHzgQ2D4zp5V9Y6+LiFmZ+eN21ev1Zu0t3s+ffnU9AA/f9gfWHLdRZfsam4zlo1//D5Zf9S3ce/W1XHv8yfzx8qu596pfAbDSmmsw8/+eHPJ6S5KktrgeODoiVgFmUaRH+3dtzMyjgKMAImIMRYDT1bA6q2u/iOjMzLF9HayvhtLPgRMjYqWuQVMUN8q3lGnTCIqBV4dk5uV9ntorfkEx8Kqr8bIbcElZHhGxJcWMFgvKB24EHgLenpkzy5OfnJndZroY4LHWAX4ErERxwQ/OzD+U6dizwCbA6hSDxC4t/x0VEYcBjwNrR8QUYA3g15n5ha4DR8TawA3AmMycXyY1h2bmtq0qWp7TZcADwHoU/St/B+wNvBH4ZGb+OSI2Bb5XnsPTwAGZ+XCr65aZl7c6l8w8p4/rtkBmzomIrwJ/j4iDKCLOH1NEmqMpPrT7AT8BbsrMM8vzmVKe720tznVdioYKEfEocDFwGrA+MAw4ITMvLFO77Sm+BRgN/BBYE/gg8AywLcW3CVc0XbdHgT2bPrOtHA7sk5nTynOcERGfA5bv73XRwltm+eV48dmZC5bnz5tHx7BhzJ83D4A7LvoFU077ES/NfI4DL72AJ7b/KPde/Svmz5vH5yadwdhPfowf7bJXu6ovSdISq7EIPnA2Mx8v78FvpEiMzsrM2yNiMnBkZt4xmMfrdTKHspvV5RQxFhExGgiK6fj2y8yNKW6QvznA415MkUwREUuX5d3etP2gevmZORO4mqLRA7AXRYy2sMc6HzglMzegaJBdEhFd86q/jSLe2wE4sYwFjwSuyMzjyn3WKMt/N7BtRKzXVXBmPkgxEG2rpjpP6qO+GwAnABtSzMgxJjPfD1xIMQXi0hQt4t3L63MScGb53m7Xrancyrn0UYduMvMfwD+BdSgaLlPLer0T2BLYmCJ1+ixARKwJrNKqkVSWdz9wBnBG2Wg7HLgzMzcBPgAcFhFvL3ffDNgR+AhwMnBN+fuiXAfwHuD0zFwP+DNwdE/nEhErU1yPu2p1+nNP9dVr46WZz7HMcqMWLDc6OhY0kgB+PfF0Zj0znXlz5nDf1dfyto02WLDt3L0P5Kh1NmbPM09h6REjhrTekiSpPTLzgsxcPzPXyczvlOu2qzeSMvORzBzTQxn9agX2Z9a7c4Ddy5/3AM4DPgOsHxFHAF8BRvXw3p48DjwbEe8GPgxcV9u+Zw/lL7gRL+t03sIcKyJGAWtn5i8BMvNWYDpFYxDguszsBO6jSJxauSkzp2fmy8BfgZVr288GPlvO2741RcOzN//IzLszcz5FP8xfl+sfpUiV1gHeAVxR9q08AehqUPR03fp7Ln3pBF7MzAuB/4mICRRz0b+pPNYUir6gYygahT8ZQNnbAAeW53QTMJIiHQK4JTNnZuaj5XL9mgD8JTOnlD+fS5E49aRr0N9LA6ifXgN/veVW1t/uwwCs9d5Nefze+xdsW2b55Tnyvlt5w8iRAMQHP8Df7pzKe/f8NB/52n8AMPuFF+mcP7/SuJIkSYOgozF0r0VUnw2lzLwJWDUi3kZxI34OcDPFt/x3UnT1ejVneDFFOvQp4Ge1bT2VfxOwekTsBDycmU/QPz0dq9X5N3ilS+JLAGUDoydzm37upPu1uBj4UHn8yZnZ18357F7Kh6Jb2kOZObbsW7kJxUO3oPffS3/OpUflg7tWAP5adr/7LvAURUPpfqBRln0uRUN6N/rXkG0+rz2bzut9wK/KbZVrkpn1awLV69RB9+vW/P7pFN04K2OkImLLiKjPt6/X0NRLr2TOSy/x1Vv+h12/920uPuRrbPqZXdniC3vz0syZXP5fx3LIjVfznzdfyxN/eoD7rrmOu395BWtstAFf+c01HHztpVw84WvMffnldp+KJElawvR3evCfAIdRpC1d3a/GAy9TPMhp2Ks49sXAtcBLmXl3RGwIEBEr9VR+ZnZGxLnAKRSJyUIdqxzr9FBE7JSZv4yI91GMd7mvl7LmMoBp1TPzhYi4BvgWxYCzhfUAsFJEjM/Mm4F9gD3KxuNg/F66KbsifheYVJ7Ph4AfZuYFETGOojtj17EmAb8F/tSPhuxcihn1oBjL9UXgCxGxGjAV+JeBVTPGZuZU4PPANX3s/13gpIj4eGb+o+yOdxJFd0ANkc7OTi744iGVdf+X/7vg59vOv4jbzr+osn32Cy9w5m57D0X1JEl6/VoExygNtf4+cHYSsC9wdvlt/I+BP1GMBVmOYma5kQM5cHkTPYNXUoOu9X2VfxFFt6zLFvZYpT2BgyPiXuBUYKfMrKc6zW4H3jfA5OEiYOZgjH8pu/jtSnGTfw/wOWDfwfq9NBkXEVPLrnB3Uly/L5fbJgJHlddsIsWEE2uV9fs7xfTxk/pxjJsoGnkHUTw9edmIuI+i0fT/MvOvA6jvdOCYiPgT8Gb6GDeXmWdQfAHwPxHxR4pBgZMy86ze3idJkqTXh0Zn52s1K/fgi4gOipnS3pWZB7e7Pv0REcMousE92a7nBQ2ViGhQzF3/G2D9slE3FMcdQzH945ihOF6zAxvLLz5/QFIvzuic2fdOkqSh0vY4Z+7BOwzZPc7wU65o+/m20u8uZP0VEcsCv+9h85GZecVCFP9LilnmPjIExxosd1BM4b0DQES8g2LK8lb2G+xpDXsSEbsBX2+1rT/zyvdgZ4pnLX2xq5EUEd+lGKNVd0dm7vcqj9MvEXEjr0z20OyMMlGSJEmSWlqsEiVpUWOipCWFiZIkLVLanrDM/fInhi5R+u/L236+rfR3jJIkSZIkvW4Metc7SZIkSYu5DvMUr4AkSZIk1dhQkiRJkqQau95JkiRJqvKBsyZKkiRJklRnoiRJkiSpykTJREmSJEmS6kyUJEmSJFWZKJkoSZIkSVKdiZIkSZKkKh84a6IkSZIkSXUmSpIkSZKqHKNkoiRJkiRJdSZKkiRJkqpMlEyUJEmSJKnOREmSJElSlYmSiZIkSZIk1ZkoSZIkSaryOUomSpIkSZJUZ0NJkiRJkmrseidJkiSpyskcTJQkSZIkqc5ESZIkSVKViZKJkiRJkiTVmShJkiRJqjJRMlGSJEmSpDoTJUmSJEkVDR84a6IkSZIkSXUmSpIkSZKqHKNkoiRJkiRJdSZKkiRJkqpMlEyUJEmSJKnOREmSJElSlYmSiZIkSZIk1ZkoSZIkSaryOUomSpIkSZJUZ0NJkiRJkmrseidJkiSpyskcTJQkSZIkqc5ESZIkSVKViZKJkiRJkiTVmShJkiRJqjJRMlGSJEmSpDoTJUmSJElVPnDWREmSJEmS6kyUJEmSJFUtomOUImJ34HBgKWBiZp7Ww37bA6dm5lrl8ubA94ClgWeAfTLz0d6OZaIkSZIkaZEXEasDxwFbAGOB/SNi3Rb7vQU4EWhu7f0U2C8zx5Y/n9LX8UyUJEmSJFUNYaIUESsCK7bYNCMzZzQtbwPckJnTy/ddAuwCHFt731nAMcDx5X5vAA7PzHvK7fcAB/VVLxMlSZIkSe00AXi4xWtCbb/RwLSm5WnAW5t3iIiDgbuAW7vWZebLmXl+ub0DOBq4rK9KmShJkiRJqhraWe8mApNarJ9RW+4AOpuWG8D8roWIWB/YGdiaWgOq3L40cC5FG+hbfVXKhpIkSZKktim7183ox66PAeObllcFnmha3hVYDbiDYtKG0RFxc2aOj4hRwBUUEzl8IjPn9HUwG0qSJEmSqhbNWe+uB46OiFWAWRTp0f5dGzPzKOAogIgYA0zJzK6G1fnAg8CBmTmffnCMkiRJkqRFXmY+DhwG3AhMBS7IzNsjYnJEjOvpfRGxEfAJYHPgroiYGhGT+zpeo7Ozs699JPXgwMby/gFpiXBG58x2V0GS9Iq2xznzvvflIbvHGXbIf7f9fFux650kSZKkqkWz692QsuudJEmSJNWYKEmSJEmqMlEyUZIkSZKkOhMlSZIkSVVD+8DZRZJXQJIkSZJqTJQkSZIkVTlGyURJkiRJkupMlCRJkiRVmSiZKEmSJElSnYmSJEmSpKqGeYpXQJIkSZJqTJQkSZIkVXU4RslESZIkSZJqTJQkSZIkVTlGyURJkiRJkupsKEmSJElSjV3vJEmSJFX5wFkTJUmSJEmqM1GSJEmSVNVhnuIVkCRJkqQaEyVJkiRJVY5RMlGSJEmSpDoTJUmSJElVPnDWREmSJEmS6kyUJEmSJFU5RslESZIkSZLqTJQkSZIkVfkcJRtK0sL4wf/e2O4qSAutMXpteOHZdldDGhwjVmh3DSQtIWwoSZIkSapyjJJjlCRJkiSpzkRJkiRJUpXPUTJRkiRJkqQ6G0qSJEmSVGPXO0mSJElVHU7mYKIkSZIkSTUmSpIkSZKqnMzBREmSJEmS6kyUJEmSJFX5wFkTJUmSJEmqM1GSJEmSVOUYJRMlSZIkSaozUZIkSZJU5XOUTJQkSZIkqc5ESZIkSVKVs96ZKEmSJElSnYmSJEmSpCpnvTNRkiRJkqQ6EyVJkiRJVc56Z6IkSZIkSXUmSpIkSZKqHKNkoiRJkiRJdSZKkiRJkhYLEbE7cDiwFDAxM0/rYb/tgVMzc61yeQ3gfODNQAJ7ZObzvR3LREmSJElSVaMxdK9+iojVgeOALYCxwP4RsW6L/d4CnAg0F346cHpmvgu4Aziir+PZUJIkSZK0ONgGuCEzp2fmLOASYJcW+50FHNO1EBFLAR8o9weYBOza18HseidJkiSpaggnc4iIFYEVW2yakZkzmpZHA9OalqcBm9XKOhi4C7i1afXKwMzMnNv0vrf2VS8TJUmSJEntNAF4uMVrQm2/DqCzabkBzO9aiIj1gZ2Bb/TxPprf1xMTJUmSJElVQ/vA2YkU3eHqZtSWHwPGNy2vCjzRtLwrsBrFGKSlgdERcTPwQWCFiBiWmfPKfZrf15INJUmSJEltU3avm9GPXa8Hjo6IVYBZFOnR/k3lHAUcBRARY4ApmTm+XL4Z2A24ANgLuKavg9n1TpIkSVJVo2PoXv2UmY8DhwE3AlOBCzLz9oiYHBHj+nj7v1HMknc/RSp1eJ+XoLOz3l1PUn91Pninf0Ba7DVGr93uKkiDZ8QK7a6BNBiGtN9bK/Ou+uGQ3eMM+9gBbT/fVux6J0mSJKlqAM83WlLZ9U6SJEmSakyUJEmSJFV1mKd4BSRJkiSpxkRJkiRJUpVjlEyUJEmSJKnOREmSJElS1QCeb7Sk8gpIkiRJUo0NJUmSJEmqseudJEmSpConczBRkiRJkqQ6EyVJkiRJVT5w1kRJkiRJkupMlCRJkiRVOUbJREmSJEmS6kyUJEmSJFX5wFkTJUmSJEmqM1GSJEmSVOUYJRMlSZIkSaozUZIkSZJU5RglEyVJkiRJqjNRkiRJklTV4RglEyVJkiRJqjFRkiRJklTlGCUTJUmSJEmqs6EkSZIkSTV2vZMkSZJU5QNnTZQkSZIkqc5ESZIkSVKVkzmYKEmSJElSnYmSJEmSpIqGY5RMlCRJkiSpzkRJkiRJUpVjlEyUJEmSJKnOREmSJElSlYmSiZIkSZIk1ZkoSZIkSarqcNY7EyVJkiRJqjFRkiRJklTlGCUTJUmSJEmqM1GSJEmSVNVwjJKJkiRJkiTV2FCSJEmSpBq73kmSJEmqcjIHEyVJkiRJqjNRkiRJklTlZA4mSpIkSZJUZ6IkSZIkqcoxSiZKkiRJklRnoiRJkiSpqsMxSiZKkiRJklRjoiRJkiSpyjFKJkqSJEmSVGeiJEmSJKlqEX2OUkTsDhwOLAVMzMzTats/CRwDDAP+AOyfmbMjYgzwE2B5YAbwucx8tLdj2VCS1Dbz58/nmNPP4YGHH2XppZbimwd/gTVHr7pg+7W33M6ZF19BowGf+ujW7PqRf2XO3Ll87eQzePz/nmLYsA6+cdB+vP1tq7fxLPR6N3/+fI7+1gnkX/6XpZdemm8eeRhrrvG2Bduvvf4GfnTOuTQaDXbbaUd23WlHAHb89J4sN2oUAG9dfTTfPubIdlRfkhYbEbE6cBywCfAy8LuIuDEz7y+3jwROBTbOzP+LiIuAvYEfAd8ALszMH0TEQWU5e/Z2PBtKaqlsdT8M/CgzD2haPxa4G/h8Zk5qS+UGQUSMAw7MzP3aXZfXs+t/fwcvz57Dz046lqkP/C8nnPVTTj/yKwDMmzefkyddyCUTj2PEMsuw/Re/yjbv24S77v8L8+bN46KTjuGWu+/lez/5Od8/7JA2n4lez66/8TfMnj2bn/3kbKbecy/Hn/zf/GDiiQDMmzePk045jV/89FxGjFiW7Xbeja3/dStGjlgWgPPOOqONNZekXgzhGKWIWBFYscWmGZk5o2l5G+CGzJxevu8SYBfgWIDMnBURYzJzTkSMAN4M/LN87zCKNAlgJPBiX/VyjJJ68wzw0YgY1rRuN+CpNtVn0GTmHTaS2u/O+5Pxm2wAwNh3vZP7HnxowbZhwzq4+owTWW7kCGY89xydnZ2MWHYZxqy+GvPmzWf+/Pk8/8KLLDV8WE/FS0PizrunMv5f3g/A2A3ew333/3nBtmHDhjH5lz9jueVGMePZZ6Gzk5EjluWBv/wvL770Evt88SD22v+LTL3n3nZVX5IWBRMovqCvvybU9hsNTGtanga8tXmHspG0LfB3YGXgunLTEcB/RMTjwFeAE/qqlImSevM8MBX4AHBjue7DwPUAEfEl4LMUrfLZwGcyMyPiEeA2YCwwPjOfrBccEUsBZwPrl6tOz8wzI2IS8CxFpLo6cGxmnlN+K3AmsCEwHzgRuAB4AnhHZj4XEb8DLs/MEyLiM8AWmfnvrU4sIrYCjs7MrSJiCnA7MB5YBTgoM68Z8NXSgM164UWWGzliwfKwjg7mzpvH8GFF42f4sGFcd8vtfOMHk9hy07EMHzacEcsuw+NPPsW2B/wnM2Y+xw+O+mq7qi8B8PysWYwqu9BB0cifO3cuw4cX/4sdPnw41/36Ro49/jtsucXmDB8+nGWWWYZ999qTXT/5CR7529/4wpcm8KtLL17wHklqu6EdozQRmNRi/YzacgfQ2bTcoLgvrCjv494UEd8CfgDsDpxLMV7p8ojYGbg0IjbIzM76+5sPJvXm5xSRJhGxKXAPRaNoeWBHYKvMXB+4CvhS0/uuycxo1Ugq/QuwUmZuBGxP0Ujp8rZyeQeKBhHA0cAz5bE+WC6vC9wAbBkRo4A1gC3L/T9a1qm/ls7M9wOHAN8cwPu0EEaOWJZZL760YHn+/M4FjaQuH958M37zk1OZM3cul91wE5Mum8zmG2/AtWeezGWnHs/Xv/cDXp49e6irLi0wauRIZr0wa8Hy/Pmd3Ro8H976X7np2quZM2cOl101mbXWXIMdtvsojUaDtdZckxVXWIGnnn5mqKsuSYuEzJyRmY+0eM2o7foYsFrT8qoUX5oDEBErRcSHm7b/FNggIlYB3pWZl5fH+0X53pV7q5cNJfXlCmDbiOig6Hb3s3L9TIrW+acj4tvAx4FRTe+7rY9y7wMiIq4FdgWaY4Hrytb9fcBK5boPAj8GyMyngcuBrYDJwNYUqddPgfXKtGo8r6Rg/fGrpnqt1NuOGjwbrxv85g9TAZj6wP+yzphXBsA//8IL7HnoscyeM4eOjg6WXeYNdDQ6WGHUSJYbUaRQKyw3kjlz5zFvfrcvk6Qhs/HYDbnpt78DYOo997LO2u9YsO35559nz30PYPbs2cXneNll6Wg0uOSyKzj+5P8G4P+efIrnZ81ilZXf1Jb6S9Ji5Hpg64hYpexttDOv3MNBkTCdHxFrlMu7Ar8FngZeiojxABGxOfBcZvY6nMSMX73KzOcj4o/AFhSNla8Bn6ZIfX5PMbPINcA/gI2a3trrALnMfCYi1gM+BGwH3FUuA7xU7tMZEV1vqTfqGxSf32uA/wDmAr8G3gXsC9ybmS/Rf137dpZlawh86P3j+N3d9/LprxxFJ518e8IBXDnlFl548SV223ZrPr7V5uz5/45l+PBhxJg12OFft+Cl2bM5bOIP2eP/HcOcOXM55HO7MWKZZdp9Knod+9AHt+KWW2/j05/bl87OTr51zJFcec2veOGFF9lt50/y8e0+yh77HsDw4cOJd67NDttvy7z58/n6kcfwmc9/gUYDvnXUEXa7k7RoWQQfOJuZj0fEYRRfhi8NnJWZt0fEZODIzLwjIvYHroqITuB+ism7OiNiJ+D7EbEs8BxFI6tX/ldZ/fFz4HjgjsycWzZeZgEPZub3yg/csRSD5volInagmJJxN4pvAramaHz15AaKBtDBEbEyRbe/nTLzqYh4kSLROgYIisF6Rw/kBNUeHR0dHPOlfSvrmqf63m3brdlt260r20cuuwwTv/7lIamf1B8dHR0ce/jXK+vesdaYBT/vtvMn2W3nT1a2Dxs2jJO+bS9fSRqozLyAYpx687rtmn6+DLisxftuB947kGMtek1FLYqupJiY4WdN62YDHRFxP3AX8ACw1gDKvIYidfoTxUQK52dmb9M+HQusFBH3AjcBx2XmXeW2yRTTRz5P0aAaDVw9gLpIkiSpWUfH0L0WUY3Ozh4nepDUh84H7/QPSIu9xui1210FafCMWKHdNZAGQ9uHAcx/4PdDdo/T8a73t/18W7HrnV4zZZe83/ew+cjMvOI1Pv544Ps9bN4uM5/oYZskSdLrWmNopwdfJJkoSQvBRElLAhMlLVFMlLRkaHsrpTNvHbJ7nEa8r+3n24qJkiRJkqSqRXDWu6HmFZAkSZKkGhMlSZIkSVWOUTJRkiRJkqQ6EyVJkiRJVY5RMlGSJEmSpDoTJUmSJElVjlEyUZIkSZKkOhMlSZIkSVUd5ileAUmSJEmqsaEkSZIkSTV2vZMkSZJU5WQOJkqSJEmSVGeiJEmSJKnKB86aKEmSJElSnYmSJEmSpCrHKJkoSZIkSVKdiZIkSZKkGhMlEyVJkiRJqjFRkiRJklTlGCUTJUmSJEmqM1GSJEmSVGWiZKIkSZIkSXUmSpIkSZJqTJRMlCRJkiSpxkRJkiRJUpVjlEyUJEmSJKnOhpIkSZIk1dj1TpIkSVKVPe9MlCRJkiSpzkRJkiRJUo2RkomSJEmSJNWYKEmSJEmqcnpwEyVJkiRJqjNRkiRJklRlomSiJEmSJEl1JkqSJEmSakyUTJQkSZIkqcZESZIkSVKVY5RMlCRJkiSpzkRJkiRJUo2JkomSJEmSJNWYKEmSJEmqcoySiZIkSZIk1dlQkiRJkqQau95JkiRJqrLrnYmSJEmSJNWZKEmSJEmqMVEyUZIkSZKkGhMlSZIkSRWNRXSMUkTsDhwOLAVMzMzTats/CRwDDAP+AOyfmbMjYjXgLGA08AKwR2Y+0tuxTJQkSZIkLfIiYnXgOGALYCywf0Ss27R9JHAq8KHMXA9YBti73HwecGVmblT+fEJfxzNRkiRJklQ1hIlSRKwIrNhi04zMnNG0vA1wQ2ZOL993CbALcCxAZs6KiDGZOSciRgBvBv4ZESsDGwIfKss5B/h1X/UyUZIkSZLUThOAh1u8JtT2Gw1Ma1qeBry1eYeykbQt8HdgZeA64B3A34CTIuIPwCXA7L4qZUNJkiRJUk1jCF9MBNZq8ZpYq1QH0Fmr5Px6zTPzmsx8E3AV8AOKXnQbUaRRmwKXA+f2dQXseidJkiSpbcrudTP6setjwPim5VWBJ7oWImIlYFxmXleu+inwM+AfwHOZeVW5/gLglL4OZqIkSZIkqarRGLpX/10PbB0Rq5RjkHYGftVca+D8iFijXN4V+G1m/hV4rOySB/Bx4M6+DmZDSZIkSdIiLzMfBw4DbgSmAhdk5u0RMTkixmXmM8D+wFUR8UcggEPLt+8EHBoR9wFfBvbp63iNzs7OvvaR1IPOB+/0D0iLvcbotdtdBWnwjFih3TWQBkP7H2L0z2lDd4/zxtXaf74tmChJkiRJUo2TOUiSJEmqWSRDniFloiRJkiRJNTaUJEmSJKnGrneSJEmSqgY2bfcSyURJkiRJkmpMlCRJkiRVGSiZKEmSJElSnYmSJEmSpBojJRMlSZIkSaoxUZIkSZJU5ax3JkqSJEmSVGeiJEmSJKnKRMlESZIkSZLqTJQkSZIk1ZgomShJkiRJUo2JkiRJkqQqxyiZKEmSJElSnYmSJEmSpCoTJRMlSZIkSaqzoSRJkiRJNXa9kyRJklRj1zsTJUmSJEmqMVGSJEmSVOVkDjQ6OzvbXQdJkiRJWqTY9U6SJEmSamwoSZIkSVKNDSVJkiRJqrGhJEmSJEk1NpQkSZIkqcaGkiRJkiTV2FCSJEmSpBobSpIkSZJUY0NJkiRJkmqGt7sC0utVRPwWODUzL2paNxL4GxCZ+XRt/0nAlMyc1EuZjwAvZ2Y0rRsOTAOuzsy9B7H+g36siNgM2DkzD42IvYGt6uVExFbA0Zm51aut+2CKiGOBOzLzinbXpS9+5lqWudh95gaqrP9VwINAA1gGmAwckZnPD+JxPgask5knD1aZAzz+XsBBwFIUXwSflZmntKMukpYMJkpS+5wN7FFbtxNwQ/2GdYBGRMR7mpa3BjoXoryhPNa6wFsWrkpDKzOPXBwaSSU/c90tdp+5V+mOzBybmRsC6wMrAz8Y5GOMA5Yf5DL7JSL2ByYAO2TmWOADwJ4RsW876vNai4gxEdEZET+srR9brt+7TVUbFBExLiLOanc9JBMlqX1+DpwYEStl5vRy3WeBW8pv/kcAKwKHZOblAyj3F8AuwL3l8m7AJWV5RMSWwHHN5QM3Ag8Bb8/MmRExBpicmesu5LHWAX4ErATMAg7OzD+UScWzwCbA6sCxwKXlv6Mi4jDgcWDtiJgCrAH8OjO/0HXgiFgbuAEYk5nzy2/ND83MbVtVtDynK4AHgPWAR4E9M3N6RDwF3AGsBmwKfAX4FDAMuLYstzMiDgEOBOYBV5YpxCRgSvlqWX5PF69MSC4EPgTMBb5RHvudwFcy8+dl+bOAjSl+X/9F8TnZELgsM7/SU/kt+Jkb+s/cZbzymbgL+B2wN/BG4JOZ+eeI2BT4XnkOTwMHZObDra5bZl7e6lwy85w+rtsCmTknIr4K/D0iDgLmAz8G3gqMBq4H9gN+AtyUmWeW5zOlPN/bWpzruhR/G0TEo8DFwGkUjbJhwAmZeWF5A7898KbyWD8E1gQ+CDwDbAusygD/loDDgX0yc1p5jjMi4nO0qeE2RJ4BPhoRwzJzXrluN+CpNtZpUGTmHRSfQamtTJSkNim7vFwO7AoQEaOBAMYC+2XmxhT/o/jmAIu+mCIlICKWLsu7vWn7QfXyM3MmcDXFDSjAXsC5g3Cs84FTMnMDipvjSyLiDeW2twHjgR2AEzNzBnAkcEVmHlfus0ZZ/ruBbSNiva6CM/NB4GFgq6Y6T+qjvu8BTs/M9YA/A0eX61emuJEbS5FQbELRYNqI4kZ0j/Jm9t+AzYANgE0iYpN+lt+bf2TmuHL/rwEfBvYEvt60z+jMfD9wPHAOxQ3pWOALEbFCP44B+JmjPZ+5DYATKBq2m1M0st5P0UDevzyHs4Ddy+tzEnBm+d5u162p3Mq59FGHbjLzH8A/gXUoGi5Ty3q9E9iSomF+NkVDmohYE1ilVSOpLO9+4AzgjLLRdjhwZ2ZuQpHuHBYRby933wzYEfgIcDJwTfn7olwHA/hbioiVKa7HXbU6/bmn+i4hngfupri+XT5M0dAlIr4UEbdFxH0RcVdERLn+kYj4WURkRLy5VcERsVREnBcRd5evL5TrJ0XEf0fEbyPi4Yj4fLl+RET8tDzWPRGxV0QMj4gnI2K5cp/fRcSh5c+fiYjTejqxiNiqbJgTEVMi4jsR8fuIeDAiWn4xIb0WbChJ7XUOsHv58x7AecBngPUj4giKdGHUAMt8HHg2It5N8T/N62rb9+yh/AU3RWWdzluYY0XEKGDtzPwlQGbeCkynuDEHuC4zO4H7KL79b+WmzJyemS8Df6Vo0DQ7G/hsRIygaOD0lYL8JTOnlD+fS/EtdpeuG6ptgPcCd1LceI2j+FZ7S4oU6dnMnJuZ22TmnQMovyfXlP8+CvwmM+eWP7+xh33uy8wnM/M5iuvZvF9/+Jkb2s/cPzLz7sycDzwG/Lpc3/U7Xgd4B3BFREylaFR1NSh6um79PZe+dAIvZuaFwP9ExATg+xRpzyiKlHR0mYztRZEw9dc2wIHlOd0EjKT4OwK4JTNnZuaj5XL9msDA/pbml/++NID6LSl+TvllQ/llzj3AbIokbUeKMXfrU4xR+1LT+67JzMjMJ3so91+AlTJzI4qG9Pimba0a6UcDz5TH+mC5vC5FArtl+be5BsV/RwE+Wtapv5YuG/KHMPAvcqRXzYaS1EaZeROwakS8jeKm6BzgZopvXO+k6HbTeBVFX0zxP89PAT+rbeup/JuA1SNiJ+DhzHxiIY/V6r8vDV7p8vsSQHmz15O5TT930v1aXEzRbW0Xim5bfd0oNZfX0bycmS+WPw4DJpbjOcZSNJqOA+bQNBYmIkZHxIr9Lb8Xs3t4/0D36Rc/c0P+mZtdW67//oYBDzV93jYBtii39fZ76c+59Cgi3gKsAPy17H73XYouW98H7gcaZdnnUjSkd6N/Ddnm89qz6bzeB/yq3Fa5JuWXA3X9/lsqu+Q9RPGlxgIRsWVEHD+AOi+OrqBIPjsofkddfw8zKb58+HREfBv4ONWGdl9J231ARMS1FAn0V5u2tWqkf5Ci+ybleMfLKZLXyRRfKHwA+CmwXkQsRdHQunEA59n12VmYLwakAbOhJLXfT4DDKL757uoKcyRFivAJihuOgbqY4iby3Zl5d9fKiFipp/KbbopOoe/uRH0eq+xa9VB5E0xEvI9i7MF9vZQ1lwGMnczMF8rz+FY/6xwRMbb8+fO8ktQ0u4EiMRgVxYxql1HcFN8MbNe0/kJqN2b9LH9R4GfuFa/1Z64vDwArRUTXN/b7ABf0dt0WVtkV8bvApPJ8PgT8MDN/SjEj3timY02i6Or5t340ZJuv5Q3AF8vjrUaRdKwxsGoO6G/pu8BJEbFq+eaVKboxPjiAYy52yu60f6RoXH+QstsdRerze4qxbddQ/B6bG9ov0ovMfIYiAfw+RSJ7V9MXQ60a6fX7ya4vKK4B/rV83QhMBfYF7u3HlwzNuvZt9eWF9JqxoSS13ySK/3GcXX4z+mPgTxT98pejmOVr5EAKLG9oZvDKt3Bd6/sq/yKKLjKXLeyxSnsCB0fEvcCpwE6ZWf+GvdntwPsG+C3wRcDMfo5FmA4cExF/At5Miy4cmXklxYQBt1HcYE8Fzs3Mu8pz+D3FjclNmXl97e19lr+ImISfuS6v9WeuV2UXv10pbvLvAT4H7DtYv5cm4yJiatkV7k6K6/flcttE4Kjymk2kmHBirbJ+f6eYPn5SP45xE8V4voOAY4BlI+I+ikbT/8vMvw6gvgP6W8rMMyi+APifiPgjxU35pMx8Pcyc9nOK8Yt3NKVzs4AHM/N7wB+ATzKAhnZE7ECRIF4NHEwxHuptvbzlBor/pnQ1UnekeLTAUxSNso8Dvy33O4KBdbuT2qbR2flazeAqaXFSdt04EHhXZh7c7vr0R0QMo+iS9GT28eyWcpzFlMwc8xrV5TUtf0m0pH/mFncR0aCYCfI3wPplo24ojjsG/5Z61XyNyvE/TwIfz8xfRzEr4h0UKeTqFAlM1+9wiyhm29wqMx/ppfylKCYZ2ZQizbk0M78RtWerRURnZjYiYnngdIpJS4YB38tXZks8Atg+M98XxdT+9wCr95ZQRtOzy8pJHY7OzCl+NjTUbChJi5GIWJYi0WhloZ7nExGXUXSN+UhmPvVaHmuwRMTdFNMp75CZL0bEOyjSoFb2Ay4Z6oZSRNxI6wkXzii/BV+k+ZmrGuhnLotpjoeiXrtRnSlxgXKM0KspcxeKZy19MTMvKdd9l6KrXt0dmTko0zkvqX9LkhY/NpQkSZJeZ9r9xUQ5Lu/7PWzebgCTu0ivGRtKkiRJklTjZA6SJEmSVGNDSZIkSZJqbChJkiRJUo0NJUmSJEmqsaEkSZIkSTX/H9R6pJBBlKnAAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 28% |####################                                                    |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  26.59450035759143\n",
+      "The outdoor Demand prediction R2 is  0.24753993784999162\n",
+      "The Total Demand prediction RMSE is  21.55771350687681\n",
+      "The Total Demand prediction R2 is  0.7636078896058817\n",
+      "The Total Demand prediction MAE is  19.389179633584718\n",
+      "The Total Demand prediction MAPE is  0.08737087552311436 %\n",
+      "The final set of features for May are ['Val_May_Monthly_precip_mm', 'Val_May_Monthly_mean_Day_temp_C']\n",
+      "The coefficients for each feature are [-1.04939159 14.28103895]\n",
+      "The best training parameters are below with their scoring method:  RMSE\n",
+      "     snowfeatures  conservation  cor_threshold  colinearity_thresh     RMSE\n",
+      "143          True         False           0.45                 0.9  26.5945\n",
+      "The model is automatically selecting features and calibrating the  Jun outdoor demand model.\n",
+      "The automated algorithm automatically optimizes the respective model by looping over input parameters within\n",
+      "the training data. In addiiton, the algorithm checks for colinearity between features, removing the one with\n",
+      "less correlation to the target.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 42% |##############################                                          |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  17.425846461104445\n",
+      "The outdoor Demand prediction R2 is  0.623344921813956\n",
+      "The Total Demand prediction RMSE is  27.50513462914986\n",
+      "The Total Demand prediction R2 is  0.09908431953793373\n",
+      "The Total Demand prediction MAE is  24.78847358813916\n",
+      "The Total Demand prediction MAPE is  0.06050822126230424 %\n",
+      "The final set of features for Jun are ['Val_Jun_Monthly_precip_mm', 'Val_Apr_Monthly_mean_Day_temp_C', 'Total_snow_in', 'Val_May_Monthly_mean_Day_temp_C']\n",
+      "The coefficients for each feature are [-1.98521823  8.94782618  0.11946181 14.90519373]\n",
+      "The best training parameters are below with their scoring method:  RMSE\n",
+      "     snowfeatures  conservation  cor_threshold  colinearity_thresh       RMSE\n",
+      "136          True         False            0.4                0.85  17.425846\n",
+      "The model is automatically selecting features and calibrating the  Jul outdoor demand model.\n",
+      "The automated algorithm automatically optimizes the respective model by looping over input parameters within\n",
+      "the training data. In addiiton, the algorithm checks for colinearity between features, removing the one with\n",
+      "less correlation to the target.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 57% |#########################################                               |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  18.997649796041955\n",
+      "The outdoor Demand prediction R2 is  0.7296490500037287\n",
+      "The Total Demand prediction RMSE is  10.38453139290509\n",
+      "The Total Demand prediction R2 is  0.9514530601835467\n",
+      "The Total Demand prediction MAE is  9.38337205169241\n",
+      "The Total Demand prediction MAPE is  0.021094176864790506 %\n",
+      "The final set of features for Jul are ['UrbPopulationDensity', 'Val_Jul_Monthly_mean_Day_temp_C', 'Val_Apr_Monthly_mean_Day_temp_C', 'Val_May_Monthly_mean_Day_temp_C']\n",
+      "The coefficients for each feature are [-0.07505973 31.14216318 -3.91199583  9.54456925]\n",
+      "The best training parameters are below with their scoring method:  RMSE\n",
+      "     snowfeatures  conservation  cor_threshold  colinearity_thresh      RMSE\n",
+      "283         False         False           0.25                 0.7  18.99765\n",
+      "The model is automatically selecting features and calibrating the  Aug outdoor demand model.\n",
+      "The automated algorithm automatically optimizes the respective model by looping over input parameters within\n",
+      "the training data. In addiiton, the algorithm checks for colinearity between features, removing the one with\n",
+      "less correlation to the target.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 71% |###################################################                     |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  37.75960403765589\n",
+      "The outdoor Demand prediction R2 is  0.2613483429291603\n",
+      "The Total Demand prediction RMSE is  26.803285577731657\n",
+      "The Total Demand prediction R2 is  0.7869311763314432\n",
+      "The Total Demand prediction MAE is  25.576755284673066\n",
+      "The Total Demand prediction MAPE is  0.06301563370257961 %\n",
+      "The final set of features for Aug are ['Val_Aug_Monthly_precip_mm', 'UrbPopulationDensity', 'LitCotCrk_AcFt_WR_Apr', 'Val_Apr_Monthly_mean_Day_temp_C', 'Val_Aug_Monthly_mean_Day_temp_C', 'LitCotCrk_AcFt_WR_Mar', 'Val_Apr_Monthly_precip_mm', 'Val_May_Monthly_mean_Day_temp_C', 'Val_Jul_Monthly_mean_Day_temp_C', 'Val_Jun_Monthly_mean_Day_temp_C', 'Val_Jun_Monthly_precip_mm', 'BigCotCrk_AcFt_WR_May', 'LitCotCrk_AcFt_WR_May']\n",
+      "The coefficients for each feature are [-1.81635380e+00 -3.16349847e-02  6.32041057e-03  6.30736831e+00\n",
+      "  1.50479154e+01  1.07992194e-02 -2.87978896e-01  1.14849889e+01\n",
+      "  1.44172944e+00  1.22865885e+00  4.07734087e-01  1.41719261e-02\n",
+      " -2.36343423e-02]\n",
+      "The best training parameters are below with their scoring method:  RMSE\n",
+      "     snowfeatures  conservation  cor_threshold  colinearity_thresh       RMSE\n",
+      "255         False         False            0.0                 0.8  37.759604\n",
+      "The model is automatically selecting features and calibrating the  Sep outdoor demand model.\n",
+      "The automated algorithm automatically optimizes the respective model by looping over input parameters within\n",
+      "the training data. In addiiton, the algorithm checks for colinearity between features, removing the one with\n",
+      "less correlation to the target.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 85% |#############################################################           |\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  11.657120389719092\n",
+      "The outdoor Demand prediction R2 is  0.8215668623621867\n",
+      "The Total Demand prediction RMSE is  18.88354715238881\n",
+      "The Total Demand prediction R2 is  0.7252475778329739\n",
+      "The Total Demand prediction MAE is  15.222645075153688\n",
+      "The Total Demand prediction MAPE is  0.04427967524078643 %\n",
