Magnetic_null_point_in_3D.ipynb

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   "source": [
    "%matplotlib inline\n",
    "#%pylab inline\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import athena4_read as ath"
   ]
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    "# 1.basic procedure for the null point identification\n",
    "\n",
    "considering the B-components are on corners.\n",
    "* (1) **reduction**: loop over all corner $B_x$,$B_y$, and $B_z$, if either of them are always positive/negative: remove them from null candidate list.\n",
    "* (2) **selection**: For each faces, calculate the intersection curves for $B_x=B_y=0$, $B_y=B_z=0$, and also $B_x=B_z=0$. Given their bilinear properties on the cell faces, it reduces to solve \n",
    "\n",
    "   $(a_1c_2-a_2c_1)+[(a_1d_2-a_2d_1)+(b_1c_2-b_2c_1)]x_i + (b_1d_2-b_2d_1)x_i^2=0\\,,$\n",
    "   \n",
    "   $x_j = -(a_1+b_1x_i)/(c_1+d_1x_i)\\,,$ where subscript 1 and 2 $\\in \\{x,y,z\\}$ depends on the cell face(e.g., 1='y',2='z' if choose face $x=0$)\n",
    "*  if the solution to above quadratic formula is complex (no intersection), $< 0$ or $> 1$ (intersecting outside the cell): does not represent an intersection point. Otherwise, we found an intersection point!\n",
    "*  Count intersection point for $B_i=0$ and $B_j=0$ for all six faces. A null point has to satisfy the following criteria <font color='orange'>(is this sufficient?)</font>:\n",
    "*    (I) for each pair of intersection curves (e.g., $B_x=B_y=0$), the intersection points are paired (even number) \n",
    "*    (II) the third component (e.g.,$B_z$ in case we considering $B_x=B_y=0$) should have opposite signs in half of those intersection points. Could be all zeros I guess. \n",
    "\n",
    "         \n",
    "  "
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wn9IedMrdq/0OtX12x7ZVq//v7nLc7dX/qwN/m0mcEREREU0zSsnm7sBdwMs6SwYlvQu4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\nTNPNgBVZugPXkyjJ1Jld9l/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+Adi47fdwbeAzbYPUbwrs32pfXc35/o3W8ZKWTTV8RHRK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\nNCcIEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ad687295950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = (8, 8)\n",
    "plt.axis('off')\n",
    "#plt.arrow(0, 0, 0.5, 0.5, head_width=0.05, head_length=0.1, fc='k', ec='k');\n",
    "plt.plot([0,1],[0,0],c='k',lw=10)\n",
    "plt.plot([0,1],[1,1],c='k',lw=10)\n",
    "plt.plot([0,0],[0,1],c='k',lw=10)\n",
    "plt.plot([1,1],[0,1],c='k',lw=10)\n",
    "plt.text(-0.1,-0.05,r'$f_{00}$',size=20)\n",
    "plt.text(1., -0.05, r'$f_{10}$',size=20)\n",
    "plt.text(1.,1.05,   r'$f_{11}$',size=20)\n",
    "plt.text(-0.1,1.05,r'$f_{01}$',size=20)\n",
    "plt.text(0.155,0.6,  r'$(x_i,x_j)$',size=20)\n",
    "plt.plot(0.15,0.6,marker='o')\n",
    "ys = np.linspace(-0.1,1.05,40)\n",
    "xs = 0.24/(1.0+ys)\n",
    "plt.plot(xs,ys,'k:')\n",
    "\n",
    "xs = np.linspace(-0.04,1.05,40)\n",
    "ys = 0.69/(1.0+xs)\n",
    "plt.plot(xs,ys,'k:')\n",
    "plt.text(0.1,1.1,r'$B_x=0$',size=20)\n",
    "plt.text(-0.2,0.8,r'$B_y=0$',size=20)\n",
    "\n",
    "plt.text(0.06,0.45,r'$f(x_i,x_j) = a + bx_i + cx_j + dx_i x_j$',size=18)\n",
    "plt.text(1.05,0.4,r'$d = f_{11}+f_{00}-(f_{10}+f_{01})$',size=20)\n",
    "plt.text(1.05,0.7,r'$a = f_{00}$',size=20)\n",
    "plt.text(1.05,0.6,r'$b = f_{10}-f_{00}$',size=20)\n",
    "plt.text(1.05,0.5,r'$c = f_{01}-f_{00}$',size=20)\n",
    "plt.text(1.05,0.28,r'$f \\in \\{B_x,B_y,B_z\\}$',size=20)\n",
    "plt.text(1.05,0.18,r'$i,j \\in \\{x,y,z\\}$',size=20)\n",
    "plt.arrow(0.3,0.05,0.3,0,head_width=0.025,head_length=0.05,fc='k',ec='k',lw=2)\n",
    "plt.arrow(0.95,0.6,0.,0.3,head_width=0.025,head_length=0.05,fc='k',ec='k',lw=1.5)\n",
    "plt.text(0.68,0.03,r'$\\hat{x}_i$',size=20)\n",
    "plt.text(0.85,0.9,r'$\\hat{x}_j$',size=20)\n",
    "\n",
    "plt.axes().set_aspect('equal')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time =  2481.862\n",
      "face_dimensions =  [65, 257, 257]\n",
      "-5.000000e-01 -2.000000e+00 -2.000000e+00\n",
      "box_origin =  [-0.5, -2.0, -2.0]\n",
      "1.562500e-02 1.562500e-02 1.562500e-02\n",
      "grid_spacing =  [0.015625, 0.015625, 0.015625]\n",
      "cell_dimensions =  [256 256  64]  num_cells =  4194304\n",
      "SCALARS\n",
      "start loading scalars !! \n",
      "loading array_name =  density\n",
      "is,ie =  297 16777513\n",
      "data[density].shape=  (256, 256, 64)\n",
      "VECTORS\n",
      "VECTORS\n",
      "start loading vectors !! \n",
      "loading array_name =  momentum\n",
      "VECTORS\n",
      "VECTORS\n",
      "start loading vectors !! \n",
      "loading array_name =  cell_centered_B\n"
     ]
    }
   ],
   "source": [
    "t,x,y,z,data=ath.vtk('Unstra.0080.vtk')\n",
    "bx = data['cell_centered_B'][...,0]\n",
    "by = data['cell_centered_B'][...,1]\n",
    "bz = data['cell_centered_B'][...,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. reduction with nested for-loop in series: too slow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77860\n",
      "[[0, 1, 11], [0, 2, 11], [0, 3, 11], [0, 19, 11], [0, 20, 11]]\n",
      "time cost =  210.98925209  second\n"
     ]
    }
   ],
   "source": [
    "nx = bx.shape[2]\n",
    "ny = bx.shape[1]\n",
    "nz = bx.shape[0]\n",
    "\n",
    "import time\n",
    "\n",
    "def positive_negative(inputlist):\n",
    "  if ((all(item>0 for item in list(inputlist))) or (all(item<0 for item in list(inputlist)))):\n",
    "    return True\n",
    "  else:\n",
    "    return False # elements with diff sign or all zeros;\n",
    "         \n",
    "\n",
    "#print nx,ny,nz\n",
    "candidate = []\n",
    "tstart = time.time()\n",
    "for k in range(nz-1):\n",
    "  for j in range(ny-1):\n",
    "    for i in range(nx-1):\n",
    "      corner=bx[k:k+2,j:j+2,i:i+2]\n",
    "      is_bx = positive_negative(np.ravel(corner))\n",
    "      corner=by[k:k+2,j:j+2,i:i+2]\n",
    "      is_by = positive_negative(np.ravel(corner))\n",
    "      corner=bz[k:k+2,j:j+2,i:i+2]\n",
    "      is_bz = positive_negative(np.ravel(corner))\n",
    "      if ((is_bx or is_by or is_bz) == False):\n",
    "        candidate.append([k,j,i])\n",
    "\n",
    "tend = time.time()\n",
    "print len(candidate)\n",
    "print candidate[:5]\n",
    "print 'time cost = ',tend-tstart,' second'\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. reduction using parallelized for-loop\n",
    "* using 8 processes, takes 38 sec compared to the above 210 sec..\n",
    "* part of the 38 sec is used to clean-up the None in the list (Any way to not including those None in the first place??)\n",
    "* change the candidate_list to save tuples in the list instead of list in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77860\n",
      "\n",
      "[(0, 1, 11), (0, 2, 11), (0, 3, 11), (0, 19, 11), (0, 20, 11)]\n",
      "\n",
      "time cost =  31.4930899143  second\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import multiprocessing as mp\n",
    "from itertools import product\n",
    "\n",
    "\n",
    "tstart = time.time()  \n",
    "nx = bx.shape[2]\n",
    "ny = bx.shape[1]\n",
    "nz = bx.shape[0]  # shrink 4 times for test/debug purpose.\n",
    "#contruct list of argument for parallelization\n",
    "klist,jlist,ilist = zip(*product(range(nz-1),range(ny-1),range(nx-1)))\n",
    "#print klist[256*64/4:256*64/4+10]\n",
    "\n",
    "\n",
    "def positive_negative(inputlist):\n",
    "  if ((all(item>0 for item in list(inputlist))) or (all(item<0 for item in list(inputlist)))):\n",
    "    return True  # elements with same sign (+ or -)\n",
    "  else:\n",
    "    return False # elements with diff sign or all zeros;\n",
    " \n",
    "\n",
    "def f(location):\n",
    "  #candidate_pl = []\n",
    "  k = location[0];j=location[1];i=location[2]\n",
    "  corner=bx[k:k+2,j:j+2,i:i+2]\n",
    "  is_bx = positive_negative(np.ravel(corner))\n",
    "  corner=by[k:k+2,j:j+2,i:i+2]\n",
    "  is_by = positive_negative(np.ravel(corner))\n",
    "  corner=bz[k:k+2,j:j+2,i:i+2]\n",
    "  is_bz = positive_negative(np.ravel(corner))\n",
    "  if ((is_bx or is_by or is_bz) == False):\n",
    "    #candidate_pl.append((k,j,i)) \n",
    "    #print '[k,j,i]=',k,j,i\n",
    "    #print 'length of candiate=',len(candidate_pl)\n",
    "    return (k,j,i) #candidate_pl\n",
    "  else:\n",
    "    return None\n",
    "\n",
    "result_list=[]\n",
    "candidate_list=[]\n",
    "#create a pool and map the target function with multi-arguments\n",
    "p = mp.Pool(processes=8)\n",
    "result_list = p.map(f,zip(klist,jlist,ilist))\n",
    "candidate_list = [item for item in result_list if item is not None]\n",
    "\n",
    "tend = time.time()\n",
    "\n",
    "del result_list\n",
    "\n",
    "print len(candidate_list)\n",
    "print\n",
    "print candidate_list[:5]\n",
    "print\n",
    "print 'time cost = ',tend-tstart,' second'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p.terminate()\n",
    "p.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check if the above algorithm is correct. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "k = 0; j=1; i=11\n",
    "corner=bx[k:k+2,j:j+2,i:i+2]\n",
    "print positive_negative(np.ravel(corner))\n",
    "corner=by[k:k+2,j:j+2,i:i+2]\n",
    "print positive_negative(np.ravel(corner))\n",
    "corner=bz[k:k+2,j:j+2,i:i+2]\n",
    "print positive_negative(np.ravel(corner))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dump/restore with pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77860\n",
      "\n",
      "[(0, 1, 11), (0, 2, 11), (0, 3, 11), (0, 19, 11), (0, 20, 11)]\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "## dump the candidate_list\n",
    "#pickle.dump(candidate_list, open( \"candidate.p\", \"wb\" ) )\n",
    "\n",
    "\n",
    "# for the following development, we could simply load the \n",
    "# list below and carry on the calculation\n",
    "candidate_list = pickle.load( open( \"candidate.p\", \"rb\" ) )\n",
    "print len(candidate_list)\n",
    "print\n",
    "print candidate_list[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 3. selection\n",
    "## 3.1. finding intersection points by bilinear interpolation over cell faces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $B_x=B_y=0$ on one face ($x=0$): $f(x,y) = B_x$, or $B_y$\n",
    "$B_x=0 \\Rightarrow B_x(0,0)+[B_x(1,0)-B_x(0,0)]y+[B_x(0,1)-B_x(0,0)]z + [B_x(0,0)+B_x(1,1)-B_x(1,0)-B_x(0,1)] yz = 0$\n",
    "\n",
    "$B_y=0 \\Rightarrow B_y(0,0)+[B_y(1,0)-B_y(0,0)]y+[B_y(0,1)-B_y(0,0)]z + [B_y(0,0)+B_y(1,1)-B_y(1,0)-B_y(0,1)] yz = 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# function which takes the cell location and solve for intersection points on six surfaces\n",
    "def bilinear(x,y,f00,f01,f10,f11):\n",
    "   a = f00; b = f10-f00; c = f01-f00; d = f00+f11-f10-f01\n",
    "   fxy = a + b*x + c*y + d*x*y\n",
    "   return fxy\n",
    "\n",
    "# test if a list has equal number of positive\n",
    "# and negative values; zeros belong to both.\n",
    "def issym(b3):\n",
    "  npos = 0; nneg = 0\n",
    "  for item in b3:\n",
    "    if (item >= 0): \n",
    "      npos +=1\n",
    "    if (item <= 0):\n",
    "      nneg +=1\n",
    "  if (npos==nneg):\n",
    "    return True\n",
    "  else:\n",
    "    return False\n",
    "\n",
    "###############################################\n",
    "# intersection curves Bx=By=0\n",
    "###############################################\n",
    "def intersect_bxby(location):\n",
    "  npts = 0 # # of end points\n",
    "  b3 = [] # third component values\n",
    "  sk = location[0];sj=location[1];si=location[2]\n",
    "  f = bx; g = by; h = bz\n",
    "    \n",
    "  for nface in range(6):\n",
    "    if (nface == 0):  # x = 0 face (y,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj+1,si],f[sk+1,sj+1,si]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj+1,si],g[sk+1,sj+1,si]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj+1,si],h[sk+1,sj+1,si]\n",
    "    if (nface == 1): # x=1 face \n",
    "      f00,f01,f10,f11 = f[sk,sj,si+1],f[sk+1,sj,si+1],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si+1],g[sk+1,sj,si+1],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si+1],h[sk+1,sj,si+1],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 2):  # y = 0 face (x,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj,si+1],f[sk+1,sj,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj,si+1],g[sk+1,sj,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj,si+1],h[sk+1,sj,si+1]\n",
    "    if (nface == 3): # y =1 face\n",
    "      f00,f01,f10,f11 = f[sk,sj+1,si],f[sk+1,sj+1,si],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj+1,si],g[sk+1,sj+1,si],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj+1,si],h[sk+1,sj+1,si],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 4): # z = 0 face (x,y)\n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk,sj+1,si],f[sk,sj,si+1],f[sk,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk,sj+1,si],g[sk,sj,si+1],g[sk,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk,sj+1,si],h[sk,sj,si+1],h[sk,sj+1,si+1]\n",
    "    if (nface == 5): # z=1 face\n",
    "      f00,f01,f10,f11 = f[sk+1,sj,si],f[sk+1,sj+1,si],f[sk+1,sj,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk+1,sj,si],g[sk+1,sj+1,si],g[sk+1,sj,si+1],g[sk+1,sj+1,si+1] \n",
    "      h00,h01,h10,h11 = h[sk+1,sj,si],h[sk+1,sj+1,si],h[sk+1,sj,si+1],h[sk+1,sj+1,si+1]\n",
    "        \n",
    "    a1,b1,c1,d1 = f00,f10-f00,f01-f00,f00+f11-f10-f01\n",
    "    a2,b2,c2,d2 = g00,g10-g00,g01-g00,g00+g11-g10-g01\n",
    "    coeff = [(b1*d2-b2*d1), (a1*d2-a2*d1+b1*c2-b2*c1), (a1*c2-a2*c1)]\n",
    "    sol1 = np.roots(coeff)\n",
    "    sol2 = -(a2+b2*sol1)/(c2+d2*sol1)\n",
    "    #  determine if intersection points\n",
    "    for s,t in zip(sol1,sol2):\n",
    "       if ((not isinstance(s,complex)) and (not isinstance(t,complex))): \n",
    "         if (s>=0 and s<=1 and t>=0 and t<=1):\n",
    "           npts += 1\n",
    "           # if yes, calculate the third components on those points\n",
    "           b3.append(bilinear(s,t,h00,h01,h10,h11))\n",
    "\n",
    "  #  determine if null pt: (1) intersection points in pair (2) third component flip signs\n",
    "  #  if yes then return True; else return False\n",
    "  if (npts>0 and npts%2==0): \n",
    "    return issym(b3) \n",
    "  else:\n",
    "    return False\n",
    "\n",
    "###############################################\n",
    "# intersection curves By=Bz=0\n",
    "###############################################\n",
    "def intersect_bybz(location):\n",
    "  npts = 0 # # of end points\n",
    "  b3 = [] # third component values\n",
    "  sk = location[0];sj=location[1];si=location[2]\n",
    "  f = by; g = bz; h = bx\n",
    "    \n",
    "  for nface in range(6):\n",
    "    if (nface == 0):  # x = 0 face (y,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj+1,si],f[sk+1,sj+1,si]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj+1,si],g[sk+1,sj+1,si]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj+1,si],h[sk+1,sj+1,si]\n",
    "    if (nface == 1): # x=1 face \n",
    "      f00,f01,f10,f11 = f[sk,sj,si+1],f[sk+1,sj,si+1],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si+1],g[sk+1,sj,si+1],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si+1],h[sk+1,sj,si+1],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 2):  # y = 0 face (x,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj,si+1],f[sk+1,sj,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj,si+1],g[sk+1,sj,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj,si+1],h[sk+1,sj,si+1]\n",
    "    if (nface == 3): # y =1 face\n",
    "      f00,f01,f10,f11 = f[sk,sj+1,si],f[sk+1,sj+1,si],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj+1,si],g[sk+1,sj+1,si],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj+1,si],h[sk+1,sj+1,si],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 4): # z = 0 face (x,y)\n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk,sj+1,si],f[sk,sj,si+1],f[sk,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk,sj+1,si],g[sk,sj,si+1],g[sk,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk,sj+1,si],h[sk,sj,si+1],h[sk,sj+1,si+1]\n",
    "    if (nface == 5): # z=1 face\n",
    "      f00,f01,f10,f11 = f[sk+1,sj,si],f[sk+1,sj+1,si],f[sk+1,sj,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk+1,sj,si],g[sk+1,sj+1,si],g[sk+1,sj,si+1],g[sk+1,sj+1,si+1] \n",
    "      h00,h01,h10,h11 = h[sk+1,sj,si],h[sk+1,sj+1,si],h[sk+1,sj,si+1],h[sk+1,sj+1,si+1]\n",
    "        \n",
    "    a1,b1,c1,d1 = f00,f10-f00,f01-f00,f00+f11-f10-f01\n",
    "    a2,b2,c2,d2 = g00,g10-g00,g01-g00,g00+g11-g10-g01\n",
    "    coeff = [(b1*d2-b2*d1), (a1*d2-a2*d1+b1*c2-b2*c1), (a1*c2-a2*c1)]\n",
    "    sol1 = np.roots(coeff)\n",
    "    sol2 = -(a2+b2*sol1)/(c2+d2*sol1)\n",
    "    #  determine if intersection points\n",
    "    for s,t in zip(sol1,sol2):\n",
    "       if ((not isinstance(s,complex)) and (not isinstance(t,complex))): \n",
    "         if (s>=0 and s<=1 and t>=0 and t<=1):\n",
    "           npts += 1\n",
    "           # if yes, calculate the third components on those points\n",
    "           b3.append(bilinear(s,t,h00,h01,h10,h11))\n",
    "\n",
    "  #  determine if null pt: (1) intersection points in pair (2) third component flip signs\n",
    "  #  if yes then return True; else return False\n",
    "  if (npts>0 and npts%2==0): \n",
    "    return issym(b3) \n",
    "  else:\n",
    "    return False\n",
    "\n",
    "###############################################\n",
    "# intersection curves Bx=Bz=0\n",
    "###############################################\n",
    "def intersect_bxbz(location):\n",
    "  npts = 0 # # of end points\n",
    "  b3 = [] # third component values\n",
    "  sk = location[0];sj=location[1];si=location[2]\n",
    "  f = bx; g = bz; h = by\n",
    "    \n",
    "  for nface in range(6):\n",
    "    if (nface == 0):  # x = 0 face (y,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj+1,si],f[sk+1,sj+1,si]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj+1,si],g[sk+1,sj+1,si]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj+1,si],h[sk+1,sj+1,si]\n",
    "    if (nface == 1): # x=1 face \n",
    "      f00,f01,f10,f11 = f[sk,sj,si+1],f[sk+1,sj,si+1],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si+1],g[sk+1,sj,si+1],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si+1],h[sk+1,sj,si+1],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 2):  # y = 0 face (x,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj,si+1],f[sk+1,sj,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj,si+1],g[sk+1,sj,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj,si+1],h[sk+1,sj,si+1]\n",
    "    if (nface == 3): # y =1 face\n",
    "      f00,f01,f10,f11 = f[sk,sj+1,si],f[sk+1,sj+1,si],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj+1,si],g[sk+1,sj+1,si],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj+1,si],h[sk+1,sj+1,si],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 4): # z = 0 face (x,y)\n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk,sj+1,si],f[sk,sj,si+1],f[sk,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk,sj+1,si],g[sk,sj,si+1],g[sk,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk,sj+1,si],h[sk,sj,si+1],h[sk,sj+1,si+1]\n",
    "    if (nface == 5): # z=1 face\n",
    "      f00,f01,f10,f11 = f[sk+1,sj,si],f[sk+1,sj+1,si],f[sk+1,sj,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk+1,sj,si],g[sk+1,sj+1,si],g[sk+1,sj,si+1],g[sk+1,sj+1,si+1] \n",
    "      h00,h01,h10,h11 = h[sk+1,sj,si],h[sk+1,sj+1,si],h[sk+1,sj,si+1],h[sk+1,sj+1,si+1]\n",
    "        \n",
    "    a1,b1,c1,d1 = f00,f10-f00,f01-f00,f00+f11-f10-f01\n",
    "    a2,b2,c2,d2 = g00,g10-g00,g01-g00,g00+g11-g10-g01\n",
    "    coeff = [(b1*d2-b2*d1), (a1*d2-a2*d1+b1*c2-b2*c1), (a1*c2-a2*c1)]\n",
    "    sol1 = np.roots(coeff)\n",
    "    sol2 = -(a2+b2*sol1)/(c2+d2*sol1)\n",
    "    #  determine if intersection points\n",
    "    for s,t in zip(sol1,sol2):\n",
    "       if ((not isinstance(s,complex)) and (not isinstance(t,complex))): \n",
    "         if (s>=0 and s<=1 and t>=0 and t<=1):\n",
    "           npts += 1\n",
    "           # if yes, calculate the third components on those points\n",
    "           b3.append(bilinear(s,t,h00,h01,h10,h11))\n",
    "\n",
    "  #  determine if null pt: (1) intersection points in pair (2) third component flip signs\n",
    "  #  if yes then return True; else return False\n",
    "  if (npts>0 and npts%2==0): \n",
    "    return issym(b3) \n",
    "  else:\n",
    "    return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. identify 3D null point by number of intersection points and the values of the third component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time cost for the 1st pass =  7.85764408112  seconds\n",
      "time cost for the 2nd pass =  0.426918983459  seconds\n",
      "time cost for the 3rd pass =  0.845993995667  seconds\n",
      "\n",
      "find  536  magnetic null points\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import multiprocessing as mp\n",
    "from itertools import product\n",
    "\n",
    "\n",
    "tstart = time.time()  \n",
    "\n",
    "#create a pool and map the target function with multi-arguments\n",
    "p = mp.Pool(processes=8)\n",
    "\n",
    "# first pass: finding intersection of Bx=By=0\n",
    "mask_list = p.map(intersect_bxby, candidate_list)\n",
    "cell = [candidate_list[i] for i in range(len(candidate_list)) if mask_list[i]]\n",
    "               \n",
    "\n",
    "tend = time.time()\n",
    "print 'time cost for the 1st pass = ',tend-tstart,' seconds'\n",
    "#print type(cell)\n",
    "#print cell[:5]\n",
    "#print len(cell)\n",
    "\n",
    "tstart = time.time() \n",
    "# second pass: finding intersection of By=Bz=0\n",
    "for cell_one in cell:\n",
    "  if (not intersect_bybz(cell_one)):\n",
    "    cell.remove(cell_one)\n",
    "tend = time.time()\n",
    "print 'time cost for the 2nd pass = ',tend-tstart,' seconds'\n",
    "#print type(cell)\n",
    "#print cell[:5]\n",
    "#print len(cell)\n",
    "\n",
    "\n",
    "# third pass: finding intersection of Bx=Bz=0\n",
    "for cell_one in cell:\n",
    "  if (not intersect_bxbz(cell_one)):\n",
    "    cell.remove(cell_one) \n",
    "tend = time.time()\n",
    "print 'time cost for the 3rd pass = ',tend-tstart,' seconds'\n",
    "#print type(cell)\n",
    "#print cell[:5]\n",
    "#print len(cell)\n",
    "print\n",
    "print 'find ',len(cell),' magnetic null points'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## save the location of null points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "536\n",
      "\n",
      "[(1, 186, 23), (2, 56, 11), (6, 167, 19), (8, 154, 26), (10, 79, 2)]\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# dump the magnetic null\n",
    "pickle.dump(cell, open( \"null.p\", \"wb\" ) )\n",
    "\n",
    "\n",
    "# for the following development, we could simply load the \n",
    "# list below and carry on the calculation\n",
    "cell = pickle.load( open( \"null.p\", \"rb\" ) )\n",
    "print len(cell)\n",
    "print\n",
    "print cell[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3. Visualize the spatial distribution of the null points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xloc = np.array(cell)[:,2]\n",
    "yloc = np.array(cell)[:,1]\n",
    "zloc = np.array(cell)[:,0]\n",
    "\n",
    "#\n",
    "# try to visualize the spatial distribution of null points\n",
    "#\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.gridspec as gridspec\n",
    "%matplotlib notebook\n",
    "\n",
    "# create the scatter plot\n",
    "matplotlib.rcParams['figure.figsize'] = (8, 8)\n",
    "fig = plt.figure()\n",
    "fraction=0.025;pad=0.08\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "#ax = Axes3D(fig)\n",
    "ax.scatter(xloc, yloc, zloc,marker='o')\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlim3d(0, 65)\n",
    "ax.set_ylim3d(0, 255)\n",
    "ax.set_zlim3d(0, 255)\n",
    "\n",
    "ax.set_xlabel(r'$x$',size=20)\n",
    "ax.set_ylabel(r'$y$',size=20)\n",
    "ax.set_zlabel(r'$z$',size=20)\n",
    "ax.view_init(elev=22, azim=130)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4.  find null points on current sheets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "826  current sheets\n",
      "536  magnetic null points\n",
      "\n",
      "[(1, 186, 23), (2, 56, 11), (6, 167, 19), (8, 154, 26), (10, 79, 2)]\n"
     ]
    }
   ],
   "source": [
    "import cPickle as pickle\n",
    "pathdir = 'data/64H/'\n",
    "jlist_sorted = pickle.load( open( pathdir+\"jlist.p\", \"rb\" ) )\n",
    "print len(jlist_sorted),' current sheets'\n",
    "\n",
    "\n",
    "null = pickle.load( open( pathdir+\"NULL.p\", \"rb\" ) )\n",
    "print len(null), ' magnetic null points'\n",
    "print\n",
    "print null[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## the number density of null points on current sheet is twice higher than uniform distributed over the box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "{(39, 187, 23): 7, (52, 211, 14): 22, (36, 238, 25): 16, (72, 235, 45): 15, (92, 29, 30): 88, (56, 211, 14): 22, (45, 231, 11): 7, (75, 243, 35): 154, (131, 243, 0): 9, (74, 216, 53): 15, (123, 93, 56): 4, (36, 234, 26): 16, (90, 162, 54): 190, (51, 221, 4): 7, (36, 236, 26): 16, (66, 140, 6): 29}\n",
      "\n",
      "current sheet volume / whole box volume =  0.0156700611115\n",
      "number density of null points in whole volume =  0.000127792358398\n",
      "number density of null points in current sheet =  0.000243438569798\n"
     ]
    }
   ],
   "source": [
    "# search for the current sheet given a null point? \n",
    "identity = {}\n",
    "jvolume = 0.0; numnull = float(len(null))\n",
    "for i in range(0,len(jlist_sorted)):\n",
    "  jvolume +=len(jlist_sorted[i]) # obtain total volume\n",
    "  for location in null:\n",
    "    if location in jlist_sorted[i]:\n",
    "      null.remove(location)\n",
    "      identity[location] = i # store null pt location:index of current sheet key:value pair\n",
    "print len(identity)\n",
    "print identity\n",
    "print \n",
    "# calc the number density of null points on current sheet or in the whole box\n",
    "nx=64\n",
    "ny=256\n",
    "nz=256\n",
    "totvolume = float(nx*ny*nz)\n",
    "print \"current sheet volume / whole box volume = \", jvolume/totvolume\n",
    "print \"number density of null points in whole volume = \", numnull/totvolume\n",
    "print \"number density of null points in current sheet = \", float(len(identity))/jvolume\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## show null points embedded in current sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 39 187  23]\n",
      " [ 45 231  11]\n",
      " [ 51 221   4]]\n",
      "\n",
      "[23 11  4] [187 231 221] [39 45 51]\n",
      "(2064, 3)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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tH1uTUigpayM7bbPj+bP59Tbb/PPhQv5aMEC3p09m+p581j++ussfuUVKuLiKJg6lfqkJIonTuTjmBjeGj2aXTExZD39NKVjxpAzciTvBgdT4uNDjYcHFW5uVAwcyLr+/Unt1YtyLy9S3dyoGDOGEoOBXcnJ7Fq8mIPFxexYvJj65GTejYzkQHo6G2yf/1q8WAKQA7N33xUOwt6FKBzfuZeTnr1v5+wAdO6t7L/1DJ8rLQCdOnXKoQLQ2UtKO99/ny927ODr3btpaWnhw6oqGqurqbRa2RIfT+64cdRbLKzSaqm3WHgrMJBdFgsLlErqLRZyNRqawsMpHTWKBouF1aGh7LJYqAwNZavFwtqxY9lusdAUF8dGs5n9sbGUm800xsbSYFteKw8NPfMZEsLJ+fPZNnkyP8ybx5oXX+TookXkGgwcSUnhg6lTOZaaSt306XyRnMw/bRuwLyUAbbW94aqLj2fPokV8HBNDQ3Q0FRMnkqPTUfHCC8zz8WG1wcAbAweyOiiI2L59KVapsD7xBOkDBzK9e3cye/fG+sAD5D78MCkPPMCi++6jsHt3rN27k+XqinXQIDL1elJGjSJnwgTin3+e9FdfZenUqaS/8grpL7/MymnTyJwwgaXjx5P4/PMseeUVEkwmCmNiyJw+vW2CbFtlJdvXrZNN0XZk774rHIS9C1E4rp9++ulXLydtbm5uCxutN6P/3jN8JABdvqepqeln/7/PXh5r2byZLfHxZ/b+2PYEbTYYaImKokirpSUqijLbZ4lWS1N4OJsMBprCw6kzGNgTHk6VwXDpAcj2ud329YoXX+RkZiZFRiMnMzP5Z2QkPyxdSt306exKSOA92wbs3ZmZfJqdzefLl/N5bi5f5eXxkS3wXCgANURH8/nUqdQYDFT6+bFFrSbP2ZkPPD1JeOQR3ndxYeEDD1DVsydJd97J0i5diLzzTtLvuYeE225j5d13k9m5Mysfeoiaxx/n7X79eGfwYLLd3MjVaFip11P5j39QM3Mm/8rMZFtuLrWJidRarVRbrfzL9kboU9uUWK3VysHiYjbYPg+98w5s3co+eSNkV/buu8JB2LsQheP5LaPsrTefNzc3X/CQw98SgK6kc4AkAF1aAFql0dAcEcEmg4HmiAiKgoPZP3s2y/T6MxuzJ01ib2ws681mtkZFURsRQX1yMjsWLODDefMoDw+n1GKhbPJkcl94gfenTydj1Cjqpk8na9Qoip59lkQ/P5J8fUnz9ibHzY2FLi4U9utH7ZAh5Dz+OJU9e5J0993kX/834hQKlikULL7qatJuv53E22+n8p57KLnzTrZ07szKe++l8qGHKOnalWW9e7Nq8GBWq1SsNRj4ZOpUPp01i2+WLKF+4UL2FhVxoLiYg8XFbUte+2x/fbC4mB3JyVBZSb3t86BtSUwCkH3Zu+8KB2HvQhSO4feOsrfeU3U5QsvRo0clANk7AAUGsic8nBy1mv3h4SR7e/OxyUSRSsU6rZZ8b28KNBrme3jwVmAglaNH89bIkazUaJju6kq8UsmbLi4UqdXE9u1LoVpNVN++lGq1vDloEMVaLbEDB1JtMBDj6kpRSAgx3t7khoQw29ubotGjSX76afKefZZMjYZ0lYpEb29iXVyI7d+fWU5OxDk7M7lrV+IeewzLgw/yWpcuzLrvPsLvvpu4zp2x3n034XfcwZxbb2X2nXcy66abSO7QkWkKBXmKG1mguJWVintIU3TiDYWCmZ1uoPbBB1n9wANs7taNvIceYpWTE8v69aNIpWJlQADbx4+nbvJk9s6ezcevv84XKSn8c/58vszPZ09BAftWruT/Vq1iX1ERe4uKaCoqkgDkwOzdd4WDsHchCvu62OWk5wso546yn33nlgQgx3nODUCt+4O2rF5NY3U1a+bNY2t8PCX/+Afbo6LI0+tZHxZGpl7P9uhoErVaqs1m3nB1pVyrJdnVlTw/PxIGDWKJRkO4qyvZAQGk+/mR7e1Nurc3swYMIF+jYdHgwbwTFETsU0+xwseHV3r0INHTk0mPP06ipyfjuncnYcgQJj3+OCleXkzr04fFQ4fyao8eWAcOZHzXrkzr2ZPE3r3J7N+ftH79WNqnD8t69WLhI4+wsEsXEjt3JuuBB5jfpQtvPPAASzt3JvG220i9+WayOnXCev31LOjYkVk33MCsq64iUaFgmuJ6FipuJlpxM4mK25ipuJ1Jis6YFAqy772X/O7dKXNyIsfJicoRI8jx8mLd2LEUh4RwIDKSOouF/bGx7IqOpnHRIrbGx/Nlfj5f5+fzf/n5fJGfz1e2z//Lz2dvYSEfJSby3/JydixYwE9r1tBUVCQByAHYu+8KB2HvQhT2cSmXk5448euj7BKArowAdG4Qar3za41t3Dw/JoYtWVlUJSVRl51NutlMbUQEcX5+lIeGstzPj0qtlnhnZ5ar1bzauzfLvb3J9vKiTKXineBg0nx8qBg1ikWenuQrlbzp5MQWtZrFvXrxZWgoS/v1oyEkhFm9e1Pu7c2cJ56gwtsb6yOP8HafPszr3Jm8xx9n1n33EXn//STcdx+p991HzD33MPf225lz++0k3HUXS++6iyV33MGqO+5gxR13YL31Vpb97W8s6tiR0uuuo7xjR5Zcdx2ZHTsy5/rrmaNQME9xLa8rbmSJ4hZiFbeSrLgTq+Jepige5h+KO5h19bUUPPYYK/r0Id3FhZrQUJL8/Cg2Glmk0VASEsJ8lYolej3xgYGsmTGDJS+9xLbkZHYkJ1MdF0dVXBy1Vit12dnsLS5m68KFrJkxgw/mz2fRCy+weupUlrz0EhWxsaycPp21c+aQHxvLRxUVbK+pkU3R7cjefVc4CHsXomhfp0+fvuTLSY8cOdJ2P9eFzvBx1AB05MgRmpubLxqAWsNAnS0MVGRksCYjgy2rV5+ZmrJdBPpnCkAXes49lLA8NZU9BQUU/eMf7I+NpSo4mPqQEFKdnChQKpnl5ESVUkmelxe1SiX5np6kubrylocHqc7OrPHywtq7N/UjRrD00UfZNWIESY88wvseHsQ88ADVvXqx4N57KevenehbbyXlrruw/u1vpN92G3NvvJG4m24i86abyL75ZqJuuAFrp04kduxI7nXXUXLddeR27EhJx45kdejAG1dfTXaHDmRccw21HTtSde21LOvQgYwOHZh67bWYFAomK67jJcVNRChuJ0JxB9MUd/Kq4h4MigfQKDqjUSgI69KFaU5OTOzXjzcDAviHqyuJWi2z/fxYERxMekAAa4xG4jUaii0Wwv38SDMYsOp0pI0dy/yQEKxjxmAdN47FkyZhHTOGZePHs2rKFFJCQig1mUgMDKTQZGLJqFFUTphA6ujRfDB/PhVz57Jl+XLWnOc8I3H52bvvCgdh70IU7aN1lL01GFxomeu3jrJfyQHo3H0xewsLaSwo4OviYrZkZVGXm9t2js75gtGpU6fYUVd33tOOLxacHDEAnftsWb2als2bqQoPpyU+ni06HS1GI5UuLjTqdKT37s0uX19Wurjwqa8vpf37U+niwvbBg6kaOJBGpZLUvn3Zo1ZT8uijHFarWdmjB40+PmR17crnTz3Fms6d2dOzJ8V33EH5XXex9uabqbn5Zso7daKwY0eqO3SgpGNHEq++moVXXUXGVVdRctVVrL36avIVCjIVCpIUCqYqFMxTKJitUJCmUFCkUFDeoQOrr7uOjOuvZ6ZCwSzFHUxW3INJcQ+TFPfwsuJ+TIruTFC4olf05yXFVWT5+VH/6qusMhj4KiWFlUYjJ9LS2BARwbcpKdRNncrRlBSqLBZOvPUWFVOmcDIzk7rXX6clK4vt0dHsT01le0LCz0b3GwsKqE9IYO+iRdRHR7M/NrZtIq7cbD7z37Utp31TUMDJ99/nq7VrOfnFF3yxc+fPLpQ9+2oQR7xQ9kph774rHIS9C1H8b5090XX06FEOHz7cFgwuFnwudZT9zxiAWhvXHlvjaikr48OFC2ksKGDNvHlssZ08vLmsjJVJSeypqWHrqlU0VlfzUV4eddnZvwhOdZfwRumKCkB9+vCRry85/fuz3deXnD59qHJ2Zn2vXrzVuze1Li7MeuQRSvr1Y97991Pi5MSce+9lUZcuRN50E4m33sqsTp1YccstxHTsSGLHjmRdey1pV19NlkKBVaEgS6FggULBawoFbygUvKxQEKdQMF+hYI5CwesKBdMVCiYqFExTKIi0BaLSq66i8rrrKLvhBopvuYVIhYKZijuZqXiUSEUvEhQ9mafwIFzhwTTFaIIVrrx8TSc2GAyctFqpNRppycqi2Pa5JSqKlqwsGqKizlwoO2UKx1etYrXFwrcpKWycMoVvU1LYEhHBV0lJ1M2bx1d5eW0bpC8pACUk0Gg7+6j1AtqWxka+rK//xYWyhw4dartLrTUY/fe//5Vg9BvYu+8KB2HvQhSXX+tE1/fff/+LM3zODgYnTpwZZT/7DJ/fOsp+9l6bP3MA2mI7b6b13Jm9hYWc+uADalJSaNm8mbrs7Lb/jZ+dT5OcTGNBAXXZ2TRWV5/ZhNwOQei3BKDz3cm1ZtkyPlyxgtSxY8kbPZo5AweS7e5OTLduFLi68nrXrqwZNIi4Rx+lsHdv4rp0IatXLxK7dmWO7QDBsM6dee3vfyfilltIuesu5tx4Iwk330xmp07kXH89iddey5rrriNSoeBVhYIZCgWvKBQk2p6lCgUrbCFnnu37hQoFVQoFBbbvZ9rC0tJrriGpY0esHTqwqlMnym6+mdzbbiPzrrswd+jIeMX1vKDozcsKN2YrBjFbMYrXFCMIV0wkWHEf2QPd2PhrASg6mpasrDP3qpWVnQmHWVnUvf76mTdBM2bwdVISW+Li+CQzk4aMDD7JzORf2dl89OabfLVwIdsjI9k7e/YlB6DzXTjb+nt7bjA6+zk7GLXXhbJXEnv3XeEg7F2I4vI5e5T9QocXth422HqNxfluZZcAdPkC0JazfvyvNbbL9Xy9ezc733+fzWvWsGX16ranYvnyM5/Llv1sA3Su7U6u1ru6ahcsIDc6mvwpU8gJDaUkJIQSlYr3RowgrW9fitzciOzalYxevZjepQvFTk5Mv/9+snv3Zl6XLmT27Mnczp2Zdc89xN52Gzm33UbBrbeSdtNNLLjpJko6dSKvQwdSr7qK2muvJVGhYK5CQbYt+BQoFJTYlrNyFApiFAqibG9/8mzfy73qKmZfdRWzOnQgukMHFnbqRMrttzPz1lvJuvdelj30ECt69mSVuztxffvyTIdOBCq6o1E8jUExnNGKIEIUnoxR9CD4uhv4aMIE3nvxRU7Om3fBALR+0iQaY2MpMRppXLSIgnHjaIyNpWLiRFqysqieNInt0dGsjYigLj6ezfPn89GCBXyYlERteDg1FgslL75IyejRLBw+nFqjkQXDh/O+ycTb48dTPXUqGyMj2VtYyPa8vAvWyYEDB35x8ezZz8WC0alTp/j3v//9i+W0vxp7913hIOxdiOKPO3uU/WKnNrcGlv3791/0VnZ7BqADBw5c9gD05SefnHezc+tTnprKR3l5VFit1CUnsyYykqrwcKrCwyk1mWhctIg1M2ZcMQGosaGB5h07+OLdd2nZvJlviovZU1BAyZQp1CckkD12LLUREeQ++yybwsJY8PTTbAgLY6lGw4awMDKefpocvZ6UESMo0GhY7OdH5tChJA8axBxnZ+IHDCCqZ0+S+/XD+sQTVLu5MeOBByh2cyO2SxcK+/Zlwf33U/7YY6TcfTeFnTuz9LbbSLvlFiKuv56kTp2I6dCBaR06YL3mGmZccw2zFAqKr7mGRNvSV7otDFltAWie7Y1PhkJB8dVXk3zVVYRffTXzO3Yk6cYbWXzzzaQ9+CCRXbow98knmeXszExPT94cPpxYpZLXhw7F1PlBhikUBChuw//qzigVCiY81I2EESNYodOxdMQIlgUGMsPdnYJx44jw8iJ33DisAQGkBgczzd2dHJ2O2CFDKDYame/nR7FKOigAACAASURBVGlICLOHDiUjOJhId3fy9Hrihw+nxGQi22Bg8dixzNXpSAgMZP6oUcSPGkVCYCDThwwhc9QoYr28WDlqFG8qlSwMCiLSz48ss5kFEyaQEx3N8thYPigtZUtlZdubw18LQBKMfp29+65wEPYuRPH7/dZR9oMHD7aFnz8afP4XAaj1ZOnLHYC+2LHjvG969pWUUJedTWViIrnR0cx//nkyx44lPjCQFc8+S/5zzxHn6Ump0UjM8OGUGo3kPfccDdHRbIqM5JuCAkpmznT4ALSnoICWsrIzSzdZWWwIC6MlPp5NtsMPF/v5scdsJsfNja0hISx1cqJUqSSuXz+KlErm9OtHpVLJEmdnlru5seSpp1javz9Le/Ui7cknKerbl9mPPELJwIFYu3ZlZd++pN53H5ufeIK0O+/k/a5dybv9djbcdRcZN9/Mjttuo/yGG3jr1lsp69SJrBtu4M1rriHftuE5r0MHkhUKkq6+mrSrriLh6quJUChYcM01xCsU5HfsiPWaa4i//npSrr+e1Ntvx3rrrVgfeogJDz7Iqz17Mt3LC+uYMcwMDmbJSy+xZOxY3jaZWBQUxDR3d6KGD2e6Ukm8Xs8bw4eTrteT+eyzrDQamaVU8o7JxHyVigK9nqwRI6gMDWW+jw+bwsJI9PNjQ1gYqSoVq0NDSVapWBcWRnJAAPXR0WQFBLApLIw8nY4io5EsvZ5Nkyez4dVXqY+OpvKFF1igVFIUEsJSjYaKsWNZqtWy9dVXydLpWP/yy5QYjbz7yiusfu01Ns2dS8nMmaxfupTihARWJSVRunjxL4LR762X1mB0vnD0Z92Abe++KxyEvQtR/HYXu5z03OBz7ij7kSNHLtt9W1d6ADp3yWtrfDz7U1PZERNDfXQ070+aRKpKRbXZjFWtpsJsJlGrJX/cOOJHjWLF5MlMf/ppUsPCmDduHLnR0aROmnTmBvLp089smm49NM+2x6N1L9CHVVUOE4BSfXxosVhY4+5Oi9HIehcX9ut0LOndm8+HDcP62GNsd3cn76GHqOrThxxbyJnTtStpPXqQ3a0bqV27UtmzJyUPP8ymxx8n9+9/p+6hh0i+7TbW3n03mTfeSPUtt5B1ww3U3nwzWR07MufGG3n92muJue46pl1zDYWdOpHUoQNZN9zAnGuuYW6HDsy+6irirr6aVxQKsjp1IuHaaym76SYSr7uOtNtuI/qmm0i9/34SO3cmt39/rE5ObH3mGSpDQ2mIjqbUaGRrVBRrX3mF7VFR1EVFsTEigvqEBNZFRrI/NZW6qCgOWq1sj46mfsYM8g0G6qOiqJ00iT2xsXxi269TarvaoygkhJb4eOptX18fFkZLVhYFrUtmtr+/DRbLmSUys5k9sbGsDwujKS6O3RYL62xXi9TYrhZZa/v8wPb1TywWPpkyhSqzmXcnTqRk3DiKx4+nzGikatIk3pkyhc1z51IyaxYbbcGoNCWFMjsEoysxDNm77woHYe9CFJfup59+4uTJk7+6zNUafA4cOEBTU9PPRtkv54Wjf9YAtD06mj22O602GQy0xMdTZdusWmXbALs1IYFTb7/N5tmzz1zOmZzMnoIC8qdNozw2lrnPP09qWBgzAwPJMBhYGBLCKouFDLOZqrg4Fr/6KuWpqVRkZPBhVRV1VVV8aHta9+dUnDVBdrHvn3tm0WULQH36sF2pJO7RR2kcMoTyhx9mz+DBvPPQQ9T07Mnc++6j6NFHKX7wQcoefpiKRx6hvEsX8jp3xnrLLSy//XZe+9vfiL7pJkydOhF3ww281rEjczp1Iu6aa5h/ww2kXXcd2TfeiLVjR9bdfDOpHTvy1i23YO3Uiaw77mDZjTey5u67SbjxRt7u3JkpN91EzgMPMOO226hwdiazRw/qfH0pc3ZmZ0gIy3182GU08v7YsWfuFwsLozE2lirbfWWNtpOc96emUhsR0RaAmuLiaIyNZe+sWbzz0kvsj42l2hZc7BWAdlksbLE9uywWtlos7LZY+PAf/6BqwgQ2Tp5MwbhxVEyZQsW0aXz45pv802pl9/LlrEtJYdOSJRQnJFCyaBFlqam8X1rKh5chGJ27nPbjjz/a+4/G38zefVc4CHsXovh1l3o56aWMsv8VA9Dmigr21NTwrtVKXXw8ayMiqIiMZGtCQtt4+x8NQOcueW21bZ5eFxlJ46JFlJpMbI2KYqlez3qzmRy9nvqoKCpefpmquDhKpk+nPDaWgilTqI2MZG1EBO9FRlKfkkKt1UqN1Up5bCx5r71GbWQk1eHhbIiMZGdKCvnTp/9s9D5/7lzemj+f5TNnsjU7mxqrlb2FhZRbLNRHR5Oj11NrNrNQqaTWYGD24ME0GI0sdnFhg1pNyuOPU+XtzewePdjg50d09+7s9vdn1WOPsdPdneUPPcSG/v2Z/9BDlPfqRXaXLpT16kVily7kP/EE6Y89xtKuXSl75BGWd+nCmi5diL3nHhLvvZeZ99zD8gceoOjWWym8/35Sb7mFvPvvx3LLLSy87z4m33wz+Y89RvTdd5PTvTvp99xDRffuJN99N+t79mT63//OZg8PErp1Y59ez7uenuw1GNioVNJoNlOq1bI/PJwGi8WhA9CGsWPZZbFQFBrKVouFlSEhbLdY2Dx27EUD0GcWC1tMJnZOnsxu29f2xMZSFxXFnkWLzozbFxaeGcHPy2P3ihV8smwZn2Znc2TtWhpKSmjesYOGLVva/iz5/vvvf9eeopaWFglA4spm70IU59c60XXs2DEOHz580Tc+546yX+wMn8s5aXVu0HDYALRmDY3V1VRZrWxPSKDcZKLEZOJtk4lC21Nq+9o7JhMlRiPrJk5kQ1gY748d+4cCUK3trcsHtqa41vbWpSQ4mL3h4byt0VBr2y+yNiyMkjFjaLBYWPfSS9SazdROmkSR0UiR0ciaiRMpN5v5eMoUtr32GhvCwtgweTJpwcGsmzjxZ6Hqw9mzyTCZqIqLw3rW26g0g4HZajW548Yx09eX3JAQpru6kq1WM713b97y9WX+k09Sq1Rifewxaj09mfjgg6zs35+Yzp1Z1rs3r997L8VPPYXlvvso6tePxd26UTFgAAsef5ySgQNJ79OHdwYNoqJfP8rd3Cjr1480Z2dWDh5MposLJW5uVDo5scXHh8KePfnA05P4xx9nq1rNwt69+VdAAGn9+1Pr70+OkxPvDhtGupMTu0NDSezXj/qQEBKcndkaEkLK4MHU6vVke3lRbzKxTK3mQGQkm158kXqLhbdHj6bcbCYtOJj3Jk7k3YkT2TB5MtujoykxmaiNiKDAZKIhOprayZNZP348+c89R63ZTGpwMBVmM8tHjmSLxcICpZJyg4EYDw+KQkKwenlRFBJC/NChfBAWRtzQoZSOGcMSlYqSkBCs3t6sGj2amUOHUjJ6NPGenuT4+7PM35+Up58mV68nSatlhV7PQrWaxBEjsHp7k6JUkqzRkBEcTJ7BQLnZzK4pU34WgOptb4MuFIDO94azZfNmvq6u5uQXX/DZ1q1t54KdvTewubmZI0eOcOzYMb777rtfDUYSgMQVzd6FKH7u3ImugwcPXvCgwd8zyv5XDECtS2CN1dW0bN5MfUoKW+PjqbVNexXYwtBKk4niCRPIfO45lur1pGg0xHt6kqfTMVelYmNEBJnjx7O3sJBtKSl/KAAVBwTQEhXFe1otjbalj8azlkJqDAZ22f7Nf5fFwidnvQ1oOGs5ZLfFQpXBwN7wcGoNBr6ZMYPK0FA+ePll0oKCqJo0iVS9nrqoKLKffZbtUVEs1WpZHxbGHG9v8tVq3nRxYYW/P5F9+/JuQACze/akXKnkzW7dKHFxwfLgg5S4uPBm585UDBpE1D33UOXsjOXee3n7iSfI6NKFnF69iH7ssTNnBTk5ke/tTb6bG8uHDWO5tzepw4aRrFSSOXIkc9zdSXN3J9PHh8UDB1IREEDS4MFsNBhYoVKx22gkT6OhODSU9BEjqDWbWTZyJNstFtKGDWOjwUC8hwerQ0KY4+1NaUgIc9zcKA8NJdbVlXy1mhgXF/I1mrYQMmfoULL1epKefpoMlYpMjYb5w4eTGhzMLLUaa1AQCU8/TX5oKHOHDyfzmWcI9/IiXqtlpocHWSNGYHV1pVStJt7NjQKdjugBA8jT6XjN2ZlEpZIZrq4kK5Vnfs6QEGa6uVEUEsLUgQPJ9PcnysWFFJWKFI2GdL2ehVotmc88w2K1mmSViniVilSNhuV6PctCQ1n27LOk6vXMVatJsn0/+emnifP1JXvkSJZrNBQGBrIyMJAlGg1VZjOlJhP1CQl8lJx83gB0oU34p06d4ocffmj7M6L1n58DBw5cMBi1LoH99NNP9v5j8zezd98VDsLehSjOaA0+x48fb7uj6/jx421/8JwbYs4OPr9louuvEoDOXur7fPv2nwWgvYWFbef8tDaIvYWFfJScTH1CAu/ZlphKTCbyTCbWzJjB4pdeIu+114gKDiY1LKztrcoMjYYVkyczLySE8vBw0p59lgqzmaXPPMP2qCiW6/U0xcWxdvTodglAe8LD2Wow8Nnkyaw3GNgfHk61wcC+yEhy1Gq2mUyke3lRrdeT5OZGXUgI2e7uNBiNzHrqKTbodET16EHR4MFYu3Wjon9/wh98kA1eXiR17kztoEHMuvtuyp58klfuvptFjz5KYteuLO7Xj1e7dyfJ1ZXpPXuS5uzMQmdnrK6uJAwZwgK1mlkqFakhIcT6+lKg0ZA3ciTJQ4eSPmIE0z08mO3nx+yhQ8nU6ZitVrPEYGCeRkOOwcCigABydDqiPDzI1OmY4eXFSqORWF9fikJDiRw4kDR/f6YPGMASf3+mDRxIQUgI0YMGURoayuyBA0lUKokbMoRcjYZlI0fy9ujR5Ol0ZOp0pGg0LPTwYIVazTwPD9aMHs1CjYa3nn+eBSNGUBEaSr5Gw/qxY8kfOZKPpkwhRamkduxYEr292T5uHDnDh7PBYCBLqWSjwYDV2ZkqrZa5rq7U63Ss8PRks8FArlLJ6pAQ3vTwYIm/P4ne3lQEB7NSp2N1SAiFOh0rdDoyNRoWqFQkq1QsValIVasp0OuZ6+FBiUbDEjc3CgYPpmDwYJLd3Fih0ZCs0VA8YQIrzwr1ayMiWB8ZyZ6CArZd5HyhC+31OXnyJN999x3Hjh37WTBq/fNHApC4Ytm7EP/qWkfZL3Q5aetVE2ePsv+RM3wkAF04ALV+tn7/3H+DbiwoaHuz05CSwt7CQpYaDGwID8c6fDgrdTrSvLwoUquZPXAglaGhzHZzoyI0lCRXV4rUamJdXMhRq5k+YAAbDAbS3N2pN5moDgpig8FAjkZD4ZgxpOt0bJo0idLRo9kwdizvvfgiK0NDWRkayjtjxrAuLIzPp05l24svUqTV0mA0UhoQQL3RyHtBQb8IQHvDwynTamk0m9mgVLLHYKDK05Ovn3mGZf360RAUxMwePdjs50fiI4/wpVLJW48/zjdKJTO7dmWnry/Jf/87NX37Yr37bqqefJIZ995Lfq9eJD38MCudnZnZrRvZffsS/+ST5PftS27//iwdMoSFHh4kKZXM9/FhoUpFrLs78R4ezPHyIk2lIn7oUJI0GrKeeYY8vZ5yk4mUUaMoefll5gcGkvnssySNGEFJSAgLhg5lvdnMooAAaiZOJF6ppNpgYMnQodQbjazy92dveDiZ/v7sDw8nX62mKTycTUFB7LUFy722vydN4eFsMhjYabGw07bf68vXXmPdc8+xPzycdWeF0abwcDbaQuqW1sCp1dJsNlOsVNJisVCsVNJkNlOj1dISFUWZry8tFgu5w4bRYrFQrlTSbDazQaulKTyccq2WPWYzq0eM4OvQUJZ5eLBRqSTfzY0KX18q/fwo0mopHTWKjQYDqUolFSEhvOHkxGJPT+Y6OZHj5sZyDw8yvL1JHzaMRSoVSzUaFqlUpKhUzFOrSdPrydXrKXn5ZUqmTGFrdjYVGRl/eDN0azCSACSuWPYuxL+qSx1lb25uprm5maamJg4cOHDBW9n/DAHo+PHj7N+//38WgD7Ky2NvYWHbG57q8HAqIiPZEh/PDtuVFZcagFpPiH5n8mROZWae2SMUH8/2F1+kOSKCRX5+1BgMTHdxIUut5o0BA8j09ydm4EDKQ0OZYXszEdm3L5k+PkT360eitzfhLi5YVSqmeXqS/eyzLFSpKNRoKAkMZOnTT7NYrSbWz49YlYp0jYYUDw9innqKIl9fFvbvT6VSyXJnZ0qGDiXDzY0NOh3L/PzYbjSyzM/vFwGoUaejysWFwzod6b160aRWk92jB/v8/Fj18MN8NXgwMZ070zhkCFmdO3PU15flDz5Is68vSfffz9p+/Uh59FEqBw0ipXdvNvn48JabGx+r1ZQMGcI7SiX5Pj6UarVUBQVRbTC0BbYirZbdRiNrAgLYZDCwcfx4NoWFsSs6mmKjke1RUdTYRtF32jYcV9o2IteazeyNjaXG9kZtjS10rLG9WSuy/XW57fM9rdZhA1CZUslhg4ECT0++1Gqp9vTkc62WXWo1dSEhrNXp2Gs2UxIQQNPUqRSNGMFes5maESPYHRrKeqWSD3Q6SgIC2GAw8KFtg3XrGP0qg4HS0FCW2M4lKnz5ZfKnTKEqKantvrrfe2edLIGJK5q9C/Gv5tzLSX9tlL11c+Kv3cpuj7csV0oA+nLXrjP3W2VksCUri4IpU3g3MpIik4lVtqfEtlxQajKxMSKCmhkz2J6Q0HaX16UGoK2tS1y2Jlhia8iVQUHsDw8nzdeXWoOBGf37s8Lfn5m9e1Po64v1qaeo0GqJcXamVK3G6upKmkpFopcXCd7eLFapyFCrydRoSNNoyHnmGTL1epZqNES6upLm7U1U377EOzkxp1s30vr2Ja1nT9YrlSx3d2e30cgylYrt48ZR4edHnU5H4aBB1KnVlDg786VWS2bfvjQMG0biY49R6+5O+kMPsc3FBev993PY15cVDz7It76+ZHbuzF5PT+Z37kzDgAEUPfYYe4YOpcDJid3DhrGyf38atVoqvLzYFRDAZrWahpAQduh0fDxuXFsDXxsczOGwMDbpdPzLaGSDwUC9xUJTXFzbBNZa26c9AlBZUBD1RiNvBwaywWBgmVpNrcHAKo2GeouFWr2e3UYjOX5+fGI0kuLlxecmE2VqNc0REaxWqWgym1ni40OT2Uyhjw+HJk1qC0AVQUE0GI3k+/jweWgo2e7uNAYFXXIAqlYq2W8w8JFaTUNoKNU6HTvHjWPLWcunWywW6iwWmiMi2GqxnLmI9Zxb61tr++vi4l/cWfdrYUgCkLii2bsQ/yp+S/A5e5T94MGDl23M/H8VgC7XWP3/KgB9//33befjnL0Uti0lhfqEBDZERlIbEUGpyUThhAkUTZhAvslEkclEru15y2SiwnY1xt5Fi3gvMvK3BaDWtwK2z9YG/Y6fH01mM6W+vjSbzeT4+PC50cja4GC2WixsePFF1hoMlAQFsUGrpdzPj2I/P2qDgsjRaMjXaFjo7U2+SkWSmxs5Q4aw4KmnyHN3J9nJiRxvb2b170+KUsnsoUNJ1+lI12ioNZt5a+RIag0GilUqtuh0pA0eTLVSSfJTT1GnVpPfqxf1vr4s7NGDeqWSxG7d2OTry5yHH2Z7QABzH32UhhEjyOnThzpfX5J69qTW25tkJye2aTSsGjyYXUFB/5MA9M6YMeyyWCgLCuJLo5Flnp7sNhpZ6OrKznHjWDhkCGt1OuIHDKBEq2V2796UBwczZ/BgigMDiR00iDRvb6weHixVq1miUpGpVJKtVrPU25sMb2+m9ezJIi8v5ri7k+3vT5xtz07soEGkKpUkuLqS6u7OjL59WalWE+7kRKFaTaKr65k9Pq6urFCrsTg5sUKtJqp3b5Z5ejK/b19KNRoWDBnClhdfJNPPj89NJha6u1OpVJLi5ESRpyf5gwZRolazSqtl38SJbQGoWKNhq9FIoY8P9aGhVCmVfDx69B8OQOe7jPXX9gtJABJXNHsX4p/Z2ZeTfvvtt79rlP3QoUMOHYAu57lC7R2ALrTk1ZCSwtazglGxbTy+yGQiU6NhS1gYORrNmXu0lEo2mM0kqlTstFgo0mhoDA+nKCjo0gKQbZmk2rZM8tawYRwOC+NdrZatFgvrDQbqxo2jWqfj69BQPlKr2axWszs0lCqdjs06HZVKJV+HhlLt7099UBC57u68r1JR6eND2fDhLPH2pmzMGOYHBpI/ZUrbqdVF06dTYLFQ/vLLrDEaydbr2RQWRv7IkWwwGCgdPpz3dToWurlRERzMzAED2DJuHFZXV7aPG0e2uzu7Q0PJcXNjm17PEi8vPjGZWOrtzb9MJvL9/Nis01Hq50dlUBBlajWbXniBTJWK7SYTOSoV72q1pHt7U6LRkOzpycrAQCpHj2bZyJGUhIQwz9ubHJ2OZG9vakJDsQ4axFu2N2QFajVWd3dy/P15zcmJXLWaSd26ke3pifWppyjx9WVBv35UabW8/vjjVGi1xPbrR5mvL3P69mWptzdzvb3J0euJ9fEhR6Nhgbc3aV5eZHp7E+viwkqNhkV+fqwdNYq0oUNZrlTyhrMzmQEBZ35tWi1vDh7M6qAgYgcNolSr5c2BA8nR6Zjm7Nw2BZbu78/swYNZqdUSNXAgqcHBWAYOJL11imzYMCx9+pDn50eskxOVKhXL+/en2M+P+EGDyFcqme7sTKZWy3wfH2pfeok8jYaPTCZKg4KoMhhYrNFQNHo0OTodGyZNYr3ZTN1rr/3PA5CcBC2uWPYuxD+js0fZf2vwOXcv0OU8aFAC0KUFoNbPcxtD9RtvkPPMMxSOHs2bHh6k+/vzmrMzBc88w/RBg840bFdXlvv7M9PFhQK1mtkuLnxgMJDr60tzRATLhw+n0Wwm08eHvWYzBa6u7DUYKPHwuDwBKCCAMi8vdvr70+DvT0NoKB/odOyLjKTYaGRnSgq50dHkvfkmuTEx5MbEkB8Tw+JXX2XeuHHMHzOGxFGjiPPzI9HHh3SlknmenmeuAwkIYGtUFItsb5DSVSoqbHdh1YaFkRIQwPboaJK0WsrNZqwqFSVnNeScgADef/llUpVKakJDSRgyhPyRI0lSqSh7/nnmeHqSFRDAYj8/EocOZaGfH5Z+/UhWqZjt6kpOQABzBw+mTKslxsWFbI2GmQMHUhwSwvSBA8n29yfSyelMqHJzY51WS0r//qzTaol67DFW+vgQ3bs3FUoli11cWBMQQIKHBwUhIcxwcSHdy4t5zs685ePDSpWKLG9vStRqFnp5sczPj7TBg6lSKlnp7k7jhAkUaTTU6vWkDhlCnq8vyW5ulKpUpLq5UTl2LLNdXHg7KIjYAQPYqNOx2N2dzTodGYMH84nFwgJvb8pDQ0n08qIiNJTXbSP8U3r0YJmPDzOeeII5/fsT/dRTpHl5EevqyvLgYOZ7eVGg05Hm60upWk2KlxcFej0LAwN5LzKSZePH05CSwnu2AzWLTCY+mDSJYqORhpgYaiMiOJCRIQFICHsX4p/JpV5OeuLEL0fZL7QJWgLQH/91te4Bar0Nvjw1lbrsbCqsVvaVlFxyANoaH8+u6GgabadEfx0ZSaKnJzUGA1EuLmT6+xPr4sIytZpIV1fKQkOZadvsHDtoEMvUasJ792aVry8xTzzBZqWS7O7d+cjTk5Tu3al2d2dmjx7kDxvGLGdn8p9/niUjR5IXGEi8uztLXV1JdHJivrMzeT4+pA8fTllQECXDh1Ph50eqmxuLXV2Z/9RT5A8YQKWXFxt1Ojbr9ewND2e92Xzm5OP4+F/s+1gzbx7VcXEsGz+eEpOJHL2exXo92c89x5tqNeljxxI9ciTWMWOYNmwYGQYDsSNGUGQ0sjgwkFKjkbn+/mQZDET4+pJjMDDP3581RiOZQUGsNhqJHTKELJ2O11xdSQsOJs7Hh3Sdjlm+vmTqdEQNHMgSb29SvLxY4edHwfDhLPb2ZvmIESzw8iJXqyXLz49avZ6UQYOoN5nI8PSkwWgk27aUmD9sGN9MmsQqX1/2mM285ePDHrOZJW5utFgsZHt4sN9goNTdnfrQUPKHDmW70chyW/DcEBjIgfHj2aDTUafTsV2nY61OR31ICFX+/uwxGFivVHI4LIwKrZbPjUaqdTq2hoSw3t+fhqAgat3dqQ8JIXfQIHaHhrLcw4ODL75Itrs7nwYFsbxPH/6p0bDEyYn6kJAzm9RNJuK9vdlmMpEycCClI0aQ4OREuVLJcg8P1mm1ZHp4UBEScmaKbtgwYr29SfP3Z6aPD7njxhGtVpNrsfCGTnfmqIYXXiB10iQWT5pEVVwc2RMnUh0XR7bZzCqLhfljxrR9v8JqpdL2z8O+khK2Zmezp6aGPTU1bFm9+oL7gCQAiSuavQvxz+D06dOcOnWKI0eOXDTMtJ7x81tG2SUA/fFfV+sU2DfvvceemhpqkpKotp2QnGZrAItffZXUSZPImz6d/ClTWDNjBhXh4aw5a0rs3ADUGB5Oqb8/p15/nXLbUtfqwEAaw8NZ6ONDlcHANBcXcmzBKM/fn5l9+rBBq2X2k09SpVQy/9FHKe7fn+hu3Vjh7s6MJ57gLa2WGU5OpOv1zPPyIs3bm4QBA8j38CDPzY1M9/9n793jmrzzfV/WPuuctffrzDlnrdn775k1e2Z3LrVVqnIRwjWEGAghBZ6GROFRJDWGKqaKQQSiQvASAggBUeQiQkDk4oVLFFGsF6RFWunY6Y0pU0qt03Y60xnrmjUz7/MHT9hqb7bFamf4vl55RZ4nhCfJT35vvpfPR0bD0qWURURQrlBQFhJCWUgIlXI51RERVIWGUubnR0NQEDXBwdRJWZPWJ5/kms3G6by8T8HdkGQDcs1mY9JuZywri16zmZH8fM5mZzPsdM74nY2WlPDugQO0SQrKPUYjV/Pz6TSZmKisknFoxAAAIABJREFUpGfLljtsQE5lZzNeWUmL0cgbhYUcX716Rhxy0m6nW2poHhBF3jQaGTMamTSbuSQIDAkCg4LABUFgwmzmdFISE2YzJ6KiuG4231E6vGWx0CwBz1lpMmtQOr4vMJBbFgt1Xr+zsDCmRJFepZIJqffq/YwMzj75JNdNpq8NQFfj47kSFsaEKHJMJuN9UaRRJuO6KNIWFsaUIHDaz49JQaB38WJuGY30REczKWUFvdc+Lor0hoczKYr0Sz/vZHw8v9m0iV69ngnJLuOW00m/V6Xcar1DhPNKeTkTLS105uTQX1REVUYG3XY7TRs3MuR00mu3M97SQntOzh2K4btSU2neuJFmyZ6lt7T0c5ui5wBoLr7T8aAX4nc5vKPs3sbm999//zMnorwZoa8zyj7bADSbkPFdAKCPPvpo2vfo5Zd5vbeXm+fP85bbza1jxxh2OplsbeWdzk6G6+tpy8mhw2Jh71NP0aTTUaPRUKhUsk8UqX/6aTosFoY2bPhCAPL2+PRJX7dJXx9PSmLCamV/RARXjUZKAwLwCAIlCxcyqFaza+FCBgSB0oUL6YuPJ9fXF6dSSd7ixeyPiGBPUBCdCgX7Fi2iNiiIKpmMMn9/qiIiqAwP54BKRU1CAsVyOVUKBfkLFrDb15fdTzxBxcKFHI6Opi4qip6UFOp1OvqtVjwS3F2pqPgUAF2VGmgnJe+s8cpKRqTM0WhJCVNVVQzl53Orro4xm41bdXX0SxuwVw3bOyk3Kt17srO5WVvLhU2b7h8AKRQMpafTIpczbjbTGhnJuNnMvpAQpsxmKgIDuSGKHJfJeDct7X8DkELBL41GOuLiGFu5ki6tlheWL2dQq6UrMZHj8fHUR0YyKAgcUyh43WikT6djRBQ5mphIp1ZLs1xOX3Q0Z0JCGNPrOR4RwVsGAzXBwTwvCOxdsoQXpKm7V+Lj6Q0IuK8ANHLX5+A9PnyXavnVux5/UgLXXquVCelzH6mv5+3nnvtUSWwOgObiOx0PeiF+F+PzJrruHgn3jrJfv36d69evfy0Nn9kcM/9HAiDvJN27777Ly5cv89G1a7xy/DgfnDrFLw8c4AO3m3NFRbxx8CDjhw7xhtvNpeJiJisrGbPZmLLbuSZlQQY3bGCfKFKm01GoVFKq1XJAraZTr6cmLIx3N22iNynpCwFo5rh033/XlJj3cc95gUmrZcJqpSshgWtGIw0yGX3R0TjnzaNswQJKfX1xBwfTuXQpXRoNzUolrWo1VUFBHAkKovrxx2letAh3UBBumYxOhYI2hYLe+HhOJCZyJi2NttRUhvPz8WRn02218mJBwdcCoAuZmUza7XQYjUxUVtItbZyD0qTcQG4uE1IG6NeFhfSkp/O2w0FPSgqjFgt7NRrOiSJuhYJehYI+rZah5GS6FAoG1GoagoI4GBREZ1QUFf7+nNRqqVy8GI9WS2NICBcEgd0BAXRptdjDwvCYzdQplQyKIrUKBedFkZ0hIXQZDFh9fTmh1bLziSdwy+XYfX1pi45mx6JFPL9qFbVRUZwXRfYqFBxRq6kIC6NSraZKpWJvdDRujYb6qCjqIyLYGRrKfpWK8rg4agSBupgY3Go13SoVrdHRuPz9ORgWxg4/P3oMBlzR0QyKIo2hoXQqlZT7+nJMqcQhTaoVBQdzShRpVak4I/UKDSUncygsjNfWrOGwWs1kTs59ByCPZN8yIn2+k5WV02Xiz7DRmAOgufhOx4NeiN+V8E50/fGPf/zcUXYvDNw9yv5NNHy+CwA0m8KKU1NT31jo0dtb9e677/L73/9+pgQ2cfr0tCGk283Nri4uORy8efAgr9fXM1Zby5mCAl4vLmYkO5uJ7dsZy8zk4vr1vGu3M5Kfz8sFBXRbrfRs2UKXycRBnY4KmYw+g4HSsDB6DQa2BwZyVhTZs2QJZwUBV1AQVw0G9gcHM2E2czg6+qsBkFY7k+m4IYqcCgtjTKvFExvLKwYDFwSBV4xG+uLjGUlPZ0AQeNVgoC8mhpPR0fTExFAnl1OrULBXoaAtIYFGyWerJyWFMZuNq4WFXCkowJOdzZm8vOnGWaOR/sxMxmw2Xiks/EwA8kjg02swMGm10qTRMGKxsP/JJ7lqsVCrUnE+I4P9MTG8ZrGwLyqKl9PTOSxBRlVgIJ0KBYX+/rgVCvYGBdEsk+FWqTiqUOBasIAjQUGUPv441TIZDQoFu2Qy9ms0FPj7UxMRwa6FC6kNCyN//nwaYmNZ5+eHQ6ulVC6nU6+nWC6nQRCwhobiTk9nw+LF7IuNpUgmo1urxREURINazbr586kRBDYsXIhDoSAnIICa+Hi2hYezT6dje2Qk9XFx1ERFUR0Rwf6ICArCwqjR6ahbvpy9BgN7VSqqFQr2R0SwNyKCCmmyzCGXU6vT4VCrcapU5Pv5UREezs4nnqA7Pp7S0FAaY2OxR0SwLymJirg4agWBSrWaMZuNVsnD7eCyZQzn5uJKSGAwM5PjklhkpyTNcEoCzm8LgLw2GXMANBff2XjQC/Fhj7/97W/8+c9/5uOPP/5SDZ8PPvhgJuvw3nvvfaEr+98LAM22svTXBaC7e6u8mbc//GHaDX7i9GlOOxyMOJ30W62M5ufTajIxUlLCFUns8FJxMb9xuRjdsoW3tm3jxYwMBtesYTw3lwubNjFeUsK5Xbu4WFrK5eJiPLm5VCUl0bJqFTalkpb0dKx+fhxRq9nzxBOcVSrZ/dhjtMtkbP7xjzmhVJL7i1/QIJezy9eXXq0W26JFnBFFXHL5jGXDrACQUsmLsbGMGwyckx7TLYrT5TubjSsFBZzLzuaqy0V/URGnHQ7cBQXTDeJNTfTs3s3l8nJeqKjAU1REr8NBs3TeU1ZGb1ERzc88w1B+Pi6NZrrR288Pj1bLTl9fuuRynJK447OPPUaHVsuWefPoVSop/8UvqAkMZPtPf0pLUBAFCxZwVKFgv58f9UFBVIeEcCAoiJ3z59Mgk7ErMJDapUupX7qUyogIXFFR2BcvpikigvKgIE4kJeEKDuZUUhI7Q0JoTEhgm58fNRJUtKemsjsykvaUFHb6+dGl1VIYGEiHVotDJmNQFCkICKDLYMAZHMzZlBRqFQoGRBF7SAjHDYbpJvewMKr8/TkUHU390qXs1WjYn5hIoyBQrlZTEhpKTUQErogIyuVy6uLi2KtUUhcbyz61mr0qFTUxMRRHRFAbF4dTJqM7MXFaBkClYpe/Pw2xsTglk9ai2FgOpKVRbjDQYrGwLSmJCpOJLfHxtFksuAwG6kQRl15Pm8XC7hUraLLZ2LthAyP19RzJyWGkpIQzeXmMV1bikXqxeiVQmi0A+i7Gg9535+IhiQe9EB/WuNuV/Ysmum4fZX/33XfvyZV9DoBmB4A+r6n89uu62NPDeH8/HoeDkZISukwmBjMy2K9WcykzkzqdjtY1a2YED9uNRgYyMxkwm7m8cSPv2u08n5fHRHk5gzt28HJdHddqa/lVbS3PFRbyu9ZWjmVn82uXi4M6HcMWC5VRUXRptWzz9aVPqaTgf/5PjgYHU/rYY/RHR1O4cCENsbFkPv44jYLAlkWLqFEqcQQG0ik5tF9ctYrioCD6dTpKFi7koEzG9kcf5YC/P3uXLGEwLo6TWi2/MpnoS0xkQOpH8Wi1NIaGcj4mhlFB4JxO9ykAGq+snN747nIIf+u11xgfG+OyNDF3tz2C9/ywx0N3VRUj9fV0ZGUxWVlJz4oVTFmtnNBqmTSbqYuKYjg9nS2+vgwIAlt/9jNagoMpe+QR3MHB2ObNo0upxPb449MCgI8+yp4FCyhbuJCqwED2LF5Ml1xOdUAA7XI5TUFBuAMDaQ4K4lBQEANqNS1yOWc0Gg7J5QwKAg0KBa8ZjXQqFLy1bh2HtVqG09Pp1moZSk/nqELBe5mZ1EdG8quUFGqXLOGawUBjUBA3zGbqg4J4x2SiJSiINw0GXJKRaX1wMOMGA93h4Yzq9RwMCeGYUkmjXE6/VktvUhJ9Wi1Xli3jqmTzcdVo5Hh8PCOiyNm0NK5aLLy2aRM9BgMDaWk0xsYyKIr0CQLXjEa6ExP5bW4uZzMyOJ+ZSYvRiCc7m8HcXF5yuejIyuKF0lIqUlNnhDtH8/M5sXYtoyUl9DkcXGlunpE2cKxcSYfFQpVOR5fRyP7ERLqMRgpjYzkgimxLSJiZFuu229m7Zg1DTie1oshgRgaNycl0Go3UpaVxyuH4lH/YJ598wieffPKgf1V/rXjQ++5cPCTxoBfiwxa3m5N+2Sj7Rx99dMco+2yqIt8OQLP9nN+0zPQwANCXTdPdfl13m6G+WFHBRGUlndIGUqPTcdxkmhE8bFy2DLdOR61Gw16VimZBoG7ZMs5mZ3Nq+/aZEfnfuN08V1jIH44cYXDHDj5saeHk+vV86HLRazTyhx076NBqmVqzZloV2WDAFRTEgCBgX7IEjyhiDwigTa9nV1gYHXo92+RyqpKSWOfvz76kJCzz5lEbHs7ORx+lJzSUPb/4BcdCQyn186NJoWB3UBClkZEUhISwS63GoVJx6Mkn2RMWRqdCQWtICOX+/tRHRFAUFkaX0Ujv2rWMlJQw5nJ9CoC+ihmmV1dppL5+OpNgs3HL6eScpIbtLesdksu5ZbHQJ5NxMz2d/uBgJkWRA9J0lDs4mA9EkSthYVyLj2cgNpZRvZ5jSiWvGAwMKJVcNRimFaXj4zmvVnNOreaCWs2wWs016d+TokiLUsmU2UxnVNS075lkIzEiCIybzRyVmqfbJBuJvrAwbhmNdMhk02Pyixdzc906jknTYu6wMD4QRdwyGTdEkRPSNfeGhzMWH885tZq3DAYGBYHzgsCI1Kztke7PCgJvGo2ckVSuX7NYZjzFvCa1l26z5fAKFk7a7Qzl58+AqleG4c3ycgalDM1Vqfn8XHY2t44du0O6YbK1lWGnk3dqaug0mRjJz6dTmtY7LJXMzmzbxnhLC6ccDk46HDhSU2mzWCgTBDqNRrrWrWPY6aTX4WC8v5/LHs+nnOLnAGguvtPxoBfiwxL3ak56r1mH2brNZpPx3wMA3auMwBcB0GRr6x0p/yFpGuxyeTkjJSX0SgrQnRIUdZpMNEu3Vq9dhsnEQHY2h1au5G2XC49UVhi0WrlZW4vHZOKPu3bRbTDw+02b8MTG8tGmTRxWq/ljTg5HNBpu5uVxVoKE3sRErlut1Gk0jFksOCIi6DMYKPHzY0CrpUJSNs7+8Y9xBgaS/eijlPr5sWvhQg5FRdEQG8vB5cupUqs5FB+P3c+PJilTUhkYyBGNhj1xcbQ+8wy1aWnsffppilNTabLZcO/axdDx4/S2tNyhl+S9Xf4cg8zvIgAdlya4Dkr+W53SqLpXiLI+MJApk4nWwEA+EEUaZDI+XLUKd2goL+n1NAQGTmv8yGS8nJj40APQZGUlo1Ipy/v43ruapb1ioN7/D94eIa8o4nh//2cC8hwAzcV3Ph70QnzQ8de//pWbN2/ek0fX7aPsX5Z1mAOg2b22r6qf9HUAaPwuMcSrLhcjTidda9fSbjSyR63mQEwM+1QqKjUa6nU67EolvWvX4oyLY8BsplZSSa5SqbhmsdCg0Uw7iycmcjMvjyMaDX/KyaFFreb3mzZxMiaGjzZt4phazZ9ycnDHx3MzL4+jEjwcUSiYMptpkJqpy554gi6lkgPBwZwXBFqVSnoMBtqSkmgWBErDwqiMiKBo0SJqZTIOKRTsjYqi8cknKVWpqE1Onilt9K5dy1WXiwFv6cRmo9fhoDUra1onZts2hpxOmjdvZqiubqYHyOsc7t65k5H6eurNZjxWK/U6HW16PfaICNx6PbbwcEYtFkqCg7kkCNQtWsRwbCx758/ndGwsBb6+vJCcTIW/Py8mJ3NMJuN0TAxuuZxzOh1uhYLh5GTcMhntUVE0BgdzOCSEmsBAmuRyGuRyPHFxdAYHUxsczOGoKBwBAXi0Wg7IZJwXRVrVao5ptTRGRNCkVlMREkKXXs/ukBB64uOpCQmhXxAo8ffneHw8+QsXcjkzE5dMxnlBoEImY1gQcPn705+YiNPfn8tpaexZsoSBmBgagoI4plBQHRqKW62mU63mmslE/dKlHNFq2RMZSYNWi1OppHPFCi5mZPCc0ciIxUJnSgr9ZjPNgsDFjAyOiSJjFgtXNm78XAB6Yw6AZiUe9L47Fw9JPOiF+KDi9lH2L5rS+iqj7HMANPvX9lXB57Ouy6sEPXT8+B3N0Ke3bJkZ2/Y2Qt8BQCUlTFRWclUSPxySDCZfzsrifEYGfStXsjMqimKViq3+/hxUqykMDKRFrycnKIg2vR6bZNFQ5OdHk1pNrr8/50WR/XI5N7KzGZTA6JhGwx9zcjikVvPRpk0ciYnh95s20RoWxs21a2kPCuJmejo94eFMSAJ+U2Yzx+PjGbNYuCo5mp8RRcaMRoZFkZfT07lqNHJeyj68ZLEwZrEwarEwbrdzNT9/eoOULBGGnU7GKyu5ZrPxnsPB2YwMrmVlUS8IjNls9JpMTFVVcWb7diZbW9n39NN4srMpSUig02ikSqulXhDIDQ2lJT2dzaGhHEhKwhYQwDGtluInnuBwRAS2+fNp0WqxzJ9PXWwsWwMDOazXU+jnR1dCAjuCg2mRvLWcERHkLV6MQy6nJCKCFrWaUpmM+qgonEuWUBcZSfnixVQpFBzSaHBFR9Oo0VAeHk6lSkVZSAhHNJrpiSxBwKVQ0CII7AoL40BEBIVPPMH+8HDy58+f9hJ79FHqExOxBwRwLD6eQpmM6thYcqVrzFq8mAZBmG6a1mopWbKEo4mJ7AwOplbyEnNrNOwMCaHtySfZGxNDT1oae+PiqJe0papVKhqSkynR6diXlkaZXk9NWhq1y5dTrVJRGhNDvU6HQxBwWyx05uRw2uFgYOtW2oxGKpOSaElPpzQhgYHsbOpXr55R9Z4DoHuLB73vzsVDEg96IX6bcbs56XvvvfeFG6oXfN59912uX79+T6Ps/4gANNvK0t5r+7rgc/tncfX553l1ZISLPT0MHT8+7X9ls1G8ciVui4UyvZ4DokhRbCy1aWnUpqVx9JlnGMjO5rncXM5lZzNaUPApABqzWHhF+vfZNWu4uH49F0WRd3JzadNoeGHVKvL8/WlUqymSwGh7QACH9XqsS5bQrtdTJFlj7PLzw61WU+jvzzlRpEQm402jkVaFgutr1nBwyRJe0ukoe/xxXk9OpiEggDdTUjgeFcXbJhNHNRqurl8/6wB0vahouhRjtXJKFLnldHIxI4NbdXUMSBmHXquVW3V1nF63jkm7nZ6UFK5ZLJRJxqV7IyLoNRiwBwbSrFaTtWABjVotOQsW0BwVRdHChRzTatkVGMghQSDz8cc5qFazaf58qsPD2blwIa6ICBz+/tTHx7NbJsMdFUWJnx91QUGUBAZSplBQEhJCTXw8ZdHR1MTHUxoVxc6QEBwqFfawMI6nplKt0XB85Ur2qVQcTkigNCRkWstnyRJ6ExOpDA7mRFIS2xctol2nY0tAANWxseQEBNCp11MQEECDBGvH9Xp2LlmCW61mu68vR9RqbAsWsFMmoyAgAEd0NLbISBoFgWKFgsOCQFlkJOVKJSUREZSoVNQYDFSlpbFbr+fgqlVU6XQ06nTsU6lwxMXRbDKx9+mnad68mb2ZmRSvXMm+tDRKtVr2PfUUJ0wmSg0GmrOycKxcSZPNRvWGDezNzGTXqlVUZWRQ/fTT7EpMpESrxZ2eTolOh9tiwZaUNG2JsXo1525b650S4J7Oy5sDoLn4x4gHvRC/jfgsc1KvPs9ngc/tGj5fZZR9tmHgfkHVbE6q3Q8A+jqK2Z/1vr104QIfv/oqb3g8M+ant44d43xuLlNVVZwwmbhms3FAo6FeEKiOi2OfTscBnY5Dy5dzSKejIyWFLoMBT1oa3aLIpVWr7gCgwdsAaPK2Po4WqYTlkRSg6xQKBkWRHD8/DsXGsluyxigKDKRLryd/yRLa9Hpy5s/nkFzOhp//HGdgIOt+/GNqZDKe+cEPcMtk7Jo3j7MKBc758+mKj8ceHEz36tUcS0vj+XXrOCmK9Kek0KvTMZiayllR5II0gXS/AKjfbOaW08kFqffHK/p4IjGRcauVuthYXjCZ2BUQQLtKxY7HHmNIraZ60SLOCwLlMhkjFgu5/v60a7XYAwK4IAg0y2Qci4/HtWgR3fHx5Pv6UhcYiMvXl7YlS2gKC6NOraY0NJQWrZYCf38a5HKqJNXs4089RaNajeepp2iIjeWCKHIiMZFRo5GuxEReNpk4Ik1r9Wm1jBqNdGo0vJ2TQ71azRWTifKwMEaNRg5HRzNhNuNeupTrViutajVTVistajXXrVZ6pdd6TXpvu81mrhcVMSDdj1osTFitvGaxMGG3M5qfP5N9nKqqmhHfvF5UNFP6GnE6P5WNvGKzcSEnh1FJwHLI6eRKeTlvdXTgKSrihYoKmiwWutato8Nk4pBOx+HkZHpMJhpXr+a0w0HxihU0Z2XhfOopmtLTqV62jH06HdbwcKpEka3SdFj1s89OlzwPHvzMHrBPPvmEW7duPehf718rHvS+OxcPSTzohXg/wws+3ozC7RNdN27c4IMPPrhjI7/dlf3raPjMAdA3e53vvffezETdN81QfREAnZN6KM5KG/d5qSn10qpVeMxmOkWRBkHAERHBgYiIaYNOuZza2FjaEhI4Zzbzq6ysewMg6f60dN/u/TopietWK1UKBWdFkTw/P+rUavIef5w6uZyNP/85XUolWx55hBPh4RT95Cf0hYdjnzePBrmc7PnzadHrWb948bQInwRu+5KSaNTpcK9cyaGVK9mblERNYiL7BYG6p56iURDoEEWG8/O5WlBwXwHouHTfEx/PpNmMR6lkMjWVjuBgfpecTPPChXwgirQGBnLDbKY2OHi6ETk0lAlp4mpUr8e9ZAkjej3VISFcF0XOqdWMGwxcEgTGjMaZsftjSuXM+bcMBgYEgTOCwCVB4Jwg8H5GBkOCwJTZTFd8PGNGI2cEgetmMy9IxztjY/nNpk10abWMm82ckDzFBiSrDbdcPt20Lr02rwVKr1b7rQDQSE4Or27bxpTdzuhtCt3jLS3TvUKVlYzk53O9qIhxu/1LQfet3bvxmM2M2+20pKczXlnJlYqKL8z8zAHQXPzdxINeiPcjbh9l94LP3RukF4DuHmX/JmAwB0BfH3y8GZ+pqamvfW2vv/wyr46M8OrICNeGhzntdvNaXx+XGhvvCYAuiiJDUobkJYuF/rQ0nhdFLq5eTbMgUBYXhzM6GqdKRY1GQ61KRbFSSV1iIp0JCVyzWLiwatU9AZBXCbpD2kC9x08oFEyYzewPDuYVgwHHE09wSa1mz7x59CiV2H196YmPZ+v8+TSr1ewKCKBWEKjSailJSMDx1FMcXLWKvaJIndHIvpUrqV6xgkpR5NDq1VQKAnuefJJyQaAoPp4KUWSnKNKYl8eRvDw8RUWc376dc9nZdJhMdBiN7NfpGMnPp8tkYryykjPSxJsnN5fJyko6jEam7Hb6DAZuZGdz7DYxx08BUEoKHcHBfCgItEtmoMekEfQDS5bcYVh6WTIsPSWTMSmKVAcH33cAagwN5TmdjobISN5cs+bvHoDGJduTW07n9P+Hurrp3qA5AJqLf4R40AtxNuOrjLK/9957XL9+fdYyDnMA9M3Ax/v+f5Nre3VkhI9ffZXxgQFe6+6mu6CAKw4H1StWMJCdTceaNYzabLh1OsbtdjpSUr4QgIakUte45Id1eft2BrKzGcjLw2O10m4yUbdiBZUGA664OMpiYtgZEUG5UslmPz8GRZHS4GBG09NpkMkY0espDwxkRK+nTrLGaFAo7gSgqChuWSz0SBtul7QBd0RFcSs/n667Nt7uxESGLBY6U1PpNZup0ulwp6dTv3IlA1YrXWvXMpibS/vatXQajezXaDgQE0NjUhL7kpPps1o5uGEDHXY7NVlZlGVkUL5mDRVr1uCyWGjMy+NwXh69DgctOTm4s7IoTk2dFtBbsYL+oiIOms2MlpTQJIr0m80Ux8TQpNdTEBJCvyhSpVQyIIq0L13KcGIiNQEBDCiVlPn6MqbXUxsUxITZzAGZjEmzmT1BQVwzGDgcGMi1lBTagoK4lpJCSVAQg4JAfWQkF3Q6jmm1vGwycSIpiWtGI26FghFBoF0u52pyMt1aLWeXLeOwQoFboeCEVosrLIxOrZY9CgUtej0NsbG0a7UUPP44y//b/0u0z38h4Z/+O2qf/5N13/tXSh97jEuCQJW/Px6tloJFi7gkiuwPCZm+l0qbu0JC6ExNxa3T8WJBAS0mEy8WFOA2mfBkZ9NiMjEg2Ve8WFBAv9XK2zU19GzZwkhJCaeysxnJz2dUWmefB0Av5OTw2vbtDw0A/cd//MeD/rX/teJB77tz8ZDEg16IsxWvvvoqdrv9nkbZvaUWr2/XbIHFbKss/z0C0OeBz2xcmxeA3ujr4/dnz3Jx1y7GKyo4tHo1I/n5NOp0nDOb2SuX02MwUBAYiFutZpu/P/VxcewKDaVlxQp6zWauZmV9CoBuH0meaGnhJZeLc4WFeHJyOCsp8/ZKN0dkJL0GA/kLFtAdFUXVz3/OGZmM8kceYTgsjNL/9b8YVqvZMW8ew2o1Jb6+XDMYaHjiCW6IIu2hofcEQCcky4wLosiU1cpVi4Upu50zWVm8U1nJQHY2k5WVDOXnMym52L9iscxkGiYqK6c3xJYW3m5pYaK5mTcOHeJ1t5uzVVV0FhVRk5VF6erVNK9fj3vdOk7n5TFSXk7z5s30ORzszcxkb2Ym2xMSqBNFdsXG0mk0UqxQTGsZyeVUK5VUR0RQp1BQuGgRh9Vqtku9Pa7AQPq0WrbNn0+nWs3mBQvo02pxLVxIl1zOnoUL6dNqsS5YQKdGgz0gALdGQ6GfH/UKBQ5p4mpPWBhndToOyOX0JCXo7rHDAAAgAElEQVRRGhREWWQkhYsWUS2X49ZoqIqOpkmjoVwyMS0NDcW1cCEJPv+M0keG3EeL3GcZIT7xyHwWo/LxoeDxxymQerVypV6t0ogI2vV6tkVEUJ2URHZoKIeNRnbExrJPFCnW62mxWNialES11MTcYbFQpdfTsGoVB59+mg6Lhe1PPklnVhaVej1HTSbcy5dTmpBATVoajhUr2JuZSWdODvVPP80Rk4k9KhWO6GjqdDrKBIH6tDQOrl7Nkawseteu5UhyMrVxcbQIArU6HS3p6TSmptJvtTKQm8uI08mp3Fw8Vis9kkDiObOZKbudwczMOQCai3+seNALcbZibGyMFStWfCH43N5ce+PGDd5///1ZBYv7AUD3I6s02wB0L6/ZC55f1tw8mwD0Sm0tf+ro4KLDcYcr+QmTias2G3U6HR6zmRK1ml6zme2RkVTpdJRoNJTGxOCMiaFcreaw0Uj32rUMO51cdblmAGiipYWxAwd4w+3+lH5Qm7S5FKtUnBVFyoOCOCsI7Jk3j76wMHJ/+EM6Fy9mww9+QMfixWz+wQ/whIdT+KMf4QkPZ/dPfkKHTMaWn/6Uo1FRFDz2GJdEkdqoqOmmW8l9fjYByDsOPXGbnoxX8fqiw8F4eTmjeXmMWq0Mmkw0iSKtaWm0p6fj2biRrtWr+WVBAa3LlzOYmUl5TAwNGg0FISHs12golMtxJSSQHxrKQWn6q0OrxRkcPD2CPm8e3QYDmxYsoE0up3bxYrqVShxPPEFjbCyr582jQ69nqzRVtic4GLdaTXFEBA2CQGFQEE1qNbuCg2mRy9nt60tlRASFixdTq9FQoVTi1mioUyjYGRJChUqFTSYjxuf/IMRHTZCPSLTPMpQ+y1D6LCfaZzkKnwie9Plnypcu5WhKCkXh4XTp9VRERHDMYKAwMpLBjAz2aTSczcjgkE7HaH4+7owMhp1OalevZrSkhLq0NDxWK0dMJnqtVjySxpJDr6fbaqU0IYF2o5EGnY42k4mBrVvp2b2b5s2babFYKBEEGnQ6XEolZUol9cuWUZ2Wxu7kZKrT0iiWgGePSkVZXBwNOh37li2jOSMDR0ICtcuWUZ6QQIlOh1MQKE5IwKlW40pMpE6jocdoxBkbS4MoUrxy5R16T8Nnz34mAN28eXMOgObiux0PeiHOVrz66qsYDIZPAcnnjbL/9re//YcFoOvXr8+KUeuXvebPAs8vy7jNFgD9YXCQa3cB0Kike3K3O/bJvDymqqpoXr6cHqORw8nJ1Ot0HNTpqF++nBaTiaMmE20mE8ckBeiT0l/T53bt+hQA3a6p0i01Ch9NSWHSaqVXoeCawUCFry/DajXbHn2UC2o1W3/yE86EheH44Q855ueH68c/5phMhvUXv6BLq2Wzry+dej32gAAOqtXk+fnRqFazzc+PC6tWcVij4d3c3PsCQJOtrdNikZWVM70qv1y/novr13N5/XpG167lNZuNU6tW8UZuLqfT03nNZmNk7VomsrIYSE1lcssW2g0GLhiNuORyOjUaGiIiOJWYyP6oKM6lpWFbvJiWxERyfH3piY5m/6JF9ERHs+eJJzgnijw7fz7tWi27/P25JAi4Q0I4o9WyPySEYwYDu0NCOKhWUyGXc0kQaA4N5Zwo0qhQTJfiYmM5K4p0JyVxLi2NSbudnWFhLPvnn5PgU4DCp4AUHwfLfBzE+xSg9KkhxCeX+H9ehEu5lEm7nWGpXHTaYOCW04lbr+eW08mYdPy81CjeLWntDEj9ZmOSBs+I1N/j1drxnh+Vzl+12abLWNJnMuR0MlVVxZBU1prYupWJggKuSp+dR+ohGrXZmLDbeXHjRjxmMwPr19MueYm1m0yM2my8UljI89u3M2qzcXLdOlqMRlokhXNPbi5VaWkMO52cdDiYOH2at5977gszQDdv3uTPf/7zg/7V/7XiQe+7c/GQxINeiLMVExMTJCYmzmy8d4+y373x3k8Ams2y2ncRgL4O+DxIAPK6YvdLm8mLBQUM5efTJYpUJSXhiIujKiYGV0wMNRoNTTod1QYDzSYTB1euxL169XT5y2rlTG4uV0tKOL1lyx0AdDYjg1tOJyPSpJS3lFUrNT27goK4qtez39eXS2o1Bxct4pIgULBwIR6tlrwFC2hVqyn196dFrWZrQADH9Xpyg4Lo0uvZFRhIq1pNRUQEbp2OHWo1z+XmcnD1akYrKujbsoV+q5VOydpjIDubni1bGHI6eb68/CsD0DWpR2pYGq2fsNsZzMjgncJCzq1Zw68LChhZu5Y3LRY8ycm8nplJT3Iyk9LXU1lZDKam8s6mTQyKIhOSieq42TxT9jsj9UEdlcp/DZKlhvf8yeho3s/IYEDKgp2Snue09B6fjI9nymrleel4r2Q5cUFSXJ6027H8fB5JPnFfCEAqn2Vsnuf7nQCgUUkUc9Ju56rNNv0YyQV+qqqKYel5pqTzU1VVDDmdTEg/63b9ny/ziJsDoLn4zseDXoizFdevX+fJJ5/EbrfzzjvvfOko+/vvv89vf/vbWQWLP/xhdkUGv2sA9E3A55sAkHf663htLUcrKji4dSsNOTnYDQaaMzNx6nScy86medkyzmdk0KrTcdVmo33NGsYrK+mRYKX/rs3kmsXCyG2b/IsbN3I+I4PulSvZJwiUazRUKBRUxcRQFxeHW6ejaflyjppMNEiTU9XLljGUn88+qfG622Bg3GqlS5qUOiKBkBeI+qTensG7psa8vlonJb2ZqqgoBkSRzX5+1MfGUiAJKrrkcrqMRnKVStwWy4xI3t5nn6W5oIDmggJeaG7mheZmnm9q4vmmpplyR++BA3RXVTHc1MRQXd2MsvBXAaApu53zGRlMSM21k1YrZ0SRtzdt4pQoMrlhAx4JhE7r9YxnZnIqOZnXLRaOajS8YTLRGR3NJ+vXfysAtGHe4yT5RH8JACWRu2DRwwFANtscAM1CPOh9dy4eknjQC3E24s033yQtLY3vfe97iKJ4T03D3xUAuh9ltfsBQN8UfL7Jtd2e+fnjuXNMHD7MeGMjlaJIb1YWedHRVCUlsS08nH2SrYFbr8ceGUmv2YxTo8FjNlMWE8OZjAw6U1N5SdL5uR2ARi3TZpZj0vGXLBaGTSbGJGuM3hUraBAEDup07NdoaEhOxhYWRqfRyJbwcGoFAYdcTrtez47QUAZFkYqoKKasVroSEu4JgDx3gZJ3jP6MpNUzJG3AXZmZfHL0KKMVFUy0tEzbf9TXU71hA002G023+Xpd9ngYPnuW8bExeg8epLuqagaWmmw2PGVl9OzezWhFBWdyc2cySc0mE03p6ZzLzGQ0P59zGzYwkp9Px5o106UXo5GBjAxOpKZyzWJhIC2NqawsTqakMGax0KHXM7p+PW06HRczMzmWmMgFUaQ2IoLhlBT2BwXxvChSEx7Or7OyqFcqeW/NGpoUCn5pNFIbFsY1k4lWtZq3t2yhPy2NIYuFttRURiwWqpRKXsrKoi8lhZezsmhPTeViRgbNgsDZzExG8vOpSkhA/U//xlIfE8E+z6DwMaHwMSHzWckSn1xkPutZ+k//gx0yGY2CgDMmhlMZGTgVCk6ZzRRERtKu11OuUnEgKYldS5fiyc5mt17PSEkJ7WvXMpSfzxHpPXE89RQnc3PZl5aGJzub+uXLOSmJFnqys3EKAp7cXKrT0ujNzcUtlV8PG40MZ2Yymp3N5I4dXMzM5KrNRofJxLnsbLqMRp5bv54Bs5mxrCyubdrE+YyMadA3GmdKYHMANB0Pet+di4ckHvRC/KZRUVHB97//fTZs2EBwcPA9b5pzAPTNAcjbYzU1NfWNwWc2AejXTU183N5O1/r13Kypod9k4pbTyXNpaUxZrdQqFAyLIg5/f46p1ZT4+dGpVpM3fz7dSUlUyOX0p6UxKIqcTEv7UgDyWlF4jw9v3MjZjAx6163jQEwMvamplElNsw65nGZB4FlJv2fjokXsVSrZFhg47SXm58d5UaRBoWDcaqVdyhR9XQDy9iLdXfa7IjVz9zocDNfXU5WZSW9RES1ZWQw7nfTZbHiKimjPz5/JIFVlZlKdmcleCaL6SktpzsnhZF4e7meeoVoUqU5LY9eKFTTZbHTm59NfVMT5bdvolbJiBxMTqZa8w/aLIt1WK7WiSEt6Oq06HY2CQE1MDJfWrGGPXM7A6tVUqVScSk+nRKHAnZjI1vBw+p5+mt1hYRzR6SgOC6NLr2eHXE6NZFB73GikMCyMVp2O1uXLOW40skOl4pjJxO7YWDpNJhqXL8eRkEDIv/zfLPRZyAKfpwnzWU+4z3pkPk8R5SMi81mE4v/6bzQZDLh1OvZotRw1mbCrVDTodOTKZDQLAvu1WrqMRpwaDR0WC9sSEmizWKgwGGgQRar0ejosFnKio2kQRXbHxtJjNNIQH0+VVjtzPlupnG5EVqvpNBpxaTQc0uko12qn3x+djkOCgDM6moMGA46EBA6bTJTGx9Og01Gp0VCjUrFfpWKfWk19cjJ7tFqqdToadDrK4+LYHxNDW3Iye3U6GletYu/q1bRmZdEu2cC0Z2UxXF9P74EDn6kAPQdAc/F3E9/2wvvhD3/I448/zoIFC1i8eDEAH374IVFRUTzyyCMoFAo++uije36+d955h48++oi//OUvXwmAvJNg/4gA9Hk2IF/lmm7P+Mzma/48APKWuS6cOMFzHR0819HBhRMnZo7dCwCdk0ojJySI8CoW9yYmMmW14ggMZFAQcPj6UhcWxv6gIHaHh9MgCPStXMlgRgavb9r0pQDk9Qwbslh4XlKGviiKvJuby2G9nisbN1IQFkabXs/u8HA69XocYWF0GAzkBwVNg9KSJTRLTc6HYmPJ8fdnQBSpjopiwmqlTwKjrwtAn9X/dKuujvPr13O9qIhTGRlcs9k4l53NiNPJqGQW+1ZHB8P19XjKyqbBKDOT1qws2iwWzm7bxpjLxUmHg77SUppsNpoLCnAXFLA3M5PmzZtpfPZZOrOyaF27ln1pabizsnCkpuLOyqIlJ4emjRtpe+YZWk0mdqhU01ChVtMgijhiY+lMT8cWHU1LWhplajW9q1ZRoVLhFkUK5XIOr1zJNoVi+rxWy2Gjkfa1azmVnc3O2Fg6jEYKli6l3WikXqebtotYvhzFf/1/CPWZT7DPSkJ8NiDzeQqNjy8J//I9hqQm+VcKCzmVnc3VkhI8VitX8/PxSKPjdzuzDzudvFNTg8dqZbKykrPZ2UxVVdG+Zg236uo4k5k5Uy4cyc+nX3pci8nErbo6hjMzuV5URPOTT9JhMFASE0ODIFCdkMCRtDQaRJFeq5UaSf/JoVKxTxDYpVazX6ejatkyWk0m9i5bRqMEP00SQO1PTubwunW0ZmVRu3o1VWlpVKWl4TaZ6LZacefkMN7fz2WP5zPBZw6A5uLvJr7thfejH/2IDz/88I5jWVlZ7Ny5E4AdO3awadOmr/y8f/3rXx8KAJrNEfOHDYBuB5/r16/PKGl/GwDkzfK8dluZ63JNDcMNDTTk5uLZsYO2Z5/leYeD05s3M7FnD80rV94TAPVIENGgUHDDbOaoQsFFUaRTo2GfTjfjlVSu0bBPpaJapWK/Wk2zIHDUYGBwzRrGsrK+FICmbutT8fb+9CUmMmm1UhsVxZAostPPj0NqNTuknp6tAQF0GwxsW7KELoMBm6Rd5PD3p1mtpsjPj0uiSFNCAlN2O5elDflrA5DUrD0kTZONSX0k3p4Ub7P03U3Ttx8bv62B2ltKmTh9mvH+fi4ePMiv6uunJQUkTaXTDseM7sxwUxMTLS3TejXSBJtXeXpAUp7ulWBhQIKK53JzudnVRW9WFh/v28el7Gx+53IxlJXFG8XFXCgq4vdtbXiysrhZW8ulvLxpaMnPZ6qqirPZ2Yzt3k1lbByJ3/sfaP/Lv7D0v/5/uFSxXNu5kwmpf2aqqmraYf02p/Whu5zWbwegycpKhu863240TtuJSCrk125Thr5VV4c7PZ1bdXXTICut2XGrdebzGMrO5q2KipnpvtH8fG45nVyVJvuGpdfkVZgeuq3kNSb1F41XVjIiiSKOSAKLk5WVTFRWTl97ff0Xlr5uB6D//M//nJV94duOB73vzsVDEt/2wvv3f/933n///TuO/fSnP+X69esAvPvuu/z0pz/9ys/7t7/9jaCgoL9bAJrNrNJXBaC7wed2OYE//GF2s173CkC/bmriTel2qbSUyw4HrU8/zXBuLtU6Hd0mEwWhoRzV6SgLDaXPYKA6NJRhUaRG0tLxWjZ4AcgLJZdEkUm7nUuZmXiysxnIzeWsZA3hHRvuNJmoWbaMvQkJlMXG4lCpKFap2KVWU5WURO/KlXjMZl6QrDE+F4Ckn93n/dordJiUxJTVSplcjkcUyffzozE2lq1+fjTExrIjIIB2vZ4tS5bQotezNTKS6qQk7CoV1aJITkwMp+x2OnNymGxtZczleigA6LW+Pj48fXrmsbcuXWKkvn4GgMb7+6e/R9qcb5/e88Kc95pH7prq80ivYUSCiZfy85l0ubjscPD7tjb6LBY+dLk4k5XF71wuns/O5q2yMk5nZfGGy8VbLhcvOxxMulx4rNY7wGAOgOYAaC7+DuPbXng/+tGP8PX1ZdGiRezfvx+Af/3Xf73jMf/2b//2lZ/3qwKQV5jvYQeg2QaMrwJAXwY+DwsAvd7UxK+bmvBs3jyt/Pz001zesoXqxEROmUzsiY1lICODPXI5XQYD9oCA6bKSnx810r1HFNkTFcWk1crQqlXTmjk220yDqHezH29p4XJ5OcNOJ57cXPrWr+e4yTSjAt1lMtG4bBmHdDr2xMWxJyKCaqWSkrAwOvR69mu1DFkstN/V9Hw3AB2Xjh++a0qsJzGRCauVZoWCC6KIPSSEboOBqpgYzmdk0CaKXC0pwZWWhsduZ/eKFVRlZFC8cuWMfUW33U5zTg7jLS0MV1Rw69gxTm/ZwmRlJcclXy+P2cy7Dsd3GoC8Tb7ex3knri7l5XGztpYrW7bw67IyPBYLLxcX84rDwZjDwXhJCd0bNjBeUcFvJDD6MgA6J5W0eoxGJqXprKv5+ZySMnLe0qJ3OvBAUhKjFgtHU1J42+H43wAk+a11GY2M2+206fW8lZv7vwHIamXC5XpoAOgvf/nLrOwL33Y86H13Lh6S+LYX3tTUFAA3btxgwYIFnDt37lPA8/3vf/8rP+/f/va3r1QCu58ANFsTVg8KgO4VfL4NAPL2/jzX0cFrfX2cr6nh121tnwlAww4Hf+ro4ILDwcft7ZwrLORPHR305uQwXlnJUUmduUGnYyAjA6dazamMDAojIzlsMGAPD6dNr8elUnEyM5MOk4nL5eWfAiDv5j7e0sKvamv5jdvNREsLV10uhp1O+nNz6bZaaTKZOCLd6pcvp16no1StplSlYqufH4elUtagKLJfgq8+SeH58wCoVzr+nPdrqfenX/Jx8mYgBgsK+OTo0RlYeFGaBuvMyaG/qIjqzEyabDaqnn12updnwwaG6+tpy8lhyOmkR4K/lqws+hwOmmw2nm9qYri+nnc6O2cA5p3OTkbq6xmpr2eoro7h+nre6ui4rwA0LPUj9RYVMdHSQr90f2rrViYrKzm9ZQsjTicdFgtjLhce6fzZbduYqqqaFrEsKaHNYuGKy8XhrCyuVFQwWl5OR14eJ7Zto2HDBhoyMzm2YQMNRiMuvR6XKFKUmopr7VoqTSaq0tLYu2wZVTodm2UyqpKS2BIWhjs9HUdCAvuSkiiUy6kRBHJlMvYJAtbQUFqMRgoVCpr0eko1GlqMRuxqNftEkY1KJW0WCzvV6umpsshIDksZvn06HblyOYdXr2aXSkXr8uW4dTpOpqTQEBtL/8qV1Ot0DK5fT7FGQ71Oxz6p76fLaKRr3TpObtnC89u30yLpVrWuWUOnycRZKdM52drKC83NcwA0F/8Y8SAXoc1mw+Fw8LOf/eyOEtjPfvazr/xcXgC61434fgHQbI6Yf9sA9FXB59sAIG/m53m3m8s1NbQ8+yyHMzPZv2IFh9esoTcri56cHC47HPTm5HwmAA1LQojeTMHJ3FzGKyupWbaMs2YzzrAw3Go1BQEBuJRKNvr5sU8QyAkLw/3MM/RarTOb5mVJNPCzAOjzsiCjFRVcLSnhudxcTknKvOVqNQNmM6VSVsoREcFhg4Ed4eG06/XYw8IYtViojYu7Y0z+6wLQ3SKQd0+BNUtj8e5du+6wQbjs8TA+NjYzJn/Z42Ho+PEZ3aCh48cZ9ngY9nimx+ml891VVVxpbp5RFH77uecYOn6c88ePc6a1le6qKkal86NHjjBx+vTMz/N+v6esjKG6OjxlZXdco/faeg8enL5vbJy5pjvOS8e9j/uy80PHj9Pf1sblvj4uezz0HDjAcH09PYWFXCkrY7ikhJ7CQtwbNlC/Zg0VKSkcSUujdcUKWpKTubJtG03Ll3M2M5MaQaA7JYW9MTEMmM0cXL6clwoKqDEYGMzM5IBKxUmDgYOSDcXumBgGsrPZLvmo7ddo6DUYqJbLOSwI5IeG0mU0kh0cTG1CAjvlcvYlJVEcF0edIOBQKKbH9CMjp33WFi+mWqWiSqOhQaejVhSpS0vDpdNNC2UqFDQvW4Zbp6MtOZnetWvpzMpi5B6mv+YAaC7+buLbXHR/+tOf+PjjjwH44x//yJIlS/B4PGRlZbFjxw7g6zdBA3MAdI+3Gzdu8MEHH3xj8Pk2Acg74dWfl0dPVhaHn36azlWrOPjUUxxKTqZz9WpKYmPpNpmoEAQubtlC48qVjFdU4MnJ+cxSySmpD2NQao7ulRqSq6Kipp3cQ0JoFgRqtVr2CAINq1axV5qYOfzMM3RbrRzfsIHni4t5vrz8cwHoszJIfVu2MFFZSZfJxJjNxhHJhqN46VI6jEY2h4VRnZREQWQknXo9u8LD6TUYqIiI4JokGjhpteJJS/taAHR3D9Dt2Zp7+ev/y25vvfYa42Njd9zeeu21GZX2zzv/Zd//Ta7pXm5TU1N88skn3Lp1a/o9GB+/IyvldWZ/0+XiDZeL8YoKXt2zh9MWC78sLmZ40ybG8/I4n5HBxzt30m8ycbO2llObNs2UzCYkS5L3cnM5bTAwZbdTJwhM2u20pKZOO7OnpTFutU4LZ9rt7EtKYsJup1Wn42ZxMVdWreKaxUJrSgoDZjN1gsBgRgZdUs9QmyAwYrFwLDUVj9lMU3o6/VYrHZIP3uDGjYzabJzLzOSU2YzHaqXbbme4qelLp7/mAGgu/m7i21x04+PjzJ8/nwULFjBv3jyKiooA+OCDD4iMjOSRRx4hKiqK3/3ud1/r+b8KAN0Pl/X7BUCz3VfkBaBvCj73A4Duzk7dDUBXHA7eqKhgeOtWhnNzaU9NxZ2YSJ1aTeGiRbTGxLA9IACPTseu0FAGTCbKpB6gQzodw/n51Oh0TNrtdKSk3AlAXm2d2ya0rlksnEpJwZ2eTsPy5exLTuaATkez9Jd0kyBwYvVqOkymmSZpz102E58FQMOf08cymJvLVFUVRyUwql22jHajke1KJR1GI0WhoRwWBPbK5fQZDFSoVNPifsuXz0xEPQwA9Hk3LwDdb5C53wA0LvXSTEj35yS15bH8fCYLC7mcmckfd+3CYzTyocvFifXreb24mMHsbN7Yvp0XMzOZ2rSJ56UMXrukKO1Vlj4jrckR6XyndLxVELhZXMyI1FQ/JKk+95vNXC8qmrFcOSWdf006P5yfz2Rl5Yza+VWbjXG7fVpd2m6ffh0tLdNlyHv87OcAaC6+8/GgF+JsxsMCQN9EY+fbAKD33nuPGzdufGPwuR/Xd68ANJSby68LCng+M5Nr0u1UairvbN5MaWQkvSkpbF64kEaVigJJS8exZAldej25gYEMiiKOJUu4KAg0BAXxusFAc3Aw181mGhWKmYbkG9nZjIjiHaPtL0rKz30rVnBAo6EqLo5ylQqXSkVtXByHk5M5uGwZbSYTh6UeoKOS/1Zvbi7DTienJFC5G4BGPwdQeqWyXYfJxFB+PrU6Hb1mM8VaLS1GI9tjY6kWRbYlJFCVkUGhwUCv3Y5782YmW1t5SZoCmwOg+wxAklWI1zLjrKTLdGrTJn7j+v/ZO+/oqM4z/3Py2805yW5+azsn3mQ3Xv+ySQSbkBjW2BhLMqZIdESzEM3IFGFRbCMbiWIZ7IAkbNFMt0HIINGbaQLTmyk2lgMEbGyaIVOk0cxoqijS9/cH806uLlPuvfPeed8Zvd9znsOhje7ceXSfj573KYtx8u238d177+Hc+PG4OXEiTg4eDPusWViXng7X+++j3JfhORQEgNYLAKIi1nFXiBOxdkSaSk5OZg5AkQ4Z1BMw5JObIwUfGtcnH3C4deFCbF+wADuWLcPxrVvx6fLl+H7vXpz95BNFAHQ7Nxfr+/SB5+23saFHD3jy83Gof39UTZmC9ampOJeZifw//xmfpqTg3ebNsSU5GX/5/e+x98UX8W5CAvb17ImZLVviaK9eWNy6NU6mp+PT1FR8NnKkH4AqfUMPr+Tk4POxY3F+4kSczcnBmUmTcGjcOOwcMQJr0tOxKC0Ny3r0wPLu3VHim+i7YtgwrM3OxsLevXFk3DisycjApRkzUJGdDcPSpTgqAyMCKCRjRACGzMTZP2WKfyfU7SVLcOjdd3Fz/XqszcnB/oICLPUVO5PpzcvfegunV61CRXFxoy4wAUB18Hq9jQCIHMOd3rkTNw8exDlfAfiFxYtxbt48nJs3D5W+X0+89x4O5OejIjsbe7KyUJqejhPjx2N5nz6onD4da4YPx9asLCzNyMDSl17CrA4dUNKzJ+YkJWFvZiaK2rXDtt69kff001j/0kuY8fTT2NurFxa3a4f9w4ZhcefOODF8OIqTk3F47Fgs7tgRJ0aNQkmfPjg+ahQ+SUvDzpdfRplvrcii7t1xatQobE5Lwzjb8r4AACAASURBVLFRo7Bl8GCceOMNbM3KwvmZM/0A9E1uLipzcnBu+nScnjsXp1etUnUEVl9fz/qxr0ms464QJ2LtiDTVvn37RrUtoUyPJaO8AhABH6PR6M/6WCwWLq5P3t5+uaQEtZs2oXL+fFwvL8e6vDzsmTkTRZmZWJSdjRl9+2LxqFFYPHw4Dk6ciP3Z2bj85pshAehAWpp/OWbd9OkoTU3FtXHjsDwxEReGDMFf/vhH7H/xRbzz5JPY8+yzeP+JJ1Dx7LMofvJJHOnUCQv/+EeUp6ZiW2oqPklNxfa+fbH7pZdwIjMTu4YN8wMQyRIRO+ezr3xLVLe/8gpKfEtU32vbFp/06oW327XDtsGDsTQtDeemT8f27GzcXLIEn/lAKBgAnZMdofmP1Hy/npo1C95PP8U1H9hcO3AAddeu/aOoWVLMLC0MPuv7e73qbWIBgOR/TkBIWuB9pqwMZ8rKcLa8HHt8U6+XvvEG1ufmYvnIkViWmYm12dmY2b07VmZkYFZq6oNVFH36YE16OuZ17oyNaWn4MDER67t0wexWrbAxJQWT/vxn7BgyBNNatcK2tDTMadsWO3r3xpx27VDapQumtm6NTZmZ/uzmnMRE7O3bF/OSk/Fhjx6Y1bUrVmZmIv/FF1GWloaPU1Oxq29fzG/XDjvT0rCkc2eszcjAx75uxMXdu2NNv37YkpWFw++9h3OlpTirEIC8Xq8AIKHYFmtHpKnU1FT8/e9/FwAUBHxIxqe6upp7APrK19J+RtLa7t66FXunTcOp4mKsGzsWq0eORFHPniju1g3zu3XDX9q3x8aMDMzv1Am3cnOxMS0tIACRGp91qakwjRuHhYmJ+OvgwZjXujXO9uqFFS1b4q+9euGj3/8eZ198EbOffBILWrXCvJYtsfjpp7EqMRHrkpOxOzUVJR07YnNqKlb7wOjQgAE4m5mJs6NH+wHonC9bdN63PuNSTg5OZGbCnJ+Pj3r3xvFRox4cnw0ahPe7d8fKzEy826/fg3k9U6fi5vr1+NKXqYkUgPTK7Cg1u92Ompoa5rCjBoCUANFXa9fiYHExKufNQ3lmJrZnZWFdRgZW9eyJg0OGYH3//vguLw/bMzPxxauvoqRrV2xITcXKtm3xaUoKSpOTcSwjA0VJSTg6ahQK27TB1j59UJyYiD19+mBecjIODR+O4qQkfJadjSLf/Kc5HTpgT0YG5nXogPL0dMzr1g1bR43C0l69sGPQIKzr1w8nMjOx0NdtuKR3b2zMykKZLxO5b/JkVEyejC8WLsS50lLcOn5c1RGYACChmBZrR6Spnj174saNG0wBSN5hRcPUFlYHAx/y97EMQPumTsVXxcU4MnUqDubmYuurr2L7mDHYNmYMSgcNwqqMDLzTti3WdO+Od1q3xroePVD8zDP+WTvSBaNkts4W2aydXampuDluHEoTE1E5eDBmt26Ntd27Y2FiItakpqLkhRewIikJJW3aoPjPf8aK1q1R9PTTWJSYiNUvvIA9ffrgwIABOJSZiUOjRgUFINPkydjtW5VxLicH1woKcH76dPx9xQpsy83FweLifxxh+eb1kCOsPR98gGvr1+MrHxgJAIoeABEjNUJkfceByZNhKCjA7iFDcD4nBydGjcIxXy3OYd/nvN/365HMTFzKysKp9HSYxo3DqfR03B43Dhv79oVp8mQckPnmNh+8nxo8GJ45c7BjyJAHNUGDB8M7Zw4OZGbiem4uvnztNdycMQOHRo/G9/n52Dd6NGoLC3H69ddxe+ZMfPXOO7i6ePGDOibJOActx58CgIRiXqwdkab69euHb7/9tskCUDjw4RGATu7ahSt792J3URG+KC7G3okT8cW0adg2Zgx+WLQI+6dObQRAp4qLcd03+JAMQbxaXo5z8+fjq+JiHJ42DaUDB+Kz7Gy836ULDmZnY45v4eicF1/EtsGDUdCxI7YNGYLiDh1wIScHn6SlwSSZtXNctqZia9++D8Cpf3+czszEoQED8FmfPtiZmoplHTtibefO+Dg1FQuSk7E4MRFlqan4uFMnrOndG3N69cK60aNxdOJE7Bs3Dudzc8MC0O0lS1Dp25NF5vWcLS3FzYMHH56B4zvCWjtzJs6VluJgcTFurV+P07Nno27HDgFAKszj8cBoNKoGoNOrVj2AT99U5hO+dScnMjOjAkB1c+fikO9r/tU3EfrE+PH4+6xZOJydDc8HH+C0D4xO5+XhQnExzs6ahb+VluK7NWvwXXk5PCdP4oZKHxEAJBTzYu2INDVo0CCcP39eUeDVY8koKwBSCj48AtDOkhKcWbEC6958ExW5uVjWvz/WZ2RgVseO2JmdjY/S0/F5fj7WjBmDa4sWYe/UqQEB6Krvz66Xl+NMcTFcW7Zg+5tv4vtFi7BuzBhU+lrKN2ZloaBrV2zPykJ++/YPpvX6pu0WvPACTmRmYi2Zykw2rvsAaH9mJi7l5OCyrwj6Uk4Ojo4di+MjR+JEZib2DhiA7X36YM/AgVjUvTtKMjJQPGAA5gwciLJhw7A6IwMbMjKwPj0dq/v3x/mcHFSMHBkSgMgRVziQkR7LnNqxAxuLix8UtJaV4dqBA7hx7JgAII4A6OjIkTiSmYkdffrgfFYWdvTti7OjR+OTbt3wXVYWdqam4rusLCzv0AHfZWVhZceOqJoyBYf69IF58mSsS0vDhZwczOnSBZU5OVjVpw9uvf22H4BOTpyISzNmYGdWFkyFhdg1fDi+y8vD0fHj8dX06TiSl4ez8+bh208+wcVVq1C9bx8ubt8Oy/nzuHjmDOx2O5xOJzwejwAgofgVa0ekqczMTHz55ZdNBoDUgo9eABTJ7KPjW7fCdewYvpw/H9cWLUL54ME4MGwYil94AdsyMjC/QweseeklTGnfHhtGjkRBaiq2jB2Lz3Jzsdc3Afrc/PkBAejYzJn+7JH0mOhofj5uLlmCbb5ZO6szMnBi/HjM79wZu4cMQbFv4/rMZ5/F6l698M6zz+LIyJH4pG9fXMrNfQiAKidOxHlJ4fONWbNQ8eqr2JyZia2jR2Nffj4OTZmCjdnZ2Dp2LFYPG4ZFvXtjcY8emNWhAxZ17YoFaWlYN3o0tk2YgHNz5+LrRYtUAZDUvF4vvj579qFBgtEaJthUAahy5kzcLijAruHDUTFuHFa+9BK2+JbibnvlFWwdPhzF3bvjo/R0LPN1gy3o2hWbBg9Gef/+WJuWhlnPPINlL76IWf/7v9jSty+mtmmDdX374s1WrVDasydmPv00Snv0wIznnsOmwYMxJTER6wcPRkGHDg8gvlMnlAwahILu3bE9Oxuzu3VDSUYGFvbqhW1DhmBW585YmpGBd7t2xfKRI7EuNxdrp07F6VWrsOvjj3Hlq6/w7YUL/mXRRqMRRqPR/1wjYOT1euHxeNDQ0MD6sa9JrOOuECdi7Yg0lZWVhc8//5wpANGGCwIY0sJqKfhoKbqurq5GdXU1VwD0RXExPCtW4Oi4cXBOnYqK/v3xQ24uNvfujfNvvIGC9u2xy7fZvXTgQJQOHIiPBw1Cua/+57PcXBzx1Qftmzo1JACdlxUOH/RNZd6anY2z06ejZNAg7Bs3DvPT0nB4/HgUdeqEiiFDMPO551DSvTtWdu+OZb1745P0dKwaMgT7xo7F+UmTcCknB+t69ET2vz2Ovv/n39D7xwlI/ad/Q+9f/gY78vLwpWQlxoHJk7HNNyPowOTJOOSbD1Tp29lFBidqBSA1tSwCgCIDoDNlZf6RAqfnzsX6nBycWbgQS7OyUJGfj3WvvYblmZmY278/Fg8ciNWjRmHbhAlYPXQoFnfvjg39+qGkRw+s6NEDH3TsiE/69EFhcjK2DxiAwuefx46+fTHrmWewdfBgFLRvj31jx2JBWtoDoOrYEduHDMGcTp3wSXo63m7fHluysjDjxRfx0UsvoaBTJyx/6aUHR7GDB6OwUyccHT8ey3r3xp6sLBzPz8e+wkJUrl0bsPuLgI7T6YTNZoPFYoHZbIbBYIDFYhEAJBTbYu2INDVhwgQcOXJEcfClvWJCLwAikCMHH63QYbFYogJAZMaP1L67eFEZAPk6uA75fl3fpw88+fnY2q8fKrKzsS4zE8v690dRr16Y060blmZkYM6AAVjzxhtYMmoUrpSV4fjs2YoA6Jy8xVy2M6zcN3xweUYGtvo2vm/Ozkbp0KFYMGAA5vbqheXdu2Nay5bo0uyfkdKsO5KavYF2zRYhqdkStGk2Ge3/+d+w5rXXG21QJwWot7ZuxbnSUnxRXu5vSSe1PWRez42tWwUA6Wxutxsmk0nxvyfHjmR/GTGyY2zt7Nk4V1qKA4WFuLB4MQ6/8w6O5ufjRH4+jkyZgp3Z2SgZNAj7x4/H0YkT8dnrr2P78OE4OnIklvfsiX2Zmdg6YAAuZ2dje1oa/jppEuZ26oTKnBxsHj4cp3NycHLkSBjz87HDt1Ljk/R0GAoKcMg35fzAyJE4m5OD9YMH49rkyVjdqxeuTZ6MAyNHwlBQ8GA7/Pr1D5bVqjge9Xq9cLvdAoCEYlusHZGm3nrrLezduzfuAMhkMvnT0ZGAT7QBiHR4XTt0yD/M8NNFi7Bj2TL/oMPV776L6+XlODR1qiIA2paWhso33sDZN97A+TfeQOUbb+DrN97AjdmzsSc3FwfeeQfvDx2Kxb5pyIuyszE7MxMHCguxZdo03N6wARd8U5HDAVCgYYTk/1fOm4ej+fnYM3Eitrz6KjaMGIEuzf4JLzQbiuRmE5DYbAKSmk1A+2YT8HyzfLRr9iqS/+knODtnzkMAFKwD58aVK/5Wa2JnFMzp4RmAbDYbrFYr8+ugAUDhjGSIbh48+GDr/bx5uLlkCW77JkjfXrIE+3wTpG8vWYJz06fjSk7OgyJoXx0Rmeh8NjMTV/PysK1HD9RNn46jvp1fJ3x/f9Q3MXqrryj6oK9Ien9mJm5PnowjvsnS+3xF1fszMyMCoLq6OnEEJhT7Yu2INDVt2jTs2LEjbgBIOrmZ5o6xaAMQWWNBipavl5fjC1/dzuJRo7ArNxd/6dIFJf37Y2ZyMlZ164aCZ57BgeHD8XGnTvg+Nxfr+/cPCUDXZ87EF/n5+GHRIpz1dYodnj0bV8rKUJaXh90FBSh+5RUsHT8eH/h+LR4xAgcKC7HNB0bH33uv8XRlX3Da4xtKSABICjAXSkpwfd06LB32Mjr8n2fQrtkiJDZ7Fx2a5aNDswno2uw1pDbLxwvN3kWHf/ojlgwapBiAtJoAoPgEoO8FAFET67grxIlYOyJNvffee9i8ebPiwE17xxYtuJAfddHeL8YDAJ31tbXvnDoV1xYtwrpXX8WX+fn4aMAA7BszBh927IgdGRmY3bYt1vXogenPPovS7t0xrU0bbB42DBuGDsWFN98MDUAzZ8K1ZQvO+o7ASIbnS1+dzfpp01BRWOiftVM8YgTW5+TgwyFDsG/yZJQOHYoD48ZhuW+J6obsbFTOm4evff9fCkA5bV/Ac82y0abZVrRpthXP+uy5ZlvRptkutGhWjt81G4ahyR1RsXKlfwLzF+XluHnwIG4dPy4ASAAQvsvLw5WcHHz68ss4Mn68f6XGvpdfxrmJE7G9Tx9UTZmCPS+/jEPjx2OtZAv87YICrElPx/mcHKzu2RMnxo/Hoq5dcWrUKHzctStOZWaiNCUFRzIzMa97d6zPzsbaSZNwpqwMp3fuVFUgL4qgheJCrB2RpoqKilBeXh6zABSsxod2Z5keABQI0JQA0EnZwMOdkybhyvz52OYDolUDB2JfdjaKe/XCzuxszOzYEduGDUPBiy9iRbduWNmtG1b06IFto0Zh09ix+Kq4GF8sWBASgOSb2MkerNOrVqHu1CkcKi7G7Q0bcDQ/H5dmzEDp0KHYRtrns7OxeMAAbJ0wAfunTMGnb72Fr+bMwVvPJOK5Zq8EBKBWzc7h/zU7hv9o9iayX37FH0RIDQntLi0BQNrM5XLBbDbrBkBf+Yqkv1y0CLsLCh5M+p42DbsLClC5aBH25+fj2JQpOJGfj225uThQWIjN06bhQGEhjrz3Hvbm5WHdiBEPCuhzc7HPl708UFiIg+++i3Pz5mH5yJHYN2UKFgwciN2TJ2NBv37YPWQI5nfogIohQ7A4JQUb09OxOCMDBwoLsXbmzAcrUI4cUe1jHo+H9SNfs1jHXSFOxNoRaWrevHlYtWpVzAEQ2Y8UrMaHNgCRFldarxesE00LAJ0sKIB940b/JGjy7yqmTsUPixZhy5gx+CI/H8vT07FpzBhsGjMGm8eMwZYxY7BhzBjsHDMGW31dYatGjMDVhQuxd8oUeLZvx19le7WCAdC50lL/9m9pDdBnvvb5Xb7OrW3Z2VgzYgQ+y8vDivR0dPnnJ/Fss2VBAOgofvuT32FDSYnugVwAEB8AJF+ZQTJ/pJbrzL59OOv7lexlq5DsZDsr+Tend+7Ero8+wpaFC/1/T/5OWie2Z+VK3Dx4EPs//BC3N2zA4fx8nJs+HaUZGTg0bhxKMjJwbvp0HM7Px+0NG/Dl2rWaMo9erxder5f1I1+zWMddIU7E2hFpasmSJVi2bFnMAFA48Il1AJJvet+xbBk+Xb4cny5ahJ2zZ+O78nKcmj9fEQARUCJ/fqq4GFfLy/Hl/Pk462t935Gbi7WvvoqNPls4YAD2ZWdjef/+OJmXh/WvvIKrc+ZgT24uHJs340xxMTzbtysGIAJM5+fNw8316/HVwoU4PGsWTi9ciN0zZ6Ldvz6KJ5oNwH82W4GEZpvwx2blaNNsF1o1O4f/bjYBz/+/P+Dq+fMCgJoIANE2JUd0JOt07cAB1J06hdsbNqBuxw4cmDwZdatW4ez06ahbtQqGpUsf/DBQWioASKjpirUj0tTKlSvx4YcfKg7cNAuL1cCFUvCJdQAKZt9dvOiHIgJEq999F2dWrMDOmTPxXWkpKn1gFA6ArksmQX/ns2tlZThZXIyKt97CvpwcbBo5EudnzMCm7Gx8v3gxKqZOhX3jRpwsKEDN2rW4VFKCmgMHcHT5cjiPHsXZkhJ4P/88LACRGiDPiRO4efAgLn72Gf7j/z6Of/1xOn7abBP+rdk2/LzZHPzHP3fEk//333GsoiIqgwh5BiCr1SoASKMJAKIn1nFXiBOxdkSaKisrQ3FxMbcApBZ8iNFurWcNQMGg6Ntz53Bk2zYc3rABny5ahBMrVmBnURG+Ky/H5z4gCgVAV8rK8LfSUnxTUoKLq1bh8sqVuFZWhi8WLMDN9etR4VsPsXbmTOxeuhTlM2fiVEkJTq9Zgyt79+JkWRku7tyJLQsXYv/atdgyZw6OLV2KHQUFuLF2Lc4tXPgQAF2UAFDdtWv48uhR5OfPwH/8RwJ++tNH8etf/w9mzixQNWAvUuMdgGw2G/PrCGROpxNVVVXMryOYKQE0AUDKxDruCnEi1o5IUxs3bkRBQQF3AKQVfJoSAMnfq/z4TJopOrFiBXbOno3rmzbhenk5vi8rw7effIILK1fiYkkJrvkWPBIA+r6sLOAQwUBFyKQQ2ev14uqlS/jmyy9xZPt2HF6/Hps+/BCb5sxBaX4+jpWU4PjHH+Nva9bAffw41Tb2SE0AkDaLBwCSD2fcvXTpA+ifOhWn585FxbvvPvi1sBCnV63CnpUrNRXfCwASiguxdkSa+vTTTzF9+nQmgTsQXEQKPnoBkNVqhdls5hqAgv29FIyObdmC7YsXY/OHH2LLnDn4fNUqfL5iBW5u3NgIgK6Wl8P7+ecRb0QnnS/fXbyIS2fP4si2bdi/di32r12Lw1u24MLp0/jm/PmwSyT1NgFA2iweAEhuwaZVn1UwUDOUCQASiguxdkSa2rdvHyZPnswUgKxWK0wmExXwiRUAolmjpOS91tbW+u8zacEPlDHatGABti9ciM9Xr8bV/ft12YhuNpvhdDrhcrn8YwzkSyTJGhOXywWv16t7oBQApM14ByCero/4cayKddwV4kSsHZGmDh8+jDfffJMZANXW1qKqqgoGgwFVVVXUjtcEADW+din4hJrkbTAYcOXCBXx77hyunj+vy0Z0s9kMl8sVMEC43W44HA7/EkmTyQSDwQCz2QyLxQKbzaZLtkgAkDYj/sf6OoKZACB6Yh13hTgRa0ekqc8//xzjx4+PeuCWHnWR4EwLLhwO+oMLYxGAbDYbzGYzjEYjampqFK0wka460SvzEgyAQgUOvbNFAoC0Ge8ARL7XWF9HXZ0AIKE4EWtHpKlz584hKysraoFbCj5VVVWw2+3U4aKpA5AW8JECEJnzxAsABQsmNLNFPANQTU0N7HY78+sIZLEAQLxcnwAgobgQa0ekqYsXL2L48OG6B24CPuSoSzpM0WaziQwQBQCy2+3+7IjFYtG0tFY66JJnAAoVZFwuF+x2+0PZourq6qDZIgFA2ownwOD9+rxeL+7cucP6ka9ZrOOuECdi7Yg0deXKFQwePFi3wB0KfGIJgGhfI20AItmP6upqTeATLwAULPAoyRYZDAamnWjBjGcAIt/frK8jFq5PAJBQXIi1I9LUDz/8gP79+6sKtkqKi5WAj94ARHNuD48AVFtbi+rqahgMBphMpojAJ54BKFRAkmaLDAaDomxRtE0AUHxcn8fjwd27d1k/8jWLddwV4kSsHZGmzGYzevXqRQ2ApEE5HPjoCUC0BxfSvsZIutTkcFldXU0t29WUAEgenAwGw0PZIml2jWSL7HZ7VOcWCQCKj+sTACQUF2LtiDRlt9uRmpoaceDWAj7E7HY7jEajACCF4CMtIHc46B73NWUACrV6Q2ttEQ0TAKTdyOfF+jrq6gQACcWJWDsiTdXV1aFDhw6KA6Q82EYCPgKAlAEQaf8ONiRSABCd4KR291i0skU8AxBPgMH79QkAEooLsXZEmrp//z6SkpJUAxAN8IklAKJ9jWqmN5NZScGGRAoAohOcaC1fpZ0tIh19rO9RIOMJMHi/PgFAQnEh1o5IU/X19aoASPpTbqTgIw30BoOBKgDRbluPJgAFWlsRqsBZABCd4KTn9vlIskUCgLSbzWaD1Wplfh11dQKAhOJErB2RphoaGhQBkLzjiAb4CAAKPL3ZZDLBaDSGBR+9AUivQNAUASiYBcoWkW40ki0ix54sO9GCmQAg5ebxeHDv3j3Wj3zNYh13hTgRa0ekqYaGBiQmJoYM/NKjLtrt5U0ZgKTQEsn05lgEIKfTyTwgyYMTCwAKZPJsETk+Y9mJFsx4AoxAZrVaubk+AUBCcSHWjkhTwQBICj7V1dX+oEi7tqapAhCBFhrTm2l+JgKA2F+L3IhfKMkW1dbWwu12Ry1bFAsAxMseNY/Hg/v377N+5GsW67grxIlYOyJtJSUl+QNvMPDRI9jKAYjGID8pANHs2rLb7VQhTVoDohV89AIgUmytZw2QACDlFqoGiPXcIgFA6nxMAJBQzIu1I9JWUlISvvnmm0brFILV+OixuNThcFAHINpt67SyVHpMb6YJQNJuM70AqKqqSgCQCtNSBO31euF0Oh/KFplMJqrZIp6OmAIZTyMEBAAJxYVYOyJN/f3vf0ebNm3wyCOP4MCBA2GLmwUAaf//ek1vFgBEJzjxCkDETyJ9HT2yRTxlWAKZACB6Yh13hTgRa0ekIbPZjAkTJuDRRx/Ff//3f+Py5cuKAqQAIG3go+f0ZgFAdIJTvANQMIskWyQASJ2PCQASinmxdkQaunbtGiZOnAij0Yjk5GTFbe167O1yOBoX38YDAEVzerMAIDrBqakCUCCTZousVutD2SICFhaLhesjMJ5mKAkAEooLsXLA+vp6tG7dGr169QIAWK1WpKSkICEhAampqbDb7Zpe98UXX1S8liFWAIh215ZSAAoHPnpAiwAgOsFJAFB4k2eLSHs+7doiWsYbANXX19MMCVEV67grxIlYOeDcuXMxZMgQPwDl5uZi9uzZAICioiLk5eVpet0uXbrg9u3bAoDCWKhjOrXTmwUACQBSajwBkNxqampgs9kUZYtYzC0SAERPrOOuECdi4Xy3bt1C586dcfjwYT8ANW/eHCaTCQBgNBrRvHlzTa/du3dvXL9+XQCQRgAi90QJ+OgBLTTrsgQAsb8WufEOQMFqbOTZIrPZ/FC2yOFw6Jot4uneCQASiguxcL4BAwagsrISR44c8QPQI4880ujfPProo5peu3///vjmm2+YgYU88MYKAEUyvVkAkAAgNfeLlyAuN7VFxkpri2hliwQA0RPruCvEiaLteLt27cK4ceMAoFEGSA5Ajz32mKbXHzx4MP7617/GHQDRni5NAIi36c0CgOgEJ54BiLf7RYy0ztO4/3pki3iCRwFAQnGhaDvelClT8MQTT+A3v/kNfvnLX+Jf/uVfMHToULRo0aLREViLFi00vf4rr7yCL774Iq4ASI/1GmSGD2/TmwUA0QlOAoDUm541NjSyRTzdOwFAQnEhlk4oPQKbNGkSioqKAERWBP3qq6/i5MmTzMCCdwCSL4TlbXqzACA6wUkAkHpjUWSsJlvE09oVj8eDhoYGarEg2mIdd4U4EUsnlAJQTU0NOnXqhISEBKSkpMBms2l6zddeew2HDh1iCkBms9n/4OIFgKRrK8ivtAq1aUILbQAin4MAID6Mx/tFjJcuq2jXFmkxAUBCcSHWjkhbkyZNQkVFhQAgyf8NNL2ZZqcarwAk/RyaEgC53W4BQBqMpyLjQGYymWC325l2ohETACQUF2LtiLT19ttv49NPP1UFFjTXVugBQA6H+vUa8iGGctgRABS/Ad3tdsNkMjG/jli5X8R4ByCz2QyXy9Xoz9Rki2h+DwgAEooLsXZE2vrLX/6CjRs36gYWvAOQ0unNAoDiN6DzDECBgjgvxjsAmUwmuN1uRf9Wr0406esLABKKebF2RNqaPXs2ysvLmQJQVVUVampqogpAgaY3h3o9XgGI5nBKKQDpVSvBU2syMQFA2iyeACiQECaLYgAAIABJREFU0cwWCQASiguxdkTamj9/PkpKSpoUAKlZW0GMZqeaACC+gqYAoPj5LKUWKQAFM7XZIq/XKwBIKD7E2hFpa+nSpVi6dKniIEl7bUU0ASiS6c20AYgWtMQaAPGYNRAApM14PM6UmtFojFrXV7hskdZl1byIddwV4kSsHZG2SkpKMH/+/LgDIOl1RgI+egAQTWgRABS5CQDSZgKAwpvH44HD4YDL5WL9qI9IrOOuECdi7Yi0VV5ejg8++IApAFVXV8NisVAHIKvVGvHaiqYKQE1hQSUxAUDaTACQMiPHYLEs1nFXiBOxdkTa2rRpE2bNmsUEBPQCILILjAwxpFGzJACIjgkAUmc8AxDP11ZXVweDwaD7fB8lJgBIKG7E2hFpa8eOHXjnnXfiAoCk05sNBgPV66TZqs87ANntdtTW1sLlclEPIAKA1Jlehbw0TACQMhMAJBQ3Yu2ItPXZZ58hLy+POQCRwKgVfCwWi39fl91up36dTQGATCaTv6NFrwFxAoDUmQAgbeb1egUAURTruCvEiVg7Im0dPXoUOTk5qoIk7aGFFotFEwAFW1tBrlMAkPL7WFNT42/ltdvt/sDh9XqDtvxaLBbYbDZVO5YEAKkzAUDajAAQ6+sg1yIASCguxNoRaevUqVMYP348ExDQCkBKpjfzDEB2ux1Go5H5a5FhkOQ+kkyPwxG6Bsjr9cLlcvk/h6qqqkZzUMh9DzQ1VwCQOuMZgHi+Nt4AqK6ujvWjPiKxjrtCnIi1I9LWV199hdGjR8cEAEkDdjjAEQAU2shoAOkwSOk4ArVHB4HmoBiNRn92Tvo1eNggLjUBQPF3bR6Ph5sFtwKAhOJGrB2Rtv72t79h+PDhTAGIZBHCgY+a6c0CgIL/ezIaQD4TKRIACvbgJ3NQbDZbowJ18rX0KrZWYwKA4u/aBADRFeu4K8SJWDsibX3//ffIyMhQHED1GFoYCoBIjQuZ66O0pZ02qNF83ywASNohF2w0AG0ACmTka5O6IovF4q8rMpvNsFgsumzjDmU8AxAvs2wCmQAgZeb1enHnzh3Wj/qIxDruCnEi1o5IW7du3UK/fv24A6BIpzcLAPoH+AQrFGcFQA7HwzVApK4oVLG1w6HPhGoBQPF3bTx9pgKAhOJGrB2RtqqqqtCzZ09uAEh6RBPJ9OamDkDyAmclx4HS98jDKgxpsbV0qrfRaAy4dDIegqXceIYMnq+Np8/U4/EIABKKD7F2RNqqra1FSkqK4oCrx9oKUt9DuokiXVvR1AGIHBuqHVnAGwAFMqXF1mrqingKlnLjGTJ4vjaePlOPx4O7d++yftRHJNZxV4gTsXZE2rpz5w7at2/PDIBqa2sbDd+jsbaCNrDQfr3a2loYDAbqABSqwDleAChUkCHwZ7FYAg5xDAZFPAVLufEMGTxfm8vlgtlsZn4ddXUCgITiSKwdkbbu37+PxMTEqAOQdHozacemBSt6ABBN8KMJQOS1SIFzJNmzWAagQCYd4hiq2NrlcgkA0mC8TFoOZAKA6Ip13BXiRKwdkbbq6+uRlJQUNRAIVJRrtVphNpsFAGm8l9IVILTuWTwAUCALNsSRQKS02JqH4C4ASJs5nU4BQBTFOu4KcSLWjkhbDQ0NqgAokrUVwaY301wN0RQASF7gTCubFC0A4m0QotPphMlk0r3YWovxDBk8X5vT6URVVRXz66irEwAkFEdi7Yi0pTcAKVlbIQBIuckLnGkep0UDgMgRHeugJLVAxyXSYmsyxJHMo4rmEEeeIYPnaxMARFes464QJ2LtiLRFAEhp3YjatRVKpjfrAUC0i7VZA5Ddbg84F4kmAEnfY1MHoFDBzOl0hiy2pjnEkVfI4GnXViAj/sz6OojP3Lt3j/WjPiKxjrtCnIi1I+ohNQAUbm2FFGiUrq2IFQDScvQXCoCU3HPpBOdABc4CgCK3SAtmpcXWwYY4Op1OTfdUAJA2EwBEV6zjrhAnYu2IeogWAGmd3kxzLo5eAKS19imYhQMgaYFzdXV10AJnAUCRmx4dQ4GKrckSXzV1RQKAtBlvAHT//n3Wj/mIxDruCnEi1o6ohyIFoEinNwsAagw00pqpcJ1dAoAit2i1TCsd4iiFIgFA2oz8AMH6OurqBAAJxZFYO6IeUgNA0pZ1Aj6Rzp/RA4BoA0s0AEh6dKh0k70AoMiN9cwY6RBHUmxNRhsYDIaQQxxZXjMvy0YDmQAgumIdd4U4EWtH1EPJycmKZ8iQwuZwm8UFACkHoGAFzpHAlAAg5cYagIIFTfLZhhriyAqKBACpu1cCgITiQqwdUQ916NBBcWeXdG1FpIP3pK9LK4sRSwBkt9upTHAWABSZ8QhAdXUPHzORuqJQxdZ6fnbyoM4zAJF7xPo6yL0SACQUF2LtiHqoa9euuHXrVkhAIdObyRENTViJBQBS0v2m9v3SyqAJAIrMYgWAgv0bt9sd9SGOAoDU3av6+nrWj/mIxDruCnEi1o6oh9LS0nD16tWAgVpekKtHy3pTASDp/TQYDNS21QsAisxiGYCC/b9QxdY0hjjyvEC2rq4ONptNABBFsY67QpyItSPqoQEDBuDy5ctBwUfvtRVq5uJEE1hovp50KKTNZoPRaKR2hCgAKDKLNwAKFYjDDXFUCkWxAEBWq5X5dZD7LgBIKC7E2hH10JAhQ/D1118HnN4sD5J6ABDNIM4bAElnI0mHQgoAYh+YiDUVAAr2NcgQx0DF1sGGOAoAUm4CgITiRqwdUQ+NGDECH3/8MT777LOw05v16NiKRwAKNxtJABD7wESsKQNQsK8rH+JIiq2rq6ths9n8zwGeWvOlZrVaYbPZmF9HXZ0AIKE4EmtHpK0zZ87gt7/9LZ588kls2bIlbCBtqgAknX8UyuQTnIO9J14BiNRNCQBibzwVGsuLrUmmSFpsLR/iyNIEANEV67grxIlYOyIteTwe9O3bF//5n/+Jjh07Yt++fYqCZKwAkFJgofV60rqpqqqqsHDDIwBJC8cFALE3ngAo0D0zmUwhhzjSKLbWagKA6Ip13BXiRKwcsK6uDs8++yxatWqFli1bYsaMGQAAq9WKlJQUJCQkIDU1FXa7XfFrrlu3Dh6PB7m5udizZw9TAKIJBNEGIHmBc7TfL63XEgDE/lrkgZNnAAp2z5QUW+s9xJF8Hdb3idyPhoYGvUJDVMQ67gpxIpZO6Ha7AQD3799H27ZtcebMGeTm5mL27NkAgKKiIuTl5al+3fz8fGzfvl1RkNSjZT1WAShYgbMSUwNLAoDYBXPWgZNXAHI6narumZIhjoGKrbWaACC6Yh13hTgRa0cEHoDQ008/jbNnz6J58+YwmUwAAKPRiObNm6t+vZkzZ2LDhg0CgBS+XqTLX5s6APESmIgJAFJvTqcTVVVVEb1GoGJrMmw10iGOAoDoinXcFeJELJ2wvr4erVq1ws9+9jNMnjwZAPDII480+jePPvqo6tf94IMPUFZWxhyAaAGBXgBEcwdaUwUgngITMQFA6o0GAAWycEMclRZb85RpFAAkFDdi7YgAUFtbi44dO+LixYsPAc9jjz2m+vUWLFiAlStXKg6UtAuWaQOBw0F3XlFtba0ffJQUOAsACm48ApDa45xoGc+zdvQCoGAWrthaPsRRABBdsY67QpyItSMSvffeeyguLkaLFi0aHYG1aNFC9WstW7YMS5cuFQAUAHysVqs/LU+z+FsAEPvAJA3mAoDUmcPhiCoABTJSbB1oiCM5nta72Dqceb1eAUBC8SNWDki2rwMPWtiTk5Oxe/du5ObmoqioCID2IuhVq1Zh3rx5AoBk/19a4EyOwHh8v3oAkNvt1iUgCABSbrwDUHV1NfPrkBupKzKbzaiqqgpYbK0n4Ae6Ho/HQzUWsBDruCvEiVg54Pnz59G6dWs89dRT+NOf/oSZM2cCAGpqatCpUyckJCQgJSUFNptN9WuvW7cO77//ftQDrhwIaC0HjQSApAXOZI4JDaDS8/0KAIrMon2co9QEAGk34sd1dQ8PcSTf39IhjsTnaWeLvF4vvF4v7XAQdbGOu0KciLUj6qHNmzdj5syZTAHIbDZTByA1R1bSOp9ABc60AYjm+6WVTRIAxP5apCYASLtVVVXB6XQG/ftwxda0hjgKABKKK7F2RD20c+dO5OfnxxUAKR3YSFZXhJvgTHsApAAg9kGSmAAg9UZ+YGB9HcEsHAAFMyVDHNVAkQAgobgSa0fUQ/v370dubm6TAiBpgbPZbA4LEAKA6JgAIOXGOwBZLBbm1xHMtAJQIPN6vUGLrcMNcRQAJBRXYu2IeujYsWN44403oh5wpUbSztEAIHKcpaYOpykAkHSBrAAg9sbrfKK6Ov4ByGw2w+Vy6fb6gYY4kmLr6upq//ej2+0WRdBC8SPWjqiHTp8+jXHjxsU9AAUrcBYAJABIAJA6a+oAFMiCFVvX1tayfsRHLNZxV4gTsXZEPVRZWYlRo0bFFQBJJ1ZLC5y1rq5oagCk1wZvAUDKjWcAIju9WF9HMGMBQIHM4/GIIzCh+BFrR9RDly5dwssvv8wkeOsNQEoKnJUCEM0VIKSokjcAMpvNfliUtwnTmJ0iAEi5CQDSbiaTSbcsphojP0TEuljHXSFOxNoR9dDVq1cxcOBApgBUXV0Ni8VCDX5qamr8hYo04ID2DjSawEcDgGpra/11DKSANFibcCRQJABIuQkA0m4CgOiKddwV4kSsHVEP3b59G3379o0LAJIWONMElngGIDLlmnTEORyBa4Dks1PkA+WUTNkVAKTcBABpN6PRKACIoljHXSFOxNoR9VB1dTV69OjBJHjTAiC73e5fXUGCLM2VHTwDkFYgld4zq9UKi8Wiugg6GBTJu2EIFAkAUnddvAKQzWbjHoCite4ilHm9Xty5c4f1Iz5isY67QpyItSPqIYfDgc6dO8ckAIUqcBYApO6e0eoCC9YNQzJzei9cVWs8AxCP11VX9wCArFYr8+sIZrwAkMfjEQAkFD9i7Yh66O7du3jhhReYApB0CJ/SIG6xWPyrKwIVOAsAevg91NTUBC0K17MNnkBRVVWV3+SZomDD5PQ2XkGD1+uqq+MfgAwGA9Mt8MQ8Hg/u3r3L+hEfsVjHXSFOpNZxtm3bhlatWqF169Zo3bo1WrVqhR/96EfYu3evHn6qSfX19UhKSooJAJIGcbPZHLKzi/bWepqvF20AktZGBasXitYcIJvNhrq6wJki+TC5aEARr6DB63XV1QkAUmoCgITiSpE60kcffYQXX3yRhk9Sk1oAotmxpQaAlATxWAEgmvcwFACR7i4lwx+jDUCBTApF8gm74dYOaDVeQYPX66qrq4PVag35ObI2AUB0xTruCnGiSJzo22+/xa9//Wvcvn2bll9SUUNDA9cAJC9wVgohtHeWxRIAyY8IlVx3NABIS+BUCkVaAx6voMHrdWn9HKNlXq8XBoOB+XXU1QkAEoozaXWge/fuoU2bNti0aRNNv6QiHgBIGnylQTySCc5NFYCkS17VvH+r1RqyDZ6nwBlqF5NaKOIVNHi9Lpqfox4mAIi+WMddIU6k1YHy8vKQmZlJ0yepqaGhAYmJiYqDu9qCZbUApCV7IQDI+lBbu9prjSUAChb4pFBEtnYTKLLb7QGhiFfQIH7C+jqi/TnS8AMBQHTFOu4KcSItznP48GEkJCTA7XbT9ktq4gGAyHoILdmLaAEQrdejCUAmk8kf7LXuOosHAAoWDANBkdls9kORzWbjct4OzwDE4zwnYh6PB0ajkfl1kGu5d+8e68d7xGIdd4U4kVrHsVqt+M1vfoPTp0/r4ZfUlJSUxBSAyFEXzUWrRqOR6tJWmkBFA4CkKz9MJlPE1xaPABTICBSRacZkargUiiKpKaJlAoC0mQAg+mIdd4U4kVrHKSwsxL/+6782aoNv3bo1Nm7cqIefahYrAJIe25hMJqpdW7S31tMGoEjuobQjjtZi1aYCQHJzOBz+7eEEigJlilwuV1ShSACQNnO73VwB0P3791k/3iMW67grxIlYO6JeUnMEFqhgWQv4kPZsi8XiDzq0YIV3ANIKkdL7RjriaM0UasoAFAg05JmiaEORACBt5na7YTKZmF9HXZ0AIKE4E2tH1EvJycmKg3skABSswFkafAUAKb9vDge9oYoCgML/WykUWSyWRlBEoIAWFPEMQKTWjPV1BDIBQPTFOu4KcSLWjqiXOnbsqBhqtABQuDUMAoCC37dwbe0CgCKzSEFDLygSAKTNXC4XN0XtAoCE4kqsHVEvdevWDT/88IPqQKn034eb4ExqWmgCkNYt6dEAKiUAJG9rD/bv9AAgl8ulS0CIRwAKZF6vF06nMyIoIlk/1vcnkAkAUmYCgITiSqwdUS/16dMH33//PVUAIu3FSubSCAD6h6kdACkAKDLTA4ACWTgoqq2tbQRFAoC0mQAg+mIdd4U4EWtH1Evp6em4dOkSFQCSFzgrKa4WABT+mFAAkD4WLQAKZHIokrbkV1VV+bvTWLfky434LuvrCGQ8Dbb0eDyor69n/XiPWKzjrhAnYu2Iemno0KH4+uuvFQXKYLAizVyoneDcFAFIWkeldtGr1GgNVRQAxP5a6ur+AUUEiAgUEdCVZ4pYmAAg5Z+lACChuBFrR9RLI0eOxJkzZzTBitbMhdTsdjuMRmOTASBSSB6orV3ta9EEIPK5CgBib9IjMI/HA6fTCZvN1ihTJIUit9sdNSgSAKTMRAZIKK7E2hH10tixY3Hs2DFVACTvUIoEDvQAIFpHQ3oAlTSIRbLvjCYAScFWABB7C1cDxBKKeAYgnj5PAUBCcSXWjqiXJk6ciAMHDigOlAR6tC7ebKoARKDRYDBQa6sXABQ/AVNqJLOqNuAGgyLyfUoDiqqqquB0OpnfI94/TwFAQnEl1o6ol/Ly8rB7925FoEK6V7Qe2QQDA4PBENcARLriTCYTqqurI56mHasAZLVamQcmXgOm1MgEahpBmEBRdXU1TCaT/7haKxQJAFJ+7wUACcWNWDuiXnrnnXewbdu2kIBCCpyrqqqow0o8AxC5d9KuOBrrRAQAxV/AlBotAAoWmMlnrgWKeAYgnsYHeDweNDQ0sH68RyzWcVeIE7F2RL00a9YsrF+/PmDwtlgsjQqc9Tiu0gOAaIGBVgCSF4dLs2VNFYBsNpsAIIWmJwAFC9ahoMjhcPihSACQ8nsqAEgobsTaEfVScXExVq9e3Sh4Bytw1gNW4g2Awk2/prn6QwBQZCYAKHQAl0KR0WiE0Wj0D+mUQhHr+8XTfZPePwFAQnEj1o6olz788EOsWLGiUSA0mUwBC5z1BCBaNUV6AJCSIzX5+opg70cAEPvgREwAkPqgbjKZUFNT0wiKqqurH8oUNfX7JgBIKK7E2hH10vLly/HBBx9g06ZNYWfS6AFADocjpgFI7foKmgCkdrO8AKDGJgBIvZEJ1eT3JFNktVqDQpHH44kKFPF03wQACcWVWDuiHrLZbOjevTseeeQRzJo1K2zw1iNboxcA0QCDUACkdQikACD2wYkYrwDE470iJgcguXm9XsVQFM/3TQCQUFyJtSPS1pIlS/CLX/wCHTt2xDvvvMMMVvR4TVpgEAyAIllfIQCIfXAiJgBIvZlMJrjdblX/x+v1wu12B4Ui8j0UKRTxct8IBMaDWMddIU7E0glv3bqFDh064A9/+ANatmyJBQsWAACsVitSUlKQkJCA1NRU2O12xa9ZUlKCCxcuYMuWLfjLX/7CFIBoDQbUG4Bqa2sjXl8hAIh9ECcmAEi9aQGgQCaHIvJ9FQkU8XLfBAAJxZ1YOqHRaERlZSUAwOl0IiEhAZcvX0Zubi5mz54NACgqKkJeXp7q1969ezfefvttAUBhAIgMgYx0fQWvAETGGwgAYm883ititAAokAWDIjJANBwU8TJt3Ov1wuv1Uo0BrMQ67gpxItaOKFVaWhr279+P5s2bw2QyAXgASc2bN1f9WgcOHEBubi4zWOEdgGivr5AvlOXhfUrnOzUlACIF7KyvIxbuFTE9ASiQESgi4zlCQZEAIPpiHXeFOBFrRyS6fv06nnzySTidTjzyyCON/u7RRx9V/XrHjx/H66+/HlcARGPYoLSt3Ww2UwMqAUDsgzgxngbn8X6viBmNRl0KmNVYMCgyGAz+ujyn08nsOgUACcWdWDsi8OD46+mnn8b27dsBPAw8jz32mOrXPHPmDMaOHcsUgLQUE+sFQIHa2mm21QsAYh/EiQkAUm88AFAg83q9/u9TAkUEiCwWS1ShSACQUNyJtSPeu3cPXbp0wfz58/1/1qJFi0ZHYC1atFD9ul9//TVGjhzJDFZ4AaBQbe0CgOIzqPMKQLwc5QQyXgGorq4ONTU1sNvt/t9LM0XkmRANKBIAJBR3Yu2Iw4YNw8SJExv9WW5uLoqKigBoL4K+fPkyhg0b1qQBKFxbO825QjQBiNZeMQFA7K9FajwDkMFg4BaASMY21L9RCkWRDG4UACQUd2LphCdOnMCPfvQjPPXUU2jVqhVat26NiooK1NTUoFOnTkhISEBKSgpsNpvq17527RrS09ObJADZ7XZFbe00i6p5ByC9Fl0KAFJuvAMQT/u/pKYEgAKZ1+uFy+WiBkXk38WDWMddIU7E2hH1ksFgQJ8+fZgCkNpt6+EsXKs5CXxK29ppAxCBDQFAbE0AkHqLRwAKZHIoImMwCBTZ7fagUCQASCjuxNoR9ZLFYkH37t2ZwUq0AUi66V5pMTdNAJLCBo8AZLfb4XK5qAc5AUDKTQCQNiPfo3q9fjAoMpvNjaCIHBHGg1jHXSFOxNoR9ZLT6USnTp3iHoDk29rVvF5TACCDwdBoxgr5Pa0t3zabjZtFlcQEAKkHgKYMQMHuicvl8i9iJVDkdrtZP9qpiHXcFeJErB1RL927dw/JyclxC0Bqt7U3NQCSHweSAOLxeOB0OmGz2ajsbhIApNxqamq4BiDW1xHMWABQIBMZIKG4E2tH1Ev19fVITExUHDADbUaP1Gi/Jqmz0bKtXS/Q4BGApN1vBoMBDkfwGqBAawqU1kTU1dX5f0JmHZykxjMASdu5eTHeAaiqqkq3GjY15vF4cOfOHdaPdipiHXeFOBFrR9RL8QhAJKNBq2A73gCIZMUIJNpstrAAFCwgSluKpTURpKuO1BMJAFJuAoC0GU8AdPfuXdaPdipiHXeFOBFrR9RLDQ0NSEpKigsAIm3tZHcXraWtPAOQmsWq8mGP5P7U1tbCYDD4C5Udjgc1Px6Pxz/TRE2QJDURFovFn10ymUz+a420noiWCQBSH9h5BiCz2SwAiLJYx10hTsTaEfWSWgCiORWZFgDJ61isViu1WTu0AYjABo3XUrNZnhSBB8qKkQxQVVUVbDYbXC4XnE5nI3O5XJqhyOPx+GEtUD0Rq71NAoDUB3aj0cj8OoKZ2WzWbZCn2vskAEgorsTaEfUSASCl2RI9AEjra5JliPK2dprDBmMdgEIVgZNjKYvFgqqqKn+XHMkQWa1W1NbW+gEoGBQpASLpEZiSeiI9WvHlJgBIfWAXAKTsPgkAEoorsXZEPRWLAGSz2fwZDXkHGc1jJqWgwSMAETgMVARus9n8iyOdTifcbjfcbrcfdMjfSwfAkeyaw+EICEUEiAJBUbgaoFAzVkg9Ee2jMwFA6gM7zwBkMpngdruZX4cAIKG4E2tH1FOxBEDSAt5gbe1NHYBCzTwiIEKCrBR+gpnL5fJ/verqan9dDxkAR47U5BkiKRSRNni19UROp/OheiLpfCJyJKclUPEKQDQnGtM0t9sNk8nE/DqCmQAg+mIdd4U4EWtH1FNqAIjmTBwpAIV7zWAFvIGsqQKQtBYq2HEX6fpSAj7hoEi6O0l6dEbgikARyUSRbFOk9UQOhyPofCI19UQCgNSZACBl5vF4cO/ePdaPdSpiHXeFOBFrR9RTrAEo3GuG29YuACj0ig/pcZfD4YgIfEIZydZIj7BIRx4J6jTqiaQWST2RACB1xjsAGY1GLjbVezwe3L9/n/VjnYpYx10hTsTaEfVUcnKy4nk50QQg6bZ2UpAbbcjgHYBMJlOjeyS/f2qPu2iYy+XyZ+sIeEmPsAIdnQWrJ4qkFT9cPRE5WmMdMOUmAEibCQCiL9ZxV4gTsXZEPdWpUyfFAZ5mR1QwAIp0fQXPAORwOGAwGCKeUVRbW+vPdsg32tvt9qBFznpbbW2tH8oCfV0CJoHqiaRHZ7Rb8YPVE0nBkYfgWVfHLwC5XC6YzWbm1xHMBADRF+u4K8SJWDuinurevTtu3rzJDIDIa8rrfLSur6ANQLTb6iMFIOmRoPyoT5r1cTj0O+4KdPxFanLsdrvmozN5PZHaVnw1UGSz2fyzj3iaTyQASJvxsqhVAJBQ3Im1I+qpvn374rvvvmMKQKSlncb6ingFIJL1ISsspAMfWR53kd1rFoulUSt8JK9J7jvNVny5yY/ApKs9WM4nEgCkzQQA0RfruCvEiVg7op4aOHAgLl68qCgI0z4OIkcmBoPBX59B4zVpHDPxAkDSzJj0uItcF6vjLofDAbPZ3GjFhZ6gRfxPayu+/OhMyX4y6WqPaM0n4mWrudx4BiCyp0wAEF2xjrtCnIi1I+qpYcOGobKyMqoAJG3ZlgZRVpDBKwCF6oCrqanx1wCpPXaKFEYIkFmtVipZH63XEa4VP9TRGSnO1lpPRMCTHEXK5xNpDaC8ApDT6URVVRXz6wj2mfCyp8zj8aC+vp71Y52KWMddIU7E2hH11KhRo3D69OmoARA5ujGbzf4FnAKAGpt8Y3uwFRZk4KG8kFivTJDdbg9Z5MzaArXiS4/OyH0kAEdqfSJpxSdBj/g2+dzI1yVfQ+lrCgASAMSLWMddIU7E2hH11Lhx43D06FHdAUg+oZgEI94BiPZcoVDXFm7gI5moLJ/pIz2mCVZIHEmmJpIiZ5YmrycisEgyZ0pa8bXvsq8tAAAgAElEQVRAkbSeiHwmSuuJBABpgw5e1nQIABKKO7F2RD2Vk5ODzz77TLdsSLi29qYGQAQiAn2dYBvb1RY5hyokltbMhIMiPYqcWYEQASCbzQa73d4oW0NWbOjdiq+knog0GbAO5AKABACxjrtCnIi1I+qpyZMnY9euXdQBSGlbux4AFAwyeAQgJRvbaaywkM/gCRT4pa/vcESvyFlPI59fdXV10PtHoxU/kvlE0noiAkW8zSdyOB6srWF9HQKAoifWcVeIE7F2RD01ffp0bN26lSoAqVlfQbvGJpYAKNTGdtKmrecKC3ngJ8MBSSC2WCxc1voofW9aj+3CteKHOjqT3i8t9UQEfmjUEzUVAHK7+ZlSLQBIKO7E2hH1VEFBAdatW0cFBqTrK5S2tTdFACJZL55WWEizJWazOWS7Oc9HYeSYifaxXagp1lpb8QMZKTAnvw9WT0S+brTmEwkAUg5ADQ0NrB/rVMQ67gpxItaOqKfmzJmDTz75JCIYkLa1y1czxAMA0RqsSGYUBauHYjXTJ1S2RBr4g7Wb8wJEpE7FZDL5a2r0hq1IWvEDQZEcgAKZtJ5ImqEiX1eP+UQCgAQACTVRsXZEPbVo0SJ8/PHHmgEo1CbyeAAgWpOlyX0yGAwhsz4OR/Tqbcg8HJKxUwoy4drNHY7oZomkxdpq3oce1yHdORasFT8YFDmdTn/NlVp4kdYTkQwVzflE5Icc1oARyHga0igASCjuxNoR9dRHH32ERYsWqQYgeVt7JBkWmkdMDoeDykoNWgAk32pPOpHk4BPt4y6Hg16Rs7RmJtjxkLy1nMf3oRcUkXsjL3SWHp3V1tb6uwAdDsdD9yvS+UTEB7XWEwkAEgAk1ETF2hH11OrVqzF37lxVMBDJtvamAkDBjgXJtbE67orWJOdwx0ORziaSZq9YTqSO5N5Id47J5wQ5HPq04rvd/6gnkrbih6snEgAkAEioiYq1I+qpDRs2oKioSFVAj2Rbe1MAIHKsF+hYUA4BLIqcQ7WE6xn0pS3fwTqrlIAM+Yx5nUit1EjNEvETVq34SuqJ5MtjeTKeZhQJABKKO7F2RD21bds2vPvuu4oCOknf04SVeAIg+cb2QDN9yD2kNaVZaaAlR1M8TXIOteQ00LGgNHtls9liKusjf9/hapYibcWPZLVHoHoiMiKBt/lEPAGQ1+sVACQUf2LtiHpqz549mDZtWlA4kQd0mlOWtWZYlABQJHVJcgv3noNtbCcmXWFBfpKPdEqz0kCrpciZNRwEyoSQejNea32UmrRTTe37CNeKT4Ax0lb8QEaO6+T1RNIaL1bb2B0OPjrUCADFi1jHXSFOxNoR9dTBgwcxadKkhwJ6sLb2WAAg6SRdvQEo1NBHpUXOgaY0R1ovQ+5DvAADyZrpCYx6ml6danq04gcy4svSYO92B64nko5HiAYU8QRAXq+X9SOdmljHXSFOxNoR9dSJEyfw2muv+WEkXFu7AKB/XHeoje1WqzWiFRah2qlDBX353ivewSCUBRtoGCwTEmytB2uLJOujBYgiacWXH50RgJEDUDAAkNYTyT8XPeYTCQDST6zjrhAnYu2Ieurs2bN49dVX/dAT7viI5owdKQDRhCo9ASjcxnby8Kc900dJvQzJMLAocqYNDOQ9Kh1oGGythzToRxsGeZpP5HCEHlPgcASuJyLXTIBeSyu+tJ6IZKikM6MirSfipUNNAJBQXIq1I+qp48ePo2vXrmjZsqWiKc60AShYhoVHAAq3sZ2AT7QyENKf9MmQRXlwiaUMEM3t80rn7+h1f6KZ9dF6f8ItxyVQJC08p1FPFGw+kdZ6IgFA+oh13BXiRKwdUQ/dv38fS5cuxc9//nM899xzuHr1qiIYiAUAIg9xmtdHsgrBVljQ2NiuJYhJi5ydTqf/ekIVybIOvqyAIRprPXjJ+mj9DORZNPnqFr1a8d1u7fVEAoD0Eeu4K8SJWDuiHpo+fTpeeOEF7N69Gy+99JJiGGhqAGS1Wv0P40Ab21mssHC7lRc5ByuSZXk0JL8+lp1qNNd68J71UWPSmVFWq5VJK36weiLit+S1ldQnCQBSL9ZxV4gTsXZEABgxYgQef/xx/OlPf/L/mdVqRUpKChISEpCamgq73a749ci8CqPRiLS0NMVAQHPGDs8AJF31YTAYGsGPFHxYZH0iKXJWcjSk19oKuSmFuGialrUesZz1CfT+Q9VfqWnFD1ZP5HZrX+0RqJ6IDB11ONjOJyLvJ17EOu4KcSLWjgg8qNWprKxsBEC5ubmYPXs2AKCoqAh5eXmqX9dqtaJbt25MAYh2VikSAJKOACBpf+n1sVph4XbrN8k53NEQ7SxRtNZx0L4/gVrNSWYkHrI+xNfV1l8Fm91EuxU/EHB4PB7/Z0CO7aQrRaI5n0gAkFBcirUjEt24caMRADVv3hwmkwkAYDQa0bx5c9Wv6Xa70alTp7gDIIvFovr/BRsBQAJ1tIuciUmPVpR2RUUa8IO1UkdaQFxbWxvzayxIloiAMskSalnrwYNJs4o0JoXTasVXCkU2mw1Wq9UPIcTPAtUTEZjXA4oEAAnFpVg7IpEcgB599NFGfy//vRLdu3cPycnJiiGBdoeVHgBUXV2tCoDkG9vlM32kO5lYFjmzrtUJ1zUU6t7E03yiQLU+ocYU8FyATn6o0RtIabTiu92Bj86kABQMTELVE5HPMFIo8nq9uHPnTsTPeV7EOu4KcSLWjkgUDoAee+wx1a9ZX1+PpKSkJglAoSZek4c1SetHu1aG3Gue6mMCgYCSAmKWS1hpB3E1tT6hjoZYF6DzsFdNSVce8aFQWSKr1RoSgAJZqPlEBMbU1hMJABKKS7F2RCI5ALVo0aLREViLFi1Uv2ZDQwNzAKJ9rKYEgLSssAj1wKYV0GI5UyLPgpAsEQFMXrMgSoxGh1eoBafRXOshhWvePg8lAy2lWSIC19Jaskha8Yn/aqkn8ng8AoCE4k+sHZHo+vXraNmypf/3ubm5KCoqAqC9CFotAEnTxrEIQOE2tpNaHyXHXeQhLA9oWrNE8ZgpIfeBpwnNWt+LHseQ0VzrIT1SjYXic3LNgaCR1OoZDAb/DCy9WvGV1hN5PB7cvXuX2vOetVjHXSFOxNoRAWDQoEH41a9+hR//+Md44oknUFJSAqvVik6dOiEhIQEpKSmw2WyqX1cLANEcMqgHAFksFlRXVz8EPqE2ttNaYaElSxTtImc9Ldx74akNX817icQntHxd2ms94mlGEclgke+raLbie73eRgXecmAVRdBCcSfWjqinCAApncOjFwDRzCrJASjccZee3V2hskTV1dX+o6JIVz+wtkgyJaHazFlkiXib6xPpWg+SWeThvURqwd6LHBoDjXLQoxWfZH4cjgdHZ+IITCjuxNoR9VZiYiJTAKJdV0QAKNTGdhJQWK+wkO7w4qE4VouRz5BWwXYwaIxGrUysZEoCdeUFKiBWu1SWV5PWxil5L6F8SI9WfHEEJhS3Yu2IeksNAKltMWcBQNXV1Y1S5IFWWJCsT7QDQ6AiZ6XHQqyDUKD3Eq2aknABP1Jo5C3ro8WkWRDiQ/L1EbH4vhyOf7TqR/oZk+8zta34SuqJBAAJxaVYO6LeUnMExjsAkRS5wWAI2d3FAirUFDnzdiwUKiixgrNQw/bUZIliJeujxORDDcMFfF7qrYK9FwLYenVEKmnFV5olEgAkFJdi7Yh6izUA0ThWI8dd5AFPjmOIsVxhQaPIOVyWKFot5jzMjwl3jwINIwxU4xUPWR+pESgNBdihwFqPtSdajSWUBppvFaoV3+l0wmq1YtKkSaioqGD9OKcm1nFXiBOxdkS99cILLyjOwATqsGINQPIVFuThLs/6RHsOjd6TnIMFM71azMkai1hq0w/UUUUyV6QwPh7qY7RCabi1FSzWemjdSabn/ZX+8EHuUXl5OYYPH46ioiI8++yzKC4uRn19PevHOTWxjrtCnIi1I+qtzp07+3/SiiUAkm5slwIc+cmN5XEXgYVoTnLWK0tEe1cUS3M6naipqfEHeXJcyvvKimDmcNAfashqrYfaQmeW5nK5UFlZiddffx2tW7fGr3/9a/zsZz9Dhw4dUFBQwPqRTkWs464QJ2LtiHqrR48euHHjRswAUKCN7dK/l7aW81DkzPpBHUmWiNQt8fLTeCQW7Cgy2pk0Wp9rNIca6r3Ww+FgX1OmxqxWK1577TW89NJL/vlrFosFe/fuRVlZGeMnOh2xjrtCnIi1I+qtfv364cqVK8wASE1dEZnpI9/YTjJCNTU1/jR1oBoQvQJFrOy8UpolcjqdcdVCrXaHV6h7xLp4mIAcyz1xtNZ6SD+bWJlOffHiRSQnJ2PJkiVxdeQlF+u4K8SJWDui3srIyMCFCxcUAYi0viaaABRuY3uwIudQCztp1TfE+iTnQOP+yX3iqTCW5WcTrlsoWveI56GGwdZ6hCpCj6XuO5fLhbKyMjz33HOorKxk/djWXazjrhAnYu2Iemv48OH46quvuAQgEpiDbWyXFjkreYgGWtipNZDpXeQcbZPCgs1mC9o+HQt1MtHY4RWseFiPYY0ulysmM3LB1nqQ+xUrR14WiwVZWVkYNmwYHA4H60d2VMQ67gpxItaOqLdGjx6NU6dOcQdAWja2R/KQDrXQVP5/WBQ562VKQE5eAyJvDaYZ7CM1Vhm5QMMa5YtNtdwj3rqiIv1syLiKQHu8WHSdhbPKyko8//zzKCkpQUNDA+vHddTEOu4KcSLWjqi3xo8fjyNHjiiCFavV+tCMnUhNXlcUamM7ASM9JyQHO+6orq5uVATKQ5FzpOZwaFtjEWqqLouRA+SaeJvrE+oINhw4xlP3ndTX5FkfJWs9WGSJXC4XVqxYgXbt2uFvf/sb68d01MU67gpxItaOqLfefPNN7Nu3jzkASTe2V1VVhdzYzuIne9J5RoxlsKfxnsi9plV8GihLpDTYR2qxUoeldFijwxF+qGEsmdpC53DgqHfNVVVVFV5++WWMHj0abreb9SOaiVjHXSFOxNoR9dbUqVOxa9cupgAkHUzH2wqLQME1VLDnMY0vNXJ8p3f9RTSyRDxmfbS8B3mwJ/eJ1zZ8Ne+NfPaRHBUr2eNFqzPv7NmzaNu2LdauXdukjrzkYh13hTgRa0fUWzNmzMCWLVsUwQqpy6EFPyQYGwyGkMddJIMQ7Ye30iLnYD/Z87RigIcZRTSzRLGS9VFq0uNIaWcjb234Sk3v2iXaaz2cTicWL16MpKQkXLlyhfVjmblYx10hTsTaEfVWYWEh1q5dG1UAImf+RqOxUTFxoKwPiyMmaZZEK3iF6xKKZhDjdUaRlixRPGR95O8n3FBD3pfjyq+VHK9Gs3Yp3FqPUIBtNBoxcOBATJgwAXV1dawfyVyIddwV4kSsHVFvzZ07F6WlpVEDILLCghx3BdvdxeK4S88sSahZMnoFMdJ1E0uFtMH2d5EidOI78ZD10TrUkAT7SAcR6vF+aK/miMRC1Vy9//77WL16NT799FM888wz2Lx5c5M+8pKLddwV4kSsHVFvLV68GMuXL9cdgIKtsCCBjvVxF8kqRKvdWB7EaM7bYfF+9A5ipH06HorQ3W76Qw3DdVPpnSWKlYnO5Bh2xowZSEpKwk9+8hP88pe/RL9+/TB79mx88803rB/JXIh13BXiRKwdUW99/PHHWLhwoeLsjdFoVA0/8o3t0r8jwZ/VkD2eaknkRx1aamR4ej96fT7RmPCt52ccraGG0dj0HotDGm/fvo2+ffti0qRJqKurw9WrV1FeXo7XXnsNe/bsYf1I5kKs464QJ2LtiHprzZo1mDNnji4AFGxjO8kI2Ww2/3EQjaFxah/cvE9yDlYjE+g+xcL7UWtKl7GGK0KPhj8pMdZDDZW24at5PwSoeLi/Suzw4cNo06YNdu3aFdUjr4qKCjRv3hy///3vUVRUFLWvq1Ws464QJ2LtiHpr48aNKCwspApA0uMupSsslBQN0wxEsbR9WmqBsh/kuCNeBjTSWMbKU5aI56GGoWqugmUdpbBts9mYvwel77OgoACdO3fG7du3o/qMra+vx29/+1vcuHEDd+/exVNPPYXLly9H9RrUinXcFeJErB1Rb23fvh0zZsxQXMdjMBgU1QmF2tiu5KfNUBOZtQ7W46EVnKaRjhuS7eC9Q0iJKc36aLlXLLJE0ixJLMB2uJk7ZBYYL4XOSuzmzZvo0aMH8vPzce/evag/Y0+dOoWuXbv6f19YWMh9Foh13BXiRKwdUW9VVFRg6tSpEQNQqI3t5IEaaGM7jQdzuAAWT0XBxBwOR9AsVqAOId6XmdLI+mj5mnpliaTt4LGSJQn1XsiKGmkherQmM0di+/btQ5s2bbB//35mXV6bN2/G6NGj/b9fs2YNJkyYwORalIp13BXiRKwdUW8dOnQIb731lmYAkq6wCHbcJV1hQTP4Bgtg0mMzh8MRV0XB8sCqJPAEmyPDyzJTvbI+Wu4tjSyRwxF471WsmjRzSmBH+kNNsCXCLD9Lh8OBd955B926dYPJZGL6jBUAJBSzYu2IeuvkyZOYMGGCKgAikKN0Y3u0ZvpIj82kawVMJpO/3ojXn1SVGK3jFF6WmbLI+mi5RqVZIiVDDWPNSKYxXKFzqDlX0Z6GfvXqVaSmpmLWrFm4f/8+60csTp06hS5duvh/L47AhGJGrB1Rb3355ZcYM2aM4s4ug8EAu93unzUSbIVFTU0Nk5k+bnfjImeyVkDtsRlPFo0i2kA7qfQsGuYl66PlPkmzRCTQk25HnmFO7fuMpNA51GRmvYY1ulwu7Ny5E23atMGxY8dYP1r9un//vr8I+s6dO3jqqadw6dIl1pcVUqzjrhAnYu2IeuvChQt45ZVXVGWAQm1sZ7nCgswkIaAQ6OGq5NiMp4DMChSCBfpIf6KPhayPWrNarX6wJpAdK3OJgn1GZEI1ze/hQMMaaRWi2+125ObmIi0tDdXV1awfqw+poqICCQkJ+N3vfofCwkLWlxNWrOOuECdi7Yh669tvv8WQIUPCwo/dbvc/3OUzfXhYYaG1yDlctxmrAk8eQSHS/WaxmvUJ5TuBPiPWU5kjMdoTqpX6VKDMo9L6tCtXrqBDhw6YO3cu6uvrWT9S40Ks464QJ2LtiHrr5s2bGDBgQMisD1lDQGb7SNvbpUXOLI67aBc5yws8o31sFksda0r3m/EIc5Ga2qGG0T4O0vJZSgudWV5HoEJ06fefxWLx/9vNmzfjmWeewenTp1k/SuNKrOOuECdi7Yh6y2QyoXfv3gHBh9QAkFoah8PhP17i4biLdEPpXXAarD6G9rGZdO0DC5ikcZ8C7Tcj94tFdlCP90ijHounLJF0pAKPwC39/quqqkK3bt3wxBNP4LnnnkOrVq1w4MAB3Llzh/WjNK7EOu4KcSLWjqi3bDYbunTp8tCRVrAVFiQ9HslMn0iN9SRn2pvd43GNhTTrY7VaA04a5iHzocXv9Fr9wKJoWFroHCufw/nz59G1a1eMHj0aWVlZeOqpp/DTn/4UkydPZv04jRuxjrtCnIi1I+otj8eDjh07+rM+gTa2S8GIdLqwgB8SVEMVObOwSI7NyMwYs9kck1mfQBaq1ifcfjMe62NYDTXUM0sUi9lGl8uFsrIytG3bFpWVlY2eY06nE7du3WL0FI0/sY67QpyItSPqrfv37yMpKQknTpwIurFdetxltVphsVga/TSvdwdVLNXFSK851LGZw+HwH6XEy8wYrbU+PHfm8TbUkEaWiPVSVi1msViQlZWFoUOHwuFw6P5cHDFiBB5//HH86U9/8v+Z1WpFSkoKEhISkJqaCrvdrvt1sBLruCvEiVg7ot764Ycf8PTTT+O//uu/cOPGjYdqgUKtsNBjX1ewABTrBbTBhjSazWbu1wkoMZodXqEyH9G6V7Ey1FBNloiXQme1VllZieeffx4rV66M2jqL48ePo7KyshEA5ebmYvbs2QCAoqIi5OXlReVaWIh13BXiRKwdUS/dvXsXc+bMwc9//nP8z//8z0Nb3rWssKB5vBHNIudoBivpMlaW3Wa0LBodXvLiar33m0nn4MTK8ZD8+uX3ioAR+ZxiwbdcLhdWrlyJdu3a4eLFi1F/Rt64caMRADVv3ty/VsNoNKJ58+ZRv6ZoiXXcFeJErB1RL+3atQudO3fGN998g8TERN1m+gRK2YcLXKyLnPUwkiEJtcZCybEZT4GL5VwfvfabkaPWeClGJ5ks8oMIaW6IdkZNrVVVVWH48OEYNWoU3G43k2ekHIAeffTRRn8v/308iXXcFeJErB0xmCoqKtC8eXP8/ve/17RXpqGhwW9JSUlRW2EhD1zywWckmMVKBiScyQu3tdwrlkdBod4TT8eSSvabhbpXPL4nGvckUKFzuIwa67qrL774Am3btkV5eTmzDe5AeAB67LHHon1JURPruCvEiVg7YiDV19f7d8vcvXsXTz31FC5fvqzptQgAsZrpQ4I8GbZIjAR53rIeat4X7cLtYHN2onlsFkvTnJUuMo2l96TU1BY60x7toPXzWrJkCRITE/Htt99SfmqqlxyAWrRo0egIrEWLFqwuTXexjrtCnIi1IwbSqVOn0LVrV//vI90u/O///u8YO3Ys1q5di+vXr0c1CMiLnIMFeZ7bpAM9yGlOpw4XuKKxxDQeMiTyKcNS4K6uro4J31LyHkntXKSDGqOZJTIajcjIyMD48ePh9XppPCYj1vXr19GyZUv/73Nzc/3PWVEELdQkxNoRA2nz5s0YPXq0//dr1qzBhAkTNL+ex+PB0aNHMWvWLHTv3h1t27ZFZmYmli1bhgsXLuiSEVJT5BzoJ3m9CmAjfU+sBxqGWmKq9Sf5eM2QkDqzcEGe9bUqNafTqWvLvl5ZopMnT+KZZ57B5s2bmR55STVo0CD86le/wo9//GM88cQTKCkpgdVqRadOnZCQkICUlBTYbDbWl6mbWMddIU7E2hEDiTYAyXX//n18/fXX+PDDD5Geno5nn30W/fv3R3FxMT7//POIMwCRFjmHqiNiVSzM60BDJcdmoYqyYz3rE+g9hRpqGG60A69ZInLcGs2OyUizRE6nE/PmzUP79u1x7do1as8vocjFOu4KcSLWjhhIp06dQpcuXfy/j/QILJwaGhpw7do1lJaWYuTIkXjuuefQpUsXTJ8+Hfv27VO1EFJrQXC4BzHtrIeWgBor7fpKjs1ibfCkEtMy1FDJlG+WWSIppPIA3uGyREePHoXFYsHt27fRr18/vPXWW2KPF4diHXeFOBFrRwyk+/fv+4ug79y5g6eeegqXLl2K2tdvaGhAVVUVtm7diokTJyI5ORnt27fHm2++ic2bN+P27duNgqbT6cTNmzejGlCjUSwsXSIZS0clgYIWAUiSUSNQxHPWQ837oznUUL6ck1UGMhYmOkuzREajEa1atcJPfvITPP7440hLS8P69evxww8/RO3ZJaRMrOOuECdi7YjBVFFRgYSEBPzud79DYWEh02tpaGiA0+nEgQMHMH36dKSkpKBdu3bIysrC7Nmz8dxzz2HMmDFMf0INN2NHDcDIj1F4DT5qTVrrw3PWQ41FY6ihPAOpd+E+rUJnFp9FQUEBUlNTsWnTJhQWFqJ37974xS9+gVWrVjF9hgk1Fuu4K8SJWDtirMrhcGDkyJH46U9/ip49e6Jt27bIyMjAhx9+iC+++IJ5AA1V6xHqp3jpRnDW74HmvSBHh8FqfXjJeqgxlkMNw+3s0tpBRQqdzWZzTPnfzZs30bNnT+Tn5+PevXuNnhUNDQ0P/VkkunXrFjp06IA//OEPaNmyJRYsWACgae3yilSs464QJ2LtiLGqXr16oV+/fv4NzfX19fjmm2/w0UcfYdiwYWjbti169uyJWbNm4fDhw1Hdsh3IAg3Sk9Yu2O12XeqXWJvWDi+WdVfhTNqyz0NdDLlfkXZQsSh0pmH79u1DmzZtsH///qh0eRmNRv+2eKfTiYSEBFy+fLlJ7fKKVKzjrhAnYu2IsSqr1Rry7xsaGnD79m2sX78e48aNw/PPP4+OHTti8uTJ2LFjB0wmE/OHPPkpXl4XE0u7ukIF5HBZHy33K9CxWTTHFcRKy36o7jz5/YrVbjyHw4Hp06ejW7du/gGCLJSWlob9+/c3qV1ekYp13BXiRKwdsamooaEBdrsde/bswZQpU9CxY0ckJiZi3LhxKC8vx7Vr15gcY0gDD806IpYWLUiI5riCWN10Hu5+GY1GPxjFUjH61atXkZqailmzZuH+/fvMnivXr1/Hk08+CafTiUceeaTR38XzLq9IxTruCnEi1o7YlOX1enH8+HEUFBSgR48eaNu2LV5++WUsXboU58+f1w04lK6xCDczhre6GD2yPmq/vh7HZtK6LJ7ud6T3ihQ6V1dXByyu5jEL6XK5sHPnTrRp0wbHjh1j+vxwOp14+umnsX37dgBNa5dXpGIdd4X+f3t3HhT1ff9xfJLJ9I92mjYzndak9aCxrXWILEV2AW/FgLGWhGBTI44mHo0T45F4NR4xaiXGo2p11MRqEhutRyQZD2I19Yx3IhrUMVFQqYoHCAIaBfb1+yM/toALAi7fzwd8Pma+f7Co7K7v9f328/l8329LmA5E/E9xcbGOHDmi+fPn67nnnpPb7VZ8fLxmzJihzz//PCBJvewYi5qeH6lsIKcNYzxs3Rq6l22z+no3VHXek8oOOlc236x0FdJk0Z2bm6vRo0crLi5OV65cMfpvRVFRkWJiYjRnzhzfY/fTLK97ZTrvwhKmAxGV83q9ysjI0Pvvv6+BAwcqIiJCTz75pCZOnKiUlJQaJfu6GmPh724gJ8/FmF71qc3zrc62WW2aGtaHq7RQrW4Mll2FNHkY/euvv1anTp00e/ZslZSUmP6nQX369NGIESPKPXY/zfK6V6bzLixhOhBRfV6vV1euXFFycrJeffVVtaRd0uwAABabSURBVG/fXu3bt9eIESO0Zs0aZWZm+k0GTo6xqCzB18WASVtXfWr6flU2wPTy5ctWbgPV9nUG6gxTVeMpAl10FxQUaO3atQoPD9e+fftM/xMgSdq9e7cefPBBhYSEyOVyKTQ0VCkpKcrOzr5vZnndK9N5F5YwHYioPa/Xq4KCAv3nP//RpEmTFBMTo4iICA0YMED/+Mc/dPToUY0aNUqDBw82dmtxZedi7uUcUX1b9alJYi/dnszOzja2qhboq7SjeF2dYapYdAfqrNq1a9c0bNgw9ezZk2KigTGdd2EJ04GIwCoqKtKBAwc0dOhQPfzww3K5XOrdu7fmzJljTYPG0v/B1+YcUUNY9fH3nlTV1NDG4bjVfV2l265O9sGq7KxaTTp9p6WlqX379lqwYIEVW14ILNN5F5YwHYgIvClTpqhRo0ZatWqViouLdfLkSb377rvq27evPB6PunfvrqlTp+qzzz5TTk6O8URZnXNEDXnVp6ZNDau628z0YfSyr6uuR3TU5LrbgNwzZ874CskVK1bI4/H4mg2i4TGdd2EJ04GIwNu/f7+uXr3q93ter1cXLlzQqlWrNGTIELVp00adOnXS6NGj9cknn+jixYvGk6e/FY+yw0ttXfGo6RXI1SzTh9H9vS4TIzpqEmNli8i4uDg98sgjcrlccrlc2rhxowoKChz+5MIppvMuLGE6EGFWaYPGTz/9VK+//nq5Bo3Lly/X6dOnjSWxsqs+OTk5xu8ECuTrquumhia2zepzs8bU1FR16dJFw4cP16uvvqrIyEh9//vf14ABA0x/RFEHTOddWMJ0IMI+3377rXbv3q233npLPXr0kNvtVp8+fbRw4UKlpqZaMfKhsv46tjbQK71MNTWs622z0oPO9e22/YKCAi1dulSRkZFKS0sr9zm4efOmzp49G/DPltvtlsvlUnBwsCZNmiSJQaZOM513YQnTgQj7FRcX66uvvtKCBQvUq1cvud1uPf3003r77be1a9eugP5vv+yqT02a/1U1xsOGO6dsbGoYiG2zigedbS08/V1XrlzRCy+8oAEDBqiwsNCxz1PpzyouLpbH49H+/fsZZOow03kXljAdiKh/vF6vzpw5o+XLl2vQoEGKjIxU165dNX78eKWkpNR6daN01ScQqyP+xniYunOqvjQ1rOm22b10FTd9HTx4UJGRkfrwww8dmeDuT2FhocLCwnTgwAEGmTrMdN6FJUwHIuo/r9erq1ev6uOPP9bIkSPVoUMHtWvXTsOHD9eqVat07ty5KouN2q761DS518Wcrrv9zPq6OuLvPcvKyvK9Z6XvYX1rR1BQUKBFixapbdu2OnnypJHPS0lJiVwul374wx9q7NixksQgU4eZzruwhOlAtMWLL76on/70p3riiSfKPT5v3jy1aNFCwcHB5Zalp02bpubNm6tFixbavHmz00/Xal6vV4WFhdq2bZsmT56s2NhYRUREqH///lqyZImOHz/uS5rJyck6cOCAkUGfdXmOqOy8q/q2OlLVdf369XKrQ/WpSWNWVpZ69eqlIUOG6ObNm6Y/JsrLy1Pnzp2VlpbGIFOHmc67sITpQLTFrl27dPjw4XIF0LZt29S1a1cVFRVJkm8A4vHjx+VyuVRUVKSMjAw9/vjjxpbR64uioiJ98cUX+tvf/qaEhASFhYXJ5XLp0Ucf1fLly60oEqo6R1Q6xqM6f0ZVTQ3r8+VvK89fF+bSbTObWhbs2bNHbrdba9euteqzOnnyZM2cOZNBpg4znXdhCdOBaJMzZ86UK4D++Mc/6rPPPrvj1yUlJfmGDkpSbGysNXOC6oMtW7aoadOm6tmzpxYuXKh+/fopIiJC3bp10+TJk7V161YrGjT6O0dU1YiF2jQ1rA9X2aLubiNVKuvCbKpJY35+vubMmaMOHTooPT3ddOjrypUrvju8bty4oXbt2mnjxo0MMnWY6bwLS5gORJtULIBcLpfeeOMNeTwedezYUYcOHZIkDRkyRB9++KHv1/Xv318fffSR48+3PiopKdEzzzyjTz/9tNzjXq9XFy9e1OrVqzV06FC1bdtWnTp10qhRo5ScnKwLFy4YX0nwl9xLz8SUFkj17UxMdV7zvR50NtWk8fz584qPj9fIkSN169YtQxFf3tGjRxUaGqqQkBA98cQTmjp1qiQxyNRhpvMuLGE6EG1SsQAKDg7W0KFDJUkHDhxQUFCQJAogJ3i9Xl2/fl2bN2/WuHHj1KVLF0VFRWnw4MH64IMPdOrUKSsKjcrOxFR35pTNV15eXp0UdXU1vLTstX37doWHh2vDhg1WbXnBDqbzLixhOhBtUrEA6tatm7Zv3+77unnz5rp69aqSkpKUlJTkezwmJoYtMAfcunVLe/bs0fTp030NGhMTE7VgwQIdPnzY8WLDX1PDssm9snNENhRudytQnOxZFMhts/z8fL311luKjo5WZmam6ZCFpUznXVjCdCDaJCMjQ8HBwb6vFy9erIkTJ0qSTp48qSZNmkiSjh07JpfLpVu3bik9PZ1D0IaUlJQoLS1NCxcu1PPPPy+Px6O4uDhNnz5dO3furNNb6qtbINT0HJHpq/TuNdM9i2qzbXbu3Dn16NFDEyZM8N24APhjOu/CEqYD0Ra9evXSo48+qu9973tq3Lixli5dqqKiIiUmJio4OFhhYWHlVoOmTZumxx9/nNvgLeL1enX27Fn985//1EsvvaSoqChFR0dr3Lhx2rRpky5fvnzPxca9jnyw7ZBw2au6B51NXJVtm73yyiuaNWuWlixZorCwMG3ZsoX/jOCuTOddWMJ0IAJ1xev1Kjs7W+vXr9eoUaPUsWNHtWvXTkOHDtW//vUvnT17ttqJvi6bGpqe5F7aiLI+3b1WWhDNmzdPsbGxeuSRR/Twww8rJiZGkydP1pdffmk6/GAx03kXljAdiLg7f00aU1NTFRERIZfLpfDwcB08eND3PZo0+lfaoHH79u2aMmWKr0Hjiy++qHfeeUfHjh3zW9iU9r9xqqmhk5Pc6+qgsxNXenq6YmJi9Ne//lXFxcW6dOmSkpOTNXLkSC1ZssR0uMFipvMuLGE6EHF3/po0Pvnkk77iZtOmTerYsaOk/51Poklj9RQXF+vLL7/U3LlzlZCQILfbrZ49e2r27Nnau3evFixYoPbt2xstEO42xqM2BZGNw1lr8tw3btyo1q1ba8eOHY7GS0lJiUJDQ9WjRw9JTHGvr0znXVjCdCCieireoRYbG6vVq1dLklasWKHevXtLoknjvSopKdGpU6c0d+5cNW3aVI0aNVJ8fLzefPNNbdmyRdnZ2VYUAP7GeFT3HFHZMR317Tb9vLw8jR07VnFxcb7O7E6aPXu2evfu7SuA6nqKe2ZmpoKCgnx9gXJychQUFKSzZ88G9Ofcb0znXVjCdCCieioWQCdOnFCTJk3UuHFj/eIXv9C5c+ck0aMoEPbt26fHHntMr732mm7cuKFLly5p7dq1GjZsmNq1a6eOHTtq5MiRWrdunRUNGkuLmuqcI7L5oPPdrm+++UadO3fWrFmzVFJS4nhcZGZmKjo6Wtu2bfMVQE5McZ8xY4YGDRokSRo0aJCv4ELtmc67sITpQET1VCyAhg4dquTkZEnSmjVrFB0dLYkCKBCysrK0bds2v98rbdD473//WxMmTFB0dLQiIyP10ksv6b333tM333xjRWHh7xxR6ZWdnW3d7fd3ey3r1q1TeHi49u7d62wwlJGQkKDDhw9r+/btvgLIiSnuRUVFCgkJ0Zw5cxQcHKzi4uKA/4z7jem8C0uYDkRUT8UC6Ec/+lG575d+XXELjCaNde/27dvau3ev3n77bcXFxcntduv555/X/Pnz9cUXXxjfZipt2Fi6RebvHJHJ2++ruq5du6YRI0YoISFBOTk5xv6ON2zYoJdfflmSyq0AVSyA6mqK++bNm/XAAw/4nU2ImjOdd2EJ04GI6qnYpLFly5a+vkRbt25V69atJdGk0QYlJSU6fvy4Fi1apMTERHk8Hv3hD39QUlKSduzY4dih44q37lf8vr9zRGXHeJguiI4dO6YOHTpo/vz5Rra8yvrLX/6ixo0bKygoSI0aNdIPfvADJSYmOjbFfdiwYfr5z3+uOXPm1Mmff78xnXdhCdOBiLvz16Tx888/V1hYmFwulyIiIsr1PaFJo128Xq/OnTunFStWaPDgwYqKilKXLl30+uuva8OGDbp06VLAi43aHHQuKChQbm5ulWM8nCrcVq5cKY/HY2U/n7JbYKNGjarzKe6HDx9WcHCwMjMz1aRJE1/BhdoznXdhCdOBCNxvvF6vcnJytGHDBo0ZM0YdO3ZU27Zt9corr2jlypU6c+bMPRVEubm5unjxorKzs+/pz6lqjEeg+xGVXtnZ2Ro8eLASExOVl5dn+q/Kr7IFkBNT3CMjI31bX/Pnz/fd8YnaM513YQnTgQhAunHjhnbs2KGpU6fqqaeeksfjUb9+/bR48WKlpaVVa/WloKBAV69e1cWLF5WXl1cnKzNlx3gE+hxRamqq2rRpoyVLlrBt+//eeecd/elPf/J9XVJSorCwMO3cudPgs6r/TOddWMJ0IKJ+y8zMVKdOndSyZUsFBwdr7ty5kqpuEEen6rsrLi5Wamqq5s2bp549e8rtdishIUEzZ87U3r177+hIffr06Tsm0ztxBeIcUUFBgZYtW6bIyEilpaWZfutxHzCdd2EJ04GI+u3ixYs6fPiwJCk/P1+//vWvdeLEiUobxNGpuna8Xq9Onz6t9957T/3791dERIRiY2P1xhtvaMSIEfrJT36ir7/+2vjB5ZqeI7py5YpeeOEF9e/fX4WFhabfZtwnTOddWMJ0IKJhiYuL05YtWyptEEen6sDwer1KS0tTaGioGjdurNjYWHXo0EGvvfaaPvroI50/f954MVRaEFU8RzR+/Hg9++yzGj9+vFq1aqUPPviAIhiOMp13YQnTgYiGIyMjQ02bNlV+fn6lDeJo1BgYOTk5euyxxzRu3Djdvn1bXq9X+fn52rp1qyZOnKiuXbsqKipKf/7zn7Vs2TIrVodKC6IjR45o2LBh+t3vfqdmzZrpxz/+sZ566ilNmzbN+O3uuD+YzruwhOlARMOQn5+vsLAwffzxx5Lu7Ihb2iCOAihwTp06VeX3b9++rf3792vmzJl6+umn5Xa71atXL82bN0+HDh0y0qAxKytLvXr10pAhQ3Tz5k1J33XeXrdunaZMmVIn71PTpk3VqlUruVwuhYeHS2KI6f3OdN6FJUwHIuq/oqIixcTElGvSVlmDODpVm1NSUqITJ05o8eLF6tOnjzwej37/+99r2rRp2rZtm99miYG89u7dK4/HozVr1ji65RUUFHRHF+m6HmIKu5nOu7CE6UBE/denTx+NGDGi3GOjR4/22yCOTtX28Hq9+u9//6uVK1fq5ZdfVps2bdS5c2eNHTtW69evV1ZWVkC2zfLz8zV37lx16NBB6enpjr/OZs2a6erVq+Uec2KIKexlOu/CEqYDEfXb7t279eCDDyokJEQul0uhoaFKSUmpskEcnart5PV6de3aNW3atEljx45Vp06d1LZtW9+2ZUZGRo0LovPnz+vZZ5/VyJEjdevWLSOvKygoSKGhoWrdurXeffddSc4MMYW9TOddWMJ0IAKw182bN7Vz505NmzZN3bt3l8fjUd++fbVo0SIdPXq0ynNEO3bsUHh4uNavX290le/ChQuSpMuXL8vlcmnnzp2VnlHD/cF03oUlTAciUNcqNmucN2+epO/mOLVo0UIhISGKj48vN3qBZo3+FRcX68iRI/r73/+u5557Tm63W/Hx8ZoxY4b27NmjvLw85efna/r06YqOjlZmZqbpp1zOpEmTNHPmTMeGmMJOpvMuLGE6EIG6Vlmzxi1btvhuux4zZozGjh0riWaNNeH1epWenq73339fAwcOVGRkpBo3bqxx48apqKjI9NPznUGSpIKCAkVFRWnz5s2VnlHD/cF03oUlTAci4LS4uDht3bq13GPJyclKTEyURLPGe+H1enX27FlrCsb09HTf+bTg4GAlJSVJcmaIKexlOu/CEqYDEXBS2WaNZfXo0UMrVqyQRK8ioKEznXdhCdOBCDilYrPGUlOnTlV8fLzvawogoGEznXdhCdOBCDjBX7NGSVq2bJmioqL07bff+h6jWSPQsJnOu7CE6UAEnOCvWWNKSopatmx5R5M8mjUCDZvpvAtLmA5ENCxt27ZVSkqK7+vVq1erW7duBp+R/2aNmzZtUvPmzdWkSROFhoYqNDRUgwcP9v0emjUCDZfpvAtLmA5ENCxpaWn67W9/q1u3bik/P1+/+tWvlJGRYfppAYCP6bwLS5gORDQ8Y8aM0ZtvvqnRo0dr6tSppp8O6qnc3FwlJCSoRYsWatmypfbt28cUdwSE6bwLS5gORDQ8hYWF+s1vfqNWrVrp9u3bpp9OvVCxW/XcuXPLfX/mzJl64IEHlJ2d7XusoXer7tu3r5YuXSrpu0Psubm5THFHQJjOu7CE6UBEwzRx4kTNmDHD9NOoNyrrVi19VxzFxMSoWbNmvgLo+PHjDbpbdV5enn75y1/e8ThT3BEIpvMuLGE6ENEwTZo0SbNmzTL9NOqtst2qExISdPTo0XIFUEPvVp2amiq3261+/fopNDRUAwcOVGFhIVPcERCm8y4sYToQ0TBRANVe2W7Vn3zyie/2/bIFUENv1njo0CE99NBDOnjwoCRp+PDhmjBhAlPcERCm8y4sYToQ0TBRANVO2W7VN27ckMfj0fXr1yXdXwVQVlaWgoKCfF/v2rVL3bt3Z4o7AsJ03oUlTAcigO9U7Fb91Vdf6Wc/+5mCgoLUrFkzPfTQQ2ratKkuXbqkpKQk32BPqWF2q27fvr1Onjwp6buCevTo0UxxR0CYzruwhOlABPAdf92qy2rWrJlycnIk3R/dqlNTU9W6dWuFhITomWeeUW5uLlPcERCm8y4sYToQAfjvVl22o7YkBQUF3XEbPN2qgZoznXdhCdOBCACAk0znXVjCdCACAOAk03kXljAdiAAAOMl03oUlTAciAABOMp13YQnTgQgAgJNM511YwnQgAgDgJNN5F5YwHYgAADjJdN6FJUwHIgAATjKdd2EJ04EIAICTTOddWMJ0IAIA4CTTeReWMB2IAAA4yXTehSVMByIAAE4ynXdhCdOBCACAk0znXVjCdCACAOAk03kXljAdiAAAOMl03oUlTAciAABOMp13YQnTgQgAgJNM511YwnQgAgDgJNN5F5YwHYgAADjJdN6FJUwHIgAATjKdd2EJ04EIAICTTOddWMJ0IAIA4CTTeReWMB2IAAA4yXTehSVMByIAAE4ynXdhCdOBCACAk0znXVjCdCACAOAk03kXljAdiAAAOMl03gUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABQf/wfxnl7CV5Wz9MAAAAASUVORK5CYII=\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jsheet = jlist_sorted[7]\n",
    "points = np.array([k for k in identity if identity[k]==7])\n",
    "xpt = points[:,2]; ypt = points[:,1]; zpt = points[:,0]\n",
    "print points\n",
    "print\n",
    "print xpt,ypt,zpt\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.gridspec as gridspec\n",
    "%matplotlib notebook\n",
    "\n",
    "# prepare grid points with sheet found above\n",
    "locs = np.array(jsheet.keys())  \n",
    "print locs.shape\n",
    "\n",
    "# create the scatter plot\n",
    "matplotlib.rcParams['figure.figsize'] = (8, 8)\n",
    "fig = plt.figure()\n",
    "fraction=0.025;pad=-0.1\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "ax.scatter(locs[:,2], locs[:,1], locs[:,0],marker='s',s=30,c='r',alpha=0.1)\n",
    "\n",
    "ax.scatter(xpt,ypt,zpt,marker='o',s=85,c='b',alpha=1)\n",
    "\n",
    "ax.set_xlim3d(0, 64)\n",
    "ax.set_ylim3d(150, 255)\n",
    "ax.set_zlim3d(0, 50)\n",
    "\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "ax.view_init(elev=20, azim=30)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 3.4.  Test the algorithm\n",
    "* set up a null point $\\mathbf{B} = (x,py,-(p+1)z)$, $p=1$\n",
    "* construct the above field structure on 3d grids $8\\times8\\times8$\n",
    "<img src='download/spine_fan_cartoon.jpg' width=300>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.5        -0.48989899 -0.47979798 -0.46969697 -0.45959596 -0.44949495\n",
      " -0.43939394 -0.42929293 -0.41919192 -0.40909091 -0.3989899  -0.38888889\n",
      " -0.37878788 -0.36868687 -0.35858586 -0.34848485 -0.33838384 -0.32828283\n",
      " -0.31818182 -0.30808081 -0.2979798  -0.28787879 -0.27777778 -0.26767677\n",
      " -0.25757576 -0.24747475 -0.23737374 -0.22727273 -0.21717172 -0.20707071\n",
      " -0.1969697  -0.18686869 -0.17676768 -0.16666667 -0.15656566 -0.14646465\n",
      " -0.13636364 -0.12626263 -0.11616162 -0.10606061 -0.0959596  -0.08585859\n",
      " -0.07575758 -0.06565657 -0.05555556 -0.04545455 -0.03535354 -0.02525253\n",
      " -0.01515152 -0.00505051  0.00505051  0.01515152  0.02525253  0.03535354\n",
      "  0.04545455  0.05555556  0.06565657  0.07575758  0.08585859  0.0959596\n",
      "  0.10606061  0.11616162  0.12626263  0.13636364  0.14646465  0.15656566\n",
      "  0.16666667  0.17676768  0.18686869  0.1969697   0.20707071  0.21717172\n",
      "  0.22727273  0.23737374  0.24747475  0.25757576  0.26767677  0.27777778\n",
      "  0.28787879  0.2979798   0.30808081  0.31818182  0.32828283  0.33838384\n",
      "  0.34848485  0.35858586  0.36868687  0.37878788  0.38888889  0.3989899\n",
      "  0.40909091  0.41919192  0.42929293  0.43939394  0.44949495  0.45959596\n",
      "  0.46969697  0.47979798  0.48989899  0.5       ]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
