{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import scipy.io as sio\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import h5py\n", "import math\n", "from sklearn.naive_bayes import *\n", "from sklearn.metrics import confusion_matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is an analysis of rat hippocampal recording data; the original dataset is posted here.\n", "\n", "## Unpacking the Data\n", "This experiment contains **neural spiking** data and **rat position** data, saved into a `.mat` file. Because of the size of the data, Matlab saves it in a compressed format (hdf5) that must be read using the `h5py` module. We'll work with Cicero here, but if you'd like to explore more the Gatsby dataset is a different animal running on a linear track." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ " f = h5py.File('Cicero_09102014_sessInfo.mat','r');\n", "#f = h5py.File('Gatsby_08022013_sessInfo.mat','r');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This file contains a struct named `sessInfo` with fields `Position`, `Spikes`, and `Epochs`. `Position` and `spikes` contain the position of the rat as it runs along a track (called a maze), and the time of recorded spikes during this and other behaviors. `Epochs` gives the start and stop time of various behavioral assays (we'll ignore this for now and focus just on the maze-running experiment.)\n", "\n", "### Position data\n", "**`sessInfo.Positions`** gives the location of the rat while it runs on a (in this case circular) track. It has fields:\n", " * `MazeType`: the type of maze (ascii code, see conversion to string below)\n", " * `TwoDLocation`: the X- and Y- coordinates of the rat in the maze\n", " * `OneDLocation`: a 1D representation of the animal's position (ie how far around the circle it has run)\n", " * `TimeStamps`: the time at which the animal is at the given position (we'll use this to align to the spike data)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "mazeType = ''.join(chr(i) for i in np.squeeze(f['sessInfo']['Position']['MazeType'][()])); #converts ascii values to a string\n", "time = np.squeeze(f['sessInfo']['Position']['TimeStamps']);\n", "pos2DRaw = np.squeeze(f['sessInfo']['Position']['TwoDLocation']);\n", "framerate = 1/np.mean(time[1:]-time[:-1]) #compute the framerate of position tracking as the mean difference between timestamps\n", "T = len(time);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The position data has some missing values (where the tracking failed). We can use `np.interp` to fill these in by linear interpolation. This works pretty well, although there are a few (rare) timepoints where the tracking does jump around." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "pos2D = np.copy(pos2DRaw);\n", "x = lambda z: z.nonzero()[0]; # an anonymous helper function for interpolation\n", "for i in range(0,2):\n", " nans = np.isnan(pos2D[i,:]);\n", " pos2D[i,nans] = np.interp(x(nans), x(~nans), pos2D[i,~nans]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's what the position data looks like when we plot it:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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ZtYfuqfFMu/WEhm6OjY2NRXAsSYpxkVvs3ef/9pjTODlo4ayU8otIcBlJB/4XXEYCliilplvnTrOWkQIcxstI6Rm5XPnmz5yvZjNeOjHuujEgWki4vfTDevSN37PWaMfz0oxPbxwUMR2r12Oz6FL6Bxk0Z0dJM3o9NquCeI5kAXrEuK9RgAsfp2uL2SoeUk5siwS8/Pjrn8wNHMN/1BEA9G8dzR/bCzkqtaktTGwiipET5rE1cwvn6UuZkjmE/k9+z+KHhjV0s2xsDhvKi+OVWfmIUvRs7mDb1k0sXb2W6EARTvxE4cUlPtz4yceLiwB+8VOoolmvEthNAjMliUG9unJE23bklvgrCOuVWfkI0C01wRbZEUbE7hzYr18/VWNydm8RW358ixmuMxnYMSVib6hJi7bwzcrtnNm9FaMHtOPBL/5g2+LpvOt6FoBcvRlfeXozKXAKa1VbLtXn8LTzbQD6lb5O81ZtmHnH4Hpv97Dn57JxVxFGudujjWQzz30H2SqBYz2votCYcXk7evhWsmvVj+xav5Dn/BcxT+vHR9cNrNffpCbR3n7c15yv/YyBkCSFPOL8oNp6upS+yxL3TWxWLTjH+zS92iREjHgWkXSlVL+Gbkd17LPP2hw07cd9zYvOCYzUF3C6ZzzrVDtuHNyBccO7NnTT9knwmdIxOZbv7x7S0M2pFyK5z9r9df/5eOFfrPxqAgNlJc0lj+bk0kLyiBHPQdXrVxo5xLNLJbBLJZBNovm/9S+HBHJIJLllG7p3bE9cjNu2YtcBte2vEbtz4L7ImPsuaQsf4cjAl1wx5y7ev+74iLtZPv51I6d/exK/+q7kgT8HAWbUZDRmJ3vdfw4djb85X/+Fyx0/7PX+/7me5fKdj+x1vK4Z9vxcWufM5yHHt8wxenG0ZPC76sj9jk8ASJF8+ss6nna+TadPTXf2Jo54mlJAX1nHbF8fFm7KqbffIz0jlysn/kQ/tYr/GL32On+qls7zrjcqHDuu9BW8OPCLk9muu0iWPQAkk0+clNBDNgPwe2Z+nbffpuEYP3MN367awRndWjYK8QkQSykAXSSTdaod78z/K+LbPuz5uVyc+wbJjnzuzL6VYc/PPWzEs03jJz0jl99++ZYT14/nUsdmthopZNGMleoIZhtJ/K2S2KkS2UkSe1QMXpx4cVKqnPhw4LFe+9GIo4QUyaObZHCstobu2l8cLRm0kDxaSF7NDdlt/vMqnfWqLdmW0BYS2WAJ7IWSSI8evWiTdiS5JT5bSNcBjVY4f+U4DZ8/nTsdU+nqWc/UpWkRd3MsWfgTl0oBdzk+ZYZ3EJMXb+G4Ds0owQvA5MAQNvtbcZE+h2edb1Z470v+UdyqT2OC40UInAO6s97avSl7D99HmRbxk/QVAFzETxXKDNZX0EnL4gP/qTgHXMdmrR1jfzuNGEpRQEGJr97aO3VpJi9r/8fJ+nI2GKk0l1z+Vk3prG3jJf8o7nB8UaF8qXKynWbmCwVNKAmdS5TC0N+CgSZh2VzTJgIZP3MNK+ZN50TJ4o2fTwOIeAEa59bZHjDv3a5aBtONQXgDkblqWJ4/s4u4LuobAO703cqf2UUN3CIbm9ox9edlGN//i5v0n8iSptzsvZ2ZxgAqJxTTBByaMOSo5mzdXUxBsZejmpTSO/cbzgrMobO2rcbrFKqokPjeqRLJU004QrbTU9tIvJRUKOuSAN0t406VrIHtq5uyyOjKh7N7803P4XRo19a2SIeJRiucB3ZMZvyPPbmTqTSREqYs2cr5fdpE1A2RVrwKgHWqHWBaL3/PzOcq3Rw08lUsABlGy9B7bvPeyhbVnN9VR7arZox3vgUz7oQRE0CqyvwXXu78ZBm9ZEPodZFyE1tuGeoy7z/50PVvUsV0UZ9j9OLU5t344OvVjNMKudLxPa/6R/LmL5vqTYQsXP83T+vLAeikmRbweDEfUpVFM0Avz8QKrx/yX8Nzzv8CVHDjiMKLO6pJnbTZpuF5Z8Fm1rmeBuCDwGm8s2BzxAvn4zom41jvB6C55AIQ5WxskztF1VlMbWwih/S/dpH5w2ucmvkGMVopb/jP4WX/KIqJwqEJ151wBBt3FbFzTykJ0U627MjmNFnEWRtm0FOzch/stirbRxedFejHB4FhzDO6U13fcOOljWTTTnaSJn9zhGxnmJ5OquwmT8Uyx+jF7EAfcoins2TST1vPidoKRsp8AitfZekfnZlr9ObRH3pzTN/j6dY60RbSB0ijFc5905Lo3r4VZJquD/6AYurSzIi5AdIzckkoyQAHnKEvZrM+mjVGWzJUS7pKBgD5mKJsuerI14Fjed5/EZtUaqiOT42TGT+4Kfz8LCS0hqEP1Hm7v1yexV9RjwGw0WjFqd7/0IwCLtF/JEXymG90w680WssuADw4yS32UuQNQJRZR19tPd8YA7jzk2W8eEnvOm3vpEVbUHmbwV12bLJ/CFMCJ1GCm9H6bBKliJf9oxAUu1U8pVbhzePPYuSEeXyWeRK/Gx353n0fA7S1oXqi8aLrjU2U2NQWj98IPQFjKaHIH92wDaolQX/KJpbLRmxU43qMR+OhJPiwsLGJQL6dNZPU+Q8yQtvEAuNoHvZfzUbVGl1gzLHtOK9PG9btKGDh/B+4T/uY4/VVZW+uNGR84h/C1MBgVqr2lOAmmT20k79pKztpJzvppGUxWFvB6foSlhsdedk/ijmqNyN6tqZzi7iQ73JBiY9fN6Xwc1YbAtYq02P+KzleW8mF+k8M135jlD6f5UZHPgkM5Z++6yjBTU/ZyFB9GUO15dzrmMy9TGbb782Yu7QXy1QvJtKds/t1toMQ94PG9cStxLyMEtDhdddLHFH6IdOWZvL0qB4N3SwAPl+aSZ9Kyytdta10tTZZXGQchWH1sHYpSdySfWeFstEOjTVPnglqOOzJgp+egfhU0pNH1GnmivKLvo/7r0ChsYsEJgRGhY7nEF8mnJWTpBhXhTqaW35aX63IqnPhPHnxFpIoACBTJRNPEQ/6r8Fv3doP+a+t8n3B+2TarScw7Pm55GXvbVmOEQ8HF/JhE8mUt+ukSB5FqpEIZ+uujKVkHyUjBwf+0N8xtnC2iVCWr9uEd9a/OC1nBtmSwG3eW5lhHAdIyMocF+1k9h+Z9Fj0D6Y5K25F8VPgGN4KDGee0R1VSUF3at6E7qnx5BSlIHRkxoZdGAYQACd+ztd/5mb9S/7neo6VRnte/WMkKzzDUaKTEufmvD5tGDe8614p7wpKOnHXvJ7E+fYwUp/HJfocxjvf4iHHh8wy+jEzMIAJ/lG8wEU0J5ch+nJO1pYzQp/PGJmNRzlZuLQrPy7pzVyjFy9pLbmwX1vOi7AV/EiiUQvnHJ/LTIAHpJDPTl/k/MjZBR7iqxnY5gR6crfvptDr7+8ewp2fLGPu+myGHJlSUWyKwDkvQuEOjBn/YH1gOlP8Z/OKo1W1mSuqrasWaMBvRheO1dbxk3EMYIrMJ2asosRvAFCooumobQfAg4unvl4NwB3em3nJ9Ro9tY0QAKt4neJ2aMSK6fpyi/d2fledqi174+AOrNq+J5ThJMj3dw/hyHFf7lU+Cg/KqYe/0TYRgVMvk84p5LOZVqRn5Eb8YBEMLm4ipsXZ7Yj8ezSJMp/mGCklj4QGbI3N4URloVmV0Sl9cw4ZP/yXoVtfI44i/hc4gxf951NIDLrAKV1boIAZC5ZxgfqB+5xTQ9rjDf85TPSfxW7iQ/UJ5YWyd68xp3K75qzbyZQ1J/NZYDAjtAXc4pjG684Xydr0PlMDg/ksMJhJi7YQH+NAF42L+rbhlqFlY92wbi2ZujSTD5bE8473DHrLBi7W53Cm/hvn6/PYo6L5wejLl4Hj+TRwElMCQ3Hho7+2jqGaaY1+zPke8B7rjdY8tfgyLl7ci8dHdN+r3TaNXDjnUWYlbCPZ7FRJjJ+5JmL8FMsHnV3keZjf1FFU9l9KaWJaa2sUuLqTZQNfwrH+bC7VZ+NROo/5rqoyc8Ww5+fSKedHRst2Xls+ct91VyIuxkGCv4hvA/0BIcqpMXpAu1DnaT/u65BoBijFRZE3QK82CXyZeQIjAgvoLJm1vl44CFqcg/eDJnDDifuXostbRVeIwUNGfuOx6tnsH1IuZiBZ8kHB9e8tZukjpzVgq/ZN0FUjmF2jdUJkW28nLdrCU863Qq9j8RBQNIpJik3jZsWqVZROGctgVcCsQH++U/15SdqGLKoAC+bP4YR1T3Oe/MlvqgsP+64OxSXpmtCnbQKF639mtMziv/oiNDHXZQtVFP08r1OKGw3olBJL01gXnVrE1Sreqm9aUqjM6AHtSM/I5Y2fNjJt7UlM857AMG0JF+lzuVn/ktsc01hkHMUMz3EsMzrxxs9mgoHgGBes6/w+bZi6NJNdBS15aN2RPOy5huO1lQzXFnGavoTz9HmsM9pwv+8GXGn9iYlpw7Prj+Fp7+W0lR2crC1jtP4j77me4VX/uTw07eJQ+2zKaNTC2Si3FNJZ28bSwJH1GpRWE3nFXppIcej1LhKoSjTXdvOCBVtLedX7MKujriHWylxR2UXiircX4du1gdfdLwHwWmAk05bvn7uEEVCkSB6LjS4AOLW9AxUWBI5mkG5amT2Y2T6m3XoC7cd9TY6K50jNFM5uve4DgLbll9LdsjjnqSa0Topm/v0nH1BdhpLQQxHgddeLHO95mRPGz2beuFPC0l6byKFlfBTeIh2XBDhBW0mGasHq4vYRL+iiraw8sZYrWGKl50Ck8eXPi5msp4dex1iCP5JiUmwOQTbOofO0q/DjYT1tuMf5KffwKRuMVGal9+e5JccwTBZzsz6LXOK423sjU40TEQSXLgxs7SJt21eM2f4DRzm2Vqj6df85POu/GNA47egWjD2p40Hfy33Tknjzin6kZ+QydWkmU5bofOs7lhbs5nz9Fy7U5/Kk8x0APvSfwkcrr9tL6wQFdHpGLslxbrZt30FMXjxzC3uyVHWmvfzNufp8PnU9xvNbL+RN42z6pjWjU4s4uqf2YGXWsZy7+FQe1t7lFsd0ovHy0LTLAVs8l6dRC+emMU6GlDzPXPfd9JQNTGYoAWXurtXQm1bsLvJWsDjvUgloApv+fdYB1TewQzNekCg2GKlEizlwrswqyzE8adEWCjb8ykeul0PHXPjwsn9p7JyeXJpGFbJZmZk+Cj2BCuddunCf/wbm6aZPtkc5cVkCOa1pDLv3xNHUsgC3TKh7n1GPP0CCFGIoYQ8xOP2Bfb+pChKjHXQpeY8/o64IHWstOdysT+e1vBHhaq5NBNGlZRy+jQ5cBBjjmM0Yx2zal07imW/WMOXGQQ3dvGqJtoRnMDgw0hlW+GUFm0GslIKCDX8XNFyjbA5dDAPmPQ8/PgWJR3Jh6VjW+1qSTC6n60s4Q/uNWxxfcgtl7nn3escy1+iJIJyWksvF8h39//6OOGcJa4y2rDbS6CBmxqZH/FcxJTAUTeDJkT3CLigrW483/J3E65ub8lrgXNrILi7Xv+N6fSZnBdbBlmbQbkCF909atIW3v/yBy7VveUD/yXTpqmJuPc75CfepyZyc8RyLM1ox9sQOPD2qB91TE3homoMSorjW8Q0OAjw87UrAFs9BGrVwfvPK/pz/uo9VRhp9tA1sjhptukRkdm1w8dw01kXcnjLhvIcY3AeRoSHYmUqyXCEfx10FZaFrr875k/nuf1V4TxNK2L0fwnnSoi101cyMH6tVGlAxWBDg0XO788AXZUc9OHFb6bCyC0rJVXHEiIcoPGQX1E9GiiQKySd2r2CM/WH5v06n/bivudw7jnscU0LphO5zTmabagYc2ITHJnLJK/KErLflWbNjTwO0pvYEXTVMy22E53D2FHIhFTd3Clqcdxft/d3b2BwUJbnw+Vj4cxb0uJDoc17iqe3ekC/xl0vTaJq5J5QFI0fFEUcx77j+U1ZHAQSU8KVxPNtUMsO1RXTUtjMv0I1H/VeyQbWpM9FcnvKuHEE3jmVb3PxXXU2TzmczZsd4eOcMOP4Olna4kc+W76RZ9iJ6bpvE985l+NGYYQxiemAQO1QSfnT66H8xOHojnT2rOUrbiiaKue67AXhnwelMyzmB7n1P5ZSuLXlyzWX40LnR8RW7pjDuAAAgAElEQVQO/Dw8zQy0t8VzIxfOfdOSSIpxUOiPDqURu8oxi998XfljW8Pv+BZHCZP8Q3kncCYg9GxzcAExiTEuSnCHhHN5cot9e6XBiZNiSvTEWtf/7Ky1jLTyH/9pmP5fvSq1efSAdvx75urQaw9OEpzmbeQJGOSIGSCRLPlk+tzUNW5dI1EKybNyYh/M5GRw52R+/vMYfvEeQwq57CGWd53P8pxrImw6AzqcFK5m20QAhUWFaKLYrprSSnaHjqt6CGo9GIL93yEGbupvo6ED4vePSZCKm50Es4I0jY1sFxObRsb232Hy5WYWquHPQf/rQIS+aebY8N2i5fxj+wMMdK5mauAEHvFdTRHRJFLAz+5/EF/OtVIXRUfJ4jx9HhuNVlzjvYcfjd6AoGvCE/UcNBd046iA5xyY9SDM+z+O+OVNHsJHjHjYJfG8EhjFh/5TyCYptDGL6dd9IQBn/3cBMUYRF+g/h/YuuNoxCzbOgo0Pc3XgaPzaOTzjvxQfDm5zTCNKfDwy7RrAFs+NWjgDtIyPpjCnzCVAxxz1EqPrb6e9qigoLMItPrapFP5Upgi9/8yD873OK/ZSotyhDp5XXGax8QeMvYUzxeS5ai8k84p9DHSuoUBFk21FvVdltS8fVOXBRbHPdI9waBpZhrmrWWtyyKR5nedyjo9yklhUSB5xodcHyvvXDuCKtxfx2+bdFKpm+AIGzyQ8yLToJ2HyZaw6Ywpz81LsPJeHCK1iDCiE34yjGKEvCB33GRFsxVWKGDwUqGjipCRkvY1IjAAsfI0VdOYl77m84HydBCk2XTWIfN9sm0bE0g/g67shNhmu/gba9g+dmrRoCz9M/5D/OF4jWryWL/NgNKBNgptbit4kXoqZ6D+LV/0jGKit5kz9N7rKFh7xXcmkwCn4cSDAsDD5M4cFdxyc+zJfeXtz9sqydLYne55nD7HomnCjlT6v8pg1eewgpi7N5NeMlhy541Tuc3zCdQ5zZ8+fAsdwpJbJO/ozrNLTeMZ/Cc/5LuQe56cM1FbzxPQrmbv2LFLiow7blHWNXjj7AgZF5XKC+qwcMR2bN+yOb/GaOTgUYor6tKYxB32DbcsroQQ3zckNvQ7iDSgqe2XESQn5xf5aBzvFUMoZejAvZfWBfXHRTkbnP8AIbQEGGnHWJOXM7i1Z9rvpB/a48x1O9z6738GJ+8sej59EKSRHxYdeHwzvXztg74N5PfFOPIWm00bzjvdpXnEkVpsK0KbxkOI2J9lLjCNDwlnDQNciN72bjh+HGOwy4omTEmLFU2ECHVGs/xZ2b2Jjp6eYvfIIBnomsCbqmsgW+zaNC18pzLwHln0AHYbA+W+b4hnTvWHakr9IW/4c/3N+zRqjLbf6bmejah3KyRy14DkudczhFf9InvdfBMB3xrHMMo4NXUIwLbaRmpqtVf+RHJ+uM999GwAroq6nfekkTjmqebWJEsq7gYyfuYanf7mc7wP9GO+cyEn6Cib7h7BWteVy/Xvedz3DR/5TuMz7Tx50fMTrzv/jhw1zedR/FZ8s3lrv1vdIoNELZ6euUVhh4wIhmlLSt+Q2aGo6V8BcngyKemcYMkx4AgbFuENpqDyBmteU4zEt02/8tHHvZZ5KTFq0hVRrU5OZgWNrLHvLkE488EUJC4zuoddgpr3rvHwLAF20TJz48dXxLebxB0ikkI2khl6HncS2fN35cUb9PpbjZCUz/YOqTAVo07hoGWPeK7kqjt2qCU2lEBc+0ppGbo5ht2H2/V0kcAR/E0NphQl0RPHrq5DQjlGX3si6WX/y/q+bMJQQK/a2QjZhIHczTLnCdNE48R5zZ11r0jtp0RYmfvkjLzpeppe+kQ/8p/Kk/zL84mLMgHZ0S00g/+fXucnxGZ8FBvO8/8KQ33KXlnFMXZqJQKPYTa9vWhK3jBxC+y8+4gXn62xT5sQhOa52rpLjhne18kC34+zFHblD+5Rr9ZnsoCn/9o2mh7aJ6/WZnKit4H7/DRwtm7nL8Rnfu+5lYuBsnvmigLnrdkaOJb4eaPTC2RcwQlZdgM6SyZqoa3jIdzVv/GymemsI8ZwSpaAISpR584bLn69ARdNE23ugrCptXJzl0rE6a9/+3v+b/1dox7/3/KcDUJ3WD84uv1m5fa/E7j4cfOA/lcsdP9BSctiqWuzz2geD6eNcRJ7RJPS6LujcqQv8Di786JowsEOzOrmOTf2xbUc2AMW4edU/koedH+LCT58Ifvi7lCWclSnum1DC+rzSyEuht20pZMyH058G3cG44V3ZtKuI4o1u2+Jsc/CsnwWfX2/+felk6HJG6FR6Ri4Lpr/