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@pckujawa
Created December 11, 2013 01:47
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hw4 big data - "known A" code seems to be working
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{
"metadata": {
"name": "hw4-tuesday-afternoon"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": "### Algorithm\n\n* Choose some profile x, x ~ (0, F). Do this by creating x from $B * \\mu$, where $\\mu$ ~ (0,1), so $F = B * B^*$. $x$ **must** have fewer elements than $\\mu$, so B needs to be constructed correctly. You can start with a vector $b$ and do 'full' convolution with $\\mu$.\n* Create a matrix A. \n* Simulate a measurement $y = A x + \\nu$, $\\nu$ ~ (0, S). y *should/must?* have more elements than $x$. You can start with a vector $a$ and do 'valid' convolution with $x$.\n\n* Assume A is known\n * Find $\\hat{x}$, $V(\\hat{x_i}) = Q_{i,i}$\n * (optional) Repeat measurement of x N times. collect meas info \\hat{x} and its variance\n \n* A not known\n * generate calibration signals \\phi_i\n * simulate measurement $\\psi_i = A \\phi_i + \\nu_i$\n * collect calibration info ???\n * compute A_0, J, R, \\hat{x}\n * (optional) arrange K cal., N meas of x\n \n\n"
},
{
"cell_type": "code",
"collapsed": false,
"input": "%pylab inline\nfigsize(18, 4)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Populating the interactive namespace from numpy and matplotlib\n"
},
{
"output_type": "stream",
"stream": "stderr",
"text": "WARNING: pylab import has clobbered these variables: ['f', 'inv', 'svd']\n`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
}
],
"prompt_number": 157
},
{
"cell_type": "code",
"collapsed": false,
"input": "from __future__ import (division)\nimport numpy as np\nimport scipy\nimport scipy.linalg\nfrom scipy.linalg import (toeplitz, svd, inv)\nfrom pprint import (pprint, pformat)\n\ndef pprintGlobalsSubset(subsetKeys):\n pprint({k: v for k,v in globals().iteritems() if k in subsetKeys})",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 158
},
{
"cell_type": "code",
"collapsed": false,
"input": "mu = np.random.randn(100)\n\nlenB = 3; lenA = 9\nvariance_nu = 1 # S",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 159
},
{
"cell_type": "code",
"collapsed": false,
"input": "np.matrix(toeplitz([1,2,0,0], [1, 0, 0]))",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 160,
"text": "matrix([[1, 0, 0],\n [2, 1, 0],\n [0, 2, 1],\n [0, 0, 2]])"
}
],
"prompt_number": 160
},
{
"cell_type": "code",
"collapsed": false,
"input": "# b * \\mu, 'valid', is equiv to B*\\mu when B is a Toeplitz\nb = [1.0/lenB]*lenB\nprint 'b', b\n\nbMidpt = int(len(b)/2); print bMidpt\nfirstRowB = [0]*len(mu)\nfor ix,b_ix in enumerate(b): \n firstRowB[ix] = b_ix\n\nfirstColB = [0] * (len(mu) - len(b) + 1)\nfirstColB[0] = b[0]\n\nB = np.matrix(toeplitz(firstColB, firstRowB))\nprint B.shape\nprint 'B[0]', B[0]\nprint 'B[-1]', B[-1]",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "b [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n1\n(98, 100)\nB[0] [[ 0.33333333 0.33333333 0.33333333 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. ]]\nB[-1] [[ 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0.\n 0.33333333 0.33333333 0.33333333]]\n"
}
],
"prompt_number": 161
},
{
"cell_type": "code",
"collapsed": false,
"input": "x = np.convolve(mu, b, mode='valid') # x needs to have fewer points than mu\nxFromBMatrix = np.inner(B, mu)\n#print 'x', x\n#print r'B * \\mu', xFromBMatrix\n\nassert np.all((x - xFromBMatrix) < 1e-10), \"Uh-oh, different answers for b conv mu and B * mu\"",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 162
},
{
"cell_type": "code",
"collapsed": false,
"input": "# a * x, 'full', is equiv to A*x when A is like a Toeplitz but with more zeros\na = [1.0/lenA]*lenA\nfirstRowA = [a[-1]] + [0]*(len(x) - 1)\nfirstColA = a + [0]*(len(x) - 1)\nA = np.matrix(toeplitz(firstColA, firstRowA))\nprint 'A', A",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "A [[ 0.11111111 0. 0. ..., 0. 0. 0. ]\n [ 0.11111111 0.11111111 0. ..., 0. 0. 0. ]\n [ 0.11111111 0.11111111 0.11111111 ..., 0. 0. 0. ]\n ..., \n [ 0. 0. 0. ..., 0.11111111 0.11111111\n 0.11111111]\n [ 0. 0. 0. ..., 0. 0.11111111\n 0.11111111]\n [ 0. 0. 0. ..., 0. 0. 0.11111111]]\n"
}
],
"prompt_number": 163
},
{
"cell_type": "code",
"collapsed": false,
"input": "y = np.convolve(x, a, mode='full') # y should have more points than x\nyFromAMatrix = np.inner(A, x)\nassert np.all((y - yFromAMatrix) < 1e-10), \"Uh-oh, a conv x not equal to A * x\"",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 164
},
{
"cell_type": "code",
"collapsed": false,
"input": "nu = np.random.randn(len(y)) * variance_nu\ny += nu\n\n\nprint mu.shape, x.shape, y.shape\nassert len(x) == len(mu) - lenB + 1\nassert len(y) == len(x) + lenA - 1\nplot(mu, color='gray'); plot(xs, color='k'); plot(ys, color='blue');",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "(100,) (98,) (106,)\n"
},
{
"metadata": {},
"output_type": "display_data",