+      "The final set of features for Sep are ['Val_Sep_Monthly_precip_mm', 'Val_Sep_Monthly_mean_Day_temp_C', 'Val_Aug_Monthly_mean_Day_temp_C', 'Val_May_Monthly_mean_Day_temp_C', 'Val_Apr_Monthly_mean_Day_temp_C', 'Val_Jun_Monthly_mean_Day_temp_C', 'Val_Jul_Monthly_mean_Day_temp_C']\n",
+      "The coefficients for each feature are [-0.96277559  8.09783096 11.93886891  3.30959512  2.97667022 -3.80143281\n",
+      " -7.28232441]\n",
+      "The best training parameters are below with their scoring method:  RMSE\n",
+      "     snowfeatures  conservation  cor_threshold  colinearity_thresh      RMSE\n",
+      "261         False         False           0.05                 0.8  11.65712\n",
+      "The model is automatically selecting features and calibrating the  Oct outdoor demand model.\n",
+      "The automated algorithm automatically optimizes the respective model by looping over input parameters within\n",
+      "the training data. In addiiton, the algorithm checks for colinearity between features, removing the one with\n",
+      "less correlation to the target.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100% |########################################################################|\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The outdoor Demand prediction RMSE is  20.247451744791704\n",
+      "The outdoor Demand prediction R2 is  -0.3613861494322399\n",
+      "The Total Demand prediction RMSE is  18.486774202431338\n",
+      "The Total Demand prediction R2 is  0.3062923569550261\n",
+      "The Total Demand prediction MAE is  17.59460641330412\n",
+      "The Total Demand prediction MAPE is  0.09536622302206858 %\n",
+      "The final set of features for Oct are ['Val_Oct_Monthly_mean_Day_temp_C', 'Val_Apr_Monthly_mean_Day_temp_C', 'Val_May_Monthly_mean_Day_temp_C']\n",
+      "The coefficients for each feature are [ 5.73659506  4.22206448 -2.02079688]\n",
+      "The best training parameters are below with their scoring method:  RMSE\n",
+      "     snowfeatures  conservation  cor_threshold  colinearity_thresh       RMSE\n",
+      "259         False         False           0.05                 0.7  20.247452\n",
+      "yes\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "<ipython-input-11-5f63722fb25d>:92: UserWarning: constrained_layout not applied.  At least one axes collapsed to zero width or height.\n",
+      "  fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/' +str(plotname)+'.pdf')\n",
+      "<ipython-input-11-5f63722fb25d>:103: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total R2 is  0.9766188032681973\n",
+      "Total MAE is  16.64387014491631\n",
+      "Total RMSE is  19.547113475292928\n",
+      "Total MAPE is  4.365156659505094\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x720 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Ryan\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:132: UserWarning: constrained_layout not applied.  At least one axes collapsed to zero width or height.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Create the list of parameters to search through to optimize model performance\n",
+    "p_space = {\n",
+    "    'snowfeatures': [True, False],\n",
+    "    'conservation': [True, False],\n",
+    "    'cor_threshold': np.arange(0,0.7, 0.05),\n",
+    "    'colinearity_thresh': [0.65, 0.7, 0.75, 0.80, 0.85, 0.90]\n",
+    "}\n",
+    "\n",
+    "Outdoor_Months=['Apr', 'May' , 'Jun', 'Jul', 'Aug', 'Sep', 'Oct']\n",
+    "IndoorMonths = ['Jan', 'Feb', 'Mar', 'Nov', 'Dec']\n",
+    "\n",
+    "\n",
+    "\n",
+    "slc_val=copy.deepcopy(slc_test)\n",
+    "# calibrate and predict with the outdoor model\n",
+    "pbar = ProgressBar()\n",
+    "for i in pbar(Outdoor_Months):\n",
+    "    print('The model is automatically selecting features and calibrating the ', i, 'outdoor demand model.' )\n",
+    "    #put the month, use conservation_goal (-1: no, -2: yes) correlation threshold, colineariy threshold, CV, aplpha, model type, tuning method\n",
+    "    #put in the params, month, scoring method (R2, or RMSE for now)\n",
+    "    PerfDF, cv_results, cor, X_test_RFE, coef = Demand_Optimization(p_space, i, 'RMSE')\n",
+    "    \n",
+    "    colrem = slc_test[i].columns\n",
+    "    slc_val[i] = slc_val[i].reset_index(drop=True)\n",
+    "    slc_val[i] = pd.concat([slc_val[i], PerfDF], axis=1, join=\"inner\")\n",
+    "    slc_val[i] = slc_val[i].set_index('Date')\n",
+    "    slc_val[i] = slc_val[i].drop(columns=colrem)\n",
+    "\n",
+    "#Calibrate and predict with the indoor model\n",
+    "for i in IndoorMonths:\n",
+    "    slc_val = Indoor_Demand_Model(slc_val, i)\n",
+    "\n",
+    "\n",
+    "#Put it all together and display the results\n",
+    "#Demand_Forecast(slc_val)\n",
+    "df = pd.DataFrame()\n",
+    "Pred_Obs = Demand_Forecast(slc_val, True, df, 'y_pred_tot', 'y_test_tot', 'GPCD', 'Short_term_pred_gpcd', 'SLC-WDM')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#save predictions\n",
+    "np.save('ModelOutput/Slc_Pred_Low_Ave_High_WY.npy', slc_val)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slc_val = np.load('ModelOutput/Slc_Pred_Low_Ave_High_WY.npy', allow_pickle =True).item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "yes\n",
+      "Total R2 is  0.9766188032681973\n",
+      "Total MAE is  16.64387014491631\n",
+      "Total RMSE is  19.547113475292928\n",
+      "Total MAPE is  8.419832806068381\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-12-18238038290e>:103: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Put it all together and display the results, need to adjust the term forecast depending on training data\n",
+    "df = pd.DataFrame()\n",
+    "Pred_Obs = Demand_Forecast(slc_val, True, df, 'y_pred_tot', 'y_test_tot', 'GPCD', 'MLR_Seasonal_term_pred_gpcd','SLC-WDM ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas.tseries.offsets import MonthEnd\n",
+    "cols = ['y_test_tot', 'y_pred_tot']\n",
+    "monthorder = ['Jan', 'Feb' , 'Mar', 'Apr', 'May', 'Jun' , 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+    "Pred_Obs = Pred_Obs[cols]\n",
+    "\n",
+    "#input population into DF to calculate total water demands.\n",
+    "Population = []\n",
+    "UR_gpcd = []\n",
+    "\n",
+    "slc_pred = copy.deepcopy(slc)\n",
+    "\n",
+    "for i in monthorder:\n",
+    "    slc_train[i] = copy.deepcopy(slc[i].loc[:2010])\n",
+    "    slc_pred[i] = slc_pred[i].loc[2015:]\n",
+    "    slc_pred[i]['UR_gpcd'] = np.mean(slc_train[i]['Obs_gpcd'])\n",
+    "    Population.append(np.round(slc_pred[i]['Population'],0))\n",
+    "    \n",
+    "UR=pd.DataFrame()\n",
+    "for i in slc_pred:\n",
+    "    slc_pred[i]= pd.DataFrame(slc_pred[i]['UR_gpcd'])\n",
+    "    slc_pred[i]=slc_pred[i].reset_index()\n",
+    "    slc_pred[i]['M'] = datetime.datetime.strptime(i, \"%b\").month\n",
+    "    slc_pred[i]['D'] = 1\n",
+    "    slc_pred[i]['Date'] = pd.to_datetime(slc_pred[i].Year*10000+slc_pred[i].M*100+slc_pred[i].D,format='%Y%m%d')+MonthEnd(1)\n",
+    "    slc_pred[i].index = slc_pred[i].Date\n",
+    "    slc_pred[i]=slc_pred[i].drop(columns = ['M', 'D', 'Date', 'Year'])\n",
+    "    UR = UR.append(slc_pred[i])\n",
+    "\n",
+    "UR=UR.sort_index()\n",
+    "Population = np.sort(np.array(Population).reshape(36,))\n",
+    "\n",
+    "#place in to prediction df\n",
+    "Pred_Obs['Population'] = Population\n",
+    "Pred_Obs['UR_gpcd'] = UR['UR_gpcd']\n",
+    "\n",
+    "\n",
+    "\n",
+    "#Now we can form some acre-feet predictions.\n",
+    "gpcd=['y_test_tot','y_pred_tot','UR_gpcd']\n",
+    "for i in gpcd:\n",
+    "    Pred_Obs[i+str('_AF')] = Pred_Obs[i]*Pred_Obs['Population']*9.33454e-5\n",
+    "\n",
+    "    \n",
+    "remcol=['y_test_tot','y_pred_tot','UR_gpcd', 'Population']\n",
+    "Ann_Eval = Pred_Obs.drop(columns = remcol).copy()\n",
+    "Ann_Eval = Ann_Eval.resample('Y').sum()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no\n",
+      "Total R2 is  0.9765992771958528\n",
+      "Total MAE is  537.3515654137589\n",
+      "Total RMSE is  630.708116168728\n",
+      "Total MAPE is  8.419832806068387\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-12-18238038290e>:103: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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RCCGEEEIIIXyEJGhCCCGEEEII4SMkQRNCCCGEEEIIHyEJmhBCCCGEEEL4CCkSIoQQQgghhCjzkpJg+nTYsweioqBvX4iPtzqqkpMeNCGEEEIIIUSZlpQEb74JJ05A/frm8c03TXtZIz1owvel/gZHlkB4HNjjILiK1REJIYQQQggPuZSesOnTISLC/MD5x+nTy14vmiRowvetewoOznW9sEGVxhARD+Gun4hWUDkabNIhLIQQQghRlrl7wiIi8vaEPfNM4YnWnj1m/8Dsc/Sd/leWdX4GR50O7NnjvdhLiyRowrc5nRxKTuFE6BPEJFwDJzbCySTzs3cG4DT7BVWB8JbnE7bwePM6OMzS8IUQQgghRPFdak9YVJRJ5gYueYwWW//H1tjb2VypA1FRno+5tEmCJnxb2g6Gfvg6iVtvZsXKYFq27H1+W1Y6nNxyPmE7uRF2T4XkD8/vU/82uHq69+MWQgghhBAl5u4Jy81up8iesL59Ye1DH9Ju3WR+7TyCJXX7c+IE3H+/52L1FEnQhE/LPLiKn7f04kxGMLfdBmvWQHi4a2NQZYjsYH7cnE44s88kbNsnwf7vwZEJAcGWxC+EEFbbuBHq1YPISKsjEUKIorl7wtw9ZwCpqRTZExZ/agkt1w5je5Mb+bzpGBpEmOSsrM0/Aw8naEqpMGAZcIvWepdS6i/ABKAqkATcq7U+p5RqDXwMhAG/AkO11llKqSjgC6AmoIF7tNZpSqlw4EvgCuAI0F9rfciTn0VYY8Uvf5CWUZWnn3Iw8b0A7rkHvv8eAi423cxmg8oNzM+5E3BgDpxOBnuMV+MWQghfcOQIdOoEQ4bAv/5ldTTCU+R+S5QnffuaOWdges5SUyleT9jkydgaNaLpqq/4d3igx+P0JI9VVVBKdQSWAM1cr8OA6cBDWusWrt3cv+ovgGFa62aADXjQ1T4JmKS1bg6sAV50tY8BFmutY4CPgHc99TmEteb/UoWAgGxeGBnAO+/AnDkwenQxD7bHmsfUrR6LTwghfNlHH0FGBqxbZ3UkwlPkfkuUN/HxpiBIRATs22ceiyoQkpQEo6M/5akrFzHqnfAyWVo/N0+WvXsQeBQ44HrdE1iutXb/yh4DZiilGgIVtdYrXO2fAXcopYKBq4Fvcre7nt+M+UYH4L/Aja79RXmSfZbEVS1oF7ufiAh4+GEYPBheeQW++64Yx4cp83hqmyejFEIIn5SZCZMmmeebNoHDYW08wmPkfkuUO/HxMGoUfPKJebxocuZ0cvCJsXz8ygGOpwZSpVndMr3+mZvHEjSt9QNa68W5mpoAaUqpr5VSG4DRwEmgLnAw134HgfpAJHBKa52Vr53cx7i2nwJqeOqzCGuk7t7Mqh3t6dk9HTCjFz/4ANq2hYEDQesiThBU2ZTflx40IYQfmjED9u+HPr3PkZ4OKSlWRyQ8Qe63hF977z3qTHyOaw99QUSEmQLjrgA5vQzXiPPmwlFBwPXAc0BboDIwwhWDM9d+NsBRQDuudvc+udlybRPlxMK5h8h2BNHz5uo5baGh5h9cSIgZo3z6dBEnCYuRHjQhhF967z1o3NjJiL/0AUyxEOEX5H5L+IeFC+Gpp1gXdSvrEp7Js6k4VR99mTcTtEPACq11itY6G5gGdAD2AXVy7Vcb003/B2BXSrln+dXhfPf9ftd+KKWCMJNgj3n8Ewivmr8gmEoVzvCXhLxf1kVFwdSp8NtvZuK7M/+fldzssXDqN3BkezZYIYTwIevWwZIlMOzenbSstQibzVGmh/uIEpH7LVH+7doFd9wBzZrx091TOHkqb0pTnKqPvsybCdo8oK1SqoHr9S3AWq31biBDKdXF1T4QmKu1zgQWA3e62gcBc13P57he49q+2LW/KEcSVzahW+tthFTI/wUeXHMNjBsH33wDb7xRyEnsMZCdAWd2ey5QIYTwMe+9B5Urw33dplCpwp80rbNTEjT/Ifdbovz7xz8gKwtmzuTmAWGcOGEqPToc5Dzv29fqIC+d1xI0rfVe4G/A90qp34BqwOuuzfcAE1ztVYCJrvZHgIeUUluBq4CRrvYXgU5KqS2ufR71zqcQ3rJn+0l+338FPbuduOg+Tz8Nd94Jzz8PiYkX2SlMKjkKIfzLH3/AV1/BvfeC/bSZhBFffz1JG7OKOFKUB3K/JfzC5Mnw44/QrNklVX30dTZnoePDyh+bzRYNpKSkpBAdHW1tMOKiPnl7E/c/3ZJNC1cS173jRfdLTzdr/Bw4YBaxbtQo3w7nTsI3EdB6HMT+w7NBCyGED3jtNRg5ErauOUCMrgd1buDVSe146ZtXOX0aqlSxOkK/1AjYZXUQ3iT3W8Ij5s6FhARTlKB8KPDa4M0hjkIUW2Ii1A4/SIvOhS8wXbmyqVTmcJiu7DNn8u0QEg4V60ihECGEX8jMNNVur7sOYsJmmcbYfxDfwIxv3LzZwuCEEOJy/PQT3HILjBljdSQeJwma8DkOB8xfHkWPNmuwhYQVuX+TJvDll6ZC2dChBRQNCYuVIY5CCL/gLq3/+OPA/jlQ5Qqo2Z1WTXYBZXtdICGEH0tOhrvughYtYMQIq6PxOEnQhM/ZuMHJ0VQ7PbseLvYxN90Eo0fDf/4D77+fb6M9BlK3FVHuUQghyr6JE6FxY7ix559w+GeoezPYbDRsYqdqpXQptS+EKHvS0qBPH7PI2cyZfjFOWxI04XPmzzkOQI/rKpTouBdegN694amnYHHuJTvtsZB1Gv7cX4pRCiGEb1m7FpYuhWHDIODoL5D9J9S9CQBbRBzxDZJISpIvqoQQZcywYbBtm1lj6YorrI7GKyRBEz4n8adztKi/mbotWpbouIAAmDLF/Nu9/XYzzAcwi1WD6UUTQohyKqe0/n3AgdkQWBFqdTcb7XHEN1hPUpJTBhMIIcqW556DTz+FHj2sjsRrJEETPiUjAxavrk6Plr+APa7Ex9vtZg7GmTMmSTt7FtODBjIPTQhRbv3xB/z3vzB4MNjDnHBgDtS6FgJdlc7CTQ/aqVMB7NljaahCCFE8O3ea6SlKwaBBRe9fjkiCJnzK0qWQcTaEnp13Q0DQJZ0jNhY++wxWrDBrpVGhBoRUk0qOQohy66OP4Nw5MxKIUxrSdkK9m8/vYG9BfJSpECKFQnyTUipIKdVNKfWQUur+XAtKC+F/fvsN2rTxi4qNBZEETfiUxJ+yCQrMpFv3S0vO3Pr1gwcegI8/hoyzNtOLJj1oQohyKDMTJk0ypfWbN8cMb4Sc+WcAVKhOy2ZHAEnQfJFS6lHMWkgTgZuAG4B3lVK7lVIPK6VsVsYnhFelppqiIBUqwL33Wh2NJYp9F6yUCgEcWussD8Yj/FziTxn8pclaqkS1uexz3XqrSdCWL4cEeyzs/bYUIhRCCN8yfTocOACTJ7saDswxQ8QrR+XZr2rtaK6ovY+NG+t7P0hxUUqpb4H1QHut9cF822oBQ4EZQB8LwhPCuxwO+OtfYccO+PlniIoq+phyqNAETSlVExgB9AUaAE6lVArwP2CC1vqI50MU/uLoUVi/qRKj+yVC9Qcv+3xXXWUKhyxcCAl3x8DZY5BxBEJrlEK0QgjhG3JK698IZJ6CP36FmKcv3NEeR3yDdSQl1QOkQ8aHPKG13lfQBq31YWC0UkqyauEfRo2CH34wayZdfbXV0VjmokMclVIDgbnAYeA2oDoQ5np+HEhUSvlnv6PwiAULwOm00bPtOqjU4LLPZ7dD27YmQZNCIUKI8mjtWli2DB57zHwhxaH54MzKO7zRLTyO+Prr2b7dFFISvsGdnCmlXs2/TSn1bu59hCj3OneGv/8dHnnE6kgsVVgPWjVMd7sjX/tmYLNSagLwmMciE34nMRHslU/Rrn0I2Ern292EBJgwAc4ExVIJTKGQWt1K5dxCCGE1d2n9wYNdDftnQ7AdIjtfuLM9jvioOTgcNrZuhXbtvBmpuBil1GggArhTKWXPtSkYuB54wpLAhPCmjAwIDYUbbjA/fu6iPWha63e11g6lVPv825RSPbTW2VrrdzwbnvAXTickJmaTEPMzQbUv+F/ukiUkmAn0S9fXg6Aq0oMmhCg38pTWt2MupAfmQJ3rC66Ca4+VSo6+aSVwDHC4Ht0/+4B7LIxLCO84fhxatYIPP7Q6Ep9x0R40pVQbzCD1KUqpuzk/YD0Y+ABo6vnwhL/YsQN27w7kHwmJUL1fqZ23a1cICoKFi2z0vDpGSu0LIcqNyZNzldYHOLEeMg5B3ZsLPiC4KldEO6gUmkFSUqjX4hSF01rPAeYopeZqrVdZHY8QXpWdDQMGQEoKxMdbHY3PKGyI48NAT6AuMD1Xe1a+10JctsRE89iz5XyoPrbUzlulCrRv75qH1isWDiWW2rmFEMIqmZnwwQdw/fWu0vpghjdig7oXHx4UWC2Wlg01SUmtvBKnKJG9SqnZmC/AuwL/AQbnr+woRLny/PMwb55ZzPEvf7E6Gp9x0QRNa/0QgFJqjNZ6pPdCEv4oMREa1jpMk2bBEBxWqudOSIBx4+B0UCuq/vk5nEuFEHvRBwohhI+6oLQ+mOGN1dtDaM2LHxgeR3zd1Xy7MR6n01Za031F6fgXMBMYBpwANgAfAxfpEhWijPv6a3jjDXj4YbN4rchR5ELVWuuRSqnblVKvKqUqKaUGeCMw4T+ysmDBAic94hKxRXYo9fMnJJge9KW/ub6ZkWGOQogyLk9pfTBLiBxbWXD1xtzsccRHbeD4cRsHDng8TFEy0VrrjzBrzmZqrZ8F/HMRKOEf/vgDunWDd6SkRX5FJmhKqRGY4Y53AhWBl5VSL3o6MOE/1q6F1FQbPWO+h+odS/38nTtDcDAsXKdMgxQKEUKUYWvW5CutD3DwJ8B58flnbuEtiG8ghUJ8lEMplXNfppSqSjHu04Qosx5/3CxGHRJidSQ+pzj/8O8CbgLStdbHgE7A3R6NSviVxESw2ZxcG/ezRxK0SpWgY0dYuDQcAipID5oQokx77z0zvzantD7AgdkQWguqXVn4wWHNaRm1BZAEzQdNB74E7EqpvwELgGnWhiREKcvMhL59Ye5c8zow0Np4fFRxErRMrfVZ9wut9Ukg03MhCX+TmAht1D4iw89AeJxH3iMhAdautZEa2E560IQQZdYff5hpGzml9QEcWaYHre6NYCviz3pgKBF1atCg5hFJ0HyM1vqfwFxgNaZI22TgFUuDEqK0DR8OM2bAkSNWR+LTipOg7VVK3Qw4lVIVlFIvALs9HJfwE2lpsHw59Gj5i/nmNyDYI++TkAAOByxO6Q2p0oMmhCibLiitD3B0BZw7UfTwRjd7HK0abpIEzTf9DxgD9Ae+1Fo7LY5HiNLz+efw7rvw97/DoEFWR+PTCiuz7zYMU+o1HkgHVlDMIY5KqTBgGXCL1npXrvZhwO1a6+6u160xlYrCgF+BoVrrLKVUFPAFUBPQwD1a6zSlVDhmGMAVwBGgv9b6UHFiEr7l119Nb3fPpl95ZHij21/+AhUqwMItV3FL/RGQdQaCKnns/YQQorRlZsKkSaa0vlK5NhyYDbYgqN2zeCeyxxFfdwU/zk7g7FkbFSp4JFxRQkqpTphhjllAZ2CjUqqX1npZMY6V+y3h21avhr/9Da65BsaPtzoan1ecKo4HtNbXYv4xR2itu2qt9xR1nFKqI7AEaJavPRYYkW/3L4BhWutmmAWxH3S1TwImaa2bA2sAd3GSMcBirXUM8BHwblHxCN+UmAihoQ66Nl3g0QQtNNQkaQvXNgeccEp77L2EEMITvv0WDh408+rzODAHanQt/vIh4XHEN9hIVpaNbTKgwJeMB3oAx7TW+4CBFOP+Ru63RJkwbRrUrg1Tp0JQcfqH/FtxqjhWUUr9C/geCFZKfaiUqlKMcz8IPArkFPJVSlUAPgReytXWEKiotV7havoMuEMpFQxcDXyTu931/GbMNzoA/wVudO0vypjEROh65X5CQ85CpOcSNDDDHDdsDed4WoTMQxNClDnvvQdNmsANudehTt8LJ5OKLq+fmz2O+Cip5OiDKmmtc/44aa3nULyRTnK/JXzfG2/AqlUQGWl1JGVCceagTQROArWADExP2uRCjwC01g9orRfna34d+ATYmautLnAw1+uDQPi0+NgAACAASURBVH0gEjiltc7K157nGNf2U0CNYnwW4UMOHoQtW6Bn62VmYdVKnl3uJSEBnE4bv/7WXSo5CiHKlDNnTGn9u+/OVVof4KCrElq9EqxlXLUJTevupkJIpiRoviVTKRUBOAGUyjOQ9aLkfkv4tNdeA63BZoOaNa2OpswoToLWRmv9Aqaa4xngHqB1Sd9IKdUTiNJaf1pADLknwdoARwHtuNrd++Rmy7VNlBHz55vHns2+NsMbbfn/s5auDh2gYkVYmNxHetCEEGXKTtdtdvPm+Tbsnw2VG0JYTPFPFhBEUERTWjTcLQmabxkD/ALUV0r9FzOnbExJTyL3W8JnTJ4MI0fCl18Wva/IozgJWna+14Fc2j/OAUALpdQGzATVdkqpqcA+oE6u/Wpjuun/wKwF4l4goQ7nu+/3u/ZDKRUEVAWOXUJMwkKJiRAZ6aBV9VkenX/mVqGCWbR60dar4ZQkaEKIsiM52Tw2bZqrMfssHJpvqjeW9AsuexytGqyTBM2HaK1/APoCLwNLga5a628v4VRyvyWst2yZKTd7/fXw8stWR1PmFCdB+1UpNQ6oqJS6HlNhaGFJ30hrPURrHaO1bg08AKzRWt+ptd4NZCilurh2HQjM1VpnAouBO13tgzDrgwDMcb3GtX2xa39RRjidpgft2s6HCQhwenz+mVtCAiTtiObo/hOQfc4r7ymEEJfLnaA1bpyr8Y9fIPtMyeafuYXHEV93GYcPw+HDpRKiuERKqYfcz7XWyVrrD7TW72utL2ksvtxvCcvt3w/9+kFUFPz3v7IY9SUoToL2LJAGpAKvAUnA8FKO4x5gglLqN6AKZt4bwCPAQ0qprcBVwEhX+4tAJ6XUFtc+j5ZyPMLDtm41c9B6tl1rGqq198r7JiSYx1+2dYXT273ynkIIcbmSk6F6dYiIyNW4fzYEhkKthJKfUAqF+JKh7idKqZkefi+53xKe9+qrcPo0zJqV76IlisvmdBa8BqJSqnZ5XOvCZrNFAykpKSlER0dbG4wfe+cdePJJ2P31EKIqL4dbvFO0IzMTIiKyGdz5A97/uDZE3e6V9xVCiMvRowekpcGKFbkav2sKVZtCwpySnzAthSNfdKDmw0d48014+ulSC1UUrBGwq6ANSqn1Wus2+Z+XdXK/5ccyMmDTJmjvnS/fy7gCrw2F9aDlXPGVUkM8EJDwY4mJ0KyZk6igH7wy/8wtOBi6doGFWxOkUIgQosxITjYl9nOc+h3SkktWvTG3yg2pUe1P6kSmSg+abyn4W3MhyoLvv4eTJ83is5KcXZbCErTcM46HeToQ4T/OnYNffoEe3U7D2SNem3/mlnBNIFv3t+DwrgNF7yyEEBbLyIA9e/IlaAdc36FeyvwzAFsA2FsQH60lQbOeJGWi7Fu0CG67zVRtFJetsAUQ85diFaJUrFgB6enQs53rrsCLPWhwfh7aoqVh3NnLq28thBAllpJiCivlTdBmm9L6VRpd+ontcbSqv5J35nQgM9OMMBCWaK6UcqfJTXI9B0BrHW9BTEIU3549cMcdpszsP/9pdTTlQnFWqAf5dkeUosREU9AnQc2BQ6EQ3tKr73/llVC1cgYL1zTlTkc2BEh1ISGE77qgxH5mmqngqJ64vBOHxxFfdwXnzj3G779DixaXdzpxyW60OgAhLtmZM9CnjxkeNXMmhIVZHVG5UFiCVlMp9VQBzwHQWr/tubBEeZaYaBaNtp/9BSKuhADvfm0bFARXdzjCwq1XQ3oKVG1S9EFCCGERd4KW04N2aD44Mi99eKObvQXxDT4BTCVHSdCsobX+JX+bUuoW17poQvi2556DDRvM/DOlrI6m3ChsDloi0NL1k/t5SyDO86GJ8ujkSVi9Gnpcmw0n1nl9eKNb924Ofj+oOKB3WPL+QghRXMnJEB4O1aq5Gg7MhqCqUKPr5Z3YHoeqqwkOzmbjxssOU5SuV6wOQIhiGTECPvsMbr7EgkWiQBftQdNa3+fNQIR/WLgQHA7o2SkZUjO8XiDELeH6ajAKFi04y93tLAlBCCGKxV3B0WbDTEY7MAfqXHf5ow8q1iGkUhViGh4kKal+qcQqSo3M/Re+7bffzLjrOnVg0KCi9xclUpyFqnMopdZ5KhDhHxIToUoV6NRokWmo3sGSOFq3r0p45ZMsXCJjpcXlOXvWrMWZmWl1JKK8ylNi/+RG+PMA1C2Fb6ttNrNgdcPNUsnR90iZYeG7du6Ezp3NgrbCI0qUoCHf6IjLNH8+dOsGwanLoUINqBxtSRyBgXB1q60sXNPYkvcX5ceLL5r50X/9K2RlWR2NKG/OnYNdu3IlaDnl9UuprkR4HPF1lrJ/Pxw7VjqnFJdOKVVRKdUS6KWUqmR1PEJcIC3N/NEDeOIyCxWJiyppgibEJdu9G7Zvh549gWMrzfwzm3U5f0LHQ+w42IC9e6RIqbg069fD22+b4grTpsGQIWYIrxClZdcu8/9UToK2fzZUawsVa5fOG9jjaFVvJQCbNpXOKcWlUUp1AnYAs4G6wF6lVGdroxIiF6cT7rsPtmyBqVOhsXzJ7SklTdAe8EgUwi8kJprHnt1PwanfLJt/5pbQPRuAhT8dtzQOUTZlZ8NDD0FkJCxeDK+8Av/5Dzz8sPkbJkRpyFPB8ewxOLbi8qs35hYeR3yUGd8owxwtNx7oARzTWu8DBgLvWhuSELmMGwfffANvvOH6tr1oSUkwapT5AnPUKLnOFFeRCZpSqopS6l9KqZ+BFKXUh0qpKl6ITZQzP/8MdetCTM1VpsGiCo5uLTvUonqVoyycn2FpHKJsev99WLMG3n0XIiJg5EhTbXjyZPj73yVJE6UjzxpoB38Cp6N05p+52VtQy36YGtXOSCVH61XSWm91v9Baz6H469UK4XnXXmvmnT31VNH7YpKxN9+EEyegfn3z+OabkqQVR3H+4U8EDgK1gAwgDJgM3O3BuEQ5tHw5dO0KtmNmOA3V21saT0B4LN1ifmHhkh6WxiHKnj174IUX4MYboX9/02azwWuvwZ9/wjvvQGgojB1r6SheUQ4kJ0PVqlCjBrBsNlSIhGqlWHq2QnVsleoQf0UKSUmyEJrFMpVSEYATQClZVEr4iLQ0U+GtfXto356kJJg+3fwtjIqCvn0hPv7Cw6ZPN19gRkSY1+7H6dML3l+cV5whjm201i8AmVrrM8A9QGvPhiXKm4MHzRy0Tp0w88/CFISEWxtUaCQJ8avZfcBOSoq1oYiyw+mERx81j5Mm5U3AbDYzJ23oUDMCZPRo6+IU5UNOiX0ccOgnqHMDBASW7pvYWxDfYAObN5uhu8IyrwG/APWVUv8FlgFjrA1J+L1Tp6BDBzOOn5L1iu3ZA3Z73ja73bSLwhUnQct/uQ4EZBq8KJGVrk6zTh2d5wuE+IDuHQ8BZn02IYrj22/hhx/M36roaFej8/wl0WaDf/0LBg82Cdq4cVZEKcqLnBL7p343c9BqJZT+m9jjiK/1CxkZ54dUCkv8CPQFXgaWAl211t9aG5Lwaw4HDBwIv/8OV18N5O0VCwg4/3z69AsPj4qC1NS8bamppl0UrjgJ2q9KqXFARaXU9cB0QG5nRYmsXAnBwdBG7YGMP3wmQWsRH0qNsCMsWiQThkTRTp6Exx+HNm1c1YWz/oS1T8L/7HBoQc5+AQHw8cdw110wYoSZpyZESWVlQUqKK0E7tsI0RnYq/TcKj6NVg9WAzA2x2GqtdbLW+gOt9fta621WByT83OjR8N13MGECdO8OlKxXrG9f08N24oTJ9dzP+/b1fOhlXXEStGeBNCAV0/2eBAz3ZFCi/FmxAlq3htA09/wzaxaozs8WHkv3mIUsXOCQog6iSM89B4cPw0cfQdDJVfBjG9DvQGAFWHY3/HkoZ9/AQJgyBW67zRQNmTzZwsBFmbR7t0nSmjQBjq6A4DAIa176b2SPI6buNgIDHZKgWStdKVXf6iCEAGDGDDNUZPBgGDYsp7kkvWLx8fDMM6aHbd8+8/jMMzL/rDiKUyTkFa31c8Crng5GlE/Z2bB6tSmxyrGVEFABwn3kX2dYDAmx0/nfyv7s2JFrrSEh8lm6FP7v/+DJJ7JpG/QyJL4OFevBNYkQWht+6gDL7oGEeTlzhIKD4euvTZI2dChUqAD33mvxBxFlRp4S+0dXuNaO9MDypfZYQkPOoqKPkZRUo/TPL4qrMqZa9l7MF+M2wKm19pE/mMKvnD0L3brBBx/kmWzdt6+Zcwam5yw11fSK3X9/waeJj5eE7FIU50p/i8ejEOXali2Qng4dO2IStGpXQmCI1WEZ9lgSYs2IXZmHJi7m3Dmz5llU/XO8clVX2PIaNBoEN22C2j0gPA7avQ+HF8CWvHP6Q0LMvLVrrjFfUkydatGHEGVOTon96HRI3eSZ4Y0AwVWhcjTx0dul1L61nsCsg3Yf8BgwzPUohPe4hxPddZe5MQoNzbNZesW8ozg9aDuVUvOAJZhvdADQWr/tsahEubLCNXWiU4dMWLcOmvzN2oByq1gXFXWQ2tVTWbjQzoMPWh2Q8EXj38hm69ZAfhh+B1VsKXD1LKjfO+9OV9wHhxfBptFQ4yqofU3OptBQmDXLlOW/5x7Tk9anj3c/gyh7kpOhUiWoHbLaFKKp7qEEDUyhkHqr+PrnzqSmXjjHRHjFDuB5rfUjrhL744ChFsck/El2Ntx+uxn2MWhQnp6z4pbWF6WjOAnacddjI08GIsqvFSsgMhKuqLYZsv/0mflnANhs2OwxJMSvYeHCa3E6Zd0qkdfv61J49ZW69O/4DTf3CoH2myE08sIdbTZoPwmOrzbz0W7cABVr52yuXBlmz4aePc3aae6ETYiLySmxn1MgxIPFlcLjiK+1EPg7mzaZNSuF130GfOd6vhtYBHwC3FTUgUqpMExZ/lu01ruUUg8Bj2PWVFsD/E1rfU4p1Rr4GLOm7a/AUK11llIqCvgCqAlo4B6tdZpSKhz4ErgCOAL011ofyv/+opwYORJmzoSb8v4v5y6tHxGRt7S+9Jx5TpFDHLXW9wGfuh6fBma5nhdJKRWmlNqslIp2vX7I9XqTUupTpVSIq721UmqNUup3pdTHSqkgV3uUUupXpdRvSqlZSqkqrvZwpdRspdQ21/baFw1CWG7lSjO80XbcVSDEkzcZl8IeS4Kay6FDoLXVwQif4XTg/G0iQwftJjQ4g3ffqwBdpxWcnLkFV4Gu/4PMU7Dsr+DIu0pJ1arw448QF2e+fVyw4CLnEYJcJfaPrYCqTaFCdc+9mb0FrRqsBaSSo4UitdYTAbTWGVrrd4A6RR2klOqIGeXUzPW6GaaYW2cgHnOv96hr9y+AYVrrZpg5bu5xI5OASVrr5piE7kVX+xhgsdY6BvgIkJq0ZVhSEowaZYbbjxqV79/6tGkwdqyZMJ1vOFFJSuuL0lFkgqaUGgO4l1utBIxQSo0sxnFywRCcPAlbt+ZaoLpCJFT2sc7YsBgSms4EZB6acEnfDQt68PmEdSzc0p03xjmp3b5P8bpXw+Og3Xtw+GczVy3/5nCYN8/cePfqZf59CJFfdjbs3AlNGjtdBUI8OLwRIDyOetX2E2E/KwmadYKUUnXdL5RStTD3REV5EHM/dcD1+izwiNb6lNbaCWwCopRSDYGKWmtXlyyfAXcopYKBq4Fvcre7nt+M6UED+C9wo2t/UcYUusD0xo1w333QpUuB68LIgtPeV5wiIX2A6wC01vuAbsBdxThOLhiC1WZpHZOgHVlmbjJ8bQyhPZbGtXZQr85ZSdD8ndMJOz6B2S05kpLC09P+jy5dnDzwSHjJznPFEIj+K2weDYcv/J8qMtIkacHB8NRTpRS7KFf27jXFaZo0OAYZhz1XIMQtrDm2gADimx6QBM06bwMblFJTlFKfA+uA8UUdpLV+QGu9ONfr3VrrRAClVA1MsZFZQF3gYK5DDwL1gUjglNY6K187uY9xbT8FSKnPMqjQXrDERKhWDb75xlS2ykcWnPa+4iRowVrrzFyvzwGOog6SC4YAM7zRZoP2LY/B6d+hRherQ7qQPQabDRI67GXRImQ9NH+VdQZ+6Q0r74dqbXnq502cTg9l8mQbASWtbG6zQfsPzLC0pXfDn4cv2KVOHXj5ZfjpJ5g7t3Q+gig/ckrsR7qyJU8naIGhULUp8VFbSUoyi8oK79JafwL0BNZjRg1dr7X+svCjLk4pVQ/4Gfi31noR5p4v9184G+Z+Ln87nL/Py/+Nqo1i3AMK31NoL9gzz8CmTVC74BlDsuC09xXntmOpUupLpdQ1SqkE4FNg5aW+oVww/MuKFRATA/bMpabBFxO0Sg0hsCIJrdZy5IgMOfNbe2fAgR+g9TgSHT/zxddVGDECYmMv8Xw589FOwvIL56MBPPooNG1qetEyMws4h/BbOSX27b9AYEUIb+n5N7XHEV93GenpkJLi+bcTF9Jab9RaT8BM8YhTSl3S/ZZSqjmmaMjnWmv3Orb7yDunrTZmlNMfgF0pFehqr8P50U/7Xfvhqg9QFTh2KTEJaxXUC9Zh/mt0DnANYAu/+EgRKa3vfcVJ0B4DDgETgDeBw5jKQCUmFwz/4nSaBK1TJ+DoMggIhmrtrA7rQgGBEKZIUD8CMg/Nb6XtAGycafA4Qx8OoFkzeP75yzxneEto+x4cmg9bX79gc0iImQPw22/w4YeX+V6iXElONssz1LXNg+rtzfXT0+xxxNecD0ihEKsopSKUUs8Cu4APgJ8u4RxVgXnASK31W+52rfVuIEMp5f6mdCAw1zVKajFwp6t9EODu15/jeo1r++J8o6pEGZG/F6zxsv9wy/KR3Hp2WrGOj483hUU++cQ8SnLmWcWp4piutX5aa91Ka90WUzBkcEnfSC4Y/mfnTjh2zLVA9ZGlEHElBFW0OqyChcXSqPJCGjaUBM1vpe2ESvV49Z+h7NxpEqZ863Nemsb3Q/Q9sOlls05aPr16mUWsX37Z/OEUAkyC1rixg4DUdZ4vEOIWHkdc/U3YbE5J0LxMGR8AezH3QRWBaK31S5dwugeAWsDTSqkNrp9XXNvuASYopX4DqgATXe2PAA8ppbYCVwHuYnAvAp2UUltc+7iLu4kyJncvWNCGNQxY8CBp7bpT45NxVocmClCcddCAnKGJj2GKf5zAfLNTErkvGE+72r5zXXzuAT5yreOxjrwXjM9dVSP3AANc7S8Cn7kuGCddxwsfk7NAdftzoFdDMx++rttjYfdXJHTL5LsfgnE4KPm8I1G2pe0k6Y8ejB9vShB3715K57XZoP3/wfE1sHSAa320Wnk2T5gAbdrAK6+Y50IkJ0OTqJPgOOf5+Wdu9jgqVfiTptGnSEqSlaq9RSk1G2gHTAW6a63XKKVStNapRRyah9Y62vV0guunoH02AhcsRur6srx7Ae3Hgd4liUP4rvh4iK91GNrdBvVrU2XONFOtSvicIhM0pVQH4EmgH2ae18PAlOK+gVww/NeKFWZx3hZ118G2s745/8zNHgNAQsd9fDalEZs2QatWFsckvCo7dRcPTfyUatVgfJF100oouAp0mQbzOsLygZDwI9jOfwMQHw/33w/vv2+WoFGqlN9flCkOB+zYATd0cE0Eq+6ltSOrNoGAEOIb72ZDkoxf8qIrgbXAZsA1+/CCOfhClI6JE83wpqVLoYbU1/NVF+0jUErdoZRahhlOeBToChzUWn+qtb5wtrsQ+axYAe3bQ+BxV4GQyM7WBlSYMFMJIqHVOkCGOfqd7AzmrmjFyi2NeestU2241EXEu+ajJcKWC+ejvfoqVKwIw4d74L1FmXLgAGRkQJNq66FSFFSqW/RBpSEgCMKaE99gIzt2QFqad95W0ACzlNAA4KBS6n+YIY5ClL5XXoFly8ywDZdCF7AWlihsENdUzDc5zbXWj2mtVyHf6Ihi+vNP2LDBvf7ZUqhyBVQsuHyrT6jaGGxBNKi8lsaNJUHzO+m72byvBQB9+njwfRrfDw3vhk0vweFf8myqVQteeAG+/x7mz/dgDMLnbd9uHpuELfTe8EY3exzxtX7B6YTNm7371v5Ka52ltZ6mtU4A2mKWEQpVSm1XSg21ODxRXsyaBfv3Q2AgtG6d01zoAtbCMoUlaLdi1iLbrZSaqpS6meKtaC8E69dDVhZ06ug0FRx9ufcMTIW0sGZwaivdu8PixbIeml9J28n2Q02pXfMcVat68H1sNujwf1ClCSwbABl/5Nn8xBPQqBE8+aT59yP8U84aaPal3k/QwuOIr/UzIDdoVtBab9VaPw7UwyxS/ZDFIYnyYPly6N8fnn32gk2FLmAtLHPRBE1r/b3W+iagJabc66dAfaXUS0opTwwAEuWIu0BIx7jdkHHYt+efuYXFQOo22rc33yDJOkB+JG0nvx9qRtOmXniv4KpmfbRzJ2DZX80C2S6hofDGG6bn4t//9kIswiclJ0NISDYNqu/1XgVHN3scDSN3U7VKliRoXqKU6pG/TWt9Rms9WWt9pWuf67wfmSgXDhyAfv1M99jEiRdsLnQBa2GZ4pTZ36m1fhaoDwwBrsdUVBTiolauhIYNoXbAr6ahLCRo9lhIS6Zt63MArFtncTzCe9J2sv1QM5o281I1q4h4aDvRzEebURfWPAYnzXiyfv3gqqvgxRcvXFRU+IfkZLii7jECgwKhWpuiDyhN4XEEBDiJV0clQfOeHkqpmUqpm1xruwKglApRSl2vlPoBkARNlNzZs+aPyqlTZohjAROsC1rAOjXVtAvrFFYkpELu11rrc1rr/2ituwCdC9pHCLc8C1QHh+UU4fBpYTHgdNAyejvBwbB2rdUBCW85dfggh1Nr0Ux5cRR3kwehx2KoezMkT4Y5LWFeF2wpU5gwPoOjR+G117wXjvAdycnQpHYyRLSBwNJYjK8EKjeEoMrERyeTlCRDvb1Baz0CeB2zlNEJpdTvSqlk4BjwBPCa1voZK2MUZdSYMeaGbMoUiIsrcJf8C1i7n/ft6+VYRR6F9aDNUkr1VUoVtM8mpdQdwHceikuUYQcPmq7xnAWqI/8CAYFWh1U0u0kiK2RsIS5OEjR/krzdAeCdIY651ewKXb6EPvuhzZtw9iisuJe2e+ty7y0refddBzt2eDkmYSmnE5KTnTSJWOv94Y1gln+wtyC+/mpSU2HvXu+H4I+01iu11jdiRisNAPoD9bXWN2mtl1sbnfBFxaq8+Mwz8OWXhWZbuRew3rfPPD7zjGkX1ilsHbTbgXGYFed/xlR0DAAaAwnAj5gLiBB5rFxpHju1PQ37tkBUGfnfpGozc3NyahtXXgkzZpibJZuUxinfnE5+32EqWns9QXMLjYSYp6H5U/DHItj+Ia/d0J//zdvCPwav5dsp+yCqn/d7U4TXHToEZ87YaFLzN4jsak0Q9jjia84HnmTjRhnq5E2uxanl60FRKHflxYiIvJUXcxKrzZuhcWMzmezuu4s8X3y8JGS+prAiIWla60eBLpiLRR2gJrAG6KK1HlrSVe6Ff1ixwixM36b+csAJNXy8gqNbUEWo3AhSt9K2LRw/Drt3Wx2U8Lizx9h+oD4ATZpYHIvNBrUSoOvX1B2ymhEPrWf6km788slkmFkf1j0Np7TFQQpPyimxXzvZ+xUc3ewtiKu5GICNG60JQQhxcYVWXtyzB665Bu6/3+owxWUorAcNAK31PuBfXohFlBMrVpglNkJPLwZbIFTvaHVIxWePzUnQwAxzjI62NCLhaa4S+w3q/knFij60NmxoTZ4eV5PJM508OfN7Vvd4kEA9EX57G2r3MJUgQ8KtjlKUspwS+1EnoHK0NUGExxFW6TRxzdOYNasKL7wgIwmE8CV79pies9zsdji480+47Taz0v1LL1kTnCgVRVZxFKIksrJgzZpcBULCW0FwFavDKr6wGDj9O/FxWQQFSSVHv5C2k98PNqNpk2yrI7lAxYowbpyN9ZvDmLJzKvTZB3EvwaH5sG+m1eEJD0hOhqDATBqqetZlRXZTTODh/mtYs8YsoSSE8B0FVl486eSRjQ+ZhWi//BKaN7cmOFEqJEETpWrLFkhPh04dsuHYyrIzvNHNHguOc4Rm7aRFCykU4hfSd7L9cFOaNffNorR33WW+8Hj+eTidVQtavmwqox5daXVowgOSfz9LoxopBNW2cORBxToQEsGghBmEh8O771oXij9QSi1USi242I/V8QnfU1DlxXbL36NV0hfwyivQq5fVIYrLJAmaKFU5C1THbIWsdIgsA+uf5RYWYx5PbaNtW5OgSZnp8u3Y/j84nladpspLa6CVkM0GEyaY4hHjxmEK2VRrD8dWWR2a8IBknUGTWhbOPwPzP509jiqZa3ngAfj2W6nm6GHvY6aSHADSgfeACcBRTIE2IfIoqPJi+5duNI3PP1/gMcWq+ih8xkXnoCmlBhV2oNZ6SumHI8q6FSsgMhKuqLzINJSFBapzs7sStNStXHnlrXzyibkxkSpm5dd2nQVYWMGxGDp1MoW43noLHnwQGlbvANvGQ9afpriNKBecTkhOqUDXrjug2n3WBhMeB7u+YtijTt5+28a//gVjx1obUnmltf4WQCk1HOistXa4Xs8GZICpKFBO5cUTJyA8HGxNodd4kpJMsZA9e8y9i7vCfqFVH4XPKawH7Q7Xz2OYb3b6AbcC7wD3ej40URatXGluJm1Hl0Kl+lC5gdUhlUxwVajUAFK35SkUIsqv7TtCAN9O0MDcHNtsMGIEUL0DOLPgxHqrwxKl6MgROJ3+/+ydd1iUV9qH76EoIoKABRWxAUeMIkJQMcZIlDRXjSYma0w3PW5MMe1LWdOb2SQma3pzs6nGFFPcYDdRrCjWQxEVCyJdQRSY+f44M4gKOiLwTjn3dc31zpx5Z+ZHMr5znnOe5/f4EN693Pja3YC+UFlCtw57GT8ePvhApa9rmpR2QO1eGm2AIIO0aJyBsjIYPhzuvRc4br9fVHRiIDZr1mlcBwn0+wAAIABJREFUHzUOyels9kdLKUcDeUCMlHKslPIqIAaoai6BGuehuBi2bbM2qM7/y/nSG234R0HpVvr3B09PbRTi0pgrydgdiIeHmZ49jRZzerp2VaudX38NKzKt/bF0mqNLYWuYHi5aG6wEtYMGULyZqVPVRO+LL4yV5AZ8CawSQjwjhHgWSAE+MFiTxlGxWFS+4ubNMHYsUL/9fkqKcnmsTUCA2mXTOCb21KCFSSmzbA+klLtRne41mhNYbZ0rDo7Jg/I9zpfeaKNtXyjZQquWVfTpo3fQXJqy3aTvj6B7l8O0aGG0mDPzyCPQuTM88Fg7zD5ddYDmYmSk5QIQHt3ZYCVAwHnqWLKFCy6AuDh4801lSKBpGqSUTwNPAIFAW+BBKeVrxqrSOCyvvgrffgsvvQSXXgqogKuuQMxkqsP1sUSXbzgy9gRo+62rOT2EED2FEC8DO5pamMb5WLVKXQTiw1SDU6dzcLQRdD5UV0DJFm0U4upYe6BFhDtHUoCfH7zwgloMWbRrsnJK1bgMmVvy8DBV0z26j9FSoGUwtO4GOXMxYWHqVNi+HZKTjRbm8uQCW4CHgWKDtWgclfnz4fHHlc3vww/XDNdpv1+iSk9Odn0sKjpen6ZxPOwJ0G4C+gEbgVSgO2Bw9bLGEUlJgT59IODYUvD0VT3QnJHgeHUsWENcHOTlwd69xkrSNA2Ww9lk5EYQ6aAOjnVx1VUqdeWvrBFweAdU5BstSdNIZKYfo3uH3bRoF2m0FMV5T6h+ljlzuOYaCAnRlvtNiRDiFuBT4BEgAPhJCHG7sao0DomnJyQmwscfn9AvsS77/aIiuPvuU10ftUGIY1Ovi6MNKeV+QMfYmtNisagdtLFjgYN/QbtB4HHGr5dj4tcLWgRCwWpiY28D1C5aqE7sdTkO7MrjUIU/EVHOk7fVpg307Qsrt/aBIUDhGuh8udGyNI1A5k5fwrsWgqmH0VIUPW+F9Hcg9RFa/m0099zjw9NPq5003QO3SfgHkAAslVLmCSHigPnAh8bK0jgMFosKyJKSYOTIU5rZ2+z3a7s4Tp58PBDTAZnzcMYZtBAiAXgR5SRU802QUur/zZoasrKgoAAGx1dA8Ubo87jRkhqOyaTSHAvXEHOR2q1Yt66mBlfjQmTIYwBERDpXS8jBg+GbbwIxmz3wKFitAzQXwHLsEBl7Q5kUm260lON4eELsG7BoBGx/kzvvfIznn4e334Z//9tocS5JtZSyVAgBgJQyRwhhV/61EMIfWAH8TUq5UwgxEvgX0Ar4Rkr5pPW8GOAjwB9YBtwlpawSQoQBXwAdAAlMklIeFkK0Bf4L9AQOAtdIKXMb70/W2I3ZDBMmQEKCisJOCs5s1Njva5wae2Yl7wPzgKmo1R3b7YwIIfyFEJuFEN2tj0cKIdKEEBlCiOdrnRcjhFgrhEgXQnwkhPCyjocJIZYJIbYLIX4SQvhZx9sKIX4VQmyzPh9yVn+1ptGxNageLDaCpdp5DUJsBA+E4k34tjhCVJR2cnRVMjLVGlWkg2SU2UtCApSUmJCHR0G+rkNzBQozUykpb0t47zZGSzmRkIshdCxseYEObXKZNAk++0ylTWkanUJrAGUBEEJMAgrP9CIhxCDgTyDS+rgV8AmqNVIUEC+EsK3ifAFMkVJGohbdbSmUs4BZUsrewFrgKev488ByKWUUaidPJ7kaxXPPqa0xZ3C00pwz9gRoVVLKf0kpl0gpl9puZ3qRvmC4FykpysCgT2AyYIJ2g42WdG4Ex6tAs2hDjVGIxvXI2NkGb68qp3OySkhQx5U546BwtXaxcQEyNyrvrfBoB8yljnkNzEch7SmmToXycvjoI6NFuST3o+ZDUUKI/cBzwH12vO524F5gn/XxQCBDSpktpayyvucEIUQ3oJWU0rqkymfWcW9gGDCn9rj1/ijUDhrAV8Dl1vM1zclPP8H06XDTTfAPu/ZINE6OPQHaZiFEvwa8t75guBGrVkF8PHgW/ansmVu0NVrSuRFkNQopVEYhubmwb9/pX6JxMo4Vk743jJ5dS/BysnLJyEhV5L0yYzAcLYCybKMlac6RjM1qoyQ8ysF20AD8IyBiCmR9TP+wDQwfDu+8A1XOYX7qTGwH+gOxQBIggD1nepGU8jYp5fJaQ52B/bUe70e1R6pvvB1Qap2b1R4/4b2sz5cC7c/qr9KcG1u3wvXXq0nWe+/Vm9qocS3sCdB6AuusaYlpttuZXqQvGO7DkSOwYQMMHmSB/JXOn94I4NsZWnWGgtXExakhvYvmYtgcHHsdM1rJWWMyqTq0lZu6qwGd5ujcWCxkZlRjMpnp4SD+IKfQ7yloGQTrH2TqfRZ274YffzRalMuxTkpZLaXcJqXcLKWsBJaf8VWn4oE1TdKKCTCfxTjWcds5tTHVek7THKxZo5qZzZ0LPj5Gq9E0E/YEaE+gVnJu4yxr0Or4LH3BcEHWr1crqYP67YLKUmjnpP3PTiY4HgrW0L+/mhDrAM21MJfuIPNAOBGRnkZLaRAJCbBV+lBytKNuWO3slGWTua8TYZ3LHHf+1SIQ+j0DBxYzOvZnevTQlvuNhRBioRCiFIgWQpTWupUBDemjsQfoVOtxCCqbqb7xPCBACGG7GHbiePbTXut5WP0B2gAFDdCkaSg33QRSaitpN+OMAZq13mwTqjl1NrAbaEiFor5guCirrIv3g7ovU3dcYQcNVJrjoXT8WhTTu7cO0FyNvVkHOXLMlwhHTCmzg4QEsFhMrD7wdx2gOTv5KWTmhhMebrSQMxB+JwT0wXPjNO6bUsWff+rrYiMxDohGuSr2q3WLBC5uwPutAoQQItw6h7oO+F1KuQuoEELYfqRvsI7bduqutY7fCPxuvf+b9THW55dbz9c0NS++CL/+qu63bm2sFk2zc8YATQjxLHAAFaBJIBNl3Xq26AuGi5KSAt27QwgLwKcj+PU0WlLjEDxQHQvXERennRxdjQxZAUBkn1YGK2kYAweqnd2VOy+DovVg1pdApyU/hcy8CMKFg0/CPLxgwOtwOJNbhr6Hn5/eRWsMpJSlUsqdwJXAddZ5EcDDKJv8s32/CuBm4HtgK6q2zVbPPwl4QwixHfADZlrH7wHuEEJsBS4EnrSOPwUMFkJssZ5z79nq0TSA776DJ56An382WonGIOwpjb8RCEMFZQ8DiSiTjrNCSlkhhLgZdcHwQQVZtS8YH1r7eKznxAvG50KIJ1E7dxOt408Bn1kvGMXW12sMIiUFhgwB8v9S6Y2uUsAafL46FqwhLm4EX3yhzEJCdFMHlyAjQ31PIyIMFtJA/P3hvPNg5fb+MKwCijdBUKzRsjQNoCh7CwWHggl3hu9i58ug02UE7HqSW2+8lXc/9OWVV6BTpzO/VHNGPkVlKoGa21hQTtXX2fNiKWX3WvcXogxHTj5nI8q07eTxXcDwOsYLgTH2fL6mkUhLg5tvVmkSM2ee8XSNa2JPgJYnpdwvhNgG9JdS/kcI8Zi9H6AvGK7Nvn2QkwOD40rh8A6IuMdoSY1Hi0DwC69xcgSVzjPqrJcnNI5IerYfPi2O0aWL8/aUSUiAOXM6YDabVMNqHaA5H1VHyEovA3D8FEcbsa/Db9H8Y8SrvP3udN59F5591mhRLkGElPIqACllCfCAEGKjwZo0zUlhIVx5pTIF+f57aNnSaEUag7DHJKRSCNELld54obXmy1HLmDXNjK3+bHCktRDBVerPbATHQ8FqYmK0UYhLYa4mI6cj4WGFeNhzFXRQEhKgqMiT9MJBug7NWSlKJTO3O+BEAVpAHwi/i/Cjz/O3S0t57z2oqDBalEvgbc0kAkAI4ceppmgaV2b2bNi7Vzk26m1pt8aeqclLwAfAL8BVQA6wuClFaZyHlBTw9oaYDvPBoyUEDjBaUuMSHA/le2jjlUtkpA7QXIYje8nI7UVkzyNGKzknBlv7wa/cey0UaKt9pyQ/hYxcldvY05nKd/tNB6823D/yBQ4ehK++MlqQSzAbWCWEeFYI8QywEtUDVuMuTJ0KqanHL+4at8UeF8dfpJQjpJRlqPTEy6SUtzW9NI0zkJICAwaAT+kyFcx4uth2vK1hdcEabRTiQlQVZ5N1oBcRkc69OC0EtG0LK7MuhJJtqs2FxrkoSCGzIIYuXcDX12gxZ4FPO+j3NIkhr9K39yHeegssJzfG0ZwVUsqXgEeBAJSBxyNSyoaYsmmcjR9/hG3bVKpOnz5Gq9E4APa4OIYIIR4TQrwKTAcmWe9r3JyqKli7FgYPrIKida6X3ggQNABMnjV1aHv2QF6e0aI058ru9Dwqq1sQ0dvBXfPOgIeHWmhN2RoJWKBQb/E6HfkpZOb3dU6zmoh7MflHcH/SK2zcCEuXGi3IObGlNQohgoA/gWeAF1C7aUFGatM0A+vWwcSJ8OijRivROBD2pDj+jDLwMJ1007g5mzdDeTkM6pOpLL5dpUF1bbxaQ8B5NTtooNMcXYGM7cqUIbJvoMFKzp3Bg2Hzdj9Ky9tAvk5zdCrK90J5Dpn7ujpP/VltPFvAgBlcF/s6wW2P8OabRgtyWpZYj/nAwVo322ONq5KXB+PGQYcO8PHHRqvROBD2uDi2kFKOb3IlGqcjJUUdB/dcotqEu2KABip1M+cHBiRZABPr1sHllxstSnMupKerY4Sw5xLo2NQ0rN43jpHaKMS5KFhFaXkb8gp8nTNAA+gymlZdh3DXxbN48YcH2bHD5Fy1dA6AlDLWenRiyyLNWVNZCRMmwMGD8Ndf0L690Yo0DoQ9s5N1Qoi+UsrNTa5G41SsWqWuJz1a/gZtIlVNgisSFA9ZH+PvmU1kZE+9g+YCZOzwxa9VOR07OlPRT90MGqTKFlJy/sbIggeMlqM5G/JTyDrYG3AiB8eTMZkg9g3uGXEFr/x0P2+/7ckbbxgtyrkQQtx4uuellLObS4umGZk5E5Ytgy++gFjdIkVzIvYEaH8BG4QQ+4FK26CUUq+RuTkpKTBokAVTwQro4sJt6YKPG4XExvbkr7+MlaM5dzJygokMO4jJ1M1oKedMQICqKV+Zfj4k7lVpc75djJalsYf8FDIPJwFOHKABBEbTOe4Krhn0LR9/fA3PPOOJv/+ZX6apYYL1GAL0BhYBVUAikIpyd9S4GlOmQFiY2kXTaE7Cnu30h1Fd7K9EXURsN40bU1QE27fD4Jh8OFrgmgYhNtr2Uy0ErHVoOTkqI0HjpFQeJmNfNyJ6lBmtpNEYPBhSNoUqFz2d5ugcmCuhcC0ZxSo1vFcvg/WcK9HPMXXUBxw65MlnnxktxrmQUo6WUo4G8oAYKeVYa8PqGFSgpnER0tJg1t2bmDKpiOkvtSRN6Om0pm7sCdCKpZTfSinX1b41uTKNQ7NmjToOjlyr7rhq/RmAhzcExtQ4OYK223dmjhXtJDuvBxERruMJnpAAhUXepOf20QGas1C8CaqPkHkwipAQ8PMzWtA50qojA6+8jISIFcx8oxyz2WhBTkmYlDLL9kBKuRsINVCPphFJS4MPn93Pdf+5jPtTrqWoCGbMUOMazcnYk+K4SAgxA/geOGoblFLqKaobk5KiSg/iu/wKhUHgL4yW1LQED4QdnxCbVA14sm4dXHqp0aI0DSF72wHMlr5ERrUyWkqjkZCgjin7rkYU/GmsGI195K8EIHNvZ+e02K8LMZX7x07j2hlD+HVeNaPHehqtyNnYb21Q/RnKLfsOYIehijSNxk/fHuXBv67C91gxyZfMINBqIjx3LkRHG6tN43jYs4N2HXAV8CUqSPsemNOUojSOT0qKqnvxL1+ods9MLm4+FRwPVWUEmLYRHq6t9p2ZjK2HAIg4L9hgJY1H796qFm1l9kgoWAPmaqMlac5Efgq06kRmdkvnrj+rjacP4+9OpHPgXj6btdtoNc7ITUA0sBFYD3QHbjFSkKbxuPC7++iRu5Ifx37GgY4qIgsIgN36n4qmDs64gyal7NEcQjTOg8WiHBzHjamA0u3Q47QGVK5B0HGjkLi4vjUtBjTOR3q6Sm2M6OM6LgYeHsrNceW286DqEBySENDHaFma05GfQpnvRezfb3KdAA3w6jGeS+LmMS9lGGaz+m5q7ENKuR8YJ4QIlFIWGa1H04h8+inD0z8g+fzH2Xre8bqzkhLlE6LRnMwZL51CCD8hxDtCiIVCiCAhxPtCCGfPltecA5mZUFgIg6KszaRc2SDEhn8kePtD4RpiY2HXLigoMFqUpiFk7GhJUJtigtuZjJbSqCQkwOb0QA4d8dN1aI5ORT4cziSr/BLAyR0cT8ZkYniiBwWlbdm85oDRapwKodgKbBZCdBZCbBNC9DZal0bViU2fDrfeqo5nXTd2+eXkTX6cL8RzFBWB2azM1oqKYLzuNKypA3vWtmYCJUBHoALwBz5oSlEax6amQXWPRWDyOr675MqYPCAorsbJEbRRiLOSsSuQiK55RstodBISwGw2sWb3cB2gOToFqwDILB4IuFiABiSOUTXJS+alG6zE6XgbmArkSSn3WR/r+ZbBpKUpM4+iIggN5ezMPfLzoaoKQkLo8NGLPPSIJ4GBsGcPBAbCtGm6/kxTN/YEaAOklE8AlVLKcmASyvpV46akpCjHsT5tfoSgWPByHbOF0xIUD8Ubie2vvHJ0HZoTYjGTvieUiO6HjFbS6AxUc31W5oyD/FXGitGcnvwUMHmSkavcQZzeYv8kwvpG0DNkN4uX6PzGsyRYSplseyClnIVaFNcYyNy5KpgKDFQpu7b7c+ee4YVHjsBll8G119YMRUerHbhPPlFHHZxp6sOeq+fJ1eaegDbQdWOWLYPBg8x4lqyCdm6Q3mgjeCCYKwlkIz176gDNGTlSmEtOQVciermeiUZgIERFwcrMIVCcBlVHjJakqY+CFGgbTeaOFrRvr4wCXI3hgw6wdEMU5gpdSnUWWIQQPoAFQAgRgppzaQxk9+5T/42e0dzDYoG77lIThZtualJ9GtfEngBtmRDiFaCVEOJSYC6wuGllaRyV/fth82YYOWQPVFe4R/2ZjWBrKqe1H5oO0JyPrM25AERGtTBYSdOQkAApm3tgMVdB0Qaj5WjqwmJWKajBg8jMdL30RhuJlwZSVBZE2sKVRktxJt4F/gd0EEK8BKQAs4yVpAkLU2YetTmjucfMmTB7NjzzDIwZ06T6NK6JPQHao0AZqg7tBSANeLgpRWkclwUL1DGp/3J1p70LN6g+Gd+u4NOhpg4tO1uZpWich/StpQBE9Ak0WEnTMHgwFBS1JPNAeE2dk8bBOJQFlaUQHE9mJq7TA+0khv+tJwCL57tevWdTIaX8GHgK+C/gDdwupXzXWFWa8eOPG3rYZe6xaBE89BBceSU8+WSzatW4DvbY7FcCz1pvGjfnjz+gXTuIafcjFPeAVp2MltR8mEyqDs3q5AiQmgojRhgrS2M/GelVAEREhxispGmwNaxeuWsUEdooxDEpVjubR3wGsGeP6+6ghXb1ILxrHotXtueBqiPuU6t8DgghFkopRwDLjNaiOU50tDLzmDtXpTWGhcHkyaepHwsIgJEjYfZs0jZ7nPC68eN13ZnGPk4boAkhxgGPAH2BcmATMENKOb8ZtGkcDItF7aCNHGnBo+BPCBlptKTmJzge9v1G7MWHgDasW6cDNGciI8ubjm3z8A/sYLSUJqFPH/D3h5W7LufGgnuNlqOpi8JUMHmxo+A8wHUDNIDEYcf49ocLqN67AM9uo42W4wy0FUK0llKWNdYbCiGuBx63PvxdSjlNCDES+BfQCvhGSvmk9dwY4COUMcky4C4pZZUQIgz4AugASGCSlPJwY2l0BqKj7QisqqvB0xPi4mD+/Br3x8DAE90ftXOjxh7qTXEUQkwAXkXZ7A8ChgP/Ad4WQpxT1wYhxPVCiC3W2wzr2EghRJoQIkMI8Xytc2OEEGuFEOlCiI+EEF7W8TAhxDIhxHYhxE+6N1vTs3kz5OZC0oX5UJHrXumNNoLiAQvBpvV0767r0JyN9OwAIkJzjZbRZNgaVqfIGDicpfptaRyLolQI6ENmtqqDdOUAbfhlIZSUt2XD4lSjpTgLZcAuIcQiIcTPtltD30wI4Yuaw10E9AcuFEKMBj4BxgJRQLwQ4nLrS74ApkgpIwETcLt1fBYwS0rZG1iLSsPU1MZigeuvhwcfrBlqsPujRsPpa9CmAiOklF9JKbdKKbdJKT8HRgEPnuZ1p0VfMJyXZKv5b039mTs5ONqwGYUUrNZGIU5Ixp5ORHQrOfOJTszgwZCW3oHDFa2hcI3RcjQnU7QBAgeQmakeunKAljhCJeksXlQN5iqD1TgFHwMPAZ8D39e6NRRP1DyvNaqmzRsoBTKklNlSyirUHGuCEKIb0EpKae10ymfWcW9gGDCn9vg5aHJNXnsNvv4aOhzPzmiQ+6NGY+V0AVobKeUpXyMpZTpqW7yh6AuGk5KcDEJAV+9k8PaHgPOMltT8+LSH1t1rnByzsqC42GhRGns4VHSE3OKORIYfM1pKk1LTsHrHIN2w2tE4kquyDwIHkJEBQUFqRd1V6dQJRK9SlmyKh/wVRstxaIQQfYFDwAIp5ee1bw19TynlIdTi9XZgD7AT6Azsr3XafiD0NOPtgFLr3Kz2uMbG/Pnw2GNwzTXw6KM1ww1yf9RorJwuQDtdoyBTQz9QXzCck6NHYelSSEoC8har3TMPN23PEhxf4+QIyihE4/hkblKpjRGR3gYraVoGDVLHlTljdIDmaBRZLxaBMS5tsV+b4Rf7sEwOoyr7J6OlOCxCiFtQNV+PAhuFEJc00vtGA7cC3VDzqWogEmufNSsmVG9bDzvHQffCPU5mJkycCP36qe7TpuPT47N2f9RoamGPzX6joi8YzsmKFXDkCCRdmAelEjo1yu+HcxIUD2U7ie2j6nt0mqNzkL5FbXVG9HHBrsC1CApSO90pOy5SVvuWky+VGsOw9aazBmiuarFfm8QRLTh0xJ/U5Zn6u1g/9wF9pZSDgNHAY430vpcCC6WUeVLKo6hso+FAbfvlEGAfasG8rvE8IEAIYVuR7WQd1wBs3w5+fvDjj9C69QlP2dwfAwNhzx511AYhGns5nYtjtBCitI5xE+BzDp9Zc8EAEEJ8BkzjxB07uy8YUspq9AWjyUlOVuZEwyN+h224d4BmrUNr57GGsLDLdYDmJGTIowCE9+tosJKmJyEBfvlJYKkowFSWDX49jZakAbWD5teTo5YAdu92kx204eq4eJ0gvngjBMYYqsdRkVLusx5XCiHaN9LbbgReFUK0RjlxjwZWAZOEEOFANnAd8ImUcpcQokIIcYGU8i/gBpTrY6UQYjlwLfAlcCPweyPpc37+9jdlqe9T97TYLvdHjaYOTreD1gvoV8etL3AuPysbgZFCiNZCCBPHLxhCCBFuXaW5DnVh2AVUCCFsbhQ1FwzAdsEAfcFocpKTlfmA/+FfwDcU/KOMlmQcQXGASRuFOBkZmZ6EBu3BN8g9ArT8olZkHegF+TrN0WEoTIXAGLKz1WaSOwRoHTtCVO8qFm+7GHJ+NFqOo3Ly1mKjOKpIKf8AvgLWAWmomv/pwM0o85GtqHITWz3/JOANIcR2wA9l6AZwD3CHEGIrcCGguy+//LJKaYR6gzON5lyodwfNGhw1OlLKP4QQA1AXjEpgNeqCkYy6YPgAv3HiBeNDIYQ/sJ4TLxifCyGeBHYDE5tCrwYKClQQ8s+nzZC7ALqOPyHP2u3wbgMBUTV1aD/8oAp/T3Zr0jgW6dltiAjdCybXL1etaViddRHhBaug+9+NFaSBykNwOBN63uQWDo61SbzYi9mfDqNy5xN4R083Wo4z0Gi5oFLKV4BXThpeiHLRPvncjcDAOsZ3oVIjNQA//wyPPw433AC33OLe8yFNk3HaRtVNhb5gOBcLF6rV3ksGbYOiYvdOb7QRFA/7fycu1gKYSE09nsqjcUwy9nTk6ouyjZbRLPTpA23aQErOKG4oeN1oORqAoo3qGDiAzAXqrtsEaIkwa5Yv61K9GXzxDp1yeyonl5T4Wh+bAIuU0t8gXZqT2b5d9TuLi4P339fBmabJaHaTEI3zkZysdofiQ+YCJggZabQk4wmOh4o8YnvvBWD9eoP1aE5LYYGFgtJAInodNVpKs+DpCQMHwsr0gVC0HsyVRkvS1HJwzMhQ19TgYGMlNRcXXaSOi7cmwh7t5lgHJ5eU2B73tR41jkBJCYwdq1Iaf/gBWp1LxymN5vQYsoOmcR4sFhWgJSaCV/7/IOh8aOkms4rTEaSMQjp4riI0NFTXoTk4GVuKgUAiItxnTSohAV56qTNlZR60Lt4EQbFGS3JvijZAy/bQqnONxb67LL63bw99+8KSzNE8nvMY9H7AaEkORVOVlGgamXnzIDsbFiyArl2NVqNxcdxntqJpEJmZsGsXJCUegfwUnd5oI7A/eHhroxAnIWNLAQARUW0MVtJ8JCRAdbUHa3bE635ojkBRKgQOAJPJbXqg1SYxEf7cej7HcldDRZ7RcjSas+f661WK47BhRivRuAE6QNOcluRkdUyKWQ6Wah2g2fBsCW2joVAZhaSnw6FDRovS1EfG9iN4mKrp2cf1HRxt2BpWp+wcoQM0o6k+BiWbITCGw4fVoldkpNGimpfhw6G8ogVrMuNg7zyj5Wg09jNvnmoGC9BT109qmgcdoGlOS3IydOsG4S1/AC8/aJdgtCTHIXggFK4jLtaMxQKpqUYL0tRHeoYH3drtomVwd6OlNBvBwSoIWJmdpAM0oyndpuoAAwewdClUV7vfIrytDm1J1jhtt69xHjZtgokTlWujbrSuaUZ0gKapl6oqWLQIkpLAdOAP6HixSuvTKILiobKUuMgsQKc5OjIZ2a2J6LwbvHyNltKsJCTAym39sBRvhcrSM79A0zTYDEKCBrBggfIYGDrUWEnNTXCwati7OGMM5CartgMajSNTWAhXXgn+/vDVV+5TNKpkyyXAAAAgAElEQVRxCHSApqmX1auhtBSShubC4R06vfFkgpVRSEevFLp00U6OjorFAhm72xHZLd9oKc3O4MFwsKg12Qe7Q6FeQTCMwlTw9AW/cJKT4cIL3bO3bWIi/JXWi6NHLbD/f0bL0Wjqp6oK/v532LMH5s6Fzp2NVqRxM3SApqmX5GS1YDSi969qIEQHaCfgHwVeraFgDbGxegfNUcnLg9JyPyJ6lhstpdmpaVidkaDTHI2keAME9mdfridbtsBIN+1UkpgIFRWerN59CeT8YLQcjaZ+Zs9Wk6BZs9RKl0bTzOgATVMvycmqF2Pw0XnQuge0cTPbsTPh4QmBsTVOjtu3w+HDRovSnEzGdtUDLCLC/dJT+vYFPz9YufMyyF9ltBz3xGJWFvuBA1i4UA0lJRkrySiGDVOLfot33wr7flXmKRqNI3LzzfDzzzB5stFKNG6KDtA0dVJaCikpkDSyGnIXqfRGnX99KsHxULSBuJhKLBbYsMFoQZqTydhaCEBklHvVn0GthtVZQ/UOmlEczlb1f4ExJCernmD9+xstyhgCAyEmBpZsvRAqSyBvidGSNJoT2bwZcnLAwwNGjzZajcaN0QGapk6WLFFOY0nxW6DqkK4/q4/ggWA+SlzENkCnOToiGdvK8PKspJtob7QUQxg8GDZmdqO8uBDK9xotx/0oUqs2lrbKIGTECDX3c1cSE2HFumAqzEGwR7s5ahyIgwdh1CgYP147NmoMx41/JjSnIzkZfH1hSNe5YPJUDo6aU7EahXTyXkmnTtooxBFJT7fQs8MOvNq6Z/8aW8PqtTvOh4I1RstxP4pSweTJlr392L/ffevPbAwfDkePmkgpnKICNIvZaEkaNyItDaZPh1tvVce0NOsTlZVwzTWqaPm993TGkMZwdICmqZPkZFUv0LLgNwgeBC3aGi3JMWndA1oG1zSsXqPnvw5Hxo5WRHbKhFbu6cJlq29XaY66Dq3ZKUoF/ygWLG4JuG/9mY1hw9QO4pKsq+DIfr1ooGk20tJgxgwoKoLQUHWcMcMapE2bplKHPvhAFd9rNAajAzTNKeTkgJSQlFgGhWt1euPpMJkg6HwoWM0ll8C2bfCjztpxGMxmyMwJIqJrHpjc83LXrh2Eh8PKnZfoOjQjsBqEJCerxuFhYUYLMpaAAIiNhcWpUWDygj3azVHTPMydq+ogAwPVIoHt/vbnvoOZM+GBB+CGG4yWqdEAOkDT1EFysjom9f8TsECnSw3V4/AExUPJFu66rYzoaJgyRZmsaIxn3z4or/AhonuZ0VIMJSEBUmQslvw1OqWsOanIgyP7OOYXx9KlOr3RxvDhkLLKmyNtL9F1aJpmY/dutUBQm4AAWNLyUnjmGXj1VWOEaTR1oAM0zSn88QeEhEDfgO/Au63aIdLUT3A8WMx4H0rlww9VUPDkk0aL0gBkZKhjZIR7ByUJCXCg0J+d+4OhdLvRctyHwlQAUnZcSFmZTm+0kZgIx47Byrw7oFRCyTajJWncgLAwKCk5/ti37CDlBUfoEO4PTz8NXl7GidNoTkIHaJoTMJth4UJISrJgyv0fhIwAD33ROi1WoxAK1zBwINx7L7zzDqzS5T6Gk7FN7ZxF9PYxWImxnNCwOnehsWLciWLl4Ji8OgoPDxWYaGDoUNUCYvE2638QvYumaQbGj1d1Z0VFYKo8xtX/vZLb51zC+HHasVHjeOgATXMCGzZAfj4kXbAPyvfo+jN7aNUJfENrit1feAE6d4Y77lDGUBrjSN96CB/vI4SGu6fFvo2+faF1a1i5e5Su+WlOClOhdTeSF7Vi4MBT06vcFX9/5cOw+E9/1apEB2iaZiA6WnmBBAbCsO/vo+f+Ffg8NIXo/tqxUeN46ABNcwK2+rORUb+pOyE6QLOLoPiaAM3fX+2gpaXBG28YrMvNyUg306tjFh7+7mmxb8PLC+LjIWXHRZC3DI4WGC3JPSjeQLHXBaxZo9MbTyYxEVavhrKgCcq8Rvfo0zQD0dEwvdP7JMr34bHH6PbItUZL0mjqRAdomhNITlar7Z2qf4A2keDX3WhJzkFwPBzOhKOFAFx5JYwbp/qs7NhhrDR3JiOrBZEh6eDXw2gphpOQABvSO3PkqDfsnWe0HNen8jCUprM4Ywxmsw7QTmb4cJVhsGLvNWpgz0+G6nFVhBCjhRBrhRDbhBBvWcdGCiHShBAZQojna50bYz03XQjxkRDCyzoeJoRYJoTYLoT4SQjhZ9Tfc8789Rf84x9w+eXwfM2fXn9/NI3GIHSApqnhyBH4809IGlEFeUt0euPZUFOHtrZm6O231c7F3XeDRae4NzvV1ZCVE0BElz3g7W+0HMMZPBiqqjxIyRkDOTrNsckpTgMsJK8fSOvWMGiQ0YIci6FD1fVxyZow8Bc69bYJEEL0BN4DrgSigVghxOXAJ8BYIAqIt44BfAFMkVJGAibgduv4LGCWlLI3sBZ4qvn+ikamY0cYNQq+/FIVQnKG/mgajUEYEqDpFR3HZPlyOHoUkuI3Q/URba9/NticLms1Xe3SBV56SblifvmlQbrcmN274VilNxHdDxktxSFITFTptx/8+Qjk/gFV7t16oMkpUgYhC1aGMnw4tGhhrBxHw89Ppd0uXgyEXgkHlsCxIqNluRrjgG+klHuklJXAtUA5kCGlzJZSVqGCsglCiG5AKyllivW1n1nHvYFhwJza4834NzQOlZVqpTQ8HH74Adq2rXmqvv5oc+caqFfj9jR7gKZXdByX5GQ1iRjWYw54eEOH4UZLch5atFUpoYVrThi+6y61cv7AA1Cgy36alRqL/XDt1ALQpg3cfjt8t3AAuw+0h33zjZbk2hSlsqu0PxmZ3jq9sR6GD4c1a+Bw26vAUgV7fzVakqsRDngKIX4WQmwA7gE6A/trnbMfCD3NeDug1BrM1R43HLvTEi0WdfGbNElZVZ9Eff3Rdu9ubMUajf0YsYOmV3QclORkGDIEWpf8Cu2GgLfelDwrguMhPwXMVTVDnp7wwQcqZeLhhw3U5oakS/VDHCFaGqzEcbjvPgATMxc8qlPKmpqiVJIzbgB0g+r6SEyEqir4a3uccsPVbo6NjRcwEpgMJACDgJ5A7aR7E2BGzQftGcc6bihnlZb4zjvw+ecQGam2yE7i5P5ooB6HhTWNdo3GHowI0Fx6RcdZOXAANm6EpOGHVGqOTm88e7peBRUHIOfEvAibte+nn1rTeTTNQsa2w/j5HCKkezujpTgMYWEwYYKJDxfdQmnGUqg+ZrQk18RcCcWbWbB5BJ07Q58+RgtyTIYMAW9vWLzEQ6U57vsdqo4YLcuVyAUWSCkPSimPAD+gArZOtc4JAfYBe+oZzwMChBCe1vFO1nFDsTstcckSlcIyZoxqRl0Htfujmc3H748f3+R/hkZTL0YEaC67ouPMLFigjpfELFd3tEHI2dNlDPiFw7bXTnEFefpp6NkT7rwTKioM0udmZMhKIkIyMLVxb4v9k3ngASgt8+WTBVcpMyBN41O6HXPVMRau7c3IkWDSbZbqpHVrGDiwVh1adTnkLjBalivxC3CpEKKtNcC6HJV5JIQQ4dax64DfpZS7gAohxAXW195gHa8ElqOynQBuBH5v1r+iDuxKS9y1CyZMgIgI+M9/6tw9gxP7o+3Zo47TpqlxjcYojAjQXHZFx5lJToagIBgQ9A20DIbAAUZLcj48PCHqIeXkmLf0hKdatYL33lN1US++aJA+NyM905uIjhngpwO02gwcCEMvqOat/91PVba2Nm8SClPZsCuG/EIfnd54BhITYd06KPUZDt4BkDPnjK/R2IeUchXwKvAnsBXYBbwL3Ax8bx3bzvFykUnAG0KI7YAfMNM6fg9whxBiK3Ah8GQz/Qn1Ylda4p49yhnpp5/U8TRER6s6tk8+UUcdnGmMxsuAz/wF+FwI0RY4xPEVnceEEOFANmpF5xMp5S4hRIUQ4gIp5V/UWtERQthWdL7EQVZ0nBWLRQVoI0ZY8Dz4PwhJApPuwNAgetwEaU+rXbSOw094KikJrr8eXn4Z/v53nfbUlFRWws69rfl7bBb4XmW0HIfjwYc8GT++Oz/+cIyrE8z633tjU7SB5C1XALr+7EwkJqp2VH+ubMEVYdfAzi8gbia0CDjzizVnREr5CcqErTYLgf51nLsRGFjH+C5geFPoayjjx6uaM1A7ZyUlKi1x8uRaJ11wAUip+jloNE5Gs/8qu/KKjrOybRvs2wdJQ/aoGipdf9ZwvFpB5BTY9xsUbznl6X/9S7np3XFHnWZSmkYiOxuqqz2J7FYIHvrH+WTGjIGeYYf410+3QP4qo+W4HkWpLNg+hr59oVOnM5/uziQkKPfgxYuBiDtVi5ed/zValsbBOW1a4uuvq1QVi0UHZxqnxZBvrquu6DgrycnqmHTeb3AQtYOmaTgR98DWl2H76zD4xK95+/bqt+OWW+DDD1VNmqbxsVnsR/TSJhh14ekJ99/vyX0PDmHlr++QcHOC0ZJcB4uFIwe2s3zLAO6+x2gxjk+rVqqJ+pIlwGtxEBgLme9DxN26eE9zWqKj60hF/OMPeOQR7fChcXp0XouG5GTVu7G75xwIOA98uxgtybnxaQc9b1WpOuWnlkbedJNK63n0Udi/v47Xa86Z9HR1jIjUq6f1ccvtvrT1O8QbH/U8xdRGcw6U7eLPzX05ekz3P7OX4cNh/XprTVH4HVCcBgWrjZalcTayslT9wHnnKdtkHeBrnBgdoLk5x46plcukEZWQt1ynNzYWvR8ASzWkzzzlKZNJGYZUVMDUqQZocwMy5DECWxcS3KWD0VIcFj8/uHNiJt+vuJTstHSj5bgORaks2DwSb28zw4YZLcY5SExUKd/LlgHdJ4JXa8j8wGhZGmfi8GEYO1b9wP74o7rAaTROjA7Q3JyUFCgrg6T4TWA+CiHaXr9RaNMLQsdDxntQeeiUpyMj4ckn4bvv4JdfDNDn4mRsP6ot9u1gysOd8fAwM3NGkdFSXIeiVJI3J5Ew2KLniHYyeDC0bGlNc/T2h27Xwa6v4VjJmV6q0SiWLYPMTPjmG9XTRqNxcnSA5ub88YeqR0nsNQc8WkKHC42W5DpEPQyVJZD5YZ1PP/KIcnK89161+KdpPNIzPYkI0Rb7ZyI0oiPXXrSIj77vd4pltaZhHMzeQerOWJIu8TzzyRoAfHyUWcjixdaB8DtUTzRtFqKxlyuugB07tG2qxmXQAZqbk5ys+iK1LfsJOgwDL1+jJbkO7Qaq/6byTTBXnvJ0ixbwwQeqseb//Z8uA2osKiogZ18r3QPNTh646wCHj7Tmo38XGi3FJVi0XPVb0vVnZ0diImzYoKzSCYpTvTgz39cXRs3p+e03+Plndb9zZ2O1aDSNiA7Q3JiiIli7FpKGl0LJVuik0xsbnaiHoTwHdn1T59MXXAD33ANvvw1/+xvs2tXM+lyQrCywWExEhu6BFoFGy3F44i4bwkVRS3jrHS+qqoxW4+RU5JO8LpaANhWcf77RYpyLxEQViy1bhqojqjELWWO0NI2jIiVMnAjPPgvV1Uar0WgaFR2guTGLFqnC7KT+y9WArj9rfDpfAf5RsG1GvSvBb72l+qMtXapSHv/1L/REuYGYzTB7trof0bNCu3jZQ5twHrp6Djn7/fn+e6PFODeWog0kb0ri4qGleOoMx7Ni4ECV6vj779aB7tcps5AsbRbirqSlwfTpcOut6piWVuvJkhIYO5Yqz5a8ceFcbr3d89RzNBonRgdoborZDHPmqKbJg0K+Ap8QaNvPaFmuh8kDoqZB8UbIXVDnKV5e8MADsGWLWkV+6CEYNEjZTmvsZ/9+uPRSePVVuGbo7wzor3ug2cuo8cFEhKTz+muVOqPsHMjckM3ugm4kXa5Txc+Wli3huutUf8ilS7GahUyEnV9psxA3JC0NZsxQmT6hoeo4Y4Y1ADOb4YYbsGRm8kr8d+w0h516jkbj5OgAzQ3JylKBwNdfw403mPHOn6/SG/VuQ9PQfRK06gTbXjvtad26wbx58O23sG8fxMerYE0biJyZX35RDUv/+gs+/MDM1/eMwzOgu9GynAaPsCt54PI3WLPOmxUrjFbjvCQvUNtmSZdr+8aG8Oab0KuXCtQOHuS4WciuL42Wpmlm5s6FwEB18/A4fn/uXFTN2bx5/H7Jm+T1vqjuczQaJ0cHaG6E2azS6fr1g40b4eOP4e1n1sPRAp3e2JR4toTI+yA3GYo2nPZUkwkmTIBt2+D221W643nnqTpot+RYMVSV1ZseWlEB990Ho0dDly6wbh3cNmk/JstRbRByNgTGcOOlSwjyL+X1140W47wkrwijW8cD9OpltBLnpE0btUCVnw833wzmtudDYAxkaLMQd2P3bggIOHEsIECNM3YsLFjAnI731n+ORuPk6ADNQaiuhvR09cPUFGRkwEUXwf33q92zzZtVXrfpwB/qhE7acqxJibhT1VNsm2HX6W3bqmbWy5erfpujRsG110JubhPrdCTk2zAnCL71g6+94LtA+Kk7/BYNyUPZ+umdDOq7i7ffhqkTl5Py0YtEecyEjHfV61vrAM1uTCZah1/GXRfP4scfLWRlGS3I+aiqKGdx2vkkXbBHJyOcAzEx8PrralHqjTdtZiEboXCt0dI0zUhYGKe0/vDZsZU4ny1gMpHWfgQ7sk3MmaP65x04oM4pKVGv1WicHR2gGcDRo2ql/6OPVA+sIUPUqo8Qahfghhtg1arGWTCsroY33lDpX5s3w+efq3Sw0FDrCfv/p+yMfTqc+4dp6qdFIPS6XTVfLbN/eW/oUEhNheeeg59+gqgoZc1vNjehVkegaCOkToOOF0PMK9DncehxA3QYjqV1L97/7SrOv/st9uf78ev/TeLNsZfik/4ErJsKW15QtX8BfYz+K5yLruO4d+RMvLzMvPWW0WKcj7VLdlBS3pakEdpN7ly5914YNw4eewxW5d0Inr7Kcl/jNowfr2rKiorU713F/iIm/zyG234dR1pqNTNmKFd9Ly8oLlbp7RkZ6vzx441Wr9GcOyaLm6UNmEym7kB2dnY23bt3b/LPKy1V6YTr16uJdmoqbN163KWvTRu1YjhgAPTvr8799FM4dAjOPx+mTFE7Jz4+Z//ZUsItt8DKlcrC/f33T2oTUnlI7VBEPQQxLzfK36s5DWW74OdeKt0x7l9n/fL0dLjzTrVaOHSoCtSiohpfpuFUV8D881Xq7RWbwKddzVOFhSr1c+5c1Wfq88+hUyfb645B1SGoLAUPb/ANrfv9NXVjroYfOnHTR3P4fvkwcnJUPYfGPp67fzX/nHk+edl7adetq9FynJ6iIvW7aDJB6of/oG3BJzB+vzIPcU56ADuNFtGcnOt8Ky1NXetzdlbz+J+j6LlrER5LlzD9jyEUFanrU24ubN8OeXnQoQPMnKkWpDUaJ6LOa4PeQWsC/vc/uOYaiIhQO2PDhqnUwvnzVYD08MMqzz4jQ638LFumasNuvVUd9+6FWbOgvFzl4YeGwuOP298jq7oaXntNBXzbt8N//qNqak/p4XhgCViqdP1Zc9G6G4RdC1kfqtqqsyQyUrVG+PRTFeT376921lxujWXD41CyBQZ/dkJwtnSp+pvnzVPf7/nzawVnAJ4toGUw+PXQwVlD8PCE0DE8mPgYZWXKTU9jP8lLAxnQPY12Yfq71xgEBsJXX0FODtw+8xksVeWwU5uFuBPR0cpe/+OQJwjP+h8e786CIUNOqE8LCYHhw+Hqq6FnTx2caVwHHaA1Ab/8ohpAR0erCfQvvyhXvtxc1ePlxReVEUR4uHIeOpk2beDuu1VK4qJFqnbs1VfVxWfcOFi4sP5J+bZtqvnxI4/AZZcp6/brr6/DoNFigT1zVepI+wsa/b+Bph76PAxVhxucrmMyqaB9+3b1g/T00yr4d5kgbf8fIN+EyH9A50sBtdv81FNw8cVqJ3nFCpg2re5/O5pzJHQc/UNXMmJoPjNnQmWl0YKcg8OHYeWmHiQN3KbdcBuRhAR44QWYMy+I91c+p66bLnOx09jF/PnwyitqUnTbbUDd9Wm69kzjaugpThPw9tuwYwd8/z08+aQyeDhhpd9OTCZl6PH995CdrfLx//wTRo5Uzn7//rdKhQQ1iX3lFZUSkpEBX34JP/xQz+eW74Olo2HHZ9DtWuUyqGkeAmMgZCTIt6D6aIPfpn17+O9/Vf+0mTNVQO7085ajBZBys6odi3kFgJ071Q7088+r2sz161Xqr6aJCBkBXn48OP6/7N0L331ntCDnYOniaqqqvRh5ke7X1dg8/LDqb3j/h4+xcYNZm4W4GxdfrFIm3nyzZujk+jTbfV17pnEldIDmJISFqZXEnByYPVvtsk2ZokxFbEYjjz2mgsGtW2HixHp2zXZ8Dr+eBwcWQewbMFDnMTU7UQ/Dkf3nnK5jMim3s3vvVc05n3qqkfQZgcUCq++Ao/kw5L/g1YoVK1RK45YtasHhs8/U917ThHj6QOcruCz0RXr3tvD66y4Q+DcDyb8V4+N9hKGJ+gva2Hh4qN+8oGAPrn3nWw6nfW60JE1zUFCgbi1aqJSJFi1qnoqOVkOBgbBnjzpOm6bTGzWuhTYJcWJWr1a7aF9/Df7+6v6ECfVk2JTvVRPgfb9B+6Ew6BPwj2h2zRrUjPf3AWCpVCYYpnNbJzGb4a67VM3Qs886aaCW9SmsuhUGvAZR0ygsVOY5LVpAcjL06GG0QDdi59ewYiIf7EvnzocjWLJEpVlr6qevKKZzi9X8sbwztO1rtByXZNEiGDnSzI3DvuSz5DHOaBaiTULspaoKLrlEeedv3KisGjUa10WbhLgaAwcqF7u8PGUgcs019e2afWbdNVsMsW/CyKU6ODMSkwmipkHJVtj3+zm/nYeH6pl2002qJu2VVxpBY3NyKAvW3QcdE6H3g1gsMHmyqtn85hsdnDU7Xa4AjxbckPARwcGqWbqmfvbtgy3pbRnZbwn49zZajsty8cXw1EP7+Hzp9cx+Q6c5ujQPPwyLF8Ojj+rgTOO26G++C2BzMzqFE3bNLoTBn0Cb8GbVpqmHbtfCxsdh22vQZdQ5v52HB3z8sTJ1eOwxtfP0wAONoLOpMVfBiuvB5AWDPweTB+/Ogh9/VOmbcXFGC3RDvP2h4wha5X/LPXe/zPMvmEhPVy6izYHZrJoUh4SomlpPz+b53IayYIE6Jg3ZBR76J7UpeerFLiyZv5Z7/jmYQWMsiN7akMUehBAzgHZSypuFECOBfwGtgG+klE9az4kBPgL8gWXAXVLKKiFEGPAF0AGQwCQp5eEmEzt7tqo3mzqVtJgbmTsddu9W9fbZ2WpB2sNDLdxddNHxurO5c9V5YWFqTKc7apwdvYPmilgsKmXMtmsW9xaMXKKDM0fCwxvE/ZC3FArWNMpbenqqHdWrr4YHH1Qprw7PlhegIAUGvgetu7Jpk9J+2WXKnVJjEF3HQ9lO7r1+C97eNFvj6sJCVUc7ejTEx6u+RhMmqJ5/O3Y0j4azZcECC+3a5NM/zs9oKS6Pl7eJL2dtwcernGuuPkJFhdGKHB8hxAjgJuv9VsAnwFggCogXQlxuPfULYIqUMhIwAbdbx2cBs6SUvYG1QNMl0a9dC3fcAYmJpN3wGjNmKPOP0lL44w/VC/TQISgrgw0bVJnH//0fPPGEOi80VB1nzFA91DQaZ8bQAE0IMUMI8Zn1/kghRJoQIkMI8Xytc2KEEGuFEOlCiI+EEF7W8TAhxDIhxHYhxE9CCP3rCFC+B5aMUvU8gf3hijQQ951znZOmCQi/Xe1WbJvRaG/p5aUMNcaOVSYyDt3LKj8FNj8H3a+HbtdSXq6asgcGqkBT2+gbSOgYMHnQ8eh3XH+96r134EDTfmRqqnLoXLhQBYT//S+MGQMrV6oG7b16qdudd8KcOco/wGgsFliQXM2I8xbg0S7GaDluQZdBV/L5PXeQtsWXhx4yWo1jI4QIAl4AXrQODQQypJTZUsoqVFA2QQjRDWglpUyxnveZddwbGAbMqT3eZIK7doWrroJvvmHuPG8CA9Xvwdq1qsWKxaLK01q3VlkiW7fCwYNqVy0wUP1m2F4zd26TqdRomgXDpkBOtarjDFgskPWJ2jXLWwpxM2HEYr1r5sh4+0P4XZAzBw433vaAt7eq3briCjWZ/dwRTc8qD6vURt9QOP8dQO2Y2Rqrd+hgsD53x6cDtLsA9vzAgw+qSVH//iowagpfqdmzlRPtsWOwbBncdx9cd50KDHNyVH/Ht9+Gvn1V8+IJE1SrifPPh8cfV0GdEbspW7bA/lwvkvolQ1sdoDULLQIYNa4tD/3tLWbNUm1oNPXyPvAEUGR93BnYX+v5/UDoacbbAaXWYK72eONy7Ji6yHTsqFZm2rc/oRn1oUPQsqVKf7Zdf1q2VONHj6pbbQICVLqjRuPMGJIwf9KqTn9qrepYn7et6mzl1FWdZ4QQH6FWda6sNb4UeLS5/obTcqwESrc13+eZj8GWl2D/fOhwEQz6GNr0ar7P1zQcMRXkG7DpGYi4++xf37JdnUF4y5Zq4jJmDNxyiwrarruuEfQ2FuvvV0HpyKXQIoDvvlO7fY89pvr8aRyAruNg/YOcd2EWKSm9uO02FRhdeSW8845q8XGuHDumaiVnzYLhw9XCwsnBuckEvXur25Qpah63erWq/UpOVulML7+sVtgvvFC1nRgzpnn6RdfUn/VdAIEzm/4DNYrwO3lxwoUs3/13Jk/uSGysNhM6GSHEbUCOlHKhEOJm67AHUHuJxQSYz2Ic63jjcv/9IKVqSu3tDahasqIitRvWpg0cOXJiVsXRo2q8ZR1tXHXTao0rYFRFs21Vp6v1sWOu6jSUFdfDvl+a9zM9fSHubYi8R6czOhO+nVWK345PIXt2w96jw0UqjbXLmBNMCnx8lNnGqFFw440qJeTqqxtJ97mQ8wNkfQx9HkExZhYAABzoSURBVIcOF7JzJ9x+OwwapNoEaByEUBWgsecHYmOnsXq1cnT85z+hTx/VO/a22xqeirp3r/o+pqSoHkYvvWSfYZuXl9ptGzJEuZYeOqR23ZKT4eefVQB5wQXKzfSCCxqmzV6SkyEidC9hPX3Bq3XTfpjmOMEDadEuiq+m3s6AB35m4kRYvrxmbq9RXAt0EkJsAIIAP6AbUF3rnBBgH7AH6FTHeB4QIITwlFJWW8/Z16gqP/wQ3n0XHnnkhP+B48erxRdQO+XJyWrRxdNT1aBVVSkTKQ8PNV5UpHbOSkrU/cmTG1WlRtPsNHuA5lSrOg1l0IdQtKF5P7NtP/BthCVtTfMTNxPCruXUr7QdFG+CjH/D8qvANwwi74Vet0HLIAB8fWHePGW6MXGi+v0bO7Zx5Z8V5ftg9e0QFAf9plNZqXb2LBaVuqYnWA6EX3cIjFEBddQ0vLzUHGrcOFXHf+edqt7xgw/O3uFx6VLVFqSsDL79Vu3MNZQ2bdQixKhRakL3yScqiBw6VH3XX3xRBZSNSU4OvP++Sq2cnLhQ/XfSNB8mE4TfQc/iKXz0ZhbX3NqL2Fi1a2KrQTrTzde3eXZZjUJKmWS7b51rDQfuAjKEEOFANnAd8ImUcpcQokIIcYGU8i/gBuB3KWWlEGI5Ktj7ErgROOveMG++qYw+wsLUv9d585Tpz/nHVvBN3r3s6nUJ5X9/EdJOdGMcMwY2b1aLMJdccqqL48CBdbs4Tp6sXRw1zo8RO2jOsapzLrQKgVaXGa1C4yx4+0HnSxv22s6XQe8HYe88SJ8JGx6FTdOh+ySI/AcERuPnp2zLL7lETYR//FHVpzU7FjOk3AJV5ZDwBXi2YPrTygTi6691ipJDEjpOfZ+O5KrrGhARoZoGf/yx2vmKjlYB0bRpZw6wLRZ44w0V6IWHq1ZHjRk8eXmp4HHSJGU08sor0K+fSvOdPl25vDUUi0Xp/fe/4af/b+++46OqtgWO/5JQLXS9oAJBgQWKolK9V68oVsSKij4RQQWx4vMh9n69FlQQCyrYUK5YuFi4ojxAfCgSkCIgumyABUSRJiUiZN4fa485hEkyQjIZkvX9fOZD5pwz5+ydDGeftesb9r5rl03ccNyNUPuqEsuDS1L2eTDnWs464H4GD36SceNs7cTPPrMWlDVrih4vWbmyBWq33mrdYisCVc0NwdoYoBrwNvkTgJwHDBeRGsBsIN5n9zLgeRG5GfgWOPfPXnfNGpv/Y8YMa+2uVw9qrf+BYau68S2NOHvLaH4+JYvq1a21rGlT+xu++Wb+PaY4HpC58iYjVhojvpNUsFYHOAqr1RmH1eq8KiILgEtU9UMReQobqzZIRP4DjFLVf4nITcBeqlrsbXa7V7Z3bmewej588SgsegG2bIQ9O4Xujyezem0lOne2iQ1ef91a1VJKh8Ks/tBuGDTrx+TJNt7swgthxIgUp8UlZ/V8ePsgaPcENLtkm93LlsGVV9p4x9at7e/Ytm3iU61bZzXbr7xirXDPPQc1apRu8lesgLvvtqAqKwv697e1b2vXTv4ca9faJCaPP24P/3XrWtfOfv0gu9pkmNwZjpoADY4t/mSuZE2/EL59FU5fCpV332rXli32t1u1qujX2WdD584pSW0TYHFKrpQm4s9b/fsvolatbJ57Dlavtgl9WrGAkZvOoTsvszDjAMAC6mrVIDvbWjg3bLAxqUOHegDmyrWE94a0CNDC4omdscUT47U616hqTERaA8OxxRNnA71V9bcwLezz2OKJ3wLnquqqRNeJ8gDNVQi/rbRxXl88Chu+hV0bQ7PLWFm7D0edUJt586w1oUOH/FebNjZ9calY/Sm80wbqHwNHvsXPKzJo3drGDHz8cSle1+2YWAzeamYT0Rz1TqGHjR1rrRDLl9s6dnfcYQ9YcarWFenzz63L4cCBqe1etmiRtZSMGgW1atnaSVdcYQ+DhVm40AK7kSMtuGzXzvLYvXvkc589CHMGwBk/QbU9UpIXF7FiOkw4DNo/CU37lnVqilPhA7TBg23sWG6u3fM3rMtjS2zrQayZmfaqWdO6K+bmWqVPsi1pzu2E0i9AKwseoLkKJW+zdX/UofDTFMiqzqo6fRmZcwU5c+uQM2tXvlls02BlZcVo1XIjHduup8Oh6+nQZj0tmufu+HpksRjkXAwbl0KX+cSq/YWuXW38Tk6Otby4NDbnWtCHofMUyEowZVqwek0WA2/fm+Ej92Df7N946qEldD5yHa9P2IeeffegatUMRo9OWWtFQnPn2rT877xjXa7uugt69LDWNbCHxzfesFkqp0yxGeK6d7fArH37BCecdj4sfw9O/z6V2XBxsRiMbw2ZVeCEj8s6NcWp8AHakCFwwfrHkLzPubH6YNasrxQ51l7xR9IGYXBLs2ZWRtSubd2UnSuHPEADD9BcBbZqHnzxCCx+EbbkLxr189p65HzVgZyvO5DzVQdmfNOeNRtqAVCj+hra7TuTDk1z6Nh0Oh2bTmePGiu27/pHvgV7d2XIEJta/ZFHrBXDpbkVOTChY9KHv/dpJ/o+/RRfLW/GkS2n8P5nnWi770zGDLiQRg03Q7X6Np4t+m/056r1IDOrFDNkY+gGDoRZs2xttdtus+6LTz5ps0s2bgyXXgoXXRijXt08iP0OeeEV25z/85QTYbem0OmtUk2vK4I+CrOutACtTpuyTk1RKmyA1qvXIho2zGbhsPd5acUxTKp0AmdWfoP1G/Nr/+KVJFu2WKBWv761nnXvbmsefv+9TQDkXDnkARp4gOYcv/1iXYNiiSc+zcsD/Xo3cubUImdObXLm1mbeZ7uzZUsmGRkxDm+3km5dlnH6CT/SaO+NyV1zl32gziHMng0dO8KJJ9o4uPI8i1q58tNU2LQ66cM3bszkzoeb88CT+9HrjC95ZOBYqsV+sMlGcn/M/3fzum0/nJEJVfewVpFSlJeXwWsfncSNLw7k6x+zATiu9WQuP344Jx08jqyM3ywIK06r2+Cg20s1ra4Im1bD2L0gs+o249CS0nIgSEpqiipsgNa//yIqLc3kptfbsrZSHbrWy+HbNTVZt87Km8zM/G7DGzbY+/r1rRWta9f89dC8Bc2VUx6ggQdozm2PDRustWHyZJsQYv58296uHXTrZq+m266XvZVff7Vxbhs2wCef2GQLrnzLzS16nBe/r4Pc5VsHbRt/tG2xzUV8sOT8vjmLt6cdSMsmP9E8exVkVILMyvmvjMq2vuAfP4f3GZUhqxrs1QWq1ExJWl0hvhlpXbi3R3YPqH90iSanEBU2QFu0cCHZ553Hli+/5oajZ/BbtlCzJsycaUtuVK5s94ro+LOaNeHvf7f7x6pVPgbNlWseoIEHaM6VhC+/tEBtzBib5AOs8IwHa/vvv23rWK9e8MILFuQdeWTKk+ycc2Wp4gZor71G9gUXwOjRzGvUdas1y6LrosXXN2vRwsaibdpkx5xxhgdnrlzzAA08QHOupC1ZYouEjhkD06ZZwSqSH6wdcogtaNyjh82kd8cdZZ1i55xLuYoboC1aRPZuu9kCaM65gjxAA8jIyNgP+Grq1KnssyOrljrntvHTT/DuuzB+vM3QmJdn0/mvXGmtai+9ZIsJO+dcRZKVldW0YcOGX5d1OlLJn7ecK15h94aKGKAdDkwt63Q455xzrmK47rrrjrj33ns/KOt0pJI/bzlXvMLuDRWxLnsmcASwDNhSxmlxzjnnXDnVoEGDrJ49ezaoWrXqzLJOSxnw5y3nClHcvaHCtaA555xzzjnnXLrKLP4Q55xzzjnnnHOp4AGac84555xzzqUJD9Ccc84555xzLk14gOacc84555xzacIDNOecc84555xLEx6gOeecc84551ya8ADNOeecc84559JERVyo2m0nETkTuAH73mQCI1V1UAmcdzHQSVUX7+i5XMUkItnAF8DCsKk6MA24XlWXl+I1p6hqdoJ9zwK3q+qS0ri22/mISCtgPnCmqo4p6/Q4l45E5D1gT+D3sOkSYHfgIey+/rKq3hyOPRgYAdQA/g/op6qbRaQR8GI4jwLnqeq6lGakGCJSAyujuqrqYhE5hhLIo4jUAkYB+wI/A2er6o8pzt5WEuT1WeBwYH045A5VHVse8lqSvAXNJUVE9gYeBI5T1dbAYcA5InJK2abMuT8sVdWDVfVgoAXwI/BaGaXlKCCjjK7t0tOFwKvYA6dzrgARyQCaA60j9/J5wDPAqUBLoJ2InBg+8iJwhao2x+63fcL2x4HHVbUF8DFwSwqzUSwR6QB8gOUVEalOyeXxH8BUVW0JDAceLv0cFa5gXoO2wN/jf2NVHRu279R5LWneguaSVQ+oDOwC/BJqLy4AcqMtYCLSCWs56CQiU4CVwAFAd2AC8G/gr8CvWC3I4vgFRKRXOE+v8H4KcDvwFVZLsiuQB1ylqtNLM7Nu56aqMRG5DVguIlcBPYAsYAFwKXYzb419nx5Q1ZGFff9UdYqI3AOcCawAlgFvAlOA6iIyGmgFrAJOwwqVvYC3ReQIVf0lJZl2aUtEKgPnAUcA00RkP+y+2EdVTw7HXAk0Ba4BBgGdsO/sc6o6ONxb7yf/e3wj8DRQC/u+Paeqt4ZrPYHVUP8AxIC7wvf4euDscI53getUNVb6vwHnkiLh3wkiUhe7T88HvlTVRQAi8iJwlogsBKpHngWeA+4QkRHA37F7cXz7+8B1KclBcvoAlwMvhPftKbk8nhT2AbwEPCYilVU13iKZalvlVUR2ARoBz4SK/7HAHUBDdv68lihvQXNJUdVPgDeAb0RkhojcB2Sp6lfFfHSeqoqqzgX2AD5S1YOA0cDQJC9/ETBOVdsCt2IPHs4VSVU3AV9iLWnNgaNV9QIs6P9FVVsBRwO3i8hBhZ1HRE7GvnMHAF2AQyK79wAeCudaDpyjqvcCS4EuHpy54CRgiap+AbwO9AXGA21EpHY45hysBrkPgKoeij24nSoiR4Rjot/jc4GXVLUjcCBwtYjUA/phlVktgN5AOwAROQFoE94fAuyNBY3OpYvawCTgdKAz9l1uhFWKxS0D9sEqJRJtrwesVdXNBbanDVW9WFWnRjYVlpftyeMfnwn712LlVJlIkNf6wGSsR0FHrNLqIspBXkuaB2guaap6KZANDAMaA9NF5IxiPpYT+TkXGBl+fh57OE7GRGCAiPwLqAs8mmyaXYUXAzYCqqprwrajsZYHVHUFVvHQqYhzHAu8oqqbVHUV9oAdt1RVZ4SfP8UKE+cK6o3V8AK8HN5nYLXH3cIYi7qqOhM4BjhFROZi9899sAAMIt9jVX0A+FZEBmBde6pggdmxwChVjYUxkJPCZ48BOgCzgNlYN6MDSi/Lzv05qvqRqvZU1TXh3vw0cCd2H4/LwHo+ZCa5nbA9nSWbl2TyWLBrfQZplH9V/UZVT1fVZaq6AXgEq/gsd3ndUR6guaSIyEki0l1Vf1DVZ1X1HOAqrOYjRv5/lMoFProx8nNepDtNJrC5wLHR8/xxLlX9ENgf65LTHXhrR/Pjyj8RqYJ1mdmTrb+HBe97GVh374TfP2BLgs/ERb/DBT/vHCKyJ3Ai8D+hO/gIrKXgDKzbT/fwGhU+kgUMjIzB6YiNT4HI91hEHsTuwUuwsRgrsO9fYd/XLGBI5LwdgLtLLKPO7SAROVxEOkc2ZQCLgQaRbfWxHgrfF7L9J6CmiGSF7Q3C9nRWWF62J48/hOMQkUrYBCtp05NDRA4UkW6RTRnYhDDlLq87ygM0l6wNwD1h5rr4YN6DgTnYg0G8JvbUIs6xS+guBlaDPL7A/hVASxHJEJEmwEHhWvcDPVT1eeAK4NAdz44rz0QkE+vXPh34usDuyVjFAqFL2GnYeLKE3z+sBbebiFQJs1F1ZdsavYI242N8nTkfmKSq+6hqtqo2xgKjfmG8xV7hmHiANhnoIyKVRWQ3bIB9xwTnPRYYpKqvYhURe2NB2ERsAqcMEdkLax2OhfOeLyK7hYeZ17Fxlc6li1rAIBGpJiK7AxdgYy1FRJqGh/T/AsaH1uFcEflb+Oz5YfvvwFSs0gOgJ9s+a6SbHEouj2+H94T9U9NsTFYGMEREaofxsn2BseU0rzvEAzSXFFV9D3vgHSciCnyO1dTeBdwGPCwiM4HVxZzqLBGZBxwPXF1g30TgO2wa1YexBxOwJvAzQ5efseT/h3Quai8RmRu+J59gD6znJjjuTqCOiMzHpvK9W1VnU8j3T1X/E46bA/wHq73bmOC8UeOwSUKa7HCu3M6uFzYLWdRjQHsRaYF1efxVVb8J+57Axk7OwWYse1ZVpyQ47z3ACyKyAKu4+hhoAjyFTcI0H+tKvgTYqKpvAWOwh8EFwNyw37m0oKrjsHvsHKwr7jOq+hH2f2gMtozK5+TPznseMFhEPgd2I39c+2VA3zDJxhHAzanKw/ZQ1VxKLo+3AB1F5NNwzOWpyEOyVHUedu/6EMvrXFWNd/8uV3ndURmxmE/g5FJDRGKq6l3A3E5FRA4Dmqvq86HG7yPgwlDQOJdWROQkIENVx4lITexht62qrizjpDnnnEuSt6A551zRFDhXRD7BJlcY7cGZS2MLgetDS/L7wK0enDnn3M7FW9Ccc84555xzLk14C5pzzjnnnHPOpQkP0JxzzjnnnHMuTXiA5pxzzjnnnHNpwtfpcc6ljIh0xKbYrYtVEH0HDFDVT0vhWp2AR1W11Z/4TBbQH1uHphJQBVsY/VZV/a2E0jUAaKWqvURkBDbpyEQRGQ48oaqzottL4prOOeeKJyL9gEuBytj6gbOBm1T127B/MXCmqn5cVmksSnzZjURLc4jI3sAw4FRVjYVtV2LT2R8W1mUszbRNARoDa6LbVfXgHTjnBOC/VHWFiDwIvFXIsiQ7HQ/QnHMpISJVsfXBjgvrjiEiPYDxItJEVbeUaQLNMKA20FlV14jIrtgCwiOwhTNLlKpeHHl7LPBkgu3OOedKmYg8ALQGuqrqdyKSCfQAPhKRDqr6fdmmcIcNB26LB2dBP6yMuxo4JwVpuFZVXyv+sKQdG/n5TuADEWmvqsWtVZr2PEBzzqXKLkAtbAHKuFHAWiBLRI4g0uIVbQETkduBpkBDoAG2yO7Fqro21Gi+hN2oawEPquqw+AVCkPU90EFVvwjbJgKPqOobkeOysYUyG6jqWgBVXR9qVP8WjqmJLTJ8MFa7Oh64UVU3i0gucC9wXEjj/ao6LKydNjSk7ydgOaEGMdQoPgocAuwFjBKRnsB9Ie+vichp2GLwmdgCxNeo6ozwO8kO12oM/AD0UNVlInIpVvBuAnKBS1R1YVJ/Jeecq2BEZB/sntlQVVcBqGoeMFJE2gA3kL8Q8uUi0hqoipU3z4jIbsCzQDMgD1to+xJVzRORk7HFlasAG7BeIx+Fe/hh2L1/AXA4cJqqzgppehmYEsqRm4BuWDmwGLhMVZeKyP7AM1j5+jmwayH56wDsqaozI9s6AXWAgcDXItJQVb8L++oDTwAtQn6eUNWhocxaGbYPA0YCDwMHYq2Ok7AgbPOf/P3XLOw8ItIy7KsLZAFDw+/82fDx90SkSwiqPwT6huN3aj4GzTmXEqHQGwi8IyLfiMgLQG9goqpuSuIURwJnYwXDZuDWyL46QDugE3CniBwYue564HngYgAR2Q9ojrXmRbUBPo0HZ5HP/6iqY8LbocAvWCHSFqttHRD2VQVWqOpfgTOBwSJSDbgsXG9/LEhrlOB3cxOwFDhPVXPi20WkBVZIdlPV1iHPb4hIjXDIEcBZqtoCWA/0C900hwAnqGo74Cms4HfOOZdYB+CzeHBWwES2voduVNVDsfv5PSJyAHA6sHvortcuHLeviDQD/gl0UdVDsODh36HiEKxy7RBVPRcLtHoDiEht4BjgX6HS7kCgfTj/21ivDrBKzuGqehAWlDQuJH9nsW2ZdxkwSlWXApOBKyL7Hge+CGXLYUBfEWka9q1S1f1V9RFgMDBLVdtgFY31gGsKSQPAIBGZG3l1CdsTnkdEKgGvAdeHfUcCA0Sko6r2Dp89Kh5YAhOAM4q4/k7DAzTnXMqo6kPAX4CrgGXAdcCcUHtWnFdVdXmo1XwaOD6y7zFVjYUuKO9grVhRjwM9Q2tWX2BEgi6VeRR/TzwRa9mKhTFpT4RtcfEWudlYwLYroZBV1U0hWByVRF7jjgYmqeo3AKo6GWuFaxP2T4kElHOAOiFfrwLTRORRYDX2+3LOOVe4yoVsr4r1mIiLd0VfigUEnYEPgANCC9P1wBBV/QoL4hoAk8Li8aOwsiYe7EyPtDY9A5wtIlWAc4E3VXUN0BXoCHwcznElICJSFzgIa8VCVT/EWuISaQF8FX8TWshOwyovCf/2iQSOx2CVe6jqGlVtFfIDMDVy3q7AJSFds4D2WDBZmGtV9eDI6+1iztMc2A94Jux7H6iOBXGJLAKkiOvvNLyLo3MuJUTkb8BfVXUQVpM3TkRuxAqUY4GfgYzIR6oUOEW0y0QmsCXJfajqFyIyDzgVmwCkQ4Ik5gAtRWR3Vf01ku69sYLqzHDuaEGdydaF+sZwvZiIEMlPNF9/putHVoHrFbxmtJ99LH4dVe0hIq2wQvZ6bPzc2X/ius45V5FMB5qJSH1V/bHAvqOAaZH30fIlE/hdVReFFqZOWMXaRBHpi93DJ6lq9/gHRKQh1mPidGBdfLuqLhGR2Viw0hsbF0Y4x33xrvthPHftSBqSKV9ibF0B2SdseyuUVZlADeACrEJzM5GyR0T2BVaEt+si58nCenF8Fo6rBcRE5BRsTBjAUlXtQtESngfrcbImOpGIiPyFAhONRPxOgfJ/Z+UtaM65VPkZuFlEol1FGgA1gflhfyMR2VNEMth2wPKpIlIzDNzug82uGNcTQEQaYa1n4xNc/zFgEDAj1HxuJWwbhdXU1Qjnq4EVVr+EQcfvAleISEYoJPsC/1tMvsdjrXfVQpfH7oUct5lta3AnAceHwhERORobh5dDIUSknoh8F9I8BBv70K6w451zrqJT1R+wLuwvhUo5AESkNzb2677I4b3CvkZYJdikMO73WWCCql6HlRWHYvfw40J3dUKXvnlYK1Aiw7GeJbuGFjHCuS6OdG2/E3hBVX/BWpvi3fcPpfDWK8VaouKzFfcB+qlqdng1wrpi9g/l70Tyu1vWDPloluC87wL/HSkT38RmkXwz0kpWXHBW6HlCujeKTSgWD24XkN+LZAtbl5tNsLF4Oz0P0JxzKREm6DgN+GcYg7YQeAXorWYh1nXkY6w2c1GBUyzH+t5/htWe/TOyr4mIzMK6N16lqpogCeOwCUqeKCKZlwELse6Bc7FAaCGhAMS6Zu6JBZTzscLj7mKyHs/TAqx7RsF8xf0beFFE/uieGX4nl2FjFhZgk5CcHLq9JKSqK4B/YA8Ns8Jn+hSTRuecq9BU9QbgRWyc7wIR+RILwA5T1SWRQ6uFlq63gStD2TYSawVaGO67NbHJLBZiFXmjReQT4C7gFFWNtkJFvYlN/jQism0EVn5NF5FPsW6NvcK+c4FzRGQ+cAtWPibyGnBC+Lkr9vxfsLv9YKA+0AULjlqGnicfAvfEJy8p4CqsK/98LPCcD9xfSBqKkvA8YXz6qViAOg/rUnpLJHh9FXg/9Bgh5PHV7bh+2smIxQr2nnHOufQSZruqp6pXJNi3mCTWpRGRw7CCrpVuPc2wc845V66JyLtYcDOjrNNSGkIL4zSgrarmlnV6dpS3oDnnyj0ReR4YDVzkwZlzzrkK6BLg1tCFsTy6Hbi6PARn4C1ozjnnnHPOOZc2vAXNOeecc84559KEB2jOOeecc845lyY8QHPOOeecc865NOEBmnPOOeecc86lCQ/QnHPOOeeccy5NeIDmnHPOOeecc2ni/wFwjhahRN7ZdgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Pred_AF = Demand_Forecast(slc_val, False, Pred_Obs, \n",
+    "            'y_pred_tot_AF', 'y_test_tot_AF', 'Acre-Feet', 'MLR_Seasonal_term_pred_AF', 'SLC-WDM')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no\n",
+      "Total R2 is  0.6283371213177069\n",
+      "Total MAE is  63.03597863247863\n",
+      "Total RMSE is  77.93344228795345\n",
+      "Total MAPE is  30.78863740465362\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-12-18238038290e>:103: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Pred_UR_GPCD = Demand_Forecast(slc_val, False, Pred_Obs, 'UR_gpcd',\n",
+    "                'y_test_tot', 'gpcd', 'Stationary_Demand_Forecast_gpcd', 'Stationary Forecast')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>y_test_tot_AF</th>\n",
+       "      <th>y_pred_tot_AF</th>\n",
+       "      <th>StationaryError</th>\n",
+       "      <th>SLCWDMError</th>\n",
+       "      <th>StationaryErrorAF</th>\n",
+       "      <th>SLCWDMErrorAF</th>\n",
+       "      <th>S_Perror</th>\n",
+       "      <th>SLCWDM_Perror</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-12-31</th>\n",
+       "      <td>100550.276769</td>\n",
+       "      <td>94468.369531</td>\n",
+       "      <td>139.858017</td>\n",
+       "      <td>-112.889766</td>\n",
+       "      <td>12207.162705</td>\n",
+       "      <td>-6081.907237</td>\n",
+       "      <td>12.140357</td>\n",
+       "      <td>-6.048623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-31</th>\n",
+       "      <td>81646.774954</td>\n",
+       "      <td>81484.779554</td>\n",
+       "      <td>512.522236</td>\n",
+       "      <td>4.603096</td>\n",
+       "      <td>32430.298904</td>\n",
+       "      <td>-161.995400</td>\n",
+       "      <td>39.720245</td>\n",
+       "      <td>-0.198410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-31</th>\n",
+       "      <td>86505.457871</td>\n",
+       "      <td>83638.300459</td>\n",
+       "      <td>455.384862</td>\n",
+       "      <td>-48.858969</td>\n",
+       "      <td>28891.579705</td>\n",
+       "      <td>-2867.157412</td>\n",
+       "      <td>33.398563</td>\n",
+       "      <td>-3.314424</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            y_test_tot_AF  y_pred_tot_AF  StationaryError  SLCWDMError  \\\n",
+       "Date                                                                     \n",
+       "2015-12-31  100550.276769   94468.369531       139.858017  -112.889766   \n",
+       "2016-12-31   81646.774954   81484.779554       512.522236     4.603096   \n",
+       "2017-12-31   86505.457871   83638.300459       455.384862   -48.858969   \n",
+       "\n",
+       "            StationaryErrorAF  SLCWDMErrorAF   S_Perror  SLCWDM_Perror  \n",
+       "Date                                                                    \n",
+       "2015-12-31       12207.162705   -6081.907237  12.140357      -6.048623  \n",
+       "2016-12-31       32430.298904    -161.995400  39.720245      -0.198410  \n",
+       "2017-12-31       28891.579705   -2867.157412  33.398563      -3.314424  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Pred_UR_GPCD['StationaryError'] = ((Pred_UR_GPCD['UR_gpcd']-Pred_UR_GPCD['y_test_tot'])/Pred_UR_GPCD['y_test_tot'])*100\n",
+    "Pred_UR_GPCD['SLCWDMError'] = ((Pred_UR_GPCD['y_pred_tot']-Pred_UR_GPCD['y_test_tot'])/Pred_UR_GPCD['y_test_tot'])*100\n",
+    "Pred_UR_GPCD['StationaryErrorAF'] = Pred_UR_GPCD['UR_gpcd_AF']-Pred_UR_GPCD['y_test_tot_AF']\n",
+    "Pred_UR_GPCD['SLCWDMErrorAF'] = Pred_UR_GPCD['y_pred_tot_AF']-Pred_UR_GPCD['y_test_tot_AF']\n",
+    "Evalcol = ['y_test_tot_AF', 'y_pred_tot_AF', 'StationaryError','SLCWDMError', 'StationaryErrorAF', 'SLCWDMErrorAF']\n",
+    "Eval = Pred_UR_GPCD[Evalcol]\n",
+    "\n",
+    "Annual = Eval.resample('Y').sum()\n",
+    "Annual['S_Perror'] = Annual['StationaryErrorAF']/Annual['y_test_tot_AF']*100\n",
+    "Annual['SLCWDM_Perror'] = Annual['SLCWDMErrorAF']/Annual['y_test_tot_AF']*100\n",
+    "Annual"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>y_test_tot_AF</th>\n",
+       "      <th>y_pred_tot_AF</th>\n",
+       "      <th>StationaryError</th>\n",
+       "      <th>SLCWDMError</th>\n",
+       "      <th>StationaryErrorAF</th>\n",
+       "      <th>SLCWDMErrorAF</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-01-31</th>\n",
+       "      <td>4709.274282</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>7.342278</td>\n",
+       "      <td>-14.631378</td>\n",
+       "      <td>345.767994</td>\n",
+       "      <td>-689.031718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-02-28</th>\n",
+       "      <td>4888.568674</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>4.579880</td>\n",
+       "      <td>-17.762379</td>\n",
+       "      <td>223.890584</td>\n",
+       "      <td>-868.326110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-03-31</th>\n",
+       "      <td>4493.545735</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>16.816118</td>\n",
+       "      <td>-10.532955</td>\n",
+       "      <td>755.639941</td>\n",
