1.3.FunctionAndClass.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.3. Functions and Classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random as random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_sum(n1,n2):\n",
    "    S = n1+n2\n",
    "    return S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5803"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_sum(125,5678)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_product(n1,n2):\n",
    "    P = n1*n2\n",
    "    return P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "281231"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_product(71,3961)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.choice([1,2,3,4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def roll_dice():\n",
    "    side = random.choice([1,2,3,4,5,6])\n",
    "    return side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "2\n",
      "1\n",
      "5\n",
      "5\n",
      "6\n",
      "3\n",
      "3\n",
      "2\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(roll_dice())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def roll_two_dices():\n",
    "    d1 = roll_dice()\n",
    "    d2 = roll_dice()\n",
    "    p = find_product(d1,d2)\n",
    "    s = find_sum(d1,d2)\n",
    "    return d1,d2,p,s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dice 1 :  6 Dice 2:  6 Product :  36 Sum :  12\n",
      "Dice 1 :  3 Dice 2:  4 Product :  12 Sum :  7\n",
      "Dice 1 :  5 Dice 2:  4 Product :  20 Sum :  9\n",
      "Dice 1 :  3 Dice 2:  6 Product :  18 Sum :  9\n",
      "Dice 1 :  2 Dice 2:  1 Product :  2 Sum :  3\n",
      "Dice 1 :  4 Dice 2:  1 Product :  4 Sum :  5\n",
      "Dice 1 :  1 Dice 2:  4 Product :  4 Sum :  5\n",
      "Dice 1 :  4 Dice 2:  3 Product :  12 Sum :  7\n",
      "Dice 1 :  3 Dice 2:  6 Product :  18 Sum :  9\n",
      "Dice 1 :  5 Dice 2:  2 Product :  10 Sum :  7\n"
     ]
    }
   ],
   "source": [
    "with open (\"dices.txt\", \"w\") as file:\n",
    "    for i in range(10):\n",
    "        d1,d2,p,s = roll_two_dices()\n",
    "        print(\"Dice 1 : \", d1, \"Dice 2: \",d2, \"Product : \",p, \"Sum : \", s)\n",
    "        file.write(\"Dice 1 : \" + str(d1) + \\\n",
    "                   \"Dice 2: \" + str(d2) +  \\\n",
    "                   \"Product : \" + str(p) + \\\n",
    "                   \"Sum : \" + str(s))\n",
    "        file.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Math(object):\n",
    "    \n",
    "    def __init__(self,A_value,B_value,C_value):\n",
    "        self.pi = 3.14\n",
    "        self.A = A_value\n",
    "        self.B = B_value\n",
    "        self.C = C_value \n",
    "    \n",
    "    def find_sum(self,n1,n2):\n",
    "        S = n1+n2\n",
    "        return S\n",
    "    \n",
    "    def find_product(self,n1,n2):\n",
    "        P = n1*n2\n",
    "        return P\n",
    "    \n",
    "    def poly(self,x):\n",
    "        pl = self.A*self.find_product(x,x) + self.B*x + self.C\n",
    "        return pl\n",
    "    \n",
    "    def circle_area(self,r):\n",
    "        A = self.pi*r**2\n",
    "        return A\n",
    "    \n",
    "    def circumference(self,r):\n",
    "        C = 2*self.pi*r\n",
    "        return C\n",
    "    \n",
    "    def roll_dice(self):\n",
    "        side = random.choice([1,2,3,4,5,6])\n",
    "        return side\n",
    "    \n",
    "    def roll_two_dices(self):\n",
    "        d1 = self.roll_dice()\n",
    "        d2 = self.roll_dice()\n",
    "        p = self.find_product(d1,d2)\n",
    "        s = self.find_sum(d1,d2)\n",
    "        return d1,d2,p,s\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1368"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = Math(A_value = 10,B_value = 14,C_value = 6)\n",
    "M.find_product(24,57)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.find_sum(21,67)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.5"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.circle_area(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.200000000000001"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.poly(x = 0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Circle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def area(r):\n",
    "    A = np.pi*r**2\n",
    "    return A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "314.1592653589793"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def circumference(r):\n",
    "    C = 2*np.pi*r\n",
    "    return C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.83185307179586"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circumference(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Circle():\n",
    "    \n",
    "    def __init__(self, r):\n",
    "        self.r = r\n",
    "        \n",
    "        \n",
    "    def area(self):\n",
    "        A = np.pi*self.r**2\n",
    "        return A\n",
    "    \n",
    "    def circumference(self):\n",
    "        C = 2*np.pi*self.r\n",
    "        return C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "380.132711084365"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Circle(11).area()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(78.53981633974483, 31.41592653589793)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CC = Circle(5)\n",
    "CC.area(),CC.circumference()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Circle at 0x104539b00>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CC.r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "radius:  2 area :  12.566370614359172 circumf :  12.566370614359172\n",
      "radius:  3 area :  28.274333882308138 circumf :  18.84955592153876\n",
      "radius:  6 area :  113.09733552923255 circumf :  37.69911184307752\n",
      "radius:  24 area :  1809.5573684677208 circumf :  150.79644737231007\n",
      "radius:  25 area :  1963.4954084936207 circumf :  157.07963267948966\n",
      "radius:  46 area :  6647.610054996002 circumf :  289.02652413026095\n",
      "radius:  567 area :  1009987.480609929 circumf :  3562.5660691708254\n"
     ]
    }
   ],
   "source": [
    "for r in [2,3,6,24,25,46,567]:\n",
    "    CC = Circle(r)\n",
    "    print(\"radius: \" , r, \"area : \" , CC.area(), \"circumf : \" , CC.circumference())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Class Gravity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gravity(m1,m2,d):\n",
    "    F = (m1*m2)/d**2\n",
    "    return F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gravity(5,4,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Newton():\n",
    "    \n",
    "    def __init__(self,value_of_G, value_of_g, supplied_info):\n",
    "        self.G = value_of_G\n",
    "        self.info = supplied_info\n",
    "        self.g = value_of_g\n",
    "        \n",
    "    \n",
    "    def gravity(self,m1,m2,d):\n",
    "        F = self.G*(m1*m2)/d**2\n",
    "        print(self.info)\n",
    "        return F  \n",
    "    \n",
    "\n",
    "    def gravity_pot(self,m1):\n",
    "        F = m1*self.g\n",
    "        return F\n",
    "    \n",
    "        \n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "N1 = Newton(value_of_G =6.7, value_of_g= 9.8, supplied_info = \"great job\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "great job\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(6.7, 9.8, 0.2378698224852071, 117.60000000000001)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N1.G, N1.g,N1.gravity(2,3,13),N1.gravity_pot(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "great job\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "98.26666666666667"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N1.gravity(m1=11,m2=12,d=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project: N-charge system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ E = q * \\frac{\\hat{r}}{r}$\n",
    "\n",
    "$ V  = q * \\frac{1}{r}$\n",
    "\n",
    "$ V  = q * \\frac{1}{\\sqrt{((x-x^{'})^{2} + (y - y^{'})^{2})}} $\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printit(x):\n",
    "    print(x)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10101\n"
     ]
    }
   ],
   "source": [
    "printit(10101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Class Charge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Charge:\n",
    "    \n",
    "    '''Data incapsulation'''\n",
    "    def __init__(self, q, pos):\n",
    "        self.q = q\n",
    "        self.pos = pos\n",
    "        \n",
    "        \n",
    "    def line(self, x,y):\n",
    "        '''create a vector from charge to observation point'''\n",
    "        self.vector = [x-self.pos[0],y-self.pos[1]]\n",
    "        '''norm of the vector'''\n",
    "        self.norm = np.sqrt((self.vector[0])**2+(self.vector[1])**2)\n",
    "        \n",
    "    \n",
    "    def V_point_charge(self, x, y):\n",
    "        '''recall length'''\n",
    "        self.line(x,y)\n",
    "        '''Make sure to exclude source itself'''\n",
    "        if self.norm > 0:\n",
    "            self.V = self.q/self.norm\n",
    "            \n",
    "            '''if length is zero, set V equal to 0'''\n",
    "        elif self.norm == 0:\n",
    "            self.V = 0\n",
    "            \n",
    "        return self.V\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example :  Lets use charge q = 100 at posiotion x =5 and y =5 to find electric potential at different points in 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = Charge(100, [5,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 | 14.14213562373095\n",
      "0 1 | 15.617376188860607\n",
      "0 2 | 17.149858514250884\n",
      "0 3 | 18.56953381770519\n",
      "0 4 | 19.611613513818405\n",
      "0 5 | 20.0\n",
      "0 6 | 19.611613513818405\n",
      "0 7 | 18.56953381770519\n",
      "0 8 | 17.149858514250884\n",
      "0 9 | 15.617376188860607\n",
      "1 0 | 15.617376188860607\n",
      "1 1 | 17.677669529663685\n",
      "1 2 | 20.0\n",
      "1 3 | 22.360679774997894\n",
      "1 4 | 24.253562503633297\n",
      "1 5 | 25.0\n",
      "1 6 | 24.253562503633297\n",
      "1 7 | 22.360679774997894\n",
      "1 8 | 20.0\n",
      "1 9 | 17.677669529663685\n",
      "2 0 | 17.149858514250884\n",
      "2 1 | 20.0\n",
      "2 2 | 23.570226039551585\n",
      "2 3 | 27.735009811261456\n",
      "2 4 | 31.622776601683793\n",
      "2 5 | 33.333333333333336\n",
      "2 6 | 31.622776601683793\n",
      "2 7 | 27.735009811261456\n",
      "2 8 | 23.570226039551585\n",
      "2 9 | 20.0\n",
      "3 0 | 18.56953381770519\n",
      "3 1 | 22.360679774997894\n",
      "3 2 | 27.735009811261456\n",
      "3 3 | 35.35533905932737\n",
      "3 4 | 44.72135954999579\n",
      "3 5 | 50.0\n",
      "3 6 | 44.72135954999579\n",
      "3 7 | 35.35533905932737\n",
      "3 8 | 27.735009811261456\n",
      "3 9 | 22.360679774997894\n",
      "4 0 | 19.611613513818405\n",
      "4 1 | 24.253562503633297\n",
      "4 2 | 31.622776601683793\n",
      "4 3 | 44.72135954999579\n",
      "4 4 | 70.71067811865474\n",
      "4 5 | 100.0\n",
      "4 6 | 70.71067811865474\n",
      "4 7 | 44.72135954999579\n",
      "4 8 | 31.622776601683793\n",
      "4 9 | 24.253562503633297\n",
      "5 0 | 20.0\n",
      "5 1 | 25.0\n",
      "5 2 | 33.333333333333336\n",
      "5 3 | 50.0\n",
      "5 4 | 100.0\n",
      "5 5 | 0\n",
      "5 6 | 100.0\n",
      "5 7 | 50.0\n",
      "5 8 | 33.333333333333336\n",
      "5 9 | 25.0\n",
      "6 0 | 19.611613513818405\n",
      "6 1 | 24.253562503633297\n",
      "6 2 | 31.622776601683793\n",
      "6 3 | 44.72135954999579\n",
      "6 4 | 70.71067811865474\n",
      "6 5 | 100.0\n",
      "6 6 | 70.71067811865474\n",
      "6 7 | 44.72135954999579\n",
      "6 8 | 31.622776601683793\n",
      "6 9 | 24.253562503633297\n",
      "7 0 | 18.56953381770519\n",
      "7 1 | 22.360679774997894\n",
      "7 2 | 27.735009811261456\n",
      "7 3 | 35.35533905932737\n",
      "7 4 | 44.72135954999579\n",
      "7 5 | 50.0\n",
      "7 6 | 44.72135954999579\n",
      "7 7 | 35.35533905932737\n",
      "7 8 | 27.735009811261456\n",
      "7 9 | 22.360679774997894\n",
      "8 0 | 17.149858514250884\n",
      "8 1 | 20.0\n",
      "8 2 | 23.570226039551585\n",
      "8 3 | 27.735009811261456\n",
      "8 4 | 31.622776601683793\n",
      "8 5 | 33.333333333333336\n",
      "8 6 | 31.622776601683793\n",
      "8 7 | 27.735009811261456\n",
      "8 8 | 23.570226039551585\n",
      "8 9 | 20.0\n",
      "9 0 | 15.617376188860607\n",
      "9 1 | 17.677669529663685\n",
      "9 2 | 20.0\n",
      "9 3 | 22.360679774997894\n",
      "9 4 | 24.253562503633297\n",
      "9 5 | 25.0\n",
      "9 6 | 24.253562503633297\n",
      "9 7 | 22.360679774997894\n",
      "9 8 | 20.0\n",
      "9 9 | 17.677669529663685\n"
     ]
    }
   ],
   "source": [
    "for x in range(10):\n",
    "    for y in range(10):\n",
    "        print(x,y, \"|\", C.V_point_charge(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total Electric potential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def V_total(x, y, charges):\n",
    "    V = 0\n",
    "    for C in charges:\n",
    "        Vp = C.V_point_charge(x, y)\n",
    "        V  = V+Vp\n",
    "    return V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Charge at 0x1a19b08ef0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Charge(q = 20, pos = [23,34])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: Lets use collection of charges to find a electric potential at point x = 4, y =4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.892446541150622"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_charges = [Charge(q = 20, pos = [23,34]),\n",
    "                  Charge(q = 25, pos = [13,48]),\n",
    "                  Charge(q = 40, pos = [3,14]),\n",
    "                  Charge(q = 80, pos = [88,60])]\n",
    "\n",
    "\n",
    "V_total(x=4, y=4, charges = sample_charges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation - 1 : Lattice of charges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''first charge to be at x=1,y=1'''\n",
    "q = 100\n",
    "\n",
    "'''Dictionary to collect charges, x and y xoordinates'''\n",
    "Qd = []\n",
    "\n",
    "'''List to collect Charge objects'''\n",
    "charges = []\n",
    "\n",
    "\n",
    "'''use for loops to construct collection of charges objects'''\n",
    "for i in range(5):\n",
    "    for j in range(5):\n",
    "        \n",
    "        '''This dictionary is collecting charges and their coordinates'''\n",
    "        Qd.append({\"q\": q, \"x\": i*20, \"y\":j*20})\n",
    "        \n",
    "        \n",
    "        '''charge objects are being collected'''\n",
    "        charges.append(Charge(q , [20*i, 20*j]))\n",
    "        \n",
    "        '''change the sign of charge alternatly'''\n",
    "        q = -q\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open ('charg-lattice.json', 'w')as f1:\n",
    "    json.dump(Qd,f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot of lattice of charges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Plot the lattice of charges'''\n",
    "\n",
    "plt.figure(figsize = [10,8])\n",
    "\n",
    "for item in Qd:\n",
    "    \n",
    "    '''Sctaeer as red dot if charge is positive'''\n",
    "    if item['q']> 0:\n",
    "        plt.scatter(item['x'], item['y'], c = 'r')\n",
    "        \n",
    "        \n",
    "        '''Scatter as blue dot if charge is negative'''\n",
    "    else:\n",
    "        plt.scatter(item['x'], item['y'], c = 'b')\n",
    "        \n",
    "plt.savefig('charge-lattice.pdf')           \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,\n",
       "         3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,\n",
       "        16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,\n",
       "        29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,\n",
       "        55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,\n",
       "        68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,\n",
       "        81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,  92,  93,\n",
       "        94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106,\n",
       "       107, 108, 109])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " np.arange(-10,110,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find Electric Potential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Create X and Y coordinate'''\n",
    "X = np.arange(-10,110,1)\n",
    "Y = np.arange(-10,110,1)\n",
    "\n",
    "'''Initiate vacant V-list of list'''\n",
    "V = [[0.0 for i in range(len(X))] for j in range(len(Y))]\n",
    "\n",
    "\n",
    "'''Calculate Electric potential at each x,y coordinate'''\n",
    "for i,x in enumerate(X):\n",
    "    for j,y in enumerate(Y):\n",
    "        v = V_total(x, y, charges)\n",
    "        V[i][j] = v       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "VV = np.array(V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.56757626, 4.85433988, 5.15256208, ..., 1.9676212 , 1.90204466,\n",
       "        1.84132998],\n",
       "       [4.85433988, 5.19909434, 5.56531782, ..., 1.98223735, 1.91468171,\n",
       "        1.85231669],\n",
       "       [5.15256208, 5.56531782, 6.0138754 , ..., 1.99386203, 1.92459953,\n",
       "        1.86083363],\n",
       "       ...,\n",
       "       [1.9676212 , 1.98223735, 1.99386203, ..., 1.52817829, 1.50192297,\n",
       "        1.47624715],\n",
       "       [1.90204466, 1.91468171, 1.92459953, ..., 1.50192297, 1.47728094,\n",
       "        1.45311692],\n",
       "       [1.84132998, 1.85231669, 1.86083363, ..., 1.47624715, 1.45311692,\n",
       "        1.43037562]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save electric potential data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open ('potential-lattice.json', 'w')as f5:\n",
    "    json.dump(V,f5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot Electric potential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [18,14])\n",
    "sns.heatmap(VV,annot=False,cmap='YlGnBu')\n",