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0x6ysxEtPXQI+0XeeQfORbt26npaayVB4iEKch8fOBrVq0OhduiAyTXDhyOy+eKLaNCfPh1L/N56C5G4bt1QtlarlvkywJo18bsTJiATMmwYMicdO+I9qlWDgjTNSj35ZN6dmUqV8Hrjx2P3x3ffQeDHxzv/fhRJTETZytixqG82/Sx+fvg+Zs6EsTNpEq5DxYpwYpyFJCE71r27+nyLFEEE96efUBaxZQuivnmdDOYGijNHORoTg+dInAxUpQqcO1fIeFy5gu9cbK41PZYscfZZ2o4kcX7/PufHjyPL9MEHkFOdOyNzK8skU2PX0lGtmvLfcila1aoI9rRrx3nPntCZr7+O51qUo7Nm4c+nn4YcrVdPPT2KMWRApkxBad7YsZDJ48bh51u0gJwtU8Y8eyLu58jNmSlbFkMpRo2CTtmwAb0Ld++6lhzNyID+njZNHdAR9Vj9+nDqV6xA5qlbN+iJESOce+4XLyL7Jeo7T08EArdsgX7YuhX348WLeXtNN5CjhJthUXHGxeGh9vJCZEUWXD//7HwBde8eogn16pkLJF9fc8P44EHnnm9eMBoR5d+5k/OPPoLgato071mL5s2hGNu0QUnQpElQLF9+CYPx2DGUXz14oF0mITYWjonciDdpkuWfM71fJAmRoOhoND4fO4ZzXLECka0JExD9atYMpQoeHnnLpAQFQWGPGQNjKSzMfDGWM8jK4vzwYc5ffRUOX26fo1Mn556vzMmTuUd7mzbN22u5geK0KEdTU+EoFysGx1kOFqxc6fxsrMEAB8g06yUbdmKWhjE4yq6OJOGZ37cPkf8JEyAT85JBYAyBmEqVYMz37o3fnz0bdf5btiAQ9PffcChTU7WRLSkpiPjL/Ylt2lj/bKZ/T02FY3XlCp7XnTsxfGXePMjjfv04b9UKn0nWi3Lfl7XD3x/VDkOGcP7++9D5N244P9sgSSg5mzdPsUlyOkqUcO75yty4ofTL5nTN89Iz6AZylHAzVIozLY3zRYvUBt9TT6EUxpkCKCMD0de+fc3rkatXh+F5+zZ+VpKw2friRc4vX3beOVsjMRElUytW4LxDQy1P5WIM0S450lajBqI7EyfCSfn+eyid2FjnGtmPHsGBEG0vgwG9H8HBiDLac35ZWShdOXECZWbz5+O6tW9vPlXFdNQsY4hQdugAh3rlSkRBnVWGIEkwYrp2NXeU5ePpp5V72RXYvt18Cph8PPNM3l7DDRSnSo4ajRjIIWYm69SBIevMUkKjEfff2LHmMic4GPL13Dn8rCThOb56Fb1/rtarlZoKmbB+PYzt9u2t36eyzvDwwHfSoQOuwbx56Kn4/XfIGGfquPR0OBBiVjEzE+PB69ZFlsWewQRGIwKLp0+jB3LhQuiSrl3VWSDZETO9hkWLwpmZMAHOaHg4d+qSwatXkZW1pjvr1ct7ZsERnD6t3jNlasPk5d5zAzlKuBlBjDGemGjg332n3olRtizn//ufc2uT793j/N13ISTFB7Z8eUxpcUUHQyQ9HSVCy5dD4YlZm3btzAVRsWKIgE2YgKhYWBiatR35HUgSHL6kJCisqChEca5cwfW+ehV///dfnNudO/j7n3/CuJ89Wz0hhzF95/8/foyo5M6dWDD43HMoVbA2g75OHSjbxo05f+EFNNJevOh4A8toRJmatahYx46IZrrCJuGsLMvlKU88kbc9P26gOP9zQA4fVpfD+PujMdmZfRMGA3qq2rRRl4EVK4ZSn+PHnZ8pzInsbDyj69ahPLRFC8WBt5QlLlIE2bmRIxEI2bkTssuRzp8kwYFITkbmOToaMvPKFWR+r17FslBZjt6+jRG0Fy4g0v/xx+Y7qvSYUCeTkYHzCQvDtL8XX4SOspZ99vaGjK1ZE6VcX3wBXeeMzN5HH1mXo40bI6jqKkEd00Z1xjDOfe/e3J9BN5CjhJsRxBjjoaEGlfB+6y3nTYmSJM7/+AM1nGKUuG5dNHft2uUaddOWiI5GdGnqVESLLE05kYX30KFwShYv5vzXXzmPjNTeCLh+HRNT1q9HScLbbyMTMHw4Iu2tWkGZly+P6KGfn/U68M6drQv53I4mTdDj0KsXUvvvvIN66KVL0UsQHo7mw5gY7RyB7Gwo+R9+QKRz4EA0A1oqOWEMpWRdusCB2rEDRoPeSBLqw+VzKFHCfPt19+64Vw4edG6ENibG/NzkY8QIOKHWcAPFGcQY4717K3LUwwPPd1SU474jU65fR128uK+gbVs4txs2oPzHFXn4EKU/c+fCwRA3UJseTz+N52P+fNTVX72qfcDm/n0Madi4EUGOefPQlzBqFEq12raFfmreHMEXf3/zXjf5yGvPm6WjVi04BT16oEzqrbfQU7JkCb7PsDBksKKjtdORkgTd9MsvyJqMHAmjvm5dy+fo6wtHd/p09OhFRmpzHrmxZo2iuzw9zc8vNBSydNs255Y/ZmRY10GdO8P2sYYbyFHCzQhijHHGoDhHjnScwDAlPR0CvlUr9UMZHIxaVFcc93jvHgzoF15QZoFb6lmoXRvKatkylA04aiPw7Nm5KzVrilJLxWl6WErxi4Zb2bIQ0nqQnIzxmUuWQIlba+CvVo3zRo1gwO3cyXlCgj7nI0nI8nl7wyGTy2QmToRDIqbta9XCeTtr6eHx40pQYNEi9aQfb284lpbOzQ0Up0qOdu6slDI5GqMRE/969TJ3sF95xTWnsCUk4BmbMQP1/bIhadqDFBKCseALF8Ihd1SQbMuWvMm1vPR4tWmjnRzNTSaXLIms+/372l+TjAxsMV+xAn13depYPoe2bXFdnn8eulJPPf7ttyjJGjcOcvXCBVRKVKqkLs8tVw4BMGeN9Y2KUnpxXn8dgwvEa/bqq+jZNMUN5CjhZgQxxnjbtgZ+6pTjH0TOYbC8+y6EgvgQPvEE+hyc3awpEhcHZfTii9YjQCVLQth+8AGaIZ1ZQrN0KbYV166NEpqePRGtnjoVxuLSpciObNuGLMyBA2gKP3MGJQ83bqDEKi4OvR4JCZg4FRcHpXb3LoTpxYtotN68GRkO0/6G5cthsL79NiJjr72GqVstW2K6iVgWIhrbjiI6GtHTmTMRcbU0ccXTE87xrFn4XrWOHlsqD0lNxf1mWoLo44PyLS13deSVEycQDeUcxu6WLfiu5EbfwECUvYhOthsoziDGGK9Z08B37nROOVNSEspgatdW3yt16uD5c/ZiQ5GkJNxDr7+ODKylrKu/P6bdzZmDbEh0tPPONzxcCQaEhqIvcuhQTP2bPRvlUmvXol9q9278/O+/o+7/r79gTN6+jSziw4eQow8fQo7GxChy9No1yNFduxDUMs38zJmDAMS778JZmzULQ0fatsW5WcsUOWop6sOHyMK89x4c4FKlMNXL9Hzq14cO+vFH7fWjJTlqNHK+f79l3eSsrMiNG+g/krPahw/D5ilXDpUI3t7IsomZeDeQo4Sb8V8PiKO5dQvjBosVU0o7fH1RtnDmjMNPxyKShEjm/Pkw4Fu0MBemxYtj2sjSpa7ZrOkM5A3PXl4wTHNDbni9fh3jIH/8EUMHnEVmJuqtFyyAgrLUT+LjAyPh448dE1W+fh0OkukUm1q1OP/qK/0yNHkhMxMGmBhEqFIFEUmj0S0UZxBjjMfHO16OPnwIg69kSXX2uHdvGIPOnl4kc+MGmpe7dTMfHsEYnrGuXZHtPnrUtff+OIqHD+F0BARAz+TlGU9JQS+JPBVr3Tr9z9MaRiP68774AuXTloYEeHig8uLttx2z2yMmBsEw04bwcuXQS5LX0eJ6IElwYMXgZvHicDrT0txCjhJuRr42+GrBlStwMsRpVj17ImPgrNISkZQUCO4XXjCveffygqDq04fzTz7BVnFyOMyRJGRT1q517cVXeSU9HRGqd99F2YMcRZOnlDGGyPPrr+Pn9BwakJ6O+fFiVqRxYzjy06fD+XMWBgMMJtFhw8LSQq84HS5H799HeUmxYsq1btUK98D16w47DatkZqKU8PXXLWeLGzVCtvG991ASmZbm7DN2Tc6fxzAYPcqoHE12NnTmkiXo3ZEnWIk9ERUqQPfu2qVv5sZoRKZKzIp07Ij/HjnSeSWUnOPZWbFCvRS4WjXO160r9HKUcDMcpjgjIlBzL6bbq1fHpB9nR7tiYtB/0quX5XKghg2R7v79d9dtgHcF5I3a4sQkPSe3OIvkZJSsvfKK5brvkiXR87N1q7516tevw+AXyxw8PVEe4qySSs4RRRwzRrwmhV5xOkyORkZikIQop0qVQubg0SPd3z5HkpIwwGHYMMulN5UrI+v9yy+OKwsqqJw+jcy6fO1Wr3b2GWlPRgayXe+9Z3nnkL8/rsGaNfo6YDExyHab9rF07ox71VlZxMRE2B3Ks17o5ShRiCjJGEtgjP2Ww8/orjiPHTNviGzQADsdnDni9+FDCLbu3ZX6fvn8fH0RoVm+HOnswsbjx6hJjohA9PGXX1C/vGkTynk+/xxp6nnzUPbzwQcwtl99FbXHr7+O6OusWZwPHozJLI0bm4/fZQxTvk6fxhjMO3dQRuAqZSFaIEkoM1iwAJE803r2p57Cvozvv9dv6lByMsocTJ2hJ59E/byzsnRnzyLKPXBggVacLiFHr17FiGmxhj04GNPtnJllTE3FpLlBg5D5ErPGHh54Jj76CGO6XXnUry2kp6N/Qx5BvmcPyke//RbZ3+XLEe2fPx8Tq+bORV3/9OlqOTpzprLbSJ5KaCpHJ05EMOfvv+GExscXvux7ZCQi/08/bT5Bsm1bDC9ZuVK/CYWZmZjcZbpVvl496EVnZelu3UJWpkGDAi1HiQJEMGPsB8bYFcbYVcbYV4yxwHy+xlLGmJE5SXFGRECQiONbQ0NR2+8sA9RggJHdp495M1pwMPoWfv65YJYNpaaiiTE8HHW/n3+OaOPQoSjXadoU00BM+xkaNjRXdqaHqUC252jdGte+YkVMvHn6aSjfN99EWdumTWgYvHKlYEZJY2M5//pr1DwXLapuCg4IgCL55Rd9MmnZ2TAGTRcy1qoFo8gZAx0kifP7952mOAu8HL19G1N9unQxzxw7yyBKT0eJzKhR6hIwxnDPT5iABtu4OOecnz1kZiLodPgwsuJLl8JpGDEC429btMB+DtPPndNkP/moUEE7OVqxojIxsFEj9NaMHAnHZtEiDBf59Vc0wTtrpL49JCUhKDZ2LAapiGVa3t7ocdq0SZ/9OpKE71/MQDGGa71ihfP67ZwoRwk3oghj7DJjbPX//92bMbaXMbY/H69RlTF2mzF2mjlYcd64gT0T4kPbqVPeFu3ogawsBw40L68qXhyTVvbtc242Ji9kZ2Np1e7dMD5efRUjKUNDzRuTGVOmEuV2NG+O61ChAgybBg2gZNu3R3aoXz9M/Ro2DPWygwfjWj77LCaw1KuHvphixSxPszGNZOV1DKU81rhCBSifUaMwuevrr6Ec7txx/QhgaioiokOGmDt+pUrhuh4+rI9Dfvw4vicPDxgrXl5oDF+92vGOiJOaJwu0HH3wAMakry/uFy8vPJMbNzpHVhmNKEGdMMG8mbhIEciirVtdd7eIjCRholZ4OHZnvPkmZNsTT2DMr6kMs7aIz/To0AET4MqVg4NSrx5ka7t2cA769IETM2wYgkKyHB0wAHK0RQv8bvHi6v5I0egW/25tq7bp0b49/ixZErpiyBBkXFatQqDnxg3XmjRpiexsTPybNMk8y+7vj+u5Y4c+pdxXr0JO+/lBl8k7s95/3/GT5agJnXAEUxljmYyxUsK/NWeMSYyxp/L4Gt8yxqYwxg4xBynO+/dRmywKyhYtIOSc4Xj8/TeMdNPoiRyF3rXL+b0nlsjIwLn/8ANS98OHI3shGrA5GfG+vjDe+/fH55w2Da+zYgVGpoaHo7Hxzh3tMwwPHqAsS24uZEzpm3n0CO8pj+zduhVlQ3PnorygXz9kR6pWxWf1989duXbpAkdr1ChE/X79FRFjVyz1SEpS+oxMDYz+/eFc6bGD53DgMwQAACAASURBVJ9/cI1lQ5YxNDSuWeO4fiYnKc4CKUeTk1HyKBq+Vasio+AMhzsqCudTrZq6xE+OQm/c6FojfmWys2Fc79qFMtBx4yBfxOua03JVLy9c9w4dYLS/9BKe0c8/RzZx715Ma7x5E8+2ljInNRUTw8SpcsuX4zMZDHCgLl9GefOPP6JJfd48jAUeMABOT82aCAo1aJC7HG3QAP0PAwfidbZvRzbdFQM8GRn4TkeMUOsZxvC5X3oJ+z+0Ji4O95HofJcsiWfDUfc/OSCEI9jPGDtv4d8TGWMr8/D7LRhKDryYAxRnYiIMHFEY1KoFA9PRpVYpKSg/Mt00WqwYIk7btrlWWU9aGup6ly9HfXePHpajX+JRtCgiPgMGwMFauhQK4/RpNNO5Qn9FbCxqnl980bbomtEIZ+b0aThNCxdiMkq3boj8ydfI2iKu4sUR+Zs8mfMvv0RDtis5m3FxMBrk869XD396eMBB2bFD+yj3nTu4HmJGqnp1PC96OyJOUpwFSo5mZCBIINb/lymD5aWOvnczM3EP9u1rvqh0yBDUw8fHO/acciIrC30Y69bhHm/TxvLIV/Hw8YHj37cvjNZFi1D/f/w4nC5XML5TUlCWOmqU7Qv8EhNhkP/8M/qFpk6FjKlXT6kIEIeGmGYXQkPhvH36KYJJepQ82UpKCr6zfv3gELdrpw7SrVunfUYuIQFN86IjW6oU+pz0vjbkgBCO4B5j7CcL//4nY+xYHn4/nDE26P//WzfFmZmJcphSpZQHsUIFNIk5clKUJCESNWkSUuCiAK1fHwa6Xk1r+SEzE30xX30FY7p5c/O0uqenYiCWKgUj+oUX8BnCwhAhdwUHw9lkZSHyGB6OazN+PMa9WsqayIrC1xdKafp0ODWRka6RKbl5E+l80+WHFStiNr7WWZHISDwr4r1XowYi7HqV9jhJcRYIOSpJMA7FUbXFiiES7ejMwj//YLGeaa+CfC+6wkAOoxFlMZs24Vlu187ycy9PNAoIgGwYMwaDIn7+GdF9Vy+5dQRGI7Ipv/+OgMiUKQiKWJpgxhh6CD08MHDkhReg/y9fdg2d9PAhgk2mpcdBQfpkRR49wlh20REpXRrBMr2CnOSAEI4ggzH2jYV/P8oQkcuJnoyxk8LfdVGchw6h8Y0xPPDFi0O4O7L+NyMDRpN63CcyBOPHI5LlTANTHjH4/vtoAn/6aevRuDp1UC712WeodY2NdQ3juKCRnY3RtD/9hLKzwYORVbJ23YODkUlavBhZEmeOWM7OxhSdZ59VZ8HkrEhYmLZR2Vu3YETIjkinTnim9+3T7j1knKQ4XV6OXrqErB5jKPXx8cG0OUfuQzIacd+NH29egtS/P3rOnGmsG40I3Cxdimly3btbf54rVcLz/NFHuI9dtRzT1ZEkZIH27IFsHD0aWRJr2fkSJaDf5s3j/LffnNsHJEmQ5RMmmJdotW6N8rnUVO3e7+FDOOdy8LNpU+iVr7/WPotGDgjhCGxVnB6MsQuMsQ7Cv2mqOKOi0EQnKqk5c/AQOor4eCgYeZtuQAAETcuWaKxzVj1yRgZqcj/8EErSNConO2xVq8IwXrQIzoYzN1i7C48eoWb7vfegKC2VZzRtishznz4oe/n7b+cZL3fvorZYzIqEhqK08X//0zbCdvMmlLU4yKBXLxjHWlHAHBDd5ajBgBGsYhZq1CjHZhhSU5GNrV9fOYdatZANW7DA9pIfezEaEa1etgwOkGnTsRydL1sWPSjz5sFJKgyL+lydx4+RLVmyBP0iljbad+oER7pTJ+jCkyed58AmJkJeijtGOnfGvTN/vraVEfHxKEVv3lx5r2bN4JBpBTkghCO4zyyXDlxgOZcOjGOM/WLyb3lSnFOnTuUzZszgM2bM4Hv37jW78dPTkVoUIwpdusBIcxTXriFFLBr2Pj6oTz171nHnIXL9Ompjx441j7bI0etmzTDNZscO19j0TsDIuXIFoypffBHfkdikLWZIxoxBpu3uXcefZ3Y2Mh8jR6qn85QujfS/lvfT33+r9/V4eeF5s/U99u7d+59MmTp1qjMUp8vJUUlC07ZY4lS/PucHDtj55eWDmBjcO+KWZcbgeIeHO6ec5t49DEV49VV1Sa941K6NjN1338FRo8yG85EkZJm2boWOe+IJ9dhx0WkcMAAlUteuOf67E7MiYmO/vz9k3PXr2r3XvXsY5y/K62eewee2BReQo4SbsZ8xFmHh3xNYzs2TXzNE9iL+/zjPGEtmjCX9/98tjZ/MNXIXFqYWKiEhEDiOECLyPO5nnjE3wN5+2/FRuqwsLO574w11zXbHjupI+vTpqDV2ZGaIsI/4eAwpmDTJ8nZzxjB55a23oMwcrUT/+QcNpKIDXqQIzvfqVe3eZ+9e9W6YoCCUYdizc8KJTeguI0fPn1dGojKGko1PP3Vc2d/FizDAREfbzw8O+JUrjjkHGUnC9Xj/ffUCWHF4SM2aMOS+/RaZd6JgkJICm+G116yPgn/qKRj+4eGOL3uNi0P2Q8z4enjAQTp+XLv3uXBBKa9kDNnOV16xb3gDZUAIR/ASQ/mApfGR3U1+tlwur2Vz6cDduxAiYrZh9mzHLOmTJCxr69tXXXdaty7KrBw5ySohAVusR460vOm7ShWMnv3pJ9eaDGOKJKHs4u5dRLuPHsU4w40bMVryvfcQhSTAv/9iZ8bgwUpUVkyvh4TAITh40LElBvHxKM8SI3lylO3MGW3eIysLn118j2rVUBNui+PlJMXpEnI0ORmlKOI0qTFjHBc8OXMGfUViuUz58riHHDmcIy0NY7InT0avhqkcLV0aDsfGjZjY5spkZCCTdOUK5ydO4HNt3owpZh98gIMAMTG4NuPHK9+7uLSxZEk8Dz/95Fi9npoKGScPKxCdYK12lsl2jBisLFEC+2ds0RnkgBCOoAhj7BJjbBVDPbIvY2wPwxItkbmMsWzG2BM5vFa+FackoTSlRAlEyEJCOO/Z0/YUYn6QJBg54ljANm3QxP3LL44rD5B3NvTsab7wycMDm6Y//BCjH12hFMBoRA306dMQ5J9/jgVbw4djqknVqupIo7WjQwdnfxLXJDsbZX7z56Ncy/S6lSqFMco7dmjb4JgTaWlwGOURvmXK4Hnt2RP3gRYYDAg6FCmi1FH37Yuyi/y9jtMWETpNjnIO51TOpjVvjmt49Kg2301unD2r3uTcvj2mF61f77ixvvLOhuHD1Vlisfxs5kxcE1cYeytJcPAvXEBfycqV6HEcOxYlx7Vr5zzUQiw5IsyRJGRrly/H/WA64tnfH87yN984rnrAaITclkeie3pCXzZvjntXC/2emYnPXLo0yi/9/bEjLb+l4+SAEI4imDH2A0MpwFWGbb6BJj8ziTEWzxhrZOH332AoHUhiSunA6xZ+TqU479xR14GXLw+DVm8jW5KQtjVdsNe3r3bGVG6kp0MQDR2qXvr35JNodB8wAJMtYmIccz6WiI+Hsl6zBk2svXvj+zLdNm7pMHVAPDwQfapRAw3O3bvjNYncuXkTU3k6dDDfnNytGxqK9+93jFFlNMJYGj1afR79+qHMRQsiI9VTkgICUD6U1yieExWnU+SowYDSJvl6FS2qLJLTm4gIZMPEe6FlSwRwHBEsMRpRNmu6tbptW5ShdOuGBvN//tH/XKyRlITM0MaNcDAGDkTQS5T71g5Lu4eCgmC0NmuGJudBg1wjMOXqxMVxvnYt9Ly8k0Q+GjaEftu61b7yz/zwxx/I0Inn0bq1dhmRhAQss5QdL09P9D3ldX8IOSBEYSOIMcYTEw38q6/UM63HjNE/CiFJGJfYtq36oe/TxzGOR3Y2plRMnGg+GSkgAIbkL784TgDKJCVBia9Zg8bLDh3Mm0blQ6xllSPhzZrBAJ0yBRNtNm5ENPbcORjPCQmuMbu9MBAbi++pd2/U17dooXwXISEoz9NyopQ1JAlOj6kTP2iQdsMidu3ivHJl5bWbN89b2ZcbKM7/HJB9+9TTyzp3doyxff48osfidx8a6hjHQ+7pePNN8/IqeWP6pk2YSuRI0tOhRzZt4nzaNARZLJV/iZkL0alo2BBT8yZORInqunUwRk+fxpb1+HjaJ6IVSUnowRsxAtdedPSKF4dDe+yYYxy7Cxcwgc3U8dRqotXZs2o9Ubky5zt35v57biBHCTcjiDHGO3c2/PcwVKwIpaU3x4+rR/oyhmj+qVP6v/fNmygtkUfjisqyXz/0fDhqlnlaGiIvy5cj1V+/vhJV79TJXEl6eCBr0acPMhZffQXn4sYNx5X/3LmD97tzBxmhhATU77pCGYUzMRhQPtC9u3lmJDSU8y++0L/uXq47FhWchwcUuxZllMnJmHgjRvGmT885iucGijOIMcZHj1bkaLFiGAGqt6N//Tq+D/Fea9FCu/KRnIiPx6buvn3N5VSHDihhclSfibwNfe1aRLFDQ5XMsKWshRwg6N6d85dfRv9GWBiCBY4a5R4bi+8vMhIltA8fQu9kZbl3BiU9HVndIUPMpxPWrIlSWEeMrD5zRl0RwhhK8Y4ds/+1s7Kw9ysgQHntAQOwGNIabiBHCTcjiDHGGYPinDBB/70Ut26hzEk2yhhD3foff+j7vtnZiDL06qUYh7Ji6tQJDWmOaCK/fRsRuUmTLG9Dlw9PT0Q0hw+HwN26lfO//nJ8NsYSvXtbjyJ6eqLGtU0bRHaaNUMPz5AhKEuZMwdGy/r1+D7OnIHQLWwZmTt3MLpa7tEQndyXX0bmT8/PLEkonxSd7I4dYZxpYRSeO6c8v4whshwWZvln3UBxquRo9+7ab7A35dEjOB4+PnjOvLwgT3bu1Nd4lSQYYKNHK2Uz8nSvpk0xMS2/PUK2EB/P+fbtmIZobRu6GL0eMABBp40bkcHIa9mLnrz1lvVz9vBASVi5cnCUGjWCnhowABmZmTNxrdeswXP+xx/Qrc5cpqoHjx5h8Iw4IU3M7m7ejF4jPTlxQj3Rql49ZEhu3LD/tW/fVvdqBQYi02ZJN7iBHCXcjCDGGA8JMXALY+s1xWCAwBVrPdu3178p8+5djHsUS0cYw1SfTz7Rf+JKTAwyKi+8gOiN/P6mo15r11a2oR875txtsrkhCmNrR+PGuf+MHK1lDIZU9eooWXnuOdTKrl2LUY03bhRcxSpJMHimTlWmacllUnXrIvOlpzFkNHK+ZQsMGHkSUokSqMO395pmZ+N1ihWDA+Lnh0yLqYPjBooziDHGAwMNfM0afR0AsaFVlB0//6zv+xoM2ONg+lyXLo3SK713QiUmIio+Y4Z6sZxp+a64DX3/fhiwroo4ZdLaIW/Yzu3o3FkJAFWqBN0qjw1fuRKTui5dclyWXA+uX4fDWbWqElBhDI3d8+bpP1nu8GFk9uTxwj4+uPftzZhJEuc//ojqE39/fH9PPmm+n8QN5CjhZgQxxnhUlH455+xslAmZjvTctk0/hSlJMFwHDlSP8fX0RINmWJh+0eeEBESkXn6Z8wYNLCuL0qURiV64EEvICto29ORklA9ERaG+/fJl1M2eOgWH8sABTDPbvBnG0vz5mIE+ejQyUG3awGgqVUpRJjkdrVpB2DdqhLK999+HwL56tWDVYGdkIGprmtYPDESNupb7PEzJykIkUewlqlcP35O93LmDEary65Yrx/kPPyj/3w0UZxBjjF+6pJ8clSQYkWJGrUQJBCz0jABHRCB4IpaKyMGjTZv0y8impcGBeOstNAKbTkxiDM3948cjWLFrV8Hbhp6aikbs6GiUFF25gjKy06cRhDp4EOWUW7einO/DD+G0PPccoubt2uF+KFfOvPfL0hEcjOtYqxYi+HPmQEb/+afjJqNpgTzo4IUX1ANYvL3hdJ04oZ9tYTTiXqtVSy3v1qyxvwTZYMC9LL+unx+CpPLruoEcJdyMXBdo2cOBA+plRIGBnC9apJ/SysyEUmzUSD1rPDgY23/1ynbcuoVIcJcu5iU3jKGprl8/GAsXLhS+ciN7SE+HE3PwIMqy3nsPRkW3bhDyvr5QtNaUqjwiduRIRGgPH3bt7JHM1atwOkwjnD16INKrp4M8Y4a69K93b/udH0nC0jhxk/WQIXBU3UBx6ipHL15Uj3/18sK9o1fJqCSh2bpLF5RPivJ76lSUgupBXBwmDfbvj/vI1OkpUgTn9MEHMND1Lr0pSGRlQb8dPYrncMEClLz27IlAWECA9YCYfE/VrYug3RdfIDBREBbp3r+PAJe454b9f2Z9/Xr9bI2MDDgH4uCe5s05//13+1/78GF1tUTbtnBO3UCOEm6GLorz7l1EaeSpIp6e6HnQa4RtSgqEpjh9pk4d1GL/9JP25TuShBr4d9+1vO21WjVMT1m0CNGsghSldzWMRkQIDxyAk/fCC3BIxIk1jKFmWq4Dl+vhp0xBU/i1a67b1JmUhCyRuKyKMWR61qzRz8i6elXdy+PtjZGQ9oqC+/dRAiO/bpkynH/9daFXnLrI0eRkfCf16yvXs08f/TaXZ2WhXFTcdRMUhAzkV1/ps4T2xg0Ych06mGc5WrXCs/7225hA5Ar9bwUVSYKDd/QoMqHTpsG5LF/eXH+J91vdutDlK1di0pmr6rKMDNy7psGqPn1QjqfXYIHYWOgkcejIkCH2BztTUvDsy69bpAjn8+cXejlKuBmaKk6jEYJKjgo88QScAL0iZvHxiJiL9dDe3hCYttYkG41KeY9IdjaE99Sp5v0kjKFRcMoURA4LUjq7oCJJcEz27cNOjilTkAmxVKrBGCKqvXsjS/LHH66nSI1GfJa+fVFaIztTVargnPUyvsLClKxd3bq4t3/+2b7XlCT0nSjlXoVecWrugPz6qxJQadgQx/79mr28itRUlPiYLl3t1QvRWFud999+Mx8uIknQB3Pm4DOZPqclSmD8+bZtjptG5e48eIDv+csvIUfbtzefPiUfRYuiGX7xYtyPrthTcu4c5+PGwWiXS3xLlECfiF49QRERSk9KyZJ4388/t78s69gxcVt7oZejhJuhmeK8ckU97rBCBf36PG7fxujPokXVgvHVV+2fwDJzpuJQZGTAkZk1C383VZiNGiE6d+aM60bY3Y3kZBg+Cxag30fsPWJMqZUuUQJTVFatcsxIx/xw7Zp5fXPFiijhe/xY+/fLzER2SRzdO2SI/RnL2Fi8zsiRhV5xaiZHY2Iw+U6Ua0uX6jOEISEB0WHxGfH0RDnjhQv2vfa33+L1fHwwESwqChnhBg2QvRTv7SpVEJE/eLDgDpsobKSno6dv2TLcj9WqqeWoPCLezw/lgR9/jF4SV9KD9+9DP4tlUoGB6CuKi9P+/SQJvW89e6rLp+zdA5Wayvkbb3DesmWhl6OEm2G34szIQA2mGDGZNEmfxurYWCiqLl2U9ypdGu+vRT302rXmpVSmZT69esEg+Pdf+9+P0B9JgoPx3XdohLc0zpEx9JtMmYLov6MXplnj9m1k3MTJceXKIfqox+Ss6Gj1Fu2SJTnfsMF+oyI+vtArTrvlqCSh/0HcHt6zJ/rLtObxYzjoLVsq7+Xnh3tNC2f82DG1Pqha1XwvTv/+yF6fP+9aRithnfv3Od+xAwb8oEHqAS9i4HHMGIw7dpWhAAkJGFwi9qcVLYqGfj0mZyUlYQiNfM/7+uL97S2ndQM5SrgZdinO48fVjW1163J+5Ih9D5klDAb0W4hNiW3bIsWpVcPx3r2WBSpjKFFZuFD/kb2EY4iJQYR27FgoTNPvu1Mnzp96CnXvekTK8su9e1CWYsavfHn0PWldmiVJmLpTtqzyXj162GcIu0HzpF1y9MYNdbN32bKYTqS1YZ6ZiRJZsWG3UydEiWNjtXmP69fN+7PELN6bb+pXkks4FoMBe2emTsVUQ9Pvu359TDBbssQ1ssxJSQjeiLKtSBFkAfUY6HDsmHooTePG6Am1FTeQo4SbYZPiTEuDRy/X2/v4YHyc1sZQejrKTsTRoeXKYWutVs25x4+rF6qZHlpEgF0ZSUJE9MEDRNyvXkU96/HjaPzetQsG6fffY6rIxo3IJmzbhgb/nTtRr75vH37+5ElM7YmOhnPo6tdOrkn/9FMMDvDzg9IUS1K6dkWNvLMjenFxiD4WK6Y0W1apAmdK66lZ8fGIZMrXISAADo8tNc1uoDhtkqNGI2SZ2Aj83HPaG0PyLhhxdGjRopzPnatdtu/ff9FjZZrpkI8ZM+yvh3d10tIwOSoqCo7YhQvogZHH6W7bhimN33wDObp5M2Trjz9Cjv7yCwJhBw7g9y5cgEw2GFxfjnIOJ2P1as4HD0aJq+kG+tBQBPK0WOBnD48fw64IDlb6K4oXRxmZ1jZMWhqeM3nqoKcn56+/blsprRvIUcLNyLfivHhR2a7cvj1KWrReQpWdDcNfnGoVGIjxi1pMYpEkzg8dwoQMa46H6IAURNLTIegPHoTzsGwZNgFPmIDP3aoVrm+RItbLksTDUoTL0mE6hcTHB05j3brIWvXsyflLL0EIf/45Sp4iImB0uYKSTUuDQzVhgrochv1/CV6nTphadfeu887x4UM0VPr5KefWsiUaSbUmLEx5DkuV4vzZZ/OfCXQDxZlvOXr/vjJa94kn0AAeHp7PLycXJAmBAbG3x9sbz59WzvRff5lPAbLmgBREsrPRw3LkCOr7V6xAsG3SJDwL8ncXEKDOYuV0WBuUIR5imTFjyM6XLg0nsmVLZGiHDkVG6ZNP4NycPIlsqSuMeZeHtsyYYXloS9Om2Gui5+6j3EhLQ2BJHGJTrRr0pda66MIFJdDp6QkdfPZs/l7DDeQo4WbkWXFKEgwvuR7dxweCT2tht38/jGMxRfraa+bblW3h8WOU1cgOlKiUn3oKQnzPHkT4V65ETbYrz5pPS4Pzt2MHvovJkzF1rFo1CDnTqTbWDtNtwvLh7w+jMyQEP1OnDl6zShX8W/nyEN4lSiAq7+eH8be5vZ+l6TdyVLZePRhmEyfiO9i5E9FVZyjVzEwYcC+8oM7CydfsqacQwXTW1LPbt9VZCsZQW6+1Uk9KQu+V3DNQsmT+JmW5geLMlwOye7e6DOSll7QfcXvhApayiffGiBHYuWMvWVnIfsrbt0UHPTQUgx327sWz8dVXeI712lmiBZmZCNaEhUHHTZ+ObE6dOkqzvDyVLqejfXvL/16kCGRkxYrY71C7Nv6sWhVytEIFyJcSJRBo8/c3zx5YOsQAhGnQp0YNODHPPYfM5dataIZ2RpO/JME5euMN877KypUhV1audF7vXWIiBs2IvXatW8OB0pKsLGRZ5GlZPj7IvOdVt7mBHCXcjDwpzthYdbagXj1ErbXkzh00tjGGB9TTEwvp7J1qxTnKgd580zyi3bUrDClXLw2QJKT1d+5E5LtfP0Tfcos6+vjgqFQJhkGvXhhPOGsWBN+33yLqGhGBkoGoKBgKqam2G/yShJKByEg0mP72G0oM1q6F8J07F+//zDPYNyA2BubkqBQtCsd0/Hic+759yEI4KmuSlYXSiMmTkdERy7RKl0akT+tMYF45d05tDHp7owlS6/6V/fvVPTMvvZS3MZxuoDjzJEdTU1EvL1+/MmXgjGiJwQAD2tNTmfjWsyeeRS1ee/FiZbSpfDRpgp01ekxo05r4eNzHixYhgzBsmPXeP/GoUgX3ftOmCDyMHo0s7uLFyJKHhWEa4tWr0FkPHqAE1VbdIkn4/agoZJmOHEGgaf16DEF5911MfRw0CHLR0j4P+RCX8vr4oBdhxAj0PuzcCafUUQEeScJ1mjULTpLotPn7w2E6etQ52fDISEyAE6/dwIHQjVpy6RK+A/k9evXKWw+WG8hRws3IVXGGhamF24svaqtoMjIgxMUG8969tTHmbt5E9LpECcXQ9fdH+vziRftfXw+MRpz39u2Yld+zpzpaKh9ihCwoCGUWw4bBwF+/HkLcVdLxuZGUBKEcFoYa4rlzEdnv1AmZlZwMg65dkTGZPx/OlB7ToUzJzsa5Dh2qHifKGEoy1q3TZ2lbTkgS+nXEhYY9e+J6ankPxMZCYcrv0ahR7s+qGyjOXOXon3+qHeoePbSdwCNJyNyKDmKTJnCa7SU+Hs9kUJCyeNXTE8aZPXtC9ObePZRTfvABlmOaOk6MqZcu+vnhfn72WUTrV61CCevt264fpOIcGXG57Hb9ekwZmzABckosM7J01KkDnTJzJpwSRwzfkCQ0ZU+Zoh6Vyxjk2McfazccIT+cPq122nr0wKAGLftDTIMRFSrkXoLpBnKUcDOsKs70dERY5AekVCmk3bXk0CH11tVKlWB426vQ/v0Xgldu/GIMGZyPP0b9vCshSdj7sGoVHIiQEPNMjXwEBCBiNG0aGhlPnICicFUDQAskCZGpX35B1HL0aBgNcrpcVBSyYdSsGYT75s2Y3qTn9XnwABFJcRocY3Cc3nrL8Y5uZibqmsVenCef5PzyZe3ew2jEZxbLU1avtn6d3UBxWpWjkoQSGPl+9fXFtdPSKbx8Wd0zEBiIni97l20+eIC+MTEI0L49oteRkdqcu5bcvYus7vPPo8TJWjmojw8CNnKZ56FDyJIXhGCNrUgSpv8dOIB7Y+JElJHK3604kll0SsaNQ3br0iV9r8/jx8gkmZaeyYuFf/vNsXpOkpBxat1aKXWrXRv3ipb8/LOi7z088GxZK5NzAzlKuBkWFWd0NBRN06ZKlDk6WruH7v59bLsVhcybb9ofNb5xAwJTTKnXqIGItKssuJIkpLzXrME1qFjRclQuKAhlNa+9BqV65UrBiMI5iqwslDts24aSk1at1A6neFStitKtb7/VppfIEpIEh3DCBGVcrqxMe/VyvAJ99AjZP9HomjdP236Vc+fUwwmGDLFcx+0GitOiHE1OxkQgeWlbvXralELJpKTAYBHv+xEj7B+QEBeH1xWz0qVKoWTHlbaT37+PhuFJk8Rt0Wpnyc8PpWhTpkDmRkS4dl+f8KTLiQAAIABJREFUo5EDPLt3I+PeubP1fpeSJVGC+r//6bOfRubKFdgDcuZf7plo2RLT3Ox1rPNDRgYyaOJemwkTtA1k3rmjfEbG4PRYur5uIEcJN8NMcR47ppRcVaiAsXlaRT4kCVHpwEDlYevUyf5yq+vXsdNBdDxq1kQa2hUcj9RUlMfMmWN5IghjcERGjUKvxLVrhTsapxePH6MkZMECzvv2VcruxOyEhwcMkvfew6ZfPa6zwYCMljh9iDH04Xz/vWMV6O+/q2fRa72rJzkZEUr5Wa5Rw9zIdgPFaSZH//1XqfP29YWTrGXp6v79aqO7Xj2U3thDbCwMP3HfTOnS0AGOKG3MjexsPN9z5qgz5+JRogQGMSxbBmfDkc9aYSEzE30an3+O8q2QECUDL07wql8ffTAHDugziCMjA/2DAwaov+Pq1ZFV1GoHWF64elUJJDCm/a6e7GxltUFoKJy9XbvUP+MGcpRwM1SKc/VqpayidGlEbbUiOhp7FhiDAVi+PCLS9jzAcXGIyIiCoXZtlCfZo3i0EKaPHmHm+6BBSiTR31+5vuXLo+Rq1So4HIW5jMpZGI2Ipm3YgEi0aZ0xYyiNqV4dZRtr18IxefZZTGspX96+pWmShGlA3bur37NcOTQ7xsRo91lzIj0dn0uM4k2ciHtUKzZvVq6vnx8myMm4geJUydHwcMX59fVFBlYrkpMR8Zcjw0WLojTRnqh+aiqivGJvT9myWCBnT1Y6K8t+B0AO3owfr55EV7OmUm7Wpw+mAJ47R1livbhzB9mHCRMsL2/18UEQbdgwjCpevBjZuPr1sWPjxx/te/9z5/B6YpAxKAh9bo4qc5UkPMtiifTTT2u7ZPHIEbVzPWeOck+7gRwl3Iwgxhh/8MDAJ09WlwBpmWL9/nv1Q/vSS/aN3MvIgMKRDZ5KlRAB3LTJPoV37x4e+NKlbRNq0dGcf/klDE7TciAfHyj4lStRT0sOh+PJzISAnzVLaabN7fjhB23eOyICTofpDoBKlXBfPPUUSra6dtW2TEfk8mV1z0z//hg7rRWXLqmzLRMnonHTDRRnEGOMJyYa+KefKt9xcDAWymnFsWPq0dr2Gj+SBNksZ2X9/JAh++QT+6LLSUlY9FatGhzT/GIpeCNmMNu1Q4bj9GnKcDgDSYI8++gjlLnlNo2RMTRxa8GtW8gmmt4XpUujWfzpp+GYP/kkmun1IDZWPS2rQweUpWml02NjOe/WTXn9rl3xb24gRwk3I4gxxtu2Nfx3sw8bpl2pwMOHeD35tcuXRzOxrUgShIq40bdsWWRu7FFEFy8iwmYaIc4LCQlwKsaPNxe6gYH4/Fu2uFbtNAGiopBBy2mE5ebN2pbxRUZa3xcgH/Pna/d+phiN2M3QoIFyv7/6qnYTXpKS1M988+acX7hQ6BVnEGOMDx2qyNG2bbWbcpWejoEGsmPj749Ahz0Gz6lT6kEFfn4Y7WpPxiMqClOUihdXXrd167z9bloadlWMG2c9eLN6tXYLFAntePgQGdac9k7NmaNtGd/Dh8iI5LTUceBA7d7PEmFhCLjI+uOZZ7TrMczOxuQ5+bOEhHC+f3+hl6OEmxHEGOOMGbiHB9KmWnnxYWGIAMoP0ODB9j2cf/6pjgr4+KBe2dZMiiShVEIuC5OP4GDUPOdUnmI04ndHjlQmZBQrhnMqXx4lEnv2OG9BHZF//vrL8rhjObo2eTL6KbTqGbl50/JYUA8Px+wTiY5WP09NmyKDoQXy5CfZkCxevNArzv/kKGMo59Pq2f/zT3W2rk0b+/YSREebL68cOdK+fUvnz2M6neg4BAZigEZOrytJKK2ZOlWdIa9WjYI3BZW4OPNFv/Lh74/vdOdO7QYBJCQo+25Mj40btXmPnEhKwvMu2g9ajL6W2b0bfU2Mce7lVejlKOFmBDHGePHiBh4Wps0Dk5LCVeVcxYsjnW6rY/PwIXaPiJGOZ5/FxCtbyMpC2YFpCU7jxugVyEkw3ryJKKGp4RgUhHP8/feCWYP8+DEMhcuXYRAcOwYHa9cuRCXXr0eKedUqjBBdsQJR9A0bsHtg+3b8bFgYGmFPn0bT3sOHBauZPjpavauhVi3zJWXdusFR12Kzc3q69fGX27bpf+2MRtT5y4ajvz++Y62CEMePI3JXu3ahV5xBjDHu5WWwOzMhYzSit0PuGfP25vzDD23P9KaloclVbDBv3RqT22xBkmBoiU4sYygp/PjjnANDcXEo0TKVwb6+mKT2668FM3iTkYEJZFevwik7cQLycPduPM/ffIPn67PP4KAvX46szvr1yLT+8ANGv+7Zg2t76hSCArGxBUuvpKSo+4kqVVLfd4xh19fMmdqUekuSuVPNGHqGPv/cMffStm2Ko+Dhgc+mlZN18yYyyUFBhV6OEm5GEGOMR0RoE2K6dg3jUOXRnN26oXnNFuT65IoVlddr0sT2SS9GI4zp2rVRUykLqR49sFXbmtGQmYmIjThnXzRGN2/O2zZoZ5CWhibsPXvQFLxgAeevvILJJh074lrIE8lM569bOixF7C0dYp+BlxeyQo0a4RoOGYIeoMWL0dB35AhKVVylJ+bhQ5xniRJwcuPiUO7Svr16yZ+fH8ruzp617/3u3lVnCkVHu0WLnO9NrThzRl3W+Oyz2jhYnMN4iogo9IoziDHG9+zRRo7GxyMzK5dINWiAwICtHD2KZ11+xkNCMADEVgf3yBE8D088odwzzZvnXK4oSfi9QYPMl3e2aAFjXKt7TmuysjA6/cABBNOWLEF2Z+RI6JIGDZShA6JuyemwNupWPESd4+GBTGy9epCvAwci0z53LmR7eDiMeVdxVDIzIR+LFEFgKjkZ90efPpCtcvmnhwfn/fpBztkTcElLg+0hXy95746stzZs0P/a3L6t1n2hobCJtCAtjfMjRwq9HCXcjFw3+OaVHTuUpvBatdAkaKtAuXsXDbLyg9yqFSLutggQSYIBLi6lKlkSY3v//NP67yUmIpJXqRIiKXKUuFo11Oi7yiIuecHUkSOIps2YgehSjRowZitVyptCFI0JWUGWKgVjpVYtZIi6d0e6u3lzZApq14Zwr1gR1ygoCIa56fhZS4dozDOGCFmTJlCsM2fi+z50yDk135JkOdJ84wYa2E23CrdtC4PO1kjbH3/gWvfvD4U1fLhaifXtq+2kFUskJ6v7mCpWREZPC9ygeVIzOXrunOLoBwZizKmt/TnJyVhaKjcJV6zI+Tvv2N5gHhGBqUPivT9kSM47bjIyYPw1bgx5JI90LV0azcQXLth2LnqQkMD5yZM439mzMf61fn3FYbK2IDYnOVqkCAzuihUxuatRI+iztm0hJxs1QsazWjVcm7JlUTXg7597r5gc4BGDFj4+kK19+6K3a8UKTOLTeyGrNSzJ0ZgY6Nbq1dWfpXZt2A0JCba9V3Q0dFXDhtDPM2Yojk5wMJyDU6fs+zy5kZ2NiXJy5jwgAE6rFriBHCXcDLsVZ3Y2GsxkIVKliu1RYUlCOlpOZTKGaH1srG2vd+yY+absESNyLt+6cwdKX9xV4u3N+csvQ9E6s6RIkrBfYPNmGBYTJqivlaXDwwP9KbVqIQI6eDB+96OPkIH49VcYFrdvo+8lLc1+RZWdjczBpUuY2799Oxr1338f7z18OL6H6tVzbiKUnc/gYETJ5s+HMxkXp831tJW0NBgppuVTZcviWbDFaTJ1riMiEAWXGzv9/fGd6b1EbcsWGEDFisFg+uwz++8HN1CcmjggX3+tRG4DAzn/6SfbX+vAARi1omF85Yptr3X1KuSweK937pxz+VZCAkrITBetjh8PeeDsZYD372ML9axZuQ+ikI969eActm2LLOHkyWi+Xr0aWfJTpyCf4+NR1mqvrpAkyORr16DLfv4ZyxQXLIBxPXo05GK9euoBKpaOrl2hK7p3x0CD7dsh852Zec7ORnmaqVNbtChKmm3JHmRnqz9TZCTuudatFX04ZYrtTk5eOXECz5+HB/TElCn2DzNxAzlKuBl2Kc4HDzA+VBYc3bvb3mh++7a6Ibx8edtnh1+4gFSvKNT69Mk52hYRgUWAYiNlUBCi8VFRtp2HvRgMMCQ+/BARLXEOPmPqsikvL0SQ+vXj/I03sNPi2DH9Nn9rRUYGFM2vv6Je9+WXoZDk/gu5tMH0qFIF2ZKFC+HkOHIplcipU6g/lg2A6tXhLEydqs19s3u32pCsXx+ZodzIyLC9bCcyEo6qaDTaU0ftBorTLjmang6DS77eDRrA6LeFxERlT4jsuC5bZlv2+M4dNNiKfVChoViCaM1wvXULkfdixdRR+fHjHbevwZS0NBiES5eiCdq0lNS0abpKFei1adNQfnngAKLrrlImaonsbFz78HAEe15/HRnVhg2RlW7a1LIcLVsW/RrvvAMZrOWG7/xw/TqcKnmCmr8/9iUNHmzfLiaZs2fVJVrlyuV9D9nJk7a9Z2Ii9JnotNujj91AjhJuhs2K8+xZtSCfPds2JWc0QmCKCmvsWNsEYUwMJmOJc8k7dEANtDWOHTOv261SBcrK0dNXHj/GmOKXX8Z5W5uvXro0yqwWLkSU9PJl50cU9SAjA0bLhg24Jm3aqGt7RcHu64s/P/oITfCOroWOjUVkUnTIfX0R+bK1D0rm8WM8X2Lt/JgxOWcG33wThuOCBbZFYtPTMRJVjKDbWg7nBorTZjkaFaWe4jNkiO2jcPfsUZdcdu6M3oX88vgxso3iVLh69RA1t2awXbxo7qyUKIH79u5d2z6PrWRloXxw9mxkW017TuQjIADXaNYs9BueP++8QIaeGI3I+m/dCrnQpYvlpazNmikR+9mzkfF39DCAlBSU35puPx840P5yvexsDFMRx0R37ZpzZnDTJvzcxIm2rSeQJGQCZV1erZrtDpUbyFHCzbBJcW7YoE2pQFwcGvnkMqJKlRCFyS+yYJFfp3VrCNM9e6wrzLNnFWMxNBR/tmgBReSo5VaShEjnZ5+hGV40ruVz8vbGf0+dCmF444ZrR+L0JjMThsKaNYgah4Yq6XXxKFUK0bPVq/XvnxBJT8e9KBqCPj4o17Bn1CnncDQ7dVIbeKtWmTsYptO1unWzzQiUJNybcplcpUq2lVe6geK0SY4eOaIY+V5eWABoy7OdlgYHXe6rKlYMQR1bHM/du5XAUvv2CMZ8/bV1mRgZiYCRh4dyb1arhmymPTtF8kt0NLK+gwapDUyxBLd+fWRiVq9G/587LzE0GpF53rwZmYcOHdSyRT6KFkVG+tNPYTg7SvcYjaiAMM3cDBhg/6LWmBiUr4ny+e23LQ+TEX+ufv2c+0ZzYvdupaw7IADldPnFDeQo4WbkS3FKEmrcZUFlT6nA0aNKQ2JoKOcvvGDbTo9z59QGaLlyGHdoTflev25ezzx8OMpaHCFcU1MhjF56ybwJTxaGXbuibOLYMdedsOVKJCfDcX31VfUYXfFo2xaTa06dcsz3nJ4OI1DeMi1/ty++aN8AA0nCfHvZcA0ORnmeqXOTno7PK793mTK2LwHdt09x7v384KTnBzdQnPl2QDZuhJPo44Pv8rff8vml/D83biDYwhh6dnr2tM3RvX0bfQ3y/SL3HFmLgMfFoYlc7D1o2xZRdkcY9tnZcOBmzTIf58sYHKJWrfAZ9u3Tv+a/MJCRgWv69tvIylnqzwsOxve+f79jvmejEca6fI/Lx7PPomzaHg4eVJz2IkVQAn76tPpnJAmN/HJwsEgR/N0WHXLpEp5R+TN88EH+XscN5CjhZuRZcWZmqksyJk+2bbOp0YgRrHKqvkQJTNDKL4mJqNGVhaTcXGZtgeC9ezhnscejZUttlwZZIysLk0jGjIHTZdowWKUKDNMdO7TdFuuu3L0LJ3TUKKW5VJxOU706GjHPn9ffGcnIQMS1ShXl/Z96CqUQ9pT4PXqEe0ZWzMWLW3YM9uxRl9JMn25bWcW1a+rJZXPm5D3C7gaKM89yVJIg/+TrOGiQ7b1C27crpTTe3rYNDMjMxPmIexr69rW+nyEpCY3XYslstWrIzupd9ihJCCC88goCTXXqqOVo6dLIqG/a5PxBFYWBR4+QhZg8WTGcxWtetiz+36FDjvnud+xQT7Ns3Rrfd3S07a+bno4eSzlT5u2NslXTz/Pnn8iAyO/9zDO2jY1++FC9O2fIkLyX/bmBHCXcjDwpTtPFQtOm2Zbej49XN4e3apX/8hhJwqQecXdCixbWx+slJKCeVZy7Xrs2FgfpaXxKEqIp06ebT1hp2RIN+59+iqiIK5ZUSRKM55QUXMO4ODhxt2+jrvz6dUx8iY1FbawrfgbOcV5//gnjTC5rE486dbBcUqst4NbIyEBtc716SolIhQo5Z+vyQni4etLQmDHmjs39++relGbNbMtcJiYqcqBzZ5Qn5GWyixsozjzJUaMR8kD+Hnr2tK1MKT0dMlh+ncqVMco5vxw9qm7ArlwZ0WZLz3J6Opbnic5s2bL4N737BG7cgNMj74OSj/btYYTOm4dGYVfZgSEiSXhGHj/G8/PgAeTonTuQn1ev4s979+DcufLi1ps3UebWrZt5diQ4GI7h8eP6fgZJwtLb0FBld1FAADJdto6r5hzyXyz36tjRPJOYkoJKDflnQkLyNhDElKwsXCvG8J5dulgPnIq4gRwl3IxcFWdcnLrEadEi24zNkyfVUeBp0/KvuO7eVR5cxhD9++ILy4pHHun7zDNqIbl6tf3j8HLin3/QwGkanfP0hBG4YYNtpWb2YjRC6R09ip6dVauQAp42DZNhunaFMVK+PJwjU0Pd9DCdTiWP+61QAYqhWTNM6Zo+HQ188+crC7OuXrWtoU8LbtxAxKtxY/PP1KYNHBU9J8Gkp2MGvjjmuU0b89R/foiPR5OmmOE5flz9M6Zbz7t0QQQ9v2Rno0RDbqrs3z93xe8GijNXOZqeri79HDvWNjl065Z6mk+fPvmPxKakwOmWX8PbGxk5a85QeLi6PKtYMTgEemZrY2Mh28UGfTGAs2wZjHZHI0l439OnYQivXYthIDNmIOPaowdkX0gI/js3OSo7fuLfixZFhqdGDZSXtWuHkt3nnsOzt3o1MpsXLzpHl3COPooVK8zH3MufZ8kSfXdlGY2cf/edUsbNGK7Xzp22B8PS0zE9TH694sUR7DRl2zYliFS3LuS5Le/51VdKaWvTprimOeEGcpRwM3JUnDdvKlEnLy8Yz/lFkqAs5EkkgYGc//BD/l9nxw6k2D08EEUePty6Arp6VelT8fJCr8qiRfoZvdnZiBwOGqSeAiNnZ5YudZyyfPQIzt4336BMZtAgOBZFiuRtkZYcWcztZ0QD2tbXKlUKgnfYMAj+b75BWZSjJq9cugRDTC4tksu0/P0x0cfeGuOcuH9fXdLIGPa65KaErCFJ2OsSEKA4vPPmmddpnzoFA1b+/j780DbluWiRct5du+YcyXcDxZmjHE1MVG+1fust2675zp2KweLlhdKp/Eabz55VgiNPPIEggbXxuLGx6ibc1q0RUNCrvEmSECAZOlQdrJKNy3fesb3nML+kpEAWbdmC4MnIkYi6y8+N6ShfS4el4Rh5CeZYOiwZ+qIsbtAAPQxz5iC49McfjhsCEBUFHSc7i3K5lqcnAhTh4fplyFNSsA1eHODy9NO277zhHOcrVliMHWueVY6MhI0h3wdjx9qmt37+WSnJrl075/4tN5CjhJthVXFGRCCaLUdk9uzJ/8OVkYGojax8mzXLeQmgJR4/Vs/IL1cOTdyWSE9HZE7ssRg3Tj+FaTAgYi42k7dogZrouXMxtUhP7t1Dje4bb+AaieUR1o6AAPxcgwYooxkyBBO25s/H9Kbt29EQe+IE52fOYPThpUvoAbh5E8omJgalBDExyPhcuICG+b178fsbNmB+/uLFWD44ejSEdY0alhdmiY15jCEi26gRlP7ixZyHheGz6qXEJAmf4Z13zJ20du0wKUavMccnT6oj2kFBKM2ztZzk+nX163XsaF4j/eiRug559GjbyhdWrlQyIW3aWM8cuYHitCpH791TSjs8PDAdKr/IozxlOVqxYs6jxS0h997JgSA/PzyjlhwY2ZkVDeNu3XBv6UFGBvo2xBLJ9u0RcJo6FbJIzxLPR48gY959F+8nTrCzdtSvD1lRpw6cuIEDIYPfeQeZmy1b0F94/DiyJefPc/7335Cj//6LbPT9+8he3b8P2frXX/is4eEwTDdtwjP28cfoSxg/HtnpunXVPTvy4eGh7nH08EA2euBAyPeff8b76FkidfMm5Jepg1a3Lu59vTI2//6rztJ5e2MIh61Zuvh49Sjg6tXNx+dmZqp37rRvb5utER6uBI4qV7a+gNEN5CjhZlhUnMePKyUqZcpY76/IicRExcjx9ETkLL9GTkQEsh3yA967t/UI8eHD6ibZOnVsq8/MC//8g88jZgE8PSHo9ZqmlZGB72HZMsvLtNq2Vf+9cmUoq6lTIfj37kX5hrNHTxqN+A7PnoVC/OILKP7evdXpdEtH37747KtWQUjrcZ0fP4bx1aKF+r3LlUP5gx5LKY1GlAvKvUItWsAY+/tv214vMxORUHk0atmy5gZrZiYaSEVHK6e9ItbYvFnJ+jVubHlXiBsoToty9NYt3LOMwfG2VM6RG1lZ6gDM2LH5/56iotQZmKZNrfc8XbkCp1X+2TJlYAjr8azFxSEDJ0abZad5+3Z9SmWNRjxXa9Yg4yg2FlsKhpQrh+zDxIkoK9q5E1kYR+/HMEXekv7nn5hut3IlnvmBA+F0WNshxRiW1fbpg7HP587p0zuTnY1rZVqGFhAAuaPXUsp9+xSbISQEgbbwcNteS5JwnxQtimyhry+eBdOfEUeVV6tmm9w+cULJbpYrZ3nUsBvIUcLNMFOcp07BsK5YEYaQNW88J+7cUTc3LlmSPwVmNCLqI0frihSBoWrpNeLjERmS38vXF6Un9jSkWUKS4Fz0768W7kFBKB+yNjXGVoxGZCDmzYNS8fOzrEyKFYOjN28eDJyCvkwrPh4ZmGXL8L2GhirpddNShYoVUXe9di0iYFoaSZKEMoZRo8xHjT73nG0L3nLDYEDUTq4JL1IERoKtBsLBg1BmjOFZWrtW/f8lCc6pqDxtMQx27VK+o1q1zGu/3UBxmsnRqChk/AICkPk9eDD/1zU5GY65fO9NmJB/o3z7dnVWb8YMy8ZzWhoCAeLSvgkTbJv0kxsXL8KgF2Wajw+cq3PntH+/a9cQlZ88Wb0jRDx8fWFkvvoqMrgnTxbs0b3JyZBfq1ahf+TJJ5WJaaa7NUqUgF5btgwOjdYZkmvXELATr33FinCEbN0ynhOZmXAKxP6hl1+2vQT7yhX19K2ZM81l8q+/KgHJwEDbKkYuXFDkdfHi5n18biBHCTdDpTgjIhQvvHp12zY4nz+vTOWxJep37566CbpRo5yNopdfVkfO7Kn9tMbhw0ivivPma9XifPlybRsxU1LQ6zJxojoqKI6QrVMHBvCqVVAWrjj5RWuysvBZ165FuZC1TEmVKshS7NyprQMaG4spK5UrK/eAlxf6RLR2PDlHyYaYzevQwfZlirdvq2foT59ungXbs0etPG1ZBvrbb8po1tq11eU6bqA4VXL0/n2lzyIw0LYJVffuqbNw77+fPwf78WPcn/LvV6iA6LA1vvpK+dm6dSHztObyZQRTunZV3qtsWTg+ljJntpKZifvxtdfUw0DEPopKlVB+unQpvh9nZzQcgSRBXn3/PZwxUcaIR5kycFq++UZbJyw5GXqrcWNkXOX369PHtgWnuREZiTJjUXfa6vAkJcFhEs/ZtOLy4kUEcRhDUGfZsvwHxa5fV/qfAgI4//135f+5gRwl3Iz/FOfff6PmljEYWrYYVnv3KkZIyZLqhycvnDyJ6IwcBXjlldwNyQcPIFjWrdO+TCAiAqMyZaHTrBmU565d2kWJbt9GLXavXupGOvlo3BjGxy+/6BONLIhIEgT16tUYRiCOOZYVW2Agshc7dmjnjGRmQinL4x8ZQ63xiy/av+XclNRU9cjWYsVQDmDLPZ6SgmEE8mv16GFuWPz9t9LL5OvL+bff5v99Tp7Ed9GiBRxo2QlxA8X5nxx98EBZhlm0aP57NThHeZRshHh7457LD5GRaICWs9B9++Zem56VhUDHe+9pb4xHRqIXT860hYRArn39tXbPZnw8ymOGDbOc5ahSBQ7Jtm36lFEWVO7eRRnl888jY2fqrPn4wNjesEE7Z0SSYCuYlg3372//lnNTjEZkQ+Rsm6cn+jNt6ekzGjHSXz7fBg2QeReJjVUPXlm4MP+2wu3bSm+Rn5+SPXUDOUq4GUGMMX7unOE/I040HPLDunVKLXj16rlPK3n0SC1svvtOMcBbt85fClPrLMD161BkonDs3Bl1mlqQkICa3U6dlLS4WArQsydGHOo5xrAwIUkw2lasQOOgWEYiOyMjR6LnRAuDJysLPRvi8AEvLxjeZ87Y//oiBw+qJwL16WPbRDWjEWV6YjTQ9BmNi4PSkw2DTz/N//ucP68EMkJCMHTCDRRnEGOMR0Ya/ivVKFLEtiWnhw4pBnRQUO6vkZaGCL5s5Jw4oQRwQkKsl65aQms5Ghtrvi29QQMEBbQI4KSnY/N6z57mZUUeHghGLFyIyLSr7ilyNW7fhrMxebJ62aQezogkQc+3bKl+n5o1MSpeSy5fVr9Ps2a296Fs3qwuC/7tN/X/T0/HPiZZjo4fn/9n6949PCuMYSrjoUNuIUcJNyOIMcYrVjT8l3q1ZSHbihWKId2qVd5GicplRXPnomxGFgwNG9pebmIvUVFYNCSO0g0NRemCvQrMaIQxMWqUuva5dWuURzz/PAxkR41OLMwkJEBJ9ulj7owUK4apYVoMC8jMRFbCdI5/mTIoI+zVC+cwZox923oTE9V9Tg0bYmqPLWzbpizlLF7cXHk+fqzenbNgQf7f4/wwM3/EAAAgAElEQVR5pV+nUiXOz58v9IoziDHGQ0MN/xlqttSA79ypjD2vXDlvBpI8SGDAAGQUZGO/bFlMpnMGBgPKqky3pX/zjTZOTkQE9heJPWHt20MHDRnC+caNyIwT9pGWBmdx1Cjzses+Pvj3H36wf1iAvFxQ7LOQZXXHjoocHTzYvlKtzExk+GT9HhSEZ8YWPXD6tFIm7e2NAKzpZ3rjDeWzjBqV/wEwMTFKQ33Ropz/+muhl6OEmxHEGOOMGXjJkmiCyi/yPoBGjTAGLy8N0JcumafIGUPDZS7LhHUhORlpWrEEqm5dCFd7jdRbtxB5Np1aFRSEEX7Hjrn29tuCTkICDJ++fRVn5MknFUP+f/+zv48nIwMNu6bbgcXjiy/s/yw7dqDEqUkTRHgXLLDt/oyIgIEbHIzsiulenowMdcnWe+/l/30iIpTm55CQQq84/5OjXl4IJOSXHTtgyFSqhPvz7t3cfyc11fI+nsaN9elNyo3sbJRByRkwxpCN0WJbenw8BiaI/UyyITxkCPqW9BqVTVh2RuQyrZAQlAnb28cjSZBplkqR5eP55+3/LGfOYPqZXCo1bpxtmfG7dxFw9feH/WPapyVJ6uDq0KH5d9bu3VN6dfz9C70cJdyMIMYYDww02LSJ+ZNP1A9XXj18cQuvfDz5pHMaqvfsgXNQvDgMpkqV0Oxsz7haoxERHbF/RD66doWSdtYmcHdGzoyYjoYMDERE1d4BBgaDuixLNJK06hGJjlaPSR082LasWUyMch08Pc17PrKy0F8jv8/cufl3Qs6dw1ALN1CcQYwx7uFhsGnU7u7dinPcsmXey1t+/NH8XgsJcc527AsXcO7yotigIIzYtTeje+wYsjym+4OaNYNjQ31xjictDdm6CRPU1QI+PpyPGIHpTfYE7rKylEXCpsf+/dp8huRkDHMRKxFsyVKnpqJqQn6d2bPNP/v77yv/f8CA/DvK9+7JwxQKvRwl3Iwgxhjfty//aYdly5SHatCgvHv2kmRessIYFJcj0+YxMRCW8vsXLYosiD09AnKTsjxbXh4DWKUKnC5nlZYR5ly6hEkvpnXO3bsjgm2rA5qUZO6EyI2PWjmdmZnY7yJGvG25txITlaZ9Dw/zMb3Z2SgfE69NfrN1Z85wvmdPoVecQYwxvnJl/uXo/7F33eFRVN37bhJIaKGXELqgNJXQm3QEe/nxKYoVy6ciKtjRz15BxYqKBUVULNhAQZEmSq9K7x2pIYEkJNmd+f3xejh3Z2fu3NndBMzu+zzzKNk7s7NT7rnnnPe8Z+pUXlxnZKA2TheyRK+8FVX/Izvk5Jjmgw/yQtTng0xpJI6BYeC6UISdFqOVKyNQsGxZ1E4/jgixaxdsmywEQg7i+++HP+cFAsFqmLRde214/YrsQDLk9OzWrBkqfauDwsLgefKee0KdkBdf5M+bNPFeQ7N7t2l++GGJn0fjiDE4dvBV4c03+WW65BJvHv3QoaGTSkoKpGeLg4pkGOB9ytr4/fpF5hzk5uKayDQrnw+8/enT4xSrUxlZWZBTlhteCgERgnAzYcuW8aJS7hnToAGi3dHC++/z91SpEl7R89GjwVKVb74Z/LnfH+yoN2vm/ZrEQPFkWPPo9OlMNznrLG+L9m++CZ1HExLglHhxYiLB9OnByklnnRVe01qC348aJWstQN+++HssSOX+W5GfD3lfWQGKbOvTT4dHc923L7RJpRDIqo4ZEz3GxMyZTBssVQqS1F4RCARnQv7731C7/8orwb/Bq5hIDMyjcZwiSBNCfCWEWCuEWCeEGCuEqKCxX38hxPdCiDVCiL+EEIuFENcqxns2nO++yy/RhRd6cz6eeSZ4gX7BBTAsxVV4vWFDcEfg6tWhaBFuujgrCzUwpDojBHjcN9xQNP1ITiYCASwAjh1D5PzAAagmHTtWMhRmDAML+EsvRTaM+uE0aQKFNq9O5HvvYUH46KPoISILD9x9d/SiePPns5FOTEQWz+v9yM0NpguOGhX8eSAQ3Fg0ORkG9uef9d7/k2g4T9l5dOZMfiZatHCXyZUxZUrwgqx9ezxvXo4RCQ4cQNNAOYD0wgvhFyPn56OIV+7ZIQScqXBkjE9lBAL4vTk5mEcPHsRckJVVMuZR04QIxc03ozaC6heqV8cC3CvDYPZsZKkvvxziAtWr8/Nx6aXhiebYYevWYCW122/3TpUyDLQOoGNcf32okyQHcxISEOjSXQPFHZA4igPJAobv3X/+nSSEmCaE+MVlv3JCCEMIcZ/0t4uFEAEhxP0O+3gynB98wC9P//7eIlLff8/R4OTk8AreI4Es8ysEnIRwaQJHjqD+RdabT0lBdifa/SCijcJCpHOXLEE0fuxY03zySUy4l14K2li9epiMy5ZFREiO4ssb1SIkJUH5qXFjFOX17YvC0FtuMc3778d3fPcdjEW0O9QXBTZtClVDO/NM/AYviwTZgG3ZAoe9bFk06qxVCxHkaGD37mBN/See8J6lOH4c2Uw6hrWgMhBwjka69ak4SYbzlJ1H58zBc0AZJR3VQMLKlaxiJgTmteLEggV41+n7e/eG1HI4KCjAorJOndBFWbT7QUQbgQAchxUrQBcbNw4F1EOHoi6rSxfIyZ57Lu516dLOIhWkfJaQgMx8o0aQ9O7VCwvvwYPRw2TsWMgOL1v271BL3LcPARiZ5pqejmaEXpzV/Hyeiw4fhq1KSEDGrUyZ8PsjWXHsWLD8/uDB3qlShgFKopxJt/5WEkGRt+Rk0HRViDsgcRQHhgghCoQQVaS/ZQgYxb6K/coJIf62+fscIcQOh320Dee337ICSd++iJrqYtIkLFKFAM8ynD4jkWLtWhiBxo25sY9XBAKgb9WowTLCqammOWJE9CLa0YJhQJXk558R0b7mGkzYvXvbG0Hrdtpp7mPatdM7lrzA8PlAVevTB4Zk9GhEdDdtOvWoahs3QvVFdsDat0chZDgGzzBM8+uvWT7U50PRYqQylqYJB2LwYLyjCQlYuHiN4BUUQEyCfusbbwR/vmiR/f295BL1cU+S4Twl59Fly0BLEQIRfy80jGXL+NkpVy58KeZIkJWFRWSVKhB0CHfh9/PPcL6oqWepUoianwzb4IbDh+E0vvEGAhMdOgR3qVdtVAeo2qwKiU5b+/bB/65VC0Ggm25CBmrSJAR5ojGfRBP795vmvfcGZ4EbNYIYS7g0qrlzgxvCXnlldMQXDAPXskED2PaMDO+1qYaBIBCd2113BQeEsrJ4PSRvDRqojxt3QOIoDvwihFhu8/cjQoi3XfZNsvnb50KIbIfxWoZzyRJEGsqUwYvupbDsyy85ktymjWkeOqS/b7QxYwY7Tl7PY8GC4AV3aioiXidDccYOmzYhOjZsGJwMOVVtF22j33DGGagBuOoqRNlGjYIi0pQpoCTNng0lmoULoWq0ciWM3Pr1WCysWIGi12+/RRRw9GjIDt99N2gaF10EdRy77sTy1rMnon8XXIDrOmeOu5NbXJSFv/6Ceol8vj16oHt4ONixgwtshUD2IhqyqYYBuhcd97zzvAUKTBMLguuu44yKXBNiGKGOabVq7ovGk2Q4T7l5dM8eOOM+H+6NF9WdxYuZFli7tnuj16LEwoVM9zpyxFu2bfPm4ExbUpJpPvTQqdOdfO9ezH0jRiBjaSeYIkSwKlfZsngvunZF9mPoUMxh48bhWNOnY478/XfYkSVLMG+uWoV5dP16zDG//Qb1xPHjofD11FOYk2+8EfPPBRcE033ttowM2OkePRBR/+kn9yi+YRTPXLp7N4JO8uK7efPwRROys4OLvxs2xPWNBr76ipXpmjcPr/nriy8ic162LCT35Wssy5yTA+5Wwxd3QOIoDuwRQnxj8/eVQojfwzjeEiHEFIfPXA3n7t0weEJgseolJfnZZ+x8dOgQne6p0cDXX2NBbO1/YIc9e4Ll+nw+RJy80CaKAocO4fxvvZVVl6wFgEIgtdu6NShnr7ximtOmwVkpbhlgw8Ci5fffYZhHjABNq1UrRHPtIoWlSsF56dMHyjrLl6PT84svwhhXrIgomE7vmWhg8WKOXrdrh0zDPfeER4coLISjRrSMihXhrEcDr70W7Nh5PT+/HwspIXB+33/Pn/3vf8H3qHJlPE8qnCTDeUrNo3l5/IxXrOitRmzBAnbg69YNn/IUbSxciKjt//7nPvboUbzzMg22X7+TXyuXk4NM0vDhWCwKAeqTdS5KSEDGZuBAOBdTpsBpOBm1G0eOYC767DPQZ6+5Bs9W5cr286jPhxquXr0gaTx7NhygN99ExjMtDc7t+vXFc/5btsCmJiSA7lu6NDK2Or1v7PDxx7Ah5NC++GJ0sulTp3LWpnHj8OjVciZEbur69dfB9ygxMbQprBVxBySO4kC+EOJjm7/PFSim9IIMARpCW4fPlYYzN5ej/pUre0uPf/IJL646dz45DQatOH7cNO+8k1/6Fi2cU8D5+aY5cmQwf7VjR9OxX0pR9zA5fhyRohEjcE/s6jJ690bh5sMPQ5Fk9erI+pkUFwwDRumzzyAvSzQiVaRP3iJtgOUVv/0WTGWrXz98Oszs2aC00LFuvTU6zuF77/Ez0qmTd+c/L4+5ymXKcGRx40YY5SpVmA50+unqeqqTZDhPmXnUMEzz6qt5oeGll8Eff3Djt/r1Tw0pb8NAppMixKmpzjQVw4DQh/yMN2qESL/dwr2o59FAAIv3556Dc27tLyIEMvW9eiG48OGHyPx6zSSeLOzdi4DBAw/A7tr9Pqdt2rTiPdc1axAYo++vWBEiN+E4D+vXB6unnXtudIKEs2axc1Ovnnfn3zBALaTzon5LubnIhpYqxUpyFSuqs+pxBySO4kC0DGeKQNTuAcUYR8NpGNyILCnJW93ElClY9JQpA6pJpJ2mo4FNm4J5u+ef72w0/f7gjru1aiEtbjcx5uWB9tK8efSzCgUFMNTXXmtPqSpXDr9j9GhMXCVFRcU04bD+/DP4s3JnZetWsSJoYcX92wsLTfPll7mYWAjUi4SjRHTwIKhqdJxrrokOJWXCBM5AhsNlPnQoWMWGMh07doAXv2gR//4uXZwFBv7lDkhE86hpBtPirHU1KqxejcVwrVrIcm7b5u3+FQUOHw6mUJ19tjpyLlNkypXDwt/uOQkEUFPSoEH0nSzDQM3ALbcEizXQlpSEWoqnn4aj/W8I2ugiLw9Z5yefDK7Fs9smTy7+ZsCGgUx+rVp8Ht26hUcxPH4c1F86zoABcDYjxfz5nIFMS/OuvFVQwCqDpUrxWurgQdAwd+9mql+9es50r7gDEkdxYK+wpw6sEPrUgQQhxNdCiNddxqUKIcwhQ4aYw4YNM4cNG2ZO+ycMInfvfPtt/ZdtxQqOGFxySfHRY1T48ktE6SgCOXKke5TlqacwWTz4oL0DZRiINMk6+C+8EPm5GgYibnfdFex0ZGQgK9C+Pbi9s2d7LzL+NyMvD40DZWUqeWvcGFzyxYuL1xnZsoVpWULAWRo/3vs5GAZ430Sjq1MHFI9IMWkSR6pbtPDOZd6yhZuMNWkS6sR8/z1nqx54gH/3tGnTTswpQ4YMORmG85SYR+V+Hbfdpv9c7NuHxbgQEI84FdT1FiwILpi+7Tb3zMD337Nz7lTzsnhxsGNw1VXROd/Nm0FzlOdoer+aN8didcqUf4eiVLTg94OiJAdO5K1mTdzXX38tXkfs8OHgTEFyMmT7w7FxP/zA2dty5VAHEymWLuVAWLVq3lXasrM5Q5OaGjq3r1rFTs6AAfxenQLzaBwxhl+EEMts/p4p3IsnCR8Jlp9UwTZyJ/MT77xT/yXbvZujLKedFv3O5hMnYpGpa8Tz8lD0Rr+lbl39Tqe5uc6Us3XrgvsmlC4NpyASZ2v3bhiGFi2CDUJiIoohJ04svgZjpzLmzQsuxJQjZ0KAMtexo2m+9VbxLSwMA5RDWZ60b9/wshhz5nCxccWK0elq/dNPoE0lJWFx51WAYfFiXrB06hS66HzjDVxzn88+WHESi9BP6jy6fDlft1699NWJcnN5QV6hgmn++ae3++WGhQuRZdOVUTcMZPuoeLhCBcxHuvs6nf++faink+mkN9wQGaUyMxNytXZSp126QALWS/F/ScWWLcG9feyK23v1QuakOOsdZ88OFkpp2TI8SeaNG1klKzERTVsjxapVbG8uucR7JmTPHmQ4KMBkfQ5nzEDNY7lycAKtiGdA4igO3CFAH7CTj+xjGVvDZv+3hRDjLX97RwhR02ZsiOFcu5Z15vv21Y+CHDvGFKdKlaKv0vL55xxp1cnI7NgRTGu58MLw+34QsrLQ14IiyjQRuRXhOiEQQO1A//6hNQ8ZGaBWFaW8byAAx2f+fChojRoFBZcHHsA59eoFo92uHaKwTZsimpiejuxMy5b4W6dOoIINGoT9H3sM5/7RR5hU166NrjTk9u3IBFWtikX+mjWgT7RqhQmcrmHFipB/jIbClA4OHGDKSaNGMDJTpng/zurVbKhKl45Or4eZM7me65xzvHeVnjyZn9ErrwzNIN54I0cuV64M/uwkGc6TOo8eOsTZgsaN9Z2+QIB7ESQmRl9qd+FCzgbbLXKsOHo0uLlaq1aRS+UWFJjmq68GK+O1bYt5KFzMm4ffIxe5C4Es0mOPFW3hvmHg3V+6FGqAr76Kovb774faWe/ecIjat8f1a9YMAbo6dbDw79MHi+727ZFNHTgQgbMRIzAnv/8+noPly6Nbi5KdDTWmlBQs/HftQha2e/fgrFHp0igaX7Yset+tQl4eAnpJSXB2TzsNwhpes8r79wcX5T/2WOTZ8Y0bUV8iBN5vr87y6tX83J91Vuj9fPVVPl+rSE7cAYmjOJAshFgtYOx8QojSQoifBJpoyXhECOEXQnSS/vaKEGKnEOI6IcSgf7ZrBBpy1bP5riDDmZMDzne3bpgkdSPufr9pXnwxXpqkJHc1B6/49FNe/HTp4l5TsmYNJvczzkA04aWXIpt4qH+DHG0/44zwFwd+PxaVFIFq04aj+ffdF/2I59GjiKS/9BKKnHv0gIGRHSl5s4se2m1O+1ujaPRcNGsGtZNHH8U9XbYs/LoZw7B3jletQiE70QCFwLNz2WUwssVBz/r552DJ5scf915YuXt3cGfekSMjP/fvvuNo81VXeT+nd97Bde3Y0TSffz74s2PH4IwKgf/K2cCT2IjwpMyjhgGVtnbtMA95CcY88gjf8zFjvN0fNyxYwM5H/frujvmBA/gN1aphoXzHHZE3Ep0/PzjLW706GtyGU3hsGAhw9OyJY3Xvzhmam25CNjGavYXy81H39PrrEPro2xd2QG4MKW+kquW2UZ2VaqMeID4f5u4LLoCt+OADXNNI5ODt5tEdOzBvWTMjXbtiYVwc9Kw//+R1hRAQcvDKMsjJCT7GjTdGHgz76y9+j9q29X5Os2bB6evZE/OwPK8bBoKlFECT66HiDkgcxYU0IcRXAsWS6wRoABUsY24VQhwUQrT85991BaJ7AZvNLzQM55AhHMX00ql8+HB+wT/80NvL6IYJE9j56NrV3fmYP5/VeSpVitwZyswEJ5OchAoVsJAPh5uanw+DIaeYaVKcOjU6k7rfjwny/fdRdHnWWcHZFeKVW7datWDkBgyAcX30UWQVXnwR2YwxY3DMTz5BtuSbb+BEjR2LMQ89ZJr//S9kHfv2xcR82mnB/S7sNp8PEeLrrsN3bNgQHSchMxO0EevvPftsZGaKulnXvn28MBICkVCv1KesLFxLOsbQoZEXib7+Oh/v4Ye970/vemJiaMR65UqOQN94o/w7TprhPCnz6NixfI2//lr/2n70Ee93zz3e740K8+ezmlaDBu7Ox7ZtvDBOSoJ0diQoKEBmtX59OLGJifiN4UizGwYyctaC8n79MCdFQwxEVua7+258l5xdsStmFwK2JyMDmfF77oFD+eSTqA985RVQQ997D3ViEydiHv3qK9iFl17C+DvuwMK0f39E8E8/nZ0r1ZaWhizQyJHIxkTD+Tp+HDK31saL9eph3i9qmmteHpxJ2anzms3y+4Op2P36RS6MM306UxIvusj7vPz++85rpgMHWDWuQwe2VXEHJI6ShhOGc9o0fiFefVX/RXr77cgWNCrIUr46zsdPPzHnunZt70W8W7ZggUp1JvPmBRdd3nFHeA2J8vJgeIhWIwR+1zXXeOeRWmEYyCSMGYNsAy0yrFtCAn7b0KEwiOPGIXq4caN3Oo7X89uzB9/15ptwcnv1Cs4mWTsB160LLvgnn4Re79WrvU32fj9oET168PE7d0YU8auvijYjUliIRRd9b8OG3vnMBQXBfWjuuCNy8YF77uHjjR3rbd/jx7mYskGD0MjrmDF8bJKcjAHDeWIe3biRs2+DB+tf11mzOKN48cXRVSOaNy/Y+XBT01q1ihdAZcp4L+I9dgy0oyuuAMVk8+bgLt4DB4Y37wUCeGdlhUIh4KTPmeP9eFZs3oyF4YUXOjdyFQIBpCFDEKQZOxbytWvWFP1i/NAhKFq99x4CAeedFxxg8fmCnaQqVRBQevvt0MDOxo3e6FyGge/+z39YCKR+fTg9r71W9AGdsWNZUrhiRRSaewF1OKdr06dP5JTsDz/k4915pzdbYhhwMoXAmsXaD2fOnGBxD9OMiXk0jhhDqhDC3Lo1y0xLw8Peu7d+5OSPPzjN/J//RDfdLTsf55zjPrlPmMARiTPOCE+yUu4R0r07T7Tly/Niygvy8kBbkRfbpUpB7SMSTnIggEXFvfdyE0Jrx970dNCdXnwRk9mpoEZmRWYmfsf776N2ghpeWrdmzXBv1q1D6js9Hc6u18ZZK1diUUhUISFA6YtW91wnfP0195NJSUFE0QsMA83eatXCNbryysgWqH4/d3YPp85g/XpeZF95ZSiFgLr8li+PhU8MGM5UIYR56FCW2akTL/R1ex9t386iFhkZ0V3Iyn1EdKR8//gDPZ+EwH/nzfP+nXIGqE0b/v6EBKgLen12AwHQB5s1C54XLr4YNS2RYPVqnBM5NVR3QN9RuTLuzeOPwxGLdNFaFMjJQRDq448RoDj9dPt5lAI7ixeDWlexIrLWCxZ4Wzxv3w51SJmq26QJAj1FGdBZtCjYzj36qPdn6ZNPYENatECWPtJMiNyY9eWXve2blcW1NmefHUpvfPJJPva0aTExj8YRY0gVQpiXXZZ1IrKwY4fey3PkCCL6KSlY6EazOO6zz5ir3q2bu0EePZpf1HbtwlPfystjwytvbdqE5yzMmBGcOk9JQfZB9/paUVgIOtmQIfYL9ZYtERWbNOnfq/JiGIgEvfkmFsikBkXbp5+GNmDs0gURQS+NLrdsQXRWPs5VVxVtn4U1a4Idn2HDvEcNR43i/b1IutohJ4cLNMuX90a5NE0sduhcrAozmZmc1Wrd2jT37y/xhjNVCGH+739ZJyLRv/2mdx39fp4j+vSJ7ru7cGGw8+Em5TtlCtcz1KkTfnbWjp6Unq5/TWSsXo0AFAW6fD44vVahA12QzPmIEcHvo7xIv/deLFSjRQc9Gdi5E5Q+u8DOuHGhxfrNmoG65SXDf+gQsqlyLWD37qa5ZElR/SoUlstNYPv18x5c++EHDlb27BlZXZNhsPCIz+eNcmmaeEfpXIYODf7M7+fMffXqprl+fYmfR+OIMaQKIUwhYDi9RPmvuQYvRoUK0W0c9euviAZUqeLufBgG6FI0GfXtG370cOJE+6jRjz96O86+fXxtyPG4777wpQz/+gvNu2SJV9ratkUxsNdMwL8Ffj+idS+8gBoR04QD98wzLLFIW5kyMAQzZuhn4ubNC14sJScjshdJQacKpDpDhvqSS7zT3+Qi5Ucfjex89u3jCFzHjt6fUXrOy5SBgyVj/nxkV8qWNc3HHy/xhjNVCGEmJmIeJcqEDkaO5Ps5fbq366/Cpk0ISjRujHvs5nx8/DFnfJs2Db/vyJo19vPoU095O05uLpwEeXF7++3hqyvu3In6J7sauCZNYEcWLfr3OhwqWAM7WVkQmBkzJlgsQwg8AxdcgIW0LtVz40YEIeXjXHtt+ME2NxQWst3v0QPZK68NYD//nINZl14aWf1lfj47CjVreheRkeeA778P/mz3bth+n880b765xM+jccQYTjggV1yhP/nKi/VIixNlbNrEWYjevd0jG//7HzeWGjgwfG58YaG9DroQ+hz5QABj5SxKjx7hGUzDgJISyf2R0fT5kPYePfrU6Ip8MkHdjQcPZnqTEIgmnXsunlEdR8QwMFZemFSrhuLQoliMGAZocfRd55/vLQJnGKBN0P6jR0d2PuvWwZGtWhVUEy+/OTubHcGzzgr9HS+/TJSJEm84T8yjZ52l71SuWMEL7GgWnWdns9JUw4buAaIJE7iZZocOkfVvsi5oaRsyRP8YU6cGy8A2axZ+jcfSpZAHT0pCMIiog2eeaZpPPIHFYkl0Orxg1SoEyajhKG0XXYR6UN35ac6cYBn0lBTUPhZVw9yJE7kupEUL75K4cr3a4MGRPQeHD2Mt0rAhHFovgdBAgN+/KlVCs6CTJ5NiZomfR+OIMaQKIcyaNbO0FXp27mRqzP/9X/Qmb9lopqXB81fhrbcwNiEB1KNw609ycoKNHRntu+5CRFLn9/31FztCtIANpxv28eNQQ7E2IzzzTETvImnQVZJx7BgiuD16BKu1tGwJSprOs5GXh0hUairoQ8nJyFBEu5kmQVaj6tPHm3KP38+9IoTwXlNiBXWrFgJNBb1gyRJeRI8YEXqenTubZkpKiTecqUIIs1SpLO3oZ14ev+ctWkQub0sIBPDcUkbPrUbi11/5/g0aFH4GORDggAlt1atDDe3LL/Xohnv2BFMjU1JM89lnvS9gAwHQbGThCSFAK3vxxcj7mJRUFBRgsXv55VxbKAQoXG+9pedYBwKgr9WpA2cvLQ005qLK0k+bhudECNR+eu9rmPsAACAASURBVKUwPv00/8777otsPbNmDZ/LzTd72/fvv9kBvOiiUJt1661xBySOkodUIYQ5aZIegT4Q4L4OaWnRK8jzajS//ZbTp1dcEZnz0bw5T0D163ubKPPzuWESHePmm73Lre7fD4qCNQJ13nn6TlAcwKpVqOeQa0VatcKCROc67t8fLNmYloZFWlHg3Xf5e7p397b4y8/nqFliYmjq3isoq5KS4p37/9JLiHxXrhxa0L95s2kuX17iDWeqEMJ8+mn9QiRSIitd2nv9jQqPPsrP1Pjx6rErV3I/g9atI3M+Bgzg761QAfVquvNWIIDAC52LEHi2vTZ4zcmB4pO1CLtNG9QVFrVSU0nCrl2oU6MFtRCo+Rw7Vu865uaCKku0vrJlUStWFLZsxgxWvzztNG/0QcOAzDL9Rmt/I6+QsyrffONt359/xnqkXj3UNcrIzjbN6dNL/DwaR4whpIOvCi+/zC/XtGneXi4VZCUJN6M5bx5Pit26hR85PHwY0Vn63gsv9Lb/9u0wbOSQtWgBSpDXcxg5MniST06GE7NqlbdjxRGMVauCF0VCYJE8daqeEfziC+5Y6/Ohq3FRUAk+/JCdpc6dvdWfHDvGNSzJyaiXCRfHjvHC7eyzvdWm+P1c0N62bWhAIAbUW1KFEOaRI3rz6C+/8DM5apSHm+SCL7/k4957r3rsjh0stdugQfjZ1YKC4Jq3pk29zcnZ2ahLoJ45tWqBWuNloXr8OOob5GJrnw9BrTlz4gGcSLB7NxQIiepEDIFx4/TqJubNC86mDBig3+DYC377jam49esj8KGLQIALyYWA2Em4kBsJVq3qzuSwgnqeVKsWep1iYB6NI8ag7YD8+SdPQla1hkjgxWiuX4+XWghvndqt2L2bu5AnJHhvnjhrFuvElymDNK6XxWkggGgQFZY3bYrjPfEECoPjiB5WrODsGm1XXw3ZSjds3x7cSLF16/CLYFWQm222a+ftuT50CM9y586gPURC01uyhLN5bu+iFYsXsyNljd7FgOHUnkcPHeKFcs+e0ZMuX76co8D9+qnlSTMzmf5VpUr4z3RODgqW5fnby+9Zvz5YWveuu7w3Jpw6lRu7du6MazBkSJxmFW3s2IEsqZztb98eUXs3ZGUFL/Dr1DHN2bOjf47z5nEWrU4db89AQQGoT2edhXrQRYvCP499+7im9Nxzvb0T+/czxf3OO4M/i4F5NI4Yg5bh9PvhmSckwGBES3LXi9H8+2+OpKSlha/SsmEDFxwnJ0NfXheGgaI8SitXr+69OHLRouBCzUqV0MgpmjLGcYRiyRIUfBMlKykJ2v5ujqPfH0oleO+96EdVv/ySjfsFF3jLhGzciIWkEKgniWRR+9xz/Gx6pZ6Bp4wggUxDjAHDqe2APP44KEpeJM/dsG8fNzlt0kTtwB4/HiwN/vvv4X2nNYM8cqS3/SdP5sVicrL3OqYtW4IDC6VKoTeQV/prHN6wdSsKthMTuQ/IDTfoOY6ffsr33OdDzVi0aXGLF7MQTJMm3sRajh5luedGjSJTQ/zxR342vTR2Nk1k8yg4KtMzY2AejSPGoGU433+f+ZWRNn4iHDmCRlI6RvPoUdCdiF/stZs0YdkyzlykpnqLwuTmBkdx2rXztoDYvx/UKooSQ1bPu3xgHJFhxgzOfpEzosPBnz+fxQqaNUO0NhLpRjt89x1Uo+rWhRPipcnW5Mn8m557Lvxz8PtBbRQCFB0vC7oDB9j4y6pHMWA4tebRhQuZXhGpcAAhEMC1pjnN2lHZOnbgQJ5/Jk0K7zv37Ak/gxwIBDdYq1vXW9+I3Fw4cTJttX//kitFfqpi3bpg0YH0dDRqdMOWLey4Vq0K4YNoy54vX44MyJlngl3gpUfU2rUcFLU2WfWKO+5gB9uLNG9hITIxQiADT+cQA/NoHDEGV8N59Ch38r7llvBfRituvRVRlHPOURtN6vXh8yFC/Msv4X3f+vX4rqpVkR7VoeAQtm1Dh2KabG+8UZ/nXFiIiIbcVK9du/AduT17EOk7FTvy/luQn4+6I8pqJCVhUeQWjSMqAb0Pl14aPfUiwoQJ/Jw8/LC3fYcNw36JieFHtk0TzzvVvzzyiLd9ZXU6cuxiwHC6zqOGwRHjDh2il0GjaGm7du5CBGPGcGPC118P7/syMyGRfvrp3jPIWVnBWYsePfQDMIYB8RFZLrthQ/zmcK5ldjbEE5Yu9b5vHIBhIDgpd44fPNjdoSgshBNJ9LtWrcLvk+WEBQvYSb3wQm9Z4Y8+4t+jK8Nvh5wcbnZ55ZXesj2//RZakxID82gcMQZXw/nYY3gJypWLngzs7Nn8cr39tnrs2LEY16IFlFLCwYEDyN4IgQJbL9zQuXO5ViMpCQssXYO3axeK7igqXK0aJuxwKDIbN8JpozqcJ57wfoxYwZIlWGDojJOzIRkZ7h2WDQOKZbRP9+7Rj+DJzTU//1x/v/x81uGvWzcyOsr48XhXhPDmzPj9WFAIgQW3YcSE4XSdRydN4nv6xx/h3xcZ27dz4e1tt6nH/vEHnMJ69SA3Gg4KCrgLddWq3oJBmzcHdx4fNkw/g3j0KBa2pFiYkoKAQTi01f37oRRGAaHLL/d+jFjBhg2Q3XfD9u1oAkz3tk4dPZGaL75gCejTTvNWOK6Dzz7jc7JKhLuB2A4pKZDZDxdLl7JAx5gx3vYdNIgp59nZMTGPxhFjUBrOXbtQZC2E9062TsjN5eZl3bqpF+MbN3LjqBtvDO/78vK4R0e5ct7oW9OmYRFWsSIkcr2oXM2YwYVoGRkoKAunaH7pUkgNU5EyTUjvvOP9WLGAzExkKNLT0c3XzVk8fhxRfsqGlCoFHXu3/d5+m+l00Y7g+f1c3FumjLco7aZNHJG89FI9ZzkvL1R61zC4AN9rxH7uXH5WJ0yICcOpnEfz83nOGzBA/zqqYBhc05SerqaZZGczfbB9+/B494bBCj1CmOZXX+nvu3YtHP1GjbCg++QTb/uS49GgAWi7W7d6Pn1z61ZQ1cieEZ33wQfjKll2CASgsFe+PGoY3JxFw0CwUM6GPP+8e43d9Ols42vVcg8AecWDD/L5TJyov9/Ro6wK2Ly5Xp+mQMB+fXHnnThO9ere6GC7d3OA4f77Y2IejSPGoDScN97IBs5LozQVKLqbnKzm7RYWmmanTmx4vLy4BJnznJBgmlOm6O87Zw4bq1699CJB9J3PP88OQ/Xq3ot5DQMOjLWx1+mnI4PiRSI11jBzZnBn9PPPd+8EbZooXmzRgql2N93kvlD78suii+AdOYLGWpTN8KKONnEi/363xoK//46oeP36oXQyqlnwarxNE9Ks6elwpP7+u8QbTuU8Ono0O7dee1s4QY7u/vCDeuzNN2Nc2bLh10o8/zx/34sv6u+3eTOrfjVp4k0q+quv+F1OTsbc5xV//olIMgUYhEBg6LnnvCtuxRK2bAmWNc7I0KMNb9sGIYy6deFY9OzpHnhbtIhZBhUrgn4ULfj96KdFwRwvAcjly5lx4NZYcMsWqCQmJ4cK5Ozfz8X3XjMxI0fimvTta5orVpT4eTSOGIOj4Vy+nCO8H33k7aVxwrJlbAjcCmWfeQbjfD77CWn3bnf+vdyUy0uH5wUL2PA1baq/+MvM5MJ6IeBAuTkuL76IRTKN27wZcoCyPGXbtojmeylK9oq5c7FQKsrvKC7s3Gma//d/fP3KlNGLxh0/zkpOQmDSd3N8p0/nZyXaEbx167gWo2tXb1LPtOisWlVNOdy2DUZTCPueFFddxUEAL47vjh0cDX366RJvOB3n0cOHmYI5bJj+9VPhwAFesF15pXqs3OXeju6alYXjqfDFF3yMW27Rzxjs2ME1G1Wq6BfiFhSY5vDh/J0NGrhnAb/+GjUlJJ966BCCCNSnSQjUjIwZU7SKg2vXmuYLLxRNr4viRlYWmvRRMM3nQxbJjXJqGJhLaP3QrJl71mrdOlZyS0lxd6q9IDOTgzn16nkL5rzxBgcwp093HpeXx+d/zTWhn7/wAv82L+I1x48zTfiKK0r8PBpHjMHWcBoGc31btYqOVn1hISIEdExVdHnxYpYkffDB0M/9fqSHmzZ11useN44Nz91365/n8uXMD27UCDQ0HaxYwXUmQqBXituCcccOntxbtgSvmbIuzZsjkvTrr0VPEXj9dT6Pzz6zH/NvpClMmRJctNqihXt0zTDgFNI+Z57pbjAWL+YF4eWXq0UVvOKnn9iQe3mOc3LwDtevjzoV1f174AEcv1KlUHGDrVvZQXnpJW/nTvSHypVLvOF0dEBoIV25cvQkYqnxX5Uq6sXUvn2s+nfeefbPwC23ICPw7bf2x5g3j+9/37769K2//2YKS2qqfuZjzx4u1qcMptt1KygALVUIvIevvsr017Q0vMOffRZ91TorfvyRgxEPPWQ/5t84jy5dGiwdX6sWatPcfsukSVwIXqOGewZl1y6m2/Xrp6eqpYt16zgL0auXfqDNMBCEadkSGd1jx5zHygIiVoc5N5cdlGuv9Xbun35Kxy3x82gcMQZbwzltGr9IM2Z4e1mcQIu6hAS17KKsHOHUkfmVVzgiY1fQ+fvv7MBcdJH+ZLN6NS8k69bV5xpPm4bCO6I5OC3irZA7wMtbgwZosFXUKCxkfiptXbtCoeSmmyBv2bIlFqYZGUhHV6iAa5SeDoereXN81rEjFltDh5rmu+/iHpwKFIecHCwG6Hk491xcdzeneuJEXnilpbmrpq1fz057o0buUWUvePFFPBNNm2KRo4s5c/i+qiRXMzO5wec994R+rnJQVDh0iDI4Jd5w2s6jW7cyRW/0aP3rpsLUqXxPVVK+hsHZ2KpVsbC3Yvp0Pta774Z+vmsXz4ctWuiLLRw8yFHbsmX1RQyWL2cJaJ8PDV51gl9ff20/j1aqhJ49xZHVffPN4Dq9hg2Rxb/tNtigjAw4g+eei2eifHncl9q1MbZpUwTmOnRAoOGWW9Af6tdfIf5ysh0Xvx8CLLSIz8gwzeuvd6dmL1gQ3LTXydElHDpkmpddhvEVKniTr3XDjz/imnfogIy4LnbtYmnexx93HhcIcLuAHj1C75nKQVEhEKD+JCV+Ho0jxmBrOM8/H4bg0kvVL8bHHyM97xZZ2rWLpUvvv189duhQjEtOtlef2LiRswR2i6XMTBi/du0wSR49qv4++bgURatVS18p65tvsLht0QJp3lWr9PYrKEBhu9Votm6tjrJEAzt3oqutndF22qjbsGqjyVfe0tNhdIcPh4rZn3+eHJrXqlWI4FJE7tJL3ZWyfv+dF+blyrkv/les4Aho167Rq9UxDEQEhUBGw8vzQVS0Ro3U5/P66xhXqhTeBRmyg+IlC2OaWIR17VriDaftPDpsGAr527RRZ0NnzMAC1u2+5uayoMa556oXpe+9p3Y+jx7Fs0QRYeuxAgH8vXt3zFO6Dd2OHOF5IDlZv/5t+XJkdNLTcV46HbYJMs2KturV7Z2uaOLQIQ6G6W7t27uPkTNAtFWtCpt8xx1wBObPj74EuA727IEoCtnz1q3dM8SbNzMFyudzb8y3fz83Ha5XL3rqm6YJBTghYAe81GNR75oyZdS06lmz+J5Nnhz8WSDAKoV2DooKP/xgmk2blvh5NI4YQ4jhXLqUX6D5851fiLw8TnO7FSUOHoyxffqoIyaLFyPqXqYMJnYrAgHu4tuokb3BvuUWfJ6aql9wuXs3p0erVtV3Ir77jiPr7dp5i3pTkyLr1rSp/jF0sX07ZFUHDw6miTltXbogujViBDjTP/yA4u6ZMxGB/e47OJ7jx2Oh8+abpvnyyxh/6aVwVog2ZN369cOC5vbbsfAqalqEjMLCYG55ixbuheMbNrCC0QUXuHOTJ0/mSOi110YvcrllCzvebk68jM2buZDSrsaDkJ/PTqadUhNxoZOSvMlY+/2meeRIiTecIfPokSOs7mOXXZBBUf9Bg9TjXn0VDm7nzurs7IEDCCJVr4732A6U+Sxb1v4doPvt8+nL7cqKg6VK6Yt+rFgB50MIZAS8UBg/+cR+nklKin4g58ABOHN33YVGcU5zHG0tW4K+c//9yGZMmoQ5dNYsZM2//x5CFp98ggL7t96C3XvmGYinnHkmZ9DsnJTy5XH8SZOiJxKji3feYdtXo4Z7luvQIbbdXbvCXqiwejVnWzp0iF7dTk4OOzduTrx1P2I52NV4yKDMY9OmofZNbkFgdVBUMAzTPHy4xM+jccQYQgzn4MEczVa9nNQdPTlZzUPeupUnKrdi9j59MO6cc+xT72PG8Ms7c2bo5zNm8Oe6DYQKCrAobt8edBHd1Oj337NxaNvWG91IXgSTkW/fHgt4a/Q5XBw9CueAMkFWA0aL0po1mWpEmxe5YSfk5OBajh8PCs8FF4BGRJEw2qpVg9P488/hyYOGg48/5t9fpYo7zfDAAaYFlCnjzmUm1SMhsJiIFkaOxDETE/W6txOIQpWaqm789s03fN5WamNBAe7dOefY12WpEAPykSHz6Guv4TpWrKheCC9apPfe5eWxKtHw4errTfNLWpp9/YTc5Oy110I/37qVnac771R/l4xbb0U2wufTl+lduZKza2lp3pxbuWEcbc2awUFYsED/OCoUFOC96N8/VJWQHC2aR2QJWiHgUETj+1evhqPy2GPIaDZtypF02sqWNc3//AeBId2sf6SYM4cpeqVKwd6ocPw4qL0UoHGTY/7lFxatufLK6NSimmYwxVyXLm2awRQqlQ1Yu5bP20744aKLsL4aONBbgCoG5tE4YgxBhvPgQaaofPih84sQCLBKk1t39Ntvx7gGDdQLTNl5sFsQbtumbrp17Bhr3ffsqf9ik1JWYqJe8yTTROSCDE/r1vqKJ4bBjR3pO595JrqN7JYtA71KlqLt0AFG/rLLsOBYsSJ4Mi8oQF3O668jmxGtid4Ou3Yhutq9e2gEsXJlSD+vW1d0309YsIApd4mJ+O2qZ+bQIa5NqlZN7SgaBp5R+l1ffBGdcy4oQORVCDisulS2rCzmYasa1sndujt2DL0e1BQ0JcVbQXUMGM6geTQQ4AJsO5qojCuv5HdU9fy9/TYHfHbvdh63YwcHFOyyyDk5nNHr0iX0XTcMXmjXq6fX0NM0sUBWOTV2+OsvXsCmpXl777/7LnjuGDw4ur14tmxBQIioRkIgI1G2LK7Ps8/CSZepdYGAaa5ZA6rps88WrSOQmYngzsUXhwaQUlIw18+ZU3TfT9i2jRuWksOqsvMFBXDmyGlxo+jRcy8EaveiBVL3q1FDfy4LBJhC17mz+n0lhkONGqFKirK8uRdHOQbm0ThiDEGGc9QojuaoUp4//sgvkCpdvmsXR5pVNATD4Be7Tx/7z8ko1q1rL41KUb8yZfSzCHPncjTmscf09pkyhX9TRob+5OX3Q8KQrluNGvq8ajccO4bFoTUqVq4c5Fh//71onYpwsXcvDEzv3sEFnJF0nvWC3bv5uTvjDCwaVNi6lRckDRuqM38FBdwduHz56DlVCxaw4/bmm/r7vfsu9klIUF/fBQuw8OzZMzQQkJvLkrJuFAoZMWA4g+bRX37hZ1kV0d+6lSOlX37pPK6ggOs17rhDfa2Jglq3rn2NAHHgk5Ptn0k5q6ArhLFzJz8Xus0vV61ip7hWLW+0K7ngu0wZUJqigcJC0Jn69QsOjiQmQt1u8uTiy9J6QVYWIvmXX84BRCGgnlQcOHYM2RdaOwwdqr5OR4+yImaFCmpRGtOEE0+/yUttkAp797LapVsQVca8eXwuqt5I+/ZhHu3Vyz4T1rEjjnHddfrfHQPzaBwxhhOG0+9nbqQbx7xnT4y74AL1OJo40tPVBbDffssvtZ2s7uTJWMwlJdkbxQUL2CDpSoVmZbFEa4cOeoZl0SLuUN2qlb7zcewY0q70G6+7zltPByf4/Uh7d+wYzBXOyABHN5zmjScL+/fjtwweHFndxF9/eds/Lw88eYrEutUzLV3KiihuDnhmJu5F69aoN4nGPTdNdmQrVNCXifb7SUnFvXcEUSH/7/9CP7v3XnzWpIm+UxsDhjPIASEOeL9+6utC82ODBupaqA8/xLikpNAmZzLWr2eHxq5p37p1kOOtUMH+Od+7lx0J3YVRIMDqb7Vq6dXB7d3LDWJr1kTWQPe76PkTAtm6aEkb//QTAjhUC0j35dlni76YPZo4ehTO7MCBkc3/69d7E9EwDNN84gmuMbzmGvX8sHdv8LX+/HPnsX4/apo6dYLTEK37QUEZIbw1P6Rn121Op6Co3Vz58cccCNBVFoyBeTSOGMMJwzllCl4In0/dOXrZMn5pVZGnffu4aFaVkvf7Wfv78stDPzcMrmH4z39CP8/Px0QgBOoddIuar7uOswQ6GZPMTHZYzj9ff9LYuzc4M/H449EpTJ4zJ7i2o0MHRHK8dBouaZg/HxP6oEHeFGICAe6tIIQ7l/mDD3isz4d0/ty59vd15UqugXr6aW+/xwlHjoCykpysloW0YupUPCeVKqk7Ak+axJFfK91nwwb+7brFyTFgOE/Mo1u3cjBEVWSamck0Sbf5kShTN92kvs5E5zrjDPt58PrrmUpk9/mAAZyd1Z3fXnqJnwcdCqvfzwGsjh1R36CD3Fw+P3Kio6ECtXYtd8oWAnP15Zcj0n4qZo2LAzt3wpns0sVb0z7T5IZ7QiATorJ1f/4ZnLHv0wfUOrtn8+BBVo20E8kIB4EAiybccIP+/d62Dc9uejpocE6Q50or1Swvj4UX9IOmJX4ejSPGcMJwEi/zwgvVL8GgQRjXurV6cqEmZDVqqOlcFAlISLA3RuQYCWFfePv44/isVCl9zXC5s69dpNAKw4BREh7pNFu2sNOSlKSuq9HFtm2c7qYF8K23ejcUJRGPPBLskHmRbywowLNPz6JbEa3cqZ62Fi3sixPpvEqXjl6TwgkTQGHx+fQph4aBzJ0QeGacIDd2s3OaiFp22WV63xsDhvPEPErzXoMG6hodEhSoVEldK/DZZ/xMqu6zHBiyo3Nt3szZEbviX3I6nfa3w4oVTEe96y69fZ54gr9Ht9Hc0aPg3NN+DzwQuXNw+DAyUBQcEALZ7eKoPzvV8fnn7ETXr48gihfcfz9f0yeeUI+VAz+0pafb183Jdvv7772dkxOWL2fmx3ff6e9HznzHjupxlE22c5qIDnnaaXrPcwzMo3HEGFKFEOayZVlaUawdO9iIqdQjDh7k6N7Ikc7j8vN5gW4nF2kYWEgKAQqTFXv2IHqVlqYfCZb5ypddppeNoB4Jbr9bxr59cNJatgTlYfp0vf2cUFAA7rPM8e3eXR3JjkW8+SY/o3XrujcQlJGbC5UncmhVEf6ZM0MNpxAQALAiL4+Lkp0U3rxCjozffrv+fkQ7KFdOLX5ATTLr1g1dSBNlMiFBrYlPiAHDmSqEMPftyzqh6OQ276WnY5xTx2zTxHNC2V036U+K4rdubf983XorL3asEebCQsyFp59umpdcojcn5ubyubVooSeTOmMG11boKqnl52MR160bnjc7VSEvMAwsNIlyKQTEJYqj8eu/CVOnsgxu+fLeFvyGgWwdXd/XX3cee+CA/TzaqZP9cYnKnJ4ePYoxKRx26aK/z4IFfK4qG0NNMpOSQgNiGzfyMXRqW2JgHo0jxpAqhDCffTbL7NoVzXFUi6NXX8WYdu3UNRNjx4IX3LixOro3aRLGVapkr2svd+q1iyyPGIHPatfW00E3DNO8+mrsk5amx1devJjrK3SL1fLzWU2oalV32VY37NsHA9y8OQx4gwaI0Bdld9ysLBTc/fADVKteew2qOi+9BP74889DwWvCBBj0detOnQLNX37hAsOyZdVdwK04coSzBOXKOfPT8/KYYkhbw4bOfUVk/Xe3vhC6IIWYlBS1vK6Mo0d5YfHGG87jtm/nKKi1n0NhIWvi64g3xIDhTBVCmF99lWV26gQaj4rCNHUqggctW6oVrWbPxuK7QQM1VenPP1FvUq2afQBp506ew+wyvhMn4rPERH1K1HPPcVZPRxJ6716m0HTpojdXGAYX1SckRF5UTfVeNWpgfqhUCfNaUc5bublYoP74I4Ijr70Gme6XXoKT+vzzqDP55BNE+FeujF7Pi0ixZg3XdPh8oFfp2hy/n5ugCqHun2SViU9Nda7J2LGDg5tuggy6kAvLrfLjTjAMLqRXrQsKCli4xE7khMR13Jo+m2ZMzKNxxBhShRBmmzZZJ1LbKlCHW7dxlLUYOlQ9jtKTV19t/zk1Ljr33NDPjh3jTMbzz6u/h/Djj3AIMjL0+MqZmZyePfNMfcNAXcZ9Pn2agRMWLeLFns+HTE80DZRhwHmgfh3nnx9cHNipk32Eija5+3mpUqAmXXaZaT78MOh1ukXS0cb69dxYr317d815GX//Def5nHPwXycpUmtfgPvuUx9XbpKpWnjqIjeXlYR0VdxMk5vQNW+uXlAQJc2OlvnUU+zIuy3gYsBwpgohzBtuwDzqVnxOvZbcxlHWont39Tgqdj3zTPv7edddnM2yFs0aBgJKQqC4Vgdbt+IZ7tABjogb/H7uVl61qnvnbILc90nVRFMHO3YE1+Ldequ3xrE62LWL+3VcfjkySjKVSTWP9ujB/+/zwe6cfz7u7dixmKOLMuDkhIMH+dzq13dXCpRx/DhsfJs2cD6dFOFkypYQCEqqfuubb/J10nUY3EABQx1HgPDeexzkUvUBIwpu/fqh2WSSk05IcH8vYmAejSPGkCqEMIWA4Zw92/nh37lTL0qwfTuPUzXW2rWL0/F2VBe5WZZdNOStt/jl11FCMQyWvnNT7yI88wwKfcuV0+cGy7rlbopKbhg3jjXeK1aEAxUNGAaoWw8+iOhqv37OhrFtWzhsbdtiwdGpEybrbt1QTNqjB0fUrVvZsohWZWQg2qe78IgWDh8GpaRsWUR3vTiDf/7JaldOES7qwkyOTlKSmi+dkK/R5gAAIABJREFUmYloWEKCN4dBBXIEqlbV74a8ejXfI9U7LwtTWCWj9+yBA9Orl31TUBkxYDhThRBm7dqYR92KyslpHDPGeVxhIdOEVA3tCgs5wmq3SN+7l2mbdrLNc+fys2CnQGgH6nHTsKFe9mDCBP4tunPY7NlcnzFoUGSL71mz+JonJSHzF63F/JYtCIC1aqUO1tSujX4ZbdogINKpEzJBNI9ecgmfo93WogX6XI0YUXwy5YSCAsyB9Jx5keA+cIBp1p0729dFLVuG+Vl20lS9kwIBvtbXXqvfD0mF77/nuU7X1h87Brvs9s5v28ZrHevzX1gI29qrF5QrVYiBeTSOGMMJByQ1VW1MaMFfo4b6hX/5ZZ5wVXQuKsJMS7M/Hi2K7aJ/Mv9dt1Pvr7/y5KbT/GfJEv4dOoXqpgllKjKaV18dvpELBJA9ko1PNDqkr16NhS/VI9CWlobf2a8fIm4ffojFiG4jLcPAgnTmTCyqhg5FobK1L4kQcF7eekufMhQpcnI4I1eunLvmvAw5Auu0cDp4ENQO4sO3bq1WYpswARkmn89b12cnHDzIjpKXviAU1bziCucxfj9nw+ycacpQDhum/q4YMJxBgRzVff39d36mVNlBmq98PrWYAnV19vnsj0fR5Vq17FWjLr0Un59zjvN3yJB7O40d6z7+4EEEKCpVcs+cE7ZuZYelTZvwM76GgXeYasJq1IhOc76dO2HnqIcQbUlJyMp37w6p7DFjEDzzIhV88CACfB98gHt34YVs6+StZUtkI5won9GGYWCxT9+v6oFhxW+/8QL8hRfsxxw+jPmGlM6qVVPbiBUrWAjkm2+8/RY7BALcaNZLX5C778Y+TZuq7T3J9w8aFPoZ2fpevdTfFQPzaBwxhhOG007z/8gRNKaaNg2RGiHcpSBpsWenirJ6NSblWbNYevfee0PHbd4MWkDr1vbFgVQE6/OZ5qZN6vMh0IKrb1+98aT007atniOxYwdHsFq3jsxoXn8903sGDIi8o+6SJShSpS7atJ15JoxYNBbCdjAMODLDh3PRLW2JiWrt92hi/3424jVrqmWmZRgGPwdpaeqFxKJFyGwkJsLJcILcJdtL8bgKZMAaNdKPBpKijF1xpIxnn8VzcvbZ9p+Rg6xCDBjOE/No48ahv9/vx7P+3XdoDErzigpEv+rRI/SzvXvhxP/0Ey/Y7Bq45ucjUtypk70DuWEDLwx1FYCIzlWnjl6fCOrbUbWqWvSAkJPDnbVr1tQTOXDCSy9xj5J27SI7lmnCMbr22lDqZb16cBaWLCk66d4NG5DttFPfe/TRovlOK/Lz+XqWLu3NmaPnoHRptVrl33+bJ4Qc3IRlSJmyc2f981Dh/fdxvORknIcO1q7l+6DKBE+ahCxZ5cqhmerJk/naHDvmfIwYmEfjiDGcMJwffBD6wD/wQOhkd+aZzilKN/qVXUR84MDQzAtlR+rVs1/8k1KRXd8QO8g0A51JU86WWPW7nZCbi6xHjRqRUY2osF4IZCsioQps2sRNk4QAFer003Fc3WLTaCEQAK3iv/9lA2MnPOCEjRv1+0447U9R1TPO0O9zsHMnp9mvuko99qGHQEupVk1Nh3rnHRyvTJno8NC3bIHx6tLFnQ5FyM8HdaxXLwQZnLBoET8/1gZgixfrRfNjwHCemEftAi+ffho676Wnh3aaJ7jRr2SFIdo6dAiV4qbsSGKi/XNGDS0bN9ZbOMt0LpWAAWHHDqaQ6tJ2AgFIt5YujWxRuPj8c742N94YWc+QgweR5aPMT8OGcI7uugvZiuLsF2IYiP4/+CBTllQF3lYcORJZMf+RIxzIqlRJ347k5XHQ8eyz1Q38xo1DUMMtSywXj8+b5+ln2OL4cWQKMzLce0HJ6N0bmX0VrTY7m9kRVsWro0dZJEJFE46BeTSOGMMJw2lXFPvqq6GGTmVM3OhXlO63blbePKW2hw8PPYa8INI1UBSx6tbNfaxhsKOkmy2R943E+SCaG2WQwnU+9u9HVFzujt62LSa+k1HIaEVBgbo+yIo9e2D0S5Xylvq3YsECLPqTkxGR070W48frRbl06VC5ubzAfOopb7/BCdTH58Yb9fchsYT+/Z3H+P2sKGYt5A8E2JkcN875GDFgOE/Mo3YZ21mz7Oc9p27jbvSrxx6zP571/lC2pXfv0GMcOsTPqqrGRAb1LahVSy/DS8X2det6dwAimUdnzOC5r08f9WJXhdxcFNnLNW4NGmA+OBUU/wwDDpDu78vNZSZDJA1xd+1iYZTrr3cW6bBiyRJehL/6qvO4QABBIiHU/YpMk/vD2DE4wgE9461b6+9DVN20NPU1pUL3++8P/YwYGvfc47x/DMyjccQYUoUQ5pln2gtqb95sH7lzih6r6Ffyiypv/fsHOyvbtqmjGtTtt0MHvQl04UI+nk4vji+/5PFLl7qPjxa+/ZbpEAMGhF9YN3061FPoNzRujN90Kjge4WL/fpY89PnUxbtu+OorjhyqFs0yDAPNHzt0ANdXdW+IDtWwoboWhBqyVa8enY7OJH5Qvbr+s0PN58qWVdNpiOpg16uHMmyq7FAMGM5UIYSZkpJluzAvKGDFPtpSUpypKCr6lWli0WmdR08/PbgvQmEhO4d2vTOIPlelip54wYEDqKESQq9z8+rVrACl+55FAytXssPQqlX4vSLWrEFNHDnfVati0axDOztVcfw4v8tCIAMWbvbmr79YrtytBkzGo49CDKZyZXX2V6ZDqSii33zDdkGXjq2CnFXRpext2sT7qDJCNOdnZIR+RrLWzZs77x8D82gcMYZUIYQ5fLjzLE3FtfSSO1GYdNSv5BdVCERRrM7MK6+osyhz5yKT4tapmkDGvGNH90V4QQErGl15pd7xo4E//mBqwznnhLcgNQzwvMnod+2KhfqpEKmLBrKygqUqn3oqfKeKuthWrapPgdqwga+t6tnbupWLXlXZmv37+Z67FfPOnAnHRqUMI6vU6dIRDh9mp1elhkWBg/T00Gv+4Yf4rFo158VMDBjOVCGE2bev8zw6aFDw3OfkROuoXxUWorkpHSs5OVQZacYMnrPt+Oxr1qDY1q7TvR2oGWvVqmqeOoGy3c2bR0elSAfbt8NuCIEgg5UyqItJk7jXROvWWDRHq+ndyYbfz5kxChyEmyEiW52QoN/wNSeHayX/9z/ncUSHEgK0ZCfIgjRDhqi/e+VKHEulTimr1Ok2vTQMVvpSqWHJAhRWu0OiNyrHJwbm0ThiDKlCCHPaNOfZlbqECqFWMZk4EZ59nTrqqAo1b/P57BWJKKXq1kNEB5mZiNqddZaew/Ljj/j+xEQ19/Tzz+E0RCOrcOQIousVK6LAUFdSeNMmvs5Hj+IYdJ86dYpOn4lTDXl5vLApXdq0rVvSwYEDiPwK4a377RVXYJ82bdT3/qqrePGiGkdyphddpB53ww0Yd/HF6vOjLNHDD6vHyaAeEKpC1vXr+dmy1n/Jjo+TwlgMGM5UIYQ5apTzPCpnf7t2db7fS5ZgDipTRh35lZWRxo8P/fz22/GZWw8RHRgGvq9FCz3K4Nq1oL0mJamL22fMQAGuKlOoi0AAC+v69fFur12rt9+2bbwA9/vx7tB1bdAAcuUlDYaBejX6nY8/Hl4mpLCQmwimp+s7Ms88g30qV1bTt154AeMqVVKPo3frjDPUgi2jR/N91Zlvzz/f/bcQyKm76CLnMQUF7Nhag0kyndXJrsXAPBpHjCFVCGFmZjobTmoWmJCgjmSRRJ9TU0ECRYdbtQr9bNcunhSjIZdIHX7LldNLnZNUnkqaNC+PF6+68rwq3HMPZ3x0JRUff5xTuZ9+GtzH47bbwo9o/RtQWIgMBmXmrF26dTFqFF+zJ5/U22fpUt5HpXS1bBmPU4kYrFjBC0lV6p6e4/Ll1feWnouWLV1/ygmQ6EHHjs5jDAM8fiHsa1uouNSpKV0MGM5UIYR56JDzPPr88/xMqMQXXnoJY846y3mMaULsghZnVvj93HVcp1jcDWvW8LmvWeM+nhbxZ52lXuhRsMmJsusF48ZxNsiuDscOREGsV880332XG3QKAQdKV6ji34pRo9iB0G3ma4Vck6mqJZNx+DDT+VT3PjOTM32vvOI8LicHwRc3ZUX5OVb1+iBaV3KyXrbPNHmOrlBBzTqg5q52Ur9EZ3ViX8TAPBpHjCFVCGFmKfLLpI1dt676BSTpRBU/+NAhngBefz30c0rz16wZnbQ9OUU63U0PH+bCxe+/dx732WccgY9UwWjVKnbIRo/W30/OSgmBaGm5cuFnBKKFw4dByVHJLEYDfj/LQVapEl7BaiDARbhCQHlNh65Gqfby5dWLqz59kNFS0QcMA7UiQqhVgg4eZPrXrFnO42QHSVdqeOZMDjCouvneeKPzu3TPPcikOHHBY8Bwus6jlKEsVUp9P667DuOcCtQJpMg0YEDoZ3Pm8HMQjUwoOesNG7pnfQ3DNE87DeNV9K5Vq/gcI+1mfeQIO2ReRBiIk09bYiIy0Q89VHy0MTvk5SFCrqtoFwmoj0ViojdhEBmyNPBtt+nR1ahO0edTO3r33gtWxfXXq589yozb1akRDIP7GqmoUkeP8vulK0+9fz9fA5U4Dgn7NGwY+tnYsVhH/ec/9vvGwDwaR4zB1XA2aoQXZuBA55cqP58X76pCb5kDaddk6K67UAMRjYiY388pTR1JPeKyp6aqazB69sQ4N0lWNxgGJFCFQDTfS60GRVHkLTm56AsknQzAX3+h1oYW9G59IaKBv//mKG/nzuHVujz6aPA1PPNMd2W1F1/k8X37OqsBffyxXvaNqDJuimsk8PDgg85jDIP7ragMrIy8PKZFfvut8ziSkrWrzaLfWq+e/b4xYDhd59GLL8Y1UhWZmiYHclQOaVYWP4N2TdhefRXUKzfKni6o4aQOLVbmsquizLTwbd48ciorZZFTU/X7N5imaT7ySOg8KgSco6KEYdj/5p07EbCgGqBSpcLvJ6WL/HymYaanhxdU++234OtXuzYcKNV9lYMldeo40w2XL+dxKlodSZvXrKmmk1FN6HnnqX8TqQoOHqweJ4OK8p94wnkMOd5JSaHS5StW8G+1CwbFwDwaR4xBaTgNgxVFVIuTlSvVjgWBMhynn27/OUUnohHJl9UsdKKANOFce63zmA0b+JiRRqdktS2vx5IpV7RdcEF09eg3b8Y9f+opUNKaNYOCSnIyuLspKSxpaz2XJk2idx4qzJjBRdSqgkYnyA6xvH38sfM+e/YEj83IsM82HD7MWQunfg+miWwbOZAqNSKiV9lRF2XcfDOylffdpx4ng2SqVRKQmzcjale2bChVkFSZfD575z0GDKerA0K9i1SLE91AzuzZfL3tnhkKUNx9t/MxdJGZyVnaadPcx1PvKLvGlYS8PFYFU8mx6kDOIns9ll2fq2bNoltwvncvKGGjRiGr1bo1gg3UNb1MGcylpUvzXCZvRe2AmCbmL+p1dMkl3vc/etR+HnWbg2SZ+Nq17cUz5AJvFUtg61Y+lqpuh+hVZcqor+0bbyCAaZdhdML99+PYPXs6jzEMzOGVKoUGD+TraFdPFwPzaBwxBqXh3LGDXwhVUTb1SahVS/2CkvqQXZ1IZiZ/1+LF6uPogLjtOnreBw+yPrmqpoAMVuPGkUXtDAOceyHU9SZOkAtQfT5IakaDMpCfj6Z07dox9c6aIbAzNHYOSHHJVT72GL6vbVs0G/QCqyobbc8+67yP/E7QVqUKuNBWdOqEz1XiDdnZek2o5s/n71Op+3zwAUczdfHcc7iG/fo5jwkEmJZgrWvZt4/Pza6WJQYMp2sghxbcKjEM3UAOqQ851fqQzHQ0AjlyHZ2bOp+8WHSqBzJN1E+R060juqECRbS9ZpFNM7Qv1U036XP+VTAMCJp068aBLXkjupjblpgY+fXRxaRJsN/nnOOtsSFBprPS5qYkKSu5CeHc54n6FanmJ9Nkm/XCC85jjhxxbggoQ26yqlsL9M03KISvX1+9PmjTBse1y3KS8pddLUsMzKNxxBiUhnPqVLwMKSnqBe699+pNEC1bOr941K3cKarnFURl0ImMv/cexlas6FzkKxd2qmT8dJGdDa6x1/qF3bt5YkxOjk4H2KNHsaih5lLkbKSmgt703/8iIvTjj1h8TpwI6tDFFzPlR94qVgR1zy0NHw3k57ND9t//etv32LHg8y5dGlFU1bO+caP9YuH220PHEsfcraCYaH0q6qHfz4vYzz5zHicXhaoUYWRQXVPt2upxJFFtpTTKmVK7+qkYMJzKeVTOmqmKuCmQk5amvg8k6WtXJyLTs+ycYq+gOjqdyLjcc0kVDKD6LTfBEh0UFoJuqJKRtkMgwI6/zxfaxDHcc5kwgTuFk5OYkoJA2PXXIxMyaRKy3t9+iyzB//0f5kvKmMoL8ipVMCcVh7DIRRfhe3Uk662guh+6nsOHu88/dk5L586h4779ltchqqwF0fqc+ucQKBuporPm5nJGSrdGafVqPadlwACMufPO0M+oWaFd/VQMzKNxxBiUhpMUWdxoH337ukd6c3J4grUzFiSlFw36zqFDzGtfuNB9/MMPg14yaJDzmIUL4dRkZISvLx8N0LUWwln2VIaqsNg0UdQsOx6lSqGQc8kSfSO0alVwjw6ZStChg/fMhFeQA1m6tN69kalqVOMkBFLuboZeNjL0W6+4wp7mt2CBXtaC6kqcqImE666Ds6UynPICVLeRpkxXVGWuiKplV1hPEsB2wYUYMJzKeXT6dH63VFF66sKsG+m1q/OR6XCRBnIMA8+kndNph3ffxYJbRb/KzkbdR5s2CGicLBClUQi1whIhM1M9Hy5aFDw3CwFa7PTp+tnpPXtM85prgjMg9P+nnYZai6KE7ECqxC4I8jxqFUbZts19f8pE0Najhz19KiuLx6pogBQwTUpSy/Y++SQyFW5OtdeGtXJAS2Wbiap1wQWhn5HYh10xfQzMo3HEGJSGk14G1cLcNDml/OmnzmPkRY7d11Ex7uWXq79LFzk5MHA6dREkMaqS1X3zzeg5SOHi4EGmwfTu7T5+3z7cm7vusl+MvPwyjtW2LSgWw4frd3+1wjAQ/b7ySkSsSFKwbl3U9uim9VevtldIU+H4cW5Adv/9zuNmz0bfj3bt+G+7dyMVn5SEDJebEkxmJhSwyJlW8X39fpZs/ugj53ELFuCcunZVUy6GD8ex3IqLKY2vypTIkCP0KqolUSHsBBioR4pdJigGDKdyHiXlGzd5ZJ1ATnY2O/h2gglUjNu4sfq7dFFYiIWvTnGyTu2JnOnWzdBFG4EAZ27r1HEfX1AAB/uSS+yLpSdPRja6VSvMC9dcE5kS4KJFsLnjxkFZrlQpBNSaN9frQm+aeKefesp7XSAJo6ic4E2bEIyoWpVpednZcFrq1gW1SmfRTvLdQmDuVTl43bphnKrrem4uz6Pz5zuPI9GMBg3U50cBl4ceUo+TQU0Mv/7aeQwFW+0EKZ59Fp/Z9aeKgXk0jhiD0nC2b4+XQaURvncvTyLWbrwy3nhDHeWl1OPjjzsfoyiQk8Pnr5q0broJY4qzQ7oVVINSsSK4rCoYBlL7FNmX1WEMA1Eg+t19+8JZiTYWLuRnKDFR7aCaJpxUnw+b6lmyA2XratRwjuJTdNguRU49YIYPd/+uffvgrArh3pvj6qtBK1C9Q3LWQlVASSIObpQuMta671IgwGICv/ziPI4ag9n1DCFFITs1rxgwnMp59JZbcG1USoKmqRfIIcWhhAT7eoU77sDnl12m/q6iAFH03n3XeQw9w02bFt95WUE1KEJAecgNTz3F19yaUf/iC47Ot2ih3xHcCzZtgvND5/zoo+rF+sGD3GdD1RfDDpStS0hwlvLOzHRmMwwbhs/69HH/rqysYMlolcrVk08iUGYX4JBBsuaqgA99Z2Kiugnm0KHe3yVSFFM5ij/9hDFly4bexy++YIfMihiYR+M4RZAmhPhKCLFWCLFOCDFWCFFBc99mQoifhRCr/tn/BSFEKYexSsN58cVYbKgKJ5csgUxj27ZqesELL6Ao96abQj8zDFbhmDTJ+RhWBAKooThyJHwFKFn67vBh53FEMVEVuFmxaZM7BUoX2dm8SNaJghGvX4jgrquGwSlgIaA5XpT84oMH2QmpVUvt5AUCXOjuNROWnQ1aR/36zs9Qfj7zjq0KJNQZWCezZJrIVCQkINKsWsRQHYgqU2Ka/PyrMkWkmFWxovpYtOD1IhVNVJuxY53HqIwjNYKziyqeRMN5Ssyjd96JiKaqL8bRo3hG2rdX1258/TWOde659p8Tv91rIGf3bjzT4YpZFBQwZUhVj0FZdS/P5o4dcPqjUU9mGOhVkZTk3mvFNBEQIAfDSj0cN44X4m3bFm3jwvx8BL+EQLBAZZNNk+uETj/dW6d5w0D2o0kTdYNWKqS2qrp9/jn+Xq2a3v2i3hy1a6t/E9WBVK2qPh4FX1Tv2rZtbP+2b3ceR6wHN+lsGdTvx66+g7B2LX+/VTZalie2TidxB6Tk4I+TfQIKJAsh1ggh3v3n30lCiGlCiF809q0hhNgrhHjkn39XEEIsFzC8dkgVQpi//WZvOKmOQiUJOW0axlSooH