Main_Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "temporal-backup",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np             \n",
    "import pandas as pd           #Python Data Analysis Library handle data in a user friendly way\n",
    "#import random\n",
    "import matplotlib.pyplot as plt #for plots\n",
    "%matplotlib inline\n",
    "from sklearn import linear_model # A very popular Python library for Machine Learning\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import train_test_split #needed to for assessing prediction\n",
    "from sklearn import datasets ## imports datasets from scikit-learn\n",
    "import scipy as sc\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols \n",
    "from datetime import datetime\n",
    "from pandas.plotting import autocorrelation_plot\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "import pmdarima as pm\n",
    "from scipy.stats import norm\n",
    "import requests\n",
    "from io import BytesIO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "black-publicity",
   "metadata": {},
   "source": [
    "# Appendix\n",
    "## Introduction\n",
    "\n",
    "In the code below we implement the steps discussed in the main part of the report.\n",
    "\n",
    "First we introduce the necessary functios for calculating model evidence using Bayesian Linear Regression. For finding the best possible predictors, we use AIC, BIC and Bayesian Model Evidence.\n",
    "\n",
    "We find the coefficients for linear regression for the best chosen predictors. And perform prediction into the future, up to the year 2030 for the predictors, using corresponding models for them.\n",
    "\n",
    "We figured out, that the best predictors for our model in terms of AIC/BIC/Model Evidence and common sense are the ```(Petrol+Diesel)``` and ```UK Population```. Using  ```(Petrol+Diesel) = Total_Cars * (1-EV_Fraction)```, we focus on prediction of the ```EV_Fraction``` and ```UK_Population```. We obtain future values for UK population according to the prediction of the Office for National Statistics.\n",
    "\n",
    "The future values of the ```EV_Fraction``` are obtained from the SI model discussed below. We consider three scenarios for this model:\n",
    "1) UK predicted scenario with 43% of EV fraction on the road in 2030;\n",
    "\n",
    "2) Scenario where the current Social Awreness measured in Google trend scores affects the parameter $\\lambda$ of the model;\n",
    "\n",
    "3) Scenario where the UK follows the exact steps which Norway implemented for increasing the usage of the EV in the country in short time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inclusive-collection",
   "metadata": {},
   "source": [
    "## Predefine Necessary Functions\n",
    "##### Bayesian Linear Regression Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "isolated-moral",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for Bayesian Linear regression, return posterior mean and 95% CIs\n",
    "def BLR(X,y,mu_0,Om_0_inv,a_0,b_0,ind_names,N):\n",
    "    col_names = ['posterior mean','lower 95% bound','upper 95% bound']\n",
    "    n,p = X.shape\n",
    "    XtX = X.T.dot(X)\n",
    "    Om_n_inv = XtX + Om_0_inv\n",
    "    Om_n = sc.linalg.inv(Om_n_inv)\n",
    "    term1 = Om_0_inv.dot(mu_0)+X.T.dot(y)\n",
    "    mu_n = Om_n.dot(term1)\n",
    "    a_n = a_0 + n/2\n",
    "    term2 = y.T.dot(y)+mu_0.dot(Om_0_inv.dot(mu_0))+mu_n.dot(Om_n_inv.dot(mu_n))\n",
    "    b_n = b_0 + term2/2\n",
    "    sigma2 = 1/np.random.gamma(a_n, 1/b_n, N)\n",
    "    betas = np.zeros((N,p))\n",
    "    # draw N samples from the marginal posterior of beta\n",
    "    for i in range(0,N):\n",
    "        s2 =sigma2[i]\n",
    "        cov = s2*Om_n\n",
    "        betas[i,] = np.random.multivariate_normal(mu_n,cov,1)\n",
    "\n",
    "    #find the mean of each column which corresponds to each beta coefficient     \n",
    "    mu_beta = np.mean(betas, axis=0)\n",
    "    #find the 2.5 and 97.5 percentils which correspond to each beta coefficient \n",
    "    lower95 = np.percentile(betas,2.5,axis=0)\n",
    "    upper95 = np.percentile(betas,97.5,axis=0)\n",
    "    results = np.column_stack([mu_beta,lower95,upper95])\n",
    "    results = pd.DataFrame(results,columns = col_names,index=ind_names)\n",
    "    return results, mu_n, Om_n, a_n, b_n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "royal-mirror",
   "metadata": {},
   "source": [
    "##### Model Evidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "unknown-following",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for Bayes Model Evidence\n",
    "def ModEvidence(X,y,mu_n,Om_n,mu_0,Om_0_inv,a_n,b_n,a_0,b_0): \n",
    "    beta = mu_n\n",
    "    sigma2 = b_n/(a_n-1)\n",
    "    Om_0 = sc.linalg.inv(Om_0_inv)\n",
    "    mu = X.dot(beta)\n",
    "    cov = sigma2*np.eye(X.shape[0])\n",
    "    ll = sc.stats.multivariate_normal.logpdf(y,mu,cov)\n",
    "    # evaluate log-prior\n",
    "    lprior = a_0*np.log(b_0)-sc.special.loggamma(a_0)- (a_0+1)*np.log(sigma2)-b_0/sigma2\n",
    "    lprior = lprior + sc.stats.multivariate_normal.logpdf(beta,mu_0,sigma2*Om_0)\n",
    "    # evaluate log-posterior\n",
    "    lpost = a_n*np.log(b_n)-sc.special.loggamma(a_n)- (a_n+1)*np.log(sigma2)-b_n/sigma2\n",
    "    lpost = lpost + sc.stats.multivariate_normal.logpdf(beta,mu_n,sigma2*Om_n)\n",
    "    lmodevid = ll+lprior-lpost\n",
    "    return lmodevid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optical-tattoo",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "american-grammar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_CO2_kilotonne</th>\n",
       "      <th>Total_GHGs_kilotonne</th>\n",
       "      <th>Total_Cars</th>\n",
       "      <th>Total_ULEV_cars</th>\n",
       "      <th>Petrol</th>\n",
       "      <th>Diesel</th>\n",
       "      <th>All_EV</th>\n",
       "      <th>All_ULEV_veh133</th>\n",
       "      <th>ULEV_fraction</th>\n",
       "      <th>UK_Population</th>\n",
       "      <th>Total_Vehicle_km</th>\n",
       "      <th>Average_CO2_(g-km)</th>\n",
       "      <th>Average _CO2_cars_first_registered(g-km)</th>\n",
       "      <th>Total_CO2_(NAE_data)</th>\n",
       "      <th>EV_fraction</th>\n",
       "      <th>P_D</th>\n",
       "      <th>Total_co2</th>\n",
       "      <th>Petrol_fraction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</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",
       "      <th></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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>113648.313</td>\n",
       "      <td>23757800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19620898.0</td>\n",
       "      <td>1576196.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57943000.0</td>\n",
       "      <td>4.215000e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30280.662</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>21197094.0</td>\n",
       "      <td>30280.662</td>\n",
       "      <td>0.825872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>112645.659</td>\n",
       "      <td>23939800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19499763.0</td>\n",
       "      <td>1891271.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58095000.0</td>\n",
       "      <td>4.297000e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29976.864</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>21391034.0</td>\n",
       "      <td>29976.864</td>\n",
       "      <td>0.814533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>116737.339</td>\n",
       "      <td>24841300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20051602.0</td>\n",
       "      <td>2181634.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58239000.0</td>\n",
       "      <td>4.411000e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31146.438</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>22233236.0</td>\n",
       "      <td>31146.438</td>\n",
       "      <td>0.807188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total_CO2_kilotonne  Total_GHGs_kilotonne  Total_Cars  Total_ULEV_cars  \\\n",
       "Year                                                                           \n",
       "1994                  NaN            113648.313    23757800              NaN   \n",
       "1995                  NaN            112645.659    23939800              NaN   \n",
       "1996                  NaN            116737.339    24841300              NaN   \n",
       "\n",
       "          Petrol     Diesel  All_EV  All_ULEV_veh133  ULEV_fraction  \\\n",
       "Year                                                                  \n",
       "1994  19620898.0  1576196.0    93.0              NaN            NaN   \n",
       "1995  19499763.0  1891271.0    74.0              NaN            NaN   \n",
       "1996  20051602.0  2181634.0    94.0              NaN            NaN   \n",
       "\n",
       "      UK_Population  Total_Vehicle_km  Average_CO2_(g-km)  \\\n",
       "Year                                                        \n",
       "1994     57943000.0      4.215000e+11                 NaN   \n",
       "1995     58095000.0      4.297000e+11                 NaN   \n",
       "1996     58239000.0      4.411000e+11                 NaN   \n",
       "\n",
       "      Average _CO2_cars_first_registered(g-km)  Total_CO2_(NAE_data)  \\\n",
       "Year                                                                   \n",
       "1994                                       NaN             30280.662   \n",
       "1995                                       NaN             29976.864   \n",
       "1996                                       NaN             31146.438   \n",
       "\n",
       "      EV_fraction         P_D  Total_co2  Petrol_fraction  \n",
       "Year                                                       \n",
       "1994     0.000004  21197094.0  30280.662         0.825872  \n",
       "1995     0.000003  21391034.0  29976.864         0.814533  \n",
       "1996     0.000004  22233236.0  31146.438         0.807188  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/GB_all_data.csv')\n",
    "data = data.set_index('Year')\n",
    "data = data.fillna(method = 'ffill')\n",
    "data['EV_fraction'] = data['All_EV']/data['Total_Cars']\n",
    "data['P_D']=data['Petrol']+data['Diesel']\n",
    "data['Total_co2']= data['Total_CO2_(NAE_data)']\n",
    "data['Petrol_fraction']=data['Petrol']/data['Total_Cars']\n",
    "data.head(3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floppy-calvin",
   "metadata": {},
   "source": [
    "### Standardize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ranking-attraction",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = np.array(data.T) # obtain an array from the dataframe\n",
    "std_mat = np.std(mat, axis = 1) # array of standard defviations\n",
    "mean_mat = np.mean(mat, axis = 1) # array of means\n",
    "\n",
    "mat_stand = (mat-mean_mat.reshape(-1,1))/std_mat.reshape(-1,1) # standardize the data\n",
    "\n",
    "standard_data = pd.DataFrame(mat_stand.T, index = data.index, #transfer back to dataframe\n",
    "                             columns = data.columns)\n",
    "\n",
    "std_mat = pd.DataFrame(std_mat.reshape(1,-1), \n",
    "                             columns = data.columns)\n",
    "\n",
    "mean_mat = pd.DataFrame(mean_mat.reshape(1,-1), \n",
    "                             columns = data.columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "christian-morocco",
   "metadata": {},
   "source": [
    "### Obtain the predictors\n",
    "\n",
    "Since there are too less samples, we consider juts two predictors for the model. We will consider Bayes Model Evidence as one of the approaches to choose the pair of predictors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "classified-feature",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = ['All_EV','EV_fraction','Total_Cars','UK_Population','P_D','Petrol_fraction'] #list of predictors\n",
    "pairs = [[a,b] for a in predictors for b in predictors if a<b] # split into pairs\n",
    "\n",
    "mask_train = data.index<2019\n",
    "# y = standard_data['Total_GHGs_kilotonne'][mask_train]\n",
    "y = standard_data['Total_co2'][mask_train]\n",
    "a_0 = 0.1\n",
    "b_0 = 0.1\n",
    "N = 10000 #Monte Carlo sample size\n",
    "\n",
    "K = len(pairs)\n",
    "Evidence = np.ones(K)\n",
    "\n",
    "for i in range(K):\n",
    "    predictors = pairs[i]\n",
    "    X = standard_data[predictors][mask_train]\n",
    "    n,p = X.shape\n",
    "    mu_0 = np.zeros(p)\n",
    "    XtX = X.T.dot(X)\n",
    "    Om_0_inv = XtX/n #unit information prior\n",
    "    results, mu_n, Om_n, a_n, b_n = BLR(X,y,mu_0,Om_0_inv,a_0,b_0,predictors,N)   \n",
    "    Evidence[i] = ModEvidence(X,y,mu_n,Om_n,mu_0,Om_0_inv,a_n,b_n,a_0,b_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "optimum-findings",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table for the Model Evidence\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictors</th>\n",
       "      <th>Evidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[Petrol_fraction, Total_Cars]</td>\n",
       "      <td>-39.594235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[Total_Cars, UK_Population]</td>\n",
       "      <td>-39.620303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[P_D, Petrol_fraction]</td>\n",
       "      <td>-39.640187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[P_D, UK_Population]</td>\n",
       "      <td>-39.684615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[All_EV, EV_fraction]</td>\n",
       "      <td>-39.976824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Predictors   Evidence\n",
       "13  [Petrol_fraction, Total_Cars] -39.594235\n",
       "9     [Total_Cars, UK_Population] -39.620303\n",
       "12         [P_D, Petrol_fraction] -39.640187\n",
       "11           [P_D, UK_Population] -39.684615\n",
       "0           [All_EV, EV_fraction] -39.976824"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evidence_table\n",
    "model_evidence = pd.DataFrame([[a,b] for a,b in zip(pairs,Evidence)], \n",
    "                              columns = ['Predictors','Evidence']).sort_values('Evidence', axis = 0, \n",
    "                                                                               ascending = False)\n",
    "print('Table for the Model Evidence')\n",
    "model_evidence.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "monetary-envelope",
   "metadata": {},
   "source": [
    "### AIC and BIC for model selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fresh-conservation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total_co2~All_EV+EV_fraction 67.63650100460357\n",
      "Total_co2~All_EV+Total_Cars 79.72224269316641\n",
      "Total_co2~All_EV+UK_Population 76.3860738115662\n",
      "Total_co2~All_EV+P_D 79.70999134756876\n",
      "Total_co2~All_EV+Petrol_fraction 77.03776140178539\n",
      "Total_co2~EV_fraction+Total_Cars 79.34884880033474\n",
      "Total_co2~EV_fraction+UK_Population 76.49369787527468\n",
      "Total_co2~EV_fraction+P_D 79.33248902803794\n",
      "Total_co2~EV_fraction+Petrol_fraction 77.0743939118195\n",
      "Total_co2~Total_Cars+UK_Population 51.879858700370924\n",
      "Total_co2~P_D+Total_Cars 78.70759150605552\n",
      "Total_co2~P_D+UK_Population 58.806082560918\n",
      "Total_co2~P_D+Petrol_fraction 52.29274635982891\n",
      "Total_co2~Petrol_fraction+Total_Cars 46.87492891198832\n",
      "Total_co2~Petrol_fraction+UK_Population 76.5192105354318\n"
     ]
    }
   ],
   "source": [
    "target = 'Total_co2'\n",
    "select_list = []\n",
    "\n",
    "for i in range(K):\n",
    "    predictors = pairs[i]\n",
    "    formula=\"{}~{}\".format(target,\"+\".join(predictors))  \n",
    "    aic=ols(formula=formula,data=standard_data).fit().aic \n",
    "    bic=ols(formula=formula,data=standard_data).fit().bic\n",
    "    formula_select = [formula,aic,bic]\n",
    "    select_list.append(formula_select)\n",
    "    print(formula,aic)\n",
    "min_aic = min([i[1] for i in select_list] )   \n",
    "min_bic = min([i[2] for i in select_list] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cosmetic-algebra",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smallest aic 46.87492891198832 Total_co2~Petrol_fraction+Total_Cars\n",
      "smallest bic 50.76243951000131 Total_co2~Petrol_fraction+Total_Cars\n"
     ]
    }
   ],
   "source": [
    "for i in select_list:\n",
    "    if i[1] == min_aic:\n",
    "        print('smallest aic',min_aic,i[0])\n",
    "    if i[2] == min_bic:\n",
    "        print('smallest bic',min_bic,i[0])     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "athletic-disclaimer",
   "metadata": {},
   "source": [
    "## Main model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "described-watts",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Total_co2</td>    <th>  R-squared:         </th> <td>   0.586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.552</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   16.99</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 17 Mar 2021</td> <th>  Prob (F-statistic):</th> <td>2.53e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:11:50</td>     <th>  Log-Likelihood:    </th> <td> -26.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    27</td>      <th>  AIC:               </th> <td>   58.81</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    24</td>      <th>  BIC:               </th> <td>   62.69</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>         <td>-2.203e-15</td> <td>    0.131</td> <td>-1.68e-14</td> <td> 1.000</td> <td>   -0.271</td> <td>    0.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>UK_Population</th> <td>   -2.2504</td> <td>    0.402</td> <td>   -5.602</td> <td> 0.000</td> <td>   -3.079</td> <td>   -1.421</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>P_D</th>           <td>    1.9151</td> <td>    0.402</td> <td>    4.767</td> <td> 0.000</td> <td>    1.086</td> <td>    2.744</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.384</td> <th>  Durbin-Watson:     </th> <td>   0.446</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.304</td> <th>  Jarque-Bera (JB):  </th> <td>   1.793</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.461</td> <th>  Prob(JB):          </th> <td>   0.408</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.137</td> <th>  Cond. No.          </th> <td>    5.95</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              Total_co2   R-squared:                       0.586\n",
       "Model:                            OLS   Adj. R-squared:                  0.552\n",
       "Method:                 Least Squares   F-statistic:                     16.99\n",
       "Date:                Wed, 17 Mar 2021   Prob (F-statistic):           2.53e-05\n",
       "Time:                        14:11:50   Log-Likelihood:                -26.403\n",
       "No. Observations:                  27   AIC:                             58.81\n",
       "Df Residuals:                      24   BIC:                             62.69\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "const         -2.203e-15      0.131  -1.68e-14      1.000      -0.271       0.271\n",
       "UK_Population    -2.2504      0.402     -5.602      0.000      -3.079      -1.421\n",
       "P_D               1.9151      0.402      4.767      0.000       1.086       2.744\n",
       "==============================================================================\n",
       "Omnibus:                        2.384   Durbin-Watson:                   0.446\n",
       "Prob(Omnibus):                  0.304   Jarque-Bera (JB):                1.793\n",
       "Skew:                          -0.461   Prob(JB):                        0.408\n",
       "Kurtosis:                       2.137   Cond. No.                         5.95\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "predictors = ['UK_Population', 'P_D']\n",
    "\n",
    "y = standard_data['Total_co2']\n",
    "\n",
    "X = standard_data[predictors]\n",
    "X = sm.add_constant(X) \n",
    "model = sm.OLS(y, X).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "independent-sperm",
   "metadata": {},
   "source": [
    "## ARIMA Model for Total Cars\n",
    "\n",
    "For predicting the Petrol+Diesel, we first build ARIMA model for Total cars prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "impressed-findings",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f8df7e610>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main = pd.read_csv('data/data_quaterly.csv')\n",
    "main['time']= pd.to_datetime(main['Quarter'].str.replace(' ','-'))#.replace()pd.to_datetime(df['Quarter'])\n",
    "main = main.set_index('time')\n",
    "# main[' Cars'] = main[' Cars'].str.replace('..','')\n",
    "cars = main['Total']\n",
    "cars = cars.fillna(method = 'ffill').dropna(axis=0)\n",
    "cars = cars[~cars.isin(['..'])]\n",
    "cars = cars.astype('float')\n",
    "\n",
    "plt.plot(cars)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "excessive-tractor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -2.719409\n",
      "p-value: 0.070739\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the stationarity\n",
    "from statsmodels.tsa.stattools import adfuller,arma_order_select_ic\n",
    "from numpy import log\n",
    "\n",
    "car_diff = cars.diff().dropna()\n",
    "car_diff_4 = cars.diff(4).dropna()\n",
    "plt.plot(car_diff,label='diff1')\n",
    "plt.plot(car_diff_4,label='diff_season')\n",
    "plt.legend(loc='upper left', fontsize=8)\n",
    "\n",
    "result = adfuller(car_diff)\n",
    "print('ADF Statistic: %f' % result[0])\n",
    "print('p-value: %f' % result[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "supposed-adapter",
   "metadata": {},
   "source": [
    "The ADF Statistic is far from the critical values and the p-value is greater than the threshold (0.05).\n",
    "Thus, we can conclude that the time series is not stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "meaning-being",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=1381.784, Time=0.20 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=1385.704, Time=0.02 sec\n",
      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=1385.998, Time=0.07 sec\n",
      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=1384.719, Time=0.17 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=1417.957, Time=0.02 sec\n",
      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=1383.495, Time=0.22 sec\n",
      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=1386.184, Time=0.18 sec\n",
      " ARIMA(0,1,2)(0,0,0)[0] intercept   : AIC=1384.191, Time=0.11 sec\n",
      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=1382.622, Time=0.11 sec\n",
      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=1355.067, Time=0.34 sec\n",
      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=1356.395, Time=0.53 sec\n",
      " ARIMA(2,1,3)(0,0,0)[0] intercept   : AIC=1358.877, Time=0.81 sec\n",
      " ARIMA(1,1,3)(0,0,0)[0] intercept   : AIC=1370.111, Time=0.45 sec\n",
      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=1358.661, Time=0.39 sec\n",
      " ARIMA(3,1,3)(0,0,0)[0] intercept   : AIC=1356.776, Time=0.95 sec\n",
      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=1386.030, Time=0.32 sec\n",
      "\n",
      "Best model:  ARIMA(2,1,2)(0,0,0)[0] intercept\n",
      "Total fit time: 4.926 seconds\n",
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                  104\n",
      "Model:               SARIMAX(2, 1, 2)   Log Likelihood                -671.533\n",
      "Date:                Wed, 17 Mar 2021   AIC                           1355.067\n",
      "Time:                        14:11:56   BIC                           1370.875\n",
      "Sample:                             0   HQIC                          1361.470\n",
      "                                - 104                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "intercept    199.5762     30.387      6.568      0.000     140.020     259.133\n",
      "ar.L1          0.1286      0.046      2.825      0.005       0.039       0.218\n",
      "ar.L2         -0.9352      0.045    -20.727      0.000      -1.024      -0.847\n",
      "ma.L1         -0.2915      0.089     -3.272      0.001      -0.466      -0.117\n",
      "ma.L2          0.8626      0.078     11.012      0.000       0.709       1.016\n",
      "sigma2      2.558e+04   3057.108      8.366      0.000    1.96e+04    3.16e+04\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.39   Jarque-Bera (JB):                22.88\n",
      "Prob(Q):                              0.53   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.50   Skew:                             0.71\n",
      "Prob(H) (two-sided):                  0.05   Kurtosis:                         4.82\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
    "                        FutureWarning)\n",
    "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
    "                        FutureWarning)\n",
    "model = pm.auto_arima(cars, start_p=1, start_q=1,\n",
    "                      test='adf',       # use adftest to find optimal 'd'\n",
    "                      max_p=3, max_q=3, # maximum p and q\n",
    "                      m=1,              # frequency of series\n",
    "                      d=1,           # set the difference as 1\n",
    "                      seasonal=False,   # No Seasonality\n",
    "                      start_P=0, \n",
    "                      D=0, \n",
    "                      trace=True,\n",
    "                      error_action='ignore',  \n",
    "                      suppress_warnings=True, \n",
    "                      stepwise=True)\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "# arma_order_select_ic(car_diff, max_ar=3, max_ma=3, ic='aic', trend='c', model_kw=None, fit_kw=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "elect-student",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Autocorrelation')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5), dpi=100)\n",
    "plot_acf(car_diff)\n",
    "plt.xlabel('Lags', fontsize=10)\n",
    "plt.ylabel('Autocorrelation', fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "accompanied-corrections",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Partial Autocorrelation')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(car_diff)\n",
    "plt.xlabel('Lags', fontsize=10)\n",
    "plt.ylabel('Partial Autocorrelation', fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "constant-memorabilia",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                D.Total   No. Observations:                  103\n",
      "Model:                 ARIMA(2, 1, 2)   Log Likelihood                -671.799\n",
      "Method:                       css-mle   S.D. of innovations            162.766\n",
      "Date:                Wed, 17 Mar 2021   AIC                           1355.598\n",
      "Time:                        14:11:57   BIC                           1371.406\n",
      "Sample:                    01-01-1995   HQIC                          1362.001\n",
      "                         - 07-01-2020                                         \n",
      "=================================================================================\n",
      "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "const           124.1744     13.461      9.225      0.000      97.791     150.558\n",
      "ar.L1.D.Total     0.1436      0.049      2.902      0.004       0.047       0.241\n",
      "ar.L2.D.Total    -0.9499      0.038    -25.123      0.000      -1.024      -0.876\n",
      "ma.L1.D.Total    -0.3518      0.101     -3.498      0.000      -0.549      -0.155\n",
      "ma.L2.D.Total     0.8661      0.067     12.860      0.000       0.734       0.998\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            0.0756           -1.0233j            1.0261           -0.2383\n",
      "AR.2            0.0756           +1.0233j            1.0261            0.2383\n",
      "MA.1            0.2031           -1.0551j            1.0745           -0.2197\n",
      "MA.2            0.2031           +1.0551j            1.0745            0.2197\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                D.Total   No. Observations:                   68\n",
      "Model:                 ARIMA(1, 1, 0)   Log Likelihood                -459.824\n",
      "Method:                       css-mle   S.D. of innovations            209.123\n",
      "Date:                Wed, 17 Mar 2021   AIC                            925.649\n",
      "Time:                        14:11:58   BIC                            932.307\n",
      "Sample:                    01-01-1995   HQIC                           928.287\n",
      "                         - 10-01-2011                                         \n",
      "=================================================================================\n",
      "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "const           124.8365     21.444      5.821      0.000      82.807     166.866\n",
      "ar.L1.D.Total    -0.1857      0.120     -1.544      0.123      -0.421       0.050\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1           -5.3852           +0.0000j            5.3852            0.5000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8036052265097938"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build the arima\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "# 2,1,2 ARIMA Model\n",
    "model = ARIMA(cars, order=(2,1,2))\n",
    "model_fit = model.fit(disp=0)\n",
    "print(model_fit.summary())\n",
    "\n",
    "# Plot residual errors\n",
    "residuals = pd.DataFrame(model_fit.resid)\n",
    "fig, ax = plt.subplots(1,2)\n",
    "residuals.plot(title=\"Residuals\", ax=ax[0])\n",
    "residuals.plot(kind='kde', title='Density', ax=ax[1])\n",
    "plt.show()\n",
    "\n",
    "# Actual vs Fitted\n",
    "model_fit.plot_predict(dynamic=False)\n",
    "plt.show()\n",
    "\n",
    "#predict\n",
    "fc, se, conf = model_fit.forecast(4*10, alpha=0.05)  # 95% conf \n",
    "\n",
    "#if we spilit the data as training and testing data, and calculate the MAE\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\n",
    "\n",
    "test = cars[np.round(2/3*len(cars)).astype(int):]\n",
    "train = cars[:np.round(2/3*len(cars)).astype(int)]\n",
    "model = ARIMA(train, order=(1,1,0))\n",
    "model_fit = model.fit(disp=0)\n",
    "print(model_fit.summary())\n",
    "\n",
    "# Forecast\n",
    "fc, se, conf = model_fit.forecast(len(test), alpha=0.05)  # 95% conf\n",
    "\n",
    "# Make as pandas series\n",
    "fc_series = pd.Series(fc, index=test.index)\n",
    "lower_series = pd.Series(conf[:, 0], index=test.index)\n",
    "upper_series = pd.Series(conf[:, 1], index=test.index)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(12,5), dpi=100)\n",
    "plt.plot(train, label='training')\n",
    "plt.plot(test, label='actual')\n",
    "plt.plot(fc_series, label='forecast')\n",
    "\n",
    "\n",
    "plt.fill_between(lower_series.index, lower_series, upper_series, \n",
    "                 color='k', alpha=.15)\n",
    "plt.title('Forecast vs Actuals')\n",
    "plt.legend(loc='upper left', fontsize=8)\n",
    "plt.show()\n",
    "\n",
    "#measure the performance of predict\n",
    "mean_absolute_percentage_error(test, fc) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "senior-elephant",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2020-10-01', '2020-12-31', '2021-04-01', '2021-07-01',\n",
      "               '2021-10-01', '2021-12-31', '2022-04-01', '2022-07-01',\n",
      "               '2022-10-01', '2022-12-31', '2023-04-01', '2023-07-01',\n",
      "               '2023-10-01', '2023-12-31', '2024-03-31', '2024-06-30',\n",
      "               '2024-09-30', '2024-12-30', '2025-03-31', '2025-06-30',\n",
      "               '2025-09-30', '2025-12-30', '2026-03-31', '2026-06-30',\n",
      "               '2026-09-30', '2026-12-30', '2027-03-31', '2027-06-30',\n",
      "               '2027-09-30', '2027-12-30', '2028-03-30', '2028-06-29',\n",
      "               '2028-09-29', '2028-12-29', '2029-03-30', '2029-06-29',\n",
      "               '2029-09-29', '2029-12-29', '2030-03-30', '2030-06-29',\n",
      "               '2030-09-29'],\n",
      "              dtype='datetime64[ns]', freq=None)\n"
     ]
    }
   ],
   "source": [
    "#create the datetime index for predict\n",
    "import datetime\n",
    "\n",
    "def create_assist_date(datestart = None,dateend = None):\n",
    "    datestart=datetime.datetime.strptime(datestart,'%Y-%m-%d')\n",
    "    dateend=datetime.datetime.strptime(dateend,'%Y-%m-%d')\n",
    "    date_list = []\n",
    "    date_list.append(datestart.strftime('%Y-%m-%d'))\n",
    "    while datestart<dateend:\n",
    "        datestart+=datetime.timedelta(days = 365/4)\n",
    "        date_list.append(datestart.strftime('%Y-%m-%d'))\n",
    "    return date_list\n",
    "\n",
    "len(create_assist_date('2020-10-01','2030-10-01'))\n",
    "new_index = pd.to_datetime(create_assist_date('2020-10-01','2030-09-29')) #2030-12-29 equals to the 2031-01-01\n",
    "print(new_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "happy-messaging",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                D.Total   No. Observations:                  103\n",
      "Model:                 ARIMA(2, 1, 2)   Log Likelihood                -671.799\n",
      "Method:                       css-mle   S.D. of innovations            162.766\n",
      "Date:                Wed, 17 Mar 2021   AIC                           1355.598\n",
      "Time:                        14:11:59   BIC                           1371.406\n",
      "Sample:                    01-01-1995   HQIC                          1362.001\n",
      "                         - 07-01-2020                                         \n",
      "=================================================================================\n",
      "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "const           124.1744     13.461      9.225      0.000      97.791     150.558\n",
      "ar.L1.D.Total     0.1436      0.049      2.902      0.004       0.047       0.241\n",
      "ar.L2.D.Total    -0.9499      0.038    -25.123      0.000      -1.024      -0.876\n",
      "ma.L1.D.Total    -0.3518      0.101     -3.498      0.000      -0.549      -0.155\n",
      "ma.L2.D.Total     0.8661      0.067     12.860      0.000       0.734       0.998\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            0.0756           -1.0233j            1.0261           -0.2383\n",
      "AR.2            0.0756           +1.0233j            1.0261            0.2383\n",
      "MA.1            0.2031           -1.0551j            1.0745           -0.2197\n",
      "MA.2            0.2031           +1.0551j            1.0745            0.2197\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
    "                        FutureWarning)\n",
    "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
    "                        FutureWarning)\n",
    "\n",
    "#predict\n",
    "model = ARIMA(cars, order=(2,1,2))\n",
    "model_fit = model.fit(disp=0)\n",
    "print(model_fit.summary())\n",
    "\n",
    "# model with whole data and Forecast until 2030\n",
    "fc, se, conf = model_fit.forecast(4*10+1, alpha=0.05)  # 95% conf\n",
    "\n",
    "# Make as pandas series\n",
    "fc_series = pd.Series(fc, index=new_index)\n",
    "lower_series = pd.Series(conf[:, 0], index=new_index)\n",
    "upper_series = pd.Series(conf[:, 1], index=new_index)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(12,5), dpi=100)\n",
    "plt.plot(cars, label='actual')\n",
    "plt.plot(fc_series, label='forecast')\n",
    "\n",
    "plt.fill_between(lower_series.index, lower_series, upper_series, \n",
    "                 color='k', alpha=.15)\n",
    "plt.title('Actuals & Forecast')\n",
    "plt.legend(loc='upper left', fontsize=8)\n",
    "plt.show()\n",
    "\n",
    "#the predicted value of number of cars at the begining of 2030 is \n",
    "predict_2030 = fc_series['2029-12-29']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "twenty-confidentiality",
   "metadata": {},
   "source": [
    "##  Total Cars Prediction until 2030\n",
    "\n",
    "```fc_year_1``` is the prediction from 2020 Q4 to 2030."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "empirical-browse",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0\n",
      "0   36375.183716\n",
      "1   36852.722540\n",
      "2   37347.097967\n",
      "3   37841.919033\n",
      "4   38337.266959\n",
      "5   38833.161780\n",
      "6   39329.571967\n",
      "7   39826.426783\n",
      "8   40323.629756\n",
      "9   40821.071910\n",
      "10  41318.643617\n"
     ]
    }
   ],
   "source": [
    "# transform the prediction of number of vehicles from quarterly to yearly\n",
    "\n",
    "car_num_series = pd.concat([cars.iloc[[-3,-2,-1]],fc_series])\n",
    "\n",
    "fc_year = []\n",
    "for i in range(0, len(car_num_series), 4):\n",
    "    fc_year.append(car_num_series[i:i+4])\n",
    "\n",
    "fc_year_1 =[np.mean(i) for i in fc_year]\n",
    "\n",
    "fc_year_1 = pd.DataFrame(fc_year_1)\n",
    "print(fc_year_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "harmful-satellite",
   "metadata": {},
   "source": [
    "### ARIMAX try: taking into account COVID19\n",
    "\n",
    "We use this model to obtain predictions with COVID19. See details in the main body of the report.\n",
    "\n",
    "refer to https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_sarimax_stata.html\n",
    "\n",
    "https://www.kaggle.com/poiupoiu/how-to-use-sarimax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "prospective-collaboration",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                  104\n",
      "Model:               SARIMAX(2, 1, 2)   Log Likelihood                -685.150\n",
      "Date:                Wed, 17 Mar 2021   AIC                           1384.300\n",
      "Time:                        14:12:01   BIC                           1402.743\n",
      "Sample:                             0   HQIC                          1391.770\n",
      "                                - 104                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.1553   2.69e+04  -5.78e-06      1.000   -5.26e+04    5.26e+04\n",
      "x1          -326.5778    251.699     -1.297      0.194    -819.899     166.744\n",
      "ar.L1          0.0460      0.053      0.865      0.387      -0.058       0.150\n",
      "ar.L2          0.9491      0.056     17.032      0.000       0.840       1.058\n",
      "ma.L1         -0.1026      0.099     -1.040      0.298      -0.296       0.091\n",
      "ma.L2         -0.8435      0.098     -8.647      0.000      -1.035      -0.652\n",
      "sigma2      3.376e+04   4555.481      7.412      0.000    2.48e+04    4.27e+04\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                36.82\n",
      "Prob(Q):                              0.96   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.45   Skew:                             1.02\n",
      "Prob(H) (two-sided):                  0.02   Kurtosis:                         5.11\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firuzamamedova/opt/anaconda3/lib/python3.8/site-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
    "                        FutureWarning)\n",
    "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
    "                        FutureWarning)\n",
    "\n",
    "dataAX = pd.DataFrame(data=cars, index=cars.index, columns=['covid'])\n",
    "dataAX['time']=dataAX.index\n",
    "\n",
    "#30-01-2020 we assume that it start from the first quarter of 2020 in UK\n",
    "date_point = pd.to_datetime('2020Q1')\n",
    "dataAX.loc[(dataAX['time'] >= date_point), 'covid'] = 1;\n",
    "dataAX.loc[(dataAX['time'] < date_point), 'covid'] = 0;\n",
    "covid = dataAX['covid']\n",
    "\n",
    "covid_future = np.ones([41,2])\n",
    "\n",
    "#if we assume the affect will be be removed from 2021Q4, as the new lock down plan said step four june 2021 remove all legal limits on social contact\n",
    "covid_positive_future = np.ones([5,2])\n",
    "a = np.ones([36,1])\n",
    "b = np.zeros([36,1])\n",
    "m = np.concatenate((a,b),axis=1)\n",
    "covid_positive = np.concatenate((covid_positive_future,m),axis=0)\n",
    "\n",
    "# Variables\n",
    "endog = cars\n",
    "exog = sm.add_constant(covid)\n",
    "endog = np.array(endog, dtype=float)\n",
    "exog  = np.array(exog , dtype=float)\n",
    "# Fit the model\n",
    "mod = sm.tsa.statespace.SARIMAX(endog,exog, order=(2,1,2))\n",
    "res = mod.fit(disp=False)\n",
    "print(res.summary())\n",
    "\n",
    "fc_covid = res.forecast(exog=covid_future ,steps=4*10+1, alpha=0.05)\n",
    "fc_covid_pos = res.forecast(exog=covid_positive ,steps=4*10+1, alpha=0.05)\n",
    "\n",
    "# Make as pandas series\n",
    "fc_series_covid = pd.Series(fc_covid, index=new_index)\n",
    "fc_series_covid_pos = pd.Series(fc_covid_pos, index=new_index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "posted-accordance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xAp/PB8Bdd93FvHnzWL16dZfOf9555/Hxj38cYwy7d+9m4cKF7Nmzh4SEhKO6LhEREREREek9SggMUw8++CBLliwhEAjQ3NxMUVERKSkpXHXVVYwYMQKA1NTUHh17165dLF68mH379uFyuaiurmbPnj1MnDixNy9BREREREREjoISAv0sJcHJxlvO6/NzHM7rr7/OnXfeyfLly8nPz+epp57illtu6dY5XC4XkUik9b3f7299fcUVV3Dbbbdx2WWXAZCTk9Nuu4iIiIiIiAw81RDoZ8YYUhNdfbocaWq5uro63G43OTk5BINB/vznPwNw8cUXc//991NeXg6Az+fD5/Phdrvx+XyEw+HWY0yYMIGVK1cCsR4Br7/+ervjjx07FoAHHniAurq63vwIRUREREREpBcoITAMXXDBBUycOJGpU6dy3nnnMXfuXABOO+00br75ZhYtWsScOXM4/fTTqaqqIicnh8WLFzNr1qzWooI33XQTL730EieccALf+973OOmkk1qPf/vtt/PBD36QU045hTVr1jB69OiBuEwREREREZEeCYVClJeX09jYONCh9CnTlYr00jXGGDfg8Xg8uN1uINaVfteuXYwbN47k5OSBDVCGPX0fRUREREQOzVpLXV0d+/bto76+nqlTp1JQUDDQYR2W1+slMzMTINNa6+3OvqohICIiIiIiIsOez+fjwIEDlJeX43INj1vl4XGVIiIiIiIiIp0Ih8NUV1ezb98+mpubyc7OJjExkYqKioEOrc8pISAiIiIiIiLDSiQSobGxEa/XS1VVFY2NjaSlpVFYWDjQofUrJQRERERERERkyAuHwzQ1NbUmAXw+H9ZaUlNTyc/Px+k8/PTtQ5ESAiIiIiIiIjKkWGsJBoM0NzfT3NyMx+OhqamJ5ubm1iRAbm7usEwCtKWEgIiIiIiIiAwJ4XCY2tpaKisr8fl8BAIBABISEkhKSlISoAMlBEREREREROSYFolEqK2t5cCBA3g8HpKSkkhJSSEzMxNjzECHN2g5BjqAFsaY7xhjrDHmd/H3CcaYXxhj1hljmowxB4wx9xtjijvstyy+X9vl4Q5tso0xS4wxnviyxBiT1aHNaGPM0/FzVRtj7jDGJPb1dQ+UJ598kmnTpjF37lzWrVs30OG0s3r1ah555JGBDkNERERERAa5aDRKdXU1GzduZPPmzfj9fgoKCsjOziY5OVnJgCMYFAkBY8yJwBeAtW1WpwLHA7fGf34ImAw81ckh7gFGtFm+2GH7Q8Bc4Pz4MhdY0ub8TuBZIA04BbgC+DDw66O5rsHsrrvu4pZbbmH16tXMmjWrS/uEw+E+jipGCQERERERETkSj8fDpk2b2LRpE01NTeTl5ZGdnY3DMShuc48JA/5JGWPSgQeBzwN1LeuttR5r7bnW2kestVustW8CXwNOMMaM7nAYn7W2vM3iaXP8acSSAJ+z1q6w1q6In+siY8yUeLNFwHTgk9baVdbapcD1wOeNMe5evWBrIdjUt4u1hw3h2muv5bXXXuOmm25i4cKFPP/88xx//PHMnj2b008/nY0bNwKwbNky5s6dy7XXXsvJJ5/ME088wbZt27jwwgs58cQTmTNnDn/84x9bj7tixQpOPfVU5syZw+zZs3nyyScBuPHGGznxxBOZO3cup59+Otu2bQOgqqqKRYsWMWvWLGbPns2nP/1pKisr+f73v8/SpUuZO3cuX/rSl3r14xcRERERkWNbKBRi7969bNq0CY/HQ25uLjk5OaoN0AODoYbAH4BnrbVLjTE3H6FtJmCB+g7rFxtjPglUAM8BP7LWNsS3nQx4rLUrWxpba980xniAhcCWeJv11toDbY75ApAEnAC80lkwxpikeJsWGUeIH0I++GnxEZsdle8egMS0Q26+4447WLt2LTfccAPz589n+vTpvPLKK8yaNYsHH3yQyy+/nPXr1wOwdu1a7rzzTu644w4ikQgLFixgyZIlTJ06FZ/Px4IFC1iwYAFjx47lgx/8II8//jgLFy4kGo1SX18PwE033cSvfvUrAB5++GGuu+46nnnmGR544AHGjh3Liy++CEBtbS05OTnccsstPPPMMzz66KN9+zmJiIiIiMgxxePxsHfvXmpqasjMzCQ1NXWgQzqmDWhCwBhzBbHhACd2oW0y8HPgIWutt82mB4FdQDkwE/gZMAc4N769CKjs5JCV8W0tbSrabrTW1hljgm3adOY7wA+OFPtgtnLlSubOnds6bGDx4sV85StfoaysDIDJkydzyimnALBlyxY2bNjAFVdc0bp/Q0MDGzdupKysjOnTp7Nw4UIAHA4HOTk5ALz44ov8/ve/p6GhgWg0itcb+/UtWLCA3/72t1x//fWcfvrpnHfeef123SIiIiIicuwIhUKUl5ezb98+rLUUFhZqaEAvGLCEgDFmFHA7sMha6z9C2wTgYWJDHL7cdpu19p42b9cbY7YB7xhjjrfWvtfSrLPDdljflTYd/Qz4TZv3GcC+w7SHhNTYE/y+lND1LJm1ttNCGy3r0tPT27XNy8tj9erVB7V/9tlnOz1+aWkp1157LW+99Rbjx49n7dq1nHXWWQCcfPLJrF69mqVLl/LYY49x8803s2rVqi7HLiIiIiIiQ1sgEKC+vp7Kykpqa2vJysoiJSVloMMaMgYypXICUAC8a4wJG2PCwOnAtfH3TmhNBjwCjAPO7dA7oDPvASFgUvx9OVDYSbt83u8VUE6HngDGmGwggQ49B9qy1gastd6WBWg4VNs2B4515+/LpRuVNFtuyjdt2gTEuvSPHDmSoqKDO0ZMmTKF1NRU7r///tZ127dvp7a2loULF7Jp0yaWL18OxKp91tbW4vF4SExMpKioCGstd955Z+u+u3btIj09ncsvv5zf//73bN26lcbGRtxuNx6P56Dzi4iIiIjI0Ncy/Hjnzp2sXbuWLVu20NTURGFhoZIBvWwghwy8DHQsb/83YDPwC2ttpE0yYBJwprW2pgvHnUHsRr4s/n4FkGmMmW+tfQvAGHMSsXoEy9u0+Z4xZoS1tmW/RUAAeLdHV3eMyM/PZ8mSJSxevJhIJEJWVtYhK/y7XC6efvpprrvuOm677TYikQj5+fk8+OCDlJSU8MQTT3D99dfT0NCAMYZbb72VSy65hI9+9KPMmDGD0aNHc+6557Yeb9myZfzmN7/B6XQSiUT41a9+RWZmJmeffTa33XYbc+bM4eSTT+auu+7qr49DRERERET6WTQaJRAIEAgEaG5upqqqqnWYcXp6Om63W9MH9hFjj1CRvj8ZY5YBq6213zDGuIDHiNUYuIj2T+prrbVBY8wEYDHwH6Ca2EwBvwaagROttZH4cZ8Dinl/OsK7gT3W2ovj253A6vg5bgRygPuAf1trv9aN+N2Ax+Px4HbHJifw+/3s2rWLcePGkZyc3L0PRKSX6fsoIiIiIgOtpa5Yc3MzjY2NNDQ0EAqFCAaDWGtJTk4mPT0dl2tga+BXVFQwZcoUCgoKBjSOI/F6vWRmZgJkdqFHfTuDYZaBQxkJXBJ/vbrDtjOBZUAQOBv4OpAO7AWeJTbLQKRN+8XAHcCL8fdPAV9t2RjvjXAh8EfgDWIJhYeAG3rtakRERERERIaxcDhMXV0dFRUV1NfXY63F5XKRlJREWloaWVlZ6gnQzwZVQsBae0ab17uJFfU7XPu9xOoOHOm4tcAnj9CmlFhPBBEREREREeklgUCAuro6ysvLaWhoICEhgZycnAHvASCDLCEgIiIiIiIiQ0MkEqG8vJzy8nKamppISUkhPz9f0wUOIkoIiIiIiIiISK9qamqitLSUqqoq0tPTKSws1HCAQUgJAREREREREekV1lqqqqrYs2cPfr+fvLw8DQ0YxPSbERERERERkaPm9/vZt28fZWVlpKSkUFhYONAhyREoISAiIiIiIiI9FolEqKurY9++fXi9XnJyckhMTBzosKQLVM1hmHryySeZNm0ac+fOZd26dQMdTjurV6/mkUce6ZVjPfXUU9x4440A7N69m7vvvrvd9rFjx7J+/fpeOVdPfO5zn+O1117rdNvVV1/NnXfeCcCzzz7LvHnzSEpK4oYb2s+G2dTUxKc//WlmzZrFlClT+Pa3v421ts9jFxEREZHhLRwOU1lZyYYNG9i0aROBQIDCwkIlA44h6iHQz6y1NIeb+/QcKa6UIxbsuOuuu7jlllv46Ec/2uXjhsPhfhn/s3r1ap555hkuv/zyoz7WJZdcwiWXXAK8nxD4whe+cNTH7S1/+ctfutRu0qRJ/PWvf+Vf//oXfr+/3baf/vSnAKxdu5ZwOMxFF13Eo48+2q3frYiIiIhIVwWDQWpra1unEUxMTCQvLw+n0znQoUk3KSHQz5rDzZz00El9eo6Vn1hJakLqIbdfe+21vPbaa2zZsoXf/va3fP/73+e73/0u4XCY7Oxs/vSnPzF9+nSWLVvGN77xDU477TTefvttvvnNbzJ37ly+8Y1vUFlZSTAY5Itf/CJf/vKXAVixYgXf+ta38Hq9WGu59dZbufTSS7nxxhtZtmwZoVCIzMxM/vKXvzBp0iSqqqpYvHgxZWVlGGM44YQT+MUvfsH3v/99vF4vc+fOZcGCBdx1112dXkdJSQlvv/02xcXFfOhDH6KiooI33niD5uZmSkpKKCsr4x//+AfPPPMMjz76KF/60pcoLS1l7ty5jB49mqeeegqAxx57jC984QuUlZXx2c9+lptvvvmwn+/f/vY3br/9dqy1JCQk8OijjzJ27FiWLFnCL3/5S4wxjBo1irvvvpuSkhImT57MP/7xD0444YTW/Z9++mkef/xxzjjjDG644QYuuugi9u/fz1VXXUVVVRXjxo0jEom0nnPy5MkAPPHEEwfFs2bNGq666iqMMSQkJLBo0SKWLFmihICIiIiIHLVQKEQgECAYDBIIBPD5fNTX12sawSFCCYFh6I477mDt2rXccMMNzJ8/n+nTp/PKK68wa9YsHnzwQS6//PLWbvRr167lzjvv5I477iASibBgwQKWLFnC1KlT8fl8LFiwgAULFjB27Fg++MEP8vjjj7Nw4UKi0Sj19fUA3HTTTfzqV78C4OGHH+a6667jmWee4YEHHmDs2LG8+OKLANTW1pKTk8Mtt9zSehN/OGeddRZLly7lk5/8JOvWrSMpKYmGhgaWL1/OiSeeSFJSUrv2d911FzfccAPvvPNOu/X19fUsX76cqqoqJk6cyKc//WlKSko6PeeyZcv4yU9+wmuvvcaIESPw+XwArF+/nhtvvJF3332XkpISfvKTn/CFL3yBZ599lquvvpq//e1vrQmB++6776Bu/xBL1Jx22mn84Ac/YOfOncyZM4fzzz//sJ8BwIknnsgjjzzCZZddRiAQ4IknnsDr9R5xPxERERGRjqy1+Hw+PB4P1dXVBAIBQqEQ4XAYYwxOp5Pk5GRNIzhEKCHQz1JcKaz8xMo+P0dXrVy5krlz5zJr1iwAFi9ezFe+8hXKysqA2JPpU045BYAtW7awYcMGrrjiitb9Gxoa2LhxI2VlZUyfPp2FCxcC4HA4yMnJAeDFF1/k97//PQ0NDUSj0dab1QULFvDb3/6W66+/ntNPP53zzjuvW9d5zjnnsHTpUqZNm8Zxxx1HYWEhr776Kq+++irnnHNOl4+zePFiAPLz8xk/fjy7du06ZELg2Wef5aqrrmLEiBEApKbGemK88sorXHTRRa37ffnLX+bHP/4x1lo+9alPcdxxx/Gb3/yGvXv3snXrVi644IKDjv3KK69wxx13ADB+/HjOPvvsLsV/00038Z3vfIf58+eTnZ3NwoULefnll7t8/SIiIiIiwWAQr9dLdXU19fX1hEIhkpKSSEpKIi0tbdhNHegI1DNmxxKY8H8DHUqfGl6/1UHAGHPY7vz9zVrbaWavZV16enq7tnl5eaxevfqg9s8++2ynxy8tLeXaa6/lrbfeYvz48axdu5azzjoLgJNPPpnVq1ezdOlSHnvsMW6++WZWrVrV5djPPfdcvvvd7zJt2jTOOeccCgsLefnll1m2bBn33ntvl4+TnJzc+trpdBIOh7u8b4uOn2Pb1yUlJRx//PE89dRTrFmzhiuvvLJX/0FNTk7mt7/9bev7n//850yfPr3Xji8iIiIiQ5ff76eiooLq6mp8Ph8ul4uMjIxhWxjQRIJkbf0nOevvxRlqpHHtKCgeukkBDfYY5lpuyjdt2gTEuvSPHDmSoqKig9pOmTKF1NRU7r///tZ127dvp7a2loULF7Jp0yaWL18OQDQapba2Fo/HQ2JiIkVFRVhrW6vmA+zatYv09HQuv/xyfv/737N161YaGxtxu914PJ4jxl5cXIzb7ebPf/4z55xzDmeeeSZPPfUU+/fvZ+7cuQe17+pxD+fiiy/m/vvvp7y8HACfz4fP5+Pss8/mP//5T+v6u+66i7PPPrs1MfCZz3yGe++9l/vvv5+rr76602OfddZZrYmM3bt3d/kpv9frbR26sGvXLv70pz9x/fXXH81lioiIiMgwUFtby+bNm9mzZw/GGAoKCsjNzR2eyQBrSS9dyphnPkr+qjtiyYD0cYQK5w50ZH1KPQSGufz8fJYsWcLixYuJRCJkZWUdcso/l8vF008/zXXXXcdtt91GJBIhPz+fBx98kJKSEp544gmuv/56GhoaMMZw6623cskll/DRj36UGTNmMHr0aM4999zW4y1btozf/OY3OJ1OIpEIv/rVr8jMzOTss8/mtttuY86cOZx88smHLCoIsV4CzzzzDOPHjwegsLCQefPmddrrYfbs2UyZMoWZM2cyfvz41qKC3XHaaadx8803s2jRIowxJCYm8uijjzJjxgx+9rOfsWjRIoDWooItLr30Uq655homTZp0yKf3t99+O1dddRX/+te/mDx5crthD8uWLeOTn/xka8HGhx9+mD/+8Y9ccskl7Ny5k8svvxyXy4XL5eK3v/1tpwkRERERERGIzR5WVlbG3r17cTgcw74eQHL1evLf+y0p1WsBCKfkUT37GralzWNKydDueWs0X3nvMca4AY/H48HtdgOxLji7du1i3Lhx7bqmiwwEfR9FREREhrempib27NlDdXU1mZmZpKR0vf7YUGEiAZKr1pBWtpLUshUk128DIOpMpm7aldRO+yQ2IZWKigqmTJlCQUHBAEd8eF6vl8zMTIBMa223qourh4CIiIiIiMgQF41Gqa6uZs+ePfj9fvLz83E6nQMdVt+zFldzJYne3STWbSet/C1SKt/FEQm83wSDd/yF1My+hnDq4L75721KCMigVllZ2doNv61zzz23dSrDvnDJJZdQWlrabl12djavvPJKn51TRERERKQveDweDhw4QHV1NSkpKRQWFg50SH3G6a/FvetZkmq3kNhQSqJ3N45w80Htwil5NBWdhG/EAnxF84kk57TbXhOoYbl3Obn+XAoYukkCJQRkUCsoKOh0VoO+1pP6AiIiIiIig4nP56O8vJzy8nKsteTm5g7Z6QMT63eQveUfZOx6Dkc02G6bNU6CGaMIucfQnD+HphEnE8ycAG3qJgQiATZ6N7Las5rV9avZ17wPgIKqAqaPHrp1BIbmt2EQUq0GGQz0PRQREREZ+oLBIFVVVezfv59AIEBWVhZJSUkDHVbvs5bU8pVkb36ItLIVrav9OdNpGHUGQfc4gu6xhDJGguPgW9+aQA0ra1fydt3bbPJuImRDrdscOBidNJpU1+CZMr4vKCHQx1rG5QSDwWFZsEMGl2Awli0dFuPFRERERIa4aDRKIBBoXfx+Pw0NDfj9fnw+H+np6WRlZQ10mH0ipXIV+e/eRnLdVgCscdA48nTqpi7Gnze73dP/tioDlbxZ8yZv1r7JloYt7bblJeYxN2suc7PmMtM9E1+tjykjpvT5tQwkJQT6mMvlIjU1laqqKhISEnA4HAMdkgxT0WiUqqoqUlNTh2xXMREREZGhLhAI0NTURENDA7W1tQSDwdaHPi3TYicmJg7ZqQSd/lryVt1B5q5nAYi6UvFMuIT6KR8jlD7yoPYRG2FbwzZWe1bzXt177Gja0W771IypLMhZwPHZx1OcXNzuM/Ph69uLGQR0V9DHjDGMGDGCXbt2sWfPnoEOR4Y5h8PB6NGjh+T/HERERESGKr/fj8fjoa6urrUHgDGGlJQU0tLSyMrKGvp/30UjZG5/nLw1f8IZasBi8Ey8jOo5XyaalNWuaWWgktX1sVoA6zzr8EXev7F34GCaexon557MSTknkZOYw3CmhEA/SExMZNKkSa2ZO5GBkpiYqF4qIiIiIscIay01NTWUlpbS2NhIYmIiKSkpuN3uoZ8AaGEtKVWryX/vtyTXbgLAnz2FyhO/jT9vZmuzcn85K2pWsKJmxUG9ANJd6czJnMPcrLmckH0CmQmZ/XoJg5kSAv3E4XCQnJw80GGIiIiIiMgxIBQKsX//fvbt20diYiJFRUUDHVL/sZak2o1klC4lo/RlEprKAIgkpFM95xo8Ez+MNQ4ONO/nzZo3WVG7gl1Nu1p3d+BgcsbkWD2AzLmMTx+P06iGVmeUEBARERERERlEvF4vpaWl1NbWkp2dPTRnCGjDEWzE5SsnoamclMpVZJQuJaHpQOv2qCuFhjGL2DX9SlYH97Nm192srl9NdbD6/WPgYGbmzNahAOoF0DVKCIiIiIiIiAwC0WiU8vJy9u7dSzgcpqCgYMgN93Q2V+Pe9RypFe/g8pXjaqrAGW46qF3UmUxjyamsLz6Ol51BVnnXsX399Vjen0bbZVzMcM9gYe5C5ufMx53g7s9LGRKUEBARERERERlgTU1N7N27l8rKStLT08nOzh7okHqNiQRJ2/8a7p1Pk1a2AmOjB7WJJGUSSi0i6B7LhqK5vOwK8EbdW+yreLhdu1Epo5iTNYe5mXOZ5p5GslPDso+GEgIiIiIiIiIDpGVq6NLSUvx+P3l5eUNmimhX4wFyNj1Axp4XcAa9reub82bTMGYRQfdYQmmF+JPz2OIvZU39GlbWrmRv5T/eP4ZxMTdrLiflnMSczDnkJuUOxKUMWUPjmyYiIiIiInKM8fl8rb0CUlNTKSwsHOiQekc0QtbWf5K35k84In4AQqkFeMddiHfchQQzRlPmL2ONZw1rDrzCOs86/FF/6+4u42JO1hwW5i7kxOwTSXOlDdSVDHlKCIiIiIiIiPSjUChEbW1ta6+AnJwcEhISBjqsXpFYt5WilT9unSLQV3ActTM+i69wHvv8ZbxR8wZv7Pw1+5v3t9vP7XLHhgJkzVUSoB8pISAiIiIiItJHwuEwfr8fv99Pc3MzDQ0N+Hw+mpubh1SvABP2k7v+L2RvegBjI0QS0qk67utsKTmRN2pW8Ma6hyn1lba2dxkXUzOmttYDGJs2FocZWgUUjwVKCIiIiIiIiPSCcDhMIBA46OY/EAgQiUQwxpCQkEBSUhJ5eXk4nc6BDvmomUiA9D1LyV3/VxIb99JoDK+OPon/FU5hdcPLHFi9pLWty7iYkzmHhXkLmZ89n1RX6gBGLqCEgIiIiIiIyFFpaGigrKyMhoaG1pt/oPXmPysra8gUCmyR0LCPzO2PkbnjaXZYH/9OS+GNkhLWJbqIUgbVZQA4cDArcxYfyPsAJ+WcRLorfYAjl7aG1rdSRERERESkn0QiESoqKti7dy+hUIi0tDQyMzOHTD2AVtEIzkA9Tn8NiQ2lZO54iuqqt/lXeirP56eyPTGzTWPLiOQRzMmcw+ys2cx0z1Q9gEFs0CQEjDHfAX4K3G6t/UZ8nQF+AHwByAZWAl+x1m5os18ScBvwcSAFeBn4srV2X5s22cAdwCXxVU8BX7PW1rdpMxr4A3AW0Aw8BNxgrQ32weWKiIiIiMgxrKmpidLSUqqrq0lLSyM7O3ugQ+oVrqZy0g68QWrZmyQ0HcDVXIMzUAc2yvaEBF5JTeb5tFQ2jip+fx/j4ris45iXPY/ZWbMpSCoYwCuQ7hgUCQFjzInEbvrXdtj0LeCbwNXAVuBm4CVjzBRrbUO8ze+Ai4ErgBrg18AzxpgTrLWReJuHgJHA+fH3dwNL4vthjHECzwJVwClALvB3wABf68VLFRERERGRY1g0GqWyspK9e/fi9/vJzc09tocDRMOkVK8lbf8bpB14gyTPjtZNVU4Hb6YksyI9mzdTkqlyvV/zwIGD2ZmzOSXvFObnzFcvgGPUgH9zjTHpwIPA54nd8LesN8A3gJ9Yax+Pr/sUUAF8AvizMSYT+CxwpbV2abzNJ4G9wDnAC8aYacQSAQustSvjbT4PrIgnFrYAi4DpwChr7YF4m+uB+4wx37PWevv4YxARERERkUHO7/ezZ88eKisrh8QMAWn7X6fg7Z+T4KtoXbcvIYGn88fxQkoiO6Ltb4MSHYlMz5jO/Jz5LMhdQGZCZsdDyjFmwBMCxLrpP2utXWqMubnN+nFAEfBiywprbcAY8yqwEPgzcAKQ0KHNAWPM+nibF4CTAU9LMiDe5k1jjCfeZku8zfqWZEDcC0BS/ByvdBZ4fLhCUptVGd28dhEREREROQbU19eze/duGhoayMnJOabrBDj9deS/+2vce14AoDIli2cKJ/NComVjqALwQ9SPwTA+bTyzM2czJ2sOUzKmkOhIHNjgpVcNaELAGHMFcDxwYiebi+I/KzqsrwDGtGkTtNbWddKmqE2byk6OX9mhTbvzWGvrjDHBNm068x1iNQ5ERERERGQIikajlJeXU1paCkBBQQGxzszHIGvJ2PMiOe/exjbr482sTF7LHcPqqJco5RACg2Gmeyan5J3CiTknqhfAEDdgCQFjzCjgdmCRtdZ/mKa2466drDvo8B3adNa+J206+hnwmzbvM4B9h2grIiIiIiLHkGAwSGlpKWVlZaSlpZGefuxOmVdTv5Fta3/DquB+Vham0+B0xzZE6wGYmDaRU/NPZWHuQnIScwYuUOlXA9lD4ASgAHi3TYbNCZxmjPkqMCW+rggoa7NfAe8/zS8HEo0x2R16CRQAy9u06WxwT36H45zUdmN8ZoIEDu6h0MpaGwACbfY5VFMRERERETlGRKNRGhoa2Lt3L7W1teTk5JCYeOx1lfeGvKwof4nl+59mvW2I3d0kpAKQ6kxlpnsmszJncVzWcYxIGTGwwcqAGMiEwMvArA7r/gZsBn4B7CR2o34usArAGJMInA7cFG//LhCKt3kk3mYEMJPYDAUAK4BMY8x8a+1b8TYnAZm8nzRYAXzPGDPCWtuSfFhE7Gb/3V66XhERERERGaT8fj8+n4+Ghgbq6urw+XxEo1EKCgpwOBwDHV6XNUeaeafuHV6v+C+rvOuIxDs8G2uZG01gRtE5zCg8nQnpE3Aa5xGOJkPdgCUE4tMGrm+7zhjTBNRYa9fH3/8O+K4xZhuwDfgu4CM2jSDWWo8x5q/Ar40xNUAtcBuwDlgab7PJGPM8cI8x5ovxU90NPBOfYQBiRQk3AkuMMTcCOfHj3KMZBkREREREhoZwOEw4HCYUCrX+DIVCeDweGhsbCQQCGGNITk4mMzPzmCgcGI6G2d64nbX1q1lbv4qtTTuJEG3dPi0Q5LxoOvMmXk3KqEWgXs1HFA6H8Xg8OJ1OnM6hnTQZDLMMHM4vgRTgj0A2sJJYzYGGNm2uA8LEegikEOt5cLW1NtKmzWLgDt6fjeAp4KstG621EWPMhfHzvAE0E0s63NAH1yQiIiIiIv3I7/dz4MAB6urqWpMC0WgUYwzWWhISEkhNTSUzM3NwDwOORkho2EukZh0rat7gjcBeVjn8+DqEPCoU4oJGH+e6isiYcR1NxR9QIqALWhIB4XCY3NxcioqKyM7OHuiw+pSx9kj1+aSrjDFuwOPxeHC73QMdjoiIiIjIsBaJRKiurmbfvn00NTWRkZFBQkLCMfXkN8G7h6yt/8JVu5G3/aX8J8XFstQUAm2GMWRHIsxv9nOSP8CJ0SQK0kZTN3UxTSWnKRHQBaFQiIaGBkKhEDk5Oa2JgGNlqIjX6yUzMxMgs7s93Ad7DwEREREREZFu83q97Nu3j5qaGlJSUigqOtxs4oNQJETKxnvZveNh/pGcwPPpqdRlvD8F4FhSOCt1Mie4ZzLKPZVoSj6R5GyCDpemPTuMaDRKIBBoXQCcTieZmZnHXCKgNyghICIiIiIiQ0YoFKKsrIwDBw4QiUTIzc3F5To2bnuC0SBbG7ayqWwpmypfY4PLEi58fwrALGc6p+SfzukFZzAudVzr8IbQQAU8SAUCAZqbm4lGo0QikXbDQwCSkpJITU2lqKiI1NRUkpOTSU1NHVaJgBbHxn8ZIiIiIiIiR9Dc3MyuXbuorq7G7XaTmpo60CEdUcRGWFu/lmVVy3ir9i2CNhjbkABgKHJmMCNnPifnnczszNmaGeAQwuEwjY2N+P1+kpKSSEtLIzExsXVpGSaSkJBAcnIySUlJAx3yoKCEgIiIiIiIHPMaGhrYuXMnXq+X/Pz8QV8jYE/THpZVLeO16teoC9W1rs8LR5jv9zM7fSpjZ3yVfPfEAYxycLPW0tTURFNTE8YYMjIyGDVqVGsyaFAXiBwklBAQEREREZFjWm1tLTt37iQQCFBQUDAobwSbI81s9m5mnXcdq+tXs8e3p3VbpjX8P6+XSxqbmJRUROX8H9NcOG8Aox38mpqaaGhoIDU1ldGjR5OVlUVGRsaw7PZ/NJQQEBERERGRY5K1lsrKSnbt2oUxhvz8/IEOqZW1li0NW1hVv4r13vVsa9xGpM3M6C7j5GSTzUfKt3JqUyNORyK1M66mdPpVWKe6sx9KIBCgvr6epKQkxo0bR0FBgbr/HwUlBERERERE5JgTDocpKytjz549pKamkp6ePtAhAVDpr2RZ1TKWVS2jIlDRblt+Yj5z0sZzfDSZ87e9SH7TLgAai0+hat71hNJHDkTIx4RgMEh9fT1Op5OSkpLWgoBydJQQEBERERGRY0LLmPH6+noqKytpaGggKyuLlJSUAY3LH/HzZvUbvFq5lLWNW1vXp+JkIRmcFAizoKGOMY2rMPbd1u2htBFUnnA9TSNPH4iwB71AIIDP5yMYDOJwOMjLy6O4uBi32z3QoQ0ZSgiIiIiIiMigFY1GCYVCeL1eqqqq8Hg8hMNhUlNTKSwsHJAx44FIgC2NW9jg2cCmmjfZ0ryPcLxsgbGW+f4AlzY0co6vmZT4VHet1+NIJJw+gobR51I7/VNYV3K/xz8YhMNhotEo0WgUa227n36/n2g02jpbwKhRo0hLS8Ptdg/K+hDHMiUERERERERkwDU1NVFbW0sgECAUChGJRFpvGsPhMH6/n8TERDIyMkhMTOz3+GoCNbxa/Srv1b3HtsZthG34/Y0GRoVCXNrUzPmhJAoT8whnTSFQlEtTaiGh9GJC6SWE0oqJpOSCGZ6F7wKBAE1NTQSDQZxOJy6XC2MMDocDYwzGGFwuFyUlJa0zBSQnJysJ0IeUEBARERERkQHT3NxMZWUl5eXlBIPB1ptEp9PZeqOYlJREZmZmv98YhqIh3q17l5crX2Z1/WqiRFu3FcSnB5znDzC18AySZ3+JSGo+AeOgtF+jHNyam5tpbGwkEomQlJSE2+0mJyeHtLQ0EhISWn/HDoej9bX0HyUERERERESk3wUCAaqqqigrK6O5uRm32012dvZAh0UoGmJH4w5W1K7gf1X/wxv2tm6bkTiCS6v3coqnkpHhMM0FJ1B5yvUEsycROcwxh6NgMEhdXR2JiYkUFhaSlZVFWlragNd7kPaUEBARERERkX4TjUYpLy+nrKyMpqYm0tPTKSoqGrB4QtEQ2xq3sdG7kfXe9Wxp2EIwGmzdnp2QzZm5C/lI+TZmbVka2ye1iLIF36Bx1FmgJ9rtRCIR6uvriUQiFBYWUlxcPGhmgJCDKSEgIiIiIiL9IhKJsHfvXvbu3dtaFHAguohHbZT13vUsrVjK23Vvt0sAALhdbmZmzuT0/NNZGISSN28hoakMaxzUTruK2pmfHbbFAA/FWktDQwM+n4+cnByKi4vJzs7WEIBBTgkBERERERHpc5FIhD179rBv3z6ys7NJSkrq9xhqg7W8UvkKL1e+TEWgonW92+VmRuYMZrpnMt09nVEpo3BEQ+Su/TPZm5ZgsATTiilfeAv+/Dn9HvdgFgwGaWxsJBgMkpaWxpQpU8jNzcXl0q3msUC/JRERERER6VPhcJjdu3dz4MABcnJy+m2WgKiNsq95Hxu9G1lVv4r36t5rLQyY6kzltLzTOKvgLManjY89ybYWV3MVSfv/R+7aP5Ncvw0Az/hLqDzhm9iEtH6Je7ALhUI0NTW1zvzgdrvJy8sjMzNzQBI90nNKCIiIiIiISJ8JBoPs2rWLiooKcnNzSUhI6LNzWWvZ0bSDDd4NbPRuZLN3M42RxnZtZiSN5PyM2ZySOplk48JRu5ukHS+SXLuZpLrNuPy1rW0jSZlUzP8ejaPO7LOYB5NwOEwgECAYDBIMvj+MwhiDtbb1vdPpJD09nVGjRrVOD6ihAccmJQRERERERKRPBAIBdu7cSVVVFXl5eX3Wjbwh1MCyqmW8VPkS+5v3t9uWbFzMCYQ5oame85p8jA+VAssPeSxrHATd42jOn0PNrM8TScnrk5gHC7/fj9frxVqL0+kkKSmJjIwM3G43SUlJGGPaLQAul4u0tDQcDscARy9HSwkBERERERHpVZFIhNraWg4cOIDH4yE/Px+n09mr57DWsqlhEy9VvMSKmhWEbAiAZEcys9wzOS7i4AP73mJW7U4SgEhCOuHUMQRan2QbLGCdiQSzJuLPnkIgZyqBrEnDomBgOBymrq4Oh8NBcXExbreb5ORkkpOTNf5/GNFvWkREREREekU0GqWuro6ysrLWOegLCgp67UlyZaCSzd7NbGrYxDrPOsr8Za3bxqWNY1HBuVzQ5GfUxvtJbCgFIJKYSdW0xdRPvlw1AIglUurr6wkGg+Tn57cmA2R4UkJARERERER6xFqLtZZoNIrX66W8vJyamhoSEhLIy8s76l4B/oif5TXLWVO/hs0Nm6kOVrfbnuRI4pS8U1hUuIhJCQWMePNHpO//HxBLBNQqEdBOY2MjDQ0NZGVlMWHCBHJyctTtf5hTQkBERERERI6oqamJuro6PB4PkUiEaDTamhCw1tLc3IzT6eyVKef2+vbyQsULvFr1Kr6Ir3W90zgZnzaeqRlTmZYxjVmZs0h1pZJYv53i/15NYuNeoo5Eamd+lropH1MigFivjcbGRpqamkhNTWXixIkUFBT0aXFHOXYoISAiIiIiIp2KRCJ4PB6qq6upra0lFAq1FppzOBytReYcDgc5OTlHlQgIRUO8VfsWL1S8wAbvhtb1RclFnJZ3GtPd05mUPolkZ/vx/Rm7X6Bw5Y9xRPyE0kZw4NRfEMiZ1uM4hopwOIzX6yUYDJKens7kyZPJzs4mOXno10eQrlNCQERERERE2gmFQtTU1FBRUYHX68XhcLRWne8tDaEGtjZuZXPDZrY0bGF743YC0QAADhzMy57HeUXnMTtzNg7TSbf2aJj8935H9tZ/AtBUtICyD9xKNCmr12IcTKLRKOFwuPVnJBIhEom09tAA2vXYMMaQnZ1NQUEB2dnZKhQondK3QkREREREWoVCIXbs2EFlZSXJycm9UgugRbm/nBfKX+Dd+ncPmh4QICshi3MKzuHcwnPJS+p8uj9nczVpB5aTuf1xUmpiPQlqZnyGmllfAEfvzmQwGDQ3N+PxeHA4HLhcLpxOZ+v0gElJSTidztbeGi09NxwOB+np6bjdbtUIkMNSQkBERERERID2yYC8vLxeeaoctVHW1K/hufLneK/+PSy2dVtxcjFTMqYwJX0ys8MOxofCWJebaEM5kYCPSKKbaGI6SfU7SNv/P9L3v05y7cbW/SMJaZSf/COaRp5+1HEONsFgsHWmhjFjxrQ+5W9ZdKMvvUEJARERERER6fVkQE2ghhW1K3i+/Pl20wMel3UcZxeczQz3DLLDQdy7niXz7T+3ThPYFf6c6TSWnIJ3/EWE00YcVZyDTTgcpq6uDoARI0ZQVFREenr6AEclQ5USAiIiIiIiw9zRJgOC0SA7m3aytWErWxu2sqVxC7XB2tbtqc5Uzsw/k/OLzqc4qYD0/a/hXvcD0spWYGwEgKgrBX/OVByhJpzBBhwBL85wU2ybMxnfiAU0lpxCU/EHiKR0PpzgWNZSwDEUCpGbm0txcTGZmZmthRtF+oISAiIiIiIiw9jRJAO2NmzlmbJnWFm7krANt9vmwMG4tHGcXXA2p+WfRoozBVfjAUqev5Kk+u2t7Zrz5+AZfwkNo88+eJrAaBhnsIFoQhrWmXhU1zlYWWvxer34/X4yMzMpLi4mJydHQwKkXyghICIiIiIyTAUCAXbt2tWtZEA4GmZF7QqeLXuWbY3bWtdnJmQyOX0ykzMmMyV9ChPSJ7SbIjC5ag3F/7sRV6COSFImnvGX4plwMSH32EOfzOEikpx9NJc4qDU2NtLQ0EBGRgaTJ08mNzdXswFIv+rWt83E+qucDpwKjAVSgSpgFbDUWru3twMUEREREZHeV1dXR2lpKR6P54jJgGA0yO6m3az1rOWFihdahwO4jItT8k7hwqILGZc27pDd2zN2PUfhyltxREP4s6dw4PRfE04t7JPrGgjWWnw+X+v0f0C7z6JlKsBoNEo0GsVaSyAQIC0tjUmTJpGXl0di4tDsASGDW5cSAsaYFOA64MtALrAG2A80AxOBy4B7jDEvArdYa9/sk2hFREREROSohEIhysrK2LdvHw6Hg8LCwoNu5Pc172NLwxa2N25ne+N29vj2EImP9YfY9IDnFZ7HosJFZCVmHfpkNkru2j+Tu+FeABpHnkHZwluwrpS+uLR+F41GaWhooLm5mbS0tE6TABBLDrRMFeh0OnG5XKSkpJCXl0dycvKhDi/S57raQ2ArsBL4EvCCtTbUsYExZgzwCeCfxpgfW2vv6b0wRURERETkaHm9XkpLS6mpqSErK4uUlPdvzCM2wjt17/D0gafZ1LDpoH0zEzKZmDaRhXkL+UDuB0hwJBz2XCbsp2jFD8nY+zIAtdM/RfWcL4M59sfGR6PR1nH/breb0aNHt04LCLTrKQDgcDhUHFAGJdPxy9ppI2NmWmvXd+mAxiQCY6y127rQ9hrgGmLDDwA2EOth8Fx8+6GC+5a19lfxNsuIDWNo65/W2ivanCcbuAO4JL7qKeBr1tr6Nm1GA38AziLW8+Eh4AZrbfBI19HmGG7A4/F4cLvdXd1NRERERKTXhEIhwuFwa/f0aDRKJBLB5/Oxf/9+wuEwubm5rUXr/BE/r1S9wjNlz1DuLwdiQwGmZExhYvpEJqVPYmL6RPIS87p0U+tsriZr22Nkbn8cl78W63BRMf+7eMdf3KfX3R+i0Sgej4dAIEBmZiYjRowgJydH4/5lQHm9XjIzMwEyrbXe7uzbpW9uV5MB8bZB4IjJgLh9wLeBljKjnwKeNMYcZ63dAHScVPQC4K/AYx3W3wN8v8375g7bHwJGAufH398NLAEuBjDGOIFnidVDOIXYsIi/Awb4WhevRURERERkQAQCARobG6mrq6O+vp5IJEIkEmkdrw6xp9YZGRlkZWVRFaxiR+MONjdsZlnlMhojjQCkOdNYVLiIC4ouIDcpt905nP5akuq2Ek1II5xaQDg5Fxzv304kV68na8vDZOx9GRONzTgQSi2kfOGtNBcc10+fRN/x+Xx4PB6ysrIYP358ux4BIseqLvUQaLeDMecDjdba1+PvvwJ8HtgIfMVaW3dUARlTC9xorf1rJ9v+DWRYa89us24ZsNpa+41DHG9aPLYF1tqV8XULgBXAVGvtFmPMBcAzwChr7YF4myuA+4CCrmZZ1ENARERERPpLMBikoaGBuro66urq8Pv9OBwO0tLScLlcOByO1mVn405W1K5gZ+NOdjTtoCHc0O5YRUlFXDjiQs4sOJMUZwpYi6vpACmVq0mtWkVK1WoSvXva7WONg3ByHuHUAkw0SHLd1tZtzflzqJv8MRpHndkuaXAsCofD1NXV4XK5KC4upqioiISEww+XEOlPfd5DoINfATcBGGNmAb8GfkOsq/1vgE/34JgtT+k/CqQRu1nvuL0QuJBYL4KOFhtjPglUAM8BP7LWtvwrdzLgaUkGAFhr3zTGeICFwJZ4m/UtyYC4F4Ak4ATglUPEnBRv0yKjC5cqIiIiInJUmpqa2L59O16vtzUJ4Ha7O+3Sv71xO99d/912RQGdxsno1NFMSJvA8dnHMy97Hk7jBCCl8j0K37yVxMZ9Bx0r6B6DiQRx+SoxNkJCcyUJzZUARB0JNIxZRP2UjxHImdZHV96/vF4vPp+PvLw8Ro0aRUaG/tyXoaUnCYFxxJ64A3wYeMZa+11jzPHAf7p7sHhSYQWQDDQCH7TWbuyk6aeABuDxDusfBHYB5cBM4GfAHODc+PYioLKT41XGt7W0qWi70VpbZ4wJtmnTme8APzjMdhERERGRXtXc3MyOHTvwer0UFBQcdlx/IBLgju13ELERpmVM49S8U5mQPoHRqaNJdBw8zV3b6QGtceLPmUZzwXE058+lOX8O0aTMWEMbxemvxeWrIMFXiSPURFPxQiLJOX112UfF7/fT2NhIJBLB4XDgdDpJSEhorfhvrSUcDhMOh4lEIq01GFJTU5kyZQp5eXk4nc6BvgyRXteThEAQSI2/Pge4P/66FuhJP/ktwFwgi1iC4e/GmNM7SQp8BnjQWutvu7LDbAbrjTHbgHeMMcdba99radbJeU2H9V1p09HPiPWKaJFBrC6CiIiIiEivCwQC7Ny5E4/Hc8RkAMADpQ+wv3k/2QnZ3DTlJjISDvGE21py1v+VvHV/BqBh1FmUL/g+NiGt8/bGQSQlj0hKHoHcGUdzSX0mHA7T2NiI3+8nKSmJvLw80tPTCQQC+P1+AoEA4XCYQCAAgMvlIikpieTkZFJSUkhMTMTtdrebiUFkqOlJQuB14DfGmDeA+cDH4usn04Ob4XgRwpaigu8YY04Evg58saWNMeZUYEqbcx3Oe0AImBR/XQ4UdtIun/d7BZQDJ7XdGJ+ZIIEOPQc6xB4AAm326UJ4IiIiIiLdFwwG2bFjBzU1NV1KBqypX8N/ymMdeL8y8SuHTgZEQhS+9RMydz0LQO20K6me+9VjdnpAv9+Px+PBGNM6JaDb7SY1NbVdO2stkUiEUCiEMaa1x4DIcNKThMBXgT8CHwGusdbuj6+/AHi+F2IytB+XD/BZ4F1r7Zou7D+D2I18Wfz9CiDTGDPfWvsWgDHmJCATWN6mzfeMMSOstS37LSJ2s/9uj69ERERERKQXhEIhdu7cSXV1Nfn5+a1TBh5KY7iRO3fcCcD5hedzXFbnVf4dQS/Fr91EasU7WOOkct638Ez6UK/H3x+stdTV1RGJRCgpKSE3N5eMjIxDflbGGFwul2YKkGGt299+a20pcFEn66/r7rGMMT8lVgRwL7Hu9lcAZ/D+9IAtlfs/Clzfyf4TgMXEahdUA9OJFTlcBbwRj2uTMeZ54B5jTEuvg7uJ1T7YEn//IrG6CEuMMTcCOcBtwD3drdIoIiIiItKbwuEwu3fvpqKigvz8/C49xf7Lrr9QG6xlRPIIrhxzZadtUipXUfjWT0j07iHiSqPslJ/iK17Y2+H3i2AwSG1tbWuPgJycwVnLQGSw6VE6zBjjACYCBUC7lJu19n/dOFQhsAQYAXiAtcD51tqX2rS5glivgX90sn8QOJvYEIN0YomFZ4nNMhBp024xcAexG3+Ap4j1dGiJOWKMuZBYz4c3gGbgIeCGblyLiIiIiEivCofD7Nmzh7KyMvLy8rr0NPuN6jd4rfo1HDi4duK1JDuT2213+SrJW3UH7j0vABBKLWD/6b8jmD2pT66hr3m9XpqbmykuLmbUqFEkJXXsbCwih2KsPVzNvE52MGYBsZvlMcRu1Nuy1tphO/Am3pvB4/F4cLt7Ul9RRERERCQmGAyye/duysrKyM3NJTHx4FkBOqr1V3Ld2htojDTxkZKP8PHRH2/dZiJBsjY/SO6Gv+EIN2MxeCZ+kOrZ1xBNzurDK+maSCRCMBjE5XKRkJBw2LbRaJRAIIDX6yUlJYXRo0eTn5+vml4yLHm9XjIzMwEyu9vDvSc9BO4C3gEuJDZOv3sZBREREREROaxAIMCOHTtaawZ07BmQtv8N0ktfwhOoYUe4nu22ka2OMKsTHTS6XEwLRrh+439g97uEUwoIJ2fj3vUfEhtjNcCb8+dQecKNBHKmDMTlAbHeD22r/RtjSEpKwufztSv0l5iYSEJCAsFgEL/fTzQabW1bUFDAyJEjSUs7xGwIInJYPUkITAI+Yq3dfsSWIiIiIiLSLT6fj507d1JbW3twzQBraV57O/8o/w/LUlOpTnLGy3E7gFgPAnckws8rK0gP7Yfaze2OHU7Jo2rutTSMPR8G6Gl6IBCgvr4eh8NBSkoKeXl5rdP7JSUltU4F6Pf7aWhooKmpCZ/PR2JiIkVFRaSnp5OSkkJycnKXek2IyKH1JCGwklj9ACUERERERER6UWNjIzt27MDr9VJQUNCuQn6pdyv/Wf8zXsZD1B2bQtAAJa5sxiWNYFzaGMakT2JK2gScM5rY76vE1bpUEMoYRd2UK7AJA/c03ePxEAgEGDVqFLm5uaSkpBzU+yEpKandE/9IJEI4HCYxMVFDAkR6WU8SAr8Hfm2MKQLWAaG2G621a3sjMBERERGR4SASieD3+/H5fOzdu5fm5mYKCgpab363NWzj8dJ/8JZ3TbyCl2F+YgnnT/gsUzKmHFQ0EGJzZweyB244QEeRSITq6mpSUlKYMmUKeXl5Xb65dzqdXZpZQUS6rycJgcfiP+9ts84S++fJAvqvVURERETkEMLhMF6vF7/fj9frxefzEQgEaA42U2WrqHfVU7qnlD2+PZT6SqkL1QFgrOWc5hCXTPoSxaMvHOCr6Dq/3099fT15eXmMGTNG4/1FBpGeJATG9XoUIiIiIiLDRH19PZs3b8ZaS0JCArW2lmdrnmV57XLCNnxQe5e1XNTYxJXhdBJO/SOhjFEDEHX3BYNBGhsbiUQijB07luLi4i5Nmygi/afb/0Vaa/f0RSAiIiIiIsNBy7TfYXeYh/c9zOvVrxMlCkC6K50xqWMYkzKKGdVbmbN3OZOCIWzhfMrO+BmhxIx+ibGxsZGmpqZDbm+p8p+SktKusF8oFMLn89Hc3IzL5cLtdjNixAhyc3P7I2wR6aYepeiMMROAbwDTiA0T2ATcbq3d0XuhiYiIiIgMPXsa93Bvxb28u+Pd1kTACVkn8JGRH2FS+iQSmsoY8cZ3SanZAEDttCupnvNlcPT90/VoNEpNTQ0ul4sxY8aQmJiIw+Fot0QiEZqamqirq2v9aYwhGo3idDrJyMigpKQEt9tNWlqaCgGKDGLd/lfFGHMe8BSwGniDWO2AhcAGY8zF1tqXejVCEREREZFeZK3lf9uq+f3L29hV3cTJE3JZNKOIM6bk405O6LXzvLihnFMm5ZGa+P6f3Peuv5ffvfs7LLFeAidmn8hHR36UCekTAEjb9z+K3vwRzqCXSKKb8gU/oGnkab0W0+EEAgFqa2vJzc1l9OjRuN3uQ7bNzc1l1KhRrcUQm5qacDqdrUmAtrMjiMjgZVq6LHV5B2NWAS9Ya7/dYf3PgUXW2uN7Mb5jijHGDXg8Hs9h/wEVERERkf5nreW1bdX8bulW3iutP2h7gtOwYHwu504v5LwZRRS6D67e31XPry/nmgffZWZxJg9/YQFpSbGkwKq1D3DVql+wMJrJp1OnMz55BNGENKKJ6SRXryNn80MANOfOoOyUnxFOG9HjGLqjZTrAkpISSkpK2g0DEJHBzev1kpmZCZBprfV2Z9+e9DuaBlzeyfp7iQ0jEBEREREZNDpLBCS5HHxywRjOnlbAa9uqeWljBdsrG3ltWzWvbavmJ89u4vcfP45FM4q6fb7Ve+v5xj9XYS3MGZVJauL7k3AdF4rw7N4DjA6XEpvB+2B1U66gau614Dx8b4VAIIAxhoSEhB53yw+FQtTW1pKamtrt6QBF5NjXkx4Ce4FvWmv/1WH95cBt1trRvRjfMUU9BEREREQGl311Pr77xHr+t7UKeD8R8MXTx1OQ0b4HwK7qJl7aWM5Taw6wfr8Xh4EfXzaLT5zU9T9v99b6+OAf36C6MciZU/K556p5uJxtus+XraVxzb+pK9tDRqLFEWrCEWzEEW4CC/VTr6Bx1FmHPUfLTXxLIiAYDALgcDhITEwkMTGRpKSkw3bbDwaDeDwerLXk5eUxevRoUlNTu3ydIjJ4HE0PgZ4kBL4PXAf8HFhOrKjgKcBNwK+ttT/u1gGHECUERERERAaHaNRy/4rd/PKFLfiCERJdDj550hi+dPp4Co4wFCAcifK9J9bzz3f2AvCNcybx9bMnHfHJuac5xIf/tJztlY1MH+HmkS+dTHrSwR1yq6qq2LJlCwUFBd26Jmtta9f+/Px8SkpKcLlcBAIBAoEAPp+PhoYGAoEAfr8fgKSkJFJTU1uHAASDQerr6zHGkJubS1FREZmZmeoVIHIM6+8hA7cCDcD1wM/i6w4APwTu6MHxRERERER6zfbKBm56bB3v7qkDYP7YHH7+4VmMz0/v0v4up4Off3gWBe4kfv/f7fxu6TYqGwLceulMnI7Ob5yD4SjXPPAu2ysbKXInc+/VJ3aaDOip5uZmPB4P6enpjBs3jtzc3NYeACkpKe1jCQbx+Xw0NjZSW1tLU1MTtbW1ACQkJFBYWEhBQQFut1uJAJFhrts9BNrtbEwGgLW2odciOoaph4CIiIjIwAlFovz51R3c8fJ2gpEoaYlOvv3/prF4/mgch7iRP5IlK3bz/ac2YC0sml7IHR8/juQEZ7s21lpufHQtj767j7REJ//60kKmFx/6b8Hu9BAIh8PU1tbicrkYMWIERUVFJCUldTl+ay3Nzc34fD78fj9ut5uMjAwlAkSGkP7uIdBKiQARERERGQze2V3Ld59Yx9aKRgDOmJLPTz44i5KslCPseXhXnjyW/Iwkrn14NS9urODsX7/KmNxUstMSyU1LJCctkQqvn0ff3YfTYbhz8fGHTQZ0lbUWr9eL3+8nNzeXkSNH9uiBkzGG1NRU1QcQkU51KSFgjHkPONtaWxefdvCQ3QqG87SDIiIiItJ7rLXsrG4iOzV2490Zjy/Ez5/fzD/eKgUgJy2R/7toGpfNLem1p+DnzxzBks8k8rn732F/fTP765s7bfejS2Zw5pQjP/V3OBw4nU4qKipISUkhNTUVl+v9P8vbDg9oqfx/uAKBIiI91dUeAk8Cgfjrf/dNKCIiIiIi4A9FeGr1Ae5bvpuNZV6MgZnFmZwyKY9TJ+VxwphsEp0OnlpzgFuf2Uh1Y6zK/uXzRvKdC6aRfYjkwdE4aXwu/7vxTNbt91DbFKSmKUhdm58njc/hkwvGdOlY2dnZTJ8+nYaGBqqrq6mrqyMajZKUlEQoFMLpdDJmzJhuDw8QEemuo6ohIO2phoCIiIhIz+2vb+aBN/fw8Ful1PlCACQ4DaFI+79XUxKcjMpJaR0eMCE/jZ98cBYLxuf2e8xHKxKJ0NTURENDAzU1NSQmJlJSUkJGRsZAhyYix4h+nXZQDk0JAREREZHu21fn4+fPbeY/68qIxv80LclK4cqTx/CxeaMIRaK8vr2a17bFlurGWMfVRJeDr505kS+cPp4kl/MwZzg2tPxdroJ/ItIdfZ4QMMbUcZi6AW1Za3O6E8BQooSAiIiISNeFIlHufX0Xv1u6jeZQBICTx+fyqYVjOWdaAS7nwePmo1HL5vIGNhzwMH9cDmNy0/o7bBGRQaU/Zhn4RneDEhERERE5lHd21/K9J9azpSI2adX8sTn84JLpzCjOPOx+DodherG7Vyr5i4gMd11KCFhr/97XgYiIiIjI0FfvC/Lz5zbz8Nt7AchOTeC7/28aHzlhpLrKi4j0s65OO9jlFGx3uyiIiIiIyLGprinI3a/tZFdVExfPKeb8mUU4HZ3f1DcHI9y3fDd3vboDT3OsYODH5o3i2xdM7ZNZAURE5Mi6OmSgniPXEDDxNsd+RRcREREROaQGf4i/vLaLv76+i8ZAGIDnN5QzNjeVL5w2gQ8dX0JyQuxPwmA4yj/fLuWO/26nqiFWDHBKYQa3XjaT+eOGbekpEZFBoatFBU/v6gGtta8eVUTHMBUVFBERkaGsORjh/hW7+dOrO6iPTws4fYSbD0zM5ZF39rU++c9LT+Izp4ylMCOZ21/eRmmtD4CR2Sl889zJXDq35JA9CUREpHs07eAgoYSAiIiIDEXRqOVf7+7lthe3tj7ln5CfxjfPncIFM4twOAxNgTD/fHsvf3ltJwc8/nb756Unce3ZE7nixNEkug6eOUBERHquP6YdnA2st9ZG468PyVq7tjsBDCVKCIiIiMhQs3ZfPf/35AbW7K0HYk/5v372JD54XEmn0wKGIlGeXnOAe17bRV1TkCtPHsOnPzCW1MSujlQVEZHu6I+EQBQostZWxl9bYjUDOrLW2mFbQ0AJARERERkq6pqC/PKFLTz8dinWQnqSi6+fPYlPLRyrp/wiIoPI0SQEupqqHQdUtXktIiIiIkNQNGr5x9ul/OqFLa11Aj54XAnfuWAqBe7kAY5ORER6U5cSAtbaPZ29FhEREZGho64pyHWPrGbZlthzoKlFGdxyqWYDEBEZqro8mMsYc1pX2llr/9fzcERERERkIKzdV881D7zH/vpmklwObjp/KledPKbTOgEiIjI0dKe6yzJitQOg8/oBxLcP2xoCIiIiIscaay0Prizllqc3EoxEGZObyh8XH8+M4syBDk1ERPpYd1K+dcBe4FZgEpDdydKt/mTGmGuMMWuNMd74ssIYc0Gb7fcZY2yH5c0Ox0gyxvzeGFNtjGkyxjxljBnZoU22MWaJMcYTX5YYY7I6tBltjHk6foxqY8wdxpjE7lyPiIiIyGBU0xigtMaH1x+ibUFpXzDMNx9Zw83/Xk8wEuXc6YU89dVTlAwQERkmutNDYATwQeAzwLeA/wB/BZ63XZmqoHP7gG8D2+PvPwU8aYw5zlq7Ib7ueeDTbfYJdjjG74CLgSuAGuDXwDPGmBOstZF4m4eAkcD58fd3A0vi+2GMcQLPEiuceAqQC/ydWE+Ir/Xw2kREREQGVCgS5fcvb+POV7YTjf+15nIYslITyExJwBeMUObx43QYbjxvCl88bTzGHKojqIiIDDVdmnbwoJ2MGUXsJv1TQBKxm+cfWGvDRx2QMbXAjdbavxpj7gOyrLWXHaJtJrGb+Cuttf+Mrysm1pPh/1lrXzDGTAM2AgustSvjbRYAK4Cp1tot8V4JzwCjrLUH4m2uAO4DCro6dYOmHRQREZHBYld1E9/452rW7K0HIMnlIBCOHtQuLz2JOz9xHAvG5/ZzhCIi0hv6Y9rBdqy1e4FbjDFLiPUS+DaxJ/O1PTketD6l/yiQRuxmvcUZxphKoB54FfietbYyvu0EIAF4sU1sB4wx64GFwAvAyYCnJRkQb/OmMcYTb7Ml3mZ9SzIg7gViyY4TgFd6el0iIiIi/clayz/f3sstz2zEF4zgTnbxkw/O4uI5xfhDEep8QeqaQtT7gjQFI5w4NpusVI2SFBEZjrqdEDDGJAEfJjZ04GRiXe0vtNb2KBlgjJlFLAGQDDQCH7TWboxvfg74F7AHGEesfsF/48MBAkARELTW1nU4bEV8G/GflRysskObirYbrbV1xphgmzadxZ5ELGnQIuMwlyoiIiLSp2qbgnz7sbW8uDH2Z82C8Tn85vK5FGelAJCc4GREZgojMlMGMkwRERkkujPt4HxiwwSuAHYR605/eU8TAW1sAeYCWcQSDX83xpxurd3YMgwgbr0x5h1iyYELgccPFy7vz4hAh9dH06aj7wA/OMx2ERERkX6xqczLp//2NuVePwlOww2LpvD5U8fjcKgmgIiIdK47PQTeBEqBO4B34+tO6Vh4xlr7VHcCsNYGeb+o4DvGmBOBrwNf7KRtmTFmD7FZDgDKgURjTHaHXgIFwPI2bQo7OXU+7/cKKAdOarvRGJNNbDhCBYf2M+A3bd5nECuUKCIiItJvVuyo4Qv3v0NDIMz4/DTuuOI4ZpZopgARETm87g4ZGA3832G2W8DZ83CA2FP5pE43GJMLjALK4qveBULAucAj8TYjgJnEZkKA2HCETGPMfGvtW/E2JwGZvJ80WAF8zxgzwlrbcuxFQID3kx8HiQ9bCLSJr1sXKiIiInK0nl1bxnX/XE0wEmX+2BzuuWoemakJAx2WiIgcA7qcELDWOnr75MaYnxKrE7CX2NP1K4AzgPONMenAD4HHiCUAxgI/BaqBJ+IxeYwxfwV+bYypIVbU8DZgHbA03maTMeZ54B5jTEuvg7uBZ6y1W+LvXyQ2E8ESY8yNQE78OPd0t0qjiIiISH+5741d/OiZjVgL588o4ndXzCU54WifzYiIyHDRo1kGelEhsAQYAXiAtcD51tqXjDEpwCzgKmL1BcqIVfv/mLW2oc0xrgPCxHoIpAAvA1dbayNt2iwmNtShZTaCp4Cvtmy01kaMMRcCfwTeAJqBh4AbevNiRURERHqDtZZfvrCFPy3bAcCVC8bww0tm4FS9ABER6QZj7eFq5sUbGXOytXbFERvG2qYBY621G442uGONMcYNeDweD263e6DDERERkSHIFwzzvSfW88Sq/QDcsGgyXzlzooYuiogMU16vl8zMTIDM7vZw7+owgPuNMS8ZYy6Pd+U/iDFmenwIwHbg+O4EISIiIiJHtrWigUvvfIMnVu3H6TD88iOz+epZk5QMEBGRHunqkIHpxKr+3wI8aIzZChwA/EA2MBVIIzYV4LnW2vV9EKuIiIjIsGSt5V/v7uP7T67HH4qSn5HE7VfMZeGEvIEOTUREjmFdGjLQbgdjjgdOJVbkL4VYkb9VwCvW2treDvBYoiEDIiIi0tuaAmH+79/reTw+RODUSXn89mNzyUvvdFImEREZZo5myEC3iwpaa98D3uvufiIiIiJyeMFwlHpfkFpfkNqmINWNQW5fupUdVU04DFy/aArXnD4Bh4oHiohILxjoWQZEREREhjV/KMKPn93Ik6sO0BAId9qm0J3EHVccx0njc/s5OhERGcqUEBAREREZIHtrfXzpgXfZcOD9Hp4OA1mpiWSnJpCTlsjEggxuWDSZXA0REBGRXqaEgIiIiMgAeGVzJd/452o8zSFy0hL51Udmc8KYbNzJCRoSICIi/UIJAREREZF+FI1abn95G3f8dxvWwpxRWfxp8fEUZ6UMdGgiIjLM9EpCwBiTZa2t741jiYiIiAxV9b4g3/jnapZtqQLgkwtG838XTSfJ5RzgyEREZDhydHcHY8xNxpiPtXn/CFBjjNlvjJnTq9GJiIiIDBH1viAfv2cly7ZUkeRy8OuPzuHHl81SMkBERAZMtxMCwBeBvQDGmHOBc4ELgOeAX/VeaCIiIiJDg8cX4pN/XcmmMi956Uk8/uWFfPiEkQMdloiIDHM9GTIwgnhCALgIeMRa+6IxZjewsrcCExERERkKvP4QV927kvX7veSmJfKPz5/EpMKMgQ5LRESkRz0E6oBR8dfnA0vjrw2gPm8iIiIicY2BMFff+xZr9nnISk3ggc8pGSAiIoNHT3oIPA48ZIzZBuQSGyoAMBfY3ktxiYiIiBzTfMEwn/7bW7xXWo872cUDnz2JaSPcAx2WiIhIq54kBK4DdhPrJfAta21jfP0I4I+9FJeIiIjIMcvTHOKLS97h7d11ZCS7eOBzJzGzJHOgwxIREWmn2wkBa20IuK2T9b/rjYBEREREBoto1LKzuom1++rZXN7AxPx0Lp5TTEpi56Mkw5Eo/3irlN8u3UZtU5D0JBf3f2Y+s0dm9W/gIiIiXdCTHgIYY64kNtvAeOBka+0eY8w3gF3W2id7MT4RERGRfvXunlpe3FjB2r0e1u/30BAIt9v+k/9s4mMnjuLKBWMYlZPaun7Zlkp+8uwmtlXGOk9OyE/jto/O4bjR2f0av4iISFcZa233djDmGuAW4HfA94CZ1tqdxpirgU9Za8/s7SCPFcYYN+DxeDy43RojKCIicqx5aWMFn7//nXbrkhMczCjOZHJhBq9tq2JfXTMAxsDZUwu4dG4Jj723j2VbqgDITk3gunMn8/H5o0lw9qR+s4iISNd5vV4yMzMBMq213u7s25OEwEbgu9bafxtjGoA58YTATGCZtTavWwccQpQQEBEROXZtr2zgsj8spzEQ5swp+Zw3o4jZI7OYXJiOK35jH4laXtlcyd9X7Oa1bdXt9k9wGj518li+dtYkMlMTBuISRERkGDqahEBPhgyMA1Z1sj4ApPXgeCIiIiIDytMc4vP3v0tjIMxJ43K4+6p5nT7ddzoM50wv5JzphWyvbOSBN/fw0sYKZpVkctMFUxmXpz+FRETk2NGThMAuYlMM7umw/gJg49EGJCIiItKfIlHLtf9Yxa7qJkqyUvjj4uO71NV/YkE6P7xkBj+8ZEY/RCkiItL7epIQ+BXwB2NMMmCA+caYjwPfAT7Xm8GJiIiI9LVfvrCZV7dWkZzg4M9XnkBuetJAhyQiItIvejLt4N+MMS7gl0Aq8BCwH/i6tfbhXo5PREREpM88uXo/f351JwC/+PBsZpZkDnBEIiIi/adH0w5aa+8B7jHG5AEOa21l74YlIiIi0rfW7/fwrUfXAvCl0ydw6dySAY5IRESkf/UoIdDCWlt95FYiIiIig8vr26q59uFVBMJRzpiSz43nTRnokERERPpdlxICxphVQJfmJ7TWHn9UEYmIiIj0kWjU8qdXd/DrF7cQtTCzxM3tVxyH02EGOjQREZF+19UeAv/uyyBERERE+pqnOcT1j6xm6abYSMfL543klktnkpzgHODIREREBkaXEgLW2h/1dSAiIiIiPWGtZfmOGv7y2k7W7fcytSiD40dncdyYbI4flU1magIbDni45oH3KK31kehycOulM/jYiaMHOnQREZEB1e0aAsaYE4kVElzZYf1JQMRa+05vBSciIiJyKMFwlKfWHOAvr+1kc3lD6/rXtwd4ffv7ZY4m5Kexr66ZQDjKyOwU/rT4BGaN1GwCIiIiPSkq+AdiUw6u7LC+BLgJOOlogxIRERE5lKZAmPuW7+bvy3dT2RAAIDXRyeXzRnHBzCK2VTbyXmkdq0rr2VXdxI6qJgDOnJLPbz82l6zUxIEMX0REZNAw1napVuD7OxjTCMy21u7ssH4csNZam9GL8R1TjDFuwOPxeHC73QMdjoiIyJBT4fXz6b+9zcYyLwCF7iSuXjiOT8wfTWZqwkHtaxoDrCqtx+GAMyYX4FDxQBERGWK8Xi+ZmZkAmdZab3f27UkPgQBQCOzssH4EEO7B8URERESOaHtlA5+692321zeTl57I9y6cxoWzikl0OQ65T256EudML+zHKEVERI4dPUkIvAT8zBhzqbXWA2CMyQJ+Gt8mIiIi0qve3l3L5/7+Dp7mEOPy0vj7p+czOjd1oMMSERE5ph06pX5o1wOjgD3GmFeMMa8Au4Ci+LYuM8ZcY4xZa4zxxpcVxpgL4tsSjDG/MMasM8Y0GWMOGGPuN8YUdzjGMmOM7bA83KFNtjFmiTHGE1+WxJMYbduMNsY8HT9XtTHmDmOMBhmKiIgMsOfXl7H4LyvxNIc4bnQWj12zUMkAERGRXtDtHgLW2v3GmNnAYmAO0Az8DfiHtTbUzcPtA74NbI+//xTwpDHmuPi244FbgTVANvA74ClgXofj3AN8v8375g7bHwJGAufH398NLAEuBjDGOIFngSrgFCAX+DtggK9185pERESkF1hr+fvy3fzomY1YC+dMK+T3Hz+OlETnQIcmIiIyJHS7qGBfM8bUAjdaa//aybYTgbeAMdba0vi6ZcBqa+03DnG8acBGYEHLVInGmAXACmCqtXZLvFfCM8Aoa+2BeJsrgPuAgq4WZlBRQRERkc7tr2/m36v288KGcrJSE7lo9gjOm1FEZsrBhQA9vhCPvbePf7xVyrbKRgAWnzSaH10yA5ezJ50bRUREhq4+LypojLkEeM5aG4q/PiRr7VPdCaDNOZzAR4E0YjfrnckELFDfYf1iY8wngQrgOeBH1tqWCYlPBjwtyYB4jG8aYzzAQmBLvM36lmRA3AtAEnAC8EpPrklERGQ4a/CHeG59OY+/t483d9a22/a/rVXc/MR6zpiSzyVzizl7aiEbyzw8uLKUZ9eWEQhHAUhJcPL1cybxxdPGY4xmCBAREelNXR0y8G9iNQIq468PxQLd6sdnjJlFLAGQDDQCH7TWbuykXTLwc+ChDlmPB4nVMCgHZgI/IzaU4dz49pa4O6qMb2tpU9HuQqytM8YE27TpLPYkYkmDFsN2ykUREZEW2ysb+dOyHTy77gD+ULR1/YLxOVw2t4TqxgBPrTnA1opGXtxYwYsbK3A5DOHo+70WpxZlsPik0Vx6XAnu5IN7EYiIiMjR61JCwFrr6Ox1L9kCzAWygA8DfzfGnN42KWCMSQAeJlYE8csdYrunzdv1xphtwDvGmOOtte+1NOvkvKbD+q606eg7wA8Os11ERGTY2FLewO//u41n15XRMiJxfH4aHz5+JJfOLWZk9vuFAL961iQ2l3t5avUBnl57gL21zSQnOLhodjGfOGk0x43KUo8AERGRPtaTaQd7lbU2yPtFBd+J1wn4OvBFaE0GPAKMA87qwpiI94AQMCn+uhzobALifN7vFVAOnNR2ozEmG0igQ8+BDn4G/KbN+wxixRBFREQGNWstu2t87K31sb++mf11zeyri72ORC2fOGkMl80t7tKY/Q0HPNz53+08t768dd2i6YV86YwJh72xn1rkZur5bm48bwo7q5vIz0hSbwAREZF+1KOEgDHmbOBsoIAOUxdaaz9zlDEZ4t3w2yQDJgFnWmtrurD/DGI38mXx9yuATGPMfGvtW/HjnkSsHsHyNm2+Z4wZYa1t2W8READePdSJrLWBeBvix+3SBYqIiPREbVOQpRsreG17NePz0vjcqePI6MYNdCgSZeXOWl7cWM5LGyso8/gP2fa90nr+8Mp2rj17IpfMKcHpaP//uHpfkBc2lPP0mjJe314NgDFwwcwivnrmJKYXd724rjGGCfnpXW4vIiIivaPbswwYY35AbIq/d4jddLc7gLX2g9041k+JFQHcS+zp+hXEpiE8n1ghv8eITT14Ee2f1Ndaa4PGmAnEpj/8D1ANTAd+TWzawROttZH4eZ4Dion3OiA27eAea23baQdXx89xI5BDbIaBf1truzztoGYZEBGR3lbu8fPChnKeX1/Oyl01tBlmT156It88dwofO3HUQTfsLZoCYV7dWsWLG8r57+ZKvP5w67Ykl4PROamMzE6hJDuFkdmplGSlsLfOxz3/20mdLzab8Pj8NL5+9iTOmFzAy5sreHrNAV7fXk0oEgvGGLh4djFfPWsikwtVTkdERKQ/Hc0sAz1JCJQB37LWLunWjp0f66/EehqMADzAWuAX1tqXjDFjiRUL7MyZ1tplxphRwAPEigmmE0ssPEtsloHWcsbGmBzgDqBlhoSngK9aa+vbtBkN/BE4i1hC4SHghngvgK5ejxICIiLSKzaXe7n1mY28sb1957gZxW5Om5zP8+vL2VXdBMQK8H3vwmmcOikfAE9ziJc3VfDc+nL+t7WqtWI/xJII50wrZNGMQhZOyCM5ofNawI2BMH9fvpt7XttJfTwx0NHUogwunlPMxbOLGZ2b2mkbERER6Vv9nRCoAeZba3d0a8dhQAkBERE5Wo2BMLcv3cq9b+wmEu8OcMKYbM6fUcR5M4pab7yD4SgPvLmH21/ehqc5dsN++uRYQmD5jvef3gOMyU3l/BlFnDu9kONGZx+yN0FnGvwh7nsjlhjw+sNMyE/j4jnFXDS7mIkF6uYvIiIy0Po7IfALoNFae2u3dhwGlBAQEZGestby/PpyfvT0Rsq9sbH9588o4nsXTmNUzqGfvtf7gtzx8nbuX7G73bR9kwrSuWBmEefPHMG0ERlHXefGFwxT2xSkJCtFNXNEREQGkT5PCBhj2lbSdwCfIta9fy2xiv6trLXf7E4AQ4kSAiIig4+1lgpvgF3VTeyuiS/VTZTWNjM+P40vnTaBWSMzu3y8aNSyu6aJdfs9rNvnYWtlI43+EL5ghOZQJPYzGMFh4NTJ+Zw/o4gzpxaQntR5Hd9AOML6/R7ueHk7r26tAmB0Tio/umQGZ04t6HJcO6saeWhlKVmpCZw/c4Se3ouIiAwT/ZEQeKWLx7PW2rO6E8BQooSAiMjgsa2igcfe28+/V+1vfeJ+KGdOyeerZ03ihDHZB23zBcO8tauWFTtqWLOvng37vTQEwp0c5dASXQ5Om5TP+TOLmFWSycYyD6tL61m9t56NZd7W7v2JTgdfOn08Xz5z4iHH9ouIiIi01a9DBuTQlBAQERlYNY0Bnl5zgMfe28+6/Z7W9U6HYVR2CmNy0xiXl8bY3FRGZKXw/Ppynly9v7Vy/8IJuXz1zIkkJzp5Y1s1r2+v5r3Sunbj8SFWnX96sZtZJZlMH+EmOy2R1EQnKQlOUhKdpCa6qPcFeXFjRbvif4eSk5bIyeNzuX7RZMZr+j0RERHphgFJCBhjJgITgP9Za5uNMcYO8+yCEgIiIgOjtMbHb5du5ek1B1rH0bschjOnFvDh40s4c2oBSa7On7jvrm7irld38Nh7+w668W9RkpXCBybmMm9sDrNKMplUkI7L6ehSbNZatlQ08Pz62NSBe2p8TBuRwdxR2cwdncVxo7IYma1x+SIiItIz/V1UMBd4BDgTsMAka+3O+BSC9dba67t1wCFECQERkd4TDEfZX9/MqOyUQ958VzcGuPO/23lw5Z7Wm/lZJZl8+PgSLp5TTG56UpfPt7++mbtf3cHDb+8lJdHJwgm5fGBiHqdMzGN0Tqpu2EVERGRQ6u+EwP1AAfA5YBMwJ54QWAT81lo7o1sHHEKUEBAROXrRqOXfq/fz6xe3sr++mYwkFyeNz+HkCXl8YGIukwsy8IUi/OW1ndzzv500BSMAnDopj2+dN7VbBQI7E4laDODoxtR8IiIiIgPlaBICnZc8PrxFwHnW2n0dnpZsA8b04HgiIiJYa3l1axW/eH4Lm8pi/y8zBhoCYZZuqmTppkoActMSsUBtUxCI9Qj49gVT+cDEvF6Jw6lEgIiIiAwTPUkIpAG+TtbnAYGjC0dERIajdfs8/Oy5TSzfUQNARrKLL58xkatOHsOOqkaW76hh+Y4a3t5VS008ETA2N5UbzpvC/5s5Qk/zRURERHqgJ0MGngXes9b+nzGmAZgN7AEeBhzW2o/0fpjHBg0ZEBHpnnpfkJ/+ZxOPvLMPiE27d9XJY/jKmRPJTks8qH0wHGX13noaAyFOnZRPQhcL+4mIiIgMVf09ZOBGYJkxZh6QCPwSmAHkAB/owfFERGSYsTZWJ+DHz2xqfeL/weNK+Oa5kxmVk3rI/RJdDuaPy+mvMEVERESGtC4nBIwxlwFPW2s3GmNmA18GIsSGEDwO/MFaW9YnUYqIyJCxp6aJm/+9nte2VQMwqSCdn31oFvPG6kZfREREpD91p4fAo0C1MebvwL3W2u/3UUwiIjIEhSJR7v7fTu54eRuBcJREl4Ovnz2Jz586nkSXuv6LiIiI9LfuJARGA58GPgXcYIxZAfwVeMRa29QXwYmIyNCwfr+Hbz26lo3x2QM+MDGXn1w2i7F5aQMcmYiIiMjw1e2iggDGmNOBzwAfAizwCPBXa+2K3g3v2KKigiIynESjluU7anA44MSxOZ0W+AuEI/z+5e386dUdRKKWrNQE/u/C6Xzo+BI6TF0rIiIiIj3Q30UFsda+CrxqjPkKcAWxngOvG2M2W2tn9OSYIiJy7Pjf1ip+/tzm1if+mSkJnD2tgPNmFHHapHxSEp28V1rHtx5dy/bKRgAunDWCH14yg/yMpIEMXURERETietRDoN0BjJlALCFwDeC21ib0RmDHIvUQEJGhbt0+D794fjOvb48VBMxIcpHgclAbnykAICXBydxRWby5qwZrIS89iR9fNoPzZ44YqLBFREREhqx+7yFgjEkFPkps2MApwE7gN8B9PTmeiIgMbntqmrjtxa08veYAAIlOB1eePIavnDkRd7KLd/bU8cKGcl7cUMH++mZW7KwB4EPHl/D9i6aTlZo4kOGLiIiISCe61UPAGPMBYkmAjxJLJjxOrHbAK30T3rFFPQREZDDyhyIkuRw9GrNf6fVzx3+38fBbewlHLcbAB+eWcN25kxmVk3pQe2stGw54WbGjhhklbhZOyOuNSxARERGRQ+iXHgLGmK3ABGAVcBPwkLXW052TiYhI/zhQ38xTaw7w5OoDbCrzkuh0kJOWSG56IjlpieSlJ1HoTmbuqCzmjc0mL739uP56X5C7Xt3Jfct34Q9FATh9cj43nT+V6cWHTngaY5hZksnMksw+vT4REREROXrdGTLwPLHeAGv6KhgREem5el+Q/6wr59+r9/PWrtp224KRKOVeP+Vef6f7js1N5YQxOcwbm01tU5C7Xt1Bgz8MwAljsvnWeVM4aXxun1+DiIiIiPSfoy4qKO/TkAERGQilNT7+uGw7j723j1Dk/X/T54/L4bK5JZwzrYBQ1FLbGKS6KUBNY5DapgC7a3y8u7uOrZUNdPa/gqlFGdx43hTOmlqgKQJFREREBql+LyooIiIDb1d1E394ZTtPrNpPJBq7o582ws1lc4u5eE4xxVkp7dqXdHjfwtMc4r3SOt7dXcfbu2sJhKNcvXAsl8wpxuFQIkBERERkqFIPgV6kHgIi0h+2Vzbyh1e28+Tq/cTzAJw2OZ9rz5rIvLE5AxuciIiIiPQr9RAQERkmXthQzlcefI9wPBNw9tQCvnb2JOaOyhrYwERERETkmKOEgIjIMWLFjhq+9o9VhKOWUyfl8a3zpjJrpKr5i4iIiEjPdCkhYIy5tqsHtNbe0fNwRESkM+v3e/j8/e8QDEc5b0Yhf/jE8bicjoEOS0RERESOYV3tIXBdF9tZQAkBEZFetLOqkU/d+xaNgTAnj8/l9iuOUzJARERERI5alxIC1tpxfR2IiMhwFAxHufeNXUSilvNmFDKxIKPd9nKPnyv/+hY1TUFmlri5+6oTSE5wDlC0IiIiIjKUqIaAiMgA2V3dxLUPr2LtPg8Av3phCxPy0zh/ZhHnzxjByOwUrvzrSvbXNzM+L437Pj2fjOSEAY5aRERERIaKHk07aIwZCVwCjAYS226z1n6zd0I79mjaQRHpqidX7+d7T6ynMRAmKzWB2SOzWLGjmlDk/X+TE10OguEoRe5kHr3mZEZmpw5gxCIiIiIyGPXrtIPGmLOBp4BdwBRgPTAWMMB73T2eiMhw4guG+cGTG/jXu/sAmD82h9s/PpcRmSl4/SFe2VzJCxvKeWVzFc2hCJkpCdz/2flKBoiIiIhIr+t2DwFjzFvA89ba7xtjGoA5QCXwYHz9n3o/zGODegiIyOGs3+/h2odXsbOqCYeBr501ia+dNbHTAoH+UISVu2oZn5fGqBwlA0RERESkc0fTQ6AnZaqnAX+Pvw4DKdbaRuD7wE3dOZAx5hpjzFpjjDe+rDDGXNBmuzHG/NAYc8AY02yMWWaMmdHhGEnGmN8bY6qNMU3GmKfiQxratsk2xiwxxnjiyxJjTFaHNqONMU/Hj1FtjLnDGNNuOISISE/sqm7iGw+v4uI7X2dnVROF7iQe/NwCrjt38iFnC0hOcHL65HwlA0RERESkz/SkqGATkBR/fQCYAGyIv8/r5rH2Ad8Gtsfffwp40hhznLV2A/At4JvA1cBW4GbgJWPMFGttQ3yf3wEXA1cANcCvgWeMMSdYayPxNg8BI4Hz4+/vBpbE98MY4wSeBaqAU4Dc/9/encfZUZUJH/89SWchW2chC0kgCZGENYRFNtllG0EQcWEAWVxgxnccRURexHlHZ9HBAUU2RxGGMSMqyKKgQARBZDdsIQJZIEBCdhI6K1m6z/tHVZPLpRPS6dtd3X1/38+nPt236rmnTj256br3uedUkRU9AvhSM49JkgCYs3Q1V94/k9ueeYP6hmw01vF7bMe/fmx3Bva23ihJkqRibc2UgTuA36WUrouI7wEnAzcCHweWpZSOalGHIpYCFwI3kBUcrkgpXZpv6wEsBC5KKf04ImrJPsR/JqX0qzxmODAH+EhK6d6I2AV4ATggpfREHnMA8Biwc0ppej4q4S5g+5TSvDzm1Py4hmzpsAunDEgCWLxiLVfcN4Obp8x55yKBR+48hK8ePY7dR9QW3DtJkiR1Jm16UUGyb+z75L9/K//902Tf8p+/Fe0B73xL/0mgN9mH9THAMGByY0xKaW1E/Ak4CPgxsA/QrSxmXkRMy2PuBQ4E6hqLAXnM4xFRl8dMz2OmNRYDcveSjYTYB3hga49LUnVpaEicdcOTvDA/+1t88Ae25avHjGPvHQYU3DNJkiTp3ZpdEEgpvVLy+2rgiy3pQETsQVYA6AmsBE5OKb0QEQflIQvLnrIQGJX/PgxYl1Ja1kTMsJKYRU3selFZzLv2k1JaFhHrSmKa6nsPNk6fAOi7qVhJ1eH30+bzwvzl9O1Zw3Vn7ssBOw4qukuSJElSk5p9UcGIeCUi3vMONyL6R8QrTT3nfUwHJgIHAD8C/icidi3ZXj6nIZpY957ulMU0Fb81MeUuBupKlrnv0y9JnVh9Q+KK+2YC8LmDx1gMkCRJUru2NXcZGA10bWJ9D2BEcxtLKa1LKc1KKU1JKV0MPAd8GViQh5R/Qz+Ejd/mLwC6R0T5WNzymKFN7HpwWcy79pO32Y33jlAo9V2gtmQZuZlYSZ3cXVPnMWvRSvr1rOGzB48pujuSJEnSZm3xlIGIOLHk4bH5HPxGXYEPA69WoE9BVlyYTfZB/WjgmbwP3YHD2Hh7w6eA9XnMzXnMdsDuZHcogGw6Qm1E7JdSejKP2Z/sA/yjJTGXRMR2KaX5+bpjgLX5PpqUUlqbx5C3u9UHLaljq29IXHl/NjrgC4fsSL+e3QrukSRJkrR5zbmGwB35z0R2S75S68mKARc0Z+cR8R3gbrK7AvQlu3Xg4cBxKaUUEVcA34iImcBM4BvAarLbCJJSqouI64HLI+JNYClwGfA8cF8e82JE3ANcFxHn5bv+CXBXSml6/ngy2Z0IJkXEhcDAvJ3rmnuVRknV6c7n5vHy4lX079WNsz80uujuSJIkSe9riwsCKaUuABExG/hgSmlJBfY/FJgEbEc2B38qWTHgD/n27wHbANcCA4AngGNSSitK2jgf2EA2QmAb4H7g7JRSfUnM6cCVbLwbwW+Bfyg5tvqIOD7fzyPAGrKiw9cqcIySOrkN9Q38sGR0QF9HB0iSJKkDiJTe7/p82lIR0Q+oq6uro1+/fkV3R1IbufWpuVxwy3MM6NWNP190JH16bM0dXSVJkqTmW758ObW1tQC1zR3hvjUXFSQiDouIOyNiVkTMjIjfRsQhW9OWJLV3dWvWc/+LC1mzrv492zbUN3DVH7PRAeceOtZigCRJkjqMZr9zjYgzgP8GbiMbhh/AQcD9EXF2SummynZRkorzwrzlnDtpCnOXrWFg7+6cc9BozjxwNLW9smkBtz/zBq++uZpBvbtz5oGjCu6tJEmStOWaPWUgIl4EfpJS+kHZ+q8CX0gp7VLB/nUoThmQOpe7ps7jwlumsmZ9PV27BPUN2d/L3t278rf77cBZB43mtJ8+zpyla/jGR3bm3EPHFtxjSZIkVZuWTBnYmoLAWmC3lNKssvUfAKallHo2q8FOxIKA1DnUNyQunzydax98GYBDdtqWKz49kYdnLeFHD77MSwtWvCt+2z7deejrR9Cru9MFJEmS1LZaUhDYmnevc4APA7PK1n843yZJHVbdmvV8+ZfP8OD0xQCce+iOfP3Y8dR07cJJE0dw4p7D+dOMxfzowZd5YvZSAP7usLEWAyRJktThbPE72Ii4AfgycDlwZURMBB4FEnAwcHa+XZI6pKlz3+LLv3yW2UtW0aOmC5eeMoGP7TXiXTERweHjh3D4+CE88/oyZi9ZxccmjthEi5IkSVL7tcVTBiKiHtgupbQoIk4GLgAarxfwIvCfKaXftE43OwanDEgd07JV6/jPydP5xZOvkxIMr+3JT87cl91H1BbdNUmSJGmz2mrKQDT+klK6Hbi9OTuSpPamoSHxqylzuPSel3hr9XoATt5rBN88fhcG9elRcO8kSZKk1tXcSa/NuwKhJLVTz815i//3m2k8N7cOgJ2H9eXbJ+7G/jsOKrhnkiRJUttobkFgRkRstiiQUhrYgv5IUqtKKXHtgy9z2eTppAR9e9Rw/tHjOPPAUdR07VJ09yRJkqQ209yCwD8Dda3REUlqbSvXbuDCW57j7mkLADhxz+F884RdGNK3au+WKkmSpCrW3ILAL1NKi1qlJ5LUil5ZvJLzJj3FzEUr6dY1+PaJu3Pa/jsU3S1JkiSpMM0pCHj9AEkd0n0vLOT8Xz3LirUbGNqvB9eevg/7jBpQdLckSZKkQm3VXQYkqb17e309L8xfzr3TFvDjh14B4IOjB3DN6Xs7RUCSJEmiGQWBlJJX25LUbs1dtppHX36T5+a8xXNz3+Kl+SvY0LBxYNNZB47ikuN3pXuNf8okSZIkaP41BCSpXUkpceOjr/Kd37/I+vp3z2wa1Ls7e27fn4/vPYITJgwvqIeSJElS+2RBQFKHtfzt9Vz066nv3DVgz+37c8CYgUwY2Z89t69lRP9tiHC2kyRJktQUCwKSOqRpb9TxxZ8/zetLV9Ota3DJR3bhrINGWwCQJEmStpAFAUkdSkqJnz/xOv9y5wusq29gRP9tuOb0vZm4ff+iuyZJkiR1KBYEJHUI9Q2Jh2Yu5mePvsoD0xcDcNQuQ7jsk3vSv1f3gnsnSZIkdTwWBCS1a68uWcUtT83h1qfeYMHytwHo2iX4v8ftzOcPGeMUAUmSJGkrWRCQ1C499vKb/OC+GTw5e+k76wb06sbH9hrBafvtwE5D+xbYO0mSJKnjsyAgqd25ZcocLr7teTY0JCLg0J0G86l9t+eoXYfQo6Zr0d2TJEmSOgULApLajZQSV/9xFpf/YQYAH91zOBf/zc4M779NwT2TJEmSOh8LApLahQ31DfzTb/7KL558HYC/O2wsXz92PF26eI0ASZIkqTVYEJBUuNXrNvClm57h/pcWEQHfPnE3zjxwdNHdkiRJkjo1CwKSCrWg7m3O+9+neG7OW/So6cIPT92L43YfVnS3JEmSpE7PgoCkQjw/t44bHpnNXVPnsb4+0b9XN64/a1/2GTWw6K5JkiRJVcGCgKQ2s6G+gckvLOSGh2cz5bVl76zfd9QALv3EBMYO7lNg7yRJkqTqYkFAUpt4aMZiLr7ted54aw0A3boGJ0wYzjkfGs2Ekf2L7ZwkSZJUhSwISGp1D05fxLk/e4p19Q0M7N2dM/bfgTMOGMWQfj2L7pokSZJUtSwISGpVf5qxmHMnZcWA43YbxhWnTqRnt65Fd0uSJEmqehYEJLWah2cu4dyfTWHdhgaO2XUoV522F926dim6W5IkSZKAQt+ZR8TFEfGXiFgREYsi4o6IGF8WkzaxXFgS82AT239Z1s6AiJgUEXX5Miki+pfF7BARd0bEqohYEhFXRkT3Vk2C1Ek9MmsJn/ufv7B2QwNH7TKUq0/b22KAJEmS1I4U/e78MOAa4ADgaLIRC5MjondJzHZly2eBBNxa1tZ1ZXHnlW2/CZgIHJcvE4FJjRsjoivwO6A3cDBwKnAKcHmLjlCqQo++vLEY8OGdh3Dt6XvTvaboPzeSJEmSShU6ZSCldFzp44g4B1gE7AM8lMcsKIs5CXggpfRKWXOry2NLnrMLWRHggJTSE/m6LwCPRcT4lNJ04BhgV2D7lNK8POYC4MaIuCSltLxlRyt1PvUNiXlvrWH2klW8+uYqXlmc/Xz8lTd5e30DR4wfzLVnWAyQJEmS2qP2dg2B2vzn0qY2RsRQ4HjgrCY2nx4RZwALgbuBb6eUVuTbDgTqGosBACmlxyOiDjgImJ7HTGssBuTuBXqQFSge2Oqjkjqhh2cu4fybn2XxirVNbj9s3GB+dMY+9KjxAoKSJElSe9RuCgIREcD3gYdTStM2EXYWsAK4rWz9z4HZwAJgd+C7wJ5k0xAAhpGNPCi3KN/WGLOwdGNKaVlErCuJKe9zD7KCQaO+m+i31GmklLjx0Vf5t9+9SH1DonvXLuwwqBdjtu39zjJ2cB/2HTWALl2i6O5KkiRJ2oR2UxAArgYmkM3f35TPAj9PKb1dujKldF3Jw2kRMROYEhF7p5Sebgxror0oW78lMaUuBv55M/2VOpW1G+r5pzumcfOUuQCcsvdI/v3k3b2NoCRJktQBtYuJvRFxFXAicERKae4mYg4BxgM/3YImnwbWAzvljxcAQ5uIG8zGUQELKBsJEBEDgG6UjRwo8V2yaQ6Ny8gt6JvUIS1a8TanXfcEN0+ZS5eAbx6/C5d9coLFAEmSJKmDKnSEQD5N4CrgZODwlNLszYR/DngqpfTcFjS9G9kH+fn548eA2ojYL6X0ZL7v/ck+xD9aEnNJRGyXUmp83jHAWuCppnaSUlqbb288ni3omtTxPD+3jnMnTWF+3dv07VnD1aftzWHjBhfdLUmSJEktUPSUgWuA04CTgBUR0fgNfV1KaU1jUET0Az4JXFDeQESMBU4Hfg8sIbtTwOXAM8AjACmlFyPiHuC6iGi8HeFPgLvyOwwATAZeACZFxIXAQOAy4DrvMKBqNmvRCj7148dYs76eHQf35qdn7suOg/sU3S1JkiRJLVT0lIG/J/uW/kGyb/Mbl0+XxZ1KNpf/F020sQ74MNkdAaYDV5J9uD8qpVRfEnc68Hy+bTIwFfhM48Y89njgbbJCws3AHcDXtv7wpI5t3YYGvvKrZ1mzvp79Rg/kjv/zIYsBkiRJUicRKW3qenlqrnwkQ11dXR39+vUrujtSi10+eTpX/XEWtdt0Y/L5hzK0X8+iuyRJkiSpxPLly6mtrQWobe7o9qJHCEhqp556bRnXPDALgO+cvIfFAEmSJKmTsSAg6T1Wrd3AV29+loYEJ+81guMnbFd0lyRJkiRVmAUBSe/xb797kdfeXM3w2p5868Tdiu6OJEmSpFZgQUDSu9z/4kJ+8eTrAFz2qT2p3aZbwT2SJEmS1BosCEh6x5sr13LRrVMB+PzBYzho7LYF90iSJElSa6kpugOSWu6Z15fxs8deY0NDy+4aMmvRSpasXMe4oX342rHjK9Q7SZIkSe2RBQGpg0spceGvpzJr0cqKtNeta/D9T02kZ7euFWlPkiRJUvtkQUDq4B6auYRZi1bSp0cNXz16HBEta2/CyP7sPqK2Mp2TJEmS1G5ZEJA6uOsfng3Ap/bdns8ePKbg3kiSJEnqKLyooNSBzVy4godmLCYCzj5odNHdkSRJktSBWBCQOrAbHnkVgGN2HcoOg3oV2xlJkiRJHYoFAamDWrZqHbc9PReAz37IqQKSJEmSmseCgNRB3fTk66zd0MBuw/ux35iBRXdHkiRJUgdjQUDqgNZtaOBnj70KwOcOHkO09NYCkiRJkqqOBQGpA/r98/NZuHwtg/v24IQJw4vujiRJkqQOyIKA1MGklLjhkexWg2ceMIruNf43liRJktR8fpKQOpgpry1j6tw6utd04bT9dyi6O5IkSZI6KAsCUgdz/Z+z0QEf32sEg/r0KLg3kiRJkjqqmqI7IFWLxSvWsnD52y1qo27Neia/sACAc7zVoCRJkqQWsCAgtYEZC1dw0tWPsGZ9fUXaO2SnbRk/rG9F2pIkSZJUnSwISK2sviFx4a+nsmZ9PX171tC7e8v+2/Xq0ZULjhlfod5JkiRJqlYWBKRWdsPDs3luzlv07VHDH84/jGG1PYvukiRJkiR5UUGpNc1esorLJk8H4Jsn7GIxQJIkSVK7YUFAaiUNDYmLbp3K2g0NHPyBbfnUvtsX3SVJkiRJeocFAamV/PzJ13ly9lJ6de/Kdz++BxFRdJckSZIk6R0WBKRWMHfZav7j9y8C8PVjx7P9wF4F90iSJEmS3s2CgFRhKSW+cfs0Vq2rZ99RAzjzwNFFd0mSJEmS3sO7DEi5lBJzl61hfX1Di9p5aMZiHpqxmO41Xbj0ExPo0sWpApIkSZLaHwsCUu57907nRw++XLH2zj9qHGMH96lYe5IkSZJUSRYEJGB+3Rquf3g2AH171tDS7/T3GzOILxwypuUdkyRJkqRWYkFAAq55YBbrNjSw35iB/OrcA7wjgCRJkqROz4sKqurNXbaaX/1lDgAXHD3OYoAkSZKkqmBBQFXvqvtnsb4+cfAHtmX/HQcV3R1JkiRJahOFFgQi4uKI+EtErIiIRRFxR0SML4u5MSJS2fJ4WUyPiLgqIpZExKqI+G1EjCyLGRARkyKiLl8mRUT/spgdIuLOvI0lEXFlRHRvtQSocK8uWcWvn54LwPlHjyu4N5IkSZLUdooeIXAYcA1wAHA02TUNJkdE77K4e4DtSpaPlG2/AjgZOBU4GOgD3BURXUtibgImAsfly0RgUuPGPPZ3QO+8jVOBU4DLW3SEateuvH8m9Q2Jw8cPZp9RA4rujiRJkiS1mUIvKphSOq70cUScAywC9gEeKtm0NqW0oKk2IqIW+BzwmZTSffm6M4A5wFHAvRGxC1kR4ICU0hN5zBeAxyJifEppOnAMsCuwfUppXh5zAXBjRFySUlpeqeNW+zBr0UruePYNAL7q6ABJkiRJVaboEQLlavOfS8vWH55PKZgREddFxJCSbfsA3YDJjSvyD/TTgIPyVQcCdY3FgDzmcaCuLGZaYzEgdy/QI9+HOpkf3j+ThgRH7zqUCSP7F90dSZIkSWpT7ea2g5Fd2v37wMMppWklm+4GbgFeA8YA/wr8MSL2SSmtBYYB61JKy8qaXJhvI/+5qIndLiqLWVi6MaW0LCLWlcSU97kHWcGgUd/NHqTajekLVnDX1Kz2c/5Rjg6QJEmSVH3aTUEAuBqYQDZ//x0ppV+VPJwWEVPIigPHA7dtpr0AUmlTFYopdTHwz5vpg9qpH/xhBinBR/YYxq7D+xXdHUmSJElqc+2iIBARVwEnAoemlOZuLjalND8iXgN2ylctALpHxICyUQJDgEdLYoY20dxgNo4KWADsX9avAWTTERbStO+SjWpo1BfYbP/VMrdMmcNf57Xscg7r6xu4568LiICvODpAkiRJUpUqtCCQTxO4iuwOAYenlGZvwXMGAdsD8/NVTwHrye5ScHMesx2wO/D1POYxoDYi9kspPZnH7E92zYJHS2IuiYjtUkqNbR8DrM338R75lIW1JX3bgqPW1vrrvDou/PXUirX30QnDGTfUWR6SJEmSqlPRIwSuAU4DTgJWRETjXP26lNKaiOgDfAu4lawAMBr4DrAEuB0gpVQXEdcDl0fEm2QXJLwMeB64L495MSLuAa6LiPPyffwEuCu/wwBkFyV8AZgUERcCA/N2rvMOA+3Dr5/KBl/sObKWQ3Ya3KK2enbrwun7j6pEtyRJkiSpQyq6IPD3+c8Hy9afA9wI1AN7AGcC/cmKAg8An04prSiJPx/YQDZCYBvgfuDslFJ9SczpwJVsvBvBb4F/aNyYUqqPiOOBa4FHgDXATcDXWnB8qpB1Gxr4zbPZRQC/ctQ4jth5yPs8Q5IkSZK0OYUWBFJKmx1jn1JaAxy7Be28DXwpXzYVsxQ4433aeR044f32p7b34PRFLF21jsF9e3DITtsW3R1JkiRJ6vC6FN0BaUvc+nQ2XeDkvUZQ09WXrSRJkiS1lJ+s1O4tXbWOP760CIBT9h5ZcG8kSZIkqXOwIKB277fPvsH6+sTuI/oxfph3BZAkSZKkSrAgoHbv1qffABwdIEmSJEmVZEFA7dr0BSt4/o06unUNTpo4oujuSJIkSVKnYUFA7VrjxQSPGD+Egb27F9wbSZIkSeo8LAio3dpQ38Dtz+TTBfZxuoAkSZIkVZIFAbVbf565hMUr1jKwd3eOGD+k6O5IkiRJUqdiQUDt1q/z6QIn7jmc7jW+VCVJkiSpkvyUpXapbvV6/vDXhQB8wukCkiRJklRxFgTULt05dR7r6hsYP7Qvuw3vV3R3JEmSJKnTqSm6A+o8GhoSdz0/n3umzWdDfWpRW1Pn1gHZ6ICIqET3JEmSJEklLAioIh6ZtYT/uPslnn+jrmJtdq/pwkl7Da9Ye5IkSZKkjSwIqEX+Oq+OS++ZzkMzFgPQp0cNZx00ihH9e7W47d2G92NI354tbkeSJEmS9F4WBKrM8rfX87NHX61IWzMWruTOqfNICbp1DU7ffxRfOvIDDOrToyLtS5IkSZJajwWBKrPi7Q1cNnlGRdv86J7D+dox4xg1qHdF25UkSZIktR4LAlWmV7eunPrB7SvSVo+aLpyyz0gmjOxfkfYkSZIkSW0nUmrZ1eC1UUT0A+rq6uro189b5UmSJEmSWtfy5cupra0FqE0pLW/Oc7u0TpckSZIkSVJ7ZkFAkiRJkqQqZEFAkiRJkqQqZEFAkiRJkqQqZEFAkiRJkqQqZEFAkiRJkqQqZEFAkiRJkqQqZEFAkiRJkqQqZEFAkiRJkqQqZEFAkiRJkqQqZEFAkiRJkqQqZEFAkiRJkqQqZEFAkiRJkqQqZEFAkiRJkqQqVFN0Bzqj5cuXF90FSZIkSVIVaMnnz0gpVbAr1S0iRgBzi+6HJEmSJKnqjEwpvdGcJ1gQqKCICGA4sKLovrRDfcmKJSMxP23JvBfDvBfDvBfDvBfDvBfDvBfDvBfDvBdja/PeF5iXmvkB3ykDFZQnv1kVmWqR1UoAWJFSck5FGzHvxTDvxTDvxTDvxTDvxTDvxTDvxTDvxWhB3rfq38iLCkqSJEmSVIUsCEiSJEmSVIUsCKitrAW+nf9U2zHvxTDvxTDvxTDvxTDvxTDvxTDvxTDvxWjTvHtRQUmSJEmSqpAjBCRJkiRJqkIWBCRJkiRJqkIWBCRJkiRJqkIWBCRJkiRJqkIWBLTFIuLQiLgzIuZFRIqIj5VtHxoRN+bbV0fEPRGxU1nM2Ii4PSIWR8TyiLg5IoY2sa/jI+KJiFgTEUsi4rZWPrx2KyIujoi/RMSKiFgUEXdExPiymIiIb+W5XxMRD0bEbmUxPSLiqjyfqyLitxExsixmQERMioi6fJkUEf3b4DDblbbMeVnss/n/rYmteHjtVhu/1sdFxG/ymOUR8UhEHNEWx9neVDDv5+brl+ev4/5l20dHxPURMTtv4+WI+HZEdG+Dw2x32irvJXGeV6lM3iNiYP43Znpk73dej4grI6K2rJ0B4TkVaNu8l8R7Xm3b17vn1VwF/77/OLJz5ZrIPjv9JiJ2LtlekfOqBQE1R2/gOeAfyjdERAB3ADsCJwF7Aa8B90VE7zymNzAZSMCRwIeA7sCdEdGlpK1TgEnAfwN75nE3tdZBdQCHAdcABwBHAzXA5Ma85r4OfJXs3+aDwALgDxHRtyTmCuBk4FTgYKAPcFdEdC2JuQmYCByXLxPJ/i2qTVvmvNH3gHmVPYwOpy3z/ru8/SOBfYBn85hhFT+q9q9See8F3AN8ZxP72Znsfcd5wG7A+cDfbSa+s2urvHtefbdK5H14vnwN2AM4m+yceX3ZvjynbtSWeW/kebVt8+55daNK/X1/CjgH2AU4Foi8ncb3M5U5r6aUXFyavZB9qP9YyeNx+brdStZ1Bd4EPp8/PgaoB/qVxAzIn3dU/rgGmAt8ruhjbK8LMDjP2aH54wDmAxeVxPQA3gLOyx/XAuuAT5fEDM//PY7NH++St7t/ScwB+brxRR93Z8x5yfq/AV4Eds33M7HoY24PSyu+1rfN2z2kJKZvvu7DRR930cvW5L3s+Yfnz++/Bfu6EHil6GNuD0tr5d3zauvmvSTmk2T3DK/JH3tOLSDvJes9r7Zh3j2vtlneJ+TtjN1MTLPPq44QUKX0yH++3bgipVRP9sb84JKYRPYHhJL4hpKYvYERQENEPBMR8yPi7vIhNFWucYjW0vznGGAY2egLAFJKa4E/AQflq/YBupXFzAOmlcQcCNSllJ4oiXkcqCuJqVatlXMimzJzHfAZYHXrdL/Daq28v0n2RvHMiOgdETVk1fWFZNX4arc1eW/Jvpa+b1R1aK28e17dvErlvRZYnlLakD/2nLp5rZV3z6ub11p597y6eS3Oez664BxgNjDnffbVrPOqBQFVyktkUwS+G9mcue4R8X/JXuzb5TGPA6uASyOiV/7C/k+y12FjzI75z28B/wacACwD/hQRA9vkSNqxfGrG94GHU0rT8tWNQ7EWloUvLNk2DFiXUlr2PjGLmtjtopKYqtOaOc/bvhH4r5TSlAp3vUNrzbynrIR+NNnUphVkhcnzgeNSSm9V8DA6nBbkfWv2NRb4EvBfW9tGZ9HKefe8ugmVyntEDAL+CfhxyWrPqZvQmnn3vLpprZl3z6ub1tK8R8QXI2IlsJJsqsbRKaV1m9jXVp1XLQioIlJK64FTyKYOLCWryB4O3E02VJeU0mKyIUYfJXtR15FVsZ5ujGHja/LfU0q3ppQa586k/LnV7mqy4UJ/28S2VPY4mlhXrjymqfgtaacza82cfwnoB3y3JR3spFot7/nJ+VqyN+aHAPsBvyGb67jdJluoDpXOe5MiYjjZvPdbUko/3Zo2OpnWzLvn1U1rcd4joh/Z3OkXgG+/TxubbKfKtGbePa9uWqvl3fPqZrU07z8nK7QcBswEbo6InuUNteS8akFAFZNSeiqlNBHoD2yXUjoOGEQ2tKUxZnJKaSwwBNg2pfQZsqGMjTHz858vlDxnLfAKsENrH0N7FhFXAScCR6SU5pZsWpD/LK/kDmFj5XEB0D0iBrxPzHvu+EA276m8glkV2iDnR5LNKV0bERuAWfn6KRHxPxU4hA6pjfJ+AnBqSumRlNLTKaUvAmuAsyp0GB1OC/PenP0MBx4AHgPO3YqudiptkHfPq02oRN7zi3/dQ/Ylx8n5lyOl7XhOLdMGefe82oQ2yrvn1TKVyHtKqS6lNDOl9BDwCbILCZ5ctp8WnVctCKji8hfu4shuObgvWYWwPGZJSumtiDiS7MX/23zTU2TXGHjn1hwR0Q0YTTYloepE5mrg48CRKaXZZSGzyf6wHF3ynO5klcRH81VPAevLYrYDdi+JeQyojYj9SmL2JxvF0RhTFdow5/9IdsXvifnykXz9p4FLKnZAHUQb5r1X/rOhrP0GqvC8WKG8b+m+RgAPko0MOyelVP5vUDXaMO+eV0tUKu/5N6WTya6VdGJK6e2ydjynlmjDvHteLdGGefe8WqKV/74HG6/dVpnzanOuQOhS3QvZrbsm5ksimxs0Edgh3/5JsmkCjbcefBW4tayNc8gqt2OBM8guQnJ5WcwVZFdEPobsDcxPyaplA4rOQUF5v5bsqqOHkVUSG5dtSmIuymNOJvvgcxPZrXb6lsT8iOwiJB8mG3p0P9ktYbqWxNxNdmvJA/JlKnBn0TnozDkv2+9oqvhqyG2Vd7KrIS8BbiV74ziO7Hom64A9i85DB877MLJzwufz1/Eh+eOB+fbhZMMd7ycbGfbOvorOQWfOex5zBZ5XK5Z3squnP052jhxb1o7n1ILzXrbf0XhebfW843m1NfK+I3Ax2cWSdyC7UOkdZJ+dhuQxFTmvFp4wl46zsPGWRuXLjfn2fyR7E76O7FuHfwW6l7XxH2QVsXXADLL7b0ZZTDfgMrI3K8uBP1ByO8NqWzaR8wScXRITZBeMmk92IZc/AbuXtdMTuCr/Q7IauBPYvixmIPC/ed6X57/3LzoHnTnnZfGjqe43Lm35Wt8XuDePWU72bd7fFJ2DDp73b22uHbJ7Vze5r6Jz0Jnznsd4Xq1g3tn0+6EEjC6J85xaQN7L9jsaz6tt9Xr3vFrZvA8Hfp//3V5H9hnr55TctpQKnVcjb0ySJEmSJFWRqpvTIUmSJEmSLAhIkiRJklSVLAhIkiRJklSFLAhIkiRJklSFLAhIkiRJklSFLAhIkiRJklSFLAhIkiRJklSFLAhIkiRJklSFLAhIkqSKicx9EXFvE9u+GBF1EbFDEX2TJEnvZkFAkiRVTEopAecA+0fEeY3rI2IMcCnw5ZTS65XcZ0R0q2R7kiRVCwsCkiSpolJKc4AvA5dFxJiICOB64H7gyYj4fUSsjIiFETEpIrZtfG5EHBcRD0fEWxHxZkTcFRFjS7aPjogUEZ+KiAcj4m3gjLY+RkmSOoPICvmSJEmVFRF3AP2BW4F/Aj4ITAGuA34GbEM2aqAmpXRk/pxTgAQ8D/QG/gUYDUxMKTVExGhgNvAqcAHwDLA2pTSvbY5KkqTOw4KAJElqFRExBJgGDAI+AewF7J9SOrYkZiQwBxifUprRRBuDgUXAHimlaSUFga+klH7Y+kchSVLn5ZQBSZLUKlJKi4CfAC+mlG4H9gGOyKcLrIyIlcBLefhYgIgYGxE3RcQrEbGc7MM/QPmFCKe0wSFIktSp1RTdAUmS1KltyBfIvoi4E7ioibj5+c87yUYMfAGYlz9nGtC9LH5VxXsqSVKVsSAgSZLaytPAKcCrKaUN5RsjYhCwC3BeSunP+bqD27aLkiRVD6cMSJKktnINMBD4RUTsFxE7RsQxEXFDRHQFlgFvAudGxAci4kjg+0V2WJKkzsyCgCRJahP5nQA+BHQF7iWbCvBDoA5oSCk1AKeSXWtgGvAD4MJieitJUufnXQYkSZIkSapCjhCQJEmSJKkKWRCQJEmSJKkKWRCQJEmSJKkKWRCQJEmSJKkKWRCQJEmSJKkKWRCQJEmSJKkKWRCQJEmSJKkKWRCQJEmSJKkKWRCQJEmSJKkKWRCQJEmSJKkKWRCQJEmSJKkKWRCQJEmSJKkK/X84OMCp+lMkrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(12,5), dpi=100)\n",
    "plt.plot(cars, label='actual')\n",
    "plt.plot(fc_series, label='forecast')\n",
    "plt.plot(fc_series_covid_pos, label='forecast_with_covid19')\n",
    "plt.fill_between(lower_series.index, lower_series, upper_series, \n",
    "                 color='k', alpha=.15)\n",
    "plt.title('Forecast based on Covid-19')\n",
    "plt.legend(loc='upper left', fontsize=8)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total Vehicles (Millions)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "certified-conflict",
   "metadata": {},
   "source": [
    "## Get the prediction for petrol+diesel "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hollow-breakdown",
   "metadata": {},
   "source": [
    "As mentioned before, we follow a simple formula for predicting the Petrol+Diesel:\n",
    "$$P_D = 1-EV_{frac},$$\n",
    "where $EV_{frac}$ is the fraction of electric vehicles on the road.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "peaceful-password",
   "metadata": {},
   "source": [
    "### Main approach for EV Fraction model: SI-type model\n",
    "\n",
    "Since the EV fraction of the cars on the road is directly connected to the population, we suppose that it follows SI (Susceptible-Infective) model trend. For example, the same trend one can see for the people owning smartphones.\n",
    "\n",
    "We can represent this trend as time series which is given by the differential equation:\n",
    "$$f'(t) = \\lambda f(t)(1-f(t)),$$\n",
    "\n",
    "where $1$ represents the 100% of EVs on the road and $\\lambda$ is the \"infection\" rate. This parameter depends on different factors. We will present two scenarios for the parameter, representing how it can affect the fraction of EV cars on the road and the CO2 emissions derived from it.\n",
    "\n",
    "By the constraints of the Road to Zero, it is predicted, that there will be 43% of EV on the UK roads by 2030. With this restriction, we found the \"infection rate\" for the SI model should be $0.506945$. But this model is presenting very optimistic scenario. We also set the infection rate to slowly decrease after the goal of 43% is achieved.\n",
    "\n",
    "We also created two possible scenarios, where we suggest the factors that affect the infection rate and build the prediction for EV fraction into the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "broad-shower",
   "metadata": {},
   "outputs": [],
   "source": [
    "infect_rate = 0.507#664 # was just found according to the curve, tried several values\n",
    "series = np.ones(31,)\n",
    "series[0] = 0.0102\n",
    "for t in range(1,31):\n",
    "    series[t] = infect_rate*series[t-1]*(1-series[t-1])+series[t-1]\n",
    "\n",
    "series\n",
    "\n",
    "future_ev_fraction_uk = pd.Series(series, index = range(2020,2051))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "sealed-angle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(future_ev_fraction_uk,  linewidth=2.5, color = \"blue\")\n",
    "\n",
    "plt.grid(b=True, color='DarkTurquoise', alpha=0.2, linestyle=':', linewidth=2)\n",
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "plt.xlabel('Year', fontsize=15)\n",
    "plt.ylabel('EV Fraction', fontsize=15)\n",
    "plt.title('EV Fraction future projection')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pointed-begin",
   "metadata": {},
   "source": [
    "### Petrol + Diesel Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "happy-minneapolis",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0\n",
      "0   30055.007998\n",
      "1   29505.222338\n",
      "2   28944.011153\n",
      "3   28357.797144\n",
      "4   27746.604837\n",
      "5   27110.407715\n",
      "6   26449.142534\n",
      "7   25762.723915\n",
      "8   25051.057747\n",
      "9   24314.052354\n",
      "10  23551.626862\n"
     ]
    }
   ],
   "source": [
    "#assumption 1\n",
    "fraction = data['P_D']/ data['Total_Cars']\n",
    "fraction_2019 = fraction.loc[2019]\n",
    "fraction_2030 = (1-0.43)\n",
    "fraction_future = []\n",
    "for i in range(11):\n",
    "    fraction = fraction_2019 + (i+1)*(fraction_2030-fraction_2019)/11\n",
    "    fraction_future.append(fraction)\n",
    "    \n",
    "fraction_future = pd.DataFrame(fraction_future)  \n",
    "P_D_future = fraction_future*fc_year_1 #fraction_forecast*cars_forecast\n",
    "print(P_D_future)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advanced-spare",
   "metadata": {},
   "source": [
    "#### UK Optimistic Scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "elder-european",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0\n",
      "0   36004.156842\n",
      "1   36288.188519\n",
      "2   36489.375740\n",
      "3   36542.325361\n",
      "4   36376.067236\n",
      "5   35890.928163\n",
      "6   34953.411964\n",
      "7   33398.232554\n",
      "8   31048.004640\n",
      "9   27765.378739\n",
      "10  23546.711493\n"
     ]
    }
   ],
   "source": [
    "# UK scenario\n",
    "fraction = data['EV_fraction']\n",
    "fraction_2019 = fraction.loc[2019]\n",
    "fraction_future_UK =  list(future_ev_fraction_uk)[0:11]\n",
    "\n",
    "fraction_future = pd.DataFrame(fraction_future_UK)\n",
    "P_D_future_UK = (1-fraction_future)*fc_year_1\n",
    "print(P_D_future_UK)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "living-black",
   "metadata": {},
   "source": [
    "### Social Networks Impact Model\n",
    "\n",
    "As the score for the impact we consider current Google trend of different terms connected to the EV: \"Electric car\", \"Electric Vehicle\", \"Hybrid cars\". This trend gives us the infection rate for our SI model.\n",
    "\n",
    "Since at earlier time steps the model is close to an exponential model (i.e. $f(t) = e^{\\lambda t}$), we can find $\\lambda$ for each of the terms we have. \n",
    "\n",
    "\n",
    "We finally use this parameter $\\lambda$ to build another SI model for the trend of EV cars which are affected by the social media. The time series for EV fraction may be then presented in the following way:\n",
    "$$f'(t) = \\lambda f(t)(1-f(t)),$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "demanding-hartford",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orig = pd.read_csv('UK_pivotGoogle_orig.csv')\n",
    "df_orig = df_orig.iloc[:-2]\n",
    "\n",
    "df_orig = df_orig.set_index('Row Labels')\n",
    "df_orig.columns = ['Hybrid_cars',\n",
    "       'Electric_cars',\n",
    "       'Car_cost',\n",
    "       'Best_EVs',\n",
    "       'Electric_vehicles', 'Total']\n",
    "\n",
    "df_orig['Avg_EV_trend'] = 1/4*(df_orig['Electric_cars'] + df_orig['Hybrid_cars']\n",
    "                               + df_orig['Best_EVs'] +  df_orig['Electric_vehicles'])#.plot()\n",
    "df_orig_lambdas  = np.diff(df_orig, prepend = 0, axis = 0)/df_orig.shift(1)\n",
    "df_orig_lambdas = df_orig_lambdas[df_orig_lambdas>=0]\n",
    "df_orig_lambdas_column_list = np.nanmean(df_orig_lambdas, axis = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "further-track",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot((df_orig['Avg_EV_trend']-np.mean(df_orig['Avg_EV_trend']))/np.std(df_orig['Avg_EV_trend']),  linewidth=2, color = \"green\")\n",
    "#plt.add_hline(0, 0.087030, linewidth=2 )\n",
    "plt.grid(b=True, color='DarkTurquoise', alpha=0.2, linestyle=':', linewidth=2)\n",
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "plt.xlabel('Year', fontsize=15)\n",
    "plt.ylabel('Score', fontsize=15)\n",
    "plt.title('Google Trend for EV connected phrases')\n",
    "#plt.hlines(0.087030, xmin = 2010, xmax = 2030)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "increasing-decade",
   "metadata": {},
   "outputs": [],
   "source": [
    "infect_rate = df_orig_lambdas_column_list[-1]\n",
    "series = np.ones(11,)\n",
    "series[0] = 0.0102\n",
    "for t in range(1,11):\n",
    "    series[t] = infect_rate*series[t-1]*(1-series[t-1])+series[t-1]\n",
    "\n",
    "future_ev_fraction_SA = pd.Series(series, index = range(2020,2031))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "secondary-texas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data = pd.read_csv('data/GB_all_data.csv')\n",
    "new_data = new_data.set_index('Year')\n",
    "past_EV_fraction = new_data['ULEV_fraction']\n",
    "plt.plot(past_EV_fraction,  linewidth=2)\n",
    "plt.plot(future_ev_fraction_SA,  linewidth=2)\n",
    "\n",
    "plt.grid(b=True, color='DarkTurquoise', alpha=0.2, linestyle=':', linewidth=2)\n",
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "plt.xlabel('Year', fontsize=15)\n",
    "plt.ylabel('EV-Fraction', fontsize=15)\n",
    "plt.title('EV Fraction Projection')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "available-soviet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0\n",
      "0   36004.156842\n",
      "1   36384.870555\n",
      "2   36757.278294\n",
      "3   37098.914127\n",
      "4   37402.154093\n",
      "5   37657.563982\n",
      "6   37853.595491\n",
      "7   37976.276042\n",
      "8   38008.921778\n",
      "9   37931.923865\n",
      "10  37722.686183\n"
     ]
    }
   ],
   "source": [
    "# SA scenario\n",
    "fraction = data['EV_fraction']\n",
    "fraction_2019 = fraction.loc[2019]\n",
    "fraction_future_SA =  list(future_ev_fraction_SA)[0:11]\n",
    "\n",
    "fraction_future = pd.DataFrame(fraction_future_SA)\n",
    "P_D_future_SA = (1-fraction_future)*fc_year_1\n",
    "print(P_D_future_SA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unnecessary-suffering",
   "metadata": {},
   "source": [
    "### Norway Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "juvenile-tampa",
   "metadata": {},
   "outputs": [],
   "source": [
    "norway = pd.DataFrame([0.1, 0.7, 0.9, 3.2, 5.7, 14.5, 21.1, 26.6,33.6,49.1,55.9,74.7], \n",
    "                   index = range(2009,2021),\n",
    "                    columns = ['EV_fraction'])\n",
    "norway['EV_fraction'] = norway['EV_fraction']/100\n",
    "norway['lambdas'] = np.log(norway['EV_fraction']/norway['EV_fraction'].shift(1))#np.diff(norway['EV_fraction'], prepend = 0)/norway['EV_fraction'].shift(1) #equation for finding lambda\n",
    "norway\n",
    "infect_rate = norway['lambdas'].copy().iloc[1:] # get the lambda  # change to np.mean to see faster increase\n",
    "future_ev_NOR = np.ones(11,) # change to 31 for stopping at 2050\n",
    "\n",
    "future_ev_NOR[0] = 0.015 # the starting 1.5% for EV cars in the UK in 2020\n",
    "for t in range(1,11): \n",
    "    future_ev_NOR[t] = infect_rate.iloc[t]*future_ev_NOR[t-1]*(1-future_ev_NOR[t-1]) + future_ev_NOR[t-1]\n",
    "\n",
    "future_ev_NOR = pd.Series(future_ev_NOR, index = range(2020,2031)) #change to range(2020,2051) for stopping at 2050"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "express-spice",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0\n",
      "0   35829.555960\n",
      "1   36163.091253\n",
      "2   35778.264816\n",
      "3   35373.138685\n",
      "4   33653.289260\n",
      "5   32526.247405\n",
      "6   31702.740209\n",
      "7   30648.876388\n",
      "8   28318.947655\n",
      "9   27561.288970\n",
      "10  25270.085575\n"
     ]
    }
   ],
   "source": [
    "# NOR scenario\n",
    "fraction = data['EV_fraction']\n",
    "fraction_2019 = fraction.loc[2019]\n",
    "fraction_future_NOR =  list(future_ev_NOR)[0:11]\n",
    "\n",
    "fraction_future = pd.DataFrame(fraction_future_NOR)\n",
    "P_D_future_NOR = (1-fraction_future)*fc_year_1\n",
    "print(P_D_future_NOR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "governing-correspondence",
   "metadata": {},
   "source": [
    "## Prediction for the Future Population\n",
    "\n",
    "The population model is an exponential growth model. But we use the values provided by the Office for National Statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "subsequent-knitting",
   "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>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>67200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>67500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>67800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>68100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>68400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>68700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>68900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>69200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>69400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>69600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>69800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      population\n",
       "Year            \n",
       "2020    67200000\n",
       "2021    67500000\n",
       "2022    67800000\n",
       "2023    68100000\n",
       "2024    68400000\n",
       "2025    68700000\n",
       "2026    68900000\n",
       "2027    69200000\n",
       "2028    69400000\n",
       "2029    69600000\n",
       "2030    69800000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = pd.read_csv('data/UK_population.csv')\n",
    "population = population.set_index('Year')\n",
    "# print(population)\n",
    "mask_population = (population.index<2031) & (population.index>2019)\n",
    "uk_population = population[mask_population]\n",
    "\n",
    "#uk_population = uk_population.reset_index(drop=True)\n",
    "\n",
    "uk_population"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hollywood-kennedy",
   "metadata": {},
   "source": [
    "## CO2 prediction with Population and P_D\n",
    "\n",
    "\n",
    "The formula for linear regression is the following: \n",
    "\n",
    "```Total_co2 = -2.2372*UK_Population + 1.9087*P_D```\n",
    "\n",
    "\n",
    "We assume that:\n",
    "\n",
    "1. The linear relationship stays unchangeable during the whole period.\n",
    "\n",
    "2. The number of cars/population keeps the trend into the future (using arima model).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "manual-promotion",
   "metadata": {},
   "source": [
    "### Standardize the data\n",
    "\n",
    "Since our model is built using the standardized data, we first standardize the Population and P_D predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "hearing-japanese",
   "metadata": {},
   "outputs": [],
   "source": [
    "beta_UK_Population =  -2.2372\n",
    "beta_petrol_D = 1.9087\n",
    "\n",
    "\n",
    "std_y = std_mat['Total_co2'].iloc[0]\n",
    "mean_y = mean_mat['Total_co2'].iloc[0]\n",
    "\n",
    "uk_population_std = np.array((uk_population-np.mean(uk_population))/np.std(uk_population))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "pending-liberty",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the prediction for 2020-2030 function\n",
    "def prediction(P_D_future):\n",
    "    \n",
    "    P_D_future_std = np.array((P_D_future-np.mean(P_D_future))/np.std(P_D_future))\n",
    "\n",
    "    y = beta_UK_Population*uk_population_std + beta_petrol_D*P_D_future_std\n",
    "\n",
    "    y_unstandardize = y*std_y + mean_y\n",
    "    y_unstandardize = pd.DataFrame(y_unstandardize, index = range(2020,2031), columns = [\"CO2\"])\n",
    "    decrease = (y_unstandardize[\"CO2\"].iloc[-1]-y_unstandardize[\"CO2\"].iloc[0])/y_unstandardize[\"CO2\"].iloc[0]\n",
    "\n",
    "    print('Prediction:\\n',y_unstandardize)\n",
    "    print('Decrease for CO2 emissions:', decrease)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "forward-desktop",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:\n",
      "                CO2\n",
      "2020  35797.222243\n",
      "2021  35137.069029\n",
      "2022  34439.261160\n",
      "2023  33674.075990\n",
      "2024  32809.256003\n",
      "2025  31799.497503\n",
      "2026  30847.207479\n",
      "2027  29351.091744\n",
      "2028  27756.692689\n",
      "2029  25738.497924\n",
      "2030  23294.851458\n",
      "Decrease for CO2 emissions: -0.349255333275596\n"
     ]
    }
   ],
   "source": [
    "# UK Scenario\n",
    "prediction(P_D_future_UK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "alternative-jesus",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:\n",
      "                CO2\n",
      "2020  30857.736044\n",
      "2021  31145.249887\n",
      "2022  31409.272092\n",
      "2023  31586.262665\n",
      "2024  31654.658891\n",
      "2025  31587.778165\n",
      "2026  31616.042612\n",
      "2027  31173.765870\n",
      "2028  30739.929251\n",
      "2029  29995.989459\n",
      "2030  28878.038287\n",
      "Decrease for CO2 emissions: -0.06415563846865756\n"
     ]
    }
   ],
   "source": [
    "# SA Scenario\n",
    "prediction(P_D_future_SA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "aging-examination",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:\n",
      "                CO2\n",
      "2020  36612.116771\n",
      "2021  35997.093332\n",
      "2022  35006.819953\n",
      "2023  34005.942643\n",
      "2024  32318.294566\n",
      "2025  30940.310820\n",
      "2026  29983.968173\n",
      "2027  28644.210367\n",
      "2028  26900.959723\n",
      "2029  25979.014300\n",
      "2030  24255.992574\n",
      "Decrease for CO2 emissions: -0.33748729345723144\n"
     ]
    }
   ],
   "source": [
    "# NOR Scenario\n",
    "prediction(P_D_future_NOR)"
   ]
  }
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