    "plt.savefig('potential-lattice.png')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation - 2 : Random Charges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''first charge to be at x=1,y=1'''\n",
    "q = 20\n",
    "\n",
    "'''Dictionary to collect charges, x and y xoordinates'''\n",
    "Qd = []\n",
    "\n",
    "'''List to collect Charge objects'''\n",
    "charges = []\n",
    "\n",
    "\n",
    "'''use for loops to construct collection of charges objects'''\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        \n",
    "        rx = random.randint(1,100)\n",
    "        ry = random.randint(1,100)\n",
    "        \n",
    "        '''This dictionary is collecting charges and their coordinates'''\n",
    "        Qd.append({\"q\": q, \"x\": rx, \"y\":ry})\n",
    "        \n",
    "        \n",
    "        '''charge objects are being collected'''\n",
    "        charges.append(Charge(q , [rx,ry]))\n",
    "        \n",
    "        '''change the sign of charge alternatly'''\n",
    "        q = -q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Save charge configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open ('data/charg-random.json', 'w')as f3:\n",
    "    json.dump(Qd,f3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot charge system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Plot the lattice of charges'''\n",
    "\n",
    "plt.figure(figsize = [10,8])\n",
    "\n",
    "for item in Qd:\n",
    "    \n",
    "    '''Sctaeer as red dot if charge is positive'''\n",
    "    if item['q']> 0:\n",
    "        plt.scatter(item['x'], item['y'], c = 'r')\n",
    "        \n",
    "        \n",
    "        '''Scatter as blue dot if charge is negative'''\n",
    "    else:\n",
    "        plt.scatter(item['x'], item['y'], c = 'b')\n",
    "plt.savefig('plot/charge-random.pdf')      \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate electric potential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Create X and Y coordinate'''\n",
    "X = np.arange(-10,110,1)\n",
    "Y = np.arange(-10,110,1)\n",
    "\n",
    "'''Initiate vacant V-list of list'''\n",
    "V = [[0.0 for i in range(len(X))] for j in range(len(Y))]\n",
    "\n",
    "'''Calculate Electric potential at each x,y coordinate'''\n",
    "for i,x in enumerate(X):\n",
    "    for j,y in enumerate(Y):\n",
    "        v = V_total(x, y, charges)\n",
    "        V[i][j] = v       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Save electric potential dta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open ('data/potential-random.json', 'w')as f4:\n",
    "    json.dump(V,f4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot potential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "V = np.array(V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [18,14])\n",
    "sns.heatmap(V,annot=False,cmap='YlGnBu')\n",
    "plt.savefig('plot/potential-random.pdf')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Much finner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Create X and Y coordinate'''\n",
    "X = np.arange(-10,110,0.5)\n",
    "Y = np.arange(-10,110,0.5)\n",
    "\n",
    "'''Initiate vacant V-list of list'''\n",
    "V = [[0.0 for i in range(len(X))] for j in range(len(Y))]\n",
    "\n",
    "'''Calculate Electric potential at each x,y coordinate'''\n",
    "for i,x in enumerate(X):\n",
    "    for j,y in enumerate(Y):\n",
    "        v = V_total(x, y, charges)\n",
    "        V[i][j] = v       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "V = np.array(V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vNlWE2D6rC5RJk81I8iJsrT1DbF8F4KoU5RJCpgMfGIQQQgghswul0ZvDHiKEEEIIIYQQsq3YcjF+KYeurh2x1x/yFF2svNHn5L1GZ9taDj3XyvYXxfZSS+UTFXTFZ9FO361suS/aMQhob0QjvTJpIOBRWrvzFVWNribXjJBs2CdRDpWn55zPi3QdJcVFmLV2p14DQ9I3x1O0kEN3um4+LZWWSAl0q+WU7uQrIpsOyUN941q1fHeS1G3uTrI9RT2Ge6XHCbxLp/AoXSWBx1dyiXswooSnDfrZ4zOVyps8ja53nGdUTzznB84zVWX0mEBp79Le56ba9027rfJ8JWTabLkXYUI0cy0+IQipI0XtgAkhZFLIl2BCZokGhb+bUupF2BjzOgDPXt+9ylr7SmPMkwC8HcASgA9aa19dso2EEEIIIYQQQkgyCv+pYP2F9ykAHgngEVgLm/QcAJcBeAaAnwXwC8aYp6doKCGEEEIIIYQQkoIyX4TvAPAya20HAIwx3wFwDoDvW2tvWj/2NwB+G8DVZRs6LepmFzxJm+D48gK2PrH2xx4bnlBdORthYRcsQyQtKmn0voXM6ubkBdcCZ+dclvdoN6vgzlU1E8S+LKGt7H78dlXFQho4ZXjshXW+FLZDofLknPTZPQXLVvt1D8cwS0Tb60fa0q6q8ZWmjN1etn30iFtvVxrVt+WID7GwkBUitzHntqMlTgvZXZYNoUNbu/GpOpRUEZPZomHEfNcStO+NDLOUwl44vozR+eoYZqnI7wTAbxfcVj5CpM+QduSPup4YU12vlEr3oBk/lF8wFKLYTvFsTB1qMHX7qmQ72lXTa/TmFH4RttZ++/i2MeZsrEmk/y+svSAf5w4ADyjcOkIIIVsW58WXEEIIIWSClP5TgTHmIQA+AeAVAH6IvMvbuv+RiBBCCCGEEELINqKss6zHAvgHAH9srf2AMeYJAE4RWe4L4PYydWxGcplv2uJmLkSSc07pWtPga4c+LvelTEqHSJJy6Ifuc4VNp+7M9Ji3Hs1iwHzrHvdWkeGUujKEjPIu6Ui5dSgkmc8bMSlOQh0fcsFfXtUSp7Jy7e0oaypCrKmCG6LEPUeHH/EdPyQUzz1xIwxuPuTkm//mnVnamXs3tvsAeg88IStf6Ba1R2nZ9mEglkus1LOIBHaWqVq+XCUp2l5ECu/MuciQSzrMkk+ZmEKiHCuTnmUazjPUxQmfFDCHkvtzUiatnylie1U841f68KOe+fIXhf+57oZTKmJSpEltDkW2BtMKxTZLFH4RNsacCuCjAM631n5q/fAX15LMWQBuAvC7WHOeRQghhDjIl2BCCCGEkElS5ovwywEsAni7Meb4sXcDuABrX4kXAXwMwIdL1EEIIYQQQgghhCSljLOslwJ4qSf54UXL3QpsBzl0rDfaYmUXS3PzSSmUmyY9Q0spNAA87992bGxf9vhjG9u3HHH1bvOrQq7Vyxqlx60pZClSJpX38iwSpUwqd72jtVYhT5MISKZ8HiRDUuuQRLmsB2ld9yw7GEjhkbMseqwc84HmaLkg4EoJd7X9o3BHM3uEHDiQVda68YCT73EXnbmx/dk3/0Dk24/Or56+sd8fZC1sq7nqzDO5rdpUVvJc1ut0FdRF1lyXdsSQwpt4Edl0rt6SHqWHquFFpI6xXtaLzmnn2ZH4t0soooTjKVpsz6v1bPdcNggnLWbbu9Qic0Q8y+9acYxHvG3NPVM9fVEoMsSI8uvMrD27t4OcnF6jN4c9RAghZCrIl2BCCCGEkElSylkWIYQQQgghhJB60eD3zk2ZyRfhOnqKLtKmacmfc2WUL8ItL1q+LLer06VoycuykD/dJjxDA8Bf/8rKyLTlnnur+GQ0ISlqrAfpWImO22duebHtc53v+mVcIcmTT16k5/fQkYxt3lYST+z9I/OFpIT75oVn9RM6G9uPPNH1sn7lrQsb23cfXdzYPvyIezv5PvO2mze2++bErD23H0FfeJFuiYkWLWVWE6jhWYBivf7GMksyYWCy7Z1W37iS4vHPCZ2n8/kcl4e8QZeVSa+lDUVaQxwPt7cIAefstUOaeiwor9FSDn3uSdlz/Yzdrjvomw+3Nra/dFdmNtVRnqHlfvTvHbUvn6OxJkWppcdFpNuEbDX4pwJCCCFTQb4EE0IIIYRMkrJxhF8H4Nnru1dZa18p0l4M4FnW2vPK1EEIIYQQQgghJB46y9qcMnGEnwTgKQAeiTVXth83xjzTWvsRY8yDAVwE4MY0zUxP2alRVCZUVg49TSl0lfLl1EiZz2rfbbf0Bvnt/e4tcOvRTPp5pJul3bPqdvxq3++xOYawV2aPB2kg6AHaKV+eFvRWPbq8ZmM4s1IpPU+LXEdRufas9llbNXtJeIp+zL0zKeGz/n6Pk+/y/3R4Y/tb+zOZ9OGfWXLyHdp3xsh6WwDm5oW36Zb0wO7mLbL2xXrEraOn6BBVSo9Tl62l65WSWL8bklr7vEvrJni9QavFuMjvVZ9MOlhvAgl1PvrA+GUUIWd6JM2NPB6kAWCHkEpLOfT5nzrRyfehJ969sX3Dgez539bRIDzbuTZGPqNpHjR96i79J9VR5n3wDgAvs9Z2rLVdAN8BcJoxZgHAJQBem6KBhJDJM6svdNOEfTY+8iWYEEIIIWSSlIkj/O3j28aYs7EmkX4sgP8F4DIAN5VuHSGEEEIIIYSQsaA0enNKe402xjwEwFUAXgHgDACnWWv/1BhzXtmyJXWQLUzSM3QdPWPXDS3zlfs90e0Hu03HK25TOIruDt2euStzGg3pT1J6mgaAjkcaHStXjpeWqYzyq2PDn8/3dTIkyXbb4y8vLOuW57j5fJ4xg56sA22tg7SsLl+BnfEpsObovpXS/68J76kfetYhJ991d2RyaDm+u3a6s0Q+jPuuo1aHZsBrdFlZcgpJ6Kx5ipakaHtZmXPl/VfITiXtPRxap4LeoD0epavwBu2jqPx7UoSGV6b1VL5jYj2TnqGlFBoAbjqUpR0Rz/x7Lw1w+7EWRpH7HTLDa0RKYn9rEDJtSr0fGWMeC+BaABdZa98H4DkAHmKMuR7AewE82hjzwfLNJKQ4OjQMIYQQQkgMvpdgQsjsU8ZZ1qkAPgrgfGvtpwDAWvs8kX4egIutteeXbSQhhBBCCCGEkDgaW1IPmpYy0uiXA1gE8HZjzPFj77bWvrt0qwghhBBCCCGEkIoo4yzrpQBeGki/DsB5RctPTfGQQeOfU8QuuKjNTZV/64m1NZykvVDITkmm9QdZojLvhYiQhN7ATfRdc1fZgrp2wf4OCIUncvDY/sbaG+XHQJ7oD/XkzJ+gDfNQpKk+E9s+e+FRZU6DFHZKqe2Ci49xkbrEfSEMAjvqPjjQyaSAX707S/vm/jmVLxt9aVfcUvFLFjJTYvS67gX77EaL2kJKO9ZGoNNmLWSSZFohjsrWW7WNcGy4LIfoOHR+QuGJfGtiyB7XZy+sz3PrdQuU4ZSCtskF02JIEWZpMPpRtp4m1jPRZ121nsmQiV8SPg9kiCTAtQu+ZzU7p9t3y+s5z39v0wkhM0BpZ1mEEEJIEWbZ+RQhhBBSa+g1elPYQ4QQQgghhBBCthW1/SJch3BJ4xArhy5yXVX/taJIuJXosiPzuXJTrRkbXZ6WJA1Exp4sQ8maBqKQotItb3giLd0S7Wh6Qx8BzjUHQgv58sVKfmPlysGQRpGhlUJt8oVS0nVPUnbmq6uoFDp12+NDc8XdS9osQCJNC471/fmkHHql77/bpaxSy6YHfTkXvEUUIiT5DcmmnTJm+Kt1kXBHsdcbm29q0tHI+yUYnqiAbDokLw6Zjnglz2oh9YVWytcrTAQ8MulcXZGhmsJj6g/l5lxKIOSbLL/lXdtc8w4ZHGIlt2ZlnSbNQNqq3p4oX8qhV9RaKddO3Sbfc2+SYZbqEFqQkFmhti/ChBBCtjbyJZgQQggh6WhQGr0ppV6EjTGvA/Ds9d2rrLWvNMY8BcBfAGgB+BqAC621nXLNJIQQQgghhBBC0lAmjvCTADwFwCOxJkL6uDHmmQDeCeAp1trvGGM+DOC/AnhvisaOS5G/g8RKY1NLoVP8zaZKiXO+rolV5RCSfTrqJSkb1v0iMzbdtGZes7x+PI5cPllcU8q4lNTK2RP5PO3R+aDlu43R+XIenz3dqWVcIalVyKN0TBn6XtJS6Y3z1b5XgpbAq3NsGXWRnfm8qeevQ4yVPF9L/+SOkAjqPpcSwW5flqfuK9kmebzVcOWY4rwUEt2Qp9sisuFYOXVqirQ1V8YEJc9F5ORVS9DlXOgPRx8HxngWh0IYeOr1yaR1EWW9NWt8Mulx6krdJknIRGkYeJbL8+Ra1AmY9khpdGise56ydVrORCsyooTE98ybJCGTJ0K2MmW+CN8B4GXHv/YaY74D4DSsfQneY4xpYS3O8HLpVhJCCNlyzLL9LSGEEFJn9B++SJ4ycYS/fXzbGHM21iTSjwVwC4DrABwCcBOAD5drIiGEEEIIIYQQko7SzrKMMQ8BcBWAVwA4DODPATwUay/Bb1//96Ky9VRJ3eXQk5Q8p8aRQeq0OGVZfF2ym0JeEx2voEomJr3bNv397huTkLdl6dU6JNeWstS2kn/3PFLpeOemcfKnkAw52vN0ZBnTkhfnJW3j920dCbV34PFIHnuNOl9fDOpAbGtPt77yG43yMtpYOWcsqeXUkySF/LmI5Dm17Dp3Xqxb/AA+nzE5b8uN0WnBZ1QBmXTBIpy+8HmQ3qyuWLyerHMRG3z4TTN80SB0ebGRIuQztROIFCFlzaHfYLINWhrtPtfrRx2XqdiIEoRMmlKmqcaYxwK4FsBF1tr3AXg8gG9Za39grR0AeA+A80q3khBCyJaD0mhCCCGkGhpo1vJfnSjcGmPMqQA+CuB3rbUfWD/8LQDnGmPus77/DABfLtdEQgghhBBCCCEkHWWk0S/HmjOstxtjjh97N4DXAPi0MaYH4EYALyjVwjGZ1t8ZfFKm1PLnaXlrniQhT7eO0iqUT9AZNtD2yYMDHkNDUjW5P+fI5/zjKMvrqmxOfDGPTBpwnVy7UmNVoPS06W2RPi+kxxtd71oZozPmPFSL7VhpVAoJteMdOYFH3DrKzooQex3yq63+gut4wQ14fA6W4TmvarmtTzqauryipPhaXsQDdOic2PHxSZmHBS8qiXJASood+bM7cFJu7CSph7n32R5ygRyg4VnrdD2ORNkjkwZCsuZQZ47f1hTkoxnI54g4rton5dBSvtxWZk0yLfY3VPAZEMg38Dx7kzx74ooghIxBGWdZLwXwUk/y+4qWS0hq2okf2oSQNNTd5pYQQgiZVRo+5whkA/YQIYQQQgghhJBtRWmv0SmZpOy3rKfo0Pmhvy7EehsOUeSvF1tRUjNAXF/0hqp/Hc+52otytj0nCtf5pBx6qZ2lzbfcfC2x3RWStFXl1VJqoKVMetD3S8a0TMxFahhdWZhPrpXvC4+cLOAxNNZDtTNu2gu1OG/o8eScAi3HczyBFvCcG6IuofyKyI3d7aE336DvliE9SjtlDPxl9AMLVWzbY/u6yDqa2kN1CorIn0NlREujnfH1j2nMcc04QgFfm0LIjyQ6OoDcbbaEzDVnEzJys/C9XsQTuk8mDYQ9Sjv5ArJpKRuPHrtg6ugHSSgSQei3kS9ShPby7D43i+Gbk7nnSMQ5QLXPNkJIPLV6ESabw0/4GfH215U2Y6aI/YOIL5QQITGEXmgl9BpNysD5QyYBLTjIzFKXv8LXmFIvwsaYNwB4Ftb+tHeptfbtxpgXAPij9WNfAfBCa20nUAwhhBBCCCGEEDIxyoRPegKAXwPwMACPBvASs+Y++hUAfnn9eBPAixK0kxBCCCGEEEIISUIZr9H/Yoz5VWttzxhz//WyVgD8obX2EAAYY74J4LQ0TZ1NQuF0pGS3CplvKASD95z44scmYGIVtAlyZbr+0Efw5dP1BkLyeMNgqePSFnj3XHZlJy+6V7kk7rAj3ayQu1bcEfHZp/aVrEWe5QulFCI3D7zzKdJeOJ81ULY/tNKkcEJb5MJeZNtFwvhoXNu7uHxFKdLG+DA5/rBI0i44FIJn0BdjryarM/cL2Ajr/ou1tSwSwqvuUtwiIbFxxYycAAAgAElEQVT0fsjm1jcXcuNdMsxSiGC9gRBekqZYCAdqQWuJNbslCmmp8AOOfbw4nnvkRxq8+uZq6BkVmuu+vg2FWcoTvbiPJBy6z5/PG2pPNacVuXZ6f0NEEnpexU7hkE0wpdekMmhPuSmlusha2zXGvB7ADQCuBfAja+0nAMAYczKAFwP4/0q3khBCyJaDPwAJIYQQMi1K/63AWvs6ACcDOBXA8wFg/QvxtVizG76ubB2EEEIIIYQQQkgqCkujjTEPArBorb3eWnvMGPOPAB62fvyfAbzTWvuXqRo6KXzhkoD4kElF5NDBsgPlhXAlwHGyHK8kaYLEypVzX5NiQyTJsiPbpNuwIKTR+xaybbPXjSHzgJ0LG9u3HV3d2L5hv3vrrQ6aYjurrKMv0jOOYcm8X5LsG+98eaE56Kk8ND6e8E46nwylpMNFxX5N9MnOc9JoX7ifSD1sYwyNc6xsOjXxYXI8EthIWbOUQus07VHad16snDzU71IGGjs8Ol/qr9YpvNgXlUD7jseGRfKNY25eeB4esfdSCHe+qETRqEbXP1n77Wxi9BZaTrY5uR7NZZuNvn9Ba8aGKvInRUv/fUprfb5vvo8lR/c8IPNhluT26H4BdGihUBhDf5rbDmHqEjCbcs2wxr8Bx+my1OvFpH6HMVLEFmULeY02xrwNwEnW2guMMY8A8F4AewD8K4D/bq3tBQvwUOaL8JkA3mOMWTDGzAN4BoAvArgGwKtn8SWYEELI5AjZBBNCCCGEGGOeCOC54tDfAHixtfYcrP3p6/lFyy78Imyt/RiAqwB8HcBXAXwOwEkA7gPgZcaY69f/vaFoHYQQQgghhBBCth/GmH0A3gTgzev7pwNYstZ+YT3L5QB+u2j5peIIW2svBnCxOvyOMmWOy7QcohWV1Prk0Fr+U8SjtMb92BInbQ15mk798aaIRDlWMuWeE2qDkttGlinHZ6mdnSOl0ADwvH/L9i97fHb8psOugiPWW7VPgh+SNbllhPSwcWXkPHw67ZAp2o2pL8kviwtRRMYf8lDcl56NPd6Qc22Q92nLLxdsqoGM9WxcllglqpY6+qSyWtbs9Tas6pXn9ZV4SfZ7SG4rkZLnVjPQ70iwkBYg6K26Qil8vCdwleaRQ+fuEY8sua9kw30pWZZJIbuX2PtA1NXouQ1sHOtm24c7blona9RQuPMf3Mtds7u75rNzpMxXW3DIfemFOuDZeBjwIO2VMgckzyGH1ClMLoZaej6iDYA7xHIdHAzcjE1PP8WaQ+XbEfkbQJYdmS9YXvS6OjlP0XQ8SEZSU2m0MWYvgL0jkg5Yaw+oY5cAeBXWfFEBwP0A3CHS7wDwgKJtoWNtQgghU0G/QBFCCCFky/PHAG4a8e+PZSZjzIUAbrXWXisON5H/tFL4W12pL8KEEEIIIYQQQkgkf4U1SbNGfw0+H8ApxpjrAewDsAtrL8GniDz3BXB70YZsmxfhop46Y8+T8tCQrFmmtYWkT+dz0qKDxvv3pQQ4/2eT0ZrVSUptQtIln/wrL6H1eI3UnpKllDfUpoB0qyckX0e72fatR1adfFIOLdOOdt1bT5YXqtdHTtoRKZ9zyvBKnMN1OeMQlGE3Ridp+bcnbaDLi5RQy/ZJueBAXWRPSHZ7Xb88VCqNWm2x0wfawgFtU2zHenRNQbw81u+h2ZXKRpYnvfIGvAjrr8AdoWDtin6H/losJm9L3D6DltuZLefW8nsdr1IPNSnp+zjtCHn/9s2F3DgKqWyvJ+6RjpIor0rddGACzUmNu7hPW2pwRKOk9+bGiquzbxzK1tjWD9zfU/1bfpyl/Yz4DXX2CU6+wXx24w7EDa2fN9LXdB3Gu4rnday82ucFP28uEmn24pE8h59fgTTP8dRSaCAshyZk4tRU97suf9YvvaPyPfn4tjHmAgDnWWv/wBjzLWPMY621nwXw+wCuLtqWmnYRIYTMFu3W5nmIS6ezeR5CCCGEEMF/AfAOY8x3sfaV+J1FCyr1RXjdI/SzsPZn90uttW83xvwS1hxm7QbwDQDPtdby5w4hhBBCCCGEkLGw1l6OdTm1tfbfAZybotzCL8LGmCcA+DUAD8Na2PkbjDHXAvhHAE+11n7DGPN3AP4bgHelaGxVxHoeLIKW60jJc1vU21bf5p00x7u0m09KdnrqMnpCotOTGZV0J1YeVNZLb648j4Io55XYU15ICuXKwrWk2n+elCjLenuqrmXR2XetZDm/vd+9pW4+kkn3pBxanrNW3uj2aZlVEW8ARcwCxjlnIL3MeuT4ANCLlOA7sjhx/Xlv1WLbaY+/rY7sU3kv7qxmiUePDUceB4C5+azipaXs+CqAhYUsbU7KBRvFJIJ1Iyeh9khvc96Gxf3SzRz7otEAOscyGW3zYCZtbSy7AzRcFDLV3Zmn3+GS+yne37du43NS6SwhKSlk8aFzkngH9sihtYTa8awu5NDNo10nX+PASrYt5MvDeXeshrszD83DxWx9bMypBsrrdx562mt0VlfvZtdk7L6vedzG9k/f+IUs4dQ9bl3iGsNmAQH7DiefyFVgHHMemkvKsGNNHfS+NH0IldEUEvfcMErzm8iHjLwd9W+1FNE1yhIrhY6VVzO0OknJcEZ/a0ySMnGE/wXAr1prewDujbWX6kcA+Ly19hvr2V4C4COlW0lICYrahxMyDvIlmMQhX4IJIYQQQiZJ2TjCXWPM6wG8HMDfY81z1xFjzAcAPAjAZwG8rHQrCSGEEEIIIYSQRJT2Gm2tfZ0x5i0A/glrMaCeCuAXAfwIwKUALgJwcdl66oD8fK69GvokOrl8YlvLoeeFbHpBeFtcVJ4XpbhMfk9Z6btfpFadjy0emTRciWAKz5PREiBPvpz8W6YFzxeyZuUB2e9t2i1EKKMdqXlz4DbqaC9riZQGL6sxkFJ4KbteVR/C5HkdjwdpvZ/3mh2HTwYSmtMhfG3qaf2cuC4pk27nJNSjy9btKRuCdm6+gZWVrBC5vXrTESdv+zt3b2x3zL6N7cYDdzvn794lPd9O/wtxCtmsHINGw5U9++SS7RbQFQMp5bbS23Bzvgncubyx3/rePdn2jfvddpy5Nyvj7GwMBq1FN18rWyEbYrLnZZ+ezhlUO26OoDagyfJ54h2rLo+naO+1B84HgL5Yt6Rn6MZB11u+9Njcvem2je250+/vlvcz2ZhCeoruDlwZtX5YHm/fjjlXei3k8+0z7ufk/ckbP5dVJbxG9xeVlzux0CSRHvvGUS3ujQLSpSTzwiN/BoCeGG/H87u2wxJIr+1aWr/QkOtjdlxfutxviWfofDPtMyoXAcFzfrMx2SgahCRj+j9Dak9habQx5kHGmEcAgLX2GNZsgy8C8AVr7U3W2j6ADyGRMfNWRy/wPuiYdnwojc6IveG3Q5/JF98Q8iU4hHwJ3qpo218f3d7meQA4L8GEHEfbEvvQ4ZPI5qT449h2gy/BhGxdyvgXOBPAe4wxC8aYeQDPAPACAI8yxpy6nuc3AXy1ZBsJIYQQQgghhJBkFJZGW2s/Zow5F8DXsabQ/Qdr7fuNMfcA+CdjzCKA67FmP0wIIYQQQgghZBJsB3lfSco6y7oYyv7XWnsVgKvKlLuVaTphkUbbBAPAveYyDeLJi9n2zrab76iI63OnCslzENKONTs+yMVjGN0+bYPqU0Wmlg0Fywvd0855fjtgN0nZp/qMUnONGh3WZ1V1UlP0dci+V5pc9aUtbc5GeHQHhBSrRWzbWwFbLC0jcdsu8oXG0WMvvHbeaFvvXNgvkeY9B27bQ2GLpD1cW9iqPu0VD3Dyffxt/7Gx3T99N3w0AvW6ad4ivFQtbyxSfrRvAGkfv28JrdsPb+xKu+BTXvcI57zbL/7axnZDhLxpqIql/etw4J+4vjA0IfvZFP3u1CVtbvVEmGA8GK/NqL5euS8StURZhi66z6t/ZWP7J2/8Vydf65RdWXFy8dBGQLJrxA2dk1CLcEz9B+51khoPyO7V/o4ssI8MxaXLrEOYsxTht4Ihk8T9kwt7Jubnqggjt3LMLbDRzTK2dmT9t3OXO4ml/bAMraSn+px4eEgfKW19i3hCX+aer86e/zdOaqoMmTTLcu1Y22xCJsG0Qq8RQgjZ5siXYEIIIYSQSVLaazQhhBBCCCGEkBpRA1VL3eGLcAU4MtJAWAC5rb1G71vIxCKPPDET9pyx2427c/PhTIb09bvd4ZQhebriZujo9ortWIlKfIik8W9Cn9xJ1xsKs+SXSavEXJInzJSqTMo7pVq9qwr0mWfkwiJ50mLl6Rp/iCR3X0rNpBxtXkn1F0SaLqMnGiXn3GrfbYUrzxflBfrMLcEf9mvoPce9RhH1CnNzbr6lpSzjgYffZ2P76nfe4eTrP+LeG9u7FrNz2nPqOmR4ENWo7fxskv0yPHW34zm6f9YJG9tSCg0AwwdmIZOGC1khw0gbKH3Pxcqhhs79GJcvhBz7ltMIf7ixXBmRjdfha0ojmyRD4Sy6zx4ZukjKoXPhk+R5sjNUuKSha98wanM9XyaN1m1yFs+5rHwtr26LdUHet3qa+WTTdb+382HEsu2+mi+9rpBDH8kS2z9wQ5tJk4bez564sb1sTnDyLS6O7py2+v0j5dC72oORxzUy7GBPz3vHFEeiO2O0yRMhZOtCaTQhhJDpwPBJhBBCCJkSSb4IG2PeBuAka+0F67GF3wtgD4B/BfDfrbUM9kcIIYQQQgghk6DmCpU6UPpF2BjzRADPReYp+m8AXGit/YIx5lIAzwfwrrL1bEXk53ilqsSuuUyyI+XQz7lun5Pv787LvNt+96Ar8dJl+nBluePfNak9L4bKc71a6zRRhkzIqanG9yjdU5U5dQnZVQqJRUiS5ZNmhmTibUeG7NLyyKH3zrut2LcwWqoGAIe6Wal3Cc/lBzpuvsFQeDGXXrddtb8zxtIbtF7QHTVd09Fdu/k87qubC35PzvPnLG1sd8441cnXFrfZYkgaHZBVxkrmfWk5SajnvNh8KXDuCVWvVL22WiLxvjvQXRYT4OxMSjkQnqEBV8I6FN6BtYxWSlYbgTFwyo7sdzlXtYfdkLdpSVN6PRZzqdXU+YRXZjWQUvIcK5OOJeTRvCXa21/wjAdcj82t+2eeoftKhizPk1LmYSswgaTX7ZbyGh05v5uBfpfzM2jesAX1dHoKO2tOR3iGFl71AeBR//Psje2v/MUPNrZ7ynO39FAt5/ScWqP3OFEzsnr3zrv5VsX9uL+TDcjRnjLLkdvCfKeo/HkY+I0yS56iq/aaTcgsUGopN8bsA/AmAG9e3z8dwJK19gvrWS4H8Ntl6iCEELI1cV6CCSGEEEImSNkvwpcAeBWA459M7gdAepa5A8AD9EmEEEIIIYQQQioi0pnkdqbwi7Ax5kIAt1prrzXGXLB+uAlXnNTAGAqQkEdg7zlq3/eJOySjlTKXRsBjcSxFpDFddc6RbtamHwrP0H/7BFeSJNPkObrMQjIcvR/wZhw6ryyOV+uglMenD/Vn0+W5Xr0Dno2Ho/Npyq5BobnkSFED8zuE7FvpGVpKoQHgYfu6G9un73LvsluOZGnX3525XNVeozuikU0pJ1f9J/s6fI3igOM22s0osy2JtEU19rvkarhD1As/A0dLOPTOz9C95FLMA7JTgrO2RRUXjZbKNsQANURlOp+UA7eF2Ud7roVuJ9vvnSg6XruwFYU2hQy9rWS0UsJaxINvzqO7+Gjd74u2as8XwlVtQ+TTMt/mfNbANuSzxy3OvS3UnJYemxMsuE7dAQm+HMe+kKQPd7ou2B1PzH25WKqK54S8WpTXVGYGIbl2LA2Pt+ncNXpk0z4v0WXa5GMYGINQWllyfSHXEiFd75/jmmh99S03ZmkPOWlje06NY0u48JcWDdob9L2Eac65J2fPl4ee4N50PziUDdZX7srmYFfZZkgv0j25jsAltfSYEFJ/ynwRPh/AKcaY6wHsA7ALa4/QU0Se+wK4vUQdhBAyE9DeanzkSzAhhBBCyCQpbCNsrX2ytfah1tpHAHgtgCustX8AYMUY89j1bL8P4OoE7SSEEEIIIYQQEkOjUc9/NSJJ+CTFfwHwHmPMHgBfA/DOCuqYCq4nYi2pHf1lQ0ttejKwuyhipe+WJ73vfu2utrMtPUpLObQ8R5fp1KtkQ67kedRVhCni5ThEyANyEJ8sdajGJ1pdLbxLBmS5Wjbt5Kv0g5doQ+CanC+VARn3nJg+P11u4szdmQxNyqF//192O+e9/wmHN7a/fzCuTU7/5bR/nnx6fD0y9tycc/Jl223VFzuEN+xd7SxtoeXOwGXhkfRA173njombWkrDe4F1wC3dzej7yhy6R1LIoX1S2ULnw5Wbiq5Fe6mBnpAeSyfAw6HyCOwpX3vvbXjGO9aDds5zrpRVCmVm42jXydc40sm2V7KMwyUlGxaekns7s7VdS29Dba8SZ275h8CRvQ6U92bHO3TIvEN6ZZbjpia47/pD/RLy6uybIzotdp5NkrL15iT4Yl970Jae8BeF1cKKkkb3zsy8Q88tZYO6Y6db2Zy4FebFw7Gt2rRbrMUP2ZvdS+f/8wlOvr9/2v6N7W8fyO4lXR4hhPhI8iJsrb0cax6iYa39dwDnpiiX5JEvwSSOkA0vGY18CSZxyJdgEkePTqMJIYQQMiWq+CJMCCGEEEIIIWRa8O/zm1LbF+E1Oevk6x0OG47naJ8n6wG0N2OxI/J1Bg1HpjMQZUu55OFu05EKHRSlLyvZtPS22FO64Y6QXK6K8zoiX7MxRE9ILqX8MtZTdLwXXBdfIHpfn2+GHpON407bdJ0hl7vyPFWXJ2NINh0r6y4014duG3xfvpt6PjZHt68rduzBNu6/MzsgPUO/8VEHHan0zYezfEd7mSzulB0D/MfRzd0QzDeHzrz10W4qr8yeYdR96cihlVZ9h/BWeuJi9nny0Se5stcH3Sv7Qv6t/dmyKT2VAsBPluWSmvXLApQXbdGOnuNBOySV9ps0+Gg04qTSoXxOfzZcZ84+GWm75UqKnZtHLIittuuVeaDGJ1bmHSud9XnclcdbTfcahzJRLLjDhRaady9v7LdvzGSajR+43v2HD8ykpD2TbQ+EJ97V/hDzC+MvBFqy6/MiPYzsTEcqq+eFRyrdbDacfM1I7yOx3qAbkZ7AY9fRkDzY5x16OAjLrTfyDePky6nzhQj2mePV3J0jc2I5k/2yuNhw7lt5g7fdJRELYk63WnKtyybq3atNnLiQ7cvfPN89mDXi4sccxoOFVPr7B7MJuSwUOSct9vHj5SzNZ2LSbri/jXw0G2k8SheK3kFBGyGVUthZVtVMK/RVbPik2I6LtVWZjzQmbQcqjnmZAOC8BIeIDgNV8iW4KJOcI7HjPS0Zdmy9+kXQh3wJ1ujwST5iXoKB+Hmb2ivzjlZcX8iX4BDuS7CLDiXlI/aHUvw9lzafjmjkYxCbLyCNLvsSXLS82GuUL8HBeh+4b/NMQKGX4FxdiWPXpZ4/dSR2/sS8BI9VXuJ8kyR030oWIue0fAkOIV+CQ8iX4BAxL8HA9F6CCSHVk+RF2BjzNmPM5erYbxhjbkpRPiGEEEIIIYSQSJqNev6rEaVfhI0xTwTwXHXsPgDeBqrTCSGEEEIIIYTUjFI2wsaYfQDeBODNAB4ukt4L4PUA/rxM+UXwmaXFouW7Pql0TuYizguFctGhi3z5pAx01Qk14z9PS56l/bBM022Qtsqh8nz1ppA8FyljEGnrm68smBhVnmM77hx3M8b+4cu1RY/VhIrtgB20HG49z/oiUdpl6VBc19+dGX7JEEmAaxcsz9O27bKugbJvljj9CT+xfSvl4NIEYVFJo/cJed7PnZBJ8J6tQnZ88KmZLeiNh7Il9GDHLU9KvntKOzroj14v9DjWTU4XtLkVA9LU95FnYW42XRm1LCO1zDfkGyBahi0ma2PV1YdKu+D7XfzzG9t3vP56N98Z9xpd9hh2q7Ey3bLkwmDJnYD6tKysPXRvB22Jo+XG/oy+vk0hUQ72i5xbBb6YFDURcEJJtfzzrCnNapQdsHPvi3Pm5918bU+4LB1e7mAny/jFO7Pj9qA76aRd8P5OVrE2t5FLSUjm7MsX+n1CG15CZpuyzrIuAfAqAKceP2CM+SOsxQ/+QsmyCSGEbGFibYkJIYQQMibU5W5K4b8rG2MuBHCrtfZaceyhAP4zgD9L0DZCCCGEEEIIISQ5Zb4Inw/gFGPM9QD2AdgFYD+AUwB8BcA8gPsZY/7NWvv4cQvPh6Qp0dJNyi8mX43Ll/trjBOJQ2qSVBlCdReuS0hMA+2Qdel8vjQ9Bj550CSlQbIvgnKl5LLpYuX5JNQhBo7MPpRT6lJ1kpTC+yduU+jkVoRc955Vt7jlngiPoS5ESvClHFp7SnYk+PDjS9OScZ8XaZ2v6aT56+2Itt90OJPgffhp+5183xPyvG6sh+FcG0enhde90MIiyohrkipDhS2KLcHXDNXRDTHPnPFoaQmi2C4o9fQS8E4uTWDyEmUh125lrR/ucB+fg7NP3NiWcuj+2a7X6OGOTFfaEkW01dO4JUIOhMIROX2WSxuOzFcUHVopJSlkzU6+BPLxInJo3c+OVUANvs4UlpYHBtxZz6T8WT9vPGY6Wsp8REieBytZgQc77qD6ytDl+X/jVDsgFLzEEWsORUgVFH4RttY++fi2MeYCAOdZa/8EwJ+sHzsDwHVFXoIJIYRsfWhfRwghhFTDsI7x12pGbeMIE0IIIYQQQgghVVDWWRYAwFp7OYDL1bGbAZyRovyixHqQDkmepfy2EfAG7ffgq+ScnjYN+n4JkSw8JA/NeZ528o0+J5RPS48HkdJW3zkhoiVjseUFpaMFZM6RXiNDHp8LSeSC1+ufZ7752AtMXJnUV9KyjkgMeS7vO56S3Xyu53J5frG/WEZ71xa4XtHdtAOdrB1fE16yv3PAXSaPCtnewW52F+vyQsi1pOnxOL/W3un8Nbfh0arpdVTO6ZD01vdH6VZDnRfwcO4rP+TJ2iHwEJDd3lL5hmL4B/Piftmz4OTrn5NJoPvSM/QO18Xu4F7ZeUsLWXlz8+6FSA+7oWvs9eVxNX+c+wxe5D3d8GyPQ6x82Xt+5J/o6/KxI1Z2HvpNUuRaQufEPmMK1asa73ie9niQXmvT6I7SzxtppjMIrI8+dGSMgSctZE42LU/RVMkQMlmSvAgTQggh45LCbpUQQgghI6iDY4KaQ2k0IYQQQgghhJBtxcx8ES7i5Tk1Ppk0EPAUHZQ2Cqm1SvH+hWIMuY7Po3TIG3Ss/DkktS5CrLw4OiZaSAZYRDYdXW9ijXfc9MmNgePFU8pwdYGi4wdC39bRZQzi5GkhL+Y+OXRqL5GxHt21fE56xv5p4FOllPFJCV+7oSV9Ma0NM0veNIMSS4+72IaWRstTchWMLCKakLy4KTziDvpu4Y5UWsiX+0338dlfbGEULeVtd2leFCek0fNKGj0nitdtl3Lo/oq4h9Uk6XRGNinfF6Lp8npDHrSd4xVLmScpgfbNR318GHlDNnz9qeXFsnx5j0S65w6te0FP0QXGTs8Dn5w+OuKF6lzp9Tn028/nhTpYl3Pcf457flw+XX4dmZa5DSF1ZWZehAkhk4NqmvFZ6bPTxoXSaEIIIaQi+LNkU5K8CBtj3gbgJGvtBcaYnwdwCdbiCN8K4PestQdS1EMIIYQQQgghhJSl9IuwMeaJAJ4L4Kr1Q/8ngNdaa682xvwlgJcDeHXZesoSKz3WEhjflzHtUdDrUToobU3rlTDkAdGpNtIbdKzUOteOsp6iI+XkofJC4x30nuoZsKCnackkJdTR3eT3Lt2THslzc99Xd1zbQxKsFPMn+nyfB+2ch10hDR/EtV3KvZuNoSvpi2nsGMTKAOuIKwkVJiYAhuJi5JAEpcwhyWWxJmZlt/TaLtLETdJWSmjXdEaeo7xBCyfSC0Iavbjo5tuzkF3JvHpg3bOaHeh2sjYtL7sTY3hIaKOF5nm45D76pSxbe8324ZX86nwVei9OQUiVINO0FHrgmbeahlwjmqPniC4jRV84cuWQx/Tg2Hmk8DnJ/OjtWGVR7p6V66+453LP55LS5hSeoVOs87O2nhOylSj1ImyM2QfgTQDeDODh64dbAPasb+8AcE+ZOggpCz3CjU+R0ETbndDLMxnNkL8ACSGEkGqoS4y5GlP2i/AlAF4F4FRx7E8BXGOM+SsARwE8pmQdhBBCCCGEEEJIMgp/LDPGXAjgVmvtteLYEoBLATzJWnsKgP8HwP9bupWEEEIIIYQQQkgiynwRPh/AKcaY6wHsA7ALwOkAlq21X1rPcwmAPyvXxDwpQilJu47QXwNi6/KFVgq69JdtiLWlGUNJGBsWyRcKKTZfbBv1NfpCJuXsex3bSGHTmqth/NBHugxv22PtYidqSxxnP+z0c2SYJV1vCi/SdVPB6vBJsXLwgWNn7C8vNhRZEXRb6xASI6TA8tlQNkKLQkFi/7orx0S2PdfWpkyL62dZnrYlbs9lidJG+KQld5Y89ITVje1zT+46aVfftrix/fmD2WO88aPDTr65r/9kY7v/M/cS23udfANhuBxaY312wSGbUUmKdSRW6Rey25XTLGSrK+2CB2pudsWQ9EXILV2vtAtuz/kdiDSE7XjsHG6GxsBTyDg2wb68ofEuMsZBvw5iXQ3dfvJ3V+x9umk7yMSZpZCBMwFDgGxK4Rdha+2Tj28bYy4AcB6APwHwXWOMsdZaAM8A8OWSbSSEEEIIIYQQQpKRNI6wtXb/+kvxh4wxDQA/BfAHKesghBBCCCGEEELKkORF2Fp7OYDL17evBnB1inJjiA13FCxDnj9GXb56i8hyQpLa2PJiZdihsEhS6qnllq40WrYvqnnoq31XTiXkeLnLjZQ8e/upWAii6JBLBcY+iYQ6Up7uyGhD8vRAeXVU1xTxbB2UWnmuv6gpQeoHMtQAACAASURBVBGKKoUnFVpJSyJj75FQGbHrR1C+HCjfh3etLzjXvSFkVDimORk+SWzvnnNn0BPum4U++oP3Lzhpb3lWJpv+wi3ZY7z1fTdIw6P+j7M3tr/6Z3Zje3D/3ar1WUNcua1fKhuSRvvWi7qEUioyb3vqAdbtCinu4Uwn3VjpueftyPp2uFuMlZqA8tmRYn77zvGFRNqs7CLjHcKZ7bm11/PMCo1VheYhVUh0pyXDdszQamBSQyqGQ7wpjCxDCCFkKsS+BBNCCCGEpIYvwoQQQgghhBBCthVJbYTrQFmP0jmPxQXqDeFrU0hSG1t2am/QPZVRfr0ZDOTxoTdfSGolvWnKyx/kZFc+2bTqGK8yurzn5SAFxj6FhDra67jHe/jaeaO9dQc9fAeqiqWOUmuf/C1kSlBHL6Oyb/s1bJ+k0VDrhVwTAp2bwnNwapmubHtT3CRaAtv0TP6OmoBf+Gkmqb3091adtI8Jr9Gy3t6DTnTyffWtN25s902WNhRyXd0mV9bttkmmtTwepDVF+i8FObGtZz7p9WzorNNifVTS6OHRTALdvnF/Vp6Sp/fP2ZdtizHoz7s/v9qe52ZIhuzkUxdSRA6dYhy9kQgK4gxbpClT4boSlEFIrajazmQLwC/ChBBCpgKl0YQQQgiZFqW+CBtjPg3g3gCOe4p4IYCnAXj2+v5V1tpXlqmDEEIIIYQQQghJSeEX4fXwSOcAON1a21s/9iQATwHwSKwJRj9ujHmmtfYjKRo7LmVl0oBfKlP0U3pZKWVIuhOUcEbmk3Logaqs3x8tE+sHJNQ+KR0ADFtZRp80by0RHvyeJh0vuoHitNdE16Oir95w+V5qKKGW1x/ypOqTUI8q00cRL9y+NgTrmZISqAl9n01fklTUQ7OP1F9wtTQ6NYU87EZOIF12rEdlibwPjvbche6Gg/Mb2987NO+k3b2a5ZUy7LkzXG/QnROXNraHYgGeW3Trkp6sW23/WuyTQwcltYH+nKhqTz5jxOGBsh/wS4rVRO1md3vzhwc2th/82gc72W5483c3tvtn7hUp7s8vV+YceB7KcwLydNn2opJnX5oe0uhnQGQ+57kUeCYXeg5PiTqa0dQd/dOv7mNcSyiN3pQyX4TN+v/XGGNOBPAeAJ8G8DJrbQcAjDHfAXBauSYSQkj94UN6fCiNJoQQQsi0KPMifAKAawG8BGtBCK8DYK21nwAAY8zZWJNIP7ZkGwkhhBBCCCGEkGQUfhG21n4ewOeP7xtjLgXw6wA+YYx5CICrALzCWvv90q1MQEhSU6i8cqcXrzfwBUVLMX1yaJ3PJ4fuK8lYrye2u1lat+dkc8qQsr228kDanmuItKw8Kc3T5YU16QEZmy+bJlrOOr5MN7WE2qkn0sV1rIQaUG33SKjX0uQ5cf2e2ktf6H4uUlfYI7eU6rn94p43Ot84pCijSsrK/SqXRhdY6GOlokW9+Upk/6309bqcLZj63lzuStlrVsjOXW4Z88Izsexn7blaeoeWa3ZOAiuluAkkz5NU7cm6nL5o+U1shuIX0pzraBudndmBvvDWfcOfWydf/2eFt+6dmcQ99zwU+47nbj0GnnzaM7Svb0OSfo3fTCV0zuRkHinM36qk7nLouj9fSALoEnlTCneRMeZxxpgnikMNAF1jzGOx9qX4Imvt+8o2kBAyeer4o6LuUBo9PpRGE0IIIWRalJFG7wXwBmPML2NNGv1cAC8G8FEA51trP5WgfYQQQgghhBBCSFLKSKOvNMY8BsDXAbQA/G8AzwKwCODtxhz3pYV3W2vfHVOmz1NgFfgkK3X5EhYrqfF5hg7mU2XLrzIDkSg9QwOuHHp5efQ2APRWhcx5Pqt3xw63cxfFtuPhUjVQyvictiJEnDfonBdlT2l5OfDoiaLnbSFpVIE5mJP++zLmvGSPztYfupJLh0hpebAvEsikm5FlFFpLIu8r3YaBZ36GvF+6EvT4JtYNeW+GvvQG0xJ0QFGvz5sd12m6Gp8cOiRFlX3R0dLowLxwzE9Evrk5twzp5TnU716P15Hy50lKoQtHgPBEM9D94npb9ixaALAn69zlh2Ty597ZJzjZWgtZvl1LWRkLC2qshEmQlEk3lYTaJ4eOlTwXkUJvllYHikQimHQ7SDmcyBNTa8WMQa/Rm1IqjrC19jUAXqMOv7RMmYSkZpI2S1sF70sw8cIfQIQQQgghswPNqAkhhBBCCCGEbCtKfREmhBBCCCGEEFIzqIzelNq+CGsbzEnJW2PljSlsTopIKcdxce8rP2QjLLf7yghDhkmSdsFDe8DJt/C9uze2e+dktlPHztnr5JM2US0xE4eq3qEY++HQbxMlca8xzl5YUyz0UYowOYWKcAiH/xH5pL20TAjYEvdzNnXS+C5UxvgXJs/JhRGJzOeWF1fvYOgfj4G4yJ4nlJKuK8WYFrGPqotXZmeNqaGGvIhdcM4m02MXrMPaxDLwrMsaaUPaUvl8/hXydrGjy05h+5viWZnCzC0XJcmD44dCNF7b6s6JZ9aSsP0dDF2hnbx+55mnypP23G6IJH9YpNh562vPZvjDJ40fPrAoKXzHVPm7robLWSHyPj34BkW2B6VehI0xnwZwbwDd9UMvXN9/HYCdAK6x1tJmmBBCCCGEEEJIbSj8ImyMaQA4B8Dp1tre+rEzAfwjgMcA+AmATxljnm6tvTpFYwkhhBBCCCGEhBnW3d17DSjzRfh4fKRrjDEnAngPgAUAH7TW3gYAxpjzAayUa+IakwytFMMk5TAhiUqsRLJIe7V8Tobs6K1kO1IKDQC/8D8euLH95bf8YGN79fQ9bnn9TAsWG3ol2D6xHZal+vuziES5kIRaU6EMKSz/FtfrHPeXoT3sNUV/DoRUONfNJe/hXL0eOXS74c/nHnf33dBH4ppUvp7IKK892GeqDfK8fqBv6xAiwpGKRppV6HtTyqFTy7W1BFTWJcP/FJX5+uTQDTVYRcLaFJX8+spoqye6DIGXRJ5foL1FrlH3bRFSyKnbwULGn1vO2YlDTsWOTdHfxanl0HX01LpVZM4pcH8L8WWKbF3KvAifAOBaAC8BMAfgOqzFE77GGHMFgNMAXIl8eCVCCCGEEEIIIWRqFH4RttZ+HsDnj+8bYy4F8DAATwJwHoAjAK4A8FwAl5dpJCGEEEIIIYSQSFJIY7Y4ZWyEHwdgwVp77fqhBoC7AHzSWnvnep6PADgXiV+EQzKNOsimU1BEipLzbptYztIUWqbWQrYjPUMDrhxapslzgLwXzpS4stRx0uL6rKyX51y94xeRBN/1NrX3WZ9naABwpL1Ss+nvmNAYuPV62gC/HLqtGt92ZKRxgyXluz3dF/IaA5rxQV9KxutNXso8/nkhz9Cxpg+x8yJEEcmpzzN0Lp/07Ksy+soLSa1jCQ2HbMdA9btcY+X0jB3fopSVNheXjMedGBof31yYb+l1Jdufl89Gtf7IIekKO4uesrmQQ+KYaVQsSy3mzb9gXcVOIyRHkSgKhIyijDR6L4A3GGN+GWvS6OcC+CMAlxhj9gI4DODpAD5aupWEEEIIIYQQQuLgB+FNKfwHOmvtlQCuAvB1AF8FcJm19l8AvBXAZwDcAOAWAH+doJ2EEEIIIYQQQkgSSsURtta+BsoZlrX2MgCXlSm3DF6pZw0l04XkzxW0Q+LIwtSfSdpCZrdjR5bx2Dl7nXzSO7SUQy8tudcry5N1FZXFScmlLENLl8MepUdTVELtllFOTj0OyWXxkV6Z5YKib7l4r6bSK7P/fNmOeSFH1NJEmdaWEsZAG7piMnWUhNGVZGVpbQzREXJoR9atypdNHMiOSjBusXNrGOm+eRCQNfvk0AO1UIXK8JYX6IpGoG99FTdaxfq24VmbQpLalpRQR3ox1xSJFqDl2q4kXfaFv95Yr96pTc9iZc1F2xArhZemFXLtWFDS6F3tbH/fQuaee0nlWxZrwqFuNjGOdN2ZuyrySXMMbZohSf0cSRFpZbvLn8v2YR09V9ODNNnKlHoRJoQQsoZ8CSaEEEIImSqMI7wp2/2Pd4QQQgghhBBCthnb5ovwtDxN10VGEpIDu5KxbEdLTIdzWdqiON6ec69xMMj+viLLnlOzrdWWdYk2qD/PFJHMhZik9Mjt9+rmQuiaYmWpIVrSM626DOkxVcp8m+p6Bx7Js8bnKTokYZxrjZYzAq5UcUnIGZUTc6euVXGRhztuxUdF66VEdb45xIrHU/Rgxr4W++ZJ3rv0aDl0X2l35XmDvk4Tkt3Ave56bBblqXw+BbRuu6+qoBdhMQlzpiOOHFrI8SOl0Xp9GARMKWRe18Owm8+VRjdGHtdMK9pGkXpD54S8dYe80c870ujsuJRCA8B9lnob279wUndj+2H7ek6+b9yTPfi+evfcxnZuTOWq6KwX2gv1aPOLKsym6vClpI5mbVV+ZIste1oSaj0edfiNq+cpvUiTcdg2L8KEEFIlKzP2slsHYu2UCSGEEDImjCO8KaVehI0xvwXgdQB2ArjGWvtSY8yTALwdwBKAD1prX12+mYQQQgghhBBCSBoKvwgbY84E8G4AjwHwEwCfMsY8HcAlAJ4A4FYAVxljnm6tvTpFY6uiDtKOWJqIl334PP3lPCCLbcfzqdL0NZqjJYztgBzPaY/Sr/jk0NrzaZE/aKX40BTyPF0HQm1yJZGjtwFgIAoZqonV8Cj1WjntqZxbw1GHc/hkiqF8bSXJ8nmN3qkm5N757ML2LQxHHgdcr7AHOtnF33FMTdyVbLM3zNLmmkMc7cWJCZuJPUXH4pU8BxaV0PyR+1IOrb1G94Xr25BsWspAW1q67uTzS5R915iil4t4G9bSaGlyEjJbcDwHK3sEKZvWaU4+z3G9ptbhw3xqaXTeW7fcDknXs+05z5gCwL3mst59xImZNPp3PuZGUfjgrx/Y2L7xUOau+2DHnbidweh5oW1RvJL5GfgdU0eZcyx18zkUas9kzb/oUZrMNmVMQJ6JtS++t1lruwDOB3AMwPettTdZa3sA/gbAbydoJ1mHtg9kEmg7bbI5sS/BJKMOL2CEEBKibi/BhETTqOm/GlFGGn0WgI4x5goApwG4EsC3Adwh8twB4AEl6iCEEEIIIYQQQpJS5kW4DeBXAJwH4AiAKwAsw3Vx2AA/YhJCCCGEEEIIqRFlXoR/DOCT1to7AcAY8xGsyaBlcIz7Ari9RB0E6f+SEJT5CHVnXraYnTg3NwzkG00+LEnDmxY6ryyx7ZX5irahyr8Cha5D2mhKz7x9N7IHeuJuHfT9Yyrtxefn3UQ5jj0xudqqkBZGo+3Gmk7a6G3Atd+TIZK07e99lrL9++3Ith+w0813wsJJG9v/cfSuje3B0F0mpQT6qOhPbcPcdGyn3baj5pJgn+3qUBmf+eyCe103X7cr0npumhNOSdpnqrBsLTEMc7m/t4o9ac8euG9993cofJvM11Y2o/Oe+bjYcvO1PTbCHWULKve16N7pQpEYsheW3RQKoTctuXqRNTbWv8Da/uhQbOH1x98Zy8JxwncOZJPzQ79xwMkn05YjPcvL9uXMLr1Tv+aLyowxy3LoutgPTwt5D2/7L3GzPJEnRJkX4SsBvM8YsxfAYQBPB/BhABcZY84CcBOA3wVwWelWEkII2XLomMKEEEIIIZOisHcXa+0XAbwVwGcA3ADgFgDvAnABgH9YP/ZdrL0cE0IIIYQQQgghtaBUHGFr7WXIf/G9FsDDy5RLipELizSUaX4X917lRE7KnG0PE7jJLyt5Dkn4iqbFnlNljPJYmWI4rM1oOXRXSaNXV7N8KysiJIuSti4sZI3auVPNHzGBBj79M5Tk0J/NlTCK4zrMiZSYLoh69yrptpRD31dsn7LDOPle+aXbNrZf//MnbmzfdtSVOsp6Xenk9CRIPrlb1feB3JfSeimFBoDOctbvzUOr2TaAxrEs83AxeyT1ds+7de3IBlnKlef0uueR02tZdyOkm/bghvNy02T4rT0itM4JC+5n790ivJeUyh7sunfFoY7/LhmI86RCW8u15SXHhjapg5QwVs0XlkYrybOcF5Flyj7rqcl/WIzX1+6e29j+3kH3Z9WRXmPkOVoKXwTnOmqogNwOMtxZw53fqctmKKXaQWn0pjDeByGEkKkgX4IJIYQQQiYJX4QJIYQQQgghhGwrSkmjyWQo6gHPJ4EJecL0ycJ02iRx5X3jnx+SDW+W9zhaCu16nB2/Y0KnFJGz6muSToikZ9+ukjwfOyok1D88tLHd+v49Tr7lh5ycpZ21y0lbXBrdKD1fpGzT2c55d5XbcXN1QZS3d8G9S+6/U3qNzuTQg6GrE3/ruVnI89uO2o3t5b67TBaSmw71/vhzZloeyGPTpNfofl/JUoUcumXdudW6cX923gP3Zttnn+Dk683tyM4RUviBkgM3pFRf3qcBWbdzXHV0Q9TlSKNVvYtiX8qhf/HkjpPv5/Zl8+7b+7O59ZW75px8nb6U5bpt6osFZCAvTM8rr1dh//yrUjoZS9FnTWi9iK3Lub/FcS1lPiK8x/dEv+9XbZBpzpiq8nx93Qh4o3ee66NPnziyz2LHsS4S6ip/46QenxTPg0nJpNfKn84PyO3uQZoK9c2py9pJCCFkmyFfggkhhBBCJkmpL8LGmN8C8DoAOwFcY619qUh7MYBnWWvPK9VCQgghhBBCCCEkIYVfhI0xZwJ4N4DHAPgJgE8ZY55urb3aGPNgABcBuDFNM0lZikh+YmVmKWQFUrKSl+iMbryWuUhpputR2S3QSVOFxEqjnTQhicznq06XEuv1N+RdutPJDsx/9+6N7V975elOvk+9I/Oo3D1jp6ohu0Ypc55T0tF5sT/veF52Sysrp9NS673z+za2//SL2XX85WPu7+S75Ugmh/7xsWxW37nizvBVj2R1oTXEUSGXnGUZll+C79+X49FX8YGbK5kcWH8FPuW1WZCBO15/fVbeqXvcQoTGfyDck+faJNcBMXT5fEORzz/pfNeokUUsCg/SDz3B7Yzf+Vgm//7gr2ceyb9zwH0cu97J3fZJuayb5pcjNr0y6RD10NWlkDwXeU7J8dZS5hWMTgtJQp3yEphL1MUhbKxE27cmhq6jStl0Ff03KZllkX4OlhcwESAzTF0WiRpT5p59JoAPWmtvs9Z2AZwP4IvGmAUAlwB4bYoGEkLILCBfgkkc8iWYEEIIIWSSlJFGnwWgY4y5AsBpAK4E8BoAf4m12MI3lW8eIYQQQgghhBCSljIvwm0AvwLgPABHAFwB4EIAp1lr/9QYc17p1pEck/zmFOuxV7epiLQ1JI3ueeRpIemOlEMPlEyz75FQj9o/jpY8y32pAM7l88qm/Z1U1KN0THm67Pn57ED/Z0/c2P7EPx1B63uZd9/ez2Veo3csuIW0hRZ5qZ119p45V6C1o53th7xV+iSCuXkh5sKykCvfs+rOyNuOZlLcNz7qXuL495x8tws59C2HM+ntPatue472sv2uaMN8c4iV/uj5mZPxi+2hlE7qfN6+iJPy5tPS6t2c+0Bu60VhMXvU9M/KvEHf9rc/cjyUy7ThvHDXrCsT6HnRlPe+GJ/cvekxGQh5mffJXAF3nVoW6oBv7Xev4wNCDn2DkEPLeTWqfB8hr6i+tFgpahFJctWkUPrFXpe8N3taTi6fRU55sZ7k1Xg7aVFFOGjv0j5S9F/unvOZL+XyibTIulJ7Nk4yf8oX4ZaXWBqeQjadvt9Hr6OkYgpENdlulLmffwzgk9baO621ywA+AuCXADzEGHM9gPcCeLQx5oMJ2kkImSDyJZjEIV+CSRw6TBchhBBCyKQo80X4SgDvM8bsBXAYwNMBfNRa+zwAWP8ifLG19vzSrSSEEEIIIYQQQhJR+EXYWvtFY8xbAXwGwByATwD46zKNCUk26IamOnwysZDXTZnWUt6B245HU3+9rgdNITft+yWCzYAEzfHuGpBDS4+2g77fo7REq0uareyA9Ezb0hPV0SM6NanyRXmJ1Yiy7W2lNt2xM0tcOUt46T1rD/qZo1/sWsry7drpdsbu+ewi94rt+yz1nHx757ML64p+Odx1O21ZjI+UP3cC/XKgI7y2HnUvsifq+uHhIxvbq313+TvQydoh5dWHuu71Stlrx/EW634VDsn4Hdl05HjXwYtn0Hu6SNuxo4FOJ9vv7J7Pds7O5M/9s09AYzmbJ1IOPZTnAIC452LNB6QH6UGo7WId6KvC5fI29KxZgDv2ci596S73OqR36GW11h3sZNcv55b2WByLs17GyhFnWNhQ1jM0APf6HW/IbsdIqbRPGjxWvZH4JNChZ23efGn8yv0eyF1p+CDQZz7v0kXkurq8qikyt9LI+OPypTCliGlDHZ5DRQn9PNuy0Gv0ppSKI2ytvQxrjrFGpV2HNfthQqZKf1usdmnp9zbPQ1wojc6QL8Eh5Evwdke+BBNSFXW0+647/BBDyNaF9zchhBBCCCGEkG1FqS/ChBBCCCGEEEJqBj93bsrMvAj71K0c4/EpGhZJ2gKLiDmYVzbC863hyDRtqiBtN1f6WW3HVL1y7HsB+akMcyJt+frKXrgjjE17XbftA1GZNBVstd1652QFbX+bpM1wSDgr295I4O6+Ie2WZcKcsn8UifNz8nx1vWKlWFpy03aKsEgnLmSdfe7JXSffz52QyWBvPJRV/JW75px8q/0szRc6CwA6A7ktbSvd1ULa+C61/HO/I+aWtN1cVXNuRdote2yCATVvlX3mMDJEVKyqP7XdVijklkSGSWqKOdNquw2aW8rGp9tacgvp+246HaZrdBu0Tb0bHm30mgAAw9boC2uohU/6GxiItI6aF23R9gPd0XbkAHB3c/Rg6Xxyfut54Js/IRvK0Lrv2H96c6WX1U4yjEohu1i5EwgZ1A8UHRvWyDemofPDYQxHP3uLhzsUdsAqzVn7pD2p6jTZZz574VHl+4i1Xa3STDK27Nh5EIueL752BPtF5ousN4Wdtr4XGU6JTJNSL8LGmN8C8DoAOwFcY619qTHmKQD+Amu/v78G4EJrbaTFGCGEkG0DDfgJIYQQMiUKf1A1xpwJ4N0A/hOAhwH4eWPM0wFcCuB3rLUPBbADwH9N0VBCCCGEEEIIIRE0GvX8VyPKfBF+JoAPWmtvAwBjzPkAVrD2JXiPMaYFYBHAculWBmDIpTiKyKFz4ZM8cuhFJTfdMzc6nM6Skkse6WUF3r2SHR8M3ZHrqxA1McgPTV0lf15ZyfaXj6owRscyOe9wMbs9Fnao2STkwVLypGXNUjbU8IZSKr8u5OXUok1C/qxlnzK8jJR4N5UD27ZI29F27zo5/rvFGD94r+sR+Nkfz8LmfOhp+ze2v7nflUZLiegxJ1SR2yY5T9pyPirJ6lGhr44N7SWlfj116/jC2uh80eGTnONxE2FaISxyYcTk2iHmTDskx3M6voVB5Edhn1x7qM6XVcskPfdbTifKNcYf2ix0r680RgsN9Vi1PTe77gZ5npZB+uZPUWJD6KWm6Qm7U0dy/RwdrmYyP/r0GMqxk9Y77THCHfpCHObCeYkyfTJpwJVK+2TSdSFsIuBPKxLeqghadi4ZhkJdeU7blqGFCEG5F+GzAHSMMVcAOA3AlQBeA+APAVwH4BCAmwB8uGQbCSGEbEFiX4IJIYQQQlJT5kW4DeBXsBYr+AiAKwD8DwAXAHgo1l6C377+70VlGkkIIYQQQgghJJKqZT1bgDIvwj8G8Elr7Z0AYIz5CICXAPiWtfYH68feA+BDpVtZkGgveJW2YnoUkbs1HfmhX0LVDkij9wnPwY8WnoPNvVz3zd85kE2/z/80O76S89Ir5FRBr9HZtvzS1HOdFzty6PZ373bSWt+/Z2O7/8BMyrtq9jn52u1MB9oS3me1Q9jhcPR2aGlK4UHad17ucHN0WlPdFNITeMBJtuNt+YYD7vLyASGH/rZIu2fVLfCYKOOw8L57TI19z5HMZ+3boebjgseLuZYI+jyQarmyKxf05+s5Y6/K8JSnCbUjJQFlvStJVotHQ8xVZ86oSSLLGKh+d708+9s49OTT5wxEp2mP8RLphdodA912eY1izqmBk/OxIy5Yj5ucd0WfPUU9vEadM/4pwXpiZa/O/VegDePUFVtG8Lo8x3MS0wLXH5svZMrki+ygn9dyXz/z5bq1Iu6lFSikGYgjd9/6P8J1n/nGMfnvzODclN7y3YyxnrZjSVFec0pzpojXbLL1KHNvXgngqcaYvev2wE8H8H8DONcYc5/1PM8A8OWSbSSEELIFodNoQgghhEyLwl+ErbVfNMa8FcBnAMwB+ASAd2FNJv1pY0wPwI0AXpCioYQQQgghhBBCNmdYMw/NdaRUHGFr7WUALlOH37f+b2Yo8lFiK8mpfdcSK9XSEtOdc9n+g4Qc+tn/fIKT7wNPyaSy37gnm4ohD9dFGCrtZGMl82YspdAA8NCLzMb2t9783Y3t/ul7nHz93Zk02pFpam2QpxO1p9tGa2S2ICnWN58n3raaFFJmpy9JSoAPCinzl+50vUF/e382xlLyfPeqW9n+1awz7l7J0o4qD9+rq1knzouqlne65e1cELJAMVfnlESwHTnvpHQr1tOv9ijtkzyn/kAakhqHkGPc98ik9b4zL4ILpJAXN937MyR5lirnodjp95WcczlLbB4RIew7rk66v3N+Y3uwN9vW81uaPrTEAA2U7Foq9ztSt6jm2UBK+sXxWM/+KUjtETdEK1Celm1mCXFl6/sltp9iJZyxMmnnnLhsQQl1Ckm7z3xp95zbwn0L2f5O5e79qIjscI+zTru1yRKld2ltKpR6fUs9jrG/hRoBT9tV3tPO9QbMbVwzA7/cPSRrpmyYbBf+f/bePciWozrz/fajH+d9DkIICQFHAk4KYyPARmLGw4gwshkHYGLCls0FPCNsCXwtDAQPDx7LPCwzBtsIhvFchkAiZMAGyRD2xcYCjHGcJwAAIABJREFUheWrMVgWQjAgDCglgYTRA6Sj81Sf0717P+4fe59da63amSe7qnZ3dff3i+iIrMqsrKyszKxdXd9aayO9zxFCyJpRxxAgdcf+k4oQQgghZLUo9UWYEEIIIYQQQkjN4OfOk8IX4YJsdo/UEuktFQAWlrPtOw9nMtdrX3RQlZNeo6UEq2qvgVu3NXH8mJBfzmfn7T1Ne4NWcmiRJ48BgNYa3dhUOXSRclYmZSXvMk/SEff/SCfrmI7x8ixlYuqYZd2Zh0QdR49kM23/vUuqXPtfHh6nDz/jseP0nifP6wbuzOrrZwpYmFLoic5IlYDmvEFLyXNg/3B7shw6Wm4QLheTFJdFjhFbt/QirTxI20qC86Whvb33wo0PtaO/bLy2Hs3GSevuQ1n6e3r96e07ZZzunpPN9eX2nCo3I8aMbF/PaBubolFS/txt2HmQpdUci657xSSWRaSesXLh+oz8O3EN1x6Gs/1WTl1EQh17RhfxdFvcI26gPlsucK6mdQ+sZLmRcSHS0mu0lEIDwHmnZuYDT92p8+4+Ik1dsomwZCM7iO1pPhrXg+qmGZBN22gLqSZgwWdFbs5l6W5k/sm5HpxXhGwiNsN7GtnkyJdgkkboJZiEWQ8/0uoGldGEEEIIWSsKfxF2zl0C4HVi11kAPg7gdgCvx/B/tbcBeK33vpOvgRBCCCGEEEJI5VTtaXEDUiZ80lUArgIA59wzAPw1gI8B+HMAPwngKIBrAFwG4P1lG7peicmf6k5OpikkWl1xYR1T8IDw+ntrpl7FNw9qL8JHhIT6sJDDLhrZVa8/WRo03x5gsTt5kks14tZtDSwtCQ+aW7PMxX1aGt174o5xWsqhZ7dqt87Kk6y4qY3ERadR8UCISaFjebK5Wi7YCEq37JiW92dR7O/0wydeFnnHzP1e6mTnPX48S7dvf0iVe+kbHj9O/+37H8yOedwTVbn5+awOed/sreorSZuRs+YvYXhMYD8Q9gy9nkkdZ42G/tqr+s90Zl90YlPcn1SZdC5vKXPn3Lo7k0M/4Z3nqnL3X3H7ON3bm3mFHwy0NFp6hx6IJ6b9mh2Sp+diJYvrt2YlQSWGGT9FPOs31f0ZBPNiXm9D5XIkrh3alCDijT2ggW41tLyzHxpzE+pcLUJKkdz6I/MS65N9Zvs8JNH90fEWzt6RRU54ipBD//Lf68gO116YzZ9vHYx4SlbnrvdaV8gT+CD8rLT3SuZJSbqVRsu5HpPqy99aMvqAXTtktzcjtyN5PBaYS7E6Umkqc4nVG0vr6bc5qZaqbIQ/BOC/AvgRgN/03h8BAOfcNwE8qaJzEKIIvQRb5EswSaPqEFbrGT4gV06q5LnPuBxjaI6wcmjjuHLkSzBJgx/VMlKXbJoKkfVC6Rdh59yFALZ47/9ytOv7o/2nYiidvrjsOQghhBBCCCGEJJLqOXUTU8XHjtcCuFLucM49AcCNAK723t9UwTkIIYQQQgghhJBKKPVF2Dk3C+ACiK++zrlzAHwBwAe99+8r1boNSKod0GqipC6RsA3ShlLaqiz29JX0hS5yUdix2OvVdWTlrG2pLBeT26hQQOJkM2aUD+azgm0TB6m7PbMPlFktE8+jJeqU57L/fLN2kyk0EgsWsQsuKvGKhfiRQrt+L3wCWYe0b7JhlgbiBE3R4N7TtP3a377vgay+p2ehcOa0ObcOtSPtrayNlbiPTWVHtvIwF7m86HHhOtaTvKyRGJNG2aC2JtjQnqjPDOKWuCnq1pm5qcOjZWNG2gQP8yaHR8vN4cSFepC4uIdsFG2XFZmrMXvFRsBmFADaBUK+pNoLR9cO6f8hEEppWEcghIyxHZbPrNS5U4VdYxFi9zv0TAbyoaUmHgO9lnTFIih9cwDAd0WIJGkTbPNkiMOuvT8FJOqhUHOxctMmNIVj4fTs86EdmD/zLV1Obqu1zZxqKfDbaFEXU3NJtck+U0SaoZQIKS+NfiaAO733CwDgnNsB4AYAv+u9/3jZxhFCCNm4hF6CCSGEEFISGriflLIvwmcDuE9sXwLgNABvds69ebTvs977t5c8DyGEEEIIIYQQUgmlXoS999cBuE5svx+bOFTSSlkrmXRRCZ48rouwpk/KpGIhdEKhAJbNMSHpaMwzrZLtGS3ZjDiw2bR5k+uzcslQyCRbruw/46bt56BI+2Ly3ZiMWEv1Jh8PaCnqnIhk03vyNlWuc3q2vWVWHmMltaH2RLZNKClJkVBKsXOnygqrlgiqsRWJj9GcvDtOot603QqHGmpGJO4yb2ZGn2t5lxg0Ijxab+8uXd+WLJxbY2c2gGZ0lLdC5g2DQVhy2AgsndP+x31qyBcp55w1nqzbgXAwsXnflfPeDCB53m4gTB4AdDFZ6hkbj7n+TJTihqg6lIvts9SxIMeT7BfrdFzJaIW89pElfff/+aFs7H/zgK7k0e7k42yIQ/Usj/Rzqvw2tNZVcQ+aEQl+qN9z7Y6ZGYgbIeXPO2Z0bzx2Ptve1s7SC119fw4sZSc4upzlWdOefmB+RyX468j0hpBpUVX4JEIIIWRFpIZZIoQQQsgKoTL6pNTFXxMhhBBCCCGEELIq8ItwTbASoir+QyFlRHHJqtiQ/z2ykhqR2ZPyJ/Mfp25IohM570DJqU19iXIo6WVWeUi1nSk0fY1esU9SUg4d8xpdB6qQXAbHSK5cxHNyosRd9uHMbLax3Ujc++LeyWPaRiprJbaT2mPblBv8qoHhrFj94XOlHSOp+5fU3DwI6PEaDX0tsfkjTRzakfvTaGQ3vDc3P07bPpOSeTnOrIf4ZqIZRFlyEvxEb+Ux1Doo9ret5Dkgh455up1rZQt126yxchxLGa01e1nsZWlZhTWp0dXLa7IecdePF9y4zFdOinCWvH7rybkpozcoKbPuTemV2HoJl3WGpNY2T15XzPwk5k1cH1PtfUz9XVQU2btyLj1mTv+wOe/Uzjh99o4s73tH9f259eFMur4k5kvHzGF1/2s+9gmpE3wRJoQQsibU/YWeEEIIWa8M6DX6pBR+EXbOXQLgdWLXWQA+DuDPMXSYtQPA7QD+s/e+k6+BEEIIIYQQQghZfQq/CHvvrwJwFQA4554B4K8B/BGALwN4kff+dufcJwH8OoAPVdDWTUXVHqVLy6RNnmqTkeHITaHkicqftBfhmNfocB0S6XnYetOEkjKbtqfWH/Aka//5pspVIKEOHbeakuxUT5txuVuWznnaFhXOCDfe1vvzQErcA/1s64v1k25veL4U8bSZKhlfSZ5ktb6s5pxBB8qtZB4pb9BSQW3Pre6rGCM5j+5p8lg5FlpiLFlTitYamT4U8SidXM5sS6n0rJA/W2n07tlsRZdeb3cad/sLAW/Dhzvhp9miSOfGlZDedsXu3Nquxo/JLCAXTfbovuKaT0JEXizXI/UDzsjJ5dcHFfHBlJP3PubsXR5nZdjdQHt71rNxoG5L0HQkfEgy2qNyRHcesZQJWNvkzyXq2GLm0llCDv0rN2be7a994QFV7lsHs3IttTjxSx8hVVCVNPpDAP4rgOcC+Gfv/e2j/b9V4TkIIYRsICiNJoQQQqYEpdEnpfTHRufchQC2eO//EsBTATzqnPuUc+7rAN4F4FDZcxBCCCGEEEIIIVVRxdfa1wK4UtT3IgDPA/CvAK4G8DYA76zgPJuWqj1Kp3onjcoykyWmafWFpFXD7cl5qV+Tmi1gIDox5sk6lZgculh99fqvXX/QSPaomSrfTZUZqr6NyJoHYgDE+q9s11bttXRY58qPWauvp7L/ipgO5JD3FMAg0Bmt3P0OtMlK4SMeyUNtlIpDK8GX3qqjZhARk4siFJU5h+qQ89nWrTxKi3Jb2nqFPG1Ltv3sUzKR8t4duty/Ppqd4BsHZsbpbi4iQHbmqAmMaGBfeCzOmy2EO01dc8B78UoIHVdkbueIOpSWMvHJXsEB3U99kdm1996GfQgQkzUPAs/oWKSIqLlIUosK9nVi38Y8d/cCUn0A6AqpuYyA8ajp+LsOZwLrTwk59N1H9J1c6Gbbvf7kfh5u1+s3BCFV4Zx7B4BfHm1+znv/26OPsFcC2ALgWu/95UXrL/VO5ZybBXABgM+Odv0QwC3e+3u89z0A1wE4r8w5iIaBn1fOoHIDro3PNMJKEGIJvQQTQgghpCSNRj3/Ehm98P4cgGcDeBaAn3TO/V8APgrgZQCeDuC5zrmfL9pFZb8IPxPAnd77hdH2DQDe5Zx7ovf+BwBeAuCrJc9BCCGEEEIIIWSd45zbDWD3hKxD3ntpUvsggDefiD7knPsOgH0A7vLe3zPa9wkAFwG4vkhbyr4Inw3gvhMb3vsfOOdeC+BvnHPzAL4O4C0lz0EEsY+bRb8Wp0pq5FfCKuSwMalVihzaepwN0WimfxUuq2i0x1sPxlW2IVYuJqtMkVyuRBpdllRPxHlp9Mrl0DkP1YFyqZ6xU0mfL+XOU5TkuRQpl78/Jy/XaDV0ucQOCDiQjlYRnRNS1mwKNgNeo/uDvIx6EoNB2pzuD6br1yQkk7bbwoF2ziP3tnZW7qwdWfo//aP+PfPxCx4dp7//aDajd8/28b2j2c+O472sDin73N4eYFHKQEX4AXkdsw3twVh6ka5aCBSrr2rzkJyb4kA7mnLwD/S8UM9rI38ODdvY+It6eQ6UKyp/rmIdDJ+7WN9KkwEph7aPyY7o62NC1rx/UZf754ey9LcOyjmh271feF1fFHndgtE19O+plS84TaTNrWZj7Z5nZF3wRgDvmLD/XRDmtN77b51IO+eehqFE+n9g+IJ8ggcBnFm0IaVehL3312Eof5b7Pgfgc2XqJZub1B8LqfaKlEZnpIdhWb9PsNR/JFQdkmY9kzyX1qhcHUl5CQaqH4/rGfkSHGMx0W7VhvHZzKT+z3UzmFdVbS/bXsfPw6qZqv02qZ76TvgPALhmwv6JDpZHYXo/B+CtGP4vap/IbqDE/z4Z2ogQQgghhBBCyNQZyZ+Togo5534awGcAvNF7/ynn3AUAThdFHg/ggaJt4YswIYQQQgghhJDa4Jx7IoC/BvAr3vt/GO3+8jDLPRXAPQBegaHzrELwRXgDMQ37YVV/AblRqh1QyCZ40vYJrKxyUEBnmbNPTQ3JtAlkjGVJDWUSs8ctonWpOhJVqr1wUSlY7Lgi0uHVlBvH7qMkFIIpd68SjRRjIZ1s2KXguWSeOG/TLJahkEnW/j/VLn2aNKztb+JxoW7vme0lIVn+14Vsv7QJBoD7FrIjl3rZzwxrctEWJ55pCRtM+wyQ4ZPkfmNPqUJEGePxXmB8Rv1TTD4kd1zqs7GILXGq3W7O7tvYuGb7w6SHzCtm+6vrSC1XfgKl3qvQemaV+u0iTeronpf2vvuFcbvtP2lz3JE2wqZR2k47bQzXnZWERyMBahaaswBvATAP4Ern3Il9/wvAxRh+JZ4H8HcAPl30BKVehJ1zrwLwO6PN6733b3HOPQvAVQB2AvhHAL/hvbeh1gghhBBCCCGEkBze+zcAeEMg+9wqzlH4Q6FzbiuAD2IYR/hcAM8fxXv6BIDXee/3YWjAfGkVDSWEEEIIIYQQQqqgzBfhFoYv0tsALACYAbAMYIv3/pZRmWswdIX9oRLnIRWwVvKYuBv/yXJoe4yUPlYt+6xCzjpNmXQVHmfXs6fomPS2iOKnintVTWiPcsfX3dtyaiil2HH2GBvWaLx/Be0KnSuUBowcuoLQZiFWMv+qXnPU+iv2d82D44AI5SJDvty3oNve6bXG6YVuWOY7JyWhooqeKSjzpLws1w+pctu0YsE2DLcn34TU0DVFzyvR62Oa5DefJx+w05VRFjKvqrjPihzXNBNaqpLtWAqFJ+qb8BVS8pw696Uc2p43lJf/PVVg3Ca1jtQS2vGdlMJfhL33RwH8HoA7MIwlfC+ADiqM7UQIIYQQQgghhFRNGWn0MwH8GoAnAzgDQ78aPwf9P9lSsZ0IIYQQQgghhJCqKSONfhGAG733DwGAc+4aDL17VRbbiawPinp/jMmhQ2iZ9CCc15+8356rqMQ0JH3M1SfaYb3MVnneokxTDp3q8THVc/d6VvgUGd8blVTZdGysx/qpiHw55Bk6V645ef9we+UDtI5jWkosO8Yz7ZFluZXJn490jMfiwLoipdAA0JqRx2RpKyPtD7KOlx6lrXdpJWFdRROG1GdZFV7g5SWGPGFbrNdjiZX91pkq1tEiedZ7uuyyZdN/My0ph5bSf/0DoCPmQruRJpMu4tE8JIW25cgGpY4PmZpR5qf5NwBc6Jzb5pxrAHgpgP8NYHEU/BgAfhXA9SXbSAghZAOyGV78CSGEEFJPytgI3wDgkwC+CuB2DJ1lvQfAKwG83zl3B4DtGHqWJoQQQgghhBBCakGpOMLe+/cCeK/Z/Q0A55Wpl9SD4p4XV+6VUGK/EoW+GuXKBeTQPaMhKuKFOqYek9WvpgqliKfoOnqGtsQ8Ra8n1nPbV4tGo9hX4Sq8N8fk0NOkDkq1qJmK0BT3jKa2IzSiC4HjAWA2U01jezvL3D2rF+O2+Ff8oY7YOGZkpOIEzZjOtwC5vkgtF5BDVy3LLVpOkvr8Sp0vdSH1t0Eor28Gbug3RGwtb5nPSf121lHdlkzrcrPyN4pai3RHNxKf2TEJtGrfKnpWJ2vPoI4Tt2ZUYLVICCGErBxKowkhhBCyVvBFmBBCCCGEEELIpqKUNJpsDMrKWUJS6JPVHZKTxaTR1lN0qFxMDt0XrjZj5wp5lQWAhtA3NsW/k6ycqLVKqpS4TDrcZ1XINFPHj2xHbMysZ4rMpWlKIlezviIUlUbbOqZZLuQp2nqJDq0XVSjTpj6HZTrmlVnIkmWbmsatrpRG75jJyj1tly545ras4L1HM5fUnd6MKnd0OTvvcdmeFVx7qmdnXa4xcb/dTjW3KfIsq5q4/DmcWfU8q4Jks6nB5Gd+34zbrtjuicHftW6jBVYaPTMr0mIY56TL4td3X0mjrQd2MefCzSgN45xuUPi586Swi8iGpw4/+MnGh/ZRK4dzkxBCCCFrRakvws65VwH4ndHm9d77t4i8FwP4U+/9WWXOQQghhBBCCCGEVEnhL8LOua0Yhka6AMC5AJ7vnLtwlHcagD8BsDH1j4QQQgghhBBSVxqNev7ViDJfhFsYvkhvwzCSwgwy852rALwLw7jCpAZULdssYuNZRRuUjZUxagnZbPWMoZu0H+5HbIlVeBXzL6OW0nROthfO1YeVY9eLVJs4Zb8Xsx9ObEfMfmia4Y7qHkqpLm0K28pNt4Gh668iTEwVxJ63cmzV7Lm8poRsZIfbwm5S5C3n7GezHTPNLH3mNr3ivPKmHeP0xy84ME5/86CuL7SGxex2qwgTE6svZBecap9qCYXuibUpRqjPojbCzXDlMft4SegZOm1i9tchu2Br+9sVA3lpKUsfP67r64q8mXk9prdtl+fNOsCGQerIzmmJ57X5pdCG7FAkEXuu0xaYEE3hL8Le+6MAfg/AHQDuA3AvgJudc68H8DUAt1TRQEIIIYQQQgghpEoKfxF2zj0TwK8BeDKAwwA+AeByABcCeCGAM6toICGEEEIIIYSQFVBFWIENThlp9IsA3Oi9fwgAnHPXAPhNAKcDuA3ALIAznHNf9N4/v2xDycmZtkwzVQ6d2o4ioQ/i5UQbRIikmPxpuWvqELohGTZlxsyUwUzWF1K6ZCVjsi8asu4WgqTKyeT6lg+5INLROlJvViREVuC8sXGwnkMp1UUOvVpUEd5pPXmHbtQklkKRZlT9m8fKKKWViQyl1DXryFIvyzuynF3JPSJEEqDl0N87mi2KRzv6QjryXFHpdrZtw9X0A+Xy8urJefGwfjIdluXG5M9lwzFZ+onSaGUCNAjnKWMgUzD8zEobkKnPvHhfRMJRSXOowO8EQP8eWFgQz/W7Dqlyc3c+Mk53zzlF5S3s2zNOt8Vzvm1iKUozqq7ogLbp277qw7QwapQ/E5JOmRfhbwD4I+fcNgDHALwUwFe89y8GAOfcXgA38SWYEEIIIYQQQkidKPwi7L2/wTn3bABfBbAM4FbQORYhhBBCCCGErC2URp+UUnGEvffvBfDeQN69APaWqZ+cnI0i00yWXwYkToD2AC3TPSN/XlzM0t0Fmyl01HOZrqm3XU+V+YAcOuJ0sxJCcmi71klZZaoH6TgxTZqQEqrz6mIbZaxWQRVy4yqxHthDlDV7OFleKkqyGZFzSoqO/Tp6lE6VTau5H9ifz0urW0qUG0aHK5fVh45ntX+tN6PKfedQVsmhpazcw0v6Co93s/pDMmnAyp9N3iBULmzOEvcGLdNhz9DSS3Gq1FpKdmPlUtEen02euOFWTi3lu3JcDIy5kapf3bqYF2opYw8WK0S6jF2XU/0ufgvM+EdUuee+7Snj9Ffe+12Vt7R39zjdEx0a8zoe9dQu56rIs3NY1sF3H0LSqYk1FCGEEEIIIYQQsjqU+iJMCCGEEEIIIaRmUB1wUvgivM5YTXlpFV6iV7O9A3GyntC+LRv9nJRDt+48oPLkdm/fY7JjRBoAusKNdFt5BTUX3ErrwyJyzphnaOl5MiZ7bAS8Rkc9ruZLi+RkmfR6ZjNKuotIt6My0go6UUo4Zf2rKV2Wcs46SqYt2pRicnq4Pdl8IiYZk7d02Whq5frREQWPLIc7TUqepRQaAI51s5Z0hEfqrjlvLyKb7ga8Qcck1HFp9GT5ct/IhtW5Yl6je5OfX7lyBRZWKXG247YlTmDz5H0cROpQ46QfyrCkyaajNVSsqVbze16YRpnn/1fek8mhu057jW4L9X8Va0RI8mx/n+lIDJOPWe+s52gTpL5QGk0IIYQQQgghZFNR6ouwc+5VAH5ntHm99/4tzrnnAPgwhnGEfwDgVd77Q6E6CCGEEEIIIYRUx2AjSQKmROEXYefcVgAfBLAPwCEA/+ScuxDAOwC83Xt/vXPufQDeAuDyKhq7WdmM0swUCnveFd4gW3cdVFk/dvnTx+lv/8F3xunek3aZOtsindaOVGJyqpAX2LZxV90WdbREXtt6i1VSK5kzUNIjJTM0csSNIoHeDBQZq6meblXaLFoxiWmInEyzF5Bwmgd9yKO0Hacb5fdBI7AmWOTaYdeBWXGgXUskoWeRlShLX/xN0fHHjcFaaI3pmPqkHFrmWUm2rCPmDbqb6M035Bna5oU8QwNhOXTPaLd7gUgH/X64nDxvw9z8hnoGZOmmGfjiURaVTUtjw9wzqjkxmZt0so0x84bqJc+T001jutSeyc67ZYuQ6u/bo8p192a/B1pzuuPlce1MXb1h1htCNhplpNGt0fHbAMyM/o6P9u8cldk62kcIWUfQ/oYQQgghhGxkCr8Ie++PAvg9AHcAuA/AvQBuBvAmAB9xzj0I4GcB/K/yzSSEEEIIIYQQkkSjUc+/GlFGGv1MAL8G4MkADgP4BIYS6JcDuNB7f6tz7k0APgbgxRW0dVNBOfTJsXOpobRHWuK0uCg6VHqDfJqWPH373XdkedJTpDjGnlulK9Y/5b27ynRYEjkj5I3zrSw9ayXUYlvLGwdKgrgopIn2Y7E8d9Xjdq3mAedfGiE5tPVsmyqvDs0rICwttLvXzqP06p0rRNNInqWJxGxgTQCAObHdjlyHlCJ3pETXlOvKPOFu2JYLyZVjHp+jnqEjef2A1+i43H+yZ2gAGKhrTKsv5Bka0HJoGemgu6zLLXekvlpUYgZgc0ZIdMVNbbV1A2X7WrGbL56pVlLcD3iKzn1pkc0NyKSB6c4luY4MTAOllBnzov9mdIMG/ezApv5pABFQQh1nyymJdtQcKrQ//JBaKxl22NSKkPpSRhr9IgA3eu8f8t4vAbgGwPMAHPfe3zoq82EALyjVQkJKol6CSRLWRo+QaVC1fT0hhBBCSCplXoS/AeBC59w251wDwEsB3A3gic45NyrzMgBfKdlGQgghhBBCCCGpNBv1/KsRhaXR3vsbnHPPBvBVAMsAbgXwRgCfB3Dd6OX4IQCvrqKhhBBCCCGEEEJIFZSKI+y9fy+A95rd14/+yAqgPUV5QuEiZozdU297NuyXpR0wTJgkYRc8s11PFVmnPFfMrrGhyumCRWyimup6bTiUbHv7TGaYtWtGG6Zta2fbC92sgYeXtVhE2vlZ27uc0XAC9Eq9foiFPgrZBVv7x74K+RK2UdQ2wnqMtMQUlHPOhmqSE6MOdrtriQqZJPpsm7ETfex8X+Rl6eM93YEHlsQ6INYIaxMubXrjdruTj7G3NGQX3E0MkWTzYmNahivSNvC6XKiOnH18wC7YluuJctIOuLfQU+Wah5fG6cbxzIB4MKuNUAc7ZsfpzrYs3bbXIexYbdiisM2wCcPXknVk+3Mhy+RGwF7Y1lFkDsfCO8nztsx5GwGb3pgJR+6ZL0ORyTXL2FXLciHfHyuhZh/ZCFk3lHoRJoQQQopCG2FCCCFkSvAfJCeljI0wIYQQQgghhBCy7qjVF+GYPJiyj/VJqjv9uPxJSB2F5LdpZZpSKiwkXW3z2Wle1Dczo6dAr59tS9mUlTW1ZyafKxfSKRIOJkSRsW6PmRXhUKQc+qce21HlfnxPJrv7l4OZFuzW/bOq3LGuOEEvrYH2fofk0HUxC6hLO+pAqC9ioWGk7FOGggHi4WCkHFPKodszthFi/Ihpm5tzttGbGLkuSHOJXbNatPqcU7Kb8rRd2Zrw3SP6f+W3PpytCwviPloZckesETEps5Q8a5m0KqbWDpln15T+YHIaCMuXrRw4JIfOSahDUut+pL5AKCUA6Io5010Sz7lDi6pc+84DWR13/3Ccbu19nCrXO3t3Vk6eZ8sMwliTnclzM3eUKDeIPejWaHKq53BE8twIyKZXJI0W9TfFBGyaz07BUIi2PqSVqxu2fVU/X2Vf0NQqjB13JA+7iBBCyJpgX0IIIYQQQlaLUl+EnXNvw9Ar9BKAa73373bOvQx7v7jXAAAgAElEQVTAuzD83989AF7tvT9YuqWEEEIIIYQQQkgFFH4Rds5dCOAVAJ4LYAHAXznn/hOA9wB4rvf+fufc7wN4J4A3lG2olFXUXRKy2SgqgZGSotQPQ/KYZitcTjIzoxsYk9SEvFXaa5RyaCk9yXmhDGgupu3NVp5WyqSfsUd7IL3obzMv2Z968eFx+usH9A2R/TSIyREDx1jqIEOuQxtORpE5spqE5KbLRtu6tCikk0e0PL+xmGlCB/PZI6m3U8vzMS+OERNyYD3O9ifLNNebB2k5l+wyIvNiy2BojG8xXqPP3pmtCxd9Yc84/amf0//D/tojYu0U+62UuSMkz8sRr9FSUq3XDl2uH/AoHZM/x2T8UomQ94Q++ZjUc8Uk1P2Y1Fp6Ue5mG41j2pZAyqFP/d3njdMPXnGTKtc+ZUtWx3zYlqAb+Rmo5dBCJm2eqf2AB2g759TvOJVhzpuoVYzJtdPQN6GR+JuiEVlXQuZQ7Zw0erLM2V56qhx6mvJO6/2brB/W23NvLSgzd54N4Ave+yPe+x6G8YMvAnCZ9/7+UZnbATypZBsJIYRsQORLMCGEEELIalLmRfhrAF7knHuMc24ewC8AmPPe/xUAOOe2AHgbgL8u30xCCCGEEEIIIaQaCkujvfc3OueuAXATgAMA/h7A8wDAObcLwF8B+Ib3/s/KN1ND79Lrk1QJdaoH6ZysSXqNjsizpKypiCR7ZeeafN6T1Z9CTCIopYkLy1kDv7ZfT/m/EHLor+zPvIke7uj/kS0KL7Ado5PqKM+vkyWMdaGObVpNykqtrUdc5Tk34jV6cDSTd7bv0nLb1l2ZF9zeUzJZbvecU1S5XjuTSg/EMLb3dCN6gLRmBlIuOYjMObkt5+nhJd1L3zmUaUL/4mez+/Otg3q9OCTWBelJftF4kl9S60VYGl2F5DlcLuy9We0361lIDh2VWhdYWKLzT3bnjNbrNp+ceYd+4Pf/YZyeP+NMVU4awUSVF8J7dRfao7R8ZqkIEFbKLKW9AdMEW64IqVLo1PMUlVbHokFob9AyrW94O5AX8xodI/W3b5FnoGxDUZl0agQRUi2URp+cwr8ZnHM7AHzGe/9M7/0LMHSY9V3n3OkAvoihLPqSSlpJCCFkwyFfggkhhBBCVpMyXqPPAvAx59xPAdgG4NdHf38D4Drv/R9U0D5CCCGEEEIIIaRSykijb3fOfQbDL78tAO8H8DgAzwHQds790qjobd77VfsyTO/S1WGlPKlBy4tIYFIlm1rmYeSCLSkXFKWMlqdI++xYUtKoRBl2TE6VivbQnFXSM9d4TOji9gsZ5M0Pzalyt+3P5KaPdnWj9i9lkryFblZHx8ggY/JBCSU60yM0f6z0LzVurxzvPVmfnQi9yfXZ00hpZvPuTHo7aDTw1N97xnj77iu+lR2zN/NoPqxTSKPVCSqQNybO4WmSX1/DcsmQp/a89+YsLeXL+400+p8fyvr268Iz9NFlfeKDnWxNeFSsCce7xpRCyKHlemHXqXTJc9o4ixHyBm0JrWcxaXRsfxHzm4Gwtxns0N7Te2dl82L2lKeP0znJqhg0jcNLE9MAMNiVPRNsHcut7NwyUkLLFhTq7bXybh+bpkWmcOyYkPx5uF3OG3S+nGxTEQm+NatIOy702yjmwX6tKPpbdTOwWs+v9UypOMLe+ysAXGF2b0QTLUI2FfIlmJBpIV+CCSGEEEJWE760EkIIIYQQQgjZVJT6Ilx31pNMOtWj8loi5SdVyKRDecWVHJM9SjfMx00p3bL/CbIeRMd1RP5lFPMMXVaVkh8HwgOr9Jrd1yeSEsnlXtYBB7UqTpU7ZuSNnWVxv6ULUuutOyArbZpBPUi8x5tByVPEe3NZj8/TQMn2xDhomvnS3Zp5o+0/NfMMfecnH1BSaZk3mNePp6YaZ5O92Q7bMdkcoY7PgNRnVG5ZGqStAx25IRwHd/t6UbRzf1zOjDNZbkF5jTZrhzhXT8jnYx6aV5PY8zUkh85JnqV35ALXEYsw0JwRkQ22mJ9pu+fHyZ6YI9YzdPNIdveb9x8dp5cefECVmz37SdnGPn0fe1uz+gdizOQ9clc7udK9Pq/8+FTJcywv1ctzejnZPt23ZdetPsKDM3bfUk3IiniUXg+/dzcKm+H3VFn4RZgQkkO+BBMyLeRLMCGE1JE6/hONEFINpb4IO+feBuDVGIZOutZ7/27nnAPwYQB7APwQwMu99/y1QwghhBBCCCGkFpSJI3whgFcAeC6AZwM43zn3iwA+C+A93vtzAfwfAG+roqGEEEIIIYQQQk5Oo1HPvzpR5ovwswF8wXt/BACcc58HcDGABe/950dl/huA3aVaWBEx+9Q6EmpfXWwpytoLD4+bnGevMRwaJnwuaUOYC+Wi6jP2ronOkkMu6auwfY3ZDeq+ESFKIrbE8phuT5daXMwyjx3LLHwWj+sK22KlmJvXjZqdzbbbsv/apm8T+yz1Hm8UytoL2+Ni9el7EDGWF8ZesbBf2iY8S8/M6HK9HdkA6p5zSpZxzinKtlHaBTe368eTHINNMc5ibap6/KzmeNTrqr2RMitgLwyg35froFgvcg+SyRe2bG2OxfZxEVqpa4yJl5ezdE/kxdbiXGiuULmK70E8bNPK6ysSMhDQY7ol+nl51jq5EHWIPsudVtgILz5w3zh9xtt/RhV7+N23ZBsmZFmjpB10KnEfHJNt/vPlJqctqXbAen/YbjcWFqkty5m6W01hPxw5V+i8sXEm147cbwiVTrMfLhJykpD1QpkX4a8BeL9z7g8BHAPwCwBeBOB659zVGL4ofwfAb5VuJSGEkA2HdfBDCCGEELJaFJZGe+9vBHANgJsAfB7Al0ZZLwDwIe/9cwB8D8CVpVpICCGEEEIIISSZRrOef3Wi8Bdh59wOAJ/x3l852n4rgH8B0Pfe3zYq9kkAny7dyimwnkIrSaIhNtZIsmKlPGVDK60ojEhJQnLd4vVVWy4m6e9GOqPfl+mskiUTPunokazg8e89Ok63v7VflVvc95hxevnsnSpv+44s3ZgTMjYjw2625MWkySA3s0waKB9aqWh9Ejnm7NRutrIdLVH5YEYXnBfp5ZbUTc8E22vl1W1Rp5TgyzbYOmL7Q2G/UteEomYQqSYhOs9WKExT5DFmXgn1JfS393ADpcq5a6TRy6KSjrDHWDa2GXKdkeGTLC1x7+T60DZq4JDsNVVOHSN1TgzMDYqFVkrBjjMVakeMzbb5ldbFZPudhmlff/vsOD139t5x+qF336zKtc4+I6t7q550g1bWEG2OEJ5zVT8DU+soIofOhTSKhDtS5US63RwE85T82dTXTpRQh6TSdk2Qt7+vTNfMgfK4iiXPRUIpEbLWlHkvPwvA/+ucazvndgH4dQC/AeBU59y5ozIvBfDVkm0khBCyAalLPGRCCCGEbD7KSKNvB/AZALcDuBXAB7z3/wTgPwL4iHPuWwB+BsCbq2goIYQQQgghhJCTs9beoTe612h4768AcIXZ92UA55WplxSjLrLpVCmPPiZc31rJpqug6gnfD8jx7Jc1KYfuCTljd1kXPC68Q7e/+fA4/e/esleV+9If3TNOd56wQ+X1tmYXKSXZA/Nvts0mc66Csh6lY156tTzW6nJFWtzTVuxfp0Lf17BeVsXEbRvJc+i6muZc8tzNgKzZbldh9rJWYzXuPX5yo4qu8/K4nrjfVtbcyRwRY2kpyztuvMwfP5pV0lgQLqTntKy3tS3bnhNmFVZaL++3HAcN6/Vfjn1USxXqhVj75DU2VTeFZfEhmbSlJ/v9ydozdHdr9jNwsHNO5bXFPVH9buamHJ9Srp7zjJ1qthDwFJ06F/Py4lC5NG/QK/EaLeXQUvJsJdQyb1YeY6XWAfly3wzIrvQeL0wa8u4I5Q+HyTJpQHuUDnmQtm0qStVeqYtENSGbl5qZLBNCCNksUBpNCCGEkLWi1BdhQgghhBBCCCH1Yj05A14r+CK8SSgyGaqWU8cCxReRTce8rCqmIOWpG/bL2kBJqCanAe21tfe0PeP0l97zPVWu5zKv0VbaulrS0bjMd+MTu97Ql9WY1+iwTBpQ8rmIK9CQVDrnVVZIbK1kPkTey7OQ50Wk0UqmqSSb4fpDUsyTtWm1qELyLMmbUsi0kN4aXaU0rehIafQR7SK+/e3M63zrzgNZ3U/Zo8p1xZqztHsWIdozkzs+9d5Pg7JqhlwIESFnbTVjlU8eqz2tp0a/nZ2gYbxBSwaiXGtWd5rsd+XhOzqXJqctVYRQCZlBRD0+RzxDh5oUKxeTYTcDnqEBYL7VF+ms3FxL33t5nPTovmw8ui+aKA1jTDk5pVPXFWnqMkiNCmKbkXYqQlYdSqMJIYQQQgghhGwqkr8IO+d2ArgZwEu89/c65y4EcCWALQCu9d5fPir3LABXAdgJ4B8B/Ib3Pm+vTwghhBBCCCGkcjabcq4ISS/CzrnzAXwEwL7R9hYAHwVwAYAfAPicc+7nvffXA/gEgEu897c4564GcCmAD02j8WS6FLUtKCLji8mmdd1h74Wp7amjzURqn5WV41kp65YtWWcce0rmTbT/lF3oCumjlMzNzxv5nFDkVbHo1t27dF28s0tC/VRcTj5Zfjkw8zR0r3LSW9FpMRl/qiw5JoENSS5TPdOe7Liy5STTHi8hz/IxL/N9IbHsGbllV2wvC2fQzaMdVa7lMzn02Zc/Y5y+513/osr1ztie1b09k0bnzC+kJrQdvm+DyLhYq3U/eVxIr9ERj+7yOqSH76a5wH5LzjnZZ8ZsQWy2zC9CubZL5bV9jjRbk++J7fOwhDrcppjUuqwcOiaJjP0m0euPuT8iHfIMDWg59I6Z7EY+dl6LiLe3s3LHelmF+xdt6ydfTe73j/oNFfAgDShrmY1oTkbICVKl0ZcCuAzAA6Pt8wDc5b2/Z/S19xMALnLOPRnAFu/9LaNy1wC4qML2EkJWAfkSTNLgj4WVQ6/RhBBCCFkrkr4Ie+8vAQDn3IldZwB4UBR5EMCZkf2EEEIIIYQQQlaBOqrq6kZRr9FNaL1OA0OncKH9ZBNRVoIW+7KWKqHWx1QfVL16j9pp54nJT0Plmkr6pk80J6ZrU+rddmiJpJSbzphVQ9YZ8+Y7TYlpFZQdt3WR3PcTpcYxwuMs7F26IaSTOUl2P5xXpE1qf8SDrd4fll+m7C9abtrE+jMkh+6bhUXJoaUH6Z4pF7iPgxl9E/pn7R6n73nnN7O6z96tykF4LJa/HPpGki09jds2SVlujF6BeVHU/CaFaBuCMmlgIDpezdOovi98MvWsMBcs5dBKJl3Qa3RDPR/CEvdQHTEPzXp/WE4eI9WjdCNSri0k0KE0oL1DSzn0805dVuX27sgmw71Hs5tw635tP9AR3qFletrPJVk/VVFkPVLUa/R9AE4X24/HUDYd2k/ImlH1S/BmwP4QJYSQupH6EkxIGRhehZCNS9H5/WUAzjn3VOdcC8ArAFzvvf8+gEXn3E+Pyv0qgOsraCchhBBCCCGEkAQajUYt/+pEoRdh7/0igIsBfAbAtwHcAeDTo+xXAni/c+4OANsBfLB8MwkhhBBCCCGEkGpYkY2w936vSN8I4NwJZb6BoVdpQgpRhU2LtFUpYlc8rCPckGnaQRch/w82YSMkbJHshJf/mZP2YAPTwEYzbM8l7cWkmXEojE1Rqv4nYl1sequmyHWlOnLIhasRcyQWBsmGXdJ1JJ480g6dNzmzaIikIuOu0D1YQT+E+iwWmkralg7MDe8HbIlj90atF9tmVV7vKZktcP/0bVm5uZYqNxAhk5o6K9i+hrIlNuuUtDtNtB23NKZo8xjz8RC0WTf9Mgg8l+w9DbYh0b4X0Ou5XOetJF3lyfqK2hIH7IKLhEgCwl98YrbEzcD+XB3hrGDdgA6ttHMma4e0CQaAX/mHU8bp6174yDj97UP6ad6W1x9pX+gaad9LNitFnWURQgghpWD4JEIIIWQ6VP1BYiPCLiKEEEIIIYQQsqngF2GSTN3/ayKVYUVlr2Ul1akeqm37QrKk1HKp0k4rPwyF31iJLLwRkMLl5W6T5dWr6TdhPcuhp9n26DiLSEVDUs+87DNNQl2EKsIdVR1OZzWJ9buSQ0fLhfNCSNnszKzumOXd89nGDi2bVojwSa3IAya9fVI3HdbHFhmDVa9TeTODtHOF8uySnRoerBlZi/WzI2IeE3jGFH0GhOTQsWdySPKbLxeWUKdSifmWSD/azSqUIZIALYeWefIYAOgGntnrLX4pwzGR1YYvwoQQQgghhBCygaiZg+Zakvwi7JzbCeBmAC/x3t/rnLsQwJUAtgC41nt/uSn/YgB/6r0/q8oGE0IIIYQQQgghZUh6EXbOnQ/gIwD2jba3APgogAsA/ADA55xzP++9v36UfxqAP4ES1ZG6UgfJcxUy5CLXYWVDqZKnsJQ5fB1FvFDb8yjZkNgf80DaFAflJJGNydLJlfSllriFr7HIfyar+G9mHeWskiraV9Qzeri+yftjY1iO1ZikLSZtrdqDdJFyqfejCvOLWH1VywJTvQrH0OtKlm7lfklkBfv9tNUktnbItkufunGZr/EoraTHiTraRFZT4l/k+FT5c8MMwpB82crYw96gw96lQ56h7XaqlFl7eU6TUMfryNJ2LBWha+bfUi87wYGl7My37p9R5aR3aCmHlscAwLKorxtpbuo6TchmIfURcCmAywA8MNo+D8Bd3vt7vPddAJ8AcJEofxWAd1XWSkIIIYQQQgghSTQa9fyrE0lfhL33lwCAc+7ErjMAPCiKPAjgzFGZ1wP4GoBbKmslIYQQQgghhBBSEUWdZTWh3DOiAaDvnPtxAL8I4IUYvRiT+lG1FLpqWeaqnjfRy3MVEuoisumYXDIkkwZW4hU17Am0CFV45i3bjrpLoYFibSwq91PlVn5aRd+0ITgezXhWcrxI/SEv1DHqInmW96dyM4iCS12oD219yitz4smk7LXVNMeIXxYNIdmMe7UOuCo3xzUia9sg4rVeyW1leyPepWPjMZRXdP0qu24VXXulHDrXZ6IvYvJq7Sk6XF9IDp2XRk+WQ9v1K7Qmpsqfp41cB6xcuSMWwqOdrFWLPd34trhG6Rl62ZRbFOO4G0gD9fciTbk2WW2Krgn3AThdbD8eQ9n0RaP9twH4OwBnOOe+WKqFhBBCCCGEEEKSWWsJ9IaRRk/gywCcc+6pAO4B8AoAH/Xe/yWAd2CYuRfATd7751fRUEIIIYQQQgghpAoKvQh77xedcxcD+AyAeQy//n66wnaRCqibBLoS77giXY3EJ/GaYt5yI4elyhuLyCpDstSTtUlSRIqaWt80j7HUXQ5dhdw2tb6Y99TQcbG1Qo6l3BgW9XXVuA2fNyabDtUdK1e8b0P7i00EXV9aHbIvpu01ugpCczXqiThxjVHy7MTFKCfRFf2Za6uQ78pBbT0CN+XAi0ity1J03KbeA5UXkT+H7hUQlkNLKfQwb3L9TbOwhOTQ7aa9B7Lcyr1GpzKN50bIDMTO546QNsu8tpXqB7BSayWHjniG1l6jRRsQLhejjuuUJPW3Fdm8rOhF2Hu/V6RvBHBupOy9APaG8gkhhGxu6v4jihBCCFmv1P0jQR2oQwhZQgghhBBCCCFk1ShqI0xqSF2k0FV7sC0k50xteqStqR6fU2XTsX4p4l06JrsO5a2mk4K6SAnXiml6ho6NfVlHOyp1TJMc9pVkNSxrtm2XUj0tu9PnDcnVYmM6Rnq5lcvOVbnENsTNFGILVbghvXX0JT0meZabvZ7MMJXE7ECUFFeMLXOD5NgdCGlvK1cu4FE6csOr9lxehYf9InLoXNfKckom3giWC3maBrQEOraeqXIIU/Uzwcrki6DkxtLjs/XeHFgjuqnnMdtqvU300p+KXfcJ2UjwRZgQQkil2B99IWizRUrB4bNi6uaxlRAyPTjfT07yi7BzbieAmwG8xHt/r3PuQgBXAtgC4Frv/eWjcs8B8GEAswB+AOBV3vtDlbecEEIIIYQQQggpQJI61Tl3PoAvAdg32t4C4KMAXgbg6QCe65z7+VHx/w7g7d77cwF4AG+putGEEEIIIYQQQkhRUr8IXwrgMgAfH22fB+Au7/09AOCc+wSAiwBcj2GQgp2jclsBHKistWTqpNoFV21HJ6nCTqelQnZEGlvMRC9oG2mlnqHrt3Y7ZcMsVWErVU04qpWz3mx/qybVLji1DmkX3DJhSWRezA4vNB67xsZThkzqKXvKsL2wsulsDCLjvfzAKNK3qfbXyW0w20E7/9yR4XWl6rBnqYTOZfdru+BwuZ40iOxlPdCwRtDdwOpkbmJ/Juvtflv3fLst64zYz6pTZxuNxPFor7FqaWIwfFIzPEaKhEga1inKJdocR8MiybyIj4K2alOaL4NUqvZUn3uWh85l2q7seHuT14ToeW1YpNRygZBJ0+4XsjZQGn1ykl6EvfeXAIBz7sSuMwA8KIo8CODMUfpNAG5wzn0AwAKA8ytpKSGEkA0FwycRQgghZK0o6mi4Cf0trQGgP5JMXw3gQu/96QD+HwAfK9dEQgghhBBCCCGkOop6jb4PwOli+/EAHgDw4wCOe+9vHe3/MIArijePnIwqQiZNUw4dOyYkgbbHlL3GvjmP/ArVMudSMupICIKQdNT2Zcgrbk4uGajbUjbMUry+cDlycqbdf3pehSWHWn6o65gVUsX5Vpaeben6pGxRyp8Xe7rCRRHypiMzrNdocV4pk242YhK6aj8Xp64rsb5NOf5k9JV5g8iwa0UkrI1ELVlVyxtjUeMiJxv0J5dTIZIANJblAMrSjSVTUEqj1QKu70JjLouLNJjXP2+6yPKkTLphxnRIKjyIaGDXSn5o5dCF6giESMrlRSTUrUDIpLzkeeUmHM0C0uiYHDg1tFlRtYqqU8qQc+HgxDog96eeJ9K+mDf+VDl0yKSMKp71RRVrxEan6LP8ywCcc+6pzrkWgFdgaB98N4AnukxD/TIAXynfTEIIIRsN2pERQgghZK0o9CLsvV8EcDGAzwD4NoA7AHzae39wtP8659ztAH4NwKsraSkhhBBCCCGEEFIBK5JGe+/3ivSNAM6dUOZ6DL8Ok01CzOOsxEqhlYRK7U+TJqaS96442WvicFt60p0sk84R8fgcki/HvEvHvpKV9S4dqy8G5VD1Ro4fKTO0XlulHHrnTDbSds/qUbdtJiu3sJwNkkMdPduPBP6XumjngZImam/VIS+mdmymjsFUKXNIchlbz2Ie7YvMJelp25pwaAl52OQiJlltJC5hkpi8OpRn740s15c31Xp/lnLoY5kL6caCEtqjeURsi2MGW/RPmMHu+ckNBPQ63cxk0k1zw+W1yG6vu/fV2L0PeXU+eR1p4yzkKbptxm1IDj1r1qlZcU9i3u0lepjpBkpv9zFzhGImSmltyjFF6XERyXcsukZqm6jwqR91X7fqQBUmpoQQQsiKif2ThhBCCCFkmvBFmBBCCCGEEELIpqKo12iyRtTFS3RqOxoRj4+yjpj8SXuhDNenAtQH5JaAliBaCZWSz4Vk0uZcqr0FPT4rr5bhKir3Lh1qg6WOjgfrJtfuD1avn3JzKSDtzXmNFtLox8xlEtPzTl1W5fbtyvLuPJzJSG95aFaVWxTzR3qUtu0bBNrXbAy0BFjkxTy1p5LsaTtynlZgbbKy89C8za0/4rCuOFcv4mnbeuGWm1XMgyLepuUx9vi+aFRfOIBuLOvekN6hG8ezMdh8ZFGVa957aJw+9KM7xundZ/yYKtd7yu6sTTPmjsxm41i2adDSxcp63o5JEVMltbINtj7VvkiFoXbk5M+RB06ojvz6M3mOxOZSyIO93ZZpu57Jy+/Itcg2WOR1VYbV9Id/N4QoarIxTRlxapuqkENXQd2e5RsJSqNPDr8IE0JIBdTxnwV1J/ePKEIIIYSQVSL5i7BzbieAmwG8xHt/72jfDIDPA7jCe3/TaN/LALwLw29j9wB49cibNCGEEEIIIYQQsuYkvQg7584H8BEA+8Q+B+CjAJ4j9u0E8CEAz/Xe3++c+30A7wTwhgrbTGqG/RIWkkNb+YH2Ljl5/zBPesFNa1PXaG26QgKkvkKZc6m8gEwa0FLpoEwaYSlzzLt0VKIcqDtXLlBFqqfplbBWDo/q5vF6NaXRRZFzSTrclVJoAHj5DXvG6U/9XPZ/zG8eNHNzKW2u9zCZdnMQNGkoSkwOHS6XpVux9UeUm22Fy8WQa5FwgIyOLRjxKC3XQdlnVU9FKxMe9CfnDcxipDxF98SGlUYvCk/Rj2bS6MbRJVXuofu/Ok4/7rdfNU4feN+nVbndp/+brA5zroFshxh0eSl0WieWlRwmS2qn2AZbR8NUmOp5Omi+hHA5OZesNHqH8Gh/ylyW3jmjyy0I24IDS2pGI4iYV51Ub8hVeHJeI/lvTP4sKdo+eoquN5RGn5xUafSlAC4D8IDY9+sA/hjAl8W+GQCXee/vH23fDuBJZRtJSBm69ExLVoG6vwTXEdqGEUIIIWStSPoi7L2/BACGH4HH+357tO+NYt8jAP5qtH8LgLcB+B/VNZcQQgghhBBCCCnHVLxGO+d2YfhC/A3v/Z9N4xyblT6m6+GsCk/RoWNyXltFuh3wJglo2dRcK1yHlOgs9eSFDJRHW0XOG6vU+02WSQ/PNdmjdGEPkgEvz6kyaUtIrrSSr5bp17Lyz3qrKaeOSdKrJC93n965ou2I5El1xMJylpaeoQEth5Z5P7FnWXmODqkt5lsDHAvNOUGzUf6epK5LMTlnQ8mfdYNmmzKd5W1t656Wa1Nsfsu16Zho/XxLr1Oy9rbp55CzYCtB11LXage/bEPea7Rog5AoNzraZ6/UhkuZ9GDnHBqPHB9vn7JzbJWFB//walXFGY8/f5zutSKTfYoazphsWDUhcgtC3qp7Zr+6pfKa+gM0xVgdiILymJchxcgAACAASURBVF4faAUmTc6jdOC6Ys/yVDOD2YBnaEDLof/N4zKjgafs1Dfxu0eyM9+2P1uXls1zvaMOy/Jmm0BXmWZgIitZp1KlyHWAcuiND5VqJ6fydyrn3OkAvoihLPqSquvf7NDNd0ZqXwRfgkkQSlbJJGz4pBApL8HA5hhnqdfIdSpDvgTHkC/Bm51m4gMx9BK8Gekmzs3NsE6lwpdgstGo9Iuwc64F4G8AXOe9/4Mq6yaEEEIIIYQQQqqgamn0L2DoRbrtnPul0b7bTtgYE0IIIYQQQgiZLvQafXJW9CLsvd87Yd8LRPqvQPVuLSkaGidcX5ZuWHuhwMSz+3X4pLC9kAyl8Nj5LL29rcs9KkIp7F8Mh1JQ4Vpsm8Sq0RU2dak2rblrl+dKPC5kL2wpYj+cKmuahi2xrr/YdZVlteyFV4K83tR5GjN/lFVY6V9HyG8PdbJRYiXPMkyStCWWx9j6YrZ2Mdu7rp2EUyL2YFLh20xzQnbBu2b1bJJrkwzzIvsPAB4WYV70PDPrlExb+/OAjbANfyNppMZeizAIGLLmdsvtWPikBRkyKbMFbRxbVuVa23eM06fv/nfjdH/PvD6tjAnWKj+uVPggM4CkFFnfg3B9soqeuQfSrtqGo1JtknbAIp36g9deRyNgS2zRNsI2LxwyKViH2G/nnJw/0i745Tc+RpW79oUHxunvHBK26OYim43AGlvwWVN3O+Cqn21Vy6Hr8uwlBOBLKyGEkDVitV6CCSGEEEIsU/EaTQghhBBCCCFkbbAKEJKHL8JkqsQkU1Im1RaZNkSSlBye99hMMnfWTh0s5J4jWZiXW/fPjNOdXCiFyXLOGLm1REpshQ4wJplKVSamyneLyItT18SVSKFCMuqi8qfQda1myKUqCIW4KVqHCuFlw+nIeSbyOpEb2RejYdHMkeaiLJdhPRtLabScV3YehGS+zcZA3dcqJHhqzYn0uzXpCB0jTTi2CHOM0+Z1a597arY2nb0jW5u+/6gOTXXb/uyx2xVV2K/jcm2yefI2dCOS1dX64J6TTCv7kyzZiEijm/cdHafv+9E/qXKn7n7GOD17xuOz824xP2Fms74etK2WeWLTc3JyJYeWklpzfCsgS26YmzDoy+cDJqYBoCdueFc82vomJlZTDKcZcfn2OpoF1l8rjQ6FTIo9y2MS6hD2WbEgBrUMkSSl0ADwvaNNcUyWzq0/ymwjLdxh4dBC61T2S2/QZLPC/xUQQghZE9bbPzcIIYQQsnFI/iLsnNsJ4GYAL/He