{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#縦孔内輝度値反射シュミレーション\n",
    "\n",
    "#   均等拡散反射\n",
    "#   円筒形空洞\n",
    "#   影あり\n",
    "#   カメラの捉える輝度\n",
    "#　　解析\n",
    "\n",
    "#ライブラリのインポート========================\n",
    "import math\n",
    "# from tkinter import N\n",
    "# from urllib.parse import DefragResult\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import datetime\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "#=========================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#パラメ-タの設定************************************************************************\n",
    "#縦孔の形状\n",
    "r =  50.0          #縦孔の半径[m]\n",
    "d1 = 50.0          #縦孔の壁の高さ（厚さ）[m]\n",
    "d2 = 50.0          #縦孔の空洞の高さ[m]\n",
    "R =  0.1           #壁の反射率[]    \n",
    "\n",
    "cr = 2.0           #空洞壁の位置[]（rの何倍の位置かどうか）\n",
    "\n",
    "#太陽\n",
    "J = 1.0            #太陽の入射エネルギー][J/s/m^2]\n",
    "theta = 45.0       #太陽高度[°]\n",
    "\n",
    "#その他\n",
    "PI = math.pi       #円周率[]\n",
    "\n",
    "#セルの幅\n",
    "dx = 0.5           #x(太陽方位に平行)方向の分解能[m]\n",
    "dy = 0.5           #y(太陽方位に垂直)方向の分解能[m]\n",
    "dz = 0.5           #z(鉛直)方向の分解能[m]\n",
    "dr = 0.5           #r方向の分解能[m]\n",
    "dphi = 1.0         #φ方向の分解能[°]\n",
    "\n",
    "#計算範囲\n",
    "#縦孔底\n",
    "FL_x_min = - 2.0 * r        #x軸の最小値[m]\n",
    "FL_x_max =   2.0 * r        #x軸の最大値[m]\n",
    "FL_y_min = - 2.0 * r        #y軸の最小値[m]\n",
    "FL_y_max =   2.0 * r        #y軸の最大値[m]\n",
    "FL_r_min   =   0.0 * r      #r方向の最小値[m]\n",
    "FL_r_max   =   2.0 * r      #r方向の最大値[m]\n",
    "FL_phi_min =   -90.0        #φ方向の最小値[°]\n",
    "FL_phi_max =   270.0        #φ方向の最大値[°]\n",
    "#縦孔壁\n",
    "WA_z_min =   d2             #z軸の最小値[m]\n",
    "WA_z_max =   d1 + d2        #z軸の最大値[m]\n",
    "WA_phi_min =   -90.0        #φ方向の最小値[°]\n",
    "WA_phi_max =   270.0        #φ方向の最大値[°]\n",
    "#空洞壁\n",
    "CW_z_min =   0              #z軸の最小値[m]\n",
    "CW_z_max =   d2             #z軸の最大値[m]\n",
    "CW_phi_min =   -90.0        #φ方向の最小値[°]\n",
    "CW_phi_max =   270.0        #φ方向の最大値[°]\n",
    "#空洞天井\n",
    "CC_x_min = - 2.0 * r        #x軸の最小値[m]\n",
    "CC_x_max =   2.0 * r        #x軸の最大値[m]\n",
    "CC_y_min = - 2.0 * r        #y軸の最小値[m]\n",
    "CC_y_max =   2.0 * r        #y軸の最大値[m]\n",
    "CC_r_min =   1.0 * r        #z軸の最小値[m]\n",
    "CC_r_max =   2.0 * r             #z軸の最大値[m]\n",
    "CC_phi_min =   -90.0        #φ方向の最小値[°]\n",
    "CC_phi_max =   270.0        #φ方向の最大値[°]\n",
    "#***********************************************************************************************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#カメラのパラメ-タの設定************************************************************************\n",
    "i_CAM = np.array([0.0,0.0,1.5]) * r     #カメラの位置座標\n",
    "s_CAM_i = np.array([0.0,1.0,0.0])       #カメラの向きの初期値(単位ベクトル）y軸＋方向（北向き）\n",
    "\n",
    "elevation_angle = 0.0                  #カメラの仰角[°]\n",
    "azimuth = 90.0                            #カメラの方位[°]\n",
    "\n",
    "h_CAM = 1024                              #縦のピクセル数\n",
    "w_CAM = 1024                              #横のピクセル数\n",
    "\n",
    "pix_size_CAM = 13.0 * 1.0e-6          #ピクセルサイズ［μm］()\n",
    "f_CAM = 10.22 * 1.0e-3                     #焦点距離［mm］\n",
    "\n",
    "pix_CAM = np.zeros ((h_CAM, w_CAM))     #ピクセル数の配列\n",
    "#***********************************************************************************************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#カメラの計算====================================\n",
    "pix = np.zeros((h_CAM,w_CAM))\n",
    "\n",
    "IFOV_rad = 2.0 * math.atan((pix_size_CAM)/ (2.0 * f_CAM))\n",
    "IFOV_deg = math.degrees(IFOV_rad)\n",
    "\n",
    "FOV_h_rad = 2.0 * math.atan((pix_size_CAM * h_CAM) / (2.0 * f_CAM))\n",
    "FOV_h_deg = math.degrees(FOV_h_rad)\n",
    "\n",
    "FOV_w_rad = 2.0 * math.atan((pix_size_CAM * w_CAM) / (2.0 * f_CAM))\n",
    "FOV_w_deg = math.degrees(FOV_w_rad)\n",
    "\n",
    "#============================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#関数の定義====================================\n",
    "#rootを求める関数\n",
    "def SQRT(x):\n",
    "    return np.sqrt(x)\n",
    "\n",
    "#角度からsinを求める関数\n",
    "def SIN(a):\n",
    "    return np.sin(math.radians(a))\n",
    "\n",
    "#角度からcosを求める関数\n",
    "def COS(a):\n",
    "    return np.cos(math.radians(a))\n",
    "\n",
    "#2つベクトルよりcosθを求める関数\n",
    "def DCOS(a, b):\n",
    "    over = (a[0] * b[0]) + (a[1] * b[1]) + (a[2] * b[2])\n",
    "    under = (SQRT(((a[0]) ** 2.0) + ((a[1]) ** 2.0) + ((a[2]) ** 2.0))) * (SQRT(((b[0]) ** 2.0) + ((b[1]) ** 2.0) + ((b[2]) ** 2.0)))\n",
    "    if under == 0.0:\n",
    "        return(0.0)\n",
    "    else:\n",
    "        return (over / under)\n",
    "\n",
    "#2点より単位ベクトルをもとめる関数\n",
    "def VEC2(a,b):  #bベクトルからaベクトル\n",
    "    I = a[0] - b[0]\n",
    "    J = a[1] - b[1]\n",
    "    K = a[2] - b[2]\n",
    "    under = SQRT((I ** 2.0) + (J ** 2.0) + (K ** 2.0))\n",
    "    if under == 0.0:\n",
    "        return((0.0, 0.0, 0.0))\n",
    "    else:\n",
    "        return((I/under), (J/under), (K/under))\n",
    "\n",
    "#2点よりベクトルをもとめる関数\n",
    "def VEC(a,b):  #bベクトルからaベクトル\n",
    "    I = a[0] - b[0]\n",
    "    J = a[1] - b[1]\n",
    "    K = a[2] - b[2]\n",
    "    return(I, J, K)\n",
    "\n",
    "#2点間の距離の2乗を求める関数\n",
    "def LL(a,b):\n",
    "    return (((a[0]- b[0]) ** 2.0) + ((a[1]- b[1]) ** 2.0) + ((a[2]- b[2]) ** 2.0))\n",
    "\n",
    "#グリッドの設定\n",
    "def grid (d,max,min):\n",
    "    l = int((max - min) / d)\n",
    "    r = np.zeros(l+1)\n",
    "    for i in range(l+1):\n",
    "        r[i] = min + (d * float(i))\n",
    "    return r\n",
    "\n",
    "#反射パターン\n",
    "def Ref(pattern,Inc,Emi,Nor):\n",
    "    if pattern == \"Lambert\":  #均等拡散反射（ランバート反射）\n",
    "        return DCOS(Nor,Emi) * R / PI\n",
    "    elif pattern == \"end\":\n",
    "        return DCOS(Emi,Inc) * R / PI\n",
    "    else:\n",
    "        print(\"stop\")\n",
    "\n",
    "#2次関数の解の存否の関数\n",
    "def dis(A,B,C):#A,B,Cの値\n",
    "    D = (B ** 2) - (4.0 * A * C)\n",
    "    if(D < 0.0):\n",
    "        return 0 #解がないとき0を返す\n",
    "    else:\n",
    "        return 1 #解があるとき1を返す\n",
    "\n",
    "#2次関数の解の公式の関数\n",
    "def kai(A,B,C,s):#A,B,Cの値，sは−1or1\n",
    "        return ((- 1.0 * B )+ (s * SQRT(B ** 2 - (4.0 * A * C))))/(2.0 * A )\n",
    "\n",
    "#円筒（縦孔壁面）とのあたり判定\n",
    "def HitCyl(s,d,r,hmax,hmin):\n",
    "    A = (d[0] ** 2) + (d[1] ** 2)\n",
    "    B = 2.0 * ((s[0] * d[0]) + (s[1] * d[1]))\n",
    "    C = (s[0] ** 2) + (s[1] ** 2) - (r ** 2)\n",
    "    if dis(A,B,C) < 0.0:\n",
    "        return 0\n",
    "    else:\n",
    "        t1 = kai(A, B, C, 1.0) \n",
    "        t2 = kai(A, B, C, -1.0 )\n",
    "        if t1 <= 0.0 and t2 <= 0.0 :\n",
    "            return 0\n",
    "        elif t1 > 0 and t2 <=0: \n",
    "            z = s[2] + (t1 * d[2])\n",
    "            if z >= hmin and  z <= hmax:\n",
    "                return 1\n",
    "            else:\n",
    "                return 0\n",
    "        elif t1 <= 0 and t2 > 0: \n",
    "            z = s[2] + (t2 * d[2])\n",
    "            if z >= hmin and  z <= hmax:\n",
    "                return 1\n",
    "            else:\n",
    "                return 0\n",
    "        elif t1 >= t2 :\n",
    "            z = s[2] + (t1 * d[2])\n",
    "            if z >= hmin and  z <= hmax:\n",
    "                return 1\n",
    "            else:\n",
    "                return 0\n",
    "        else:\n",
    "            z = s[2] + (t2 * d[2])\n",
    "            if z >= hmin and  z <= hmax:\n",
    "                return 1\n",
    "            else:\n",
    "                return 0\n",
    "        \n",
    "#円盤（縦孔開口部）とのあたり判定\n",
    "def HitDisk(s,d,r,h):  \n",
    "    if d[2]== 0.0:\n",
    "        if s[2] == h:\n",
    "            return 1\n",
    "        else:\n",
    "            # print(\"yoko\")\n",
    "            return 0\n",
    "    elif (((s[0]+ ((h - s[2]) * d[0] / d[2]))**2)+(((s[1]+((h - s[2]) * d[1] / d[2]))**2))) <= (r ** 2):\n",
    "        return 1   \n",
    "    else:\n",
    "        # print(\"soto\")\n",
    "        return 0\n",
    "\n",
    "#ベクトルの角度を比較する関数(radで入力)                \n",
    "def CompVec (A,B,c):\n",
    "    a = math.acos(DCOS(A,B))\n",
    "    if a <= c:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0    \n",
    "\n",
    "#ある点（位置ベクトル）と最も近い点を参照する関数\n",
    "def SerchNearPoint(P,F,E,n): #P(x,y,z)点,F[i,j](x,y,z)(検索配列)，E[i,j]（輝度）,n[i,j]\n",
    "    L_max = np.finfo(np.float64).max\n",
    "    E_max = np.zeros(4)\n",
    "    for i in range(np.shape(F)[0]):\n",
    "        for j in range(np.shape(F)[1]):\n",
    "            if L_max > LL(P,F[i,j]):\n",
    "                L_max = LL(P,F[i,j])\n",
    "                E_max[0] = E[i,j]\n",
    "                E_max[1] = n[i,j,0]\n",
    "                E_max[2] = n[i,j,1]\n",
    "                E_max[3] = n[i,j,2]            \n",
    "    return E_max     \n",
    "\n",
    "#円筒（縦孔壁面）との交差点を求める関数\n",
    "def HitCyl_2(s,d,r,hmax,hmin):\n",
    "    A = (d[0] ** 2) + (d[1] ** 2)\n",
    "    B = 2.0 * ((s[0] * d[0]) + (s[1] * d[1]))\n",
    "    C = (s[0] ** 2) + (s[1] ** 2) - (r ** 2)\n",
    "    if dis(A,B,C) < 0.0:\n",
    "        return 0\n",
    "    else:\n",
    "        t1 = kai(A, B, C, 1.0) \n",
    "        t2 = kai(A, B, C, -1.0 )\n",
    "        if t1 <= 0.0 and t2 <= 0.0 :\n",
    "            return 0\n",
    "        elif t1 > 0 and t2 <=0: \n",
    "            z = s[2] + (t1 * d[2])\n",
    "            if z >= hmin and  z <= hmax:\n",
    "                return t1\n",
    "            else:\n",
    "                return 0.0\n",
    "        elif t1 <= 0 and t2 > 0: \n",
    "            z = s[2] + (t2 * d[2])\n",
    "            if z >= hmin and  z <= hmax:\n",
    "                return t2\n",
    "            else:\n",
    "                return 0.0\n",
    "        elif t1 >= t2 :\n",
    "            z = s[2] + (t1 * d[2])\n",
    "            if z >= hmin and  z <= hmax:\n",
    "                return t1\n",
    "            else:\n",
    "                return 0.0\n",
    "        else:\n",
    "            z = s[2] + (t2 * d[2])\n",
    "            if z >= hmin and  z <= hmax:\n",
    "                return t2\n",
    "            else:\n",
    "                return 0.0\n",
    "\n",
    "#一つの位置ベクトルから4隅の位置ベクトルを求める関数\n",
    "def VecOneFour(F,u,v,h,w):\n",
    "    A = np.zeros([4,3])\n",
    "    A[0] = F + ((h**2.0 + w**2.0)**0.5 *0.5 * ((u + v)/np.linalg.norm(u + v)))\n",
    "    A[1] = F + ((h**2.0 + w**2.0)**0.5 *0.5 * ((u - v)/ np.linalg.norm(u - v)))\n",
    "    A[2] = F + ((h**2.0 + w**2.0)**0.5  *0.5* ((-u + v)/np.linalg.norm(-u + v)))\n",
    "    A[3] = F + ((h**2.0 + w**2.0)**0.5  *0.5* ((-u - v)/np.linalg.norm(-u - v)))\n",
    "    return A\n",
    "\n",
    "#3点から面積を求める関数\n",
    "def CalArea(A,B,C): #3点の位置ベクトルを入力（A(x,y,z),B(x,y,z),C(x,y,z)）\n",
    "    return (1/2.0) *  np.linalg.norm(np.cross((B-A),(C-A)))\n",
    "\n",
    "#ベクトルをx,y,z軸回りに回転させる関数(x→y→zの順)\n",
    "def LotVec(A,t,p,o): \n",
    "    Rx = np.array([[1, 0, 0],\n",
    "                   [0, np.cos(t), -np.sin(t)],\n",
    "                   [0, np.sin(t), np.cos(t)]])\n",
    "    Ry = np.array([[np.cos(p), 0,np.sin(p)],\n",
    "                   [0, 1, 0],\n",
    "                   [-np.sin(p), 0, np.cos(p)]])\n",
    "    Rz = np.array([[np.cos(o), -np.sin(o), 0],\n",
    "                   [np.sin(o), np.cos(o), 0],\n",
    "                   [0, 0, 1]])\n",
    "    R = Rz.dot(Ry).dot(Rx)\n",
    "    B = np.dot(R,A)\n",
    "    return B\n",
    "\n",
    "#底面とのあたり判定\n",
    "def HitFL(s,d,n,x_max,x_min,y_max,y_min): #s(始点の位置)，d(方向ベクトル)，n（底面の法線ベクトル）\n",
    "    t = -(np.dot(s,n))/(np.dot(d,n))\n",
    "    x = s[0] + (t * d[0])\n",
    "    y = s[1] + (t * d[1])\n",
    "    z = s[2] + (t * d[2])\n",
    "    if t < 0.0:\n",
    "        return 0\n",
    "    elif(x_min <= x <= x_max) and (y_min <= y <= y_max):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def HitFL2(s,d,n,x_max,x_min,y_max,y_min): #s(始点の位置)，d(方向ベクトル)，n（底面の法線ベクトル）\n",
    "    t = -(np.dot(s,n))/(np.dot(d,n))\n",
    "    x = s[0] + (t * d[0])\n",
    "    y = s[1] + (t * d[1])\n",
    "    z = s[2] + (t * d[2])\n",
    "\n",
    "    if t < 0.0:\n",
    "        return 0\n",
    "    elif(x_min <= x <= x_max) and (y_min <= y <= y_max):\n",
    "        return t\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "#空洞天井とのあたり判定\n",
    "def HitCC(s,d,n,p,x_max,x_min,y_max,y_min): #s(始点の位置)，d(方向ベクトル)，n（底面の法線ベクトル）\n",
    "    t = ((np.dot(p,n))-(np.dot(s,n)))/(np.dot(d,n))\n",
    "    x = s[0] + (t * d[0])\n",
    "    y = s[1] + (t * d[1])\n",
    "    z = s[2] + (t * d[2])\n",
    "    if t < 0.0:\n",
    "        return 0\n",