NdOdEAG7y3sE3xgAzJ3O7RDbsyMO/5H3+7XiT740+jPNdh65pFSynEdWXakGw3fdOu5mAMrNfnW/lqa4NQQt099QEHvnSzTeBAYx3TmSC6xXSjc5MDJzF2fpCPnI+zduBMznL+zT3OKZwh+Nzrta/4d31p3P9mjPp17XTYSGgG71wbhrromh3matGFyuH8HBtER8GhjFt+bYGEc4uKwCm1PKfCIc/n9uhU0CMZUlWuB06XR/6hhK/gbuK7ABxlsV5X5ZpgLwSLz0sF5CdmAGFNaXkKr8pSnmcuvC1MZDL+YEjZAc7tJa1+WgHTsBPvBSTp0zhHKgj/1SlmV1FF8PczdGmUTN+5hqys7eDC3areNqKKaI1DLqlRr7FOeiaFC0eULVbVapXFr4GrjjofXmFw8VEhVbMbGz2GyMAc8fDz89Cyx5w0QfQ9IjQ6aUbt5P3+T1McH7FMqMTt/luJYvmtE1w0rZgOWdk/sYZ+mKaO/NYaHTlVt/t+HEwun/bRu9uMHpAO7q0jGPhppwDtpAH65i6tC2j0jswIjCX2x2fc6PjK/aoaDRRXO+YyRDtd+7y3cQE/yhuc3zO7Y5pXKN/y1vrh3PJ2vN5bMQxjf77rIlGL5wTY1wVLM5BijEtvW2bxtR3k0jPyOWv7TngMnfWg9ovm9REvNvBnj0xOMQgBg/bcoX+spYrnN/xhO/yvcrHVbPld1W4dY2mYqbhylHxJDdxHVDHO6tHK35cXghAd/mLpj3O3O869ovCv8ENOdZ2pzl1lOJqydZCegAOAvj9hu2q0ciZkp7JUCvfeA5xBKzIWicGuRHqM5yekcuKv7LACbutYNgoa8L8V3ZhQzatIvmZsOoLGHAjRMVXOFWk3MSILZxtDoCiHJh6LWyaA70ug7OeA2fZ2P/V7Ll0+ulW+mhbecN/Ns/5LyIgOidry7iv5GO6uDIpVm7mGD35NnAs3xn98ODC5dD2yzobyZT3XT7YOsw80u05JX0IJxhLuVifwxBtObooOmvbmOF+iNf853KH71Ze8m/ndsfn3On4nFb+3Tz45Q10aRl3yI6RjV44AxWCA4PsVKbVdNxBZrI4ED5fmonLGtBKlQtNCEvH3FPqYw9map14ijhCtvOp+3EAnFT07S1W7pCrhtux72Cn+CgnMVZe6CKiaN3kwIT+i5f05k4gY20rTk/aTs86DAyctGgL8ZiCIbh0XZXLSjiIjzEf0A78GOy9a6NN40ITy1oLFKso/FZQsUYgYn/bz5dm4rQszruDFmfrOdMhpWGDoSvw20Qzp9+AsRUO5xV7LYuzJ/TaxqZWZC6BKVdCUTac+wr0KduoKn3zbv6aPZHhW16gRFxc5b2PuUYveskGxjk/ZqC2hr+MFtzhvZlZRn9KLaOaJnBa1wjKkhFhVBbQN6UfS7Inmwv0n7jO8TXxUsLNjunc7JjOjd47udV3B3+pKdzmmMYcby+mLm13yH6vh4RwLlF7D3RNLKtGQ/xw2QWekCXIg5N+YZgFAiDCHmVa0OOkhKOtzUqAUDaM7wJ9ec5/ER+6/h3aubBbq/i966qEL2AQKx48ykkAHV8t3Duq48VLesNng0jbsuiA66gNkxdvIVFM4ZyHKRyuOK59nVxrt8d0AXESQCBirZI2taNDcixRW63JLa5yFudAxP622QWe0OYnu5VlcbZ2ER3SpXmDtasCnkJY8i4cPQISKy7V7i7yUozb3gDFpvYoBYvfgm//CfGt4NpZkFpmjJkyfw3ub+/mAn0+vxpHc4fvFqLEywTny5ytL2SXiudh31V8HDgZPw40gWPTkujcIu6wTaW2v1TeyfD1JclM8IzkRO0P3nE+iyaKN1wvAnC/73q2Ginc5/iE4Uv6cv4h+h0fEsK5qswNiRQS5ayfnesqkxznZo+1MUEpLo5pEReWeru1imdPfpnFOZG9l2c/D5zIetWWUuXEJWYbajOoNo11EZ1bGnJxOehgxtTesHIqFO6EJnUzqHv9Bq2t5fY81YSmMc4682cvszgHUNgW50OB4OS2FBd+ZVmcxYjo3za4+UlwoujGF1kTueUfgScfjrt1r1NNY1148py4reeSvQGKTY14i+Crf8CKydD5NBj1X4hpClgBgPN+5Oz1D9BO+5sXfBfwceBkbnZ8yWX6D/hw8JJ/FG/6z6JEYjjl6BakxLltsXwQVBbQny7R6eD5iC6yhVnucQA843wzVP4R4x3un5rAM+cfc8h954eEcI6twmcuXopQgYbZyKB7agJLpczi3D1MwUZjT+rIY2vMfLPxUkxTKQidy1YJpEg+3xn9rOu6QruKraxFVg0wd/QKCueDJmgVyFoOR54Wnjor4QsYJFq7kuWqJnU6EO8uNe8lh21xPiTYXeQlSrz4lE4A8x9EtsUZynbdy7d2ynThi5yJnLXhCW0HQJu9AxUTY1x4cIXSZNoboNhUy64NMOVy2LkGhj5oppvTTEPYpIWb2fjV89ynTyKXOK713Us32cyP7ruJxsOUwBD+z39+aNe8ut4a+3CjvID+fGkmOwtacOrOaRyz+1tecL0RKjfa8SNH5W3hzom3c2K/PofUpKXRC+eUODerrO2hfwj05lR9GQAJFOFpIOGcW+zFYe1gGFCOsA3EfdOScMcmgh/ucExlvdE2dM6Fjz+N1hhoJEY78QScIeFcG6/f3UVeosUTSp930MuoLY8xr7y97oSzU9dIDFqcaULTOkpFB5AQa7rIOMUfOULF5oAxV1i8oeBdv+WqoRHZFuegq0bQ4uzCHzETuZ9nvM/g3M380v52TqymjBcHLg5ukyKbQ5f0jFzWzvmI87c8jcPpwnHZVOh0Sujcd4uWc/zKRxjtWMHcQE8WGV0Z73yTlpLLd4G+POO/hI2qdcRvWnIoUD4QMT0jl0snlrCgtBvPOicyWP8DgD7aBr6W+7ll8R2MWdrrkNk4rGF8GcLIeX3asFx1onfpG1znuyd0PFGKqs1DXNckxbjQrWA9H1pYB+LMIvND9dI20VdbT7YVFJcgxaEApyFdUvBQJpxrk16raayLWEpDgZYHbb2NiofkzpC17ODqqYE9Hj9xUoJX6XhwHfSugTWxcvse/ErDYf2utbXi20QmiTEuovCG0kU2FotzVNBVQwWFc2RYnCct2sKeJZ+wQyVx5a/NmbRoy15lUuLc1nPJG3ptYxMkPSOX9956kTGbHyTKKGJo4eOkO/sA5v311sSXGbvqMvrLWp70jaGJlHC/8xOyVDMu8DzCDb67yZA23Di4A/ec3oXJY4+zRXM90TctiY9vOI7LTx/EhNRneMh3dehcnJTwknMCTX3ZTF2a2YCtDB+NXjj3TUti6k2DcMelAEL70km84h9JPEWce0wd5xCuhtxiL7plcVZoYR2Ii1TZYNNR287cQM/Qa6+1gPDXriI8qsyXsDYiLzHGRYx4KLFcNcKyjJrau06FM0rhIFDm467qboVhxvJtOMTgZn166HVjRkTOEJF1IrJBRMbVUO4CEVEiEkFJgsNDlHjx7GVxjtysGgBR1mS4mCgCSnCJOVls6Inc5MVbiKeILNUMA43Ji/cWzt1SE/AqZ6jNkZwvO9I4HPrrwk05ZPrNbFjfBfqyNdCMz5dmMmX+WvSvbuN15wtsU8mc432S/to6+sif3OMby3nex1jGUYwZ0I7JY49j3PCu3DK00yFh2WxM9E1L4pahnbh/+NF8bAzjGm+ZITNJCvnI9SRfLPmL9IzcBmxleGj0whnMH2zhg6cy9aZBdG0ZR4kehy6K/xvVqUHakxTjQrOEsz/MFueSSj7Ii1RZMFyS5bbw166iCpad2hreo/FQrMJoBUrtDQXbYc/28NVZjtaJ0WZeZcta2Dpx73ze4aLAY1qaNVEVXjdGREQHXgXOBI4GLhWRo6soFwfcDtRtepQGwo2XUisjT5nF2WhwEVoTQdHpQ8eLE9d+uGPVJW6HRrSUPT/cjr2Hltxib2glLFLcSxoDh0t/LSjxcZq+BIC3/MMByP9zAQO+O5cLtZ941X8u53kf5xJ9DqfrS3jCfxmfBU5CE+GJkT14alQPWyxHAH3TknhiZA/mqj7MD3QLHT9C+5uZ+j3cPWV5lStSjYmwCOdImQ33TUvimzsHc9/IgQC8/+PyBpndlLc4G2G2OAdFYpBFxlGhv9tp5u5ne0r9ePfTVSOv2EssZVk1wpJjtVUv8//tyw++ripIjHHhwh+ytNdlsJEuphVki5ESet2IORbYoJTapJTyAp8AI6oo9wTwLByaW72ZrhoVhbNGgM/SMyPWKuLEj0c5AKngL9zQ1tvEGBcx1LxilRTjCon9SHAvaUQc8v01PSOX9PmzuEH/mkn+k/lNdeVq/RteLBqHToBLvA/xH/8lXKL/yHWOb3jHfzrvBM60g/8ilNED2vHkyB68Y5gboL3kPw8wxfMXhZcx/ouFjVo8H7RwjsTZ8IYCU0h9Ou8Pxry1sN4HwfIW50CYLc6OSgFwO1XZDHuFUbb1aHkf59pY0ILBgcUqKvT6oGnZA0SrU3cN0+Jc9zGubqdOlmoWyuLhdu57U5kIpjWwtdzrTOtYCBHpDbRVSn1VU0UicoOILBGRJdnZ2eFvaR2RHOeuIJybJ5pZKhwYoZ0hIxEn/pBrknc/+3hdE11OOFdFbrEXLw7cERTQ2Eg45Pvru3NW8oz+Olk04yn/GO5xTOZfzg+YbfRhuOff/Ka6coq+lEcd7/F9oA9P+C+3RXOEM3pAO/QjT2ez0YJB2kq6lb4NmG4bK6Ku5/e5U3l1zoaINVLURDgszhE3G16RY36sOIrw+up/EKxLH+dYV0XB5rGCmwDGeB8ETGtoeR/nXdaOgDXRNNZVIR1dWFK7uZtAcpc6E87JcW6c4sdn5eANx7bm1RHr0slR8cRLMU78e/0OjYyq7OUhB3ER0YD/A+7eV0VKqYlKqX5KqX4pKSlhbGLd0j01gSjxUarM/pOWbG4SpEsgoneGLC+cPZT5C0fCAki0eENZeaoiaHF2iw+FitjvOAI5pPvrlPlruWzTPaTJ39zrG8tgbQW3Or7kY/9QbvLdSZHEcnHrHF5xvMIq1Z7bfbcimm6L5kZAcnw07wdOo7+2nvaygyGe50Pnnil5lM0//LdBjJsHSziEc9hmw+HCHWcmSU+ksEEGwYEdmuHSzOeaOBwM7NAsbHUnN3HzT9+15Y4IH/hP5Vrv3RRgpkw7pWuLChbn2kSvJ8a4KliMwub2kNrbzOVcB4F73VMTKgiJcOXLroojkmND2503oZgjkmPr7Fr1QCbQttzrNkBWuddxQHdgrohsBgYC0xtjwFF1rMrKr2Bxzsw3J7eRnKc7Oc6Nq7zFWTlCPs4N7aoBEFPO1asqVmbl41Vm2x0EIsJK3kg4ZPvr5PlrSZt1Jf1kHXf6bmGHasqzzoksMzrxiP9qEI3Lj3Zw166H2U0c13rvoZQoLu7f1hbNjYDz+rThc4ZQpNxc4/iGzaoVt3pvC53/t+MtBgaW8uIP6xuVeA6HcA7bbDhcy0i7vaY1MApvgw2CQVcNQ4XXFnTNCR34NHASAPnW9tsP+69httEXgMGdkxnSpXmFDVBqM6jqyo9b/OENDgRTOBfthD1Z+y67n6zKyq8QHLiqDgfixBhXSBTE4GnsmzcsBjqLyBEi4gIuAaYHTyql8pVSyUqp9kqp9sBC4Fyl1JKGaW74yS7wVBDOnoDZT3WMiPW/DU4UveVcNYI+zpEgQqPw1uiqIZRl/gnmn7apFYdkf508fy3tZl1NP1nHP3y38L3Rl9ecL+JD5xbv7Riakwu7J3DJn+bGJtd47yWbJHRNOL9Pm4Zuvk0t6JuWxFn9uvBh4FTO1+dxvPYHXxnH8YH/VAAcYvCq8yVyNy5pVJbncAjnsM2Gw7WMFBdjWgOd0jDbIy/clIMoM+uC15CwuoqMHtCOzi2TeMR3Jed5HwOgZZybxBgnI3ul8v61A1iZlV8hq0ZtBlWXYXrQFFt5nMNGaAfB8LtrZBd4Klics2vhknKgpMS5QxkYosTbqHPQKqX8wK3ALGANMEUptUpEHheRcxu2dfVH+XR0HmU+Ch0E0CQyLc6rsvIruCaZwYG1d8eqSzQVwC1+SlT1z9puqQl4LdcyF76IsJI3Bg61/pqekcvTU36i3ayrOVbW8A/fzUw3juNJ5zt0kUz+4buF7SRzbNsmnLVuHB0li5t8d7JetUUTeHxEdzt7RiPivD5tmGBcwAYjlXecz3KlPosn/Zex0mgPQKx4eNExAc1XErGxJZUJR1RVaDYMbMOcDY8OnlRK5QPJwdciMhe4py5nw7s9psHb2UBBKAM7NGPhHEVACbquh9VVA+DJUT24eGIhfqVw6MKrl/Wt8CARTB9nlwTQMGpl2XGrEoDwbbkdpGV3EN0Uzl3PDmvVyXFunOUsznXp4xzndrCjnMU5zt24N91USs0EZlY69kg1ZYfUR5vqm/Lp6GKj3FBkBgc6NAl7nw0Hir2DA4PCOSxZcA6CkqI9QM3PD3MCb7bdiT8irOSNhUOlv05esI5tM//D7foM3OLjLt9NTDeO5yJ9DhfoP/OS/zx+MnqiiWLEthcY7PiDe303MN/oYQcDNlL6piXxzxH9uHDao/zH8TqPOd/jWG0N9/uu5xPXk8RJCZ20LO5zfMyCre1Iz8iN+InRQVucI3E2HBfcHpmG2x7ZoQIE0OrEt7dvWhKTbziOe0/vwuQbjtvrJuuWmhAKGqytZcelTItVaQ0WowPCGQ3Nu9aJxbl7aoK1AYoeel1X/LopJ2SNj8HDD2v+rrNr2dQPUfhCrhqGmPeQTgAkMp0Iuqcm4Cq34Y9XOUIBwGHJgnOApGfksm6r2R9KLeFc1SS2vKuGW2xXjcONpZv+5phvL+Aux6fMM3owzPssXxoncKRs5QnHu/wS6M7L/vPQNeFGbQaXOOYwwT+CTwNDbNHcyBk9oB33jjyOsf67+bfvUk7XljDB+TLP+i8OlbnK8R2la79rFC4bYcnjrJSaqZQ6UinVUSn1lHXsEaXU9CrKDqlr36vdVt6OhrI4L9yUA8rAQCNgqDpZfgju0lPVzCzoqgHgxlcry05JsZlmLSgkwkpqL1M4h3kSkVvsxSV+fMpR579zi/io0KQiWkrZkF0U8Z3bpnrK0tGZ/aQk5OMcIBCIzHR0ucXeChbn0nJxDGHJgnOALNyUE9oKvFi50YUqfVCDOweC7apxOFI0fyJdtS3c5r2VG33/YCutODIlmmecb1JIFHf5bmFQ5+bc1m4L9zk/YXrgOJ73X2iL5kOE0QPa8cTInkwMnMNo74OkSg4DtLW87x8WKvMf53+J9uVH5PO3PIfEzoGViQ9ZnBvGx3lgh2Y4NUUADV3X6n3Zd8PfBSHfTTc+NvxdUGP59IxcNmbtAupKOPeGkt2QF96E50kxrlBwYF3/zmNP6lghOBDgjZ821tn1bOqWHi1jcUogNBnq2c70JnNgoEeoq0ZSjKtCcGD5zDkNGaw6sEMz4h1mO7zi5omRVe/gtjIrP9R221XjMKMkj+My32KB6s7XxnE4NOGcnqmckPsFvbUNPOa7gnxHEv2T/YzZ/jTrjdbc57sBzU47d0gxekA7nhrVg8V05WX/eZytL2Sx0SXk79xC8njC+T9+35Ib0YapQ1I47y4xM1q4pOES7Wt16KqxL7blleCxLDtu8bItr6TG8p8vzQwNwEHhHFZ/4WCAYJh3EDSzapRZ4Ooyq0bftCSiYuMAiBFTOK+2B/5Gy/ptZtae4P2+Odf8TR0Sua4aZcGBZRbnKCsAuCGDVfumJfHsuZ0AuHN4r2pFjp1V4zDml+dwePJJHPEMd59+FI+P6M7SFSu4W5/MnEBPZhiD6NU6nm5LHiSeYm7z3YYHt5127hBk9IB2PDWyB28aZ7PKSONh54c84LuWAhUNwNn6IqLXfR7RLhuHpHBOinXjUY4G83FeuCkHoW5dNWrCEzAquGp4AkaN5bMLPESJOQB7lBOh6qXWA6ZFd9CcYfdzNoOlynw+63qKEsyTHU8xwD6/V5vIZU+BGcwWFM5/7TaFs44Rsa4aCirkcS6/yVFDuz0c1cxsU+c2LaotY2fVOEzJ3QyL/gu9RnN0nxO4ZWgnVmXl87D+HgAP+a5BRGiX+SWn6MsY77+EdaqdnXbuEGb0gHY8OrI39/vHkkQBVzi+Z5zv+tD5J5zvkuzbGZHPYThEhXNusRcfjgbNquGQhnPVcDt0SixBEIMHXy0EXtByVYqL/u2TwhvV6nBDi6PDLpyDeW399RAcCLDdY1r1HnB8BEBhqb9Or2dTdziNsvsdIC7G/G0dBCLWVaPyhj/lLc4N7vbgNSeTOKOrLbIyKz9kLXeJnznrdtZHy2wamh8eMzMrnfwQYLoG7kqfxjA9nZf857FdUmiudvOI4wMWGUfxbuB0O+3cYcDoAe1IPWoArwfO4QL9Z4qIYl6gGwDReHjW+QbbcyMzluiQFM5JMS786A2aVUO3ggMbwlWjW6t4ijAHsFgpJb/Yz6RFNfsXB3M+e3DWjb9kau+wBwjmFntDWTXqY4JUYhr3cIiBEz+lPtvi3FhxKTOCOOjjbBD5WTWCwYFeq62luIm2+m1D53HGFxTO1e+oWdlVY/aavyNyUP+vOhQAACAASURBVLQJI1sXw6rPYdBtEJ8KwIwlG3hIe4/1RmveCZxJq4Qo/u18EwcB7vWNBdFsv+bDhJQ4NxP8o1hvtOZp51u8EzgDgHWqLYP01aj0dyPSZeOQFM5Bi7OrAbNqCKaPc0O4aow9qSOFykydFospECYv3odwtpZ8w56OLkirXlCazwffzA1bJ0iKcYV8Put7gtTEctewaZwE0y8Gd7rbZcVF6Bj4/ZHpqhEMDgxanEuUC7f4QruUNii+fVucz+vTBp+UuWoYCqYuzayP1tk0BErBrAegSQs4/g7AtDbHLZ9IWy2bh33XYGhOBhV8y1D9d57xX8IW1YJTu7awRfNhwnl92hDQXNznG0sLcjlZW85vRheayh6WGp240zEV3Vcccc+JQ1I4J8W48NJwPs4N7arRNy2JxARziSsWMzDQ6695cC3vqlEXrBYzeOi3+bPDNoM0LXBmVo36niAFtzq2aZx4SgqBsvtd00wx6iCAQWRuub0qKz+UfhHKNhuJpoGtzVDmquGq3uLcNy2JI1s1BcysGhABlnKbumP1l5D5Gwx9ENxNAJix+E+u1mYyO9CbRaorzY1dPOz4gIVGV94PDEMXuPGkjg3ccJv6om9aEk+M6M4KOvF2YDhjHLNZbHQhVXbzp9GGFMnnGn0mn6VnRpTV+ZAUzrnFXnzKgVPMh3ND+NI5CBBQDeOqAeDTTctPEzEtzvvyc65r4fxTXjIBJXSWrfjCZNELpqPzUj8WZ10DQ5nL+G7xoR+SvefQJz0jl4wd5v0X3CJad5iWUB0jYrfcrhwcWFIpPWKDUguLM0B0jBlga088D3H8/8/emYdHVZ79//OcM0v2hQQCGAhGUFkFwq4g1F1RUWtrsVVrq7burT9febXVt9ZWrFqt1RZ3rdXiiqIVdxAXghBEIaAIgbCEJcskZJ3tPL8/njMnmawTMkuI87kuLzlzzpw5mTnL/dzP9/7ebvjgDhgwCib81Hr52L1L6CfqeMR3LiBZaH8cHYObvVeC0Dq0MozTd5k/dSh3zRvLg/4fst3IYa5WyLdGLtO1Yt73T+QK29vY/E29ahawTz76M5MceLExXdvEAFy8v2l/lxrfcFJYUomQRsykGgDOZFUol2RKNbpqkBDpwHnK8IE04SAJT9iKrwKaTx86GpEPdoZkJnG991pATTUPyUyK6OfFiQzKfrH5fNcE/HDyMEANeHtry+1AcaDHkmqowDlB9JbAWYAtodPNfCJgk+mNwkHFiRlrnlBuGqf+ETSlyV+8ahsnVixmtXEs6+TR/EhfwYn61yz0/YRdcYnG95r5U4fyu3mT+F/vleRpB3DiZahWTrVMIU00cKq2plfNAvbJwDmgcc4R1bzt/F8AHlmxNWqfr6QaqjgwFlINgKED+wOqOBBgRE5qh9tmpzpJEB68UsePHl4P5xZ4sWELY/HVtPwsU6phw2aL/Pd8/PDs5pbB+Dh+eHZEPy9OZCivdVtFdU04mJSXyTGD0vFL0ft9nFtknFs25NlVFWPNvacB7EldfndpqWrK3mH6xsfSfzpOhGiogo//AkedBMNPBtQsz/r/LmKQqOIfvnMZTAW/t/2bVf5R/Nt/clyiEYf5U4eSNnI2//KdwjBtPwCTtG/ZI7M4X1vJnW8VW3KNF1bv5GdPro5qQrQlfTJwzkxy4Dft9bOF8mt11Ud36lXDiFkDFIBRR2TilbqlJezMM3XM4HQS8Frez5GwdSssqbScTsJZfKUCCT0q3/PowektOjJ6SHXaIv6ZcSKD0/Qtb5ROMpIc1vlp68Utt5VveVupxknal2zeVxuzhwigMs6OrmdgRgweADTPcMW9nPsgK+8F90GVbTZZUlTKldpSNhrDWGmMZaH9cTQMbvbFJRpxmrnqxKP4s3EJL/lOBOBIbT8SwQnaRrJMX+cXVu/kozee4avvdnDrkg0xue/1ycB5Y1mN1QkvgDSiF8D2BqlGoL2tHb+13BHFZTU48VgyjUh04GtZsBmu4qvV2w6gCYlX2vBF4Xt2NXiswYVD+Hj8k5JeVbAQJ3QCBXWBc35afpY5sOvFPs6D0nAIfwtXDRU4/4/9RZx4eOD9b2N3cF4z49wFX+9XxcqB+3PM/afjhJfKbfDF40rXnKM8eYtKXVSvW0K+to9/+M7hx/oKZukbuNs3n91yQFyiEceiIC+T288dzy2+K1novQiAXFGBJiTn6Z/w3f5aXvikmCcc97PU8XsAnvq0JOrH2ScD54pat5XRCOCLYuZ3Wn4Wdk1ioGGPgoSgPSpq3VYTmMByR6jOgV4riCiPQKV7y4LNcDlg9EtQp68PHUNGvjhQBVcBqYYXf9xO67AlcH9obKHp94VZStQeC9/ezOx7l7Pw7c3dfm9NvZJjeExXjTqa9cQpNFIVy4LGEAPnA3V+vFK3NM5xV40+xgf/B7pDOWmYFJZUcpJYS4VM4wOjgBttr7LaOJbn/SehxSUacVoxf+pQrpp1FIv857DUP916/f/ZX+b19XvYVqNiuYCco7ELx7BI0CcDZ4AU0Ri0bIuibrEgL5NjBiSR6HRw+9zRMZmC6p/qxIvNyux0pSVMwINb2iN2PIGCzXBaBB6srwdUU4Vo2NEV5GUy/kjVUjhQXNY71bBxuiKxVTFsYUkl3ghIiVqy8O3NfPPpqxzneo9FK0u6HTxnJaizzWs2QKkhxVqXKNzIWNo5e0KTaoBqstQ6sRGnD1C6CjYvVZ7NqQOtlzMT7UzVNlFojGSsKGGgcPGs71QkGieNzIlLNOK0YcGZI5k3fjA3eq8Jev1223M0epuToE48+OOBc3h4b9N++om6oNdstuj9qUWlLkoOHOSg2wgStEeT0YPTLWlEYLkjslOdOPFa+t1IFAduLKsxA+eupSOh0jLjHC2/7glHqgeCzfw74hrNw5NEoQaK0rwFBgZ2kfRxfuLTEp5x3MvfHP/Ajo9FK7s3xRgYKHoDA0XZHDgn4Y5tGxRvY0gZZ1CBc2spXZzDHCnhvdsgdRDMuDZo1d4dmxgsqig0RjFV+waAVcaoeLY