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KSgr+8Y9/YPPmzdi8eTMAYPv27cjIyEBWVha2b9+OV199VZJFE+IJtoMkAUClUiE2NhaN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JngcrkIDAwEwFymraxMBmAfnzgcPx4DJaUkpKT4wNMTqKjQxuXLl9G+fXv07t1bKMjw9u3baN++\nvdR07poYMGAArl+/zv/3ixcvEBMTg6lTp+HMGUJMPn1KAgmPHSPy/8mTVeHszEFCwjUUFAgTBubm\nwPbthdiw4RoKCghZsHgxWdACJIRx5syZQvdwunMFQMjN69d1ERAwFKamFEJCgOnTgYwMkjg/dCj5\njJhMRAEiq42MjERlZSWjz4MeC5o0aVJrnPH19cWiRYuE7sfq6uq4dOkSgoKCRMrLKYrCnDlzYGZm\nhoULFzI6h7pC3G9tzJgxUrvnaGoCbdqU4MaNOTh+vBt27ybhewwbMYiFLNYKgIzxjo6OCu/2ExER\nATs7O8b3cVnsFQkJCejUqZNcizamYZIaGhro2LGjzMcXRFlZGS5cuIDJkyejRYsWmDNnDjgcDk6c\nOMG3U/br14+/PdPWjLNmzYKfnx+j3J5mzUhXrZQUMgeZPRuwsKiGmdkBaGtbiN1PWogkRQEHDxLL\nz08/ATduEKKUKeQhHbp27SpEOsgbdCkOz549g52dHf/fOjo6mDdvHkJDQzF+/HhcvnwZJiYmGDVq\nFE6fPo1Bgwbxty0uLoaKiorE9qKqqqo4ffo0fvnlF6GcHXnh5gZs356Gu3fnIjRU9DayKh0k3esp\nisLr169hYmIi8VqVlOtAUVSj0uE/wIMHpBvTkSNkbG9EI2g0KNJBFjg4ONRrmOTFixdhY2MjUZLG\nNAugpr1ClLVCHjg4OGDfvn0YPny4UCBfly5ksD9+nPQ0nzNnOyoqNsPLawJOn9aHjQ3petGrFwkz\nu3GDBLGJKqSLkvQygaJCJGtCU1MTgYGB+O2335CUlCTTvmlpafDx8cGRI0egp6eH4uJi8Hi8/wnS\nIT8/v8GSDkw6WNAqB8Hfg5WVFd68ecN44SgPROU5AERBYmRkhOzsbEbHoSfnmpqa2L17N5YuXQou\nlyuzN5zL5aJVKxVoaOzDX3/dQps2QOfOwO3bbPz111+YPn06HB0d8fjxYwCyWyto9OjRA8XFxfxr\nZOPGjfD03I5Bg9Th60uqVsHBhEwQBJ3rIKqqZ2RkBIpKhZ8fqTRWVJD2atOmleLyZSVQ1BRs2QL8\n/jvpILFqlS1mz7ZFhw5A8+bAkSMs2Nt/wqNH2bhwARg7tnYfeyaSW4D85szMzPDw4UNGn4eamhpU\nVVVrVQYTEhLw+PFj/Pzzz7X2MTAwwLVr17BmzRp+1gWNgIAAPHz4EIcPH67XTBdBfGtfLLz4Gzhw\nIN68eYMPUtKE6fbAVgrqvUjnOciqABg6dChu376tsOs+KysLHz9+RJs2beqFdEhOTpYpQFIQTEkH\nRWDdunXYsGEDunXrhsePH+P58+dYt24d7O3tRQZ0MyUdBgwYgLKyMsbXGgAYGADe3sD16x9RXT0B\n7dq58ttshoYSi4QgJKkIsrOJXcnfn6ih5s//ZnViCg6Hw+hvFUSnTp3w9u1blJaW1kvnipqkAw0N\nDQ20b98eISEhiImJQVRUlBDhAECiykEQysrK8PX1VVh3rZ9+agVl5R8weTIlMkNMkfaKjx8/QktL\nC4aGhhJVSU5OTnjy5InIbQoLC6Gqqtqg53f/13DvHrEtHjtGiIdGNEIQ/7OkA9MOFjWzFJgiICAA\nM2bMkLiNLPaK+iAdAOKRnTVrFoYPHy5yErV27VpcunQEjx+vw44d2rhxg/iR798nAXfSCGANDQ2U\nlpbK3NpQUSGSomBra4t169Zh3LhxjCcSFEVhxowZWLRoETp27AhlZWWoq6ujpKTkf4J0ANBgSQdp\nSoeMjAx8/vwZNjY2Qs9raGjAxMREZvJIFogjHQDZLBYFBQXQ09MDi8VC37590atXL+zbtw/q6uqo\nqKiQKP8URElJCTQ0NBAbG4teveywYwcZnKdMARYtYmHmzAXw8/ODu7s7zp07h4sXL8pFOjRr1gy2\ntrYICQnBzZvJuHx5HM6fH49Zs4gE2slJ9H7u7u64fv06MjMza1X1mjRpguLiYlRUVKBVK5JZkJAA\nvHnzHPr6i/DihSby80mAYu/eFGxsHmPLFtLK7sMHYtMYPjwPlZXiCSpZMnhcXV0lymprQldXt9ZE\nfceOHZgzZ47Y+7iZmRkuXryIKVOm8FvoxcTEYOXKlTh//ny9da0RBTabDTabXatiK8piUROlpaXY\nsWMHVqxYobDzyc7OhoaGhsxjWefOnWFoaIioqCiFnMfNmzcxYMAAVFRUKNxeUVxcjPz8fH74rayg\nSYfv0Rr44cOH2LRpE+bOnctIfcKUdFBSUsLMmTOl5miJwvr16/Drr11x6pQm3r8nVc9168i8Y+JE\nYu0sKhJPOly+TIooNjZElSUvX8bhcFBRUSHzPu3bt8fLly/rpXNFbGysyBwCwZaZ7dq1ExmOKi7P\nob6hpaUFI6NUHD78CfPmkcKWILS1tRldV3TemqT5c0JCAiwtLflERmgoMGcOsH076UoUFwcUFxMC\nsXv37iI7NjVaK74voqKIappWLzaiETXxP0s69OrVCzExMRIn+/IqHd6+fYu4uDipLeqYkg46OjpC\n7K8iSQcA+OOPP2Bra4uffvqJ/3lQFAUfHx+cPXsWt2/flvvGS+dZyFqVkiWJWh7MmjULFhYWcHFx\nwb1796Ruf/ToUeTn52Px4sX853R0dFBQUICqqiqJMsX/GvTn2FAzHXR1dVFWViZ2Anv//n306tVL\npJ3I1ta2Xi0WqampYhPhZSEdCgsLhSZ5kyZNQlhYGFgslkwWJC6Xi/LychQUFPBVVM7OpLvF+/ck\nO6BtWw9cu3YNCxYsQOvWrRkH7ApCVdUQbPYo7NxpjqFDm8PR0RBv3ijhl18kVwlbtmwJU1NTPHv2\nrNYiQElJCQYGBkKqFh4vAwkJwxERoYXDh0ma/JIlgKdnEbp0SYezsxo6dvymoqJzHcRBFtJBnlwH\nwe8wIyMDFy5ckBpG2bNnTwQEBGDEiBF4/PgxxowZg4MHD6JDhw6M31tREFeh9/T0rJXhIYjDhw/D\n3t4eXbp0Udi5yJrnQMPY2BgWFhYKy3UIDw+Hs7Mzn9BjAg6Hg+rqaqnjWnJyMtq1aye3FVJfXx8s\nFktq28y6gqIoPH/+XKbvVxbLwS+//IJLly4hP595qHRSUhIuXLiApUuXAiD2z2nTCHnw4gXJZjh+\nHGjdGvjjj+4ICWmL1FSyb3ExsV95ewPnzpH8GVVVxm9dC/LYKwCS6xAbG6vwPAeKosQqHcR1rxAE\nU6VDfcDS0hIs1gvcugWsWEEUKDSYKh3Ky8uhoqIiUoFDIzExEZaWluBwtLF+vR5mzSJZIZ8+ERvx\njz8Sgrt5c+Ddu+NYvNgQW7aQohqNxs4V3w+RkcTOffo06RjViEaIwv8s6WBgYIAWLVrglYTm0PKS\nDocOHcKkSZMkLkTpyQqTFlqampooLy/n97hXNOnAYrFw4MABfP36Fb///jufcAgODsatW7fqfNOl\n1Q6yoKCgoF6ZeBaLhVOnTmHy5MmYOHEiXF1dxWZ8ZGRkYNmyZThy5AjY7G8NW3R0SBcBdXX17yaR\nlgdaWlpo2rTpf1LZYAIWiyVW7VBQUIB3796JTZa2sbGp1zBJcZkOgHxKBxqOjo549OgRqqqqZJJq\nc7lcpKSkwM7OTmjCpa9PJtcLF5KgwujoboiJeSLUDUISKipIlWHVKuJ7trbWxbt3Q5CfHwsNDUdc\nuNCZsX9/0KBBePfuHVRFzPKNjIyESIfFixdj5syZteTndOeKmpDUNhNgnukAkO8gKSmJ8YJu4MCB\nQt109uzZg4kTJ0JfX1/qviNGjMDy5cvRs2dPTJgwQSohXV8QZ3dzdnZGQkICPn36VOu1iooKbNu2\nDStXrlToucia50BDW1sbtra2uHDhQp0VABRFISIiAs7OzjIp1lgsFqPrNjk5WSiHRlawWCzGYZJ1\nQXp6OtTU1GQa65kqHQAy3xo+fDj+/vtvxsdftWoVFi1aJHLcatWKBFZfvQpkZFCwt3+A1FRt9OpF\nwm1tbUmb1+fPCTlRV8hLOtC5DoomHTIzM8Hj8URm9QgqHcShvudXkkC3zbS0JGGe27YBO3aQ15iS\nDkzu84mJiTAy6oFVq/rh3Ts2nj0jOUK7dhEFTEICUFJCWlOvWMFFYeElJCeTvKGdO4Hy8sbOFd8L\nN28CY8aQTlQCDbMa0Yha+J8lHQDpuQ7ykA6VlZU4duwYpk+fLnE7pioHgEw8BPsXK7pdGEBCg4KD\ngxEaGorBgwfjwoULCiEcAPnCJAsLCyW2OlIEVFRUMGXKFCQlJWHUqFHw9PTEsGHDhHprUxSFWbNm\nYfbs2bCt0cybJh0asrUCIL+fefPmQbOuqW/1CHG5Dg8ePEC3bt3EEnjfQ+kgjnSgF9BMrBE1f89N\nmzZFq1atEB8fLzPp8Pr1a5EkDItFshAePiSy4ylTjFFWZoPHj0kobHg4EBJCyInjx0mw2pYtRMZo\nYAAsWkQ61WzcCOTmsrB06S24ur6At7enTCoZe3t7sZYXQ0ND5OTkACAhl/fu3cPy5ctrbScudE1f\nX1+iFYdppgNA7nn9+vVDREQEo+1btGjBv9a/fv2KQ4cOyRQCOWfOHNy8eRPr169nvI+iIe63pqqq\niuHDh+P8+fO1Xjt+/DgsLS3Ro0cPhZ1HRUWF3EoHgCzmlJWVhYL65EFSUhKUlZVhYWHBz1hhCmnX\nbXV1Nd69e1cn0gH4PrkO4qrmkiAL6QAQdaG/vz8joujJkye4d+8e5s+fL3Xbqqoi2Nm9x7FjbGRm\nAocOkQC6w4cBRTkK66J0qA/SgbZWiCp2qKqqoqqqSuK49F/ZK4BvpAMAtGtHiIeDB4GZM4HCQjVU\nVlZKVRAxmT9HR+vjzz/Hw8WlCMuXP4QoblhJiRBYU6eao7o6AD4+73HnDukiZGUF3LihjWbNGu0V\n9YmwMKI6CQoC/u3m2ohGiMX/NOkgLdeB9pfxeDzGx7xy5Qos/l979x0fRZ3/D/w1m83upvdKCiWB\nFEISQkiEAwPSPIqgcsqp3ztFpZyKiOXufqjYsHCnKGfDO87vqSeeha8FpUmRnkASAiEhoYQS0vum\nJ7u/P+ZmSdkyMztbsvt+Ph4+HpLdnf3ATmZn3vMusbEmO0kLCTpwa+GCDlJnOnD8/Pywfft2BAYG\nYu/evZKVNwhtJtnd3a13NJ2lKBQKLFu2DKWlpZgzZw7mz5+PRYsWoaCgAFu3bsWlS5f0Xhh5eXmh\nsrJS0OdI9NOX6dDa2orTp08jIyPD4