Robotics-at-Home-with-Raspberry-Pi-Pico/ch-13/2.2-moving-with-encoders/analyse_output.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [\n",
    "  [1932, 1928, -0.017763, 50.8663, -0.017763],\n",
    "  [2355, 2259, -0.426371, 60.8024, -0.426371],\n",
    "  [2033, 1938, -0.42193, 52.3291, -0.42193],\n",
    "  [2310, 2194, -0.515198, 59.3528, -0.515198],\n",
    "  [2039, 1940, -0.439696, 52.4345, -0.439696],\n",
    "  [2387, 2263, -0.55073, 61.2768, -0.55073],\n",
    "  [2044, 1943, -0.44858, 52.5399, -0.44858],\n",
    "  [2387, 2263, -0.55073, 61.2768, -0.55073],\n",
    "  [2045, 1945, -0.444136, 52.5795, -0.444136],\n",
    "  [2321, 2204, -0.519642, 59.6296, -0.519642],\n",
    "  [2051, 1947, -0.461902, 52.6849, -0.461902],\n",
    "  [2393, 2269, -0.55073, 61.4349, -0.55073],\n",
    "  [2050, 1950, -0.444136, 52.7112, -0.444136],\n",
    "  [2389, 2276, -0.501874, 61.4745, -0.501874],\n",
    "  [2049, 1955, -0.417489, 52.7639, -0.417489],\n",
    "  [2394, 2276, -0.524083, 61.5404, -0.524083],\n",
    "  [2054, 1958, -0.426371, 52.8694, -0.426371],\n",
    "  [2399, 2279, -0.532964, 61.6458, -0.532964],\n",
    "  [2058, 1956, -0.45302, 52.8957, -0.45302],\n",
    "  [2401, 2278, -0.546289, 61.6589, -0.546289],\n",
    "  [2058, 1956, -0.45302, 52.8957, -0.45302],\n",
    "  [2339, 2221, -0.52408, 60.0908, -0.52408],\n",
    "  [2462, 2336, -0.559609, 63.2271, -0.559609],\n",
    "  [2331, 1953, -1.67884, 56.4537, -1.67884],\n",
    "  [2059, 2218, 0.706179, 56.3615, 0.706179],\n",
    "  [2407, 2278, -0.572939, 61.738, -0.572939],\n",
    "  [2327, 2207, -0.532964, 59.7482, -0.532964],\n",
    "  [2058, 1956, -0.45302, 52.8957, -0.45302],\n",
    "  [2403, 2283, -0.532964, 61.7512, -0.532964],\n",
    "  [2061, 1960, -0.44858, 52.988, -0.44858],\n",
    "  [2410, 2276, -0.595142, 61.7512, -0.595142],\n",
    "  [2413, 1952, -2.04747, 57.5211, -2.04747],\n",
    "  [2067, 2286, 0.97266, 57.363, 0.97266],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12862af40>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(np.arange(len(data)), data[:, :2])\n",
    "ax.legend([\"left\", \"right\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what does this show me - that the left side is moving consistently faster than the right. That the peaks and troughs are likely to be timing discrepancies. Note there are some points later where left and right cross each other though.\n",
    "This will be why it thinks it drives in circles. In reality, it is not driving in that much of a circle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1286dadf0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FF8EwDNx1112Or3nDDTdgdHTU+rVz507v74IgCKKJ0XXDarN1KrvQcDliOuEp+Jg7dy6OPfbYsj875phjsGPHjrI/Y4HHW2+9hfXr1ztmPQAgmUyip6en7BdBEARhs3t0EhO5IuJRDQtnTO10ATjBKZVdiGmAJ8HpmjVrsHnz5rI/e/3117Fw4ULr9yzw2LJlC377299ixowZYo6UIAiiRWFZj8UzOxGv0ukCAAOdTPNBmQ+i8fEUfFxzzTU49dRTcdttt+Giiy7Cs88+i29961v41re+BcAMPP7sz/4Mzz//PH72s5+hWCxi7969AICBgQEkEgnx74AgCKLJ2Vqn0wUA+jqo24WYPngqu5x88sl46KGH8IMf/ADHH388br31VnzjG9/AxRdfDMA0IHvkkUewa9cuvOMd78DcuXOtX08++aSUN0AQBNHsvL6vttgU4FptG1jzYRiNP3dmdCKPn774NjL5opL1cgUdj7y0GwfHs0rWaxQ8ZT4A4Nxzz8W5555b9e8WLVo0LS4ugiCI6YQlNp1VK/Nhll3YcLloRFNybG752wdewpPbDuJnnzwNM7qSYR+OI3c+vhXf+q83cMv5x+GS1Yukr/ezP+zGZx54CRe9ax7u+LMTpa/XKNBsF4IgiAbGMJxnuvAwwaluAKkGHC73q1f3Ys9oBr9+bX/Yh1KTt0cmy/4rm92ldViA2SpQ8EEQRMOza3gC//qbLRhRpGfYsm8Ma/9pIx55Kfy5VHtGMxjPFhCLaFg4o9Px5+LRCLqTpeFyDab7yOSLGMsUAABPbjsY8tHUhh0n+6+q9XYNqwl2GgUKPgiCaHi+9V9v4Ku/eh0PbFLjA/T45gPYun8c/9kAwQd7Il40sxOJWO1bNvP6UBWkueXAmK1neGLboYYuz49l8qX/qgk+UqV1DoxllelMGgEKPgiCaHjY5nVwXM2mam9A4ZcvtrgQmzJso7Hwj5tn/1jG+v8DY1mrjNSI2JkPNeeQX2fPaKbGTzYXFHwQBOGZ327ej7976GVlT2qqN4SU4tR7Lba4aLNl9Hc0ptcHn/kAgCe2Nm7pRXXmg19n1/CEkjUbAQo+CILwzDfWv477ntmhbBNhG0JK8YbQEMFHnZkuPI0632V/RfDx5LZDIR1JfcLMfLzdQroPCj4Iogn4+4dexqXffhZFXU0tnVl4q7LyVi8CbIyyi2HwM13qBx/MaKzRBKf7U2bwceL8PgDA028cUnateqFQ1DGRM7N5YWQ+VHXYNAIUfBCEYIq6gavuex7/vGGLkvV03cAPnt2Bja8fwM7DatK2qjfnlPKyi51pCVMcuS+VxVimgGhEw+KZzp0uDGaxPtKgmo//Z9kguttiSGUKeOXt0ZCPairjWTsQUNWunOKu6VbqeKHggyAEs3X/OH7+hz34t//apmS9dK4A9hCp4mnNMIwQMxFqn0aLuoHJEDsQWMll4YwOJGPRuj9vCU4bLPPBNB9ze9uwarE576sRSy/89ZXOFZVkZ8oyHxR8EAThF/YkM5ErIl/Upa/H37xUZAYyeR2F0k1ZxdNhrqAjW9CVrQdUntPwdB+W2NSF3gOwjcYardWWaT5mdbdhzVEs+Gg80Wmq4vszLvmz58s8AAlOCYIIAB8AyL55metxqWIl6+W5/2++9aauGV4Jg2U+lrrodAHs4ONwgwpOB7uTWHPUTADA7988jGyhsXwtKq+vymBENHyZBwD2pjJKHlgaAQo+CGw/mMa///cbytomh9M5fOu/tpX1/jcTqp+a+c1R9s3SXIN7f1m1603m5WeT+LJS5fqqYZmPo9xmPjpZq23jaD6KuoFD4yzzkcTRs7owsyuJTF7H82+NhHtwFVR+X2V/f9nrt8UjSEQj0A1gb4t4fVDwQeCrv9qML/38NTz2yl4l633/mbdw26N/xH/893Yl66kmVbZxyd8EQg12JtWuB8jPJvFlJXP9cIKPsk6XGgPleAa4soveIN0kh9JZ6AYQ0YAZXUlomoZTjzRLL081WOml8lqTnfVi94eetjiO6G8H0DqiUwo+CBwotcFVGgHJgqVgVa2nGvWZCLUlAtUaE/VPo2o3ICcOjGcxOplHRAOWDNbvdAHsVlvdUHPtuYG12c7oSlqTdpnu44kGE52GlfnobovhiD4z+GiVdlsKPgjrJqXOVKdQWjd8AycZqM5EpJSvF57mo3J9GVS+vorsTjVYyWXhjE60xet3ugBAIhZBV2m4XKOUXg6USi6DXUnrz0490tR9vLRzZIruIUyUX2slAXV3W9wOPijzQbQKqoMB9oVrlCcz0fAdGerLIGozESqumco15AcflU+/4VynbKaLW70Hg+k+GkV0yjKrs3rs4GP+QAfmD7SjoBv4/fbDYR3aFMLMfMyzyi6t0fFCwQfBWVc3p1ulasIsSzSjwDWsDUHVek4wvcdSF86mPP0NZrHOhOWzupNlf76mlP1opDkvqgNP9vo97bbmg8ouREtgGIaV9lQ3QroxrKtlEWorqoLuE/49mR4ccrukpmowWkPzYXt8uBObMqzgo0G8Pg5wbbY8p5ZabhtJ98E+60Q0Uvq9mkC3h9N8kOCUaAnSuSLnjkmZDxGUlSUUl0HUBDthZyJkP42Gn/kwDAOv7/dXdhnobKzggzcY41m9xBSdvrYn1TAlIvZZz+0zj1V2WXEsy8ouccwb6AAA7BmdbJhOJZlQ8NHihGngNJbJhzo3QxZhBgOqBaeA/ACr8vVVZz7CEEYfSucwMpGHpgFHDnoLPvo6Gsvrww4+yjMfg91JLCuZpz3VINkP9tkP9baX/V72et3JGGZ3m91A+aIxZQpwM0LBR4ujeuPSdbvMoxtm5qXZUF8GCU9wWu33stbrSERLv1eT+ZhRyiCEUXZ5vSQ2XTDQgfaEu04XxkCDaT6cyi4AcKrVctsYug/22Q/1tZf9XhYssO1uiyEWjWBur5lxaQXRKQUfLY5q8SA/BK1y/WZBeevrpOpMi+JMRCmAYxuC7NZXFsAxAWAYmY+t+73NdOHp62wci3XDMDjBaduUv2ei0ycbRHTKruUjrLKLulZbc93WEZ1S8NHiVG6UsssgU2YnhOShIIsil9kB1M9ayRV16Tb5lZ+Z7Bv0lKdRydkkewNSk3qvhm2r7k1sCvAup+EH9mPZAjJ50w6/WubjlCUDiGjAm4cmGmLDtcouijIffKstgJZyOaXgo8Xhv1wqxoerFg+qptIwaawZyyClzb+3PV5aT1Uw0Fb2e1mkFKfeq8HKLl7bbAHO56MBBKfM3bQ7GataPuppi2PFvD4A4Wc/irphlYGHFAWefKstAMzrN0WnFHwQAID/7+m38MPf7wj7MKQQfhtjc2U+VIsVdd3AeC6cTMQRyp4O82XrSe9AqHj6Hc/KzwhWstXjTBeefm6+S9hYeo+eqVkPBrNafzJk0Sk/M2hIUaBbmfmYR2UXgjGWyeOLP30Ff/fQK8qmvqok7DbGZnM5VX0+x3MFsH1xZsm+WuYNk5/4OqQoGGCvz1LSqjMtRd3AhEJh9KHxLA6V9BpHznI304XHbrXNh96y6WQwxmPpPrYdDLX7jd2LErEIZnSaxzuRK6IgcYqy7fNR0ny0kMspBR91GJ3MwzDMG1CzbZTA1Bv5qGwxXwO0McqkslMiW9CRK8i/eSWiEczskt+dMZkvolja0JgdtMwOm0y+aJ0/u/1RTVlpsLvNGoSmMkPHsh7zB9rRkYh5/ves1baoG6FnFu1Ol6liU8bKhf1IxCLYl8pi24G0qkObAm/41dVmn3dZs2fyRd0qc1uZj9J3avfIZFPaEPBQ8FGHMvFgk4kjAfVP6o0yN0MWbCOe02vfbGW+R1stH7OenmRuOOy1oxHNmtWhYj0AmNsrP9gxX9/ehNimoPI6fT1AyQUAkrEoOkv6irCNxg44eHzwtMWjeNfCfgBm9iMsLM+Ntjji0Qja46y1W871zb8uGwY4t7cdmgZk8rqV/WpWKPioQ5oLPpptowTC8Gxocs1H6am5ryNubQAyszvW01p73NooZW7O7PPrSvLBjpr1mMBVZjaJHzfQ0x633qPKrOfWktjUT5sto5+124YcfDgZjFWy5ijWchue7qNSf8H+Oyrp+8Su7Y5EFLGSnXsiFsHsbub10dy6Dwo+6jCetWu9zVYiAMIQnDZ35sO6gSXjloJdxebcXfaULu8z5E2RrGBH6vuz1+NT4bLO6UTOLiuVv0d13302UO7o2f4yH0DjiE6Z5qNamy3P6iNN0elTbxyyzr9q2INDZfAhO/PR3VZeWrMGzFHw0drwCmgV7pGqYTfVtjgbpKSmtczKCjRZKcvORKgJBsbKggEVwY49i8IOrtS8v2hEs9LTsjeEaERDezyq5DOs5PV9/g3GGFbmIx3uPeuAw1yXSlYc0YvuZAyjk3m8ujul4tCmwD84AJD+fWJBO8uuMewBc80tOqXgow7lZZfm2iiBqQZO8h391HYuqCbF1Y3VBANsNkQcPe3yn9LLNSYqgqtyB0j5T6P2+9M0TclnyDOczuHguLlhex0ox9PP5ruErBuwyi41Wm0BIBaNYNWSAQDhWa1XZiJkB9dOmQ8mOm32dlsKPuowVuZW2VwbJTDVQ0GV5iNMAyeZjFUrS0jM7qSqZD5UlEF6QlgP4IMPWU+j1ev+qq7TrQfMrMcRfe3oTHrvdGGwskuYgtNsoWi5rA521Q4+AOBUq+U2HN1HyjHQlaX5sLOIPFR2IQCUZz6asexiZT5UtTEqNqhSDT+rQYVYkb+BqSnz2KlivrtGVlvg1A1B7jnlM0kAlIhqeZiz6dE+nE15GiH4ODhurp2IRqz231qwIXO/335Yanu6E1MyHwqzbDx22YWCj5ammcsuvLJfnZ1wedml2bJJ1TIfcgWgvOBUpebDfn8ybfmdOhBklZbCznxsEaD3AICBTlZ2Ce/7tT9li001Tav788tmd2NmVwKT+SJe2DEs+/CmMPVaK32fJPl8sIxoZeaDWay/3eReHxR81KGZyy68sl/VBE/VZR7V8JmBboW+G2ZbqFoNRkciaplwySotVaamZXuZVM7asLJXirKeQWzVeRqh1Xa/ZTBWv+QCAJqmYXWIpRf+uwuY82gAeZ+9vV71zMd4ttB0gnweCj7q0MxlF3YDj0U06wahquzCz80Iq7VOBuWaCPkGVWNVMx9quk9MQaZsDYZT+6PsOnw4mQ/hZZcQBacHPAYfAHDqkWzOi3rRqerP3klw2p6IWm7FO5u444WCjzo0c9mlmkeEzACrqBtWJokFH4A8++IwKGtFVeiD0cN1n8hcL1WRiZBdBql8OpQdYE15+lUQ0DFGJ/JWtiBIpwvAaz5CLLu4NBjjYXNeXtgxUnbvVcHUzirJ+qJseZaN54gWGDBHwUcd+JtOs5VdUlU2SplP6XyQMaMzgUTMvPyaKaPEB3RqfDCqtfbKE4BWiuSYMFNVR0BYmQ8V3/2tB8ysx9zetik6AK/0lzQfIxO50HQDB6yhcrU9PngWzOjAvP52FHQDz755WNahVaVRWm0BfsAcBR8tS5obV95s9beyjbJ0s5M5Ppytl4hF0BaPKplFopJCUUc6Zw+KUm8yJl8A6iTIlJf5qN6BIF9jor7sYolNAzibMljmo8BlG1Xjp+wC2KWXpxTrPpw/e8lt3cmpgaYlOqXgo3VJc/bqzWaIVc0dUzdgbaCy1uup2Eia5bzymR11JmN2ZoAXgMpvDywJMiVbyFcKQO0OBMmttlPKLvKvURHOpoy2eBQdbLhcSLoPP2UXwJ7z8sRWdbqPom53/oVhaFeJXXYhzUfLwl946VwRhaL6/nNZ8BtXWzyCmLVxqU6hN0fmg72PtngEiVhE+vsrv2GWC0DlKfSdTL8UZT7a5a5ntz+WB8jj2QJ0ycLoLfuDD5TjCVv3sT/lzt20Ejbn5dU9KWWBU/mDA/vs5WZmnVptgdbw+qDgow6Voqdm2SiB8si7fOOS9WWr7FxQPzFUJrUMsWSUsvi5QyrKIJUTXwH5rahTA1bJG0KF4JS9TzMjKPe7v1XAQDmefsvrQ33mQ9cNyybea9llVncbls7ugmEAT7+hpvRilYSjZkkYsL9Lk/ki8hIeOmtlPuYNkOB0Cm+//TY+8pGPYMaMGWhvb8cJJ5yATZs2WX9vGAa++MUvYu7cuWhvb8fatWuxZcsWoQetimq18+YKPthTrJoUs6Xutm7szZn5qHxqzhcNZCU4NrKNMhmLIBkr3TAlCkArJ74Ccl0gM/kicqWbflittslYBPGo3FIWYH6We0ZNgWbQThcGy3wcDiH4GJ7IoaAb0DRgpgtr9UqY1bqqOS/VxJ9dnL296M8+V9Cte0LlYDnAznyMTOSbqhuQx1PwMTw8jDVr1iAej+MXv/gFXn31VXzta19Df3+/9TN33HEHvvnNb+Luu+/GM888g87OTpx11lnIZDLCD142/IfOBjU1y1M6MDXylh0MTBF0Se6UUE3lU3pnIgZm7Cjjuqk2G0LmZ1g58ZVfW8ZnyM6ZpgFdicpsmRpNS/lwOXmbwBsH0gBMfURvldZLP4Rpsc70HgMdCcSj3hPslt/HVlWZj6nBRywasXQzoq9v/vW6qmQ++A7EZhWderoq/vEf/xHz58/Hd77zHZxyyilYvHgx3ve+9+HII48EYGY9vvGNb+Dzn/88zj//fKxYsQLf+973sHv3bjz88MMyjl8qrOQSj2pW9N5cbaHVgwF5czPCNXCSDbs22E0jInkEfDWHRJmlrMoyHft/cz15wU5XIoZIpHy9XEFHRnBHT1lZqeycyhdGHyxt1nN73bel1mOgM/zgw2vJhbFqyQxENOCNg2nsGZW/+VYGnQxZug/r2k7GLJF4JbbNenOKTj0FH4888gje9a534cILL8SsWbNw0kkn4Z577rH+fvv27di7dy/Wrl1r/Vlvby9WrVqFp556quprZrNZpFKpsl+NArsRdSZtzwbZ9uMqcTKMkl1PV2Xio5pqNVyZorVqT2syP8PKNltAbrdL1VQ4l00S/R7TuSKYppTfhFQEySxA6CtlK0TAhrmFITj122bL6G2P44QjegGoyX44eW7I8nmp5fHBaHavD0/BxxtvvIG77roLRx99NH75y1/iyiuvxNVXX43vfve7AIC9e/cCAGbPnl3272bPnm39XSW33347ent7rV/z58/38z6kwIKPrmRM2kUYJlMMoyQHA1M6CdrlekSoplJDA3A3LwkZM6ahKSu7KCiD8L4EMjfmak+j5dkkOanwWERDW9y+NcrOCALAaOn6cDP91S1W5iMEzcd+HwZjlZzKWm4V6D6cxJ+yru9aYlPGvFLwQWUXALquY+XKlbjttttw0kkn4YorrsDll1+Ou+++2/cB3HDDDRgdHbV+7dy50/driSbNBR+qB0ypQHXra5jW1SoYy1bJDEh8j5XBnLmefM1Hd5Uyj8z1WJDKkHVO+fPJT2FVmvkQpPcA7CxKGIJT1mbrN/MB2FbrT249JN2ltTILzJB1fVdmgathtds2aceLp+Bj7ty5OPbYY8v+7JhjjsGOHTsAAHPmzAEA7Nu3r+xn9u3bZ/1dJclkEj09PWW/GgXWymiWXZpLnwDwT86VdsKKraubJKCr9qQuUy9QGczxa0vJtFR5fz0yMzsON2jZT6OVszbskqu863RkgmU+xJVdBkqvNRJG2WXcn8EYz7sW9SMRjWBvKoPtB9OiDq0qdcsugq/vaiXMSuZR2cVmzZo12Lx5c9mfvf7661i4cCEAYPHixZgzZw5+/etfW3+fSqXwzDPPYPXq1QIOVy3lZZfm0icAtRxH1XQSqBDyqaTaDUVuWSKc9aoJXMdz4k246m0I4ssu9daT9+AxIqHswnw+DocgOD3g02CMpy0excqFfQCAJyRbrTsHunIFp7UyH81use4p+Ljmmmvw9NNP47bbbsPWrVtx33334Vvf+hauuuoqAGZb2qc//Wl86UtfwiOPPIKXX34Zl1xyCYaGhvCBD3xAxvFLpXrZpTkyH4ZhhFB2qQx2mqzsUuWGIjNorZYqllsGmZoZYNeMYZgBiEicng5lndNqmhZ+PZlB8kgpQOgXmPnotzIf6ofLMc3HoA+PDx679CJX91EtsOZ/L0tfVFNwWiq7HBzPCu/sagQ8BR8nn3wyHnroIfzgBz/A8ccfj1tvvRXf+MY3cPHFF1s/89nPfhaf/OQnccUVV+Dkk0/G+Pg4HnvsMbS1iWshU4Xd7RLlyi7N8ZQ+mZ9qGCUzZQ9MrXM2W/BR6eAKyPbdcF5Prq+IvV5bPIpEVM50Yvt8qiq7VA92ZGcEAbs00isy81EKPvJFQ7lRFet2mdUT7L7PRKdPvXFIqr29UzAga7JtNXF6JX0dcctnpBmdTp3DLgfOPfdcnHvuuY5/r2kabrnlFtxyyy2BDqwRGC8NletKxqU+wYZBdcMoxSZjFfbFfsyIGomaGgxFwYAKAejUG3QMB8dzCoMBOW3vTqlwJWWXUvAhMvPRnoiiLR5BJq9jOJ2vmeIXSTpbsIZTBtF8AMCKeb3oTEQxMpHHq3tSOL7Ufiuaup+94EGGbjIfmqZhXn87Xt83jreHJ3HkoBjn20Zhet/tJWOXXaLSR3mrprphlLz0cqGoY6J0Q2JPE7yzXzNkP9RrMJwFrnJNxlTVxetlPtRsCCrLLiK7XQBbdKrSaIwZjHUkouhMen6+LSMejeC4ITPgePOQPNGpar0P20cqyzyVNPOAOQo+alDNZEzWKG/VVDWMkrhRVpsaGZdoXxwG1X0+ZPpuOLtxypjC6qzBkNMR4FSHl93+OHU9uZmPXEG3MgUiMx8A0F/y+lApOrVKLgGzHgwVA/IcA92kpCxbFY+eajSzyykFHzWwul3aYk2Y+ShtJMmpG6XMzoW2eKSsvKIipT06mcdV9z2PDa/uq//DPskXdWsIYbXMh4zrppoAlAU+hoQprE4aDEu7Izo1nZ1axjLXl9vtMqXVVrIWamTS3FQ1rXYa3g+86FQVIgzGeOwZNfIeUOpmPiQF1vU+7yOa2GiMgo8aVOt2GZM0Hl011VLMcjsXaqfsZXp9PL55P37+hz24e+M2aWvw4+27yrJJ8jJm1W5gyVjEEoCq0mCoFoDKCujCarW1xKbtcWuGjSiszEdaXWZRhMEYT7/kGTW6blj3O+fOKvWttgCVXVoW3ueDPQ3pBqwU6XTGdnO0L365nQv1ZifIy3wwh0eZTo8suGqPR8syO7JKWbyGhv8MzSmssuZRqC1LON2gpWVa6gTIMjKCgByxKYNN41ZpsR50qFwlst/DeK4A9jypLstWX3AKcBbrTdjtQsFHDXjNRzIWQTxqPpU0gyOn+o2kegpd5iwSBru5yxTdOVmB8/oEkRmzahqayt+L/AzLJ77KL0uYPjSq523UDpBlZAQBuyTSK1hsCvAlixA0HwEMxnhkl13Y5x6PakjGyrdE9tCZLejIFXRha6YcSnyVsLLLvlRG6PqNAAUfNeDLLpqmcS1+zRB8OLVNynb0C8E9snTjHZ3MS/MKcC4rme+vqBuWJkQEThoa/hhEBnROE1/534vMXmXyOvLFch8ahvxrtPz98RlBGdepnfkQH3wMSC5ZVEOUwRhDtm6Fz3jxM30AWEMM+Z8LSiZftAKJepmPmZ1JJGIR6AawdzQjZP1GgYKPGrA6PrsAZd30wiCsNsapaU35AR17YtINees4BVcdiSiiEZYxE3fd1BpMJcPYzGniq3kM4q8Z9lqaBnQmnO3VRWaTUjVS4TJHATDBqci5Low+q2Sh7oFJlMEYQ7ZNfC3xZ7RsirKY7xN7HU0DuhK1g49IRMM8a8Bcc3W8UPDhgK4blraD9ao30yC0RhEPqhjYxz/1yU7dVgYDvAZD5MblpKEBuPZAgdcp//lVPh3ag9dEBld24F8pwmTnOF80kBWUitZ1u6xUO/gQf50OW0PlmiPzIbzVlmU+JAVQ9fQXoj97tl5XYuq1XY0jmnTAHAUfDvBtilbmQ1HZJZ0tSBcYOY6QTsp5j05PlSo1H4A80WmtG5gMUa1TJkn2etUyLTIzH9XeX2ciCnbPFhVg1RIdAlyAJeHBw5po2y5DcMq6XdQEH/mijkOltcQJTs33MJYtIF8Ur3uoZjvAI1rA7bbNljGvSdttKfhwIF2yVo9yaWYVT+kA8Jff