+       "      <td>-473.303171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-04-30</th>\n",
+       "      <td>5114.524352</td>\n",
+       "      <td>4643.726952</td>\n",
+       "      <td>24.996996</td>\n",
+       "      <td>-9.205106</td>\n",
+       "      <td>1278.477435</td>\n",
+       "      <td>-470.797400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-05-31</th>\n",
+       "      <td>8583.758986</td>\n",
+       "      <td>8054.101919</td>\n",
+       "      <td>13.335806</td>\n",
+       "      <td>-6.170456</td>\n",
+       "      <td>1144.713444</td>\n",
+       "      <td>-529.657067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-06-30</th>\n",
+       "      <td>12890.020382</td>\n",
+       "      <td>12143.083636</td>\n",
+       "      <td>14.946044</td>\n",
+       "      <td>-5.794690</td>\n",
+       "      <td>1926.548117</td>\n",
+       "      <td>-746.936747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-07-31</th>\n",
+       "      <td>16379.389681</td>\n",
+       "      <td>16521.121898</td>\n",
+       "      <td>12.453508</td>\n",
+       "      <td>0.865308</td>\n",
+       "      <td>2039.808677</td>\n",
+       "      <td>141.732217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-08-31</th>\n",
+       "      <td>15197.197243</td>\n",
+       "      <td>16351.696472</td>\n",
+       "      <td>13.006844</td>\n",
+       "      <td>7.596790</td>\n",
+       "      <td>1976.675810</td>\n",
+       "      <td>1154.499229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-30</th>\n",
+       "      <td>11502.006931</td>\n",
+       "      <td>10581.546063</td>\n",
+       "      <td>10.339270</td>\n",
+       "      <td>-8.002611</td>\n",
+       "      <td>1189.223590</td>\n",
+       "      <td>-920.460868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-10-31</th>\n",
+       "      <td>6784.423105</td>\n",
+       "      <td>6071.879773</td>\n",
+       "      <td>13.992224</td>\n",
+       "      <td>-10.502637</td>\n",
+       "      <td>949.291652</td>\n",
+       "      <td>-712.543332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-11-30</th>\n",
+       "      <td>5299.251908</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>-0.312916</td>\n",
+       "      <td>-24.135659</td>\n",
+       "      <td>-16.582211</td>\n",
+       "      <td>-1279.009344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-12-31</th>\n",
+       "      <td>4708.315488</td>\n",
+       "      <td>4020.242564</td>\n",
+       "      <td>8.361965</td>\n",
+       "      <td>-14.613994</td>\n",
+       "      <td>393.707671</td>\n",
+       "      <td>-688.072925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-31</th>\n",
+       "      <td>3747.489710</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>36.470100</td>\n",
+       "      <td>-3.444037</td>\n",
+       "      <td>1366.713253</td>\n",
+       "      <td>-129.064919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-02-29</th>\n",
+       "      <td>3545.403337</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>45.887264</td>\n",
+       "      <td>2.059609</td>\n",
+       "      <td>1626.888577</td>\n",
+       "      <td>73.021455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-03-31</th>\n",
+       "      <td>3241.788769</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>63.817536</td>\n",
+       "      <td>11.618154</td>\n",
+       "      <td>2068.829713</td>\n",
+       "      <td>376.636022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-04-30</th>\n",
+       "      <td>4525.764752</td>\n",
+       "      <td>4947.391246</td>\n",
+       "      <td>42.911118</td>\n",
+       "      <td>9.316138</td>\n",
+       "      <td>1942.056267</td>\n",
+       "      <td>421.626494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-05-31</th>\n",
+       "      <td>5202.834938</td>\n",
+       "      <td>4895.104544</td>\n",
+       "      <td>89.172399</td>\n",
+       "      <td>-5.914668</td>\n",
+       "      <td>4639.492745</td>\n",
+       "      <td>-307.730393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-06-30</th>\n",
+       "      <td>11629.504953</td>\n",
+       "      <td>11983.593435</td>\n",
+       "      <td>28.896039</td>\n",
+       "      <td>3.044743</td>\n",
+       "      <td>3360.466324</td>\n",
+       "      <td>354.088482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-07-31</th>\n",
+       "      <td>13113.950619</td>\n",
+       "      <td>12621.761487</td>\n",
+       "      <td>42.098780</td>\n",
+       "      <td>-3.753172</td>\n",
+       "      <td>5520.813183</td>\n",
+       "      <td>-492.189132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-08-31</th>\n",
+       "      <td>12143.936026</td>\n",
+       "      <td>11371.123706</td>\n",
+       "      <td>43.074404</td>\n",
+       "      <td>-6.363771</td>\n",
+       "      <td>5230.928053</td>\n",
+       "      <td>-772.812320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-09-30</th>\n",
+       "      <td>10495.557894</td>\n",
+       "      <td>10994.630258</td>\n",
+       "      <td>22.335182</td>\n",
+       "      <td>4.755082</td>\n",
+       "      <td>2344.201933</td>\n",
+       "      <td>499.072364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-10-31</th>\n",
+       "      <td>6268.234299</td>\n",
+       "      <td>6579.050919</td>\n",
+       "      <td>24.823425</td>\n",
+       "      <td>4.958599</td>\n",
+       "      <td>1555.990459</td>\n",
+       "      <td>310.816620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-11-30</th>\n",
+       "      <td>4167.182691</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>28.251982</td>\n",
+       "      <td>-13.168559</td>\n",
+       "      <td>1177.311686</td>\n",
+       "      <td>-548.757899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-31</th>\n",
+       "      <td>3565.126967</td>\n",
+       "      <td>3618.424792</td>\n",
+       "      <td>44.784007</td>\n",
+       "      <td>1.494977</td>\n",
+       "      <td>1596.606712</td>\n",
+       "      <td>53.297825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-01-31</th>\n",
+       "      <td>4299.786860</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>20.317090</td>\n",
+       "      <td>-17.889861</td>\n",
+       "      <td>873.591554</td>\n",
+       "      <td>-769.225876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-02-28</th>\n",
+       "      <td>4049.243978</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>29.212750</td>\n",
+       "      <td>-12.809379</td>\n",
+       "      <td>1182.895524</td>\n",
+       "      <td>-518.682994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-03-31</th>\n",
+       "      <td>3543.906179</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>51.586028</td>\n",
+       "      <td>-0.376567</td>\n",
+       "      <td>1828.160440</td>\n",
+       "      <td>-13.345195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-04-30</th>\n",
+       "      <td>3936.532388</td>\n",
+       "      <td>3548.014077</td>\n",
+       "      <td>66.203610</td>\n",
+       "      <td>-9.869557</td>\n",
+       "      <td>2606.126535</td>\n",
+       "      <td>-388.518312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-05-31</th>\n",
+       "      <td>7427.320860</td>\n",
+       "      <td>8476.514535</td>\n",
+       "      <td>34.048489</td>\n",
+       "      <td>14.126139</td>\n",
+       "      <td>2528.890489</td>\n",
+       "      <td>1049.193675</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-30</th>\n",
+       "      <td>14063.436455</td>\n",
+       "      <td>12753.703882</td>\n",
+       "      <td>7.821565</td>\n",
+       "      <td>-9.313034</td>\n",
+       "      <td>1099.980875</td>\n",
+       "      <td>-1309.732573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-07-31</th>\n",
+       "      <td>16247.673230</td>\n",
+       "      <td>15969.874949</td>\n",
+       "      <td>16.018969</td>\n",
+       "      <td>-1.709773</td>\n",
+       "      <td>2602.709812</td>\n",
+       "      <td>-277.798281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-08-31</th>\n",
+       "      <td>11051.688592</td>\n",
+       "      <td>10505.278926</td>\n",
+       "      <td>59.033664</td>\n",
+       "      <td>-4.944128</td>\n",
+       "      <td>6524.216672</td>\n",
+       "      <td>-546.409665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-09-30</th>\n",
+       "      <td>8903.103479</td>\n",
+       "      <td>8856.254476</td>\n",
+       "      <td>45.885378</td>\n",
+       "      <td>-0.526210</td>\n",
+       "      <td>4085.222721</td>\n",
+       "      <td>-46.849003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-10-31</th>\n",
+       "      <td>5193.040924</td>\n",
+       "      <td>5875.854692</td>\n",
+       "      <td>52.410841</td>\n",
+       "      <td>13.148631</td>\n",
+       "      <td>2721.716423</td>\n",
+       "      <td>682.813769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-11-30</th>\n",
+       "      <td>3925.935013</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>37.708201</td>\n",
+       "      <td>-10.070825</td>\n",
+       "      <td>1480.399472</td>\n",
+       "      <td>-395.374029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-31</th>\n",
+       "      <td>3863.789912</td>\n",
+       "      <td>3530.560984</td>\n",
+       "      <td>35.138277</td>\n",
+       "      <td>-8.624406</td>\n",
+       "      <td>1357.669187</td>\n",
+       "      <td>-333.228928</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            y_test_tot_AF  y_pred_tot_AF  StationaryError  SLCWDMError  \\\n",
+       "Date                                                                     \n",
+       "2015-01-31    4709.274282    4020.242564         7.342278   -14.631378   \n",
+       "2015-02-28    4888.568674    4020.242564         4.579880   -17.762379   \n",
+       "2015-03-31    4493.545735    4020.242564        16.816118   -10.532955   \n",
+       "2015-04-30    5114.524352    4643.726952        24.996996    -9.205106   \n",
+       "2015-05-31    8583.758986    8054.101919        13.335806    -6.170456   \n",
+       "2015-06-30   12890.020382   12143.083636        14.946044    -5.794690   \n",
+       "2015-07-31   16379.389681   16521.121898        12.453508     0.865308   \n",
+       "2015-08-31   15197.197243   16351.696472        13.006844     7.596790   \n",
+       "2015-09-30   11502.006931   10581.546063        10.339270    -8.002611   \n",
+       "2015-10-31    6784.423105    6071.879773        13.992224   -10.502637   \n",
+       "2015-11-30    5299.251908    4020.242564        -0.312916   -24.135659   \n",
+       "2015-12-31    4708.315488    4020.242564         8.361965   -14.613994   \n",
+       "2016-01-31    3747.489710    3618.424792        36.470100    -3.444037   \n",
+       "2016-02-29    3545.403337    3618.424792        45.887264     2.059609   \n",
+       "2016-03-31    3241.788769    3618.424792        63.817536    11.618154   \n",
+       "2016-04-30    4525.764752    4947.391246        42.911118     9.316138   \n",
+       "2016-05-31    5202.834938    4895.104544        89.172399    -5.914668   \n",
+       "2016-06-30   11629.504953   11983.593435        28.896039     3.044743   \n",
+       "2016-07-31   13113.950619   12621.761487        42.098780    -3.753172   \n",
+       "2016-08-31   12143.936026   11371.123706        43.074404    -6.363771   \n",
+       "2016-09-30   10495.557894   10994.630258        22.335182     4.755082   \n",
+       "2016-10-31    6268.234299    6579.050919        24.823425     4.958599   \n",
+       "2016-11-30    4167.182691    3618.424792        28.251982   -13.168559   \n",
+       "2016-12-31    3565.126967    3618.424792        44.784007     1.494977   \n",
+       "2017-01-31    4299.786860    3530.560984        20.317090   -17.889861   \n",
+       "2017-02-28    4049.243978    3530.560984        29.212750   -12.809379   \n",
+       "2017-03-31    3543.906179    3530.560984        51.586028    -0.376567   \n",
+       "2017-04-30    3936.532388    3548.014077        66.203610    -9.869557   \n",
+       "2017-05-31    7427.320860    8476.514535        34.048489    14.126139   \n",
+       "2017-06-30   14063.436455   12753.703882         7.821565    -9.313034   \n",
+       "2017-07-31   16247.673230   15969.874949        16.018969    -1.709773   \n",
+       "2017-08-31   11051.688592   10505.278926        59.033664    -4.944128   \n",
+       "2017-09-30    8903.103479    8856.254476        45.885378    -0.526210   \n",
+       "2017-10-31    5193.040924    5875.854692        52.410841    13.148631   \n",
+       "2017-11-30    3925.935013    3530.560984        37.708201   -10.070825   \n",
+       "2017-12-31    3863.789912    3530.560984        35.138277    -8.624406   \n",
+       "\n",
+       "            StationaryErrorAF  SLCWDMErrorAF  \n",
+       "Date                                          \n",
+       "2015-01-31         345.767994    -689.031718  \n",
+       "2015-02-28         223.890584    -868.326110  \n",
+       "2015-03-31         755.639941    -473.303171  \n",
+       "2015-04-30        1278.477435    -470.797400  \n",
+       "2015-05-31        1144.713444    -529.657067  \n",
+       "2015-06-30        1926.548117    -746.936747  \n",
+       "2015-07-31        2039.808677     141.732217  \n",
+       "2015-08-31        1976.675810    1154.499229  \n",
+       "2015-09-30        1189.223590    -920.460868  \n",
+       "2015-10-31         949.291652    -712.543332  \n",
+       "2015-11-30         -16.582211   -1279.009344  \n",
+       "2015-12-31         393.707671    -688.072925  \n",
+       "2016-01-31        1366.713253    -129.064919  \n",
+       "2016-02-29        1626.888577      73.021455  \n",
+       "2016-03-31        2068.829713     376.636022  \n",
+       "2016-04-30        1942.056267     421.626494  \n",
+       "2016-05-31        4639.492745    -307.730393  \n",
+       "2016-06-30        3360.466324     354.088482  \n",
+       "2016-07-31        5520.813183    -492.189132  \n",
+       "2016-08-31        5230.928053    -772.812320  \n",
+       "2016-09-30        2344.201933     499.072364  \n",
+       "2016-10-31        1555.990459     310.816620  \n",
+       "2016-11-30        1177.311686    -548.757899  \n",
+       "2016-12-31        1596.606712      53.297825  \n",
+       "2017-01-31         873.591554    -769.225876  \n",
+       "2017-02-28        1182.895524    -518.682994  \n",
+       "2017-03-31        1828.160440     -13.345195  \n",
+       "2017-04-30        2606.126535    -388.518312  \n",
+       "2017-05-31        2528.890489    1049.193675  \n",
+       "2017-06-30        1099.980875   -1309.732573  \n",
+       "2017-07-31        2602.709812    -277.798281  \n",
+       "2017-08-31        6524.216672    -546.409665  \n",
+       "2017-09-30        4085.222721     -46.849003  \n",
+       "2017-10-31        2721.716423     682.813769  \n",
+       "2017-11-30        1480.399472    -395.374029  \n",
+       "2017-12-31        1357.669187    -333.228928  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Eval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Pred_UR_GPCD' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-87dd60c0ef69>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Get monthly stats for UR\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mURcol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'y_test_tot'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'UR_gpcd'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mPred_UR_GPCD\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPred_UR_GPCD\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mURcol\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      4\u001b[0m \u001b[0mPred_UR_GPCD\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Month'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPred_UR_GPCD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mUR\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;31mNameError\u001b[0m: name 'Pred_UR_GPCD' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "#Get monthly stats for UR\n",
+    "URcol = ['y_test_tot', 'UR_gpcd']\n",
+    "Pred_UR_GPCD = Pred_UR_GPCD[URcol]\n",
+    "Pred_UR_GPCD['Month'] = Pred_UR_GPCD.index.month\n",
+    "UR = {}]/9++++\n",
+    "\n",
+    "Irrmon = np.arange(4,11,1)\n",
+    "month = ['Apr', 'May', 'Jun' , 'Jul', 'Aug', 'Sep', 'Oct']\n",
+    "for i in Irrmon:\n",
+    "    UR[i]=Pred_UR_GPCD.loc[Pred_UR_GPCD['Month']==i]\n",
+    "    \n",
+    "for i in UR:\n",
+    "    \n",
+    "    print( calendar.month_name[i], 'Unit Rate Performance Metrics.')\n",
+    "    T_r2 = sklearn.metrics.r2_score(UR[i]['y_test_tot'],UR[i]['UR_gpcd'])\n",
+    "    T_rmse= sklearn.metrics.mean_squared_error(UR[i]['y_test_tot'],UR[i]['UR_gpcd'], squared = False)\n",
+    "    T_mae= sklearn.metrics.mean_absolute_error(UR[i]['y_test_tot'],UR[i]['UR_gpcd'])\n",
+    "    T_mape= sklearn.metrics.mean_absolute_percentage_error(UR[i]['y_test_tot'],UR[i]['UR_gpcd'])*100\n",
+    "        \n",
+    "    print('The Total Demand prediction RMSE is ', T_rmse)\n",
+    "    print('The Total Demand prediction R2 is ', T_r2)\n",
+    "    print('The Total Demand prediction MAE is ', T_mae)\n",
+    "    print('The Total Demand prediction MAPE is ', T_mape, '%') \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "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>y_test_tot</th>\n",
+       "      <th>UR_gpcd</th>\n",
+       "      <th>Month</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-04-30</th>\n",
+       "      <td>160.030</td>\n",
+       "      <td>200.032692</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-04-30</th>\n",
+       "      <td>139.970</td>\n",
+       "      <td>200.032692</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-04-30</th>\n",
+       "      <td>120.354</td>\n",
+       "      <td>200.032692</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            y_test_tot     UR_gpcd  Month\n",
+       "Date                                     \n",
+       "2015-04-30     160.030  200.032692      4\n",
+       "2016-04-30     139.970  200.032692      4\n",
+       "2017-04-30     120.354  200.032692      4"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "UR[4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-39-18238038290e>:92: UserWarning: constrained_layout not applied.  At least one axes collapsed to zero width or height.\n",
+      "  fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/' +str(plotname)+'.pdf')\n",
+      "<ipython-input-39-18238038290e>:103: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
+      "  FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n",
+      "C:\\Users\\Ryan\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:132: UserWarning: constrained_layout not applied.  At least one axes collapsed to zero width or height.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total R2 is  0.6235333383547133\n",
+      "Total MAE is  2043.3946037944281\n",
+      "Total RMSE is  2529.7467407378826\n",
+      "Total MAPE is  30.788637404653628\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Pred_UR_AF = Demand_Forecast(slc_val, False, Pred_Obs, 'UR_gpcd_AF', \n",
+    "                             'y_test_tot_AF', 'Acre-Feet', 'Seasonal_term_pred_UR_AF', 'Stationary Forecast')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''I am putting a hard code on the limits here to make both figure have same color scale'''\n",
+    "\n",
+    "plt.rcParams[\"axes.grid\"] = False\n",
+    "plt.rcParams[\"axes.facecolor\"] ='white'\n",
+    "#plt.rcParams[\"axes.edgecolor\"]['bottom'] = 'black'\n",
+    "\n",
+    "def gradientbars_sliced(bars, ymin, ymax):\n",
+    "    ax = bars[0].axes\n",
+    "    xmin, xmax = ax.get_xlim()\n",
+    " #   ymin, ymax = ax.get_ylim()\n",
+    "    for bar in bars:\n",
+    "        bar.set_zorder(1)\n",
+    "        bar.set_facecolor(\"none\")\n",
+    "        x, y = bar.get_xy()\n",
+    "        w, h = bar.get_width(), bar.get_height()\n",
+    "        grad = np.linspace(y, y + h, 256).reshape(256, 1)\n",
+    "        ax.imshow(grad, extent=[x, x + w, y, y + h], aspect=\"auto\", zorder=0, origin='lower',\n",
+    "                  vmin= - max(np.abs(ymin), ymax), vmax=max(np.abs(ymin), ymax), cmap='Spectral_r')\n",