OssmenQ3d2NiooKREVF6X1coVDAx8eH153ygeUVAHv8OXz4\nMO+gg1arRVtbG86cOWNwXj0AjBrFBhkmT2aDECtWAH/6E3tHafNm9m7C7t1sb4bqanY8blkZ++f1\n69lMCZWK/Uz+/Oc/47nnnjO5tr6Cg4PR2tqKS1yu84DHampq0N3djUceeQRvvfWW3gu9vpMr+uLK\nKwxduHR2dvIOOgDCSyw4H330EWbMmCG4dGXatGlmTR0yl7Fjsb4pFj09PXj11Vclz3I4fPgwYmNj\nRR/jhw0bhokTJ4oqsVCr1Th48CA2btyIRx55BDNmzADDMIJ785jq63D58mUEBgaaHey1RtAhPz/f\n4kGHSZMmQaFQYN++fSaf+6c//QnPPvssr8+j7wW9TMY2uM7K4r0sXsQGHZKTk3HmzBlUVlZKGnQw\nFiRiGMbkZ1NfX2+z8oq4uDhd0AFgy2OOHGEn4Ywdy+DAgdm4ds14XwdjweWuLras5ty55fjkk2Ys\nX96K1lbj25PJZJg5cyZ27dqFhATghx+Av/2tCz/9lIZFiwJgYtAdEemnn4B77mF7bBjqE0VIX0M+\n6HD06FGDj7u4uMDd3Z13N10A2Lx5s8ksB4D/uExO3wkWlgo6AGzjs88//1zSL0ih5RWNjY3w8fGx\nesmCSqXCI488gvPnz2Pq1KmYNWsWHn74YWzZskVvqri3tzeqq6vtPtNhKNAXdMjJyUFiYqLRrJ4R\nI0agoaEBjY2Nkq/p6tWrCAsLM1oCxbfEQl866+TJk3HkyBHeQYfOzk7I5XKDDU5kmCYAACAASURB\nVMT6ksuB558Hzpxh00cPHeqf6fDJJ8BHH7FppHfcoX98XFBQEJqamgT/HtbV1eHmm2/G9u3b9W6z\nuroa77zzDiIjI7Fo0SK92zBUXqFUKqFUKg02+BWS6QDcaCYpJE2/u7sbGzduxFNPPcX7NfbC2L42\nc+ZMnD59GhXcHFMAn3/+OSIjIzFlyhTJ1tDU1IScnJxBHfWFCA8PR2JiIv76178iPDwcmZmZWLx4\nMdasWYONGzfim2++wYkTJ3Dt2jUcOHAAb775Ju655x7Ex8cjJCQETz31FEpKSrBkyRK88cYbACCo\npwNgOtPB3NIKjr1mOgid6MAwDJ5++mnMmzcP48ePx4MPPoj3338f2dnZ/c4P9uzZg7KyMixdupTX\ndqXOItBHzPQKgL0xER4ejvLyckkbOZv6vIz1dejo6EBvb6/NMh/j4+NRXFzc75gbEMB+F+XlAR0d\nPpgwwRuvvcaOStbHUHnFpUtsOU1JSSc8Padg7tyAflnCxgwMQCcmVmLduu+xdCmD228Hlixhg/PE\nfJ2dwNq17NS7b7+VpgSKOIchHXSIj49HTU2NLt1XH67EQqPRmDwxvXz5MrKzs3HnnXeafG97zHSw\nFKGZDvruCluTm5sbVq9ejQsXLuDnn39Genq63ud5e3uju7ubgg4SGFhe0dXVhZycHNx0001GXyeT\nyZCYmGiREgtj/Rw4fIIOWq0WTU1Ng8qFJk+ejMOHD/P+/eCeU11dLXoEnxB8RpnqU11djfnz5w8a\nEwkAvr6+qK6uxquvvop33nlHb0Cjt7cXdXV1CAwM1Lt9Y30dhDSSBNgLOrlc3u/OmylffPEFYmJi\njGab2Ctj+5pSqcS8efN0JRa9vb145ZVXJM9y4I6p5jS2DQ8Ph1KpRENDA7Kzs/Hmm2/ijjvuQGho\nKC5cuID//d//xcMPP4wJEybgj3/8Iy5evIgZM2bgiy++QGNjI44dO4b33nsPS5cuRUBAADQaDbq6\nugR9J5vKdCgpKZHk9zQmJgbnz583ezvGWKO8AmAb6NbW1uK9997D+PHjceLECSxbtgwBAQFITk7G\n/fffj1WrVuHll1/m1e8KsF7QQUymA8De2Ver1VaZXMEx1teBC4Dbqg8VN8VD33l3VBTw2GOn8PHH\nF3DyJDuS+b332OyFvrjz595edrLRoUPAu+8CGRnsnfM//jEbCQnhutJkPhMxZs6ciZ9//lmX2VxV\nVYXw8BDcfz87Oj4uDkhLY8t3iHjHjwPjxwOFhWzPlUmTbL0iMpTITT/FfslkMmRmZuLo0aO47bbb\n9D7Hx8cHp0+fxowZM9DS0oLp06fjlltuwS233DKoCdaWLVtwzz338DpxaW9vF3SxygUdNBoN2tra\nrDpqzVxCezpYo58DHx4eHkbrmLnADwUdzOfn54eWlhb09PRALpcjLy8P0dHRvJr0JSUl4fTp05Le\njQWM93PghIaGmhy9q1aroVQqB51Ec3XkarVa1yTWmLa2NtTV1SE5OdkqKfpigg69vb1oaGjAihUr\n8Pjjjw/qP8MwDPbu3YslS5YYvCCrr6+Hl5eX3uwi4EZWjL6pIkKDDgzD6LIdEnjM09NqtdiwYQNe\ne+013u9hT9zd3VFVVWXw8TvvvBMbN27EH/7wB3z99dfw8/MzKyNhoPLycpSVlWGemQPY3dzc4Onp\nicbGRkRERCAiIgKTzDh75TJkhFyIeXh4GMywqqurQ3d3tyTj9vqOzbTEhWJtbS2am5sF99dQqVSi\nLsTd3d2RmZmJzMxM3c86Ojpw5swZnDx5EvHx8Vi8eDHv7dXU1AgOmAhlTtBh+PDhuHLlimRrqa+v\nR319vW56kT7GAkK27OcAsMdcrq+Dvmw2Ly8v+PjU4csvgZMngT//mW00uXIlO5Xk8mXg1KlRuH49\nHvX1bJbE8OHsf9u3A+npwObNRbpRoVyZtKnfn/DwcPj5+aG4uBgJCQn9mkh6eLCZg7/9LXDLLUB3\nN1u2SPhrbweee47Nsnz7beA3v2H7UBEixJDOdABMl1hUVFTgd7/7HZ588kkcPnwYWVlZ2LVrF9LT\n0xETE4OHH34YX3zxBSorK7FlyxZepRWA8EwHrrxCrVbD3d3d6KggeyOmvMIegg6mcOmS1NPBfC4u\nLvD19UVdXR00Gg2OHj3K+yJi3LhxFunrwDfoUFlZaTQLylCncIZhMGnSJBQXF/Mqr2hra0NFRYXV\n7rD7+vqitbVV0Hz6uro6+Pj4wN/fH5mZmYOaNO7btw+XLl3CXXfdZXAbhkorOKYyHYT+Pgrp67Br\n1y5oNBrMmTNH0HvYC1MlAbNnz0ZeXh4qKyvx8ssvY+3atZJd6Gq1WuzcuRPTpk0zGFASIjw8HNev\nX5dgZcJLKwD239LQHVSutEKKf7uAgAC4uLiIyjriIy8vDykpKYLPKbgLWyGlSaWlpdi8eTO++uor\n7Nu3DwUFBSgvL4dWq8WECROwbNkyPP3007zXotVqrZrpIGZaSmhoKK5duybZWvLz85GcnGz038hY\neYUtx2Vy+jaTHKhvZkJaGrBzJ9sUtLgY6Olh74zfdddFvPnmWTQ1Adevsz0h/v1vNuAA3BiXCQBy\nuRyurq68snIyMjJ0U8z0jcuMjWVHO77+OptZQfg5eBBITgauXQNOnwbuuosCDkScoXPla4CxCRY/\n/PADnnvuOfzhD3/Aww8/jJEjR+Khhx7C1q1bUVVVhW3btiEhIQGfffYZxowZg8jISCQlJfF6X7FB\nh6FWWgEM3UwHUxQKBVQqFWU6SIS7s15YWAhfX19ERETweh2X6SC1S5cu6b2b3penpydkMpnRmlFj\n+/PkyZNx5swZ3kGHK1eumOznIBWZTGZyWsRAfadOzJs3r1+JBdc8ksuA4LMNfbhmkvoI7ekAsKMw\nDx06xOukdMOGDXjyySftekSuMe7u7kb3NZVKhV//+tdYunQp5HI5fv3rX0v23oWFheju7kZKSook\n25My6CB0cgVgPIAjVT8HjiX7OogprQDYizmGYXhlaXGuXr2K0NBQjB49GgzDoLS0FNu3b8fGjRux\nYcMGbNmyBbt27eJ9cd/c3AyFQmHxwL+LiwtcXFwE/V05Pj4+ukwVKeTm5pr8vPiUV9iSsaCDvrGZ\nWVlsD6KXXgIefBCIjy9HbCzb8FifvkEHALz7OnBBB41GYzD4PXIkG3j4y1/YO/bEMLUaePRRdjrF\nG28An38OWDg+SBzckC6vANiDTG5uLrq6uvrdfdm8eTOef/55fPjhh3pHDzEMg6SkJCQlJeHxxx9H\nT0+PoDuC7e3tvFLHOdyB2BLjMi1NTE+HoRB0ANjPhYIO0uDS5s+dO4fp06fzfh0XdJA6/ZhPTwfg\nRraDoUZhxk7yJk+ejM8//xzJyckm36etrQ1lZWVW7SUQFBSEyspKhIeH83p+34DB3Llz8dprr0Gj\n0UAmk+maRy5evNhodll1dTUSExMNPm4sECK0vAJgS3uSkpJw6NAhzDAyJDw3NxfFxcVYsmSJoO3b\nEw8PD5PH4sWLF+P222/H119/LdnvU09PD/bs2YOFCxdKts1hw4ZJFmwUOrkCMNzTobOzE9euXTOa\nzSNUWloaHnroIcyfPx8zZszAr371K8kutPPz8zF79mxRr+UaLPLtv9DY2IiRI0cOGj+t1WrR2tqK\nuro6fP/99xg9ejSvcg9rZDlwuGwHvn9XTmdnJ7y8vHgFsfnIy8vDzJkzjT7HWKZDfX09r1IyS4qP\nj8dPP/2k9zF9QYeBTGW06Qs6qNVqkxPZMjIy8M9//hMNDQ3w8PAw+F0yfDgbeJg2jR0z/cQTRjc7\n5FRXs6Uq0dHA2LEA30F2Wi2beZKXB+TmAh9/DEydymY32Di5hjgI0ZkOLS0tuO222xAVFYWFCxca\nvPM1fPhwjBs3DqmpqZLOCed4e3tj1KhROHXqFAD2y++5557DG2+8gV9++QVTp05FU1OTye3I5XJB\nJy1iG0k2NzcPuUwHoeUVDQ0NQybokJmZyfuCjBgXGBiIvLw8aDQaxMTE8H5dQEAAvLy8cPnyZUnX\nw6e8AjDdTNJYEC0tLQ0lJSWor683+T41NTWora01OY5XSnFxcYIu7PpeBMTExMDHxwd5eXm4fv06\nXn31VWzatAkhISFGm/eaKq/gyj4GznLXarWCR2ZyBpZYaLVaVFRUYOfOndiwYQPuu+8+LFq0CE88\n8YQkpQG2wmU6GLvrOmfOHDz//PNYuHChZO979OhRhIWFCe4bYEx4eDhqa2sFBfsNERN0MJTpcPHi\nRUREREi6n7z99tvYvHkz3Nzc8MILLyA4OBi33HILXnvtNZw4cULvjRG+xGY6AMKbSTY1NeltIMo1\n/IuOjsaECROQm5vLa3vWDjoIbZzZ3t6Orq