    "    elif(x_min <= x <= x_max) and (y_min <= y <= y_max):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def HitCC2(s,d,n,p,x_max,x_min,y_max,y_min): #s(始点の位置)，d(方向ベクトル)，n（底面の法線ベクトル）\n",
    "    t = ((np.dot(p,n))-(np.dot(s,n)))/(np.dot(d,n))\n",
    "    x = s[0] + (t * d[0])\n",
    "    y = s[1] + (t * d[1])\n",
    "    z = s[2] + (t * d[2])\n",
    "    if t < 0.0:\n",
    "        return 0\n",
    "    elif(x_min <= x <= x_max) and (y_min <= y <= y_max):\n",
    "        return t\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "#============================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#カメラのグリッド設定=========================(カメラの回転に注意)\n",
    "SUR_CCD = \"CCD\"\n",
    "a_CCD = np.linspace((((-1*w_CAM/2)* pix_size_CAM) + (pix_size_CAM/2)),(((w_CAM/2)* pix_size_CAM) - (pix_size_CAM/2)),num = w_CAM)\n",
    "b_CCD = np.linspace((((-1*h_CAM/2)* pix_size_CAM) + (pix_size_CAM/2)),(((h_CAM/2)* pix_size_CAM) - (pix_size_CAM/2)),num = h_CAM)\n",
    "#===========================================\n",
    "\n",
    "#縦孔底面のグリッド設定=========================\n",
    "SUR_FL = \"FL\"\n",
    "a_FL = grid(dx, FL_x_max, FL_x_min)\n",
    "b_FL = grid(dy, FL_x_max, FL_x_min)\n",
    "#===========================================\n",
    "\n",
    "#縦孔壁面のグリッド設定=========================\n",
    "SUR_WA = \"WA\"\n",
    "a_WA = grid(dz, WA_z_max, WA_z_min)\n",
    "b_WA = grid(dphi, WA_phi_max, WA_phi_min)\n",
    "#===========================================\n",
    "\n",
    "#空洞壁面のグリッド設定=========================\n",
    "SUR_CW = \"CW\"\n",
    "a_CW = grid(dz, CW_z_max, CW_z_min)\n",
    "b_CW = grid(dphi, CW_phi_max, CW_phi_min)\n",
    "#===========================================\n",
    "\n",
    "#空洞天井のグリッド設定=========================\n",
    "SUR_CC = \"CC\"\n",
    "a_CC = grid(dr, CC_r_max, CC_r_min)\n",
    "b_CC = grid(dphi, CC_phi_max, CC_phi_min)\n",
    "#===========================================\n",
    "\n",
    "# #空洞天井のグリッド設定=========================\n",
    "# SUR_CC = \"CC\"\n",
    "# a_CC = grid(dr, CC_r_max, CC_r_min)\n",
    "# b_CC = grid(dphi, CC_phi_max, CC_phi_min)\n",
    "# #===========================================\n",
    "#空洞天井のグリッド設定=========================\n",
    "SUR_CC = \"CC\"\n",
    "a_CC = grid(dx, CC_x_max, CC_x_min)\n",
    "b_CC = grid(dy, CC_y_max, CC_y_min)\n",
    "#===========================================\n",
    "\n",
    "#計算ループの設定===============================\n",
    "#カメラ\n",
    "M_CAM = len(a_CCD)      #観測面のセル数\n",
    "N_CAM = len(b_CCD)      #観測面のセル数\n",
    "#縦孔底\n",
    "M_FL = len(a_FL)        #反射面のセル数\n",
    "N_FL = len(b_FL)        #反射面のセル数\n",
    "#縦孔壁\n",
    "M_WA = len(a_WA)        #反射面のセル数\n",
    "N_WA = len(b_WA)        #反射面のセル数\n",
    "#空洞壁\n",
    "M_CW = len(a_CW)        #反射面のセル数\n",
    "N_CW = len(b_CW)        #反射面のセル数\n",
    "#空洞天井\n",
    "M_CC = len(a_CC)        #反射面のセル数\n",
    "N_CC = len(b_CC)        #反射面のセル数\n",
    "#============================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ベクトルの設定*****************************************\n",
    "#太陽光ベクトル(i,j,k)\n",
    "I = np.array([-COS(theta), 0.0, -SIN(theta)])\n",
    "\n",
    "#カメラのベクトル設定===========================================================================\n",
    "\n",
    "#カメラの回転\n",
    "s_CAM = LotVec(s_CAM_i,np.radians(elevation_angle),np.radians(0.0),np.radians(- azimuth))\n",
    "#カメラの基底直行ベクトルを計算\n",
    "X = s_CAM\n",
    "Y = (np.cross(np.array([0.0,0.0,1.0]),X)/np.linalg.norm(np.cross(np.array([0.0,0.0,1.0]),X)))\n",
    "Z = np.cross(X,Y)\n",
    "#============================================================================================\n",
    "\n",
    "#カメラ(CCD)の法線ベクトルs=============\n",
    "s_CAM = s_CAM\n",
    "#カメラ(CCD)の位置ベクトルi\n",
    "i_CAM = i_CAM\n",
    "#カメラ(CCD)の微小面積dS\n",
    "dS = pix_size_CAM ** 2\n",
    "#仮想スクリーンの位置ベクトル座標\n",
    "i_SCR_i = np.zeros((M_CAM, N_CAM, 3))\n",
    "i_SCR = np.zeros((M_CAM, N_CAM, 3))\n",
    "for i in range(M_CAM):\n",
    "   for j in range(N_CAM):\n",
    "        i_SCR_i[i,j,0] = a_CCD[i]\n",
    "        i_SCR_i[i,j,1] = f_CAM\n",
    "        i_SCR_i[i,j,2] = b_CCD[j]\n",
    "        i_SCR[i,j]= LotVec(i_SCR_i[i,j], np.radians(elevation_angle), np.radians(0.0), np.radians(-azimuth))\n",
    "        i_SCR[i,j]= np.add(i_SCR[i,j],i_CAM)\n",
    "        \n",
    "#カメラの位置ベクトルからのスクリーン面に向かうベクトルH\n",
    "H = np.zeros((M_CAM, N_CAM, 3))        \n",
    "for i in range(M_CAM):\n",
    "    for j in range(N_CAM):\n",
    "        H[i,j] = np.array(VEC2(i_SCR[i,j],i_CAM))\n",
    "#===================================\n",
    " \n",
    "#縦孔底面の法線ベクトルn================\n",
    "n_FL = np.zeros((M_FL, N_FL, 3))\n",
    "for i in range(M_FL):\n",
    "    for j in range(N_FL):\n",
    "        n_FL[i,j,0] = 0.0\n",
    "        n_FL[i,j,1] = 0.0\n",
    "        n_FL[i,j,2] = 1.0\n",
    "\n",
    "#縦孔底面の位置ベクトルF\n",
    "F_FL = np.zeros((M_FL, N_FL, 3))\n",
    "for i in range(M_FL):\n",
    "   for j in range(N_FL):\n",
    "        F_FL[i,j,0] = a_FL[i]\n",
    "        F_FL[i,j,1] = b_FL[j]\n",
    "        F_FL[i,j,2] = 0.0\n",
    "\n",
    "#縦孔底面の微小面積dA\n",
    "dA_FL = dx * dy\n",
    "#=====================================\n",
    "\n",
    "#縦孔壁面の法線ベクトルn================\n",
    "n_WA = np.zeros((M_WA, N_WA, 3))\n",
    "for i in range(M_WA):\n",
    "    for j in range(N_WA):\n",
    "        n_WA[i,j,0] = - COS(b_WA[j])\n",
    "        n_WA[i,j,1] = - SIN(b_WA[j])\n",
    "        n_WA[i,j,2] = 0.0\n",
    "\n",
    "#縦孔壁面の位置ベクトルF\n",
    "F_WA = np.zeros((M_WA, N_WA, 3))\n",
    "for i in range(M_WA):\n",
    "   for j in range(N_WA):\n",
    "        F_WA[i,j,0] = r * COS(b_WA[j])\n",
    "        F_WA[i,j,1] = r * SIN(b_WA[j])\n",
    "        F_WA[i,j,2] = a_WA[i]\n",
    "\n",
    "#縦孔底面の微小面積dA\n",
    "dA_WA = dz * r * dphi\n",
    "#=====================================\n",
    "\n",
    "#空洞壁面の法線ベクトルn================\n",
    "n_CW = np.zeros((M_CW, N_CW, 3))\n",
    "for i in range(M_CW):\n",
    "    for j in range(N_CW):\n",
    "        n_CW[i,j,0] = - COS(b_CW[j])\n",
    "        n_CW[i,j,1] = - SIN(b_CW[j])\n",
    "        n_CW[i,j,2] = 0.0\n",
    "\n",
    "#空洞壁面の位置ベクトルF\n",
    "F_CW = np.zeros((M_CW, N_CW, 3))\n",
    "for i in range(M_CW):\n",
    "   for j in range(N_CW):\n",
    "        F_CW[i,j,0] = r * cr * COS(b_CW[j])\n",
    "        F_CW[i,j,1] = r * cr * SIN(b_CW[j])\n",
    "        F_CW[i,j,2] = a_CW[i]\n",
    "\n",
    "#縦孔底面の微小面積dA\n",
    "dA_CW = dz * r * cr * dphi\n",
    "#=====================================\n",
    "\n",
    "#空洞天井の法線ベクトルn================\n",
    "# n_CC = np.zeros((M_CC, N_CC, 3))\n",
    "# for i in range(M_CC):\n",
    "#     for j in range(N_CC):\n",
    "#         n_CC[i,j,0] = 0.0\n",
    "#         n_CC[i,j,1] = 0.0\n",
    "#         n_CC[i,j,2] = 1.0\n",
    "\n",
    "# #空洞天井の位置ベクトルF\n",
    "# F_CC = np.zeros((M_CC, N_CC, 3))\n",
    "# for i in range(M_CC):\n",
    "#    for j in range(N_CC):\n",
    "#         F_CC[i,j,0] = a_CC[i] * COS(b_CW[j])\n",
    "#         F_CC[i,j,1] = a_CC[i] * SIN(b_CW[j])\n",
    "#         F_CC[i,j,2] = d2\n",
    "#空洞天井の法線ベクトルn================\n",
    "n_CC = np.zeros((M_CC, N_CC, 3))\n",
    "for i in range(M_CC):\n",
    "    for j in range(N_CC):\n",
    "        n_CC[i,j,0] = 0.0\n",
    "        n_CC[i,j,1] = 0.0\n",
    "        n_CC[i,j,2] = -1.0\n",
    "\n",
    "#空洞天井の位置ベクトルF\n",
    "F_CC = np.zeros((M_CC, N_CC, 3))\n",
    "for i in range(M_CC):\n",
    "   for j in range(N_CC):\n",
    "        F_CC[i,j,0] = a_CC[i]\n",
    "        F_CC[i,j,1] = b_CC[j]\n",
    "        F_CC[i,j,2] = d2\n",
    "\n",
    "#縦孔底面の微小面積dA\n",
    "dA_CC = dx * dy\n",
    "#====================================\n",
    "\n",
    "# #空洞天井の微小面積dA\n",
    "# dA_CC = np.zeros((M_CC))\n",
    "# for i in range(M_CC):\n",
    "#     dA_CC[i] = dr * a_CC[i] * dphi \n",
    "# #=====================================\n",
    "\n",
    "#*************************************************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#放射照度データの入力（CSV）入力****************************\n",
    "#太陽直達光\n",
    "E_StoFL = np.loadtxt('PPP_StoFL_t45.0h1.0h1.0_cr2.0_2023-03-10.csv',delimiter=',') \n",
    "E_StoWA = np.loadtxt('PPP_StoWA_t45.0h1.0h1.0_cr2.0_2023-03-10.csv',delimiter=',') \n",
    "E_StoCW = np.loadtxt('PPP_StoCW_t45.0h1.0h1.0_cr2.0_2023-03-10.csv',delimiter=',') \n",
    "\n",
    "#底からの反射光\n",
    "E_FLtoWA = np.loadtxt('PPP_FLtoWA_t45.0h1.0h1.0_cr2.0_2023-03-08.csv',delimiter=',') \n",
    "E_FLtoCW = np.loadtxt('PPP_FLtoCW_t45.0h1.0h1.0_cr2.0_2023-03-08.csv',delimiter=',') \n",
    "E_FLtoCC = np.loadtxt('PPP_FLtoCC_t45.0h1.0h1.0_cr2.0_2023-03-09.csv',delimiter=',') \n",
    "\n",
    "#縦孔壁からの反射光\n",
    "E_WAtoFL = np.loadtxt('PPP_WAtoFL_t45.0h1.0h1.0_cr2.0_2023-03-10.csv',delimiter=',') \n",
    "E_WAtoWA = np.loadtxt('PPP_WAtoWA_t45.0h1.0h1.0_cr2.0_2023-03-10.csv',delimiter=',') \n",
    "E_WAtoCW = np.loadtxt('PPP_WAtoCW_t45.0h1.0h1.0_cr2.0_2023-03-10.csv',delimiter=',') \n",
    "\n",
    "#空洞壁からの反射光\n",
    "E_CWtoFL = np.loadtxt('PPP_CWtoFL_t45.0h1.0h1.0_cr2.0_2023-03-10.csv',delimiter=',') \n",
    "E_CWtoWA = np.loadtxt('PPP_CWtoWA_t45.0h1.0h1.0_cr2.0_2023-03-10.csv',delimiter=',') \n",
    "E_CWtoCW = np.loadtxt('PPP_CWtoCW_t45.0h1.0h1.0_cr2.0_2023-03-10.csv',delimiter=',') \n",
    "E_CWtoCC = np.loadtxt('PPP_CWtoCC_t45.0h1.0h1.0_cr2.0_2023-03-10.csv',delimiter=',') \n",
    "\n",
    "#******************************************************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#輝度度データの入力（CSV）入力****************************\n",
    "\n",
    "\n",
    "#空洞壁からの反射光\n",
    "E_CAM_FL = np.loadtxt('CAM_FL_AZ90.0EL0.0_PO75.0th45.0cr2.0R0.1_2023-04-08.csv',delimiter=',') \n",
    "E_CAM_WA = np.loadtxt('CAM_WA_AZ90.0EL0.0_PO75.0th45.0cr2.0R0.1_2023-04-08.csv',delimiter=',') \n",
    "E_CAM_CW = np.loadtxt('CAM_CW_AZ90.0EL0.0_PO75.0th45.0cr2.0R0.1_2023-04-07.csv',delimiter=',') \n",
    "E_CAM_CC = np.loadtxt('CAM_CC_AZ90.0EL0.0_PO75.0th45.0cr2.0R0.1_2023-04-08.csv',delimiter=',') \n",
    "\n",
    "#データの結合\n",
    "E_CAM_ALL = np.add(np.add(np.add(E_CAM_WA.T,E_CAM_CW.T),E_CAM_FL.T),E_CAM_CC.T)\n",
    "#******************************************************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xa665hgFg999/Pxs3bhwrLS1lq1ev9t0+3aTjcw2L7tzlZQsWLPDdduPGjWzo0KFs9OjR7E9/+hN77bXX2PPPP8/uv/9+dtppp7H6+vqU+pYIjY2NrKamho0fP57NmzePvfDCC+yYY45hpaWlrgkCR4wYwUaMGCGVrVy5kpWUlLBjjjmGvfDCC+wf//gHGzdunOfkjal8p3XnMS+7+eabA7e//vrrWXFxMfvd737HXn/9dfuczeTkjRzd3D/dea1hzLqmHHvssUn1lTFrvp/x48ezyspKac6gribsOcgYsz87kXXr1rFnnnmGPfPMM+zkk0+2z7FnnnmGLV26tNuOg+iZdKtT1BX87//+L/bdd1+ccMIJ2vU1NTWIRqP41a9+ha1bt8IwDIwaNQq33XYbfvrTn0qWNmMMsVjMNUz3oosuQjQaxZ133olHHnkElZWVuOSSS7psvpJE2b17N4qLiwNj8ccddxyef/55XH/99fYos5kzZ+LII4/E448/jlGjRnVDbx0y/bnu3r0bhmEEjp6qqanBe++9h1/96lf4zW9+gw0bNqC0tBTDhg3DySefnJR7lCwDBw7Em2++iZkzZ2L69Ono7OzE4Ycfjtdffx2jR4+W6urmLBo9ejRef/11/OIXv8C3v/1t5OXl4Rvf+AZ++9vfhgqfpgoP1YQZXXT77bejX79++OMf/4iNGzdiv/32w1NPPRUqByYTdOe1JpHP0YtIJII777wTp512Gu69917cdNNNSbeVCImcg7rPaMGCBdq5nIDg0W8EEUimVVlPgjsaHR0drLOzM61tx2Ix1tHRwS655BKXU1RVVcVmzpyZUHuZdPcSJZXP1TRN1tHRwX75y1+6nKJDDjmEffvb3053d3sE/Pz44osvpEd8hKWjo4O9+uqrLqfoZz/7GRs8eHC3z6Lc03j++eeZYRjso48+ynRXCKJHQVODphk+t8qBBx6Y1navvfZa5Ofn469//atUvmLFCuzduzehkXe5SLKf6+9//3vk5+fjV7/6lVTe3NyMDz/8ELfddls6u9njGDlyJPLz8xN6kO7y5cuRn5+vdW8XLFiAX/7yl5RMmyILFizA+eefn9Z5zAiCAAzG0jxtci9m3bp19s2juLgYY8eOTVvb9fX12Lx5MwBruHvQ6JsgHnnkEVx66aVYu3Ztl883kyqpfK6NjY1Yv369vXzQQQf5JiMTFtu2bcPatWvt5UQ+t5aWFnuUGICkE9QJgiC6GxJFBEEQBEEQ6OZ5igiCIAiCILIVEkUEQRAEQRAgUUQQBEEQBAGARBFBEARBEAQAEkUEQRAEQRAASBRlDW+88QbOOOMM1NbWwjAM/POf/8z4/hhjuOWWW1BbW4vi4mIcd9xx0lBrgiAIouug+0L3Q6IoS9izZw8OPPBA3HfffVmzv7vuugt333037rvvPixduhTV1dU48cQTQz0YkiAIgkgNui9kgExOp03oAcDmzZsnlbW1tbGf/exnrLa2lvXp04cdeuihgQ8yTWV/pmmy6upqduedd9plra2trLy8nP35z39Oy34JgiCIcNB9oXsgpyhHuPTSS/HWW29h7ty5+Oijj3Duuefi5JNPxueff94l+1u7di0aGhowbdo0u6ywsBDHHnss3n777S7ZJ0EQBBEeui+kHxJFOcCXX36Jp556Cs888wyOPvpojBgxAjNnzsRRRx2Fhx9+uEv22dDQAACoqqqSyquqqux1BEEQRGag+0LXQKIoB3j//ffBGMN+++2HkpIS+7Vw4UJ8+eWXAKzngxmG4fu66qqrEt63YRjSMmPMVUYQBEF0L3Rf6BroyZg5gGmaiEajWLZsGaLRqLSupKQEALDPPvtg5cqVvu0k8lDO6upqANYvg5qaGru8sbHR9SuBIAiC6F7ovtA1kCjKASZOnIhYLIbGxkYcffTR2jr5+fkYPXp02vY5bNgwVFdXY/78+Zg4cSIAoL29HQsXLsSvf/3rtO2HIAiCSBy6L3QNJIqyhN27d+OLL76wl9euXYvly5ejsrIS++23Hy688EJccskl+N3vfoeJEydi69at+M9//oPx48fj1FNPTev+6urqYBgGZsyYgVmzZmHUqFEYNWoUZs2ahT59+uCCCy5IyzETBEEQ3tB9IQNkdvAbwVmwYAED4HpNnz6dMcZYe3s7u+mmm9i+++7L8vPzWXV1NfvWt77FPvrooy7ZH2PW8Mubb76ZVVdXs8LCQnbMMcewjz/+OA1HSxAEQQRB94Xux2CMse6VYQRBEARBEMDXX3+NX/ziF3jxxRfR0tKC/fbbDw899BAmTZqUkf5Q+IwgCIIgiG5nx44dOPLII3H88cfjxRdfxKBBg/Dll1+ioqIiY30ip4ggCIIgiG7nuuuuw1tvvYU333wz012xIVGUYUzTxMaNG1FaWtpj5nkgCCJxGGPYtWsXamtrEYnQFHJE99La2or29vaU22GaOYsKCwtRWFjoqjtmzBicdNJJ2LBhAxYuXIh99tkHP/rRj/D9738/5X4kC4miDLNhwwYMGTIk090gCCJLqK+vx+DBgzPdDaIX0draimFDS9DQGEu5rZKSEuzevVsqu/nmm3HLLbe46hYVFQEArr32Wpx77rlYsmQJZsyYgb/85S+45JJLUu5LMpAoyjBNTU2oqKhAfX09ysrKMt0dQoExhoamVqzavAurGnZhVUMzVm/ejQ07WtBp+n91ohED/fvmo6w4HyWF+SgtiqK0MB8lRXkozI8iL2og34ggGjGQFzXAGNAZM9FuMnTETHTGTOxtj2F3Wwd2tcTQ3NaBXa0d2LGnHXvbzcC+9+9bgJGDSrBfdSn2ryrF/tUlGD6wBIV50cBtie6nubkZQ4YMwc6dO1FeXp7p7hC9iObmZpSXl+OrZfuirDR5l7J5l4mhk9a57mdeTlFBQQEmT54sPTftxz/+MZYuXYp33nkn6X6kAiVaZxhuM5aVlZEoygJ27GnH++t34P31O/DB+p34dFMzdu7tcFfML0ZxXgT79u+Lof37YNiAvhhS2Qc15UWoKivCoLJC9O9biGika0Kiu1o7sLm5FQ1NbWhobsWmnS1Yt20vvtq2B+u27cHW3e3Y0Qks3diKpRtbAWwBAORFDIwcVIKJdf0wsa4CB9dVYPiAEkS6qJ9E4lAYncgUJaUGSkqTP/9MJHY/q6mpwZgxY6SyAw44AM8++2zSfUgVEkVEr6Z++1689cVWvPeVJYTWbNnjqpMXMTBiYAnG1JbhgJpSHFBThpGDSlBVWpQxMVFalI/SonyMHFSqXd/c2oF1W/fgs0278OmmZqyMv5pbO/FZwy581rALTy1ZDwAoK8rDQXX9MGVYJY4Y0R/j9ylHXpRyWgiitxFjJmIpxI5iLNjBFjnyyCOxatUqqWz16tUYOnRo8p1IERJFRK9i6+42vP3lNrz9xVa89eVW1G9vcdUZMbAvDq7rh4OH9sP4fcoxqir3Qk5lRfmYMLgCEwZX2GWMMWxsasXHG5rwQb3lhH20YSeaWzvxxuoteGO15SaVFObh0LhAOmx4f4ypKSMniSB6ASYYTCSvihLd9pprrsERRxyBWbNm4bzzzsOSJUswZ84czJkzJ+k+pArlFGUYHsttamqi8FkXEDMZltfvwKsrG7Hgs0Z81rBLWp8XMTCxrgKHDqvE5KGVmFhXgYo+BRnqbffTETOxqmEX3lu3He+s2YbFa7ajqUUOFw4oKcRx+w/E8fsPwlGjBqC8OD9Dve3Z0LWAyBT83GtYVZdyTlH1/usTOof//e9/4/rrr8fnn3+OYcOG4dprr6XRZ70ZuhCmn12tHXjz8614deVmvL5qC7bvkYeZjq4uxVEjB+DIkQNw6LBK9C0kw5QTMxlWbmrGO19uwztrtuHdNduwp90ZkRKNGJg8tB+OHz0I08ZUYfjAkgz2tmdB1wIiU/Bzb+OqwSmLotr9N+T0OUyiKMPQhTA9bN3dhpc+acDLKxqweM02dAiB8dKiPBy3/yBMHW05HQNK3KMgCD3tnSbe+2o7FnzWiAWrtuCLRnmo7QE1ZTh9Qg1OHV+DYQP6ZqiXPQO6FhCZgp979Z/tk7IoGjL665w+h0kUZRi6ECbPtt1teGlFA174eBPe+XIbxBHywwb0xdTRgzD1gCpM3rcf8ilxOC2s37YXr69uxKsrG/H2F1ulaQnG1JThtAk1OG18DfYlgZQwdC0gMgWJIgcSRRmGLoSJsX1PO15e0YDnP9qEd9ZsQ0y4KU8YXI5Tx9fgxDFVGEFhnS5nx552vPJpA57/uAFvfbFV+luM26cMp42vxWnja1DXv08Ge5k70LWAyBT2PEWf1aY+T9HojTl9DpMoSoFbbrkFt956q1RWVVWFhoaG0G3QhTCY1o4Y/vNZI/7x/ga8vmqL5E6M36ccp02owanj6OabSbbvaccrKxrw/Meb8PaXsljlf6PTxtdgSCX9jbygawGRKfi5t/azGpSmIIp27TIxbPSmnD6HKcM0RcaOHYtXX33VXo5Gc2vodrbCGMP763fiH+9vwL8+3Ijm1k573djaMpw+gVyIbKKybwHOP7QO5x9ah2272/Dyis144eNNePvLrfj46yZ8/HUT7nzxMxw6rBLfnjQYp46vQQkluBMEkWXQVSlF8vLyUF1dnelu9Bjqt+/FvA++xj/e34B12/ba5TXlRfjWxH1w9sH7eE5YSGQH/UsKccGUOlwwpQ5bd7fh5RUN+PeHm7B47TYsWbsdS9Zux83/twInj6vGtycNxuHD+9M8SASRBXT3PEXZCImiFPn8889RW1uLwsJCTJkyBbNmzcLw4cMz3a2cYm97J/790SY8u2wD3l273S7vUxDFyeOqcc7Bg3HY8P5d9sgMousYUFKIC6cMxYVThmLjzhbM++BrPPv+BqzZsgfzPvga8z74GrXlRfjWwfvg25OG0Ag2gsggMcYQSyGjJpVtswXKKUqBF198EXv37sV+++2HzZs34/bbb8dnn32GFStWoH///tpt2tra0NbWZi/zh0Dmcgw2WVZv3oUn312PZ9/fgF3x8JhhAEeM6I9zDh6Mk8ZW0xxCPRDGGD6o34lnl7lDo1OGVeK7h9bh5HHVKMrvXaFoyikiMgU/91avrEo5p2i/Azbn9DlMoiiN7NmzByNGjMDPf/5zXHvttdo6uuRsADl9EiVCW2cML33SgCcWr8eSdY4rVFfZB985ZAi+NXEf1FYUZ7CHRHfS2hHDqys34+/LNuCN1VvsaRXKi/PxrYn74LuH1mH/6t4RLiVRRGQKfu59lgZRNJpEESFy4oknYuTIkXjggQe063urU/TVtj14csl6PPPeBnuG6WjEwAkHDMKFU4biqJEDKK+kl7NxZwv+9l49/ra0HhubWu3yiXUV+O4hdTj9wBr0Kei5ziGJIiJT8HNvxcpBKYuisQc05vQ53HOvMBmgra0NK1euxNFHH+1Zp7CwEIWFvWNG5c6YiVdXNuKJd7/Cm59vtcury4rw3UPr8J1DhqC6vCiDPSSyidqKYsw4YT9c/Y1ReOPzLZi7ZD1eW9mID9bvxAfrd+K2f3+KMw+qxYVT6jC2tjzT3SWIHkeMWa9Uts91SBSlwMyZM3HGGWegrq4OjY2NuP3229Hc3Izp06dnumsZZVNTC+YuqcfcpeuxudlyxQwDOGbUQFw4pQ7fGD0IeTTDNOFBNGLg+P0H4fj9B6FxVyv+vmwDnl5aj6+27cWT767Hk++ux8S6Clx82FCcOr6m1+UeEQTRdZAoSoENGzbgu9/9LrZu3YqBAwfisMMOw+LFizF06NBMd63bMU2GNz7fgifeXY/XVm62c0P69y3AeYcMwXcPqaM5hYiEGVRahB8dNxJXHDMCi9dswxNL1uPlTxps9+hX//4U504eggun1GFofxq5RhCpYMZfqWyf61BOUYbJ9TyCrbvb8Lf36vHUkvWo395ilx82vBIXThmKk8ZWoyCPXCEifTTuasXfltbjyXfXS7lHx+w3EBflsBOZ69cCInfh5977n1ahJIWcot27TBw8JrcTrckpIhKGMYZ3127HE++ux0ufbLKfSF9WlIdzJg3GhVPqaIJFossYVFqEq74xCv/vuJH4z2eNeHzxV3jj8y14Y7X1qi2P56wdOgSDSilnjSCI8JAoIkLTtLcDz76/AU+8+xW+3LLHLj9oSAUunFKH0yfUoriA8juI7iEaMXDimCqcOKbKGt347nr87T1r5Nrv5q/G71/7HCeNq8ZFU4bisOGVMAwa3UgQfpgMMFOIHaWybbZAoojwhTGG5fU78cS76/GvDzeirdOKGvcpiOKbE/fBBYfWYdw+NBKIyCxD+/fF9acegGtO3A8vfrIJjy9ej2Vf7cDzH23C8x9twshBJbhoSh3OnjQYZUX5me4uQWQlMRiIIfkfD6lsmy1QTlGGydY8gt1tnfi/5V/jicXr8emmZrt8dHUpLjxsKL55UC1K6eZCZDGfbmzG4+9+hX9+8DX2tscAAMX5UZx1UC0uOmxo1on5bL0WED0ffu69u6I65ZyiKWMbcvocJlGUYbLtQrhyUzOeePcr/PODjdjdZj1+oSAvgtMn1ODCKUNxcF0FhSGInGJXawfmffA1Hl/8FVZv3m2XHzi4HBceNhRnZEnYN9uuBUTvgZ97b6+oSVkUHTF2U06fw71GFHk9dsOPG2+8EZWVlV3QG4dsuBC2dsTw/Eeb8MS7X+H99Tvt8uED+uKCKXX49qTBqOhTkJG+EUS6YIxh6bodeHzxV3gxCwcIZMO1gOid8HNv0Se1KYuio8ZtzOlzuNeIokgkgsMPPxwFBeFu7osWLcKqVau6/In3mbwQfrllN556dz3+/v4G7NzbAQDIixg4aWw1LpxSh8NH9CdXiOiRbN3dhmfe24Anl3zlmkriosOGYtqY7p9KgkQRkSlIFDn0qkTrefPmYdCgQaHqlpb2zCHl7Z0m5n+6GU+8+xXe/nKbXb5PRTEumFKHcycPpmHMRI9nQEkh/t9xI/DDY4ZLk44uXrMdi9dsx4CSQnznkME4/5A6DKmkSUeJ3gElWvciUfTwww+jvDx8YuVf/vIXVFVVdWGPupf67Xsxd+l6PL10A7buth69ETGAb4wehAum1OHY/QYhSg9kJXoZkYiB4/YfhOP2H4SNO1swd2k95i5Zj8Zdbbh/wZf40+tf4vj9B+Giw+g7QvR8YogghuSdolga+5Ipek34LFvpSss8ZjK8vqoRT7y7HgtWNYL/pQeWFuL8Q4bg/EPrsE9FcVr3SRC5TkfMxKufbsYT767Hoi+cBxl3tZtK4TMiU/Bz77WP69A3hfDZnl0mpo5fn9PncK9xinoTjc2teHqp9egN8TEIR40cgAun1OGEMVXIz8HHIBBEd5AfjeCU8TU4ZXwN1mzZjaeWrMczyzbg650t+M3Lq3DP/NU4aVw872445d0RRE+i14iiyspKrF69GgMGDAhVv66uDm+++WbOPNw1ZjK8sXoL5i5dj9dWNqIzPrVovz75OHfyEHz30DoMG0APzCSIRBg+sAQ3nDYGP522P174eBMeX2yN0OSTQg4f2BcXThmKcw7eh0ZoEjkP5RT1IlG0c+dOvPjii6HzirZt24ZYLPsjpPXb9+KZ9+rxzLIN2CS4Qofs2w8XThmKk8dVoyg/83OwEEQuU5QfxdkHD8bZBw/Gpxv5XF5fY82WPfjVvz/Fr1/6DCeNrcZ3Jg/BESP6I0K5R0QOEmMRxFgKOUU9IBmn1+QURSKJ/6G/+OKLrByS39YZw/xPN+PppfVY9MVWO1eoX598nH3wYHznkCHYr6pnjp4jiGxhd1sn/vnB13ji3fVYKcz6PrhfMc6dNATfnjw4oZw9yikiMgU/9178aFjKOUWnTFib0+dwr3GKTNPMdBdSZvXmXZi7pB7zPtiAHfF5hQDg6FED8J1DhuDEMVUozCNXiCC6g5LCPFx02FBcOKUOKzY24+ml9fjn8q+xYUcL7nl1Ne59bTWOGknfTSJ3MGHATGH0mYnc91h6jSjKVZpaOvD8R5vwzLJ6fCDMNl1dVoTzJg/GuZOH0DwqBJFBDMPAuH3KMW6fctxw2gF46ZMGPL20Hu+s2YY3P9+KNz/fioo++fjmQfvgO4cMwQE1ufkLmuj5UE4RiaKspDNm4s0vtuLZZRvwyqeb0R5/Mn1exMDUAwbh/EPqcMx+A2nOFILIMoryo/jmxH3wzYn7YP22vXhmWT2eeW8DGppb8cjb6/DI2+swYXA5zps8BGccWIvyYnqoMkFkE70mpyhbEfMINu018Oz7GzDvg6+xZVebXWf/qlKcM8m60NJs0wSRW8RMhjc+34K/La3Hqys3289cK8iL4MQxVTjn4H1w9KiBaNmzm3KKiIzA70PzPhyFvqXJh3n37IrhWwd+ntPnMDlFWcK5f34bq7Y7o9369cnHWQftg29PGoyxtWU0FwpB5CjRiIHj9x+E4/cfhG272zDvg6/xzHsbsGrzLnto/4CSAkwbFX7GfYLoCqycouTvNalsmy2QKMoSVm7ahYLivvjG6EE4Z9JgHL//oG5/ICVBEF1L/5JC/NfRw3H5UcOwYmMz/vH+13juw6+xdXc7Hl/8Vaa7RxC9nl4lipqbmwPr5OXloU+f7k9cvv6U/fGdI0ejsi9NAEcQPR0xOfv6U0fjzc+34KlFq/A/me4Y0asxU3z2GY0+yzEqKipChaH69u2LE088Eb///e8xePDgbugZcOFh+6KMBBFB9DryoxF8Y3QVJtcW43++n+neEL2Z1CdvJFGUUyxYsCCwjmma2Lx5M+6//3784Ac/wAsvvNANPSMIgiCIzGIiQvMUZboD3UlbWxuOP/545OcHD4OdMGECDjvssG7oFUEQBEEQ2UCvyuS94oorMHDgQHznO9/Bk08+iZ07d3rWHTlyJB577LHu6xxBEARBZJAYM1J+5Tq9ShStWbMGb7zxBsaPH497770X1dXVmDp1Kv7whz9g3bp1Ut2CggKcddZZmekoQRAEQXQzsXiidSqvXCf3jyBBJkyYgBtvvBFLlizBmjVrcO655+Kll17CAQccgAMPPBA33XQT3nvvvUx3kyAIgiCIbqbXiSKR2tpaXHHFFXjhhRewdetW3HTTTVi3bh1OPvlkzJo1K9PdIwiCIIhuw2SRlF+5Tq9KtPajb9++OOecc3DOOefANE1s27Yt010iCIIgiG4j1RBYrAeMPst9WZcAp556KpqamuzlO+64Q0q23rZtG8aMGYNIJIKBAwdmoIcEQRAEQWSKXiWKXn75ZbS1OQ9a/fWvf43t27fby52dnVi1alUmukYQBEEQGcVEaiPQzEwfQBroVeEzpsy2qS4TBEEQRG8l9ckbc99nyf0jIAiCIAiCSAO9yikyDMP17LMwz0IjCIIgiJ5O6s8+y32fpVeJIsYYvve976GwsBAA0NraiiuuuAJ9+/YFACnfiCAIgiB6EyYMmEjeKEhl22yhV4mi6dOnS8sXXXSRq84ll1zSXd0hCIIgiKyBnKJeJooefvjhTHeBIAiCIIgsJfdlHUEQBEEQKdPdzz675ZZb7Fxf/qquru6iowtHr3KKCIIgCILQYzIDZgpPuk9m27Fjx+LVV1+1l6PRaNL7TwckigiCIAiCyAh5eXkZd4dEKHxGEARBEATMFENnfPLG5uZm6eU3svvzzz9HbW0thg0bhvPPPx9r1qzprsPVQqKIIAiCIAjtU+8TfQHAkCFDUF5ebr9mz56t3d+UKVPw17/+FS+//DIefPBBNDQ04IgjjsjoA9kpfEYQBEEQRNqor69HWVmZvcznBlQ55ZRT7Pfjx4/H4YcfjhEjRuDRRx/Ftdde2+X91EGiiCAIgiAIxGAglsIEjHzbsrIySRSFpW/fvhg/fjw+//zzpPuQKhQ+IwiCIAgibeGzZGlra8PKlStRU1OTpiNKHBJFBEEQBEF0OzNnzsTChQuxdu1avPvuu/j2t7+N5uZm19MnuhMKnxEEQRAEgRiQYvgsMTZs2IDvfve72Lp1KwYOHIjDDjsMixcvxtChQ5PuQ6qQKCIIgiAIIuUQWKLbzp07N+l9dRUkigiCIAiCoAfCgnKKCIIgCIIgAJBTRBAEQRAEAAYDZgo5RSyFbbMFEkUEQRAEQVD4DBQ+IwiCIAiCAEBOEUEQBEEQAExmwGTJh8BS2TZbIFFEEARBEIT9tPtUts91cv8IsoA//elPGDZsGIqKijBp0iS8+eabme4SQRAEQRAJQqIoRZ5++mnMmDEDN9xwAz744AMcffTROOWUU7B+/fpMd40gCIIgQsPDZ6m8ch0SRSly99134/LLL8d//dd/4YADDsC9996LIUOG4IEHHsh01wiCIAgiNCYiKb9yHcopSoH29nYsW7YM1113nVQ+bdo0vP3229pt2tra0NbWZi83NTUBAJqbm7uuowRBZD38GsAYy3BPCKL3QqIoBbZu3YpYLIaqqiqpvKqqCg0NDdptZs+ejVtvvdVVPmTIkC7pI0EQucWuXbtQXl6e6W4QvZAYMxBLIQSWyrbZAomiNGAY8onAGHOVca6//npce+219vLOnTsxdOhQrF+/PucvhM3NzRgyZAjq6+tRVlaW6e4kTU85DoCOJRvxOg7GGHbt2oXa2toM9o7ozdCQfBJFKTFgwABEo1GXK9TY2OhyjziFhYUoLCx0lZeXl+f0hV6krKysRxxLTzkOgI4lG9EdR67/MCJyG8YiCT/pXt0+18n9I8ggBQUFmDRpEubPny+Vz58/H0cccUSGekUQBEEQRDKQU5Qi1157LS6++GJMnjwZhx9+OObMmYP169fjiiuuyHTXCIIgCCI0MRiIpfBQ11S2zRZIFKXId77zHWzbtg233XYbNm3ahHHjxuGFF17A0KFDQ21fWFiIm2++WRtSyzV6yrH0lOMA6FiykZ5yHETPw2Sp5QWZPWDgpMFo/CdBEARB9Fqam5tRXl6OS18/DwUlBUm30767HQ8f9zc0NTXlbN4fOUUEQRAEQcBMMdE6lW2zBRJFBEEQBEHAhAEzhbygVLbNFnJf1hEEQRAEQaQBcooIgiAIgqAZrUFOUUb505/+hGHDhqGoqAiTJk3Cm2++mekuScyePRuHHHIISktLMWjQIHzzm9/EqlWrpDqMMdxyyy2ora1FcXExjjvuOKxYsUKq09bWhquvvhoDBgxA3759ceaZZ2LDhg3deSguZs+eDcMwMGPGDLssl47l66+/xkUXXYT+/fujT58+OOigg7Bs2TJ7fS4cS2dnJ2688UYMGzYMxcXFGD58OG677TaYppn1x/HGG2/gjDPOQG1tLQzDwD//+U9pfbr6vWPHDlx88cUoLy9HeXk5Lr74YuzcubNLj43ovfCcolReuU7uH0GO8vTTT2PGjBm44YYb8MEHH+Doo4/GKaecgvXr12e6azYLFy7ElVdeicWLF2P+/Pno7OzEtGnTsGfPHrvOXXfdhbvvvhv33Xcfli5diurqapx44onYtWuXXWfGjBmYN28e5s6di0WLFmH37t04/fTTEYvFMnFYWLp0KebMmYMJEyZI5blyLDt27MCRRx6J/Px8vPjii/j000/xu9/9DhUVFTl1LL/+9a/x5z//Gffddx9WrlyJu+66C7/5zW/wxz/+MeuPY8+ePTjwwANx3333adenq98XXHABli9fjpdeegkvvfQSli9fjosvvrjLjosgej2MyAiHHnoou+KKK6Sy0aNHs+uuuy5DPQqmsbGRAWALFy5kjDFmmiarrq5md955p12ntbWVlZeXsz//+c+MMcZ27tzJ8vPz2dy5c+06X3/9NYtEIuyll17q3gNgjO3atYuNGjWKzZ8/nx177LHsJz/5CWMst47lF7/4BTvqqKM81+fKsZx22mnssssuk8rOPvtsdtFFFzHGcuc4ALB58+bZy+nq96effsoAsMWLF9t13nnnHQaAffbZZ118VERvoqmpiQFg5712Mbto8eVJv8577WIGgDU1NWX6kJKGnKIM0N7ejmXLlmHatGlS+bRp0/D2229nqFfBNDU1AQAqKysBAGvXrkVDQ4N0HIWFhTj22GPt41i2bBk6OjqkOrW1tRg3blxGjvXKK6/EaaedhhNOOEEqz6Vjee655zB58mSce+65GDRoECZOnIgHH3ww547lqKOOwmuvvYbVq1cDAD788EMsWrQIp556ak4dh0q6+v3OO++gvLwcU6ZMsescdthhKC8vz+rrBJG7sPjos2RfrAeMPqNE6wywdetWxGIx10Njq6qqXA+XzRYYY7j22mtx1FFHYdy4cQBg91V3HF999ZVdp6CgAP369XPV6e5jnTt3Lt5//30sXbrUtS6XjmXNmjV44IEHcO211+K///u/sWTJEvz4xz9GYWEhLrnkkpw5ll/84hdoamrC6NGjEY1GEYvFcMcdd+C73/2u3UfeJ7WP2XQcKunqd0NDAwYNGuRqf9CgQVl7nSByG5MZKc5oTaKISAHDkE8gxpirLFu46qqr8NFHH2HRokWudckcR3cfa319PX7yk5/glVdeQVFRkWe9XDgW0zQxefJkzJo1CwAwceJErFixAg888AAuueQSu162H8vTTz+Nxx9/HE8++STGjh2L5cuXY8aMGaitrcX06dPtetl+HF6ko9+6+tlwbATRU6HwWQYYMGAAotGo69deY2Oj69dlNnD11Vfjueeew4IFCzB48GC7vLq6GgB8j6O6uhrt7e3YsWOHZ53uYNmyZWhsbMSkSZOQl5eHvLw8LFy4EH/4wx+Ql5dn9yUXjqWmpgZjxoyRyg444AA7ST9X/i4/+9nPcN111+H888/H+PHjcfHFF+Oaa67B7Nmz7T4C2X8cKunqd3V1NTZv3uxqf8uWLVl5nSByHxp9RqIoIxQUFGDSpEmYP3++VD5//nwcccQRGeqVG8YYrrrqKvzjH//Af/7zHwwbNkxaP2zYMFRXV0vH0d7ejoULF9rHMWnSJOTn50t1Nm3ahE8++aRbj3Xq1Kn4+OOPsXz5cvs1efJkXHjhhVi+fDmGDx+eM8dy5JFHuqZGWL16tf0Q4lz5u+zduxeRiHwJikaj9pD8XDkOlXT1+/DDD0dTUxOWLFli13n33XfR1NSUVdcJoufAw2epvHIdCp9liGuvvRYXX3wxJk+ejMMPPxxz5szB+vXrccUVV2S6azZXXnklnnzySfzf//0fSktL7V++5eXlKC4utuf5mTVrFkaNGoVRo0Zh1qxZ6NOnDy644AK77uWXX46f/vSn6N+/PyorKzFz5kyMHz/elezclZSWltq5UJy+ffuif//+dnmuHMs111yDI444ArNmzcJ5552HJUuWYM6cOZgzZw4A5Mzf5YwzzsAdd9yBuro6jB07Fh988AHuvvtuXHbZZVl/HLt378YXX3xhL69duxbLly9HZWUl6urq0tLvAw44ACeffDK+//3v4y9/+QsA4Ac/+AFOP/107L///l12bATRq8nEkDfC4v7772dDhw5lBQUF7OCDD7aHumcLALSvhx9+2K5jmia7+eabWXV1NSssLGTHHHMM+/jjj6V2Wlpa2FVXXcUqKytZcXExO/3009n69eu7+WjciEPyGcutY/nXv/7Fxo0bxwoLC9no0aPZnDlzpPW5cCzNzc3sJz/5Caurq2NFRUVs+PDh7IYbbmBtbW1ZfxwLFizQfjemT5+e1n5v27aNXXjhhay0tJSVlpayCy+8kO3YsaNLj43offAh+We8cjk7+63/l/TrjFcuz/kh+QZjjGVEjREEQRAEkXGam5tRXl6O017+L+T3LUi6nY497Xj+pP9BU1MTysrK0tjD7oNyigiCIAiCIEA5RQRBEARBgOYpAkgUEQRBEAQBEkUAhc8IgiAIgiAAkFNEEARBEATIKQJIFBEEQRAEAWteCTOFh7r2hKHsJIoIgiAIgiCnCCSKMo5pmti4cSNKS0vpIY8E0YthjGHXrl2ora11Pf4kl6BrWmr0lPMgVyFRlGE2btyIIUOGZLobBEFkCfX19dKDl3MNuqalh0ycB+QUkSjKOKWlpQCAo3Aq8pCf4d4QBJEpOtGBRXjBvibkKrz/1/11Aa4/6+AM9yb3aG5uxpAhQzJyHpAo6qGi6I033sBvfvMbLFu2DJs2bcK8efPwzW9+017PGMOtt96KOXPmYMeOHZgyZQruv/9+jB071q7T1taGmTNn4qmnnkJLSwumTp2KP/3pT5Jy37FjB3784x/jueeeAwCceeaZ+OMf/4iKiorQfeX2ch7ykWeQKCKIXks8S9Ur5JQr1zXe/6I+JTn7qIdsgEKPmaFHBiz37NmDAw88EPfdd592/V133YW7774b9913H5YuXYrq6mqceOKJ2LVrl11nxowZmDdvHubOnYtFixZh9+7dOP300xGLxew6F1xwAZYvX46XXnoJL730EpYvX46LL764y4+PIIjeB13XiK6GO0WpvHKdHukUnXLKKTjllFO06xhjuPfee3HDDTfg7LPPBgA8+uijqKqqwpNPPokf/vCHaGpqwkMPPYTHHnsMJ5xwAgDg8ccfx5AhQ/Dqq6/ipJNOwsqVK/HSSy9h8eLFmDJlCgDgwQcfxOGHH45Vq1Zh//33756DJQiiV0DXNaKrYcwAS0HYpLJtttAjnSI/1q5di4aGBkybNs0uKywsxLHHHou3334bALBs2TJ0dHRIdWprazFu3Di7zjvvvIPy8nL7wgEAhx12GMrLy+06Otra2tDc3Cy9CIIgUiGT1zW6phE9iV4nihoaGgAAVVVVUnlVVZW9rqGhAQUFBejXr59vnUGDBrnaHzRokF1Hx+zZs1FeXm6/aJQGQRCpksnrGl3Teg4mjJRfuU6vE0UcNYmNMRaY2KbW0dUPauf6669HU1OT/aqvr0+w5wRBEHoycV2ja1rPgXKKeqEoqq6uBgDXr57Gxkb7V1Z1dTXa29uxY8cO3zqbN292tb9lyxbXrzWRwsJClJWVSS+CIIhUyOR1ja5pRE+i14miYcOGobq6GvPnz7fL2tvbsXDhQhxxxBEAgEmTJiE/P1+qs2nTJnzyySd2ncMPPxxNTU1YsmSJXefdd99FU1OTXYcgCKI7oOsakQ54onUqr1SYPXs2DMPAjBkz0nNASdAjR5/t3r0bX3zxhb28du1aLF++HJWVlairq8OMGTMwa9YsjBo1CqNGjcKsWbPQp08fXHDBBQCA8vJyXH755fjpT3+K/v37o7KyEjNnzsT48ePtURsHHHAATj75ZHz/+9/HX/7yFwDAD37wA5x++uk0QoMgiLRD1zWiq8nk5I1Lly7FnDlzMGHChKTbSAc9UhS99957OP744+3la6+9FgAwffp0PPLII/j5z3+OlpYW/OhHP7InOXvllVekGUTvuece5OXl4bzzzrMnOXvkkUcQjUbtOk888QR+/OMf26M5zjzzTM85RIKIjN8fedv2wtyxE2Zra1JtEASRe0T69EGksh9iA4uB9//Ps14uXteI3CJTQ/J3796NCy+8EA8++CBuv/32pPefDgzGGMtoD3o5zc3NKC8vx8Hn3Y68/CJE2xiKtnWgoHE3sGUHzJ1NYB3tme4mQRBpwigsRLRfBcxB/dA+sC9aK/Ng5hvo7GjF+3+7EU1NTTmdl8Ovabf8fSluPmdypruTc/DPrzvPA77PSc9eg7y+hUm307mnDcvOuQf19fVS3wsLC1FY6N3u9OnTUVlZiXvuuQfHHXccDjroINx7771J9yMVeqRTlKuwCNDRJ4KOPoXAkEIYZiXyWhkKd3SisH4HsHUHzF27wDo7M91VgiBCYuQXIFJeCgysROs+ZWjrl4dYoQEWH81l0O9SIktgKYbPuFOkTstw880345ZbbtFuM3fuXLz//vtYunRp0vtNJySKsgUD9kWST/Vg5hloLzHQXlqAXXVViHQOQv5ehqJt7chv2AU0biORRBBZhi2CBvVHW3Up2irz0FEcAeMRKv6MM8YXDUsY5f5oZiLHYQBS0eh8U51TpKO+vh4/+clP8Morr6CoqCj5HacREkVZgiqIbLFuOO9jBQZiBQZa+hUBI4sQ6RyIgj2mFW7jIqmpmUQSQXQjRn4BIhXlYFX90V7VF63989HRx4AZF0Fc/IDF3xvWe2Y4ywyOc0QQuU7YqRmWLVuGxsZGTJo0yS6LxWJ44403cN9996GtrU3Kd+sOSBRlE6Ig0ogjddksAFoLImipLARGFSLSOQD5exgKmmMo3rQHkYZtiO3YCdbW1o0HQRA9m0hRESL9KxGrqURrVTHayqLo6GPYTpAogtziJ15HqGILJYLIMCYMGCmcjInOaD116lR8/PHHUtmll16K0aNH4xe/+EW3CyKARFH2YPgLIHWdrk6swHKTWivz0LxvOQyzHHl7gcJdMRRtbkP+xu0wt26H2dIKmM5TsQmC0GPk5cEoLkZkYH+011agdWAh2soiiBVZOYAA5HCYIoIgVPF2iUgYEdlBd48+Ky0txbhx46Syvn37on///q7y7oJEUZaQqCDyr28tsDygvQxoL8/DrsF5wMF9EW0bjILdDH02tyN/214YG7fAbNpFI9wIAtbIsEhFOVjNALQNKEbLoHx09DUQK1QUTvxfOxeICx2hiiiCXOJHI4x6wBMSCCLnIVGUTYQIn3kKIvu94V0/AnT0NdDR18Ce6iKAFSHSWYloK0PxdhPFDa3Ib9gJtrMZsaZmcpOIHo2Rl4dIRTnQrxydg8qwt6YQrf0iiBUaMPmVkecBxd8D8a+WkAtklTG7itYBovAZkQOYzICRockbOa+//nrKbaQCiaJsIcD9CeMgMcOQxFBQfUSAWCEQKzTQXhFF0/C+AOuLSMc+KGxiKNoRQ9HmFuRtsuZLMvfsJaFE5CRGXh4iJX1hVJSjs7oCLdVFaK2Ior0sLoBElQInFOYSMoojBLvMGUGmCiN7W6m+uw4JIyLTMJbi6LMeMLsEiaIswc/9CRI4OndIbcOzPbXtCBDLA/YWG9hTkweMKYVhliLaCuTvZijeEUNxQ6sllLbvJKFEZB22AOpXgY6aCuytKUJrvwg6+howC51zXU2IBnyED2yN5Lx35Q3JwsjVXkCeEUEQmYdEUTaRqIBRBVES+Ui+Cdzx92YeYJYAHSUG9tbkAWNKAFaCvNYhyN/NUNjE0GdTG/I3NwPbd1KOEtE9GAaMvHwrBDaoEu2D+mLvoAK0VRjoKDEQK4DLAQIgzA/kIVSUTeRwmYcLpAunBeUZQXGMyCkiMkymHvORTZAoyhZCCBjAJ38oSBAlsl6tq+tjBOiIC6U9NQa2jy6GwYoR6ahCtAUo2s5QuCuGPl+3ILppO9ju3TB376E5lIikMPLyECkvg1HSF501/bBnn2K0l0bQVmGgs9gaVCA5QF55QBDqAdrwVlC4LHQ4LSjPSGiP8oqIbIBEEYmirCFRR8czfygRtymMG+Wxf+02EaAzD+gsBtr6A0AUbFIJIp2liLQDeS1A8VYThTtNFDVauUqstc16vltnR88ISBPJw52fygoYxUV27k9bWRSt/S3xYxYAZpS5k58FIaTTF1KZ6NKI6wNcI0Mp8N0+bJ4RjT4jsohsSLTONCSKsomQITBVEAXlIyUVTlPb0/VPu1/m2sYsYDALgI5SoGWQARhRACUwYiUwYgbydwOFOxnydzOUbGxD3s5WRDZvB9vbAnPvXnKXehKGASMatXJ+iooQqxmAjsoi7K3KR1uZgY4yAx0lDCwC5bEYshByhIWlTlxuTtwG8nN47HrQuEZB4TKhTX8XSC+MJFFFLhFBZA0kirIF8ULqKVgMua6f4PESVym6Q/5lLPw2sEIeyGeIFQGtA60VWw4ugsGKYMQqEG0xkNcKFG1lyGtl6NPQgfxd7Yhu3AbW3mE5TLEYJXpnG5EoIgX5MMrLYBQVorOqAu39CtHaPw/tJQba+hmIFQOxQgYWFfJqAMctFMNekIrcZYYlmJx8HkiiRrdNoGvktV+NmBHXe+UZ+SZg63ZKEBmARp+RKMoe4uIklCAKcpO8BE+a3SFRDAXtwy6Du8zZNt5O3CWIFTK0A9hbw+vmA8iH0VECwwTymw1EYkCfBoZIB0Px1k4U7GhDpLUDxqZtADMt4WQygJk94xubSQwDMCKW01NRDkQMmLUDwfIjaB1UjLbyCDqLDbQMNMDygI5SSySzfFHoOFddyfkxADD3AwZYXCy4hZCwPbim4Dk8hsd6pQ2xjDnnKxc3geE0qaOywHHnIXHBxrzrkigiMowlipI/EXvCJZZEURaR8AgzD7HiuT6EO+RZX7t/vRjyLINcpoqqwP3F67JC61d3rI+1fu9gXi8KoA9gAkZHBQwGFOyIAAwoaAaKdpgAA8rWtcLoiMFgsF0nMNMaNReLu069QUTFRQ4Ay9kpLbWK+xShs6oCABDrk4/dgwsAAHsHRayk5ijQXmECAFiBGKMSfmYqOT/8zq8KFUDUAsxXHKnPS3UJnbia8QypufaniB/FNUouXKYIJ8VGshOwNcKIIIjMQ6IoS0iXIEp6dFmI+qqQCVVfaF/8VxRVgeIO7rruesL6iONOtPS1bt4tBkNTfJPNx+THXSfAaB9idZIBBTsjiHRYN9PCHVaeE6fk63bk7XamGTAYEN2wBayjAypm0y4rcbwbiBQWwijp6yo3iosRq+4njchq61+EvQOdr3zLQAMdJda6WCFDR7kZbxRg+fH3DAB4eDLmvnsr4sf7YZIaF8cWCoCc/8PkUJjYgiKOvPKAtCE1YRtPseQVThM2Cg6XKcJJ+Rh6izB6+K21ePKDLZb2hoGIARiGdYYY8fdymQGeLhmJv4/E/9j8vf0vAAjvxbq8niGtd7dtQKkXLxtVVYIbTxuDaKSH/UFCQKPPSBRlD1rRElIQacWUU+4pljz26y+QQoTKlJuHVxuu7dT+IF43jBAS/7X3K9gSrjbix1EUs8vaSpzcpNZ95e0bAQAFcvttdcJBOuTviCLaHu7i4HUNMTTt6ogVMXSUa3KqDMAoiilmlwlAmD9K3LlYT3qv/jGZvE78XFn8b8JDXqLlYv8bFwM6sSKKI8MRDapzxI/JMJx2w4TUPIWQVN9Z6ZmEDUAnaHyFkWIhaYVRD8NkQFunmeluJMyiL7ZiTE0Zzp08JNNd6Xbivw9T2j7XIVGURUj3KEUQeTpA4rYhBE63u0MeYki3nVc9XzHkJYR0Qkld57UekEM1OnEFAH06YWjUS6y0A2FTv9WQECeRyF1EU5f/YhNv4s5Kfm4xZz+GWC7UF0SO1j6xnSKNQIqv1LpHfqJHaIaXSCKKty+KI7Fb2rY07pOHmLHFleoaaQSMonVSEkY9TRnNv+YY9C0ttf9OJrPON5NZZ4aVv2K95+v4erG+uB5gMIV6LH6eidtabTvrTBN2mam0wez3Vv0/L/wSnzXswh/+8znOOXgwIr3QLertkCjKElwOUZCo8RAlWkEjroOyTmgrKXfISwy59sH8+8jrKNuHcoX8xE6I7cIIIJ340d7rva6hYa0fv3Y8bSW3iDJEwWLXccSQ1Z6wH2bY5Ty52SWSpDbVvmjsFS5eVPfIfWCyOLK3i/eR99nLYWLwDqkJZS7XyBZvytGoYgmQxFCg+FH3m4gw6kHUVBSjrKxPpruRENPGVuHQO15D/fYWvPJpA04eVxO8UQ+CwmdAJNMdIGSSFURMFCmiyFEFkbrOQxA57bHAtsVf2Pb6iCCoIiygj8wRTRFI/zKDWTdVcZ0ktJjUnlWPSft12mT2NkaExfOMnTK7frzcMOIvqVx48XrCC4awnfiCu5sGgIjBtC9tfY+27eMRX0pfIfZVc1zSZ2PA+7NRP0ufz1g6V+2/NwNTt7Ff1jrdOcKUfrL4OWbXgeYc9GzH7XgGnuPQfBc9vj9+bbv2A+U7T2SUPgV5+PakwQCAe1/9HKynD7ZQYWl45TjkFGULRmKCKBGxJO5DXCdt71r2cYfEfkBedrZhntvo6rjXM1eZtezh/ASVA46boNSVXCBhXy63RnF7XKv93KCEnSKP+ppfYrxE+pVmMOn6ZBjCtvwzZsJ+uJshOEa2kxTfzmlR6YMuxCY6S0GhNV7fOUi9cyTt3cP14Y6U3ZK7x9w18k3C1jk/TP4IVecn2BUS2lZ2zMROExllxgmj8OSS9fisYRcWfbEVR48amOkudR8pOkU9we4kUZQlMPEOHEL0eK1LOn9II1b064T2oCyLokPdn0cdd3+Ucgj1lbZc5UqZNizmI4S8wmhSsSpWNOLFM4Tm1UYAqthxr9e0q+bpiOExsZ+24BHaEG7a9naiQBLDbIByomnu+KI44qu4iOFte4kjP+HiVYdrNMNrG0cYWe8NbyHEi23R5xyylzAC9OtEYSR9SnabuX9D6QlU9CnAmQfW4u/LNuB3r6zuXaKIIFGUNQgCJFmHKBFB5L2sDy1Iy2qfEewOhRJDSpldH1C28RdIgY6QjwhKRACp9zA/sZPK/U5sV+fm87ZdTpGo+wzH5jDEuupnKbg6tpOkukj2JlxQxf+mOpdIEF6eidm8Dd6mpBoY7FwiR4+Fdo1cwkgrYJizbWBdZ8FLGKniJxHHiMgOfnbS/pj3wddYXr8Tb6zegmP26x3CiGa0JlGUXQSIHlc9j7paQaSu127LgusK7Qa6Qx6CSW4/RTHUVUJIF3YT12vWedUJu84PezSZZn/OOlk8SYJAcXicsJkh/2uIQTJe5i+QRAfJuiiqnRREk1jG98v/9kwRR2ob0Aif+HvrcNyukTpCjRlOd8SWdeE0l2bRCBqt86PUDe0YkTjKGqrKivCtifvg78s24IHXv+xFoogSrUkUZQs6IcPLw4gev3VBgigoXMaXoVvP3PU1bSclhnRCRymTxFCQEEpRBPmJI68ydbtk8XOLDFvsGEKZuswkgSA6RapIMnhbokCyhU9cIAkiJb6hVQ+COBK2QVy0aMWRLqwWr2uI6zyEjyRWglwjLurE7e1iZVtX22Jde1eSUJLqeokftS45RlnJNSfuh2ff34B31mzDkrXbceiwykx3iegGSBRlCQyOqEi7Q+QlcjwEUVC4LMgdSipvKBkxpHOFkhBCOrHjLYz8naIgJyhVp0i3T2+niEl1xDBbKJEkCiQ4QlZK2uYTyfA2FPdISs7WiSNDaANCGV90BbZg78N/aH18S6EwKAnbcXQ8wmnCm2SEkW6Hqmgisod9Kopx6vgaPP/RJvzp9S9w6LBDM92lroepN5Ikts9xSBRlE74CI6RD5CuA1GWPcJm4f2jWq7lDPusDxRCgEU96MaQLkYV1hbyEUCIiKGmnSFuaGJLA8VinE06iYBITsnUiyXoviKQwAimuMrThNTB9aE0UR/FWHNzWjJSMLaEJecXbNvj6+P4lkSJ8PurunB51jTByhJdrhySMspCZ0/bH8x9twuurtuDD+p04cEhFprvUpVBOEc1TlD1oBInfOk9nB/6CyP4h4Jc/xOcIEvsiCh5dHyLu9U49Ya4hRehAO1cOhDlsnHl37PlvDNjz7VjvIc2rw+cFivD3EVNqJxLfNsLn84mLK6vMFN479Z1tTLmOIc8tFDWY68XXRyNmSi+7Hc2LH6/dF8PdX+c4nM9APWb7WVDqZ8Q/W95WfJ/SPEnC31eeB4n/XeS/ofg31p4HrvNPmONIqSud8+K5CeVc9z231e+Mx6ADr/1AXvb6Hvr98CGyi2ED+mLq6EEAgD+9/kWGe0N0B+QUZRuJXESRuCCy6jLfeoB809CGy1QxJayX22XSsl1POVao9VRnKP4+TIiM36jFOqoj5Cwn4BQJhxAJ6RQlGy7ToYbDRKJx90fFFJ0ioR2vcJroOnHHyFmnOEh2Z4z4P6IzBCf3SNjOYNZ6/ZxHTLPstOeUakJqXonSBj8V9OE07bB9A46jpNsOcC8LjSTjGPWAH9g9lp+fPBqvfdaI+Z9uxmcNzRhdXZbpLnUdDKmdjD3gRCZRlCUk9KtSrOPRhl+ej69wgmY7QcR4hstc+9MIHwh1w4qheBuG9F6oy9/a61loIRS4Lv5vRKovf+vVZa9HJaVLHLmG0scxGbSiKSqIHKeu4SmSnHpMu04WSPbO7BCVmKAthdakxGxHPEnD+flx2YocTl80wsgSPKKaZZByjYQjMeytFOHETxtV8MQricJI2o7X020Hf2EktS0ue5w7RGbZv7oUR47sj7e+2Ib7F3yJP353Yqa71GXQ6DMKn2UPYQURhDrqOpcAUdthLuEUShBp9hEULoMBJzwS37cUIhHDKMKyNkwWkcNkYojM9agLwykTQ2NiCEkMiUnhJUMOgfGQly7MFI1Yr4hhCSG+LD5+Qxf6SvUltinvy+mLrj9y300pDGfACTUGhdrUz9VeVh9xEv/bwF4PW9yKYTU5NAr5fNCdL/A416Tvg084zdCE26BxRl3bMum7GfSDRarr+X10/8ggspPrTj4AAPDix5vw5ZbdGe4N0ZWQU5RlhL6Q6rbxvFCHuKAL7526TFl2t+m6QSntWMsh3aFEnSHBCQobHnOVx9uLKOXie9H9cTtD/s6Rrk6q8JBYVGhXejyHVMe9nrtKopvE36sukoMcYtO5R9YfS07OtpOsrRYgOUcG/2XJnSNAn4ytLHuF0+w6Tmm8NQgtqVsJn6FznmidoPi+xd3r6zn9YEq3dNvJ/SKykXH7lGHy0H5476sd+NOCL/G78w7MdJe6jvRernIOcoqyBeVXo5fzk7BD5CWIxGXIbYiCiBmQH7wpiiVVEKl90f3aV5Z1ToGaQO1yhnjitCG6GI440rlCUrnBtG4QXy+6QKLbIiU7K26NzhESnRi/ly5pOkx9nYPk1Z+o4nKpbpJz7I6LZEDvIHm5R+rfQkzM5n87nXMknQ/2uSCUR5RljWukTebXuEKyO6oZbACvut3oGBFZh2EYuP5Uyy3614cbUb99b4Z71DXw8Fkqr1yHnKIswRUa0wgkP4dIXQ68gAPam4BVztwXap0gssuVMujqKTc1AJIYgnCDFNbZm/s4QzpXKKwjpLpBotOjc4+k/CLI6ByhZFyiMNuYmouPyQwnh4gX2u6QIecXGcxuI2J/6ExykbwcJNkpgsY9clwXxsSMHMSdFv43Mez2eC6QOxlbPE7NMrdijPii6P8Y/IPwGV4f4BjphuzrJnm0nZ/4fv0cI5ez1Mt/mecKB9dVYMLgcny0oQl/ev1LzD57fKa7lH4YUnOKesC5TE5RNuHzK9MlZpIVROIylHWAWxAZCJc/JG0f4A4ZsiBy8oIg56cAvs6Qv1vhuEt+jpDoBtkuieC6qEPh+WGoTk2eJm9Izfvxyjfyenltq/ZL1wfVdVKnBvA+Xvkz8XKQxL44UxvoyyJ2Xpj1t+S5W7JTKJ8HjqOkcY1U91H4XkjnpFAWlGfkG05Wvw+a76f3j4jw3+Ue8CO7R2MYBq47ZTQAYN4HG7CpqSXDPSK6AnKKsgWfi6qnIIqTkCDibUC/jecNQBFEnrNSSzcXt2DycofUvCEnp4jZ65zQDJOWnfduVyjIEfJzgwxlWX0flF/kVRYWv21NJYeIl6l5RrwN7gqJ+UjWOcDkMsFFihqiqwTbDYoI+xPzj9w5RoDzKarejLXE4s6Rmm/EXSMr9SjANYrXEy0d7eg0r+H1Uq+EHnPHSHF7nO0YtDlGwhtfx0gplw6PyEoOH94fo6tL8VnDLvxl4RrccubYTHcpzaR6Iub+SUxOUZYQJHy05cn+goW6zkcQGarzEyCIDGhzQNTcIdUdUvOG1NFk4gSKXu6EvXuNS+PlCKkuCHdCuLMiuS+aEVxeTlGeYSLPMBEBc72ihpnQS9eG3b5PH3T9zYuYdj3xWFU3zf0ZWZ+hzj3in7nq2ol1XTlHykSbYr5RaNfI8HCNdOeqUIdF3HlG4vbSOld7Ht8d13cq4Lus7IfIDQzDwC/ibtHcpeuxYUcPyy1iaXjlOOQUZROai6orGRPKsiqIDM0FX9zGtR8m78tDLGlvMna7yo1JFExB7pDgyujyhrROkKEsQ58r5OcK6XKDIpp64rLrvXIFUJ0jtb4Xah1XvpCmDdvdEfogOkMAYEJxigy3KxQRyuwcI74N75/dHlzukdgWr8MA21VyRpfxOgacK2fcEzIsx4XnG9lzGImuEfjfWnGNGP98+GfGZEsHtj8kFlj/GNJWtmNk/V/IF1Ico3hHhO18HCh1G7F7SlcpfJYbHDNqIMbUlOHTTc147J2v7ARsomdATlG2oPnFqBVEml+sThtev4DhLYjEfenEkjTyTBFEXMSogsiA7RYFukMQwlw6R8HTbZCdITnfxnE1dK6Q6pBw90SXSyS6L6LzEzX8R56JjoyuXHylUscvh0l0mbirpHO+VBdJfUSJl3ukjs7jYtM+HXhZRP5buvKNbPHsbOtyjcS5jfg5FWHyo0LE81D5XrkcI83jQXzD0sp3R/d9CfejRWkb8jZE9hONGJhxwigAwBPvrkdjc2uGe5RGyCnqvaKos7MTN954I4YNG4bi4mIMHz4ct912G0zTtOswxnDLLbegtrYWxcXFOO6447BixQqpnba2Nlx99dUYMGAA+vbtizPPPBMbNmxIvEPKxVL7q9FDEEkXZY+LradD5HGBl8WPlyCC501Jl0wNA9YweyWRWg2VuROmlWd1KTdsdSi9FNoRb+aCCBNfYruicOAiiIexVAGkChNVxPAQl/iSQ2Ax35cuXCa1pdunsiwJvfj+1e1VQSUJLUFAqp9VVPO5R4TPxy1YoYQ8If3N1SH83sP3HQcxSJTL3xu9y+kZSlPXCW25vl/qdxBqe+7vNFP3mSJZd03rwRw/ehBGDirB7rZOPL74q0x3J31wxZ/KK8fptaLo17/+Nf785z/jvvvuw8qVK3HXXXfhN7/5Df74xz/ade666y7cfffduO+++7B06VJUV1fjxBNPxK5du+w6M2bMwLx58zB37lwsWrQIu3fvxumnn45YLJZwn7SCKNSFNcSjOwIuzFpB5HUzcd2I3IJIzPfQuUOSIIrXUfOEXHlE2hutM5OzeENXc17UHCEvR0gUQmrukCqA/PKH8owYIoYpvfIiMemlijO3GyTWNd3teQgnv/5KeUaCSArz2WjndhI+b/HvoMs5Uh0/cdnLNeLnl+gacdGjnw3bHb513ivnsiDo/RwjaXv1u+QSQMx/e2VdOoVRNl7Teir50Qh+MtVyi/66+Cts39Oe4R4R6aLX5hS98847OOuss3DaaacBAPbdd1889dRTeO+99wBYv6juvfde3HDDDTj77LMBAI8++iiqqqrw5JNP4oc//CGamprw0EMP4bHHHsMJJ5wAAHj88ccxZMgQvPrqqzjppJNC9ycwsVoRSn4XYV9BJNwkAgURwggisU/ucBlfLwki8UYHSDdGdVnNG1JzhsS8H+l9vFsRj/XSv3Da1K3X1bXKTNd6Eb98ojC5RoA7z0ddFzFi7rr2ckTID3Lyh3R5RlEIOUZCXb5e/fVkt8O3N6ztnVFrcOUcuUfMGbBzhQzY+UeAk2tkxPOS+Ag1vhaAnWvkHp0WP++YsCwk79g5RkzohpJ5JOYY8bPJXsdzjMR6Up6Spi1hX9p8I7G7SZJt17SezrSxVair7IP12/fiqSXrceXxIzPdpZThc4ylsn2u02udoqOOOgqvvfYaVq9eDQD48MMPsWjRIpx66qkAgLVr16KhoQHTpk2ztyksLMSxxx6Lt99+GwCwbNkydHR0SHVqa2sxbtw4u45KW1sbmpubpZeIK4/IS+yIggiabRRBJBFCELlmCIZaJzh/iLtDXoJIHamkc4cMuEM30kgoyamQQ2Sq8xHGEcpT/hXriu6P6PS4Rp555QF5hNH8Xmq4zG8/7mWhv3FXSeckuRwijYPkN2+T+jcQnaOoR9jT8+9tnwvCecNFLi/3Cqcp57A2z0g4v2W3SPg+Sd8LYVvx+wNdPblaqHwluNclS7Ze03oqhXlRXP0NSwg98vY6NLd2ZLhHaYByinqvU/SLX/wCTU1NGD16NKLRKGKxGO644w5897vfBQA0NDQAAKqqqqTtqqqq8NVXX9l1CgoK0K9fP1cdvr3K7Nmzceutt7pX8Atn/L12nSpihPVBgkgUPmEcIijbewoiw/mlrxNEkhiKtyPf+MK5Q46L44ghvp3qDInr1DLRFXI5RkodnROkc4/EbUT8nSLTc52IyZzfLeo+3KPFlGXFFYoYMdv14e2qzhD/1x55Jjgc4jp19JrtFhmCs2S/N2DGnR9m79+Av2vEf3UKM2LHt7GcFsc18naMAEjLGsdI/FVh2KWejpHBC/k6Q63neF3WaDq1XFgQe5kGYZR117RewGkTavDbV1Zhc3Mbnl22AZceOSzTXSJSpNc6RU8//TQef/xxPPnkk3j//ffx6KOP4re//S0effRRqZ5hyFcqxpirTMWvzvXXX4+mpib7VV9fb22TwK9M3zqG63qbkCBKyCGyf7kz//whg0HMDQnrDsnJv8Isy4Y+Z4U7Hqrzo3OFVJdEdYN0TpDXSDT3fEH6PKD8SMx+qc8783qJ23jlKHnlNvm5QmIfVRdJTNZW3SP1c1YTs/nfQnTnVHfPTlgXzg+vBHspCVs4P7R5RuL56HPOWue0c77L7pJVx8sxCg5nK9+tMN9ZcV0KZNs1rTfQpyDPDps9tGgtWtpzPO+KEq17r1P0s5/9DNdddx3OP/98AMD48ePx1VdfYfbs2Zg+fTqqq6sBWL+campq7O0aGxvtX1rV1dVob2/Hjh07pF9WjY2NOOKII7T7LSwsRGFhoXfHdBdX1zqfuYiEuqrw8RREHtta7/0FkXjh9xNEXvMOBblD4lxDYohGrKu6RuDlmlwhP1coTC6RvM5xe8SZpMX6OsLmE3G88oq4MxSN5xXF7Hp8u4jdD9sFgjw3kdgaAIA7U8JqySESc48MJ0+I5/6Ifbb2J+QUAbZrZLcNf9eIv5eeqQbEXRg5z4g7RvYXwnD7N/Z7vxwjXsvQba04SbqvkCFuo8ySpBym3W4awg5Ze03r4Zx10D64Z/5qbNjRgn99tBHnTR6S6S4lTfxyndL2uU6vdYr27t2LSEQ+/Gg0ag9fHTZsGKqrqzF//nx7fXt7OxYuXGhfHCZNmoT8/HypzqZNm/DJJ594XkB8CSlwfH+tauqHcYhcv5qh1vEQRIb1y9ywnSL4CiJx1Ji0DL07JDlCghshCiLVseBuicsx8nGFvHKAZAfFcWtEtyffNfJMl//jP5LM7+U1Wk11hvIN0+UyOe14HZPbQQrKQbJdICgukSHnHPG/qfi3s6YIkF0jXa6RmnMGCKJaOGdUx4i7lr4j00Qxrzv3FbEf5Bi5vnMCnj9i1O+57nufIFl5TesFlBfn47+OHg4A+J8316AjFi40npWwNLxynF7rFJ1xxhm44447UFdXh7Fjx+KDDz7A3XffjcsuuwwAYBgGZsyYgVmzZmHUqFEYNWoUZs2ahT59+uCCCy4AAJSXl+Pyyy/HT3/6U/Tv3x+VlZWYOXMmxo8fb4/cSArXRTikHe/a1i2IvPaXtCAS+hReEPH6zs3NL3fIyx1SHSCvfKEwrpCXI8TdINEJ0rlGYl21voqfi6RDdYQ4fs6Qtey4Q6KbFDGcujoHyfnXykFCfBSbqZ5AhuAQGe6co0g810gdqRYBJNfIO9eIP0dNnh3b8V/gcoxkJ0aXZxTCMbIRZqrmJXZ1jWNktyN81bTbanKQ0nAzyeprWg/n3MmD8acFX2D15t2Y/+lmnDq+JngjIimuvfbahLe58cYbUVlZGapurxVFf/zjH/HLX/4SP/rRj9DY2Ija2lr88Ic/xE033WTX+fnPf46Wlhb86Ec/wo4dOzBlyhS88sorKC0ttevcc889yMvLw3nnnYeWlhZMnToVjzzyCKLRaGId0okgDb55RH7CR9pe/lWckiAS3CG7CwGCSMoNgTtc5pVILQoY0RkCYDtDXmKICxb/EJpeCMnD8OU6HJ1oEgmbWK1D/O0vJV7H9ymKpphw4qjih2/DRVIYgWRtIyRoI+JaL4bVxPPPdPXdCanxRGwuqnhILWw4zU8YGQaz9E5cRKVFGEHYVIfhLYx4b7VfaqF+qm5R1l3TehGDSotwyRH74oHXv8SDb67ByWOrEYmk+AfNBKnmBXVDTtG9996Lww8/HAUFBaHqL1q0CFdddVVoUWQw1hNmFshdmpubUV5ejpE/m4VoUZFV6OESeVrwIV2iLhNE8e0SFUSi8FEFkZ87pOYNuRwjDzEk1uHrnTK9ENKJIJ1YUuuI26pEEV4gxXwi3GpekCiYRFdHFEq8jiNoDKmOySKuddztcd5H7PVyedxDYYZrHZ+nyGSWcJHq2uthO0L2ejhljOnewxJFjDtFvAxOjhMTyw3B6lfe23VhjUqTwgJxx8he71EOpY667NkOYLa24vPf/jeamppQVlaGXIVf03L9OBJl/ba9mHr36+iIMTzxX1Nw5MgBSbWTic+P73PI3b9CpLgo6XbMllbUX/vLLu17JBJBQ0MDBg0aFKp+aWkpPvzwQwwfPjxU/V7rFGUtQYJIVx/u+t0miEQxBKduGEGkiiEI5aI7pLpGOndIFUpezpBO7AQJIS8XyGvyRp3oSTS52tmfe3JGez+GLJrscBe44yIPu4+3Ei/TO0SAqV9nOGEwkzHfsJqJ+FB+H9eI+z28nIfTEG/fEMWR/dmFd4ys1XHHKF43nY6Rs2VAGE39sxte7XTLj2yiC6nr3wfnTR6CJ95dj9+