diff --git a/.vscode/launch.json b/.vscode/launch.json index f1c5e8f..7dcb00f 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -9,7 +9,8 @@ "type": "debugpy", "request": "launch", "program": "testing.py", - "console": "integratedTerminal" + "console": "integratedTerminal", + "justMyCode": false } ] } \ No newline at end of file diff --git a/balloon.ipynb b/balloon.ipynb new file mode 100644 index 0000000..da7354b --- /dev/null +++ b/balloon.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from ambiance import Atmosphere" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('data/balloon_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time = df['Time']\n", + "pressure = df['Barometric Pressure (mBar)']\n", + "altitude = df[ 'Altitude (Meters)']\n", + "\n", + "plt.plot(pressure)\n", + "plt.show()\n", + "\n", + "plt.plot(altitude)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "modeled = Atmosphere(list(altitude)).pressure / 100" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(modeled, label='model')\n", + "plt.plot(pressure, label='measured')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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2vduJzm6n5GoCC4MKUR/lje2os3bBoNNgfnac7HKIKEhEG3Uw6NxvuWfaOasyEgwqRH3sOnEGgHsTwjC9VnI1RBQsFEWBOVwPALB02iVXE1gYVIj6qLe6f9OZmBQluRIiCjbanp43ISQXEmAYVIj66nkF0bCJloh8zPOywqAyMgwqRH14Xj8YVIjI1zyvKwJMKiPBoELUh4u/6hCRn7n4MjMiDCpEfXhyCmdUiMjXND3vuIK/EI0IgwpRH57fdJhTiMjXFLhfWDijMjIMKkR9eM4daxhUiMjHel9XmFRGgkGFqA/hnVFhUiEi3/K8rnBGZWQYVIj68Jw7Zk4hIl/zvKywRWVkGFSI+vD2qIBJhYh8y/MLEK8uHBkGFaI+eq/6kVsHEQUfhSvTjgqDClEfLp76ISI/8fwCxAXfRoZBhWgAXEeFiHzNc0qZMyojw6BC1Id3RkVyHUQUfLjXz+gwqBD14X0B4YwKEflY7+XJTCojwaBC1IdLcME3IvKP3h4VGgkGFaI+vBMqPPlDRD7Gy5NHh0GFqA/BGRUi8hPvL0DMKSPCoELUh+CmhETkJxrOqIwKgwpRH73rqDCpEJGPccG3UWFQIeqDMypE5C9sph0dBhWiPlzeJfSZVIjItzyvKjz1MzIMKkR9eJa2ZkwhIl/T8NTPqDCoEPUhOKNCRH7SuzItk8pIMKgQ9SG4KSER+Yl3rx/JdQQaBhWiPlzeZlomFSLyLS74NjoMKkR99K5MS0TkW9yUcHQYVIj64F4/ROQvGm5KOCoMKkR98dQPEfkJX1ZGh0GFqA/OqBCRv/Dy5NFhUCHqw/sCwl99iMhPeOpnZBhUiPrgjAoR+QtnVEaHQYWoj96rfphUiMi3eHny6DCoEPXRuzKt3DqIKPh4XlYYU0aGQYWoD65MS0T+0nvqh1FlJKQFlZMnT+LOO+9EdnY2wsPDMWHCBDz88MPo7u6WVRJR76kfJhUi8jEu+DY6Oll/cHFxMVwuF5577jlMnDgRRUVFWL16Ndrb2/HEE0/IKotCnOfcMWMKEfma5xcg5pSRkRZUrr32Wlx77bXer3NyclBSUoJnn32WQYWk4e7JROQvnlcVNtOOjLSgMhCLxYK4uLghj7HZbLDZbN6vrVarv8uiEOJijwoR+QkvTx4d1TTTlpWV4emnn8Z//dd/DXncxo0bYTabvbfMzMwxqpBCCYMKEflab48Kk8pI+DyorF+/HoqiDHkrLi7u95zq6mpce+21uPXWW7F69eohv/+GDRtgsVi8t8rKSl8PgUJY74JvTCpE5Fsa9qiMis9P/axduxarVq0a8picnBzv5zU1Nbj88suxcOFC/P73vz/v9zcajTAajRdaJtGA+IsOEfmNZ8E3F19oRsLnQSUxMRGJiYnDOra6uhqXX3455syZg02bNkGjUc2ZKApRnFEhIn/hgm+jI62Ztrq6GpdddhnGjRuHJ554AqdPn/Y+lpKSIqssCnGeGRXmFCLyNc8vQJxQGRlpQWXr1q0oKytDWVkZMjIy+j3GRiOShZcnE5G/sJl2dKSda1m1ahWEEAPeiGQR4IJvROQf/AVodNgUQtSHy3vqhy8oRORbXPBtdBhUiPrgpoRE5C8KF3wbFQYVoj5c7FEhIj/xvKywmXZkGFSI+vDuniy1CiIKRhpPMy0vUB4RBhWiPjynfrikDxH5mgKe+hkNvhwT9eFdR4VzKkTkY7w8eXQYVIj64O7JROQvChd8GxUGFaI+BC9PJiI/6Z1RkVtHoGFQIeohhECbzQGgt+mNiMhX2Ew7OgwqRD3+cbAGFU0dCNdrMSXVJLscIgoyup4u/c5up+RKAguDChEAm8OJJz8sBQD892UTkBBllFwREQWbSclRAIAjNVbJlQQWBhUiAP/vg+Mob2xHQpQBdyzOll0OEQWhWZkxAICDVS1wsaN22BhUKOTtr2jG8ztOAAA23jwTUUZpm4oTURCbnByNML0GrV0OlJ9pl11OwGBQoZD3v1uPQwjg5ovScdXUZNnlEFGQ0ms1mJ5mBgAcrGyRW0wAYVChkLb7xBnsKG2EVqPgB0smyy6HiIJcfs/pn0IGlWFjUKGQ1dntxPo3DwMAbpubicy4CMkVEVGw8/apMKgMG4MKhaxfbS1BeWM7UkxhWH9dnuxyiCgEeILK0VorbA5epjwcDCoUkvadasYfdpYDADbePAPmcL3kiogoFGTEhiMu0gC7U+BYbavscgICgwqFnC67E+v+fhBCALdclIHL85Jkl0REIUJRFO+sSmFFs9xiAgSDCoWcRzYfxYnT7UiKNuLH10+VXQ4RhZj8jBgAwMEqi9xCAgSDCoWU3SfO4G+7K6AowC+/OhPmCJ7yIaKxlZ/JS5RHgkGFQoYQAr/cUgIA+Mb8LFyWy1M+RDT2PDMqJxrbYemwyy0mADCoUMj46FgD9p1qRpheg+9fOUl2OUQUomIjDRgf714O4WBVi9xiAgCDCoUEl0vgiQ/csymrFmYj2RQmuSIiCmX5XE9l2BhUKCT863AtiutaER2mw12XTpBdDhGFuN6G2hapdQQCBhUKetYuOx7ZfBQA8O1F2WygJSLpepfSt0AI7qQ8FAYVCnq/+uA4TrfakJMQibsu42wKEck3Lc0EnUZBY5sN1S2dsstRNQYVCmr/LmnAC5+eBAA8fOM0hOm1cgsiIgIQptdiSqoJAHCwkuupDIVBhYKWpcOOB944BABYWTAOl05OlFwREVEv73oq7FMZEoMKBa1HNh9FvdV9ymf9dVNkl0NE1I+nobawokVqHWrHoEJBacuROryxvwoaBXj81nyEG3jKh4jUZXZWDADgcLUFDqdLbjEqxqBCQaeyqQPrXj8IAFj9pRzMGRcruSIionPlJEQhyqhDp92J0oY22eWoFoMKBZVuhwv3vHwA1i4HZmXG4P9cnSu7JCKiAWk0CmZmcN+f82FQoaDyy/eLcbCyBaYwHZ6+fTb0Wv6IE5F69a6n0iK1DjXjqzgFjX2nmvCHneUAgCduzUdmXITkioiIhjaLQeW8GFQoaDz77y8AAF+dk4Grp6VIroaI6Pw8QeV4fSs6uh1yi1EpBhUKCm8fqMaHxxqgUYA13MuHiAJEsikMKaYwuARQVG2VXY4qMahQwDvZ2I4H3zoMAPj+lZMwMSlKckVERMPXe/qnWW4hKsWgQgGt2+HC914+gPZuJ+Znx+F7V0ySXRIR0Yh4Gmq5lP7AGFQooD31USkOV1sQE6HHr78+C1qNIrskIqIR8Sylz4bagTGoUMD6pKwRz37sbqB97OYZSDWHS66IiGjkZqSboShAdUsnTrfaZJejOgwqFJBqWjpx99/2w+kSuOWiDFw7PVV2SUREoxIdpseknt46Lvx2LgYVCjhOl8B9rxaipcOOGelmPPqV6bJLIiK6IJ4NCrmT8rkYVCjgPPvvMuwpb0KkQYunb5+NMD03HCSiwMYVagfHoEIBpaSuFU9+WAoAeOSm6RifECm5IiKiCzfLe+VPC1wuIbcYlWFQoYDy2HvH4HAJXD01GTdflC67HCIin8hNiYZRp4G1y4GTZ9pll6MqDCoUMD4+fhrbS05Dp1Gw/ro8KAovRSai4KDXajA9vWcnZfap9MOgQgHB7nThp5uPAgC+VTAeOYlcfZaIgou3oZYLv/XDoEIB4aXPTqGsoQ1xkQbceyVXnyWi4ONZ+O0AG2r7YVAh1bN02vHkR+4G2vuvmgxzhF5yRUREvjc7MxYAcKzGCpvDKbka9WBQIdV77L1jaOmwY2JSFL4+L1N2OUREfpEZF47YCD26nS4U17bKLkc1GFRI1bYV1+PlPZVQFOBny6ZDp+WPLBEFJ0VRejcoZEOtF1/1SbWa27vxwBuHAQDfXpSNi3PiJVdERORfnobawooWqXWoCYMKqdZD7xThdKsNE5OisO6aXNnlEBH53aysGABAIWdUvBhUSJX+ebAGmw/VQqtR8Kvb8rlMPhGFBM+MyonT7bB