4upKWlIS8vT5J18Pm8jK3VluMyOUIzHQYydv6sVqtR\nU1PT71jD9XUwJSUlBSUlJbh48aLR7yCAvSDfv59tdLlhg8lNDwnnzgHLlgFjxrBBhxdeYBtoBgWx\nAZZHHwU+/BA4fBhoaAAuXGCnYf35z8CcOUBoKJCSAmzaxE4e2bKFDTxQwIFIRXTQ4f3330dUVBRK\nS0sRERGBDz74QO/zGIbB/v37kZeXh+zsbNELNYYrseju7sbSpUuxY8cOHDlyBLGxsbrpFVITGnRw\ndXWFQqFAdXX1kAs6CCmv6OzsRHd3t83GOQk1fvz4IZd5Yq+CgoLQ3NyMSZMmCb4bKnVfB7VaDbVa\nzasRnDlBB5VKheTkZFy9etXkxdPZs2d10xasZcyYMaiuruYVFAEGl0ZwJRZPPvkkli1bhtjYWHh5\neaGnp0fvMaG7uxvNzc1GT4plMhn8/PwGlVhwdyHF9LuZPXs2vvrqK6xZswYzZsxASEgIxo4di9df\nfx3Xr1/H9OnT8c0332D16tWCt21PFAoFZDKZ0X3Nzc0N69atk6xvkFqtxtGjR03enRVKLpcjLCxM\nknp5MT0d3N3d0dnZOeiCv7S0VPLpMq6urvjVr36FdevW4dChQygvL8fq1atRWVmJ3//+9wgODsZL\nL70keLutra24fPlyv7vCQghtJsknizE5ORklJSW8zhlskekgBLe+1NRUSYIOra2tKCsrM5mpYCjT\noaenB62trWZNjpFCVFQUGhoa9AYX+AYdDAWXi4uLERsb26/ZMt/yCqVSiaSkJBw+fJjXd39UFBt4\n+OgjYIj2FoZWCxw4ACxYAEyZAoSFscGHr75if15XBxQUsIGFkSOBnBxgzRr27z5tGvDpp4BSyTb6\nPHmSzZLYuZP998jKsvXfjjga0We/2dnZWLt2LZRKJR544AG8+uqrBp8rVS2cIZMmTcIXX3yBHTt2\nQCaTYd++fbqLXpVKhd7eXnR2dkKpVEr2nm1tbYLTI728vFBeXj7kRrUJKa/gTkqGas00ES8oKAgp\nKSkYO3as4NdyJRYLFiyQZC1lZWWIjo7mtR+GhoZi7969Bh83VUM7efJkFBYWorW11ejd0aKiIlH/\nNuaQy+VISkpCfn6+yZIXfQGDefPm4Te/+Q3c3Nzw0UcfAWADycHBwaiurh5057umpgb+/v4mp3Nw\nfR36nhiKKa3gpKenY8GCBQgODsaaNWswbtw4hIeHO+RxiDseS/l9Zsy+ffuQkpJikburUVFRuHLl\nitlp6+3t7YKz6xiG0QXUuRsBWq0WpaWl+JWFB897e3tj3rx5uikgpaWlSE9Pxx//+EdB6f8FBQWI\nj48XXDLAEZLp0NvbC7VabbAMjePm5obRo0ejoKCg34QLfWpqaniVpknBnKBDYGCg7vhnjoKCAiQk\nJJj8vAz1dGhsbISPj4/NG5HLZDKMHj0axcXFg0aSK5VKaDQao+fcxsorBpZWAPyDDgBbYpGTk8O7\n5Cgion+pxf/7f7xeZnM9PcDXX7O9KZqa2BKRrVuBgbFXhmEDEWFhgMRxY0IEE33kysnJ0aUJx8XF\nGcxiYBgG06dPx8KFC/Hdd9+JfTujJk2ahB9//BERERH49ttv+91lZxjGItkOQkdmAuyJRlVV1ZDL\ndFAoFOjt7eXVhGko9XMg0pLL5bjttttEjYOUOtPh4sWLvC9k/P39oVar9Z6Q8jnRnjx5Mq5cuWKy\nmeT58+ctPhpOn9TUVOTn5+vmlxtSU1Oj67TPmTx5MjQaDd56661+x1Uu6DCQqdIKjr6+DmImV3Dk\ncjk2btyIZ555BrfeeiuGDRvmkAEHwPQECylVVlbi3LlzmDp1qkW2HxUVJUlZlZhMB2BwX4eKigoo\nlUqrp6/HxsZi1KhRus77fJlTWgEICzq0tLTA09OT1/F9/PjxOHnypNEbTtaaXMGxh0wHPk0kAcOZ\nDrYel9mXoRILhmFMlkMYyxQ2FHTgU14BsEGHM2fO8Poe4oSHs4GHTz9lMwJE9Bu1mt5e4O9/B2Ji\ngL/9DVi7lp0Msnz54IADIfbGaKbDzJkz9aYdv/LKK7yzFw4fPoywsDAUFRVh/vz5mDhxosG0p3Xr\n1un+PysrC1k8c3tGjRqFw4cP46abbtJ7oskFHUw1oeFLq9UKLq8A2EwHjUYz5IIODMPo7q6ZusvR\n2NhoN1+KZOhISkrC+vXrJdse334OAHvXJjg4GFVVVYiKiur3WHNzs8kT7UmTJuHSpUsm78RcunQJ\nEyZM4LUmKYWEhMDLywsXLlww2pFf3wWAq6srLl68OOj3nptUMlB1dTWvi4iAgABcvHix38/ETK5w\nRkIb+4ql1Wqxa9cu3HzzzRb7XCIjI3H9+nX09vaKClZyxPR0AAYHcEpKSiQvreCL60siJMvCmkEH\n7i47H9HR0dBqtbh69eqgYyqnpaVFcC8tc4gNOowaNQpRUVHo6OhAZWUlr7R9Q/Ly8nhNLzKU6WAP\n4zI5fPo66Gu2rtFo0NXVZTALoqioaFCzX749HQC2vOfy5cuCz0PDwtjAw29/yzZP/Ne/2At7e3L4\nMPDYY+zUj88/B266ydYrIs5g//792L9/vyTbMprpsHv3bpw+fXrQfwsWLEB6errugFNUVDQoxYoT\nFhYGgD1ALViwAN9//73B91u3bp3uP74BB4C9KDZWRy51pkN3dzdkMpng2mzuxH2oBR0A/s0khZyY\nEMKJi4vDpUuXBDf6MkRI0AEw3NeBz3iy4OBg+Pr6Gs3U4Dq7W2tc5kB87tQZGnWpL9BoLNOBT3CX\nm3TSlznlFc5EX6YDd9f4+PHj2Lp1KzZv3mwys8WUkpISqNVqi5YDqlQq+Pn5oaKiwqztiBmZCQy+\ngyr1qEwhZs2ahZ07dwp6TX5+vtWCDk1NTbyzGBmGwfjx4402lLRmlgMgvH8FcON4xv19zM12EBJ0\n0Pe52MO4TI7YZpLccd7Q+bq55RXu7u7o7e0VNY43JATYvZsdEXnTTcAHH7A9E2zt2jXgnnuAu+5i\n+zEcOkQBB2I9WVlZ/a7PzSG6vCIjIwNbtmxBe3s7tmzZord2r62tTXegqKmpwc6dOzFnzhzxqxXJ\n29sbzc3Nkm1PTJYDwB6IuayBoYZvM0nKdCBiKJVKjBo1yuBJjFBSBR34lguNHTsWJ06cMPh4bm4u\ngoKCbBZwHDt2LC5evGg0LZ9vwAC4kekwMONNSHlFXV1dv9dT0IEfLtOhqakJ+fn52LZtG9588018\n9tlnqKqqQmJiIrRaLcrKykS/R29vL3bv3o1Zs2ZZvH6c6+tgDikyHdRqNerr6w3embe0SZMmoaio\niHfT1+7ubpw9e3bQ+EohhNz9F3pDITk5GcXFxQZvVgg53khBaKYDN7mCC7qaW2LR1dWFoqIiXp+X\nsfKKoZTpoI+xjLauri6UlZUNyjYSUl5RVVWF+Ph4waVKHJmMzSY4eBD4xz+AW29lx0jaQkcH8Mor\nQHIyO+azuJjNxHDQykHiBESfTaxYsQJXrlzBmDFjUF5ejuXLlwMArl+/jrlz5wJg60GnTJmClJQU\n3H333VizZg0iIyOlWbkAUmc6iA06eHt7w9PT0+ZNgMTgm9JLPR2IWFL2dRDS0wEwP+hg6oQ0Ozsb\nUVFRNvvdV6lUGDNmjNF/XyEXAR4eHmAYpt+JYHt7Ozo7O3ldmLi5ucHV1bXf683p6eBMPDw8cODA\nAWzevBnnz59HVFQU7r//fqxatQoLFixAUlISxo4di7Nnz4p+j9OnT8PHx0fQ6FuxzA06aLVaSYIO\npaWlGDlypFllHuZQKpWYOnUqfv75Z17PLy4uRmRkpFnTl4RmOggJOnh4eCAmJsbgyN7a2loEBgby\n3p65FAqFoKADl4nB3ZE3N+hw9uxZDB8+nNd+ygVIBgZ17amnQ2xsLC5fvqx3ko6xcghjx3nueDaw\n9IJrCD9wzLI+VVVVGD9+vOigAycuDjhyhM0oSE0FvvjCrM0JotUC27YBCQnAiRPsxIlXXgFo0BoZ\n6kSfAXt5eeHbb7/FlStX8H//93+6L77w8HBs374dADBy5Ejk5+cjPz8fP//8Mx544AFpVi2QvQQd\ngoODMWbMGMnWYU1Cyiso6EDE4CZYmEur1QrOdAgODkZNTc2g8Xl801knTpyIM2fOGHw8JydH0Hos\ngTtp1tePp7OzU9AEAIZhBvV16JuKzMfAZpKU6cBPWloali5diieffBJ33nkn0tLS4O/v3+/fPSEh\nAcXFxaJLLAoKCqw2ZYkLOoidctXd3Q2GYURNcBgYdLBVaQVHSImFuf0cAOE9HYR+txtrKFlTU2PX\nmQ4Dyz9SU1ONlouYwre0AmD7DCkUin6fjUajsatMUoVCgejoaJw/f37QY6YyHYQ0kQTY7xu+2Q5V\nVVWYMmWK2UEHAHB1BZ5/Hti+HVi3DliyBOCZiCRaTg47ZWLtWmDzZjb4YOZwH0LsxtC75S6C1EEH\nMeMyuXVwWSBDDZ9Mh46ODmg0GrpbSUQZN26cJEGH2tpaKBQKQXfluOfX1dX1+7mQ8gq1Wq03WwIA\nTp06ZfMLmujoaPT09KC8vHzQY1wDSCETHwb2dRCaLs2NzeRQI0l+VCoVQkJCjH5Wfn5+8Pb2FjUZ\norm5GRUVFVZrqOjt7Q2lUjmoxwdfYrMcgBtp2729vbh48aJVMjuM4ZpJ8gnA5OXlISUlxaz3s2Sm\nAwCMGDEC3d3dg445Wq2Wd9NZqZgbdBg9ejSqqqpEn0vynVzBGfjZtLS0wN3dXfR4VEuIi4vTW2Jh\nrAeDseO8oaCDqW1yNBoNampqMHPmTJw8eZLXxDU+JkwAcnPZng9JScDTTwO//CLtlIszZ4BFi9j/\nFi8G8vOBGTOk2z4h9sApgg7e3t5oaWkxu7EWR8y4zKHOzc3NZKYDd4HmqKPqiGUlJSVJUl4hNMuB\no6/Egm+mg5eXF0aMGIEjR44Meqy9vR1lZWU2DzowDGMwRVhMfTWXHdJ3G0IuIri+DhzKdJCWsZpr\nY86cOYO4uDjBjZLNER0dLXp0pthxmcCNTIcrV64gMDDQrFIFKYwZMwYMw+DcuXMmn2vNTAetVisq\n6GCooaRarYaLi4tVz6PMDTq4uLhg3LhxyM/PF/X+Qj+vgX0d7Km0gmPoGGOqkaTQTAeAX1+H2tpa\neHt7Izg4GMOGDUNhYaGJvwF/bm7Axo1s1oNKBTz+OBuEuOceYOtWoKFB3HbPnwfuvRe45RZgyhSg\ntBRYtozNsiDE0ThF0EEul8PNzY13IxpTxJZXDGV8Mh0aGhqotIKIFhkZifb2dr2jGIUQ2s+BMzDo\n0NXVhc7OTl4XIh4eHoiMjMThw4cHPVZQUIDo6Gi7mOqSnJyMs2fPDqrDFdNJPigoaFCmg5DZ6FRe\nYVkJCQkoKioSHGw/ffo0kpKSLLQq/czp62BupkNraytKSkpsHhQE2It0PiUWWq3W7MkVAHshzifo\noFaroVQqoVAoBL9HSkoKioqK+l3wW7uJJCB8eoW+nhNi+zpoNBqcOnVKcKZD3xs99jQuk2Mq6KAv\nY0dMeQW3TVPn8FVVVbqRppmZmZKUWAyUkgK8+CKb+XDqFDte87PPgOhoICsL+Otf2SaUFy+yjSAN\nuXaNDS5kZgJjxrDBhyeeYIMbhDgqpwg6ANKWWDhj0IHP9Ap7qjckQw/DMJL0dZAq04Hr1s4nc8fd\n3R1hYWF6gw4nT55ETEyMXWRHeXt7IzIyclCTQbGZDtXV1dBqtbp0aaHlFQODDs52XLWkgIAAeHh4\n4OrVq7xfU1NTg9bWVgwfPtxyC9PD3KCDmHGZwI1gur0EHYAbJRbGlJWVwdPT0+zyBL6ZDkLGZQ7k\n6emJESNG9DuuW3tcJiAs06GrqwttbW2DAsVigw6lpaUIDAwUdH408LOxp3GZHENBB6VSCYZh9P57\nGwouazQanDt3DnFxcXrfi0+mQ1VVlS7wnZGRYZGgQ18REWzg4PvvgcpK4MkngZIS4JlngGnTAG9v\nIDCQnT7x618DDz3E9oZ47DH2Z35+7POffRaw0WArQqzKaYIOvr6+aGxslGRbzhh04JPpIHSkFiED\nmRt0aGxsxKeffor09HTBr+WCDtzdGSFBNLlcjujoaJw+fXpQGVJubi5GjBhhF0EHQP+Js5igg5ub\nGxQKBZqbm9HS0gKZTCbo4s/Pzw9qtVpXd0s9HaSXkJAgaIrF6dOnMXbsWKtPWQkICEB3d7eoGwPm\nZDq4uLhAqVSiq6sLYWFhorYhtVtuuQUHDx40eoEsRWkFwP/uv7nf7QNLLGpra+066MCNphz4eyC2\nmaSYz0tfeYW9ZTrExcXh3LlzerOpDJVYGDrOX758WdeLRh8fHx/k5ubiq6++wsGDB1FSUoKmpqZ+\n2RSVlZVWDTr05e4OzJsHfPghO/Xi8mU206GwEPj4Y2DlSrY3hEYD+PqyPRxeew2ws4+UEItymqDD\nwPphczhr0IFPTwd7i8SToWXcuHE4evSoqNd2dnZi4cKFmDFjBu68807Br+fG2XInSkLLhfz8/DBm\nzBicOHGi389PnjyJiIgIuwk6jB49GnV1dbrjYWtrK3p6euAl4lYLl+0gYniIhAAAIABJREFUtLQC\nYDu0+/r6ov6/7cCpvEJ6XIkFn8aEWq3WJqUVAJvlJDbbQWxjZ46HhwdiY2PtpheRv78/EhIS9GZN\ncaQKOri6uqK3t3fQ1J6BzMl0AIBRo0ahra0N169fByC8/4sUhAQdamtrERAQMOjnY8eOxYULF3hN\n8upLyOQKzsDyCnvs6eDt7Q0/Pz+9v7eGgg6GMtqMlVYA7LnBvffei9jYWHR0dCA7Oxt///vf8frr\nr+Of//wnfvzxR5SXl+vKK5KSklBWVobm5mYz/obmkcnYvg+pqWxAYtkytjTjxRcBO4lxEmJVThN0\nCAwMNLtWnOOMQQe+5RXU04GYY9GiRcjPz8ezzz4raISeRqPB//zP/yAoKAhvvvmm6AuIviUWQvdn\nDw8PpKam9rtY6OzsxLlz5xAUFGQ3QYeBDdG40XVi/s36Bh3E1Gj37etAQQfpBQYGws3NjVeJxbVr\n1yCXy3Un7dZmTtBBbHkFwGZBGkrpthVTJRZSTK4A2GAPnxILMU0kB74Pl+2g1WptUl6hUCjQ2dnJ\n63ulrq5Ob9BBqVRi9OjRRscj6yN0cgXQv7xCq9XaZU8HQHgzSUPnz6aCDgzDIDQ0FMnJyZg5cybu\nvfderFmzBo8++iiysrLg7++P9PR0XaaEq6srUlJSkJOTY8bfjhAiJacKOogdyTWQuXdWhiKVSoXu\n7m6Dd0S0Wi0FHYjZQkJCcPDgQfz444945JFHeDXB02q1WLNmDSoqKvDJJ5/AxcVF9PsPDDoIubPk\n4eGBpKSkfkGH06dPIyYmBj09PXYTdADYNOH8/HxoNBqzmroFBQWhpqZG9J1LbmxmT08PNBqNXY2D\ncxRctoMpXJaDre74mxN0MOd36+6777baeFC+Zs+ebTLoIEWmA8CvmaQUpZMpKSkoLCxEQ0OD4FIs\nKcjlcjAMw2uMoqGgAyC8r4NWqxX1efUNOrS3t4NhGLs874yPj0dxcfGgnxtq/Cg26GCIh4cHRowY\ngczMTEyfPr3f8ctSzSQJIeI4VdChvr5ekrGZzjgyk7sjYiitsL29HTKZjO5UErMFBQVh3759OHPm\nDO677z50d3cbff6bb76JXbt24dtvvzV7/+sbdBBaXuHh4YHRo0fjyJEjuuNMbm4u0tLSzL4bK7Wg\noCD4+vri/PnzZt11NKe8AriR6cBlOdhLirsj4fo6GLvD29vbi8LCQpuUVnDCwsLQ2NgoOHXdnJGZ\nAMwKUlrKxIkTcfHiRVRVVQ16rLq6Gm1tbZI1++ST6SDFDQVvb29ER0dj3759Vs9y4PDtYSFl0IHL\nIBLaM6RvTwd7LK3gGMp08PT0NFheMfB7ur6+Hjt37hTVi8kYa/d1IIQY5zRBB1dXV3h4eJjdTFKr\n1TptwzNjzSQpy4FIydvbGzt27EBzczMWLVpkcL/7/PPP8fbbb2PHjh2SnJT1bSYpJtPB3d0d3t7e\nKCkpAcD2cxg/frzZd2MtgTt5NjfToba2VnRjOC7TgSZXWE5QUBAUCgXKy8sNPufChQvw9/e36YWN\nTCZDRESE4GwHe/zdMperqyumTZuGPXv2DHosPz8fKSkpkgXoTF2Ia7Vas8srOOPHj8eZM2dsFnTg\n09dBq9UaDToMbIppCldaIfTz6nuTx15LKwC2maQ55RVcaeTixYsxYcIESdfGBR2ElGoSQizHaYIO\nwI0TZHN0dXXBxcUFcrlcolUNHcaaSQq9K0yIKW5ubvjmm2/g6+uLOXPmDOpsv3fvXqxatQrbt29H\nZGSkJO/p7+8PtVqtey8hwUUPDw+0trZi8uTJuhKL3NxcJCcn22XpQGJiIi5duoSKigrRQQelUgl3\nd3e4u7tDqVQKfj2X6eCsgVxrSUxMNDrFwlYNJAeKiooSNOITML+ng70yVGIhZWkFYDrTgXtMit/P\nmJgYeHt723XQobW1FTKZzGAgKzk5GWfOnOFVpgGI/7yGeqaDvqBDT08Pent7+30XvvHGG2hoaMDr\nr78u+doiIyPBMIzocbyEEGk5VdBBimaSzlhawaFMB2Jtrq6u+Ne//oVx48Zh2rRpqK6uBgCcOnUK\nd999N/7zn/9IerEkk8kQHByM4uJi+Pr6Cro7NTDo0NXVhcLCQsTExMDd3d3uSgeUSiXi4+N1WWBi\nBQcHiyqtANhjikwmQ11dHQUdLCg+Pt5giUVXVxdKS0uRmJhog5X1FxUVhcuXL/N+vkajQWdnp0Pu\nO1wzyYGfmbWDDtx3uxTHL5lMhkWLFomq3ZcCn6CDsSwHgL2YHjZsGM6dO8frPcVMrgCGTqZDSEgI\nent7B51b6ws6cBlt3L60f/9+bNy4EV988YVFgvIMwyAjIwPHjh2TfNuEEOGcLuhg7thMZ5xcwTE2\nwcLckVqEGCKTybBp0ybMmzcPU6ZMwaFDhzB37lz87W9/Q1ZWluTvFxoaiuLiYsF3lgYGHc6ePYsR\nI0aAYRi7vRObnp6OhIQEs7YRGhpq1sSDwMBAlJeXO+SFo70IDg6GXC7XjSzsq7i4GFFRUXaxj0ZE\nRKCqqspkHxcOlyEjkzneqczIkSPh4eGB06dP9/u5VJMrOKaCDlJ/tw8fPlzUeF4p8A06BAYGGn2O\nkL4OYiZXAP0/F3vOdGAYRm+2A9fToW/QrG9GW0VFBX7729/iX//6FyIiIiy2PurrQIj9cLxvaiOk\nynRw1qCDsUwHKq8glsQwDF588UUsX74cU6ZMwZNPPonf/OY3Fnmv0NBQXLlyRfD+zAUdEhMTUVlZ\niZ07d9ptPwdOeHg45s6da9Y2pk6diilTpoh+fUBAAK5du+a0x1VrYBhG11ByIHsprQDYzKbg4GCj\n/Sf6suffLSkMHJ2pVqtx9epVSUd8mroQl2Jyhb3gG3QwlVWQmprKq69DTU0NmpubMWLECEHrBG6U\nV9jzuEwOl0nVl0KhgFwu7xfQ4jIdenp6sGTJEixbtgyzZs2y6NpoggUh9sPpgg61tbVmNZVx5qCD\nm5ubwZ4OQpvuESLG6tWrcfHiRTz++OMWe4/Q0FBotVpRQYe2tja4uLggMzMT7733nm5yhSNfGMnl\ncrN63AQGBqKqqooyHSxM3xSL1tZWXL16FWPGjLHhyvqLjo7mXYPt6L9bs2fPxs6dO3V/LigoQGJi\noqSp6HwyHRwp6GBqUgefTIfx48fzynTgSmHEZOK4uLjAxcUFbW1t6OjosFl2CB/z5s3Ds88+i02b\nNvUbqz6wxII7f3722WehUCiwdu1ai69twoQJOHXqFO/sKUKI5ThV0MHDwwMMw6C1tVX0Nhz9JMcY\nQ5kOXKd/ynQg1iDmrpEQXFNFoUE0Nzc3dHZ2ore3F5MnT8aVK1fsPtPBHgQEBECr1VLQwcJCQkIg\nk8l0I2EB4MyZMxg9ejQUCoUNV9ZfZGQk76CDueMy7d20adNw7Ngx3feu1P0cAOuXV9gSn0yH2tpa\noz0dADbTIT8/3+QNLHM/L5VKhevXr0vWU8NSFi1ahAMHDuCbb75Beno6srOzAegPOpw5cwafffYZ\nPvvsM6uMq/Xy8sKIESNQUFBg8fcihBgnOujw5ZdfIjExES4uLkbTzH755RfEx8cjNjYWmzZtEvt2\nkuGyHcRy5kwHQ9MrWltboVAo7OrElRCxFAoFoqOjBXdYZxhG1/dk8uTJANiTUwo6GMfdVaSgg2Xp\nK7Gwp9IKTlRUFK5duwaNRmPyuY7+u+Xt7Y2UlBQcPHgQgG2CDo5WXtHV1WXwcY1Gg8bGRpOlDEFB\nQfDw8EBZWZnR50kVdLDn0gpOQkIC9u7diyeeeAK33XYbli