/9jmOGNEfQaP6iMR5+OGHUV5eHrr+X/7yF9c0FH6QKMoWdA6QR71E84i6QhDp+5a8IPIKl/m5Q6r745UzlIgY8g+dya4TIAsgd45R6k4R4AgftT0rpCWLpqjhbMNnuxbnJtIJJFuuMHd4TVyn/t1dOUeG4/Co+UZ8b3wkmt0vg0nCiPGQnmc4LZww8lUYRghh5KFh9XlB4fKLtCJJbJPunznPZUcNw9yl9ViydjtWbGzGuH3C37yzAu5cprJ9FzN9+vSE6vN8ubCQKMpWdC5RkCDyaUMURAl1oxscItUdAtyCSJdIrQuV+YkhUfCEzSNShZCXCFIFS1e4RcFOkZAHhJggULioimkFkm9+kcGkdapzJLYLFnGdX6JrJCZiO48icZKwRXdIHLqfrDDynOAxzJ/B0LtFCdk5XsIobJ4SkXOMGFiCk8dW4/mPN+FPr3+BP104KdNdSowcEEVdDYmibMHL3UmoDcUl8iARlyjsfuWHenLxkrgg8gqXJRIqk4WS3hnyd4z0QkhN0BbXqeUAEPVJrg47Ak0MTUWFv2mMh8YUwSQ7RUxyk7iTxF0kvYPkdo+cLpiuclGIWWnYTjK2jYdrJAojgLtUcjity4RRd4TR4I/qOpFT1DO4/OhheP7jTXjxkwZs3NmC2oriTHepR1FZWYnVq1djwIBwOVt1dXV48803MXTo0FD1SRRlOaFdIlUQebhEac8jUhwiTiqCyC9c5uUOuYWSLHqCQmTJCCGpTE2y1ogeP5HkBR/vE2NywrUdGoMjhqQ6zHTKhfAbF0leAikSFzSqe6Q6R1ZN/tBYSO0DZqBrxJd1Q/d7lDDycYsoiahncnBdP0wZVol3127HnDfW4JYzx2a6S+FhRmrnZTec0zt37sSLL74YOq9o27ZtiMXCzzROoihL8E2uzhFBlErIzCtcphtZFjZUFsYZ0okhVQjp3CBR4IgCKIw48qqrEmNyErWILYaEuqJQsh0jFpFCaY4Q4snS8T4Ytm/jJGnb1ze3QyTlHDE1mVtoR3GNuDASQ3C6cFpWCyPNn9SpmpgwkuqSRuoxXHbUMLy7djvmLl2PmSftj5LC3LjVxi/rKW3fHSSaV5QIufGX6i2IYqaL2s81QaQTPqI7pIbKdGJIn0vkiCEvV0gt5yLGSwSpAkgnehKZr4jXNRWXyOqbWzSJQsleZwiOES8ThJCXe6SKI1EEyQ6RM1pNvOv7ukZ8n4prlDXCKEidWI1SXhDhybQxVRg+oC/WbN2Dxxd/hSuOHZHpLvUY+AztXUWvfcxHttMlLpGOLBdEEcN5qCt/LIfqGjmPonBCZapIssuFx17kGzHbGeKPsOBl+Yb1WA3rcR7xZcTXG6arTCqPdCI/0mnvi6+32mYJv8Tt8w3xUSPWfsT+qP3UlsUfGaIeK3/JZc4jP+RHhzCIjxYRy9yPOXEeLSI+TsUwxL+T/DcXzx/nXwjnk3M+ep+H/JyOiyO7zDm37TLpO+F8H8Rl3aNA9N/TgMeAKN/TwBGnRM5hGAamH7EvAOCRt9YhZuaIamZpeCXAAw88gAkTJqCsrAxlZWU4/PDD8eKLL6bnWJKEnKJsQXeRFC+iHnVTDpu5+tH1gsh2ckIIoqBwmZ875BUmEx0gXdhMdYQCHSJxYkflquDlDKn1vIhp/kjiHERWX2LaelrHCE6ojYfZYiwCMFMbWvNyjvQOkd41shAeOGvIpRl1jIQpBCS3KEwYTVeS4E3BajreQxJGPYrzDx2C372yCg3NrXjh400448DaTHcp6xg8eDDuvPNOjBw5EgDw6KOP4qyzzsIHH3yAsWMzk4tFoihXEH9xJkDCeUSu/abfIQKSE0R+uUP+ydVm0mJIJ4TscJywzk8YqesTQbVyuRgSn4MWgyEIiYjznDMYiBimVQa3QIKwLIojq0zJOxJyjuAarabmFSllhpNIrQundZcw8n7MRgL5RQGiJ9HcIgq79VwK86L4ziFD8OCba/HwW2tzQhSJBmmy2yfCGWecIS3fcccdeOCBB7B48WISRb2drkqu1uIliHQuEdIriNSnpos5QWEFkS5PyMsdSlQMebpEPkIoyB0KM1kj74s6F5FK1DDdj/4Qc47EfQsCSieQbLcoviyKIzs5Oy6OInwWa8MZrQYl30h1jfTD+uPrulkYMWY9XDah/CLdzcEIdoucFhJMuianqMfx/aOH438WrcX763fi4w1NGD+4PNNd6haam5ul5cLCQhQWFvpuE4vF8Mwzz2DPnj04/PDDu7J7vlBOUQ8hobCZiJ8gigsdqXoKgsjrmp+MIIoaVj6LWmblrsQkQaTmy/Aynosj5tvIuTdWXhDPBZLyebRlncg3Ou2cHCcHyNm/mh/k5BqZru2C2nC2sfYr5i8F9ZfnIvH8KzFHyv5clM/LvSxuL+cV6fKPdHlGYllX5Rjx8zZMfpH63ZBcVXh8t0jMEB4MKivCiQdYj5h4+O21Ge5NCPiQ/FReAIYMGYLy8nL7NXv2bM9dfvzxxygpKUFhYSGuuOIKzJs3D2PGjOmuI3ZBTlG2kKJLpG9TWKde4NXtPASR6hJJzScoiCIGQ1RItA2akNFPEHk7Rnp3SFwWw2Jal0hxgLyWrTL3nEViub2cZPhMbS8m5RIpD4xlwj7if1sp/0gTNnOF1wxT6xzZ+UVGRM43MpjkGnEfRx2hxsNp3eUYMcF2sdwZ//wi7goZcSdI9XHs9152jpG6W9Rt45mJbuUHxwzHK59uxosfN+C/T23DgBJ/xySjMKQW0o1vW19fLz0Q1s8l2n///bF8+XLs3LkTzz77LKZPn46FCxf6CiPVidKRl5eHPn36hO873y7hLYjMo4omL5dIs51n2ExXVRFEWpcIiY0yU0klf8gvd0gUWWqoTA2T6cSQtzDSTNwoCCD3PEUeidYhb4Ax5SZsD9OPSwVRLEmzWUtzE5nxMifniOchmXHx4xyLXhw5oTAxv0hcdgSOOodRdwujGA+VacJoUsBMEk4JDNO3RY1PGI30DRHn4Lp+GDmoBF807sbTS+tx5fEjM92lLoePJgtDQUGBnWg9efJkLF26FL///e/xl7/8xXObiooKhHnYbt++fXHiiSfi97//PQYPHhyqPySKsoVEXKKANtIRNrOr+Qgip07iw+4dIeIWRCpBgkjcTg31hBVDQa6Qa84iDyEUkcrdx5Low2BFoSO3E5MFk3xvtreNiWJGrCe6RfAXRxDyjyxhY7rbNvQj1LpTGMWECFdQfpG9JLhFvs9Is/smJF0jLoM8ErgTdYsoDNcziUQM/NdRw3DdPz7GM+/V4wfHDEd+NEszV9LkFKXUBcbQ1tbmW2fBggWB7Zimic2bN+P+++/HD37wA7zwwguh9k+iKBdQ3CBPl0i7bUDYDG5BJLpE1nqNQyQIHrGdMIJIdn/ChcsA93B7NVymc4dUESSNLPNwhoKEkE4EiQJI/zDY1MJnfD+mR/gsGn++mfVoDlNykriLJDpIdsJ2gDiSBJGfa6QM31fDaV7CKBIPv3WaSEkYOe6Qf+K1lXANaMNo6gXdECVLsHPE17nCaOQa9XpOHleN259fiXXb9uKN1VswNZ5nlG0Il/2kt0+E//7v/8Ypp5yCIUOGYNeuXZg7dy5ef/11vPTSS77btbW14fjjj0d+fn7gPiZMmIDDDjssdJ9IFGULfi6R73YBydWafagukdiWKIgSzSPi64MEkZpHxElVEHnlDrmEUYpiKEgIuUefeV8pdLlG6nPO5PrynEQRmPKM14YjkmwnSbxDC3UsIeMvjnjOEYSQWgRMco30w/fDCSM+C7f1WSfnGHFBlFJ+Ufxz8hyNpvsTim6Rs+BRL4RbRE5Rj6WiTwG+PWkwHnl7HR5956usFUXdzebNm3HxxRdj06ZNKC8vx4QJE/DSSy/hxBNP9N3uiiuuwPbt23HSSSfhrLPOwqmnnoqKigpt3ZEjR+Kxxx4L3ScSRTmAr0ukogubKSExcZ06/J6vSzaPiHchjCDiZXmRmKcgCjPcPihcprpDYmhMfi8IqgAh5CWCRAGkfcxHqOH57jomHOGjPiRWFEqiSOLODRdI3EFS3SMvcSS9B9zruGukPj8tQBhFDOfBsiazno3W6Uid0MKI50xZgijJ/CK7SBNGc4XPfNwipZ426Zoco17N947YF4+8vQ5vrN6ChqZWVJcXZbpLbro5fPbQQw8ltZs1a9bgo48+wnPPPYd7770Xl112GY488kicddZZOPPMM7HvvvvadQsKCnDWWWeFbjtLA5u9EC+XyO/Xo84l0rTrO0mjLmwmrlYEkbrOb3LGsBMzqvjmDwUIInVouT3cPD68njtCfIg6H0rPt7eHuvNh6bBe/FEhzpB0eah7FAwFwqM+xG35K9/olNYn8lLbikBeXyD0w3mUiWn313nUibKt8hnyof3i8H37cxKG8YvTF/DHhuj+HhG7vnfOGD9fnFf44fqiQHf+VU5/n3NYO0xfWuf+/ri/XwEOrdBe4OM/iB7LvgP6YtLQfgCAJ9/9KsO98YCl4dVNTJgwATfeeCOWLFmCNWvW4Nxzz8VLL72EAw44AAceeCBuuukmvPfeewm3S6Ioy0nZJZLWa5KrxU0NOWzmTN6oD5sBbkHE13OCBJFYZj0bKxaQUO0viKR5doRwmSgYROHgmoNHECHWfEVxsSMIBukZZgZ/7linJH5EAVRgxCTRon2+GWLIh3cd3oYovOz5iRTRJe7LnpcoLvwKjJhLHBUIotD5PE3ps5I+P8+wZHhhlMf/rvwcUESQnzCSzi2NMOLnpW7+ImedcI4L6/iXwXPuIvF7xte5vmdwCyASPQRgPw/t2fe/BnMlsRHJUltbiyuuuAIvvPACtm7diptuugnr1q3DySefjFmzZiXUFoXPsgRmOzZpconEC7falho2U7bVTX7Hm0w0j0hcH5RYrSPM/EOqINKNLNOFysScIV2YzNmXPCJNrSuWifU4fiEzNcSmm4/IjbJObEOMENllahhMSNIWRqXxsBoMcUZra3s7RCckYkv5RWo4LSCUZjJrLiN9zhHHHUqLxfOIhK2s9wH5RTE71CZ/SEzWQonNXaSE1wJziwC45jMSc4tINPV4po2pQnF+FF/vbMGbn2/FMfsNzHSXJOK/i1PaPtP07dsX55xzDs455xyYpolt27YltD05RdlOsi6Rph3tnETCL2kxfOA9M7A+jwhI30gz7hjlR2JSfXU/foJI53So7pATWpLDZNwZEtvgDo3ozOhCV/mIeTpGqnPk5x7pnCGdKxWBKTlMfv1Uw2tezpE067eHa+RMmqn77MM7RrplnWNkn65KHfUcVMNo9nmrOaed85l/P5j820Fwi/TPBRS+T+QWESEoyo/i25Os+XL+8f6GDPdGQ5pmtO5qTj31VDQ1NdnLd9xxB3bu3Gkvb9u2DWPGjEEkEsHAgYkJTxJF2YJXnkFA/ZRdInGVEjazysIPvxfL1bAZJ8xM1V7kGzHkReSbrpcg0uXE8MdoiKEyLh7EnCFRBETttt15PLbI8Mjz0YbJhEdvhH3la8STX/6SGtoLI47EnCM130jMoxKFkf35drEwAuScI1UY8fW6MJp0rsIRQ8657T7nXY8AEcpd3y3pC5R6bhHR8znzIOvBsK+ubMTOve0Z7o1CjuQUvfzyy9JcRr/+9a+xfft2e7mzsxOrVq1Kqm0Kn2U7OpdI6wTxf8O5RGHnJOLowmZ2OSDdoAD45BH5CyJ9HpETygIQShB5hcvEUWVcDDltOmEydzhNmdRRKRfX8X2KJDppo4o9OkxAfISHOAKNh8H4dIp8nV1uxOcxkobhx0etGXJIzYoUiaEwSCE07fsEQ2l+8xiZ8VFzuhFpsENiTAqjcdQwmljfOTg4Q/a5uI/PXWT/KomH3PRzGVnrEhqJlgUhBiJzTKrrh1GDSvB542688ulmnDd5SKa7lHOo+VjpzM8iUZQt6Fwir6un6hIZunX+LpHcnntkjppcbZXJYTN7sjzFNdL9uteNNEtEEIUNmakhHnWYvSh21LwhMWcorBjyEkKuZ5+l5SeURliJf0tpviInfyjeO3e5oWxnmO58I0PONeJl0kSPXSCMTGZNPOAIbVkYmcwQcpLk/CImLPNh+jHmTBTJv1f2zNZc2Kg5RoZdKgknyBLHlceVzLxF3fULm8g8kYiBU8bX4PPXPsdzyzdmlSgSfh8nvX2uQ6IoFwgjlOCxLoxLBDWUINSFPNoMkEMU4j1ZDZvZZYLAUUVSdwsiNZHayx0KK4bUCR/Fdc7n4pNo7XE3jLmUq5hULM5ZJDhEopukSa6WWol/PjHRgZHEDbxdIxZ/lpooftIsjEyDeSZemxBmsxYcJQ4/Z1U3iR+9b9J1V7tFHl9f+wcR0Ss4d9Jg/OG1z7Hoi61o3NWKQaVZMmdRqiGwbhJFhmFAffZZmGehhYFEURbiGoavorpELqcocZdIKlLEkZpcDbjDZs5NRz9jtS5sJq9LvyBSR5fpwmVe7lCiYsg/dOZ9pUjKQVJFVlwA8RCZVUde5+sciU6H4hq5Q2dWmTo6LR3CyBQ+ioghiyTAET7iucOFjy2S4K4rTurIWFycGM5Bc5HDw2oJu0V+D4vVCChnaw8hRfR4hlT2wfh9yvHx10144aNN+N6RwzLdpZyCMYbvfe97KCwsBAC0trbiiiuuQN++fQEg8NlpfpAoyhIYuIPjsV4UOzr8BE8SLpEuuRrwd42CwmZ+idXdJYjUcJnqDunEUKJCSJrV2kP0qJ+ra318O1Nz04yCyU6SfQyii+T033rmmfJcM1EceYXUJMEUkYSR88w1Jc8oSWFksqhGCDnL3B0KE0aDSxCJ7hADnw3bPjRVICXqFokxMPXv5bcu3lUxGEf0Hr41cR98/HUT/pVNoijF8Fl3ncTTp0+Xli+66CJXnUsuuSSptkkUZSt2uAxaIeQ5ysWjPFGXSNzOzyXiqGEzAK6wmVPXHTYTSYcg8sofUsNlXu6Q6AyFEUNeQshL/IR2iDR/e5MZ3tuLggSOeySFu6R6gK9rpAuniaE0AK4E7ASEEX+yvdecRdw9kj9HOeeIh9Hk56h5z13ElJBaWLdI+shFt0gbTnOXiwKIFBBx0rhq3PbvT/HB+h3Z89iPHAmfPfzww13WNg3JzxbC5A35iCS9EILbQVJcIl7PK5fIqSOHyQD9LMJ+Q6nFsJlfHhHfLhVBZPdREUTqMHtxmLo8Vw+T2o1CLvMaKp+v5E7pZ6+2jiPMK18cYp/IPuyZrK0+FkCelVs73QDkYfyqS8aH7ovzGXF0Q/a9huuLBD0ORDdMXz5/3Oea15B817ksfVfUWa395y2S8Pgh4jc83/nBIwzPJ3oV+1QUY/LQfjAZ8OInmzLdHSIOiaJsx08o8Qup6yKtLwt2hTQhtLhLxFFdIlHwiHXU0Wa6B7sC3mEzZ31yDpEaMhPDZepNnwsiSSDZ+5EdI1FQ8PKocsMWxYlO6NhzIoV42Z+d2oa4D41A4v2w/h5K/hSUuZjEz0QnGlWBqAgj2dXzFkbi+eE3h5F4zuiemaarK85d5HWuqo6neI67vwPOOe/lqHoNbLD+VcvhCCAvSBj1Ok4cUwUAeOHjLBFFLA2vHIfCZ9lG/Nel569HA+5h+PFynSsE9SYA/ouX3ygSd4nU5GqxjusXvlLmFTbzulH6oY4yE/ESRM46xx0C3LlDqhji78V/JfHGy5SrQlRzDIkkV0cNph2J5tlEvGoEzA6z2duL4TMxIVtJrjYRsfrIw2mG9dgRuUyfY+R/LP75RSai9jHrwmhi7hAQLxM+X13StankEqm5RbqRaOq8RUzIKTJgpVDbn39APhEDg+GRZ8TgbQoTvYMzD6rF7Bc/w3tf7cDOve2o6FOQ0f4It4akt891yCnKFgz3NdVZ523D+yZYi0XKL9sgl8gq07tE4rKXS6QuB402E9eFDZuJiC5RsoJImuFZcEcAb0GkOkJWe4LbA/fL6ke4l7gP6SXsQ+cgqX3k/3q5RvZ+NOE0nYvm5RglEkZzhToFB4g7RM46U3Nume5zK0G3iP9I0H0X7GVAGzJzjQAOCm17rqMQWm+lprwYIweVgDHgtZWNme4OARJF2Y2XUAqbYM0v7kKZ6BLxZUO54HvlDnkt+7lEvL462gyQE7HTFTZLRBBpb/RQBZI+VKWKIS8hpBM6+YY1QWKYV76hF0tBAsmrv3IojC+7w2n25yUII/53SYcwUs83NYwmrVNEkupAimXSYAClTvCy+4eCe+SmXEf/vfMIocXX0Sh8QuS08TUAgP+sIlGUDZAoyiaMkKEzTbm2LWXZnViqLutnrwYSyyXyWna2U/KKfMJmXSmIrH3LITjVHQothjyEEOAWOs6xBbtEXtvrBJJVN1gcicfpiCPT9blJeUZpFEby31UOm8p/d498oiTdIo7qgNohZWG967sBBW3env676fmd9XOHiV7D4SP6AwDeXbMNpqk5V7qTVHKJ+CvHoZyiLEEariui+yWqrrOX4bb1DSBU0mic7nCJdKPNAP0zzVQSEUR2n0MIIjmfSBQOSthMOAax3FoX76Py+Xr98oh6lAeingvM9capo16kDF2ZletjT/4YzzOKwYjnAQGAM2+RqRyRtY17uH7M4wJp5QbJ+UW8rt0WnD452wDOnEZCLlH8LS/jeUI8t0idt0id5Vp9Jprn8HzDPUeREc8bkucyAlw5RuLfTPc3ATlIvZWJdRUozo9i6+52rGxoxpCSzJ0IhhxISGr7XIecomzFN8coRJkmLyJwpJmwuWuUDhDwiz1g2XYrvJOrxbph8ogAaIWTuC7dgkh1hqzyuGPj4eRYbbhfcl8NRBW16rWNlxOl5iFZ6xQx5+MaqXlGLmEpOEZRn7+J0//wYTTVLXQ+l/BuEcfQnYtBQl/ot9fwfGdZCaHFy0J9L+PtkQAiOIV5Udstmv/p5gz3hiBRlGUkPeqML4aw9dULuv8QZf88DD+XiJd5DcEX2xBdIpEw8xF5hc2SEURiuEkXKuPbW/uVxZAc0lJecdHj9XI+H/96arsRn36oOUdiSM353N3CSMwz8ssxSjaMJv19lTCafF55D9EXzz1XSFdZFpOq5XMz6NyXuho+hKb5jvqF0AjilHHVAIAFn2VBXlEvDp0BFD7LHnQXU3tdyPqaX69+oTM1dyKibO8lfgDrBqXOXi3VFZyWIJeI4zfRX6J5RLpRZmo7OkHk9M0dLhPFkFUGadlqX3iv3FVT/QViKm3GGLP3FxPbd4XOmF0e44/FEOsyPvO1FdbijwpRH+ERs8t5+AvSeh42c816zUwp5GafB8Js1zF7HQ9p6YfoS88/Y+Is1lY7fNkwmGuW60SG56cUQhM+VwmPkKZn6JzoNRw5cgAA4JONzdjd1pm5jqQqbnqAMCJRlKV4PuvMy6J35Qkl6A4J63QJ1s664F/mTt3EXCKRoLCZ2KZOELnrqaPMwgkinTvkJ4aChFA0ydtfDExqSxVI/C7NxZEp9NMSD1zMiGWGUyZ+/B45Rl45RWHgokXMJYoxp52o4cxdxMVQhIshTW6RmkekPixWXY7x+YsE4cIf/cHzmcSPgZ//gXMW+QkhQxFKYj1VRPH6RK+ktqIYg/sVY8OOFiyv35np7vRqenX47Ouvv8ZFF12E/v37o0+fPjjooIOwbNkyez1jDLfccgtqa2tRXFyM4447DitWrJDaaGtrw9VXX40BAwagb9++OPPMM7Fhw4bkO+VhqYcJnVllSj1tvoS8LK9j2nW6BGt7nSp0oIqjxF0ip67/aDMvXEPvhTbUtsMIIjFEBWhyhYQwmDufyLBfzr6NUC94tOHOKzKk/qg5R2JIzSoTEt+Vv5cu1OVy4VIIo4l/I/3f3Du3SOxP0Dkmop7LXutCzVkkbaxZ1ji2+u+0uyxVsvKaRoRi8tB+AICPMiiKDJb6K9fptaJox44dOPLII5Gfn48XX3wRn376KX73u9+hoqLCrnPXXXfh7rvvxn333YelS5eiuroaJ554Inbt2mXXmTFjBubNm4e5c+di0aJF2L17N04//XTEYjHNXn1INbEamhwIn2W/0Jkub0h8r97ApHUaZ8her3F9glwilUTDZrqEYDV/hvfdNdTezini/XLnDFnlslCx9uGImLBiJ4x4UrcR9ysKMzEpm/edL/sJI32OkfI3S0AY6fByAP1yi6Rl1zmlX/ZLuA4zZ5GI7whOdRnw/mGjksa8oqy7phEJMbHOEkUfbtiZuU6wNLxynF4bPvv1r3+NIUOGSE/b3Xfffe33jDHce++9uOGGG3D22WcDAB599FFUVVXhySefxA9/+EM0NTXhoYcewmOPPYYTTjgBAPD4449jyJAhePXVV3HSSSel1kmvC6b2l6ji4qhukIfLowudOe/doTPxva9wUn7Ve81LBCTmEvkhJlPrECdmjCo3S9nB0OcPie6QVWZIdax1sqCBx7ogdHVjYFKbJmN2PR5ek8JqzH2exBjvL4sPuWfaUJodruLhLmWofujj0OYaOXJJevSHJreIh7bUsBnPMzJZVKovhtV0IbT4x+B8JEqIKyiE5ps3pFuvyzUCXDePdMxonRPXNMKTg7NBFBG91yl67rnnMHnyZJx77rkYNGgQJk6ciAcffNBev3btWjQ0NGDatGl2WWFhIY499li8/fbbAIBly5aho6NDqlNbW4tx48bZdVTa2trQ3NwsvbR4/aoMKlN/4aqrPUJn6nIqoTN5nSxyIhpxJJKKS+TU04fN9I6RO2Sm5g+FEUSSc+Pr6iT+H5R9+O3HK6QmHo/9PsAxkoQiPFy5kGE0lSC3SPyb+51DuuH54row57RuFJpI2O+MVeA6VD1dEDrL+msa4cuY2jKUFeVhd2vmHDkKn/ViUbRmzRo88MADGDVqFF5++WVcccUV+PGPf4y//vWvAICGhgYAQFVVlbRdVVWVva6hoQEFBQXo16+fZx2V2bNno7y83H4NGTJEruAVRgubT+Sz7DXcWDdvi9f7MKEzrwRrXV2AixHZJQLcI85UEg2bie2qeTRiDpG1DNd7P0Fk9cctUqx6boHD2wkaoq9uz9vwE0dS35IQRlab4vQEevfNa8ZrbV0lt8jdjn7eIqc/+uH5Yn/Fdeqyrp667C+MhHWA/N1Sv4th84qM9M5XlLXXNCIU0YiBMbVlme1E2BCZ3yvH6bWiyDRNHHzwwZg1axYmTpyIH/7wh/j+97+PBx54QKpnKDcoxpirTMWvzvXXX4+mpib7VV9fL+xMs4GhyUVINJ/I8EmuFqrpJmx01vmIGo/31rI7h0haVtZ7OggalygsathMdUB0SdVW35SE6rhgcfKK5JwhQC+GnGP1Fj6uPvsIpSBxpLpGiQoj1aVT84u8nDyrjXBuUdC8Rfay8rfWJVyLdcOcl2pY2OsHgfZZaNJ2/ssJ5RWlgay8phEJMaamPNNd6PX0WlFUU1ODMWPGSGUHHHAA1q9fDwCorrYm01J/HTU2Ntq/tKqrq9He3o4dO3Z41lEpLCxEWVmZ9AJCukNAYD6Rul3YEJlKmF/QyYbOdKPP9A8K9c8lCuMSicPv1ePjgkhsz08QWf3kZY4Q4cs6MeQnahIOnwWII+fY3K5RWGEkfRaGWyA5+/AOo3mhTroptaUZgRaUcC2t8wmhie+9HVGnP+6Qs+gCBfxACRNO8/zxoylPgGy7phGJQ05R5um1oujII4/EqlWrpLLVq1dj6NChAIBhw4ahuroa8+fPt9e3t7dj4cKFOOKIIwAAkyZNQn5+vlRn06ZN+OSTT+w6SRPmAqn5Zeo3fDjZofj2+5BhiKDQmbSdpxjSCBkllygRRJfIL7HaWk5cEFnr3WLI6bs+hGatM6SXe31E27bTL71rJPfNXxhZ5Uz79xLDaLrRaCo6t8h1TD45RXI99/kivvcLoXFU8R9G8Kc1r6gL3SGRrL+mEYFMrKvI6P6TzSMSX7lOrx19ds011+CII47ArFmzcN5552HJkiWYM2cO5syZA8CymGfMmIFZs2Zh1KhRGDVqFGbNmoU+ffrgggsuAACUl5fj8ssvx09/+lP0798flZWVmDlzJsaPH2+P3EiKMBdR14XYf9kvn0gkbPhBfK86Qvr37l/+YcSQOC+RjjAukZeA0uURySIB0jqrzFsQ2dspYkjeZ/DdUK1jSkIgEi/jT6w3EGNMKo/CsEeq8RFq9uSP8fpRwB7pFTVgT/BoLTP7F5/XSDPdpI6AM3rMfUzeD4vlM2TzyRz5A2OlkWXKDNfiw8QjSl1x1Jn9Hsye3ZqvEx8cK85uLT4glimTPTJhBJt0pqoFrgoawtRJgKy+phGhqKvsg7xIFyjmsKTq9pAoyl0OOeQQzJs3D9dffz1uu+02DBs2DPfeey8uvPBCu87Pf/5ztLS04Ec/+hF27NiBKVOm4JVXXkFpaald55577kFeXh7OO+88tLS0YOrUqXjkkUcQjSb3DPTQSdY+BM1P5L2d3mXyFkTev+r96rnCJz6hMxWvEWdBeLlEfJ2aWC0mVYcVRMmIoaihN2tjTAxLOttygSSJoPh+uTjyE0a8n+IjQkx7PwAfqm/3D0y+edtD572Ox/34D3HmaqddSyTFWFTa1hSWLbECadkZcu9+7If8Hh7l+n6LYkgWSXA/0sP+LJj8hQ1chvumYTAwGGn5hZ2t1zQiPPnRCAZXFmNtpjvSizEYUyczIbqT5uZmlJeXo2727Yj0KZIf72EALOK8B+LrInIdI8LsZcNgMOLrDQBGxLQTRiMRZk/ayCer43kW0fi6iMEQjVi/qfl7Xo8Pl44YDHnxofMRgyEvYto5RBHDRL5dj7+3lvONmJRPlG/EpJFHVv2Y5PREDIYCo9MVOlPzifIR88wl0oXOvIbfe4XNkhVEqhjyEkFBiCIJkN0jUxCPsfjXmZdxIWTajwFhdu0YY7azY8KaE8iE4w7FmAETBmIwYDLrX+t9JP4+4rxnEZhQ3sfXWXUiiDEDHSwvvp28fQeLWu/jzy/rYFHrfbxepxmxt+tkUatv8fUdLGK/74z