02uUWoxIMKqQ6DdYuPPROEQDg7ssnYmbPf1wiomAXF2lAVlwEAOBwFddTAVQSVGw2G2bNmgVFUVBYWCi7HJJICIEH3jiElg47pqeb8L0rJsouiYhoTM3yblDYLLcQlVBFUPnhD3+ItLQ02WWQCrzyeSW2l5yGQafBr26bBT2v8iGiENO7kzJnVAAVBJX33nsPH3zwAZ544gnZpZBklU0d+FnPMvnrrs7F5ORoyRUREY29WT0r1BZWtkAI7qSsk/mH19fXY/Xq1Xj77bcRERExrOfYbDbYbDbv11ar1V/l0RhyuQTWvn4Q7d1OzB8fh28vzpZdEhGRFNPSzNBpFDS22VBr6UJaTLjskqSSNqMihMCqVauwZs0azJ07d9jP27hxI8xms/eWmcmVSoPBnz4px57yJkQYtHji1nxoNdwZmYhCU5hei7xU94xyIff98X1QWb9+PRRFGfJWXFyMp59+Gq2trdiwYcOIvv+GDRtgsVi8t8rKSl8PgcZYaX0rfrmlBADwo6VTkRU/vNk1IqJg1buTcovUOtTA56d+1q5di1WrVg15TE5ODrZt24Zdu3bBaDT2e2zu3LlYvnw5XnzxxQGfazQaz3kOBS6704X7XzuIbocLl+Um4vb5nCEjIsrPjMFLuys4owI/BJXExEQkJiae97innnoKP/vZz7xf19TU4JprrsGrr76KBQsW+LosUqlntpXhcLUF5nA9fnHLTCgKT/kQEXkuUT5cbYHTJUL6dLi0ZtqsrKx+X0dFRQEAJkyYgIyMDBkl0Rgrqrbgme1lAIBHbpqGZFOY5IqIiNRhQmIUoow6tNkcKG1oRV6KSXZJ0ki/PJlCU7fDhf/z+kE4XQJLZ6Tixnyuo0NE5KHVKJiR7r5MOdT7VFQTVMaPHw8hBGbNmiW7FBoDf9xZjuK6VsRFGvDITdN4yoeI6Cxc+M1NNUGFQkd1Syee+qgUAPDgl6cgPorN0UREZ+u78FsoY1ChMffIP4+g0+5e2O3mi9Jll0NEpEqzMmMBAMfrW9HR7ZBcjTwMKjSmthc3YMuRemg1Cn66bDpP+RARDSLFHIZkkxFOl8CRmtBdhZ1BhcZMl92Jh/9xBADw7UXjkZvCvXyIiIbiWfitsKJFah0yMajQmPnzrpOoaOpAssmIe5dMll0OEZHq5fdZTyVUMajQmOjoduC5j08AANZelYsoo9T9MImIAoLnEmUGFSI/+/OuUzjT3o1x8RFsoCUiGiZPUClvbIe1yy65GjkYVMjvuuxOPP8f92zK96+YBJ2WP3ZERMMRG2lARmw4APdq3qGI7xjkd+8ersWZ9m6kmcNw0yyuQEtENBIzM3pO/1QxqBD5xZ93nQIAfGNBFmdTiIhGaEZ6DADgEGdUiHxv94kzKKxsgV6r4Gvzss7/BCIi6sfTp8JTP0Q+5nQJPLL5KADgtrmZSIzmUvlERCPlCSqnznTA0hF6DbUMKuQ3f99XiSM1VkSH6XD/VVw3hYhoNMwReoyLjwAQmpcpM6iQX7R22fH4luMAgHuvnMSNB4mILsD0nlmVQ9UtcguRgEGF/OKZ7WVobLMhOyES3yoYL7scIqKANjOE+1QYVMjnyhvb8aed5QCAHy2dAoOOP2ZERBdiRs8lyodC8BJlvoOQzz36r6OwOwUunZyIK/KSZJdDRBTwPKd+qpo70dzeLbmascWgQj61s7QRHx5rgE6j4KHrp0JRFNklEREFPFOYHtkJkQBCr6GWQYV8xukSeOz9YwCAb148DhOToiRXREQUPEJ1g0IGFfKZl/dUoKjaimijDvdcMVF2OUREQWWmt0+lRW4hY4xBhXyipaMbj28pAQCsvXoyEng5MhGRT033XvljlVzJ2GJQIZ/49UelsHTakZscjW9ePE52OUREQWdamgmKAlS3dKKxzSa7nDHDoEIX7IvTbfhLz8aDP7p+CjceJCLyg+gwPXJCsKGW7yh0wTa+WwyHS+CKvCRcMilRdjlEREHLu0FhCK2nwqBCF+TTskZ8eKweWo2C//vlKbLLISIKajMyYgAAhzijQnR+LpfAT//VcznygixejkxE5GeeK38Oc0aF6PwOVrXgWK0VEQYt7lvC3ZGJiPxtaqq7obbO2oWG1i7Z5YwJBhUaNU8D7VVTkxEbaZBcDRFR8Is06jAx0T17HSobFDKo0KhUt3TiHwdrAAB3Ls6WXA0RUegItQ0KGVRoVDbtLIfDJVCQE4+ZPc1dRETkf96l9BlUiAZ2ps2Gl/dUAAC++6UcydUQEYUWb0MtT/0QDezFXafQ3u3E9HQTLp3MdVOIiMbS1FQzNArQ0GpDvTX4G2oZVGhEuuxO/PUzdxPtmksnQKNRJFdERBRawg1aTEqKBhAafSoMKjQib+6vRlN7N9JjwnHttBTZ5RARhaQZIXT6h0GFhs3lEvjDzhMAgG8vzuaePkREkvQu/NYit5AxwHcaGrZ/H2/AidPtiDbqcNvcDNnlEBGFLO+VP9UWCCEkV+NfDCo0LEIIPPvvLwAAty/IQnSYXnJFRESha0qqCVqNgsa2btQFeUMtgwoNy5Yj9fj8ZDPC9BrcsWi87HKIiEJamF6Lycmh0VDLoELD8qed5QDcq9CmmsMlV0NERDNDZOE3BhU6r8NVFuw52QSdRsG3CsbLLoeIiABM9yylH+RX/jCo0Hk9v8N9pc8N+WlINoVJroaIiIDeGZWiIG+oZVChIVU1d+Bfh2sBAN+5hJsPEhGpRV5qNPRaBU3t3ahu6ZRdjt8wqNCQNn1yEk6XwOKJCZiWZpZdDhER9TDqehtqg7lPhUGFBmXptOOVns0HV3PzQSIi1QmFDQoZVGhQf/3MvflgbnI0vjQpQXY5RER0lhnpMQAYVCgENbR2eRd4+69Lc6Ao3HyQiEhtPCvUHqoK3oZaBhUa0OPvl6DN5kB+hhnLZqXLLoeIiAYwOSUKBq0Glk47qpqDs6GWQYXOUVxnxev7qgAAD984DRoNZ1OIiNTIqNMiLzW4V6hlUKFz/P5j97opS2ek4qKsWMnVEBHRUKZ7Tv9Ut8gtxE8YVKifmpZO/ONgDQB3bwoREalb34XfghGDCvXzp53lcLgECnLiMTMjRnY5RER0Hp4ZlSM11qBsqGVQIS9Lpx0v96yb8l3OphARBYRJyVHQaxW0dNiDcoVaBhXy+tvuCu+6KZdNTpRdDhERDUPfFWqLqq2Sq/E9BhUCANgcTmz6pByAexVarptCRBQ4pqWZAABHaoKvT4VBhQAAbx+oRkOrDSmmMNyYnya7HCIiGoHpQdxQy6BCcDhd+G3PKrR3Ls6GQccfCyKiQOLZNPZIDU/9+Ny//vUvLFiwAOHh4YiNjcWyZctklxRyNh+qxakzHYiN0GP5xVmyyyEiohGakhoNjQI0tNrQYO2SXY5P6WT+4W+88QZWr16Nn//857jiiivgcDhQVFQks6SQ43IJ/GZ7GQDgO5fkIMIg9UeCiIhGIcKgw4TEKJQ2tOFIjRVJpjDZJfmMtHclh8OBe++9F48//jjuvPNO7/1Tp06VVVJIev9IHUob2hAdpsOKgnGyyyEiolGanm5GaUMbiqotuDwvSXY5PiPt1M/+/ftRXV0NjUaD2bNnIzU1Fdddd915Z1RsNhusVmu/G42OEALPbHPPptyxcDxMYXrJFRER0Wh5rvwpCrIrf6QFlRMn3PvJ/M///A9+9KMfYfPmzYiNjcVll12GpqamQZ+3ceNGmM1m7y0zM3OsSg46n51owtFaK8L1WtyxKFt2OUREdAE8DbXBtpaKz4PK+vXroSjKkLfi4mK4XC4AwIMPPohbbrkFc+bMwaZNm6AoCl5//fVBv/+GDRtgsVi8t8rKSl8PIWS8+OlJAMDNF6UjNtIgtxgiIrogU3tmVKpbOtHS0S25Gt/xeY/K2rVrsWrVqiGPycnJQW1tLYD+PSlGoxE5OTmoqKgY9LlGoxFGo9EntYayemsXth6rBwCsXDhebjFERHTBzOF6ZMVFoKKpA0drrFg4MUF2ST7h86CSmJiIxMTzL78+Z84cGI1GlJSUYPHixQAAu92OkydPYtw4NnX622ufV8LpEpg7Lta79DIREQW2KanR7qBSGzxBRVqPislkwpo1a/Dwww/jgw8+QElJCe666y4AwK233iqrrJDgcgm8utd9yuz2+Vw3hYgoWExJdZ/+OVbbKrkS35G6aMbjjz8OnU6HFStWoLOzEwsWLMC2bdsQGxsrs6yg9+kXZ1DV3InoMB2+PCNVdjlEROQjvUEleBpqpQYVvV6PJ554Ak888YTMMkLOK5+7e4CWzUpHuEEruRoiIvKVqT1BpayhDXanC3qt9AXoL1jgj4BGpKm9Gx8ccTfRfm0eL+0mIgom6THhiDLq0O104cTpdtnl+ASDSoh5bW8lup0uTE83eXfbJCKi4KDRKMhLcV8gESynfxhUQki3w4VNn5QDAFYWjJdbDBER+UWw9akwqISQfx6sQb3VhmSTETfNSpddDhER+YEnqBxlUKFAIoTA8zvc2xasWpgNg47/9EREwSgv1X3qp7guOC5R5rtViPhPaSOK61oRadDiGwu4dgoRUbDKS4mGogCnW21obLPJLueCMaiEiOf/455N+dq8LJjDuUsyEVGwijDoMC4uAgBQEgSzKgwqIaCkrhU7yxqh1Si4Y9F42eUQEZGf5aYEz+kfBpUQ8Mb+KgDAlXlJyOxJ2UREFLxye/ZwK6kL/IZaBpUg53QJvFNYDQC4ZU6G5GqIiGgs5Ka4r/wpqW+TXMmFY1AJctuLG1BvtSEmQo/Lc5Nkl0NERGPAc+qntL4VLpeQXM2FYVAJci/uOgkA+NrcTF6STEQUIsbHR8Cg06Cj24nK5g7Z5VwQvnMFscqmDuwobYSiAN+8eJzscoiIaIzotBpMSooCEPgNtQwqQezlPe5dkhdOiGcTLRFRiOltqGVQIRXqsju9QWUFZ1OIiEKOp0+lpJ5BhVToncJqNHfYkR4Tjqumpsguh4iIxpg3qHBGhdRGCIFNn5wEAKxcOA5ajSK3ICIiGnN5PZcolze2w+ZwSq5m9BhUgtCuE2dQXNeKcL0WX5vLfX2IiEJRsskIU5gOTpdAWUPgrqfCoBKEPLMpt8xJhzmC+/oQEYUiRVG8syqBfPqHQSXIVJzpwIfH6gEAqxZmS66GiIhkCoaGWgaVIPP8jhMQArh0ciIm9lxDT0REoSkYGmoZVILI6VYbXttbCQBYc+kEydUQEZFseQwqpCZ/2XUSNocLs7NicHFOnOxyiIhIskk9i77VWrpg6bBLrmZ0GFSChNMl8Pq+KgDAtxdlQ1F4STIRUagzh+uRZg4DELh9KgwqQWJnWSNqLV2IidDj6mnJssshIiKVCPSGWgaVIPHa5+7elGWz0mHUaSVXQ0REapHrvUTZKrmS0WFQCQJlDW14r6gWAHDb3EzJ1RARkZoEekMtg0oQeHpbKVwCuGpqMqammWSXQ0REKjK5p6G2uK4VQgjJ1Ywcg0qAq2ruwOZD7tmUe6+cJLkaIiJSmwlJkdBqFLR2OVBr6ZJdzogxqAS4Z7aVwekSWDwxAdPTzbLLISIilTHqtMhJiAQQmA21DCoBrKq5w7vA2w+u4mwKERENLJBXqGVQCWB/3nUKLgEsnpiAOeO4wBsREQ0sN5lBhcZYa5cdL++pAADcuZibDxIR0eA8MyrFDCo0Vl7aXYHWLgcmJkXh0smJssshIiIVy+tZS+WLhjY4nC7J1YwMg0oA6rI78ced5QDcmw9qNFwun4iIBpcRG44IgxbdThdOnmmXXc6IMKgEoDf3V+N0qw1p5jDcmJ8muxwiIlI5jUbpt55KIGFQCTBCCPzpE/dsyp2X5MCg4z8hERGdX6A21PJdLsDsLGtEWUMbIg1a3DY3Q3Y5REQUIAK1oZZBJcC8+OlJAMCtczMRHaaXWwwREQUMz54/xwNs0TcGlQBS1dyBj4obAAArCsZJroaIiAKJZ0aloqkDHd0OydUMH4NKAHmnsAZCABfnxGFCYpTscoiIKIDERxmREGWEEMDx+jbZ5Qwbg0qAEELg7QPVAICvzE6XXA0REQWi3BT3L7kldVbJlQwfg0qAOFprRWlDGww6Da6dniq7HCIiCkC5ye6F3wKpoZZBJUC8tNu9XP6SKUkwh7OJloiIRi4QG2oZVALAmTYb3thXBQD4VsF4ucUQEVHACsRdlBlUAsBfP6uAzeHCzAwzFmRzl2QiIhqdSclRUBSgsa0bjW022eUMC4OKytkcTvzls5MAgO9ckgNF4b4+REQ0OhEGHbLiIgAEzqwKg4rKfXCkHo1t3Ug2GfHl6SmyyyEiogCXG2B7/jCoqJxnJdqvzcuCTst/LiIiujDehloGFbpQByqasfdUM/RaBcsXZMkuh4iIgkBuSs8lygFy5Q+Dior9ZdcpAMCN+elINoVJroaIiIKBZ9G30vpWuFxCcjXnx6CiUu02B94rqgMALL+YsylEROQb4+MjYdBp0NHtRGVzh+xyzotBRaXeL6pDp92J7IRIzM6MkV0OEREFCZ1Wg4k9+8UFQkMtg4pKvdVnXx9ekkxERL4USA21DCoqVN3SiU++aATADQiJiMj3PCvUBkJDLYOKCr2xrwpCAAU58cjsWZiHiIjIVyYH0FL6UoPK8ePHcdNNNyEhIQEmkwmLFy/G9u3bZZYkncsl8PeefX1unZshuRoiIgpGnlM/5Y3tsDmckqsZmtSgcv3118PhcGDbtm3Yt28f8vPzcf3116Ourk5mWVLtOdmEiqYORBl1uG56quxyiIgoCKWYwmAK08HpEihraJNdzpCkBZXGxkaUlpZi/fr1mDlzJiZNmoTHHnsMHR0dKCoqklWWdK/vdc+mXD8zFeEGreRqiIgoGCmKgryehd+Oq7xPRVpQiY+PR25uLv785z+jvb0dDocDzz33HJKSkjBnzhxZZUnVZnPg3cO1AHjah4iI/MvbUKvyPhWdrD9YURR8+OGHWLZsGaKjo6HRaJCUlIT3338fsbGxgz7PZrPBZuvdmtpqtY5FuWPi3cO16LQ7kZMQiYuyBv87ICIiulCB0lDr8xmV9evXQ1GUIW/FxcUQQuDuu+9GUlISduzYgT179mDZsmW44YYbUFtbO+j337hxI8xms/eWmZnp6yFI42mi/ercDK6dQkREfpUXIEFFEUL4dKH/06dP48yZM0Mek5OTgx07duDqq69Gc3MzTCaT97FJkybhzjvvxPr16wd87kAzKpmZmbBYLP2+T6A52diOy574NxQF+HT9FUg1h8suiYiIgpil0478n3wAADj446thjtCP6Z9vtVphNpvP+/7t81M/iYmJSExMPO9xHR3u/QU0mv6TOhqNBi6Xa9DnGY1GGI3GCytShV7fVwkAuHRyIkMKERH5nTlcjzRzGGosXTje0Ip54+NklzQgac20BQUFiI2NxcqVK3Hw4EEcP34c69atQ3l5OZYuXSqrLCmEEHj7QA0A4NY5wXMqi4iI1G1yADTUSgsqCQkJeP/999HW1oYrrrgCc+fOxc6dO/HOO+8gPz9fVllS7DvVjOqWTkQZdbhySpLscoiIKETkevtU1HthirSrfgBg7ty52LJli8wSVMGzdso101IQpufaKURENDYCoaGWe/1I1m5zYPMh92mfr83jaR8iIho7ucnuJtaSulb4+Noan2FQkexfh2vR3u1EdkIk5o3n2ilERDR2JiRFQqtRYO1yoM7aJbucATGoSPbm/p61U+Zw7RQiIhpbRp0W2QmRANTbUMugIlF1Syc+O9EEAFg2O11yNUREFIpyVd6nwqAi0TuF1QCAi3PikB7DtVOIiGjs5SUzqNAAhBB4a787qHyFsylERCQJZ1RoQEdrrShtaINBp8G101Nll0NERCEqL8V95U/Z6TY4nIOvDC8Lg4okbx9wz6YsmZIEc/jY7q9ARETkkREbjgiDFt0OF06eaZddzjkYVCRwugTeKXSvnbJsFk/7EBGRPBqNgknePpU2ydWci0FFgk/KGtHQakNMhB6X5XLJfCIikis3OQoAUFKvvj4VBhUJXt3r3in5hplpMOj4T0BERHJN7plROa7Chlq+S46xM202fHCkDgCXzCciInXwXPlznDMq9Ob+atidAjMzzJiebpZdDhEREXJ7ZlROnmlHl90puZr+GFTGkBACL39eAQD4+rwsydUQERG5JUYbERuhh0sAZQ3qaqhlUBlDn59sxonT7YgwaHHjrDTZ5RAREQEAFEXx9qmobeE3BpUx9PIe92zKjflpiDLqJFdDRETUS619KgwqY8TSYce7h2sBAF+fz9M+RESkLt4ZFQaV0PR2YTVsDhfyUqKRn8EmWiIiUhfvjApP/YSmN3uWzP/avEwoiiK5GiIiov4mJ7mDSo2lC9Yuu+RqejGojIHyxnYcrGyBVqPg+plsoiUiIvUxR+iRYgoDAJSq6PQPg8oY+EfPvj6LJiYgMdoouRoiIqKBeU7/qGnPHwYVPxNC4J2D7tM+N+VzNoWIiNSrN6hYJVfSi0HFz/ZXtODE6XYYdRpcPS1ZdjlERESDUuOVPwwqfrbpk3IA7rVTosP0kqshIiIaXG6fRd+EEJKrcWNQ8aNaSyfeK3JvQHjHomzJ1RAREQ1tYlIUFAVo7rCjsa1bdjkAGFT86i+7TsHpEliQHYepaSbZ5RAREQ0p3KDFuLgIAOpZoZZBxU+67E78rWfJfM6mEBFRoFDbnj8MKn7y0bEGtHTYkWoOw1VT2URLRESBoffKHwaVoPbG/ioAwFdmp0Or4Uq0REQUGLxBhad+gldNSyc+Pn4aAHDzRRmSqyEiIho+z5U/pfWtcLnkX/nDoOIHL+46CadLoCAnHhOTomSXQ0RENGzjEyKh1ypo73aiuqVTdjkMKr5md7rwxj73aZ9Vi8bLLYaIiGiE9FoNJiS6f8lWw5U/DCo+tq24AY1t3UiIMuKKvCTZ5RAREY2YmlaoZVDxsdc+rwQA3DInHXot/3qJiCjweBpqj6vgyh++k/pQvbUL20saAAC3zsmUXA0REdHoeGZUihlUgssb+6vgEsDccbFsoiUiooCV1zOjcuJ0O+xOl9RaGFR8RAiB1/e6m2hvm8fZFCIiClzpMeGIMGjR7XThZGO71FoYVHxkT3kTyhvbEWnQYumMVNnlEBERjZpGo6jm9A+Dio+81jObcv3MNEQadZKrISIiujBTUt1B5VitVWodDCo+0G5z4N3DtQCA2+ZxJVoiIgp8U1JNADijEhTeL6pDp92J7IRIXJQVK7scIiKiC5aX0hNUOKMS+N484D7tc/PsdCgKNyAkIqLANznZffVqjaULrV12aXUwqFygmpZOfPrFGQDAstnpkqshIiLyjZgIA5KijQCA0oY2aXWw63MQW47U4T89OyCfTVEApwvo7HagvLEdQgALsuOQGRcxxlUSERH5z+TkaDS02lBa3yqttYFBZRAHK1vw0u6KYR9/21yunUJERMFlUnIUdpY14ovT8tZSYVAZxOKJCTDqtOfcLyDgEoBGASIMWoQbdEiONmLJlGQJVRIREfnP6kty8O1F2UiPCZdWA4PKIBZOTMDCiQmyyyAiIpImTWJA8WAzLREREakWgwoRERGpFoMKERERqRaDChEREakWgwoRERGpFoMKERERqRaDChEREakWgwoRERGpFoMKERERqZbfgsqjjz6KhQsXIiIiAjExMQMeU1FRgaVLlyIiIgJJSUlYt24dHA6Hv0oiIiKiAOO3JfS7u7tx6623oqCgAH/84x/PedzpdGLp0qVISUnBp59+itraWnzrW9+CXq/Hz3/+c3+VRURERAFEEUIIf/4BL7zwAu677z60tLT0u/+9997D9ddfj5qaGiQnuzf0+93vfocHHngAp0+fhsFgGNb3t1qtMJvNsFgsMJlMvi6fiIiI/GC479/SelR27dqFGTNmeEMKAFxzzTWwWq04cuSIrLKIiIhIRaTtnlxXV9cvpADwfl1XVzfo82w2G2w2m/dri8UCwJ3MiIiIKDB43rfPd2JnREFl/fr1+MUvfjHkMceOHUNeXt5Ivu2IbNy4ET/5yU/OuT8zM9NvfyYRERH5R2trK8xm86CPjyiorF27FqtWrRrymJycnGF9r5SUFOzZs6ffffX19d7HBrNhwwbcf//93q9dLheampoQHx8PRVGG9WcPl9VqRWZmJiorK0Ou/4VjD82xA6E9fo6dYw+1sQPyxi+EQGtrK9LS0oY8bkRBJTExEYmJiRdUmEdBQQEeffRRNDQ0ICkpCQCwdetWmEwmTJ06ddDnGY1GGI3GfvcNdvmzr5hMppD84QU49lAdOxDa4+fYOfZQJGP8Q82kePitR6WiogJNTU