03_Numpy.ipynb

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    "# Numpy\n",
    "\n",
    "NumPy is a fundamental package for scientific computing with Python. It contains, among the other things:\n",
    "\n",
    "  * a powerful N-dimensional array object\n",
    "  * sophisticated operations on arrays\n",
    "  * tools for integrating C/C++ and Fortran code (not covered in the course)\n",
    "  * (pseudo-)random number generation (today? if not in the next lectures)\n",
    "  * linear algebra, Fourier transform, ... (in the next lectures)\n",
    "\n",
    "NumPy can be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined and this allows NumPy to seamlessly and speedily integrate with a wide variety of projects.\n",
    "\n",
    "Link to the [NumPy documentation](https://numpy.org/doc/stable/user/whatisnumpy.html)."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import numpy as np # import naming convention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arrays\n",
    "\n",
    "Numpy arrays differ from the python lists, because Numpy provides:\n",
    "* full support for multi-dimensional arrays\n",
    "* closer to hardware implementation (efficiency)\n",
    "* designed for scientific computation (convenience)\n",
    "\n",
    "Similary to other programming languages that have a specific focus (e.g. \"object-oriented\"), arrays in numpy are so central that developing scientific applications can be considered as \"array-oriented\"."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "A demonstration of the numpy efficiency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "216 µs ± 36.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    },
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     "ename": "KeyboardInterrupt",
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_13019/2166748812.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# numpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'a**2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m   2349\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_local_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2350\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2351\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2352\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/decorator.py\u001b[0m in \u001b[0;36mfun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkwsyntax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcaller\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextras\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1171\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1173\u001b[0;31m         \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1174\u001b[0m         \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m         \u001b[0mworst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/timeit.py\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m             \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# standard python\n",
    "L = range(1000)\n",
    "%timeit [i**2 for i in L]\n",
    "\n",
    "# numpy\n",
    "a = np.arange(1000)\n",
    "%timeit a**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A numpy array is a grid of **omogeneous** values (all of the same type), and is indexed by a tuple of non-negative integers.\n",
    "\n",
    "All standard data types are available ([documentation](https://numpy.org/doc/stable/user/basics.types.html)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 3.] float64 \n",
      "\n",
      "[1 2 3] uint32 \n",
      "\n",
      "[1.  2.  3.3] float64 \n",
      "\n",
      "[1.+2.j 3.+4.j 5.+6.j] complex128 \n",
      "\n",
      "[ True False False  True] bool \n",
      "\n",
      "['bonjour messieurs dames' 'Hello' 'Hallo'] <U23 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# The type should specified when the array is created\n",
    "a = np.array([1, 2, 3], 'float64')\n",
    "print(a, a.dtype, '\\n')\n",
    "a = np.array([1, 2, 3], 'uint32')\n",
    "print(a, a.dtype, '\\n')\n",
    "\n",
    "# Alternatively, we can let python to infer the type: \n",
    "a = np.array([1, 2, 3.3])\n",
    "print(a, a.dtype, '\\n')\n",
    "\n",
    "# Also complex numbers are allowed\n",
    "a = np.array([1+2j, 3+4j, 5+6*1j])\n",
    "print (a, a.dtype, '\\n')\n",
    "\n",
    "# Booleans\n",
    "a = np.array([True, False, False, True])\n",
    "print(a, a.dtype, '\\n')\n",
    "\n",
    "# Non-numerical types are also allowed: strings\n",
    "a = np.array(['bonjour messieurs dames', 'Hello', 'Hallo'])\n",
    "print(a, a.dtype, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimensions\n",
    "\n",
    "The number of dimensions is called the **rank** of the array.\n",
    "The **shape** of an array is a tuple of integers giving the size of the array along each dimension.\n",
    "\n",
    "- 1D arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array: \n",
      " [0 1 2 3]\n",
      "rank: 1\n",
      "shape: (4,)\n",
      "size of the first dimension (axis): 4\n"
     ]
    }
   ],
   "source": [
    "a1 = np.array([0, 1, 2, 3])\n",
    "print(\"array: \\n\", a1)\n",
    "print(\"rank:\", a1.ndim)\n",
    "print(\"shape:\", a1.shape)\n",
    "print(\"size of the first dimension (axis):\", len(a1)) # not recommended in >1D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - 2D arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 x 3 array: \n",
      " [[0 1 2]\n",
      " [3 4 5]]\n",
      "rank: 2\n",
      "shape: (2, 3)\n",
      "size of the first dimension (axis): 2\n"
     ]
    }
   ],
   "source": [
    "a2_3 = np.array([[0, 1, 2], [3, 4, 5]])\n",
    "print(\"2 x 3 array: \\n\", a2_3)\n",
    "print(\"rank:\", a2_3.ndim)\n",
    "print(\"shape:\", a2_3.shape)\n",
    "print(\"size of the first dimension (axis):\", len(a2_3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 3D arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 x 3 x 4 array:\n",
      " [[[   1   10  100 1000]\n",
      "  [   2   20  200 2000]\n",
      "  [   3   30  300 3000]]\n",
      "\n",
      " [[   4   40  400 4000]\n",
      "  [   5   50  500 5000]\n",
      "  [   6   60  600 6000]]]\n",
      "rank: 3\n",
      "shape: (2, 3, 4)\n",
      "size of the first dimension (axis): 2\n"
     ]
    }
   ],
   "source": [
    "a2_3_4 = np.array([[[1, 10, 100, 1000], [2, 20, 200, 2000], [3, 30, 300, 3000]], \n",
    "                   [[4, 40, 400, 4000], [5, 50, 500, 5000], [6, 60, 600, 6000]]])\n",
    "print(\"2 x 3 x 4 array:\\n\", a2_3_4)\n",
    "print(\"rank:\", a2_3_4.ndim)\n",
    "print(\"shape:\", a2_3_4.shape)\n",
    "print(\"size of the first dimension (axis):\", len(a2_3_4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arrays can be created from lists, but also lists can be obtained by arrays (however, remember that the two are not equivalent because a numpy array is **not** a list):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3] <class 'list'>\n"
     ]
    }
   ],
   "source": [
    "l = [0, 1, 2, 3]\n",
    "a = np.array(l)\n",
    "print(a.tolist(), type(a.tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialization by using the numpy and linspace methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 3 5 7]\n",
      "[0. 1.]\n"
     ]
    }
   ],
   "source": [
    "# evenly spaced:\n",
    "print(np.arange(1, 9, 2)) # same as \"range\": start, end (exclusive), step\n",
    "\n",
    "# or by number of points:\n",
    "print(np.linspace(0, 1, 2)) # start, end, num-points (to be used for plotting f(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialization of the most common arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]] \n",
      "\n",
      "[[0. 0.]\n",
      " [0. 0.]] \n",
      "\n",
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]] \n",
      "\n",
      "[[ 1  0  0  0]\n",
      " [ 0 20  0  0]\n",
      " [ 0  0  3  0]\n",
      " [ 0  0  0  4]] \n",
      "\n",
      "[[0 0]\n",
      " [0 1]\n",
      " [0 2]\n",
      " [1 0]\n",
      " [1 1]\n",
      " [1 2]] \n",
      "\n",
      "[[ 4.  5.  8. 13. 20.]\n",
      " [ 2.  1.  2.  5. 10.]\n",
      " [ 4.  1.  0.  1.  4.]\n",
      " [10.  5.  2.  1.  2.]\n",
      " [20. 13.  8.  5.  4.]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 3x3 matrix of 1's\n",
    "ones = np.ones((3, 3))\n",
    "print(ones, '\\n')\n",
    "\n",
    "# 2x2 matrix of 0's\n",
    "zeros = np.zeros((2, 2))\n",
    "print(zeros, '\\n')\n",
    "\n",
    "# 3x3 unitary matrix\n",
    "unity3d = np.eye(3)\n",
    "#unity3d = np.identity(3) # np.identity calls np.eye, so the two are equivalent\n",
    "print(unity3d, '\\n')\n",
    "\n",
    "# generic diagonal matrix\n",
    "diagonal = np.diag(np.array([1, 20, 3, 4]))\n",
    "print(diagonal, '\\n')\n",
    "\n",
    "# from list comprehensions\n",
    "array = np.array([(i, j) for i in range(2) for j in range(3)])\n",
    "print(array, '\\n')\n",
    "\n",
    "# from a function\n",
    "fromfunct = np.fromfunction(lambda i, j: (i - 2)**2 + (j - i)**2, (5, 5))\n",
    "print(fromfunct, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "?np.eye"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "?np.diag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshaping and Concatenation\n",
    "\n",
    "An initial 1D array can be rehaped to a multi-dimensional array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original: [0 1 2 3 4 5] \n",
      "\n",
      "reshaped: [[0 1]\n",
      " [2 3]\n",
      " [4 5]] \n",
      "\n",
      "flattened array (back to original): [0 1 2 3 4 5] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(0, 6)\n",
    "m = a.reshape(3, 2) \n",
    "# N.B.: if the parameters of the reshaping are not appropriate, reshaping fails\n",
    "\n",
    "print(\"original:\", a, '\\n')\n",
    "print(\"reshaped:\", m, '\\n')\n",
    "\n",
    "a_from_m = m.flatten()\n",
    "print(\"flattened array (back to original):\", a_from_m, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The operation of concatenating two arrays is trivial in 1D, much less so in nD:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1D concatenation: [1 2 3 4 5 6 7 8 9] \n",
      "\n",
      "default 2D concatenation: \n",
      " [[1 2]\n",
      " [3 4]\n",
      " [5 6]\n",
      " [7 8]] \n",
      "\n",
      "concatenation along the first axis: \n",
      " [[1 2]\n",
      " [3 4]\n",
      " [5 6]\n",
      " [7 8]] \n",
      "\n",
      "concatenation along the second axis: \n",
      " [[1 2 5 6]\n",
      " [3 4 7 8]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 1D concatenation\n",
    "a = np.array([1, 2])\n",
    "b = np.array([3, 4, 5, 6])\n",
    "c = np.array([7, 8, 9])\n",
    "print(\"1D concatenation:\", np.concatenate((a, b, c)),'\\n')\n",
    "\n",
    "# nD concatenation\n",
    "a = np.array([[1, 2], [3, 4]])\n",
    "b = np.array([[5, 6], [7, 8]])\n",
    "# if no axis is specified, concatenations occurs on the 1st dimension\n",
    "print(\"default 2D concatenation:\",'\\n', np.concatenate((a, b)),'\\n')\n",
    "\n",
    "print(\"concatenation along the first axis:\",'\\n', np.concatenate((a, b), axis=0), '\\n')\n",
    "print(\"concatenation along the second axis:\",'\\n', np.concatenate((a, b), axis=1), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Copies and views"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Being **mutable objects**, numpy arrays can have **copies** and **views**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 2 3] [7 2 3] [1 2 3]\n",
      "[7 7 3] [7 7 3] [1 2 3]\n",
      "[7 7 3] [7 7 3] [9 2 3]\n",
      "Are a and b the same object? True\n",
      "Are a and c the same object? False\n",
      "[[7]\n",
      " [7]\n",
      " [3]]\n",
      "[42  7  3]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "b = a # this is a view\n",
    "\n",
    "c = a.copy() # this is a copy\n",
    "\n",
    "\n",
    "# The changes applied to the array a modify also the array b \n",
    "# (which, as a matter of fact, are the same PyObject)\n",
    "a[0] = 7\n",
    "print(a, b, c)\n",
    "\n",
    "# ... and viceversa\n",
    "b[1] = 7\n",
    "print(a, b, c)\n",
    "\n",
    "# changes on c do not modify the array a\n",
    "c[0] = 9\n",
    "print(a, b, c)\n",
    "\n",
    "# The python object is the same for a and b, but different for c:\n",
    "print(\"Are a and b the same object?\", np.may_share_memory(a, b))\n",
    "print(\"Are a and c the same object?\", np.may_share_memory(a, c))\n",
    "\n",
    "# Exercise: check if np.array.reshape returns a view or a copy\n",
    "d = a.reshape(3,1)\n",
    "print(d)\n",
    "d[0] = 42\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please remember that, with numpy objects, the `id()` function does not always work. To check whether two variables point to the same PyObject, use `np.may_share_memory()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing\n",
    "\n",
    "The items of an array can be accessed and assigned to the same way as other Python sequences (e.g. lists):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2 9\n",
      "[2 5 8]\n",
      "[9 8 7 6 5 4 3 2 1 0]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "print(a[0], a[2], a[-1])\n",
    "\n",
    "# reminder: the [start:stop:step] works as well. \n",
    "# step can be negative and a reverse sequence is thus obtained\n",
    "print(a[2:9:3])\n",
    "print(a[::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hQgghhBBCiICSIkQIIYQQQggRUFKECCGEEEIIIQJKihAhhBBCCCFEQMlihX7mjnK0goLWYGzX41wHC3TNeEdLIu6ceR8pyg/qlNT3/JPyKjIhLNww3YN99hHcWpz75YI24Z7gGA7AoevuN2SdFCGEEEKIq48UIf40gFSqr78DRj2PqdYv8HUK2gI0NfMuZt4CINciJGVi6DWqx9MFhI5EaAJGOAPJBBwB83YtxXgZMzi56C+miMaGRXg0YwIAMPisFueenNfpBtx1QL6vHWmrnRe4CFGAZswY4k7VU3VgbuFDCyGEEEIIf5IixI889fQHAVwH8Arn1OTvCt0A0Tlw2ptoYv4+r1M1Nl8D4W4AQPr6k5T2Ry0Q3eEJ1R81ox39CtobYxW3Zkwl4MH0RF8ByLMI0aCfNuCJBQAG1ydgUGHvaZjKdJXM/wMhCrG0QHpDhBBCCCGuLlKE+MsAUlFffx4AwJhRtEb4Jy3ONakQF7gA2gjCl2zySSjQYeI2ItzHwDUKsBSxdDNi+K+i5Skcd5SjFREeKNRFUy6f1oBJAOCJ0roxKYUuQpzxKd8a0foaAvVyu/VHdGBeYdsQQgghhBD+I0WInxjXancAqEvAd2q8e7f/72hO1jzG/XiRf89y4FXPBOdyZl7FQD0jVXtcA6b5Pw9AhJkAUkC0Dcx3B+KeV+6tLGbmXgQMhxQhQgghhBBXFZkdy0+Y6MH0P1cE4n7aFPeuHAoQAIA6JfUDAIcBgEE3BSKPZ4JzIIDWAM0m8NlA3DMzNdW1DkAyGDe7nnc2DfT9hRBCCCFE7rw9IakTQ65VPZ77QLgFRBEAFGIcMoGDusP9HmL4Un6NGRP1jjCVu5n5Bz3e9TpiqbzhdowC8+0AKgJ8gkj5rzol9cOcrndFORsrhIcAM1mLc8diBDndZfVhZKIXCDVAdJpNXqE73QsQw4bPxSPI6Snt6AriHgxcA6A6QCdBOAA2P9Xi3FtyDD2CnEZZxyQwyFTxruOF1K9ze33uCY4niSmCmHap8Skf5fqNGEOlyKH3BgD2cECKkAI4BuAmIlzj9zs9Q6FmqP4SAb9qDteLHrc+3e/3zOo//Dei9Q0A9VJMPARgTMAzCCGEEEKIHCkA4J7gGKqa5nEQxQHUC4zmYNzMwKMEvOpx6XtTJ4Y0yLc1E60BHkuEwZgQVsvj0g+B+XkAbQE0BqgHM692j3e0zOlyNW0Q91iARiKWShtl9K3EmAXC7QAagflOIszzpOrZxhkYpfVTTPwBA48C6ASgEcBdwTwBoE+NaOckEFG2m87mVDB3BHis6uF/5/raYqkiMWYAPBYKZ28ncxZNawOgFIBk/YQ7oAPB8xCR9gf/7O8bGaHaswTUAngqYvg3f98vDzsBAMx32phBCCGEEEJkoQAAMZdJ394O5hgiGkQwuzPhaQJOMlBPNc09eI6qF7RhwzTeZiASwB4wEkD0AsAfAvBARZ5v4gHAk6pPBXArGF8T8AbAzzPjPaRN15odoQyAXwl4jYEnic17CPQAExYCYIBjjGjHlJwuZWBJ+p/9MIDUnM5xu/TeAHQAv6t/uD7JM7yi3Jb+1RdYxp78Xqu/GdH6XQBuBQBmyrEXyjITwsIBGsvAac1hvOLXe+VHwaH0r5pgDJWyNYsQQgghhPBKexxLwSF4uK0W796Z+aAKALH0juHSjwGobOjawxrwUgHabQJCGBOe0Ke4Xst8wB3taKYb2oV8rg9jwhAwZmjH3c9lfiOfMj40UlU85bNWCkyI0t3u/+Il/jPzMRVYYkTp34EoFszDMYJewGxOzXytDvcyA/pMANWMBloHDfg0ayAi6g9mEGFV1uuzIbQGAwTvm+DAGUa6UVVrAwBkKjVB3BGgfwEgECXqx1zv+PP2bjbiCSgFxlOI4RR/3is/msc4ZJAOAKqhay00YJudeYQQQgghRBoFALTJ7k+zFiBeaY/TLE4/vV0B2y1LwKKsBQgA6HGug3jx8ql8rncA+FyLd43O2pMQMjX5pB7nOpyt3SmuBLzEf+bUmAZjTvqXFd2l9MbZTojjn5FeeJBJ92U7HksVwdwFAJg531XLibkeADD4TH7nWq4yKsKkLTBpC4MXM2MIgF8ZGKHprj7+7Jlxj3e0JGAwgG/0426/FjsFEs8XAaQAAIPq2ZxGCCGEEEKkK9DsWEw4nv5V+YI27FGpWIORiYp3vY+0N6O/AQApOb+GTI9k9UUs+Uxd7E7V+wDQwPhFO2Zk6yXJoa2KAMBEOc5WZYMqRHR3quGs48+bkIIEAESsjL8aHkMDABCl/dyJK9sdRQghhBBCpPF5s+2JCukJMvswUAdABKf1SICA0gAARkGLkOS8ZpkqCLdH+SzHwRl5iaZqBjlGgM3GIKpDjApm+sLhBJQBACIlx9egO9yrDJf+OoBKhkvrrAHrM44RUX+AQYTl+b65TluksBwAKEyBH5Qdxz9raY9eEaJDanvYbGeC44m5m8q4OXV8SHvn1JTjVt/WE+28H8BtAHbmOXNYoDH/jrSZ0qrYHUUIIYQQQqRJK0JiqaLh0teC0gYvZ8g6epwIIQVs93Qxc5khl1IKtbaEO8oxjEifAeYwgNKGoiP7awByeQ0x/Aei9bUA9SWi+5BRhMRSRUDvDBTsUayrBjMD+EEFflBj6RMjVf8axDVUheMBDLD0XrEUYkJ/idLuO9bStoUQQgghRNBRAMBwafOQNnvSXyB6Ecy3aXDX1BzuCprDXYGBkYVs92Ixc/2Juewu6Mnu8Y6WRHgFQBiAHUQ0wGRqpJnuihmvAcD5/NohVtIeyWL0QSw5gCuPYjFwSptq7Mo3TFpPye8AYFLBH1/zqxi+AIUzJhToi1gqa2XzHpejJwG1CThOpNTyRDvvz/wfM+qmncnlM/YhmqpZmSEP6T8DzvfnL4QQQgghAkNLnRhSXwX1BQAiZZA6JWVN1pMoWi+fY5+C/3ChziaMRtr0ubs0h7sTYtjweZQrbYxHxfyaUf9yrTXK6L8DKO9JVe9UgURSaACYQaCl6b0LBYiDiwyUJ+ZyhXod/sTemboUl8dRxwF8aWHrjrRboB6QV28RRXD6cQNaFw3w+5olAMoBADPlNyObEEIIIYQIEEXzcJP0ry/kVIAAABH5dUBzcRGhCQAw4d1sK6kDSDFCaiF9xuE8zeZUIqwCAJOU+xBFldJXewebBX8Ui0HHAYBAtQt6jb8Rc6j3aw8XerhN3o0jGWkFRW7/JaefaXj3MVyWZshJLFVG+uN3BLZ8HIwQQgghhCgazQRXIwCUPntUNiPIyWX0boGNVWjVgNwHgmumcW9Be3KYeAmY/kVAT7ei7yWGBuBbfaqrEGt+mLsBuouBmwt+jX8xqbekdzCxnuw+mt/5xgS9PTPVBQBm2ueIT/0mt3PVKakrAazM7bhngmM+M/4PwBEtztW08OmLxnBpGd9/j+Y29gfqvkIIIYQQIm8KUdonxAxEYmxYRNYTjLLaE0h/k38Vy5hCOPs6JrFUEYUY06IdNbaA6ByAssSYDAAger9QaUzsTv+qadbpfv0hdXxoXWOi3iX34yH1AB6dvnkUMzg5t3MzMNMwAhYQsEBRzLstCxtI7C0Cv8Z/+G9bswghhBBCCC9N8xj7DVW/BKCioRkrONoxXHe4v0o1nNeqJj8M0HikDeq+iqc4pQ0At2ZgmHuC43+67l4EwDRcWmuC/irSxoNcRtrA9bwtYw+iHUsBPI30Qc0moVBFiGYYOw2H/jeAUm633kIH9hT2FRWGSkYtmLTRiHJ8DeKlBOUYs/kDKUpVZrRSFR4BoFTa2VzYSQZKLkVpB2aAaIPdUYQQQgghxBUKXuTfmTEufbsVAYcMl+5STfMIwOMBHGVgqp0h86M5XNMI+B6ASoxXDJf+q+HSfwPoEwbqMfA0UPCFA5nSFi5M28CXjsmp/ytUoP/w35w+toTSB/0HBKEJQC8w+D0Q7WLm1ek/w1IATIDitDj3+vyaSWuKvQUbmVTc2c4CbyyVAfMdAGAqsH/1diGEEEII4aUAgB7vepNY6QXgf7iyvEYyAW9ppru1wuZRANtMYG9ejREppwBsI+BgUcKwYl4AsA1EOwp1YQxfVtndkoF3APyREQeML4mpjx7nmg+YuwBsYzZ/ya85XXfvB/BXWiu8uFBZMm7O/G76n/2Kcn1haE7jMwINZMK7nLZGi5np8M8gWscmWmhxqRMK0WyL9D8vqCnuZcXJx8xHAWwjwmfFaacwPJrjHgBOEA45Xki1ciYwIYQQQghRTFTAWWf/UTzR2j0MJRGAqZFWG1MuF2rhRABALCmGSz8CoD6I22lT3AUurNzRjlEEzADhkDbF1azQ9y6uCWG1DDZOAQCYY7R49wsBzwDAE6V1Y1LWArisxblKFeZaY4JzDZi7M2GYPsU1z08RhRBCCCFEEfh90HRJxFCeTv9yc5EKEACIYZOiHJOZ8A4YowAUrncnLUhNI9r5IgAw8wk93vVmkbIUkoc9bdK/vKzBmBOIe3pNCK1jsPlvAAApdYvShCvK2VAhvoeBU7ruftvKeEIIIYQQovgUuwNcbdzjHc0B3AEARLSwOG2px91LABwFqHfqxJBri9BENYDHAjyWCAOLk6Vw+DYAIOC/iOeAjgcxYIRnvGYA/YvShko8GmmP48Ujhv2/HokQQgghhCgU6QkBgGiqZsAxCjDrk0K9AICA71Xd9UGx2l3GHo5yPAwFrRXTLPjq6Sa2sopRmXcpoNPFylIIDL7ITEsNVmdYu6ph/jQ2vneT7vPawXAXpg2TcADAl7rDvcDKbEIIIYQQwhoyJgSAK9p5vQLOPHj5ooeV25zxKd/aFkoIIYQQQoggJT0hABxwXTSgzwKRm0zsVdm1WZvKv9qdSwghhBBCiGAkPSFCCCGEEEKIgJKB6UIIIYQQQoiAksexhBBCiAAhokgA5W2O4Q8hAFLsDmExBWnvk4JtlsUQAKlIW5w6mATj30EgeF/XOSlChBBCiAAgov6KorzndDo9dmexktvtdjAzHA5HML1ZJ2bWTdNkVVULNUPj1cw0TUVRFI2ZTSIy7M5jFZfLpWqapiqK4gEQNP++UlJSdEVRyOFwuBFERWNKSooeEhKSImNChBBCCD8joo66rm9YunSp3qdPH7vjWKZly5b8zTff0O7du3HDDTfYHccyVapU4bCwMDpw4AAqV65sdxxLnD9/Htdccw1atGiBjRs3Qtd1uyNZYteuXejSpQsGDx6MuXPn2h3HMtOnT0d0dDReeeUVDB061O44lhkzZgxmzpwJj8cjY0KEEEIIfyKiGxwOx7rZs2cHVQFy77334osvvqB169YFVQFy7bXXsmmatHXr1qApQFwuF+rXr89169ZFYmJi0BQgJ0+exJ133ok77rgDr7/+ut1xLLNixQpMnDgR48aNC6oCZPbs2Zg1a5b3ZyU9IUIIIYSfEFGd0NDQg6NHjy4/efLkoPng75lnnsGrr76KJUuWoF+/fnbHsUzr1q3x+eefY9euXbjpppvsjmOZqlWrckhICO3fvx9Vq1a1O44lfv/9d9SqVYsbN25MW7duRUhIiN2RLLF3717cfvvtGDRoEN58802741hm1apVGDhwIObMmcPdu3enGjVqSE+IEEII4Q9EVCksLGzbwIEDywZTATJ79mzMmTMHM2bMCKoCZMCAAThw4AASExODqgCpX78+G4ZBW7ZsCZoCxOPxoG7dulyjRg365JNPgqYAOX36NLp06YJOnTrhjTfesDuOZfbs2YMHH3yQo6KizMcee4wy9svAdCGEEMJiRBRWqlSpze3bt68xb968oPl/7UcffYRx48Zh9OjRePLJJ+2OY5nnnnsOq1evxrvvvovbb7/d7jiWadeuHc6cOUM7d+7Etddea3ccy0RGRrKu6/Tpp5+ifPngmGzur7/+wvXXX8+NGjWi5cuXQ1VVuyNZ4sSJE7jjjjv4gQce4JiYGJ8PY4LmF6MQQghxNSAiNSwsbHWjRo0arly50hEsbyYOHTqEgQMHon///oiPj7c7jmVef/11zJo1C9OmTcN9991ndxzLDBo0CHv37sXatWvRrFkzu+NY5sYbb+Tf/RNEjQAAIABJREFUf/+d9u7di/DwcLvjWCYyMpKrVKlC69evR2hoqN1xLHHp0iXccsst3KFDB547d2623mApQoQQQggLhYSEvFW9evV269evdwbLm4kff/wR7du3R9u2bfHf//7X7jiW+fjjj/HMM89gxIgRGDlypN1xLBMVFYUVK1bg7bffxh133GF3HMt07doVR48epU8//RSNGjWyO45l6tSpA1VVacuWLahQoYLdcSzhcrnQtGlTs27dulixYoWiKNmfSJUiRAghhLCI0+mML1eu3H1btmxxVqxY0e44lkhOTkbjxo25fv369MEHH0DTguOtw5dffol+/fqhT58+mDZtmt1xLPPmm2/i5ZdfRnx8PAYNGmR3HMsMHz4cmzdvxooVK3DbbbfZHccyzZs354sXL9KePXtQq1Ytu+NY5sYbbzRDQkJow4YNlNuYneD4TSKEEELYTFXVx0NDQ5/dvHmzXrt2bbvjWKZOnTpcoUIF2rBhA8LCwuyOY4mff/4ZrVu3RqtWrbBw4UIQUf4XlQCbNm3CiBEj8Pjjj+PZZ5+1O45lXnrpJSxYsABz5sxBz5497Y5jmR49euCrr76iTZs24frrr7c7jmU6dOjAFy9epIMHD1JePTtShAghhBDFRET3OhyOWevWrdOaNm1qdxzLZKyZsWXLlqBaM+O6667junXr0po1a4JmzYwjR46gV69e6NmzJ2bMmGF3HMssX74ckyZNQlRUFIYMGWJ3HMs89dRTWL9+PZYuXYp27drZHccygwYNwoEDB2jfvn2IiIjI81wpQoQQQohiIKL2uq6/v2TJEq19+/Z2x7HMrbfeyufOnaNdu3YhMjLS7jiWiYiI4HLlytHmzZtRqlQpu+NY4vz582jZsiW3bNmSFi1aFDQ9O3v27MEjjzyCwYMHIyYmxu44lpk5cybmzZuH2bNnI5gWMB03bhxWrlyJzZs3o3HjxvmeL0WIEEIIUUREdL3D4fh45syZet++fe2OY5n+/fvj0KFD9Mknn+DGG2+0O45lMtbM2Lp1K6pUqWJ3HEtkrIZep06doOrZOXXqFLp06YLbb789qFZDX716NaKiovDcc89h+PDhdsexTMbaQcuWLUPbtm0LdI0UIUIIIUQREFGt0NDQraNGjQp5/PHH7Y5jmWeffRYffvghFi1ahE6dOtkdxzJt2rTB2bNnaefOnahbt67dcSxTu3ZtlC5dmjZv3owyZcrYHccSf/31F5o2bcpNmjQJqjUzDhw4gAceeAADBw7E5MmT7Y5jmdWrV+OZZ57BK6+8wr179y5wN5wUIUIIIUQhEVHFsLCw7f379y8XFxcXNKuhz5kzB6+++iqmT5+OAQMG2B3HMgMHDsRnn32GdevW4eabb7Y7jmUaNGjAKSkptH//flSrVs3uOJaJjIzkqlWrBtVq6GfPnkXHjh3RoUMHvPnmm3bHscy+ffswaNAgHjNmDIYNG1ao5wClCBFCCCEKgYhCS5UqtbFdu3Y158+fHzT/H/34448xZswYPP300xgxYoTdcSwTFRWFlStXYuHChejSpYvdcSzTsWNHnDp1ipKSklCvXj2741imdu3aQbdmxuXLl9G4cWNu0KABrVy5Mmh6dr777jt07tyZ77vvPp4yZUqhP4wJml+eQgghhL+lr4a+qkGDBk1WrlzpCJY1M7744gv069cPffv2xUsvvWR3HMtkrJnx4osvYuDAgXbHsczgwYOxa9curFmzBi1atLA7jmWaNWvGv/76K+3ZsyffmZVKkjp16nClSpVow4YNQbMa+q+//oqWLVty27Ztef78+UXqDQ6O355CCCFEAISEhLxZrVq1Ths2bHAG05oZbdq0wW233YYFCxbYHccyGzZswIgRI/Dkk0/imWeesTuOZZ5//nksXboUb731Fu666y6741imR48e+Prrr2nz5s1o0qSJ3XEsc8011wAAbd26FcGygGnGauh16tTBqlWrlKL27EgRIoQQQhSA0+mcXLZs2UFbtmxxVqpUye44lkhNTUWDBg342muvpdWrVwfNaujffPMN+vTpg169emH69Ol2x7HM22+/jWnTpmHKlCl48MEH7Y5jmYw1Mwozs1JJcMstt/D58+dp9+7dCKYFTG+++WZT13XatGkTFadnJzh+2wghhBB+pKrqsJCQkLGbN2/W69SpY3ccy9SuXZvLlStHGzduDKo1M1q1asW33HILvfvuu0GzZsbmzZvx73//G8OGDcNzzz1ndxzLTJ8+HfPmzcMrr7yC3r172x3HMn369MHnn39OGzZsQDAtYNqpUyf++eef6eDBg1Tcnh0pQoQQQog8EFEvh8Px6tq1a7UbbrjB7jiWCeY1MyIjI4NqzYxvv/0WPXv2RPfu3ZGQkGB3HMusXLkSEydOxNixYzFs2DC741hm1KhRWLt2Ld577z106NDB7jiWGTx4MPbt20d79+61pGdHihAhhBAiF0TURtf1ZYsWLdI6duxodxzLtGnThs+ePUu7du3KeGY9KNSuXRtlypShzZs3o3Tp0nbHscSlS5fQvHlzbt68OS1evDhoenb27duHhx56CA888ABeeOEFu+NYZvbs2Xjttdcwc+ZMBNMCplFRUVi2bBk2btyI66+/3pI2pQgRQgghckBEjR0Ox/qXX35Z79+/v91xLHP//ffjs88+o48//hg33XST3XEs06BBA05NTaWdO3eiatWqdsexhMfjQd26dbl27dq0du1aOBwOuyNZ4uzZs+jcuTM6dOiAuXPn2h3HMh9++CHGjh2LZ599Fk888YTdcSzzxhtvYPr06XjvvffQvn17y9qVIkQIIYTIgogiQkNDtz799NOhTz75ZHB89Axg/PjxWLVqFd555x107tzZ7jiW6dChA06dOkU7duzAtddea3ccy0RERKBUqVJBtRp6xpoZDRs2DKo1Mw4dOoRBgwbhvvvuQ1xcnN1xLJOYmIinn34aCQkJ3LdvX0t/F0oRIoQQQmRCROXDwsK29e3bt/zUqVODZjX0uXPnYubMmZg2bRruv/9+u+NY5sEHH8Tu3buRmJiI5s2b2x3HMo0bN+bk5GT67LPPUL16dbvjWKZOnTpcuXJlWr9+fdCsmXHu3Dm0b98e7dq1w/z58+2OY5n9+/djwIAB/Mwzz+Dxxx+3/MMYKUKEEEKIdEQUEhYWtqFt27YRb731VnCMagawfv16jBo1Ck899RRGjhxpdxzLTJw4EcuWLcOCBQtw55132h3HMp07d8Z3331HSUlJqF+/vt1xLBMZGQkiCqo1M5KTk9GwYUO+7rrraNWqVUEzzfXJkydx++23c79+/dhfH8YEx3dKCCGEKCYiUsLCwlZed911N6xatSpoVkP/+uuv0bdvX/Tp0wfTpk2zO06RuQ7tAdj0bn/88SdIej0B80f+G/0bXQPXwV02prPOSy++BOPQHiQuWYSWLVvaHccyLVq04AsXLtCePXtQq1Ytu+NYpk6dOlyhQgXasGEDgmUB099//x0tWrTgVq1a8VtvveW33mBiZn+1LYQQQpQYoaGhb1arVu3B/fv3h1SuXNnuOJb45ZdfUK9ePW7ZsiV98sknJXrK2p/bRYJdLrtjBEy5B++Gs3FwzFy2dOn7OHbsGAYPfghBsc6O5oTS4Tlce+21/Pvvv9OBAweC43UBcLvdqFu3rlmpUiXs2rVL8Udhde7cOdSoUUOKECGEEMLpdE4qU6bMuP379zsjIyPtjmMJl8uFatWqce3atWnnzp0lfsraf1oRUvbGP+Co8s95vSVKSDm0W1ePv/jiC9q9ezeCaf2gpk2bmn/88QcdPHiQKlWq5Jd7ZBQhwdHXLIQQQhSRqqpjmTlm4sSJOHnyJE6ePGl3JEv069ePU1NTacKECdi/f7/dcYqtgckImmnKRIn2999/Y9++fTRx4kRcunQJW7dutTuSJWJiYvjHH39UDh48CH8VIJlJT4gQQoh/tHLlyv3t8XiCY5qedKZpQlXVgL5nZz+/ofimcVly/IOqEOkJuXr94QJqv+4I2MKR/v63leHy5cu0fft2tG3b1q/3kZ4QIYQQAkBKSgrt3buXgmnhvhUrVqB///4I8AeNfn1H9k97HEtc3RYtWoTevXsH6nZ+r3Z+++03VKhQAREREf6+lVfQzH8uhBBCCCGEKBmkCBFCCCGEEEIElDyOJYQQQogSjRxOODvcZXeMQnH/7wt4zpy0O4YQtpEiRAghhBAlGpUui/JT3rA7RqH88eJYXC5AEaJ0ngiUrub/QH7w7LPP8h133EF33VWyCkQAMNeMBEzD7hhBTYoQIYQQQoirFDUdAKrSwO4YRbLw6/Go2q0lut4y1O4ohWauHS1FiJ/JmBAhhBBCCCFEQEkRIoQQQgghhAgoKUKEEEIIIYQQASVFiBBCCCGEECKgpAgRQgghhBBCBJQUIUIIIYQQQoiAkiJECCGEEEIIEVBShAghhBBCCCECSooQIYQQQgghREBJESKEEEIIIYQIKM3uAEIIIcQ/RXJyMkJDQ+2OYbk//vgDly9fBgCUK1cuKF6jx+OBy+UKiteSWbD+Hcxw4cIFGIYBAKhYsSIcDofNiURupCdECCGE8KN9+/ZhwIABiIiIQKlSpdCoUSM89thj+Omnn+yOZgmPx4NmzZqhRo0aqFGjBhYuXGh3pCK7ePEiRo4ciSZNmiAsLAylS5dGvXr1MGDAABw9etTueEV2+PBhDB8+HDfccANKly6NSpUq4c4778SKFSvsjmapvXv3olq1at6/i7t27bI7UqGtXr0ad999d77/rVu3zu6oxSY9IUIIIYSfrFq1Cg8++CCSk5O9+44cOYIjR45g48aN+OSTT9CoUSMbExbfkiVLcOLECbtjFNvmzZvRr18//Pbbbz77T5w4gRMnTmDNmjWYOXMmhg8fblPCotmyZQt69eqFP//807vv0qVL2LhxIzZu3IgnnngCCQkJ0LSS/ZbQ4/Fg+PDhME3T7ijFsmnTJqxfvz7f83r37h2ANP4lPSFCCCGEH5w4cQL9+/f3KUAyO3XqFO6++254PJ4AJ7POwYMH8cQTT9gdo9guXryIwYMHZytAMktJScGTTz6JgwcPBjBZ8Rw+fBhdu3b1KUCymjNnDmbNmhXAVP7xyiuv4PDhw3bHKLavv/7a7ggBI0WIEEII4Qdz5871+VS2T58+mDNnDpo1a+bdd+rUqRL7WMX69evzfYNbUsTGxvo8HlehQgU888wziI2NRXh4uHe/x+PBU089ZUfEInn77beRmprq3b7lllswb9489OvXD6qqevfHxsaW6J/j2bNn8fzzz9sdwxL/pCKkZPe9XYWqbl1eWldCn8zpmEs155xv07Pk/isvASKSEjszo2XW/Qrwxen23Uvm/+mFECVOamoqFixY4N1u2LAhVq5cCSLCPffcg3r16nkHz86dOxc9evSwK2qhnT9/HiNHjsSSJUvsjmKZTZs2eb8mInz55Zfe4mPMmDGoVq2a9036vn37kJqaCqfTaUvWgjJNE8uXL/dut2zZEnv37gUADBkyBL1798aHH34IAPjzzz9x+PBhtGvXzpasxTVq1KgSXURlOH/+PM6fP+/dfvnll3HvvffmeG6lSpUCFctvpAixWIjuLOsxMTWnYw4PLwZQ4H8ldXasreBmjgcRAQCBfzrbtnusRVEDImJH4kMm6LaMbTI9c8+273nIbzck9ADwdNbdJuhtAFKECCEC4qOPPsKFCxe820888UTGr3LUqVMHffr08b5B/Pjjj3Hu3DlUr17dlqyFcfnyZTRs2BCXLl2yO4plLl68iCNHjni3W7Ro4dP7ERoainvuuQfvv/8+AMAwDBw9ehQ33HBDwLMWxoULF9CpUyccPHgQR48exaOPPupzvGvXrt4iBAB+/PHHQEe0xCeffOJTbJVkWXtBWrdujcjISHvCBIAUIVcxw+RJRBgO5oxd/wNQYoqQ8O0f1gKpbxDYOxcgE20E4L8iRAghrgJZZ1KqXbu2z3bDhg29X5umiWPHjpWIIsTlcvkUIHXq1MGTTz6JMWPG2JiqeHRdx9KlS3HmzBmcOXMGt9xyS7Zzatas6bMdFhYWqHhFVrVqVSxatAhAWvGYdeD5jh07fLbbtGkTsGxWyRink6FZs2YlasxOVlmLkAYNGtiUJDCkCPEzhdDJMIyTAHDuvLvAHzOEb01sCBWP+y1YIJA6DUBAJyMnVX/Bk5qcAACqqk1noG8g7y+EEADw888/+2xXrFjRZ7tChQo+2+fOnfN7Jqu1b98ey5cvL9Fv+gCgbNmy6N+/f57nrFmzxvt1aGgo6tat6+9YlspaNB06dAgrV670bjdq1AgRERGBjlVs8fHx3pnZGjZsiJEjR+Khhx6yOVXRZS5CSpcujVWrVmHDhg344osvcNNNN6Fjx44YMmSIz3iekkyKEH8zcfanjr1PFvYyUjCDS/DPJ3z72ttAuD/Q9z3d+q5LAC4BQHhS4l+Bvr8QQgCFL0JK0pohrVq1wvPPP49u3brZHSUgDh8+jGPHjnm3b7rpJihKyZzXZ9OmTRg6dCi+//57777KlSvj3XfftTFV0Rw9ehQvvfSSd/vVV1/1GU9REmUuQv766y8MGTLEu3306FEsXboUy5Ytw/vvv4+qVavaEdFSSvVdHxTrVVTb/FE1O68PRjV2runKhK7pm5znyVchAoiIE9I3S1x+IYQormDtCSlfvjz27NnzjylALl68iAEDBvjse+6552xKU3xJSUk+BUi1atWwfft2NG/e3MZURfP444/D5XIBAAYMGIDOnTvbnKj4CjIz1pYtW9CtWzcwl/y3V5rq0U+FJyUuIcKsM227f17oBpzK1vCktUcV4oTTbbtvLfT1DuWN8KTEygxO+Olcymru39+WCdNbHDign7t87k6w2ZOJIgFkPJz7HQPfMTz//aldr2/8nYO2btVqajQjffNPAFsA9PT3fa0UviNxMCNthioCL2bQg3ZnEkKIQPrll198toOlCPknSUlJQc+ePX16QW699dYSvUjcDz/84LP9yy+/YPTo0ZgxY4bPOKWr3eLFi/Hpp58CSHtsacaMGflccfX7+eefcfHiRe+2pmno378/WrVqhaNHj2L+/Plwu90AgAMHDmDJkiV44IEH7IprCQ2AE8C/mPGv8B2JW2BSwo/t9ycyYgq25CRDAbiXyegVnpT4OQizwv5Ulxzr2jU1/4sBgAjgtgRqW7N62MmI7Wtfdbqc80906fJ7kV9VEfx0+dy3AEciffaSTG4gAAT1qfAdiXH+np2qpvbX42Ck/SYgiiPTrMvZM+XdRtKavgrIO5E5Mz452777i9YmzVn1DRtKqaHe2cHOmazEE3Ghi5CaSYlrFaBUxrahugecu63PL3ldI4QQVwuHw+GzbRiGz76MNxO5nS/sZZomBg8ejF27dnn3hYSEYPbs2TamKr6IiAiMHj0a+/btQ1JSEpgZH3/8MQ4cOICkpCRcd911dkfM12+//YbRo0d7t2NiYnxmMyupTNPEyJEj8fXXX+O7777D7NmzfXoc77zzTvTp08e7HR8fX+KLEN+HGhmdQPxhzaTm39bcnvh0lZ0flSlkezeC8dbl0p5T4UlrYwv/qBVHMvH0FGfKmfCkxFdqbU+sX8j7FwPn91p1MCZF7Fp3k78S1Nq9viIYk9I3T4T9qSTkdX6umCIY6JDxHwgB+3hDCXONA1ATAAg8XjGVIk3cTUC7zK9BNZSre0J2IYTIJOvz2r/++qvPdtYpbkvCzFj/JFFRUVixYoV3W1EULFq0CC1bZluGqkSZMmUKpk+fju3bt2PZsmXe/b/88ou/HjMr3KeoBRAdHe193LFx48Z4+ulss/KXSDVq1MDMmTOxYcMGHD9+PNsjj71790bTpk29299++613raGSZtq0aQgJCYFCQH+kTZua+eGya4mQ4DCVMxFJa2ZW377mmtwaUgjDQLQMgCvT7qoAP685lFM1tycuDN/+0c25Xg/P88Q8D77rZ5QG8KRJOBqelLgmIikxUA/6nQMjloh7ECkNFFZuAPMYAN5HxNjk8f66uWm4YwFk9NE/W/DepKtDzV3r6hDjWQAA8/6z7XostDmSEELYokqVKj7b+RUhNWrU8HsmUTBLly71GfAMADNnzkTfvsE12WK/fv18ph7+8MMPLV//hQr5JEd+jh8/jjfeeMO7XbduXcTFxWHSpEmYNGmST+EIpK0YP2nSJKxevdrSHHa56aYrn4MbhoGTJ0/aF6aIZsyYgddeew0pKSnQzrTrvgLAiuo71tbVmB9j4F+4Mh6iLINGqoQR4UmJH5nghJ/a9diWubH0cSBbayR9XEWB5xEAQwBk9GA4iPAQoDwUnrRmOxMl/NT2wIeZH/U63a7nFwCGVd26fLSmhg4k5qEgapF+mAB0Z6B7xI7Er9jkBIdZZvH3HTumWP5dIX4q7E9tVQ5v/L8M37G2JZjTRqYxsk8gboEaSR82VqAOT9/cfLZd9xL3L4ZMngYgJG1LeZoBLnkT/gkhRPFJT0jJ9Pnnn2db1G/8+PEYMWKETYn8h4jQrVs3zJ8/37vv+PHjOa6TcrW4cOECTPPKaIHExEQkJibmev7ChWmfhT788MMleixPhqxjy3777TebkhTN+++/j3HjxmHy5MkYN27clcexzrW957sz7bpH/egpXYuZ+gJYjyu9IwqA3gpoa3hSYlJODf/Uruv5s+26/+fHdt0bEHNnAEvh0ztC7YmxqmZS8+OV9n5cNuv1v3Ts/9eP7bq/ebZ9j5ZgsxkDbyBT7wgzrgfRfJf61+kaO9c0seB74eNs2x7vHevaNZUQq9TanRhea8e6VjWT1t5Vc8e6u8F8ZaQQuCb5oXtRgToDaWN0PAoro6xu39/Cdya29RZqoPfOtr9nV95XCCFE8JKekJLn4sWL6N27Ny5fvuzdN3z4cMTHx9uYquiOHz+O+fPnY9y4cejfv3+2BTSB7FND67oeqHgii19++QW7d+/GsmXLMGPGDJ+/hxnOnDnjs12SVlPfvHkzHnroIYwbNw4PP/wwgBzWoeCOHQ0AqwCsqrF1dSQp+mNE/CiAjN+QjfK6CQOM9j0+BfBp+NbEylD4ERANAZAx2uka3ZMSAuCP3No4277nIQD/rr5hw7NqSOr9TDSU4O2BqAwTlQvxuguk+vY112jAuJrU/GETcAJmWqWRfQo0R/WkjyujXVfLJqOOSFp7D4C70jfnnW7f7cvitKfqWAEPvDOdmQb8Ou0KAVTTRMb4lctgY2yxG1XQTeErfz9D/nLIoHQhRInRunVrn+1t27ahZ88rEx1mXuCvTJkyaNLE8s/WRCEYhoEBAwb4PN7Sq1cvzJkzx75QxXT48GGfdSYiIyPxn//8x7udnJzsnWEKSJuNqXHjxgHNKK6YOXMmXnzxyhxCZcqU8fn5GYaBvXv3ercrVKiAypUtfzvsF4cOHUKPHj0waNAgfuGFFyhjNsASuxielWptT6yvEh3ktLEo+dJVlAVgWRFigp9P71r521R4TtWt67z98g6VMq84rlXduq66DtM427H7hdzaO926+1kAZ63Kl5/wHWvuYVD6JOP8X7epuzNeg64YVTLPf0CM8lW3rqteLpl+zWvMy9k23Xf4ObYQQvhNp06dEBkZ6X1TO3/+fMTExKBs2bLYs2ePz5u/Bx54AKVKlcqlJREICQkJPj8TIK2QfOedd3I8/+67777qH6G7/fbboSiK9/Gld955B4888oi34H399deRnJzsPf+GG26A03l1zwFz8803+6xzktWaNWt8Hp177733cOutt6J06QK9vbPVHXfc4VOEJCQkoE+fPt5CY9asWT49IY888kigIxbJ999/jy5duuC2227jt99+2+dJomxFCG3dqtVQ/u5JxEMVVbsT4KyPHh3O62YEUPj2NZ2YaChU9AEo67yDRz2sJud4cbrw7R/dzKQMVUPxAEBlsgT4iVix9FNxE3gHvgWIAWA7gX80SfmbGNcD3MbKe2ZGQPn0L0spJn2lqFeed8zSD1NfV82fAPwPwFXzcYXJSnnyJqWndNV86srRLKvKEs3XVRN/l+Z+AFYGKqMQQgQSEeHhhx9GbGzarO5//PEH6tWrh1atWmHjxo0+5w4bNsyOiCKTzDNFZRg3blyu52/ZsuWqL0IqVqyI++67D++99x6AtMd9rr/+enTu3BnJyck+0w8TEaZPn25X1AJzOp15PoKU9THI6tWrl5hHltq2bYu6deviu+++AwB88803qF27Nu6//36cP3/eZ+yLrusYNerqf3L//Pnz6NixI2rWrMmbNm3KNpTBW4RkDEyvqfoMTM9gAFihkJJwum23vchBxsD0msAQJsppat0NpsIJ59r0+IRzWEW76tblpa8MTFdaZEvKvJ9BCTVL1Vi2v3lzd9bDRZW+rkWrTLt+I1JanWnb7duMHeE7EmPA8FsRIoQQIvgMGTIEb731Fk6fPg0A2d5IAGmffmae8UYE3k8//YT9+/fbHcMv5syZg23btuHHH3/07tu8eXO284YOHYpOnToFMprIwuFwYPHixWjXrp136t3k5GQsWLDA5zxd17F48WLUqlXLjpgF9vfff6NLly5gZj548GCOY6m1iKTEfkw0VGXuwtkHXF8CYR4Z6pwzHbueyamBWjsSO5qgfyvg3gCy9nokE/O7HjJn5bbaeK2kj25gpid0NXQggDJZFgv0MPABwUw4277nzoK97MJRQ10N4Pu612YuQAAATJE51E1XrVq7E8Ph8c5QBtPAubMdux8p+PXLQ+EJ9RZmboMunOt4z1dW58xL+M7Etj5jQv5Ud5e0KYuFEP9s4eHh2L17N7p27Yovv8w+1K93795YsmSJDclEZmvWrAFnH/8ZFCpUqIBNmzbh0UcfxZ49e7Id1zQNkydP9tcaIaKQbr31VqxYsQJPPPEEzp7N/lR9aGgoVqxYkW0Nkavzx+6vAAAgAElEQVSNYRjo1asXzp49ixMnTlBuEx5oDCzPPviaviE2Z5Oe8s7p1v3zfHTKZMwF2HeJTcZZJsxRXO55Zzr3uZjLpWnXQ30BxL2y7P4NoPms0qs/3tbth7yuLy5SFZM9Po8/+TwQ2eLAAR3ge/yZgQnjFOZyOR0zQQ8T0DF98ycCRzEozznZPG70I8KVhQ5VLATwSIEDuUMjTMIW7+UKrwHQM7fTTcIujflfuRyuwKAZ3i3CHGLez6qa98dOJtaZgHcByculXLUBnC7gKxBCiKtCeHg4du7cicTERGzbtg1HjhzBzTffjDZt2uDee++Foij5N3KVa9SoEWbOnOndbtOmZD04cM011/jkL4h69er5KY31GjVqhJ07d+L999/Hnj178OWXX6JixYpo3rw57rnnHtx44412R7RMs2bNfH6WJennlKFXr164/fbb8dZbb+Hzzz/HsWPH0LBhQ7Rv3x6d/p+9+w6PqkofOP49MymE3gkpIEgX6YJAggEBgYQqIFh/wiq7VrAALqtYQNFFQFixoAiugggsGJIgVUgCiNJVijSBJPQWSALJzJzfH0nGTNqUZDIJvp/n4TH33nPuvHdmEu97T+venaCg0r/4wf/93//x888/s2/fPqpUyff2FrAdE6KV5nsNsxK7ha918XW3o/QHSeZKS7Nm2XLW72hmm2/4LDjTu3eKizE4JT095Yi38c+x3wr61tsSGXCy64AkgNNpp98BahVUvzgkhUSsKOhYUOyqzlqpsKzNKwmh/Re4MxZXnAkJPwYcy+9Y0KbVQRjN1iREa/1DYmh/GQsihPjLqFSpEiNHjmTkyJGeDsUt6tevz9ixYz0dhst69epFr169PB2GWxkMBh588EEefPBBT4fiVk2aNKFJkyb2C5ZylSpVKrMrwU+YMIH//e9/xMbGUr9+/ULLegGpwELMzE5wosvOn5QJ9BJl0LMSuvbP29Znl9bAeoWalRgaHpPfeBF3Ohc27HpgXNR6oGfWrgpmi+GPoNio9dpAAzTNSjIeIYQQQgghyprZs2fzwQcfsHTpUjp06GC3vJeXUkEnQsIv2y1ZAIOXV+ipzvddsl+yoPreo4tSvzhYDHqswaJ+Aspn7fLWir5Z6VCSVmqV0vovM32JNlDeJhVUqtAudUIIIYQQ4q9r6dKlvPTSS8yePZv+/fs7VMerKAkIQFETCE8nIACnu/b/LXhTZGuLUc0B1Y3MZOQKqM1mbRlnULRR5GgR0RmFjpMpVgZ1SGk2A1g0jo2PUTpBoTZnb2qNUy1c2qI75JwgQBuUy6s1mY3cNEKOWIwOra+iIc4A1onzTV4WGZQuhBBCCFHKbNq0iYcffpiXX36Zv//97w7Xk8UKs5wKG3AE6FvA4eNAgeM2CmNWqmHgpigjQBIVjzg7ViYhJGIGMMNuwRySMsdcFGHchery54/8kNS1n8tzF57OXFk+zNl6SaERLk0G4L91RW1jund1AIwUPBpKCCGEEEIUyb59+wgPD2f48OF66tSp+U7FWxBJQtxMob/HmPlzgL5aNmZ4Un+uiaIxvOfJUJzlZfH+pzZSNkdzCSGEEEKUESdOnKBHjx507NhR//e//3UqAYE8y1mLv7qgDStqAE2zNvclhfT73pPxCCGEEEKI0uXixYuEhYVRp04d/cMPPzidgIC0hBS79JRyVw1+GePyO2aypBdp/E1JMJfz8jdYWAJg0Hzp6XicZdZ6hcLwR+79ymL+zQPhCCGEEELcUlJTU+nVqxcmk0nv2bPHpQQEJAkpdlnrm8yyW7CUOt21/2/ACE/H4arTof03k2MgvBBCCCGEKB4mk4nBgwdz4sQJjhw5UuBq6I6QJEQIIYQQQghh19/+9je2bdvGnj17qFatWpHOJUmIEEIIIYQQolCTJk3i22+/5YcffqBhw4ZFPp8kIUIIIYQQQogCzZ07l+nTp7NkyRI6depULOeUJEQIIYQQopTSu79CV6zl6TBc8uSd6bRP34Zl62xPh+I8i9nTEZQay5cvZ+zYscycOZNBgwYV23klCRFCCCGEKKUsm9/1dAgue7MLcHM1lujVng5FuCg2NpYHH3yQcePG8fTTTxfruWWdECGEEEIIIYSNX3/9lX79+nH//ffrd98t/mRYWkLcLHBTVE3lYwhKT+fMubB+ZxypU3tTjL+Pt/m2nPssZsPVpG7hBxypf/v69VVu+qU1z7lPW1R6YmjELocDFx5Td9PK24zeRv+c+0zacO5MSPgxT8UkhBBCiL+OkydP0r17d9q1a8eiRYtcXgukMJKEuJuRb7TZcq+XUt0Ah5IQH6N5pLaoGTn3KYP+AejhSP0035t3K4uyXelccRbwz79GwWpvivH3NuowADQnE7uFb3WmfuDOqPKkqgEAWqkrZXEF9uD4mE4WrRsAGLx03KnOEYn5louNamxRqn1h53LkPTAYvV7UFp7Juc+o+Bz4m5OhCyGEEEI45dKlS3Tv3p0aNWro2NhYtyQgIEmIW9WNW3WPAXUvsD3pnvA4F05xXcFOALTe60J9rSAWQFu45EJ9vL30B2g9HADFUsCpJIQ0Xgb9OoAB/StQppKQoA0ramgf79VANQCLicFAvkmIRak+oAsdfae0/gV774HisNKZCy5qRTM0dVyJXQghhBDCGWlpaQwdOpQbN27o/fv3uy0BAUlC3MoAbwIomO7iKQ4nhEaEFSGE9KLUD4yL7AqG4a7WD94WFYhmvKv1SwPt6/0GGmdX49FAegHHCtpvlRgSMRuYDRAUt+q/GvWwk68vhBBCCOG0hx9+mOPHj3P48GHl6+vr1teSJMRNAmMj26IM3YBL/uXrfufpeJylQAVoNQvFdTK7kTVy9hzapKeBKo9mD4o2xR+le9WN+66FAeMYYDfQ1omqvyaGRrRyU1hCiOLnHRkZyZ49ezwdR7HZsWMHAAsWLPBsIMWot9lS4Gw6N9LSyty13nHoEMGeDkK4bOPGjVy5csXTYRSb1NRUAH7//Xd2795NjRo13P6akoS4iVaGh7PasL7b0b59hmejcV5gbPSjWqkOoN4E3RMnk5DguKiOGvUQsBSDSkbrMpeEGDDOAJRB8y+LItrT8Qgh3MPX11fNnDnT02EUK601FStWZNy4cSjl1h4V1tdzt72N/fAp4FKuXb/GCy+8UOyv6c7rer2ONw9UKfg2bOkhL44fddvLlwiz2YzBYCiR72BJSsuw8OW+L/nqq6/c/lol8buVrVy5cnz11Vc0auT0c2eXWL/9ClRAbHRnreiolA7SWhlQlt1asetM1/77dWYXk0L5x0c3NKCaGOHyqZB+2wGCNkd3txh1D2VR1ZVBH/XS3ov/CL3vdL71t66obbD4tjNoS0ZCaMQGgKD4qNZoNUCj62o4pcysSAyLOJhf/cBNUTW1l+6rNA1A+QN/KNjpbTbtPB42KN90teX+pT6XLlXoAaBMlh2JYREXCrq+4LiojmZlqG4x6YQzYeG/FlRO8YYhgPYjASwGvbSgcqWV/9q1FYx++m3gfLrBPN3HYujp7DksMAswGzSTLIoJxR+lewXHRvVDcR/whdnL8JsyWzwdkhDCTW7evJm+fft2vzZtytyzkgItW7aMYcOGlegNjLudDb0NnZ5/j9ZatWpzaYcrQyc9J3naBFJX/LfA4w+8+Q3l7ulTghEVvxo1auiXX35ZTZw40dOhFKvKlSvz5ZcLinXhPk+7cuUK1apVo3Xr1iX2ml4Adbes6htgUR+jqKcANCg0aIXSEBi/6n+BfuqRxPYRqYWdzKD1Awr9tgW2NV69untqRcsSDHqg0oDSaA0ZZEwJ3BTVLr9Ewmj27gKWFRquANUC4qJnKBibOb4aFICRt4LiogcmhIbbPJkOjItahZE+SiubxwoaSDd6XQncHDk88Z4B63K/5q8thqUHxUV9qKEhXrwOvJHftbXcv9THgt9apS1VDF5qDFBgEhK4pV1HDXUBs5fhxqbC3rPSyFA+fSKaAFDPne864FpgXJRT9QPjV40E1VnDx6e6RRwOjC9bjQhq0yavAC/eR3MDbZ4MBllPRwghhBCiGBkAjGZDC6AecA3FD0rrT0FNR7MCMGmthpBKXODOqPKOnjilkuUD0AOBDDR7QMcClwA/g5HK9uoHxEY9r9DjAI3iILAROA0Y0dTMp8q9gBeo/RqWAf8G5gGHgaoYDDGBcdEj8nsti1aLAdA8UFA8ly/53QdUATKMRq9lhQZvoXPWTwdPdR6WVmjZUiZwS1Q9pXkROF6tRuonztYP3rbUD63eBVJNZkO+CV1pF+B1/Sk0zYA5id0GnnL+DMoYGBvdJTB+1VMB8aseCY6LbNVh507vYg9UCCGEEKKMym41uKCU/pfvDb//HO3Z82rOAgHx0e2V1tuAdiqNocCXDpw3WGl9F0p9m2FKHX0ubNh1ALV0qbFuHb9nTEadYqe+j1L8E9hmNFiGnuw6ICn7QFB89DCLUtfy1NB8j5EZiV3D43Pu7rBzp3dS6ulFCoaCfhP4Jk9VZVqkME4CmgfHxtx5qlu/X3KXUZphWY3aa091vq/Q6W4tqC4KUOjddq6z9LHwLuCH5l+/thhmdyan3LSp/Eugg7VWUx1dnLE0Cd62pjqaycAVL6Xece0sugWKLWiFAiwoTqeePhMYG/VYYreItcUZrxBCCCFEWeQFkNAtfGFBBZJCwncGxEYtVopH0QzAsSQkCMUPiSHhNi0LetgwM/CBA/XLAxfKXzd2P9w34mbOAwkh4fmOsUjsFjEkv/072rfPCN4U+YrFaBgKNA6IjW6ee+Xx06ED9wfGRe0FWpsNluGATRLSePVqX12RgQAalSeJyU0pWmb2ZlNH7JUtTQJjo7ugGIFmT1K3iMXO1q+3JTJAY5gAXLjpa3jPDSG6ncWU/jqo6ig18URI+OUinOomcA2srXb+KL4PiI0al9QtwpHfASGEEEKIW5ZDfd0VOvumPL9uUPmzMM2VgLJpzYzDffvetF/SvoSwAUeBzG5R+XflAlgEoPLpkpVaydwbqAykmcypK+2+oKYGgLZQZuZuU6C00jMBtFITHZmIIDeT2fAOUEGjpl7s1De52IN0s4DY6Oag/oEm0WBMLXTRwfwobTmrUK+ZtW6YFBrhlxgaUcvHbKqG5hngOqCU4u3gbVGBxR+9EEIIIUTZYTOIu/amGH8fg75PK0t9UEGADwBKNQbQUNXB85qSzqZtKEpgysIaV+oFbo7spZWhBUrVV1ANtArIeV6jzv8aDHyTlTg1DoyNbJvYbYC1K5XSerjOHBYfld21rMC4QQVAdQCD0lcLK1ua1I1f9bBCdQQ2JoWGO/3e19286i6DQT0CnKhw3fBR8Ufofgb0+xq8ULzuyliehG79v829L2tWtg+D4qIOalgPlNcZ+jVgTDGELIQQQghRJnlB5liNgLp+U7yNPKehfNY8VPmp4OB5k7K6XrnMnO7j1IDg4PiYThZt+RCDoX1m9AU9yNcV89ub2DXiZGB8VDyaUFAPkLlAXVZXLDUAQGv7XbHa79zpdRqMAFpRLC057ha4M6q8sqh3UGhtMLg0na7BYJgJWmmlXy2uFqySFBAf00cp+gIHks6kfVHc508IjdgQFBu1Wiv6aqU6268hhBBCCHHr8gIIqOv3LzSZkzhrftSKSKX0H2iV+TRY0RPN0xSSneSS7zogTrh2pndve4PXrepsiKzj5WNYBdQCktHqK6X0LxbFWaWt2ch8oFph51GaRRpCUWo4ZL4fqRUs95HZFSvZ11Ihxl4sO9q3zwiMi7oOVFRaVXH0GjxJpeoxWqlA0N+bMkiovSnGP+dxb4P2QSnQlMs+dj7s53OayRaAgLjo+xS6K3DES+kNeep7aT8ADV7Zx2rXTrnkysB3dzFoy5uZM0nrD2rVqlCr9qY/P2ovb2pZNxTVam+K8ff1Nabbm6AgN63Uz6D7ArcrUK50eRNCCCGEuBV41d4U4+9t5FUAhXotoVv4W7kLBcZHN3XyfqmoK7s51Ypi9DG8TmYCkmBKt3Q4e++AszmPK1ABcQ5ML5yesRQf79lAg+C4qI6nQiN+UsoyLKsr1orjYWE3HAzpElBRq7KRhGAd66P6eBst+SSQKvs//bOPB29pG0hXsmcty75Jb2S2GBK9jbk+fm39b7Ps+lcu+vUEitRlrzhpVC3QaKU+9jZaPrY5aLEpON/baEGbLJuBMKdeQ5GoMt+L8vXio6tStIHvQgghhBBllsHbYGlFZveh1MTQHVPzLaV1/RKNynntsv47O3cCAlA/bo0/4GvvJAn3Dr4IrAUwwwONV6/21WR1xVIGu12x/qROAWhNHcfriFufDs764VoRZ94SQgghhCjTvJSioQZQnMzuXpObUoTqUtxxREHDrP8ez+94hs7o5nBHMs0iFOEKhqVWNMeR2RXrwmlT+fWOxqPR2xV0VejWjtbxJG1WM4H/FljAyCKgLejvMatxAKfMlc9lH77pY4gsl2ZuXuD5jbyVuU4LhzGTmdRV4mRhMQXujCpvuWaqDWDG54a71xwxK+41mrImYshFGXSAViq71eZZzKzX3qQ6+xpK0xEArQ+5HqnjGmxaWfVG1mQSXr5+yc52HxNCCCGEcBcvrUhEA5r6wduW+uWeFajullV9DVq19FB8jkoEaoLKcyOsQAUqXnQ0hzLf8PnO6JeeCgSjeIfMjvvLdFiYydFgDLAt6/XaOlrHkxLDIi4AFwo6HhgXlfWdUNcSwyIO5j6eNR1vgVPyBsZHX0VrlOJmQj7186NTVB+D0Ws5gAHLVqCrI/VcdSYk/FhBxwK2xqQpszU/T8jvPai9aWlFX1/MBc2qFRgb1RtFbwCUYVPRI7Yv3eg9zoB+DcBiyvgIeKokXlcIIYQQwh6DMhl3AybAz2Lym91y/1Lr0+DA2KjeBotaBGR4LELH/Ayg0eOCtsa0yd5Ze9PSioFxUUs13EXmNdqVNSA+kswTNss6rxNdsSAj3RJH5kiCasGxUY2dqSvKJqMq19xi8jsZGB81OWhrTJsa21dXBgiM/S44KDb6RRTZ68ucK3fTd4oHQxVCCCGE8DhDQljfBI2ak7X9t8sX/U4FxkXFBcZFnUaxBvBG8b4ng7RHmY1vAClANW227AiMi9oTGBe109vod0nD/cBHwHnHT5i5cGGWpDOhu+KciefsvQPOktV9x6IY7ExdkclgyJzmGEBDqZlFy46aaF7XZsvucunmq4FxUTdRxpNa6emAH5kLZo452rNniawfo/Wf76FSZeY9FEIIIcRfgAHA11zhnxo1k8yn97WBEMAfOGKAHlj0EQ/GaFdCWN8EDPQBjpA5yL41WYPVFXpWkrnic86cr65f3e/JXOEajVpS0FiZwmgsmWMstB7mbF0BWpOzC+CiAguWEgYvdVbDJmxb3HKOMdmmlKFNYmjESkqIUvpO68+69L+HQgghhPjr8ALImnr2hdvi1vz7piG9jdLUVFofSwodsDVrLYOfgM/tnSwpNOId4B1Xg8m6QXN0CLlt3a4R8S33L73j8kW/lhrVQmG5ZkrXP+aYLSug0BPkcObm2TuAigAGo/rSlXgsqb7/M/qlz0apDvW2rr79ZJe+R105T2mQGBpRpPEYiSHhfwP+5lwtnf2aZypcN7r0GRSXpC79TmDne5nYNeIk0L1+fHQ1kza3AEMtha5qhuPGdNOvWTOvlbTOAAo2nwqN+MkDry/EX9JPP/2ExZL57Oquu+7CaDTaqZHpxIkT+Pn5Ubt2bXeG55K0tDT27t0LgK+vL23bOj7kMT09nV27dgHg5eVFhw4d3BKjKy5dusTvv/8OQPXq1WnSpIlD9VJTUzl27Bj+/v7UrFnTnSG65NSpUyQmJgIQGBhIcHCwnRqZkpOTOXPmDI0aNcJgMLgzRJccOnSIy5czJ5ds3LgxNWrUcKr++fPnOXo083asUqVK3HHHHcUeoyt27dpFenpmh4U2bdpQrlw5D0dUMrxybvwRet9pir7QoMdkLX63K+ufy7RZP5/1476ELv32uHKOM717pwTFRc/Q6DfNJvNzwPN2K+VVOSA+pk9mTOaLp+/p/7OT9Q3Z9Q1mfTPhnvAfXIihxKmlS40B/n6dADTMLksrsGdNvbulKOcIiI1ujkHVB1Aoh5PnnIJjoxqjMtdvUZr3ihKPEMJxMTExhIeHW7cvX75M1apVCyyfmprK888/T0xMDElJmUsv3X777YwYMYI333yz1NwIzp49m4kTM9c0rl+/Pn/88YfDdb/44gv+/ve/A1ClShWuXLnijhBdMnbsWP7738yOCwMHDmTlyoIbqy0WC1988QXvvPMOx44dQ2dNG+rv78+QIUOYNm0alSpVKpG4C6O1JiIign379gEwYcIEpk2bVmD59PR0Jk+ezJo1a9i7dy8Wi4WKFSvSuXNn5syZQ9OmTUsq9EJduXKFjh07kpycOQ/O4sWLGTFihFPnGDZsGJs3bwagU6dO/Pjjj8Uep7P27NlD+/btrdsHDhygWbNmBZbv27cvp08XfqteqVIl4uKcGkngEV72i/y11I1bXcuAHgmgtGutINnSfAwflEs3j0MxqsGmlZOPhw1y9i/v7UpbVgMoo/oB6OFkfe/s+trIWTK72JV6gYEV7tRmS0Xgmq/Z9JGn4ylpSumn0PqZopxDK90VFErx66nQiBj7NYQQRWUymXj99dcdLn/+/HkiIiL46SfbhsqjR48ydepUDhw4wNdff+3xp6JXrlxh5syZLtVNTU3lvfdK53OQ3377jUWLHOupajKZ6Nmzp/UGNqczZ84wd+5coqKiWL16NS1atCjuUJ3y7bffWhMQe65du8agQYPYuHGjzf7r16+zbt06OnTowMKFCxkyZIg7QnXKu+++a01AXPHll1/m+/l5mjN/M1JSUlizZo01AS5IlSplY61sSULInEXLy6tCiNK6sQH9DJkLG6amW9TXRTnvxU59kwPjo19CMyBdGe4C1jlSTyt1BK2+s9mH/sXR11XafA5ltKmPpuwsjmextEJxVmn9uQuJW5mnYZ/C9vPHonc7dw7VHMVZbXG9e6QQwjljxozh558db7AeN25cngQkp//9739MmzbNqZuU4nbjxg0GDhzI2bN51gG2y2QyMWzYMI4dK3AGdI85ffo04eHhmM1mh8pPnjzZ7g3syZMnGT16NFu2bPFYC9bPP//M6NGjHS7/4IMP5klAcrp+/TqPPfYYd999NwEBLjXMF4slS5bw7rvvulz/8uXLvPTSS8UYUfF47bXX+O677+wXzLJ//367CUhZIkkIYKD8bdktBlnMaDWyOBbISwwJnw/Md65OxCpglcuv2W3AbmCQq/U9LSEk4kvAo+NAPCkpNGIeMK8o50gMjZgATCieiIQQhbl06ZJNtx5HXLhwgWXLllm3b7vtNt566y0uXbrE5MmTrV2WPv/8c1599VWHx5UUpyNHjjB69GhiY2Odrnvq1CmefPJJvv/+ezdEVjRbt27l8ccf58SJEw6VP3PmjE13Jm9vb4YOHcpDDz3Ehg0bWLBggXWcwo8//sjSpUt54IEH3BJ7YZYsWcJTTz1FSkqKQ+XPnz/P6tV/3voEBATw/PPPU69ePWbNmsX27duBzETknXfeYc6cOQWdym3MZjOzZ8/mlVdeKdLN98SJEzl/3vFJUt3t+vXrTJo0idmzZztV77fffnNTRJ4hSQjgjfmGBcNetM7AoLYrs1peVsZPCCGE8Jyvv/6acePGOX2DM3/+fG7e/HO42/Tp07n//vuBzATlrbfeAiAhIYGYmBj69+9ffEHbkZGRwfTp03nzzTe5ceOGU3UtFgsfffQRr7zyCteuXXNThK65du0ar7zyCnPnznXqhjY+Pt460QDA6NGj+eijzJ7C4eHh3HHHHfztb3/OvfLjjz+WaBJy6tQpnnrqKaKiopyqt2zZMpuWoAULFtCrVy8AQkJCqFevnvV98sTYid27d/PEE0+wc+fOIp3nxx9/ZN68Ij3XK1bR0dE89dRTnDx50um6v/76q/XngIAA6yD73JRyaY6nEidJCHAqbMARoI3dgkIIIUSWL7/8kscee8xmn9FodKiLz8KFC60/16xZk8GD/1xS6qmnnrImIdllSzIJmTBhQp4xII5e1/Tp05kwwbYR1tG67vbAAw/YPPl3NK74+Hib7QEDBths9+/fH4PBYE1USvppdadOnWwGKjt6XQEBATz66KPs3buXlJQU7r33XuuxoKAgmjZtysGDBwGsLT0lJSkpibvuusvmOlz5HpnNZv7+97+Xmi5Ma9euJSIiwmafM9eVMwlp1qyZx8eLFVXpmHZDCCGEKGNSU1NttseNG8fIkSMdqnv8+HHrz/7+/jZjCPz9/fH19bVul/SYipzX5e3tzccff0zLli0LqZF/XYPBwLRp0wgJCSn2GF2RM7aqVavaJCSFefbZZ4mOjuaTTz7h1VdfpXv37jbHfXx8bD4/Z6eNLaqc19WoUSOHuwUOHDiQhQsXsmfPHg4fPmxzDUePHuXQoUPW7bCwsGKL1xHp6ek2N+YhISHMmDHD6fPMnj3bOrV0cHAwjRs3LrYYXZH7b8YjjzzCM884Pg9N7iSkrJMkRAghhCiC6tWrs2DBAmbMmOHQ2I3k5GTS0tKs29WqVctTJuc+VwaFF4cGDRqwfv16xowZ43Rdf39/Vq5cmadVpDRo164d27dvt3Y9suf222+nX79+PPnkk7z55pt5nj5HR0djMv25Tq2n1p7o378/27dvp3nz5kU6z6lTp5g8ebJN68F9991X1PBcYjQaeeaZZ9iwYYPT6+ckJiYyefJk6/bMmTMpX758cYfokooVKzJjxgy+/PJLh1szrly5Yl37BTK7aj700EPUqVOHgIAAhgwZUirHYBVGkhAhhBDCBdWrV+ett97i+PHjebplFSZ3UlG9evU8ZXImIefPny/R7iQNGjRg3rx5HDp0iG7duqn3C7gAACAASURBVDlV19/fn5kzZ3Ls2LES7ULmiDZt2rBixQp27Njh8KKEjsjd8tCxY8diO7cj+vTpw7Zt24iMjMz3u+TsuerVq8fXX/85Oej48eMZNmxYUcN0itFo5LHHHuPgwYPMmTMHHx8fp88xduxY67ik3r17W8dceVLFihWZMGECx48fZ9y4cU7Vzd3NLzIykkWLFnHu3DlOnz7NihUr6NevH2+88Uap6X5mj4wJEUIIIVwwfPhwl+o5m4RkZGRw6dKlEuvmU5TWi+xFCUujWbNmFfs5X3vtNdasWWPd7tixI7179y721ynMN998UyznsVgseabrffnll4s0Na6rgoODWbBggcv1V69ebZ19zsfHxyMze+WnZ8+e9OzZ06W6ObtiFURrzeuvv05gYKDNZAmllbSECCGEECUo90xa+XXHyr3KemmaXlRkmj9/vs0EAkCpXZjREUlJSWRkZNjs++CDDxgzZgzp6ekeisp5aWlpNuMsXnzxxWJt+fKU3ElISEgIH330Ed9++22ewe6vvvoq169fL8nwXCItIbeggPjo9krrqbl2X04MjXBsxKTwqMD4VU+hlc30Kxr1fVJouEOP8YLiV0VorWxGumnU/qTQ8BccqV9rS2QlX60WaG1ITgrdMVoz2WK/lhDCURUqVLDZzjlVb7bcU+PmriM8a+3atXnGyowaNYp77rnHQxEVXUZGBpMmTaJmzZrMnTuXw4cPk56ezqeffsrp06eJjIx05bQlPlfs1KlTrZM5BAcH869//aukQ3CL/v37U6FCBQ4ePEhwcDAzZ87EyyvzNn7o0KFEREQQExMDZK5ts3jxYp544glPhlyg7O5ikoTcijQ1AdtRZIpCRzYGbInpoCyWr+ydOik0orkGhzsbBsfHdLJoy8KsuH5P7BYxwE4VjwmMj5qMptBETaNiCrqZD4iLflih7f21O5QYGjGw0BJaNSfX56cUCXbOm6O+IRi0bX10ZUern+864FpQXJQG/X8Bse2/pxtLHH5tIYRdtWrVstnOb/rTS5cuWX9WSlGnTh23xyUcc/DgQYYNG2YzGL1Xr158/PHHHoyq6Bo0aMCUKVOAzBnBhg4dysqVKwFYtWoV69atc3gwv6ccOnSIf//739bt0jQYvah69+5dYFc/pRQvvfSSNQkBrNMrlzZaa5577jn8/PwkCbnFZRgUvQEsFlV4W6pJ+2Ggqf1TvqFgskNJiAIVYLHMQmWeVxnIsFfHk7TGX1H4e6DQewo+RjXs1AfstmkbzJYP8DIsB7Cg/onWTv3VNxjM32ltOABg0TwEON0xVMF7Gu5HqdcUbyyV1hAhik/uWX7sJSHVq1d3aWCuKH7JyckMHjyY5ORk675WrVqxbNkyvL29PRhZ8TIajYwcOdKahAAsXrzY6SSkpBfNmzJlirXrWPny5Vm3bh3r1q2zHk9I+PN53vHjx61jmN54440yn+jnnpXtyJEjHoqkcC+88ALR0dGkpaVJEnKLs5wKidjkbCUN/1S6oNYOxxIQgIDYqJEo7gZuAGVpRZ19aBbne0TpAw7UP4Pmg3yPGNQ5e5WzFs88AhAUt2q0drI1+2TXAUlAEkBAXFRnV/4XcCo04qfAuKhtoDvXjWs3mFCWu3AaIUQ+atasabNtLwnx9/d3e0zCPq01jzzyiM0T5gYNGrB69WoqV3a4sbnMyD0tb0Grc5cm2bNhQeaaHJ988kmBZc+dO2c9Pnbs2DKfhFSsWNFmO+c04KXF9OnT+eijj/j000957LHHJAkReZ0O3fluUZ98B29b6ofymwYkgdoMuiyNRzmQ2C1iWhHqny9i/VJBo75V6M4GrUaDJCFCFBdfX18qV65sfZqeOwnJyMiwGVRat27dEo1P5O+NN96wGRdRt25d1q9fT0BAgAejct2HH37IoUOHOH78OLVq1WL+/Pk2xw8fPmyzfSsmWmVFeno6sbGxnDx5khMnTnDbbbfx+OOP25Q5deqUzfbtt99ekiHa9fXXX/PPf/6TDz/80NqtTJIQ4RYWU7mXgWCt1BgFHR0fRSJKC2XQ/8PCTK3oXXtTjP+5sH5nPB2TELeK7t2789133wGZN3tHjx613jTEx8fblO3Ro0eJxydsRUZG8uabb1q3fX19Wb16NQ0bNvRgVEXzzTffWL9rSilee+01brvtNuvxVatW2ZRv1apVSYYnclBKMWTIEGtLT9WqVXnggQdsxrvknCoaKPKilcVp3bp1PP7440yYMIEnnniCM2cybydkil5R7IK3RQWCmgAcOn069XNPxyNck9g14iSwGzD6GM1lqSVLiFJv1KhR1p8tFgvTpk1Da016erp1cDCAl5dXnieeomRlZGTw+OOP2ywA17RpU1asWMHrr7+e599nn33mwWgdl3N8h9aa8ePHk5SUBGSOJ8i9TkdISEhJhueSlStXorUu8F/r1q2tZTt16mTd36xZMw9GbZ+3tzdhYWHW7StXrjB16lTr+Jdjx47ZTA9doUIFHnrooZIOM1+7du1iwIABjBw5Uuee0lpaQkSx0ybeAcprrf6phw0zB8ZHezok4SKt1E9K67Ya1Q2Y6el4hLhV9OvXjzp16lgXLvzss89Yt24dKSkpXLhwwVpuwIABMibEw3744QebMToA+/btY9++ffmW79SpU5lYKG706NHMmjXL2h1w6dKlrFy5ktatW/PLL7/YTB3dt29f+vbt66lQBZkLgeZsnXr77beZP38+nTt3ZuPGjVy9etV67IknniixxU0Lc/ToUe699166dOmiFy5cmGeIqiQhIo+A2PbLAonqiEKB3odW2zMsadPPhQ2zu/JN3c2r7jIY1MPA9qRu4f8rgXDdoX1gXNQWoBlwFtQetP4usVuEo1PV1g2Mj16L1m0UXNOavSg2JoXunFvWZpkyWCy7debsJp08HYsQtxIvLy/ee+89Ro0ahdlsBuDEiRM2Zfz8/HjppZc8EZ7IIecMUbeSwMBA5s6dy8iRfzZ0Z2RksGPHDpty2euGCM/q168fTz/9NB9++KF135kzZ1ixYoVNuUGDBjFtmueHpZ47d47u3bsTFBSkN2zYkO8cOdIdS+SlGIwiEAgA1QfFZG+j3+6ALTEd7FU1GNQsQFnQE9wfqNs0AroA1YHmoEei+CYwLmpxje2rHRmZVzNrWt1aGhqiGAzMCYhvvykw9rtgdwZe7IzszfqpbmY3OyFEcXn00UdZtmwZ5crlnTywdu3a/PDDD3Tu3NkDkYlsWmvr2J1b0YgRI1i+fHmBrW09e/Zk7969NmNFhOfMmDGDN954I9+/GQCPPPIIS5cuxdfXt4Qjs5WSkkLPnj3RWutdu3YVOEmntISInDLQRAH7lFJntbbUR6leQDugkbJYomtsX934Yqe+yflVDoyLHgF00RBzOrT/5pIMvBid0Up9p7Q+qjXpKnONkwfITEhGlEs3pwGjCqmfrDUrUeqQ0vqqMtBYazUQ9G1oQsH4LVBm7ipM2nDOmDWrgLboYCDRsxEJUbo999xzDBo0yLptb6XzQYMGceTIETZt2kR8fDzly5enc+fOhIWF5ZnK15Nmzpxp7e7h7OJvU6ZMsXYxK21raeR8ipzfLGQ3b960efLsiOrVqxc5rqJo0KCBzXU1adKk0PJDhgyhR48exMTEsG/fPpKSkrjzzjvp0KEDYWFhJb7WR0FCQ0Ntruuuu+5y+hw5v8ee/pyyPfroo9x9993W7aCgoALL+vj48Nprr/HII4+wdu1afvvtN27cuEFISAjdunUrFcliRkYG/fv3JykpiSNHjqjCfuclCREAKK0PmNItwWfvHWCzsrpaunRSgH/510C/BtQud9P0T2Bi7vrB25b6gd+7gMWI5ZUSCrtYGZRhpr9fned2tG9vs6hi7U0xb3obLcvJbB15LDA2ck5itwG7c9fXRhVpSk/9Ine3Nf+1aycZ/dI/Bx5AcXdgXPSIxNDwb9x6McXE62b6Ve2T+QdEa1XLTnEh/vLatWtHu3btnKoTGBjIQw89VGoGkuane/fuLtctzQOacyaM+SlXrpzdMqVNlSpVnI65atWqPPjggzz44INuiqroAgMDCQwsWoN8Ub7H7tKiRQtatGjhVJ0GDRowZswYN0XkOq01jz32GDt27GDv3r1UrVq10PLSHUsAkBgWcSF3AgKghw0zJ4WGvw5sBUCpJ/OrrzP8XgTqKfj6VOiA/EfrlXIJIf1+z52AAJwL63dGKcPjZK52bgBjviMOk7r0O5HfuJkzvXun3PAxPgmcAdDo0veXowD+VevlbPUqPY9lhRBCCFGqjB8/npUrV7J+/XoaNGhgt7wkIcIuDRr4ImuzWuCmKJub0XpbIgO0YiJw02I0vFriAZaAhJB+vwNZE6rrwtu285HVhW1ZZnUaF2twbpRw8aKPdUOr0rf8qhBCCCE8btasWcyePZvFixfTsWNHh+pIdyzhEAv6sIHMfqHKWzcCrHNImizGHgpdATiFRb8SkGtKXoXuCqA1gQHx0R8DKJOelRgWcbCk4i8OWqnDSuseoBq5dALFYTSgCAjcGVU+sX1EavFGWPy8K6VXtZgyf1bo856NRgghhBClzZIlSxg/fjwzZ85k4MCBDteTJEQ4xGgxGLQhc4Cy2YS5gGLBShfa1aha9nFlZClQppIQpXVWy6Eu6PoLpbUyqMxB3trnWsUyMVWvyaKrWpNPbTjn4XCEEEIIUYps3LiRRx55hBdeeIGnn37aqbqShAiHWIw0VlmLxaaX8zqU85gy6D+wUOAaGgru0tAQuAp8D2AyqzzjT0o7DY1V5n8P2S2cD6VpnHU/f+J4WNiN4ozNXQwWld11LKNcivrdo8EIIYQQotTYs2cPERERDB06VE+bNs3padQkCRF2KVABWmdPS3s69xS9iV0j4skeL5GPwPjoz9C6oVKcSgiJGOHIawbER7dXWj8LoJQ+nBDSf6qr8ReHwE1RzZSRrpnxcMDZ+revX18FXz0MQOmy1AKk2maOCtJ7D/fte9N+eSGEEELc6o4fP869997LXXfdxaJFi1yax1mSEIHatMkrwHj9bYMXH5zqHJGY55jh2hSUyl4xe2aJBGWhPorHALRWWwG3JiFBsat6aIOqX6162te/thiWbnNs0+ogbWShAm/gptFg/Ch3/cAtkU0xq/43fL0+zZ2k1di+urJfOdN8sqa41TDLnddSrLRuiwIMarunQxFCCCGE5124cIHu3btTu3ZtvXnzZpcXkpEkRNC+UiV1OvX6yxYTYwPjojaiOKI1iUqroAAjYaCyJ7A+ULd83bJzA+0UQzBaz7980W9qYFxUPFofU8qQApYm2qj6K6iSVfC9k136Hs1d22JSlQ0G9e9y6eZXA+Oif9BwDLigtL69HNynlQoEULA8oVvE2pK8Mle13L/UB+XXLXNLrfRsNEIIIYTwtOzV0DMyMvTvv/9epJUsJQkROXkD96G5TwFkDwLJtAFtfjy/dTTcQukaObZcGoPhorrAMJRCZ01llcWEZmqSpeIUO/Urgx5orZXz11Mz/6bRMrZ4w3Wfi5fL3WvITL4SkkJ2bIRwT4ckhBBCCA8xmUwMHDiQkydPcvjwYeXj42O/UiEkCRHsaN8+I2hrTFttNg8AOoOqRebCdGeAX7TSsadD+n+VtV6I8yxEAgng+GB0BZ2zftQWg37fpdd1grelwpIMY8oFi6I/WjdVUAsoD/yO4lc0ixK7RewqqP6Zczd2+fuXC1OoAQpakVm/qobjCn5FsyqxjLSAZDNow/2gQfGVZnKZmM1LCCGEEO7x+OOPs337dnbv3k2NGjXsV7BDkhABQEKXfnuAPe44d2K38Egg0slqXbP+G3O6a//fijmkPLJmq4rO+uc0PWyYGdic9a/Mq7F9deVyWg8F0lHkGQMjhBBCiL+OiRMnsnz5cjZs2ECjRq4tl5abJCG3Np/AuKgfATRcTAqNKBP9aQI3RdXESBMArQ3veTqekhYUG/2iVpkzaYG63dn6AbHRQ5TS4wEUBLgSg99NyxNaUUXDx0ldI066cg4hhBBClH1z5sxh5syZLFq0iM6dO9uv4CBJQm5tCugEoBRlZ10OLzpndfzantStX6yHoylxWunbyPrcXGFQ1NFFqN9h505vrfRYIF0ZeMfV8wghhBCibFu6dCkvvPAC06dP5/777y/Wc0sScgtKCg1fg+2Q6DJFGQynMFv+gWKbp2PxhMTQiGeBZ12tnxAa/hG43oUqa/KBYFfrCyGEEKLs27RpEw899BDPPfcczz//fLGfX5IQUeq4c3yKEEIIIYQo3L59+wgPD2fAgAH6/fffd8uDbUlChBBC/CW9s2vAe0CrRz7o4POz/pJDR26d5XDO1D3D4/+5myVHXgdgSMOJeBvKeTaoIlr/xG2YDfkfU97e+GRda1lhan8Sc1CTPPsrnb9B52/K7lC8zUlfcSb1CAD3v90C3WC/9Xt4q3jg3604WXsNGZY+Zf73Kj8nTpygR48etG7dmmXLlrmtZ40kIUIIIf6qOgL33N6xBhf0ES4kezqcYuQHjTvX4lhy5sziZm3G28MhFVVis0qYvQq5H0oucBb10qkGUKNynt3VE8r2rdmZ1CPW791tHaoCV6zbt4qGd1UnhcRb4vcqt4sXLzJ06FCqV6+ut27d6tau/WX7my6EEEIIIYQoFkOHDuXGjRv6wIEDbh9bLEmIEEIIcYubNm0aZBg9HUaRlO/n6QhK1pJvvuH4Dz96Ogyned99BENtT0dRMm6F36tsN27cAODy5cscPnxYlSvn/m5mkoQIIYQQt7iPP/6Ym9dN7n4Z7c6TT+wXUmZnfXTFqqgoNlw3u/Ml3PJ5DQtoqRrUruaOU5c6JfR7lc2tv19aa4xGo1q1ahW1atVy50tZSRIihBBC3OL++OMPyhkruPtl3JokvPdTf3eevtT56quvKHdPH3e+hFs+ryVHXr/lxoAUpIR+r7K59ffrypUrVKtWjeDgkpuhv4B5JoQQQgghhBDCPSQJEUIIIYQQQpQo6Y4lhBBC5OP+hv+kim/ZGGG7/OjbXE0/5+kwPMY31cJD7Wd7OgyHpJmusfjwq54Ow2MGNXiZ6uUCPR2GQ/7qv1fuJkmIEEIIkY8a5YKoUS7I02E4xMtwq61W4ByDRVPHr6Gnw3BISsZlT4fgUdXLBZaZz+qv/nvlbtIdSwghhBBCCFGiJAkRQgghhBBClChJQoQQQgghhBAlSpIQIYQQQgghRImSJEQIIYQQQghRoiQJEUIIIYQQQpQoSUKEEEIIIYQQJUqSECGEEEIIIUSJkiRECCGEEEIIUaIkCRFCCCGEEEKUKElChBBCCCGEECXKy9MBCCGEELeCCxcucODAAYxGI82bN6datWoO171x4wa7d++mYsWK3HHHHRgMpecZYUJCAr///jtVq1aladOmVKhQwan6qamprF27FgBvb2/Cw8PdEaZTLBYLx48f59ixY9StW5cmTZrg4+PjcN2TJ09y5MgRateuTZMmTShXrpybI3ZMRkYGhw8fJjExkQYNGtCgQQOMRqPD9Q8fPkxSUhJ33nkn1atXd2OkzklNTeXQoUNcunSJJk2aEBwc7NJ5/vjjD/bs2QNA9erV6datW3GG6bQrV65w4MABMjIyaN68ObVq1fJoPCWt9PyVE0IIIcqgZcuW0aRJE2rVqkW3bt3o2rUr1atXp0mTJmzevLnQuufOnaNXr15UrlyZLl260KpVK6pVq8bo0aO5ceNGCV1BXiaTiTlz5lC7dm2Cg4O59957ad++PZUqVaJr164cPHjQ4XN9/PHHDB48mMGDB/PQQw+5MWr7kpOTmThxIpUqVaJRo0b07t2bO++8k/LlyzN8+HDOnz9fYN3U1FTeeOMNqlatSoMGDejVqxetW7emQoUKREREkJSUVIJXYisxMZH/+7//o3z58txxxx307t2bxo0bU6FCBV5++eVCv0tXr17lwQcftCZUYWFh1KhRg6ZNmxIfH1+CV5HXvn37uO+++6hYsSLt2rWjZ8+e1KtXjypVqjB79my01g6fy2w2c//991u/i+PHj3dj5IVbv349bdu2pVq1anTp0oV77rnH+ru2cuXKQuu2aNGCqlWrFvqvfv36JXQlRSNJiBBCCOGiJ598kmHDhnH48OE8xw4fPkyPHj2YNWtWvnWPHDlC586dWb9+PRkZGdb9ycnJzJ8/n549e3Lp0iW3xV4Qs9lMz549ee655/LclGut2bp1K23btiUyMtLuuS5dusT777/vrlCdcvnyZVq1asW7775LamqqzTGz2czSpUtp2bIlv/32W566KSkptG/fntdff51r167ZHLNYLERHR9OyZUt++uknt15Dfn755ReaNGnCwoULMZlMNsdu3rzJ9OnTad++PVeuXMlT98yZM9xzzz0sXrw4z2f9+++/0717dz799FO3xl+QZcuW0bZtW9auXZsn2UhOTub555+nf//+WCwWh843d+5cdu3a5Y5QnTJ16lR69eplbZHJKSEhgcGDB/Piiy/mW/fq1ascOHCAq1ev2v1XFkgSIoQQQrhg5cqVzJs3r9AyFouF8ePH88svv+Q59uKLL3Ls2LEC627ZsoWpU6cWOU5nvf3223ZbcG7cuMGTTz5ZaJKUkpJCv379PNpCkNM//vEPTpw4UWiZc+fOMXr06Dw3ti+++KLd1p/Lly8zatQo0tPTixyro9LT03n44YfzJFW57d+/n5dffjnP/hEjRrB3794C65lMJp599ll+//33IsfqjKSkJMaMGWM3wYiOjmbOnDl2z3f69Gn+9a9/FVd4Ltu2bRuTJ0+2W27GjBls2LAhz/78EuSyTJIQIYQQwgWTJk2y2R4yZAgbNmxgyZIlhIaGWvdnZGTkKXvy5Emio6Ot261bt2bjxo0sX76cunXrWvcvXLiQmzdvuukK8kpNTeWtt96ybvv4+DBmzBi2b9/O3LlzadasmfXY2bNnC2zl2blzJyEhIWzfvt3tMTvil19+YcmSJdbtKlWqMHHiRHbu3MnUqVOpU6eO9dj27dttusScPHmSTz75xLpdoUIFxo0bx44dO3j//fdtur789ttvfPXVV26+mj8tWbKEffv2Wbfr1avH9OnT2bFjBy+99BIVK1a0Hvv88885evSodTshIYHY2FjrdsuWLVmyZAnbtm1j4MCB1v3p6em8++67br4SW++//75NgtumTRsWLFhAXFwcjz/+OEop67FJkybZtCTmZ+zYsSQnJ7stXke9+uqrmM1m63aPHj2IiooiMjKSvn372pR95ZVX8tT/9ddfbbbr1KlT4L+yQAamCyGEEE7KHoSerVmzZixfvty63aNHD/z9/a03HLm7gcybN8/mZmT69Ol0794dgGPHjlmfWl+8eJHly5fz4IMPuu1acvr5559tbuiGDBnCxx9/DEDHjh1p1qwZPXr0sB7PfV2pqalMnjyZmTNn2lyfp+W82QYYN26c9Yl0u3btqFChAmPHjrUe37VrF0OGDAEgLi7Opu4TTzzBjBkzAGjfvj1169a1+Xx27drFqFGj3HIdueW+rtmzZ1sTiPbt25OcnGztTqW1Zvfu3dx+++1AZgKTs5vTJ598QpcuXYDM72dUVJT1M9y5c6fbryWn3Ne1cuVKa7IXEhLCb7/9Zu36lpKSwqFDh2jZsmW+51qzZg3ffvutewN2gNlstknKq1atyvfff4+3tzcA9913HzVr1rR299u3bx8mkwkvrz9v1XMmIfXr1+ePP/4omeDdRFpChBBCCCdt2bLF5gZuwIABNsdr1qxpvaGDzIHD169ft24vWrTI+nPdunXp2bOndftvf/ubzbkWL15cbHHbs3XrVpvt3NcVGhpqM+vXoUOHbI5Pnz6d6dOn2yQgzs6m5Q65B1jnvq7shCNbzuuyV7dfv342N4q53xN3yhmbn58fvXr1sjmes0UDbGPr2LEjkyZNol+/foSEhNh8X2vVqkWjRo2s2yU5SUJKSorNeIlWrVrlGWhd2HXldOPGDZ5++uniD9IF+/bts/kbEB4ebk1AILPVsU+fPtbtmzdv5uk+mLM7Vs5WybJKWkKEEEIIJ/Xt25ejR4+SkJBAQkICd999d54yaWlp1p8rVapk0zUmISHB+nPuaTmrVq1K+fLlrf38T548WdzhF2js2LEMGTKEhIQETp06lWc6XbPZbNNS4u/vb3M8Zx9+X19f5s+fz6effmp3jIm7ffLJJ0yaNImEhATOnDlD27ZtbY7nvsnOeV3vvfcezz77rPWzztnVLrtuzuvO/Z64U1xcHKdOnSIhIQGz2Uz58uVtjqekpNhs54wtNDQ0z7Vk++WXX2xu7HMmye5Wvnx5Tpw4YX2/a9asmadMzt8tKPg9f/vtt61d0O644w7Ac+MqWrVqxR9//GH93cqv5SbndSml8nSrytkSkp2EJCcn4+XlleezLwskCRFCCCGc5OPjQ8OGDWnYsGG+x0+fPm3ThSX7Bggy1wbIOXg5v/UYqlatak1Czp49W1xh2+Xn50fTpk1p2rRpvsd/+OEHm6e5BXWBadGiBZ999hmdO3f22OxKOVWuXJmWLVsWGG/uaVFzlqtUqRItWrSgRYsW+daNjIy0SUIKeg13qFmzJjVr1syTVGUr7LryY7FY2Lp1K1OmTLHZ379//6IF6gSlFAEBAQQEBNCxY8d8y6xatcpmO+fvV7ZDhw7ZjGX5z3/+Y9PlrqQZjUbq169f4PS5aWlpNoPR69evb/Pg4vz585w7d866vXv3brp27WrtlnbnnXfy97//nSeffNJNV1D8JAkRQgghitn8+fNtumvlfOKcO6nILwmpVq2adVapCxcuYLFYSsUChp9//rnNdkhIiM12o0aNWLx4McOHDy8V8Trixo0bfPnll9ZtpRRdu3Z1qK7JZOKLL76w2Zf7PfGUpKQkm8kPqlSpYjcJad26tc3TdqUUs2fP5r777nNbnM7aQkauXwAAIABJREFUsWOHzYxed955J1WrVs1T7qmnnrIm+yNHjiQsLKykQnTJokWLbFpCcn+Pcg9Kzz1uZvfu3YwZM4Zt27bxySefOLz4pidJEiKEEEIUo8jISF577TXrduXKlW2mR82dhOS3snrOfWazmQsXLlC7dm03ROu4qVOnsmzZMut28+bNGTFihE2Zhx9+uKTDKhKtNY8++qjNDd7QoUMdbs34xz/+wbZt26zbvXr1cjiBcafr168THh5us17E+PHjCx2fYzKZbCZbAJg4cSLPPPOM2+J01qlTp/KMyck5m1u2r7/+mo0bNwKZLVnTp08vkfhctWXLFpuxK15eXrz66qs2ZRztRrZgwQKaN2/u0cUYHVU2HlMIIYQQZcCOHTsYOXKkTfec8ePH24z7uHDhgk2d/J7iVqlSxWb74sWLxRypcxYtWpTnpmjq1KkYjUYPRVQ8Jk6cyNKlS63bXl5eeboiFeSdd97hs88+s24rpZg2bVqxx+gss9nM8OHDbQZ3+/v72+2KlJiYmGdGs3feeYc+ffrku9BhSUtOTqZfv36cPn3aui8kJCTPIPUrV67YLPY3efJkAgICSixOZx0+fJiBAwfaTMU9atQomjRpYlMud0vIkCFDiIqK4qeffsoz+P7tt9/Os/hkaSQtIUIIIUQxOHHiBBERETYLx3Xq1CnP6seVK1e22c5vobncA4pzJyUlKTY2lscff9yme9mIESMYPHiwx2IqDh9//DHvvfeezb4pU6bkufnLz+LFi/Os/fLyyy/Trl27Yo3RFc888wyrV6+2bhuNRubNm2d34HJ2uZo1azJnzhxrS8KaNWvo06cPW7du9VgXO5PJxNChQ21uxKtWrZrveKNXXnnF2tp4xx138Pzzz5dYnM66cOEC/fr1s3nI0KhRo3wXKR0zZgxt2rTh4MGDBAYG2rSu3nXXXSQnJ/Pf//4XyFxZfcmSJaWqFSun7IRLkhAhhBCiiFJSUujfv79NV6vGjRsTFRVFuXLlbMrmng3r8uXLec6Xc6E2g8Hgsa5Yx48fZ8iQITYD6Xv06MHChQs9Ek9x2bhxI88++6zNvmeeeYYJEybYrfvzzz8zatQom6Rs5MiRpaIVZM6cOdZ1XbJ9+OGHRERE2K0bFBRknR560KBBjBo1yjreZfv27Sxfvpxhw4YVf9AOeO6551i3bp1129fXl++++47mzZvblPvpp59sEpP//Oc/NtMnlyYZGRncf//9HDlyxLqvTp06rFmzJt8Zwdq2bVvgBASQOQYmOwmBzBaW0ig9PZ2HH34YPz8/6Y4lhBBCFIXWmscee4xffvnFuq927dqsXr0635uJ3AmFvSSkZs2aHrmRSklJYeDAgTZPadu0acOKFSvKxKDXghw/fpzhw4djMpms+4YOHcoHH3xgt+7Zs2cZMmSIzZS+3bt3Z8GCBTareHvCpk2beOGFF2z2TZo0iTFjxrh0vtwtXblnpCop8+bN46OPPrJuK6X48ssv6datW56y77//vrUrpJ+fH9OmTaNPnz7Wf8eOHbOWPXjwoHV/zimzS8rYsWNtBpdXrFiRmJiYAmfcs6dx48Y22zmTm9JCa81DDz3Enj17SEtLk5YQIYQQoiimTJlis1p6lSpV+P77760rU+dWs2ZNlFLWJ+n2kpC6desWc8T25ZdYNW7cmO+//z5Pd7KyJCUlhUGDBtkkVj169OCrr76y29UoIyODoUOH2tywtm/fnpUrV3o8KTt58mSexOrJJ590eHxLfrp3726znfMGvqRs2bIlT5ei//znPwwfPjzf8jnHVaSlpbFmzZoCz3316lXr8ZzTTpeEefPmMXfuXOt2dstOUbrz+fr62mznbKkrLcaOHUtMTAzLly+nb9++koQIIYQQroqMjGTy5MnWbT8/P1atWlVotwlvb2+qVatmTTRyDzq/efOmzTgRTyQhuROroKAg1q1bl2fxtLIkO7Hat2+fdV+HDh1YuXJlnhu4/Dz33HM2K5Q3b968VCRlaWlpDBo0yGYg8gMPPGDTepCfiRMnsn//fo4fP06dOnVYv369zfHdu3fbbJd0l8CEhATuv/9+m66AU6ZM4amnnirROIpb7sTKy8uLJUuW0KNHjwLrpKWlsXjxYuvClE2bNuWll16yKZN7dfWCHoJ4ynvvvccnn3zC8uXLadOmDSBjQoQQQgiX7N+/n4cfftjmieMXX3xR4CrUOfXt25evv/4agKNHj7J3715at24NYDOoGKBfv37FGLV9uRMrHx8foqOjC1xkrayYOnWqTWLl7+9PTEwMlSpVslt33rz/Z+/M42yq/z/+PHc2jH09c+xLlH2dQ/mKIoosaZH2VXtavmhRWvxIESVtvhIlWYoolYQIxzITURFROA5Zwxiz3PP749x75+5z78xdhj7Px+M+Zs45n8/nvM9dP+/zeb9f7/c9ci2cq13+wu1izd133+3hMKSnpzNz5sxCV3Y2bdrkcjy2bt3Ktm3bPIr+eYdftWzZMoJWByc7O5sBAwZ45FjdfvvtPmIA3lSvXj3o+1TXdXJzcwHrfe108JOSkiJgdeH4c6xee+01H4Uvb1JSUhg2bJjrhkVqaip33323h7Le559/7tEnlq9XYXz00Uc8++yzTJ48md69e2MYBiCckIhjqEoS0C7A4QxZ03MCHDsvMFSlDeDvltJvsqbHX+NPIBAIIsQTTzzByZMnXdupqaksWbLEx4lwMmnSJJfK1Z133ulyQgBGjRrFjBkzyMrK8gihKV26NLfcckuUrsAX0zS5//77PRyrGjVqMGHCBL/tK1SoEFIuRbw5cOCAh2MF1nW5Kwy506JFC5eqWVZWlo+8bY0aNTxqwbjTsGFDHznjaLFy5UpmzZrlsa9UqVLcc889ftv37t3blVzevXt3j9WPhx56iHHjxtGhQwdWr17NtGnTPPp27949wtYH5t1332Xjxo0e+44fP87tt9/ut/3dd99N586d/apludO6dWtXocM2bdqwbt26iNgbKiNHjvRwrBITE8nIyAh4XS+++CJ16tTBZrNx+eWXM2fOHMAKK3z88ccZO3Ys1atXJyMjw+MzWrlyZQYPHhzVawmVb7/9ljvuuIMRI0b4VHMXTkjkqQasDXCsDrA3hraEhaEqaUAfoDGWrbnA78Bm4AtZ0+1BujtZBNT0s78P8KWf/QKBQHDO8c8//7gkTJ2cPn06qGrU2LFjXU5It27dqF+/Prt37wZgwYIFpKWlcfbsWY+4/uuuu85vHZFosXHjRleldid79+4NeF01atQ4J5yQxYsXe9RuAdi8ebNH5W13evbs6XJCvv32Wx8Z5R07drBjxw6/fVVVjZkT4n33GyxJZe9q2k5kWXY5IXfffTeTJk1y1d1YsWIF6enpVK1a1aeWzeDBg2NaCd7fdS1YsCBg+65du5aYSvWBsNvtLF682GNfXl4eM2bMCNhn6NCh1KlTB4DHHnuM+fPnu2q5fPDBB3zyySc0a9aMLVu2uFZ4nG2DFaaMFZs2baJfv37ceOON5ksvveSj3CDUsQQAGKqyHNgPvAc8CVwP3ASMAj4HNhiqosbNQIFAIChBfPXVVx4hFeEiSRJvv/22R+2G06dPezggVatWjXnV44ULF8b0fLGiONcVbPIbb4pjW5UqVZg2bZqPqpe3A1K/fv2AK2HR4PDhwx65N+cLa9as8Xluw6Fjx44+q3nZ2dls2rTJwwF5+OGHCw1biwW7du2ie/fuXHzxxeaMGTP8SscJJyT6jADucDziW/I2OOlAMH3BtsASQ1VqFTLOYxRc79lC2goEAsE5SSQmpj179uT777+nSpUqPscaNmzImjVrPGL0Y8EXX3wR0/PFgqysLJYtW1akvvn5+XGTpi2Mn376yScZOVx69erFDz/8QJMmTfwev/3229m8eXNMBQkWL17sU7n9fCASn62RI0fy4Ycf+tQaAitvZOTIkbzxxhtxl4s+dOgQ3bp1o2bNmuayZcsCGiPCsaLPZ7Kml8yKMf4xsMLJfgIqY62GODPvKgFvAQEzqGRNn+saSFUm4z8/RCAQCM5pRo0axYgRI8Lq4y+JWVVV9u/fz8aNG1m9ejWlSpUiPT2dtm3bhqTYFGlmzpwZlrRnKAm9U6dOdUmgJiQkFNm2oiJJEmvXBoqS9o9T8So/Pz9sByZWYTB16tTxUbAqDH/OROfOndm8eTNr165l27ZtHDx4kBYtWtCuXbsi16woDj169Aj7upwhS4Xx6aefcubMGSB2r5OT++67L+w8DX/O4a233kr//v1dr1d2djadO3cmPT3dpzBqPDh16hTdu3fHNE0zMzMzqDcknBCBkz3ARGC6rOmudT1DVcYBW7EcEIBuhqpIsqaXPAFqgUAgiBEXXnhhxMZKSUnhkksu4ZJLLonYmEXFqdAVSRo1ahTxMcOhdOnSLknQcElOTi5y32hTuXJlKleuHJGxUlJS6Nq1K127do3IeMWhZs2a1KzpL7W0+ARa8YkFkXToypcvT8+ePenZs2fExowEubm59O3bF13X+f3336XCblIkGqqSgTX5nF0U5SZDVZYA3wDTZE3/pwj93wMOAlNkTT8Qbv9IYqhKJaAXUA+QHbv/cDyWypqeHaDr+UArWdPzvHfKmq4bqvIJ4BTmLoeVtF68NWCBQCAQCAQCwXmBsw7Pxo0b2bJlC5UqVSq0TyLQBvgQGGeoyhTgHVnTD4Vx3gbA68CLhqpMA96UNX1XGP2rA/cAwwxVmQNMlDV9Uxj9I4Lj3P2BQG7bLkNVHpA1/dto23JQlfuYJLR3btvtLFU27P8xmuf054C4ccJrW+R6CAQCgUAgEAgAeOaZZ1i4cCErVqygXr16IfVxT0yvAbwA/GWoyjRDVcJdky0HPArsMFRloaEq3cLsnwzcDGw0VGWVoSoDDVWJZfDoZQR2QAAaAgsdMrbRxbRdDebzzofNRrzX6Du4/X9M1nQjbpYIBAKBQCAQCEoMkydPZvz48cyaNYsOHToU3sGBDSvR2P1OdwqWstFPhqosN1Sln6EqwVS0/ge4i4rbgL7A94aq/GSoyh2GqgTLrpsDbPfa1xmYh7X68IShKhVCvJ5IsBIYDwwBHsGztkUpLPnafw2GqjQB3Mv/Lg7UViAQCAQCgUDw72H+/Pk8/vjjvP7664VWfvfGJmv6Q4CC5Xh4y0d0BRZgrW48aqhKOe8BZE0fB9TFCmX6CnCvBtQKmIa1uvKCoSqyn/6zZE2/0HGuWXiG+tQFXgP2GarypqEqF4R1deHxKdBa1vSusqY/KWv6e7Kmvylreh88n5feUbShRGGoioRVN8TpROZi1Q0RCAQCgUAgEPyLWblyJYMHD+bxxx/ngQceKLyDFzYAWdOzZE2fLmv6xUAL4E3guFu7hljJ6/sMVRnrPYis6Xmypi+UNb03UB94CavwnZPqwHPAn4aqfGioio+GmKzpK2VNvwmr2vbjwG9uh8sCDwHbDVVZZKhKxGUTZE1/UNZ0j9KphqqkOGx1L90ZHcmGkskwoIvb9kRZ0/+IlzECgUAgEAgEgvjz888/07t3bwYOHGiOHevjGoSEj0SvrOlbgUcMVRkOXAfcC66chPLA3VgF+Pwia/pfwHOGqryAtWpwL3AllsOTDNwK/BfwqzQla/oRrET31w1V+Y+j/7VYoVAS0AdohKeTU2wc+SfXA3diqWPVcpzTm7KGqpQvihJYyEj2RZDgUgqz24lqUro/DFW5Gvg/t11LgKdibYdAIBAIBAKBoOTw119/cdlll9GuXTtz1qxZRa6MGLBOiKzpZwxVmYmV7/EKVsXskJE1PR/4wlAVHcsBuTJc42RNX2WoygEsh+XucPuHiiPM7EesVaBQqApEzQmpoRmLiWPuhaEqzYGPKRAu+Bm4wfGaCgQCgUAgEAj+hRw9epSuXbtSrVo1c+XKlcUqze7XCTFUpQZWjsg9WBK87hQqz+qY1N+EtYrRxutwHhB0MmuoSjIwwNG/G9YKiDth1zMphCn4OiDZwGHgNFARSz3MSbGe9JKMoSpVgS+w1M7AqqDeR9b0k/GzSiAQCAQCgUAQT86cOUOPHj3Iyckxf/vtt2LPhV1OiCMJuQfWxL8vvnK1e7GUtN4LNJihKh2wVKUGAaleh48B7wOTHSFX/vo3xnJ8bgOqeR3OBj4CJjlCxiKCoSqJwA1uu+wOG2Y6K4cbqjICGBOpc5ZUDFVJAuZj5fUAZAF9HSF2AoFAIBAIBIJ/Ifn5+VxzzTXs2bOHHTt2SMnJycUeM9FR9+IOrHCn+n7arMNKSp/vr6CdoSrlKVj1aO2n/3ZgEvChrOlZfvqnANc4+nf101/HWql4V9b0wyFcU7g0wtPhWixr+jSvNrWjcN6SyNsUJKLnA4NkTd8QR3sEAoFAIBAIBHHmnnvu4ccffyQzM5MqVapEZMxE4C98w7LysOp0TJQ1XStkjDVAMz/7v8VyXr6WNd0M0v8jrMRzbzY6+s9xrkhECe8aJP5WacItvFgsDqYr75oS9xbskYbL2v5xIffvWLOfaZoLCvaYr8jagYBiAgCGqgwD7nLb9YCs6YtCPadAIBAIBAKB4Pxj5MiRzJ49m++//56GDRtGbNxEPB2Qo8C7wFuypoeqPuW+inAGmIkVMvVLEfrnY8nhTpQ1PVaKULu8tq80VMUma7odwFCV64GLYmRLXHCokLnrq/0FNPAnx+xgigjREggEAoFAIDi/eeeddxg3bhyffPIJHTt2jOjYTgfkF6yQqZmypp8pwjj7gcnAe7KmHy1C/+PAVODNWE9uZU0/bKjKDqCxcxdWtfiPsJLy7wrY+fyhHp7J9nWA4UHaf4HlqAgEAoFAIBAIzkM+//xzHnnkEcaPH88111wT8fETgZ6ypn9bjDEeBFb4yxcJkfHATbKmny6GDcXlCcA99KgFliwxWKFp3wOXxdqoomK3mwmSu0sh2SKtJiYQCAQCgUAgOE9ZtWoVgwYN4tFHH+Xhhx+OyjlsxXRAkDX9u2I4IMiavirODgiypi/GSq73zgc5CvTHs2J6iUeymc3dNk1TMj+JmzECgUAgEAgEgnOGbdu2ceWVV9K/f3/z1Vdfjdp5AhYr/Lcha/osQ1UWYiXZXwD8CvzkzA3BCjeLCTXW60OwpI6LhGRKlxQoAZiL09Ye+DVYe1nTZ2Ll8ggEAoFAIBAI/qXs3buXgQMH0rp1az799NOo1sUTTogbjhWZ9Y5HpFhjqIqzOGNbWdP1CI7tiyTZzPQ0V+aQKdlCVtWKBIaqZACKY7NMLM8tEAgEkeS9Xx6ItwmCEDlTNoExGX3jbYYgBKb9OjTeJgiCMHDgQCpXrmyuXr066oW5hRMSfaq6/Z8Q7ZMd7FSjGfmUt7bMtWnr9NXRPqcX1fGsLi8QCASCODN06KPYz9ribUaxaHR3vC2ILW9PmcKvH38WbzPCRu59jNL/kupq58PnyklOjpU+nJCQYG7evDnqDggIJyQaZAMLAxzzKdYYaex5tpaSxEEAybTFo8r713g6Xk4OxtoQgUAgEFh8+823nD2dX3jDEswjd7eNtwkxZeOmTawoil5pnOmbfgF1apePtxkx4Xz4XDmx263sg6VLl0opKSkxOadwQiKMQ6K4f7zOn7Ze/xj4OF7nlzX9X3avSiAQCEo+v/z6K6USUuNtRrEYt/7qeJsQU/73v/9R6tJe8TYjbD7dOYo//smItxkx4Xz4XDk5fvw4lSpVomzZsjE75/mxhiQQCAQCgUAgEAjOGYQTIhAIBAKBQCAQCGKKcEIEAoFAIBAIBAJBTBE5IQKBQCD4t3IUOHj6WE71ypUqS4lJ589PYnZ2NidOnKBGDUusUCImYjdRpfQ/ueQnBrgOmw1bpSqxNaiYmKdPYWb7Zp6nnCpy/ecSQamEsqQmVQTg70N/Uya1DKmp50fehJNDhw5RoXyF8+JzFU/On29cgUAgEAjC4Km2X1wDkJKSkqVpWunWLVrH26SIMW/ePK677jpM0yy88TnC9S/8humQEfXGVrka1Zd8GWOLisc/Y4eT9fn5Vye4X/0nXf9XqVLF/O9//ys9MmJEHC2KPOXLl2fGjBmkdBDl0IqDCMcSCAQCgUAgEAgEMUU4IQKBQCAQCAQCgSCmiHCsKGKoypNAZ+AlWdM3hdB+GHCZ1+55sqZPDaFveaAuoMuafqQo9sYaQ1VKA/WBE7Km74/heZMd5z0L/ClresjxCoaqJDr62oHdsqbbo2Olz3nLADWBJGCnrOn+YxL895WA2kCqo29udKyMLIaq1ACqA7tkTY96oc9IYKhKRaAOsFfW9GMh9pGAJX4O3Sdr+p4Q+o8GmgHDZU3fHoa5AoFAIBDEDeGERAlDVeoAo4FDwJYQu7UEenrt+znIOZoDQ4BrAdlt/0FgJvB8tCZvjol8B6CL41EPkICJsqa/E6RfLeBu4GaggaMPhqqcAL4EnpA13YiCvVWA24E7gAuBBMeh04aqrAQelTV9Z4C+FYBbgLuA5hR8bs4YqrIGGCpr+tYo2NzBcc6uQBO3Q/mGqmzDmnR+HaBvMjAQuBfoCJRyHMozVOVnR9+lkbY5EIaqXAR8hvV6H5Q1/VI/bSSgO3Af0AMo5zhkGqryBzAGmBaO0xiGfQvxfI798bKs6R/56dsGy+YBQDW3/QeAqcBoWdPPBhlXwvdzDwXXXxjbgaeB08BNIfYRCAQCgSCuiHCs6PEMkAxMKsKd5y+Bbo7H2/4aGKqiYjkoD+HmgDioATwJbDVUJS3McxeK4w71CWA18H9AL6yJfROgapB+ZYA9wPNAQ/CQlagADAZ+NVSlU6RtxnquXsO6Y5zgtj8VuAr42VCVAQH6rgXeBFrj6biXBi4HMgxVuTXiFkNfLCfTe3KcgOWwLjFU5dUAfacDs7AcmFJu+xOBNsC3hqq8EkljC2ECBe+RRgHaPAl8C1yD5wRcwnq/TAW+MlQlGt9b9R22BXtU8u5kqEoPIAPL2avmdTgNGAn8ZKhK5UAndqymOT/vfYtg+yfAPmCQoSoXFqG/QCAQCAQxRzghUcBxt/8OIBt4vwhDHJA1fYXj8UeANkmOvyewJnjXY02U/gN84DhWHwi4KlEMEimY2J4FfgBCuTstYU2gc7FsvB1oAbQHXnLsrwhMN1SlVIAxikqSw8Z5WBPGdlgT+cex7iCXAt43VKV6gL4Ai4D7gXSsFZEHgeOO428ZqlI3wjYD7MBaUevrsLcx1srXT47jTxqqclUQmzXgCQpWUwYBux3Hhhmq0j0KNntgqMqVWI5qdiFNnTbvwpq8XwVcAFyJ5QjiGOexKJjpZBHwVIDHGj/tnTYfAcZhrT41Bi4FZjuOXQi8Eeykzs878GO4BjtucryF9X3+VLj9BQKBQCCIByIcKzrcjDU5+VLW9BNROkcWVnjKq16x5zuA1Yaq5AH3AH0NVekga/qGCJ/7RWAFsFbW9GxDVXIp/P1kYt2hf8FPrPsmR/jKFKxJ3C0UzYELxBdYoWLe4W0/G6ryG/AVUAUYihXa4s5S4EZZ0zd67d9mqMpPWCtCZYHhwAMRtHmcrOkj/ez/3VCVb4BfsHI9hjrsd2cLMEPW9EVe+3cYqrICK4SnAtbr+F0EbfbAkUMzHstZm48VXhaI/VgrP9NkTXcXyt9pqMp3QCaW8zfKUJWJsqbnR8HkpbKmvxlG+5PAKGCCrOkn3fb/DvzgCDG7AbjJUJWXopizMQfr++A6Q1UeljX9nyidRyAQCASCiOB30vh3t+plE0/mJVTaeDQiE+hfmjdPrpx8pIKcceDvIg1wvZRwcH+NKjXWHPw7JNHzgvaHMc2YJA57cbPj79xonUDW9AysMJBAjMNyQsAKI4qYE+Jwep4vQr8srBWiQLwLjAXKY9kcMWRNDzj5lTV9iSPHopm/88qaHtCxkDV9jaEqP2IJEETa5pNBjp0yVGUu1kpOMz/HXwrS96ChKjOxQvlaGqoiRSPPwsH9wEXACCwnLyCypn8Y5FieoSoTsUKyymKFZ+2IoJ1FQtb0VcCqIE3GYTkhYL0/ouKEyJr+h8Mhbg1cB/wvGucRCAQCgSBSJALsaVO/YunknIF207xBkqR0SKyQn5CIIxk0Q7LZX6ux1tAKG8xQ024D2/2SZG6psU6/95Baq5Md+9jKqVwCUoKhKiclk/8lJOc+V3X13z4TrAPpShdJksaZknkybZ3e4++OtS+wm/mvmKT1BpKN9LRsU1U+y09IGFZrzV4PNaWDnWTVnm+7UZK4GtLqAIlGetopVOUnkJbJZfSXWW76lCE1VCUFKw49BRgVKNHX0fYTrBCnGbKmTwnQpiXWpDAfK7QjXux22JCAlQBe4pE13e54z7XGmmTGkt+xXreiPFc7sZyQWD/PBx1/A+bhBOF3x99UrByiaIgBVMJaJdiPFY70QjGH/N3t/xLhhISAu9hBtN8fC7E+OzcjnBCBQCAQlHBsAKWSsu8yMadKEj3ArOB2vAFwrWm3rTjYseagwgaTJEkBUzVNs7nRMa2bHfv3WMpJzkTgcqbE0LyziRcFGKAymKrNpP3fHWtfkG/mrzYtxZlkR4tSEgxOzM/ziWM37bblksSjDpudKzxlgc5gPm9kyV/vaVO/onc/h2rNcUAF7gx0bYaqNMCKp1exwkIC0dXx99dgd7JjQHUKnvdzYbLmRHH8jbXUqDOBvyjPVXH6FoeWjr9/FqGv83n+hwJnJtKMAipjqbSdicB47iIL58p7OpY2r3f87WSoSlLQlgKBQCAQxBn3cKxjmNKbJuayJHvu7/m5Cf9IpZMa2u35zyEx0DTNTw5eLG+rscYIKBnrQpISTJN3JTBNibE2pHUm9n9MpFaSyROhGJZv2l8HqknwLqa03C6ZOhIXSab5YOBe5jLgXUx+Me223UmlEirm5eRegSS9BtLlpZJz3qUgNMKdWViJv70NVUmVNf20nzbXOf7+CawLYvrFjr/BHJVY0M5sh1SeAAAgAElEQVTtf+9chhKJI6HfmRheaF2VCJ43iYIJfVjPlSPmv21R+hYHh5LXjY7N94owhPP9sSlKkrcXYuXH/IqVBxQJnDYfkzV9V4TG9GawoSr3ALWwEuR/AqbKml7oSnAAYvk5dH7npACtYnA+gRumafLrr7+yYcMGFEWhXbt2VK4cUBTNh71797JmzRrKlClDp06dqFq1KAuckScvL4+ffvqJLVu20LhxY9q0aUNqampYYxw9epQZM2YAkJKSwv333x8NU8PizJkzbNiwgV27dtGyZUtatmxJUlJovvuhQ4f45Zdf2LlzJ7Is06JFC+rUqYMkSYV3jjLHjx9n7dq1HDlyhHbt2tGkSRNsttA0iP755x/WrVuHYRi0adOGpk2bkpCQUHjHGKDrOmvWWNog7du3p169ekUaJyMjgx9++AGAtLQ0brjB35QwduzatYt169ZRsWJF2rdvT40aNQrtc/LkSfLzg6dESpJEhQoVgrYpCSQC5EsJi1KSzr7nJ0RqC9dLNxh/pv0ENCffdgdWDHpwTNrZ4KRd4j9p6zyK9C3/u1v1qaePpwZNYDahPJi9JEkaUGPd/oVuh1bRTZr29+lqPt/OpsR/0tYd8J64ZgHTD6o1c0zMj8Hsv+/i2pVrrdl71KvdIuAU1srJ1RSo2rhzvePv7EImbarjb6i1QSKOY2I8zM2ObfGyJUyGO/4ew3/xtmjxCJbcbi7h5/HciSXNasf/+yYiGKryGFYieRUsp8fp7M4GJoU5VgcKimJ+EikbvRiP9f3ydCQSyA1VqUpBUnu0bAarpoqT9o7H7YaqvIhV7yPkHDNDVRKwZIcB1siaXpQVq5CRNf2AoSp/Y70f0xFOSMwYPXo048eP59gxz/qU1157LVOnTg06Gdi5cycDBgxg61bPUkOXXXYZc+fODcuRiSR5eXk89NBDzJw5k6ysgnJTCQkJPPHEE4wePZrExNC0bSZNmsSLL74IQIUKFeLqhBw/fpzBgwfz3XffkZtboJ5fpkwZJk6cyD333BOwr67rPPPMM8yYMQO73fOroF27dnzwwQe0aNEiarYH4/fff2fQoEFkZmZ6pM7KssyMGTPo0aNHwL579+7lhhtuQNM0j+uqWLEiU6dOZeDAgVG1PRjLli3jnnvuYffu3R77mzZtyqeffkrz5s1DHis7O5sbbriBnTutKFlVVePmhHzwwQc89dRTHDzoGYjQvXt3Pvroo6DOyAUXXODTz5sKFSpw/PjxiNgaTWwANbV9O/zlaAAwx8w3TSu3wYSmIY6bYJrSG14OCADVlh86VS9zd2HPjA343MsBsVhu5lVbf8gnft3fuZzY81mApcyUnJiX55Nr4EiYXuDY9HlHGqrSkIK73YVNgGo6/h4qpF00uR1LqtcEhsSqqndxMFSlHQXKUk/Jml40EYPwz1sbK2wIYLys6b+G0bcq4Ky18Z6s6euDtS8mj2OJATyE5YDkALfImn5jOHVoHBPjd7A+Y2uxEr0jiqEqPbHkddfKmr6gsPYh8hpWaNdBrBo80WAr8DqWUzoCa4U0D8uZepHgogr+eAirJkseVjHDWOD83qkVo/P9qzFNk0ceeYRnn33WxwEBmDdvHm3btmX//v1+esPGjRvp1KmTjwMC8P3339OpUyf++uuviNtdGNnZ2VxzzTW8++67Hg4IQH5+PuPGjeOyyy4jJyen0LH+/PNP3nwzHMG56HHgwAG6dOnCkiVLPBwQgKysLO69916GDBnit+/hw4dp164d06dP93FAADZt2kT79u1ZsiSW988sMjMz6dy5MxkZGT7aPYZh0LNnT15//XW/fX/77TcuueQS1q5d63Ndx48f59prr2XUqFHRMj0on332Gb179/ZxQAB++eUX0tPT+eorb2HIwIwdO9blgMST1157jTvvvNOvI/Hdd9/RunVrfv3V/1Tk8OHDhTog5xL+1+iulxIOd66jGJ3S6hmd0urZJE5ZBySfnIqAA9vy/CZuh26Z+VZxuh/tWKX8gUuUukantHpSkr061t11bDZboGuY5fh7paEq5b2OOUOxfpU1fXOgcxqqUo6CugFxcUEdxQnHOTbfkzU9WOhYicBR3fs9CibGRQkvKsp5JSxJ4LJYifwvhjnERKyViYNEvz7DWmAlVnhTHlae1HuGqgwL2suXoVgOdR6WgxrRUCyHJO8Ex+bwYG3DGLM7cJtj8zFZ06Px2bpR1vQWsqY/Lmv6m7KmvyJr+k1YKwrOmx4vG6pSNkSb62HVvgFLvrfwMNbI4FQ09C6cKIgCc+fO9Zlge4e+/PHHHzz55JP4Y9iwYRw+fDjg+Dt27GD06NHFNzRMXnnlFRYt8tRV8b6uVatWMW7cOIJx5MgRevbs6ddBiwcPPvggP//s+VH0vq733nuPxYsX+/S99957MYzg+h05OTkMGTKEkydjmw567bXXcuiQ531P99Aw0zQZMWIEO3b4pqUNHjyYvXv3euzzfk5eeuklMjKCiXFGniNHjjBo0CDOnj0b0K4zZ85w7733cvq0vwh6T37//XfGjh0bcTvDRdM0/vvf/3rs874uwzC49957/YrB+rthcS7junKjbVq1g6oywVCV3cafadl5uXn7sUu7sUu7TatYGkhmSAFmEhyvvq54rlpibl7YickH02tefUBVfjyoKsdyzJQTUh57nNeAdRcVO/ZA17AUOIwVT93P65grFKsQE9wrKsdcp9+h9PUZllrSr8B/g/coMbyDNTE+DtwaRblYb0YCfbDCsG4OJ3naUJVHgJuwwrBui9LE2IWs6dfLmt5V1vSmWO/l17FCyF4xVKXwEEnAUJXLsCSQwQqTisbEeAjWiulih3xtsTBUpT4Fn7tZsqZHJRRL1nS/IYuypmdSEFIlE0JFc0NVUrGUqsphyWiHLWddDJxOSMlIKDjPmT274CchJSWFCRMmcOTIEebNm0e1atU82nlP9LZv387y5ctd25deeim7du1i06ZNNGrUyLV/1qxZMZ/Uzpo1y/V/WloaH330EUePHmXKlCkeE6Zx48YFVM3/5ptvaNeuHdu3x1pjxD///POPxypFs2bN+OabbzAMgxEjRni09Z6s7tixg88//9y1Xa1aNSZNmsTff//tExK0d+9eV/5LLFi/fj1//FFQ07h79+5s2rSJnTt3MmhQgZ5QTk4Ob7zhWTN1x44dZGYWpK9ecsklrFmzhj179nDXXQWq9na7nQkTJhBL5s+f77Fadeedd7Jr1y42bNjAxRdf7Nq/f/9+Pvmk8J+FBx54wMOhiRcff/yx639JknjxxRc5dOgQX331lUeey+rVq105MO5s21bwU2Wz2VBV1e+jQ4cOUb2OSGEDONhBbk6StMu0KhHXw7pLusOEzSZsBqxvT1MKKWvLNKW9hbcKhpRbddPhsCRDDVX50JTMLyS42LSqbh8Ec4vbNeQBSKbN7zU4iqPNcWy6QrIcoVhtHJuFvdPdK0KnhGN/hHgHK6b9H6B/nNW5QsJQlUexwlzswE2ypsdkrdRQlf4UhGE9Imu6v2rYgfpejpXzAPCMrOnfRNi8oMiaflLW9MeBaY5dIx2rcAFxqLvNwQot+lTW9FcjbZehKhWxZHjtRGBlyLHqsBBrtekn4O7ijllEZlHw2b4gWEPH6tqHWEIHh4EBsqYXVik+kji/dyKhRiYIgvektn///jz22GNUrFiRgQMHctNNN3m0/+mnnzy23333XY/tV155hQYNGtC2bVuPlZNTp055OAXRZuPGjR53zB955BFuuukmVy5Hly5dXMdOnjzJn396pjodPnyYW265hV69evkciycLFiwgO7vgo/jyyy9zxRVXUK1aNUaPHk316tVdxzZv3uzhXK1evdpjrHvuuYdHHnmEqlWrcv3117vyXdz7xwp3R1iSJN577z3atm1LgwYNGDdunMeKiLvD4d0XYPLkyXTq1InatWszfvx4UlJSAvaNNu62VatWjXfeeYd69erRvn17nnvuOY+2hdk2e/ZsvvsuajV5QyY/P585c+a4trt06cLIkSOpUqUKV155Jffd5xm1671qB54rIQ0aNGDdunV+H0uXLo3ehUSQRNpLSaZNmQVmOWC7CY+mrT/wrfsn0EhPux9JCj28SrKfKo5REubpkIoSOjio1hwM3Gptma+Qy3jvwoiGqhymkGJpWJONB4AehqpUchTlc66CZMia/nvgrgC4J7zHVJbAcTf8dgom8yVewtRQlR4UTOZHypoeenBn8c7bEpgJSMD7sqa/E0bfRhRM5ufKmh7P9d1JWInxFbFygPw+fw4HxTmZ30wQKepicrHjHIeAhwxV8T7uTPquaKiK8zl/z1F40wPHZH4G0IKCyXxcJtayppuGquzEqtYe1AnBWvUYiLW6dr2s6bEO6HeGm8Ykp+rfTGJiInPnziUjI4PMzEyfCUSzZs182rszf/581/+1atVCVVXX9s033+wx3meffRYwVyHS1K1bl08//ZTMzEwyMzO59dZbPY43b96cFStWuLbdJ6pgTWQ/+ugj13ZSUhLly5fnyJEjUbW7MC699FJmzJhBRkYGO3fupHfv3q5jNpuNxo0bu0KakpOTMU3TNYHftMkz5bRvX88F0Z49e5KcnOzKkXFfmYg2d9xxB02aNHFNxOvXr+86piiKR4Ky93vw+uuvR1EUMjMz+eeff2jduqDeboUKFahVqxa7du3y2zfavPjii2zcuJHMzEyaNWvmoVxW2GfLnRMnTvDYY4+5tiVJCmd6GVHsdjvTp08nMzOTjIwMbrzxRo/j3kn23p8t8HRCmjRpEh1DY0jiflvNZgmSvQWAmS/1T9u4/zfvRiZS7fgLzwXGdEiVSvBxDe3ACO/jB9rXLCMlFOqAAKzBkuCtC/QHPqAgH6TQ9T5HVecTWA5IyPkzxcVQlT6A8872MFnTfQNaSxgOCde5WLVMZsqa/n8xOm8NLDW0ssByIIjks0/fisBirHCoDRTkKcQL9ziHOv4aGKpiwwpnag4cAK52CDFEk+pYYVmBKON2fBlWyJI3/4dVI+gs1qrenkgaWAScMSg+BU+dGKoyiILQqwdlTV8eqG0UcX7vxFMY419BmTJl6NOnD3369PF7fObMma7/k5OTufTSS13bpmly4MAB13aVKp4/T6mpqaSmprpi3XVdj6TpQalWrRrXX389119/vc+xvLw85s4tEBBs1qwZaWlpPu2cVK5cmc8++4znn3+elStXRsXeUKlbty633HILt9xyi8+xP//80yP05YorrvAIO3vrrbd49dVX2bdvH/v27SM9Pd2j/+HDhz2S9OvWrRuFK/BPixYtAipyLVmyxEMhyVsh68ILL+TCCy/023ft2rUuB8Rf32jTuXNnOnfu7PeY+2cLgtv27LPPunJ5VFXlzJkzbNkSH/HSpKQkevXqRa9evfwe9w4r83dd7uFYztfujz/+IDExkTp1/E4DSjSJNuxOl/JPfw4IgCQVevcvzpjNAOwSfsNiEhJtjexm4Sqhjjuen2Ap49xgqMoqrFAsE/g0RGN+xqqeHZPnzFCVFlgrODasu/rjC+kSdwxVqYI1ma8ArCZGYTaGqpTCUkGrg1U4bmCoylKOhOu5QBOs8MS+8boz74b7r3+g0LvxWEpVZ4B+sqYXM1QyKAcI/jlpBVzosOULxz6flQJDVW7D+gwC3C1r+o+RNDJcHI6cU1XPb4C7oSoq1k0LsFTW3o+FbV42pAC1HZvniiz3eYVpmmzYsIH58+e7ahEA9OnThzJlyri2jx496hHv7k+Gt1KlSi4nJN5qOLm5uaxcuZKZM2d62OLPUXHSs2dPJk+e7JHfUtLIysri66+/5u233/ZQhnLPpXBSpkwZGjduTOPGjX2OLVzoKeTpfac+1hw+fJhFixZ55HGkpKTQv3//QvseP36cxYsXM2lSgfJ7QkJCXGV6nezZs4cFCxZ42Fa9enW6du3qt/2mTZuYMsUK4rHZbLz11lseuS4lhS1btrBw4UKP1dFOnTpRq5anyKGu6x4iD8uXL6dRo0YuZzEtLY0hQ4YwcuTIkGvDxJtEySYlYy1N+a1Ic/ASuTrY/N/qKSFIkGwCEpLfNbl8e/6dYdQQmoU1AboccIqZ/xjG5G0tlhPSprCGxcVxV38xVgLsMgokbsMdJwVwD1Ae4qgkH3EcSlifYU3q/sAKsylc59H/WK8ATjHt0SGEy03DCgk6CvR2hNuFyptAd6x6Mn1kTQ8rZ8mJoSoPAs6MsZmypi8ryjgO3G+n+Oj5GaoyBEsNy8RK+N9QlJM4VtqudWz+IGv6NH/tHEncvr/eBeOMw3JCjsma7redoSr/oUAd7WVZ0z/y1y4Em1tSUNNoj6zpo4oyjoObKci18LlRY6hKHaxwt1JYzlW4imWRojkF6nxFLa4oKAZTpkzhoYce8tjXq1cvj/Ak8HUqAjkh+/btAyyloPz8/LgVjnvggQeYOtVTzfuhhx5i5MiRPm3bt2/P2rVr6dixo8+xkkbPnj098j1sNhtTpkyhXz9vbZrAZGVleTw3kiRx+eWXR9TOcKlfvz6nThVExaemprJgwYKQamq0aNHC9b4Dy3mZPXs2nTp1ioqtobJz504uuMDz3m7t2rX57rvvKFvWV7TQbrdz3333uZzLIUOG0K5dO5928Wb+/Plce+21Hvs6dOjgV6HNWxnLW7HswIEDjBo1ih9//JH58+dTrlzQVNESgc1mSs4f1VqH1Fqe77JuUiL5tvFYP64lFrvknICZ13kfMzqkpUtS6HfaHYpBW7Fi/h917A6nCN1ax9+oOiFed/W3A9c6kuuLQhJWaJHzEc3gz3eBLlgqPn1kTQ+sT1k411Bgc9Ayo4aqPI8VtpcLXBNOArwjef4+rHybG2VNL85ablcKbL4oyDlLG6ryvqEqfn81DFVpD4xxbB7EywlxJM9Pdmw+I2v6vGLY3NLN5kuKMU5QHMnzn2HJD88BngveIyi1KLD56kLOe5ehKlcEONaRglDHHcCXXsfLYoX31cBKnh8cx7o8zu+cvbKmHwjaUhAVvOsZyLLM1KlTKV26tMf+UJ0QJ3a7nb//jl+aj/d1tW3blrFjx/qtEN6nT59zwgEB3+saNGhQWHfK7XY7gwcP9gjvue6662jVqlXEbAyXw4cPezggAE8//TTdu3cvtG9OTo5P6N+jjz4allMWLfbs2eOxLUkSU6ZM8bsyBfD222+zcaNVr9UpPlAS8X4PpqamMm3aNL/fCaHK8y5dupQxY8YU3rAEkFg9f/9GIyHtV+AiO/YvDnRURtvstp/tkr2RRNpNWAmvmcTgzn5RkUxzJkjdMbnSSFfmmZL0kc0k324zL5Zs0lCsSVoOnhK6wfgES5Y4AcgHwpnALcWq1F7ZUJV2sha4iGIxGURBom8qsMJPIrCTH2RNfySSJzdU5WPAfc3Z6bg8aKiKu1s/RNZ0zdGnNVbyPFix9Z8EsfmgrOk9I2RrRQpi9bOBSUHOC9DOWeXbEYrjDHHLxqoV8XKQvl0jJNdrwwpTu9tQlfXAL8AurFyW5lghVs4ZwH1+QsNew3pNTKzQwmBlYV2vUZx5lgJp2ZZAZpDX6XVZ0z+M0HlVYKqhKr8Am7BW6BKxnuc+FKwSP+Jn1e5Oh61gfb/8GMTmr2RNfzpCNvvDOcuIVIFIQZh4q0EZhkHDhg2ZMGECDzxQsFDtXTejfHnv0lT43MU8evQosixH0NrQ8b6ujIwMateuzZw5c0Ka3JZEcnNzPfJywJImXrNmDV9++SVNmxZem3no0KEeoViJiYm8/HKwn4fo40+R7JlnnmHx4sUsWrTIJ//Inb179/oULBw3bhzffPMNX331FYoS9Hczqnhfl2maXH311QwePJjp06d7JK4bhsEzzxTUtH3llVc8nPqShPd1nT59mjZt2jBy5EgfFTBvJ2To0KHcdtttVKtWjU8++YQRI0aQn2+lHrz++uvcf//91K5dm5JMIhvNXFNVhkhWWE9VyeR1U7JTcH9DmmOa5neSFJsCckVBXm/MPJCedo0E/ZAYKGEONCWQLAGEYzZsN9ox51vzsZBwOiEAy2VNDzkgV9b0U4aqLAAGY4WwRMsJcV+tqEXwCsmFhZK5fyvtJTR5z8ZYMf7eyI6HE/dfUvdYgioEVyvzX2LYgUM9yXmrIBdr4hiIBAom7OXwb7c7ktf/TrvLhNC3sFUk92sOJqBvx7quJKyCeel+2pwCng9Qldxph0ThNhe2ZutecyKaov/uz53/bMkCqhdyPNTn2Z2mjoc3R7ESzf3lnLnbXNfxCETU8jQcK6NOuZ/YFSkQeHDPPfcwfPhw1q9fz9ixY9m7dy9nz57lwQcfpHr16q6wC+8Jkfeda8CnNoi/O6OxYuzYsTRo0ICFCxcyceJETpw4wbFjx7j66qvZsGFDSGE+JY3c3Fzmzp1L3bp1mTp1KtOmTSMnJ4c9e/bQrVs3tm/fTsWKgfVlJkyY4FOocsKECT4hQ7GmevXqfPXVV5QvX54JEybw+eefY5oma9eu5corr0TTNL8rWABly5Zl0aJFVK1alTfffJPZs2djt9vZvHkz3bt3Z/PmzR6T/RCJiK5R+/btWb58OVlZWYwePdolJDBr1ixKly7tERL3+OOPc+KEVTKpU6dO3H777ZEwISpce+213HTTTezcuZOXX36Z7du3k5eXx/PPP0/lypU9wjtffPFFBg4cyLZt21AUhZtvvtl17Mknn+TPP/9k8mQrACI7O5v58+czdOjQmF9TKDi/3xIB0jR91f6OtZolmObTSGY7TKqaEn8AM9I0/SO9vdJaknhBMj0kaH2wI62S4AWJItYJyec3EqQXzHA17k3TTIP+Rsead2DaB4CtqYR50sRchY0J1dfu22Okp41HspXDnh9KgbaDwGmsFYaixKTPpMAJiVYl7QysmgyhUNgkzD0Mb0KI4STv4RWaEgB350AndJsLK/bYhAIn5BNZCyofkxXGecFyANz/D6dvQOUpQ1USKHAmtsiaHlDIW9b0M4aqVAV6AFcADShwNLdjTWjfCnLdUygkRM2NwvQkne+Pk3jmDoWLc5Uw0Gv7eQi2OCksWf1it/9fK6TtU8Aq4EqsxG6ng7MNS2jinSDhTWsJ/f0RTUmWK7BWyX6RNX1jFM8jCMIVV1hRfe3bt6dv374edyGfffZZBg4ciCRJHoUMwXdlBKyVDycJCQkedSxijTMpuU2bNnTo0MGlCJadnc2LL77oUfvgXKFMmTJcc801ALRr145atWrx7LPPAnDo0CEmT57s2vbm66+/9ql6/eSTT/Lwww9H1+gQqF27tut9d8kll3DLLbe4cpI2bNjA559/7rpub2rUqOF6bTt27EjlypVdk9pff/2VmTNncued4Sm8B3J4wsU9xO2qq66iRYsWrpWB6dOnM3z4cC644AK+++47l8pUQkICU6ZMiZgN0cCpmtexY0cGDBhA1apVXfVsXnrpJe666y5XOGetWrWoVauWh7y0O3fccYfr9QKrSnxJ5NSpUwwYMICUlJSCu3g11+3bR4DEZsUKxyq0Uk3auv2rsdSOioRDnWtUUfvL6/Z/QIFCjeex9QfCUY26BcsBOQ3ML6StP77FCp9paqjKfyJROdobR20Ff9KmRcE5YTsGTA3W0O38Yav/OCZyo8LtFwCnzSYFMfuBznumqOd1VG8vUl8/tMJ6XwGMC+Hc/2C9/8J+D8qa/na4ffzhuMPe1rH5nqzpJ4K1L8SmpViOSKDjn2M5IpHA+f5Y5q8Oidd5j2DdOJgZrF2AvmspyAOLJ86ZwcS4WiFwUatWLdq1a+eqMbF9+3b++usv6tat6+NQFOaEVKtWrcSo3Vx++eUe8sHnSlG0wujXr5+H07Fs2TK/TsjOnTu58cYbPcKWbrzxRsaNK/QrPS7069fPQxhh6dKlAZ0Qf33dJ7VLly4N2wmJFv369XM5Ifn5+Xz//fdccMEFvP12wU9fQkKCj9KZe27J5s2bXTK3S5Ys8aixEg9SU1O5/PLL+fJL6/7uoUOH2LJli0cNoWA0bNjQY9tdYrmkkJubS58+fdB1nbNnz1IyvtVKHs78ic9kTQ+78KJjJcF5d/TJYG0D0M9QlXWORyxurTiTjacU5XrjhHOSuUTW9NCyteKP0+a/CF3yOd60w0oUzwVej7MtIWGoSnmsfA4IwdmLN4aqSM7PO/iXGS+kfxOgL1aNo+kRNk8QAjk5OX4LoHkrCv31l6VIXaVKFY+7s4U5IcFqcUQT9wrjTkqVKkWbNgUposePH/cJHSvp+Luu5s2be+ThOF8rd5x3cN1rb1x55ZV8+OGHJeJu+9mzvqKWgd6D3vh7TrwFBgL1jSamaXrUYHES6LqcORFgfS63b9/u8XB/jrKzs/3ujwV5eXketjoJ9fXyh/d3UBFC56KKaZrccsst/PTTT66bF8IJccNQlSRDVR6hIC68OLHVc7FUtq42VMW/fENgqmEly6oEKEIXKQxVScVKrM3GkqE9V3BO6Ev8JNMNp80TiqFkFmucDuosWdOD5umUIFSs77afZE3/Nt7GhIBEwee9fRH6P+4YY3SodW8ExWfJkiV0796devXqUbp0aZYsWeLT5scfPaMGnbr/3uFVzkrdTrKysjwmhTVr1oyk6UF5//336dKlC4qiULZsWfbu9Yyuzs7O9pAGrVChwjkhBfrUU0+hqipVqlShfv365OV5fgVv2bLFw5ny5/jdcccdHsnBnTt3Zv78+XGb7NntdgYNGkTr1q0pV66c3zCdQO9BgLvuuot27dpRsWJFnwKMgEcBR+++0UTXdfr27UvTpk0pXbo0Tz7pey832HWVVDZs2ECvXr1o1KgRpUuX5sMPfbVVAl3X6dOnee6557jjjjvo0aOHRxV4J94KYiWtRs8TTzzBokWLWLFihes7LZpSrOcMDmnQWUA9CuLovwG+L+qYjsKHVzjGOxJit6fxjSHuAGkAACAASURBVF+PduXjUsDDwL5wEvDjiSMpfSJwVtb0+JbhDY/PgB8oQthPHFmPVS/Hd4ZVcjmAZfP6eBsSCrKm2w1V8ac+GGpA70TgbaybHoIYUa5cOZYtKyjz87///Y+rrrrKtb1//34yMzM92terV8+13b9/f95910qx+uuvv1i1ahX/+c9/ADyKlgEMGDAgGpfgl/z8fFatKoggnj59ukc9kO+//56srILUt0DVuksax44dY/36gq+EhQsXehTg867L4F1wcMyYMcybVyCUKcsyCxcu9JFfjiU2m409e/awefNmwHptdu7c6TH59L6uli1buv4/ePCgy6H8+eef2bBhAx06dAipbzSpUaMGP/zwgyu5/OOPP+bVV18lJSXF1SaQbU2aNAkavvTzzz+73r9ly5Z1vc6xeB2rVavGN98ULHb/73//8whvy8rKYvny5a5tSZJcog+pqalMnTrVpei2cuVKhg0b5uEsf/qpZ4BF27ZtKSmMHz+eKVOmsGDBAlq3bu2qYo9pmv/6x4H0tOYH0tNMx8N+ID3t+wPpaWXjbZd4iId4iId4RP+RnJyclZmZaYZDbm6uWb58eRMrL81MSkoyn376aXPfvn3mnj17zJ49e7qOAeYNN9zg0V/TNI/jXbp0MXft2mVu2rTJbNy4sWt/+fLlzdOnT4dlm2ma5ty5c03rJz48duzY4WFXlSpVzNdff908duyYmZGRYTZt2tTj+JgxYwod89JLL3W1r1ChQtg2OTE61zXdfqs9Hgd7tQzad86cOR52N2jQwJw+fbp58uRJ8+uvvzarVavmcXzBggWuvidOnDCTk5M9jtetW9fs2bOn38fQoUNDup4TY4YFvJ4D6WnmmRVLCh3j6aef9rCrY8eO5pdffmmeOnXKnD59ulmqVCnXseTkZHPHjh2uvpMmTfLo27p1a/Ozzz4zT58+bc6ZM8csW7as65jNZjM3bdoU0nW5U6VKlZDeI94MGDDAw7ZrrrnG1DTNPHbsmPn88897HFMUJeTPSKtWrVz9VFUN2y4n5cqVMz///POw+zVo0MDD9kcffdTcuXOnqeu6eeONN3ocu/TSSz363nLLLR7Hr7rqKnPz5s2maZrm4sWLzTJlyriOpaWlmdnZ2WHZduzYMRMwd+/eHfZ1BWPWrFlmUlKSOXXqVNe+AwcOWLaaJeAHIN6PA+lp5Q6kp/U/kJ7W+0B6WrV42yMe4iEe4iEesXsUxQkxTdOcPHmyx6Qg0KN8+fLm/v37ffo3b9680L4PPPBA2HaZZtGdENM0zbvvvjuk62ratKl59uzZQscrCU5IXl6eefHFF4d0Xf379/fo+8knn4TUL9zJbSSckMOHD5uKooRk16hRozz6nj592sPhDfZ46KGHQromb4rqhGzdutVMSUkJybZ58+aFPG68nZDPPvsspGtKTk42t23b5tF369atZunSpX3aVqlSxWffm2++GbZt0XBCli5daiYlJZnPPfecx36nEyJyQgBZ00/Kmr5A1vQvZU2PX1lagUAgEJwzPPjggy5J00CUKlWK9957z2+htw8//DCo9G6jRo0YPnx4se0Ml9dff50mTZoEbVO9enWmT59OcnJyjKwqHgkJCXz00UdBi/UBXHTRRUyZMsVj34IFJbf+Z5UqVfjwww8LfR169OjBU095VgwoU6YMM2fOLDQUKT09PeYVx5s1a8arrwYVvgTgvvvu8wirK+kMGDDAo3CpPxITE5kwYYJPwcxmzZoxcaKvAOKRI54R/y+//LJHfZF4kZmZydVXX83NN99svvCCfyV74YQIBAKBQFBEFixYwPjx4ylTpozPsRYtWrBhwwZuuOEGv33btm3LmjVr/Bb7u+SSS1izZg116kRVm8QvZcuWZdOmTTz22GMkJCT4HO/VqxdbtmzxyB84F6hfvz7btm1zFY305p577mHjxo0ecfY5OTl+RQdKEt27dycjI8NHzQoshaQxY8bwzTff+HVU0tPT2bJlC926dfM5lpCQwNNPP82PP/5I+fLlo2J7MB5++GGWLVvmN8G6YsWKzJ4920OS91zhrbfeYsaMGX4d4gYNGvDDDz/w4IMP+u177733smzZMr9FMdPS0njrrbc8qsXHiz/++IPLL7+czp07m9OmTQsoHSeZphlLuwQCgUAgKFGkpKRkaZpWunXr1kUe49SpU2zbto1ff/2VSpUq0apVK49E9MLYv38/q1evplSpUqSnpxdblnfevHlcd911FPc3/ujRo2zdupUdO3ZQp04dWrVqRY0aodZBLRjDKbNqs9mKXHjx4H/qYfqRawWwVa5G9SWbQx5L13W2bt3KX3/9xQUXXECrVq38VkjPz8/n77/DC5BITk4OqcL9P2OHk/V5YJ2SiuOmUerSXiGf1zRN/vjjD7Zt28bhw4dp3rw5LVq0CDnpes+ePWzbtg3DMGjatCktW7YkNTW18I5BqFq1Kk8++SQjRowo8hh5eXns2LGDrVu3kpOTQ6tWrbjoootITAxfW+nIkSPk5loigqG+Tv4oX748M2bMoH///kXqD5bS3C+//MK2bdsoU6YMrVq1omHDhiHJPefm5rJ9+3Z+++03zp49S6dOnWjQoEGRbQFLbrtSpUrs3r07rO8ub/7++2+n6pq5ZcsWvxdjGAZpaWlCHUsgEAgEguJStmxZVFUNubCYNzVr1gy4YhJPKleuTJcuXejSpUuxxihpKIriN0TOm4SEBGRZjoFFxUeSJBo2bOhTtC5U6tWrV6zJZ7RITEykadOmPuFJRaGwcLxYUqpUKdq2bVskFaukpCSaN2/udxU1npw+fZru3bsDmBkZGYV6U8IJEQgEAoFAIBAIBEUmLy+Pvn37ous6O3fulEJZqRJOiEAgEAgEAoFAICgSpmly2223sXHjRn7++WcqVKgQUj/hhAgEAoFAIBAIBIIiMXz4cBYsWMAPP/wQlpiGcEIEAoFAIBAIBAJB2EycOJE33niD+fPn065du7D6CidEIBAIBAKBQCAQhMWnn37KsGHDePPNN+ndu3fY/UWdEIFAIBAIBAKBQBAyy5cv59Zbb2X48OEMGTKkSGMIJ0QgEAgEAoFAIBCExObNm+nTpw833HCD+dJLLxV5HOGECAQCgUAgEAgEgkLZs2cPl19+OaqqmjNmzCi8smIQhBMiEAgEAoFAIBAIgnL48GG6deuGLMvm999/XywHBIQTIhAIBAKBQCAQCIKQlZVFjx49yMvLMzMzM4vtgIBQxxIIBAKBQCAQCAQByMvLo3///uzdu5fff/9dSkpKisi4wgkRCAQCgUAgEAgEfrnrrrtYt24dW7ZsoVKlShEbVzghAoFAIBAIBAKBwIenn36auXPnsmLFCurVqxfRsYUTIhAIBAKBQCAQCDyYPHky48ePZ86cOaSnp0d8fOGECAQCgUAgEAgEAhfz5s3j8ccfZ+LEifTr1y8q5xBOiEAgEAj+lYzJ6Ps40PjqEc2StpqLMP5aE2+TIsahqn/Q/+mWfP3XFAC617qbRFtynK2KLHtbVGBvi4oASCmlKO241nOFnKYGeaXrApB6LIdWSw7E2SKBwGLlypXcdNNNPP744zzwwANRO49wQgQCgUDwb6UvcGm7frXYa25g7+F4mxNBykP6wLpkHv4agK41byOR88sJOVw3le3/qea24+v4GVMUagG1LPsr78sSToigRPDzzz8zaNAgBg4caI4dOzYiUryBEE6IQCAQCAQCgSAirNF3MzFjBQAtXv0vq1JTyVj8QXyNijBtJz3DB/kHmHUeXZfdtNNl6hjG//0L6QP7MGvGrKg6ICCcEIFAIBAIznv69OlD3hkz3mYUi5k5uUSmOsG5wchnn2VDwth4mxE2tsa1SejZEYDE8mXJAXLOZsXXqAiTVL4sWdjJOs+uK7liOQBGjR0Tk/MJJ0QgEAgEgvOcM2fOkJtlj7cZxSPq92VLFmfPniULW7zNCJukszmUibcRgnMC4YQIBAKBQHCes2zZMkolpMbbjGJx8D/1MHNy4m1GzBj36quUurRXvM0Im+V7f+f/1n/r2i6TdJraFf6Mo0XR4/cjTbCbCfE245xFOCECgUAgEAgEgqiQknCWmuX2xtuMqLDraGPs53aUY1w599b5BAKBQCAQCAQCwTmNWAkRCAQCgcAPV9QeQrmkKvE2IyS+3fsuJ3OPxNuMEkWbqlfSoHybeJsBQHb+ab78c1K8zRAIShTCCREIBAKBwA/1yrWiSqla8TYjJFboH0JuvK0oWdQoU4/GFTvG2wwATucei7cJgiiQZLMhce7lhOSbduxm/OPIhBMiEAgEAoFAIBCEyZw+d56Tgg+TMlew+I9t8TZD5IQIBAKBQCAQCASC2CKcEIFAIBAIBAKBQBBTRDiWQCAQCAQCgSAu1CrbFLV6/3ib4Rch+BBdhBMiEAgEAoFAIIgL5ZKqlBgBAW+E4EN0EeFYAoFAIBAIBAKBIKYIJ0QgEAgEAoFAIBDEFOGECAQCgUAgEAgEgpginBCBQCAQCAQCgUAQU4QTIhAIBAKBQCAQCGKKUMeKMOMy+yn5pvmHv2MJiXmNhrX8al+gvi+s6JaYWLZs2wTJVtcOCjYO2sz838/+c3rb812XZ0fP6sgxevPVNRPypGZAHSDZbkq/m6b02zMdFuyNt22xQZJeyezT0jRpYJekWhIct5v8nnRG2jbskoUnC+s9Zv01VUjM2+/vmD3BbPhMq0V+jwkEAoFAIBCcSwgnJDqk+NuZezZZ8rf/lU1969htvJhcvlxfoJKJiQRggomN5PLlDvxfRt+nn2676EMwzSjaXWTGZvS718S824bUwXS7SkkykTAZk3H1bJspDR/e7ou/4mdl9HhBu6p8UlLiGImr+wMKgOR4pWxAfmnznzGb+r6UU7rUG883nZNTyHB+3z9JUr7f949AICh5jB49mr///huAAQMGcOmllwZse+bMGTZu3MiaNWsoU6YMnTp1onXr1iQmlqyf6LNnzzJy5EhycqyvsIceeohGjRoV2m/fvn289tprAJQuXZoxY8ZE1c5w2bt3L+PHj3dtjxkzhtKlSwdsv3HjRrZu3crOnTuRZZkWLVrQvn17UlNTY2FuyKxbt47Zs2cDULlyZZ577rmg7Xfs2MEPP/yAruu0adOG9PR0atSoEQtTw2L+/PmsWrUKgIsuuoghQ4aE3Hfp0qV8