+    "    ax.axis([xmin, xmax, ymin, ymax])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Pxyld02fXbd4DdNW2/FJK4Puz+vidTHlNfYQyaezrlID2LsoXlt78ktJO/5syhrrbd4CrI+J6Slf3tpQs6w6ZOSciTqC0y79Rvkz17H2QFokZSKmf1PFlb6JkvX5OGX/0IcqlQ46o68ymZBLmUAKvn1Le/PdehEOfDhxJCV6nUQJF6r7PrP9vpHwQfwqI+XTj9fRRyuSS31Jmwg6ndo9l5gWUyQ1HULKdbwHelgtxnbwsE2P+C3glJbg9gdL995UFbde0/R8oGbIVKVngOyiBzanAl+o6T9VjjK3ncgblceuekf4JyqVn/kSZufwXyoSj7qzNLpQg5lpKYDWZeROGvkOZwHNi3ffqwJvzPycl9eZ4yuVl/o/y/I2lBF4b9eXc++CDlODkt5SZ8rdRgqJeZeZ5lOf10LrueyhZvJvrKp+iXKbol7XON1AuQTWV0l4+Q+mS/xIlsJoBbFy334uSKb6TEqh+JuvlaSiB/0col076BWWC07coWbbPALvW19f8nEPplr+4qewESvbvtHrMHSiXxOl1skw997dSJmRdTXnNbE/JNH+zx+qnU17rN1OGrWyXmc9kmfS1PaUr+WbKVRD+jxKEQrk8z68os9D/SPmi8fbM7G1ISHe9nqO8nzzO88c7X0uZuLMP5bn6FGUiWPev5hxV63ku5XJLi8WvYWnx0ZgzZ84LryVpSIpyvcUjs+k6eJIkDTQzkJIkSWqJAaQkSZJaYhe2JEmSWmIGUpIkSS3xMj7V4YcfvtTIkSM32WOPPaatuuqq8/s9VEmSpCVBF+V6rn+kXLbreQwgq5EjR25y0EEHTWp3PSRJkoaQLSmXtnoex0BWq6222iu7urruvvrqqxkzZky7qyNJkjQUrE35MYrnMQNZTZ06dRbAzJm9/QyxJEnSEqnXYX1OopEkSVJLDCAlSZLUEruw+2D27NlMnTqVZ555pt1V6ShLL700Y8aMYdgwv8dIkrQ4MYDsg0ceeYRGo0FEGOz0k9mzZ3PffffxyCOPsPLKK7e7OpIkqQVGQ33wxBNP8PKXv9zgsR8NGzaMl7/85fzzn/9sd1UkSVKLjIj6YNasWYwYMaLd1eg4I0aMcNa7JEmLIQPIPmo0Gu2uQsfxMZUkafHkGMiFMX06jBzZtv1ed9117Lfffqy++urMmTOHGTNm8N///d988IMf7P86SZIk9WAAuTBGjoSByJ618KtA66+/PmeeeSYATz/9NNtttx2bb745a6+9dv/XS5IkqYkBZAf497//TVdXF8suuywTJ07ktNNOY/r06Tz33HMcffTRvPa1r+W0007jwgsvZNiwYWywwQYcccQRzJo1i29+85tcf/31zJo1i5122om99tqr3acjSZKGOAPIxdStt97KDjvswOzZs/n73//O+PHjWXHFFTn33HP54Q9/yEtf+lLOP/98TjrpJE444QROPPFEJk2aRFdXF1/+8pd58MEHufzyywG48MILee655/jwhz/M+uuvz+tf//o2n50kSRrKDCAXUz27sPfee29OOeUUTjjhBC6//HImT57M9ddfz7Bhw+jq6mLjjTfmfe97H29961v5n//5H17+8pfzhz/8gTvuuINrr70WgH/9619kpgGkJElaIAPIDrDMMsswfvx4rrjiCi688ELe/e53s8kmmxARnH322QBMmDCBm2++mauuuoq9996bb3/728yaNYvPfe5zvP3tbwfgscceY+mll27nqUiSpMWAl/HpALNmzeL6669n5MiRNBoN9ttvPzbddFP+7//+j1mzZvHYY4/xzne+k3XWWYdPfOITbL755mQmm222GT/96U+ZMWMGzzzzDLvtths333xzu09HkiQNcWYgF1PdYyAbjQYzZ84kIvjqV7/KwQcfzPjx42k0GmyxxRbceOONvPSlL2WXXXbhfe97H6NGjWLNNdfkve99LyNGjODee+/lPe95DzNnzmSnnXZi0003bfepSZKkIa4xp4VLx3SyRqMxFpg8efJkxo4d+7xld9xxB+PGjZtX0ObrQHaS/3hsJUnSULImMKVnoV3YC2OggrwlLHiUJEmLJwNISZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktWRArwMZEcsB1wDvyswpEbEv8HFgDnAD8JHMfC4iNgJOAZYDrgL2y8yZEbE6cBawMpDA7pn5dESsAJwNrAU8DOycmQ9ExIuAHwGvB54FdsvMOwfyHCVJkpY0A5aBjIhNgauBder9dYDPAW8CNqjHPqCufhZwYGauAzSAfWr5BGBCZq5LCTgPqeVHApMycxxwMnBcLf848Ewt/yRw+oCc3KzpA7Lbvu536tSpRARf+cpXnld+xx13EBH8/Oc/JyJ63Xabbbbhne98JzvssAM77LAD22yzDR//+Mf517/+tcjVlyRJS4aBzEDuQwkQz6z3/w3sn5lPAkTEX4DVI2INYFRmXlvXOx04PCJOAbYCdmwq/x1wELBdXQZwDnBCRIyo5V8ByMyrImKliFg9M//er2fWNRJ+0ujXXQKwW98v6r7CCiswadIkZs2aRVdXFwCXXXYZL33pS19w25NOOokxY8YA8Nxzz7Hbbrtx0UUXsdtuuy1cvSVJ0hJlwDKQmbl3Zk5qun9vZv4fQESsBBwIXAysCkxr2nQaMAZYEXgyM2f2KKd5m7r8SWClBeyr4yy99NKMGzeOP/7xj3PLfv/73/OmN72ppf089dRTPPXUU6ywwgr9XENJktSpBv23sCPiFcBE4EeZeWVEbE4ZE9mtAcymBLc9U3Kzm9ZpNr9tGk3bdJzx48fzq1/9is0224xbbrmFiKAvP02577770tXVxaOPPsoqq6zCBz7wAcaPHz8INZYkSZ1gUGdhR8S6lEk1Z2TmV2vxVGB002qrAPcDDwHLR0RXLR9dywHuq+sREcOBZYFHF7CvjrTNNttw1VVXMXv2bCZOnNjnIPCkk07iF7/4BYceeiiPP/44//Vf/0WjMQBd8pIkqSMNWgAZEcsCvwYOzsxjussz815ges1EAuwBTMzMGcAkYJdaviclcwlwWb1PXT6prj+3PCK2AKb3+/jHIWTppZdm3XXX5cYbb+Taa69tufv6He94B1tuuSVf+tKXBqiGkiSpEw1mBnJv4OXAZyLi5vp3RF22O/CdiLgTWAY4vpbvD+wbEbcDWwIH1/JDgM0i4ra6Tvds7u8BS9Xy4ynBaEcbP348xxxzDOuvvz7Dh7c+IuETn/gEN954I1deeWX/V06SJHWkAR8DmZlj683v1L/e1vkz8IZeyu8Ftu6l/DHg3b2UTwc+uPC17aNZ01uaMd3SfrtGtrTJW97yFr785S/ziU984j+WbbzxxnNvr7rqqlx66aX/sc7LXvYy9tlnH775zW+yxRZbLFQQKkmSliyNvky6WBI0Go2xwOTJkyczduzY5y274447GDduXDuq1fF8bCVJGtLWBKb0LPSnDCVJktQSA0hJkl7I9AH6BTJpYQyB9uiAN0mSXsjIkeDlzjRUDIHhh2YgJfWfIfCtWJrL9igNGDOQkvqPWRoNJUMgSyN1KjOQ7TK7Y39hUYsjMzWSpBaYgVwI06eXRMsiGfafsfv0p2YwMv/cp82fmT6db59zDlf/5S+MWmoplhk1io/ttBNvXH999jjySA7caSc2XW+9Raxk/4nddyfPPvs/FzzyCAyhei6xzNRIklpgALkQBqqXbs6cEX1cbw77ffvbjFtjDS795jd50fDh3D5lCvt+61scc8ABL7wDSZKkRWAAuRi6/o47uP+RR/jxl79Mo0ay640dy0d33JEJF10EwE8vv5yvnXUWAF/8wAfYdL31+MOtt/Ktc86BRoPll16aYw48kJcuuywXTZrEGb/8JbNnz+bVa67JoXvtxVIvehGb7bcf66+5Jg8/8QRjVl6Zd2++Oe94Q/nBoJ2+/GWO3Gcflh45ksNOO40nnn6akS96EYd88IOsN3YsUx9+mM9NmMC/pk9nw7XXbsvjJEmSBoZjIBdDf/nb31h/rbXmBo/dNll3Xf5yzz0AvHjkSC46+mi+vt9+fG7CBJ6bMYMJF13EYR/6ED8/8kjetP763D55MndNncpPr7iCcw89lIu/9jVettxy/Kj+5OHjTz3FPttvz8Vf+xo7brEFl15zDQBTHniAf8+YwXpjx3LQD3/I53bdlQuPOoqvfvjDfOr73wfgq6efzk5bbcXFX/sar11nnUF8dCRJ0kAzA7kYajQazJo16z/KZ8ycOTeofN/WWwOw7uqr87Lll+dv99/PW1/3Og78znfY9vWv562vex2bv+Y1nPXrX3PvAw+w86GHzt3HemuuOXef3dnDN2+8MUeccQZPP/ssl1xzDe/efHOemT6dW//2N7544olz1//X9Ok8/tRTXH/HHRxz4IEAvHvzzTn45JMH5LGQJEmDzwByMbThK1/Jmb/6FTNmzmTE8HlP4c133cX6a63F7Nmz6erqmls+e/Zshnd1sdf48bxl44254k9/4lvnnMMt99zDi0eOZPymm3LwBz8IlMk5zcHpyBe9CIAXDR/OWzbemMtvuolfXncdJ372s8yePZsXjRjBxV/72tz1H3j0UVZYZhloNOj+nfVGo8GwXiYNSZKkxZOf6ouh16+7Lmu/4hUcfeaZzJg5E4BbJ0/mBxdfzP477gjAL37/e6B0dz8zfTprrLIK7//KV3hm+nT2Gj+evcaP5/YpU9h03Dj+74YbePSf/2TOnDkcduqpnPHLX/Z63B222ILTLruMFZZZhlestBLLvvjFjF1lFS6++moAfv+Xv7D7V78KwJvWX5//reW//uMf+feMGQP5kEiSpEFkBnIx9f1PfYrv/PSnvOsLX6Cr0WD5ZZbhWx/9KJuutx7f//nP+df06ez4pS8xbNgwjjngAEYMH86nd96ZL5x4IsOHDePFI0dy5D77MHaVVThwp5344NFHM3v2bMatsQb7br99r8d8XQRP/etf7PrWt84t+9YBB3DYqadyyiWXMGL4cL7zsY/RaDT4ygc/yOd+8APOu+IK1l9zTZZe5OseSZKkoaIxx+u/AdBoNMYCkydPnszYsWOft+yOO+5g3Lhxc+/3y3Uge9HKdSA7xR2PPMK48ePbXQ315/uAv0SjoaK/P99s2xoqBjd2WxOY0rPQDORC6Lfg8YYbnr/fftqtJEnSQHIMpCRJklpiAClJkqSWGED2kWNF+98cgNmz210NSZLUIgPIPhg5ciSPPvqoQWQ/mgM8OnMmI+++u91VkSRJLXISTR+MGTOGqVOn8vDDD/fvjh95pH/3tziZPZuRd9/NmMMOa3dNJElSiwwg+2DEiBGs2fTzfv1mvfX6f5+SJEkDbEADyIhYDrgGeFdmTomIbYFjgVHAeZl5cF1vI+AUYDngKmC/zJwZEasDZwErAwnsnplPR8QKwNnAWsDDwM6Z+UBEvAj4EfB64Flgt8y8cyDPUZIkaUkzYGMgI2JT4GpgnXp/FHAqsAMwDtgkIrqvIH0WcGBmrgM0gH1q+QRgQmauC9wAHFLLjwQmZeY44GTguFr+ceCZWv5J4PSBOj9JkqQl1UBOotkHOAC4v95/A3BXZk7OzJmUoPH9EbEGMCozr63rnV7LRwBbAec3l9fb21EykADnAOPr+nPLM/MqYKWaxRxypk+nXEneP/+GwN/06e1+RUiSFicD1oWdmXsDRER30arAtKZVpgFjFlC+IvBkDTaby5+3r9rV/SSw0gL29fd+Oal+NHKkv4qloWPOnHbXQJK0OBnMy/gMA5o/phrA7BbKqeXd6zR7oX1JkiSpnwxmADkVGN10fxVK9/b8yh8Clo+Irlo+mnnd4ffV9YiI4cCywKML2JckSZL6yWAGkNcBERFr16BwN2BiZt4LTI+Izet6e9TyGcAkYJdavicwsd6+rN6nLp9U159bHhFbANMzc8h1X0uSJC3OBi2AzMzpwF7ABcDtwJ3MmyCzO/CdiLgTWAY4vpbvD+wbEbcDWwIH1/JDgM0i4ra6zgG1/HvAUrX8eEowKkmSpH7U8Of5ikajMRaYPHnyZMaOHTtIxxyUw0gvqF/fBmzYGir6+/PNtq2hYnBjtzWBKT0L/S1sSZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUkuHtroAkSUPd9Okwcs6cdldDAmp7HNneOhhASpL0AkaOhEaj3bWQiqHwXeYFu7Aj4qu9lB03MNWRJEnSUDffDGREHA68BNglIpZvWjQCeAfwiQGumyRJkoagBXVhXwdsAswGHm0qnwnsPpCVkiRJ0tA13wAyMy8DLouIiZl5/SDWSZIkSUNYXybR/CMiLgVeBWwBnAnslZnTFvagEfEB4Iv17sTM/GxEbAscC4wCzsvMg+u6GwGnAMsBVwH7ZebMiFgdOAtYGUhg98x8OiJWAM4G1gIeBnbOzAcWtq6SJEl6vr5cB/IE4CLgWeBx4GZKQLdQIuLFwPHAm4ENgS0jYnvgVGAHYBywSUSMr5ucBRyYmesADWCfWj4BmJCZ6wI3AIfU8iOBSZk5DjgZcMKPNEimPzunTA/0z78h8Df92TntfklIHasvAeTYzDwZmJ2ZMzLzIGD1RThmVz3u0pQJOSOAJ4G7MnNyZs6kBI3vj4g1gFGZeW3d9vRaPgLYCji/ubze3o6SgQQ4Bxhf15c0wEaOatBo4J9/Q+Jv5KhGu18SUsfqSwA5OyLmrhcRy/Zxu15l5lOUbOGdwFRgCrAq0NwlPg0Ys4DyFYEna7DZXE7zNnX5k8BKC1tfSZIkPV9fAsGfUzJ6y0fER4DLgZ8u7AEjYgPgQ8AalGBvFrAO0NzX0KDM/h7Wx3Jqefc6zRpNyyRJkrSIXjCAzMyjgYnAH4G3AScBRyzCMd8B/DYzH8rMf1O6n7cGRjetswpwPyVD2Vv5Q5SAtquWj67lAPfV9YiI4cCyPP8yRJIkSVoEfe2K/hllcsrOwNmZuSgjk/8MbBsRS0dEA9iecs3JiIi1a1C4G2V29r3A9IjYvG67Ry2fAUwCdqnle1KCXIDL6n3q8kl1fUmSJPWDvvyU4WbAPcCllC7nf0TEmxb2gJn5a8rklhuBWyiTaA4D9gIuAG6njI/sniCzO/CdiLgTWIYygxtgf2DfiLgd2BI4uJYfAmwWEbfVdQ5Y2LpKkiTpPzXmzFlwMjEiJgEfoWQeN46IdwKHZ+Ymg1HBwdJoNMYCkydPnszYsWMH6ZiDchjpBb3A20BLbNcaKvqzXYNtW0NHf7ftF7AmZcLz8/SlC/vFmXl79536CzV9uQC5JEmSOlBfAsgZEfES6qzniIiBrZIkSZKGsr5kEo8EfgesEhHnAG8H9h3QWkmSJGnIesEAMjMvqRNY3kb5FZkjMvOOAa+ZJEmShqT5BpARsW9mngSQmXcDdw9arSRJkjRkLWgM5H7dNyLiooGviiRJkhYHCwogmy9YsMZAV0SSJEmLh77+Es3gXnFIkiRJQ9aCAkiDRkmSJP2HBc3CXjcibqm31266DUBmbjBw1ZIkSdJQtaAAcvyg1UKSJEmLjfkGkJn5u55lEfGuzLxkYKskSZKkoayvk2i6HTEgtZAkSdJio9UAsvHCq0iSJKmTtRpA3j8gtZAkSdJio08BZESMiojXANtHxIsHuE6SJEkawl4wgIyIzYB7gEuBVYF/RMSbBrpikiRJGpr6koH8FrAt8GhmTgX2AI4b0FpJkiRpyOpLAPnizLy9+05mXsaCrx8pSZKkDtaXAHJGRLyE+tOGEREDWyVJkiQNZX3JJB4F/A5YJSLOAd4O7DugtZIkSdKQ1ZcA8pfAHcDbgC7giMy8Y0BrJUmSpCGrLwHkHzNzI+DuAa6LJEmSFgN9CSCfiYgxdQZ2v4iI7YFDgaWBX2fmJyJiW+BYYBRwXmYeXNfdCDgFWA64CtgvM2dGxOrAWcDKQAK7Z+bTEbECcDawFvAwsHNmPtBfdZckSVrS9WUSzdLA5Ij4W0TcEhF/iYhbFvaAEbEW8ENgR2AD4LURMR44FdgBGAdsUsugBIkHZuY6lJ9S3KeWTwAmZOa6wA3AIbX8SGBSZo4DTsZLDkmSJPWrvmQgP9HPx3wPJcM4FSAidgFeBdyVmZNr2VnA+yPidmBUZl5btz0dODwiTgG2ogSh3eW/Aw4CtqvLAM4BToiIEZk5o5/PQ5IkaYnUlwzkPcAumfk74AHgU5Qu44W1NtAVEf8bETcD+1N+4WZa0zrTgDELKF8ReDIzZ/Yop3mbuvxJYKVFqK8kSZKa9CWAPB24s96+F7iS0t28sIZTftnmw8AbgU0p4xXnNK3TAGbX+vWlnFrevU6zRtMySZIkLaK+BJArZubxAJk5PTO/C4xehGM+APwmMx/OzGeBCykBZfM+VwHuB6bOp/whYPmI6Krlo2s5wH11PSJiOLAs8Ogi1FeSJElN+hJADo+IVbvvRMTL+c8sXysuAd4RESvUAHA8cH7Zdaxdy3YDJmbmvcD0iNi8brtHLZ8BTAJ2qeV7AhPr7cvqferySY5/lCRJ6j99mURzLHBzRPyy3n8r8LmFPWBmXhcR3wSuBkYA/wf8gNJNfgEwkhIEnl832R04OSKWA24Cjq/l+wNnRMTBwN+BXWv5IcDpEXEb8ETdXpIkSf2kMWdOz6GE/ykiNgS2AWYCLwG2y8xNB7hug6rRaIwFJk+ePJmxY8cO0jEH5TDSC+rD20Cf2a41VPRnuwbbtoaO/m7bL2BNYErPwr5kIKFk+JYCPg0sw7wsoCRJkpYwCwwgIyKAT1LGHk6h/ErM2Mz854DXTJIkSUPSfCfRRMSllJ8OnAFsnZnrA08ZPEqSJC3ZFjQL+7XAjcCtwN21bHB73SVJkjTkLCiAXI1yEfFdgWkR8TNKF7YkSZKWYPMNIDNzZmb+NDPfAryO8vOAIyPirojYb9BqKEmSpCGlLxcSJzNvz8yPA68AvgXsO6C1kiRJ0pDV18v4AJCZ/wJOqn+SJElaAvUpAylJkiR1M4CUJElSSwwgJUmS1BIDSEmSJLXEAFKSJEktMYCUJElSSwwgJUmS1BIDSEmSJLXEAFKSJEktMYCUJElSSwwgJUmS1BIDSEmSJLXEAFKSJEktMYCUJElSS4a38+AR8W1gxczcKyK2BY4FRgHnZebBdZ2NgFOA5YCrgP0yc2ZErA6cBawMJLB7Zj4dESsAZwNrAQ8DO2fmA4N7ZpIkSZ2rbRnIiHgr8MF6exRwKrADMA7YJCLG11XPAg7MzHWABrBPLZ8ATMjMdYEbgENq+ZHApMwcB5wMHDcIpyNJkrTEaEsAGREvBY4Cjq5FbwDuyszJmTmTEjS+PyLWAEZl5rV1vdNr+QhgK+D85vJ6eztKBhLgHGB8XV+SJEn9oF0ZyBOBLwOP1/urAtOalk8DxiygfEXgyRpsNpc/b191+ZPASv1/CpIkSUumQQ8gI2Jv4B+Z+dse9ZjTdL8BzG6hnFrevU6zRtMySZIkLaJ2TKLZBRgdETcDLwWWAdYAZjWtswpwPzAVGN1L+UPA8hHRlZmz6jr313Xuq+tNjYjhwLLAowN2NpIkSUuYQc9AZubbMnP9zNwI+Arwv8B4ICJi7YjoAnYDJmbmvcD0iNi8br5HLZ8BTKIEowB7AhPr7cvqferySXV9SZIk9YMhcR3IzJwO7AVcANwO3Mm8CTK7A9+JiDsp2crja/n+wL4RcTuwJXBwLT8E2CwibqvrHDAY5yBJkrSkaMyZ03Mo4ZKp0WiMBSZPnjyZsWPHDtIxB+Uw0gvqz7cB27WGiv7+eLNta6gY5NBtTWBKz8IhkYGUJEnS4sMAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLhrfjoBFxKLBzvXtpZn4+IrYFjgVGAedl5sF13Y2AU4DlgKuA/TJzZkSsDpwFrAwksHtmPh0RKwBnA2sBDwM7Z+YDg3ZykiRJHW7QM5A1UHw7sDGwEfC6iNgVOBXYARgHbBIR4+smZwEHZuY6QAPYp5ZPACZk5rrADcAhtfxIYFJmjgNOBo4b8JOSJElagrSjC3sa8JnMfC4zZwB3AOsAd2Xm5MycSQka3x8RawCjMvPauu3ptXwEsBVwfnN5vb0dJQMJcA4wvq4vSZKkfjDoAWRm3tYdEEbEqyhd2bMpgWW3acAYYNX5lK8IPFmDzeZymrepy58EVhqQk5EkSVoCtW0STUS8Gvg/4HPA34A5TYsblKByWB/LqeXd6zRrNC2TJEnSImpLABkRmwO/Bb6QmWcAU4HRTausAty/gPKHgOUjoquWj67lAPfV9YiI4cCywKMDcyaSJElLnnZMolkNuAjYLTPPrcXXlUWxdg0KdwMmZua9wPQacALsUctnAJOAXWr5nsDEevuyep+6fFJdX5IkSf2gHZfx+SwwEjg2IrrLfgjsBVxQl13GvAkyuwMnR8RywE3A8bV8f+CMiDgY+Duway0/BDg9Im4DnqjbS5IkqZ805szpOZRwydRoNMYCkydPnszYsWMH6ZiDchjpBfXn24DtWkNFf3+82bY1VAxy6LYmMKVnob9EI0mSpJYYQEqSJKklBpCSJElqiQGkJEmSWmIAKUmSpJYYQEqSJKklBpCSJElqiQGkJEmSWmIAKUmSpJYYQEqSJKklBpCSJElqiQGkJEmSWmIAKUmSpJYYQEqSJKklBpCSJElqiQGkJEmSWmIAKUmSpJYYQEqSJKklBpCSJElqiQGkJEmSWmIAKUmSpJYYQEqSJKklw9tdgYEQEbsBBwMjgO9m5gltrpIkSVLH6LgMZES8AjgK2ALYCNg3ItZra6UkSZI6SMcFkMC2wOWZ+VhmPgOcD7yvzXWSJEnqGJ3Yhb0qMK3p/jTgDX3Yrgtg6tSpA1EnaUibMqXdNZD6n+1anWow23ZXV1fXaqut9h/lnRhADgPmNN1vALP7sN1ogC233HIg6iQNaWuu2e4aSP3Pdq1ONZht+6CDDhr99a9//Z6e5Z0YQE4FmqPAVYD7+7DdH+t204BZA1AvSZKkxcLo0aO79txzz9FLLbXUH3tb3pgzZ05v5YutOonmakq39TPANcC+mXl9WysmSZLUITpuEk1m3gd8GbgCuBn4icGjJElS/+m4DKQkSZIGVsdlICVJkjSwDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLOvFC4lpMRMT7gC9S2uEw4MeZ+a1+2O8UYOvMnLKo+5JaERFjgb8Ct9eiUZRr0X4hMx8cwGNemZlje1l2GnBYZt47EMfWkiMi1gf+ArwvMy9od33UfmYg1Rb1gu/HAG/PzA2BNwL/HRHvbm/NpEV2f2ZulJkbAesCDwDnt6kub6H8nKu0qD4E/Az4SLsroqHB60CqLSJiQ2AisGlm/qOWrQ9MB35DzSBGxNaUDMrWEXEl8BjwamAX4NfAz4E3AU8Bu9dtpgBbd/9l5l51/1cChwF3A2cDS1N+J/3jmXntAJ+ylgC9ZQMj4kXAg8ChwAeALuBW4KPAycCGlHb47cz8cUTsRS/tNjOvjIivAe8DHqH87Or/AlcC11F+PGF94HFgR2Af4AhKe98yMx8dqPNWZ4uIEcz7meBrgE2BA4D7MvOYus4FwFl1+YnAapR2/cXM/E1EHAZsBqwOfI+SpT8KeDGwAvCpzLw4IsZQ3p9fQsl4vjkzx0TEMsAJlDbeBXwjM88Z+LPX/JiBVFtk5p+Bi4G/RcT1EfENoCsz736BTW/JzMjMm4GVgD9k5gbAucDxfTz8h4FLMvP1wFeALRbqJKQ+yMzngLsomch1gG0y84OULzOPZub6wDbAYRGxwfz2ExHbU9rqq4F3Ahs3LV4JOLbu60HgvzPz68D9wDsNHrWItgPuzcy/AhcB+wJnArsCRMSylF6kS4HjgFMz83XAu4ET63KAkZm5Xmb+APgYsHdmvhbYGziyrnMccF59Xz8feEUtPxi4se53K+DLEbHWAJ6zXoABpNomMz8KjAV+AKwBXBsRO73AZtc13Z4O/LjePoPyIdwXvwE+GxE/AV4GfL+vdZYW0hzgWSAz85+1bBvgR5TCRyhfqLZewD7eBvw0M5/LzMcpH+Td7m/6ydbbgBX7r+oS/wN0Z/vOq/dvA0ZGxNrAe4Bf1C9L2wJHRMTNlF6mEcAr67bN798fANaPiEOAzwDL1PK3UYJTMvNC4Ilavi2wX93vVZQepFf350mqNU6iUVtExHbAMpl5HnAacFpE7EPJDs5h3ritET02fbbp9uzM7B6DMQyY2WPd5v3M3Vdm/j4i1gPeRekK34vypiX1u9qFHcDKPL/99vwC36C8J/faboFZvWzTrbnt99xeWmgRsTIwHnhdRHyC0rZeAuxE6bLehTKM6Ot1ky5Klv2xuv1o4CHKsIrm9j+JMuziSuC3wE9q+fzaeRfwgcy8qe735ZQhTWoTM5Bql38BX6tjxoiIBrAR8CfK+K7ub5Y7LGAfL67delC+EU/ssfwRYFxENCJiTWCDeqxvUt6IzgAOBF67yGcj9SIihgGHA9cC9/RYfDnlCxMRsSLlA/ZK5tNuKZnz90bEiyJiOcoXoBcaxD4TEwVaNHsAv83MMZk5NjPXoIxd3I8yVnEXYG3g6rr+5cD+APWL+q2UcY5zRcRLKcM5vkJ5396BEiBCaee71fXGU8ZHdu/3o7V8NHALZTyl2sQAUm2RmVdQPlgviYgE7qR88/wqZbLBcRHxR+Z1X8zP+yPiFuAdwCd7LPsN8A8gKeNqut/gvge8r3aFXAjsuYinIzVbNSJuru3rz5QxXLv2st4RwEsj4i+ULrmjanal13abmZfW9f5EGWt2P8/P6PTmEuCyGohKC2MvYEKPshOAN1C6kR8Bzm/qDfoYsFl9Xz6P8mX9qeaNa3byR5Ru8DuAZSkJgaWBT1C+KP2JEpw+UTc7HBgVEbdSgsnPZ2bPL2UaRM7C1mIrIuZkpl11WiJExBuBdTLzjDor9g/AhzLzljZXTeo3EfFx4DeZeXtEvBY4uU6c0RBj14YkLR4SODQiPk3pPTrD4FEd6C7gnIiYTZkouU+b66P5MAMpSZKkljgGUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJL/C1sqYNERM/fJn0UuBj4ZGY+1YYqzRURWwNXNBXNotTvV8DnMvPBut5ewJGZOabefwdwGrAcsBnwbuCLwGOZucZg1b+/RcSVwJt7WfRMZi4zyHU5DDh0AascDkyh6Xnpp+MuC3wJ2AVYFZgG/Bw4OjMfreuMBSYDr8rMu/vr2IMhIs4CZmbmXu2ui9TfzEBKnWdnYDQwBngX8Hrg2LbW6PnGUOq3JqWu6wKXR8TSdfl5wMZN638D+CWwPvAgcBTwGWDzwarwAPou5bFo/lurDfX4dtPx31jL3tBU9u3+PmBELANcBYwHPgYEsHc97rURsUp/H1NS/zEDKXWexzPzgXr7vog4jhKo7NO+Kj3Pg5k5s97+R0RsBySwH3BMZj4LPNu0/vLAHzJzSkR0ZxyvyMypg1flAfNM03PVNpn5NPA0zA3sAB5urltE9PdhvwosDby2Hh/g3oi4GvgD8B1g1/4+qKT+YQApdb6Hm+/UbsPvULqCV6B0D34pMy+oy98HHEHJhP2D0p14Wl22PHA8sCMlyPtf4DPd3eMRMQU4PTMP62vlMvPhiLgQeA9wTHMXdlOX/EkRsTvzunz/GhGHZ+ZhEbEjJSu5JnBnPZdf1vpcCdwK/BcwCtgQGAl8H3gbpQv9J8BXMvO5euy9gV9TsmIzKRnRT2fm7LrPjwGfAlYGrgf2z8w767J9gS/UZTcDn8rMP/b1sWhWu5VfCywLbEQJpq4EDgN2B14KXA4cmJn3NnX17kB5jlYETgROr3/rAr8Fds3MZxamTrVehwAfB0YAPwI+m5lz6rI+nX9EdAEfojxXTzcvy8x/R8TXgJ9ExAFNi3aqj/3ylCENn8nMmRExop7ve4FlgN/XxyTrsbagZOBfA/wN+HpmnlmXnQ40gA2A1YDfAS/PzC2a6noQ8L7M3KQP7X9L4HuUbOrF9TFq69ARaaDYhS11sIhYkfJhf1ZT8XeAccDbgVdTuhFPjoilImJlSkD1HcqH4NHAKRGxbt32VEpgsiWwXV3n9KZ9b8LCdXfeDqzXS/loYCqly3pnSvcmlG7Wb0fEhsCZwNcpAcJJwIURsVHTPv4H2IsSWD0KXAg8DryOEoi9C/ha0/pvqHXZEvgyJZB8B0BEfJgSrH6ZEtRNBS6OiEZEbE/Jqn2K0gU/kdI1P7r1h2Ou7YELKIHz74EfUgKlPSnjQYcD/1sDsm5foHw52A/4NHA+8HlKV/GbKYHbwnoFZSjBFsBHKOe6HUCL578OZUzr9fM5zqR6bq9rKtsH+G/KY/IeSiANcCClLW9HCQSforbJ2g1+GXA2pX0cAXyv1rXbB2r5eMpz+6YedX4/cG69Pd/2HxErAZdQvnxsRPkys9N8zk9a7JmBlDrPLyJiFiWz8mLgMUoQ2e1q4LjM/AtARHybknVblZKRHAHcl5n3AqdFxL3AgxHxSsoH94qZ+Vjddk9gSkSslpn/yMznZTtb8E9Kpu15MvOBei5PZuZDEfHiuuiRzHw6Ij4LnNqdUQLuiYhNKUHfh2vZxMy8utb3rZTM6maZOQu4s2a5fl0zTVDeFz+Smf+syw+kBMYTKUHZ8Zl5Tt3fx4BDKMHQ5ynZrYvrfo6KiG3rY/vV+Zz35yPikz3K3tKUtXs0M79fj/USYA/gXZl5RS3bnZIl/i/gtrrNkZl5C3BLHb5wbmb+tq5/JSUTubBmAvtk5pNARsQXKFndS2jt/F9a/z8+n+N0l68I3FVvfzozf1/P4xDgGOBgYCwlGzilZrM/CryqbnMAZbjDcfX+3fXL0CeBX9SyP2Xmhd0Hjog7Ke18QkSsSQmG3/NC7Z8StD8KHFQzsodFxLvmc37SYs8AUuo8HwGuqbdfQsmy/SEi3pCZfwV+DOwYEftQgonuLE8XpdvxJ8ClEXEP5UP29Mx8PCI2pwSlf+9lPNw6lEBmYS0HPLkQ240DXlMzg91G8PzM1pQe668A/LPpHBrAi4Du8ZWP1OCx25N1n1Ayk0d3L6jrfRYgIsYBR0dEc7C0FCVLOT8nU7K9zZofx+a6r0PpNbqu6fiPRUTW8+oOIP/WtM2zwL097i+1gPq8kEdq8Njtn5QhAdQ69PX8H6v/VwF6m1m9av3/aFNZc1f4TcDLatbv+5TA7v46fvJiShd3d53GR0RzN/lwnj+sY0qPY59HyRxOoGQfr8nMf9RgcEHtfz3gL93d+dUNzHt8pI5iACl1nvt7XO7kjxExntIF+DlKALk5pev3B5RLp/wBoH747R4Rx1AyKu8G9q9dfsOBZyjdcz1NW8Q6b0AZq9iq4ZQu89N6lP+76fb0HuvfRem27qk7cHuul2WNBSxr3vdnKF2YzZ7uZd1uj7/ApWmmz+d2s676121mj+WzF7D/Vs3qpaz7sWnl/O+iBIevp2TEe9qEch43UcY1wvPPo3v41XOZeVdErEPpgt6OMrxg34h4Xa3TOfxnBrT5PHo+rucAB0fEyygB5OlN57eg9r8T8x6LbjMwgFSHcgyktGRoAMMjYjlgN2C3zPxK7brr7k5sRMS6EXFsZt6UmYdl5msp49HeQ5kpvTTQlZl3NwU+x1IyiAslIl5KmZTws4XYPIG1uutT67RHre/81l+N0jXcvf4qlDGQfXk/vIumSwxFxNIR8UBErN+97x51+Qyw9UKcV2/upgRVmzYd/2WU7trsp2Msij6ff52FfzKlC/95QxfqpJiDgZ9n5iNNi17TdPsNwLTM/GftRt4xMy/MzL0pz884ypeSBNbpUad3ULrVez+JkqX/S11nI8oY0u7zW1D7vxXYOCKaEzPNl6OSOooZSKnzvKTpGnqjKJMm1qYEaM9Ssig7RcQ0Stfb9+u6SwGPAB+JiCeBMyjjyzagjKO7IyJ+CZxZx/5Np2QwuzJzGsydSPBsz5m1Pby8jmscUY//DUr275SFONfvAFdHxPWU7vZtKRmoHeaz/q8pXbxnR8QXKWNETwH+nJnT+3CpmuOAEyLiz8AtwFcombTbKYHEqXUM3dWUyRkfosyEXmSZ+UxE/BA4vs52foTy2N1HuU5mu6+b2Or5fxV4C3BVRHyJ0gW/JmVyzNLAJ3qsf3wdqrAMZdJL92St5SkZw8eAv1K+QDxdb08APl5ndZ9KGa/5TeqwgwU4h/LcXtl9gfsXav8RcS7zJul8l/Kl6I303kUvLfbMQEqd56eULrVplMDmbcB7M/OazJxB+WB/D3AH5fqQR1GCkI3rdf92ogRgt1Nmb/+AcrkWKB/Od1ECsd/V7ZqDtT/ywh/OU2vd7qbMmr6aMnFkfl2085WZ11LGeO5DCUA+BfxPZl46n/VnUbrlZ1HGif6CkmGdb0aqx/ZnU8ZAHgf8iXK5mndn5uzMPI8yA/rQWpf3UDJjN7d6Xgvwecov95xf6/9vYJuFeez6W6vnn5n/ArahjFk8jpLhO4PyuG7Sy/UxvwdcRPkidCbzxo6eQBnCcBpl5vMOlIlGj9eJYO+ifLG4lTLx5tDM/MELnM65lCD23B7l823/mfk4Jbv5WspY4rfUekodqTFnTs9fPpMkSZLmzwykJEmSWmIAKUmSpJYYQEqSJKklBpCSJElqiZfxqQ4//PClRo4cuckee+wxbdVVV+3tYrmSJElLii5gNOXqGv/uudAAsho5cuQmBx100KR210OSJGkI2ZJefjHKy/hUq6222iu7urruvvrqqxkzZky7qyMtts5/5W7troIEwPvu+Um7qyB1grWBe3oWmoGspk6dOgtg5syePyMrqRXP/uOhdldBktR/eh3W5yQaSZIktcQMpKR+Nayr3TWQJA00A0hJ/WrYsEa7qyBJGmB2YUuSJKklZiAl9athfi2VpI5nACmpXw3rsgtbkjqdAaSkftVlBlKSOl5bAsiIOAJ4HzAH+FFmHhsR2wLHAqOA8zLz4LruRsApwHLAVcB+mTkzIlYHzgJWBhLYPTOfjogVgLOBtYCHgZ0z84HBPD9JkqRONui5goh4M7ANsAHweuBjEbEhcCqwAzAO2CQixtdNzgIOzMx1gAawTy2fAEzIzHWBG4BDavmRwKTMHAecDBw38GclqduwroZ//g2JP0kDZ9AzkJn5u4h4S80ivqLWYQXgrsycDBARZwHvj4jbgVGZeW3d/HTg8Ig4BdgK2LGp/HfAQcB2dRnAOcAJETEiM2cM9LlJchKNJC0J2vJWn5kzIuJw4Hbgt8CqwLSmVaYBYxZQviLwZGbO7FFO8zZ1+ZPASgNzJpIkSUuetk2iycxDI+IbwC+AdSjjIbs1gNmUALcv5dTy7nWaNZqWSRpgXkhckjrfoAeQEbEuMDIzb87Mf0XEzykTapp/rHsV4H5gKjC6l/KHgOUjoiszZ9V17q/r3FfXmxoRw4FlgUcH8pwkzeNPGUpS52tHF/ZawMkRsVREvIgyceZEICJi7YjoAnYDJmbmvcD0iNi8brtHLZ8BTAJ2qeV7AhPr7cvqferySY5/lCRJ6j/tmERzWUS8AfgTJet4QWaeGxEPAxcAIylB4Pl1k90pAedywE3A8bV8f+CMiDgY+Duway0/BDg9Im4DnqjbSxokdmFLUudrzJnTcyjhkqnRaIwFJk+ePJmxY8e2uTbS4uv/1hz/witJg+Btkye+8EqSXsiawJSehf4SjaR+ZQZSnei3r3xnu6sgPc9b77msrcc3gJQk6QW86XenMWrMy9tdDQmAZ6c+2O4qGEBK6l9eSFydaNSYl3PFq8xCamh4y13tzT6CAaSkfuZPyKlT2balecwVSJIkqSVmICX1K7uw1als29I8BpCS+pXdfOpUtm1pHr9PSZIkqSVmICX1K7v51Kls29I8BpCS+lWXFxJXh7JtS/MYQErqV8O62l0DaWDYtqV5TMhLkiSpJWYgJfUrfwtbncq2Lc1jACmpX9nNp05l25bmsQtbkiRJLTEDKalf2c2nTmXbluYxgJTUr7xWnjqVbVuax5eDJEmSWmIGUlK/MkujTmXbluZpSwAZEYcCO9e7l2bm5yPiNGAL4JlafnhmXhgRGwGnAMsBVwH7ZebMiFgdOAtYGUhg98x8OiJWAM4G1gIeBnbOzAcG6dSkJV7DD1l1KNu2NM+gB5ARsS3wdmBjYA7wy4h4D/B6YKvMnNZjk7OAvTPz2oj4EbAP8ANgAjAhM8+NiEOAQ4CDgCOBSZm5XUTsARwH7DIY5yYJhg2b0+4qSAPCti3N047vU9OAz2Tmc5k5A7gDWL3+nRoRt0TE4RExLCLWAEZl5rV129OB90fECGAr4Pzm8np7O0oGEuAcYHxdX5IkSf1g0DOQmXlb9+2IeBWlK3tLYGtgf+CfwCXAh4FbKQFnt2nAGGBF4MnMnNmjHGDV7m1qV/eTwErA/QNzRpKa2c2nTmXbluZp2ySaiHg1cCnwucxM4D1Ny74H7AncTunm7tYAZlMypz37EmY3rdOs0bRM0gBr2M2nDmXbluZpy/epiNgc+C3whcw8IyJeExHvbVqlAcwApgKjm8pXoWQSHwKWj4juH5YazbwM4311PSJiOLAs8OhAnYskSdKSph2TaFYDLgJ2yczLa3ED+G5EXA48DewLnJGZ90bE9IjYPDN/D+wBTMzMGRExiTI55ieUbOXEuq/L6v2j6/JJdaylpEFgN586lW1bmqcdXdifBUYCx0ZEd9kPga8BvwdGABdk5jl12e7AyRGxHHATcHwt3x84IyIOBv4O7FrLDwFOj4jbgCfq9pIGSaNhN586k21bmqcxZ44vCIBGozEWmDx58mTGjh3b5tpIi6973vmudldBAuCVl13Sr/uzbWuo6O+2/QLWBKb0LPSXaCT1K3+tQ53Kti3NYwApqV85TkydyrYtzWMAKUlSH3gZH2keA0hJ/coPWUnqfAaQkvqV3XzqVLZtaR4DSEn9ygykOpVtW5rH71OSJElqiRlISf3KS52oU9m2pXkMICX1K3+tQ53Kti3N4/cpSZIktcQMpKR+5UxVdSrbtjSPAaSkfuVMVXUq27Y0j9+nJEmS1BIzkJL6lTNV1als29I8BpCS+pXdfOpUtm1pHgNISf3KiQbqVLZtaR5fDpIkSWqJGUhJ/cqLLatT2baleQwgJfUru/nUqWzb0jxtCSAj4lBg53r30sz8fERsCxwLjALOy8yD67obAacAywFXAftl5syIWB04C1gZSGD3zHw6IlYAzgbWAh4Gds7MBwbt5CRJkjrcoAeQNVB8O7AxMAf4ZUTsCnwDeDPwD+DSiBifmRMpQeLemXltRPwI2Af4ATABmJCZ50bEIcAhwEHAkcCkzNwuIvYAjgN2GdyzlJZcw5ypqg5l25bmaUcGchrwmcx8DiAi7gDWAe7KzMm17Czg/RFxOzAqM6+t254OHB4RpwBbATs2lf+OEkBuV5cBnAOcEBEjMnPGAJ+XJOzmU+eybUvzDHoAmZm3dd+OiFdRurK/Rwksu00DxgCrzqd8ReDJzJzZo5zmbWpX95PASsD9/X4ykqQlhteBlOZp2ySaiHg1cCnwOWAmJQvZrQHMplxmaE4fyqnl3es0azQtkzTA/JCVpM7Xrkk0mwMXAJ+sYxjfDIxuWmUVSsZw6nzKHwKWj4iuzJxV1+nOMN5X15saEcOBZYFHB/J8JM1jN586lW1bmqcdk2hWAy4CdsnMy2vxdWVRrA1MBnYDTs3MeyNiekRsnpm/B/YAJmbmjIiYRJkc8xNgT2Bi3ddl9f7Rdfkkxz9Kg8ffC1ansm1L87QjA/lZYCRwbER0l/0Q2IuSlRxJCQLPr8t2B06OiOWAm4Dja/n+wBkRcTDwd2DXWn4IcHpE3AY8UbeXJElSP2nHJJpPAJ+Yz+INe1n/z8Abeim/F9i6l/LHgHcvWi0lLbRhPYchSx3Cti3N5S/RSOpfXX7IqkPZtqW5HNEhSZKklpiBlNSvGnbzqUPZtqV5DCAl9S+7+dSpbNvSXHZhS5IkqSVmICX1L7v51Kls29JcBpCS+lWjy44NdSbbtjSPAaSk/mWWRp3Kti3NtdBfpyLiq72UHbdo1ZEkSdJQ13IGMiIOB14C7BIRyzctGgG8g/n/yoykJYEzVdWpbNvSXAvThX0dsAkwG3i0qXwm/u60tMTzWnnqVLZtaZ6WA8jMvAy4LCImZub1A1AnSZIkDWGLMonmHxFxKfAqYAvgTGCvzJzWLzWTtHiym0+dyrYtzbUoAeQJwEXAgcDjwM3AKcB2i1wrSYsvP2TVqWzb0lyLclGrsZl5MjA7M2dk5kHA6v1UL0mSJA1Ri5KBnB0RcwPQiFgWfxpRWuI1hvk2oM5k25bmWZQA8ufA2cDyEfERYG/gp/1SK0mLL7v51Kls29JcCx1AZubREbEnJev4NuAkyhhISUsyL3WiTmXbluZa1J8y/BnwJ+A2YGRmzln0KkmSJGkoW+gAMiI2o3RjzwTeBPw5IrbPzGv6q3KSFj8Nu/nUoWzb0jyLkoH8FrAtcHZmTo2IPYDjKL9S84IiYjngGuBdmTklIk6jXE/ymbrK4Zl5YURsROkaXw64CtgvM2dGxOrAWcDKQAK7Z+bTEbECZWzmWsDDwM6Z+cAinKekVtjNp05l25bmWpQpZS/OzNu779RfqOlTQBoRmwJXA+s0Fb8e2CozN6p/F9bys4ADM3MdoAHsU8snABMyc13gBuCQWn4kMCkzxwEnU4JaSZIk9ZNFyUDOiIiXAHMAIiJa2HYf4ADKr9cQES+mXEPy1Ih4BXAhcDiwGjAqM6+t