+T3efcdvpdb4whLzOavJJWo+Ju0br6y6cS3HQnZTE7s513pKF18erJlpkfAZ1np/mmanwkUFWGy9RDSCttK4AR6Z16k1VK5TguC0k+kl1FgEHBw3sx6xiGa5qwalpz0OlmyTkcGpN95etIbKbZstg7XbNlvHCwUfDlidLomobT8uIZ1djdf2plDQDWw7MC5tjXqCU/FtmvVabSVmPrh0rbyyS/1MhAwNRvWndAllnhqZHRndLvUGb4nfEOqk3iUEkAze50M0LADIFXUlFgGs5DKzKynMsyQa0Szb+REJ399aWT3zz0UHuu7abBl22YU0Hy0B7/HBsCeGyntKLxR168YrtTXUwd5X2iwDhy+47Mm2+aKOMa4tVZ5i3vnpSUZApzrzUetpTcZnWO8GLVr8XS8VLtOPhn3PZQhO2xNRq31UhdeHaIMxhszyUb3PXvT17eSk64RtsU6Zj5YgzVmrM5ToEyblP6UD/JOlmrkZzpkPucFH5ZOSrICu1qwGKZkPF+JIdcGO+WdsOrHs9fg/Fy0CdH76lXedjk7Ka7UF1IpO9wsWmzKYGFfGw8O4w72QIVrTVCuLWA1WdtkzkkFRklVAGFDw4QBvMMaQOaeDwW+Wsp5UTAMnB8GppI6eej4YsgK6ypuV/G4X57KEDEFm9UyE2jIPH6CPC1qzViuxeRyCN4Qamhb+z2WWXWQITgE7o6JCdCraYIxhB1BhlF3EBp71NCaVzO5pQyyioaAb2JdqHq8PCj4cqPT4AOTpIXhGytpC5dwsJvNFK4Ke2n0iy+fDyQFUbkBXebOSJjitUZaQkfmoLQCVJ3Ct9nTITydW1REg2h9GdbslI5MvWuZzfRIEpwAw0ClPL1GJaIMxhswAyn2WTfS15u7zjkY0zO0zPVOaqfRCwYcDrNW2LPiQNGSIh98sZd0s2JctGtGsTYMhS4PhaK9eCuhyRR0ZgQ6gjCmZD1leAZPOm5eU4KOW4FSqBqO2KE/c06Hq9WoLXGU9eLCSSzSioTvp7knYKyozH9ZcF0EGY4yBTnkup/W6T0RPNPfaagvwA+aaR3RKwYcDtcouoscr8wwryHxYJjfJqYZRMmrb+aKOTL66nXBXIma10cm0rmb22LLOaa3NS2Y3SE3Nh6L1zD8XnYlQrflwtwGJvkYtsWn7VGtvUQxItifnEW0wxrCcWiU8kNXTYFj3REGDDL1mPgBOdNpEXh8UfDjQGGUXOevUqjnKMYyaatjGiEQ0y2JYRj2d3dwXzjC/vPKySbUyH2ozEewY0rmiMIGaWw2GuKfDOt0uyssucsTmVputJLEpYHt9yLIn5zkgeKItwxouF2rZRXC3i4/MB5VdWoB01VZb1WUX2W2htTeugrDOBfM9dSSiiEWnXnJy2xjNtRfP7ARg3oBFmy3lCrpl813bcVRtqy0gTgDqNvMh2ojJqR1RdECnegNiWAZjEtpsGf2K5rsYhiEt8yFzOm+9TISstm6nEl81mtFinYIPB8az5XNdAPsGlCvI0ScA5QFHOFbg3MaVFfNlq9dJYH+5ZXQSmOdw8aAZfOQKutDpskD5cXfVzCaJ992odgNLxCKWK+90L4Ooan+s1c1jridHC2V3usjLfKhqtR2dzCNXemARn/mQI5o1DHumj+przYvmoxkt1in4cGC8VN/jNxPZ+gSg/OlkLFMQln3gqXVj5zcu0R4Kzjd2mdbV5trz+toRj5ofnuhyFjvuTm6CLY9oPUS+aAdQqjQY9QWgssogdVwnBZV56ltsm38+ni0I9VoYVlB2USU4ZWLTvo44krGpFvVBkFU6SueKYB9n/Wtb9LXmQfPRZ5aNd41MQm8Srw8KPhxgs126kvaXKMIp0mXpPiqfTkYkiFvd9rWL27jUpux5ePfIPkl147p6iHZ74xJx4yjT0CjIRPATXytbpRki5x6V+9CoFQE6l3k4LxNBGUHAnjsks+xiC07lll0sd1PBbbaAfX5GJ/NCgz/2uce4GV6ViMx4m9e298zHnN42aJp5DAfT2UDH0ChQ8OHAmDXbpbojp6z5LqMVryu3taxR3CPleX2wG25/R0Ja6tbJw4TBMj6GUT4t2f96toYmXkVDA4gtE6TrTHzl/1xEADmZL6Jg+dCo6T6p951IxmybcpHf/VEFZRc2sE6G3omHeXyINhgD7G4Xw5h6jwwC/7k7dRuJzHhnCzryxeoeS7VIxCKYU2pfbpbSCwUfDlSzVwfEi48qqcx8yHH0c5uJUHNjl2lbb2c+4nbmQ3BAVy+4SsYiVslHxDl14xMg0iyOrRePatYGXInIAJKtF9HMUlY1+O+hiA3VzaRRGboP6/rslCk4LQ2Xk6B34rHFpmI9PgDTyI5lnUV+f920vYrsyGNZUk2b+mBbD9vrg4KPpqZatwvAtRRK2CgNw7CCDSbYklGndd82qUovIKfbxTAMW9DXEecyH6LLLrWDAU3ThG5c9c4nINZbhN+YnZ4OewSWQWr50DDYuS7qRuANtciVlVQFdAwVgtOORBSJUtAoU/exX1KbLaNfgtGYW6tzUdlg9u+7kjHPU38t0WmTtNtS8OEAa1HsTDqVXcRnPibzReRKLZusNTTcsovotkk14kjGRK5oqe/7OxLS5kO4eWoWGbS6yXyILJ25qVGLXM+NIK89bot7g67JazhUvUcGXxaUhaZpStptZQ2VY7D3cFjge3DrNioq4+2nzZZxRJN1vFDwUQXDMKzafKXlsS2sk1EiMF8zHtUwr5RiC2OQkvgR0uF0uzCxbqI0e0R+2aXWxiWutOSuRFA6pwLEkW6mcIrM7LgZOW5mk8Rk6NhnkohFanZpiBa5ArbgtE9itwsg1yeDcYDNdZGc+VBddjH/Xsx934/YlHEE63hpEot1Cj6qMJm3268qMx+inRV5WBdGWWdGmJkPQar+upoPST4f9vmMVzz9CS671PEx4f9OVSZCpI7G+vySNTI77eJKdW5v0KLKdezzq+c4KTpI5sussoMPFV4f0ssuEmzi3WY+RAXX9rXm/fOmsksLwEoumoYqg9dUdGbE7UmUEtKk9Q2c5LhVqnePLL+x2wGd6LJL7cwOIFbX4mazFLqeqzKPhMxHvQ0hKeY6dfP58X8v6jrN5HWrzNonsewCyLUnZxxIyROcAvb3WGzZxd1nL8qlOFDmg3M5ldm1pAoKPqrAasBmi1W5KEimG2dVT4oQ7ITFGziV1nN4cpYl4uXPJyDnyQlwt1mKLbu4CXZECk7rp6YtLwQB04ndpsJFeYu4f/oVK8Rm12csojl29YjCbreVo/mYzBWtTKmMVltAzoA8z5+9omutGqzbZSJXlO7ZogIKPqqQrmKtzpA5h8Se8xCXVqP1YuAk2ppb1ZwOBn8++f8Kz3xkvXSfqGm1FVvmqS0YBsq9EIJeN27KWObfCxIBuvj8+L8X9d23M3MJaRNtGbIzH6zNNhmLTNHJiaJPiubD67UmKvPhvezSFo9iZsnArRlKLxR8VGGsirU6Q8Z4dEY1QyzRG2Umr4dg4FQ71cins0WmE4crOgnkCU69BAMCMhEuNksZmZa6XghJUZkIb5qP4BuC13ZLMd9JVWJTQL7glDcYkxVIyejY8S44Daj5CJD5APjSy/QXnVLwUYVamQ+ZJmPD3JNQn7QSgblGLQMn0ZoPt3MzRHg28PBPloB98xI9M8dT94kqkzGBduduNRiiNBFuzie/XvA6vFqvBwav8ZKNbMGpTIMxxoCEAMq9z4eozEf9Tq5azGui6bYUfFTBNhibujnLGI/O4MsEA5ahTl5oNoB92dwYOIm4yWYLtneJ02bSwQ1kEynkrSy79HJfeJEzc1gWTFX3iRsfDJFdWao1EaqNn9yYtvF/LyzzwYbKtcsVmwJ2dkWWz4fV6SJhrgtDRubSfaAr+lrzl/lgFgxUdmlSxhzcTQG5ZZdha7NMWDeLgm4Ia3kF3KUZRar6y4agOdSCec8GkUJe3lodAGLRiBWAyBCt1XqaCcv0K5PXkQ+Y5bGDKzXdIF47EES1P9Y1mhKc+RiuCI5lIjvzIXOuC4OJZkU+kLkt8Ym+tv1oPoDyjpfpDgUfVWCZj1pll3SuKHzcPT9euy0eRXvczLyIFIl50SdM5oviNq5krOq4+co1RQp5hyvKLoB40Wkmb7uo1i6DiMtEuNks+UBPdTeIKg2GqGyS61ZbwSVXNiBNpeZDlr36AcnupoD9HkQ+kLlu61Z8bTsxr4lcTin4qMJ4xjnzIWu0NsCXCco1CiJFp27sffn3qGzjEuTZwFN5PgE7EBF1E2bvT9NgDZ+qhozMR63PMBaNWJoeVQp9YfMvFHefeG+3FNRqmy5vBZcJcwfNFnRM5sQPl5NtMAZA+AOZYfAzfdRea/WCHSeayeWUgo8qjNcou8SjEesLINpobLhCgCanxlk/zRgr2ZHzP+9/PbdfbvGZD6br4NPaoofLWUPQErUHRYlqC80VdGRLGpr6ZQkx7b1e51+IEoCqchx1H1yZfz8hKOs5ojDz0ZmIIhEtDZeTUHrZL9lgjCFyPtNEroii7m68PZ/5CFLyEVV2SWUKUrymVELBRxVqlV0Azkpa4Idf1A3r9azujE6xGyWgXtnvuqYq2LytqBtcWpsvu4h1OfV6PsezBeuG5289+7irtYJXWzPIOdV1A+M5d5bQIq6Zch8alwFr4Nku3oIdQEzWk32v+xQITjVN40SnEoIPBZkPgBPOCrgnss89GtGsB0on2GefLxpW8O8VNx5L9ehKxqxzMN1FpxR8VKFW5gMQb8IFmPVfFlD3WaZYzBgojL726d3GmOLOJ9/lIjqb5L5Tgtu4ArxHdn46uQ6hemsGySaN5wrWeXTbjhjkmvH2NCq2/bHeZxiPRtAWj5T9myCobLUF5IlOi7qBw2n5mg9ArFka/2BUz5ukU4CJnhuPJTcwp9Ndhyn4aDrqBR8y5ruwG0J3MoZ4KT0qww7caxtj0PfodnMW2YoK2OezKxlDImZf5lbZRVBA58b9EwCSsSiSpeMIsjm73SjNYwp+Ttl6iWgEbXWeDkW4uPJPo5VzlaauZ2eTxKTC6z+NinzwYGWXXkXBhz0bRWzwcWg8C90wvYNmSGy1BfjJtiJb1ut/7pGIZjm3+g082TVTy2PJDUc0SbstBR9VqF92kSGOLJUIOqfqE0TWaL0aOAV9jymXm7PoeTJO00JFWzR7qeGK0H148QkQIZLztjEHL/NYGpoaPjQM9j3UDbP7zA+Fom79W2/vMdh1ahhGVUG0THjvIJGwksuMrmTdbFxQRGq2rGu7xrRmnqDfX/5eEcQFdl6/KTptqeDjpptugqZpZb+WL19u/f3evXvx0Y9+FHPmzEFnZydWrlyJBx98UPhBy8Z92UWGIdbUzgyx3S5eravViPlEZz6cbuz2zUut5gPgfSKCb86qggG3Hhjl6wUJrtwFq4A5RyQe1Upr+nuPvHbDVTZJkHB4IldEvmhma1QITgF57bYHFBiMMUR2q3nVXwT9PgW1Vmc0i8W657Nw3HHHYcOGDfYLxOyXuOSSSzAyMoJHHnkEM2fOxH333YeLLroImzZtwkknnSTmiBXA7NWdBH122UWkIVYVcWSoglPRVtnhWFdX3thFz7jwckMRuzm7ybQEX89LZkdEt4uXp1HTnC6Ow+kcxjIFzO31s555btrikbLynBOiRK7s+kvEInXFjqKQNdVZhcEYY0Dgw4OXEiYQvKzodT0nmsXrw3PZJRaLYc6cOdavmTNnWn/35JNP4pOf/CROOeUULFmyBJ///OfR19eH5557TuhBy2a8hr06IGe+i61858suEgSnWZeGSsJMdbxaV4t2j6zMfIjudlG9OXvQfAjIJvnL7ATXfHh9GvUbDLjVJDFEZeis4Lg9WAreC0wvcVh02SWlRmwKcO9BoODUredG0MyHl6xlLSzBaasFH1u2bMHQ0BCWLFmCiy++GDt27LD+7tRTT8UPf/hDHD58GLqu4/7770cmk8F73vMekccsFd54xknzIWO+SzWrZRlPKqoNldhTgts2RlHn1DHzwWWTRFg0e9ksxWYiVAUD3jUmQQSgXp8Og55T/6n3YEGy0/Upk35JrbYHxtW02QJiu9VUf/ZBDcYYLPNxKJ2TYhinCk/Bx6pVq3Dvvffisccew1133YXt27fj9NNPx9jYGADggQceQD6fx4wZM5BMJvHxj38cDz30EI466ijH18xms0ilUmW/wiRb0K1WP+duF/HzXapbgbMnFfWCU2FlF7eZFsHZJHuuS/XMhyiLZnYN1Ht/gBgXV2/BTvBMi5shdvZ65jEVdQMTPm+KKa9Po8lg79F3sBPw2hmZVOduyuiX1GqrymAMsCfbiim7eMt6BW3tDmowxuhtj1t703QWnXoKPs455xxceOGFWLFiBc466yw8+uijGBkZwQMPPAAA+MIXvoCRkRFs2LABmzZtwmc+8xlcdNFFePnllx1f8/bbb0dvb6/1a/78+cHeUUB4AVqng122zLILn/lgnS+ZvI6MgFHzpsmNW9MvUYLTcKyrnTwU2uJRy6tBRLttWJkPV8GOwDKIm/Xa41HE2HRiRanpoO/RDh7driem7DLMlV1UIdIjg8fSfCjJfNgdgEEzl14zH7a5pN9rTUzmQ9M0rvQyfUWngVpt+/r6sHTpUmzduhXbtm3Dv/7rv+Lb3/42zjzzTJx44om48cYb8a53vQt33nmn42vccMMNGB0dtX7t3LkzyCEFhhlAdSaijnbZUsoupY2QPZ0ApucHu5mLeFrJFnRLYa9q1opXt8rxbAF6AAdQBnuyrNbGKFJ06jazA4jpkvLWfRI8SPYSDJRPJ1Yjygv6IOA32Ana6TaquM0WsLMGIrvnALVlF9YunCvomAz4QOYlq8f/nIhW26CwjpeWyXxUMj4+jm3btmHu3LmYmDAjsEik/CWj0Sh03dmONplMoqenp+xXmNTTewBc2UXK+Hf7ZlRuiSzCVCdfel3nrA5DnFW2y0xL6ZwaBiw77yCw81Wtph5W3ViELb89dM39eqrKPObPBU1Nq21/9JLZ4X9uWmo+SpnUyXxRmFbAMAylZZcObkZN0CDKf5ZNzbVdi2boePEUfFx77bXYuHEj3nzzTTz55JO44IILEI1GsW7dOixfvhxHHXUUPv7xj+PZZ5/Ftm3b8LWvfQ3r16/HBz7wAUmHL550HY8PAOgVVJLgGa0yBM38vTjRKTvermTtIWiAGL1AJu8+05KMRaybihjr6lqZj3Da9cRkItxvliIGy3kp8wBcgOW7HbG0nstyRNDrdCzrL9gJ3mo7VeMlmy7BmVTAzB6wWScqMh8iZ9T4Daz9XmteMy21aIaOF0/Bx65du7Bu3TosW7YMF110EWbMmIGnn34ag4ODiMfjePTRRzE4OIjzzjsPK1aswPe+9z1897vfxf/6X/9L1vELx2qzrXFB2jf1YBMOeVS0hnrbuIKnl9lGEnGRaSlP2Qd/r04Op4C4souXzA7/M2IyEe4/w1zRv2bIqzFSUAGo1/WCdvSoFh0yRi3BqbrMh6ZpwkWnzGCsOxlDewDLcC+ImlFjZxFVdVaJabUFmsPl1NNZuP/++2v+/dFHHz0tHU15rLJLjc2Sbd7M1rlWlsQNmXwRmbz59DDFDlygxbqnNs3Sk6c5wr2IZMz7jSXlIdPCjutQOhfYYj2TL1r14GpPlvZkzGAbCJ/ZUW/6Vf8z7CoNwzIMc816s1lqr6e69VXNep6DK0GttsOKh8ox+jviODCWFeYdxMSmgwoMxhiivr+eBaeK27pr0QwupzTbpQI3mo+2eHBbZx4Wwcci2pRAxiq7KLYT5o9juj1VsnJKNKJVVZbbMy6Cpm35zE79jV2EQZXXYVhdyWDZFu8dAUFbX73W4UVpTFRrPsxrr7ddXdkFEO/wyzIfKjpdGCK6dvyMtw98rXnMtNSClV32j2WRKzhrKhsZCj4qSLuoATNbZ0DMIDReHFnpdsjabcWUXdxf/NGyjUtNpC+qvdfyUHBwjxQ1H4LP7LhxqQxqz5zJF60bjWdL6GliwuW19TVoMMDW8/r+JvNF5Iv+b/phCE4BcSULhjXXRYHYlCGidDSZL1p+Tn4CTz/ldva973Uxt6geM7sSSMYiMAxgz+j0LL1Q8FHBeGmuS6eDtTrDmu8iYrS2gyEWwJvqTL9BSl47CUS199bqdAHECU79liT8blz8Buu21BfkMyzqttuvVwGon/V4d+FGrcPzWrBxn2sahoERS2CuNvMhcjAbYE+0VZv5CC44ZdeLl/H27Bop6IZVJneLn2u7FpqmcaUXCj6aAsvno87N3fYXECeOrFb/FZkm9Vvf9vukbrlVuoz0RXko1ArmAHHn1GswF3Tj4sfNux1dHmRzLp/4Kr8uns4VwSxeVHWfeA2Q49EIOkqble+AJ1uwnrrVZz7EdXoBwP6UOoMxhggRPv9dcjtbpyMRtb53Xu/7Ezk+0xI88wHYpZfp2m5LwUcFVtmlTvARdGPmcbICN/+MCU7V2wkH1Sh4Xk/A4DWgvpivT1jmw/vGxSaY+tm4/MyGCPIZsn+TiEVcC46DjB5g60UjmutJr9Y8mZw/czqvn6G5ZrCs52jpumuLR3yJgIPQLyvzoVBwKuLhwU/bq6bZpWivnz27zrxc2/VgHS+7pmnHCwUfFTCDq7qZD4FGY9Ws1Rn9gsSRgPr6fVhDu2oFc4C4zIfXzA7/s36uGz8juYMEyX6CnSCfIb+e26dRtp4fc7p80XbJ9PI0GtTvwZ5oq7bkAsgTnA52qdR8MB1c8LKL1yyE3+wsX94TNcWYGY39cU9qWopOKfiowHXZReAIeHsOibMhloh5DH67T4IP7fKqJg9oXe1g2MZg53kiV0S24N/p0Y9dcpBz6scnIIgGwxZjen9//oIP7+u1xaO+zen40lctX59KggbJdnCstuQCiBechpr5CNAu7NU8j+H3+vZa8nbDggEz8/GrV/fhnbeux9/c9zx++uLbVmat0RF3JpoE72UXkYZYznNIUpkCCkUdsaj/eNHrk2xwDwV/gszA7pHp2pmP7rYYIprp0zIykcfsHn9pUD9PTyIyA37W86Oj8bNekMyO3xt0T3sMB8dzpQ2l3fW/Y++vPR5F3MP3KmiQzMSmYQQfIsc1ZPJFK9APQ/MRJBscNPPhNZi37oVJcZ/52mNm45LVC/Hoy3txcDyLn/1hD372hz2IRjScsmgAa4+djT89ZjYWzOgQtqZIKPiowI3PByBOnwDULrv0cl0Go5N5zOjy/yVXbifsYQgaIMYHA+A1H9WDj0hEQ19HAofTOQxP5DC7x1/K2F8w4H/j8hrMBV3PyxwZIev53hDipeDD69OoP8fJoF1gtaz/ZSMy83GwNFAuEY2U3adkw85bupS59GOA6Ndt1G/G23rwE9Bmy2hPRHHL+cfjpvOOw0u7RrDhtX3Y8Op+bN43hqfeOISn3jiEW3/2KpbO7sLaY2Zj7bGz8Y55fa4MH1VAwUcFbuzVAb7VVqTgdOoXOBaNoKcthlSmgOGJoMGHVwGomDZG90O7BPl8uEhr93fEzeAjwBOgv2DA/8blJzMQZLicFQx4eFrjpxMXdcN1V465nj8TJr8ZM7/BR9DrNCyPD3NNu+SYyRcDCV73j9nTbEXpGNzQ3WZ2exV1w3fm0o9+CuA/e6+CU3EGY5VEIhpOWtCPkxb047qzlmPHoQkzEHltH57Zfhiv7xvH6/vG8f8+vg0zu5I4c/ks/Omxs7HmqJnKLPGrQcFHBW4GywFcVkBA2WWkzpCp/s5EKfgIZ5CSugmlYjQfbmrq5tNTWrloLYjRmHqNif8yD2BqKno9bLBeM2WVa/p/GvW6AQX7XgyH5G4KmJsnv3HP6Q0QfKTs4EMlkYiGvvY4DgXIXAYvu/i71kRqPpxYMKMDf3XaYvzVaYsxOpHH46/vx/pX92Hj5gM4OJ7FDzftxA837URfRxy///u1nkqOIqHgo4K0ZTLmtuwiLvPhlIbt60jgrUMTAqc4qslE+B3cFLSUVUvAy+gT0a436S2zY/5s8EyEl9RtEI2J3c3j/v0lY1EkYxFkCzpSmbyn4MO3CNCnOZ3fp9+gbfajIc11AUrD5ToSODiexeF0DnN6/XepHBgPJ/gAzAeLQ+mc75ZhP1lL/ue9PnT6vbaD0tsRx/nvOALnv+MI5Ao6nt1+GBte24f1r+7D8jndoQUeAAUfZWQLReRKzpP1Mh9BNhEeXTdcdGcE96XI5O33prrV1rVVdmmTm8gVfYtr3bpHijin6gWn3m+YQQLIIBqM7HhW2dOhiPZHb+uVgp1ssMxHGGUXwLz2D45nA7fvHwjBYIwx0JnAtgNp39/fZs58OJGIRXDa0TNx2tEzceN5x2IsG/zBOQjUasvBsh5AfctdUbNdUpm85eroVHYZEPCUzi5+TTOnnbohSMq+fHCTt8wHUO6u6QW37pHWfIggFs0+BkUF2bjUm4z5rIv71Jn4fTq03YZ9ll0UbUAMu9tFfdkFsK/9oJOybWt1dR4fjKA28UEDT++BbvjBB4+macqzMJVQ8MHB+v7b4pG6T91BWgp5WGdGZyKKRKz6mn0i7YQT7sbbA8FusrydsNsyQTwaQVvcn2cDYyTtzj1SxFhuX8FAABFvkO6alI9hWF6HrlVb0wuq2x/9C1yDlVxHLZOx8DIfQHDvoANj4ZVd7Myl3+DDu7MtEKDV1odnTrNDwQeH1eniQt3PnrZyBR2ZvH+jqnpzSACxg5T8iCPHMnnPG5dfO2H25Rz1KeStp59hBPUK8JPZAWx9QhD7cT/dNUXdsNw8va7nNTPgvyPArwbDXzBgCVxdDumz1wtWcrWu0c5wMh92u22wB6cwhsox+gO+B7+BbvAsGwUfDAo+ONI5FnzU3yy7EjGw7rIg3RmWOLLT+aLsE9CbH8SaO180kPVo3+vXTjioyLWetTrDCuh8nlN+JLc6Aaj3G2b5MCy/mQi/3SCN7bvhx8ekfD3vnyGv8Qov8yFmvsv+sZLmQ6G7KcN2OQ1advGZ+fBYNvV7bTczFHxwuLVWB8x2L79DhnjcPKmLEUd6v/g7uQDL63v061YZtL13xGUnQZ+V+QhmoOY3s+P1Kd3MtHi/YWqaFqAsEVAA6rkjwOfT6DRqtR3LFCyNl5dOIJGIcAjVdQMHx81/H0bZJYgOrjxrqfZao+DDhoIPjnGXHh8MEfNdalmrM0QKTr1c/HyA5f3L5s9OOLCYz2PZxe859ZvZ8RsIZAs68sVSpsVnMDDqURwd/OnQb+bD77wNvwGyv+Ajk9c9D/QamTSvt45E1Jczpwhswan/h5nDEzkUdQOaBswMYHzolyCarUxeR8HneHvei8hLKVqmydh0hYIPDrcGYwwRs0gszUeNpy8RglO/N3a/I9JT1lOlP/tiv9mkYZfukazMNTKZt8onXvA/h8RcN1vQPQ21Y+dD08yMlBf8+GAUdQPpnHl83oMd7+vpumEF/941Jn7r8P5S4bz7sdeAZzhksSkgRkPGDMYGOhKheEX0ByhFjwX5LnEaqomcu++vYfi/tpsZCj443M51YYiY72KXXWq1hdrKbq/CT4b6vnafwU5AS3c31uqAPc7cMAKKPz1mdvjA1st7ZD/blXTfrcTw8xnyE1/9WlB7aUNP5wpgl7bvTIuiVHg0olmt+F7XdCMwl02QjZvB9B5hlFyAYJqPVIDvUnvcu4YqnStapTbKfNhQ8MHhdq4LQ1XZhX3RCrrh2xhGtXgwiEGV+e+DZT7qlV0SsYgVCPh7evK/cfkpZQVRy/tR6LOAOhmLOLaAO+HHH4YdWyyiWe3W7tcrnyfjfk3/rpN+RwGEOdeFMRBQrAmE22YL2A9rbNq3F4J87n40VOzhxs+13czQmeDwWnbpEVh2qdXt0haPWhftiM9BaP5NddQaRnX71Jgw3Ha7mD/jv27sV6wI+Dunfj8//+v5C1bL1/MRXLXHPQ8p449x3OWaZou8N8ff8jX9fS/CnGjLqJwK64cwDcaA8mnfI4qEzYwej6LxINd2M0PBB4dnwamIskvandthcIGkWjfH4FbZ/s5pPat6niCq/yCtc37Oqd+ha4A/HY0118XPej6+F0GCq0QsgmQpO+N2TT5ocPt95/Fr6c42yrA6XQDz2FnpwG+3V9iZj1g0YgUgXr+/QYMPr4FnkGu7maHgg2Pc5VA5hogR8KMu5pDwf+87+PBhBc7/vOdWWx9D1wD/Jj4MdZkP/6lbPyLeIGr5IJkIVcFV8A3B23XDfq4zEfU1Q8i+Tr1mPtwHx7JgU2EB/14fB0I0GGOwc3jYYzY4aOeJ1+ub2myrQ8EHh112cdcC53fCIY8bwSnAi05VO/qpznwEs65mZSnZmY+wNmc/mQh/6/m/QfvxwUj5FPBaa3qcJxOkrMT/O8+ZD6u7LbyyCxBcdBqmwRjD72RqUYGu2weyoNd2s0LBBwerF7uxVwf4+S7+NspsoWi1a9V7Ug8+SCmodbUqq2z/1tX5om4Jct1kPoK4nAYLPoKUJdQEA0Henx8fDL+tywzvmY9gqXC/16nbVnDZ2KJTfw8zTPMxGILHB4PZxHsvu4j67CnzEQQKPjjsVlt3mY8gToeAncWIRrS6T7TBBymptZL2q4mwSxLeAzp2PjWtXJDmRBD/FNVlkCCbs58JzEFu0H58MIKmwi3xt+unUVF1f3+ajzBbbc31g40XsMouPeEITgH7PXgtu/g1l2N4ve8HzbI1KxR8cNizXdyajPnfKAH7i9/rQgXdH9BoTLXdueoJpYAdmPW0xS1BXS2sAVsBvAJUtb6K6D7x0/rq5/358cEQJwL0mvlQswEx3PrQyCbItT+eLVgZ2zA1HwM+y6aqrzWa61IdCj44rLKLW5+Pdu83dR6708W9PsHPk0q2ULTS337LIF4DrKCOql4dQAH7qdKtmC/I05/fcfP8v1HVDRIs0+JXg+GvDOLXAdKri2tomY8GEJwCXBnXx7W/P2XqPToSUdfifBn41a2M+xTfM/wGun7a8psZCj44rLKLS8vdoCZjXnr+gwhO+ePz2lZovUcPUxzLrLI92quXp+y9nVf2FOc2pW0LTv2f0yBlEF+CzACtr6p8Rfh/57711b9vCr+eqsyHn+CjqBvW+egNWXA6EOB+0gidLoD/sksQ8bb57/yVXchavRwKPkrki7o1Nt7rbBevzoqMYQ9PQX6V3YB98XclY67KETx+2ol5q2yvaXu/DqCAd/fIINmkYAJQtQ6n/KA33eV1Kk4A6rUu7nNDsLxFvBo/qdmAADNbxr4XYZddggjYwzYYY4RXdvF3rVHZpRwKPkqkOdtyt6lEP86KPF48KYLMMgiWsrezO27nyrAvWzyqWeZP3tb020ngzT2S9/nwMjMn6KCoYA6g/rtPDMPWNdVfT0xmwP0NWlQmwpvA1U8wZ67nvdWWlQW7krFQhrHx+N24Aa7TJfTMh99WW0HXtkuLBZpoWx0KPkqwzSThYZZFIhZBe9wU1vnRfYy49PjgfyaIFXgQvUBRNzCZd6fB4MWRfuyE/dbTvbYxsppxrqC7fm9A8EFR/lpf/d/AkrEI4lFvw7BEpabd36Cn19OonwB5uEHEpoB97fvRfITtbspgpWiv90T14mbKfFSDgo8SXq3VGeyCGvVhNDbiYqgcg90sJvNFZDxslECwjasj4X2KY3C9gD/zttFJb5mPzkTU2pS93MDY+4tH/Q2K8rpRGoYR6AamaZpnfZJqTYQt4FWV+RCzAXnJfIw2iMcHYD/MHBrP4ZW3Rz3dUxrBYAwoz964LScC4gzmvA6Wo8xHORSKlfA6VI7R0x7H/rGsL9Gp2wmsANCdjCEW0VDQDYxM5DGn150XCRCsfs+mOI5M5JGazGO2i77+VMCUtl/b+mEP7qaA+d76OhI4MJbFcDqHI/raXf07e85K0MyOWe6p9xqZvI5C6eYaZHM+lM55FoCqtjtX3v7o03WSvb9cqSsrGav/fRxuEHdTAJjRlYSmARO5Is79l98hogGLZ3Zi+dweLJ/dbf53TjeO6GufMnb+QAMYjAH2Q5tumJ+nm4e4TL6IXNH/QEHAvj+NZwuuvr+U+agOnY0SXue6MLyaG/F4KbuYG2UcB8dzGJ7IYU6ve7FX8Ei/FHwo20j8uap60dAw+jviODCW9aT6F9UJki8ayBZ0tMVrb1xsvYgGyz/D+5run9YKRd3ycfDbfeLF7lzXDYznAgpOFQtc+YeUsUwBya76n4tXQbRMetvjuPn9x+HRl/fgj3vHMDKRx7YDaWw7kMbPscf6ua5kDEtnd1nByPI5Pdg9MgkgXIMxwCx7dyaiSOeKGJ5wF3ywz13TgC6XXY2VsO+Sbpgl2FoPrCKu7WaFzkYJ21rd2809yHwXy2TMtS9Fwgw+PIpOA2+WyTiAyWnjoeDl5u7H7yDoxtWZiEHTTAFoKpOvG3ywIKwrGfM9ktvLOR3nxNcqAsjxAN1R9npqW21ZV9Z4toCxTAEzXWQBGsVgjHHJ6kW4ZPUiGIaB/WNZ/HHvGP64J2X+d+8Ytu4fw3i2gOd3jOD5HSNT/n3YrbaAWY5O5yYxPJHDYnTW/Xn2uXclYlMyOm5pi5saqnzRwFgmXzP4EHFtNysUfJQIUnYB/Hl9jHgou5g/N10EVkHr98EyH27PJ+BP9R80ZR+JaOhOxpDKmBvXrO566wW3Z/aiUWCfs3mT9ScL81I6Yz+TiEZ8dUeZ65nnZiJXRKGo151UaznU+my1NddkwYe763TE5QRr1Wiahtk9bZjd04Z3Lx20/jxf1LH9YBqv7Ulhcykg2bx3DG+PTGJ2TxKLZtTf7GXT35HAruFJ1w9kIkogZik6jsPpHMYyBcztrb9eIhqp+5DRalDwUcKe66Km7GIYhuebUdDWsqBtharq934m6RqG4SvzYSnmPRgVibiBdbfFkcoUXGXMghpwAd7KEqMCBHJeBK58Zs5vZqfSnI4JtKsRxPGXp7stDoxmXLv/suvTzdyhRiAejWDp7G4snV0eHY9O5tEej7ruCpSJ7XKqds5Kd1sMh9O5ut/foFnnZib8q6dBYMGH7xHwnu3HbWMyt5tlWKY6nod2TYoKdtwHBJOckMzLk6WfgE5EMOAlmyTiBublOhUTXLm/ZkSsF4/abe/1zmkQx18erx02fjJzjUhve7whAg/A+8BNUcGA2+8viU2daYwrqAFIe7RWZ3gR1vGwL0t7POo6HdcXUl+7V3tuUcGOl8wHOyeJaAQdHkSZfqYFi7iBecsMBLdn9rJRijBF8pItE2XC5DbgCeL4W209t9fp6KT3zBxRm36PTq2iggFWcq13rVGbrTMUfJTwW3bxr0/wPmDKrx34WECNgu9BSoHdMT0YOKVt8a6X1L2d+fAzZ0VE5kNVMOBdgxEk2OGD8nrusXbrspqn0SBDAcvX8/bd99ONRdTG67RvEd9d899T5iMoFHyU8Ft2sUoSHssuIz7bQgHvFuvTzdHPz8A+v9NC+32UssRszt4zH4EyLR6yVyLLPPmigUxel74ev2a99yjs6dfj96KRWm2bBXvgpuLMh8t7FGk+nPEUfNx0003QNK3s1/Lly8t+5qmnnsJ73/tedHZ2oqenB2eccQYmJyeFHrQM0n4Fp+ym7mHqK8Btlp3e20K9ll2Cdkv4HxKmRuAK+H+q9NNBpFoTIeJJ3dbteAh2fGbKANOPhFU06l03Irp5APcBnbgyj/vrtFDUrZ/rmyaC0+mA1wF5ogSnbsvtVlcVlV2m4Pludtxxx2HDhg32C8Tsl3jqqadw9tln44YbbsC//Mu/IBaL4aWXXkIk0vgJFnZR+u528Zj58LNZDnT6FZyKMcVy+x4th1OfbYxeHUABvo3R25fcn+A0nDKIGA2Gi8wHG5oXoA1V00wfjFSmgFSmgFk9NdYTnIlwq/kIOuLci96LH78wXbpdpgO2CN/rkDdVmQ8xwU4z4vkTiMVimDNnTtW/u+aaa3D11Vfj+uuvt/5s2bJl/o9OIWzaZ7fn4COY5sPLU5Cfp3TT/tlMe/u3O/f2HkU4qgJ2yr7dhYB0JO2vk4Cd07FMAfmi7srXIsh4e4YXvYCIpyd/3TXBMxGml4m7dsSgT4duhcri6v7uMx/sO9vdFqvrQUK4h5Ww3JoECgs8XQe6VHZxwvO3YMuWLRgaGsKSJUtw8cUXY8eOHQCA/fv345lnnsGsWbNw6qmnYvbs2Xj3u9+N3/3udzVfL5vNIpVKlf0Kg7Rfe3Uu1etlLPuIj7Y79pSeyuRRKNauozP4G3+XAs1HUTd862cYnYmY65Q9Y9jDkD6e3vY4WGLF/dOTAA2GTx8MFesFdahluB2gJ74Or0bz4aUF3evQQ8Id/Vw22M39l5XHSXAaPp6Cj1WrVuHee+/FY489hrvuugvbt2/H6aefjrGxMbzxxhsATF3I5ZdfjsceewwrV67EmWeeiS1btji+5u23347e3l7r1/z584O9I5/YZRev9urmRVXUDWsehhu8jn8H7CyJYbifomu9L246rVe8pOxFWHNHStbVgPupoX6tq2PRiLUxuy1nidBg2KUsNT4Y7N+OZ21/GSdEtQe67egRlflgWUtVqXAvQTmJTeXAyi75ooG0i/uv6kA36JDNZsZT8HHOOefgwgsvxIoVK3DWWWfh0UcfxcjICB544AHouvkk/vGPfxx/+Zd/iZNOOglf//rXsWzZMnz72992fM0bbrgBo6Oj1q+dO3cGe0c+YYJTryK79ngUsdKm7qX04ifzEYtGrC+NSke/yimOtdcr+W3EIq4mfTrhVeQ67GFIXyVeylnlg6IUlUEEPK3x/3Zc0dOa22yL6u6TlPC6f/3rxm9mjqhNeyJqWfK76QJUHXhS5sOZQMXHvr4+LF26FFu3bsXcuXMBAMcee2zZzxxzzDFWaaYayWQSPT09Zb9UU9QNTOZZ2cXbhqlpmq/5LtZm6aHbBfDeGiqybZJNcay9nigxn7uUPSPIzd2L6JQfFCXinLrpkhJxTs1g0Py6q6pT97jM7ojrQHCno7EzLepaba3MHIlNhePF/0i94JRMxpwIFHyMj49j27ZtmDt3LhYtWoShoSFs3ry57Gdef/11LFy4MNBByoYvFXjVfADeUugMNkvEc2uox1kGIp7y2BRHoP57DGqtzvBqXT0aYGiXF5dTUYOi3IojDcMQvjm7fVoT9xmqzUS415ioE5z69aEh6uPlnihKz+RecBq8c6xZ8RR8XHvttdi4cSPefPNNPPnkk7jgggsQjUaxbt06aJqG6667Dt/85jfx4x//GFu3bsUXvvAF/PGPf8Rll10m6/iFwEou8ajma6qmn44Xv5ulXSJwqU8QcKNlUxwBdWlGrxbrwcou7m9e1lNzwJuJW6HyRK5oaTSCrum1FTXwZ+jS2Ex82UXNhsCu0VxRRyZfOyM4MskceKnsIhq35ouiBgry/348W4BeQ0NFrbbOePr27dq1C+vWrcOhQ4cwODiI0047DU8//TQGBwcBAJ/+9KeRyWRwzTXX4PDhwzjxxBOxfv16HHnkkVIOXhS8wZifqZpep7DmCrqVbfHryKlyhDT79+YI6To3dmFqcvf19KJucHMz5JZdRNeMmVDZKePG1otGNGtwmv816weQ+aJulSBFZT5qZSLKu6NU1eHFXKOdiRg0zRSAj2UKNTNhflrrCXfYmY/a319RAwUB+1ozDLMUW+27IqLzr5nxdEbuv//+uj9z/fXXl/l8TAeYqZLfC9IaMuSy7MKegjTN+w2+z6PXh+ihXaozH26MzVKTeUuH4cfAySq7pNW4jQKmUDka0VDUzbKKc/Bhr+d33Dyjx0VmgBej+m3NZrgJIEV0RzF6XAasoq5R1pU1likglcljsDvp+LOjPhyNCXe4FYyLGigIAG3xKBLRCHIl59pq93GR13YzQm43sDMffoMPlvlw3xZqfkl62+OIePwSeBecihGAup3iqLqVDbCfeLqSMV+jvvtKT05ujIpEvT+zlFU/GBBVo+Zfo1YAyf6uPR51ZbjmZr1a3wu+OyqIhoZfL5PXkXfwwRGpoQHcd/RYjsbtVHYRjdtssGjDr3rfX/agErTzr1mh4AP+57owvGo+hn26cQLuU4wMYZ0LLgMs8YJT9+6Rfj0UBjwEdEEnBPN42ZxFrOcmMyBK/Fm+Xv1gJ2hwDJQ/PDitmS3oyBVZ3V9kQFf7u08+H/Jw2+0iuu213j1K5LXdjFDwgfJ0nB8slb3L2SdBNkuvFuvilf1qhoR5sR8P6h7p5ZyKzES4CwbEZz5qBTsigw83XWAisxCxaASdJSt+pzXZ+9M0oCuhJpsEIJAmiagNK2XVDz7Etr3WuydSm21tKPiAyLKLS82HD4Mxht+yiyoDJ9WZFoBvW/b3Je/zlPlgnRKKMx8CbmBuAkiRwYC79USnwmtnW6wHjUTMc8mz9nrO7zGIwJyojyUYr6PZEhnIA/UbDSjzURsKPmAbZ3k1GGN4mZsBBMt8WIOUXIgjzWNS230iXvPhpuzifUIwD3tyGpnIu3ZwFZMZcB8MiPAJcBfsCMzstNvOuE7tiCIzLfzrOD+NigseAXct4SzroWn0FCwDt2VT0W2vtg7OIfgQ1PnXrFDwAb7s4u8i8WoyJirz4WqQkmLfDfHdNfXPaVADJ3ZOC7phdT45IaqsZL6GGwGouNkQ7jQY4tcznXFrPx2K0LQA9QMsWZmWWgEd+773tscDd1kQU2Hf33qCcVmCU6f7Plmr14aCD/BlF5+ZD5e2zowgmyW/UY7X2SjNYxJbdqnrcCrYwMlNQBc089EWj6Itbn4V6rXbiryBudF8iLyBedNgBF8vGbOdceulpsWXXdRsCG7O6QjTe5DHhxT6SpnLTL622Zu8a03Ntd1sUPABux87aLeLVzdOP5tle4LbKL04cga15vY4y0DUem6G2Y1MBst8mP/Wm2JeRB3XTTZJbPDhXoMhoiyhaVrd6yYlcD3+dZw3BFmiQ+fPkHW3kbupHLqTMWu4Z63vr8isHuC+1ZbKLtWh4AN28OHXVMl72cX/HBL+39XbKHMFHdmCmLZC1fbqXobZBSljMdy6nMoQgNbORIhcz0OwE9ABsnJNp6ygtEyE44Ygq92yfuaDxKZy0DTN+v4eruH1obrVVrTAtdmg4AMiul3Mm0q2oCNbqL1RAsHmkABw9UUDym+IouyEa91kC0UdE6VAIehm2RaPWE8z9XQfQbtdAPczc0QNXQPcBQMiN8teF4PlxAtA3ZZBRD+NqtJ81P8MaaKtfAY40bgTooOPemVTarWtDQUf4MouPvv+u5PmjAfAmylWr2+BZP0vGn8sHYkoYoLcKmu9P5F2wrwDaD3/lJGAmg+Ad0lU081jvoaXYEBcsDOZLzo6gIoOBuq3I4oNBuptCCIFw2XrZWtkPqzuNiq7yMJN5lKkYR9ArbZBoeADwcsukYhmGRbVK70YhhG4TBCGo5+1UWYL1pTVSliQIMKaG3A/FXVYwLhyu93W+ZyKHhTlxh9GtQOo6FRxvblHom/Q7l0n1WxAACc4pbKLNNxMtlUvOKXMRy0o+EDwsgvAd7zUfkofzxZQKG3evoMPy9HPbWeGuKdmAI5dNvI8G5zPaSZftKawCsl81Din/NA1sSZcakzGYtEIOkoOoKpSxfVaX0WL8tSXXep/hiI0SURtBjrrf39FZ73c6pko81EdCj5gbypBgg+vMx6SsQjaE/5ae926nIp8im2LR62hbcraGF0Ms2MGTtGIFuhL7iZty44jGYv4GmBXSb2bl2HYmRbVmYHp2o5Yvw4vr9XWqSuL5rrIx53gVG3gKbJk2oy0fPCh6wbncBog8+Fyvsuw0M4MdZkPoH5rqOqnZoCfFhoPNHLejY5GljjSyQE0nSuC/bFojYLz05rYdkS7LKEq0+LOdVJcu6X5OgXdQCZfXUczTJoP6djf3+rBB9/5J7rVdtyhFE0+H7Vp+eBjgjOlCVZ2cTffRcRTkJv6JtAET7EuNB9MIOpXvMtwo6OxPTDEPqUbDg6gbL1YRLO8XYJSS8SbL+rWBirqPdYKBopc4C8+s6Om1bYzEUXEEptXX3OUul2kU69sWtb5J/haA6aWoss7/yj4qEbLBx+s5BINeIN341YJiMl8hGGIBdS/scsz8ZFfT+8LIfORjEWQiLJS1tT3yG+UQbI6PLVaX/ljCNqaba/nfM2I1tDw69W3VxeznqZp1rlyWjPILCfCHfXuieza7kxEhVncJ2POpejyzj/63KtBwUfWviiD3ODdt4WWOjM6g3RmMM2H6rJL7ZS9KGt1hhtHThGdLua/d6/5EBXMlbUTVw0GxNeMawV0bD0RrdmMWg6nojU0gH2uclU8dwzDkJIKr3WdihJEE7WxRfi1gw/RgYDT9c1+3xYXd203Gy1/VkR0ugDu57sEnUMCeDfEEu1WqUrz0eMioBuZDH4+ATv4mMgVHedDyNm4agUDYoM587VqBAOCSxJA7Rk9MjaEWu3EmbxudZqJsnPnX6vaOWWC6Igm7ntITMUS4Tv49IgWmzKcdHAkNq1PywcfQT0+GG5nn1iajwA3vz4XGyXAiesE3Whdd0oIusm6KWUFnWjL6G6LWbV7p4ySaJMic13n95iSsp7qTIvz92JMcCYJMMun7Pqb+jRqBwKdPjvNqlHre8EbjEVooq00WPAxli0gV5gq/JVlde40WoPEpvWh4CPgUDmG2/kuIjQfPW0xq26p0k64u07ZRfXsBMAW3QbNfEQimuu6sdDMQA2TKinr1Qx2ZL4/Nevxr1f5XWTrdSXFaWgALrtT5T2Stboaetrj9sPD5NTvryzDL9t8seJao6FydaHgQ4DHB+Cl7BJcfKZpmnUzq61RUDs3Q3Sq0dXEUIFivr465Sxb0yIwM2B5mdQKPtRqPmRkPtK5IgoVlu7SNwRFqfBa2SsSm6ohGtGs2UXVHshkZSKcvk9kMFaflg8+WItj4ODDddlFbHeGq1kGgg2cnLI7ojdnN+3LIt0jbfM2dXXjWhkzGevVuk5lalqAqe2I8jcENanwWgHdqCBNElGf/hpGY7IEp87BhxyNSTPR8sFHaGWXAN0ugG0nrLbs0njW1baAV0Tmo3bZRXQmiX+tWt0gIp+eams+xD+txaMRtMeZpXv1G7So1mxGvQ1B1XoAl/mgsot07C5A57KL6EyEUyna1r/R5+4EBR/Cyy51Mh/W+PegmQ8PpljCyyBqNR9ODoKA3U0gJvNR2+tDZuajlu+GegGonM3Z8QatekNQtB5AE21Vwr6/h6t0vMj67B1bbdlYBIGdas1GywcfwlptXWyU+aJuXZRBN8t6GyXvVimu7OIu8yHaZAyoPszOnBAsMPhgw6kcnGPlCE7rl0FE3sDcBTuyBKCVGgxJ3gsOIl7boVaN1wPACU5J8yGdWg9kTBCqvuxCn7sTLR98jGeDz3UByi+ycYfNmT2laxoscZRfatU3AVlulc432VxBfLCTjEWRLBn0VNVEcBOCxQpO1Ti4AvWCAfE3sFoZOtWCTFl1cfWZD+fPUFQrOFGfgZplFzEPmZXU66wizYczFHyUIuKuZLC+/0QsYtmzOwkk2Zeipy0e2OK3XtlFiltlDQFo2ewEgV/wWgEPK2G1xSNoiwf3bXDbaisy+LDbNNUKQM1gsdwjRlpq2iG7E1YHgqrMDmBfS71UdpFOX42yi7y27uqBNbXa1qflg490KfMhYthQPftxkW13A5319AnybKQnqrZN2jb1ooIdgC/1VGtjFNfpYr6OcweRrEFRbmatCHUATcSgWYPQ1KSKnTQfqjMtsjYEJ68HgNck0SYkG7tbTZ3Ph1MpmkzG6tPywYc92yX4RVJvvosoQyz+NZw7M+TNBQFqtU2q6yQYmRQr5qvVasu/X1FTMYHa7y8loSwRiWjoSqhtRa13gxbdgeBsea32/QG8ozFlPmRTK3MpW2xMrbbeoeBDkL06UN9oTGT9t74nhfgvWzxql5ZUfdlqnVPb40NMwGMJTqvcvFhA2R6PIi40s1P95qXrhnVtqipLyNuc62k+prcIkN+ADKNcbC6yFZyoTX8NzZb8Nuvqgbzo9ZqJlg8+RHW7APWNxkSWCey2MrVTHJ3EfLKtsqt6KKTFll3YBjE6mZ/SsSQjC8G/XmWX1HiuALaPib6BOWswJLfaTjply9TU4WVrPoq6YU2wBcyJttnSnBEKPuQz4PDwIKPzj8FeL50rln1/qexSn5YPPkSZjAH1jcbEWoGbX7RUZupGCcjsJFDbxshMempZV/cKurGz1LhhqBsU5dQlxdZLRCNWx4+4NadqMMwR9OYNWnzwMVUTwWtoZLW+Vop4rUGLgt9fRyJqCcj57wXbBGMRTXiXBTEVdk+sfHgo6/xT8P3NF3UrCKXMhzMtHXwYhmFlPkRMYq1fdhH3pM4CGMOwRW088urp1S3WZXk22Buls4eCqLJLIhaxNonKpydZwVUiZgcX/HXDB48ih6CZrzk1oCvrVhJeOpsasPIaGpmtr3wZRNZ3QtO0qun3Ee5hQ/RnSEzF6Z7IPhPRJVOgepfjuMRgp5lo6eBjMl8EC5BFZD7qlV1Eaj7i0Yh1w6susAqrni6rTdM58yGq7AI4e33IKmMB1csgMtO21T5DFtx1ck/xwtarMjyPrd0WjwjfENj7yxcNK5tjGIbUz5CtOTo5NfMR1NOHcIfTPVF2CaSyFJ2SGOw0Ey19ZtjTl6aZqdOg1C+7iB0yVbu1TFxGh8dZPCjZQ0FBtwvgfE5lqterPTXLdEisVpaQuZ71/rjvhaw2W8DsXGOJBrbOBFeTl/IZVikPjkoIjonaWB0vnBZOll6LURnMk97DHa0dfDDXu4SY1Lb7bhfBvhQKZxmoH9pV37papIeCU+YjJSllD/BPTmozH6my1LS89aq5qspcL8JpLCo3hGhEE/KgUUm174VIjRfhjmodLzIzXvzrsnVkBzvNQksHH2lB1uqMerNPRLfdsSf+w9VaQ2WXXRSPR6+WTRKdSQKqPzkBsjMRUzMftiGWvGBnrGrmQ01mR7YDZGUJlK3dlRSvoQGqn9ORSfHXJ1Gbau3ysjMRld9f2cFOs9DSwQdTv3cGtFZn1HI45YegiQo+7OFy6kx1nAWncjIfTmUegJ8QLG5NJ5dTWWUsoLYGQ2oZhA8GpK5nvma2oCNbKJbWlpdJMtcsD1plz9qoFkCOWgZjtAmpotrDg6ysLIPKLv5o6eDDtlYXc1Fas0+qOJxO5IrIlSzJhZVdrChfzVAy8/XCmZtRuZ7ICcE8TufUnjArT4NRrvmQZ1KkWuDKt5lWZiLkBQPVMx+yNqBq59Ty9emkzIcqqpVN5Wc+yr+/sjrjmo0WDz6YwZjYzEetOSSJaERYzdmV4FT5xFBZmY/y4INlkURMCOZxOqcy67jVAiwVZZDqrb3ib5jRiGZljCqfDuU/jVamwmWLDqe22lK3izoGqnx/ZQe6laJ49vApK6vXLLR08DEmcK4LUC4crLRZltHz70ZwKs9OWFWrrfl6k/ki8twwu9FJcROCefrqlV0kliVSVTIfUgSgVYMdltlRUwYZk2QdX7ne1FS43GCHF9WKLrMS9ekrZZkOp6tpPmQLTsszH6T5qE1LBx9pgXNdAPvGXWmzDIifwAo4C055hz1lI6QlbZbVUvYA7/Eh9gvuNDNHReajWuurjMxALZMxeZmI6mUQVRuCrUmS6/VQlvmYFP+dJ2ozUOX7qyrrlaoMdMnVtiaego+bbroJmqaV/Vq+fPmUnzMMA+eccw40TcPDDz8s6liFI3KuC2CaysRKT+GVug8ZT0FOJQKZDnuWroVbI5MvIsesuQWnmGNcmYq/sTNBWa/gG7vTZEy5ra9qNRh8KYtl6Nj1Kq0u3l5elpAtAJ26IahNvQOc/T+VXZRRTTAu01OGf10rsM5Sq60bPJ+d4447Dhs2bLBfIDb1Jb7xjW9MCzthdrGICj6YzfLwRB6pTB5zetusvxNprc7o76ztxinDYa+WOFLTYI1rF0l3WwwTuWJ5G6OkzAcvWDMMw7qOZWYGqrbaKjD9KpQydB2JmPQb5tTMh9xUeKXnjmqvB8MwbJMxEpwqo6/Kw4Os6dAMZ30RBZ218PxpxGIxzJkzx/HvX3zxRXzta1/Dpk2bMHfu3EAHJ5u0wKFyjJ72OIYn8lPKElaZoFNO5oPfKFWUCDJ5Hfmijng0YnsoJGKICLbmBswNf18qW9beK6OMBdgbRa6gWxszPxVTbhlETeaDDUIr6qbleEcixqWKZWsiSpkPiT4m/Ouqan+s3ID47jZqtVXHANetxu6J6j57ZjJGrbZu8PxYvGXLFgwNDWHJkiW4+OKLsWPHDuvvJiYm8OEPfxh33nlnzQCFJ5vNIpVKlf1SRTonNvMB8D4Y5WUXmYZY+aKBdM7WmKhvm1SbQgd4a3WxN/bORBTxqBlAMdGazKmYQG3HURnteprGO4Cq6QaptHSXXwZR2/5Y2ZXFrk+R3W1Efdj9oKgbU641aW3W1GrrC0/Bx6pVq3Dvvffisccew1133YXt27fj9NNPx9jYGADgmmuuwamnnorzzz/f9Wvefvvt6O3ttX7Nnz/f2zsIgOiyC1C9jRHgNB8CL8j2RNSaiFrNVEdG2q+aBkO1mhyQU8YCzI25r0K0xtaVMXQNmLpxFXVDWap4ajCgtvVVlfGTSpMx01CQaZJooq1K2uJRtMfN+xP7DJQF1pPlrbaU+aiNp7NzzjnnWP+/YsUKrFq1CgsXLsQDDzyAwcFB/OY3v8ELL7zg6QBuuOEGfOYzn7F+n0qllAUgUsouVeZ0ABLLBB0J7E1lMDyRw/yBDgBqvmy8BsOO9NWkNQG7vVi05gMwFfMHxrLWZ2bfTGTpE8rbiSe4LJbcG+akss25u+IGLTv4qGwnVhUg6waQzhWlaZKI+gx0JvD2yCSGJ/I4os/+PskOrNn3V3ampVkIpEbs6+vD0qVLsXXrVvzmN7/Btm3b0NfXh1gsZglRP/ShD+E973mP42skk0n09PSU/VKF5XAqVPNRfRaJrCFT1Rw5VdkJ29bVkud0VBnYZ40rl9DGWOmSKLtEwF9/45mCtV4iFkEyJidlz3+G2YK8biUG3+0isxWcMaXVVrLGpC0esTrdxjJ5LtNJYlPVWN/fdM7KIAISv7/c6w6nc8iWvkuU+ahNoLMzPj6Obdu24aMf/SguuugifOxjHyv7+xNOOAFf//rXcd555wU6SFmMC/b5AJwdQEckWS1Xm+8SWl+7IkEXIK/bxXzN8hbmlET9BWCXsiZyRaQyeetJTaZDIi9yLdO0SPIm4NeT2QrO4DOQhmFwAbmc9fhOt7FMgQuO6elXNXy7PLu22+IR4Z1/jHg0gvZ4FJP5InaPZqw/l/VdahY8nZ1rr70W5513HhYuXIjdu3fjxhtvRDQaxbp16zA4OFhVZLpgwQIsXrxY2AGLZFywvTrgbAfONBmyTLHKNB/S9QKVBk5qZycA8spYANfCnFaT+WCvzUpZ6azcEgFQrlFgWYGuZEyKpgUo10LJbAWvXK+oG5jIFTkNjbxz2t1md7qNTlLZJSz4bLDsrCyjuy1mBh8jkwDMjrKYpGu7WfB0N921axfWrVuHQ4cOYXBwEKeddhqefvppDA4Oyjo+aRiGLeoT22o7tezCK69Fj9euPkhJlXhQtVjR9lCQ1e1ivma5V4CKvv1u1k6cyWMiK7ckAZQPQlMxhZPXYMhsBWfw7cT7UhnopWkHsgNIwAzG2cOA6O87UZ9+ruyiasJsd1sM+8eyVvBBJZf6eDpD999/v6cXr5xv0khkCzqKpTuS2G6XqYLTUS4QEd3zX82RU5V4UNWQsJ6KUtYk56gqJfNRUcpSvTlP5OSeT6B6JkLu+7OzVyqCD9ZOPDqZx+4RMxUei2hWJ4QM+KynzOCYqE21sovszAcL5ncNm8EHiU3r07J5IV6IJGqwHFDdrZIFBt1tMeGpuNojpOWKB8MyjGLvVZaHgp35qBTUqtVgqHhKH+MErrIzO4AZGKvaENh7fHtkwvq9zLZXvp2YBKfhYT882GaPsifMsmv5bcp8uKZ1g4/SDbAjERXqymm3FMr3pABsRz+VI6SnmuqozbTYc13keChUZpNUtM7x3Sdqgg/7M1QZ7BR1A/vHstLXM1+/tCEMT5b9XvZ6qckC952nJ2DV9HOTbVWWXQBwZRf63OvRsuHZuOChcoxqg9eYcFFGCrZa2UV19wmbCyLfMMpcR7aHQuVwKruspCbzMZFXITi1MxEqRHm8BoPdoGU7QPZYmQ+z7KJqAxrL5K2yC3W7qKe87FK6tiWNDWD0TAk+WnZrdU3LZj5ET7Rl1OrMkCE+s3va1ft8qLJXt3w+Jpl1tVwxH3tyGklXZnbUdJ+ozESoyrSwVlTAzkSoSoWr2hB43Y7MbCdRm+qaDzXXGivVUuajPi0bfMjw+ADsDT+T15EtmF0LKj0pAAWZj6RT2UVusJMr6sjki/aQPmmZD/OcjmULZY6FqrpPbA2N/DIPrzGRLZJTnZq2nkZH1ZZdyjQflPlQDq+DS6nSF1U8xMoOrJuBlg8+RIpNgfJgxhZISvSkKL1mOldEtlBEoSjfTth2HDUNnNhmKctevSsRA5N2jGUKGEnLfarsbY9b641M5JVkPqxgIKte86FCUAvYwY0VfEg2YWLvZ4/issue0QwKpU46EpyqZ4CbTL0/pfazd/o9MZWWDT6YtbpIjw8AiEY066Za2Z0h4ymou802hhqZyCuxE+Zr25m8bt1oZW3OkYiGroTdYcPOp6x6ejSiWRvlyEROUbcLK4MUpJfNzNc21xvP2pkW+WUQ8/X3KtsQzPPHRtvLzuywoHznYbO7JhmLoJ0m2iqnIxFFotRVuOOw3ekkk0r9Ek20rU/LBh/jWXkbSuXsk9FJeU/qkYhmeYeoshPmzZTYRhnRzKmvsuDLEirq6f1VU7cSgw+ulKVC4MrOp24A+6xgQE1Zwjb8UlPmYagKrvaUzieVXMLBnExtnns7+FBzbdu/p8xHPVo4+GCZD/EbZuUgNJndLvzrDqfV2AlbT5QFHQfHzUCgK6nOQ2FYQRsjE7PuS2WkD13jX7vc50PeeslYBPGo+Xmp6gapzDyofhpVtQExb0USm4YHK72wIW+qAk/r95K7a5qBlg0+0hKs1RmV811kaj741x1RpO7mO4RUiQd5gaTtHik/88GenDQNVulHBtWySfK7T8z3eHCc+W6ozUSoX0/tBtRLqffQqHzQa7ZrrRlo2eCDmYzJEL1Vll3sbhc5myXbhA8rshPmdS1vK/NssM3bZJ9P/rVZ/b4rERNqRlcJf82kc/Jnu1R7fVWtqNbvJQmUGVNT4Wo3IMp8hMdAxfRw9Vk9Cjzr0brBR05i5qOy7GL5fMg1xVJrJ6zWVIfPfKgsu7x1SI1grVIcyf+ZLFSXQSrfj6rWXqffi6by/ZDmIzwqs6LNFsg3A60bfGRkll3sjXIyV7TqjrJuRizKVzvFsXyWgSrDqNFJe1y5TPfIyrKL7ECg0uwuGYsgEZP79ZwiyJSdvWoPN9Miez1eRwPQRNswqXwwUf39pcFy9WnZ4INpPuSUXewSAXtKj0U04W6qDH4Qmio7YdWGUWy9XcMTlqBPpocCczndM6oms8OXsgA1rXr8e5KtaTHXK39Psr4PTuvJPqeappVtOpT5CA++5KUikI9FI2XdfqLNK5uRlg0+xmUKTrn5Lry1uqxuEH4EvPpBSoo6JdrLMxFdyZjUGwq7ebG2UNXBgIq0Lb85y9a0mOvZ76kjERU+4bkS1WWlyjX6SHAaGnzwoUp/wdbpStreS4QzFHxI7XbJSx+CBvCZj5wyO2G2Ge8bM4MPVfX7naW5ILKfKqembdUGAypumD1l68l/f6rXa4tHEOM2ARWp8O6yzAeVXcKiv9P+HFRZnbNrmvQe7mjZ4MMqu0gxGbMHocluszVf2zbEUtGmyb++YZT/Xt565ns8UBrHLruTQLVgrXINFTfM8kyL6syO/PX4YXbxqIak5NQ7UJH5oLJLaJRnPij4aERaOPiQY68O8GUXNQOmLMFpCFMcnX4vfr3y9yM989Gp9v2Za6guu4RX5lG3IcSt/8o0wbPXs98XtdqGR5hlF2qzdUdLBh/ZQtFqaZQhsuNNxlRYgbOndL4TpNlMdaa2Mcq9sVd+XkrKEpxGQIVDouoySHlmR9WGoPZptJsEpw0BZT4an5YMPljWA5Bjr84bRllD5Trl3YjYTc4w1Le+2r9X2zYpU0MDAG3xKNri9tejGTMffOurivfXFo9aIuFm3RD4dcjhNDz4gZthZNmI+rRo8GEPX5OhuLfmdGQLOFwa/y6zLTQejVhtmkwTId+gSq1HROX7USHm45+e1GgwOJGcku4a9WWQHisYUJsKV5VpYeu0x6Noi9NE27DgB26qutZYMK9K4Drdacngg+kiuiSltvkbObPnlv2kXplZUV0GUT24Sfb5BMoDHDWdEuFpPlSNAO+xggFVwQ7bgNRmPqjkEj7Mq0fVZ//+E4fwJ0sG8MGV85SsN91pyRAtnWPBh5wnk2QsimQsgmxBx1ul4EP2k/pARwI7D09av1dvJyz3ZtuRiCIa0VAsGW+oEPMNdKrNDKhutQ1HABpOGUTZ029pHWqzDR/2gKLqsz9uqBf3X7FayVrNQEtmPmR6fDDYk6TdGio58zFFIKm2DCJ7M+HbJgG51uqMPsWK+Z4QMx/N2hGwaEYHAGDhQIeS9RaU1ls8U816hDNLZnYBUPfZE95oyczHuFV2kRh8tMWswAOwU4Cy4IMb1XNBohEN7Qrq291tMSUTbRn8OW1GEy7VviIAcPrRM/HCjmG8c2G/kvU+vGohls7pxsoFatZbtXgAD3x8NZbO7lKyHuHM5889BhesPAKnLBoI+1CIKrRk8MEEpzKDj6kCSXWZjzA6M5R4KCTjAMzSkgrNh+p2PdWtqHx5UFUZ5OPvPhKXnbZYurU6IxGL4NQjZypZCzAzdKcsps2uEehui+NPlswI+zAIB6jsIolKAZ/MbhdAfWdGZyIG5lytrJOAaw1VUVNna0Q08/3KJmwTLlWoCjwIgmhcWvIuwIIPmZMH+QBA9hA0oNyRU8XGFeGm9KreKKMRTUmAxbIrXUn5Q9eA8uBKVUD35yfPwzvm9+H4oV4l6xEEQQBUdpG2hmqnw7DshFOZgvo2xnY1VtnsnKo6n/0dCWsipqpzet1Zy3HdWUqWIgiCsGjJ4MMqu0hMpZeXCJpPn8Cvo7qNUUWnCwAcf0Qv+jriOPVINXXjtngUD155KqIRKk0QBNHctGjwYdqryy272Bukis6MPsWdGYB6AydWalE1sGuwO4nf//3asrHsslk2p1vZWgRBEGHRksGHXXaR1x7KaxKUWIF3qi+72HbCaj0iVHS6MOKUgSAIghBOS95Zmc+Hqm4XNW2h4XVKqPKIOGPpIJbM7MS5K4aUrEcQBEHIoSUzH+MKBKc9ZYJT+ZmP9jjv2aAmM/CeZYP47eb9OPUoNT4Ky+Z04zfXvkfJWgRBEIQ8WjL4sGe7yOx2sV9bReZD0zT0dySwN5VRlvk4/x1H4P0nDinpPCEIgiCah5Yuu0gVnLarFZwCtu5D5UhnCjwIgiAIr7Rm8KGi1VaxzwcAfHjVArxzYT9ZChMEQRANTcuVXfJFHdmCDkBl2UVN5uOjf7IQH/2ThUrWIgiCIAi/tFzmg7XZAnK7XToSUSRKbZoDkifaEgRBEMR0ouUyH6zkkpA8dl7TNHz27GXYO5rBvP52aesQBEEQxHSj5YKPNHM3lZj1YHzs9CXS1yAIgiCI6UbLlV3Gs3kAaoIPgiAIgiCm0oLBh5n5kKn3IAiCIAjCGU/Bx0033QRN08p+LV++HABw+PBhfPKTn8SyZcvQ3t6OBQsW4Oqrr8bo6KiUA/eL5fEhca4LQRAEQRDOeH78P+6447Bhwwb7BWLmS+zevRu7d+/GV7/6VRx77LF466238IlPfAK7d+/Gj3/8Y3FHHJC0Amt1giAIgiCc8bwDx2IxzJkzZ8qfH3/88XjwwQet3x955JH4h3/4B3zkIx9BoVCwgpSwsQzGKPggCIIgiFDwrPnYsmULhoaGsGTJElx88cXYsWOH48+Ojo6ip6enYQIPQM1QOYIgCIIgnPG0A69atQr33nsvli1bhj179uDmm2/G6aefjldeeQXd3d1lP3vw4EHceuutuOKKK2q+ZjabRTabtX6fSqW8HJJnqOxCEARBEOHiaQc+55xzrP9fsWIFVq1ahYULF+KBBx7AZZddZv1dKpXC//7f/xvHHnssbrrpppqvefvtt+Pmm2/2dtQBoLILQRAEQYRLoFbbvr4+LF26FFu3brX+bGxsDGeffTa6u7vx0EMPIR6vPVTthhtuwOjoqPVr586dQQ6pLiz4UDV2niAIgiCIcgIFH+Pj49i2bRvmzp0LwMx4vO9970MikcAjjzyCtra2uq+RTCbR09NT9ksmacp8EARBEESoeAo+rr32WmzcuBFvvvkmnnzySVxwwQWIRqNYt26dFXik02n8x3/8B1KpFPbu3Yu9e/eiWCzKOn7PUNmFIAiCIMLF0w68a9curFu3DocOHcLg4CBOO+00PP300xgcHMTjjz+OZ555BgBw1FFHlf277du3Y9GiRcIOOghW2YWCD4IgCIIIBU878P333+/4d+95z3tgGEbgA5JNmuzVCYIgCCJUWnC2Cyu7kL06QRAEQYRB6wUfGVZ2qd2FQxAEQRCEHFoq+CjqBibzrOxCmQ+CIAiCCIOWCj7SuYL1/6T5IAiCIIhwaKngg5Vc4lENyVhLvXWCIAiCaBhaagfmDcY0TQv5aAiCIAiiNWmp4GOMBR8JKrkQBEEQRFi0VPCRprkuBEEQBBE6LRl8kNiUIAiCIMKjpYKPsQwFHwRBEAQRNi0VfKRprgtBEARBhE5rBR85MhgjCIIgiLBpqeCDlV26yFqdIAiCIEKjpYIPVnbposwHQRAEQYRGSwYfJDglCIIgiPBoqeCDmYx1kc8HQRAEQYRGSwUfdtmFgg+CIAiCCIuWDD7IXp0gCIIgwqOlgg8quxAEQRBE+LRU8EFlF4IgCIIIn5YKPsbJXp0gCIIgQqdlgg9dNyyHU8p8EARBEER4tEzwMZEvWv9PwQdBEARBhEfLBB+s5BLRgLZ4y7xtgiAIgmg4WmYXHufEppqmhXw0BEEQBNG6tEz9obc9jk+deTQiFHgQBEEQRKi0TPAx2J3ENX+6NOzDIAiCIIiWp2XKLgRBEARBNAYUfBAEQRAEoRQKPgiCIAiCUAoFHwRBEARBKIWCD4IgCIIglELBB0EQBEEQSqHggyAIgiAIpVDwQRAEQRCEUij4IAiCIAhCKRR8EARBEAShFAo+CIIgCIJQCgUfBEEQBEEohYIPgiAIgiCU0nBTbQ3DAACkUqmQj4QgCIIgCLewfZvt47VouOBjbGwMADB//vyQj4QgCIIgCK+MjY2ht7e35s9ohpsQRSG6rmP37t3o7u6GpmlCXzuVSmH+/PnYuXMnenp6hL72dIbOizN0bqpD58UZOjfVofPiTLOcG8MwMDY2hqGhIUQitVUdDZf5iEQimDdvntQ1enp6pvUHLAs6L87QuakOnRdn6NxUh86LM81wbuplPBgkOCUIgiAIQikUfBAEQRAEoZSWCj6SySRuvPFGJJPJsA+loaDz4gydm+rQeXGGzk116Lw404rnpuEEpwRBEARBNDctlfkgCIIgCCJ8KPggCIIgCEIpFHwQBEEQBKEUCj4IgiAIglBKywQfd955JxYtWoS2tjasWrUKzz77bNiHFDo33XQTNE0r+7V8+fKwDysU/uu//gvnnXcehoaGoGkaHn744bK/NwwDX/ziFzF37ly0t7dj7dq12LJlSzgHq5B65+X//J//M+UaOvvss8M5WIXcfvvtOPnkk9Hd3Y1Zs2bhAx/4ADZv3lz2M5lMBldddRVmzJiBrq4ufOhDH8K+fftCOmI1uDkv73nPe6ZcM5/4xCdCOmJ13HXXXVixYoVlJLZ69Wr84he/sP6+1a6Xlgg+fvjDH+Izn/kMbrzxRjz//PM48cQTcdZZZ2H//v1hH1roHHfccdizZ4/163e/+13YhxQK6XQaJ554Iu68886qf3/HHXfgm9/8Ju6++24888wz6OzsxFlnnYVMJqP4SNVS77wAwNlnn112Df3gBz9QeIThsHHjRlx11VV4+umnsX79euTzebzvfe9DOp22fuaaa67Bf/7nf+JHP/oRNm7ciN27d+ODH/xgiEctHzfnBQAuv/zysmvmjjvuCOmI1TFv3jx8+ctfxnPPPYdNmzbhve99L84//3z8z//8D4AWvF6MFuCUU04xrrrqKuv3xWLRGBoaMm6//fYQjyp8brzxRuPEE08M+zAaDgDGQw89ZP1e13Vjzpw5xle+8hXrz0ZGRoxkMmn84Ac/COEIw6HyvBiGYVx66aXG+eefH8rxNBL79+83ABgbN240DMO8PuLxuPGjH/3I+pnXXnvNAGA89dRTYR2mcirPi2EYxrvf/W7jU5/6VHgH1UD09/cb//7v/96S10vTZz5yuRyee+45rF271vqzSCSCtWvX4qmnngrxyBqDLVu2YGhoCEuWLMHFF1+MHTt2hH1IDcf27duxd+/esmuot7cXq1atomsIwOOPP45Zs2Zh2bJluPLKK3Ho0KGwD0k5o6OjAICBgQEAwHPPPYd8Pl92zSxfvhwLFixoqWum8rwwvv/972PmzJk4/vjjccMNN2BiYiKMwwuNYrGI+++/H+l0GqtXr27J66XhBsuJ5uDBgygWi5g9e3bZn8+ePRt//OMfQzqqxmDVqlW49957sWzZMuzZswc333wzTj/9dLzyyivo7u4O+/Aahr179wJA1WuI/V2rcvbZZ+ODH/wgFi9ejG3btuHv/u7vcM455+Cpp55CNBoN+/CUoOs6Pv3pT2PNmjU4/vjjAZjXTCKRQF9fX9nPttI1U+28AMCHP/xhLFy4EENDQ/jDH/6Az33uc9i8eTN+8pOfhHi0anj55ZexevVqZDIZdHV14aGHHsKxxx6LF198seWul6YPPghnzjnnHOv/V6xYgVWrVmHhwoV44IEHcNlll4V4ZMR04S/+4i+s/z/hhBOwYsUKHHnkkXj88cdx5plnhnhk6rjqqqvwyiuvtKxeygmn83LFFVdY/3/CCSdg7ty5OPPMM7Ft2zYceeSRqg9TKcuWLcOLL76I0dFR/PjHP8all16KjRs3hn1YodD0ZZeZM2ciGo1OUQ3v27cPc+bMCemoGpO+vj4sXboUW7duDftQGgp2ndA1VJ8lS5Zg5syZLXMN/c3f/A1+9rOf4be//S3mzZtn/fmcOXOQy+UwMjJS9vOtcs04nZdqrFq1CgBa4ppJJBI46qij8M53vhO33347TjzxRPzzP/9zS14vTR98JBIJvPOd78Svf/1r6890Xcevf/1rrF69OsQjazzGx8exbds2zJ07N+xDaSgWL16MOXPmlF1DqVQKzzzzDF1DFezatQuHDh1q+mvIMAz8zd/8DR566CH85je/weLFi8v+/p3vfCfi8XjZNbN582bs2LGjqa+ZeuelGi+++CIANP01Uw1d15HNZlvzeglb8aqC+++/30gmk8a9995rvPrqq8YVV1xh9PX1GXv37g370ELlb//2b43HH3/c2L59u/HEE08Ya9euNWbOnGns378/7ENTztjYmPHCCy8YL7zwggHA+Kd/+ifjhRdeMN566y3DMAzjy1/+stHX12f89Kc/Nf7whz8Y559/vrF48WJjcnIy5COXS63zMjY2Zlx77bXGU089ZWzfvt3YsGGDsXLlSuPoo482MplM2IculSuvvNLo7e01Hn/8cWPPnj3Wr4mJCetnPvGJTxgLFiwwfvOb3xibNm0yVq9ebaxevTrEo5ZPvfOydetW45ZbbjE2bdpkbN++3fjpT39qLFmyxDjjjDNCPnL5XH/99cbGjRuN7du3G3/4wx+M66+/3tA0zfjVr35lGEbrXS8tEXwYhmH8y7/8i7FgwQIjkUgYp5xyivH000+HfUih8+d//ufG3LlzjUQiYRxxxBHGn//5nxtbt24N+7BC4be//a0BYMqvSy+91DAMs932C1/4gjF79mwjmUwaZ555prF58+ZwD1oBtc7LxMSE8b73vc8YHBw04vG4sXDhQuPyyy9viaC+2jkBYHznO9+xfmZyctL467/+a6O/v9/o6OgwLrjgAmPPnj3hHbQC6p2XHTt2GGeccYYxMDBgJJNJ46ijjjKuu+46Y3R0NNwDV8Bf/dVfGQsXLjQSiYQxODhonHnmmVbgYRitd71ohmEY6vIsBEEQBEG0Ok2v+SAIgiAIorGg4IMgCIIgCKVQ8EEQBEEQhFIo+CAIgiAIQikUfBAEQRAEoRQKPgiCIAiCUAoFHwRBEARBKIWCD4IgCIIglELBB0EQBEEQSqHggyAIgiAIpVDwQRAEQRCEUij4IAiCIAhCKf8/r9qHxwfQGn4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(np.arange(len(data)), data[:, 3])\n",
    "ax.legend([\"overall speed\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1287793d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "rot1 = ax.plot(np.arange(len(data)), data[:, 2])\n",
    "rot2 = ax.plot(np.arange(len(data)), data[:, 4])\n",
    "ax.legend([\"rot1\", \"rot2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1287e73d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_rotation = np.cumsum(data[:, 4]) + np.cumsum(data[:, 2])\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(np.arange(len(data)), cumulative_rotation)\n",
    "ax.legend([\"cumulative rotation\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-35? That is not enough for a little circle. It is clearly pulling to the left though. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is this down to how I'm adding stuff in? The next data I should collect is one pose, following it's path. Let's see how that changes over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x, y, theta, left, right, rot1, trans, rot2\n",
    "new_data = np.array([\n",
    "  (1150.09, 192.206, 66.9911, 9, 8, -0.00444137, 0.224023, -0.00444137),\n",
    "  (1172.14, 244.418, 67.2132, 2138, 2163, 0.111037, 56.6778, 0.111037),\n",
    "  (1192.47, 292.049, 66.5559, 2002, 1928, -0.328659, 51.7888, -0.328659),\n",
    "  (1217.07, 347.724, 65.7653, 2354, 2265, -0.395283, 60.8683, -0.395283),\n",
    "  (1238.91, 395.394, 65.0103, 2032, 1947, -0.377517, 52.4345, -0.377517),\n",
    "  (1266.02, 452.31, 64.0509, 2446, 2338, -0.479665, 63.0426, -0.479665),\n",
    "  (1289.42, 499.497, 63.1893, 2047, 1950, -0.430812, 52.6717, -0.430812),\n",
    "  (1318.48, 555.725, 62.15, 2460, 2343, -0.519639, 63.293, -0.519639),\n",
    "  (1343.51, 602.222, 61.2706, 2053, 1954, -0.439696, 52.8035, -0.439696),\n",
    "  (1374.49, 657.551, 60.2402, 2464, 2348, -0.515201, 63.4116, -0.515201),\n",
    "  (1401.1, 703.261, 59.3341, 2058, 1956, -0.45302, 52.8957, -0.45302),\n",
    "  (1434.11, 757.778, 58.286, 2477, 2359, -0.524083, 63.7279, -0.524083),\n",
    "  (1467.93, 811.4, 57.2289, 2465, 2346, -0.528521, 63.3984, -0.528521),\n",
    "  (1497.94, 857.197, 56.2962, 2130, 2025, -0.466345, 54.7538, -0.466345),\n",
    "  (1533.72, 909.792, 55.2569, 2472, 2355, -0.519642, 63.6093, -0.519642),\n",
    "  (1564.36, 953.251, 54.3775, 2067, 1968, -0.439696, 53.1725, -0.439696),\n",
    "  (1602.09, 1004.88, 53.3027, 2487, 2366, -0.537405, 63.9519, -0.537405),\n",
    "  (1640.58, 1055.54, 52.2368, 2474, 2354, -0.532964, 63.6224, -0.532964),\n",
    "  (1674.6, 1098.74, 51.3219, 2138, 2035, -0.457461, 54.991, -0.457461),\n",
    "  (1714.96, 1148.2, 50.2559, 2482, 2362, -0.532964, 63.8333, -0.532964),\n",
    "  (1750.48, 1190.18, 49.2699, 2142, 2031, -0.492992, 54.991, -0.492992),\n",
    "  (1792.59, 1238.2, 48.2307, 2482, 2365, -0.519639, 63.8729, -0.519639),\n",
    "  (1829.61, 1278.96, 47.2713, 2143, 2035, -0.47967, 55.0569, -0.47967),\n",
    "  (1874.64, 1326.7, 46.081, 2557, 2423, -0.59514, 65.6255, -0.59514),\n",
    "  (1914.43, 1367.25, 45.0062, 2216, 2095, -0.537405, 56.8095, -0.537405),\n",
    "  (1961.38, 1413.23, 43.7893, 2562, 2425, -0.608464, 65.7177, -0.608464),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x128d0aeb0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_rotation = np.cumsum(new_data[:, 5] + new_data[:, 7])\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(np.arange(len(cumulative_rotation)), cumulative_rotation)\n",
    "ax.legend([\"cumulative rotation\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So - we should graph the change in rotation (rot1 + rot2), vs the change in rotation in the pose. That should give us a better idea of what is going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2000.0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets plot the x, y, theta\n",