3zvaNwPg8wCu8N7fNNrnAHwYwB4APwTwcu/9wWqbTQghhBBCCCFkEvQafXKSXoSdc+cD+AiAfWKfA/BRDMMlndjXAPBZAG/w3n/eOfceAG8D8F+qbHTVVCFhJOUI+3jW3qGlHPpX/uEUVe7an3lknP72oWxot61X68i5Ql4ti8t8J9dhzxuSJcXGYxHZdLL366RSozqDbUg8PlWebq5pPX1NjN8rWc7IG8U1B2XSJq+rMoyUOXBMp5fWl9aUoCva24uYHISkicA0PJKu3Au3llOHveBLD9K753TLpXfb//yPe8bpj1/wqCr3g4WsnPTCvdTT55Wy9o7RuS6Lvq5a1hWT74bKxTIbkQqlVPpHj3x9nH7cW1+jyj38x1eN02cIaTSM/Hkg9crWa3QjTbOrPEDL6qw0Wrg6DnmQBvS8kBNj0NXlloWj7M5RkbmotdGDrdmzbbAt00k3tQIfbWliJPrC/jCOdYs0CwjJpIsi1whrmnFAeFa/9eHMo/23DurzHhfrlowUYU04uqly6MD+mNR6vbFWEuj13GdkY5P6DL0UwGUAHhD7fh3AHwP4stj3HAAL3vvPj7b/G4D/WbaRhBBCNh60SyOEEELIWpH0Rdh7fwkADD8Cj/f99mjfG0XRpwL4oXPuagDPBvAdAL9VVWMJIYQQQgghhMSJxZYnQ6r2Gt0G8AIA/957f5tz7goAVwK4uOLzkA1GP5AGgEeFB0npGVpKoQHg3qNZ3vGulGmu3kJgPdEOlEwz229lQiFpRqp36dxxARlSqqRtJbLjIm1Xx0dkvvHjpGx4/S72MdOMkMzX9lFXaKWVitZIavtCMtgTD8hORA7cV8rWiDfWwP7hdppn6PR7P/m8QNikoQqzF1nfkpFf3reQbf/5CzK32z88pqWtbaFhlWYfR1v6Sto9IXs17dDrzOQ1piiV/G4SlQxE2v4oG8xl13ja4549Tv/wyj9T5U4/9aeyY7ZkEQEGM6ZnhFw56gI5UkxJhaU0uq0LSo/NM7Nh6XFP3P4lmdHRg31pMdtuf/dQdl6vn3M9l5kELe17zDg9O5sbJeOUunTrXToi61aRHSLe2OWZQ97YgbCJRMd4xj4KaTKQlWuZ9UzKzqXX+pw0OmC2YX8bhNa6InLqzQ6l0GS9ULV50Q8B3OW9v220/UkA51V8DkIIIYQQQgghpDBVvwjfDOBU59y5o+2XAvhqxecghBBCCCGEEBKg0ajnX52oVBrtvT/unPuPAD7inNsG4D4Av1rlOTY7fRT770URT6qpWLkkApLVfs7rb5buCn2RlRxKb5C37s9kcdIzNKAl1NLr5HxrgKPCO2s3IOe0bZomqXLg2L0uKptOOu8KxkhIllxF29ebTLqItDf9+Nh1iQOlk1rjjVXdVyUbTuuzqEQwIn9O7ZfUdaqI5Nm2STrZ7UdkkH3h6VjKL6XHZwC483C2Hi10M//cbdO3u2ezlhydy/KOLOtyC2KhahtJaLMf6qdqx35sWMi8nB2a7BrhsVhKoQFgsDPzCNzbu2ucftypF+j6ZB1bxLpvXTnHGiwGipQD55ouyrVFc2fMr6X5+azcKfOZtndrS9+ro8vZyfYLN9RLS2ag9bJx0fpuFnHy3MudKvaNP7wrO+Ts3bLlqlxDXq+4jqZVk4vtWTvOxNhqp3VtMsqzvFmn5FztqIlrpcyT1xzrBV898yNznXJoQjYfK3oR9t7vnbDvBWb7y6AcempU/Ql/M3C0w14j04c2UYQQQggh64eqnWURQgghhBBCCFlD6iZDriN8ETbkvPnWbBDFPKRWUn/i9cfKhWSL3X5DSa26St6XlTuy3DASrewqO0ZCJT1ZSg+Qy0YatSi9RvYT5U8FZa7yOOlBMychD9SRfN60YlOVUA/rmJwZkyjbtofaWMSjdLMxSJJHNxvFvuJW8eW3kHfkRNl0fm4Kb68h6eyUr6mIXD39GHNieZiROkqJ8SBgtnGs18B8c/I6tSzSP1hoYZeQOUsTju0zuk1n78iks6cJr8db28vjtNvdxT//KJMKHxAuhtfqOdRuAV3h0VdLoIVsdgboLossWUxobwe759F4NJONSw/Q2Cnuzc45NI53szwhG5Zy6MbxLgbzQvc7WPlAblh1dUA23Z7RN2HPXNYxz9qj/EHj+Y/PrvF/Pzg3Tn/1kSz9mHng/oNZnTNbsuvonZN5hv7aXx9A647Mc3TPZZ6iG+KYhYUBdu7KGq+8QYsB1O8Dc6Lb5bM2L43O0jFv0OnPkcnjpzMwa514fncjv+RDXuttXreAHLoK7/bTpna/T2vSLyFSfxuQzQU1oyRI6iKbXi5tlbQP4xDtxPoW+6u38NX9QVBHUu2titiZVlHfeqZqfwDrGWtnG2I+sZx8CY4hX4JjyJfgutBNa7p+CY4gX4Kj5eRLcAT1ElwT5EtwDPkSHEO+BMeQL8Ex5EtwXaj6hS51bY/9c7ru1O0leD3Al2AyieQvws65nRh6hX6J9/5e59xrALwew3/r3Qbgtd77jnPuWQCuArATwD8C+A3vfdpTjRBCCCGEEEJIKSiNPjlJ/0J0zp0P4EsA9o229wF4K4B/C+CZo3ouGxX/BIDXee/3YSiQurTiNhNCCCGEEEIIIYVJ/SJ8KYYvuh8fbS8B+E3v/REAcM59E8CTnHNPBrDFe3/LqNw1AN4F4EOVtZisGVIIWMg+1chS+gF74VwdQuYkbYRjbZDhEmzdsbxphkUoYi8coy62xEVCMKkwOYltSg45lQvTVe5fomspoS57bnvtqyWnm3afadtpe1GTQ0nlwiKJdEPZE+py3cD6c6yrz7sgtpeEpPhxW/QIf/lN28bpj/37A+P0XCs2X8LbsXAw00Tbj5o8ccny9gxmjJR5Juuohgyt1NGa7MaiuFvKdtjGNMq2BwXshQF9LU0RtsmGHdrezuq/4PRMDv3Kv9yuyv35RY+O03ccyXTJcyaU1Lbt2bmOuj3jdOdJO3X7hBx827bsmFkjeZ4R2zL003xLr/Ry3MVshCX58VjOB0DMr0HMf0EofFJRO+CqzW9ilDVbKTrX10qGXgeq/m1ANgZJL8Le+0sAwDl3Yvv7AL4/2ncqgNcBuBjAGQAeFIc+CODMylpLCCGEEEIIISTKRrAld869AsDlAGYAfMB7/z+rrL+Usyzn3BMA3Ajgau/9TaP65L9cGmDscUIIIYQQQgghiYzeM98N4N8BeBaA1zjnfqzKcxQOn+ScOwfAFwB80Hv/vtHu+wCcLoo9HsADxZu39oRCAdWFQnLlnFwyFP4GplyoPlNOhnIJyBQBI0sWkqy+CX3UFxdmZYshQtLBXN4KjquSWCiKVE+WRcZjNAxSah0F26HnkrjfiWGWYuetgzyroBKzEnoqZFe4XFhOnnaeOvTzyZkcoqVvFiBpmtEUY9CuMR0x8NpCsds2HX2ok23/6Hg2cu86rH1F/pmQQ999JJO5HuroGSjPa0046uDtVl5+TBrdleGOjCwXMyK0kpAhN7q6XOPBTF588KHvjNN7Tte/h3pbxE+aHWEv3LG2S+SctvP7mHhO/ZPw+P3JX35UlfviD7M8FTLQTLr5edmmrF962/S4aApFtZRDz82Z0ILtbHtrO+vPbW19ITJvPiLPl223YQy7A7meQ6SLmR6Fngmx9Sckf7bnLfKML/o1J2r2k/ycr3bRXR9rONnoOOd2A9g9IeuQ9/6Q2L4QwD947w+Mjvs0gF8C8PtVtaXQF2Hn3A4ANwC4XLwEn5BMLzrnfnq061cBXF+6lYQQQgghhBBCkmg26vkH4I0A7pnw90ZzCVM3uS36RfgSAKcBeLNz7s2jfZ/13r8dwCsBfGQUbulrAD5YvpmEEEIIIYQQQtY5H8DQobLlkNmeusntil6Evfd7R8n3j/4mlfkGgPPKNYusJsqDb6IMJybJVhLYkEwaUApGJRmznv0SPUWHiM2YVO+Xq2noHpNNF2Gg7m+xOvQ9TTzGbIekzEW9S0/znqTKx1Ll0Kspm5bnSo0h2Ku4fUWvN7W96fderD9mnIVMM2BlnyK9qHKMZFUcdv+xLH3cmHr8n0eyx+7R5ayOHx3XXoSPdbO8vBR1cjrvLVekK5gwjYCtQsNMzoZsr7ystikoZNMqbS7k8MN3jtOP/51fHKf3/9HfqnLbnvycSc1eEbrPso2edmSNw0LK/vUDmfz57iPafbOUUC90wwO8LftJyJwHRuEt54g8pj2j694yk7V9i5BD757VF3LKXHYjd83qQSLH7lFxvY+a61jsZXlyvqR7YQ7nxSTPqo5IfSHv0itpRyrqORcrl1hfkd9nJI1UEy1SPSP5s33pncR9AJ4vtis3uS1sI0wIIYQQQgghpH5sgH+e/D2Ad44iFC0A+EUAr6nyBKW8RhNCCCGEEEIIIVXivb8fwO8C+P8AfB3AX3jvb63yHPwivALWkwdpoKCMOCLDCV1/7LwhmfTwuMkepXNerRNltKlM01NkXcZFX8lji/VZEUl1soQ60bNxsnfpVfQgHZP9FpEET1M2vZaerItQRNZtCc1b63m5LRaWoEwayEmlw+edLGWW8mdAzwspa5ZSaEBLapetNLov5aJCRpuTRk8eAKnjInYPYp6X5XZLyHd7VhotvEZjLis42KblxbuemHmH3v9HfzdO7zhznyrX2yqOM+dSXqkTx5bsp652/o3jwkv4w4Psp9RBO34EnV74Xklv0LEfZqpvhWfotlbWY1a0Y6vwBr1nTs+Q5z2uM07/xB59kXcKr+a3PZz1bXegT6a8RvdivyEmy5xjXtCLRHkoGg0i9dkRfx5OrsQeE1qnUj1Nb4AvfYQk4b3/CwB/Ma36+SJMCMlRh/As64319tJJCCGEkI1LXT7O1JnkF+GRF+ibAbzEe3+vc+41AF6P4b+/bgPwWu99R5R/MYA/9d6fVXGbCSGEEEIIIYSQwiS9CDvnzgfwEQD7Rtv7ALwVwE8COIqhC+zLMPIk7Zw7DcCfICqAXN9YCU0d/+tSRHqjjrcSZSVTjRwY6gvbZ6JgT0kijZwqcMxGpRLD/bLd1BgUkhgn35+I3C1V5hxwYJsvV9IzZFEpdDGZND8rn8B2RaMxWVYak7nGxkXQU72VQgc8Stu6pcy5o+ShpjpxLtkG6xl6WeWFz9UVeXlpdDivCFoOHZHAhmTTLdMZs1I3ndUx2DOvivWER+ltZz47279V/4Tp75zL6pgzWmFRRyMyaKR37Z5IN43X6O6yaK8054j8GND3I3xDmq1glqpfys5nW7o+oZpWZgA72nowPUPIoX/lC3tU3nX/4eA4/Z1DWV+3l0ybws0tREgObZ8H3YDU2q7zOi9LFzURiJvJpf0ACg2TVBO32O8zsnLYf5uX1PXrUgxfdE+4rF4C8Jve+yPe+wGAbwJ4kih/FYB3VdZKQsiqMm07W0IIIYQQMj2aNf2rE0lfhL33lwCAc+7E9vcBfH+071QArwNw8Wj79QC+BuCWyltLCCGEEEIIIYSUpJSzLOfcEwBcD+Bq7/1NzrkfxzDG0wsBnFlB+wghhBBCCCGEkEop/CLsnDsHwBcAfNB7/77R7osAnI6h86xZAGc4577ovX9+6ZbWnLqHVpIUDbOU6ro/NcxSIMpA1M40Zks8Taq4p0XkIIXtVsp6fS4cuiatveoeJ9ubh0NxVEGqHDxkVxa3F06rvI4mwoMiccRWQCN5YoQ6R4+DkP2wHS59ZUOZbXTtOJN2wWI+Nk2FPXGyjiyXsy+Uts6TbRwBY3McCZ8UswOe9r07gbWhlDau8hpbxo61NyMLiqSpsCHtfeU1GZtjaRc8mNc/b2SbZPWxtV32Z7+n295tTL53LRM+KX18T26ftWcOtd2eJvR8PN7T9UnbX2kTDAB3Hc46bWFZzBEbYWzimeJrdGzsh+yCuxHbXzkneqZB6j4WsPuJ3YPYg137u7B9EV4j6kYV7aO51dpA2+eTU+hF2Dm3A8ANAH7Xe//xE/u99+8A8I5Rmb0AbtoML8GEEEJWTpGXBEIIIYSQKij6RfgSAKcBeLNz7s2jfZ/13r+9mmYRQgghhBBCCClC3dUGdWBFL8Le+72j5PtHf7Gy9wLYGyuzUVlPMmmgWJilmOSpbJilXGiq0HkqCKWUKq8uGi4r2PbIeSuRYafegxAFZcdRKTxkXtYoG1FFSuaUSjXXJKWlDJYrcv2pEuWicuigvLoCKWsd5dUhBr1wnpXbqqEgJpbtZ31c2jiWIYia5sRqTEcktV2E80LIddTe+pDsE9DSz75Kp5ms5CXUBeSi4h7Y5Uxe/0CFAgrLQ3sNUdAuCkoaHX7ADuRxbb36NmV7U6O8RfpMS6Ujz8PEUF+p6oj0tgupvpBDH+7o2Ey37c/Sdx7WPwmPi0F9SBxnpfqhcEeWImtxP2Y+EJBD900jekLL3YuEG5OIaFtotlawxgTuY2xNiP1m1OEjV4+qf7umXj8hqw2FaYQQQgghhBBCNhWlvEYTQgghhBBCCKkX/Np5cvgiPGViko+6y6ZTVZp2oqV7803Uw5SUUFuUbC/S1iJeqWNtaEY8yYbqKDpGkj13h9qwgmsPjfGotB4RSaj0phnxLr1KDnHzMtKg1+hBUjkgLIFOlTWvlZRs2rJrJTmMyW2FpDouoR4Ey0lPsKnepaVsOjqHi0ij7fiOyXKVvDPLtOMqJu0tguonJfm1XnUn93tTq3IhF3d5TL+pVw8ldY0okuVhpgo0xU2JyZBVf0buj1wiYzJpOZ5aBeTZqdglJeR1/MhyM1ju4FLYM3+qF/Mq1qZQHfa3hZJkiw7oLusKloXEe1nkWQm1vCcz4tdxq63P2w54IAeMh2rEkGvJ2muD1/L3aOjclEyT1YD/LCCEEEIIIYQQsqlI/iLsnNsJ4GYAL/He3+ucew2A12P4/9nbALzWe99xzj0HwIcxjCP8AwCv8t4fqr7phBBCCCGEEEIsdVee1oGkF2Hn3PkAPgJg32h7H4C3AvhJAEcBXAPgMgw9Sf93AG/33l/vnHsfgLcAuLzylm8A1kr2UfXEKCxRXSUJdV66LfIiTZCyuCIy6WH9k+XQtk0qL1FCHUNJxsR1xPqiKEXa2I94te4q2XTAM3TuvLK+1Vv5Y3LomAdoJZ+Lep4u2LAE6uhdOionD9xWK01U4yLiclXeO1lHXuY7uQ396DALS6hD9zsmwc/nDZLKheTVcW/n4TyJ6pdInpQD98ycaAY8SufuaXPlg9XexyLIORy73y3lxTxcTj17cpmYmJl6GTFp/XKk8f1BdjI7VnX0gYiMX9UXNnspQqwOtY6KRkkpNAAsLmZ5xxZEuUVde3sua/uWLVl6fl7Xp8wqTGeETC7qSN1fklLXTkLKkCqNvhTDF90HRttLAH7Te3/Eez8A8E0ATxrltQDsHKW3AjheUVsJIaS21PHFkhBCCCGETCbpi7D3/hIAcM6d2P4+gO+P9p0K4HUALh4VfxOAG5xzHwCwAOD8SltMCCGEEEIIISRIUTXjZqKU12jn3BMAXA/gau/9Tc65LQCuBnCh9/5W59ybAHwMwIvLN5VURW3kJcnK6JIS6tjhEc+0qWgvz7rCkBzanqctpH/NwP4YeamakKdFZMjKo3Skn2NeLQtJo0V13ZxsL8uUMulc85K9c6YRko6mylIBLaUsIn9O/aq8ml+fBzVZMBqBgZbz2tqYnGenkjpOaaPCcvcissfUsRkbZ7k6e7Kc2J/q4bwfvsYi5LwwqzmdVd7KydNFHXIt6sEg1oGCbQ3dr5g3aNl2G2EguF5EFtmYR+FW4ngK3W+7fksvz011kPG8LJtqzlVWOpuTHKoIBvKZZ8xeZDpx7ZRjptfVB0k5dP/Og+P0rD+gyvXcY8bp40/bPU63Z3T7WnKdt57aQzL5msiQ6y6HjqF/r6xdO8jGorDXaOfcORg6z/oz7/0Vo90/DuC49/7W0faHAbygVAsJIWQdELMJJoQQQggh9aLQi7BzbgeAGwBc7r1/n8i6G8AT3QkNNfAyAF8p10RCCCGEEEIIIak0G/X8qxNFpdGXADgNwJudc28e7fus9/7tzrmLAVznnGsAeAjAq8s3k9SBqr3xau/NERkgZLlIhdI7cqocOFdH4rkKIOuzkudZsd2OlJP/udKeOvW5pDKsKz2fGn2W7ts0rVFePifa3gyXC7bP9HNHSvciblb7vck3KKfSDLQhVVq1Es/QIdniSjwCq3IV67+qllQXqS82H2M0AiezklcpoQ55fLZ5cprl6xN1BDxN59uUdlEx+fNqjrOqUVLpgEzaouZtS+eptid6Y4/mRfTA0rO18gZt70FgmbLLqLr6yDhTeWp/2CtxTBYfND8xzxS5jrZyz5u0NTYVdVxAJg0AzcCzPFVfbL2TL3eyOqQc+nn/5WxV7svv+e44vfSkXVn7IvNvrUh+XtfspaMqKJMmVbGiF2Hv/d5R8v2jv0llrsfQbpgQQjYNdfhxtN7YqD/SCCGEEFJ/SjnLIoQQQgghhBBSLwo7gtpEsI8IIYQQQgghhGwq+EWYlLb9rcI+o69sgmIlIydThlVpMZOmLc0MhVZqG/seaSM818rS8y1jOxWwi+mYkBjLIpRER3RM1+h3Y+E39HnDfSbbPisuOG9vliFthJesrW83KynbFLMtVSaJkSGSbhccyUsMkdQPlBvWMTkEU13k1VX0U0q5lTjaljaVyr7X2vQG7Hibkfg0sRAyAzGXwiGXzDEFbmTM67itLmQXHLURDow5u51qcyyJmkSLfmoYG089l8R9M+fpBW7QSvosmGfqaAbvq7HVDYRFis31RmvyMcNMeYzYHbGXbkTsJNXaKTrUhqtTfihMng5FFjBiRvg5l/p8zYXQE23qizzrM6Mb8Adgw3TNzGaZvaefMk7f8t7vqXK9c7K89tzkuidtT5MiX6vKhoGcBtMMqmCvlzbDZCUkvwg753ZiGC7pJd77e51z/zeA12G4JH4OwG977wfOuZcBeNdo/z0AXu29PxiqlxBCCCGEEEJIdaQ6VdvMJP0TyDl3PoAvAdg32j4LwJsAnAfgJwD8WwA/O3pZ/hCAF3vvzwVwO4B3Vt9sQgghhBBCCCGkGKlfhC8FcBmAjwOA9/4e59yPee+XnXOnANgF4BCAGQCXee/vHx13O4BXVtxmUoAi8udUeUnVMpRouKOohHqydCsXTkf1RSzWhawjp7+0J5/YJhUySYVF0uWkpHjXTCYieuy8FhRtn8nO++hyVuGBJV3hYXHVKsySlaApIqFNxGGzRp4mpdw72ln6MXO67VtE3hHR9v2Luu3y/qgwS/ZWJUvh0whJKWMSSyUtj4W46YVDMNlQH6nnlkxTqpcsMZX7o9dUbMGQYWNkeJ6cqlKG8FJ5YWlrcpglWVsvXK4IqePMlk2V5cbKpUqtUwnJ2HP1JYZZklJXOV9izbPjTM7P/5+9d4+S5KjvfL9Zj+6e9+gFEnqNhDzBYmyeB8G1Aa1tYPciw3KNDT54wY8RthF418b3WncvwmAD8h4vhjXXYK4QIAOWYMHaNYux2TXW8pCFLEDIsiEQ0oyQrScazVM93V2P+0fVVPzilxXRUVFZ1dXd3885fU5WZmRkZGZkZGbn9/f96WvV25aQItdlZ1BPS4W4pmXf1OeqCBzP1P6ij1noeOr6/LAXWUG4XCk7kVgWiSxQYT9yvl9OSpvldhuq3HLgrSNmXgAAIABJREFU/JSfDcTzgDhvjaZf4dZtbvqxi3a77Zyz02+fkENv2eKmm+rcyzGmUPfyUN+vgtjXvVQ59FoZA6VutwoJNVMrkVFIehG21u4DAGOMnLdijLkMwH8CcAuA26y1ywBu6JfdAuAKAO+tuM2EEEIIIYQQQgIwReHqjPXPIWvt1QBOA/AAhATaGLMLvbjhb1prrx1nG4QQQgghhBBCSJVkuUYbY84FcJ619ivW2pYx5noAv9pfdhaAvwLwBQC/XllLychULYcOLcuRTo5CN1lqFJDWRRwuc5HHIuc/bto1er7uBEGnzrtlzzi95ZW7YLvTY+4/5rRg33zEv5SXhMywLab92pRbblT6Fi4npdJSDv2sM1a8cueLtt991LX95ofmvHKLLSmzS3UTH514vxWS0ojkOSo3FdJJLX/2ZJpCYpsqGy4iF4KW6o1Lqsw5VXpbriOtHdK1Vkpl9aHoSmfeyLGQ7tIxR2mJVLiXTsGYY8IoxyjYB1UloXK6P4brC7epiJyDUPcsnSu5LSlnVE7yqddFR+x/Sw92LbczxYq8AJWEWsSttOfdODWnbiTemFjBfU42I9YdPXdtcUBL15xXd1h23gndN+HvY9e7EYfLSfmzznog7xVyHZ314IQ8/+I8tlW5OVG/XNZsqOtgwS2T0vfOdt+SW/bPRkRqLcefmrrYQ32/HDY1/FhULV2uor7YeFa19NiLlqiiPsqkySrkpk/aBeDjxpinATgM4BUAvmyMqQP4DIBPWmvfXlEbCSGEbEBmJV0UIYQQstFYq5jw9UTWi7C19g5jzFXopVNqAfgSgHcBeCmAZwBoGGNe0S9+68kYY0IIIYQQQgghZK0Z6UXYWrtHTH8AwAdUkRvAf0CsGTlS6N56o80HVnEqDUqoK3CLjbi7djx5VqozdETfOEG5kkY2d5twhpZSaAB41Y2nDaY/8S8fGUx/97Av8WoGXa2VpE3sZOx81wLTgO+ALdu+Z4ff9p/5a9f263/Mtf3vD/oCqHqt2qOdel0kS54jTtFSDt1qh8u1W8PLpUtRw666VacNzHIbjjloJ8p3NbJbtMUFU1d3sYZ3vYfPvTxm8rylyqRj5Ej6RgkxCcmhY+VC5wrw5fmdSH2+g7YIW1DnICRdL5TGMhY+IJHt8KSOkXJSCg0AteMuVKM4uuwWLPnjVHer2Jmd8666WtOvT/STunSynuITUKzPFEFJqDoHnq11eFnsHiB/S/nzdiVR3j3njrW8Vxxd9ms8KH53xAFtqR2WmQSaMiRCSZkL0aa6LJd4/Opqh6VDdSwsoIpwnpBTdDSUKVrfeO3JrS9HllzK+DF6FX59Mcd0smnJlUYTQggR6BRJZHUojSaEEEImA12jV4dfbwkhhBBCCCGEbCr4RXidMy1naCBdOiol0L4jbtp29X+wpAzUlyQpF08pdYxIwaT8OVfWUw/I80rHL6P+4ytuJekMDQDXX+IkxdJ5+XjL39BKoF/o/pIqDeoEpgFfgei1/Yjf9k/8WKjt/v/jpPtn7Nh2Q/s4dG65jli/jRGSpRZFWA7dWvHrXhbKzBW5LPJVuSbkfnUl3+2KkTwmzQs5mqZem6VjJs13pTxUFfRcstWxKGQHanmVeOXaTddnWnNuuhkZA6VMWo8X3pgj5mvnZXkQvdCMiGy4Erf8RMl4jJCMv+OrgdGWkn7h0qsl/ZKGkLbWG8pVV/xuRp4y2qIfF6JflNztvXMXPhjyOHnO0PDl0PXvPurqPvCwV67+xMe5duw91ZVb8HdEy28H25mRLzCeLF7Mj8lDc78eyfXmxMUkpdAA8GyRSWDvLrfs2yq05xaRSUA6SJ9Q46PMKiDdqjul8AZ5DYel/94aXraKsDO0Pt+hDAulsKRA6FVICh1bJ1Yuhg6xGZfQPRmoxnmajtJkEvBFmBBCKqDVXr0M8SlaVTzOEEIIIUQT+8cK6ZH8ImyM2YmeS/Sl1toDxphfBfAG9P7V9lkA/5e1tmuMMeiZaJ0C4AEAr7LWPhqqlxBCCCGEEEIImSZJL8LGmIsBXA1gb//3BQB+A8DTAJwA8EUALzTG/A8Afw7g31lr/9IY83sArgDwWxNoOxmBHNlH1Kk05gIrPvK0A9JJIGwupJ1FvST3UsZVT9Mht+G7PsZcR6VrZuw/aZ4EKFKuE5CntZSEaKntGvj9E27+rQ/7TqXfOuQu2UWhHzy45AulpJysFWiDbl8qrU647Q8vufm3fN9v+x2PurYfWZH7q9ou6pfbioUB5Mr9Q3iS38TroF7zvwpLZ+gVpwjs/V50BWuH3AkvTigHWyEB7u5wcsGVLXroFv1WHE7tYJsqje4GFMolt+GAw7CW3hYrbkax7C8sTriOLOWrulx3i+tPnd0Lg+kVcVwAX8Yor/Ui9sU+5rpdhXY0g6iLdOC4547ZUg69tCTk5MdVxxVj9spOd9wXFvzjsnWr+33KvOtMy2rsOLY8/HiWpPVCORuTa/srKQm+6Ged/Q8Ops96y3O8cg+8/Va3zvm7ZKP8+hIdVkIy2pikNobfjDSHdLmtVkdvS4yxqvOH7l+xUyDvm3qYknLoV33+lMH0dS/yv5X8/cHh4Uv6GIXlxaqc52Ke5hqtCZ27aJuK4fsxCuPKoVPlz1W4P8e2lSqbXiuZNNm8pJplXQbgcgD3AYC1dj+AJ1trjwPYDWAXgEPo5RA+bq39y/567wTwR5W2mJAR0akPCJkElEaPjnw5IYRMHrrIjs6sxH0TMiq1Yjb/ZomkVwRr7T5r7ZfUvBVjzGUA7gZwP4DbAFwE4AFjzDXGmK8DeD+AYxW3mRBCCCGEEEIIyWasb2XW2qsBnIZeLPBb0ZNaXwLg/dbaZ6D3kvwH4zWREEIIIYQQQgipjizXaGPMuQDOs9Z+xVrbMsZcD+BXAfw1gDuttSeDbK4D8KlqmkomRWpMhh9vJmLUVIBGO5A2RktHWyL8TNZX8zMpoC5+y5QVfgSqTosgY0v9cn6saTilQScSfuXFp3hxVCpFi2iTjHddhk8t8GtJHdtGIEWUjAkGgBW5LS/m1q9PHgsvxQZ0ObnQP2Zy2x0RlKrbJJEpknS5UHxzS51HL/Y5cr4lOfGU6XGXfjnvOlCNrx12wdT1O118XO3OR7xy3Qtd+pbWE3eLCrb42/IuGhEj6zcpmHgmvo8iLUkpzl82wi3UqWsgfms5tIyRrh047Fa59z6vXPOCc9yPi1x8YWfeHzDaDfe7JtKo1FS/lWNEcqoimS5L6bu81D0Z0q/UmOAY0Xj2gHcD4PfP4pgbnRp3HfLK1fa7362nujRDOPVUr9zeXW5wf9HZ7vx+7RHtG+DijB+V170KTc5Cnx+R/qh+weMH0/f/7i3+aiJ9klzHG3yhfCdEF2yo+5ffB4PNmyihVEqa0r0yED9cvqe6ael/IdPpAX6aJBkXbFX6pGOttPFcIuNxG6VPPMPry02ZE49bTozJDaxTqi+hDUA4PjfWz7K+hGXGEoeez0ptGjNeGMiLGZ41uW5VMDJwdXKP0S4AHzfG7DbGFABeAeDL6LlKn2GMeWq/3E8C+Nr4zSSEELLRkC/BhBBCCCHTJOtF2Fp7B4Cr0Hvx/SaAxwC8y1q7CODlAK42xvwDgB8D8KaK2koIIYQQQgghhIzNSNJoa+0eMf0B9PIF6zJfBfDssVtG1oRUiWCsXFvIJVeECvLECb/g4mNCjrfkVuoqqeO2bcM1KzWdZklsV0oTSylfItlQYvLgUB11KeWJlPPTJ/nlPHmxmL/cUaLpgHxHpzRqBeRfOm1TSHoUlc/pbQW2i4g0Wm5Xp1RZDsi6tZwqmNojUm6UdBkhpEzVq1sdtFg6obrIfSXl0Ge+5eleufveJlK5nL3d1a007h2RX0ZKgGMStFBbe/W5GW15gpWm1pNAS/mz1t5K2bRIi9Td2kTt4ccGvxcP7B9Mn/Xmn/CqePgq59dYP2eH2K5Ks9QVKacCaaBi6GuiHjiEWq4spdJV9LMYyVJub51YahM3LaXrtTsPeuV++LefPJj+5nvucgue6UujLz7DSf9/82NuDPujn/eDQu466qTSh8SgXU6hJ+4VMo2NGqSl9LjV9BfK9GNtIa3HuTu9cq2t7rGou2t+MD0357epLjpGUzxJ1Rt+OS2VnhTl85uo9fSkqOHQHn8c1fcbt1Deyw4u++fglofcOZApkhZbfn2HxXrLgXR6o+DLlcfXwIZSJEXLRdqUKsv10sGp7YbqiG03i1gaw0h7ZP/RbQ/dp2J1kDTGPt+bAMrHCSGErAnyJZgQQgghZJrwRZgQQgghhBBCyKYiyzWabE5CTtElB8n2cFmllEIDQOM7TnZXF9Ptvb7M7rj43RCu0drBVhgWK5lYTKLrL6t5UrA0d2nPoVqVk66bLblMS7yEnLUjpGU1hNvnbUdL2rxlQ1cprZfuzhlepiXfKdsqu0G7ctJdWpcL1Zfugq7kWYE+XYnMVR2z7oKQ7z7R9e/7f8f3FiwuPN39mBMay0QtXTy8oRssJ6/bQkqPT/gy5GLZFZThDUF7asCTSXdOXUAhHLS3nnP+YPqBd3zBW21hj1vWluETORbNUFL2yL+EQ8cwc7PJpDpFp1IU4fFMLuoIp+TOD/hj8e3v+PZgui1doxVffdhJit/9bxcH019+cM4rJyWxOdeZPgdyH2tz/kntiDwDRdlW2CEk1bUtrp/N+U3HvNtFLCy47cp7lG5jTNLeCZxvHXJRePe54dvpLUu9BwYXBTMitFTjC3k/E2OHzCIA+LJpqSDXY/tyIKtA+R4QankauU7BOXLoHDdpXV+R6C6dKrtO/RLmS57VM478GQvFibhBV+0o7WX1CBfbFGxUN+wq4RdhQggha4J8CSaEEEIImSbJX4SNMTvRc4m+1Fp7QMx/A4BXWGsv6f8+D8DHADwOgAXwamvtsQrbTAghhBBCCCGEZJP0ImyMuRjA1QD2qvlPBnAFgO+K2e8D8D5r7fXGmCsBXAngt6ppLplFYpI2KffypJMA6tbJoZ/+H35gMP2Nq+70yrXP3+XqEzIpLRlLdYOMukZHlnnl5A+5jmqDdN2UsqGWllALKZgvcfK3mypzSZU858iGonK0mKOkJ18e7pINhOXQ2jE05BSt25fjsJtDo+k7ptfFiWyokba9w+kqW8LBtpBuyAC6Qg7d3ea0mV3liFtL1PaE5NBt5WrtuUMLl+fiuO/6WzvkvugWj60E2wcht5XLujvmUMhQivOcg+/82c6hGAA625y0tbtd6FTVtmKuwl65RIlpKlLKrF2PZw3dPOmA3BbHVvZNAMAFuweT3Z1KKyz4zmF3rh4+4fqwdog/tiyudXGxy/AaIP26rQXOKQB0hBy6Ww93jLq4VpvCKVpLnufn3e/d29z8XXMtr5zc5+Mtt93FFb++5IwNoUFb7ZIvyQ7LpD13e1WlN+bWZMFw//ZChdSOLItrJCZfbQXuAe3Ee0CMKlx0U+XGeRJqv1xIDp3qQh2TWqcei+jzhNfeUGiZakeio3Rq1gMSZsZvRTNBqjT6MgCXA7jv5AxjzDx66ZPeIuY1ATwfwKf6sz4C4KeraCghhMwypZdJsipFe4L/mSCEEEIIiZD0Rdhauw8AjDFy9lUAPgRgv5h3OoAj1tqT/xK9H8A54zeTEEIIIYQQQgiphizXaGPMCwGcZ639DWPMJWJRDWW/0M1u2rZhkI6chZBJFZEzXBO6jK50egXQNs6R9BvvvHPofL1ekaFb1DKz1CoSTWXjdYQUaSXJj5AUxbY7plRoFJfNVKmZt07itrsRqVXI8VrXneoUHZIZViGT9mRmDaAlvgrLfialpwDQ3er6dLu+xa0T+ULaFdLOWsSZNuca0aO2bIcMaagd8c2t6vsPDaaP3XvXYHr7Ey70ynWE5LnbFBLvegE0xbiwRTgWa2dfKROXUus5f1yRY45/XBBk0g7QKVTtEg2EZeI1talmU5QTK3WaYflzoxlchMdEhoCVZXnf8MtJObRUVLQreGrQ10G9HnZJl4T6T8PvZtiyxS18yiknBtM//gT/Gvnbh9wx/NYhITvv+BXK8BjtaC8JyfhLkulaoJx2y4+Fs4jpoEwa8KTSnWDmBWClI49tbB8DGQFUOR0uMymqcF6Ouzy7aX1cUl2opQu3XKehzlWOk3Usy0MozKkU/lWBG3TOOpvdQZqOyKuTe4x+FsAPGmNuA/BBAM8yxnwCwEMAdhljTo7wZ0HIqQkh64Ocl+DNTovS6NFp1lcvQwghhBAyAbK+CFtrf/HkdP+L8Futta/s//4SgFcC+FMArwHwufGbSQghhBBCCCGEVEPWi/AqvB7AtcaYNwP4Hnpfj8mEqEVkSH45iHJVt0H9FjLQesNtbMtWv+DiXieBls7QWkK9bZt07hy+HSAsD+120111Q5SPrdSnDZ8N+C7SnYD8WZfzpDy6vgyXx1RSZUOp/UdL7kLrpUqt9PpSFufLrnU75HSa5DCHRt2XdEqJZaOhKxfnu+b6eyfx4Gopb5YcOoZsh5RJn/A/ex//J2cR8fj/8K+8ZQ+84y8G09vOeppbIAeMdgfdLeKiFtd+V13f8uuxlENrR255ra+VY2aOg3RRK8aWR+tNSaW97CJ67JQDl5RJx64JLff3tisu6pj0Vu5vR1w78WtYtKFWjYxaIq9BmaVAnxopOX3OGc5N/bKPzXvlrvk5J5W+60hYT+45ukeOhdeOWJaDztBiAHyJutzfWqSvBmXS8E2kZURD/P6Vdl3E7mWhU1/FM07u2JHq3iwJOUPr9WTdDVVO9sdGMXy+XhZqKxCWQ9fUQQ9ldpgr9L1dXsRh92+JlonTRXp0qnBJ3+iM9CJsrd0zZN6NAC4Rv++RvwlZa8Z9CSYkhaofyNcz8iU4hvcSvMmZRIzwRofX3OjEUooRUhX6H9yEzCocEgkhhBBCCCGEbComIY0mhBBCCCGEELJGrFV40HqCL8IbCB0LkBon6sUPJ24rlpakLkJ8m7KHLfgFmyJYRaaO0HFKMi642ZTlVJsS9Q2THBhKx0/G1kRiokLxw+W4nWobnxvvm1JfaiolvU/B9EmRmONQDKHGKxdpYGq8cOw6EKG/pfpkXKu3j+3xz2+87WkpWryOJ2JBuwsqfv/cCwbTD131+cH0jvN+wCvX3hJId9TuAFubQ5d1m+qCFsGH8vjp8UIe91haqVBYdRXjQ2pccGydVKm0nxonvCyGjBmOXRde3ZHxtu3FuIb3I3QNd1QbUtsUw6tfhrorfXUhGrUk4tJryuD82GPumP31/QuD6T981Qmv3N/c72KGj664g7aiXOY7gfRRqWNR9N5TRAoG4oUBlTJR9jPdr0QdsZRGXsxsxnU2K9EDOSmISnWkXpuBuGC9figueKHut68pls3JuGKd2kz0k6W2W3hCP8fImHovw1ZaHLBmon426jcjKwiQKI02xuw0xtxhjNmj5r/BGHPjkPJPN8Ys6fmEEELIgK2MESaEEELI2rDqi7Ax5mIAXwawV81/MoArhpTfCuC9AOb0MkIIIYQQQgghk6U2o3+zRIo0+jIAlwP46MkZxph5AB8A8Bb0cgVL3gXgPQB+pKI2zgzT1NpPUgIUs8mXaFmdlGhJyVStHmmslDIraVlHpJSR0tuyxHS4HLqupdEBmWrVmWUALdONpCDA0GKlciHpcaeU+GI8ctMPxPpjagqLkKxbrx+SQ7e0XDJRGh2VAAe2G0NKWLvt8EqxdDVe/+xE+k+g+uguZcgqS6p9KYMU8tDOTj81DC7cPZjc8oQfHky3lYS6u138X1R9Bfbk0FI2rY5ZSA6txxU5LnjHOXG80Exi/BiXmBxakhX2EpH0h9qgketFLpHwNVy61rvDy42QKk3KrQuhSy4WW34lj60MJmviAjyxe8ErdrBwffobcH36rqNhlcNjy2I8W/Hb1xLSaC0ND5Eqi/fCbXQl0YVp6Y6klDuijEYt0Gmm+Ww16XQyoZRJeh9DKZP0KQjJyUtpkQKS53n1fLar6U7WqfNueot6Gzi+4jb2oFD7d7p+C1viHh2Tvvtr6QtXhMkhjDxm3cB2e22MVEKIYtUXc2vtPmvtl9TsqwB8CMDdcqYx5qUAtlprP1VdEwkhhGxEvBdfQgghhJApMvIXamPMCwGcZ639sJp/JoA3A3hjRW0jhBBCCCGEEDIitWI2/2aJHNfonwXwg8aY2wBsB3CmMeYTAP4HgNMAfNEYAwDol3metfZoRe2dOLNygkLtGEXyISVAMbfh1G2FpXDatXV4I0vylcSD7cltAo6wgC+XXCs5oz5mQWliopwxV8ocIlcylOoA7c8Pu0amukGHXGX175gkMiS5TJYNJxKTxZf6Y01OCqm1alQR2v+YFEz+ezNy4jzptvqXaEfoiwuhhu4q64eOdHaOSCy7c+JWM69k08KutBZxhQ/JoWMhElKSXsWYEJVQV3zz8CT4GQ7SgN9ncmTSqeReS+FrM3wNdwLrlMrpnZQa7WUhjT7s+3rW9x92P+55ZDBZM4/zyj32lDPEL3ddLC76J6Eu7XzFwK/PaarzfTAEKLH/RUyjV1k4XJ7ea8fw60z3x5BMPian9pqTeYn5TsRSUpvWcScd0xhzoQ4ta6hyspvNiZW2qeexMxbcSX7qaS4s4Pztvo35/qNukP3qw07uf0JlNmiIk6eCDAhZF4z8Imyt/cWT08aYSwC81Vr7yv6sD4plXWvt08ZuISGEkA1JV+fsIIQQQgiZEswjTAghhBBCCCEbiGLC5nAbgeQXYWvtniHzbgRwSaD8jIiMV2dW5NApxNoaU8/FJECdiPueVy7WMEFIJqXlbrXE69OXWoWljiFJVsm9MON8RxVjGcTqm6bjYeo5jbpGJzo+huTQZXdpN53jEFsqF5BDV3GcdZ/ztt0dXq6Ed/J1wdGle53QAgBdoX7zryW1HSEp7opKStLbZuBKUBdZVzpAKy2zJ3MOOMTrcl64REkanSbTDI0RsXLe/Bm5caQ6B6fKbbuJ/TZVDh0rF5JDp4dB6HIRF3dhbVwIjW6xuOKVa99932D6CW/53wbTD77ja1652jk7BtPHtznpqB5XmuIpSzuXB9sak+AHLqVCh1UEZNOlMAgxrZvnjxeyPt2q4eeuiOxITt/U0upQ9TEX4ZBMurdsejff0PARe16JuTJL1+imdJBW0ugdTff7wh3uBP/UX5/mlbv+X7qwgL8/6DpxrAtXHXJByDSgLo0QQiqg6pjjzYBOfUQIIYQQMi0ojSaEEEIIIYSQDcSMCJdmGr4IbyCqkE3nyKRjTqXwyhVJ5crrpc2PyaFDVCGFirlQho57zGF4rUiVP5eWJdYXkkPrcjkyyJD77LD1QqRuN4SWRme59GqNjieVDktHq6bmmUELmbT+hBtwiNcdPNXtXW63tCkpAw2ES8S2VdpuyH23YmfoKuTFershF+nUsTgmHU2VrMYIrVe+NgPXcCxcIvFi0q7RodOgc1nXzz9zMH3f79zk5l9wVnBbhXChXmn49XWkvDjQhzWp0v9uPVyuHoi36agj4Z3v2D1VLtDnUa4jCpZc8JPl0MXQcrH+XUTGW18OPXz+KMj7fEnKHNhuLFYzx726JI0Wv6VMel6N0XL/Dwhn6OuEFBoAvn3IvR48suS2vKRco1shJ3AqpMg6gdJoQgipAEqjR6fGf1cTQgghZI1I+iJsjNkJ4CYAl1prD4j5bwDwCmvtJf3fewD8CYCdAA4BeK219p5qm0wIIYQQQgghJAS/dq7Oqi/CxpiLAVwNYK+a/2QAVwD4rpj9uwCus9a+3xjzRgDvAPBz1TW3Ojbbh4hU2XRcojNcNl2SwMo1Eh1NU0mVk42ybC3Qx2ya7UuVLIXk0Fp2FqpPrx+SQ6c7xHbD5QLO0LH2xbYr0ZLnmCw1WYo6vNiQFeVKw2XSJSb5ZVp3iYjLc9DFPVE2XY/UVxPbzXWPr1oOXfV4FpWOijaF+mNsW7ru0HieKrWOEXKG1vXFHN3T3eMj7ZA7KfNXb5vzyrXP3zWYLs7a7uZv9R+Xulub4oebbPkm1OiInUntI6FwAQBoNDwhclJ98lrqtrt+OxL7tzdmRfpPyGl6yGqunHa37w4/ZuVniNHbnps1YlovFHo7vgy7GyxXF3LoLUIOvb0Rvii+8Yjr05//Z/86OLzitrDYcgftREka7X63xD2qVviy6VDWCGDtQsOC2RbIpiLl2r4MwOUABjkFjDHzAD4A4C2qbB29r8EAsA3AYgVtJGQsZu0leC0Z9yV4M1JFmpjNRvIDP12jyRjwmhudnH/YbHb4VW10QrHDhMwaq34RttbuAwBjjJx9FYAPAdivil8J4CZjzK8BmAPw3GqaSQghhBBCCCEkhWnmxl6vjPyPLmPMCwGcZ6398JDF1wJ4nbX2bAC/AuAGYwz//0gIIYQQQgghZGbISZ/0swB+0BhzG4DtAM40xnwCwBsAPMla+98AwFr7aWPMHwM4HcDDVTU4l1mUx66V3CaWWsArV4oDCkb4BOvrJIYzpUp209MixZbF0hik1R9CS4pT/xu3VqkGUiXQss+kplkqp09y0+kxf+GUQaG44Fh8Ycr81ZalkpOGJipb9OL8sprkETu2OvVMsEmBdDA6DtiP7/XrCKdFUtsKxAWnplGLlfPmRwaBacpKk1PNROKFQ3VUEfubQ+xaj/kGyBREqb4BJeROixjhror9RVMsk41q+Hfsrvwtj2fbv3i89Emyvna4sR3Z1xf8C6brjdnjxQv3NjZ+PqHgcJF6X4+mUhLTpYem0fd/3Hjhqhj3+U8/W8j6tjXdsr27Wl65s7a6I/Dxu7YMpj99x7xXbtcud3Aev92ts1APbzf1OYGQWWLkF2Fr7S+enDbGXALgrdbaV/a//J4wxjzPWvslY8yPADhqrV3zl2BCCCGzR+mhnBBCCCGVMIsfAWeNnC/CQ7HWdo0x/weA9xpjtgA4CuCnqqqfEELhiyv1AAAgAElEQVQIIYQQQgipguQXYWvtniHzbgRwifh9C4CLK2jXhmEWP3gktyn5P0lpKZdixNIxhcqlspZmAVIqPAumBTlS6N56aXWGUiQBsbRIutxwyW5HyYFTpdahNsRITUmTi9ff5XaTJdT+edQS0cF8dSJlKhcpf9bH1pN3xnIQYXixsqzZTUfTIon6tQwyVH9pW4nlQhLoVPlz1TLpqPx5ilLm1LFYEg9biEiAA9d3Wao/fEyISvpj44Ans1fS45BMoZR/a3ixQh+0Fdeo4oSTqRbH/TxLxbI7AN0t7tGss3vBK9fa4dI2yXGgUPcXTzKfOD7m5vUbtz/GIjG8a1gV9MIqhL5ah2aE6iv372rv14WX+ihcruqvdk1R37nb/IP2rz/p0oO95CmuP/7ppYe9ci/54NbB9NxTnGz6lHn/ZtHwbg+xaz3x2WPtH5PIJqOyL8KEEELIKDCVCyGEEDIZKI1enVn8YEkIIYQQQgghhEwMfhGeAFX8d2EWZLRIlLLE1c+prslSkpS2WU3OMZvmf8umKZNOlSF560ywbk2q86uUS5ZcqAOu0amy5iqcnHPrCK1XxbZi63hy6GUh2VxS2mjpaCtkpNJRFwA6QlYacpDWv0tS5oAculxOTFfgBr1WEujU7aRKpT03aLWP8lrIcTGvgnSHeDfdUdew128T5c9RxMGQMmkAcQvjlKqVG3TREtfZkaXBdP2AL0XtHHjILbvwTNe+vaf45ebdNdepu+moPD0iB9a779cxnqN0bCzOGTtrkcZKgXtXy8Tl+R7daHokpvVMoe/DLbFjx1tu2T8c8h/zP/HyI4Pp/89uG0z/5J/u8MqddrYbcGve2OFvtyOPdWboVSp624RUAV+ECSGErAmxWD5CCCGE5FNfvcimJ/lF2BizE8BNAC611h4wxnwYwI8CON4v8jZr7Q3GmNcB+DX0/ld7K4BfttYuV9xuQgghhBBCCCEki6QXYWPMxQCuBrBXzH4WgOdba+8X5fYC+D8BPBO99EkfAXA5gHdX1N6RmObHhqqTo6evN+aGo6RahkYaUYnzdLXMwkeoKuTFVRCSKI3i3Bhyio7LIIe7wAK+HLodkD/3yiU6yQb2ZRb6AZDu0utJWxPrjh3b4jHnGFo7uuSVKxbdsu6CcLDdOe+V82xbG+EDGpIyd7tdT+4Yk1dP0g06d9m45MifRynnOQdHOlfVsunQ9d3WLubyWhfXc7vll5Mreq7Muq1yP0ouz2nu51l6Wb1jEimNPuF2uLX/Pq/YmVf+6GD6obff7NY5x5esFt64J1zg1T2llqgBTu5bGSEnsbE4FNqikWNCXbWhHhlzJLHxYlxyn/1ynvm8DA2q86903LKDS65V3/h+0yt3q/gtuiaestffk5bY1lxN9OGYM7SY1sclVw5NyKRJ/SJ8GXovtB8FAGPMVgDnAfiQMeZsADcAeBuAJQCvt9Ye6Zf7+345QgghxCMW80cIIYSQfGbCb2jGSXoRttbuAwBjzMlZZwL4AoDXAzgM4L8D+CVr7dUA7umXPQPAGwD8fKUtJoQQQgghhBBCxiDLLMtaezeAl5/8bYx5L4DXoCefRv8r8ecAXGOtvXH8Zs4e03SGTpVwVp0LS283LG2J7EdEAuzJaPhhaCqkypNqhZY8ZziGJjrEdrVcMiCf6yg31nbASTZV2tmJKCc9FWWkjlyZXUiKGrvmfHdgVV9gn7Ujd7HipJnFcWfdUL/7kL/du4SD7RMf5+q7yHewxYK04nDTpfZFnF+Dxz3x/KS6Qa+V/DlGqkv4KI7SOdudlot06VoXF7uUQxfLysVcuJrLPqw7SVe4mhdN3yam6/0shk5qapGL33OvliECHdX2hlvWFddLY88TvGIPvv0mV90FZw2m2wvK7mbMvlo61xXI4j3H74g0Wp5jKYVvKSm8XK8unlKbSgo9J549pDO0Hjt8qb6QF6v2JT9rZZSr4vlMdrlW6WY+fL8W28pdWtz4pPx5R9OvcFnfIPuk7vsoUuhRQrEIqZqsF2FjzA8B2Gut/XR/VgFgpb/sSQD+CsAfWmvfVUkrCSFThTcmMg3W6gWUEEII2ejwI9Pq5KZPKgC8xxjzBQDHALwOwLXGmB0APg/g/7HWfrSiNhJCCCGEEEIIIZWRK42+3RhzFYCvAGgC+LS19jpjzK8DeDyANxlj3tQv/ufW2rdU09z1T0wOPa4EOnX9mOtfNGF5aLXodvNk0xK6DVZHDeM7RcfKxaR1WqZ7kpKTbEAO3VKKw5DMLtY+fY1I6WPIvbjdHcFwNoMcd+D0usMrefLT/Y94y85+67MH0/f/ztcG0439j6D9ggsGvz1fWrGtdgtoCLNSmS+45AYtz0ExfD7gS6BTZc6xcrPwn/Jkl3DtBh1YFpM8pzpI56Alz5KYVFZe657kuQCKo066XxxxrubS7RzwXc2xY87N3+pvqyicxLgbSa4Zcy4PlfNCM+qq486JjQnX9bYKMyjO2+mWyX3qAp3dbr2ukHzL66rdAprimgtd+101nsnzk3pNxNygPZdwJXleabmFy0tizD7mF5Rj08oWdyza2/1HVtmnazVRdxtoNMWyQHfvdOLy90E5AI3Ac1M5hGP0kLdaYH60Ter5qSVOQivyUNYJSMMX6l1PDj1X6w5dJ9qmpFKEzBYjvQhba/eI6fcBeJ9a/m6sUaqk9c5axQGTzQVvVI6UByCA8l2JfAmO0WiuXgZIPwdkcyFfgmN4L8EbFPkSHKOZeM1thvFMvgTHSB1/Qi/BG4lQTDBZ38zCP3xnHT6GEEIIIYQQQgjZVPBFmBBCCCGEEELIpmLj64oqJOe/BlUks06NH4nFpiTLIyJ1FF4ckAxSi9XnJsupCtJiUELHfZoy3/X8H6NJH6eccMNuJN1RKC64teIXXBbqyRWxTGcvkX2wIUY8LZ9ryIY0ZOyZap+Ylou05DBVghiL8ZT1x86jjG+uiwpV5gwvvrC7RWgpn3i6V+6B3/nG0GXeOtDxim6+DpOU6ZT08QzFBcfSIlURBzwtiWgs7jvWvk6kX4T6TGos8aRJTmHmxQi7H8UxXxpdv8ul91q+597B9Nx55/j1XbDbTTf9jtZtDg8MLsWiJ/atULmuOqkdkVYM4jotybq9nEG1YDkv9l5UPYn+nNNnvFRK6ubQWhHLjrgfje8+6pWr3XlwMN3ee+pgeuVJp3nlmk13bGRqpdy+Lp9JYvd8f9z3NxaO/R3/AvQOp061J56hYveNUFpEvb9+esvRn9UYhjUb1Kd0n1vPJL8IG2N2ArgJwKXW2gPGmA8D+FEAx/tF3matvcEY81z04oR3ALgdwGuttWkBP4QQQjYNjF8ihBBCyFqR9CJsjLkYwNUA9orZzwLwfGvt/aLcTgB/BuDFfWfp6wD8EoD3V9dkQgghhBBCCCEkn9QvwpcBuBzARwHAGLMVwHkAPmSMORvADQDeBuCFAP7WWnt7f703jrCNDUOqBCb2NSQkhx5F/pwqy5HrxWSuHaHF6Yj6dNVB2XRmmqWQFCdVrpwr0VnPcmjJLMqVpHRNy9h8aZ2bXla6kqXH3MLaoROD6fqiTq/i9IPtHSINyVYllZyT6XlE/1aaw6oliKn1xc6jl95JXND1hn9wG2LFtnCjbSnJYbFn12BaSjO7Ij0NADTFMZOy85oajEIpkvQyPx2KXy50nMrpS4aXK6Z4QUvpf+z8psqm9bickz4pRvDYqt+y3/mpmVR9qSpQWU6ERBQn/Gt45Z5/Hkyf+eZLBtMPvP1Gr1zjCTtEY9MaEQtpiKUfC62j08QV4hrpirxNnblwDifZ9xv18LUkQxBK+xFYVgq/qHg8S02hJ89x7TsHvXJ7r/zBwfR33vmPg+n2nt1euU63IaZHbmp6to7IMUt97oqXG75OLvI6zc2UlvpcSGYbqq5WJ+kl1Vq7DwCMMSdnnQngCwBeD+AwgP+O3pffUwEcM8ZcD+BJ6OUZfpOujxBCCGH6JEIIIYSsFVlfa621dwN4+cnfxpj3AngNgFsBvBjAcwB8D8A1AK4A8NZxG0oIIYQQQgghhFRB1ouwMeaHAOy11n66P6sAsALgAQA3W2v398t9EsAbqmjoRiEkU9AfRlLlDI2IpEbW0YjUF3IE1HKYjlfOzW8pzXNH6N1qcpmuL9DWXHfpEPzolEbqOYhp3NsVSKjaUr7bchWuKNfo2qOLg+mGddK67ncf9soVFzrX45ZwIG3Xt3jl6nXhqCxUi1W47WoH5GQCmrRSnw508oZe3ZN/u+G/veDLNNvCeVs60zbVQNIUDrZ16Vyt7ixSwllyja7LNmHoNKBDRMR05AKfljN0iUibcmTTuvuEHKVjfdXblqqwm6F9TN2u3JQ2dJd9qysdlZU7efMC5w794Nu/OJieO/9cr1x7q7SFV67RUmIc6WdSDp3qIO1tJ3KP8oyhlTI66Iqu+lLoWppECEeO63jcaXu4a3ZHjMsA8J23/8NgWrpGQ41TMYlyTvtCEmV9OReRZXK9RqR9IQl0xbeKIeXCz3gp6HbnPJOR6VGF1H6jk/uOUAB4jzHmFGNME8Dr0IsT/jyAZxpjTt6dLgXwtfGbSQghZKNRY24HQgghhKwRWS/CfTOsq9CLAf5HALdZa6+z1t4L4JcBfMYY8230YoavqqqxhBBCCCGEEELIuIwkjbbW7hHT7wPwviFlPgvgs2O3bEaYBVltEXMeDMhwAKBRG75Mztd1doT+qdXxKxQqVbXMr09KpYMyabVaqpvxuDLpzU7MBdYrF5FixsiR0sXwHKSVNLp+fMUt+84gixse9+bneOUefPtNg+naWdtd3TvnvXKdjjs6qfJGqP0NOSBrkmWLog7vilMnJKR+KprKcVYoC+tiHND7K6v35IIRWXPdqztcriR5DjhKp7rgalKdfqvGc8SNNSFwU+mqizF2LaU6ulZ9Pabin4PwcZHnvjUvQhN2+ddm+6JTBtP1s9013F7wH2G6252reXchrM+XfVX3l5CBW6rreG7v85zfo9JtOS1c1hOl1jHpbfy6ctOpEnztHt9oivv3Tid/bylptOdav8Wdx+Z2/5w2xPgWctPWbZKkOzmH19PS09CzVmxbqcSek+jyTIZB1+jVmYX3PEIIWffQAXl0eMwIIYQQslbwMYQQQgghhBBCyKYiyzWaVENIhgOsIocOrKclz3Pi90LdTc/XY9JoN73U9jd8oj1cY9FREupaQDqa6iicKsule+H4yL6UI0/vMfy469Phu7FWYcUsZHFnO2fo7/32X3nFtp17wWC6VYyvHwtJDrvdsAQvJm/MQl0k8pc8tp2I3FbKFlOljjEnZ0/irEywYnXUPTl5uFxI8pwq56yCuLw4cB2olUISUy29jblLh9qRWy5VVxkaL0rbDSzT8l0pya/Nudo78F2jC+kA3V4Y3iAAmBPy6qa/MelkLvu+ViWkSvDHlRym9tuSG3SgfbExJlcOnbpOqF/o890U14hsU6vhn+9u1/2W563kWi/PaST8IuQEXs60MdwNul7T8mf3e071Hz8kLRbW5qZjtyJvWeQZJ/X+TTYX9dWLbHr4RXidwRNGyGyyZql61jE6lo8QQgghZFokfxE2xuwEcBOAS621B4wxzwXwbgA7ANwO4LXW2mVjzNMAfBDATgBfBPAr1tpW9U0nhBBCCCGEEEJGJ+lF2BhzMYCrAezt/94J4M8AvNhae7sx5joAvwTg/QA+BmCftfZmY8w1AC7rz9/wTCtxddSlUH2VkpLoHc2eeGa5U+AJW9uD+Q8s1nHRjt7/Kr5zpDFYtv9oY7AOAJxo1wf1LXeKwbaW1XZboo1aruxJYrvFQB7U6bqv3ZT4rB3yHGi5m3eulKxLnu+Ty9pYRa53UrPTGuJGHJL8KX1Ztymcnk/pSyYPAFsvvMgVWmmjc8oW97spNbx5n3FD7dNO0SnSRSmp1vJq6UTd6fjl5DHzlsHJBDudLuri4uysdNHoH/eVlpMWLq/4MkO5rNVW08Kptd3qDpxbO223Ld2+btd9/e10gcbJ+lpAs+mXOylb7Xa7Jfn2sP0fdizkMrmO/ALd7rjf7U78HKSeq3A5uR/dwe/etCunOSmD1Y7SGnltxohta1yCLt8dd07bna5/DuCPA41+/1leBurzQiq91EZ3W6+jFEeW0d3Rc4cuji97TtHFchvFQq/CbhtoqL5Vrw/vn1qWPkxW3FH9J3a+Q/1R903Zvzudrt/3T7a1E29fauhCaP3Yspy+D3F/6EAdi7aTOrfbQHOuV7DV6mJhi6tw8bEutmw92Re6WFjo958WMOdONwDfObohdKBalh1yp487Qotpv1g0E8fJ56RWB94z01bxPHaiU2Chv95j7WJQ7kS78MLaTrQLnIwMWG67bbU6RSmLhnf/ThwTyMaGrtGrkypMuwzA5QDu6/9+IYC/7ecTBoA3ArjBGHM+gC3W2pv78z8C4KcrautMs15eggF4L8EABi/BeplcR9cX227oWOi40NhNhkyfWmAaWO3BYXgcVNJLMCp+CYZ6CQZm+iV4tXLew3oxfH5pmUxppC5O+aDYbAyfji1rqnRM8iFUbku3T75ANLz6/HJ+7Gb4eHoPtbr/BI6ZlmHXA+Vyz1W4nN6P4fGKmtR0PandeJLS/ViqK3key2m13HRD9B/5Egxg8BIMYPASDPjpkgAMXoIB/yUYcC/BvTYNbysQjq2NpeSJXZuhvqr7t9f3ZbzrGr0Ej7Kt0DErjQPy2p9zBeVLMIDBSzCAwUswsL5egvX0VuXNshDwcJnT8chy7PTSMannKTnNlx9Ckkn6Imyt3QcAxpiTsy4CcMwYcz2AJwH4CoA3AXg6gPvFqvcDOKeqxhJCCCGEEEIIiTOtj3TrmdwPcQ0ALwbwfwN4JoBtAK7o16c9g6l0JYQQQgghhBAyM+SmT3oAwM3W2v0AYIz5JIA3APgwgLNEuTPh5NSbkiokKjHJi/xvj5boyDRJpy+4/0c8+4wVr9wFO5wcev9Rpym65WFfW7Ys0iQti8CTlvqHU02mtRHLcv8jkppmgDhy/gsYSz8V/Y+Zt5rsj359vmOelEv6bQ1JbGv18D4Vh5cG0yf2H/CWLZx/vvtx1jZRYZpkNSZDlnLoVFnhkE2nEVkndF2UYkHHHJBS90NLE+NS4dWnS/VH5I3hdcLp1mJ1dPS/dofNRzlmMoSXMsnbyW64nL4AO8PLlVLPBerTBEMQMlIplesT81V1Us7ajvSRuiiXm+orlHaonLpw9brLy9I6YarcPfU6qCJFUqpkXod+eIh+4rU98TqItSkU9gHo+4NcJxJWEZE8h1ImlVJTBuTPgC+Bnq+7K0Op/SED1JZF51/p6ItYPGtFbgI5z0apz1Z8BiMbmdwvwp8H8ExjzLn935cC+Jq19h4AJ4wxP9Kf/28BfG7MNhJCCNmAMOUUIYQQMhnqxWz+zRJZL8LW2nsB/DKAzxhjvg3gVABX9Re/GsC7+/O3A/jDKhpKCCGEEEIIIYRUwUjSaGvtHjH9WQCfHVLmmwCePXbLKqATkYwl1yGmZ93ZOOaAuL3hDoaUQgPAK//6tMH0J378kcH0Pz7qd48cOeI00akENiKTND7IrduXSIZ1cbLPtLx0MkoGKS5c6fzaUA7ILWHjWnvg+GD6wnf+hFfuvrfd6n48yfX1bsO/oqWjqZRn5zqpapfZUB1VkPof1py0OanOxjnyZ/071Z3cnx9uX1zSL8aLSDF5H4nJqb1yse0KwjJpQEqlU2WkyfLnSDuqILQtLW3ttN2G6wFnX6AaOWbM0V4S68fBdTIkz6nLUkMEYuvFr+HxB6NQHbk1h9oec+72Hef9cv64EnFeDsimy67RQhqtZNM7RbaNx21pi/l+ueMtt7FDyzUxrY+azD8VlknXVGrKAaWTUO1zUui5KxLNQchMkRsjTAghhIwFpdGEEELIZJjFj1azxqx/5CSEEEIIIYQQQiqFX4QrwpfMSamIX26S/53R25K/jwkZjnSGBnw5tFwm19H10TlwOlQtha7axbxEpL1ysGl5UjUto5WyabGOb3aOI6cuDKbbP3TGYPqB//j3Xrnuk88cTHd2u3Ua8/52m6KBUlqn5XhS3hmT4Pnu0khi0l9IQxLYZOfYSMEc+bP+7csWVTmklQuRKtUrjaNCW9iJjO0hIWiOTBqIO0rLnelGNpDj9hp0kI5UkiuDlNdS13PCVpJVyGWJlSvC0uNIn56g5Dm1XNVu0KNIoasej3KOhTc+RN3ow9sJhVyUHcOHy6a1a7SMqplXrtEyQ8czTne5Ei5UIWn3HHOV3H7QxQAttf1O1xIdviafMyPPlpWEEohpyprJRoYvwoQQUgGUII0OjxkhhBAyGXiPXZ3kF2FjzE4ANwG41Fp7wBjzXADvBrADwO0AXmutXRblXwLg/7XWXlBxmwkhhBBCCCGEkGySXoSNMRcDuBrA3v7vnQD+DMCLrbW3G2OuA/BLAN7fX/54AP8J+aaBRCBlKWX5szvELaVfWRJJ2r9/wgldbnm46ZWT7tBSDi3X0fW1RNJ37TyY455aBRvFKboKOXTOfwFjKsC4PG+4TDVWX0fIyRY6fuUnRCUhmXTvt9PJHf3hxw2mW0/c7Recc+Ua21xfn5/3izWablsNIcGrK7fqWsTdNiSHjksYp9dvU51kU9aP1ZEqfy4tQ0453aY02XRIPthW/TEkh+6o21vLky+HN5wrlQ5RRDSMOVL45O0munWn7m8hrjntJC9l01VcLlWHKlRRLnXMznGXjsmhq97HnHVyx1E5dsbDL8SYkNakaPtkHVo2vU24Q0s59Kv/16leuT99waHB9F1HpAw7vK3UtgcdpAE1NIXHrND4OAlHd0JGxRjzI+h9jJ0D8AiAX7TW3mOM2Q3g4wAuBPAwgJ+x1j4Qqyv1uroMwOUA7uv/fiGAv7XW3t7//UYAN4jyHwTwtsS6CSFk3UMH5NGhbIsQQgiZDLViNv8q4OMA9llrn9af/sP+/LcD+JK19l+g9wH3P696jFK2Zq3dZ639kph1EYBjxpjrjTG3offSewgAjDG/BuDrAG5O3BlCCCGEEEIIISSIMWYewJvFx9jbAZzXn34Jei/GAHAdgH9tjGkiQq5ZVgPAiwE8B8D3AFwD4ApjzKcA/BSAHwdwTmbdE2Ga7s2peJKxkimo1AaJJOrdwvvvhe8o6Ndxoi130q21rKR/IbfBpbZfTtbXkm6+Ebm2RMvdUiU1lN6sTq5zbmodMblpSCamFMWYq8k6wif1sZarcU7okAulZ6yJZc05cY3sWkAI6TraUA6kUgIdc42OuUH70uPR5Yhr9VU5V8IY6jOpsmZdVpbT/Uf2QblslG1JQiEnyizfWyZl0y01aDcQWja+TLpch6s/Vco8toO0qqQbqaQInIPcUJmiPnz+KA7Sk5T25l4/OfUltymwsUk6XGuyHa8Dcmg9ptYC5ZKd6cNNSD5X+jJYdEbROCCycHz8BY965e455jawKJ6tUq/N0rgXcq3n8xOZQfoS5t1DFh2y1h4aMn+AtXYJwMf69dQAvBXAf+0vfgKA+/vlWsaYIwDOgFM0l8h9EX4AwM3W2v39hnwSwBvQu1ufBeBW9HTbTzDGfMla+7zM7RAFEz8TMptQGj06VacHI4QQQkiP+uzeY/89gN8eMv9t6L3YAgCMMT+NXiyw5NvW2p8wxswBuBa9d9l39pfpJ7ECq/wPNvdF+PMA3maMOddaey+ASwF8zVr72+jvmDFmD4Ab+RJMCCGEEEIIIQTAewB8ZMh872uwtfa/APgvupAxZjuAP0fPKOtl1tqV/qJ/BnAmgH8yxjTQy2z0SKwhWS/C1tp7jTG/DOAzxpgFALcB+M2cugghhBBCCCGEbHz68ueoBHoVPgbguwB+xVorv/j+BYDXoPeF+JXoGWetDFl/wEgvwtbaPWL6swA+Gyl7AMCe0PL1iDzS0dgzEQurpX8yXiMrBiUSv9bqhCuUbVruhGPqYvW1AstKKZ0C7YsxzTRLs0iORLTquOAi0oZYTKZcNifTItX9ctsa7izvarrpeRX/d2jZVfjgohui9P4ekTHCwgphRQ15nUDqFS1lrgWWpcYB58be5Uiqq/Y4SO5LkT6SEwesy9Zr4XKNQIywTl8SWybxxjo5Prb9cp6ngqxPj48ibteP19NtGD0eMJVSWiDR92PxtKE+GF0nEnQs1wvFC6vmZTHpcIRJXptVxOPG0iIl11FBO3L2OZbCKmeMrWJMDHXp2DOO9lw5uOR27JsH3f3r7qN+JTIu+OCS8BfQ6dsC7Sv7yoTW0XkH5cH1Fvjl5MLElJg1L9UcY4VmgY0YTmmMeTqAlwH4RwBfN8YAwH3W2v8dwJUAPmKM+Qf0XrRfvVp9udJoQgghZCw24k2aEEIIIZPBWvsNBFworbUHAbx0lPr4HEIIIYQQQgghZFOxab8I50iUJ0lJbiLbJ6V0Sr7SikjwOl6qD1FfKX1SmpylFZAKtSJpkWTKpKrTJVF6Eyb0H650Caz+HU6LJOWnMkXSViWNftyC6+XPOt3ply/c6WtRv3XIaaW/8qCbH5dkuUYt1VV/9OShsZQv46UbqUK2V73kOU97G0yLlLitmPxZL/Pk0GJ+TPIs+9mc6mcLYlmzPrxuwB/PZKq4x1RJ2V4vJV0tPN56iyLhLJ4UE6MwfNzPSaWk2xTdqlRLJkqtQzJpINyfciXTOfLvXKYpgfbKZaRFSi03zbRIsXVCcuhyuVD78sa9UBiaftZoiYI6zeThlZoo55bpcSoUhraU2PnL5yAUmuEzi+mUclK7kTRm4f1m1uEXYUIIIWtCPRLDSwghhBAySZK/CBtjdgK4CcCl1toDxpjnopfbaQeA2wG81lq7bIx5BoAPoJdH+F4AP7dacmRCCCGEEEIIIWRaJL0IG2MuBnA1gL393zsB/BmAF1trbzfGXAfglwC8H8B/BvAWa+3njDHvQiMjrB4AACAASURBVC+t0psn0fiqyHJyVr+DEi8lqfFlyGnb9copyYuUSrfUsppXv9Qr+/VLWWlMliL3JeYMnSqHzpG/bXY5dI7MJS5jGy5nLbv+huvzXHrF9HzdP8OnzrvfZrcT6//M/zzNK3f9jx8cTNvDbp3Dy359y54kzbVYS7eXPYfz8R17q5Y5p8r4KnFFzVknwylatzXUz4B0N2hPDi02tqXh9wvpSH7qfFeU8+s7vuI29uCJyJFpCamjDA9RISay7R1vvA3fAybpIK1JlTWH1omtp+W6XbEzse2GlmVlVECe/LmK6ypnu+NKoUeqY43k0DnO0LnE2pcz7vn3ChWS5mXhUNsSkT6yfzbaafuon4tC40Jpn+TYGXsuDI1NmeESqbLmkKs1mSyURq9O6hfhywBcDuCj/d8vBPC31trb+7/fKOqqA9jZn94KwD3VEkIIIX0ambF8hBBCCCHjkvQibK3dBwD9XE0AcBGAY8aY6wE8CcBXALypv+w3AHzeGPMeAMcBXFxlgwkhhBBCCCGEkHHIdY1uAHgxgOcA+B6AawBcYYz5j/3pn7DW3mKM+Q0AfwLgJSmVxmSvuS6Ao6KlHTlS6ZgMR+5jSCYNwJOpeJKSUhL1sKN0J1CuxJhuzt2Ia3SoPal195ZVq+2oQpYzay5zqe0p1HUUlraqcrFlUtoaMT+S7pr3HnPO0J9+4SNeubuPuGWetEx1A+nC2eqGr0DZvlaWi3lM2ppGskx65JpP1j/e+Jjbvponb0x1jQ67QYccyHu/3fSCOPc7lOT5rK2uLzz1VCfB37PDdye/S/Szv3u4OZheVpLnZdFRGlJmr/ZDrhYLNwlJCUtOzmI6V8osD3w3ceAb1xka8OW8IZm0Xi+mjg21Y5qyv1z1bhUS6Kz6QnLlEfYj1KQcZ+hRth1yii65jk/p/Mfkyi09XgSub62MTh475W9RTg/5NXGfkllCGuVYOFdONDB+n5MXu18dZc6zTZ2qq1XJfe56AMDN1tr91to2gE8CeDaApwBYtNbe0i/3AQCXjN1KQgghGw79zw1CCCGEkGmR+yL8eQDPNMac2/99KYCvAfgugHON01C/DMDfjddEQgghhBBCCCGkOrKk0dbae40xvwzgM8aYBQC3AfhNa+1jxpifB/BJY0wB4CEAv1BFQ0OS4kkzrqN0jkxabzemavZkKiVpi5tuexK0tOOnJc/B9ullSbVHJNQVSKEnLddJPcfrlU638GSqMdfo0HWhz+Pxlvt9x6Nu6Nl/tO6VOyrcfKWzr8Z3GxZS1JI8W1xnXSkFi7fXrR4LOchjkk7RVfTHsCRydAdpwJc866/A/rLhztC9326ZdCTXbtC759yyC3e6jf3sjad65T7+AufjeKeQSWsH6ZB0uxUZb2NS8HB4TK47sJAhp2qoFanOr1nO0wGZ9Cj15ciSYw7VVZPlGj0lKfQodeQ4QwNhOXS8TUVSuWniZbyIhK5JOXSt3lXLxA/vgKqQEFlMPuSVQkfCsmmJdLYuRPvaSrodeqbVt01KnjcGdI1enZFehK21e8T0ZwF8dkiZzwH43NgtI4SsGbFYX0Kqgv2MEEIIIWvFRvyYRQghhBBCCCGEBMl1jV5T1qtMGgj/5yHm2Bd1HY1tPHRopig9znXmrbINk2YWZdI5LsC6z4XqiDkHS/T5WRTS6IOiliMrfieR7tJLUoJWktTKadH24c0B4KvOSpKxwFhSdmpPo2qn6NT6UkMfUoltN9R/SucqIC/uLRsuh9au0fNCgjgfOPcAsCLO6z8dd07R111y1Ct34KirRPZNTdA9NUJEEelnAQitg/SxcxaIuUHn1JGp8A7WVwVV1DdNOXQq03Xernb/xyX3+UzeK7RrdCjNR60WrqPuhfmocU/UJ8NA9HlbETuzKAzyH2v55eRDf8e7v+pMEUImnnE91opu5Rk/yOhQGr06s/LcThJhpybTgP2MTAP90EcIIWT9w5dgsl5I/iJsjNkJ4Cb0HKKfDOCdYvHZAL5qrb3UGPMyAG9D71/n+wH8grX20eqaTAghhBBCCCGE5JP0RdgYczGALwPYCwDW2r+w1j7NWvs0AP8KwBEAv95/WX4/gJdYa58K4HYAb51EwwkhhBBCCCGElKkVs/k3S6R+Eb4MwOUAPjpk2e8D+GNr7Z3GmNMAXG6t/ef+stsBvHr8ZoaJyS8mGT8ci9uNrifXidYfqnCEfZpgZ8uJGcmVysxKLPB6pUhMvyApxwEnpvuJ1CGR6bwQSe8g0bGvMoZJxqDqkC0vL4RcqGJLazJ1RoYfwCiMm54oWndGe1KJ9QPZPp0iqRaJgWsE4oLnVVqSLeK3jJVrqm0tihjzOw+7tEj3HPPLHV52t79Dy64Ry6X+OF4HmMXY3ypierO2qw6GTqc0KBc55JNu67jxqalxwLnbnWb8cKiOULqkqrY7yfpSid0D5DL9fOIvE/eUxGe3hjq2O0Q6uDMWRNo4NT4ea7kVvy9SwOlLTKaIUuHDhGxKkl6ErbX7AMAY4803xvwAgEsA7OuXewTADf1lWwBcAeC9lbWWEELIhkE/9BFCCCGETItxXaNfB+B91tolOdMYswu9F+JvWmuvHXMbhBBCCCGEEEISqc+YDHkWGfdF+N8AeJGcYYw5C8BfAfgCgF8fs/6xmKZsOiR3i6mkYpLfVBt/f1t+I6YlwavCHXCjyJ9T0zFUwVp9TCunWZDLwuUaAdmrlrbKr4SyD+s0FbK+RkyqJqSynkRXxQ7IdBay6d3M/p2axsg7ZtFyo0vcU4mNFenpk+S0TpEkptVOSjl0U/SLbQ3/TG5rCmm07D+qPrkvB5fcwhXVf6SE+phIn1RKq4XJ4aXCi4TbtKc0lq8H1koqGyNHDr1WUuhYU6d5bHO2lTvupYekyXJC1hy555Wl0aKRkTSYIen1vArZOXXe/f7BU5yY+fzt/p7cc2xlMH3bI83B9GLLL3cieB9Je7akGzTZaGS/CBtjTgewxVq7X8yrA/gMgE9aa99eQfsIIYRsUHSuYEIIIYSQaTHOF+ELAfyTmvdSAM8A0DDGvKI/79aTMcaEEEIIIYQQQibLJE2DNwojvQhba/eI6VsAPEctvwFrp9gciZC8Y9KS6Rx36RieRGcGJSuTlBVOWvo9TYt3ee7WauCKSmCjy4SkWEzPq5FASlsfL9wv5XwAOLrsNvawsCddUXaxUuoakxc3Aq7RLe3i6UnaxPzI+ch1l/YlxWHJc0h6XKovsc+Exojc6zQkh9ZtledA7lOrU2CLkEAvCMnzjjl/n87a4srtFE6qS21/Y9IB+viKW3a85ZdbFL9PiDpaEQnjLDg+V8G0XKJXQ0qKQw7Ss8ok5dBVU8W9LMcpuphBHbs/BirJc6C5JVkzwvfrkKN0bveWYSBSDv3qG3d65T72gkOD6TsPVxtGM4vPloRUxbp4aSWEELLx2NLYKO4AhBBCCFlvjGuWRQghhBBCCCFkhuDXztXhi7Bi0k7TqfKYqiXUs84squJynXS9OuQ6Y7VmunXnyqnkeg1lhHTavGvxk4X75bnb/GHonmNu2R2PumVS5goAJ6RrtDgYrZK8OE1C7cnkEqVgqcep5LQdaEeqpDhWX6zf5sj2UmX73rmPtE/u00qn8KR/cvqMeX90+xe7RZ/ZPj+Y/ufjXvY+fOewmz6+Uhfb8tu7JH5Lp2jtTj4LY5NWmM6KtHlc1pMcOkcKDeTJoat2ip50HRsFb4gQXVOnoImNnaGxuKNcmWUIRlvev9T4c1Tc9w4cdQWlFBoA7jk2fB1dn8yCME3Jc+xZdR0NA2QDwn8WEEIIWRPkiy8hhBBCyDRJ/iJsjNkJ4CYAlwJ4MoB3isVnA/iqtfZSY4wB8AEApwB4AMCrrLWPVtdkQgghhBBCCCEhpmn6ul5JehE2xlwM4GoAewHAWvsXAP6iv+xMAF8B8OvGmALAnwP4d9bavzTG/B6AKwD81gTaPnVSZSTTlFD72x17s5WwGWQuOW7Bk5QyV02rO34b9XGRX//O2eYkq6/54jav3LXPdxqvu46M7n5ZKuedK1Gfvp69n+N34lh7GwG35YZaR8qIGwF5MaDc4+W02g0px2uJglq25/XVxPEstb/IckvtAvOiXzTFQu0mfs625mD6F764MJj+8PP9+qW0vuPtr5YmDneK1hK+TsVSQnk8U+ubtBQ6dczeKJLsVHLl0FnbqnhTs/I8UCW5WTjGpdvV46O4hvXYGbi+OxF3aTn+LCoX/IdPuEHxG4+4MfA7h/36pCu+XEfXFxrr9Fi0GZ7jCAHSn10uA3A5gPuGLPt9AH9srb0TvRzCx621f9lf9k4AfzR2KwkhU2XWX9TJxmCe0mhCCCGErBFJX4SttfsAoKd6dhhjfgDAJQD29WddBOABY8w1AJ4O4FsA3lhRWwkhhBBCCCGErII2eiNlxnWNfh2A91lrT9p2NtB7MX6+tfZWY8zvAvgDAD8/5nbWFTnyubWSU68HJulsWPVx3yhytA6U3FYeJu2g6UlHXcGW0pgea7ka7zm2MpiWUmgA+N6xtljHScFy+3dINhyTUOt9HHe7elshOfSc+kI6JyTQ8uvpnJJGN0UdbTH/hJLFrYiFy5DS4LBszzu/8AkpB0r7K+sW04tt3zVaOjtrl/B7j7s+86HnPebmi74EAEeW3W1tScihV1T/8fdx+LRub2j9jUSO/Dl1nfXgEj2uHHrSLszTdHkuNog0KHj/Kj1bdIeW66hQIflyUQ6lcNMtb4G/LVnfcuScdpaHy5yb6tllRezLsii3pKXRnkO+2E64CcFsA6VlFT+raUk6IZNg3GHu3wC4Xvx+AMCd1tpb+7+vA/DsMbdBCJkyG+T5h8w4dI0mhMw6vB8SsnHJvr6NMacD2GKt3S9m3wTgDGPMU/u/fxLA18ZoHyGEEEIIIYSQEagV3Zn8myXG+UfXhQD+Sc6w1i4CeDmAq40x/wDgxwC8aYxtEEIIIYQQQgghlTJSjLC1do+YvgXAc4aU+Soohx6ZScbBAlXFwm68eI2qU2KlpneIxVrKNuntTjIeObXuUpoFEVfVFvFHSyom6vsipcPtB13s791H/KMh44IPLrk6dBqIdmf0A1BTcV+hZbHsSalpsGR9+jyGUiE1VDkZF7yr6bZ8xoJ/zLY1XDmZRuORJb+Fh5cDLdaphbxfok2R6yV2jYTizxbbBZpi/+U5fli1/Y6D7na1/6ir8fiKfxs7KNZbFDui0yfJ/uPHuQcau8qyWUDH6nY7w5fp/QjF+MZifzdSXDAZjWnGKeeg71FVf4Hqer4J6h6N4TcSPQbKNEZohw+ovHxW2vLZIFxObkuPe8syLjgy7q2n5z0OMWQcxjXLIoQQQrJo1vgEQwghhEyCjWLgOknoAUAIIYQQQgghZFOxbr4IT0v6sFH/e7KeZC6zSK7Uaq1SK3nbRVjGlSrd9iRUEbm2lHvp1D2Hlt30Utv9D65Z8/8fJ1PoSHn1spJ4jZu2oSw7Hy47K0mog/WF609dNqcqXxDSaCmHfsbpfsqg87e7cvcccxXe9kjTKyfTashzpT/Men0mY+yNyc7lsqV24cnEl0V6p8NKGr0oJN9NsUhvS5ZbEn1pRcu/IylBJOPee3R/iW0rh25OvqNofcOno+tQmzgyayUvnnVZs0b2wVjbc+61pWvRu48MT6WkF9XUQu/+KOpr6XgbOR6JAbij7ptyfKwFpjWxdHCtgBy6FPIUGB9TL/VJjqmEVMm6eREmhBCysWiss4dyQgghZL2wUT/uVUnyi7AxZid66ZEutdYeMMa8CMDvA6gD+DqAfdbaZWPMeQA+BuBxACyAV1trj1XfdEIIIYQQQgghZHSSXoSNMRcDuBrAXjH7GgAvstZ+yxjzKQCvAfBBAO8D8D5r7fXGmCsBXAngt1K2MwtyiVloA5keuf8tC0lxo865EelWyIk4VZIdc6GO4a2X6FZdklp15HpiQdsvJ502l0UlsbZK+ap2iQ5JW6u+hif9H1W5/4U67k3xe0fTTUspNAC8+n+dMpj+0xccGkzfeVi5VQsJXq0InDcg6Jodk5Onup23hJSw1VVO2UIWqPv0Yjtcv0RK/2SfaalVQo6pMZm9J03Uy2QdAXnkKMT6cdDluRMuJ+sruUsnyqFTJdAVq7WDTELmK/exWEefU9ZRUysjL9NBOGQlZR0ACBhD9xZ1Q+W0M783YATb5CmoM85x3AU/4hqdUcd6o+owFbI+SX1evgzA5QDuE/PqAHYaY+oAFgAsGmOaAJ4P4FP9Mh8B8NPVNJUQQshGQqeLIoQQQkg11Gb0b5ZI+iJsrd0HAMYYOfv1AG4EcATAfvRefk8HcMRaezJ74/0AzqmorYQQQgghhBBCyNhkmWUZY84E8HsAnoLeS/Af9P/eibKojuoDMrOkymiTXShTpcwxV105P7qt0duXStz1N+yS2fIarzVeQpImZbSxdkTaFJN15SDPVa7cK+QGXZLRBqrvlpw73e/jwg35e8f8clIOfY9wZFhs6/pWn54EofqXu4U6FmH3VHkMaxFNbKrbaejGpK/ZcR20qyAqV47KFoeXi0qjIzs5LclzKrH2rDd35BAbZT+mSez+FSuHyJidihzDO1Hn6eGO0uWQi0gISwahy7ucKSK0fmTszWsSIWtK7rX+PAB3WGvvstZ20IsfvgTAQwB29eXSAHAWfDk1IYRsSFJTahHHZoxrJIQQQqZBUczm3yyR+yJ8B4BnG2Me3//9MgB/Z61dAfAlAK/sz38NgM+N10RCCCGEEEIIIaQ6sqTRfafoKwH8jTGmBeC7AF7XX/x6ANcaY94M4HsAfraSlm5A6FC9NlTtvJjqPFnFF8Oc+jwJtdJndQIOmp1u4UuF5UolibKsT8i91EHriH8DVpE/dlwZVkS5vYr79/jn1Ds0RWA+gCWxkweX3P8tv3mw6ZX77hG3opRQf/+E/79OKZXWMuxg+7z5aScuXk5K0P3zIB2la9FrLrXvj9/RfEn2cHl/aZnYD2WeXgndgCY41Q26VE6c8FQH6WmS8wVBtz2rjgk6SKe2r4r92CjEjkWOg3TM3T7m+Iyo5Fm0Q44D+r4ZGC/0uOePK5Nj0s7Qqc+7sfsSIZNgpBdha+0eMX0tgGuHlLkHPZk0IWSdQpkvmQaURhNCCCGTgbfY1Zk1F2tCCCGEEEIIIWSiZEmjNyuUMm8Mcs5jXGqVtl6qJCvkIA2E/3OV6n6ppaheHV65Qm1ruIT6ZNmTSPfLRkQz1hJzc78KZp1Hub5eFpEo++WqHQhkO1paGt12LT603Bk6v9cmUZ+oQ7tGL3fEuRLlWplytFR5ut8HfRdm361brBNpkpb4j9oG/ds7fqX+3R2+UGkYO53h8sbSfgS6T6PWRUtvPIGuOAm634Zkzh2l0/SWZTpUS3Lku7HzXYU7tKxjkvLiWZcyd7uz1yZN1ecqeH+NhDdo93mvf3rr6RMe2K5qk+dGn+gunRq+UwXJYTATbQUhk4cvwoRsIlJfOikVIdOAEnxHzkvwZmfWX+hmER6z0WEIB1mv8HpfnaQXYWPMTgA3AbjUWnvAGPMiAL8PoA7g6wD2WWuXRfmnA7jZWjs/gTYTQgghhBBCCCHZrPrhxxhzMYAvA9grZl8D4FXW2qcA2IpemqST5bcCeC+AuWqbSgghhBBCCCGEjE/KF+HLAFwO4KNiXh3ATmNMHcACgEWx7F0A3gPgR6pq5KSZxdjfWUlbsRHJkYrkp08aXi41NmmaqZlSKcUEJcY3R+OqAkTjRJNTZ8hjK46ZKjXJWKfYsZAxqC0dIOZ1PNfiZdUhQ8dCp6Lw4oI7w2PP9G8vphd5hNYrx6JLYims3LQ+38nyf5mWJHEdkZkKy2qnvOMk6tbjTU3OSIzHzU6LFIgLbnfC5XLSJ+l9TI3xDKW/0YTOT27scM56XdXAqtMpjYs+fpNsXiWpqSqIAw7VETsWsbEjdE+NeXBEUyuF1lHFgvHC8MfOcJzy9Ei9B8zic/VmhGFuq7PqMbLW7rPWfknNfj2AGwHcB+B0AJ8CAGPMSwFstdZ+quJ2EkII2WDwJk0IIYSQtWLk5xBjzJkAfg/AUwCcBeBmAH/Qn/9mAG+stIWEEEIIIYQQQkiF5LhGPw/AHdbauwDAGHM1gE8C+AaA0wB80RiD/rLbADzPWnu0mubms1YyDUqcZ48qzkmOpK9qSVbsv1jeehFFck4Kh5gMu5OYVqLqL4Gx1Ef+sR4ukx6lvlTSJe7hZVIq3WmH6wu1V++jnyIr3IZUOfS442opLUmgDUCsz4QbIevW/bYhljXrIu2XKifrkOmnai11bKXE3btegs1T66eVA8Ipkzparh2QQ7fbfjmvvsSGyDGwUNrWYswLXEtlQ00aJeVS1WmWpFQ6JpOeVtqmjUry+YiUyxunYicr7V4ZXEevF5BJa6L36ERSL02mRdoYFMzMsCo5L8J3AHiXMebx1toHAbwMwN9Zaz8I4IMnCxljutbap1XUTkIIIRuMGQuzJIQQQsgmYuT/21prvwXgSgB/Y4y5HcCzAPxm1Q0jhBBCCCGEEEImQfIXYWvtHjF9LYBrVym/Jv/r3ygSaEqq155c11FJnrwoUYbsNSjsQl01uXJtv47wPtYS6/Ml5GEpavgcaIvd4XLgsow90qhEQueuLOsOOBuXzndgOxE3aH9+moQ6lZgrekp7euuosnJZxrZ0fVIOvaPhaj913l9/XpQ7suIqeWix7pVriUFBSqiXh7ZmdXz35m5kmZhWBzQkh263/HItsayjZNMSOSbWxO7Xa8pR2ZOpupVikmnPTDvR1XkUp+QcifI0Zc0bUUKt+62W0LtyUOVC9eWVC62T6i49pBa5tfAir1ER2+hY+JJ3XYTdpUPraCh53lxskKFkotC0kxBCCCGEEELIpoIvwoQQQgghhBBCNhU5ZllrDuXP1dZRNRtH1jX6OnrfQ3K3VElf2dk4IJWNEHSQ9qsLO0hnbiu2Tky6XQuUa8TaIGXSalFLyFTjOiGpMU1zTU4lOmZFzUmHy6b18UuVPHvLIpvNqS9G3noxfaOb1P/NDfVBXW5OyHmlHPoZp6945c7f7rTCdx91euCbH/L3abE9XO4fc4FNRY9FIado7Rot5dCtFSHdVnrtFbEMLVGJGtBqTfe7Lq6rrnqSqIuDXZOy6Y46Fon/ig/JpkdxB64F6ohtK5VpOkiPW0eum/Y0yXGKluSGNY0bygRExumou3SsnKxPzFZjahHINkFDws3NLF7fswa/CBNCCCGEEEII2VQkfRE2xuwEcBOAS621B4wxLwLw+wDqAL4OYJ+1dtkYswfAnwDYCeAQgNdaa++ZSMsJIYQQQgghhJAMVn0RNsZcDOBqAHvF7GsAvMha+y1jzKcAvAa9HMK/C+A6a+37jTFvBPAOAD9XRUOnJYeeFflzNbLp8SoJOTyuvt2xNrspCUmyyo7FqXKqNOfpEN1u4UutAnXn1i/RkueGkFLKZdqZ1nMOltNadi6mlyMy6bUKuciRTedKlJO3O+a2RjmW6WEBknDfz5E5bWm4+vbs8G2Tf/p/njaYvv7HDg6mv/lIuD9KUiXo+pj5btCxZe5HW5XrtN2yFaH4Xln097F26MRgunis5epe8B8RujvmXB1b5DJ9EWMoNXUNe1JpcQBTQ0xGkTinykVTtxV0LFYnMiaV3mzIZ5LU54tx3b5HIXVb0fuNt16iu3Tovh6pQncrKZUOyaSHrUc2Njzdq5PyzHAZgMsB3Cfm1QHsNMbUASwAWJTz+9PbxHxCyDqimGD6JUJOwocyQgghhKwVq34RttbuAwBjjJz9egA3AjgCYD+AT/XnXwngJmPMrwGYA/DcCttKCCGEEEIIIYSMzciu0caYMwH8HoCnoPcS/Af9v8sBXAvgddba/2aM+SkANxhjftham/R5aT27QefUkbrOuBLn1bYbTkpf7XZzpdY5VN32dCqQrEaWjfsFrXSNBVx1tTQ6tc6oU7SclttV22kK+eRC3U3PK1llQ1Qo2yDdewHghJiWA15LOdjKNnn7hDw30RxJcaobdNXku0GPu930sv75CUsJO9L9XIwDLbUtef6PrLjp/UfqXrlP/Pgjg+m7xDK5jq4/6tYd2Oe5ehfL7dHPgyeT1spj0XFbooG1w0teufqdjw6mizsfdgsuPN0r13ribrHSFredun/MauLYetEIpX0f7ijdBVBkaNxTpcw57tKpcu1UYu2rwl26CudgKcmPnY8q2pvUnine1lNl9yUJfngtvYWhi1K7/axLnsthXWnryecO7YxNRmfW+sUskhNO9TwAd1hr77LWdtCLH77EGHMGgCdZa/9/9s48zpaivPvfMzP3ctmVJYCKqBEeDcoiUV/FNSLGaDRuaDQaomiMG0QWyRsjErNoomjERIxiRNHgjguLGoiKG4gYiYT3EQ1GEYgoIgjce2c57x/dc091ndN1avr0mTkz8/vez3xun+56qqq7q6t6+dXzfArA3T8O7A3sUZ+VEGISkTS6Rxvhk4QYRpOH4LVKk4dgIYQQYqk0GW6+CzzEzPYqfz8V+CbwM2CzmT0SwMwOB25z95sGZyOEEEIIIYQQQiw/S5ZGl56i/wL4dzObA75PIYfumtnTgdPNbHvgNuAZ7VZXCCGEEEIIIUQK6YyGk/0g7O73CpbPopgPHKe5DHhoKzUbM6PONcmf35ubLi9hK/OZY61nyzK05ZpznMtYjllAKOOL97F+3lde95SW5S49NEMq1FF1Xmy1fgtB/m3MOQnziMMnbQzmAu+yobe8x6bq0dgxCHlz+1wvk5u3VBv0LVt7y+Gco4X4WNQcp9z5s+2ENJq8YavtecrJOfCN6hHN1a2Zdxq32y1BBKGfbe6V/I2bNlTSfefm3jB522wv3c+jdrY5kDaH84Xno7no4TkOl+N+KuxL+rcNXy7KDsoN9nf6zrlKuqlrevOg937dYduWhNxPpgAAIABJREFUrz/18kq6zt136pU1P3r9IqugstVjVu1jg/VLmLdbV3ZumKWmc3rDcEpth1Jarrm546BJKKU2ykqny8uvOke4U7ut0p33dW65481oc4knhTofHG3nvZT860IwivXFaruWhBBCCCGEEEKIkViyNFoIIYQQQgghxOSy2pQiK8G6eRBezhBJbcih6zalJLqN6xFI5ppcNLGHzybyoiY0PaepY9goDNZ8/baKhKpynBJxjBLUVX05XeSnlPXhtj6JV40sO677dkGGu23Xy/Hg3WYr6fbrqTT5n1/1lr9zc1XaeudcL8PNlTrkhU8aB9nSrWU6r8spf27DrtLm+uoeSJTD6yzhlXluay/HsL1A1YP6bCDZ3RyH6Zrv2W0N0sVhm6rXSG85JS9uSqWPDSWbm6rhjrr32W3bciiH7kThk9gY2I2xcfaNjcHxrJNJL4UmYZbi3a2TJScl1AmZdJP8RI8m8uf4XqAui1SoojjCQtg+pzLjCS7UD0sjyzZzL9M25KGSF4vViKTRQgghhBBCCCHWFdlfhM1sF+BrwJPd/YdmdjRwEjAPXAwc7+5zZdiktwIbgZ8DL3T3/2m95kIIIYQQQggh+pCAZDhZD8Jm9lDg3cAB5W8D/gp4sLvfYGb/BLwKOA34IPAUd7/SzF4IvJ0i1vCys5rk0H2yuBqNSaqcVuSNCSlYSFXulUiX0ByM04l0U/lzSqqYk0csWwt/T9VI3wCmphPaqET+i+R6miQ7XTXVdKItLGR6pc4lzCP0DB1KoQH+4Et32bZ89qNv2bb8/VurdQjbcapNh+e+DU/RbVyP45Zot0kbsrjs/U2dnprzGDkirtQ39JK+ua8NhzLn3vLWSBodyqFDT9HxPlXbWVDtJfTtuX1nxTt7MNrP77xdJd3c/nfdthx6hq5IoYGFwK470+vcc+WXfRLTmnT9Hp/rPUpX7BKy6ZTcuDa/xHSJOo/SS/FkPQlMQv3ajiiRHLuDC38+IY1eCLZ1Ex3TVDQ4TgVtcCYYv2Lv0nXjTRtjaIpxykBzvTDnenmOZefdCYyqIFY/udfEi4GXA9eXvw8Cvu7uN5S/Pwv8npltB7zW3a8s118J3LOtygohhBBCCCGEEKOS9UXY3Y8BKD4EA/Ad4DQz25fi4fiZwN7uvgU4u0w7BbweOLfdKgshhBBCCCGEqEPf0IfTyGu0u3/PzE4GPg3cCXwEeMjidjPbCJxV5v83LdQzm1EVNivlGTpXyrsUiVyTYxGqd+YT0rKUlCBXNl2xydQm5ObXVP7crdkWS6OypdGBBqgb7ON0tL8LgefpUCYdy6kqbWFwFYo8Wu79KpKkhtKtOm+5cZueDcq6fa63/MNfVdN94NG/CLb10t02W935uYRMtW1Wk5R5pRi3ZLwieU5IWxdqvUhHEsaacucimV4oh966UJ8ubI+5Es5cUlMzpgMJ58L2kdfozqbe8i6BbDr2bBzkMbUh8OTcQn+TP16lTn6el96QlJQ5RV07y/XyHI8plbGiZal1yvt126yU1LqJHHohusYWgpue2bn6dGF+1WlNsKFyVx1eI9H5HvHgxOcxzL8yHSiZR7sDVkXinUrXV48wj1arJMRQGk0XMLNNwGXufqi7Pxz4CfCDcttOwIUUD8FPdffZ+pyEEGJtoAFcCCGEEGL10DSO8I7ARWZ2ILAFeCVwRrntbOD7wEvdXWHFhBBCCCGEEGIZGbcCZC3QVBr9czM7FfgGsAH4kLt/yMwOpfAQ/V/AFeWc4uvd/XfaqvBqYDnl0HXLRf5L/0QVyrNSyp2kLLcu70R+TWSAfXk0kEPHNqE0aiHTu2RKCjYTyKZCaTQz1YTTNd5OU/uUPD8VOWaNZ2iIvEgH+95fxSDvuCKDt8WntE4OPRfltzmQrP7vnb0Dc8XPNlTSXfPLnmEoob5pc7UF3hnkF8pXY8lqXd37vf4uXWo97jeCazEgfK7n7nQeLdSjJr/5SIY7V2nTQTtbqG9n1Ws9b9yICfuBPm/0QX8eerCNva8u7NCTSscy0Ep+NWWlZJ6Vvj2eElJXTn0VhtBANp1ZWEqiXCeTjtMl8wsaRqfB3WuuJLsp4XnMncq0UsT3E3Vj6kI0/yuUQ2/d0tt2553R1KjNvblMU9E0gx12GHwPNR2d09C7dG4/lZY5D04Xy5/H+2BUvyNt9Oeh/FsepEVbLOlB2N3vFSyfCZwZbf82mps9VsYZcmitIsnq0ql7CBZCCCGEEGIt0FQaLYQQQgghhBBiAtE3jeGs+gfhNr6QtuEpuqmsrS5dRaaZCvo+3x24Ps4jRUXuFmSS8oCc8k46qjfNpVB3bHPl5LE0KjzWc4En57nZarpwW9Xjc7Xc0IPkho2BXHK+y0yYdmbwsZ1f6FbyDCWI4X7MdyOv3glZc440ai5OF3odjbvWQK5UkQ1HkqxwWygj3Rq176ngeP4yEHn9cnaKTYHUfEOQ/2wkk9oayKG3BMtbA3nkVFSPlOS5iaxrOR0kJNSny0ZcbpP9Tx3nZvk1MKIqu8uV+8/V2Ex1urWeoqvTKjosLNT3500IJaydoPLT0cjfCa6RqZppGkPLSki0t+W3UK1TXf5xfxaSat9pb8s1Y1sDT9NxHilv/nX9bbZ36ZbHzdwxYDXTmaqfbhUez6npDvPBIDAfSKNvv7164Ge+34tSMP29m7ctz9nulXR32l22LYdj/saWlWmxF+pcOfRUzfqlUNuvRvnF/eA24qkZNXVq2n/LC7VYCqv+QXjSaT8cU/O6jIvlDJEQspqP2cz08DTQ/2BdR+45yB34JvFGadN03oncWhsWp0o8N7mONuY2rTdWs5fE3LlnuTdY8Rzh2vxW8R1bbv+TO7d0pcaU9cAk9u1tk+tzZD5zEAgfglcbbZ/vbL8Yq7c7W1PEL0xEP9kPwma2C/A14Mnu/kMzOxo4CZgHLgaOd/c5M9sHeA9wN+AO4Hnu/sO2Ky6EEEIIIYQQQjQh6/2smT0U+ApwQPnbgL8CHufuD6TwHP2qMvkHgM+4+6Hl8pvarrQQQgghhBBCCNGU3C/CLwZeTvFgC3AQ8HV3v6H8/VngZDN7P3Aw8Phy/b8AF7VU11ZZKblsKNlJhUhaqEsXzWkNw/rMz1c2RWXV7/DU9OD5YdPRa5KpykTR+vnCo4aVaEpuiKRK/SIJVd284M2bo+N+W28yUWdrz6i7qXpJze/U+52awzvVoO7p8Enhr5rJvgPq0YRKWYm5OXVhaGLp6OaadLOR5LluHlCsipurhEyqLzdXzpsqazUxTslcK/OFM8tKkZI51+WRDvvVyUoXtq20v4J2O8GU74apukn/EakQf02mo1Tm0uY2hMQr+vlEHTqJXayfq1tNWDt/OHO+cEzdddG2jwzJyeupu9b7/KqEIay29Mb1cE4wwINP/vVty9980w8q27bcpzdHOHWPF1I3h7dvW+oeIpDAhtviCBB16XJJ+s+ouRcYZFepU5hu6VUSCdQtDCfrQdjdjwEo4wIDfAc4zcz2Ba4HngnsDfw68CPgLWb2SOBG4BUt11kIIYQQQgghhGhMIwej7v494GTg08AlwJXAVooH60OBi939wcCngLPaqaoQQgghhBBCCDE6jbxGm9km4LJyHjBm9izgBxRfgG9z98+WST8EvL2NioYsp6y5rqw2wiXVSXnj36Ecej6W8s72lmcjD4hhKICK5CfSUM1sCELShDqaROsIZdLdKL86iVYsjRlVmtk4NEpg2CfzCaTRs8GxDaXQADOBVGrqmt7y/AG7VdKFoRVmg1hK05FeabpGQtWJpYmBrCmUfTaVxVWy7yZ0hSnJYbixJpQSwFxoWJEcRmGWamSl/VIwBtIv3QqXB8ukB9W3Nr/BxQ61G5U2pMx1ecThNppRLwdO1WFUL6S58uemUus6OXRsUyeHTvXtlfVRBdtuP5VQSom+uFKNTI17OrRgRuVim/nhaWDAtJxwW1T3UFKdE2quL8fY+3fLHrArxykcrxte+E1k0+MIcTgJVMI4JsJ8hdMHOtv3QjbE43ooh567XzV8UhgyaTox1azaBrsDlyGW03cHri/sesuV27hofJ1pII2u9qPRlKIgv8p0o/i6r5hFY35NH54aKzo190Kiylq5hsdJ05CTOwIXmdnOZrYReCXwYXf/AXCdmT2xTPe7wLdaqKcQQog1hkJsCCGEEGKlaCqN/jlwKvAN4LsUUugPlZufDrzGzL4LHAu8sI2KCiGEEEIIIYQQbbAkabS73ytYPhM4c0AaBx4zasXWCrleN3OlZbFn6FAOvSXybNy9deu25c7mnrS3z7PxLht7Pzb1FmNJViix6CSkf3XEEo2U7LVNliLNCyXvoUfp8PhBVQ59wGsP3Lb8vb+6qpJuYb9de8vzvePeJ5cMD8b00g9G3z6ukBymzoM0UNGk1cqkiWSa2RLGzDpV1nfy0jXIezWQ6z009ba0emwSJ79GojzVaXYMQylcUtYcLjeQP8d2C4ly873Wd7PSVW3qt6WoeGxu6G25jty6V21Gv2DCqT3Jfcrc325SshosT8UnPM+jdMUkWI5NmsiXm8ge256iNInEsvjwwFfObzTWhtPEdtiht+1Ou2sl3ZZ79zxDb9hULWyHHXt2MxuCiBzTlWQVqXTKG/R00O7Skufe8sbQJpEu3JZqwpVoC31TQoIfU+H6vDEgRh6k26Wp7Hc9oWMkhBBiRVjNLxKEEEIIsbrRg7AQQgghhBBCiHVFI6/RK8Goiqrl9DTdBnWejUMP0lD1DB1KoQFmvCffnf7BL3o2v16V+cxZzyPi3PR2PfvpallTE6ChSn1BauMch7K7ilfCSE6+sH/vmIVy6HB9bFfx2jrmQ1l3nFKnsN6DNFR0TYlNoQfphaTn6RqZNNW3c1NBujaaX66MNtcmXVZehXM9NjeZStDvaXuwHLrfA+nSPYtWvCZH+1TnUTkljY7XjyqH7pPC19knvIlX84vrN3y5b1tC+5eUSo/4KT2OHNBEslzdx27ttpz1Syy5dkuqX61M7Qn7+ZSn5FDOGp2rilQ6IZMeta+Pz3VTL9KrlaoUPu/c93m/DmXIYbuNztWGDYPHm1DiHDMV5bEx8Bq9MZh1Nh3LsCuS58Hy57geU4l0MzVy6E3Rfdx204O3Rcptwll4m+d7hW3p8+ge9I/BtnhcW0iO5d2B6cToyGv0cPRFWAghxIogabQQQgghVoqsL8JmdgpwVPnzPHc/ycyOAE4DtqcInfTaMu0hwHuAXYAvAy9197kB2QohhBBCCCGEEMvO0Afh8oH3SOBQCv3ChWb2+8CbgEcDPwbOM7MnuvsFwNnAMe7+DTM7E3gx8M5x7cB6IJT5zGzoMDcbejbuLceejUM59L3/4gHblq899buVdPP77RLk19PyxEHKa6V/sXSrgdfjNjxI10mjpqai+gY2fdLRQB+0YUNveX6n6qUyd0BPAt25Z+/4xRLq6R17GW4INsVyleWSu/XLjgZLYBdoJhepnMdIQ71QJ69O1CnlZXVUxi15biO/XNl0LnVy6FzPojF11+1cJCOtKEcjiVzshXSpZHt5TqSrrq/3Jl7nGTr+nZIN17GcX8dz65SKelDn/Rqi6Aa5+9X2AYj71BoP0HFfXDsFKNaOBsemVibNgO6t37zIY3CyRt6kl2I3CcRS/VHTpRpdOMbH2YVS6bAdbKCe6ejETQfjfCiH3m6mWqeNNd6gp/v64lyv0b3fYd7bRdLoXTf0Wt6em3rLO0b1u32uV9hNm3s7+cuopYaX7VxlfElEg5ASaNlYRd3AipFzb3kDcLy7b3X3WeBq4ADgGne/tvzaezbwLDPbD9je3b9R2r4PeNYY6r1uCR+CRR6rbX74JKA5E2I5GPUhWAghhBCiKUO/CLv7Nk9AZrY/hUT6dIoH5EVuAO4B3K1mvRBCCCGEEEIIMRFke402swOB84ATgTmKr8KLdOipKbsD1gshhBBCCCGEWAZW0xSJlSLXWdbhwMeB49z9HDN7NLBPkGRv4Hrgupr1q4amMtpxym/TISGC0ALR/NQwTNK1b+jNC57fvxo+KbSbDiZy9M1jXSMXVDgftxOduDAUQhgyYVOUx9xMb9bQwkJvOQ6lEM4zDvOL5xWFxzacS9OZEI1ydd5kfVikVHUr4W8S4ZgqYRYq66vpRp0/u1bDNNSF24D6ecEbo/lm4byycFs8fTI8J1vme7nfEc8Pr5lCubEDWzNflVbyqFlf/B48LziVrq6c+Hd2WKTE/Nlw3m3uuDHu6R25+Yc+KRaC/ViIQ6rM9zZ2KgewQeXGwELoxyLqjLvTdecuai/hXNOwO4vnnSa2rUUmYR/Tc4mD+eHxvO+AqcR5q4ZZrG6bCfJM9bEbg2Y3XRNKqfg9OL++dEF+Yd5x+KS9tu9dm4fsPrtteb+dqul+eFsvk2//vHePuCWaAz8bhFaaStwmCDGpDL3NNrN9gXOB57r7OeXqS4tNdl8zmwaeC1zg7v8DbC4fnAGeD1wwhnoLIYRY5eQ+BAshhBBCtE3OF+ETKD6InWZmi+vOAI6m+Eq8CTgf+Fi57XnAu81sF+AK4O0t1lcIIYQQQgghRIIJEGhMPDnOso4Fjq3ZfPCA9N8BHjJivVYddaF72qAidYxCE81s6BU2v8vGyrY564X4CUMkxRLq7k49u+lKiJ9qWeHPyvIyhf6BZi74lyJViyXLvXTVhNM1kqpYylyRWgc28XnMlZPlh48YjdzQHhCdh4Q0aqpyjdTIpGMSu1snoW7KSn2cTB/bUHYWyFITxzZZVk0ojjjExs5BKI09ghAb28chNmaDEBtbwnpX9yoMpxTWddM0bA2kdqEX6Th8Wx194Y4q2xLpasMnVX/Xh0WK0w2W1KZCEOWGY0qRStdkLKrUL1X3QA7d2Rppo8MTPh9IUaMKdev6s+RFEZQb72DuoBB29BuicDAbwxh69RLYkGrfVq1DJ+jEkudjnd2xNhnL2hj+2ig3/D0TtdU6OfRMlMeGcFsiXbgtLCru80OpdCdY3i6SZIdhkkI59PO+VJ0y98FH98Jvfu+XvXTxrU91Kk6wTD3xNgmDxEoyITMQhRBCrDe2Lqyzu38hhBBCTAzZXqOFEEIIIYQQQkw+yyjYXLXoQXhC6JPe1Hg27vNKHOpoItfGc9PbbVteWAjkz9GVEcqhNwTLU5EGpuLNuOWLq8nFmvJgm5KqV+qeyCRUz/XVL+Ftsq6sUA4dn8ewrDoJ+rgJj1/qfMQS0zrvzX1ypxpZ4FRCE5iUTYes4s4+JQures0eLJNuSphH7NE0lEMftkfgWXTnqgT2h7f1LoRLb+q5SN88Xz0hM8GX39BB1sapbvZX4ZQHaGq2VTxIZ6po096gw+VubbpxM86yql6tqwXN18ihO5vnqplsCbbNxi6lA0Jd6XRmxxfWab5av85sKMmuv7I6QbnxVKGwYSwEt0gpz8GVsSfhJTzlpTjMYzqzP1sPHqmb7Ff+VKP6bXWS3+J3vZQ57EunM71G18mf+8oK70ky6xv3c3cGffP//Kq3HEqh4223z/WW46u52t8Gy9QjKbSYJCSNFkIIsSJIGi2EEEKIlSI3jvApwFHlz/Pc/SQzOwI4Ddge+LC7vzayeRLwDne/d5sVFkIIIYQQQghRj141D2fog3D5wHskcCiFuPFCM/t94E3Ao4EfA+eZ2RPd/YLSZi/gzegc9FGRygbrI4VXrTy2z6tx6OU50u/MBJ5gQw+sKYlXKIeO5bt1nqJTHhVzpUfjZCnSqk6gSUt6Y63RVcbnoNbTduqYLaNOI1duG0qZcj1I96nOa/JIeUDO9hycK6GeQNq+DnIl7qk6hN6hQzn0sy/evZLuw7/1823LV/2i1xnlNuGNU92KjLpOZhdTkTyntiXyS3mArk832DN0n01FXhzXL68OVc/To7fvNupU8QYdyp9/tbWSrHNLz4V4585ANh15aO7u2JPTdwNvzd0N9S2oEw6Wkey6c8dcsDxb3RZIubs79Mpd2C2aUxQSaFHno1AB4fiY7008zwt1UvoenKzljNjQBrkem9uQNuek65c8h8vd2nQzCWn0huC+K5RDb4o889d5lG467aVuTN0SqW5+trmX8sqbe9fBNb+slhvKoX++pWcTT3uZG71rWjEk0RaQd79yA3C8u29191ngauAA4Bp3v9bd54CzgWcFNu8BTm29tkIIIdYM8U2VEEIIIcRykRNH+KrFZTPbn0IifTrFA/IiNwD3KNO8CrgC+EarNRVCCCGEEEIIMZROC4411zrZXqPN7EDgPOBEYI7iq/AiHWDBzB4APAN4HOWD8WojltCM6p0zlrl2a7QYSQlxxYVk5CUz2BR/XJnKlE1V5NoJ+W5Yj5XybJyibndjVWETOVWf5+kat54pyXPq2HZqvUbnSa3bZqGbL6utk1enJM8pqXWdCjRVn1wJ9bhpMug0k5PH+1sv46uWNVg2PBfJ526f7f0OPUOHUmiAa2/tbbs1sInlcqGiNqzDxqn6r8L9kufB21LeoOskv/HvtDR6sBy6ri+P07Wgas6mbW/Sff1eKMsNZMmdW6vS6Olrb9m2fNt139u2vPNe962kW9h3l96PXXqRDejWu1eueIaOvFVPBZLsqR/9srLtpp99d9vyHnc7ONhy10q6+UCiXZFez0Sy7uDuqRteP2vUBek4p+y0IYVuMg0r5Q063DYT9euhlHlDQvIcyqF3iNJtP7Mw0CZmLugv5xcG998x4Xi4Neqnbg1mDGyZ753U+FiEY0LoaXq2TxrdGWiT66W/Kakxfzn7XLH6yerazOxw4CLgZHc/C7gO2CdIsjdwPYU8eh/gcuB84G5mdkmrNRZCjJ1VNvVsItCb16UjabQQQgghVoocZ1n7AucCz3b3i8vVlxab7L7AtcBzgfe6+0eBU0q7ewFfdPdHjqPiQgghhBBCCCH60avm4eRIo08ANgGnmdniujOAo4GPl9vOBz42hvpNLLFkNZTPpSS11Tx6y33y0Bqb6ZlOraQv/iLVRCZX5xm6b1sDidJKfWXsk5tWjlleHk1lyCk5dJu0nXeurBnqJbux98s6CXDKc2PKu3Quy9XuliLPrvt63FROXsmjkl+1TgtBuaF8eUv0ZfamwEvopTf1PIuGnqGhKocOvZHG+VXkc0G5M1PdSizhlPSv3ht0tI9NvDK34A26rg59eWRKsieSsL6B9+ZOJFG+7brvb1ve8+Qnb1u+8a8/UUm3656/2cs68CBdkT/HhN6q76yW27mtJ42+/sZLK9t+7aQXblv+2Wkf3ra8290fUc0jKLsif+/G42uNpD++hgemGiA7n4A71qZ1mLSxLdXn13mG7tuWyG+64vG5XhodyqF33Vj1cP5r2/ca1y4beununKsWdsvW3u/Qk3Moa4b6KSELUV8c9rGbEyqmbk2fHcd+r0x7CdZPdfqn3AyqX/G7PgqAEOMmx1nWscCxNZsPrlmPu/8QuFejWolaVt2NkxBC1BDfVAkhhFj91D0ECzFpZDvLEkIIIYQQQggx+UyC0mTSWaP+DYUQQgghhBBCiMHoi/AyE4YgSIXfyJ1DWck7Cukz6hzh3G1NwhYsJY9ccvc3HV5m9DxCqnOuM9MFP1bz27x47mYqtFI1XZBHIv/ct3jjDKXQdP5x3fzCeO5w9rzqSn7d2nShXC08fpvjefRbe1vvCOalzUTp6uYZ3zFXPTtbg21xHcI8UvObK+kqITuqdaqbg9s/RzgvLFJdv5I7Dzh3W1/9MhvusoYKCU9JMN50N1VvJXa+ey9M0k//9tPblu+yz/0r6Ra2qwmTFO9UOB95azBHeMt8bbrdd7XKphvf9M/blu+x1+E9kxacCGjK0mBiXyrt55+Xrm5ecF/4JGrSJfKL+8Twdxg+aY9N1Y7lIXv04hjdZ5deOw7D1QFceXPv2pq7o7dta3yN1Mzp7aOyLRWCaHB+cbHZ4ZMSVaqvQwOjVH7tZifWCFkPwmZ2CnBU+fM8dz/JzI4ATgO2Bz7s7q8t0z4IeBewEfgx8AfufsuAbIUQQqxjkjdsQgghhGjMKv6WsmwM/ahSPvAeCRwKHAIcZma/D7wXeCpwf+DBZvbE0uQfgNe5+8GAU3idFkIIIYQQQgghRsbMDjWzLcHvjWb2ATO72syuMLP7Dcsj54vwDcDx7r61LORq4ADgGne/tlx3NvAs4AJgGtiltN0BuHkpOzUuYgnNckmZUuVWpLIJOV5KEpkK1dS2KmnUsEjjlvnmhq1K0UQll5ST17xqim1yJWQrJZWuk+jCEsL6JEIr5ZZVV24ubTtFaDukUyyZ7tTIyVPnoCKRq+rgKh1+6LG5X0IdlpWSzwU2Qblb+8InDV6G+pBJcZ3qwielwiItBAn7pcc1Ng0lz01scvNL7WMTcqfAxOkWpoMraENPptndZWMl3fx97rJteae9D+vZRw23u0MvZBIbEldnGE4pkEZzx2w1XRBOabsdd6tsuvtuj+nV466benUIwjYBFcl3pcNIHLRKKMQxj4dxWMPVykpNw8plKnFOU+PITBA+KQyztNNM9aK9dyCHfs6/775t+SOP/Xkl3TW39q6z6rgZhY0LlzPDEaX6uroQdanpNun+e3B+w+oYkhuyTKwPzGwH4HQKBfIirwJud/f7m9mjgPcB/yeVT074pKuCQvenkEifTvGAvMgNwD3K5VcDnzeztwG3Aw8dVoYQQoj1x7xCbAghhBBjYVI9IpvZXYC7DNh0yxKm074FeBtweLDuScDrANz9y2a2p5nd091/VJdJ9jEyswOBLwAnAv9Ndcp9B1gws+2BM4Ej3H0f4J+A9+eWIYQQQgghhBBizXIccO2Av+NyjM3sKcAO7v6xaNPdqP9QO5BcZ1mHAx8HjnP3c8zs0cA+QZK9geuBBwB3uvtl5fp3AW/IKWNSyZXbhtLWbgPdWp+EtkYrkpSeLrnUpdG2bHicpMptQxZfK93K9Aw34iZfAAAgAElEQVTdJO+lpFsp9dw4ZdIxTfaxjWukjTesdfsV71OtFIxUZxRmEEmUA7vKzIxYytzgwq3In6MvveG2+CtwVTad8DpayaO3HF/PoRx6YT6RLtO79Khky5/HrO+rRCyInC2H7a4bpJuKGuR86NA28Pjc7Val0d2ZXiadLcG23Pk7s9UrtRPYdTb3Kt+9ofrh4Cc3Xbpt+dfu+sDKtg136d2ydHfq1am7fVUa3Q09WSc0z8s1tjWREKdYI8rqJE33MTUW5VKVAPeW74j62NA79DmBHPq/I6/Rv5rt2c12w/vMSF5c630/Vb/EtJdMm/xy806KPEWvG95GIVuOqXTqZvYs4K1Rmv9HMQX3iAH2Uwz4UJuqyNAHYTPbFzgXeLa7X1yuvrTYZPeleIJ/LoXzrO8D+5qZubtTONP65rAyhBBCrD/kNVoIIYQYD5MagrOUPw+VQLv7R4GPhuvM7Bjgz4Avm9niuv8AHglcR/Gh9gdl8sUPtbXkfBE+AdgEnLZYIHAGcDTFV+JNwPnAx9y9a2ZHAx8xsw7wU+CPMsoQQgghhBBCCCEG4u7vAd6z+NvMuu5+SLl8PvAC4Ctm9ghgc2p+MOQ5yzoWOLZm88ED0l9A4T163ZKSSWdLrUMZW/BRPymHza1gC0zqW6Zx0djDZ633yzyZ3XIe51y58jjLXUrZuRKqtqWA4/RWnSsFz/UuHQcRrJzjihfq2C3xsJr255eSxYVy6PgrcJ036FheXSdlXogKC+XQKQl1nRy6DYlyrmfftuXQudER+vufQHqc8Bo9Hag25zdWJZzVhIFhmK6vYQzWu/cdvc1h6IReupt+8d1Ksr1f89Jtyz978/sq2/ba7+7blrubgjptF+1H4A274sk64ek/PSVm6R1Qk35/vY3JkN+3h/1bxRt0y/WBan+2JZBD/2xztbRLb+pJ8q++pXcrfudcdadu3tL7vXm+vh8Np5WEXv9THprTXqPrp6nk5Jcru07lXzc1aFidxLrmdOBdZnYVsAV4/jCDrDnCQgghRNvkzhsTQgghxFJZ+2Osu3eC5c3AHy7FflI9awshhBBCCCGEEGNh3X4RzpUoj2qTm19MmH9KdpUrm14pUnVvQneFXADmOjtNkSuRa9tLaBMWup1aiXIsSaqTp8WnKl8ePDjDpnLtJhKqlZJTpz1tp+yW7l266uF7dI+e3YSUbi4hmavb1ud1NJT71XiGhqocumIzX82wsq0FmX3l2mxBt5c7xoR1iout6y/irOucI3cj1XBFqjjTy2WeKGEojd5Q0btX0wXnpDMbeIOOK7ypd6vS3aEnKd37br9ZSXbTWz+8bXmffR5aLWrnwZ6iKzJpqh6vu9O95eloF+sk5E3770n05lw3fqf3sX7