{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy - multidimensional data arrays\n",
    "\n",
    "Based on lectures from http://github.com/jrjohansson/scientific-python-lectures\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `numpy` package (module) is used in almost all numerical computation using Python. It is a package that provide high-performance vector, matrix and higher-dimensional data structures for Python. It is implemented in C and Fortran so when calculations are vectorized (formulated with vectors and matrices), performance is very good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating `numpy` arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a number of ways to initialize new numpy arrays, for example from\n",
    "\n",
    "* a Python list or tuples\n",
    "* using functions that are dedicated to generating numpy arrays, such as `arange`, `linspace`, `zeros`, `ones`, etc.\n",
    "* reading data from files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2, 2), 4, dtype('int64'))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = array([[1,2],[3,4]])  # from list\n",
    "M.shape, M.size, M.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.+0.j, 2.+0.j],\n",
       "       [3.+0.j, 5.+0.j]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array(((1,2),(3,5.0+0j)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.+0.j 2.+0.j]\n",
      " [3.+0.j 4.+0.j]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((2, 2), 4, dtype('complex128'))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = array([[1,2],[3,4]], dtype=complex)  # from list\n",
    "print(M)\n",
    "M.shape, M.size, M.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2,\n",
       "       2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5,\n",
       "       3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8,\n",
       "       4.9, 5. , 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1,\n",
       "       6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4,\n",
       "       7.5, 7.6, 7.7, 7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7,\n",
       "       8.8, 8.9, 9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arange(1,10,0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2,\n",
       "       2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5,\n",
       "       3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8,\n",
       "       4.9, 5. , 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1,\n",
       "       6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4,\n",
       "       7.5, 7.6, 7.7, 7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7,\n",
       "       8.8, 8.9, 9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array(range(10,100,1))/10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,\n",
       "        2.1,  2.2,  2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,\n",
       "        3.2,  3.3,  3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,\n",
       "        4.3,  4.4,  4.5,  4.6,  4.7,  4.8,  4.9,  5. ,  5.1,  5.2,  5.3,\n",
       "        5.4,  5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,  6.2,  6.3,  6.4,\n",
       "        6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,  7.4,  7.5,\n",
       "        7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,  8.6,\n",
       "        8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,\n",
       "        9.8,  9.9, 10. ])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linspace(1,10,91)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e-03, 1.83298071e-03, 3.35981829e-03, 6.15848211e-03,\n",
       "       1.12883789e-02, 2.06913808e-02, 3.79269019e-02, 6.95192796e-02,\n",
       "       1.27427499e-01, 2.33572147e-01, 4.28133240e-01, 7.84759970e-01,\n",
       "       1.43844989e+00, 2.63665090e+00, 4.83293024e+00, 8.85866790e+00,\n",
       "       1.62377674e+01, 2.97635144e+01, 5.45559478e+01, 1.00000000e+02])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logspace(-3,2,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.  0.5 1.  1.5 2.  2.5 3.  3.5 4.  4.5 5.  5.5 6.  6.5 7.  7.5 8.  8.5\n",
      " 9.  9.5]\n",
      "[ 0.   0.5  1.   1.5  2.   2.5  3.   3.5  4.   4.5  5.   5.5  6.   6.5\n",
      "  7.   7.5  8.   8.5  9.   9.5 10. ]\n",
      "[1.00000000e-03 3.59381366e-03 1.29154967e-02 4.64158883e-02\n",
      " 1.66810054e-01 5.99484250e-01 2.15443469e+00 7.74263683e+00\n",
      " 2.78255940e+01 1.00000000e+02]\n"
     ]
    }
   ],
   "source": [
    "x = arange(0,10,0.5)  # linear mesh start:stop:increment\n",
    "print(x)\n",
    "y = linspace(0,10,21) # linear mesh start,stop,number of points\n",
    "print(y)\n",
    "z = logspace(-3,2,10) # log mesh, 10^start, 10^stop, number of points\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 3, 2), 18, dtype('complex128'))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z=zeros((3,3,2),dtype=complex)\n",
    "\n",
    "Z.shape, Z.size, Z.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 3), 9, dtype('complex128'))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z=ones((3,3),dtype=float)*1j\n",
    "\n",
    "Z.shape, Z.size, Z.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 3), 9, dtype('complex128'))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z=ones((3,3))\n",
    "# Z *= 1j   # issues an error, but would not work\n",
    "Z = Z*1j\n",
    "Z.shape, Z.size, Z.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random number generators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17129319280423427\n",
      "[[0.68136812 0.48474291 0.19586362 0.05304944 0.13935424]\n",
      " [0.61944048 0.73399677 0.5378894  0.63996137 0.18797896]\n",
      " [0.30404113 0.98573661 0.46519993 0.86791191 0.85096155]\n",
      " [0.16435511 0.31602604 0.96019131 0.06160088 0.21851716]\n",
      " [0.02242947 0.50900989 0.79989502 0.42830787 0.23565606]]\n",
      "[[ 0.50124616 -0.18421149 -0.94434126 -0.09408017 -0.76212889]\n",
      " [-1.25422183 -0.48305494  1.91924483 -0.72006581 -1.12775672]\n",
      " [-0.83642159  0.12483796 -0.06792395 -0.87661959 -1.00020029]\n",
      " [-1.69659347  1.58098831  0.24925687 -1.34600216 -0.09466769]\n",
      " [-0.90416851  0.41066708 -0.77173271 -0.22797163  0.96824553]]\n"
     ]
    }
   ],
   "source": [
    "from numpy import random\n",
    "\n",
    "print( random.rand() )  # uniformly distributed random number between [0,1]\n",
    "\n",
    "print( random.rand(5,5) ) # uniform distributed (5x5) matrix\n",
    "\n",
    "\n",
    "print( random.randn(5,5) ) # standard normal distribution random matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File I/O\n",
    "\n",
    "Very common form is comma-separated values (CSV) or tab-separated values (TSV). To read data from such files into Numpy arrays we can use the `numpy.loadtxt` or `numpy.genfromtxt`\n",
    "\n",
    "File `stockholm_td_adj.dat.txt` contains Stockholm temperature over the years. The columns are [$year$,$month$,$day$,$T_{average}$,$T_{min}$,$T_{max}$]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011 12 22    -0.4    -1.0    -1.0 1\r\n",
      "2011 12 23     3.7     3.1     3.1 1\r\n",
      "2011 12 24     3.2     2.6     2.6 1\r\n",
      "2011 12 25     4.2     3.5     3.5 1\r\n",
      "2011 12 26     8.2     7.5     7.5 1\r\n",
      "2011 12 27     8.3     7.6     7.6 1\r\n",
      "2011 12 28     2.6     1.9     1.9 1\r\n",
      "2011 12 29     4.9     4.2     4.2 1\r\n",
      "2011 12 30     0.6    -0.1    -0.1 1\r\n",
      "2011 12 31    -2.6    -3.3    -3.3 1\r\n"
     ]
    }
   ],
   "source": [
    "## Check if file exists\n",
    "!tail stockholm_td_adj.dat.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77431, 7)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = loadtxt('stockholm_td_adj.dat.txt')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inline figures from matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# time in years when we have year/month/day\n",
    "t = data[:,0]+data[:,1]/12.+data[:,2]/365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1800.08607306, 1800.08881279, 1800.09155251, 1800.09429224,\n",
       "       1800.09703196, 1800.09977169, 1800.10251142, 1800.10525114,\n",
       "       1800.10799087, 1800.11073059])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe0a8dde4f0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t, data[:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a bit more extended in x-direction\n",
    "plt.figure(figsize=(14,4))\n",
    "plt.plot(t, data[:,3])\n",
    "plt.title('temperature in Stockholm')\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('temperature $[\\degree C]$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets save the data in the form [t,$T_{average}$]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=[1,2,3]\n",
    "b=[4,5,6]\n",
    "vstack((a,b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77431, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstack((t,data[:,3])).T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77431, 7)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "savetxt('StockholmT.dat', vstack((t,data[:,3])).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.012060273972602772e+03 -4.000000000000000222e-01\r\n",
      "2.012063013698630130e+03 3.700000000000000178e+00\r\n",
      "2.012065753424657487e+03 3.200000000000000178e+00\r\n",
      "2.012068493150684844e+03 4.200000000000000178e+00\r\n",
      "2.012071232876712429e+03 8.199999999999999289e+00\r\n",
      "2.012073972602739786e+03 8.300000000000000711e+00\r\n",
      "2.012076712328767144e+03 2.600000000000000089e+00\r\n",
      "2.012079452054794501e+03 4.900000000000000355e+00\r\n",
      "2.012082191780821859e+03 5.999999999999999778e-01\r\n",
      "2.012084931506849216e+03 -2.600000000000000089e+00\r\n"
     ]
    }
   ],
   "source": [
    "!tail StockholmT.dat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More efficient binary storage of data to the disc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 38048\r\n",
      "-rw-r--r--@ 1 haule  staff  1756407 Dec 18  2020 Scientific-Computing-with-Python.pdf\r\n",
      "-rw-r--r--@ 1 haule  staff   611082 Jan 20  2021 00_Introduction.html\r\n",
      "-rw-r--r--@ 1 haule  staff   615891 Jan 20  2021 01_Basic_Python.html\r\n",
      "-rw-r--r--@ 1 haule  staff   627779 Jan 20  2021 02_Numpy.html\r\n",
      "-rw-r--r--@ 1 haule  staff  1303621 Mar  4  2021 03_Scipy.html\r\n",
      "-rw-r--r--@ 1 haule  staff  1040661 Mar  4  2021 04_Scipy_example_Hydrogen_atom.html\r\n",
      "-rw-r--r--  1 haule  staff   210051 Jan  3 17:57 02_Numpy.ipynb\r\n",
      "-rw-r--r--  1 haule  staff   826208 Jan  3 17:57 03_Scipy.ipynb\r\n",
      "-rw-r--r--  1 haule  staff   533268 Jan  3 17:57 04_Scipy_example_Hydrogen_atom.ipynb\r\n",
      "-rw-r--r--@ 1 haule  staff   430767 Jan  3 17:57 double_pendulum.mp4\r\n",
      "-rw-r--r--  1 haule  staff     2038 Jan  3 17:57 pendulum.py\r\n",
      "-rw-r--r--  1 haule  staff  2864984 Jan  3 17:57 stockholm_td_adj.dat.txt\r\n",
      "-rw-r--r--  1 haule  staff      264 Jan  3 17:57 ttt.cc\r\n",
      "-rw-r--r--  1 haule  staff    24263 Jan  6 16:06 00_Introduction.ipynb\r\n",
      "-rw-r--r--  1 haule  staff      752 Jan  6 16:06 mymodule.py\r\n",
      "drwxr-xr-x  3 haule  staff       96 Jan  6 16:06 \u001b[1m\u001b[34m__pycache__\u001b[m\u001b[m\r\n",
      "-rw-r--r--  1 haule  staff    52785 Jan  6 16:07 01_Basic_Python.ipynb\r\n",
      "-rw-r--r--  1 haule  staff  3889864 Jan  6 16:07 StockholmT.dat\r\n",
      "-rw-r--r--  1 haule  staff  4336264 Jan  6 16:07 ST_data.npy\r\n"
     ]
    }
   ],
   "source": [
    "save('ST_data',data)\n",
    "!ls -ltr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=load('ST_data.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allclose(data,data2)  # how close are the two sets of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(abs(data-data2).ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amax(abs(data-data2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating data\n",