4xb7gB426+OfSzffsQOW3WTN0R2orDh/ll1enQaweSw61e3XlMQQFTn1TKGVZQRPDDD8M7Nxk//IBjVanirtTi9+N6p6eHNjWaPJmLDW+4oXgabmVnY3GVng4nRMXP/e47vqe6NQwEKm5VyT8SzcVa8E1qP1Wr6i90SPFHRTcYOxZjzjhDfSxyvOw6jRPcdOIJlA3SUYAjqOo7CHKBu5XSR5H59PTQQMRJMpzFPo9+8on9PEqKd6rAgUyDUzWRHD0aY2QaoYwBA5Ah06XfEYiGaqdCpIN16/j8VfVO1Cth5Ej9Y1MmVyUTrQtafJYurSfgMWIEFqAtWwZnVles4AL9rl2jK93rBL8fTlOTJnCKVBmDDRvYIdStYSAMG+aczSSQ8Ez37sF/lxfX1j4XTqDAmur+yvUpKjU2qqGx6zROKCzkazNnjvM4ygaVLq1vJym4d+GFzmPy8vi3WANylA2vUiV0TRN3QEoOcoQQ55/sk3DAECFEgRCiivS3DCGEIYTo67LvKCHEfiFEovS3y4QQASHEGTbjU4UQ5uTJobOnYTDXWMWLJ11tu6ioDIoM2PGDZePlJYokRzLCrV14+mnsb8e5JMgLP5U8pgyVbGk4IPqVdcK3g5zWlptq+f1YCAuBlH40+4a4Ye9ell1U1WkYBqJr3brp850J1Fiwa1fnMcSxtdKY5GdQ91nq3Nk9K/bjjxhTvrz6WNQZWFVgLktVq7Iukyd7d6ZIzlSlTHTgAH+/NVAgd7a2vsM2hvNR79OiZxT7PPr88/arUFp0v/WW87WVJaFVzfSeeQZjnJR+iEqn6j1jB8rAhVsYTtm5ChWcn7njxzmboApqWaGSLfUKol9Vruw+/x04wAtVq0NHSkZt2kRHulcXBQXcefzcc9Xv99VXw1HVaSApg4JyqanO12jKFM7GyA6B38+BS5WkvQyq/1RlxXbt0nPQH34YY/r3V38nFZirej5t344xCQnqTImMt97CPm71Xmlp9s+VafK6wdofLO6AlBxkCyF+F4iGtTzJ52LFLwLRNiuOCCHedtl3gxDiW8vfKgsY3QdtxqcKIcypU0MN5/Hj/MKrFB2oQ7JbQRctsB55JPQzWf/eS2Hin386pyt1QVEsVcdTopd46a0gT5ibNoV3bjK6dHG+flbcdRfGduoU/PevvsLffT7TXL8+8nPyCorMn322+n49/zzGuamvWUFGMSXFmVY2cyYKT887LziK7/czPWvyZL3vI+UXVd2I7LyqfvNtt7kbYdPkhaKd3C1h3jwsHipV0u/H8tRTKHZVUd8MA9Hgs89mLnzDhg1N0wyWJ7bWE1kMp08Isc/LhBgmin0eHTnS/gaT06/qy/HXX3rPCVEF7SKshsHHsCtQd4JusEmFUaN4Qe4EmV7iJdBEQg5e6LlOoMWuKkJNoDqvSpWC55O1a/l62XWiL2r88gu+u1Ytdeafag1Kl/ZGs5V7HTl1hT9yBDVLPXqE1ut16IB9Vf2UZJAimYr+WljIdDfVM/r22xjjRpsiqtZTTzmPyc/neVQ3G//DDzjPZs3U4/r1QxBs/PjQz5yYJ3EHpORg6T//vULA2IwVSLurcE2RnhFjjxDiG5u/rxRwmpyQIhChe8XmsywhxASbv6cKIczp00Mt3pEj7pOQabIEqu4Lb9egSqZ2eGkmJXeyDjearxPFfucdFBi6qQ/ZnVtCQuQ1Fnl5HBWZOlU9NhBgWV1rBoFocOE0P4wG8vMxMft8alrajBk4z6Qkb52ADx7k58GJRy8bV2tUq107REZ1G7iRKpRKJW7/fv4+FWWCMjOq7I1pcn8bOwlWgrzQc2seRyClJjenjyLYRF2oUKGCuXHjRnPLFv5Oq1KQZDi7CSEW/TNPFTWKfR595RV7z4GiraricpliopoDqTDWTgwjP5+P4aWZq9yBeeVK/f1kkAOtyqDNnAmqoUqm1wpZoCEaBd7Uo0Onlu+88zDWWitCjSCbNSveLLKMvn0RMFApjskZS690VlIJU8kvU18ia8DmpptQBK/KdMsgVSjdrIFc02gFBaHKl1dnh667zj3wWFjI109Hzt80Uf9Edl8Fqim1U3wsXx6fWenecQekZKK0EOIBIcROIcRDAtxhO+wppvPJF0J8bPP3uQLFkE5IE4jQPWnz2U4hxFSbv6cKIcxZs0IN599/60XwX3sNY9q2Vb9wlEa3e+FkaUYvhYak++1Wf6JC9+4omHv7becxpIKiW6Bsmpzqr1s3/HMjbNiAGorq1d2L2vftg6PUvHnwYiIQgMGoUUMtEVjUoPoTFS1AphotWODt+O3aIQLnRHuTjbI1C0QLDpUMqoweJWmWAAAgAElEQVTHHsN4FVfaMPSoeO+9h2hbr17q7+zbF4b4jTecxyxb5t0B0X2PrVHIMmXKmPXr1zeHDXvSFKLAFCKU+71z504ynIX/zFHF4YAU+zz6xhv2Dgg1MFVFy2fNwphSpdTXn4q47cQ8srP5vnuRgZWbm27dqr+fjNtuw/w0ZIjzGKrvqlZN/7hyllslEqKD/HxkPurUUau9EQYNQt8na7CkSxfcU50mt0UFopt266YeR3VqKmUyO1x+OWy2aj+SPrcWjxNd2K2fDeHrrzG+Rg31uLZtMU7Vt2XpUmTAGzdWsynuvBPfd8cdzmP8/vAdkNKl1eMyMjDOruaPmrr+9FPw3+MOSMlGNSHEHCHEDiHETJvteDGdR7EbzrlzQw3n1q388tk1WSOQNOc556hfOBUN4ddf8VlKivoYVpAqjxdqlBVU/PvOO/af5+eztGWdOuqFn4wPPsAE3aFD+OdGkOVj3Rw0+b7JxaByJNGrxG00QYt2t0h/q1Ywrl644qbJiz0nuoZqkaaiCdqB+L5usrjt2kHdyGpQZJB6jNuz3KmTfXZLhuyA6GaQqCeMncSuDGt34Fq1apmZmZnmVVfdbArR3BTiN3PHDh4/fvx4s2bNmmQ4M4UQtwkh/vYyIYaJYp9H337b3gFxWlDIILpMaqr6+lO/FauIgmkGZ9u8UCzlwmFVHxoVSHXJSSbV7+f3JSUF75hOtnvuXCyiK1eOXAVLzvToOGhUs2ZtfqeT0SpqTJrEz4sqC3PJJZh7VOp2diBRCpWaGkneTpgQ/HcVTdAOFIBMSFAXew8YgMyBSqhjzRq9Z5my19de6zxGdkB0A2G0lilbVj2OMtljxoR+Rpklaw1N3AEpuWgoINdoKLbiiNoJAfUVO+rAChE+deCIUFAHBg4cYg4bNswcNmyYOe2f2VbXKNGC0i3aQbQgu/QpFeq6yYtaQfQvN76lCqTbbUdpueUWRCTpOtCmUnEiEKXGLUKlAy8LSiqcs0ai5b+HW7AfDbzyChbsqoZSpglpRjvj5gYnWgChoICvg5WmRQpZVmlJJ4wfj6icinZimkwd+PJL5zHEN3frj0AFzSojvHy5dweEurGrHEO5xoAUhGb90zIZUfS5phAtzSuuGGy+/PLLZp06dUyfz0dG0xRCNPln3nlWdzKMAMU+j/bpEzqPmiZnwFTdpSkKXKuW+j6Rk2ynVrZzJ98fnU7UhHApsDKoT5IdpeWVV7gvhbzp1JsQpcarbbCD1wUl1VtZHcdKlewXiMWJ336DxO5VV6mznNQcUjerS+jRA/up6jioGN7awJcUsnT7Z6xciXmtf391f6v+/XHcRx91HqP7LBMFS6WWFQh4d0BIFdQtkECBTzvmRdWq+Oz7701z2rRpJ+aUIUOGxB2QEoJR//w39Z//zxNwMv4Szgope4vhvIRA8eQym79nCr3iSavRrSTw2x6wGZ8qhDCXLAmN3MmLGLsu2oT77tObbFSTNilpuaVgrZgwAQtA3VSvHagB4osvBv/dMLirqbw1aKAXiSP51Wg03PKiEibXOKxbx3/ftg3ZmNq1i6bpoC5I4lF1zwIBrjWwKoG4gRY6ThQLw2Aa0W+/BX9GRveZZ/S+iwoeW7RwHqNLwXKTVyU0bYpxqjoV3XdXBjnMTupKpqk2yEzZzDQbNGhiJiQkmAkJCWa9evXMiRMnngzDWezz6LhxofOofM1UPXDGj8eYRo3U94lq6ewWhrKSll3HbicsXIi5QfUcu4GyxHbOOHXXlreEBD1KlVwboyuooAKphOnMK2Sz5EyHYeAe1K9/cgrQCR99xA6ryh5RrcGrr3o7vk6m1YlGRCIoVgl4J1CG3+dTOw0ULFRRsKh+MDlZ/Z2qTCJB992VQQ5zlSrqcS1bYpwdxY0akn77bfDf4xmQkoO/BWQa9wsYlb+FEP8VQiQo9nEzWtHCHQL0ATv5yD6WsdbC+ZECCjPy77hMCOEXCvnI5ctDDefSpaCDVKqkTou+/DLqKFSRBNPEwiY93b7ZENFPvNZLfPgh9jvtNG/7yejTB8ewa7pE5yVvt92md1ziO7tNRDqQnQqZ3mKHAwe4qZxc7JmXx4tztyZWRQly+FRRLLn+yKtaFzkuTs3Q/H5E2/r1Cy1qJaUxqzPqhP/9D+NVNSDHjvFvUXU5pzojtxoQon+o+jzIylu6EqFPPonxqg7Gdpzo2f9caNB/JplC1DN9Pp+ZmppqNmjQwLzwwgvNjRs3ngzDWezzqF0fkLw8ZH+rVFHzyL/9Fvf+vPPU9+mmm/AMPPZY6GerV/P98VIvsWSJd4fViuHDsb+ditrSpbzwp61zZ73jypRS3b4SKjjRW+zQtSsWxRUqBF8XWnir5rCixpAhOIeLLlKPI0qqKvtqB1XNJqFfPzyz1ppClTNqB1JndJPyP+00jFNJ5+rWGVEtokr23C7j6wb6frdgavPmGGdHjatVC59Zr2vcASk5IEpVnkCHW93uuMWBZCHEaiHEOwKSlaWFED8JNNGS8YiAQewk/Y0aaI3459+pAlHAd4U9UoUQ5p9/hhpOWRpXlRalhYtbp2dK19rVWkyfDiPtNQNC6c6UlPD5wZSidmrARhM9bd99p3fcefN4Hy+daO1w5AjSz61aMffeDjk53G+FNrlvyaWX4m8nSwUrOzs4veyEmTP5/L3IMh87hihuz57Oym1yvwprbRPJR6oibDKIdnL99c5jVKpbMqiB1SWXqL+TomOq6Kv87uo6IORMqfTzZfoaqSylpaWZGzduNHv37m8KkWAK4TPPP/9Kc/fu3WZubq75wAMPmNWrV7cazoGRTZFaKPZ5dMKE0HnU73fOuMnQ7fQ8cCDG2dVa7NiBDErp0u6BChl79/J93bBBfz8ZI0Zg/wsusP+cMsK0qeRPZcgOfDRUsK6+GllzO+49IRBAjYp8vnLGhGjHZ5wRne7sXmEY7ASprmNODgdkVHbDDt26oa7TaZ4uKGCn0kpRu+UW93lRBglgZGSox1FGSiWT/sknGPOPOrgjKPCoqnExTe8OCFEpa9dWj3Oir5kmK5BZnca4A1JyYAghvhBCNDjJ5+GENIGalLVCiHUChs/qJN0qhDgoQvuYnCFAP1j9z/7PC3QBtkOqEMKcNy/UcMpqQSqtcarDcOv0TF2q7V54WTbUy4JT1s4Pt3iSuuw69XI4fpy7BAuh328kXDqEHQyDu6o7cXKzszlFLW9ytkPu2RLpOYWDO+/kKKnqPj/4ICKPugaMQOnv0qWdj09UFzuufV4e7q+uchSpZj38sPMYmQ6lcgZIxvSaa9TfSQXNKjnmefNQeNq/v/5voQZeqsJRuTcQyXqWKlXKTElJMX0+n5mScrrZps30ECWlefPmkeGcKVBvV1yKgsU6j77zjv3kQGpBqi7julQjyjRcemnoZ/n5oSIBOpApj6o6FRUoEOWkFGgY3PHaSyDHNDlza5UlDQfUQNBOxpjOk5w8eZPtA9mdKlWic05eQTa3bVt1rQ/N9127epMzl+sFnXqAyUEOK6U3Px/2SDf48eCDaufVNIMlcVXPNkn6ugmDUCZeJcecn49MUN+++vLUEyfiuPXqqceRKI+dHH2PHniPrDTiuANSclBc9RynOlKFEObXX9t3QqcCbFVXXVr0uRVdXXMNxtlRtQ4d4slF1XPEisOHeb9wlZ1eeAGTlZNBMk2O0HiJEGZnI1rdsKH36JMdqNbGSVWLIqjWTV6oHD3K96FPHzW1LtqQu7Or+Mi5udx4TJcKRaDUv0ou+e673Y2dLkjJRKWMJnOSVdFSNxUhAr2TThQz02TqoBulQcbw4bjuqp4mmZlwcs89l3ua+Hw+s2zZsuYddzxjCpFvG0SQDOcwIcRugcxzSUKqEMJ84gl7B4TkQ0ePdr62cgG5SgqXGv61b2//OUVPvSo0UZAlXGWnL7/E72zd2nmMLPc7apT+sVu3RtDESanQCygAkZpq359p82b7ebRxYx5jGMj4JCQg46RiCEQbO3ZwEELVw8I0oWgnhPqdtsM772C/GjWcFbaI/lynjrdj24GaAato3HJAVK5ttILENLp0UX8nUcxU/ZRkSuPff6uPR/jwQ2Qx3bI5ffqAvmbN5hw/7hxEiDsgJQdnnewTOEWQKoQwX3vN3nCSEtEnnzi/SLJCkyqqTdrgTh18SfLQi7KIYXDXUKtUoi7efDPUwFghyzeqqENWkNLFZZd55+Ba8dtv6ozK8eOg8ZBxoq106WAKGDmMQpjm009Hdk66yMvD9SXDoJKNJDWolBRv3ZIDAe5krqIlUOQrGhr+JFKg6tD88ccYk5amPtagQbg2KoN48CC4zR07qh1hooh0767+ThkU9VX1Z5GzOdSPJjU11dy2bdsJWVA77rXFcNYSKAQvSUgVQpi33mo/jxLfXNWYraCAFx4qugdFtZ3q5aiOSbcLNYEWqyNHetuPIPdyUqkEUpO1e+7RPzY55127wtmPpPnfwYOmmZiI49kJVRgG3kHKWsmbTGtbu5YzMwMGFB8Vi1TQatdW94SS1aC89lKibLsqG0rUZC/NeZ2gYkcQ5IyLTCu24pFHUECvatBomrBHrVur1xs0p1WqpH9/n3sO+6jULwsK+Bm0BpI2buTfaaUIxx2QOEoaUoUQ5gMP2BtOMkqqSLRcMKxaFJHKj1OEjFQh3nrL+Rh2oIWtbvdqK2Tqg6pwk85P10Dn5fFiVwhQHLzQy6woLOT6CVX0MDsbRpqkX8lwy7j3XkTSVQWG0YRhYOGUnq4uKg8E+DrffLO37yApZyGc6Ul+Py8avFBA7HDoEGpyzj1X3VPgkUeQ/bjxRucxhsG1HePHO4+bPh1jSpWyj94SqA5IVzDBNDmbo4rSf/llqJNxzj8NgKiPSJs2ofvZGM6lJ2/KKxKkCiHM88+3n0ep54BbJJqeAVWjUKqPSkqyX4hffTU+v/VW9XdZQVRUt4WbE44d41oXVXbuqqu8ReUDAcwFsiOg2xTOCd27u2cQCgqQSSTZa/FPRlEO5lA2ZejQyOv8dDFnDmyeW6COqGZOmTInyME2lVQ6rQ3sxFu8wDBQa9K7tzqQ88UXeL66dFFn7qm24777nMfs28e/ccUK53HPPIMxnTq5/w4CZXNuuMF5zPr1zk4G9REpXTr0/Y47IHGUNKQKIcyrr7Y3nJdf7h6tCgTYm58zx3kcNQ10igRTMbgqSmgHMia6RY1WHD/OtBZVd1zqQDxwoPsx585lKoS8RSp/+9prSHnXrevOrw0EmGcqRLCzUVCgnniLCqpFs2kicv//7F13eFTV9j3plQk9kNBBQHoRpIkUUVARBTsgVizwFBS7Dxs2FHt5iiAC9oKKaEBpIlJUpENQegmEkgzpycw9vz/Wb3Pundw5c87MJGBm1vfd7z3JnTt3btlnl7XXrlYNz53OJGfDEOpQERHeM6TLl6M03q9f4Io6H3+M70tOlnP2KQh96CHv++zaJe6TLECbOhX7+CrvUxAnq6aY4XKh4iQL3jgXUr12CzJJb44YUf5vIbBwOhhjvGNHezuqKtRBzq4sMWCW5LbLBFMz+IUXyr/LE3T/rrxS73NmUMVXliChRFSbNr6Pt3s36LGedlRlirkM33yDdyQ2Fr16vkDrIGPlpWVJjKEy4cuOfvkl1uRLL9VPtJBQCWPer82xY0hY9Ovnf88QwUxzklGriF4rU4lzuQSTQqb0SOI1sbFy2V+qvPmiuplBdEtZbwmxEJKSyldWiAFgx8gIATsaRojBwRjj559vv3BS07Av1SRytj/5xPs+RCHyNu2UvktVO5zw5JMod/qSAZaB+KAy2sIbb2AfX832nAt1JM9NNlFeBfv2CZqRCoWopATcaTK2qkoepwPffSeuk24QSk2MjMkn0BLNSCej5Q20OMnm3xQUiOBWRhGkYMbXtGc6f9mC6HKJuSOqjpqZ9y5rar3xRuxzww3l/3bZZfjbffdZ/33fPs6XLq3yC6eDMcZr1bK3o6pCHUSvkQWrTqc8e0tqUy1byr/LE7Nng5MeyEylm2/2vV5QL1hkpG/J39dft7ejsonyKiguFpKuqhQiogkxxvkHHwT2/RWJLVsEzW3wYD26GjVQ0+aNAks0o1q19Jrb7UBJlebN5baPZGtlbAyzwqFs2O5zz3mv1ppBM1RU+5UMQ1x7GVWbgnCzTD7hscfwN0859JMnOV+woMrb0TBCDA7GGG/e3H7hpIzn/7MsvIKifpl86a5dMDJt2tg3WfpqrvQGUr1IT/efh0sKRHbKMoR167A4t2wpl1PlHH8n42XefH1OBVQWjovDNfUFp1NkxM8+Wy4ocLrw+edChWfAAD06g1kggLajR8vvt2+fqNR99llg5+tyiUZ5GfWPKFO+6HeUffY1A4IqWrKmdzOHWHXiPdHXEhPlDgtVc+x6hyhT7Zm9hw2p8gungzHGGXPaqo6pCnWoyDobBoZRtm1rn91euBDH0JUmp2Fw3t4fFdBgTpkEqtMJAYj27X1n5/PyIEvt+X5/951/52fG/PnieLKqH8HtFtWBWrUQ6J0OCV4ZNm0SPYDNmukpQy5aJOwjbXZrRUmJ6I959NHAz5kGa8qof2aa99q13vej6kF6uvw7aQihjOZr7ilU7Us1C0nI6OjUP2NXbSQKl+e54Xmt8nY0jBCDgzHGExKctsb0gw+EMZPh/vsRpHhmP80wl0ftOM4LFkBhp08fvazNjh3ipSdlHl3MmIHPp6V5X1RcLtCfGFOje5WU4HqYDbpsErYqioqwwDOGxVnlWu3ejUwfLU7jx6tLJFYkTp4UqlTkNMkaDD2xdKl9htTuOlOVpGHDwPnav/4qvksmZ0x0GF8Vl+7dsZ+MT52XJ5qUf/3V+35USUpOVneQXnoJn5EpGHEuhprZycnSs+XpHA4eHBIL56kA5O+/y18bs1CHrEH77bdBa7GjsZlBU6rtqqD//CPUqHbulB/HjNJSYZ/9DdDNAw1lwS+eCTW6l2GgL5Cqer6cVVUYhpDRbtNGzR7m5eHeUN/h0KGcHzoU+LkEitJSkVWnTYdiu3+/6I0zb9Omld+XRBBiYgKv6J84IRcEIBB9u1o1ue2mXiFf7w812cv6TffsEddBhabHOX4DY+WFXzxx4YXYz06+/bzz8LcpU6z/DhGfKm9HwwgxnFo47bIla9dC9rVGDbmBfvppvDSdOslf0Isuwn52Sjsq2uN2MAyh1iXLDMuwZw+a26Ki5IohVB5t2lQ9SCIlDcYQYAUD336LLGJMjLzZzow9e0S2iYJK2WC0isSxY3CePBe98eP1juNJGaDtpZes++XniwnI/qr8mPHQQzjWOefI9yNHUTYnpKhI0LRkMwUoQx0RIa+mEKXB17mZQZl3WWOwmfpDM0AIOTnib+beHZeLApMqv3CesqPmgXWE3Fw4FrVrc75qlfdrTMFjfLy8r4iGVtop7bhcQqzCbsaADKTW5S+dtaQESZHERM7ffdf7fiRmEBOjnnDYsEE0udeo4d/5eWL7djiD1auD/ibrByA4nULKnM7lo49OTzWksBBrHk3Opq19e73jbNworq15Gz3aup9hCLaDr3lFKiC5cF99dMRQ8CWdTpV+GWXKbMdklGTqE4mLU5erp2p8u3by/SiBaPd+khz23LnWf8daUuXtaBghBgdjjLdr57RVFsnLExkKu4WVsGyZcI5kmujEHbXjPnIueJ6e0b8vEPfY1xRpGWgKtqwsa+bK61CZiNsdERG8AYA0kEzHqXa7YSRJujg5GddswYLKU3HhXEyBNW8JCfrn8MMPYhovNaEzVp4KSE55YqJc6UwVtAg/8YT3fQoL1cQNSPWEMbmsJv0GX9x+co48nQcZevXy/d6Zh4V6ZvHN8rzmQZ0i81/lF85TdtQbJY/oczKVMfM8JNnsIKJ0xcXZD5okpSlfvXueIAfK1xRpGWgKtizZUlwsgiTZ9fAErR+MBd78TJg1y+pwqyaW5s0TqmWMoXI1d67vvpZgwm5gImPyZm477NolgpgGDcSa70kF/OEHEajoJAm9gRIfw4fL91MJLI4eFee2YoX3/YiiGBEhT6pSn4ivgYZmUKAk62MtKfF+nqWl9n8Ta0mVt6NhhBhOZe7efNP+hSFnS0Y7MjtbsgZBoq5ERNg7guRUe8rG+gI18Toc/jvS1LyZkIChhB9+aL9f//6+s8WeyM0VzWmyOQs6cLtFIzRjWEhVkZkJp5MCI8ZAP3voIbkKUzDO+ZNPhOKSedO951u3IvuYlobhfW43KAFLl1ozVjt34tq3bi3vUdJBQQGcQF8Ul6NHUQGTLXQTJyIz7ivrPGgQgiyZUAJVA9u2lQtCeH6GgjeZDObs2eh7sRtuWFqK3hPPJAU5tM2aVfmF85Qd9SZ/S86WL3oIOVsyFZ2cHEHHs6ti0uyZGjX07KFZkUiHvmXG6tXiGFdf7T1InzBBZItVqwduNwJwqv4Eq+rw/PPinGU0Yk8cPYq1gihltAbdfjuuQ0VWRZYuLV/5YIzzlBQ9CvPx40gIJSVBwjk3F8/XkiXWSmteHqhn9etbp8IHgrIyPL+yvg767owMeQ/lrFlIMA0dKm+MnzQJ9taXTPWQIb7trScGDcI9mDzZ+z4bNojA1XO4oWHgNy5fbk3yENU4IqLK29EwQgynFk5v2TJaKHzJOlIWVUY3KSkRzqddIyFxKKOi5NlgT5ib1GQUBxl27rQ24aWl2e83dy7+Hh+vd4408DA2NjjN6JzjetLil5aG71Bd9FwuONGDB5cvv198MRymadMQUO7eHdjwL8NAlYVmTdB1MH+nTHPeEwcPCtpderp1QJgZZWXiuWzUSO9+VQaKi7EYMmbPtyYcPCjukSzAN8+0UXUgibffuLGczz52LPaTzTPxBM0iGDmyyi+cp+xo69b214ImR6emyt/RO+/Efr7oJp07Yz87QQCzPdRRvjMMMT9IRqGSIT/fWhlgzF5NaeNG8XeduR408JAxOWVRB4Yhkl8JCVjzVOhYhNWr4bx7Jlb690dF8plnOP/6a/Qo6hzXDn/8IXoI7LaBA9WPVVgoBlfGx8urbvT+JyT432tZkSCBDJmAg8slnm9fAT6tT/PmqX3/yZOoSNauLa/OvfWWqBCqrtVPPYXPdOxY5e1oGCGGUwunt4Xxyy/x8Ccny7NpxIv3lcmmCoJdpqmoSChPyIZx2aFDBwQQMlqMN3z7LTJHZkMeF2e/b2EhnNkePfS+q6QETl7t2pAxDVZ2LD8fpXjijl53nbzR1Q7792ORpOZK+l/zlpzsW3/eEzt2gDpkrrRERKBy8/ffnL/6qgj6ZNl3M3JzheJSSop8Xgj1JkVEyIejnS6ocuFJIa5ePfk7SKIHvuQlzaC+JhnVwDDE8/Xqq2rHNQzRtP7WW1V+4TxlR+0ym5xbxTLsGtUJxIuvXl0e9JN4w6BB9n8nFT5ZNtYOpMJz/fV6n+Mc2V1KDJg3b1XV3r1BfdX9rsGDkbEfPDhwGViC240eNJJk79XLe2LDG3JyoARGohLmQbS0xcTozTjiHImBd9+12lHGoBi4ejUUkmj9kk0TN8PlgoQ4Y0huyGRjv/lGfKfuoODKwLZt4vxU6VcykQSqICYnqz9fX3whAjQZDY/mnenMFqHKyh13VHk7GkaIwbJw2i0UWVni5V63zvtLQlKesbH2vGTC44+LDIAdLrkEf9edhP3OOzDCjRrp07DIKHhu3n6HuadAZ5H66SfRDO1vw7wd8vKsjZFnneXfoEHDwOeefx6BTKdOol/ElxIaYc0aVMHMfR4xMajQDB0KJ8WMP/6AfKJKhaWoSASwsbHyTNOaNSK4efBBtXO3O4Zu0KUDWlhknGHDELQcGT3ELMYg08r3BB1b5qhu2iTupUxe0oytW0V1Z926Kr9wOhhjPDkZdtRuCJp52r2MLnnggLjWnu+KGSRukZRkn1UnBTZdWfOffsK7GhOjr/D06KP2dtRbhYMafRmT9xh6IjNT9NQEMv/JEy4XkkpEb6tZE869P9i8GeqKN92EZJU5waWSINq+HX0I1JtIW9euoEX/9JN1/127oKLmtFfUt8DtFpU2xjj/3/+875uVJd7jIUP8S5xt2RKc3jtvoMRLq1by87v+ennQTiB/QCcwJjq0rA+1qEisp7JBiWaUlAja4ezZVd6OhhFicDDGeGoqFs7p0+1fAhraJJvQ64uXTFiyRGRd7ErzNPCvQQM9Y7d3r6CpqGbTCZmZ9nM77DKZnKPqQBlh3UZPapiPiZErbunCMLDgkYGLi4MjKgsGVeB2Y3FTnfhLGUTamjRBtjbQsv2hQ8gotmyJ50wmFXrsmKjidO7sXxCxaxecu44d/efD+zo+XSNZk7pZwlWWOTVz71Xmw3BunRkiSy6Yh4WpgmRB+/XjPDe3yi+cDsYYv+AC2FFvfV40UdtXcoVUcrz15XGOipnMeafBrxERenM9SkoETUU1m044flwoaZk3b0M4DUMkFNq106Mnmef/6PS/qWDxYlG9Ywx9AzozNexgGLBhqsqDN91kvYa1a+PfVq4MrHpeUIBkVadOOK5soK3bLZQra9f2T0AlPx+2OC2tYlQXS0pEgORL/YrWxjlzvO+XkyP6WVXpV6WlIsCUDamkCkxUlFysxwzyl5o04Xz37ipvR8MIMTgYY3zECCyc3qT1xoxRc7aJ4++rYX3oULywdguH2SmSZQDtMGIEPmcnT+kLZWVQWaGBeIzJeflEn9HN3hUWiuvUsKH3abP+YtMmVB9at8Z3pKcjMyaTOQwmnn4awdyTT+L+BYNqtmyZaLisU0feYH3yJKgdXbrAidi6Vf/73G7Ozz8f39eokVpWURf//a9YWFSoNr4krikLqCO/S/M/GjWS3ydyEnUEFOgzd93FudNZ5RdOB2OMP/447Kg3OtvLL+OaeOsTIdxwg+/KGOcIZOrVQ8+CJ0pLxWwWT0lPXyDOed26+nbDMCBLa+4PJO0AACAASURBVM74Dxjgff/Nm0WlUpXeR99z1VX4XEKC/lrhC1lZ6KeoXRu/xeFAlbCy+si+/RYB/333wXFXlYKVITMTEr3kBL/8snzu1U03ofKdnu57aKQ3jB+P74uPr5jeEaI+xcRwfuSI9/1o1ldyslwUxEy/Uk3e0cDZyEh5sE/9tDr+CX2md++QsKNhhBgcjDE+bZrzlCNih+nT8RLIBvVxDof9nHN8O0G0wHrrFxk1Cou4TmMy5yLrxxikQf2BuTkyIcH77/XM3unQvv7+WzgHgwcH1uBth/x83AuSqKVg53//q1hKEefB/S2GgSoOOSg1a8qnFjudouk8MlJtwrEdXn1VXLdgDI/0hMuFRZ0x+yZiQmkpAi7G5Ape/tKvaOiVLLBwOkVQLmuAN8M8YCwjIyQWTgdjjC9a5DxVdbDLmJtFAmSOyrx5uDepqXIO+rRpOFatWvaBwkMPgf5zwQVq941w5Ihowp09W++zhMOHrckcGS+enKyUFLkT6QmnU9BTzjor+IkClwvJjpo1xe9ISQFNSzWD7S/c7uCqaH35pRg0GRsLyrIs+KA12lciTobFi8UxdOSWdUAVGl9DLSmhdOON8v2IAq5Dv6LJ5r4CC3pWZQ3wZhiGqIa+8EJI2NEwQgwOxhhfvdp5ylDs2VP+RdizBw7Q2rVyo7h2rRpdxKxkYpehpsysw6G3qBiGKC3rNHl54sknxfnJyrDm7J2uxOvXX4sMOMnIBhsHDiADZVac6tsXU1V/+61ivjNYOHRINEkyBi67TD0sNxfOFgUfH33k3/du3y7UbHQHI6pixgxQ1dq2lTdDzp8vspXe6ICc+0e/OnJEUBZlM23oOY2PV2/IpGnJNGAsBBZOB2OMHz3qPEXzsFP5Ky3FrJWlS+WJgJwc8QzKnuOjRwVdxG46PakK+lNNpqp3167+O8I//SRouTLBjtxc0R/jy0H0xMaNSBTVrAn+vcpEc104nUgUVK8urmfLllCGysio+KROIDh50jozqlEjueyty2XtJ3z0Uf/uv9MpkiLnn18xa82ff8KGdu3qnebHOVQcVdgKZvqVasXHMEQySeYD/POP/ru4ebP4zLZtIWFHwwgxOBhjPDfXeYpH+d57ai+Ht5exbVscR1a9MAxBEbJrrHU6RYVA17H/4AN8Li5OfcquHYiv3bSpvBR7zz3oM2jQQM6jt8Prr4tFbcyYihsGuG8fgpzYWCHfyRjoG7ffjkpBZVG0fCE3Fw20CQlCTWbcOPn55eaKZs2oKHtnTAVlZeI4LVqoOTP5+eUbQmXIyxMce1+9ABSA+ZJkpQyyDv2KKAk1asi597feiv2GDFE/9jXXWLOSIbBwOhhj3Ol08gED8NvvuUf9etmBGmZ9yapefTX2s6tyGIZQjJPJk9rBPHhSJs/qC5TMSUiQJxBmzgTdp1kzeX+XHb75RvR89e5dcdWJ3FwEUikp/NR9pkTZtdfivCuCrukPSkrQT1mnDgKBuDi8wzLKb1mZGGLJGChn/gafNPcmKUktKWIYCNpVv88whMpY9+7yz5E4QqNG8kCIpLKrVVOnX/3+u7hesl5B6m1NT1f/jTR486yz8JkQsKNhhBhOLZyPPYYslC9nxxeIFlCnjtyxoSpH7dr2zuWkScJo6DQnFhUJ2oquBKUZO3cKCtOzz3rfr7BQLPING8oz1Z4wDM7vv18YsOHDKzYQOHIEyio0XNK81asHStnYsWjm+/ZbVKcqKzApKsKzY6Y7pKf77q/ZuhXOV2Qkgo/PP/f/HJ54QlRQVJ2uG28UmUIVkDpRcrJcZWjTJmTBBw2SU8lOnsRzN3CgXvKAJD1lE9PdbpHdU1VtKykRyQMa5hkCC+cpOzpjBpIWjRsHlvUlXnlEBDK43mCuctg5erNn428xMaiI6oAcPF/0FhkKCkQm/JprvO/ndgvnNyEBAZAOPvhAVPQ6dw4s+eQLOTn4voEDrbOj6Dr36IGA75lnYI/++ss61K8iQcNemzUT55ScjEqa7Hk8cgQVJKJp+SNnT6DBwIypz5Mh0Yrhw9UScZ9+Kr5DJr2bkwOKYu/eclEHw4AyZ58+oC6q4oEHcA6+JqbTvC4d1Taq6FOSNgTsaBghhlML53ffiQyyrvyiGUeOCO6vjL6UnS3KnXZO47594jgff6x3Dq++Cg51Skpgv4WcxUaN5PKj27eLpstevfScdsMALYOM6YUXVgyNwBP79sEg0yJqp1lPznjTpmicb9sWUofNm8PBSk8HTz0QSeH8fDjO5vkBKSngycp444aBnhaivFx6qf7sGDPeew/HGTQIszFUQA2LjIFT7Qu7dqkFtZyLBat9e3kD6jPPiCysqlLPnj3Y/4IL5MPcfv4ZCYLBg+2pmXawa8gMgYXzlB010z1k1DZfcLvFOyFzBt1u0Di9BcElJSKI1JWj/u47vA/x8XoDDT3x2Wf4/sREyLV7Q06OkNdt0EAvmcM53n9aU1q3ltMbg4XjxxHkDR+O30eKkXZb/foIjtq1w/m1aIF716ABEkB33un/eZSW4veb1RxjYlCJ8xWMZWQI1a+ePdV7FOywdCmelx49UMVTyfavXSvu2x13+N6fZnExhsqTDBQgpKTI+66I7soYpOFVUFyM5E/v3hB68YaDB8F0GDBArnhoRlaWoC8uX45/CwE7GkaI4dTCWVoqjJBMzk4Fl1+O4wwdKt+PVEy86XKTtnaXLnql4NxcoZrkz0AtQl4eDFxiInpLZA5xRobIwN10k37p+vXXhQHs06e803nyJBa6iuDSnjgBwzh5Mn5v164ii82YaITztvlypj1RWgpHZORIlOhpZkhcnJrk5bFj4hljDAu5jNfsCx99JIz9xRerZeC2b8e5M4bnWOV+02RwX7Q+kmtkTB4g5OaKuTI61T5SpmnSRP5bSVJVp4n5P//BZ847T/xbCCycp+wo54KeI6suqWDyZHGfZO89DdxMS7O/ny+8gL9Xr643pNTthkPKGBIQ/lJEDQNqaKmpqE7LZidlZgpaas+e+hXYjAyRlGjcuLzzbRiYOxSsAYZmFBYiGz9lCiogvXoJiVjazHbVc/PlTHvCMFCpHTcO19XhEAmOkSN9U5+Ki0GVpu+vWVNdetYOq1ej2sIY523aqCk85uaK9aVDB7X7Qiptvmh9u3eL6yHzaQxDBG6yOR6eIHGe2Fh5opMoYGlp6myO998X94TeuxCwo2GEGCwLJ0l5tm0bmALHt9+KaopMO9zsaNlJ9JlnIMiGztnBXAYOJBP5668iOzNypPy6kMwmY/6pfsyahWtmN232wQdx3HPOCSwbqQrDQDVrxQrcz7ffRpVg5kwEQh9/jMrV11/DGVc53sqVcEQ8F+WEBMwckC0mhMWLYcjpszfdFBi9Yd48QaPo319tATTT7po2VeOcL1smzllWqXG5xLEHD5YfkxbilBR1edAjR0SDs4yS8PffIij7/nu1Y7vdcPo8F/wQWDgtdpRoTwkJgfUEmGfFyGzY/v0i+WH3bOXkCMdQR+qWcwwmpWPrftaMfftEo/k55/gOwOk7b7xRfy1asQLO+O23l/8srQtNmuBaBVNpyhtycpAg+fJLOJbvvoserA8/RPLjs88wu0o1ibJlC6rzVPmiLTISQ2BVehG3bhVy8GT7dCl6ZqxfLwLH5s2R9fcFwxBJmaQkNZne/fuREFRJulAfVZMm8ufNPOldVT3T5UJvBmOgLXtDUZFY7555Ru3YnIthiOYkRgjY0TBCDJaF0zzxWHXwnB3M1ZSpU73v53Yjk92+vfeG3IEDcRzd3hTDEJnIVq0CUyp55x1xXWSLsGGInoDUVL1sI8FuGr1hoDxPDiEFQ5VBMQgWtmyxLpZRUZBQnD1b7Tr98w+MMRn9lBT/m80JixYJhbAePdTvF00RjolRcxpKS0Xzf79+cqfnjjuwX0QE3kdvyMkRtL8nn1Q7b84FrbBuXXmwdffd2O+ss9SrbkuWIBM7eDACGEIILJwWO5qfLxz+999Xvzd2IKlvbzOaCOPH4xn21pBLme7GjfUrGSRyUK2ammPpDcuXC1qtryox9QTExsJ26OLvv+2pi6+9JgJweh+DPUOkIlFYKCqvtJ17LiroKpS1Y8dASyKBj+hoUK4CmTOyfbvou2zQQN6zZIZ5XVWVe6agIj1dTlWmCgJj8rlRbrcIxIYPVzsHzgWtMDISa5M3kNhHfLz6QNBDh1DZHjDAKn0eAnY0jBCDZeHkXDQo33WX+stoh0mTUD4cPFi+4JGhiIyE7JwnfvwRf2/YUJ9ms22bqF7o0oTMMAwxwTwqSl6NKS6GAoiqHKoOfv9dzLlgDJmgp5+uGDpBRaBjR73FknNkTseOFY5LmzYIXFR7Erzhl18EVaNTJ9DQVGAeQKla5br/fvC/e/VCptAb6FlnDLQ0GR5/XNBqVFV/nE4RtMjeB6dTNKO+/rrasTnn/LLL8BnPwXMhsHCWs6NkL7zNOlLF7Nl4Tnv2lM/IMM8YsVOR2rMHtqtGDVQxdeB0CuU2XZqQJ8wTzGW8eRLoqIhq7+7dgv5La88dd+hNjD+dGDUKSbWnnrIG+jLk5qJiQO919eoIbgOhrnKOdY56jOrWtU+g2WH9ekGPUpVenjMHgf2AAfK+0O3bxb2Ni5MHul99JfZVDUTNcv+y98EwxOBHX4qHZtCQ2vr1rYnTELCjYYQYyi2cb76Jh79GDXUpOjvs2yf46bIsoMslpHsvvbT83w0DDmhkJFQqdDM1lPFNSFDPzNihqAjfz5hvHnNFwjBgfMnoU1Zz9mw9tbDTAZ3m+qwsZOHNM0yaN8dU50D7YL79ViiMtG6tPvxswwZR2bvsMjX6hnmB81apKCqyavUzhs95w4kTgksuG2ToCeoFcDjkQQsNYnQ41KtCZsqW5wyMEFg4y9lR86wjmYCFLxQVierZ3XfL9yX542bN7Cu+U6YgaVG3rrpgAcGsOqQjO+0Jw0Alk7Lvv/7q/7ECxbJlVhpS9epQZ/Sncl2ZyM9Xp47l5SHZQGsxY6AEvfRS4ImrdesEy6BGDXUH/tgxsZa2bq22LtC8F6pU2P1+txsJE/Oacd113o/pdosAQUfpzZwokiWUliwR+8nmoplhpmxNmWL9WwjY0TBCDOUWzuPHxQsciKQp55Cco7KszNgtWCBeVLvqwurVwrnR7a0oKBC89BEjAuP8mnnMl16qnjWvCOTnI1NCdIJmzRAYTZjw76IUmGEYMOgPPSQWG8agePL++4EHWC6XaAhkDOV8Vd7zhg24vo0bY9FVabDMzBQZR28T77dsEU345k3W0/HYYwjIa9ZU7zEoKhLCDDKZSZdLSHjKZvl4gihbLVqU/50hsHCWs6OGIRSRVCWavYGSQjEx8iTKzp2i4mtHFc3KEjz9m27SOwfDgBgBY+hRCkSpr7BQBFVduwYWoAUKlws9GeT0de8OitOYMViLzuRhrTLs3g0HltYrxlD9nDIlOAHWBx+ItWfECFTgVHDkCJx+hwMVGBXHPDdXUG/POss+eZKdLeja5m3ZMu/HnTMHAbkvuqsn+vbFsS++WL7fsGH2FWEZiBESH19eQCEE7GgYIYZyCyfnojzt6wXzhf37hZF66SXv+5n7Nbp2tTf65OAkJenTb+bPB20nJgZzMALB8uWiQaxTp4rVm1fB7t0orRNFibbOnUF3OJNoBd6Cv3/+QSafHPEaNfDc1KsH5ysYs0iOHYPEMV2fgQPV792ff4r5JKmpapz0/HzIbVKFylvAcskl5RdNxrwHW2vXgkrTvLkelYb41vHxcvobCUhERMgHa5mRmyt6HuwkmUNg4bS1o6RO1bhxYMFzSYlQCrrhBvm+1K9Rq5Z9EGvmxuuKc2RmIgipUQPUk0CSOXv2QGUtPh7vlI4DWBHIySnvsDOGBubHH68YSq2/8HbdjxyBvTTTdBs1wrv52GPBSZgVF6O5n45/9tlqzeOco3+IBhDHxVn7G7zBMMRA1oQE7wHLww/b21FvkrpHjiDorF1bT0Fw5Upx7F9+8b7fzp3eK8LeYBhizbCjbIWAHQ0jxGC7cC5diua8mBh1VQhvoEF7NWvKaR9//CFebDt+Jw1cYwwTXXUXv3HjhGMl06JXwbx5okp09tmBNWYGC0eOoDpECkq0xcTAgH/1lVxGuKLx7LMIkqgX4oorxP303Nq3xzMQrPP94w9RBWMMi5UqlW/VKtE3kZamrvg1ahQ+ExsrF3T4/nsrZYA+Y4eiIhGkde6sLqxQVCScEl+9XRSk6chRkvpbSoq9IlkILJy2dnTfPtiq5GQxlNFfzJ0r7JfMWT92TDyvdnM/DAO2nTEEsbo0nHffFc/pCy/ofdYTf/0lKg+1aqnPX6hI5OVBjZCukXnr1w9/U6VsVgS++QZBW61a6NccNkzYfLNICWOwr2+/Hbwk2b59onmdMSQqVaspe/eKafUJCerzMKZOFd83d673/TZtwjXxvGd2wZFhCBn32rXV+xENQwzL7N1bvu8994h3TLWK9vPP4rzt+mFDwI6GEWKwXTjNuti+Znn4wvHjYkH0NeCN5n40aWKf9f7+e3mQIkNpqSidpqSoN8t5Q0aGlf7kb39JVlZwedCGAW7u3XdbDXKLFsicd+6MDNaMGTDagaifqKKkBI6YXbBhzjQ+/LA6V1YFhgHpYGp2rFYNssGq+OUXkdlv1EiudmLGW2+J3+VrErA5I01VrHr17PedNEkEKDrXiZoaBw2SP6fffw9q18CB6pPgXS4hB0oTez0RAgunrR3lXKjiNWsWWBXE7RaO5mWXyfelXp+4OHtp6x07xDuhM/WZQBnwiAjw4QPB1q2iwd3h8N8WlpYi0xxMWd2dO1H9MMvdkgJVq1boZXnjDVQlg1Gl9QWXq3yvmOdWqxZU+lasCC59bNEioXQVFQVGg+q13rVLXMOkJDktyoylS4Ucs6/EycqVYl+zypldcDFnjvj7F1+onQvnkFFmDL4RDQe0w44dSPz16KGu7sW5YFZ4m4sWAnY0jBCD14XT3JexerX6S2QHmtaclCTPNuzeLbLB3qhS1GhZp44aD9+MI0dE1r1168A0+jmHISUHtUED/4KaMWPw+VGjApvaboeSElQ+hg4VQ+U8t+RkZPYeeABB3e+/w9E+flwvOCkrA90rMxOO+4wZWAi7dSuf4ffc5s0Lvh7/1q2gi1DGrk0bvfvz889Cb75ZMz3aHw3j8zXDYNMm0esyciSe/0mT7BvQV6wQGU4dGuGmTaIvQCbXW1wsMpR2YhDeQE32kZHer1EILJxe7eiuXSKwfO899etqB3MCRhYgFhWJSdHeFIaefVY4k7JGWjuUlEDdi5I5gfZw/POPqFAmJuLd08W0afj8gAH22eNA4HbDGR4zxrsdjY2FrRk/HtWu5cthb7Kz9WSP3W5Qpf75B9XXuXMR2PftK9Yab9szzwRfiOTwYTxDRA2qW1dvJldmJtZGCjBVExucgzYaFQXlRFmAd/y4eN67dkVS76mn7PugDhwQCVFZg7oncnJED92IEfJ9iVbbooV6YLpjh7iP3hgaIWBHwwgxeF04DUMoBXmLyFWRny/Ug/7zH/m+U6agvBkfb081OHxYKHqoyveZ8eefwukbOjTwLNGaNaKxMzVVbQgUobhYLOQUDEydGtjMEm9wOqHK8fzzUBGhRcG8UdMsbRER+G1NmyLrc8EFKF2PGIGFvnNnOA6e033NiiuexzP/9333BT/wyM1FlpCcPhrOpTOs8IcfRIa4ZUv/BnT5orzl5yMoou+QURny88W96dFD3aFxucQ73KaN/Ll67jnhSKlKe5p7t2QqMiGwcHq1o5yLikHDhoFlyg1D2IvzzpO/O59+ivc1MtI+oC0tFRWVc87Rr4QePixsyNlnB97YvG+faDSOi0Mvkg5uvVXYlagoUGBUh3PqoLgYCbnXX0fSqGXL8naOAn7zVq0anOSOHZHwGT4cfTSDBuH6N2sGu+lpIz1nfjBWvt9vyJDgrxmlpQjqzLZ9/Hi92VNbtginvUYN/2aLLV8uV5w0DCH/Xa2avEptGBADYQznpaMER4I6KSnyRKE5SaA6wJVzXFtaC7z5JCFgR8MIMUgXTjMnUVZyVAHRUtLS5E28+fnCqLdrZ89RnjkTf4+PBydWFx99JH7Xf/+r/3lPrF+PikyNGig1z5mj/lnDwP5kqBlDeT8jI/Dz8oWDB1F9ePhh0G5ItUO2+apmkAPQrh2ON2UKfsvRo6LaQ4FoMIMPtxvKLBTokmOkyjWmY0ybhowbY5CHzsoK3jkSzHNl4uJ891lR/1J8vFoPCuGNN0TgJ8s8HjggHJ2HH1Y//nff4V4PHChXwQmBhVNqR/fvFwGtXZO+DkjeNylJTic0DJGJrVHD3olbs0bQVvzp5/j9d/G7hg0LPJmTlQW7ERWFgPnJJ/UCoyVLhKQ7ZetnzKh4JavjxzG9/emnkdSi4ZG+NnNfmrftggvQl/XII7jf+/eLag9j+Fsgcvl2WLTIqsyXmoreF53r+PXXwo7Wrh14L6k3vPSSOE+7+TdmvPee2Hf+fPXvWLZMfE5GqTVXkXUGJ2dmIqjs1Uve5xICdjSMEIN04TQ3LPbtG5jDWFICzmx8PAyTLIv7558iizRunP15jR2LjFJKiroKhxnEp2/bFqXeQLFtm1DrYAzOtk7W3enEOZmzW8OGVb76SlERMjxbtoCPPX8+eKyvvgo+9KOPosLw5JPIAs6di5LxqlVwjrOzvdMANmxAgDZpUnCDj7VrxWLHGLJ2L7+sR0fIyrKqZN12W8UpiD38MCpLnTrJh7FxbqVC2lEKvGHfPkHZ8MWfpsbK9HT1Z9a82F5+uXzfEFg4pXaUc9GUWq9e4OIKkybBqatXT05Dzc4WiY3zz7d35qdMwXMYGemfOMfs2Th+o0bBSSocO4bnlZ75fv30RD5KS/GeEM2GMVCjVGVig4XSUlz/7dthG3/4AYmvN98EPWjiRAQskydDPOTDDxHQ//orbG9Wlvdq2bFjuGfDhwdXWGT3busaFh2NKrXqoFPOcT5mlazrrw8+JY7w0UdIiJ13Hr5Thi1bhD3UkaAuKhIJ0fPOkwdh/lSRORe9Hy1byitZIWBHwwgx+Fw4zQO1AhlARceiErOvAWrmLI+djN3hw4L32by5fj8INfRRoPPaa3qft0NODuhJ5kqGbuZn2zarI+xwIAjTyXyHAgwDz+PQoYK6QYuLqqoJ4fvvRYMlVWeCnVUkUD8UY5BMlTlsO3bA0Tz/fGRVVTOQhiEWtfR0ea/TL7+I8/nkE/XfQeo0sbG+m/NDYOH0aUezsgT1UyZJroIDBwTt88or5c/QokXi/noONuMcTu555+Hv1ar5J4f70ktC8OK22wKvOJSWoieNzrt2bT06C+fo96MqI22jR2OyerBpn/92/PUXrpU5iTNoEProdI9DMruMwQZVlEz9vHmolDGGCqws2ZSbi0Rj795Yk3UCKpobFRsrX4PNVWQdYQfzUENfCYAQsKNhhBh8LpycY4YGYzBQgRrvhx4S2RWZ7KLbLRzxWrXss2AbNoisRt+++jzYwkKrOtPzz+t93g6Gwfn//ieUOOLikPXSuW6GAQPbpIl1uNIFF+DfdZoaqxry81Gxov4JxtDncO65+lnOoiLRMO6vo6ODV16xOkMyRy07W/R91K+vJ/352Wfie2RcepdLTILWqXBmZYkBiyqLbQgsnEp2lJzq2rUD75n45BNxj31RPqnaGxWFbLwnsrPFnJHGjfVlZg1DVHjo2Q6GjcrIsCYG7r1X38avWQMhjJ49xXE6d+Z8+vTTK0t+ulFWBgUoCj4ZAz26Qwd9URC3G7aN6Ln+rHk6yMgQ39Wjh/xdKikRa2hcnJ7U88aNgpHgK2F6/fX6VeTSUhGwDRnie/8QsKNhhBiUFs41a/CStGvnm2fpCyUlwulp3VquQ5+VJRaggQPtHbbvvhNVlZtv1jd6xcXWsvPkycExnBs3Wnm0l1+uX6UpLES/S6tW1kxew4bIpJ9OPfrKxq5doANQ5tecqZs/X/+ebd6MeSPm4wRbhcwMM/94xAi5g1ZYKJrHk5L0hA0OHkTFhLLjMtCE7chIPSWkm24SdCIVRzoEFk4lO3r0KBImTZvKFclUce21uA8pKfZyu4SSEiGr3rSpfQZ482bRcNyrl34F0DCsw+CuvDI4jdGHDlmTMOeco6+65XYjsWAe0McYbMnEiad3Entl4+hRUIU8ZzB17oweOt17dviwaOxmDJWGYEqpe2LZMpHc69RJLjRgGNa+w88/V/+eoiL4BRER8Htk18XcI6IzHoASUtHRajTyELCjYYQYlBZOzjHUijE0pOk60p7YtElkMO6+W76vmQPvrVHS3Ij24ov651NaKnjwjGF4YjCCkPx8zm+5BcdMTARV6J139LODhgFBgCuuEE2jjIE+dv31MIAV3Wh5OnDyJNR8hg61qsMkJYEnrksR4FxwxGkRi4nB81OR12/OHHH+vhRrXC5wuyljrTItmFBUJGSHL79cHlDt2IFsd7du9n1W3rB2rbgPs2apfSYEFk5lO/ree7iv0dGo4AaC48eRcWXMN0UvM1NQREaOtN/nxx+FfRk1yj8bSNPfGcN7Gwwqo8uFhAtRbtq3h3iITo8d4a+/oJhFdDjaLroImf+KUCA83SguBl311lutMzKiojBMcMUK/XttGOjBME+OHzdOf7ClDlatEoyHNm1807sef9w/v8AwUMWjCsvatd73dTpxLuedh8SP6nXMzhZ9ShMnqn0mBOxoGCEG5YXz0CGRfR49Wu2FkcEcNPjqLaHy/oQJ9n83qwpFROhLOHKORY4yu2RMg+WUfvKJVWGqTRv/B3jt2wdeqtnwd++OwHD0aDSFVxTvtjKQmYkG8oEDRX8OVcyaNsXf/JHXNAxkwFq0QPDbsCGa/v78M/i/wYyvvhKOU//+vhfoiRPFffU1xNAMw+D8YjLUTwAAIABJREFUhhuEY7F4sfd9i4qs1zQ/X/07iMrSrZv6+xECC6eyHS0pETMVunQJnKpk7vF45RX5vqQeOGSI98DgtdfE8Z55xr9zMtv2Cy8MHtVp5UpBdaEK3IwZ/g1TPXECtoSEFBhDpblaNQTv//uf/8NlzwQcPAia2eWXC6edJJxr1UK1SiZvK8Mvv4gKbefOOJ4/a64O/vpL+B/Nm/uuVn/wgbivd92lF2CZ+09lqnWGwfnVV4sE486d6t9Bjfq1a6uvZyFgR8MIMSgvnJwj40kvZqBcebdbKGylp2NB8IaiIt9yqiUlgnqSlOTf8ES3G8Pz6DfeeWfwFs/sbBhCckYp6+ZP0yfnyGp9/DEa6845x5rNi4jAQKZHH8ViEezhVMFEcTHu7T33WJ0B2uLjQb367jv/p7YvWQKHmY4ZHY0man8yqDqYNQv3gTE47r6+7/XXxTnqTqh++WXx2ddfl+9Li19MjF7fzNy54jt++03tM6WlnOfmVvmF08EY47m5anb099+FHXj2WfXr7w3UxxQXJ1ccMgzYbZkzZhic33GHf5QSM0h2nTE4acGSsy4oQDO9eT5Gx47+DS/kHDZ/4ULMkqBeR/PWsiWq9AsWnNk9Iy4XKgSPPYagwPN3REaCjjRzpv9Vis2byw9ivOcePZUyf7B8uVjbGzb0PRT2p59E78all+oF+RkZogp4663yd+Xtt/WrwZyDUksVcdUkk2FwfuxYlbejYYQYHIwx/tVXagunYYim7bS0wAc97dkD3nFkJF72QCsOx44hO9KrFzIL/gw+Mgw0O9Ji1L59cBWotmzh/OKLrQvD7bfrKzeZsWcPDNnw4eWHAjKGfxs1CvKPX3yBhSSYVAOnEw38X34p7wnIzUUW89134TSdf375ng7GQA266y44S4Es+uvXW/nJjIE370u1KVC4XKDxUbAzZozvd+Xzz8WidO21eu/CwoVi0bzlFvmi+fHH6hlzM2hGQ8eOeJZU8cgjnJ9/fpVfOB2MMf7UU2p2lHMhxhEb6x+V0IyCAtEndtllgQfWpaWoQHboAJqIDnfejJkzRZIpNRWJgGAhKwtrhpmaeeml/kmyE44exfsxZox1LhNtsbEQAnnkEQTjf/yhXj1UgcuFvqyZM+X9fYWFqNx++CHszJAh1n422mrVwrv6ySfyBJ8v7N8PdoCZ/tu3r39JPl1Mny4q4Vdf7btfZ80asQZ27ap3f3bsEOtRr17yoaHr1gkauc5A5JISJA179kQPi2pS7dNPOW/dusrb0TBCDA7GGG/QwKmsyrJvn1DAufVW9RfPGz7/XGRsHngg8OPt2SMa7KpV82+AIilZkYFJTtaTKFXBwoWCikHn+sQTgTdCl5aC0/voo6iM0ALdt691cYqKQk/KZZehv+eDD5BB27MHzrJO1shcso6NhYG96SYsft264XfaTV5nDDSUqCic3wsvIDgKtP9m927Q0czOyYAB/gWkunA6xQA4xkC98+UQzp2LjG7XrrgOOtOy//5bfdHMzBR0jGHD1K+zWZGubl31HrBNmygTWeUXTgdjjMfFOXlmptq1KSoSQUOPHv5X9wi//w4hBcaQ4AiU2pWTIwbqRUaC6uQPMjKERG9kJKoXwey3Wr8eQYHZrk2aFFggwjnejfXrYZP69xdOcKdO5W1Yo0Z4P+65B+vG8uVIchw/rpfoMQ/+jYjAd91wAxzcPn0gatK8eflp6YyJe9WpE2z/b78F/kydOIE12dw30q6d7ypaMFBWZlVW69zZN2Xst99gC/v0QRJLp+rmdArRmAYN5AlBp1MoFLZpoxfkmBXpVHvATpygAbtV3o6GEWJwMMY4Y06fw8rMePddYRh0Jk17A72UjAVnKODOnUJWMj7evwFbnGNRb9JEnNuddwZ3PoTLhabUunWFHF90NDLg/jQG2iE7G82CTz+NwKBmTftAgIyi+b/j41FJatoUGbaePbHQ3ngjqhedOuFviYnej+m5RUTAeA8bhoVy4cLAsnMEw0CG9dprrXr2nTrhOypD+//vv63KZ48+6tvZevVVqxOhU1U0L5rp6fIFNycHcr5UYdK55mZOv+oE4bIy0DVatOC8Vasqv3CesqN9+qg72CtXCmfy5ZfV74c30FBAxkCjCvSZz821yrTqVMzM2LfPKoM7eHBwh3waBmx869aY+k5B9gUXcP7NN4E74pwjifDdd3gXBgwAA0Bm58x/j42F3W3UCCpR556LCtPNN+NYXbvCJtpVg2Vbejoq9ffdh9954EDgv5NzOMZ33YX1gr6rYUPQjIJxLX3hxAkRTDOGRnlfTv6CBUJYQIWmZYbbLeYmxcf7Hg9AlOfERDAaVJGRIX6TTn/VnXciAHM4qrwdDSPEcGrhZEy9WmAYMJzkzASqae92Q7aRsmTBmMVw4ICYFREd7b988IkT1gbyLl30ms1UcPIkgjrPsn/HjghQglnmNwwEJcuXI1s3YQIWscaNrQP9ZBs1M6pstWtjMZk1C4Y92Dzq7GwonJjPPT0d937u3MpTB1u8WAR38fG+ufOGASoHnfOgQXrUGZcLDab0fb6qOxSoRETo9X38/rvI/v7nP+qfe/JJcW5Ll1b5hdNiR998U/06TZiA65SQoDc92dd1Zwx9ToGioMBKY3ziCf8Cm9JSOMp0nAYNEIAFE6WloIF6ypY3agTp2WCLc+Tm4l2aNQtV5GHD0DMSGyuqPrKN+hpUtpQUiI28+SampQdKf/ZEfj6oX+bkTXQ0Ejgvvlhxg1k9sX27mDzOGJ5nX8/b7NkiedaqlVyS2g5m+eiPPpLve801Yt8PPlD/jsOHqYoBWqJqIPf11+L7Zsyo8nY0jBCDgzHGe/TAwtmihbqDuGsXMgDVq8MxCTTbVlgosmRJScFRJzp6VDQBR0Zy/v77/h3HMEAzosa2lBRINgYbJSXgepqzjowJvfpgOCgyFBbCeG/eDH7vzz8jszZnDipTL74IacPXXweV4o038LcvvrDSAiIiYNQramCiYXC+dCmqHUSTo61/f1zDYDfeu914fo4fL/+3t94SC2Bamly2kXNcl1tvFed8zTV6tKuyMshGN2mCYGvuXO/7ZmdbRQquuEL9e06eFMIAHTqoOyFr14rr8cgjIaHe4mCM8VtucZ6yX6oKSgUFyH7HxCDTG2hvlmGgQkn3O9C5TZzjnK66Shxz4kT/7f28eUJ+NDoaFYVgVydJtvzyy619C7GxoDTpDizVRWkp+ia2bcO7sGQJqicff4xE07RpcKxffRX/+9prCGLmzStPVb3qKr3J3brYsAGKj569gx06wK4Fmly0w4IF9rNCFi4Uz0ZiIoJJXzCLb3Trpl9ZmzIF62ubNnIKeEGBkOZlDAkvVbjdIoivWVO9UpWVhQQeY6jQhICYRxghBgdjjP/5p/MUz3PSJPUXa+5cUWp+6y31z3nD0aPC4alXTz+TYQen0+rQB0J1WLlSLBDt28MgBUvdxRMbNnA+dqyV3pSWBr74M8/AgFcGrUgVRCGrV89/RRoZDAOB0UsvWTNkVGWZNIkr8+91sX69kJ0cO1b8+/HjcPaoJ6pbN9+KMDk51t6f8eP1qjRlZdYsnDdaTHY2FtS4OLFvcrLed9EQr4QE9UbpggJxfzp3hvMaKgHI/v3OU/1ngwapv58rV4omYvPz5S88pz//+mvgx/SUKb/lFv/pODt3isGIDRogaaBDZdHB3r1IhpAjR4FP+/aoWvz6a+XQilRBsq4JCUh4VISN37sXCSWyabQlJoIWtnp1xXzvgQOC6dCzp7BFxcWgqxJtumFDyO7K4HZbe+0uvFBffMFcLbz1VvvnoKAAAaO5ohUZqRfomHskVeWKDUP8vtq1UUEJATsaRojhlAzvCy+Il8tXBtf8kowaJYy6Pw3fntixQ7zsbdsGp9RsphHEx8Mx8zc7f/Qoss/Ep3c4kMmqqGx/Tg6czBYtrM2WjCEDPn48Mkc6GfSKwIoVyHYHczq704k5GrfdJoQFzD05/ftDHKCifnteHhTRzL0xd9yBxW/ePEGZO+88vAe+5C0zM62Zxqee0lvoS0utmeinn7bfb/Fiq0wpbTrDuD76SHzuvffUPzdunHB8yakMgYXzlB398Udx3WbOVL9uU6eKzwWjDy43VwS6tWoFZ9q3221tDL7xRv+z80VFsF0UiERHI4lQERl3+r7Zs0Ex8qww166NysgXX8DmnE5kZmJ9ClQZzQySOr/3XkHFjI4WiROqdlRUpaWsDGsY9eYwBqGEnBz4Gm3biqrC4MG+FSHz8zlv1sxaJdKtHJqHFF53nf36vXOnfa/PVVepf88ffwgKq87AV3OfLbEtQsCOhnEGoD5j7AvG2DbG2HbG2HuMsWoKnxvMGPuWMbaVMbaJMfY7Y2y0j8+cWjjLygRdqW1bdaeusFB8rk6d4FQtVq4UmdsrrgjOdNWSEgyxIprXeef537RnGKAeEaeTKiK//BL4eXqD2w2Vj/vuK18BoOz2iBHImu3adWZVR1RhGMh8Pfcc1KCI8mbemjfH3yuq2kHn8fXXVjpEhw64/tnZ1gpESgocTW/X+59/wNv25HvfcIPeOZWW4v7S52VNjC++WP66MYYgUQVbtgjn5Mor1Z8lc5Plq6+Kfz+NC2dl2VLLPCWqHFWvrj4jwTDgCJFzGAxbsnevSJR06hSceQ2GwfnkyUIVrUUL39lqGZYssQo3pKUhqVCR9mvrVmS/PecnMQZncdAg0EzXrz+zqiM6ILtzySX2IiGpqUiArFpVsdd6zRqrclhaGqhVBQUItIgiFx2N/iJvgcThw5Advvxya0Koc2e9qq5hcP7f/4rPjxzpPXloHvJp3lTZHjk5Yq1u317dj/n7b3HPbr5Z/Hs4AAmjohHHsOi9+///Hc0Yy2CMLfLxuSTGmMEYm2T6t8sYY27G2P2Sz1kWzo0bwZPt1QvZKVXs2ycmc3fuHJxG488/R3YsORnOaDAyU243jBz1K9Sp43sKuwy5uWgiNRvEUaMCl9JVQWYm6Ejnn2/9fipj16gBGsb992NBz8ysvIZsFZSU4HmbOxfneNFFQtjAvCUkIFv2+usV3wPDOdRTSBGFMVQSpk1DAPDJJ1Yqx9Ch3p260lLvzfrXX693TiUlCMTp888/L99/2zZ7p0Mlw3nsGBbNnj1Bq1NVyzp2TDi7Awdan7XTtHBWpi212NHjx1Ed694dz7VqdbSgQEiS163r/6RqM9atA7+9YUNkjXftCvyYnCMBQ89YXByqZP46sqWlsGXmDHn//vKhisHCwYM496FDhYoSY1gHIyPxG2k9nDkTQcmZNNjV5UJ168svERhefnn5Cg9jWCP69EHiYt26il8LcnKg3kRrbWQk1sqTJ0F7MyfRunaVS9Ka++XM29ln6wcfZuGP0aPlAWZurr18vAqlsbQU7367dqjaq1IMy8pEkrRpU2tFMByAhFHRGMcYK2WM1TT9W2eGBXGQ5HNJjLHDNv++nDG2T/K5cpPQ33tPvGizZ6u9NJwjY0cZ6+uuC05W5YMPhAE75xzQn4qLA5dtXbRIOJIREQhKAsl0bdxoNfrVqqHXpLKUQ44fB2Xm2muFMpLdlpyM87znHtA85s9HifjQoYrJ9LlcqBhs3sz5Dz+AZjJqFKoJVJY2b0QZadUK55iREZzqlwqOHwcP2UyRGj4cTuChQ9brWqsWGkplz/iJE+Ub5BnDYqTzbpSUWFXYfCkbHTok6AnVqolKRrNmvr+rsFBIbyYkqGe2DUNQw6pXRwOuGadp4axMW1rOji5fLmzXffepXUfOUbWoU0c4ZsF4/lesEM59ejoqAG534HTJzZut1YvRowNT7DtwQFSBKCt+333BV3zyhoIC2MSxY61JCM8tNhbr0dixmIb99dfom9i7N7gDXgmGAXuyfTv66954A0559+72iYaEBASFaWnIoH/xReVdw6IinJ9Ziax7dwQ9+fmw6/RexMaimu0rQKeZG573QIcyZhhi+CdjqFLK1ryiIjFbJSpKsB0iI30nQw3D2i/1xRfq5zllivBLPCvW4QAkjIrGIsbYXzb/nssYe8fHZ6Nt/u0TxthJyWfKLZyGIbKt8fF6alTvvKPuKKni009FYNOqFRa8hAR7FQ0dHDhg1TkfNCiwBdkwkM2nvoCWLSFt+8YbwZeelcHtRvn9889hcC+80F4S0rNHIDISGewuXVC2v+02GMMnnkCpfsoUzp99FgvGCy+A5jNtGv73qaeghHbNNTDabdvCiTIr0HTrZr+Y16uHc5w0Cfc6WBlaVRw5gmZUctD69cN9o3kX+fmiuscYHG2VqfV//GH9nL/vhXkGx7Rp8n1PnBDNzMnJkNDduRPOii9pa7dbULwiI6Hao4rXXkNDa3S0vfzwaVo4K9OWlrOjnFt55jqDTJcvFzZv1KjgJHPWrBEy0TVrignlX30V2HHz8kBjod/Zpk3g/QtLlggJ9Ro14Pz997/BnR3iC4aB6sj8+aBrDRsm+tDMG/UvmLfatfEeXnQR+mQef1zY0aefRhXCbEdfegkJq6efRpVg5EisRx07wiabqai9etnb0erVwRQYPx5JxG3bKpeGm58P+0TrX9++oKe+8w4cfcOwrrc9eqg9J/v34zp4/t5Ro/TOz0ynuukmefBRViYSThERWJdycqD+9vbbvr/riSfEd/mqVpuxeDEqoCkpWLs9EQ5AwqhoHGKMfW3z7xsYY7/6cbw/GGPfS/5uu3A6nULVqHFjPcM/dqx/TowMCxZY1XwYg1pIoCgtBf2HjpmWps6T9wanE4bKPHirTh0sOpWVhfKEYSCT/803KNOPGoVAjiQPvW12pXxfgYy37ZJLYFzHjMFi+/PPwW1Y18XBg7hPZtpFzZo4N88s7hNPIJhQkYU0DPQ/UIXHXOmJitJXTaPeD19D4JxOIbQQG4vFTAc0j4IxvSboRYsEBfDBB72d22lZOCvTltraUbNST2Ki+uRjzsEzp/uhIx4gw6ZN5WcNtW4dOB3HMDBTiCp+SUm+5yn4QmkpHFpzJSIhgfO77w4ONc1fZGdD9OO555CMuOACKy3TbjPP1pBtnvfGbuvfH7b7mmuwpsyfj+txunr+nE5cC6raMYbE5cSJ5e37vHm4h9OmqVXcv/vOOjjXnNBaskTvPKn345Zb5M97WZkQ0tC1hZxzPmOG+Oydd6rflx07EGwzhgSwXQ9uOAAJo6JRwhj70ObfVzA0UuqgMwMF4RzJPrYLJ+co9xJ944IL1HnMJSXIdPTtiyxssBqzb7/daogjIoLXiPztt2IKbc+eKPvrSvp5Ys8eVAXMDm61ami8qyjpXn9QUICKyYoVmBnw6qtwJEePRrl80CBw+vv3R7/JeeeBS9yrF7JY/fqhd+Pqq5F9e+opOCNffYVjZmYi8DpTmuL37MGUX3NAW7cuKhPe7nlJCXocfOHgQc4vu0wct107ZPkefBD/PXKkf+fs69odOoQsYd26UJLRnVFj1tJ/+GH1z2Vmivfm3HO9Uw5P08JZmbbUqx3NyRHS4s2aqdNHDQOVqy5d4NDPmKF+X2R4//3yTm2gVRDCn38K+l+HDrAJgfbDnTiByoCnjO6NNyLLf6agpAQUrFWrQMl66y1QOm++GcG9px3t29dqR889FxTa4cOhtDd5MprIP/sMM4+2bEEi8Ezp4ztxAokZcpopyJ40Sb6+qTwPhYUINOm4qano1ZwzB0FIhw7+XQfDkNvSggLY77g40B+nTNE7fkaGSMZcdpk6rTknR1DW0tO99xWGA5AwdDGGgXMs29yMseH/v3+wFs14hozdAz7287pwco6MORkB2ZAeT5w4IbjByclQDwoEGzZYsx+0XXppYMc1Y9cuZJUoY92wIX5/oMjO5vyxx4SjxhgM3B13VE5TdRhYdFavRkBlpjOkp4M+FChFzuWCbGKNGkIR7vbbrfz97dsrRi54+3ZUKany8fXXep///HPByR45Uj1QPHFCNJI2aCB3OoK0cJ7JtlRqRzdtEpXCIUPUHRNz/09EBBywQOB0Wh1G2po2DV6CICcHFBfizDscoKEG2mNWUIB31UyDioiAw756dXDOPQzfyMxE4EHJSUqsPfJIcChyCxYgiCW61qBBVsrr7t0VI5V87JiVtfD883rvxLp1gsbbvbv6mlJWJhTlEhJA3fWGcAAShi6qMcZaKmxJ/79/FrOnDaxn6rSBSMbYl4yx1xX2lS6cnMN5ppfy88/VX8gDB0TzmMMBPrq/MDd0em533eX/cT1RVgY+rplWNGxY8AYiTp1qLbN3747qwaxZULsgGs19951ZSiv/Vhw6hGtOwTDxtRs3RpUmGAHB2rXW/pa2bdWoWsHAqlWiv8fh0KclLFsmKkEDBqg30JaWipk0iYlYfGUI0sJ5JttSn3b0s8/EM/LYY+r3qKBANMNGRgY22Twry1qR9aQJBSu7bhg4T7OtO+ccvX5Cbygthb0kijCde+fOoCkePozfcfPNqCgEKlgSBtal6dNF/0n16niOatRAf0wwrvHu3VaxjWrVQH+qjIrPnj3ieYqMxG/Vwc6d4llv3lyPWmyu9Pjyr8IBSBgVjUWMsXU2/57DfDdOEmYxIT3pCw7GGB83bhyfOHEinzhxIs/IyLA89C6X4JbXrSuP0D2xd6/IztaoAQlDf7F9O+gDd9whGm1p698/uIMA9+61GsOkJAQmwfiOoiI4v+eey09Nn6fsh5krPGRI4DSwUERJCQKASy6xyhOTo/LFF8EJ7o4dQ5WDAuOICDybx48HfmwVzJ8vnMn69fXfreXLkflu1Ajvk46ijJkj7Y2+k5GRccqmjBs37nQsnJVpS33aUc5Fv1lkpF4yJy9PZISjovQpdmYcPIj+jHvvhUNpriw3ahTc7HJuLmiZZinWe+4JzrBBtxvVvh49QDuk3xAVZW3Ubtv29PaM/FvhdiOhMXp0eaWts86CQmUw7mNRESh25rXwkkvg1FcGNmwQwwYTEvT7VnfuRCW4QwfQBHVYDeZhg48/br/PGWBHwwgh3MVAHbCTjrzAY9+6Np9/hzE22+Pf/scYS/XyfT4zd5wjw9G/PxyWunX1ei927hRa2rVrB0/bfetWq7pTv35q6kQ6+OYba8m/Y0dknYMBlwu81tGjvTdyt217ehu1/y0wDKj83H13ecWv1FTwkoP13LndyJCZv+ecc1AJqSy8/74Irlq1QgZPBwsXiuCld+/ysrkyvP22+N3eJrF74jRl7irTlirZ0bIyCAp06QK63KJF6tfd6UTFlDHQRBcsUP+sDOaJ6eRcBnv+xtq1Yr4J+3/q45dfBof2ZRhIit19t7UR2rzVqaMnABDK2LYNFKsmTazXMDkZDdy//ho8ut6PP4r+KMbwncESrlHB0qVCdr1mTX2q+LZtInipX993Jdjzu4kOfNVVapWecAUkjIpGHGNsC8NCF8EYi2WM/cAwQMuMRxljLsZYT9O/vcwY288Yu4ExNvL/t1EMw7gaefk+pYWTcwQdJCvasKEeLWnHDlGiTE1FNSMYKCvDUDfzwhasAIGQlwdKFDl83brBYARzkc7LA63AbvJ3ZCQW75dfrryZGP8GHD8OecQxY/BsmeUwY2LADZ8/P7iVsV9+sVapatZEFquypiUbBigP9P09e6o1x5sxb55QK2rdGlRJVcyfLxbca69Vd0RO4yDCyrKlynY0J0fIiiYmcr5ypfr1P3FCOPJxcXoBjC9MnizoeElJeLeCibIyCFwQT75FCwhaLF0avO8oLcUz6k2VqnlzyJuGaVkCBQUIZsePxz1xOKxVsX79MIE8kPkunti+3TpXKTYWClWVKVf/6afCDjZurC9o8NdfIuCtWVOPGbJlC5JWjKFnUPV3hwOQMCoD9RljXzA0Sm5noABU89hnLGPsGGOs3f//d0MmmjA9NxcLQgDCOWge1Ex91lngMrdsqcbt3bJFvLBNmgTXgV+wQJxXTAwUSIKturR+PRriKJCKiIATFqjmPWHNGvtF07zFxOAcXnwRGb0zRVmqMuB2I5P61FNwvD1FCSIjcW1efTX48wJWrBCNgqRWctttlTuXIDeX8yuvFI2SQ4fqL9gffSQC6Y4d9aprCxfCQU1NhbqRTjB8GhfOyrKlWnb0yBHRwJ+SAnpny5Zq/R3HjgkKanw87kuwsGGDdejbhAnB70U7cADPMYk1kJO7fHlwjp+XZ5/IMW8REaBuTZ6MjH4o9dsZBgKAV16BTfOUt2cM9OPJk4NPg9q2DRLwkZHCjg0eXLliLKWlUCXs0kXYQW+qU96wapXwN1JTITKhim3b8Jn4eNhwnQRQOAAJo6pBa+HkHGVKT8rQ5ZerfZb4ll26IGsQ6MwNM3butA4sGj06OBxVM9xuUAfMlIWICFRhAq3q/PWXcKpTUzm/+GIY665drdO5PelFo0YhQ7VxY9VaSE+eRMXhlVcgZ2xHr0hOxmL5zjtoYgwmDAMcaBrYRtuYMZWvuvPnn1bHcMoU/crOe+8JLv655+plgRcvFhzt9u31A68QWDi17ejeveUH2zVrpkbFOHIEg/rOPx9JiQ8/1LsfMuTkWGdv9Omj5ySp4tdfIUtr/v0DB+LfA8HJk8JeOhygDt94I575WrXsxUwcDtiRt99GIqgqVZqLiyEA8+67CCg9qVWU2OrfH4IdGzcGP7G1aRMUJs3Xvl8/9PBUZhJt/37rMMS779brfeMcFTvyfxo2BLtDFTt2gKrFGNazLVv0vjsE7GgYIQYHY4xnZOh1Hj76qNWA6QxY27pV0Dji4oJb6i8ogIPIGDKKrVoFbw6JGW43mkjNtJ/ISAQDOgbJE7t3w0jaGWWnE/NKxo8X2VPaiBseG4vg7uabIX25YkXFSBYGG7m5MOzTpiGYa9XKuliZ+ePt26OZd8kSddUmHRgGssrmhYox6Pb//HPlLpiGgWoeUQUSE/2TYn3lFevCrxOYL1sm+kXatNHvSfr7b849d+qqAAAgAElEQVR/+63KL5wOxhh/6y29l+3LL8s7g6pKZseOiSwuY6DmBevZdLvR3xMRAae9aVNUzyri2f/lF6HyRdugQYFJtx8+jOfOLpgrLsY1fugh6/WjYItseZs2sEVTp4Lqlp3t//lUFgoLEUC9/Tb6NTp3tg5CNQ/1a9wYohnffBP8RB1h3TpQYc3X+Oyz8SwFkxqrgh9+EH17ERHoc9Glzi5YIBIxLVro9d798w8o4ozhPHSqJpwjMTB/fpW3o2GEGByMMe5wOJVVdDZutBo1c1ZWFfv2WZWsdDW3ZTAMZHvMVYpx4yrGyLrdCKBI5pWM2803Y2GtSGd1zx40Q195peCTetuaN4c2//33Q0v/q6+wUB04UDn9C2VlyPiuWMH53LmcP/ssFr8hQ4Scq91GjsCdd+K36jRL68LtRoBHwRxtF1wQPHqIDpxOUJ3oPGiooQ4MA9eajjFkiF52d8UKke1r3Vpf5GHXLghQ1KhR5RdOB2OMR0Q4lRWqcnPtq3ojRqhf37w8MWmdMWT6gxmUZ2QIBUTG9CkjOli6FEG++VqMHo2epYp0VrOzOf/kE9hHs/Kh3ZaWhurQhAmgzX3yCd6R3bsrJhniCbcb0uKrVyMB9uKLqA4PGwYWgqfqn3lr2hTP1vTpoAFV5Nr02294Vszf36EDzrmyBymWlSHYpPNITUUiSRcffSToam3b6g3Y3LVLVDtr1tRXLHQ6UcGLjq7ydjSMEIODMcYZc/LUVDUu5o8/2hu42Fg9WkduLjJd9Pk77gjuQrN9uzWL3bhxcJs2zXC5OP/4Y2TuO3US39mmDeevv47sRUUjKwsOw/PPozeldWtrFYGye55bVBScxB49EMzceScW2IcfRlZ16lT8hvfeQ/b9iy/gqH/2GeczZ6LS8vzzaCK87z7cx9Gjkfnq0wfSnrKFkShOUVEISseMwfetXBncxkdvOHAA2d4mTayDqIYMCXyApr9Yt85Kubr5Zv1+j7w8OByMgaozYoSek/TBB4JLf9ZZ+hOt9+4VdI+UlCq/cJ6yo7GxoKz5wp499okcxvQa08vK8M6aA2ZdWokM2dmwJ3T8lBSosFWEA2sYuHZ9+sBRpOuTlgaZ0opMQBBycpBweO01BCWeVQRzsslzS00FZXbYMMgc33MP+g2eeAI28rXXkBz78EM4499+C3s6axYqnVOn4nfefz/mW40ZA5s8ZAjsAVVC7TazfW/eHMmLF16A0mJlSIPn5uI3dO5sTf516YIg8nRMcN+/33pd+vdXZ2oQXC7cQ7KjXbvqUVBXrRJDG6tX15+Fk5dnrsxVeTsaRojBwRjjtWo5OWNwGFQyXKtWwUE1U2MYA5dWRx2rtBRGnj5/8cXBnX3hdsOZNeuY33xzxQUELhdUWDwzeYmJKIkHMozRH+TnI1v2v/8hEz50KBYEUjOz2zw13+02T3qSt80z8IiMRLDTuzfn112HzNTHH+N5qkwFlLIyLP6XXmptZq9eHc5WZUrqmuF2o5/FTLnyh9+/c6e1wjhpkl5w/+ab4rM1auhnvT2HkC5dWuUXTgdjjLdtCzuanKz2DG3fjveyb1/rcxgdDeqbKgwDzit9vl274M+++OYbwV+nQCfYfVcEw0Dwf8UVVhsSGQnnPiOjch3akhL0L374Ia7z8OHISKenlxfDMN8DX/bRkwLmbfOk3FKw060bApR770UyaMmSykl2EQwDPTtjxpQfcHnttZx///3pE0r59luhhhYRgaZ63Wp/Tg6CP7N/opMUW75cPL/R0frriucQ0pkzq7wdDSPE4GCM8eXLnaei9LZt9TImWVnWzHHdunpZBsNABpo+37mz/lwDX9i1y9rwWL8+DFRFYssWNLmlpFgNc9euqCacbinI4mJcl19+QRAwdSoc1RtuwKJ26aW4Zr1745zbtEGTbFoanID0dCyMnTsjQ3PRRViYR43CgL5774V08Jw5MMR79pz+Jvldu9C/RD1ItKWlQdFt167Td25bt+I6Jifj+WzTRr9JkXNkPInrHRuLSoYKTpzgfMYMq1MUFQWqng6ysoRSWHIyHMkQaJ50MMb4jh3OU4FXrVp6lLncXFQO6drHxupTNT77TNBE6tcPfiB94gQSOHSOSUmogFZkMHDgACoIxJ+nrWlTVBVO95DBsjJk2letQjXjlVdQCb73Xsi1Dx2KSn+fPqDKtmuH4Dw9HSpU9eujn6BjRwxQHDQIdKrrr4fS3oQJqJzMmAHq0I4dGN53OnH0KPr1PKtBDgcqNzrzMIKNw4dFxa59e/gjP/2kf5ytW60DLh95RC2AKSxEb5eZusgYqoY6KCoSDJGICM5nzw4JOxpGiOGUesuyZaLB6txz9SsR990nKD/NmulL682Zg1J3hw6Qug2mvCTnCHSmTxcKKXXrwtAHayaJNxQUYPHo1k0Yo2bN4GBccgn+VplyrqGGffuQ0Tc7d5RRGjoUg68quyHSjOJiOFhmesUTT+jTzwwDAR9lZNPS1NS6qNfEjg6kO6PhyBEhzJCYKHpnQmDhPGVHd+0SlYIGDfQqwpxb5wE5HHqVEM6Rka5VC99dpw6qn8HOQi9cCGolZXYHDQquoqEdyspQhRk8WKwziYlYs/r0wbNfURWZMBB8zpkDxoInFax3bzy3lVnF9oRhYC2tUUOc14036lNHOceaQAnZxETQ5VS+f8IEMefGvE2erPf9RUXW3q4ZM/DvIWBHwwgxWOQjv/tOlAwHDYJzpIPFi8WLW7euPt9x+XJrVuXhh4PvHO7fj+z+eeeJLO+ddwZ/irod/vgDWa2LLy7vDPfvD0e5opo8QwWGAbrEU09ZZw3UqYN73aQJKm5nwnVesQK9OnSOrVr51/BeWGgNsHr0UF94ly61p30MHap3DllZ6H/q3BlOobnRMwQWTosd3bRJOEItW+orh2VmomeNMVQ0vvpK7/M7dlildK++Orh9IZxD1OOuu6x9fMOG6Q908wc7d4K+adc03qUL5888UznnUdWxZw+qL/37W6lw9esjyJ040b8qbbCxY4dVLr1OHVT1dQNvs/obY1grVKuQx47Z29HGjfXOo7AQ/gHRnN9+W/wtBOxoGCGGcvr1c+bgwU9P1x84xjmCDuoxSE7WV5w4cMDaQ9G7d/DL7IaBYMsc7CQnw2mtjMbnggI05o0eXZ6iRQ7kc8/BkT4dzXv/NpSVwZGeMAHUDM/rmZrK+a23gm52JlzPnBzQ1Oj8YmLQfKob8HMOeUdzuf+WW/SOU1Rkf81UhuIR9u4FjYQxON6e73wILJzl7OhvvyF7mpwMqo1u8+vBg6KPJzISlQwd5OUJSXKqulZED9ratWjOpe+JioIQRWUkdMrK0Pcwfnx5ihZjsO+PPhp6wwb9hWGAPjV5snWmFm3VqsEnWLDAP1sVbJSUINg0D1O86SYEA7o4fhzJQTrOgAF6zATDEMNqzdsjj6gfIy8P30ufnTnT+vcQsKNhhBhsB2hNny4M+vnn68+S+PtvLHjkXH38sd7ny8pgBCkTUbMmAoZgo6wMqiSpqdbszvTplSNPyzmM6MKFcEjNzeHk0NWqBarYyy+jglJZ53Umo6gIwcRzz6FU7TlHgDFUFh58EI7gmRB0cI5F6rPPrM28vXv7l0UkuemkJGTZatZEBU0n23byJHp3PK9dfLw6BfPvvwUdJynJfo5FCCyctnZ00SJBvWzeXJ8ilJMjKrWMIUjVzerOmiWEJWJikNEONiXLW0LnyScrJ6HDOd7x1as5f+ABq4JcfDxoYomJaJx/+mlUGU93H8WZgLIyJAxfew2BRY8e5W1BWhoYAhkZZ0bQQfjlF2ujf4sWaupzdsjIwO9MToYtvecePeaFywWpf7sKiCoLJCcHPUCMwe95993y+4SAHQ0jxOB1gu+774oAQFd6jnNByYiKgqF47DF953nxYvSD0Ms8cWLF6K3n5YF3b57w3rYtOMeV6by6XDCsEyZgcqydQXM4oMzx3HNwrkNhIT16FPfi/vsheODJQW7SBM9qr16Qnqzovh5/sHQpeqvq1UPGzuFAVtuf5ysry8oRbtxYv+H4wAFrlnPKFM5HjsT/HzlS7RhbtohgKiXFu2xxCCycXu3o4sWCF56Wph9sFhYiAcEYnv3rrtN36rdts6qiDRtWMdKs3hI6779fuc6rYWBe1eOP43rZ2dG4OFTaH3sMgWJFDeM7k5CXh4bsJ55AMObZrxARgQpmu3aoHK1de+YkbwhbtuD5jY3F+xQdjSqDP9PrCwqsgUNKCqaz6yA/3zrzZMwY2FLGcB1Vgv2jR4X4R1SU92GzIWBHwwgxnFLBssOnn4rm1Nat9XXYnU7rC37RRfrl0cOHrTzjgQP1B7Kp4tAhzseOBeUhMRGyrG3aoBR6OrI/e/fCGN12m70UI2PiHK++GhSyr78GJ/bfWCkpKgLt7NNPUQG76iqro+25WHbsiOfrk0/O3Eb+9evLK6I89BAoNv7gyy/FRF+iHOhWKNeuFQ5pbCyGbHGOxXLtWjUH96+/hMxlrVryTF8ILJwOxhifM8f+RqxZI5TJatXSDxbLyoRTQ47Njh16xygstNL+mjf3byCbCjwTOmefjUDkhReC34uigqNHQXmdMAGOnp10brt2qNpfdhkc2o8/RhBTGQMGg42yMiRh5s2DzPPo0VA29DaPqUULNGxPnx58BcpgYd8+2DrzvbvlFqwX/mDNGuuaOmCAPtV73z5rr5W5Qrlxo9qalJUlhDtiYuT9XiFgR8MIMTgYY7xaNadXFZMffxQa340b6y98npNImzTRb053u5HxJ5Ws2FgscBUVFGzdCklaz1L06VpACVlZUOQYNw4OJNHc7Lb4eCy2N9wAacg5c5CN3b799GX7DAPOcmYm1H3efx+UiaFD4RDZOQbEL09IANXqscdQMj+d90EFu3ahkmAeBjlggP/SqLm5uJd0rDp1UBXSgWFgLk5MDJzC9HT/mt5XrULgyxgqOps3y/cPgYXz1CT0WbPsr8HmzUL+OTnZnqrmC9OnC767w6F//zlHcF+tmuizu/nmihtUd+hQeYW3atWgmFgZQwW9ITcXfQwPPgjaUXy8UEf03KKjkeC56irY/5kzYX82bcJ1O11zLgoK0Iy/ciUSMJMnI8ho29Z+YCFl2KOjOe/eHWyCr76qnF6dQHDsGJ4Xc59H27ag/Plz7UtLca0oGIuP5/zVV/UrPQsWIKlQpw78IlW5czPMvXPx8TimDCFgR8MIMZya4JuQAMNqhxUrRLN0aqq+Pj3nyMyTQlZcXPkGKxX8+adVNejss9FgWFHYuhVZFs8FdNKk07uAEk6cgCPzxhvIbvbubd/U3r17+X+rVg2qSwMGYHbHo4+ioe+VV0ANmj0b2fYFC0AfWr0aWZ3Nm3H/16yB87poEYYvfvklMukzZ4KG8eST4A5fcQWoI02blh9WVaeO/aJfty4CjzvvhGFfu/bf00R65Ajn//mPVda2Uyf0+fjrrPz8s+izYAxZWl1lpdxcOCh0jPbt4cDoYuFC4aw1aqQmtx0CC+cpO8oY3kc77NolkgZxcf7NIvr9d+uzoDqfwIw9e6zNrnXrIjCpKGf60CEkocy2KToaAfXGjRXznTooKAB9cPp08P8HDrTSyGgz9+OYEz3NmuFv11yDzz/5JOZkvPMOenA+/xw2cvFiBO/r14NKtGEDbNuKFXjHv/8eQcHHH8PuvfMObPJ//oMA6LzzMJuC1lHaPAcC0+ZwIMC66SY8k0uXnl6pXB0UFKB6Y35mGjXC9fS3ur9pE2ax0PG6dtVnU5SWisnolIBZuVL/XLZuFT1T3nrnPBECdjSMEIODMcbPOst5qgT45Zf2D/9ffwmH0eHwL4O3fTuySfTyjh2rX8UoLkYGw+zg3XFHxWbEvS2go0djQTldWTA7GAaCox9/5Pyll1Bav+YaZGnsZj3QpjK5lzFrAOhtM8vfetuiokBNuugiUCPeew8LsT8KJmcC9u5Fps48yK9ZMzgT/vKoDx3CM9apk8icz5yp/7z9+ae1MfeWW/xzRN5+G/eNHCFVukYILJwOxhjv39956hpPmWJ/nw4dEvS3qCj/Jt0fPWqlpQ4apE9BNAxUIKmSxRjojrpzS3Rw8iQc8wYNrLZgyBDOf/jhzKONZmfDaX/zTawxY8ciY+2ZSPFM7KjYUfPwXm+bWazC2xYRgSQPJWxefx19HgcPnlnrkipOnMCASfPzXbMmnht/+x3z8yHp36QJAv+oKM7/+1/9pNb+/UIelzEEqv5UkBYuhC/Rrh2qot565zwRAnY0jBCDgzHGd+50nsqiREZ6LydmZiIL0bcvnNn33tN/+fLy0K9AL3G3bv5lYjdvFqoRjOFF1m0g04XnAkoKN+3a4d91s9KVDbcbi+r69ahsTJ+ObN3YsZCyPP98/CbiQ9evD0NprgCZG5ejopC9qVkT+zZpgqrKiBHIsF5/PSYCT52KisqiRch4Zmefec2N/uKPP9DoSiX9rl2RUX7zTf/546WleJ7Mzsztt+tPajcMzt96S9y/xETcB12UlVl7ubp1U3d4N2/mfPHiKr9wOhhjPDvbyYcPF9dp0iR7J/D4cWSmu3TBczNpkr7z7XKh+mHODq9apXcMzkHrNNvjpCRQUioyGCgtBSWUAjGq6DRogEqs7hDbyoZhQLVoyxbYtFmzUKkYNw7Bfb9+EJxo3x4BS1oamrvNNCKzcl9kJIKa6tWRUW/cGP0Jffpgrb36as7vvhsVgZkzEaytW4dg9nQOUQ0mdu3Cb6S+oXr10GP26KP+JxcNA5U9c8B7/fX+vSc//ih63iIiQC3UfUcMA4pjRDVu2FC9ApOdzflnn1V5OxpGiOGUektuLgwevaivvWb/Ihw6ZC1j3nWXfibBMOBgRUXBYXM4EPT4Mzjorbesjtq111Z8Ix0toJ4KK9HRUOj45pt/D2VIFS4XgsecHGTPz7RsZWXC7Qalwjz8ijE4Dq+8EhjN4eefrXKm9epxPneu/rvhdFqV1Nq08U/u98QJKOaY3y9VxZkff8S7XatWlV84T9nRsjLr/I2xY+3flbw8oTrGGGYInDihf3+++QbXuGlTVMgef9w/2zN/vtVR694dNMuKhGGA9jt+vLVXijFU2WbOVJeD/rfA7UZG/sQJ/G9VCSD8xerVoJeZ+/9q1EDFIpCE3qZNVvuclITKii7joqzMGujXreufeENpKWyBuQKmWj3ZvBnJvejoKm9HwwgxWOQjCwqscwG80Qjy861Zs759EaHr4pdfrBn1oUP1B3ZxjtIoTcXt0wcZ3/vuq7jmSjM2bkRDn2c/Q926yP6fCRznMIKDwkL0t3jS0Lp1Q6YtEGdi715rj0Z0NDLjugpXnKM3x6weNmaMf/MYtm8H1YqO8/TT6oHQG2+YM31VfuG02FG3G7x9c9bVzvFxu0EFof1atPAvSMzMBA2HjtOlCxwwXZw8ifOOiBD9Dtdfr1958wd79+L58hTWSEoCjXT58qpTNQ11uFxgK5jpTIzh3r/xRmCzY3JzQek1K35de61/PZvbt0MCm4Ljfv2QgNXFsWPWYGj0aHU62Y8/igRrcnKVt6NhhBjK6dcXF4NCU7MmSoSjRtm/LIaBkjC9nI0aoU9EFydO4IU08z0//VT/OIaBJj5SlWAMJe2pUytnVkZJCWQPL7vMavw6dkRG+6GHzqyheGGowTDQQ3H33VbaREQE7vXy5YFxranZ0swr91dq+uRJ8MDNNA9/xB44B7WE+gMSE733hnnCk67Vsyfn//xT5RfOcnbUMKDYFh0NG9C7t/eM5xdfiGGB1ar5N3S1pATBDNme2FhkfP2pVK5ebZ3qHBuLJEtl9Ge53VDIGzNGXBOqBKann5lD8cJQw86dqNB5Djzs1QvN94FU1V0u9FOZh/m2a4ceHl2UleHdIcrcBRfgXfYnwbR1q+i/i4jAcf1J4jRpwvmqVVXejoYRYrAdoFVWZs3Mde+OyL+wEA1uZkMxf76I0BMS/AseOEdGxFxFuPpq/2Y7FBdjajjp7lNwNHt25Tn/WVloAG/bVshd0paayvmtt8LJ8Gd4UhiVg8OHQRM0D3CLicFCcMcdyDoHgqIicO1TU0UmsGFDOPr+BDQZGVZ1pD59/DtHwxDN5oyBlrNundpnc3Otjuv11+N3hkDzpNdBhLNmWTnff/0FO7RkiVUOe/16PFvkqHirPvvC2rVWCl/Pnv49B243bHnTpuJYKSlwoCrLbp08yfmMGXiWzTRACtSuugr0RH+oa2FUDk6eRBLEcx085xwkOlUbsL2BntPWrcU8DYcDttUfKuKGDVYRlRYt/JMq5xy+ESkGJiWpy2aXlYHabg7QjhwJCTsaRojB68JpGGikJUekfn3R+/HQQ9Z9t22zDvWZPNm/bEF2NoyS2Vn3R6qSc/QqPPggZBLN1YiFC/07nj8wDBi0J5+0qiPRlpiIEu/7758Zsr6hjuJiBACXXlp+aFfnzuiLCpTWV1yM94pmQpBj99xz/lEPjh+39hwkJSFz5k+wffw4nseEBNAhzj1XnRK5c6fV8TXTtUJg4fRqRzkXvTD0zl98Mf7/xRdb9zt61FplGznSPwpeURHoe1SdTkiAOpI/z0RJCZ578/DLBg3Qs1eZfWC7d8Op7N+//LsZFYV/f+UVUM/+jepPVQkUYN9wg7WKxRgC2ieeCLxP0+2GraaggzEE+v/9r3/KVMXF+Gx0tDjW/ff7F2wXF4N+TYFWo0bqAxNzcqxJHDMDJQTsaBghBunCyTkarmrUsBqR+Pjyk5xzciCn6HDAuerZ0z91K8OAdCl9Z+vWgTV079sHDjEtxjEx6HP54YfKX6j270fT/IUXWiVxKePStCmcyRkzMPAxvJBWPIqLUT144AFr1Ywx0cfj77RdM0pKMF+lYUPre3Tvvf4PA/v6a1BT6HiDBsFR8wfLl1ubkB94QJ26uGQJaGP0mz77zPr3EFg4fdrRbdus/TS0LVtm3a+0VPRhdOwIm+BvlnjFCtFT4XCUt9k6yM1FY7A5oXPRRajwVLbgxvHjqHxcdRUa7800LXpvr7wSgdNff4W2YEZlweXCc/rwwxDjMD/j1MezbFngLATD+D/2zjs8qqLtw086EBKqVOm9g0jvRQWkSBGQjgqKKIIvVUUF5RXsgBQFLIAC6iugKB1piqDSRIrUEAIESWBJQuqe+f74Od+Zs+dssuVsye7c1zUXYc/u6fPMPHXgSRBzR0NC4G09fdq1fR48qFVk6td3fcHYM2e0xsZ+/RyX76dOaQ2wtl7QIJCjkiAjz4GTMSykZDtwjhun/15ODqxUfLIfE4PQJ1cm0gkJiLE3qxLLsWNQkEQLY6NGWDzPF5VILBZM1IYO1SYMi61MGYSiLVyI8w/2iilmkZSE93LAAHUCU6AABsqICAwa331nzsQqKwseLnFQjoxETokrCY2cVavU/RUt6tr6IIzhnXr1VTVMKDbW8TBKRUGoYWgo4qU7dDDur0EwcDokR0+c0K/F06qV8XP75hvVehwWBquxK/0/JQXhHK6s1GxEfDxWT+clyImgVL//vm8qVnEDwrhxTFMCWWxFikDGzp2LSbLMHzGHtDREKDz+uDb3gkdDdOqEvAwz3gue42m7xtTAga4VbuAcPqzKvvBwyEJXyqcrCq6VlxEOC4NX21GFa/16NZS9c2fMuWwJAjkqCTLyHDgVxXjxuZAQ+yuA7tmjtfQOHgwPiT+wf79+wl+5MsJifLVKrNUKBWPhQghU0arNW8GCmJA0b44ckvnzkWDnjUpfvkRREAa0YwfCSJYudW0/Z88ip6NDB/shHJ9/7lrekRFJSYzNmwdrGl+HIyICk0Ezwu3u3oVFvU8f1y3bcXHa1Z1btnS84lFqqrbMb4sWjF25YvzdIBg4HVJAxPsltg8+MP7+mTPaCVfr1t6pSOUIp09jhW1RoSpeHOG3ZvUhZ1EUxs6dg7I1erR28U2xVayI3K6hQ9FHN2/Ovwv3OUNyMsa/jz9mbPZs1/Zx/ToMKr16ab1hojd/yRLXPbG2pKcjIqBhQ23Yar9+5nimFQWhkPff73rFSotFW1IbCeOO/TYnB2ud8N9WqoSiJ8bHCXg5KgkyHFJAjPIXiGA5+Owz49/duqUdcCtWdD2ZyxMcP474SnEyWrIkYtd9PalXFIRgrViBkKwqVew/AyJUh+neHTkvq1fD1X3xYv5ai8RqxYR42zZMyMaORXK2bfhf7drO7/v77/X3LDYW7+cXX5ibxHriBM5drGrVtSs+M3t9mn/+cX3S9L//qfc2JAS17h19X86ehWLFr++pp3K3KgfBwOmQAjJqlP0+PHWq8W8yMxHWInqVV63yn8lyfDzCCLnllxtLnnvO8+sxOUJCAjx6zzyDd9a2XLrYSpSAIeL556HEbN8OOeyNKopmoShIWN67F8aa555DeKTtquqhoc7nN1y6pF+vJTKSsW7doHSYmceYkICJOV/8j3tUevd2vCiGoyQnux5dcPCgtnT0oEGOL5yYnIx7J44TuVWaCwI5KgkyHBo4MzOx4vPKlUhAt/WITJtm7GpUFPyGuxadneh4g4sXIaTFCWPLloyNGYM4UH8Z6BMS4IL+73/hUapXT2/JJ8KCc+IgU6ECqsgMGYKJzNKlyH/57TdMJP/5x7PPw2qFhej8eQyKa9Yw9vbbqNX+6KMIQalYEe7vFi3sTw4iInDNQ4a4tihfRATuxbPPQslxdZVye9f4/ff6Sj3FiqFvxMWZdyx3SU5GFS8e3lO2rHMLa23ahJAWPvlYvjzv3wTBwOmQHFUUJEl/9RVCPVq10r4v3bvbnxTu3q31Kj/2mP94lRmD0eb117UT/IoVYanevNl/8jCSkxnbuRMhY6NHw2JvZMm3XaOidGl4nx99FGtMLViAsJlff4U36Pp1z4Z2KQo89HFxsK5/8w0MNVOmQCa2bw+PT4EC+vdKbCEh+F7v3q6tucdN7i0AACAASURBVFWzJjxdI0bgHMRKbmZw4ADebZ4MToTwzscfR6U4fyEzE32Yh9YWKuRcGOyxY1rFZdq0vPtIEMhRSZDh0MBpxKZN2kn7Aw/YF2jnz2uFYq9eUGj8iX/+gUApXlzrbWjUCOFZ/jTYc9LTkWT5+ecYFLt2xbkbKSZiM5roR0fDk1KvHgbfhx/GwDZoEBSeIUPgMRo+HF6Z0aMZe+IJDICDB+P77dvj+DVqIIxMTBB1pHXujEltgwY47qxZGOROnnRfSTp3znxlMjkZExlx7Rki3MOPPvJdSJ8RioKJb+nSOMeOHfHMHF1A1GrF8+AW0HvvdTw/KwgGTpfl6MmT2vj5evXsLyKYnKxdALZ7d9fWDPEkaWmQl5Ura/PtKlVC2I8/VvvLzkYC8Lp1sLr36oW+wUMn7TWxRDFvBQpA9tWuDUNWt27wEA4cqMrRoUO1cvTxx2GQGTIEx+7UCSFBtWpBJsfGateDyEuONm+O7/MQzRdfhGf8yBH3SyhfvGh+LmJ6OgrPNG+uvY5y5RibM8e1RY49yS+/qEnrDRrgWZ065fjvv/xSnTtFR2MdIEcIAjkqCTJcHjgZg+B49llVYNxzD6zrRmRnI5GSW4lDQ+HqNtuC4i6pqYwtW6ZNsiSCwBgxAjG0/uIVsUd2Nixle/ciXGPOHIQAPfQQBsbu3R1TCGzDn4yamEOQWytZEs+8XDkMNH374t2ZOxfnuGsXlAR/T7TPysKk79FHYZnjympICCYP27f73/sRH49z48+iaFG8446e57VruF7uNenQAWEejhIEA6dbcpQn8/MJb4ECqJZn9Hx4sqvY7/r1s59/4yuys1Gt6KGHtGE7oaEoc71xo//3dasVhSJ+/RXKyVtvYZHNXr2QkyAqWLm1xo3z/o5Y1Sm3xnOC7rkHpcF79oSC8/rrsMBv3Qql1t/DxhQFVdrGjoVHtW1b9RpbtoSn3J8iJRiDJ338eG1FzZkzHfemp6aiWEL58vh9jRrOJdAHgRyVBBluDZyc77/XxmpOmmTfHf3771oPw733wpXtjxw5gthhXsOftzp1kDDub4O+M2RmwrL099+wZG/dikH2o4+QmDljBtzCEyZAURg/HsLzqacQnvbkk7DczZyJ7TNmIDxswQLET3/zDarT/Pwz8m0SEvx/wmEPRUHI2nPP6WPIO3aEIn32rK/PUo/VCms0D4EkgiLhTOjFt9+qa0C0b4++7exzDIKB0xQ5euQIrN78WfXpYz8mPC4Ok0/+3ZgY9D1/CXUSuXgRcoJPvEQL92uvYcKcX8nORvjZ+fMY23bsgOxbvhxK5csvw3M4YQLkhyhHx45V5ei0aRhrpk2DMvHBB8gBXLcORr19+/B+xMXl7ypeZ8+iUIEYfkSEsLChQ82remk2332nLVPesiXy/Rzl0CG1OljduqjA6GiuCCcI5KgkyDBl4GQME0y+FgARrET2YjazsyFgxRCdPn2wZoc/kpaGZHsxLpgvyti6NcJw/PXcJa4TFwelyjbnKSwMk7916/x3NfsTJ7Rhj+XLO74SL2PwTI4erfVuurooaBAMnKbJ0dRUhDaK1nN7XmVFQTEBsTrQ/febn6RrFtnZmMj16qWGFHFPTr16CIF1ZlInyR8kJSFJ3Sg3pU0b5CX662r23PvLz7dwYRgfHVX0s7OhgPKw6AIFXF8UNAjkqCTIMG3gZAydau5ceEPKlEEi2YwZ9idp8fEIxeGdOzqasffe829L+V9/wUplu9gSt4q8845/VH+ROI+iwFvzxhvIk2nSRB/+MH++c+FH3ubOHVgYo6LU837mGedW1N6/Xxvf3rOn64slMhYUA6epcpQxKLfFiiHUhwjWYXux8BaLunghD3OaNMk363I4ypUrsPQbhTHVqQOPyfHj/hfOKHGMixcx0X7gAX2YbtWq8HydO+frs7RPVhbOv2JFrRx0xtB49izmBPz3TZq4t2ZJEMhRSZARS0Ts1CnzBk7G4CrmAyePdbRd8Vdk40ZthZe+fRFL78/k5KCs8HPP6UscEiGHZP58VLuQg6j/kpmJqljPPadXKtu1g9t9+nT3Bg5b0tLMf7+zsxE+xxOa27fHRG7/fsf3kZmJhFVunS5UCPt09/0NgoEzlojY3r3mytH4eORQ8PexRIncS/AeOqRVmrt1QxVCd1ef9iSKAk/5yy9rw894q1kTyes//+zfhqlgx2pFrsxLLyExW3yGtWsj7+ypp/AczRoPc3IY++EHc8dXRUFIOfd6t28Pmbp2rePHURSstcJLU4eEwBDrbuXFIJCjkiAjlohY8eIWtmWLe53DlqwsJD+LltgxY+xXk0pJQT15ceGthx7yr9J79rBaEaP7/PPaOGde1aNMGVSQ+vxz91a/lpjDtWuYmA0cqM/vIcJEaMoUTN7NnLxdvIj9FisGl7xZFYG2bNGuyxETA0+iM7HiR49qc7NatEB+kBkEwcAZS0QsMtLCFi82f0K0apWah8Plor2F3rKz8exr1FAnQI0bQ8n2d3iZ4ldegfIsGgKI0Ff79EGC/t9/S8OOr7l9G8bDJ59Uq+vZ5vg89RSUBDPLnvNFXrnByKx3++hRbRh5eDiqkzkTHnb1KmP9+6v7qFwZcwMzCAI5KgkyYomIEVkYESy9ZluZTp+GFYF3yLJlEbdsjz//1Fr9QkJQrjC/5FhYrSjTZ6tMia1+fYRIbN7sX6VaA5XLl1GGcswYNRFQfDahoajs9M47WH3aTBQFFb4eeUT1LBChqpq7xRdOnNAuZBUaijU+nAkRS03FInjh4ZjohYUhZtlMORAEA6dGjg4c6FzImyPcuKFdbblQIeSe2YtFT0jAwodiBaoHH8wfBh3OX38hVEcM0xVbpUro01995fvFY4OBmzchsyZOhLEiNFRbvYqHGb3yChLyzVYQjx2DsiOW/w8NRdi3OyQkoBCA2Fd698bcxVFycpAbEhur5oqOGmWuHAgCOSoJMmKJiLVta/n/jte2rfm12q1WhHKI1uZRo+xb8RiDVUMsX1igABQkZytH2KIo5k8yc+PCBVz7gAHGZW1bt4a1ecIErMrtifUqgglFwT3/9FO8Y0a1+olQinjgQFiXc1t91lVSU5FcKXom+KTp7bfdmzBduwbLoqjQdO/ufALv999rw86aN0e1L7MJgoEzlojY0KGqHK1WzTPJ4D/+qI1L79ABoVf2OHZMq6SaadA5c8Z7sioxEesnjB6tr6ZF/4b51q+PBP6PP0b+iD9WBMtPXL8O5W78eH1YlSgzunVjbPFizxgJs7NRVaxDB+1xixdHLqY7+ZYpKTC28PLi3Fu4a5dz+/njD7UoDTey2isc4Q5BIEclQUYsEbHkZAt79VXVAlCihGc6UEKCas2qVw/hWdOn27cSWK0IlRHzQ0qUQAUtV+uc//KLqmitWuXdeuk5OSgz+MYbEKgREXoLEhGS+Hv0gHDcssV/K4T4GqsVk6B16/AePfQQQgFECxlvxYohfOO992Cd88TkhJfrHT9e+84SYZHFDRvcO+716wjhEhMb69dHCWVniI/H2hGiN2buXM/V3Q+CgfP/k9A//1yd0ERGogyy2ZP0lBSEe4aEqPJj+PDcDUfbt+sNOlOnYgFWV7h5E/K7Th3IY2/KKEVB6d7587FgYHQ0ymHb9vnChfH5tGkoJ52Q4L1zzE8oCpSH777DmPPIIzBMcG+x2AoVwlpes2cjB9JT4ycv19u6tfb4DRuixLE71QdTU+Htrl9fXXG9XDkYrZyRzxYLDIfcEBQSgpL07hpJ7R8v4OWoJMjQVG/ZsUMbyzl1qmcmJVu2aFd0LVUKXgJ7nT89HTGfRYqoykPZshAizlZ6mT5db0l54QXn3K1mkZKCez5rFhQOMc5bbGXLwurZrRtCtz7+GPkJeVnRLRZc2+ef+3ciqiMkJ0N5W7ECQr5NG/srrd93H96pAQPgFj9+3LPXb1Sut0MHTOyfesr+ytaOkpCAsAdRseraFe+BMwNmdjbCdsT79vDDuXsizSAIBk6NHD15Ul0pmci1mv+O8PvvWmW0YEGUsk1NNf6+1QqjC/egtGiBd2HqVOfWhmEM3lqxzxUoAM/KL79434ObmcnYgQPwLA4YoFf+xVaxIpSSceMgG3bsQNx+buesKAgFe/dd/y277SipqfDMffkljBldu9ofdzp2RD5Z9+6Mvfkmnq2ZuRy28HK9otLRrh0m+P37Q+Fx5926cwfXIa5Z9tBDGH/t9RkjFAWrl4vlr5s0yd0TaQZBIEclQYaufOS1a7DWEmHC37mzuRWAOOnpsLqKi6Q1aJB7daCbN2HNEgVI8eIQII5a4PgKrMOGaRPkiVAScu1azwrZvM7t3DkM7hMmYIIQGam3AomtVCl1QP3wQ7irf/0Vk9bly9XvNW2KCiT+iqJgIrBvHyxRL7/M2ODBqCZWvDiuwXY9DvEd6NKFscmTkevhjQTVO3dwnp06aWOH+b1etsx9q3B8PJQt8T0tXRqKtzMDJmO4r2KFpPLlkYvljcliEAycOjmalqau5xESAoV0xw7z7y33EothSeXKYd0ie0o3N+iIYSMFCsBz50xIy19/QU4VLaq3Ui9aZH4ejDNcvYp8henT0UcLF9YavWxbkSJQ5h5/HOVX16xBn7l0CfKUf69SJUw+/TlMNikJxpovv4SnYuRIGGzKlME12I57vEVHY6wZPx5jx9Gjnq88lpkJz3C/fhjrxPOpWhUKQ1yce8e4fRsln/k4wq91yhTny4ufPg2jDd9PTAw8cd6o0BYEclQSZBjWr8/JwaSeVx+JjISy4IlOdv26Pp69Z8/cPRLXrkF4iJbcmBgoJ84IlJs3YdWydTU3aYKBVbTmffstY4895v3a5RkZKGv86ae45p49EV9uO+kl0q7Uyic+tt+pWhXJrNOnozrJ4cNYxTcpydznm5WFCciVK7AI//QTQqUWLIByMWYMQqJatcI5iYs82mvFimEC0KcPLJIbN2Jw8tZkIDUVk5qRI/VhXmaW6710Ccnk4oBcrhwGOmctsGfOqOFWTZqgn02cCAXKWwTBwGl3HZDVq2FB5s/x6ac9c+/T0vTx7E2bMrZ3r/3f3LkDrwGfmBIhJGXUKOfy5PhCraI3hhsGhg9HOC/3pB86BC+FM+WhzSAnh7FTpzApnzkT51C3LsJgbeVMs2Z5y6IyZRCqNHky8iR++w2Gjxs3zDVg5eTgOSUmYkzctw+Gg8WLIQPHjYN3oF07jGPceJhXa9IERQmmTYPR7cwZ7+XMZGUxtnMnPG+23hdernf/fvflelISwrh45ASfJ7z4ovOhh4mJUMx4sQ4i3PcrV9w7R2cIAjkqCTJyXUDrwAFtbfbmzTGZ9ATHj2PRIn6sihXznhAnJUEIi8ndBQpgTQdnwkp4paJBgzAgifHElSpBUHKvS5kySOz0NXfvwkK1Zg2E7MCB8JjwdSBcbYULw5pauzaefd268Ew1bowJTbNmmGi0aYPJRbNmCDepWhX3JjZWjavNzdpm2/iCZKGhsFR26YKB6K23MOAeO+abhdUSE2EN7NUL7xaR6pEqXBjKyM6d5gzehw5BORRd+xUqYLLhbKz19etYgJCvwEuEFX3/+MP983SGy5cZO3Ag4AfOXOXo+fPaJNrKlfHOeIIrV7TVryIi8i79nZ6Od0wsSBASApnibCL9sWN472JitPltJUqgP3OvS4ECyDnwNVlZmNh/+y3Kxg8bBoW9fHmtUczZVrAg5GHHjqocrV+fsUaNECIqytHHHoPsbtAAxqVy5TARt/UIiHLBXhMNOeXKYbI8ahTyDtesgYzxRcWwO3egqA0dqo7XfCIfHo6qU998Y05OydmzMLKIimSRIghPdPba09Jw78RIjaZNPZMjmxsWC2Nbtwa8HJUEGXmu4Hv3LizvXBhHRXnOG6IosMrXrg2Lv6PcuYOJKs9fCQmBRbpvX4Q9OGNJSUzEYMzX8DBqMTHet+A5Q3o6Jj19+ujPPTQUk9LQ0NwHWEcGX6PET6MWGYlBplw5KDIPPgjl5T//wXP77DMogGfOeDf8bfNmxBzv2aO1iP34IxSLli31XqTISEykVq92PgzKiMxMhNy1aKG9r1WqIIzL2fuRmoqwC9E7WLMmPDfeDBtRFNyj6tUZa9Ik4AfOPOWo1YoQSdFDMW6c55TqP/5A+fOJEx3/TVYW+qJodGrSBPtZu9a5fMCUFPzmwQe1SrDYwsKQn+avZGdDgZ4yRX/uISGOyVF7YaNiE/t+bq1uXRy3VCkoM507Q3l5/nnkoC1fjvzKP//0bnn3335DPs3WrVCAuZz54w94/Dp31itTISHwIC1cCK+Ru1itOP7DD6sy+/774YV74w3nc7BycnA/RaWvVCnMDTxVrMMee/ZAhjdoEPByVBJk5DlwcrzpDcnKci1p+O5dDPKPPKIfBBYscF4InT8Pq5g9r0Ljxs5XzvAm48drB7nVq7UTWkXBPbt6FeEJBw5gAPv6a0xEVqxAovOSJbiv8+cjifmddxBDvnQpKuB89BGSW//3P0zed++Gpe3ECTW8y9+S4G/e1D/PqCjjyUSxYrCMfv21eeEz167BeyeGv/B+5exkjzEonSNGaCd7pUrh2Xl7wExM1FbZ6tIl4AdOh+WokTfE2bKfjqIorin0OTl413v10r6bZcrA2+psNanEROSE1K1rLEcrVoQhwgyF3hN89JF6rtWrQ+bZjiUZGbjOv/+G7Nu+HfdwzRpVji5divuwYIFWji5ahOp8S5dCIfv6axjidu1C/snx47DqJyb651hjm/TPDU62z7lgQXg6Vqxwbq2i3LhzB2OTODfhz2n+fOcVfKsVnhLu7SaC0eCVV7wbtsoYxuZJk1SFql69gJejkiDD4YGTd4jJk9VJWqFCmKA7s+KyN8jOhku9a1etUIqOhkXG2apEtrHNtq1QIVjM162zv9K7L7hyBdafgwd9fSb+xZ07WANDHGRsW3g4Y0OGYBJglrdPUaDkDR2qjT2PiICC4+hzOnoU4Qu1a2PyJoYHcOviq696f8BkDEroPfeo5zJ4MGMXLwb8wOmUHLVaYfkVvSFTpnhmPRp3UBTkkAwapJ1QhoUhh+Knn5zzqj31VO5yNCwMXutly7wbW58XKSlQkH74wf8MKb4kIwPWeXFdGtsWEoJw2g0bzPXKnD4Nz4+4thgRKkU6+pxu3EB+Vp06MAQULar3eI8YkXcIoyc4eFCrVLVuzdjhwwEvRyVBhlMDJ+eXX+AS5CE41auj0/sjp04hJ8RWULVrB2uTI1a3++7TWgGbN4fQNcpvCAvDvt98ExNFf66WEkykpCD5sGVL+yEhRAjj++ILcycaV68i2bdePW2MdpkySBx2tgTqnDn2z79WLd+sdp2crF2pu0QJxHwzFhTJky7J0XPnEN7EY9VLlMi9HLkvuXoV76ptHkK9evCyORJGM3q0+rsiRXDd1avDMGT0LjdujD67f7/3vXgS+7z9Nox7Rustica+N94wN6TWYkFYVLt22hDpwoVRLdDZUvqbNtk//xIlUOTE22RmMvbSS6qRNzIS9zsnJyjkqCTIcGngZAzekFmztLGdvXohxMAfSUnBQMlXpuYru0ZHIx9h61b7A39iIsKKjNzGZ8/Cvd6nj34grV4d1uC+feFi/+0375Trk+jJztZWQ4mMhOVLfF49ephX5z89HaFU3btrw7oqV0blry++cG1wvnsXOR5Gg6Yz8f5m8uOP2olp797aanRBMHC6LEetVoRxiiVC778foTf+SFYWQoS48alECSj04eGo0Ldunf0+lJICr8mlS3rDzNWrqFA1fLi2zDoRPJVFiyKf5PXXIYvz+3oc+RmxLHxIiD60rm5d87wGOTkYm4cM0So8kZEIK54/37Vyzzk5CEm2zU0hgsfGFxw9ihLW/DyaNkUYMycI5KgkQChGRLeIaFce33N54OScPYuJG+80UVEI//DXAUJR4DaeOlVvwSlbFgv3HTnimuciIwOxv5MmwRLNKzuJrXBhVPuaPRuDsb/ep0Bk2jRYVLdvRzhASor6DvTr5761TlGw1srYsVplh3tWXnjB9QpqtoUWbFvXrt4PD/nnH+QZcY9SkSLwKtr2nXw+cDoiS92Wozdv4r0RQ0CeeMKcBF1PceIErNy2pVRjY7Gexq5drr2TOTlQwF55BcqYUZJ2RAQmwtOmwZLtT6Gvgc7ixShTv2GDutYRN+g1aeJ8iVsjTp7EsxXXt+Hj5+jRUEJdebeMCi2IrUoV3+R6zJyphq6Gh8O4a+v1y+dyVJJPKEtEXxPRKSI6TUQfE1GMk/t4j4is5AUFhDFMOL77Dp1XtPRu2ODfIUh37kAYde2qj/2sVw+VRc6edX3/Fy9iQvbkk/YFXmSkZ1ZJljjG9u2Ix3fVM2W1YrI0dao+aTcqCqVMf/jB9f3HxzM2Y4Z2IC5YELlMvJxl0aL4nrewWhEKwa32nTpBqb582fj7Phw4vSVLTZGjjMFLKoaXFC2KJFt/9pxmZiJc5dFH9WGp996LieQff7g+FiQmokTr888jHNaoUERICEKDJb7h2DEk1Lszlp08ifDSAQP0z/aBB9yrPHjrFqIQRO9NaChy1HhURGio96tbbtmC0stECM+tV89+qXSpgEg8TRQRnSSij/79fzgRbSGibU7soxIRxRHRIfKSAsLhYVlicu/Qoc7XkvcFV64g1lJ0gXKloX59xGUeOuSelfn6df1AWru2edcg8Q5ZWVBcnnlGG3rEQ7patUJFG1dXQlcUWPgGDFC9Cx06INl8+nQ1FPDrrxmrUQMJ9d7i+HHtIF6iBEKIcptc+mjg9KYsNVWOcgVPDEUaMABlo/3ZoMMYJnrLl2srfcXEwKpbqRLy8XbscC+nw2LBvXjxReQDREVh//5aRUtijKJgTJ0xQ1uuuHBhGFlq10YupTvGlRMnYKzhBR/atcO78vjjqFjGGCb8tWujMpm3uHIFCrtorJo1K3cvvFRAJJ5mPBFlEVFx4bMmRKQQ0QMO7mM1EY0jop/IywoI58IFlMIVXeeDB7vnTfAmx46hKk3fvnpLW7lyEGibN7tf/ctiMWflbInnSUvDWhojRmgXvuTtvvsQn+7OO56WhnwiUQnmVuT333ddoTGDlBSs2yIm8D/xhGOVm3w0cHpTlnpEjiYnI8StenW1ClWHDvnH0h8XBy/yY4/p+0vRoojrX7fOtRh+kfT0/GHkksCTt2sXFNF779W/F9WrY+w9fNh1ZZtXwbQNgS5SBN64S5fMvSZnz+2DD7SVCx980LFxQyogEk+zjYiOGHx+m4iWOPD7+wjhBmHkQwWEs3MnEql4RwsPx+Td2TryvkJRGPv9d8Rn2k4KuWVvwADUNbcXfiLJn1itSAp85x3GHnpIa9Hl7voOHTCYuDugnT+PyX3RotpjdOgAj5kvw28UBYO5OFmoX9+5UAUfDZzelKUelaMnTmCBNfHd6NNHm6Dq7/z9N/pSu3b6EKqICPSxxYsRhuPvXh6Jc5w7B49w//7GeZGNG8P6f/y4e8/+n3/gMbEtC1yvHo7vaw/Zr7/iWvl5lS0LBdzRa5YKiMTTXCWibw0+P0ZE+x34/XYi6v/v3z5XQBjDRO6rrxAqIsawT5+e/xIHL1zAhLNTJ9USXLOm1nozdiyqH5m10JI3URTEpJ465esz8Q2XL0OZfOwx/eKTjRsjX+fhhxFi4m5ycGoqFm/s2lXrKSxYkLExY1xPVjeTEycYGzVKPbfoaIQpOhs+46OB05uy1ONylDGsySGWcQ4NxfPxpUXXFW7cQNjeI4+oRSAKFlTXxilTBt6R5cuRR5cfOXDAfyuZeZqbNzHmjxmjzQslQt5YRARjbdsy9u67GFPdITsbRQgefVRrJAoNRWERZ9eq8QRXrzI2bpyaHxUaijBsZ8WFVEAkniaTiD43+HwfwRqXG92I6Ffh/36hgHCyshBeIsbMFysGq5izq5X6A0lJmEBOnaovG8lbgwYQNBs34vveIjERFvV58xj75BMI6EOHMFHJbTGo7dvVpL9HH3V+wcb8hKJgcrNmDVz+oiIptho1MHisX+9+qIjVigFx1CjEOfNjNGqEgfqdd3wbZsW5ehWFE7ilunlzhCO66uXz0cDpTVnqNTmqKMj54YmzRFCMJ07Ulj7OL9y9iwImL72EHBGjPlilCsL9vvjCu8UWGINsmDMHY9eGDQh/O3cOBUzsTWyvXVOLmrRvj0gAX0+CPUliIsa4119HxINtQRdu7R8+HEVZzKjsduwYqgqKVQFLlICndvp0hP/5mpQUVHLj+ScdO0KWuhouKBUQibOMJMQc59asRNTv3++7OmiGENFRImonfOZXCgjn7l2UE+Vx9K1bwyry6qv+txKwo1itEIjvv49a+LYrU3PLT82aEMILF2KlU0+tIH/okPFALlqyq1TBpLJDB5RRHjAA4RG2361aFVb/gQMh2BcuZGzBArT58+ER+uADXPtrrzE2eTLC7IYPx/4feABJ2Q0bQhnzFampSOyeOxeW1zJl1GsUr7tkSeQrLV9unmX57FmE8VWurL23BQvC0pvbGjTeJCUF/VBcobt+fca2bXNvvyYNnP4sS70uR3NyGFu5Un2n2raFhXXcOPetyr5CURCOuGwZ+qCtF5Ir6/fei3CeefOg0HuqbGp6eu5yNCqKsQoVIBvbtcMq3P36YQyw/W65clhfYuBAlLC1J0cXLUKJ9qlTUeRi5Ehc60MP4Rk3bozj+IrMTFRqW7AAsqtqVfUaxeIUhQvjPnzwATypZihgiYkYZ8QwJu5R6N4dkQfp6e4fx12ys7GgqKgclS2LipvuyHmpgEicJYaIajrQov/9/jUyDhs4SrmHDYwiok02n/mlAsK5dQuCVkzojY6GVePKFa+fjqlkZ8P9PmcOBh3bBe9Ey2Xz5ljFdeVKJKSbEe9//Dgm2a1bIyzMdk0K3ozyWlxtYhUTe61nT/evxsp/hwAAIABJREFUzRFSUnD/P/oIylCTJvZXPy9WDJO2t9/G+i9mr6Uxc6b+mO3bI9TLB93OED5gikpZ2bJQwtwZMHnC5dy5pgyc/ixLfSZHMzIwGRQnZWFhjA0blr9yRIxQFFzD/PnIeSlRwti4ExoKRfnxxxHr//vv5kxEb92CYaZ9e8g3cbFI2/5slhwVJ/H2WoUK7l+bI2RkIA9u1SpUHGvTRlvhUmyFCuFevfIKY/v2mb9y/dq1aiEG0Tjy9tvmLXroLtw7KY73hQvDM+RO/omiIBdw3DipgEg8yzYiOmzw+S3KPXHyE4JV7/C/7QgRpRDRnX//b6/0ZCwRsfHjx7NJkyaxSZMmsS1btpjXIx0gKQmKiLiYVWQk4kfzS9WsvEhPx4R4wQJMDOyF+7RogWtv0ICxQYNwX/73P+RkuKuYZGQgfOGPP1DBa+VKTDr/+1+EP0ycyFjnzsbnFREBa33p0qj2dN99cLU3bYqFwpo1w/l27QrFZ9gwxp56CmFgr76KQWLJEvct6bZkZWGCsmYNrqF3b33Mseh1CA3FRO3pp2GNOnPG86ERmzbh2FWrwkN0/rxnj+cMfP0e2wFz9mz3Ezbfe28LK1lyEiOaxMLCxvti4PSmLPW5HE1Px+Tb9v3v0ydwchGys2FcWb4c40OjRsaGhbZt0ddr1MD1v/giwreOHnVfMcnKQojVsWMIWf3yS+SzvPkmjA0vvAAvh5EcDQuDHC1WDHLISI6OHAk53Ls3PAxjxmBh25kz4fFZtAiTcTOxWhFWtn49JssDB0ImiPfWdjHImjVREXDxYoQUebpQRlwcQrtKloQn3Z11ZTzBoUMIsRKf9dNPux8W+cknW1ilSpCjRD6Ro5Ig4hlC6IBR6ciuNt8tlce+8rLaEfnQcmdLaircq+KCa6GhmNj+/ruvz858kpIQevP661jArlQp+4oJV8oaNECS+4svQnnYupWx06fNW039q6+0xxs/3vsx17akpGDSsXEj3o8JE3C/Hn5YTVq11ypXxvfffBOhGr7INcrOhkXQnwZLbqlr1kxNajZrwLxxA/H6Yh9++mmfWO68KUv9Ro5mZ2OyLeaIEKFwxrZt/vUemkFqKvrXu+9irKhSBQqIPZnAFZN+/WAg+fBD9IU//zQvlOv0ae0xBwyADPMl6ek4r82boTRMnozQrrZttSGXRq1MGYSDzZyJRVV9FSrtCc+Kuxw6BM9+3brq/erd2/1CLnfvwngnLuzZu7f0gEg8SxQR/UVESwmxyJFE9CNhAS2Rl4goh4ha5bKvfKWAcDIyYOHiVbMKFEAIUZs2sDbltlBPfkZREHq2bRvCVsaMgTveNnzKnou+dGlYqQYOROLkhx/ifm3bhtCi+Pi8c04SElSrV7t2nl0V9u5d5FgcPIgJwIoV8MZMnIjBrnlzxu65J/cwBP53yZKYYE2YgPjxAwc8Fxeen1EUJNLed5/WuzVypPsDZlYWQmVq1EAoJRFyf44c8elChN6SpX4nR61WeLdatVKfdZ06CCVauNB/wv88wc2bqBi2ZAnWm+jcWRuPT4TQVCO5UqwYQjYfeQSy6MMP4S3evBm5D5cu5e0dtFpVj37NmvBimx3aycnMxLjBPduff44cy8mT4Y1p2xZGPaPEcN54WFtMDN6XsWPxjvz0E0rbSvQcOIC8E/E+Dh3K2J497u2Xh1tVq6YWZaheHdUpZQ6IxBuUJaKvCWEApwkr+cbYfGcsEd0kovoGv59MCBu4Q2rYwH/sHMvvBk5OTg4s8iNG6CfaM2fm/zwRR1EUKAbbt0Mxee01LFxUq5b9eFwi/ZoS4mBTpQom+E8+CevciBGwfj/9tP77NWrgO88+i2OvWoW2ciUGu88+QwjCypUIMeMhXc8/j/0PHgwLUadOUGqqVTOO4xabUTJ8kSIIW+CWyyVLcE+8UfknPR2LZ738Mqz7+Q2rFYNao0bae9qnjznexa1btWFcPXpAoeSTLh8OnN6SpX4rRxUFxRdErxQRQu2eeSa4FkLlisnSpQgz7NkTnqLc5JFtn+GtYEEYQe67DxPP/v0Rejp2LBQXroTzVq4cjvfss1gMb+VKYzm6ahWUnrlzMc5NmoR9DhkCy3qXLjA0NW1qvCCq2Iw8QQULwlrfsyeUs/feQ/+9dMnz3rGcHChxc+fCg50fjYn796OwinhPW7Rg7Mcf3b9/R49qw7hatcL6KDxsUCogkkDDbwdOjqKg9OGQIdqQm7AwTIx37w68sAJHURRMwH/9FQsazZuHSUWfPhigKlTIXUmxXVzPnWakNNg2eyFTRYrAUti2LQby2bNxLV9/jQmyt0vT8iIC//0vBnzxHoaE+EepXEfIycF7YRuO068fPBPu8vffCIfj+w0Px+TL9v4EwcDp93KUMXi5nntOP+Hu1Mn3C176EkVBSOzhw8iDeP99vMePPKLmlYlls21b8+bmyVHbXAt7Taw+xVt0ND5v2RLn/dJLCPH94guModeve3es5EUEFizAvbQ1iu3d671zcZfdu/U5kq1bQ3lz957euAHjn7hA54AB+jVwgkCOSoIMuwNnQgJWQPanyf316xCoYp4IEXIj5s+X7mIjFAUhAxcvwvr044+wur37LqxfEyciYXz4cOPykbyFhqplJ6tUwUBXrRrcwzVqwNPRpAkUke7dIUBHjUIeybRpeG7vvYdj//gjFIvLlz1XitgZ0tMxQC9ejAl1bKzx9TdvjlLE/r7IZGoqklXvvx+5PFxxevRRcxY4tFgQ5icqlA89hFWsjb8f8AOnXTmamQlvkD/Frt+5g3e9Xj3tO37vvbBO59fF/zxNejpCWY8cQWjrl19i3Hn/fYQ7Pf00whkffVQbuy8aL0JD0SfLlTOWo337wuvSpg36VL9+8FCPG4c+N2sW1gtavRp5cb/+irLLvl7lmzEosEePYu2pIUO0VfXEVqcOPEH+7n3LzoYBp1Ur7ZyjXTvGduxwf26UlYWoBlExa9QIyo4RQSBHJUGG3YHz8cfRIdq29b8qKllZsI5zd2WzZqqF/ZFHYMXKj+5dX5OcrB0omjUzx7XsTygKqqutWoVBsFkzdSJt6ymoVw95JRs2oCSnv3P5MtYPEAe0jh2xsrsZJVmzsxEqIq7PUKMGKn3l9o4EwcBpV47On4/7VLMm3iN/6kuKgjj//v3hURbzqjp0wERS5lK5hngvq1RBeFWgeZgSEpDfMnUq3hcedhYTo805qVQJ84nVq/2nZG5u3LqFyo0VK2rlaKdO9pUDZ1AUjKti6fqSJVFYJrey50EgRyVBhuHAmZOjLyU4aJB/LnD155/ITbCt0V6yJMINfv/dvwZ9f2fYMAjavCaVviI1NffV3EXE8pLz5sEzI5Z7tm2NGiH5/8sv89eq0gcPwgMlls2MjoaC9fff7u/faoUlsFYttVJbbCwssY4o+kEwcNpVQF55Rbt+Qbt2eF7+Rnw8lCXb1cgLFYJM2LbNPxbLzC/MmQMP5PLl/uX94mRkOF6IgBdI2bwZyen9+8NbZk+OVqgAz/fHH6PsuD+OI0acPQuZKebvhIVBtpplhN27F0bdIkUgQ8PDkefjiIErCOSoJMjINXb5558RT8o7Y2QkEoD9MQY+IwPWmN699QsW1auHUqxmrWwt8Q1JSVA0IyOxGvCiRagPb7WiCsznnyO0bNQoDP5ieUnbHJWSJZEIOXs24nj98Z3ODTE8wHbwf+stc65HUaCI2i5yN3OmcwpaEAycucrRv/9GKI34nB57zD9DnaxWFFwYOVKfSH3vvQhBPHYs/0wqJcY0agQvRatWyHXjq5WfOwfv8MKFCClr21brURXnA0TIjenUCe/F+vX5w8MhoiiM7dyJ0FvRa1O0KDw7ly+bc5zff0dInXjvJkxwrvpgEMhRSZCRZ/KkoqAalZj0VqwY4vn9IX7fiMRExFY2aaKeM59ENWuGOOdAWeQwWMjJQR14e1a33FpsLOKoJ0xAQua5c/l3AnX2LAb7MmVQaECcGKxbZ16Yx08/6Us+Dx6MxRudJQgGToeS0Pft0yYsR0aiXKq/Kr8pKcjZ6tJFnZzx8LuaNRmbMUN6mPMbigIlIa+1P4xagQIw2jz5JMqdHz+ef71i16/DUMOLn4ghpR9+aN6aUX/9pTc+dOgA466zBIEclQQZDldvyciA0sFL/0VEYAK0bJl/51scPw6vjVGCdcOGSOrj1h+Jf7FvH6xGNWrkveggb7VqIWRk7lwsmhUXl/+fbXo6FKdOnbTXytd9OXDAvGMdOqQvM9mrF5JLXSEpibE//gj4gdNhOaooWMlaXLG8dWsUafDnIlpxcQgrGjxY3+cqV0YS9s8/e269C4nrxMWhD9er57jiUb48Com89hoiC86cyb/KBicnB2Fk/ftroyTKlmWsa1esm2PW+3v+PAq7iF6VZs1cXww0I4OxvXsDXo5Kggyny0cmJ2NCL05SKlf2f0XEasUA+cIL+jhnPnGdMQOTufwuaAOF7dvzHijDw5HbsWmTWi89UDh2DHlMtvX+q1VDSKGZ4Q5792L9DjHxsnNn15WbzEwkVWJht4AfOJ2WoxkZCBcU6/4XK8bYG2/4tyLCS6vOmgUDjm1/LFcO8f/btgVef8yv3LjhmNLRogXy3/JDwQ1nuHQJq4qLhQGIsNDt5MnuL8IqcvIkQoB5YRwiKH7r17umePDFY6tUYax48YCXo5Igw+X69XFxiBEVLdP5QRFhDJ3699+hcPCkWiJ10anixZF0/+mn+S+mNZC4ehXPaNkyxOlevIjYdP686tSBhyuQuHoVycB9+2oHy6golLbctcs8K53VilXo27RRj9OuHSYiO3a4tk8eslmtGpR6IsaKFg34gdNlOXrrFiZHYunn/KCIcM6ehbdRnHDxgiCFCkGpXbAAeTD53ROZX1EUlEJftAgegNOnUeVJnIhv3uzrszQXiwU5gU88ofVChIQgf/Cbb8ydpxw8CJnNj1WrFjz3q1e7btDctw85OjxhPSIi4OWoJMhwewGt/KyIMAbh/OefmAQMHWpsGWrUCAlpu3YZX1NmJmNbtjB2+7bXTz/o2LYNE7QxY/yj9r0ZJCWhYkznzupiVDy5vEEDTOCSksw7XlYWEk1tyw536ICJiKsTxd27tTkOEREILbp4MeAHTrflaHJy/lZEGMNY8P77mPSJFdl4q1oVC6Vu3Ghc3peXBU5I8PqpBx3nzyOPrHv3wLnfd+/C+NGvn7oOC89drVAB4WRxceYdT1FQwMQ2NLZuXSg/rubjnTyJxYTFfU6axNjRowEvRyVBRiwRse+/t7CtW93rjJcuYUE7URHp3BmTJzGh69Yt8ypLeIKEBHg+Bg3Sl/YlwgTh4YdROWTfPoRSvPMOtlWqhMX+JJ4lECypd+5ACXj4YX3VtlKlECp48KC515qWhgTLypW1x+vdGwsxusqJE/ocq0GDkOzPWFAkT8YSETt82MI++cS9EE4jRaRWLcZefllbeezuXXNKLHuKW7dgZX7ySX3oC1dO+/fHat1btqA/bNumythvv/X1FQQ+gSBHMzMRfjt0qH61+uhofL5jh7lh1Tk5KPghFrkhQiGQjRtd91AnJMCwJq6I3r69WgI4COSoJMiIJSJWrZqFESHh19VkU46oiPCQpmLFEEpz+TJCnsLCEBPp7+TkoPO/9hqES2io3mocFaVVVCIjEfseCMJdYi5JSXDJP/00KsqI71HRolisa/t28xcsu3wZEz2xDGR4OCqDubNA4ZUrOGdxwOzYEYnsIkEwcMYSEevZE3K0cWM8R3dITsYaIrGx8ExxWfPkk7CQ8hC9OXPcO443UBRUA3r3XeQORkXhusT3JjSUsdKltX1iypTAW7xP4j5paUgYnzZNbySMioIH5KuvHF8vylFu3UIhnu7dtcfs1g3eX1fHfIsFBgaxQEDdugiPFfcZBHJUEmTEEhHr2hUDZ4sWiGEcMkQtU3v6NGJ8nU1Mi49n7Pnn9Yv68L8LFHDP6uoLkpIgFJ59Vq2jbhSyxeNqH38cK57KQTR4OXMGHrL27dX3n+dcFCqE9SC++878ktaKgpDBfv3U4xYvjvVPJkxwb02c69dRiKJ6dfV9r18fVceMBuEgGDhjiYgNG2ZhISHwMIWGapP4b95EOJWza38kJ0P+2k7Oxfb5564/S1+Qlgar9LRpqACWW4W7woVRjWnVKnh9JMHJ1asIU+3ZUzXe1KmjGlO6d0c/8ES44rFjjI0dq1UQqlVDRbgjR1zfb2oqSgFzAwMRijgsX248ZwgCOSoJMv4/dnnrViQMisrCmDFqknbXrq65MfkAWq6ccTiTK+sK2ENRvOt5uHULA6O9wVP0irRogfjnFSsgtPxxddxAJzUVIXJmrWprRHY2Y3v2oLqKWOCAt5gYKKZr13omhyU5GWGPdetqj1uhAsIGb950fd9XryIWuWBBVZEqX57lGXYUBAPn/8vRo0chN21D3Lp1U2PSXVn3Iz0dExPb58pl9Y8/uv5cjfCmHL17F7kfeZWIDQlB5a3Ro7FQ3i+/mG/lluRNZibyJt318uWGomCcnDULi8oahfA9+CDG03/+Mf/46enwVrdvr/dUv/ACcmhcJSUFc6KSJbHPGjWw3zlzcn+fg0COSoIMTfKk1YqJUY0axgPAjBmud7pTp4w9BqGhsHCZkRdy/DgmfS++COHljUF09279NRUvDstdbh6SqChYUEaMQEnVDRvgbZLeEvfJykJo0dq1cG336QOLFX8enTqZdyxFgRK9ZAneY7GiFG+VK6Oc7rZt5hRmSE7GauQzZjA2bx4sgzNnMla7tv7YXbog3NGd9yohAV4TMWysTBkkHDtilQ6CgVOXhG60kCNvPXu6Hid+9642P0RsrVsz9scfru1XJC0N3q3x43Ed3ihLnpysv56iRXGtRgnt4vjRowdyjl59FX3+2DFZAtgMrFbkcW3YgGISgwahpCzPWStRwtwxNiEBBr1Ro5AbZ/usS5ZEFcRvvjEuYuAsOTnwQEydCuPMkiWoDtasmTY8kAiK77Jl7im8FguUjBIl1P1GR8MT6IhhKAjkqCTIMKzekp2NzhYZqRcCq1e71vk+/jhv69bw4SiP6yqvvKLdZ7Vq6NyeXK3XYkEYzfDhuDdioihjjF27htCU2bMxERYTMo0mqxERsHL264e4/VWrYLG/dk3mlYhkZWFw3LoVA8fkybhnvXrlvWhh06buHfvKFbj7R45k7N57tftu3x7vcsuWGGyOHzf/uc2fn3dfGjsWcffucOgQBmPbRbs++MC5cJggGDgN5aiiIEHWqJjF5MmuPZNff8392XOlc+tW15Wc//1Pu79SpZC3tGOH5wwkioKQ3QED0J9tLczJySjF/fbbkLeid1EMYRH7QNWqmMhOngxL+Z49qIIkjTwqViuMfz/9hHv00kswjA0enLdHqnRp17x5nKQkvGvjx+uNJzx/tG5djOH795uvCBsZD21bjx4oNuOODI+LQ/4d9xwTwUA5Y4Zz3psgkKOSIMNu+cg9e+x3ytatna/AcuMGyjOOGoUBYd48hHOIceTiBHHZMm31LEc4cwYu2wYN9PusXBnHPXBAPzCnpcGd7C2rWWIiKr8sXIhJbPPmCM0xutfVqql/R0XBO9W1K+7l7NmYCO/ejcl4oJSltVqhyB05gtCSFStgoRozBpOrKlXsW0XFyUjJkvB2PPccFOBffnE+RlhRkC+xZg1yf/jaFratfHl4s778Uq+EmonVytjixcbnEBqK5GRnvYmnTmHi160bYqk7dNAvfli+PN5XV/pIEAycduVoYqJ9hbhKFUz8nCErCyEgw4ZBfs6ZA8t/w4Z6j2vVqgj1SEx07hjXrkHJbdtWv8+SJdEPt27Ve/OsVigp7kxKncFiwTi1fDkU7vbtkXtnr29wq3ZYGCoWtm8Pw9HLL2Mf27ejL9y+HRjGHkXBJP/ECVzbypUYd59/Hv28Vi1jIyOR1jgWE4Oy4GPGILxz1y6M586SmIgqUa++ijHeKEKgWDEYkpYuVavoeQJe8tm2AiFvLVs6701MTsYYwOVo164w2Ij7LVwY75srZdWDQI5Kggy7A+f69XlbWcePhzXYXW7exCJJTZtqjxETg7yJP/5wfkA4fRpJn40ba/dZoQKEwujRjH39NQabiROx7f77fbfwoKLABb1jByZ648cjiVXMy8mt1auHfwsWxOB6//347ciRqCbz1lvwpnz7LUKBDhxAHO/Fi7DCZGSYP+jm5GCScPkyjrVvH5L4V69GOdg5c3Bu06YhLKVpU+QK2VMujD6PikIyYs+eCBP69FPcw+vXja8nKwuTq08+YezCBf32tDSsCj5vHibzZcqoxxLfpeLFUUZ08WK8a56esJw9i4HLqKQpH9hc9Xi8/LL99yo0FJWX3FHOg2DgtCtHz57Vh3PYtp490T/c5e5dKMAPPqjdf0QEwmd27nTeK5KQgL7aoYN2whgeDjk6YAD60tWr+JcIXkF3qym6w82bkDXLlkFZ694dE+q8ngORGjYXEQGlu3Fj3M+hQ6HwvfkmlJVvvoERaf9+hHydP48Jdlqa+bLAaoUxLiEBFdB++QWGmTVr4C16803Gpk9H69sXk+dKldS1MGybUYh1eDiMXQ88AG/XokXw3l26ZHw9ioLx5MMP8e7avldZWYg8WLgQ946vx0GExU7534UKwTvw1lv4vqfD/a5fRzU2IyMll3fffefavr/+Ovd3q2tX95TzIJCjkiDD7sDJVwvfsQNWpgMH0DF5iAnvVJGRsIzwqlnu8ttvmPBw92/hwhCk9erBmudKrsjZsxDSTZvqFw0KD9e6RitU8L/VtdPT4d3Ztg2D6ssvw3LXvj0GmrAw/doORk1cJM6ohYcj7rpxY0wiKlXCwFGjBlzk9erB0tqkCWMDB8JNXrs2vnPvvbA+FimC+8kHe3vx6mITByTbFh0NL1m7dijv/PLLUDL27oXy6+yE6ttvtfsvWxbXW7ly7iEHxYrBA/LWW1CIzVqNPDeuXEG4k60SGhKiKpz8ue3c6doxsrOhbBldc4MG5lSVCYKBM9eFCE+dggV6zx5MHnfvhpXX1vrap495BRLOnYNiL3oEKldGP502zbXyy9euYcLbpQuKati+L0WKaBXiH34w51rMIisLBpfdu+E5njULBSG6dIGMiYxUFwDNrVWqlPv20FDIvS5doMRUrAhvV/Xq8DrUrYu+1bgx+nbjxjCiVKuG8adUKcjhQoVUo4u9CbPYGja0vy0qCs+/VSt4QKZORbn47dthiHE2LO3cOb18bNwYY0FMjP38x0KFEAXxyivoD95YrPjWLcY++wz32tazaxv6tXCha8dQFHh3jDwqpUq5l7TOCQI5KgkyXFrB984dWBHKl9cK3cGDzbN83b4NK4zt6uQhIVAiVqxwbeXxS5dgte7ZU6t4iC0sDNbtkyfNuRZPk52NyeqRI1BSVq9GvfIZMxCq1bs3rGL9+0MY5hXbKz5Xe61t27y/Y7uvkBAMrJUrQ3np0AGTrunTMSC+/z6SSPfsQYifsyF4uZGYiHvz3HN5nzOvtjN2LJSd06e9o3Awhuc4f74+P6huXQzus2fjHc7MxMSGCBZZZ7FY0If5Pmxb8+bmTQ6CYOB0SY5mZSEsxnZy2bkzJoZmWNIzMtCnhg3TP+PGjfEOuOL1/ecfWMAfe8w4x4W39u3hjcgP8NDPP/+EQr92LUKOZs6E8aNfP8i97t1huLBd+M6oD+Ula3gp2bya7b5iYqCs1K+Pc+rZE7Jt0iTV271jB7yiycnmeWVu34bx5913cy+ywlv16nj3Fi2C4cZb+Te3bkHJfPhhbQhku3YII5w4UZ2rcI/hhAnOHyczE33YNtKCtzJloLibQRDIUUmQ4dLAKXa+FSu0Lt2YGFgafvjBvEnbsWPI37At5VugACzx33/v2joK6emY+OYmQGNiEA711VfOx1I7yz//wKXvjYTznBwokgkJ8K788Qcm/ps2Ifxu7VrGvvgCwvXTTzHJ/egjKG8LF+K5L16Mz1euxMqw69fjue/YgUHq118xmMfFYcLr6Ul8djYsc5s3Y+Lw7LNQwMQwKnutQAFYLJcsMafCijMkJOB8jWLuy5TBdRw4oH8nEhIYO3zYuWOdPImwFNFaHRamloklgpLo7HoVuREEA6dbclRRIMNsre/duqFvmbVGzKVLCHu0tfqGhiL0ZuVK1zxeOTl5lyOPjISy8umn5r5bRmRmIk8hLs7zIT1WK3Lvrl+Hp/3IEShcmzfjma5di7C4VatghV+xAvloS5YgfOnjjzE5X7YME+a1a+Gl3bRJ9ZodOIAx8MIFKBPeuKb4eNzDpUux5s8TTzjmZQ8Ph7I0a5bnx0tbbt/GO9yzpz7vKiYGitDmzXrDyu3bGHedGXMTEnCNtmWxO3ZUDXyhoc7neOVGEMhRSZDh1sDJycnBBLRxY3RA3hlr1IA1V9z9li1b3DrO9u3IaxCtT1FRsEiNHAk3qDMVeqZMMRai9gRs3brIS1m3zn7+i6vXuGaNepzoaFhGH3kEA8DixUj8PHvWO27r3HDnGbqDosCy9eefGEg+/hiT6V69ENpglOzbrJl2EtS4sT5R9T//0VrmPH19ioIJxZw5UNZtlY7SpaH07t5tzmQjIwPKpFjTvmbNLSw2Fop9XBy+N3o0vIKbNrl/TJEgGDhNkaOKgmf+4IPa97ZUKVjhExLU77rzjvLw2okT9Qsc1qsHmbNypXPx6qtX6/tebuVzK1ZEwu4nn0CmGU3+XL3Gkye1fb5WLfSzZ5+Fl/W77+AZ8PUaIr6So4xBaTp9GsailSvhLe/fH55fIw+5aPwLDcU9FQukEMHTbtZY7ygXLsAg9tBD+vCqmBhEUGzcaE6BGasVeT99+4qhcVtYRATmHseO4XsffACZ/u677h9TJAjkqCTIMGXg5CgKBFrPntpJVeHCcA+fOcPYpEmTTDlWWhpnEBk8AAAgAElEQVQsSz166HMIoqMZe/RRbM/r0o4eRRjQxIlQKniOyd27sFjNmAGXu5G7vUIFtP79EUf/00+wnrt6jVxw5WVlKlkSk+hGjXBuTzyB3IjFi1Gz/dAhTCpTUz3jSTHrGXJyclBV5dQpWKK+/x6Wt5dfRrxw166w2kZHa+8DL9Vo5M1o0AAhE7Nnw/p4/Li6+OO0aariumqV56+PMbyv33+PBE/b0r3VqmGS+cwz5q678PffUDDEuvNECPt44IFJur6hKJ5RboNg4DRVjjIGuTRqlLZKUXg4vAgHDjA2caI572h2NpT5oUO1eUX8eA8+CGt9XmEkiYlIdBfDFhUFk7YNG9CXBwwwrlLVvDne0e7dUSHphx8gD1zthzt25G5E4q1xY0xa69aFjBkxAuGg8+cjofjnnxG7b7HkDzlqtUJpPHsW3ufvv4fHZdYs5Gl27w65WLSo9j7YC6cND0e544cfRmjX8uWMHTyoKm5ffKF+95VX9B5uT8hRvtDrlCl670Pr1hinhwzBO2dWVcvr15FDWqWKXilr23aSxjDAkXJUIskb0wdOztmzmNTbJiFXrjyJbdtm7rFu3IBw7NFDbwWPjMTny5e7V6Y2OxvC9623sD9bYcRbSAhjJUpMYqNHY+B2tpRgejom4ps2ISzn+eeh0NWpgwmzvcomto0PKhERmNzWrInE0W7dkKszbhwm4m+8AUvNokW4R6tXo8ILDwHYtw8KzcGDGNQOHGBs0KBJbP9+bNu7FwPCTz/BQ7N+PQamZcswkL/5Jqy3//kPjslLQDZvjom37WBIZP/eiq1QIYTf9ewJL8iSJYjbvnw571Cvf/7BROfIEePtZg2cvLpbjx7aRfx4q1UL525mjXtFQbhg5876fjBkCJ6XonhmcmCPIBg4PSZHExPRR23DT0uXnsRWrTI3rDElBRPvxx7TlwUPCUFe0rvvuldmWlHgfVi0CEaiUqX0pUp5i42dxAYNwjGdLYmanQ3r+PbtMGZMmQKDRKNGqjHJqAS8beOVGUNDketSrRoqDD7wAM5/7FjI0XnzGHvnHVjjly2DYeOrr+Bt2bYN/e7gwbzl6O7dkKMbN8IjvmIF9jlvHmTWlCnwjk6eDM9v69aQIyVLGlf5ys0LxeVC//5QNMePhxHsxx8xfueVr5GVBS/u1q3G282SMenpuJ+DBxuPF+XKQcHassW56Ie82LMHz1icU4SEYAzli7tKOSqRuE4sEbH4+HhmsVg80q5csbC337awatUsjMjCiMazxx7zzLEsFgu7fNnCli+3sD59LKxgQX5MC4uIsLD4ePOOk5RkYb/8YmELF1rYqFEW1qCBhYWGqtfIjztvnnnHvHXLwk6dsrDt2y1s1SoLe+stC3vhBQsbMsTCOne2sDp1LKxYMRy3bVv12u21QoXy/g6RhbVsafvZeN13+HGd35e+Va1qYbVq4ZqGDbOwadMsbMECC/vmGws7cMDC4uIs7PZtz71D48ePN2U/v/6qva7wcAvr1MnC5s61sMOHPXf+LVpo7+Ubb1jYhQueuUZHWnx8fKAPnB6XozdvWtiKFRbWrJnaBxs29Fw/uHHDwr7+2sKGD7ew4sW17/HOneYd5/Zt9IXlyy1s3DgLa97cwqKi9HJm2DBzj3n+vIXt3m1ha9ZY2PvvQ8aMGGFhDz5oYQ0bWtg99+C4rVs7JtfKls37O82b5y1HiSysaNG899WqVd7fiYnB+9KunYUNGoSx4p13LGztWgvbuxf3ID/I0cRE7VhOZGFNm1rYiy/iOjx1DU8+qR6vZEncv2PHPHONjrQgkKOSIKM84YWWTTbZZPN0K0+BiZSjsskmm7daoMpRSZARQniZY2WTTTbZPNjKE+RNICLlqGyyyeaNFshyVCKRSCQSiUQikUgkEolEIpFIJBKJRCKReIViRHSLiHb5+kQkkgBH9rXART5bicQ7yL4mkfgBZYnoayI6RUSniehjIopxch/vEZGV/LMzu3p93YhoIxGdJKI/ieg3IhruoXN0FHeeVR0i2kpEJ/79/VwiivDAObpDID0rIwK9rwUzgf5sA6lvSjlqjD8+KyMCva9JJEFBFEHYfPTv/8OJaAsRbXNiH5WIKI6IDpH/dWZXry+aiBQimix81psgsKaYfI6O4s6zKkVE14jopX//H0NERwiC218IpGdlRKD3tWAm0J9tIPVNKUeN8cdnZUSg9zWJJGgYT0RZRFRc+KwJQRA94OA+VhPROCL6ifyvM7t6fdFEdN3g8z1EdNm0s3MOd57V20R0g4jChM/6EgaXWiaeozsE0rMyItD7WjAT6M82kPqmlKPG+OOzMiLQ+5pEEjRsI1hwbLlNREsc+P19BDdoGPlnZ3bn+sINPltDRHfcPSkXceda/iai9TafFSMI7Wnun5opBNKzMiLQ+1owE+jPNpD6ppSj9vG3Z2VEoPc1iSRouEpE3xp8foyI9jvw++1E1P/fv/2xM7t7fbb8TkSb3Doj13H1WgoQLHTvGWyzEKxB/kAgPSsjAr2vBTOB/mwDqW9KOeocUo5KJBKPkElEnxt8vo9gJciNbkT0q/B/f+zM7lyfLU0Irt/73T0pF3H1WsoSLHSzDLbFE9Fm90/NFALpWRkR6H0tmAn0ZxtIfVPKUcfx9bMyItD7mkSSbxlJEJK5NSsR9fv3+6525hAiOkpE7YTPvNGZvXV9thQgWIKmunTW5iAHTsfwh2dlRH7ra8GMlKNSjtoi5ah/kN/6mkQSNMQQUU0HWvS/379Gxu7Mo5S7O3MU6d2y3ujM3ro+kVAi+oaIFrh81ubg6rXkFjpwm/wndCCQnpUR+a2vBTNSjko5aouUo/5BfutrEonEDtuI6LDB57co94SuTwjWhsP/tiNElEJIVjtMzpXE8ySuXp/IZ6SW/PMl7lzL36QX2kUJFj1/sXAF0rMyItD7WjAT6M82kPqmlKN58xn5x7MyItD7mkQSNDxDcGkalbTravPdUnnsyx+tCe5e3xIiWmnz2VIiKm3WCTqBO9fyFhElEixbnL5ElEP+Uz4ykJ6VEYHe14KZQH+2gdQ3pRxV8fdnZUSg9zWJJGiIIqK/CAImhIgiiehHwsI+Ii8RhGyrXPblj53Znet7jxDbO4KIhv7bhhEWQaro0bM2xp1r4Qtovfjv/2MJVh9/snIF0rMyItD7WjAT6M82kPqmlKMgPzwrIwK9r0kkQUVZIvqa4J48TRCmMTbfGUtEN4movsHvJxPcmXdIdWf+x1Mn6wKuXF8FUhMxbVsO+U4Yu/OsahHczH/9+/s3ybjuuy8JpGdlRKD3tWAm0J9tIPVNKUfzz7MyItD7mkQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEkk+5hIRJRHRVYN2g4iUf1sHH52fRCKR+DuXSMpRiUQikUgc5iIRtbOz7UXCoLnBe6cjkUgk+Q4pRyUSiUQicYILRNTe4PM6RJRORLeIqKxXz0gikUjyF1KOSiQSiUTiBBEGn4UQ0c9EZCWiMd49HYlEIsl3SDkqkUgkEombPE8IGdjl6xORSCSSfIqUoxKJRCKROEglIkohojQiqurjc5FIJJL8iJSjEolEIpE4wXZCyMBkX5+IRCKR5FOkHJVIJBKJxEGeIIQMHCLEL0skEonEOaQclUgkEonEQcoQUTIRZRJRAx+fi0QikeRHpByVSCQSicQJ1hNCBl739YlIJBJJPkXKUYlEIpFIHGQgIWTgJBFFGmwvTET3ePWMJBKJJH8h5ahEIpFIJA5SnIgSiSiHiFrZ+c5IIvrUa2ckkUgk+QspRyUSiUQicYLVBKvdV4TSkUbtP0T0ia9OUCKRSPwcKUclEolEInGQGoRB0+pAW+Gjc5RIJBJ/RspRiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEonEzwghovJEFCubbLLJ5sFWniBvJBKJRCKRBDnliYjJJptssnmhlacAhfkBxYszRqS2sDDGfv9d/727d7X/HzVK/c2AAfrvP/CAuv3RR9XPV67UHu/aNe3vvv5au/3JJ/H5qVPaz8PDGUtMVH8XH6/dTsTY2bOM5eQw9s47jNWooX5epIj2mLGx6jaLRbutXTvtPr/9Vt3WuLF2m7jfMmW02xYsULc99pj+XLOzGTtyRP85EWOvvYbfLV6M/0dFabdXrYp/xWvkbdMmxh58EH+XL69+vmWL+vfDD+Nf/j0ixiZMUP9+8UX1723bcC5Xr6qf3Xuv+veMGep11q6tP58yZbDtwgXGBg5UP3/iCf071L27ur1sWf12xhj773+xPSIC74BEj6/lnERiJrFExOLj45nFYjFsly5ZWNOmFkZkYSEhFrZ4sf47t25Z2IQJ+E54uIUNHmxh8fHG+xs/fjy7csXCevSwsFat8JsOHSzswgXj79u2Gzcs7IUXcC5EFhYZaWFz5uAcHPm9xWJh585Z2JNPWlhYGPZBZGGNG1vY+vWO7yO3Nn78eFP2468tv1/fmTMW9vrrFlanjvr8q1bFu9uvn4X17eve9SUlWdiCBRZWurS6/1KlLGz+fAtLTnZ8P1euWNjo0RbWsiX2UaSIhS1f7to5LV9uYVFRFhYTY2ENGlhYnz76azx6FPeByMIqVbKwjz7S7+fWLQt74gn1uiZMyLvvxcfHcwUk1sfyzmP4emJy44Z+gvjSS479tlUr9TcNG0JxUBR1e8mS6vYjR9TPX3tNO3G2Ze5c7fn8/DM+375d+/mxY9rf/fabdnvNmvg8MVF/jW3aqL/LyWEsJETdVqUKY7duqdurVNH+9tw5421lyzL2yCP4XFEYi4zU/m79evV3HTroz4kxxg4cMFZAFi3C9jlzjLdzBaRmTf22desY69ULf1eurH6+c6f6d48e+Fd8ZuPGqX+LisLGjTiXrCz1M/FdePxx9Tq7dlU/5/ejUCFsmzZNe5783onMn69uL1BAv50xKG62yrFEi6/lnERiJrFExCwWS64v/Z07jLVvrxeitrz3nirAGjViLCFB/51JkyYxxhizWhmbNUvdZ6VK2sEtLw4c0ArpIUNgJXOGs2cZGzxYKzy7dMEA6A78Go3IzGTsuecY27EDAjc/ktv1+Svp6YytXQtLXGio9pk3aoR398YNfNfV61MUTN5q18bEiIix6GhM1FJSnNvXrl3qJKNgQcYGDXLNKmi1wpLJr7VOHcb+/lt/jb//zlipUuo5b96s31dODiYlomVVnKgasWoVY2+9ZZEKiIf56SftO12vHmMZGY79tkgR7W/DwrSTc95fuMWbM3Kk+puhQ/X7FWVrtWrqu/Lpp9rJbGam9nfff689H/6qnjihn5TPnq3+LilJu61cOXWbrSIRFoa+wbnnHnweFaU9l9RU/THF8aF5c+220FB8bvs8RCWCMcZefTV3BaRWLfWz2FiMdUeOwANlq6Ds36/+zRUUsQ0danysNWvU6yhRAp8NGKBu791b3T56tPp50aLaa920SbvfVq3078KZM9rvZGXpvyPJG1/LOYnETBxSQBhjLC1N69Z96y3j761Zowr6ihUZO3lSu9124rNxI2MxMepE64svHO+MaWmYBMXGMla6NH7/wQfagcUR/vhDe218cvXXX87th5PbBPbHH9VjlCzJ2JgxcIXnJ4GcXxSQjAzc76lT9ZOs0qUZe+EFxo4e1f/O2etTFMa2bmWsaVN1/2XLMvb00/qwlLxITWXs2WfV/URHI1wjr4m+ERaLdkLSowdjt29jm3iNW7cyVrgwvnPPPcYKeHY2lHy+r6lT8z6nb7/FRA/eEqmAeJKZM7WTa0eNKP/8o508E0EmiZw5w9jw4Yx99pn2czGkafp0/b4bNlS3z5mjfv7GG+rnjRvrf7dsmbav7tiBz3ft0k+iRaPT2bPabf364XNF0XtPbJWp6GhVLogYhYOJfbpRI+22iAh8vnmz9p7ytmsXjBFPPOG4AjJqFGOXLjH25ZeMtW2Lz+rWVbf/8ov6N1dQxNazp/q3qIStWKFeB9+feF6id+mVV9TPmzWDQsnD9ZKTtZ4nI2+YoqiKCxHCviTO42s5J5GYicMKCGOY0PXuDQHSujUsu0aTkJ9+Uid8xYoxtm+fum3Lli267586pRW4L7zgnHdg716tC71NGwyazrJzJ4Rr0aJQikJCYD1ydl9G1ygeo0MHrcAmggXqiScQz+vvykhu1+drkpIQmz5ggDqp5oNrZCQG6E2bcn+/nLm+X35hrGNH7bMcOJCx06edP/d9+zCw8/20b8/Y+fPO74cxWLDr1dMqDDk56nZ+jStXIgafCMc28iJmZjLWv7+6r1deyVv52LIFkzFMWKQC4mnEyb6RMmAP0XpOhPCYK1cc+225ctrfPvCA9r3ghiUixi5fVj9/+mnt5NqW6dO1Cjj3kKxbp34eG6t6RjgHD2rPhxvJLl7UKwItWmh/C0UZ7+z776ufHzum/V1IiNbAxXMjuDzn4UXr1+P/vG/xdvw4Y7t3GysfogIihlD168fYJ5/gb35Pxee9b5/697Bh+n22aaP+rnRp9XPxOjt1wmfPPKNur11b3f7xx+rno0frn1mDBur2okX12xlT81OI3I8yCFZ8LeckEjNxSgFhDJPjSZNUQTJlivFk5M8/1US5qCjGvvkm9/3evq0qN0QIhfrnH8c7ZkqK1nJcoABj776rnXQ5gqIgBECcvIWFYaC8cMG5feXGtWuwbHfqpB8cS5TAQPLZZzIZzxEuXMBg2rGjOpEQn12XLrD2JSebd8zjx/XhDt26wZvmLHfvok/xSYyrnjzOzp1qQnJUFMKgbFEUbYz+/fdrE4E5GRna6/zvf/M+/p49uAYiWLjj4qQC4mm4961gQcdDrxjTTiyJEM/vCBkZ6vvK/7VNPuaf2yYdi++TOAnm9O2rbhfzCRYuVD9v21b/O+514G3PHnx+9Kh+Ui6ea2amdtubb6rbbJUF2xAtbvgqWZKxDRugeDOGSAAjBeTaNcZ++CFvBYSHhPGxkHvOuVJ/333q9vfeQz4Gvy5RWSLCWFaxIv6uU0fd/vLL6nXwcLkxY9TtpUoZ39u+ffX3XlRcwsKM5wSffaZ+58MP9dsleeNrOSeRmEksEbHr1x1XQBjDpF6snPLMM8aTpfh4xurXV4WhWD3ECKtVm9jYpo1xFZfc2L1ba0Vu1QoeFmfJyYHLW4y1DQ9nbOxYrTXPDBITGVu6FIl+YWH6iiy1a0O52rBBDaEJZq5fR67Fc89pJyu8xcTAC/HFF+YqHYxB8Xj6aa0Hq3VrdbLjLHv3aivEtGrlmveOMQz6S5eqlXXKloVV2JacHG0/69bNOEclLU2b8/Hee3mfw6FDqrW1dm3k1VgsUgHxBsnJjB0+7NxvxPyAokUd7y+2Mf1E2mTyrCxVCeXVrziifBs3Ti9Pxe3Ll6ufi2FARlb4L75Qt4eGIpyRMfRN23MVw4+Sk7Xb/vc/ddu332q3FSumPSY3slWpon6WkaFOtm0T2LOy9NXBxMarUIkKyP33w7Ahfk8MwSJSq3+JCecLFyJX8vZtNVRMTDIfN0495+ef145zRPA+ccT8GyPljytcvBnJE4tF3T5smH67JG98LeckEjOJJSLWvbvF6YRoq5Wx8eNVgTJypHFYy61b2kohs2fnbdnduBGCvVIlTKY+/ti5GPjUVMYmTlQniVFRsLS5kvSdnc3Y55+rlik+qDzzDOJyzebGDYQaDByoJgaKLSwMg8jMmcgdMXuC7Y9cugQr/pgx2lA9IsaaNFEH7meeQT6DMxZgRzlwQOuha9QIYRDff+9afsatW1Bm+bkXL46QEWc9dpy7dxkbMQL7a98eoYRGRSDS0hjr0wffa9cO/dYo5M9iwfaICFhbFy/O+xz+/FP1vFSpoobySAXEfxGNNfby+oyw9TZ07Gj8vbt39f3RtlzwhAna7aLcE3MFxMm16KXgzJunNdpwNm5UP+djguhZvnxZez5//onPs7P1FZ4qVdIekxduqFMH/1cUKOBcCRcVEF7aV/QE2GviPapRA30pt+8XK4Z/n3tO/YxXHWNMDRMVcx0HD1a38xK4RGroang4tvHyx0b3lmObKxMXp/8OYxjLzTbgBRO+lnMSiZnEEhEjsrBRo5yfSCkKYsu50Hn0UX1FE8YwAA0cCEEaHY2KPunpue/79GltGNTIkZg8OcP+/Wo99Xr1MGn89Vfn9sHJyoI1rlIl7K9FCwjo4cP1ZSTNwmqFRXPePMRXFyigFfLc7V61Ku79m29CKUlK8sz5eBpFwaR561aEzz3zjBo6YNtiYuA5eOcdeMlcUQIcOZ8dOxjr3Fl77IYN4Y1yJURKURCOWLasur82bVzLGeFcuqQNyRgxwricZWKitmrPzJnG9y0pCQoMESzJorXYHn//jWfVrBlyA8TcFamA+C88ZDEqyrkSqAsWaPuEWJo2NzIy9H1ZnCgzplrgbdf4EPOQjEJ6xdwSsYSsuF7JyJH63508qT2fmzfxuVFpY9ukee55CAnBxNrWmyIqINWr4zd8DRDewsL0oaOiAlK6tLZUrqMKyM6d6nlyT7GYkN69u7p9+XL1c65UhYRgm6KonxHpE/g5Yt6Ks544iWP4Ws5JJGby/woIEWOTJzvfIRQFXg0ueHr2NFYurFbUpRcnXVzQ2yM1VRsi0LAhJjrOkJaGCTwX8CEh8Ny4GsqUmcnYkiX6xONu3SDwPTER5qSnY0I8fToSE23ji8VWuTIG7LffRmjCzz9jcu/opPmTTxBP7KrClhdJSQg/WrQIls127dRBlDex9HOJEogHf+89hCN4soSx1QoFw7bEZqtWSGB39Rlfvqz1osTGImTK1VwPxrCmArcYh4fjfhqd3+nTqhcvLEwb2iJy/bqaUBoejtLFeREXpyqKZcroK99JBcR/GTMG/U6csDuCKJcrVHDcc3fxorZPVaigf//F/ibSurW6beJEfX6cmOQsVuwSc0eM1kY5dEirDERFoRJXXJxerj7wgPq7zExt/l5Kij5USlRA7r8fffPtt9XPeClbcYFALu/43zyx3dZzJDbuceHJ5ESQVRyeGyIqIC1bqtvF0se1auE+iApgt27qdjE0S0T8jnhsiXn4Ws5JJGYSS0Rs3DjL/wuOefNc6xjvvqsKn0GD1PhbWz78UBXaNWpo680boSiwGHFBHhurjdF1lCNHtBPKsmX1C245Q1YWwoLEaiREsESvXeudNT7S0zF4LlmCicR996lJirxxSzZvUVHIa3nwQYQAvfkmFJTt26Fs/PUXJspiDHKnTvBK5HavFAX35PZtPNN9+xj76iskU0+bBqt8167wRBUrprf42Q7ajRpB0Vq8GPHH7kzSHSUtDRNzUfEhwr3avdv1dyUnB5MgHtpABOXQKETKUaxW3FceUlKmjLbanMi+ferkpXBhNVHWlsuX1ZynqCjGvvsu7/O4fl31MhYpYmz5lApI4MFz+4gwoXYUsWKTkdErJweW+fLl9Z437n3mbf9+7XaxEpOY9/f66+rnRqGERuV99+zRr9hOpPWgHD6sfh4SAvlgmwthmwMyfDiumf+fhzlx4xzvz7bht5mZxquR2x5HDFFdvVo9V35MMd+ML/DImFYJE0OzOKKCxK/VFlGxevdd/XaJ+/hazkkkZhJLROzWLYumfJ8jIRdGLFkCi1bp0rBWiavQinz3nRo+VLIk4uvz4tAhbTjOf/7jfLnanBwoQGJ5yIcfhlXOVfgaEF26aAeEypVxP7ydNJ6RgZCkjz9m7KmnEJpVvry+7K/YxIE7txYaigEzMhLPrUQJ3MuoKHX/tgqPvVatGvZXqxYm46++CoXw1CnvL9B47hzeJ+6B4QpIv37ul4s8fFhrmS1fHt4Vd7h2Df2M77NVK/vKzLp1alJ6uXL2F/s8d06d4BUqpK69kBtJSeq7Ex2tD6XhSAUk8OAJ5qGhzuWhffmlVg44079EBb5wYb3850aT0FCtwUKc8Bsp1V99pT2n2FjsW1QwSpeGAiD23Q8/VLfz6lhiCWEivUGISBtKFhICpYT/jstRW8XFKB/QqInrkixahLC6nBx15fV27bRGC47o7RG9PJxvvtEexyjJ/K+/1O1GC1NK3MfXck4iMZP/L8OblaVOlEJDHY/pteWLL1QPR5Mm6urStvz2m1qTvECBvMv0MvZ/7F13eBTV175JCCSUAKF36S300AlFaQIiHUWaVAWUjiAqIKCAIgg/BEQUpUsRpUhTEBSVzhc6UkRIL7ubZLN1zvfHyZ0zd2d3s7tZDMK8zzNPNrPTd+bOeU95D6ZsKcO8Q4b4JlP78KHY8TV/fkztcVa/4g3OnQN4+WXy7jdpgtseMQKjC48yPSs7mEyYvnb4MJKTt9/GY23ZEtOCuEGR04mnSQQE4AuuUSNsgDdqFNYcrF6NL/HLl7OvA3qUsNtR2rJbN5GchYZiHrWvTSg5kpMxtSwwEPPGAwJQycwLxWsZmZlI0lesQAlTZdpHmzbOi+4lCYUXlCTTVfHn1avU0yEsTO1ZdgaDgSKKefO6JywaAXnywO+runW9W0/pSa9Y0fMx0WoVx5lu3dTLcKLt2IdCqdh4+DDu84UXMNqfmIhRWkeCAICEms9r2xYdTcpUswED6PvwcJzHBR745CzSqyQgfOLXxVGSnU+OjVT5so5REaLzFykAACAASURBVKVjolQp3P+lS1R3EhFBtYRhYXQumZm0XpMm6mt75464H2fvXUkSiZAG/yO3xzkNGvwJoQ9IRgbWZnCjwlVKR3bYtYs8P7VquW5sdfcu6ZIHBGDYNrsXkt0OMG8eKWsVK+Z7vum+feT1bdUKvfE//ujbtpS4exdg1ix1TUODBuiVelyldK1WjFr9/TfJQfIpLAy9ZyNHovDA9u3oTTx4EFMYfv0Vo1RXriDB+7ejGJ4iJQXvM6X6D2NYH7F0ac5VxWw2rOtQeiwbNvQ9kvLTT9Sl2XFyVlALgN7bMWPwHAsVwtQ3V/fcuXOYYhYQgMfsSR8To5FqoIKCUGXIHTQC8uThwQMk6keOeLeesgfIrFmerxcXJ977zlJ8uCMhIkKcr3RaBQdj7Rn//84dddSC10cdOSLOL1ZMfD8pazLKl8d5ytQ0V4TCmXQ472TvKjXVlRiHYx8X5f54xOjLLyk1LDiY5jv2NOHEhBfLKyFJopOKK4U5om1brC3xNYtCg3vk9jinQYM/oWpEmJKCdQ1RUWh0ukrZyA4HD9KAVbmy647OqalUONe6NXqNPTFejxwRu7pOnepbBCM9HWDBAjHk3b2798Xurra9fj0qZilfEjwq8uefuRsVcQfu3S5aFD2F3iqQPU6wWLDIcuBAURI6IAA9qQcO+KfG5NQpUY0qPBwjPr5K6wJgMawzw+OFF5wvr9NhCgVfbvp016mKv/5KKj7dumFUKjuYzbgsv35btmS/jkZANHAoif///Z/n60VHi/f/xYvqZe7cwVorpfoTABZ/K50BvJFu06b4PW/CxyfuMPvhB3F+uXJibYnS2OcyvI5OJ2cERClEwad589wTEMe+H3xyTGlTphdz59r48fg+5vP5Mx8UROdis5HTxJXKlbI/y4kTnv92GvyH3B7nNGjwJ5x2Qn/4EGsYGEMjP7tCcVc4cYIGxLJl1eo4HGaz6IXq0sWzKEFsLHp3+XrNmvnerfz6dbFALzgYjTdfUmac4eJFlJXlgz+fXnwR83Nv3fLPfvyFK1fQeHZVx/O4Q5KQEIwfL8pDlimDueJTp/p+XzsiNhajEUpi89pr2au8eYK7d9XpF0FBznvQ3L0rSld/8IFrgnv4MNVhVark2f1ns2FNUevWeAzr1nl2DhoB0cDBveyhod45X44eFY1sTxwGdju+g5Q9nIYPpzSlJUtwfHesj3v2WXx2t29XG/zc86/TifNbtsTzcSQcrVtTahiflO8ZxrCubsEC+uyMaLiqrXM8RqVcLh8LWrZEZxefzyOqXGYXQFSoLFTI+fVUNiXdvNnz306D/5Db45wGDf6EUwICgBEAXtRXpQoaWZ7AMdJx5gyFqosXd53iIUk0CPPB05PicLsdDXg+8Bcu7Fk9iSvs3y92Py9VCkPY/lJh4lGRZs0wV1kZeWnUCFWp/GUYP424cQM7JjumWAUFoed+yxbv+h24g9EIsGiRuiDckzQmT3D4sEie+DRkiHrZP/8k4yNfPjRMXGH3blE1x5M6Krsdu0/zY1izxvPz0AiIBg5Ophs18m69NWtEA94THD+OyytJwaRJ9PnuXbHDt3LMlyQcpx2/e/dd3PaBA+L8bt2QtDgu36uX+H++fOq+QiEh1AjQVQ1IVJTz+evXiwRHqRTGm7SGhoq9TooVQ+n8oUNR/ANA7JWSN6/z6/nVV7TM/Pne/X4a/IPcHuc0aPAnXBIQADSkeASjQYPsoxJLlmDkwDEnPDoaw7qMYXqIu9qSrVtpQC1Z0vM+FCdOYIicD5DjxvluaJrN2OBOGc5u0QJfaP7E5csopVq5svrF0qQJpj75GtF5WmC3I8mdNw9TrByvY/PmmJYRH++/fdps2GeAE4+2bdFo+fpr/xBVXufEPbMlS2I0KiQEPaSO+dc7dpBnuUQJ96py33xDaR4NG3p2XSQJu1XzazpvnnfnoxEQDUqYzd7XwSnvP2dSus7Ao5JKo75/f3qfSZKaSDCGaVSrVomysnzatw8Ju1LEhDE05s+ezZ6AlCqlTsktVAgdT+4IiKPKovJZVJKOatXos2PKFP8cGEi9UcqVw+tw5oy4XUlCARllBPzmTfp+0CDvfj8N/kFuj3MaNPgTbgkIAObTcm9p27aulYtMJjSaGUMSsnev+P2tW5T7GhrquhcBAKbO8OhLSAjKJHqCxERS8qpRA/PxXRXLeYK4OAw7BwSQNGu3bs7zj3MC/gKYPl2tdc9fYjNnYhpCbipHPS5ITcV7YvhwsQ6Ip1pUrYqePX/U8SghSShSoJQtDgnB9AV/CgtER1MqRps2lJN+44Z470kSRmD4sdSu7Z6wrlpFy7qTyXYEL5BlDGDKFO9SZx4+BLhyRSMgGnIGZe2Wq3pCJQwGSjFUpliVKYN/g4IwzVh5byunmTPtMGOGTTU/IUFsvMunGTPUPUAYU6ti1aqllj0PD0dnEz8uZ8fTubPz+aNGiREVpTOrcmU6X8dozrZt9Pn6dZTVVX7frx8SleXL6Zra7fR9ZOQj+6k1uEFuj3MaNPgT2RIQAPSw8kG8Vy/XReIpKVSEmzevWp3qn39INjA4GLfrCnfukEIWY+7z2ZWQJFRIUTZU+/TTnBV7nzkj9nLgHqBHkSolSRj1mTIFlVXy5aMXKTd4O3bEF9a5c/9Og77cht2OhvfixWiIOHtJR0RgNOnMmUdT2H/2rPiiDwjAlCRX0rY5xYoV7nvdmEy4/5YtyUPqjlB88gkde8eOrhuFOmLJEtHY8ebapqdjVKpUKY2AaMgZeHRbWTjNcfMmGtTKZ+WLL3B55djpqCYXGir2CFFO+/YZYfjwTGFeqVISSJIEEyeql//0U1T1cpyvVP1iDCPpypoU3C5FWxzHNp6y1qWL8+Ps2hWE41HWgBQpQp3P33hDXG/xYorg8oiSsj6RR4+iosRrzbMTWrV6ZD+1BjfI7XFOgwZ/wiMCAoApIHxwGjfOtSGSnEyh37x5McStREICkZTAQPdyfamppOhTuTIaQM56HjjDuXNiV9iuXT2vY3EGScKoDc+rZQy91K+/DhAT4/t23cFux/N49100NJ0Z3sWKYVrBunUog/skREgSEjCCNns2GsthYSQPzaf8+TGitmYNygY/Kty6hWRTue9u3bxT8PE3EhIoJ5yLJbgiKpKE3tlSpdCIe/FFz+8RZd79Sy95p+Zlt1P6SVSURkA05AwDB6Ix7iixC4DvI07COfh48fLLagLC1Z569cKGeY5jakiIBImJadC7t1mY36OHGVJTU2HgQItqnV271OlWjKkLztu0oXRkPpUvjym/7giIqwhI3bqiUp6ypjA4GCPBnDAov+vZk0hG377oPFRKr2/YQI4W5XvzcZVXf1qQ2+OcBg2eoihjLJUx9rObZTwmIAAYei5VCj04s2e7Xi4piTqy5sun7q2h04lFdcuWud6WxYL5v9xb06qV5/n8GRlIEPh+ihfPvmdBdrDb0dumzLUNDUXvV057SGQHnQ6P/403xOgQYyQ1GRSEL6XBgzES9PPP7o9r9WpUisotFS69Hht+LV+OxoKjd5BP9evjNZ84ERuCPWqidesWRhgqVSKjIDIS4NixR7vf7BAdTQp1gYEY2XDlDLDZsB8Iv4avv+6aqDhiyxaKer7wgufrcShV7Vat0giIhkcDnY6IxerVOO/6dbr3lOlSvL6Ce/G/+UZ0KPGpfXsLxMTEQLNmJmH+woVG0Ov10KYNEZA8eSQoWFCC6GgzRERIqm05iki0aiXWFjKGzzOPUDqqYPFlXdWAhIXhGK+cx5/bgACSEs6fX1TTK1aMisrDw9XSwL/+SkSNX1cNuY/cHuc0PB0owxjbwRi7xhi7zhj7nDFWyMttfMIYszM/EhBJEkPWK1a4XjYxEY1GPuAfOiR+n5EheofmzHFtSEkSGqj8BVKxond1GD/8QDUljAGMHet5CoorWCwAa9dSB2nG8GU2a5Z/C57d4cEDLHwePJhScVxNlSqhx2vyZHzZffstvmS4V4xHc3ISJXIGScLr8dtv+MKfMwe9ji1a0G/C7xPlFBqK3sKpUzFV71FGOZS4dg2vp2Ph6rZtuZ/utn8/GSSFCrlvwGk2i0X5EyZ4fvx79qBRVL48pp15S/a495QxLmWtERANjwaffkqGeFoazuPk11k9HREHjNYXKcJJA5GHWbP0cOxYApQpYxXW+f77RIiPj4dKlag2ZPJkI6SmpkJqaioUKWJ3OwYzhqlUjlGO6tWpGzsnIJxE8KaBbdq43uaVK+L/fB3GsAaLf3aU81YSF2WHesZwzB05Ej8/+ywWrbdu7bz+RpIAfvnFuzFGg2/I7XFOw5OPfIyxq4yxtVn/52GMHWSMHfZiG5UYY38zxk4zPxIQAPSqDhhAg+S2ba6XTUykgrvwcJQVVcJspm0xhuTG3QD244+Up1qgAMB333l82BAbKxKeyEiULs0pMjIwn1bZ/C0kBAdjZ70aHiVSU9FD/8knqMpSr574snMl5aicAgMxj7hhQ/S6vf02yiN/9BESzrVrkfRs3YqfP/oIU8QmTsSi8D59MG2qaVPchvJl6Grq2BHT5YYOxXzkc+e897jnFP/3f3gvKgtWq1dHY9ofx7Jvn+fpg46QJPxNOSmqXNl900BHcv/uu57Xbhw5QqT0+efJqPMUJ09i6gdj6FW12TQCosE/cGw0a7dTrd/EiTjPaqXCa2XhOp94jURgIEDlypJAPPj044+JAsnAd50Et2/HQExMDOTPT0RjxQodJCcnQ2qqDoKC1GTGceIqXMqpTh0cW5UERDke8/eVq21GR4vjvHIfer065YtP69bR9XMkIB9/TAphQUGUnvXBB+rf5d49Gjcdsx00+Be5Pc5pePIxnjFmYYyFK+Y1YoxJjLFOHm5jE2PsdcbYMeZnAgKAhhQvyA0ORqPFFRIS0BiNiEDD3JGE2GxY2xESgssMHere4Lt6Vezx4GlxOgAut3IlRmSaN8fBfdYs3w1DJfR6VCRSFgHmyYNRB1cNGP8NZGZiYfa6dXh8L76IamVK9ShfJseaDFdkhr8YAwIwja59e/y9P/wQozDnzqFiTW7h/HmA3r3VBsGWLTnrYM6RmEiRiFmzvF/fbMbrpSSRCQmul09NFX+bTz7xfF+nTlHRbv363qcU3r5NKScNGhB50QiIhpzCYsFxf8QI6ltz6BDd5zdu4Lz9+2ns4UREOSkL0QMDJQgJEUlI4cJ2SErSQb58IomoUcMGKSkpkJiYCAEB9N3Fi3EQExMD0dGx8rzQUNeRkFdeManmNWhAsriuGhHWqaOW6OVj68GDIsmYMYM+79ypFlDh07Bh2DCVMaxrU6YVjxqFYw+PmnCHRv36zn8f/n3Pnv/K7fDUIrfHOQ1PPg4zxi44ma9jjK32YP3GDFO3gtgjIiAAaHDz/NmCBVElyBXi46kra0iImrBIEjY24oPfCy+47+GRlATQoYNyUPcuTeT6dZIMZgyP7cwZz9d3B6MR5U55nj43vvv08d8+/IX799UvpTx5MFpVrhx62jt2RNLQsiX+3nXqoCEwYABet5YtMa2gf398aU2ZAjB3Ltb1rF+PhPPatcerON5mwzSj557D8+HnXr8+ph74K41g926RkPbr59224+NxHU4KXn3VPVmOjycBiOwEHhxx4QKq5qCxhRLU3kCno2tZqpSYMqcREA05xddf0/jECQhXmOrSBaN8W7eS7K1jrw1nU7t2NvjmG4Mw74UXrPDggZokvPqqGQwGAyQkKJeXICEhAWJjY+HLL5Pl+WXLWl3uc8yYTNW8xo3tsGKFTSAVjlP58up6Ev68rluHKa1KAsIjEhMmYGNWZ9usWhXHO/4OnzCBvuvQAa/x4MH4vzIF7MoV9e/Da00CA/+9dNmnEbk9zml48hHDGNvtZP4lxtivHqx/hDHWN+vzIyMgAGik8GhEiRLu+y7Ex5OBEhKCPS2UkCRqxoQvB/e9FSwW8t4whkpFDx96fuwWC6YW8XSRoCCAd95Rh/l9hcWCNQ9KA7dSJRzIN2/2T9Qlp9DpyKtWujSmkvmzn8XjhpQUTC1QksMWLZCM7tnjP+KRlCQqZxUtir+5NxK2586R8ELLlpjq5m79e/dQzYYxTKHaudPzfV2/TvU4FSp4b0BYreRlzZdP3TxUIyAacgJJIgfWsGE4784dMrK55C4nKIxRhJ7LvjMGUKYMRi6Cg/Hv/PkG+OWXeMEoX7LEAqdPi+pXjAGsXm0Eg8EA166lyfPy5rWDwWAAvV4Pb79tlOdHRqoJDJ/efDNdNS8y0gpLl6ZnGfA8eiJGYAoVEs8FiQ69+5SpzDVrEjlp3RqFS1wdj7ITvFKqe80avM7ffUdjCn/Xv/ee+jey2bA2kzH3AjUacobcHuc0PPkwM8a+djL/JMPIhjt0ZYz9ofjfIwKyerVvBAQAe2FwL2/lyu4laePiyCAPDcUmh45Ys4ZeLI0auS/oliSA//2PoiGlS2MaiTe4dElUQqlXD40/f8Fux0G8aVMxJ7lECexbkdudzvfuBdi06fGKUPgb0dGoBqXsCRAYiN7So0f92zfk++/FdIiePb2Xad66FZ8PxjAFYv9+98tfuUJ9Ep59Vp3m6A737lF+d8mSlMriDaZORWntsDBMXXOERkA05AQ8rYobzAAkhlKlCknU8tql8HASa1D24aCCc5x++CERPvxQJ8wLCpJg1Sq1zO6ZM+lgMBhg584MeV6FCjYwGAxgMBigTx8iLb17q0kGn6ZO1avm1a1rhSVLjG4JCJIJcZ6y6SDv98Hfg1zYo3RpFCtxdTw7d1LUdM4cGnd4faXRSGlrffrg3xo1nI+ZCxbg96VKZe/IkyTMhOBNVjV4htwe5zQ8+fCVgAQwxi4yxqIU8zwiIPny6XPU3fv8eRzwmzVDQ9udFz0ujiRkXZGQ7dspMlGjRvbF3D/9RJKBwcEYkvYGFgvAvHnkPcuTBwdjf0VDAHDAPXkSpWb5uTGGZKtbNyQC/qg50IAwm/ElqkzVYwyjEdOnA9y969/9JSYCDBmiNHYANm70jtzYbEhKlZ7M69fdr/PHH2hw8XP7/XfP9xcbS3nfRYogGfcWSsWr+fOdL6MREA05AXfc9OiB/2dk4L3OmFg8zeV1u3YlQtKhg/OC8PLlrVnriN8XLqwmIMHBEuh0SDROnKAISN++ZpmANGpEaVeTJhmc7hOjB5yAKPdLn90RkC5dxOOqVo2WUYpOFCpE/U2Cg3EMclXzN2kSps0yhqm2fDtjxtD150Xtyl4kzpx0sbH0btu+HZ1vrpqj8rq4N97w++3yRCO3xzkNTz5imfMUrIvMfQrWcMbYPod5HhEQxsZD4cKTYdy4yXDw4EGfHozjxzG1ijH0wrpLMYqNpXByaCjKATri4EHyWJcvn30h99271HuEMZSU9ZZAXLhA24iKQqJ04oR32/AE8fGYbqZMBWIMU7QWL/731bOeFEgSyv2+/joSUqU0cb16AJ9/jsaLv/e5aRNGtHgedvfu3qUDAuCLWmlEdO+efTrcoUPknSxb1r0yliNSUtDz2aoVbsMb4sLx++/kde7dW0xhO3jwIEyePBkmT54M48eP1wiIBo/w/vtiCt8ff9AzwcdinnIVGkrGs3LihKVPHwlKlpSylrULhKNtW1OWwS8a+i+8YIPZs8UajpYtrWAwGCA11QBLl1Kq1VdfGWUCUqYMFZ7PnatTHROfPvrIqJqnLGp3R0D69hVTw0aPpkhLmza0fN68YuPghAR0cjk7ngIFyIGRNy91ZK9YkZwn27bhvPz5SdVy+nSMmjs2GubEom5dVBHs39/57/zZZ0QavR0rn2bk9jin4cnHYcbYeSfzU5n7IvQvGUZIzmdNFxhjaYwxQ9b/zmR8wxhjEBqqzxp8c5YHv20bpU8NHOh+W0oS0rSp80jIqVOUy9quHXb6dof0dLH3QVSU98W0ZjOSA6VayqhRqBnvb9hsmF7QowddNy6V26IF5uTygksNrnHtGtbvODYxLFIEI07Hjvk3zYrj1i0s0uf7q1oVIwLe7uvaNZLDZAy9utlFw5RRwurVvYvopKURWcqXz7fmig8eUKpZvXru5Xq1CIgGT3DmDD0DnITwuqYmTTBSLUmUMjRihFrpKiKCItnbtpmhfn27U2P+xRfVRIAxgI8/tkLv3qIE77RpJjAYDDB5skkgLHfuGGQColS+ckzrUk4rVzrbL20zKMg1ARk0SFx33jzaT5UqNnmdgAAJ7tyh49m4EYv0XR0TroN/lRHNq1dxHPr1V4ou8chKuXIkia+MnB4/Lm43KAjFThxhMlHa6KRJ/8799SQgt8c5DU8+xjFMw3Imw9vRYdmS2WzrGPMgArJ+PeWlOtP59ga8MRRjqM3uzhiLiUFPb4EC6M1yZgj93/8h+cifH5fLLr9dklBulg+o5cv7pj51+bIoZ1qiBHq6H4UhC4BRj9mzkYw5vhzatEGdeG9rCZ5kxMSg0pYzffwWLVDW8lE1hDSbMd+Zv5T5i9lbsguAaWK8YDZ/fiQW2eGzzzyvk3JEZiZ1VQ4KwuJ7b2E00nUvViz7OiaNgGjwBLxeo2lTHGdv3qT7PDIS3xHjx9Mzpyya5lO/fmiEFywoQadONvj88zSoV0+MHJQoYYPOnZ0Xip85Y4Y6dUQZ3W3bMsBgMMArr1AKVECAJJMPvd4gEIaPPkp1SjICAiRYuDA7AuKslwh+Hj5crC1ZuJAICMoJ0/KpqakyWRo50g7ffee6NwljVEA+cyY5ct56C5W3AgJQaYwxsWM6r0GZNo1+Q0miOk/eA+rtt53/3lx6OCTE/w1wn1Tk9jin4clHPsbYFcbYGoZ1HXkZYwcYNiNUYjZjzMYYa+lmWx4REL1eD2++iYNBYKBaocpbKPPYFy92v2xMDHl/XZGQO3coVz04GIt0s8OPP1L0pHZtgC+/9P487HasJ+HbYQwbDv71l/fb8mafJ09ibqxjA6mAAMzTXbMm94vX/21YreiJe+cdNEaUspOMYRRg7lyMSjxKnDwpKptVrepd0TeHzUYdm/PmRVJw4YL7dZwpxXkjYGexkExpQAASam8hSaTwlSePZ9ETjYBoyA7nztF9zUUXxozB/2vUIMWnZs3wb1SUmLKI7wYJIiORPFSrhlGM9u0zYdYsfdb7BY3wQYMyoGZNtVRusWISGI0mKFpUNNZv3UoDg8EAnTrROoUK2WUCcucO1YUEBEjw8GEs/P57rLCNkiXtcO+eAVasUMvwZhcByZ8fP48dKxKQBQtcR1piYvQQHo7batzYCtHR4rKO59i4Mf5t3hxg3Dj83LEj1drwruiFCuFYyxiluJYtK0ZseVNF7qApXty5yElmJkWwpkzx7D552uskc3uc0/B0oAxjbAfDlKrrDLuiF3JYZgxjLIkxFuFk/WkMU7AMjFKwpjpZTiYgZjPmg/MBw1nY1FNIEjYU5IPb11+7X94TEhIfT4NkQAB6T7LDzZv4wqpUCdcbPdo3tae4OAo9c4/N/PmPXkrXZsNr8frrJJPKGGmyP/MMpiFs3vxkRkfu30cC2LcvNcTiU4kSGMJ/4w3saP+oIlMciYliQ8A8eTBi5a5fjSskJIipW5064fbdwW5Hnf46dTBS0quXd/ey3U6a/oxhrxpfsGgRbeOzzzxbRyMgGrID96xHRuKzHBtLBiyvS0ADHf+uXEnREXqOKHWKp0RVrGiFqlVtWc41NLq/+irZIWKAU58+NjCZxDSrEiWIaDRoQNuvXZsUsH7+mYhB/vwSxMbGwm+/ifK+Vargdj780HsCwonEm2+KUZuZM/UOTQ9pnR9/TIKGDS1ZY5UELVtaoWhRXPb999PhxRfFqFDTplLWNaKaj7x5KaW5e3d1k0ROThgTnTA6HdVv8nU2bHD+uy9fTu/97CK5P/+M6l6OdSdPE3J7nNOgwZ8Q+oA8eECSugMH5kwFymIhNZI8eTAi4Q5KEpI/v3MSotdTugpjmNeaneGp15OKB2OYS+yrAtLhw6SF3rw5RmW+//7RG78AGAE4ehRJlLIplHKqXRtTFHbvfjQ1K48SdjsSxm3b8Hfluv+OU5MmGNI/cQKvyaOG1YpSz8WKUc1S69beFXwrcfo09fdgDElMdl49k0nU+Z8+3btzlyTyajLme5rlgQN0DV57zfP1NAKiwR3On6d7c+9enMfVrUqXBliyRCQfZcoAzJrFPfJkdA8caMlyoHGioCYZISES/P57nFOj/dNPLXDrlgkWLiTjvEsXi0w0SpYkY3/IEFLA+vJLSqsqU8YOsbGx8P33icJ+GzVCwvLee85Sv5wVoYOwTcYAJk0S1x09Og2qVrU63c7HH6fCSy8RMQoOluDZZ/G8xo0zwaJFIhEKCJDkYvitW22QNy9+5tkM+fOT00Sp4sjrcYYOFX/T0aM5eaJx29l70mgkha7p093fJ7yQvlYttC+eRuT2OKdBgz+hakR47BimdgQGYg1HTpCWhvm8gYFIHLIrIveEhJhM2B2aD4BjxmRvwEkS1gtwb0x4ePaEyBWMRkwD4lEVxnBgjo72bXu+QJLQAF6xAj3hyhQxPtWpgwSpVy883u3bsV9EdgP37dsACxdmrzqWE9hseCwbN2IBYtu2pNvPDXz+uUQJjD5t3Pjoajpc4fBhkQi1bIlKWr4INUgSrstVo8LCkLxmB0fSPWuW94R39mxa/623vD92AOwPEhaG0b+hQ71zTmgERAPHw4dqaWmeFsiNVIOBxrQPP6SeFjziMWcOpe6UKME99xIUKICGevXq6j4ePOLx/PMWFTng04UL5qw0LTLkFyzIlOs8qD4DYMsWUsCaPp2IQcWKNvjnn1hYvz5Z2HaHDqikNW2aewJCERBSx6pVCwnVxIniur16GSEqiuYpIzdjx2bAp5+KaVdjx6I8cIsWJjh4MMXlcbz2Wia0a4fEZuRIG+TJg/N5p3RlJKRtW/xboACKwHAoqjjsAgAAIABJREFUSSWfXPXoWrqU3vmXL2MhuzNcv077Xr7cL7fjfw65Pc5p0OBPOO2Erswz37UrZw9MQgI1SSpRIvv6CU9IiM0mdkHv18+zVJiTJ+nFFRCAvT98Vf26dYtenDj4Y6pUdqk0jwI2GxbaL16MWu2hoVRo7DgFB6Nq0aBB6AnftAlD29HRmPbw0kt0ffr3B5/6w1itqN71xx94/6xYgYbv4MFYzKhsCOg4hYdjt+MFCwDOnvVfd3JvcOOG2MAsKAhTvZKSfNue0Yipcnx7EREY7ckOcXFik8xPP/V+30uWoBMgb16AsWN9i9bp9dS7p0IF74mgRkA0cIwYgc/Tu+/i/0pDdfJkrG3jxeUFC6JCnDLVKjgY4Kuv+HMpQdu2pP7EJ2VvD27E83krVhhhxQp1kXjp0hI8eKAmBwcPYgH633+LvT3+/hvJx9mz6XJqExrnEty8GQdLloj76NsXIynjxqm7rCuPv2BB2lbhwji/USMkIG+8IR5fmzYmGDCAGiMq5XyffdYCt26Jxzx/vk7ex507MU7T0BgDqFPHBlOmYISkShUbtGuHxzxokEUlW1ykCMnvO9aUNW+O84sXp7HDWWQ+PZ1SjPPkwYiIK7n0SZNov7nxrs1t5PY4p0GDP+GUgNjtFO4MC8t50fXt25TaVbVq9gaMkoQ895xzEiJJ6AljDD3m7dq5bnqkRGwseW0Yw0LGnKQrHT2KBqVyQF62zL9NDL2FyYTG+5o16LVq354aNSonx5xeV1NYGCqjVK+O98XAgUj6evfG3O3u3bFLbmQkErzAQNfb4i8lxvBF060bRmh270YlsH8jnc0VUlOxGFKZYtClC0ZrfMXNm7gNLus8aJDoKXSFv/6idD9PhRcc8fnndB5vvulbAafdTkQ7JATvK29w/DjA1q0aAdGAUVU+Nnz1Fc7r1Qv/5++HDh1QuZAxgKlT1fKxL78sQZcuaJD36GGFlJQUeOcdPdSqhVGPSpXsWc+MMzUpLCgfOTJNNS7172+DP/4QyUFAgAS3bxPRUBIGHv3Yty9DmN+mjRViY2NhxgzR+B89GlO2hg83C8s7HoeSgJQubc8aMzEaMWqUWSAZNWtaHJoe0neVKmHKlzJqo0wVO3UqGZo1c0aG1BPfR+nSdmjd2qLaX9u2kjxWKsElfZXvGa4k6TjO8zQ7Pn38sfN7KCWF3mXjxvn9Fn3skdvjnAYN/oRTAgKA3l6eq96okW/F20qcPUuyfE2buu8bAIAkhKdauYqEAOAgxz1kERFYx5IdLBaUDuSDXffuvjVi47BasfET9/QwhgTq36oP8QS8sPPIEfQwjhiBBKJsWdHg9nXiKjWOU/Hi2NyxWzfMC16zBmDfvseraN5sxmJ3x99v376c/X7btlFqWdu2GAnyZHvnz1NedMGCvqlsKXvy9Ojhe870vHl0Tb75xrt1k5L4faEREA3opGAM00NtNlR94/cWj7DxYvQ8eTDK7Og42brVJBvhS5fqYevWJDh/Pg6KFbNnbRv/Vq1KhjyPfjRsaM/qWq42vP/3Pwvs3KlO3Vq5ElOwDhwgohEWRoXpX3xBRn3FinZISDBAbGwsjBghKla9/Tb2EhkwQG3AuyIgzzyDn6OikIDUqmUTCEh4uE2Q/FV+V7iwXVW3sn69Ub5OX3xhVKV0MSamcXHyMm0akRxlITzfX5s2Fnnd+/etIGUNckYjFarz+p06dVAYZvNm8d5ISxPH3xIlXDtqeBPDwMB/N/X5cUBuj3MaNPgTLgkIAOZscu+FN0WnrnDoEG3v+eezN4piYrLvmA6AaT5cMaVCBc891jt3YsoSf+F99FHOUn5SUzGNIE8eeqE2bYr1Jo8LEXEGSULlklu3MFri+FIKC0NvfEQEpmi98QaGwqdOxdSqt99GdaSlS9HwPXkSUyketUpYTmG1Aqxfj/U8+fKh0e+PCFZmppgiWLGi5wT36FFqRlmihG89bPbvp+esXTvflLoAUL2On4O3zcJ44XvZsgBhYRoBedqh7GjOe8/07o3/N2hA4zevrRs2DKXTleNQw4Z2eOstNHbLlbPC5MloGHNJ3aAgSa5XqFuXDG9loXpkpA0KFVIXel++bIZly9QE5NSpdBXRqFuXFLBef50M8tatsc4jNjYWevYU+30sX441Iz16eE5A6tTBSE+HDnh+nDwoCcA33yQ7bI+2GRtrgGbNSLnrww8z4bnncFsTJpjh88/d9yThBKNjx0yoXRuPe/RodfSoQAEJSpTAY5s/PwNSU1MhLS0NTCYTTJrEO9ATacDfQf1OVKZ+M+Zawt9qpayDjh0f73erv5Hb45wGDf6EWwICQAVijKm9Fr5AadSMGJH94KHsmB4a6rxjOgCqIvHCxaJFsWeEJ7h5U8yz79Yt57mlN26IDbPw5eSaQD1OGDaMjrltW7yuTxpsNixq571lGMMo0IIF/vntGzSg7fbs6XmK3+bNeBwhIfhi9aROxBEnTlBOdmSkd31ClNi/n84hKsp7xTFuPBYqBPDNNxoBeZohSeTYaNEC/794ke4vx/QbxtCzzYvP+TR4sEX26I8alQbt2qHxz6MdERH2LIeJJBenO0516tigVSsTFCxIhnm5chJkZppgyhSxN0hwsAQpKUg0lApURYrYISYG53fuTIRi2DCzTEBatxajC1u3Yi3Js886V60iY55IRpMmtiwj2+Jy+R07xIL6mzcpWnHqVDqMGkXRnkmTTLIkceXKNpgxQzxGZQRFOYWESDB+vCHrGlugcWPaJid8nTqZ5Our1+shJSUV4uNT4MwZ171KHN/RBgOpZvGxY9QobNbqiKNHaTlPxDyeFOT2OKdBgz+RLQGRJMoBL1AAiwJzig8+oMHjvfeyXz42liIKoaGuGyVevkz5wyEhzgcuZzCZ0KvPj6lsWddKHN7g7FmqpeFThw6ek6PcwOXL6L0/fPjJ8yzZ7QDffkv3EmOYGjBqFNaf5BRbtlCaYXAwRlI8uYaSJBphjRr51hn43DmMVjGG5+grmTp8mDyVefJ43+H99m26DoMHa0XoTzsOHaJ7m4+rPB2rYUMqOufiFD16YMqtaLCKqUEdOxrledwI5gSkXz8iF/w7nk7UqpU1a19k6L/8Mvb/6N/fJuyzQQOMaMybZxKiD40aWeUICI9SMAaweHGmTEBq1BCjKfPm4XctWhABCQmxw+zZohyu8rjatMFllSTHcfruO5GAnD+fJvcN2bjRCBs2UOpYz54WKF9elChWRoOUxft84ilZvICdMYDJk2mb1apZhL+MAUycmA5Vq9pg8WIj6PV66NBBPH7eWLF3bzvY7XY5ZQsAnUCOxzB4sPP7itsl1ao9/tF2fyG3xzkNGvyJbAkIABZ+Va6Mxv+AAb6ndHAo+xKUK4dpMNkhLo4Mx5AQrGVwhvv3qVN1YCDWZniKXbsoihIYCPD++/7pvHrqlNh4jjEs2MtJ3YkGzyFJSEaVHtXAQJST9UdX+4wM6trMGDaI/PNPz9a12UTy27kzegK9xbVrpCTzzDOe1UIpIUlI7Dt1Eu9Tb4vfrVZqaFqxIqb2aQTk6cHdu2ITW7udGsjWqYNSqpcuKQ1oUZiCMYzi9ezJDWRKK+LG8LPPZiqKzMmo5d8vWqTsWC4ux4165bR2rQVMJhM0ayamOL32mknVAZ0xgF27MmQCwlOPGAPYuRPn//prAuTNK25r8GCzqplheLgdEhMNDudB6/HIR5cuzggInteWLUmCmtX332dAZKQti/SY4PZtg7xsrVo26NlT3Fb9+rhsgwZWmDoVoxictOFYgt+PGZMmR594ZIYxgNq18doof4+6dTFCEhFhgdjYWPjqK51wzEpyc+GCDvR6PRiNRrBYLJCaalfJypct69yRc+sW1S9+9NG/dovnKnJ7nNOgwZ/wiIAAYC46V6Z6/fWcP0g2GxYllyiBxuC+fdmvExdH5CIkxHVxbkoK5dE/8wxGWTz15t+9K74Qn30Wtev9gePH6bgYQ0OtXTvMifYH0dEgwmgEWLsWiWuLFnjNAwKwjsUfkTwALKStU4d6l/TqhfefJ8jMxC7v/H4YOtS3YvG//0ZPctWqWMdy65Z36z98iLVKjkZO3breH8v8+XSdf/kF52kE5OlBjx7oqOKOn+3bxXuqSRMSF2nYEAmKUiWpRQsJrl+3yulAW7akwTvv6LKUq3DekCGiIhVjAE2b2rOMegmWL3ceMaha1Q7VqqnrP65fN4HJZFKlba1fj4SiYUObsD9OPgwGg2D8nz6N9SLbtyer9rFwYSYsWpQpFIWXKIFF68rllIY8rxdxHgHB5ZYvT4UKFej4/vc/o9yQccgQs9xDhDGAIkUkVSf21q2tWe9JO+zfnyFsG68rfl+9ugWGDlUreIWH26FAAfG6KWWJjxxJgPv3Y6BMGTG6xFPNxo83gU6ng5SUFEhKSoKEhASYOVN9HK7SUadPx+/Dwv79PlG5gdwe5zRo8Cc8JiAA2PyHDwg7d+b8YTIasbkbYxh+96TglpOQYsXQsDx0yPlymZlo0PGUrMGDPS8stlhoYKteHbvx7t7t+Xm5gyRh9KZzZ0pTwZcjqiRlpw6mIXvExGDzPaWCTpUqaPj83//5Zx92O3rduAcuLAyNLk+JbnKySEZnz/Yt5S0+nhwDlSr51rdlxw61scYY1n95gz//JLUbZcNDjYA8HThyhO6dAwdwHK1eHf/nMrtDh9Iyu3dTTw8+ffutBSZMQKO3QgUbbN6cBNeuxcLUqUg6ypeXICJCXcjNi85HjrTBiy/aHO5lXGbcOKvg3cdnRgKTyQTp6SZVDcSFC0gyypYlg7psWbtAQJTr8LqQ1avVfUY2b85QzSta1A7//CMSEN6BnDGAfv3Q4HeMwDAmyURl9my9TBIYA5gyxQSzZpmyrpVdSBHLk0eCgwfF41BGhKKj01RpWMptr1yJRevidZKgTx+LvH0+n0dOxo41Qnx8PEydKp4nT4nDniRJkJKSAomJiRATEwM3bsRDWJhIFD/6yAJWq1VI1wLAGjd+b40ejTbFrl0oP/8kOvVye5zToMGf8IqASBI1FSxcGKMFOUVioviSun07+3Xi40m9Kl8+gIMHnS9ns2H/Az6IdejgWa8Qjv37UTKQr//qq76lxziDJKGHuFcvsdFW4cIoEfz33/7Zz9OE8+cBhgwRZYVDQ7GmxV8RDwBssqjsTl63rneG/927lA7mbZqgEjodCSjkz++603B2sFoxtdLRQPKmVik9nZ7jhg1Fsq8RkCcfNhvd05074/i2di03NkkFiUcKGzRAEl+zJt1v1arZ4eFDo5w29dJL6Vnk3g5lyuC8MWPUKVQYucDvDx40OylAx//XrVPL7w4divUfN286StJK8PChDvR6gxCV4B3NDQYDPHwoGtUtWuB3CxboVfv56Se1ehRjAPfuuY6ADBpkztqnGAEpUkSCbt04OTEKdRx9+lgExS7Hc3IkPPXq2eQIxubNRjkiwqdy5exQogSSib59zTJxQIIhQb58EqxalaEiJk2akHJXUpIBLl0yKCR+paxztWcdgxmuXImFmJgYiImJyeqjkiksW7u2FVJTU2WFrczMTJmQrFsHWfvHrAh+DE9iinNuj3MaNPgTXhEQACQMvOdDy5a+9xdQQtmosEYNzzpOx8eTFF++fOhxc4Vly8jIr1vXO+M+Pl7sil25sv+LyP/6C4mSMiISFIRG4YkTas+4P675kwKzGfPI27UTX7ZlyqDYga/dy11hxw7StmcMfzdvaqLOnME0qVatkBxxSVJvkZFBEZTgYN96hXBkZlKePp/y5PHuvLjscEiIWgZbIyBPPrgRGBiIUcaMDHpP8GJhpaNl1y6U61bec926WaF7dzSsCxWyw+jRSEB4cXlgoATDh1uzxnwydqOi8HvezbxLF1tW0TgtU6CABLt2qQnI+vVY//HTT+J3lSrZQafTqQjC1KkmmYCcOiX2+hg0CLudT5licNiPBCdPpqv2jSTHcVmahg1DUhQaqi4OHzoUv2veXCROkZFWOHZM3JcymnD3rgFKlaL/w8Pt0K4dXtOJE80wfbpJPjZOKvr2RYJRuDCt17s3dUXfsiUDihQRj7FAAUkmKxs2GKFxYxtUrChGppTntWFDskxAYmJi4OrVWEGSOCBAgnv39KDX6yE1NRVSUlIgJSUFUlNTQa9PhwYNcNl27SRo2BDX8UTg5r+G3B7nNGjwJ8IYY3DunHdancePk0rOrFn+ebBOnyYVlFatPDN+EhLI65Y3r/s6kp07SZ60TBn0lnsKScKu0vz4AgOx94W/u53rdNgBtmJF2k+xYpg+9O67KPF68CCe6yuv5Lw55H8VkoTe/nHj8PrwuiDG0JDeuNH/v43BADB8OO2ndGnXkTdX+OEHuoeKF/e8UN0RFgupqwUGIinKCUaOJNKxciVezylTPF//wAEigCtWqL/XCMiTDYOBGmeOGYPzFi/mRials3LiXr8+Rj+6dxdTeTjJwO1kQFQUGqxVqtizCIoNihcXl0OnEq8nsMKRI2bZYFX+7dnT5rTPx19/mSApyQSvvip6/qdMsYBOp4PTp0Vj/ocf0mUCsnWrmM7Emxa++iqtkz8/dg//8Ud1ClZQkAQPHrgmIKNGIRlwLKRnDGDCBIwQNGsmEpAyZezw99/iNtu3p/P+8ssMVVE7b0jYooUVFi3KVO1rzhxd1vigbIJoh/bt8ZoNGWKGIUPU9SGNG+P3ERGOKXHKaAn+feEFC8THxwskZOJE8TyGDUuHhIQESE5OBp0Oi9c5Idm3j5bt2RP3GxkpqVK2/uvI7XFOgwZ/IowxBq1b673OP58zh7xaOfG+KrF3LxGbPn08y+FMTKS+C8HBaOS5wm+/UV1AwYLYINAb3LwpFqg3bgxw9ap32/AEVivKxY4YoX4pKb3vUVGe95h4EnDzJnq1qlRxfJEDjB2LKW2P4n1z/LioYvbii0h+vcHKlXRvV6/uu/qWzQbw8st0LF984dt2OD7/nLb16afer5+aikp2jGF+v2MjT7MZIDVVIyBPMmbP5l5vrNFLSSE1wVmzxHRFxtAZdOGCuhicFKAkiI42yalB3FidMYMaDvJ1lCpUx4+b4dNP0bguXJin+eDfzz6zwPjxIsmoWtUOJpMJfvtNHRnZsQOLo/ftE4kDr/MwGAzw9tuisX7uXBoYDAbo3VtMgWrd2grffqsmIMHBEty/75qAjB6dmTXmq6/V9Om4j9q1LbLsLr82Op1BmDdnTqZs6G/fngHz5omk5dNPcVv58uHxONbCrFyZIhA+PvGu6OHhdti9W31+vHg/IECCd94xCQX7PG2MzwsLk+QakISEBNDpdHDrVoqgCpYvnx2uXhVTtZSEpFcv/O1LliTCc+sWKWzZc9Jl+DFBbo9zGjT4E2GMMWBM77UhY7VSCkipUt73CnCFNWtoAJs3z7N1kpIoFz442H1ay40bZMAGBaG33BtYrQBz51KxbZkyWKz7qArebt3C/Smb5imnsmW9i+b81xATg8a7o1QnY6hs9b//eU8GPIXBQHLR5cvjff75596RHJsNYPJkOuY2bXxPC5MkVKDj28qp9OSZM3j/MgYwaJBv5O3VV7lB4Ty18a23AJo10wjIk4q//6bIMmN4f86aRc6SuDjx+3r1JDAaTTBgABqLERE2aNUqE15/3QAtWqBB+8ILNjh7ViQF5cpJ0LWrLWsb5MHnxnLRothMcMwYm2DY8unOHRN07ix64l95xQomkwm2bFE3+rtzxwg6nQ6+/NIo7Ou990yQkGCAuXNNQhpYSIgEer0hq9mgSEyGDzcL2+FTaKg6WqGcRo7E61G8uJqAvPMOGvz589uhYkXx+xs30gT1q9mzTVCzJv4/f36mbKjzSVkgf+RIOtSrJxK1sWPToEkT+j04GRk92ixf/z17MlTHGRQkyfPef98E586lQfnydmjXzgI7dqhT0g4cSJEJiMFggORkA0yeLJKlKVPSITY2Fq5fj4UjRxKEiMnp0wnyb8L/rlmT4ZCu9d8mJLk9zmnQ4E/IBKRIEe9JxP372Lm0SROAgQP9532eNQu7cKN3xrN1kpMpjz1PHveRkPh4NGj5PmbMUHtus8Mff6Anm6t4NWuG3XsfFSQJYMMG5y8qxrB4vU8fPK7/svqH0YgRtRkzkFTWqiWeZ9WqGH3zpUu4Nzh8GFWllMTBW3nbjAyA3r1pGy+9lLO0uXfeoXs2p6mPKSl4fqVKAXTvjkXk3mLfPjo3Zw6My5e5xKpGQJ4EGAx4/ylTXV95hcZcxjBVNDQUPy9erK7z2LzZBL//Hi8bsZ99hkbnsWNJ8jK7dpmhRw+bbMQyBjBpklVeJzJSHbFo2tQOW7daoGVLtbHesCFGOsqXF0lJlSp2yMw0KfqGSFkGvwRGIzbRc0xJKl3aDjqdAcaNE4+BNy00GAyq41u4MBNWrlSnNhUqZIe7d50RECyq5ylYymgGvwZz56ojDnw6ejQdOncmEjFihFkmHcOHm1X1Gp99ZoQaNWxZv6M60hERYVapWDEGUKOGTW7s2K+fGVq2pH3yyAUvRi9a1A56vQFSUnDS6x3ljQHatzcJBGTqVBNERtqEYypYUIIVK4wQFCRBhQo2iItznrbF75s+fTIhJSVFTtdylPxNTk4WCtofd+T2OKdBgz8RxhiD4sVRtWPgQO8fiL17aQBZudI/D5ndTi+2gADPC3VTUgAiI5EMhIa6lwrOyCA9ehyocJ43yMgAmDSJiiuDg9E49nf9AceyZa4JiHIqUgTP57PP0FB/nMdVux07xi9aBPDcc6SWo5zq1QMYPx5VTR71ueh02BmdXqRY1+AtQY2LE6M2s2Z5vw0llL/9u+/m7DrY7SSsULAgNofzFikpFD3p2lV9PJJEZKlmTY2APAngtRwFC2IU788/1c8qH1PLlsXxkUfI0GC1g16fBsOGofe7ZEkbNGlihmnTjHJxeb16dli8WPTABwZKcvpV6dKSUAzNJ+6JL1hQjEowBjBzphXS0kyqNKK6de0QH2+CcePE/fXoYQOjESMgvDmf0pg3GAzwyitiFIE3LTQYDFCzpvjd9u0Z8MEHagJSrJgd7t5Vq2NxwlGlii1rPKfz5cb4vHmuCUjbtlahwLtTJ6sszduqlRXGjBEJ0ttvm2DwYFe9RiQoWtQGlSqp1ccYA/n6OKZtlS9vz7oPiGQcPpwuSBivXm1U7Ad/5/Pn4yAhIQFiYgwQFqYmQ4whieKfL15Mk+tAkpKS4M6dBChVivaJUsdITuLj4yEpKUlelqdxKSMkjgpbjxtye5zToMGfCGOMwfr1JBvoSUNAR/Au0M4UcHyFyUQGTGio5wW7KSnUiTkoCBthuYLdjp52fu6RkZjy4y1OnRILoevWxUiEv/HzzxjpqFUL+5osX46/1zvvYBSKF4I6ThUrYmrE4sVIGG/fzpkx7CvMZvSKb9+ORG3MGIygOR5vUBDKdc6di4pj/5bq1/79VM/AGOaueyIL7YjLl/EeqFYNz+Xzz3N2XF9/Tcc0cGDOI1y8QBiNI9+2wfs5FC6MssSO+OYb2sf+/RoB+a/j2jWKcixZggSTj7M8pbVYMapzWrsWl1EWUA8daoGRIzPl9BieqhQZaVekzFhUzQK7d7dBzZo4b8oUKwwcaIQ8eSS5mR2X3XX07PPpl1/McOWKo8wurpeZaYLu3UVP/IIFVpmAvPSSaKzzbue8SSCf9u4l47psWdFYP3cuDd5+W73/0qXtcPu2moDwviO8l4ay0R/vEzJnjiMBoWV4uhWfIiJssHGjUb5Gx4+L6U+9e1vkCA0nafzvV18Z5HoNJbnjpIhUs8TjUC7Lpz59zAIBSUgwZKmV0TITJxogMTERDAYDHD2aDs8/T9eSH1OBApLc0HH58kxhmwaDAdasEdPd9u5NFKIkyik2NlZOzXKmsJWWlgYmkwlsNttjQUhye5zToMGfCGOMgU6nl1V1KlTwvhleejo1Q2vQAMmDP5CcTDrxJUsC3Lnj2Xo6HXW+DgwE2LzZ/fLr1tHLtUIF35q5mUxYIM23ExCAuf++pLbkBLGxAJs2AQwbRjKY+LIVXwahoZiyNngwytXu2YOGc0KC7xEcScIUqthYrEvZuhW99X37ImnidTN84uIBjCGBe/NNTJ3zQhXaL0hIEGsrChUiA8pbHDiA6zOG5+yt0IEjvv+erluXLjmPrp04QdubMMG3bfzwA12rr75Sf5+SQvfb0KGaCtZ/HZIE0KkTN27xHvz2W7oHnnsO//KUxerV0Wnw449khHLjkXvKS5Sg9J3WrYlI7N6tTq/68EMy9k+eNEPr1mYYNixNNtArVcJtOtZ4MIbpVBkZJtixQ+3h79sXa0Dq1RON4IMHzTIBiYwkAzhfPgkSE9HIbd6c5ufJI0FKChnAyghNUJAEyckGePNNtUpUpUo2+OsvNQHhql+8w7my6J4TkGnTjELPEGV0R3lsnOicPUuk48aNNKEje0SEDf78UyQlPIIxZYoRunRBcsKvt5LktGljhUaNbFnX2u5wHjifH2doqARxcSJZmDZNJDBFitjh/v1EYZlTp9KhSxcLXLyYJhOW2rVx2716WVQERKczQJMmdKwzZpjkRodxcXEuiYgrhS1OSHQ6HaSnp4PZbM41QpLb45wGDf6E3Afk3j1UMUEvhPcPxpkzZHxPm+a/B+7OHeoRUqsWGjeeQK+npleBgeiRdYejR9Gbi94b3yJBAACXLmEkhQ9+1aujZz03IEnouVy/HglJ06b0GzubOGnDly0akVWrIlFp3x6gZ09UYBo0CBtSRkUhiahcGSMZ/PdnjEQBnE3FimF0a/p0rGt58CB3ro/NBrBqFRbLcu34Ll18awIpSVivxD3AzzyDhC4nOHaMUtJatsw5mY2PJ1LatKlvjoLkZJQgZgxrR5y9gzmZ43VlGgH5b2P3bnp2Dx3COqbKlfH/AQPUUcxvvwVOWxj3AAAgAElEQVSw2exQq5ZokObNSx28Z8zQyylS4eH4d9YsK/TsKZKIkiUl2SBv1swOBw+aVQY3/zxihEgWGAMYNAgbDY4erU4hWrbMDCaTSVbM4uQgIcEE6enpkJycLKhstW9PdR5Vq9Jxzp8veuGVxKBiReycPnKkmoDUqGGDmzfVBIR3L1d2YKfmfbxOxS6kPdWoQQ0FObHjU2goEiROXvbsyYBhw+h4ihbFuhbldahb1yaTmYULdQL54ftjDMnRjBlIIsqVswsRje7dLcJvwRjA558bhWt15kyaKjVu6VKDilTwaf78TOFYihaVIDWVvk9KMkDjxjYYMIDOr3JluwNB0akkf90REmX9CG+GmJKSAv/88w986ot0YA6Q2+OcBg3+hNCIkOeZ16mDefneYuFCXD8gAOCnn/z30P35JxU2tmvnueGUlkZpXAEBzr21Sly9SukEgYFonPri5LBaUaEoJIT6I/Ts6bv0qj9htwPcu4ee+o8/RqnfFi3Qa6+UmnU1cXnN7KZOnZA4tm+P9RurVqFB/agUq7zFqVNi873ixbFmyJff22KhRnyMIfHN6XmePUuRlIiInMst22zkqS5aFLux+4KpUzFKWKSIc+J4+jTVRH32Gc7TCMh/FxkZFNlo0ABJ7JIl5KT4/nvxuW/SRAKz2Qr795NnOyBAgmHD0uGNN7BAOCzMDlu3JsnfccP6t9/UdRrK9KOVKy3w4Ye8wzY30skQfeUVIgV8Oxs3WsBoNDktrj5zxghxcaIHvmxZO6SlpUFsLMq9KtdbvToD9Ho9GAxi6lBAgCQ3KIyPF4u1n30WSQtX/VKSifr1rXDjhpqA8KiLMkrBiQc/HkeVr61bjfDRR5kyeVCmowUGSmAwGOR+HLVr26BtW6tw/Hq9AZ57jubxZYODJdi7N151jPnzS3Ka1euv0zX84gsjBAZK0KCB1anSVbt2ROLOncOIhmOfkBo1rLB+vRG6drWqyF1srEEghYwB/PILpb/t2eOsNkaCK1fShO1wyd/4+HjQ6XSQnJwMCQkJ8u/uOMXFxUFiYqJQ0H7p0iXo2LGjX5+3H3/8EUqWLAnzXEiA5vY4p0GDPyEQEN5jIDAQvfje5prbbCTNW66cf3tUfPcdGTZDhnhuKKanA3ToQCRk3Tr3yyckYG5zWBi+eMeO9T3t5dYtUQUpb15sYPhvp2V5AknCnio3b2I066ef8Jp//TUWYS9YgPUyb72F0rQzZ2Lq1qpVKGX8ww/YL+P8eayb0Oly+4ycIz5eLIwNDMTz8fVeTUkhw57fmzlNQbx+HQkReu98q0tyxHvv0THu3evbNg4dIsNzyxb193z8KFECIyx8/NAIyH8XyvuGMYwMh4XhZz4GKL/fv98CGRlGaNXKlmVwmuDatVi4dy9ONqjfeCMNZs/GukNeLP3yyzaYOdOaNU6KTer4vPh4EwwYwOsicFtVq+LfoUNt0LChY3G6BMeOmeHWLXX9RXi4DdLTM+D0aXXK17hxaRATEwMPH8YojkGCq1djZc+40qvPGMCyZWgoX7kiEooJE7DuoVs3dQpY8+ZWuHZNTUA4OVD2/+DXhKdjOfYG+fJLI2zbhsZ3kSKSHH3gU3KyAfr3p3khIZKQ2vX33wahTqV8ebv8/bZtSYpu6rRO164WeVk+b9o0Ezx8SIZ+69bqyFN0NJKBl192VviOE1ftatPGqoqC8IJ+Tl7nziUBgCtX0uDdd01yWhhjqND14IHBKQHhilt8Uha0u4uS9OvXD1577TXo1q0bZCqkDfl3tWrVgpo1a8Lo0aPBYDBk+5zZbDaYNWsWNGzYEAIDA7MjIK0YY78xxi4xxq4wxqbn2sCnQUMOIBAQANGDuXat9y+se/foBdW/v39Vi5YvxxSiJk3wxegpMjLIux8UBPDll+6Xz8zETtD0QvDdoy1JaPBVrUrbK1cO6yMeg5q2pwZWK6q0KSM4LVvmrIfKzZtUo8QYRgBz+pv+8w9A5854n5cq5VnULCXFvbPgyBFyDLz1lm/HlZ6OaWWMYXTL2XnypoZhYUhiOTQC8t9AYqL4/+3boipdeDg6ZBjD6KZeT84dxgDat7dDRoYR9u6lIuAffkiEpKQkWLnSIhu+ly7FQceOoirUL7+YoVQp0chWGt4vvYSpVNWri4Y3j3Ts3m1REZeCBbH+Y/9+sTs6YwDduxshPT0ddu5UG8EbNiTD/fvxcg0DYwB58thl4/PBAyUx4cdvgNRUPbRrJxrcvDu64/w+fcxw+HA6XL2qJiC8U7mygD801C6cr2MU4LPPjPDbbxRxmDtXvL5nzqTBnDkiEatWjc5v06YMoZlgYKAEjRvj95MmGWDAAHVkYdIkk/AbMYapWUqD3pn88KxZpiwSYIBBg8Trz3/7Bg1w38q6Gz7FxRmE6JAyNU45XbuWBitXZsr9WcQC+ASnBMRxckZILl26xMczYIxBSEgIdOzYEVauXAm1a9eGMWPGAACA1WqFLl26QKdOnbJ99n755Rd47bXXwGw2Q0BAgDsCUocxls4YeyVr6CnLGPuHMfZ2rgx8GjTkACoCAkAvmfBw9UvJE2zahOkjrVtnXwDuLWbOpMEqu7oOJYxGlAvldQ7ZpW5KEqoFcTJWqRLWd/gKkwngww/FGoyoKN8K3jV4hyNHqB4IX96YjpcTJbAjR6gwNzvJZ0+RlERqak2bAly4kP06JhPe088/77w+Ki6OajaGDkUi5gumTqVzdaYMlpyMtT2MobqZEhoBefyh12N90IAB1A/qxRfx98yfH/926yaqXGVkiH0jNmwwwaFDadC0qTmLpNigUiU7fPqpBSpXxuXGjrXBgwcxgrRsy5Z22LjRIhugPL2oaFHa9oEDZkhIEA3oihXRSC1cWIKLF9VRjvHjsch8+XKLioDMn48FxZ98IhrARYvaIT4+Ga5fFw3nypVtoNPpICkpCW7eTBC+CwmR4P79GIiOjlMdw8GDqJrFjXk+7dmD8y9fVhOQPn3UtRMFC9qFc3BUj1q6NFNoashVr/i0fn2GHCHhE68DqV3bCqtWGaFyZTsoidWrr+I1bd7cBKtXp6qOc+pUk0AWiexQStT9+wZVtKhSJaw5QQPfAAsXZoIy0sSX4+d66FCGihh8+CH9Phgdc00inE2eEhBnhOTatWswceJEqF+/PgQEBMhEpGnTphAcHAzJinD6+fPnISAgAA4fPuz2+VM2RcyGgOxgjF1wGH4mM8bSGGOF/+VhT4OGHMEpAUlKImNi9GjfXmg8zaVoUYCHD90vK0mey/fabFj8yhj23fjlF8+PKTOT1mUMayCyw759lItfoAAWZOYEDx5QjxMcZFH9yZfCZw3u8eeflB4VFYXG04QJngsZOIMkASxdSoZYz56+1Us5Ij2dyHFICKpVeQLuLAgMxBQ4Jex2LKrnzgRncrme4OxZOl9X3dd5x/iiRdVOC42APP544w38/QoVwvH6wAEao7jTpH59/BsRgUT2u+/IWCxenCs7kae/WDEpy5C1yuTi2jUTHDhATQcZA9iyxQJt2qBBPWAARjrmzrXAhAnWLKKB3dMPH+bpUrjd6tXRqH/lFRts26aOZJw/j0Xmjn0+GAM4ejQB0tPTYfx4MQVrwgQTmEwmOHZMJDQTJ1ItQnS0SBpatDBDTEwM/Pxzgmo/V64kQnJysly0zacTJ9KdbosxgKFD1REbSrniUrQiAWnYEJWseNRk27YMyJ+f1p82zQSXLon7mj3blEVuJHj/fXWk4t13qeD7zBl1HUibNlZo00Z9bd95h1Ki7t5Ng+eeU/82+/aJpGLjxgwoW9YGv/4aBzVrWrPGLDzH994zCcveupUGixZlClGg779XkxR3E49mcMlfX6adO3fCqFGjYM+ePfDGG29Aw4YNoWHDhqpnq3DhwvDaa695/Cy6IiBWqxUYYxmMsWUOw08jxpjEGBv4rw98GjTkAE4JCADWSnAD2ZeeFklJ1JfClVoOX657d3zJ3bjh2bYNBpJwDQ/3rkO1yUSePcYwbSY7XL0qplC9/37OU21OnkSlKN5JPTgYjbjcUoR6knD5MkCvXuIL7/nncxbBAsAo2uDBSsPDP/UZZjMRhaAgz2s0vviCjsUZMeDFwoxhjY4vsFhIIaxxY+cRlAsXiKCsWqX+XiMgjzfOnKFI74oVOEZWr47/82aa/HvGsBbIbrer0qEYo1Sh1q2Vkq04b+BAG1y6ZIa8eWm98uUl2LOHSMC6dRZYtMgKx46ZoWJFKctQxkjGkiWiscujL7t2WWDYMPG78uWxx4fJZIKOHcXjLFrUDg8exEBycjK0ayca3pcuIWnZskUkJqdOURHzL7+IxdWTJplAr9fDRx+JBn5AgAQPH2LaVpkyIgGJjsZtOZICxrCpIf/MIwzYXE9Zt6GOPGzebJTlaT/5JFMgBz16WECnMwikZMkSOvejR9VqVIsWZcrzdu5Mhvr1bcK+CxaUVPUYjGH6lMFggNOn0+GZZ+xCJ3e+/5desoBeb4ALF9Lg/n28FjExWAC+bFm6cO4dO4opVpygKdOwRo82e0Ue/EFANm7cCNOnT5efozJlykDv3r1Vz1f9+vWhdevWHj+PrgjIjRs3gCHReNNh+CmaNX/Bvz7wadCQA7gkIHY7dhRnDGsufGl+plRIcVV3odORpGOjRp4X8P7zD3VirlHDuyJiiwXrU/ixzZmTPaFIThaLjfv2RSKUE9hsmKLGVWYYw5zrSZMoDUKD57h7F9OMlMZS8+bYwDGnuH8fnwO+3ZEj/dPvxm7Hwm2+3a+/9my906dR1AANO/X9+8cfJIvsi6w2B29aGBQEcO6c+ntJAmjThnthnY8TGgF5fGG1kmQ2Fx758ENyivAms1yF8PnnJbBarRAfbxSazTVpYoapU6mh7ZQpXK2Kljl71gyjRomGeGSkXa4hiIiww7x5uJ6S3PToYYO+fW3QqROtW6UKLleokAQ6nQlKlxZJxtixGEkxmUwykeFT166Zcj1HhQpkpJcqJcnrLF1KXvuAAFHq9ZtvxPSmTZtQWnbKFDFqEhIiyYYuFXHjdOtWLMTHx8OZMykqIqHsjcFJFlePUvbzcIyCfPxxJnTujOczYoRJ6OJeqxauryzOXrgwUyYHX35pVKWJTZpkgkaNrFnvZrNcbB4Sgqla4eF2OH9eTaAYA7h0KQ0uXEiT1bgc+8CEhkpyDQqX5uUKVA8eJAspZoUKUZ8Vvd4A48aZValfRYvavSIP/HdJSkrymYCsXbsW5syZIz9LefPmhaFDh6qesTZt2kCtWrU8fiZdEZBTp05xAvKqw/ATlDV/9b8+8GnQkAO4JCAAmHrBjbk1azx+fgQMG4brh4W5TjPy1Vg6e5byk9u1806tymrFfhZ8AHv77exJiNWK6VKMYU1BvXreRV9cwWwGWL1a7MKdPz+qTvlSg/O04cEDTK0KDqbrFxGBBNgfhf4nT1Ivmjx5fJdndoQk4XHzY1661LP14uMBypen83RUVUtNpYJxb0i9I/76C9PB0ChyvsymTXT8J086X0YjII8vli+n3++dd9Cxw8fUmTPFZqFBQRL88QcqCM2cmZllSNph3bokiImJg+bN0aDs0gUJA2PYxwOJiw0ePDAJ9QBKrzljKLPLu5JzAtK6NREULrmLn9EwfuklG2RkmGDYMGuWohYus327BUwmE6SkmFT7mTdPJxMQ5fEMG2aVCcj06URMwsIkwfCcM0eMmty4gdGRIUPEqEmxYhgJ0On0QnRBGRn59Vd1atPcuUbVvBYtkEAoO6dXry6mNk2bZhL6jbzwgkVxDnaIjRWL4SdONEOHDlb586pV4n47d7bAhAlGmTDQdxL8+SdFhJTpZbx+Z+FCTFn74YcMFVngURDe72TsWLNAQFJSUlQd1pVSu5yIde1K51KmjF1oBpndxBsS5oSALFu2DBYvXiw/SxoB0aDBO7glIADUVKxECd+M4dRUMqw7dnRtuC1aRIONNzKhe/YQSRo2zDvD0GZDjzkN4J6t/8032KyQMWxe6Gt6iyMyM7E4nqeuMYb7mTsXu4trEHHxIsDw4Vg8y8lH5cooC+xLxM4Z1qyhbZcooa6zyAnmzaPfeeZMz9axWrG3Cr/3HAmwJFF0z5u0RkdIEvU2qVIFC44doddTgfvgwa63pRGQxxP//EPjGGMoNPDSS/i5XDm8d5RG4NixNkhISIDo6DjZ+z59OhKS7783KYxFs1wTwqeffjLDe++p6wU4OShYEBsAlighNtwbMsSada9LCiJB21671gIXLpiznk/ytufNK8GKFRanMruHDmEB8j//iNGHX381ywRk8GAyqmvXFpWdeAoQY9gQkM/v0oXOLyTEDt27Z2YZu2JvkHz5JEhJSYFOnUwqEsEYwAcfqAu+GzVCApKcTNvq0EEkQoMHW2DuXPodevSwyNcqIECSDX4+DRxogcmTcfn27a2QkmIQyNozz9hhyxa9fE0Zo7So7dup5oKrYTEGcsSjWTO6Zrw/CZ/q1bNm3WN4DzVpYlMRkJs304SGjh98kOmUBGzfngGvvWbymjz4g4B88MEHsHLlSvl5Kl26tNMUrAYNGvglBev69eucgDimYBVhWgqWhv8gsiUgycmYYlGtGjaU8wUHD9Lgw5uTOcJuJ1WhYsW8q4VYuhTXq1nTs8Jyx/2OGoXrV6qEJMQTdaQ//8SGbPy83nnHf0ZvRgbm9PNeEM2boxH88ssAv/32dMv32mwY2VDKfzKG/VY++8z3ni2OMBpRgIE3pmzUyL9CAWvWkOLVqFGe/6ZcHjogAAUSHMGlcBnzTiXOEd9+C1leSsz5dwbeA6JgQfe1MBoBeTzRp4/4DPFGtIwBbNsm1hgVKiTB338bIT4+HkaPRi98eLgNHjzQgdGYCc2bozHZtasNzp4Vjf5Wreyg05nkaAgZ6RJUqIDrvfaaDa5eFb3ehQpJ0KwZfs8jI0rSEhQkQXKySVbQomgG/t271wybNjka+BIsXZqaFX2gdKbAQJTs5QTkuedof927WwTDMyqKiIayPqFpU2V3dJ1s3CqjFoxhNMJgMMAzz6hraBgDWLEiWTWvRg0LJCUlgU6nkwvNX35ZVLXq2NEKX3xBUYyoKKusFMYYCMSJk44NG4wycdDrDYL0cL58Ety5kyxEb3hqFI9apKQY4M03abtKAsMjQ3q9AYYPp2VKlRLPO29elNrlUSlOQPr1o9+uRw+Lz0TB2cTJTnJyss/bePfdd+GLL76Qn6dOnTpBo0aNVM9ZkSJF/F2E/onD8NOQaUXoGv6DyJaAAGB6EA74WJDtC3gucYECzmU8AdDLz1Nd2rf33KCXJIBZsyh1wFulKrsdYPp0ijyMHu0ZCUlIEOtCOnfGonp/wWBAo0BJdLgxvH49GslPCwwGjA4pxQAYw7qDr7/2Tz0Gx61blHpSqRIqujmLAPiKHTuQQISGYoTRU2ncHTvQEcAYRk+c4aefMCrhJBPAY6SlUdRywADny9y5gzUojRplL2mtEZDHD3v3is9RkSIAdevS2CtJAF26kDE5aJAV2rWzwK5dSXJKVL58EvToYYNdu8i4PHnSDB9/LEY6vvvOAmvXojEZHCxB//5GyJ/fDitXkgF9/rwZNmzAZXjdQ//+ZAyPHauOnnTtimRB3cAQU4Z0OhO8+646wnDwYAI8eBADXbvScVesaIeUFCIgNWqIRrLSA1+1Kh3XvHk0X9lXY/XqFJmAXLwoEpCSJZGA8AiA47Rpk7quolIlq6pu5fXXxchKvXo2OHyYrmndujZITjbIUYsFC8RIRNWqNrhwgfYVHZ0G48aJJPDu3WSoX1+M+CAhsoFeT40GlalsXGL544/p2iQlYQfz0FAJpkwxyQpmfPr553T5/O7fT4F69WxQqxYtExYmydK9jwsBmTp1KmzdulV+platWgV58+Z1KsN75MgR4fmLj493+WxmI8P7LWPsvMPwM5lhbxBNhlfDfwoeERCrlby13bp58npTw2BAY65ZM/RWu/L4KqMl8+d7vn2LhchA/vzeN5iTJLH54JAhnhmGVqvYm6RSJf/IsiphNmPjQmUvC8ZQ8nTaNNeE7klAdDQW5fPmluhlQ5Wr48f9Hw3avZv2FRyMzQv9uY+ffqLi8XbtMO3OE1y7hpGGkBCU3nVHkOPjkUT4ihkzyFngSrqXp+pUqeKe/F26BHDwoEZAHiekpwNUrIi/H0+ha9yYnEzR0QAmk10mAkqvds2aFsHIrFnTLkcpnn8eC79feIEMx4gIOxiNJqhbF5cZPhzTuO7di4E+fdCwjYqyO5XL5fK9zzwjQdu2SmMdj2f9er1qf3zq0sUG6enp0LOnWNdQrZodYmNj4fx5dc+OK1eIgBQoIEZrDhyglCNlD5PDh2m+UpVpx44kmYAoIy2MYU8Rg8GgigTwaffudNW80qVtstFcrx7+BlOm6IVlihe3w5UrBsU6SHSqVMH9LF2aKahgFSyIRn1YmJRFfIywdavR4bxTYexYIinKvi/XrqVl9fAQ7xGuvtWunRUSE/H8dToD/POPQa7T4KlinBx99BEJA+zcmSbvR5mGdfp0us9kwRUBSUlJ8Xkb48ePhz179sjPlclkgjp16sDYsWNBkiQwm83w/PPPQ5cuXYTnb8GCBRAUFASnTp1y+nx60IgwjTE2KGvoKcsYu88Ym5VbY58GDb4ijDEGMTHuCQgAwI8/0qDkKiUjO/z8M21j/XrXy02bxgdqgN9/93z7yckkH1m+vPd1E5IEMHs2HWP//khsPMHu3dQvJF8+bHT3KFKlzp9HBSZeHMwN8iFDUDr5SVDPunoV6164R5ar9BQsiCIFnnQH9xYWC913jGHUyRf5aXc4f57ukQYNUAHOE6Sn07WoXDlnfUyyw9WrJAixZInzZf74g67Tt9+6317HjgCMaQTkccL06fjbhYRQjw+ucvXmm6hydeJEpmBUBgVJEBVlko1/3qtBmRr1669myMgwCR28ly2zwObN5EH/8UczxMUlwMWLcbJxuWkTFoxHRpJBXqOGXTacZ82yCoYzHq8d/v47EUwmk9zkUGkgL15shJiYGFkti08rVlggNjYW9uxJFMhMgQIk26vTqVPBkpPJ8CQjXGyApyzUbt3aJBOQgwfFVKmICEzbcmwm+OKLZhgwwAzXrhGJ4L9B8eJ20Ov1oNPpoF07vJ6TJxvAUY73zp0YOWUqJEQCvV4PnTpRoXm3bmJEKCnJIBOGESPMckoWn0aNMsJXX4kpYYUL43GvXm2E5GSDLP3LJ76PwEBJvlaXLqUJxvuNG6Ls78CBZpmApKamwt69GXI9CZ9mz3ZeB+LLpEz38nUbr776qiqyERMTA/369YNatWpBzZo1YcyYMWAwiHKZa9euhWLFikF0dLQwPyoqCho2bAiBgYFQpkwZaNSoEWzYsEFYJmu4ackYO8UYu8QYu8oYm5ZrA58GDTlAGGMMFi7MnoAAUK+CiAjf6x1GjOBhWtcEwWxGD2uBAuhhNXghd3v9Om6bMayd8NTDrMT8+cqXgufpPdevY6QoLAwJ0ODBOZfqdYXkZKx3qVwZDXOefhYQgDU7H3/8aAz1R4Vr1zCtKCJC7RHs3x/gk088N9i9xcOHJCXLGEDXrv5NpQPA34Kn+FWu7Hn/EEmi3iP58jmXwvUXJAng2WdxX7VrO6+nUcrutmjhnmQfP86vqUZAcgtWqxjJvXQJoxyMoSODjxmMoWJVQoIFjEYjGI1GOH8+BcLCbDBpkgHOnYuH7t2tWeTDIqfcVKtmzzI6Mfpx6hSRjfBwCcLDJbkgvVMnO5QqJUHhwnYYMACN8lKlJDAYTJCaahK868qUq3PnzIJ8K2MAPXr8P3vXHSZFtXzvLlHCkpNkRHbJGSUqkiUoCKIgICJJnigGEBQMiGAAE6AiKCgoSTA8kSDwQAlKlrRIDrsTd2enJ+6Ert8fNbere0JPz2x6z9/U9/WnzNzuvtMz3VvnVp1znGAymcBsDnVARzBkgIyMTMUKenKyCDabG3Q6HSxdqiSgd+3ql6of588rj/ngg8g/sFq5cSAes2RJUsfKyVGCgTZtciWPic2blQCkc2cEIBUqKBPszExBOo8c+GHST+fiTulPPGEPSOLSMY4c0UP16nTtrl/XwaRJjsA188CHHyrbsM6etYWYMfLWsG7dvHDqVBacO6dTfDbedjViBF6XX34JBlg+CTxwVax165whCTyXDMbfkU8BQARBgGPHbNLvizGABg18cYOFggAgjzzyCBw4cKBQ7+eifs4lIhH5GSmMMahWzaqpx/30aTIcW7EivhsoK4t4HpH6ywFQgYWvyk2YENs5du2iP7KjRsVXiXjvPXqg9uunnQNgs6GaDD1YCzZp9Pkw0Rs3Dk0Zg/8Qt2gBMHcuziH4OhiNSDYtKJCkFi4XVrdeew3ljIPn3bgxSiOfPFmwpPtdu+j3mJSEJpNa+D+xhE5HZPZq1QD+/lv7vp99Rtfks8/yd17BsX49nWv37vBjtmyRJ3nqx+vVC8HK4MEJAFJU8eOP+Fx48kl8Vtx9N353LVuiqIj8nluxAsGHw+GQfBJu3UKzNrkz+AsvKJWRGAM4cAAVpObMCeVq8O3zzynRrV4dV81feskbcB6n95KTRRg1Ct/v1AmBgcPhhvHjvRLP45NPssFoNML+/aEqV/XreyEjIxPOnFGu3LdtiyBJp9PBzJlK/sSLL5IE7549ymMuX859KpT7cC6HIAhw5YqStzF8uEMCIMFVhX79MHEPbvPS6ym55cCJf94yZUQwm9FZnBO6mzXzQJUqyurDypU2qFOHvoNTp/SwYEEOMIZE9mPHlG7te/bYYcUK5fzuuw8BzujRHjCZTJCZmQnNmlHlZMQIT+BZ5pd4GY88Qu+XLi1K/+ZAZPbsUKWqYD+Vc+d0CgAiCAJcvy5A/fo+aN4c+Sr5AT6sVmsI2IlnGzJkCJw4caJQ7+eifs4lIhH5GUEYlWIAACAASURBVCmMMWDMCh98oO0GmDyZP3zjT1w3bKCHzg8/RB63fDmNk7Vaaoply2jfN9+Mb55Ll9Ix7r1X++cVRVSQ4QCqRAnU2y9o9SqvF9vcpk+nHm/51rUrus6/8grAd9/h/zOGho6rV+d/4s3D78fq0Jo16PbeoQNJ2/L2KjnoOHGi4K+Vx4Pn4k7PVasiGMnvsFrJSbxcudj4QUePEl9kzJiCvSaCgHLGjKGxYbjweKjF8aGH1I+3bx99rwkOSNEFr3L07YvKa3LwyL1ieKJvtysN4XiC5nS6oFs3XIm+6y4vTJliC/yeMbkcOJBM/7hZHd+4d0fz5n74/HNMSrkpX3KyCBcv4n5vv01Jc/Pmfmnl/4MPPHD+vBsuXnTDunUeKcG9eFEHRqMRli+nxJdXUB5/3A45OTmwZYsSSMyfjyBDr9eHKEhx3xC32x2inPX335j4njkjBxki/PorJcSHDil5GzNnChIAWbpUWXV45BFUkJITtxkDRTsXv7ZlyvgVQIQxpVN6/fpeaT6MQUiL1cGDFtiwAWV9S5US4datTKhWjUDLypUWOHBAKfvL/UyaNPHB5ctmmDLFpmgve+wxuq4HDiAv49Ilm2KOn37qlL5jxtCTRJ68GwwCjB+fK/FPGAP45hszZGZmQk5OjmJsVpagMIL8bwEgffr0gQvx6pzHGUX9nEtEIvIzJABSq5a2diW9nvrY58yJ7yYSRYDBg/EYtWtjkhZp3P3308pxrPwG+QpfLN4i8li5ElfGu3cH6NwZfU20xtmzypaiQYMKz1hQFLHqMXcurnaWKIG+EcGgRL41bIjtZ7//juTjWNrsnE50Cz92DDlCq1Yhn6ZPH2qJC7eNHYsKZsePF5688N9/A3TsSHN44onIZOu8hNuNimqYQAD8+qv2fbOz8fvAhCzUbDC/g3OfypaNLIH98cc80YtexeGCED17JlSwiip8PqruLVxI9+GkSQBnzsjvQxF++80FJlO2lJjpdDqw2Wzgdrvhhx8oqd22TYDmzYOTXPLPaNUKXbKTk0WYO5cqFp995oHx432BZzlv26L9Bg0KldlNShLh1i03jB6N73FOyKBByBcwGo0K4jrfb+NGZ4iXB2MA+/fnSgCkWzdlm9WlS0RAnzuXjlm7NlU59u8nkBFsTvjDD0pAs3x5tgRAFi1SApCnnkIAIq8gMaasgHCHcs63kLenyZ3SP/rIArt32yRVqdGjlaBr1y57oG0M/338eJbU/sYYwIQJNrh5M1NqlcL5UWXi9GkzVK2qvI59+njhjjtwXm++SbyM2bP5vERYutShqPA0aKB0KudAiX9OxtBTJhwAye9NDkDycq7u3bvDrVj8AvIhivo5l4hE5GekMMagVCksqcs8dVRj4UJc3e/bN/7E7cYNMsF66qnI43Q68sMYNCi2JNXrxTl2744ciVOn4pvrpk1EzG3TBluXtIbTSYZuHHDlp5md1rh5E6sczzyD14ODyGhb+fLIZ2neHKBLF4ABAzBh798fKxn16xORNXgLfr1GDYAhQ7AitWtXwXE6IoUoIjDi80pOxhYwrTK4ANiGGM5/Izj8fmwxZAygRw+s+sUyzyFDcN9y5bB6VJBx6RK26XTvjv4z4SInB/15GMMKm1r8/jt953v3JgBIUcWBA/Q9PPgg/rd6dQS3r7xCid9dd/mgfXsPPP44SqHq9UZo08YP48b54OxZDiqQ53HxolJ5adAgqn7wbeVKD+zalSu1Y9WoIYLV6obUVGV1ZPNmq7QPEY4paeWqWi1aKKsAn3/uCMxTD127KhPu4sXRG8TtdsPIkV4JlJQpg/wPDkDq1SOQUbEiEdDdbjcMGEAJ9xNP5EoJ5/ffU+LepImSj/D558p2ou3bjRIAmTVLCXbmzXMFOCPK5+OlS3S8mjXxM3OiutxN/JVX6Hjr1iGHgTuaP/mk8nps2OAAi0WQrt333zsUnI02bXJBp9NB+/a03+TJVM349ttsmDVLOdc6dfyS43qvXuSDYrEIMHKkBy5cwH8PHaoEqhkZ9Pl27HCEVIDuustdKAAkJycnXwBI+/btIbsgFUHCRFE/5xKRiPyMFMYYTJlilZJjLYRrp5P69idPjv9m4i1OSUnq/eTyvvNY++Czssg7oUGD8BUIsxlN/tQqLFu2UNtQ06axGSUCIIjhFYjkZIAFC/LPNC+e8PsJ2MW6demi/n6ZMtgC9vjjyIfZsAHg2rWiNVDMysK2IT7Hhg0xQYsl/vwTE/WSJdXbtUQRgR4/10cfxXaexYux5a9YMeRlFHQMH47zbNw48m9y4ULkc1SuHL2Kx8UqunfHa5EAIEUTXE6Zt80xBvD11yL4fD6ZUzklgVWr+sBkssDBg5TgLlniCTyjRThyJBdWrlSu9B86lBsCQNxuJJXzc7z6qhdu3VIm4Y0aecFoNIPb7YZLl4KJ5LjfN994ICfHrUi+S5YU4cYNTCAzMjKldi45MTwryw1ZWW4wGt0wZQqCiT59iGSekaFXHLN3byWIkhsE7thB0q9ykz+5AaEgCJIcLd/S03USAKHWJBEYE2HlSofC0Zxv167R8erXxznUquWXrj8HU6+/Ttfr88+xhejRR/F7GjMmV9Eu9dln2FaXmorXYfFiF2RnE2G+QgWsTEycSMd88EEntGuHAOOFFwQ4e1YntYLh3y8R1qzB30GpUiJMnJgLEyfmhiToX3zhlH1urMbI3//6a6dCaa10aREyMjLBarX+TwCQZs2agTs/Dag0RFE/5xKRiPyMFMYYpKdboVQpfAhEcioPDu64XLx4/D4Ufj+2NVWqhKvFapK348dTcnvxYmznOXuWqi09eyrPI4rUjtO+vbp/wi+/kPxto0YAV6/GNo+rV5EEes89eIzWrZFkXVQhb8lq2BCJ9+fOYaK9dy/AwYP4mdevR+D3zjvYqrN4Mf73/fcB1q5F75Zjx9ApPD8N+/Ir9uwhYz38Ix257S9S7NtHVaPbb1c35HznHTrXSy/Fdp4jRwjozpoV277xhLxa8d134ceYzXT/LFqkfjy5RC8HaQkAUjSRmorfAxen6N1bhNxcJJpPn+6REsOqVX3wxBN22LXLAS6XG954A1fTmzTxSxK3jz6KSbq8xWfwYB/cuhUKPtxuNyxbRnyNW7fcsHkzXw3H4732mhXMZnOg5YlWynnyXKECGgn+/rtyRX/gQB+YzcgVOHky1Mvj4YdxnitWeBTHe+MNIpkfPaokYo8f71VUQKgdSVRwD+SVh2nTlKTqZ56heZYoIUJmJpL333jDLSXZ99xDoEWvDwUgN2/S8XhLFXcyT0oSJdUsuaHgBx9gAj1jBs6tb18vtG9PFZy5c3GeAwfi9Zg6NRdMJgJF5cphK9myZQSuOnf2SjyT++7DqsSkSUqS/c6dJkULWdmy6KQuvyYZGYKCF7JkSaiM7jvvKIHb/v2GQgUg8Z7LarVCkyZNwF9QxMkIUdTPuUQkIj9DMiL817/wAVC3rrYqiMdDrtRjxsR/Q50+TSvx770XeZwgUE/8+PGxywD/8AM95IJbSHbupBarAQPUgdB//kPJWJ06sbfHeDzIs+DnK14clZe0+o3kZyxaBDBsGF6beGWV/5vD6cRry2VGU1IAvvkm9uP88gsJCjRsiC7gkeKrr+h3NnZsbFUfq5XuqVat4pOQjiVEkUj4vFoRLmbN4ivk0YUYuLBB5850vAQAKfxIT1cmt6VKifDHH9lgMBjAYrGA0WiExYst8O23ZsjMNILT6ZQS8O7d/YGE2Scl1OfPu8Hlcit69u+5xw+33y6C2Yz7ffyxB6ZO9cEvv+RCWhqOe/JJBATPPUctT7fdJsK5c1ghcLvdsGlTLlSrhiv8vN1r/Hjcb+lSqsAwBvDJJ+SY/e235pAk/tgxrMjMnKlU49q7lyo1W7cqJXiTk0W4do0ACEn+KnkLo0cTUOKVBb7xCgQCPp8EQORmgwMHEhH71q1QAKLT0fGaN8drT+7qokTwlwOQefNw/Ntv42utW/tgyhQCQ48/jpWJ6dNzpevYv78XXn0Vx7doga1kcrPE2rX9sGoVApKKFf2QkZEJx44ZFYBjzZosRdsWYwCnTmVBdna2Iqnv35++hwkTQqskgiDApEm50vewYkVWgQMQi8WSbwBELOSyflE/5xKRiPwMCYDcvIkrrx064Kq2lli7FgIPNawyxBtctrZcOXV/hN9+w8QsGliJFHJ/j2AjRHniOH68euL4xx9YtWEMe6rj4ZYcO6aUn23XDsFYIvIn9u7FlqLkZFwJ7tYt9ooVAMDmzdpb73bsIGDZv39soFIUUTKaMazyqVVY8ivksrt//BF+jMFAHjORjAl5HD1Kx/vlF3o9AUAKP95+W5nczp3rkaR15Vt2dja4XC4p+TaZyI+D8zKeegqrBydPUsIpb51ZtgwVpLhS1rBhtAJ/6hQm/nffTYn46NFuKUGXV01276YKw65duN/EiXQsBEKk0PXMM8pV+Ro1iMvx0EO03223odcIP89HHyl5LPfdp2zB4tyE1q2VbVY9enhl81O2E913H73XpIlH+nzydqiRIwmAXLsWCkBMJjpe27Y4fw5EGBMlw7/58wmATJuG81izxhn4e+SXtT4hR0OvFxT+H+3b+yQ+S8mSImRno9wtBxjFiolw4gRd299+M4DRaISHHiKQ9dRTTpgxw674LWzYYJa+G4PBAIcOZUOHDnRdOnYM7+ORkyPAG2844ObNvIGCBABJRCL+t0ICIADUt56Wpk2S1ecjh+ZospxqYbWiFCz+cVIf++yzOK506dj8FAAwyeP97iVLYouRPBYupD8G8+apH+vUKVKYidc12+3GVibuWVKiBMBbb8VGik6EMiwW9Dzg32OpUiiuEE+FZ80a8r1p21ZdfODoUSK3d+ig3soXLr78MjI4LohwuUiG9dFHI4/jiwM1akRvr5s0iScaSgCfACCFH1270u+pSRM/WCxO0Ov1CpUrQRBCWqd4qxQHIWXLinD9Or43YYJSDalePRE2bvSAy4Xu4Txxl5PWOR9EruL07rsOuHULVazk5543zysd1+nE1zp1IuDSq5dLSuwzMzOhVSvlCvyECQQk+BwQYPgV55k+XSmZyyV4b950w6RJPsmDY8MGhyLpvOMO+vybNinfI6AA0LOnK3CdTUHzowrA5ctK8MQYKNzWW7TwBu4lD5QuLULFin7o1AnP8frrBCZGjcJ57NzpkMDAkSPKY3/8sQt+/pm4O/Xq+eHvv2nMjh32EE+S9HSbpFb24YdYMfvjD7puqak+GZkd9124kNSl/v5bBxUrKvk5pUqJkJ0dnnMhBwUFCT4EQYDs7Ow8n8tqtUJqamoCgCQiEXkIBQCRl+21KP0AKAnieTHc49UUxrDfPlLY7WTq1r177N4VNhtVUWrWVK5oiyIqcvF5RCO8p6cjwbNFC0w+Y5FZlceff6KDOq0UoRdGImKLLVsIyDKGXJt4ZdrlPjJduqjLL1+6RETfxo2xahBLnD9PVYZHHy0csj7nqZQqhQIB4SIzkzhP77+vfrwLF3Bc69ZofiePBAAp3DAYlBWKbdtckJlpVIAPecuVfJs0ySetgjMGMHs2cSfOnnVDy5Y+SEoS4bnnBLBYaL9du6iNhp93+3asYvz6KwGFmjXx/cmTbWAwGOHGDTe8954X9uzJlaRdZ83Cc9rtbkUFYckS8mzIzJS7nHPAgEDC5XJDmTK036uvehWfsWdPqrSgmzm+Hsw3OXVKmXRWqkSgpnFjZXtW1ar03oQJ9kASrmwRe+454o2kp4cCEG7qJwiC1LrVpg1VDXr1QlCCfA7uwYK8ilOn6HjnztkU3/8LL7gV5ytdGvkavML19ddOqeLCt9277ZJU7rhxdjAajSAIKA9cooQIgwd7wGxWmilOmZILVqsVsrNRgvi116whn/HgQQPo9XowmUyKdq38AAWFCUDMZjM0a9as0O/ton7OJSIR+RkKAAKAUre4iqPthhBFXPFlDPkT8YYoolwpY9iapFYF2LuXHmhLl8Z+ritXUFK0alUkv8s5Lz4fSVYmJ0f3D7l6lVS2SpYE2Lo19vkA4Ir0rFl4zpYtsSryzDOx+Y78fw2dTqlwlZKCIgnx8ANFERXKOnfG1sJevdQ9OIxG/P7vuAOB6KVLsZ3P5cKknTEE1rGS4+MJk4kECNSI7tOn45jbb0c+jVpMncpXRkOvewKAFG68/z4lw2lpPmjTJhc6dsyVkq6srKyw4MPtdkukc8YAqlQRwWhUvp+dbYfDh7GVS9669eqrmBxXrszbl/xSO9Ts2aHu6Pff74SMDANs3KgkizMG8NdfueByueDAgRzptWLFRLh50wFut1sCIM8+Kyh8MvhcL19Wqmr9+qtSqYsTuxkD6NqVqiNElEfZXovFqmjRkROqR4+mdiqLhVSlEKwjyfnoUaUT+6uvEgBRmhriJidx166Nn4tzNASBZG1796br2a0bHtNgoJau//zHLlMHw9Yvq1VQeH3odAJ06eINgEx3iFfJV1854dVX3YG/xx5J0ctoVCbi/frRXOSSvIKArVX33KP87j/7jJJ/ebuW0WgsdADCq4DxbDdu3IAOHToU+r1d1M+5RPz/iFqMsU2MsfOMsXTG2ArGWHkN+/VnjP3AGDvHGDvNGDvCGBujMj4EgMiT++PHtd0U27fTPrFKm8rjr7+oHSmaMztPeMqWja+3f88ecn6eOlX5ntNJUrN33RW9vUqno6pKcjL6bcQbhw+T+SJjaMC4alXBuZT/L4fPh9dGbnT44IMAGRnxHU8UqeWIMYCnn1YngjscqGrGf4dHjsR+zhkzIJBAYSWsMOLNN5FMX7VqZC+WmzdBUsaLBvKzsoik/8knoe8nAEjhhCiK4PF4oGlTpd8GT+AvXTKpApCzZ5WJ+zvveEPG2O1EApcDkF69yN2cMYBVq8hZPNj/Y/x4N9y8iYnnrFmYoNaqhft16OAHp9MJRqMRPv6YHLqbNvXDgQO5CgCi0+lg2jTcv3t3AhI7dlAlo1Qp9CCRfwalYzeBk48/JgDSrp0frFYlAJFXFT76iBSd5O1U1av74Y8/MJnes0dJdl+yhIjrJ08GAxBUo3r4YQ/06OGF6tX9EoDk+4wdi59LnvS3akVtXbyisWGDQ1LRYgxVrQRBgLQ0eu2vv2ySl8fQoR64dMmmqF7Nm+eCn36itq4RI1ywYoUzJBFfuJCAS716/pD3z5+3KdzOR47MBbPZrGgHDN6MRiNkZWUVmB9IVlZWngHIhQsXoHv37oV+jxf1cy4R//woxRBAfBb4d3HG2HbG2M4o+5VljImMsRdkrw1hjPkZYy9G2CcEgIgiEqIZA3jsMW03hShi5aR7d/xvXoLzUFJSMLGPFIKAXhO4IhRf28q779IfgGDQYDYDPPAAVjWqV1dXPgJAcy+5P8aHH8Y+Hx5eL+4vl8nt1CkyUfj/W4gikpxbtEBp3MqVkaOweXP87UteLxos8us9bpx6FU5eKStWDODnn2M/57ZtyMNIS4tsAJjfcesWAoukJIBPP408jlc/6tSJror31ls4tnLl8DyRBAAp+PD7MQF3Op1gMDigfn0vMCZC+/a5MGOGHbZvd4Fej4kXl78N3j74gBLw2rVFRYsV3xwOh5Qo8jYum82taMW5/XZqa3I63YrEffZsL1gsOdLKd58+mGjz1fl333WDTodE8yefpCQ9OVmEEiVE2LUrF2w2G1y4oIOMjEy4806uDEVgiUsAM4ZKXfL522xukFcr9Hp67+WXKbF/7DGfAoAYjUrS+B9/EAn98ccJ8Ny8KUhk/x9+sCj2WbaMeCNHjyp5KByAcA8SDkDuuIMAyLRpHIDQ56tfn6oO3Ovjgw9cMGOGS6qCcDf3IUNov9277fDeewgeUlPxHLzFCxdyPJCRoQRdY8eGqljJeSFJSSIYDKHJupwUX7UqgRR5uxb/zoO3cO1a+QVA9Hp93Mc4efIk9OvXr9Dv86J+ziXinx/TGGMexlhl2WttGYKLPir7lWWM6cO8vo8xdiPCPiEABID4GMWLa3c637Ur9spJuLBYiNw9c6b62B076JwrV8Z+LjkpvXTp0HlfuUJzSU3FlV61sNvReZ3P6bXX8tbPbzAATJhAMrKMYZIcK8fgnxQnTiDgpMQE4PXXEQDGGy4XwNChdMxnn1WvOIkiwLRpNP7zz2M/p8mEHCTG8DdTWBUuPu86dSJXd3Q6nFv37tF5ULm5VEmcMyf8mAQAKdjw+/3gcrnA6XSCzWYDvV4Pf/xhgPR0HWRlZUmVCp54RQIgPXpQpeKRR3zQqpUfDIboAGT/fiV3Yv58AgMuF3JAqlXzw/TpWBWxWq2BFWi91LKFf29EOH1aL61Ot2hBClCMYXVEENxgs9mgbl2vos3ozz+pkjFjBiXSr7yirOL8+itVeSpUUIITOcl+wQKPAoAcOkRgqEQJUcHX4NyQ4sWRW8HbidauVXIgUlL88MEHWDk5fDg8AOE8GO6EXr8+JewvvYRzv/deL8g9XPj7XKXrgQc8cP26ALt2kUqV2SzA88/TZ9+wwSER04sXF8FkEiTpXcYAmjbF4zZtSteyZ09vSCJutSr5L4cO2cMm7I0b+6BMGRGmTHGHfZ//NjkwjVQdMRgMYDabwWKxxA1IuI9MXgDIwYMHYejQoYV+rxf1cy4R//zYyRg7Eeb1HMbYJ1H2LR7mtW8ZY0KE8WEBiMeDCQpj2s3QRBGN/BhDXkVe4quvkFeSlBSdjM1XrVu0iN2dHAArKWlpeIyGDUNBxh9/UHtJjx7RV4PdbgI1jOFKcl6Tyz//xAoI/eHEFjU1bsI/LW7exKqEHIzdfz/AmTN5O64gANx3nzx5ig4a5UaDc+fGfk458K1YMb7fbTxx/TpW9BhTr37MmYNjatWK/nv/+muelEVufUsAkIINn88HTqdToSQUTuWK974Hy99iWxM5jstbceQGfrzCws/hcCAn4623KEktU0aEzMxQcMNVreQA5I8/lIaAvXu7pCTTbncoWqUqVEAvErfbDQaDkmRdq5aoMBLs35+AxPPPe8Fup/f0ejc0bYqVgIEDldyQ+++n/bZuzVUAkM8/p+ScVwz4xtW/+Oo+ByDLl4dK7Y4fj1UEue+GHIA0aYJzuP12ZfVCEAR46y1XAIj5oFQpfL9sWXp/xAhlhePCBQJNJ0/a4JNP6DMsXeqCK1fo/UOH7KDXkxRv2bI4n8ceo30aNVK2WF25YoPhwz0KhbOvvgpt0xIE5KgEGxWGAyD8d2u1WsFisai2a+l0urjatfIDgPz6668watSoQr/Xi/o5l4h/fmQyxraEef0UY+z3OI53lDH27wjvhQUgAJRkVayoXVJ00yZalY5VIlcefj/KnvLVYbWwWJD8Xrp0/MDn3DkyFxwwIBQwfP89Jb6PPhodUPh8WLlgDKBNG0yc82o06PcDfPEFVmTq1sW2n6pVsf2lMIjLRRVWKybEXI2JX9N4FcfkYTaj4hiuEqLyVbT45htlm1Y8FS6etDMWnzFivMFlcuvXx8pFuLDZiFOzcKH68eTtmmpmpAkAUrDh9XolaVqewHNwIN84QAmWv3W73fDzz7kgb0267TYRpk3zwoULwUAiFIDIE/4pU3whxw4FO8jhWLGCcyTEACjOBrPZDC6XC86cUfJRuMKV2+2GAwecivfGjlWes04dpToWV9XiW1oaAqY331Reo+bNaSU/Pd2lACDTp9N8xoyhVqScHAIZHJhwAPL226FE865dsYqwdy8BkHvv9ULv3vh6s2Z4LevWpUoIP9fSpdQyxQ0hS5QQpfflbuxbtzogJ4eI8+vXOxQGhS+/jN8Dl9pdtQqBQ8OG/sDzEI/7/vtUxSlZUln5ycwUpEpN8HFj3aK1ReVnuxYHIAaDIW4A8tNPP8GTTz5Z6Pd6UT/nEvHPj1zG2Jowr//GkJQeS7Rl2M7VIcL7EQGIxYIJbo8emPhqCZ8PoEkTfBBNmpS3G03e0rVzp/rYDRto7I4d8Z2PgyfGsHUqOD78kN6fPTv68UQRW4O4N8SAAflTsbBYABYvpuNykDhvXvQWsf+l0OkAXn0V24D456xTB6tj+dGudOMGQJ8+vPUDYN266Pvs20emhH36xAcqr18nbs/IkbHvH29cuUImiWrtiu+/j2PKlYuuwLZvH303am2XCQBSsCGvfMhbrmIBILw9qXRpEWbP9sLNm+HBg8vlks5lt9vB4ZBL3orw0EO+ENWpSABk6lRK0MuX94NOlyPNfd48Ws0fN04JIL74wiWdjzGAtWsJnDgcVMnhSfPFi/jen3/mwhdfeKTqyc8/KytEvB2sVCkRnE6XlPgKghBoe8Jjfv01cTkyMgiAcHDBAcjLLwdXOZDbIQgCbNlC78krAy1b+gKLBPjfatUIgHCzwVq1/NCggTew2EcARE4IX70aAQVv6VqyxCVVahgDeOQRVPHiKlUvvIDfy4IFLmjQwAcTJuC/9+1TSglfuGBTJOLyqgo+Fz0xJ/PxVCVycnIgKytLoZ6ltV2Lg/W8AJANGzbA9OnTC/1eL+rnXCL++ZFfAKQ0w+rHTJUxEQEIAPWLt26tfaV35Ur+4Fd3NdcS/frRine0nnyeTDZurK5cpBYvvkir4du3h77PTRAZi94bz0PernPXXbjqnh9hMgG88gqS9fnxy5VD3oxenz/nKIo4dgxg7FhqFWrcGMnNCxdGl4LVGmfOIJipXh2Pr8Xz5tw55EV06oS/x3iqTn4/ylszhrwJrYBx0aK8qcsBUKtio0aRgZPHQ8IOzz0X/ZhPP41j77lHfVwCgBRseL1eMBgMYLVaVRP/nBwif0caY7OpVy+CAcjhw7SqzqV069QJle8NB0C6dqWqwrhxnqDzuGHjRje0b+8LUbGaOZPOmZwsgk5H76WnKysn06YReHnhBa9ivxs3shUtYrz9qEkT5NTIAUjt2sRHycigRFTe5jRs35PJ2gAAIABJREFUWK4CgDz9tEMxF75dv06Ect56xbc2bfA8DRvifytVIgCyZYsj8JwXoX17j7T/pEm5MHOmG778ksAAV+nq2RM/84svuhVu7Rws8arIwIHhgYNOZ4By5ajKEewAn5MjKDxE5C1jBQlAgqsjsbRr8e+He5vEs61evRpeeumlQr/Xi/o5l4h/fuhY+Bask0x7C1YyY2wzY+yjKONSGGPw1FPTYMaMGTBjxgzYLsu8T56kh6ZWiVC3m0ip0Ujk0eLkSWp9+vpr9bEXLlDS+sYb8Z3P68Vkqls37H+/cUP5vs9HZOXkZO1tQKtXk7xwWhqugudXWCwoq1qlCn1XpUsjmDp5snBM7fIaXi8qWHXrpvxDXaMGVqMiScXGE7//Ti1GFSsC/PZb9H30enINr1cvfs4Gry7EUqk7fpzugd274zvvxYv0+1OTiF63jipCwb/94MjMxGPWqQPwww+h72/fvl16pkybNi0BQAowRFGMWPWQb5x7odfro45V23hSZ7PZYPp0pc/DbbeJ8PHHHgUnI7iFy2AwwK1bmVC2LCW2O3eqV03k2wMP0DnvvlvZfrVxI4GTkiVFuHaN3hs5khLl1FSPQo74xg0CLsWLi/DGGx7IycmRuAW8NapdOyUR+9gxAiBPPeVWAJDx45XVAb79+qtdSvx5qxPf2rfH85Qvj9emVCl6n5PKGQO4/35lFahRIz9s306A5403EICMG4fnGTnSI3FIGCN1rQ8/dEnAYcMGB1gsykRbr9dD9+50bVauDOV47NhB501OFlW5HgUBQMIBkmjtWhyUxKuutXz5cngj3kQjD1HUz7lE/PNjJ2PseJjXLSw6CZ3HakYyvmqRwhiDHTsiL+dy8nMs7Y7vvcfL6tqM9Oz2yF4b48ZR4hetsjF3LiXgly9rn688MjMx8cVVotDVYocDX+/SBT/f6dPajvvvfxOZvXZt7ftpDZsNrzufO+/Nb9ECV9CjJZRFESYTVoj4qjvf2rfHVit3FAJ0rPH998QlqVNHG4Hd6cTKFf89//VXfOc+f558NaZN07aPKFLFpF27+FvPxoyBwMpuZGlhUSRDRC3y2wsX4thatdTliq9fBzhyJFEBKcgQRVFT4p7fAEQQBOjYkUBEy5Z+OHEiMpCw2+3SCvX+/QZpvypVRPj6a4/m86el0TnnzVPu98QTBE7+9S9l61bXrrTfww87FADkzz+VSl4LF9oUrTzc8HDdOmUCvm+fXbaPSwFAhg1TmvvVqYPH+OQTJzz5JDnHy4/XqZMSgMg5HnLJ27Vrc+CBBxwwZIgzsJgiwokTcjCE1Zi5cxE8dO7sVexfoYI/BNQwBnDxorLFSq/Xw7PPUpuZ3ExRvrVpQ9f91ClbzAl9fvAyIm15adeKtC1evBjee++9Qr/Xi/o5l4h/fjzFsA0rnAxv76Cx1cPs/wlj7Kug1z5ljNUIMzaFMQYTJkQGIJ9/jg+VsmVRMUhLCAKtMr//vvrYfftw9b5y5fAcievXKXGL5pXgdKKSFa4Qxb/6/+uvtOocju8hXxGvX197y9PBgwCVKsW2+h5rOJ24yt2mjTKpT0oCuPdebJELBwpFEeDatYKVgxVFrMosXIjcojvvpPkVKwYwYgRWKAqiarNiBVatGANo1kwbIPP7yWG9WLH4+UU+Hzqr9+iBLvfhvDLCxQ8/0PXZuze+c1++jIpyrVujvHak2LkTZAmE+jFFEZ3fI90f8kCjxQQAKcjQCkAEgQz88gJA+KqyICCH4vXXPTBwoA9ycqKDH759/DGZ9JUqJUJysqjgckTabDa3QnXpt9+UlZ9mzTjIEEN4LPXq0X4LFuRIACQ72w2DB/tkz0oRTpxAoJSRkQkLFpAj++7dZgXRmUvZMgawZo1TAUD69JG3g4lSC9SMGW4YPz438FxRAhDuTl6xoj/k/XPnbLJ7FBXNtm0jt/UbNwgoDBuGLVVcvat2bb/CDZ0fV74PXk9li5Ver4evvqJzjBsX6gUiCAIcOGCDWrX8MHu2C3S62EFCfvAytFZHuMyvWnUkmrrW/PnzYfny5YV+rxf1cy4R//woxRg7yxA0JDHGSjLGtjE0I5THy4wxH2Oss+y1JYyxm4yxsYyx0YHtMYbGhvXCnCuFMQaVKlkj9oXbbKQQpZX3AACwYAGu2tatq75CmpVFhOpI5n0zZ1LSHq1v/uef6WG6dav2+QYHr6YwFp4PcuYM8S/uvls7P+HsWZI4LlMG4Kef4p9jtDh1CluxatdWgpFSpZBn8fbbCADtdqpadeoU3fk9lrBYADZuBBg/HlfL5fPg127mzPxtS5MHFwPg5+vaVTv3gnOCGEMAE29wAYOkJARYWsLjIUGHBx6I/9ycp9Gokfp9OGIEjtPirbVnj3zFNPI4nw8AvU4SAKQgQysAsdloVT8vAIRXMaJxTtxu5Ixw+V9efbFarfDAA8r2pNq1RUhPj37uU6eoUlGpkg9sNkfQ+dywaZMbZs1SgplgcvrPP5skALJypUcxl7ZtcyW/lKtXsxTvXbigTFpXryazwV9+cSgAyN13y6sqIkydSnyLMWPCA5Du3RGAVKlCzvL8vZs3CSwsXSoEpIxN0mt//22DEiXwM3bvjq1iO3eSm7nZLECLFl5pPlzRqnZtqgxt3uxQzOfMGQOMH09Vkk6dQr1A8mMrLAAiCGQUaTKZNLVr6XS6EHWtOXPmwGq1ftYCiqJ+ziXi/0fUYoxtYkg6T2fYTlU+aMwkxpiZMdYi8O+6DKsk/jCbj6kAEMasqkRcLt/Zvr32G+XmTeo737JFfSwnd9erF54ga7FghaRLFwQ20YLzNOrW1b7aHBw+H/JBGAOoVi28x8H27fQZH3lE+8r9jRsATZsi5yE5GeDjj+Obo9bw+ZA/8MQTCJqKFaMKFV/dl/+bMYDRoyP7OgSHKCJP48wZvCYrVyKA69aNro98S03F73z79vgFA7SE10u/XcYAhgzRDhQ//ZT2ywuX6do1AthPP619v48+wn2KF0d+UzwhB/dqv7H0dFT36tQJ1eeixahReMyePdXHUVUlAUAKOmIFIFo4I3kFIE6nU9H2YjKZwOl0gsPhgCpVqOJQo4YIp09r44CsX09goV8/Z1i54XDb5ctKjsfVq2TI2KWLUkp2wQIr2GzYRvTXX5T0V6jgDyE6L1xI1ZHHHnOAyWSS3m/WDJP9kSNzYfFiFyxZgi1ZTZr4YNQoBCDyFitBINI4d0KXc0QsFprLyJEoBnD5MnmpHDxol0wRW7RAjkd6utIL5Kmn6DqcOoXHlTugL1umbDH7/XeTwnNFrspVEAAkL8TwWAGI2WwOeS9au9aePXugS5cu8Mgjj8Dbb78N/kDLQGZmJgwfPhzS0tIgNTUVJk6cCIKgrWXk3Llz0LdvX2jevDmkpaXBrFmzwBOUCK1evRrq1asHDNvzjzP0ijvOGHulSB98iUhEHkICII8+GvkGOXqUHs7Hjmn/ozhsGO7Tu7f6uBs3SCI0Etn83Xfx/XLloq9gX7+O3IfWrcNL6mqNW7dQipgxbF/y+ULHLFtG12bePO3HzspSSsw+/zy2/Ozdqy0JjDecTmwlmjwZrw9vS1LbUlNxroMHI59gyhSAxx/H7zUtjSpk8o236DCGlZ7Bg/FaXbmS98/w99/RK0c2G8CgQXSNJ01SrwDI45dfCDgNHx5/W5ooAvTvT2BY498jyM5GwM0YGlnGG2+9xVeK1SWgn38eAolRdBCdlUUtkdGki2fOxLH33psAIAUdWpJwu92eLwCEJ3A5OTmq55In6haLRTqn3e6QVupLlhTh2DHtBPR58yhZLl5chPnzcyMS3uXbf/5D1YiWLb1SAnrtmktS7+KVgcuXEdQIggD79lEC37o1GRDylfPnnqPqwKhRdkWyWqcOgqwVKxxgtVrh3/8m53FuGliypBKAcDBQqxa1ksnf51WcgQPdgYqSQfL6WL3aIfmD1KvnDyTUgnStf/zRAadOCTB4cC58+qkDjEY85tNP07WZO1fJ8dDr9TBwIFWrihWLj2QebcsPZSqtG/9dhgMg8k2ursV/8/PmzePPMmCMQZUqVWD48OHQsGFDmBTwHvB6vdCvXz/o06dP1PvWYDBAzZo14c033wQAAEEQoE2bNjBx4kTFuNWrV8OaNWsSFZBE/KNCAiBlyqgbDnJjwClTtP9RlHt5RFvFHTuW/3EInwQJAiVlr74a/dy87aZsWfSUiDd++YU+QyQwM306jVHrsw8OlwsrJ3zfLl2oVefkyfjnHEvYbAh66taNDkT4xlXOwm1lyiBgeeABgJdfxt+AO5/I5B4P8kdKlcJKTqQKTUYG/V4ZQ9d4rdWpM2dQ0jk5ObbWunCxdi3NYds27ftxQFCxYvyyzW43tbzNmRN5nMtFCmpaOJW8MlOpknr1KjcX/U7KlgVYsSIBQAo6cnOjJ/EOh0NKjp1OZ4EBEC73y9tXBEHpt8HNDCdPtsHWrbHNo1cvksNlDGDECK+m/daupcrJ44+7pIrM779nSQRzfH5RO5sgCPD99wQwhg4lqdpOnXwwbJgHhgyh4y5ZYpOuzeXLmZCSgsddsyYLdDodnD5Nnhr9+uF+cpUrQRCgb19vYMGCPqf8fQ6W7rknV2pZ4q7pL71E1Q25fG+jRvj+0qWusIn28uUEMCZOVHI8dDod/PILtXkxBnDpUuwk8/9FABIOkOzatQsmT54MtWvXVgCR4sWLQ5ZsZfT48eOQlJQEO6OYmL3wwgtQrVo18MlWN7ds2QLJycmQnp4uvZYAIIn4J0YKYwxuuw3dTtWkbj/5BH0T7r9fe1Lm91Mf+4wZ6mPPnKEH3M8/hx/zxhuUmEXzYbDZALD/HFf78xKzZkFgtSq8H4PPh9eFj9Ha5w+A1+ill0ITeTVn6YKItDTl+Tt2BHjwQUyGly/HxH/WLLyWzz6L7Vzz5iE3Yts2VIfKzi442d9jx5TE+saNw5vfnTpFHJsSJQDWrNF+DqORRAz69QMwGOKfr9FIif2oUdr3u3aNKkpLlsR//i+/pN+jmh8Pl94tWRJVydRCFHGBgLHo7WTbtvEVU4CrVxMApKCjMAEITxYtFovidZfLJTla8+Q4XItUODd1rVv16lStqFHDBzdu2DXtt3AhVU4++IAqFefP6xTPvdGjCdAIggBffkkk82efxddOnqSqCCeNMwawZw8SuA0GA7z5JrVmbdlilgjtXN2qY0esOpQuLSpUl+6/H4EJ9wEJBiAcLHXs6JGucatWOHb+fJcEzOTARu4FEi6xlruyDxig9APhvIg2bTyyz2OPKXH/bwUgWVlZcR9jxIgR8NNPP8GXX34JtWrVgtatW4fckxUqVIApUVZs77zzTnjwwQcVr2VnZ0NSUhIsWrRIei0BQBLxT4wUxhgMH24NPHwi3yjy1ovvvtP+h5F7H1SsGJ2PMWgQju3RI/z7FgsRv7VwQVasoCTo3Dntcw4Ojwfn1rQpVgrC+VIIAiZndepgL/21a9qPn5WFPBP5H8LixeP3m4gnnn0WE+Zp01Ay9r8lHA4kg/OWqGLFsLUnHAj+5RdK3itWjE05KjeX2rWiySsfPRr9eKNH47GqVEEwojUefxy5UAMG4JziCVHEFkTGkPyvFpznpAUknTxJz4BoSlnc+LBPn4QRYWGEFgCSl8Q/GgBxOBzS6j9fXY7U5hVsZqj1vE6nW8FH+PrrLM37Dx/ulSXQRIrPzMyE1FSlAtb69R4JgCxZQtWBDz7ACsK77yKX4/bb/dCyJZG6ufqT0WiEl16ySvv99psgtfG0bZsbWEDhFRC/ZJKXnZ0Ngwbh6489lgtLlzpgwwYlKZxzQ1q0IADCAcbMmW6p6iLnjnAvkHr1/LB+vSMkmc7MJG5J8+Y+xXscgGzaRJ9n/PjwSlj5AUBMJlOBAxD+mfICQAYNGgSnA38katWqBUOHDg25J1u1agVdu3aNeM+6XC5ITk6GGWFWZ1NSUmD06NHSv1evXg0jR44ExthuxthpxtgfDE2mixfpgy8RichDpDDGYONGq5Tcqa36DhmCD6CHH9b+hzE7m/wvvvhCfexvv1ECfuhQ+DEvv0yJnVpfOwD2/DdrhuMHD9Y+53Bx5Qolt2PHhh9z7RpVfNq00U6AlytuybchQ7Ttn5Hx3wUa8it271ZySdq0icxB+vRTAikNG8Z2PUSREuakJHVn9B07cNzIkZF/f3z1nzH0M9Ealy7RZ4ilchMc27fT+dW8Ts6fp3H79kU/7owZuAAQ7f73eKhd8rPPEgCkMMLjiS5hG2/iH7xxwnB2NjqJ22w2hYJQTk6OKsck3nmcO+eWVvi7dUMOhM1m07RvtWoEXEqXFmH/foN0/rFjEYDwMaVLi7BvHybZr7xCXh5btzokUFKrlh+aNfNJXI3ixSnhNxqNMG0aJfWnT9sgPd0GV68KMHIkgoHKlanCIQdDgwcj4Jk82RnWk6JePTzfHXd4JQDC+SQTJuRC8+YEiDhXg3uBMAYwZ074KkjVqv7A31V/2GTdbM6Cnj298N57Trh1K/9BgVyZqrAASHZ2dtzH6NWrF1y6dAkAAEqWLAljwyQF3bp1g7S0tIj3bGZmJiQlJcG8MOTROnXqQP/+/aV/r1+/HsaOHQuMsQqBR04nxlgGY+y7onvqJSIReYsUxhiYzVZo3x6J1l9+GfmPHG/XKFMmevIvD57cdegQfWyXLmim9/zz4d83mUjZR0vP+r//TUnWf/6jfc7hYtUqOtbmzeHH/PYbtv7wJFVLS9L+/SiTGg6ERFvBBkCzxmLFAKZOzVvb0H9LZGQoeTWlS6OZYjiFNL8f4IUXaOzdd4dWHAwG9e9h8WLaX+03ZbFQe1evXuHJ6S4XqkO1aoVtXLG0pPH7pHFj7YT5cNGnD2+nUB+HHh3Yfhdtnn4/ffZ33lEfy9WvkpPx2icASMFHUQAQbtwm53toAQTyeWgFEFRpccPMmV44c0anef+zZwm48OpBRga2+7jdbli6FBP4OnVEyeSwWjURTp60SX4djAEcP47ch7vvpiSfu7mXL68EIOPGUVvTtWsCPPQQnqNTJ9z3ttuoUiEnOXN54ieesEvXlHtSWK1WqVpTp45PAiBc3veBBzwwfDi1St28ifPhXiCMAUydGr560batL7AIqSSZ50ey/t8GQPhvLy+fqXPnzqALkEsLA4DwCHrsTGaocto5zCMpEYn4r48UxhhYrVYYPx4fUAMHRv4jJwjkJL1+vYa/ioGQq2idOKE+dvNmTFyKF4/cu879GWrWjM5HkbtJd+iQN6M9UaQqUJUqkefHW78YQyUirZGRgZ//iSeU8rX9+0dOSJ1OgObNaWy5cgBvvhm//HBRhsEA8Nxz+BurXZsrKKHqVbiwWgEefZSUykaMCP09nD+P3KXZs8Mn2T//TEpg48erJ+LjxlGLViTfEq48VbJk5HmHi8uX6TsPJy//1FPYJhdNRSw9HatwnTurq6m5XFSl0MI1OXiQfmPR5sClj7lMbwKAFHxoASButzvuxF++cddqucqVwWCIiVeS13nw/QVBiDp23Dhqv2IMYMWKHCnZdbuVLuj79yPP5I47/HDihB369uUJvQgmEyafc+ZQRaFHDy/s2GGHf/+beBFGoxEeeoi4I9nZlOA/+qgnAM5FCYDw/axWK3Tu7Akclz6jfGvd2hMASARAeIWja1cvvPMOgY2dO3FO3AuEMYCHH/aEJNPLljkVHik3bhQdAImVGF5UAKRt27ZgDRBRa9asGbYFq3Xr1nG3YFWoUEHRgsUj6LHThqH1wowieOQlIhF5DgmAbNlCq81qySt3hg5zv6nGyJHofD1tmvo4jwcrIIxF5nnodASEtHhoyAHQt9/GNm8enI9hMBBfY8CAyAnrtGnU0vPjj7GfT6dTGgj26RNZftjrxRYkft0Yw32/+CK8dPB/W2Rno1ITr2zx+W/ZEhkwpqcTcb55cwQYwWMvXiQlqOrVQx3rz5xBMMEYepa4VdS6vv+e5rZqVfgxGRn0GV5+WfvnBwCYMAECrRWhYDMzk6pqWqRvGcN7TQ1MbdyI40qV0mbMyKsl0aqYPh/dH8uW4WsJAFLwoRWABLuY56UCwresrKyYZX3zCkC0fg673Q4vvkj8hfLl/WAyZSsAiN3uhnLlsPpx6FAuHDmSC9euuaFLF2qxKluWgMJXXylNFNetU3pnGI1GGDDAKVUUBEGAihUxwX/7bZdiX7nRoCAIcMcd/sD965M8KeTcms6dEWxUqOCXKiRLlmC1JTXVB1euCDBjhgu2bLGD2YzHlHuBcINC+bZjh0MxpyNH7PmarGvZ4lWminWzWq0Kaeh4j5OamgrewIO6T58+0LZt25B7smLFippI6MHgxWKxQFJSErz99tvSa6aAQkjQY6clQwDyQhE88hKRiDyHBEAEAVdtGVNPmOWJSzQlKnlw+c5q1aK3l8yejWMbNIicgM6YgaThu+/WlmSPGoUr3cOHh2/liRS3biEBvXJlkkT94Qd6WH/6afj9PB5cvecr5vGQ4P1+pRt348bqxxEE5JOUKUP7tGqFogH/jUDEakVlswoVaL7Vq6NsrprE608/kRhB6dLh1duuXUMyN69WBZPKTSYUC+C/MzWiuMmE8+IVwkiJPZeSvv12dUnr4LhyhXxwwrVAvvoqVfzUiOly8L5wofo5+/fH34YWnxG/n2Sao7Vf7d1LwJtXCBMApODD69UmR5tXACJPSjMztbmhF8Q8tOzPk802bdyyBFuERYscihYst9sNWVnKfTdupMoH3/e11/DzHzpklx0Pyd16PSWnJpMJevRwB55PImRnC9CokQ/KlPHDggXKakOwE/qdd/oCCzB+eP11NyxZ4pIS5+zsbOjRA6s1t93ml76DVavQrb1yZT+YTKYQ7khOjiBVXFJTfSHJtNUqQN26JEX8zTdEVE8AkPDHaNKkiWRCuGzZMihZsmRYGd5dQWVoQ1CP9IsvvgjVq1eXjgWAMrzFihVTyPA2aNAADAZDMAAZx7AFq3cRPPISkYg8hwRAAAD69sUHUMBPJ2w4HLTKqybbGxx6PbW6bN+uPvbKFXq479gRfszly5jkMBZZtlcely4B1K+P42Mh+JrN1KoiX8zg/fplyuBKe7gwmeicjRvjSn888d13dM3Ll49uwpeRgSvqyclYAUhOxnksWhRdarUwIisLE2QuU8sY+kosWqTOLfL7EbDw771evfCk9Js3iVNTsWKoXK/XiyaKNWoAtGunrngligha+Rwjtd0dPkyfJRbiOQDAk0/ifo0ahYJzt5tAxeuvqx+HV2mKFVOX3jWZqN1Ly70TS/vV00/zlVZ6LQFACj60AhCtLubBm8vlguxspXqUPIH/bwAge/bkwrffesDpVM51+XIuiUuJ/9ChTrh6NfL8772XyOV8n59+wsTcYBAkNS5uAjhzJpG7TSYTdOyIACQlBUndfPyIEbmSLwc/vjy5TUvzBRZNcExamhIwcKPBYsWIvL51qykA+kW4eVP5/WRlZUFOTo5EMq9ePbyT+bx5VJl54QX6LAkAEhmAiIGVKLfbDc2aNYPJkyeDKIqQm5sLAwYMgH79+inu0TfffBOKFSsGBw8elF7jRoQLAu0eVqsV2rZtK5ka8mjQoAFMmjQJGGMlAo+choyxS4yx/UX31EtEIvIWCgDy8ce0gqvWvsHN8wYNiu2PZO/euF8kFSl5cDD00EORx3Dvjfvv13b+xx+HwEM9Ni7Ip5/Sqi5PeAWBPCOGD49cYTh5kioSEyfGX4k4dQpX6vk83norOnH4r7+wJUfudl6qFF7/P/6Ibx7xhtWKxnxDhmA7UWoqAarXXgsvbSwPQUBfEv457r03fNVCpyMlsvLlw39O3qbEGMDWrern/fbb6O17fj/AXXfhmLvuiu23dfUqVT/CtXZ9/TUEEp3QFrLg4PykaIpvnKNUsaI2qV+t7VeiiKpzHTpQ+xVAAoAURmgFIFpczIM3p9OpaLviySJvYYpnixcIRdrfanVDaiom2c89R34nOG+XVHXo3Zskd5s29cLZs6HHNhrdUL8+jueVg+LFRamlSRAEqF/fH/gb6IH77vPCsWM20OuxmmAymaBlS87VwIS/bFkxsBDlg/79kZPSv3+upKzFt2bNcH4cMNSsqQQM3B8kKUmUOCBHj1KL1ZkzBgVI5FuzZp5A5UTpO8K3S5dsEkDr3JnatAobgORFGlfLJjfJzMnJiRuApKamSgAEAAnlw4cPh7S0NEhNTYVJkyaBIAiKe/Szzz6DKlWqSPK9PNLT06FPnz7QrFkzSEtLg5deeklq7+Kxbds2eOihh4Ax9hdj7Cxj7DJjbDFjrFyRPvgSkYg8hAKAXL1KyVYkuVMATNrwAY7KQFrjiy8oMYxGHt+8ma8QRXYy//lnSsgvX45+/vR0Wj2PpGIVLnw+XClnDFu+eIK5fz9yB0qUAFi6VP2zdO+O59bi4h4pTCbybejSBQGglorG1atoIiivODCGZoPvvIMgKS/k/EghCADffIPAgftH8O3++3FOWpy+//6b5JQZQzJ2uDY+o5HGlS0b3hCStxAyBvDKK+rnNRgIzIwYERnwffUVHfPw4eifRx7z52M7WcOGoa2BoojJvBbQnplJVY0fflAfyxcCtCisyduvZC3JYePiRboO8lbBBAAp+CgoAGK32xVk8+zsbKm6kJcKSH4DkHnzMKkvUUKEffuM0lw5N6V7d0zop071wQcfOKTKRYUKIuzdq/RQOX/eDaVL88oH/vfOO5WViDlz3DBjhhv27LFLqlFDh3qgQQM/fPSRIPl81KmDAKJOHb8EaLhqVd++oXyMFi0QYPBKR6VKygpJhw6kwMUByLVrJPnbv79H4o5wb43MzEzo3dsl7XfrFu7HqyP82CNH5sKrr7rgwgU6X37wJbRs+eHNUVgAJCcnJwSAFFYU9XMuEYnIz1AAEAAyMHvttcg3gdNJidnMUrrDAAAgAElEQVSmTdpvHouFeCbR9pP3s0dSkvL7qQrx4ova5jBiBI5v1y42iVR5G4q8T/9f/yJQlZEReX++6p6UhIZ58YbHgypXfC63365dXtjlwvYzzn3o3JmOU60aCgV8/jkCFnkcP46k8HvuwfcjgU6nE0Hel1+iSAEXCuBbmTJ4ju++iw5AeXz7LcB99xHfI1J7U1YWGQmWLg2wZ0/omNOnqZVtwIDo1ajRo7Ht6r77IgM9m42I7loqe/LIzsZrUqYMwMqVoe/Lf3PRzA8XLSKeiBrHymDQ3goJgH48fA7R2q+WLw9fQU0AkIIPn8+nKXGP5GIebpO3q8jbnbj0bl4ASDyVmEgA5MyZXChVSgwsTghhgc2iRZi416sngsWSA9u2maB2bR80aiSCXh8MuuQABLeRI9XN97KziWi+dKkANWsikEhJEcFiEaBjR6q8TJuGAKRBg9B2qNatcVy5clSBkb8/cCBxUzgAyckRJKCUnCyC0ahcrbdYLPDkk0Q0P39ep/he9Xo9mEwmyM7ODqmO/JMBSLhKkJbNYDBAy5Yti+Q+L+rnXCISkZ8RAkBeeklbu8XAgTjuySdju4GGDsX9hg2LPpbPpWHDyCv077wDgRUjbUnt8eP0RyVWIMCliqtXpyQ8JwcTLsawFStSeL3o8M4YViIiybhqjc2bibydnAwwb15s3hFHjmDSeuedSpDAtx49sJ1o8GCstsjfK1ECAUnZsghKOnRQOrm3aqUEHQ8/jPONRRrYagUYM4aOM3hw5CQ8JwfncOedCAbCJdYWC33WRo2i83H27KFzr10bedxHH6HRZtmy6gA0XCxZQq1Q4bgvvNVRRckRADDZ5wsCs2apj/3kE/oNahFjmD8fgapaKyQPfm+PG6d8PQFACj60ApBgE8Fwm8vlgqysLEWCKndO50mcwWAoMgDC97dYLPDnn1aoXt0HSUkizJwpgNEY6nFy+jQ3ABTh3DkEVidOGKF7dz/89FOoi3yvXpyngYl906Y+hTxt8CZXkjp71gwVKhBXQxAE6NePpIBHjvQEFqOUYEEQBGjXDgEIB1TBHJHJk4lQf/06fgeCIECZMsQr2bQp1O180SJS7jp61KKojgRvvDoi93gpLABS0K1e8s8ULwC5du0a3HXXXUVynxf1cy4RicjPCAEgv/+Oq/StW6v3nHNVq7p1Y6skyFW0ovX9X76MY9PS0OAvXJjNtNKuZqIoD84dkRNltYReT0m/XD1o0yZKVtUI4pmZVNW56y5t/fdqcfWqsorRvTvAjRuxH+faNeQfjBpFak9t2oQHJlq2ESMQjG3cGJthJY8DB6iyxRgmv5GkYgWBrkHx4uFdzP1+bFfjgOjUKfXz5+ZieyFjyDWJ9PvOysLKV9Wq2kwxg+fEXd6fey70/Vu3iBuyYYP6sfbvp2t14YL6WK7MNnGitnnyFrBo7Wo+H90bweIUCQBS8JFfAMTpdCqSU5PJFCKxm58AREslRm1/g8EACxbkKJ4/NWuK8OGHHhAE5T779uWC3U7znzPHFqiYinD9unIsb+lq0MAPJUogGGjXLjII2brVAU2b+qBCBT8YDCbJaLBcOaxyPPwwgo4BAzxw/jxxNtaudcClSzbpOB07eiXgwish8vP8+acA06a54O23BbhxA6tQgiBAjRoEQJ54Ijco6SbyOmPkD8KrI2azWdFmF24zm81xJ+z/NABy7tw5uPfee4vkPi/q51wiEpGfEQJAvF6AmjWjJz4XLoTv944WTiea5WkFDA88gGPVlLk4ubxjR21z+P13mvv+/dr24fHhh1R14ImsKFJFqF499aR7715qgXn66djOHS48HpQt5tyWypWjcwDUQhSRvL51KzqEz5xJ5OZw24MP4rhNmwD+/BNBWrytsV4vVnL49SlbFnlDkY7ncBAnplixyLye116j+WrxgeHtTMWLq/+2X3mFqgmxyO4CAGzbBoFV0PAqau++i5Wn7t2jVypeegmvw5Ah6uN0OvqdqJkU8jCbafy+fepj5SpgwZytBAAp+NAKQLiJYFZWVsh7NptNSgQ5OAjn78Fbs/R6fdwAJJZWMPm2bp0HLlxwKxLms2d1EqeD/5cxgIYNRfjiCw84HMpj5OTkwMmTesnFfNasUP7Mzp25gee8CCtXOiU1rPvuC+VtCIIAs2ZRZWLJEkECLTVqIAA5etQOe/fa4cIFBBtyQHD33XRM7rLOVbNq1QqvWsWBJAcgXL43KUmE5593h4zv04cqMMuXO8FqFWDtWid06uSD69cFCZBw7oj8dxBcHTGbzXHzJyJthUV2l6ujxXuMY8eOwf1alW/yOYr6OZeIRORnpDDG4MIFpaFH//74oPrXvyLfCKJIqkxanJTlMXUqtvhMmBB97NKl1PYUqWf/yBFKfv78U9sceOLav7/maQMAJsktW2JbzJgx9PrVq6R29cIL6sfgCa7WhFhL7NqlNCKcOTN6hUlrWCxK0JGWhvyE/IzLl5Hgz8/RsaO6k7jLheaMPImPZNC3fTt91+EqDcFx/Tp9j2rtTLz6wVhsbvc81BTcuJoUYwgu1MLvpwUDNSEEAFK50+LFA0DVyrJlo1frOC+pRYvQ9xIApODD7/drSuB5a5XZbJZec7lcit54nU6nahDIE0adTleoAOTw4VwoUUKEChVE2LaNVLlycnJg1Chf4Lnhh6lTfRIAYAygeXM/bN7sAZeLAMi4cfbAvSCC2Rx6LovFLR1jyxYHrFvnhCpV/PDrr/awSekPP1ALVkqKX1LPatIk1HdDDjTwbxABkK5dla7tTZuG3z8YgKSmEsekbt1Q0PLJJ9SC9eyzbrh5kzgrM2aEAhbuO6JWFdHpdBG5I/9kAPLbb7/BcLV+6wKMon7OJSIR+RkpjDFYs0YJQBYswAdV69bqN8PkyTguSO46asSS2GRk0MNYjWw9cCCCGq1k9B07UH2oVy90xI4l+Oo1Y9guxOPdd2k1/sSJyPv7/chp4NcgHpPCcKHX43fRsCGS/WvUQNJ5XhWuRBETy2LFMCFWMwmMNXw+TIyrViUw8fLL6qv+ublUcWIMqyThQqejlrIxY7Ql3ZzHULeueiVr7lyqOAlC5HHh4uJFqixs2xb6/smT9Nn++kv9WHKSeLT2uyeewHvk2We1zXPiRDzuwIHRx3KQN2NG6HsJAFLwES8AcblcUlWEr3DL+R4FBUC0cFHkW3Y2yeympXng8uVMxefYvTtXug9OnMiF8+fdMHq0T6ok4EKZR6rgLFhggWLFRChWTIT5870Q3K7ldruhVy8v3HOPV/L/yMgQYOrUXLjvPi/s3KnkWZw7R21VjAG0a+eGKVPssG5dKB9DEAQYO5bm++ijHun1OnU4kMB5h3MuDwYgly/bFJWfsWNDCfNXrwqyY+L53nwTlbFKlxYhPd0Wsk+wZ0Z2drbm6kisgKSwuCYcgOh0uriPsWPHDhgjX30sxCjq51wiEpGfkcIYg6efVgIQ3lOelKQus7tlCwQeYNpVjQCQAMzbbMKpFQUHJ0GrtSzx1d3q1bUlmqKIAIsxgGee0T53vm+3brhvr170usdDBOxoDu3Z2QgUypZFZaj8Sur9fgQd3CmcMbx+aoBIS9jt0X0oYo19++g7uPdebF+L1urj8RBIwHaC8OP8fqqQVKmCnIpowWWdcdUz8rjsbLq+Af+omOK553DfO+4IDw65YpoWoZVZs3BsNNEIp5OkkH/8Mfpx5RXODz5QH2u3ozABY+GNDRMApOBDKwDhCZjJZAKHwyFxKXgyH67lKniz2WzSPoUFQCZOxMpAqVIi7N5tVKyYI5ByQ9Om/sDfCWqpOn48F4YM8UHFiiLodG4JgPTtS8Z7CGr8sGOHkowuCDZFq9HVqwKUKYNJ/LvvuhRJaU6OALfdplTO2rgx8mr+/Pl4/uLFRbj3XgIgdesiAElOFqFxYz+MHk3vHTxoh2nTcmHixFzp+ul0RujZU1k1WbzYFfac99zjhaFDPbB9O1ZxDAbijjz5ZChokQMQfh2++MIJf/0lSNwR+e8nuDpiNBo1VUfywxxQ68YBeF4AyPfffw+TJ08ukvu8qJ9ziUhEfkYKYww6d1YCEJeL5HLVnJJzcsh7IJJjeaTg7TbRVHsAkGPAGCovRVrNz8ykVeXdu7XN4YMPaBU7VgAgV0mSV2YOHUKTvZYtoztiHzlCSZ6W9qBYQq8nbgz+QQOYNi1+N/b8jJs3AR59VDm3Z56J3jLm8yH5n1Y0I499+20aF805HgC/f+6efv/96jyWV1/FcZUqoVpXLGG3o+pVpPnLfTcWLlQ/llz96s031cfu2kXVOS1zlnt6nD2rPvY//0GuSr164bkwCQBS8CGKoqZEnpNw9Xq9YiU7JydHE/jILwCixkUJ3q5edUl8jSFDnJCebpZauOQA5r33vIG/E6LE+9Dr3fDQQz7Yv5+OZ7VaYd26rMAimygRvrEa4ZMI6Xa7XQFAXn7ZHfh74QedLjQxbdnSF1hYwHl07BhZunfDBmrZ4l4hgkBGg1w9S76tX4/73HabCBkZ+PlnzrQrwAdWVcNXXeSbxSLAokVo0Fi8uAjvvecMGRPsmcHlg7t08YLFogQQWqsjFoslBJD8rwGQb775BmaEK/UWQhT1cy4RicjPSGGMQenS1pCWl65dtfWg80pArAk0JwZHa/MCQJUm/nBV4x7wNhCtixNZWbQqHA8XgysK9eihTFjHjcPXGzQAcLvVjyHng2gFTrHEgQMAbdvSOapWRS+PgjAejBYuF1YMOMeCX7uTJ6Pv6/fjdS1RAlf71ZLzw4dJQUprdeu99/A3X7++uqmlxUJqT/Pnazu2PDZvRoBRpkx4MLhvH12ba9fUj3XuHI2N1kbIqypdumibZyRPj3Dxxhs4tlOn8O8nAEjBh1YAEtzXr9PpwG4Pla1V2+x2u7S/VtASLwBxOBxw+LAZKlcmjkOxYiLce28uLF5sgStXiEOSmemGJUs8Cl+PRx/1BX7HIlitBECuX8+EKlUQ1Myc6ZWMChkDqFhRhI8+8oDVSgDEahWgVStfxGqBIAgwbBgqXQ0b5oIxY+yQnm4Es1lQOKjz7fhxatlKSSEAwsnkwf4fgiDAlSu0z65d2XDhgg6qVfMH/o56Zc8C9QT68GE7dOhA15MxgIEDPSHjggHIjz8SaHrjjVDeCAcTOTk5MVVHChOA8N+dXq+P+xirVq2Cl19+uUjuc5VH0DXGWBZjLDPMZmSMiYHtnoJ9EiYiEdojhTEGjFnh+HHlD517cETzIHjnHQQhQ4fGdiPJe9cjOZ3Lg8uBPv985DHLllGSrdUTg6/Ey1uptIZc/lQOHq5dowrSRx+pH8PnIwO9unVjc5bXGj4fXhu+8o4riSiXmp98jkhhNiNg4DwSXs369lttilmiiCpofO5qbvI5OVRVats2OgAEQA4H56BE4xBx4FyxYnwkf+5CPm1a+Pc5r6pbt+jHeustHNu4cfTr2K5d9Gsnj0ieHuGCq6Q99VT49xMApOBDCwBxOp2KpNBoNILT6YwZPOQHAAlHhsdju2HzZo9UadHpdHDpUiZUqoSJNvfH4FuJEiIMGOCDVas8YDQqz/HNNx5p3Oefe6TXBQE5B+PHY/Xgrrv84HK5YdUqD1SrRsf/5RenBECysgRo3hyT9jJlRNiwIbTKsGaNE2bNcsN335FM8Zo1TihTRoShQ5UJfna2IPFT5NWORYuc0LWrF0aNCg9yatfG6/Duu/gZTp0yw9SpufDYY6TCdfYs8TkOHrTDunVY3TCZBJg50y0j6IsSWX7jxtDPE841nFdBSpQQ4fffwxPygwHJlSsWMJlMEasjckWz/wUA8vHHH8OCeHpv8yFUHkFXGWPdI7w3hyH4+D7fH3yJSEQeQgIgn3yi/KHzfviSJdWT1F9/xXHFi8fGA/H5sIWFMeQsRIuFC6mqECnZ0umIW6JFZhQAgQN/cF+6pH3+PDjPoEsX5byefRZfr1YtOkn56lVSVHrssdjnoDWMRjSOrFABeRHYToDVq2j+EfHE2bOYUN92G13j++8HmDNHuz+IKCrbrmbNivz9iyLyaRhDbo3Wz8QVnMqXR7AUKRwOlIWuVg1X/WMNk4kAWDgTzNxc/D4Yi8xtkQd3tI+mumYyUXtiJD8defj9KGZQuXKop0e44E7wkWS1EwCk4CMaALHb7SFeD/GCB4fDIR0jHgCjBkCee84bAL65kJFBK+bvvuuWAMjKlR4YOdIF5ctT1YK/N2SID7Zty4WrV91QubIYWGzxSQpYcgDy449mad9z5/A9nc4NEyf6YNgwX0gLVpMmyqrBrFluyMkJTVA5P8NgMMD48Ziw9+sXSiZPSaH537oVPfE9dcoGSUkilCghwmuvCQoVrG7dqALClbq++MIJSUkiVKwowtatDsX809J88NxzeE0rVBDDVmnCAZBDh2xS5WjcOHV3eKtVgIULXVChggiHD9ulY6pVR/hnysrKKhDfEQ5AuIFjPNs777wD77//fpHc5yqPoCuMsR5hXm/KGHMxxiyMsVoF8exLRCLiDQmAPP648odusVDSouaVkZOjrT0qXIwYwftuo4/9+286j5oyUM+eOEar0ZrfT73/c+Zo20ceBw/SvOQO3EYj+Z1oSVa//JKOE814Lq+Rno6qULz9jG89e+K5462KiCJyBz77LNQ7pEoVVLeKxS1cFLEiwY/xzDPqK/0rV9JYLaAWAH/nvDI0d6762DVrILACit9vrLFiBVVPwqm//fgjgXmTSf1Yt27RZ/39d/WxXHWuXDlt7ufp6XRstXY0AKVK3enT4cckAEjhRKRknyfcwavO8QCHggQg69dTxeL55wUpUXQ6nWC1uqFOHTHwbPeB2WyGK1cy4euvBXj4YR+ULUuVi3ff9cJvv+VCmTIiJCWJMGmSFw4dypVACF0PHdSvL0KlSiJs3UoVko8/9kCXLn44etShACAvvOAOLFTQufr29Uo+GuEASIMGmKwvXBhKDG/UiNqtTp4MVaEKTuZ79yaQ8f77VgUAmTePSPVffYXVjOvXBahQgQwNeeVi9mw3mEwC3HcfHm/UqND2KzkAOXtWB4sXO6FjRzkAE+Hxx9UBSGamAI0a4edv1MgfYuIo545EAiN6vT4idySeTf7dxHuM1157DT777LMiucdVHkElwryWxBg7wBjzM8YmFsiDLxGJyENIACQtLfTHPmwYtoIEV0eCIy0NH0offhjbzbRqFbVMaeEkPPAAQJ066v3/n3xCCa+WZAuAZIdr1dLeuiWPAQNw/969lQkyb9cpXz56wiqK1PZSqZI21aa8htmMRGhOZOZbxYoA7dujT8vSpZjgcnKx14sk5sxMrBgdPoyJ+bhxRJ5mjPg4zZsj5ySW6hiPefPoeFOmqIOPq1exla1mzdiqSPwcFStGb3/jvKixY7UfXx68WhYM9nnMmYPneOCB6Mdavx4V12rVUldbA6D2tUGDtM1z3Tq6L6O1dn3/PYGySPNIAJDCieAk3+VyKTgfer1eIqHnRULX6XRKx4wm2Rtpk6txud1uOH06V0rse/Z0wa1boapcS5d6pCT62LFsBYDJzsaWq2HDfHD5shs+/dSjeKYxBpCa6od587xw8qRNuganTuWCXIL3yhU3pKTgPCZM8CgAyLZtyIFIShJh4kSS0W3Y0A+HDtlDktxLl4zQujVKAYcjhnfp4g08e4gDcvWqALt320P8Rr76yqn4LNu2ZSsAyP79REZ/5RUCO/PmUeWobVsf/PmnXToPJ99v2kRzy8kR4NgxG5jNAqxda4cBA1wKTxXGyKV90iR1ACIIyDfhymH9+3vDVozklZaCro7kBwCZNWsWfK2lNFwAEeNj6RmGrVd7CuCRl4hE5DkkAJKUFKqOM3astj7wMWNw3OjRsd1MN29iy1Tr1uQqrhb/+heeZ8CAyGMMBmrD0qrMlZFB+2iRKA2OQ4cAOnfGlpXDh+l1QcB2HcbC+yMEh9FIZoJ9+8bvKA4QPSmVhyiiqtfIkSR5HLwVL068FvkW7rVatbBVaufO+D8D5zcwBjB+vDpAFUVsGWIMwZRWbobZTK1v0QjlZ89qrziEC3n7VThlOVFEwjdj0QE/AMCoUdqrhw0b4thocro8ZsyAQMIQfSx3g1fjrCQASOFEbm6uAiTIV5ZNJhM4nc58UbDKbwCSne2GtDRcKa9Z0wenT+vCurDbbG5o2FAMLAK4pIT1xIlcePppr0Qyd7vdcPWqW/LGuP12f8gzql27XHjjjRxJ8YpvDz7oCzzDRLh1S9mCZTYLUiXhiy+c8OWXTim5fvrp3JAk98QJk3S+nj09YLUqE9m33nIFPjMBkCVL8LVmzciAMCNDgFq1lJ/h/HmlEaHFIkBKigi1avlh9WoCFN9/74BixbANa8cOAjU7dzqgalU/VKxI7VeXL9vgrru8ULKkKFVO+FahgghPPJELTz+NgKZ0aREuXVKv2vDtyy8JPM2ZE0pe56A4MzMTrFYrXLsmwOzZLjCZssFkMoW0DsoBtclkiqk6EmzgGM82ffp0+O6774rkHo/hkVSfMWZjjDkYY40K5qmXiETkLVIYY9CihRWqVw9tteLGeu3aqd8U3IPjzjtjv6H46vuyZdHH8laS8uXVE+zevbE/Ppo0qTwGDcJWrJkzte8jDy4rHOzu/uGHlKhfvx79OP/+N47v1Alg7Vrle1YrmskFCwYEx82bWHmIR1VLELCVbNEiBCSpqdiKl5oaHpgwhgIBDz2E3+H583kDTgDYwsXb/0aNig6m1q6VrwxqPw/30KhSJTpPhyflTZvG9/k+/1y9/UquaHXxYvTj1a+PY6NxRa5fR78RxqLL6fLo0QPHaxF64cBPDWAnAEjhBAcgwXwPeTIvByDxckBcLpd0jFgVtPjGk05UQxKgUyciUVeu7P8/9r47Sooi6r6WLGEJkjMKLkFyBkWQpCiIBIkCgoiiJAURUYmCiIqKoAiKASSoBAVJCqhkJIcl7gILG2Yn7OSe0H1/f9RUV/dMz0zPAsv3fb9559Q5MF1dXdM9XftuvffuRefOIiZP9mHVKi/OnRPk9Knvv/diwgQfLlygAObaNaMMXvr186uu8corFExUrSrh8GEP3nzThxo11I51vnwSnnjCD6dTwPr1PGqyZo03pAbEZrOhRw9vYLPNK+/wDx7sxaVL9hAnNyMjSyUQuHSpmup2924etThyxI6UFDu+/94VAkoY/W+RIpIMBrKyDBEd6dOn7Xj2WWUUSMLixerrm802HD3KQUlqql0FPPLnl9C9uxvLl5thMFhhNNpQrRq912PGRI5+GI027N3rwEcfuTFwoFce9/HHQ6MgSgBy86ZVTtt64w0OVnJycmAymXRFR4KfmbIxUH47AGT06NHYplXElwcWw5K0i9DUq8l3acmLW9xu2xIJIahVywpCqJOktG3bEFj4IjuBhw/zBT1WnYmBA+l5I0dG75uRwa9z7Fj4fgw4aaWVhbMffuB58tHU2bWMpZMVLaqOJAkCdRYLFNBP28oK2MuWVacFsbqKunVpQXQ4YyrrBQvqKyKOZnY7dV5376bP+uxZICWFRpscjtsHHEpjTGbt2tEaoWgpcQYDL6gfPFj/dTIzOR3whx9G7ut28+Lw3NYedusWOZrIQHz16tHvp7L+I1rkcO1a2q9aNX3PSRR57dLGjZH7ShK/96tXh+8XByB5Yx6PR5XOwrQOlE75nWCwUgIQu90e8/nnzgkwmXgq2KlTmShQQA0MglvJkhI6dBAxYYIP333nxcGDFty4kY4+fWjEoHBhCjKU17l8mTM+LV3qDcxdwK5dTowY4ZCpfbt1oyKOf/3lCTj5tJh9/Xo3jEa1M/vxx2489JAf06dz5zgz04aKFUV07UrTu5RpPg8/zOsmiheXcPo0ByppaTbVd5w2TcCWLc7A2i3JERODwYa33hLQqxcFFE2b+lWO9IkTdlmX4+ZNGyZOFFTAh//NjB6xoFoj9NwJE/hztlqtWLLEJc/t/Hk+ltVKAc+337owdqwHrVr5Q9jKWKtYUUR2duQIyKRJHIyuWBGqT2K1WmGxWHIVHWH3LTs7O9cAZMiQIfgnUmHsXTSdy9EoQlOvjhBaBxK3uP2PtERCCLp2pQAkmE0nLY0vHJcuhX8pBIErIe/cGdsLxcBCo0b6+tepE90RPHqUz1uverdSnT03kQOHg6fzfPWV+tg339BUrMKFqfZINDMYuNbExIn889OneeH4yy+HPz89ndOuEkKpVx9/nDrBgg5a2ntlDMQRQkGUnrkOHcqjGLEUhi9aRJ9XhQqRwRwA/PQTj2JFYskKZ0YjT7/askW7T+/e+oH4unW0b2Ji9OgQo9PWSzOdnMyfwY0bkfumpOhbH+IA5O6bJElyYTdzfrXSo+5EAbkgCLkGIMnJlJ3qySe9uHCB07JOnUqjAQUKSJg3z4vXX/ehUycRpUuHByb9+nFdii+/9Gpeb9QoCgBq1pRgt7NULhoFungxHZs3e/HXXxS4jB3rC7lGuXJ+vPqqC4cOMY2LUIf0/fcpCLrvPgmXL9tVTEssCsGK5Nu2VYv4MSVyQoDRoz1Yu9aBihVFNG/uR3q6+joffuhGrVoinnvOC4PBgJs30zFggBv58klYvtyFTz91o2xZPl7NmiKeeIJ+pyZN/LodbAYA8uWTsGkTU6znkQlWfH7ypB3duvlCFOCVrVYtEU895ZUB0auvhkZOlACE/t+G7t19YFGfPXsi0/2y6MjNm+GL2bOysmAymWTAcjsApE+fPjh69Og9ec91LEUVCSFmQoiHENLw7q56cYvb7VkiIQRjx1plp0/9R40zBEVLeWQ6HbGkPQHAnj0s3KuvUHnUKNo/ku6Iz8d3cX/+Wf9cWJFxJK2RSPbii/T8ChV4Tcgvv9DvxSiHI6l3K42lbuXPT6MPokhpZRct4ov75s3hz7fbaQ5/8B+EaLok98p+/JGnXaUmoE8AACAASURBVHXvrg98bN/Ov1c01XmlsYhGsWL6IhqMWS2WCIvS1qxhu7ja0TWfjwPOSJEEZoyWuHv36H1ZipTe3/SPP9L+5ctHj5hs3Uq1XTp0iFyjEwcgd998Pp+ssxBcvH03AAi7VnCEJVIzmwU0biwG1kg/TpzIDDi3ZlitbtSrR4+1aSPC5WLRFgEXLghYs8aLN9/0oVs3UdbqYMXQHTqIOHnSA4cj9JoXLggoUIDqXezcyVPUFi2yoHZtH44eVUdmmDZH48YelUI6jTx4sHChHampfFf91i2bTEs7caIQSD/iAGTKFCEwHi1Gf/xxn4oN6tFHOeh59lkvxo7lu//BkQJlxIHt5LMIkLJGJDFRwty5bmRl8dqRuXNDWbiU7coVGsEYPtyDGjW45sqOHQb5OW/f7pSjPDYbTdlS3p/ixSV07uzD1KkCfv7ZidRU2o9FbqpUEUNAlc1mUxElsM9SUzmDVrlyoirFTasdPepA7doiVq92Ro2OsAgJE0KMFYA8+eSTOH/+/D15z3UsRRsJTb2ac5eXvLjF7bYtkRCCRYsoAHnoodAfPFM6nzUr8osxdiyNgrz0UmwvlJLG9+DB6P0ZFer990d2kJjjNW6c/rkwNqx69fSfw+zQIerQBjv8zPFjNQQPPaQvFcbrBerXp+fUrs2pgt94gwOLsmVptCPSGIyNirXy5fVrcOSVrV/Po08dO0aPSAAUYLE6iFgL9hnLk56IhpL+ec8e/ddQ2uDBtA4jnPigMoVRjyhn8+a0rx56Z0ZqEFxPFM5Y+l8kogdmH39M+0arEYsDkLwxu92OnJyciKlVd6KAPDcAxO0WMHAgjUYULChh82ZWJ8HZuPbs8cgA4NNPtSMabKyDBy2oVEkdsShUSEKjRiIGDvRjzhwffv3Vi+Rkyoh15gxPzzp61In77qPO7ZAh6rqRp5+mznLr1gJOnszCe+/ZUaeO+jpFi4q4eDEDWVlZePttZ+AzCampthAA8vXXNG2pcmURO3c65bSqXbscSE21Y9Qozqb1yCM+NG7sl8FVJCeYAZD9+02ymGCNGiJGjfIgJYU669u28QiRUpzw5k0bbtywYc0aF15+2YP69dX6Jso2ebJNBQy0oiXVq4uBvy0ikpPVQOHnn/kcVq1yqeZw6JAD69c7sXChE2PH2tGrF6X6rVhRlH8HhAClS4tRC9579uSRJiUjmdVqhclkgsFgCCuEyKIjkWpHlK1jx464du3aPXnHoyxDzxGaenWeEFJI43hxQki5O732xS1uubVEQgi2bLHKu+3BO7Qvv0wXgf79I78YX31FncjbKUT/4ovofVNToVhUw/djLEqNG+ufx/HjfOzUVP3nATTSobWA//QTPa7UVti9O/p4Xi8tjA8er39/6qQydq1u3cLvPl+7xtPClE1PcXFe2cKFHHy0a8fpfqPZggXMGaCpQLFYx470XD0MUosX0x3+hg1zX+tSvTq9XjjqePZbffjh6GOZTPw5RksVTE/nfc+e1TdXtuEQTRMFoJsOetaGOADJG/N6wzvt3Hm//QJyQRDk3WWr1aqr/0cfcSf+gw9yZGcwWI9kzBjqDJcoIeHKFf75kiVeWSxQEAT8+69VdrxZcXa4VqyYhJYtRQwf7seBAx450lKrlg9ZWW75vpjNZmzaxJmr9u6lOiA5OVb8+acdw4e7kZgoonNnfg9XrjShSBExACA8WL3ajqwsXgNy5IgDQ4d6MXu2IIMPs9kmpy2NHMkBSJ06frkWplQpfQDEYDCgd29vYHPLr0rvYuCmTRufHDkZPJhGdpQOPmuJiRJ69PCiUyef/AzOnMmICEBsNhvOnePihM2a+WEw0M+zsmyyDkq3bj6kpNjRsKEfpUpFfl5abceOUBpjZUtNtaFWLVEGYgyEqSNHVvl3Fw6MZGRkBJjZwkdHWrVqhexoQk13ySIsQWUIIVmEED8hpG2YPsMJISvv9NoXt7jl1hIJIbh0ySq/6MnJ6h/8F1/Qz6MVdDNF8YIFY9fSGDSInhtOH0FpksT1JiLRle7bR/skJOgvjJckruqsR4k6+FxGW6xsSuf48cfpZ889F308JdBStldeocd//51/Fo5eVSluGNxi/X53w2bP5vOpW1c/fW5qKi3qb9aM7sLHYhcvxhbRaNmS9p02LbbrMFPWUYUT6mO/iwkToo/30Ud8vGDa7GD74w8EHDR976Qk0XSz9u2jF6ADvLA+2r2JA5C8sf+pAOT6dbdcA1Clig+DBrkwcaIbM2ZY8cUXOfj9dw8OHvTg8mUB168LqFZNwsSJPphM9PwNG7yBnXAJJ07QSIbFYsG4cTQFqFAhCbt2efD77x4sWODD8OF+tGwpqsQJWXv6ab98zo4dBrjdbrjdbjlqcetWOpo3p9fr18+n2hXPyLBh2TIXzpyxybvqL7zgCLlGrVo+zJ2bgytXMjWd2AMH+DkrVvAIgVLg8MEHI9dsGAwG7NhhwNCh7N5KGDbMo0rbSk62Y948txx5MBhsKFeOp2oVKSKhUycfZs4UsHu3A2azDRcu2GVw9N57PF0v3DzS0mw4eNCBmTPdMiBkNSLHjtlRq5aIIkUknDplh9lsC0lrK1ZMQt26fnTu7Mbw4U7MmiXg229d+PFHp0x53Lt3KIWxVjtyxCHfw/btfZrq7ux3y7RE9ERHjEaj6nfQqFEjOPWE6e+CRViCVhEa/VhPKAWvVnuDEPLtXV4K4xY33ZZICEFOjhWJiXRB2LRJ/YP/+2+2OxI5L//aNW2nW4+xVI6GDfX1Hz6cOoaRdmkFgTpehMSm7TFyJPtDpf8cZh4PFcPji6t615zRCBcooC/V5u23Q4GD8ju/+ioCf0y12ZCcTsrw1L8/TedihdCs9ekTm17InTCPh4Kntm35PIoUiU0hnT2jmjVjZyxjyup16kSPaGRn87qU3JKesILxkiW1I1UeD9f/0PM7bdCAP/NoxlIKW7bUN1ezmT+T06ej92f0vitWRO4XByB5Yz6fTxd4uB0GK9YYHWpOTk7IMVa/Qf/twtWr2ahRI7TAO1wrXlxCrVoSWrUSMWmST6Zw7dhRlOs8LBYLUlLS8eCDdNx27UTVddk8kpMF/PqrF3Pm+NC5M081atdOwOzZOVi/3o29e424ciVDrkdZtYpGDjp08KtYsFi0YsAArhy+b59dNaYyspCYKOLll+04eTJbdmKtViu++ILWbZQrJ+KJJzhVLlVtpzUgzZv7Fddw4K+/aFpRdrYNX3/tkkGSsiUmSjCZIjvp69Y55SjLc8+FKqAPG0a/Y+XKIq5eNePff7Owbp0RX37pwvTpAoYP96BzZx/q1vWrAJOyLVvGU60MBpoKxv7/xRduzJvnllmyunXzwWAwqVjbzGYb2rb1BQCriGvX1HO0WGgK2blzdhw86MDOnU788osTK1e68NJLPI3vxRdDC94Z0DCZTCHREYvFAqPRGLZ2ZNGiRRg5ciQ6dOgAq2L3Jz09Hf369UPdunWRlJSE0aNHw2aLwusesPPnz6Nbt25o0KAB6tati6lTp8KroaC8f/9+tGvXDoSQU4SQc4SQKYrlpw6h4EPU0b7Jw2UxbnGLaImEEFitVnmnd8EC9Q//1i1OtRmJ6cbv50xYu3bpevdk27uXnpcvn778/w8/RGCRjtyPpdoEs3tFsp9/pufcdx8tVo7VzGZ6LnOslebx8Jz899/XN97SpdwJDgYgLhetV2nRgoKJaA611wt8/TUHZoTQ3fdYnP/cmtFI0/kYnS1r+fJRDQy9dvEiB1IrV8Y2B4+Hp65Fo94FOPtViRL03uXGJkygY4QT9Tt2jB6vWpUCnkj299/8t1CqVPRr9+9P++qty2JzISR6KpzXy5/D3r2R+8YBSN6YXgCSmwJyvQDE5RLw1FN+vP++D3Y71SO5fDkD1apRh7J0aQkDBvjRrZsPTZp4UK2aTxbz02qMBatqVQlpafw6jG540yaz3Pezz9QRIMZ6xdrFi0LE9J+yZUW0bCmib18/hgzxYfFiARs3OnDjBnXcWb+FC9UF3Yxq99VXPTh+3IZRo5woWpRHGjZuzFal+AwfTutCGjRggIjP6bXXPIF1maZNvf02BSRt2vjwxhuCnOrEWqNGPlSpQj8bOza6MrnNZsPnn7vl87/8koOFI0ccchRjyRIXli4Nje6Ea0WLSvK5Xbr4os5BKU44YYJLBUDeeUeQAdmcOW40b+7HQw/5UamSdlQrXFu+PDRtKxwA0UrXMpvNquhI+/bt2TqGwoULo2vXrliwYAHq1q2LlwKLrM/nQ/fu3dG1a9eo72tWVhYqVqyIuQHmHpvNhiZNmmD06NGqfufOnUOxYsWwatUqBJabyoSQNELI2/dswYtb3O6AyQBkyBC6oxxcHyCKdMeekMgpK2YzLwp++WVa6KvXsbVa6Y74Y48BJ09G7795M3cMIzndM2bQfm3b6psHQNOAmGP199/6z1Pa6NF8EQwGVCyqUaOG/ugDi5wQQiMZSvvnH35s7Vp941mtvEifEAowf/9d37m5tVmztP9IxCr8yHRjkpJiT/Vj97FgQaphEs2GD6f9e/eO7TpKY+xw4QrGGetU5cqRx0lJoaQD7L4VKhQdFNWuTfvqTbdjdUxly0bve+UKn8vNm5H7xgFI3lheAhBWg2CxWFSfv/02j3QwCtf09HQcOuSQU3v69/fDZlMrspvNFCDs3+/B5s1efP21F40aiYHfuoR9+9QaHwyAZGVlYfRovwxWjEaWaibg2Wf9GDHCD4uFfnb2rEfWBalRw4f69b0oXjxUJT24rVrllFOXOnf2haQDMRrecuVEmM20CD05OQMzZljRowdlzrp2jaf4TJliRdGiIvLlE0NSkjZvdmLLFid276YRj127OAho1swn34/+/d34/fds7N7NAdi//4bS1VqttD7i0CEHNm1y4quvXJgxQ8CDD9J7VqSIJLNV7d7tQIMGftSrR2tJNm9mOiV0jg8+6MfAgV5Mnixg0SI31q934sABB65ds8nF+6VKSbh4UZ9C+htvcMavJUvMyMzMxMGDDvmeTJokYM0aV9Tnk5AgITFRQpUqImrV8ssRkJ49QyM87LdvNpt1zVEZHZk/fz46dOiAfPnyyUCkePHiKFiwIEwKfv3jx48jISEBO6NoEkyePBnlypWDX+EIbNiwAfny5cOFCxfkz/r164cmTZoAABRLziRClc5L3pMFL25xuwMmA5Bp08Lv1LKai3BMOmfOcH0KZevUKbSv202dqWBVcOZcRaP7BdQF3ZFYoP74gzrslSvHlmrUty/9zuGcRkmiKWfh0lSU8wtOq7l2je5ilyoFHDigf04sZSkhIZSumDnltWrFpvOxbRtlxWJzHT8+d1EfPXbhAqeaVbZYoh+nTvHz1q2LfQ5TplDCg379oveVJKBiRXqtYF0XveZwcDAbrmCcvXdduoQfx26nBerB9+7QofDnuFxA165A69bAv//qmy/T5GnRInpfRoFcpEhkCl4gDkDyyvQCkFgLyCMBELPZLH/GajUIAV55xS4XmbNak2XL+PHPPnOrAEhwO3nSI6cKlSghoXp1CUlJIpo2FdG2rYiOHb3o2tWNXr3ceO45P6pVkzBokA9Tp/rw+edeLFzIgdD69Twy8sEHPtlp3bQpGxkZmbhxw4n9+z1YvdqL99/3YfRoP7p2FVGnjoiCBSU89hg9p0wZUdO5vnzZLu/+//yzU64nyczMxMKFFJzUqeNHVlYOjEYjnntO6VSrAciGDTkq8Tyr1YamTf2Bv6c+zJolICXFLt//sWOdgY0pP44ft+OFFzxo2dKHFi38qFZN1BQkVLZGjfzIzFSnNzGRwfR0M06ezES/fnS+BQpI2LUrFOQwgUJCKBXxjz+6sHy5C0uWuPDJJzTlauZMAW+9JeD11wWMHevBqFEeDB7slWmCCxWScOyYATk5FNC1bu2H0UgFDufNc2PRIrcc6enZ04sjRxxITrbj1i2brM1y86YNDz3EdF9CU7fod0qPGYAEg5GHHnoImzdvxtixY1GzZk0ZHCitZMmSeDmSWBeAOnXqoHfQ7pbZbEZCQgI++OAD+Z0uWrQoJgYEwRRLTlNC064G3KMlL25xu22TAQjTndCi1GzThh4LTs9idvq09uI2bFhoX6bjEawIzahF9ehkeDzcsYsUlVEKpQUDnkg2ZgwCO3Xax1lufbt24cdg0SAtXZQ+feguvFJkMJqdOKHckVMfS0mhO+J675/SMjKoo8rGbtyYFvDn1sJFpE6fhlxnxFpSUmxjMyX4xo2jO73B5vFw5XPGTBbJTp7k84yVEY2ZMrUwXEoTEyAcPz78OD/8oP1+LVwY/pzLl/WBdKXpZbUCuGJ9gwbR+8YBSN6Y3+/XBR4i1W/obUztmwGQc+cEuVajfXsBN25Qhial1ojbLWDIEOogKjUm3G43zp8X5KJzli6VmBg7WxLdiBFl8PLii37F9d0wGExo3pymOT3wgA8GQ/g6GKfTib//tsngokIFEQ0b+tG6tR+dOvnw9NNePPecFyNGeFC9uoiqVUUMG+bBvHkOHDiQheTkLDmFbMwYnh61YAEFJVqpZ0uXck0Mg8EAk8mEZctcspPO6GizsrJw40a6HJmZO9eNrVsjp0wVLiyhRg0RLVv6ZQHEMmVEXL+u7WwzYcuUlAw0akSfW8WKak2OU6fscrH4wIFeTXYtPa1GDS/S0jJVQCh4PrNn84jJF1+4g4ABp+ItUkTCvn3a4oXs3losllwBEIvFgroKRp5KlSrhWQ1RskaNGqF9+/Zh31W32418+fJh0qRJIccSExMxZMgQAMDFixeRkJCAzz77DIAKgJQmFIDMvUdLXtzidtsmAxAmllalivpl8HiAHj3osSFDgP/+09aRYExWynbkSGi/d9+lx4JTJJ99lu2ghH1nVcYU0cNRmwLqupRY1M0//xyBXSXt4xs30uMlSoR3hFnuv5YC9dy59FjTpvrnBHDWIa3d8tdfp8cSE4E5c2gxfNeu+sQdRZE6swUK0DS8hATgqadoCpoe6lm/H9iwgQIyrfqf8+d57UX58rwORg/VK7Nz52jaWIUKuUsXY2QKCQn61OgZza+WNo5eY0A10nNmFNSRGN0yMuj7EawzE5QqrDIGfvLn1x/9Y++5nrS4mTNprVOvXtH7xgFI3lheAhC2y28ymWAyCUhKYloQfmzebMD+/RYcPSrgyBEPDh3yYP9+D/7914Nt2zyoUUNCsWISpk+3Yv16Iy5edKN2bRH164s4fZqmWplMAurUYU6yhM8+82LpUi8++YRGKaZNc2PsWDtGjXLihRf8GDjQj969/ejY0S/XCTRtKiInh4MPBpr+/tsgF0FPnKiuGzl50oM5c3xwOikAOXnSilKloqdpBbeVK0148UUanShVimuE2Gw2bNjglAFB48bq4vzSpf2oXt2HatV8qFrVh8qV/ahY0S+DoAULXLBarcjKykJycgZGjHDh/vspKEhJscvAolgxCR995MJvvzlx5IgD16/b5NQxBmgIAT7+2I2TJ+04f96OK1fsuH6dsn2ZTDYYjbw4/MwZuwym2rThDFPDh1MwV62aiLQ0KnxYsSJtBQtKqFnTjxYt/Gjf3odOnXx48kkfevf2YsAALwYP9sg1LYmJIk6eNESJPtjQp49XBmN//slBxsyZHJx8/bUrzPnW2wYg6enpqohHoUKFMExjp/WRRx5RAZVgS09PR0JCAt57772QY1WrVsUTgVSUAwcOICEhAd9++y0AFQDJTygA+fIeLXlxi9ttmwxAdu+mL2/BgtzpzM7mzqKyaWlrpKWp07Bat9Z+8ZYupceDNQ+Yw96nT9h3VmVMI+P11yP3S0qKDlSCjVEKFyigzbKkjKyEY/zatAmB3ZjQtChWt5GQoJ96FgD+/JNfN5gQYMOGUJYrQmjqk147cgR44QX1+a1bUy2M//5T7+JnZVGwMXs2rzUgJFSd+/JlTm1cvjyleU5PB779Vh/hADPGXlW3buzRD4ADXz3pRQD/fcUiZBlsb74JNGkCvPaa9nFB4BooeuqNvv6a9i1blgpbRnq2q1Yh4Bjon2+9emwXNnpftuEwZkz0vnEAkjemF4CEq9/ILQDZu5czK+WmPfII3V2/7z4J//3Haz3OneNF448+KqqKypkzqBQydDoFdOggBjaHJPz6qwd//eXBb78JWLnSgi++MOOjjyyYP9+BTp3cgXdJwujRfgwb5sfp04JcdzJsmB9OpxPXruXITv2DD/qxcKEbc+a48dZbAsaPp6lEgwZ58cwzXnTp4kO7dj40auTDkiVmuZbhww/dsFgo/e4nn7jx1FOhDFZ6WuHCIhYutODatQy5lsFgMMBsVqeDMae+W7fQepVLl+zyPQ0uaNdqhQpJKF5cRJkyIkqXFsFSxhYvphGI7GwbJkzw4I8/1AXfjz/OSQfOntXS5LBhwABvYJNEwuefW3D4cDaSkymQunmTjh08/4wMXvRfqZKIy5ftyM62oV49f2A9Cl+IfycAyNWrV9FWUVQaByBxi1vuLZEQgvR0K778ki86X34JbNlCi0uZg6Rs4dinXnmF91m0SLsPc8zLlOGfffUV3wkuVgxo1Ijm6Qc77kp74w3aPxJdriRRRWdCaHrJ9ev6ahyU2g2bN1OBQuV5osgF/oJpi5mZzZyxKNi5dLs5WNu6Nfp8lNdlkR+msM6sc+fQ56RVLxLNJInWhnTqxMdhLGiEABUr8mgGIfTfCQn0d9K3r7quJTUV6NmTjxFOByOa+XyQ6zH0sFdpWbt29Py33orel9V/1KlDWdFyaw0bIvDHWvu4Ms1Lj67VW29pgzwtYxGcNm30zVWSOHvbtm3R+z/xBO2rJ4oVByB5Y6Io5rp+I5a2erUXaWkmuQh8505DSDF1tJY/v4SCBSUVcPnmm1Adk02beFrP+PG8xkULgJw+7ZEjG7lp/ftTJ7ZAAQn793vgdFIhwi1bnPIcZs4Uwu7Os38bjUZ06CAEAI6Ixx/3hU0nK1IkVBQwXz4J/fp5sGKFC99848S0aU7V8QIFJCQlefHMMy689ZYVP/xgwYkTFlgslDJ47Vre/6OP3Ko5MvBTvjxNJ8vNfcqfX602rtUuXeLRmGbN/Cp9EpvNhvnz3Yp7EBkIFSwooXhxCWXKiKhUSUSVKqIcEerblxaa37plw9Spgqb2hxYA0at6HtzOnDmDzoq0hooVK2qmYDVu3DjXKVglS5aUU7AuXLgQLgWrFImnYMXtf7klEkLQt69V88XfuJFSeAZ//uOP2i/V2bO8TzjH/PBh3ocBjGBqVtYi5d4vW0b7hFNeF0Vtp7xChcgUo6mp2vMJThljDm24QnWA17XMmBF6rEMHemzq1PDnaxkToitTRg2Kdu9W0+sSQutQbseOHKHpXEyngl2XgdKCBamT/cUXwNWr6nPT0mhRPCE0dezYsdzPY8sW9odZfz2D0qxWHh3SQxGdmcm/bywRpGArVSryuzB5MvuDrm+8fv1o/1dfjd533DgE/kDrGzvW79yqFe0bbqNBaXEAkjemF4AE12/E0n75hTqw9er5cOoULWZPTU1HixY0FadoUQk7d3qQlSUgO5uyW+XkCLDZaITC7eb1FZs2ZcsAZNSo8NGb997jaUqff+7FkSMeXL5sxzffmPD++1Z5zIsXhRCa1oQECUWLirj/fj+qVxdRty4tZG/dWsBjjwno2dOHAQP86NqVO+Nz5/rkOTJHddw4j+wML1/uwpdfujBunAcLFrhx4oQdzZr5sXUrjQKcPm0O61Tnzy+hSRM/evXy4u23Bbz5plt1XKnq3q2bD6mpdvTq5ZWvHclRL15cRIsWHgwb5kKrVvScwoUlHDlCwUJqqh0PPEDntWqVC1lZVE+jWzd6fx9+2I9Dhxw4cMCBvXsd2LbNil9+MWLtWjPWrHGicWO/PI8//ogMQGw2qv/BgOmoUTwy8fvv/PPnn/dEVbKP1L79NrJCurIx5rTbASBHjhxBz5495Xeua9euaKqRY1uqVCldRejB4MVisWgWoTOgolhympB4EXrc/pdbIiEEr7yiDUBOnqQOn9KxrVYtPP2nIPB+4cTJlNEFBjDGjw+99iOPRHx3VTnuWvORJPXOPWtFi0ZO/TlzRnuhe/JJdT8W7Ynk4DEHs0OH0GPvvEOPxUIRDNCdchY9CQaC27dzymRCIhfJx2o5ORQ8bt1Ko0JnzoT/HaSn80hNiRKR2Zr0GHO8e/TI3fm//YbAH2N9ESGWIpc/f+xCh8zsdv4cwoGv1q35PdJjTZrod/r79KF99airA/R5lilDa8D0pAWytLvvvoveNw5A8sb0AhBl+lQs4CM5madEdeggIC2NOnPZ2dm4edONBx+kzm3lyhKuXIk81rVrLpQt6w9slIhYsMCLd9/14bXXfBg82I8ePfxo00ZEUpKI8uVDowRTp7pRsqQY2ASigMHhEPD441zpfN26bNy6RdmonE6nfO1gNfjr1wWUL8/FDl0uCpgOH3ZhxQonJk8W8MQTXk0A0KqVHz16UGe/Vi1Kw5uWZkRSEv2sQgURbdv65LkSQhmacnJsyMqyyVEC1sqVEzF6tEf+f/nyovzdV6924fvvXTKQ6d3bhWbNvBF1VAgBXnjBLbNqZWba8NVX6hqJnTudckRh6lQe4WFF6JmZmbI+ibIVKyahZk0RLVrQezB8uAeTJwtYsMCNb791YcsWJ8aNC63N2LDBiZIlJTRv7ofBYMP169k4fz4D+/YZ5WjRpk1O/P23A7t2ObBlixO//urE2rVOfP+9SwZkhABz5rhzBUC0FOr1tL1792LAgAHyO7dkyRIUKlRIk4Z3V9BuV1YQ9/uUKVNQvnx5iIqc4g0bNiB//vwqGt7+/fvLIEex5EwihDhInIY3bv+LLZEQgosXrXL6hZbDrYyCREu5YA7whg3ax71enprE0nUMBqB4cfX1o6lCX7tGd2HbXQd0TAAAIABJREFUtw/PcMXSUJRNT07/M8+Enrd9u7oPS1kLF4EB6M53+fI0ZUbpyJ46pVYCr1SJOn47dkSfGwAMHRreIf/9dz5u+fL6xruTZjDQ4n0G9vRSwIYzo5EzfK1fn7sxJk2i52sRAmjZihW0f+3aubseQAvv2XMwGEKP2+0cSJYuHX08SQKqV6f99RThM3CjN2Vt3z62Y6yPeIBFCfWot8cBSN5YrADEaDTqBh9Wq4DmzamzXLGiH6dPZ8rOqdvthiAIOHPGgzJlqCPbuLEoa3Jotb173bdVN1K1qhhw8P0qgcKsLDcaNGD1ByIOHTKpmLiCAYjd7sBjj/EalKee8qNePTHq3KpUEdG1qw8DBnDHfN06uhufnW1E//7OkHMKF5YwcqQHQ4d6sGyZC3PmuOXPGV1uoUISbDYbfvzRJbOKEULZqoxGG4xGm0xHO2aMHdnZ2cjJseHkSRtWr7bjrbcc6NnTjZo1fWD1GiVL+vHUUy68954NW7fmID3dEuKAT5lCgUK+fJxql/1OPvkkR54H03LJTStfXpRTpI4ft+PCBVobwlICz57NlgFZq1Z+TVX3L7/kBfSjRnlCakQiNYvFctsA5I8//sCIESPkd04QBNSvXx9jxoyBJEnweDx48skn0T0oT3bu3LnInz8/DihylJkQ4fsBVWKr1YqmTZvKoobMzp07h+LFi2P16tUMgFQmhNwghEy7h0te3OJ22yYXobP8ctaUjqMybSpcOgkzphmyZk34Pkx74pdf+GezZ/NrVKsWvdDY4eD9T53S7uNy8doBumukj1I1OVld+/Lww6FO2f793GHTYgUD6LXYGDdu8M8ZY1Vwmz8/+twAmqP/8MO0GNlmCz3O0mPy5ctdwXZuzWSiBAWE0KhZLMxj4WzTJsr61aRJ7jVK+valdLGR0uWU9uab4QGeXtuxg98HLYd+5Ej+3O+7LzpTlduNgIOiL6JUpQrtv3q1vvlu20b764nGiCLfRNBD2RwHIHljkiTpAhNsZzsWADJmjD+whkoqZW+DwQBBoGlWly4JWLLEKzvvTz7px+uv+3D2rICRI/3YudOjACCCvLNfpoyIJk1EdOpEVchHjfJj8mQf3n/fh6VLvVi71ovt2z04csSDS5cETJ7MtTzWr+ffweVywWAw4OjRTFSoQOdbvbqIZcu8mDHDh7VrvcjOFnD+vCDP/+pVh8zupNXKlRPx2GM+vPyyB59/7saGDQ6kpbEIAdedYAKFqal2vP66EyVKKCMbEgYM8OLSJTv++YdT5VasSPu8/LIHdepwVfSBA724cMGO06ftSEriqWGdOvlw5QrVxSAEKFZMxOXLxhAnOSXFLs9LqxUsKKFJEw9efNGF5cvtOHWKAptmzbiGxq1bFIDs3m2QIz/PPOOFwWDDxYt2/PuvAx9+6EbLlj7MmuXG+PG0GL9LFx8aN/ajcmUxJGLUubNX1uxQNgZADAYDtmzh0ZgJE9QF5b/9xskOunf3qYrv8wqA/Prrrxg7dqzqvUtPT0e/fv1Qt25dJCUl4aWXXoIt6A/zsmXLcP/99+NMUCHkhQsX0LVrV9SvXx9169bFW2+9BZ+Gwu6BAwfQtm1bEEJOEULOE0Im3+MlL25xu22TAYjZzJ3uYCXk9HS+iEQrymWFt5FoRZmTqizOVQIKLTHEYJMkPt9Iu+zLl/NxH3ss+rjMWPE6IZR9KNhsNn788GHtMZQpaUpK4hMnQov78+WjUR095nBwB1DruyvV0bWokO+G5eRw1e9ChbQLmW/dilx/o2WslkGRdhuzsVoUPelCANfmiEWnJdgiRVHWrQt1CqJFirKzed+LFyP39ft5zYsedi3lnIJpuLXMbOZz0SMkGQcgeWN6AYjZbJZTp/T0/+47vsvfvr2AIUOceOIJD5o396BmTR9KlIiWAuQPrHESTp2iIMRmc6NzZ7f8+S+/hBaga7WdOz2ygzp+vD1QiO7G7t1uLFqUg1desaNrVzeqVvUjWOSvXz8/Xn7Zj/vukzB/fk7gHjhQqRLtV7q0hBde8OOTTyjguXbNFbFWgAGBAgUkbNvmwGuveVTpUCVKUPYoQmgqVkqKHZ9+Ss9hDFSFCkm4cMGOoUPZPabnz5ghBKIQNlX6U/nyItatc8rUwNOmOWA28yL469dtsmbHffdJWLbMhS++cGHYMAH16vnC6nSUK+dHhw5cKX7QIA+MRiNu3UrHm2/a0bSpHxkZ/LtfvmyXC/6nTQtfmD9tGq9xqVnTj3HjPPj4Yzd+/tmJo0cdyMxUAxCbzYa33+ZpW+vX06jShQt2uZC/SRM/0tNjBw9KAJIb8GGz2bBq1SpMnjz5nr3j93qdi9v/H1aJEPIzISSZEHKBEPI1IaSEznPrEUJ2EELOBs7/gBBSMExfGYAAXKk6mCLX6+ULVSThP4DqTxACzJsXvs/zz9N+n3+u/pylbykinBGNzTcSk5Tfz+f+4ov6xgXozjE77+xZ7T6dOwOPPx45DaV0aTpGcJ/g4n4NIo2IxihTg+8hs7p16fFYi9xzYzYbTykrUIDWiGjZiBF0tz8QcdZlbNxZs3I3N1HkWjC7d+s7h6WQ6aGjDWczZ7JdS/XnJpO2Inwwq1mwXbvG+966Fbmv0UgJEJo0Cf/bDTYGmCIwR8p25QqfS2Zm9P5xAJI3FisAYdGLSM3tdmPJEluIMx+plSghoVYtCa1bi3j0Ub+8az16tFoU8MoVXrxepIiEv/7yRJzLrVsCypWTZEe8TRsB5cpFZ3IqXVpC27Yihg/nxexz5uQEUrDssjo6IUDv3n5YLAL+/NODiRO92L9f2xG9epU7xA8/7Fexb91/v4hp06y4dCkLJ0/aUbasiBkzBFitNgwbxov1CeHF2Rcv2rBhgwMTJ1Ln+4EHRFV60bp1TjlSk5AgoXVrOk7ZsiLefVdA69Z+bNjgRMuWvAZmwwZeoH3mjB1Dh3qxYYMDmzbZMW2aE126CAFq3dB7VqiQhP37uaJ7MIuVMm2rQAEJf/8dWSE9UitXTkSzZh48+yxVSl+0yC2DqNKlJZw/b4fVasOcOW7UqKEWQoylsd/97QCQr7/+WpM6N6/sXq9zcfu/b4UJDbEtC/y/ACFkOyFkp45zyxNCMggh0wP/L0EIOUEogNEyFQBhaRtaNLt6lMcBTrsaScyMiQ4GO12sDiSSwJrS9KR7ATxaEMuO9qFDfIE8eFC7T6NG9PiSJeHHYUAhOIqSlaXWTYl2X4NtyJDIYG3OHL4DryenP7fmdAIDB1KnOl++8BEyQeCOdziCgmDz+TgBQix0xUpTRu8uX47e3+/nNSd//pm7awLAqFF0jGCK+CtX+O9R2bS0dZSmZJgLvK5h7dYt3levivuiRbR/y5bR+x45wsfXU6QfByB5Y3oBCNsJ1gIgO3Z40L+/H5mZNKUpKysLkyfbAs+bFg4PHerH+PEC3nnHis8+y8HmzV4cOEDToywWZa2JgNq1qYPboIGoOiYINA3q3LkMWcOheHEJ773nxeTJPpw7JyA9nbNmCYKAY8ciMyZVq+ZD164+jB/vwxdfeLFrlwdpaXQMi4XPpW1bETdvpssARBAEzJrFQQirx6B/x9yazvezz3rle6IEHvPnu3HjBi/eZlEJdh7TsGCOe7BGxvHjdvn49u1OpKdTpiqbzYbz5+1o04bPk0WClHUZVaqIyJ9fwpo16kLzxx6j5z36qFobJCfHiiNHcrB4sRVDhzpRrJgakCQleTF8uBPLl9uRnKy+B0ajTabyrVePFpSzYz/95JLn1769D3PmuDFmjAdPPOFD/fr+ELaySG3KFB5hUUZhcgtAMjIycj3Gp59+KjNU3Qu71+tc3O6dvZNH13mVEOIlhJRRfNaUUMq1rlHOXUgIMRAqUsPsWUKISAhJ0uivAiAsetGvX+gPn+le/PFH5Bfk+edpv6A6KpU99xztE8zQw1KzohXOpqTQvmxnu1YtSvUazkllCtJz5kQeV2lKWtLTp7X7sELfjz4KPw7T09CqP2BFxYTEDhI+/pie16iR9vHkZHq8RQv9qV2xmstF6YnpHyoqfhfONm+m/QoW1KdEDtDaHnZ/9Oy0axkrriZEXw3JzZs0qlW3bii1cCzGVOunTw89lpxMUwNZxK9y5ejkDkpAHK1eRBktuXlT33ynT0fAQYned+9eWmekEASOaHEAknemB4AwNqCsrKyQCEOVKkxfwSeL3SUnZ8iOZtGiEvbu9ciaCpmZmWGvM3QoPadIEQnHj3tgtQo4ccKDdeu8mDPHh+eec6J5c4+m0viKFV7UrSuid28/btyg450965HpW2vWFDFxog1Llpixc6cBqalZKqar4PbGG9QBL1xYwunTHqSnp+O//zKxaJEb3bqJKtDBgEWrViKWLnWiTx8vOnXyybv8ZrMNdetyIMEAQO3afhiNavYo5rjeumXDzZs2lV7K8OHaonnt2vkCgIqlWSmZqWx44w1BNdfSpUU88ohaUb1PHy+OHePgZts2XhS/eXModW12tk0lkBhO16VGDR8GDXJj8WInjh+3Yf9+h5y2xWo2cnJsaNWK3p82bXzIzFSnZS1d6sJrr3mQmmrHnj0OLF+eg+nTrXjhBRe6dPHhoYeUESV1JOd2GnsutwNA5s+fj8XhhJ3ywO71Ohe3O2MpMfZPIIRk3Y2JaNhOQqMWwZZDoqteXiKEbAz6rDSh4GWqRn8VAGHgQUvdmBWO//pr5BeE5ew/91z4PoMH0z7BegaPPUY/j5S+BXBnNriFYzlq0IAej0UN3eXi4x4/rt2nY8fowGbgQNonqG4NAKfiLVhQ/7yYMeX6/PnDO9aMqSgacUBuzOOhbGEMhH3zTeT+U6fSfk89pf8a33zDdjZzP8++fflz1GMXLvD+eoGSlo0cSWmQw0XHlKmB4SJsSvvzTwScueh9L1/mY2sxcGkZ23yoVSt6361bad9SpfSNHQcgeWe5BSBut4CePanTWLKkhMOHs2RnzW634/p1HkEoVUrCgQOOECFAZVuxgjuz9euLeOABSd4RD98k1KghoWtXEc8+y1OJjh3zyHNkQII68A7cukUjOYyJS6vt38/rRsaO9eGdd3xo2DBUjfy++yTUrSuieHEJf/zhgcvlwsGDNrlu4ocfeFThpZfUNRuEAE8+SXU7ggGI1WpDr15eOdJDCKuH0U4lWrqUpi6x1DUmtqdsa9daULw41+U4fNiBTZucaNqUXyN/fgnDhnlw/jy9DlMnb97cr4qCZGbyY3SzjAr6/fGHGdOnW9GlixuJidqpWhUq+FG/PicF2L6dgoW0NBtGjvTI0RvW1q3jQGjTJto3KytLJkWw2Wi9R82aXNH+33+ja47kFQB57733sEJvCP8u2L1e5+J2Z8xGCKmts29zQsgRQqMIeWHphJANGp+fIoTsi3BeEULn+InGMSshZJXG5yoAwtJGRo4M/eHXqEGPRdrlBoC5c6kzE0l/YNgw7ShJ9+7082gplm43B0TKFm5urDg6ICaqyySJj6vF9LNxI/DAA2y3iTqRWgXWEyfyPsG2fTs9VqyY/nkxM5lo1KF7d+DSJe0+rJbhq69iHz+aMRanfPmAb7+N3r9lS6qaHkv0eulSqgczcGDu51m1amxRJiV9rsWS++syzY5wAMRi4dc5dy76eJs20b7BBBFadu5c7N8hKYn2r1kzet84AAk1fXfi7pvHE7mOQhAEzejFZ59xh3zZMrMMUJRRhUuXeISkZEkJL79sw/TpNrjdAkwm9TVWrvQiXN1IyZISWrYU0a+fC1On2vDDDy78959HTtHas8cjO/1z5vhU4zqdLrzyCndi27UTMGCAD7VqSSFzoMXugiy+p6XjUa6ciBEj/PjlFy/MZnoOo/V1uVzo08cTAFF+5ORQMPHDDy6ZwYq1evX8SE62qxxdBkAYoFC2QYNCQQVr6ek2VYpS48Z+1fHkZDt69+bF3U8/zceyWimFr5IFq3BhCa++6sHGjfy+rV3rhMVCz+nXjz97pXJ6dnY2Ll7MwOLFVpjNVvzzjxXz5tnRs6crbO1N27ZenDkTnmHKauXpYNWqUbatzMxMGYBcuWKXGcFKlJDw1193BnxoPZfctMmTJ2NNtJzvu2j3ep2L250xFyHkGiHkPRK5QHsJIcRHaAQhrwCIhxDyvcbn/xJaVB7OKhE6z1kax9IIIds0PlcBkJdfpovI88+H/vBZUfPy5ZFfEEbn+/TTocf8fprqwUTqmjWjKtpXrtDjTH8jUv0Is1mz1AtfixbhKWcfeYT2WbAg+rhKK1qUnrdzZ+gxViegbOXLh1LyfvAB+0MZOsbBg/zcaGk1webx8HP/+0+7z+OP0+O5LeCOZCdPUkDxySf6+jM19XXr9F9jyhR6zjPP5G6OSk0UQvTR1yqddz2CfOGsWTM6RjiSgOvX+XXS0qKPx0gR9ACEkyf52OEoooONpSnef3/0vnEAEmr67sTdNz0AhDq5PHrx338eOeVl0CAn0tLSceyYCb//7sGiRV6MHetD164iataUQkBF+fJ+fPedF9WqSdi2jV+bF3ZL6N7dj6VLvfjzTw+uX+d1HczxtFqtigJ5Hmlp1UqEw8EjNCdPuvDRR1b07u0KqVUgBNiyxYPvvvPi9Gk+j9RUXrjOWv36IsaNs+P336mGRrj7dOyYWwZC33/vwtGjDnTsyCMFRYpIaN+eM0v170+BwPTpTsyenYOjRw04edKO4sWlwOaBKN8TZXqUVnv+ec58VayYJEcsUlPtKnDSrJkXe/aEOulmMwU+LI2LEFpjw5z7Bg38qFPHj1GjPNiwwYFKlUR8+aW6buTUKRPKlKH9//hDnQZlseTg8GEzPvnEGmAzU9/j6tV9GDzYja++cuD8efXczpzh32HUKI/8O7h40YT69Xmq344ddyb1ijWma3I7AOS1117DpruRUqDT7vU6F7c7YxmEkFKEkOWEkHOEkEeDjj8f6CMRQiyEkJcJIZl5NLd7BkCYIrlC6FO2pk0jO1TMmNPYu3fosf/+0w69169Pjw8YQP8/fnz0F9FgUFPZRqIb7dKF9pk5M/q4SitbloWKQ4+1axf6PcqUCS3KZexCWkXGyt12szm2uTmd/NwTJ7T7DBpEj7/ySmxjh7M9e9RK4kajvvMkiRfcx1LYzfQ4evWKaZoAKHhgpAqsBaf8admZM7y/TUNjRa+1bEnH+PRT7eOnT8d2nWXLaN9ghjotUxaJh1OrVxrTACGEpvRFSz2LA5BQ03cn7r7FCkDMZgE1aogBR1dEnTpeFaNTuJaYKKFZMw+eesolq6P368dZrkwmAR060HETEiSsXBlKs6sFQF591Sc795s3e7B4sRf9+/tDIg789yrh6ad9WLzYi3/+EVC4sIQCBSRs3crofgVUrkznV66chJ07aSTn9dftOHaMOqLh7lP//nQuSUmUPlYpTti7txfnzlEQsWmTE02a+JGSYofZbJM1QJYutci1ENWqiYG6EQmlS4uq4vOzZ+04d86uqpXYuVMtZMiiKzabDU88wUUGW7f2qs67csWO1as5kMjOtuGDD9wK8IMQsJCQIOGJJ7zYscOpSs0yGLLRrh2tN2nRQp22xdr27U6ZhrlwYSlsqlatWj4MG+bGihVOXLpkx0cf8QjOL7/QyMS+fRaULSuicGEJv/12Z8HHnQIgI0eODFE4z0u71+tc3O6MdVT8+xFCyBlCyDeEkPaEkD2ERjskQsgvhDr2hBDyfh7NLYNop2CdJLlPwcohOlKw3niDLgha6UIDB9IajWi5/kx1um/f0GMeD1ChQujiNGgQPT58OP1/pAJ2pTGK28TEyP2efpr2e+stfeMyY0XiP/0Ueuzrr0O/x/ffh/b79lsEdpxCj+WGrYiZTaFDEqRtJBsDlLFS/GqZJPE0nXDXC2dKjZdwYEnLGJjNjSDgq6+GPp/774/O2qQEBrFqliiN1cZ8/LH28ZMnKTBu1YqyfUWzb76h4EOPRg5L1yIketqZz8dT9ViLFimMA5BQ03cn7r55vZH1NKxWAXa7XaYjvXXLhjp1fCHvCnMoGzSgheBTpvjw9dde7Nnjwc2bAux2Wn/RsSN1JCtWlHDzpvpaJpOARx6hDmm+fBLmzKFAYfVqOkeW+5+TkwNBELB9O9/1Z1GD4Faxoh/9+/uweLEHf/+dheTkDDlNrHdvJqQnISeHz2PTJq9Me0vBFf33m2/awgKQkyd5GljJktypfughv2YRN3PO9+7lQoNjx9plB3/bNifOnrXLxd1jxvACdEY5q0x/slptePBBfl2lQ379ug1LluTIdS1du/pgNNrw778OJCRISEiQQqhqb92yYfp0QaYOpqDCJ0eb+Gd+/PCDCxYLTcH6449s+diqVeoIya+/OuUC/Jo1RZw6ZYfFYsW+fVa8/74dTz7p1iQYIAR48EG/rHpepYofly9nwGQy4cgRxx0rOg9u2dnZcmphbscYNGgQ9u/ff8/e73u9zsXt7lgpQgu4xUC7TgjpeY/mspMQclzjcwvRV4QeDF5KEQqm3tTon0gIwauvvopJkyahZctJIGS7pugbSymJVkcRKYoCULYf5UJ03300HQWg9LuJidoF21rGAEK9epH79etH+8UqLMfSzrRAV04OpyamfwS0nb3Fi+lxLcYgux2BP87hmbbCmVIMLjlZu8+LL1In8fXXYxs72CSJAss2bWjaVazuVlYWdXLvvz98vYqWsZqgGjViux6gHaEiJLpytzJ9yemM/brB11+4UPu4MtISjVYX4L+jpk2j9/30Uz52NFu6NPQeVasWGRTpASDbt2/HpEmTMGnSJLz66qtxAJJHFgmApKUJqF5dwoIFgkxDm5KSjvvv58XnM2d6sWWLBxcvCnC5wgMZh8OBDz7IkX8zGzeqr3vjhoBffvFiwgSfyuklBHjkEZpatWOHSQVANm/2hgjlVa4som9fJxYutODQISOcTlcgJcstgyiHw4GtWzl4Wb+ez+X8eQFTpvhQpox63Pvv92PevBxYrVbN79m9u7rGoVgxCe+/74bRGNlBnTvXHXiHfDLYeOMNGnXKybHh669daNDAjytXOEBgQCM4BWrWLM529eGHbtWxrKwsfPyxRT7ep48XRqNNFjj87DN3yNxycmwYN46NKWHzZidycmxYu9aJ9u3VILRmTRHz5tlx+XIGevWi36l2bVFWH790yS5TIicl+XHhghrwZGfb8N9/DpjNOdi7NwezZ9vQrVu4YnYJK1ea5CL0u9XuBAB55plncCKWXbQ7bPd6nYvbnbHHFP/uQyjgkAgt1k4lhPxOCKlwD+ZFCCFjCU3D0qLh7RLUt3zQ/z8klK0rn+KzZwkhfqKDhnfuXOo4aaVAhdOzCLaxY2m/wYO1j9+8qdZBUNYnvPAC/UyrCF7Latak/aMpnL/yCtC+PTB/vr5xmfXsSWl0w9UtsGhOQkJ4ytbZs2mfYEE6gKtbFyumT6NCaQYDv4fhzmVq7lOmxDa20iSJ3jcGlGbPjp0y2O3mKVh//aX/PMZeVqJEbNcDaHRp6VIOYooWpZGUaEXZJ07w+6pMN4vVWN1RuKL7WGtAvvsOAScgel/2DhISPQWrTRttoBbpOW3YwDcP9Fg8ApJ3Fg6AuN0CevWiTnWZMhJOn86UHfjffjPLOfm1a4tISYnOpHX6tAv33UedyWHDfNi/n9aLDBjgR61a4VO4qleXMHy4X64RGT7cAbPZIkcqWL9Onfw4fNiEW7fSZdV2JdOVEoBYLA7Uq0fn0rmzCKtVwOrVXjz+uNrZzZdPknfsly6lWihXrthQrZqEd97xwWhkhe4CmjdXn9u/vzZlbnDr1s0XWLPEwMaTPwS0KFOZzp+3y9GZb75RA5CLF+1g0ZonnlAXrbP0tdmzecTlhRc8GDKE3sNu3Xyq/mlpLHWL3V8fUlPV89q924E+fbwqtrJSpUQMG+aSwdSCBW6sXOmC0WjD4sVuOfVMOc6uXQ7UrCmiRg1RpaFitVqRnW3Gn3+aMWOGFUlJdK5Fiog4ciRLBgcmkymiAv3tAhCmuJ6b1q1bN1y8ePGevd/3ep2L252xdEJZsLYRnm61lhBSmRByHyFkAaGO/AuKcwbm0dwKE1qX8hWh9L+FCCF/ECpGqLTphAKLtorPmBDh24H/JxIaTVlGtE0FQCZMYItt6A+fOfs//hj5BRkzBoE/SuH7sF30ggXVTh6jTJ00Sd/LyGh7tbQWlPbUU7RfrClYrIYgHOnFQw/R45GYiSKlpCl3220x1hsoBfZSUrT71K6tDzRGsvfeo2O0b09JCnIrati8OR1HrxaLJNFoGAN4uWWkYsXbeqMox47x+6pHNyScRaOUjpUF69dfad8KFSL3kySaqsXG/u67yP1//52mKLLferFi9FlFUlufO5c/Fz0WByB5Z+EAyPLl3LlfsSJHdt6NRiPcbjf+/NMjg5A6dUTs3y9g3TqvZnTA6RTktKFChaSwNSNFi0p49FERkyf78OOPXqSm0vMvXhRkp3vMGDssFos87siRPPLQr58TX31lRr9+Aq5dcwcBKg5AFizgitzDh/tCis5r1pQwa5YPqakCUlIEfPutV3bgp02ju/vly/O0LZdLQOfO6gjIkCFemTUqXDMabXI9BHWsJfz3X2QWp3//5QBi1arQ1KNy5URQAUg1oFAyRzFFckKAnj3pcy5cWMKtW7y/kmZ3wgSPHMlQgiIGjE6ftuOVVzzyM2Lgja4PXIl+yhRBk0r47Fm7rKsyb15oJMZms2HGDD7n9u0FXLmSIT9P1qgCezYsFgus1vDMWnqbwWC4bQDy6KOP4qZecaW7YPd6nYvbnTEvIcRNKPC4SEIjC4QQ0pAQcoAQsosQUotQ0JJXVokQ8jOhRecXCAUQJYL6vEQIMRJCHg76PInQNK5zgfPnE6qmrmUqAPLii2wnJfSHz3b7o+mARKLyZfbgg7RPlSrqz1mx+IwZ+l5GtnsbSQjUPanyAAAgAElEQVQQ0CcYGGyiyIXiwqmUV6pEjzdrFn6cESNonxdfDD3GWJqi1bBo2dGjlGK2SxdttiavV796fTj74AOgYUM6xogR4VnG9BjTh9FTwwCo6xgIiU7NHM7YPS5dWl//s2dpXUaDBreXgsUEKMMBrlh1QHbu5JGcSKYsKCeE1u3oeW7TptH+enRa3n6bj6/H4gAk78zn84UAhosXBdkx7tvXpXLyHA4HUlMF/PqrF0OH+kIE6M6c8eDq1dDajiZNQmlYa9cWMXiwH59/7sWhQx6ZwSo4EtOjhz+w/tPcfwZABEHAtWsOdOwohIz9xRdeTQBy+nSmylFmrWBBCX37+rFli0cTRGVmZiIlJV0uzn73XXrfzp3jdSvKNmiQV6X0Hdy++86Fhg3VaUwffBB9F/+ffzgAYboYrG3d6kT+/BIKFaL6Gps2OXH5sj0EgFitSl0Srh/CdEvOnLHLz795cx/S0vg1cnJsmD/fjfr1/SFsWpcuZePtt62oWFH5rCXVbyRfPglPPeXFxo00nYudO348nU+pUpJKCd5qteH11/nzffppL1JTeZTLYDDIApjBzWAwwGQy5RqM3AkA0rx5c5hjZYy5g3av17m43RmTCCFOQqMIhaL0fYUQcotQwPJ/zVQAhInmjRsX+sNnu9HRlNBZIfno0eH7ME2RYMe9VSv6uV5qV7bTG03nggEerSLxcGY08j8k589r92GRnCFDwo/DqIW10qBY/r1WgXo0Y6xI1atrH1cK6uVmw4YprRctSlPyYqUJDra1a+l4JUtGH0uSOOsaayVKxM4UBlB2NEIoGNMTvVESA9xOpJ2B6UgUyIz6VovmOdj0UDaLYuh9I4Rq1kSzmTNp365do/dlpA6EUMAWzeIAJO8sGIC4XJyNqlIlP/bty8KqVSZMmUJz8itV0i4SJoQWgn//vQclS0ro398vF5m7XAKSksTA+iBi0SIhpAA9XFu7lkdiVq2ieiRnz1rw6adePPZYKAAiRMKjj/qxdasHqakC5s3zyXoh6enp2LHDINciEAI8+KCIOXN8snp6uJaZmYmFC2kNReHCEq5dE/Dhhz45RYtuxPkwcKBHvheMOvfoUYfKid+3zxECgjp3diM9Pbrg3Z9/cgCya5caAFy4YEfjxn40bepHhw6+wN8RIQSAMCAxYIBaXHHAAG/A8bZh1CgOUGrWFFXifs2aUYAxcqQ6zYw57FevGtCiBb1+hQoiTpyw4733BFStqv7tPPCAiHnz3Lh2jRbKM3a0ceP4uN9841LNz2y2yQDDbDYHQIoVFosFRqNR/p5a0RGj0RhTdISRHmRnZ+cagNSvXx+CINyz9/ter3NxuzNmJYTUiKF/RUKLwP+vmQqAMMdi2rTQH37Bgvp204cOpf0iUb8yoBFca8KKvvUKjdaqxf6QRe7HwNPWrfrGBahjxRbKcI4vi4BosWQx69CB9tFKxWG7znqjAkpjQoBa6XIABTc1a1JQGWvaFAM3dMdUH0tTNEtPp9TMVapEV2YP3sVnLTd6JsqaDj0RDUniwGDLltivx2ziRCpUGUmQk/1+fvkl+njK32M4fRIGtoJbly7Rx583j/bt0CFyP7OZ359Ivz+lxQFI3lkwAJk1izulrNhcqxUtKqFdOxHjxlG2