MLP_Monthly_Demand.ipynb

{
 "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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FLD+HWDasBtXtprx+Uyk3dygzKywMbFIdc25ePfDu7M5zoK/Pr6rf/wQezszb6pYXqUoUPgxsFhHbUm6wXbxSWnx/Zt5a/f1XYMfq740ppcYzKSWh76riOXoO5yVpzn5GKYW8kVKL8gVg/2rbCpQvhucDVLUIv6IkSIsA51czsRARp1FK62DO95zZ+U5EHMIrTZMupBQIrAAsB5xWd1+cF3g7cDfw72pe7Z76iv+qXh53bzX4OJT71pOZ+RLwaFWTs0hmXl/FuFtELEeZKrF+Lu5LMvPF6jW5nVfm756ZmRdVcdwCrBoRK/dxXjf04/XSIJhAtq8bgLdGxOKZ+XCPbe8GatXCg52ntK+5SHtuq5+jFOYwT2lmPhwRf6UkWp8G9gMWrdulr+PPbi7XsZSEd6vahoiYSPkG//E+wrkmMzebzbaxwL6ZeXF1vPkpJXo1szvPgb4+L9b9/XLPg1WlqH8DLqAkn7Uq89prMbt5b2fUx1G9Hs/147wkzdlZwC0RcSywYGbeUZfMzGku597uubXHNXJtfikzf9lzZUSMpSRzk+vWvZHS4WZd+n8P6xl/X/cw6P0+9iFKUvtd4DeUL+v17Uh7u4+96h5WHedt1bbZnZeGmVXYbSozH6S0BzknIt5UWx8RO1FKzr5drXoEWLPathGldK5mBjAuMx8DbgF2rvZbnfKND/qei/QSYI+IGFe1a9mT0olnIH5KqZZdKDPv6LFtdsfvay7XK4D3VVXvVPtMoaribtAfgL0iYu4qjlOBI/vxuKF4feq9FVgQOCQzf0f55j4Pc5779nLg0xExJiLmoVT5vIvGz0tSJTMfotxjTqOURtabCrwcEZsDVFW4n6DcBy6mVM8uXF1/29c9bqivzaS0zf5UFcdEypSCa8zhcX3F36hNgN9l5g8ovcQ/xpzvYQl0R8QmVRyrU6ZJbPS8NARMINtYZh5MacT9m4i4IyLuoVRXrldXrXAgsG9VPbs9JVGsOR84M8rQM9sAW1dVBl+lVGuQfc9F+g3gYeDWav9xwL4DPI1fU9oH9rzxMrvjZx9zuVbx7gqcGxG3AUdQOqD0WRo6B0cA91FK/+6ifOv9Yj8eNxSvT70plCqpqVHmvv1wFc+cqucPozQ6v41yDr+vqqQaPS9Jr/ZTSjvGs+tX1t2r9o2IKZQvc4dn5lWZ+XtK0nkz5Qt5fanZkF6bVTXyR4GdqzguBb6amX+ew+NmG3+jsVDa1G9Ufdb8lVLV/eYqUZ5dHC9S7vNfr+75pwCbN3peGhrOhS1JkqQBsQRSkiRJA2ICKUmSpAExgZQkSdKAtP0wPocddtg848ePX2v77befvuSSS85sdjyS2s5YyugEN/HaYUjalvdGSYPU572x7RPI8ePHr3XggQfOacBqSZqTDYFrmx3EUPHeKGmI9HpvbPte2BMnTlxu7Nix/7j22mtZaqmlmh2OpPa1PGVIkVHBe6OkIdLrvbHtSyCnTZs2E2DGjBlz2lWS+jKqqnm9N0oaIr3eG+1EI0mSpAExgZQkSdKAmEBKkiRpQEwgJUmSNCAmkJIkSRoQE0hJkiQNiAmkJEmSBsQEUpIkSQNiAilJkqQBMYGUJEnSgLT9VIaS1I4iYkHgOmCzzLwvItYDjgMWAKYAn87MlyJiMvBjYEHgT8Dumen8hJKayhJISRphEbEOcC2wQrW8IHA+sGtmrlLt9tnq95nAXpm5AtAF7DLC4UrSa5hAStLI2wXYE3ioWt4EuD4zp1TLewMXRMQywLyZeUO1/gxgi5EMVJJ6YwIpSSMsM3fOzGvqVi0PPBMR50bErcBhwBPAksD0uv2mA0uNVJySWsw//gEHHACzZjU7EttASqPdlClw/vnwwAOw9NKw+eYwaVKzo1IPcwHvB9YFHgD+DzgIuAzortuvC2j+J4ekkfXSS/Cd78ARR8A888BOO8FKKzU1JEsgpVFsyhQ45hh4/HFYaqny+5hjynq1lIeBGzLz3sycCfwcWBuYBixRt9/ivFLtLakTXHMNTJ4MhxwCH/kI3H1305NHMIGURrXzz4cJE8rPmDGv/H3++c2OrM6DD0Jms6NotkuBNSJiYrW8GXBLZt4PvBAR61frtwcubkaAkprkyCPhuefgwgvh5z+HJZdsdkSACaQ0qj3wACy00KvXLbRQWd8y7rsPdt+92VE0VWb+G9gN+F1ETAUWAY6sNm8HHFetnx84sTlRShoR3d1w5plw771l+bTT4M474UMfam5cPdgGUhrFll66VFtPmPDKuiefLOub6q9/hT/9CT7/eVh/ffjVr5ocUHNk5rJ1f18EXNTLPrdRqrMljXb33AN77AFXXAH771/aPS6+eLOj6pUlkNIotvnmJYF8/PHSaa/29+abNymgZ56B/faDtdYqN8anny7rF1mkSQFJUgt48cXSQWbVVeGmm+D734ejjmp2VH0ygZRGsUmTypfYCRNg2rTye//9m9QL+7e/hZVXhuOOg113LVUyCyzQhEAkqcUceSR87Wvw0Y/C1KmlFHLs2GZH1SersKVRbtKkFhi25+GHYaut4K1vhfPOg/XWa3JAktRk//sfPPIIRJTmPOusA5tu2uyo+s0SSEnDY+ZMuOCC0iB88cXhqqvglltMHiV1tlonmRVXhG23LcsLL9xWySOYQEoaDrfcUr5Nb7556SwDsO66MG5cc+OSpGb6+99hk01g++1hueXg9NOhq6vZUTXEBFLS0Hn66VIVs/baZXzH886Dd76z2VFJUvNdd11pT3TzzfCDH8Cf/9wC7YsaZxtISUOjuxve/e4yRM/uu8O3vlWqZSSpkz3xRLkXrrUW7LMPfOELsMQSc3pUyzOBlDQ406aVNo5zzQWHH166etvOUVKne+wxOOAAuPjiMv3gQgvB0Uc3O6oh05Qq7Ig4PCLuiog7I2K/at3GETElIu6JiG80Iy5JAzBjBhx/fGkIfmI1OcoHP2jyKKmzdXfDz35W7o0/+Ulp7zgK23+PeAIZEe8C3gNMAtYE9o6I1YDTgI8CKwFrRUR7dUeSOkmtk8wXvlDaOH78482OSJKa7+mnYeONYYcdYPnlS5Oeb38bXve6Zkc25EY8gczMPwLvzswZwBso1egLA/dk5r3V+jOBLUY6Nkn9cOyxpZPMQw/Bz38OF10Eb35zs6OSpObp7i6/558f3vjGUdFJZk6aUoWdmS9HxGHAXcAVwJLA9LpdpgNLNSM2SbMxY0b5vfbasNtupU3PFlu07RAUkjQk/vhHWHNNuO++cj88++zSkXDM6B7opmlnl5lfBxYDJgIrAN11m7uAWc2IS1IP//43fOxjZQ5rgA02KPO02sNaUid77DH4zGdgo43g8cfhP/9pdkQjqhltIFeMiMkAmfkccD6wEVDfp31x4KGRjk1SnVonmZVWgksvhWWWaXZEktQaajPJ/OxncNBBcMcdpV14B2nGMD5vAQ6LiA0opY4fBX4IfCcilgfuBbaldKqR1Ax33ll6Dv7tb6Vn9cknw7LLNjsqSWoN110HK6wAp5wCq67a7GiaohmdaH4PXAT8DbgFuC4zzwV2BH5FaRc5FfjlSMcmqTJ+PDz5ZOkkc+GFJo+SOtuLL5Zxbm+4oSx/97twzTUdmzxCkwYSz8xDgUN7rLsCWK0Z8UgCfv3rMuDtKaeUOVr//ncYO7bZUUlSc/3xj6XjYCa8/DKsuy7MO2+zo2q60d1FSNKcPfAAfPSjZSzHG24o026ByaOkzvboo7DTTqWTzEsvlS/YRxzR7Khahgmk1KlmzIDjjoOVV4bLLy9TbN18c5mKUJI63U9/WjrLHHxw6STzgQ80O6KW4lzYUqd6+mk46ih417vsJCNJUKqpH3oI3v1u2HvvkjSuvHKzo2pJlkBKneSpp0rSOGNGKWm85RY7yUjSiy/CYYeVmWP23BNmzSrzV5s8zpYJpNQJurvhggvKzfDLXy6NwgGWWsqZZCR1tquvLonjoYfCJz4BV1456meRGQq+QtJo98ADZSaZzTeHRRctHWXe+95mRyVJzfeXv5Tq6pdfhksuKdMQLr54s6NqCyaQ0mjW3Q1bblk6yRxzTOkks/bazY5Kkpqnu7tMlgCw1lrwf/9XOsm8//3NjavN2IlGGo1uvrnMkrDggvDDH5Z5q52KsKVExILAdcBmmXlfRJwObAA8W+1yWGZeUE39+mNgQeBPwO6ZOaMZMUttLxN23x1uvLH8PXFimc9aA2YJpDSaPPUU7LNPKWU86qiybrXVTB5bTESsA1wLrFC3ek3gnZk5ufq5oFp/JrBXZq4AdAG7jGy00ijwwguljeOkSXDrrXDCCfCmNzU7qrZmCaQ0GtQ6yey9N0yfXnoRHnhgs6PS7O0C7An8DCAiXgcsDZwWEW8CLgAOAyYC82ZmNX8aZ1TrfzDSAUtt6/nnYfXVYepU2HZbOPZYeOMbmx1V2zOBlEaDb3wDvva1Utp4wQW2c2xxmbkzQETUVi0OXAl8DngSuBD4LHAHML3uodOBpUYsUKmdPf98mXJw3nlhu+3KffF972t2VKOGCaTUrmbMKFXWiyxSvlW/7nWw774wl5d1u8nMfwEfry1HxEnADsBdQHfdrl3ArJGNTmoz3d3wk5/AAQfAb39b5q4+5JBmRzXq2AZSakd/+UvpPfjpT5eb5XLLwRe/aPLYpiJi1Yj4RN2qLuBlYBqwRN36xYGHRjI2qa1MnVqG5dlpp9KRcKGFmh3RqGUCKbWTp54q7RzXXRf++99yk9Ro0AUcHxETImIcsCtwQWbeD7wQEetX+20PXNysIKWWdtRRpRnPbbfBj34Ef/oTrLRSs6MatUwgpXZx883lZnjyybDXXnD33WVwcGeSaXuZOQU4Evgzpdr61sw8p9q8HXBcREwF5gdObE6UUovr6oIttiilkLvs4mwyw6yru7t7znu1sK6urmWBe++9916WdT5fjUbd3eXG+PjjsM02cPjhdpIZHm8G7mt2EEPFe6NGvUcegf33h802K4lj7V6podbrvdH0XGpVM2bAd78L73xn+XvChDLVlsmjpE7W3Q2nnw4rrlimHnzggbLe5HFEmUBKrajWSWb//Uvi+PTTzY5Ikpqv1knmM58pTXpuvbV0INSIM4GUWsmzz77SSeaRR+D88+E3vylJpCR1uttugylT4NRTSyeZVVZpdkQdyzE/pFYybly5Ke69NxxxRJnLWpI62ZVXwv33l1EnttwSNtmkjH+rprIEUmq2++4rN8annoK55y7V1yecYPIoqbM98gjssAO8971w3HEwc2Zp52jy2BJMIKVmefllOOaYUgXz85+XYXoA5pmnuXFJUjN1d8Npp5VOMueeC1/5Ctx4I4wd2+zIVMcEUmqGG28snWS+9KXy7fruu+E972l2VJLUfFOmwM47w8orw9/+Bt/4RpnPWi3FNpBSM3z96/Doo6WTzMc+5vATkppuypRyS3rgAVh66TJPwaRJI/TkL7wAl10GH/5wmU3mT3+Cd7zDwcBbWEMJZERcBcx2BPLMtChFqtfdDb/8ZRnDcZllSvXM/PPbzrFNeQ/UaDNlSmlRM2ECLLVUmbfgmGPKSGLDnkRefjnssQf885/w97/D8svDBhsM85NqsBpN7b8HnAw8BDwLnAQcBzwK/GNoQpNGifvuKzMlbLll6RwDsOSSJo/tzXugRpXzzy/J44QJpdCv9vf55w/jkz7yCGy/felVDXDppSV5VFtoqAQyM38FEBFfAt6RmbOq5YuA64cuPKmNvfwyHH88HHpoqaI+9tgyPI/anvdAjTYPPFBKHusttNArk7wMuRdfhNVXh//8Bw45BL78Zds5tpnBtoFcFBgPPFctLwDYv14C+Na3SvL4kY/ASSeVRkUabbwHalRYeulSbV0/Z8GTTw7Dbev++8tB55kHvvOdUj++8spD/CQaCYNNIM8GboyI84EuYAvgR4OOSmpXTz5ZqmWWXx722QcmT4aPfrTZUWn4eA/UqLD55qXNI5SSxyefLAnlZz87RE/wwgvlS/VRR8FZZ8EWW8DWWw/RwdUMg+relJlfA74CTAAWBvbLzO8MQVxSe+nuhl/8ooxbtvXWZXnCBJPHUc57oEaLSZNKh5kJE2DatPJ7yDrQXH45rLpqmV1r661ho42G4KBqtqEYxudh4E7gDGD1/jwgIr4ObFktXpSZB0TExsCxwLzAeZl5yBDEJg2/e++FPfeEiy8ubXpOOcVheTrLgO+BUiuaNGkYelx/8Yul/ffyy5dE8r3vHeInULMMqgQyInYCTgcOABYCfhMRu8zhMRsD7wPeDkwG1oiIbYDTgI8CKwFrRcSmg4lNGhE33FBmkvnTn8pUWzfeCGuu2eyoNEIauQdKo96sWWXaQYD11oOvfhVuv93kcZQZ7AidewPrAU9l5n+BNYDPz+Ex04EvZuZLmfkycDewAnBPZt6bmTOAMyltiaTW9PTT5ffqq8Muu5SZZD7/eZjLsfk7TCP3QGn0uuuuUkV97LFl+ZOfhMMPh/HjmxqWht5gE8iZmflUbSEz/w3M6OsBmXlnZt4AEBFvpVRlz6IkljXTgaV6ebjUXE88AZ/7XOk1+NRTMPfcZWzHiRObHZmaY8D3QGlUev75UtI4eTLceScsvnizI9IwG2xxyf8iYjLVjAwRsR3wv/48MCJWAS4CvkS54a5Qt7mLklRKraHWSWbffeG//y3jOTrFlgZxD5RGjT//GXbcEf7xD9hhh9Kde7HFmh2VhtlgE8jPA78AlouI6cDzlHaMfYqI9YFfAZ/PzHMj4l3AEnW7LE6Z4UFqvmeeKbPI1DrJXHghrLFGs6NSa/g8DdwDpVFlzBgYOxauuALe4yyenWKwCeRUYDVK6eFYIIH5+3pAREwEfg1slZlXVqtvLJtieeBeYFtKpxqp+eabr8yQcPzxpbe17Rz1igHfA6W2N2sWnHYa/OtfZWzH9dYr1dZjxzY7Mo2gwX4S3pKZq1M6wgAQEdcAb+vjMftTZm44NiJq604BdqSUSo4Hfg/8cpCxSY27/voy/MQ558Ayy8Avf+nQPOpNI/dAqX3ddRfsthtce23pLPPyyzBunMljB2oogYyIK4C1gNdFxFN1m8YCN/X12MzcF9h3NptXayQeacg88USZk/WUU+BNb4IHHywJpMmj6gzmHii1peefh29+E44+GhZYoJRA7rij98YO1mgJ5Mcp872eBuxUt34Gr+5NLbWPn//8lU4yn/88HHZYuVFKr+U9UJ3lP/8pY91uu22Zw9pOMh2voW6kmflUZt4HfAzYNjPvrzZ9iTKTjNR+Lr+8lDredFMZw8zkUbMxFPfAiFgwIu6IiGV7rN8rIq6uW54cETdHxN8j4scRYSNcjYz//rf0qO7uhmWXhb//Hc44w+RRwODHgTwdeH319xOUoSxOHeQxpZHx8svw7W+XhBFemUlmdWejU781dA+MiHWAa3n18GVExMrAQT12PxPYKzNXoAxx5kw3Gl6zZsGPfwwrrlia9NxdNfF905uaG5daymATyLdm5v4AmflkZn4BWGXwYUnD7Prry1A8Bx0E559f1s03nw3BNVCN3gN3AfakbriyiJgH+CHwtbp1ywDz1iZfoMy37SxdGj533gnvfGeZYWvVVWHKlDJxgtTDYBPIcRGxYG0hIuanfEOWWtMTT8Aee8D665e/f/1rOPLIJgelNtbQPTAzd87Ma3qsPpLSpvJfdeuWxFm6NFJmzIAPfrCUOJ5+Olx9dSmFlHox2LY0PwVujIhfUKpuNqdU6Uit6f/+D370I/jCF0onmfkdsk+DMiT3wIjYBFg6M/eLiI3qNo2pjlvjLF0aen/8I7zjHWU4nnPPheWXt52j5mhQJZCZeSRwILAQZfDcAzLz2KEITBoy//oXXFMV9uy9N/z1r/Dd75o8atCG8B64DbBKRNwK/BhYMyLOA6bhLF0aLg8/XHpVb7RRafMIZVBwk0f1Q6PjQC6YmU9FxCKUhuDX1m1bJDOdC1bN9/LLJVE87LDSg/DOO2HuuWE1hxvV4Az1PTAzP1P3+I2AQzNzq2r5hYhYPzP/DGwPXDwU56AOVuskc+CB8Nxz8PWvw047zflxaropU0qz/QcegKWXhs03h0mTmhNLoyWQV1e/HwUeqfupLUvNdf31pTf1wQeXNj2XXVbma5WGxtXV75G4B24HHBcRUymlnCcO8fHVaXbbrfysthrcdhsceiiMH9/sqDQHU6aUUZUefxyWWqr8PuaYsr4Zurq7u+e8Vwvr6upaFrj33nvvZdlll21yNGoJN90Ea68NEyfC974HH/lIsyNSe3gzcF+zgxgq3hv1Ks8/DzNnlqY7N9wAU6fCpz/tTDJt5NBDS9I4YcIr62rLhx46rE/d672x0SrsHfranpk/beS4UsO6u+Gee2CFFWDNNeEHP4BPfcp2jhoW3gPVVv7wB/jc5+D974fvfx/WXbf8qK088EApeay30EJlfTM02gu7Ng7Z4sCKwJWUKbzeDfyN0jNRGhn/+le5OV5zTflWPXEi7L57s6PS6OY9UK3v4Ydhv/3gnHPKl+stHEK0nS299GtLIJ98sqxvhkanMvxwZn4Y+C8wOTM/mpmfACZTbqLS8Hv5ZTjqKFhlFbjuuvL3kks2Oyp1AO+BankXXwwrrQS/+lWp35wyBd797mZHpUHYfPOSQD7+eOkHVft7882bE89gexUsnZn/rC1k5gM4yK1GwvPPl5lkap1k7rqrDNHjTDIaWd4D1Vpq/RpWXLGM7ThlSullPc88zY1LgzZpEuy/fymBnDat/N5//+b1wh7sQOLTI+IwyvRaXcCuvHoWBWlovfhiuRHOOy98/OPwzW/Chz/c7KjUubwHqjU89xwccUSZReaCC+DNb4aLLmp2VBpikyY1L2HsabAlkJ8GJgG3AX8FlgUcTEpDr7sbzj673BRvuqmsO+wwk0c1m/dANd8f/lDmrT7qqFIs9dJLzY5IHWBQJZCZOR34eERMyMzHhygm6dX++c8yf/Vll8FaazlemVqG90A11aOPwj77lE4yEXDVVWVWGWkEDCqBjIgALgAWioi1gCuAj2fm1KEITuLYY+ErXylztJ50UkkkbeeoFuE9UE0111xl0oTDDiuzytjOUSNosFXYJwH7Av/NzIeq5R8NOiqp5oUX4EMfKu169trL5FGtxnugRtbtt8NnP1tGoVh44TJ02de+ZvKoETfYBPL1mXlZbSEzvw8sOMhjqpM9/niZYuv888vyQQfBL38Jb3pTc+OSeuc9UCPjuefKqBOrrw6/+U1JHMHEUU0z2F7Y3RExHugGiIjFAYuIRpkRmby9u7u04/nCF+Cxx0pnGXD+arU674EafpdcUiZLuPde2GknOPpoWHTRZkelDjfYT+cfAH8A3hARRwI3AN8fdFRqGSMyefs//1mm2NpuO1h2Wbj55lLyKLU+74EaXjNnwgEHlJLGq6+G004zeVRLGFQCmZn/B3wVOAsYB+ySmT8YisDUGs4/v4wKMWFCKQys/V2rYR4Sf/kL3HADfO97ZUaZyZOH8ODS8PEeqGExaxb83//BE0+Udt+//S3ceiu8613Njkz6/wbbC/uKzHwv8KchikctZtgmb7/2WrjvPvjUp2DrreG974U3vGGQB5VGlvdADbnbby/twK+/Hp5+Gj7/+VIzI7WYwVZhLxwR8w1JJGpJSy9dJmuvN6jJ2//3P9h1V9hwQzjyyFI909Vl8qh25T1QQ+O550rTndVXh3vugZ/+FPbdt9lRSbM12E40zwL3R8QU4Jnaysz8yCCPqxax+ealzSOUkscnnyztID/72QEeqGcnmf33L/OzOiyP2pv3QA2NffYp1daf+UzpJPP61zc7IqlPDSeQEfE24DeUBuTThiwitZTa5O31vbA/+9kGemHffnvpJLP22mXaLds5qs15D9SgTZ9eamGWWqpMmLD99rZzVNtoKIGMiJ2A7wL3AMsB22XmH4YyMLWOhidvf+mlMrXW+99fDnDllfDOd1rqqLbnPVCDMmsW/PCHpcr6Pe+BCy4oQ5fVhi+T2kCjbSD3Ad6WmesAHwYOHLqQNCpce20pZdx0U/jHP8q6d7/b5FGjhfdANWbKFFh//TKu41prlepqqQ013ImmmraLzLweWGzIIlJ7+9//YJddSieZ556D3/0Oll++2VFJQ857oAbsN7+BNdYoX6p/9jO47DJ461ubHZXUkEbbQHb3WJ4x0ANExILAdcBmmXlfRGwMHAvMC5yXmYc0GJua5aWXSg/CadPgS18qnWTms4OqRqVB3wPVQZ5+GhZYoLRv3GsvOOQQO8mo7Q22F3ZNz5tpnyJiHeBUYIVqeV7gNOBdwL+BiyJi08y8eIjiGxYjMsVfO3jwQVhySZh7bvjGN2DVVWG11ZodlTSSBnQPVIeYPr2M4zh1aplha+GF4bjjmh2VNCQaTSAnRcRTdcuvq5a7gO7MXHAOj98F2BP4WbW8NnBPZt4LEBFnAlsALZtA1qb4mzDh1VP87b9/ByWRL71U2u984xtw9tklg/7Up5odlTQSBnsP1GhW30nmxRdLiWO33zE0ujSaQC43mCfNzJ0BIqK2aklget0u04Ee85+0lvop/uCV3+ef3yEJ5DXXlNkS7r4bttwS1l232RFJI2lQ90CNYtOnly/TN9xQZtj6wQ9s56hRqaEEMjPvH+I4xvDqKqAuYNYQP8eQGrYp/trBQQfBt78NyywDF10EH/xgsyOSRtRQ3AN7aQe+B7AX5f53EXBAZnZHxGTgx8CClCkTd89M21y2qkUXLW2/f/azMvZtV1ezI5KGxWCnMhwq04Al6pYXBx5qUiz9MuRT/LW67u4y4C2U4XkOOADuvNPkUWpA1Q78Wl5pB/5mYD9Kc55VgXcAm1S7nwnslZkrUJLLXUY8YPXt4ovhHe+AJ56AcePg8stLcx6TR41irZJA3ghERCwfEWOBbWnh9o9Qaigef7z8zJr1yt+bb97syIbBPffAJpvACSeU5a23LiWQ9rCWGlVrB14bCuheYOXMfBZYGFgIeCIilgHmzcwbqsedQWkfrlYwfTpstVX5Iv3EE2VZ6hAtkUBm5gvAjsCvgLuAqcAvmxnTnNSm+JswoYxaM2HCKOxA8+KLcMQRpVd1rQehpEHLzJ0z85oe616OiF2Af1Hagd9KG7YP7wjd3aVt44orlrEdv/ENuPVWWGmlZkcmjZihGsanIZm5bN3fVwBtNfZLw1P8tYMbb4Sddnqlk8zxx8MSS8zxYZIal5mnRsTpwOnAoZS2kG3VPrxj/O53sPbaJZF0sgR1oJYogVQLmjGjlED+/vdw3nkmj9IwioiJEbE+QNVB5lxgEm3YPnzUevbZ0oHwvvtK28bzzoNLLzV5VMcygVTR3Q1nnllmj4EyV+vUqWUua0nDbSHgrIhYOCK6gE8C11a9vV+oJZfA9rR4+/BR6fe/h1VWKW2/L7qorFtgATvJqKOZQOqVTjLbbw9XXAEvv1zWjxvX3LikDpGZdwBHUob1uQ14DvhutXk74LiImArMD5zYlCA70UMPlSY8H/oQvO518Kc/wZ57NjsqqSU0tQ2kmuzFF8tMMt/8JowfX9ry7LorjPF7hTQSerQD/yHww172uY0yvI9G2tFHw29/WzrJfOlLZbpWSYAJZGd78EH41rfgYx8r87PazlFSp7v11tKk5+1vh0MPhb32sp2j1AuLmjrNY4/BiSeWG+Rb3lJ6WZ97rsmjpM727LOllHHNNctvKEOXmTxKvTKB7BTd3WVqrRVXhP32g7vuKuuXXbapYUlS0114Iay8MhxzDHzmM/Dznzc7IqnlmUB2gr//HTbeGHbYoXyb/utfS49CSep0F1wAH/4wzD8/XHMN/OhHsMgizY5Kanm2gRztZswoPayffBJOOQV22cVOMpI628yZcO+95Qv1ZpvB978Pn/2snWSkATCTGK2uv74kj3PNVcZ3nDoVdtvN5FFSZ7v1VlhvPdhwQ3j66TJc2R57mDxKA2Q2Mdo89lhpw/OOd8Cpp5Z1G24Iiy/e3LgkqZmeeQb23790krn/fjj22FJtLakhVmGPFrVOMl/8IjzxRJly69OfbnZUktR8Dz8M66wDDzxQmvF8+9swYUKzo5LamgnkaPG5z5U2juutBz/8Iay6arMjkqTmevFFmGceeOMby3i3W2wBG2zQ7KikUcEq7Hb24otl7DIo0xCecgpce63Jo6TONnMmfO97ZZiy++4rc1afcILJozSETCDb1dVXw2qrwcEHl+V3vMNOMpJU6ySz994waVJJHiUNObONdvPoo7DTTvDud8NLL8EHP9jsiCSp+bq74YADSieZBx6Ac86BSy6BZZZpdmTSqGQbyHZy2WWwzTZlTMeDD4ZDDoHXva7ZUUlS83V1lSY9O+8MRx5pJxlpmJlAtoPu7nJzfMtbYPJkOP54eNvbmh2VJDXXgw/C5z9fRp9Yd93S7tEqa2lEWIXdyl54AQ49FLbcsiSRyy0Hl19u8iips82cCSedBCutVOaxzizrTR6lEWMC2aquuqp0kjnssDJTwosvNjsiSWq+WieZffYpnQfvvNMxb6UmMIFsNf/7H+y4I7znPfDyy6UR+Nlnw/jxzY5MkprvD394pZPMxReXpj2SRpwJZKvp6oIrriidZO64A97//mZHJEnN9bvflapqgP32g7vvhq23tspaaiITyFaQWWaSmTGj9BzMhG99yx7Wkjrbgw/CJz4BH/kInHhiWTdunD2spRZgAtlMtU4ykyaV6pi77irrTRwldbL6TjIXXwxHHQUXXdTsqCTVMYFslvpOMltsAVOnlkRSkjrdH/7wSieZO+6AAw8sJY+SWobjQDbDzJllmq2ZM+HSS2GTTZodkSQ11zPPwF/+UjoQbrppaQv+7nfbzlFqUZZAjpTubjjzTHjqKRg7Fn7zG7j9dpNHSfrtb2HllUtbx8cfL0nje95j8ii1MEsgR8LUqbD77vDHP8J3v1t6ES63XLOjktREEbEgcB2wWWbeFxG7AvsA3cDNwG6Z+VJETAZ+DCwI/AnYPTNnNCnsoTVtWqmqvuCCMkHCuefaQUZqE5ZADqdaJ5nVVoPbboMf/ahMuyWpo0XEOsC1wArV8grAl4B3AJMo9+Y9q93PBPbKzBWALmCXEQ94ODz6KKyyShnr9qij4K9/LW0eJbUFE8jhtPfer+4ks8suMMaXXBK7UBLEh6rlF4HPZeZTmdkN3A4sHRHLAPNm5g3VfmcAW4x0sEPqwQfL70UXhW9/204yUpuyCnuoPfJImUFmySXhoIPKPNa2c5RUJzN3BoiI2vL9wP3VusWAvYAdgSWB6XUPnQ4sNYKhDp1nnoGvfa0Mz3PNNbDuuqVpj6S21FLFYRGxbUTcFRH3RMSec35EC+nuhtNPhxVXhL32KuuWW87kUVK/RcSbgCuA/8vMqyn36O66XbqAWU0IbXB+85sypuPxx5eamBVXbHZEkgapZRLI6sb5TWADYDKwa0Ss3NSg+mvq1DLcxGc+U3oSHnFEsyOS1GYiYkVKp5qfZGbtJjINWKJut8V5pdq79XV3w3bbwcc+VjrH/PnP8P3vw8ILNzsySYPUMgkksDFwZWb+LzOfBX4JfLLJMc3Z735XBgCfMgV+/OPS03qVVZodlaQ2EhELAJcCh2Tmd2vrq6rtFyJi/WrV9sDFTQhxYGbOLMljVxesvXZp63jLLbDees2OTNIQaaU2kL219Vm7SbH03wYbwM47l97Wb3hDs6OR1J52Bt4IfDEivlit+21mfg3YDji1Gvbnr8CJTYqxf265BXbbrXSM2WIL2HffV22eMgXOPx8eeACWXho239xJuKR21EoJZHu29ZkwoVTJSNIAZeay1Z/HVT+97XMb7fBl+umn4atfLZ1k3vAGmGee1+wyZQocc0y5bS61VBkz/JhjYP/9TSKldtNKVdjt3dZHkjrVxReX9t8nnlh6Vt99d5lVpofzzy/J44QJZUSz2t/nn9+EmCUNSiuVQF4OHFoNYfEs8Alg1+aGJEmao6efLpngL35RhueZjQceKCWP9RZaqKyX1F5apgQyMx8EvgJcBdwKnJ2Zf2lqUJKkOdtiizKTTB/JI5Q2j08++ep1Tz5Z1ktqL61UAklmng2c3ew4JEkD0NUFc83542TzzUubRyglj08+WdpBfvazwxyfpCHXMiWQkqTRbdKk0mFmwgSYNq38tgON1J5aqgRSkjS6TZpkwiiNBpZASpIkaUBMICVJkjQgJpCSJEkaEBNISZIkDYgJpCRJkgbEBFKSJEkDYgIpSZKkATGBlCRJ0oCYQEqSJGlATCAlSZI0ICaQkiRJGpDRMBf2WIBp06Y1Ow5JbWzs2LFjJ06c2OwwhpL3RkmDNtt7Y3d3d1v/ABsA3f74448/g/k58MADN2j2/cx7oz/++NNqP7O7N46GEsibgA2B6cDMJsciqc0sscQSY3fYYYcl5plnnpuaHcsQ894oqWFzujd2Vd9UJUmSpH6xE40kSZIGxARSkiRJA2ICKUmSpAExgZQkSdKAmEBKkiRpQEwgJUmSNCAmkJIkSRqQ0TCQeNNExLbAIcA44PjMPLnJIQ27iFgQuA7YLDPvi4iNgWOBeYHzMvOQpgY4TCLi68CW1eJFmXlAB5374cAnKbMS/F9mHtsp567G9HKf2BXYh/I/dDOwW2a+FBGTgR8DCwJ/AnbPzBlNCrtPvZzTHsBeQBdwEXBAZna3yzn1PJ+69XsBn8zMjarlybTh+UTE6ZTZmJ6tdjksMy9ol/OBXs9pPeA4YAFgCvDpZl5HlkA2KCLeBHyT8g86Gdg1IlZualDDLCLWAa4FVqiW5wVOAz4KrASsFRGbNi/C4VElS+8D3k55r9eIiG3ojHN/F/AeYBKwJrB3RKxGB5y7GtPLfWIF4EvAOyj/R2OAPavdzwT2yswVKInYLiMecD/0ck5vBvYD1gZWpZzbJtXuLX9OPc+nbv3KwEE9dm/X81kTeGdmTq5+LqjWt/z5QK//cwsC5wO7ZuYq1W6frX435ZxMIBu3MXBlZv4vM58FfkkppRnNdqHc+B+qltcG7snMe6tvO2cCWzQruGE0HfhiZr6UmS8Dd1Mu6lF/7pn5R+Dd1Tm+gVJrsTAdcO5qWM/7xIvA5zLzqczsBm4Hlo6IZYB5M/OGar8zaN3/o1edU2beC6xc3fsXBhYCnmijc+r5HhER8wA/BL5Wt64tzyciXgcsDZwWEVMi4rCIGNNG5wOvfY82Aa7PzCnV8t7ABc08J6uwG7ckJbGomU5JqEatzNwZICJqq3p7DZYa4bCGXWbeWfs7It5Kqco+iQ44d4DMfDkiDgP2B35Bh7zvakzP+0Rm3g/cX61bjFLtuyNt9H/Uy72vdl3sAhwD/AW4FViDNjin3s4HOJJSs3Bv3bq2eI96OZ/FgSuBzwFPAhdSSuvuoA3OB3o9p+WBZyLiXGBF4M/AFyk1Y005J0sgGzeG0p6npguY1aRYmqWjXoOIWAW4jFId9y866Nwz8+vAYsBESulrx5y7hkbV7OcKSjvaqxkF94/MPBV4PfAwcChtek4RsQmwdGae3mNTW55PZv4rMz+emdMz8znKF/4P0qbnU5kLeD9wMOWLynyU5gZNOycTyMZNA5aoW16cuuqADtExr0FErE/58DsoM39Ch5x7RKxYNdCmuhGfD2xEB5y7hk5ErEjpDPCTzDyiWt2211BETKzuCVTNOM6ltO9s13PaBlglIm6ldMZYMyLOo03PJyJWjYhP1K3qAl6mTc+n8jBwQ9V0aCbwc0qtZ9POyQSycZcD742Ixar2Fp8ALmlyTCPtRiAiYvmIGAtsC1zc5JiGXERMBH4NbJuZ51arO+LcgbcAp0bEPBExN6XjzA/pjHPXEIiIBYBLgUMy87u19VXV9gu1RAzYnvb5P1oIOCsiFo6ILkr792vb9Zwy8zOZuVJmTgZ2Bm7OzK3a9XwoCePxETEhIsYBuwIXtPH5QLmG1qg+jwA2A25p5jmZQDYoMx8EvgJcRWn7cnZm/qWpQY2wzHyB0pbpV8BdwFRKZ6LRZn9gPHBsRNxafUvfkQ4498z8PWWIkr8BtwDXVUn0jozyc9eQ2Rl4I/DF2vVTDQ0FsB1wXERMBeYHTmxWkAORmXdQ2gxeB9wGPAfUkuO2PKc+tN35VB1NjqS0E7wLuDUzz6k2t935AGTmv4HdgN9VsS9COUdo0jl1dXd3z3kvSZIkqWIJpCRJkgbEBFKSJEkDYgIpSZKkATGBlCRJ0oCYQEqSJGlAnMpQLSEidgf2AMZRRtX/K/CVzHwgIu4DPpmZNzcxxNmKiDsoE9lf3exYJI0u3hvVqiyBVNNFxDGUgdg3y8yVgVUpUwZeHxEtOU+pJA03741qZY4DqaaqboJTgYmZ+XiPbSdQSsk/RBmwfTVgHuC7mXlaRMwPnA68lTL35y3Abpk5KyI+DBwCzE0Z5Hf/zLw+Ig4F1gOWBO4ANgA+lpm3VM95HnB1Zv4gIr5CuXmPAe4DPpeZD0XEysBpwOuq2NcCdvJbtqSh4r1Rrc4SSDXbOsDdPW+QlcspNzGA5zNzdWAT4MiIWAX4OLBANf3WWtV+b4mItwLfAj6YmW+nTGN1fkTMV+2zDPD2zNyGcrPbCSAiJgAbA2dHxA6Ub/trV8f/PWWOWICzgFMzcxJwQnU8SRpK3hvV0kwg1QrGzWb9PJQ2P1DmXyYzH6LMCfpe4FpglYi4GjgIOD4z/0G5kS4BXFFNO3gW5Vv48tWxbsjMGdXfpwFbVvM8bwP8NjOfpMwzui5wc3WMvSnzP78emAT8tIrnz5Rv65I01Lw3qmWZQKrZbgDeGhGL97Lt3ZS5ZgFm1q0fA7ycmfdSbnxHAgsCl1fVM2OBKzJzcu2HcsOr3cyeqR2omoj+r5Sb4k688k16LPDtusevCdQmqwfoqvt7BpI0tLw3qqWZQKqpMvNBysTv50TEm2rrI2InShubb1erdqzWL02pSrkiIvagtPO5NDMPBP4ArA5cAbwvIlasHvNBYAow72zCOBU4EJiv+tZMdaydI2LBavlw4GeZ+RilPdHO1bFXp1TnSNKQ8d6oVmcCqabLzIOBM4HfRMQdEXEP5Ua4XvUtGGB8RPyV0t5m78z8O6WqZCxwV0TcAiwEnJiZd1Ha9pwbEbcBRwAfycxn6N1vgWV55Rs21d8XAjdExJ2Uqpkdq23bAFtHxO3AV4G7B/saSFJP3hvVyuyFLUmSpAGxBFKSJEkDYgIpSZKkATGBlCRJ0oCYQEqSJGlATCAlSZI0ICaQkiRJGhATSEmSJA2ICaQkSZIGxARSkiRJA2ICKUmSpAExgZQkSdKAmEBKkiRpQOZqdgCShkZEjAMeAG7NzE2H6Ji7A3sA44Bu4K/AVzLzgX489lTglMy8pZdtdwB7ZebVQxHnHOL4HvBoZh463M/VXxHxDPC2zLyvbt1BwNbV4vLAI8CT1fLpwHKZuc8QPX83sBjwDmDjITzu1cAyvBJ3zTcy85cNHG9H4JOZuVmD8SwEXJCZ76mWbwU2yswnGjmepFeYQEqjx+bArcCaEbFSZt49mINFxDHAasBmmfnviBgDfAq4PiLWycxpczjEJsAPBxNDJ8nMo4Cj4P8nYt9rJOka4HP+FvjtEB/2S8Md9wBMANauLWTm5OaFIo0uJpDS6LEHcC7wT2BfYPeI2Aj4NnA/sCLwPLBjZt4dEWcAiwDLARdm5oG1A0XEUsDuwMTMfBwgM2cBP42INYCDgT0j4j5KCdHN1ePuAz4JfBxYEjgrInYAngZOA14HTAXmq3uujwFfpzSpeRrYLzP/UpWoHgu8F5gJ3Ah8ITOfrp7nRmAS8OXMvKDueAsCP6Ykv9OBGcC11bY3Ad8DlqaUqp6bmd+KiGWBK4HLgDUo98avAbtVr9vNwDaZOSsivgx8FJi3Oo/9M/OCiDgUWBZYglIK9yDwqcycHhEbAidRSnFvYoDNh+pL4qrk8hZgXeANwI+AxYF3VfFsmZm3V6VvJwCrVud6BSW5m9HHca8H1q9en8uBXatzfgfl/2i+6r04LDMvHOA57Ap8ODM/XC2vWMW0NPBpyms9N+V/8qjM/EGPx19NXVJdvxwRn5nN408H5q1KHteg/C8slpmPRsRXgW2qdX+nlIg/3NfrMJDzlUY720BKo0BErAysB/wC+AmwQ0S8vtq8JnBSZk6ifKD+rO6hr8vMVeqTx8o6wN215LGHy4EN+oonM78CPARsl5k3AmcBp1YxnEBJsGpJxCnAJzJzNUrS9psqCTyEkoSuVv2MAb5T9zR3ZOZK9clj5TBKorwisAUQddt+BpyWmWtQSqY2jogtq21vBi7KzDUpCcQJlARjFWBDYN2IWAbYmFINOgn4CnB43fE3BLbIzBWBZylJ/NyU9+WLmfl24CpK8jkYy2bm+pQS4aOBq6u4LwH2rvY5DrilOte3A4sC+83huMsBG1ES802Bd0XEBMr/zfaZuTolef5BRCw9m2N8JyJu7fHzeuAcYIOIWLzab6fquPMCuwAfrF6frapz6peImL+Px+8EPJ+ZkzNzZt1jdqrOb63qfbwDOKOv16G/8UidwhJIaXTYg1KK+BjwWETcC+xKSYRuy8xrqv1OA06uSy6v7eOY42azfh5KSVq/VM81CfgpQGb+uWoDCfAe4IrM/Fe17cqI+C+ltGhTSnvLl6vjnAT8uu7Q19C7jYHPZ2Y38EhEXFA9fj5KIrBIRBxR7Ts/MBn4C/Ay8Ltq/T+B6zLzqeqxDwGLZOZ1VYnqdhGxPKUUcP6657669hjgb5TSsFWBlzPziuocz4mIwVbtn18XJ5TEsba8UfX3ZsDaEfHZark/SevvqpK2pyLiH1X861FKVX8d8f9z8W7Ke9pbW9jZVmFHxPnApyLiOGA7YMPMfCYiNgM+FBFvpbwf8/f2+N40+PhNgdMz89lq+QTgK1WyD72/DpLqmEBKba5KjLYHXqyqdgEWBPaiVL3OqNu9q/pdK415ZjaHvQF4a0QsnpkP99j2buC66u/uumNCqUKcnfr9ajGN5bXJ6BhK8tpzW219zexi7+u5uoB3ZOZzABGxKPACpXTupSrprHm550EjYnXgN5TSvUuBPwL1Va3P1/1d/9rUx1MfU6NerF+oJdk9jKWUht4NEBELM+fEv7f4x1JKo9epbYiIJSmdfAbqVEqV+93VMe+tmktcX62/FvglJfntqdf/tQE8vl5v/1tz1R1/du+jpIpV2FL72w54DFgyM5fNzGWBt1BKYRYDJkfEpGrfXSkla0/0dcDMfBA4ETinajcI/P+qv09Q2sNBSSLWrLZtRCmpqpkBjKtKRW8Bdq72W51SKgelDdz7I+It1bb3ABMp7RsvAfaIiHFVB549KW0U5+Ri4LMRMaaqfv1odU5PURLj/arnWhj4c217P70TuDkzj6Ukjx+jJCN9mQJ0RcQHq+f9CKVzx3D7A/CFiOiKiHkonWX2auA4tS8T7wSIiMnAPcCb+npQbzLzBkoy9jVKMgnl/+cR4BuUpHyz6nl6vq71/2srU0pA5/T4GcDYiOiZAF4CfKb68gWwD/CnzHwRSf1iAim1vz2AY+vbeFUJ4onAF4CHgW9GxO2UhGf7/hw0Mw8GzqS0SbwjIu6hVA+vl5n3V7sdCOxbdVLYnpIo1pwPnBkR76O0Jdy6iuGrlBIoMvMu4HPA+VW19lGUjhZPUhKChyk9y++mlD7u24/QD6WUHk6lVEnfXrdtW0pbxtspSeo5mXlWf16PyjnAohFxN3AXpRR0kYhYYHYPqEoHPwYcUb1OmwP/HcBzNmofSqeX2ylJ7O0MoG1hTWY+QvnS8J2IuI3SjnT7+iGIeuitDeTX6rafSvmC8+tq+VJgGpCU93lpSkK4fI/jfgN4X/V/cjjwp348fjqlecKddc02AP6P0pb3L9V7uTrli5ikfurq7u53UyZJbaYqFfxeZr6t2bFIkkYPSyAlSZI0IJZASpIkaUAsgZQkSdKAtP0wPocddtg848ePX2v77befvuSSS86c8yMkSZI0B2MpI2vcRI+hw2AUJJDjx49f68ADD5zdgMKSJElq3Ib0MulE27eBnDhx4nJjx479x7XXXstSSy3V7HAkSZJGk+V5Zdar/6/tSyCnTZs2E2DGjMFO7CBJkqQeem0eaCcaSZIkDYgJpCRJkgbEBFKSJEkD0rQ2kBGxIHAdsFlm3hcRGwPHAvMC52XmIc2KTZIkSbPXlBLIiFiH0iV8hWp5XuA04KPASsBaEbFpM2KTJElS35pVhb0LsCfwULW8NnBPZt6bmTOAM4EtmhSbJEn99t9Hnm12CNKIa0oVdmbuDBARtVVLAtPrdpkOOKijJKnlvWGx+fjk9uc1Owx1iF/+bKtmhwC0zjiQY4D6Ec27gFlNikWSpAGZNaar2SFII6pVEshplPkWaxbnleptSZJaWrcJpDpMqySQNwIREcsD9wLbUjrVSJLU8maNNYFUZ2mJcSAz8wVgR+BXwF3AVOCXzYxJkiRJvWtqCWRmLlv39xXAas2LRpKkxtgGUp2mVaqwJUlqW7aBVKcxgZQkaZAsgVSnaYk2kJIkSWoflkBKkjRI9sJWpzGBlCRpkKzCVqcxgZQkaZDsRKNOYxtISZIkDYglkJIkDZJV2Oo0JpCSJA3SrLFW6KmzmEBKkjRIlkCq0/iVSZIkSQNiCaQkSYNkL2x1GhNISZIGySpsdRoTSEmSBskEUp3GBFKSpEHqdipDdRg70UiSJGlALIGUJGmQrMJWpzGBlCRpkEwg1WmswpYkSdKAWAIpSdIgWQKpTmMCKUnSIHWNbXYE0sgygZQkaZDGjOludgjSiLINpCRJkgakpUogI+JTwMHV4sWZuX8z45EkqT8sgVSnaZkEMiJeB5wIrAA8Afw5IjbOzMubGpgkSXNgAqlO0zIJJDCWUqU+H/AsMA54vqkRSZLUD2PGmkCqs7RMApmZT0fEV4GpwHPAH4HrmhuVJElzZgmkOk3LdKKJiEnAZ4BlgCWBmYBtICVJklpMy5RAAu8HrsjM/wJExBnA54DvNDMoSZLmxBJIdZpWSiBvA46OiPkoVdgfBm5qbkiSJM2ZbSDVaVomgczMSyPi7cAtwMvAX4CjmhuVJElzNqZlGoRJI6NlEkiAzPw28O1mxyFJkqTZa6kEUpKkdmQbSHUaE0hJkgbJBFKdxgRSkqRBshONOs2QNPuNiLkjwmRUkiSpAzSc9EXEG4CDgM2BiUB3RNwL/AI4LjMfGZoQJUlqbVZhq9M0lEBGxPbA54GfAx8H7gVeAt4CfAC4LCKOy8yfDFGckiS1LBNIdZpGSyAXAdbKzFk91t8B3BERxwF7DyoySZLahAmkOk1DCWRmnjCH7TOB4xs5tiRJklrbYNpArgXsB7yNMvXg7cAJmXn7EMUmSVJbsBe2Ok2jbSDfC5wJnAj8DOgG1gL+EBHbZOYfhy5ESZJam1XY6jSNlkAeDLw/M6fUrbs4Ii4BjgTeO+jIJElqEyaQ6jSNjgP5hh7JIwCZ+Rdg4UFFJEmSpJbWaAnkzD62dTV4TEmS2pJtINVpGk0gvVIkSapYha1O02gCuWJEvKYKm1L6+JZBxCNJUtsxgVSnaTSB3HRIo5AkSVLbaHQg8T9GxDhgnsx8BiAiVgWmZubLQxmgJEmtzhJIdZpGx4FcCrgS+DpwTrX6EGC1iHhPZj40RPFJktTy7ESjTtNoFfZ3gNMys5Y8kplbRcQhwNHAp4YiOEmS2oElkOo0jSaQb8vMbXpZ/y3gjkHEI0mSpBbXaALZazvHzJwVES8MIh5JktqOJZDqNI0mkE9GxJsz8976lRGxHDBj8GFJktQ+xo5tdgTSyGo0gfwu8LuI2Ae4jjIl4rrACZRqbEmj3KMvPMOi4+dvdhhSSxjTZQmkOkujw/hcGBELAj8GlqXMTPN34PD6jjUDFREfpvTsng+4NDP3bfRYkobXouPnZ49rz2p2GOoQP9hgu2aHIKlOoyWQZObZwNkRsQgwKzOfGEwgEfEW4BRgHeA/wJURsWlmXjyY40oaPmO7mh2B1Bq8FtRpGh0HcjxwOLAiZTzIE4cglo8D52XmtOo5tgLskCNJankmkOo0jZZA/oBSzXwxsBWwCPC1QcayPPBSRPwWWBq4EPjqII8paRj5oSkVY7wW1GEaTSDXzMxVASLiXEop5GATyLmAdwIbAc8AvwU+DZwxyONKGiZ+aEpSZxr0OJCZ+XhEDMXHyMPA5Zn5CEBEXACsjQmk1LIsgZQKrwV1moY70fQwawiOcSHwk4hYGHga2BT49RAcV5KkYWUCqU7TaAK5cERsXre8UP1yZp4/0ANm5o0RcTRwLTAOuAw4vcH4JI0APzSlwuYc6jSNJpAPAHvPZrkbGHACCZCZpwGnNRiTpBHmh6YkdaZGBxLfaIjjkNSGLIGUCq8FdZqG20BGxFLAfJmZEfENYEHKPNhfzkzHb5QkdQwTSHWaRgcSX5vS6eULQFLGgjwTWB/YkzJXtqRRzg9NqfBaUKdptATyCGCrzLyqWn46Mw+LiGWAX2ICKXUEPzQlqTM1mkC+pS55BOgCyMz7q7mxJXUAO9FIhdeCOk2jCeSLPZY3rPv7iQaPKanNWAIpFV4L6jSNJpDPRMRSmTkNIDOfAYiIicBzQxWcJEntwARSnabRBPJHwNkR8cnM/C9AREygDPz9/aEKTlJr80NTkjpTo+NAnhYRywP3RsRdlMHDVwROyMxzhjJASa3Ldl9S4bWgTtPwOJCZ+eWIOB5Yj9KJ5sbMnD5UgUlqfZZASoXXgjpNo+NAbpyZl1fV17+ZzT7vy8xLBxWdJEltwARSnabREsiNI2IvSlvISzNzBkBEzA28mzIv9lTABFIaxfzQlKTO1GgbyIMiYh3gUOC8iJgOjAHeCFwDHJGZ1w9ZlJJa0tgxzY5Aag1eC+o0g2kDeSOwaUQsBCxP6Ujzz8x8cqiCk9Ta7DggFV4L6jQNJ5A1VcJ4yxDEIklSW7I5hzrNoBNISZ3LD01J6kwmkJIaZgIpFV4L6jSDSiAj4sjMPHiogpHUXmz3JRVeC+o0gy2B3AwwgZQkdTRLINVpBptA/isiLgWuBZ6prczMYwd5XEltwA9NSepMg00g/1f9fnPduu5BHlNSmzCBlAqvBXWaQSWQmbkTQEQsA4zLzH8MSVSS2oLtvqTCa0GdZrCdaJanzIW9JDAmIh4FPpSZU4ciOEmS2oElkOo0g63C/h5wdGb+BCAidgK+D7xnMAeNiGOARTNzx0HGJ2kY+aEpSZ1psAnkG2vJI0Bmnh4R+w3mgBHxXuDTwEWDjE3SMBvbZZNnCbwW1HkGm0DOFRGLZOb/ACJiUQbRiSYiFgG+CXwLWG2QsUkaZrb7kgqvBXWawSaQJwE3RMR5lMRxa+C4QRzvh8BXgImDjEuSpBEzxhJIdZjBJpCnA/cAHwDGAp/LzMsbOVBE7Az8OzOviIgdBxmXpBHgh6YkdabBJpA3ZeZk4KohiGUrYImIuBVYBJg/Io7LzC8MwbElDQM70UiF14I6zWATyGcjYqnMnDbYQDJzk9rfVQnkRiaPUmuzBFIqvBbUaQabQM4H3BsR/+bVUxlOGuRxJUlqG3aiUacZbAJ5APDiUARSLzPPAM4Y6uNKGlp+aEpSZxpsAnl01QZSUgdy7Dup8FpQp2mZNpCS2o8lkFLhtaBOYxtISZIGyQRSnWawCeS+QxKFpLbkh6YkdaaGEsiIWDozH8jMP/ay7QODD6u1vTDjGcbPNX+zw1AHaPX/tTGNz1wqjSpeC+o0jZZA/hpYHSAifpWZn6jb9i3gkkHG1dLGzzU/104/o9lhqANssMSOzQ6hTw6eLBVeC+o0jSaQ9ZfKW/rYNmo5aKwkqcbPBHWaRhPI7tn83dvyqGTbL8kPTUnqVENRAimpQ/lFSiq8FtRpGk0gx0TEBEoiObbub4CxQxJZi3PQWMnrQKrxWlCnaTSBXBV4lFeSxsfqtnXEVeS3TUlSjZ8J6jQNJZCZOWaoA5HUfvzQlKTONNiBxDuWnQckrwOpxmtBncYEskGO+SV5HUg1XgvqNCaQDfLbpiSpxs8EdRoTSEkN80NTkjqTCWSD7DwgeR1INV4L6jQmkA1yzC/J60Cq8VpQpzGBbJDfNiVJNX4mqNOYQEpqmB+aktSZTCAbZOcByetAqvFaUKcxgWyQY35JXgdSjdeCOo0JZIP8tilJqvEzQZ3GBFJSw/zQlKTO1FIJZER8HdiyWrwoMw9oZjx9sfOA5HUg1XgtqNO0TAIZERsD7wPeDnQDl0TExzPzguZG1jvbu0heB1KN14I6TcskkMB04IuZ+RJARNwNLN3ckPri3UKSVONngjpLyySQmXln7e+IeCulKnv95kUkac780JSkTtQyCWRNRKwCXAR8KTPvaXY8s9PFmGaHIDWd14FUeC2o07RUAhkR6wO/Aj6fmec2O56+dHVZ8iJ5HUiF14I6TcskkBExEfg1sFVmXtnkcObIb5uSpBo/E9RpWiaBBPYHxgPHRkRt3SmZeUrzQpLUFz80JakztUwCmZn7Avs2O47+6rLzgOR1IFW8FtRpWiaBbDddXZa8SF4HUuG1oE5jAtkgv21Kkmr8TFCnMYGU1DA/NCWpM5lANsjqCsnrQKrxWlCnMYFskCUvkteBVOO1oE5jAtkghy+RJNX4maBOYwIpqWF+aEpSZzKBbJDTVkleB1KN14I6jQlkgyx5kbwOpBqvBXUaE8gG+W1TklTjZ4I6jQmkpIb5oSlJnckEskFWV0heB1KN14I6jQlkgxzzS/I6kGq8FtRpTCAb5KwDkqQaPxPUaUwgJTXMD01J6kwmkA2yvYvkdSDVeC2o05hANsjep5LXgVTjtaBOYwLZIL9tSpJq/ExQpzGBlNQwPzQlqTOZQDbIIRskrwOpxmtBncYEskH2PpW8DqQarwV1GhNISQ2z1EUqvBbUaUwgG+TNQpIkdaqWSiAjYlvgEGAccHxmntzkkGbL6grJ60Cq8VpQp2mZBDIi3gR8E1gDeBG4LiKuysy7mhtZ7yyBlLwOpBqvBXWalkkggY2BKzPzfwAR8Uvgk8DhTY1K0mw5jI9UeC2o07RSArkkML1ueTqwdj8eNxbg4x//OPPMM89wxDUbT43gc6lzLdjsAObA60AjxWtBKkbmWlhsscU46aSTGDt27NiJEye+ZnsrJZBjgO665S5gVj8etwTArbfeOgwhSZIkdaYLL7yQAw88cImjjjrqnz23tVICOQ3YsG55ceChfjzupupx04GZwxCXJElSR1liiSXG7rDDDkvMM888N/W2vau7u7u39SOu6kRzLaXa+lngOmDXzPxLUwOTJEnSq7RMq9/MfBD4CnAVcCtwtsmjJElS62mZEkhJkiS1h5YpgZQkSVJ7MIGUJEnSgJhASpIkaUBMICVJkjQgrTQO5JCJiKWBM4E3AAlsl5nP9NhnGeAOoDY45n8y8/0jGugoFBHbAocA44DjM/PkHtsnAz+mDKX/J2D3zJwx0nGOdv14H74OfAZ4vFp1as99NHgRsSBlSLLNMvO+Htsm47Uw7ObwHngdjIDqdd6yWrwoMw/osX0yXgvDqh/vwYCvhdFaAvl94PuZuSJwM/DVXvZZkzJU0OTqx+RxkKqxPL8JbABMBnaNiJV77HYmsFdmrkCZbWiXEQ2yA/TzfVgT2Lru/98PzSEWEetQxrZdYTa7eC0Ms368B14HwywiNgbeB7ydcj9aIyI+3mM3r4Vh1M/3YMDXwqhLICNiHPBO4JfVqjOALXrZdS3gbRFxa0RcGRGrjlCIo9nGwJWZ+b/MfJbyHnyytrEq9Z03M2+oVp1B7++NBqfP96GyJvDliJgSEd+LiPEjHuXotwuwJ73MqOW1MGJm+x5UvA6G33Tgi5n5Uma+DNwNLF3b6LUwIvp8DyoDvhZGXQIJLAo8VVf8PR1Yqpf9XqB861kdOAb4dUTMPTIhjlpLUl7vmp6v/Zy2a2j0+TpHxPzA34AvUf7/F6b3UnoNQmbunJnXzGaz18II6Os98DoYGZl5Zy05jIi3UqpRf1+3i9fCMJvTe9DotdDWbSAjYgvguB6r7wF6jo4+q+djM/PQusXfR8SRwErAbUMZY4cZw6tf+y5e/drPabuGRp+vc9Ue+IO15Yj4LnAaZSYojQyvhSbzOhhZEbEKcBHwpcy8p26T18IImd170Oi10NYlkJn5i8xcqv6HUs+/UESMrXZbgt6rkPaOiNfXreoCXh7+qEe1aZTXu2ZxXv3az2m7hkafr3NELB0Rn6nb7v/+yPNaaDKvg5ETEesDVwAHZeZPemz2WhgBfb0HjV4LbZ1A9qaq378G2KpatQNwcS+7vgv4LEBEvAsYC0wdiRhHscuB90bEYhHxOuATwCW1jZl5P/BC9Y8MsD29vzcanD7fB+B54OiIeHNEdFHaiF3QhDg7ltdCS/A6GAERMRH4NbBtZp7bc7vXwvCb03tAg9fCqEsgK5+j9Dy9C9iQMpwJEbF7RBxe7bMvsElE3EFpA7lNZlpsPgiZ+SClyPsq4FZKL/e/RMTvI2LNarftgOMiYiowP3BiU4Idxeb0PmTmI8BuwO8ow1x1Ad9tVrydxGuh+bwORtz+wHjg2KrT6q3VZ7HXwsjp8z1o9Fro6u7u2VxQkiRJmr3RWgIpSZKkYWICKUmSpAExgZQkSdKAmEBKkiRpQEwgJUmSNCBtPRONJDVbRJwMrAGsn5kzq3VjgT8BV2XmIc2MT5KGgyWQkjQ4XwTmAw6uW3cwMBP4elMikqRh5jiQkjRIEbEq8GfgnZRBeC8C1gI+QJnYYAzwGLBXZk6NiBWAk4EFKNO43QpslZkvRMSLwG+A1YDtMvPmET4dSZojSyAlaZAy83bK7D8/Bk6jTJO6PPBpYMPMfDtwNK9MD7YL8JPMXLfa783Ah6ptcwO/y8wweZTUqkwgJWkIZOZJwLPADZl5MSUhXB64LiJupSSQEyJiEeBA4JGIOAD4AbAkZQq3mmtGMnZJGig70UjS0LkX+Gf191jgZ5l5IEBEjKEkio8D51Luvz+nVHcvTan6rnlmpAKWpEZYAilJw+MPwDYRsUS1vDtwRfX3+4HDM/O8ankdSsIpSW3BEkhJGgaZeWlEfBu4LCJmAU8Bm2dmd0R8GbggIp4FngT+SKnulqS2YC9sSZIkDYhV2JIkSRoQE0hJkiQNiAmkJEmSBsQEUpIkSQNiAilJkqQBMYGUJEnSgJhASpIkaUBMICVJkjQgJpCSJEkaEBNISZIkDYgJpCRJkgbEBFKSJEkDMlezA5DUfxExDngAuDUzNx3ksa4DXgfMDQRwe7XpzszcbjaP+RpwW2b+Zg7H7gYWy8xHe6y/D3gReB7oqn7OA76dmTMaP5uhFRH7A2/LzB17rF8W+CevvFb11s7Ml4YhloWBq6vF+YE3AVktXwasAuyfmXcNwXMdCiyamXtFxO+H8LgbARfzStw1j2bmxg0e8z7gk5l5c4OP3xmYOzO/HxG7Awtn5lGNHEvqRCaQUnvZHLgVWDMiVsrMuxs9UGa+A/5/UnRHZk7ux8PeAww2odiu9qEfEfMBZwHHAXsP8rgj5fl+vlZDIjOfACbD/0/EvjcSz5+ZHxziQ/5zJF+3ftgAuAMgM09pcixS2zGBlNrLHsC5lFKwfYHdq6Ti28D9wIqU0r0dM/PuiDgDWARYDrgwMw+c0xNExELAyZSkpZtScvRlYDdgTeA7ETETuLPabwFgCUpiu1VmvtDfk8nMZyNiL+CfEfGVzHwqIj4LfI7SxOYxYK/MnFqdy3PAqsAbgd9W2z8MLA7snJlXRsQKs4srIl4AjgLeV207OjN/UJXsnghsAvwX+A/wZH/PoyYidgQ+C8xXPf4n9cuZ+e6I+CqwDTAD+Ht1fg9HxNXA/yjv4Q8y86R+Pud9wCcppZNHUkqoA3i2Otd9quVfZeYXqsd8GDiEUvr8HKWk8fo+jvtN4F/A24BxwG6Z+eeImJvyv/cuYCzwN2CfzHxqAK/ZWOA+4GOZeUu17jxKqev5wA8p7/filP/xLTPzv3WP34iSVL+t53JEvLG3xwPrAx8BNomI54HFeKXkdRXge8DrKf//383Mn1bH7fV16O+5SqOJbSClNhERKwPrAb+gJCY7RMTrq81rAidl5iTgdOBndQ99XWau0p/ksXIiJTFbtTruapQE42TgZuBLmXkBsAvwk8xcF1geeDPwoYGeV2ZOA54qpxjvAj4NbJiZbweOBi6o2311SinoO4EvAs9UJaknAAdV+/QV1zyUatN3UJKj4yJiPCVhXQFYmZJELt1HyPNGxK09fk6u274KsFFmvrvnckTsBGwKrFW9V3cAZ9Q99vHMXLm/yWMv1gKOqkr6ngIOrs59dWDPiFgyIt4KfAv4YPUa7wqcX5UGz846lETq7ZT/r29V6w+iJMJrZOZqwEOUpLU3y/Xyun0lM2cCpwE7AUTEBGBj4Gxga+D6zFwPeAsl2d1+AK9Hr4+v/n9/CxxX/V9TPfdc1fratbQp8K2IWG8Or4PUcSyBlNrHHpRSxMeAxyLiXsqH//WUdonXVPudBpxcl1xeO8Dn2RRYPzO7gRcj4hTg87w2MTiQUoJzACX5WpJSWtWIbsqH+xaUpO+6iKhtmxARi1R//y4zXwYejohngUuq9f+klLT2J65a+82/UhLK+agSlqoN40sRcRYwaTaxzqkKe0qPErj65U2B0zPz2Wr5BOArVUkewDUMzr2Z+bfq739SSj1fAh6NiKcor9E7KaWvV9S9xrMor/vs3J+Zt1Z//xXYsfp7M2BhyusNpUTzv/Suryrs04CbImI/SunsbzPzSeCEiNiwWv9WSsnfjX3E+SqZOdDHrwCMz8zzq8c/FBG/Aj4AXMXsXwep45hASm2gKh3anpLQ3VetXhDYi1IqWN8Bpav6PbP6/cwAn24MJaGrXx7Xy37nUO4hPwcuopTadfWyX58iYhlKgvdPSjXoz2qlpRExhpIAPl7t/mKPh7/cQFzPA2Rmd5X01LbV7zOYDj09X+/65bG89rWdq+65B/pe9dSf12cscEVmblVbERETKaWHH5/NcZ+v+7ubV+IdC+ybmRdXx5kfGD/QoDPz/oj4KyUh3YnyhYWI+DawNiXBvIryf9jzf6y7x7paMt7fx9fr+f7Aq///Z/c6SB3HKmypPWxHqVZeMjOXzcxlKVVy81Pab02OiFqJ2a7AdVXni0b8AdgrIroiYp7qeJdV22bwyofp+4HDM/O8ankdygdwv1U9jE+itFl7oXrubSJiiWqX3YErBhh/I3FdTGkSML6q0t5qDvs36hLgM3XVxfsAf8rMnonfcLoCeF9ErAgQER8EpgDzNnCs2v/K3FWyfyqlHWYjTqWUHs9X167w/cDxmfkzSsnmJrz2vXwEWDoi3hARXZRqa/rx+Pr/5ZqpwMsRsTlARCwJfIJX/v8lVSyBlNrDHsCxVXsxoPTOjYgTgS8ADwPfrHpU/5eBtRPraR9KUnc7pTTnEkrnASjtw46sqly/DFxQVSU/CfyRvqtBa86qOi7MpHyY/4qqLVlmXlqVGl0WEbMo7fg2ryst7I9G4vphtc8dlET9nj72nTcibu1l/Y79iO3/gInAX6qE6x+ULwcjJjPviohdgXOrhGsG8JHMfGYAr3HNEcAxlM4zYykdlr44m32Xm83r9r6qU8xvge9TOuXUHA4cExFHUEpTr6XHe1mdzw8pJfHTgQsppY5zevzFwLH155yZL0fEx4ATqyGN5qJ8Gbmq6kQjqdLV3d2ztF5SO+nZC1WSpOFmFbYkSZIGxBJISZIkDYglkJIkSRoQE0hJkiQNSNv3wj7ssMPmGT9+/Frbb7/99CWXXHLmnB8hSZKkORhLmXTgJl47xmz7J5Djx49f68ADDxzs7A2SJEl6rQ3pZUaztu9EM3HixOXGjh37j2uvvZalllqq2eFIkiSNJstTZgp7lbYvgZw2bdpMgBkzBjPzmCRJknrRa/NAO9FIkiRpQEwgJUmSNCAtVYUdEVcBb6DMWQqwW2be2MSQJEmS1EPLJJAR0QWsACyTmTZolCRJalEtk0ACUf2+NCJeD5yamd9rZkCSZu++p3/U7BDUQZZdYNdmhyCpTislkBOAK4C9gXHA1RGRmXlZc8OS1Jul5tuGucYs0OwwJElN0DIJZGZeD1xfW46I/wM+CJhASi1orjELMO3ZHzY7DHWIpebbrdkhSKrTMglkRGwAzJOZV1SrunilM42kFjS2q9kRSJKaoWUSSGBh4PCIeAelCvvTwO5NjUiSJEmv0TIJZGZeGBHrAH+jTOB9clWtLallWQQpSZ2oZRJIgMz8KvDVZschqX+6nItAkjpSSyWQktpLV5clkJLUiUwgJTXMEkhJ6kwmkJIa1mUbSEnqSCaQkhrW1WUJpCR1IhNISQ2zBFKSOpMJpKSGWQIpSZ3JBFJSwyyBlKTOZAIpqWH2wpakzmQCKalhjgMpSZ3JBFJSwyyBlKTOZAIpqWGWQEpSZzKBlNQwSyAlqTOZQEpqmL2wJakzmUBKapjjQEpSZzKBlNQwq7AlqTOZQEpqmJ1oJKkzWXwgSZKkAbEEUlLDrMKWpM5kAimpYfbClqTOZAIpqWH2wpakztRSCWREbAscAowDjs/Mk5sckqQ+WAIpSZ2pZRLIiHgT8E1gDeBF4LqIuCoz72puZJJmxxJISepMLZNAAhsDV2bm/wAi4pfAJ4HDmxqVpNmyBFKSOlMrJZBLAtPrlqcDa/f3wVtvvTXjx48f8qAk9eWJZgegjrFwswPowxPNDkAdZeEReZbFF1+cc889d7bbWymBHAN01y13AbOaFMsczMIhNDUyWv1/beFmByC1gAVp7etUGnqtlEBOAzasW14ceKi/Dz733HNZdtllhzqm2eqedfmIPZc6V9eYjZsdgqR+6J5xabNDUIfomut9zQ4BaK0E8nLg0IhYDHgW+ASwa3ND6sOsFi0c1ehioYbUHrr9TFBnaZkEMjMfjIivAFcBcwM/zsy/NDms2fNmIUmq8TNBHaZlEkiAzDwbOLvZcfSLNwtJUo2fCeowLZVAthVvFpKkGj8T1GFMIBvlzUKSVGO7eHUYE8hGebOQJEkdygRSkqTBslZKHcYEslHeLCRJNX4mqMOYQDbKm4UkqcbPBHUYE8hGebOQJNX4maAO0+8EMiLmBmZl5oxhjKd9eLOQJNXYsVIdps8EMiLeABwEbA5MBLoj4l7gF8BxmfnI8IfYorxZSJIqXQtv2+wQpBE12wQyIrYHPg/8HPg4cC/wEvAW4APAZRFxXGb+ZATibD2WQEqSpA7VVwnkIsBamdkzU7oDuCMijgP2HrbIWp0JpCRJ6lCzTSAz84S+HpiZM4HjhzqgtmECKUmSOtSc2kCuBewHvA14DrgdOCEzbx+B2FqbbSAlSVKH6qsN5HuBM4ETgZ8B3cBawB8iYpvM/OPIhNiiLIGUJEkdqq8SyIOB92fmlLp1F0fEJcCRwHuHNbJWZwIpSZI6VF8J5Bt6JI8AZOZfImLh4QupTZhASpKkDtVXAjmzj21dQx1I2zGBlCRJHaqvBLJ7xKJoR3aikSRJHaqvBHLFiHhNFTal9PEtwxRP+7AEUpIkdai+EshNRyyKdmQCKUmSOlRfA4n/MSLGAfNk5jMAEbEqMDUzXx7qQCLi08BRwH+qVRdl5leG+nmGjAmkJEnqUH2NA7kUcCXwdeCcavUhwGoR8Z7MfGiIY1kT2C8zz5njnq3ABFKSJHWovqqwvwOcVp/QZeZWEXEIcDTwqSGOZS3grRHxZeA2YO/MfHyIn2PIdNuJRiPA4Q4kSa2orwTybZm5TS/rvwXcMQyxTAeOAa6rnuN7wHbD8DxDwwRSkiR1qL4SyF7bOWbmrIh4odEnjIgtgON6rJ6amRvX7XM08M9Gn2NEzHKUI0mS1Jn6SiCfjIg3Z+a99SsjYjlgRqNPmJm/AH7R45gLRcQXMrOWWHYN5jlGhCWQkiSpQ/WVQH4X+F1E7EOpVh4DrAucQKliHkrPAAdExHWZeSOwF3DBED/H0DKBlCRJHaqvYXwujIgFgR8Dy1Jmpvk7cPhQ95TOzJkRsSXwg4iYt3qeHYbyOYacVdiSJKlD9VUCSWaeDZwdEYsAszLzieEKJDOvAVYfruMPOUsgJUlSh+prHMjxwOHAipTxIE8cqaDaggmkJEnqUH2VQP4AmA+4GNgKWAT42kgE1RaswpYkSR2qrwRyzcxcFSAizqWUQppA1lgCKUmSOlS/xoHMzMcjwkkx6plASpKkDtVnJ5oezJjqWYUtSZI6VF8J5MIRsXnd8kL1y5l5/vCFJUmSpFbVVwL5ALD3bJa7gc5OIK3CliRJHaqvgcQ3GsE42o8JpCRJ6lB9toGMiKWA+TIzI+IbwIKUOaq/nJkvjESALcs2kJIkqUP1NZD42sCFwBeApIwFeSawPrAnZa7szmUJpCRJ6lB9lUAeAWyVmVdVy09n5mERsQzwS0wgmx2BJElSU/SVQL6lLnkE6ALIzPurubE7m1XYkiSpQ/WVQL7YY3nDur+fGPpQ2owlkJIkqUP1lUA+ExFLZeY0gMx8BiAiJgLPjURwLc0SSEmS1KH6SiB/BJwdEZ/MzP8CRMQE4HTg+yMRXEuzBFKSJHWovsaBPC0ilgfujYi7KIOHrwickJnnjFSALcsEUpIkdag+x4HMzC9HxPHAepRONDdm5vSRCKzlWYUtSZI6VF/jQG6cmZdX1de/mc0+78vMS4ctulZmCaQkSepQfZVAbhwRe1HaQl6amTMAImJu4N2UebGnAiaQkiRJHaSvNpAHRcQ6wKHAeRExHRgDvBG4BjgiM68fkShbULdV2JIkqUPNqQ3kjcCmEbEQsDylI80/M/PJwT5xRBwBzMzMQ6vlhYGzgLcAjwBbZubDg32eYWMCKUmSOlSfCWRNlTDeMhRPWCWjxwLbAEfXbfoGcE1mfigitgdOoMy/3ZpmmkBKkqTO1K8Ecoh9FLiH186l/SHgndXf5wAnR8S4zHx5JIPrr27bQEqSpA414glkZv4UICIO7bFpSWB6tc+MiHgKWAx4aEQD7C9LICVJUoeaYwIZEUdm5sEDPXBEbAEc12P11MzceDYP6epluXWL+UwgJUlSh+pPCeRmwIATyMz8BfCLATzkQWBxYFpEzAUsADw20OcdKfbCliRJnao/CeS/IuJS4FrgmdrKzDx2iGP5PbAD8C1K55lrWrX9I2AJpCRJ6lj9SSD/V/1+c9264cievgqcERF3Ak8A2w3DcwwdO9FIkqQONccEMjN3AoiIZYBxmfmPoXji2viPdcv/Az4yFMceCd2WQEqSpA7Vn040y1Pmwl4SGBMRjwIfysypwx1cS7MNpCRJ6lD9qcL+HnB0Zv4EICJ2Ar4PvGc4A2t1lkBKkqRO1Z8E8o215BEgM0+PiP2GMab2YAmkJEnqUP1JIOeKiEWqNopExKIMTyea9mIJpCRJ6lD9SSBPAm6IiPMoiePWvHaA8I7jVIaSJKlT9SeBPJ0yd/UHgLHA5zLz8mGNqh1YAilJkjpUfxLImzJzMnDVMMfSXkwgJUlSh+pPAvlsRCyVmdOGPZo24lSGkiSpU/UngZwPuDci/s2rpzKcNGxRSZIkqWX1J4E8AHhxuANpO1ZhS5KkDtWfBPLoqg2k6lmFLUmSOpRtIBvUPdNhfCRJUmeyDWSjLIGUJEkdqj8J5L7DHkU7sg2kJEnqULNNICNi6cx8IDP/2Mu2DwxvWK3PYXwkSVKn6qsE8tfA6gAR8avM/ETdtm8BlwxjXK3PEkhJktSh+kogu+r+fksf2zqTJZCSJKlD9ZVAds/m796WO84sO2FLkqQO1d8SSPXQbQIpSZI6VF8J5JiImEBJJMfW/Q0wdtgja3Hds8yvJUlSZ+orgVwVeJRXksbH6rYNugo7Io4AZmbmodXyu4DzgX9Xu/wtM3ca7PMMF0sgJUlSp5ptApmZY4bjCSNiIeBYYBvg6LpNawLHZOaRw/G8Q22WJZCSJKlD9Wcg8aH2UeAe4Ls91q8FvDEitgHuA/bMzH/ToiyBlCRJnWrEE8jM/ClARBzaY9MTwM8z8/yI2B04F1h/ZKPrv+5uSyAlSVJnGrYEMiK2AI7rsXpqZm7c2/6ZuXvd36dExFERsVBmPjlcMQ6GJZCSJKlTDVsCmZm/AH7Rn30jYgxwMHBUZs6s2zRjOGIbCvbCliRJnaoZbSBfIzNnRcTHKW0jfx4ROwA3ZuazTQ5tthxIXJIkdaqWSCArnwZOjYivA/8FdmhyPH2yBFKSJHWqpiWQtfEf65bvBN7RnGgGzjaQkiSpU7VSCWRbsRe2JEnqVCaQDbINpCRJ6lQmkA2yDaQkSepUJpANsg2kJEnqVCaQDbIEUpIkdSoTyAZZAilJkjqVCWSD7EQjSZI6lQlkgxzGR5IkdSoTyAZZhS1JkjqVCWSD7EQjSZI61ZhmByBJkqT2Yglkg6zCliRJncoEskGzrMKWJEkdygSyQZZASpKkTmUC2SDHgZQkSZ3KBLJBJpCSJKlTmUA2aNas7maHIEmS1BQmkA2aNbPZEUiSJDWHCWSDLIGUJEmdygSyQZZASpKkTjXiCWRErA8cB8wNPAZ8JjPvj4iFgbOAtwCPAFtm5sMjHV9/zbQEUpIkdahmlECeBXwkM6dExGeAE4GPAt8ArsnMD0XE9sAJwFZNiK9f7IUtSZI61YgmkBExD3BIZk6pVk0B9q7+/hDwzurvc4CTI2JcZr48kjH216yZlkBKkqTONKIJZGa+CJwJEBFjgEOBX1eblwSmV/vNiIingMWAh0Yyxv6yBFKSJHWqYUsgI2ILSlvHelMzc+OImBv4SfX836q29Zxcugto2TTNEkhJktSphi2BzMxfAL/ouT4i5gd+S+lA89G6KuoHgcWBaRExF7BAtU9LsgRSkiR1qmZ0ojkT+Aewe2bWp2G/B3aglEhuRelQ05LtH8FxICVJUuca6U40b6f0uL4L+GtEADyUmR8EvgqcERF3Ak8A241kbAM103EgJUlShxrpTjR/47VtHWvb/gd8ZCTjGQxLICVJUqdyJpoGORONJEnqVCaQDbIEUpIkdSoTyAbZC1uSJHUqE8gGOQ6kJEnqVCaQDdr8f5c2OwRJkqSmGNPsACRJktReTCAlSZI0ICaQkiRJGhATSEmSJA2ICaQkSZIGZDT0wh4LMG3atGbHIUmSNKqMHTt27MSJE1+zvqu7u73HM+zq6toAuKbZcUiSJI02Bx544IZHHXXUtT3Xj4YSyJuADYHpgDNUS5IkDdISSywxdocddlhinnnmuam37W1fAilJkqSRZScaSZIkDYgJpCRJkgbEBFKSJEkDYgIpSZKkATGBlCRJ0oCYQEqSJGlATCAlSZI0IKNhIPHXiIilgTOBNwAJbJeZz/TYZxngDuCf1ar/ZOb7RzTQUSgitgUOAcYBx2fmyT22TwZ+DCwI/AnYPTNnjHSco10/3oevA58BHq9WndpzHw1eRCwIXAdslpn39dg2Ga+FYTeH98DrYARUr/OW1eJFmXlAj+2T8VoYVv14DwZ8LYzWEsjvA9/PzBWBm4Gv9rLPmsDZmTm5+jF5HKSIeBPwTWADYDKwa0Ss3GO3M4G9MnMFoAvYZUSD7AD9fB/WBLau+//3Q3OIRcQ6wLXACrPZxWthmPXjPfA6GGYRsTHwPuDtlPvRGhHx8R67eS0Mo36+BwO+FkZdAhkR44B3Ar+sVp0BbNHLrmsBb4uIWyPiyohYdYRCHM02Bq7MzP9l5rOU9+CTtY1Vqe+8mXlDteoMen9vNDh9vg+VNYEvR8SUiPheRIwf8ShHv12APYGHem7wWhgxs30PKl4Hw2868MXMfCkzXwbuBpaubfRaGBF9vgeVAV8Loy6BBBYFnqor/p4OLNXLfi9QvvWsDhwD/Doi5h6ZEEetJSmvd03P135O2zU0+nydI2J+4G/Alyj//wvTeym9BiEzd87Ma2az2WthBPT1HngdjIzMvLOWHEbEWynVqL+v28VrYZjN6T1o9Fpo6zaQEbEFcFyP1fcAPSf4ntXzsZl5aN3i7yPiSGAl4LahjLHDjOHVr30Xr37t57RdQ6PP17lqD/zB2nJEfBc4DfjKSAUor4Vm8zoYWRGxCnAR8KXMvKduk9fCCJnde9DotdDWJZCZ+YvMXKr+h1LPv1BEjK12W4Leq5D2jojX163qAl4e/qhHtWmU17tmcV792s9pu4ZGn69zRCwdEZ+p2+7//sjzWmgyr4ORExHrA1cAB2XmT3ps9loYAX29B41eC22dQPamqt+/BtiqWrUDcHEvu74L+CxARLwLGAtMHYkYR7HLgfdGxGIR8TrgE8AltY2ZeT/wQvWPDLA9vb83Gpw+3wfgeeDoiHhzRHRR2ohd0IQ4O5bXQkvwOhgBETER+DWwbWae23O718Lwm9N7QIPXwqhLICufo/Q8vQvYkDKcCRGxe0QcXu2zL7BJRNxBaQO5TWZabD4Imfkgpcj7KuBWSi/3v0TE7yNizWq37YDjImIqMD9wYlOCHcXm9D5k5iPAbsDvKMNcdQHfbVa8ncRrofm8Dkbc/sB44Niq0+qt1Wex18LI6fM9aPRa6Oru7tlcUJIkSZq90VoCKUmSpGFiAilJkqQBMYGUJEnSgJhASpIkaUBMICVJkjQgbT0TjSQ1W0ScDKwBrJ+ZM6t1Y4E/AVdl5iHNjE+ShoMlkJI0OF8E5gMOrlt3MDAT+HpTIpKkYeY4kJI0SBGxKvBn4J2UQXgvAtYCPkCZ2GAM8BiwV2ZOjYgVgJOBBSjTuN0KbJWZL0TEi8BvgNWA7TLz5hE+HUmaI0sgJWmQMvN2yuw/PwZOo0yTujzwaWDDzHw7cDSvTA+2C/CTzFy32u/NwIeqbXMDv8vMMHmU1KpMICVpCGTmScCz/L/27hiXwiAMw+gbVkBHoZJ8S7AEvc4OrMBNlDorsAANGqUgCpGIUjuFWIBCCC2KuXcBI65Eck41003158l8mfzJfWvtPD0I15PcVdVDekAuVdVykkmS56raTXKYZDX9F24zt395doBRHtEA/J6nJI/T9WKSo9baJEmqaiE9FF+SHKd/f0/Tx91r6aPvmfe/OjDAT7iBBJiPiyTbVbUy3e8kuZ6uN5Pst9ZOpvuN9OAE+BfcQALMQWvtsqoOklxV1WeStyRbrbWvqtpLclZVH0lek9ykj7sB/gWvsAEAGGKEDQDAEAEJAMAQAQkAwBABCQDAEAEJAMAQAQkAwBABCQDAEAEJAMCQb5tcUqy0ue7yAAAAAElFTkSuQmCC\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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kxkiCJiwzfz7YbDB3LowYAaNGmemOQUFWRyaEEELUTkqpxUALINex6Q6gMfAC0AD4XGs92bFvHPAOEAL8BNyptc7zeNBCVMWxY/DqqzBhAkREWB1NjXJrgiYnCVGW5GTo2RMuvhjefReuvx7+7//g9detjkwIIYSofZRSNiAWONt5DaWUagBo4EJgBzBPKTVUa/098DFwq9Z6uVLqXeA24A1roheiEgoKYPRo+OYbuPBC6N/f6ohqlNuKhLicJHporeO01nFAKjATGA50AvoopYY63vIxME5rHQvYMCcJUUcdOQIpKSY5A7juOnjgAXjjDXj7bWtjE0IIIWop5XhMVkr9oZQaB5wDpGuttziSto+Bq5RSZwMNtNbLHe95H7jK4xELURWPPgqzZ8MLL9S55AzcW8VRThKiVEuWQF7e6QQN4Omn4ZJLYNw4WLbMstCEEEKI2ioc+AG4AkgE7gSigV0u++wCWgNRpWwXwrt98om5aLz1Vrj3XqujcQt3TnF0niTuAQKBJcBzyElCYNafNWwI5557epu/P3z6KZxzDlx5Jfz6K5x1lnUxCiGEELWJ1joFSHE+d0xbfAJY6rKbDSjA3KS3l7BdCO919Cjccw9ccAG89popZlAHuW0ETWudorW+UWt9WGu9H3CeJEo6GchJwsckJ8OgQcULgoSFwZw5pvT+FVfA8ePWxCeEEHXFnDmglFlPL+o2pdR5SqlEl002YCvQ0mVbJJAJZJSyXQjv1bixqSj39dd1uqqcO9egyUlClGjzZvjzz6LTG1116gT//a8pw3/77WC3l7yfEEKIstnt8NhjkJYGq1dbHY3wgDBgqlKqvlKqMTAGeBhQSqn2Sil/4Hrge631NuCEUmqA472jge8tiVqI8uTkwFdfme/j46FZM2vjcTN3rkGTk4Qo0YIF5rG0BA3g8svhiSfg449hxgzPxCWEEHXNDz9Aaqr5fs0aa2MR7qe1ngvMA1YDvwEzHdMexwJfAxuATYDjSpcbgBlKqU1AI+BlT8csRLnsdrjpJrj6ali/3upoPMJta9C01nOVUn0xJwl/4DWtdYpSaizmJFEf+I6iJ4n/KKVCgN+Rk0SdNX8+REdDbGzZ+z3yCPzxB0ycCF27lp3QCSGEKG76dNMeKD9fEjRfobV+FHj0jG0/AD1K2PcPTAE3IbzXU0/BF1/Ac89Bly5WR+MRNnstnz9ms9ligC1btmwhJibG2mBEufLyoGlTuOaaipXTP3bMFBLJyDBFQ9q1c3+MQghRF6xfb25uPfkk/Pwz7Ntnpo4Ly7XBLPmoteTaS3jM11/DqFGm59kHH9SVoiDlngPcOcVRiGJWrjQ90Co6GtaokVngbrPB8OGmeI8QQojyvfACNGgA//gHxMWZhO3UKaujEkKICtq7F8aMgX79zF39upGcVYgkaMKjkpPBzw8SEir+njZtzMj2pk1w442mebwQQojS7d5t1vCOHWtmLcTFmeRs0yarIxNCiApq0QI++ghmzYL69a2OxqMkQRMeNX8+9OkDTZpU7n2JiWYtxezZZrqOEEKI0r3+OuTmwvjx5nlcnHmUdWhCCK934gSsWmW+v+IKiIy0Nh4LSIImPCYry0xxHDKkau+/914z0j1liknUhBBCFJeTYxK0YcOgQwezLTbWTHeUUvtCCK9mt8Mdd8CAAbB9u9XRWMZtVRyFONOiRWZ6YlWrMdps8OabsHGjWSu6fLnPFPMRQogK+/BDOHAA7r//9DZ/f+jeXUbQhBBebto0cxJ7/HFT8rsCUlMhKcnkc9HRMHKkOd/VZjKCJjwmORlCQuCcahT0rV/fTEVu1MgUDTlxoubiE0KI2q6gwPSO7N0bzjuv6GtxcSZBq+XFm4UQddXcuTBpkul39uij5e+PSc6mTTOztFq3No/Tpp3u/1hbSYImPMJuN+vPEhIgMLB6x4qKgjfegL/+MqWjhRBCGHPnQlqaGT07s+BZfDwcOgTbtlkTmxBClGrLFrjuOujZE957r8IVG5OSIDzcfPn5nf4+KcnN8bqZJGjCI9LTzUVBsfVn65+B9DcqfbzBgyEgAH74oWbiE0KIumD6dDPFZ9So4q9JoRAhhNc6+2x4+GHTWyk4uMJv274dQkOLbgsNrf3L1yRBEx6RnGwei6w/yz0Ka6fAqnGwf3mljtewoWmLsWhRjYUohBC12qpV8NNP8M9/mhtYZ+rWzdxhlgRNCOE1Tp2CjAxzcnroIWjVqlJvj46Gw4eLbjt8uMLL17yWJGjCI5KToV07aNvWZWPm91BwCvyDIeVGyMuu1DETE+G338yUHSGE8HXTp5t1vrfeWvLrwcGmmqMkaEIIr2C3w7hx0KsXHDxYpUOMHGnWnWVlmTW4zu9HjqzhWD1MEjThdqdOweLFJVRvzJgN9ZrDBXPg6J+w+oFKHTchwfxn/PHHmotVCCFqo+3b4csv4bbbTJJWmvh4KbUvhPASr7wC//mPuatU2Qa5Dt27w4QJZt1ZRoZ5nDCh9ldxlDL7wu1SUuDYsTPWn+Wfgsx5EH0VRCZAx/Gw6QVoNQyiKtYorW9f09dn0SJT0VEIIXzVyy+bx3vvLXu/uDj49FNzs7qK10NCCFF9yckwfry5gHvyyWodqnv32p+QnUlG0ITbJSebHjyDBrls3LMIco9A6yvM8x7/htDOsOImOFmxYe569eD882UdmhDCtx05Ym5CX311+esunIVC/vjD/XEJIUSJ0tLMCatrV/j4Y7P+TBQhfyPC7ZKToX//M6bdZMyCgEYQmWie+9eH/h/DiX3w610VPnZCAqxbB3v21GzMQghRW7zzjknS7ruv/H2dCZpMcxRCWKZlS7NIbM4c09hWFCMJmnCr/ftNIY8i68/sBZAxB6KGmsTMqUk8dJsC2z+HrZ9W6PgJCeZx8eIaC1kIIWqNvDx46SW44ALTnLo8LVqYXpJSKEQI4XF5eZCdDY0bw8yZEBNjdUReSxI04VYLF5oiPUUStP3L4cSe09MbXXWeBE37mVG0nIxyj9+zp+l3IdMchRC+6KuvTIGQ+++v+Hvi4iRBE0JY4L77zNqUnByrI/F6kqAJt0pONhV1itzZzZgFfoEQdWnxN/gFwLkfmfL7y282o21l8PeHgQOlYbUQwvfY7aa0fmwsXHZZxd8XFwcbN8KJE+6LTdQMpVSQUkoKuona7623TNXGhIRKNaL2VZKgCbex202CdtFFJpEq3LhjFkQkQFBoyW9s3B56TofdCyDt9XI/JyEBNm+GrVtrLHQhhPB6S5ea5tTjx1dujX18vJlptH69+2ITVaeUaqGUekEptRU4DpxQSqUrpZ5WSjW3NjohqmDJEtPvbOhQeO45q6OpFSRBE26zYQPs3HnG9MbD6+HYXyVPb3TV/g5oORTWPABHdJm7JjrqjMg6NCGEL5k+HZo2hRtvrNz7nIVCZJqj91FKjQa+B/YAVwBNgRDH9weBBUqpMdZFKHxJaipMmQI332weU1OrcJDNm+HKK6FDB9Pjo/COvShLucPmSqnumBODAvKBTcBXWuuyr5qFz0tONo9FErQdswAbtC6ncZnNBv3ehXld4ZfRcPEyMy2yBJ07m4XvP/wAN91UI6ELIYRXS0+Hb76ByZMrP1uobVuzRl8SNK/UBOijtT5zfv86YJ1SagZwj+fDEr4mNRWmTTPLVFq3hqws87zSTaBtNlNO/913TdGAasaUlGTW3UZHm0KQda3/mVOpI2hKqWZKqS+BTzEnjJXAciAc+FIp9blSKsIzYYraKDkZOnY8oy9Pxixo1h8aRJZ/gAYt4Zw34eCvsP7pUnez2cw0x0WLzAxKIdzq1CHI3mZ1FMLHzZgBgYFwV8W7khTy84MePaTUvjfSWr9UQnLm+nq+1vpFT8YkfFNSkknOwsPNOcP5fVJSBQ+Qn28uytq0gR9/hPbtqxWPM2HMyiqaMFZpVK8WKGsE7T3gea31zyW9qJQaCLwLVGJpsvAVJ06Y/4+33eayMXsbZK2GuOcrfqDoqyDmBlj3pCkq0rRPibslJsJnn4HWJikUwm1WjTON1kdkgE1miQvPO3AA3n8f/v53iKzAvS4A8k8UaWsSF2eOUVAgPWK9jVKqD3Af0BXIAdYCL2mt11oamPAp27ebRMhVaKjZXiEPPmh2/u9/IaD6dW5cE0Y4/ZiUVDdH0co6LQ8vLTkD0FovAYbVeESiTli6FI4fP3N642zzeFY568/O1PtVM5qWMhrySi7N6uyHJtUchVsV5MLOuXB8F2T9YXU0wke98YY5v1akMTUA65+BpEg4trVwU1wcHDtmlocI76GUSgS+AVKBScAUYDswXyl1oYWhCR8THQ2HDxfddvjwGbOiSvP++2Z4q0WLGknOwOR6Z86QrFTCWMuUmqBprQuUUjal1MVKqfuVUv9QSp135j7uD1HURsnJZvrNha6/TjJmQWhXU6WxMoLCoN/7pljImgdL3KVNGzj7bOmHJtxs3y+Q6/iNtXuBtbEIn3TiBLz6KlxyCXTpUoE3HPwNUh8zP7dprxRulkIhXushYIjW+hmt9Xda6++11k8AI4DHLI5N+JCRI800wqwsM9Lu/H7kyHLeuGwZ3HGHKeE9Y0aNxVOthLEWKmsNWgTwO/AK0BcYCLyvlEpRSoV7JjxRWyUnw3nnQaNGjg0n9sG+nys/euYUmQix95oLjF3FL4xtNjPNcfFicyIRwi0y55liNY3aSYImLPHJJ7BnTwUbU+efgJQboX4LiPob/PkfyD0CmOQuIEDWoXmhFlrrYqtqtNYrgTAL4hE+qnt3UxAkPBwyMsxjuQVCtm2DK64wd8y/+KLGRs+gGgljLVXW39y/gC+01s+4blRK/Qt4HritxHcJn7d7N/zxBzzj+pOzc65pOt16RNUPHPcs7E6G5TfB39ZCUNH7BAkJMHOmuSPcs2fVP0aIUmXOgxYXQmg3SH8d8o5DQAOroxI+wm6HF14wF0jO9iJlSn0UDm+Agd9DvWYwvw/89S50HE/9+tCpk4ygeaH8Ml6zeSwKITDnmkqt78rMhJAQU2I2vPJjOWVVaXQmjK6v33JL3Vx/BmWvQbvgzOTM4Umg5EoNQgALHAMLRdafZcyChmdDeHzVDxzQAPp/BCf2wKriVYYHDTKPMs1RuMWxLeZiN+pv0HIwFJw0o8JCeEhysmkuff/9ZtZAmfYuhY3TTU/JqEugaW9ofj7ol6EgDzDTHCVB8zpSi1jUXv37w6ZNVarWVpEqjd27m35sM2eax7qanEHZCVpuSRsd685kEpkoVXIyNGt2eo0DucdgV7IZPSv3qqIcTXtD10dh639h+5dFXoqKMneEJUETbrFznnmM+hu0uAD8gmSao/CoF1+Eli3h2mvL2TH3GCwfAw1jIH7a6e0d74PsrZBhCjbFx5sb3nv3uitiUQUdlVKpJXytxfSjFcL7PPooPPWUGeav4rTGapf1r2PK+luUuzii0goKzAja4MEupZt3/c+MNrSu4vqzM3V52Ew1W3kntBwCgSGFLyUkmOJBp05BUFDNfJwQgPmZaxwLIR3M8+YDzHrIagwKC1FRdjv88osprV/uuW31RDPie9GPENjo9PZWl0OjtrDpBYgeVaRQSJEZD8JKQ60OQIhK+eQTk5zdemu1DlPtsv51TFkJWgulVGlFfJu7IxhR+61daxawDxnisjFjNtRrai5oa4JfAMQ9Bz8Mgj1LoPXpbg8JCfDaa/DrrzCghj5OCPKyYc9i6ODSFThyMPzxMBzfAw0irItN+IS9e+HIEVDljaFkzoc/34SO90OL84u+5ucP6v/gt3th/3J69OgHSILmTbTWPyqlAoF6WutjAEqpbsAmrXWJM5uEsMzKlXDzzXDBBebiqxqzpKKjzbRG16VrdblKY3nKmuK4AOhWytdC94cmaqP5883j4MGODfmnTIGQVsNMYlVTmvUH/wawu2jjs4EDzflBpjmKGrV7kRkFbvW309siHT/ke6T5nnC/tDTzGBtbxk6nsmDFLRDSCXo8VfI+bW+CwFDYNIMmTczFj6xD8x5KqdbAeuByl82TgbVKqShrohKiBDt3wogRZt71119Xe9qSr1VpLE+pV8xa65s8GYioG5KToWtXsx4MgL1LTP+dmpre6ORfD61F5rIAACAASURBVJqfV+ziuEkTs67ihx/MlGghakTmXAhobIosOIXHQ1ATs74y5nrrYhM+IT3dPJaZoK26F07shgtmg3/9kvcJbGQKh2yaBse2Eh8fI6X2vctUYKbW+lPnBq31NUqpyZgK2n+3LDIhXKWkmMaMzsID1eRrVRrLU9YIGkqpxx1d7Z3PP3SU2ReimJwc+PnnM6bK7JgFAQ0h8qKa/8DIRDi83kwxc5GQYM4bOTk1/5HCB9ntkPmdqdzo73KH0M/f/AzuXmD2EcKN0tIgMNC0FyrRjiTY+jF0mWyKKZUldhxgg7RXiIsDrSE7u6YjFlXUVWv9bAnbnwakgYzwHqNGwebN5q58DfGlKo3lKatR9QPAxcBWl82vAJeUsTZN+LCffjLFOQrXn9kLYOccaHmJe3pFRTjuHewpOp8xMdHE8csvNf+RwgcdSoWcDIi6rPhrkRfD8Uw4stHzcQmfkpYG7duDv38JL57Ya4omNekFXR8p/2ANz4Loq+HP/xDXNQe7Hdatq/GQRdWUVUH7hIdjEaK4qVNhtqkES5j0TneXskbQbgAu1Vr/5dygtf4VGAGMdXNcohaaPx/q1YPznbPADqyE47tqfnqjU3g8BIYVS9DOO89Uef1BlgaJmpDpLK9fQnG1lo51aLuk3L5wr7S0UqY32u2w8g7IPQL9PgC/wIodsON9kHeU+PBPAGSao/c4rJRqc+ZGpVQ7IM+CeIQ4LSkJHnjgdIIm3KasBC1fa5115kat9R7K7nQvfFRysink08A5WLZjFtgCihZWqEl+/hAxsFihkEaNoG9fKRQiasjOedCkNzSILP5aw7OhcQfphybcqqAA/vwTOnQo4cWtH5tKuT2egrAuFT+oo3F19NF/ExZml0Ih3mM68K1SKkEpVV8pFayUSgBmAzMsjk34sjVrYPRo6NcP3nzT6mjqvPLWoBUryaKUqgdU8Bad8BUZGbBhg8v6M7sdMmZBRAIEuXEIPCIBsreYnj8uEhNh1SpTolWIKjuxH/anmObUpYkcbIrh5J/yWFjCt+zYASdPljCClr0DVt1jiteo8ZU/cMfx2HK2EtdxnyRoXkJrPRez3uwdIAc4CrwGPO1aOEQIj9q9G4YNM5XYZs2C+qUUIRI1pqwEbS7wvFKqsKmB4/tnAZk8JopY4BhAKFx/dmQjHE2Hs0a494MjS16HlpBg7jr/+KN7P17Ucbv+B9jLHgWOHGz6pO1P8VhYwreUWGLfbjcl9e150O89M6OgsloNg0ZtiWu5iNRUyJe5MV5Ba/2J1rot0AxoqrXuJMmZsNQnn8CBA/DNNxBZwmwSUePKakz1b2AOsFkptQKTzPUBNgFXeiA2UYvMn2/+zxYW89kxyzy2Gu7eDw7pBPUjTZ+qdrcUbu7Xz0y1XLTI3PQRokoy50H9CFN8wcFuN19+zttbEYPA5m+mOUZcaE2cok4rMUH7803zM9fnDWjcrmoHdjSujo/4juPHryUtDTp1qna4ohqUUvWBJ4COwCLg5SoeZxrQTGs9Vil1EfAC0AD4XGs92bFPHGakLgT4CbhTay3r3ERx48fD8OHQrornGlFppY6gaa1Paq0vAcYAK4FfgOu01kO11hUuYK6UmqaUet/x/UVKqVSlVLpS6imXfeKUUquUUmlKqXeUUjXY0Vi4W36+GUG7+GKXJvIZs6BpPwh2c19Nm81Mc9yzqEip83r1TLEQWYcmqqwgDzL/B1GXgu30qfKOO8yF8ubNjg1BodC0r6xDE26TlmbW1hbeuD76J/w+AVoOMT3NqqPtTcS1M7XAZJqjV3gDiAG+xxRlm1LZAzjaI41xfN8AmAkMBzoBfZRSzopHHwPjtNaxgA24rZqxi7rmP/8xJV5tNknOPKy8NWhdMUPsn2utX9RaL6/MweUk4RvWrIGDB13Wn2Vvh4O/wVluqt54psgE05z18IYimxMSYO1a2LvXM2GIOmZ/CuQeKrL+bN488/tqyxYYNMglSYscDAdXwalidZWEqLa0NFMgxGYDCvJh+VjwC4K+77rcFauiwEZ0uuB8ggJOsmalLNr1Ar211ldrrd8ArgAur8yblVJNMDOgnnZsOgdI11pvcYyOfQxcpZQ6G2jgcl33PnBVTfwBRB0xd665Izl9erm7pqaavmU332weU1PdHl2dV1YftJswQ96TgD+UUheXtm8p75eThI9YutQ8Xuic3ZUxxzy2dvP6M6dS+qElJJjHxYs9E4aoY3bONSXLHaX0Dx82v6u6djU99o4edUnSWg42ff92y5CtqHnp6S7TGze9APuWQe9XILhVjRw/sMvddG29jtUpu2vkeKJaCvugOSppVzYDfwt4BHDeLYoCdrm8vgtoXcZ2Icyo2XXXQXw8vPZambumpsK0aZCVBa1bm8dp0yRJq66yRtDuxXS074u5g/NgJY8tJwkfsXy5+U/Z2vmvljELQjtDSElNe9ygUQw0agt7itau6dkTQkNlmqOoosx5pjpeYAhgWr/s2gUzZ5o2Dj/8cDpJ23LkHAhoLNMcRY07dcqM2MbGAofWQepkOGskxNxQcx/S8CziuhxhzcYm2E8dqbnjippQUNEdlVK3Aju01q6/DP0Au8tzm+OYpW0Xvm7/frN4v1EjmDMHgoPL3D0pCcLDzZef3+nvk5I8FG8dVeYUR611puMxBWhe0YPKScK3pKRA//6OJycPwN6f3NecujQRCbBniZn+4xAQYEb1pGG1qLTsbXB4PbS6DDCjsG+/DffdB336mF3i408naQMTAtlScA3sSrYwaFEXbd5sKtLGxgKrxpm2JX3erP7UxjPEndeOfUeas2v55zV6XFFpYUqpkc4vIPSM52W5BrhYKbUGU2hkGHAr0NJln0ggE8goZbvwdc8+C5mZphl16/LHS7ZvNzfDXYWGmu2i6spK0OxnPK9MZR85SfiIXbtg2zaXBG3nt2DP99z6M6eIRMg9DFm/F9mckAB//WViFKLCds4zj1F/Izsbbr0V2reHxx8vuluRJO3+6WzZYoejf3k+XlFnFVZwbHfKTG1sezPUr/D90gqLGxANwOoFKaZAjrDKduAely/X5+PKeqPWerDWuqvWOg54DPgGGAoopVR7pZQ/cD3wvdZ6G3BCKTXA8fbRmMIkwtc9/bS5K9m3b4V2j44u3nP28GGzXVRdZaolnpmwlUprPdj5vVJqLDAQuBNIV0q1B7ZgThIztdbblFInlFIDtNbLkJNErZLiaP1UmKBlzIbgsyC8p2cDiRhkHvcsgqZ9CjcnOpanLV4MY8d6NiRRi2XOg0btISSWyePNKMaPP5Y80yM+HhYuhIsSGzLo34tZ0mMZMYOk2pWoGc4ErUPEBtiSV6TlQ03q0cM8rtEt+VvGbIge5ZbPEWXTWg+s4eOdcFyHfQ3UB74DvnK8fAPwH6VUCPA7VSzpL+qIpCS44AJo1szloq58I0eaNWdgRs4OHzbr0G65pez3ibKVlaB1V0q5TkYPdjy3AXatdUhlPkhOEnVTSgoEBZmLVPKyYdd8aHdbjU+/KVeDCAjtCrt/gM6TCjd36QLNm5tRDknQRIXk5ZhEv/0dpKTASy/BP/5hfm+VpmdPWLjQj4sGhjHw+sEsSYGYGI9FLOqw9HRzvRRe8KvZ0MQ9N79CQqBdOztrdg6ATU9JgmYhpVRroKHWWjtaEoVgZjE9rLU+UZFjaK3fxxRdw7HcpEcJ+/yBKeAmfF1yMlx1Fdx5Z7lFQc7UvTtMmGDyu+3bzcjZLbeY7aLqykrQauQWsJwk6raUFHNxWq8esGM+5J/w/PRGp4gE+Os/kH8S/OsBJk9MSDCFQux2z+eNohbaswjyT3Cy2eXcMhTOOguee678t/XsZWPhqy9z0d33MHCgnSVLbJKkiWpLS3OsPzv4OwSGQsM2bvusuDgba1ada1pM7F8Ozfq57bNEyZRS5wBzgfGAxiwZ+RgYANwNlF/zXIjK0Bquvtrc0X722Sodont3SchqWlmNqre5fmHu4DQFwhzPhY87dQpWrXIZCd8xC4KamMp3VohMhPzj5sLCRWKiWe/qnCokRJl2zoOARjz51kA2bjTFQRo3rthbew5ULHwokSOH8xk4ELZudWegwhcUJmhZqyE83q13meLj4c9tYRzJbQ2bZrjtc0SZngSu0Vr/1/H8qNb6cUx/2GutC0vUSVlZcPnlZirUt99W/JedcLuy+qA1UkrNVko97Nj0PWZ64o9KqcGlvU/4jjVr4ORJR4JWkGv6RrW6HPwqs7SxBrW4AGx+xcrtO/uhSTVHUS67HTLnsebobTz7vD9jxsCQIZV4f0QiPdusYeGb73HkCJKkiWo5dszcXIrtkA+H/nDb9EanuDjzmJr/EOz4Co5tdevniRK11Vq7du+0gblpDjSxJiRRZ913n/kllZQEZ59tdTTCRVlVHJ/BVA9yLP1jn9a6DXA1cJe7AxPer0iBkL0/Qu4h66Y3gik/3aR3sYbVbduaOdHSD02U69Baco/s4uYXHqZZM3jhhUq+v34zCI+nZ5OPWLgQSdJEtaSnm8cOrXaa6eNuLr7kTNDWZF0H2CDtFbd+nijRyTOeu05JOeTJQIQPeO45k5ydd16xl1JTYcoUuPlm8yiNpz2rrATtEuA+rfWpM7YvAOLdF5KoLVJSXBpU75gF/sEQebG1QUUkwv4VkHuscJPNZqY5Ll5s+gkJUarMeUybN4HVG5rx+uvQpCr3q1teDPtT6NntKAsXmopWgwZJkiYqz5mgxTZfY75x8whaVJQpqrR6QzhEXw1/vQO50rjaw445ioQAoLU+BqCUOgvIsSwq4TVqJHH66SfIzYUWLeCyy0r8jGnTzAzI1q3N47RpkqR5UlkJ2nGttWszlKkAWms7chdH4NKg2l5gyutHXQIBDawNKjIB7Hmw7+cimxMS4OBB+OMPi+IStcKm5Wt5fNYURo0ypYOrJHKw+Rnc+6OjuiMcOiRJmqg857rZ9qFLzQ2wxrFu/TybzYyirVkDdBxvkrO/Zrr1M0UxbwOfKKVaODcopcKB94DXLYtKeIUaSZwWLzZ3rZ94otRdkpIgPNx8+fmd/j4pqfp/BlExZSVoQY6mhgBorT8BUEoFuT0q4fUyM0051f79MdXFjmdC6xFWhwXNBoBfkCm378K5Dk2mOYrS5Occ4Jbn76ZhcD6vvlqNAzUfAP4NYNcCAHr1KpqkZWbWTLyi7ktLM1VEg4+vgPAe4Odf/puqKS4O1q2D3JA+0Pw80C9J42oP0lrPBJYCW5RSvyqlVgLbgBSt9afWRiesVu3E6a+/YNQo6NDB1MYvxfbtpqeZq9BQs114RlkJWjLwYAnb78FMcxQ+rMj6M2fVRGezaCsFNIBm5xYrFBIVBR07SoImSvfac1v4JW0ALz69i4iIahzIv54pWLM7uXCTM0nbvbvM34lCFGEqONrh4Gq3rz9zioszFXo3bQI63gfZW80MCeExWuuHgTbAU8DTgNJaP2ptVMIbVCtxOnLEVGwE+Oab4gdyER1tpue7OnzYbBeeUVaCNgUYrZT6Ril1t1LqLqXUl8BNmDKwwoctX+7SoPrgKqgfAQ1aWR2WEZkIWWvg5IEimxMSTk+7FsLVli3w0PNdGRq/kL/fWQN9piIHw5FNkJNRuKlXL3jgAfj0U/NzKER50tKgw9mHIe+o29efOcU7VpivXg20GgYNWsL2Lzzy2QKUUhcBaK33aq3naK1na613nbGPxYu9hVWqlTjdeqtZ2PrVV9C+fZm7jhxppk9mZZm1+87vqzz1X1RaWX3QDgK9gSXARcDFwK9Af621rBr2cSkp5oKzXj1Mgtakt/d0gY5wzGfcs7jI5oQEU7b6118tiEl4Lbsdbr+tAH9bLm899h02v7LuW1VQpKMTya6ikw0mTTK/SO+5B/Jk1pgow4ED5oIoNmqL2eChBC02Fho0cKxD8/OHlkPNz7FMc/SUixwtji5VShX2rFFKBSmlhiil5mKux4QPqlbi9MgjMHOmmWtfju7dzWyP8HDIyDCPEyZIM2pPKrVhlVKqnqN60AuOr9L2ObMkrKjjnA2q774bUy3xyEaIvsrqsE5r2gcCGply+9GjCjcPHGhyyB9+gHPPtS484V1mzoSFP/jxxk0TOavnRTVz0LBuZlR59wJod1Ph5uBgmD4drrrKNMC+SxqWiFI4C4TEtlgDfoEQ0tkjn+vvD926ORI0MMWfNs+EAyuhuZw43U1r/aBSqi9mFtPnSqldmJvpEcDPwJNa6xQLQxQWciZOSUlmWmN0NNxySzmJk7PbfY8e5qsSnyUJmXXK6ig8Ryn1NjBba12kOLlSygaMAm4FKtPGVdQBRRpUZ60xVRyb9LY6rNP8AqHFhcUKhTRtatZXLFoEj8psfoEp2HH//XBhr83cnjgTIp+rmQPbbGYUbdd88//DdnpU7sorzWju5MlwzTXm51KIMxUmaKE/Qmg38Pdcfa74ePj8czO6bIu8yPz87vqfJGgeorVeAQxVSoUC7QE78JfW+nDZ7xS+oFKJ07Jl5hfOjBlyR7CWKWsuzyggEVNJaKZS6mGl1GSl1HvAFsdrV3siSOFdihQIOeiYL9ikl2XxlCgiAY6mFVkDBOY89csvcPy4RXEJr2G3w513mhHhd+68B7/I8yCo9EXTlRY5GE7ug0NF6x/bbPDyy2a99uTJNfdxom5JS4OAADsx9f7nsemNTnFxpuro9u1AUDg07QeZ//NoDAK01oe11r9prX+X5ExU2rZtZu5jdDRce63V0YhKKmsN2jGt9d3AAOA3oCXQAlgFDNBa3yknDN+UkmJKP7dqBRxYBcGtoUGk1WEVFZloHncXLduYmGguyJctsyAm4VU++wy+/RaefDSL9sHfQdTfavYDIh3TJXclF3upSxcYNw7eestRjEGIM6SlQZuYPALz91iSoIHLNMeWl5i1xif2eTQOIUQVHTsGw4aZ6U7ffgtNmlgdkaikclfDa60ztNavaa3v0Vrf6/h+pyeCE94pJQX69XM8cRYI8TZh3aBes2Ll9s8/HwICTJ9G4btOnYJ//hPOOQf+74rPzcZWl9XshwRHQWgXsw6tBFOmmOmN995rRvOEcJWeDrHRjkq0Hiqx79StmxnpLbIODXupP8tCCC9it8OYMaah4eefmx5DotapgXJlwpcUaVB96rCZRuiNCZrNz/Rl27OoyNVvo0bm4kMqOfq2P/+EfftMcuS/ey40ageNY2v+gyIHw96fIa/4nNqwMHjmGVi61JTeF8KpoMCRoEVtNueysG4e/fyGDUEpl9HdJr3MDa/M7z0ahxCiCmw2uP56eOUVGCJlImqrsoqECFFMkfVnWb+bJ037WBZPmSISYPuXcDQdQk5ffPfubdqA2O3e0xlAeJbW5lG1OwHbFkG729zzw9DyYtAvwr6l0HJwsZdvvtlMc5w40cxGadSo5kMQtU9mJuTkQGzz1RDSCQKCPR5DXNzp8z02P4i8uMSiN6JmKaUWY4qClEhrneDBcERtc/Cgmc545ZVWRyKqSc6yolJSUlwaVB9YZTZ6W4EQpwjHOrQ9Rdeh9e5t+oZs2WJBTMIrFFbIC/sJ8o9Dqxpef+bU4gLwCyp1apifn7nJmZkJ//63e0IQtc/pn88fPT690SkuztQYyMpybIgaaoreZMmiSTd7FXgNyASygVeAGcB+4E8L4xLebuVKiImBb76xOhJRA8rqg3ZjWW/UWn9Y8+EIb1e0QfWv0LAN1PPSOuGN25sCJrt/gA53Fm7u7ZiRuWoVtG1rUWzCUlpDZCSEHJ0NAQ1NWwZ3CGgIzc4tc+1Ov35mucALL5gRtQ4d3BOKqD2cCVqH8BRocr8lMcTHm8c1axx9bVs6eiNn/s97b8rVAVrrrwGUUhOBc51tjpRS8wDpfyZKtnMnjBhhFjb37291NKIGlDWCdpXj6x7M3ZwrgeHAi8AY94cmvM2pU/Dbby7/9w+sgqZeuP7MyWYzo2h7F5tpOQ5du5pRwFWrLIxNWCotDZSyw855Zp2Yfz33fVjLwaZf4PE9pe7yzDPmpsf48e4LQ9QeaWnQoH4+rcJ3eryCo5Ozn21hoZD6LUxitkvK7XtIM6C+y/PGgJTiE8Xl5Jjk7OhRM3rWvLnVEYkaUFaZ/cu11pcDe4E4rfVwrfWVQByQ56kAhfdYvdqlQfXJA5C9xTsLhLiKTDSxuvSiCgoyFx+SoPkurSE2Jgtyttd8ef0zRTrWnp1RUdRVy5bw2GMwb575Er4tPR06RO/Hz88O4XGWxBARYX4uCxM0MOX296fAqUOWxORjPgFWKKUeV0o9ASwH3rY4JuFt7HYz9eK33+C//zVV0IDUVFMp+OabzWNqaplHEV6oImvQorXWfzmfaK23A63dF5LwVkUbVP9mnnh7ghbhWE+9u+jFce/e5nxWUFDCe0SddvAg7N8PqoXjyjPqUvd+YHhP0+y3nBLl995rKueNH29uhAjflZbmqODYqD0EhlgWR1xcCQmaPb/Y+VTUPK31Y8AjQDgQBtyntZ5qbVTC69jt0KmTmYYxbBhgkrFp08z60datzeO0aZKk1TYVSdB2Oe7gtFFKtVVKPQtsdndgwvssX+7SoPqgs0CINdNvKiy4FYSoEguFHDliyq0L3+Jc36Maz4fweNOvzJ38/E3T6l0Lymx4FhQEL71kRk9efNG9IQnvlZsLmzdDbLPfLT+/xsfDhg0uNwya9YPAUJnm6Dm7gfXARECGLUVRubmm0tS//gWTJhVuTkqC8HDz5ed3+vukJAtjFZVWkQRtDNAN+ANYDcQAN7kxJuGlUlLOWH/WOBaCwiyNqUIiEmDvT1CQW7jJtVCI8C3OEvuxDee4f/TMKXIwHN8JRzaVuduQITB8ODz5pKnsKHzP1q2QlwcdmvxqeYIWF2diWb/escEvwHGz4X/SXd3NlFI3Ae8BDwChwByl1G3WRiW8xpo1EBtbYlPX7dshNLTottBQs13UHuUmaFrrXVrrkVrrEK11qNb6Wq31fk8EJ7xHkQbVYEbQvH16o1NEIuQdgwOnT2SdO0P9+pKg+aK0NAgIKKBNs78818PPuQ6tnGmOYKo55uXBAw+4OSbhlQpL7LdMMyO8FopzLH8rNs0xJwMOry/xPaLG3AP0B45orfcCvYD/szYk4RX27DHTGfPyzLSmM0RHw+HDRbcdPmy2y9q02qPcBE0p1V8ptVgp9YdSKtX55YnghPcosv7s+B7I2eHdFRxdRQwEbEXWTQQEmOk7kqD5Hq2h7VlHCQzIg5DOnvnQRjFmPdGu5HJ3bdvWNK7+739h2TL3hya8S3q6efSGBK1dO2jWDObMcdkYdYl5lGmO7pavtT7ifKK13oEUaBMnT8IVV5iF1HPmmH4xZxg50qw7y8oy6+yd33ftKmvTapOKTHF8C/gW+Cfmjo7zS/iQlBRTBjw+Hpf1Z7UkQavX1FRCK2Ed2u+/Q36+RXEJS2gN6qwM8KsHjTzYCK/lYNi7BPJPlbvrgw+aX6DjxsnPp69JS4Pwxsdo2iIY6ltbLtvPD+66C7799vTUYIJbQ2hX0w9NuNNBpVQcYAdQSt0AHLQ2JGEpux1uv91ckH34IfQseQp09+4wYYJZd5aRYR4nTIB162RtWm1SkQQtT2v9gtZ6idb6R+eX2yMTXiUlxZwLgoJwJGg2y+/uVkpEAuz/BfJyCjf17g3Z2S4XHqLOKygwIxSxkRpCOpoCHp4SORjysmHFLWY0t6D0zKthQ3Nnc80aeOcdz4UorGcqOP6Fral3FGC6+25zc276dJeNUZfAvp8h95hlcfmA/wM+BjoppXYBTwL3WhuSsFRurul19vjjMGpUmbt2726mMM6caR67d5e1abVNRRK0dUqpbm6PRHitEhtUh3aCwEaWxlUpkYlQcMokaQ5SKMT3bN9uZoioZish1EPTG52iLoV2t0DGLFh0EcyOgl/vNgVs7MX7PVx9NVx4ITzyiGkNIHxDWloBHZqlmvYMXqBFCxgzxtyw3+Pstd7yEnM+3bvEytDquk1AD6AnMBhQQIalEQnr2O3mDvlXX8Gjj1bpEGWtTRPepyIJWlvgN6VUuqxB801FGlTb7Y4CIR4qrlBTmp8PtoAi69CUMiMVkqD5jsIS+01+8XyC5l8P+r4DI/fCeV9Ciwth83uw8EKY3RpW/RP2/VKYrNls8PLLZp3AY495NlRhjZwc2LHDzyvWn7m6/35zo+6VVxwbmp8H/sEyzdG9ftNa52utN2qt12mtc4GfrQ5KeFZqKrx213p0ywuZMX47qev8zC+HKihtbdrIkTUctKgRARXY5xG3RyG8WtECIZlwYnftWX/mFNgImvUtkqD5+5tpm5Kg+Y7CEvst0yB0vDVBBARD9CjzlXsMds6F7Z/Dn29B2ssQfBZEXw1nX0P3br256y4br79ulh50725NyMIz/vrLPMZGpkGTO60NxkWHDjBiBLz+Ojz0EDRsWM9MG5dCITVOKfUD0AcIVkodcXnJHyheU13UWamp8OZT+3li/jDqFeSQddiPadPMerKq/C5wrk1LSjKzSaKj4ZZb5PeKt6pImf0fgbWY5tRbgO1AkJvjEl4kJcWlQbWzVH1tqeDoKiIRsn6DU6f7ffbubdb55EltLJ+QlgYhjU4REbrH8yNoJQlsBDHXwgWz4Mq90P8jCOthErX558A37Xh81OOEh+UxebL0narrCkvsn70fGri5gXolTZhg7rbPnOnYEHUJHPsLjv5paVx10BVAd+AnTA9a51cskGBhXMLD5nx5ivG/jCIseyefXTMbv+jW1S7qUdLaNOGdKlJm/wlgDyZB08CfwAtujkt4kSINqg+uApu/uYisbSISzPSxvadr3PTuDcePw8aNFsYlPEZriI3eg80/CBq1szqcogJDoM3fYeC3MHIP9J0JIYommU9xdc+3WbLohFR0rOOcCVqHziFVnsbkLueea76cffpo6Si3n/m924ja5QAAIABJREFUpXHVNVrrI1rrrcAI4Hqt9TbHSxOBBpYFJjzLbueCL8bRYeePzBn2Ljtb9wWkqIcvqcgatBuBaOAroAMwFpAOlT5i507YseOMBC20KwTUwt8TzfqBfwPYfbrcvhQK8S1ag4r6E0IU+FVkhrdFgsKh3U0w6HsYuZtzz8nhaHYD1q2VUbS6LE3n0zIsk0atvGB0twQTJ8LWrfD110DjdtC4g0xzdJ/3gKaO7w9hyu3/x7pwhEcdPUrHQ8tZ0Psh1na/oXBzRYt6SEPq2q8iCdperfUuYCPQQ2v9EWa4XfiAIuvPnAVCauP0RjBFGpqfB3tOr0Nr3x5CQiRB8wXHj5s7j6r5as81qK4J9ZoyIMH0w1q25HA5O4vaLG3jca8rEOJq2DCzHm3qVPPrgJaXwJ7FkH/C6tDqog5a6wkAWuvDWuvxQBeLYxKeEhLC3jkpfNzxqUoX9UhNlYbUdUFFErRcpVQ7zPTG85VSAUB994YlvMXy5S4NqrO3wckDta9AiKvIRDi8Ho6betF+ftCrlyRoviA93TzGNl0JobXrOiemWwdahmWy7EfpO1WXpf/p7ygQ4h0l9s/k52cqOv72G/z4IyZByz8Oe6W4oBsEKqVCnE+UUo0A75r3Kmqe1nDTTZCdTbd+Dbl/ol+xhtPlrRtLSpKG1HVBRRK0Z4C3gbnAlcAOYLE7gxLeIyXFJDCnG1QDTWtZiX1XEYnmcU/RaY5//GHKSIu6q7DEfstN3lEgpBJsTXpwbmwKv6xsaHUowk2ysmDfwQbEttoODdtYHU6pbrwRmjc3o2hEXAh+9WSao3t8CKxQSj2hlHocSAHetzYk4VZZWWaYet482L8fqFpRD2lIXTdUpIrjXK11otY6G9M08RKt9a3uD01YrViD6oOrwC/IrEGrrcLjITCsyDTH3r1Nn7f1srKyTnOW2O8QmV7rEjQCGjKg+59szQwnM9PqYIQ7FI7wxuJ1BUJcNWgA99wD330H63VDaHGBJGhuoLV+BpgEhAKNgAe01lKgra7Ky4NrroEtW8xQ19lnV/lQ0pC6bqhIFcdIpdSDSqnngSnADY7vRR1XpEE1mBL7Yd3NWq7ays/f3PXdc3oQuFcv8yjTHOs2raF1xCEaBudC4/ZWh1NpA/rmALBsmcWBCLdI22RKdHbo3NjiSMp3110mUZs+HTPN8fAGyJbb8zXBOa1RKdUEWAo8DvwbM5rWxMrYhBvdfz8sWAD/z959h1dVZQ0c/t30UBISIKETCGTTDBCagHQQEBFEcVQsfIptHMVRwDLOiDPOqCOWQR1UsPcRUbFA6EVCb6HuJLSEHkihped+f+ybkFATSHJuWe/z3OeSk5ubhYaTs85ee6333oPrrruqt5KB1O6hLCWOs4GumNrnkg/h5ko3CCmEtPWuvf+sSFg/OLXb7KkDmjeHWrUkQXN3CQkQ1XAf1IwCL1+rwym3Dl1rE+CbRdyy01aHIipBwtbjeNkKaN7W+W9z165tusN98QUctN1oDh6KtTYo97HE8XwMSC3xKPr4shxlkduVUtuUUk86jg1USsUrpRKVUi+VeG0HpdQ6pVSCUmqGo8+AqEqHD8NXX8GTT5p/WFepaCB1efeuCedSln+IflrrK8q7HTPUbsW0h/1Qa/2GUmogZo5aIPCt1vp5x2s7ADOAIMyAxoe11jI+2EJFA6obNABO7IK8TNft4FhSPceszyOLoflYbDZT5igJmvuy280K2h3dt7peeaODX70OdI1cw4rlLjiDUFxW4o7TRNQ9iX/9DlaHUiZPPgnTpsHUj1vySvfGpsyxxQNWh+XytNYxjuey3EA/j1KqD2agdTTgC2xXSi0EPgL6YPoI/KqUGqq1ngN8AYzTWq9SSn0IPABMu/q/iSizevVMyVL9+hX2ltHRkpC5urKcANYrpcq96eick0Rn4DGlVHvMSWIE0BroopQa6viSL4A/aa2jMCt0cqa32HkDqsE9VtCC24J/nVJljp07w5YtkC3dot3SsWOQkQGqzlqX6+BYLKQjPaNWsHFbTc6csToYUdESknyIqr/LrPC6gObN4ZZb4L33bZwMHgmHF0BhntVhuTyl1D2Xelzu67XWS4F+jhvcYZgb8bWARK31HsfxL4DRSqmmQKDWepXjyz8BRlfKX0ycb/dueOUVU4fYqBF4e1sdkXAiZUnQVgCblFIpSqndRY/LfZGcJFzbBQdUewe47OpDKTYvCO9nOjnazeDfzp0hL88kacL9FDUIiaqnXfdn2LcmPdrvIz/fm7VrrQ5GVCS7HRL21SaqWYbZJ+siJk40zQdmLBkHeSfg2EqrQ3IHox2Px4B3Md2zRwBvAfeW5Q201nmOzo/bgYVAA+BQiZccAhpd4riobCdOwPDhph3qoUOXf73wOGVJ0CYCdwIjOXviKFPyJCcJ11Vq/xmYBC2ko0vu3bmg8H5wZj+c2gWYBA2kzNFdFSVoqoF2rSHV5+jRvRCQRiHu5vChQk5lVaOli/Wu6dIFeveGNz9uR15BAByUbo5XS2s9XGs9HDgKdNBaj9Ba3wJ0AMq87UNr/QJQF2gMRGG2mhSxAYWYa8ALHReVqaAA7rzTbIyeORMaNrQ6IuGEypKgZWit/6e1Xl/yUdZvICcJ17RyZYkB1YUF7tMgpEh40T40Mw+tSROoU0cSNHeVkAB+vvk0rXsAara0OpwrFhqhaN1gOyuWy9A+d5KwycxOiGobdJlXOp+JEyElxYv/bX9O2u1XrCZa611FH2itkynDjWulVCvHnn601meAWUBfoOQGp3rAQWD/RY6LyvTMM2bW2dtvQ79+VkcjnFRZErRFSqkpSqnuSqmYosflvkhOEq6t1IDqkxryT7tXglYzCgIbFO9Dk0Yh7k1raNHwIN7BkeDtZ3U4Vy60Ez2i4li5ykah3MJyG4lbTAFJVEfn7+B4rhtugNat4bUfHsSethGyDlsdkrs4pJR6USnVTCnVXCn1CnDZ7SVAc2C6UspfKeWHKY98H1BKqRZKKW9MVdQcrfU+IFsp1dPxtXcDcyrh7yKKJCXBW2/Bo4/Cww9bHY1wYmVJ0O7E1EB/BXzveMwsw9fJScJF5eScM6D6uCNrcYcOjkVstgvuQ9u2DWnA4IYSEkDV2+m6DUKKOBqFpGf4snOn1cGIipKw/TT+vtk0bu1iNY6Al5cZ4bRZh7Nw2wA4NM/qkNzFvZgma5uBDUAE8H+X+yKt9W/Ar8BGYD0Qp7X+BhiLuX7bDuzk7HXcGOBNpdROzEDsqRX5lxDnaNHC3AF/802rIxFO7rJt9rXWza7kjbXWvymlumJOEgXA91rrb5RSqZiTRADwG6VPEtMdQxo3ICcJy2zcCLm55+w/86kONZWlcVW48P6w90szZLVWWzp3NqXhmzeX+LsLl5efD0lJdm4astF1G4QU8atFzw5mIPCKFdDGxf86wkhI8qVFgwN4+0VaHcoVuesueP55O6/99hcGDpsOzS/bbFBchtb6EHCzUipEa51ezq+dDEw+59hC4LwZHVrrzZhZt6Iy7dtnLq5Gjjy76V2IS7hsgqaUqgG8gmmLPxp4GXhKa33qcl8rJwnXtMrRS7N0g5AYl+ouVibhjtrvI4uLEzQwZY6SoLmPvXshL8+Gqr8TgodYHc5Va9muNnWCjhMXV5sHZBiJ67PbSUiug4o8YXUkV8zfHx5/3MZzz/Vj86rJtO9e4H6/L6qYUkoBPwDBSqkumEZrN2utZe3c1Zw6BSNGmCStTx8zOVqIyyhLieNUIBMIB7Ixg6Q/qMyghLVWrjRNMxo0AArzIX0j1O5idVgVr0YzqB5R3CikQQMzL1L2obmXhATzHFUvwfVLHAFbaAw9WvzOit8LrA5FVICCkynsOhxBlGuMP7uohx+G6tXymPLDONNUSlytt4HxwFGt9UHHx3Lt5WoKC+Gee8wMn6+/luRMlFlZErSOWuu/AHmOZh9jMO1ehZsqNaA6czsUZLtXg5CSwvvB0SVgL5RGIW7qbIv9JJfu4FgstBM9o1aQmOTN0aNWByOuVvKWneTm+xPVJtjqUK5KSAiMuy+fb1bdTspGmQNRAWprrecXfaC1/i/mBrlwJS+8AD/8AK+/DkNcv4JDVJ2yJGjn3qb1Rlrgu63zB1Q7JuK6bYLWH3LTIX0zYBK0HTtMRYJwDwkJEFLzJLUbhIC3v9XhXL3QGHpExQFn5xUK15UQb7oeRnVw/VlIf54QiN1u4z/vh1sdijuwK6UCcIwgUkrVw1x/CVexfj289BKMGwfjx1sdjXAxZUnQlimlXgUClVKDMe3yF1duWMIq5w2oPr4OfIOhpmtuXr+skvvQMAma3W728gr3oDWoBruw1XL98kYA/GvTuV0qfr55MrDaDSTsyAIgqrXr3zxo2hRuG7yDD367kcyjaVaH4+qmAbFAmFLqZWAV8F9rQxLl0qmTGUT97rumc7QQ5VCWBO1p4DRmH9o/gXhgYmUGJaxTNKC6Q1ERa9o6CO0EtrL8qLigag3NTDTHPrROncxhKXN0H1rbUeGbIch9Wh4GhLelU+QW4uKsjkRcrYQkX2pWyyIszOpIKsaEp+yczAri/TdTrA7FpWmtPwT+CnwJ+AIPaK2nWRuVKJODByE+3vz5llscA2WFKJ+ytNnPA/7ueAg3V2pAdUEOZGwG9Werw6pc4f1g71dQmE+9ej40aiQJmrs4dQoOHrQRdZ2G4GusDqfihHaiR/NFvLOwIzk5Nvxdf/HFM2UdJmF/Q6KancBmC7Q6mgoR068t/dst5T8zonniRbk2vVJKqYVa6wHAMqtjEeWQlWU6Nu7fD7t3Q6B7/LsWVe+SyyJKqZuVUiuVUieVUkeUUguUUrLL0U2dN6A6cysU5rnXgOoLCe8P+SeLO49JoxD3UdTBUdXXbtHBsVhoDD2jVpCTY2O9NMxzXekbSTzckqgoNyp/8vJm4tg4Dh4L4euv7FZH48pqKaWqWx2EKLv4zXa2dL2PwnXr+brv+8QnSnImrtxFEzSl1Gjg35g2+92AvsDnwNtKqVFVEp2oUucNqD7uyFJC3bDFfknhfc1ziX1oCQmQmWldSKJiFLfYr58EQS7ex7ykkLONQqTM0XXlHN7M3mMRRLV17Q6O5xo8sgHXNI7n9deyrQ7FlZ0G9imlFimlZhc9rA5KXFh8POix/+Kard+woP/LrAq7iSlTzlY6ClFel1pBGw8M0Fp/rbXerrXeobX+FBgGPFk14YmqdF6DkLS14F8bqje1LKYqERAGwe2K96EVDazesMHCmESF0BpstkJaRBaCd4DV4VScwHDC6/kS2fCINApxYbu2HcZu93KLBiEl2Rpcz7i+M9iyPZA9e6yOxmV9CDwFfAp8X+IhnNCG1xczetPzbI6+i7jrJhESYkZPzJpldWTCVV1qD1pNrXXyuQe11glKKVm3dUNxcSUGVINZQQvt7Bndh8L7w67pUJBLp05m08T69dCvn8VxiauSkABN6h4iMKyF1aFUvNAYeqqVzFkxErvdM/6ZupuE7aaDY0s3GM9XSmB9BvXcD5/DwoWmy7goO6VUO+AksFprfcDqeMTlrbBdR4MBr7Lm2seLT8bBwZB83lW0EGVzqRW0c+eflSSXAm6moMD8Iu3f33EgP8vsQXPX+WfnCu8HBVlwfDV16kBEhOxDcwdaF6LCt0Gw+3RwLBbaiZ7N5pCaCrt2WR2MKLfcdBL2mtJGt0vQgFZdFfVrHWTBvDyrQ3EpSqn/wzQGeRrYrJS63uKQxKUcPQqpqTSM8OXXtpPI9zlbqZGZaW56C3El3LR3uiivdesgPR0GD3YcyNgM9gIPStD6ALZSZY6SoLk2u93RYr/+TvdqEFIkJIYeLU19o5Q5uqA00yAkrE4OtWpZHUzFszUcysB2C1i0qIDCQqujcSmPA+201t2A4cAzFscjLiYnB0aOhL59GXVTPunp5jqqsJDiP4+Sjg3iCl0qQYtWSp24wOMk4Eb9qgVAbKxZlR840HGgqEGIu3dwLOIXAiEdSzUK2bXLnGCFazp8GE6d8iaqfoLbrqC1abidWkHZkqC5ovQNJByOIirKTe+T1unOgOiVpB4PYMsWq4NxLVrrg47nlUBdi8MRF2K3w4MPms37kycTHePDhAlm39n+/eZ5wgSIjrY6UOGqLrUHLbLKohCWmzfPzD+rU8dxIG0dBNSDwIaWxlWl6vUHPRXys+jc2WyzXL++RNIqXIrW5lk1SISaytpgKkNgfbyqhdG9rSYurr3V0YjySttIwpF7uKGHr9WRVA4vXwb0L4T/woL5dtq3l50RZXTubIJ8S6IQl/b66/DZZzB5MoweDZhkTBIyUVEumqBprfdVZSDCOpmZsGoVPFOykCJtrec0CCkS1g92TIFjccTEDABMmaMkaK6puMV+82zwccO+RjabGVjdYhlzPm9Perq5aytcw4kDmsPpYW65/6xIow7X0qrBDhbGNuSpCUFWh+OqZJics4mNhUmTTGL2179aHY1wU25aWyHKY9Ei0yTk+qKtyHmnIHOH55Q3FgnrBTZvOLKIkBBo0UL2obkyrSHQP4vGkW6ctYTE0LPJT8DZMRnCBeSdIjHR/DHKjcbznafBEAa0XcjSFQHk5lodjMsotb2kxMcnHR8Lq3XqBI88Ap98Al5yGS0qh/xkCWJjoWbNEvPP0jcCds9pEFLEt6YZyu3Yh9apkyRorkzvLKRleCJeIa2tDqXyhMbQtflKvL3tMrDalWRsJvGwGf3g1glaYH0GdtvNmSw/Vq+2OhiXEYnZ51/0KPq4HbL/31oZGZCba/aCvPsuVKtmdUTCjV1qD5rwAHa7SdD69wffoq0QaY6sJLSTZXFZpl5/2P4q5J2kc+eafPstpKZCXdmm7XISdB7t62n37OBYJLQT1QPO0KFVKitWhFkdjSirtA0kHIrCZrMTGeneZeR9rw/B658FLIjNo1cvNxoWX0lke4mTys01HRsDAmDOHM/a/iEsIStoHi4pCfbuLVHeCKaDY7VGEFjPqrCsE97PjBdI/Z3OjgXE9eutDUmUX24u7N7ri6qv3bODY5FqjcG/Nj3bxrN6NeTJyCnXkL6RhNRomjSBQDfcHllSrVb96dx8HQvmnrI6FCEuKD7e9Pq47z7zHB9/zgvsdnjsMVi6FO6+W5IzUSUkQfNwsbHmuXj+GZgVNE8rbyxSpwd4+cGRRcTEmENS5uh69uyBggIvouonQlArq8OpPDYbhHSiZ/N5ZGXB5s1WByTKJG0DCUeuoWVLD7jQq92NgdG/s3pTCCdkB5VwMvHxMGWKGanTqJF5njLlnCTtnXfggw/g2WdhzBjLYhWeRRI0DxcbC5GR5gFAbiacTIDaXSyNyzI+1aBOdziymKAgUEoSNFdU3GK/+Unz/9SdhcbQo+H/ABlY7RIKsrFnbCPhQBP33n9WxMuHAX3OUFDgzbKlMrFaOJdZs0z325AQ0++j6M+zZjleMH8+PPEEjBgBL71kaazCs0iC5sFyc2Hx4nPKG9Mc9XyeuoIGpswxbQPkptO5syRorqgoQYtq5WdtIFUhtBONQvfRpFGOJGiuIGMrx07UIvNUoGckaECP65sR4JvFwt+OWR2KEKUkJ0NwcOljwcHmOADh4TBsGHz+uXRsFFVKfto8WFwcnD59gfJG8MwGIUXC+wN2OLqMzp3hwAE4dMjqoER5JOhC6gYdJaRxhNWhVL5QU4vbs0MKK1aY7RLCiR2LI+GQycw8JUELiBhEL7WcBQusjkSI0po0MbNgS8rMhGYNcswH0dEwe7ZpdS1EFZIEzYPFxoKPD/TrV+Jg2jqo3gz8a1sWl+VqdwXvQDi8SBqFuCi9Pds0CAly4wYhRao3A99a9Gi1joMHS9z5Fc7p6HIS0nsAnpOgERjOgC6arUlhHD5sdTBCnDVqlNl3lp4OhYXmOfN4Pk8sHA7jx1sdnvBgkqB5sHnzzOyzoKASB4+v87wB1efy9oe618GRRXToYKoapMzRtSQkeRFVL8G9OzgWsdkgNIaeTX8GZB+aU7PbIXU5Cem98fGBpk2tDqjqDLzeTPVZFCvdHIXziI6GCRPMvrP9+83zFPtT1Fw1H9q3L/P7XLYTpBDlJAmahzp6FDZsOKe8Mec4nN7j2fvPioT3g8yt1PA5SuvWkqC5ksxMOJIa4Gix78ZDqksKjeGakB+oUUMGVju1k4mQfYSEo+2IjDQVDJ6iQ7+OhNY4zoJfj1odihClREebpOqjj2Bygw+o89VUePJJk22VQZk6QQpRTpKgeaj5881z6f1n0iCkWHh/83xkSXGjENnb4xqKG4REZIBPdWuDqSohnfCxZdGt0ylZQXNmqcsBSNxf33PKGx2863ahX7sVLFgWLOdS4ZyWLoVHH4UhQ+Df/y7zl122E6QQV0ASNA81bx7Urg0dO5Y4eHytefbkBiFFQjuBT004spjOneHIEdMsRDi/hATzrFp50OmtqFHINUnEx8PJkxbHIy7s6HIKfcNI3O3vcQkaXt4MvO4YKUdqk5Qo7faFEzpxwlwUffMNeHuX+csu2wlSiCvgQVcwoojdbhK0QYPOOQcdi4OaUeAXfNGv9RhePhDWG46cbRQiZY6uQe8sxMtWQGTrEKtDqTo1W4BPTXq0XEFhIaxebXVA4oKOLmM/I8jOtnleggYMGGqaTy34KcXiSIQooWhJd/hwWLXq/GzrMi7WCbJJkwqKT3gkSdA8UHw8HD58Tnlj3gk4vAAa3GBZXE4nvB+cTKB9ywN4e0uC5ioStp+mWdge/Oooq0OpOjYvCO3ItY1+xGaTRiFO6cwBOL2H7RlDAWjZ0uJ4LNCiew+a1N7HwnmnrQ5FCKOgwLRyfO898/EVzDq7UCfI9HRzXIgrJQmaB5o3zzwPGlTi4P6foTAXmtxqSUxOybEPLfDEYtq1kwTNVWhd6Dkt9ksK6URwXhzXXGOXBM0ZHTX7zxbGd8fPD7p0sTgeC9gC6zKw02YWrWpEQYHV0QgBPPss/PjjVW0yv1AnyAkTzHEhrpQH9ZASRWJjoV07aNiwxMGUmRDYAOp0tywupxPSHvxCHPvQ7io+h9tsVgcmLqawEBL3BNKvTwIE97Y6nKoVGgMFWfTolMaXM2tTUFCubRSisqUuA58azF0STq9eUKOG1QFZY0C/PD6aF8TGVRl07lnL6nCEJ/v0U3jtNfjjH+GRR67qraKjJSETFUtW0DzM6dOwfPm55Y0n4eAcaHyLKZUShs0LwvoW70M7fhz27bM6KHEpBw7AmSw/VNNU8K1pdThVy9Hcp2e77Zw8CVu3WhyPKO3ocvYzgq1bbQwZYnUw1hkwojkAC3/eY3EkwqPFxcGDD8KAAfDWW1ZHI8R55GrcwyxbBrm5cP31JQ4e+BUKc6DJaMviclrh/eH0Xjq3MS0cpczRuRW32G/pgX28a0aBT3V6Ri4CZB+aU8lJg8ytxO68HcCjE7TwVu1p12Q7CxZKAY+w0Nq1EBEB//sf+PpaHY0Q55EEzcPExkJAAPTqVeJgykwIqAd1elgWl9MK7wfANXXm4+srCZqzS9CmfbdqG2hxJBbw8oaQDkQELqBePWRgtTNJ/R2A2PXdaNgQ2ra1OB4r2bwYeG0yv8e3IPuMbEQTFhk/HjZuhNBQqyMR4oIkQfMwsbHQpw8EFl2/5p+Gg79B41HmAk+UFtwGAsLwz1hIdLQkaM5Obz1Bdf9TNGjRyOpQrBESgy1jIz17SKMQp5K6nHx7IPOX1WHwYNnHOnBwINm5gcTFJlgdivAkhYXwwAOwZIn5uFo1S8MR4lIkQfMgycmwc+c55Y0Hf4OCLClvvBibDcL6mX1oneysXStDgJ1Zws4couonYKvloUsUoTGQf5qenY6ydy8cPGh1QAKAo8tZk3ovGRmevf+sSO+b2uHjnceCX49ZHYrwJC+8ADNmwKZNVkcixGVJguZBitrrl2oQkjwT/OtC3V4X/BoB1OsPWQcZe1sKJ0/Cc89ZHZC4GJ3kZ1rsB7e2OhRrOBqF9Gi9EZAyR6eQfxrS1jN3+2i8vGDgQKsDsl7NOrXp1mo7C3/3oGHywlpffw0vvQT332/KG4VwcpKgeZDYWNNav03ReKj8M3DgFylvvJwwsw/t2ia/8ac/wbvvyoWvM8rOhr0HgolqfBh8g6wOxxpBrcE7gI4NFhEQII1CnMKxVWDPZ+7aGK691sxIEjCwdybrElqTfijV6lDcjlLqBaXUNsfj345jA5VS8UqpRKXUSyVe20EptU4plaCUmqGUcr/uLWvXwn33mc33//2v1BgLl1CpCZqcJJxHQQEsWGDKG4vPTYfmQsEZKW+8nJotoFojOLKIf/0LGjeGceMgJ8fqwERJu3aB3e6FaunB/2O8fKBWe/xOrqFrV0nQnMLR5aSeDGPd5mApbyxhwLBwCu3eLPlpm9WhuBWl1EDgeqAj0AHopJS6A/gIGAG0BroopYY6vuQL4E9a6yjABjxQ9VFXss8+g/Bw+P578POzOhohyqTSEjQ5STiXtWshI+Pc8sbvwL8OhPWxLC6XULwPbQk1qhfy/vuwYwf8859WByZK0jsdHRxbefgv4NAYSN9Ij+52Nm6EM2esDsjDpS5j/q77sdtl/1lJ3Qa2pHrAaRbOz7U6FHdzCHhKa52rtc4DdgBRQKLWeo/WOh9zvTVaKdUUCNRar3J87SeA+92xnToVVq4k/lBdJk82i2mTJ0N8vNWBCXFxlbmCJicJJzJvnskzivc/5GeZ8sZGN5u77uLS6vWHnFTI3MaQIXDXXfDyy7Bli9WBiSIJW9MBaNm2tsWRWCy0E+SdoGfMIfLzzc0ZYZGCXDi2itjtI6lTBzp1sjog5+Hn70XvjkksWBUBhdJuv6JorbcVXUsppVoCtwGFmGuyIoeARkCDixx3fXY7PP887NkDNhvxqfWZMgXS06FRI/M8ZYokacJ5VVqCJicJ5xIbC507Q+2ia9dXFzxOAAAgAElEQVTD8yD/FDS51dK4XIZjHhpHFgPw5ptQq5YpdSyQawunoLefoX6tgwQ1irI6FGuFxADQPWo1IGWOlkrfQGFeNrFrohk0CLxk13cpAwfY0Qej2L9lo9WhuB2lVFtgPjAR2A3YS3zahrke87rIcdf3r3+ZMpcffgBg1iyz/zMkxPw7LPrzrFkWxynERVT6rwuPP0k4gYwMWL36AuWNfqFnEw9xadWbQo3mcGQRAHXqmKqJNWvMs7CeTvAiqn6C53ZwLBLcFrz8qG1fSatW0tDGUkeXsTm5PUdSA6S88QIGjmwOwMLZ+yyOxL0opXoCC4FntNafAvuB+iVeUg84eInjru2HH8zq2Zgx8Oc/A2bMUHBw6ZcFB5vjQjijym4S4tknCSexaJFZ5SlO0ApyYP9saDQSvHwtjc2lhPeHI0ugIBuA22+HYcPOVlEIayXsCUI12g9+tawOxVreflDrGkjbQM+eJkErlNtd1ji6nLn6buCc+ZMCgHYdg6gbnM6CxQFWh+I2lFKNgR+BO7XW3zgOrzafUi2UUt7AncAcrfU+INtxrQZwNzCnyoOuSJs2mT0IXbuamWeOrmhNmkBmZumXZmaa40I4o8psEuLZJwknEhsLNWtCt26OA4fmQf5JKW8sr6a3Q14m7PsfYM7706aBtzc8+KApeRfWOH4cjmfWJKr5aatDcQ4hMZC+gZ497KSnw0apIKt69kJI/Z25W4bRsSPUq2d1QM7HywsG9DjEwo0dsZ85bHU47mICEAC8oZTapJTaBIx1PL4HtgM7gZmO148B3lRK7QRqAK5dE/LCCxAaCj/+CAFnE/9Ro8y+s/R0c8Oq6M+jRlkYqxCXUJndIUqeJIqOvcfZk0QA8BulTxLTlVJBwAZc/SThJOx2k6ANGAC+RYtlKTPBtxaED7A0NpcT3h+C20DCVGh2N9hsNG4Mr74Kf/wjfPopjB1rdZCeKUGbSmnVSubbAKZRyK7pjByUzGPVm/LWW/D551YH5WEytpKZWUjclpZMnGh1MM5r4JAafDOnATuW/0CbwTdbHY7L01qPBy42ibn9BV6/GehaqUFVpa++gv37oX79Uoejo2HCBLPnLDnZrJzdf785LoQzqrQEzeNPEk4iMRH27YNnnnEcKMiF/T+Z8kZvD29HXl42G0Q9BmsfgWMroW4PAB56CL7+Gp58EoYONeNWRNXS8WlAHVS74Mu+1iOEmkYhIfZ1PPhgU6ZOhZdegqZNLY7Lk6QuZ9G2/uTne8n+s0sYMLwxjIcFczJoM/jyrxfiPHa7KWe55x6oUQPOLgqUEh0tCZlwHdJTys3Fxprn4v0PhxeYMj0pb7wyze4G32BIeLv4kJcXTJ9u5k099piFsXkwvTUDH+88Ito0tjoU51DrGrD5QNp6nnjC3Ft46y2rg/IwR5czd/st1Kxpp3t3q4NxXhHNbEQ2PMLCFeFQmG91OMIVvfEGPPqoKWMRwk1IgubmYmOhRQto3txxIGUm+AZBvUGWxuWyfKpD5P2QPBPOnO1joxT87W/w3Xem9F1UrQRdSGTYLnzreHgHxyLeAaabY/oGmjSBO+4wNxHS0qwOzEPY7diPLiM2fjADBtjOlpeLCxrQ5zRLtvUk//Cqy79YiJJ+/RUmToTRo+GRR6yORogKIwmaG8vJgcWLS3RvLMyD/T9Cw5vA29/S2Fxa1KNgL4Ck90odnjgR2rc3+9EyMiyKzUPp3dWIapgMfiFWh+I8QjtB2nqw25k4EU6fNlVAogqc2o3eHcS+w3WlvLEMBt4YzomsYNYt2GJ1KMKVbNtm7j517AiffCKDBoVbkZ9mNxYXZ8ruzpY3LoLcdClvvFo1mkPDGyHxPTOywMHX13T1PXIEJk2yMD4PU1AASSl1Uc0kKy4lNAZyjsGZ/VxzjdkfOXUqZGVZHZgHSF3O3M0mMxss+6ouq9+g6thshSxYIPMgRBnZ7TBuHFSvDj/9BNWqWR2REBVKEjQ3FhsLPj7Qr2gWdcpM8KkB9eWK4aqpxyEnFfZ9W+pw586mWcj06bBkiTWheZrkfXZy8vyJallgdSjOJbSTeU5bD5ibBkePwmefWRiTpzi6jLlbhtOqlZ2ICKuDcX516kCHVkdZsK5tqdJxIS7KZoNvvoFffoFGjYoPx8fD5Mlw333mOT7esgiFuCqSoLmxefOgZ08zA82UN/4ADYeb/Sni6oQPgKDWpuX+OQPQXnwRIiPhgQdktaIq6PhUAFTb6hZH4mRqRYPNC9I3ANCnD3TpAlOmmFVHUXmyUtawdMd1DBkiYx/KauBAb1Ymduf0rgVWhyKcmd1uVswKC01b2k6dij8VH2/Ob+npJmdLTzcfS5ImXJEkaG7qyBEznLa4vPHoUsg5Dk1GWxqX27DZQD1mVieOld7YXq0afPABJCWZO3iiciVsOQaAig6zOBIn41PN3ERwrKDZbGYVLSlJGtlUqqxDLF3XiOxcf9l/Vg4Dh9UhN9+f3+ftszoU4czefRdGjjTzzs4xaxaEhJiHl9fZP8+aZUGcQlwlSdDc1Pz55rl4/0PyTNOBsL5cMVSYiPNb7hfp398MwXz9ddiwwYLYPIjekUtQYCZhLVpaHYrzCe0EaWd/AG++2azuvvrqeQu/oqIcXc7c+CEEBBTSu7fVwbiO63rZ8PPNY8HSYFPxIcS55s+HJ56AESPgzjvP+3RyMgSfMwozONgcF8LVSILmpubNM3X9HTtiZsukzIIGw8An0OrQ3IdvDWh+HyR/d8F9E6+9BnXrmkQtT643Kk1Cki+q4W5sAbWtDsX5hMRA9mE4sx8Ab2+YMAHWroVlyyyOzV2lLic2fih9ekOgnG7LrFo16NE5g4VbroPUOKvDERY7dy/ZztkJcNtt0KYNfP75BTs2NmkCmZmlj2VmmuNCuBpJ0NxQYaFJ0AYNcpzDUpebhhZS3ljxLtJyH0xpxbvvwqZNZiVNVA69tzZRTY9ZHYZzqj8YsEHCO8WH7r3X3Dj497+tC8ud7d2SxM6DiiFD5ddreQ0YXJONe2M4tm2J1aEICxXtJUtMhF27YOa3BXj94VZyCnxg9mzHxvrzjRpl9p2lp5vroKI/jxpVxX8BISqA/AZxQ/HxZg/a2fLG78A7EBoMtTQut1Qz0qxMJr1fquV+kVGjzGPyZFizpurDc3dnTttJSa2HanH+f3sBBLeCpneAfhuyjgBmVefxx+G332CLjJ2qWLkZxP7eFED2n12BgYNNA6vF809YHImw0qxZppHR1q2QnQ21w7x5ueG7PBT2A/EnIi76ddHRpkIgJAT27zfPEyaY40K4GknQ3NC8eeb5+uuBwgJHeeMNZg+aqHjqccg+Csn/u+Cn33kHateG7t3hz3+GkyerOD43lrjFJB2qtZ/FkTixa16AwmzY/mrxoT/+0ZSUTZliYVzuKHUFc+MH07RRNkpZHYzr6dwZgmrksGBNFJxOsTocYZHkZJNgBQTANfkbsdlgd8NerPG77rINP6KjzQ3Rjz4yz5KcCVclCZobio2Fa66B+vWBYysg+4iUN1amegMhqBXo81vug/n/sHUrPPgg/Oc/0Lo1fP+9NGmoCHrTYQCi2oVaHIkTC4qCZvdA4n/hzAEAQkPNGIivvpIN9BUp98AKFm4bwJChPtikw365+fhA3165LNg6EPad36VPeIYmTSA1FUanf8CMDTF0SYslO9uUZsv5SngKSdDczPr18Pvv55Y3BpgyPFE5bDaIegzS1sHx1Rd8SUgITJsGcXGmecutt8KNN8KePVUcqxvZvmYf/3wtCH/fbFp2aGp1OM6t3d/MXslt/yo+9Oc/m5sEb71lYVxuZuWyDE5mBTHkBh+rQ3FZA4fWZPfRSPYs/hpy060OR1hg1Ci4Nmcp4xMfZXXIEH4PGEh2NjRsKA0/hOeQBM1NZGXB009Dt26mnO7++wF7IaR8D/WHmo6DovI0uwd8g8xen0u49lpYtw7efNN00WvbFl5+GXJzqyhON1CYuYupf/6CTteFcTC1JjPfnE312nWtDsu51WgGkffDrulw2syZatoUbr/dzOxLl+vgq5d/hrnLI/DxKaB/f6uDcV2DBpnnaXPGwLaXrQ1GWCK6xm6mpd5Cin8LHqv7DX6B3rRta1ZYpeGH8BSSoLmBpUuhfXvTle3//g+2b4dWrTCtirMOQZNbrQ7R/RW33P/fBVvul+TjY0a57NgBN9wAzz0HHTpI2/PLOrWHA7OfYkiv3Yx/6y4GdNvD1ni48dHbrI7MNbT9C2CDrS8VH5o0CU6fNqu74iodX01s/EB6ds4gKMjqYFxXq1Ywbhy89utEfv4qCU5LTZtHyc6Gm27C36eQ3Jk/c8MdwURGQlSUNPwQnkUStEpSWAhJSfDTT7B7d+V8jxMn4JFHoG9fyM+HBQtg+nSoVcvxgpSZ4OUPDW+snABEacUt998v08sbNYKZM+GXX8wKaJ8+JsE+Jh3jSzu9D1Y/yP/+8hzX3PkXViT25r2pmfy8rA3hEbJyVmbVG0OLh2D3x3AyCTAXO0OGmL2R2dkWx+fiDm/fwMa9MQweVs3qUFze229DTIdc7v7vR+yeM9XqcEQlOXfWWXw8pjPI+PEwcyatbmwhDT+Ex5IErQJkZZnBr9Onw6OPQs+eZnp9y5YwciS0aAFDh8LPP5vWsRXh119NedwHH8CTT5p22QMGlHhBcXnjYFN6JypfzRamW+ZFWu5fzLBhsG0bPPssfPEFKAUffmiSfI92OgXWPELmt524e0If/jD1a1q2rsHGzf489FiwNGG4Em2fBS8/2PL34kOTJsHRo/DZZxbG5QbmxeYDMGSYTKe+WgEBMHOWHzZvP26ZMIasg/FWhyQqWNGss/R0c7MyPR2m//2QSdIeeACpExaeThK0ckpNhfnz4bXXYMwYkyTVqAFdu5oufZ9/Dt7eZiXkww9Nw46//c2cjG66CZo3h3/9y8wpu9LvP2aMaTARHGyaTrz+OlQ/t4P+sdVwZr+UN1Y19bjpmpn8Xbm+rFo183OxaZP5mRo3Dnr3Nt0fPc6ZA7D2T/BzC5b9qon+i+brlXfywgvwe5wfUVFWB+jCAuubld59X0LmTsCswHfubC6WKuoGkscpzGPu8maEh56gfXurg3EPzZrBF58VsGlfR/407pDV4YgKNmuWaZ4VEgJeXtB336dM+akFK99Zb3VoQjgFSdDKYN8+k1w1bAhhYWa+2KRJsHw5REbCX/5i2qbv2gUZGWYv0dSpZtm+Z0+zNL93rylna9HCvL5xY7jjDvMeZWm3brfD119Dmzbw3XfmPTdsME1BLihlJnj5QsObKu4/hLi8egMhSEHCpZuFXEzbtmZP4ccfw86d0LEjvPiih7TkzzoE65+A2ZHk7PiYSb/Mpu8/FuJXozYrVtiYPBl8fa0O0g20nmQG12+ZDJgmpJMmQWKiKckW5VeQupF58f0Z3DcNL/mtWmGGjazJ8w+v4qM5g/nwje1WhyMqUHKyuckM0DgljuG/PMiBRteyLlfqGIUAkF7AZZCZaRKvgQNNM4cOHUxTjtByjF7y9YVbbjGPnTvhvffgk0/gm2+gXTuzl+yuu7jg5vL9+83nf/nFrNR9+KH5mouy2yF5JtS7HvyCy/vXFVfD5mVa7q/7k1nFrHOxDPoSb2GDsWNh+HBTij95MuTlwUsvXe4rXZTdDltegB2vQWEeWwuf464pf2HzFn8eeugiK8TiygXUBTXetNxPfw5Cohk1yqzuv/oq3HwzUj5aTuuXJHL8VFeG3ORtdShuZ/J/OrJq5XIefaYrMX0L6RgjGbA7aNLElDVGeCXzh29v5kRQY6Zf/x0N65+9Cxcfb1bakpPN60eNkn1ownPIma4MoqPNqtinn5rZQf36lS85O1erVmb20IEDMGMG+PmZvWsNG8If/2j2k4HZg/T++2bVbOFCc6EaF3eZ5Azg+Fo4kyzljVYpbrl/dZvba9c2JbMPPgj//Kdpx++W4v8KW/9BYYObeXPvYTrf/3cOHfFn9mxzI0OSs0rQegL4BpvEGFOWPWECrFljVvVF+cyNtWGzFTJoWIjVobgdbz9/vppxhLo1j3LLyDMyEsJNjBoFp4+eZvQXN+GTn817w37mQFZocRv9C+1RmzLF0UhECA8gCZqFqlc388rWrYPVq83w4o8/Nglhr15mb8jDD0OXLiZpe/JJcyF1WSkzweYDjUZU9l9BXIhvTWj+f5DynSnbuwo2G/z3v2bf4XPPme5mbkW/A9v+yf6aE7n+xS958rk6DB5sft6HD7c6ODfmFwKtnoT9P0Ka2fMxdizUrWvGdYhysBcSGxdJl9Z7qVPH6mDcU91Oo/ju+efZf8iPe+4ukAZKbiA6Gh6f4MeB5r2Z1vsbciNbl2qjf+4etaI/z5plbdxCVBVJ0JyAzWZKFz/+2JQzTpkChw+bi9Tp0037/MjIMr5ZfpajvHGguQgT1oj6ExTmQ2LZWu5fire3KYe9+WZ4/HHTctgtJM+E9Y9zOOAeuj70KqtW2Zg+HX780ez1FJWs1RPgFwrxfwMgMBAee8x0iPXI5jRXKH2fZlVCZwb3P2F1KO7L5sW1d9zNm2P+zC+/evPKK1YHJK5aVhbRnXzptnoqf5439Lw2+iX3qBUJDjbHhfAEkqA5mdq14amnICEB0tJMN78y7Qc5tQc2ToIfG8HpPdDs3kqPVVxCzRbQYCgkvQcFuVf9dj4+pknM4MHmZ+KbbyogRisdWQJxYygI6cmdb39ERoaNFSvK8fMurp5vELSeCAd/g9SVgCmxrlbN3CQSZbNgdjKFdm+GjJDls0pVbyB/vDuJO6/7jr/+1c6CBVYHJK7Yt99C69aXHBLbpInZ/19SZqY5LoQnkATNSdlsZbhQtRfCofmw9CaYHQk734DwfjBgMUTcXiVxikuIurKW+xfj72/KO3r1grvvhtmzK+Rtq156PCwbATUieXH5PBYv8WbaNKQ9uRXUYxAQZvYBYm4QjRsHX35pVvPF5c2d50et6hl07dPQ6lDcni3mVT74v/+jdbMj3HGH/Iy6pHXrTD1148Zm4/1FjBpl9p2lp5v9+EV/LtqjJoS7s9ldvH+3zWaLAPbs2bOHiIgIa4OpKnknYPenkPgunNDmAivyAWjxEFRvbHV0ooi9EH5pDX61YPDqCnvbkydNR9FNm0xnz0GDKuytK9/pfTCvO+DFPO8NDBkZxtixblS26Yp2vgkbnoQBSyC8D3v3mnEgTzwhK2mXYy+006juEXq238P/FnW3OhzPEHcPevUmOv9tE+3aebF0qWm05WKaAXutDuJqXNG118GDZlO9ry+sWUP84bDiLo0nT8KePXD0qNlz1qwZKGVuVB86ZDpp16plumhLN0fhBi57DpAVNFeSuR3WPgo/NIT1j4NvLej+OYxIhvYvSXLmbIpa7h9fY1ruV5CaNWHuXFMhMmKEC3Xdyz4GiwdDfhYHWy3grnFhtG0L77xjdWAersXDENjArKLZ7UREwB/+YDrIZmRUXRg//WTKl1q3Nl1tZ86EY8eq7vtfia1rD3AwrR5DBmZZHYrnaP8PVP0EPn52GqtWme6jwgVkZcHIkaZOcfZs4g+HFXdpPHEC5s0zWztOnoTTp80NyORkSEmBggJTYREdLd0cheeQBM3ZFeZDyg+wcAD82hZ2fQiNb4HBa2DwKmh2F3j7Wx2luJjm94JPzSseXH0xISHmF1qTJjBsmKkacWr5p2HpjXBqL/k9Z3P7Q604c8YMXa9WzergPJxPILR9DlKXw+H5AEycCKdOmefs7Mr99qdPw0MPmWu30FBz5/yzz2D0aNNVsn17M95k9uyqTRjLYu4PRwAYPKKexZF4kOpNQT3GrRGP8eSjqbz9ttmfK5xcdrYZ9PrllxAdXapL47p1EBBgRmLm55sO135+sH07pKaaVTXp5ig8jQyqLgt7IRxdDgWVfKVyrvQNkDgNzqRAtcbQ/mWIvN8MmhWuoajlftI0aHwreAeW/z1qRECQOu9wWJiZj9erl2kesmQJXHPNVUdc8Qrz4Pc/QNpauG4mf327F8uXwxdfmJmAwglEjoPt/zaraPUG0aGDjccfh6lTzc/VtGmmrLairVtnRkgkJsLTT8Pf/24uzPLyzOcWL4ZFi8w8vLfeMhdoMTFmFmX//nDddVCjRsXHVVaxCwO5psk2GrZpbV0QnqjNs5A0g1dG3MeazT8zbpxZXWnb1urARElFg6ZT9hXSuGkIo16fT3R7s7k+OdnMNwOzala9urkpVLT33t/fHK9V6/z3lW6OwhPIHrSyOPAbLB1WOe99OeEDTMv2hjeCl+TTLulEIvzaGuwFV/4eDW6