5ThteWL2Tcx/+lCsfeQu3u0l1X27B5bZ3GCL2W8tjxHb21bqjHmP1ycAZ4CrPjQBsNoYAkJvRub9oOFEaZ9U5MFyexd1lY1kNnhZe1p0FqmMGp2MLFNkRmeJAJR3Rw9r4oLZBTV1Hy44O4JsK5VIS8KGNazQPT5x4gmQargYPPqljC6OUqCVFpS6yjeb7wOP2+wG45MnVIe+jn5lx9mBHAg0tpBoBL+eYFQh660MOnJtwxAPnvsbGV2FPEfzg90FNcIpKXVRu/BBQwfJk7Ru2GEfgIo1JeZnxbHMcixsXf8lnbzzOovKf8Vj5xdy87TImii3UyuDY7Qn7/RQbeQBM1L4DYNHH26J6rH3SFsCmwbvGFJb6pzNa7ABgSL/oee5Oy8/Cs1zG1I4uoHF2hBCoFpfVMMTswBdYDjcB6UggcA6HDVVBrsq4eaNkRwdgmD60gQFJXKpxeJKIO8izXGWcVXFguGcvXli9kzuWbuC7hOus12brX5HqbWDldxUUlbpCCiC27lWBd8ASUbY4+xKFGyS8uGZnbAqtPA1dNj8Bdd27pZ0E09UkbkfXB/A2wQd/gJyxcNxFQateW7ebyWIT5TKdbXIwI7Q9rDWOBiCjF/nyxokNNy7+kmUb95Hmd/Gw/W9MdagZidXGsRwhKnjF8Qc0EVyfdozWXFc0UfsO/LC9PFhhEGn6ZMZ565/PQqCqzhPN5gDRvkHb8OOT0bFJ64iWgWpnlNe6sYvmwDkSxYGjB6erQF50LR0JFWGofUXzez5qkArOHSFIYOL0TqobPCQIj9U1sLrBozLOph1dODPOC/9bTNEbj2DzN7VZ97ZDecz/bsmGkPalSXXOBVputyTQ6MgZRUlaEN7GTtttB1CWjs1Sjfj10wf44lGo2Qmn3WU1OwlQfrCJ6aa+2Y6fQVRSKlWdSHUv7M4ZJzoU7aji5LveoPzrd/lMv5I1zl9bMh6ACpnGae57+I//B53uZ5QoBbpu8BZu+mTgDPCn88bSiMMqAormDbqwpBKBgR8RMzs6UFO6oba2taGq3SPF8m8P4JHNxYrhaIP+9c5yQA0QomFHB1DVpAJ0B96oFCTGCT9V9R4S8dBk+iBX1XvITHLgMzX44co4F5W62PbZK9zvWMTmhMvbrB+iqfN3y4HakPZ3bLaSZgTs6Pol2bnUcwvQLNWIWRYvRKnGxrIa3Dis4tq41Okwp74SVt4PI06D/NlBq4pKXWzf8jUDhYtVxmhyRTm6kOw0VOBcFeXeCnF6B0WlLh584gk+8F3C8467rV4bAbYag+lHHfUkcpvvF0HOGgH+65/Cs75TGCiqEBhRv+/12cB5Y1kNTTgtv9Zo3qB7Q+dAIKg4sCts+CyrtUiwvbyOgySTSoO13FMyzWvFgy0qdnQAackqqxZOd5A40cWua6TQSL2pEbbrmlm8qmMLo3b9nmWbSRf1XW7nD7Gqr75RXTtedARw+thBbDUGA8Re+uBtDEmqUVHrNl014nZ0fYKP7wFPHZxyZ5tVhSWVTKYYUPrmsWI7AKVSNcE5sn9Km/fE6fsUllRS6DsGv2xf6DhcK2O6vomvnb9gR8J8ztFXWetW+scCcJb+Bcdqu3AKH1mElngIJ302cBZAE3YShJdgNWB00GVsOwcCeKVuSSM6IzvVabau1qzlcGPXNVwyhQzT7cSu9/zU236gGohucWCggUM43UHiRJeS8jqyxEGqZKq1LMCSakB4tOsbyw4i23k4POSbd0j7y2gxUJSoIt4fTT8GaM44x0T6YPjB18QXe7qubv96VzXZ1DBd32Qtxzk82fh1Ef41T1B+9EUw4Ng2698v3sfP9XcA+LW+lPvt/2SX0Z+N8kgg7qjxfWVafhZSt3OU+3me9p3W4XZprSyFgSCXjYC0Y7a+nvc37Y+qs0afDZxHD05X2ldAx4iRVCN2nQMhdI2zctXwWxnnSLhqHGzyUk0KGdQhMCiraXtRdIeiUhfLN5cBZsATpeJAaerAHfHiwMMWn9/HMLHP0lq6/TIiGnxdYNVYAPzI/XtOct/LX30X8rDvXHxSQzXSDo3SA+rB4MVmDRQrfapYNSkGM2sBvixR1+GHWw/yk8cLO32AVdS7yRUVQctxDj+KSl3se+V/aDTsnFM8s81v/sLqndTu2cQIbQ8Ac/VCXvLPZp7nTppwMnlY3FHj+0pBXiYvXjmdYVlJ/MF3Cf/ynWKt+9g/jt0yG4B/+M7hbPddzHQ/wLCm59lk5DFb/6rN/u6zP8pQsY9Ho+is0WcDZ1eDB78Z5NjwR1WL2nukGqFpnIvLaszAOXKuGvsPNlEtk9GFZCAuahp9PbLOem3dbjS/mu71YGP20f2jciMefUQGbmnDES9uOmwZrLlIEF62SSVzsGmmz7hszjiHIwCdMDTTKtoD+EKOZJs8AhAclEnYhEEibvqndC33KSp1sfJbc6Aom11k9tep4w2nzWN3WbZO+VHXk4DHZ/BaJ900s1Oc/MsfeFBKslPirhqHI+98sYGTtbU85T+dvb60Nr/5U59t5yxNWS2+7Z/CKPdT3Ob7BZWkowlYcMbIWBx2nF5CQV4mK26ew/jcDG73/Zxl/skAnKh/TX+qud5zLX/xXcQGmc8umQMIzvP8gdPcCznB/bc2+3vKfi+rN23jyn+t5dYlGyKefe6zgbMq9lF/no4RdS2qRmylGgH7N0cInQPLa92qODCCrhp+A9YYajrvfP0TAG5/IzQ3gfaQgE2ooMEn9YjIS9rD1eCxMvnx4sDDkwF2NdsRaCCSkeygotYdJNXoaQBaVOqicOt+Rmmq6nta098BlYUePiCFOlQhXQqNTBja9YDvtXW7sRltB4rZaUn4pbDsEWOhca6rVYOMgL1fZ/ePcbkZVMk0QLWtH5ebEfkDjBN2GmvULOo2U2Pf+jcvr2tinv4poDKHIEhLsHHqqBxe/tWMeLY5DgCvX3sC43PTucZ7A1uMIwC40HMHS40ZqkHOrHxuPu0YXv31DO44rwB/9kgq9Bxe8p0YtJ8h4gD/tD/Its3reGH1Tn7y2KqIBs99NnDeWFYTlHGO5hRmYUklmoytVKO7nQNtwm9JWyIRhA7vn8zX8iiapJ0Rpg9jTzpljhmcbmXTvdgiIi9pj8wkh/W9Hs7FgUKI04UQ3wohtgohFrSz/rdCiE1CiK+FEB8KIfJicZzh5oXVO7F5lM7+IKrQMyPB3mKgGZ4AtLCkkgu15czTPwdgH2rW6Y/zxnLPBePwCXWtJWg+rgpB6ynBGgR7sFvXaEBiEsp1HikcUgVNDTK07yzQeCZQuB2na3rb9Zog1eCznuaC0KJSF48s30pRqQuvT5Kv7QOwNM1P/3wKj10yKR40xwni9WtP4IQRA7jWez0+qXGR/hECuHJmPgvOHMk1c4ZTkJfJ/KlD+eCm2Tx/xTQe9gfXiTzim8cMfRMfOm/mEv1dvP7Ixl19NnBWxT6BjLM/qlrUgFRDomGPkva2Nc2dA7ueer5gYi52M+Ns0wUXTMwN+/G8f9NsBLBTDrCCk2THodvfuRo8VoGeDz1qmd9AxtlxGGechRA68AhwBjAK+IkQYlSrzb4EJkkpxwGvAH+J7lFGhn8s/44C7VsADkqV9b38hHxGD043O9qFx75yWn5WkFE/wLzxg63mJIFGOk4ttBmplgNFd4uB4sayGnMgF7tiVU+DqmpvMO39uhp4B7ZLigfOIdEbr9eD1Sqbp5xpJK79u/jL48/yyftLuGHRmzR5WkoEBQl2LR4wx+mQf/1iKpfNO5PXHOcw37acidoWnlm1o92scUFeJsk5wyk3Z64AKkjnXf8kAH5reyXiSa2wBM69bTQM6sHXnHGObnFgQV4mWUk2MlOTuH3u6JjcMATBxYFdDRzs+PCbRUeR4k/njTV11ypzdttZre/9oZOZ5EA3gwUvtqhlflWHOVVEdhhnnKcAW6WUJVJKD7AYOLflBlLK5VLKBnOxEAj/aCoGOGq28z/2lwCoMTPO86cOZWNZjdkwKTz+wgV5mRT0U/rmazzXowEjcpSLR2FJJU2GWbhseELKjLgaPJYHuhe7NWDr7nUebl5YvZPt+1SxXxNOdI1OB97VDR4azcx0onDHm2CERq+6Xm/8zzqur/srAP9x/Ili5+W8XH8ZL9ruYLHjLlY4buA1x+0AVJrONY4wuCjF6dvMnzqU6qk3sVf244+2pzF83g7vjRPzMvmp51ZreY72Jb/y3gjAGuMYNBHZpFaPz+beOBqG4OJAu4hucWBRqYuDDU3sq/Vy51vFUbVJCWC5BIRQxFZYUmlpnCPZSKRZH9zz7mzFZTUtpBp6RAoa28PV4MEj7Ye7xvkIYFeL5d3max3xC2BZeyuEEFcKIdYKIdaWl5eH8RAjw1Fij/XvQMYZlKa5EQcJZsY5HEV2R2oHWC4n8o6chsPePPM0LT8LKZSDjVMYIc1IZSY5LB2zWzYPFGMt1XhxzU5LctEgnRyZldxpoqCq3kOjmXFOxM2e6p6563xP6DXXa1Gpi/oNb1rOKB/6J/Cifw53eC/lMs/N/MRzGy/4T2K8phwOisz22odpgiFOlCkYMYS7jUsZpZXyM/39Ds+b8yfmskUO5VnTkeNk/UskgmIjDzt+HBGe6Q/HMLBXjYYDqIeTCpydmoyqXKKwpFIVB0oNr8+IicY5MIXrCKGhQ2aSwwqcI9lIRGVrVQFWT7O1EqxmFV7T1zYa9BGNc3uJyXa/QiHET4FJwL3trZdSPialnCSlnNS/f/8wHmJkOKKFFVqgQC9AE47w6W6lJKluJ6NGj+e3px7D87+cFhRQes3AOVDg2hVqoNiscQ4MFAOtwh0iNgM5p00j2XQOacDZZevbfskOS6qRiIey6qaYJBYOM3rN9frEim943KGyzRe6b+cX3pu503cJzxmn8SkTWC1Hc7vv59b2YzXV9GT/wbYt5+PEaU1BXibTzvo5K4zx3Ki/zKK3PulQrnHVrHzu8F1mvfb/bC+xXQ5ihP1Am/ttuAlH4NxrRsOt8ZuBsy56UIV2CEzLz0LHwBCx0zhX1LqDigM7y6C5GjyWHV0kH76uBg9eacMehof8mMHpQa4a0SoOVFlz/bDWOKOu0SEtlnOBstYbCSFOBm4DzpFS9glBap7Yb/3baHX7a8JBgvAiCMP9om4/eBvIGTbKKm4JUFhSSZNffbbm73g6siXltW5rEOzBZrkYZCY5VC1DjAZyGUkOUs1GBbUyqcvWtyNyUmmQqmNjsmhEAq92Yl8XB+gl12tRqYtJ3zVbga2Rx5Kd4uDP543lplOP4cWrZvDyr2YgkHjMQnOv+f+mnlSCx/le4Wr08n/eS3Hg42b+xYMfbGk3eF5w5kjG52bwun8GANfa3uCgTCLHvx9MB6JIEY7AudeMhltSWFKJx1CHJozQHk7hoiAvkzSHoH9a7DTO/VOdeKUNXUg0jE5dAjIT7diFaoASyYevsgjUsYch4xws1bBFTaoR0Dgf5hnnNcAIIcSRQggHcBGwtOUGQogJwKOoh/CBGBxjRBgm9uOTGie51S1Ia3H3apLqtwxoiXtElfI2pt+RbVYFZl5AFS6Hcg5VN3iCMs4BbXBA/mQLg/zpUMhOdZKKmkysJanLwsDzJ+Za2vIMVDvyeNvtLukV1+sXW/fyC5vKeT3mOwtdwKM/m8T8qUODBodHiT04hJ+Hfedyk/fXANj1eKuoOKExLT+LfbZB/MN3LmfrhYhty7n4ifYbK50yeiBfG82uRPNtH2ETBn/+97Jeb0fXK0bDrWmpI3SI6Eo1ikpdNHk87K6JvcYZlI1VZ9rHmnr14PPKyGeclVduz7O1Ae9pUBrnSHhPt4eSwNitDnOHY8ttKaUPuBZ4F9gMvCSlLBZC3CmEOMfc7F4gBXhZCLFeCLG0g90dVoxLruJdY5LZiERZHgVoqb3tMYHAObNt4Kyug4DG2RfSdVBV7yFZNOGVOh5sVNWr98R6IHfBxFwytEZ8UsOnJ3TpyFOQl0lKhkqKZArlxrGrqqGzt3zv6S3X6w9SSq1/fybH8cd5Y9skhV5bt5sZWjEAi/0/YI1U3v2/OL7tdRAnTnsU5GXy/C+nseHIy9hh5PB/tqcRvqZ2k5+ZSQ7Lj78lo5rWccE/P2fh25sjcoy2MOzDGg0De1Cj4fktN2gxGj49mtmrgFTDFmWpRmFJJUebDVACGudoZ503ltWQaAXOvk4DvH6JavzkR494xjmQHQvH59ha2NFFi4paNx5pI8Wcnj5cs2VSyreBt1u9dnuLf58c9YOKNH4fWZ699MudzbCDSZw+eiALzlQdzKobPGSY/sIJeHvu9lBVAkKHjKFtVk3Lz2K5pq5Nu/CFdB30S3aQ7Goy7b+EpSWOtT1iQV4mDLLRcCCJy4/PD+k+t7PBjoEgQyg/7d2ueODcFb3hevVu+cj69w2XX8rEowa12aa81s0FWjE7jf7slmqANG/8YOs6ixMnFAryMrnmlDH88YnLeVK/myv0t6htPLbNdq4GD9VCObf83HMzTzvUTOIf7c8wVy/kxytvZ2hWsmUDGi56HDhLKX1CiMBoWAeeCoyGgbVSyqUEj4YBdkopz+lwp2HAkmroIAxfVIPXaflZ6CtUy+1YapwHoLxi7fg6DfBq6lUQ6I2CxjktTPrg7FRnkFQjWp0DA58X0I7HOYw4uBsML9MnT2bFxDlBq9w+w5JqJAo37p5qMqtKVNCs29td7Zbq1qtLH79/YwPHDEzt9P6UkeQgRQQCZywtcUD2EauM8wurd5Kwdx/9RSKLVpaE9JBKTXRS05hMP1TGOT2x/e8oTu/hhdU7Gf7dx6DBSv9Y/mdxMSkJ31oDuO8O1NLkNUi2wb3aJpb5pwAwZVgmD140IZaHHucwpSAvkxHTz+WDVe/wa30po1bOa3N/mZafxXIzcBZIjml6hm8TLgNgqvYNTjws27g37IFzWMwVpZRvSymPllIeJaX8k/na7WbQjJTyZClljpRyvPlfRINmCHbVsGuhWT6Fi4K8TJyawcDMlNhqnFtknDvTOPdLUKeBLwoZZ0+YMs5jBqe3kGpEr3MgENRwIs5hRJWq8KdffptVP5481GoZnYiHH0/u4Y22qqTdz4FgH2cbfvwGLPp4W6e7+3RrBUk0UW8W1gVpnMNUcHso/H7Jes7XP0UTqqzltiUbunzP1XNGUC7TyRY11nKc3s2yjXutc2+WvoF7m+7gctffmLjrWbJ3LuM093tcbLxJ/6btpIsGVhsqw9xVsWicOJ3xStEu0kQDVaQi0Xjq05Kg9QV5mUwfY1oeUocbB+tbaJ7HiRLOGNN2ZqSnhEOq0WvxByyfwlEl3w2KSl0cZ/gpdbl5+K3iLrNJkWD04HTWyoCPta9TjXNtg5oqjYarRn+pY9PC4+OcI3wYUmCgRa04EOIZ58OWTnTH86cOZWD5sbAWbjhxCKf2JEMhpQrSc6e0u3pafhYvmrNBAd/oTZ2cv/Me/pR5/vc40/4FmwzVO2pPjbL3CsifkmmMesZ54dubOU6ogN+QqvgrFFvI+VOHsveLIQyvreHPc8eGPRsUJ/ycMWYQH5QUMEf/im+NXFJFI6dpa8gydeoBAm4ap+trWGLMjOvX4xwyL6zeyajGtUxxfMvvvZepF0XbItOhubnwTXPNhDcjHw6q+9Ldw4sZPvWmsB9bn23nU1hSiduvvmQtyq4ahdsqsAmlcfbEyMfZ1eDBa7b17UrjnOlUp4H3MHLVkIADv5VVj5aPcyCT7xReaznOYUJVCdgSILX9DMQPxqoA7tQRqT37nIZKcB/sMONckJeJ7lSfkSxUAOz2dzy4/2pPDdfZlgAwSjMLtKQ648PZVKi7PLNqB9O1TQDc6L2mW+8dlHskIxLr4kHzYcL8qUO5oN8ODsgMTvPcwzzPHylwP8oNnqt5yne6td27skg6sAAAIABJREFUxmQATtPXAnH/5jiHziMfbeEm28vsltks9v8AgMvbKTI94HHikxqZohYBGJnN993hu16NyLH12cB5Wn6WpS9M0KMr1eiXpII5n9Qi2lCkM6blZ2GIZo3zK0W7O3T3sDLOh5GrhpJq+Cxbr2hJNUYPTrd8cwHq3fHM8+HAjYu/ZPmq1ezVBoLWwW3Pnqj+7+1hN7tOJCEBZEIaAKmoz3LaOi5wFcAOYyAAt3ivAGCUeb4r+VNsNM5NXoObzfblO6Q6vj+fNza0N6cOhNp9YMT9fQ8LDIOJvvXU5c7iuNwM0p2C39me42+Of3C57R0AVhvHcp33em7xXsEi39kADB/Q1vEgTpyuKCp1kXbwO47TSnjKdwZebAxIdbQ70M5MduIihUzqkIAv46jgDWT402p9NnAuyMvk9nmqKOHE4RlR/exqs9jOjxazJhkFeZlMHaEeZg68+P0dZ74znCoz742Sq0Y4Ms6uBg92M+Mcze94+bcHsAs/OaKadOp4fX0ZL6zeGZXPjnNo3Lj4S35UfDVzWMOGxixuXPxl+xvazU6C3h5OL1sezh0HzgMyUnFLG6lCfdYR6QkdbpuaaGOgqORN/zRe9Kuixl+dqB4OzRnn2Gica8y25VWoDHrIGeTUQWB4Ofe+pRGzjIoTRvZ9BY1V5E89mx9PHsps7yf80hbcx+wqz28AeNE/h4W+n6BrcMsZcTeNON2nsKSS7TKHGplkzWrNOCq73W1dDR6qZSoZog4B7NEHB63fUNx13UV36bOBM4A0LZ9WbdnboYF2JOiXqLJHkbZ364ppI9SUtAMfuiY6zLqX7K8GlMZZI8KdA8P0kM9McmDHF/GCxtas2lrBD/WVAFampXXBQpzexevry5ihq5tvqczh9fVtbOYVYcs4lwACMvM63CQjyUEdiVbzkM6KqIQhGSSqKJPq+k1x6FbNhJI/2bCL8Fg8dgcB7JVZvOOfDIh2O2F1xJLv1EzNhQf/xaKVJfHgubezzbSiy5/Nso17+cwInll42nca1ebg6c/njeXm047hpatmxKQwPs7hz7T8LHxaAo/65nKKXsQUsZk3vyprN4bLTHJQRSr9RC0SKKoNPueeWvyfsMd+fTpwXr9HPZR06bf8lKNBTYuMM8SuLbPU1UPULvztiupBTYl8VKxa3vrQsUXQPi9gnRUOV43ishps+C2pRrSKA+s9zW4aAfuy6qbItveM0zOcNF9/pTKn4w0DGWdfD3WZVSWQPgRsnevfm3BYloqdoburSRBe9sl+6n2+5nNQuWrEpgW8w6aRRBMNZuMYhy30x8kb25RE46e2DwF4ae2u8B9gnLBRW/weZQkjuPX9/ew/cIC/2/8etP5B3wUA5PVLatNJME6c7lKQl8md547haf8ZlMl+3GZ/HkMa7boPuRo8VMk0sjiIAD4uDb5/T2MT9ywL78C8TwfOY/OUAbsdH0IQNZ1zQ6P64Qzz661tjE1gtWGfCuAdePF1MHAoLKlESPXw9mHjhwW5EbvhuRo8+NGxCQNdGD16yEuUW4hXRrc40KELbvBcDWAF7Z3pU+PEnnSztTNgZW3bxco4h0Gq0W9Yl5t5pQ2H6PzeUFTqIgd13e41j71lHWGzxWP0Nc4JDo1E4bEGkAmO0B8nJU3JQcuuhvjgs7fy0mff4Ny7hsoGL6euu4b33D9lujmDA7BHZlGD0jL/9cfjY3WYcfoY86cOZeaoodzr/THHaSWco33OB5v3t5FGZiY5OCDT6S+qkYCnlQf/hfrHfLHDFVZJZZ8OnLdVqsDMJvz4DPh2X20X7wgPGd8sBuBsfRUAq2LgqgGQnKgCATs+DNp/qE7LzyJBU2Gn1G1dtsztCapgUQW6CT301h4zOB07fqtrYLSKA39+/JEsNWZgSGHpU9OcfdrV8bCmqNRldagD2CUHdLyxLYxSjU70zQFCsTW8Z9lmBooqACvjnJbYfL5tLKux6gYCy9EixWEjEbeVcU5xhH4d7G01gInWwDdO9ygqdfHi2+/hEH7GajuYrX9lrSs0vZqPEM3Pt3iWOU44uerEo3iTE9hgDONm+0s4pIfb39gYJL3YWFZDucwgQ9TjwEuj1+AbY4i1PuAxv2zj3rAdV58OnJ9fswfAmg6NlhZ1uPcbAEu/2HoEFC2qPUqe0ZWm2GZ+Pz4Z+cxpwD7O3sM26K4GjynViG5x4IIzR1r61DTz9/V2YiUWJ7Y8+vE2MlCB89985/OdzGVYVlL7G2sa6M6eZZwbXdBY1WXg3D/ViQcbDjPg7cjWcGPZQatRSLlUg8NbTm8uuBJgtdwOLEeLI9ITSGoROB+RkRjye4Vu5xHfOZbvr0OP5pHHCZXCkko2GHlc5rmZv/vm4ZMaxUYeU5oe4UqzGBBARLlXQpzvB0qyMY67fReTKyq4XH8HvyGDZs8FsB81YMsV5QxIdfK6/3hr/ZO+MwDC2gilTwfO5W51Mw40GYiWFtVhqPbWge5JXiM2+ZSUJBUgdGZVVVhSiWaoh67H0CKqA1cd08xTzufp0WcFigMj7QTSHmkJDqqkKkYA8Prj+bLeSvHeg1bG+T3/JADu/1En08n2xJ5lnEOwogNla9gy49xhgyIpSUZJv+pIxK4FO1eo/ejmfmSnjY7CTXaiQBOSRhncBjwUhvZLwiPtOIQfgcHQfh0MZuLEFDVLqDNB28p1ttf5zBjDjzy3kyHq+Iv9cWu7QMF0vMgzTriZP3Uo4044m/f9E7na9gaZHAySv54/MZevUd0DJ2nfsvdgEwcczTPnZ+mFCOCYgT30529Bnw6c/Talows8eKKlRZWJavRzp+9nAORnJ3e2ecRwqfgdRyfteDOTHGhm1ssj9YgGoAE7OgAdf4+LA5Wrhs1ajhZV9W5VxctBazlO78Tt85MulMa5WiaTnmTrfDrZntSzjHMIVnSgphc9LQLnjiQW/VMTrPtXAwkMzggOMF0NHnzShiYkNnpWN9BdmhrU+d9I96/j/P4puGlu0JTfP+7321v5P/1pbrAtYbFvNnf6fsaf7E/yjmMBx2sbedB3Pt8aufyv7QVSaeDRlSVRc6+K8/0hNdHOQt9PSMTNjbZXeeyTEkuzXJCXyaSCqRyQGZyofYXPZ5B6xLHWewcKdT6GMynYpwPnlAQn9dIZdS3q6KRqVhmjcOPApguuOvGort8UAQIZZ0cnGeeAHzIoV41IPngDxYGBY+ppcWBLV41o5nylhEqZRrY4aC3H6Z24PX6rOLCGZIyuZgd6mnF2mRnnzGGdblZR68bTojiworb9wZddFySLJtzSpjLUrSQNgeJA6PlgtDsUlbrYsms/gCXVyO5GF82rTjzK6myapHlido+M0zkvfbiKn+of8C/fKeyQA3nXcQunaWt5zD+Xme4HWW8MZ7CopJ+oY4TYjSS8AUqcOKBmPkpFLi/4T2K+/iFHsoffv77BGqSNPiKDd/yTmaN9hZMmRo06Luj9/ezusJpD9OnA2es3grxSo6VFTWksY8Qxo7n5tGN48crpMSuYqHKrIMGBF010nHHWzcDZiy0KGWcV6OqiZw/5MYPTg1w1olUcCKo9crlMt7SnnbVLjhNbGr0GGaIOn9SoI5FGb+e/VQMOSvZWHHrWrGq7au7h6HqWKZTiwH7JDpJpop4Ea7klAW90AGcPB6Pd4bV1u3Gigv1G6UQTdLuw2CvU35Kk+bvYMk4sKCp1UblNtc5OEY0ssC/mHWMys9wPstw/nscd9/OM4y9UyxRu8FzNOjkCiJ57VZzvDwF7uod859OIk1tsi/FLeHWdstJ1NXh4x5hCknAzW/uaD7YeDHr/BUPq29vtIdOn7QD6JTuorUkiRTRayxHH54bavWQXjOCa2cN7vDuv18vu3btpauq+t+zkgU42n/YSp5HM8STRP8XN5s3BGrThDi95c09ns5jBAplJkqOazZvDe5IFf9Y8NnMSd/Xws4Y7vDjOuAcbgidkOmkRPO7W/POsgaSKmyingSdkNiDafK89ISEhgdzcXOx2e9j2+X0l2amT4a2jmhRAkOzsWK5VVOpCq/BRJ6v4xWOr+M+hDHpDdNTon+o0i/o6Lw7MSHKQLJpooH0dcWaSg33mbdyGL2oZ5/JaN4lm4NyAk0l5md36rlS9gw46CLPeIe7I0Lt4bd1uBqIcXc7XP2WF/zj+4TuX++3/ZJa+gX0yk9u8l/OSf7Y1eJs1Ijv+O8aJCPOnDmXFt0fzypZZzNdVQ57A/Nu0/CweFiOpkGmcoa/mxs1ToMUttbr0Ky5+IpvnfzktLOdnnw6cR+SkUlfW7H4wIid84vCO2LipmDHAdn8WR4Zhf7t37yY1NZVhw4YhOmhi0hH1TR6Sq3zsk5mUk0l+djLJreQqlXVuGqrLGaKVI4xc+mekkZUS+pRrt47H7aOyYj9DxQFsMpfB2Zltjqc7+9IqvsWLDRsD2/3bIoWxp4YMapQNkzEUKWyMPCI8GW8pJZWVlezevZsjjwzHGfT95picVDL21FMjk63ljnh13W7Okg4ShBuPX/Lqut2HFjiPOKXLzUYPTsezzt51cSCqRqNOtu9YsbGsBiPgVIMvqnZ0SYGMM85uFQaCCvi3mLNFNhG9gD9O6JTXuhlr6kO9Ume3zOZNx23UkMwfvRfzb/8puHEggBSnzskjc3jwogmxPeg4fZrZxwxg97fpOIUXJx7rvlmQl8nMYwbx3pZJnKN/jl0Gz7ztlDl4vEbYBuh9OnA+f2IuDV8mkiyacNg0zo+gRzGojNVfX/yQ5+3wu49c/Ha4q8c/UlNT0yEFzQD1HgNrwlhK6t2+NsGl35AIEdB9CvwRdgCR1hix558jkDHxf9U1gc+vgwA7fvxa+DLDQgiysrIoLy8P2z6/76QTyDh3TkWtm0Yclia6I91xh7jroG4/ZHY94HE1eEjEhgNvl3aKLbvztUaA5WVuN4uAo0WSaJZqdLe0z9XgwWd6ujuF/5AlJgvf3sw7xfs4ffRAFpw5sus3xAmZ6gYPY4XS7PvQ+antQ/7rn8Kt3l9SK1I4eVQO/VOdnD8xck2z4sRpiavBQx2qdiuNxqD7Rv9UJ28bU5lv+4hZ2tfW61uMIyg0RmLTRNhkRH06cC7Iy8Q1bDBG5Tb+86PwpOg7455lmzlCqsKIMiOTe5Zt5qVfzejxfg8laAYV4EnZHGDqWtv9tHxNItrdJly4GjwYEhAgpMTV4OlRxjkdMxDvYFAQKRLsOoZflQcIJAn28Lq1HOrvHactVfUe0kU9FaYHclV9xwFa/1QnTTgtCUJH8okOcYVmRQdmUZ+0Yde67viXIho7zDiPHpxOoZm5deCLqh1dwOazo6C+M6blZ7HRLA5MEL5DeqAtfHszk1f9GsM4lkUrzwaIB89hZE9NExO1LQAkCg/v+Qu4wXstPmycMjKHxy6ZFOMjjPN9Q81UqcA5RTQE3TdHD07nRdMC+HHHX63XX/efgEBw57ljwhYD9uniQIDMjH5k2bxRGRFvPVBHjjm1tV9msvVAXRfviCx+QyIRCDMv21422W9Ia73sYBtd1xk/fjxjxozh7LPPprq6+pCOx+eXQRlnn+lw8OCDD9LQ0D0LMBXgq/21HBSsWLGCuXPnttl+6dKlLFy48JCOuzWpCTbr79izaydnzJrS6fY7duzghRdeCMtnx+keZTWNZLTIONv1jm95owen0ygdJAiPtdwtQrSiAzWIDNjRdZVxTsSDuwPLt+XfHrD0pTb8PPrxtjbbXPLkao79/TIueXJ1139DiGSnOkmybPKc3XLUCOAVgWZIh1Yc+PgnJZykf8mt9v9Yy3HCR32TlzSzPqhWJnKd9zrL/vOoGFmsxvl+42rwUCfUuZdOvSVNKyp1sfT1l9jivLTNe/bLTM4dPzjI/76n9PnAGWcKeKLTajsnLYEk0fwwyUlLiMrndoSuqaAyEBh3lHFuDpzbzzgnJiayfv16Nm7cSL9+/XjkkUcO6XiUlZbav7CWOw+c/f72H6qBgD8QwHYlMTnnnHNYsGDBIR13e5/dLG6RGF340cUD59hwyZOrSfS4GKqVW9nRXa6OB2gby2poxGlt2229sBU4dy3VCHiad2YVGcCOD3cHk4Pby+vwm7fxDOoorWoIakJxyZOrsW17j+vkC6z8riJswfOYwemWVKNJOrvtalNYUkmj33TY8XsPycLM3+q6i/chCi8ev6TarA1YaxzNX+yPsSNhPhdoK1m0stlHN06caDEtP4t6M3BOFQ28UrSbolIXr67bzeOO+6z22i0Zpu3j060VYT2Ovh84O1KU9jAKDOmXhB0/bmkDBENi1A2rqNTFI8u38uXOajPjrOg446yQIWicp0+fzp49qpV5XV0dJ510EhMnTmTs2LG88cYbAPzlL3/hoYceAuA3v/kNP/jBDwD44rOP+c311wDNEoeHHnqIsrIy5syZw5w5cwBISUnh9ttvZ+rUqaxatYo777yTyZMnM2bMGK688kqklOiaYNv2nZx30aVceOoJnDxrOtu2BWfb1qxZw4QJEygpKeGZZ57h2muvBeCyyy7j+uuvZ8aMGeTn5/PKK68AYBgGV199NaNHj2bu3LmceeaZ1rqWbCn+irmnnsT0sy/l388+jV8q6ciOHTuYOXMmEydOZOLEiXz++ecALFiwgE8++YTx48fzwAMPdLhdnPDy2bZKrrK9BcBMbQMA9e6Os5sVtW6acJBoBs7d1jhXbYekbEjoOogM2Mg5usg4Vzd4cODFYzYLqW61Xb9kB2mmT/08/VMAXlq7y1r/+bYKnnLcxzW2pdZyOCguqwly1ehuA6LMJId5nwT9EIsDnQR3go0LnMJLkkNng6EGgXP0rzhXV/cpTShLx2Ub98bs2OJ8PynIy2T6aDWjl0YDPp8q+BPAuZ4/cp3n2jbvOVLspaYxvF2j+37g7EwBw8s/P9wUlY5GNvxWsU4sKCp1cfEThdz/3rdc88I61u31dJpxTnbarPUI0alO2O/38+GHH3LOOecAyjZtyZIlrFu3juXLl3PTTTchpWTWrFl88sknAKxdu5a6ujq8Xi+ffvoZk6ZMUx+FpLbJx/XXX8/gwYNZvnw5y5cvB6C+vp4xY8awevVqTjjhBK699lrWrFnDxo0baWxs5K233sJvSC697n+5/NKf8sp7n/LWe8sZNKi5F/3nn3/Or371K9544w3y89tOne/du5dPP/2Ut956y8pEv/baa+zYsYMNGzbwxBNPsGrVqna/h6uvuoK7Fy5k1ZvPqhdMjfWAAQN4//33WbduHS+++CLXX389AAsXLmTmzJmsX7+e3/zmNx1uFyd0blz8JVPuWMKZf1vZ7nU9+a738RuSRlN/e6nnFgActs5veY04zEzqIaQvQ7Sig2ZPcx1/pxnnqnoPduG3/Mpba7RH5KTyoV85GWyVqvhZa3Gday308k48Qcs9QdnRqWNpxEl5NwcZG8tqLImJAx9Lvtzd7WNIJbhRTbIjdvfdvkh+djJF8mhreaMxjKObnuVl/2wAzhgzqIN3xokTOYaaz/k0UY+BuneePzGXXdoRTNG+abP9XH01Xr8Ma/zX5wPn1zaqTMiT76/nwkWfRzx4jnXgXFhSicdnYEjw+Q0K97g7zThD15maxsZGxo8fT1ZWFlVVVZxyirLbklJy6623Mm7cOE4++WT27NnD/v37KSgooKioiNraWpxOJ9OnT2ft2rV8UdgycIbaJi/17rYNIHRd54ILLrCWly9fztSpUxk7diwfffQRxcXFGJ5GyvYd4MwzTgMhyEpPIcnslLh582auvPJK3nzzTYYObV/XNG/ePDRNY9SoUezfrzqgffrpp1x44YVomsbAgQOtDHhLampqqK6uZvbsEwE4/4Lz1TFrAq/XyxVXXMHYsWO58MIL2bRpU7ufHep2cdrnxsVfsvWrT/lCXMbCyut5+rG/UrT9gLV+8l3vM7XhY35jexkNA0MK1pkBwM9nDOt0341SBdqBbGq3qNoecuDsavBgoOMQfjQhO8w490t2YMdndQds7UV//sRcDpreOYFGT/kt9KctC03TaAhr4WmScNMk7RiH8BgR0CJw9rJmh6tbU/+n3L+CtQm/tpY1DOJtiMJHUakLUfo5N9pes14bIg6wJeFSLtY/AOCL7fEOgXGiT7lPSWBTabAauxXkZZJiM/iZ7YMO3iXbrf84VPp04Lzw7c3sL9sBwHn6JxgSfrdkQ8Q+LzvViQOf9UA4lIKZnjItPwuHTUMXYNM1phyR0GnGud7tU1phqVpHtxfIBjTOpaWleDweS+P8/PPPU15eTlFREevXrycnJ4empibsdjvDhg3j6aefZsaMGcycOZPly5eza8cO8keoACbg9NFewJCQkICuq8FHU1MTV199Na+88gobNmzgiiuuoKmpCSmbCxpbM2jQIBISEvjyyy87/J6czubfxtpXCL2zpZQIIQg0C2w5KHnggQfIycnhq6++Yu3atXg87QdDoW4Xp33eLd7PCKHkQuO07Txsf4ijF58An/wVGqoor/PwsOPv3GBbwmX6u9b1OHJgaqeuC/1TndSh3CtSaOqeq4a3EQ7uDjlwnpafhV+oc9ypyQ5dJTKSHDjwWn9Da7/kgrxMcvulUi+dpJqSjdqm5mu45TmdJurx+MIXXibSZGX0u8v5E3OtwYDdbALz4prQA+fvyoPld048ffthFmVeXbeb4/XgZ2W6eX4NFKopyootccvMONGnYHguPqmRLhosi7mFb29mnu+dDt8zQuxhUxg97vv0vead4n285VcZzsANfkdl5LrLjRmcjg2/9ZCLZhvoAAV5mTz/y2n89tRjeGT+RCYMag6c23XM6MCdoj3S09N56KGHuO+++/B6vdTU1DBgwADsdjvLly+ntLTU2nbWrFncd999zJo1i5kzZ7Jo0SImTBhPolNpNQPH5PNLUlNTqa1tv4Az0DExOzuburq6Zj2yLYHcQQN4+533kVKy31VrFRhmZGTw3//+l1tvvZUVK1aE/N2dcMIJvPrqqxiGwf79+9t9b0ZGBunp6XyxuhCA15cssb7HmpoaBg0ahKZpPPfcc1ZhY+u/r6Pt4oSGwyZIF+o6Lmj6J7/w3ITsNxw+/AP8dSR32x63tk0VjVab9wldOOuMHpxu2b4li0ZSu2Nv6DLP/RADZ8ByKLCJzoNZBz5L49we+2oaOUiy5T9d0iKobJlhfstxGxKVsQ8HSbgPyYoOzIA/W/0eiWaRobMLGU1LbARfMwl4EH36aRZdKmrdXG97vc3rRzc9y/2+HwEw++j+0T6sOHFACGpJVo3thOC5VTv418pN3GF/rsO33GV/CncYkwZ9+lZz+uiBlEmVydHMibwjI2ijU1xWg0348EndWo4FBXmZXDNnOBOGZmC0sKNrLyhudqdoXu6MCRMmcNxxx7F48WIuvvhi1q5dy6RJk3j++ec59thjre1mzpzJ3r17mT59Ojk5OSQkJDBz5kw08+nWcsb4yiuv5IwzzmhXGpGRkWHJGubNm8fkyZOtdc89dBdPPPUsPzzleOad/gP27dtnrcvJyeHNN9/kmmuuYfXq0JwELrjgAnJzcxkzZgxXXXUVU6dOJT297eDn6aef5uabbmL62ZeSmOC0vrerr76aZ599lmnTprFlyxaSk9W5Nm7cOGw2G8cddxwPPPBAh9tFEyHE6UKIb4UQW4UQbexGhBBOIcSL5vrVQohhUT/IDhic3twNtJoUtmbMJO2qt+HXq2DcjznPLJILEOpA9rlVO1pknBtZtLIkdGlXNxw1QEmq3IZ5LZgtp9tFSjNw7lj+5TUk+2Umg0SltRwgPbE5+E80bfaWri8L6Ri7IlF4LGnLoZDeb4D6vxnwd6f7YEBfHSABLyMHph3ysfR2esP1usg31xrAzRs/ON4lME5MKCyppEYmkWrOoH331ecscdze6Xumat9QU1cf5DjUE/p0A5QFZ45k6579sKfZrP+n04ZF7PPKa93Y8VsZru4WzISbQBDcmcY5YFkX8NZoL7iuqwueFn3zzTetf3dUQHfSSSfh9TZXsm7Zooz0d1fUmMfUfCzXXXcd1113XYefd9ddd3HXXXcFvVZV18QR+UN5+aX/UC7TOSIjkawUJ/n5+cyePRuAoUOHUlxcDMDUqVO57LLLAHjmmWfa/fs0TeO+++4jJSWFyspKpkyZwtixY9v8bQUFBXyyqpDM2i3skVlc9ts70DXBiBEj+Prr5o5Fd999NwB2u50PP/wwaB/tbRcthBA68AhwCrAbWCOEWCqlbCm2/gXgklIOF0JcBNwD/DiqB9oBXr9BmqinTibgR7dsDckZBec8xK93n8kRe97hLvvTAJZdW1cD2V2uBpKk8nvOFgdBwsJlm3k5lCZG3fBwBlXQUm7eJwRGh8WBGgaakHhkxxlnmybYLfszSuywlgMcNyQT/zaB3sKmKVx5l8QeZJwBmrQkDCksV5Du4GjlqJEo3MybENnOsLEiFtdrdqqT//qncJb+hfVavVTa0lNHxVtrx4kdmUkOakm0ioOfcSykvzhord8ts9kjs0mnnmO1Zoehk7UiHv3EzimjB/a4r0dYMs69YTTcETlZGYDKSMAheLN2g+xUZ1BxYCw0zi1RQXGoGefQ/JB7SuBzxKG4FrTAbxjm/gLL4TnuuXPnMn78eGbOnMnvf/97Bg4c2O52vnY0zocRU4CtUsoSKaUHWAyc22qbcwHTNoRXgJNEL2lp2C9ZtcU+aLZebV0wd+1ZU/m3/xT+7P0JADrB50pHnDwyhx1S/d75QlltFe3oRsY5IR0SQ7shuxo8+M37RGctp3OS1VfeWd2EQ9eokGlkmQ8PvcXPNOeYAexFzbqVy/BKxxLw0NRBY5ZQ2OlqopZE0syM866q0APoJC04cE7AE9F7e4yJ+vV6wcRcioxjgl4LzMbE+rkW5/uNq8GjpBqmXO8P3uCmJ7migqnaN0FBM0ATDqSE19Z138EJ09ilAAAgAElEQVSnNT0OnFuMhs8ARgE/EUKMarWZNRoGHkCNhqOD0HFLu6Wji+STf8zgdOz4LO1iLDTOLWn0+oMaoDR622ppVQOU5qAiki23oTlw1noYONtE8P7CddwrVqxg/fr1bNq0ycpQt4euN7fcDufnR4kjgJZ3ld3ma+1uI6X0ATVA9/siR4g00UCNbF/iUpCXyau/noHXDOoCgXNX1+ODF02gkjSqZTL5QskZQs7OBqzoQoxVpuVnYZid85yav8PiwDEDVbDSmdzEa0hcMpV00YATD40+w5KYuBo8lt9xtZlNH5YVHn95p/DikYc+aVlSXkeNTLb06iXlofvtH5OlfttX/ScApsb5kI+k1xP167UgLxOfHnyeBALnWD/X4ny/mZafZWmcdQ3eMqZzg+fqLt+3T/YDDslotA3hyDj36uzV6MHpNGG3pBrdbqPbDYrLarDjt/SIsdI4B1ABsQhabk1zy+3oZJwDbhmBgNOaZu8mgYxztI67Nc0Z544LL3sx7X3prf+AULZBCHGlEGKtEGJteXl0quw37T1Iuqi3bNhaexuDevDPGm36GpuGaZ21tQ6Q5NDZLfszyHQOSHKEeIt0hW5FF8BntZzuODxfvUVlvgMOFO3dU6SUDDb1zRfoyj994TKl5ctMcliBs9O8Bx6dk9qt4+wIB94OW4GHgscvqSCdHFzWcqgMSVO/y0Fz8OQU3oje22NM1K/XolIXA4x9Qa81SfVb/7twRxeHGydOZDlIEmmiAV3TmDUiOyi7/HffvHbfc0BmIlCzKT0lHIFz2EbDkXgIt26j++e3N4VNIN4aCdhaZJxjHUol2PUgqUaCvW2BUaDldrQyzhlJarpEoGzdulMQ1BIz4WtlBKOd8dW1QMY5Np/fQ3YDQ1os5wKtK8asbYQQNiAdqGq9IynlY1LKSVLKSf37R77K/sbFX6K5DzJN22w9yO16+7exYTmBwmCJzaZ1mNVtSWaSA5dMIUPUWctd4vNA9c5uBc6FJZW4/aZsqYOW00WlLgq/Cw6c27unOO06j/nPApqLoL/Zq2QbLrPzIKjgMpw4WvhLHwoS2CEHMkzbZy2HQlGpi10VKtiuMQdPCXhY/u2Bzt52OBP16/XRj7dxvFYc9NrfHQ8zR/uSTXtrOeX+FYfyd8SJ02MKSyqpMZJIox6fz6CmwcPpWrMW/2uj/ftwgnCTlerosb4ZwhM4h200HImHcEWtmybpIFG4OVMr5GjPZhatLIlI8DxmcDp20axxjvWUlt+QQYFzRy23oTkzHenMabLTBkKQ4tDJz07utFNhZxiBjLOMTcbZb0hrABCLz+8ha4ARQogjhRAO4CJgaattlgIB8dgPgY9kKEbXEeatr/dyvdmU4URdFVhu7WCK39CVFlPHUCbloSAENaSQQZ213CU1u0Aa3QqcVedAde4L/O0G6K+t243NDHo90o4m2s+WXDxlKNvlIHxSI0eogLLWbC1+77vfkGAGzAmE1y+8ZSvwQ0ETsN0YyBGi0uxq2P52Dyxdw1ULH2fJvx9h91v3UPzEFfyq8UmgWX6SiJuPvjkQle6wMSDq1+uXO12W9rwlTzvu5Xe259heXsMlT4bmVhQnTjjJTHJQI5NIFm4EfpK9VRypqSZmDdLJd7J13lbxW9srVNR6wnKPCEfgHLbRcCT4dl8tXmwUiO/4h+MhXnP+HwBPfbY97J9VXFajfJxjbEcXIOCY0VlWtDnjHF6tcGcIoZHs0A45aIbmAqhoHnfQ55uFlxyGGmdz1uda4F1gM/CSlLJYCHGnEOIcc7MngSwhxFbgt0Cbot9Y4DckSWZXv1JD2Zl5O5ji31yhAkUNA58hO7Z8a4FNiKCMs808z4pKXTyyfCtFpS6+3LqbM257nGEL/sv4P7zbbUcNUJngwADbQfvFgZLm5iBebJw0MqfdbMmCM0dioGETBtfZXmeZ4xbs+Bi24C1O19YA0CibJRsB7n1rPXP/8uYhJxEcwtcjjXOCTbOKMYeJfRgSjvndsiCf6Vue/4wriubyaNP/47ytt5K79s/8RPuISZrp0iNVgiVNNOAP8Tc+3IjF9drk9fOlHNHm9Vf9M/mlbRlna6tY+V1FXx2oxOnFuBo87DMFC8NFGYluFTR7pU6ZzOKAVPdId6t70/mmTekF//y8x8cQjsC5V2evdlc3MELbwxAtWPrRHT1dqKgHXXPnwFin50LNOLcsDmxvm5SUlC4/68EHH7QakHSJECpDdwg888wzXHvttVbGub3jXrFiBZ9/3v2LY9iwYVRUVIS0beC71Q7PjDNSyrellEdLKY+SUv7JfO12KeVS899NUsoLpZTDpZRTpJQlsT1iRU6q0zqff+X9DQAdyeQTE9U0vo5qQR+K7KKq3k21mXEWGFTVuykqdXH/40+T/uH/kPjUiUz492iW2f8fp2hrqW70ccczyp7xtR2hy45Uxlk3j6/9jPOYwemWzMKDjTnHDAhp3yO1XXyXcAnfOS9hkeNBAD4yxpuZZ/XdPbi0kJvXnsiS+ksPeQbOiadHGWeEYLsZOP9QX8mD9ocZ5N/D6+vLGLbgLYYt+C/rNm4kRTTxsO9cznDfzXFNj/OC/wfWLnbIHAAW2p8A4PUwVMz3RqJ9vR47KI3nfCe3eb3YGAZApjmwDIdDQZw43WFafhZrGA3ADG0jWq2Ss2kY7JHZ1qybU7Ttghwuehw49/bslT98zWK6RLlq9B6pRih2dIGMMz3M3HYvcNZAyh51zAsES+1lnA81cO7W52vBhnqHU8b5cGbckAycwstOoz+bZR4AV8xsP9Nb41W3N03IkIsDfX5JlUxFF5J+1FLn9vPaut3coT3J+fqnVBhpPOxTtc+PO/7KjoT5/I9tMXUygd++XcYLq0NrG93Sjs4hfO0eW3FZDQ7Uzd+LrcsZrBtbVZbbhbq+Co2RFBuqMYsTL06jgTlrr7a2SaWBRSu7H2c5e6hx7pdk5zuppCdX2N5mnv45K5w3sSNhPjsSLuZb56Ussj8AwCf+cWyWedSKZCpb2OodkBlB+/yuPHKdYb9PLDhjJFukmkje3+I7Pkf/DFCDPYh9r4I43z8K8jI5ftJEthsDmaOtt1rA60KyWQ5lufOmiB9DWHycD8fsVQf1RD3C1eAxW27rCEJ7UEeSZju65uXWtOwcKOg8c7pixQpmz57ND3/4Q4499lguvvhipJQ89NBDlJWVMWfOHKv733vvvcf06dOZOHEiF154odVkZNiwYdx5/z854YwLePnll5k9ezY33ngjM2bMYMyYMXzxhRL5V1VVMW/ePMaNG8e0adOCGoYAuH0+3nzvY+adPZcfnz6L8+aewf79+9mxYweLFi3igQceYPz48XzyySeUl5dzwQUXMHnyZCZPnsxnn6mbf2VlJaeeeioTJkzgqquuojuTIOq7bR6UtPfdxokMjhazOnn9klhw5sh2txs5pDlDG2pxoNeQbDGDuWO1nUjg+dU7GSCqecU/i0u8/8t9vh/zqO8s6z3Jwk2pzAEEty7ZENLfMC0/C7SAHZ3R7rFJ828FcGPvdAYrI9HG68YJDGt6gV97buAT/xgAPvaP41rP9bjNzHAa9fym/HZGi+3UmQ0thglVnNedgq/sVKfpqmG3lrvL1XNG4MbBKr9yL33Jd2LQeqfwcpSmskm7TEnGkMwk/uE/h1f9M7nFe0WPXD3idExBXib//tVsfGhBto/jNfXo1sPWRidOnO5z/sRc3mUa/5+9M4+Pqjr///vcWTMhCQMJS1jCLggoEBRUirgXbSuK1UpdarWu1a/WLmjdq61d1Nban9Rq3bG2omKLVsENUUAJUAmLgGAQwpKE7Mus5/fHufcmk3UmyczckHm/XrySO/fO5M4w99znPOfzfJ4Ttc1M0hs/JZIjunMgQKbbzjX+W/ir85GIx8cN6B5LpqZ4PU7TVUMSZUV+LLy1EA5Ed2MGyAmG0MJ+bIQYJd3K+s0e6azhzZmIf+oVKnMrRIe64w0bNrB582Zyc3M56aST+Pjjj7npppt4+OGHef/998nOzqa0tJT777+fFStWkJ6ezm9/+1sefvhh7rpLtcV0u92sevOf0G8UixYtora2lk8++YSVK1fywx/+kMLCQu6++26mTp3K66+/znvvvcdll13Gxo0bAdU5rqYhwKzjp7LkjYuo0zysfOMf/O53v+Ohhx7i2muvpU+fPvz0pz8FYMGCBdxyyy3MmjWLPXv2cNZZZ7F161buvfdeZs2axV133cWyZct44oknov5sDas/I3BO5Zs7IByGr1by+P9CvLxT45sTB7UZ8LaHEbAZEoGTxma3/SdtjcFcOMpJUb90B5urRwAwXWzHoQUZI4rxihpKm2Q6HwxezPHaF0zVdgLwSXhirG/FDP7tbdjRTcrNYo++3BiQtnZXsDbefRZT7n2bivogb4Vn8F54KueGP2ZJaDYhbGajkiedDzGydje