+eWXANSrV4+hQ4dGxcai8Pbbb7N9+3YAunfvTp8+ffy22717N2fPng06VkJCAhdccEHEbYw0Jesb7vzkMyROAuTlZZ/21yDfRjPJ5LYgY6RJ8MHYjL5NR7RlWFSsLCYm3Au0C9xCGmS3ccW4zH4thrVZqMfMsBiRkJJQQ7LzQJAm5ZF4NSn77MXANYEa5ZRNPJt8Nu9DAEzKBWsrEAhKJnPmzOHZZ591bderVy+gE7J+/Xr69OnjclicNGnShK+//pp69epF09SwGDt2LK+++qpru3///iE5Ia+++ipvvPEGABUqVChxTsgjjzzCggULXNujRo3y64T88ssvPProo3z33Xc+x2rXrs37779Pz549o2prqOTk5HD77be7JrV169YN6IRs3bqVvn37snv3bo/9SUlJjBkzhieeeCLq9obK/v37uf322zl16hQAPXv2DNkJOXHiBLfeeiuGYQCgqmqJcULWrVvHg//P3nnHN1W1D/x7krRlFxkiiIiCKCijKUPlFQXRV0ZThoATXwcqojgQaIpY+goNS0VFFCcqLlCRFPmpICCgCNIUkCl7CCqbMrpyz++PNGlWM9qkLb7n+/n0k557z3Puc5Lb9Dz3PGPYMGTRc+YqVaqUaIR06NCBo0cDF1CMj4/nxIkTEdcz0qiYkChj18kUc4L1P+YE63/Srvv2WIhiu0AuRgoP1y2JHDEpO7lLFNSMNKclrESwBvcyP5I6dinfrzi1ypU/QC4GdrgfFMi+GTbTf0oSSms157TzfrFLfaU0OBUKRcnk5OTw9NNPh9R3yZIldO3a1ccAAdi2bRvXXXcdW7ZsibSKpWLHjh2lMh7WrVvH22+/HQWNIsOCBQs8DJCS2LdvH507d/ZrgIBjN+W2227jo48+irSKpWLKlCkuAyQQK1eu5IYbbvAxQAAKCgp49tlnefDBB6OhYql46qmnXAZIuIwZM8ZlgFQm7HY7jz76qMsACcSff/4Z1AA5n1BGSGVBcEzAcxQa6pmN1mZmY+YtzXfFNUXymlsvnV3KQRWmY3C+B61LvjExPtVovcGcYO2k05OAwN34ujV91W11KkzD6HIWKV7S7LomZqO1sdmYeYvZaL1CwjPunQTcWVEKKhSK6DJ27FgOHgxtszc1NZWzZ8+62tWqVfNwwfrzzz+ZMGFCxHUsDcOGDQvqAuLNzp076dGjB+fOnYuSVmXj7NmzPPHEE0H7SSm59957fZ4sx8fH+/R9+umnOXLkSMR0LA27d+8O+b556KGHPOZlMBh85vXee++xZMmSiOpYGr799lu++OKLUsmuXbuWN954I8IaRYZXX32V9evXh9R348aNUdamfNFNtJleGr/BdFlpB7Bkm9Inrk1OKK18RlbykxnZSTeXVj7SZGT1b2jJTrpuoi2p70RbUt9J2Ult01f3rBXNa8bqDVn6s6JpitE6wdzxK5eJO2DAHHuN+MKRgHtA8zXR1KW0CLjDbLT+22xcsCKNNM15fHRb6yaQL7v3rVIlrlLOoSzYY6sekgXicnPi/BHeQfipRuvLwGa3Q/+4+SsUCsdT/+nTp4fUNzs7m9WrV7vaJpOJgwcPsnfvXtq0aeM6/sUXX1T4k8/PP/+c77//PuT+UkpmzpxJYmJipXzy7OSFF15gz549Qftt3LiR5cuXu9pNmzZl/vz5HDt2jNWrV3PDDTe4zh09erTCd0OeeOKJkAy/9evXe+yW9OnTh507d3Lw4EHGjh3r0XfGjBkR1zMccnNzefzxx0slq2kajz76KJqmBe9czhw4cCBorI47mzZtcv1uMBhITk72+9OrV69oqBtxDBKe1hfyZIYteb5eMG10wvzlwcXc0MQdUieft9hMPyKZlp+YaHVfhAZHdhVSTLPYTL8heCX/ZM7HFZEJypJlGo7gXiFojxQ4N8WkhNgYA5Zs0yeGQvuIkR2+ifg36rNtvvq7pHNPNF+YZ7GZ9gOtAIQUQQ2iCeuTLtYX4IpIkkL8aU60bg1Vn4m/9b6APNHW2daEOJWaaLUFkkkxzt9R0jmJbkfxOwqalFE16gAyskxGnft14uT6lNYLjocqP35t8pUGtIbOti7WviNQZrO0VnNOAwH2iOUOEK2KGjVD1UOhUJwfSCkZOnQodrs9pP7uT2WFEGRkZBAfH098fDyjR4/m7rvvBhzB4LNmzaowv/xTp06F7F4GDretBx54wBU4XFnZvHmzRzB6ILzn8uCDD2IymQDo2LEjqamp9OjRw3V+w4YNkVM0TObNm+cKvA6GM2jdyeTJk2nSpAkAZrOZl19+2eX6FOqT+mhhsVjYuXNnqWRnzJhBVlZWhDWKDOG6l7nvhDRv3jwkV8LKjNMdSyeQfTUpf7TYTLaM7KTB6ZsHxoY51o0I5sVmZe3IsJmeLsXuQWsk78TWqrnPkpX8QkZW/4bBRSKI4HmgfYnnJXcV6vW2l1cNLDllRhR4bUfPOKA48k8nN5fc24Eo1N0udWKp8wcdKeFcU8vTt3eXF4I3w9fcTR+Nqz3bWtA5lBUhmOE+B62AjuHIG3QyxV3eXmi4vYwaFb8Hgsrh5K1QKCLG22+/zS+//AJAbGzwf58LFixw/X7ppZdy9dXFXxG33+75dRPqojIajB07lkOHDgGhzWv27Nkei/aaNWvSsGH5/jsPhaFDh1JQ4AhZDDavP/74gzp1ir2Ik5KSPM5369aNuLjipIb791dMRvozZ87w5JNPutrB5vXUU0+xcOFCLBYLqampHtmUqlSp4jHnunXrRl7hENm+fTuTJk1ytWNiYkKW/fPPPxkzZoyrHco9XF4sXLiQL7/8EghdL3cj5Morr4yKXuWJDskmr2MJQooPYvNy9020JaVN3dDvwoAjCOnpoCa4TMBLsTGGA5Zs0yvjs/o2C6yB3Ay4Pzqqj5DPCVGwd6LN9NFEW+8AGZeiwhkQWUg+Bb4C3J1AG+bG5g4pT2VOnzT0AFx3p9D4qTyvX1bSSdchpPs39sG89h32VJQ+FcGkrD6tAdffgZDn12eoUCgCc/jwYcxms6v9zDPPBOjtcA/5++/iDXD3xR44FiQ1axZvmFaUS5PNZuP1118HQKfTeSxwQ+Gyyy7j559/pkWLFtFQr9R88MEHLveq2rVrBw28njBhAkePHuX06dNs3ryZtm3bepzfs2ePR7xMKBnDosG4ceNcBlDr1q255ZZbAvZv0KABPXr0ICUlxSeG5IcffmDfvuKM+sHGiibu8Uh33HEHjRo1Cln26aef5tSpUwDcdttttGzZMio6hsu5c+c83MtC3el0d8e66qqryMvLY8mSJSxfvtwjvux8QWdOtF6j6XSdEXwAFDsRShpIxLiCwsJ9E22m9ydlJ7X1N4DZaO2P0F0NTPMKQK6JZLhe2H+32JLmZ2Qld/Unn5qQmao3FDYVyHGA++ODGAn3SHRrM2ymFROzTf3nzh2oL/OMS2avlOKpGENeA7NxfntzovUus9HaP/9UTn1gT3E3cX8UdfAgffPAGghecR2Q4kBeTs575XX9SBBryxoOuO4dIeQL4bnrnd+kk66TQnvL7dBZTcZMKVFAoVCcd4waNYpjxxz//jp37szgwYMD9j9y5IiH25a3EQJwwQUXuH7/66+/IqRp6Dj96J16PvrooxiNxpBkY2NjeeSRR1izZg3XXFO5QuCOHTvGyJEjXe3x48dz4YWBn7U6qV69ut9FrLdLTEXMeePGjUybNg1wuPfNmDGjVHVmdu7cydSpUxk2bJjrWPXq1Rk4cGDEdA2Hzz//nEWLFgGOXbVQXejAURPE6XIWFxfHa6+9FkSi/JgwYYIrI1nz5s0ZPXp0UJl9+/aRk1McIjxnzhzq1KnDzTffzI033kh8fDwPPPBApU0E4Q8dwJh2X/9sTrD+J1+nbyQQTwC/ufWJk/AfTYp1lmzT0gk2r31IwJzw9Waz0fp0/smciyXiXhDuDpQ6ECYh5BKLzbTeYku639uYGNVm4YEUY2Z6vjGxqZSiN0grbrsjAv4lJV/saJa7MyMrecTUDf+OeEWgfGNih9TE+a882+Y7j1oeaTctLUTId10HhLwk0tcuiZjc3BdxVB4HQAo59nypnA6OuApgvOuAFL/X0Rq9W7LEP48Y29oREq51tiW8nJr45aGK1EmhUESOFStWMGvWLMBRIGzGjBkIEbiuqLdR4W5w+Dt2/Phxl+tQefHmm2/y66+/AlC/fv2Qsy11796d7du38+abb1KvXr1oqlgqUlJSXCmRjUYjQ4cOLdN4R44c4c03iz2WDQYDt912W5nGDBdnPFJhYSEA9913H//6179KNVbnzp0ZOXIkv//+O+C4DxcvXkzr1q0jpm+oeMcjjRs3LuRdkLy8PA9DatSoURW2Q+XN1q1bPertvPbaax7ufCXhnRlr9+7dHrsfhYWFvP/++3Tu3LnCk1mEioeZnNZu3glgOjB9gs10rc5RgG4QUA0AyU06RGvA7zdL0QJ5NjDbkmW6SggeljAYcDoTtgHx3t4rC78BfIKx00jTSOQb4JsJ65Mu1hfqHpBCPkTxQvxSIeTUAnvcWuDHMs7d59qTskxNpOBeTdBUSBoLqC1BgHAvGVo3fVnXKtE2BixZyUOF4GHXAcm7qYnWWaHI6nTygJRu749GyEHpAFLPceEmL6UIO4Zh4m+9LzDodJkSnAbjSb2w93nYOLN8/pMKbEhcn5FOpwu1RotTfivu76GQJQall8REW/JtAuGWWF8uLjh1ely44ygUispJQUGBxyL28ccfp02bNmzdGvgr19sICbYTIqXk77//5uKLLy6jxqHx119/efjRT548mdq1a4ckW9rFb3mwatUq3nnnHcDhXvbGG2+g05W+UkFubq5Pkb8HH3yw3Be7s2bNYuXKlYDjvpk8eXKpxsnNzfVwEwS4++67PbK1lSfu8UhXX301w4cPD1l24sSJbN++HXBkM3N3l6xoHnvsMfLz8wHo378/t912G7m5wZeU/tLzCiGIjY31cAfMzs7m+eefd7lSVmZK3KsbY7T+AvwyKcs0ThPMA0Lbhy2iKBvTM5OyBqZrInc24L/0Y0nXb5v5B/DCW1mPTDwqDr4EonS52UJCiAxb0nQhGALEiKJETiWVjTHEV2sA7I2WNhlZyV2FkK+6HVpSl4YhP65JSbB+CXxZ2uuPSZi/FriptPLpy7oaYmvVmAOuDF2FQuhuH5VgLbeAbHOCNVD18qCkJFgnAZOCdiwBS5bpKgSfAXoACVsKdIYBaTctLSyLXgqFovLw8ssvu3y0L7roIv773/+GJOf0UXdSo0YNnz7Vq3tu+J86darcjJARI0a4akdcf/313HfffeVy3Whit9sZOnSoqyDcQw89RMeOYeUr8UDTNO655x5WrVrlOlatWjXS0tLKrGs4HDt2jFGjiuvaTpgwgfr165dqrH379vkUzJs+fTpffvklixYt8kieEG2ys7M9FtGvv/56yO5l3oU1X3nlFapWLdecQiUye/Zsli5dCjj+xp0udKHgboQYDAamTp3KvffeS82aNZk7dy5Dhgxx7Yy89dZbPP7445UmBqYkSvxEXTshwm0nJAxcOyHCYyckZDx3QkST4BKlx2IzjRTIkBetUosN39EyRMZn9W2m18kvkI7PRsC2PJ2+/8PtymkHIQLE1qo1DWR3Z1sghqUkfO2/zOw/kIm/9b4AocsEnBWfDus0rXea0XoikJxCoTh/2Ldvn4fRMWXKFGrVCi0ppHemIW+jBODkyZMe7fJybVqyZAkf1LA/0AAAIABJREFUf/wxELp72fnAK6+84kozW69evVJVf3fn2WefdWU2AscT6Q8++KDcM4GNHj3aVRyxffv2PPLII6Ue68ILL+S3336jXr16vPbaa7z66qucPn2aQ4cOccstt7Bz585yWcxrmuaR7vruu+/mxhtvDFnePZC9V69ernTKFc2JEyc8AtDT0tJo3LhxyPJvvvkmzzzzDFu3bqVhw4Ye78ldd93Fli1bGD/e4QFfWFjIggULKq0R4tzh8lhMp6/rWztO0+6RyId14O0AKIHvpCZeKmnQ9GVdq8TUqnW7cBgvN/jsJAjWCJh2pk1bv+VE00nXxWTZegihPaxD9JJC6j3F2a4hX6maW3VNiPMMASEgyaMqj5DiQyns79hlzME4Lf9Mgd7wqEBG/fFG+uqetWJiDJlI6gBIOGKX+l5FbnLnBROzkx4F4XLEFIIXUxLmvxVI5p+EYxeo5lyK0yrnaTpdnzFG666K1EuhUESWJ598kjNnHCGEXbp04Z577glZ1jsQ+vhx3xJGzkB3cDz1LA8jJD8/n8ceK34eN2zYMJ9MUOcjBw4c8NihmDRpkl8XuFCZNWsWL7/sUYOXKVOm+KRWjjarVq3i3XcdYZaRcC+rXbu2y+1uwoQJFBYWuly7Dh06xDvvvBNShfmy8vbbb7sKedaqVYupU6eGLOteWLNKlSq8+uqrQSTKD7PZ7HJ3a9WqFU899VRY8tWqVaNdu3a0a9fO7/lBgwa5jBBw7AhVRv7880969+6NwWBwGCET1vW5Xie1h2MlAyV4m7lnpRQfCuQrJRW8s2T3aYXUhsTG1xyMlN5/2YXAVwg5zZyQucqf/OQNPRtrhfoHYxEPIrgEvJ+6yMVS6qaZE60LHeUDI8ekrKRLELjvha9KSZzvsfc80WaqFdGL+mHu3IH6uGaGzyQ4zdZzQkjTc8Z5pavOUwE43MhEcfoJwdy8hMRRAUT+ccTG13wVyc1FTU1Ice+Ydl//XKFKKRSKiJKXl+eREennn3+mSpUqrra3S8vIkSNJSUmhbdu2rF692sddJpgR0qBBg3LZjcjOzvaooP3GG28wc+ZMV9u74vStt96KTqfj3nvv5e233466fqXlm2++8SgI99hjj3kYW86AbicXXXQR4Mic9eyzz3qcW7t2rU8w+/DhwyukmOScOXNc95qmaT7xOO7JDPbu3eu6RzMzM0NKuZuUlOQRX7JixYpyMULciyiePn2apk2bepx3j39YtGiRa1579+71kM3Ly6NVq1Yess5YDIA1a9a4ZDds2BD1VNLuum3bts0jDbc3U6dOZdq0aVx00UXs2bMnpPEvu+wyj3ZpiztGk5ycHLp164aUUhYWFgqDJcu0USfw5+i3Xwhex6C9ZQ5QadpiM30J9AO8gyiOg3xbs+unj+nwdYmVezKykzKENIyiyHfejVwpmI1deyW1/QLfaJwIUajXNda5fbFKz8xgjmOChBIDRCLEjmbnpoFwlly163TcObqdf6MtGBOzTf2lpPibQvKtOdE6MVT5CdnJ7XVSuh49SCm2pCbODxiTMn5t8pV6nfySot01Cctr1iy894lSpuOdaEt6VyJctTXyT+V0DyeewpJtmoHE9e0jdIxMaWf9NeTrZ5tGS0lxCVwhp5sTMr8IJJORlfykELjeJynFM+bE+XNDvaZCoTg/8DYyCgsLfRay/s47F0916tRBr9e73E2CGSHl5eLjPa9gGbmc58s7c1e4eM/LfRHrD+d578/077//pm/fvh5BxIMHDw7Lrz+SlHZe7umhwVHkMCcnx2V8OfHeBfvjjz9Kq2pYuM9L07SA83I/L6X0kJVSBpR1P+9tYEcDd93sdrvP5+CO83woAetOvPuG6h5aXuTn59OrVy8OHz4sV61aJa644goM+BogqyRMKziV81VIiz4prkF4/CFsRYpX85EfpCVmBq+coolWCA8D5KCAGTGSmSOMVr9uWxGlgP3uVxeSzu6nJ603XY0kdGfEUmDJSh6KcAu8F3wlNZE30Zbsm+dP03JT2mcuCzSeponGQshinXXudU6CI+xcgK54zkLIgDFB6ev61o7VyW8AZzqX0wjxxplTMV0n2pJ9+usMBRtHtVkYMNuURHTAzSWwYc0W4T0GlBiBTs6mpmnh7b1LrgK3z12Kr0vuXJQJS0iXq6KElTrBNr+fIZBnNH7/v1QvRaFQFKPT6WjYsCEHDji+Br2LEZ46dcrjiW2TJlENi1SEQGFhIQMGDHB9ZgD9+vXjvffeO29jZnr27MnatWs5fPgwzZs35/fff/eYizOA2snll19e3ioqijhz5gxPPvkk+/btY+/evbRt25Y5c+Z49PHe+WjWLHCt8PJESsndd9/N+vXr2bRpk3AmGXDGhBQimatJOW1M+8zSxFtIEN8LmJZitH5XSpeptQI5rY5sNOfhxPILwh7TYf4Biy3pBOBwhBRcbbElT9c07UOh110uJC/h6x8WWYR2m8clJAMkcoDfvjpxCAi9XGg5YJBac9wqggM1hJSflnQT2O2Gh4B/VL0QKbkFgcsZV8C/JPL/ShTIyqpOIudfeVOFQoFer2fQoEElns/JyWHhwoWudkJCAi1atPBwK7n99ttdT9APHjzIt99+66ovMXv2bI/xAl0rktSrVy/gtfbt2+eRDapr165ceOGFdOrUqUSZykDz5s0Dzmvjxo0elaj79etHTEyMhyvPM88846qy7hxz9uzZ6PXRrKEcGKPRGHBeK1eudO1eVKtWjaSiMm/OnbVq1aq5aqbs2LGDZcuW0bVrcV3pzMxMj/HKK1Vvt27dfHZl3MnMzHRlgbroootcAdpVq1blhhtu8HCN9Oa7775zZX6rW7cu3bs7cuiUx65B//79XXFk3miaxty5xY4TLVu2pE2bNq4YnerVq/PDDz+4XLN27NjB7t27PVywnPWKnFx33XWRnUAZeOqpp1i4cCE//fQTjRs3dj14MUghLVLH60UpccNHyA/0aPNGGReUKv2qQHyHxhRz+/k/ler6ZUZKiem/AtwC7uUwnU4Mc7Ol9uFWNFChUCgU/7vExMR4+Hd7s3XrVg8jZPDgwT5BqA888ICHG8+YMWOIj4/n+PHjTJxY7D1bv359+vTpE0HtS6Z58+YB5/XZZ595GCHPP/88N910UzloVja6d+/uWmz6Y9y4cR5GyLvvvutRF+Xw4cPMmDHDQ+b48eMkJCT4Ha9du3YB38dIMXjwYAYPHlzi+T59+riMkPr16/vo1LNnT48MX/fffz8jRoygZ8+ezJs3z2NRGxcXR3Kyr2dDNHj++ecDnm/atCl79zqqJLRt29ZjXsFic9q1a+cyQoLd75HGmUTAH7m5uR5GiMlk8vgeAEcM1ltvOfL8aJrGXXfdxdixY7nhhhv48MMPPeKymjVrVm7fG8GYPHkyM2fOZN68eT5B9YbUhMzUsgxuNlozyiSfOP+NsshHgnqy4fSj4s+WIId4n5NCWkAcEpLKk2LhH0466bpY90rxsLI8d8cUCoUi2rRu3ZoOHTq4qpLbbDauv/56n37/+c9/iI2NLW/1FG5kZmb6+O8fPXq0xKrUoRZ2rGgGDx7MzJkzWbPG4QCzd+9ehg8f7rcooNlsjnrgtiIwY8aMYc6cOS4j6pdffqFXr14+/YQQZGRklClTWqSYPXs2zz33HDNmzKBHjx4+56NW7+J8omiB+/AEm+k9HaKLFPIKgdwihbYktd036yasT7pYV1AcsG4/fSKi0VlSk2N08HLwniD1BI48A6RB+0JXwHpXW4g/A/X3RhdnX0uecO3JakL4JrF3I+Ys27Q42TVQH3f0UgtYTrhKVvbVmnDV2EAiwy7/KiWP6aR07a+KuOL3IxQKNTHRgPaBs62LtQfMdWdHN8OgFWYG6uNB+/ahR5spFIp/JJ9++im33noru3b5z+DdoUMHRo4cWc5aKbxxz4T2T8JgMPDRRx/RpUsX/vrrrxL79evXr1JVHP9fpUmTJrzzzjvccccdJSbCMBgMvP/++wwcOLCctfPl+++/54EHHiAlJYWHHnrIbx9lhLjhrBLvc9zhqlYqw8OgiZctNpMjN2ChYZi541c+j04inf2rLPoCpDiyoS0Ltf+ozvNzwukfDE1o7o8DN48xLlgQ7hipiVZbWXR4rv38bcC2oB2d/RPn7QTKnA8vffPAGrG5ue8AGAQ1op0aWqFQRB6dTkdcXJyrXVKl52bNmvHzzz9zzz338OOPP7qyTFWtWpW+ffvy9ttvU61a2LWCo4Zer/eYV6hPWmNjY11ygfz1KwqDweAxL/fg7HPnzrFixQqP88EIp280cX/fS9KpRYsWbN68mREjRjB79myPxe2FF17I+PHjGTLEx0mkQomLi3PNJ9xdQnfZyvI5geOec9cnJibGb7/+/fuzbt06HnvsMY8YJYPBQGJiIqmpqZWiOGNWVhbJycncddddHkVdvVFGSJSR4Nor04Q2EvC/f6twIaS4Xroyrsmpka4NU5mJPV0Yh4FB4J3xWqFQnC+0aNEi5NSaDRo0YNGiRZw7d441a9a4CpKVtAipSAYMGMCAAf5zpgTCWTyusvLcc8/x3HPP+T1XtWpVvymUzwe8syeVRJ06dXj//feZOXMmmzdv5uDBg7Ru3ZpLLrkkyhqWDvdaNuHiLIJY2YiLiwv5O+Pqq6/mxx9/5OTJk2zatInc3Fw6depE9erVo6xlaOzcuZPu3bvTuXNnOWvWrICJnZQREmEKNYMUouCk35OxdpWSNQSkkM40yX/kV6n6cYUqU87k28/KWEOs//vHHqPuH4XiH0rVqlVdWX4UioogNjY2YEVuReUiPj7ebxxZRfL3339z00030bhxY7l48eKgmWWVERJhUhO/PIQz3a+iVNg10UsvpNDrxKm0VnPyg0v8c0i77ttjqPtHoVAoFArFecTp06e5+eabEULI7OzskEpbKCNEUekoisdQKBQKhUKhUFRyCgoK6N27N4cOHWLHjh2ipDg4b5QRolAoFAqFQqFQKMJGSsm9996LzWZjw4YNYaWoVkaIQqFQKBQKhUKhCJtnn30Wq9XKjz/+SNOmTcOSVUaIQqFQKBQKhUKhCIuXXnqJ6dOn88UXX9ChQ4ew5ZURolAoFAqFQqFQKELm008/JSUlhWnTppGUlFSqMSq+prtCoVAoFAqFQqE4L1i8eDH33XcfI0aM4LHHHiv1OMoIUSgUCoVCoVAoFEHJzs4mKSmJ22+/XVosljKNpdyxoozFZnoAMAGvm43WRSHK9AUe8Tq83my0jg5RPhGY4HX4uNlovTNEeT3QBIgBdpqNVnsocn7GaQycMxut5V4l3mIz1cYxh/1mozXscrcWm6k6cBlwxGy0/hlp/UK4vh64FNADu0r7GZTy2m2AyV6HT5uN1tvLSweFQqFQKBSVi127dnHzzTfTsWNHPvnkk5BqgQRCGSFRxGIzxQNTcbzP/wlDtCnwb69jsWHI1/Mj/1cggaJF9x3AEMCIwwAByLPYTFnA02ajdU2wCxctnicCycAVgLTYTBuAD81G60thzCFsLDZTAvAo0Beo73b8EPAuMN5stOYFkL8ceBi4E4cB4zx+DPgKGG02Wo9FR3uw2Ew1gLuAh4B2eH4Ga3B8Blnhjjspy/QvKXgHQMIms9HaP4hIHXzvnxPhXlehUCgUCsU/g8OHD9O1a1caNGggf/zxxzIbIKDcsaLN08AFwDtmo7U0i7gdQNein6dLIV/gJt8vSN9vgHeAThQvfgHigOuBVRabKRQd3gKexWGAAAigLfCixWYaFbrq4WGxmW4GbDiMiPpepxsCzwHrLTZT3RLkL8bxfo/GzQApog4Ow2CLxWa6JpJ6e/E9MBPogO9ncAOw2mIzPR7ekEJoQkyTcKWEK3Hs7gRjHcX3zdjwrqdQKBQKheKfxJkzZ+jevTt2u12uX78+IgYIqJ2QqGGxmWriMBwk8Goph8kxG63LyqCGFoa8c9G7GLACWcAx4DrAAjQAJllsph/MRusGfwNYbKYRwANFzcnAdKAGDgPgriL5TWaj9ZtSzCVU/Y/h2PVYDWzAYYA8imN340ocn8XdfuQNOAymc8AHwEocRk0tYACOz/JC4H2LzXRtlNyjnHP4HsgE1gIncRiBFhzG1VSLzbTEbLRuDmXADFvSfQKZCOQCVUKRKTKYl4HLrU2hUCgUCsX/IIWFhZhMJvbv38+OHTtEbGw4jjmBUUZI9OiHYwG72my07qlgXULhZyDVbLT+6HV8q8Vm+gVYj2ORPBbHotwfQ4peM93jV4riYtoA1xT1iYYRchoYB7xsNlpPuR3fDiy32EzgMETusthM481G6xYv+UJgBjDBbLQe9Dq32mIznQBeANrjcDX7KvJTYAUOl6uVXse3FLlj2XDsijiNuoCkbx5YIxYygH3AL8DACOurUCgUCkWZ2HlqLW9tHlrRavjlRF5AT/b/Ce677z7WrFlDdnY2derUiejYfo2Q9M0Da1Q5h3504pyTkbhI+uaBsbF5p+PNCQsPl0Z+7tyB+r1XFtZ9ts28wyBlqP3PtGl7JI00rTTXjAD3FL1+UUHXDwuz0ToywLktFptpAY5Yi3b++lhsprY4dhrAKyjebLTmWWymqcAs4DaLzVTLy1AoM0ULd+/FuztTcBgh4HAP8zBCzEbrH8CwAPIvAek4XBjbEQUjxGy0PhPg3G8Wm+k7oBcO/YMSk5ubAjSUcL9Oiq5SBP3TUSgUCoWiXMm3n+Oo/Y+KVkPhh1GjRjFv3jx++OEHmjdvHvHxDQDp6/rWjpX2/sAgJB1jIV4TYLGZdiGFDcRUc+LXq4MNZslKvg8hh4LYYDbOf9iSnXSdlGJiLHQGg95iM+UA7+rPiedHdZ6f4y0/KTu5iyblZJA5ZmPmLePXJV2h03STRDPZi0JiLbakXITpK00nR41pm/mH57X7dBLCfqdEJNGMJhRiiLVlnc7AtA7EDwWnTo1Pu2lpofc1X9vRMy7nlOF7AXECMS7FOP/bEudnS/oUxGVC8mFKonVGyf1MDYBuRc15wd6384TtRa9NLTaTzmy0eht3zt2Rszie2Huzoug1Dke2sNmRVzEg291+bxausNloPWuxmf4ALgEuj5hW4eGcQ9DrT8judakO/QhgY4Ex8cO4LFvX6KqmUCgUCoXin8Irr7zCK6+8wieffMJ1110XlWvoAGLs2oNI3kFyCxDvdv5yhLwdoS3LyE6+I9hgAhoBnUBek5GV3BUplgjogiPNKEBN4KmCOK2lP3lNk3Uc8qL9+HVJV+g1sVIg+1KcGaoKkrt0hbruvhfXlkrEkzgWaM4dnhoC/iWQabG1an6bvq6vj3/7E80X5gnkCaCTFPIB7/NOJq7tfTmIO4BOUiezg7wVXXC8t8fMRuvOIH3PFxoVve7wY4BA8S7Ir2ajtcD7pNlo3UVxhq4rvc+XAw3dfv89XGGLzWSgOOA9bPkI4ZzD9oC9AJ3UT8IRA2KuwN1AhUKhUCgU5xk//fQTzz77LFOmTKF//2AJNUuPe3as4wj+qxPiRiljGsUY8mpIRFsJXwJVhJSfTsrq0zqkUQV6nZAzAYlgok5HHylFN+BppDgQyhAGTbyMY9E3UyDvAK2LQDyCI9i4JH4QUgzU6bkmX1JdL8TFEu4HjgI3x2r2mf7V5RMAJL2mbvh3db8j63XOJ/17zQkLfgmivtNkDGasnE8kFr2WlCLWuUA/FGAM5znv7FXlQaLb72tLIX81xYHdpZGPBM45BLy+ZW1yZ2AQiBVmo3VB9NVSKBQKhULxT2Hay9N48sknGT58eFSvYwAQhsJM/WnDW35cpDbMnTtw0I5mueuAa6TQ7gdK9Ft3IUmUkCPQbkhJWOC+aF2avnngO+QXBAuIryXhNp2OvqPbWee7HV+Rvqzre9WqV6vnLSDQbkgxLvBeIJ8FZmVkmfKF4GOgT/qq2+qkXfetR62HPCkyYwWngRr59ipJwGc+U5KOoF4p+SyEuJRri17XB+l3XmCxmZIA5+7VJyV0cxoWJ4pkYnAETx8G/s9stEocmZ7c+5YLRbVLnDEvq8xG6+5SDOMMtP8DWB4RxcLAYjP1B5wOmSV9BoAQ6JJedvyqhVTcUqFQ+GfOjnT0uvMjf4sKoPVlxaFP+fVva0WrAYAm1Ya04vyhU6dOcurUqRFLxVsSBgBz229KdC8ZMGCOPSM7KVNIcY1EtApxXD2CV70MEADSWs05HYK8Dim+GN1u/nzvE0VxHT4VrP0YIC4K4OtYR6rc2Liqsc1wpHEtHjPRenaizfS1hHuElIPwMkLGZ/VtphcYAfQ6+WkI+l9c9Pp3CH0rNRabqRrwWlHzK7PRurCErk7DwmloPOImdwuO1L8nvPqWF8NwFGAsxJGuNywsNlN3ioPanzQbraHcwxGjqIjhK0XNz8xG6+KS+mZk975XOOqMfG1OyFxVLgoqFP9QTuT/cxb2ra+5hrwz0cgsXn6svKiQ2DCWRWcKTnCm4PytszpkyBB+zI36OjDi1ExoSaPBpopWo1w4X/+uGgz4N7Wv95tnCIBnRz5bLjee30c8c+cO1O9untdAE7pYACFF0aJLhlwzQOaLEgO3Q0FI7fWyyKev7lnLECsu0ItYEafXI6X9OJI6WqHwOwdN8ImQ3AP0SF/ds1Zap4Wu7E167AMAJGwZnZAZyu6GsyBeRLKLVTDjgUuBHCDQvlyNotfcotc2bueuwWGE5Hr1jToWm+lSHKl1AaaVVOMkgHwNHKl7ARaajdYvI6lfiEzEYdieJEDRyqkb/l1dyDgLYNejpZaXcgqFovKTYDRizw3erzKj+2M1/A/tKFzRvDlnq0Y2JWp5UHBpA85VtBLlxPn6d5V7YX3yK1oJ3IwQS3bP+kIazBL60ozGSAxIH+su3vtACZxI7TS/TI+QNBG7LVyZCbakJD2kSESr2BhDbSRIrzkIveZ3DgUncxbF1Kp5REC92FhDMvBRsZDDFUtIXzctb4rckJxxJee1EWKxme6keNH7aFEa25I4CjTGkXwAHAX/euNwx5pTdKymW9+oY7GZqgPzcdRryQaeD1Ne4MjidQWO3bchgSUij8VmGkxx6uAhZqPVZxfQSb49brSARkjeHZW4wLsOikKh+B/mww8/pIref8jj+cJfNzRF5leGpVP5MGr0aKrceFtFqxE2S/dvJ2PN9672idwLWLbnFlc7uVlrHm59fUWoFjEuvPBC3n33XcwfRy9oO5q8kr2MBbs2VbQaDiMkY23va4TO8LMsXiTmIsUuhHQas3VwpCaN8TeIH/aXUa+C1MSvSlxs+WNiVvIHOiEGu4I1BH8h+QuHGxY4AosNAuF3Dmk3LS3MsJnmAI8JySCKjJAiV6wEALs+JFesQsCOIyNYXDhzqExYbKZEHJXHwVEAMEAcAuAwNhpTZKiajdafLDZTE7PR6p4WOd6tb1QpMiBm4aipcQToazZaw304k46jMGEB0N9PEcOoYrGZOgFvFTUnmY3WuSX1nfBrn0t0ep4FzmkGmVYuCioUCoVCEQSJQEp37x4DBl3kqm5XBIX5GkLqg3dUBMTwVtYjMUKn+wSoKWCbhvZkqvGb792Dry1ZyUMRMhz3qrL6zJ8JpSihk4ws011CMBhASiYJXeGL3oURLTbTEYrdpPwiNPEJOvmYhFsm/tb7gpTWC44b0AYWKWJ7rl1m0NSoZqNVWmymYzjiHkJ2X6tMWGymi4CvgarADxQHdQfC+X67dpq8DBAofj+iboTgqOx+Ow6jcJDZaN0bjrDFZhqIozI5wBNmo/XnCOsX7PoX46gxEwd8CwR0r9Lp7d1BVAX26uxibFGFeDfEtQASLrHYTG8CCMTUFOP8HZHXXqFQKBQKhSIwhiP6A1cLTd8aQEr6pCYu2OrdSeq0S4SsvMFRQjiDhsXHqYnzU7zPp2eZqsWKwAYIgLm99WeLLWkvcKksEH2A96WQjtS8klB2QZwc5Tw1Qiw2U1UcLkyNgW3A7WajNZSoK6dhcUUJ4xqApl59o0KRATGuqDnMbLQuCVO+A45dFAG8YjZa/aZ2jhZFyQCsOOqCbMJhRIXqCH0pjqQAnhRVSxdQz3leCD4BlBGiUCgUCoWi3NHpNN3VRb/vNSdafQwQAKHp/C4sKw0SxxyE/M7f6RghQqw1LyXCaWzoBk20JTcHEgCpabrPw9Dot6LXyv2+eeHmwtQRhyHV22y0hppaZGXRa2uLzeQv7iaB4liZlX7ORwSLzdSRYgPiJbPR+lZgCR/5xjiMsKrAN4SSkjqCuMWhGHEYa73NRuupwFIgNHYDnwf42VPU9bjzmKbJ8z57m0KhUCgUivMTg3RWI5fCr3Pb1A39LkTI3uWqVbgI5xyk32xfOrQHJKHt5Og03Sea0FJA3iylGIoACT+N6fB1OHEuq4ABOBbe5xPpwEAgH+hnNlrDeUr+JTAdRyxMNxyuRO44q9wfIoQ6GxNtvROl0D0BICTbU4zWCcFkLDbTJRQbEJmE5kbmLl+9SK4hjqKYd4axA+FvvAkIR7pmvdQmjTKGFCyeAfQF8oBks9G6J5RrpbTPXAYsK+n8xKzkD6SQTYE9ZqP1jlDGVCj+B+gNGCbeuuTQ4sWLq7RuHVo93vMB6/z53Pef/3D8+HEAquirVbBGkafNd39y9Q+OHDi6C+pR74uoPd+KCjkvp3FugeP5pgjZAV2h+OdgQLAVCQjZ2JKddJ17bYH0ZV0NcbVqvkhxpehKitwC4mKBGAC8735mwtqkjjqdeCjUkUYnfv2bxWbaCFyDkE8WjR80K5YXzviBKy02UzWz0Xo2TPlyx2Iz3YUjjgLgYbPRGlZBPrPRethiMy3BURPkJYvN9IPzCb7FZmoGmIu6fhHKwl6ivxQp7ytq/QwENEKKUulmAhfhKBJ5VzgGhMVm0gEfA+1wZMJKMhut3sU7w6UPklYAGroPgYBGiMVmug9wuhM+YDZaVZ0PhSLGPr+4AAAgAElEQVSKOGv+jMuJkzFUOe+zR7mj02LJzSn4R83JG32Bhr7A8buuqnbezTW/UI/93PlXY0KhiBSGulqjtUfEoS0CWkoprBabaYIU8jchRfPYWrXulsgbcKQ3rbRP9aXgIyHpLqFHhs30hZDM1oS064S4XqcTTwF/4Xi6f0GIQ36KY9GrB+wU6L4IU6VfgYNAIxy7AgvClK8IphW95gNPW2ymEutR4Ngl2eXn+AtAFxyxHyssNtO7ONLjPoQj89phiovuRZr/4MiEBY7PeaVvcLaL/zMbrWavY11xZMIChyuXNYD8NrPROqj0qpaI8zPIA0ZZbKZRAfommY3WsmahUygUCoVCoagQDA8nziyw2Ho/AroFRUGrLxcHoUuAOSAXgwjLt748SU1Y8FFGVlI/IUgW0B9Bf12RHxVwHCHvRIqQi8zZDXyqL3Q+eZdLUztZw6p5YjZaNYvN9AnwLA63rPPBCHG648VSvJgvCb87Y2ajdUVRbZG5OIoVuhscOUAPs9G6MzR1tLoUudBJRCg1Y9xd8ZoU/ZSEvx0Jd/kGRT9lQ1C36B6024U9lHk7dYgj+Gdw3qZ/VigUCoVCoTAAmI0LVkze0PNqu92QCiQiqQdyF0J8aE7InD1xramdpiNdSHks0GACuUJDpAshS/uEdqtEpOuK65OEiJSpifSx2JLuF4i+ElohRQ7IFUKvfyml3bw9GVnJLyKoKe3234KNZi/gL73gDFAdqZtdyrl8hMMIMVlsphiz0VpQynHKi0lAqE7DJWa3Mhut8yw2U2ccuwrdgbPAIuBLs9F/4gP/iOuKfpE6PS+GIPALjpiWUPB3D+wIQz5oDZvxG0yX6aXTkJFzxyR8E0qK4AxCNy4C/i16I5HzJGK3QB4KR06hUCgUCoUiGrie/o5qs/AA8Ji/TintycbhkhWQ0YnWlZQh81FRdq5xpZY3Zr6PV0yIk9TE+aEsZAGIFdyLI5PTmfyqcSHvoHjqYt1gsZmW4nDz6YcjI1GlxWy0To7gWKuB1QSpbREQKTojJAIWjm5rDVrW02y0/oLDECkVRTs040or742uUHR2bsVJKaaEqIMlUtf3GTvR+jWO2i8KhUKhUCgUFY7fbFIKhhe9fpXWak5ZCi+m4TBCRlA6I+RKi83kXFhnmY3WYWHKx7rJHzUbrb1KoUO582KWqR6CFgBCiIgZR+WJEPJ6hw0iF6cmZtoqWp9wsNhMRsBZnDTUOCqFQqFQKBSKkNFVtAKVibeyHomxZJmGgyOjkRC6D8syntloXQEsBjpYbKYbSjFENaBT0U/LUsgLN/nEUshXCLlCOl2xVo9OmB9Wlq5Kg+R6AKnpzkcjqhbF902LCtZFoVAoFArFPxC1EwJMXNv7cqnTfYKOprj8+MV3eQkJYVXaLoE7cVQfDxpH4MZsYKnXsXDSxf6Mbzazyh6T4kLotP1C6oYKR72V8xT5uhQ6e2r7+YsqWpNSsBbf+6ewIhRRKBQKhULxz0QZIYAG1QR0crrwA8vyq8TdnkZaqQvVOTEbrUeAI2HKHCZA8HcI8jnAutLKVzSp7b5Zx3msP4DZmPl2RetQWopqJ5zX779CoVAoFIrKjTJCAEOefq+9iuwrEQVCV7DGnLCw1AaAQqFQKBQKhUKhCIwyQoBRnefnoDIHKRQKhSJMpJR89913QfvVqVOHjh07+j134MABli9fzurVq6lWrRodO3akS5cu1K1bN9LqhszJkydZtSq4R2yTJk1o1apVwD7bt29n7NixAFSvXp133303IjqWhoMHD7Jhw4ag/a6++mouueQSn+N2u53/+7//Y+PGjezYsYMLL7yQli1b0q1bNy6++OJoqBwS27ZtY/fu3UH7XXfddcTHx/scP336NKtXr+ann37i0KFDtGnThk6dOmE0GqOhbsj8+uuvHD16NGi/7t27YzAEX9LOmTOHr776CoArrriCF154ocw6loYlS5aQn58fsE/VqlW58cYbfY6vXLmS06cD50yKiYnh5ptvLpOO5YEyQhQKhUKhKCV79uyhR48eQfvdeOONLFu2zOf4/PnzufPOOzl3zrM8VoMGDfjmm29ITKyYnCJLliyhX79+Qfs98sgjvPnmmwH7WCwWPv/ckSAyPj6+Qo2QWbNmMWbMmKD93njjDR599FGPY0uXLmX48OFs3LjRp3/NmjWZMmUKDz/8MEIIn/PRZuzYscydOzdov1WrVnHttdd6HFuyZAl9+vQhJ8c39PTxxx/nxRdfJDY2NmK6hsM999zD77//HrTf8ePHqV27dsA+f/31F4888ggnTpwAoFOnThVihJw5c4bu3bsjpQzY79JLL2XPnj0+x3v16sWpU6cCysbHx7vmWZlR2bEUCoVCoSglmzYFLWNUInPnzqV///4+Bgg4Fkw33ngjq1evLot6paYs83Jn0aJFfPTRRxEZKxKUdl6bNm2iZ8+efg0QgJycHB599FFeeumlsqhXako7r6+++ooePXr4NUAApk+fTnJycllUKzV5eXns3LkzYuONGDGiUizMt2zZEtQAKYn9+/cHNUDOJ5QRolAoFApFKSnLYn3cuHHY7XZXu2HDhh6uMmfOnMFiiVoN04BEwgj55Zdf6NevH4WFlSe5XklGRCDsdjv33HMPubm5HsebN2+OTue5jEpLS2Pv3r1l0jFc8vPzQ9ot8EZKyVNPPeXhFlSrVi2aNm3q0e/bb78NaZcl0mzdutXj76MsLFmyhI8//jgiY5WVsvxtleb+rcwodyyFQqFQKEqJ+4KiYcOGDBw40G+/5s2be7SXL1/O5s2bXe3777+ft99+m/z8fG699VZWrlwJwIIFC/jjjz/KPd7AfV5t2rSha9eufvt17tzZ51hubi7p6elMnTq1UhkghYWFbN261dXu1q0brVu39tu3TZs2rt/XrVvHunXFCQNbtmzJrFmz6NixI/v372f48OF8/bUjrPTMmTN89tlnjB49Okqz8GXbtm0e7/OgQYO46KKL/PZt2LCh6/dVq1axf/9+V3vIkCG8+OKL1KhRg5kzZzJ06FDXudmzZzNgwIAoaF8y3ov1oUOHlugWFhcXV+I4+fn5PPbYYxHVrSy4z6tWrVrcf//9fvvVqVPH55i7ERIXF+fjMuikatWqZdSyfFBGyD+QSVkD4wtFrkdxQyHJT020nleVuxUVQ/rqnrUMMQbPSFMhCsckzF8bkvyyrgZDrZrtvY/Xlw2zHk6cGVK9GkuW6Sqpt1epZ2+8KVQZhaIicF9QdOnShWnTpoUkN2PGDNfvBoOBtLQ09Ho9VatWZfTo0S4jxG638+677/L8889HVvEAFBYWsm3bNlf7zjvvJCUlJSTZtWvXcuedd7Jjx45oqVdqtm/f7vHUf8SIEfTs2TOo3IoVKzzaDzzwgCvJwCWXXMLTTz/tMkKg/J9We1/vpZdeolGjRkHl3Hc39Ho9aWlp1KxZE4AHH3yQ1NRUjh8/DsBvv/0WQY1Dw/1vKz4+3uNvJhwmTZrkcT9XNO7zSkhICPk7Azw/6xYtWoQlWxlRRsg/ELs4e60O3bceB3X8Bfh/NBKAKb/2uqjAYLgJQAqxb0y7r38OSUan74aOrkLjHLBER5WloxPnnAz3+pEgY23yLeipC2A4yzdF2dD8MvG33hfIAtFHoGupIS8XcEQKNmmafuFzifMi55waBpOzkxsVQhcAKeWeMUbrL+HIp28eWCMmL683gE6Tx1ISrd8H6h+n1xuld7FMKU8AF4Ryvep1LqhTUFjok1bnCEcaAYdCUlrIYULTP35EHHwceD0kGYWinNE0jS1btrjawbJEubNkSXEt3CZNmnDppZe62j169EAI4fIb/+GHH8rVCNmxY4fHYj2ceS1YsMDDAGnUqBFxcXEhZW6KNt5P1q+88sqQ5PLy8mjdujUHDhzg+PHjmEwmj/OdO3cmLi6OvLw8AP78M5zaxGXHfV41a9YMyQABmDp1Kg8//DDr16/n5MmTHrttQgiqVKniajdu3DhyCoeI+7zCuQfd2blzJxkZGa52jRo1gmaWijZlmZe7bKj3b2VGGSH/bCSwHEBKcaw0AxTq9a8IKQcCCORcHNXYS2TiOlMHqdcvEVADCTiShDyhkbt/wq99Oo/p8PX+QPKRJsOW3Ebo5LdIR/xTfpy4CvB5JJK+eWCN2Lzc6UjdQKCqROLMbyIk6IV98kSbaXyznVUmDhgwJzJOqiFSKJkukH0BBOJjICwjJDY31wykAkhBNhDQCNEM2gmh6X8EQFIPwdXhXO+MJvNjwSEvqIrEf17SANgN4iV9IUOF1KW8tqPnO080X5gX7hgKRbTZvXu3R1B5y5Yt2bJlC0uXLqV+/fpcd911fhdvdrvdI+2ot9uFXq+nVq1anDzpeG5TkYtacCx21qxZw6pVq2jevDnXXnttSOmDExISyMzM5O67764URoi3K0ujRo1YsmQJGzZsoF27drRv354aNWr4yI0ePdrlXnX27FmqVavmcf63335zGSBQ/otD93ldeeWV5OTksHTpUvbt20eHDh1ISEjw68ak1+tp2bIlLVu29Dm3YMECDh0qfmZ06623Rkf5AHgv1v/++28WLVrE2bNnufbaa7n66qt9YnK8GTZsmCuW5+6772bjxo2sX78+qnoH4vTp0+zbt8/VbtWqFbt27WLx4sVUr16da6+9lmbNmvmV1TTNw4XzqquuYv/+/SxatAiDwcC1115LixYtoj6HSKKMkH82+Waj9abSClvWJndGh38HZ3/9s0xXIfg/oAZwFMnXCGoCvYBL9Hpt0YtZpn+NSLSGVUG+LAjky4SQgMGQd6YuUn9fUVMCOwEbUAu4HqglYfyOZrkSyChhmIiTkZXcVQiHAVIaJmT3ulSH/plwZIoq1t8EYMky9QHmhSOf1m7eCZf8+l4tsOvD3gd/ro11t8Vm+gIhB+Wc0j+E2g1RVEK8F+tDhgxxGQ5OHnroIaZPn+7hs3748GE0TXO1/fl+X3DBBa6x/vrrr0iqHRTveXXq1MljXjqdjvHjx2M2m/3Kx8fH89RTTzFy5EiqV68eVV3DwX2xbrfbqVevHmfPnnUdq169Ou+9916JcT2AjwECeLhiAVxzzTUR0DZ03Oe1efNm6tSp4xEj0rhxY7744gs6deoUdKzly5czZ84cD1etevXqcccdd0RW6SCcO3eOXbt2udpz587lvffe88gq1a5dO+bNm+cTSO9kzpw5rho+tWrVYsqUKSGl044mmzdv9pjDc889xxNPPOHRx2Qy8dFHH1GrVi2P47t27fJ46PHaa68xfvx4jz7dunXjs88+o379+lHQPvKo7FiKEhACnZwGnAZCc+4VTADqAic0uy7BnGh9yGy0DtKgO2CXcGWe4Kno6ezJpHWmZKAbkB2iSJ5AvmKw2xuZjdYrzEbrILPR2kPTy1ZI4YxKfN6yvle5PGpIJ10nhHwZOAXsCtbfHzqpnwzEARX36KeUCMl7jlfd8IrWRaHwh/di3dsAAXjnnXfo0qWLx5Nyb6OiJCPEfVx3+WgTbF6appGamsp9992HN3369GHPnj2MGzeuUhkg4LlYLyws9DBAwBFUPmjQoLAyku3evZs33njD1a5SpQq9e/cuu7IhcvbsWY9dprNnz/okAzhw4ABdunTxMZb80a9fP15//XX+/vtvAC6++GKWL1/uk1gh2mzdutXDUD916pRPWtt169bRvn17v/Eqp06d4umnn3a1x40b5xGUX1GE8p1htVrp2LGjKx7HiXfsjz/ZJUuWkJiY6LHbUplRRojCL5Ys02CgPYKXBPLvYP3TNw+sAfQEEIjR7m5XY4zWXwS86jjHoGjp7KVPrKYxFcExIZgSrH+cvvCIZtddkWLMfGpkh288fB/GtM38Q9MxxNlV2A3lsi8dl5X1ANBWwGQgbHc6y9rkzsBABJ8KKc47I6QODZcCxxGyhSU76bqK1keh8MZfqs2LLrrIxyd/zZo1HgGk4Roh/mSiib95XXbZZT46ffjhhz7V4tu1axe0aFxFkJeX5xMsL4Tgyiuv9NndSEtLY/v27UHHPH78OD179nQt2AGGDx9ervETW7Zs8Visg6Na9lVXXeVRQTw/P59hw4b5GF7unDlzxqc6+S233OIKVi9P/N2D1atXp0WLFh7FII8ePcrw4b7PqcaOHcvBgwcBx86U925DReFvXnXr1vWICQNHxrP//ve/QWVr167N5Zdf7nFs//795ZqdrSwoI0Thw9QN/66OkBnAYf1ZMTUUmZhzuSagCoAmDZne5zUpnMeaZ2SZjBFTtgRiz+UOB5qjYUGK48H6P9vmuzOB4lWKMkPlAGhI/zkdI0j66p61pI7xwKE8ycvhj+Daycq363ku0vqVBw8nzixAYAWQmm5wReujUHjj/rSxcePG/PTTTxw6dIg//viDTz/91GMRmJGR4VrgnTlzxmMc9wDgko4FWjxGErvdzh9//OFqt2vXjm3btrFr1y6OHj3K2LFjPfqPGDGiXPQqK7t370av17vaycnJ/Pnnn2zdupUjR454pJ8tKCgo0dXMSX5+Pv369fNI+Vu3bt2gcpFm165dHnERI0eO5NixY2zZsoUDBw54VEc/ePAgL774Yolj7d27FyGEh9Exa9YsWrZsyY8//hidCQTQxUlMTAwzZ87k2LFjbNu2jY0bN9KkSRPX+WXLlpGZWbzssNlsvP56sQfv9OnTPf4WKxL374zatWuTmZnJ33//zZ49e/j22289dg9ff/11D5c09/ekevXqfP755xw5coSdO3eyfPlyj4cEn332Gb/++muUZ1N2lBGi8KGgoEoK0EgKXgiUScodneCWol93pCZ+6ZMBqWp+3C9AAYBO5+obFSzZPesjeA7Yn5+TMz2CQ58GEI6YkagSYzCkImkApKclWsNefbjtZL35XBtrxUeElp4fAISQfSpaEYXCm+XLl/Pnn3+ydOlS1qxZw/XXX+86d8cdd3Dvvfe62qdOnWLVKkfSuHr16nmM48+twruy84UXXhhJ1UtEr9dz7Ngx9u7dy/fff8+KFStcwa5CCP773/9iNBY/R9q0aZOH0VJZueqqqzhz5gzbtm3j+++/56uvvnK9p1WrVuWdd97x2MFZtGhRwPEeeughli1b5mrHxcXx1Vdflfsu0IABAzhz5gwbNmxgxYoVTJ482RVc36BBA95//30PI+X770vOS3LZZZdx8uRJjh49yltvveV6f06fPo3JZPJxD4omY8aM4dSpU/z666/8+uuvPPzww67g+latWjFliqeDg3Nemqbx6KOPuooc3n333dx4443lpncw5syZw5EjR1i5ciVr166ld+/ers/n3//+t8euTkFBAUuXFieqfOuttzh+/DirVq1i7dq1DBw40GVY33DDDT5ptBcvXlwOMyodW7duRQihKSNE4cGkLFMThByBZHdBXJWZYYg2KHr1myD96evmnMMR7I3URHT/m0rDC0A8yLS0m5bmBu0fApbsnvWBhgBCElK9jNIycW3vy4XgKaT4Pf9UzrvhyrvtZOVA4figApUYadec8TwXTcoyNQnYWaGoABo0aMBNN93k19/cOzbAWdXa26Dwt7g7dqzYAzMuLs6vy1a0EELQpEkTbrnlFr/ZopKSkjzalakGQyAMBgMtWrTglltu8cmqVKtWLY/F6qlTpzzcrNyZOnUqH330kasthGDWrFl06dIlOooHoUqVKrRu3Zp//etfPueuuuoqj2xLgT6rqlWrUrNmTWJiYhgyZIhHscJTp07x5ptvRlbxINSsWZP27dvTtm1bn3Pef1vOeb355puuHYBq1aoxZswYTpw44fpxr8But9tdx71d2qJJ3bp16dy5s98sWN7pn70/r9q1a3Pttddy1VVXBZUNxaWwIti2bRvJyckFMTEx6coIUXigCTkJqCrhubRWc/KDChQhoT6AANfju3Q87y/XOSGjlrZhUlaf1sBDSDblG9t/EKlxJXpXSg1hwKcGRiTRdDpHMLlOG5N209Kwyw07d7KQTDUnLDwceQ3Lj4LTZ7YCuQASETy1i0JRifCODdm501FqyDtzTTAjpEGDBj7nKxJv/3XnvM53vOe1Z88enz6LFi3yeeI8adKkcs8eFQ7u9+Hhw4fJyQnJwcFnUVuZ3HuqVatGfHy8q+28B913es6ePUurVq244IILXD/uwd1r1651HXc+IKhoyvK3Fcr9W9EcOnSIbt265eXn58/Ky8v7rzJCFC4mrOtzPYg7kGJdamLmp2GKF/1XlScBMtb1ahdry9pusZnWZKxObgAgESc9+0YeTWgvA3pNSHMaaRF5tJG+umctIcVEAAGzR7e1+kaHRQhLtulGAf2BX80JmV+EK+/ayRL8lV+1yktRULFcKTLC9gBInSzfvJcKRYjY7XaP4n5O/r+9Ow+K6sr3AP49vbEpcV9wmeBK1LiAyyRTxgUlCgIuiabQmBit6GjyNPEJKL74LImARmNFrRjFOEPimhhFFLGsOGp8SUwCKqPRygRHJ8Qo7kQberu/90d3X7l0NzTQdIP5faqosvuec/tcurH63HN+v1/l2A97bY3mzZsr9qhXNwnxVVYfo9GouHNsV1paqnjsTs2QhqRimtOKKl9X5W1zly9fxksvvaT4nSQlJWHRokWeH2QtuLquip9DPz8/RdyBXq/HhQsXUFhY6NCvcsCzt1NF2zm7LiJSxEk1ts8gEcn1Sypy52/L3b6VP7++VlpaisjISMO9e/eOlpeXzwE4JoTJhFCR9D4AkLAkA1TTuIdW1r7WiYYg9esAugAYBB3Zkq6TbRIi6mUSknYmPg5AJAGnUsJzHILja0unVb8L61asuxqNpt6iMJdjuQq2IHQiUavUFvJKFtGKZb32+LYsrIdUWF1rHInP2R/ChQsXMGzYMISGhsLf399php7KdzHt2y+EEIq7lr/++qvii+3NmzcVaVYrfxmsTzk5OXjmmWfQoUMHBAQEYMuWLQ5tKm8RcVVcrSHZuHEjBg4ciNatWyMwMBDffutY87XidWk0GsV79PDhQ4wfP14xOZw9ezbS09Prd+DVeOutt9CvXz80a9YMzZo1c9hCZrFYFHfEQ0ND5a1o/fv3R1BQEPr06YOxY8c6TDjz8vIUj71VhJGIEBcXh6eeekoeX+XtUr/88gtMJpP8uDF8BktKSjBy5Eh069YN/v7+TlfPXP1tPXz4EKNHj0aPHj0QEBCAMWPGuN23ITAajYiJiTFevXr1nF6vf4GIJICLFTKbtDOx02yVrY8tCT94pNoOjowAgkC2zxSJK/b4bRWJfwEAQWgFCAC5vc3LXZvzZ2uFoPcIAKlUHstNtzI/PkYIMRcACHj7v/t+UW264trSncmfAWAAgLwlEdn/qK59Ze+eHf+sCuIlAEWtKGSzxwfoI9YVNAKk+pm8MlYboaGhOH36tFy/49NPP0VGRoa8RYSIsHnzoz9DtVqtiBlISEjAihUrAFgnHXv37pWL5GVmZipea9q0afV6LRWFhIQovqBv3LgRc+bMkR+XlpZi585HC+Xt2rVzWnG7oWnSpAny8/Plxxs2bFBkjiosLJQTBwDWQN+K2bRmzpypqEcRHh6ODRs8mfek9iquYmzevBlLlz5KiLhr1y7cuvWoPnBkZKT87z59+sjVw69du4bc3FxFvE/luiJ9+/b1+NidEUKgrKxMzjx2+fJl5ObmKuJAPvpIGbI6YsQIAMCkSZOcxkvYbdu2TZ6ohYSEyMkjvLGS0qZNG1y6dEmuRH/w4EFcvXpVMdl1dV1BQUEoLi6W4zxOnDiB8+fPKwpjuurra0SEqVOnmgsKCv6j1+ufJyK56BFPQhiW58cF6gTSAJAkancHHiRuQlBzApoBgJFog1bQQwHVb8nhB44AgADZNnCSx+MUbquuvQkS3YmQndJ//9eeOGdaflyYENgBQEUCmUsGHPibJ87rzKr/i2+KAKQCIJWg5Go7OBBCRbHvA4AApbwe8ZGpuh6Nh2QABISgAF+PhDG7wMBAjBw5EocPHwZgvVMZFRWF5ORkdO3aFampqYovvS+88AJCQ0Plx6+++ipSU1PlAmxLliyBxWLB3bt38d57jzKjd+7c2eldz/oSHh6OkJAQucbC+fPnERcXh0WLFkGtVmPBggWKzF3z58+XsxY1ZFFRUVCr1fLd/t27dyMgIADz5s3D9evXMXv2bEX7xMRE+d/FxcXYvXu34nhhYaHToH3AWmHeWylto6OjFTVoMjIy8ODBA8yYMQOnT5/GvHnz5GMajUaxdWzcuHHYvn27/HjatGmYNWsWoqOjsW/fPsU1BwcHV1lJ3tNiY2MV2Z1mz56NhQsXIjY2Fp9//jkyMjLkY23btpULZ1bMSOdMXl6ePAnp1KmT11eyxo4di48//hiAdZUqJiYGycnJiIiIwLp165Cbmyu3HTZsGAYPHiw/jo6OVqSEnjBhAhYtWoThw4cjMzMTO3bskI/1798fUVFeKWlWrQULFkiHDh26U1ZWNpyIFGn/eBLCoBWYDaADgDyd2Vy8+vuYdhWPk1qtAwBB8Lcf0w8aXKKIuVDRTRB6CIgnAMCWVnYjFMQTAIGE8OhqwvLjIzS64Kb/A4BUAh9UHj806ub2pLpqoNXq72Pu6yXSLxuSW+p4Nts5z05o5idwgIBgAKdNfv7zXLX1BCmQ5oLQTgA5KrN0w+Ea1GotABBIfg8WDcq9Yd82927BuHEqYDCASxLpTjq8hxp1gO13oLEfe0LV8XbjmKwI261lcbuahox51dq1a3HixAl5b/p3332HiRMnOrRr1aoVli9frniuS5cuiIyMlL9oFRUVISEhwaHvzJkzHTI51SchBD788EPEx8fLz+Xk5CjqMNj17t0bc+fO9drY6qJ9+/Z45513sGzZMgDWiumZmZkOq06A9QtwxYmfs0rjZrPZoTK5nTer248ePRoTJkzAvn37AFjT6WZkZCi+pNUy8CwAAAvySURBVNslJSUp7rpPmTIFW7dulT+DpaWlWLt2LdaudQwnXLlypUOihfo0Z84cbNu2DWfPngVgXalZuHChQ10alUqF1atXw8/Pz2tjq4sVK1YgOztbrhl04cIFpxOnJk2aOLyHKSkp2L17t5wS++eff3aYPAPWuJ+KNzJ8adWqVbRp0ya90WgcTkQOubw5JoRB2OI5AIwxq9W/Vf4BMBAACIi1PxdwpkD5JZlgX92oIhKKbMc8vhKigXUFRhDwZeXxE+HR7QEVnTKr1b9pNVrH/6Ftlh8fodFJ5s8I6A4SxUTaCTXJFFYbRLaYmgq/40rvQT8AEMAk+3Nr8mPl9WPVo997mBCmaw79CfZKXE/bn7urut44qpCTbRKi8vwKGmN1ERYWhk2bNlW5EhASEoKTJ0863U+/c+dOxZ3OymJiYnxSDDAuLs4hA1RlAwcOxIkTJxAcHOylUdVdSkqKQ2rXyiZPnoy9e/cqnnM2CWlIMjMzFdtynFm6dClSU5UZ24UQ2LZtG7p37+6ynxACb7/9tiJdrzfodDrs3LmzyqQMGo0GWVlZ1a5+NCQhISHIyspyuYoGWBNXHD16FEOGKBNCtmjRAjt27KiyFk1QUBAOHTqk2HbnK5988glSUlJMRqPxeSK66KwNT0KYRwjAWhBP0CBAiMrHbRmy/gQAKlJd8ergakjbNHg9IEYBeKBSSeOcFV9kXiSoLQBIknBZ0Z4xX3n55Zdx7tw5jBkzRpF1qGvXrpgxYwZOnTrlMmaiVatWOHbsGGbNmoWOHTvKz3fu3BkLFy5Edna24pzelJaWhpMnT+LPf/6zPMkSQqBv376YP38+vvzyS7f30Xfu3Bk9e/ZEz5495cKHvqBWq5GTk4Pt27cjLCxMXmHS6XR49tlnkZqaip07d0Kr1cp9ysrKcOPGDXn87vw8+eSTXr2uFi1aoKCgAOnp6ejUqZP8fFBQEEaNGoUtW7bI8UeVdezYEYWFhUhOTlakgvbz88PgwYORl5eHNWvWeHU1zi4sLAyXLl3CG2+8oUhr3bJlS8THxyMnJwdTp06t0TlDQ0N99j7ZRUdH4+LFi5g8ebJiEt+pUydMnToVx48fV8QrVfTcc8/h0qVLmD59uqJCevv27TFlyhQcO3asQUxAjhw5gtdee81sNptfJCKXW+R5OxaDjvC+EfjEZQNrXMQAAHkgvAUAZaWlii1VBGkfoHoLhBYZ52J7JfWDIo2t0ElDQQIAJMmE7OrGtDw/LtBPrW4DAGqTsXzRoEPXXbVdNvy4IS0/1nVkpKChgNgMACQQLST8G2Y4Lf26Mj9+vhA0B4AkgRIWD8g5V91YXVl1Jj5EEiodABju3bteZeFEYV4FSeO6MKHAZwD6CCCHCIkA0Pmy/11EWA+rKOALCeUu65cQkC4E4gH8CMIkANAZ/K7W/Kq8y1okUtMeAEgtGk6SesYqCAsLw+HDh0FEuHLlCnQ6HTp06OBW36CgIDkD1dWrV+Hv799g6oIMHToU33zzDcxmM4qKitCmTRvFFx93ZWVl1cPoai8hIQEJCQkoLy9HUVERunTpgoAA5yFnAQEBioD0hkqr1SIpKQlJSUkoLS1FcXExevTooUgF7Yq/vz/S0tKQlpaG27dv48aNG+jevbtiMuYrwcHBWL9+PdavX4+bN2/izp076NGjB4TjvU632Let+VrHjh3lmJvi4mJYLBaHOh+utG3bFn//u7UM2rVr11BeXu7VDHrV+eGHHzB+/HgzEc0jogNVteVJCMPCiAO3ANxydTy9ILaMIACB3xeHH7jkrM3i8EOn0gpifwXQQbKIvwJ449FRIUDj7BsXjy8Zkl1tsnGdEGNIsuwFAIta9TWAv7huTbQ4Ak7HZR1//JNkC4iQLOLy0oHZTkvGpv0QGy1UYo11yLQoZUDd0vxaiI6CLL0AwC+4aSSAY67a2ooKutxulFYQZy/YV7o4Itt6rRGPjidF7LkP4L6zvgCQnh9/3/Y7MCyOcP4eNkRC0gywzl1hMt+7X+Dj4TBWJSGEIvi8ptz9EuJtGo3Ga+lZvcnf3x+9e/f29TA8Ljg4GL169apV35YtWzbYmhutW7d2KPT5OKi4ClpT3ozTcUdRURFGjx5tlCQp3Ww2V5ulkychzEOIgNjtgEgEaN7KgrgfTKW/fxrs11pb5h+7WACjAICqWnHxodTCuFC1SuwCoAZwDpK4nJYfN95ZW4K4X5sUuqw2xBCAAIHvqlxJYowxxpjPlJSUYMSIEYby8vLtBoNhmTt9eBLCPKZJsOWdB6WaCACRAtimC266qhzl/oLQ1NpCbFkSnv03d84lCGrbHXCQEPUaFA4AKrPUA1DZxol+EHC5ZitA5wD0r+6cAlDbKz4KQr1fw+OIrFvIIECf+nosjDHGGHP04MEDjBo1ynD79u1j5eXlr7vbjwPTmce82S3XYPT3Hy+AQwBMsFa4bgpATwKZxvDwOVWf4RFS0aNUH0Q7qmjaIL3/zeQAslaMBwj/7nLZ32W8BnMu/eyEJwGKAGCAhnZX154xxhhj3mUymRAfH28sKir6p16vn0hEFnf78koIq1ZyeE4V8RhKy3rteQBg3PIfJzfR6cueE4DeEBjwdY1T3JIcA3K9SbClTlGNyeHZeQCqjGKzVYmvXaSbE0Z/wyAQtAAghFj74ot73P6jdGZx+IFBdemfHJH9CoBX6nIOb5Mk84sCAiB8kfz0QaeJBBhjjDHmG0SEV155xfztt98W6/X6KCKq0bZpnoQ83lTpBfFjAEAiGLwZx2CbjORW29CJzz6brEZXDAEAQeKDN7vleq/yk4eQhL/YpjS3DUQf+3g4NbL8mzEt/Pz8rMULVBhgL/Todv8fJ+v8yg0jAQBC7V6KoMrnsBagfAMAEUkra3MOxhhjjNWfxMREaf/+/ffKysqGEVGNbxbyJOTxpiXQYQAQKtwA0K6a9g3Cv7o/fFpI6iYAfjeoVR/6ejy1QYKeBQCC2LAsIlvv6/HUhJ9W29f+uanpBAQAgszmZqY69AcAbdOmkwF0BonPlww8eL52Z2GMMcZYfVi3bh198MEHZbZq6MW1OQdPQh5DKkldIglS1OJQwXldjAaJVH0hcAOErcv677vn6+HUCiEUAr/4EW3w9VBqSgLdAgnF50cI8cDd/haTxkAwO9SCUekMbi/TChV6koRstYb+190+jDHGGKt/u3btQmJiotlkMo0hogvV93COJyGPoeSB2WcAOE0v2xgsGZCTBaBhVbeqocURB/pU36phsq081PrzY6tZUqfP3+IBB9xK78cYY4wx7zl27BimT59uNplMk4noVF3OxdmxGGOMMcYYY1U6e/Ysxo0bZ7JYLP9FRPvrej5eCWGMMcYYY4y5dOXKFYwaNcposVhWWywWj8Tr8iSEMcYYY4wx5tStW7cwYsQIw8OHD3cZDIalnjovb8dijDHGGGOMOdDr9YiKijKUlJScKC8vn+nJc/NKCGOMMcYaN8kC85WffT2KGpEe3Pf1EBirktlsxsSJE40//fTTj3q9fnxNqqG7gychjDHGGGvUpHt3cGvKc74eBnOD3mzEDf3vvh5Gnfi1ao7fYWm016E3mdxqN2vWLPNXX311Ta/XjyKiMk+PgychjDHGGGPMK45cuYgjVy76ehh10n91IrIs15F1uFFXE6jS0qVLpT179ty3VUO/Ux+vwZMQxhhjjDHGGAAg73AeVq9eXW40GkcQ0X/q63U4MJ0xxhhjjDEGANj68VaL0WgcS0T/rM/X4UkIY4wxxhhjDADwRK9ua4joZH2/jiCi+n4NxhhjrMEKDAy8RkSthRCSr8fiKRaLRVgsFq1OpzMDeCyu61KfZjqd8PUovOevVx+aj5SaGt171/qZAaoecxN4u3/jNuHopHl1roheHf6QMMYY+0MrKyvrDyDE1+OoB9qysjL30uA0AlrR7DQAna/H4S0vPKFJ3H+j9B++HkdNtRoa8TyAdF+PgzV8PAlhjDH2h0ZEJQBKfD0OVrXrQ0JO4A80CYls5vc9EZ319ThqavTejWG+HgNrHHgSwhhjjLEGr93pa1G+HgNzyy0Ap309CFYn9ZKSt7L/ByikTWipSwq0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"numpy_indexing.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For multidimensional arrays, indexes are tuples of integers.\n",