    "fig, ax  = plt.subplots()\n",
    "u, v = np.cos(np.radians(new_data[:, 2])), np.sin(np.radians(new_data[:, 2]))\n",
    "ax.quiver(new_data[:, 0], new_data[:, 1], u, v)\n",
    "ax.set_xlim(0, 2000)\n",
    "ax.set_ylim(0, 2000)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That actually looks coherent. There is roughly an arc, with the headings changing in the same direction. That is good."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not the little circles being plotted... What?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "poses_raw_data = \"\"\"\n",
    "  Received data: {\"poses\": [[1150, 192], [155, 870], [1413, 665], [112, 620], [738, 209], [518, 344], [661, 1441], [1315, 9], [1077, 998], [1433, 197], [957, 729], [589, 569], [1492, 1089], [1345, 61], [982, 258], [385, 1024], [478, 675], [582, 210], [1359, 686], [922, 937]]}\n",
    "  Received data: {\"poses\": [[1172, 244], [103, 894], [1363, 693], [124, 675], [789, 184], [551, 298], [713, 1463], [1293, 62], [1068, 943], [1394, 238], [1010, 748], [540, 540], [1490, 1032], [1292, 82], [1036, 243], [352, 978], [508, 626], [639, 212], [1311, 716], [890, 891]]}\n",
    "  Received data: {\"poses\": [[1192, 292], [91, 944], [1312, 698], [77, 697], [801, 133], [597, 274], [669, 1489], [1263, 104], [1105, 906], [1405, 289], [1028, 796], [488, 537], [1537, 1008], [1241, 72], [1061, 197], [382, 935], [560, 621], [687, 232], [1358, 736], [847, 863]]}\n",
    "  Received data: {\"poses\": [[1217, 347], [90, 1005], [1268, 656], [64, 756], [764, 85], [654, 252], [608, 1496], [1277, 164], [1161, 883], [1350, 315], [1037, 856], [447, 581], [1550, 949], [1208, 20], [1066, 137], [436, 907], [598, 573], [727, 278], [1306, 767], [786, 859]]}\n",
    "  Received data: {\"poses\": [[1238, 395], [90, 1057], [1282, 605], [63, 809], [712, 78], [677, 205], [570, 1460], [1300, 211], [1203, 914], [1339, 367], [1075, 893], [451, 633], [1517, 908], [1229, -28], [1049, 87], [484, 887], [578, 524], [756, 321], [1254, 775], [736, 843]]}\n",
    "  Received data: {\"poses\": [[1266, 452], [90, 1120], [1340, 581], [64, 872], [653, 55], [651, 148], [585, 1399], [1328, 267], [1204, 977], [1340, 430], [1138, 884], [464, 695], [1455, 900], [1275, -71], [992, 60], [511, 830], [524, 493], [815, 343], [1207, 732], [688, 803]]}\n",
    "  Received data: {\"poses\": [[1289, 499], [90, 1173], [1380, 615], [65, 925], [644, 3], [611, 113], [633, 1378], [1351, 315], [1157, 1002], [1341, 482], [1159, 836], [494, 738], [1406, 881], [1294, -120], [948, 88], [488, 782], [487, 456], [855, 308], [1219, 681], [668, 754]]}\n",
    "  Received data: {\"poses\": [[1318, 555], [90, 1236], [1377, 678], [66, 988], [676, -52], [594, 52], [682, 1418], [1377, 372], [1100, 975], [1343, 546], [1174, 775], [558, 741], [1394, 819], [1273, -180], [923, 147], [440, 742], [491, 392], [884, 252], [1277, 654], [669, 691]]}\n",
    "  Received data: {\"poses\": [[1343, 602], [90, 1289], [1328, 699], [67, 1041], [700, -99], [634, 17], [681, 1471], [1398, 421], [1098, 922], [1345, 598], [1212, 738], [590, 699], [1419, 773], [1242, -223], [956, 188], [425, 691], [532, 360], [933, 233], [1318, 687], [693, 644]]}\n",
    "  Received data: {\"poses\": [[1374, 657], [90, 1352], [1273, 669], [68, 1104], [676, -158], [693, -5], [623, 1497], [1423, 479], [1147, 882], [1347, 662], [1272, 716], [580, 636], [1447, 716], [1246, -286], [1016, 206], [472, 648], [581, 320], [988, 265], [1317, 751], [741, 602]]}\n",
    "  Received data: {\"poses\": [[1401, 703], [90, 1405], [1275, 616], [69, 1157], [625, -170], [726, -47], [576, 1472], [1443, 528], [1195, 904], [1349, 715], [1310, 680], [531, 617], [1427, 667], [1294, -310], [1069, 201], [521, 628], [575, 267], [1017, 310], [1269, 773], [791, 586]]}\n",
    "  Received data: {\"poses\": [[1434, 757], [90, 1469], [1328, 580], [70, 1221], [563, -187], [711, -109], [578, 1409], [1465, 588], [1210, 966], [1351, 778], [1304, 616], [468, 606], [1364, 654], [1357, -303], [1113, 155], [560, 578], [524, 230], [1070, 344], [1213, 744], [855, 589]]}\n",
    "  Received data: {\"poses\": [[1467, 811], [90, 1532], [1383, 610], [71, 1284], [539, -246], [662, -149], [629, 1372], [1487, 648], [1161, 1006], [1353, 842], [1246, 591], [405, 614], [1303, 638], [1419, -313], [1093, 95], [544, 517], [474, 191], [1125, 311], [1213, 681], [912, 616]]}\n",
    "  Received data: {\"poses\": [[1497, 857], [90, 1587], [1381, 665], [72, 1339], [569, -291], [644, -201], [678, 1397], [1504, 699], [1111, 984], [1355, 896], [1191, 592], [382, 663], [1281, 588], [1448, -359], [1046, 67], [501, 483], [473, 136], [1145, 260], [1257, 648], [950, 656]]}\n",
    "  Received data: {\"poses\": [[1533, 909], [90, 1651], [1327, 700], [73, 1402], [602, -346], [680, -253], [676, 1461], [1523, 760], [1098, 921], [1358, 960], [1136, 560], [405, 723], [1315, 534], [1463, -421], [986, 89], [478, 423], [520, 93], [1192, 217], [1314, 677], [978, 713]]}\n",
    "  Received data: {\"poses\": [[1564, 953], [90, 1704], [1282, 672], [74, 1456], [581, -395], [730, -271], [632, 1491], [1539, 811], [1140, 889], [1359, 1013], [1111, 514], [457, 735], [1341, 488], [1504, -455], [970, 140], [508, 379], [559, 56], [1241, 236], [1313, 730], [984, 766]]}\n",
    "  Received data: {\"poses\": [[1602, 1004], [90, 1768], [1275, 609], [75, 1520], [522, -419], [773, -318], [576, 1459], [1556, 873], [1202, 906], [1362, 1077], [1063, 471], [509, 699], [1316, 429], [1567, -444], [971, 204], [568, 356], [561, -8], [1282, 285], [1261, 767], [974, 829]]}\n",
    "  Received data: {\"poses\": [[1640, 1055], [90, 1832], [1329, 575], [77, 1583], [460, -435], [750, -378], [569, 1396], [1572, 934], [1214, 969], [1364, 1141], [1002, 491], [542, 644], [1256, 407], [1606, -393], [1019, 246], [610, 308], [509, -45], [1342, 306], [1205, 736], [947, 887]]}\n",
    "  Received data: {\"poses\": [[1674, 1098], [90, 1887], [1376, 604], [78, 1638], [440, -486], [705, -409], [615, 1365], [1585, 988], [1170, 1002], [1366, 1195], [992, 545], [557, 591], [1203, 394], [1653, -366], [1071, 229], [589, 257], [461, -72], [1381, 267], [1197, 682], [910, 928]]}\n",
    "  Received data: {\"poses\": [[1714, 1148], [90, 1950], [1380, 667], [79, 1702], [468, -544], [676, -466], [670, 1398], [1598, 1050], [1109, 982], [1368, 1259], [1003, 608], [519, 540], [1179, 335], [1715, -383], [1113, 181], [536, 221], [444, -133], [1398, 206], [1250, 646], [858, 964]]}\n",
    "  Received data: {\"poses\": [[1750, 1190], [90, 2005], [1332, 695], [80, 1757], [495, -592], [710, -510], [674, 1453], [1609, 1104], [1101, 927], [1370, 1314], [986, 661], [464, 543], [1202, 285], [1755, -421], [1098, 128], [511, 173], [479, -176], [1439, 169], [1298, 673], [807, 985]]}\n",
    "  Received data: {\"poses\": [[1792, 1238], [90, 2069], [1274, 667], [81, 1821], [475, -652], [767, -537], [619, 1485], [1621, 1167], [1148, 884], [1372, 1378], [941, 706], [424, 594], [1232, 229], [1818, -432], [1042, 96], [548, 121], [524, -222], [1502, 183], [1304, 737], [744, 997]]}\n",
    "  Received data: {\"poses\": [[1829, 1278], [90, 2124], [1273, 612], [82, 1876], [422, -666], [801, -581], [569, 1463], [1630, 1221], [1199, 903], [1374, 1433], [917, 756], [445, 644], [1214, 177], [1855, -392], [987, 94], [598, 97], [513, -276], [1540, 222], [1257, 766], [689, 996]]}\n",
    "  Received data: {\"poses\": [[1874, 1326], [90, 2190], [1327, 574], [83, 1941], [358, -681], [781, -644], [567, 1397], [1639, 1286], [1216, 967], [1377, 1499], [949, 813], [484, 698], [1150, 162], [1865, -327], [938, 137], [636, 44], [455, -307], [1601, 247], [1197, 740], [624, 985]]}\n",
    "  Received data: {\"poses\": [[1914, 1367], [90, 2247], [1376, 602], [84, 1998], [335, -733], [733, -675], [612, 1363], [1646, 1342], [1171, 1002], [1379, 1555], [1006, 811], [528, 734], [1094, 150], [1893, -277], [952, 192], [618, -10], [405, -333], [1642, 209], [1194, 683], [570, 966]]}\n",
    "  Received data: {\"poses\": [[1961, 1413], [90, 2312], [1380, 668], [85, 2064], [364, -792], [702, -733], [670, 1395], [1653, 1408], [1109, 982], [1381, 1621], [1065, 783], [592, 719], [1067, 90], [1957, -262], [998, 238], [563, -45], [384, -395], [1657, 145], [1246, 642], [511, 937]]}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first term looks like the term above. Let's decode parts of it, and plot their path - we don't have the heading info here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['  {\"poses\": [[1150, 192], [155, 870], [1413, 665], [112, 620], [738, 209], [518, 344], [661, 1441], [1315, 9], [1077, 998], [1433, 197], [957, 729], [589, 569], [1492, 1089], [1345, 61], [982, 258], [385, 1024], [478, 675], [582, 210], [1359, 686], [922, 937]]}',\n",
       " '  {\"poses\": [[1172, 244], [103, 894], [1363, 693], [124, 675], [789, 184], [551, 298], [713, 1463], [1293, 62], [1068, 943], [1394, 238], [1010, 748], [540, 540], [1490, 1032], [1292, 82], [1036, 243], [352, 978], [508, 626], [639, 212], [1311, 716], [890, 891]]}',\n",
       " '  {\"poses\": [[1192, 292], [91, 944], [1312, 698], [77, 697], [801, 133], [597, 274], [669, 1489], [1263, 104], [1105, 906], [1405, 289], [1028, 796], [488, 537], [1537, 1008], [1241, 72], [1061, 197], [382, 935], [560, 621], [687, 232], [1358, 736], [847, 863]]}',\n",
       " '  {\"poses\": [[1217, 347], [90, 1005], [1268, 656], [64, 756], [764, 85], [654, 252], [608, 1496], [1277, 164], [1161, 883], [1350, 315], [1037, 856], [447, 581], [1550, 949], [1208, 20], [1066, 137], [436, 907], [598, 573], [727, 278], [1306, 767], [786, 859]]}',\n",
       " '  {\"poses\": [[1238, 395], [90, 1057], [1282, 605], [63, 809], [712, 78], [677, 205], [570, 1460], [1300, 211], [1203, 914], [1339, 367], [1075, 893], [451, 633], [1517, 908], [1229, -28], [1049, 87], [484, 887], [578, 524], [756, 321], [1254, 775], [736, 843]]}',\n",
       " '  {\"poses\": [[1266, 452], [90, 1120], [1340, 581], [64, 872], [653, 55], [651, 148], [585, 1399], [1328, 267], [1204, 977], [1340, 430], [1138, 884], [464, 695], [1455, 900], [1275, -71], [992, 60], [511, 830], [524, 493], [815, 343], [1207, 732], [688, 803]]}',\n",
       " '  {\"poses\": [[1289, 499], [90, 1173], [1380, 615], [65, 925], [644, 3], [611, 113], [633, 1378], [1351, 315], [1157, 1002], [1341, 482], [1159, 836], [494, 738], [1406, 881], [1294, -120], [948, 88], [488, 782], [487, 456], [855, 308], [1219, 681], [668, 754]]}',\n",
       " '  {\"poses\": [[1318, 555], [90, 1236], [1377, 678], [66, 988], [676, -52], [594, 52], [682, 1418], [1377, 372], [1100, 975], [1343, 546], [1174, 775], [558, 741], [1394, 819], [1273, -180], [923, 147], [440, 742], [491, 392], [884, 252], [1277, 654], [669, 691]]}',\n",
       " '  {\"poses\": [[1343, 602], [90, 1289], [1328, 699], [67, 1041], [700, -99], [634, 17], [681, 1471], [1398, 421], [1098, 922], [1345, 598], [1212, 738], [590, 699], [1419, 773], [1242, -223], [956, 188], [425, 691], [532, 360], [933, 233], [1318, 687], [693, 644]]}',\n",
       " '  {\"poses\": [[1374, 657], [90, 1352], [1273, 669], [68, 1104], [676, -158], [693, -5], [623, 1497], [1423, 479], [1147, 882], [1347, 662], [1272, 716], [580, 636], [1447, 716], [1246, -286], [1016, 206], [472, 648], [581, 320], [988, 265], [1317, 751], [741, 602]]}',\n",
       " '  {\"poses\": [[1401, 703], [90, 1405], [1275, 616], [69, 1157], [625, -170], [726, -47], [576, 1472], [1443, 528], [1195, 904], [1349, 715], [1310, 680], [531, 617], [1427, 667], [1294, -310], [1069, 201], [521, 628], [575, 267], [1017, 310], [1269, 773], [791, 586]]}',\n",
       " '  {\"poses\": [[1434, 757], [90, 1469], [1328, 580], [70, 1221], [563, -187], [711, -109], [578, 1409], [1465, 588], [1210, 966], [1351, 778], [1304, 616], [468, 606], [1364, 654], [1357, -303], [1113, 155], [560, 578], [524, 230], [1070, 344], [1213, 744], [855, 589]]}',\n",
       " '  {\"poses\": [[1467, 811], [90, 1532], [1383, 610], [71, 1284], [539, -246], [662, -149], [629, 1372], [1487, 648], [1161, 1006], [1353, 842], [1246, 591], [405, 614], [1303, 638], [1419, -313], [1093, 95], [544, 517], [474, 191], [1125, 311], [1213, 681], [912, 616]]}',\n",
       " '  {\"poses\": [[1497, 857], [90, 1587], [1381, 665], [72, 1339], [569, -291], [644, -201], [678, 1397], [1504, 699], [1111, 984], [1355, 896], [1191, 592], [382, 663], [1281, 588], [1448, -359], [1046, 67], [501, 483], [473, 136], [1145, 260], [1257, 648], [950, 656]]}',\n",
       " '  {\"poses\": [[1533, 909], [90, 1651], [1327, 700], [73, 1402], [602, -346], [680, -253], [676, 1461], [1523, 760], [1098, 921], [1358, 960], [1136, 560], [405, 723], [1315, 534], [1463, -421], [986, 89], [478, 423], [520, 93], [1192, 217], [1314, 677], [978, 713]]}',\n",
       " '  {\"poses\": [[1564, 953], [90, 1704], [1282, 672], [74, 1456], [581, -395], [730, -271], [632, 1491], [1539, 811], [1140, 889], [1359, 1013], [1111, 514], [457, 735], [1341, 488], [1504, -455], [970, 140], [508, 379], [559, 56], [1241, 236], [1313, 730], [984, 766]]}',\n",