A1pMgrPd53WP27DFJWn6+GageFXWV8VYkux1W3w+7P4Yu0/gt8WGGDYMHHoAPPrA6OFFK0gxY8wD0+dmcbzAjPh55BJKSTCL1+usQHn7136qgAF59FV54AerVMwPZ+/a9+OtzcmDVKpOwLV4MK1eaJM7Hx4wo6dcPbrwRrr326mMrq1OnoHZILo/f8guvfSOdC6rcjimwcSIH2ywnZsh11KoFa9eaEnAX4PZ70IoGTfdJ+4EBW97i/SE/cCArtHjW2eTJplwxJMSMkcnKgjNnzNfWrm0+Dgw8e74ZOvTsexd93eTJlfk3FKJSXfYcIAlaWdjtkL6p6lfQAsOhRvOq/Z6icpxIhJwr2VBjhyOLQE+FnFSo3Q3aPG2GkNvOVijv3Am9e5u7jsuXg9P0y9n8PGz7J7T7GykhL9Khg2netXKl+eUrnEhhHvzSCnyDYci64p+v7Gx4+WV45RVTjvrKKybB9rrCAvl9+0wX0uXLzV63adPMxVZ5nDkDcXFmdW3xYnNhXlBgGub84x/QrduVxVYev3yfzvBbQ1jw4TcMuE+65la5gmz4WUFAXZYGrGHAQC9GjTId3F1gVIfbJ2iTJ0O1hE08MbMnR8Pa8cnYpaSeDChOrIoSuJAQOHAA5s83l1re3ubGS34+dO9uzjM2GzRvblbOMjNNglZyqLUQLkgSNCHcQn4W7PnE3DU+tRtqRpkZVs3uLt6DuHmzWYUIDTUXvw0aWBox6Hdg/WMQOY68jh/Qp6+NrVth/Xpo2dLi2MSF7f4MVt0L182EJreU+tTOnWY1bckSc+H0/vvlL6n98ksza81uh3ffhbvuqpiL6RMnYMYMk0geOwbDh5tyyQ4drv69LyQlBR6+9xBL4oJIS9qCf6MqXLoTZ+35HFbeAz2+5rXvbmfSJHPzoEMHUxoXEmKeSz6c5MaQ2yRo48fv4cSJCJo0MauXP/9sxpv5HD/C8pwu+HkXMuUPazlRvT7bt5tGjmPGnG2Xf6kujn36nP+6Jk2ki6NwC5KgCeFWCvMhZRZsf9XsUQyoB62eMJ34/IJZvdrsFWrcGJYuNU0WLJE8E36/DRoOh17fM/FpH6ZMMXe3b7vNopjE5RUWwG/twOYNQzeDl3epT9vtphzxqafMXewnnzRlitWrX/ptMzJMZ8avvoKePc17NGtW8eGfOmX2zb32mvmeo0fDiy+azpBXKz8f5swxiemcOXbsdjvP3PQ6//p+PHi7Xp93t2AvhDkxkHcC+7Ad3HufP59/fukv8fc/P2mrVctUHfz1r5f/Wa4gbpOgjR27h8aNI1i3zpTY16kD+adz+C6tP+0LN3JHo+XsCOwEmBX44GCTQMsqmPBwkqAJ4Zbsdjiy0DR2ODzfdIps+TCoJ1i6tgFDhpgZMn/5i9mj06RJFZb9HFli2umHdob+85n9WzVGjDArJ+++W0UxiCu371tYcTv0+Aoi7rjgS44fh0mTzPy6pk3NqIQbb7zw2y1bZkoaDxwwpU3PPGNKmCpTRga88Qa8+aYphxwzxiSSkZHlf6+UFPjwQ/hwRgH7D3hTLzSN+3u/x7i+HxDRrR9c+3HF/wVE2R2aZ843MW9Bq/Hk5Jj//0WP9PSLf1zyzz4+ply2IvZYloHbJGjjx++hVq0I3n/flB/m50ML3338kjWAZ+wvM9M2GjC/soKCzGxqf3/TDKhjR9MASAgPJAmaEG4vbQPseA2S/wc2H2h2N3P3v8DosY05dcq8pG5dk6h16XL2uU6dSoglPR4W9ILAhjDod/YeCqVjR7N/IC7O/GIWTs5eCHM6QEEODNt2yeZEy5ebFvk7dsAtt8B//mMuwAByc01C9sorJjH64ouq2RtW0rFj8O9/mwQyNxfuuw+ef97csLiUggLHatm0XH6b64PdDoOj5/Fgv/e58bpt+EaOgia3QUhHl9jw5PYWDYL0jTB8F/gFWx1NWbhVgpadHcHHH5uk68wZx3k+O5tsAih5ientbVYoW7Qwf05LM6WLsoomPJAkaEJ4jFO7YcfrsPsjKMghp85w4g92Z+32pqzd3pQ12yPYsbcedrvp7hBR/xhd2+6lS+t9dG27lxiVQo1qOVcXw4GfAC+4Po5c3yb06mX2Lm3YcGWrF8IiKT/A8lHQaKQpo72E3Dxvpnw5iH98NBRf7wJeevhnBl67i3tevI/128K5/+403vqPHzVCrOuHf+iQ2Z/2/vvm44cegmefhfr1S78uJQU+mp7NjOkF7D9cnXq1DnF/nw+5/4a5NOvSwyRloZ0kKXM2aRthboxpv9/hX1ZHUxZulaBt2hTBhg0wIOc3huX/xHivt8kq8MNuN/vJvL3Nypq3t5l3Vrt26e6M0o1ReCBJ0ITwONmpZjD2ns+gIKvUp05mVWf9rvasTerA2qQOrEnqyL5Us5zg5VVAm0YJdGmxiT5t4rix83xq10wv3/f2rwM9v4Va7XjiCbOi8v33Zzd6Cxdht8PSmyBtTZm/ZNfhpvzxg1eZt7kfAKE1jjNj3Dhu7vKjeUFAGFRvbjrTFj+amefAhuftd6sMycnw0kumNNPPD/70J7Ofbu3KM7z/dhq/LamP3W7j+mvm8eCQHxh+Sy18m98CtbtIUubs4u6ClO9heGSQAjYAABbCSURBVCJUa2R1NJdTqQmaUioIiANu1FrvVUoNBN4AAoFvtdbPO17XAZgBBAHLgIe11vll+R4l96Bt3BhB/bRtfJvSnT1eLejFck4Wms18/v7mn05OjknQ/PzMXrQGDcxYjLw88+9RCA8jCZoQ4tKOHjVtyteuhTVrzPOxY+aXae/eMHIkjBhh9hqV1axZpuRt/HgzYFh4BrvdNIJZtNDO5OfSaRC0y6zsFj/2mOczyaXnAnr5QvUIqN4MfCq/S0PS/nq8+MltfLmgd/GKcr1ah7hvwEzG3XuCZt0HQu2ukpS5klN74RcFQQpqtCj/1/uHQswb4BtU4aFdQKUlaEqpbsB0oBUQBRwBNNAHSAF+Bd7SWs9RSm0FxmmtVymlPgTWaa2nleX7lFpBW1CDL5K6Uc1+mmF117IhtTF5eeafT7VqprtjZqYpM/b1hbAws3Lm5yfzzITHsjZBq8q7OJKgCVEx7HbTCv/HH81j2zZzvGNHk6zdfDO0a3fxa9dduyAmBlq1MnuU/KTBnThXYR6cSTkneXMkcIVXWWZbDtuTm/PpwhF062pn+Jg2+NbvWmq+oHAxie9B4n+v7Gv9Qs14iYDK2Jx7nspM0GYAnwKfA32BpsDftNYDHJ+/G+gHvAgs0lpHOo73Al7UWvcvy/cpvvbSmjpjHsRvwyreuXUpGaobSUmmOZDdbuYo+viYFbS8PJOcXX+9KXWUTo7Cg132HFBpvbRK3MWJcnwcCHxEibs4SqmhWus5wBeUvovzAFCmuzhCiIpls0Hnzubx0kuQmAg//QQ//GDudL7wgmn6MXKkefToYVbbwPwyvu02s+/g228lORMX4eV7tszRQm2AVx+2NARRkVo+bB4eTGs9DkApVXSoAXCoxEsOAY0ucbx8EhOpsXM9yf+YwYncbuxPhqgoU9a+cKHpjJmRYRKz9u3N/rPcXLP/8/77JTkT4mIqs9nxA8CjmLs4AF2BRK31HgCl1BfAaKXUdiBQa73K8bpPMHd2JEETwgm0bGnuck6YAIcPm0GkP/5oOuO98YbpEHnTTSZZ++UX0xDkp5/MXCEhhBCW8gJKlkrZgMJLHC+ftm1h1y6ahIUx+ZxP3Xprud9NCOFQaQlaFd7F8QbYv3//lYYqhCiHQYPM4+RJMwx73jyzWvbhh+bz48aZu6J791oaphBCOCVvb2/vxo0bV9W32w+U7FdaDzh4ieNldfbaq1EjOeELUQ5lOQdU8rjQUirrLk59gF69el1tfEKICjBjhnkIIYQ439NPP13/lVde2VVF3241oJRSLYA9wJ3AR1rrfUqpbKVUT631CuBuYE453leuvYS4QmU5B1RlglZZd3HWAr0wK28Fl3mtEEIIIUSVq1+/vvc999xT39/ff21VfU+tdbZSaizwPRAA/AbMdHx6DDDd0dBtAzC1HG8t115ClFN5zgGV3mZfKbUX00noMJCI6R60B/gFcxfnO0er14e01iuUUh9g9qq9VqmBCSGEEEIIIYSTqbJ+wlrrbGAs5i7OdmAnpe/ivKmU2gnUoHx3cYQQQgghhBDCLbj8oGohhBBCCCGEcBcykVMIIYQQQgghnIQkaEIIIYQQQgjhJCRBE0IIIYQQQggnIQmaEEIIIYQQQjgJSdCEEEIIIYQQwklU5aBq4aSUUrcCz2J+HryAzypiDl3RDDyt9d6rfS8hiiilIoAEzLgOgEAgDnhGa32kEr/nEq11xAU+9zEwWWu9rzK+t3APSql2wBbgVq3191bHI0R5OIZZxwE3aq33KqUGAm9gzr/faq2fd7yuAzADCAKWAQ9rrfMrObYXgNscH/6qtZ7kLPEppf4O3ArYgQ+11m84S2znxDkFqKO1HutM8SmlFgNhQJ7j0ENATWeITyk1HHgBqA7M01qPr8j/drKC5uGUUg2B14Hrtdbtge7A7Uqpm6yNTIhLOqi17qC17gC0Ag5zdq5iVesH2Cz63sJ13Ad8h7nAEMJlKKW6Ab8DUY6PA4GPgBFAa6CLUmqo4+VfAH/SWkdhzosPVHJsA4HrgY5AB6CTUuoOZ4hPKdUH6A9EA52Bx5RS7Z0htnPiHADc6/izM/2/tWF+5tqX+H0f7wzxKaWaA+8BIzH/f2MccVRYbLKCJuoAvkA14LjW+pRS6l4gu+QKmFKqL2aVoK9SagmQBrQF/gDMA2YBPYCTwJiSq2ZKqbGO9xnr+HgJMBlIAr7E3H0oBB7XWq+qzL+scD9aa7vjDuoRpdTjwF2AN7AVeASYDrTH/IxN0Vp/drGfSa31EqXUy5g7nseAQ8BsYAkQqJT6BmgHpGNOzA8ADYDflFK9tNbHq+QvLVyKUsoXGAP0AuKUUpGY8+cDWuvhjtc8BrQAngReA/pifo4/0Vq/6TgH/5uzP9vPAR8CtTA/g59orf/m+F7vAdcBBzB37v/h+Nl+BrPS4A3EAk9rrWUYqricB4BHgc8dH3cFErXWewCUUl8Ao5VS24HAEr/HPwFeBKZVYmyHgKe01rmOWHZgLuotj09rvVQp1U9rne+4Ge6D+fdqeWxF/r+9M4/2azz3+OckiBpuiOHWPPuGRBA0oazrIjVUxTyVNLkiDVW1ekMNrasUJaVmWsRCI1ENl6ZSmrjuukiCDITwVWOp4aKt2ZVw7h/P+4ud098vOchpfpzns1ZWfnu/w37effbaz36G930l9QDOBM4i9GQz/W1V/r9T0kqELp/VJPLtQ0TIXihyHARstChlywhaJ8f2Q8CtwNOS7pd0DtDV9pMLafqwbdmeCawCTLbdBxgLXNTOyx8BjLe9NXAq8UGRJJ+Yopz/SETSNgZ2sv0twhHwuu3ehCfzNEl9GvVTUha2Jz6e9yC8sjVWAc4vfb0CHGz7p8CLwB5pnCUL4OvAc7afAP4TGAZMILz9K5Y6BxNe1iMBbPclPpYGStqh1Kk+24cAY2z3BzYDjpO0MjCccHr1BIYA2wBI2g3YqhxvCaxBGI1JskBsD7X9P5VTqxOGUY2XgDUXcL4jZXu09uEraSPCAfFRE8k3R9KPiZT8SQuQ4R8uW+EXwCmE05Emk29F4p7tA+xMvNvWbhL5NgS6SrpN0kzg6AXI8KlkSwMtwfZRwLqENb8OMEXSvgtpNrXy+33guvL7WuJDuD1MBEZIugFYCbikvTInSR1agfcA236jnNuJiDJg+zXCGbHjAvoYAPza9ge2/0p8TNd40fb95fejRPQ5SdrDEGBM+X1jOW4BbgH2k7Q2sJLtB4BdgL2K0p9KKPLNStt5z7btnwF/kjQCuBBYijDMBgCjbbeWeZGTSttdgH7ANGA6kXLVq+OGnHyB6UK8b2u0EEZRo/MdjqRewB+A44Gnm0k+2/9BOPjWIpwsTSGbpKHA87YnVU43zd/W9mTbg2y/UfT31cDpTSLfEsQ79QhialA/YP1FKVumOHZyJH0dWM72jcA1wDWSjiQeulY+nluzZJum71V+f1RJk+kCtJ34WO1nXl+275W0KbAnkSo5mPi4SJJPhKSliHSIVZn/2WzrhGoh3nt1n0ngwzptalSf67btk6QuklYFdieiZd8jnpsVgX2JlLEzyvHo0qQrcILtm0v7lYG3gf5Unm1J5xEfBDcQjoRdSt+NnuGuwAW2zy/tV+Dv39VJ0h5eAFarHH+ZyCRodL5DkfRVYBxwnO2xZe7XYpdPUk9gadszbb8r6WYiff7DxS1b4SBgteIM6gEsRzjpm0I+SdsD3SoGZAvwbAM5/tHyvQxMtP1qkfUW4AAW4b3LCFryLnB2WaWuNilzC2AGMQen5mEduIA+limpYRCe4Qltyl8DNpHUImk9YkIlks4FDrN9LXAM0PezDyfpbEjqQuRzTwGealN8F+FsqH3o7k3MJ6v7TBJR3f0kLVVWLduT+T1f9ZhLOruSxhwOTLK9pu11ba9DzPkYXlKzVi91agbaXcCRkpaUtByxOEP/Ov0OAEbavolwTqxBGGETiYWeWiStTkSMW0u/h0taTtIShFG3f8cMOfmCMxWQpA0ldQUOBSaUiO37xWCCeK7bfg8sUiStRTzLh9oe22TyrQ9cKalbcSIOJFIKm0E2bA+w3bssvnEqMd9692aRj5ivN1LS0pKWJxYyOblJ5BsP7CpphSLH7sRCZYtMtjTQOjm2/4v4uB0vycDjhAfgDGL50AslPQD8bSFdHSDpYWBX4Lg2ZROB5wETqTj3lPMXA/sX780twKDPPqKkk7C6pJnl2XmI+Dg9pE6904EekmYRS9ueaXs6DZ5J278r9WYAvyO8XO/V6bfKeGKRkPU+86iSLyKDgcvanLsU+ErxsN8IvGX76VJ2BTGfcgbwIHCN7bvr9Hs2cL2kRwgH14PAesAvicWaZhEp588B79n+LRFlmEosMjKzlCfJJ8L2+8RzPY6YW/U4H6+i+03g55IeJyIy7Z2T/mkZASwNnF/RCYObQT7btxN6ZAaRWnxfMSIXu2wLkLlp/ra2xzP//Rtle3IzyGd7KrFo0z1FjueIaUKLTLaW1tZcwCn5bEhqtZ3pXsnnHknbAhvbvrashjcZ+DfbDy9m0ZKkXZS09Rbb4yV1Jz5utrb9l8UsWpIkSdJOMoKWJEnyMQYOkfQQsZDC2DTOks8Zs4ETSyThv4FT0zhLkiT5fJERtCRJkiRJkiRJkiYhI2hJkiRJkiRJkiRNQhpoSZIkSZIkSZIkTUIaaEmSJEmSJEmSJE1C7t2TJEmHIak/sRz4SoRD6HlghO1HO+BaOwKX2O79Cdp0Bb5H7FeyBLAU8FtiYYX/W0RyjQB62x4s6Spi4ZGJkq4ErrA9rXp+UVwzSZIk+WxIGg4cBSxJ7CU4HTjF9p9K+bPA/rYfXFwyLojaFhz1tumQtAaxLPxA262S1iG2XNoOmEOM+SbgNNtzin6dQCyk1UpsGj0X+HHZwgNJKxBb2+wIfFTqXWL76lJ+N7ER9htFjCWJZfTPsP1W2evs18C+the2vc0XnoygJUnSIUjqRuwR9u+2+xTDaTQwoRhGzcDlwLbAzmWzzm2ITX+v6oiL2R5aMcIGEEqu7fkkSZJkMSLpZ8B+wJ62NwU2A/4ATJa05mIVbtFwJWEYtRZjbQpwLyDbvYC+QE/gvEqbp2xvYXvLoi+PAMZIWkXS0sSqsS8AfUv53sBJko6o9HF86WMLYAtgeeAGANtvAWOIfXg7PRlBS5Kko1gGWIHYlLHGaOBNoKukHahEvKoRMEmnARsCawGrEZvqDrX9ZvFajiEMnBWA82xfXruApGUJJdHP9hPl3ETgYtu3VuqtS2weuZrtNwFsv1O8pl8tdboTmwpvQXgDJwAn254r6X3gp8DXiozn2r687J92UZHvf4FXKB7D4kG8BNgSWB0YLWkQcE4Z+28k7U1sEt+F2HD4+7bvL/dk3XKtdYA/A4fZfknSUcBw4APgfeDbtme366+UJEmSzKMYYMOBtWz/FcD2R8B1krYCTgK+U6p/R9LmQDdCF42StBxwDbAREUmaRryTP5L0DeCHRLbGu0RGyeTyft+W0AuPANsDe9ueVmS6Ebi76JhTCOOxC/AscLTtFyVtCowidO/jwLINxtcPWNX2A+XUicA421fW6th+W9IxwP6N7pPthyS9S+ijXsDbts+tlD8n6cAy1nrt50j6PvCypJ62HyciaOdIGmn7lUbX7gxkBC1Jkg6hKLYTgN9LelrS9cAQYKLtD9rRxb8ABxJevLnAqZWyHkS0a0fgdEmbVa77DnAtMBRA0gbAxkQ0r8pWwKM146zS/mXb48rhRcDrhPd0a2BzYEQp6wa8Zns7Qon9vHgRjy7X25Qw0tauc29OAV4Evml7au28pJ7AFcB+tjcvY75V0j+VKjsAB9juCbwDDC/RyAuA3WxvA/ySUO5JkiTJJ6cf8FjNOGvDROZ/v75nuy/xrj9bUi9gH2D5SlYGwPqSNgLOAvawvSUwDLi5OBUhDJ0tbR9CGFpDACStCOwC3FAcepsBXyn9387HGR+jgStt9wEuLP3V4wDm14c7AHe0rWT7JdsXN+gDSfsSBuhsQj/eW6eP6banNOqjpDI+UcaE7feBB4A9GrXpLKSBliRJh2H7fOCfgWOBl4AfADNKZGph3GT7leK5vBrYtVJ2qe1W2y8AvyeiWFUuAwaVaNYw4CrbH7ap8xELfwfuTkS2WsuctCvKuRq1iNx0wmBblqJIbX9QjMXR7RhrjZ2ASbafBrB9FxGF26qU310xKGcAPcq4bgLuk3QJ8DfifiVJkiSfjiUbnO9GZFPU+AWA7ReBO4GdgXuAXiVj4kTgAttPEkbcasCkspH8aEIPbVj6mmJ7bvk9CjhQ0lLAIcBttt8A9gT6Aw+WPr4LSNJKQB/guiLPvUQkrh49gScrxy3VMUk6XtLM8u9lST1K0QaV848SunWg7Xdpnz5tRCsRTazxDDHVoFOTKY5JknQIkr4KbGd7JOGtGy/pZEJpDABepczBKrRNg5hb+d0F+LCdZdh+QtLDwEBiAZB+dUScCmwiafmS+16Tew0iCrV/6buqjLswv+J+r1yvVRKV8VTHVZV1YXRtc72216xOnK5N1Mb2YZJ6E8bhicDhRPQxSZIk+WRMATaS9GXbL7cp+1fgvspxVfd0AebYfkbShkSGx07AREnDiPf7JNsH1RpIWovIptgHeLt2vqQHTicMsiHAcaWoK3BOLa2/zPVesSJDe3RPK/MbU/cVWceXa48ERpb+q3WfKlG7ekzh47TPeUjaC9jB9vH1GklaBtgEqC4cNoc2Or0zkhG0JEk6ileBH0qqpoOsBnQHZpXytSWtKqkFOLhN+4GSukvqAhxJrK5YYxCApLWJ6NmEOte/lFAy9xfv5nyUc6OBUbUUwvL/ZcDrJfXiDuAYSS1FEQ4jJooviAlE9G7pkvJ4UIN6c/l7L+0kYFdJ6xd5diLm4U2lAZJWlvR8kfkCYn7DNo3qJ0mSJI2x/WcivX1McdgBIGkIMffrnEr1waVsbcJBNqnMCb4GuNP2Dwg90pd4v3+tpLIjaQ/gYeBLDUS5ksg6WbZExCh9Da2kvZ8OXG/7dWKuWy21vy8lbbDeEIENKsdnEtG6QbUFvCQtUeaPQUTHFsY4oLukEyp9rA+cDzxWr4GkLxHp+RNsP1spWo+YQ9epSQMtSZIOoSzQsTdwVpmDNpuYADzEwWwiPeRBwvv2TJsuXiHy6x8jFtk4q1K2nqRpRHrjsbZdR4TxxAIlVyxAzKOJ/Pn7SrrI1HI8tJQfC6xKGJSzCMV25kKGXhvTI8SqVm3HVeNm4FeS5qVnlntyNDEv4RFiEZJvlNSWuth+DfgJ8WEwrbQ5ciEyJkmSJA2wfRLwK2IO8COS/kgYYNvafq5SdekS6bod+G7Re9cRka7Z5Z3cHbiovN+HAWMlPUSsVriX7bepz23EwlDVVYWvInTblJJm2IdiJBKpkAdLmgX8iAaGEfAbYLfKWF8g0ia3J6YgzCJSIPcB+tv+S+M7Na+PD4j70wuYVTJYxgE/sT2qUnVkSZGcTujbt4Fv1QpLSmd/5nfIdkpaWlvbZtMkSZIsXsqKVivbPqZO2bO0Y+8ZSdsSyqy37XzRJUmSJAkg6Q7gR7bvX9yyVJE0GOjVKCWyM5ERtCRJvnBIuhYYCxyRxlmSJEmSzMe3gVPL9IKmoGxPcChw2mIWpSnICFqSJEmSJEmSJEmTkBG0JEmSJEmSJEmSJiENtCRJkiRJkiRJkiYhDbQkSZIkSZIkSZImIQ20JEmSJEmSJEmSJiENtCRJkiRJkiRJkiYhDbQkSZIkSZIkSZIm4f8BMIpdpDV+LKkAAAAASUVORK5CYII=\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": []
  }
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