3hm5gbXAsH7v/j1O1DWwKjYopWztpcCbOjUEzcO7M6tqCGcO5/bVNXBy4A0N+/afg+VxqX05BeByDRRn3OZ5lRWgqxbqm8eGLpjD/8U+4NXCd/io9SyLVk8gf0Y+gK5PCupGMY1/EPiNwTjVDSZEM8vO8HJh9KfZVr3Oy7X/m49fa/9PmcxYGrgIgzdH1rOkRHzg/fcXx3L+ouX5PxmS2Hy3ldX6cIkRQWiPjrAmjS1/kdkskMsrFh+OPP56hQ1VGbsqUKXz11VfMmjUr4pg1a9awZcsWTjrpJAD8fj8nnHCCuf+ieWdHaJwvvlh1eJs9ezZVVVVUVFSwatUqlixZAsCpp55KWVkZlZVqqToYUi4Je/cf5Lpr76K0tBQZCjJy5MhWz3nFihVs2dLYnbaqqorq6mpWrlzJq68qh4ZzzjkHrzd6mxpl9dfoqtGa1V+KJtQfJvT8fAYETyAvdCKLVh7Lu1sPsvzWOTG9zKTcLBzrGyUC7QVs/91WjhHOBkOSV9fv7dCK6ObTj+L21zaxKjSRWxxLIvbtRxnoZ7hsVPvgPP+95FKGXYQ4qBekRGtLvmZXGQ0hAfZGO7rm56akGobG2dGhVGPj3WcBMOb2ZfjCTv4ZOsXcZwS4x2q7+GXgSsKTL2DfxmL2ymyGawchxgWTrcWHI1qBd7YQ+qsHz2HEwmXm9j5yeDC4wNx+JXQydajMuF1rbG5z6ZNrqAuE0RA8ETyHq+3LcOFneE7XraZSNGJPy8QTsJm+n1vDw5mg7Ym6qVCKFPHiKy2P7eEhjNP24ZMOLvTfyTHaLn7leKbV478M5wJw57diT3I054gPnPPzvGbHI4PHHX/k5sANnPHQBzHfuNvDyDj7ZZwyznMfjOnw8qoGRPU++lHDLpnHoEw3AzLdLY7pU60rZ6Sk1hdsN+vscjVmGGw2G8FgSwG+lJIzzjiDl156qdXXSE/3RNiDNe+FI4RoVTZhHCeE6nZ4452/49KrbuKCCy9i07rV3HPPPa3+vXA4zOrVq0lLS2uxr7N9eOoDIdIR7cpgUjQhPZv/Bqcx3/YR820fscB/O5+UTOLmf2zgj9+bGvXLbC6uZHgTqUZ7AdvBZpL7aPSYRhb07uAPeEr8gRG6zRHA+yF1nsGwZMl1JzL/8U8oJttMetoEfPmbc1p72RY0taNrqzjQKDYGlZ2ONre689fnMO+xVWzaV8mADBfD+nnw7VEB7tbwMF4Mncb39Wv8oPQy37aKhYGrzfOJBltYJQXa85eOlq8ebPzMjPO2awJfSEYEzTt/rY7Lz/Oy5Vdzzec8/JuPwQfT+wd4sRvH8xRQp6XjCjdeSIXhEUzQ9uDQV0KS7RyVovdS3RDkf+HRjNP2sUMOYanrrnaP3y0Hc+bRA1kwY3iX//YRHzgD1MrIYHGu7TP+HCxmS0n3BraGxjmILepipHhi0wShKIsDjf6BnS1yy8jIoLq6muzsbGbOnMkNN9zAzp07GTNmDHV1dezdu5dx48bpR2sRgfPLL7/MKaecwqpVq8jKyiIrK4vZs2fz4osvcuedd/LBBx+QnZ1NZmYmoAJzgaSyqoYBgwYTCkueffbZiHOpqqoyt88880wee+wxfvaznwGwceNGpkyZYv6NO+64g7feeovy8uitlVJSjdh5OXQK59iUhn24OMQnwNubD7T/pGaUVPtwiJBpg9ZeMBwQnbu+0+waXwaHcIr/IdLwYyNMABsNqEnjWRMHkZ/njQj4YqW8zk9YqFUeByEKWwlAJuVmsVoPnP3YY8ruvf7jxlWgq59bZ2acDasm43Pbr7ehHSJK+EoOjvr1Jw90QyGmZKa7Mo9NzztafvKd4+Fff+HFyyZ1yzmkUBQUleMv05jIDg7RlwGigqO1IgAG6W6yqeLAFMlixdaDnKonNiZpX3V4fJW9L9ecPLpb/nYcSuSsx2Ey8Ek7n4cbl/KNZgDd6UM5c1R/01Uj2mKkeKIK/VRw11bhX2MDlMjtWLn66quZO3cup5xyCjk5OTzzzDNcfPHFZnHftm3bGg8WApoUl3i9Xk488USuvfZannpKNTa45557WLduHccccwwLFy6MCIxVxllyz63XcNN1V3HuN08jO7tR6/rtb3+b1157zSwOfPTRR83XOvroo1m0aBEAd999NytXrmTatGm88847DB8e/Uy0r8dp2tF1pQNib+Kj8GTz9xwqALDHOFHLznDhImBmR9vTWJbUR752tBPZrffPJc2uIdGow001HjNonjclN6YMeVvMHNUfqbfcthPilYK9LcYi1fWvMePclYl4WB/qDccfUJO9l3U5RzYqcI/WFWTnfuWX3F4r8IRhV4mRf63ZkfIV7kbW7CrjD4Hv0iAdDBDqep2oB85DhbLtTGmcUySDgqJygqW7mCZ2mI+9EDytzeO3hYdx8rgB3dI1EHpJxnn80AEcu/dvDBTlfOj6CQAe4QMJS6LQPcaCyjjbo+9UFkfSXXZqqgVCqJtkaxKMaBqgGI4Yc+bMYc6cOebjjz32mPn7jTfeyI033mhun3rqqXz22WctXuurr75SDSOCjZmK+fPn85vf/CbiuH79+rF06dIWz//BD37A2ed/j4aqUs49aw7jz7gEb2ZGhARl3LhxLVw4Xn755Rav1b9/f9555x1z+5FHHmlxTFuku+yEnA5CoRCjvJ3vgNibGJ2Twfml9/Cq6x7zRhxrbf6k3CwcG6LTOH9dGSkj2l9RH/Xf2Xr/3I4P6gL5eV5OnzwUtimpRjAYbqFz9nqc5pK4Tzo6Lf3KyXBRqn/SIT2Arqjzc83sUXz00VcA9BPVIOHlz/ZEtZSpGVINXeOczNFu++Eg44BX1uxgw6cOXrr6hJjG9MVr93D7a5vIpIYGXPhx0DfNzsa7z+KMhz5ot2hyaF83qxa2fcPuycwc1Z8/28Zzpv93bHNfEbFvqCghixqWr/2cxWv3dNuEMkWKaFiyfi/X216PKKy+xP6u+ftPA9fQh3p2ycE84vh/fCGH8eH2EgqKyi3TctvyvP7jWWT0yaBIDuICn9LBGEU3pd241LRmVxkOgvixRd2pLJ6ku+x401VGYFR/T6vBXVM7OmM77git0w1QwAj2FRKRNA9lm82GU5OpoDlKfjhrFOvlOLaFh5mBc4M/Nm345uLKiCZD7WU6g7rd2/awasEqO6lnjxejB6oB3EGIMC1rIsrr/Ga3v2AXMs7nTxtq+u4agfPu0loWnj0Bh1tZjaXp3Rj9weiuywk5aqIazQQm3rz3pZJluYQff0iyJIamHIvX7uFPr33Ip67r+dx9Ndvdl3OJbTkV9UFGLFyGKN3GVtcPeN95C7+2P6l7TKt/R4k97K1oYNaD73b8h3og+XleXrxqJpNHDGJFKDIoHqaV8D/31Xzmvp5FjkdYu3FTRMfHFCniRUFROcvXbeFC+4ct9q0OHc2Ihhd5JXQyz4S+yXHaF/QX1bwUOtVMTnQHvSJwBvjsjjO4dvYoavAAjf6o3Yk3zYFDqIxztJ3K4o3ToW5sHkfrQYMRhCa0dbUQZkb+gw8+YPr06TE9PRSWCNEoMUla1z5h69IEoLexYMZwbAJKZBbZQgW8sX7fDG/jaIrSJg7OZJ7vPi7032VuW4mKBvXdsYtQqy48RrExgA97p8eT/Dwvex2qWcx/QsrdxrhmXB5ly+kRKnAORNkx6quDh/Xz6pqrRncQFCo5YMjvYvlG/XHFF3zH9ok5kQO43/E0x4gvAfiN40nShJ+R2kEW2N+LeO7ZtrUA7Kto6MLZW5v8PC/jBmYwSLQtgfmm7TM8ooEPtpe0eUyKFN3Fml1leMMVLR4vkVlcHvgFIBja182p/Q5zje3fLAl9gzXho9E00W3y2V4TOAMsPHsC00erFq9Gxrk7qaxV1ccBaY3iQEBld6HNAM/IOBu3m8QEoRpdWdxt7HaY3Iwzmg3CKTeNWMhw26kknSzU8rfHGZuN36TcLBwiSEB2nOm85uTRFGpjqSADu010W2FId5GRribxDoKtuvCojHP3aJxPnHESRzU8w7LwTAAunK66woVsymnGo2ecHVF2hqrW5VtGcWAypRojBqmboVsf0yfGkP32VR/ml47FAGYjFoAlznvIooZ8rVFD+UboBC71L+Q/oRnc4r+OR4LfBWBI38ji8yONl9buabP4akTDYkY3PM8uOZg543ISe2IpeiUzR/VnlzaM2b5IaeWH4WPx48ChwZ++N5Wra/4fdbj5dWABNgH3nTsppXHuLEG90t4luj9wnjEiCz6i24sDpZSdtk1D6IFJG4GzEXTKZttxRYguZWpVsK+QiCRnnEMqe96NMoBYOhj2NNLdDqqqPWRqdeZ2LGwuruSMKKUa+XleXr76BNbsKmPmqP7dWsvQHRw2Ms60nXH2N3HV6MoKltFs5r+bD0Q0n6kOq9c0pBrRZJwLisrZtOcQ2FXG2W4TzJ82tNPn1lUO+1Ww7xL+mPzzR9+2jLec9wLwm8DF/DX0bbyBKla5/o904eNZ528B2BEewmX+hezXm7B8FD7GfA2nTRyxGmdQ9oDX2l5vdd+YhucACGFLaZxTJIz8PC+nHjWAAV+8E/H4rrBKig7ISuPLd5/iQm0LCwNXcZgsFhw/vFts6Ax6YeCsbtSuOGScRdjQI9q6rTjQ7XZTVlZG//79Oxc8R5lxlonMOAutS5+PstlrlGokL+Pc5LMV3dMARUpJWVkZbveRmcXKdNmpqk4nU884Z8aoDy+p9kW0eu7IDis/z2u5gNkg06Myzjb927zjYHXE/s3FleSKAAFpQ6J1WQ6x8OwJLbo1ZvXx4K+2mVKNfukdB+evrt+LTR/r/NLOqeO7r1q9M/TxKJ22s43MfVuEJOQJZWf1Quh0AMrJZL7/XpY5b2OKpuQadwavMIPmpswem81zV87ohndgXb7Yd4jXXf9s8fg7oXxVBA9MGZqVCppTJJSKOj8/sn0c8VihVK5pXmo4bc+jFMixvByag8MmOL+bJ/a9LnA2vF3jETiv33WIqajskFEc2NUbytChQ9m7dy8lJZ3UjwV9UHMISgFHy2Cs1hekpP4gtVRSKSvxexyUxbvYraFS/avY0qlMbUWdH+GvpoJaDsgw1QftybGD89dA3WEo36pkG92E2+02uzMeaQRCYaqlB7cI4MIftabWIDvDhYNQVHZ0VsfIOGeJWsaLPby+EY4f2d/MjKgGKI3vNV5T2npcZsY5GozJCzRKNZLJ5kMNnE+j/K41T+y2OEwmHwUnU6s3yRqbk862kuF80/9bzrGtYXHwNA6hxvA0uxZ3txWrEdacZmdGgKLwAPK0QxynfQGA2651yns7RYrOUlBUTnHRdo5zbTcf2xYeRgA7gjALqp8my1bDLwO3I9H47vRh3T6x73WBc9/MPkBjcWBON954+7lVENidxYEOh6PNVtJRUbwRllwI31sM41s2bPjL+zu58oMFPBM6i98GF/DTs47ihlPGdOGMo2DVI7DiHvjlAXC07ObXEbe/tomcdX/nFscSzm54gYtnjODX503o+IndTeESePuHcP1aGDA+8X+/B9Iv3Un1YfV/nk5DVBnOpkzKzcKxMUhA9yPuyS1/s9LV53CT/XVusr/OmIbn+PWyLWbgPCk3C39BIK7OFYdr/dThNjXOh2s7ljlkZ7gobyIhSfbk5WCtmoAYY3q0TkluW5gBlJvZZJsGy2+dw4NvbuXFtTb+6GucvNq1+FsUWpErThrNCx+dbgbOT4bO5lfaM3iF0rh/c9KgZJ5eil7IkvV7+Y72ScRj47Wvecn5AL8M/JAF9vd4IngO2+Rw7Fr3Z5uhlxUHAnwnXwWhLuHH3s0p/Oo65RMbwBaT1i6uONUyJv66Vneryn3VtCUubcJb4esqdaPbuPtgB0e2zqTcLOwiRFCqJhVJC55c+t/1Vbd/XAqTvh4nNVIFjH1EPZv2VfHgm1ujfv6WfeV658Dkuzl0lU37I/2Bs6mkpok9n9EARWVS4jOe9Et3Ui+deESDud0Rk3KzzBU7P46kT168ZjJEnVO0yRCv1oBNSCqkGiNdemHkwrMnsOneb3Lt7FGM6O/h2tmjzHbfvY2FZ08g29G4Ors5PCJi/+7Stj2uU6SIB6XVPmZqW1rd94Dj7xTLfvwxOB+tmwsCm9LrAmeEhl/acBJE62Zf174uPeMsExeEdohD6SgJtD7Aldf6sIswoQQF+wVF5TzxSTEANzz7cac6fRlevkYXtKQFTy5l5YWv5wZvycBYFv+p/Z/MCq1l0cpdUQfPmlQ38XjLFxKBEIKAbJT49BOREzCvx4lTBPHhiNt4MnZgRoRUY+zAjA6fU17nN4ur/dKR9ATBhCH9CUlhnlO0rhojverzNOQmef3TI/YvPHsCH/zslBa68N5GtrtxMjdcHDJ/70MdTnvvCyFSJJeKWp8pFWqNewKXU4eb++dN7taCwKb0um/9ml1l+HHgJNCthtgANXUqqxu0VMZZD5zbyDj3S1NfgUCCgv0l63qkHNIAACAASURBVPdSG1ZBjwj7Y2pWYGBoP6Px8o0rbt0XOJVxjolqPXD+jm01f3M+DMDTn3wV1XMnDVTfZys03ugqtb4gDtEYlBhZXGMyWV7nV81eZPzGk4m5WdThMqUa0QSdXo8Th+kv3fmOht1FYXGlPqYHze1oGN5XfYeM79Kwfp74nGAPpqConMraxo6b47S9PB08C4DfOp6g1tf9/RBSpGiNgqJyvvPIu5y578+kibbHwnfC0+mbZo9b0Ay9MHD2epz4cOAi0Gq3rq5gZJz92C2UcdazKG1knLcVq5t0orK3AvChPhc3/piaFRhMys2KyDgnT6qhZ+caqpLz93sohlSjKb4oO9bt2K8muv4o7OiszvItkVIlt34zMCaTXo8TJwH8ccw4FxZXUi9dpqtGNEFneZ2fdJS0oxZX0hMEpdU+/Nhj7gab7TGSBj2/0DRerNlVxufhUdRJ9dmMFft4MHgxAOfYPmXb/sqYpFYpUnSGB9/cyv2LnuUP5Tdylf2tNo8rlv0AQbzNwXpd4Fxe5zezE92dxampVzNzS2Wc7U7Q7G1mnDWpMgZGEBrv7O3E3CzTSsxFIKZmBQabiyuxEzTtkJIn1UhlnGMlJ8NFDbEXhBpUVquiJEOq0ZEdnZWpD0Q2z3E1C/xiaS/eWUqrfTTgbPG326O6PkC6qCcsBfW4qK7vfoeiWMjJcOHDGbPGOcuhRrvAEbB6ES9mjupPyObmQ927+gxbAT6chKVKeXxLW8NfV+7qlOQuRYr2KCgq58JFnzBy4TK2r3qF11x3M07b1+K47eEh5u/X+H8CwPhBHUvOukKvC5y9Hid+accpAt2exemrv1TQShlnUFnnQOuB80R96TtRN4/C4sqIwDkW6yiDkmpfhFQjacGTsw8gwJfKOEfL+dOGUt1KxtnjjG4oyjGyhEeAHV3z1RZXs8AvlvbiXcGPHTvRd8Bcse0QfWigFjcgWLHtUIfPiScTc7PwY8clguZ2RxQUlfPG+q8AixVzW4z8PC8X5A/l/sAl5mOX297m1sC1ADzqfAyJ7FbJY4oUlz21lp8v+hfn7f0du90L+LvzDy2OqZdOPg0fxY8DN5mPbZKjAPjF3PjWJfS6wLlRDxe752dHFJWo1wpgoZbboHTO/talGjv1pe9ESTVKq334dEcElwhEvazaHIcIEpTd553cKTRNyTVSGeeoyc/zUkZjYLNXZgOQmxVdFnp8jvIi90fRctvqNA+EXajxwgj8JuVm4RTBuE9qA9hNzXI0VNT78dBAHS5zO5kUFlfik7GN6Wt2lTU2ccGOTRPd1un1SGP+tKHsI4dL/LcBcK/jWaZqO839c7SNLZr3pEjRWW7+xwam7HqCd10/42L7+xH7QrIx3VBOH67338yDjr8BcG/gUgDOPHpg3Bsy9brAuVEPF5vnZ0cUFJXz8RfKLSIg7d3acrvLODxtZpyFLtUIJEiqkZPhogGjCU3nbrjZGS7sVmmC4cpIaZxjJIzGn4PzKJb9yEB9L6P1c9598DDQ6ITQkzXOzS82wxXCCPyM4kCfdMQ1IxqQdhwi+sDZZdNwC7+pezVs3JJFZ8Z0ZcNpeFE7uGrWSMt2mEw2+Xlerp09ilXhyTwVVF7Wl9mXm/tvsC9l6cbilFwjRbfwwfYSysikVrp4JDCfi/2/NANmm2gcNG8L/IgGHEzTJ3H/CJ0CwDUnj477Ofa6wDknw4VfLw40truDNbvKzCA0iI0L8odaZyB2etrUOE/I0aUaCcrgnT9tKEHNKA4M8N4Xh2IecCflZuG0QnEgKJ1zSqoRE2Nz0nkoeCGvh2bpbg7RT9eqa9X3OFETvXgiBFzjv5lr/TcDyikGGiUcRnFgIM7Sr0CToDMahvRNw6UXLRrbycQY02PROG8urjQdTQLSTnXKHaJdFp49gTOPHsjvghexLTwsYt9x2nami2389cMvk3R2KY4k5ozL4cXQ6Uz0Pc2fQvNZHZ7Id/wPcE/gMr4MDzaPq5dOLrO907iNm+NHeBMSd/W6wNnQwzlj0MNFg9fjxCb1gRibtZaQHeltumok2kIvP8/LtFGq25QLP8GQjNmSThUHhszgKalZR1dGKnCOkeW3zsFhE9RINw4Rwkkwqo51BUXlbCxSrecD2LHbBPPj0BUqUZw0Jpu3w8ezJqz0eEbgZ4xJKuOstPxxzThji0mq0dejigmNWoWktLtvgjmmE/2YbujHAXNikqJ9rjl5NMLh5pbA9fibyeSuty9lxdaDqaxzii7zx+9NZd6UXJw2DY/TxticdDbLEbwQOh0bYaqkSvaN1/bwI/ubAPwnNAOAeVMTcz/odYFzZ/Rw0WDc5ABC2K2jb4Z2M8599eRMvLNaTcnsozp9GUvTsVrSSYiQaiT1pufOTEk1OsGYnD5mI5R06nFEsdz/6vq9TXSpDk49aoB1VnU6wXNXzmD22GxCQgWgzcekRGScczJcERrnaFfgnE0yzsmmsLgSv3TgFNGP6WrVqrGZjqUSHRYlP8/Li1fNZNrx3+CR4Hcj9p1i+x8T+CqVdU7RLfzxe1PZ/sBcttz3TZbfOofZY7O5wLaSEdpB0w7xRG2L2fp9hFDWnt1Zs9YevS5wjpfGOVIzZ7OOowa0q3E2LPQSWVk+erAqCHMRW6cvA6NoyhpSjVRxYGeoagiYxWXpooGqho4tzUqqfWawYxQH9nSeu3IG845TleDGmGRMJDcXV+IUwbjquSfmZkUEztFeiy4RMIt8k40xpsdiqWdY/YEqDuzRWvkEkp/n5YHzJvOu98IW+66zv8GKLamsc4rupaConLU7irnJ/irrw2P4MKSsEU/W/ke9dPLX4DmMF3tIp54NCfru9brAuaLObzZAMba7g/I6v3njC1ou45zepqtGMiz0Nh1SNzZXJ7P+5XV+lXGOY0e1qOlhGmchRD8hxHIhxA79Z4uUrRBiihBitRBisxDicyHERXE4EbMRSjoN+EIdN0DJznCZwU4Ae4+2omvKhNx+AGaBnhG8mnZ0Mn567vI6v9I4ixACGfW11FSqkWwqdG9+47sRzZiuLC0bC6Ot6gdumeu1GT/4xlhTm2/wLdsa8sR+Xu1EN9gUKdpiyfq9fN/2LrniML8PXoQm1L0iTfhZFZ7Eh+FjsYswx2lfsPVANYvX7on7OXUpcLbqRd0e+yrqacBFmt4pa19FfQfPiA6vx4nNLDbpORlnw0LPjz1hFnoHa1UI4NZdNWLN+hvZ/UTKS9qk52WcFwLvSinHAu/q282pAy6TUk4Evgn8UQjRtztPYuLgTN0HWAXOZdX+DjNVqmOkusb8R9DyeuH+KnzSgatZy2ijQ2Y87egMX3sAG6GorqXsDJfuL+0wt5OJLxiOWEWMtgulUecSsPbqhSWu1+YsmDGcAuf0Fo9fa/s3i9fuSUjwkqJ38PX+Q1xvX8rHoYmsDk/ETuP1/X54KgXhcQSkjXxtOwBvFe6P+zl1NeNsyYu6XYSgVrrx6C1jEZ1p+tySzcWV5sDtx2GtpT9neqsa56YWekFpS5iFXr/MdIJSMzXOsTqbqOx+iGCci6aiwp2lJiWh5HZPi4FzgWf1358F5jU/QEq5XUq5Q/+9GDgE5HTnSVxz8mjqpB44iwYkdFgkqq6xRl2qpa6xLqDa0De2jI6QajTREsfj/ZbX+U3Jk5NgVNfSpNwsXPjNjHOyJzAnjOqvnJL08eSEKMewppMwC2OJ67U1Rg5q/Jz/EZwDwEX2DxhIGXe+vikl2UjRZQqKyhlb/BrZooqHdF19/7TGsPW90BR8ODkg+zFUqMLxiYMz435eXQ2cLXtRt8XEwZnU4dJtsLrvQy6p9pnSAz92ay39GRlnGbnYmywLPaPtdmc1zkbG2Y/NGhln6ElZ54FSyv0A+s8B7R0shDgecAKtVv0IIa4WQqwTQqwrKSmJ+iTy87yMyB0IqIwzdLzyIGnMEvqPICeEtnTGEiIyzvF4v16P0wwc7QSjupZM7bVMcst7nYw0Bz7ZmHHOSItOQtJU9mNhLHG9tkZfj5P9UsmMAtip0qVX19nfICRJdRNM0WWe+GA7V2j/5bPwONbLcWgChmY2Xq8H6M+I/h7CTSwGor3+u0JXA2fLXtRtMeeoAdRJN24RwEaIOUe1e8oxYS4VWkT7Z+L0ABICkbIUr8eJXTYGIonKHL3/xSE9cPab27FgNIawRMbZpU+8LKRzFkKsEEIUtvLv3BhfZzDwPHCFlLLV9W8p5RNSyulSyuk5ObHNh50eNekwAueOMKQLoJbXk53p7C4au5k2k2oMzsApGlvLx+P9qoyzen0XoaiuJUmkxjnZExivxxkx8YhWbmIVvXxPuV6bk53hYndYWYueYSvg5sANAFxuX46XKtamAucUXaCgqBz79v8wTCvhyeA5AEzP81J2SK2Sbw0PB8CuCX0ir8ajRCTSOpxqCyFWAINa2fXLWP5Qk4v68vYuauAJgOnTp8dlPC4sriRNr+b34OtW+xKXUDcdyy39OdLVz0CdHkQrDD9kUBnnRGWOdpfU4MNpZpx3l9TE9Hyvx4lDhAjKVMa5NaSUp7e1TwhxUAgxWEq5X78mW521CCEygWXAHVLKNfE4zwZNfRfTRXR1BpFyqCNLquGXdtNOzcidbNunAo94Zna9Hid7Ysw4T8rNwrUxgE/vAJrsCUxhcSUjic1idFJuFnubBM7JfA895XptzqTcLErWK9XlIFFOhexDueyDV9Rwrf3f/GbH93nwza0sPHtCIk4nxRHGog92coNtGV+FB7I8nI8mYOzADEbt3QjAO+F8ABw2DWeTiXwiLOk6zDhLKU+XUk5q5d9S4KB+MWO1i7otSqt91OlFSR4aus2ODhor4KXVzEqMYLmZs4ZaCjaatiRu6btfuhOfdODWJxrRtls2MFw1/DJxBY1t4tYzzj3Hy/kN4HL998uBpc0PEEI4gdeA56SU/4rXiTQI9b3sE2XGOdIJwWJyqC7QllTDJhs9qyF+rhpB3bXDKaLTODdqr60h1VAacYdebCyj8oU3Vq1AJQ0s5YIUiWWu1+ZsLq7kkFSBs1/a+KbtM6713wLANfZlpFPPEx/tSmmdU8RMQVE5lds/Yor2JU+F5hJG4/QJA5mYm8WZ2joA1ofHAeC0a3qxshqPuqdqrX26GuFZ9qJuj9omRUndiZWaAkTg0APnZs4aEUvfCe522FTjHCszR/U3W24nqqCxTSyYce6AB4EzhBA7gDP0bYQQ04UQT+rHXAjMBn4ghNio/5vS3SfizexDUGp49OuwoyJRw80Bkr+83p0YUo0WrhoDlWY07q4aMWackWFcImgZqcbE3CzqpBubkLgIRFUzoVatggSkjTCatVyQIrHM9docCWbgvEGOZa72KWvleFPr/D3b+4RTWucUnWDNrjIu1/5LhUznldBsbJrgmpNHs3nfYYZpSsZrjFsjs9Nx4Tdjr+7qBt0eXQ2cLXtRt0fTjHN30nTWYymculSjmbNGeZ0fh26hF5KJ854+XOunoYlUI5p2y80xOwfKJN+2XfpFaiGNc3tIKcuklKdJKcfqPw/rj6+TUl6l//6ClNIhpZzS5N/G7j6XiUP6Uovb1Dh3NOA1negdSXZ0AnQ7tUipxs79ulQjjpldw8cZwBmlxnnyIDURNzSFyf5/eP+LQ6a1oYeGqGomjAYoAYtkzdvCStdrcyblZlGiB87rw2MZppUwUXzFJf7bAbjT8QIA1fU9xnEohUUIVR3gTG0dr4RmU4+bH80aSX6el4zS/5nHGCtlhfsqqSGNgUKtbMRaM9UZuhQ4W/mibouKOn8T/1gfK7Yc7LbXVgUzFsxcmBnnSKlGZLdDe8KyLv3SnU2WVmOXaqzZVaYHzjaCYZncjIaZce4ZgbOVKCxWA54ROHekTVNuDgECuhzKqsFOrEzMzcLfilRDCzda74F1XDV2FKuMj5FxTvb/w6GqBg5I5QZ0vLaNQ1UdJ0SM5jIB4tdc5kinvM5PCSpw3hgeTVBqzLV9yudytHmMjRCrUxnnFDFQUFRO3acv4BAhXgqdCiinjIKicrK+ftc8zrDR9PkDSARjhbIzjeb67yoWE+PGH18wbEo1Xnb9imzKmXDHW93y2k4RsGYrYFPjHJlxbtp2NpDA4sC+HqVxNqzF+sYYsHs9Tt1Vw0ZYJrk4sOdpnC3De1sOUifdZnHgex1MYg1NfjwDyWRQWFypFwdGSjUm5CgpijGmxNtVw0kwqsIaW1hNeK0i1bjouOGsCOfzRXgot9sXc/G01mrZI5mUmxXRxCXZWfOeyMxR/Tmst2RwEmRteAJztU8ByWe6/vRMbR3+KBvSpEgB8OBbWznT9hl+aaNBqnt7dX2AJev3MkdsMI8LojoHX5XxCf1FNU8F5wJqPIg3vS5wvui44VTQx9w+Svua+mC4y52OlP6yUeNsKf2l7qrx9sbIQg0JpqtGIosDAV3T2TlpSHmtD4dQAVTS7ejsbtDsPUnjbBlKanzUkEYG9eZ2exhSjXjasyUDJdVwtJBq7D54GIhvZnfmqP5ITX2eDhHklYK9HRZzTdQDeqs0QFkwYzi/Om8Krw+8gTztEN8L/7vD5xjFgX4rjCE9lPw8L2fNOBaAHFHJG+ETGa3tJ19s5+XQKQDMt61k28HqVIFgiqjZ9PVhNMI4RYgzbAUAbN5fRaj8ayZoe8xJWQiNDGq4qPJpDnmncXjUufz6vMksmJEKnLudBTOGs0c2ejcbOueXP+ta4Ky6aQUsczNpyqYSdUN+9/PdXPzEanMQa6oZDfag4sBstwrxA9ICLbeFUF7OKalGzNg0QYVMJ0vUmtvtsbm4EhcBy+tSY6VNVw0zs6u+3/GY2ObneZk1PhdQDUGCwXCH0qe9xfsAqJJqQm6F/4cFM4bzixuuh3FzYeUfoLr91YvG4kALjCE9mJKgB5+0M0BU8EboBKqkhyvs/2WfzAbgdNsGpIRXO+gKmiIFwOK1e5grVzFF28Wa8ASeD50BqEZ1zt3LAVgZOgZQMcutjldwBioZcNGjPH/VzIQEzdALA2eF4E/B8wHMDoJdXU4yPGatYtHUlKVbKgBIw4c/JM3Wxirr0ujjnKisS06GKyJwjrXltnv/pwBsk8OSb0cHSuecyjjHjBCCCvrQlxpzuz2MTnr+I1Gqgb1FA5Rx/dQkPN6T8bwcpQ92ECRMx0FkWkidX5lU+n5L/T+c9QAEffDefe0eVl7n1zXOqYxzV5BCsFUOZ55tFU6CPB86nbO1T832xwCZ1LDjYGp8TNExbxXuZ5/MpkE6mCa2c6P9NQRhPtxewslsoCg8wEx8jhX7uERbTun478OgyQk9z14ZOI/NSecfQbWUZOgKA6GuBc4ScIlAXD1XO8v+WvXfnKZPEgzvaq/HiV2ECEotoZZME3Oz8DfROMdqH3NCYA110sVqORmnI8l2dKB0zimNc8yojHMf+ooac7s9JuVm4WjS6tlKqzpdwZBquJo1QPm6RK0M+aQjrhPE7WVq1c2YRHekcz5lqBpPysnEaRPMnzY0LufVKfqPhhnXwIYXoXhDm4cZdRKGRC2Vce4c86cN5d7QlfSnivsdT/OM/Ba1uLnG9h/zmFlaIeuKylNyjRQdMnfSYD6VE5jle5S3wjO42f4q52ur2HWgjJO0zbwXnsox2m580sFV9jept2Uw4DvtT5LjQa8MnJffOge7U2U5DV3hqJw+7T2lQ8xiEwve1APCSVgK0kSkhtQoDjSqUxOVJS8sroxouR1Tp59wmAH73sU34hRuOHMyL141k/w8b5zONEpcmamMcyfI6+ehknSyRB0aYfL6edo93ljVORKlGj7ZmHGemJtFQVE5a3coSYQPZ1z9ysNCTfaba6zbYmSa0qRffMo0Xrr6hORff805+efg6U/N6z/lL+/taDVgM8Y+f4LHviON/Dwvd1z9fQpGXs23bau5dfQ+/h6ayxit2DxmtvY54ZRcI0UULJgxnGtnj6KULP4vcANbwnlcb1/KSVohacLPe+GpnKgV4hIBpmo7WZF7DXj6Jfw8e2XgDHDTmZMATLnAnKMGtHd4h5TX+U07Osst/QlBHS5TlmKQLJcCo9OXESjE1Oln/waoLsY77TxuOGWMNW7arkzwpW68sTKsn4cKqSasWdQwrIPA2egcaHxfj5TOgYXFlREa58LiSl5dvxeHrnFuwMGccTlx+66PHKT0qEYnzw5XgOpKQdi46vSp1rj+muPOomjKT+hzaB1bVzzLhYs+aRE8G7KfI82hJRnk53mZeen9MPQ4LjjwCP+VMyP2z7Z9Dsgj5npNEV8Wnj2BJdedSHYfF48Fz2W0tp+HHIvwSTsCyQTtawA2hUfwxeB5STnHXhs4l/pUuOYk0C2Brtfj1PulW3Ppr76VwNkoDjS8TBOVJTd8a41JS0xSjW3LQNhg7JlxOrtOkNI4d4qvD9dRJpWdX39RxeovS9s93ugcaAQ7lnKu6QKtuWoY0i9QGed4vtfDfjUWuqIdC+vKVJZHs+7t4+dfHsPX4Ry+bVtNSCqLq6Yo2U+IgAVXCHskNjuc91fsMsjTg5bwl+B3zF254jBjxL4knlyKnkBBUTl/eX8ni9fu4cX31jOlfjXjNWXa4BU1uESQ55y/NY+/M/BDCg/UtvVyccW6I1+cyUpXFeEuEeyWQFctIzdqnK229FcnXS2kGoYlUzDBBTKFxZX4pBOHCKERjk2qsW0ZjDgpKcszbeLKSGmcO8HBqgYO6o0rBolyqn0hbv5H27pUY6JnReearmBMJJtKNQyXHlAa53i+1z7pqsjPhT+6sbC2FDzZcTuf7mBTcQ175AD6C3Vdbt4XOcakigPjQP/RcNavGVS2ln59+0bsOln7nPe+OJTSOadolYKicn70xLvw7n3MWHYmDxedz5OOh7jJ/rp5TJ104dO7lU5seIqNcgxzJw1Oyvn22sC5vD6AX9q6LeMsUYWGhsbZakt/rUk1VOfAEP4EZ8lLq32mM4KTgFms2PETd0LJNhj/rTieXSdw6xrnZLf/7mGMGdCHvTIHgGFCtUn9z+fFbR6vOgcGCcgjS5dqSjVECKFPJA3rPVCypni+188PquvPrf+9DieydWWQbu3AGSEoJwMv1eZ2U4ziwESPfUc8+T+AsWcxv/Yf5kMN0sFs7XOCIZnSOadolSXr93IGa7jBvpRD0suDge+xOKi6Bu4IDyEkBZvkSKpI47+h46gljeNHeBNmP9ecXhs4ez1OAtixE+qWgdPKPs45GS5dqtFgboNeICOCZs/3RAYixucUk5fzF8vUz6POjsMZdQFXBoQDEIx/q88jiV/MncAhvWXvQKEyUe25Qh6putTSah9+aRToBSmt9iHBbEnvwxHX93qwJkRIClPj3OFE1pBqWJhJuZkclhl4RbW53RRjtS2Vce5mhIDv/JmAlmY+5BYBZmhbceFP6ZxTtIoADuv2lr8OLsBGmAX29/gkdDTz/fdwb/AyZmjbyBFV5rh4KInfpV4bOKtWszbshNBE1wdOK0s11FJwS/s3o3Og4aqR6M6B0FjJHxXblsHgY6HvsDidVSdx6TfllM45JvLzvISFnRKZyQBU4NyeI92R3CbZ16TtNegTcdGYcY7rexWCBpzmDalDeoBUY97UoRGrbPOmRlrmpezo4kjGQAqOuSfiIbcIcLy2jQ9Sco0UrXD+tKEcEso16Gnn7/iZ45+8GprF5YGFVJPOc6Gz+GXghwDMsf0PUFK/ZNFrA+eZo/qblex2TXTZ6klCRAMUK2XDlKa4seGIsRRrBCJGBi9RgYjRAAVUAVRUDVCqD8LXn1pPpgGNgXNK5xwzs8Zkc0h6GSgqzO22ULrUwBGZJTSuQcNZw5BqNEgHIOI6EW9ckfKZ220SDkF9ueWlGoXFldRLF24RaLWOorzOj0ukMs7x4u3wcS0em619TjAsO+xMmaJ34uqj6l2yRRV/Ds7jJ4HrCAk7D5w3mdljs7nL/jwALwZPA2BkdnrSzrXXBs4AQezK9L+DjmXRMHlwHxwihE+qzIWVsmFKU9xYtW8sxZbX+bETIoAtoTcPowEKKKlGVK4a298CJIw/J74n1xncRsY5FTjHynNXziDgGcggrZzZY7N57soZbR57JOtSm6/AlFT7cOE3J5jxXOI+f9pQ9jCQsdo+nHaN89traFJ3GJCWzziXVvuoQ00A3PhbyE/M75K0HXHfJStQUu1jY3h0xGOnaBsJy9RnnSKSxWv38OO/vsnd9coxo1Rm8lDwQkBw/7zJLJgxnOcGvGSuwN0ZvAJNwK/mJbZbYFN6beC8ZlcZAWnDLoIEg+Euz4K/2KestKzYchuIaHFtYBQHJnq50miAAipwjspVY9sy8I6AAUfH9+Q6g0tps1KBc+eYMuEoJmY0tBs0g65LFSH88sjLEhpFxYacqqKJL7yxHS/y87wMPfokptqLeOnK49r3Zq7Tx0mLa5wr6vzU64GzB1+Lzy+lcY4v2RkuloZOjHhsjFbMIMosd29MkTwKisp5bul/+ZfjLiZqRQD8PngRABMGZajiv02vQMHTAGy8eD23njWBf117YlI95Htt4Oz1OE0LqDBdDxorqlTbYH8CMkSdwYe9hZ7YcClIdOfA0mpfhKZz58EOtMG+atj1gZJpdMPqQLeT0jh3DXcWNHT83TO80o80XWpOhitCqpGT4eJwrR+X8Jv2S4dr4xvYDThqJo5wPfnpJe0fWKd7bVtcqnG41k+9VIFzmmho8fk1rl44jqjvklWYP20oL4VO5ZBsZktn+5yXP9uT0jmnAGD9yn/zsuNuXAT4PDySBungvyEl85mW54W9BbDkSnXwZW8w5ajRlmh81msD58LiSrM40NjuCnapglIj42w1fNJhLnUYGMWBhr1XonTZSuOsblQuAqwrKm9/IN2xHEJ+a8o0oDHjnNI4dw53FgTrIdR+oeiRmiU0inehUbrUL92pNM76ddIvPc6BXe5U9bO4bR9tbUzpiwAAIABJREFUQBUGguWlGv3SnaZUIw1/i8/vSP0uWYX8PC+TRwzigcCCiMd/6/gbwTApnXMvp6ConJf//jCX77yFEtmXH/h/wRhRzOuhk6ikDwK4eFQDPKks6Zh5A4w6Oann3JReGzgLiGhz29U8pkM2WkdZEX+TFtcGRkOJYIKLA8+fNpSAoekUAcJS+Ti2ybZl4OkPw9pfyk8abvW5PfrWeh58c2sHB6dogf75dTTxUBnnIy9LqKRLjSsw739xiDEDMyLsLccMzIjvSWSPBWefjgPnHpJxBqjXJx0efFQ3RI593jSHWRx4JH2XrMTCuRP4d/gkVoci5XVjxV52dLTKmOKIZfGaIt79221ctOdeNsgxnO+/h5naZjzCx3Mh1RH4u+M0Jr12unpC1nA48/4knnFLem3gPDE3iyA2VRxIjG2fWyHHo0JvY2nVau2AW9M4Kwu9YMJ12fl5XkYPVhpJV7OCxRYE/bDjHThqLmi2hJxfrPzuPdVONlBbwaKVu1LBc6yYriQV7R62ubhSX14/shqgCDBXYNzCz7tbD5LpsifWF16zKavHDgPnw+pnmrU1zr5gmHrpBiBN+Nh6oDpiVauytg7giNTLW4X8PC/3n3cMd4d+aK5qAix3/ZylG4tZvHZPEs8uRTJ4afUuwst+ws/tL/FG6AQu9d9GDR4usy3n0/BRbJEjyKKG+ypua3zSlW+DZq1Q1Vpnk0DK6/wEdKlGdwyc47PVjc+qHrN+HLia+bQqC71G7+lEWui50voAkEYHWvCvPlJFd1a0odP5x/r91EsnfUQ9AP9c93WSz6iHEaUriZBBbEISsGh3zs5y/rSh5jXoxk9YwupdZbhFAJ90JC6wy50KBza1L5mpLQVXFtitnaG96LjhTaQaaoxpuqrVz60SHQFSrhrxZMGM4Uw/biZPhiKbVl1he4s7lxamtM69iJc/3kbOm1dyiW0Fi4Lf5v8CNxDAwcna/8jTDvFs8Czc+PhnxiO4q3arJ130ImTmJvfEW6HXBs5ej5OAtOMQwW4ZOIsOqUyMZV01pAObkNj0DDs0+jj7EizVAGjQPACkiw5MzLctA4cHRs2J+zl1FpsmqCGNDOrM7RQxEKVUY/Ig9Z1JtO94vMnP8zJqsGo9bkxu/cEwLvw04ExcYJc7VXW/PNTOikldKaR3zfM+ESyYMRxvlvp+GP7UTYuQq/WMc0rjHH/mTxvKIuazTzZ+b+5yPE//8GF++1Zqda438MpHmxj/9sWcom3gzsAPeDB4MRqS87SV/MrxNAekl3fDU/l/jj9xVED/TuRfAROsmTDrtYGzURzo6KbiwANlauZsWCBZyVUjJ8NlBvQuAhEtt516VsvYThRG4NyH+rYPCofhizdhzGngSGv7uCQzMjudKukhQ884J9OYvUdiSjXa//5tL1YFRVbsztlVXGnqO2N076tuCERINRLyXqMpEKwrs3xhoEG17qphTM73VTSONf2UiiOlcU4A+Xlezskfw32ByyIe/9R9Axu/OpSSth3h/P4/Gxm7/DLGiz1cG7iFF0KnM19byXLnz3jYuYgK2Ycf+2/kAcdTnGrbqJ6UPQ7O+nVyT7wdem3gLFDZ4e4oDiwoKufrg6poxrBAspLGeWJulqmhdDZpOCJRgXQypBo+oQJhI9hsleINUL3f0jINgL4ep55xrje3U8SALtV48OXlnP2nlW0u31ZVqYyhMQm00uS0q5T51FDsNtps6xlnXyKvzX6jlAyjvcC5tqxHFAYC1Ilmk/MmVpZf7leTsIROTHox86cN5V2OY3loWsTj/3Ley19XfpmSbPQgrnjyY6bc+TqXPbW2w2MffHMrGWt+z7HaLm4K3MiH4WN5zvEgDzkXUYuba/w38y3/A5xpK2C+bZV6ks0J858CpyfO76Tz9NrAWRUH2k07uq4UB766fi8u3VWjHieaUAOFVSgsrozIODdvuZ0MXbYUGjXSTXobGeeConI+eONpQtjYmGZRNw2dnAwX1TLN1DhH1UI8hcltb6oioYXiOXwHtjH/8U9avZG6pPp8a6R1Vx86y8E6FdQZGWeJakef0E6kQkDulA4yzqWWb35i4M1SHsJG4Dwky23u0wwXJJn4pEFvJD/Py33nTuanoRvYGB5lPn6stotrbf/mF0s+TwXPPYDLnlrLvKL72Wi7nPU79jDvsVVtHltQVM66j97iatsyFgdP5e3wdH5lf5pv2Aq5I3AF3/Y/wNvh47nG/h+uti9DCt3K9/R7YPAxCXk/naXXBs7ldX5dqhHsssatpNpn6ujqcDE9z5t0g+6mlFb7zBuEUwRMBwvlqhFIii47J8NFDWnmTa1psFlQVM7NT/yHOYee54DsywXPbrX0oDoxN4saPKbGuasOLb2NZdtrzN9naYUA3PBCQYvj/HVKA20UfR1JpKerYlkjcBaQeKkGKLnGwc0QbCWbL6UqDuwhUo2wsFEvna3WUUzob+1i7iORBTOGc85xR3G5fyFrw+PNx3/h+AfjSldwweOfpGQbFmfljlIz2fUnx1/YuLeyzf+z371ZyG8cT7JXZvNA8Pv8wPY2F9k/4NHgPF4InYEQgruHbeA2+0swcBJC02D0aTDjukS+pU7RpcBZCNFPCLFcCLFD/9lmtCiEyBRC7BNCPNaVv9ldGJ0DHXRPcWCaUDeaBumy5FJ90wYLBqVVddhFGL9MfLfD86cNpZY0MkUdTrvG+U0y9K+u38s8PjC3gyHZvs9zkiksrozIOHdVLx9PrHjNetMbM8ijRTEAB5p9FwuKyikqPgRADUdextkvdDs6PXAOhMK49eJASOC1OWQahANwsLDlPl+12tdDpBqHa/0Rk/Om3QONYm6rSzWseL12hfnThuJzZHKJ/3ZeCc02H/9/zke51/40f1u5gwsXtb7ilMIarApPBuA02wZutL3KopW7OO7+5Vz93Drz/23x2j3k7V3KWG0fDwQv4RhtF3fYX+CdUD6PBC9AAM+cUMYVpQ9TPfB4DlfXEnBkwLzHLWc91xpdPcOFwLtSyrHAu/p2W/wK+LCLf6/bKCyuJCht2EX3FAemNck4Ww3Vqc8InBsbATiS2O0wP8/LgEFDmeL189KPZkZk6EuqfWyVeQDc4r8e6HqDmngiIELjbOVzxYLX7FGDGpt75ImDrR7z6vq9pOmZwzrdn9dKdQRdpSEEPmnHLVRwF5IyIuOcMNorEKzrGV0DDfqlO6mVbjPj3LR74IEy5RlufL4W1stb7nrtCvl5Xl68aiYXzhjFzwLX8OfgPHPfZfblfOm+FFm0mgse/4QfNQnEUlgDp03wYfhYPtGb2tzqeIVPXdczt/7frN6ym+/qk57XPt3BLfYlrA+PYXk4n1/Zn6ZIDuSWwPVING4eV8qJG24FGWLz/hr61e3mx7VXUnDYeknH1uhq4Hwu8Kz++7PAvNYOEkLkAwOBd7r497qN0mpfROfANhtwREkahsbZejfz86cNJaQZgbOf9744REFROQPS9aYtJKdpS8bAkeSKw63KWozMeDkZaIKIjLTVUMWXDpz6OVtcqmG5a/brw3Xm70NFSavH7DhYTbo+Oa3BjcBadQRd5YRR/fHhNDPOmQ6BXYRNiVVFouzSsoapLp2tBc61eptkj/Xt6EAV6dbjMpMaxkpgQVE5uw+ogMyve4JbeBJmueu1q+TneXngvMk8cN4xPBy8MCJ4BviX6z4KXVdQtvUjLnj8k1SjFAvxw5NGslsOZkHgDi7w3QXAAFHBfY5nedr5O8IS7nhtE9MP/ovB4jC/DVzMN7VPGaMV83Dwu9SSxhnpO/m/PTeZ3ZYniV08GPgeK4JTekwr9q6mGgdKKfcDSCn3CyEGND9ACKEBDwGXAqe192JCiKuBqwGGDx/exVPrmEATO7quUFHnxyOUjjiMlribXJTk53k5etgA2A9OETSlD9MHpsHmJOr8ModAdTGEQy26AhrSl3qcltOMN6ewuJKBOHRHBGlpqQYWvGaLDteZU/gsUQtAmiNyTn+41k9ek4xzbl+3pb8TsVLlC9KA0/Rx9vnqQWuc1O6r7MDvvLsQQmWdize23Ge22+4ZgTOoFcDmTZZeXb8Xh/E5W7CYuxmWu167iwUz1N//5etKonW27VNzX7rw8arrHnaEh/CHpRfy4ba59M/0MH/a0CPquu9pLDx7AqCafK2rG8+1/ptZ5PwjANO17UwQRRQf6M+1rqW8F5rCWjmeNx2382V4MCUyi5ed9zEjtA2AEpnJH4IX8e/QCdTjxuXQmDmqZ4wtHQbOQogVwKBWdv0yyr9xPfCmlPJrIdpfxJZSPgE8ATB9+vS4FzoHsONsIl3oLIdr/bjxUa/rEZtq6axCn3TlE2tkcgWNlkxJa9qSNQTCQag5BJmDI3YZmbd6i2rGmyLA1Ik79E6UyaSnXbPBUNgMnA25S78+kRlAh00zA6B6nAxyJ1jCEGdKq33US6dpR2cP+0DD1DgjE+j7kDsVPnoY/HUs3lDKW4X7mTtpMAucRsa5Z0g1AOqlE4+IDJxLqn3mOOjDkfSJeU+7XrsTI3j+ydIbGSru4hhtN88HT+dS+woAxmr7+KvzEXZ/uZgnQ+dw6bqTmTd9dCqAjiOL1+5pvOZntJxcLTx7AgvPnsBlT63F92XkOPyWq7FV9ifhidxmX8zRWhEA/3T9ytx3s/96Xg/PAsBuE1w8fViP+j/tMHCWUp7e1j4hxEEhxGB9JjwYONTKYScA3xBCXA/0AZxCiBopZXtarYRQhxuP8CEId+l1HDYNDz5T3+ywWU/cnjegH+wkQk6wddNOoNGSKeE6v0w9y1O1r0Xg7EFl2KwofWnOxNwsduuXUlOf7GTR065Zu01QKjPJFlW4RAA7QTJdkUOTUSwHKpgMhLp2zVqRhiZSjSy7mtAbGechfRNYEJk7DWSId95bQXjVMxwtB3D7jm/TZ9jnfAd4eUs9F52UuNPpCvW46U91xGPZGS7q9ISJHztjBma09tSE0dOu1+5mwYzhHDUog8JNv2d8wQJyRRkjGl7kv86FjNe+BtRK6QOOv3OjfI0n1n2Lyz87jdvPzW81sEvReRav3cPS1//JLxwvcdEOJcVo7TN+8M2t1O9cxfPOP/J5eCQL/L/ETYDr7Uv5of2/ANzheNE8fld4EKO0AwBc4Lubz20TOHN8DtkZrh4VMBt0NcJ7A7hc//1yYGnzA6SU35dSDpdSjgB+CjxnlQu6Vm9WYuiTO0sgFCZN+MzmJ1a8qX9RpoJiI9NSWFyJXdcY+RNdgGSQNUT9rPy6xa60JkGS1Smv85ufoZuA1dv3Wu6anZSbxam+P/BUcC6gVhuaX0MOm2Z+d/04Igq9jhQacJpZ9XSbkpAZk9qErrroBYKbCz7gEvu73OZ4SZ3DgU8A+MW/d/YY3WkdLvqKGkDy+deqIHBSbhYuo9GMdFrdis5y12s8yM/zsuBbZ+A861ecZtvADZkf8x3//eb+IaKM6/z/x+7wYO5yPM8HjpvY++8HuOeVtR0WEBYUlfOX93emCg2j4C8f7OQ+x9NM03YyRuzjkeVftDhm8do9fPTRuzzl/D17ZQ6X+39BDR7sfXI4WiuiXPbhG75HeDZ4BgDPBs9gn8wmKDWuDv2MccefwUs/mskTl03n1+dN7nFBM3Q9cH4QOEMIsQM4Q99GCDFdCPFkV08u3tTqtlZtNeGIln7p6oZnZEeteFM/pNdfGRnn0mofDQ3qwYRX7htkGoHzvha7PKKBBl0zbnW8Hqf5GToIWL19r+Wu2V/MnUAVfdgt1Wq1m0DEqk1BUTl7DhximCghIG2EsFlevtMZVMZZXZ/OJhrchJM5GPoMYpR/e8TDs22b9N8Ev162JfHn1Qn6UM9gcZhLbcs5UO3j5n9sYHNxZaOWHIdlreh0LHe9xpXjr6YqdxY/9j3JatePI3Y97vwTFaRzV+ByCsMj+bn9H1y36UJ+/7dnuf21Ta0GxgVF5Xz38Y957Z13mZ/yie6Qqno/+6XSGeeKUkpq/BGf6+K1e/jb0nd41vlbqkjnEv9tlJPJ7LHZnN2wlJnaVp4Mns2fHY9xuX05ldLDIFHON2yFvDb8Nq656oYeGyw3pUtRiZSyTEp5mpRyrP7zsP74OinlVa0c/4yU8sctXyk51Oq2Vq0Z5MdCdUOQLFFLlfSY21YjK0M1WDAyLQC79kd6mSa8sjzNCw6Pkmo0ITvDRQb1VOsTGwtXvAMqe29kBl0iYOniQCtes/l5XoZ408zvoVtEZpxveKGAV5z3cKH9wx6xAtFZfNJh2tE11eAmhdypTGJXm7tr/V0vqo43FXV+Bgh10z/XprLlb28+QEm1z6xt8WO3shWdJa/XuKJpvDr8dgrlCD4IH8vN/ut5PtioZJlr+4x77M/xUuhU5vnuo066eN52H/Z1T/L9J1e3CJ7veG0Tl9neZoXr55ykbWLRyl09ZrUkGQzt66FYD5wHCxUf/PXDLwE1Cbl/6Xr+Yn8UjTCX+G/jAP3J6+eh/67Xucv+PKtDR3OObS1TNPWcLFHHWbZ1/Em7lO9e+fMeHzAbWD+dF0cMTXI6XRs4tx+sZiDllJJlbluN0YNVQY9xQ66o85Oud7qrkWnJqSwXArKGQmVkc5NJuVmkiwaztbLFl1JVcaAe4Dj1TpQpYmPi4Ewa9PbSLvyRnrvVPiboWseQPmRZfTLVGZpqnLWQz3wsKQyZxmgROaE9KPvyUvAUALwe6xdn7i6tJaDXHhjfmz66dr5xYnLkTsR6KpMnHM33w/dxa0AVkN0V/EFEp0FNSP7seBSB5Fz//XwYPob7HM9wP4/zl3ciM887DlVzmU059B0tVJHa3z/endD305MY1s/DPqlihSFCuehs3q86ti5Zv5eF2gscrRVxa+A6vpKDsWkwrnIVv7cvYkN4DGmiwSwGNPhr8Bz+nX5BYt9InOm1gXNFnZ9a9Iwz9XxxoPPBrpRhhohSiuRAAEJJr1VuSeFBJUcxpBr7KhvMFtHVeJJXWZ45pEXGubzOTx/qqSENja61Q08EE3OzTGcSKxQH9kSuOXl0RPe8jXsrW1167avb1Vl9MhUrORmuCDu6oF9dr8ZKRsInCkYjlCak4TMD+W9OHtxiv9Wo9YdaSL2M5IBL+AlLQQBba09NkUTy87y89KOZfH/GcM48eiD9M9y8HJwDwOLgqQA4RYgnnA+RKeq4JngrfwzO5wLbSn7y9Y38/Mk3zLFDE8LsdDdR+wpIoCd6DyQnw4XUUz9TNd08IBhi8do9lK97lcvsy/lb8GzeD08lu4+TuX2+5DH7n9guhxHAxmQROSl5P3QsDwYv5oezRiX8vcSTXhs4+4JhavWMpkc0UHS4rtNLONmiGpuQlEh1M7db8FM9oOINM9NS2xAgQ28RXa1LTJJC1pAWGueZo/qTqTVQSxrOHuDtWFj8/9s70/AoqqwBv6fXJBBCIIEAgbCDArILKuAGuIyjuIwiriOK+8g4+omgjjou6LiNywwoLrgALgi4OwIC4rAjO7InEJawBZKQpDvddb8fVd3phAAJWbqb3Pd5+unqW7eqT92qW3Xq3HPPORySmTGyXTUilZ5pifTtYCo1sXjw+YxgMHyHTfCpkp1qzoayggtEL52aJlCoXGW6aoRjNOjen492xYjFGxyli4YXFxvgsFwyAomucjzmd0NyOUQdIj7PZy0lkCTl7Zt70aN5Ikli3lOn+fvxb9/lACRLDu85X6SOKuCbxJsZ7n2IFrKXL2yjmfnNZDPJV7yb6X4zBMwQy11Hn/Jj06lpAo0tF42+tvX0lA3sz/Xy7vQf+I/zlWC9Fxxv803RHbzpfRy3FNFAcjjTtoGVqk2wzlojjTuK/sYV3VJPuegnEaji1QzX9W4RYnE2h0Xfm39sn75jsSwjmwaYnXq/pTg3rhdTRVJWHYbYKVJ2Yq0Hc2GRQb2gxTm25hIslKZeKuRlga/YCtAzLZFODYXGycl8cnvfiPeL2p/rCbpquKWIzRHoqhMNpCY3ACBGijAgOMny7DYNOUC9EnWXbz+1Zsiv2XW4hKtGaOi9cIwGfb+1pOLsxIdT/MHIQRE+oQ4Al8PGVP8AoPjevD/XQ1K8mzTZw3ZrhPBUdPs5lUiKd/O7akGeiuFz99N0l80cUmZegva2nYx3vsqO/YeYo3pwufcZslQiD+8dzbJ37yfO5mO5ah/clw2DIwWRNwcpUliz6zCN5RDbjWT2qvo843yPB+xTmeV+OFjnVvuPXOeYQ4oU34MDyz0sK3WOiuNq75PUj4/jtaFHj15FO7VWcR7WpwW4zM5X17K8HiosOt4mZTJ+7pbg23Dg5nx6BFpjDuV7ySEu6J7hMwziJR+PcuDBhccXpsk+Cc0ABbm7SxTHqXxaNU2JeKUZzOGt4ORAiliaka1DH50E2UXmsLkbL0Kxi06f1g05qErG2j2UX/G+GslszsqlICSqRiBzZqFyRUQEkeLkM6aSGckT6gJc26s5H/kHkamSgkYSMK33aba9pKvGuOwSyVkDNZjna6F042zPm4z130Cn2P1Bly2As+zr+KdjHDGqgHSVwpXep5jiP48Rtq/5+MgI7rB/wxbDdC260jafAp+ho2scAwFS5CDpKoVXfVdzmm0Hf3VOBeBz3wDO87zMRP9gilRJF6d9qh6bDDNK1majKf09r1GImwcHdqjpQ6gRKptyO6rxOuPBBwnkndT2yzKymb1uJ7+4xwOQhank3XVum+NtFhY2ZOWSo+KCKY19BsTb8smjBhMrlEUgJF3OTkhMKy735IG7bnhkqiBX9Uhl5ZLA5MAiDGVOpIgGpT+SqBNnugy58KEotjiHhvsL4DMicCJBJdh5qIAC3MGETMHMmWFKANSwrovueeP43v0oKZLNUPtsoDgx0Y6D+WGRqyKMuvQ0fli7h9zcWOIsxf+ndVnc1a85qbYD7Em7gskXnqX7aYTTMy2RySPOYuHWA/RtPZB6qf9iwriXGZj1Hi1tWQBcbl/A5fYFLDHa86vRmWn+/mxXjRnlnMIY56Tgvl52jePHwl6Mm7eVQZ2iwzBzLJZlZPPC9+tZvycXZShrlM7Jvee3O2nXiE5NE2i8IptNRjMm+y+gHvk86pzMPH8XdpHEN64x1A2JQpZhNOLBortZpjoAii6yje2qEYepy6DTG59yLhoBarXifMjvxqdsQWWyoolLxs/dQh/b+mDYlsDkwEjsjIcLfGQ4U+gumwEFCA0kl2zLkufzhSlpS4Jl7SkVWQNPLrjDm9GrvPRMS6Rd0wawn2CYq/1RYJGLNLI9pvOhi6ISFufsfC9uq13v9P41XOJVLyLkW24QMXiDFt5CXGFxJfjroA6MnuZlou8iHnFOCYZzCyjyWTlhcu2qII3i3RTkuulp28jnrie5xzuSv73zNT+7DXr36AUReK/WHE3PUu5K3S+7k8Fvd+FS33xec/07WN7btpHeto2MdHx5zH294XyD4UUPM37uFt6+uVe1yl1dLMvI5oFxM3jd+QYGNjJUIzYazZl1uDujpxXwwo/rMfwKn6G4qFNKud0l5vy+m2s5xB4acJ5tBX+x2rG7bTMDZDV7VX2cyodbfEzync8zvpvIJwabwOVdm/HDWjtFPoMYhy0iDYhVRa1WnA0l5BBHAqbibFRQd9y6L482VvKUL/39iPRZB4uNjpznXEkMXgxsXGpfHAzzk+cJk99XMAlKiOLs84LfA67oUJwBYmNNt5/ApC5Nxalbx2xDlxxtcXZRxDf+vvxo9AagW2rkuUNVhmYJMeTnFM+5CLhsFBKezHbD+rRg+m+ZFO4wLf1zjK50smUwxW+Go2vTKDpGg9buyqFAuUmy5ZAkOZxmy8CGdaNv0Cq8wmlOGtMKfTZfLm/BJUtb8r3z/46qM853GZP9F5ChGuPAz3r3n8kjlvPtKxmtPuHZdTcxadH2qLSKTl2eyTD7LHrYNrPEaM9ZtnVcbZ/Po0xmm9GYed4zSJX9bFZNeX7FDQDHVZ6XZWTz5fJMcn7/BYfb4H7H9BLrA4EEGskh9qkEHvHewWyjB3abMKx382Da7GUZ2dbIQMOINCBWFbVacW4Q5+JwXp2gxblBBX0JzTTApsL5lu8KAFLrR97EQIA4l50cvzkUHk8+D1hvkr3FTKkZLoMz7roQU79kSDqv5ToTJRZnAJ9YrhqiFeeTZd3eQoZQMi08mBZnFz481u0qJd7N9Pv6hUvMaqF+nCtocY6TQmLFg0c5MLCFbSLekO6prNphylTfMi4ELM6jLjktLDJVlMIiP3scDYK/Y/GSYo0Q0uDUCpFV2whYoUcDQxc/zkTX82SqZGYaPbjGPo+7HN9wjm0tsxOv5cPDXdlmpJChUtihkrnd8T2LjY48PkPokBIfdUrebxnZ3GZbynx/J24sGgNACgcYaF/OQNtybrTPxC6KpsYBngd+XJt1zH1NWrSd12f8wt/t7/Ose0mZdX7xd2aVas1aoyXzjc7kUBe7wD+u6FzixaP0yMCpSq1WnJ124TB1ghZnp71iFmOXw4ZbilPjOmwwf9SFVS5nVdCvbRJ5v5v+zHWlkDZiTsa7rejh421WMySklgxJ57GiUkSR4ry/0Lx2Ar6pOlZoxdl7xPRbDri7TFq0nZkr0zHEwTVShNcwX07+MrD9MfcRzQQmsMXhIQZvMGZyuCbirdl1OJiUpr7kBtOdn9kyeh6OyfFucvOL53HE4CFNsigghtg6yWGUTFNVXN0jleuWnM6DRffwuvMNzrKt4x7vSNrYdnFfzA88cPgFbo9pQp3C3TRUOdzlGck5tjWMckxmtrd7tblsXPfGTBbv9CACl3dtWmXRJZZlZOPN+p227l18WDQIAJtAtj2Zj32D+Ng/iHvt03nY+RnP+kxrcx330fHKl2VkM3XmL/RMH89C1/wS6x4uGsGN9pm0l0yu9z7GCtUWAIdduOD0RiTFu4NW5tpIrVacG9RxkXOoDvUCFuc65bPBE8JVAAAgAElEQVQ4j/1uPT+s3UP9WGfwIe9RDm7vH9kWjMBEwLoUkCSH+d7fmzlGNyDMQ9/1mkGO6arRecx0+qhVvOsiaiYHAuzON29MgUlIB49oxbmiBK32FPGM413a2nbRl/X85OuBy+bDi6OE7/OpRHK8m+0hCZli8QStu+GaiLc/14NYynsieUFFPhKifJSXM1Lr49xYHDEoTkzFOcvRlJYS2a51mvLRMy2Rp6/ozBMz4PYiN/9yvsWn7n+w3GjLCwVX4BUXI2N+pgO7aSi5fOr6Bx/5B/GM832ut8/m43WDq9xl40/PTeJz790QY87LmL7CdDGrCuV56vJMrrb/gl8JP/rN/T4zpAvD+rQwrcfT5nCvYwaz/d2CyV+6t0gMRnr6cnkmhZmr6L/3Y56z/4/QHEDbjWRuLXqExxwf01m2MaLoQVaotpzZMpG2jeNrtbIcSq1WnAFyqEMz9pe7/tjv1jP3l58RXKxQTehuN4eVvTiJj43sNLQBxTle8kmVfcwzzgDALoR16Hv54Tja7E2n+6hv+adzAlfbfwHg1knr+eCZK8ImV4VwmCHpAr5gTnutjfR40hRJcUi/Gx2zguWD7MvJV268OEv4Pp9KXNUjlWeXBPpnATHiDVp7wzkRL5CSOlHyKCSy729lsWrHIQaHuE/FWRbng642tAyfWJoqZlifFnRIiefpr+txVmZHrrHP4xb7j/zL9W9WGa34w+Gn6WXbyBTnP+hl20g3MeMN/8P5AdP8/Xhixpoqc9mYtGg7sXnpBLK5j3e9SofCD5i+Yhcp9WL4Ye0eLu6UwqhLT87daU16Fg/ZZ/NfoxdZNOC0lPig0p+d7yVFsnHj5TzbSiY6xzLJfwGz1/Xgl/U7uEQWM9Qxmz623ymdNPPfvsvZoZKZ6HyB5rZ9jC4azs+qJ89d2SUq/cCrk1r9dD94xEuOiqWe5Ad/n4j3/5fO9+5H+dn9NxqQE7Q4e3FE9AM9Od5NnjItWu0lk1jxslk1BSCckb3GfreemTudJJCLC09QaQY45A9zqLwKkFNYRC6x1LXiZOcUFrEsI5u3ft58wpjOr361mNvHvstL3/xWE6JGLErs+JSNBpJz1DoXRcG05tGQfKOi9ExLJKmh6TpQlwJi8VJgPXnDmVApkPAkQfKCSnQ0JQzZl+fBGxJztg6FNJe97JDGYZRKUx30TEvkiT92wmOPY6L/Ii70vsSjRcM5w7aN5+wTyFMxvFJ0DQC/Gp2D262JuZ3zWMrCLfuqRI5Pl2wnkZJJsDbE3IodP4d/fQfnwY2Mm7eVkVMqdr9flpHNnRMXc/mBCTSQPD70DwagR4iy37d1Q1bTln6e13ndfyXtbZmMd73GEvfdLHPdyauuf5tKcwhz/WfwuW8A19l/5nnnu+QQxzDvaKYYFwYt2ZqS1GqLc4M6LnKy6wSTgpTHVcPjM4Kt9rxzAmuNlgAU4YzoIeSreqSywLJoPen8ECAYsDycfLggnYuUmVK7qRwosS6PyJxoWRZ5Xh+5KjZocd55qJAHxs2gIYf5p2rL1LvPDlozJi3azvdrdrNtXx47DxUyyLaECa5X+cOvz+KzxZy0JSLaSY43rcq3OH46ap1DDLxWkplTK4JzMVle8/jqSgHJcigYKrJ7GIdGA1bmRPLYqZKA6Ei3HSA+xsGLBUPJoQ53Ob6htW0XbvGx1dco3KJpqoGeaYl8OuIsvlyeyaasXKakX0AX2cqf7PO41jGXbGW6/zWSbDoXTmBNzO0ATHC9zM5fPyWj6G7SLhgOzuMbbQLRIxLjXKzddZh9VkbKq3uk0rheDJt2Hp1UZ0vMTcHlCzwvMX2Faaz7cHifEx7XsoxsXpowkUflA85wbGOibxALjNMRMZ/tocf/jyFdeGz6al7zXcMbvis517aS+x3T6W5l9Qvwq78TBsJZtrU4MJhl9OA9/8UsME7HJqKV5uNQqxVngFwVR4wU4TqJMGIpcpBN0gyfsuHDHtEW555piaQkJUOIMW+l5fDfv11SmKQCv4JtyszqdJ395xLr8lQsyzKyo8KnyjBMV5i6FHCWbS2POKbQzbYFgJaFk7j3k2UsHD2QIW/OZ33mPtpLJvWBFrZ8zratBczMkx8sSK+1inOnpgl4VjiDfuKlCaQ1jybFrSJk+80XxXjyaSV7+M7oE/bMdgE/61jxkqvMqDxzNuyNmgfq/118GqOnrWasbxjX22fTUXYAoHQoulOW0MgOkxZt54kZI3jJN5RLXcsYXn8FiTmLOc22gxXuEUzxncdQxxwAmvkzYcEYche9yOLkq0g6/166dmxXYt/LMrL5ctkOflu2gAtkKefYlnKZ7KYIB5/4L+TuxRfRsHEq61UaW4wmeHFgQ9HBVjJPwWz3Q3QtfJt5m+DmdxcdV3letW4dnm9GM9n+M7tVAx7w3sMM4xxAuLN/66OejwG3lVe+W0nLvbN4wvg3bikON7vGaIkXB+fY15KrYvnYP4gP/BexXTVGAIdNeLpUtAxNSWq14nzwiJfcgN8v+eVy1QjGAAUyVRIufHhxRsWkpT2eYh/F6f6zKbJOf3neeKuLSzqnMHuFOaz1J/vcEutyqMMdE5ew/InB4RCtQpyWEk/ezjjqSgFvOf9FAwnNRqnYk+Nh5JTfaLPra6bHjCtzH/tJwF8UrriA4WfNrsMMIIbEY2TyPGIpcaeiqwZAPjEYSmgu+0iUPDJtTZk8PHyZ7ZLj3fxOsTEgV5n3yrW7j3aliVQCD//35m8lPyeGjjZTcW7d4YxwiqWpIQJKpBlb+CJapSXy1ITP+HvmHTjECCrNocQbh7kw632Y8j7fuy7iM+cVeOo2o6s9nYY7fuJ2WUIrpxne7TejLVP9A2gm+7nPPoM77d/y5f5+TJBLmWN0Y7jje67wPM1LzvG0s+0s8T8TXS9wvXcM8zbtL3Ny4vItuzn400ucvfsjbBj8y38lb/v+iNceS9fUelzXu0WJbQKxmG0HNtLn4Nd8kj/NXGHNgS1QLvJx09mWzlYjhaeKbuJz/7nkEYdd4K4BrYmPdZ7yMZirAq04W1aUepLP5n1Hjll3WUY24+ZuwU2xcnxY1SFZDlGAKyomLeX4HfiVYBcVHPYO91vla0O70/V3MzTeAZVQQuHMJ4b8/OiIizykeyq5O2PpwI5SSjMkkks29Zi+Yhfpx1Cas1R9/KVna9QyNmflmsqZQL4y00+HErB+Lj+Bz3i04nY6yCuK5QzbVgD2xzQP6wPsqh6pzFy8Mvg7B/Ne6fH5j7VJRDKsTwvW7DpM/m9uEPAqO0sOxjAk3IJpaoTSsYU9DU/j/7bdwYvOd0647SXeH7nE+yNWxFq8NjsLjE5MKPoDM/09yaJ4v61kN8Pt33GNfR7XO4pHTwfalzPMO5rPXE/TylYcT7mLbOVL198ZUfQgj1n5Rob1acGy9INs/PkjBqS/Tg/ZzzdGH8b6hrGLZM5pl8TIge1LHE8g9XZG+hYmul4IvhyWxkURvxhd+NA/mF+NTihs2AQGndaYu85to5XlClCrFec8jy/ot3eV/Rde9l1L+zHfsfHZS0vUW5aRzahxn9FUDrA+5oVgeXPZx5m2DXzhH4CNyLc4I0IesSSQHwwtFQnD3h1S6pO7O5YkOdqSGC0Bo9bsOkxPYkmz7T1qXZrsJVvVO+72M/09q0u0qGHr/iPkWspZLrFHuWx4rJe9aEn3XFGaJcSQmx9LFzEVZ2+98LoT9ExLpHlyg6B7V56Knsm6pdmfGxLeTzViX16YMqVqws7VPVK5dvH5LPZ0ZKh9DtfY55JUxoTkssgjlqn+/sy0ncWQPi3p3DSBtbsOsykrl6UZ8JhvOK/4/sSN9pk86PwCgPsd0/nR34sbvGP4zP00qWJG8ZrqH8Bg+1K+dj3GI0UjeGyawf/mz+amw//hetvvrFNpPOi9m0XKdN0TzOt43NwtxBgFNPVuo5l3G0bWWt6yLSA5puxj2K/q8an/PCb5LmQnyQhgswkXdGykFeaTpFYrzn1bN2Tepo7sVA3pYPm+ef1HTz168NMVzHUfndKzv30NhhLe8V+Ky2mjb+uG1S5zZcklroTiHAnD3gePeMkjliaBjF4h1HFFhxV2c1YuHY6hWEx3P8Hlnn+UsCh3LxxHNvGkx5gB6mf4zwbM0IC1FY/PCI4A5ag6bDKa0c++lln+7tSRQuYYXYHoSfdcUbJyPDSzJsj6lI01R+qHWSLYcNAffErkW5N13VEaajHfUpwzlI6oUZsJTKB7fIYw1nc9L/v+xCDbUi60L6dAudmlkjhIPC1lD/1tq+lkywhu20DyeN31Fjmuz6nX6H7oegtYo7YBV4nlGdm8vucqxvsv4xr7PDrJNjarZhTiZph3DFNdT5Ish7nWMZfRRcO52f5fxrteNf8gF7DBa76rGO+7jN62DTxin8zV9l9oJIfgEOYnlGM8IlcZrXjfdzHfGn3B4eaaPql0bppAdr5Xu2NUklqtOH84vA8tR33LBqP5UREdQtl1MIdjBXj4weiNI7kdn1x9RlRciEdUDEjxbPlwZSULJa/Qh4OSw797lNmWEiXP6OXbs7nZbr7x/+LvzJ1FD1KEg00xNwMwwvEtl9kXAvBE0S1kY1qgOxa+jw87PqsrdmkW/hGAcNEgzkV2rhlJIoc4xviGc66x0gq7VPxGES3pnitK5qH8YOzXHSqZ9MPhd1PK9Qm5djNaTCCzYbP60Wl5DoTW04qzJuD7HIi+8UNGX74r6ntUvQ/i3TjyMvmDbSGPOicHy+t598JPj5ufvvdC37vomdaixKTEx6ev5hP/wOA2Avjrt+Ti7LHcYJ9FJ1s6vxqdqEMBY2yTSvzvSMeXjHR8WeHjKlJ2vjP6MNE3mOWqHS6HnWt6p+rEJVVMrVacAdwOG7tUQ7paERDKIhCbtyze9l1G66Q6UXFR7s/10iHGnN2rIiiE955cD8kxxZbvG7yP8rthvsWflnJ8F4dIwWfA55xHB8nk/qL7g9a5qz1/Z6r7qRJuKGsMcwg+3m0nz+MOhlfrlpoQ1kQ04cZpFw5aIdiyVV0yVAof+lOC62McNj65o29U9LWTQSmY5+/CAPtqMlQKKgLi7sU57eQSSzwF5Ftx4KMpc2AosZbPvFacNVDS9zlgLd6UlcvOw4XEOmzc1q+16XOckc34uZ1pte6PXGWbx8uuUvNUFr5lftL6weCnoVlPhvUxJ+6NnPIbM9dn0Twxjmeu7MKGPbmMnlbA6/6r6GZsZoLzZdrZduJXwrv+S2kqB4IGlgDjfX9gmr8/m1Qz4sknQY5QnzwS5AgvOcfTSA6x2mjJc74bWGm0IZ8YXA4bw3pqhbm6qPWKs9Mm7PIn0VByec35JiOL7mPQy3P46W/nBesEEqSE4lV2tqkmrFBtuSGKEgIESBUz2PvGrNwT1KwZVhmtOMO2DYBfrTShdhs8EiXWxdT6Mcw91JW53q7BsliHjWW+Dkz19w8mdnmu6HqWq/YA3NAnrdaGniuLBnVcHDxoKs6ekCx17ZLrlOiPpypOmzDH6MYA+2oOEo/TFn6/nc7NEqi724xNfhDz3ERTApRQkq0x7nTVmOhU/TXVRekJhKXXvX1zLyYt2s5j06HQ6+IVZ8kQbwBkzId3LjCXU7rABY/z2oWtWN67K1+s2s8bU2eRdmAe013zg6FKA9hFMcLx7VH/fZFnLBuUaURqlhhLs4QkEmKdGEf20XLntzSSQ7zvu4infLcAMKRbU9o1jteuGNVMrVecDWCnlYBjiP1/vO27jIL9bshpbwZBdxYnSAnlnqKRzDR6IJQMQB7J2ASu945hsutZCqx0vrsOFYRZKlPBvNH7KKtiRpQo/+zOs6Om888fdSH9xs4i81AhInBF16a8NrQ7HR/7nqn+/nSQHTjx8ZF/EGD6MmuluST141wcsFxYYq3oNUnxrlqhNIMZ01zEDEeYp2LxhTOlZwj1rKQ+vxltEQhrXOmTZcfBfJKtUZ/tqjHug8ceRdRoyiIQgeqJGUIPTzdaOw/w6uD6tHXsh00/wtY5xZX3rIZJ1wLQw/oAlM5a/6W/H7/6OxMrHp50TERQPOe7AUHxmPMTBtqWc6X8ysXN/bR0HYacnbBvN/g94ISf/D151mfOk7lrQGv9TKkhar3i7LILh33Fk42+c482F175W7Bshquk5edu7wPMNHoAwp0Djg5AHqk8M6QLo6cp/uK9l5mGGcUhIdZ5gq2qn/XPXELLUd/yaNHwEtkMo6VdA8wfdeFRZZ/c0Zer/2Nwmfe5YJkAW57/Qw1KFh0kx7uZa5wOwHLDTDxQPyb812dN0aCOk81HTKV0gXE6fkXYEwCFxrbfpJrRtH5M1PVLMCOxHFD1qCuFZKpk6p6ikVk01UvJuNANaRvoC2fdY34rBbtXwsQ/gufEkTquss/nUtsiAA5Sj3u8f2Gp6sjd9q8AeNj5GV4cuApTwdUMUntDvaZ8vU2Ylu5grtEVP3YGn95YK801SK1XnJPiY1hQcDoTfYNYabThCLHE4uG1q9pDUT4LN2SybHMmPhxsMZqSrhqzSrUBYEC7pKi6WIf1acHibQeYvuKcYNlfB3UIo0TFdEtNYHLmhSV+nwr0