{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e08ec366",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f17ee96",
   "metadata": {},
   "source": [
    "# Импорт"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "38906a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = pd.read_csv(\"../results/test_processing_camera1-2020-10-14---19-38-42---093390448_scaled.txt\", sep=\" \")\n",
    "table2 = pd.read_csv(\"../results/test_processing_camera1-2020-10-14---19-38-42---093390448_scaled2.txt\", sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a92a7977",
   "metadata": {},
   "outputs": [],
   "source": [
    "unpr_table = pd.read_csv(\"../archive/test_processing_camera1-2020-10-14---19-38-42---093390448_scaled.txt\", sep=\" \")\n",
    "old_table = pd.read_csv(\"../results/v_and_angle_inertial2.txt\", sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "229279d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>angle</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.079121</td>\n",
       "      <td>-0.000958</td>\n",
       "      <td>-0.002339</td>\n",
       "      <td>-0.001163</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.118682</td>\n",
       "      <td>-0.001008</td>\n",
       "      <td>-0.003687</td>\n",
       "      <td>-0.001495</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.265381</td>\n",
       "      <td>-0.001311</td>\n",
       "      <td>-0.009058</td>\n",
       "      <td>-0.001354</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.312755</td>\n",
       "      <td>-0.001672</td>\n",
       "      <td>-0.010727</td>\n",
       "      <td>-0.001087</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.305157</td>\n",
       "      <td>-0.001148</td>\n",
       "      <td>-0.010342</td>\n",
       "      <td>-0.002309</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>-6.236240</td>\n",
       "      <td>-0.003986</td>\n",
       "      <td>-0.216678</td>\n",
       "      <td>-0.015653</td>\n",
       "      <td>2486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>-6.232910</td>\n",
       "      <td>-0.003874</td>\n",
       "      <td>-0.216552</td>\n",
       "      <td>-0.015807</td>\n",
       "      <td>2487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>-6.235240</td>\n",
       "      <td>-0.003893</td>\n",
       "      <td>-0.216643</td>\n",
       "      <td>-0.015660</td>\n",
       "      <td>2488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>-6.234920</td>\n",
       "      <td>-0.003943</td>\n",
       "      <td>-0.216628</td>\n",
       "      <td>-0.015702</td>\n",
       "      <td>2489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>-6.239260</td>\n",
       "      <td>-0.003925</td>\n",
       "      <td>-0.216792</td>\n",
       "      <td>-0.015543</td>\n",
       "      <td>2490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1354 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         angle        v1        v2        v3  frame\n",
       "0    -0.079121 -0.000958 -0.002339 -0.001163    352\n",
       "1    -0.118682 -0.001008 -0.003687 -0.001495    353\n",
       "2    -0.265381 -0.001311 -0.009058 -0.001354    354\n",
       "3    -0.312755 -0.001672 -0.010727 -0.001087    355\n",
       "4    -0.305157 -0.001148 -0.010342 -0.002309    356\n",
       "...        ...       ...       ...       ...    ...\n",
       "1349 -6.236240 -0.003986 -0.216678 -0.015653   2486\n",
       "1350 -6.232910 -0.003874 -0.216552 -0.015807   2487\n",
       "1351 -6.235240 -0.003893 -0.216643 -0.015660   2488\n",
       "1352 -6.234920 -0.003943 -0.216628 -0.015702   2489\n",
       "1353 -6.239260 -0.003925 -0.216792 -0.015543   2490\n",
       "\n",
       "[1354 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a03edbda",
   "metadata": {},
   "outputs": [],
   "source": [
    "v = np.vstack([ np.vstack([table[\"v1\"].values, table[\"v2\"].values]), table[\"v3\"].values ])\n",
    "v = v.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "addd5a16",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_x(x, name):\n",
    "    fig = plt.figure(figsize=(15,5))\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    ax.set_aspect(\"equal\")\n",
    "    ax.grid()\n",
    "    ax.plot(x)\n",
    "    fig.savefig(fname=name, dpi=300, format=\"png\", bbox_inches='tight', pad_inches=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7548e7d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DauHFH3e4VHKzM9nGgZPpPH9rExpUr1SiY4UG+jKuZzjL9pxg7qaEUopQLN19nGV7TvBsn0b4e8sgMa7IYlEMbhtCbEIqU1def2AZUf5c6O925ydrsSrFtBFRRIVWNexp+RM9GvKPfo1Zsvs4N7+znMU7zTkvaUXxwZJ9TF4Wx+0ta7Hsme7cFC6DIglxIySBE6Wqup8nL90ewYZDp1l/MNnocAD7CFdTtmURWs2HAZHBpXLMp3uF07BGJeZsPEKeCyWqZhVz+DSjZ8YQWMmDh7qEGR2OuIYHO4fSs0kNJizazaQ/ZMqGikRrzZNztvD6L7sI9vfi46GtaV7b+EFEHu/ekOkj2qGBsXO2sCA2yeiQxBVOnMnins/WMmnpfvq3qMk7d0fi7SFzqgpxoySBE6XunrYhVPZyY8Ki3YZ3LrfZNCO/juZMtn3icT+v0nmyo5RiWMd6bE9KY9xc6X9REsnnzvPP77fjZlG8MzjS5UcxrejcrBb+b2Az/L3d+eTPOFIck8yK8i3Xpnnymy0s3HaUx7s3YPU/byEqtHgDQTlTjyY1WDTuJhrX9OO5+dv4dlOC3FxzEfGn0uk1cQVbE9IY060+H9zXSpI3IUpIrpREqfNytzLx3lbs/OsMd32yluRz5w2L5ZuNR1i57yQ1fRXdSnn+qhGdQ3mwcygLtx3l6XmxpXrsikJrzeu/7OJwcjpfPtSOW5pIXwgzCKniw7djOpFr04yXv/1yb+W+k4xdlsHC7Ud5rHsDnu3T2CUHnKjp78W04VE0r1WZf3y/jf6TVnEmK8fosCq0dQeSGfjfNWgNC57swvP9m+LpJsmbECUl47UKp+gVEcT97eswZ2OCfZQwA+ZNiztxlpcX7KB9WFUebZSFh1vp3694+bYIjqVlsXjXMfYeO0vjmn4lPmZaZg4/bUki+dx58rR9cmmbTZNnc7zWGlshw7hfWG2fdvrSRND5t11ac+X6wtfporznOsco6DgHTqazNSGVBzuHSl8Ik2lc04+/927Eu7/v5cDJcyXuWypc0287jvHorBgCvRVvDW7JHS1rufTosDUqezFvTCc+XhbHpKX7GfTxGn58oov0qy1jmdl5vP3bHr5aG081Xw9mje5I0+DKRoclRLkhCZxwmjfviiT+VAYz1sVzd9sQagd4l+n5521KQCnFx0NbsytmvVPOYbEoXrkjgujDKTw1L5ZF47qW6OJmR1IaQ6eu50yWvUmm1aKwKLAo5XjtWLYoCjvLhfOri8uXli6EduV7Cwr5yjNcuU9B5y/K755/F3erhX8PaMrwTqHXfZ9wPYPbhjDpj/38fV4sb90dKRdo5Uhmdh6f/hnHR8vjaFijEs9G2ujXqrbRYRWJu9XC070b0bKOP6NmxHD3p2uZMKg5HepXMzq0CuF8bh7PfBfLou3HGNiqFs/2aUydqj5GhyVEuSIJnHCq/xvYjNs+Ws3UlQd59Y5mZXbeXX+dYfqaeAa0CKaGnxe7nHiuYH9vxvdsyEsLdrIpPoX2YTfWL+TAyXMMnbqeSp5uzBndkWa1jB8cQIhrCapsH8hi9MwYBkxexbJnuhMa6Gt0WKKEEk5nMHbOFmITUmkXWoWpw6OI3bjW6LCK7ZYmQXz2t7Y8+91W7puynpdvi+DhrjJIkjMdS8viiW82E3M4hfE9w3m6t0wJI4QzSB844VSNgvzo3TSI+TGJ7Dl2pkzOabNpPl1xAAW8NrBsksY7WtWmlr8Xj82KuaFBHXLybPxn4W6ycm3MHiXJmzCPPs1q8s2oDtg0LNpx1OhwRAkdS8ti5Neb2H/8LBPubM6cUR0J8PEwOqwb1jsiiCV/70bLEH9e+2UX01bJ9BfOsnzPCQZMXkXM4RSe6d2Ip3qVfdcJISoKSeCE0z3btzFe7lbu+3w9O5LSnH6+r9bG87+tf9G5YWCZXXj4e7vzyd/akpyeTbd3lhdrVMqcPBtPzY1l6Z4T3BdVhzB5giFMpnODQNqHVeXTPw9wNC3T6HDEDVq57yS3fbSK+OQMPh7ahgc61CvTybmdpYafvV9c98bVeWPhbsbO2ULC6QyjwypXluw6zpiZMVTx9eC7Rzsxtme4S/eVFMLszP/JLFxeWKAvL/RvQlpmDs98uxVdyAAcpWHPsTNMWrqfOlW9eXdwpNPOU5BWdQJ4+bYIzp7P5ZdtfxX5fR8vi2Ph9qM817cx/1eGzUyFKE1v3x3J2axcftgsc3CZ0Xu/72XElxtJy8xh+oh29GhSw+iQSpWXu5VPHmjD8E71+N/Wv7jv83UcOpVudFiml51r481Fuxk1I5p61XyYN7oj7VxoegkhyqsSJXBKqVeVUklKqVjHv/75tj2vlIpTSu1VSvUteajCzO5qE8Lrg5qz9/hZftnmvGZWExfvQ2vNVw+1J6iyl9POU5gHO4fSsEYlXl6ws0h3eLXW/BSbRJeG1XiiR0MsLjg0txBFERboS9t6VfhqbTyJKfJ0wyy01rz5624+Xh7HbZG12PZKX7qGBxodllP4eLjx2sDmzBrZgTNZuQyZso4jyfK3eqOSz53nrk/X8PnKg7SuG8D3j3emWiVPo8MSokIojSdwH2itWzn+LQJQSkUAQ4BmQD/gE6WUTPxRwd0XVYfIEH/e+nWPU57CbTiYzNI9J7g3qo5hQ5pbLIopw9pyPtfGdzGJ193//cX7OJycwR0ta5VBdEI414Q7m5N+PpehUzdw8OQ5o8MR13H8TBb3TVnP5ysOcnvLWnxwb8sKMcFy1/BAvhnVgbTMHPpNWslnKw44tWVIeXTibBYPfx3N3mNn+ej+1vz4eBcqe8lUDUKUFWc1oRwIzNVan9daHwLigPZOOpcwCQ83C8M7hZKUmsnK/adK9dj2jvfR1A7wZnS3+qV67OKqX70SPZvU4JPlccQcTil0vwMnz/Hx8jh6Na3BPW3rlGGEQjhHk5qVeb5/U46czuDDP/YbHY64hm2JqfT5YCUxh1N4sHMok+5rVS76uxVVZEgAv43vRlBlL976dQ+v/bKLPJskcddjs2m+j0mk53sr2H30DO8MjuR2uQEpRJlTJbnrpJR6FXgQOANEA89orVOUUh8D67XWsxz7fQH8qrWeX8AxRgOjAYKCgtrOnTv3huNxlnPnzlGpkkxSWxoyczUvr8nkVKamcVUL4QFW3Cxw8bpBg+3CS8ck1IX9BC5ObL0yMZesPHi0pScdg6+eHaOsyzAjR/Pi6kxSzmvGtvakbdDlMWXlap5bmcHZbPhnOy+aViv/d71LQuqguXy18zyrE3N5saMXYf72v20pQ9exPyWPDzdn4W5RPBPlRR2/6ydu5bX8tNZM35HNqqRcutdxY2gTDzys5bMpe0nL8Fi6jVm7s9lxKo8gH8XQph60rC6zUZWV8loHK5LilGGPHj1itNZRhW2/bgKnlPoDqFnApheB9cAp7NfTrwPBWuuHi5PA5RcVFaWjo6OvGY8R/vzzT7p37250GOXG6fRs3v51D6vjTpGUWrQR65SyTxytHBNZK5R9neN1o5p+PN0rnJsbVS9w5CsjynDzkRTu+mQtN4UHMnNkh8u2PTF7Mwu3H2Xa8Ch6RQSVaVxmJHXQXBJTMrj3s3X8lZbFs30aEVTZi6OH9tIismgDCxX18rmoo9wV53K8qAPnXTnRfWGCKnsSFujrEk+3MrPzeG/xXr5YfYiQKt5MHR5V5MnXy3Md1Frz0oIdzFp/BA+rhT7Ngqju54lVKSwWdfl3jwKLyr9OXfybsd9c1Bdfw6Wbja7gcHw89UJDi/4GrUnLzOFQcgbxp9I5cjoDN4vi2b6NebhLGB5uxv9NVyTluQ5WFMUpQ6XUNRO469460Vr3KuKJpgK/OBaTgPxtwkIc64Sgqq8HbztGiNRak2fT5DqarlxIyOxflJd+mlGbulUYe0tDPloWx8z1hxnWsR4Ah5PTWbj9KGNvaSjJmyiXQqr48OodzRg9M4b3Fu+7tCFmk3FBGSjY34tZj3QwrG8uwPncPB76aiPrD56md0QQL/RvKlOWOCileGNQC25tHszcTQmsO3CK8zk28rRGO1p52Ft/aGz60k9TOlC8ps2VPN0IDfShZZ0A7m4TwtAOdanuJwOVCGG0Ej37VkoFa60vDCl4J7DD8fpn4Bul1ESgFhAObCzJuUT5pJTCzapwK6ctCMf1DGflvpNMX32Ioe3rYrUoPliyD6VgSPu6RocnhNP0aVaTvW/0IyvHxvEzWaxat5E2bdpc931FvS4ueuv/ol9pF/WYxYlxR1IaH/6xj0Efr2HpszdTw6/sR8dNOJ3BqBnR7Dl2ltcHNmNYp9Ayj8EMujQMpEvDoo/AqfMleBduNNqfzNm3u9rNR3mCI0T5UdLGy+8opVph/z6LB8YAaK13KqW+BXYBucATWuu8Ep5LCNNxt1p45Kb6jJ2zhbd/28O9UXX4KfYvHuvegNoB3kaHJ4RTebpZ8XSz4u/tzl8BVlrXrWJ0SGWufVhV2odV5baPVvP12nie69ukTM+/eOcxxs3dQlaOjbfvbsF97eTGUWlRjuaTlmI10hVCiJIrUQKntR52jW0TgAklOb4Q5cHtLWuxev8ppqw8yJwNRwD4m6M5pRCi/Gte258BLYL57/IDRIYE0LdZQd3KS9+SXccZPTOG8BqVeHtwJG0qYAIthBDlkQwfJEQZ+NetTfDxtJJwOpPqfp7y9E2ICub9e1sSm5DKmJkxTBnWlj5OTOJy8mxMW3WId37fQ/3qvvz0RBd8PeXrXgghygv5RBeiDFTx9eCV25sZHYYQwiBe7la+GdWBm9/9k6/WxtM7IsgpfaQSTmfwzLdb2Rh/msZBfsx/rJMkb0IIUc7IGLBCCCFEGahXzZd/3dqEtQeS+XptfKkf/3ByOo98Hc3WxFRe7N+UBU92wc/LvdTPI4QQwliSwAkhhBBlZEy3+rSqE8CXa+PJyM4tteMuiE1iwOTVJKZk8NH9rRnVrT5e7uV0eF8hhKjgJIETQgghyohSise7N+CIo6mjrRQmFHvhx+2MnxuLn5cb00a0c2r/OiGEEMaTBE4IIYQoQ32a1WR0t/r8uuMYY+dsueHjaK15/oftfLPhCA90qMuK53rQqUG1UoxUCCGEK5KezUIIIUQZ+3vvRmw5kspvO4+RmJJBSBWfYr0/7sQ5xsyM5sDJdO5vX5dX72iGu1XuyQohREUgn/ZCCCFEGfN0s/L23ZG4WRSPzdqM1kVvShmbkEr/Sas4fuY8z/VtzIRBzSV5E0KICkQ+8YUQQggDhAX68vytTdielMa2xLTr7q+1ZuG2owyZso4qvu4sfrobT/RoiMVS+tMRCCGEcF2SwAkhhBAGGdS6NpW93Hh89mbOnS98VMozWTk8PS+WJ77ZTHU/T74b05laAd5lGKkQQghXIQmcEEIIYZAAHw+mjWhHUmombV5bwun07Kv2ST+fy+OzNvNT7F880KEu3z/WmbrVitdnTgghRPkhCZwQQghhoPZhVfn3gKZk59n439a/Ltu2++gZBv13DavjTvHawGZMuLMFNfy8DIpUCCGEK5AETgghhDDYyK5hRARX5s1fd3PoVDoAO/9KY9B/1xB38hzv3dOS4Z1CjQ1SCCGES5AETgghhDCYUorPh7UlN08zZ+MRAKavjsfNoljxbA8Gtw0xOEIhhBCuQhI4IYQQwgXUqepDv+Y1mb76EOsPJrN41zFubREs/d2EEEJcRhI4IYQQwkW8eVcLgip7MWTKes5m5RJVr4rRIQkhhHAxksAJIYQQLsLPy51JQ1pdXG5e29+4YIQQQrgkSeCEEEIIFxIVWpX/3NmC2gHehAdVMjocIYQQLsbN6ACEEEIIcbmhHeoytENdo8MQQgjhguQJnBBCCCGEEEKYhNJaGx3DRUqpk8Bho+MoQCBwyuggRIlIGZqblJ/5SRmam5Sf+UkZmpuUn/kVpwzraa2rF7bRpRI4V6WUitZaRxkdh7hxUobmJuVnflKG5iblZ35ShuYm5Wd+pVmG0oRSCCGEEEIIIUxCEjghhBBCCCGEMAlJ4IpmitEBiBKTMjQ3KT/zkzI0Nyk/85MyNDcpP/MrtTKUPnBCCCGEEEIIYRLyBE4IIYQQQgghTEISOCGEEEIIIYQwCUngrkMp1U8ptVcpFaeU+pfR8YirKaXqKKWWK6V2KaV2KqXGO9ZXVUotUUrtd/ys4livlFKTHWW6TSnVxtjfQAAopaxKqS1KqV8cy2FKqQ2OcpqnlPJwrPd0LMc5tocaGrgAQCkVoJSar5Tao5TarZTqJHXQPJRSTzs+P3copeYopbykDro2pdR0pdQJpdSOfOuKXeeUUiMc++9XSo0w4nepqAopw3cdn6PblFI/KqUC8m173lGGe5VSffOtl2tVAxRUfvm2PaOU0kqpQMdyqdZBSeCuQSllBf4L3ApEAPcrpSKMjUoUIBd4RmsdAXQEnnCU07+ApVrrcGCpYxns5Rnu+Dca+LTsQxYFGA/szrf8NvCB1rohkAKMdKwfCaQ41n/g2E8YbxLwm9a6CdASe1lKHTQBpVRtYBwQpbVuDliBIUgddHVfAf2uWFesOqeUqgq8AnQA2gOvXEj6RJn4iqvLcAnQXGsdCewDngdwXNcMAZo53vOJ48anXKsa5yuuLj+UUnWAPsCRfKtLtQ5KAndt7YE4rfVBrXU2MBcYaHBM4gpa66Na682O12exXzjWxl5WXzt2+xoY5Hg9EJih7dYDAUqp4LKNWuSnlAoBBgDTHMsKuAWY79jlyvK7UK7zgZ6O/YVBlFL+QDfgCwCtdbbWOhWpg2biBngrpdwAH+AoUgddmtZ6JXD6itXFrXN9gSVa69Na6xTsycNVF6TCOQoqQ631Yq11rmNxPRDieD0QmKu1Pq+1PgTEYb9OlWtVgxRSB8F+Y+sfQP6RIku1DkoCd221gYR8y4mOdcJFOZrytAY2AEFa66OOTceAIMdrKVfX8yH2DzubY7kakJrvSyx/GV0sP8f2NMf+wjhhwEngS2VvBjtNKeWL1EFT0FonAe9hv1t8FHudikHqoBkVt85JXXRtDwO/Ol5LGZqAUmogkKS13nrFplItP0ngRLmhlKoEfA88pbU+k3+bts+XIXNmuCCl1G3ACa11jNGxiBvmBrQBPtVatwbSudR0C5A66MoczXUGYk/EawG+yFMY05M6Z25KqRexdxGZbXQsomiUUj7AC8DLzj6XJHDXlgTUybcc4lgnXIxSyh178jZba/2DY/XxC82yHD9PONZLubqWLsAdSql47E0/bsHenyrA0ZwLLi+ji+Xn2O4PJJdlwOIqiUCi1nqDY3k+9oRO6qA59AIOaa1Paq1zgB+w10upg+ZT3DonddEFKaUeBG4DHtCXJmyWMnR9DbDfCNvquKYJATYrpWpSyuUnCdy1bQLCHSNxeWDvPPqzwTGJKzj6XnwB7NZaT8y36Wfgwmg+I4AF+dYPd4wI1BFIy9fkRJQxrfXzWusQrXUo9jq2TGv9ALAcGOzY7cryu1Cugx37y11mA2mtjwEJSqnGjlU9gV1IHTSLI0BHpZSP4/P0QvlJHTSf4ta534E+SqkqjiexfRzrhEGUUv2wdym4Q2udkW/Tz8AQZR8FNgz7YBgbkWtVl6G13q61rqG1DnVc0yQCbRzfkaVaB92ut0NFprXOVUo9if0/0gpM11rvNDgscbUuwDBgu1Iq1rHuBeAt4Ful1EjgMHCvY9sioD/2DsAZwENlGq0oqn8Cc5VSbwBbcAyQ4fg5UykVh73z8BCD4hOXGwvMdlxAHMReryxIHXR5WusNSqn5wGbsTba2AFOAhUgddFlKqTlAdyBQKZWIfSS7Yn3vaa1PK6Vex54EALymtS5oUAbhBIWU4fOAJ7DEMTbQeq31o1rrnUqpb7HfXMkFntBa5zmOI9eqBiio/LTWXxSye6nWQSU3zYQQQgghhBDCHKQJpRBCCCGEEEKYhCRwQgghhBBCCGESksAJIYQQQgghhElIAieEEEIIIYQQJiEJnBBCCCGEEEKYhCRwQgghhBBCCGESksAJIYQQQgghhEn8Py3B86nnhWPIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_x(table[\"angle\"], \"testing.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dc342e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_x(table2[\"angle\"], \"testing2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4b9795d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t8suw0LZRBHMeHMQj01fywa9b+WrFbr69t7/lg5m4CsUYGRrEf27syYL0gzwyfSV/+3IV8zYe4O1re2gFTvlc6vYM7p+6gl2Hs3hmWCduqOAlEhWVkrKf5ORzqnWfgcznRybGmBnGmHbGmDbGmOd8vT9Vulv6tSYqLIjHvlhJxolcq8PhwPFsrnx3IeHBdh4d2uG0dVpx8x8XdYrl27Q9pG7PsDqUahGILW/5usRFcXWvFnyTtptVu45aHU7AGJ+ymYOZuTw2tANhwae6rF3RvRmD28fwy6aDzN900MIIVXkULcMOu41xo7rx5CUdyTiRy/C3F7B2zzF8OWjb2bi9MdoKtWqc17YhvzwymIeHtOeHNftJfmUuC9L1c+dPdhw6yfvzt/Ls/9by1Ner+duXq5i0cBvGmCq7VSVjDE6Xmxyni5O5To5n53HkZC57j2bx/aq9/GXaCoaPX8iRk3m8dmVitVfcVPkFXluhqpTQIDuvjkzk1klLGfnOAmY/OMjS/v8PTkvDbeCF4V0JD9GPo796eEgHvl+9j9dnb2Lizb1qfMU6vztToJ7xvv/CBGat3c+lb81nxn0DOKeJDkNemsVbDjFu1kYGtYvh/A6NTlsnIrw6qht//vd8bvhgMU/8qSNx9cIQ7zopeF2RewoeFL4r+L4t+r7C7yk6rcS6Qy6CNx8scX3BNk/bVtHtC3WC7bSJiSDYEVgtyhVxqvX89DJ8S//WJDaP4qYPl/DHN37hpvNa8eQlHS35Tivc8laYiHBXchvi6oXx969Wc8MHv/H3SzpyQ9+WfnO9Xm21aMsh7vgklSMn8wgLshPssHE8Ow+3gSe/DsyOZcF2G9f2bsEjQzsQFaZTowQCPVquhS7sGMtVPZszdclOvl25l0sTm1oSx5JtGcxPP8gjQ9tbFoMqm/rhwdwzuC3//G4dD32WVuMnPne63QA47IH5NzaMCOGd63tw2Vu/8u+fNvHWNd1rdL4qY9/RbG7/JJWIEAcPD2lf7EF8/fBgxl/XnQemruDZ/1k0TemSxVWymSZRoUy8pRftYv1/3s/KcJZyAqZHy/r88MBA7vwklY8WbGPrwRPcPiie89pU76UEJVUwwVOBG9atGX3iG3DNe4t46ps1fPjrVv59dXe6xOm14NXN7Ta8NTedV2dtJK5eGJ+M7l0wh6zT5Wba0p38fjynyvZX1Q3CdptgtwkO773d5pmzuGOTurRvHFkwMJkKDJqtWurpSzuxYucRXp+1kT91aVLtLQxut+GBqStoFBnCNWWYCkBZb3T/1nyeuosvlu9mVM/m9IlvYHVIPlPaQVWg6NY8mtsHxfPuvC2kbj/sdyPM+osJP28hM9vJj38ZSHxMyfMMdY2LZs5fB7HnaDaHvV3OTZEpIM6cOsIUeZ6/NVPk+ZmvKfzeFStWkJjY7bR9cca+Cm+r+Hh2ZJxk7Mz13PFxKrMfHFSjW9DdJbRq5WsaHcZXd/fjlR838Nbczczb+Dvjr+3OxV2aVFuMBd8zpZxYia0byoz7B/Cv2Zt4O2Uz13+wmDev7k6/tg30hEw1cbsNT3y1mim/7WBAQkOev7wLzeufmnbCYbdxbe+WFkaoahutvNVSoUF27hrclvumLOe+qct5bVS3au1K89Bnaew+ksWzwzoRXUdHxAsEIsL0O89j4Ni5PPRZGt/c05/QIFtAjtR0NoF8zVth956fwORFO7h/6gr+d29/HX2yiDV7jvLhgq1c3LlxqRW3fCJCs+iwah/oInuHnb5tquZkSd1QB/dPXcETX63i2WGdcdTQETRPleGS/z4R4eEhHRiYEMPd//X8Fj6UcZLr+rSslm78ZT1JFOKw88jQDgxIiOGBacu57v3F9G5dn3eu66Fl2sd2Zpzkjk9SWbPnGNf2bsE/L+uslWZluZp31KXK7NLEpqRsOMAXy3bTN74B1/WpnjNH36bt4Yvlu7k0sSlXaatbQAkPcfD6Vd24/v3f6P7sLMBTwbHZBJtQMDm153ogz8GRMYY8pxNHyg9nbK+4n8CSfhgLX8R9Wo+SYlsvTu9zEhJkJyzITveW9bgruc1ZrwEL1NEmi4oIcfDKyK7c8ckyrn5vETMfGGh1SH7jRI6TWycuxWETnhnW2epwqs2liU2Zve4AU37bSXzDCG4bGG91SD6RX4bLUoR7xzdg9oMDueOTVF74fj0/rt3Phzf3pG6ob6//yW8dLGsLaN82DZhx3wCmLd3JuB83MuT1n7n3ggSur6bf7tpmx6GTXDVhIRknc3l4SHvuSm6jFTflF7TyVsu9OjKRJdsyeH22p/ukr8/iZee5eDtlMy0b1OG1K7sFdLe02mpAQgwf3tSTH9bsI7ZuKE63G5fbcyDidns6bLmNwRhPJUpE2L17F3Fxcadtp6x9+vO3UVRxAz0UXp6/xAA5ThcZJ3L5Nm0Pa3Yf5et7+hFZyoFZTWl5AxjauQnDu8cxfdkuvlu5lz91rb5uYf7s/75azd6j2fz1D+1oGBFidTjVRkQYNyqRhZsPMXHhNq7s1dznlRQruMrQ8lZYdJ1gptzWh/fnb+W5GesY/dESPry5l0+vBXJ5Lq0ttdtkUQ0iQrgruS2tG4Rz5+Rl/P2r1azdc5THhp5DVJ2al0erfL1iN49OX4nDZmPyrb3p0VK7nSv/oZW3Wk5EeP3Kbgwfv5D//raDuwe39en+xs3ayLq9x/jXVVpxC2SDOzRicJFR+UqTkvI7ycmdfBhRGePYcICbPlzCW3M389jFHUp8ncs7YElN+Yw+d3lnVu0+wuuzNzKkU2yN7SpXVlt+z+TL5bu5bUBr7r0gwepwql2Q3ca4UYnc8MFvfJG6i5v6tbY6pCrnLBiGv+zvERFuHRBPZKiDR6evosvTP7DwsQtoHBXqkxgrc23txV2asPofQ3j6mzVM+W0n05bs5LGLO3DbgHhtHaqkt1PSGTtzA20bRfDKyES6NY+2OiSlTlO7f8EV4Bl5a3D7GF7+YQNfLt/ls/3M33SQCT9vIT4mXEeXVJZIbt+IC89pxKSF21iyreQ56/JHqqsJLW/gucb19oFt2HQgk/umLicv/5R/LfXo9JVEhDi4pX/Nq7SU1cB2MZzTpC6v/LiRpaWUhUDlcrtx2KRCFZkre7bg8Ys7YAyMenche49m+SDCyg+MFBHi4OURXZk6pg/ntWnI8zPWc/vHqRw4ll2VYdYax7PzuGtyKmNnbmBQuxi+v3+AVtyUX9LKmwLgpRFdCXbYePKrNZzMdVb59nOdbh6YtpwmUaF8eWc/PTOoLPOPYZ0JcdgY+c5CdmacLPY1NWG0yaKG94jjiu7NmLFqH9OW7LQ6HMs8P2MdS7Yd5o5B8TSJqt6BR/zNm9ecS2aOk5s+XEJ2nsvqcKqU020qNZrm7YPa8Mno3uw/ls2gsSk+ObF5ap63im9DROgT34CJt/RiePc4fly7nyGv/8ym/cerKMra4VBmDiPfWciMVfsY2SOO929MIqiW91BQ/ks/mQqARpGhTL61N8dznLz4/foq3/7787dyMDOXf17WWfvlK0s1iw7jPzcmAfD67E3FvqbgmrcAneetJK+OTCShUQQvzVxfpXMSBYrZa/cz4ectJLePqdWtbvnaxEQwblQimTlOJi7YZnU4VcrtNpVuOe+f0JBv7ulPbFQIf5mWxqdVfNKjYFCVKjiZabcJr45K5PM7+uJyG/7w2s+Mm7WREzlVfzK2plm24zBDXv+F9AOZvHNdd14emVjru5Yr/6afTlWgZ6v6XH5uMz5P3cWOQ8W3SFRE+oHjvDRzPf3aNmBw+7JfJ6WUr/RoWZ+bzmvF9GW72HrwxBnra8pok0WJCG9e053j2U7+Mm3FGaNy1mR5Ljdvp6TTuG4oE65PqpFTXFTEZd2a0a9tA96am05mDTrQd7pNlbSct28cyYz7BtC9RTSPTF/J7R8vJSu3alopzzYXXUUktarPlDF9aNmgDm/M2cQtHy2pUXn1hb9/tZpjWXlMuKEHQzvrgE7K/9WsIxNVafec3xa7TTj/1RS2FXNQW15Hs/K4/eNUAB64sF2NnhRWBZbbBsYT7LBxwweL2V/kGpGaNNpkUe0bR3Jnchvmpx8sseWxJvrX7E0s23GEu89vW61zWvo7m024/4J2HM9xcsP7izmalWd1SFXCVUWVN4DI0CD+e1sfru7Vgh/W7OfKCQs5nl35/5Ovumd3ahpFykPJPHlJR37blsFVExayds+xKt1HTXLgeA7DezTj/A6xVoeiVJlU6hdMRJ4Wkd0issJ7+2OhdY+LSLqIbBCRIZUPVVWHNjERPPXnTjjdhhe+X1fpM/PvztvM5t9PMPGWXvRspUPtKv/RLDqMl0d0ZWdGFo9OX3naupo22mRRD/6hHYnNo/nPL1uqrBXBn83fdJA356YTVy+Ma3VuyTP0al2fh4e0Z9mOI7w9N93qcKqEswq6TRYWGmTnhSu68PKIrqzcdZTBr8zj27Q9ldpmQeXNB9eAiwi39G/NqyMT2bDvOJe99SszVu2t8v3UBEez8mrkdBmq5qqK04+vGWO6eW8zAESkI3AV0AkYCrwtIvYq2JeqBiN6xHHfBQn8sGY/M1fvq/B2Nu4/zn9+2coFHRoxqF1MFUaoVNUY1q0ZN/ZtScqG35mzbn/B8prc8gaeoeIfGdKeE7kuXppZ9de4+pPsPBcPTFtOs+gwvrq7n7b+l+Cu5LYMSGjIpIXb2fJ7ptXhVJq7ClveChuZ1JyPR/ciNMjGvVOW88acTRU+yVkdAyNd0T2O7+8fQNtGEdw1eRn3Tlmuo1EWkp3nItfppm6YVt5U4PBVp/9hwFRjTA6wVUTSgV7AQh/tT1Wxewa3ZXrqLu6dspzrt2VQv04weS43NpvgNp6WCafL4HQbnC6399773O0mz+Vmxqp9hAXZee7yLlb/OUqV6PE/nsPsdQd4O2Uzye0bYbdJjRxtsqh+bRtyaWJTpi3ZyYnWNuK7nCQi1IHgmehcEJD8x54z+YJncAXxLreJVHg49pLkHwgXPh727E/OeJ0x3snhDRhOf+42hqxcF098uZqDmbl8PLpXrZqMuyJeHN6VQWPn8uZP6bw6KjGgRwX2tLz5pnvsgIQY5vx1EHdPXs64WRuZn36QG+PLX4HLH23S1ycU2jaKZMptfRj7w3omL97Bql1HmHRLb1o0qOPT/QaCY95uwlFaeVMBRCrTLU5EngZuAo4BS4G/GmMOi8ibwCJjzCfe170PfG+M+byYbYwBxgDExsb2mDp1aoXj8ZXMzEwiIiKsDqPaHc528/6qXNYcclH0U2IXz+SndvEMc2wXT9cPm4DD+zw6VPhDyyDObeT7gQFqa44Chb/n56cdeUxam0tSrJ27uoWwcI+T91blMnZgGI3q1Nzrow5luRmflkP6kYrP+5b/PVDwHWFOu6PIYor+5JT1F8jmrUQa7zbK88s1tJWDK9sHB2xlpDrLzydrc5i9w8ng5g5u7BS4ld13V2aTftjNy4N8V0FxG8MXm/L435Y8mtQxPNEnnIjgsn/GVhxw8vqyHJ7sG0p8VPV0Tlpz0MWbK7KxCVzUMohL2wQFbLkoj5LK0O5MN0/Mz+KOxBD6NNFBjKzi78cIVhg8eHCqMSapuHVn/aSKyGygcTGrngDGA8/i+R19FngVuKU8wRljJgATAJKSkkxycnJ53l4tUlJS8Me4qsPlQz0TVwbZbYQG2XG7TbFnwa1Wm3MUCPw9P8nAgY9TmblmH85G55AQkQerVtLvvL40i67Zc4FdMdTw0Tc/ERTbFqfLfVrlqHArWH7LlsFz0GqMp2tattNV0M3U0zbnaSnzPKfI8+LX5y8o6fVuY3C5DS5jCk4SiQi2gsenP89vHbTbhE5No+jZqp7ffWeVR3WWn34D3PzpjV+YuzOTv404j4TYyGrZb1X7Yu9y9uUe9fn/7fzBcMnqvdw1eRmPL8jlpeFd+WOXso1YmLtmHyxLpVdSEp2bRfk0znzJwEUDjvPYF6v4Mv0wofUb8/SlnQhx1OwrW0oqQ3PXH4D5S+jbI1Ev77CQvx8j+JuzVt6MMReWZUMi8h7wP+/T3UDzQqvjvMtUAIosdCGvXi+iaqp/X3Mug19J4alv1nBnchug5l7zVpiI0DrKTnKfllaHovxAkN3Gx6N7c/4rKTz2xSqm33me1SFVSFWONnk2Qzs34W+9Q5mwxnDX5GW8PKIrI5Oan/V9VnXPToiN5LPb+/L8jHX8Z/5Wfk0/xPjrutOpafVUIP3F3PUHuG/KchpGBNMtLtrqcJQqs8qONln49NLlwGrv42+Aq0QkRERaAwnAb5XZl1JK+VKQ3caTl3Rk79FsPlm0HajZ17wpVZLYuqE8cGE7Urcf5oXv1wXkABdOt9snoziWpG20nVkPDqJ7i2ge/nwld09edtaRXF0+mOetrGw24f8u6cgrIxPZkXGSq95dROr2jGqPwyoTft7MLROXEOSwMeW2PkTV0WveVOCobAffsSLSDU9Pmm3A7QDGmDUi8imwFnACdxtjav541EqpgHZ+h0b0ia/Poi2eg5ja0PKmVHGu7dOC+ekHeXfeFt6dt4UmUaEEO2w4bILDZsNuE2w2bxdVONXXtZpEhwVxfZ+WXNix+Lm5qrPlLV9UmGc+uKe+XsO0pTvZdfgkn9za+7TeK0VjBM//0CojesSR1LIeN374G1dNWMS1vVvy1J87BnQ349LkOt28+P16Pvh1Kxd1jGXsiK5E1wm2OiylyqVSlTdjzPWlrHsOeK4y21dKqerksNt47cpu9H3hJ0C7Cavaq06wg49u7knq9sP8vOkge45k4XS5yfOOMOxynz66Z3XbuP84Yz5eypU9m1OvTjDBDs912XVDg+jcrK5ntEl79Zff0CA7L43oSs/W9XnoszQGvzKPl0d2ZXD7Rme81m1hy1thrRqGM/3O87hvynI+WrCN5TsO89HNvagXXnMqNS634df0gzzz7Vo27D/OhefE8sbV5xIaVLOv9VM1kw6to5RShTSJCuP7+wfwxbJdRIboV6SqvUSEpFb1SWpV3+pQznDkZC5PfLWab9P2kp13atCcwro1j67+wLxG9IijaVQod/93GWMmLWX8tT3OaCV0eQd59YcW/oYRIXwyujdPfLWKKb/t5NxnZzG4fQxNosPIyfMEWngwoMLObKSTUtdLKevyFR6V1pSwHAxut6f7qdttPAMb5Z9QcHsGOXIbyMpzkrr1JNmuxTSMCGbs8K4M7xFneaVZqYrSIxOllCrinCZ1eeJPHa0OQylVgug6wbx1TfeC5y63ITvPRcaJXJ77bh0z1+xjxc4j1gUInNe2IT88MJDr3l/MmI+Xcn2flvz9ko447DZvzN5KkZ9UImw24ZlhnWnbKJIF6QdZv+84K3cdLWidMsYUXKeX72xTf5w5G5UpcZ2htIqdFLs8f/RZm80z2qzddmrEWbt3Dspgu3BeUwcjBnalX9uGOqebCnhaeVNKKaVUQLPbhPAQB+EhDsZf153zX51nactbvkZ1Q/n09r489FkaExduZ0fGSd67IQmH3VbQ8ladA6ucTZDdxuj+rRndv7XVoVSplJQUkss4hYNS/k4rb0oppZSqMUSEOQ8Oqu4xVEoUXSeY/9zYk3/P2cSrszZyyb/n8+LwrgWtWLZKjfutlKpttPKmlFJKqRrFX7oiFnbP+W1pHBXK41+s4tr3FpHcwTOIiT+1vCml/J+e71FKKaWU8jERYWRSc+b8dRANIkL4buVeABza9KaUKgf9xlBKKaWUqiYtG4QzdUyfgudad1NKlYd2m1RKKaWUqkZNo8OY/eBAZq7eV+Ik3kopVRytvCmllFJKVbO2jSK55/xIq8NQSgUYbaxXSimllFJKqQAg5swZFC0jIr8D262OoxgNgYNWB6FKpTnyb5of/6b58W+aH/+nOfJvmh//pvk5U0tjTExxK/yq8uavRGSpMSbJ6jhUyTRH/k3z4980P/5N8+P/NEf+TfPj3zQ/5aPdJpVSSimllFIqAGjlTSmllFJKKaUCgFbeymaC1QGos9Ic+TfNj3/T/Pg3zY//0xz5N82Pf9P8lINe86aUUkoppZRSAUBb3pRSSimllFIqAGjl7SxEZKiIbBCRdBF5zOp4aiMRaS4ic0VkrYisEZH7vcufFpHdIrLCe/tjofc87s3ZBhEZYl30tYOIbBORVd48LPUuqy8is0Rkk/e+nne5iMgb3vysFJHu1kZfs4lI+0JlZIWIHBORB7T8WEtEPhCRAyKyutCycpcZEbnR+/pNInKjFX9LTVRCfl4WkfXeHHwpItHe5a1EJKtQWXqn0Ht6eL8b0705FAv+nBqnhPyU+ztNj/F8p4QcTSuUn20issK7XMtQeRhj9FbCDbADm4F4IBhIAzpaHVdtuwFNgO7ex5HARqAj8DTwUDGv7+jNVQjQ2ptDu9V/R02+AduAhkWWjQUe8z5+DHjJ+/iPwPeAAH2AxVbHX1tu3u+0fUBLLT+W52Ig0B1YXWhZucoMUB/Y4r2v531cz+q/rSbcSsjPRYDD+/ilQvlpVfh1Rbbzmzdn4s3hxVb/bTXhVkJ+yvWdpsd41Z+jIutfBZ70PtYyVI6btryVrheQbozZYozJBaYCwyyOqdYxxuw1xizzPj4OrAOalfKWYcBUY0yOMWYrkI4nl6p6DQMmeh9PBC4rtHyS8VgERItIEwviq40uADYbY7aX8hotP9XAGPMzkFFkcXnLzBBgljEmwxhzGJgFDPV58LVAcfkxxvxojHF6ny4C4krbhjdHdY0xi4znKHQSp3KqKqGE8lOSkr7T9BjPh0rLkbf1bBQwpbRtaBkqnlbeStcM2Fno+S5KrzQoHxORVsC5wGLvonu8XVg+yO9ihObNCgb4UURSRWSMd1msMWav9/E+INb7WPNjnas4/cdSy49/KW+Z0VxZ5xY8rQD5WovIchGZJyIDvMua4clJPs2P75XnO03Lj3UGAPuNMZsKLdMyVEZaeVMBQ0QigOnAA8aYY8B4oA3QDdiLpwleWaO/MaY7cDFwt4gMLLzSe8ZMh7a1kIgEA5cCn3kXafnxY1pm/JeIPAE4gcneRXuBFsaYc4EHgf+KSF2r4qvF9DstcFzN6ScStQyVg1beSrcbaF7oeZx3mapmIhKEp+I22RjzBYAxZr8xxmWMcQPvcaprl+atmhljdnvvDwBf4snF/vzukN77A96Xa36scTGwzBizH7T8+KnylhnNVTUTkZuAS4BrvRVsvN3xDnkfp+K5jqodnlwU7lqp+fGhCnynafmxgIg4gCuAafnLtAyVj1beSrcESBCR1t6z1lcB31gcU63j7Rv9PrDOGDOu0PLC10ldDuSPaPQNcJWIhIhIayABzwWvygdEJFxEIvMf47mofzWePOSPfncj8LX38TfADd4R9PoARwt1FVO+c9qZTi0/fqm8ZeYH4CIRqeftInaRd5nyAREZCjwCXGqMOVloeYyI2L2P4/GUmS3eHB0TkT7e37EbOJVTVcUq8J2mx3jWuBBYb4wp6A6pZah8HFYH4M+MMU4RuQfPj6Ed+MAYs8bisGqjfsD1wKr8YWWBvwFXi0g3PF2LtgG3Axhj1ojIp8BaPF1b7jbGuKo55tokFvjSO3qvA/ivMWamiCwBPhWR0cB2PBcnA8zAM3peOnASuLn6Q65dvJXqP+AtI15jtfxYR0SmAMlAQxHZBTwFvEg5yowxJkNEnsVzEArwjDGmrIM4qFKUkJ/H8YxYOMv7fbfIGHMHnlH1nhGRPMAN3FEoD3cBHwFheK6RK3ydnKqgEvKTXN7vND3G853icmSMeZ8zr70GLUPlIt5Wf6WUUkoppZRSfky7TSqllFJKKaVUANDKm1JKKaWUUkoFAK28KaWUUkoppVQA0MqbUkoppZRSSgUArbwppZRSSimlVADQyptSSimllFJKBQCtvCmllFJKKaVUANDKm1JKKaWUUkoFgP8HuxxcAYOk9NAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_x(unpr_table[\"angle\"], \"unpr.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8194d252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_x(-old_table[\"angle\"], \"old.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3bb5a06",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}