    "\n",
    "Note:\n",
    "* in 2D, the first dimension corresponds to rows, the second to columns.\n",
    "* for multidimensional a, a[0] is interpreted by taking all elements in the unspecified dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing\n",
    "\n",
    "A slicing operation creates a **view** on the original array, which is just a way of accessing array data.\n",
    "When modifying a view, the original array is modified as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0]\n",
      " [0 1 0]\n",
      " [0 0 2]] \n",
      "\n",
      "1 \n",
      "\n",
      "[0 0 2] \n",
      "\n",
      "[[1 0]\n",
      " [0 2]] \n",
      "\n",
      "[[ 0  0  0]\n",
      " [ 0  1  0]\n",
      " [ 0  0 10]] \n",
      "\n",
      "Are a and b the same object? True\n"
     ]
    }
   ],
   "source": [
    "a = np.diag(np.arange(3))\n",
    "print(a, '\\n')\n",
    "print(a[1, 1], '\\n')\n",
    "print(a[2], '\\n')\n",
    "\n",
    "# slice the original array, creating a view\n",
    "b = a[1:, 1:]\n",
    "print(b, '\\n')\n",
    "\n",
    "# modifying the view also modifies the original array\n",
    "b[-1, -1] = 10\n",
    "print(a, '\\n')\n",
    "\n",
    "# check if b is actually a view of a\n",
    "print(\"Are a and b the same object?\", np.may_share_memory(a, b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fancy indexing\n",
    "\n",
    "NumPy arrays can be indexed with slices, but also with boolean or integer arrays (masks). This method is called fancy indexing, and it creates **copies**, not views."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original array: [ 0  2  4  6  8 10 12 14 16 18 20] \n",
      "\n",
      "the mask: [ True False False  True False False  True False False  True False] \n",
      "\n",
      "the filtered array: [ 0  6 12 18] \n",
      "\n",
      "Are a and filtered_a the same object? False \n",
      "\n",
      "the modified array: [-1  2  4 -1  8 10 -1 14 16 -1 20] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(0, 21, 2)\n",
    "print(\"original array:\", a,'\\n')\n",
    "\n",
    "mask = (a % 3 == 0)\n",
    "print(\"the mask:\", mask,'\\n')\n",
    "\n",
    "filtered_a = a[mask]\n",
    "# equivalent to a[a%3==0]\n",
    "print(\"the filtered array:\", filtered_a,'\\n')\n",
    "\n",
    "# verify that fancy indexing creates copies\n",
    "print(\"Are a and filtered_a the same object?\", np.may_share_memory(a, filtered_a), '\\n')\n",
    "\n",
    "# Indexing with a mask can be very useful to assign a new value to a sub-array\n",
    "a[a % 3 == 0] = -1\n",
    "print(\"the modified array:\", a, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing can be done with an array of integers, where the same index can be repeated several times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "selection of indexes:  [20 30 20 40 20] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(0, 100, 10)\n",
    "l = [2, 3, 2, 4, 2] # l is a python list\n",
    "print(\"selection of indexes: \", a[l], '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When a new array is created by indexing with an array of integers, the new array has the same shape as the array of integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6  8]\n",
      " [18 14]]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(0, 20, 2)\n",
    "idx = np.array([[3, 4],[9, 7]]) # note the shape and the ordering\n",
    "print(a[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# an exstensive example\n",
    "from IPython.display import Image\n",
    "Image(\"numpy_fancy_indexing.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operations with arrays\n",
    "\n",
    "### Basic operations\n",
    "\n",
    "By default, all operations (additions, subtractions, multiplications, ...) with arrays are performed element-wise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3] \n",
      "\n",
      "[ 0  5 10 15] \n",
      "\n",
      "[1 2 4 8] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# operations with scalars\n",
    "a = np.arange(4)\n",
    "print(a, '\\n')\n",
    "print(a * 5, '\\n')\n",
    "print(2**a, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3] [2. 2. 2. 2.]\n",
      "a - b: [-2. -1.  0.  1.]\n",
      "a * b: [0. 2. 4. 6.]\n"
     ]
    }
   ],
   "source": [
    "# operations among 1D arrays. Also in this case, all operations are element-wise\n",
    "a = np.arange(4)\n",
    "b = np.ones(4) + 1\n",
    "print(a, b)\n",
    "print(\"a - b:\", a - b)\n",
    "print(\"a * b:\", a * b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Be care ful that, since operations are element-wise in all dimensions, array multiplication is not matrix multiplication:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the original matrix: \n",
      " [[1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]] \n",
      "\n",
      "the \"*\" product:  \n",
      " [[1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]] \n",
      "\n",
      "the proper matrix product:  \n",
      " [[3. 3. 3.]\n",
      " [3. 3. 3.]\n",
      " [3. 3. 3.]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "ones = np.ones((3,3))\n",
    "print ('the original matrix:','\\n',ones,'\\n')\n",
    "\n",
    "print ('the \"*\" product: ','\\n',ones*ones,'\\n')\n",
    "\n",
    "print ('the proper matrix product: ','\\n',ones.dot(ones),'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other operations\n",
    "\n",
    "- array comparisons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "equal?: [False False  True  True]\n",
      "greater than?: [False  True False  True]\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# element-wise\n",
    "print('equal?:', np.array([1, 3, 2, 5]) == np.array([3, 1, 2, 5]))\n",
    "print('greater than?:', np.array([1, 3, 2, 5]) > np.array([3, 1, 2, 1]))\n",
    "\n",
    "# array-wise\n",
    "print(np.array_equal(np.array([1, 3, 2, 5]), np.array([3, 1, 2, 1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- logical operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logical OR: [ True  True  True False]\n",
      "logical AND: [ True False False False]\n"
     ]
    }
   ],
   "source": [
    "# logical\n",
    "a = np.array([1, 1, 0, 0], bool)\n",
    "b = np.array([1, 0, 1, 0], bool)\n",
    "print(\"logical OR:\", np.logical_or(a, b))\n",
    "print(\"logical AND:\", np.logical_and(a, b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- application of functions on arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sin: [ 0.84147098  0.90929743  0.14112001 -0.7568025  -0.95892427 -0.2794155\n",
      "  0.6569866   0.98935825]\n",
      "log: [0.         0.69314718 1.09861229 1.38629436 1.60943791 1.79175947\n",
      " 1.94591015 2.07944154]\n",
      "exp: [[2.71828183e+00 7.38905610e+00 2.00855369e+01 5.45981500e+01]\n",
      " [1.48413159e+02 4.03428793e+02 1.09663316e+03 2.98095799e+03]]\n"
     ]
    }
   ],
   "source": [
    "# mathematical functions re-implemented in numpy\n",
    "a = np.arange(1, 9)\n",
    "print(\"sin:\", np.sin(a))\n",
    "print(\"log:\", np.log(a))\n",
    "\n",
    "# works all dimensions\n",
    "m = a.reshape(2, 4)\n",
    "print(\"exp:\", np.exp(m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- matrices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]\n",
      " [12 13 14 15]]\n",
      "a: \n",
      " [[ 0  1  2  0]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]\n",
      " [12 13 14 15]] \n",
      "\n",
      "transpose: \n",
      " [[ 0  4  8 12]\n",
      " [ 1  5  9 13]\n",
      " [ 2  6 10 14]\n",
      " [ 0  7 11 15]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# extracting off-diagonal terms\n",
    "print(np.arange(16).reshape(4,4))\n",
    "a = np.tril(np.arange(16).reshape(4, 4), +2) # play with the last parameter (sign and value)\n",
    "#a= np.triu(np.arange(16).reshape(4, 4), -1) # play with the last parameter (sign and value)\n",
    "print (\"a:\", '\\n', a , '\\n')\n",
    "\n",
    "print (\"transpose:\", '\\n', a.T , '\\n') # a.T is a view"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "N.B.: a linear algebra package exists in numpy and is called `numpy.linalg`. However, in terms of performance, the package included in `scipy`, also called `scipy.linalg`, is better and more efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reductions\n",
    "\n",
    "Operations that reduce the dimensionality of arrays are called *reductions*. The reduction functions operate on the elements of an array and return a (set of) scalar(s), and are available either as methods of the array class or as numpy functions.\n",
    "\n",
    "In dimensions higher than one, the axis has to be specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n",
      "sum: 21 21\n",
      "min: 1 1\n",
      "max: 6 6\n",
      "index of the minimum argument: 0 0\n",
      "index of the maximum argument: 5 5\n",
      "mean: 3.5 3.5\n",
      "median: 3.5\n",
      "standard deviation: 1.707825127659933 1.707825127659933\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(1,7)\n",
    "print(a)\n",
    "print(\"sum:\", a.sum(), np.sum(a))\n",
    "print(\"min:\", a.min(), np.min(a))\n",
    "print(\"max:\", a.max(), np.max(a))\n",
    "print(\"index of the minimum argument:\", a.argmin(), np.argmin(a))\n",
    "print(\"index of the maximum argument:\", a.argmax(), np.argmax(a))\n",
    "print(\"mean:\", a.mean(), np.mean(a))\n",
    "print(\"median:\", np.median(a)) # median is not a method of the array class..\n",
    "print(\"standard deviation:\", a.std(), np.std(a))\n",
    "# and many others..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]] \n",
      "\n",
      "sum along the columns: [ 9 12]\n",
      "sum along the rows: [ 3  7 11]\n"
     ]
    }
   ],
   "source": [
    "# these functions work in higher dimensions, too\n",
    "\n",
    "m = a.reshape(3,2)\n",
    "print(m, '\\n')\n",
    "\n",
    "print(\"sum along the columns:\", m.sum(axis=0))\n",
    "print(\"sum along the rows:\", m.sum(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting\n",
    "\n",
    "We have seen element-wise operations on arrays, which work only on arrays of the same rank and shape.\n",
    "Nevertheless, it’s also possible to do operations on arrays of different sizes if NumPy can transform (*broadcast*) these arrays so that they all have the same size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"numpy_broadcasting.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original array: \n",
      " [[ 0  0  0]\n",
      " [10 10 10]\n",
      " [20 20 20]\n",
      " [30 30 30]] \n",
      "\n",
      "broadcasted sum : \n",
      " [[ 0  1  2]\n",
      " [10 11 12]\n",
      " [20 21 22]\n",
      " [30 31 32]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = np.tile(np.arange(0, 40, 10), (3, 1)).T # check what exactly np.tile does on the documentation \n",
    "print(\"original array:\", '\\n', a, '\\n')\n",
    "print(\"broadcasted sum :\", '\\n', a + np.arange(3), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More on shape manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new dim along axis 1: \n",
      " [[1]\n",
      " [2]\n",
      " [3]] \n",
      "\n",
      "new dim along axis 0: \n",
      " [[1 2 3]] \n",
      "\n",
      "resizing: \n",
      " [0 1 2 3 0 1 2 3 0] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# adding a dimension (useful for linear algebra)\n",
    "print(\"new dim along axis 1:\", '\\n', np.arange(1,4)[:,np.newaxis], '\\n')\n",
    "print(\"new dim along axis 0:\", '\\n', np.arange(1,4)[np.newaxis,:], '\\n')\n",
    "\n",
    "# resizing\n",
    "print (\"resizing:\", '\\n', np.resize(np.arange(4), (9,)), '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random numbers\n",
    "\n",
    "There are two modules for (pseudo) random numbers that are commonly used. When all you need is to generate random numbers from some distribution, the `numpy.random` moodule is the simplest to use. When you need more information realted to a distribution such as quantiles or the PDF, you can use the `scipy.stats` module, which will see later in the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy.random as npr\n",
    "npr.seed(123) # fix seed for reproducible result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uniform: \n",
      " [[0.69646919 0.28613933 0.22685145 0.55131477]\n",
      " [0.71946897 0.42310646 0.9807642  0.68482974]\n",
      " [0.4809319  0.39211752 0.34317802 0.72904971]] \n",
      "\n",
      "normal: \n",
      " [[-0.67888615 -0.09470897  1.49138963 -0.638902   -0.44398196]\n",
      " [-0.43435128  2.20593008  2.18678609  1.0040539   0.3861864 ]] \n",
      "\n",
      "integers: \n",
      " [[42 36 29 39 34 22]\n",
      " [31 28 35 34 13 41]\n",
      " [ 4 43  6  1 12 35]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# random values from a uniform distrubtion in [0.0, 1.0) for a given shape\n",
    "print (\"uniform:\",'\\n', npr.rand(3,4),'\\n') # shape=(3,4)\n",
    " \n",
    "# random values from a standard normal distrubtion  for a given shape\n",
    "print (\"normal:\",'\\n', npr.randn(2,5),'\\n') # shape=(2,5)\n",
    "\n",
    "# random integers between \"low\" and \"high\" edges for a given shape (size)\n",
    "print (\"integers:\",'\\n', npr.randint(1,50,size=(3,6)),'\\n') # shape=(3,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reshuffling: \n",
      " [1 5 0 8 7 9 3 4 2 6] \n",
      "\n",
      "choice w/o replace \n",
      " [18 15 11 12 19 14 17 16 10 13] \n",
      "\n",
      "choice w/ replace \n",
      " [[15 17 19 12 14 18 11 12 11 11]\n",
      " [13 15 19 10 18 11 16 13 13 15]\n",
      " [19 17 19 12 13 13 13 18 16 19]\n",
      " [17 16 13 19 16 16 16 11 13 14]\n",
      " [13 11 10 15 18 16 18 19 11 10]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# randomly shuffling a vector\n",
    "x = np.arange(10)\n",
    "npr.shuffle(x)\n",
    "print (\"reshuffling:\",'\\n', x,'\\n')\n",
    "# npr.permutation works similarly\n",
    "\n",
    "# Generates a random sample from a given 1-D array (without replacement)\n",
    "x = np.arange(10,20)\n",
    "print (\"choice w/o replace\",'\\n',npr.choice(x, 10, replace=False),'\\n')\n",
    "\n",
    "# Generates a random sample of a given dimension from a given 1-D array\n",
    "print (\"choice w/ replace\",'\\n',npr.choice(x, (5, 10), replace=True),'\\n') # this is default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probability Density Functions\n",
    "\n",
    "numbers can be drawn from basically any common PDF (normal, binomial, chisquare, etc..)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exponential: \n",
      " [[24.05386637  2.4638149 ]\n",
      " [ 4.51149008 19.30834077]\n",
      " [12.42685226  4.99134912]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# exponential\n",
    "print (\"exponential:\",'\\n', npr.exponential(10, size=(3,2)),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frequencies of 6-faces dice tossing (two players): \n",
      " [[3325 3355 3263 3355 3372 3330]\n",
      " [3359 3383 3350 3299 3278 3331]]\n"
     ]
    }
   ],
   "source": [
    "# tossing a dice\n",
    "roll = 1./6\n",
    "x = npr.multinomial(20000, [roll]*6, size=2)\n",
    "print (\"frequencies of 6-faces dice tossing (two players):\",'\\n', x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.142212"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# toy example - estimating pi inefficiently\n",
    "n = int(1e6)\n",
    "x = npr.uniform(-1,1,(n,2))\n",
    "4.0*np.sum(x[:,0]**2 + x[:,1]**2 < 1)/n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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