TEtk6t1n8+CnK9h9uIC+rRuGNeFMJHNVj1SmLNlO78K3yLbcAm7r1zrMUtUcIwd2YPQ0LwM8rwazeC3ceiCsimrr5LrcdGAUjTiEwkanCAhfeTK0bVSXGzLGcLqk48VJ21M0Moum+jmeWwci0LQbPLqDmyYsJHXbZ7SSPcw1zmCl0YY84nBRRKrsZ0ByHqnsxX8wnboU8pbvCnaRhADv+C9lttGNfao+Dw05m2F900r8TdOMbOa/sxDDMHA5bNx5bpvqP3BNkFqvON92TitGT8vj774/B8sGtEuCXqZy42yazRtbFlJYKqPbgHZJUakAvTa0O2e2asj3a3ZzSecmYU+AEmD6ff0Y8uZ81uzKoXPTeqfUJLmeaYnM/b/zwy1GxNMzLZHP7jybcXO3sDen8KjMWKc6w/q0YPuBI7z9izkq4Y6AEJd3ntuGoRv2UuRXOO0StQ/oRy45jWvHHyLTSMZuk6iZO6GJXi7p0pTRm4uNQS0bxpFiE1on1+XOc8+lZ1oiyzKyuWHCQrw+A7EJg6zYyhv25PL9mhRuOcYzumdaIpPv6Bu0fEfjKFA0I6oSU7dFpAHwKdASSAeuVUodldZLRFoAE4DmgAIuVUqlH2/fvXr1UkuXLj1p2SrCpEXbeeGH9Rzx+Dm7zdEWwWUZ2SzceoDcgiLW7s6JKIVTU3sQkWVKqV6V3Mcp0WdPZQL3m0h5IEaaPCdLOI6jsn1W99foZtKi7Sc0Up0q/etUoLz9tbKK84vAQaXUWBEZBSQqpR4po94c4Fml1E8iUhcwlFLHnZ2hO7VGU5IqUpx1n9VoaogqUJx1f9Voaojy9tfKBvO9AphoLU8EhpQhyOmAQyn1E4BSKu9EHVqj0VQbus9qNNGD7q8aTYRRWcW5sVJqN4D13aiMOu2BQyLypYj8JiL/FJEyk0SKyAgRWSoiS/ft21dJ0TQaTRlUaZ/VaDTViu6vGk2EccLJgSIyE0gpY9WYCvxHf6A7sB3TX+tW4N3SFZVSbwNvgzmMVM79azSaEGqyz4rICGAEQIsW2u9fo6kour9qNNHFCRVnpdTAY60TkSwRaaKU2i0iTYC9ZVTLBH5TSm21tpkO9KWMTq3RaCpPTfZZ/bKr0VQO3V81muiisq4aXwG3WMu3ADPKqLMESBSRZOv3BcC6Sv6vRqM5OXSf1WiiB91fNZoIo7KK81hgkIhsAgZZvxGRXiIyAUAp5QceAmaJyGrMEKXvVPJ/NRrNyaH7rEYTPej+qtFEGJUKR1ediMg+IKOc1ZOA/dUoTkXQspSNlqVsKiJLmlIq+cTVwoPus1WClqVsolWWiO2zFeyv5SGSzlFpIlk2iGz5apNs5eqvEas4VwQRWVrZ+LZVhZalbLQsZRNJstQkkXTcWpay0bKUTSTJEklEcrtEsmwQ2fJp2Y6msq4aGo1Go9FoNBpNrUArzhqNRqPRaDQaTTk4VRTnt8MtQAhalrLRspRNJMlSk0TScWtZykbLUjaRJEskEcntEsmyQWTLp2UrxSnh46zRaDQajUaj0VQ3p4rFWaPRaDQajUajqVa04qzRaDQajUaj0ZSDiFKcReQ9EdkrImtCyj4VkRXWJ11EVljlZ4aUrxSRK63yDiHlK0QkR0RGWuueFJGdIesurQpZQta3EJE8EXkopOxiEdkgIptFZFRIeSsRWSQim6z9uqpTFhFpLiI/i8h6EVkrIg+E1A1Hu6SLyGprm6Uh5Q1E5CerXX4SkcRqbpcav15EpKWIFISsGxeyTU+rXTaLyOsiIhVtl5qigses+6vur7q/RjnlPS4R8Ye011ch5eXuR9Uhm4h0E5EFVp9aJSLXhaz7QES2hcjdrQpkKvN+ErLebbXDZqtdWoase9Qq3yAiF1VWlpOQ7UERWWe10ywRSQtZV+b5rWH5bhWRfSFy3B6y7hbrOtgkIreU3rbSKKUi5gMMAHoAa46x/mXgCWs5DnBYy02AvYHfIfXtwB7MoNYATwIPVbUsIWVTgc8D/2H9/xagNeACVgKnW+s+A4Zay+OAu6tZliZAD2s5HtgYIkuNtotVlg4klbH9i8Aoa3kU8EJ1y1LT1wvQ8jj1FgNnYWb/+h64pKLtUlOfCh6z7q8Vk0X312PIUtPXC6dIf62KT3mPC8g7Rnm5+1F1yAa0B9pZy02B3UB96/cHwDVVKM8x7ychde4BxlnLQ4FPreXTrfpuoJW1H3sNy3Y+EGct3x2Q7Xjnt4bluxV4s4xtGwBbre9EazmxKuWLKIuzUmoecLCsddab/LXAZKtuvlLKZ62OAcqa5XghsEUpVeHsSBWRxSobgnmC1oZUPRPYrJTaqpTyAlOAK6ztLwC+sOpNBIZUpyxKqd1KqeXWci6wHmh2rP+sTllOwBWY7QE10C6lqLHr5Rj1mgD1lFILlHkH+JDi4y93u9QUur9Wnyy6v+r+GqGc9HFVtB9Vh2xKqY1KqU3W8i7MF/jqyuxY5v3kODJ/AVxotdMVwBSllEcptQ3YbO2vxmRTSv2slMq3fi4EUqvw/yst33G4CPhJKXVQKZUN/ARcXJXCRZTifAL6A1mBix5ARPqIyFpgNXBXyIM5wFCOvvndZw09vFeJ4bMSsohIHeAR4KlS9ZoBO0J+Z1plDYFDIfIGyqtTliDWcFB3YFFIcU22C5iK039FZJmIjAgpb6yU2g2m8gA0qgFZAtTI9WLRSkR+E5G5ItLfKmuGeS0ECL0uqqpdagrdXysnSxDdX4+J7q81T3mPK0ZElorIQusFCKq2H1VGNsB0H8O0Zm4JKX7Wun5eFRF3JeU51v2kzDpWuxzGbKfybFvdsoUyHHNEJUBZ57cqKa98V1vn6wsRaV7BbU+aaFKcr6fUTVIptUgp1QnoDTwqIjGBdWL6Tl2OOcwX4D9AG6Ab5hDNy1Uky1PAq0qpvFL1pIxt1XHKq1MWUyCRupjDnyOVUjlWcU23C8A5SqkewCXAvSIy4CT/sypkqenrZTfQQinVHXgQmCQi9aja6yLc6P5aOVlMgXR/1f21hhGRmSKypoxPeS1+YLZXL2AY8JqItKEK2quKZAuMFnwE/FkpZVjFjwIdMe9PDTBf3CpDeY63Ju45ZVHu/YvIjUAv4J8hxWWd36qkPPJ9DbRUSp0BzKTYcl/t/dJRlTurLkTEAVwF9CxrvVJqvYgcAToDgYkrlwDLlVJZIfWCyyLyDvBNFcnSB7hGRF4E6gOGiBQCy4DmIfVSgV3AfqC+iDist8xAebXJopR6U0ScmA/hT5RSXwY2qOl2UUq9aQ2ToZTaKyLTMIdm5gFZItJEKbXburntrW5ZrPU1dr0opTyAx1peJiJbMH3vMik5HBZ6XVS6XWoK3V8rL4vur7q/hgul1MBjrRORch1XyPWyVUTmYI6YTKWS/agqZLNeer4FHlNKLQzZ925r0SMi7wMPlbV9Bcik7PtJWXUyrWsvAdOFqDzbVrdsiMhAYAxwrtUPgGOe3y2lt69O+ZRSB0J+vgO8ELLteaW2nVOFskWNxXkg8LtSKjgsJubsXIe1nAZ0wJzAEuAoi5fVmQJcCayh4hwli1Kqv1KqpVKqJfAa8Jx1g18CtLNkdWEOLX5l+cP9DFxj7eIWYEZ1yiIiArwLrFdKvRK6k5puFxGpIyLx1n/XAQaH/OdXmO0BNdAuIdvU2PUiIskiYreWWwPtgK3WjTtXRPpa5+tmio+/KtqlptD9tZKy6P6q+2uEcsLjEpFEsdwcRCQJOAdYV4X9qDKyuYBpwIdKqc9LrWtifQumf/TJXD+hlHk/OY7M1wCzrXb6ChgqZtSNVpjX3OJKylMh2USkOzAeuFwptTekvMzzW4WylVe+0P5+OeY8EIAfgcGWnImY96sfq1Q6VY0zIyv6wbwR7gaKMN8ahlvlH2D6RIbWvQlz0sgKYDkwJGRdHHAASCi1zUeY/pWrrJPQpCpkKbXdk5ScjX4p5oz4LcCYkPLWmB1hM+Zwo7s6ZQH6YQ5XrLLabAVwaTjaxTr2ldZnbal2aQjMAjZZ3w1q4BzV6PUCXG0d90rr2v1jyLpemDfsLcCbEMzuWe520f1V91fdX3V/rY7PsY7LaocJ1vLZ1nlYaX0PP5l+VE2y3Wid8xUhn27WutmWvGuAj4G6VSDTUfcT4GlMZRTMidKfW+2xGGgdsu0Ya7sNWNFaqvhcnki2mUBWSDt9daLzW8PyPR/SL38GOoZse5vVppsx3XGqVDadcluj0Wg0Go1GoykH0eKqodFoNBqNRqPRhBWtOGs0Go1Go9FoNOVAK84ajUaj0Wg0Gk050IqzRqPRaDQajUZTDrTirNFoNBqNRqPRlAOtOEcQItJQRFZYnz0isjPk9/+q6T+7i8iE6tj3ye+Ly5MAAAOkSURBVCAiH4jINcdZf5+I/LkmZdJoNBqNRqOBKMkcWFtQZiacbgAi8iSQp5R6qZr/djTwTDX/R1XyHvAr8H64BdFoNBqNRlO70BbnKEFE8qzv80Rkroh8JiIbRWSsiNwgIotFZLVYOeOtbFdTRWSJ9TmnjH3GA2copVZav88NsXD/FpIt7GFrH6tE5KmQ7W+2ylaKyEdWWZqIzLLKZ4lIC6v8AxF5XUT+JyJbA1ZlMXlTRNaJyLdAo5D9j7XKV4nISwBKqXwgXUTOrJaG1mg0Go1GozkG2uIcnXQFTsPMab8VMyvSmSLyAHA/MBL4F/CqUmq+pbz+aG0TSiDrVYCHgHuVUr+KSF2gUEQGY6b7PBMQ4CsRGYCZuWsMcI5Sar+INLD28SZmOtOJInIb8Dpm+lKAJpgZ0TpiZvb6AjM1bgegC9AYM3Xne9b+rsTMBqREpH6InEuB/lRtClKNRqPRaDSa46IV5+hkiVJqN4CIbAH+a5WvBs63lgcCp4tIYJt6IhKvlMoN2U8TYF/I71+BV0TkE+BLpVSmpTgPBn6z6tTFVKS7Al8opfYDKKUOWuvPAq6ylj8CXgzZ/3SllAGsE5HGVtkAYLJSyg/sEpHZVnkOUAhMsCzR34TsZy+m8q3RaDQajUZTY2hXjejEE7JshPw2KH4ZsgFnKaW6WZ9mpZRmgAIgJvBDKTUWuB2IBRaKSEdMK/PzIftpq5R61yovT7720Dqhcssx6gRk8WFauadiWqx/CFkdY8mu0Wg0Go1GU2NoxfnU5b/AfYEfItKtjDrrgbYhddoopVYrpV7AdIfoiOnicZvluoGINBORRsAs4FoRaWiVB1w1/gcMtZZvAOafQM55wFARsYtIEyyLufV/CUqp7zBdT0Llb09JFxONRqPRaDSaake7apy6/AV4S0RWYZ7necBdoRWUUr+LSEKIC8dIETkf8GP6Gn+vlPKIyGnAAsvtIw+4USm1VkSeBeaKiB/TleNW63/fE5GHMd1AThQ6bhpwAaabyUZgrlUeD8wQkRhM6/RfQ7Y5B3gKjUaj0Wg0mhpElCrPaLvmVEVE/grkKqUiJpbz8RCR7sCDSqmbwi2LRqPRaDSa2oV21dD8h5K+x5FOEvB4uIXQaDQajUZT+9AWZ41Go9FoNBqNphxoi7NGo9FoNBqNRlMOtOKs0Wg0Go1Go9GUA604azQajUaj0Wg05UArzhqNRqPRaDQaTTnQirNGo9FoNBqNRlMO/h8q7SkBEl/QxgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(1,3,figsize=(12,4));\n", "inds = range(5000,10000);\n", "ax[0].plot(time[inds],pos2DRaw[0,inds],'.',label='Raw tracking data');\n", "ax[0].plot(time[inds],pos2D[0,inds],label='Interpolated');\n", "ax[0].legend();\n", "ax[0].set_title('X Position');\n", "ax[0].set_xlabel('Time (seconds)')\n", "\n", "ax[1].plot(time[inds],pos2DRaw[1,inds],'.',label='Raw tracking data');\n", "ax[1].plot(time[inds],pos2D[1,inds],label='Interpolated');\n", "ax[1].set_title('Y Position');\n", "ax[0].set_xlabel('Time (seconds)')\n", "\n", "ax[2].plot(pos2DRaw[0,inds],pos2DRaw[1,inds],'.',label='Raw tracking data');\n", "ax[2].plot(pos2D[0,inds],pos2D[1,inds],label='Interpolated');\n", "ax[2].set_title('2D Position, ' + mazeType);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also to make life easier during decoding, we can try to decode the *phase* of the rat's trajectory around the maze, rather than its (x,y) position. This is given by:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "linearPos = np.arctan2(pos2D[0,:-1],pos2D[1,:-1]); # for circular mazes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spiking data\n", "`sessInfo.Spikes` contains the recorded neural spiking from the experiment, which includes both the maze-running session as well as several other behaviors we won't be considering here. It has the following fields:\n", " * `SpikeTimes`: the time (in seconds) at which each spike occurs\n", " * `SpikeIDs`: which recorded neuron fired each spike in `SpikeTimes`- eg, the tenth spike recorded happened at time `SpikeTimes[9]`, and was fired by neuron `SpikeIDs[9]`.\n", " * `PyrIDs`: the ID numbers of recorded neurons that are hypothesized (based on spike waveforms) to be pyramidal cells\n", " * `IntIDs`: the ID numbers of recorded numbers that are hypothesized to be inhibitory interneurons" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "spTimes = f['sessInfo']['Spikes']['SpikeTimes'][0];\n", "spIDs = f['sessInfo']['Spikes']['SpikeIDs'][0];\n", "cells = np.unique(spIDs);\n", "N = len(cells);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The original dataset contains spikes from a series of experiments; we only want to consider spikes that happened while the mouse was running on the maze, so we'll extract those out:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "mazeTimes = np.logical_and(spTimes>=min(time), spTimes<=max(time));\n", "mazeSpikes = spTimes[mazeTimes];\n", "mazeIDs = spIDs[mazeTimes];" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we're going to convert the data in `mazeSpikes` and `mazeIDs` into a *spike raster*. This is similar to the vector of spikes we constructed in module 1 (spike-triggered averaging), but now the spikes come from multiple neurons, so instead of a vector we create an $N$ (neurons) by $T$ (time) *matrix* of spikes." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "raster = np.zeros((N+1,T-1));\n", "for ind,i in enumerate(cells.T):\n", " raster[ind,:],_ = np.histogram(mazeSpikes[mazeIDs==i],time);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's what that spike raster looks like:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,4));\n", "inds = range(0,400);\n", "plt.imshow(raster[:,inds],extent=[time[inds[0]]-time[0],time[inds[-1]]-time[0],0,N-1],aspect='auto');\n", "plt.title('Spiking of ' + str(N) + ' recorded neurons');\n", "plt.xlabel('time since start of maze experiment (seconds)');\n", "plt.ylabel('neuron number');\n", "h=plt.colorbar();\n", "h.set_label('spikes per ' + \"{:.1f}\".format(1/framerate*1000) + 'ms time bin');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's the data unpacked! So **at time $t$, the rat is at position `pos2d[:,t]` (or linearlized position `linearPos[t]`), and the spiking activity of the N recorded neurons is given by `raster[:,t]`**.\n", "\n", "Before going to the next step, we'll adjust `time` so the maze run starts at time 0 instead of 17,375, and then clear some variables that are no longer needed to free up memory." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "time = time - time[0];\n", "del f, spTimes, spIDs;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're also going to define a helper function that we can use to smooth our spike raster with a moving average (you may find this useful for decoding.)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def smooth(a,WSZ):\n", " # a: NumPy 1-D or 2-D array containing the data to be smoothed (row-wise)\n", " # WSZ: smoothing window size needs, which must be odd number\n", " aSm = np.zeros(a.shape);\n", " if(len(a.shape)==1):\n", " out0 = np.convolve(a,np.ones(WSZ,dtype=int),'valid')/WSZ;\n", " r = np.arange(1,WSZ-1,2);\n", " start = np.cumsum(a[:WSZ-1])[::2]/r;\n", " stop = (np.cumsum(a[:-WSZ:-1])[::2]/r)[::-1];\n", " aSm = np.concatenate(( start , out0, stop ));\n", " else:\n", " for i in range(0,a.shape[0]):\n", " out0 = np.convolve(a[i,:],np.ones(WSZ,dtype=int),'valid')/WSZ;\n", " r = np.arange(1,WSZ-1,2);\n", " start = np.cumsum(a[i,:WSZ-1])[::2]/r;\n", " stop = (np.cumsum(a[i,:-WSZ:-1])[::2]/r)[::-1];\n", " aSm[i,:] = np.concatenate(( start , out0, stop ));\n", " return aSm;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Principal Component Analysis\n", "\n", "This week, see if you can perform the following analyses:\n", "- Use `sklearn.decomposition` to perform PCA (check the functions `pca.fit` and `pca.fit_transform` here)\n", "- Compute the principal components by taking the eigenvectors of the covariance matrix.\n", "- Experiment with whitening and smoothing (look at effect on PCs and covariance matrix).\n", "- Plot the PC loadings of neurons.\n", "- Plot the projection of data onto the first few prinicpal axes.\n", "- Look for position/behavioral correlates of PCs " ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import *\n", "\n", "velocity = np.sqrt((pos2D[0,2:]-pos2D[0,:-2])**2 + (pos2D[1,2:]-pos2D[1,:-2])**2)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }