tahoe-redux.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:5a78288f0c804720c503fb4646650b0a5c89af8dd356af891c5fd01f18ff5c11"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "\n",
      "import numpy as np\n",
      "from scipy.stats import gaussian_kde\n",
      "import rasterio as rio\n",
      "\n",
      "import pandas as pd\n",
      "\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "import l8\n",
      "from l8 import timeseries\n",
      "from l8 import spectrum"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get a list of Landsat 8 scene directories\n",
      "rawpath = os.path.join('/', 'mnt', 'data', 'raw')\n",
      "srcpaths = map(lambda p: os.path.join(rawpath, p), os.listdir(rawpath))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Going to examine this longitude/latitude\n",
      "lng = -120.246755\n",
      "lat = 38.791469"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extract the timeseries for all scenes available on disk\n",
      "dates, ts = l8.timeseries.extract(srcpaths, lng, lat)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get a quick look at how the spectra changes over time\n",
      "sns.heatmap(ts, linewidths=0, square=True, cmap='cubehelix')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7feae99d6f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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I48e/HpeDdGfguzihfyOnNFo8fho//kGq2pmN+C7gugzsM+qE9fhdiPjx54jI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\nTa/geumxuMZ8MvBFYDoulun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       "text": [
        "<matplotlib.figure.Figure at 0x7feab707fb50>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot loads of KDEs to see all the relationships between each spectral band\n",
      "f, axes = plt.subplots(11, 11, figsize=(121, 121))\n",
      "\n",
      "axes_iter = axes.flat\n",
      "\n",
      "for i in range(11):\n",
      "    for j in range(11):\n",
      "        x = ts[:, i]\n",
      "        y = ts[:, j]\n",