9fDoZh+gUdLl68iPfffx9bt2616gQT\naiZJiH0QHXRISkrCtm3bMHXqVKPPW7VqFT788EPs2bMH7777rtkjK81l7thMZx7tZqiRJGU5EEfz\n+9//XlS5ENfX4aabbsJLL70ELy8vh78wMpefnx8YhnHa46o19Z1iUV9fj8bGRowaNcrWy+rH3d0d\n3t7eqKqqMvq8np4enD17dkhckJmjb4mFrYIOjvL9birToaGhAV5eXrzKxfg0k8zNzRU1uYLj5uaG\nioqKIbOPMwyDe++9F0VFRXBxccGqVavw5ZdfQqvVorOzE3/605/w29/+FpMmTbLquqiZJCH2QXQh\nLp9GRtysey4wMWvWLBw/ftzsxmXm4ObCi9Xe3i56PNxQZ6i8wpFOSggxBxd0CA0N1dWrUtDBOBcX\nF/j5+VHQwQrCwsKg0WhQVVWFc+fOITEx0S4nP3CjM8PCwvQ+3tPTg61bt8Ld3R033XSTlVdnXbNm\nzcIDDzyA7u5uFBUVYdy4cZJu31jQoaurC11dXXYx2UQKpoIOpsZl9pWWloZnnnkGmzZtglarHfQf\nAFy/ft2spp9cpsPIkSNFb8MWfH198e6772LChAl49dVXcfDgQURERCAwMBD33nuv1deTmZmJv/zl\nL1Z/X0JIf+K7f/GQk5PT74CbkJCAY8eO2TToEBQUhIsXL4p+PZVXDC6voMkVhLC4oENfFHQwbfbs\n2RYdm0ZYfUsszp49i4ULF9p6SXpFRUWhuLgYmZmZgx7jAg4qlQq33367XQZNpJSWloaKigrs3r0b\n0dHRkh9LjAUduCaS9txPQAgpgw6rV6/GlClTwDCMwf8CAgLMarKrUqnQ0tIyZJt0T5w4ES+++CIa\nGhrw9ddfY/ny5Tb5LkxISEBFRQUaGhqG7L8lIY7A6NFw5syZ/ZpOcdavX4/58+dLvph169bp/j8r\nKwtZWVmSv4e5YzOdOejAnZxoNJp+J3pNTU1Om/1BSF8UdBBn9OjRtl6C00hISMAnn3wCd3d3DBs2\nzNbL0SsqKgq7du2CVqvtd8HrbAEHgM0EmjFjBjZs2CB5aQVgfKKDo2UxmppeUVdXp2skbIqPjw9u\nueUWqZamF3euOVTKKwby8vJCa2srVqxYgRUrVuDf//63TTLaXFxckJaWhuzsbMyePdvq70/IULZ/\n/37s379fkm0ZDTrs3r3brI2np6fjqaee0v25sLAQc+bMMfj8vkEHS/Hx8UFbWxu6urpENT505qCD\nTCbTBR76XkQ1NDTY1cg1QmxFXwkSBR2IPQkPD4dKpUJSUpLd3sH29fWFi4sL6uvrdXeenTHgwJk1\naxYefPBBvPHGG5JvW6lUoru7e9DNBMCxJlcA/DId4uPjrbgi41QqFRiGGbKfwcDpFR0dHTY7f+aa\nSVLQgRBhBiYBvPDCC6K3Jcm3tqGxQVzH419++QVlZWXYvXs3MjIypHhL0WQymVl9Hdra2pw26ADo\nbybZ2NhIKWuEYHCmg1arpaADsSsMw+D222/XW7pgT6KionSjM5054ACwQQcAFsl0YBjG4MU4V17h\nKPgEHbhpOvZApVLB29vbKqMlLYELOnDXCLa8aUfNJAmxPdHf3Nu2bUNkZKSuR8Ott94KgG2c07dn\nw8aNG7Fs2TLMmDEDK1eutIsDutixmVqt1qaRWnsw8E6uVqt1uBMTQsQaGHTo7OyEq6vrkD1pJI4p\nKirK7r/HuKCDswccACAyMhKPPvooJk6caJHtG+rr4EjjMgHjQYfOzk60t7fD29vbyqsyTKVSDdnS\nCgCQy+VQKpW6c0ZbTn/LyMhAdna2wZukhBDLE93hZtGiRVi0aNGgn4eHh2P79u26P998880oKioS\n+zYWITbo0NnZCblc7tQXEAODDi0tLXBzc4Orq6sNV0WIfRgYdKAsB0LEiY6OxpEjR5w+4MB55513\nLLZtQ70OHLW8YmCvEAC6Uh57KjmKiYkZ0kEH4Ea2g7u7u01v2oWHh8Pd3R0XLlxATEyMTdZAiLNz\nym9wsUGH9vZ2p7+AGDjBwtEaTRFiDgo6ECKNoKAgdHZ2UsDBClQqld4MAEfLdJDL5WAYBr29vYMe\nq62t5T25wlo8PT0RFRVl62WYxdPTE2q1Gj09PWAYxqxpHubKzMzE4cOHbfb+hDg7p/wWDwoKEh10\nsPeUVEsb2NOBgg6E3EBBB0KkwTAMli5dSgEHK9BXXtHb24vW1la7KjeQgqGsDiHjMgl/XKaDPZw/\nz5kzp18mNiHEupzym9zf3x8NDQ3QaDSCXmcPB01bc3Nzo0wHQgxQKBTQarXo6uoCALS2tlLQgRCR\nAgICKOBgBfqCDk1NTfDy8nK4f39DfR0o6GAZ9hR0mDt3Lnbt2qX7fiaEWJdjfZvw5OrqCi8vLzQ0\nNAh6nT0cNG1tYE8HCjoQcgPDMP2yHSjTgRBi7wwFHRyptIJDQQfr6ht0sFUTSU5ISAgSEhJw4MAB\nm66DEGfllEEHgO3rUFNTI+g1zj4uE6CgAyGm9A06UB8YQoi90xd0cNTvdn1BB61WS0EHC+GCDvYy\n+W3BggX47rvvbL0MQpySUwcdhPZ1oAuIwY0kGxoaHPLEhBCxKNOBEDKU6LsQd7TJFRx9f1e1Wg25\nXG4XF8WOxp7KK4AbQQcanUmI9VHQQYCOjg6bp4fZWt9GkhqNBi0tLQ6ZgkmIWB4eHrrfEQo6EELs\nnbOXV1CWg+XYU3kFAMTHx8PV1RUFBQW2XgohToeCDgLQBUT/8orm5ma4u7vbdAQSIfamb6YDNZIk\nhNg7Q+UVjhh00DcetK6uDoGBgTZakWPz9PREa2ur3WQ6MAxDJRaE2IjTBx2EpFjZy0HTlrjpFVqt\nFo2NjfDz87P1kgixK+7u7v3KKzw8PGy8IkIIMcxQpoOzlFdQpoPluLi4QKVSoa6uzi4yHQDq60CI\nrTht0MHd3R0uLi5Qq9W8X0NBB/YLRKFQoKOjw2EbTRFiDurpQAgZSgYGHTQaDZqbmx0y00GhUFDQ\nwcq8vLxQXV1tN+fPkydPxoULF1BeXm7rpRDiVJw26AAAQUFBgkosKOjA4ppJUtCBkMG4oINGo0FX\nV5fd3N0hhBB9BgYd1Go1VCqVQ5ZO6st0qK2tpaCDBXl5eaG+vt5uzp9dXV1x66234ocffhD82o0b\nN6K+vt4CqyLE8Tl10EHI2EyNRgO1Wk13LXGjmSQFHQgZjAs6tLW1QaVSgWEYWy+JEEIMGhh0cOTv\n9oFBh97eXjQ1NVGpqAV5eXlBq9XaVQBeTIlFTk4O/vrXv1LJJCEiOX3Qoa6ujtdzL1y4gICAAAo6\n4EYzSUc+MSFErL5BBzo5IYTYO+5CnOtx5aiTK4DBjSQbGhrg7e3tkFkd9sLLywsA7CbTAQDmzJmD\ngwcPCiqxfvnll/HMM89AqVRacGWEOC6nDzrwzXQ4ceIEJkyYYOEVDQ1c0KGhoYGCDoQMwP1+0OQK\nQshQIJPJ4Orqiq6uLgDOlelA/Rwsz9PTE4B9BR18fHyQkZGB3bt383p+fn4+cnJysHTpUguvjBDH\n5fRBBz49HZqamnD16lUkJiZaYVX2z93dHWq1Gq2trQ57N4QQseRyOVxdXVFfX09BB0LIkNC3xMKR\nMx30BR1oXKZlcZkO9lReAQgrsXj55Zfx5JNP2lXghJChxqmDDj4+Pujo6BjUVGig3NxcjBs3DgqF\nwkors29ubm6orKyEp6cnZDKn3oUI0cvDwwM1NTUUdCCEDAkDgw7OlOng7+9vwxU5Pi8vLygUCrs7\nX5w/fz62b9+O3t5eo88rLCzEoUOHsGzZMiutjBDHJPoI8OWXXyIxMREuLi7Izc01+Lzhw4dj3Lhx\nSE1NxcSJE8W+nUUwDIOAgACj2Q69vb3Izc1FWlqaFVdm39zc3HD9+nWHPSkhxFwUdCCEDCVKpVIX\ndGhsbKRMByIZf39/xMbG2noZgwwfPhxhYWE4fvy40ee98sorWL16NfVoIsRMooMOSUlJ2LZtG6ZO\nnWr0eQzDYP/+/cjLy0N2drbYt7MYU2Mzz507h4CAAAQFBVlxVfbN3d0dDQ0N1O2ZEAMo6EAIGUq4\nBotarZZ6OhBJubm54c4777T1MvQyVWJx7tw57NmzBytXrrTiqghxTKKDDnFxcRg9ejSv53Idke2R\nqUwHaiA5GHch5ah3Qggxl4eHB1paWijoQAgZErjyira2Nsjlcoft0M9Nqejp6UFnZyc6Ozt1PQeI\n8zEVdFi/fj0ee+wx2kcIkYDFC6wYhsH06dOxcOFCwTNxrcFYpkNtbS2qq6sRHx9v5VXZN+5CijId\nCNGPS8OkoAMhZCjggg6O3ESSw2U7cFkODMPYeknERtLS0tDY2IjS0tJBj124cAHbt2/Ho48+aoOV\nEeJ4jA4mnjlzJiorKwf9fP369Zg/fz6vNzh8+DDCwsJQVFSE+fPnY+LEiQgNDdX73HXr1un+Pysr\nC1lZWbzewxzGxmaePHkSqampcHFxsfg6hhKue6+jpl8SYi4KOhBChhIu6ODIpRUcLuhQW1tLpRVO\nTiaTYf78+fj+++/xxBNP9Hvs1VdfxcqVKx0+CEeIMfv378f+/fsl2ZbRoAPf+bXGhIWFAQDi4+Ox\nYMECfP/993jooYf0Prdv0MFa/P390djYiN7e3n7Bhe7ubpw6dcrgWp0ZdyHl6CcmhIhFQQdCyFCi\nVCrR1tbmVJkO9fX1FHQgWLBgATZs2NAv6HD58mVs27ZNbwYEIc5kYBLACy+8IHpbkpRXGOrZ0NbW\nhpaWFgBATU0Ndu7ciTlz5kjxlpKRy+Xw8fFBfX19v58XFhZi2LBhVEKgh1wux80330w1boQYQEEH\nQshQ0re8wtFvKFCmA+lr+vTpyMvLQ11dne5nr732Gh5++GEap0qIhEQHHbZt24bIyEgcO3YMc+fO\nxa233goAuH79OubOnQsAqKysxJQpU5CSkoK7774ba9asQWRkpDQrl1BgYOCgvg7UQNK4rKwsu5u5\nTIi98PDwgFwuh6urq62XQgghJvUtr3CWTAeaXEEAtmR4+vTp+OmnnwAA5eXl+OKLLwaVWxBCzGO0\nvMKYRYsWYdGiRYN+Hh4eju3btwMARo4cifz8fPGrs5KBQYeKigq0tLTY5VxhQoj98/HxQVpaGjUo\nI4QMCX2nVzhDpkNHRwcFHYjO/Pnz8d133+Hee+/FG2+8gQceeABBQUG2XhYhDkV00MGRBAYGoqys\nTPfnkydPIi0tje7kE0JEUSgUdldKRgghhjhbpkNtbS0UCgVUKpWtl0PswNy5c/HEE0/gypUr+OST\nT3D27FlbL4kQh0NX1eg/NrOzsxOFhYVITU218aoIIYQQQixPpVKhqakJPT09Dt+LRqlU4vr16wgM\nDLT1UoidCAkJQUJCAu68807ce++9BqfsEULEo6ADbpRXaLVaFBQUYOTIkdQkkRBCCCFOQaVSobW1\nFb6+vg5fFsYFHai0gvS1YMECnDp1Ck8//bStl0KIQ6LyCrBftgqFAs3NzThx4gRmz55t6yURQggh\nhFiFUqkEAIcvrQBu9HSgoAPp6/7778fw4cMRERFh66UQ4pAo0+G/AgMDkZeXh97eXowYMcLWyyGE\nEEIIsQq5XA65XO7wTSSBGwEWCjqQvkJCQnD33XfbehmEOCwKOvxXYGAgDh8+TB3nCSGEEOJ0VCqV\n02Q6ABR0IIQQa6Kgw38FBgZCq9UiJSXF1kshhBBCCLEqlUrlNJkODMPAz8/P1kshhBCnQUGH/xo5\nciSmT58ONzc3Wy+FEEIIIcSq/P39ERQUZOtlWJybmxsCAgLg4uJi66UQQojTYLRardbWiwAAhmFg\nJ0shhBBCCCEOSKvVorW1FZ6enrZeCiGEDCnmXK9T0IEQQgghhBBCCCEGmXO9TuUVhBBCCCGEEEII\nsQgKOhBCCCGEEEIIIcQiKOhACCGEEEIIIYQQi6CgAyGEEEIIIYQQQiyCgg6EEEIIIYQQQgixCAo6\nEEIIIYQQQgghxCJEBx2eeuopxMfHY/z48Xj88cfR3t6u93m//PIL4uPjERsbi02bNoleKCG2sn//\nflsvgRCDaP8k9or2TWKvaN8k9oz2T+KIRAcdZs2ahcLCQpw4cQKtra3497//rfd5q1atwocffog9\ne/bg3XffRW1trejFEmILdPAn9oz2T2KvaN8k9or2TWLPaP8kjkh00GHmzJmQyWSQyWSYPXs2Dhw4\nMOg5TU1NAICpU6ciOjoas2bNwvHjx8WvlhBCCCGEEEIIIUOGJD0dPvroI8yfP3/Qz3NychAXF6f7\nc0JCAo4dOybFWxJCCCGEEEIIIcTOMVqtVmvowZkzZ6KysnLQz9evX68LMrz44osoKCjAV199Neh5\ne/bswT/+8Q98/vnnAIAPPvgA5eXleOmllwYvhGFE/yUIIYQQQgghhBBiOUZCB0bJjT24e/duoy/+\n+OOPsXPnTvz88896H09PT8dTTz2l+3NhYSHmzJmj97li/wKEEEIIIYQQQgixT6LLK3bs2IENGzbg\nu+++g0ql0vscHx8fAOwEi7KyMuzevRsZGRli35IQQgghhBBCCCFDiNHyCmNiY2PR1dUFf39/AMBN\nN92E9957D9evX8dDDz2E7du3AwAOHDiA5cuXo7u7G4899hgee+wx6VZPCCGEEEIIIYQQuyU606G0\ntBSXL19GXl4e8vLy8N577wEAwsPDdQEHALj55ptRVFSE8+fP6w04/PLLL4iPj0dsbCw2bdokdjmE\nmO3q1auYNm0aEhMTkZWVpRsD29LSgttuuw1RUVFYuHAh1Gq1jVdKnFVvby9SU1N1PXVo3yT2orW1\nFb/73e8wevRoJCQk4Pjx47R/Ervw0UcfYdKkSUhLS8Pjjz8OgI6dxHYeeOABhISEICkpSfczY/vj\nO++8g9jYWCQkJODQoUO2WDJxEvr2zaeeegrx8fEYP348Hn/8cbS3t+seE7pvSjK9whyrVq3Chx9+\niD179uDdd99FbW2trZdEnJSrqyveeustFBYW4quvvsLatWvR0tKC999/H1FRUSgtLUVERAQ++OAD\nWy+VOKm3334bCQkJusa7tG8Se/H8888jKioKBQUFKCgoQFxcHO2fxObq6+uxfv167N69Gzk5OSgp\nKcHOnTtp3yQ2c//992PHjh39fmZof6yursZ7772Hn3/+Ge+//z7Vl6dCAAAEf0lEQVRlixOL0rdv\nzpo1C4WFhThx4gRaW1t1N2TF7Js2DTo0NTUBAKZOnYro6GjMmjULx48ft+WSiBMLDQ1FSkoKACAw\nMBCJiYnIyclBdnY2li5dCqVSiQceeID2UWIT165dw48//ogHH3xQ13iX9k1iL/bs2YM///nPUKlU\nkMvl8PHxof2T2Jybmxu0Wi2amprQ3t6OtrY2+Pr60r5JbGbKlCnw8/Pr9zND++Px48cxZ84cREVF\n4eabb4ZWq0VLS4stlk2cgL59c+bMmZDJZJDJZJg9ezYOHDgAQNy+adOgQ05ODuLi4nR/TkhIwLFj\nx2y4IkJY58+fR2FhISZOnNhvP42Li0N2draNV0ec0erVq7FhwwbIZDcO27RvEntw7do1dHR0YMWK\nFcjIyMDrr7+O9vZ22j+Jzbm5ueH999/H8OHDERoaismTJyMjI4P2TWJXDO2Px48fR3x8vO55Y8aM\noX2V2MxHH32kK+/Nzs4WvG/avLyCEHvT0tKCu+66C2+99RY8PT1pnCuxuR9++AHBwcFITU3ttz/S\nvknsQUdHB0pKSnDHHXdg//79KCwsxH/+8x/aP4nN1dTUYMWKFTh79izKyspw9OhR/PDDD7RvErsi\nZH/kyisJsaYXX3wRXl5eWLx4MQD9+6ypfdOmQYf09HQUFxfr/lxYWIjMzEwbrog4u+7ubtxxxx24\n7777cNtttwFg99OioiIAQFFREdLT0225ROKEjhw5gu+++w4jRozAkiVLsHfvXtx33320bxK7EBMT\ngzFjxmD+/Plwc3PDkiVLsGPHDto/ic1lZ2cjMzMTMTExCAgIwOLFi3Hw4EHaN4ldMbQ/ZmRk4OzZ\ns7rnFRcX075KrO7jjz/Gzp078emnn+p+JmbftGnQwcfHBwA7waKsrAy7d+9GRkaGLZdEnJhWq8XS\npUsxduxYXYdrgP3F2rJlC9rb27FlyxYKjBGrW79+Pa5evYpLly5h69atmD59Oj755BPaN4ndiI2N\nxfHjx6HRaLB9+3bMmDGD9k9ic1OmTMGJEydQX1+Pzs5O/PTTT5g1axbtm8SuGNofJ06ciJ07d+LK\nlSvYv38/ZDIZvLy8bLxa4kx27NiBDRs24LvvvoNKpdL9XMy+afPyio0bN2LZsmWYMWMGVq5cicDA\nQFsviTipw4cP49NPP8XevXuRmpqK1NRU7NixAytWrMCVK1cwZswYlJeXY/ny5bZeKnFyXAob7ZvE\nXvzlL3/BqlWrMH78eKhUKtx99920fxKb8/b2xtq1a7Fo0SL86le/QnJyMqZNm0b7JrGZJUuWYNKk\nSSgpKUFkZCT++c9/GtwfQ0JCsGLFCkyfPh0rV67E22+/bePVE0fG7Zvnzp1DZGQktmzZgkcffRRq\ntRozZsxAamoqVq5cCUDcvsloqbCNEEIIIYQQQgghFmDzTAdCCCGEEEIIIYQ4Jgo6EEIIIYQQQggh\nxCIo6EAIIYQQQgghhBCLoKADIYQQQgghhBBCLIKCDoQQQgghhBBCCLEICjoQQgghhBBCCCHEIv4/\nCfllhDD9w64AAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x756ae50>"
}
],
"prompt_number": 165
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Variance of x, F = B * B^* should be equiv to b conv b^*\nf = np.convolve(b, list(reversed(b)), mode='full') # zero is now in the center of vector\nassert len(f) == 2*len(b) - 1\nF = np.matrix(np.inner(B, B))\nprint 'f', f\nprint 'F', F\ntoeplitz(list(f[int(len(f)/2):]) + [0]*1) # looks same as F if the zeros were right len",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "f [ 0.11111111 0.22222222 0.33333333 0.22222222 0.11111111]\nF [[ 0.33333333 0.22222222 0.11111111 ..., 0. 0. 0. ]\n [ 0.22222222 0.33333333 0.22222222 ..., 0. 0. 0. ]\n [ 0.11111111 0.22222222 0.33333333 ..., 0. 0. 0. ]\n ..., \n [ 0. 0. 0. ..., 0.33333333 0.22222222\n 0.11111111]\n [ 0. 0. 0. ..., 0.22222222 0.33333333\n 0.22222222]\n [ 0. 0. 0. ..., 0.11111111 0.22222222\n 0.33333333]]\n"
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 166,
"text": "array([[ 0.33333333, 0.22222222, 0.11111111, 0. ],\n [ 0.22222222, 0.33333333, 0.22222222, 0.11111111],\n [ 0.11111111, 0.22222222, 0.33333333, 0.22222222],\n [ 0. , 0.11111111, 0.22222222, 0.33333333]])"
}
],
"prompt_number": 166