jWpF8L9QsQ7U/2xpM77httnoww20/TYxtYbotFWl01I8GFUl53w9pIn/uz2Nwfqk8cseRVFkpmtwmSmq9flm3D8JCiHqWc46wWL/Ia7QQQggxHjrrQBo9KrlxhE8Bjip/nufuJ5XrNwAXAm9w9y+W654KnEohTL8W+CN3/0XL9RZCCCGEEEIIIRqRE0f4COBI4FAKxdKFZvY04L8oYgc/KEi7C/BO4MHu/hMz+0vg9dR7nV61NPXOGXuRzsk/17t0ijYkxW3LnEetw1L2KVdmWCePiWVrFen6BMi9WpfyJqTRjfMMlps0pZTsqvW6Nsxu1PPQVEJdW98+j9yDk82PeH1AWvoWnruU1+i5Bl6e56ODFkqe54PC5ubr08Wy6ZCwD5+aDtdX002HfVNm/9D2dJtc4r68GxyblMx3qmI3WCYNMB8U0A2vzelqhp3guHdDB81RQw1z7+6+fa+cmeqO7LbvI3rbIq/W3V236y3vFEijN0a3QaGn6KC+U1GdwrYwlThmTeTFbZCb/yRIjyehDinmujBTMwUh9m4fnu7NwXLcJ4aS55nE+FUnc+7zql9TVp/X6JqicuXP/dsGRw5YSh4pOfQ4kRxaQN4X4RuA4919K4CZXQ3cE3gY8PfAcUHaDcDL3f0n5e8rgee1V10hls5y3mCuFSSNXjqTOMdv0pHXaCHEpDOjbkqsUjqT8AVrwsmJI3zV4rKZ7U8hkT7c3a8p1x0XpP058Mly/fbAyRQxnYQQQgghhBBCiIkg+1WBmR0IfAE4cfEhOJF2V+A84DvuftZoVRRCCCGEEEIIIdoj11nW4cDHgePc/ZwhafcBPgdcDPzpyDUsGedcqjbyzp7TWzMZJjV3ODfMUoq1qI7om+fWYB50PEek7nimsp5uQTZVd47bCLcxbslueAxzy0odz7bnD1fyHqPkeznnFbc+l7hhWbXzzTJDJMV51M0DLn4PDpm0EEfkCQqbnQvqMFvNcOvWMI8gjE900U0H819nFsL1eWFi4nOwvONNbn5hwl6G05kXY3zMOsF1thA08IVoXmM4f7iyT3F+YR5BpTrbR7cw4VzvqGPubteb1NsNwjERzyUOfs8EyaaiY1EfPik+FoOXY2rzm6rPTywPfSEDg/nxC9GYUvGBsFB/ssJ02eHrEnVqEgopOxxRZlikqk19frlzgscZLmn9oA5jGDnOsvYFzgWe7e4XD0k7DXwG+Ii7/1U7VRRCCLEWkbMSIYQQQqwUOV+ETwA2AaeZ2eK6M9z9jAFpn0LhRXrGzJ5Zrrvc3Y8ZuaZCCCGEEEIIIUQLdHLD+SwHjz3/q1mVGWeV28i7nTyW77w0KapteVYrx6xGHxPnXSfFjH+n6hTK5EL54NR0nK53oJpI5PrKbXDcm9mMfkLakGRPgqJ/NXjQbj98Vm66waEzliLbq4T6SIRPCiXPYcik+Sgs0mwggd66pbd8553VDBdund223Nnc01CHElqA7k4bty1vt11vPzZurCSrSKXD/iE+N8vZd+bLzoPlyjno1qabT5yrULpeLTc6B5UQVsGGKCZWGGapEmMrFfcr7jyCjrobhkiKQjBV5dCDQ2dF2VXPdxwiqlOTLtEucqXRueNIqn9I5tEg9FPd9K+UXdvjYbw+7MNTxyLc3ZTNTOUcVNtgmHYmkUe1rPo6heRKnrNtciXKqW2ZY8UkyKGXokD6zOMfuSY0xb/ceuFE3sDsuvG3J+b4TsJ9phBCiHWIpNFCCCGEWCn0ICyEEEIIIYQQYl2R5TV6PRHLcNqWDWd7eW7gXbopk+CFso1jVmeT8gwde5xdCKR2c5HkMmQmlMltqJfPheRK2lqRFE/AOW2Dtr1LN6pDpqQrxbjl1W1/WW0imatInBOeoVPXY0pGW2ezEMljQ6n07GyQLpBCA8x87+Zty9PX9Jbn73PXSrq5A3brbdutp4eOPSBP1exH3IbDdttG39u2B+lKBfu83g72KJ0qtyq1Tsl8e5l0I3fVlX56JnHlhxWJd7hG5hx7g66TQ8cetEO7cLyOi82VA4/aFlppS4lD21QOPWk0iXKQzCPyyrsQXCNzwbap6BqpjAlNIi+M0eNz07Lalj/32TWxWfeqo9Vzba4U+iIshBBiRZggFxVCCCGEWGfkxhE+BTiq/Hmeu59Urt8AXAi8wd2/WK4z4F3AXYEbgee4+y9arrcQQgghhBBCCNGInDjCRwBHAodSCDguNLOnAf8FvJciXNJi2g7waeBYd7/QzN4InAy8ps1K50rBJrGsUfNbigRpkjyCj0Lb5yAlq9waqCc3B15mYwn1dtv1lnecrj8nVflcsL5ltcokSqFjSVLrno1r1k+izKUNefVKkesxtMk+djr919YiTT26h1LcMO/QMzRU5dD3/osHbFu+9tTvVtLN33OXIL9AGh3XL9yPCWmE4TW3kFAN12cQ/a5IpetPQigjXkj03+ExC8e2ON1UoCvtJvrbUAaYnH5S480fItl0jcfnwm6whDolec7tAzuJ+o3KcqqYV5Fiuo9qfxY1yMSmUCodyqRjhWqYf2U2Qgv3OOOUEOfKn5vmX7FpVJLk0CGd1HwHAeR9Eb4BON7dtwKY2dXAPYGHAX8PHBekfRBwu7tfWP7+G+Au7VVXCCHEWqHuIVgIIYQQYtwMfRB296sWl81sfwqJ9OHufk25LnwQvi9wo5mdSfEF+Wrgla3WWAghhBBCCCGEGIFsr9FmdiBwHnDi4kNwTX6PAR7l7peb2RuA04CjR6znRDBpMunh+a+8LqlteXYjD6kJieV89EUqlEPP/vj2bcszgYwS4PZD9tq2vMMOPZ30TOTRdENFZldf4UmTzsb1acPrcRveOrPKaWi33gREbXgMrWu3sc1Cjay508n3FJ1LqAQL5axzm6rD3fyv97xDh3Lo+ftWvUZ3A7uphMy1bQVaE0/6qagHdTLpYWVVCKd6BFrPbtSYwvPYqTn3MMApdU26bk3COF3Ss3HmNJU67/7xeFonh044q06m62R2issZfWCcLOftSUWGnBqHg+Xcy7mvH63xKD3f11aDqVeJ/JucxzakwU0k0MvpDbqNctcHE94RTABZ17qZHQ5cBJzs7mclkt4IXOPul5e//xV4yGhVFEIIsRZZI24MhBBCCLEKGfogbGb7AucCz3X3c4Yk/xqwp5kdXP7+XeBbo1VRCCGEEEIIIYRojxxp9AnAJuC0IjISAGe4+xlxQne/s/Qo/W4z2xG4Dnh+W5UdREoKtprKbSKDWw00kWc3lUeG0rdQqjc9VZVAp6oUOu8J5dDPe81elXTvP6snm97poO23Le+x/XwlXSgvmgt2ay6S+i2EMqkGUtTVRmofV0rSt1J+myoeQ1eoDjF15yfV/qoepPPKSXmNbkp4fc8EI9zszhsq6eZst23L8/v1PEN3N1aHxe7OPU/RMzNL9+abkt7G5OZZly4lFa6TSUNDj9IhCe/S1TpUK1inUu0bAkIv1JltK9WPJCXKwYo6OXUqj6Wc7yb1a0KuZHy9kRvZoL/f6xn2XUuDk/UxlSlZXUhlUkNTz845rJRX6748Wr4vXiv3VjEdSaOHkuMs61jg2MT2x0S/L0VyaDFBxPOAhRCTgbxG95gAlw5CCCHEumIdvwsUQgghhBBCCLEeyfYaLYQQQgghhBBi8pE0ejhr7kF43CGJVqLcXMncap5LHJKaV1wJy5GYp10JoRKH4gjspiNNxKZNveVfBSGSwjnBAPsduce25Zfd/7Zty/9923Ql3fdv681LvHW2XoBRmTOcCHMyVZlLvDY7uOUKszQpLKc6uMm8qtx5wSmqIZJ6y3H4pFyS4WqCy2xmQ2/jdlE5c9O9sGcLC715wHGbm57urdgQTDOenqkmnErMJ62jbTl0sk9MhdqpWd+0bXaCbrBSh+imLA671LOpbxRtyNjq5gH3pxu8HP+uC5GUzC9qaKO2mXH0lXVlTUJoxnGTCqVU7ROrbbUSpixVQO5c9zE+yLQ+z7bd7MYeFmmt3kOJpZH1IGxmpwBHlT/Pc/eTzOwlwKsoLufLgT92961mdgjwHmAX4MvAS919rv2qCyGEWM2slZd3QgghhFh95IRPOgI4EjgUOAQ4zMxeA5wIPBw4qMzn5aXJ2cAr3P0Aim9bLx5DvYUQQgghhBBCDGRqQv8mh5wvwjcAx7v7VgAzu5oinNLL3P3Wct1/Avc0s/2A7d39G6Xt+4BTgXe2XfFJZhLk2U2Z9C80oSSrLxRHXaiQqlq5arMxkjcGMsjt792TToYhkqAqhz7h3F66jz//jkq6n/53r/A75np5z0ZypyaS5/Umk06xHiTUubQS3qKmPaVDey29nKbS6Eoe0cmfrslwY3Stz0z30qXCjVQksEH/MDWVSJcpqa2sb9iIu4kD33qYpSXWbRi5EuqQOjk15IcCahLCqk+C34Iceql1EJNHKrRSSO611C/DblKr8TGOqTzj3Me1em8k2iMnfNJVi8tmtj+FRPpwd7+mXLcn8ArgaOBuFA/Oi9wA3KPF+gohhFgjTPqLNyGEEEKsXbK/T5vZgcAXgBODh+C7AxcBZ7r7F8v8wlubDsvrC0YIIYQQQggh1jWdTmci/yaJXGdZhwMfB45z93PKdfcDPge83d3fUia9DtgnMN0buL696i6NlZIo19UhZhK/hixn+xxZEpmsbH3m00Grjz1KV7zCBi6l99h+vpIu9A4dyqG/9bMNlXQVb9ABfQq5UI4Yro/lVDW7Fcqki3Ttnsgwv7isSWCtSqgnQTLWpA651/ZSpNF1HnxjiXL1okl4Hw4aQ6oOteXmSp5TstkWGmQqjzrZ9HJ6l84dh7PTJaa6pGhyflKnJ+VRupKuJpNsyXwL9avWZ+k2S2HC7nH7SI1ldREL4vad+oJUl3+qH832NL2KWU6Jt+TQYinkOMvaFzgXeG7wELwz8HngtcFDMO7+P8Dm8sEZ4PnABa3XWgghxKpnEl8GCiGEEGJ9kPNF+AQK51inmdniug8DewHHm9nx5bpPu/vrgOcB7zazXYArgLe3W2UhhBBCCCGEEPXo6/gwOqnA9cvNY8//6rJVZoJ2G5i8+qwG2jhmKXnahkCCt8uGqmDprtvND9wWS6Fvne2JLu6YD7xGz1fTzQX7Esp6YplUrrxoOaVBkyiVFoNp0i6aeIpOXZvxtvB3OB71pVtg4La4Dgs16WKZcLXc+vqGNPI23FD+PM5pNSlP003KyU3XVB7Zxvmpo4m8OLUtdb6z5dQN5NC5+96GNDo1LSk/j9HT5V5adelyx65UObmOdpqOk5M+nWelvFqP+x7nvCMfMeFHPo/b5748kTdoO848amKO72QFcxJCCLFu0AtAIYQQQqwUWc6yhBBCCCGEEEKsDlIx2UXBun0QngSP0iG5MqFJqOukMA4Pl4Gj6IqUaS467hXJ81x9ReYC+c58IJvulzzXb5t0Jt2j9HqjDclY23K3un5rSV6jQ/1ScJHEsqa63e9MVzekZNN59ck7zuPwojvqeJHraTpXnp1bn8YStDHeyzWVKOd6g25j26ik5NDZeUy6O+gGxH1lk/Er16N0ql9OlbtS0uNJQN6fxXIgabQQQogVQS/2hBBCCLFS5MYRPgU4qvx5nrufZGYvAV5FEajxcuCP3X1rYPMk4B3ufu+W6yyEEEIIIYQQohZ97xzG0AdhMzsCOBI4lOKh90Izew1wDHAYcBvwPuDlwFtLm72AN7NK/HZPmkw6xVLUSZO+L+Okbc+VsTfokFlCaXB1WyhrWqhZn2K1yaIkk14e2paMtdEem/Q3KWl0fG028Ryc27fHsumVoq4/atqXN5FQtyGbrs17Mg5zhXxvy808Jdd6lx7R4/HQcjPvf1fzOVku4n4vdU7CcT73EaSpbHqtIAm0WElyrtMbgOPdfau7zwJXU8QVfpm73+ruXeA/gXsGNu8BTm29tkIIIdYM6/llXcyk3fwLIYQQa52hX4Td/arFZTPbn0Iifbi7X1Ou2xN4BXB0+ftVwBXAN8ZQXyGEEEIIIYQQCeQ1ejjZXqPN7EDgPODE4CH47sAFwJnu/kUzewDwDOBxwD3GUF8hhBBCCCGEEGIkcp1lHQ58HDjO3c8p190P+Bzwdnd/S5n0WcA+FM6zNgJ3M7NL3P2Rrdd8TDSdlzaJLJfUbtzz19omt9xKSKNonk44Z7gyhydxLFLzYEYNmTSJc2zW+7ynNhjneZ2EeerxHOEwREs30bHUhVKCRPiSAWX3ykpWc8Vp2lc2mVedss8NGdVkLvFK0XYYrDZCJDWdF9w2uSGT2vbJkUvYNyXn7Wany/NxkZ1f/aZW5g/X5r1C4+sk3ofkstrCVor2yHGWtS9wLvBsd7+4XLcz8Hngz939A4tp3f0U4JQyzb2AL66mh2AhhBDLx6Q/gAohhBCrlbUY/7ttcr4In0DhHOs0M1tc92FgL+B4Mzu+XPdpd39d+1UUQgghhBBCCCHao5OSny03jz3/q5NTmQFM0KESSyD1Qiwlawq3xVKjTDVdI5pIUVezJCnFWpVQr9T5ata28tKl+scm21JjUzK/hbx0kx6abDnHm1HLWktjY9sy31Hl0PnhndqtT5F2ZaTRTT5i5Y7J+emaNepR7w0U9XV8LEX+fMGRj1gTN1Wb578+kb3zpumHTczxzXaWJYQQQrTJWnqAEkIIISaLiXnenFj08kkIIYQQQgghxLpCX4SXQEquoy8bk0X73injDFf+hK9VOXRI7j7KS+Zgmsp/l0s2nPLSH8sym0zjSU6LyMxjpSTUTfqwtj34j+p1ehjjHDfbGAOa5LGcnqHblkPLsU6PXA/S/Xa95SYy6TY8Ta935AFaLAU9CAshhBBCCCHEGqKj1ydDyY0jfApwVPnzPHc/ycxeAryK4tPY5cAfu/tWM3sQ8C6KOMI/Bv7A3W9pv+pCCCGEEEIIIcTSGfqqwMyOAI4EDgUOAQ4zs9cAJwIPBw4q83l5afIPwOvc/WDAKcIvrXk6nd6fWBnaPgcL3d5f/7bOtr9xEpYT/4keqeM0zr9JJNVu26bb7f0tJ6lrvTM1+C+VRyq/qc74/sZ5XFJ/bee33PmPWodx5hGTe+5X0z1E0/M9CTTpH5v2+233xQuJv/VAav/X27EQ7ZHzRfgG4Hh33wpgZldTxBV+mbvfWq77T+CeZfppYJdyeQfg5lZrLIQQQgghhBAiwSp7U7UCDH0QdverFpfNbH8KifTh7n5NuW5P4BXA0WWyVwOfN7O3AbcDD225zkIIIYQQQgghRGOynWWZ2YHAecCJwUPw3YELgDPd/Ytmtj1wJnCEu19mZq8G3g88qf2qTy7yLj0+JlGGNakSWbF2aVv2vFKekZdC6NE29CCd8jxdsY/k0d0aDV1uH9NGX96GPLrJuRv3Pk5iP53LqHXPPadNy5Gn6NVJfJ22PTViVEnwuF0qSbK8Muj6Hk5W2zezw4GLgJPd/axy3f2ArwFnufsbyqQPAO5098vK3+8CHtNqjYUQQgghhBBCiBHIcZa1L3Au8Fx3P6dctzPweeC17v6WIPn3gX3NzMrfTwW+2W6VhRBCCCGEEEKI5uRIo0+gcI51Wu/5lg8DewHHm9nx5bpPu/vrzOxo4CNm1gF+CvxRu1Ve3Ug2ncekqTnGLWsSYhirQb5cR3g9p/q53HStlFXzGrhOMp0qZym03c/n9kXjlFCnmJRxre0xpckY0KQOuVLopvmPk0mrT8xCt9l5DKdDTXWaNfC663Gl7i0kXV6rTPhFOAF0upMySgGPPf+rk1OZZWaCTsOKs5yDZ9NBRw/CYrkZ54Nw07yb9Fu5Nql0ueNWo/qN+Y5wpfr6lXqRMilj21p/EG4857iBYf784yVn3diuyfloYxxv+iBcn1+r2YmGXHDkI9bEmdi68K0J6YGrbJw6bGKO77jnxwshhBBCCCGEEBNFttdoIYQQQgghhBCTT0ffO4eiB+EJYTlDdqwUkz5faCmEMkNJmcS4mEQ59CRQF0qpPx1Busy8E/cNbcimV6qvbzYXcvRyV1u/P2p/Pu6wSKOW1TScymo7jzm04fsjDp84qlQ6dc3pXkOI9sl6EDazU4Cjyp/nuftJZvYnwCsoZmKfB5zk7l0zeypwarn+WuCP3P0X7VddCCGEEEIIIYRYOjnhk44AjgQOBQ4BDjOzPwVeDTwEeCDwcODxZrYL8E7gSe5+MHAl8PrxVF0IIYQQQgghRD+dCf2bHHK+CN8AHO/uWwHM7GoKT+u/4e6zZrY7sCtwC7ABeLm7/6S0vRJ4XvvVXr+sRXnSctO2vEgyadEWq1muvFLEUs86qXQboety5avLKaGulDsBcmqYjHY87r541LG4iRS6ablN5dCriUkZh9sIrVSfd29Z9xpCtMPQB2F3v2px2cz2p5BIH14+BL8YeDNwGfAf5cPyJ8u02wMnA6ePo+JCCCGEEEIIIUQTst9JmtmBwBeAE939GgB3fzewO3AjgQTazHalmDf8HXc/q80KCyGEEEIIIYSopzOh/yaJrAdhMzscuAg42d3PMrN9y3W4+xxwDnBQmXYf4BIKWfQxY6m1EBPKQrf6J8Qg4nayXO1lNbXNTqf6l2/X2fbXtKwm5Vbym8r7a5vUfoy6T0thqrPyf20wznbRtA7LyUqVu1K03T8udDvb/tqmbgzRPYgQS2OoNNrM9gXOBZ7t7heXq3cFPmhmhwC/BJ4JfMXMpoHPAB9x978aU52FEEIIIYQQQojG5DjLOgHYBJxmZovrzgD+FvgaMEfxBfgtwFOABwEzZvbMMu3l7q4vw0IIIYQQQgixDKwHR3mj0qnzsLkSPPb8r05OZcSaZRK8LU5CHcTyMSkStbbr0WT4aGPIaVbu6AUv53DZhufptpmg24WRGOe9YRuS9zbq18YNcDMP1SMXO3IebYyv4x6j2/Yo3awOK12DyeWCIx+xJo7OfPfKlW9oA5juHDQxx3cMs5SEEEIIIYQQQojJJUcaLYQQQgghhBBi1aDvncPQg7AQK0CuRFXSpdXFpEigJ5lY9rhccttYKtpEKp2SbLa9H7kS2+WUUGu6WY+2vX6PemxXSgq9Vgn78nGMw6En6ZWSSbcxXq2mexSNz2IQelUghBAtoEF26ayVOadCCCGEWH1kfRE2s1OAo8qf57n7SWb2J8ArgA5wHnCSu3etcC39LuCuwI3Ac9z9F+1XXQghhBBCCCFETIdV9Ml+hRj6RdjMjgCOBA4FDgEOM7M/BV4NPAR4IPBw4PFm1gE+DbzR3Q8Gvg2cnF2ZTvVPiLZYre1qoZv/J5aH1Xb8J7mOTaWYnU71r1kenW1/bRDXadT6ZZc71exPDGfcx6+NNjJqG26nDsvT1nNpu68bd9+50O1U/lYTS7lHWek/IQaR053fABzv7lvdfRa4GlgAfsPdbwfuAuwK3EIRQ/h2d7+wtP0b4B+bVEyNVrTFanv4bcJ62EexdCa9H12pUErLySTWbxJDM603JiVE0lqk7fFwOcfXSQirJMR6Yqg02t2vWlw2s/0pJNKHu/usmb0YeDNwGfAfwNOAG83sTIovyFcDrxxHxYUQQgghhBBCDEIvy8CkB1cAABepSURBVIaRLfAxswOBLwAnuvs1AO7+bmB3irnAr6d4sH4M8E53fxDw38Bp7VZZCCGEEEIIIYRoTtaDsJkdDlwEnOzuZ5nZvuU63H0OOAc4iOKB+Bp3v7w0/VeKecRLr5jmCwuRjebMtIuO2fLQzlzfducLx3+jkpo7vFJzK9fjvOKV2t+2z3fbc9vFcJZzDFjN84WFWI0MlUab2b7AucCz3f3icvWuwAfN7BDgl8Azga8AXwP2NLOD3f07wO8C3xpLzYUQQgghhBBC9KEXZsPJCZ90ArAJOK2IjATAGcDfUjz4zgGXAG8p5w0/DXi3me0IXAc8v/VaCyGEEEIIIYQQDel0J8il5eMu+GpWZSRPFMOQnL5d1srxXA99x3LuY9vDx+TntzINaIKGaTGAcX50afuLzjjqOmqebddp3OPVSo2H8ii9fHzuCY9YE3c9Xa6eyEbT4f4Tc3xzvggLIYQQQgghhFg1rHLnDsuAjpAQQgghhBBCiHXFqvwinJKlrAfpoxDLTdvXVa60TNezGIVQctmGvDglUx2nbDpXOioJ9fhYTp8zky6HXu/+d8JxaTll0qEXacmkhWiHVfkgLIQQQgghhBBiMB3W+VurDCSNFkIIIYQQQgixrtCDsBBCCCGEEEKIdcVEhU8SQgghhBBCCCHGjb4ICyGEEEIIIYRYV+hBWAghhBBCCCHEukIPwkIIIYQQQggh1hV6EBZCCCGEEEIIsa7Qg7AQQgghhBBCiHWFHoSFEEIIIYQQQqwr9CAshBBCCCGEEGJdoQdhIYQQQgghhBDrCj0ICyGEEEIIIYRYV+hBWAghhBBCCCHEumJmpStgZvcDngncA1gArgcudPfLl5jPW9z9+CFpfgu4092/bmbHA48Bvgm80d23Nqm/EEIIIYQQQojVRafb7a5Y4Wb2MuAlwMeAG4AOsDfwDOBsd39Ljd17B6x+CvBpAHd/4QCbvwMeBWwArqV46H4f8LvAjLu/eMTdGRkzewLwLKovBS5w948vMZ9/dfffT2yfAV4A3Elx7N8KPJripcAJ7n5zwu7lwD2Bc939kmDb69399TU2LwI+CdwCnAw8BPgW8LfuvnmAzRTwYuAo4O4ExwJ4u7vPDjkEi/l8yd0fPSTNLsAJFMfiDOAD9I7FH7n7/yRsn0pxLM539x8E61/i7v9cY/ME4FJ3v8XMXkB5LNz9XxLlqF3QXrso80q2jabtwsx2BU4F9gU+6e5nB9v+2d1fUmNzMvCPwK8ojvuDKY7F8e7+s9z9EqItzGxHd7+9gd3u7v7zcdRJrDyrvV1M+v3Fco4hOhZCrPwX4WOBQ939jnClmZ0GXAEMfBAGbqa4Yf9riptogMcBX0qU9UTgYGA74EfA3dx91swuAP6jzqjJTX5gl32jb2Z/WW4/m+pLgReZ2cPc/YSacv4diN9m/KaZXQzg7r81wOw9wI7AJuCVwKXAs4GnAv9M8YV+EO8CpoH/BN5vZu92978ptz0FeP0Am7PK/z9GcT53ouigngy8F3juAJszKGT7p1A9Fs8H/gX4g9jAzP57QD53X1zv7vep2aezge8Be1Ach3dSDAq/B7wbOHKQkZm9EfhN4Grgz83shKCTfinFcYxt3gYcCjzbzN4APJSifTzNzA5x92MH2Khd9Fhyu4DGbaNRuyjr8Z/AJcDJZvaoYLD+zRqbc4BvA78E/oniRd3fURy7D1D0XYP2a83dxAR2S7qR0bGo2OwAvI7i5Vl8LF7r7r9M7V/AJcCDUgnMbF/gb4A7gDcB5wJ7m9mtwDPc/TsDbJblxVlpt+baxQgPPZPeLvYFTqc8FhTndb7c9ll3f/IAm/2ANwKvBbYC76d3LF4YvqAOmPT7i2UbQ9CxEGLFH4TnKL7QxmwP1A5Q7n5C+QD7V8CfufsXzew4dz+rzobiAt+V4oZ7R2AX4OdlWRsTdk1u8mHpN/rPBu7v7gvhSjP7V+C7FAPsID4OvAb4C4oLvkPRqZya2KcHuftBZjYNXOfuDy/X/5eZ1b4UAH7T3Q8u6/V+4N/M7A53f1tZ7iAOcvcHljaPKMvuAheY2X/V2DzK3e8Xrfs+8JWEzSuBv6c4H5eW9TkP+J3E/gDs6+5PKev3E3d/a7n+Q2Z2UsLuSRQvcebM7O3A581si7t/lPpj8XiK4zFvZk8CHubuW8zsnynO8SDULno0aRfQrG00bRf3dvenl3bnA5+13rSNumOxj7s/sbT5DXd/frn+ajMb+HBfshZvYqDZjYyORY8PUjwIPJriWADsQ/Hy+F8Z0O7NbDO9sbhD8RKtY2YLQNfdp2vqdxbwCYox9evAn7j7J8zscOAdwCMH2CzXizNYm+2i6Y3+pLeL9wIfAq6kaAOfMbOnuPscxYP7IM4u9/nHwEfL379DcSzOAh4xwGbS7y+WcwzRsRDrnpV+EP5r4NtmdhFFx9wF7gb8FvDnKUN3v8jMvg2cYWZPphhYU7yR4gLvACcBXzCzfwOOoOiA62hykw9Lv9HfTCF9/VG0fj9gS10h7v6O8ivfGcB73P39Znabu6e+ji+Y2QEULwZ2NbN7ufsPzWxPBr+YWGTKSlmUu99kZr8DfNXMbqL/6+MivzKzA939KuD/Ubzt/ZGZ3T2xX7eZ2YPd/ZvhSjN7GHDbIAN3P8/MvknxVfN+FC9Jtgx6Cx8xa2ZHUr4kMbPD3P1bZrb/ELvFmwLc/ZqyDf7bkGNxB/BrFG39xxQ3CVvK/+dqbCahXfwaq7RdQOO2UdcuDkjYLNZnb3e/0d3vNLOnA182s/9L/bH4hZk93t2/QNEfHuru3zazB1K0mTrW4k0MNLuR0bHoYe7+tGjddcDfmFndC7eHAW8H3urunyjL+7a7H5qoG8Bd3f0dZfpXLtq6+1fNbOcam+V6cQZrs100vdGf9Haxu5dThMzsKRQPxWcDz0mUs6OX05DMbB93P7Nc/2Eze22NzcTfXyzjGKJjIdY9K+o12t0/RPHG7hKKBrqlXH6ku5+TYX+zux9FcSN945C0Z1M8UNyz7KD/EPgp8Bp3//uE6ZSZ7VjmcRPFYHicmT2P+gsRyhv9cnnxRp/Ejf7xwCVm9gUze7+ZnWVmXwD+HfjTIfv2XxQP9Aeb2Ucp5N8pTgL+jUJ+9PsUNxMfoXQclrA7HbjCzB5XlvsT4AkUEqj719i8muKlw8copEuXmtknKd4S/98amxcD7zWza8zsy2b2JTP7HsWXhNq53O7+0/JG5BfAxcAOiX1Z5KUUsqpjKd7Yn2VmlwFfppCf1fFR4Itm9pCy7Kso5rZ/BPj1Gpu/BL5pZm+meIv/JTN7K/AN4LQam0Ht4vMsb7u4jELeVsfb6W8Xv026Xfwpy9QuyjottW3UtYsvUXxpr+P1wOXljRxeyA2fADwdOChR1ulmdjnF16pLrHjJdwHwikRZt5nZg+OVw25iKJwE/gFFH/gjypuYnBcDZvYsypuYsqyhNzFluXdSHIMjhtzEQHkjUy5/28wOLfNK3cjoWPS4ycyeZYUseLHsjpk9B7hpkIG7f5uinR9hZu81s52G1GuRW83sj83sRGDGiheCWPHlb6BcmWZj6lLH00XWYrto0iZg8tvF7OI5Ll9yvADY08zOoP6jzfVmttj3X2Jmiy8IngDUzQUdNI5cQzGOHFO3Qw3vL/6Y+vuLunHk9cC3rPBB0mQM2Yf8MSR1LNq+12rrWPw24zkWYp2yos6yVgNm9hKKh5GXuftF5br7AZ8Dfs3dt6+xexiFPPVrwO0UF/43gMOAP3b3CwbYPAc4AJineFC6juKN9B96jfOl0m6b0ybg3sDvu/uLLO206fdKm/Mo5hk9kkJq+6ghZR1A4Xn7x2Z2DEVndBmwR/lGf5DNQcAhFDf5mygeMF/s7h+sK6e0uyeFQmAKeJG7vyiVPrI9kGKOUNKT+AC7TRTH8WmemDNV3lztQjHH6m4UN3OzwGeBZ7r7cTU2PwOeRjHf7VKKG5DPejBPboDdb1Pc4NwNuA/Fw+1H3f2dQ/YldMx1DMXb/a942jHXb5c2vzCz11DcCJ6eKssKafj7gf919x8H63cCjhnULkqbv6OY53dfihudG4HPu/t1ibIeR3Gct1JcU48BvsgQz+9WeIy/w92/YcWXgv9D0W5r7cqy7vDCy/yizTUUL89SZT2fYh7exrKeP6B4QbKXu59bY/N04GgKqScUcsmXuvvXE+UcQiEL3ERVUXMn8Dx3/88629L+lRQvb/Ye8NUsTnso8A8U1+LxwJkUbXJf4Gh3/9wAm6dSSDdf5u6fKtftTXGNHOLuA29uzcyAT1H0S/9LIeW8BtiT4rr85gCb1LH4A3e/cgKPxT7AZ8ZwLPYty3oUhXy2C9yF4obz5e4eq0ti+9+luGHdzd2HPcTtB7yhPBavo5Cj3o/iuD/dB0R/aDKmNhlPS7s1d400aROlXdwuoPg6Nynt4nCKL8B/Xn4koXxh8kHgd32ADLs8Vh+guA/5MYUPjlsp7p2e7u7XJOoY3l9cN2z/I9sDKcb51FSjQXabgAcA33f3WxLpdgY2eOCgsnyB8ZS6MaRM8yCq4+nXvGb+fGQ3yrF4AMWxeH2uTWm3nMfiBuDrOcdCrD/0IJxB+UZ3S9g5lBfni+oe/oI0jyfjRt8K50uHUbztPorC6cXZ5bYr3H2gcwqrOm16FoV336RdVFaWTbntOOBVFDL0iygepD9B4Uzpq+7+l0u0+Yq7v2GATROv4Eu2GdHuCnd/kJm9nOIN5JkUUrY/BM5099MzbN5DMfD8IYV8+R0DbLY52KJwMLPoYOuJwLU+wMHWALuXlXbnpuya2JR2t1DckP2ZlxK4YQQ2/9czvV9bz/P7DPBDihdGZzHE87v1e4wfajdCWX9Gcdw+R9GOLqF4GH4RcJq7v3sJNi+kkCT22UT2a+6Grky35Ju6tXhDV6Zb8k2dFQ6m9qA4Fj/1Yq5lFuUDxpO8JzXNxsz28CGeWWvG1NoXZ+X2eDy9AfhC6sVZYLvmrpERHnpGaRd7UbSL1HSyOtuh7aJMt8EjJ2ZWOJNMOTXdnUKJNQPc6O6D5nmH6QdFYjg/NX7V2AyN3tCkrOXCqo7rPuXuXw62pRzXNXUg28TuCAoHef9B8YX4IIox8jQvnakNsTkVeOAwG7F+Wek5whNPOYBuCZZDUp3mYtrLy79FpszsngMG4ibOl5raNS3rRcBvAHsBV1F8Bd5sZu+hkFX3PQhHNt8F9oxs+h6EaeYVvInNILtOpt0ixwCP8TIsRLBffQ/Ckc2jF298Apu+B2GaOdiK7Z4MPLw87im72Ca3rGuB5wHvtOIr8mnAp72Q+uXYnEThAOczQ2waeX5vaNe0rGdTXFtdM/sXihue3zKzd1N8wRr0UPtsijmPC0uwAQbfZJnZkm/ozOwZTW/oSPSD7t4nP/XC8Vvywa9MdwVF9IChtHhD992UTcLu8mF2FC87brHCc+7rCW7ohuzb4k3dJyhu6p5D4QE+eVNXPuDcWObxrxTTHbJw9xvLcpf8IEzR96XCtD3V3T9lhSz3RfTUNJ9IPAQv2nySYkz5HYqXRbdTqC3qygq9TV8B/BnwYCtkk28c9OBo/R6qnwE8xMxOJe2hOrY7iULxcrmZ1ZX1aeC4+MEtdY0ENtnXR1C/F1B8pf44ZXg8K+Z7DgyPZ/0h9f4CeIwV87SHhdQLyzqttLsMOLGmrCmKl+a/V76IWVTTnOPuH64pZ5sNheJsK/ADM0vZ1EViOMbMHu4DIjEkbIZFb2hS1qMG5bVI2LeNYlMSOq47y/Ic1zV1ILtkOzN7E3A4hXLheoqX52dQRLB4G8V8/pFtxPpGD8LDOQ/Yn+KCih8SuxRS1Vy7Lj0nS7HdIOdLX7C0w6Gmdk3LmqKcK2Vmb44G9rq2FNq8JcfGG3gFb2KTsDt2mB2woRyEb6Y6R20rxUPCMJtQWpuyaeJga5DdDhRzs1J2TcvqejEf+dHlzfNLgH+wYv7ude4+yKPrIJvTzcwTNk09vzexa1rWJopjfXuZdvdy/a+oP8ebyrRLsVmTN3Qj2K25G7qmdtYgdFoTmxHsTqGQ9p5CMSXndIr29BIzO9jdBznKbGIDxZSNLkvzNj3IQ/U7htg0Lev/AJ+zYh5sbjinRZt3UkxbyY2fHobHexW98Hi/R314vEEh9Y5ieEi9JmW9haJvfVO5/TsUbf6VZnaAD1CPNbRpEomhafSGJnavo5jGtOioLaRL4Uy2DRto5riuqQPZJnZPoviauxvFS5HdyhfGF1B4Tm/LRqxj9CA8nMMp3tq/zN2/Oka7RedLx7v7Ze5+lRVONz5J2slRE7umZX2cwsHTYxe/epjZwRRfrAa+fW1o08QreCObEex+Rs+T8zuAo62Yh/p3FMe3LZtFB1vn0HOw9W8UzjP+LlG/JnZNy9o2iLn7v1EMcBsovnjVvShqYtPU83sTu6ZlvY/CY/bnKI7bv1ihDvkUhRfUtmxgbd7QNbVbizd0Te2ahE5rYjOKHRS+Eh66+HLUzD5L0ZZSESOWavNAX7q36aYeqpuUtehY8O+B75vZP1F8AU055WpiA83C4zUNqdfE7reC6+pzwJfd/RFWfAG/ksHqsSY2TSIxNIre0NDuiRTOMN/m7p9O5D2qDTSL+NDEZhS77dz952Z2QjD+7Ew6kkUTG7FOWVGv0asBd7+VwnveH47Tzov5R68n8GRZPkAfRhFrsDW7Ecp6HfBar8rxNgOn+ID5wU1tAttsr+Cj2DSxc/fHuvs9KGTUZ5Srt1Ds1ykt2nyG4gvI9RRvvr9Ocd6Odvf3Jeq3ZLumZTFA0u3us+7+LS/k9q3YeEPP703sRijrjRSOcm4CXl0+HN0MvMDd/7otm5LFm6yYnBu6pdg0tXsixcPa28q2H/7VPQQ3tWviibhpRICmdtt5MYViqTdnS7Ir2+sTKOaY7+fuXwRuc/cveU34tCY2I9jtaMVc0+solBaL7EC98qSJDTTzNt3UQ3UTu667/6+7v4DiJdvuFC/dfmRmX2vRBnrh8R5EGR6vrF8qbGITm6Z2M1aE6oPC2++iJ+KN1J/jJjZNInQ0jeqxZDsvvvC/EHj4oO1t2ZQMigQyLOJDE5umdv8IfMfMpt39PQBm9nCKL/91/nma2Ih1jJxlCSHEKsR6czi/R9Uj7gEULy/+vQ2bEe0O+P/t3aGLVUEUx/GfRRGD0bJBBBlYQQTBYlYwW4xW2bJJLIJJo9UuWuwGYatdtnjCon+K4cy6T3z3rnPmOtx58/003ffzN3uT9713z5FPb59bRVadS7FJxNGNAJGuZ5IOJe2fvimY/3P2QdJrmxiIFs3l112U9Eb+LPO+md2aem1NpjSX/Dn4u/m1R2b2OPnU9LfyZ3DfLZHJucj2huiE6kjX1p28yYc/3bDtU8GLM/nnD3U2oPFA/q2XY/kjDy/N7P0SmYqup/JPcL/Kv/79Qj4740jSK9uy7SCSybniDR2RTEXX700gZnay8fdzm0CKM/nnz+XfBPx4mks+wO0gv0m7SKai61B+7T6b2UlKaU/+qMj9mWtRnMG4uBEGgE4ln0x7TxsTceXrryY/uYpkanKtpMB0/0imouu6mf3c+POepKvmO8jnfq9QbuP1DyQ9sbL1c8WZ0lxK6bJ8LdGP5BO7L9j564wimX/e3lCTieRSSo+mbqxnOoozE//ONeW1iWb2/X9lSnL5DbDbkr6Zzy+5JOmKTQzlimRSYENHJFPZFdkEUpTZ1a7o+TAuboQBoEPp7yn2f7AtK2IimV3tWvv5WnZxvn661n6+ll3BzLHOtmbclPRF0nMz+zTzaXtxpmVX5fnumG+KKOkqylR2NbkWGBfDsgCgT5GJ9ktOwe+9a+3nW7JrbmNBNLNkV+R3Oi+z5Pl29Vr03hXJtNy00aqr5nwKdJVmarpaXQsMihthAOhTZKJ9qyn4PXSt/XwtuzhfP11rP1/Lrkim5aaNVl1rP18P1wKDYmo0AHTIAhPtI5ld7Vr7+Vp2cb5+utZ+vpZdwUzLTRtNutZ+vpZd0fNhXDwjDAAAAAAYCp8IAwAAAACGwo0wAAAAAGAo3AgDAAAAAIbCjTAAAAAAYCjcCAMAAAAAhvILZRKv1l+jpd0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [18,14])\n",
    "sns.heatmap(V,annot=False,cmap='YlGnBu')\n",
    "plt.savefig('plot/potential-random.pdf')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assignment \n",
    "***1. Create charges in a circle of radius 25 with 16 charge system***\n",
    "\n",
    "***3. Save charge configuration data (dictionary) to a json file***\n",
    "\n",
    "***3. Calculate electric potential for 2D grid and save data to a json file***\n",
    "\n",
    "***4. Develope a 2D plot and save as 'png' or 'pdf' file***"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}