       " '  {\"poses\": [[1602, 1004], [90, 1768], [1275, 609], [75, 1520], [522, -419], [773, -318], [576, 1459], [1556, 873], [1202, 906], [1362, 1077], [1063, 471], [509, 699], [1316, 429], [1567, -444], [971, 204], [568, 356], [561, -8], [1282, 285], [1261, 767], [974, 829]]}',\n",
       " '  {\"poses\": [[1640, 1055], [90, 1832], [1329, 575], [77, 1583], [460, -435], [750, -378], [569, 1396], [1572, 934], [1214, 969], [1364, 1141], [1002, 491], [542, 644], [1256, 407], [1606, -393], [1019, 246], [610, 308], [509, -45], [1342, 306], [1205, 736], [947, 887]]}',\n",
       " '  {\"poses\": [[1674, 1098], [90, 1887], [1376, 604], [78, 1638], [440, -486], [705, -409], [615, 1365], [1585, 988], [1170, 1002], [1366, 1195], [992, 545], [557, 591], [1203, 394], [1653, -366], [1071, 229], [589, 257], [461, -72], [1381, 267], [1197, 682], [910, 928]]}',\n",
       " '  {\"poses\": [[1714, 1148], [90, 1950], [1380, 667], [79, 1702], [468, -544], [676, -466], [670, 1398], [1598, 1050], [1109, 982], [1368, 1259], [1003, 608], [519, 540], [1179, 335], [1715, -383], [1113, 181], [536, 221], [444, -133], [1398, 206], [1250, 646], [858, 964]]}',\n",
       " '  {\"poses\": [[1750, 1190], [90, 2005], [1332, 695], [80, 1757], [495, -592], [710, -510], [674, 1453], [1609, 1104], [1101, 927], [1370, 1314], [986, 661], [464, 543], [1202, 285], [1755, -421], [1098, 128], [511, 173], [479, -176], [1439, 169], [1298, 673], [807, 985]]}',\n",
       " '  {\"poses\": [[1792, 1238], [90, 2069], [1274, 667], [81, 1821], [475, -652], [767, -537], [619, 1485], [1621, 1167], [1148, 884], [1372, 1378], [941, 706], [424, 594], [1232, 229], [1818, -432], [1042, 96], [548, 121], [524, -222], [1502, 183], [1304, 737], [744, 997]]}',\n",
       " '  {\"poses\": [[1829, 1278], [90, 2124], [1273, 612], [82, 1876], [422, -666], [801, -581], [569, 1463], [1630, 1221], [1199, 903], [1374, 1433], [917, 756], [445, 644], [1214, 177], [1855, -392], [987, 94], [598, 97], [513, -276], [1540, 222], [1257, 766], [689, 996]]}',\n",
       " '  {\"poses\": [[1874, 1326], [90, 2190], [1327, 574], [83, 1941], [358, -681], [781, -644], [567, 1397], [1639, 1286], [1216, 967], [1377, 1499], [949, 813], [484, 698], [1150, 162], [1865, -327], [938, 137], [636, 44], [455, -307], [1601, 247], [1197, 740], [624, 985]]}',\n",
       " '  {\"poses\": [[1914, 1367], [90, 2247], [1376, 602], [84, 1998], [335, -733], [733, -675], [612, 1363], [1646, 1342], [1171, 1002], [1379, 1555], [1006, 811], [528, 734], [1094, 150], [1893, -277], [952, 192], [618, -10], [405, -333], [1642, 209], [1194, 683], [570, 966]]}',\n",
       " '  {\"poses\": [[1961, 1413], [90, 2312], [1380, 668], [85, 2064], [364, -792], [702, -733], [670, 1395], [1653, 1408], [1109, 982], [1381, 1621], [1065, 783], [592, 719], [1067, 90], [1957, -262], [998, 238], [563, -45], [384, -395], [1657, 145], [1246, 642], [511, 937]]}']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poses_lines = poses_raw_data.split(\"\\n\")\n",
    "poses_lines = [line.replace(\"Received data: \", \"\") for line in poses_lines if line]\n",
    "poses_lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1150,  192])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poses_dicts = [json.loads(line) for line in poses_lines]\n",
    "poses_over_time = np.array([poses_dict[\"poses\"] for poses_dict in poses_dicts])\n",
    "# ThreeD array = time, pose number, [x, y]\n",
    "poses_over_time[0,0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the first pose... We can compare with the arc above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12671a040>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_xlim(0, 2000)\n",
    "ax.set_ylim(0, 2000)\n",
    "# The scatter will be the first pose over time.\n",
    "ax.scatter(poses_over_time[:, 0, 0], poses_over_time[:, 0, 1])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is a match... Lets try the next pose..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "for pose_index in range(poses_over_time.shape[1]):\n",
    "    ax.scatter(poses_over_time[:, pose_index, 0], poses_over_time[:, pose_index, 1])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok - so some are sensible looking paths, some are little rings. Almost like the radians/rotation is changing between poses. That makes no sense.."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think it's time to retransform them.\n",
    "\n",
    "## Standard for transforming poses\n",
    "Start with a set of poses.\n",
    "Each call will take -> incoming poses, rot1, trans, rot2.\n",
    "It will then return the next set of poses.\n",
    "\n",
    "We can then put this in a graph to see how they develop over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26, 20, 2) (8,)\n",
      "(2,)\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 2 is out of bounds for axis 1 with size 2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[74], line 15\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[39mreturn\u001b[39;00m new_poses\n\u001b[1;32m     14\u001b[0m \u001b[39mprint\u001b[39m(poses_over_time\u001b[39m.\u001b[39mshape, new_data[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mshape)\n\u001b[0;32m---> 15\u001b[0m new_poses \u001b[39m=\u001b[39m transform_poses(poses_over_time[\u001b[39m0\u001b[39;49m], new_data[\u001b[39m0\u001b[39;49m,\u001b[39m5\u001b[39;49m], new_data[\u001b[39m0\u001b[39;49m,\u001b[39m6\u001b[39;49m], new_data[\u001b[39m0\u001b[39;49m,\u001b[39m7\u001b[39;49m])\n\u001b[1;32m     16\u001b[0m new_poses \n",
      "Cell \u001b[0;32mIn[74], line 7\u001b[0m, in \u001b[0;36mtransform_poses\u001b[0;34m(poses, rot1, trans, rot2)\u001b[0m\n\u001b[1;32m      5\u001b[0m new_poses \u001b[39m=\u001b[39m poses\u001b[39m.\u001b[39mcopy()\n\u001b[1;32m      6\u001b[0m \u001b[39mprint\u001b[39m(new_poses[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mshape)\n\u001b[0;32m----> 7\u001b[0m new_poses[:, \u001b[39m2\u001b[39m] \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m rot1\n\u001b[1;32m      8\u001b[0m rotation_radians \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mradians(new_poses[:, \u001b[39m2\u001b[39m])\n\u001b[1;32m      9\u001b[0m new_poses[:,\u001b[39m0\u001b[39m] \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m trans \u001b[39m*\u001b[39m np\u001b[39m.\u001b[39mcos(rotation_radians)\n",
      "\u001b[0;31mIndexError\u001b[0m: index 2 is out of bounds for axis 1 with size 2"
     ]
    }
   ],
   "source": [
    "def transform_poses(poses: np.array, rot1: float, trans: float, rot2: float) -> np.array:\n",
    "    \"\"\"Rot1 -> degrees,\n",
    "    trans -> mm,\n",
    "    rot2 -> degrees \"\"\"\n",
    "    new_poses = poses.copy()\n",
    "    print(new_poses[0].shape)\n",
    "    new_poses[:, 2] += rot1\n",
    "    rotation_radians = np.radians(new_poses[:, 2])\n",
    "    new_poses[:,0] += trans * np.cos(rotation_radians)\n",
    "    new_poses[:,1] += trans * np.sin(rotation_radians)\n",
    "    new_poses[:,2] += rot2\n",
    "    return new_poses\n",
    "\n",
    "print(poses_over_time.shape, new_data[0].shape)\n",
    "new_poses = transform_poses(poses_over_time[0], new_data[0,5], new_data[0,6], new_data[0,7])\n",
    "\n",
    "# I don't have the rotation data - only the x,y data. Let's dump more out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# each pose is a list of x,y,heading\n",
    "# List of 20 poses, rot1, trans, rot2\n",
    "data_with_headings = [\n",
    "[[[925.478, 311.719, 208.524], [1385.53, 520.633, 305.534], [41.4205, 94.8741, 160.633], [9.37529, 1080.54, 218.524], [1171.47, 619.267, 41.4205], [629.408, 1446.96, 94.8741], [576.609, 719.446, 192.524], [524.91, 501.414, 9.37529], [698.915, 1331.59, 0.540527], [188.589, 1134.06, 309.524], [178.442, 120.2, 91.4702], [1039.41, 1341.0, 259.267], [174.547, 1006.62, 153.524], [193.563, 1226.19, 269.408], [885.456, 866.621, 6.95557], [451.586, 356.91, 184.524], [748.809, 48.4384, 216.609], [510.829, 789.432, 359.446], [465.07, 718.589, 131.524], [1095.43, 790.161, 164.91]], 0.262041, 0.593001, 0.262041],\n",
    "[[[924.88, 311.124, 241.301], [1386.2, 520.113, 306.198], [40.5783, 94.9179, 160.113], [8.8905, 1079.85, 338.311], [1171.92, 619.98, 40.5783], [629.103, 1447.74, 94.9179], [575.871, 719.038, 193.41], [525.669, 501.78, 8.8905], [699.721, 1331.83, 359.851], [189.288, 1133.59, 251.301], [178.183, 121.003, 91.9194], [1039.49, 1340.16, 259.98], [173.717, 1006.76, 74.1978], [193.793, 1225.37, 269.103], [886.23, 866.955, 7.7417], [450.798, 356.609, 127.651], [748.302, 47.7649, 215.871], [511.641, 789.662, 359.038], [464.355, 719.037, 225.301], [1094.59, 790.142, 165.669]], 16.3886, 0.843376, 16.3886],\n",
    "[[[905.23, 271.857, 245.529], [1413.42, 485.66, 333.419], [-1.23448, 108.321, 125.66], [50.2613, 1065.14, 310.426], [1204.19, 649.753, 358.766], [623.727, 1491.32, 108.321], [533.564, 707.286, 164.341], [568.771, 510.162, 50.2613], [743.604, 1333.34, 345.14], [176.755, 1091.51, 342.54], [175.095, 164.803, 124.192], [1033.45, 1296.67, 289.753], [184.107, 1049.43, 44.8065], [194.725, 1181.48, 263.727], [929.491, 874.471, 51.3198], [422.712, 390.36, 99.1461], [713.694, 20.7414, 173.564], [555.54, 790.545, 347.286], [434.643, 686.708, 197.638], [1051.67, 799.433, 208.771]], 2.11409, 43.9085, 2.11409],\n",
    "[[[879.508, 216.401, 244.703], [1467.89, 457.912, 27.8911], [-36.5139, 158.246, 97.9125], [89.5671, 1018.32, 332.593], [1265.3, 647.995, 323.486], [604.93, 1549.49, 158.246], [474.822, 724.21, 124.834], [608.189, 556.887, 89.5671], [802.577, 1317.23, 298.32], [234.937, 1072.75, 309.6], [141.106, 215.615, 185.299], [1053.7, 1238.98, 287.995], [227.789, 1092.19, 357.939], [187.608, 1120.76, 244.93], [968.04, 921.916, 109.49], [413.431, 450.783, 107.495], [652.999, 28.0316, 114.822], [615.074, 776.661, 4.21045], [376.253, 668.605, 163.515], [997.876, 770.397, 248.189]], -0.413049, 61.1319, -0.413049],\n",
    "[[[856.638, 168.865, 243.913], [1514.68, 482.267, 74.6831], [-43.415, 210.543, 122.267], [136.228, 993.715, 27.1005], [1307.48, 616.316, 316.585], [556.072, 1569.38, 210.543], [444.99, 767.716, 97.1219], [608.952, 609.633, 136.228], [827.28, 1270.63, 273.715], [268.28, 1031.87, 331.802], [88.5479, 211.105, 227.478], [1069.65, 1188.7, 256.316], [280.492, 1089.93, 322.695], [164.927, 1073.13, 196.072], [950.783, 971.765, 129.378], [397.92, 501.202, 157.455], [631.185, 76.0609, 84.9896], [667.708, 780.171, 47.7158], [325.775, 683.923, 124.043], [977.939, 721.559, 248.952]], -0.39528, 52.7508, -0.39528],\n",
    "[[[828.255, 112.102, 242.953], [1531.96, 543.334, 91.9595], [-76.8456, 264.489, 183.334], [192.965, 1022.15, 73.7238], [1353.21, 572.314, 283.154], [501.145, 1537.59, 264.489], [437.649, 830.754, 121.307], [563.493, 653.919, 192.965], [830.862, 1207.26, 302.153], [323.959, 1001.42, 26.1412], [45.264, 164.691, 273.211], [1054.12, 1127.17, 212.314], [330.649, 1051.05, 315.626], [103.798, 1056.07, 141.145], [910.931, 1021.16, 97.5854], [339.512, 526.024, 209.584], [637.257, 139.234, 77.6488], [710.799, 826.764, 110.754], [290.688, 736.806, 96.1626], [954.65, 662.522, 203.493]], -0.479665, 63.4643, -0.479665],\n",
    "[[[802.998, 63.5656, 242.065], [1530.51, 598.029, 90.5127], [-131.49, 261.73, 238.029], [208.706, 1074.55, 91.0712], [1365.25, 518.94, 228.51], [495.468, 1483.17, 261.73], [409.581, 877.72, 182.446], [510.08, 642.057, 208.706], [859.62, 1160.72, 354.554], [373.262, 1025.14, 72.8355], [47.9056, 110.041, 285.25], [1007.65, 1098.28, 158.94], [369.46, 1012.48, 282.266], [61.4574, 1090.73, 135.468], [904.129, 1075.45, 43.1665], [291.722, 499.382, 263.6], [649.374, 192.59, 49.5809], [691.808, 878.077, 157.72], [285.236, 791.248, 120.419], [904.304, 641.101, 150.08]], -0.444139, 54.7143, -0.444139],\n",
    "[[[771.821, 6.01371, 241.044], [1530.51, 663.483, 90.5103], [-166.641, 206.515, 303.483], [208.066, 1140.0, 89.4912], [1321.45, 470.299, 193.359], [485.476, 1418.48, 206.515], [344.164, 875.51, 237.008], [452.393, 611.131, 208.066], [924.72, 1153.92, 60.0044], [393.136, 1087.51, 90.0497], [64.5585, 46.7407, 241.452], [946.781, 1122.34, 110.299], [382.795, 948.401, 227.488], [15.2086, 1137.05, 125.476], [952.266, 1119.8, 338.479], [283.847, 434.403, 260.708], [692.256, 242.041, 344.164], [631.464, 903.431, 155.51], [252.6, 847.985, 181.424], [847.866, 674.253, 92.3926]], -0.510755, 65.4542, -0.510755],\n",