q1OnPHA6ed1IxYoSMjL4uIcOCbJGxP33Szh82AObTcCFC+GdfoOBU+J26SLi3XcdaNbMEzKXEiVEPPecD88/78dPP/HIx0sv0eu1aCHC7RYC4CVDlTo2ebIP5ctLOHgwMh1xZmYW6tSh96ZPHz9at+bOdK1aErZv98DhcMBoNGHhQhfWrOH1GU2b+pEvn4RPP3UjJcWO6tVFGaQQQmmAT53K1KW4/dtvHIAcOBCarpWeTql1331XCNxrEf/9Z8eqVWbs35+lKtw2mWzo0cOreqYmEx9rxQqXfK8KF6bzt1pt+PRTmoaWmCipKH0NBgPOnctAmzZ8zLFjPXIqmsVCC9i7dFFHfooUkTBkiBevvELnXKiQhDNn7PI5ffp4MWqUR46YBAOQ4JaTkwOTySQDiOCWkZEBg8EAs9kcEYzcLgCxWq1ISkqCeDupALdp93qdi9udsX9ycc6xOz6Le28qANKxI11A3n9f/aNXpotEE3MbPJgyHkUq+Gb1AMGMWswh+/lnfS8ji0BE2uFVCvYdPqxvXIAW4dLFU9uB9/m4Dsk//4Qfh6Uwffll6DEG1iJFi8IZK9AOl1bGRAhHjIht3BUrKFtVnToUkEajrY3FevWic2rVKjIoYgXXwe2112K/Zno6LT5/+OHwkaxgYyrmixbFfj1meiiQGa2xHiV5PUXrN25QEK8ECHQHMvr4LOLVpk3kfqwmiLWEhOhRkDgAyTtTAhC3242FC20I1n0oUkRC8+YevPCCA1995cbx4xRsBDvpP/3EHc+1a0NToPbsMcigpmBBWuDdsKEY1ukfNow6qsoiZ9bKlvVj6FAH1q61ICfHFXLuuXOCnFq0eDGdC0vVmTvXHcKeNXCgPyIAWbOGM0gxhfSEBAnjxvlgMlGqYmUqEEv/SU21ydfassWJRx7xyc77qlUuufZh1qycEABy+bIdp0+raybWr+d6H8HHlC0lhbNOMd2W6dOtIcxRBoMNLVvy+3zwoBrUHD3qCOiR0Gs+95wXFy7Y5MjPV19xoHXhQjYefNAn35tgJi5lO3HCjvHjPShdWv0c2P3o04cX0ZvN6kJ8TiZg0QUCzGYzsrOzw6ZqhStkZwAkt2xbDIBIuS2EvAN2r9e5uMXtTpoKgLRoQReNYPE0RhdLCHDqVOQXhNGPRgIgDzxA+/zwg/pz5jjt2KHvZWRK5ZF0gZTF2uGYqrSMFS9Xq6Z9PC2NjxuuCFySgEaNaD2AlgAfA3yx7uzbbJxJ7N9/Q48rmcb0pPcw+/FHfl7v3rdXhK1lR4/ye7Z9e/h+oki/15YtHLS0bAnkJvItSVShnhBg5Up95/TvHz2KF80YFa4W/TKzcO+blilpl/UAqTJlaN8NG/TN9/vvgbZtIwOmnByaQhfsPIaj3GYWByB5Z36/P1DQ7URWVhamTrUpnpMfR454YLcLsrNmt9s1HfT0dAEVKkiBtYA78waDgPXrvRg92o8aNUL1QxISJNy6JeCff2hERFn78fTT6ghM9ep+jBljx8aN2UhL4wXxWvPp35+eW7u2CLudfrZnTzb69XOqBAIJoYXS+/ZFTsFq3lwdealTR8Tu3TRqYrVaNR3b9PR0LFtmlq8xcqQgf+d165wwGm146inG7CTh2DE13ev06bR/jx7cGV+5kgOQq1cjq6MPH07nzHRJBgxwhjjT167xlKonn/RqigdmZNgwcCAHl0lJfjly0r49L3afO9eGhg09KFJEUkWAWNu716HSLqFOvg1ffulCixbqZ12okBTClsWc+lgAiFZ0xGg0ho2OKAvZGWCJA5C4xe1/hqkASLgUKCXlazQdB1ZvEcnZYwKCv/3GP/P59EdZABqVqVqVOpdHjoTvpxSW06O3wOyTT7jjq2WHDvFxwznGly6FBymSBHTuDDz6aPTC/mBjKUr582unFbHd7AoV9NdurF/PIzodO95eAXYkY9TA7dvrSw1bs4b2L10693UoffrQMUaN0td/+nTaX0/qUjjbvBmBndHw35NFqWbPjj6e8v3QE8lj72E0Fixm69bR/pUqhe9z6pR2ZCqaDk8cgOSd+f1+1e791avp6NyZA4X5832q6IHNpi3EN2wYdSBLlZKwaZMHM2b40L69GOLsE0IZkapVk7B0qRe3bgm4elVAsWISatcWceIET4UaNcofWLckfPKJgPT0DJw+nYlbt6jzGQ58HDjAwcKqVV7s2uXBk0+qHdxq1SRMmOBD2bISevTwywXwK1d6MWKEH4cO8XmYTIKisFpC3boiLBYa1bFYLCF1BiaTSa6HGDrUEfjNcwf+7bcdci2C0WjDs88KWLvWKEdAkpPt+PdfB5o3p89h/HheE7F0Ka+JyMiI7AAfPOhQfecWLTwqZzory4aHH6bfK18+CYsXh4IG7kxTGl3GYKZkMjt+3I4zZ+xyVKRWLV9IfcqmTU75WSpV2pVt2zYHmIJ9jx7aYOh2AYhWdCRSITuLkOSmkN1qtaJu3bpxABK3uN0hUwGQqlVZuF39o790ie7kPvYYkJER+QUpXpyOEa5YXRS5o6vcvY91h1eZkhJJRXz3brYDE1stxJtv0vOeflr7OKNFjUTBu2oV7xN87StX+Pz10LAqbdw4Wt+hJdgoSRQ0ERK5/kBpmzZxxq927W5PATyaHTjAv/fu3dH737xJ9VgqV9aOIukxBiaTkvT1Zylg4Qr89ZhSaNBk0u4zdSp13vVGWp54gj6faEQQAE9z1BNdAahAI3VcItf8bNpEU9OKFKH9R4+O/r7GAUjemCRJsNl4SktGRgYcDgesVnX04b33fMjIoMW9Vqs1xOH/7Tfu8LOaAWUrUkRC164iZs60Ys8eA6xWNYgZOJBeq3JlSdbWEAQBly4JeOghUXZcGzb0oEABCZs3myNGKzp3puc88ICIVq3Uhc/163uxfLlTjoqcP08BBou6NG1K+48YwaM4164JqFSJ34/KlSXcvOmGyWRSpVzRGhAKQEwmE8xmM2rU4A4+IUDPni5Zq4TVIjCwwgDIO+9w8T+68cbZrj7+mAMQJYtUuNaxIweTpUqJyM5W7+a/844gF/L36RPq9AdfY98+Bx54QH1Px48XkJVlw4QJDhXgHDXKgxs36Hlmsw1VqtDzmNCisl29akfTpvwev/++dvrWnQQgwePe6UJ2k8mE+vXr39N3/F6vc3GL2500FQBhkYng4ltW3BrNOXE6+SJ27Jh2n5wc3keZO375MnWaWrSgaVPRjNVoFCwYeU5sZ7dy5ehjKm3wYHqeFn0uQB27WrVoBCOcsTqAHj1Cj7FC70qVYgNGHg+t0SBEu0Zh5056T7p0iZ4uB1BnlhEMtGwZvsD5TlrnzjRFqFcvfQXunTvT+fXunbvrHTnCf3MGQ/T++/ezdJLcp6Ep0xaPHtXu8+679Hg0IU1mTAx0yZLofWOltE5J4fPVw5oWLo1Sy+IAJG9MkiSYzWZ5l9fl4rUUdruAvn2587x7tzEsAPn2Wy+C60YaNBAxcaIPW7Z4ZBYq5tgpx9izh4OXlSt53YjbLeDgQQ+GDfOEMF2NHBla88Ha5s2hRer0nRHx009m3Lql/R0EgaaBsf6sKH39ei/KllVfv2hRCWvWqLVRHA4H7HZ7wGnPgdFoxsaNVtV5Dz/sw/Xr4R3c9PR0mEwmjB4tqK6lFOfbs8eBUqUk5MsnhWhzaLV165yqOVy4YAqJbMyY4ZaPz55NwcHbbwsyS1XwmGlpNjzzDI/oVKwowmqlNRO7dxvQsiU/VqGCiO++c8FqtWHmTPq9ypQRkZXFxzt50o5atcTA32cJK1ZEisRwAHI3xAc58MoJ+4z0FrKnpaWhRYsW9/Qdv9frXNzididNBiBeL3Vsk5JCUzx++kmfE6/HiVH2UYqdsUhFNEDB7Kuv9O1qf/wxdaz1pt8wa96c5ruHo/hlACVSkTfTKdGq8WB1BkOHxjavX37h9ynYmVZGP7p3jz7Wn3/ynewmTXJHc5sbO32a12XoESZk6UwJCeHrbSKZz8fri/RQ0jKF+mCQHKux77h+vfZxll5Wvry+8Ril88SJ0fv+P/auOzyKqnvfBAg9dAhVegKIgAVBmggBREAFRDEqShUBEQVBQbGABVFBAUU/kaKCoICfgghioSkgHekQ6ibZ7G62zfad8/vj7J07s1N3ExI/f3ueZx5l9+6dsjuT895z3vcdMwaFBJ5/3tjcXi87Z62WRhqUv2KEqJ8AIMUXoVBItZ3J7fbBI4+E4NNPA0LPvN1ul4375huWcA4cGIJz59SlbMUAxONhDuKdOqFS1aVLPnj77SDceKN0lZ0SuXv18sK1a/kRYngALl6U7uOVV4ICGEpKwvaqHTv8qgBIvGVlIeC6/fYw5Of7YMQItiJfo0YY3nqrAJo1C0UWX/zw++95YLPZwOVygcvlArfbDRzHAcdx8PTTAYFQTQhAtWphOHQIV9htNhvYbDZVpaaBA1mVo0+fgCTBPXDAJbyXnY2AZ/ZsH3TuHIR33pGDBbvdKST3uFionLRT2d3kZB42bOBg5Eh/5O9CUHH8gQMuSE1FFa/PP0fAQM/FbLbAggVeqFKFF51HEHbsYMT4xYsZyNi1yw2VK/NQqRIv8zbRAgbXE4A4nU6hLYtWs/SI7BaLRXJMZ86cgW5aK47FECX9nEtEIooyBABy8SL743DxovRH/847bIVcK8ReBYGA8pjffkOgc/PNUoUlWhFo2dLYjTh1Ko7XM0577DEc9+ijxuYFQHd22pKkRnBv1AjfV1K3AsBzK1sWx/z4o/S9cJhVMYwSo2n074+fU3JWF5v3qa2609i9G6B8ebq6aawyUJRBW9xKl9Y/1lCIrbg/91x8+6NVlGef1R/L8wBVq+J4oyRupaAGmG+9pfz+4cPs+7JY9Ocz+psHAJgyhSaQxo+XEtf1ZJIBAPr0wbEvvaQ/NgFAii/CYXUVKlqJQDK5WWh7iR7jcPhg8GCWrE+cGFQ084sGMR9/HBCAwty5AbjnnpCs2nHjjQGYO9cFp0974M03Obh6FWVRz53zQUoKD+XK8fDrr4yvsW2bXwArt94agnLlePjhB7/i/sXb1as+gdcwY0YQGjdmx3H33SE4fjw/IuObCw0bIshJSuLh0iU39OoVhEGDgvD7717gOA7mz4+uwvCweTMm3AUFBQL4oGAkPz9fksx26sQqIHPmoDpWfn5+ZKwDDhxwwYULLqE9avhwvI5ZWXLHcqfTCe+8wyocb76p7LRusTihUyds16paFRWsCAGoXFleadm1yx1xXAeoUoWHrVs5CQChPJMzZ1wweLBU5pfyWm68MSRp9/rhBw5+/13bBb64AYia3K8RIvvHH38MK1euhL5GVvYAwGQywdChQyEjIwPS09NhzJgx4HQ6DX32xIkT0KdPH2jTpg1kZGTA9OnTIRBJqESPnBGEkEuEkIOR7VDkv7NK4PGXiETEFQIAoT3gSUly8PDMM8ZaYDZsgEhJVn3M55/jmMaNpa9Pm6berqQURleD27XDce++a2xeAGkLjlJLkph/cuyY8hxixafo5FLsTXH5svHjunaN8Wei2+TCYSb5q6VkBIBAsVIl9Hxo0UKf13M9wudj3016uj7pnfI4qlSJj6MyezZ+vmNHY+OfegoB94svxr4vGtTYc9w45fc9HmU+lFpQkN6ihf5YoxVCcVBjzyVL9MfSc5swQX9sAoAUX+gBELppARCfzwcc54PHH2cg5NFHQzJnc5qwFRQUQF6eT5BgFROaCQGoWTMM48a54OefUcqWVmco4dtsNsOTT+K+GjTgQayedf68T8ZR6N8/pAtA5s7FxLh8eV4AMBUq8LBoUQC8Xp8k2XzwQUbuHjWKAZ6ffvLCmjU+4d+9ewehUiX0+di+3StURziOA5fLpehXYbPZoFEjdh137ZInudEO3y+8wACLWBaXbteuOQVg17u3XzXhPnvWJRhNiqV3t2+XAoPMzGCkqsPD7t3svWizQ7qtW8cJ3ifibfNm7WqHHgCJ1+G8sABEvCkR2S9fvgxVqlQBQgiUL18eHnroIVi1ahXk5+cr3oM+nw9atWoFY8eOBQCUxu7bty9kGnB5zcvLg7S0NJgzZw4AADidTmjfvj2MiWj1ix45Iwghj5XQ4y4RiSiSEAAIbQdRarOi7UJ6yQZNerRUcaj5XvS9eN99+LpR4jT1wVi0SH2M38/4DbEQmGmyq8Y3oxK91aph4q8UixfjmGbN5O/Nnx9btYfGa6/h5+rWlbep0TY5PV+GAweYlGrTpuqeEsURx46xKpHeb6uggLVRGUmQo2PbNlZxcbv1x1MlrJtvjn1f4jnq1NE266NqVZ98oj8fbVMsXVq/TVE8Vq0aGR20qjFrlv7YN97Aa2OkhTABQIovjAIQukpvs6kTwL1eH0yejMlppUo8HDkiNfcTg5iffvJL/D3KlOHh3nuDsHKlDS5dYiRjcWsYBSBHjliE9ibq8WE2++Dll4OSth+6PfGENgDhOB/Ury/9XMeOYTh+HI9fnIyaTCZYvJjxKrp0CQnnu3WrV2gx6tYtBBcucNCqVTiyyMbD4cMeCQhxu91w6ZIFZs50wPHjOYIfBZ2jdu0QWK02TWJ0Xl4eLFnCpJOV2rByc51QqVIY6tULwpYt2lWDn392Cz4n2GIFMHu2lDS+fr0bSpXiITWVh19+YQDk0qVcWLnSCs8/LwcWOTlOmDxZyufp1Uu5vUtrE6uOXU8AEg/ZnRLZjx49Ch06dICkpCT6HANCCNyvstK3aNEiKFOmDFhF6iMHDx6EpKQk2Kqjiz916lSoVasWhESSj+vXr4fk5GQ4depUAoAk4l8VAgCZNw8fIrffLr8pqLeHkpu3OF5/HVfW77xTfczQocoJJ119/fBD7X0AYNJPuQtaniHiSoPKYoViPPgg/UOn/P748fi+mkIWACb2K1Yot1jRaxCLz4Tbjcnsbbcp990fP45VIa2E8OhR1mbTqJG81a4kgoI9QpDroRUTJkBkNS92d3eXC8Fo06bGHM7Fal1GRBGUQizFqyYhPHAgjpkyRX8+sffM2bPaY69eZWP1pLNpPP44jh85Un8s9Tlp105/bAKAFF/wPG8IgFgsFqEfXq9l67XXgrBli9xZXAxiLl/2CWDhttvCcPas1MjP6ZTL/dLV75EjEQDUr8+DyYTVixo1xHwLHl59NQiTJgUhJYWHn37yCwDo9dftsHOnlAOyd69fZEzIw0svBYXqTTQZ2WKxwB9/MADSuTMCkDvvDAlk9YyMMFy9iiDj1CkPpKWFoXPnELz1lg/cbgY+zGYzvPWWHQhBjonV6garlXE8BgzA605btQoKCiQSv3TbuDFf+MyMGW6ZStOLL/oi1Z0wHD1qg5073ZoeIh98wFq2CAHo2VMKFPbudQuVkooVefjuOwQcmzZZhc8cP648/1dfcVCqFFaF/vxT28fkfw2ARG+rV6+G7t27w9ChQyE1NRX+E+1XEInMzExor2AAVaVKFXjyySc1798WLVrAfVGtJjabDZKSkuCtt95KAJBE/KtCACCTJuGDZuhQ+U1xww343ooV2n/8KN9CazX7pptwjNgFXQwoovkSSnH5MnuYapkLUjnV+vX15xQHPV81AjoFS2+/Hdu8AJgYJicjmPj9d+Ofe/NNiPyBAMjNVR+nttp98iQjRderp5/AFleEw9gKRgj6mmgly6dOse/dqFmlOKjzfFaW/thQiPF0Pvss9n0BSE0w1VThKBfGSGtxOMzMN/WkeHmeVYyMAC4AbDcjBOV+9eKHH9jvUQ8MJgBI8UVRAxCjVRSPxwcTJjCJ2D59vHD+PJMCVvq83W6HAwdyhZatwYNDgvkhIchXmDkzCHl57DO0ioFAwyKM/f139vrOnX6h6kAIwPLlAfB6vYJCGFU9ov392dnMFb1CBfwc5UTUrs3DiRPSSsfs2axFavNmD5w4wUGbNgHo398DTZsiR2L06ABwHAdHjzIC+gcfYDXDbrfLeCMUjOTn58OhQ3nCZx55xC1TafrjDxf06OGD555zwoMP4rFMnSqXwhVvlIROCJoCipW4nE4nHDvmElrdUlJ4WLXKAyZTLlC/lDfflFdidu50Q82aYaG6Et3a9U8FIPFyTdauXQuTJk0CAIBAAH9TSlG3bl3F6shNN90EXbp0Ub13vV4vJCcnwxSF1ajU1FTIysqKBiBrCCHbCSHHCCF7CSHPE0JKl8DjLxGJiCsEAEJboKI5FeEwa2Pavl37jx9Vxpk3T/l9tQRKvLJ77pz2PgCMS/BS7ooR0i6N3Fx2LIcOyd8X+5Xs3m18Xhqvv85AgBG1LwBsP6ISyfFwEs6exbYt/IOKYOSfFOfOMQf2GjWk4gTR0a9f7N8pDdo6V7asMdJ3VhaOVyL8G40WLXCO995Tfj9WzxElAK8WHTpo7zs6vvwSJYH79NEfe/Ikuw/0OEQJAFJ8YRSAUM8Li8USNwCJBjEejxdmzmQJd6dOfjCZ1CV2HQ4HPP64O7L4wABDhQo8TJ0ahGvXtPc/eTJWLho3DoHHg9WaefOCMrPEAQNCcO2aRUhA8/PzhWM3mUxw7ZpJaFMSbxUq8LBrlxR8cBwHO3eyc3z44QB8/TW6o4v3e+CAJ+JGz0Hv3li5+e47Zd6IGIzgZhPa2fr08Sq2al27ZoIrV0zw8st4/apW5WH2bC8MGhSAr76S80by85lDOiGgqE517pwL2rZlUs3vvuuAJ57A+e+4Q141oS1dtWuHJdyReAHI9QIfFPQVFoCsWLECpk+frnsPpqSkwGOPPSZ7vWvXrpCRkaH6OZPJBElJSfDyyy/L3mvQoAH069dPDEAeJISsIIRUify7IyHkGiHk2+J+9iUiEfGGAECocVk0WTsnhz2QT51Sv+nE4EJtxVUNaMTS2w6AylNdu+onhj174rwzZ+rPSYO2zVSooHws33/PklifigO6WoTDrLpipM+exqxZEPkjE7tU7sWLTLGrRg110nxJx8SJ7LdRsyZeZ6WV9V9+wQS5dGl1hTK18HpZC5oR+VgKWCpX1gZFWjF6NM6hJuCwdy9WEVq2RKCpF0OG4HxKJpTRQVsJ1Ujw0fHbb6wSped/4vMx0Lhrl/bYBAAp3jACHmg1ID8/v0gAiNfrFf49b14BJCfz0KABryrh6/P5IDfXAeXLMzJz2bI8PP10EC5d0t+3y+UTqhQvveSBy5d90LcvS7DbtAnDpEmsIjNsmBvat/fDzJlucLvdQvJLpVhr1pQ6qycn8/DVVw5wOBwSOV6O4+DKFdayVa9eSGi7oqaNvXqFJONvvRXnHjs2IHn98GEPHDrkgdxcBCOUc/PlyYsAACAASURBVGC1WqFKFTy3W24Jakr8njyZA5Uq4diWLXE/kycrE9OXL/cAITzceWcArlxRTrIvX3ZC587sumVlIQBJSuLh7FnWYmW1OqFPnyA0ahSGgwdjb72im7gq9U8FIB999BEkJSVpbsnJyfDtt98CQLEBEKUYRwgJE0I6F8MjLxGJKHQIAETNs2D3bmwZat4cVXvUIjubPbzVvBrUgMYnn+DrzZurzy8O6sGh1UrD86xqoObDoBS0DaV7d+X3p01D8BOrfwcAwJYtEHmYa7u3iyMvj7XS6HFwouPKFfT3oOBFrQ3onxK0DY9u3bvLPSl4npnstWwZOwik8rStWum3DlksTKXKiGu7UqxahZ+vXl1ZsMDnQ4BDCMCmTfrzffghVkHUfp/iePll2vNt7FitVnbtDx7UH0/B9PLl2uMSAKR4o7gACK2i5OfnS5Jjq9UKa9b4ZaR1pQrI+PFIuE5L4+HwYeP7/vrrgFA5+c9/XJLWrSefDEFBgQ88Hg+8+aYTMjO9AiekbFkeXnjBB9u3e8DppECAk1VA3n6bJaxUNtdut0cMCjmg3iT33cfBk0+6JJ/99ltppWPhQj+0bBmGnj2D8NVX7L077wxFFsj8sspI48bU/T0ka9USJ+0mk0nYf8WK+Jm+fX0yQ73Dh12CSlmHDiEwm9UT7txcJ/Tty0AIbWdbsMArG3f6dPzgozgBSGFava5duwYHDhyAGTNmwPPPPw+nT59W3NwRdZO0tDTFFqx27drF3YJVpUqV6BYspWhPCOEJIVOK4ZGXiEQUOlIJIZCb64DmzTFh/+MP6Q+fKlvpgYNNm1jlQE0ZSk0adOpUgM6djRmsAeh7cAAgN6RWLRxnlIQLgMlat27KK+ShEHI3lCpFRmLwYOP9/jRoG1nt2sbUm2jk5GCC3qwZSh7/+Wfsx1vckZkpBSD4x08uIHDqFGsLnDs3tn2cOMHmNiJ9SwUYpk6NbT80jEg29+iB7xvxKNm8mfZx61cpvvgCx8bCgapXDz+jx/cCYN4qetW8BAAp3vD7tRN/n08qgVtYACLelCRx1Tan0wnz5hUI4KB9+zBcvmzss7Ta0aQJS5SrV+dh3TpU0XK73YLS1NWrJkhLk8vGHjvmgXPnPHDDDdL3KlUKw5UryhUHqlRFAUvfvl4YOJB5Y7RoEQaXi5O1bdFzbNsWqyP79nkEov277/pl42+7LRSpBodlrVpinxGz2QxHjlglAKpp06BM4veNN5CIXqdOGJ54wq9JWqcVjsGDPZLrctddsatcGQUgOTk5/1gAQrc5c+bA4sWLde+/zMxM6NChg+z1qlWrGiKhR4OXgoICSEpKgrffflsMQGoqPIbaEgQgU4vnqZeIRBQuUgkhsGuXQ3jIRLf40NYYPW8JalZ4yy3qYz7/HOVzo4nu1L1byTE8OsQEdK12omXLcMzNN6sDougQJ4tK/I6tW1kFQ+zibiRycpi5YaRSqxtnzyIwIwTggw+M78tsRglhCgiNJNr/hKBCCOLtxhuVK2+0UlW+vPFqEo1u3fCzRswp58xhFZN4o3FjnENNMprKKxtRlCooMN76dPgwwF134f0lUoTUjL59jQOuceNw7EMPaY9LAJDiDSMAhLak5OXlxQ1AxIkwTSBj+bzTiXK4n3xiExLo5s3Dmm1bPp8PzpzxiVSucOvWjX3O5WIKXPS4qNcFrVw0b47KVq1bM+K1eL577w2Cy4XVCOoLIQYhtEWqXbsAZGQwEKQEJjiOEzgujRqFgeM4+OYbpky1bJlPNv7hhwPQoEEY7r9f2rbldDolx2I2m8FiscDDDzNifKlSPFy8KAdOCxY44MYb8Vh79w6C3e6EggIn/PGHG9aulXNCrl3LgZEjmUcKGjUWLTCgIPZ/AYDMnDkTPjfgHLx48WJISUlRlOHdFtU3nJeXJ/n3tGnToHbt2hAWJS3r16+HUqVKRcvwZhNCakc9hkYQbMHqXWwPvkQkohCRSgiBTz5BAFK3rvxmoquzeo7HTzxhPKkTh9h1XEciGwBA8CupWlUbWFDvkuHDjR/L++/Tvl7luUeMoCtBxuekQVWs6tQx5svA87gfQtCc0WirkdXKDP7KldMXDjASgQCqdx04gCvwW7diO1l2tnFwZyQ+/FAOQAYMUN4Hx7EWoHvvjW0/tC2qbFn9xPzQIfzOOnYE+Pvv2PZDg/5u1PxAqPElIcYc6SkRXc1hnYbPx3xWjLR3ATC3dSNVOirdfeut2uMSAKR4ozgASLScrZrSldbmcrmEz//wgx8qVuShU6cw2GzanxNzO5KTeXjhBQ44jrV10Tlzc3PB6XSCy+WSKFcRgipVVHK3bFkenn02IHk/JYWHJk1CcNNNIUFml3JcXn3VDhUqIABJSwsK4KV8+TBcvGhV5I3QMTVq8MBxHKxZw45n40avAgDBc8zKCkp8RqKrH5Q3snu3XXL8f/7pUJT4XbaMSeu++qobPvuMkdipEzvdcnJy4No1EzzwgBcI4eGuuwKQnf2/CUCKotXr2WefhbUG+rl9Ph+0bt0axo0bBzzPg9/vh7vvvlvmoj5nzhwoVaoU7NmzR3iNGhHOjZT2HQ4HdOjQQTA1FD1ysgkhSwkhZSL/bkIIOUcI2VHcz75EJCLeSCWEwNNPIwCJVr/heSZFqnff3X47jnvzTQN/IUWxcyerKjgc+uOpF4SWClIwiACFGGwlodG1K35m4kT5ex4P69WPVZbV70cw0aoVgAERDQBgFRxCjJso2u1MiSwlBUFCPHHuHLaYDRvG1LPEW5cu0haptm2xqvXFF4WT9z1yBH8HdetiOxLdhwInDwAANm5kY4xKzQLgd0n5QXpqUjzPAJ0Rrw6loN9lnTrKYCoQQP8cQgC+/lp/vqee0r8HaNDftFH1tBUrGAjXi/Xr8ftKT9fm0yQASPGGEQBCE/Xc3NyYQIPX65W1XsULYsQAxOfzwZ49fsjJ0f/cyZM+qFwZHc6XLrUKJHjxKndeXh64XC5wuVzgdrthwwapFwZVhEpO5mHNGh/89JMXatdGUFGpUljUlsXD2bNu+PhjJ2zdaoajR3OgWrWw6PnHKicPPOBW5Y1QfkZqKgKQzz9nAGTnTjkA6do1BKVL89CiBQKgL7/0Qq9ePhg3ziWol4lBjsvlkvA2VqxwyngjlHBPlcfKlOFh5UoGSHbvtkuqAzk5ObBpUz5UqoTn2LFjCByO/78AZPz48fD9998bugdNJhMMHToUMjIyID09HcaOHQtOp1MyZunSpVCjRg04FtXKcerUKcjMzITWrVtDRkYGzJgxA4IR0qzokXM3IeQbQshRQsjfhJDzhJB3CSGVSuDxl4hExBWphBDo3RsBSHTbhVgB68QJ9ZuN51lyrmcmFx10FbVtW2PjaTKoBXR27WLHreWZIY5r11hry2+/yd//+mu2am63G5uTxkcfsc/qSZYC4BiaIKuZIUaH08natUqXBvjvf2M7xtOnUS2MepzQjXJe6FapEoLNaMI4Iey76d4dicmxcFZoXLmC1Q0AKQhZt04+luexQkIIQJMm2iIJ0TF5MvJI+vdXNwmkQc0Sq1VjxxZLZGcjcb5JE1S9Uop77sF9RBa6NIO63utVAQGYz0iPHsaO9eBBds31pIrPnmXKd1qmlgkAUrwRCAQMtz/l5OTEBD7EK/CUIxEvAHG7WcIudkjX2w4e9AsSsPXqhWDfPquQyB45kgtmszlCFncJSfrx42I+AwMNS5aw9qe9e9mYBx9kFZFJk1gb0ujRXOQZy+bo2BGlf9etc6g6nFM/DcoBef995svx999yqV+mRMWD2+2GV19Fsn6DBkFYtcoJVqtb9pmtW72i4/QrSvxiIm6D1q2DkedmEOrWxWObN69A+D6tViscOJArgK0mTcISFayiBiC0WnW9AUhhgM6IESPgl3jVSIooSvo5l4hEFGWkEkKgbl2HYrWAch5SUrTlcc+dQ7Ju587GfDzEQf1HjCRedjsDCVq8hpkzcYwWHyU6Fi3Cz9SurZyQ3nsvvh+rJ4TXiyRgQoxJpwJg5YEei5HefY5jrXKlSgF8843x49uzB+Duu1lbD90aNsRK0BdfIIE9O1uafIfD+NqWLVhFGDMGE+xoUFK7Nrp9Dx2KlZMmTbAaZCSRDwYZJ6FWLWUVrwsXGBjSaxMUx/nzeI6EYEuWVthsyDUhBKsZ8QRVI3vuOeX3Kchp1kx/LqM8KAAmK12+vLHWP68Xf0OEAPz6q/bYcBhd3tUAIo0EACneuB4AxOPxSEjZdGW9JACIz+eD3bv9Ag+jbt0QzJ1rg6pVQ5CUxMORI5wEfCB3gpPxRl57TcrXcDg4oaIxfnxA9AwLRaoXYeH9jh2ZbG+DBngcjz8eFKoR0byRRo2Ckfs7AHl5eTBrFpPyNZnknJH27XH+pCQe8vLy4Pvv8yXHrgRa3G4OKlXiITWVl5xbtMSvxWKBPXsKoHx5PvKsD0UqOJzMa2TiRBekpYXg8GHHdTEKpG1t1xuAFEWlZdiwYfBnCau5lPRzLhGJKMpIJYQAIQhAoqU3aVKkR46lajtqUqNqwfNshd0Atwt+/JEBIi0FIOppEovXxp134meURCqsVqa6tH698TkBABYsYK1KRojrNGEkxFg7jtcLMHIka2P78ktjx/X770zOlm5du2K708GD+hK1SsHzOO+IEYzXo7YZJfHbbFhRadYMW7MuXZKPee01rKA1bRqb1PBjj+GxNG2q7/NBr/FttxmfXxxvv02TFeV75MgRdm20qgk0jCjBAaCCGJ03WtJYLVq1wu/PiPAB9drRai1MAJDiDSMAJLr9SQ8oUFI3JfH6fPG3cdGN41jC6/GoGxaqbdu3F0DVqmF49FE3TJ7MhFRatQqBxeKWJefU3I8QgGHDguB2yxN/6tkxahSrUDRvjmCESv3WqxeGWbPY+xMn4vvJyTyUK8fDjh1svg0bvDBpkhfq1w8JFQyTyQRPPUWle3mwWOS8kYyMsDCnyWSC7GypWeKOHfK2rQMHPMI5rl4tJ7ZTMOJ0Iil7wQJW2cEqR0iRcH/sGCP05+fnyyR+/5cASGH2M2DAADh69GiJ3tsl/ZxLRCKKMgQAomQ+RonlWn4bAJi0E4Ir3bHEhQvs4Xf6tP54asinIaUtcTLXUwkSf4b6PSjxLZYuZS0vvhh8J9xuBrCM8AccDlYtGThQHwT4/di6U6YMkqSNrM7/9ReTA6Zbly5YyYgHdKhFXh7uh1asxJseaTk6zpxhXKQ2beSGfX4/c/1OTzfe+nXhAgOWeon8/v2xJ/LiuHgRRMmD/P1wmMlGG+EYGfHCoZGejmONmC8CYOtbmTL66lYArMVLS5ghAUCKN4wAEKPVB1opoQmoy+US3itJAMJxHOTk5MDevXlw9aoJ/vrLKXnGjBkjVY8qKOCgTh3G3Xj1VWW1qlGjEEz06sXamZo2pdUOTO6XLPHBokWMw/Hee3548UX27xdfxLnPnePgscdwvipV+AiICYPFYoFHH2UO8OLra7FYwG63Q+PGIcn7drsdOnViVZdorxGO4wQjxFq1wuB0cvDnnx44ckReKWFVIRcMHiwl3588iS1bYs6EGHyKNyrxWxgwQgFIXl7edQUgRQF0evXqBedibfEo4ijp51wiElGUIQAQJZnRYcMwsdNLXChv4O23Y7uZaOWkRg1jyW+7duiSrUUcpiTaqlWNuaoDMH+SGjWUP/PXXwCjRqm3z6gFXfWuUAETcr0YPx7HV66MXAitCARQGpn+4ViyRHu8240gKDmZydDeeSca7BUl8IiOvXvlfJGqVVHNLJb97t7NFJ3uuktesTh2jL1v1PkbgIka1K2rzyGhctFqruZ6QT1Fxo9Xfn/ZMoANG4xxjJYuBcjIAOjXT/860upNtPy11tz0mujNvW4djk1NVa9+JgBI8UYwGCw0AIkmdefm5gLHcYrgJBYeSVEAkOiKTF5eHjgcLhEvg4fZs/2yCgetKhAC0LkzcjFyc7H1io5ZsMAjVCpoy1aFCqzy0KpVGBwODs6f54Q2rOeeC4DZzFqq+vYNQkEBJ3nmNWqE+65WDWV4n37aD2XL8lC1aliRN9K2LVZYypULg91uj3yGgYWlS6UVDpuNmSO2bRuCZ57BsUOGiFW05CDEZOKEYyMEYNkyt0BYp8dCuSMWi0WV45KXlyeAp1iSerqf/wUAcscdd0COERLndYySfs4lIhFFGQIAGTZM+kN3uVgvuJaUq9g9WaQmZyimT8dk1Iiaz/nzbD/796uPe/BBWmI3fhwPPADQqRMmo0UVDgdbtZ8xQ3/8li3o3dGokbpfBI1QCFen6fXQA4g//sgkawnBFrXi5NJt3coqDeKtRw+AWCra69axisqjj8qT4w8+YHNv2GBsTpOJ8TvmzdMeS2WaCcHfY6xBZYZr1jTGx9AKcfXwr7+0x372GY6rV88Y6Dt1is2tZ+IpruycOqU8JgFAijeMABCt5D9a6cpsNisChMICEI/HI+wjGtwYqciIV+htNhukp2MinZTEw7Vr8mT7rbdYlSIpiYfU1DAQwgvVErvdDj/+yLgWtGohJq2vXcsS/zFjAhFgj+1cdFzTpmHIzpYCkA4dEKxUqoQqWP/5j08AJmLeCE3wO3f2RRZrwkKCvnw5c1yfM0dawdm+nVVsnnnGD1984RPOc8KEADRpEoYJEwKya8JxHPz6qzdy7DxMn+4Hp9MpAXgWi0Xmxq4k8SsGq/n5+VBQUKBbHSluAFKY/XTo0AEcRqQ6r2OU9HMuEYkoykglhMAzzzhgzRrpD33bNnyYlS6tTRj+/nuIrNTE1p7E80hITkrSb38BAJg/H/fTsKF6EuXxYD9/z576ssE0xL33Rlu2jAQ1l0tN1SeS5+QgWZsQdd8LGuEw85UgRNsLwmxmrTqEYKI9f77xylBRxtq1eC0mTZIeU6lSAE8/jYDXSLz7LvtstDwvz6OqFa1mGeWZTJ+On6leXbv6IAbbPXsam1sc4la/eCWSxUErjzNnao87eRIiq6/GZJJ5HiAtDT/z6af6Y2nr2BdfKI9JAJDiDSMARC3593g8ksTSYrGotmjFwiMpCgAi9h7Jzc2VyQFnZTFOw7Jl8halX37xQkYGa2OigCE1lQeLBas9Fy6YhEpKtEP6LbeEJFWE117DKsXtt2M1hfIvypfn4fBhj+gZx0O/fkhCL1sWAch//+sVjqFly5AAgPbuzYPt283Qrp0/slgREs7v4MFcYc6HH/aC0+kUeCNiTsqnn/rA6eSgZUs8/htvxP/26hVS4IRwsGkTApDKlXn480+3zMRRzBuxWq0yMCKW+FUCh2azWZU3UlwApCj2k5GRIcjhllSU9HMuEYkoykglhCii+pdfhsjDVfuGoMlb9+6x3Uh//y3uO9UfT70nJk1SH0PlScuVMy6VO3o0fqZdu6JrRcrPRyIvIQCzZ2uPDYfRf4UmzVevqo/leVQLo9dNa26rFVfa6dg+fXDVvCRDfH1/+w2J44Qgr6NlS3WJ2ug5Jk5k5xXNl8jNZWCud29joghWK0CVKsqgJjrElSSjIFcclPg/YkTsn42Ol17CuVq31h7H86ztbvFiY3PTSuIjj+iPvftuHDt5svL7CQBSvBEvAMGWJNZiU1BQoMkPEQOQWFWsaKWFfl7LyNDr9QqchLlzC+DoUbMkKaaAad68AuHeHDQIVbtsNhu4XC5J0k0Tc7GHx9KlNiFBbdsWQUqrVmKwArB1qxTUrFiBVYa0NKxiUFWpunXDsG0bq0h06BCCYcOCAhjhOA6ysznBG6R8eV4AV/SYmjULC3PZ7XawWCyQk5MjGCDeeqtfwhvp3p21Z/32G/I+PvrIJ+wTF+/CkuNfu9YDhPACcOrUKQiXL0ud7dWI7GoSv0ZataxWq9CqRYGB2WwuFgAS734cDgekp6dLHMpLIkr6OZeIRBRlqAIQqm4T7Q0SHRQYGDU6o/HWW/i55s31E/+cHNZ6oyUNSiVbjZBnATDxpO03//mP4UPXjVGjcKV70CB9IER9UAjBapJa8DxWCujY6dP1r9uYMQhqVq26vjyPeCMYRD4PJUmXLo0VDr1jDYXw2tIKSjSpe9Mmdp3mzzd2LHPm4PhKlbTdyClnBNsbjBO7adB2qNRUbSU3IyH27NATcaBAu18/Y3MvWaJfcaRBFyvuuEP5/QQAKd4IhUIxJ/8ul0uygu10Og1xMa43APF6vWCxWODMGROUKxcWKhN5eVKZXbfbDT//zFqUKlUKw8WL0rYgq9UKTqcTRo+mxHBW4WjdOgB5eegf8sgjCBZatJBWQGj7lcUibl3C92w2DmrVwvE33RSCjRvZe08+GYCXX/ZHnhm8UEWh48uVY0R0CmKo/4YYNLjdbmjWTKqmRTfaskUIwNGjaH5YUMAJEsF0M5vFXiMMYKWm8rBvX54u+FADI2KJX6vVKlRGrFarRL45ulWLApXrXQGhILWwAIQv4T+kJf2cS0QiijIUAYjfzxLzjRvVbwavFyVxCUGeQSxBgcszz+iP1SOJA2DlgLa3GD2Wd95hrSlabWaxxO7d7GGvB2r27WNytU8/rT6O5wHeeIPNO3myMUBht2sn0/+UOH2amRgSgm1oeiZ4HIek8IEDlVWvaJWkTBl9jgQAtoC1aYPS01rAYPJkkPxBJyQ2cQKbDT1X7rhDvWXJaPA8QOPG+q14AGhMSQjer06n9lgAaYVSj+/y009YAb3jDuVrlwAgxRuxApBoxSOtaoR4K4yKVfQxiNW11NrBxDK0ffsGwW6XJsP5+VKp3XXrHIptQUuXFkSeDSw5b9w4BOfO4Tzz59P2J2nyjtUJHlJSsIpx/jzjeRw54oGmTcMRcBCWOJ0vW+aD3btZSxYFMFSVKyWF8Two4Z3+t0kTadWiT58gVKnCw6uvIlfDZrNBXl4ejB3LwNe5czlCsv3mm1Iuyu7dWB3Jy+MkpP2dO/MEUBBdMTK6RbdqiSsjFJCo8UZMJlORS/wWNQDJyMhIAJBEJKIIQxGA/PEHiB6W6jfDr7+yleBY3MHz8xlY0CK406CVjZEj1cfQikrduvrO1gA4hiZv06YZP3atCAaZoV/nztrtPw4H8lUIQZM6nwZ/hq4wd+6MCkrF+QwMhTA5z8tD48G//8aEfscObJ1TS2Z5HlfoZ89GEr7W+QFg4kpVwOjK++7d2p+x2dS/a48H4OabsfWoaVO5dK/aMevFo4/KAQghxhzuaVAOjF57o5GYMsXYXBzH1MiMGFWKuR168s4FBdr3cwKAFG8YASA+n0+xPcYoGbwoAIj4GKIBSLTxYUFBgUSGlhCAJ54IypJg2l5FCMDYsQFwu93gcDjAarUKq+1HjuTK7t+FCzmhovLzz4yfQd8vXZqHhg3DwuuXL6OqFG2Z+u9/vXDzzdSwkIeZMxkn4/hxDxw9ygBIdjbuq169UAQI8YJ7O3V4p/O2bMkAiNPJiO733CMllA8cyLxIrl1j3+m5cyaoXp1dt88+84Db7ZbICFevHjIMPnJzObh61TgYiW7VomCkoKBAorQVvRWFxK8SAMnPz4/r83a7PQFAEpGIIg5FAELbgvR6y2fORMlYI14E4li5krWh6KkBFRSwKoFaixLPM87F888bOwa6IpycjIl1UQRVSUpOBjh8WH0cz7MktEIFdfUgAIC5c9kfyccfj83oMdYIBgEOHUKeQFYWmv81a6accBPCzPAaNADo1QtVrfr2RSK4mCtBiLL3hVJ8/TVz1y5VCoFlvOd84gRAxYo418CBRXPtKMmdbklJyMWI5e/S3r3s84U11t2xg82lxR8CwGtACBowGokhQ3C8Eb5Kp0449oUX5O8lAEjxRjgcjqn6QBOzWEFEPCpW0RutUIhbvpSMD91uN7zxBkuay5XjYds2OdF8yJCgMKZ+/TCcOYOKU/R9hwOrIs2bByX38ZAhnJD4XrlSIEjw9umD4+rUCcNzzzFQQeekbVqLF/sgM5PxPO67LyiAC7dbWi05dowDi8UCDRuy8RT80KpLSgruv00bBkAOH2ZzTJjgB5eLgzlz/NCjR0jgq1SogHPZ7XaBGD5jhkP0OWy1a9eOnX/79n7DlQ80auShaVM5od1Iq1Y0b4SCTHE7lhIwjkfiV7zR/cQLQMxmM7Rt27akb+0EAEnEvyoUAQhNVPT8FOhq/0svxXYTPfAAfs6IVO6qVbSnV701Zt8+9ofk77+NHUNmJo6/917jx60VV6/iMRKi31a2fDk7Xq3VZbHi0/Dhxio7scaFC8hL6NWLHb94o1Wa6I2ueBvd9ORcxXHuHHOzJwT9K/SUxNRizRo2z5w58c0hjueew7natWPAWMuXRi1owj58eOGOJxRilQo9gvmnn+I4rVZGcXz2GXrF9OihD7AoIV7JZDIBQIo39ABIdGuT2WyOi8NxPQCImIsiVmFyuVywcyfjVbRqpZwAv/KKP+rZw0NSElYsnE6nkOQ+9pjUBbx+fSmn4pZb/NCtmx8WLWLjli5l+7/nHqy+9OoViix8BSArixHBP/zQB+npIWjcOCxUDuh7v/2GLW/NmgUEsEGPv25dBCC0Pap9e3aeq1ez/X/5JYIvKj1Myem1a0tbttxuN1y65BDma9UqAJcumWDkSNayNXw4BzabTcKpUdp++43K9TI1r3hbtWhVSswHoZURLYlf6sZuROJXCYBYLJa4AMjFixfh9qIoWRcySvo5l4hEFGXIAEgohJwIQrR71MX6/7E4Q/v9bIV71Sr98QMGYG+5Vp/9U0+pJz9KQWVJCVF2Po8nqGpQ3brYXqUWBw9iot+jByafaond4sXi1bmilc71etEIkCoy3XyzFCw0aYLqR0uWYEvNkSMo33rtGlakfD48bqcTv/sVK5DDUr++svM5BWVaVaHo8PkY6b5TJ6zEnDgR3/nSNqWkJOQrFCbCYdZuReetUyd2DtHq1bS1Q79yoReUYP7AA9rjcnJiq0iJ+ZdBdQAAIABJREFUvXf0eDS0EpOUJG/bTACQ4g0tAILJsHSl2W63xwUejKpYGQUg1FmdJqQUfNDE2G7noGxZ1ha1f7/c5fvHH73Qt29IIHjTLSvLL6mqLFtGAQGb78wZu6wtKDvbJKhIicFIaipWNp54AisJDz0UhGnTGPhZutQnmp+HYcMCQlVl48b8CHEcAUj16iyZb9IkLCT4uGjC/D5mzWIVoD178NwnTJA6mTdrJgUgDgduLVuGoEYNHubM8YLZbIbPPrMKn3njDSZvTCVzo6shdjsHbdqwa9qpU2wVkOjN6ZR6jYjlfYtK4le80d98vADk5MmTcOedd5b0rZ0AIIn4V4UMgOzbh5KkXbpou3EvWoQPorS02FpbtmxhK+h6RONLl1hCu3mz8hifjwGmDz80dgwTJiA3oGPHouFTbN3K/gBE+6mIw2plvJP0dHX+BFVKIgQBWLTrd7xx7BiSqKtXlwOOiRMBvv0WjfkKEx4PVs7UKiFt22KLn1GPjm+/ZW1Uqanx+WcEAgBdu0Lkj33Rtdzl5mILHSEoaBDrMdWvj5+NVUEuOnbswHa4pCS8Z7SiY0fcp1HeE61yzpqlPS4QYBW0aHniBAAp3lADINFKVzQpK0kAQo+BJv6jRrlg/Hi3UPWIXpXv0oXxGZ55RtlYj+M4mD5dmpgnJfGwaVO+UFURt0TRbfVqn1A1ELcwtWiBc82caZeM37nTBbNnI+i4444QvPsuAyAbN3oF0jq+HxRkd7/6ChPsbt3wXKpVYwCEVjQo4X7hQgZAhg9nbVPnz+NrYrWt6IoJx3EwfnxAAEGEAMyfj8plEyeyCsiaNQWy5J62PlHlMHqedHvpJb9hsHHggAf++sujCD6iFbeiW7XoFo/Eb1ECkIMHD0L//v1L+tZOAJBE/KtCBkAo4Vmv3ZESw0ePju0GGj0a+Rpjx+qPfeUV3EfDhurtRytXYlJ12236gAYAV3Vp68zy5bEdu1J4vYx/0ru3OqAJhdg1q1RJfTX/iy8Y6OrTp/BSrQDIORgyhHldEIJqSA89hBWg68Er+eEH1hp0yy0skaVbxYpYudKTjwXAygnlmyQnY8tTrMDRZEKwTI+nKK4rAHKOCEHPFSNEd3FQZbMKFYwbMSpFKIT3CCF4z2gFlRtOTzc2N22tMtL+fM89OHbMGOnrCQBSvMHzvCzRj64uuN1uoc2loKAgLvDg86mTyGOtgJhMJkhLYwn2lCl+cDrlLUHPPceARZ06PDgcykmvuF2Lbu3aBaCggCW7YsI6IQCTJiGg+e47D1y8yFqY7r8/EKmiMJWthg2DsG9fHixahIpaDRqE4bPP2D6XLfNBTg4b37x5EGrXRsCxYgWeV8+e+O8qVRgAadcOX6NVl48/ZvyVrl0Z+MrLw9csFmlVqEsXKQARe5k0aBCC7Gwq28sAxaFDHhnoEif2V66YoH17KaDbskXOv1HazpzxRBS9eOjUKaQJPpRatZR4I0YlfulYh8MhABAKqGLddu3aBUOHDi3pWzsBQBLxrwoZAOnQQX9V1ulk8rvffWf85vF4WPvV0qXaY8VJlZrhHs+z4330UWPH8MQTtFRdNG1Nzz+PVY3bbtNOpmfOZA/vb79VHvPNN4y83bNn4aWBT54EGDyY7ff221FqdsECY2CtsJGTg5UyWlU5fBhb6dLSsF2OEASDEyagypZW5OZiKx49l7Fj9QUMomPnTgY+YwXOamGxYLWwVStlArbeZynYLKwx4ezZOE+jRtpcoePHAe66C++tQ4f05z1wgF3zc+e0xy5YwCpq4kgAkOINMQARm/jRVWJKNi9pABJNhO/TR7rKPmRIEKxWaVK6dq24tQng22+VE2G3m/Ep6Pjq1Xk4eJCtxI8aJU2q27cPCfNmZbHqCvXwuP32EFStykfuM+SMbNyYH1kY4eHIkRxo3RrnnDsXgQOtZNSqFYKmTYMSUNG7N/47NZUBkHvuCULVqjykpIQji2QMgIh9SVwudq633MJAxl13hSTXgIKT0qXDsGqVNfIbMEPlygy0RF9jqhwmrjZcvGiCqlXZfs6dQ1K4Fm/kwgUOmjdnx9yoUcgw+FACI05nfBK/YkAVbwVk69at8KjRJOM6Rkk/5xKRiKIMCQC5epU9jPfsUb8Jvv0Wx5QrF1uSTAnBZcuihKpWbN5MS+fqbSVUBhhXcfT3f/YsKivhKpTx41aLXbtYAvnGG+rjNm5kxzljhvKY777D5DgtDeC++wq3In75MjNDpPvt1Alg27Z/hiFhMIhqVy1bsuOrVAng9deVPT1oeL1SGdyePWMHUjRJrllTX17WaLz5JqsqnT0b22epCWOpUoUDnBcvst+iVpsazzMH+ilT9OflebYQ8O672mPF3iFisJIAIMUbFIB4vV4JpyE/P19CNqfv2Wy2uAGIkoqVkS1aZtdsNsP69Uyqlm5LlvgkiWh2trR16v775VK8brcbLBYLjBghJZr/979SsLJ8uU/yfunSvMDTqFWLcSm+/hrHVa7MCyCgbFkenE4XHDvG3Nf/+CMP+vbFKsi4cS7Iy8uD8uUZSfymm0KR+wjbl/r1C0aefQyAIAhiAGvNGnb+t90WivxNlBLAH3iAVY7GjGHA6eBBdj3r1g0Kv4EjR9g1rF1bTiY/d46DK1fYtXQ6nfDXX+w869QJSRJ8s9kMBQUFEt7IpUsctG4trTBNm+aMC3xogREtiV8l3gg93lglfr/77jsYp6fKUwxR0s+5RCSiKEMCQKjhX61a2quojz+O4wYMiO3moRKmemRZALZyf/fd6mMGDKCrPsb2/9hjOL5Fi8JXP9xuJlHbqZP6fKdOoVQxIdiipXRdf/yRVZQ6dtQmsWuFzYYVhrJl2UO/dWsEQP8E4BEdgQDARx8B1K4Noj+UqNakdj15HqV5abLdrFlsnA6eB5g0CYnjZcoA/P574c/D58PfFCHYhhRL0HuO/i4Lw8Hp08fY/TV/Pk0+jFWRJk3C8d26aY/jeYB69bDV75NP2OsJAFK8wfO8LMG32WwypSuLxSK0pRQnAOE4OREe22lcQutR6dI8PPRQUHANF2/NmrHEtnRpHnr3DkJBAUuYKbD66iuLJAGePj0gS7TpexR4VKrEKgPHjmG15PhxlsiL3cPPn0dvjgoVeLjhhjD8/rsTRozAsffdhwphtGpQqhQvkM5ffdUvVDsQnDAQwCoGeBwbNjDQNGhQMLLQIQUN48ezypEYkL35JgNYt9+OgNPtdkuAV+fOcjI58lB4CQibN4/to3//gGq1IS8vD2w2G7z7LgIxsTHkzz+bDYOPTZu88MUXxtq89Fq16O9cq1VLT+J39erVMMXIis11jpJ+ziUiEUUZEgBCE/rHH1e/AcSyn3ptVOLIyWHVBzU/Dxq5uaxVRq1dSaxktWmT/v5PnWIVgcI6UAMw5a3y5dVbr5xOBACEYGtMfr58zPbtzCCufXv9ypBS8DzAl1/ivqpWxbkaNgT4/PPrI91b1OF0YgsRJXRT4KSlWLVxI/JIbr019soBx7HWvZo1i4aUvmkTO/YffjD+uYMHpSu+DRrga/HE2rU4R5kyAGaz+rhY7kUA/I1iMqHfKjdjBoLDzEz2WgKAFG8EAgFJgq9GMi8KAEL343A4DI2P9vgQH2deXp6QpIsT9eiNkrEpWCEEImDFHZUYW6B8eTambduwbC5a0ahfH/9brx4DNxSwuFwcVKyI82RmMgBCeRB2Owf79uF8s2ZRUjq2p9Wvz6oTd96JSfkzz7jAarXCoEGByN8QcQsWPX/c348/siS8e3d5xYTjOBgxgu2jVauwAMS6dGGg4ZFHvEK71KRJAcl1E8919qxHAGOpqex63XknO+8XXggI+1DjjSDR3S2cR4UKYbh2TR98FBSggAAeAw933SWvcBkBI+JWLbHcb35+flwSv8uWLYOZM2eW9K2dACCJ+FeFAEA4g07Jv/6Ksq3du8cmH/ree8ZXXamrudbYsWPpA9cYiTorC8dnZBQ+KRerXn3wgfKYcBhBSrlyWJFQkjHdsYMl3W3aKAMUvbh6lQFH/COHhohFRbIuzjCZUEGLJsedOqG8sVqb1eHD8UvYXr6MVRBMTNQVyWIJ+j00b67v/E7jt9+kAAT/WOvL3iqFz4eAihD9dilajTTCqwwEmNLcZ59pj6Wtk6VKse8tAUCKNyg3IycnR5ObQRMzi8USNwChVRYjSlpOp1Nw6aYr4TabTZK8Tp/uFO6DtLQwWCzyhHXtWh88+2xAMP6j20cfMTlZm80GHMdB377SMWfOSKV7n3suAPfdF4T+/YMSIEIIQN26YXA6pe1Pd90VFD37/RHDQUyW3W4OFi7EqkqzZkHIzc2FNm3E/AwEIKNGoRLVkCGcUAGhZovUwJBuv/7KAAglqNeoIQUglMxOCCpqud1uyM7OF7w/CAF45x1lMvuMGdKqEKpm4Xs9e2LybzJxErD31Vc+2XeixBuZOpV9l717o/yvFm/kjz880LattG0rIyN+uV/aqhXteROrxK/ZbIZFixbB3Llzr/u9++OPP0Lt2rXh1VdfVXy/pJ9ziUhEUYYAQL7/nq2eaiVj1HNArx0jOtq3h8jqj/a4cBgTOELUXc3NZgaWxK0eanHiBGvZ+eqr2I47Oux2XKXGB7Q6+KHqQS1bKvud/PEHky1NT8eqTyzB83julNSPq1n6K9T/C3HiBGuXIwSBwsaNRb+fP/5grW/33lt4NbCzZ9l8Wpwgcfz3v3IAQgi2ZsUTzz7LgLlW293XX+O4lBRjJo+PPIKywdEKV9Hh9zO1NQpWEgCk+IOuNGsBAgpA8vPzrzsAsdsRHGRluaFNmwCcOeMWZHbFyesPP0jbpj74oAByc3MF9SJx8rpjh1Tpqnz5MPz6K/IR6JjPPpPyPMSytuJt1Soch9K5bDzljdAqg5jwPXFiACZOZFWGAwccsHIl+mtUrhwGl8slJPspKTwMHUrVtLBytGiRDQhhhPacnBzIzJQeL/X74DjmEdKggbSSQ13TCeFh6lQ/mM1mOHQoFzp2ZHN98w2eh9PJqjlvv+2HU6fY/OfOiecCeP11vFYrV0qP6fhxuf/Kvn0eOHzYI4ARi6UA6tVj12rWLIckwVfijTz0EF5jcdvW0qVysBPLJhZgoPuM5o1oSfweP34cKlSoAF27doWsrCy4pqMfbzKZYOjQoZCRkQHp6ekwZswYcBpY3QqFQvDCCy9A+/btITk5WQ+A3EEI2U0IOUII+ZsQMq3EHnyJSEQhQgAg1LtB3DoRHV4vSy5iab86coQ9vPTaSzZvxvabrl3VW5tefRXnqlXL2Eo/TWZbty589WPECIj8gUHir1KI3beV2kYPHGDXsWnT2Ffxz59H3gtbqYtNjex/IWhbGV15JwST4Hgd0dVixQo2f2H9OABwDlrF0PLRofHVV3LwMW5c/GCIEsFTUrTNBr1e1q63ZIn+vNS/p3RpfZBL7zfK30oAkOIPv9+vCx5oclYYAKKnpCVW4erUiQGGypV5WLjQJ1FzwnYmN6SmsgS0deuAUDWhSbrFYgGHwwEulxsqV5aumDdvHoKcHDbftWsciM0G+/ULwfbtHli0SJrYnjsnJ8ATAjB2LFYIKAeiYUO2v8zMEHzxBUvO58yxw5YtZuHfZjPjeaSk8PDUUwhAhgzBysLnn+M+69dnpO5u3aSgau9ehwC6atfmI4taDIC43dLze+kllzDX2LFsrt27ERz89Rc7z7NnpUBC3JpFCMC2bV4JMKDfW/R3ZrVyke+Mh/R0lNpdudImmWvrVocubyQ72wlduoSEyk1yMg9XrxoDGnY7igpMnuxXBR9i8GpU4veDDz6gzy5hu/XWW2Hbtm2ye87n80GrVq1gbMRjIBgMQt++fSFTK6mKxO+//w5PPvkk+P1+SEpK0gIgrQkhbkJIVuTRU48QcoUQ8mJJPPcSkYjChKQC8vDD2spA69ax5CYWrgJdlb3xRn0ydM+eOFbN88ftZhwUPc8DALbK3bMnwPr1xo9ZKcRqVmqtKPv3s+pMv35ywHP0KCMLN2qkDmKUIhTC9ioxV2LUqNj9J/6XwmQCGDhQCraM8BZiiWnT2PyFrZC53cxgcPhw/fEXL6JHyj33YFsjvU8KU40ZPRorY3rtVXTR4fbb9ecMBNh99/772mNpVadMGXxOJABI8YcRAFJQUCAkZ9cDgHi9XgkB+J13XJKklBCA7t1DMi+PAQOkbUjr1zsUzedycnJgwACWUJcqxcO99wYlAITjOAmvgybrpUvzYLdLx9EKgxjM0GR70yavsA/KG2ncOAwHDjAie79+Hjh2jFVwjh71QFYWnktSEg/TpmGC36cPthV9+SWCl7Q0rJbYbDaJPwcCkDzhO6IE+ZtvZm1JV65IifS7duUJLWjiVqvsbBxPHdrr1OElBP8LFzgJX4YQgCtX0EW9enX2+h13yFuixBwUKrWbmcnAT4UK7FqLeSNKYGT0aHY+RtzWT570wPPPB6BOHVYF6tIlBDZbgSr4UGrVcjgcAm9EXB25cuUKrFy5Enr06AGVK1cWQMjOnTtl99yiRYugTJkyYBWtkh08eBCSkpJg69atmvdrWPTA1wEg6wghh6IeP1MIIS5CSJVifuwlIhGFCpkPiFYMGgSRFRxDwwEAV1ozMpCbsGiR9tj9+9nD79dflcd8+CG2LHXvrr8Sy/OYXOHDrHBKUFeuYH99jx6o0KU019WrqAJECLbA2O3S90+cYEncgAH6vgriuHwZV5ZpK1njxiir+/8heB4rFWIjxccfLzrgFQoxE71y5QD27SvcfKtXY/LdtSvyhYzG33+z73fNmvj3T2Wyk5O1f2N79rDreeqU/ryTJ+PYdu20x3m9TPltxYoEACmJMAJAaFtUXl5e3ABETcrX4/FIVryx3csttNfQVe7Jk+Vu5vPnYxJOPSxowk45I2J1rw0b8oXfcJkyYTCZ5ByDIUOCQnIqTrAXLJBWQR5+WNoCVLYsMzq8eJElxjSJT07m4fhxq/D6DTcEweFg57h1qxcmTmRVhZkz/aJz54VWqJo1GaeDck3odvgwA170mnXr5gebzQZOpxN++okBsPR0vwAGsWISFkATBVJPPonHc9ddQQnw27/fE2kvw31UrYrHtHUrAxLduoVknJElS6TtWU8/7YL9+3MlbVR33qkMJMStdzk5OfDXX7mCbwohALNmOSE/P1+TNzJypLhqw0cAXcgw+FACI2K/EQpGRo8eDatXr4Zdu3bBrFmzIKTQSpGZmQnt27eXvV6lShV48sknDd+7agAkGAwCIYQjhLwf9fjpQAjhCSEPFvuDLxGJKEQYBiD5+UyZasMGw/cSfP45RB7m+iTrYcNw7G23KSf4HAdA3awnT9bf9xdfsAfjn38aP+boCIXY6nTt2qgiFB0eDzPXq15d7gdx9iyu3uPqk7GEj8b33+OcuAKF514Yn5Ciim3bsOWoqNui1OLqVWzrod/p4MFoLlgU4XBgi163bljBUPOeMRI8z0QPmjTR9jaJjocfxs8VRiwhFGI8qokTtY+zTx8EStOm6c8rVuw6fFh7LD2PgQMTAKQkIhAIlBgA4ThOAhIKCgrA7UbOB1VyIgTg4YeDkJ8vTwIPHJC3Q33+OSNji1trrl0zScjWS5bYJP3+brcbNm6Uu6LTxF9swrdokU8CjgiRcjBoC9SCBSzp3r7dLCS+Zcpgok9X41eu9MErr7CKxo4dHvjzT4/QYkYBVrVqSj4guF29ihWDq1cZ0HrpJUa2nzGDEb379/cI4MNsZoCpTh05wEGuC1Zz6Hs//MCuU9euCBqeeQYT/FtukYOIXbs8Ehd2QgA2b86H555zCdcDgYQy7+bAAQ+sXesVwMioUdLqz6+/Slu2lHgju3d7oGHDMDRtyqpXc+aw33Us4EMNkJw+fRrq1asHX+jIZ9atWxfuv/9+2es33XQTdOnSxfC9qwZATp8+DQSBxtNRj59qkdfnFPuDLxH/uqhLsMx2khByihDyCSGksoHP9SOEfEcIOUEIOUYI2U8IeVTnM4YByOLFLLn2+43dSGKn8pEjtceeP89kcteuVR5D/QvKl1cGAeIQt8I88oix41WLV14R97LK3+d5JIDjHy+AX36Rvp+dzczcatQAOHbM2H79fta+his7AD//XLhzKYrIzcX2Inpc8+cX3755HtvfOnTA30upUgDvvFM0PicXLjAVqbZt4/djAcDfc/nyOJee8II4qFx0UhJyYOKNJUto+4O2WeO77+K4KlX0QW0sJobr1+O4lBSAK1cSAKS4wwgAcTgcghRuvAAkWso3WmaXqjtRwvl777Eks3x5Hs6fV1oZF7uY88Iq/o4dHklLF00wxUl7165Sd3WauLZrJ23rYs8uvyQhjn6fktYPHuQED5ApU/xQrRoe36ef2iRJ+OHDHmjXDt+bN88vgBoxmKlVKyxJ0MWyutHmfRQgiT1LDhxgymF9+jDQMH68S+DH7NzJzuXWWxE82O2c6Fj5yAIJAxa0RQznwkpHRkY4Anr8UYk5B23a4HtUtrdMGR4uXMgRWt7o62IpYbpt3eqNHAsPHTqE4MQJj6T60ahRGKxWdXdzyhtxuVzw+efsGpcqxcOxYzlFAj44joPLly9Dhw4d4DsDJMuUlBR47LHHZK937doVMjIyDN+7agBkz549FIA8EfX4KRV5/aNif/Al4l8VZQkCiKWRf5cmhGwhhGzV+VxFgj/AqaLXBhFCwkRbIcEwAOnUCW/wGCqJsHMne5DqOZVPnIjjmjZVXvl1OllyqKaOJY7Zs1kCZoQMrBa//caAkZqLOQVnhMjVi65cwVVwQpD0a9Tj4cIFNCWk82Zmxq6UVZRhtaIfxIQJUuUtQrRX2a9XnD/PKk6E4Ep7UVRidu1iSlb9+hXOsJJKTyclYbuT0ZgxA++Dhg2xshZPcByCXULQYV4tCgrQT4UQY2R0oyaGHg+b95NPEgCkuMMIAHE6mTN1UQAQl8sFV64wfobL5RI2mtCdOiVN8seNk7dgcRwHGzd6Yf9+j0SVqmLFMGzYkC8Q52mC+dJLDNQkJfFw8qRdlriKJWHFW506vFCFcbs5qFFDuqL/yCPBSDsRD82bh+GBB4Lw6ad2uOUW3OeLLzJAQQjAZ5/5oF8/ClQCEpL6pk2YiFOpXyptW64cAyA9ekiBEuVp7N/PrpvJhO1oOTk50LgxG//WW4z3sHAhcy6/776gUC2IPv9HH2XX/9ZbGZBbuNAPx46x8eJKkBiwiSsPt97qhzVrOOF7qFaNhzJleLBYpJ/78EOfpMqUlhYWeCQUIIl/F263W3A3jwYiJ0/mQK1aUqnjogIfCxcuBEIIJCUlqW7JycnwbcSsLAFAEvG/HhMIIQFCSHXRa7S/L1PjcxUJIbkKr/9OCLms8TlDAOTsWfbA2r3b8H0EDzyAn+neXXtcfj5bLV68WHnM3LkQWS3Sb+W6fJnNp8LlMhT5+ayK0rmzesJlt2NrUHQibjIxh+zKlQH27jW232++YXyH5GQ898JKxOrFxYvoyP7eewBTp2IL0V13IZeFJuRqW6VKyLW5+2683u++iwm3z6AXRrzh8zHgSgjADTcYv8ZaIVamGj8+/upKKMQ4SK1aGb8e4t+vFnjQi5dfZmBBSy2OmmpmZOifaywmhs89h21tffokAEhxRzAYLBYAQqV8acvVww+7oUGDIHzyiQcsFin4oNtNN0mdzJVkXemWlSVVZipXjodvvnFIEswff5S2WM2d6xcSV0p43r07D+bPL4BmzQKy59fcuWyFnvqB0C0jIywky9WqhYXzfPBBTLSHDw8KVQJCACZMCMDjjweF98TtX198ge1plOwu5sPQ/Q8cyPZftix7nSXsPDidLoGUX6EC2/e6dU7h+CZMYIT/0aPRoHHBAk7YnxgwcRyqhdGKBSHIX3n7bQRZDRqEwe1GR3izmX03TqdT8DYhBODZZ33Qty+CgXvuQRf7EyfYd2u3czBunPz6T5zoF8AYvSbr1yu7oNPvlPJGHnvMLVwXBE4FurwRI5vZbIbOnTvD66+/DqdOnYLTp0+rbu5Ij21aWppiC1a7du2KpAXr1KlTFIBEt2BVJYkWrEQUQWwlcoUDQgixE310W1rhtdWEEKfGZwwBkNdfxz7xDh2MJ2OXL7NERcvYEIDJ6tasqexqXVDAJENnzdLfN+0/b9gwdpdsGjzPjOWqVtV3yw6FpKvlZjNzQa9YEVfW9cLnwwoDfTDXr68tpVqYsNuRy/PUUwwk9eihDTSMbJQrQwgCl86dsY3sm28AdCTU446vv2ak5zJl0ByysC1Zr73GzuO99+Kf5/hxPCajv10aFDxUqBD/dcvLM+aXc/IkO1ct93ka1MRQT4ziu+9wXKlSCQBS3GEEgLhcTLK1sBUQk8kE997LSZ4FtWrxMGNGQKZK9cILgcjzAZPGYcPU3a43bhSv2uP4xx8PRCWLUqO8G24Iw7RpfsXEdfJkeRWgZ0+v4DXy2ms+2fs1a7J2sOPHsfVp9myc59ZbQxKX8NtvR6I2zhsCu52D9HT8PJX+pYBFnPDTSgetnhACUKUKAyDiVjTafnXhggnExPqdO72R9igX9OuHx9C2rR+WL7dGPFg42bnt24cAYc0a6XlfvsxBjx54LGPGBMDl4qByZawEdekSBKfTCUeO5EnI5kuW+IRz+vZbOYBwODi46y4xBwU/R1XP6tTB61K+vLxqQrfjxz0wezZ+t9u3S7/LsmV5OH1aaipInc1jASM2mw0yMzPh/fffBz6GPySZmZnQoUMH2etVq1YtahL6e1GPn/YkQUJPRBGEiRCyXuH1I4SQXXHM9xch5AeN93UBiN/PHKNjMQJ94QUGArTaWFwuNr+arC5NxqpW1Vc+Eiv7FEZS9f332Tx6ACo6rFZMzlJSMAGM5oQoxdWrmKzffDPus3//+JzR1SIQwJa4l1/G/VBwKN66dsW2poHPTs5DAAAgAElEQVQDUaL1lVfQ72XdOuQjHDiAQGzuXFzRZ3/wseoxcyYCGvF70VtWFsD06ZiYF2WcPo1ytnQ/Q4fKVchiCZ5nfhZJSYUzQqRgpnRpffI2DbebKaqNGBH/vqnUbnq6dhWtb18cd889+nNSE8Mbb0SgrRZ+PxVPSACQ4o5YAYjX640ZfHi9XgnZ/NNP7cL9RxPTatV4GdF81y45CFBq8XG5XJCdLVVG6thRLtvLcRx06BCSzfn553ITu59/llZLbrnFL/EZ+e67fNk8N97I5n7vvQKw2+1Cwp6aysMjj7BV/Z9/9sCCBVg5yMjAysEdd+DnX3vNH3Ws7Lyo43qvXmxftWvzQuWAji1fPiwc66+/SmWN//6bXUPaGvXhhz6hfenGG+XVh+zsPLBarTB2rF+yX5OJVV3Wr/dKuDsZGSi1O3u2QzLX008HIn/zmYN89Hb1KgfDhrHjSEsLC6CFOrr37y8HpAcOeGD48KDwu2rXLgTp6aFIXoDnOnBgQKaSpsYbUQMfdrsd7r33XnjttddiAh8AAIsXL4aUlBRFGd5o35A8DQlPHRnetYSQg1GPnykEvUESMryJKFT4CSErFF7fSZCUHkt0INjOdavGGF0Asno1W1k2ykHweFj/+VtvaY+dPx/J1ZmZygm3xcJWt/XaUYJBTKBat0a1qHhXwfftY3K548fH9lm7nXETOnZEY0W92L2bgbCaNZFUXRQtVzyP6l/jxjH3dvFWrhyqIL3zDppFxnq9Dh5Ero2SEpXNhuf+0ksAvXsz13cxOGnfHoGLyVT4cwXA392YMTh39epYjYlFbSw6fD5W0alQAeCvv+Kbx+9n5O2bbzbOK1m5kl2r/fvj2/fp0wig0tK0QdTmzWxfZ85oz+n1Iog1cn+PH58AICURRgCI2+2OG4B4vV5JT35OTg4UFLgFrwpcbAjAnDlyBSQxyZwSlvv2laosUY6DyWSCLl3Y6nylSnIjPI7jYPx4eXJdoQIPp097oublJEaHlSvzYLU6wWq1Qm5uLmRnm4TKzA03UAUotv+ePbH6cvAgA1GTJ7PkfONGL7zyCh3Pw5o1XqGta8oU/Owdd8gJ8TYbHl/37iHhuH7/HasIYt5MzZpByMnJAafTGWnvYudCK00WC2un2r4d57BapVUiBE8MzLRowa5ft25BWL4cgVrFijwcPeqBChXE8rgoXtC6NftM8+Zh4TuNJqxHX/9mzVjbGP3/9PQw2O3obr9jh1f23YqrRWKAW6oULwDUVat8ot+YOm+ECi9YrVZwOp0CIHE6nfDwww/DtGnTYgYfAGhE2Lp1axg3bhzwPA9+vx/uvvtu6Nu3r2TcnDlzoFSpUrBHhRhowIjQRQh5OPLoqUewzf6Fknr2JeKfGyMIlsa0tjAhZHBkfFEBkHIEqx/P64zTBSBdu+INb8RUjcann7IEV0uBxwix/PXXEfzUqIHjtYJWLUqXxoQ6nrBYcEW/Xj2Ae++NjQTsdLLErHRpY+7kn3zCWnRq1ECid2HD40FDyVtuEa/0sQR4+nRU0zLiIl9UEQqhEMFrr7HWNPbHBIHQqlVFIy+8ahVKOeMfWWMgUC0sFmxRK1cOQXIsxpHi2L8fz7NFCwR8RiIcZufRpUv8gHrKFKx4tWunDmzDYYCWLelKpv6c06fj2EaNtAHV6dMAP/6YACDFHaFQSBdEcBwnJGQej8cw+PB4PLJVZpoUjxrFktImTcKqrTSjR+O4m25iK/4//eSNtOo4JPPOni2tWixZIq9sbNvmhRkz/JKEnBA0OoxejWe+ILht2eIVJchO6NyZHhsCizZtGMAoU4aHa9fsYLO5hYSeOpwTAvDBB35YvZod76RJAcGM8IkncGW/d285AKHcClotqV6dtV99/71bGNe0KbY/cRwHZ84wYJKczIwF9+yRmjOOGhWA336TSxG3bx8Ci8UChw4x9/ayZXl44gk3DB6Mc/TvH5AZQ27fboaff5ZWimi7VqlSPJw7x0DfxYscHD7M/s0Uq6RqXCtWyL9Tur3zjj8CvsKS8yUEoHdvBpSUJJ0pGBHzRsS/2z179kC9evVg5MiRMGjQIBgzZozEFDDWMJlMMHToUMjIyID09HQYO3YsOKMSl6VLl0KNGjXgWJQcZrdu3aB9+/aQnJwMdevWhQ4dOsDy5cslYyKPm86EkD0EO2NOEKn4UCISIURlQkhLA1vFyPgcotyCdZgYb8FKJoR8Qwj5wMBYTQBy5Ah7wBjhMABgItO2Lfop6KlVzZlDV6GUgcqFC9jGVLeudg87APo2UNWdWGRPxREKsVaUypUxeTIaHMc4FKVKYduSVvj9dHUYt3bt9HkmenHhAvo5UM8QunXujIBEq12mOIPnsXoyZQqr/NBt0CA8Bz2ZZb04eJBJHyclAcybF38Cf/YsAgBCEDzZbPHN8847+NsoXdp4NWX3bnZtvv46vv2K72Ot3+WHH7Lfvl772sWLTB1u/XrtsQkfkOKPWAEIx3GGwAfHcRJXcjEHxGQywcaNBZL7edo0ZZWrDRu8UL9+WKg2YNIdhjNnHDJQE92yVa9eWDVZpRwD8fbKK9IV+ZUrpXyHZ58NgMPBwaFDmChPnRqIrM4HI4sY0jk//RS9Ruj7jz8ekMwllvPt3j0omBFSNar77pMDEJMJj436dFBzQofDAW++ya7pbbex9qTt2xmoSE1lgGXZMun5DR8egHff9cv2+cgjONenn+L4atXCcPVqHpw+bRLamhjJmwGwK1dyYMIEv/BvQgBataJtUOz4Ll5EF/WkJB4GDAiCy8UJ48QgonXrsGJVi275+RwsWOCTtOIRAtCoURB69cJrqcUjigYjDodDqHi98sor9NkEhBCoUqUKPPTQQzJw8E+JEn7MJeJfHluJvL+PEEIKiHGJteWEyfjqRSohBCZMmABTpkyBKVOmwJYtW4QfO+0fv+km48nbunUs6dNqf7HZmNLT7NnKY4YOpas+2gpCPI+8BUIw6Yx3FZ1K9xKCbtJGw+vF1XF63nr+Dbm5rLJECMCDD8ZmVicOnkfi8IABzEWbEFyxHzkSeRv/5AgGUX0rKwvbnKjRZLlyuBJfGAnlvDzpdc7Kil/W9o8/GKG7R4/4FL58PgSahKDiFGdQIIF6zNxwQ/zHTxXpWrdWNzh0OhFo3XyzfmsVAMD99+OcPXvK39uyZYvwTJkwYUICgBRzGAEgHo8nJgDicrkkK8hOpxOcTqdE8vbKFROkpbGEvXRpHvbtkyeCLhe2YjVuLPW9qFgxDB98UAA5Obmi1hhMZMXjNm9WVkn68ks5ibx0adbOhKBLqvhUsWIYCMFWHrebgzVrkFshBkfibfBgvG6ZmQgABgxggOP++4NgsTCy9w03hAWZ4O7dsc0sWtmLECR9cxwHDz0UjOwjKFSCxo9nXI9+/ViiLfa/aNSIgbLnn5fO/5//+ASX9+rV2fWmwIy+RwHSli3s+GvWlIKvdu38cPVqnkR6GDe8Vhs24HU+d46TqIPVrBmG1aujqx+4ffWVevUDQQMnqGuJt2+/xerKSy/5Fb1GjGzHjx+HESNGQPPmzSE5OVkAIoeNkvWKOUr6OZeIf3c8RbANS0mGt3fU2NoKn/+IELIy6rWPCSF1VPanWgGx21lFYelSYzcHz7ME68EHtcfOmgWRVRfl1dYdO4yDgW++YWONtD0pxebNLIGfOtX45/x+5J3Q/S9bpj1+3z7Gx0hKwkQv3pX5bduwtYpKvRKCniPvvKPd+vZPDasVOUHiqkiZMgBjx2J1J57w+xkvhBDk51y9Gt9c69ez38jw4fHxdI4dAyhbFucw6p9y8SK253Xrpg7W9eLvv9mxa4kzzJiBY2rX1gdIv/zCrqvWgmGiAlL8YQSAeL3MtM/tdmuOpZK9AwZ4oHnzAEyY4IdVqzxw+rRb0nNvNpthzBhMmMuWxQT0ttv8kJtrVlQjmj9fvjLfvHkQrFbpuMGDg5FnJiavbdvKnbk5jgObTepnQdWWqHIS3Ro1ik6gcfvySxecOJEj/BuVn6SKVVWq8JCXVwATJmCi3rEjAwJt2/rBbGbu6NWq8QKB+6abECQo8VXmzUNvDLqfkSN9wnczcCCrdIh9O8TGgWKn8kGDpBWWvXs9QuWhQQN23itW+MDt5gQezsKFfigoYO7nrVuHZFWqMWNcsHKlVbgm9euHBO+ORo2wknHqlEfC88C/qQFo3x5fE/NJ2rULC61jalu0QhchAEOGKFfWYtncbjfMmzcP+vbtCz6fD/Lz82H58uUwZsyYuDggxREl/ZxLxL87yhJC/iYIGpIIISmEkM0EzQjFMZMQEiLYC0jjPULIFULIY4SQrMj2CMEewUYq+1MFIB98QEu7xisK33/PHhBaHAyzmZGS33xT/n44zDgLPXpoJ+h2O7Zo4aqRseOMjuxsBEJ0f0ZJwsEg7pOe80cfaY9ftw7J8UlJWP2Jl5tw4ACruOAfCvTg+OEH9dXt/6XweLAdSEycL1UK1aDiIZXzPMCiRUz5Ky0NKxrxxMKF7JjUjCn1ghoUEgIgKjhqxhtv4PiUlPiJ9VlZOEfLluq/8bw85kGycKH2fDyPSliEYLVULRIApPgjHA7HBEBcLpfqOLvdDiaTCR54gKkxsQUCXnDrpttvv8lVrubNYyZ5ZjOCEZfLBWfPiqVhce5oCV2O4wRHcbGT98GDyv4hp09L5Wbff1++wj58eFCyT7qlpwfg2jUTrFtng0uX3NCnT0iWNBOCZPMlS/CYGjYUS+eGwGQyCQpSyck8fPyx6//Yu+7wKKqvfVOpoYXeq0loCb2FEiA06S1IR6SoIAKiICKIFIEfiIpUBREsgAKC0jtI7yi9hJZKyibby5zvj5M7d+7O7O7sshj02/d57gPZnb67M+e957znzVkOCQgt8ZKO8eN5Ivb661pRLN24Mdv+hAks8Kb7JwR9N+jr9sH/jRss4yNtm3vihJ4T01+6pId8+RgJ+ugjA9y4kQjFirH9r1mjg+7dcb9t2xrg4cMEMUsydWo2XL6cIZI76b4+/1xOIggB2LJF/tlkZupE08aUFB1UqMCfT8GCAty79/zkY/ny5dCqVSvRx+PfgNy+z/nw30cZQsgWgqLzmwTLqULslhlNCHlGCKmd83cFwgTt9sNK3CQggoAlIoSoE6TSdehMfI8ezpedPJnNsir99r/7jmUIXJUQUd+MkBDPZrYNBkZ2ypRRrz2wWgFGjmQeGp9/7nhZQcBMB72B9urlnr6E4u5dVo5DR8eO6t3V1UKnA7h6FTNPq1Zhe90ZM7At76efYhvezz7DTMvixQBLl2KHpbNnAVxYyrgFkwkbGlStys43MBC/P550zjp0iHVna9fO8zbNkyapJ51KsNlw/5QMqWm3bDAwkXjbtp5lzW7fZiTMTtvI4d13cZly5VyXmq1cCTkBmmNtjI+A/PNQQ0CMRqNTAmIwGCA9PV1cZto0VgpUvLgNQkIEaNRInomQllbRlrOFC9vg8mWmHZHqPOrV44PvChVsYlcoOv7+W+4HUqyYIFuODmmnKyUdytatclE2Hdu2sUzN9OkmMeil73fpYoHz5/Wwf78h5zklwG+/seN78CA1p6yLPhtQw1GggA3S0tLgww/lwTgV5dMxZgyaDWZnZ0OlSmxbn37KyJm0BGv4cIuYAZIG/v7+AuzZY5ARAkIAnj7VidmZChVssHIlf1z796fAjh282PzYMb1IAteu1cF332Xl3JcFuHQpCW7dws9TapBYtKgATZrg90BaltWggZXLfmi1OvjxR2NOlkaA/v3N8M47eHzSDl5ffum8ZEvNWL9+PTRp0gQyn6dXey4gt+9zPvjgTSgSkD17sHa+bFk0KVOD/fvZTersWcfLPX3KaumVgvbsbJbRGDHC+T5PnWJlJcuWqTtOe9DynMBA5XaySrDZAIYPpzNezsmHyYREhV6bdu1ce5nYIzkZy3UCA0Fy88ZOVs+Dx4+xrGjhQrwObdow53c66GflbPj7827pZctikPzWW5hJ278fP3dPs9oWC3a2Cg/H1sYYEOBxm0zubev+fSTI9Hvz6afuH5fNxjQV/v4AO3a4tz4AXntqrtm7t7pjkJY8ff+9+/sEwN8UIUjqzGblZZ4+ZZ+nq/JLrZadx+LFysv4CMg/D7UEhGo6srKyZORDKjBPTU2Fp0+zuXKcpUtNDmei6Sz/K6/YoHhxXKdfP4voTC4lIdOmaWT3lIUL5VmQqlVtOcSGHUPr1lbOnZuO2Fg2a1+6tFzkrNHouMBeus22bRmp2rZNHrzPmmWChg2tnCh6/35GQM6e1XMaiVmz2Pk9fJgAM2Zkys53yBCegIwfrxVJEC0DIwRgxQoWfH/yCQbnlSrZYPt2zBicPctnn4oVE2DePFyOGv0RAhAaiqJ16rw+cKCFe79YMSs8fZoAo0YxEXrhwoLocVK4MJoFUt+S7t3NorD7+vVEaN6ckZmWLdn/4+LYedJj1ulQUN+0qb3OQxAzSZSAlCnjXLCuZmzZsgXq168Pqd402vqHkNv3OR988CYUCUhMDJ3pUf/DoB2g7Fpey3DgALbeLVdOuQ0s1YYUKOB8lttoRJF6cDBmXjwpPVqzhrXNdUYiALAL2G+/YfA5ejS7SToyTwTAGWF6LQkBeOMNx0GfEoxGnGGm5WqEAFSvjt2QvOETsmCBc2JRrBgG/LGx+LnGxiKxaNMG9QgtWgA0bYpkSMlnRDpiYgCqVcOM2p49nrUAtlox8KYElRA02FNbxkSh0QB06sS2MXy4+0TGYGCdsWrXRr8Vd0E9dggBWLdO3TpDhuDyJUqgZsZd3L/PiKyzznK0Q1vlyq6/s5MnY2Zl8mTl930E5J/H8xAQvV7PCcvT0tJAq9VCdnY2J6DOm1cQnbTtx5kzLBCWajK2bzfIxOxHjqQo3Hts8PAhbxr39Cn+W7o0X5LTrJlV7CJFh33XJyWRMjXOsy/DIgREL4rHj+Xu4b16WcQuUXTdjRuNYtZlyxYjVKnCjvHdd9mxXLuWDPPmZci22b07TxymTjWJREmqP/nlF3YeHTogecifX4C7d/G1777jsxg1a9qgb19cTqp7adQI2xMXLozbthfGx8Vp4enTRChXjq3Tpo1VzGSMGGGBv/5ix0zF5zqdDi5cyOauKfUYiY42QqtWxpzvhAWys5nOp317uci8UCFbDtlh7333nWdiczp27doFtWvXhgRvmU/9w8jt+5wPPngTMgJy9iy7ARw+rO5HIRWMq8kiZGUptyF98IDNuM+Z43wb06ezAPTqVXXHKcXJk8x/Y/Jk5zPQz56x46Img/iQcLze3bt4bIR41gb26FGc8c+XD8t0SpUCWL7cPQLjClu34ux1o0YAAweiwHnDBgymPQlu6ee6cSMSyb59MTgPDmaZCzry58fOZf/7nzxjJggYKO/Zo9w6OCsLWzzTz48QzGrcu6f+WC0WgLFjeYLkbnvdZ8+w+1hICJJqT8rqBg7E69O2Lbb7dYXkZKZXeuMN9/cHgAS6Th00WdQ5EJo/eMCIyvr1zreXlOS8W5mPgPzzEARBFQGhLXU1Go1im10qHM/OzgatVgt79/KlSxERNkX/Ba0WBea1avGBZcGCAmzZwjIrmZmZkJ2thapV5QHopElZig7WkyZJg2UMdJs0sXIz4/atewcPtkB8PK8B2LYtg9uGdEjbydaowROeqlVtEBnJH+/MmSaoUweXW7TIxPmbUAJACMCFCzpYsUKukYmJ4a/r9OkG0Gq1cOsWv+zx44zwSckBJWfDhvEC9FdftcDVq3pYs8YIoaFs+f79LXD0KNtniRL8Ndi4MQ327+fJ14gRUsd3A0yejH8HBgoQGmqD779HciD1gpGO2bNZ5mfz5meiHig9PR1OntRBs2ZWGbkkhGU/ChUSXArWnY1Dhw5BREQE3Pe0o8lLgNy+z/nggzchIyB9+uCPvnFj9QHzwIG4TuvWz/fj6tMHS78iI523Gz17ltWyOzAMdYqnT1m718hI1y1w162T30wnTHB8fY4fZ1qDfPnca+mblsaXbAUHowDZGwZ99rDZPC+LcgdWK8Dly6gZadOGLyWTnmelSphNKVSIvR4b63i7N2/ymYw8eVCr4iiotocgIAGi5Vjh4e6RGAAM1Ol3qUoV9/1LMjJYlqx+fXXtfanRJyFI/t3F06dMaO6M6NNyrbCw52tu4CMg/zzUEhBqKJiZmQlarZbLTGg0Go58UGJBRc60LOn11x13JGJBLwtwS5WyQmJiImg0GnG5CRMwaC1ShG+Pe/UqrxtJTk6GHTv4kq08eQT4+WdeF6DR6CBfPmlQjeLqy5f1oNVqITk5GR4+TBBn2ZXGuXMY7DPBOhv9+vFB9muvWUTDvnHjzNCyJSMgUrJy6JABNmyQ608aNeIzF9OnYwveXbv4bMnt2+wcpYJ8GpiHhfHn8+ab+NlI3dR79bLAqlVGmDXLlEM++HX8/QVITc2Gt982cwSA+pdUrYoaHVpaR0f58jZ48EAndh7DzxD/37SpVbwmzZuj0N9eD/Thh9mS7eF6JUvaxGMYP97zzlenTp2C8PBwuOlpB4+XBLl9n/PBB2+CIyC3brFgzJW5GMXhwywzoDZjooTff2c3sl9+cbycwcCctOvVcz8jYDAwsXxoqDrzvx495A+niROVl/3pJ5YlKV0aHbDVQBDQP6RkSbaPVq3Ua3D+TcjMxI5gw4YpkxHp6N7d+bYEAUvjpEL1Pn1Qd6IWW7eygLxECcyOuYNLlzALQkmEnfGtS5w8yQj1hAmul7fZeGNEd8vHAFipY8GCjknT7dvMbHDzZvf3QeEjIP883CUgVO/RvDmWyXTtaoH+/S2wYIFR1jqXBq5SofeGDcrC4DlzWPlR0aJWMcA9dYrfJhV0E8K3vH3zTaPMwfrBgwQuyG3QQNmETkoC6KhRwwqPHjFS07u3vA1wnjwCVKhggx07cEafCrWlw1403rChVQzYu3WzcK1w8+cXxGtVooQNOnZk73XoYIAhQ7QQFcVv75NPsPPYqlXp3OspKUwgT4N0Pz9mQshICd0OlnKtX48Ep0gRQcwUUfdye7+TmjUxm0Tb89JRpgz+/dFHJpmZIyGojfnwQzwPe9NAqlchBB3vqTt5amoqJCYmwoULSZxwHT8H9ve2bQZFrY+aceHCBQgLC3tpvT3cQW7f53zwwZvgCAgVZIeFqdMYCAJqAAjBmW1PZ9N1Oqw1JwR1J86288EHkHODc7/0ShDQnA9nVAAOHnS9jlarHCTny8dfIzqbjjd5gPbtAR49Undc9+4xB3a6/jffeEfn8bLDYkFdR926ygSkVy8sO3IFgwFn8+vXZ+uOHaueDJw5w8hfqVIA27a5dx4HDrCSsA4d3CcF0i5parxs/voLv5c1a2J5n7vIzmaZm1GjHC83ZQpqd2rU8Lz8z0dAcgdqCIhU6/HGG3ztPiEAPXrIg/s7d/Ri9oM6dxcuLMD163I9yN27fFcmalJoXzKVlcVm1Fu0YMQhOBi3Sx2sKRmRlixhZiMF0tLSICsrSwzQaYmQ/RgzJlvM+nz7rRHy5WNiZzpu3GDncvy4PGNhLxoPCRFg4UIMsuvUscGQIXzWROq/UaqUTdzfL788g9TUVFmp2uLFRsjIyIDTp9MkTuyC+Fldu5bKXSOdTgfXr8tLu1avRmL41lt4vDExWOY0YICZIx7S81+92si5rAcECKJWhBCA69f10KuXPCt0+rRe/AylXasiI23QrJlV3D+9rpRQaLVa6N/fJH6WdD3aSaxXLz08e/ZMzNK5Qz7++usviIiIgFOe9l1/yZDb9zkffPAmRAKSkMA633zzjbofw/bt0puP5z+qDz/EbeTN69xw7tQpNiM7d677+/nqK3a8S5eqW4cSLOlo3ZrvfGS14sw1fb99e3XtaK1WFJnT2XdC0OAuKcn9c/svYM8e5a5bwcFY5nfihGuSe+8eL/yvVEkd0QTAbFhkJA4/P+zg5Q5++EEaoLhHyG02RkKLFVNHXhcvZmTcmRGgI9BSLn9/x2Re2rrXmWjdGXwEJHegxgdEqvcID2dBddOmVhg92gzLlilnNqgzdefOFnGmvHFjK2Rm8jqLtLQ0iI5ms+W0JIcQgC+/5DtdDR2KQW3RovxM+MCBPAnSarUwdy5PCnr0YK7uSUlJkJaWBgcPauHDD02y2XhCALZswW2lpeng2TMd9OnDB9Q0cNfpdJCRwcqK6tfH827Xjl/e31+Ab77B8yxUSIAxY3iCUq0aIxghITYoWBDPcdMmLHPjO3IBd92p4N3PjxGQpUtZZqRoUSukp6fD8eNa2XnSLA5tBCD1FKHHPXZsNhf4//mnXiQsDRtaITWVdctq2dIKu3fLCVnRogJ8/rlJJESUMBGCTucbNhghIsIGhw4ZID4er3fevAKcOKGHAwfkxKlwYZv475UrfBleamqqKjJy9+5dqFWrFhw6dCi3f4peQ27f53zwwZsQCcj77+MPv2xZdXXoVisrherVy/Mf1N9/s5ljZ6RCr2c+CI0aqTcLpDh8mAVSw4apCw6pcRshSBIWLJAHhgYD083QwFPN7PeDB6h3KVcOt125MsDu3e6d038RZ85gAE4IlkOVKME/mPr1A/jjD+efn80G8PXX2EmNrvfWW+p0NOnpmM2j633wgXuZqEWL+HXdQXIyy0q0aOH6O240st9ggwbu/yasVhSjE4JZG0cYM4bdG5xpsxzBR0ByByaTySn5SEtL44L2HTv4QHDdOsd+Cz/8YMwhvwJs2mQQsxyTJ5tFkkDb7f7vfxliAI2TAjS45A3lTp3Sw7ffGkAuChdg82a++9GpU/yxBgYKcO+ess9Iv37ygLl4cQHu3mVZDkoe6GjXzgLR0RaoWBGdummLWNqtyd7sb+hQC9cClwbwdFSvzpbPk8cmmvf9+COeV2gof84rV7JrT8lPQIAgli4NGsT2VaWKBRISEmDNmhTpvyUAACAASURBVHTZeZ47p4e0NJ1IwuwNFRs0MMHq1Wy9gAABUlNZ+dXcuSZISdGJPijLl6N7+gcfmLlr0Lu3RfxbqnmJiGBtc9PSdPDZZyYum0KIAHXq4LWipIwQgC1bDDB4sAW+/BK9aGipoP2QmlpKvx/x8fEQFRUFO3fuzO2foVeR2/c5H3zwJgoRQuDRI40o/FVbzkHNAv39Aa5f9+zHJAisfW9EhPPAnZq/5cmDpMUd3L/PWrc2aqSuBew337CbdMmSysFrWhqSCLrctGmuiY0gYFchqhnw88PuU/8iM1YR58+jMaIzUztPcP06ErlDhzDI/uEHvM40WCYE2ye7ymzcu8e+X/igVqdTMhoB4uLYeoMHqy+pEgRm5EcIZhncwYEDTIc1fbrr5c+cYVnBefPc2xcAwL597FgdEWCpaH3BAvf34SMguQNHBMRgMMi8OFJTUyErK5vr+BQcLMCBA8ptTzMy0KG6Xz8LTJ5sgt69mbP4zp18G9+7d9NkOgMahPbtKy/xYsG6sjeHTqeD7Gy5CHrSJBNkZWWJfhR0/1u2POOWozoJat6HASvv8i7VUmzfboBx48w5AbuVI1N0lC5tg6Qk1jVKqhFp3NgKMTEsY+LvL0D58ridtWuNitoN+vr06UbxuGiplU6n44L8Bg3MkJiYCFOmZMkIyL176bBvHz0uPvAnBGDixGwYOJAda3i4jSu/unFDL5KzfPkESEzE/aem8maPr79uFq/LokVG8frQ89DpWBZFun9akkebBBCC5XlK37ns7GzIyMjgvlvSMXv2bJg7dy6cPn0aGjZsCJufR7j2kiK373M++OBNFCKEQHq6BjZtwtIVNaVDBgNAxYr0xuP5j2n9enajPHLE8XJHjjCNgLv17tnZuG6tWjhb7KxdKMWGDSwIrFlT2TgwPh5JEyVhy5e73u6zZ9ialp5z+fIYZP/bkJKCugF6jcqW/Wf2e+4ctu6VPmBjYgD+/NPxOjYblt7lz8/WmTPH9Uy+zYaNBug6asvq6LoDBjDy8/PPqk8RAJhA3M8PCYIrUF1UcDBqQ9xFly64fq1ajrMoU6fiMkWKuN+u2EdAcgdKBESv13OzydL/p6amwoIFejFIJgQzBdeuKXt9PHumk83005lsWjZDPURohygqZJYGr1IPCZ2OF67T4PTwYfkx0Pa2NHjPn1+ArCz2PnVxf/IkAYoUYQF7UJAN+vXTwcOHz8ROXzqd3F+EjshIqyi6RuIkyEgKIQAnTjANxKBBLNju1csCw4bxTu6U6C1fboT0dLnXCBX1S8X29euzwNzemV2r1UKfPvxnkSePAE+fJsCMGRqRQNgTgD/+0HEtePv0sXDlVzqdDtq0obodi9ht6+uv+YwRbTvcs6cFRozAc69WzQYaDfs8tm83QGAgCvzpelTnUa4cIyLnz7vWdkhF7AkJCfD48WMoUaIEvc9AxYoVYebMmXDlypXc/hl6Fbl9n/PBB29C0YjQFT7/nGUjHj707IeUlobeCYQADB3qeLmMDIAKFTAYGzHCvXagNhvrYBUc7DxQpdi8mc0ot2mj3NL16lWWUcmXD7UwrrB3L2+g99pr7gdy/xSsViRLd+5gy+M9e7C97YABSOakruf4QMeSMo3mn2nre/o0tueVHkPnzsreMhR376J5Yrly2PmpTh11mTuqsyAEICrKuTmmFEYjenvQ6+NOVy6LBY+1bl0kwE+eOF/eYMAWwhg0uF+K9fffWJ4YFOT4N5KezhzPp01zb/s+ApI7sCcgOp29x0cmpKdncLPIt24likEhLYkJC7PBkyfyIPDRI74Tk/T32KaNAdLSMsRlaQAfHMwEzbTMp3Jl3kvkyhW5JkApU7J8uTEniJV2zTLlZGjYeSUlJcG77/IB84oV6VyZVmpqKnTtSomPXDNCS84IYSVkxYrxy82YYRIzJD16MALSpIkZxo/P5pYND8dtLFmCbvL2+9u0CT8vqZh7yBC8BvbLjxiBZW+1a/MEqlIlKyQlJUGXLvLrSQnbvn38ex9/bBI/l3nzTDk+JOwYevRAEkI9TwjhWydv3mwQy72WL5eX8H3zjbwcDr9vuE716jaX5EOJjDx+/BhGjBgBpUqVEkkIIQSio6Nz+2foVeTybc4HH7wKtwmIRsOIw6RJnv+Qxo/HNrpFizruciQIGPTiw9B9j4Zp09hNTk2Z0G+/sY5XzZsrl10dPow+FS1bolbBVXMNvR7dv+lxFC4M8OOP7p3Hi8LTpyimnzULMwsDB/IeHJ6MgAD8fgwfDtC/P5bsHDjwYsjWkSN8CRwheK0dwWZDYTnVHOXLhyVSrkjTTz8x0lWpEvqPqIFGg6SFfn+dESR7PH7M60FcdaCSNmiYP1/9fijWrHFthLhgAbtu7hgJ+whI7sBsNovkIzs7G/bvT4U338yGrl0NEBVlhcKFBThyRF7aMmyYNue7bhFn+Vu1skB6ulz0K20p2769AWbOZGZzc+cykXlqKpu1HzqUzdTT0qxJk3iPB3s/C0IAjh/nMyU3bigF1gLMmqUTfSaSk5NBq9XC5cv8sh07mmS6gi++4DUUUlLVoYMVSpbEv6mg2968r1EjKzRrhtdDWnJVpYpFzELQERFhFa/RhQvy89i6Fc9VGvyfOoVZoE2beDI1caIZNBp5GVezZlbQah1ndtq0McCkSUiMaMZr9myWfbpxQ8+1EyYE4IcfDHDgAE8iaFareXMrvPkmfrYVKtggI4P/rmRl6aBmTemxCOKyhABUrmyVZcPUjszMTOjWrRvMmTMHzpw5A9OmTYOIiAhYvHjxP/67S09PhyJFikBMTIzXt53b9zkffPAm3CYgn3yC9feFC+MsuSeQ1p07C8Y3bGDLuXJjdrbulCmul9+9mwWZjRqhV4U9fv0Vsz54s3QdiF6/zpdctWnjecboeSAIKJ7ftg0zGV26sOBWOpo3VyYV+fPjtQkO5t3HnY1GjeSvVa2KIvLPPsOMgDdIiSBgdonub+1a1+ucPw9QvTo7rrg45c9bikOHkJxFRmLJmb17uyMkJjKPkhIlsKuUWuzbx8rc1JD9KVNoUOe+TkoNdDo8d0IA3nxT/Xo+ApI7oAQkKwsdxYcPz5b9Jtev52eps7Oz4eRJFiy/9x7TFbz2mg5SUvgORL//zgejM2dqYMwYDJADAwU4epQFlAMGYDDbrp0VmjfHAJx2eAoMFODMGVZmxdzOWVDdurVV5oQ9ZYoZvvvOCPZZi5499RAfn8J1SqpXj5Vh+fsL8OgRnm96ejqUKWMF1CGwANk+qxMdjcdPO0lJxdTdu1ugfXsWrLdoYYFJk7Q5908bfPkln7WgBOTjj02ygJ4QgJ070fdCGqzTc3/vPb7UauZMkyxrNGaMCfbuNTggaThmzNBA3bp8uduAAYac+7cVduywbwiAGpB+/fjSN7rMqlVGsWRsyRKTjCR89ZWRW54QbElMr7X0u+LOyMrKgri4OJg6dSoIdrNJVjcdVBMSEqBv374QHh4OYWFhMGrUKMhy09hp4sSJ4O/v7yMgPvjgAm4RkAcPWAD++eee/YCysnAWGR9Ejmef799nQu24OPdKe06dYsfZtavrsq2DB9E/olAhnLFWCoxXr2YzzHXqYPbAGTZuZF2YWrVCj5B/0tfDZsO2tZMnYxkP7SylRC6aN8eM1Jo12GHq5Ek0QExKUhZfJyWhjqJ8ebady5exFezRo1iS9tVX6OgeFeXYbLBePezeNGeO540MKAQBYNcu9eVHWVkodKfHUqWK61bSly4x4lKggDp9BgBmFqjHiLtu6bNns2N0ZtAJwEqx8uXDEj9PfTucYdUq/M64Y5LpIyC5A7PZDJmZaGh3/Hiy+D0KDhZg4kQjbNpkhPv3lYO6Vq1oKZEJZsxgxOXDDzWcZiQ1NQ0qVmSBd0CAAL//bhBLdKpUsYnC5d9/N8DQoRZ45x3e86FAAQHatbPCzZuMgBw6JA/KCZHrReigGgTp2LiRX3blSj5z8MUXLEim+gjWsldehhUWxrQNlMTQ97p2tcDHH7NgvmxZK+zfnyL+vXat0W5buI0pU8zwyy/yc9271wAXLzLyEBQkyD4bOhYvNsmyIoSg5uPbb3Xi+tLjJQRg+3aenBQsaINSpXDb772XJZbi0VGxog3u3dMptjWuXt0G775rziEVAqSk6ODzz43wwQemnNI/nehqLh0hIbit11/3zOU8OzsbRo4cCW+//TbYnvMBazQaISIiAkaPHg0AABaLBTp27AixsbGqtxEfHw8VK1aERo0a+QiIDz64gFsEhM7mV66srpOUEt5+mwVwjjw/LBY2G1+hgnsz5Q8fopEcIQC1a7s2ojt+nAmUu3QBSE3l3xcEgE8/ZTfMli2VRekUej3A6NH8rP+FC+qP/3lgMqFeY/Rodg3oqFsXy4BatkTPku+/x1lyNyeIOFgs6GB/4IDz5QwGzBisWAHwxhtIPAID5R4rYWFYNnf27D+jJQHAzBolioGBmJ1x9hy7f5+RkKAggE2b1O3nwgVGqCMjXWdcKGw21Lfgwxrg1i3ny585g8aBhGC2y9swm/E7RH8vauAjIP88BEEQRdi0XSk1g6NB4qVLyuJynY612Q0MFCA+XgfDhrFZ99Wr07nSJdp9iQampUvb4MABvajN6NuXiZd37lRqs8v7btByHVriJC0tql3bxgnNqQZg+nSpB4YAgwbJA9rz5/mAu359K8TFmSEtTSfOxNMgXaq9oKNKFYvD96pVs8G2bYxI+PsLcOVKkvi3fZvfKlVwf+PGmWH1ajl5OHTIkJOBoL99po2YMoXPgKxZYxTd6aXkqWhRG4wcidelaVM+01GsmADLlhnF/z96pIXly9k1PHs2GVq14onR4MFG+Ogj5nUi3df8+SaxxG7UKLOohcE2u1Z47z19zneEkRBqzhgaitkoT8jHhAkTYNiwYc9NPgAAli1bBkFBQZCWlia+dvHiRfDz84N9KmebBg0aBMuXL4c2bdr4CIgPPriAagJy5Ai7EW3Z4tmP5+hRto2vvnK83Cef4DJ+furaplJotazmvnhxzNg4w+nTLCh85RW5AaDNBjBuHDvmHj2cd0+6fRuDS7p8r17OyYo3oNfjzPjAgVgWZ/8ga9wYNQFXrrxczupGI34f3n+fL4diDyfMyhw69OKP+9YtJEUY7KBPjLPywuRk5rju54eeI2pw8CAr8+vUST2Jf/aMdZ2rU0e5MYIUtIuWvz9ms7yNXbvY56TGu8ZHQP55WK1WUXD+7Nkz0Gq18PvvfABeurTNIQnRaHTw/vtmOHxYDydO6OHXXw2icDpvXgH27GEk5Ny5JEk2w5YzUWPhOiVRQbJGwzo7+fkJYiAbFmaD9HT+GIYN4zMONNiVkhWtVgspKSnw+++p3Lk1ayZv5ZqdzXePoqNLFwvX1lZpREZa4O7dVPE8S5TgtRF+fgKcPMmXuN24oRHJyooVPMkoV84mBuvLlxvtOlRhRy1WsoSfFT2PPXt4YrBpkxGOHjVA69b8MTVsaISoKCQenTvzpKV3b4solO/fH8XtVL/RqJEVkpMzZYaQCxZkiG1zGzVi2yteXICdOw1QvLgg8xkhBKBqVYv4mYeEMKI3YoQZAgIERbG6q4Gkczr07dsXLO523XCA2NhYiIqKkr1euHBhGDt2rMv1L1y4AOHh4WC1Wn0ExAcfVEAVAbFaWWDfqpVns9M6HQs0o6MdB5WnTjHDwKlT1W/fZkPjwDp1cGb62DHny1+4wAL2qlXlnYZMJiaAJwTbDTu7z23ezMhMUBA6rb/IWfzbt9Fvolo1Vm5GCF67tm2R4KlpOfwyQBCwfOuTT9j3jI5KlbBsaf58eXbKmzAa8XrSzFulStj21xE0Gt5xfeZMdZ/3pk2oBQkLA+jdW30G6uxZRl6GDnW+L7MZu2Fh8OY6C+gJaFYmPNx1qZePgOQODAY0cdNqtZCVlQXZ2VqIjMQAkIq/S5e2weXLjjMhI0bYt9mlAbgVzp5NhuTkZEhPT4e2bU05gSmbaR83Lhv69MFgOX9+AS5cwP1QzwhCUAtBMydTp/JZiy1bmNkhFToTgqLltDQMQqmQ/NGjBI5c+PsLnCs7Ha1bKxMNqei9enX5MvnyCZCRoYMdO/Rw40YmBAYquavzJOiPPwzicS9dahTdvQkB0Xiwfn1LzjXlt3f6tB4mTmTXskYNRkBWreLJzO7dWGpm7+b+2mta8Tj58xZgyRKTWP60dq0RNBodFC+Oxzd/vgmSknQweDC/vVmzsMFAYKAAJ08mia2Np05FHUZqqg62bDFA3rwClCrFzqdBA2MOUbGK5zlgAJKey5f1olGhO+Rj/vz50LlzZzCpNWhSgTJlykAvBVflunXrQosWLVyu3759e/glp07WR0B88ME1VBGQNWvYbO/Fi579cCZPxm3kzeu8jGTLFiyJadBAvfkbAIhO7kFBrrtMXb3KNBEVK6KnhxRaLepOqFDbmcGg0chnSSpWdK0l8BQ2G5Y8deokz3J07w6wbp3njQFeJty/j+1vo6Ox4QE9T5qdcEYMnhdbtzIiGRzs3EjQYEASQY/vrbfUEYqVK9k6o0apJ6rLl7P1XHV1u3mTmQeOHKlu++7gxg2m7fniC+fL+ghI7sBsNoulKlSMTbUIAQGC2D61TBkbXLkiJyHr1rFyqaAgASpVskFEhAWKFbPmkAcLPH2q5Uq2goIEGDuWlfKsXJkGlStjIDtsmB4yMzNlrV/pzH1goACnT7PjSE1l2ozBg3lR+pw5RruWwhnQqRNPHOw1IDqdDqZONUPVqspdoeiIjlY2Gzx4kG1PqQxr6dIMjkiMG6eFqCg8twULTLBwISMUVLMSHq5MiC5e1EOvXowAUE8OnU7HERNCMFui0+nEsi52P8oWz0NK4AYNMsPmzUgM/f0FePyY72a2fz9ub+hQ9lqhQgK0aEF1QUZYsCAz53kuwLVriWK747S0NPj1V63dtcP/0+Pz8xPg6VP3S64o+Vi2bBm0adMG9K7MnNxEcHAwDFXwBIiOjobw8HCn6+7evRuaNGki/u0jID744BouCYhGwwS0o0Z59qM5fZoJuBctcr38nTuuW4JKQQkSIWjK5gzXr+MMNCHY0cd+P2lpLOitWtW52P7JE8wI1ayJy3ftiut7G2lpKGKn3ZTYrBhmWl5WPxFvID4e4MMPWetnOpo0wU5nRqP393nrFpry0X2NHOm4XMpqZZofPz/MoqghzjNnsu1/+KG64xIE9MJp3hyJtqsW0CtWsH1s26ZuH+5gwgTcdtGizomvj4DkDoYPHw5Lly6FBw8eiAQkM1Mn1t4PHGgWvSzKlrXB1as8Cbl/X8cFkkuWoAD9t99SRV1G+/ZW0GjQGZ2a8M2bZ4I2bTB4LVzYBuvWpcHkyVnw+HGCaAxYtiwLvAMDBZEU1K9v5czr5s0zwZQpJpnwOThYgL//xsA3MzNTXFYa8FatKveUiI9H/xKpd4X9aNRImRTMnImC6qdPdZKSKQEiI3G/b7+dzZVSNWlihPbtDSIZWbyYETNa/lSjhvK+rl3jCUhsLPNBoaaO0mXT03UywjR8uClnHzwxmTvXJDq7N2tmhSVLeP1IcrIOkpN1nL8K7fxFCGZcqFv9iBEGjggmJCRAt26MYObPb5Odp72rvTtj7dq10KxZM7e9y9TAUwIiCAJERkbCMUnZhY+A+OCDa7gkILS1Z6FCjv06nEGvB+jZE2eVGzVy3yTNFQ4cYDOxvXs71wvcucPMAEuWlHfxSUhA4TohWMrkrPXviRMsQ1KuHDq0e1urcPs2kj46k02D3K5dUWz+Mmk6XjQMBhTON27MP6hLlMAmAZ58N50hOxs7SdH91K/vWFMkCEgiqCdJp06udRqCgBkTun21XeV0OmwoQAm0s45aggDw6qu4bPHi7nXfUoP0dIDQUMgJvhwv5yMguYPr16/DzJkzITIyEtq2bQvLli2DR48ewWefYbYiJASN6IoWdUxCaOtcOlu+bl0apKen57S+pRNTZtBqdTB+vBny5RNg2jQzPHigE2fdo6KskJjIfEYSEhJg7FicnadEpmpVqxhAz5vHt3C9eVO5leybb2aL5EOn08Gff8qXO3oUzwfdxrHUafRos6h3UAqwZ80yyUwG6TKhoWiaR00aCQHo00eX87u3cJ4bnToZYeBAfK9vXx0sWZIhvkdND6tWVSYgt27p4bffmNajXz9GQJjhIB5jfLwOjh+Xt1hu2xY/O/uMzqZNRpGUxMWZOeKSNy9226Idw6goPyoKt1Grlg1+/tkobu/KFb1Y4peWlgb79j1TPJ9q1Swi4bFvpax2bNq0CRo0aADPXlCav3Tp0oolWJGRkU5LsNatWwevvvoq95qPgPjgg2s4JSA3b7KAXU3mQgnvvovrV6oE8Ndfnv/wlHD9OtNxNGzoPOh78ACF5pGRGDRdu8a/f+8eyzDkyYOmhEoQBCyFoaQnNBTFxd7ErVuoZ/HzY/4WRYsCvPee+2aM/0WcOYM6CKqJaNMGO5m9/753dSKCgOVF9LMuVsy56HrJEvbAbdHCdQMCqxVL/eg6GzaoO6579/D7gMGF84xLUhLL+HXu7H1d0tdfM8Lu6PftIyC5C0EQ4Nq1a/DRRx9BnTp1ICamO+TLZ8yZDTfCn38yElKunA2uXZO2w9VygWTevALs24elSNK2swsWmODhQx0kJGAmIizMlmMwh9sdOZKVg2VkZMCBA8z0jwa59erh9vLlE+DqVT74ZEJ0PsCWBrPZ2TrxPOh+Gzdms+30tchIKxw7xgu5qbs5IQCLFxuhb1+LbH9USI3fd/b/AQOQZFSubOOM9tq0scIHH+A5YQaHrdOxI+6/QgV7jQ2O+/eZezwhAKNHM30M1Y8EBtpgyxYDZGRkwpdfZsiOi7a9bdqUJznSbl3z5xu57BIV71PSQrdHieKXX5pED5cePSxw544e4uPZZ9C+vdy/pWZNs0hY9uxJgdRU3ktGzdi5cyfUrVsXEr09iyJBbGws1KtXT/Z6kSJFnIrQR4wYAeHh4VCvXj2oV68eREVFQcGCBSEkJATq1avnVhtfV8jt+5wPPngTDgmIIAB06ICZi7593dNjUBw8yG56Cxd64dcnQWoqIwzlyzt3Zn78GIXMhGDWwl7Hcu0aI1oFC2LnJSUYDChGp+cUFeW605Y7uHcPHcRpuRohALGxAN9843pG/f8jkpMB5s5lJXD085s+3btlaSdOMAO+5s0B5s1zHMivXcs+v6goeWc1e5hM+BljQIE+LGqwdy/bj7PsAwCSaXo8y5er275aWCwsazhggPJ18RGQlwc2mw0uXboETZseybl37oHVq1fDnj0pYvBevrwN/vpLD3fu3IHExESxkxIVURcpgqaBWq0OBg60iMHl5s1G2L3boNgJiRBsF8tq+RmpoC1i/f0FsctSixZGzvRw6lSp8Bq3L/XxoIM6d9O2uoQIIqGigXahQmjqR0vR7Mebb0pb4/KExr7MiRCALl0YiWjThgX7FSrYYOlSk+J6tDSrbFllAvL4Mfpo0L+nTjWJ1026rblzdZCQkABvvpnNHae0FKxsWTzPPn0sMH68GebOxWMqV84GWq0OatXisz9XrypnnAoXFuCPPwySc8CsVUQEZs7++EOehfHzE8RuWoMG6bhSLdoeOiMjwykZOXDgANSsWRPi7cWaXsbXX38NwcHBim149+/fzy2b7CLl7suA+OCDazgkIJs3s5vI3r3u/1AyM9HDgxD0DXgevwl7GI04w0wDzsuXHS+bmIiZD0KQTNkLxE+fZrPJoaGOBc6PH/PlP4MGeY8UxMdjqZXUsO+VV1BM783r5gp372LrYG+TxRcNnQ4zdFKdSKFCALNmqffbcIWkJIA+fVg53IABjj//X39l2ZkaNeRNDuyRnc0yXfnyAfz5p7pjmj+fne+6dc6X/fhjlt1z9nvxBAcOoBaKELxv2MNHQF4+JCUBXLlig/Pnz8OUKVMgIiICoqMnQP78tGORFvLkCYdly5bBF1/g7HpwMBMzlyljgxs3UHsQHW2FwoWxFavU7K9MGRusXGmEli0tYlAsdTufNg2D72rVbFCnjjUnaGfB8KJFGWKguns332GKEIASJQTR5JCO//3PZEdAcL9ffWXMKZ/CID0lhc/gSEfnzha4f58Z+FFyRAgokqvatdkxv/oqb8r4449y928M3i0510i5BOvevWSYOpU5oS9ciATk4UPeUX3r1lRISEiAmBi+Mxb9nKRu7fTat22L+xw50gzZ2Tquo9fWrQZ4/31zzvPQxl2Dt94yQ8+ejHBK9xcTY4aGDU0567Fzop9r4cIC3L+P2S9pKZ4SGdFoNJKyuj8hPDwcbrkyQPICjEYj1KxZE8aMGQOCIIDJZILOnTtDx44dueXmzJkDAQEBcNJJj3MfAfHBB9dQJCAaDZvx7dfPsx/K0KGMIDgyHPQEgoClSPhQwK5QjpCSwmbH8+dH00Eprl9nJnTlyjl24z52jAnxAwKwXt8bpSxPnqAOICiI3cirVkXtibe1Mq6wfTsrZwsM9NxoMjeRnY3ZCUooCcH/z52L7z0vzGZet1G/PsCjR8rL7tvHDC7Ll3ftGp6aii1t69fHbNzVq66PRxCYOWiePM67g+l0ABERuGxYGHZ68yb69aOzrXj/kMJHQF5+2Gw2OH36NAwcuAT8/KhWIR6qV4+FxMQMUWD+zjtMuF6jhg0ePkRRN22xq9HoOP1Ev34WSEjQiaLl6tWZM/qFC2ym/bvvDGKgS2fkQ0JscPFikihcL1mSbyVLCMD77/Ote69f18Py5UbYskXuLi4ts/rjDwNcv24/04/brFvXKinZEkTyhc8zXn9BCQ7NMnTqxJdubdrEHwclMrTzl5QoSceDBwkwciTLKKxalQVZWVlw+DB/zOfOJUFSUpK4fzqw/I2Ro2LFBMjORnE5PZctW4ywfj1PXG7d0ovbmjLF53KC0QAAIABJREFUxJG0nTsNIvFo0cIKJUqwfU6YkCX+n5Zr5ckjiGSHEihpN6uMjAxITU2VEZE+ffpA8+bNYerUqVCtWjW4quZm6CUkJCRA3759ITw8HMLCwmD06NGQZdfHfNWqVRAaGgrX7Ou4AWDRokUQFRUFISEhYgnW//73P68dX27f53zwwZtQJCATJzLyYO+PoQa//spuaN9846VfXg5mzMDtNm/uvP1nWhrzlMibV1mnQb1DnM1Sr1nDxMXFizsuz3IHOh2eR4cO0ocjXitXngrehsEAMGaM/AE4bBiWm3Xrho7lvXqhj0vt2qi3iY7G0qFu3TD4HDIEszhLlmD3pQMH0JU+N4TyGg16ikiNGWkpmzeOZ9Uqlq0qWRJLtJRw8iRAkSLsu3P+vPPtPnzISgXLlFGn98nOZh27KlRwLsa/epV5xrz+uuttu4MnT/B+QQjqvqTwEZB/BwRBgE8//RQIaQiEZOTMfj+Fjh3HQLdufwEhABUr2uDgQVZm1aCBFZKT+SzE5Ml8WdHs2SY4c0YvlgX16sW0G126WGDiRDP88otB0t1JgNBQAaZN00N8PAtMX3tNlzNBwoum//5bIyvh0Wp1nGajaFFBLM8iBGDaNCQu0va0dJQqZeM0I9Kh1H4Xn0cYaLdsyWc07AN8SkCoK72S2J0QgDp1zNz+f/rpGSQkJMCqVenccvfvJ8GjR/LSJ5q9oOLxrl0tEBtrEcvF8uTBLFCTJux4ixcXRH2In58At27pxc+kXTsrvPMOy1hdu6bjskNhYeac7wO7xu+9ZwKtVgfbtxsUPVmkZCQzMxNSU1MhPj4eChYsSO8XQAiBxo0bw8KFC0F4kcZa/xLk8m3OBx+8ChkBuXyZGQEuWeL+DyQxkZXCdOvmXdHr2rXsBjt+vOPlMjNZSUtwsHPhsNmsLFy2WpmAnhAsuXneElRBAPj5Z1aaFhCAwfyKFZ5pbNyF2Yyf79q1AB07YpmS0sNPaYSFqVuOdmhiAQIGyD17Yke11auxxOhFmOPZIz0diV7Tpux4GjRQX+LkDMeOMXF3UJBjv5CrV1F3VL48El1HZIXixg3WWapaNXWdq27fRrJVtixA//7OSSwVjWNQ43rb7uDzz2mQBXDpEnvdR0D+HbBardCzZ08ghEB09CTRpbx0aQP06rUICLHmBJbHYPPmLJEItGtnhYwMFlBevszP0vv5CfDrrwbOQG/BAjYjnpGhg1at+MxByZJWuHcPiUdycjJotVr44Qe9HQHBf/v310FiYiKkpqbC06eZsGEDZmM6d2bbDAwUYPJkNpvfpAlmOaSmiFKSodWy0iT7/SkNmvlgHapwLFumTEDq18drKXdmFyAwUIDKlXkic/AgZgo++EDDXdeUlFTYtUsrOz66H0qwqO8IHbGxVln5VUyMVTQzbNfOCjdu6MXtbNlihNRUHXzxhQm+/94IEyey60Z9Xvz8BGjY0JpzTxQgLc39TlcajQa+//57aNWqFYSGhookpFWrVrn983gpkMu3OR988Co4AmKzMQ+MyEj3y4AEAVvE4myKawGuO5C22+3WzbE2IjubnUNgoONuVs6Qmcmb/fXt+/x6j0uXWJ08IVjrP3s2til+ERAELClbvhzgjTcw8Ka6BDWjYkUUxE+ZgpqQNWvQ/G7lSvQe+ewz1FhMnYp+EGPGYNakb18sJZKWldmP5s2RfDVujL4tu3e/WEKSmIheHn5+7BgGDwZ4+vT5thsfzzu3jx+vHPzfucNIUP78WJ7lDGfPsmxCZKTrbloA2JaZEqI333S8nCBgNosQ1EQ5y7IcPoxEVe131GLB4yUEz5dmm3wE5N8DvV4PCxcuBIvFAqdPs0mKypUB4uJsEBf3EAYOnAo1atSApk2Xid/9/v0tnKM1zQjQTkyFCglw8aIeRoxgpoPU2O/DDxkxCA21iWU+Y8ZkQ0pKipjdSEpi5UPly7OA2s9PgL17U2DAAC3Qsqn4+FRYsIAnQnFxjJDUqoUC7OPHlQXXY8eaxCwPDdKpp4XS6NDBkvPc45dBnYvclK9mTTx+ej50NGyIbWppCRUdSUk6SE9Ph969mQYkXz4bJCQkwNy5meJ1kK4j/dveC2TxYhPXShmvt1k8nvXrjWImq2JFG2Rlsc/24UPeI6RQIVsOqWHXd9Uqo9vkQ6fTwYMHD6Bu3bqwe/dusNls8Oeff8LkyZPh+++/z+2fxkuB3L7P+eCDN8EREKmhnxN9lUOsWYMz+oSgo7S38Ndf7EHYoIHj+nWdDluytmyJAe6WLe7v6+5dVitPCIp3n6dsJzUVYOxYvrNVXByW23gbOh3Ajh1ojFeunNy8j45y5ZAExMQggVixAolAw4YsSB8z5vmOxWpF7c/evQDLliFJ6dIFswBSd3M6AgIwaJ06FYNpb2g27HHuHL/vAgVQyP08ZoZaLdM/BAcjCVMiDI8fsyxScDBqbpzhwAFGGKOj1RHgefPYua1c6Xi5tDSWhWvUSDn7lpLCBPfOso32OHmSHcPq1fiaj4D8e3HqFBLVuDh+QspiscD+/fuhSZNN4ufdrdsdSE/PAJ1OBytWGHOCdgHKlaNmdDa4d08HkZH4d6VKNsjIQHNAGiwHBQkwZgxz8N67l/cladcOA3epczchAK1amWHRIpYdWLIkHfbtS+GWkQrIlywxia178XV5diNvXl7vodQBiw6peWFICFtu4EALl2Wg26pcWZnM0MyM1E/E31+AjIxMSEhIgAYNGFkrVcoGycnJMGQI3yo5JMSWc59nQvQNG3gtyt9/6yWCeUE8VkKwy9nTp6zd76ef8vqN996TC+sDAgRo0MDKkSh3yceTJ0+gQYMGsMWTB7cC7PUco0aNkuk5lLB7927o3r07REREQO3ataFhw4YvDQHK7fucDz54EyIBycpCnwNCcMbcXdy4wQKWqVO994NLTMTZeHxgOS5JMRiYpsLPzzPyceQIuwZ582K5lKewWDDwlgqiIyMBjh71fJtKiI/HsprOnVl9v3Q0bowB8vz5GNi7MuxLScEZ+hdBACh0OtTkfPQRBtdK2ZICBQB69MAg1pvu8oKAfhu07TIhWOq0c+fzbfPTT5HUEYIZoLt35cslJ7MMQUAAwA8/ON/ur78y4vrqq671QYKApYKEYPbP2Xft+HG27SlTlJeR+pq40wlv1Chcp2hR/D75CMi/G9evO8+GT57Mgu/Q0IUwfPhw2LTpD3GW/MMPTaL+o0MHK1y5ooeICBscOGAQg0+pdiI83AwNG+Lse9WqNkhJkXe5qlDBBp068WVF0nKnli2N8PhxghiM2wfMr7/OxOvXrulh8GCe0Pj5CVC1Kiszss9U2A8arOP/2T5XrTJyLXHpMTgSn1MfDinZKVhQEHUwUgf5iAjUqkh1HIQI0Lo1ko3q1c0519wEb7/Nrk3FirgeJYJ0hIXh32PGsDbEAQECNG5shYQEvFbx8VmcCSM9H5oBIgRg/36D2+QjOTkZmjVrBuvXr/eK1sNoNEJERASMHj0aAJAwd+zY0aUnh1arBT8/P1gkMT777bffwN/fHxa+BO0hc/s+54MP3gSXAdmzB2eh3TUaNRpZKUq1asrlNGfP4qysO/cWrRYzHjiL49jozGRipV+EAHz7rXvHD4DZG1riVaYMHq+nuHwZswmtW9MHEmYZvNVS984dgK++Yv4L0hEcjGUzX33l3e5jLxI6Hc74T5+O7ZUDA/lzCwrCz/eHH7xHjLKykChLzQx793buJ+MKv/zCSHhoqLzrGgDqUpo0YUR51Srn2/zmG3YdBg1y/R3S6bCTFiGYAXOmW5o9G2e3mzZV9h+x2TBLRgjqS9QSwbQ0ln0bMcJHQP7rEATM/NHv6YQJl2DIkCEQErIFCAGoVSsN1q1jpUMTJ5q5ci2tVivJXmBAO3iwWQzcx45lZOHOHT2sXWuEx491sGiRkSMVGEDTblUCZGVlS4zx7DMNRkhNTYWMjEw4d04Hu3bZd81igTwhUg2FMyKC70nLk44dM0CxYixgp8JtufYDR4sW0g5c+FrJklZISEiApKQkTljfujVmGqhWhy1vE9cjBGDGDA1Uq8auw8iRRrhyRccdk9SM8MQJvZjNkG7z9GktTJrEO9gTgl3AaIexHj0sbpOPZ8+eQUxMDCxbtsxrQvNly5ZBUFCQoqfHPic1sFqtFkqVKiV7vVWrVlChQgWvHNvzILfvcz744E04dUJXi0mT2KzrmTPy91NTWcnHJ5+o26bVioJaut0DB5SXs1gwcKQ3Q3eN1qxWbOsbHo7r16/vWecvAMzCfPghIzJFigC88453ZvD1epy5b9OGkSR6zqVLo8Zh27YXm7n4p6DVAuzahZ2apJ2sCEENRVwcljA9T+kUxZ07qAehpWeFCyMZ9fQ5ePYsfh6UDCq5m2dlscCeEABXXRoXLsTji47GcihXx/boEUCpUrjtyEjHJYtWKysfK1ZMuaXwo0fsM+jfX/11WbcOoF49zFr+/ruPgPzXYTZjlg5nzbEU9MABY06wmgVVqzaEevX+EL/za9cac/QED+Gbb76Ba9eSZB2m3niDBc1//MHPqv/2m8EuK8FKuOhrFy/qYdYsU84zhBeSly5thY8+yhT/vnr1mSwr0bw5K3ei+gZp4G1PKqRlZKVK4f+/+84IFSuy7bL1lQlIy5byDEi1ahZISkqClBS+29Vrr1ngxg15K2F7cvPjjxnc31u2PINp05DwVaxog/R0LQwciNe6Vi0bHDnCiBc1ESQE4M8/k0W9h/Ra0Va7QUECXL2qd4t8ZGRkQJcuXWDBggVe7XIVGxsLUVFRstcLFy7s1NUcALMl9hgwYACEhIR47fg8RW7f53zwwZt4bgKyZw+7sc2fL3/famVOz4UKYcceVxAE9FuoVAmJiyOTNasV4LXX2P7d7dql1WJ3JpzhQe8ST8Xmx47xnaLq1Hm+LAoAXofz51FUbB+IV6uGZO78+dxpdftPwWhEshEXx7IL7OGIWQxH/i3u4JdfWNBOCBKEO3c829ajR6zUihAsNbP/jPR6FrAR4rxTGwCv75g50/UxnDjBStv69XNMHBIT2Xk3baqsB/nhB7bvjRtd7xsAz5dmAKtV8xGQ/w/Q6ZjGKm9evCf+9hv+hnU6HWzZ8iuULXsuh6SYYN68fRAWFgZ+fn7w9ddfQ5s25hxyQGfwBWja1CoGylLTwZs3WeBNOzV17Gjhgv35802wbx9rKyu9d/j7C/DTT6yl7Zw5GTB2LB/gV6/OsgAxMcqGgTjRIHdLp212Z840QWQkW1dKkJRGq1ZyAtKggQmys7NBo+G1IRMmYPtintAIkJWlE93OQ0IE+PprVn7l7y/Aw4cJUKsWXuu33sqG+PgEMUuzYIFR1ILUq2cVj7dECStMmZKV8xxnmpmCBQW4dk0H48aZYeJEs1vkQ6PRQN++feGjjz7yeovdMmXKQK9evWSv161bF1q0aOH29ho0aACvvvqqNw7tuZDb9zkffPAmnouAJCez4CUmRrk8ZPp0dnN1JbylWLCAreMoY2KzYZcmZ+THGRITmWCeEIBp0zwL5DUaJAjsYQQwZ87ztdXNyAD48ks+iKUP9cGDsTvRf5l0OEJ2NgbDXbtilqlOHXZtOnTAMqLnuS7p6Zh1kV7vhQs9M4XMyuLLAvv3l3eTMpnw9ZEjXWcWBAG1WXR7S5e6PgZp+dbixY6XO3SI6UEmTVLed1wcyxCpbaBw8yYtcfMRkP8vSEtD89dSpQAuXpS/r9EwrYGf32MgpBTkyZMHFi9eDF98gcF0kSKC6OAdE2MRS5qobuPcOR0cO6aHKlX4jIW/v8CVDrVubYW0NNY5i9diABw5wkhMp056mWhdSgK2bdOLZoxIoJT+z/6lQfzQoRbo0MGquJ7SiImxQmZmJkgzJB07srImaQnWrFkmMcPDhgBdulhgyBAkGLGxVujbl+kzKlSwwaVLrPwKW+ciQQkMFODo0WQxS0NdzwkB6NZND0WK2HLOySSSvhkzTG6RDjqys7Nh+PDh8M4774DtBTzMgoODYejQobLXo6OjITw83K1tXbx4EYKCguCcM6fXfwi5fZ/zwQdvwmMCIgjY1YgQLN94/Fi+zI4d7MaoVpj+449sneHDlQMzQcCuSnQ5NTPCUly7xoTtgYGeaUYAULhcvjw7jhYtXDteO8PDh3hetIafjvr1UWiuph3r/xekpaFAXdqxjBCAV15B/cvztPU9cAAd6dkMJO9roRZWKzP1JASzdfZNAKxW9dogackUIQDr17teZ9w4zET4+2OWxxHmzHE+UZCWhjoQZ5MNSvjkE4DOnX0E5P8THj9WbsJAcfDgI9Ft3d//FMycOQ969uwJ1as3Az8/6jXCZu3j4jCYLlfOlkMiBKhQwSYa40kDe9r5iQbUSUk66NjRCu3aWWHQIF4PsmAB05BUrGiBp08ToFQp5UzHt9+mQ+/e8mwDTlTIy6omTkRi0LKlFUaMYPulgbuj0batGRISErhtDR6MxCsxkRGHkiVt8MsvBujXz6K4nVdesYkkpUgRtq2uXS0wc6aJO97QUDznLl30cPBgCtSrZ4LQUCsUL86uRceOSNYKFRJg+HBzzjEIkJrqGfkYN24cvP766y+EfAB4j4AYDAZo0KABLFiwwJuH5zFy+z7ngw/ehMcE5Msv2c1OqeXunTusbKhdO3WzyEeOMFFwbKxy1x9BQF1FlSpYNvXBB+7V6+/bx1r6FioEsH+/+nUpMjNRdElN9woWRILg6b30779xe1Q7QggKld9+W3kW0QcGQcDPVFrORD/bd99V5yauBJ0OtUE0M9C6NQbTnmRDVqxAjVGBAvi9vXnTs2MCwHIW2u0tIMC1z43Fwkog8+UDuHBBeTmbDZsXEIK/W6Xrtm8fftebN8cspRrYbACZmT4C4gOD2WyGJk1mADU2pBlAjUYDtWolACEARYr8ArVqnc353grw7rvmnNawNjGQp67djvQU06ebOP+KhAS+tOm118yiNiQ4WIBnz9I4siAds2dnwrx5Gdz+aDeofPnkHa3eecckEqKPPpJmKeS6FemIiTHICMjkyUhAzpxhGZu33sLXataU71t6jvZGiEuXGqFOHft1qNmgQXQknzkzk1uGdhN74w2DWJb1+efuZz+0Wi1MnToV4uLiFLUW3kLp0qUVS7AiIyNVl2DZbDbo06cPjHenD/kLRm7f53zwwZvwiIBcuoT14kWLKvtF6HQsOC9fHltxusLff6NomxAsPVI6JEHAlqH0pjh1qnvkY80a5vJeqZLjrlrOcPgwy55UqICmiEriXTU4eRKge3f+4VGxIpI7R8Lhlw3372N3prAwzCzlJm7fRpE2NfEjBI8rLs5znci5c+grExLCiOGtW+5vZ+9eto1ixbA+3lNotazWPk8e/E46Q3o6ZoYwIHLc6Ss1Fd8nBLM+SiL/adNY5lCtxsnXBcsHe+j1epg2LVUSKOPr69bhZMK6dVpYvnwD5MuHhKRUqcdw4sRZaNOGzfhv2mRQbHFLx6RJvCYhI4PvLtW0qQkKF2az/Ldv6+HgQftuWDjGjtXDxYup3GuFC9tyfgtyIjF0qFkkA198YeSOkfmEyNdr184AycnJ3Hvz52OgLxXlz51rgowMneK+qd9J/vwCfPABT6g2blQ+v2LFBNBoaIZCC6+8wq4zJRwFCtige3d9znPKCklJ6ZCdne0W+Zg7dy507doVTM9To6wCsbGxUK9ePdnrRYoUcSlCpxg2bJjYxvdlQW7f53zwwZtwm4BkZaGZHCHo7G0v2hYEps0ICgI4fdr1Np8+ZUF9+fKOu1B9/LH0gaCefNhsWMtP123UyH2XdoMB6+Npt6SCBbF0y13tnCBghyepKzohALVqYcckV14PLwtSU7FcTOrh8RK0SQcAzFAtXYpCfepA7ueHRMkT8mAyYXczmg3Jnx+zGu5+9pcvswA/OPj5fGbS05kGpmBB12Tg9m3mSdOokWN38z//ZJk4JT2I0ci0SY5abtvDR0B8UIIgAAwZwrJ5hw7Jl/nzT0Esr6pS5ScoWfJL8X7To4cFYmORQBQtKg/qY2KsOYGvDh49wiCYtoslBDMX0va0P/1kBI2GJyl0dO9ukbXHlWs/2JD6Yhw5oueyEtIyMfsRG2sEjYY3FlyxAjuGtW7NyNIvvxi4jIh00GsRE2PlDBLr1bPCBx+YOJIiJSCUJNh3zaIkB31F8P9ffZUhepMkJydDerpzMqLVauGLL76Atm3bgt7RzceL+PrrryE4OFixDe9+u7KHZAVzrLFjx8KQIUO418aMGQNJ7gYOXkZu3+d88MGbcIuACALrOpUvn3IG4auvsC6/VCl1LXE1GmzXiTNKjmfR585lN8Thw9WXO5lMGHhSwtSrl/udri5dQoJA9x8d7ZnHxsGDWL4i1Rc0b45aktwUlWdlAVy5gm18V6/GxgGTJ2MnshEj8DPv2ROzWkWK4Kw7JWLS0bYtzmT+8AOSrDNnUOzv5QYnqmG1YpBfsyY7Rn9/7HbmSYerEyf4z65zZ/d9Qx4/5sXzn33m+fVJSMDjKV0af0N//+18+YMHWQYwLs7xfhctQgPLkBDlrlfXr7OOZApl1jL4CIgPjmAwICEuUgRL/JTwySd0EkGA2bNZpyo/PwO0a7cHCFE29gsNtUGNGugNUrAgBthjx0ozAgI0a8b+pqVOBQrItxURYYU//jBIiIRzLUf9+iZxO7/8wmdqwsMdE5CuXS2QkKDjXvv5ZyQgr7zCyMTly+iHorQN6gvy3nsm7jhPnNBDrVq2HJIiP4aoKCskJydDdLRRtkz+/AKUKMGc4ZOTn0FiYqJIQpyREa1WC2vWrIHmzZurciL3BoxGI9SsWRPGjBkDgiCAyWSCzp07Q8eOHbnl5syZAwEBAXDy5EnxtYkTJ0L58uVh/fr1sHHjRti4cSNs2LABIiIi4KHaDhwvCLl9n/PBB2/CLQKyZg27Wa1dK3//6FE2ezpmjOvAivaOb9oUZ9KVZsAAsIMP3e9rr6kXwGZmYlBM150wwT0zQKsVA0Q6yx8UhH+7ayh48SKrrycEH7ivvqpsVPeikJUFcOoUZm2mT8fr2KQJQIkS/ENIWr70vIO6yhcqhJ/x66+j58WuXWiQ90+RLqsV4Kef+DbJAQF4PO4Syaws5vSNQY5zcbcSMjOZLoMQzOZ5Wg599y4j8OXKATx44Hz5FSvYfp11mKPtqfPlA7h6Vb7M6tVsO64c3X0ExAdnePLEeXt2iwUnagjh/Y8IAejb94j4/xo1bsrIQdWqLGjOzMyGFSt4bUOTJixTQU0A69eXC9EDAgSoWtUmZgPs2/rajxIlrFCzJivDCghgwXzDho5b+nbvboHbt/nMxt696IHCHNYFSE/XweTJPJmy//+0aSbx2CtUsMH582y7vJ8J/r9bNwPXCUzacWvCBLPYTSxvXpYt0Wg08OyZnIzcu3cPGjduDNOnT4elS5dCw4YNIT09/Z/7UgFAQkIC9O3bF8LDwyEsLAxGjx4tI0CrVq2C0NBQuJYz8/no0SPw8/MDf39/2QgICPAREB988CJUE5Br17AtKSHYCpaSi9u3sTvT48coCicE68ddZVkFAWdPCXHeoWfZMnYj7tNHfaAmnWn283PfIyQ+HvUddN+1a7vfCenePd6nhBAU5D+vP4grCALue8MGzGJEReE1btfOOWEoWhQ/u/btMVM0cCAG6W+/jYLsMWOQSHXujFkuafkVIVg+FxmJ5XQhIaysztHInx/3N3Mm1n47c+32BqxWnNGnJYSEIGHessX9be3Ywb7vhKDg3R0pldmM2SUWeHhuInntGiuvqlYNs07OMH482+/mzcrLZGay61S9Ov4thSDg7xEDEiwvcwQfAfHBXdhrtu7dYxoqafa1b1+A8uXRyTsu7gD4+/PZA6lmpHfv+XDs2FPufWm2o04dG+h0OrGzlH2QniePIIqx5QRETkhiY5neQtr9Kjpa3jqX/r9nTwtHFAgBOHNGn9OCV0qmdNCpk9Xh/vPmFWDAAIu47379zDBjBu5XWoYmHUuXZkCfPrqc550NGje2itfh8GF2TNWqWRVLrKRkZO3atfQ3D4QQCA8Ph1mzZsEtT2pgfRCRy7c5H3zwKlQREK2WtTt95RVW9331Kj4MihdnTuLFi6vzCZD6gzjSDmzYwJbp2lW9t8bVq6zWPk8ex0GWI/z6K5YEUJ+QyZOxVEAtkpMxyJMG6PXrOy4xeF6YzShoX7QIiYPUUE866tfHADU2FsnEggUYfF+4gJoCd/HsGcCnn7Isir3XhNmMBHX7djTSGzwYj8HeUFBaklSlCnbF2bgRtUEvAhYLwHffYflS/vzy1rhqkZKCmYLgYAzSq1dHY0i1EASA2bNpUILfN0+P5cwZlr2qU8f552mxYCetAgWwdbQjQnz1Kl4fQgB69JBnrNLTWeOIAgUclzb6CIgPajFzJiMYqan8e99/L7+n5cuH9z5K3ufP5wPxokX3S/6eB/37xymWHxHC3NknTTLnTKjIfUbKllVeV6m9rtR/Q+r/ISUm9uShd28L50ROCIrjeXNCAWbNMkGFCjbFbRCC7X9LlmSvRUVZxY5Z0uOSrvfjj8/EDM/KlUYoUwaXf/ttMyxdykjTq69aHGo9KBm5cOECDB8+HPz8EoGQZCBkFhBC4LPPPvvHv1P/JeT2fc4HH7wJVQRk2DAWzEtnOqWGgYTgg8NVRx4A1IbQdcaPVy7V2rABA7smTTBYUksADh5kbXaLFnWv25DBgLP99NjKllV3PhR6PWpgpGVM1aph+Y+3y42ys5E8DBqE2hl7PwxCsDyoWzc0aTx61HOXd1fQ6zHwdqcpwP37AL//juSzXz8krkoP9rAwLFHaseP5vD2UYDZjl6vngSBgYERJVVAQCuDd0XX89BMrXaxWzXMH9oMH8TdKCHbJcpZRychgZYGlSzvu5CZ1QVcy+1y5kr2p+08/AAAgAElEQVRfsaKyV42PgPigFtLv24QJ/HtSQ0w6Jk3i7wtGI3YnpO83a/ZM8ny6DFOnToVWrbSK95qPPzbBhAlmkUxUriwnG9LsgasyLKlHh5SAdOtmLx6XEhAz7NjBv5+SooPUVD6zM2yYcrtgOl5/nX9fmgkaN06+bmCgAG+9hcSnXDkbLFpkynnmC3D3rg7at2fZlvffd91+99ixY1C9ekvx3MqWfQhhYWFwx9Obmw8A4CMgPvy34JKASGedVqzg3+vRQ37jmzLF+Q9o+3bWTahPH2U9xebNbJl27dQHzj//zLIOlSq513r15k3eebxLF3XtgwHwwbhtG0DlypgNyJ8fy3OWLXs+R3R7JCeju3XXrizQZA9aLBMbNQrLmW7dyj3xtyew2XDG/Ysv8HtFZ9bpiInBUp/evQE2bXr52hT//TdefxZkYIZILXbvxiwCIfgd8pQYbd/OhOaxscqtdCnu3kWSSgh+9x0RPFqy5e+PJo32kJ53wYJIVKSfj4+A+KAW6ensu1SkiHLWTTrBY/9MAgDYs4e9HxDwQPy/v78NZs9eCGXLrlYM2gcMsMDIkWbJd1mQidulf1etyms5atfm/2aaDZ6A9Oqls9s3ey8iwgTBwTzJ0Wp1cPEiT0pGj3ZOQKhZIB1RUUhAqlen5IInT2FhFtF9/tNPTSLRevNNM2g0Oq5r1oYNRqfk49y5cxAWFgZjxiSL6zyPj5+9lmPUqFGqxezXr1+HDh06QK1atSA8PBw++OADMP9bWk0qILfvcz744E04JSB//YUBSvPmOEstDWgFgdWdS0fBgo4D35MnmY4kOlo5q7F9O5sNbt5cfV38okU4AxsaiqJcd7oTffcdC/6CgrCUSG3G4tYtgE6d2PnnzYvH4mk9vz2ePsXjiY6Wd57KmxeD9XXrPC/deVlhtWJp2KJFSAal5JAQJHlxcWiC6U553IuEXo/ZGnqM5cq5l4E7f57pSgoUQLG+J1i/nh1D797OdVPHjzPzz1dfVZ4QMJmY70jx4vJsycWL8vtAqVLY3Q3AR0D+S0hPT4ciRYpATEzMC9uHdHJlzx75+599Jg208TX7Zw5tv42BNgueDx0C2LlT2TskMtIAa9bw5U+jRpm5ZagnBiFY5sRPAvF/U6NCJD9SnYfjDEiZMryzORokPoNff+WzNlJPEHsyERQkQPPm9kJ3XOb9983w5Ik8AxQVxfQehQrZoFo1KwwYYFH0Rjl82OCQfFy5cgXCw8Ph7NlzYhk0IY4NUF3BaDRCRESE6MdhsVigY8eOEBsb63Ld5ORkKF26NMyZMwcAALKysiAqKgpGjRrl2cG8BMjt+5wPPngTDglIVhbrGqQkQt26VR50NG6sPEMKgBkG2hUpIgJA0p5bxK5dLIPRqJF8n0qw2TANT49h8GD1pTpZWawPPSGoCVArENdq0ZSNBm/4YHHdhUgNTCYU5XfqhGSsfHm2j6JFUby/bdvLlwV4kdBqMfPRuzcjsXSEhODnvnv3y+GjsnkzKwP090edh9rOaffuMfF3QACSS0/w5Ze4jaZNUVPjjFBv3Miu5bvvKi/z5AkjR4MG8ZkVo5FlXaQjOhrf9xGQlwPPM5NMMXHiRPD393+hBERaTtq1q/z93bvZ+1WqsN/ajh1smePH+YCc/n/6dMxM9u8vJwYBAanQqNFo7ju8Z48eKlVS7lrVrh1PFiIjHXe3kpZrde1qn71g79lrTAoWtEFCQgLMn8937+rRg+2btfgVcp7DVkWDQkIA/vxTD0ePMp8Pev7UVLFaNau4rcxMJBXTp/OieapJsR83b96EmjVrwrFjx+D8eX6/ap9VGg0+43r1Qi3psmXLICgoSNHPY58LUeV7770HJUqUAKvk5rt161bw9/eHmzdvqjuglwy5fZ/zwQc1KEoIySCEHHKxnCIBEQSAAQPwxpE3L3pESJGWxgeBHTrgDd9R5iMhAR8kAQHYRlFJpH7wINtmVJQ6UbTJhJ2apOTDHaF6v35s3bg4dYRHEDDAlJKCGjXwofi8uHULS9ik3ZUIQSLyzjs4e+dpu9b/ErKyMGju1o0ngK1bo27nk0/c9+fwNu7fR0JOj23UKPXmlykp/Lpz5nhWTrd0KdvGhAnOtyE1+XTk33P4MGqyCEFSI92etMUxJYWnTuF7PgKS+3iemWSK+Ph4qFixIjRq1OiFEhDaHREDd/mkzp07ykF+585sGUGQl6lSQg4AsgCZBt179tzkXnv77d+hXTtlw78xY/jsiKOg355ktG5tsXtNmgHhCUiJEjZITk6G4cP5rMXkyVlQpgySBdoel5KJXr0s3N/Sce1aOnz7bZqMgBDC2vVSwkSJRdOmjFj5+THHdOm4f/8+1KlTB/bu3QsAAOPG8fcCtRg9mq23eTM6mkdFRcmWK1y4sEtH8xo1akDPnj2519LT08HPz+9fK4bP7fucD/99lCGEbCGE3CCE3CSErCaEhLi5jSWEEBvxkIBIW9/az8AKAvMdIARgxgznP5jMTFY+Ex2t3K7z+HHWbadWLXn3EyVkZfFeCu+9p75sioqG/f2xDeyaNeoCvNu3+Ydj/vxY7+6szt4V9HoU3Ns7owcG4mz/rl3u+478f0JGBpbQde7MOrHR69e/P8CRI7mnhTGbAd5/n5WDlC2LZoZqoNUiaScE9UwTJ7r/PRAEPjs4c6bzZemkQ0CAcukLALazpttbuZK93quXPOjKiUV8BOQlwPPMJFMMGjQIli9fDm3atHluAmI2O75fS58/hGCm2X5dacaNlqYWKcIvR8sGpcPfH59JGg17rVo19v8rV+wnf7ZB0aI/KZKKXbsM0KQJn/Vg3hlKZARfq1/fMQEpUoQnIJUq2XJc0Plsy+XLSaLPCAv0bbB2rQF698bXpeaHlCA9fZoAAwcyMtOihVU0Luzb1yK2HqZeHzqdDg4eNIhEhTqmS8fjx4+hXr16sHXrVgDA5yGtdiAEu/KpgU7HT2wajQBlypSBXr16yZatW7cutGjRwuG2DAYD+Pv7w8SJE2XvFSpUCAYNGqTuoF4y5PZ9zof/NvIQQq4TQlbl/B1ICNlDCNnnxjYqEUIeEkLOEg8IyJkzrAxq5Ej5D0Da+WrYMOc/FqORGQEGBysbDZ45g12cCMFZVDWzxElJ2M6VHod9+1dHMBiw/Sxdr0YNeXZHCRYL1h3nzcuC3P79HXcOUoPHjzE4lTpr02NasED9bLkPDE+e4Ex+6dL8Na1ZE4Mad3w6vImdO5leKjAQg3g1pMhiwZlEWkvdt6/7ZFcQ8HdMr4UzPxyDgZGlqKj/a+/Mo6Motsd/Z7ISSNhDoux8kRAgEHZliaIIio/H5vpjUd9DtgMPBHcfKoqKKIIriCgIKj5UREEQkAgKiAuKCgLKYoAEkC0hISSZ6fv746amqrp7JjPJJAPhfs6pk0x3T0+vVXV3qi9itz9hdYyIQNy8mZavW0fC/MsvI/boIbS3FMPEAkjoKYsmGRHxxx9/xKSkJHS5XGUSQM6fJ5deh4OEYzs2bNDf37p1rc+92m+qab3//FNuM3YsLVNjER5+WMaMiXTlDzwg17/6qkyAItZNm2afenfu3NP40EO6ECBS/KpuX2YBRFY0twogeu0RCmzPy8tTCipS27//lMXiEhPjxszMLGzQwGW7rwYNijAzMxMbNpRC04svFmCNGrTd8uUy1iMhwe0RMH79VVqAUlLcmvCRlZWFdet+gklJWbhkCV3Xjz7Sz3vYMP+ejTlz1OtIyyIjI3H48OGWbbt3745JSUle95WZmYkOhwOnTp1qWVe/fn3s27evfwd1gRHqfo6p3IwDgEIAqKUsSwUAAwB6+7mPJQAwBgDSIUAB5MQJWTyubVtrMcFVq6S26Z57fE+i3G6ZMtHhIP99Mz/+SMJH165UX+Tw4ZJfwD/+kINPRETJVZgFBw7QBEl0cIMG+Tch3b5dt/g0a+Y9zsUfdu6kwEkh5HXvToLN0KGUKvdiylx1oVJYSOb7tDR9IKxWjawA+/ZV/DHt368LzUOG+Pf8GQbio4/K7/XqFbgg5XLp7oYLFnjf9tgxsiZVrUp9gV1Rw7w8ElBoomKt13LkiKwNk5aGePIkCyChprSaZMF1112HHxZXiy2rBUSMIXFx9laQrCz5rIaFUVIFc2zX3LlS0FUz5qmeNSJFtMj0JtbfdBO5uYpn9IknpOa9Uyc9y1a/fvS+CCWayBQFgBgf/yS2bi0zal1xhQs7dHAVH7c1HkTEgZjjPNT4kPBwfd1VV5EAEhurB85v3Wp1C6tVy4WZmZm4efMxfOyxM5b1ffqcwx9+OOkReGJiDFy7Nh+PHcvDd945j4sXn/ds27GjLDb4wQdyef/+sgbI33//jWlpaRgeTtYZ4WrVv7/+u/5kwDp/nizE4jvi8fJHANmyheYr334r17MAwjCBsxYAfrJZfgYAXvfj++2BXLfCIEABxO2miYcYGMzpunfvlpaKHj18x1oYBvmci85kzhzrNr/8IgeGhg39C97evp0yIonJpL+F/VatkhrosDCymJQ00T93DvHBB6Wp3+mk+IzS1NIwDHIzEy41otWvT376pSkCyPjHb79RbRcxqWjfnu7psGGBpWkOBvn5uo9zixZ0fP7w0kty4paaGriFrKBAZmtzOn0X59y5U77rHTvaB5AeOCDdLK680qqh/uILebzTprEAEmpKq0lGRFy9ejV26dLF87msAohqkfjoI+t6w5DPHwCNFXYsXGi1gKhGni1brFaLYcNk3JiIERk2TCqnnE6KUxTb33uvDHqPidGtiQ88YODUqXs8n6OjT2Dz5ts9QoL5t4XFQrppid80bP8HQHz11fN4+nSeJqRERZEb1MGDeYo1BbFJExeeOnUKjx07hunpxy2/P3bsWZw8WVo5pk7V63moqX0HD5aCxuOPyyD0MWMKMS8vD0+fPo19+/bFhx6SAljHjnTdly3Ti+Ha3WMzr7+uH+uTT9LyhIQEW8G5bdu22K1bNzQM3Rom4jh9uWBVr16dXbAYxoZMAPjYZvkOAPjGj++vA4DBxf8HJIBs2SJNz8WunB5OnyYLhRAWSkr5+txzaidtXf/77zLQ+rLLdLO5N9LTSTByOBCvu86/itMuF8WoiGNJTPQvLerGjfJ8ARBTUkpXl8HtpmxVZl/k1q0pDuVCyNh0qZCTQ+4V6mDlcJAl4qefKvZYFi6UGteYGPS4LpTE++9Ly1mzZoFbcvLypNY4IsJ34oT162U67H/+0z7+ZN062WeMGmVd//jjwqWLBZBgs3DhQnQ4HD6b0+nEj4pnf6UVQAzDwLZt2+ImpeMsqwCiJg5JTra3glx1ldzmpZfs97Nrl1XAAJBJTnJyrOtSU+XYIwTkLl2kwiwyUo8JmTiRBCDxWS2U2L8/xeiJz5GR+QjwtO0x0btuaIKIPwLIu+/may5QdM3IDSo7O09zs0pMlO5R771ntZA8/fQZjI+XAsvgwQV44kSu5ztJSdL68sADUji55RYZf/LUUwWYnZ2NgwYNwsceewynTZO/L1zqDEN3g/UmQAoKCynOTT1WkYK8d+/emJqaavlOjRo1cPTo0fi//8nvVKumb9O8eXOL8HL69Gl0OBw4oyyFSUJIqPs5pnJTAACLbJZ/DWTZ8EVfAPhW+RyQAIJIAbvFKbM9FBVJzWmVKiVP1hYvlh3C8OFWS8Mff0gNU3w8CSMlsXy51FY1aeJfpeiTJymoVlg+0tLs3UnMPPywPP6oKMTp0wMXFNxumiwOHqx3qmlpZI1hN6vQUVREk4jkZP3e9OtHGtOKYscO8oUHoAKWkyb595ytWyetOfXqBS48nTlDVqDwcEp84EsgF64nAHR8dsycKbd54w19XVERuVIkJrIAEmxycnJwz549JbbcYvNVSZpkb7z99tvYr18/bVlZBRC1uK2dwgsR8d//lutbtaJ3pX9/fRu3W7eUiPbii3KbJk30ddHR5GalLqtVi5QTAJQCuE8fua5HD3JNFp+3b5c1o5KSKIZR7qsAAcYjAGL9+jlYp85B7Xfq1NGzVknBxcD69WX2KXXdihX5uHKlXodDuEcdP56HAwdKq0VYmIHHj5PQ8NJLeWiOMZk8+az2GQDx99+z8MSJE3j4cLb226+9JosNtm4tBZMFC87hsGHD8N5770W3260l/hCWjr/+0q+vndfAhAlyLHzrLes9FO7Yr776KkZGRtomT/jkkw1axshevXQz7H333Yfx8fHoViTcWbNmib4IHQ4H1qtXD6dMmaJ9r3379hgbG4sxMTF41VVXlfxAVyCh7ueYi4sRQPEbvpobAAYVb19aAcQBAD8DQA9lWcACiB2TJ5MWMzraPo5D5YsvpIa5Tx/rhOrAAcQGDWh97dr2Qa5m3nxTalnbtPEvveovv8jjaNeOgr39TV8raiJ06+afcKRiGBRwnJJC+xD1HAYP1v1TmdDjdtPER43LoEGs4gSRM2comYGYJPXs6V9Bye+/l/7rsbHk3hcIx4+TVQOArIq+ioSpAvkrr1jXi+xZzZrRO2fO8nXsGOK+fSyAhJqSNMneuOuuuzApKQlTU1MxNTUV27Vrh9WqVcPY2FhMTU0NKI2vYOdO/Z1r29ZqBXnhBeukNDzcut1111m3E/VnEOVzrjazYgiAxjahZFPdrOLiKJ20GIPWr5dWxGrVyHov92NgixbPIQAJTI88oldAj47+ofidPa/9dmysgYMGFdkKIGvW5ONrr+nbDxok3aMefFC6R9Ws6cZt287hmTNnimMg9H3Nn38eU1Nd2vEeOZKJmZmZuHTpCW3bDz/M91hZhMDUubMLBw+ejiNHjkS3240HD+rXULiTLl0ql9Wvb73/R4/K9ddcQ9dQvSfVq0sl3fnz5zE5ORlHjRqFhmFgQUEB3nDDDdinTx8cN07/fYdjJG5ROm9RiHD69OmISMkwUlNT8Z577sFbbrkFnU4nvmLXqSFit27dcNWqVQE91xVBqPs55uIiFgCu8KNVLd4+C+xdsH4G3y5YdwLAStOyMgsgqmai+B32ynffkWaodm1KyWmuBH7okJxo1ahRcmVUw6AUt+qgcvp0yS/osmVSQ1W1Kn0OBMOgauz+pvQVpKfrrlYOBxVs27kzsP1UFo4fp+Dpli39TxQQCgyDzP3C5aNuXZqE3Hhjya4Dwfr96dOlO0j9+v65++3ZQ5aT7t3JbWT58sB+99Ah6fZQp473eBi3W6bndToRV660bnP2rHz24+OtdX44C1bo8aVJXrdunbbtsRKk4JIsICUVPCwqkq6Eoi1fTrEm/fv3x5YtW2KjRqMtQgKA1W1QFZDVvlcoqoQLbmKi/M277vIugACQtU/8f8stukXk7bdJsFAn3ep+5szJ97wHiLrLZ48eZEmIisrWvlO3rhsHDLAXQL78Mt+SaeuDD6R14o479PS8kyfnY2ZmJu7YkeVZVq2agZ07uzA7Ow/XrZPWFIfD8MSMTJ6cY7rOf+OJEyfw4493epaNGfMk3nHHHZ7CfmYhUcSKTZigCxhmxo9XrxctU9N7m79jfp7uuece3LAh19NnSsGlK/5q0mru3r0be/fujcnJyZiUlIQPPvggFhUV4YEDBzA6Ohrr1q1rmf988MEHpRKsK4JQ93NM5WYtAGy3WX4afAehvwVkIdle3H4CgLMAkFP82Vsa3zgAwHHjxuGkSZNw0qRJuKa4AMDXX8sOe+BA3xPyPXtoEgNAWn/z+JWVJetcxMaWbA0wVze/6aaSg79dLsoXL77TrJl/Fpaysm2bVQv3z39WzOS1PHG5SOA7eJDchTZtotiYTZvonH/+mSxE+/dT1qMDB+jvzp0U9K0Ght54Y6jPpmQMg4RIcxG0ESPsC2cGm1WrpDtJVBRl/imJw4eltc3pDLxq+h9/SF/tyy6je2lHfj5ZBIVQb+f2dfCgtMqkpiJ+8skaT58ybtw4FkBCjC9NsspTTz2FYWFhmibZjC8B5Pz589iw4e1Yp84BHDbMe8FDkUVNtBtuKEKHw4EzZ85ERLRo10W77jr991askO+q2GbGDKkAW7ZMWAek69XQoXJbYdlYsEDuQ51Av/wyJR9RhZOVK+n/6GjqJ9Xje/FF+Q4jyvcGQKakF2OlaGFhWdio0Y/Fn3UBZOPGfLz9dl3I+PTTfI8AYq4PsnjxCczMzMTHHstRfldmtFqwQFpT1Fof11yj7+f7749iRkYGVqs2ovg65WLbtrdgvshhjDJlNwk58p6oLm7FdS895OdLBSGAjGNThbz//Mfro4eIND8wPz9VqwauNHzggQfQ4XBoLlgFBQXYvHlziyBzoRDqfo6p3IwFcsOyS8N7nWnb+BL2lQ6ltIAcOCA7ybZt7bPgCI4ckZrUhATrJOb4cfK3r1eP/pbkLlJYqPv/jhhRsm/8qVMyTgWAOrPyziy1Zw/igAF6J3jttRe+q1VREZm8V6+mwXXCBBL2rrySfK3r1ych0W7wV7PXBNKSkijwev16um6lySRWURgGXRsxsReTifvuK/9nau9ePTZl/Hj/nn01YPf55wP7zV9/ldmsmja1ptQV/P03JWZIS6P33E4o27RJBq7feqt0o2ALyIWBnSZZtUwgIs6bNw9r165tOwGbOXMmtmvXDmNjYz0uWM+bHrhXXnkFAc56JvfHjtkXPFTdnHr3Rjx1Khfr1avnWe92I44Zgxgb+5vWlzid+jOquvOItmgRWTT+/W/K3iiW33KLHB/M33n0UZmCXq0VNWWKzLYFQNr5H3+Un3Nz5TMPQDGU4v/z56X1UJwngBTURUtIMLBz50xbAWTz5nOYnq4HlK9fLwWQFi30tL1ffXUcjx49ip06SVer226TLluTJ0uXrTp1ZNB6eno+xsTIfWVmnsLVq1cjwNTiZUJwycXHH38cMzL0c0hOpvtx7px+PYrlSQ+qV4XTSff53Dm9AKG3NOFr18o+5csv9UB3s2DqDzk5OVivXj2Mjo7G/cUTlxkzZuDIkSMD31kFEep+jqncRAHATgCYCxTXEQkAnwMVI1R5BABcAHClj32VSgDJyaEsTQAkNPjS/p4+LSdqcXHWKucnT8oq6NHR1Gn44tw5snakppL1ZfLkkrUav/6qZy156KHyrRx+8iRNDKtUkYJXly4ln1tFk5dHFos33qDjveEG8ktWBwfRVE2Wt+Z0kgBiNnuXtnXqRJaRRx5B/PBD0oRdSMH5LhcFy4pJCQC5Dj73nCxkVh7k5OhVxdPSSo4Lyc3VBfAHHwzsWn73nRQ6W7WioFs79u6VqbNbt5YpL1XeeEMeh3DbZAHk0qF3794YF/eV5xkQ2U7NBQ9fflk+J61a0bIim0C9rl1fsfQdIkWrwJxBSRXiMzLk5FYIPeYEFEI4ufpq+l/N0nXzzdSPis+NG5PlUXw+cIDSz6oCi/h/xw7E+fPlZyH4mAWQ+HjEf/xDfDZMxzUN33xzi7Zsy5ZzHsGhTh1dANm16xgeOHBWyaZl4MSJhZ7tr79eWjpatJACyLFjeZ7l1aqRZSQ3NxfbtRNphsXvHMeJEyd6LD2iDR5M92LTJrls2TI9w6VhUBynWN+oES0XaY5Fs3NB/eEHWudwSLet7t3ldx59NOBHFRER586diw6HA4cMGYJ///031q9fv0QXxFAS6n6OqfwkAsAyIJeq3UBV0WNN29wDACcAoLXN96cAuWDlgHTBmuzltzQBxOWSHWFkpO9g3Px86VYVFUUZtFTOnJGm2MhI3yk/xfZifwA0QJXE8uVSqxQT47u2QVkpKqIgXKEtBqBrtWJF6CfOhkGWp3ffpcrZHTpIQUMIk+YWHU3rBg4kP+rnnkOcN48CCFevpnu/cycNtmfP6udYVEST3pMnyb3u4EGybixbRmkrzZaS+HiK/xEufSIPv7nVqEGTgEmTaPK/e3for21+PlkVRDY1ABKSly4tv2Nzu0mTKoS9AQNKznZVUIB4++3yGEeODEwQ/+orOVHr2NF7scPNm2VGumuvta8HpAaHfvIJCyCXEomJidip00ztvf7mG2vBQ3WiCkB9iB1t2txo6ScaNtSfbbXIJgDFnakTUyEgiNgP8fyqLTVVCiiqMN+pEym5xOeqVemZF5+//VYXWIYN0/fboYN8j0WdLTVzEwAJ9aK+lbnNnv059ujxhLZs+3aZOlctXOh0GpidfRb37j2H3bqRBaRZM71yebNmcvvu3aVr1ubN0srStCl9Z8mSJVilyj7ttxs1OoI7duywFHm97z66FzNm0OeUFOu9XL9e/45Iyvbvf+cqy114113jNMucYehCY3q6EAKlsNaw4Vjs2LEjvvPOOwE9r263G1u3bo0OhwN79uyJzzzzTEDfr2hC3c8xTDDRBJAHHpAv+eLF3l8Cl0tqaR0Oa6Ghs2elW0h4OE3SfXHsmKw27nBY03maMQxp6nY6acAoz5iL9etJSyeuTc2aJCD5m1kr2Lhc5Abw7LMUb6IWfVKb00mpKwcNokxg8+fLzjtQf9lAKCqi7GVCEHnsMVrudpPLxLffkoAxaRJp+OPi7I+/Vy8ysw8dSm4V/mRAKy9On6b3Iy5OVuy9+uryjTNauVLGF8XEkKXIF263PvkfMsRaINAXq1bR+9q4MQk93lzlli2Tkyq7VNuFheSqAkC+4Vu3sgByqRAZGYlDhkzQ3uOUFMRu3dK0eiNnzujv+rRpVsvG9u3bMSIiAuvXz7f0DWqCouef19fFxMj/k5IQ775bCvJiuRqjVr8+KRqmT5cCuLDsx8eTlUPtUxGlQuLTT8lVTKy/9lr9WJKTpXWxXz97AaRmTV3oUduff+qp7UmoSMOJEyfivHlLtOXx8VLYSEkRLliGJoBUrSon7WqxwXfekbEh3bq58KOPPsKUlI6WmiVjx8r7p8Z6vPoqLRdZx0aNsvY94vxFe/ppihmKjJRCTosWhiVm6OOP5XccDhpfZszQM4MdP464YsUKdDqd+NxzzwX0zI74dlgAACAASURBVK5duxYdDgc2aNAAzwfSYYaAUPdzDBNMPAJITo5MG/vgg95fAMPQs4C8/rq+Pi9PmrKdzpKzUP31lyz6FxFRshXj3Dndr7ZXL+8uI2Xlzz/1QSssjCwM5fV7vjhzhq7N8OEUn2MndNSqRZ38U0+R0GRy765wzp0jjXlJgprbTdd62TKyxvTtS+enutaJ1qoVWVlWrfIdm1ReHDyoWxrCwuh47NyRgsHu3fK9BEB84gnflhfDIIFPTP6HDrVmpPPFhx9KF5EbbrC3cCDqkz4hYKqcOCGz3t1wAwsgFyulLXioWooBEJs2fdFS8FDNECWacPnNz8/HDh064IwZM7SYQNHUmiBff20/eRdjkMim2Lq1VHaY+8+MDFlkMDGRUu+KdarLFQD1ay1a0P9vvqmPh1ddpWf4atBA/pY3AaR6dcTrr7c//r/+0jNEASAePJiNTz/9NEZEtNKWp6RIi0Z8vLB06AKImmVr/HjpmvXYYzI2pFu3LGzdujWuX3/UcjyqclB1ffv8c+p7xLktXEiCV3g4KcDUWBzRPvkE8fHHF3k+9+xJ46saM3T+vP6cNG1Kv63GuIhliIg9e/bEBg0aBPycOxyOMtW2qShC3c8xTDDRLCAnT9IEx5d2XKQ1TEuzTjzy82VH6nCUXOH5999J+wRAWqsvvvC9/eHDur/tuHHlU008N5eC51RXoeuuk3nOK4q9e2nw6dXLPnbj6qtJ07RwIQV3h9pdKdjs20duYUOG6C5QokVEkMvD7Nneg6fLi/R03b0tPp6yUJWHZenUKelqCEAuJyUF8r/0koy/6to1sAB6tQDhzTfbu3IZhm5teest6za//ir6AxZALlZKW/BQxjQIQSAHO3X6h7ZvNdZJfd7cbjcOHjwYx48fj4iyNpPaGjUiQQCR3gWRzUr02WpQsxAQwsMRO3eW3xfrO3a0umWp9T3MqXY3bJC1QKZPJ0u0WLd2rZ7lqlYtKYjfdJO9AFKtGo2l//iH9AQQLTNTz+4IgPjrr3sxPj4eAa427WcT3n///bht2zasUkXGgAghY/t2PZh92jRZ7Vyk861ZswDr1p2N+/bt0+p5iCZiHdXijACUxnvfPvl53Tr5v4iRbNxYjyHcuxcxKWk2ApClWx2/RMyQcOkS7V//0i1SAKQQEtx2220YGxsb8HPOAgjDVDwlFiJUUQMHR43SJ1sFBbop2lsmC8EPP8iOukaNkou/bdsmK6iHhyPOnevXIQfMypU0ONWtSwNDs2YVG+fx88+kXRZWIbXFxJCJe/78ip9whxqXi4Klp08nwUtoGcVEwOEgofi11/wr5hcMioooIFJ1IevalZ7t8vgtNT1oaippbX2xeDFZaAAo+DMry//fU4NM77rLXrBSY8bCw+0VCJ99hvjGGyyAVDb++su+9owoeKjWcIqKQoyI+AiHD79f2/aJJ6x9XFgY4pAh/8F7lPythw7p28THW62qIi2rqMxdo4bcvn9/+b8QetTkEq+9Zq1L8ssvUphZt04KOABkYR4yhP7/z39I2Bfrli7VNfZVqkj3XW8CSEwMeRKEh+u/A0CuRSNHys8OB+LJk6ewc+fOWKPGtcq2Bh46dBLffPNNvP766xGALAROpxtzcylm5IUXdLell16SAkiXLrR9nTqzcFdxUSChbFSbKM77xRf68rw86SpWp46eSezpp+k7atxPVBTdw6io1QiAeOed+v1MSUnBTp1usmRkXLRIxpkIpZwISkdE7NChA/br1y/g55kFEIapePwWQJYuldqLwYN1rWhREZlZxYTwtdd87+urr2ThsoSEkuM33n1Xaqhq1SLtc7A5ckQOKmLgmDMnMB/60pKZSUKH8DsWViExUI4dS4Hh5Zl96WIjN5fM/pMnWwd0p5MsVm++SVa98uboUb24WYsWZB3wU64PiDfekANvvXrkKuKLFSvku9OsGWkP/UWdIE6YYC+E5+aSBrlOHQq4NWfCQ+Qg9MqGcLENC7MKtaLg4fLlstgeBUrHWgoerllzEp98Uk95Te+PLskOHToUo6N1d6Bdu85iUZEch8SEVwggqsXU6czHsLBjCICYnLwdAWQ2N/Fsmy2sn35KWQOFMk2NKxkyhPpkANK+q5muXntNt2I4nfReAEgXLHMWrOhocns2T/YBKPZMjK1iXEKkd+r11zM0wUSQn2+gCNB2OAqwbdu2+N///hdvuOGkaTIvCxrWqFFUvH+Xp26TnYVKuPWKeBkhUCHKa9Kvn57KXfRRTz4pl6WkiGD+HI/gptK9e3esXn2Z5xqK7+3cidi+vXz+AGTqexEz9L0/lVxNsADCMBWPXwLI2rVSQ3TNNfqk3OXSfeJnzfL9Aq1cSR1ubCztS03TZ8btpk5LFGhr1UoWLgoWLhdZdtQO88YbA5uolYa8PBKs+vSxar3atCG/5V9+qXxuVeVBURHFvIwciRbf8/BwqiXz4Yfl466nsnUrTcaFxvPyy0tOwFAaNm6U1sP/+7+SK81v2EDWPAAKoN+507/fMQwS8MS1/O9/7bc7elQmnUhMtGY0YgGkcqHGxaWl6dYxUfDwzjv/o8UbpKTo2YXUgoczZ5rfWRe+/PJHuGTJEnzrrbcwMjISmzbdoG0TFpaHDgelGEeUboNCsBCTU9GEUFC9+heW9ddfLwUX0V58URYOnDpVFxratEF8/HH6v1cv0uyLOI+nnkJLhiihbHvySer31UKIADLlvJ0Akpsrj0MIVirC1Sw8XC7bsUNuX726gVlZWfjqq69i1ap/avtesYLqiWzdultbLooKqrFnQlASqMJJ5860TApe75nOoyY6HA4E+NKzrFu3DExPF8KT2xNX2awZ9aGtW4/DRo1u1tL2xsTosSTffEPCR0GBHjNUGlgAYZiKp0QB5LvvZOXS1FRdq+t2k+lUdAglZbB7/32pvU1O9u1GlJcnLRJt25IZPdga5e3b9UweiYkUCF1ek37DoEnqXXfJCaFo9epRQUA7DfLFwP795T/B94fCQrIWjRghXaNEeueEBErLWZ6VzYVAq97fIUOCn8Fr/34a+IV/+dSpvp/bbdukcFa7tn2ufTsMQ3cB8ZZgZvduOflr2VK3PLEAUrn48ku97zL3+6LgYWTkH55tbrpJ7xzUgoe7dtlNvl9Ep9NZPHEFfOaZv0zrDc87jWiN0zA3Ea/VqtVxy7pGjaxB4OPHS6vmiBF6Qoy4OJlZy+mkd0RYKe69V3f5osm2LsCbg+pFchO74z5/Xmr81fMVCEGqenW5bNEiuf3ll8vl/frpGa2Skobj/fffjw0bDtGWN2tG1nZzzacmTeS+hAvbhAmIa9ZQoguhSEtOllnLqlRx4549e3DHjr0YGSlTAD/6aIEn62bNmrsRET0CCQkbP2K3bt20uirdu8vsl02byv7OHDMUCKNHj8aEhAR0Op0YFRWFiYmJuNhXCtAQE+p+jmGCiU8BZPduqWlt1ow0nQLD0P087TLhqMybJzu0jh2psrI3srJ0wWDy5OAWF8zLI/cS0WE6HDQAlFcmo7w8cp1p00aP7YiOJuvR6tWhS+lbVr77TqaeFEXHLhTOnycLxPDhehC/00n+2J99Vn5FKzMyUAvErV6dnoFgBqlnZ+v1A269VQbm2vHbbzKOKjaWLCn+4HLJzHPdu3t3sdyyRWpku3eXLoMsgFQu8vN1C0JYmH0M34gRcpuoKO+JEAxDd4kSfWNWFsWUtGvXzpKJSsQ4AJAw7nJJK7Z5XwBSIZCQ4EaA0wig9wmqIg2A3IiElePqq6UblWgi3SwA4h9/SKHizjutFg5hHZw8mc5XTd4gxh+1Mrza3G7pCmYWAh5+WC4XNTXM+2/RQi43H9fGjVk4duxYHDJkueXcf/rJeixXX037OXZMLhP1iTZskH2r+p02bWi9GpQOQBZpkSQjIeF1NAw9qUeVKv/CUaNGa4LfxIlyGzVT54gRI7SYocpMqPs5hgkmXgWQQ4ekCTYhQXd9MgwKvhMdwwMP+Na+qib2tDTflowdOyh1oRggSqoJEigbN5Iw1bIldfxt25J2uDw4eJDqb5j9iwcNoqxB5REjUB643TSZz8mhc/rpJ/LZFYW1RGvbNtRH6p3MTNKeqcGnAPT5ySfLp8aIYVDqZDXlZ8+eJNgHi6Ii/V3s2lVXFJjZt4+0hykpFKir1lPwRWGh1NI6HN7rBH3yiZyEiFgxFkAqHyLoW7RGjawCxquv6ts4nd5dbtViguL5eeghKmw4sHh2rQaWq+2hh2gfovaM2XVItNGjSengdG71TGLFOrMFIimJstoB0Psi6vHYCSyLFlE/D0DWD7OA0bUr/R0zho5TfV9FMxdTFNcBUe8/1EzGQpkAgKjOv9UK4V27yuXCEiOaSNYh0naLNmmSfeYxoWD6/HP6HBVF123CBOlCZk5vPHQofcecyeurr+T/4eHd8f33c0y/Vw/nzt2qLXv0UbmNEHxGjx6Nw4YN056lUaNG4VFfneBFTKj7OYYJJrYCyMmT5Mfepg0FRKtuQYYhO1sA6ky9CR+GQS4vqmbFl4Z25Uqpqapenfz6g0VuLpnVxbFER9MAGWzLg2GQKXngQF0bVKUKWYzKs3CdP5w7R5PQb76hyfHs2SRADhtGlozkZPpbowb53Nql//XWIiLomenWjYSTW26hAmPPPku/9f33lL4xlHEtLhc9Z//4h35/rr2WrkF53J9Tp3QNZ716lHQgmM/ea69JrXSjRr7P48gRmc46PBzxgw/8+438fOmPHhZGBcLseP11ea7jxyOeOcMCSGVDnbyL+MCFC/Vtvv/e2kd06mS/v48+0re7917qs0VdEUQpBJhdg+rUIQFZHJNwS1TbrFkUb9e1K2LVqlTA79ZbZQKLe+7Rt4+MlFr7iAjd4jFpEr1v6uRfpOLt3l23TABI68mIEXSudvEeffpYl0VG0vbCBRqABD+BGreoxmep8Sp9+sjlojCsaKLGjxrDCUAZJsW1VBN83F+cxEwEk3fqJPsckTFMLfAIQEofRJn+uH17sqSIwP2aNQ1MSmqDtWod8nwnLCwP+/Tpg1Onmq/JqwhAXgSGgThp0iSsX78+Llq0CJcsWYJLlizBxYsXY8uWLfGv8vSzDSGh7ucYJphYBJDcXBk0Fx1NAXYqan2M0aO9Tybdbn3Cf+ut3ouaGQZNhMWEsGlTme4vGHz1lZ4asVs3ykEeTAoLaSKnBs0BUO7z558PrA5DMDh7lgL05s8nDdU111ir9Hprqs9xWZtqVhctNpasJQMG0GD+1lt0jyq6cOJff9HAnZioW0b69aPnPtiCUno6aWeFS0aXLsG1hqxZI+NeYmNJU+mNEyekEOJ0lpw2W3D2rNToRkZ6r92jFmebNo0FkIsRX+6CK1fK+xsTY19wtqBAr6Uk2mefWbc9d04XLJ59lparAsh779E6O6XIxx9TWmB9EmzY9kNxcdM9y0R82C23WPf5zTfy/8GD5f9PP63HK3TpQpZ6AFLgqHVB1N/u2ZO+a51YW60TACR4uN36dbnySnnN1NTBwi0yL0/fx80303KXSwoLDoduGTFbs1askO6jqjvb7Nm0vRDG1GQE4lgcDv14P/iAMnmJsX3DBtrHrbfKecErr5zSfr9evT8wJydHu2fR0QXocOxFAOqzMzIyPEUwzS0sLIwFEIa5CNAEkMJC6VYTFmbN4CNS77VrR4Gp3gaooiIyUYvOfeRI7772RUV6h9y9u+/4kEA4e1Y3h1epQp1oMP3+8/Op8xeFrYSg06sXuaOUV4yBSkYG3atHHiEXADsNoHkwcTjIta59e5pwjxxJpvi5c2kwX7WKNIAbN1Lg/I8/klZ982aanH/6KVkM1AJetWuT9nvGDDqWCRNI69elizVVrtrEhNbhIBeDoUPpPn3zTcVUOy8oIJcDs/B45ZV0D4MZt5GXh3jffXKQjo4m7WywfuO330joFYLF22973zY7W76jAJT5xx9OnZKTlipVqBK1GcOQMQDt2rEAcjGxezfd3//7P+/916lT+kTTnEZV8N57+mQdgJ5Pu0KaCQlym7Q0sSzB44J15Ij3PqRvX9/rVReqiIiXPJNmEbuhZpoSbf16eY5q1qdx48grQE6YKaYBgM5BtQACyAB2MUlXiySKZk5FfN11JNSdPq0vF4maXC59+Ycf0vIfftCXi/oaagxNw4bymhuGnmIYgOJ5xDimWomFkCksKcKqobY1a/TnYvt2GpvEeefn07GLhBjz51vHqwceoMK66jI1BqeiCwJfSIS6n2OYYOIRQNxuxDvukC+5eeLywgty3e23ex+YCgr0ehrTp3vXJOfkkMATFkYa8WHDgld3Y8MGvWPr0YOCBYNFbi5NHFU/3IgIxClTytfNyjAo6PLtt2lwEecocvObW5MmpM16+GGaZG/bRgN1sNx/jh0jYSMxkfyBfZGbS4PHp59SjZWJE0mbNmCANXhRNKeTNIh33kmTGXOK12BiGGQ1MKfRbNmSrDTeLHilYfNm3Ve9Rw/fKakD4fhxsrIIrerEid7f17w8mryJ45g2zT/Lz7FjFOAKQFYXu+KLhYUk2KamsgByMSHuKwBN0L09D6rArrr62KFaoAGoPzIj6mQAyMQgzZvPw9TUVM82dgVaxfYHDsgaSmo1cgA9LgLga22yC6CfszoGXnYZ/d+/vxQgBgwgC73YLiyMhBUAsviY4yeE8kVYg8xFD0U/rX4eO5bONytLH2NuuIGWZ2To24sK5WoGLADyUkAkoUJdLrJjqQHloqnCldo2b6bjEZ/NglS9etYChWfPygKq115Lv/n++/r8QNw/sez999FTyDIujpQcQthp3brEx7dSE+p+jmGCSRwA4Jkz2VqV5Zkz9Yf+lVfkusGDvU9ez53Ts/I88YT3wevwYZkFA4B8RYPh8pKfTxMuUSE7JoYq1QZLw5ydTZ2j6mcrgvHKw+prGOQuNn8+WQZEgL65de9O2rEJE2jbrVsr3qWpLOTm0gA3Zw4JoiJJgDi/KlWk68UVV9D1/vTT8jvHb78lrad6DDfeSNncgpVuOC9Pr25etSrFJQXjWT13joJGxb4HDLDXOiNalQZTpvj3Lh46JK0ttWvbaybPnkU8dIgFkIuJuXP1vuXRR+23E4XnxAQyI8P7Ps2B1xERVjfbefOs/Vp4eBFGREThyeK8zjJWw2XZduLEXI+rlFng0eMf8jEujr4v4jXsXMVq1lyAtWvvRwByqxLJVDp3pjg6ddtp0773/N+gwURtnXAJE79h50JmLk6oZpRVA+RFtquNG/Xtt2+n5ffdZ74mtHzpUn15cjItV93MVGHGbow5eFC63tkJUQMH6tXTExPpN0RdpKefpr5NuIJVrUpCozkG5fffpXvolCnU3woPgyef9PsxrpSEup9jmGASBwD46KOyaq0INBO8+absGG66ybsWOCdH18L7Kkj4889yQBAao2Dw88+yswOgCViwtMpnzpC2Rs3EEhNDAYXBzqDkctEAM2kSDaSi8zUPWIMHk3C1Y0dw3YQuFHJy6Do8/zxp0u3cuMLDSdh88kmy7gTb5W33bgogV/2RmzZFfOed4P1WerqcyAOQ+8WhQ2Xfr9utx2J07uw9Q5ZwmxTbPvKIf+f355800ahShTScdlZGzoJ1cfH339b37KWXrNsJTbYQ0kXAsR2qVl1s36uXLuiaJ/VSeJiEo0aNQsMwcPHiouJ9WAUQgB04ZsxBz+TXm6ACIMcJu4xU8jhlRfExY2QMyuWX0/upbz/C8/+yZfb7E5N2O2uvOXhbCA6IegKLCRNomcjQpQoHiLoCEIAsPIhUv0dd3r8/LZ8zR1/udJK1F0AGuXfoQL9XUCBTE5sLvgLQb6hzgJ49dYvJtm16soFGjegYxHUFQIyONjA2NsXzecsWSvUuPgc7dvNiI9T9HMMEkzgAQAASQO6+Wx8QFi+Wg8X118u8/mZOnSI/fzG4+Eqdu3q1zHRVsyYFH5cVl4viDkQHHxFBnWEwJoj5+aT5qlVLdq5xcTRBC1asCiJprFesoOJXZveBrl3Jt/jWWyneZOfOS7NCuttNE5nnnqNJuhp/IlrfvqQlXb8+uFmmMjLInUHVXrZsSZONYAh/OTmyrk7PniToevOrD5Q335Rax8aNEXftst9OJI4Q7ir/7//5dw137pQWx4YNuRJ6ZcDO1emjj/RtzJPwmjW9Wwfdbv3dadTI2ve73faa9caNi3Dw4JsxKSkJmzXrbju5b9kyA2vVSsS33tqHAGpFdAMBhNBy3rN9cjL9vflmtR8xbPcNgNi583GP1SEsjJRO6vqqVZ/x/J+eritLhHuV3bl5a6JmiJoBC4DiJRF1SwOATOm+Zo1uzRH1ucxphqdMoeVqIDkAjT2i+J8Yh8SxIJISUggq5mNetuxvdDoLPJ9btNiECxac84yZhYV6TZP77qN9qhaQhISD6HCQBemyy+iZEFnDFE+8S5ZQ93MME0ziAAC7dMnGf/5Tn2z873+yk7n6au/uG0ePygC6sDDSZnhj3jw5EWraNDgZgA4e1ANpW7UKTjVxl4s0QarLU4MG5FZ2+nTZ949IVpVFi8h0bQ4EBKAg6BkzLl2BoyTOnSOf48mTyR89MlIXSuLjSXOZnh48a8X+/RRcrQ7A7dpRZp9g3KPVq3WL1/DhwakXs3atzJBVo4bMRmPGMHSryYAB/sVlbd9OqbMBaJKhWgVZALn4UFPFJiSQBfDwYet2quVOKAC8ocaM3HWX/TZqcVu1qami7eI1UlJo3enT5NZz5oxcJ4LA1XpM4lkV6ebNAkjr1nqfXLPmYfzzT/nZXL196FC3Z+K/YYNeOV0kWQkkpbnwRDCnHJ4xg5ark/bwcL3vUYtEiir1QnAQ7eWXabnZVS05WSaiEYrCuDhq48bp8Shqi4gwsEGDYdqyK66Yj5dd9jkCkMVFzZwGQLGShYW6V0G1aouxWrXtCEC/53bLMVhkRruUCXU/xzDBJA4AMCsrW6vP8cknUkN01VXkx23HoUMy0DUy0po1S2B2BenSRRZBKi2GQW4wYlIFQC5L3qw0gexXPX8ACra7//7gpNJ1uWiSeeutZOJWM1NFRlLnP29e+RTGqyhC5Q6WkUEWorQ068CdkEBawK+/Ds7x/f67TCUp2vXXW9NWl4YTJ/SsO02a2FeaDpRffpGDeUSE7/ofIggUgAKMvSkgVLZskRrb5GQKhkdkAeRiZPVqef/DwrynRTdX1wag2Cw77r1XbhMXZ68UKCxEDAvLteyzceNcdLsR09PTsUqVRcXL3do2wo1p9WralxBURACznQtnRISaYlcKIFOn6jGKTmeeluL2iSf0/XToILN4ffghWSKcTiqcJ2phqYJBSU0UVzQvF4JDp05y2WWXyetnTsMrMtup4xmArOGjuiwDkDLPW5yhej3MrVGjo+h0Pujpa0eMQJw/fw8CUAzN7NnSSwKA+meXy5ohbcCALZ77+uWXFBco1u3fH8gTXDkJdT/HMMHEUgfk88+pU46Lo4rdZ87Yvwj79pH2q2lTyjyybp39dufPI952GwWVORzU2fsqRugPp0/rg9nllwenaOHGjbIGCg06lAUmGP74O3eSEGPWIF1zDWmzPvjg4qmM7o1du+hcIiMpcUEoycykwVrPfkNt4ECaYAQjacCOHaTdE4M3AL03Zc24ZhjkOiW0sGFh5E5RVreyI0co9XK1arKasDdeekmfmPjzfKany+w4qan0rrIAcuGxe7fvWku5ubrL0OOP22+3fz/5+ZutC3aTRXNsRHq6/T7VmhuqYCBcEhcsyC3un+1dpkQKXyEcicm6qqxS2+jRVgHkjjv0rFwApIAS5ylq+YgWEyOz2sXEIM6Z8ycC/OkRJvwVPER79FHqA8zLRb0eNWi9SRP9fojlgwZRMg1EvWghAFlp7FLwivofqqCg9pvejrdhw48wLu4rz/U0H4s5wUDdurTNpEn68vvuI+EjIuIMFhXJJB1dunh/Vi8lQt3PMUww0QSQ9evl5KFdO+8a/99/l+kJ4+Iok4Ydp0/rKU2feqrs2uctW8hFpUoV+nvbbWW3TOzdazX9DxpU9mKIJ09SViNzvnSnk1wVli4tu8WmPCkspNiE7Gz7lpVFwtnXX9N9UAerf/4z1EcvOXSINIFXXknPtxh0HQ6yOC1fXvbMVlu36oUeIyJISC6ru96ePTIjDACdw759Zdvn2bN0vP6wYIF0N+vUiZ7pkli9Wk5eu3ZFPHyYBZALibvvpmc/IkJOUO0QcT0AlGLVVxpqUU9DtPbtrX3bgQP6Nt5qSYkgaHNLTiat+R133Otz8h4RQZaA2bPps10SDwBpkZAFc6UA0rmztTr6Tz/JZBTmVN0AuoUgPn4hJiYeRQAZUxFIe+wxUtSZly9darVyJCXJa6dmsBLWkrNnrfv58Ue9Nojab6uf1eB4UayxSxdr1q7q1UdhRARZfN59l35XVDsPDz9hEdj69ycBSI0JcTgMTz2WxMRV6HJJq5KvpDaXEqHu5xgmmHgEkE2b5MSsVSvpPmFmxw7Z+dSqhfj99/bb/fWXNO86HPZZVALB5aIsVGLQqFZNdnSlJTubAuHEZKlzZxpY/J2ceePbb8nH2ZwpJCmJ/FjtfKkrAsMg17fNm8nNbNYsco0bM4bciXr3polD48ZSW+jNHF9Si48nLeL48TSYzplDLno//ODdqlYR7N9PtS7UyucANNA9/HDZzPyGQcKMOqjWrk0TgbIIOIWFdJ+EgOfLz748WLpU+q+3aeM9k5bK8uXyXe3enQWQCwm9wrQsYmdGuBmJ527xYu/7FFXI1SY04QLD0BNsOBzUv5hRMyfJZmCjRogffPAdJicnY4sWVuuHGmOxaJFMMWt2xxRNjHcjR1rX1ahBmfXUZStWkDsigH3dJdUtqlmzHz3WV1+Ztry1J56wvw6ffkrprtVl7drJa6daLYUS6PBhqwXkzz+t7k8A0sxENAAADkpJREFUMouW2F69dkIgNQtmAIhhYX09/2dkUBYvIZBVrfq1Zftnn9VrqVSt+gt26qT2Mw9qxxcML4TKQKj7OYYJJnEAgOvXZ3sCzq64gjo+O7ZtkyboevXIp9yOn36SFpLoaOlvioi4Zs2agF+6I0d07XKHDmVzcXG7SctWr57cZ/PmZQskLigggYisHWsQgLTVNWrQBH/btooJJDcMun+bNtE5Pvwwaa7at9ddEOxck+yauI96WxPwgAqg+2DXrUvXZ9gwGmzffZeuUTDibPzB5SJ3w4EDrb7ZqalrymT9Kiggi4saXJmUREGYZXkGNm4kAeDAgdLvQxDoe7hiBbnWXXYZTUT8EaLfe48mMM2bswByIaGmPRXtueesz6a5RkTHjt6fX3NBO4eD3BzN26tFL0UzZ9dC1CtfAyA6nYfw2LHT2Lp1a9y0aZPiNqX/pvj/qqvIjUxY70SBQtHi4mTtG3UcUNuLL+qfn3mGFEvqZFy08eOlhQCAYiNFMPeYMdZ9m7NIPfWUmMjT3+nTyU3O/L3162mcUpd16iSv27BhcrnIgIVotQL9/TdlqzTvX7iRxsSct6wDOFb89wVteUICYljYeASg38nL0+9FvXqP4IYN+jl/9plMDRwZmYEbN27yFFEMC8vFtLTrPfe4e/dSPOSVlFD3cwwTTOIAAOPiKA1v06beJxabNkmtSP365Bpix9q1crvatUnbrjJp0qSAXrhVq3St2eTJZatIvWWL7tISG0tpdku7z6NHaQItTMXUJuGVV1Ke/PJ2sTp+nK7RY4+R9qpOHZrs+hIEHA7q1Dt1Io3eHXdQgPbUqWSpWLyY9rl1K1m8fvtNb8OHT8Jff6UJ/Cef0GApMqGJdsUVumUlNdU+CNTcevWiQWzIEBqg0tPLv6BiZiadg0iX6XRO8moBDIQTJ8iHWWj1HA6aIJRFuAmWEBvoe4hI77Z4tpo08U8ruXgx4rPPsgByIZGTIye6YlLocFir2RcWykxIovlKhiBiIISwYoc5fSwAKSPMVlG77Ro37ol33303IlLM3BVX+O5TfvtNZriyC6BWJ+V2ihZRpVu0gQNlQHlKSqG27umnEVNS1mn9nxBIbr89z7Jv0Sf070/biYKIYvmzz1L/a/7e1q26WyRN2DfjNddcg4jUz4rl//sfXUvDsBZaLCy0uoZ9+60UQCIiDNPxys+33XZaWzdkCGJ09McIQHE3ZsGtQ4chWjA5AClRhBKsceNPEFHG0sXHr8e0tGs9nhbClYxhAYSpXHjqgNjl7xesXSt9QZs29a6BXbhQdqBNmtgLKf5OfM6f1wPU6talCW9pOXxYz9jicJAvtD/uJHb88AOlSFU79ogI+o3bbw98cucP+fmUO3/mTMpfb06BKVqnTjRANWlCk/8xYxBfeIHM97t2lV0osruHhkH+x2JQ8Ra0mpNDFrJly0ij+K9/kdubKEzZtau9wNSyJWVWeeUVKkxVHrjd9KynpQX3/u3eTcGdImNceHjwanyUltIIIIhSEdG7t//PEQehX3ioMQzt21OfYodwyRFpa2+6yfs+hXVAPON2bocHD5Kl05ySdswYfbtvv7XrB+7Bb4oDDoUgPnEirTPXywAg1ydxTHYCSO/e8n/VLU3EgkyZohcxTEpCfP118Xmvtq5Nm28wLm6G53Ns7DlMS/sDARAHDDhr+W0xbjRs+KXtRH/mTKulA4AUQoiyICC19R4BRBXIhLCYkaHvIyqKlqvxHiKT1vz50jKkNpGtsVo1qsGkrnvhBcSoqOMIQNdHF+aO4ejRozXLTLVqpDgTQlSDBndiSspVnjot0dF3YJUq/Yu3d3n1yLgUCXU/xzDBJA4AMD7+EG7fno3Z2fbt9tuzESAbmzfPxt9/t9/m1Kls7NyZtktNzcY//rDfbty4cV5/R20//ZSNMTG0v7S0bNyzp+Tv+Gpz5tC+AOg409PLtr8bb5T7q1s3Gx98MBv37g3sHANtO3bI31RbVFQ2duqUjaNGZeMbb2Tjtm3Z+Pffwf99f+7hmTN078+cCXy/WVl0X2bPzsbhw7OxVatsdDqt59uoUfmdW3nev+XL6R2qVq3sz3Moz3HzZrpX/m5/6NAhFkAuMF54QU4Ia9TwnuFs1ixrlW5vgesLFujbzZ7t/feFtlttajITl8takBXgI7ziihm4c6fc7uOPpcBj3l+NGlIbb2fhUCfF5pgwEmrWaZ9jY0mJQ5+zTdt/ig7HWOXzOQSYhQCIffpYLSBRUSRoNG/+jbbc4aCiibNmkULP/D3heqwWFoyLkxYQkUQGQFpad+3SLSbx8bTcnJoXgARBcU3VJlzYevTQXUsB9Orm06ebUygvxXXr1mnXPzW1EN95Rxw7eR/8739YLHwg9ujRFxMTVyIAZYlkJKHu5xgmmFwONDHgxo0bt/JulwPDBM4MAPgMAP4CADcADAvt4QSFcQBQCAC1lGWpAGAAQG8/97EEAMYAQDoAbAjq0QVOFADsBIC5AOAAgEgA+BwA1pi2ewQAXABwpY99XQjnwzBMOeMAmhTEcePGjVs5tsuB+huGCYQ2AHAMAOoBwD+AJuhZQM/UxcxaAPjJZvkZAHjdj++3B4DfASAMLpwJeyIALAM6rt0AMA8AYk3b3AMAJwCgtc33pwBdk5zith0AJpfXwTIMwzAMwzCMHVsA4C7l84dAQsiroTmcoJEJAB/bLN8BAN/48f11ADC4+P8LRQBhGIZhGIZhmIuaMUATbZVEADgNAEUA0KHCjyh4FADAIpvlXwNZEHzRFwC+VT6zAMIwDFNO1AQadLiTZZjyhd815kIgAQCOAEATm3Wjgawg38HF69ZXWgHEAQA/A0APZRkLIAzDMDaY/ULfAKtfaEnMAgo+vBA72dKeX18AWAEAuwDgVwD4HkIfXFmWe9USAL4AgN+Kv/8sAESUwzGWhcp0r+yo7O8ac2nwI5AQnA/kqnSbsm46ABwFekbdQPEhj1b0AQaBLLB3wfoZfLtg3QkAK03LWABhGIYxEQU0aZtX/DkcKCvG2gD20Qgo+8l3cOF1sqU9v6pAGrwpyrL+QAPqfUE+Rn8py72KBxpQHyn+HAsUTPhGkI+xLFSme2VHZX/XGKYysRYoyNrMafAdhP4WkIJhe3H7CQDOggzaDuR9ZxiGqbRUtlSDZkp7flWBtHhmNgJARtCOLjDKcq9mAsBxoIwsgoFAk/QWQTzGslCZ7pUdlf1dY5jKxFggNyy79/U607bxJeyL31eGYRgTlTHVoEpZzi/cZtn7QJqsUFCWc9kLAMtNy2oCDaYPlP3QgkJluld2VPZ3jWEqE1w3g2EYphyp7KkGy3p+Zn4Aq39vRVHac4kGsnTMslmXDaRVvxCoTPfKjsr+rjFMZYPrZjAMw5QTlT3VYFnOz0wqkAtNx7IeVCkp7bkkAlk6nrBZdwgAVpf90IJCZbpXdlT2d41hGIZhmEuUEUCTTV/NDQCDire/2FINVtT5mYkG0qjfX6qjDg4sgPjHhXCv7LjY3jWGYRiGYRi/iAWAK/xoVYu3v9hSDVbU+ak4gar7vlTqow4OpT0XXy5YZ+DCccGqTPfKjovtXWMYhmEYhikXKnuqwdKen8pCkKlTQ0lZzmUvWCe/NYAsIxeKpaAy3Ss7Kvu7xjAMwzAM4xeVPdVgWc/vdQB4x7RsLgDUC9YBBkBZzuU5oIJgTmXZQKDsLRdKGt7KdK/sqOzvGsMwDMMwjF9U9lSDZTm/WUAxEsMB4P8Vt6FAxeQalutR21OWcxGFCB8u/hwHpD2/kKwFlele2VHZ3zWGYRiGYRi/qeypBktzfg1ABrSbmwtCN6kty71qAeSus7P4+8+Aff2MUFKZ7pUdlf1dYxiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYS45UgAgCwCM4nYUAGaatvkRAHIAIA8ANlfo0TEMwzAMwzAMUyn5AADcADDOy/pvAODGijschmEYhmEYhmEqM40BIB8AjgNAnGndLQCwtqIPiGEYhmEYhmGYys2zQG5YqgtWJADsBYDWITkihmEYhmEYhmEqLbFAMSD5ANCkeNn9APBGyI6IYRiGYRiGYZhKzSggK8gyAKgDAIcAID6kR8QwDMMwDMMwTKXFCQC/AgkhGwHgwdAeDsMwDMMwDMMwlZ3eQAJIBgBEhfhYGIZhGIZhGIa5BDAAYEOoD4JhGIZhGIZhmEsDFkAYhmEYhmEYhqkwWABhGIZhGIZhGKbCYAGEYRiGYRiGYZgKgwUQhmEYhmEYhmHKndcBIAsA3ABwHgAyAWBoSI+IYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYZhLnf8Pcd3fWxZz2FIAAAAASUVORK5CYII=\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# construct a test case as shown above\n",
    "num = 100; stride = num/10\n",
    "x = np.linspace(-0.5,0.5,num)\n",
    "print x\n",
    "y = x; z=x\n",
    "#xv,yv,zv =np.meshgrid(x,y,z,indexing='ij')\n",
    "\n",
    "xv,yv,zv = np.meshgrid(x,y,z,indexing='xy')  # (ny,nx,nz)\n",
    "xv = np.transpose(xv,(2,0,1))\n",
    "yv = np.transpose(yv,(2,0,1))\n",
    "zv = np.transpose(zv,(2,0,1))\n",
    "#zv,yv,xv =np.mgrid[-0.5:0.5:101j,-0.5:0.5:101j,-0.5:0.5:101j]\n",
    "p = 1.0\n",
    "btx = xv;  bty = p*yv; btz = -(p+1)*zv\n",
    "\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "matplotlib.rcParams['figure.figsize'] = (10, 10.0)\n",
    "fig = plt.figure()\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(gs[0])\n",
    "ax2.streamplot(z,y,np.average(btz[:,:,num/2:num/2+1],axis=2),np.average(bty[:,:,num/2:num/2+1],axis=2))\n",
    "ax2.set_xlabel('Z')\n",
    "ax2.set_xlim(-0.5, 0.5)\n",
    "ax2.set_ylabel('Y')\n",
    "ax2.set_ylim(-0.5, 0.5)\n",
    "\n",
    "ax2 = fig.add_subplot(gs[1])\n",
    "ax2.streamplot(z,x,np.average(btz[:,num/2:num/2+1,:],axis=1),np.average(btx[:,num/2:num/2+1,:],axis=1))\n",
    "ax2.set_xlabel('Z')\n",
    "ax2.set_xlim(-0.5, 0.5)\n",
    "ax2.set_ylabel('X')\n",
    "ax2.set_ylim(-0.5, 0.5)\n",
    "\n",
    "ax2 = fig.add_subplot(gs[2])\n",
    "ax2.streamplot(y,x,bty[num/2,:,:],btx[num/2,:,:])\n",
    "ax2.set_xlabel('Y')\n",
    "ax2.set_xlim(-0.5, 0.5)\n",
    "ax2.set_ylabel('X')\n",
    "ax2.set_ylim(-0.5, 0.5)\n",
    "\n",
    "\n",
    "skip = np.zeros([num,num,num])\n",
    "skip = ma.make_mask(skip,shrink=False)\n",
    "skip[0:num-1:stride,0:num-1:stride,0:num-1:stride] = True\n",
    "\n",
    "\n",
    "#ax = fig.gca(projection='3d')\n",
    "ax = fig.add_subplot(gs[3], projection='3d')\n",
    "ax.quiver(xv[skip],yv[skip],zv[skip],btx[skip], bty[skip], btz[skip],length=0.1) #, rstride=8, cstride=8, alpha=0.3)\n",
    "#cset = ax.c(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)\n",
    "#cset = ax.contour(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)\n",
    "#cset = ax.contour(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)\n",
    "\n",
    "ax.set_xlabel('X')\n",
    "ax.set_xlim(-0.5, 0.5)\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_ylim(-0.5, 0.5)\n",
    "ax.set_zlabel('Z')\n",
    "ax.set_zlim(-0.1, 0.1)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4.1 First try single null points at origin $^{(a)}$\n",
    "* $\\mathbf{B} = (x,py,-(p+1)z)$ with $p=1$\n",
    "<img src='download/example_null.png' width=600>\n",
    "\n",
    "## 3.4.2 Try more general form $^{(b,c)}$\n",
    "* $\\mathbf{B} = (x+(b-j_{\\parallel})y/2, (b+j_{\\parallel})x+ay, j_{\\bot}y-(a+1)z)$ with $a=1$, $b=0$,$j_{\\parallel}=0.34$ and $j_{\\bot}=0.05$\n",
    "\n",
    "## 3.4.3 Try add two null points separated in space?\n",
    "<img src='download/example_null2.png' width=600>\n",
    "\n",
    "* two null points at (0.59,0.49,0.44) and (0.23,0.11,3.23)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95\n",
      "\n",
      "[(100, 110, 112), (101, 110, 112), (102, 110, 112), (103, 109, 111), (103, 110, 112)]\n",
      "\n",
      "time cost =  77.9246320724  second\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<bound method Pool.close of <multiprocessing.pool.Pool object at 0x2b21706c1450>>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construct a test case as shown above\n",
    "#  use the Cartesian indexing\n",
    "\n",
    "\n",
    "icase = 2\n",
    "######################################\n",
    "# (1) single null at origin\n",
    "if (icase == 0): \n",
    "  num = 100\n",
    "  x = np.linspace(-1.0,1.0,num)\n",
    "  y = x; z=x\n",
    "  print x\n",
    "\n",
    "  xv,yv,zv = np.meshgrid(x,y,z,indexing='xy')  # (ny,nx,nz)\n",
    "  xv = np.transpose(xv,(2,0,1))\n",
    "  yv = np.transpose(yv,(2,0,1))\n",
    "  zv = np.transpose(zv,(2,0,1))\n",
    "  p = 1.0\n",
    "  btx = xv;  bty = p*yv; btz = -(p+1)*zv\n",
    "######################################\n",
    "# (2) general null point not at origin\n",
    "if (icase == 1):\n",
    "  num = 100\n",
    "  x = np.linspace(-0.5,0.5,num)\n",
    "  y = x; z=x\n",
    "  print x\n",
    "  xv,yv,zv = np.meshgrid(x,y,z,indexing='xy')  # (ny,nx,nz)\n",
    "  xv = np.transpose(xv,(2,0,1))\n",
    "  yv = np.transpose(yv,(2,0,1))\n",
    "  zv = np.transpose(zv,(2,0,1))\n",
    "  a = 1.0; b = 0.0; jpar = 0.34; jper = 0.05\n",
    "  s1,s2,s3 = 0.102,0.102,-0.03\n",
    "  tx = xv+s1;  ty = yv+s2; tz = zv+s3\n",
    "  btx = tx+(b-jpar)*ty/2.0; bty = (b+jpar)*tx+a*ty; btz = jper*ty-(a+1)*tz\n",
    "######################################\n",
    "# (3) double nulls spatially separated\n",
    "if (icase == 2):\n",
    "  num = 201\n",
    "  x = np.linspace(-5.02,5.02,num)\n",
    "  y = x; z=x\n",
    "  #print x\n",
    "  xv,yv,zv = np.meshgrid(x,y,z,indexing='xy')  # (ny,nx,nz)\n",
    "  xv = np.transpose(xv,(2,0,1))\n",
    "  yv = np.transpose(yv,(2,0,1))\n",
    "  zv = np.transpose(zv,(2,0,1))\n",
    "  btx = -1.8 +(-2.57*xv+6.92*yv+0.44*zv)+( 0.02*yv*zv+0.46*xv*zv-1.40*xv*yv)\n",
    "  bty = 0.44 +(-3.05*xv+2.09*yv+0.20*zv)+(-0.46*yv*zv-0.34*xv*zv+1.46*xv*yv)\n",
    "  btz = -0.67+(-2.30*xv+8.69*yv+0.48*zv)+( 1.40*yv*zv-1.46*xv*zv-8.29*xv*yv)\n",
    " \n",
    "\n",
    "\n",
    "\n",
    "# a null pt finding script includes the following parts:\n",
    "# (1) reduction toward cand list (2) identify real null pts from reduced cand. list\n",
    "\n",
    "import time\n",
    "import multiprocessing as mp\n",
    "from itertools import product\n",
    "\n",
    "\n",
    "tstart = time.time()  \n",
    "nx = btx.shape[2]\n",
    "ny = btx.shape[1]\n",
    "nz = btx.shape[0]  \n",
    "#contruct list of argument for parallelization\n",
    "klist,jlist,ilist = zip(*product(range(nz-1),range(ny-1),range(nx-1)))\n",
    "#print klist[256*64/4:256*64/4+10]\n",
    "\n",
    "\n",
    "def positive_negative(inputlist):\n",
    "  if ((all(item>0 for item in list(inputlist))) or (all(item<0 for item in list(inputlist)))):\n",
    "    return True  # elements with same sign (+ or -)\n",
    "  else:\n",
    "    return False # elements with diff sign or all zeros;\n",
    " \n",
    "\n",
    "def f(location):\n",
    "  #candidate_pl = []\n",
    "  k = location[0];j=location[1];i=location[2]\n",
    "  corner=btx[k:k+2,j:j+2,i:i+2]\n",
    "  is_bx = positive_negative(np.ravel(corner))\n",
    "  corner=bty[k:k+2,j:j+2,i:i+2]\n",
    "  is_by = positive_negative(np.ravel(corner))\n",
    "  corner=btz[k:k+2,j:j+2,i:i+2]\n",
    "  is_bz = positive_negative(np.ravel(corner))\n",
    "  if ((is_bx or is_by or is_bz) == False):\n",
    "    #candidate_pl.append((k,j,i)) \n",
    "    #print '[k,j,i]=',k,j,i\n",
    "    #print 'length of candiate=',len(candidate_pl)\n",
    "    return (k,j,i) #candidate_pl\n",
    "  else:\n",
    "    return None\n",
    "\n",
    "result_list=[]\n",
    "candidate_list=[]\n",
    "#create a pool and map the target function with multi-arguments\n",
    "p = mp.Pool(processes=8)\n",
    "result_list = p.map(f,zip(klist,jlist,ilist))\n",
    "candidate_list = [item for item in result_list if item is not None]\n",
    "\n",
    "tend = time.time()\n",
    "\n",
    "\n",
    "print len(candidate_list)\n",
    "print\n",
    "print candidate_list[:5]\n",
    "print\n",
    "print 'time cost = ',tend-tstart,' second'\n",
    "p.close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.106060606061 -0.106060606061 0.0252525252525\n"
     ]
    }
   ],
   "source": [
    "for item in candidate_list:\n",
    "    a,b,c = item[2],item[1],item[0]\n",
    "    print x[a],y[b],z[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time cost for the 1st pass =  0.803005933762  seconds\n",
      "<type 'list'>\n",
      "[(108, 109, 111), (164, 102, 105)]\n",
      "2\n",
      "time cost for the 2nd pass =  0.00200915336609  seconds\n",
      "<type 'list'>\n",
      "[(108, 109, 111), (164, 102, 105)]\n",
      "2\n",
      "time cost for the 3rd pass =  0.00412511825562  seconds\n",
      "<type 'list'>\n",
      "[(108, 109, 111), (164, 102, 105)]\n",
      "2\n",
      "\n",
      "find  2  magnetic null points at cell  [(108, 109, 111), (164, 102, 105)]\n",
      "the first several of null points at \n",
      "0.5522 0.4518 0.4016\n",
      "0.251 0.1004 3.2128\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<bound method Pool.close of <multiprocessing.pool.Pool object at 0x2b21409e31d0>>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bx = btx;by=bty;bz=btz\n",
    "\n",
    "# function which takes the cell location and solve for intersection points on six surfaces\n",
    "def bilinear(x,y,f00,f01,f10,f11):\n",
    "   a = f00; b = f10-f00; c = f01-f00; d = f00+f11-f10-f01\n",
    "   fxy = a + b*x + c*y + d*x*y\n",
    "   return fxy\n",
    "\n",
    "# test if a list has equal number of positive\n",
    "# and negative values; zeros belong to both.\n",
    "def issym(b3):\n",
    "  npos = 0; nneg = 0\n",
    "  for item in b3:\n",
    "    if (item >= 0): \n",
    "      npos +=1\n",
    "    if (item <= 0):\n",
    "      nneg +=1\n",
    "  if (npos==nneg):\n",
    "    return True\n",
    "  else:\n",
    "    return False\n",
    "\n",
    "###############################################\n",
    "# intersection curves Bx=By=0\n",
    "###############################################\n",
    "def intersect_bxby(location):\n",
    "  npts = 0 # # of end points\n",
    "  b3 = [] # third component values\n",
    "  sk = location[0];sj=location[1];si=location[2]\n",
    "  f = bx; g = by; h = bz\n",
    "    \n",
    "  for nface in range(6):\n",
    "    if (nface == 0):  # x = 0 face (y,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj+1,si],f[sk+1,sj+1,si]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj+1,si],g[sk+1,sj+1,si]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj+1,si],h[sk+1,sj+1,si]\n",
    "    if (nface == 1): # x=1 face \n",
    "      f00,f01,f10,f11 = f[sk,sj,si+1],f[sk+1,sj,si+1],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si+1],g[sk+1,sj,si+1],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si+1],h[sk+1,sj,si+1],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 2):  # y = 0 face (x,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj,si+1],f[sk+1,sj,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj,si+1],g[sk+1,sj,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj,si+1],h[sk+1,sj,si+1]\n",
    "    if (nface == 3): # y =1 face\n",
    "      f00,f01,f10,f11 = f[sk,sj+1,si],f[sk+1,sj+1,si],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj+1,si],g[sk+1,sj+1,si],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj+1,si],h[sk+1,sj+1,si],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 4): # z = 0 face (x,y)\n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk,sj+1,si],f[sk,sj,si+1],f[sk,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk,sj+1,si],g[sk,sj,si+1],g[sk,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk,sj+1,si],h[sk,sj,si+1],h[sk,sj+1,si+1]\n",
    "    if (nface == 5): # z=1 face\n",
    "      f00,f01,f10,f11 = f[sk+1,sj,si],f[sk+1,sj+1,si],f[sk+1,sj,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk+1,sj,si],g[sk+1,sj+1,si],g[sk+1,sj,si+1],g[sk+1,sj+1,si+1] \n",
    "      h00,h01,h10,h11 = h[sk+1,sj,si],h[sk+1,sj+1,si],h[sk+1,sj,si+1],h[sk+1,sj+1,si+1]\n",
    "        \n",
    "    a1,b1,c1,d1 = f00,f10-f00,f01-f00,f00+f11-f10-f01\n",
    "    a2,b2,c2,d2 = g00,g10-g00,g01-g00,g00+g11-g10-g01\n",
    "    coeff = [(b1*d2-b2*d1), (a1*d2-a2*d1+b1*c2-b2*c1), (a1*c2-a2*c1)]\n",
    "    sol1 = np.roots(coeff)\n",
    "    sol2 = -(a2+b2*sol1)/(c2+d2*sol1)\n",
    "    #  determine if intersection points\n",
    "    for s,t in zip(sol1,sol2):\n",
    "       if ((not isinstance(s,complex)) and (not isinstance(t,complex))): \n",
    "         if (s>=0 and s<=1 and t>=0 and t<=1):\n",
    "           npts += 1\n",
    "           # if yes, calculate the third components on those points\n",
    "           b3.append(bilinear(s,t,h00,h01,h10,h11))\n",
    "\n",
    "  #  determine if null pt: (1) intersection points in pair (2) third component flip signs\n",
    "  #  if yes then return True; else return False\n",
    "  if (npts>0 and npts%2==0): \n",
    "    return issym(b3) \n",
    "  else:\n",
    "    return False\n",
    "\n",
    "###############################################\n",
    "# intersection curves By=Bz=0\n",
    "###############################################\n",
    "def intersect_bybz(location):\n",
    "  npts = 0 # # of end points\n",
    "  b3 = [] # third component values\n",
    "  sk = location[0];sj=location[1];si=location[2]\n",
    "  f = by; g = bz; h = bx\n",
    "    \n",
    "  for nface in range(6):\n",
    "    if (nface == 0):  # x = 0 face (y,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj+1,si],f[sk+1,sj+1,si]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj+1,si],g[sk+1,sj+1,si]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj+1,si],h[sk+1,sj+1,si]\n",
    "    if (nface == 1): # x=1 face \n",
    "      f00,f01,f10,f11 = f[sk,sj,si+1],f[sk+1,sj,si+1],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si+1],g[sk+1,sj,si+1],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si+1],h[sk+1,sj,si+1],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 2):  # y = 0 face (x,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj,si+1],f[sk+1,sj,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj,si+1],g[sk+1,sj,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj,si+1],h[sk+1,sj,si+1]\n",
    "    if (nface == 3): # y =1 face\n",
    "      f00,f01,f10,f11 = f[sk,sj+1,si],f[sk+1,sj+1,si],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj+1,si],g[sk+1,sj+1,si],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj+1,si],h[sk+1,sj+1,si],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 4): # z = 0 face (x,y)\n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk,sj+1,si],f[sk,sj,si+1],f[sk,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk,sj+1,si],g[sk,sj,si+1],g[sk,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk,sj+1,si],h[sk,sj,si+1],h[sk,sj+1,si+1]\n",
    "    if (nface == 5): # z=1 face\n",
    "      f00,f01,f10,f11 = f[sk+1,sj,si],f[sk+1,sj+1,si],f[sk+1,sj,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk+1,sj,si],g[sk+1,sj+1,si],g[sk+1,sj,si+1],g[sk+1,sj+1,si+1] \n",
    "      h00,h01,h10,h11 = h[sk+1,sj,si],h[sk+1,sj+1,si],h[sk+1,sj,si+1],h[sk+1,sj+1,si+1]\n",
    "        \n",
    "    a1,b1,c1,d1 = f00,f10-f00,f01-f00,f00+f11-f10-f01\n",
    "    a2,b2,c2,d2 = g00,g10-g00,g01-g00,g00+g11-g10-g01\n",
    "    coeff = [(b1*d2-b2*d1), (a1*d2-a2*d1+b1*c2-b2*c1), (a1*c2-a2*c1)]\n",
    "    sol1 = np.roots(coeff)\n",
    "    sol2 = -(a2+b2*sol1)/(c2+d2*sol1)\n",
    "    #  determine if intersection points\n",
    "    for s,t in zip(sol1,sol2):\n",
    "       if ((not isinstance(s,complex)) and (not isinstance(t,complex))): \n",
    "         if (s>=0 and s<=1 and t>=0 and t<=1):\n",
    "           npts += 1\n",
    "           # if yes, calculate the third components on those points\n",
    "           b3.append(bilinear(s,t,h00,h01,h10,h11))\n",
    "\n",
    "  #  determine if null pt: (1) intersection points in pair (2) third component flip signs\n",
    "  #  if yes then return True; else return False\n",
    "  if (npts>0 and npts%2==0): \n",
    "    return issym(b3) \n",
    "  else:\n",
    "    return False\n",
    "\n",
    "###############################################\n",
    "# intersection curves Bx=Bz=0\n",
    "###############################################\n",
    "def intersect_bxbz(location):\n",
    "  npts = 0 # # of end points\n",
    "  b3 = [] # third component values\n",
    "  sk = location[0];sj=location[1];si=location[2]\n",
    "  f = bx; g = bz; h = by\n",
    "    \n",
    "  for nface in range(6):\n",
    "    if (nface == 0):  # x = 0 face (y,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj+1,si],f[sk+1,sj+1,si]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj+1,si],g[sk+1,sj+1,si]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj+1,si],h[sk+1,sj+1,si]\n",
    "    if (nface == 1): # x=1 face \n",
    "      f00,f01,f10,f11 = f[sk,sj,si+1],f[sk+1,sj,si+1],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si+1],g[sk+1,sj,si+1],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si+1],h[sk+1,sj,si+1],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 2):  # y = 0 face (x,z) \n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk+1,sj,si],f[sk,sj,si+1],f[sk+1,sj,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk+1,sj,si],g[sk,sj,si+1],g[sk+1,sj,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk+1,sj,si],h[sk,sj,si+1],h[sk+1,sj,si+1]\n",
    "    if (nface == 3): # y =1 face\n",
    "      f00,f01,f10,f11 = f[sk,sj+1,si],f[sk+1,sj+1,si],f[sk,sj+1,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj+1,si],g[sk+1,sj+1,si],g[sk,sj+1,si+1],g[sk+1,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj+1,si],h[sk+1,sj+1,si],h[sk,sj+1,si+1],h[sk+1,sj+1,si+1]\n",
    "    if (nface == 4): # z = 0 face (x,y)\n",
    "      f00,f01,f10,f11 = f[sk,sj,si],f[sk,sj+1,si],f[sk,sj,si+1],f[sk,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk,sj,si],g[sk,sj+1,si],g[sk,sj,si+1],g[sk,sj+1,si+1]\n",
    "      h00,h01,h10,h11 = h[sk,sj,si],h[sk,sj+1,si],h[sk,sj,si+1],h[sk,sj+1,si+1]\n",
    "    if (nface == 5): # z=1 face\n",
    "      f00,f01,f10,f11 = f[sk+1,sj,si],f[sk+1,sj+1,si],f[sk+1,sj,si+1],f[sk+1,sj+1,si+1]\n",
    "      g00,g01,g10,g11 = g[sk+1,sj,si],g[sk+1,sj+1,si],g[sk+1,sj,si+1],g[sk+1,sj+1,si+1] \n",
    "      h00,h01,h10,h11 = h[sk+1,sj,si],h[sk+1,sj+1,si],h[sk+1,sj,si+1],h[sk+1,sj+1,si+1]\n",
    "        \n",
    "    a1,b1,c1,d1 = f00,f10-f00,f01-f00,f00+f11-f10-f01\n",
    "    a2,b2,c2,d2 = g00,g10-g00,g01-g00,g00+g11-g10-g01\n",
    "    coeff = [(b1*d2-b2*d1), (a1*d2-a2*d1+b1*c2-b2*c1), (a1*c2-a2*c1)]\n",
    "    sol1 = np.roots(coeff)\n",
    "    sol2 = -(a2+b2*sol1)/(c2+d2*sol1)\n",
    "    #  determine if intersection points\n",
    "    for s,t in zip(sol1,sol2):\n",
    "       if ((not isinstance(s,complex)) and (not isinstance(t,complex))): \n",
    "         if (s>=0 and s<=1 and t>=0 and t<=1):\n",
    "           npts += 1\n",
    "           # if yes, calculate the third components on those points\n",
    "           b3.append(bilinear(s,t,h00,h01,h10,h11))\n",
    "\n",
    "  #  determine if null pt: (1) intersection points in pair (2) third component flip signs\n",
    "  #  if yes then return True; else return False\n",
    "  if (npts>0 and npts%2==0): \n",
    "    return issym(b3) \n",
    "  else:\n",
    "    return False\n",
    "\n",
    "import time\n",
    "import multiprocessing as mp\n",
    "from itertools import product\n",
    "\n",
    "\n",
    "tstart = time.time()  \n",
    "\n",
    "#create a pool and map the target function with multi-arguments\n",
    "p = mp.Pool(processes=8)\n",
    "\n",
    "# first pass: finding intersection of Bx=By=0\n",
    "mask_list = p.map(intersect_bxby, candidate_list)\n",
    "cell = [candidate_list[i] for i in range(len(candidate_list)) if mask_list[i]]\n",
    "               \n",
    "\n",
    "tend = time.time()\n",
    "print 'time cost for the 1st pass = ',tend-tstart,' seconds'\n",
    "print type(cell)\n",
    "print cell\n",
    "print len(cell)\n",
    "\n",
    "\n",
    "tstart = time.time() \n",
    "# second pass: finding intersection of By=Bz=0\n",
    "for cell_one in cell:\n",
    "  if (not intersect_bybz(cell_one)):\n",
    "    cell.remove(cell_one)\n",
    "tend = time.time()\n",
    "print 'time cost for the 2nd pass = ',tend-tstart,' seconds'\n",
    "print type(cell)\n",
    "print cell\n",
    "print len(cell)\n",
    "\n",
    "\n",
    "# third pass: finding intersection of Bx=Bz=0\n",
    "for cell_one in cell:\n",
    "  if (not intersect_bxbz(cell_one)):\n",
    "    cell.remove(cell_one) \n",
    "tend = time.time()\n",
    "print 'time cost for the 3rd pass = ',tend-tstart,' seconds'\n",
    "print type(cell)\n",
    "print cell[:5]\n",
    "print len(cell)\n",
    "print\n",
    "print 'find ',len(cell),' magnetic null points at cell ', cell\n",
    "print 'the first several of null points at '\n",
    "for item in cell:\n",
    "    a,b,c = item[2],item[1],item[0]\n",
    "    print x[a],y[b],z[c]\n",
    "\n",
    "p.close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n",
      "\n",
      "[[4 1]\n",
      " [2 2]]\n",
      "\n",
      "[[4 2]\n",
      " [6 8]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2],[3,4]]); b=np.array([[4,1],[2,2]])\n",
    "print a\n",
    "print\n",
    "print b\n",
    "print\n",
    "print a*b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 14\n",
    "Three Dimensional Magnetic Reconnection at Null Points\n",
    "and Separators\n",
    "Clare E. Parnell, Andrew L. Haynes, and Rhona C. Maclean**\n",
    "<img src=\"excerpt.png\" width=400>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "btx = -1.8 +(-2.57*xv+6.92*yv+0.44*zv)+( 0.02*yv*zv+0.46*xv*zv-1.40*xv*yv)\n",
    "bty = 0.44 +(-3.05*xv+2.09*yv+0.20*zv)+(-0.46*yv*zv-0.34*xv*zv+1.46*xv*yv)\n",
    "btz = -0.67+(-2.30*xv+8.69*yv+0.48*zv)+( 1.40*yv*zv-1.46*xv*zv-8.29*xv*yv)"
   ]
  }
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