    "\n",
    "### Indexing and slicing\n",
    "\n",
    "data[lower:upper:step, lower:upper:step]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1800. 1800. 1800. ... 2011. 2011. 2011.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1800, 1801, 1802, 1803, 1804, 1804, 1805, 1806, 1807, 1808, 1809,\n",
       "       1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820,\n",
       "       1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831,\n",
       "       1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842,\n",
       "       1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853,\n",
       "       1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864,\n",
       "       1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875,\n",
       "       1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886,\n",
       "       1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897,\n",
       "       1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908,\n",
       "       1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919,\n",
       "       1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930,\n",
       "       1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941,\n",
       "       1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952,\n",
       "       1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963,\n",
       "       1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974,\n",
       "       1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "       1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,\n",
       "       1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,\n",
       "       2008, 2009, 2010, 2011])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data[:,0])  # display year for all datapoints\n",
    "array(data[::365,0],dtype=int) # the years with 365 spacings, and then last years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fancy indexing**\n",
    "Index is itself an array of integer numbers, i.e, which rows or columns?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 4 6 8]\n",
      "[0 2 4 6 8]\n"
     ]
    }
   ],
   "source": [
    "x=arange(100)\n",
    "print(x[:10:2])\n",
    "print(x[ [0,2,4,6,8] ])  # fancy indexing. Does not exists in C++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1800., 1801., 1802., 1803., 1804., 1805.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[0,365,2*365,3*365,4*365,5*365+1],0] # fancy indexing for multidimensional array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1800., 1801., 1802., 1803., 1804., 1804., 1805., 1806., 1807.,\n",
       "       1808., 1809., 1810., 1811., 1812., 1813., 1814., 1815., 1816.,\n",
       "       1817., 1818.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[range(0,365*20,365),0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1800., 1801., 1802., 1803., 1804., 1805., 1806., 1807., 1808.,\n",
       "       1809.])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[range(3,3+365*10,365),0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using mask to pick data***\n",
    "\n",
    "In addition to fancy indexing, Python allows yet another way to pick the data.\n",
    "Create a mask of `[True,....False....]` values, and pick from the array only columns/rows where `True`. \n",
    "\n",
    "How to compute average temperature in the year of 1973?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ..., False, False, False])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:,0] >= 1973\n",
    "data[:,0] < 1974"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create mask for the year 1973\n",
    "mask = logical_and(data[:,0] >= 1973, data[:,0] < 1974)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973., 1973.,\n",
       "       1973., 1973., 1973., 1973., 1973.])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[mask,0]  # All should have 1973"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average temperature in 1973= 7.414794520547944\n"
     ]
    }
   ],
   "source": [
    "T1973 = data[mask,3]\n",
    "print('Average temperature in 1973=', sum(T1973)/len(T1973))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([63187, 63188, 63189, 63190, 63191, 63192, 63193, 63194, 63195,\n",
       "        63196, 63197, 63198, 63199, 63200, 63201, 63202, 63203, 63204,\n",
       "        63205, 63206, 63207, 63208, 63209, 63210, 63211, 63212, 63213,\n",
       "        63214, 63215, 63216, 63217, 63218, 63219, 63220, 63221, 63222,\n",
       "        63223, 63224, 63225, 63226, 63227, 63228, 63229, 63230, 63231,\n",
       "        63232, 63233, 63234, 63235, 63236, 63237, 63238, 63239, 63240,\n",
       "        63241, 63242, 63243, 63244, 63245, 63246, 63247, 63248, 63249,\n",
       "        63250, 63251, 63252, 63253, 63254, 63255, 63256, 63257, 63258,\n",
       "        63259, 63260, 63261, 63262, 63263, 63264, 63265, 63266, 63267,\n",
       "        63268, 63269, 63270, 63271, 63272, 63273, 63274, 63275, 63276,\n",
       "        63277, 63278, 63279, 63280, 63281, 63282, 63283, 63284, 63285,\n",
       "        63286, 63287, 63288, 63289, 63290, 63291, 63292, 63293, 63294,\n",
       "        63295, 63296, 63297, 63298, 63299, 63300, 63301, 63302, 63303,\n",
       "        63304, 63305, 63306, 63307, 63308, 63309, 63310, 63311, 63312,\n",
       "        63313, 63314, 63315, 63316, 63317, 63318, 63319, 63320, 63321,\n",
       "        63322, 63323, 63324, 63325, 63326, 63327, 63328, 63329, 63330,\n",
       "        63331, 63332, 63333, 63334, 63335, 63336, 63337, 63338, 63339,\n",
       "        63340, 63341, 63342, 63343, 63344, 63345, 63346, 63347, 63348,\n",
       "        63349, 63350, 63351, 63352, 63353, 63354, 63355, 63356, 63357,\n",
       "        63358, 63359, 63360, 63361, 63362, 63363, 63364, 63365, 63366,\n",
       "        63367, 63368, 63369, 63370, 63371, 63372, 63373, 63374, 63375,\n",
       "        63376, 63377, 63378, 63379, 63380, 63381, 63382, 63383, 63384,\n",
       "        63385, 63386, 63387, 63388, 63389, 63390, 63391, 63392, 63393,\n",
       "        63394, 63395, 63396, 63397, 63398, 63399, 63400, 63401, 63402,\n",
       "        63403, 63404, 63405, 63406, 63407, 63408, 63409, 63410, 63411,\n",
       "        63412, 63413, 63414, 63415, 63416, 63417, 63418, 63419, 63420,\n",
       "        63421, 63422, 63423, 63424, 63425, 63426, 63427, 63428, 63429,\n",
       "        63430, 63431, 63432, 63433, 63434, 63435, 63436, 63437, 63438,\n",
       "        63439, 63440, 63441, 63442, 63443, 63444, 63445, 63446, 63447,\n",
       "        63448, 63449, 63450, 63451, 63452, 63453, 63454, 63455, 63456,\n",
       "        63457, 63458, 63459, 63460, 63461, 63462, 63463, 63464, 63465,\n",
       "        63466, 63467, 63468, 63469, 63470, 63471, 63472, 63473, 63474,\n",
       "        63475, 63476, 63477, 63478, 63479, 63480, 63481, 63482, 63483,\n",
       "        63484, 63485, 63486, 63487, 63488, 63489, 63490, 63491, 63492,\n",
       "        63493, 63494, 63495, 63496, 63497, 63498, 63499, 63500, 63501,\n",
       "        63502, 63503, 63504, 63505, 63506, 63507, 63508, 63509, 63510,\n",
       "        63511, 63512, 63513, 63514, 63515, 63516, 63517, 63518, 63519,\n",
       "        63520, 63521, 63522, 63523, 63524, 63525, 63526, 63527, 63528,\n",
       "        63529, 63530, 63531, 63532, 63533, 63534, 63535, 63536, 63537,\n",
       "        63538, 63539, 63540, 63541, 63542, 63543, 63544, 63545, 63546,\n",
       "        63547, 63548, 63549, 63550, 63551]),)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "where(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# where tells you the index where True\n",
    "indices = where(mask)\n",
    "X1973 = data[indices,3]; # This gives similar data in 1973, but not identical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(365,) (1, 365) (365,)\n",
      "Average temperature in 1973= 7.414794520547944\n",
      "Min/Max temperature in 1973= -12.8 / 25.6\n"
     ]
    }
   ],
   "source": [
    "print(T1973.shape, X1973.shape, X1973[0].shape)\n",
    "print('Average temperature in 1973=', sum(X1973[0,:])/len(X1973[0,:]))\n",
    "print('Min/Max temperature in 1973=', min(X1973[0,:]),'/',max(X1973[0,:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is the mean monthly temperatures for each month of the year?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do Ferbrurary first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.212109570736596"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Febr=data[:,1]==2\n",
    "mean(data[Febr,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now loop for all months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_mean=[mean(data[data[:,1]==month,3]) for month in range(1,13)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.0447656725502132, -3.212109570736596, -0.8321515520389532, 3.888474842767295, 9.560970785149117, 14.659591194968554, 17.318837492391967, 16.117650639074864, 11.81937106918239, 6.760057821059039, 1.9458490566037738, -1.211275106512477]\n"
     ]
    }
   ],
   "source": [
    "print(monthly_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'average temperature')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(1,13),monthly_mean);\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('average temperature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Algebra\n",
    "\n",
    "It is implemented in low level fortran/C code, and is much more efficient than code written in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.22276154 0.0009548  0.61689552]\n",
      " [0.62565466 0.0665923  0.19328271]\n",
      " [0.70376555 0.60291592 0.16846522]]\n",
      "\n",
      "[[0.22276154 0.0009548  0.61689552]\n",
      " [0.62565466 0.0665923  0.19328271]\n",
      " [0.70376555 0.60291592 0.16846522]]\n",
      "\n",
      "[[0.90610321 0.63242347 0.70666229]\n",
      " [0.94625905 0.43240315 0.91515852]\n",
      " [1.35444983 0.54499666 1.16873531]]\n",
      "\n",
      "[[0.90610321 0.63242347 0.70666229]\n",
      " [0.94625905 0.43240315 0.91515852]\n",
      " [1.35444983 0.54499666 1.16873531]]\n"
     ]
    }
   ],
   "source": [
    "A = random.rand(3,3)\n",
    "\n",
    "\n",
    "print(A * A)  # It is not matrix-matrix product, but element-wise product\n",
    "print()\n",
    "print(multiply(A,A))\n",
    "print()\n",
    "print(A @ A)  # It is a matrix product\n",
    "print()\n",
    "print(matmul(A,A))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matrix product or matrix-vector product can be performed by `dot` command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.90610321 0.63242347 0.70666229]\n",
      " [0.94625905 0.43240315 0.91515852]\n",
      " [1.35444983 0.54499666 1.16873531]]\n",
      "[[0.90610321 0.63242347 0.70666229]\n",
      " [0.94625905 0.43240315 0.91515852]\n",
      " [1.35444983 0.54499666 1.16873531]]\n"
     ]
    }
   ],
   "source": [
    "print(dot(A,A))\n",
    "print(matmul(A,A))\n",
    "# dot == A[i,j,:] * B[l,:,n] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.81999594 0.24776367 0.00199407]\n",
      "[0.39624061 0.71341642 0.88110177]\n",
      "0.7337841462337032\n"
     ]
    }
   ],
   "source": [
    "v1 = random.rand(3)\n",
    "print(v1)\n",
    "print( dot(A,v1) ) # matrix.vector product\n",
    "print( dot(v1,v1) ) # length of vector^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.39624061 0.71341642 0.88110177]\n",
      "[0.39624061 0.71341642 0.88110177]\n"
     ]
    }
   ],
   "source": [
    "print(A@v1)\n",
    "print(dot(A,v1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.39624061, 0.71341642, 0.88110177])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w=A*v1\n",
    "sum(w,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slightly less efficient, but nicer code can be obtained by `matrix` clas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = matrix(A)\n",
    "v = matrix(v1).T # create a column vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0.90610321, 0.63242347, 0.70666229],\n",
       "        [0.94625905, 0.43240315, 0.91515852],\n",
       "        [1.35444983, 0.54499666, 1.16873531]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M*M  # this is now matrix-matrix product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0.4719762 , 0.79098335, 0.83890735],\n",
       "        [0.03089979, 0.25805484, 0.7764766 ],\n",
       "        [0.78542697, 0.4396393 , 0.41044515]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0.39624061],\n",
       "        [0.71341642],\n",
       "        [0.88110177]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M*v # this is matrix*vector product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0.58466834, 0.09082266, 0.75379202]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.T * M # vector*matrix product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0.73378415]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.T * v # inner-product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Array/Matrix transformations**\n",
    "\n",
    "* `.T` or  `transpose(M)` transposes matrix\n",
    "* `.H` hermitian conjugate\n",
    "* `conjugate(M)` conjugates\n",
    "* `real(M)` and `imag(M)` takes real and imaginary part of the matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.4719762 , 0.79098335, 0.83890735],\n",
       "       [0.03089979, 0.25805484, 0.7764766 ],\n",
       "       [0.78542697, 0.4396393 , 0.41044515]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conjugate(A.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More advanced linear algebra operations\n",
    "\n",
    "Library `linalg`:\n",
    "\n",
    "* `linalg.det(A)`\n",
    "* `linalg.inv(A)` or just `M.I`\n",
    "* `linalg.eig`, `linalg.eigvals`, `linalg.eigh`\n",
    "* `linalg.svd()`\n",
    "* `linalg.solve()`\n",
    "* `linalg.cholesky()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linalg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20259783890966102\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-1.16216576,  2.94762767, -0.93336865],\n",
       "       [ 0.21797549, -2.29607639,  2.04227434],\n",
       "       [ 1.96298224, -1.68094794,  0.48053093]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print( linalg.det(A) )\n",
    "linalg.inv(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-1.16216576,  2.94762767, -0.93336865],\n",
       "        [ 0.21797549, -2.29607639,  2.04227434],\n",
       "        [ 1.96298224, -1.68094794,  0.48053093]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The eigenvalue problem for a matrix $A$:\n",
    "\n",
    "$\\displaystyle A v_n = \\lambda_n v_n$\n",
    "\n",
    "where $v_n$ is the $n$th eigenvector and $\\lambda_n$ is the $n$th eigenvalue.\n",
    "\n",
    "To calculate eigenvalues of a matrix, use the `eigvals` (symmetric/hermitian `eigvalsh`) and for calculating both eigenvalues and eigenvectors, use the function `eig` (or `eigh`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.59750194+0.j        , -0.22851287+0.27313645j,\n",
       "       -0.22851287-0.27313645j])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.eigvals(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.45257226, -0.39695136,  1.98999982])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.eigvalsh(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.33333333  0.66666667  0.        ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00, -2.22044605e-16,  0.00000000e+00])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = array([[1,2,3], [4,5,6], [7,8,9]])\n",
    "b = array([1,2,3])\n",
    "x = linalg.solve(A,b) # A*x==b\n",
    "print(x)\n",
    "dot(A,x)-b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sum, cumsum, trace, diag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81999594, 0.24776367, 0.00199407])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.06975367109378\n",
      "[0.81999594 1.0677596  1.06975367]\n",
      "15\n",
      "[1 5 9]\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "print( sum(v1) )\n",
    "print( cumsum(v1) )\n",
    "print( trace(A) )\n",
    "print( diag(A) )\n",
    "print( sum(diag(A)) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping, resizing, and stacking arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3)\n",
      "(9, 1)\n"
     ]
    }
   ],
   "source": [
    "print(A.shape)\n",
    "Ag = reshape(A, (9,1))  # this is not new data\n",
    "print(Ag.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10,  2,  3],\n",
       "       [ 4,  5,  6],\n",
       "       [ 7,  8,  9]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ag[0]=10\n",
    "A        # we change A when we change Ag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10  2  3]\n",
      " [ 4  5  6]\n",
      " [ 7  8  9]]\n"
     ]
    }
   ],
   "source": [
    "Ax = A.flatten()  # flatten creates 1D array of all data, but creates a copy\n",
    "Ax[8]=100         # changing a copy\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 10   2   3]\n",
      " [  4   5   6]\n",
      " [  7   8 100]]\n"
     ]
    }
   ],
   "source": [
    "Ay=A.ravel()\n",
    "Ay[8]=100\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorizing functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every function written in Python is very slow. However numpy type operations are fast, because they are written in fortran/C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37.21, 237.16, 225.  , ...,  24.01,   0.36,   6.76])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Temp = data[:,3]\n",
    "\n",
    "Temp**2  #  this is fast, written in C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37.21, 237.16, 225.  , ...,  24.01,   0.36,   6.76])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array([Temp[i]**2 for i in range(len(Temp))])  # This is slow, written in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we have a function that can not simply work on arrays?\n",
    "\n",
    "For example, theta function?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Theta(x):\n",
    "    if x>=0:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[76], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Does not work on array\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mTheta\u001b[49m\u001b[43m(\u001b[49m\u001b[43mTemp\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[75], line 2\u001b[0m, in \u001b[0;36mTheta\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mTheta\u001b[39m(x):\n\u001b[0;32m----> 2\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m      3\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
     ]
    }
   ],
   "source": [
    "# Does not work on array\n",
    "Theta(Temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can vectorize Theta, to make it applicable to arrays. \n",
    "\n",
    "This is simply achieved by call to numpy function `vectorize`, which will create low-level routine from your function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "Theta_vec = vectorize(Theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 0])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is very fast now, and creates 0 or ones\n",
    "positive_temperatures=Theta_vec(Temp)\n",
    "positive_temperatures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How to calculate number of days in a year with positive temperatures?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boolean array to select data with positive temperatures\n",
    "positives = array(positive_temperatures, dtype=bool)\n",
    "# keeps data with positive temperatures only\n",
    "kept = data[positives,0]\n",
    "# now we just need to check how many of these data are in each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = list(range(1800,2013,1))\n",
    "hist = histogram(kept, bins=years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist[1][:-1], hist[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yTqA6not+QzpBCko6iYwIUOK3bkROZGYyGRx33HEAgHPOOQePP/44vvKVr+Cb3/wmACc4ueaaa7Br1y48/PDDQj0BgP7+fuTzeQwNDSkqyv79+3HBBRf4vmc2m0U2m426qUcUclonDjlOZnZCd/vNvNDPdopKgOJXxSMrKMGBx73bduOr//kihicLWPOO4xuzkTOISa2CB5AVlOZdG+kc8XSSLZZwy7/+Er8ZHMVJ/T1YuaS3ptcfm3KCDAo2oiDvI/IrzvgqHhO2bSOXywFwg5MXX3wRDz30EObNm6c89uyzz0Y6nVbMtPv27cPOnTsDAxSmOnKKJw4ucWZ2UhAKSvwuZjMVeYCdvwdFClCqKCgUwOwfzTVg62YeegUP0KJGbSXdJOtsz2S+hJcPjAMA3hiZqvn1RysKylgNCgpV8bSnkyKAiqNJNpKC8qlPfQrvete7sHTpUoyOjmLDhg145JFHsGnTJhSLRfz+7/8+nnzySfz7v/87SqWS8JX09fUhk8mgt7cXN9xwA2655RbMmzcPfX19uPXWW7Fy5UpR1cPUhnxhYwWFmS6EghLDfPVMJYyCoqR4qnhQ6DuiO+wjjUlpDg9BaZbmtrrXTLIVpWLv0KQIBmrta2PbtkjxTOSif88TeVdBSc+WFM8bb7yBa6+9Fvv27UNvby9OO+00bNq0CZdddhl2796NBx98EABwxhlnKM/bsmULLrnkEgDAnXfeiVQqhWuuuQaTk5O49NJLce+99yKZTIKpHTbJMs2AFJQCp3gahuJB8TPJRkjx0OuN1rBwzQbcAEVWUCpVPE0MrIsixaMqKL89MCYeU+279GMiXxKN1sbzNSgolX3UkZlFHpS7777b93fLli0LZfhpa2vD+vXrsX79+ihvzVRhisuMmSZAsjV7UBqHUsXj2wclvEmWvpsjVUGZyntTPK1o1CYUFJrFU1FQXj00IR4zUuN3NCYFn+M1BKJqiie+VTzc7WeWwAoK0wxooSzG8G5rplIKkeIpRigzpseOHaEKCnnwTCbZVnpQqA9KqRxeDfNjVApsajHJuimelAig4lj9ye1GZwk5LjNmmkCBFZSGIwcfjTDJlmoIUGzbxnCNi2XcoB4f1FoekBWUFsziSarTjGVq9aDI320tgaic4omzB4UDlFmCUmbMAQozTZBywn1QGkcoBaUUPsVD381ohPTB3/77r3H2327Gc68Ph35OXDGZZFuioFCre20Wj0ytTffGFAWllioetw9KOuVs36zoJMvEEyXF06BWygyjQxddDlAah6yO5Etlo5cvSoqnXHlsFG/CL/ceRrFs49f7RkM/J64cHHPKq/s6M+JnzfaglMq2MLGmtVk8MqO5WhUU93m1eFDkKp44m2Q5QJklqB6U+B1ozOyALmLOBZiDlDCM54r49MZn8dhvzS3J9bt6UyWPEqCEVFAmC6XQXiFa5Bo1J6aVDA47vUX6e9vEz5JNnsUjL/YixdNIBUXqfVJLJ1lTiicfQ5MsByizBLWT7My/yDDxRL7bZx9KOH76wgH8889fxdcfecn4e90XYUrzyPu9WupG/l7CLl7j+dkToOyrBCiLpQCl2QqK/D4UAGQb6UGRnjdeSydZqYpnVneSZeKBaRbPRL5Y06RLxott23j5wNgRrxoUpMWU0zzhoD4Vfou/HuiZFFClzHiyEHgcyq8XNoUwkQvexpkEdWft72kXP0tWVIxyswIUWUFJBHlQ6jfJOj1Ron2umdKojQOUWYLaB8U5+D7yrSdw0Re34HUOUurmX5/Yg3d8aSv+8b92t3pTWgorKNGhC79fnwk90DPdycrPLZZtIdGbkL+XsBUeroISv0UqCrZtx0JBkb8vMujKHpRl8zoAOH1QarnpkZvwlcp25LT+pDQOIFMJ3jhAYaaNnMGD8uIbYyiVbaUxEFMbuw86+/DVg+Mt3pLWIt8ZFmOYs44jFHD4BXQeD4phsSlpaaAg74KstoRp1lYslUVgMtMVlJHJolh8VQ8KVfE0ZxEWJcYJS0xUlhWUkxf3VLbHrqkKR/9eoxplpwqsoDBNxGSSpTwjm2brp8TVKwDUz88DA8NRqFKaHcokqwWDQd4FNcVTfeGakK4dfspMqWzj5n95Gt96bHfV12sl+0YctXhuR1ppdd90Dwo1aauoE4DqQTl+YZfYplp8KLoyFtUoOyWNA2CTLDPtKMMCi05OkmRbNs3WD11wyke4B4VTPNHJCQXFbxBgiBSP9pgg74KS4gmhoMh3334pnucHR3H/U6/haw+bjb5xYZ+o4GlXft7sWTxiDk/CXWLlgGnJ3A70tKcB1FbJ41FQIhpl6ZhsSyWRFibZ+K0THKDMEmQFZapQRq5YBl2nplhBqZuSaPF+ZC/KbJKNTjUFRf+5aVSFJ8UTUkEJ40GR776nfBYpUnUma0hHyLy0fxR7pjHlPGjwnwCtUFDULrKAWma8ZG47etqcSTO1KCi6Mha13f2U1MwuE+NZPBygzBKmNAVFzmuyglI/dGErsYJi/DfjT3UPihp8mFKy+uIR7EGJpqDIi9uUTwBCC26QOTfM+1y5/r/w3+7aVvNrVMPUAwVofifZgkjxuEtsOmkhXQlYlvbJCkoNKR7tex2LnOKpKCjpJDKp+JpkeVjgLKBQKisnXq5QVmRb9qDUD+3fZpUpxpWioqDwcRUGClD8ArowVTx6w7VGelBklcVPQZE7CBdKZeFbiMLBsTwmCyVMFkoolW0RNDQSoaD0tFhBkUyyhGVZ+Mt3nYxD4zknQGmrBCh1eFBSCQvFso2JiHOX6HvOphOuByWG6wQHKLMA3Xk/pSsoMTzwZhrc4t2hwB6UyMjdd02E8aDQcZdJJZAvlhvqQZnIqelhE3JwNVUo1RSgyNepQqmMZMLbF6Re9lV6oCzSFZRkc6t4CgaTLADccOFy8e+e9kqKpxYPSiUgWdCdxb7hKdFrJwzOOAXn37JJNo4KCqd4ZgH6RSVXKCumqZleOhgHhIJyxKd42IMSlbzwoFRf/OXHmx7T1+HMlxkJCDxUD0r1u3P5WuHnMZG3vdY0j3ydMn3GRjA47FTxtNqDUjKYZHWEglJHimdRRSmKUmYsfw/ZlKSgcIDCTAd6AJIrlpULDSso9SMUlCPcdyFf4FlBCUe+GKy+eTrJGlQMChDmVgbgBS1qigelQSZZRUHJ13Y9kQOb6Zqca2rSBjR/Fo/JJKtDHpQwaTiZXLEkgolFPVkA0ap4yJNoWUAmmUC2UsVTKLr7ZnB4Cv/w05fx/3bsjbRtjYYDlFmA7vrPFUuaB4UVlHohafhIV1BkGTiOknAcocXEr8TV40EJGBY4rxKgBM3jUTwoEU2ypuBI38ZaFRQlQJmGQH8sVxSfVy8zbnon2cr7pAIVFErxRFNQ5LRdPQpKWyoJy7KMKZ5XD03gcz/8te/8qGbBAcosQE/xTBXKWhUPLyT1QneQR3pag/ugRIfUAn8FJfywwD5SUBpYZqyYZH2CDznFU2vKWPegNBoyyHZnU+jKqvZKUcXTrD4olc+XDqGgRDXJkuLVkUmiuxLkRGnURioZtd6nbcwr6VuvybcVcIAyC6ATnw4mT5kxKyh1Q8rJkb4oyw3DjvRgLSz5KibZcLN4nJ/1RU3xhFJQwnWSrfYYnd1vjuPttz+Mf9q2G4AaoEyH30EMCdTSO0DrZvGkAszErgclWoqHBkB2ZVPoyDgBSpQ+KHIXWQBGBYW+72SAAtQMOECZBZCCQhF5rqiVGbOCUjdFNskCUE2yR3qwFhZRZhy2k6xh8abHuApKUIpHmsUTyoOiKiim4XVySiZsgPL47kN47fAkNu0cdJ6Xn14FZZ9PDxSgdbN4gkqpRRVPRAWFgs6uNlcpiqSgSD1QAKcyDFAD46JIUbGCwtQJRcS9lQDFtoFh6Q6LTbL1U2KTLACtURsHKKGggKNsm/voeDrJGgKAoh6gTFOr+7Jt9ofIC3vYxo/0OjTrR0nxFBt/7PhV8ACtm8UTmOKpsYqHgs7ubAodGSfIiGSSpR4olcCEOsmWbffYoVTYdPSqiQIHKLMAyilSgAIAhyby4t+c4qkfnsXjoLS6Z5NsKEx3pjKeKh7DfiXFYW5HNJPsWL5oVERk9B4aJoWkFpMsbfNkZfGcnOYy4zdGcgBc46hMMtnkKp4QJtlu0agtWoqHApSuthQ6KwrKRB0KCs3iAbxjGVhBYeqGUjhkmAKAoXE3QPFrvsSEh6cZO8RJQSmWyjOis68pty/j6YNiUDzpeWRsDOriK38vtq16TEzoFSBGBUdO8YQsM6bPTe8/Oc0mWbopI5VJplWzeIJNsm4VT7UgUoaC066sG6CESeUR8hwefRt1vxQrKEzdkILSnk4K2e7QOCsojYQWhJmwIE4ncfGgFEtlvPMrP8Pvf2NbpIt7K1AVFFPwUb2Kh9Il2crAubIN38+tq3zVFi9dQTHd0NSioOgDBnPTHKAcrgQoczrSnt81fRZPqDJjZzuLZTtS6bZQULJpdFZSPHWZZKVtLGh+qaA+Ls2AA5RZgCzZUYAypKR4WEGpF1ZQHOJSxTM0UcBL+8fw5KuHI909toKqCkqIKh4KYuiu1++1TK9XrReKPsfFmOKRPkPYMmPymTRLQTk84Xg55nTER0EJWuA7MkkROEWp5CFfUXebrKBEKTN2+6AAQCJhif3jVVC4ioepE1myo6j40HjB83umdsQ04yM8QFFa3bfQgyKrBAfH8gGPbD1hPShJ0SbAvw8KKSh+r1Uu22LOCt1dV1VQtN+brhfye4W9nuRLbmBSLttKFU9+GkyyFKDMNQQoTa/iESZZ/yXWsiy3WVuESh5XQUmhs4Yy45yW4pG3s6B1PWYPClM3OUmyy6ZZQZkOeBaPQ1w8KPJ7HxzPtWw7wpCv0tyOPktH5ebCmOKpLKxZaVExHYvyfiEloVolD6V4rMpaZAxQFA9KtCoewElDT7+CUknxtHtTPJRqaVYVXiFEmTEgNWuLUMkzKpUZd2QpxVMKnX7WUzyAVGrMHhSm0QjJLp0Ud1jyhZD7oFRnIl8MvDPkacYOchVPK9Uk+WJ8YDTuCop7XAUpKLTY0CJxcCzneUxWqrgwKihS0EKLX9DAQNu2hYJCysNUQIrJ+X1IBUV6nfFcSfG2NDpAyRfLItAKVlDiU2YMSKXGkRQU57GdWsfciZDKFn0P8rGkN2tjBYVpGCIiTiWUg45gk2wwhVIZq760Fe/+ys98jYdCQTnCAxRWUKKTr5IWI0MidQXNF8vY+NRenP3/PYR//vkrsG3bY5IFqvdU6a1UiQR5UPKlsmfOj0khkb1HUat46DWns9U9qScJS61mJJruQQlhkgXcSp7hCAqK3Aclm0qAYgjdS+SHUUGpBFL0vZRK4RSg6YYDlFkAHXDZdFI56Nzfs4ISxMhkAa8PT+HlN8d902G0iBzJCopt2+o04xZ6UOQ7+rh7UApVgjoKftulFM9Trx4GAPx634hy1y/fgJjUAHnWzJz2SoonYOGS+2fM63Ieb7qhkd8rtElWOj4mCkUlxZNvcKrlcGWB721PI2FYVJPJZiso4apgqGfLa0OToV97TCoztixL+FD0aiw/SAHLSmtFWusmywoK0zBkyc5PQYl7KWYrkRcNvwCE1sMjWUHR900jgrVvbv0t7nrkt5GfJ8dGb46ZFZSXD4zhE//yNF7aP1rr5tVNqWwri2JQHxTqCporlkTQlS+Wlf2cSlrijtkYoNiyglJJ8QQoKBS8ZFMJsdCZAhBFDYnYSRZwPBJqJ9nGBrfU98mU3gFap6AEmWQBYPm8TgDArjcnQr/2qNSoDYCo5Ak70dit+vSmeLiKh2k4smRnClDK9pF9518NOVfuN+2UFRTvgljvvpgqlHD7pt/gC5t+E2lcvPPe1RWUr215CRufeg3/+sTeurazHvRUhsmkKRQUEaCURdClByjpZML1UxhNss77WZabPghUUCp33Z3ZFNoq729K8dSioOS1FM90mmSFgmLogQI0v4qHPl81BWLZfCdA2X1wPPRrU48r6vdC3qWw51BOKzMG3Hb3FFSygsI0DJNJVocrefyRF4BClYFuR3IVj76o1CuX50tlURIbpVEV4CpagFlBKZdtbH3+gPM+LTz29fOO9tn+kSmx3XT8kYKRL0kBSqms+FaSCUsstkHBTiphoSvrLGCjAQsXBS+d2aRYsEwm2VqGBcoqyUS+hKlpHBZIHhQ/BSVptWYWT9A0YwBYTgHKm+EClMMTeVFOfXRfBwC4AwNDlhqbPCiU4qHvTCgo3KiNqRe1D4r5K+VeKP6ovT2Cm18dyX1Q9H1Tb8mmrFZFPT5lBcUUoDzz2jAOVu40g9rCTzceBaVcRr5YxuVf/il+76s/Q1lKAVGKJ18si233pHgSllhsjWXG0pA3SgEEmWSpf0ZnJiWuHabvQlYewpcZywFKUQl8Gu1BGaImbYYSY8D1gjRvFk80BeXgeD6UUXZXJZDp72kTpmoxMDBksza91T3gNcmygsI0DFmykxWUjkxS1LezguKPfHfod2dX0qTPIxFdXapXLpdfL+rxKS80B8e9KZ4tv9lvfGyz0dWbUtnG6FQBQxMFvDGSQ65YFotZe8btaUF3ybli2b0bT1iwLElBCSgzTiUS6Ot0FuuDPh4dwF3UOrMpcUddbVhg2O9K96BMTquC4t9FFnC9FM3qg+IqKMELfFc2hQXdWQDhVBRKBS2b3yF+FrVZW04bFgiYPChcxcM0CFOjNsApWyRPStgR6UcixRC9PegCfUSbZLWLe8FnX/zkuUH80T2/8DWvErX4GkzPPTxR8Cx4jzzvBihRFqWHfvUGrvvHX2D/6FSk7fFDD1CKZbUSKl8sexQUdY5WWXw2Wizo76Ay44QFLOx2KkT2jwYFKEXx3lRFZOqbVEujNtmDMp5Tq3gabZINmsMDuEpAs6cZp0OYTMkoG8aHQmZaSg0BiNzunqp4jCZZruJhGo0s2ckm2c6sq6iwguKPvLj5pQN4Fo93ofe72H/rsVew5fkD+NmLB0K/XtRSeP295UX9wGgOv9w7XHU7Tfzzz1/B1hcO4OFf76/+4BCYTLLyz3Klkjim2jPe/h1yAEOLCKkBJpOs8KAkE1hYuTPfP+IfbIVP8dTgQZE+p56+mC4FZW4Vk2yzzt9CyDJjwFVDdoVRUCqPWTbPDVAW9zqB6PaXD4baNrdvlldBIdWrJFKFM6iK56677sJpp52Gnp4e9PT04Pzzz8ePfvQj8XvbtvHZz34WAwMDaG9vxyWXXILnnntOeY1cLoe1a9di/vz56OzsxFVXXYW9e1vnsp8NyMMCZdmuo8pFh3FQUzzBVTxHtEm27F1sTdCiVy3oUNMGERUU7XuQ1ZqtL6iBUZRFiQL5QxON6a2i3xgUy2XP8UaLP83OkclLKSBa7Mh3GWSSTSYsoaCMTPl3SR7Pe1M8jSszliqttDRc4z0opKAElxk3exZPNZMsIFXyhAhQKIhZJiko//2cpQCAh379Bl49WL1cWbSlkNYKurGd0R6UJUuW4Pbbb8cTTzyBJ554Au94xzvwnve8RwQhX/ziF3HHHXfga1/7Gh5//HH09/fjsssuw+io24fgpptuwsaNG7FhwwY8+uijGBsbwxVXXIFSiRfQWpElO1lB6ci4ZcesoPijdEc1XDjLZRu0xrGC4uJ3sacLYLW0ovz8qOMY9O9BLjXe9tKbANy76SgKCl2g6Y68XkyVT7IpOy95TDpMAUrJDWhosQgyyYoAxbLQ054SHrQDPmmecamKJxvgQSlpaakwqc6CNBDwkFYK3mgFhRQavxRPsxUUCirTIRZ4txdKcIBi27YIYuQUz3ELu3DxCQtg28C923ZXfT+TSTatd5KdibN4rrzySrz73e/GCSecgBNOOAGf+9zn0NXVhe3bt8O2bXz5y1/Gpz/9abzvfe/DihUr8E//9E+YmJjAd77zHQDA8PAw7r77bnzpS1/CqlWrcOaZZ+K+++7Ds88+i4ceemhaPuCRgOgkazDJcoqnOrIyYErxyHfrto0jtumdvqj4eVDoeMxXWYRkJSGyglLyV1AOVP597IIuANGqeOjO/nCDFBSTB0X+3HIKx5TiyRVLUulwJcWT9F9si9LCYlkWFnRV0jy+AYqroLQLBcVQZqy9V5h5PPLxcmh8egOUoSplxrTvmuVBKZTCL/Ckhux6czzw2nJwPI/RXBGW5ZYYE3/09mUAgH99Yg9Gq8z1MZYZaybZGamgyJRKJWzYsAHj4+M4//zzsWvXLgwODmL16tXiMdlsFhdffDG2bdsGANixYwcKhYLymIGBAaxYsUI8xkQul8PIyIjyh3GwbVtL8UgelExKmGY5xeNPtfky+kXtSC011veNX1M7EaBUCYpVk2xED4p2IZcVlJEpdfhdlO+L1I2hhiko3mOnoCsoYhaPOcWj+xmEgmI8VtXHLuxxApQDPqZfoaBkkqHLjIFwRtm8kuJRA6RGBii2bYvvq9enzDgoqJsOhIISJsVTUVBGpoqBxx2pJwO97Z6RJhcdvwBvWdCJsVwR/29HsGWCyr2VYYFaq3tRxTPT+qA8++yz6OrqQjabxUc/+lFs3LgRp5xyCgYHBwEAixYtUh6/aNEi8bvBwUFkMhnMnTvX9zEm1q1bh97eXvFn6dKlUTd71iIrI06KR1VQ2lhBqYp8sTRdOKejxftMRB9057cf6AJYLUCpy4Oivfeb0gI4WpH7qcw2yvdF3/9wgwKUvJa6dqp45H4groKSTSVgaeuB3AclpVXxmINpKI8ho6xviodMstmU26jN6EHRFZTq15MgBSVfbNw5NFUoi2Ntbmc1D0q8yowBp7ycjK5BaZ5dhvQOkUhY+PDbjgEA/PDZfb6vUS7bYl+pwwJngQcFAE488UQ8/fTT2L59O/7sz/4M1113HX71q1+J31vaGWbbtudnOtUec9ttt2F4eFj82bNnT9TNnrXIuftsSiszzrr/5zJjf5QqngDjIXGkGmW9aoCfB8U51nJV7pLlgKfeKp43Rw0KSmd0BYU+49A0pXhKmklWCUCSCbFQKL/XDJfBZcaVO1+LApTgUmPR6j6TEn1YTN+Fvg/DKCjy5zw8jVU89F2lEpbRaAzIre7tpqRow04zJpaF8KGYeqDIXHaKIw7seGXIN8CWVS0lQBEm2UoVz0ydxZPJZHDcccfhnHPOwbp163D66afjK1/5Cvr7+wHAo4Ts379fqCr9/f3I5/MYGhryfYyJbDYrKofoD+NAueCE5RidlDJjuQ8KKyi+yEGJcQAbKygAvF4O036wbVuYLKsZXxupoMgphJEpKjl1ApQofVAKDU7x6NUqBa3MWFZQUgnLM0srX5KqeISCUr3MmBbkBaLU2BygUKv7jqyU4jF8F7p6FiZlLPc60Te1kQGK3KTN70ZXVgKaoaLQ/kqHTJEsX+AEKC/tH/N9zC5DibHMkrkdOGFRF8o28FOfEn/5e2uTjjXaP7OuD4pt28jlcli+fDn6+/uxefNm8bt8Po+tW7figgsuAACcffbZSKfTymP27duHnTt3iscw0ZANT5ZlKaVjTqM2f9mWcZAXXnOKR/3ZkdqsLUyre3m+TjWTbD0eFLqA0npEHpSpQklcZPtq8KCIFM9kviF32qZOskUfBSWZsJDRZmnJAY2nzDggmBYeFApQfDwoE1KKh64VJnVEf68wpcZB339jAxQyyJr9J4BqVo16g1HLcVCIUGYMACsGegEAz+w97PsYatJ27AJzgAIAv3viQgDAlufNfXzoPEslLGXbUlqKpxTB5DudRApQPvWpT+FnP/sZdu/ejWeffRaf/vSn8cgjj+BDH/oQLMvCTTfdhM9//vPYuHEjdu7cieuvvx4dHR344Ac/CADo7e3FDTfcgFtuuQX/+Z//iaeeegof/vCHsXLlSqxatWpaPuBsZ0prW6yXGdNdESso/hQimmSPVAXFO1fGux/kQKOaB6WgpHiiDgt03ntep7MAUxUPqScJaZpvlCoeOhYKJVv0CKkH0z7TTbJBCgrgpmFEFU+gSVaV5skkW7WKJxPcB0UPRkMpKAFBSCP7oIg5PAEBipxqiRKwjk4VcMnfP4K/+v6zkbYpSpkxAJx59BwAwC/3HDZu3/BEwdikTeeSSoCy9fkDeGn/KC7+uy345L/9UvzeVMEjbyd9z3FRULx1bQG88cYbuPbaa7Fv3z709vbitNNOw6ZNm3DZZZcBAP78z/8ck5OT+NjHPoahoSGcd955+MlPfoLu7m7xGnfeeSdSqRSuueYaTE5O4tJLL8W9996LZNKcO2SCcbsCOiegYpLlTrKhkOVro4KiXUyPWAXFU83k3VfywhWliifq8UnbsrA7izfHcjg45igeI5OOItDdlhYVFJEUFGk7Dk/kxaTYWvEoKCW9UZubwnEUFEOAkqMApbpJVqgxlXWFPCh+Jlm5J4bwoBi+Cz3Iq+ZBKUm9g2S6simM5YoNbXV/eDK4SRtQu4LywhujeOXgBA6Nv47/7+qVoZ9XjKhAnLCoGx2ZJMbzJby4fxQn9bs2hlyxhD/59hOYLJSwZG67p8RY5pxlc9GdTeHgeB6//43HcHiigFcPTeC2d5+Mvs6Msc09ICkoZa2KZyYpKHfffTd2796NXC6H/fv346GHHhLBCeAYZD/72c9i3759mJqawtatW7FixQrlNdra2rB+/XocPHgQExMT+MEPfsBVOXWQK/orKKoHhVM8fhQielBMuf8jgXAKSvgARd7vkWfxVL6DRRWFIF8qYzRXFApKT3uqpuZcck+cRjRrM+0zpYqnWBZyeirhmmRlsydV2rgpnuoKCikG5EF5cyxnPLZp+zKphLjJkVUd/XUpgKqW4vFTT7orE5arpf+IctnGvf+1C89Kowt0DleZZAzU7kEhH9XoVDHUtGFCNj6HIZmwcPqSOQCAp149rPzutu89i5/vOoSubAr/8IfnBL5mOpnA75wwH4C7X2wbYuyE6CKrpRLT2rRnd/tnUIDCxA+6sNOFQ5bu2jNu6/uonTqPJJRGbQbp2VNm3KSJqHEjjAdFTvFUC4rrUVBKlQWuI5sSKsebozmMVBaRnrZ0Tc255KCpEZU8+ucqaY3aciXVg0JVd/O7s+KcphRPWqviCUpH0mPmdWZgWUDZ9vYiAdwgMp1MKNcO/bujbe6u7Otq1xO/AKSnLV15vXDf95OvDuGzP/gVPvPATt/HCA+KT4kx4JThkl8pSspPrkR7bWgy9POimmQBN83ztBSg7Dk0gfufeg0JC7jrw2fh5MXVC0RWnewUnMzpSOP3Vi4GADxcme5t6iILuAFcMWZVPPXpl0zLoRM9K1I8rKBERV5o9XkzAJcZE/qF3bTwKwpKtTJj6fWiKihyjnxeVwZjuSIOjudFiXFPWzqyglIu28pnaoSCYpxm7OdBSVpCQZnflcWhsTzyxbJopuaZZhyiiieVTGBep5MG2z+SEykfggKPjBagTBXKkDMm9LpdbU76oKqC4hNwki8obIqH+qf4paiAcB4UwDlW5NlHYZADsb1DEzhlIFwFaUFTssJw5tFOf7Cn9rhVrnQMLuppw+8cvyDU67znjKMwOlXEBW+Zh6GJAv7j2X3Y+sIBlMq2rwdFN8nGxYPCCsoMp1By74AABPRBYQXFD3nBMCso4RqUzXZoMQu6E52MkOKR93VUBYUW52TCwrzKnfPBMUlBaU8JeTrsgqQHp41od2+axaM3BlSreJzzdV5nxqOgCJNs5e8gtU/2DohmbWPeRV5O8SQTboCkB4z0XZNaVT3FY/ZgkIIS1iRL7xPUvt1N8fgrKPK2RFFA5SB77zQrKGcsnQMAeHH/mEhV0udvT4f3aCYTFq67YBmOX9SNs46eg562FA5PFPD0nsOeogoirXXanZGzeJj4QSe6CFCk3GKnVGbMJll/5DkjYap4jliTbOWiSx1HqyookVrd16agJC0L87poAc67HhRFQQl37OuN6KZFQSl5G7XJVTwiQOnKCvWTSoHTotW981yTF6psuPMV7e61Xijlsi32o36Dowcg9DgRoFQxycrKrtzCnzwoYVM89D5juaJvue9kwR14GEQtKT+5wWW0ACWaBwVw/EJL5rbDtoFn9jieGz/FIyypZAIXneAoL1t+s18o6d4UDysozDRAUqnrQTGXGXMfFH9kuVlvSAUYqleO2BSP87npmKpWZlwtKFYatWkK34tvjOJv//1XyhBAmbKUFplfCVAcBaWS4mlPu+3NQ94x62mHRjRrC2OSVap4RIrHVVCo3NmT4gmq4pEWFndgoNoLRVYHKPjxKzWmBZcCjGrDAvOSsisHKD3t0TwoFCiVbfiWfVMQqHfh1anFNK0qKBOhn1fQmuuFhdI8T1fSPJM+npEoyL1R5MGyMqQ2uh6UGVjFw8QPPcWTSSYwpyONTCqBuZ0ZVlBCUIyooBypJlm686dFzGySjZDiKcvBjLr4/MPPXsbdj+7C9596zee5znsnLAvzuyjF46eghAxQtEWzESke3Yejm2TVTrIJoXYsm9fppnhypKCEMcl6Fxa/XigFJUBxXttvojF9V92VFM1USAUlnXTLl4HoJllZyRmr+It0dBXZj1rm8ciB82uHa1FQoi3wK49yPC4vvOF0lKXzqd2nhX8YLj7RUVCee30EByueHj3goX1H33PUMunpgk2yMxw3h+wcSJZl4b4bzsNUoYSubHxNsvtHpjC/K4tEi08AIMSwQL0PSgMVlMMT+coU6vj3ARIpHgpQqvRBqaqgKGXG6mPJHKkPmSPktAh5UN4cy4kLak97KrKkX9Aep8+PqQUaipeoVNKYGrXJqsctl52IC94yH6tOXoR7tu0C4CoHeh8U03Fo9qBU5vGM6AGK+3xSH/wUV/quQntQimS+tdCRdpcZN8Vjh5rTJgdCo1MF9Pe2eR5Dylfa0ENGJmrKD6jNg2LbtpQiiaYB0DRmSutN1eBB0ZnflcW8zgwOjufx/OAoADdNS9Cx5Z3FwwoKUwemu4cVR/XinGV9ANycctRW4tPJY789iLd+/j/xhU2/afWmAKg+i0dfCBplkt3xyiFccPvDeP83H2vI6003boonpAclQhWPHkBTNc6oz12zXAY5v5tSPHnx+JoUFE+Kp3EKSkcmJbZbNwfT4ZVKWJjbmcG7Vy5GJuX2RJnw9EHxN8nKgRvh1+5etNBPWOJGwZTikRfcLkrxhCwzTqc0BUXqVaJ7fkzIgdCIz7FQCGlIrUVBkVXA4cmCUOiCkF8/ikkWcPc/GaPJg5Ot8wZmWWUC8m/2jRpfT1dQ9H46rYIDlBmOnuLRaRMpnvgoKM+9Plz5e6TFW+IgV2+YLpr6AtcIk+yuN8fxP/7pCUzkS3jmtWGj9yVu0MUr0INSVNWB4NfzV1BGRYBiXhDcAEVqdz+eEwtId1sq8oKkq2d+E2GjkC+qJsdiuay8z2TeXXST2mJG6Vm3kyy1und+H1RmLCuT1GFVbzQm90Ah2lLeFI+8/8JX8fh4UNrSnscEIb+P37Ggt1rwI5mMFrAC3utmmF4o8utHMckCbiBLn3uy8j3Uo6AAbnv83x5wUkfeTrLmVvesoDB1UTBcZGSyMZzFQxfKMHcjzUCZxWO4aOot3etVUCbyRfzRPb8QJkzb9k9lxAm6eAVV8cjVHVWreBQlQV0IaDEKpaBUPChKo7b26FU8uuLTCAWFji1apHUPyoS0v3RDpafMWFdQAkyy8mtRWkXfl3mD8tBW2U45MJDfR5hkawxQ6PnyY4KYUFI8Ph6UKtdAopYqHv0YDpPmkT9XVJMsBSJ0HtVSZmxi+XynPb6ugrrbqQ0L5CoephEID4qPlChMsjFK8YgApQE5/kag9EExXfQbPIvnF7sOYffBCczvyoics98wtzjhmmQTyv9l5OqOaqpdIZSCYl6UipKCQlU8I1NFYQLsaUtH7oNC3zOlVoYnC3V/17TA0QJTKKlVPHIgoN+tulU8lRSP8KA4vw8ayyB3APULUOQeKAS1u5/yCVCilhlnkpZQBQBnajLZTsK0u58qhAhQQppka+mDot/YhankkV+/2jbpUDqMArNcA6p4AGD5/C7l/7oHRe+DUuQqHqYRVDs5SfasVhbYTFwFxXzBaTbqNOPqnWTrLTOmi/uyeZ1Y2tcOwOsPiCNuiofUAO++kgPhsm1WpAj5+VPFkuhzYds2xiqVK34qm7wQ90pqCfUu8ZvF89L+0aqpAppfU7b9F8Ww0CLcrigocopHVlDUc9iroKhVPMGzeGQFxQmCJwsl1RBeVAMywOxBkZUuUcVjuJ7ki2XsfG0Ytm0rqoasoHRkkuJaFcqDkg+f4pmOKh76HBRU7R2axKHxPF496B+oyCnjqOt7h6ZgNUpBWTZfHTDoNyxQb3XPs3iYuihIZjQTcZzFIysofs2XmokclISaxVPvXTXlzNMJt0fFyMxRUNqFnyLYJAsE3yXLz7dt9/Un8iVxgaRARYeCRDJ49mlzWJw+KAnx2uWyjZcPjGHVHT/Fmu88ZXxN19CaFMP66k3z6ApKMSDFoy9mWW0aczrENGNT9YWcVhmX9mfecO3QUwzO+7jfITVDMykodz70Aq5Y/yh++OygcuMkm2Tb0kkREIVpdz8ZQkFx1ZrGV/GQgnLUHOdG4vHdh3Dplx7BZXdu9fUoFcVnt6pWKel4UjyVv9vqKDMGXA8K4U3xUBUPN2pjGki1uwe5zDgOwQDgBijFsl3VbNcMlFk8AZURRL2yPwWLmWTCLQGdASmeogisAjwoeoASsAjpwSDdlctBia8HRevTQGkewLnb7cqklEW6WLaFf2CPj0wvd/8kY2m9pcYFKegBHNVIVpVof6US3sUsq93lJoVJNlqZsTMI0HmuvD9N3o1FlTJeueeH23PGNXGaqnho/+56c0wp/ZUVlPZ0UqQTwplk3cdUU1AyVUyy9Sgoxy5wUiTP7B3G0EQBuWIZB8bMqmc9FTAdIsXjdM6d0gLcWunMpkQ1FxDUB6WioIjzi6t4mBBs/tUb+OA/bPc0CwrrQaEeDHFAriagzp+tRL5QmtIWjVZQaEJqNpV025DHJED57i9exfX3/EKUtsp4OslWadQGBAco+kLhjrZ3jw+/FuclWw9QXAWlO5tCImEpd38lqYOr38Ion0s0eK5hCkrGbW4ne29oP5ty/boiEMYkW7bNd76UmpFTZqabm2Mr5agvvzkufibuppMJqZGb98aCAq+JfMnXg+IEKM77hfKgVDHJliVFqlpJby2dZElBof0i45eiEuXbNaRH6Dgp287+EQpKA/okLZM+g3dYICsoTB18e/sr2Pbbg9i0c1D5OTWCqlbFA8Sn3b0sjQYNAWsWSl4+oDsnUb+C4nwPmVTCt0dFq/inbbvxyPMH8POXD3l+R/spuFGb+rOg6jE9UKDjU/Ymlcq2kgaRfw64asI8KcVDvTZUBcWdgUPeC7/tSVe6MQP1d5P1Kii2ktqY1JqwyeiKgJjFU/mxsdV9yVtmDJiNsqabG1rEdssBitQvpc1nVg/gLmpygJKWghrAuR5F8qDIKR5Duk/2e1Rr1FbTLJ6KqnfCom70dWYwtyMtjMJ+ga4+3ygK8r6azJca0qiNWC6lefRW92mttw43amMisX/EWcAGh80KSrUUD1BbqfGbYzn87MUDDUsP2batKigxCFCUVvchZvE0yoOSSSWwoIEpnlcPTmDHK97AwsT2lw/idUPrbrogHjSUPetlxmXbu0jqC1fQMedRUIqkoKgLkenOWU9lzJNSPNRrw6OgiB4P5m3KixSP5aZ46uyFQgoKqQjOLB73c9P+MnVU1gOUpD7NOCCY9lNQjAGK9D60iO0fzQm/inw3LTwSBW/KmM6dyXxJ8aBQcGZZzvWI3i96mbH3uzB1w/VDVlD2HJrA47urnyv0/fW0p7D5Exdhy62XCL9TVQWlhsU9lXQb9E0W3AClEQrK8gWyguLTB4Va3dc4S6jRcIAyQ6AUwL5hczdIv7sHy3InpNYSoHzq/mdx7d2/wBOvDEV+romJfEm5sMYjxSMHKCE8KHUGa5TKyKYSomKkESbZG/7pcfz3bzyGfcPBvRpe2j+GD/yf7VjznSc9v6ML8pApQClTusI91vSKplwUD4q2X+lirC9EpoVJHhYIqB6UnnYnGNA9KLT9fttUlBWUdlJQ6gxQNNVJr+KhY8+ooCTNpaBBZcZ+DbZ6hILifh5TBWBvRxpzK+rR7oOOiiJ7c8h/ZNve64lQUAqqgtJRURza00lYluV6UEJcj6qVGcuvUbWKR5Sdl/E//ukJXPPNx7DnUHDZMH3GbCqJeV1ZzOnIeNIhOkXDfo0CBQ8T+VJDhgUSywIUFPcz2SiXbdChxQoKU5VCqSzuaAd9AhQ/DwogGWVrSPG8UQmMTHfbtaB3s4yFghKxD0oUidiErKBQiufAWK5uleqNkSmU7erNpKiXg0m1oUXrkCG1oQ8LBLz7Qk/xBFbxaL8jOV1fiEzl6BRsJCjFI3lQSEGxLEtcYOUGadXufDPJhFv9VmeDQ1dBoT4oZePCZjIjek2ylQAlwCTr50GhtEQ1kywgp3mc40S+m5YXSv27pfNkIleUpqxb6KjsS9qnYT0otm1XreKhfZmwqi+mch+U1w5PwraBF94YDXyOmJQs3QCSwuHXT6XeHiKim6wUoDQkxTPfX0FJJ8zfK7e6Z6oij5zXFZQwTYrc3gbRL7Z0oWlUmbInQIlBszZ1mrH3czZ6Fk9euisjBSVfLNetJtHCW+2uny70pgsstWY/NOavoMhpQ4+PpFi7gmIyyQLmUmN625TBJCvPe5FlfQqI5O/4L/7fM7juH3+BctlWzqUoaYggRB8USUEx7XezgmJeRAI7yVb1oASbZAF3IfMoKAlL2Sb9mkD7VfegUHDWrgUo1TwohZKtBMAmJS1nCCD8SEnHAjW/qxbMU9AsH/NhFZRae4jIvVDIJFzPNGPimHluLxQ/kyygfq/6+IVmw9OMZwCy/L9/dArlsi0uQNVa3QOoa6IxXXQa1ehNXzzj0KxNXkSbMYuHvodMyrlT721PY3iygP2jU+jtSFd5tj/0XVWrPBEBiiEYowW1VgVF748RdMz5lhl7PCjehYm8FomEIcUjzXtJJSzkUan2KLsKCqlV/7pjD2wbGByZEudSKmm5d/l1KiiidwxV8ZRtmI4eYxWPtuimQphk/VqUB3tQ1MeSD+XlA+Nim533T4iUcb5Y9ny3copHDvaOW9SFjkwSpy3pdd4vGS740/1Mo1NFzwTksE3aAHcfj+eKYkCjXhWpI6udRLrK9guTbI3qgzwwkMqMG+FBaUsn8dblfXjhjVEs7VMbt8n7T77Wt9qDwgHKDEAuQS2UbBwcz7t33iFO0GwdHhRaRBpVARRPBUVK8Rhn8UyXguJ8Lwu7s5UAJYfjF3XX9Jq2bUsKSrUAxdnnpmAs0IMilUeLn/n4SLqzKYzmijUpKHrQGmSSpQvoPIMHBVAVlIIWiCYsiIVqqlASx0EmmRAp03oUlFLZVQDkKh5TitB0t+01yVYvM3bLr9XnCgUl503x6ErNMo+CohomsyJAMad4JvNFJXBY2N2GX3x6lUj1hFWn9GtOsWxjqlBW1AQ6hqsZZJ3td0cYENVa18t+MSIdsP+dbaq9zBiQFJR8UQT8jUjxAMB3P/I25IolpfQbUAMReb+zB4Wpiu4VkH0ofndBMlkxoTR6kEFlfLWkh0zoAUk8PCjuhcZoPNRn8dRrktUDlJ76S43l7Q6b4vG08JfMcSYFxS2fVL0dMnTHR2mW4D4o5vSQt4rHYJLV+qDIZcbdmoJC7yUHooVSWcm1TxXcMuR0MhHaJxGEvAB3SAqKSbkyLQT6dF7apsAyY1F+rf7crKCY08PLtVJjve05bZf+3ZrKjCnQ66r0pnHezzI+X4cqeDozSdFqXj8WalFQRpQAJZyCogQoqbApntqWV3kez2QDq3gAZx/owQn9nJCv9cmInXAbDQcoMwB94ZKrNMJM8myrY6Lx9CsorU/xmKoqZDzTjCMMGzOhKyjU7r6eZm3y3dxQ1QCFFBTN0CodH6bpygXpwivn84lS2ZbKMisBSshW94DXg0ILmVFB0TrJtqWT6K4YQXvaZAXFvdvVq7Xkfii5Ysk9l1JWaJ9EEPL51l5ZFJxOsuE8KHqAkgqjoNB+SfooKHIVj9TtVYYUlIPjeYxMFURqjN5XDCD1KCjO/yc1D4pO2H1L6kFHNiVMvrq65rbrr76Q0v6TX6NqgCJUJjdAcCf/Bptk0zWqD6SWyDcajajiCUKurqJrfcIyl783Ew5QZgAeBWXEq6AEp3jqUFBKjVVQKEChRSQWCkoVk6zHg9IgBSUjFJRKL5Q6So3lQGB4MqwHRf0ccoAyPFnwpLtEGa7UpVV+jOxJoO83KCj2eFAKqoLSX2m7buwgaqhWoUoe2STrbqdqTs0bFBS3WkUyydbhQZH3J00JLpZtY9BmquLx9aBUPrJpaKVfB9CewEZt6vt0ZVPC07P7zXERoKc1BUWvCixJCopoIGkwr6ZDpnjkChbyFemGab80lQkKZuWbpEPjeWU+kY5QO6UAoVqrfjeQry/FI98kNEpBCYICLzoPW13BA3CAMiOgO2uKovcpKZ7qVTzUFdN0V1wNev1aDLYm6OJAJq04eFDkC02YPih1lxnrAYroJluHgiJt99B48D4dkVI8cmlzruR+x7btVbtks6SpbbgcxFKQEKZRGw3mo/TQaM5534FeZ0BbkAclIUnQq05ehL7ODE5fMkf8TE5F6SmeghZcCT9Dym2W1YgUTyaZUKbFhq/i0Qe6VVI8NETQ8Dp66otwUzymKh7ve1Nr911vjnvUKjpu9X1D6eDJQklcL0zXpbAmWVooOzJJowKkfoYQHpSkN8UD+Btli6WyOEblAEjMrfE1ybqBbi1QiofSrGnJtD2d0DFI52Gr/ScABygzAlq4Th1wXPBGD0rAAbxkrnOhryZnmig2WEGh4Wu0Ta2u4pHNpYBP6ea0mWSdC9GCBrS7ly+W1ap4xnLyImUb/w14A1rZ/EcLbkkJUCrVSVJ78yCfAS1o1MjLTfE4xwRNkA3TqA0A/uqKU/DEp1cJ5UX+vTHFoykobrrUquozCIP8eimfQImIVMVTCcqCFBTdO2DsgxIwZG/ZfOcGYteb456qFFdBUT+HHDDReW16beFBqaagSHNoTK36gfCDAgHJg6IdT35GWXn7ZAUlJYJXnxRPnQpKe9r5rGRUb0tNv3oCuNvrKigcoDAhOFBJ6VCZnuxBcTvJ+h9MS+Y6F5vXaghQKP/cqDJjoaDMjYeC4qnQCVHF07Ay4yQpKPW3u5dnCOnKh458kZcXSz2Y0AMU0SEzkVCaXhFy18uMj5FShvYrLZ56mfHiOf4pnqLmiyD0nLmioEjfbb5UVrbNUVDcO99GlBnLi6erOJWN3oUws3iEghJYZmyuIAmaxWO6Oz+mUmr82tCkpzutnwfFdAyaGkgKD4rPTCRCTvGYFCCg+iwyGVMVD+B/4yZ/96qC4k1vyoRRtYOgFA8152xrQA+UMFDgRQFKq3ugABygxB7btnGg0qiNApQ3JK9CGJMs3YnuPRxcUmeCTsJautCa0FM81NugVehqiGlYoH7HW/c0Yy2vTQpKXSbZCAqKqZID8C7G+usUpcUvLS24hDw3JEyAQu/dma0seJqCMkAKSs4bcOnDAv1ISdspf296FU+u4JpXMyl5oF3tAUpOOjfdNuu28TXDVPGEmWbsN+StW/Jw0GOCrh2UdpsolNwyY/KgpElB0MqApc9FNx7BJtmQHpRM0qgAya9RbZIxIFfxqK9BN256GjsnpTrkipx0Inj7aUJ1raXBlOIhBaVRJcbVoHOazkNWUJiqHJ4oiAv5yqPmAHAUFFrUw/QBWNJXW4pHLjttdJnx0ZUAJV/y9lNoJvpFJpSCUmdApRv7qMx4dKpYc7WUHGhMFcqBryPfhcqfTQ8m9IGB8lj7pJQ6kd8XqAQoSfMiJkN3+7KCMlUoicAhyIMSdtpqUppgq1ZrqUrGVLGkLHZur47av2tZQUlJQYXRgxKiD0paV1AMx6F/oza3somMpkHXDlokp6TZWfSa9HhvJ1mvgmL0oIQ1yeZlBcVcxePu4+qLOG2/3gBu79Ak/s9Pf4uTP7MJ2156U/zcz4Drpv/Mx4aoPqpR+aCAhG4QmhWgCAWlEqixB4WpCsn+czvSwrcxVSiLC0AYkxgpKIcnCsZ8vh9qjr6xCsriOW2g47+VaR59sQgzi6dxCopz4aHyWACBFQVB6Bd7v14otm2rKR4t7SGjN2tzFz93wZUDnJyU4vHzKcgUtRRPrlAW22ZZwKIe/xRP2ABFLocuan6bgqagyE0PMw1I8cgLnFxNVCh77/qNVTzaOe1p1BbQ9TihKUty0EgBSj5AfRDjMYolySRb8aD4tC2QtycoQAntQSnIHhS/FA/t4/AKCkHX0xfeGMXXHn4JZRt48tUh8XvR5l6f/CuCTR8FRZh7a+uDKhSUyjk83SXGhNeD0vrwoPVbwARCxskF3Vm0pZOiIdW+Yaflvdw8y4/utrSo5KnW2llGuTtugAfFtm1x4ZrTnhGVHq0sNfYqKIa7Uu1OtX4Pinpnpg+1qwV9uw/7lBo75bTSQh2goBwaN1dMpKRGbSYPSns66TbzChwW6DyXLuRTxZJYgLoyKdH2f3Sq4EkDlnyqVXTEfpUCA/oscgnxVKEEucFWI1I8sgmVtiNfLIvutfICFqYPSjqESVZvqiajV8K4A/28d+h01z6ZLxnKjM0GaPnYFdUvBm9c6BSPmEOTqGqSDedBUbflxErX5hf3jwllJqf4kswKSjV1rV4FRe46DDSnxBhwFTpSQllBYapCvTHISEl3lYPDU8oF19RvQEZU8hyKEKBoVQ71Iue/e9vTorfBcAubtemeE9NdkXux9aoGtZA33JnRxdPkgQlDQdtuv1Jj/Q40UEHxeFBc81/KEFDRMZIN6UERZcZCQSmJBai7LSUWpULJ9r1br5Yn91dQysiV1MVI7n4a9i4/CCXFY3i9TmkBC1fFk1AeazoOXWXJez3QF/kg/wYtipNSh92knuLRblr0Y9B57QAPShWT7JSkRPT4lBmHGZZK6PvkhH7vWAlZKTZNMgakc7WaB6XOFI/4f9NMslzFw2i8fngycMEjgyz1ylhcKaHcNzylRPDVGhUtmVOp5ImgoKi+hvoVFNfZn0BbOiFmprRCQXn98CSKpbLHcyIPkiNoYc5S/4OQQUS5bON1w/423ZmlpDv9WvAoKBN5lMo29hxSjdHeHH44D4pt24q/QUx0LZcxMlXA0Hg+skm2KDwoblWIG6Ck0ZVJSS3O1e0O70Exl/c6nWRVBUVO8TREQZFMqKbt7MxWU1D0PiiU4gkRoBjMw3qaJKjMWHhQCiXxmrRPTCmectmGyZpVTx8UU4pHb9RWSx8UYnFvm+LNAdQbMX0khfs6wds/Xq8HRXte88qMWUFhJJ7YfQgX3P4w/uYHz/k+hhSUBRUjJfV4GByeVC6w1U5QtxdK+EoetUKjfgVFdJFtT8OyLKGgNNuD8szew7jg9ofxV9/faZRp9es+BQ10YQ5jks0Xy7junl/ggtsfxmO/PSh+btu2cb6HXIZaC3qgdXiygDs3v4Df+eIW/Pi5QfFz/QIfVGYse1Dk/eQ0anN9EFeufxTv+NIjkqnP9XDkQqR4aKGeKpREj5auNmd2S1fGfOccNsXj9kEpi5JUwFmc5c8kKyjpZKIhJlm6u3c8KN7zsyOygkIBivN/03GolwTL6JUwhYASXbqLnyqUhDLilhl7AxSTeuL32qTYBB0bgNkk60nx+KgcJvR90pFJiRYMHVJARuR9UmAZUWY8vSkeolkKClXxsEmWAQC8XBnG9ZvBUd/HCA9KpfV0b8W3MZpzJ4YmrOoHUy3N2pRZJQ1UUMgPIwKUJjdre2n/GAAn90wLtBwseHwpWjfJagqKbdv4y/ufwc9edCoCfrVvRHktWlfkO+R0RHVGx5Se+a/fOu//a+n9vSkeNe0BuKZduQ+KHMjIjcfGc0W8cnACQxMFPLXnMABSUNTSYRMek2yxLI4FWpD8FqZaqniCOsnKHpR00mqoSTad8iooCcs1SQNmBSWZsJTnuX1Q/E2ypgZ2hF4JEzQJnYyZk4WSCNCDPCh+KrBJ2RWt7qsNCxSepoRx2KH8GcKYZPV93JVN4k8uWo6LTliA6y5YBsDtogpUV1D80n9uiqdGk2xafV6zPCh0zIgyY+6DcmRDJ3VQ5QZV8dC8FvnOJugCo3NU5U4hUoAiKygNMMmSUkJBlrhgNllBoQvPRL4kgjD5LkUPEsgkSHdp1Uyy/+enL+P+J18T/5cbQ8l3nRmTgtKgFM/QeB4vVAJfuaJHv8DLn5UWnEUVle6Qn4IiNWo7LPlUfkkBSkpK8QQqKM7vZAVFTvHIf9ccoFju59SDMWUWj6agpENsfzWUVvfadsqVQs7nMJ/DpjQgpW+iKih6iieoC2ubZJItaK+ZEQqKe03wU5pMDSTDps+mhEnWv9V9lGugSUF575lL8K0/fqtUIVndg+K2uq+ioNTZB4VoVhUPfS5XQWl9eND6LTiCKYYIUGiRmF+p3nFzw65EHWZQVk0pHq0ss15zKC2UFKC0qoqH1KDJfFEEYbIxTfeBCAUlFU7l+N6TewEAA5WFXg7A8j4Biqug1Jji0Z733OsjIhcuBxFBJlmS3BdV0omThZK42MqPS0smUtngTMFve0aq4gkIbGk/dkoKCm2fV0HRUjw+/T50/Pug2Mp3kSuUFMOlPBCu1kaC7gJnee5G5TRS0OdQHiNSPN4eNESwByW8SZbOBzn1lQpode/XVTXYgxK8X2UPCqlsHg9KwFBCHX0fU4NAwPV5yAGKKDP2qabyLTMWU5gblOJploIiGrXNUJPsunXrcO6556K7uxsLFy7E1Vdfjeeff155zNjYGNasWYMlS5agvb0dJ598Mu666y7lMblcDmvXrsX8+fPR2dmJq666Cnv37q3/08wwSpWTeiznfxGn4KWrcnHJync2os19GAXFCVCGJgqhe23odzj1GmWHNQXF9aA0N8VDKsakJOvLMqqeT6eLPknb1RQUev3TKkPrVAXFPfnlO7qgRScM+sVe7ucwFKCgmEyyfZ1ZcREmX4l8Z+6URSeU38tkI7e6dxeHUS3FQ8f9qI93Ru/3oaNU8WidZAs+CkpKSvHYtrudO18b9iyQQah9ULx+EkVB8ZHTs4Yglo4V03FY1PwiMnolTNANjnw+TFQ+M+1LEaBI+y9SiifkIEa5bN3PExTFJKsrArJJmT6vHHTlfVI87hgE82cWAUqNKR49pdPsRm1yB91WEylA2bp1K2688UZs374dmzdvRrFYxOrVqzE+Pi4e84lPfAKbNm3Cfffdh1//+tf4xCc+gbVr1+KBBx4Qj7npppuwceNGbNiwAY8++ijGxsZwxRVXoBTQdXI2EkZBoQsinUyiP0GhpAwjq0ZPW1oEBmErefTFst4AhZQSV0FpTRWPnOIpSneRKZ80i66gmPpPKI+vPH9el6N6DRsUFL825jWneMp6MOn+/7D0/rrfR/dl0LbN7XC2nRQ89y7aUv42zf0Jk+KxbTdg6KQ+KAVJQcmSgmJO8dBmV8uT08Jf0iq2CkVtmrHU0j0jVfEAzqK487VhXLH+UVzx1Z+FngpOC2wmlYB+ra9FQXEbtQUpKJXXM3pQKpUw5EEJaHUvL5IUHNICZvIX+ZXHB5lkw/ZB6cikRKBTKqtKrnzMVsOjoGTkAEVNbwCQzOzmaip/BaVY2e7GKChN64My08uMN23ahOuvvx6nnnoqTj/9dNxzzz149dVXsWPHDvGYxx57DNdddx0uueQSLFu2DH/yJ3+C008/HU888QQAYHh4GHfffTe+9KUvYdWqVTjzzDNx33334dlnn8VDDz3U2E8Xc+hEm5RK+WRs23YVFEOAEmWSJxA9zaPLtlN1GAYBYLyiFJG02qoqHlIx5Py67KvQL5z6yPVqKgc9f17F2GzyoPj1Vqg1xUN3lqYLdXCKx6ugpJMJ9HWqAUpRSn8AMHpQiPZMUpRk+5lk5ePdTfGYPCjmFI9QCiIoKPrUarXVvZsy1YOHfLEsyrV3H5zAR771RKhgnczJy+d3wbIs5YKfTljK4u13t2oKYoLLjP33i57iCfJvJBNuu38KaDwKirSY+5XHm26ehEm2mgdFzOLxfh/6v6PM4iFMCoqS4imYz9VqrfonpOqjWpBTjEAThwXOtkZtw8PDAIC+vj7xswsvvBAPPvggXnvtNdi2jS1btuCFF17A5ZdfDgDYsWMHCoUCVq9eLZ4zMDCAFStWYNu2bcb3yeVyGBkZUf7MBuSFbjzvVVGmCmVR8koBCkX6uUIp8tTMqJU8+t2v6aI8VSjhun/8Bf7hpy9XfT33zsL5LORBMbUyD0upbONPvvUEvvLQi6GfQxeeYtkWd2mOr8K9S5MRfVCozDhkgDK/oqDIi7iroOh3ZcHGu2rQe9LgQZmoJllZQXFTPKqznxarw0YFJVFVQZHfV8ziKZQ9KR76+5DWk4WeXmsfFM80Y810LgcT+VJZubPe8coQPr1xZ+D7AsDTFdPwmUfP8WxrKqyCIgWEltUok6zqQTF1ewWc71F+vGdYYJgyY0PALDwoVRq10ULflk4q1zglQKmjD4qsVAgFpeB9bT1NlRLDAs0eILoJqVVBcbbHfW7zUjwzXEGRsW0bN998My688EKsWLFC/PyrX/0qTjnlFCxZsgSZTAbvfOc78fWvfx0XXnghAGBwcBCZTAZz585VXm/RokUYHByEiXXr1qG3t1f8Wbp0aa2bHSvkhdCU5qEprpblHuxGBSVkgNJfqQTaPxJuaq6+WJruhn+55zC2vnAA927bXfX19DsLUlJqnT8DOCXDP/nVG/i/j1YPkAi5kobSSylp4qyuYogqnpAKikjxdJKC4n4+XwUl6X9XHAZ6z/ld3gBlZKogXndMD1AMnWQzqYRIw41Mqn4FujiT3G+a+dOWTvq2Qxfva1BQAGfxByD6Uxy/0On2ueEXe/Dzl51+MvI+qjYvxLeTbNFWgqdcsayk+yzLUhqK0bFPc60eePo1Edya2Dc8iX3DU0hY7hRyRUFJWlofHPPnoMfIzw1K8ZTL6vck45YZa63uk+YFkAz5Yx4PimugJWrxoISeZpxOKoqC/L1F86Co+1De/1mTSbbgM4snIEUlDyKs1YPiPNcbPE03otX9bKjiWbNmDZ555hl897vfVX7+1a9+Fdu3b8eDDz6IHTt24Etf+hI+9rGPVU3f2LYt7hB0brvtNgwPD4s/e/bsqXWzW8rwRAGbf/WGuGgXqwQoIiWSSYl9Q3LfZMQyY8BbZlgNj6/BUJFBd9AmBUiHAhQKTIIW/D2HJvDTFw5UfU26oIzniqGrLWRp2h0L70rwhZKNweEpbHl+f6WDqvNYMihXVVDKqoIyMunOkqH3jto+uxomBYUucrbtfk4Ket1t9Soo1OkXcO8o5R4h8vaaUjxhOsnKKYEuKUCZLJRw3MIunLvMuYF535lH4Z2n9iNfKuMj33oCL+0fVY6XatdQpYqnyiweXZGUvRK0GJ+xdA4WdmdRLNvY+fqw7/s+/ephAMCJ/T1ioUoqAUo4BYUWTlM6yGySrSgohpRHl6eKhypgzO9NNxJjHg+KaqZ0Xit8FU/YMQJuiifpBIwGVS5KJaO8jzsrr0kYUzw+N4DiumVQUEgltqz6Aov2liooqt+sldS0B9euXYsHH3wQW7ZswZIlS8TPJycn8alPfQp33HEHrrzySpx22mlYs2YN3v/+9+Pv//7vAQD9/f3I5/MYGhpSXnP//v1YtGiR8f2y2Sx6enqUPzOROx96AR/51hP492deB6BeYExpjnFhkPUerFMF9wIbJv8K+De98kOXME0pHvJXTATcTRJ6AyPR8MiwiH18w1P4w3/8BV54w7+JHeBeJMu2d4x6tecA7r5IS5UWxZKNP//eM/ijex7HU3sO16ygzK8EC/lSWZz0viZZw3TgKNA29bSlxYX8pP5uYTalVI0nxWNQUNLJhOeCXdBSPLRI0vdP6hwQLkCRg4W2tGoi/aO3LxOLRyJh4csfOANnHj0HI1NF3Ln5RSW1UbOCUtZMskVpmnFl22WvhDzZllI2FISYeEpL7wDu8e78W6viqeJBCVvxFVRmLFfxyB2N/W5w6BgY9fGgyCXkpuPWr4FkGAWlIHX6pWte1nC9iOLDkxUBWbUDZJOsnParnKtRFBSpB4rfDXcY5CZvzQpQRB+UyjnvV1nWTCIFKLZtY82aNbj//vvx8MMPY/ny5crvC4UCCoUCEtpFI5lMoly5IJ199tlIp9PYvHmz+P2+ffuwc+dOXHDBBbV+jhnBgUrTtTcr83VUBcW7uOoVPIAU6eeje1C6I3ZuDZPioTvzfNE710ZHb2AU5OYnn4w+S0ZHVkPCloDKn0OkeKT5MsVyGfsqlU6Dw1NeD0qAUiNXp/S2pz0LuV+KRxh0a07xuMHqnEp65sT+HjERmEqNdY+HySSbSUkBSrGkPE6keOguvvL0s5e5Kds2qcxYH/JHyI3WLMsSSkFvexrvO3OJ8ti2dBI3XOhcaw6M5SIqKJQ6U1vbOykeySQrpUzpuHSH4rkBZjaVxJlHO5/1qT3qTZYMBS9nLp0jfpbSFBT5vK1WxSPfhIRSUIyt7p1jYTxfUlSI6gGKe44APq3uDWqC3+uGGSMg3wzRdqQNQW+uGBxkycgP8QYobkqS1E7Xg6IGCEEBlkhj15HeAbQUT9NMss73m5upCsqNN96I++67D9/5znfQ3d2NwcFBDA4OYnLSuZj39PTg4osvxic/+Uk88sgj2LVrF+69915861vfwnvf+14AQG9vL2644Qbccsst+M///E889dRT+PCHP4yVK1di1apVjf+EMYIOeDoxS9JdpGlx1St4gPqqePwqIvwI0wdFrlCZqKJgTGgzKoKGhtE2mspY1W1yn2sK8kyoKR5XvpbbzZMa44ybV2XkIJVDXjzTCdfLMSwFcoB/mXGpxioeWmxTSUsYXE/q7xb/Hp4kBcXZDqrSkZUMOUDJelI86kVLXwBPHegRx2FbOum2ivdpdEb7iV6P7mD/4K1HG2ePyMeKvDBHUVDURm2qgjJZKIkRBJSLdxciW2nadUYl6HjKR0EplMp45jXndxTMyNtC76GUEPvcrdLnlj9nykdBUfeL9/VIiS2VbcWL5JcekZu1Odvon+Ix3Zz4va47zdj/WKfzz7Lcc8V0vQhqNqejKCgBpbz0ufwUFLdRm7O/v//Ua7jk77bg+cHRukuMCVk1afawQFqnZlwVz1133YXh4WFccsklWLx4sfjzL//yL+IxGzZswLnnnosPfehDOOWUU3D77bfjc5/7HD760Y+Kx9x55524+uqrcc011+Dtb387Ojo68IMf/ABJH7PWbIFOJlrgqnlQhIJikPuKZVss+OEVlKgpnuoeFDmACDINAvLdhZpX15WagpQSqRagyMFGWLOtySSbTrqN0wqlsvgscjM3ulAGpXjkz5JKWp4AxVVQzL0Vah1O5wYQCZy7fC4yyQQuPH6+mHs0NF6ofF5nH82pBC6mWTyZVMLTWVOUYyfVxZuY057BO05ciO5sCsct7FIu6iavgR7wnLusDwu7s7jugmOMn0++e1YUlCrXULcPiq0sos7wQPf/cgxF7yWXk8qL1WlLepGwnIni+4a9FXHPD45iqlBGd1sKx87v9GwLUEnxROiDIi8WCVKFfKZuy4+Rka8jcvM+vxscPVBMCwXFfxaPvDD7NZAM40GZylcqYaRUSZD3JWonWd3A2iY9n455vyoePcD60c592H3Q8czpN2G1Iu/7pg0L1IK8OCgokXSoMCbE/v5+3HPPPYGPaWtrw/r167F+/foobz/joZOJLiRKFY/BZOr2DXG/JvnCPyotrmHwG1nuh7dRm/eCoigoVQIUuivqEB4U84VKvrurGqBI2xQ28FIClEmSr93S0mLJVVDkcfPyLB7btnH/k6/hlIEenLzY9UTJikQqaYlSajKT+isojfGgZFIJ/PW7TsGn3n0yOjIpEYgcniwgV3Sb+82tBC6mMuNM0pI8KKqCktY8KERXWwp3ffgsTBXKaM8kPTNN9LJqPRXxzWvPRr7kfRyRle6e9fRQEIqConxW26jcyZ9RpCCLrkm2LZVERyaFk/p78Kt9I3j61cNYvLJdef5TlS6+ZyydowQKigqimWR9Z/GYUjyWueKrVEVBSSQsdGSSmMiXxPHo5xMBvCZP7zRjaRaP5IGakEr3jZ/JoIS8dngSW36zH//9nCXIppKYKJBfzT0eTL4mKlUOY5KVP6ee4klVSsuLZVvyi/lU8VCZceUz0+OHJvKem7BakQOc5rW617/vGVzFw0SHTiaS8eXFISjFQ8oH4Fwc6Jrs3v1PT4rH06jNkMKRy0wnqlTy0OepluKRAw1TGauMHGyEV1CkFI9kkpXn4cgpHtM04+deH8Et//ZL3Ppvv1ReW1YkTCmealU81Xw8fsidXi3LEkEgBSKHJ/LKfiWfil+Zsd5Z0y0zVqt4iO6sU2nWrn23gNkoS4sp7XPZh2JCXpzCDgp0HuMGfkUtNeAboGgpHsfkrC5WwihbMcMSQ+N5/ON/7QYAnCWld/TtzSQtZQKvfxVPJcVjMNR6+/WUPY/R6dQmVQddO/QOpqIPiqRk0E0rXdPka5Xfa9PPy7b7Gf7+x8/jr76/Ew887RQQTEo9UPTnmcqMw6S5lSoew5wc+lz0XQu1U6/iSaneOTqnD08WpO63DUzxNKnMWO8TEwcFhQOUEFQrKw1LXldQpMXM3AfFW8VjWZY4eMk/EbYPipziCaOGeat4KhUz0v4Im+IxNTAyXagAtfV9tS6zctAUptQZ0FUXqcy4coKO5Vw/gtPlV81Fl21bXOB3vTmu7EtaBBOWc8c6J7QHpXr6KAhhYvWkXihAKQhlqjPj9imR38+9ICel2SQVk6yo4vGmHAC3hJVIJNyBgiajLF3cw+a53fkn5cBKFR25fFvetc40Y+++TiYsoXrIQVFOfG/OfjH5UKYKJXzkW09g15vjOGpOO649X01XpTQ1xdTGXoceY+qDAqjnomxf8ltcurSqrqBrh37n7g4LdMvX6RpBf8vHga8HRfrcdBxQAcHzlQnccg8U8XqUcqvZJOuvoADwGMP9phnrTRVpGw5LCko9PVAAVYFpdqt7YsZ5UI40pgol/NE9v8Dbv/BwQ+bFiBRPyeRB8S7u44YqHsA9YKMrKK6sb0rX+G0vMVUo4acvHMDKz/4YG59yhjuOhEzxmBoYyRG7/F6jUVI80sUqdBWPKcUj9UGRP9NkwaCglNxgayJfUgbZ6V4NveGZf5mx+a44LBRApLWLyhypI6zcRt5UKlkwKShV+qAQXYYLfkYKKnTCTiMWryV6YNiRnksXWV39K5Rso0lTvkj7mWQB1/z6zGuHRVD6dz9+Hk+8MoTuthTu/aNzPU3z1E6yllIdUs2DIp8r8uvI15BwCorznuRBCfJueBQUSvEY/EWyItaeVm9APJ8p6X0+nbu733Tmusk9UIhsgIISJs2dCjDJAvCkNfWgVLyOlpqmG57DE4WGm2Rlk/B04xloyQFKfCmXbdzyb7/ElucPYN/wFF6s0o8jDLpJNnQVjxaNuwoKXWTCHUidmaQwFYZJ83g8KMUS/uulNzGeL+GR550maqoHxT9AMDUwUgeyeVUN/fVN1GaSdZ8jBqEl3D4ocoAkV/GIRm22rbzGG8NT4t/Cq1HZ0b4mWU/77PpMsjRZ1aOgdLgKihjE15YyGpTluSZtolsopXhcE67pfYwBSkC7+4KP4uOHq6C4AaPJCKqTEgGKug2OgmIKUNztUTrJkgelcgwcO78T6aSFqUIZ+yt3/9srnW7/13tOxfGLur3bor12GAWFFmV58VAUFEm9o+M0YcHXm1OXgkJVPNJzSGGTU4wUBPldl+RgoiACfeec23XQCVAm8+r+Bnw8KBG6acv7zaRwuJVrqoKiBwjujYrze1JchiYal+Kh57el6uunEgWPgjLT+qAcSXxp8/P4j2f2if/TCVMPBaGceD0ogVU8Pk2FZP9EGCzLEheoML1QdAUlVyiL1Mb+kRxs2w5tkp2U2tzTCadPjCVqV1CiN2oTpaVSikdWy0wKSqlsK2mifVKAoi+8njLjEqWLzHdltZYZCwVFu6jMFSbZvPjOu9tSbkDkU2as3026d8hmk2x3m+GCH9DuPqqCQotEoVYFpagrKGYPinxMytUmooqnsh2JhIUFFYWEAhT6m9rz66Q0BUX+rvymMptSPCkfBYWqeoJKr+n8Pzxe/eZGN3rS+5rSdyVJOdSr9HQsy30+nS+kIL96cALFUjkwxaMGKKqXKQh5H5sCar1yTVfN3NdRU9OugpLHuLjO1ZviqQyHbVIFD+C9WWAFJabki2X8n8rwO4pkw3Yprfa6gKyghDPJ6icTHbR0RxzWgwJEa3evl/9OFUrizmv/6BQmJAMpEBygmMrvkgm3tLdYo4KieFBqaNRGyCZZ+f2VPigpKUCRLpKDsoKiBQoeBaWyvVEGkIVBnzZMyGXGB0anKj/LKOW3hDtePuExDOozTzyj6wMUFJMHRR8+WA3ZIBnFJOsqKOqxWSzZxsDJnOJxhwXKixWNFdg/MoVS2cbBSgPGhT3eeUj69oat4qEgT95PCemOWr6G0DEQtF86NQUlaGH369Ujb5cY20HHX8JCR2VxDnpttwleZVRFRUEplm28dnhS7MsuxXTrrfqTjd3VUBQUg0lWT2v6eVAUBUhS1w5PFDDZ4BRPsyp4AO9xw1U8MWVoIo9CyUYyYYlBX9UqVMKge1CqByjeMmPAjfTJJBtWQQHcO90wfg19QulUoeQqKKM5T/AQZJL1K7+jBUS+6MjbFkVBCROg2Fp6RmxHMiFOUCXFI5UZ0wW7pL2GoqAU1btYUWasKyg+nWSDPChBPighsWsL/hzRqK2Ap/cMAwBWDPSIShU5wJSHx2U1w6D+/ckXs7Z0wngMugGKd3+7i2m4Y1cODqMYbKm5mJ7iyYdQUBSTrOiD4h6/C7orwzdHczg4lkPZdtIrNCRSRw7qMsmEcgxU7yRrTvHIx0uYwE0PUAJTPNq5Kr+u3k2WrhXJhLmSy2876FyfkNTPXW+O45m9zrF66kCP+HlGC4ps25bSktGqeIwKSlpNa/pNHpffq1i2xU3HZKEkvD2NKjPWS5ynkzj2QeEAxQAtwnM70qK5kanENiqBfVACUzzqwU4Hf1STLBCtWRstInQyTxXKSsv0wZEp5fHBCoq36Rwg5/nNKR6nzbj/68pqSJigq1i2YYoB0pJsLZtk5e+FFgvZJAsAgyNusy59Zo1XQfGrDPCmXGQe/s0bOP1vfoK7H93l+7mcz6G+LpUZj+WKeHz3IQCOwdNkknVn0Vieu0ldAVMv9mnjNk2HSRZw1cx6FBR51ktSCxz0f8uzeOSGXqSUHBjNifTOvK6s73YpCkpCNcn6elBMjdqkhyoKSogAxfWgVL926HfvpuCN9otsknWr9AK2g26UporIF1U/0O43x0UvmTOXznXfUzPJysF1Izwo+vwpv7EUSmpaqvACIBr3NapRWzMVFG8fFA5QYslQJUDp68y404NDDMOrRkGr3qlaxZP39kEB3BNJNEQKaZJ1Xit8iocWLxGgFEtCegWAl/aPKY+n5kom/BQUeSAboftjglSUqLN4/GbDOI2avCZZ+TXpTqps+3tQKKijCyalWCjoyfkoKKJRm0+K56lXD8O2gZ2vDRt/76egdLelRd+cVytzjc5YOsdokpUNvPrF2ttkz91+k/8EMHsGCNHqPnSKx30cnYvh+qCoXgmiUHI7ycrbnzKmeNyAVFZQFna7HhQqk13YbVZPAN2DEm6a8QVvmY8TF3XjitMGxM8sy02NyibZsl096KMbBGrUFpQa0at4TAoK7UM52KNjJCj46ZYUFF2d/vmuQ3h9eAoJC0LBlrfVfU/3Ow1zDaxexWNO8ZjUTjqn8prZ+vXDU5XXr8+Dcu6yPpyyuAfvPfOoul4nCnFUUOrbi7OUg0JByUizb+o3yboleaoXBXAXwv+95SX0tqfx4bcd4/at8CkzJqJ5UMIrKHTR6W5LYXDEURPk4OG3WoASFMT5udvlPhWEHjwNTxawSJqWK6PO4gkRoPioMbJJVglQpgwKSllN8QwaqnhMCoosSfu1uvfrg6KbbHV0jwiRTDjt9qnh3bJ5HZjbmalqkiUjMwVitG/pfKgmlwPSIhai1X015GO8FgVFPzaLJTdV1N2WEvtHMclWFr28MizQ60E5MDqF/RV/T1CAIqez0ppJ1u+zHD2vAz/+xEWG17Kc5nMGD0pQdZOnzDggQPQoKNL2Z0WVl3pNSyUtV0EJCH7kFM+49t3852/2AwBOWNStXPsymupH6VTnc0RTUIx9UDwmWX9/SzqZQL5Y9twUkapcb4qnrzODH/7P36nrNaISRwWFAxQDlJ/t68w0zCRr27bHgyL3LRjPFfH64Un83Y+fRzJh4Zpzlhpn8QBAu2d4VfQAJUwVDy0iJMfKSgEAvKgFKEHD+iZ83O3pKikeIIqCUv078lvg00m31b3s9RhVFJSKe18zySoeFJG6UKt4CpX2+TmfuzJ5krIJ2gd+A9b0PiUyczsyYgGm/h0pTUGxbVsxHFITNDKl6gFmMkSAEkZBCXsRpMqPQsndlnqqeOQ7XydF5UjzahWPIcWjKCiuB2X/SE75mQl9mrGqoEQTs+n7KRs8KEH7hb4rvZOviSAFRU/xyJ2GKZgMunGi7RidKnpuLOh4OVPrxKsfT/K5HOZYqNpJVhqOKJ8Ppj4k6YSFPLw3RaaZRDMFXc2MQ4DCKR4Dh6QUT7smdddKqWyLklaTB6VYtsWCXyrb2Dc8KRYzTxVPQG64GnqK59WDE8YFRN5Oev83RvQARe0NMxmY4jG72zOGFI9HQQlodx/VJGuq4AEqjdpEFY85xSOMmppJdnjSbdCkz6zpyCTFhXF4siDme/i3uq9RQdECIxkKkgC3RbveGE4+PuUUD+AsQh4Pilyy6ZPi0Y2UMm61U/hjlxY8ullIhOokq1YjEXKZcbehUgTw6yQreVBEFY/rQVkQlOIJGBYYdTEwKW5UZhzoQdG+qygm2bRSxaMGC6Yy48AARTLr03mrBwJ0rIpt1Y4nOaAO0ytE7uth7iTrHivyMWtUUCo/G/O50WtmeXCj0M/FOKR4OEAxoHhQKMVTpwdFVghMHhQAeH5wRPxb9ndUS/GEHRYIqCmena8N46K/24I//3+/9Nlm9QKul8DuOaQawsL0QdEDFHkgG0EBAl3gghWUiAGKTzCWTiREczU5KFGm3Eo+ET3QoTSP3gfFstSJxn6ycbVW90JB8QlQ9NSSDBllAdd0qJtk5cBH7oMCOGm0CdHZ0zkW5HRFTQpKiJJYHVoUKNgI41/x86A4KR5nG3p85sfIpkyTH4FMsm+O5UTw7ldiDGgKSiKBrNxJNmJTrIQWYDr/rp42068lwQqKv+RP1SW0X+WUHR0PQf4W4UGZKorrxtF9Hcr14Sw9QKnsr7xI8bieqTDIKSpjozaR4il7zgcd4VfzuebU2+q+FejHDZcZxxTFgxJi8Q2DfMCbPCgA8JtBV5H47QEnQNE7TgIGD0qEVsiygvJUZdDZKxXzpI5ukvVjca8jawdW8RhaVwNS/w+DH+eouc6U2MAApSCneMIEKOZtlBUU4+8Tbit8R0ExByhFwyLRK83Dqdbq3m9YoD7LR8fPgwK4pcbZVAInLXaaiOllxvLrZiol1xQ8ThVKor8DmQvllvq+AUoDq3jk1xMm2QizeCjQpLeTp1TL229K8ch3ybJJlsqJZfUzkgdFMnZGVVBMZelhPCj6dxV07QhSaoUHhUYhSAre7522GBe8ZR6uPnMAfsgKCp23XW0pHDOvE4BzU3Ts/C71/VPqzUyUNveAc+25/oJluO78YxRVkZAHZMo3IKYAiN7T76ZoZqZ44qegzLwwrwnIHhRaGOpN8ch3vsKDUtIVFDdAIQXFlCv1Sq8RAhTJnLZ3yAlM/IYhFoVJ1lxGSizubcdvD4wH90Hx8dOIKh6DgrJkbjt2vTkueoiYkAOFXLGMYqkcGGgEV/H4n5DyEDndJAu4PhRTw7SeMApKFZOsmOXjkwIqlvwXfKokWnlUr9toTVdQKtuVsNwLVVsqiUKpiFyxHNgHxS/FE9jqPiAl5UdaS/GE64OiPqYjk3IqR6TzWT6+TQGKfJcslxlnUgn0dWZwaDyPXZUZMgtCelBSyYSy8EVdDEwBSpigz3P+RSgzNpVj03kgV2Wd1N+D73zkbYHbT6XpchVPZyaFgd40fr1vBGcsneMJtPQy43xAUO7HZ6861fd3cuVatfSRCF59ApRmlgc3Ch4WOEM4VGkDLXtQ6jXJFhQFxetBAVTTqRugeC/+9XlQ3BTP3iEnReO3KBYMd5gA0K9V1PQLBSV6mbHuzC+VbXHSL6koKEETjfWqnCCjrvN45316PLl4K1BmlxUU2SRLPyP3vqnclwIEx4NCCop28TeU/RK2bQuTq59JlqpxTJUTxy107kR/5/gF7ufR3s90sc9KF2w3Recd9OinoJAfY8crQ57flajZWoTUBqlOdC6GCW70xZqOPzl15+dBoX+TgpJMeFU2XTEJVFC0104lE0LRiaygkElWnsUjPCjVzalEJqA8128cg/M7NcUjz+IJg9wHZUw0pEzircv7AADvOGmhd3t8SpujXP+CoOAzVzCn9GRMFX8yput23PEMC+RZPPHk0LhjeGtkHxS5JK4gPCjqYiNL4RSsmDse6lU8UTwolOJxAxS/7qVFnxTPif3qrJGBSoASqKAU/Dwo6l2RfEdy1JwQKR5twR6r0vGX7vjmdmaUn8vDAk0kE5YwZRal+RsURLkpHq8yIE80rpriMXwX8kgBP5Os22rc+xn+4Nyj8YM1F+LPLnmL+FlavJ+qoMjKjmwaHNdMzvIi6NcH5b+dtQQA8PBv9guFQWyvaCwXIcWjeVDCiC/6wm+S3rt8PCj0HY3mCsr/ZXRTbKBJVqviAeRZOxGreAzHi1sZ5f88XZGNoqCkEt59Y5rFEwalD4qkrn74bcfghx//HVx3/jLPczxVPFWCiKjICorfHB6xLVUUlJmY4mEFZQZg2zaGKgrK3AYqKPmS+3zdg2IKMCgyD2rJTNTWB6WA1yjFYwenDXQJ/yQtQFlcCST0fgYyvn1QtDt5MV8olRAX+6AARU+9+bnqCbqgki/D3Q4rMNDTU0B0PFDO3E3xePPiFKAMTeT9B5BpAYOM/Pn9TLKuOdf7GRIJCyuX9BqrRgqagpJVAhTXNKgrYGE8KMcu6BJ3wv+0bbfyO3cxjZ7imchHUVDUx3jVR0v5mTHFM2WuMgHUgKSnLeU5N2X0WTyAq0hF8ZHJr6WkeEKMD4hikk0nLWWbzWXGqpoRWkGhMmOpD0pHNolkwsIpAz1GH41+MxPku6oFcbwXS75KJ5EK8KBYVuOCpmYSRw/KzNuL08x43s0/9nW4fVDq9aDkJQVFzOKpBAYmwxYRKsUTySTrtrp+c8zx2vgpKJQ20BegtyzsUtpt94dRUCp34O2eVvdqiocWg562lKdNvAnd01HNKEsBQnc2pXyGdDIReGGXPSiA+1mXzesA4La716t4AHf/7Bue8h1A5k4z9n4XSoDiWxIezTAoKpI0k2zGoCBMFUs19UEBgD96+zIAwL89sUfpL1OTSZZSPHlSUMJX8RAm/5a8CJmGBVLQawo+5L4nC32aCRJqFY/z71tXn4g/evsycRyFRS8TB+RpxgFpm5QaaAcFRpZlKZ4btcxYnYtTijj80U3xFMQiXy0t4tcHJUon7SDkTrJBTdoA77EhnwMd0sT2mQRX8cwAqMS4PZ1EeybplhlPhweFyhylAEVvwRxGQamlD4p8YfObT+dXxbOgK4v5Xe6dI1Xx5Etl3yoUscD5bLseoHS3pRVzqYliqSzuxOdVUjbVSo0pNdOWTiilgKlEFQVF8qAA7hgCUlDcMuPKRVN6LKWq9g5NSv00zPK5yYMif37fPijCExPuWNBNsmK7DQrK2FRR7GeaVBumDwoAXHjcfBy/sAvj+RL+7Ym97vYGlEX7Qd+P60EJX8VDmFKMcspU7SSrmmRNd8Wy5yTIfwKoQSu9zwfPOxp/feWpkRc0c5lx9dJty7KUQKDatUMO6MzDAism2RoVFN0kG4RukqVjvlEKiuy5qpY+SmtlxnJ5uX4TNlPgPigzgINSDxTAVSvqLTM2BSh00ZcVlNOWzNFaMoep4gl/IJkCHr/upXJFinyi9nVmlBNycU+7+PeETyA3Icm4MikRoDjvNVbJ93e3pTCn3fkODvs0apP9J31hAxQpQJADvXSqehVP0qCgDFSCD2ruZ5oxs2Suc3f86sEJ8Xv/Kp7gFI9vmTGlC0OqaSmtzDhnUFBo4aaqNkCu4pE8KD7DAgFnQXxfxYtCA+CAWhUUVc2MMouHyKaSkGOBTEpXUKQqncp3GCT3y+dB1QBFSfHUd/E3KSgibVYl2JGvAZkq26GcIwGt7k3eqyC6JZOs39R2He8snkabZL0eFF8FJaWmeOTCgZnoPwG4k+yMgBSUuZ3ORZcuyFMN7INS0DwocoByzLwO5UIXJsUTxYOSTFgelcYnPlHucuULVV9nBgsqCkomlUBPu5su8UvzTGhVIETaJ8XT3ZZCrzRozzb4ZEwBStgUTzaVUC4k6UTCk4PV8+9y51JSUKgaqGw7io67z9zXWkpGWqkTb5RGbWEUlKJBuQmC9js9z2iSrVywD1ZSgWmp+6kyiydAQQGAeV3e4LEWD0pGU1BC9UExDEDTm7EpCoqULtC/o2zau60LJCUxyCALqMdTvYsqHYsluYonZJrFr++LCTrvLUtNqenBQjFqiqeyDeP5kvCdmQb4yWQ0tbVg8E3Vg5ziMaU8ZSgQo+vN3I6MeOxMDVB0gz0rKDHEbXPvXGwaV2bsNbPRRbpH6sNw1Jx24VkAGp/iAbx9TXyreCQTr3wRn9uZEbn33va0IxtXAg8/pcm31b120aEZQd3ZtAjc8qWyMhSQoGAjnbTEPgyb4smm1QAllbQ8J+RcyUib1FI8tD3yvnRSXN7qlPldWe9iF6HVvdzqX+/mCzhlz/QVhq2i0JUrcxUPDZZz057u88N5UAC1YoMwmYmroXtQwpQo699pWus/ktaC77TSTE3dl21GBUXyoAT0QNG3pd6Lv2uSlZVZ9Xd+yKps1RRPmkzR5uPX7SQbTUGRg1oaE9AR1YPS4BQPHQe5YskdDmkISp33VEvQs+mEaCcwUwMUVlBmAKJJW+VgoxO0WLZ9KyjCIJsb9Vk8soKypK9deDqA4JkRRBSTLOAtCy1VqeJJJdzW55lkAp2ZpJC2adtJafILENxhgX5lxmoVT3dbCp2ZpDhJTD4U4SdJJcVFN6iSCAhI8Rg6yfZ1ut9LSjPJEvK+zBfLbj8S6bUSCQtL5rhpMMvyLlDusMBgBaVUtj0BpTyROOwdrJ5SMsnldHGmoF337BB+ZcZEl9R7h4g6LBBwg1laPMIoKLpCk9L63ejpS1MVD2FarBQPSkCbe31bop6z3teiAMX9GQUr1faLMiG4ynbQ+epJlVEflILWqC3k95lNJcX3SWMCugzpbBlfk2yD+nXIVWs0nVpWyGT0Jn7ZVFLc0MzENveAQW3kPijxQ7S5r6QM2jLuLjKpKHuHJnDRF7fgG1t/G/i6Zg+K8zMlQJnbgX7J02G6OzWVSkZBX1D8OsnKzbvo7rGvMwPLssSFmbY9aOpzSWps5i0zVlMNory6LeWZY6NDE2qz6YQyvj0IvxRPKpHw7Me+TllB8aY2AMdTQz9yOtma+0FQ2356b90UKfpaGIJg/bPrgbKsqpj6oJjQq4aoDN5UZkxBe4dimHT3RzWJvcvw3UTtmwHIZcZF8d7V8ASCiYQnCFG9SP5pGNPn7MymRGqiWopHn8VTD8ZW9yGDPtWDErwdFIjoi5XHsFqD6ZluKg4aAmATemfixpcZu8ZfahvQ39tufCwdt/KgQ0pJz8RBgYD3mOQqnhgiBgVWouGM1O3R5EN58tXDePXQBP718T2Br6t4UKSuqYAeoKgKijFA8UmThMWT4vFVUNw7FDp5KXA7Z1kfMqkE3nZsX2Wb/FM8ctCiX4T0FI+roDjbGBSgiHRNKunmtEObZBNq/4tUwnNhp1krgLu46CpKNpVU7uz8UhdklAX8Zns4P6tWZgx4fShyUBM2WBUzkPQUj2ySrQSl1FlZPu4W97ZhoLcN5x3bV7UCpbvNG6BE7ZsBSCmeOkyy6aSlfEbHJKt6Ukz/Bvx7Ylx4/HzM7UjjlMU9obel3rtTU4BSFkFfeAWlWokunSP690QVL3QO1hJwkrJGl59qqUKhtmrda6Ne//yg77dQsvFapYmlfC1Wt8XZH3S9a0snxVDOmZriSSQspfVCHDwoM1OLmkaEB6Vi7LMsS8zvMKkDJHHuPjiOqULJt1GT4kHxqeJJJy0s7G5TPCjGFI92oYza5MmT4qk23yWZEBckSnucvLgHz352tTiphYJi6ORKd7yWZeqCqy6Uch8UwN03/7RtNx7ffQgfPu8YcaciBxviLl1KI7y0fwyP/fZNfOCtR4v3cT0oSWXBTScsT/5cVVCckzWVsJCXHpNNOZ4G6p3gN2NmiaygGI4R0TitShUP4K3kkY+tsCmTUCZZquIZ9yoobekkHvnk74a6iIm5K1NF2LYNy7LcdESEiyB9hyLFU4uCojXky2gKSlCPED8/wjc+fDbypbJvAONuf+NMskmDSTas8TiKSZbOET3wqLfM2NmONIBJ8f9qC7tXtTFXxNWKfG3afdDpfNzvF6AYPDlUdThTAxTA+Z7pWsAelBhySFNQAAT2QqGTpWwDL74x5vk9oQwLLNsol21x57CochIcM68TyYSlBSjegz2RsJSTsm6TrI+CUpAm87aJAMVVFeQLMp2UJgVlIuf2QNHvtuluL6+leCiIWlTJ6//Hs/vwdz9+Hv/8i1fEc0W6Jp00pnj+5gfP4TMPPIefvXjA+xw9xZNUUzyZVEIJDunCq+f3s6mEKH+VFRT9LlYOUEx3fKJstEofFMCb4pGbtIXtpyFMsiLF4/Wg0Hd+aMIswWdSiVDN0uhOuSil+twS9vAXQT3FUouCktKqtdIpSwk80sp55VXLTFiWVTU4cbbF/7Wj4qbovKnjai9di0lWDzz0TrKmKd7V6NZuvqopKPosnlyjTbLSd7j7oNNl21dB0ZSnbDohrtt92hiNmYRs7mcFJYbQxVie1dKeody3SUFxLxDPvzGKlUt6ja+re1BkM+QZS+bgb99zKlYc5TxXrqn3O2nb00lxokY9kGjx78o6ypD/LB530aKOkmQe1gkMUESbdO9noYsLLexjUqM2APjzd56EZfM7sf3lQ/jlnsM4UHH8A+6dtKygjEsKzp5DzkWGOuYC/imelGaSbU8nld/TQqeXWjqLk3tn57fwqgqKKUBRAwYZT4BSVB8TtYICkGbxhFBQ8j7+obDIzflGp4poSyfranVPhFNQ1OdkUl4Pil8flDAelCjo04zrQZQZS7FqeAXFPYerqQ8UpOoBtx4smPr/VEMvTw9bxaOX9DcqQEkknPRfvuSWGfspKPpxlU0l8f5zlqIjkxR9f2YiznEZPoU63bCCojGkNWoD3LsIkwdF9gM8Pzji+7p5rYpHDgpSSQvXnr8MZx49FwCwqCc4xQO4i0cmaR4HHgTduRxTaa9tmsVj27ZWZuzsA33IHkF316Y+KJMFc4kxbT/gyrUjUhUPALxlQRdue9fJWH3KIgCqx0RWQ1wFxX1/CmYmlOe4vhU5YEon1EZtHZmkCEwB98IrP4Yu0vLF2j/FE+xBCdvqHlDnOgG1GRRpgSzbjnchqMyYqNX8l0hYHqNsPa3uiVAKiqEPSkZLtaidZKOneMKi9kGZjjLjcPtUrpap5t8QAYphQQZqLzN2tkMLUALmGAHq91Eo2W4n2Qa1ugfU7ziVsDC/02x81o+NtnQC87qy+NOL31LVLB1n5OMy6gDL6aD1WxAjSmUbhyuLgSlAMaZ4irKCEpTiURce+Xn6hTaTSuD3Vi7GKYt7cHSfeUaH6E9Qw4Xu7cfPx/yuLK48fQCAeVGUtzeVTOB3T1qA+V1Z/M7xC8zbQ2XGRg+KeVCgvP3eFI+q1LgmWPc7kP0kosy4sgCOS0PI5O62OanyRx0Sp/Y5ac/oCopzqiSUAKVSei3l4/1Msgu6smIxMN2J03vr6RvbtkWAQnFoXlNQRC4+YsM+8fxyWQQ9JpMsUU9uXfcI1RJU+XXfDcLrQVEVlEylLwrt2yAFxdQHJQpKeXPDqnjcn9F5XC3tFqnVvZ9JVveg1JDikRWUjkyy6nbL25ovlhtukgXUoHxRT5vvNnn3x8z1ncjIQUmYPkPTDad4JA5P5IUvZI5UWRPkQaETFAhWUPSFR36e6aT+3x86SxgKTdA21dJP4ayj5+LxT1+KQ+N53P6j36Bsw/Necsv1dNLCe89cgqvPOMp3e+jux6Sg6JNwZVJaimdUU1AIk8eE7t7a5BRP5ff7pVTQhCmo0TwoyYQhxWPo+2FSUOQqHtOwQMBZNI6a245db44bL2Z0YdCDxfF8SfysryODg+N5Q5lx9MVeDqBKZdtoONQVg3r6O3S1pYARYLQyyqAWBUVfTGsZFphOqH1Q5DTdVKGsBi/TqKDUXcUTYJKttk/VACX4saQueUcG+KV4wu8j2YMS5thKJSxYllP1kyuVhG+qsQGK+1p+6R3A3zQ800kpCkrrA5TZsVcbBPV76G1Pq4tVxn/xlZWQN0ZyODyR9zwGMAUo/goKEZS6EWPvazw5LUudLeNp/iUrKJXFM2h7gjwo+iRcGTnFY9u2CEC8BjpvIziayWIyye6X2srLqo6a4nFVKMtSqzs6PApKxYMi7YOslGYDKibZgDtJ8qGY8v5uPxj1eyD1JJNMiKDNU2YccQ6K/thCyTaWGeuBlN5/JwoeBaWGba5FQdFNzbqCQt+53IhQ/x1R712y0gel3ioeSglKx0JZeFCqpXiiV/F4/TjmFE8U34K8HdWatAHO9Uc+10Qn2QYGB7JKFhSg6DOMZkuAIn/PcfCgsIIiMbcjg7++8hTPYk0L65RRQVEXi+cHR3HesfM8j9MXFVJQkonwlRcydCLVc/cg34GWbFs5GKL21gjqg+I3hwdw88eFUhn5Ulm0bG/TgpkgBSWrKSi2bePAmKugyIGl7Ftp1/Lr8gnZnkkpHhRa6OQ7DLpIU6CSr2LcowAlKMWjDwukNvc97WmxQBe0Y66WtvHyY4sl83h5vSTcVFEWFr0XSti5MTL6ohCmkyz1dqDjSu+DQt8TfSdBaZh6FyG1iqfO1xIKivuzWhSUap+JAjffTrIiQIl+DKopnnBLUSaVcMr5S3bDTbKAmuJZ3BNeQfFrLzHTaOQ4hkbAAYrEvK4s/ujtyz0/Dywz1gOUN8wBil55EaVdtwn57r9W5ANQb78hX+zCBFBuJ1n/PigmBYWCg3xRnbej3613Gqp05GCDApSy7XxP+0fcAGVcCVDcGRsdoseD8/nSSopH7Y9Bd6zy9yVSPEn3Yu2meEwKiuMnMisoqmmVgkdSUHrbU24vF59OslHkdVLQqKIsnEm2jhSPFmDWcsftNcmG+7ypRELcIOjDAunfImWqjShIJSxxLtS7CNExYVn1352604xlk2y4/hVdtczi8ekkWyrbKJbKNY0ukBWUsMGvrKC4HpTGLaRhUzyNrvCKC/qA1FYzO/bqNEMnqbHMWLqLBxwFxYSfB6XWg6DdcEGNipyu0HuhiBLmkCd/mDJjs0m24kEp20Kh0hcRwMckSx6UdBIdGbeb7OuHJzUPihTUSN1nSaWhi55axZNSy5ANZca6ByUn90ExLJ6Xn9qPkxf34KqKOVlGPg7kEvThSTft6Keg1HonKRtz84bX0BfkalUWQXgClBpSPPrnC3tsqt6PhMeDAgDvP3cpzj5mLk7T2gQos4kaVGZcr0EWcI9Dc5lxBA9Klc/0tmPn4bQlvXjvmWrprOzHyUsBSpRjUPaZ+VUr6qh+r2lWUHza3DvvqStKs0NBkfdlHKp4WEEJQZgqnlMHevDkq4eVAOX/7diLF94YxW3vOskboBRq62FC0AWinpNT8aBo3gdxwQl5kHaESPG0p72HW0ZK8UxKbaN1OjUPAyB5UCpGx2XzO7DztRHsenNCDPsC/FUXWnCNCkpG7TQrd5Il6KKkNGqTSrN1jlvYhR/9z9/x/Fx/fEkJUBwFZU5HRizuekWY3KgtCqmEhRwcNaMQIsVTVxVPm/r9RR0uB3jTmYmQ6qNe3mtqZ/+xS47Dxy45zvueqYQ47+tP8dBxVv+dqUjxSApKOeQ+7Ypgkl3QncWDay70/Fzeh7mCHJhHUVDcQoTOCCkewCm1zze4kyyg+oyORAVFDt5ZQZkh0EJl6oNCd/GnDDhzOF6pNAcDgM//8Nf4Pz99GS/tHzN4UCpybI0Xq/Y6qniIZICC4tcR1Q9XQfGmeCYDUjzyfA0a/mcKULoyrkFU7yRJF5Vl8zoBALveHFMauk0aUjyZVAIDlQnD1BhP8aBojdqEghKQ4pGreKIGjnrZL+GmeNJSq2+9D0r0xR6QKqjKtjg+swFlxvUMQev29EGJfvzrx3rYz6soKIZhgYHvKSso9aZ4aMBiA+74hUm2hkZtnZkU+joz6Mgk0dNmbrxYjVTS7RskKyiR+qC01ZPisZGvXC8aq6CErOLRjr3Z4kGRb0jj4EGJ9M2uW7cO5557Lrq7u7Fw4UJcffXVeP755z2P+/Wvf42rrroKvb296O7uxtve9ja8+uqr4ve5XA5r167F/Pnz0dnZiauuugp79+6t/9NME2HKjBd2OwfzSGVBKZdtUdEzMlX03PXS82o9CNpF1UHtB5FikvWp4gl7MW0PkeIxlhmLoXWugiKbUwn5AkaVPG4fFOfxy+dTgDKhBChGD0oqiaV9Hdj4sQvwjWvPdrZFr+IxTe9NegOUrNRxNWpgR8gXdlnNUgIUkeLx6SQb8UIt5vGUy6E8KHWXGUNSUErRFDpADZ6AcGXGgN7BVS0zrtbkSz6/2mKuoIhW91U2M5Gw8G8fPR/3f+yCuhZWkdoslGvrJBuxzBiQbmhKtd8MBEE3O5YFMbHdhKcEfZYpKJYV/vyaTiLt1a1bt+LGG2/E9u3bsXnzZhSLRaxevRrj4+PiMb/97W9x4YUX4qSTTsIjjzyCX/7yl/jMZz6DtjY3Gr3pppuwceNGbNiwAY8++ijGxsZwxRVXoFTyLm5xwE3xlD2/ows7dQ/MFcuYKpQwni+KyoHJfMnjGxAKSo0HgcnUVwv0/no3WZE2CLl9HQGl2NQozVhmnKJF0hYBoKmcNSV1/KS7cAryaOEgBWX3m+NGD0qxVBYXcbqgnHn0XJFrlhfLtlAKiuphyZdKNeXiAarmcv5tUlB62tOepnZE7SmeioIilRmrHpQGpngqcv6o5kGpxyRbi4KSNjRqC0JWbepVUOi9GtG3QzRqq2FYIOB0aD6pv6eubaBzaEpqUBgtxVODgmLwoGQa2EmWjvkFXdnAc9jbWXe2BCheP14riXRLtGnTJuX/99xzDxYuXIgdO3bgoosuAgB8+tOfxrvf/W588YtfFI879thjxb+Hh4dx991349vf/jZWrVoFALjvvvuwdOlSPPTQQ7j88str/jDTRVAfFAo05nZkRBOh0amisohM5IsBHpTaDux6+6AQVMnhVVCi9RgIMslO5f0DDzHNuBLYAf5yaVc2halCXnhKpqROsgCwfIEToLy4f0wMfQRcBUUuCTfOw9EUFKWKx+hBqSgohotmLSd4KmGhULI1D4rzWR0FxfW6yLgzmSKaZJNek2ywgtJAD0pNpdHq5wsb3Oilk1EGbTbSJHvy4m783mmLce4xc+t6HcDcSTasB6VRdGRSGJooYDzn3oxFUfG66jDJFqRU73SYZP2GBBKe2USzJsVTqVaMSYBS1zc7PDwMAOjr6wMAlMtl/Md//AdOOOEEXH755Vi4cCHOO+88fP/73xfP2bFjBwqFAlavXi1+NjAwgBUrVmDbtm3G98nlchgZGVH+NJOgPih0krRnkiLPPjJVEKkewEkNBfVBqYWGKShCKvZJ8YRWUPxn8ZC3xJTicSVbWwQcfgFKp9YtVja8AsDyioLyptQDRd4mOUAxTxR2f9aRSVbuttXApHoVT+2ys6xoEHKKJy0FFDK1qjZyBZUpxaMvyPWkeHQPSiMUlLDPlX0u8ndqek3PewZUNUUllUzgf3/wLFxvaGUQ+bUMZca0T5slzdN1cUQyrkf5Pp3p5s6/w5pkTTcD09FJNsh/YnrPetN/ccGdORaPz1PzVti2jZtvvhkXXnghVqxYAQDYv38/xsbGcPvtt+Od73wnfvKTn+C9730v3ve+92Hr1q0AgMHBQWQyGcydq95FLFq0CIODg8b3WrduHXp7e8WfpUuX1rrZNUEXJpMBlBbJTDKBnkp7/JFJNUCZyJd8O8nWerdzbMVvQQP/asW9EzP7GsIueqSO5KU0CkEBgqkTp+yDmAxQWgD3IkYDAeUyY8AZZNgrjSigCygZa2Xfj+lOT74rotcUjaqMfVC0FI/cSbYGn4HbrE0KUKTuxvL7yNSiRgDud68oKNJ+kSc1A/WZZLs8jdrqLzMOr6BIxr+kpfy/qoISELC1ElOZcS3jA+qBbhjk7tlRjsFEwhLm97AKimyqD6r6qxVKE68YME+lJ/Rql0YYn+MAfY64KCg13xKtWbMGzzzzDB599FHxs3Ll4vye97wHn/jEJwAAZ5xxBrZt24ZvfOMbuPjii31fL2juzG233Yabb75Z/H9kZKSpQUqgB4WqH9KJiiN+EiNTRWURcQIU3SRbnwflkhMXYPMnLsKySqBSK/T2ehVPIeJCK9+J5otlZTFzlRHvSSyneCZFisd8suvzdvQeNACwbH4nfrnnMADgmHmd+PU+R22bzJfEd+K30Oh9UADnux+dKrqzeKqYZF3lqQYFRbS793pQ5nSkFXlbppZGbc42usGpq6B4qxNoPzdiWODolFoqHSWQ07+3sE0O9SoeNcUT3iQbpwAl2CTbrAClPgUFcALX0VwRnSGPLdEUsVTGQcPk+Xr5/bOX4LQlc/CWBcHX1Uam/uIEpXji4kGpac+uXbsWDz74ILZs2YIlS9wGPvPnz0cqlcIpp5yiPP7kk08WVTz9/f3I5/MYGhpSHrN//34sWrTI+H7ZbBY9PT3Kn2bSHiLF4ygolRSPpqBMGjwo9Fq1Xkwsy8Lxi7obZ5L1UVDCLrR6gCIjUjxBHpSS26jNV0GpXBDJx5AreJWZ5ZKidNScNnFBG88XpS6y5te3LHeiMS3G9N3TghA0iydXKtesZgCuuVFt1CaneNx0mExUQzMhUjxS2/BMUt03baLfjrd5XhTcRm00LDC6glhrozZ1Bo46c6mqSbaBZcaNxGySre+mJyoUxMvXuqhN6Ejx1KeX+0HXmZHJguv/a2CAYlkWTuzvrhrsp2MauNZL3BSUSHvWtm2sWbMG999/Px5++GEsX67mUjOZDM4991xP6fELL7yAY445BgBw9tlnI51OY/PmzeL3+/btw86dO3HBBRfU+jmmFaGgVOkkSz0FRqYKGJlyT9px6e5df169U03rhRZFvz4oYfO7NGkUUCc1A64h2CTFpqTKlMmAcmTAO49HbltPLJ/fJf69oLsNHVnXvCtPMvaDTkzaVvruRZmx4kHxNmoTQ/BqWMxFuqsSgNi2Le5OlQDF00k2uhohP142HHrLJ9X9UCvUNXSq4HgHmupBCajiiWKSjZPPwJSanQyolpsOKOikILqW0tSbLzsBHzrvaLx1eV+ox9MxMDg8Jf4fVn1pJEEdl2cyuueu1URK8dx44434zne+gwceeADd3d3CM9Lb24v2dqdU85Of/CTe//7346KLLsLv/u7vYtOmTfjBD36ARx55RDz2hhtuwC233IJ58+ahr68Pt956K1auXCmqeuJGmFk82VRS8qCoKZ5JkweFFJQaZ/E0CjrPdN+ImF0SctGTR9brAxQnA6pz5ABorOLx8Zsa60nx0OtKj18231VQFnZn0ZFO4jAKmMgXq6Z4AOfCkyuWPQoK7Ycgk6zSfruGE5wWHboTHssVxfei9EHxpHhqq2ZIS4qNX4BCCko9BllA9RiM54o1mYk9AUrIc8fbB0UKUKqZZFPuHWWcfAamAIW8WfV+V2Ghc4QGWtbSwn/1qf1YfWp/6MfT8TJYmVbe15GpadhqvcjK8qxSUGhwaotvnIlIR/Jdd90FALjkkkuUn99zzz24/vrrAQDvfe978Y1vfAPr1q3Dxz/+cZx44on43ve+hwsvdNsl33nnnUilUrjmmmswOTmJSy+9FPfeey+SyXhGoqLMOGCacUZTUFQPiqlRW3PlWD/8qnhqaf6VSToBil6xNBXgLZEXKPIn+CkoIk2QD1JQ3Nzxwp4sOqQZPtTrxS8AApxS0N8MjorJw66C4r2zyGgBymShBBKialNQ1BQP3ZlmUs7gwoykeMjUWjkkKyh+/h8KKjvqmGRM29aWdo6P0aliTX4JPW1Wq4KSiZDiod/HbREylRlTv596pk5HoVNTUJqhBmc1BaWR6Z0oyF6toOvJTCNuVTyRAhRbSwP48cd//Mf44z/+Y9/ft7W1Yf369Vi/fn2Ut28ZokKl6FSoiDtdrfGX7EHxmmTdQMapKKmvD0qjSBjuxIDafA1OqkNVj2w72FsiL6qUy/b3oKgKypRhRopsGl7Y3Sbk34l8UaSgTD1QiH/+H2/DVLEkcuK0LUFlxvS3XOVVy8XaPa6c7+LwhOs/AVSlRqZQg59Dfr+RqaIIiuQqKMBVpxqRNujKpjFVyGEsV4xswga8wURNVTwJ1UtTrckXBUVxC1BMZcbUgyhsyW690PtQgNKMmy06B94gBaWztlb99ZJSmjrG69ioh/RM9qAcqcgXZ1lFkZWCTCohFrWRqaLiQZFTPLTg1WuSbRR+nWTrqbKQF9B8qSyaOJlMhvJdMfkt/AIUfaKxPosHAHra0qJF9eLeNqUFfxgPiqyEAe6CTbK52YNSCVCkScu1yN0pLcVDAducyjbIbb5lam917zz+0JhTDZFMWJ59T8Fch2HQY1TIhzIyWXCVpgj7ybJUg2utCkqqBg9K3HwGZNaWhdkxoaA0KUARVTyVFE8TUmAUpB6o9Drq6/RvRz+dqNV88To26iFlUIpbCU8zDoG8oE3mS2KhlBdixyTrr6CIhm7pJIYlB3rLTbIixaP+vFiDr0FuWEZMSaXZpjsNqpwplm2MVi50fnckskm2KBkt9cd/4b+dhl/tG8GpAz3iLm8iXxQXkigXlI/97lvQ39uG31u5GIDWB4U6yUqVQkRNfVBETxg1xdOrBygN6oNCF6GhSh+LnraUJ58vzMINUVAqfTOkqo+oAXommUChRP1sQhq4pf2SSlpKiidsH5S4KShJo4LS2hRPM2626PuiALevozUKiqzmBSmyM424VfFwgBICy3LuLCcLJaXUmBaKhOV8sWSSHZ0qqK3upU6ydKGvt5Nso/Br1FbLIC6TgkJG1oTln+9PJxMolkvCg+LfSdb5+XhOHSWgBxy/e9JC/O5JCwFA8aDo2xmG4xZ248/feZL4f5BJVn6PWu5AklonWT1A8TPJ5mv0oNDjqZ9ET7v3Yi88KA0IUOj7q7WxF1AJGCqpjLDiSz3TjF0PSrzukvXzNld0ey21ziTbvBQP0SoPiqzCxS14rYdZ0QflSMRklNVTDD1yimfSvZueyLkm2TbJzwK0/kDwDVBq8DUIj4Q09FGu4PFz29MdrvCgVDPJ5oqKMhPUrrxT+t5MptqoBKV4SEFJJ62aKgvS2l2xJ0CResbI1DxBufL4Q+OOXN5j6EVBpbWNUVCc1ydvDVCbgkKEVlA8fVDCLy703cbtLlk/b+X0YrPKbkmdpAGQzaj80M/1eS0KUJQ+KDFL/9UDKygzFFMvlJxWmqmYZKW73HGpXLRDKCjxqOJxc9mNqeIBVAWl2nwd+XlCQfG5W5VTPKRApZNW4D6khXU8V2zI3bDZJOu8Xi2+Chm3zFhVUHp8Ujz/8virOHZBlzuLJ+qwwMrjD46RguK9HDRSQSEPipziibqv5AUq7KGpKCjJhBLIVfegVPrixFxBGRcl+ommlUPrXpdafFdRiYuCMms7yc7kKp4jGfI5qApKZQ4PBSiVO9DhyYIiww9LF2TdJNvqA8G/k2x0X4PZg0K9Svw/J53stG+rKSjjuaLb/K3KwuF6UErIpCiF1BgFhV5Hv2jW6iuSO7sC/imefKmMl/aP4S++9yyOmtOOM4+eU9P70mehyc8mBYXaiM/vqt+M2KXNbrGs+hSUZGgFRa3iySgpnmpVPDFVUDSTLKUXu5pkkAW8Xpdm3GxlteCrryMOAUq8gtd6oGtCq2+cCQ5QQmJK8eiNv+hOV29UNi6pLnRHGhsFRbsTK5bKsCzL7YgaIYAKDFAC7sDThvkvJuQqHmqfX23h6JB8K7RdC7qDJ5UGobS611I8RK3VDLqCcliaw+O8rtu3hCY2vz48iZML3QBqmMWT1E2y3gDluguWobc9javPPCrSa5uggYGU4qklvSnv67DPD+wkW+XuVwQoMbtLpu+O0oGkoNTbryYKuoLSDOVGv1b0dbUmQElWOmfbdvyOjXqg77DVxRsEByghCZPi6c6mxEHrB911kwLQag+K6Kdg2yiXbVz5tf9CuWzj7cfNB+C9IARhMslOGrq96ujScLUqnvG860GpdvfSQZOoCyVMVpSCxVVGqQeRMqR4dPNvrd+p2+peLTM29UGheUS2DewdmqxsR0TDqeZpMaV4+joz+OMLl0d6XT8owKSAqBb1UA4uEhE7yZJio/RBmaEm2YTWYJF6AzWrB4rpvZpxLdNnRbVKQQGcYzFfLMeuBL0e6BoU9tyabjhACUmHlCog8ppJNpGw0JVNCS9FRyapPD6ZsCSVISZVPJab4pkolMT036MrQ/ei5JXlmTRE0CRjQlccqvVBsW13kat290JVPBO5IgZHHNWhv44AJZlonoLib5Iti54XgBugRF3w9QXFpKA0ku5GKyghAzLar3QspyN4UM5/yzwM9LZh1SkLo27qtOLxoFRSPM3qgQJ41Zpm3HXr59qcVgYoCQt5zC4F5dxlfThqTjsuO8U8uLfZcIASErnEldAVFMC5yFOAsrA7i90HJ8Tv0klLXCRp4W51gEJrWrFsoyRVh+x+cxxAbZ0+ZYMwBWJBVSC6SuP32LZ0AgkLKNtuc7FqDnq6yxvPlzA47Czk9SgoikmW+qA0yIOSEh4UcxWPbJIdlY5DClaiV/Go220qM24kFGC+esg5J2qp+pCDi9AKChn/Kn/L3TKrnX8rjurFttsujbyd042niifv3hQ1C11BabZJtjubCqzgm26o5D1u/qR6OHZBF/7rL9/R6s0QzJ49O810ZNz0AmEaPidf5Hs7MsoFI51MiItybBQUqZNsQWr69EplEamlUZs+KBEITvHod/5+CoplWeIOkbavq0rOne7yDo3nMVS5c1/c0x74nCCUWTzJxqZ4UtUUFGGStUWKRyZ6HxRNQTGkeBrJ4l5nv5OqWEuJaEY6jqJ6UOjxczrSSCUszG+Rf6ER6DO06MapmSbZZMJSlNHmNGpz36NV/hOCrltxS//NJlhBCUmXSBXIre69s2Comyz9W07zZJIJcZEsi5LUmJQZl22lF0otfVpMHpSpgEnGhL7ABz2WUmj/9dKbAIBTB3oDt4k8KK9WlKz2dLKuhZj2VyaZEGpKIuG0YK+luZ2MnOIpl22PByWtpHgKnudHPZb0wHC6UzxvO7YP//uDZ4lJtBcdPz/ya8g+m6izeGj/zenI4Ns3nOeZOzSToBsdag9ARvxmNWkjurIpTBUqnqImDgsEgLktTO8AUgn6LFJQ4gYHKCEhJUTO/Ztmu8gKSk9bWklXpJMJz0U1bKnkdJGSpOJi2evura3VvRvETRWr90HRUzxBOV1SUJ7ecxgARIlttcdT2mlxb1td49lpf+nbqLRgr7XMuHIslMo2xvJFEcTSMZU1mGSV50eUu/XtnO4Uj2VZ+L3TFtf1GmoflIgeFOlYPv8t8+rajlZDCgqVpLsKSnPv5p2AiEzPzTXJ9rWoBwqRjqmBejbBoV9IaKGTJ9bmS2YPivh3e0oZspZOWZ4Fv9XlXHKKp6gP5EG0PijGKh5K8YQ0ybYHdJwF3O+B1J4zl84N3CY9J1+PQRZwPSh63lktf61RQZHKiKl9eFs6IYI7WUGRPShE9EZtzTXJNgK1k2y0Kp5Wn2uNJCWdt4Brku1oYooHUM+vZpQZy+dZqwMUOp5mk0k2bvCeDUmn6EgqlRlXFBT5otmtpHjCKCjxSPEUfRSUmjrJSoEO9SsJUlCijC6X7xDndWawtC/YT6JL3v099QUoroKiTf1Nyd9zrQqKq2bp/hP5dZ3Bit4ApX6TbPwFVaXMuA4FZaaj9y8SgwKbaJIFVM9LMxQUxYPS4gDFLUGfPcdV3OA9G5KOrMEkW/L24lBSPO1p5Q5D9qAQrfagyJ1k9Xk8QG2zeHLKsMDqZcYZKcXjZ5Al5AviGUvnVE3X6KWQ9SooSb8UTyMUlITbl8QUoMjvMTSeh07kwXt6imcmKCg1NGqL2wj5RpDSAhRKPTezzBhQFZtmKyit9qBQNeBRc2s33TPBxP+WKSaYTLI0qTcTaJJ1/59JxVBBkT0opcZ4UEwm2aDAQ36PoI6zgHoBruY/AVyTLFFPiTEgmWSDApSaW9273UHNCor7HocmvAFK9D4oqp+jmSWqtSLvg/AeFOqOOXvux8R5a5OCUumD0mSTrKzYNMWDoqR4WhtQ33HNGdh9cLyqUZ+pHQ5QQmI0yZaqmGQ1BSUdRwVFmulRLJs8KLWYZA2dZMMGKFUMZ11KgBLsPwGcRSmbSoht6u+t725H5J21z6POd6mvisdXQZEDFIOCUus0Y8AJpusxDzeLbA0mWbf/Sfw/X1jEeVtqrYLS2eQUT1YxydY/H6oe5nZmWjas8Ehh9txSTDNGk6xPozb532qAYnnu4uJSxVP29aBEMcmaOsnSzJygAEVK8YRUUCwLOG1JuDsX+TtolIISlOKpdSGkY0P2oMgBbyJhie+Lfi+nzqq1bfe8n7SgTHcFT6OoTUGZfSmepEdBaf4sHkBTUJoQAMoVf61WUJjphwOUkMgdSYlc0eRBSSn/1k2ycVNQSCou+nhQaplmbGp1HzbFE9aDcsLCbnSH9EzIabZ6PSh+ZcZZJcVT22klN2qjdvBz2tU7NNpXNO/pLQu6an5f+fEzwX8CaGXGEWfxzKYUj6eTbK5FKR5FQWmCB0X6DlvtQWGmn9lzxk4zplb3URWUTAyreMQsHttGwVBmHGmasaGKx03xhCszrjZ4ixbkS05cEHq76LvLJBN1Dxc7Zl4nAODY+Z3KzxUFpdZOstKwQFOKR38fADh+oRSgRHzftKKgzIxsb319UGaPguJvkm2ygqKYZKd//6aSCSzta0dvexoDc9icOtuZGVelGOCmeEool20kEpakoLgXzV6PB0Xqg2JQUFodoMgmWWMVT519UHIhphnL71GtzPidK/rx8C0XY2lfR+jtaq98B4t6s6FLU/04/y3z8Mitl2CJ5txvpIJSKtuYyFOAos070V77OClAiep9kbezOzszFBR5jk5Yz4xbxTN77se8ZcatUVA6mmySBYAfrLkQhZI9q6YIM2Y4QAmJfOJPFEroyqaQL3qreHo7nAu9ZVX6oEgnUTqV8HhOWh2g0Bo1bZ1kC9U7yWYipHgAZ6BVFChPXm8PFGKZpp4AjfGgiDJjuQ9Kh6agSK+dSSVw9Dx3W+oyyc4wBSVsegdw/U9hjq2ZghzM2rYt2h+01CTbpBRaKycYM81lZlyVYoA8SXciV0RXNuU7zfjT7z4ZyYSF9kxSkVwdk2y8PCh0V1m23WnGi3qyeGMkByBagGLsJEtlxpmQnWSnodSVVKx6K3iCUDuc1nahrlZmDHinucpBV+RhgQn1uJ0JUIAWJbBffcoiPH3e0fj9s5dM12Y1HTFDy7YxWSgJT1LTUzyZ5lbxMEcWHKCExLIsdGZSGM0VhVHWNM0YAD5y0bHi3+1yH5Q4pniUTrLO5xmY046DY3kUy3ZNjdqMVTwBKZ4oHpRaoIt2vRU8Qcifr9ZcfLUyY0DdV11tKeUzRVVukooHZYYEKCk3xROWOR0ZfP69K6drk1qCbJKl7taW1XyVSA6IZlMKjYkHfERFoEMzyvoFKMpz0moVj35hbfV8EFrv5DLjTDKB847tQ3dbKpLXQwQoJW+AEtwHRfagNP4Cu/Iopxz53GV9DX9tQk3x1KigSMMC3QDFXMUDOBVN/b1tWNrXjmPnd1btIeN5P60PykxA9qAcyaSUAIXa3De/l02zTbLMkcXMuCrFBOdkzIkLgqnMWMfbqC1efVBMJtlU0sK3/vg8TFa8NmGhNIfcqI2mGQelbqKUGdfC//idY/Hfz1nqUSMaSS0t2HVo0c0XyxiZqp7i6cqmkE4msPkTFwMIP5tGbKdcZjxTFJTKNh/p6QT6rsu2W8HTik7ArTDJMkcOHKBEwO2FoiooeumnjNIHJRU/D0pSymUXKh6UVMJReqIEJ4A3xVMq2+LfbQH7SE3xTE/ANp3BCaC3uq+xiqdybAxN5IWnwBOgGAZT1qo6KY3aZooHpbKf663GmunI3x0Njox6vjYC+T2PdFWLaTyc4olAhzbRmKpVAlM8MfegqMMCnWCi1qCJlKRcsQzbtpVqnsBpxnIn2RlaaaG0uq+1D0pFTaM29u3pZODMn3oXpDQrKDMWOUAjta3ZXWQBeNooMEwj4SMqAl1au/swCoqe4vF4UGISoJRs14NSa9Ak74dCyRYlxkCEMuMZMLDORDbdOAXl4JgToJhUH9k30lWnb2QmlxknZsDcoOlEvm6MVPxKze6BArCCwkwvHKBEgEaLjwkFpREelJikeCQPSq13QrKSlC+VRYmxqYOujPx+QfsyzqjDAmtVUCoelIrJ2ByguO9Tb8+LmZjiOX5RNxZ2Z/H24+a1elNaihygjUy1pgcK4KRkaVNmU6deJh7MjNummNBVkVAnclEUFFkCtQwKSnxMsuRBqVlBkRbPfLEsDQoM/oxRhgXGlWwDTLK68qI3aQO8fVDqYSaaZHvb03jstktbHti3mqRBQWmFSZbaL4zlii2/ljGzDz6iIkDBxlglxZMrVQ9Q5DuMTCrhucto9YXW7aeAuj0o8rRdOUCpZuKc7iqeZtAQk6y2300KSibZOA9KVzYlzNCdMygwbPU5EweSioLiBCitMMkCbi8ULjNmGg0rKBGgi/hErgTbtkP1QbEsC+3pJCbyJaMHpdUXW3lYYL0eFMDZF8V8CbliKdQkY2D2BSiZGgMUfb9XS/F01ZmW6W1P4xsfPhtd2eb3z2DqI5GwYFnOVOuRSSozblGAknHaL7CCwjQaDlAiQDne8XxRaUYWpKAAjvRKAUrsPChSiqdIZcZ1uPEzqQTG8yVNQQmf4pmuMuPpphGdZHV1zaigNLCKBwAuO2VR3a/BtIZUwkKhZGNowjFVd7Wgigdwq4dafS1jZh8zczVoEWSSHc8VlWZkQQoK4N7ZmD0oMQpQqFFbHdvkDgwMn+JJKX1QZqiCkqw/xaM37aumoHTPkO6vzPRwwqJuAMDPXjwAwL0+NZurTh/AsQs6cdYxc1ry/szshQOUCAiTbEUhIKpJ+mReyyQTnrvrVt91qJ1knc9UzzbJ7e7FJOMqlTmzocxYaXVfcx+U5isozMzluguWAYAwt7fKR/QnF70FD99yCRZ2T9+sK+bIhAOUCAiTrKSgZFKJqvl7Gui2oDvrWYRabSyTO8kWRZlxHQFK0u0mS2XGbVUunOnU9M7iaQaN7CRLmE2yjeuDwsxsrjp9APM63VlNrSgzZpjpJNKVdN26dTj33HPR3d2NhQsX4uqrr8bzzz/v+/g//dM/hWVZ+PKXv6z8PJfLYe3atZg/fz46Oztx1VVXYe/evTV9gGZCjZAmcq6Ckg2xGK1732m4+7pzcPYxcz0elNaneJy/y5IHpZ75QOTFUDwoVVJgSqv7Ko+NK2qAUl8nWcJUZqykeHhBOqJpSyfxofOOFv9vlUmWYaaLSKvB1q1bceONN2L79u3YvHkzisUiVq9ejfHxcc9jv//97+PnP/85BgYGPL+76aabsHHjRmzYsAGPPvooxsbGcMUVV6BUKnkeGyeonG48X3QDlBCmzv7eNlx68iJYloWkJ8XT4j4okoJSapEHhVQXJwU2MwOUrJLimcYyYznFwwrKEc+H33aMUDw55cfMNiId0Zs2bVL+f88992DhwoXYsWMHLrroIvHz1157DWvWrMGPf/xj/N7v/Z7ynOHhYdx999349re/jVWrVgEA7rvvPixduhQPPfQQLr/88lo/y7TTqZhk3S6pUfCkeFqsoNDdvmOSLSs/qwV5YCAFKNVKh5fMbcfvrVyMpX0dNb9vq8k2QkEJkeIhBSVhzdySbKZxLOxpw23vOhk/e/EAzlk2t9WbwzANpa6Qe3h4GADQ19cnflYul3Httdfik5/8JE499VTPc3bs2IFCoYDVq1eLnw0MDGDFihXYtm2bMUDJ5XLI5XLi/yMjI/Vsds2IYYGSSbZaibFO3PqgJKRW96LMuM4+KACQL7l9UKqVDluWhf/9obNqfs84kEnKIw0alOIJUFC4dwlD/PGFy/HHFy5v9WYwTMOpWU+3bRs333wzLrzwQqxYsUL8/Atf+AJSqRQ+/vGPG583ODiITCaDuXPVaH/RokUYHBw0PmfdunXo7e0Vf5YuXVrrZtcFSaj5YhljlXb3UWfH6PJ/qxUUU5lxPWkn2SQbNsUzG1A8KLWmeEKZZJ3X7p4hs3MYhmFqpeaVaM2aNXjmmWfw3e9+V/xsx44d+MpXvoJ777038t2dbdu+z7ntttswPDws/uzZs6fWza4L2YR2eMJpLx1ZQYlZmbHcSbYRHhTy5OSLZUwVaRbPERag1DksEHDUOtPQRqp4Yr8BwzCznZoClLVr1+LBBx/Eli1bsGTJEvHzn/3sZ9i/fz+OPvpopFIppFIpvPLKK7jllluwbNkyAEB/fz/y+TyGhoaU19y/fz8WLTJ3tcxms+jp6VH+tIJMKiHuYA+NO90bqzVp0/F6UOIzLFA0amtAmXGuWMZkPlyr+9mAYpJtQKv7OT7D+2hfcpM2hmFmO5GupLZtY82aNbj//vvx8MMPY/lyNe957bXX4plnnsHTTz8t/gwMDOCTn/wkfvzjHwMAzj77bKTTaWzevFk8b9++fdi5cycuuOCCBnyk6YXaOh+utJeu14PS6vEVFDCVbKBYqm9YIKBV8RTDtbqfDagpnlpb3VefLnzBcfNx5ekD+NOL31LTezAMw8wUIt2G3XjjjfjOd76DBx54AN3d3cIz0tvbi/b2dsybNw/z5s1TnpNOp9Hf348TTzxRPPaGG27ALbfcgnnz5qGvrw+33norVq5cKap64kxnJoXDEwUcmphtCkq5MR4UqYondwR5UFLS8LZGKCgm/wkA9LSlsf4Pzqzp9RmGYWYSkQKUu+66CwBwySWXKD+/5557cP3114d+nTvvvBOpVArXXHMNJicncemll+Lee+9FMhn/hYx6oQyN1+ZBsSxnHk+pAZODG4HoJFuG60GpK8VTadRWcjvJHgkpHsuycNyCLgyOTGF+V7am10iFCFAYhmGOFCIFKLZtR36D3bt3e37W1taG9evXY/369ZFfr9WQUdb1oERffOUAJS5VPOWyjUIDUjyKSTZkmfFsYeONb0euUKp5npAcvHKAwjDMkQ477SJC1RO/2uf0YumsYcR5KmEhD8Cy3BRLqzB1kq1rWKAwyZYwkT9yqngA59iot7omxQEKwzAMAB4WGBlq1jY8WUAmmcA150TvyUIBQKvVE0BVUEQVT4M8KAdGpwAAC2pMeRyJ0DExxzCHh2EY5kiCA5SIyBND//6a03Hm0dHbS5OJstX+E3kbimW7IR4UMg2P50p4c8xJg9E0Z6Y6NIuIFRSGYY50OECJyFnHzEUyYeHT7z4ZV53uHYQYBldBaf3uVzrJNrDMeM/QhPP/ZAJ90kh4Jhja935lxgzDMEcK7EGJyLVvOwb/7ayj6hptTotQLBQUqZNssYEelFcOOgFKf28bz4yJAKlXrKAwDHOk0/pb+BlIPcEJEC8PCok4JSnFU2sfD8Ct4hmedMqw+zm9Ewky2S7s5v3GMMyRDSsoLSBWCkrCVVCozLg+BUWt2GH/STQ+996V+PW+EZy8uLvVm8IwDNNSOEBpAWSEjIOCkjKZZBvgQSFYQYnG246dh7cdO6/6AxmGYWY5nOJpAUJBqaNaplGIPijKsMD6y4yJxT0coDAMwzDR4QClBVAKJRkD86jcB6UhCkpSV1Daa984hmEY5oiFA5QWECcPitxJtlCqv4onq7W1Zw8KwzAMUwscoLSAOPZBKZedicZAYxUUDlAYhmGYWmj9CnkEkopRJ1nXJFtuiAclK3lQUgkL87jNPcMwDFMDHKC0AAoK6mkp3ygSoswYKJYaW8WzqKctFkEYwzAMM/PgAKUFJGPkQZGNuvlG9EFRAhRWTxiGYZja4AClBaRi1UnW3YbGDAt0G7Ut5goehmEYpkY4QGkBcfKgmLahHvOurKBwkzaGYRimVjhAaQGpOFXxGHqxNGJYIMAVPAzDMEzttH6FPAKJlQfFqKDUvl1pKT3ECgrDMAxTKxygtIA4eVCMAUodHhTLskSahxUUhmEYplY4QGkByUR8PCimTag39XR0XweyqQSOnd9V1+swDMMwRy48zbgFUBokDgGKZVlIWE4fFKLezdrwJ2/D6FQRczsz9b0QwzAMc8TCAUoLiJMHBXC2o1xp0pZOWrDqHGI4vyuL+dxBlmEYhqkDTvG0gDh5UAA1UIpL0MQwDMMc2XCA0gJcD0o8dr9cahyH0meGYRiG4dWoBVCVTFwUlAQrKAzDMEzM4AClBVBgkozBsEBADUrSMdkmhmEY5siGA5QW0JFx5tW0p5NVHtkc5BQPKygMwzBMHOAqnhbw385egqGJAq592zGt3hQAaoqHPSgMwzBMHOAApQUs7m3HZ644pdWbIZC9MPV0kWUYhmGYRsG3ywwSnOJhGIZhYgYHKIwSlMSlsohhGIY5suEAhdECFD4kGIZhmNbDqxGjzN5hDwrDMAwTBzhAYRTVhD0oDMMwTBzgAIXRyow5QGEYhmFaDwcoDJLSUcAeFIZhGCYO8GrEqMMC2YPCMAzDxIBIAcq6detw7rnnoru7GwsXLsTVV1+N559/Xvy+UCjgL/7iL7By5Up0dnZiYGAAf/iHf4jXX39deZ1cLoe1a9di/vz56OzsxFVXXYW9e/c25hMxkeFhgQzDMEzciBSgbN26FTfeeCO2b9+OzZs3o1gsYvXq1RgfHwcATExM4Mknn8RnPvMZPPnkk7j//vvxwgsv4KqrrlJe56abbsLGjRuxYcMGPProoxgbG8MVV1yBUqnUuE/GhEZRUDjFwzAMw8QAy7Ztu9YnHzhwAAsXLsTWrVtx0UUXGR/z+OOP461vfSteeeUVHH300RgeHsaCBQvw7W9/G+9///sBAK+//jqWLl2KH/7wh7j88survu/IyAh6e3sxPDyMnp6eWjefqfD+bz6Gn+86BAB456n9+Ma1Z7d4ixiGYZjZSJT1u67b5eHhYQBAX19f4GMsy8KcOXMAADt27EChUMDq1avFYwYGBrBixQps27bN+Bq5XA4jIyPKH6ZxyGmdJHtQGIZhmBhQc4Bi2zZuvvlmXHjhhVixYoXxMVNTU/jLv/xLfPCDHxSR0uDgIDKZDObOnas8dtGiRRgcHDS+zrp169Db2yv+LF26tNbNZgxwq3uGYRgmbtQcoKxZswbPPPMMvvvd7xp/XygU8IEPfADlchlf//rXq76ebduwLPPieNttt2F4eFj82bNnT62bzRhIsAeFYRiGiRmpWp60du1aPPjgg/jpT3+KJUuWeH5fKBRwzTXXYNeuXXj44YeVPFN/fz/y+TyGhoYUFWX//v244IILjO+XzWaRzWZr2VQmBKygMAzDMHEj0u2ybdtYs2YN7r//fjz88MNYvny55zEUnLz44ot46KGHMG/ePOX3Z599NtLpNDZv3ix+tm/fPuzcudM3QGGmF/agMAzDMHEjkoJy44034jvf+Q4eeOABdHd3C89Ib28v2tvbUSwW8fu///t48skn8e///u8olUriMX19fchkMujt7cUNN9yAW265BfPmzUNfXx9uvfVWrFy5EqtWrWr8J2SqIpcZp1lBYRiGYWJApADlrrvuAgBccsklys/vueceXH/99di7dy8efPBBAMAZZ5yhPGbLli3ieXfeeSdSqRSuueYaTE5O4tJLL8W9996LZDJZ26dg6kJRUNiDwjAMw8SASAFKtZYpy5Ytq/oYAGhra8P69euxfv36KG/PTBPKsEBO8TAMwzAxgG+XGcgxCZtkGYZhmDjAAQqjpHU4QGEYhmHiAAcoDJIJ+d98SDAMwzCth1cjRu2Dwh4UhmEYJgZwgMJonWQ5QGEYhmFaDwcojFZmzAEKwzAM03o4QGFYQWEYhmFiBwcojBKUpJJ8SDAMwzCth1cjhocFMgzDMLGDAxRG6STLHhSGYRgmDnCAwijDArnMmGEYhokDHKAw6iwebtTGMAzDxABejRjVJMspHoZhGCYGcIDCcB8UhmEYJnZwgMIofVDSXGbMMAzDxABejRhtWCArKAzDMEzr4QCF4U6yDMMwTOzgAIXRphnzIcEwDMO0Hl6NGEU14RQPwzAMEwc4QGG0PigcoDAMwzCthwMURukkywoKwzAMEwc4QGEUBYXLjBmGYZg4wKsRwwoKwzAMEzs4QGGUAYHsQWEYhmHiAAcojNoHhacZMwzDMDGAAxRG7YPC04wZhmGYGMCrEaMoKOxBYRiGYeIAByiMpqBwgMIwDMO0Hg5QGGVYIHtQGIZhmDjAAQqDpOQ7YQ8KwzAMEwd4NWK4DwrDMAwTOzhAYSCLJuxBYRiGYeIAByiMUFASltr2nmEYhmFaBQcojEjrsP+EYRiGiQu8IjEiQGH/CcMwDBMXOEBhXAWFS4wZhmGYmMABCiM6ybJBlmEYhokLHKAw6G1PK38zDMMwTKuJFKCsW7cO5557Lrq7u7Fw4UJcffXVeP7555XH2LaNz372sxgYGEB7ezsuueQSPPfcc8pjcrkc1q5di/nz56OzsxNXXXUV9u7dW/+nYWpiaV8HvvKBM3Dn+89o9aYwDMMwDICIAcrWrVtx4403Yvv27di8eTOKxSJWr16N8fFx8ZgvfvGLuOOOO/C1r30Njz/+OPr7+3HZZZdhdHRUPOamm27Cxo0bsWHDBjz66KMYGxvDFVdcgVKp1LhPxkTiPWcchTOPntvqzWAYhmEYAIBl27Zd65MPHDiAhQsXYuvWrbjoootg2zYGBgZw00034S/+4i8AOGrJokWL8IUvfAF/+qd/iuHhYSxYsADf/va38f73vx8A8Prrr2Pp0qX44Q9/iMsvv7zq+46MjKC3txfDw8Po6empdfMZhmEYhmkiUdbvujwow8PDAIC+vj4AwK5duzA4OIjVq1eLx2SzWVx88cXYtm0bAGDHjh0oFArKYwYGBrBixQrxGJ1cLoeRkRHlD8MwDMMws5eaAxTbtnHzzTfjwgsvxIoVKwAAg4ODAIBFixYpj120aJH43eDgIDKZDObOnev7GJ1169aht7dX/Fm6dGmtm80wDMMwzAyg5gBlzZo1eOaZZ/Dd737X8zvLUstVbdv2/Ewn6DG33XYbhoeHxZ89e/bUutkMwzAMw8wAagpQ1q5diwcffBBbtmzBkiVLxM/7+/sBwKOE7N+/X6gq/f39yOfzGBoa8n2MTjabRU9Pj/KHYRiGYZjZS6QAxbZtrFmzBvfffz8efvhhLF++XPn98uXL0d/fj82bN4uf5fN5bN26FRdccAEA4Oyzz0Y6nVYes2/fPuzcuVM8hmEYhmGYI5tUlAffeOON+M53voMHHngA3d3dQinp7e1Fe3s7LMvCTTfdhM9//vM4/vjjcfzxx+Pzn/88Ojo68MEPflA89oYbbsAtt9yCefPmoa+vD7feeitWrlyJVatWNf4TMgzDMAwz44gUoNx1110AgEsuuUT5+T333IPrr78eAPDnf/7nmJycxMc+9jEMDQ3hvPPOw09+8hN0d3eLx995551IpVK45pprMDk5iUsvvRT33nsvkslkfZ+GYRiGYZhZQV19UFoF90FhGIZhmJlH0/qgMAzDMAzDTAccoDAMwzAMEzs4QGEYhmEYJnZwgMIwDMMwTOyIVMUTF8jXyzN5GIZhGGbmQOt2mPqcGRmgjI6OAgDP5GEYhmGYGcjo6Ch6e3sDHzMjy4zL5TJef/11dHd3V53xE5WRkREsXboUe/bs4RLmmMPf1cyBv6uZAX9PM4eZ+l3Zto3R0VEMDAwgkQh2mcxIBSWRSCgzgKYDnvkzc+DvaubA39XMgL+nmcNM/K6qKScEm2QZhmEYhokdHKAwDMMwDBM7OEDRyGaz+Ou//mtks9lWbwpTBf6uZg78Xc0M+HuaORwJ39WMNMkyDMMwDDO7YQWFYRiGYZjYwQEKwzAMwzCxgwMUhmEYhmFiBwcoDMMwDMPEjlkZoPz0pz/FlVdeiYGBAViWhe9///vK78fGxrBmzRosWbIE7e3tOPnkk3HXXXcpj8nlcli7di3mz5+Pzs5OXHXVVdi7d6/ymKGhIVx77bXo7e1Fb28vrr32Whw+fHiaP93sohHf1SWXXALLspQ/H/jAB5TH8HdVH9W+pzfeeAPXX389BgYG0NHRgXe+85148cUXlcfwOdUcGvFd8TnVHNatW4dzzz0X3d3dWLhwIa6++mo8//zzymNs28ZnP/tZDAwMoL29HZdccgmee+455TGz9dyalQHK+Pg4Tj/9dHzta18z/v4Tn/gENm3ahPvuuw+//vWv8YlPfAJr167FAw88IB5z0003YePGjdiwYQMeffRRjI2N4YorrkCpVBKP+eAHP4inn34amzZtwqZNm/D000/j2muvnfbPN5toxHcFAB/5yEewb98+8eeb3/ym8nv+ruoj6HuybRtXX301Xn75ZTzwwAN46qmncMwxx2DVqlUYHx8Xj+Nzqjk04rsC+JxqBlu3bsWNN96I7du3Y/PmzSgWi1i9erXyXXzxi1/EHXfcga997Wt4/PHH0d/fj8suu0zMpANm8bllz3IA2Bs3blR+duqpp9r/63/9L+VnZ511lv1Xf/VXtm3b9uHDh+10Om1v2LBB/P61116zE4mEvWnTJtu2bftXv/qVDcDevn27eMxjjz1mA7B/85vfTNOnmd3U8l3Ztm1ffPHF9v/8n//T93X5u2os+vf0/PPP2wDsnTt3ip8Vi0W7r6/P/od/+AfbtvmcahW1fFe2zedUq9i/f78NwN66datt27ZdLpft/v5++/bbbxePmZqasnt7e+1vfOMbtm3P7nNrVioo1bjwwgvx4IMP4rXXXoNt29iyZQteeOEFXH755QCAHTt2oFAoYPXq1eI5AwMDWLFiBbZt2wYAeOyxx9Db24vzzjtPPOZtb3sbent7xWOY+qn2XRH//M//jPnz5+PUU0/Frbfeqtxd8Hc1veRyOQBAW1ub+FkymUQmk8Gjjz4KgM+puBDmuyL4nGo+w8PDAIC+vj4AwK5duzA4OKicN9lsFhdffLHYz7P53JqRwwLr5atf/So+8pGPYMmSJUilUkgkEvi///f/4sILLwQADA4OIpPJYO7cucrzFi1ahMHBQfGYhQsXel574cKF4jFM/VT7rgDgQx/6EJYvX47+/n7s3LkTt912G375y19i8+bNAPi7mm5OOukkHHPMMbjtttvwzW9+E52dnbjjjjswODiIffv2AeBzKi6E+a4APqdagW3buPnmm3HhhRdixYoVACD25aJFi5THLlq0CK+88op4zGw9t47YAGX79u148MEHccwxx+CnP/0pPvaxj2Hx4sVYtWqV7/Ns24ZlWeL/8r/9HsPUR5jv6iMf+Yh4/IoVK3D88cfjnHPOwZNPPomzzjoLAH9X00k6ncb3vvc93HDDDejr60MymcSqVavwrne9q+pz+ZxqLmG/Kz6nms+aNWvwzDPPeJQswLuvw+zn2XBuHXEpnsnJSXzqU5/CHXfcgSuvvBKnnXYa1qxZg/e///34+7//ewBAf38/8vk8hoaGlOfu379fRLL9/f144403PK9/4MABT7TL1EaY78rEWWedhXQ6LSoT+Luafs4++2w8/fTTOHz4MPbt24dNmzbh4MGDWL58OQA+p+JEte/KBJ9T08vatWvx4IMPYsuWLViyZIn4eX9/PwB4VA79vJmt59YRF6AUCgUUCgUkEupHTyaTKJfLAJwTOJ1OCzkTAPbt24edO3figgsuAACcf/75GB4exi9+8QvxmJ///OcYHh4Wj2HqI8x3ZeK5555DoVDA4sWLAfB31Ux6e3uxYMECvPjii3jiiSfwnve8BwCfU3HE77sywefU9GDbNtasWYP7778fDz/8sCdIpDSbfN7k83ls3bpV7OdZfW61xJo7zYyOjtpPPfWU/dRTT9kA7DvuuMN+6qmn7FdeecW2bcehfuqpp9pbtmyxX375Zfuee+6x29ra7K9//eviNT760Y/aS5YssR966CH7ySeftN/xjnfYp59+ul0sFsVj3vnOd9qnnXaa/dhjj9mPPfaYvXLlSvuKK65o+uedydT7Xb300kv23/zN39iPP/64vWvXLvs//uM/7JNOOsk+88wz+btqINW+p3/913+1t2zZYv/2t7+1v//979vHHHOM/b73vU95DT6nmkO93xWfU83jz/7sz+ze3l77kUcesfft2yf+TExMiMfcfvvtdm9vr33//ffbzz77rP0Hf/AH9uLFi+2RkRHxmNl6bs3KAGXLli02AM+f6667zrZt2963b599/fXX2wMDA3ZbW5t94okn2l/60pfscrksXmNyctJes2aN3dfXZ7e3t9tXXHGF/eqrryrvc/DgQftDH/qQ3d3dbXd3d9sf+tCH7KGhoSZ+0plPvd/Vq6++al900UV2X1+fnclk7Le85S32xz/+cfvgwYPK+/B3VR/VvqevfOUr9pIlS+x0Om0fffTR9l/91V/ZuVxOeQ0+p5pDvd8Vn1PNw/Q9AbDvuece8ZhyuWz/9V//td3f329ns1n7oosusp999lnldWbruWXZtm03Q6lhGIZhGIYJyxHnQWEYhmEYJv5wgMIwDMMwTOzgAIVhGIZhmNjBAQrDMAzDMLGDAxSGYRiGYWIHBygMwzAMw8QODlAYhmEYhokdHKAwDMMwDBM7OEBhGIZhGCZ2cIDCMAzDMEzs4ACFYRiGYZjYwQEKwzAMwzCx4/8HnYu6jssDYuIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "negative = array(1-Theta_vec(Temp), dtype=bool)\n",
    "kept2 = data[negative,0]\n",
    "hist2 = histogram(kept2, bins=years)\n",
    "plt.plot(hist2[1][:-1], hist[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}