    "[[[745.731, -40.3032, 240.173], [1530.44, 716.643, 90.4409], [-137.651, 161.956, 356.643], [208.942, 1193.16, 89.6397], [1269.64, 458.41, 222.349], [437.73, 1395.11, 161.956], [314.88, 831.144, 302.613], [405.296, 586.478, 208.942], [951.645, 1199.76, 113.157], [393.494, 1140.66, 88.6207], [38.7997, 0.239159, 189.639], [928.718, 1172.34, 98.41], [346.577, 909.489, 192.489], [-15.3136, 1180.57, 77.7296], [1001.57, 1099.92, 315.11], [274.865, 382.008, 205.644], [743.286, 227.147, 314.88], [583.256, 925.834, 111.144], [199.449, 847.068, 236.137], [846.051, 727.381, 45.2959]], -0.435255, 53.1593, -0.435255],\n",
    "[[[714.391, -93.8924, 239.187], [1530.5, 778.723, 90.4971], [-75.7103, 157.787, 58.7234], [209.866, 1255.23, 89.4549], [1223.4, 416.986, 284.29], [378.87, 1414.85, 157.787], [347.888, 778.566, 355.657], [350.712, 556.904, 209.866], [927.725, 1257.05, 175.23], [395.522, 1202.71, 88.6537], [-22.4916, -9.62879, 143.4], [920.167, 1233.83, 56.9858], [285.852, 896.586, 221.363], [-1.59844, 1241.12, 18.8698], [1045.17, 1055.73, 334.846], [218.671, 355.623, 160.97], [786.711, 182.782, 347.888], [561.361, 983.926, 58.5659], [164.415, 795.817, 301.627], [890.099, 771.127, 350.712]], -0.492992, 62.0807, -0.492992],\n",
    "[[[685.801, -143.805, 241.203], [1528.99, 836.225, 88.9863], [-46.7166, 207.467, 116.225], [209.401, 1312.75, 92.5135], [1238.58, 361.503, 313.283], [325.243, 1435.65, 207.467], [405.311, 775.219, 60.7398], [301.342, 527.386, 209.401], [870.327, 1260.82, 232.75], [395.861, 1260.23, 91.4713], [-69.2668, 23.849, 158.576], [950.654, 1282.61, 1.50281], [243.356, 857.821, 286.306], [52.4955, 1260.67, 325.243], [1097.66, 1032.2, 355.651], [163.972, 373.419, 159.804], [843.155, 171.703, 45.3113], [590.491, 1033.53, 55.2195], [195.435, 747.377, 357.673], [947.021, 762.844, 301.342]], 1.00819, 57.5211, 1.00819],\n",
    "[[[655.222, -195.074, 237.171], [1532.14, 895.837, 92.1416], [-71.195, 261.913, 175.837], [208.883, 1372.44, 84.9536], [1277.95, 316.633, 288.805], [271.341, 1410.0, 261.913], [436.303, 826.239, 112.192], [248.336, 499.928, 208.883], [832.544, 1214.6, 292.443], [396.429, 1319.92, 88.4807], [-124.036, 47.5953, 197.949], [1010.35, 1282.07, 316.633], [258.09, 799.972, 309.25], [100.312, 1224.94, 271.341], [1156.99, 1025.58, 329.998], [108.706, 395.986, 203.434], [886.603, 212.64, 76.3033], [626.248, 1081.33, 106.239], [254.959, 742.856, 56.707], [976.258, 710.798, 248.336]], -2.01638, 59.6955, -2.01638],\n",
    "[[[620.105, -248.49, 236.185], [1530.3, 959.736, 90.3027], [-134.91, 267.102, 239.736], [215.054, 1436.07, 91.1556], [1298.03, 255.945, 225.09], [261.803, 1346.79, 267.102], [412.668, 885.635, 174.851], [192.099, 469.533, 215.054], [856.439, 1155.31, 356.07], [398.674, 1383.81, 83.9676], [-185.018, 28.4192, 218.034], [1056.44, 1237.78, 255.945], [298.109, 750.123, 287.819], [101.258, 1161.02, 261.803], [1212.07, 993.143, 266.788], [49.837, 371.069, 260.927], [902.274, 274.615, 52.6683], [608.9, 1142.85, 165.635], [290.508, 795.986, 111.206], [952.149, 651.593, 192.099]], -0.49299, 63.9255, -0.49299],\n",
    "[[[589.045, -294.045, 235.243], [1530.46, 1014.87, 90.4644], [-163.088, 219.711, 294.871], [214.395, 1491.2, 89.3611], [1258.79, 217.217, 196.912], [258.564, 1291.75, 219.711], [357.797, 891.035, 238.794], [146.706, 438.238, 214.395], [911.413, 1151.08, 51.2021], [404.918, 1438.59, 90.214], [-228.723, -5.19387, 178.789], [1042.61, 1184.4, 217.217], [314.549, 697.495, 224.149], [92.9489, 1106.51, 258.564], [1208.53, 938.121, 211.747], [40.6953, 316.696, 266.161], [936.069, 318.179, 357.797], [555.602, 1156.97, 171.035], [270.987, 847.55, 173.909], [898.144, 640.48, 146.706]], -0.470786, 55.136, -0.470786],\n",
    "[[[550.998, -347.757, 234.133], [1530.57, 1080.69, 90.5688], [-135.984, 159.727, 0.694336], [215.767, 1557.01, 89.354], [1195.63, 198.68, 224.016], [207.522, 1250.18, 159.727], [323.149, 835.069, 293.761], [92.0332, 401.583, 215.767], [953.151, 1201.98, 117.011], [405.31, 1504.41, 88.2508], [-294.515, -3.16525, 115.63], [989.809, 1145.1, 198.68], [266.877, 652.108, 195.802], [79.273, 1042.13, 207.522], [1152.22, 904.031, 170.185], [35.6515, 251.067, 218.6], [1001.82, 315.012, 323.149], [490.685, 1167.86, 115.069], [205.606, 855.169, 237.684], [843.478, 677.145, 92.0332]], -0.555168, 65.8231, -0.555168],\n",
    "[[[518.28, -392.235, 233.191], [1530.47, 1135.91, 90.4741], [-80.7692, 159.942, 55.9092], [216.843, 1612.22, 89.6273], [1155.61, 160.641, 279.231], [155.886, 1269.74, 159.942], [344.981, 784.353, 359.753], [46.9679, 369.679, 216.843], [928.479, 1251.37, 172.215], [407.449, 1559.59, 88.4124], [-317.989, 46.8114, 75.6089], [937.359, 1127.85, 160.641], [213.627, 637.509, 223.075], [30.0985, 1017.02, 155.886], [1097.9, 913.891, 189.741], [-7.7815, 216.975, 158.785], [1045.73, 281.536, 344.981], [467.702, 1218.06, 64.353], [175.706, 808.75, 292.819], [841.972, 732.339, 46.9679]], -0.470786, 55.215, -0.470786],\n",
    "[[[478.294, -444.653, 232.134], [1530.54, 1201.84, 90.5371], [-43.3137, 214.198, 121.838], [217.88, 1678.14, 89.4171], [1165.58, 95.4714, 316.686], [94.1679, 1292.92, 214.198], [410.903, 783.46, 54.8521], [-6.15586, 330.635, 217.88], [863.244, 1260.91, 238.136], [409.884, 1625.47, 88.5702], [-301.014, 110.517, 85.584], [875.362, 1150.27, 95.4714], [165.055, 592.929, 278.174], [-29.826, 1044.5, 94.1679], [1032.82, 903.335, 212.923], [-69.0195, 241.398, 158.885], [1109.24, 263.865, 50.9033], [496.784, 1277.23, 63.4602], [200.713, 747.748, 358.696], [887.405, 780.114, 353.844]], -0.528523, 65.9286, -0.528523],\n",
    "[[[443.998, -488.043, 231.219], [1530.46, 1257.15, 90.46], [-72.1133, 261.415, 177.145], [218.884, 1733.43, 89.6222], [1205.52, 57.2109, 287.887], [48.1764, 1262.2, 261.415], [443.103, 828.428, 120.923], [-50.0795, 297.026, 218.884], [833.672, 1214.17, 293.434], [411.705, 1680.75, 88.5021], [-296.316, 165.625, 125.522], [870.529, 1205.37, 57.2109], [172.481, 538.123, 315.771], [-33.4051, 1099.7, 48.1764], [986.154, 873.647, 182.203], [-120.453, 261.733, 213.283], [1144.46, 306.508, 83.1032], [521.891, 1326.51, 108.428], [255.995, 746.048, 53.9372], [942.344, 773.744, 309.92]], -0.457461, 55.3073, -0.457461],\n",
    "[[[403.288, -537.756, 230.153], [1530.54, 1321.4, 90.542], [-136.256, 265.213, 241.4], [219.906, 1797.68, 89.394], [1224.69, -4.11922, 223.744], [37.9944, 1198.76, 265.213], [410.598, 883.854, 176.079], [-100.47, 257.156, 219.906], [858.677, 1154.98, 357.681], [413.982, 1744.96, 88.5562], [-333.161, 218.266, 144.687], [905.827, 1259.06, 355.881], [218.105, 492.877, 286.821], [9.88617, 1147.18, 37.9944], [921.926, 871.774, 118.76], [-174.494, 226.972, 260.349], [1152.77, 370.223, 50.5977], [502.147, 1387.66, 163.854], [294.301, 797.636, 119.857], [983.118, 724.083, 259.53]], -0.532964, 64.2549, -0.532964],\n",
    "[[[367.49, -579.967, 229.247], [1530.46, 1376.75, 90.4561], [-163.133, 216.83, 296.747], [220.929, 1853.02, 89.636], [1184.4, -42.0708, 196.867], [32.9392, 1143.64, 216.83], [355.412, 888.075, 240.494], [-143.206, 221.987, 220.929], [913.959, 1152.3, 53.0181], [415.814, 1800.28, 88.488], [-378.07, 250.615, 104.401], [960.997, 1254.65, 317.929], [233.702, 439.773, 222.838], [53.7714, 1180.9, 32.9392], [895.68, 920.503, 63.6445], [-184.203, 172.484, 264.306], [1188.24, 412.711, 355.412], [449.107, 1403.47, 168.075], [267.128, 845.853, 175.173], [972.631, 669.739, 216.794]], -0.45302, 55.3468, -0.45302],\n",
    "[[[325.086, -628.297, 228.226], [1530.52, 1441.04, 90.5176], [-134.711, 159.159, 1.0415], [221.91, 1917.31, 89.4345], [1122.71, -60.1763, 225.289], [-18.8647, 1105.56, 159.159], [323.248, 832.404, 295.725], [-192.155, 180.301, 221.91], [953.092, 1203.31, 117.305], [418.083, 1864.53, 88.6144], [-393.505, 313.029, 42.709], [1008.34, 1211.14, 299.824], [186.168, 396.48, 195.845], [108.04, 1215.38, 341.135], [924.736, 977.857, 25.564], [-191.152, 108.566, 215.809], [1252.28, 406.997, 323.248], [386.321, 1417.31, 112.404], [203.112, 851.834, 239.472], [920.803, 631.691, 167.845]], -0.510755, 64.2945, -0.510755],\n",
    "[[[287.859, -669.288, 227.284], [1530.47, 1496.41, 90.4722], [-79.3405, 159.711, 56.4146], [222.912, 1972.67, 89.576], [1083.43, -99.2067, 280.659], [-70.4512, 1125.69, 159.711], [346.873, 782.323, 0.0999372], [-233.666, 143.654, 222.912], [928.096, 1252.73, 172.669], [419.877, 1919.88, 88.493], [-352.509, 350.252, 3.43018], [1035.48, 1162.88, 260.793], [132.776, 381.799, 224.348], [160.29, 1197.05, 289.549], [974.883, 1001.34, 45.689], [-236.323, 76.5384, 158.217], [1296.37, 373.502, 346.873], [365.638, 1468.68, 62.3232], [174.594, 804.369, 294.783], [866.769, 643.795, 126.334]], -0.470783, 55.3731, -0.470783],\n",
    "[[[243.792, -716.142, 226.227], [1530.54, 1560.74, 90.5357], [-43.2668, 212.963, 120.735], [223.981, 2036.98, 89.4151], [1094.74, -162.525, 316.733], [-130.573, 1148.55, 212.963], [411.192, 781.842, 55.3575], [-281.177, 100.296, 223.981], [864.379, 1261.52, 236.981], [422.162, 1984.16, 88.5189], [-288.271, 353.508, 14.7441], [1024.61, 1099.49, 197.475], [86.3671, 337.264, 279.602], [181.252, 1136.24, 229.427], [1020.24, 1046.95, 68.5483], [-295.828, 100.957, 158.654], [1358.88, 358.316, 51.1916], [396.038, 1525.36, 61.842], [201.017, 745.726, 359.043], [829.139, 695.959, 78.8229]], -0.528523, 64.3209, -0.528523],\n",
    "[[[205.157, -755.846, 225.339], [1530.45, 1616.13, 90.4468], [-71.21, 260.799, 176.135], [224.976, 2092.37, 89.6474], [1134.79, -200.807, 288.79], [-177.286, 1118.77, 260.799], [443.036, 827.175, 119.847], [-321.338, 62.1359, 224.976], [833.832, 1215.3, 292.371], [424.023, 2039.53, 88.5268], [-234.588, 367.192, 54.7886], [971.635, 1083.26, 159.193], [95.1845, 282.571, 315.845], [144.894, 1094.44, 182.714], [1040.9, 1098.35, 38.7666], [-347.269, 121.522, 212.075], [1393.93, 401.215, 83.0361], [422.559, 1574.0, 107.175], [256.4, 744.371, 54.4692], [840.299, 750.223, 38.662]], -0.444136, 55.3995, -0.444136],\n",
    "]\n",
    "\n",
    "heading_poses_over_time = np.array([\n",
    "    item[0] for item in data_with_headings\n",
    "])\n",
    "motion_over_time = np.array([\n",
    "    item[1:] for item in data_with_headings\n",
    "])\n",
    "\n",
    "heading_poses_over_time\n",
    "fig, ax = plt.subplots()\n",
    "for pose_index in range(heading_poses_over_time.shape[1]):\n",
    "    ax.scatter(heading_poses_over_time[:, pose_index, 0], heading_poses_over_time[:, pose_index, 1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Poses over time (24, 20, 3) (3,)\n",
      "(3,)\n",
      "Original pose: [925.478 311.719 208.524] -> calculated: [924.95827968 311.4334462  209.048082  ] on robot: [924.88  311.124 241.301]\n",
      "0.262041 0.593001 0.262041\n"
     ]
    }
   ],
   "source": [
    "def transform_poses(poses: np.array, rot1: float, trans: float, rot2: float) -> np.array:\n",
    "    \"\"\"Rot1 -> degrees,\n",
    "    trans -> mm,\n",
    "    rot2 -> degrees \"\"\"\n",
    "    new_poses = poses.copy()\n",
    "    print(new_poses[0].shape)\n",
    "    new_poses[:,2] += rot1\n",
    "    rotation_radians = np.radians(new_poses[:, 2])\n",
    "    new_poses[:,0] += trans * np.cos(rotation_radians)\n",
    "    new_poses[:,1] += trans * np.sin(rotation_radians)\n",
    "    new_poses[:,2] += rot2\n",
    "    return new_poses\n",
    "\n",
    "print(\"Poses over time\", heading_poses_over_time.shape, motion_over_time[0].shape)\n",
    "new_poses = transform_poses(heading_poses_over_time[0], motion_over_time[0,0], motion_over_time[0,1], motion_over_time[0,2])\n",
    "print(\"Original pose:\", heading_poses_over_time[0,0], \"-> calculated:\", new_poses[0], \"on robot:\", heading_poses_over_time[1, 0])\n",
    "print(motion_over_time[0,0], motion_over_time[0,1], motion_over_time[0,2])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The robot one and the code one I here look nothing alike. Is it the constraint? Try adding it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Poses over time (24, 20, 3) (3,)\n",
      "(3,)\n",
      "Original pose: [925.478 311.719 208.524] -> calculated: [924.95827968 311.4334462  209.048082  ] on robot: [924.88  311.124 241.301]\n",
      "0.262041 0.593001 0.262041\n"
     ]
    }
   ],
   "source": [
    "\n",
    "constrain_360 = np.vectorize(lambda n: float(n % 360))\n",
    "def constrain_transform_poses(poses: np.array, rot1: float, trans: float, rot2: float) -> np.array:\n",
    "    \"\"\"Rot1 -> degrees,\n",
    "    trans -> mm,\n",
    "    rot2 -> degrees \"\"\"\n",
    "    new_poses = poses.copy()\n",
    "    print(new_poses[0].shape)\n",
    "    new_poses[:,2] += rot1\n",
    "    rotation_radians = np.radians(new_poses[:, 2])\n",
    "    new_poses[:,0] += trans * np.cos(rotation_radians)\n",
    "    new_poses[:,1] += trans * np.sin(rotation_radians)\n",
    "    new_poses[:,2] += rot2\n",
    "    new_poses[:,2] = constrain_360(new_poses[:,2])\n",
    "    return new_poses\n",
    "\n",
    "print(\"Poses over time\", heading_poses_over_time.shape, motion_over_time[0].shape)\n",
    "new_poses = constrain_transform_poses(heading_poses_over_time[0], motion_over_time[0,0], motion_over_time[0,1], motion_over_time[0,2])\n",
    "print(\"Original pose:\", heading_poses_over_time[0,0], \"-> calculated:\", new_poses[0], \"on robot:\", heading_poses_over_time[1, 0])\n",
    "print(motion_over_time[0,0], motion_over_time[0,1], motion_over_time[0,2])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not the constraint. How else is my scenario different?\n",
    "- We are making a copy, but that should not matter.\n",
    "- Is the += operator on a list the same on the robot?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the robot:\n",
    "```python\n",
    ">>> d = np.arange(20, dtype=np.float)\n",
    ">>> d\n",
    "array([0.0, 1.0, 2.0, ..., 17.0, 18.0, 19.0], dtype=float32)\n",
    ">>> d += 0.13\n",
    ">>> d\n",
    "array([0.13, 1.13, 2.13, ..., 17.13, 18.13, 19.13], dtype=float32)\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok - I tried a bit of code on the robot  - and got the 209 (not 241) heading. Whaaaaa?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simply put, the heading diffs between pose row 0 and 1 should be the same as the motion row 0 heading delta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.27770000e+01,  6.64000000e-01, -5.20000000e-01,  1.19787000e+02,\n",
       "       -8.42200000e-01,  4.38000000e-02,  8.86000000e-01, -4.84790000e-01,\n",
       "        3.59310473e+02, -5.82230000e+01,  4.49200000e-01,  7.13000000e-01,\n",
       "       -7.93262000e+01, -3.05000000e-01,  7.86130000e-01, -5.68730000e+01,\n",
       "       -7.38000000e-01, -4.08000000e-01,  9.37770000e+01,  7.59000000e-01])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# heading_diffs\n",
    "heading_poses_over_time[1, :, 2] - heading_poses_over_time[0, :, 2]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And they are so clearly not. What is going on?\n",
    "rot1 or 2 are not lists? we dumped them above - clearly not.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay - reran code on my bot - based on the motion model - and it's doing the correct thing. What else is wrong there? This works correctly on the robot.\n",
    "```python\n",
    "def constrain_transform_poses(poses: np.array, rot1: float, trans: float, rot2: float) -> np.array:\n",
    "    \"\"\"Rot1 -> degrees,\n",
    "    trans -> mm,\n",
    "    rot2 -> degrees \"\"\"\n",
    "    new_poses = poses.copy()\n",
    "    print(new_poses[0].shape)\n",
    "    new_poses[:,2] += rot1\n",
    "    rotation_radians = np.radians(new_poses[:, 2])\n",
    "    new_poses[:,0] += trans * np.cos(rotation_radians)\n",
    "    new_poses[:,1] += trans * np.sin(rotation_radians)\n",
    "    new_poses[:,2] += rot2\n",
    "    new_poses[:,2] = constrain_360(new_poses[:,2])\n",
    "    return new_poses\n",
    "```\n",
    "- Try making it async...\n",
    "    - With it awaiting and returning a result - same thing.\n",
    "- Try setting the original poses - no copy. - same result.\n",
    "- try putting in a class and put the poses as a class member - which is now mutated instead of returned. - still the same.\n",
    "- Its not the ulab.numpy.array.tolist.\n",
    "\n",
    "Oh fgs - I'm looking at the poses POST transform - not pre - aaaarrrg! Lets confirm that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.27770000e+01,  6.64000000e-01, -5.20000000e-01,  1.19787000e+02,\n",
       "       -8.42200000e-01,  4.38000000e-02,  8.86000000e-01, -4.84790000e-01,\n",
       "        3.59310473e+02, -5.82230000e+01,  4.49200000e-01,  7.13000000e-01,\n",
       "       -7.93262000e+01, -3.05000000e-01,  7.86130000e-01, -5.68730000e+01,\n",
       "       -7.38000000e-01, -4.08000000e-01,  9.37770000e+01,  7.59000000e-01])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heading_poses_over_time[1, :, 2] - heading_poses_over_time[0, :, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.3886"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motion_over_time[1, 2]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wait - so it's still not this value... What are we now getting on the robot simplified poses code? Lets' put in motion item 1.\n",
    "- I get the exact same result on the robot. And do I get that here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n",
      "Transform: 16.3886 0.843376 16.3886\n",
      "Original pose: [925.478 311.719 208.524] -> calculated: [924.88073411 311.1235535  241.3012    ] on robot: [924.88  311.124 241.301]\n",
      "Original pose: [1385.53   520.633  305.534] -> calculated: [1386.19388733  520.11286858  338.3112    ] on robot: [1386.2    520.113  306.198]\n",
      "Original pose: [ 41.4205  94.8741 160.633 ] -> calculated: [ 40.57826324  94.91792138 193.4102    ] on robot: [ 40.5783  94.9179 160.113 ]\n"
     ]
    }
   ],
   "source": [
    "def transform_poses(poses: np.array, rot1: float, trans: float, rot2: float) -> np.array:\n",
    "    \"\"\"Rot1 -> degrees,\n",
    "    trans -> mm,\n",
    "    rot2 -> degrees \"\"\"\n",
    "    new_poses = poses.copy()\n",
    "    print(new_poses[0].shape)\n",
    "    new_poses[:,2] += rot1\n",
    "    rotation_radians = np.radians(new_poses[:, 2])\n",
    "    new_poses[:,0] += trans * np.cos(rotation_radians)\n",
    "    new_poses[:,1] += trans * np.sin(rotation_radians)\n",
    "    new_poses[:,2] += rot2\n",
    "    return new_poses\n",
    "\n",
    "new_poses = transform_poses(heading_poses_over_time[0], motion_over_time[1,0], motion_over_time[1,1], motion_over_time[1,2])\n",
    "print(\"Transform:\", motion_over_time[1,0], motion_over_time[1,1], motion_over_time[1,2])\n",
    "print(\"Original pose:\", heading_poses_over_time[0,0], \"-> calculated:\", new_poses[0], \"on robot:\", heading_poses_over_time[1, 0])\n",
    "print(\"Original pose:\", heading_poses_over_time[0,1], \"-> calculated:\", new_poses[1], \"on robot:\", heading_poses_over_time[1, 1])\n",
    "print(\"Original pose:\", heading_poses_over_time[0,2], \"-> calculated:\", new_poses[2], \"on robot:\", heading_poses_over_time[1, 2])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok - this works here, but not on the robot (example code, what about test code). What is going on?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.77719999999999"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "241.3012 - 208.524"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.77719999999999"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "338.3112 - 305.534"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Going back to simplified code on the robot, not using the constraint 360, or classes. And it behaves as above - we get 338.3112, not 306.198.\n",
    "- Add the constrain in - and it goes wrong again! Same here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n",
      "Transform: 16.3886 0.843376 16.3886\n",
      "Original pose: [925.478 311.719 208.524] -> calculated: [924.88073411 311.1235535  241.3012    ] on robot: [924.88  311.124 241.301]\n",
      "Original pose: [1385.53   520.633  305.534] -> calculated: [1386.19388733  520.11286858  338.3112    ] on robot: [1386.2    520.113  306.198]\n",
      "Original pose: [ 41.4205  94.8741 160.633 ] -> calculated: [ 40.57826324  94.91792138 193.4102    ] on robot: [ 40.5783  94.9179 160.113 ]\n"
     ]
    }
   ],
   "source": [
    "constrain_360 = np.vectorize(lambda n: float(n % 360))\n",
    "def transform_poses111(poses: np.array, rot1: float, trans: float, rot2: float) -> np.array:\n",
    "    \"\"\"Rot1 -> degrees,\n",
    "    trans -> mm,\n",
    "    rot2 -> degrees \"\"\"\n",
    "    new_poses = poses.copy()\n",
    "    print(new_poses[0].shape)\n",
    "    new_poses[:,2] += rot1\n",
    "    rotation_radians = np.radians(new_poses[:, 2])\n",
    "    new_poses[:,0] += trans * np.cos(rotation_radians)\n",
    "    new_poses[:,1] += trans * np.sin(rotation_radians)\n",
    "    new_poses[:,2] += rot2\n",
    "    new_poses[:,2] = constrain_360(new_poses[:,2])\n",
    "    return new_poses\n",
    "\n",
    "new_poses = transform_poses111(heading_poses_over_time[0], motion_over_time[1,0], motion_over_time[1,1], motion_over_time[1,2])\n",
    "print(\"Transform:\", motion_over_time[1,0], motion_over_time[1,1], motion_over_time[1,2])\n",
    "print(\"Original pose:\", heading_poses_over_time[0,0], \"-> calculated:\", new_poses[0], \"on robot:\", heading_poses_over_time[1, 0])\n",
    "print(\"Original pose:\", heading_poses_over_time[0,1], \"-> calculated:\", new_poses[1], \"on robot:\", heading_poses_over_time[1, 1])\n",
    "print(\"Original pose:\", heading_poses_over_time[0,2], \"-> calculated:\", new_poses[2], \"on robot:\", heading_poses_over_time[1, 2])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost like vectorize doesn't work on the robot?\n",
    "- Add a dumb constrain without vectorize:\n",
    "```python\n",
    "def dumb_constrain_360(items):\n",
    "    return np.array([float(item % 360) for item in items])\n",
    "```\n",
    "That works.\n",
    "\n",
    "Ok - trying plain vectorize test in CircuitPython:\n",
    "```python\n",
    "from ulab import numpy as np\n",
    "headings = np.array([\n",
    "    241.301,\n",
    "    338.311,\n",
    "    193.41,\n",
    "    51.301,\n",
    "    4.1977,\n",
    "    127.651,\n",
    "    225.301,\n",
    "    42.1525,\n",
    "    33.3177,\n",
    "    342.301,\n",
    "    124.247,\n",
    "    292.044,\n",
    "    186.301,\n",
    "    302.185,\n",
    "    9.7328,\n",
    "    217.301,\n",
    "    249.386,\n",
    "    2.2231,\n",
    "    164.301,\n",
    "    197.687\n",
    "])\n",
    "\n",
    "constrain_360 = np.vectorize(lambda n: float(n % 360))\n",
    "print(repr(constrain_360(headings).tolist()))\n",
    "```\n",
    "That works.\n",
    "- Maybe the combo of vectorise and a numpy list slice?\n",
    "`output = constrain_360(new_poses[:,2])` - yes - that does it.\n",
    "- Just printing the slice, gives the right result. Is this something todo with being a numpy array?\n",
    "    - What about storing the result in an intermediate? - doesn't help\n",
    "\n",
    "If vectorise is making strange behaviour, and if according to numpy doc it's not for performance (only convenience), then my code is simpler, book easier to explain and no slower by using a list comprehension. Let's do that.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "241.301,\n",
    "338.311,\n",
    "193.41,\n",
    "51.301,\n",
    "4.1977,\n",
    "127.651,\n",
    "225.301,\n",
    "42.1525,\n",
    "33.3177,\n",
    "342.301,\n",
    "124.247,\n",
    "292.044,\n",
    "186.301,\n",
    "302.185,\n",
    "9.7328,\n",
    "217.301,\n",
    "249.386,\n",
    "2.2231,\n",
    "164.301,\n",
    "197.687"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With vectorise: [241.301, 338.311, 193.41, 251.301, 74.1977, 127.651, 225.301, 42.1525, 33.3177, 342.301, 124.247, 292.044, 186.301, 302.185, 39.7328, 217.301, 249.386, 32.2231, 164.301, 197.687]\n",
      "With list comprehension: [241.301, 338.311, 193.41, 251.301, 74.1977, 127.651, 225.301, 42.1525, 33.3177, 342.301, 124.247, 292.044, 186.301, 302.185, 39.7328, 217.301, 249.386, 32.2231, 164.301, 197.687]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/87/nv1zkt0s2nq86764_c0jf94h0000gn/T/ipykernel_98694/638229896.py:24: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  ], dtype=np.float)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "new_poses = np.array([\n",
    "   [924.881, 311.124, 241.301],\n",
    "   [1386.19, 520.113, 338.311],\n",
    "   [40.5783, 94.9179, 193.41],\n",
    "   [8.8905, 1079.85, 251.301],\n",
    "   [1171.92, 619.98, 74.1977],\n",
    "   [629.102, 1447.75, 127.651],\n",
    "   [575.871, 719.038, 225.301],\n",
    "   [525.669, 501.781, 42.1525],\n",
    "   [699.721, 1331.84, 33.3177],\n",
    "   [189.287, 1133.59, 342.301],\n",
    "   [178.183, 121.003, 124.247],\n",
    "   [1039.49, 1340.16, 292.044],\n",
    "   [173.717, 1006.77, 186.301],\n",
    "   [193.793, 1225.38, 302.185],\n",
    "   [886.23, 866.955, 39.7328],\n",
    "   [450.798, 356.609, 217.301],\n",
    "   [748.301, 47.7649, 249.386],\n",
    "   [511.64, 789.662, 32.2231],\n",
    "   [464.355, 719.037, 164.301],\n",
    "   [1094.59, 790.142, 197.687]\n",
    "], dtype=np.float)\n",
    "\n",
    "constrain_360 = np.vectorize(lambda n: n % 360)\n",
    "intermediate = new_poses[:,2]\n",
    "output = constrain_360(intermediate)\n",
    "output2 = np.array([n % 360 for n in intermediate])\n",
    "print(\"With vectorise:\", repr(output.tolist()))\n",
    "print(\"With list comprehension:\", repr(output2.tolist()))\n"
   ]
  }
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