      "        sns.kdeplot(x, y, shade=True, cut=5, ax=axes_iter.next())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Pl7XNuhsA0GXdrW1mN2AKY+yhth6hGnO/9O8e/d6xr//YhZt6fZ++QaptxKjs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\nU/11/5mJUgEAAABsxplTp9a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9NWjwneeePDJq8Phz\nl6YGDb51/UqnGBUA0N1QG/H7CFAlm49QdfGGcw90XqcdXmMtu17rGp5qLBsQHSpCtSg3YgDA+jSH\nne5bkKr9fh34CgAAAMCyho5QbZMholONrvGppJ8AVdItQrVMgCoxkwQAAADoYpMBqmS5CNWizpx7\n68IxqvaZuH1EqRYJTzVWCVAlm4lQAQDb4fB9hWMJU7nfEYb3qps3ty9+qbYO9K2v0vsuRKiWiU9N\n0zVKNWtQNm0gtmyJfd4ATIF9O6mtj9vzL/3Q2g0A+JE7fvrV1m4jZu4GALSZu43bDWs3YECrbh7b\n5gjVKuGpWbqEDmbFDaaFDZLpcYNVwgZ337rYgR42+o3Dceu3UbN+g900xKb7PgJUy8Snku4BqkbX\nENWsNdphi4ZDhzQvPpUst05LVo9QLbJes1YbJ2u3cbPnDejTvgWp2hz+yq6w523czN0AgDZzt3Ez\nd4Pts+ps69qLi3/2OXSIql5f/iDZp290+3x0rPqOTyX9BagSEap9ZfY2bu43BQDa3G86btZusHv6\nOiO3sa0Rqlnn786yaIxqE+bFp5LVA1TJcBEqZ/GOm7XbuNnzBozN0HEq9zfCbEPteROiAvbaugNU\nyXgjVH0GqA7rEqTqM0YlRLV/DLnGzc0BAECbGwPGzdwNAGgzdxs3m8uAIfSxQWxTEapVAlRDxaem\nmRelWiZItekYlc1/4+BQtXGzfoPdsmsBqmS4CFVjW2JUXeJTyTABqqT/YGhirTZW1m7jZs8bMJR9\njVI5CJZtZ8/buJm7AQBt5m7jZu4G22fsIap1RqiS7QxRLRKfSjYToEqWi1CZO+4Gs7dxc78pANDm\nftNxs3aD3SJC9WPLhqiSccaousSnkvkBqmR+hKpLgCpxBu+usnYbN3veAIA2IaoWQy5gFZscqq07\nQrXpANVh84JU2xijMgQbB0OucXNzAADQ5saAcTN3AwDazN3GzeYyoE99hRS2LUK17gDVYbOCVOuI\nUfUdORA42DyHqo2b9Rvsjr4jVJsOUCWLR6iSYUNUjXUEqbqGp5LZ8alkdoAqWS0SmohQ7Rprt3Gz\n5w0Y2r4GqRKHw7Kd7HkbN3M3AKDN3G3czN1g+6w7RLVIhCpZLES1LxGqRcNTjT4DVEn3CNUyAarE\nnHGXmL2Nm/tNAYA295uOm7Ub7JY+z8xdNUK1SoAqWS1ClawWompsMkjVNTzV6CNAlXSLUC0aoEqc\nv7tNrN3GzZ43AKBtqD1vPlUH9kLf8alk8YHa2CJU6wxQtX/mrBjVU5cuzYxRjdG1F/7OMAwAAAAA\nAICt02dAYZF4wroiVH0FqF64cL3zYw+79d4TM7/evI6jglTN6z8qjHDp6rfnRhC6+Nb1KzODB997\n4cZSsQMA2GVji1BtIkCVLB6hSg4iTovGqA6vhVYNUy0SnWobMkCViFABwD5qH5K6b1Gq9vt1WCwA\nAAAAy1okQrXrho5PJf0HqBIRKgAAAICjiFD9WB8RquTHMaihg1SLRqcaXeJTSX8BqkSECgAA9sHc\nT9ZLKX+Y5JeTPFtrfdvk2skkf5LkjUkuJHlfrfX65GufSPKhJD9M8pFa61cm19+R5LNJXpPk0Vrr\nRyfXb03y+SQ/n+RqkodrrU/19xaBfTREeKptnRGqWTZZVgcAAAAAAAC2y6bDCauGEgAAdsm2Rqg2\nadmQ1DK6hD9XDVAlIlQAwI8PTd23IFUiSgUAAAAAy1g2PNXY1gBVYo4IAAAAsC6bjlAN4XAoapnz\nfJeNTR3WNT6V9BugSkSoAABgX3T5dP2PkvxuDmJRjY8n+Wqt9XdKKR+b/PPHSykPJnk4yYNJziT5\nWinl/lrrzSS/n+SRWuv5UsqjpZR31VofS/JIkqu11vtLKQ8n+VSS9/f2DoG9MHR4qtF3gKqLWRX2\nM+feOnV49cazZ/PUpemHhdx238n84MlrK7++Rdx238mZX3/j2aM3rPU1bBuCgRgAALCMUso/TfIv\nkzyQ5Bdqrf+p9bXeQu+llA8m+ReTp/6tWmt7xgcAAMCO6zs81dZ3hGpWCOE7zz250M9KDiIF3714\n9GetZ07dlctXrnZ6nlvvPZEkeeHC9YUe39WZU9MP4JgWWugSRQAAxm2TEapVA1TLrM0aZ+/62Vy6\n+u2Vfv5QFlljrStAlSweoRKgAgC21eEQlwNlAQAAADbvjmO3jDag/pbjp/LEje6fm5YTZ1OvL/9Z\naRN/evrG8p+XrvqzV7FIfCoZJkCVLB+hMi8EAAAA2D/3nnxg5nm8q1r3Obd9x6eS4QNUiTN3AQBg\nW839lL3W+pellHsPXX53kndO/v65JH+RgxjVe5J8sdb6UpILpZQnkjxUSnkqye211vOT7/l8kvcm\neWzyXL85uf6nSX5v6XcD7JWxxqcaXSNUmyy1rytGNS9AlUyPUM0ybZA2a2g2b1C27HAMAABgQf8l\nya8k+Vfti32G3kspJ5P8RpJ3TJ7+G6WUL9dau52cDQAAwNYZMjzVWCaasEooYSiLxKiSxQNTXV/D\nNNMiVNPMiyEcZV78YNHoAQDsunWstbrY1ghVowk+bTpItWjcs8t6a5MBqkSECgC22VgP892k9u/E\nIbMAAAAA2+nkseO59mL3/Wb33X5Xnvx+9z1dm7COIFUf4alk8fhU0j1AlSwWoVo2QJWYDwIAAAAs\nY9lzdPuwyTN2x2aR+FTSPUCVDB+hEqACAIDttuwn7adqrc2d7FeSNLsI7kny9dbjLuXgwNyXJn9v\nXJ5cz+TPi0lSa325lHKjlHKy1jp8GQXYOuuKTyWrDc66RqiG9sazZ/PUpdmHhzSRqCGCVF0CVMns\nCNW0SvyiA7UhGIwBAADLqrV+K0lKKYe/1Gfo/R8n+UoTniqlfDXJu5L8m0HeFAAAABsx1vhUo0so\nYdUgwjaaFaGaZdFgQpcYQh9EDwCgu6HXVtOMbc21ziDVomuoRtfYZ58BqkSECgDgsCZK5cBZAAAA\ngPW749gtow2pv+X4qTxxo/tnqOXE2dTr/XxuejgWtUyYqq/g1GGLBqgWiU8liwWoEhEqAAAAgG0z\nlvN0k4NzZy9c+9amX0Zny5yTO1R8KlkuQJU4axcAAHbByp+211pvllJu9vFiAI6yLfGpxiJDszGV\n2m+772SvMaohI1SzzBqizRuaLTskAwAA6FFfofe7Js916YjnAgAAYMutIz6VrCeSsMkgwplTd+Xy\nlasb+bmzvOHc4jcbAAC7b9MRqu88t/jBaV0cFYlaNE61bGjqKF3jU0n36GfXCNUyAapEhAoAtt1Y\nD/Edo/bvygG0AAAAANvh5LHjufZi931o991+V578/nB7uvqMUbUNFZXqatH4VCJABQAAAED/xnTG\n7tCWiU61jTFAlYhQAQDArlj2U/crpZTTtdZnSilvSPLs5PrlJOdajzubgwNuL0/+fvh68z0/k+Tp\nUsotSY7XWvsroQBba10Bqj7iU8ni1fYxDsiaeNSyQaqu8anGshGqaQO3RQZpAAAAQyulfDXJ6SO+\n9Ou11n+37tcDAADA9ltHgGqV+FRjlUjCrlslQjUtsDAtljArjDAvhrBsBAEAmK2PtdaiNhn+XFaf\nYakuhohPJQJUAMDRhKf6cfj36GBaAAAAgP30luOn8sSNxfarDRWjWrdl4lPJuANUiVkfAAAAAD/p\n3pMP5MK1b238NfRh0TNzBagAAIBlLfvJ+5eTfDDJpyZ//lnr+hdKKZ9OcibJ/UnO11pvllKeL6U8\nlOR8kg8k+cyh5/p6kl9N8udLviZghwwdoeorPpUsHqBK+o1QnTn31ly++M2pX29iT09d6r4RbtGg\n1CJmxaeS5QJUyeyBWpfh2aIDM0MyAABgnlrrLy3xbX2F3q+WUi4n+Uet7zmX5D8s8ZoAAADYoG2J\nTyWLB6j6iCKcvetnc+nqt4/8WhN1+u7F6TcZHA5DXb5ydeXXNOv5p1kmQJWMI0IlgAAA3d196/GN\nxKj4SYuEpxpDBKgSESoA2AfCU+shTAUAAAAwnDuO3bLSnOvkseO59mL3z0mb8NGT3++2l2vZGFWS\nrQtSrSs+law/QJWY6wEAAACcvPU1vZzHe+62u3s9d7ert9/9pl7P2220z6IdMkrVV