250OHB4RpwBbATs2lf8OOIhyIfOtavk5wAkRMSIzZ7R+ipJa1ejyUifqTLZtaZ5FCSCPpARtq0TEOcDbgX37smFm7g3QFHOuAlwO7A/8E7gE+DBwK9D804jTgDHAisCTmTmzRznAqt3b1K7uJ4GVgPtbPkNJrbObT53Kti3NtSiX8bkkIu6kXMKnCzgiM+9YyH39DXhP9/2I+B6wJ3A7NcNZNYDZlK73njO+Zzet06zRtEySJEmLqOUAMiL2zcyTADLzbuDuRa1ERLwGWCczL6hFDWAGMBUY3bTqKpRM4kOUC5h3Zeasuk53hvG+ut7UiBgOLAs8uqh1lNRHzlRVp7JtS3MtTAZyP8pFw4mIizJzx36oRwP4bkRcDjxN6Qo/IzPvjYjpEbF5Zv4e2AOYmJkzImISsAvwE0q2cmLd12X1/tF1+STHP0qDp2E3nzqUbVuaZ2ECyOZX0Br9UYnMvCUivgb8HhgBXJCZ59TFuwMn18v+3AQcX8v3B86IiIOBvwO71vJDgNMj4jbgibq9pMFilkadyrYtzbWov0SzSL88k5ljm25PoFyap+c6fwbe0Ev5vcDWvZQ/Brx7UeolSZKk+VuYANKfK5Q0f2Zp1Kls29JcCxNArhsRt9TbazfdBiAzN1j0aklaXDWGea08dSbbtjTPwgSQ4/u9FpIkSVpstBxAZubvepZFxLsy85L+qZKkxZrdfOpUtm1prkWdRNPtCMqvx0ha0nmpE3Uq27Y0V38N6PBVJUmStITorwykvzMtCfBiy+pctm1pnkUKICNiFLA2sH1EvDgz/9U/1ZK02HKmqjqVbVuaa6EDyIjYDPg5MBN4E/DniNg+M6/pr8pJWgz5IatOZduW5lqUDOS3gG2BszNzakTsARwHbNIvNetwsy/9SLurID3PsO1ObHcVJEmLiUUJIF+cmbdHBACZeVlEHNU/1ep8jbccRePFK7a7GhIAc/71SP/tzHFi6lS2bWmuRQkgZ0TES6g/bRjdkaT6pPHiFZk98aPtroYEwLDxP+jHndnNpw5l25bmWpQA8ijgd8AqEXEO8HZg336p1ZLCNyNJkrQYWpQA8pfAHcDbgC7giMy8o19qJWnx5RcjdSrbtjTXogSQf8zMjYC7+6kuSx7H06gT2a7VqWzb0lyLEkA+ExFjMnNqv9VmSeO3WUmStBhalAByaWByRPwDeJryc4ZzMnODfqmZpMWTX4zUqWzb0lyLEkB+ot9qsaTyzUidyHatTmXbluZalADyHuBLmbl/vYTPN4D9+qdaSwjH06gT2a4lqeMtSgB5OvC/9fa9wJXAqcA7+7JxRCwHXAO8KzOnRMS2wLHAKOC8zDy4rrcRcAqwHHAVsF9mzoyI1YGzgJWBBHbPzKcjYgXgbGAt4GFg58x8YBHOU5IkvxxJTRYlgFwxM48HyMzpwHcj4oN92TAiNgVOBtap90dRgs83A/8ALo2I8Zk5kRIk7p2Z10bEj4B9gB8AE4AJmXluRBwCHAIcBBwJTMrM7Zp+XnGXRTjPgWN3iDqR7VqdyrYtzbUoAeTwiFg1M+8HiIiXUybS9MU+wAHAmfX+G4C7MnNy3ddZwPsj4nZgVGZeW9c7HTg8Ik4BtgJ2bCr/HSWA3K4uAzgHOCEiRmTmjIU5yQHlt1l1Itu1OpVtW5prUQLIY4GbI+KX9f5bgc/1ZcPM3Bug6dcPVwWmNa0yDRizgPIVgSczc2aP8uftq3Z1PwmsBNzf1xMbNH6blSRJi6GFDiAz89SIuBHYBphJuaD4J4CfLMTuhlF/U7tqALNbKKeWd6/TrNG0TNJA84uROpVtW5prUTKQAH8HlgI+DSwDHL+Q+5kKjG66vwolYzi/8oeA5SOiKzNn1XW6M4z31fWmRsRwYFng0YWs18DyzUidyHatTmXbluZaqACyXrbnk8AewBTKzOmxmfnPhazHdXW3awOTgd2AUzPz3oiYHhGbZ+bv6/EmZuaMiJhEmRzzE2BPYGLd12X1/tF1+aQhOf4RHE8jSZIWSy0HkBFxKfB64Dxg68y8ISImL0LwSGZOj4i9gAuAkZQg8Py6eHfg5HrZn5uYl+XcHzgjIg6mZEJ3reWHAKdHxG3AE3V7SYPFL0bqVLZtaa6FyUC+FrgRuJUy7hH+czxin2Tm2KbbvwU27GWdP1NmafcsvxfYupfyx4B3L0x9Bp3dIepEtmt1Ktu2NNfCBJCrATsBHwWOi4hLKF3YapVvRupEtmtJ6ngtB5D10jk/BX4aEetRfr5wZETcBRyTmT/s5zpKktR+fjmS5lqkWdiZeTvw8Yj4AvABSjBpANlXjqdRJ7Jdq1PZtqW5FvUyPgBk5r+Ak+qf+spvs+pEtmt1Ktu2NJevBkmSJLWkXzKQWkh+m1Unsl2rU9m2pbkMINvJ8TTqRLZrdSrbtjSXAWQ7+W1WkiQthgwgJfUvvxipU9m2pbkMINvJNyN1Itu1OpVtW5rLALKdHE8jSZIWQwaQkvqXX4zUqWzb0lwGkO1kd4g6ke1ancq2Lc1lANlGDd+M1IFs1+pUtm1pHl8NkiRJaokZyHZqGL+rA9mu1als29JcBpDt5JuROpHtWp3Kti3N5atBkiRJLTED2U5+m1Unsl2rU9m2pbmGVAAZEVcAKwMzatFHgGWBY4FRwHmZeXBddyPgFGA54Cpgv8ycGRGrA2fV/SSwe2Y+PZjn0We+GakT2a7VqWzb0lxD5tUQEQ1gHWDDzNwoMzcCbgFOBXYAxgGbRMT4uslZwIGZuQ7QAPap5ROACZm5LnADcMjgnUWLhg3zz7+h8SdJUguGUgYy6v9fR8TLgJOBvwB3ZeZkgIg4C3h/RNwOjMrMa+s2pwOHR8QpwFbAjk3lvwMOGowTkIQBqTqXbVuaaygFkC8Bfgt8DBgBXAl8A5jWtM40YAyw6nzKVwSezMyZPcqHJrtD1Ils1+pUtm1priETQGbmH4A/dN+PiB8BRwBXN63WAGYDw4A5fSinlg9NvhmpE9mu1als29JcQ+bVEBFbRMRbm4oawBRgdFPZKsD9wNT5lD8ELB8RXbV8dC2XJElSPxkyGUhgBeCIiHgTpQv7g8B+wE8jYm1gMrAbcGpm3hsR0yNi88z8PbAHMDEzZ0TEJGAX4CfAnsDENpxL3/htVp3Idq1OZduW5hoyAWRmXhIRmwJ/ArqAEzLzDxGxF3ABMBK4DDi/brI7cHJELAfcBBxfy/cHzoiIg4G/A7sO3lm0yDcjdSLbtTqVbVuaa8gEkACZeQg9LruTmb8FNuxl3T8Db+il/F5g6wGqoiRJ0hJvSAWQSxwvCaFOZLtWp7JtS3MZQLaT3SHqRLZrdSrbtjSXrwZJkiS1xAxkO/ltVp3Idq1OZduW5jKAbCffjNSJbNfqVLZtaS4DSEn9y4kG6lS2bWkuA8h28s1IkiQthgwg28nuEHUi27U6lW1bmssAsp18M1Insl2rU9m2pbl8NUiSJKklZiDbyW+z6kS2a3Uq27Y0lwFkO/lmpE5ku1ansm1Lc/lqkCRJUkvMQLaTl/FRJ7Jdq1PZtqW5DCDbye4QdSLbtTqVbVuaywBSUv/yQ1aSOp4BZDv5QStJiw/fs6W5DCDbyTcjdSLbtTqVbVuaywBSUv/yQ1adyrYtzdWRAWRE7AYcDIwAvpuZJ7S5Sr3zzUiSJC2GOi6AjIhXAEcBrwP+DVwTEVdk5u3trVkvvCSEOpHtWp3Kti3N1XEBJLAtcHlmPgYQEecD7wOOaGutemMGUp3Idq1OZduW5urEV8OqwLSm+9OAMW2qiyRJUsfpxAzkMGBO0/0GMLtNdVmgRmObdldB6ne2a3Uq27Y0TydmIKcCo5vurwLc36a6SJIkdZxOzED+BjgsIlYCngHeC+zb3ipJkiR1jo7LQGbmfcCXgSuAm4GfZOb1ba2UJElSB+nEDCSZ+RPgJ+2uhyRJUifquAykJEmSBpYBpCRJklpiAClJkqSWGEBKkiSpJQaQkiRJaokBpCRJklpiAClJkqSWGEBKkiSpJQaQkiRJaokBpCRJklpiAClJkqSWGEBKkiSpJQaQkiRJaokBpCRJklpiAClJkqSWGEBKkiSpJQaQkiRJaokBpCRJklpiAClJkqSWGEBKkiSpJcPbXYFuEfFB4OvAg7Xo0sz8ckSsAJwNrAU8DOycmQ9ExIuAHwGvB54FdsvMOyOiAXwLeBcwG9gnM38/uGcjSZLUuYZSBvL1wKczc6P69+VafiQwKTPHAScDx9XyjwPP1PJPAqfX8vcC44D1gB2B0yNiyATKkiRJi7uhFEBuAnwwIv4SEWdFxEtq+XaUDCTAOcD4iBjRXJ6ZVwErRcTqtfzczJydmX8F/g68aTBPRJIkqZMNpQByGvBVYAPgH8D3a/mqdRmZORN4Elipubxp+zELKJckSVI/GPSu3Yh4P/CdHsV3Zua2Tet8E7in3m30WLdBGds4DJjTQrkkSZL6waAHkJn5M+BnzWURsXxEfCozuwPLBjCz3r4PWAWYWscyLgs8CkwFRjMv0FwFuL+pnB7lkiRJ6gdDpQv7aeDzEbFpvX8gcGG9fRmwZ729C2VCzYzm8ojYApiemX+v5btHRFdErA2sA/xxcE5DkiSp8w2J2cmZOSsidgZ+EBGjgL8yL2g8hDKT+jbgCWD3Wv494MRa/m9gj1p+PrApcEu9/+HMfHbgz0KSJGnJ0JgzZ84Lr7UEaDQarwTunjRpEmPGOOdGkiSpq6tr7dVWW+2enuUGkFWj0dgCmNTuekiSJA0VBx100JZf//rXr+5ZPiS6sIeIPwJbUi77M6vNdZEkSWqb0aNHd+25556jl1pqqV7nkZiBlCRJUkuGyixsSZIkLSYMICVJktQSA0hJkiS1xABSkiRJLTGAlCRJUksMICVJktQSA0hJkiS1xAuJq20i4n3AFyntcBjw48z8Vj/sdwqwdWZOWdR9Sa2IiLHAX4Hba9Eo4BrgC5n54AAe88rMHNvLstOAwzLz3oE4tpYcEbE+8BfgfZl5Qbvro/YzA6m2iIhXAMcAb8/MDYE3Av8dEe9ub82kRXZ/Zm6UmRsB6wIPAOe3qS5vARptOrY6y4eAnwEfaXdFNDT4SzRqi4jYEJgIbJqZ/6hl6wPTgd9QM4gRsTUlg7J1RFwJPAa8GtgF+DXwc+BNwFPA7nWbKcDW3X+ZuVfd/5XAYcDdwNnA0sBs4OOZee0An7KWAL1lAyPiRcCDwKHAB4Au4Fbgo8DJwIaUdvjtzPxxROxFL+02M6+MiK8B7wMeofzs6v8CVwLXAVcA6wOPAzsC+wBHUNr7lpn56ECdtzpbRIwAplJ+7vcaYFPgAOC+zDymrnMBcFZdfiKwGqVdfzEzfxMRhwGbAasD36Nk6Y8CXgysAHwqMy+OiDGU9+eXUDKeb87MMRGxDHACpY13Ad/IzHMG/uw1P2Yg1RaZ+WfgYuBvEXF9RHwD6MrMu19g01syMzLzZmAl4A+ZuQFwLnB8Hw//YeCSzHw98BVgi4U6CakPMvM54C5KJnIdYJvM/CDly8yjmbk+sA1wWERsML/9RMT2lLb6auCdwMZNi1cCjq37ehD478z8OnA/8E6DRy2i7YB7M/OvwEXAvsCZwK4AEbEspRfpUuA44NTMfB3wbuDEuhxgZGaul5k/AD4G7J2ZrwX2Bo6s6xwHnFff188HXlHLDwZurPvdCvhyRKw1gOesF2AAqbbJzI8CY4EfAGsA10bETi+w2XVNt6cDP663z6B8CPfFb4DPRsRPgJcB3+9rnaWFNAd4FsjM/Gct2wb4EaXwEcoXqq0XsI+3AT/NzOcy83HKB3m3+zPz+nr7NmDF/qu6xP8A3dm+8+r924CREbE28B7gF/XL0rbAERFxM6WXaQTwyrpt8/v3B4D1I+IQ4DPAMrX8bZTglMy8EHiilm8L7Ff3exWlB+nV/XmSao2TaNQWEbEdsExmngecBpwWEftQsoNzmDdua0SPTZ9tuj07M7vHYAwDZvZYt3k/c/eVmb+PiPWAd1G6wveivGlJ/a52YQewMs9vvz2/wDco78m9tltgVi/bdGtu+z23lxZaRKwMjAdeFxGfoLStlwA7Ubqsd6EMI/p63aSLkmV/rG4/GniIMqyiuf1Pogy7uBL4LfCTWj6/dt4FfCAzb6r7fTllSJPaxAyk2uVfwNfqmDEiogFsBPyJMr6r+5vlDgvYx4trtx6Ub8QTeyx/BBgXEY2IWBPYoB7rm5Q3ojOAA4HXLvLZSL2IiGHA4cC1wD09Fl9O+cJERKxI+YC9kvm0W0rm/L0R8aKIWI7yBeiFBrHPxESBFs0ewG8zc0xmjs3MNShjF/ejjFXcBVgbuLqufzmwP0D9on4rZZzjXBHxUspwjq9Q3rd3oASIUNr5bnW98ZTxkd37/WgtHw3cQhlPqTYxgFRbZOYVlA/WSyIigTsp3zy/SplscFxE/JF53Rfz8/6IuAV4B/DJHst+A/wDSMq4mu43uO8B76tdIRcCey7i6UjNVo2Im2v7+jNlDNeuvax3BPDSiPgLpUvuqJpd6bXdZualdb0/Ucaa3c/zMzq9uQS4rAai0sLYC5jQo+wE4A2UbuRHgPObeoM+BmxW35fPo3xZf6p545qd/BGlG/wOYFlKQmBp4BOUL0p/ogSnT9TNDgdGRcStlGDy85nZ80uZBpGzsLXYiog5mWlXnZYIEfFGYJ3MPKPOiv0D8KHMvKXNVZP6TUR8HPhNZt4eEa8FTq4TZzTE2LUhSYuHBA6NiE9Teo/OMHhUB7oLOCciZlMmSu7T5vpoPsxASpIkqSWOgZQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktcQAUpIkSS0xgJQkSVJLDCAlSZLUEgNISZIktWR4uysgqX9FRPcP3L8yM//WY9l+wA+AozLz4IjYCzgyM8fMZ1+HAYf2KH4WuBs4LDN/3sc6nQ58sKno38DfgBOB4zNzTl3vSuDqzDy43j8B+BDwR+BdwKXAJsC3u9dZ3ETEWGDyfBafkJkHDmJ1iIgpwBoLWGVN4HSanpd+Ou7GlLa1FfAi4Hbg+5n546Z1DgO2zcwt+uu4gyEihgMzgLdk5pVtro40IMxASp1pBrB9L+U7AnN6KV+Q64HRTX9vAP4MnBsRa7ewnwua9rE+8F3gSJ4foO4EfB0gIjYC9q9lO1MCyVcBGwHHtngOQ9Ebef7jOhr4YhvqsUnT8b8L/KFHnf7R3weMiG2Ba4BpwFspz+mPgO9GxPf7+3iS+p8ZSKkzXQW8GziuuyAilgPeBPypxX3NyMwHmu4/EBEfpgR22zUf4wVM77GfuyNiFjAhIk7KzPsz87Gm5cvX/7/JzBkRsTxwT2be2WL9h6pHejwebZGZD3ffjohngOd61isi+u14ETES+DHw3cxsDpjvjoibgWsi4heZ+at+O6ikfmcAKXWmi4FjImL5zPxnLXsnMAlYuh/2P4uS5ZwJEBFbA1cAa2bmlBb2czYlAH0ncEp3Fzali/y0us5zEXEvtZu1dtGvSclefQPYndKb8lvgY5n5YFM38VeATwMXZuaHImJH4Ki6/Z3AlzLzl3W/V9Z9bE7pVr0HOCgzL6vLXwYcT+lK/zfwE+CzmTmzBrfHUzK8zwL/C3wmM59q4bGYq3Yr/xT4APAEsAEl+/odypeAp4GTgCMyc3bt6l0HeISSqX0Y2BtYDzgYaACHZ+YJC1OfanREXAZsA/yd8lj/qta3lfN/F7AS5bl7nsy8LiJ+CewLdAeQwyPiZMrz/EA97qX1uK8BTgBeBzxFaU8H1eekAXwZ+CiwDCWz+rHMvKtuO4eSAd8P+AsQwKGZeUp3fSLidsqwghMiYgtK5vs1lOEXX8/MM5vW/QpwIOWxbkcmWRpUdmFLnekOYAowvqlsB+CiRd1xRLwYOAZYijImEUp3ZMvdnZk5nRLorddj0XnAe+vtMcCG9Zjd3en/AI6mdAO/C3gz5f3skho4dNsKeD3w9YjYEDiT0kX+GkoAdmHtKu/2ReBcSkByJyWo7arLLgTGUgKoHYH3AAfVZacCKwJbUrKyQRk3uCj2AP4L2A1YgRL83w9sSgmKDgA+07T+eymB5YbAjcD5lO7hrSljTb9Tg+BFqc8FwKspY1LPjIjuz5BWzn8T4K+Z+cR8lk8CNmu6vyklKHstMAG4OOalRM+iPE+voQxz2AP4cF12ILBnLduU8qXkt7X9dtsB2AL4GPAz5rU5ImJ9SlB+fkSsAlxGCVBfAxwBfC8itq/r7gt8khK8v63+lzqaGUipc11MGQd5bkSMAN5B+ZDbvcX9vDEinq63G8BI4CZgfHe2MTOfo2SHFsY/gWWbCzLz2Yjo7s5+sGaUnqZ2p9cg4EBgs8z8E0BE7AE8SgkIugPZ4zLznrr8TODUpqzRPRGxKSV46A46Jmbm6XX9r1LGer4iIpalBEevysy76/L9KFm5V1KCyRW7u+AjYk9gSkSslpnzC6r/3DThqZ52vq7p/tmZeUvd38eB6cBHMnMGcEdEjAa+Cnyrrv848OXMnBMRZ1CCoU9l5uSIOJaSiVyrPkYL46LM/FGtzzcpge3o2iXdyvm/tNZ1fh6nBKPdHgD2r23szojYDtgH+CwloL8UuDcz/xYR45vO7/PAxzPz8lqnj1Ey3e+lfJEAOCkzsy4/B5jUlLV/P3BFzWh/td7uHq5xd0SsS3k9/aLW5/jMvKTua19KVlPqWAaQUue6GPjfOiN0G+C2+mHY6n7+BPw3JcP3dkr25Zh+nF26HPBki9usRZm5O6nH+YykZI26g5YpTcvGAa+p4ze7jaBkNbvd03T7yaZ11gOe7A4eATJzIkBEvIsSWP+9l8e2uS49bU/pCu72XI/lPet+Uw0eu10DrBgR3cHWlO7Z7JRuZIB7e9xfaj516Yvmx6Z7WMTIWrdWzv8xYJUFHGdVnh/k/rkGj91uqseEkjH+HrBvREwEzs3MGyNiGUrm+uyImN20bXf76Dal+0btPp9KeV7OogSQx9TF44DxTV+koHx+do8fXY+SEe/e160RMX0B5ygt9gwgpc51DWWM4haUrroLF3I/05sCp7/WD+czI2JyZl63KBWs2at1KGP7WtH93vVm5gUz3R4GXlJvN3+IDwe+zbyxld3+3XS7ZxAHJTjqrbx5v89QZhL3NG0B2/29OSDtxfT53O7W1eP/zJ4rZObsnmWLYFYvZQ1aP/9rgU9HxIqZ+UgvyzcBmttVz3MYRn0+MnNCRFxCad/bUbq3j6TMJofyxef2Hts/0XS75+N6LvDeiPgTsDbQfZmq4cA5lIxvs+bHpNFj2X88H1IncQyk1KFq8HAJZTb29vTD+MfqW8CtlPGBi/oldDfKZYUuaXG7eygf3itm5t01EHuYMslhftc0TGCt7vXrNntQul9fyF3AcrW7GoCI+HBEXF73uzTQ1bRfal2Wa/G85ucO4LV1KEK3N1KyeQ/3vsmgafX8LwOmAof1XBARm1HG7Z7UVPzqHuNa30Dpwh8ZEccBczLze5n5X3Wfu9TxlQ8Bo5vqNJmSJdxwAedyLmUM4y7ArzKzu6s9gXV6tJ13UCYqQXk9bNJ0HmtTJu5IHcsMpNTZLqaM9/pbz4uKN1kqIv6rR9lTmfn73lbOzFkRcSBltvQBwHER8SLK2LaHM7O3TBXAyDoZAcqH639RPtCPbL6UTF9k5lN1Zu73I+IjlMklX6fMVr4LeHkvm30HuDoirqeMW9uWMkt3hz4c7/aI+A0laP4kZczmIcAPMvOOOnP4zDrObjrlYu1dmbmgDGQrfgIcDpwYEd+izMg+vB5/dn9eZqdVrZ5/Zv47ygXsL62B4UmULPJbKc/hhO7hAdUYyvP8feB9lMk0u2Tm9Dozeo2I+CLl82w8ZQIRlAD2qxHxICXA+xwlOPzkAs7lljrj/1OU2dndJgAfj4ivUSYMbQh8kzIOE+D7lMtR3UQJ9r/Hf2ZOpY5iBlLqbL+mdHFetIB1VgQm9vg7cUE7rcHlWcDhEfFyyqVlpgGrLWCz99Z1plE+5PekTHI4qi8n0otPUy71ch5lVvAo4O2Z+WxvK2fmtZQJRPsAt1GChP/pviRMH+xByfhdQ5nhfA6lS7x72V2Ux/t3wH30ITDtq8x8mhJwv5IyJvUEyuWPvtJfx1hELZ1/Zv6OMtN6pbrNrZRL93wmMw/osfpllGuC3kR5/nbIzKl12S6UcY3XUr7QTKZMioLy3PyQ8ljdQrl4/Tsy8/4XOJdzKK+Zi5vqey9ltv+2ta7HUC7584O6/EzKc3EcZRb5pZTLCkkdqzFnTqs/SiFJkqQlmRlISZIktcQAUpIkSS0xgJQkSVJLnIVdHX744UuNHDlykz322GPaqquuOr9ZpJIkSUuCLspPx/6R518vFzCAnGvkyJGbHHTQQZPaXQ9JkqQhZEvKVQ6ex1nY1WqrrfbKrq6uu6+++mrGjBnT7upIkiQNBWvz/J8yBcxAzjV16tRZADNn+utTkiRJVa/D+pxEI0mSpJYYQEqSJKklBpCSJElqiQGkJEmSWmIAKUmSpJYYQEqSJKklBpCSJElqSVuuAxkRRwDvA+YAP8rMYyNiW+BYYBRwXmYeXNfdCDgFWA64CtgvM2dGxOrAWcDKQAK7Z+bTEbECcDawFvAwsHNmPjCY5ydJktTJBj0DGRFvBrYBNgBeD3wsIjYETgV2AMYBm0TE+LrJWcCBmbkO0AD2qeUTgAmZuS5wA3BILT8SmJSZ44CTgeMG/qwkSZKWHIMeQGbm74C3ZOZMSvZwOLACcFdmTq7lZwHvj4g1gFGZeW3d/PRaPgLYCji/ubze3o6SgQQ4Bxhf15ckSVI/aMsYyMycERGHA7cDvwVWBaY1rTINGLOA8hWBJ2uw2VxO8zZ1+ZPASgNzJpIkSUuetk2iycxDKYHdasA6lPGQ3RrAbEr9+lJOLe9ep1mjaZkkSZIWUTvGQK5bJ8aQmf8Cfg5sDYxuWm0V4H5g6nzKHwKWj4iuWj66lgPcV9cjIoYDywKPDsCpSJIkLZHakYFcCzg5IpaKiBdRJs6cCERErF2Dwt2AiZl5LzA9Ijav2+5Ry2cAk4BdavmewMR6+7J6n7p8Ul1fkiRJ/aAdk2guAy4F/gTcCFyTmecCewEXUMZF3sm8CTK7A9+JiDuBZYDja/n+wL4RcTuwJXBwLT8E2CwibqvrHDDQ5yRJkrQkacyZ03Mo4ZKp0WiMBSZPnjyZsWPHtrk2kiRJQ8KawJSehf4SjSRJklpiAClJkqSWGEBKkiSpJQaQkiRJaokBpCRJklpiAClJkqSWGEBKkiSpJQaQkiRJaokBpCRJklpiAClJkqSWGEBKkiSpJQaQkiRJaokBpCRJklpiAClJkqSWGEBKkiSpJQaQkiRJaokBpCRJklpiAClJkqSWGEBKkiSpJQaQkiRJasnwdhw0Ig4Fdq53L83Mz0fEacAWwDO1/PDMvDAiNgJOAZYDrgL2y8yZEbE6cBawMpDA7pn5dESsAJwNrAU8DOycmQ8M0qlJkiR1vEHPQEbEtsDbgY2BjYDXRcR7gNcDW2XmRvXvwrrJWcCBmbkO0AD2qeUTgAmZuS5wA3BILT8SmJSZ44CTgeMG4bQkSZKWGO3owp4GfCYzn8vMGcAdwOr179SIuCUiDo+IYRGxBjAqM6+t254OvD8iRgBbAec3l9fb21EykADnAOPr+pIkSeoHg96FnZm3dd+OiFdRurK3BLYG9gf+CVwCfBi4lRJwdpsGjAFWBJ7MzJk9ygFW7d6mdnU/CawE3D8wZyRJkrRkacsYSICIeDVwKfC5zEzgPU3LvgfsCdwOzGnarAHMpmROm8up5d3rNGs0LZMkSdIiasss7IjYHPgt8IXMPCMiXhMR721apQHMAKYCo5vKV6FkEh8Clo+Irlo+mnkZxvvqekTEcGBZ4NGBOhdJkqQlTTsm0awGXATslpnn1uIG8N2IeEkdr7gvcGFm3gtMrwEnwB7AxDp2chKwSy3fE5hYb19W71OXT6rrS5IkqR+0owv7s8BI4NiI6C77IfA14PfACOCCzDynLtsdODkilgNuAo6v5fsDZ0TEwcDfgV1r+SHA6RFxG/BE3V6SJEn9pDFnTs+hhEumRqMxFpg8efJkxo4d2+baSJIkDQlrAlN6FvpLNJIkSWqJAaQkSZJaYgApSZKklhhASpIkqSUGkJIkSWqJAaQkSZJaYgApSZKklhhASpIkqSUGkJIkSWqJAaQkSZJaYgApSZKklhhASpIkqSUGkJIkSWqJAaQkSZJaYgApSZKklhhASpIkqSUGkJIkSWqJAaQkSZJaYgApSZKklgxvx0Ej4lBg53r30sz8fERsCxwLjALOy8yD67obAacAywFXAftl5syIWB04C1gZSGD3zHw6IlYAzgbWAh4Gds7MBwbt5CRJkjrcoGcga6D4dmBjYCPgdRGxK3AqsAMwDtgkIsbXTc4CDszMdYAGsE8tnwBMyMx1gRuAQ2r5kcCkzBwHnAwcN+AnJUmStARpRxf2NOAzmflcZs4A7gDWAe7KzMmZOZMSNL4/ItYARmXmtXXb02v5CGAr4Pzm8np7O0oGEuAcYHxdX5IkSf1g0APIzLytOyCMiFdRurJnUwLLbtOAMcCq8ylfEXiyBpvN5TRvU5c/Caw0ICcjSZK0BGrbJJqIeDXwf8DngL8Bc5oWNyhB5bA+llPLu9dp1mhaJkmSpEXUlgAyIjYHfgt8ITPPAKYCo5tWWQW4fwHlDwHLR0RXLR9dywHuq+sREcOBZYFHB+ZMJEmSljztmESzGnARsFtmnluLryuLYu0aFO4GTMzMe4HpNeAE2KOWzwAmAbvU8j2BifX2ZfU+dfmkur4kSZL6QTsu4/NZYCRwbER0l/0Q2Au4oC67jHkTZHYHTo6I5YCbgONr+f7AGRFxMPB3YNdafghwekTcBjxRt5ckSVI/acyZ03Mo4ZKp0WiMBSZPnjyZsWPHtrk2kiRJQ8KawJSehf4SjSRJ/9/evQfZUVcJHP/ehAhBni5gAgEGxBxQxPjgoQKLAloIPnlECURWTURAUHdXVwUFBEVREBAoF3moCPISdYFYFggSRB66QJTAETAgIQEVHwgLu4mZ/aN/Q26GhEzPvcm9d/L9VE3N7XN/3XO6qqvn9O/3625JtVhASpIkqRYLSEmSJNViASlJkqRaLCAlSZJUiwWkJEmSarGAlCRJUi0WkJIkSapl2AVkRHx+KbHTWktHkiRJ3a72qwwj4jhgfWByRKzb9NUY4C3AUW3KTZIkSV1oOO/CvhXYHlgEPN4UX4jvnZYkSRrxaheQmXkNcE1EzMjM21ZATpIkSepiw+mBHPBwRFwNvBTYGfgOcEhmzm9LZpIkSepKrdyFfSbwA+Bp4C/AncA3W09JkiRJ3ayVArIvM88BFmXmgsz8JLBZm/KSJElSl2qlgFwUEc+uHxFrt7g9SZIk9YBWCr7vA98F1o2IDwE/BS5tS1aSJEnqWsMuIDPzC8AM4HZgT+A/gePblJckSZK6VCt3YQNcBtwB3A2skZn9rackSZKkbjbsAjIidqIaxl4IvB64KyLelpk3D3H9dYCbgX0y88GIOJ/qcUBPlSbHZeaVETGJ6u7udYAbgUMzc2FEbAZcCGwEJDAlM5+MiPWohta3BP4IHJCZjw53PyVJkrSkVuZAngzsATyemXOBg4EhvQs7InYEbgImNoVfC+yamZPKz5UlfiFwRGZOBBrAtBI/CzgrM7cGfgkcU+InADMzcxvgnKHmJEmSpKFppYBcMzNnDyyUN9QMtUdzGnA4MA8gItakegTQeRExKyKOi4hREbE5MDYzbynrXQDsHxFjgF2By5vj5fPeVD2QABcDe5X2kiRJaoNWCsgFEbE+0A8QETHUFTPzg5k5syk0juou7vcDOwG7AB8ANgaa32wzH5gAbAA8kZkLB8VpXqd8/wSwYa09kyRJ0jK1chPNCcDPgHERcTHwZmD6cDaUmb8D3jWwHBFnAFOB2ZQCtWgAi6gK38E37CxqatOs0fSdJEmSWtTKY3yuAt4NfA74ObBzZl4xnG1FxCsiYt+mUANYAMwFxjfFx1ENe/+B6vmTo0t8fIkDPFLaERGrAWsDjw8nL0mSJD1X7QIyIp7tZczM+zPz7Mz8embe00IeDeBrEbF+ma84HbgyMx8CnomIN5R2BwMzMnMBMBOYXOJTqZ5JCXBNWaZ8P7O0lyRJUhsMpwfy0IEPEfGDdiSRmbOAL1L1ZM4G7szMi8vXU4BTI+JeYC3g9BI/DJgeEbOp5kweXeLHADtFxN2lzeHtyFGSJEmVRn9/vWd/R8QdmfmqwZ97XaPR6APmzJkzh76+vg5nI0mS1BW2AB4cHGzlLmx47o0skiRJGuGGU0BaNEqSJK3ChvMYn60jYlb5vFXTZwAyc7vW05IkSVK3Gk4BuVfbs5AkSVLPqF1AZubPBsciYp/yXEhJkiSNcK3eRDPg+DZtR5IkSV2uXQXk4NcHSpIkaYRqVwE5b/lNJEmSNBK0VEBGxNiIeAXwtohYs005SZIkqYsNu4CMiJ2AB4CrgY2BhyPi9e1KTJIkSd2plR7Ik4E9gMczcy5wMHBaW7KSJElS12qlgFwzM2cPLGTmNQzvuZKSJEnqIa0UkAsiYn3Kqw0jItqTkiRJkrpZKz2GJwI/A8ZFxMXAm4HpbclKkiRJXauVAvLHwD3AnsBo4PjMvKctWUmSJKlrtVJA3p6Zk4D725SLJEmSekArcyCfiogJbctEkiRJPaGVHsgXAnMi4mHgSarXGfZn5nZtyUySJEldqZUC8qi2ZSFJkqSe0coQ9gPA5Mz8GfAo8DEg25KVJEmSulYrPZAXAD8qnx8CbgDOA946lJUjYh3gZmCfzHwwIvYATgHGApdk5tGl3STgm8A6wI3AoZm5MCI2Ay4ENqIqXKdk5pMRsR7wXWBL4I/AAZn5aAv7KUmSpCat9EBukJmnA2TmM5n5NWD8UFaMiB2Bm4CJZXksVfH5DmAbYPuI2Ks0vxA4IjMnUs2znFbiZwFnZebWwC+BY0r8BGBmZm4DnIOvV5QkSWqrVgrI1SJi44GFiHgxVYE3FNOAw4F5ZXkH4L7MnJOZC6mKxv0jYnNgbGbeUtpdUOJjgF2By5vj5fPeVD2QABcDe5X2kiRJaoNWhrBPAe6MiB+X5d2Bfx/Kipn5QYCmtx9uDMxvajIfmPA88Q2AJ0qx2RxfYltlqPsJYEMWF6uSJElqwbB7IDPzPKq30NwB3A58g+HfmT2K8k7togEsqhGnxAfaNGs0fSdJkqQWtTKEDfB7YHXgE1R3Yf/4+Zsv01yWnD85jqrHcFnxPwDrRsToEh/P4h7GR0o7ImI1YG3g8WHmJUmSpEGGVUBG5WzgYeAgqjun+zLzc8PM49ay2a1KUXggMCMzHwKeiYg3lHYHl/gCYCYwucSnAjPK52vKMuX7maW9JEmS2qB2ARkRV1M9TmcBsFtmbgv8PTP/NtwkMvMZ4BDgCmA2cC+Lb5CZApwaEfcCawGnl/hhwPSImA3sAhxd4scAO0XE3aXN4cPNS5IkSc/V6O8fPJXw+UXEfKp5jz8ALs3Mv0bE7zJzyxWQ30rTaDT6gDlz5syhr6+vw9lIkiR1hS2ABwcHhzOEvSnVY3PeC8yPiMuohrAlSZK0CqhdQGbmwsy8NDPfCLyG6pE5a0TEfRFxaNszlCRJUldp6S7szJydmUcCmwAnA9PbkpUkSZK6Vu05kCOVcyAlSZKeo21zICVJkrQKs4CUJElSLRaQkiRJqsUCUpIkSbVYQEqSJKkWC0hJkiTVYgEpSZKkWiwgJUmSVIsFpCRJkmqxgJQkSVItFpCSJEmqxQJSkiRJtVhASpIkqRYLSEmSJNViASlJkqRaVut0As0i4npgI2BBCX0IWBs4BRgLXJKZR5e2k4BvAusANwKHZubCiNgMuLBsJ4EpmfnkytwPSZKkkaxreiAjogFMBF6ZmZMycxIwCzgPeAewDbB9ROxVVrkQOCIzJwINYFqJnwWclZlbA78Ejll5eyFJkjTydU0BCUT5/ZOIuCsijgB2AO7LzDmZuZCqaNw/IjYHxmbmLWWdC0p8DLArcHlzfGXtgCRJ0qqgmwrI9YHrgHcBuwOHApsB85vazAcmABsvI74B8EQpNpvjkiRJapOumQOZmb8AfjGwHBHnAscDNzU1awCLqArf/iHEKXFJkiS1Sdf0QEbEzhGxe1OoATwIjG+KjQPmAXOXEf8DsG5EjC7x8SUuSZKkNumaAhJYDzg5ItaIiLWB9wGfBiIitipF4YHAjMx8CHgmIt5Q1j24xBcAM4HJJT4VmLEyd0KSJGmk65oCMjOvAq4G7gB+BZxXhrUPAa4AZgP3svgGmSnAqRFxL7AWcHqJHwZMj4jZwC7A0StrHyRJklYFjf7+wVMGV02NRqMPmDNnzhz6+vo6nI0kSVJX2IJqSuESuqYHUpIkSb3BAlKSJEm1WEBKkiSpFgtISZIk1WIBKUmSpFosICVJklSLBaQkSZJqsYCUJElSLRaQkiRJqsUCUpIkSbVYQEqSJKkWC0hJkiTVYgEpSZKkWiwgJUmSVIsFpCRJkmqxgJQkSVItFpCSJEmqxQJSkiRJtVhASpIkqZbVOp3AihARBwJHA2OAr2XmmR1OSZIkacQYcT2QEbEJcCKwMzAJmB4RL+toUpIkSSPIiCsggT2An2bmnzPzKeByYL8O5yRJkjRijMQh7I2B+U3L84EdOpTLMvX3X9/pFKQlNBpv7HQKUtfynK1u0+lz9kgsIEcB/U3LDWBRh3J5HjvRaIztdBISAP39T7dtOx7X6hbtPR49Z6t7tOuc3YqRWEDOBXZpWh4HzOtQLsvUaIxlUf91nU5DAmBUY/e2bMd/sOom7TwePWerm7TrnN2KkVhAXgscGxEbAk8B+wLTO5vS0vUv0VEqSepmnrOlxUZcAZmZj0TEZ4DrgRcA38zM2zqc1lL193fhyLpWTY1OJyBJ6iUjroAEyMyLgIs6nYckaeTwol9dowsu+kdkAdkrHA6RpN7hOVtazAKyg7yalSRJvcgCUpKkIfCiX1rMArKDHA6RpN7hOVtazAKyg/q78fnmkiRJy2EBKUnSEHjRLy1mAdlB/f0Oh0hSr/CcLS1mAdlBXs1KUu/wnC0tNqrTCUiSJKm32APZQT4SQpJ6h+dsaTELyA7ykRCSJKkXWUB2kPNpJKl3vHDMuzqdgtQ1nAMpSZKkWuyB7CAfCSFJknqRBWQHOYQtSZJ6kUPYkiRJqsUeyA5yCFuSJPUiC8gOcghbkiT1IoewJUmSVIs9kB3kg8QlSVIv6poCMiLeB5wEPFZCV2fmZyJiPeC7wJbAH4EDMvPRiHgBcC7wWuBp4MDMvDciGsDJwD7AImBaZv585e7N0PhaLEmS1Iu6aQj7tcDHM3NS+flMiZ8AzMzMbYBzgNNK/EjgqRL/KHBBie8LbAO8DHgncEFEdE2hLEmS1Ou6qbDaHnhpRHwauAv4SGb+Bdgb2LW0uRg4MyLGlPhnATLzxojYMCI2K/HvZeYi4LcR8Xvg9cCNK3d3ls8hbEmS1Iu6qYCcD3wFuBn4AvB1YAqwcfmOzFwYEU8AGzbHm9af8DzxruMQtiRJ6kUrvYCMiP2BUweF783MPZrafBl4oCw2BrVtUM1tHAVLdOEtLy5JkqQ2WOkFZGZeBlzWHIuIdSPiY5k5UFg2gIXl8yPAOGBumcu4NvA4MBcYz+JCcxwwrynOoHjXcQhbkiT1om65ieZJ4BMRsWNZPgK4sny+BphaPk+muqFmQXM8InYGnsnM35f4lIgYHRFbAROB21fObtTTzyJ//OmKH0mS6uiKOZCZ+Y+IOAA4OyLGAr9lcdF4DNWd1HcDf6WaFwlwBvCNEv9f4OASvxzYEZhVlj+QmU+v+L2oz1cZSpKkXtSwiKk0Go2XAPfPnDmTCRNWzj03f3z6eyvl70jLs+HY93Q6BUlSFxo9evRWm2666QOD4xaQRaPR2BmY2ek8JEmSusUnP/nJXU466aSbBse7Ygi7S9wO7EL12J9/dDgXSZKkjhk/fvzoqVOnjl999dWXeh+JPZCSJEmqpVvuwpYkSVKPsICUJElSLRaQkiRJqsUCUpIkSbVYQEqSJKkWC0hJkiTVYgEpSZKkWnyQuDomIvYDPkV1HI4Cvp2ZJ7dhuw8Cu2Xmg61uS6ojIvqA3wKzS2gscDPwH5n52Ar8mzdkZt9SvjsfODYzH1oRf1urjojYFvg1sF9mXtHpfNR59kCqIyJiE+CrwJsz85XA64D3RMTbO5uZ1LJ5mTkpMycBWwOPApd3KJc3Ao0O/W2NLO8HLgM+1OlE1B18E406IiJeCcwAdszMh0tsW+AZ4FpKD2JE7EbVg7JbRNwA/Bl4OTAZ+AnwfeD1wN+BKWWdB4HdBn4y85Cy/RuAY4H7ge8CLwQWAUdm5i0reJe1Clhab2BEvAB4DPgccBAwGvgN8GHgHOCVVMfhVzLz2xFxCEs5bjPzhoj4IrAf8Ceq167+CLgBuBW4HtgW+AvwTmAacDzV8b5LZj6+ovZbI1tEjAHmUr3u92ZgR+Bw4JHM/GppcwVwYfn+G8CmVMf1pzLz2og4FtgJ2Aw4g6qX/kRgTWA94GOZ+cOImEB1fl6fqsfznzNzQkSsBZxJdYyPBr6UmRev+L3XstgDqY7IzLuAHwK/i4jbIuJLwOjMvH85q87KzMjMO4ENgV9k5nbA94DTh/jnPwBclZmvBT4L7DysnZCGIDP/D7iPqidyIvCmzHwf1cXM45m5LfAm4NiI2G5Z24mIt1Edqy8H3gq8qunrDYFTyrYeA96TmScB84C3WjyqRXsDD2Xmb4EfANOB7wDvBYiItalGka4GTgPOy8zXAG8HvlG+B1gjM1+WmWcDHwE+mJmvBj4InFDanAZcUs7rlwOblPjRwK/KdncFPhMRW67AfdZyWECqYzLzw0AfcDawOXBLRLx7Oavd2vT5GeDb5fO3qP4JD8W1wL9FxEXAPwFfH2rO0jD1A08DmZl/K7E3AedSBf9EdUG12/NsY0/g0sz8v8z8C9U/8gHzMvO28vluYIP2pS7xL8BAb98lZfluYI2I2Ap4F/Bf5WJpD+D4iLiTapRpDPCSsm7z+fsgYNuIOAb4V2CtEt+TqjglM68E/lriewCHlu3eSDWC9PJ27qTq8SYadURE7A2slZmXAOcD50fENKrewX4Wz9saM2jVp5s+L8rMgTkYo4CFg9o2b+fZbWXmzyPiZcA+VEPhh1CdtKS2K0PYAWzEksfv4Av4BtU5eanHLfCPpawzoPnYH7y+NGwRsRGwF/CaiDiK6thaH3g31ZD1ZKppRCeVVUZT9bL/uaw/HvgD1bSK5uN/JtW0ixuA64CLSnxZx/lo4KDM/O+y3RdTTWlSh9gDqU75H+CLZc4YEdEAJgF3UM3vGriyfMfzbGPNMqwH1RXxjEHf/wnYJiIaEbEFsF35W1+mOhF9CzgCeHXLeyMtRUSMAo4DbgEeGPT1T6kumIiIDaj+wd7AMo5bqp7zfSPiBRGxDtUF0PImsS/EjgK15mDgusyckJl9mbk51dzFQ6nmKk4GtgJuKu1/ChwGUC7Uf0M1z/FZEfEiqukcn6U6b7+DqkCE6jg/sLTbi2p+5MB2P1zi44FZVPMp1SEWkOqIzLye6h/rVRGRwL1UV56fp7rZ4LSIuJ3FwxfLsn9EzALeAnx00HfXAg8DSTWvZuAEdwawXxkKuRKY2uLuSM02jog7y/F1F9Ucrvcupd3xwIsi4tdUQ3Inlt6VpR63mXl1aXcH1VyzeSzZo7M0VwHXlEJUGo5DgLMGxc4EdqAaRv4TcHnTaNBHgJ3KefkSqov1vzevXHonz6UaBr8HWJuqQ+CFwFFUF0p3UBWnfy2rHQeMjYjfUBWTn8jMwRdlWom8C1s9KyL6M9OhOq0SIuJ1wMTM/Fa5K/YXwPszc1aHU5PaJiKOBK7NzNkR8WrgnHLjjLqMQxuS1BsS+FxEfJxq9OhbFo8age4DLo6IRVQ3Sk7rcD5aBnsgJUmSVItzICVJklSLBaQkSZJqsYCUJElSLRaQkiRJqsUCUpIkSbVYQEqSJKmW/wdCMCM/a5G0gQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 648x792 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Reduce width to increase space between bars\n",
+    "width = .9\n",
+    "widthM = 1\n",
+    "titlesize = 14\n",
+    "\n",
+    "Ann_Eval['Supply'] = ['Surplus', 'Drought', 'Average']\n",
+    "Ann_Eval.index = Ann_Eval['Supply']\n",
+    "\n",
+    "DayDF = Ann_Eval\n",
+    "\n",
+    "fig, ax = plt.subplots(3,1, constrained_layout=True)\n",
+    "fig.set_size_inches(9,11)\n",
+    "DayDF['Error_SLC_WDM'] = DayDF.y_pred_tot_AF-DayDF.y_test_tot_AF\n",
+    "DayDF['Error_UR'] = DayDF.UR_gpcd_AF-DayDF.y_test_tot_AF\n",
+    "\n",
+    "\n",
+    "#Annual Figure\n",
+    "\n",
+    "ax[0].bar(DayDF.Supply, DayDF.UR_gpcd_AF, width=width, \n",
+    "    color='red', label='Base')\n",
+    "ax[0].bar(DayDF.Supply, DayDF.y_pred_tot_AF, width=width, \n",
+    "    color='orange', label='MLR')\n",
+    "ax[0].bar(DayDF.Supply, DayDF.y_test_tot_AF, width=width, \n",
+    "    color='blue', label='Observed')\n",
+    "ax[0].set_ylim(0,max(max(Ann_Eval.y_test_tot_AF), max(Ann_Eval.y_pred_tot_AF),max(Ann_Eval.UR_gpcd_AF))*1.2)\n",
+    "#ax.annotate('Max Extra DC use: '+str(np.round(np.max(DayDF.ExtraDC),1))+'MGD', \n",
+    " #              xy=(DayDF.index[269], 150))\n",
+    "#ax.annotate('Max Daily DC use: '+str(np.round(np.max(DayDF.SLCDPU_DC_Water_Use),1))+'MGD', \n",
+    " #              xy=(DayDF.index[269], 130))\n",
+    "ax[0].legend(loc = 'upper left')\n",
+    "ax[0].set_ylabel('Acre-Feet')\n",
+    "ax[0].set_title(' Comparison of Seasonal Forecasts vs Observed', size = titlesize)\n",
+    "ax[0].spines['bottom'].set_color('black')\n",
+    "ax[0].spines['left'].set_color('black')\n",
+    "\n",
+    "\n",
+    "Error_UR = ax[1].bar(DayDF.index, DayDF.Error_UR, width=width, \n",
+    "    color='red', label='Base')\n",
+    "ax[1].set_ylabel('Acre-Feet')\n",
+    "ax[1].set_title('Base: Difference From The Observed', size = titlesize)\n",
+    "gradientbars_sliced(Error_UR, min(min(DayDF.Error_UR), min(DayDF.Error_SLC_WDM)),\n",
+    "                    max(max(DayDF.Error_UR), max(DayDF.Error_SLC_WDM)))\n",
+    "ax[1].spines['bottom'].set_color('black')\n",
+    "ax[1].spines['left'].set_color('black')\n",
+    "\n",
+    "\n",
+    "\n",
+    "Error_SLC_WDM = ax[2].bar(DayDF.Supply, DayDF.Error_SLC_WDM, width=width, \n",
+    "    color='red', label='Unit-Rate')\n",
+    "ax[2].set_ylabel('Acre-Feet')\n",
+    "ax[2].set_title('MLR: Difference From The Observed', size = titlesize)\n",
+    "gradientbars_sliced(Error_SLC_WDM, min(min(DayDF.Error_UR), min(DayDF.Error_SLC_WDM)),\n",
+    "                    max(max(DayDF.Error_UR), max(DayDF.Error_SLC_WDM)))\n",
+    "ax[2].spines['bottom'].set_color('black')\n",
+    "ax[2].spines['left'].set_color('black')\n",
+    "\n",
+    "fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/SLC_WDMvsUR.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Make similar figure to one above, but include RFR and MLP.\n",
+    "#Make bars side by side rather than stacked\n",
+    "\n",
+    "MLP = pd.read_excel('MLP_Models/MLP_Ann_Comp.xlsx')\n",
+    "RFR = pd.read_excel('RFR_Models/RFR_Ann_Comp.xlsx')\n",
+    "colrem = ['Supply', 'Supply.1', 'UR_gpcd_AF' ,'Error_UR', 'y_test_tot_AF']\n",
+    "MLP.index = MLP['Supply']\n",
+    "RFR.index = RFR['Supply']\n",
+    "MLP =MLP.drop(columns = colrem)\n",
+    "RFR = RFR.drop(columns = colrem)\n",
+    "\n",
+    "\n",
+    "MLP_col = ['MLP_pred_tot_AF', 'Error_MLP']\n",
+    "RFR_col = ['RFR_pred_tot_AF', 'Error_RFR']\n",
+    "\n",
+    "MLP.columns = MLP_col\n",
+    "RFR.columns = RFR_col\n",
+    "\n",
+    "#add mlp and RFR predictions to MLR/Ur\n",
+    "DayDF['RFR_pred'] =RFR['RFR_pred_tot_AF']\n",
+    "DayDF['RFR_Err'] =RFR['Error_RFR']\n",
+    "\n",
+    "DayDF['MLP_pred'] =MLP['MLP_pred_tot_AF']\n",
+    "DayDF['MLP_Err'] =MLP['Error_MLP']\n",
+    "\n",
+    "Preds = ['y_test_tot_AF' ,'y_pred_tot_AF', 'MLP_pred', 'RFR_pred', 'UR_gpcd_AF' ]\n",
+    "Err = ['Error_SLC_WDM', 'MLP_Err', 'RFR_Err', 'Error_UR' ]\n",
+    "labels = ['Observed','SLC-WDM' , 'MLP' , 'RFR', 'Stationary']\n",
+    "label2 = ['SLC-WDM' , 'MLP' , 'RFR', 'Stationary']\n",
+    "\n",
+    "\n",
+    "Preds = DayDF[Preds].copy()\n",
+    "Preds.columns = labels\n",
+    "Err = DayDF[Err].copy()\n",
+    "Err.columns =label2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Reduce width to increase space between bars\n",
+    "width = .8\n",
+    "widthM = 1\n",
+    "titlesize = 14\n",
+    "\n",
+    "colorA  = ['blue', 'orange' ,'green' , 'purple', 'red']\n",
+    "colorB  = ['orange' ,'green' , 'purple', 'red']\n",
+    "\n",
+    "fig, ax = plt.subplots(2,1, constrained_layout=True)\n",
+    "fig.set_size_inches(9,9)\n",
+    "\n",
+    "#Annual Figure\n",
+    "\n",
+    "Preds.plot(ax=ax[0], kind='bar', grid=True, width=width, color = colorA)\n",
+    "ax[0].legend(labels)\n",
+    "ax[0].set_ylim(0,max(max(Ann_Eval.y_test_tot_AF), max(Ann_Eval.y_pred_tot_AF),max(Ann_Eval.UR_gpcd_AF))*1.2)\n",
+    "#ax.annotate('Max Extra DC use: '+str(np.round(np.max(DayDF.ExtraDC),1))+'MGD', \n",
+    " #              xy=(DayDF.index[269], 150))\n",
+    "#ax.annotate('Max Daily DC use: '+str(np.round(np.max(DayDF.SLCDPU_DC_Water_Use),1))+'MGD', \n",
+    " #              xy=(DayDF.index[269], 130))\n",
+    "ax[0].legend(loc = 'upper left')\n",
+    "ax[0].set_ylabel('Seasonal Forecast (Ac-Ft)')\n",
+    "ax[0].set_ylim(0,np.max(Preds['Stationary'])+50000)\n",
+    "#ax[0].set_title(' Comparison of Seasonal Forecasts vs Observed', size = titlesize)\n",
+    "ax[0].spines['bottom'].set_color('black')\n",
+    "ax[0].spines['left'].set_color('black')\n",
+    "ax[0].tick_params(labelrotation=0)\n",
+    "ax[0].xaxis.label.set_visible(False)\n",
+    "ax[0].text(2.3, 150000, \"A.\", size = 16)\n",
+    "\n",
+    "\n",
+    "Err.plot(ax=ax[1], kind='bar', grid=True, width =width, color=colorB, legend=False)\n",
+    "ax[1].set_ylabel('Seasonal Forecast Error (Ac-Ft)')\n",
+    "#ax[1].set_title('Error', size = titlesize)\n",
+    "ax[1].spines['bottom'].set_color('black')\n",
+    "ax[1].spines['left'].set_color('black')\n",
+    "ax[1].tick_params(labelrotation=0)\n",
+    "ax[1].axhline(y=0, color='black', linestyle='-')\n",
+    "ax[1].xaxis.label.set_visible(False)\n",
+    "ax[1].text(2.3,32000, 'B.', size = 16)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/MLR_MLP_RFR_UR_compare.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#Make a function to put all of the predictions together\n",
+    "def Demand_Forecastgpcd_AF(gpcd, pdict, df, pred1, test1,pred2, test2, units1, units2, plotname, model):\n",
+    "    FinalDF=pd.DataFrame()\n",
+    "    if pdict is True:\n",
+    "        print('yes')\n",
+    "        for i in gpcd:\n",
+    "            FinalDF=FinalDF.append(gpcd[i])\n",
+    "\n",
+    "        FinalDF=FinalDF.sort_index()\n",
+    "    else:\n",
+    "        print('no')\n",
+    "        FinalDF = df\n",
+    "        \n",
+    "    months = np.arange(1,6,1)\n",
+    "    Low = list()\n",
+    "    Ave=list()\n",
+    "    High = list()\n",
+    "    for i in months:\n",
+    "        Low.append('Low'+str(i))\n",
+    "        Ave.append('Ave'+str(i))\n",
+    "        High.append('High'+str(i))\n",
+    "        \n",
+    "    Low.append('Drought')\n",
+    "    Ave.append('Average')\n",
+    "    High.append('Surplus')\n",
+    "    \n",
+    "    months2 = np.arange(7,13,1)\n",
+    "    for i in months2:\n",
+    "        Low.append('Low'+str(i))\n",
+    "        Ave.append('Ave'+str(i))\n",
+    "        High.append('High'+str(i))\n",
+    "    \n",
+    "    Conditions = High+Low+Ave\n",
+    "\n",
+    "    #adjust date range to improve figure\n",
+    "    #FinalDF['Date']= pd.date_range(start=pd.datetime(2015,1,1),end=pd.datetime(2018,1,1), freq='M')\n",
+    "    FinalDF['Date'] = Conditions\n",
+    "    \n",
+    "    FinalDF.index = FinalDF['Date']\n",
+    "    del FinalDF['Date']\n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    plotmin_tot1 = FinalDF[[pred1, test1]].min().min()\n",
+    "    plotmax_tot1 = FinalDF[[pred1, test1]].max().max()\n",
+    "    \n",
+    "    plotmin_tot2 = FinalDF[[pred2, test2]].min().min()\n",
+    "    plotmax_tot2 = FinalDF[[pred2, test2]].max().max()\n",
+    "\n",
+    "    fig, ax = plt.subplots(2,6, constrained_layout=True)\n",
+    "    fig.set_size_inches(10,5)\n",
+    "\n",
+    "    gs1 = ax[0,0].get_gridspec()\n",
+    "    # remove the underlying axes\n",
+    "    ax[0,0].remove()\n",
+    "    ax[0,1].remove()\n",
+    "    ax[0,2].remove()\n",
+    "    axbig = fig.add_subplot(gs1[0,:3])\n",
+    "    #axbig.set_title('Total demand Timeline Evaluation')\n",
+    "    axbig.plot(FinalDF[pred1], color='orange', label= model)\n",
+    "    axbig.plot(FinalDF[test1],color='blue', label='Observed')\n",
+    "    axbig.set_xlabel('Supply Conditions')\n",
+    "    axbig.set_ylim(plotmin_tot1-.9,plotmax_tot1*1.4)\n",
+    "    axbig.set_ylabel('Demand ('+ units1+')')\n",
+    "    axbig.legend(loc = 'upper left')\n",
+    "    axbig.set_facecolor(\"white\")\n",
+    "    axbig.spines['bottom'].set_color('black')\n",
+    "    axbig.spines['left'].set_color('black')\n",
+    "    axbig.text(35,600, 'A.', size = 16)\n",
+    "    \n",
+    "    xticks = axbig.xaxis.get_major_ticks()\n",
+    "    for i,tick in enumerate(xticks):\n",
+    "        if i%12 != 5:\n",
+    "            tick.label1.set_visible(False)\n",
+    "\n",
+    "    ax[0,3].remove()\n",
+    "    ax[0,4].remove()\n",
+    "    ax[0,5].remove()\n",
+    "\n",
+    "\n",
+    "    axbig2 = fig.add_subplot(gs1[0:,3:])\n",
+    "    axbig2.scatter(FinalDF[test1], FinalDF[pred1],color='blue', alpha=0.5)\n",
+    "    axbig2.set_ylabel('Predicted (' + units1+')' )\n",
+    "    axbig2.set_xlabel('Observed (' + units1+')')\n",
+    "    axbig2.set_ylim(plotmin_tot1*.95,plotmax_tot1*1.2)\n",
+    "    axbig2.set_xlim(plotmin_tot1*.95,plotmax_tot1*1.2)\n",
+    "   # axbig2.set_title('Indoor and Outdoor \\n Model Performance')\n",
+    "    axbig2.plot([plotmin_tot1,plotmax_tot1],[plotmin_tot1,plotmax_tot1], color='red', linestyle='--' )\n",
+    "    #axbig2.set_xticks(np.arange(plotmin_tot, plotmax_tot, 100).round())\n",
+    "    #axbig2.set_yticks(np.arange(plotmin_tot, plotmax_tot, 100).round())\n",
+    "    axbig2.set_facecolor(\"white\")\n",
+    "    axbig2.spines['bottom'].set_color('black')\n",
+    "    axbig2.spines['left'].set_color('black')\n",
+    "    axbig2.text(550,600, 'C.', size = 16)\n",
+    "    #ax[2].set_aspect('equal', adjustable='box')\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    gs2 = ax[1,0].get_gridspec()\n",
+    "    # remove the underlying axes\n",
+    "    ax[1,0].remove()\n",
+    "    ax[1,1].remove()\n",
+    "    ax[1,2].remove()\n",
+    "    ax[1,3].remove()\n",
+    "    ax[1,4].remove()\n",
+    "    ax[1,5].remove()\n",
+    "    axbig = fig.add_subplot(gs2[1,:3])\n",
+    "    #axbig.set_title('Total demand Timeline Evaluation')\n",
+    "    axbig.plot(FinalDF[pred2], color='orange', label= 'Predicted')\n",
+    "    axbig.plot(FinalDF[test2],color='blue', label='Observed')\n",
+    "    axbig.set_xlabel('Supply Conditions')\n",
+    "    axbig.set_ylim(plotmin_tot2-.9,plotmax_tot2*1.2)\n",
+    "    axbig.set_ylabel('Demand ('+ units2+')')\n",
+    "    #axbig.legend(loc = 'upper left')\n",
+    "    axbig.set_facecolor(\"white\")\n",
+    "    axbig.spines['bottom'].set_color('black')\n",
+    "    axbig.spines['left'].set_color('black')\n",
+    "    axbig.text(35,17000, 'B.', size = 16)\n",
+    "    #axbig.get_legend().remove()\n",
+    "    \n",
+    "    xticks = axbig.xaxis.get_major_ticks()\n",
+    "    for i,tick in enumerate(xticks):\n",
+    "        if i%12 != 5:\n",
+    "            tick.label1.set_visible(False)\n",
+    "    fig.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/MLR_Seasonal_gpcd_AF.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Demand_Forecastgpcd_AF(slc_val, False, Pred_Obs, \n",
+    "            'y_pred_tot', 'y_test_tot','y_pred_tot_AF', 'y_test_tot_AF', 'gpcd', 'Acre-Feet', 'MLR_Performance', 'SLC-WDM ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The indoor mean is  155.9054666666666\n",
+      "Apr\n",
+      "Min monthly gpcd is  131.81\n",
+      "Mean monthly gpcd is  194.49033333333335\n",
+      "Max monthly gpcd is  266.67\n",
+      "STD monthly gpcd is  34.81686894627693\n",
+      " \n",
+      "May\n",
+      "Min monthly gpcd is  162.68\n",
+      "Mean monthly gpcd is  299.2136666666667\n",
+      "Max monthly gpcd is  438.48\n",
+      "STD monthly gpcd is  61.68005071784196\n",
+      " \n",
+      "Jun\n",
+      "Min monthly gpcd is  287.62\n",
+      "Mean monthly gpcd is  460.823\n",
+      "Max monthly gpcd is  575.55\n",
+      "STD monthly gpcd is  74.677865357369\n",
+      " \n",
+      "Jul\n",
+      "Min monthly gpcd is  386.06\n",
+      "Mean monthly gpcd is  556.143\n",
+      "Max monthly gpcd is  696.01\n",
+      "STD monthly gpcd is  70.20522213007615\n",
+      " \n",
+      "Aug\n",
+      "Min monthly gpcd is  344.54\n",
+      "Mean monthly gpcd is  521.425\n",
+      "Max monthly gpcd is  632.09\n",
+      "STD monthly gpcd is  65.81317344068232\n",
+      " \n",
+      "Sep\n",
+      "Min monthly gpcd is  275.13\n",
+      "Mean monthly gpcd is  387.3406666666666\n",
+      "Max monthly gpcd is  485.58\n",
+      "STD monthly gpcd is  52.960191964237524\n",
+      " \n",
+      "Oct\n",
+      "Min monthly gpcd is  178.02\n",
+      "Mean monthly gpcd is  235.73533333333333\n",
+      "Max monthly gpcd is  323.68\n",
+      "STD monthly gpcd is  41.472093648085284\n",
+      " \n"
+     ]
+    }
+   ],
+   "source": [
+    "Months = ['Jan' , 'Feb' , 'Mar' , 'Nov' , 'Dec']\n",
+    "indoor = []\n",
+    "for i in Months:\n",
+    "    indoor.append(slc[i]['Obs_gpcd'])\n",
+    "    \n",
+    "\n",
+    "indoor = pd.DataFrame(data= indoor).T\n",
+    "valyears = [2008,2015,2017]\n",
+    "indoor = indoor.drop(valyears)\n",
+    "indoor = pd.DataFrame(data = indoor.stack(), columns = ['gpcd']) \n",
+    "\n",
+    "print('The indoor mean is ', np.mean(indoor['gpcd']))\n",
+    "\n",
+    "\n",
+    "Months = ['Apr', 'May', 'Jun', 'Jul', 'Aug','Sep', 'Oct']\n",
+    "\n",
+    "for i in Months:\n",
+    "    print(i)\n",
+    "    df = pd.DataFrame(data = slc[i]['Obs_gpcd'], columns = ['Obs_gpcd'])\n",
+    "    df = df.drop(valyears)\n",
+    "    print('Min monthly gpcd is ', np.min(df['Obs_gpcd']))\n",
+    "    print('Mean monthly gpcd is ', np.mean(df['Obs_gpcd']))\n",
+    "    print('Max monthly gpcd is ', np.max(df['Obs_gpcd']))\n",
+    "    print('STD monthly gpcd is ', np.std(df['Obs_gpcd']))\n",
+    "    print(' ')\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Min seasonal gpcd is  280.0242857142857\n",
+      "Mean seasonal gpcd is  372.48\n",
+      "Max seasonal gpcd is  445.2157142857142\n",
+      "STD seasonal gpcd is  46.17989203456475\n",
+      "Min seasonal gpcd is  64088.73823499999\n",
+      "Mean seasonal gpcd is  85248.93888000003\n",
+      "Max seasonal gpcd is  101895.85270499998\n",
+      "STD seasonal gpcd is  10569.122620112781\n"
+     ]
+    }
+   ],
+   "source": [
+    "seasondem = pd.DataFrame(columns = Months)\n",
+    "for i in Months:\n",
+    "    seasondem[i]=slc[i]['Obs_gpcd']\n",
+    "seasondem['SeasonMean'] = seasondem.mean(axis=1)\n",
+    "\n",
+    "print('Min seasonal gpcd is ', np.min(seasondem['SeasonMean']))\n",
+    "print('Mean seasonal gpcd is ', np.mean(seasondem['SeasonMean']))\n",
+    "print('Max seasonal gpcd is ', np.max(seasondem['SeasonMean']))\n",
+    "print('STD seasonal gpcd is ', np.std(seasondem['SeasonMean']))\n",
+    "\n",
+    "seasondem['SeasonTot'] = 0.00000307*seasondem['SeasonMean']*213*350000\n",
+    "print('Min seasonal gpcd is ', np.min(seasondem['SeasonTot']))\n",
+    "print('Mean seasonal gpcd is ', np.mean(seasondem['SeasonTot']))\n",
+    "print('Max seasonal gpcd is ', np.max(seasondem['SeasonTot']))\n",
+    "print('STD seasonal gpcd is ', np.std(seasondem['SeasonTot']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#making heatmap figure for methods / July\n",
+    "\n",
+    "CorFeat= ['UrbPopulationDensity', 'Val_Jul_Monthly_mean_Day_temp_C', \n",
+    "              'Val_Apr_Monthly_mean_Day_temp_C', 'Val_May_Monthly_mean_Day_temp_C', \n",
+    "              'LitCotCrk_AcFt_WR_Apr', 'ParlCrk_AcFt_WR_Jul']\n",
+    "July = slc['Jul'][CorFeat]\n",
+    "July = July.drop(valyears)\n",
+    "\n",
+    "len(slc['Jul'].columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style='white', font_scale = .8)\n",
+    "\n",
+    "\n",
+    "#subset these features out of main DF and put into cute heatmap plot\n",
+    "month = 'July'\n",
+    "\n",
+    "DFcor = copy.deepcopy(July)\n",
+    "\n",
+    "cor = np.abs(DFcor.corr())\n",
+    "cor = cor.mask(np.tril(np.ones(cor.shape)).astype(np.bool))\n",
+    "#remove colinearity\n",
+    "#cor = cor[cor.columns[cor.max() < 0.7]]\n",
+    "CorFeat=cor.columns\n",
+    "cor = cor.T\n",
+    "cor = cor[CorFeat]\n",
+    "\n",
+    "x_axis_labels = ['Population Denisty', 'July mean day temperature', \n",
+    "                'April mean day temperature', 'May mean day temperature',\n",
+    "                'April LCC discharge', 'July Parleys discharge'] # labels for x-axis\n",
+    "y_axis_labels = ['Population Denisty', 'July mean day temperature', \n",
+    "                'April mean day temperature', 'May mean day temperature',\n",
+    "                'April LCC discharge', 'July Parleys discharge'] # labels for y-axis\n",
+    "\n",
+    "\n",
+    "# plotting cv results\n",
+    "plt.figure(figsize=(5,4))\n",
+    "ax = sns.heatmap(cor, annot=True, cmap=plt.cm.Reds,\n",
+    "            xticklabels = x_axis_labels, yticklabels = y_axis_labels)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.text(7,0, 'B.', size = 16)\n",
+    "plt.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/' + month + '_corMatrix.pdf')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-83-3c16467a24d4>:39: UserWarning: This figure was using constrained_layout==True, but that is incompatible with subplots_adjust and or tight_layout: setting constrained_layout==False. \n",
+      "  plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Make a figure for July that visualizes correation process\n",
+    "Julcor = slc['Jul'].corr()\n",
+    "Julcor = pd.DataFrame(Julcor['Obs_gpcd'])\n",
+    "Julcor['Obs_gpcd'] = np.abs(Julcor['Obs_gpcd'])\n",
+    "Julcor = Julcor.sort_values(by= 'Obs_gpcd', ascending=True)\n",
+    "Julcor = Julcor[:-1]\n",
+    "index = ['Jul LCC', 'Jun PC', 'Jul BCC',\n",
+    "       'Jul CC', 'Jun Precip',\n",
+    "       'Jun Temp', 'Jun CC',\n",
+    "       'May PC', 'Jun BCC', 'May LCC',\n",
+    "       'Jul PC', 'May CC', 'Apr PC',\n",
+    "       'Mar PC', 'Jul Precip',\n",
+    "       'May BCC', 'Apr Precip',\n",
+    "       'May Precip', 'Jul Temp',\n",
+    "       'May Temp', 'April Temp',\n",
+    "       'Mar LCC', 'Apr CC', 'Mar BCC',\n",
+    "       'Apr LCC', 'Apr BCC',\n",
+    "       'Population Density', 'Population', 'Population Density', 'Housing',\n",
+    "       'Housing Density', 'Population Density']\n",
+    "Julcor.index = index\n",
+    "\n",
+    "\n",
+    "tsize = 14\n",
+    "#adjust the amount of ticks for clarity\n",
+    "import matplotlib.ticker as ticker\n",
+    "tick_spacing = 2\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(1,1, constrained_layout=True)\n",
+    "fig.set_size_inches(5,5)\n",
+    "\n",
+    "plt.scatter(x=Julcor.index, y = Julcor['Obs_gpcd'], c= 'red')\n",
+    "plt.ylabel('Correlation with Demand', size =tsize)\n",
+    "plt.xlabel('Initial Input Features', size = tsize)\n",
+    "plt.xticks(rotation = 90)\n",
+    "plt.plot([0.0, 31.0],[0.0, 1.0], c = 'black')\n",
+    "ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))\n",
+    "plt.text(30,1.1, 'A.', size = 16)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('C:/Users/Ryan/Box/Dissertation/Paper1/Figs/Julcor.pdf')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['LitCotCrk_AcFt_WR_Jul', 'ParlCrk_AcFt_WR_Jun', 'BigCotCrk_AcFt_WR_Jul',\n",
+       "       'CityCrk_AcFt_WR_Jul', 'Val_Jun_Monthly_precip_mm',\n",
+       "       'Val_Jun_Monthly_mean_Day_temp_C', 'CityCrk_AcFt_WR_Jun',\n",
+       "       'ParlCrk_AcFt_WR_May', 'BigCotCrk_AcFt_WR_Jun', 'LitCotCrk_AcFt_WR_May',\n",
+       "       'ParlCrk_AcFt_WR_Jul', 'CityCrk_AcFt_WR_May', 'ParlCrk_AcFt_WR_Apr',\n",
+       "       'ParlCrk_AcFt_WR_Mar', 'Val_Jul_Monthly_precip_mm',\n",
+       "       'BigCotCrk_AcFt_WR_May', 'Val_Apr_Monthly_precip_mm',\n",
+       "       'Val_May_Monthly_precip_mm', 'Val_Jul_Monthly_mean_Day_temp_C',\n",
+       "       'Val_May_Monthly_mean_Day_temp_C', 'Val_Apr_Monthly_mean_Day_temp_C',\n",
+       "       'LitCotCrk_AcFt_WR_Mar', 'CityCrk_AcFt_WR_Apr', 'BigCotCrk_AcFt_WR_Mar',\n",
+       "       'LitCotCrk_AcFt_WR_Apr', 'BigCotCrk_AcFt_WR_Apr',\n",
+       "       'ResPopulationDensity', 'Population', 'PopulationDensity', 'Housing',\n",
+       "       'HousingDensity', 'UrbPopulationDensity'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Julcor.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}