/y+vx9zHTatd+z8uZAWbXsd7zZZPxZcA0I2DMmrOI8XoAwAwwMz6XEePLhvOLmxnWh8rFEQMMab21zjABs6UV66+/EU1NTTmdl8Ovabl+HJnie395HY9ecXy3fn78b7b/T2YhWliUdDuxtlas+v1/5/TfvtfmFGUdCSRkysve+QyhXaEU84lE1Hwi+33IUWdymT5/RZdg7YVfLpFdB+6wmW5ixiBBJOb9qPlBUSOStCDSbS+2rwvdqQnYEUm0Ce0q+wlKuvYaiRY24Zo/+sNVrhmFJi17nItWm4nlFYVJtlZJKNnatbG82JUj0IjcZ/iAkoztm3KKSBRlL1pB5H8x9hdB+np+24QhbD6R1zaAl0DyDp2liuoSOeVqPb5fMZzkLYic7dInhlR04kjdv5cwcr1XhFJUOZ/UpOvE++qd0+U3Ck3cXp3IUeybuq3Ytva9h7APm2wt4vu9CfOdCvsjiOh11FQk79QQqUOiKBsJc3EMyDMSF8MKn2SSrIPfB+cTyet1To5+BBOv75dgrZuoUW7b2yWylnVOS3hB1FUkKowA2elK1C1y6nq7Rb79DRiFJu/fz/nzPrfCbRP83lskee8jW0agEblPTXlx5nbO0vDKcUgU9URCOkaeNwLo63i6QsowaS/COETS+hCjzhJBnahRbs97WQ2bedFdgki3D50wkup6hNGC3CKr3F9YOv2RQ2g6/B77obbl9N37PAk7iWi68JvyItEBEQSho6Y8c04Rhc9IFGUfYX5Run6VJh426w6C5idy1unziVztJTHqzAt1xJlVpneJxPdeLlF3CyLdvlRhpHOL7O3UBHBd2x4CMix+OV+J5hUFzVek3yZYPHk5qurM1p6k+p0i4UQoDCwtzHQXejUkirKI0LNa+xAwW7+22UgY9yhEbkaySdYqXvlEQXiFznz3pXVGxDbdFXzFRjcKokT36eV0qY8cEUfhpQuvvKKgeuojPzJF2DBa8PxhIbYhejXlxfmZ2zmFz0gU5Ryu5Gq/utl3hiYaQrPq+ITkQo1AY4GhM9Up0QkIr9AUd2gyIYg4fN9h3CKvkWi6ZadcHsHnNwrNs4+avCKtAA5ow6mnF+HpGoEW9vdI2B8iBBGGoOfQdSkkikgUZQ2a74F26G6CbdirEhxtE2bUTjIjz0TCJlnrCBJCfng/zNTDSfERSGFmqM4UQWGzMCE0wF+U6vcbIFJ9coS09bNQ3IcNWesruItSfe4ZQRDpgURRLpONNwsf/EJgeodIX+b30NGweCcVi33yHnHm7lfmv0qqW+SUa0SdK2SmE37eAjJcfxKbcVzet7/z57wPFuPJzLelI+mcPBI8RI5gpOGV62T+Sk6EJ4EzriuSqlMdji+STF5RV9CbvgDe4T83OsGY3D69nT5X3ZBhs64gmbmK5AbS3SOCyAAUPutV94TcIo0X2TATN3b10GbfWbBDfpPC1As7P5F/G94hJsAdOssGl4jjlVsk1QmcWiDdffIQRgFCJ5lRhlkZatNAs1oT2QgNySdR1GPo7uH2qRA0S3KYUUk6vG6yPMnae7tweIXOcpkggeS9XWLnWyphOB1h8ta6WiAlNKt1zzt1CKJHQqKIyHm8HiXhqpdDwjHdhBV0QQJRdN9EVy5dBI0iTJbumNiRIHIeCp+RKMoZsvSXZ9jZrFOhO/KLVNGgm5+ot+PvtoWbR8pdltmraOLJ1l3UEYLIFnqxIAJIFBFdRFcJpFRJxtXwSlAmCIIgehZ5me4AQRAEQRCZJ9Vk6Z4QmaafwERKmEIcz+t9qsTSeJrGQvbLZM632+ym6QGynVT/pibLvsuNSbMmEoQD5RSRKMoZ1It3Dzj50kVYoRPzuQHGGH2gYfD6rJMVrrkmSug0IYj0MXv2bBxyyCEoLS3FoEGD8M1vfhOrVq3KaJ9IFBHdTjpdJLvNABeiK/aZS3iJvpiirmMh2jKZgRisl8kioUVpGPzaSkVAidumS4ixHBN0BBFEd89TtHDhQlx55ZVYvHgx5s+fj87OTkybNg179uzpmgMMAeUUZSsMCY0wY8xIeK4icRuTGVkzZN3qi1oWQdTwv2VbboUJsAjARzrFP0e/BOsY5KHoMTDt/D0xxrJ+rqIYs47bjB+vLvSnCqHuQCd2UgmLiiLYS/Bk1IUiZ5fIRVINgSW47UsvvSQtP/zwwxg0aBCWLVuGY445JoWOJA85Rb0A0SQI8+s2XTcZrxuXSujwV1c4TIH7dH/LudDgwoMLkVxF7X26jyYWMpdIdfvCbpeLGLrvAwknopfT1NQEAKisrMxYH8gpyiVU94gZnn5lMs5RIpjMsOd4sd7D9d6PGIsgIjg/qhMUQ0Q79421XXpu2zHmfsaXCf0vBRNmzg/NF10jMyA5JpamU8f0+MwSFTyiq5QuB0hshyX43gUJGqIHkK7RZ83NzVJ5YWEhCgsLfbdljOHaa6/FUUcdhXHjxiXfiRTJ7at8T0JzImp/TSbYRjaRaLjE68ZpIpKyi6CTVWrejU448DrZ6BZ5hc7CJJHr6phI3p2LwfD9++nqy3XCjhKMeLz3cDvT5Tb6fDddH2WWfy8JwiZNo8+GDBmC8vJy+zV79uzAXV911VX46KOP8NRTT6X5oBKDnKJegugcqe+heS/mGJkw7Jwct0PE3ztOj/zenR/Esdp1iMFw3S5NRBD1SP+1bqR6R8mLGDO0OUZ+eUVeOUbuts2MPRw2rCgTw4Fhk6xjzIAZT6z2b9sSQl7uEG9LJShsJoqadE7PkC4SHpGm/QFE2onIAtKUU1RfX4+ysjK7OMgluvrqq/Hcc8/hjTfewODBg1PoQOqQKMoiDKb5AeoKmcnLjPk8esAnvNadxBBBBHqRpIbDLEEl3+AtIRNBxEP8xHhiNYvEPxt9sjWgn2lbDZmJyyZj9iM/eAiNJ1zzZRPMfip9JhAFkZdL5BU60+UT+YXOdCPPYogEj/5jEa2gUcsSySsSBVYshDPkFQqTXSWnffFjSHqkGY1QIxKkubUj011ImbKyMkkUecEYw9VXX4158+bh9ddfx7Bhw7qhd/6QKOph+IkkySESyk0ht0Z0iDxdJMHREd2imMd7lTD5RBGNbxFmBFoimACg5BWJI8wScYu4MMqkW2R6/MSzBZKHS+QVOksXXg6TmhsUFELzEjvpComFFT7Sx+UaZRbQhu5PRMKJENiwfW/G9t3dM1pfeeWVePLJJ/F///d/KC0tRUNDAwCgvLwcxcXFyXckBbLPi+7thBmVEnQhln4Rp6dbYfGc4Zr53/BcN0RmeDgLwSGaIGJMHwoS5ZYoCnSzW2sdmAzkF6l5RGKftGLHwyWKwS2ErM/aSEp08L+RzumJaVyjREJofsjnXHCOkdfXw/T4DiWUbB3mu5d5I5fIMuq3t2Ru52nKKQrLAw88gKamJhx33HGoqamxX08//XR6jicJyCnKRtSQWbJ1eFUph8hxkjxdIWFbvxwi7vZYzhFTtoG7njZ05u0Y8TpWDpDbIbLDZojEHR+f3COfEFosvi5iL/u7RV5hNLnNrnWMdCEz671etIV1ibxCZ1a4zDt0JopVz1mvmZFUkrWfgAozfUSi70M7RqFqEURirN+RuYkLuxuWhVPEk1OUraSYvOm3ebjciuTfq/glyuocI11+SszDOdJtG4vfmINmW47B0LgjYt/EY4i7QAE5OyaY5Bh1hWuUrCAK6xLxUWd+j0VJBL98okQdIvmcCx55lirydyVcPatArRDCASYIABsy6BQZjKX8ynVIFOUSARdev1+4Yc9VT5HkEQoLc5MC3PPMiDe/GNPcHBHR3txEVyIZLCHkvuHHmCIUmNtZCZrbRxdKs9pKjzhS2wkTMlP7nqhLZNrOUHihIYbOvGay1uUT+QkkNZwaJslaXfY6h5OZl0haFxje1jUQsozodfSm8Fk2QqIoW0j2wulzAVYv6klNSuexvdcNxutmpdZT2wD0CbkxnzmJxBuv9F7jFnnd2FW3yHJJeP+ZvU4UFzq3yCvHSCeOEhFIum3Eds34f7wfYpmXIDKFuom4RKmEzqy29X9LnRgKm4/ml++UrpFn6jZ+OXth5iii2awJP+p3ZC7RmqCcouxCkydkMANMTelX6qkjzqRlZVh+KiPQ+LqgvCL1PZS8Imt4vbXM84KidvvWsm6+IjDTd2h+WOwbN4MwFxNf5nWceYt0s1yr+UXWsTg5RgDsci5gxGH7yThH6ugyUYDpRJnaX34sTpnwXnHKrLqJu0TyPgWB6uP6qfuU2wifT5RKknVYVyjl6IBWJKXYJtFjaO80sbm5NWP77+7RZ9kIiaIsI9RcRYBL7Pgt+4mmRJOt1eWwj/tQk6zDzFcEAwAztQ+HleYlYnAJKXt7uOcsEo9FncxRTLqOxT8sS+gInwoDIoYhCSMAdvI1AF9xZJUFC42gIfZWfzUhNB+HiG8Ts7eRw2ZeI87CukR+o86sdiLakYUmk7exQqqGazuxvu69tZxaPlzokWcJL8NNGvOfiNxnw469kmPZ7aQaAusBoojCZ9lIWHs9hFVvrwr5SziZG4ffzSrmc2Nzh0mckJcID6EFDcP3y2NR+85zi5x9uMNoTj/dLosoONRwmrgNL1cdHB4C83u5j8/03Qfvj9o/se/yNvLxqp+bOOIsWbhLpBNKumR49ZywjkUOl/nlE4V5vIffSDPvELN8XK6/TgLfxYTqEL2K1Zt3Z7oLvR5yirIVnTsUop4rPMb0w/H5BT/Rx31EGEJN4mi5Le56TpvOshpCA/gweu/Zrbk7JLpF8gSQBrxmuBYf8WHGh+vrwmh8z1GIoTH7k7X/CXKNANk5EvF6yKxnGEx9PpsokkIKophUpgpA/SM9EnWJvEYRWm3pR53pQmdegts6Lu/comTyicLm3KlJ1ukaedYTQg9Eaiyv35nR/VP4jJyirEF7MumsTGYklGytrndd4IX3om3r92va0xmCzy/1gHAI4D8KzS/h2qmrdyTUOjoHRJzQUQwpqYnXvMxqyy1CRNdIdXX0M0fr/3P1W9leSrAW95ukINKFzdR5iZJBdIkSGXUWFDrzczRTmbRRXecXMtMmWQd8Fw3d9zeoQ0Sv4dNNzcGVuhKWhleOQ05RNuHhDqWcbK0si24S4J9XxB0gfkPxyiWS18kPhwVMx0WKJ0w7rpK3SxSUcK1zi8TJHHXPQ5PzlgxpQkcxvyiMY2TlHcUdovgdkrtGVn3ZHVLdo0RRxZI4vF6cLiAoh8gqcwsiCMu6sFmiLpHXvFKeYVLNqLOg0Jlv4jUz7O38RBQXPkzZn+/IsqD8ISB8SK0H3EiI9PDpxsyKInKKyCnKPrxOSi/XKIFlNUciTFhBXXbdtHyG5qvhDfUGJs87E4nf9CJw5SUl4BZ5oRui76xz5xc5/eL993eMRKdG5xxZ9f3dIBWv+kH7M5GcINKNNkv0wa8iOpdI+pvztjViSJ3XSj133OeW/hz1cznDhshc3xX1QHXiJ8i9hVWnJ9xEiPSwZstubN3dhryIRmAT3QY5RdkKi1sfQd8P1V1iAEO4vCJr2ZDyikwwRA3+HnoHKb7Oa2i+NToL8XUGANE5MqQh+Xx9V7tF7pFt8mg0cZg+wD9TZpeJjpH9QQL2h8ldIwCezhGHPzYkjDAC4NreOga3M2S1GS8TwmVyub8ggrhOkwjtbNc1LpHfhI18e/F9IqEz2xFyrXPaZ0ggn0izHDqfiEJohMKStdsBABPrKrA2U51INQTWA85hcooAzJ49G4ZhYMaMGXYZYwy33HILamtrUVxcjOOOOw4rVqyQtmtra8PVV1+NAQMGoG/fvjjzzDOxYcOG5DqRiLWuuxAnmFfk98vXa53v6B7Xuog71KEJjXi5RSI6t8j0mPvGbguG9katv/EbwjZux0jNMbLW8T47rpGfc6SKmERezucgtyXnMKVHEIl5RPLnmVjYLFmXyNmXkkOU4KizsKMo/RxS1+gz1zI811uFcOPxPe8KxygrrmtEaD5YvxMAcODgioz2g4fQknn1BHq9KFq6dCnmzJmDCRMmSOV33XUX7r77btx3331YunQpqqurceKJJ2LXrl12nRkzZmDevHmYO3cuFi1ahN27d+P0009HLKZ/MGkQhs8vSNcsuJq6QbPr+i2Lv6TVZV1uh7TOL6TmyudwJ9uazEsIhXl+meZmrQnx6Ga65uU6YRRjhjb5mgsLHk6LQU6CNiGul4WNKGqSealiSdpXvA88XBaT1iUniLxGmyWD2IYuBOeETg1lG/e5Y8Lr3JMFFhPWu/OF3Mvi+5TnJ9KE1Dxnsu6Cm0k2XdeIcPCRZxOGVGS0H72dXi2Kdu/ejQsvvBAPPvgg+vXrZ5czxnDvvffihhtuwNlnn41x48bh0Ucfxd69e/Hkk08CAJqamvDQQw/hd7/7HU444QRMnDgRjz/+OD7++GO8+uqrqXUs0bwiNQFUWZYWNRd8v3CB340kaERQmDmLRAfBvjGGzC3ibpGXMPIajaYKI6tM7xipwsgqg7wMtzji61X3SOcAeeHpGEl9cfbJ++HVVy54khFEIsm4RPJxyS6RNGmjcF44+wtOsA7rFOlCZ35D8V3fFcjLoZKpuzmfKGuva4QnG3e2YHWjJUwn7FOeuY5YCXSpvXKcXi2KrrzySpx22mk44YQTpPK1a9eioaEB06ZNs8sKCwtx7LHH4u233wYALFu2DB0dHVKd2tpajBs3zq6jo62tDc3NzdLLE91FV1wnLSuLyrbWsk+uhPDedaOAJlQm/lr3uSHxsJk8EsjbLdLNpGwqN111Wx1+z0Wz2nU7UTphZLtG3FGC3jWytpHFEX+Jjk6QUHK5S5qX2La4X7mO6mo5Is8+rvjx+gki8e+QStgsTC6RziVKNMHaa1l3nrqcUaFfwe4Q3OtTzScKcV6Hobuvawld0wgtiz7fCsaAg+sqMKC0MGP9SCV01lNCaL020Xru3Ll4//33sXTpUte6hoYGAEBVVZVUXlVVha+++squU1BQIP0S43X49jpmz56NW2+91b2CX1R110V+oqnr1PrMAAMLHJqPeCK2deFn0iM/InAmdOTL8FiWJnmMJ0arCddykrV+eL51bO7noYHfEOPv5ckZI87xa5KuxUkfrZu9O/HauunLEzvyz5UnX1vD861jiUpl8eRq5W9jPx4kDh/Gby8bsusUFtcT7YX38uSLmjLFHeJlogD0EkR+eURhUUOhfiPOvFyiRBOs/UZWut2gAHdIdV79lgGt8PGbnyidN5JMXNc8r2lEaBav3QYAOHxE/wz3hOiVTlF9fT1+8pOf4PHHH0dRUZFnPcOQVQhjzFWmElTn+uuvR1NTk/2qr6+X98kvqJoLpfbCqvuFGRBCs/opvnffGNSJHGUnyf0L3GsZ8HeL1KHXXsnWgJwXlEgYzW4/wDHi+9A5KGqekega6ZwZV94RfykuUuiX0o7LddI6V/pwmU4QmUz47HwEkfT3SDC5WkQNlUrngI9LFJPOIfdcRJ7LGicoaNRZUqEznaOk/S7ry1MhU9e1oGsa4Q9jDO+usUaeHTY8w6KIpeGV4/RKUbRs2TI0NjZi0qRJyMvLQ15eHhYuXIg//OEPyMvLs39Jqb+MGhsb7XXV1dVob2/Hjh07POvoKCwsRFlZmfTywvCy1D3td6UoRAjNL0ymyyHySrjmy34zXFtt8Junu11XYq2QW+QVRkskv8hqM7ww8g2nqSEnyOJIdGy0IbYEX9q2WLAYUsNl/POWj1E8bn9BlErYTHSJYB9DRP+3F0acpeoSBSVYy8uqOEL6Qmd8W491qTpGmbquJXJNI9x8uqkZX+9sQX7UwKSh/YI36EIMM/VXrtMrRdHUqVPx8ccfY/ny5fZr8uTJuPDCC7F8+XIMHz4c1dXVmD9/vr1Ne3s7Fi5ciCOOOAIAMGnSJOTn50t1Nm3ahE8++cSukwieAgjwVuEeF+Ogxw/oLvxBIYUwYsnbPXKEkC452+tBsWrStd8oNJFUhZEuz0h1jXTiyMs98nKREn35tSf2QSeGdMchihUxqVr97OS/i78gEvELm3nNXu034szLJRK391pWBb8uwdrvO2EVphg683OMvL77CZCN1zUimJdXbAYATB1dhT4FvTajJWvolX+B0tJSjBs3Tirr27cv+vfvb5fPmDEDs2bNwqhRozBq1CjMmjULffr0wQUXXAAAKC8vx+WXX46f/vSn6N+/PyorKzFz5kyMHz/eleCYEMyZgxCAK4/IYFbekJxLFK8o/tRkhtOYWI3JEzny/CJrWW7DZEAkfqOPGky6sUT5oz8MuB4SGza3yDpQoS4z4rlB8YwdO/8nJvRJyDdy5RNZy1buj/fEjlZOkLLe/qzjP3XUPKP4B+jKNbI/WCfnCBAeFRJHyj9KAfWHmCgUdXlDgPM3i7nKRfHnjDJL1CGS+6MPm+kEkegSOfUMySUSj83LJRKT/sVh+E4d75wiXVhNfuCr0XWhszSHGrL6ukZ48sbqLQCAb4welOGeIPXzsgeEz3qlKArDz3/+c7S0tOBHP/oRduzYgSlTpuCVV15BaWmpXeeee+5BXl4ezjvvPLS0tGDq1Kl45JFHEI1GfVpODIMBTBU8gCAChHWikhKETlxGOQnXTE7I9kq4tjsQr8PFkbisPv8M8TIpbGbIzxZziyRRDMF+H+X9iQsta52QGB0gjDgxvr0gjOI9lZd5nfhepX2Kn3u8TxHd30Q4ZpEYLKGUCu6cHjnUqNbzE0N8WQ2XWe0GCyKRMGEz17EoLqEqjkQRpM5L5Jc7ZNfTiZ8AN9Q3wRrccVWX0xM6s9d3MdlyXSMsdu5tx0cbdgIAjt5vQGY7g9RHkPWE0WcGYz1gYoEcprm5GeXl5dj3tjsQKS6yrpkGs+7HhvXiZfZyxHkPCOvEciMuciLMcoKMuCgymHUzj683DIZIvA5fNgyGqMHs9xEDiEbM+Htm7cpgiEaskWQRgyEC93LEMJHHyxAvN0zkG6b9PirUjRgM+UbMahvWctQwrbbFf+Nuj/yvaW9n1bXajsa9FV7XWWeVR8Hcy/Z7XRmT/hWFkSh6IsodThRpqaA6M35iSCwL6w5ZbUbcy4pDxPuSSB6RziXqYFFFHMm5RJ089CoIpE5Trmsyq4wpoikWr8cdzhivEw+NxcyInWDNRZBpRuzQGRc9JjOcUBkDmCkux0WRKYTI+OfPAEMqt9Zxp8gw5TKzpRXrbroBTU1NOZ2Xw69puX4c3cGT767Hf8/7GPtVleCVa44FkJnPj+/z0DN/hbx87yT9IDo7WrHkuV/m9N+enKIsxXKI+IK6ThNCA5yQmbhN3GkyhPqWewR5aH28ctDw/ETcIqks3rWIIYfU1DCaU98dRtMN0w9yjGBEEIWpCbvBaYc7RmK70ufudo14CC3GDEcM+ZgA1ueXmjDSzeEktq+W65wh619vd4jX8xJE9j7SIIj8wmZiqEwURGoifjpdIl2Ctf28NC6IQobIujt0RuQmL6+wkt6/NXFwhntCcEgUZQs+F0k7hKbLJeIhNNc6jzmLpNAaeANKKA1K2IwhIm4H79wiLpKckBd3arhIEoWQ8+BY8DY1YbR0CCPn8xLqimVQ6gDwzDUC4vMcaW6QHGWVlIOUAuo+/cSQtd4tiFxlGkFkL3vkEKVDEOnCZrzPanI1P1bnFZHKdI/0EPOF7Dpi+AuqENKII8Ad8hJdIqkxTd2gdSCh1FvZ3NyKt77YCgCYekAW5BOBwmcAiaLsgtkSxfm/yw2y/jFggGlzWgz5zIwLJgYhoTpenk63CPByhwQHyHZoHGGkJl1b773zi/wwE0y+jhqmnHCNeN8kFwmSiLJyg0zEWFRwiGT3CIDsIMEjBylB1OkNpCTrACGkLQ9wh3h5Vwsit0tkuBwjeQb1SCjXiIt3nUvE64VxiXhjoROs4+WBEzb6CCWi57Nw9RZ0mgwHDi7HflWlwRt0B6mKcxJFRJcjRGa8BJK90ifhmpdbOiF5t0g3Ei0Sr2vCsN0iQB9WgyHn21jr9MKIz3ANWDdwP7fIcqgCHCMA4szXtrDRhdOglEkOkRWSs4WIn3skkuIFw5VorRFCYrkcnpJDZQACw2XOds6we759WEEkEk4Qqa6RO7naLldCZaYgeETRo7pE3EFyPepGGx7zmeJCLBcFjodw6ooJG4nc5Zn3rEkuj9s/O1wiwoJEUZZgAP4CCLxQuKr6iSRexENmSngtUbcISj0xl4jX4/vmQ/Qdd8gJowE8N0YOo6WaX2Q7NDxspxNGkMtsEaMNp/E+WWUxAFFBSJmI2MnX/Ngk9whiO3D6hsRHoXkJLZ0Qst57O0NWb8O5Q7ytsIJI23fBJZKPSS+IXC6RIIjUIfgAPxc1w+6hn5fIK1wm5w75DMPXiCZ97hCvr/1Y3EKJub/ORM9l2+42LF23AwBw/qFDMtwbBwqfkSjKXpgYSIP2imknXKvrJfFkSGLGHp6foFsEyPMW2e6Q4BaJDpHqDnnNXZT2/CLxvSqM7L5YZTFmOT5qcrXJIojwcvvoxI9Ul3Nk99x5qxEKgW6SB64Hqbpco4irrp8Y4uVq7hDf3muEmbXe2yFy2vUPm0l99xJELmdII5KEZTEsJi7r5iUKyiWyVjgjzpy6cIQQk+ta/yrlcSTx0wNuHERq/O29DQCAsbVlqCkvznBvBFJ90n0PGMxOoihbUKI43uuFWi6nSL8uWbcI8Twk0S0ywZz5g5ghCSCeZ6SG0QAvoQQkk18U5Bh5TvDoHJYrnCaF36T+IVAciQ6R6CDx4F9U+CRizJnrJcgx8hJPusRk3TZ+YoiX+7lDYh1xpmm/eYiSySPyEkR+cxLpcoXU5GqnXHWDDK0QStol8sgbSiTB2uD7J3oFL36yCQBw0WFDM9wTQoVEUbYhhtDE/2vDaZbY6Sq3iNeHIIwMgznhMzijzXhd+/YvhNEA7g7JYbRE8ovCCiM5t8gRRjEWFyde4TT+2esmcgwSR9I6wOUQeRpDptcKAG7xY2/l4xqpQkhc7yWGeJs6d4j3QxwlFpRDlGhitXpsqiASR5tZdZzRZpJoEsJmXo+mcaX76MQRkJhLJG7I4qEzHQyUYE3gk6+b8NGGJgDZM+qMQ+EzEkW5g0YsaW+2KbhFfAi/6hYBkB4FoiZdA3LStdfcRcnmFyXsGAEeThLgGU6z23W25w5QDPGRavGwmp1fBMEh4iE3OPlHHFeukX3setHjh3tWa0EsBAghcdlPDPF6unAZ3zZdgijo2Wa6sJlax1MACS4RADtspgohcaJGd/J1CJfItQ7SuqAEa0Pchujx/OvDjQCAEw6owqDS5CdK7BJSPQ97wDlMoihb4L8yDcdGDxZAvF6IyRx93CIWn6War9OKIQQnXUvCiBm2WyT5IXGhE5RflIwwssWIK7dIcX+YCRNRS7gx2KIHLC50xJkzvfKHXO4R77OyHrp6yaEmKwN6ISTW9RND1rI7VMbLvdwh3o7XA16TFUTyhI16QeQ32oyfo/ZnI4TNZNEkiyOn3NC7RIJoStolguwS0dxEvZPWjhj+vszKJ/rmxNoM94bQQaIoR5Du0/AQS1zLhHCLxHmLrHXOGjGM5oTVYIfRrAkb5aTroNFoieYXJSOMrOerhUzABqDNNdKV820Buy3VPbLKHIcoBit3SHSS7OMPCJup6BylMI6RWOblDKn1w7hDdjua/CF528QFkd/8Q2rYzK6jCZuJLpCYXK26Q6JLJMGdJK0ASsIlCiF+ekLogfDnzc+3YtuedgwoKcQJB1RlujsuKHxGoihrEC12rVvkdbapbhHfyF4vu0VxOWSrLNEtAnMLJWcnhhRCAyCF0SLxg9CNRhOFkW62a+FAkawwMlnUflZaUJ6ROD+RmGvkjEbjI+0c54ivs9sC4HKPhL+Hc1w6ARQ+bOaZbK24RpJj5OMKWeuDxRDfTn1mGhdD1vbej+5IRRBJLpGHIAoabSaGzQDZGdKPMlNcIsAWNl3pElHorHfx13fWAQBOn1CDovwsfMCuyaxXKtvnOCSKcgUm6AQobhEvkOrrQ2die/yZaAxwkquZE1gT7ROeYC0mXQP+o9FE2eM123XahBHgCqd55hm53gP6vCII7Yr74cceH2Iv5AxxUcAdIy5IRHfImZIycVTXSOcGifsV+xQkhnjdMO4Q316XP8TbS1kQQRY1OkEUNNpMXK+GzdTwmfTQV53AUcVRGl0ie3uix7K5uRVvfm491uPCKXUZ7o0HqQr0HnAOkyjKFpgSIkskt4hv7+UWxdfzFbZbJLbHBLcIcDKOXKPTIIXRrHXuMJoqjEzDPUw/XcKIizE1nGayKCJgUp6R6BrJ7+ND6YV8I9E5sg7UCY3ZT71XBJLzeStukLeJkBDuSRATF0Liej8xJL7XuUO8PV24jLediiCSynwEkeoaqYLIPWmjIIhElwhwBJHOJRLh7QW5RDwcEeASSQ+AJnokj769DgBwcF0FRmXLYz0IFySKcgzP3CJeIKK6Rdo8I3fStRhGk8SQmHjDnSO4w2iiMJIcIk1+UarCyEmY1uQZAf6uEQBtIjYgtcfFkZ2QLawXBZItooSjsecsSmB+IhVdCM09LF8Mn4k5OYmJId52GHeIt+OVP2TtP3VBpLo2XoLI61EeupmrAcAzbCaiOkOaX9LS7NWq+EkEUkU9lpjJ8Ew8wfriw4dmuDfeGEgxpyhtPckcJIqyBeHXY5BbZMDt3PM2XG4RF0ZKnpHoFiUaRuOoYTRPYQR9fhGEsuSFEaALp9m5Qh6uUYzFBY6UY6R5jIcSOvMTSGI98W+iogomL7wenQEEiyDeV7GuKITE9v3EkNWmvzvE2/aagyhVQSSWxcyIryByCR8ACYfNRJdIagj2OmlZh+AAhXGJSBD1bJ59fwO27GpDWVEeThufxaPOaEZrEkU5iy60Bl6mGaLPnNq8lpR0bdfTh9EA79FoOmHkNX8R73Sqwshklqiwhs9r8owAf9cIfH+w14niiK/n4sgRWdAKJADx0WiIH688Cg3CZ8LRDbH3Q53oENCLIGu/zvtUxZDrveIO8ba9wmXO9qkJItfjOjwEkdsl0ggiNWzG0YXNRCEk1fNxiRJB3J7okfzjfcsluvTIYSjIS+x7T3QvJIqyBeGiGNotglDu4RxJbpHYuiKYJLcIsjDyegQI4utEYRQ4f5FdL1VhpJYJ4TQwScB4uUbSe3t/olskJ1L7CST7Y4kjOUlx+Ii2ZNAOy/cIoYnD1f2EkLosCiDevpcY0i/r84estlIXRGpidThB5Hw+WkGkukR2ZY+wmfaL5mzjesZZGJeI6NF8sH4HFq/ZDgD49qTBGe6NPzQkn0RRz0ESPiHCaHHHJzCMBtEdEsSQhzAymXcYDUhNGJksCtNggkMktOgXTgOgDt0PI46sw4xol0WBBCA+XN9yhSJCjhFfx/F6dEciuJ995hZBVrksfNQy8QGu1jHJYsh+73KD9O6QVd8/f4j3NxVBJLpGQYJIJ44A6AWR6BKplUVB5OUShUAbNrPf94SMDEKFP/z1tPE1GFLZJ8O9CSBVx5JEEZE24r8mFT/Heh/WLRJLvK6vksCx3ruEUdwl8gqjqcJIndvIL78ISEEY2f0PF05TXSMxKTtIHPGcI7dbJCdPRzWft+QkiftNEVNz0/R6OKyXEJKWPcJkfJ0qfngf/NwhXsdvUsZ0CCJdArafINI9ysNTEIVwhnyTq31cIqJ30dDUimfjCdbfOWRIhntDhIFEURbjNUTfU/CIMB+3yClw3kt5RbbcCSWMTDOCSMT0zC9KVRjxUWyWiOHddoSRtV+GhFwjAGq+kS2IwPODZPdINy+RWAYIM1jH23UEFFxE5SN24ecq+T0UVh6a7y2EvJbdOUjOyDJn2d8d4vv0C5eJbScjiESXKGYLoGBBxF0iFa0g8nCJ/EgouZpcoh7NP5d/jfaYiXH7lOGokQMy3Z1ADMZgpJAsncq22QKJoiyBXxgFGeMpfiyxFMItgkYYeeB6BAj0+UXq/EXO9oZv4nVqwgiQ1qjCSClTXSOTwZonyXaJnHmNuKsjOkfWAbnFkog0L5EgQtT5irweBmvVTS6Upg7R14kgV7mPGBJzhqw23M4Qf68bam+v8yjzCpc5baYmiHQOWpAg0rlE7kYgr7NFjL9LlCw9IR+DcGhpj+HBN9YAAL57aB0ikRwQviYQ8FstePsch0RRFpKWMJrnY0G83SLP/KK4MBK/0n7zF6VDGInuUEQcYSbUNZmBCBc4fq4RAN28RqI4suo44kh1i8Rlq99yiA1wz0kkukkiQUPxVbyG5vuKI0UE6cqCxJC7zHuovdVecuEy3qdkBRGD7AyZZiRhQRQqbBZCECXrEtGw/J7H/JWbsW1PO6rKCnH2xOxOsCYcSBRlC0xwgNLxk1FoIpEwmq8wsmszYbv0CyPEhU1gnpHUfcch4mViYrYlmLjjoxdHzkzWsnukDafZj/Fwyuy5j+zj1gsgVTR54fXcM2u/SvhMcqsSE0LisuoM8X3phA9v188dsssSGHKfiiByPZ4jWUGkc4kIIiSmyfCH1z4HAJw7aQiKC7LwOWcaKHxGoii7YI7TI4bRknaL/MJoQcLII5oQJvHaaiY5YeS4N/pwmsmiiDBL6NjJy74OkdW6KJi8xJGElHfEewi9YwTxWWdOOeAeNq+KprDo5jQyFdEUJIys/qUmhvh6v9whu44ihsTyrhJEnhM0qiQqiMglIhJg6brt+KJxN4rzo7jkiKGZ7k54Uj0Xe8B5TKIo25EEUPx9uoSR7369w2jpFEZ8CzFc5ptnZB+c04IopPQiyBFMOnEk5hw5LpHePbL2J4wm0zhGACShJKKKpiBU4WO3r4gkL4Ek5tvohJBabrqcJbcYEstFxyjIHeLliQ25T0EQ+blEOjIgiIieyb2vWi7RGQfWYFBpUYZ7kwA0ozWJomxB/LUouUVded0Mcot88ovSJYzsZ6zBuUnr8oxizIiHwsQPSRA5dvuWANKJIEeHKOWQ23KH0WSBBEAKsQGww2wc0U1y9iqLpkTRCSSXOPIRQeJ7nSsk7iNIDInlXu6QXOaIIbHcL38IQNcIIi+XKBm8BFFIXEKJyHm+aNyNd9ZsQ8QAfnDM8Ex3h0gQEkVZCs8vyngYrYuEkTUPkjWKjRkMBneJNMLIRNxFUlwjXu6Va6RziKJ2X+TyGIz4Osc98hNI1v7dIsnuG3PPTcSFkM5B8sPrcSDqqCvdoz3E92GEEC8XHSC+TieSRNFjbxsiXAbAVxCJYkis22WCKFmXyIsQLhHNX9QzuefV1QCA4/YfhJGDSjPcm8QwUhTnPUHYkyjKFpg86sw3vMU1TQ4LIyA4z4hPIGkP//dxjcRcI0AXUgMkqaWIJt4j+yMAoE4HIAokwBFJ4P12zWjtHKsqmJJBN2+RKox0TpFYbmryjlIRQ857bzHE21PdIV7PTxCpYghA9ggiL5fISxD5QHlFPYPGXa146ZMGAMD/O25EhnuTBBQ+S8MzB4i0EvSLMqwS1+YvSGVK2MDnJiHeRJj9b3wdeJl8c9L+mod1k42ZEeGGJ7gAwg0wxtw3UTtcA7ms0xQTfSPoNKPSus74DbvTXhdBhxlFjBnoYPH1zNCus7aLxgWD3HYHiyKGCGKIoIMp++R9EuqK9RN9ie27jjuwP1b/O1nU/vw6WMQ6Rr7OjNqfk7rOZIa9Tt0vb9s9H5HjDvHPV/1bxphh/517nCDS4fedJlHUI/jDa58jZjJMrKvAIftWZro7OcEbb7yBM844A7W1tTAMA//85z8z2h9yirIFflE05DIpvyheHCaM5us0qfu1N+46x4gxA0Y8XBaUZxRjhjacJjlEgmskTiQphtTUfCN+WDqHyAmfxacDiIe/xPAaACnEBgBmfNc81Ga9twp1TpHq7CRKkFukDtVX3SCxjs4VUtf7OUN8W3WdX+6QXUeoK23nIYhUMQQgOwSRFxrBQ2Gznk3T3g78Pf5IjyuOzUGXCNa4kQSj+67tE2XPnj048MADcemll+Kcc85JfudpgkRRluBOtIZe2Kh1kGIYTWc9MUhb8fepCCPDYJAmfAzIM1LDaTwc5pdrJOf7uMURb8sKdTkJ2ToBpBNIAFx1OKpQUsWYKpgSwUtIeQkgQC+CxLb8hBBfL4oY/q8qhpxyIfcHGpEkiBy1PJH8IU63CiI/dC6RlyDSQInWPYc//OdztHaYGDGwL6aNqcp0d5IjA+GzU045Baecckry+0wzJIqyEabKEcEt8jAaDKQgjCSlZWiSm5IXRoxZos12iUQHKWSeEeCfayQJHASLI0j74+vjywECSa1j9S0+k7XiJomozlKy6Eeg+ThFiggS63s5RukWQ/ayUC7XkR2g0AnVQPoEkR9eLpGPIEqsneAuENlNS3vMdol+Om1/GLqJ3noRzc3N0nJhYSEKCwsz1JvEIFGULTBBi6jfJ6YIo3ixJG88wm4pCSOlD7xCIsLIcrHc4TRRGMWYAUMSMpYwksJoUIQRlKH7Qt/9xJGYkG27RkK9iOEfQuPukE4kcXTiS5ydOqp+tgHEPG7cXgLIXk4gdKYTQry+ro4qhrTlIdwhXjeZ/CG7DpCcIBLxcokSEUQCiYbNKNE6t3lg4ZdoaulATXkRThpbnenuJE+q52F82yFDhkjFN998M2655ZYUGu4+SBRlE1qHCKJ+8BVGdhhNEkgpCCO+zAzhZ7BVIYwwssWVweD0VBZGjHnnGQHwzTWyQ2qGIoJ8xBE/PnFWay3xbuoEEqDLMxLFmtspsrZJzS3yDKMpt+SwoTM5F0nvCrnXeYshpqsHf3fIbkvjDkFZ1obLwJeRvEOUiiAS0ThALkEUJ6H8JCKr6YiZeGrJegDANSfsh2guPPjVg3Q95qO+vh5lZWV2ea64RACJouxF1Ce8TOMgqXVc+UW+24QURoZ7+0BhBMRVnHeekRVaE4QSFxdxEWMYzO0awck1AoQbO3d9ECyO5LwiLnrgco8AOPU8QmjWe9kZEsWSiJM0nthFx3tWa8UZChE683OExLpe9YKcIV7O1G0C3CGxTMwfCjXCDAgviFQSEUQ6+NfDSxDp6odpl8gZ/nfRWmzZ1YZ+ffLxzYn7ZLo7WUFZWZkkinIJEkVZgiFc3FUxI6b4cDGjzS/iekYVRuL/BR2UkDBi8YZDOkZ2W755RmLHHWfBUFyjWFw8cddIzCmK8PCb3ZS/OFJHqwFKaE0QSIBeSAHqc9tkZ8jLEVJzjxJFFT1OuSKONCJIfC+X/f/2zj1Iqure99/dj+mZgXEQkBlGHg4GH3GIh6AhICpEg/iISXkrxxiDmMe9moiRYI6amCowx4CmKh4rZcKpWDlgoh5SuTHKSXEtSVTUgC8URXzrKIoMAzjMDDBMT/f+3T92773XWnvtRz9munvm96lqZu+1fuvV3az97d/6rb3DxZB4ryE7LR8x5B77e4fstLzjh4Ch8xBBkwcfO/jYa22sPyyQqg/TJPzx2Q8BAD88bzpqElV+lxu+TxGLoorEvo7b54YuT7OMJgqeMGFkl6OIwsiescmt0TZQhRFyIobgJ4zEOuzO2Nv23Y7pYo0s0YPA7ftWdV5xJO5Ws8tpvUeCQAJcT5AUoyT1yxVKdh7g9QjphFMYfktmYrt+tnpBJIsgOS/YK+TJU8SQc6wIqqK9Q4BeECliyCoLf0EkEkUQqYQJIqHu0DgiRSCxIKpO7n/uQ3zc1YfRqQS+ceaUcneneAi+EQWRy+fJoUOH8O677zrn7e3t2L59O8aOHYspU4b+PWVRVCmQs9rk4PUQuQeBwkg89xNGglFkYUQQZm9/YQTDulipcUaUEzmWYCKIwsiJLZKEEqRYI9tGt6QmiiOd50j0ELk7zmTvkZ9AAtwYJOm+RLb4UcWQIpZs5GDs/LxFgeIoUAx572Ad5D3SeYXsv0SaNKGeMDEklgnyDonpUeOHrDLwF0QFeIgAnzgiURCJiAJJI66ieoyY6oCIsG7LBwCsu1fX1RR2p/pKolQxRfnw4osvYsGCBc758uXLAQBLlizBunXrCu5LobAoqiRIEDY5beArjAS0NrmTkgojoZ/e1nK2trfH8MYZAQhYTnPFkGd5TRA6uiW1MHHkBGRD4yGKIJAA+Iok6xg5G/kGjao3SBUvYd6iMOHkFUP+9y3SL52FCyEpn+QAajtNFUPWcXQxpD8HVO+Qmy4LInE3WkkEUY5IgkgUOIIgktDZCBg+6Uxl8+cXP8b7+w6jNhnDVXOmlrs7Vcv8+fNBFbTsVuULoIWzevVqnHnmmWhoaMCECRPwta99DW+99ZZkQ0RYuXIlWlpaUFdXh/nz52Pnzp2STX9/P66//nqMHz8eo0aNwqWXXoqPP/447/4Y6mQMaH9J+k3A2l+hJJeVymjsxLygi0fgr3HxIiVcvMgULnTIXQTNGMiUl0pcj4F7btqPBYF1cRUfE5I13ViVbO7xE+JxVihnP57CLp9VHpNhP8oiS958u6z4CI8MuY8QsR8HYr+cR2coL6m9XF1+L9lWV1dMbhNun9xHl8Skvtvjd94D4REoQe+RnU5wH8Fi1+MeW59F1nQ/M/Ez0H221uerfAfM3HdDEESU+w75CiLPd1Q4BhD2nZbjhvwFUVH/H5Wykl0JqLQ5bbizNucl+t9nT0NDbbK8nSkV1n/UIl7lHkDxjFhRtHnzZlx33XV49tlnsWnTJmQyGSxcuBCHDx92bH75y1/irrvuwj333IMXXngBzc3N+PKXv4ze3l7HZtmyZfjrX/+K9evX45lnnsGhQ4dwySWXIJvN6pr1J2DyDHfD634JC8fa+goQRoDwVyOMTPdcEkam4YojURjBvsCJAsj/gmkKF+ao4ki9MNsXdFUgqBd/USAFiSRVeIgCSxRMOuEkCijxpbXx1BOTnikW1idVBOUjhNT3TooNco5l0WO3a3/GjrAlwDTlc/FzFr1DoiCyxRCZ5RNEBnwCq5X/s/5B2v5xRKUQRxU3pw1jHtm+G2/s6UFNPIbvnNVa7u6UjqIEkT3BVzcGVZLfqozs27cPEyZMwObNm3HOOeeAiNDS0oJly5bh5ptvBmD9gmpqasKdd96Ja665Bt3d3TjuuOPwxz/+EZdffjkA4JNPPsHkyZOxceNGXHDBBaHt9vT0oLGxESffsArxVK0Vj5ObfUWtI6ZLeeK5QT7puWM7HXIZ1Q6Q89y6SKrbOib/c4OkJTAYuWWyXJq9zGYA7o6wXBn73D5Wz5ErY1cfk2wolyYeu3nOsVBW/CvX4aZJx8oVTN3RFrQ0lu+jPvyW0XTppKSFLaGpcULiMUG/hGaf+8UM2WXVpTHR1i92yC4riiGnfUHoBG651wgiQyOQ8okhAoIFkSeOyLbRlYHclv03e/Qo3vr1T9Hd3V2S7czlntNKNY5K5Ku/+Sde+egg/s850/DTi04tad3leP/sNr90+s1IxAu/p1Am24/HX7mzqj/7EespUunu7gYAjB1rPdm4vb0dHR0dWLhwoWOTSqVw7rnnYsuWLQCAbdu2YWBgQLJpaWlBW1ubY6PS39+Pnp4e6QXAf7K0USZifZ4RXEZsRyjjmeSVPPHCIv3ShtdGvVCRKf+iJyFf9Bo58SiCN8HPs+Dmud4I1XNkX7SzprKcJi75KC/RuyJ6TVTPic4TZHuTXM9VzLM8F3XZLGgZTfX8SG0qXiDdEprsXfN6hKSX4jlyz4M9Q5IHSfX0SecRvUOCILKX12BqBJF4Dvfc6x0S/q8MkiAS/w+GCqJBouxz2jDl0df24JWPDiIRM/B/zplW7u6UFrMEryqHA60BEBGWL1+OefPmoa2tDQDQ0dEBAGhqkh/s19TUhA8//NCxqampwbHHHuuxscurrF69GrfddpsnXZo0DTjB0wbcOd62E8xc7w/Zf4TwaCk9V8bIiRGljPuvPk/qqxqAbV90DE/PnHNp2z4Z1hjtWsgKzhbvhC3vUAPsd0PcpUbkbuEnWN4acRu/bSsGZQNw7nXk7hKzcJ9uLwdoQ7C3b5jo7GTLnYv3MIJYH8lpUn7IeonfTRudfE3daproOfLzBnnSyOsVss7dYzWAWkzTeYasY0jHRXuHoJyLY9cJIrh2RQsiiOWC6vMKH48gGgSBVAlz2nCEiLBm8/sAgCtnT8H40dVzp+YolGP3WaXBogjA0qVL8eqrr+KZZ57x5KkP9iOi0If9Bdn85Cc/cbYcApbb0n5OjCh4xOuhc23NaQRfYeTUUQJhJNUfQRgBQidziYpQknanwd2dBmdoubPcgA3Y/bQFkS1G/MWR/Saou9UAd8eaJXDcnWh+gkdKg7KrTDwGPGWh2msIEz1+RFo28xFD+YggK02uvxgxpN5zyOmPJHqE9h3h5Np4vEOCnTRIVRAJbfsvcRXpIYKYrqlfzPP0FSWlUua04cbGHR145aODiMcMfH/+Z8rdHWYQGPGi6Prrr8eGDRvw1FNPYdKkSU56c7P1UL+Ojg5MnDjRSe/s7HR+aTU3NyOdTqOrq0v6ZdXZ2Ym5c+dq2/N9WrA6UUIWPKow0tpBPI8ojJQy9r9il0KFkd0n22uk6Y1VoZHTSjmvkT0m22tkiz4YcO5rZNeQhziy0+zrj5FrW+c98ggkiNv+5btfq54kQCN8hIuzKphKjXdLvnKxC7DXxRKJHiHxPCxeyPoblgZB/LiCKi/vkNO4xlvk5EEfP5Sz8wgWnzwgTw8RFDvd/2ePoNLbFUvFzGnDDOu+RO0AgG/PPQHNjbVl7tEgUGyw9DDwFI3YmCIiwtKlS/HQQw/h8ccfR2trq5Tf2tqK5uZmbNq0yUlLp9PYvHmzMznMmjULyWRSstmzZw9ee+013wnEj9A4IkD61RktiDN40nYuh1IdhrfO3LFhGvIFSXcBQq4ONeZDOXf+7+XqE2NHnLgR0sQbaWJS1N1N6tZ+3VZysYy4ldx6qVvNvfFFdtyNPST7XBc7pIsJKvTlF2sk9kUca+AYhPGL74H83nhvbSDHMskxXt404bMUPgs53iz3eSk7y0j9vnm+f0Y0QUQGDDO/R3f4eogo4v8tv/+78AqiUsUXVdqcNtzY9PpevPBBFwwDuPqsE8rdncGBd5+NXE/RddddhwcffBCPPPIIGhoanPXyxsZG1NXVwTAMLFu2DKtWrcL06dMxffp0rFq1CvX19fjmN7/p2H73u9/FjTfeiHHjxmHs2LH48Y9/jBkzZuD8888vrGOkLIkp56LHyHHMqOVyRPUYAe53WfUVSXXa7Uven5w7PucBkhuP5jVyltQM/WNCLHNyBuvnObK8QDkvkZQGyXvkxB7l+iF6kABYjxNx8qxDyWtErndI9AKpO9B0NqUi3yU0NV/1BonHaryRzgNkn+vSnHOhnPPOkLiMJrRPap6Pd0g8lzqpLJcJdr4/IOzvkyKItLY5CvYQ+diVShABFTynDRPsu1df9cWpmHRsfXk7wwwaI1YUrVmzBgAwf/58KX3t2rW4+uqrAQA33XQT+vr68IMf/ABdXV2YPXs2HnvsMTQ0NDj2//Ef/4FEIoF//dd/RV9fH8477zysW7cO8Xiet3wnjeABnFnaiSMylDKQ52F9OflxIJJd7sQRGnaeIGocWxJsNQrL04pdhyNgconqOSxxZAiVkWhn12MLJ9Ivq9lvikcI5SGQAHn7ftYemxRHZKV5Hi5Lhid+KEw05YMqemzCBJL4YNogEQREE0J+6ZKgsdPgpgWJIacuj6fH51xMgyKIhH74iRBfQRQqYMIFkSduSLWDbKfGHRVKxc1pw4gn3urElvcOAAC+M681xLqK4eUz8H2Kyox9f4hTv78K8Vr5PkWAfCzmSddBw99OPg+4j5FtJ+S57ZPcptoHW7hI7Slpop1TB0n12TaGkmdIx0JddlGD5DRbEGrO1TTZzq3TRnfvIr/zmAEtxYohFX9x5G8X5EUqRgiJ554AaidNbsg3bsi2EcWVWDbIOySW1QkepR6PsPHUK/elEEGk9RBpzu3j7NGjeGNN6e5TVC6G432KlvzX89j89j5c8YXJWH3Z5wa1rXLep+i8k28s+j5F/3jrV1X92Y9YT1HFQrIXx89j5DhyNGU85Zz8nC9H8u4o5QxZ7IteIdshI/YNQr5YoW8QNsG9WtjeH6Xnnl1qtp1dNleXvY3f9hzB8QB5e+jULXiPrPHKHiQ7zf4r7j5TPUmqveRVEhEEiJ9wCkMVPFL1ActkVlmvABLtZPGUTx4EYRMshsgjaApYKnPOrcPQ7fa5NN/4ITvfU697bgSVCxBEYn+lr4OPICrWS8QMHv98dz82v70PAPC9s4fZfYkUeEs+i6KKwZkcRRGjCCHrJEQYhQoleRs8xHqci7qPMMo17FevJFoANx5IFExQGrMzNGJJFUfQ9d8WNLk3ishwg21z0a6u6JFFkIi7o83tqBhn5OSoQskZj4Udn6SiCqdCyWsJLaBcUUIIgJ9XSLYRykYVQ86xXGfe3iHH1iefxM9PsNWeB9+HyNOucK7tDwLKMBXH2n9+AAD4X5+fhBOPG13ezjCDDouiCkKKGxLFhyCEoJxHEkKefKsCsU6PT8W+7oh6hoxcXeT8unb0h92OKHhyHXS335PsNbKN7IGovdeJI7tN+z2yvT52O8i1Y5cXPUOKQLLH6YzXx4sk2vkJJRtxu76EIpzywe8xH07V6nkEASSmB4kgqUyAV8ixU8uTkm/bRxVDQrp+pyNc0SLZhuR76pbrNIR+BQkiXTyQJ1ZIqlOpQ5PHVA7PvX8Af39jLwDgmnOHt5cIADimiEVR5SBM1iURRqLnB/J86wgjOItVSp57DD+vUa4hSVjlOiN5jaS6fQKx7Y4TLPVhd0LnOYItfmxhIoxDeDM83iPY6a5AEkap9SJJAihEKDlDMsj32uZ6i4JFjh9+XiK/PJ0A0p0XLYSEjJKLodzfgrxDufoixw9J5xrPktq+2k/BPrKHiAVRRXPf1g8AAF85vQUnNTUEGw8HTFXNF1C+ymFRVCGIl6xSeYwA11YrlGwB4SeMHG+MjzDKiQtf75RnmUzxGnkKAJqa3FNbHOUGbHuObOEjeY/E9uC2pxVIcOuQvUghniLBzumiYi+iCpOwAOwgEeTahJcL8xRJ6VKaXgg59johJNh5Aqjtv0FpSrpWDOXayNc7BAiCyD4n3XlY3V4PkSH12R1KJA8RhL4xFcNLu7qwcYd1W4NrhtszzhhfWBRVEKIYkoSRX55dDnph5PHkCFnyuT4A2xIvuXNHFHjL2v9Kdefaj+w1cgai2AtiR67DcD0+jnfHFT5Oh+wyhitWKJfuCCRxPEI7stfIrkaOGRIDtO180V5FJ6CiEGbrFUJ55OuW0Tx5ckagEBLOi/IMeWxd+8CYnqjLZdDVERaoHUEQ+YmegHrZW1R5/CF3X6KFn21C2/GN5e3MUMHLZyyKKgZhcgwTP0Giybm4C0rJz7sk1+sNwNbaFuA1gm0DuZOGsiwmF9CJKSFNVIF2HYKIcQSSsLwG2KIHjq0okJzmHW+P7EmyystB2nKAtiqQ4CEoCNsv3w+/+SeKAFLtVBEklRPKRxJDarlSiyEl3xVIPoLJ04buXCkbaOt2PVQQaUVbgFhiKoLXdnfj4e2fAACunX9imXszlBQpiobBF5lFUaVgT4yK4CnEY+Tk+Qkjxdb+Ja0LwJZs7XONMIJdHsrWfbt9AGKgtogkjkRlp3qOnDzVLwVXHDm2hn55LVdMDNAWxZQtkgBIniStUIJQt/L+WOWhRV02y9cL5M3XJarCSDn3sfX1CImFoniF1PR8xJCmTKAY8rFRvUNSGek8xPuka085L4mHqPqvJ8OG/3qmHQBw7knHYebkMeXtDDOksCiqEAwSlIifMHKzRVONuBHyhIQgWzvZFi66OCModTsiRBA+6iNCxDJu0zqhE8FzZNcmiBlpaU1J9xNIUu9EgSS9aXA9SRDKiUIp1y+vyJHLqOTjDQpEU0+g+FHKyB4kTR1SfgFCSJeuyYscN5T7q8YOWXXIZbXeIaF+w6+NUgmiqGIJYh6rokpg5yfdeOjl3QCAH8w/0XqM0UiBl89YFFUcYcJIFDQB4oZUO/jbis26bWmW06ApL3qEpPpc4WNfoJz4JNHG04Irjqwy5PUcacp4RpKPQNJ4kax0sVrFm+R21uspglvGj6h3uQ71EvlmqJ4i/4JB3iCpbAGCJy/PkP03aCkrxAYQBFGYMCkgoFqtL0g8sYeoOrn/2V0AgAUnH4fZ08aVuTdDjKmo/4LKVzcsiioF4ZeiIxbyFUaq+AnIyx1628kJAdHrU6jXyB0LOXZSG7m/fsHYVhnDFUdiB8SLqsZLpIxQ6rkokAC4IgmQRY6Q7vEmOU0IdaitBgifkniLtJ4iNSGgXTHP14Ok8xr55EUQSYWIIW9ayM6y3F9/D1CU5TivIPLEDwWU14ol+LVD8nvBlI339h3Cn16wRNHVZ7WWuTdMOWBRVEnYF3b7OId0zbaPI3qMxGpVJ4tHpAh1yeWje40A+aJqXaiEmz46QkjXT0Ecie8FFHEk5kURSILgkcqI/TVs4aZc0H28Se6Yya1DRHrDg9HdXTsyPrZa0ZWPCBLtI4gdz1iDxJBP2bzjhnJ/JQ0cKnCKWC7z1KU59yvr2y8SjsGUmQef2wWTgNmtY3HO9PHl7s7QQ6b1KqZ8lcOiqEIwSLye2wLEvRCLgdORxI+gopx6bUHi41FS6xLrj+o1AuAJxBaX5GxrbTC2kybU6gzKtnGFTl4CSeytLk9cZnPMyalP9SbZSF4lEfv9jHClK2QZ3tfTpNYVtIzm6zUKsSlECAWUj+IZ0tn5iiFdWlAwtdbeHVakGCGNWAovy4JIx6H+DF756CCyJiFLBCKCaQImEUyCdU72ee6Vyydy7dyyrr2dn7WPhbzdB/vwf7d9DAD49lknjKxYIhuOKWJRVDGoFyXDmjRJuNp6vEJ+4kdTpbYs3HOtvXJu2UT0GuX+kZbUNKJH27ag7mwPkw7Ps9U8nQ0XQaI3SBZQkE6ku2Db9WZzClOMewJgHI7DyAgJMYASpZksjAFD/sxqCFSflUWF3ae40qafiBGPg0SQrg6NXeQlMm1ZH1vVzq+sKlIie6E0fVJsBl0QVf/1pCRc88cX8c93D5St/dMnNWLhZ5vL1n5Z4ZgiFkUVhSpQRGGkEUNBwkg9VuuWdAMp9m51bl+ENF+vkSN6hDT7+iheaJx6DUeU6Mo6trY4UsWN0+8Q75EzUKEO+5gMxyh+OAaYBihBSPYaSPYaMBOAmQTqOwjJIwSKGRiot6oYvTuL2IAJihnI1MecPqS6MogNuG5kihswa2IoBfH+LCB4qLN1caSPiVtDNYHEERMgQqY+jsNNMRgEJI5Y36P+xhiOjgNiA0AsA/QfS8jWEWIDBswkwawTKhZdF8UKIaGOQDEUZCuKITXNV6z4eJjCBJLSdv6CJ2hsLIjC2HPwqHPcdvwxiBlG7gXn2LCPY/DkG8JxPCbYavJjMbFeoCYRwzdnT0UsNgK9RAwAFkUVg7h85hU3XmEECNpAcdMoOkmeb3XCSTDyCKFAsZSLzzACltRs4SKKIztNqMc5EgSPJNg0tjrUi3PicAwUAxJHDCQOA2YCGPWJlWnWAKM/yQAEZFMxpLrSMDImzFQcRpYcwQNFz5DGrZ7oy8o2cdF1BEkkFQPFhDcDQCxtona/t+54v4lUV6753EW44aNcZtZ6n82aGMgwEO/PIpuKIz0mgcQRE9mUgcNNCST6LEF4pNlALA2kxxDMZK6KetMZm9xB9W+AtyeKcBK/l0HlJQES0maAgFHrLUYQ6cvqBZEhno9wuo6kAQD/74azcerEY8rcmxEGL5+xKKoYSDkOEUa+y2U+HiW1CY9wUpSW07ZHlCh15P6RltSCyujEUe6vKni04sq5wBhI9FkepFSX1VjNQcubAwLq9w4gWxdH6tM+UMIATMDI5kSOSK5DyV73PJY2c0te+l+Lzi7BqDEHYfOEn7AoEt/73sStT8vIkOXtixmIDZio3WddjBKHgdSBtGM+5m3AMAmUjDleqvSYJOL9Jg43J2FkCf1jYsjUARQH0o3Wly6b0ittnRjy9SDBm+4rVALqiuIdUtMie4A04/IVRGG2I5yjA1l0HRkAALQ01pW5NyMQQpGiqGQ9KRssiioJVcD4CSMbv+UyN9u3Lp3HCfB6jYK8UIBX0KjPUHP6QYrnRxRHSl0gax0tlgGMLJDsMWCYQKqLEMsA9Z0DoJiBmp4BkAHEMqYlUERxYRiO98bI5ESMziVuPafDm247XwJWvfIWR36UcCKJfAPAIMeVpg6KGc77CACpT9MAERrfHXDbNsl6L2KGtaQ4KgGKAX3jEsjWAgOjDWRrgMwoy+NEMXiVs04M+YopWQw5NkGCw0eE5O0dCsv3CKgIgmgYXFCKZU+3tXRWXxPHMXV8eWKGHv7WVQri5KgIHNmrk7vwCJkeW8UN5FcXNGmq10gbJ6R0WU5zl9QcIaQrlyscIyCWNpA4YsW61O03Ee8nKy4nS4j1Z2FkcldwQdTYQsSwjz3eFq/YMZT+SLZA0eJIqnKIdq7kfRfkPMWQ1Ua0qimW+6KYBMMkJLvTMIiQ+rTfqidLoLgBMgxk6xMgAzg6PolMrYH+RgPZOiCbEjxMavt5iCFPOZ1ggTct//ig4HwrjbT5LIi87DnYBwBobqwdmbu/yg0vn7EoqhQ8k7UhX8TF2B9py75rHrpcZqf5Lbd58kVxFFKnN831HIEAI2MglrGWqRJ9hJpeQm1XBgCQ7Ek7F1JbmHjES1jgo9hxJ00vjLT12/ZAsDhy+hPcnahixU88leyRD1FCmQLa8hVEujIhXaa4YX3OICR6reW5mu60Ux8lrDc13VgDGAb6G+PoH2MgmzIwMNqKb6K4/AEGL6X5pKlj04gbNS2KxykooFrNDxRxI5hPcp4iXjorE6aJaJNGUPnqhkVRBaEGPeu8PLCzxeU0nRjSLKeFeo38vE5wDf08TbZXyMgAsawVlxJPE2q7CLX7BxAbMBE/mgEypisoREFgGNCKERuTJGFkEHkFhZ8wUttCRHGkKef2RzkvcHNZSZ93lc98FNJuXmII0Aoiz9iCtusaBoys9Ss1dcC6MKb2CfaxGDKjkjATBo6OS+LoWMBMGsjUW8HzFCtsqcw5D0jLaznNaZeEY03d0KSxKHI8RRMba8vcE2akwqKoUhAmR20cjxiPYwuXsDgjtawmLbJXSeM1MjJAvB+I91vip6Yni+ThDOKH0voLdAz+4kHx6miXuqIKI3EQYv2ArzgCIggkTXm3b/pkh9LsyC/8R1wE8RXoqShGDAFaQeRpL6iPpolEt7UMV3PgKJw9STEg05DCQEMC6YYY+hsNZOoMmDWWUNIuleX6XZj3R5NvnztppM/X1OdJG+HYnqKJY9hTVBZ4+YxFUaUg/fKELILCvECeOCOljrDlMiFZqkBsIz5gCaDEEUKqx0Tq0wwSRwZg9GdhiC5TQTTkHVdToDDStpWnOLLbc8z8uh5VJKkMtVc54uQUumSThxiy6iuRIIok4gjIAsmDR5HsItTnylEyDjOVQKY+gfSYBI6OiSFTbwV5U1xoP6p3KPc3TFA5fdLU4W+vaXMEs6fb8hS1sKeoPLAoYlFUSchxQxaikNHuNtPFGUUVPnaaYm9kgXgfIdEHpHpNpD4dQOLwAIyBrLz8BQSKAo8nx4RcVrfzSyOMYPfRqSeXWApx5DMG9QIVWSTpKHXAaBETT6QLb1D9pRZDYe05dUVoD7CW4TIm4gP9iB/qR6oTaDABSsZBqTgy9UlLKDXGLI9SKudRAiJ7c8KWy1SbKDFOLIgs3tt3CAAwZVx9mXvCjFRYFFUKuQkzaKnMkwadB0lZTgMCvUZG1ooBSh4mpLpNpA4OIN6XQexI2orxgCI0il0GKkAYAQFeI0ArjgCl3+JFJ0ggAZFEktuGPj2w/iEgr4tsASLIasMn0yd2KLIg8sQiRWw3AGMgC2Mgi5pDaaT2EhoAUCKG7KgaZOsSSDfmPEp11l3MPX0OFEyuIIq8XKazH+HCqC+dxcddlqfo5KaGMvdmhMKP+WBRVClIEyV8hJFoH2AjLaepXqAMId4PpHpMJA6bqDmYRvxI2vICOZUHBTwjWNR4PD0aYaWrQ21Xk+YbHK0RR75tA/o3U8oPF0lqn4KIJJzyoCivQhRBUYgQAkovhoBogqiApTcAMDImEt1HkThISO2BJZSScWRHp5Aek0R6dBzpBgPZGsP9vkb0DjnnUQURivxchwHv7TsEIuDY+iTGjU6VuzsjEiITVMST7ospWymwKKoggpbPpADsCDb2VvhYBkgcJdQcMpHan0ait9+674+6dTJE2OS1DKb19GjqQEg9dprSv1BxBETzHjmJynmYSJJswxVPWS52+XhTQkxLJoQA/36FeId8+xEmiMJElXJupDNIfJpB4lNYMUqxGCgZR6YxhaNja5AeHUO2Fq43SSOA8oofYkHk8E6ndVv5z0wYXeaejGCIivP2cEwRUzJyE2awB8g11S2nGSaQSFu7wWp6sqg5mEai5ygwkMlVIC6DFem+iCKMlDa1u8WieI186xOy8/QeueUiiCTAK5TUfoVRqriiYiedqN0tQAhZ5fzajegZAqIJogKW3Qoik4WRySLZl0ZyT67+ZAKZMXVIj0mi/5g4sinDuoeSkWf8kCqWqv96UhRvdVjxRCc389IZUz5YFFUIUlwCnEMrD3qhZJB1L6CaQyZqDmaQ6O5H7Eg/nB0Eedz3J8oyWCRBExo4bY0qktcIiCSOrHoFE11Qtk2ISPL0zUn0JrmVBOQ55YfwipdnU5FidEolhILyCvUO6dJKsezmN+aBDBL7epHoJNQbBhCPwayvwUBjLdJjEkiPiulFEsK8RSNbFb2z1/IUcTxRGaEi1fkw+A6zKKogHOEhih9xqUwSQQNI9ByF0Zd2l8IGY5dTKTw9Pl4jIA9x5Fenmo6IHiQbjccs78d2FDMP+HqgiqgzqLmok1aIC72wexoV4RnyKx9RXIWWK3QyJwIyWcR6+pDqPoLULgDxOMz6FDKNKaTHJDBQH0M2abjeXbAg0vFmhyWKprMoKh+maS05FArHFDElQ5gcxXsOxQcIyb5cPFBOBBlZ64unfcBpPoR5izRpkYRRxLrs+gAfcQSUVCBZ7Sj91F38Iwolud4CP4sSXwsLurhGiCEo/H5G0YWQ1U7EeiLWG1lceeopQjRls4j1HkGq+zBSuwBKxEF1NcgcU4uj42usHW5xTczbCNZFvUcHsDt3N+tTePmMKSMsiioEA5ZAN0wgeSiLmu4BKyj6SD+MTDZ8OUxHhADoQoURUOASmO/yl6ZOv3oD6inJ/Yf8REKACK34X/p5Bk8WLILC8osVQ35pJV16i/Be5fF5G5ksjN4+1PQcQc1uA5RMuJ6kRsuTZN9ebKTybqcVTzR+dApj6mvK3JsRDC+fsSiqFOo70qg9NIDYkX4Y/bmHZIaIIMMk2VsUxTsTVRip7RcaOJ1HfXadjkm+3iO/Nvzyob/4+26hj3KxLNZ7VygF7hiJvOupUBEElEYI+aVHrTuyuCog9kiTZvh9HkQw0gOIpwcQ7+pFCgDVpWDW16BvBG+6emOPtXT22ZZjQiyZwYRME1TE8hlvyWdKRuqD/YjHQ25tH8VbVCphFLGugpa/Ii99hXiP1PpD6vPk+7brNXGKh2meCr15Wclu5hjVLmBuHFIx5Fc+iiAqpq48bIy+fsT7+lHbeTS8nmGKvR1/Om/HZ8oMi6JKJoII8niL/MqVWhgB4eIICPYcKXXk7T2y67cJakfXXr52yFNcIIKIKpCS3demFALIZrCFUEA7ecUORfUQRSmnSfP1EoXVNYLZ+UkPAODUiewpKiu8fMaiqOrQBStHFUa6ugDZTnd/n6D4nWICp/3qjuA98rShtqNrS63bpw1fu7AyOrNyzxGFTFJRyxQigsLaGPJYpCK8QVEF0TC4UAwmROTEFPF2/DJjUnGT1jD4rrMoqiSieHj80qLUl4e4iSyOhjJwWskPvRGj7kIaVSjp+hKlTBgF71IbhMkmnzojhAoECqFC84ZSDOVTvhhBVOgS3DBlT/dRfHo4jUTMwPQmXj5jyguLomGCPSEXtJRmpwHRl9RU20LFERA9cFrND/AiadtU29W179eWH4XsCBwKCm0njzjJgr1BYXn5CiG/+ny9TkUKFfYQlZS3cvcnOmH8KNQm42XuzQiHCEXdCn4YfOdZFFUaRYqYvGKMNOUje43C6lXqLklcUB5eJLVN37bV9lX8BJNfH6qFUoofm2JE0lDEIw2CdwjIUxCxl8jD63useKLTeOdZ2SGTQEUsn9Ew+C6zKKpE8lkyKzbGKF+vEaBfUtPZD3bgdJ4iSW1bKurn8YkqHsLE01BR5I7YvO63VIwIAgqPSRpMMeSXXqwgyrfPIwj78R6fOY6XzsoOmSjOU1T9W/IrZSqvan7729+itbUVtbW1mDVrFp5++uniK/WbWIuNb8i3Xr94jELq1/WTyHl52xFeOsR6w9oNslP6oXuFYlbIK4SCxhn1fYzwPgf1NbAfUdr1tFXg91SXrmG4C6JBmdc02PcoOu149hSNVIbquxYFFkVF8qc//QnLli3Drbfeipdffhlnn302LrzwQuzatav4yov81W6YVF5xNFgCqVCR5GcX4X0OExN5CagSU7K+5SEitfY6Qj63ooXQYIuhfP9fRUnzS6+ge1wN6rwmMJA10X7gMADgM8fxzrNyQyYV/cqXofquRYVFUZHcdddd+O53v4vvfe97OPXUU3H33Xdj8uTJWLNmTX4V5RvvMFiTeAH1OxehfC5EIXmhF/JCRFKUvgS9IpKPSCnFKzKFji+qfQTPVWjfixVC5RJDw0gQASWc10Jo338Y6YyJhlQCk46tK2ndTAGQWfwrT4bquxYVjikqgnQ6jW3btuGWW26R0hcuXIgtW7Zoy/T396O/v9857+7uBgBkzDSQgf9jIvxiXvJJNxH84NJ829DlZXN/gx53EbZjKyRfO4asNylvyV/odvmhqk8lH0E0GPVFnP+K2qUWlp9vvFBQXkCZkgV7+4p8suYAAOUOVs13XvOb03p6ekLb2tG+F2b/EUwedwwOHeotsufDA/t9K8f3IIMBFHPvxgwGAHg/+1QqhVQq5bEv5Bo62LAoKoL9+/cjm82iqalJSm9qakJHR4e2zOrVq3Hbbbd50p/85PeD0keGYaqL3t5eNDY2lq39fOc1vzlt8uTJkdv8CEDjTXl3dVgzlN+DmpoaNDc345mOjUXXNXr0aM9nv2LFCqxcudJjW8g1dLBhUVQCDMUjQESeNJuf/OQnWL58uXN+8OBBTJ06Fbt27SrrRFgKenp6MHnyZHz00Uc45pjqDZocLuMAeCyViN84iAi9vb1oaWkpY+9cos5rw3lOK5ZCvrPl+B7U1taivb0d6XS66Lp03xOdl0gkn2voYMOiqAjGjx+PeDzuUbSdnZ0e5Wvj50ZsbGys6ole5JhjjhkWYxku4wB4LJWIbhyVICLynddGwpxWLPl+Z8vxPaitrUVtbchDyUtMIdfQwYYDrYugpqYGs2bNwqZNm6T0TZs2Ye7cuWXqFcMwTOHwvMYMFZX4XWNPUZEsX74cixcvxhlnnIE5c+bgd7/7HXbt2oVrr7223F1jGIYpCJ7XmKGi0r5rLIqK5PLLL8eBAwfw85//HHv27EFbWxs2btyIqVOnRiqfSqWwYsWK0DXXamC4jGW4jAPgsVQi1TCOYua1ahjfUMHvRTjFXkNLjUHl3v/JMAzDMAxTAXBMEcMwDMMwDFgUMQzDMAzDAGBRxDAMwzAMA4BFEcMwDMMwDAAWRWXlt7/9LVpbW1FbW4tZs2bh6aefLneXJFavXo0zzzwTDQ0NmDBhAr72ta/hrbfekmyICCtXrkRLSwvq6uowf/587Ny5U7Lp7+/H9ddfj/Hjx2PUqFG49NJL8fHHHw/lUDysXr0ahmFg2bJlTlo1jWX37t341re+hXHjxqG+vh7/8i//gm3btjn51TCWTCaDn/3sZ2htbUVdXR2mTZuGn//85zBN96FqlTqOp556Cl/5ylfQ0tICwzDw8MMPS/ml6ndXVxcWL16MxsZGNDY2YvHixTh48OCgjq1YKn1eGwpWrlwJwzCkV3Nzc7m7xUSBmLKwfv16SiaTdO+999Lrr79ON9xwA40aNYo+/PDDcnfN4YILLqC1a9fSa6+9Rtu3b6eLL76YpkyZQocOHXJs7rjjDmpoaKC//OUvtGPHDrr88stp4sSJ1NPT49hce+21dPzxx9OmTZvopZdeogULFtDpp59OmUymHMOi559/nk444QT63Oc+RzfccIOTXi1j+fTTT2nq1Kl09dVX03PPPUft7e3097//nd59992qGsvtt99O48aNo7/97W/U3t5Of/7zn2n06NF09913V/w4Nm7cSLfeeiv95S9/IQD017/+VcovVb8XLVpEbW1ttGXLFtqyZQu1tbXRJZdcMmjjKpZqmNeGghUrVtBpp51Ge/bscV6dnZ3l7hYTARZFZeILX/gCXXvttVLaKaecQrfcckuZehROZ2cnAaDNmzcTEZFpmtTc3Ex33HGHY3P06FFqbGyk//zP/yQiooMHD1IymaT169c7Nrt376ZYLEaPPvro0A6AiHp7e2n69Om0adMmOvfccx1RVE1jufnmm2nevHm++dUylosvvpi+853vSGmXXXYZfetb3yKi6hmHKopK1e/XX3+dANCzzz7r2GzdupUA0JtvvjnIoyqMapzXBoMVK1bQ6aefXu5uMAXAy2dlIJ1OY9u2bVi4cKGUvnDhQmzZsqVMvQqnu7sbADB27FgAQHt7Ozo6OqRxpFIpnHvuuc44tm3bhoGBAcmmpaUFbW1tZRnrddddh4svvhjnn3++lF5NY9mwYQPOOOMMfP3rX8eECRMwc+ZM3HvvvVU3lnnz5uEf//gH3n77bQDAK6+8gmeeeQYXXXRRVY1DpVT93rp1KxobGzF79mzH5otf/CIaGxsrcp6o1nltsHjnnXfQ0tKC1tZWfOMb38D7779f7i4xEeA7WpeB/fv3I5vNeh5419TU5HkwXqVARFi+fDnmzZuHtrY2AHD6qhvHhx9+6NjU1NTg2GOP9dgM9VjXr1+Pl156CS+88IInr5rG8v7772PNmjVYvnw5fvrTn+L555/HD3/4Q6RSKVx11VVVM5abb74Z3d3dOOWUUxCPx5HNZvGLX/wCV1xxhdNHu09qHytpHCql6ndHRwcmTJjgqX/ChAkVOU9U47w2WMyePRt/+MMfcNJJJ2Hv3r24/fbbMXfuXOzcuRPjxo0rd/eYAFgUlRHDMKRzIvKkVQpLly7Fq6++imeeecaTV8g4hnqsH330EW644QY89thjgU+CroaxmKaJM844A6tWrQIAzJw5Ezt37sSaNWtw1VVXOXaVPpY//elPuP/++/Hggw/itNNOw/bt27Fs2TK0tLRgyZIljl2lj8OPUvRbZ18JYwuimua1weLCCy90jmfMmIE5c+bgxBNPxH333Yfly5eXsWdMGLx8VgbGjx+PeDzu+fXU2dnp+ZVVCVx//fXYsGEDnnjiCUyaNMlJt3dTBI2jubkZ6XQaXV1dvjZDwbZt29DZ2YlZs2YhkUggkUhg8+bN+PWvf41EIuH0pRrGMnHiRHz2s5+V0k499VTs2rXL6SdQ+WP5t3/7N9xyyy34xje+gRkzZmDx4sX40Y9+hNWrVzt9BCp/HCql6ndzczP27t3rqX/fvn0VOU9U27w2lIwaNQozZszAO++8U+6uMCGwKCoDNTU1mDVrFjZt2iSlb9q0CXPnzi1Tr7wQEZYuXYqHHnoIjz/+OFpbW6X81tZWNDc3S+NIp9PYvHmzM45Zs2YhmUxKNnv27MFrr702pGM977zzsGPHDmzfvt15nXHGGbjyyiuxfft2TJs2rWrGctZZZ3lujfD22287D1Csls/lyJEjiMXkKSgejztb8qtlHCql6vecOXPQ3d2N559/3rF57rnn0N3dXVHzhE21zGvloL+/H2+88QYmTpxY7q4wYZQhuJshd+vq73//e3r99ddp2bJlNGrUKPrggw/K3TWH73//+9TY2EhPPvmktLX0yJEjjs0dd9xBjY2N9NBDD9GOHTvoiiuu0G49njRpEv3973+nl156ib70pS+VdUu+jbj7jKh6xvL8889TIpGgX/ziF/TOO+/QAw88QPX19XT//fdX1ViWLFlCxx9/vLMl/6GHHqLx48fTTTfdVPHj6O3tpZdffplefvllAkB33XUXvfzyy87W81L1e9GiRfS5z32Otm7dSlu3bqUZM2ZUxZb8Sp7XhoIbb7yRnnzySXr//ffp2WefpUsuuYQaGhpG3PtQjbAoKiO/+c1vaOrUqVRTU0Of//znna3ulQIA7Wvt2rWOjWmatGLFCmpubqZUKkXnnHMO7dixQ6qnr6+Pli5dSmPHjqW6ujq65JJLaNeuXUM8Gi+qKKqmsfzP//wPtbW1USqVolNOOYV+97vfSfnVMJaenh664YYbaMqUKVRbW0vTpk2jW2+9lfr7+yt+HE888YT2/8aSJUtK2u8DBw7QlVdeSQ0NDdTQ0EBXXnkldXV1DerYiqXS57WhwL4vVTKZpJaWFrrsssto586d5e4WEwGDiKg8PiqGYRiGYZjKgWOKGIZhGIZhwKKIYRiGYRgGAIsihmEYhmEYACyKGIZhGIZhALAoYhiGYRiGAcCiiGEYhmEYBgCLIoZhGIZhGAAsihiGYZgqwjAMGIaBMWPGOGnr1q1z0pctWybZP/XUU/jKV76ClpYWGIaBhx9+eFD7F6U9IsLKlSvR0tKCuro6zJ8/Hzt37hzUfjHRYFHEMAzDlJyrr77aESria9GiRR7b1tZWPProo3jyySdhGAYOHjwYWPfatWvx9ttvO+eXX3459uzZgzlz5nhsDx8+jNNPPx333HNP0WOKQpT2fvnLX+Kuu+7CPffcgxdeeAHNzc348pe/jN7e3iHpI+NPotwdYBiGYYYnixYtwtq1a6W0VColnb/66qs4cOAAFixYgK1bt0aqd8yYMZgwYYJzXldXh7q6OtTU1HhsL7zwQlx44YW+daXTafzsZz/DAw88gIMHD6KtrQ133nkn5s+fH6kv+bZHRLj77rtx66234rLLLgMA3HfffWhqasKDDz6Ia665pqB2mdLAniKGYRhmUEilUmhubpZexx57rGTzyCOP4IILLvCIpaHi29/+Nv75z39i/fr1ePXVV/H1r38dixYtwjvvvDMo7bW3t6OjowMLFy500lKpFM4991xs2bJlUNpkosOiiGEYhikbGzZswFe/+tWytP3ee+/hv//7v/HnP/8ZZ599Nk488UT8+Mc/xrx58zwerlLR0dEBAGhqapLSm5qanDymfLAoYhiGYQaFv/3tbxg9erT0+vd//3cnf/fu3XjllVdw0UUXlaV/L730EogIJ510ktTHzZs347333gMAfPDBB9rYKPG1dOnSvNs2DEM6JyJPGjP0cEwRwzAMMygsWLAAa9askdLGjh3rHG/YsAFnnXWWlDaUmKaJeDyObdu2IR6PS3mjR48GABx//PF44403AutRlwSDaG5uBmB5jCZOnOikd3Z2erxHzNDDoohhGIYZFEaNGoXPfOYzvvnlXDoDgJkzZyKbzaKzsxNnn3221iaZTOKUU04pWZutra1obm7Gpk2bMHPmTABWsPfmzZtx5513lqwdpjBYFDEMwzBDzqFDh/DEE0/gN7/5zaC38+677zrn7e3t2L59O8aOHYuTTjoJV155Ja666ir86le/wsyZM7F//348/vjjmDFjRkHLekHtTZkyxbmX0qpVqzB9+nRMnz4dq1atQn19Pb75zW+WZMxM4bAoYhiGYQaF/v5+T/BwIpHA+PHj8eijj2L69OmYNm3aoPbhxRdfxIIFC5zz5cuXAwCWLFmCdevWYe3atbj99ttx4403Yvfu3Rg3bhzmzJlTcJxTWHsAcNNNN6Gvrw8/+MEP0NXVhdmzZ+Oxxx5DQ0NDgaNkSgWLIoZhGGZQePTRR6W4GQA4+eST8eabb+KRRx7xLJ2ZpgnAEk6lYv78+SAi3/xkMonbbrsNt91225C0B1hB1itXrsTKlStL0iZTOnj3GcMwDFNy1q1bByLyvN58801ks1ls3LgRl156qVSms7MTo0aNcoKcGWaoYVHEMAzDDCkHDhzAj370I5x55pkArGW2119/Hffccw/OP//80PJXXHEFJk2a5Jw/8MADGD16NJ5++ulB6zMzMjAozM/HMAzDMIPIww8/jMWLF2Pu3Lm49957MWXKFF9bO4g5Ho+jtbUVANDb24u9e/cCsB4BMn78+MHvNDMsYVHEMAzDMAwDXj5jGIZhGIYBwKKIYRiGYRgGAIsihmEYhmEYACyKGIZhGIZhALAoYhiGYRiGAcCiiGEYhmEYBgCLIoZhGIZhGAAsihiGYRiGYQCwKGIYhmEYhgEA/H/TrL3BorHVOAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.076545344875228e-10 0.0\n",
      "5.00343378671194e-11\n"
     ]
    }
   ],
   "source": [
    "#反射結合データのグラフの出力\n",
    "E = E_CAM_ALL\n",
    "V = 6.0E-10\n",
    "fig, ax = plt.subplots()\n",
    "plt.imshow(E,vmin=0.0, vmax=V, cmap='viridis')\n",
    "plt.colorbar (label=\"E/J []\")\n",
    "\n",
    "# plt.xlabel('x [m]')\n",
    "# plt.ylabel('y [m]')\n",
    "# plt.xticks([0,50,100,150,200,250,300,350,400],[\"-100\",\"-75\",\"-50\",\"-25\",\"0\",\"25\",\"50\",\"75\",\"100\"])\n",
    "# plt.yticks([0,50,100,150,200,250,300,350,400],[\"-100\",\"-75\",\"-50\",\"-25\",\"0\",\"25\",\"50\",\"75\",\"100\"])\n",
    "\n",
    "# theta2 = np.linspace(0, 2*np.pi, 100)\n",
    "# a = 2*r * np.cos(theta2) + 200\n",
    "# b = 2*r * np.sin(theta2) + 200\n",
    "# plt.plot(a,b, color='black', linestyle=\"dashed\") #円の描写しない場合はここをコメントアウト\n",
    "\n",
    "# theta3 = np.linspace(0, 2*np.pi, 100)\n",
    "# c = 4*r * np.cos(theta2) + 200\n",
    "# d = 4*r * np.sin(theta2) + 200\n",
    "# plt.plot(c,d, color='black', linestyle=\"dashed\") #円の描写しない場合はここをコメントアウト\n",
    "\n",
    "x = np.linspace(0, 1024, 1024)\n",
    "\n",
    "# create new axes on the right and on the top of the current axes.\n",
    "divider = make_axes_locatable(ax)\n",
    "axtop = divider.append_axes(\"top\", size=0.8, pad=0.3, sharex=ax)\n",
    "plt.title(\"h = {}[m], φ = {}[°], e = {}[°], θ = {}, R = {} \".format(i_CAM[2],\"0\",elevation_angle,theta,R))\n",
    "# plt.title(\"max = {}\".format(np.max(E)))\n",
    "plt.ylabel('E/J []')\n",
    "axright = divider.append_axes(\"right\", size=0.8, pad=0.5, sharey=ax)\n",
    "\n",
    "\n",
    "axtop.plot(x, E[512]) #marker=\"o\", ms=1, mfc=\"k\", mec=\"k\")\n",
    "axtop.axis([0,1024, 0, 6.1E-10])\n",
    "plt.ylim([0, 0.25])\n",
    "\n",
    "plt.xlabel('E/J []           ')\n",
    "\n",
    "\n",
    "axright.plot(E[:,512], x) #marker=\"o\", ms=1, mfc=\"k\", mec=\"k\")\n",
    "axright.axis([0, V, 0, 1024])\n",
    "# plt.xticks([0,0.10,0.20],[\"0.0\",\"0.1\",\"0.2\"])\n",
    "\n",
    "# plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(np.max(E),np.min(E))\n",
    "print(E[0,1023])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0 5.00343378671194e-11\n"
     ]
    }
   ],
   "source": [
    "E_2 = E\n",
    "for ii in range(1024):\n",
    "    for jj in range(1024):\n",
    "        if E_2[ii,jj]==0.0:\n",
    "            E_2[ii,jj]=100.0\n",
    "print(np.max(E_2),np.min(E_2))    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}