3CqbdH4VLLe\nAFXifF0AANhFcz99L6V8Mck7k9xdSrmY5DeS/HaSL5VSHklyIcn7kqTW+ngp5UtJHk/ycpIP11pv\nTp7qw0k+m+S1SR6ttT42uf4HSf64lPLXSa4meX8/bw3YRrseoBrKvBhV8pMBqEWiVH2YF59KZgeo\nEhEqAABgp72q9fc+Q+9fSfLJUsqJyc/4pSQfG/rNAADsolLKHyb55STP1lrfNrl2MsmfJHljJp+Z\n1lqvT772iSQfSvLDJB+ptX5lcv0dOfjM9DU5+Mz0o5Prtyb5fJKfz8Fnpg/XWp9a1/sDxmnoAFWf\nAYRFA1RJPxGqxqwYVdItSNWYFo6aF6jqGpw66nVNs0yAKhGhAoChnDh2bC2R0LF685335TvPPbnp\nl7G0ZeJTyfgCVIk1GACMnfDUOAhTAQAAAPRr3TGq5CCEtEiMKsnOBamWDU811hGgSlaPUJnfAQAA\nAIzHz915z6jO2z1s1tm0R0WqhohLzbOO+FQiQAUAAEz3qps3b85/1Mhcf/Hl7XvRwFxDRaj6rrCv\nOhBbtM6+SG19XozqKH1HqbpEp9qGClAlIlT75MSxW141/1FsyvMv/dDaDQD4kTt++tXWbklKKb+S\ng5DU3UluJPnPtdZ/Mvnar+cgWvByko/WWv/95HoTLWhC7x+ZXL81yR8n+QeZhN5rrRcmX/u1JL8+\n+bG/VWv93KzXZe4GALSZu/1YKeUfJvn/kny+FaL6nSTfq7X+TinlY0nurLV+vJTyYJIvJPmFHMRF\nv5bk/klc9HySf15rPV9KeTTJZ2qtj5VSPpzk52qtHy6lPJzkV2qt75/1mm5Yu8HOGjJu0Gd8Klku\nQNVYJETVNXgwK0bV1iVINZR58alkmABVIkK1D45bv42a9Rvshj7WasuuyZZde/UZAN2mENWy4alk\nsfhUIkDFcqzdxs2eN6Ar0ant5FBbFmXP27iZuwEAbeZu42buBruhj5nYokGqrjGqxqIxqsM2GaVa\nNTyVrC8+lQhQsTqzt3FzvykA0OZ+03GzdoPd09f5vKucx7vK2buLnru77ZYJTzWWCVAlIlTMZu02\nbva8AQBtQ+15E6ICNm5fAlTJ8sOwoWNU6zQvPNUYW4AqMSgbM0OucXNzAADQ5saAcTN3AwDazN1+\nUinl3iT/rhWi+laSd9Zar5RSTif5i1rrA6WUTyT5+1rrpyaPeyzJv0zyVJL/UGv9byfX35/kH9Va\n/6fJY36z1vr/lFJuSfLdWuvrZr0em8tgNw0VoRpTgCpZLobQd4yqMXSUqkt4Kpkdn0rmhxRmxRK6\nhBFEqHaDQ9XGzfoNdkNf67Vl1mdCVNOtEp1qDBmfSlYLUCXWX7vI2m3c7HkDphGe2j0OuqULe97G\nzdwNAGgzdxs3czfYHdsQo0pWD1Ilw0ep+ghPJcvFp5LNBagSszkOmL2Nm/tNAYA295uOm7Ub7KY+\nzurdVIgq2f0Y1TbGpxLn6u4La7dxs+cNAGgbas+bT+SBndRnhKqPAFWyviFYE3oaU5Cqj/hU0m3Q\nJkIFAAAAAMCanKq1NneoX0nS3MF9T5Kvtx53KcmZJC9N/t64PLmeyZ8Xk6TW+nIp5UYp5WSt9dpQ\nLx4YnyEiVGMLUCX9hhCO0gSdugap5oWiZoWqukamppkXn0pWC1AlIlQA0LcTx44NFg+d54ETp3pZ\nj63izXfeN4oYVR/hqWTx+FQiQAUA+0Z46sCihwIvq4/Daxd1+N+xw28BAAAA5rvj2C0rz85OHju+\n0NypCSYtEqRq4kyrBKmmhaIWCVT1FZs6bNn4VCJABQAAALAvzt1299Ln8/7cnfesdB7v2+9+007F\nqFYJTzUEqAAAgHXxyTywc/Y5QtXWjj+tM0rVNTrVmBefSvoLUCUiVAAAAAAA9K/WerOUcnPTrwPY\nXmOPUPUVPBg6QtW2aJBqmlVjU4d1iU8l4wxQJUIIANCXu2893ns0dJoH7zzb6zqsWaesK0jVV3Sq\nsY74VEOECgC2z76Fp9YVmOpqkdczVLRKmAoAAACgm2ZusspMbdEYVXIQUFokRpX0E6Q6bKi41Dyb\niE8lAlQAAAAAm3Dy1tfk2gt/t/LzbDpGlWzmTN5V9RGeSpaPTyUCVAAAwPJ8Qg8wxZgiVPeefCAX\nrn1r6e8/Kg61Spxq0djUYV3iU4kAFQAAAAAAo3allHK61vpMKeUNSZ6dXL+c5FzrcWeTXJpcP3vE\n9eZ7fibJ06WUW5Icr7VeG/TVAzutr6hBXwGqZPUI1ZvvvG+p2EE7/LRqlGqVn91Fl8hCHwGqRIQK\nAFZx4tixXkKiy8SoHjhxaqk1Wt8xquQn1y59RKn6Dk61LROfSgSoAGBf7Hp8amyxqb7Mel99RqqE\nqQAAAABmu+PYLSvHqJLF5lhNUGnZIFXSb5RqaKvEp5LlA1R9ztnM1QAAAACWswsxqmR7glRjiE8l\n/QSoEmfqAgDAPvMpPbBRfQy02pYdbB02pghVY9UY1WGrxqQW1TU+lWw+QJUYmAEAAAAAMNeXk3ww\nyacmf/5Z6/oXSimfTnImyf1Jztdab5ZSni+lPJTkfJIPJPnMoef6epJfTfLna3sXwMb1ETFo28UI\nVWPZaflvTQAAIABJREFUGFVjWhhqmUDVopGpo3SNLXQJJwhQAcB6bWuMKulvbdY2ZERqWcvGpxIB\nKgDYB7san9rV6NSijvo99HVobvt/dxyeCwAAAHBg1RhVcjC/WXS+tWyQKhl3lGrV8FSyfHwqEaAC\nAAAAGJs+Y1TJcuf2Nue/9hWkSsYRpeorPNUQoAIAAMbiVTdv3tz0a1jY9Rdf3r4XDRypzxBVHxGq\nvgJUyXBDrT5jVENZJDrV6DqAW2SwJkK1P04cu+VVm34NTPf8Sz+0dgMAfuSOn361tduImbsBAG3m\nbj9WSvlikncmuTvJlSS/keT/TvKlJD+T5EKS99Var08e/+tJPpTk5SQfrbX++8n1dyT5bJLXJnm0\n1vqRyfVbk/xxkn+Q5GqS99daL8x6TTes3WBn9Bmi6iNCNcYA1WGrxKg2aZFAQ9dwwpABqkQMYdsc\nt34bNes32E19reWWWcetsm4bap22aavEp5LNBagS6659ZO02bva8we7apQDVGKJTyxzwu4hVDsud\nx2G6LMKet3EzdwMA2szdxs3cDfZDHzO4ZWdffc2r1hmm6iM61Vh1nmZmxiaYvY2b+00BgDb3m46b\ntRvsj77O8F3l/N4+z+1trCNK1Xd0qjGW+FTiLF1+zNpt3Ox5AwDahtrzJkQFbNSuhqjWMcQaS5Bq\nmehUstgQbtHBmgDV/jHkGjc3BwAAbW4MGDdzNwCgzdxt3Gwug90xphBVnxGqZPjAwViDVIsEp9r6\njk8lAlT7xqFq42b9BrtLjGqzNhWfSgSoWI2127jZ8wa7Z9sDVJuITg0dmepLX7Gqvg7ZdcDubrLn\nbdzM3QCANnO3cTN3g/3R1zxulbnYUPOtZSNVfQan2vqYjwlQsUlmb+PmflMAoM39puNm7Qb7ZQwx\nqmSYINVhi57tO1Rs6rBV41OJABXDsnYbN3veAIA2IaoWQy7YLWMZYiXbFaJqrCNItWxs6rBFh3IC\nVHRlyJWUUv5pkn+Z5IEkv1Br/U+tr30iyYeS/DDJR2qtX5lcf0eSzyZ5TZJHa60fnVy/Ncnnk/x8\nkqtJHq61PjX52geT/IvJU/9WrfXz816bmwMAgDY3BoybuRsA0GbuNm42l8HuGFOIKuk3RrWuuMGm\nglTLBqcai0YT1hGgSsQQtplD1cbN+g122yZjVMn+BalWjU8lyweo+ohPJdZcWLuNnT1vsDu2KUC1\n7tjUtoSmVrXsQbx9HL7r0N3dYc/buJm7AQBt5m7jZu4G+2eXg1SbNLb4VGIWxvLM3sbN/aYAQJv7\nTcfN2g32zy6e4zt2Y4tPJc7SZTprt3Gz5w0AaBtqz5tP8YGdce62u3sZYm2bw5GoZcJUfYWmjrJM\nEX5dAarE4Iyd8l+S/EqSf9W+WEp5MMnDSR5McibJ10op99dabyb5/SSP1FrPl1IeLaW8q9b6WJJH\nklyttd5fSnk4yaeSvL+UcjLJbyR5x+Tpv1FK+XKt9fpa3iEAAAAAAOygE8eO9Rqj2keHg1B9halW\nDU0dZcj4VCJABQCb0vz36KrrurtvPb5UjOqBE6eWjlE165OxB6k2GZ9K+gtQJdZdALAuY45QrTM6\n1fdBu0/c6C9kv4q3HO+2tjvq/Xc5qPfwv6NlDuZt/nfQIbwAAADAvmrmIqvO6prZzDJztcOzoG0N\nU/URn0oEqAAAAAB2xclbX9NLjKo5y3WVs3yb82N3MUjVR3wqEaACAADWz6f5wMb1NcBKVo9R/dyd\n92z98GrIqFRX64hPJQJU0FZr/VaSlFIOf+k9Sb5Ya30pyYVSyhNJHiqlPJXk9lrr+cnjPp/kvUke\nS/LuJL85uf6nSX5v8vd/nOQrTXiqlPLVJO9K8m8GeVMAAAAAALAn+opRLRsu2DVDBKRWsUw0QYAK\nALZTH+u6VWJUSVYOUiXjiFL1EZ5qCFABwH4ZW4BqG6NTY4lNzTLtNXYJVB3+PS0aplr0sF5BKgAA\nAGDf3XHsll7mdqsEqRrtWdCYo1R9haeS/uNTiVkXAAAAwBg0Z6oKUvWnr/BUQ4AKAADYFJ/qA6Mw\nphgVi1smPNUQoILB3ZPk661/vpTkTJKXJn9vXJ5cz+TPi0lSa325lHKjlHLX5LkuHfFcAAAAAADA\nSIhRbd6y0YRlAgmrhhHEEACgf33FqJJsJEiVvHI9M3SYqs/oVGOV+FQiQAUA22osEap1xadWPSi3\nr+BUvb6ekGk5MX/deNR7mhenav8eF4lSCVIBAAAAdNfMRMYSpEqOngVtIk7VZ3Sqre8AlbkWAAAA\nwDiN7Tzf9hmz2xClGnt8KnGOLgAAsDif8AOj0efwatPefveb8lff+5tNv4zBrBKeSpYftK06UDM8\nY9uVUr6a5PQRX/r1Wuu/W/frAQAAAAAA+tNHsKAPD5w4tVK0YN+sGk1YNpAgQAUA49b8d20fQapl\nI6N9ruumrXkWCVQNEZs6bNX4VCJABQDbbJMRql0PT60rNDXPrNcxK1J1+H3PClMtEqUSpAIAAABY\n3BBBqqS/Gd2smdAq87mhYlOH9R2fSsyxAAAAALZBc9ZqH2f6Nue+rhqkSl559uymw1R9R6caQ8Sn\nEmfoAgAAy/NJPzAqfcWoVqmo/9yd92x8ODVG2xqfSgzP2B211l9a4tsuJznX+uezSS5Nrp894nrz\nPT+T5OlSyi1Jjtdar5ZSLif5R63vOZfkPyzxmgAAAAAAgCP0FaNaJVjAbH0EFDYVn0rEEABg3fpY\n3zVrgGXWd826Y6jQ6DriUvOMLT6VWHMBwD4ZOkC1ysG2y0SnVglOPX3jyaW/t6t7jt839WvTXvtR\ngaquYaquUSpBKgAAAIDF9RmkSoaJUh22rpjUooaITyXmVgAAAADbqK8zfZN+g1SNo86lHer836Gi\nU20CVAAAwFj5xB8YnV2JUb397jflr773Nys9x6atGp9KBKhgQ17V+vuXk3yhlPLpJGeS3J/kfK31\nZinl+VLKQ0nOJ/lAks+0vueDSb6e5FeT/Pnk+leSfLKUcmLyM34pyceGfjMAAAAAALBPxhCjeuD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TLcIwAAAO1mQTVgUXhnDgAAAKD9ZhFCNYpTm5f23F6vnNtz+4XN0/te4+KPfzhWnwbmFUI1\nqiZCqD6wcte++wihAmapv5jtl5O8WGv93aFN30rymf7Xn0nyzaH2T5VSeqWUk0nuT/JcrfVikqul\nlIf75/x0kj+5xbl+LdsL7AIHsCz/dgujAgCasnH9tXl3YaYOGgI17+uPE0I1LiFUwLzUWi/WWv+6\n//XfJ/m7JHcn+ViSr/R3+0qST/S//niSr9VaX6+1nklyKsnD/TkNd9Ran+vv99TQMcPn+kaSX57e\nHcHiWZZaW9N83wCARTHLUKh7fupoq0KoAIb5lAdLrmsLVezs76IsQAwAtMeR3iEvszMRD1QBYHoG\ntaCu1bQAAACA2RlhQbUv5u0Lqv1xKeW3sz0BdLCg2s1SytVSysNJnsv2gmpf2nGu78WCagAAAAAA\nTNnpa5fn3YW5hVBN4tid9+27z34hVKMQQgW01C8m+fUkf1NK+at+2xNJfivJ06WUR5OcSfLJJKm1\nvlhKeTrJi0luJHms1nqzf9xjSZ5M8q4kz9Ran+23fznJV0spP0hyOcmnpn1TQPcN5u8aswAA83L2\n1fV5d6FTZhVC9aGj+9fyBoRQAW1RSrk3yc8n+Yskd9VaL/U3XUoyeHhwPNvzDgbOZXvewuv9rwfO\n99vT//tsktRab5RSNkspa7XWjWncB4BaHQCwaO75qaNTrwO2MYBqmWpWG9dfG/uYZfr+QCKICui4\nwaLDAqkAgCYNHoq1OZBqrbeSja3NeXcDAGAuuh5IdfTwStavG8vNitohAADA0rGgGnRIV2t8AAAA\nAJBMNudg1PkAo4RQndq8tOf2euXcntsvbJ4eqS/z8J5j9x7ouFFCqEZRVk/suX2UEKpxCKECmlJr\n/XdJ3rHL5l/Z5ZgvJPnCLdr/MskHb9F+Pf3nrgDjurp1w9gFAKDFDhpAlQihApZDKeWnk3wjyeO1\n1mullDe21VpvllJu7nowMHVHeodavX5cm6jRAQDTtHb49gMFBjVhUA+aRiCVEKrZafL3Z7dzLer3\nDnzaAxaCQCoAYBo8TOyOSSdbNsVDVQCYrdVez0K1AAAAwFtYUA26RY0PAAAAgGU0agjVKGYRQnXx\nxz8cq09NOWgI1aiOr5zcc/t+IVSjOnnHuxs5DwCw2JZxPqswKgDgIGa5cO0Lr1yY2bX2c/c9D+X8\n2e/P7FoHtcghVBakBQZKKe/MdgjVV2ut3+w3XyqlHKu1XiylvDfJy/3280nuGTr8RJJz/fYTt2gf\nHPO+JBdKKYeSrNRaN6ZzN7C4lrHeNg51OQBgWTQVSNXG8KmBRapbzSO4TEAVi8qnPlhii7iAh0Aq\nAKBpHiYCALTboA7UtVrX0cMrWb/e3GIibXH0cDsCQgfUCQEAAADoMvUtAAAAgPaaxTyD09cuT/0a\nbTVJCNWxO/dfvHa/EKpRfGDlrn33GSeEaq032ft3FosDgO5bxvmswqgAANpjkgCqRAgVsBxKKbcl\n+XKSF2utvzu06VtJPpPki/2/vznU/sellN9OcneS+5M8V2u9WUq5Wkp5OMlzST6d5Es7zvW9JL+W\n5LvTvStYXIO607LV3PaiFgcAzNra4dvnEi6008460V7BVG0OnVo0bfjd2M3OvqkR0jU+/QEL6crW\nlkU4AIDGLOPL+4zPA1YAmK/VXq9zYVQAAAAAgNrebrz/BgAAANBek8wv2NjabKwfpzYv7bm9Xjm3\n5/YLm6cb60uT2hBCVVZP7LldCBUAQHOEUQEAy+TetQdyZuOlAx07CIo6f/b7TXZp4gCqRAgVsFR+\nMcmvJ/mbUspf9dueSPJbSZ4upTya5EySTyZJrfXFUsrTSV5MciPJY7XWm/3jHkvyZJJ3JXmm1vps\nv/3LSb5aSvlBkstJPjXtm4JFJ5DK80QAgJ0WJWyqq7WrNgdQ7Wa4z139vrNcbrt58+b+e7XM5taN\n7nUaWmhZFu+wIAcsvpXeodvm3Qd2d/X1nxi7sTDa+BCxyUmoXTbppMsmeNAKoznyzn9g7NZi6m4s\ngi7VvNavL95Y7ujh+Y/LhqkNwmTU3drN2A0A2Mn4rd2M32B/XartTZu6Fiw+Y7d2u2LsBgAMWTV2\nazXzFZiXWQRRnb52ed99ZhFEdfHHP9x3n928fPHM2MdMO4Qq2T+ISggVdJf5Cu1m7Aaja+Nc1lkw\nroHlY/zWbp6b0kaTLk569tX1sfZ/4ZULI+/7/ProdbSDBlENayKMqokAqkQIFSwLz03bTe0NRrds\ntTc1N1hO6m7tpu7Gsupi8FDbdbF2tYi/B138OdAu06q7vWMaJwVoEwuUAABN8UCN3fjdAID2WO31\nOrNIa9tCmybVtvvpyu8BAAAAAAxT1wIAAABot2UKoZq1SUKoRjVpCFXThFABALeyrP/GL9siwAAA\nk5gkROruex7qRAjVQQmhAgD2cqR3aOHrb4N7XPT7BABYZl2qXW1cf+2NP4toke+NbvOJEFh4FuYA\nAJp0pHfIy+wAAB2w2usJKAcAAACADlHT864bAAAAQNvNIoSqTS7++IcTHf+eY/fm5YtnRt53Esfu\n3H8B2yZCqD6wcte++5y849377tMEC8cBwGIzlxUAgP0Mh0mdP/v9kfZryrgBVMn4IVT/8GeOj30N\nIVQAwKiGn7UtQh3Os0MAoO3WDt8urKchXaldLdvPe/h+u/IzYrH5lAhLzMIdAAAH4wX+dlnrrcz1\n+h7AAkB7daH+dfTwStavd2+BEQCAZTeNcabQAQCAN8dEba/rNck4EAAAAIBhp69d3nefU5uX9txe\nr5xrqjsT2y+MatIAqqSZEKpRNB1CNclcCPMYAGA5DP7NX6b5rFe3bhjrAAC3tGyLlo5rGmFTuxFC\nBQAsmi6GUqmhAQBdJIxqcm2vXfn5bhNKRRv41AgsNIt0AAAAACy3LoRRLYKjh+cbDgoAMAvTHlfu\ndn7PPAGAZbQMgVTGeQAAAADdMsmCYxtbmw32ZHIXNk/P9HqDMKomQqd2aiqEqqye2HO7ECoAYJ6O\n9A51ZgFcAIC2Ovvq+lj7v/DKhSn1ZDvI6czGS1M7/zQJoQIAFt2tnsXNuzbn+SAAsEiEUR1cm2tX\nfqa7G3xv2vzzYzH5JAlLbpEX4rVQBwAwTV7cBwCA9lIbBACaNO/nqbe6vvEOALAsFjGQylgOAAAA\noHtmNXfg9LXL++5zavPSntvrlXON9OXij3/YyHkG5hVCNYouhVABAMtrsNjsMsxrvbp1w+K6AMBb\nWMS0HYRQAQDLardaVZO1OvUwAGCZCKMaX1trV36OoxNIxaz5lAkAAHBAbQijWuutZGNrc659WGYe\n3gJAN7Q9jP3o4ZWsX+/umO7oYYtiAACLqc1jyJ19E2YAACy64fFOm8dpuzFeAwAAAFhebXvf/8Lm\n6Xl3oRGjhlAdXzm55/b9QqiaNmkIlTkMAEAb5rUCALBcZhFAlQihAgC6x7M7AICDE0Y1ujbWrvzs\nDk4gFbOy5yfWUso9SZ5K8p4kN5P8Qa31S6WUtSRfT/L+JGeSfLLWeqV/zBNJPpvkJ0k+V2v9dr/9\nI0meTHJ7kmdqrY/32w/3r/HhJJeTPFJr/VGztwnspe0L8R6EhTsAAJbDpJMwAYDlsYg1MAAApqdr\nY8fh/npWCgAsusF4p81jNmMyAAAAgMUxySL/44RQnb52ed99Tm1eOnBfFsksQ6g+sHLXvvucvOPd\nI/VHCBUA0JTBuEAgFQDAaM6+uj7vLrzNvWsP5MzGS/Puxr6EUAEAAAAwLcKo9tbGulWbf14HqQM3\nVWM8CIFUTNt+b5y+nuQ3a61/XUr56SR/WUr5TpJ/kuQ7tdZ/UUr5Z0k+n+TzpZQHkzyS5MEkdyf5\n01LK/bXWm0l+P8mjtdbnSinPlFI+Wmt9NsmjSS7XWu8vpTyS5ItJPjWVuwV21YXFOfZj8Q4AYB6O\n9A55WX9JmcQJADTp6OGVrF8ffdGRtjh6uH3BoOqEAMCkuvzMNHmz/8ZFAMCiu9V4Zx5jOeMuAAAA\nANqiXjk37y5MXVdDqCZl/gIAcCsCqQCAZdDmhU0X2UECqJLxQ6gOEkCVCKECAABgNKWUe5I8leQ9\nSW4m+YNa65dKKWtJvp7k/UnOJPlkrfVK/5gnknw2yU+SfK7W+u1++0eSPJnk9iTP1Fof77cf7l/j\nw0kuJ3mk1vqjWd0jdJ0wqltrW92qDT+jgwRNTXLOWYVUbVx/rXU/bxbDO/baWGu9WGv96/7Xf5/k\n77IdMPWxJF/p7/aVJJ/of/3xJF+rtb5eaz2T5FSSh0sp701yR631uf5+Tw0dM3yubyT55UlvCji4\n1V7vjT9d0LX+AgDQnLVe+0IPAIB2U0MCAGA/XQ+hGnZla2uh7gcAYBTD75Pt9mee5wMAAACgWyZZ\n0H9ja3PkfU9fu7zvPqc2Lx24L4uiqRCqUTQdQjXJ/AchVADAfo70DhkzAAB00EHDnqZNCBUAAAAL\n5PUkv1lrfSjJLyT5jVLKzyX5fJLv1Fp/Nsl3+/+dUsqDSR5J8mCSjyb5vVLKbf1z/X6SR2ut9ye5\nv5Ty0X77o0ku99t/J8kXZ3NrsDjUad60dvj2Vn0/Nq6/NrcQqrOvrr/lzyJff57fZxbXyG+RlFLu\nTfLzSf4iyV211sEby5eSDN6mPZ7ke0OHnct2cNXr/a8Hzvfb0//7bJLUWm+UUjZLKWu11o3xbgVo\n2vAiGW1ZoMzCHQAAb7fWWxlrkioAwDJb7fVaU+va6ejhlaxf78647uhhwaAAwGJp6zhxUoP78qwV\nAGCbcREAAAAcTCnlD5P8apKXa60f7LetJfl6kvcnOZPkk7XWK/1tTyT5bJKfJPlcrfXb/faPJHky\nye1Jnqm1Pt5vP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euXnG3rb7188xd3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