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Assume A known\n\ndef estimateWithKnownA(A, S, F):\n \"\"\"\n \"\"\"\n x0 = 0 # assume E(x) = 0\n # Since A is \"known\", there is no uncertainty (J) about it, and \n # our estimate of it (A0) is itself\n A0 = A\n J = 0\n A0_star = A0.T\n \n SplusJ = S + J\n SplusJ_inv = 1.0 / SplusJ\n AsSinA = A0_star * (SplusJ_inv * A0)\n F_inv = F.I\n Q_inv = AsSinA + F_inv\n Q = Q_inv.I\n F_invx0 = F_inv * x0\n A0_starSplusJ_inv = A0_star * SplusJ_inv\n \n def estimator(y):\n A0_starSplusJ_invY = A0_starSplusJ_inv * np.matrix(y).T\n return Q * (F_invx0 + A0_starSplusJ_invY)\n \n globals().update(locals()) ######\n return estimator\n\ndef plotEstimateWithKnownA(A, S, F, y, x):\n xEstimator = estimateWithKnownA(A, S, F)\n xhat = xEstimator(y)\n plot(xhat, 'red'); plot(x, 'blue'); plot(y, 'k')\n\ndef getKnownATuple(b, a, S):\n \"\"\"a and b are treated as if they start from i=0\n \"\"\"\n # TODO put all previous cells into a function for easier exploration/sweep\n return A, S, F, y, x\n\nplotEstimateWithKnownA(*getKnownATuple(b, a, variance_nu))",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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SFAwjV7kDxwFvvQWkpQG//Xblcd6loM8BcfbsWQwePBiOjo4aj/Pi6aBBg8we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\nV67EGtXUw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"text": "<matplotlib.figure.Figure at 0x6faacb0>"
}
],
"prompt_number": 169
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Haven't gotten this part figured out yet\ndef calibrate(inputs, inputCovariance, outputs, noiseCovariance):\n \"\"\"Given inputs, outputs, and their distributions, get information to \n construct an estimate as if A was unknown.\n :returns: (R, r, Q) such that \\hat{x} = R*y + r and Q is its variance (?)\n \"\"\"\n x0 = np.matrix([0]*inputs.shape[0]).T # length ok?\n x0_star = x0.T\n # Phi and psi should be column vectors stacks left to right\n phi = np.matrix(inputs).T; psi = np.matrix(outputs).T;\n F = np.matrix(inputCovariance); S = np.matrix(noiseCovariance)\n phi_star = phi.T\n G = psi * phi_star\n H = phi * phi_star\n assert G.shape[1] == H.shape[0]\n assert H.shape[0] == H.shape[1]\n # We might have some issues with the shapes of G and H\n # For 1D data, they should be 1D, but I don't want to use ravel\n \n #assert np.linalg.det(H) - 1e-5 > 0, pformat(locals())\n H_inv = H.I\n A0 = G * H_inv\n \n A0_star = A0.T\n x0x0_star = x0 * x0_star\n F_bar = F + x0x0_star\n H_invF_bar = H_inv * F_bar\n alpha = np.trace(H_invF_bar)\n \n J = alpha * S\n \n S_inv = np.ravel(S.I)[0] # KLUDGE! but how else if S is 1x1 \n F_inv = F.I\n AsSinA = A0_star * (S_inv * A0)\n alpha_mult = (1.0/(alpha + 1))\n Q_inv = alpha_mult * AsSinA + F_inv\n Q = Q_inv.I\n \n r = Q * (F_inv * x0)\n R = alpha_mult * Q * (A0 * S_inv)\n \n pprint(locals())\n globals().update(locals())\n return R, r, Q\n \nR, r, Q = calibrate(x, variance_input, y, variance_nu)",
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'variance_input' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-170-1684d2a97072>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 44\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mR\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mQ\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m \u001b[0mR\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mQ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalibrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariance_input\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariance_nu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'variance_input' is not defined"
]
}
],
"prompt_number": 170
},
{
"cell_type": "code",
"collapsed": false,
"input": "xhats = R * np.matrix(ys).T + r\n#print 'xhats', xhats\n\n# Why do we get zeros all the time?",
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'R' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-171-4648f4db7740>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mxhats\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mR\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;31m#print 'xhats', xhats\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# Why do we get zeros all the time?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'R' is not defined"
]
}
],
"prompt_number": 171
},
{
"cell_type": "code",
"collapsed": false,
"input": "figsize(18, 4)\nplot(xs, color='k'); plot(ys, color='blue'); plot(xhats, color='red');",
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'xhats' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-172-12b2e342b764>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'k'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'blue'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxhats\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'red'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'xhats' is not defined"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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aSdoDn2fSIWcRyZy+++477NixAykpKVqKjDH2qdL5pIOzszMnHbQoJCQETk5O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\n",
"text": "<matplotlib.figure.Figure at 0x4f7aef0>"
}
],
"prompt_number": 172
},
{
"cell_type": "code",
"collapsed": false,
"input": "xs, ys, xhats",
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'xhats' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-173-edb424231d52>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mxs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxhats\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'xhats' is not defined"
]
}
],
"prompt_number": 173
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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