2oqKiA0+lEYWEhAGDixImIiorC1VdfjalTp2LFihX45S9/ibq6OvzoRz/C3XfffU4QISIiotDkt6Dy4x//GC+++KL369mzZwMAtm/fjssuuwxarRabN2/GXXfdhYKCAkRGRmLlypV45JFH/FUSERERBRi/BZUXXngBL7zwwpDHjBs3Du+++66/SrhgRqMRDz/8cEjO8HDsoTl2ILTHz7Fz7KFI7eP3+4JvRERERKPFTQmJiIhItRhUiIiISLUYVIiIiEi1GFSIiIhItRhUBvGb3/wG48ePR1hYGBYsWHDOKrqBaOPGjZg3bx6io6ORlJSEZcuWoaSkpN8xXV1duPvuuxEfH4+oqCjccsst3hWDPSoqKrB06VJEREQgKSkJ69atg8PhGMuhXLDHHnsMiqLgvvvu894XzGOvrq7GN7/5TcTHxyM8PBwzZszA3r17vY8LIfDjH/8YqampCA8Px5IlS1BaWtrvezQ1NWH58uUwmUyIiYnBnXfeiba2trEeyog5nU489NBDyM7ORnh4OCZMmICf/vSn/fYXCZbx/+c//8ENN9yAtLQ0KIqCt99+u9/jvhrnoUOHcMkllyAsLAyZmZn45S9/6e+hnddQY7fb7XjggQcwY8YMREZGIi0tDd/61rdQU1PT73sE6tiB8//b97VmzRooioInn3yy3/2qHb+gc7zyyivCYDCIP/3pT+LIkSNi9erVIiYmRtTX18su7YJcc801YtOmTaKoqEgUFhaKL3/5yyIrK0u0tbV5j1mzZo3IzMwUH330kdi7d6+4+OKLxcKFC72POxwOMX36dLFkyRJx4MAB8e6774qEhASxYcMGGUMalT179ojx48eLmTNninvvvdd7f7COvampSYwbN06sWrVK7N69W5w4cUJs2bJFlJWVeY957LHHhNlsFm+//bY4ePCguPHGG0V2drbo7Oz0HnPttdeK/Px88dlnn4kdO3aIiRMnittvv13GkEbk0UcfFfHx8WLz5s2ivLxcvP766yIqKkr8+te/9h4TLON/9913xYMPPijefPNNAUC89dZb/R73xTgtFotITk4Wy5cvF0VFReLll18W4eHh4rnnnhurYQ5oqLG3tLSIJUuWiFdffVUUFxeLXbt2ifnz54s5c+b0+x6BOnYhzv9v7/Hmm2+K/Px8kZaWJv73f/+332NqHT+DygDmz58v7r77bu/XTqdTpKWliY0bN0qsyvcaGhoEAPHxxx8LIdz/mfV6vXj99de9xxw7dkwAELt27RJCuP8zaDQaUVdX5z3m2WefFSaTSdhstrEdwCi0traKSZMmia1bt4pLL73UG1SCeewPPPCAWLx48aCPu1wukZKSIh5//HHvfS0tLcJoNIqXX35ZCCHE0aNHBQDx+eefe4957733hKIoorq62n/F+8DSpUvFt7/97X733XzzzWL58uVCiOAd/9lvVr4a529/+1sRGxvb72f+gQceELm5uX4e0fAN9UbtsWfPHgFAnDp1SggRPGMXYvDxV1VVifT0dFFUVCTGjRvXL6ioefw89XOW7u5u7Nu3D0uWLPHep9FosGTJEuzatUtiZb5nsVgAAHFxcQCAffv2wW639xt7Xl4esrKyvGPftWsXZsyYgeTkZO8x11xzDaxWK44cOTKG1Y/O3XffjaVLl/YbIxDcY//HP/6BuXPn4tZbb0VSUhJmz56N559/3vt4eXk56urq+o3dbDZjwYIF/cYeExODuXPneo9ZsmQJNBoNdu/ePXaDGYWFCxfio48+wvHjxwEABw8exM6dO3HdddcBCP7xe/hqnLt27cKXvvQlGAwG7zHXXHMNSkpK0NzcPEajuXAWiwWKonj3igv2sbtcLqxYsQLr1q3DtGnTznlczeNnUDlLY2MjnE5nvzcjAEhOTkZdXZ2kqnzP5XLhvvvuw6JFizB9+nQAQF1dHQwGwzmbPPYde11d3YB/N57H1OyVV17B/v37sXHjxnMeC+axnzhxAs8++ywmTZqELVu24K677sL3v/997xYXntqH+pmvq6vzbh7qodPpEBcXp+qxA8D69evx9a9/HXl5edDr9Zg9ezbuu+8+LF++HEDwj9/DV+MM1P8HfXV1deGBBx7A7bff7t2EL9jH/otf/AI6nQ7f//73B3xczeP32xL6pG533303ioqKsHPnTtmljInKykrce++92Lp1K8LCwmSXM6ZcLhfmzp2Ln//85wDc+24VFRXhd7/7HVauXCm5Ov977bXX8NJLL+Fvf/sbpk2bhsLCQtx3331IS0sLifFTf3a7HbfddhuEEHj22WdllzMm9u3bh1//+tfYv38/FEWRXc6IcUblLAkJCdBqtedc7VFfX4+UlBRJVfnWPffcg82bN2P79u3IyMjw3p+SkoLu7m60tLT0O77v2FNSUgb8u/E8plb79u1DQ0MDLrroIuh0Ouh0Onz88cd46qmnoNPpkJycHLRjT01NxdSpU/vdN2XKFFRUVADorX2on/mUlBQ0NDT0e9zhcKCpqUnVYweAdevWeWdVZsyYgRUrVuAHP/iBd2Yt2Mfv4atxBur/A6A3pJw6dQpbt271zqYAwT32HTt2oKGhAVlZWd7Xv1OnTmHt2rUYP348AHWPn0HlLAaDAXPmzMFHH33kvc/lcuGjjz5CQUGBxMounBAC99xzD9566y1s27YN2dnZ/R6fM2cO9Hp9v7GXlJSgoqLCO/aCggIcPny43w+05z/82W+GanLllVfi8OHDKCws9N7mzp2L5cuXez8P1rEvWrTonMvQjx8/jnHjxgEAsrOzkZKS0m/sVqsVu3fv7jf2lpYW7Nu3z3vMtm3b4HK5sGDBgjEYxeh1dHRAo+n/UqfVauFyuQAE//g9fDXOgoIC/Oc//4Hdbvces3XrVuTm5iI2NnaMRjNynpBSWlqKDz/8EPHx8f0eD+axr1ixAocOHer3+peWloZ169Zhy5YtAFQ+fr+26gaoV155RRiNRvHCCy+Io0ePiu9+97siJiam39Uegeiuu+4SZrNZ/Pvf/xa1tbXeW0dHh/eYNWvWiKysLLFt2zaxd+9eUVBQIAoKCryPey7Rvfrqq0VhYaF4//33RWJiouov0R1I36t+hAjese/Zs0fodDrx6KOPitLSUvHSSy+JiIgI8de//tV7zGOPPSZiYmLEO++8Iw4dOiRuuummAS9bnT17tti9e7fYuXOnmDRpkuouzx3IypUrRXp6uvfy5DfffFMkJCSIH/7wh95jgmX8ra2t4sCBA+LAgQMCgPjVr34lDhw44L2yxRfjbGlpEcnJyWLFihWiqKhIvPLKKyIiIkL6JbpDjb27u1vceOONIiMjQxQWFvZ7/et7BUugjl2I8//bn+3sq36EUO/4GVQG8fTTT4usrCxhMBjE/PnzxWeffSa7pAsGYMDbpk2bvMd0dnaK//7v/xaxsbEiIiJCfOUrXxG1tbX9vs/JkyfFddddJ8LDw0VCQoJYu3atsNvtYzyaC3d2UAnmsf/zn/8U06dPF0ajUeTl5Ynf//73/R53uVzioYceEsnJycJoNIorr7xSlJSU9DvmzJkz4vbbbxdRUVHCZDKJO+64Q7S2to7lMEbFarWKe++9V2RlZYmwsDCRk5MjHnzwwX5vUMEy/u3btw/4f3zlypVCCN+N8+DBg2Lx4sXCaDSK9PR08dhjj43VEAc11NjLy8sHff3bvn2793sE6tiFOP+//dkGCipqHb8iRJ/lGYmIiIhUhD0qREREpFoMKkRERKRaDCpERESkWgwqREREpFoMKkRERKRaDCpERESkWgwqREREpFoMKkRERKRaDCpERESkWgwqREREpFoMKkRERKRaDCpERESkWv8fEhzzIw0dOZsAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(altitude)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/conversion.ipynb b/conversion.ipynb index 3fe78b8..7227c8c 100644 --- a/conversion.ipynb +++ b/conversion.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 492, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -12,16 +12,16 @@ }, { "cell_type": "code", - "execution_count": 497, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ - "df = pd.read_csv('data/simulations/raw/Resultmin20.txt', sep='\\t')" + "df = pd.read_csv('data/simulations/raw/bending_Sim.txt', sep='\\t')" ] }, { "cell_type": "code", - "execution_count": 498, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -33,15 +33,15 @@ "3 9.67852498145304E+00\n", "4 9.67852498145304E+00\n", " ... \n", - "1715 9.79985543156082E+00\n", - "1716 9.81624939319664E+00\n", - "1717 9.82432436318236E+00\n", - "1718 9.82407348573446E+00\n", - "1719 9.82407348573446E+00\n", - "Name: acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket, Length: 1720, dtype: object" + "1723 -9.80224056912612E+00\n", + "1724 -9.78800336589294E+00\n", + "1725 -9.77015060305640E+00\n", + "1726 -9.74868887106344E+00\n", + "1727 -9.74868887106344E+00\n", + "Name: acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket, Length: 1728, dtype: object" ] }, - "execution_count": 498, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 499, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -108,7 +108,7 @@ "('angle_body_and_local_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth', 'Degree')\n", "('angle_of_attack_rate~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Degree/Second')\n", "('ANGLE_OF_ATTACK~Esrange#A~Esrange:Earth', 'Radian')\n", - "('angle_of_attack~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Degree')\n", + "('ANGLE_OF_ATTACK~STAHR_Rocket#A~STAHR_Rocket:Earth', 'Radian')\n", "('angular_velocity~STAHR_Rocket#B~STAHR_Rocket', 'Degree/Second')\n", "('arc_length~STAHR_Rocket', 'Kilo-Meter')\n", "('ascending_node_rel~STAHR_Rocket#TOD~Earth', 'Degree')\n", @@ -338,6 +338,7 @@ "('vy~STAHR_Rocket#TOD~Earth@Earth', 'Kilo-Meter/Second')\n", "('vz~STAHR_Rocket#J2000@Earth', 'Kilo-Meter/Second')\n", "('vz~STAHR_Rocket#TOD~Earth@Earth', 'Kilo-Meter/Second')\n", + "('V~STAHR_Rocket', 'Meter/Second')\n", "('wind_gust_p~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Degree/Second')\n", "('wind_gust_q~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Degree/Second')\n", "('wind_gust_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket', 'Degree/Second')\n", @@ -398,16 +399,37 @@ }, { "cell_type": "code", - "execution_count": 500, + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df['Time'], df['u_x~STAHR_Rocket#L~STAHR_Rocket:Earth'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2.16807271758589" + "2.5452011601288" ] }, - "execution_count": 500, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -418,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": 501, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -430,15 +452,15 @@ "5 67.885478\n", "6 67.885478\n", " ... \n", - "1715 68.072499\n", - "1716 68.072499\n", - "1717 68.072499\n", - "1718 68.072499\n", - "1719 68.072499\n", - "Name: latitude~STAHR_Rocket#PCPF~Earth@Earth, Length: 1718, dtype: float64" + "1723 68.132323\n", + "1724 68.132323\n", + "1725 68.132323\n", + "1726 68.132323\n", + "1727 68.132323\n", + "Name: latitude~STAHR_Rocket#PCPF~Earth@Earth, Length: 1726, dtype: float64" ] }, - "execution_count": 501, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -449,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 502, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -461,15 +483,15 @@ "5 67.750938\n", "6 67.750938\n", " ... \n", - "1715 67.938856\n", - "1716 67.938856\n", - "1717 67.938856\n", - "1718 67.938856\n", - "1719 67.938856\n", - "Name: declination~STAHR_Rocket#PCPF~Earth@Earth, Length: 1718, dtype: float64" + "1723 67.998970\n", + "1724 67.998970\n", + "1725 67.998970\n", + "1726 67.998970\n", + "1727 67.998970\n", + "Name: declination~STAHR_Rocket#PCPF~Earth@Earth, Length: 1726, dtype: float64" ] }, - "execution_count": 502, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -480,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 503, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -492,15 +514,15 @@ "5 21.079909\n", "6 21.079909\n", " ... \n", - "1715 21.080786\n", - "1716 21.080786\n", - "1717 21.080786\n", - "1718 21.080786\n", - "1719 21.080786\n", - "Name: longitude~STAHR_Rocket#PCPF~Earth@Earth, Length: 1718, dtype: float64" + "1723 21.039219\n", + "1724 21.039219\n", + "1725 21.039219\n", + "1726 21.039219\n", + "1727 21.039219\n", + "Name: longitude~STAHR_Rocket#PCPF~Earth@Earth, Length: 1726, dtype: float64" ] }, - "execution_count": 503, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -511,22 +533,22 @@ }, { "cell_type": "code", - "execution_count": 504, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 504, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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BQUHik08+EVVVVcLLy0usXLlSOnb48GEBQOTm5gohhFi7dq1QKpXCYDBIbRYuXCh0Op0wm81CCCHmzp0r+vfv7/AZEydOFOnp6dLzESNGiMzMTOm51WoV0dHRIisrq021G41GAUAYjcY2vc/dlVTWivh5q0XP+WuEsd4idzk37ZcLfxDx81aLf24vkrsUIiLqAB3x+93uOUBWqxXLli1DbW0tUlNTkZeXh8bGRqSlpUltEhMTERcXh9zcXABAbm4uBg4ciIiIS3tKpaenw2QySb1Iubm5Duewt7Gfw2KxIC8vz6GNUqlEWlqa1OZqzGYzTCaTw4OulHviAgBgUIweOm8vmau5eXe2LOK4qZDbYhARUbM2B6ADBw7A398fWq0W06dPx6pVq5CUlASDwQCNRoPAwECH9hERETAYmudfGAwGh/BjP24/dq02JpMJ9fX1qKiogNVqbbWN/RxXk5WVBb1eLz1iY2Pb+vU9ws6iSgBASo8QmSvpGHe1BKDtJyq4OSoREQFoRwDq27cv8vPzsXPnTsyYMQNTpkzBoUOHOqO2Djd//nwYjUbpUVJSIndJTmlnUXMPUErPYJkr6Rh9IwIQpfdGQ6MNuScvyF0OERE5gTYHII1Gg4SEBCQnJyMrKwuDBw/Gu+++i8jISFgsFlRVVTm0LysrQ2Rk8504kZGRV9wVZn9+vTY6nQ4+Pj4IDQ2FSqVqtY39HFej1WqlO9jsD3JUWlWPksp6KBXAsPggucvpEAqFAnf0be4F2nyEE+WJiKgD1gGy2Wwwm81ITk6Gl5cXcnJypGOFhYUoLi5GamoqACA1NRUHDhxwuFsrOzsbOp0OSUlJUpvLz2FvYz+HRqNBcnKyQxubzYacnBypDbWfvfdnQDc9Atxg/o/dXZfNAxJcFZqIyOOp29J4/vz5GD9+POLi4lBdXY3PP/8cmzdvxoYNG6DX6zFt2jTMnj0bwcHB0Ol0eO6555CamoqRI0cCAMaNG4ekpCQ8+uijePPNN2EwGPDyyy8jMzMTWq0WADB9+nR88MEHmDt3Lp544gls3LgRK1aswJo1a6Q6Zs+ejSlTpmDYsGEYMWIE3nnnHdTW1mLq1KkdeGk8086T9vk/7jH8ZXdrrxBoVEoUV9bhZEUteoX5y10SERHJqE0BqLy8HI899hjOnTsHvV6PQYMGYcOGDfjZz34GAPjb3/4GpVKJBx98EGazGenp6fjwww+l96tUKqxevRozZsxAamoq/Pz8MGXKFPzhD3+Q2vTo0QNr1qzBCy+8gHfffRcxMTH45JNPkJ6eLrWZOHEizp8/j1deeQUGgwFDhgzB+vXrr5gYTW3nbhOg7fy0aqT0DMb3xyqw6Ug5AxARkYdTCA8eDzCZTNDr9TAajZwPBKDc1IARf86BQgHkvzIOeh/3GQIDgP/bVoQ/rj6EUQkh+OzJkXKXQ0RE7dQRv9/cC4wkO1p6f/pF6twu/ACX5gHtKqpEjbnpOq2JiMidMQCRZFfLBOiRPd1r+MuuR6gfuof4otEqsK1lqw8iIvJMDEAk2X3qIgBgeHf3uP29Nfbb4TfxdngiIo/GAEQAgOqGRhwtqwYAJLvJ+j+tsW+LsfUYb4cnIvJkDEAEANhXYoRNADFBPgjXectdTqdJ6REMjVqJc8YGnDhfI3c5REQkEwYgAgDsKW4e/hoa5769PwDg7aWS1jjaepTzgIiIPBUDEAEA8k43ByB3Hv6yG907FADw/THuDk9E5KkYgAg2m8BeD+kBAoAxfcIAADtOVsLcxN3hiYg8EQMQ4cT5GpgamuDjpUJiVIDc5XS6vhEBCAvQor7RiryWO9+IiMizMACRNP9ncKweXir3/yuhUCikYbCtXA+IiMgjuf+vHV2Xff6PJwx/2Y3p3TwMxnlARESeiQGIPGoCtN1tLT1AB0tNqKgxy1wNERF1NQYgD2esa8SJ87UAgFs8qAco1F+L/tHNG+hxWwwiIs/DAOTh9p+tAgDEh/gi2E8jbzFdbHTLMNhWDoMREXkcBiAPt/+MEQAwKCZQ3kJkMEZaD6iC22IQEXkYBiAPt/9MFQBgUDe9vIXIILl7EHy8VDhfbUZhyz5oRETkGRiAPNylHiDPC0BatQojezZvi/E9t8UgIvIoDEAerLy6AeeMDVAogAEe2AMEcB4QEZGnYgDyYAdaen8Swvzhp1XLXI08xvRpnge0s6gSDY3cFoOIyFMwAHmwfR48AdquV5g/ovTesDTZsKuoUu5yiIioizAAeTD7BOjBsZ45/AU4bovBVaGJiDwHA5CHEkJIQ2ADPXT+j519d/itnAhNROQxGIA81NmqelyotUCtVKBflE7ucmQ1qlcoFAqgsKwa5aYGucshIqIuwADkoey3vydGBcDbSyVzNfIK8tNI6yB9z20xiIg8AgOQh9ovDX8FyluIkxjN3eGJiDwKA5CHkiZAe+ACiK2xT4TedrwCNhu3xSAicncMQB5ICIGDpSYAnrsA4k/dEhcEP40KFTUWHDaY5C6HiIg6GQOQByo1NsBY3wi1UoHeEf5yl+MUNGolUnuFAOA8ICIiT8AA5IEOtfT+9I4IgFbt2ROgL8d5QEREnoMByAMdLG2eAJ3k4be//5R9HtCPRRdRb+G2GERE7owByAPZe4CSohmALtcj1A/dAn1gsdqws+iC3OUQEVEnYgDyQIfOtQQg9gA5UCgU0uaonAdEROTeGIA8jLGuEWcu1gNgD1BrOA+IiMgzMAB5GHvvT0yQD/Q+XjJX43xu7RUCpQI4WlYDg5HbYhARuSsGIA/D4a9rC/TVYFBMIAD2AhERuTMGIA/DCdDXN6Y35wEREbk7BiAPY78Fvn80V4C+mtF9mucBcVsMIiL31aYAlJWVheHDhyMgIADh4eGYMGECCgsLHdrccccdUCgUDo/p06c7tCkuLkZGRgZ8fX0RHh6OOXPmoKmpyaHN5s2bMXToUGi1WiQkJGDJkiVX1LNgwQJ0794d3t7eSElJwa5du9rydTyOucmK4+U1ANgDdC1DYgPhr1WjstYiDRkSEZF7aVMA2rJlCzIzM7Fjxw5kZ2ejsbER48aNQ21trUO7p556CufOnZMeb775pnTMarUiIyMDFosF27dvx9KlS7FkyRK88sorUpuioiJkZGTgzjvvRH5+PmbNmoUnn3wSGzZskNosX74cs2fPxquvvoo9e/Zg8ODBSE9PR3l5eXuvhds7VlaDJpuA3scL0XpvuctxWl6qS9tibOU8ICIi9yRuQnl5uQAgtmzZIr12++23i+eff/6q71m7dq1QKpXCYDBIry1cuFDodDphNpuFEELMnTtX9O/f3+F9EydOFOnp6dLzESNGiMzMTOm51WoV0dHRIisr64brNxqNAoAwGo03/B5XtnxXsYift1o89PdcuUtxev/cXsRrRUTkpDri9/um5gAZjc3zSYKDgx1e/+yzzxAaGooBAwZg/vz5qKurk47l5uZi4MCBiIiIkF5LT0+HyWTCwYMHpTZpaWkO50xPT0dubi4AwGKxIC8vz6GNUqlEWlqa1KY1ZrMZJpPJ4eFJ7Lucc/jr+uzrAe0+XYk6S9N1WhMRkatpdwCy2WyYNWsWRo0ahQEDBkivP/zww/j000+xadMmzJ8/H//617/wyCOPSMcNBoND+AEgPTcYDNdsYzKZUF9fj4qKClit1lbb2M/RmqysLOj1eukRGxvbvi/vogoN1QCAvpEBMlfi/OJDfBEb7INGq8DOk5Vyl0NERB1M3d43ZmZmoqCgANu2bXN4/emnn5b+eeDAgYiKisLYsWNx4sQJ9OrVq/2VdoD58+dj9uzZ0nOTyeRRIehoWUsAimAAuh6FQoHRvcPw+c5ibD12HncmhstdEhERdaB29QDNnDkTq1evxqZNmxATE3PNtikpKQCA48ePAwAiIyNRVlbm0Mb+PDIy8pptdDodfHx8EBoaCpVK1Wob+zlao9VqodPpHB6eoqLGjIoaCxQKoHeEv9zluASuB0RE5L7aFICEEJg5cyZWrVqFjRs3okePHtd9T35+PgAgKioKAJCamooDBw443K2VnZ0NnU6HpKQkqU1OTo7DebKzs5GamgoA0Gg0SE5Odmhjs9mQk5MjtSFHR1uGv+KCfeGraXfHn0dJ7RUKpQI4Xl6D0qp6ucshIqIO1KYAlJmZiU8//RSff/45AgICYDAYYDAYUF/f/ONw4sQJ/PGPf0ReXh5OnTqFr7/+Go899hjGjBmDQYMGAQDGjRuHpKQkPProo9i3bx82bNiAl19+GZmZmdBqtQCA6dOn4+TJk5g7dy6OHDmCDz/8ECtWrMALL7wg1TJ79mx8/PHHWLp0KQ4fPowZM2agtrYWU6dO7ahr41aOGDj81VZ6Hy8MiQ0EwG0xiIjcTltuGQPQ6mPx4sVCCCGKi4vFmDFjRHBwsNBqtSIhIUHMmTPnitvUTp06JcaPHy98fHxEaGioePHFF0VjY6NDm02bNokhQ4YIjUYjevbsKX3G5d5//30RFxcnNBqNGDFihNixY0ebboHzpNvg5/17n4ift1r8vw1H5C7Fpfz120IRP2+1ePazPLlLISKiFh3x+60QQnjsWv8mkwl6vR5Go9Ht5wNNWPAD8kuq8MHDt+DeQdFyl+My8k5X4sGFuQj09ULeyz+DSqmQuyQiIo/XEb/f3AvMA9hsAsd4B1i7DI4JRIBWjaq6RhScNcpdDhERdRAGIA9wtqoetRYrNColuof6yV2OS1GrlLg1oXlbDM4DIiJyHwxAHsC+AGLPMD94qfhH3lb2VaG3HGUAIiJyF/w19ACFLcNfiVwBul3u6NscgPYUV8FY1yhzNURE1BEYgDyAvQeoDwNQu8QE+aJPhD+sNoEtHAYjInILDEAe4Ch7gG6afSuMTUfKr9OSiIhcAQOQm2u02nDifA0AoA/vAGu3O/s2B6DNheWw2jx25QgiIrfBAOTmiipq0WgV8Neq0S3QR+5yXFZyfBACvNW4WNeI/JIqucshIqKbxADk5o6VNff+JIT7Q6HgIn7t5aVSYkyf5snQHAYjInJ9DEBu7nj5pQBEN+eulmGwjQxAREQujwHIzR0/zwDUUe7oGwaFAjh0zgSDsUHucoiI6CYwALk5qQcojAHoZoX4azE4JhBA82RoIiJyXQxAbsxqEzjJHqAOdSeHwYiI3AIDkBs7e7Ee5iYbNGolYoN95S7HLdzVsh7QtuMVMDdZZa6GiIjaiwHIjdnX/+kZ6geVkneAdYT+0TqEBWhRZ7FiV1Gl3OUQEVE7MQC5Mfv8n14c/uowSqUCd7bsDcZhMCIi18UA5MY4Abpz3JUYAQDIPlQGIbgqNBGRK2IAcmP2W+DZA9SxxvQJhVatxJmL9Th8rlrucoiIqB0YgNyUEII9QJ3EV6OWVoXecNAgczVERNQeDEBuqqLGAmN9IxQKoGeYn9zluJ1xSc3DYN8eKpO5EiIiag8GIDdl7/2JDfKFt5dK5mrcT1q/CCgVwOFzJpRU1sldDhERtREDkJviFhidK8hPgxE9ggFwGIyIyBUxALmpE9wEtdOl948EAHx7kMNgRESuhgHITXECdOf7Wcs8oN2nK1FRY5a5GiIiagsGIDfFRRA7X0yQLwZ008EmgJzD7AUiInIlDEBuqLqhEQZTAwAOgXW2cUkcBiMickUMQG6oqKIWABDqr4Xex0vmatybfR7Q98crUGNukrkaIiK6UQxAbsgegHqGcv2fztYnwh89Qv1gabJxbzAiIhfCAOSG7AGoBwNQp1MoFLhnYHMv0Op9pTJXQ0REN4oByA3ZA1B3BqAuce+gaADA5qPnUd3QKHM1RER0IxiA3NAp9gB1qcTIAPQKax4Gy+bWGERELoEByM0IIXDSPgeIe4B1CYVCgYyWXqA1+8/JXA0REd0IBiA3c6HWguqGJigUQFywr9zleIz7BkUBALYeOw9jHYfBiIicHQOQm7EPf0XrfbgJahfqHRGAvhEBaLQKbDjEvcGIiJwdA5Cb4fCXfO5t6QXiMBgRkfNjAHIz0h1gIQxAXS2jJQD9cLwClbUWmashIqJraVMAysrKwvDhwxEQEIDw8HBMmDABhYWFDm0aGhqQmZmJkJAQ+Pv748EHH0RZmeOdMcXFxcjIyICvry/Cw8MxZ84cNDU5rqK7efNmDB06FFqtFgkJCViyZMkV9SxYsADdu3eHt7c3UlJSsGvXrrZ8HbfEO8Dk0zPMHwO66dBkE1izn2sCERE5szYFoC1btiAzMxM7duxAdnY2GhsbMW7cONTW1kptXnjhBXzzzTdYuXIltmzZgtLSUjzwwAPScavVioyMDFgsFmzfvh1Lly7FkiVL8Morr0htioqKkJGRgTvvvBP5+fmYNWsWnnzySWzYsEFqs3z5csyePRuvvvoq9uzZg8GDByM9PR3l5Z69Gq+0CCKHwGTxi1tiAAD/2XNW5kqIiOiaxE0oLy8XAMSWLVuEEEJUVVUJLy8vsXLlSqnN4cOHBQCRm5srhBBi7dq1QqlUCoPBILVZuHCh0Ol0wmw2CyGEmDt3rujfv7/DZ02cOFGkp6dLz0eMGCEyMzOl51arVURHR4usrKwbrt9oNAoAwmg0tuFbOy+r1Sb6/G6tiJ+3WhSdr5G7HI9UbmoQPeevEfHzVosT5dVyl0NE5JY64vf7puYAGY1GAEBwcDAAIC8vD42NjUhLS5PaJCYmIi4uDrm5uQCA3NxcDBw4EBEREVKb9PR0mEwmHDx4UGpz+TnsbeznsFgsyMvLc2ijVCqRlpYmtfFEBlMDzE02qJUKxAT5yF2ORwoL0GJM71AAwKq97AUiInJW7Q5ANpsNs2bNwqhRozBgwAAAgMFggEajQWBgoEPbiIgIGAwGqc3l4cd+3H7sWm1MJhPq6+tRUVEBq9Xaahv7OVpjNpthMpkcHu7EPvwVF+ILtYrz2+Xyi6HNw2Cr9p6FzSZkroaIiFrT7l/JzMxMFBQUYNmyZR1ZT6fKysqCXq+XHrGxsXKX1KHst8D34B1gshqXFIEArRpnLtZj9+mLcpdDREStaFcAmjlzJlavXo1NmzYhJiZGej0yMhIWiwVVVVUO7cvKyhAZGSm1+eldYfbn12uj0+ng4+OD0NBQqFSqVtvYz9Ga+fPnw2g0So+SkpK2fXEnxzvAnIO3lwrjW3aI/3LPGZmrISKi1rQpAAkhMHPmTKxatQobN25Ejx49HI4nJyfDy8sLOTk50muFhYUoLi5GamoqACA1NRUHDhxwuFsrOzsbOp0OSUlJUpvLz2FvYz+HRqNBcnKyQxubzYacnBypTWu0Wi10Op3Dw53wDjDnYb8bbM2Bc2hotMpcDRER/VSbAlBmZiY+/fRTfP755wgICIDBYIDBYEB9fT0AQK/XY9q0aZg9ezY2bdqEvLw8TJ06FampqRg5ciQAYNy4cUhKSsKjjz6Kffv2YcOGDXj55ZeRmZkJrVYLAJg+fTpOnjyJuXPn4siRI/jwww+xYsUKvPDCC1Its2fPxscff4ylS5fi8OHDmDFjBmprazF16tSOujYu5xSHwJxGSo9gdAv0QXVDE9YXcGsMIiKn05ZbxgC0+li8eLHUpr6+Xjz77LMiKChI+Pr6il/84hfi3LlzDuc5deqUGD9+vPDx8RGhoaHixRdfFI2NjQ5tNm3aJIYMGSI0Go3o2bOnw2fYvf/++yIuLk5oNBoxYsQIsWPHjrZ8Hbe6Dd7SZBW9Wm6/Lq2qk7scEkK8k31UxM9bLSb+fbvcpRARuZWO+P1WCCE89jYVk8kEvV4Po9Ho8sNhpypqccf/2wxvLyUOvX43lEqF3CV5vNKqetz2l42wCWDT/9zBuVlERB2kI36/ea+0myiurAMAxAX7Mvw4iehAH9zeJwwAsOzHYpmrISKiyzEAuYnTlwUgch4PjYgDAPwn7wwsTTaZqyEiIjsGIDdRfKFlEcRgDrM4k7sSwxEWoEVFjQU5h8uu/wYiIuoSDEBu4vSF5h6g+BD2ADkTL5USv0xuviV+2Y/ute4UEZErYwByE9IcIAYgp/PQ8OYVx7ceO4+Slj8nIiKSFwOQGxBCOEyCJucSH+KH0b1DIQTw6c7TcpdDRERgAHILFTUW1FmsUCjAXeCd1GOp3QEAy38s4crQREROgAHIDRRXNk+Ajtb7QKtWyVwNteauxHDEBPmgqq4RX+eXyl0OEZHHYwByA/YJ0Bz+cl4qpQKPjowHACzZfgoevP4oEZFTYAByA5z/4xomDo+FVq3EoXMm5J2+KHc5REQejQHIDRRf4B1griDQV4MJQ7oBaO4FIiIi+TAAuQH7KtBcA8j5PXZr8zDY+gIDzhnrZa6GiMhzMQC5AWkRRK4C7fT6R+uR0iMYTTaBxT+ckrscIiKPxQDk4uosTaioMQPgEJireHpMTwDA5zuLYWpolLkaIiLPxADk4uwToPU+XtD7eMlcDd2IO/uGIyHcHzXmJizbxV3iiYjkwADk4rgHmOtRKhVSL9A/tp3iLvFERDJgAHJxxVwDyCXdPyQa4QFaGEwN+GYfF0YkIupqDEAu7nTLKtDsAXItWrUKU0f1AAB8/P1JLoxIRNTFGIBcXHFl863U7AFyPQ+nxMFPo8IRQzW2HD0vdzlERB6FAcjFnWmZBB3LAORy9D5emDQiDgDw4aYT7AUiIupCDEAuzGYTOFPV3AMUG8QA5IqeGtMTGrUSu05VYsfJSrnLISLyGAxALqyixgxLkw1KBRCp95a7HGqHCJ03HhoeCwB4N+eozNUQEXkOBiAXVnKxufcnSu8DLxX/KF3V9Nt7wUulwI6Tldh58oLc5RAReQT+arqwMxeb5/90C/KRuRK6GdGBPvj1sOZeoPc2HpO5GiIiz8AA5MLOtPQAxTAAubwZd/SCWqnAD8cvYPcpzgUiIupsDEAu7GyVPQBxArSriwnyxS+TYwAA7+awF4iIqLMxALkw9gC5l8w7E6BSKvD9sQr8yF4gIqJOxQDkwuxzgGICGYDcQWywrzQX6C/rjnBdICKiTsQA5KKEEDh7kUNg7ub5sb2hVSux+/RFbCosl7scIiK3xQDkos7XmGHmGkBuJ1Lvjcdv7Q4AeHN9IWw29gIREXUGBiAXZZ//E6nzhkbNP0Z3MuOOXgjwVuOIoRrf7OdO8UREnYG/nC7qDIe/3FagrwbTb+8FAHj726OwNNlkroiIyP0wALkoaQI07wBzS1NHdUeovxbFlXVY9mOx3OUQEbkdBiAXdZa3wLs1X40az49NAAD8LfsojPWNMldEROReGIBclH0IjNtguK+HRsQhIdwfF+sa8QG3yCAi6lAMQC7q0hAY5wC5Ky+VEr/L6AcAWLL9FIoqamWuiIjIfTAAuSAhBFeB9hB39g3H7X3C0GgVyFp7WO5yiIjcRpsD0NatW3HfffchOjoaCoUCX331lcPxxx9/HAqFwuFx9913O7SprKzE5MmTodPpEBgYiGnTpqGmpsahzf79+zF69Gh4e3sjNjYWb7755hW1rFy5EomJifD29sbAgQOxdu3atn4dl1RRY4G5yQaFAojSMwC5u5cz+kGlVODbQ2XYfqJC7nKIiNxCmwNQbW0tBg8ejAULFly1zd13341z585Jjy+++MLh+OTJk3Hw4EFkZ2dj9erV2Lp1K55++mnpuMlkwrhx4xAfH4+8vDy89dZbeO211/DRRx9JbbZv345JkyZh2rRp2Lt3LyZMmIAJEyagoKCgrV/J5diHv7gGkGfoHRGAySlxAIA/rj4MKxdHJCK6aQpxExsOKRQKrFq1ChMmTJBee/zxx1FVVXVFz5Dd4cOHkZSUhB9//BHDhg0DAKxfvx733HMPzpw5g+joaCxcuBC/+93vYDAYoNFoAAAvvfQSvvrqKxw5cgQAMHHiRNTW1mL16tXSuUeOHIkhQ4Zg0aJFN1S/yWSCXq+H0WiETqdrxxWQxzf7SvHcF3sxvHsQVk6/Ve5yqAtU1lpw+1ubUN3QhKwHBmLSiDi5SyIikk1H/H53SvfB5s2bER4ejr59+2LGjBm4cOGCdCw3NxeBgYFS+AGAtLQ0KJVK7Ny5U2ozZswYKfwAQHp6OgoLC3Hx4kWpTVpamsPnpqenIzc396p1mc1mmEwmh4cr4iKInifYT4NZaX0AAH9ZfwSVtRaZKyIicm0dHoDuvvtu/POf/0ROTg7+8pe/YMuWLRg/fjysVisAwGAwIDw83OE9arUawcHBMBgMUpuIiAiHNvbn12tjP96arKws6PV66REbG3tzX1YmXATRMz2WGo/EyABU1TXizfVH5C6HiMildXgAeuihh/Dzn/8cAwcOxIQJE7B69Wr8+OOP2Lx5c0d/VJvNnz8fRqNRepSUlMhdUrucrWpZAyiQAciTeKmU+OOEAQCAZT+WYE/xRZkrIiJyXZ0+g7Znz54IDQ3F8ePHAQCRkZEoLy93aNPU1ITKykpERkZKbcrKyhza2J9fr439eGu0Wi10Op3DwxVxCMxzDe8ejF8mxwAAXl5VgCYr9wkjImqPTg9AZ86cwYULFxAVFQUASE1NRVVVFfLy8qQ2GzduhM1mQ0pKitRm69ataGy8tPx/dnY2+vbti6CgIKlNTk6Ow2dlZ2cjNTW1s7+SrJrXAGoeAuMq0J5p/vhE6H28cOicCf/acVrucoiIXFKbA1BNTQ3y8/ORn58PACgqKkJ+fj6Ki4tRU1ODOXPmYMeOHTh16hRycnJw//33IyEhAenp6QCAfv364e6778ZTTz2FXbt24YcffsDMmTPx0EMPITo6GgDw8MMPQ6PRYNq0aTh48CCWL1+Od999F7Nnz5bqeP7557F+/Xq8/fbbOHLkCF577TXs3r0bM2fO7IDL4rwu1jWiobH5v/qjA71lrobkEOKvxdy7+wJo3i2+3NQgc0VERC5ItNGmTZsEgCseU6ZMEXV1dWLcuHEiLCxMeHl5ifj4ePHUU08Jg8HgcI4LFy6ISZMmCX9/f6HT6cTUqVNFdXW1Q5t9+/aJ2267TWi1WtGtWzfxxhtvXFHLihUrRJ8+fYRGoxH9+/cXa9asadN3MRqNAoAwGo1tvQyyOXCmSsTPWy2G/Slb7lJIRk1Wm/j5B9tE/LzVYsanu+Uuh4ioS3XE7/dNrQPk6lxxHaANBw145l95GBwbiP9mjpK7HJLRwVIjfv7BD7DaBP7+aDLS+199/hsRkTtx2nWAqPOUSneAcfjL0/WP1uOZMT0BAL//qgDG+sbrvIOIiOwYgFyMPQBFcw8wAvCbsb3RM9QP5dVmbpZKRNQGDEAuprSqecJrNNcAIgDeXiq88eAgAM1rA/1wnJulEhHdCAYgF2NfBJEBiOxG9AjGoyPjAQDzvzyAOkuTzBURETk/BiAXU8pVoKkVc+/ui2i9N4or6/Dm+kK5yyEicnoMQC7E3GRFebUZANcAIkcB3l748wMDAQBLtp/Cdg6FERFdEwOQCykzNocfrVqJYD+NzNWQs7mjbzgmp8QBAP5n5T6YGnhXGBHR1TAAuZDLN0FVKBQyV0PO6Lf39EN8iC9KjQ14/etDcpdDROS0GIBcSCknQNN1+GnVePtXg6FUAP/ZcwbrCwxyl0RE5JQYgFzIpQDE+T90dcO6B+OZ23sBAH676gDOt8wbIyKiSxiAXAhvgacb9UJaH/SL0qGy1oL5X+6HB+94Q0TUKgYgF8IARDdKo1bibxMHQ6NS4rvD5fjXjtNyl0RE5FQYgFwI1wCitkiM1OGl8YkAgD+tOYxDpSaZKyIich4MQC5CCMFtMKjNpo7qjrsSw2FpsuG5L/ZwlWgiohYMQC6iqq4R9Y1WAECUnpOg6cYoFAq89ctBCA/Q4sT5Wt4aT0TUggHIRdjn/4T6a+DtpZK5GnIlIf5avPPQECgUwPLdJfhmX6ncJRERyY4ByEVwDSC6Gbf2CkXmHQkAgN9+eQDFF+pkroiISF4MQC5CCkB6BiBqn1lpvZEcH4RqcxOe/TwPDS1DqkREnogByEWUGjkBmm6OWqXE+5NuQbCfBgVnTXjt64Nyl0REJBsGIBdxlqtAUweIDvTBuy3zgZb9WIIVP5bIXRIRkSwYgFwE1wCijjK6dxhe/FkfAMDv/1uAgrNGmSsiIup6DEAugpOgqSM9e0cC7koMh7nJhmc/2wNjXaPcJRERdSkGIBdgabKhvGVDSwYg6ghKpQJ/+/UQxAT5oLiyDi+uzIfNxv3CiMhzMAC5gDJTA4Ro3t8pxE8jdznkJvS+Xlj0SDI06ub9wt7feFzukoiIugwDkAuQJkDrvaFUKmSuhtzJgG56/GnCAADA3747ivUFBpkrIiLqGgxALoDzf6gz/XpYLB6/tTsAYPaKfBwxcNNUInJ/DEAugAGIOtvLGf1wW0Io6ixWPLl0NyprLXKXRETUqRiAXMBZ7gJPnUytUuKDh29BfIgvzlysx4xP89BotcldFhFRp2EAcgGX1gDiIojUeQJ9Nfj4sWHw16qxs6gSr3/DlaKJyH0xALkADoFRV+kTEYB3JjavFP3pjmIs3X5K7pKIiDoFA5CTE0IwAFGXSkuKwJz0vgCA1785iJzDZTJXRETU8RiAnJypvgm1luZdu7kTPHWVGbf3wsRhsbAJYObne7ldBhG5HQYgJ2dfAyjYTwMfjUrmashTKBQK/OkXAzC6dyjqG614YsmP0t9FIiJ3wADk5Eq5CzzJxEulxILJQ9E3IgDl1WY8sfhHmBq4ZxgRuQcGICdXarSvAs3hL+p6Om8v/GPqcIQFaFFYVo3Mz/bw9ngicgsMQE6ulGsAkcy6Bfpg8ePD4atR4ftjFfjdqgMQghunEpFrYwBychwCI2cwoJse70+6BUoFsGL3Gbz97VG5SyIiuiltDkBbt27Ffffdh+joaCgUCnz11VcOx4UQeOWVVxAVFQUfHx+kpaXh2LFjDm0qKysxefJk6HQ6BAYGYtq0aaipqXFos3//fowePRre3t6IjY3Fm2++eUUtK1euRGJiIry9vTFw4ECsXbu2rV/H6Z0z8hZ4cg5j+0Xgf38xEADwwabjWPJDkcwVERG1X5sDUG1tLQYPHowFCxa0evzNN9/Ee++9h0WLFmHnzp3w8/NDeno6GhoapDaTJ0/GwYMHkZ2djdWrV2Pr1q14+umnpeMmkwnjxo1DfHw88vLy8NZbb+G1117DRx99JLXZvn07Jk2ahGnTpmHv3r2YMGECJkyYgIKCgrZ+JadmHwKL4hwgcgKTRsThxZ/1AQC8vvoQvt5XKnNFRETtJG4CALFq1Srpuc1mE5GRkeKtt96SXquqqhJarVZ88cUXQgghDh06JACIH3/8UWqzbt06oVAoxNmzZ4UQQnz44YciKChImM1mqc28efNE3759pee//vWvRUZGhkM9KSkp4plnnrnh+o1GowAgjEbjDb+nKzVZbaLn/DUift5qca6qXu5yiIQQzf8/f+WrAyJ+3mqR8Ns1YuvRcrlLIiIP0xG/3x06B6ioqAgGgwFpaWnSa3q9HikpKcjNzQUA5ObmIjAwEMOGDZPapKWlQalUYufOnVKbMWPGQKPRSG3S09NRWFiIixcvSm0u/xx7G/vntMZsNsNkMjk8nFl5dQOsNgG1UoGwAK3c5RABaF4j6NX7+uPeQVFotAo886887CupkrssIqI26dAAZDAYAAAREREOr0dEREjHDAYDwsPDHY6r1WoEBwc7tGntHJd/xtXa2I+3JisrC3q9XnrExsa29St2KfsE6AidN1RKhczVEF2iVCrw9q8H47aEUNRZrJi65EecPF9z/TcSETkJj7oLbP78+TAajdKjpKRE7pKuyT7/pxsnQJMT0qpVWPRoMgbF6FFZa8Gj/7eLq0UTkcvo0AAUGRkJACgrc9w8saysTDoWGRmJ8vJyh+NNTU2orKx0aNPaOS7/jKu1sR9vjVarhU6nc3g4M3sPUBRvgScn5a9VY/Hjw9Ez1A9nq+rxyCc7UV7dcP03EhHJrEMDUI8ePRAZGYmcnBzpNZPJhJ07dyI1NRUAkJqaiqqqKuTl5UltNm7cCJvNhpSUFKnN1q1b0dh4adn97Oxs9O3bF0FBQVKbyz/H3sb+Oe6Au8CTKwjx1+Kzp1IQE+SDoopaPPrJLlystchdFhHRNbU5ANXU1CA/Px/5+fkAmic+5+fno7i4GAqFArNmzcKf/vQnfP311zhw4AAee+wxREdHY8KECQCAfv364e6778ZTTz2FXbt24YcffsDMmTPx0EMPITo6GgDw8MMPQ6PRYNq0aTh48CCWL1+Od999F7Nnz5bqeP7557F+/Xq8/fbbOHLkCF577TXs3r0bM2fOvPmr4iRKjVwFmlxDlN4Hnz2Zgghd85YZj/1jF/cNIyLn1tbbxjZt2iQAXPGYMmWKEKL5Ftnf//73IiIiQmi1WjF27FhRWFjocI4LFy6ISZMmCX9/f6HT6cTUqVNFdXW1Q5t9+/aJ2267TWi1WtGtWzfxxhtvXFHLihUrRJ8+fYRGoxH9+/cXa9asadN3cfbb4O95d6uIn7dafHfIIHcpRDfkWJlJ3PKHb0X8vNXiwQ9/ELXmRrlLIiI31BG/3wohPHdTH5PJBL1eD6PR6JTzgYb+MRuVtRase340+kU5X31ErTlYasSkj3bA1NCE2xJC8cmUYfD2UsldFhG5kY74/faou8BcSb3FisqWeRTcCZ5cSf9oPZY8MQK+GhW2Ha/AzM+5gzwROR8GICdV2rIHmJ9GBZ2PWuZqiNpmaFwQPpkyDFq1Et8dLsdzn+9lCCIip8IA5KTOVV2aAK1QcBFEcj239grFokeToVEpsf6ggSGIiJwKA5CTurQGEIe/yHXd2Tccf78sBP3mC4YgInIODEBOyj4E1o2LIJKLuzPxUghaV8AQRETOgQHISUk9QJwATW7gzsRwLHp0qBSCnl/GEERE8mIAclKlVVwEkdzLXYkRUghae4AhiIjkxQDkpOxDYNF6DoGR+7grMQILH7kUgmYty2cIIiJZMAA5ISEEzl7kPmDknsb2uxSC1hw4h2c/2wNzk1XusojIwzAAOaHzNWaYm2xQKBiAyD2N7RfRPDFarUT2oTI89c881FsYgoio6zAAOaEzLb0/UTpvaNT8IyL3dGdiOBY/Phw+XipsPXoeUxbvQo25Se6yiMhD8NfVCZVU1gEAYoJ8Za6EqHONSgjFv6aNQIBWjV1FlZj8yU4Y67iLPBF1PgYgJ2TvAYoJ5vAXub9h3YPx2VMpCPT1wr6SKjz08Q5U1JjlLouI3BwDkBOSAhB7gMhDDIoJxLKnRyLUX4vD50yY+PdcGIwNcpdFRG6MAcgJnbnYPAQWG8QeIPIciZE6rHhmJKL03jhxvha//nuuNBxMRNTRGICcEHuAyFP1DPPHimdSERvsg+LKOvxqUS6OllXLXRYRuSEGICdjs11aAyiGPUDkgWKDfbHymVuREO4Pg6kBv1qUi7zTF+Uui4jcDAOQkymvNsNitUGlVCCKq0CTh4rUe2PlM6kYEhsIY30jHvlkJzYXlstdFhG5EQYgJ2Of/xOl94ZaxT8e8lxBfhp8/lQKxvQJQ32jFU8u3Y3/5p+VuywichP8hXUyJdIEaM7/IfLVqPHJY8Pw88HRaLIJPL8sH4t/KJK7LCJyAwxATuZMJef/EF1Oo1binYlD8Pit3QEAr39zCG9/WwghhLyFEZFLYwByMvY7wGKD2QNEZKdUKvDqfUl48Wd9AADvbzyO364qgNXGEERE7cMA5GTsQ2DsASJypFAo8NzY3vjThAFQKIAvdhXjmX/tRp2F+4cRUdsxADkZrgFEdG2PjIzHhw8PhUatxHeHyzHpI26dQURtxwDkRKw2gdIq+xAYe4CIrmb8wCh8/mTL/mFnjHjgw+04eb5G7rKIyIUwADmR0qp6NNkENColwgO4BhDRtQzrHowvZ9yKuGBfFFfW4cGF25F3ulLusojIRTAAOZHTF5rn/8SF+EKlVMhcDZHz6xnmjy+fvRWDY/S4WNeIhz/eiXUHzsldFhG5AAYgJ3LqQi0AoHsI5/8Q3ahQfy2+eHok0vpFwNxkw7Of78H/beNaQUR0bQxATuR0SwCKC/aTuRIi1+KrUePvjybjsdR4CAH8cfUhvP7NQd4mT0RXxQDkRE61DIF1D2UPEFFbqZQKvP7z/pg/PhEAsPiHU3j6n7tRY+Zt8kR0JQYgJ1LcEoDiQ9gDRNQeCoUCz9zeCx88fAu0aiVyjpTjlwu3S3vsERHZMQA5CZtN4HQl5wARdYR7B0Vj+TOpCPXX4oihGhMW/IA9xRflLouInAgDkJMorzajodEGlVKB6ECuAUR0s4bEBuLrmaPQL0qHihoLHvpoB3eTJyIJA5CTsN8BFhPkAy8V/1iIOkJ0oA/+PT0Vaf0iYGmy4fll+fhr9lHYODmayOPxl9ZJ2O8A4/wfoo7lp22+Q+yZMT0BAO/lHMNzy/aiodEqc2VEJCcGICch3QHG+T9EHU6lVGD+Pf3w5i8HwUulwJr95zDx77koNzXIXRoRyYQByEmwB4io8/16WCz+Ne3SHmL3fbANezk5msgjdXgAeu2116BQKBweiYmJ0vGGhgZkZmYiJCQE/v7+ePDBB1FWVuZwjuLiYmRkZMDX1xfh4eGYM2cOmpoc1/LYvHkzhg4dCq1Wi4SEBCxZsqSjv0qXsm+DER/MHiCizjSyZwi+enYUeof7o8xkxsS/78DK3SVyl0VEXaxTeoD69++Pc+fOSY9t27ZJx1544QV88803WLlyJbZs2YLS0lI88MAD0nGr1YqMjAxYLBZs374dS5cuxZIlS/DKK69IbYqKipCRkYE777wT+fn5mDVrFp588kls2LChM75OpxNCSAGIiyASdb7uoX5YlTkKP0uKgMVqw5x/78drXx9Eo9Umd2lE1EUUQogOvR3itddew1dffYX8/PwrjhmNRoSFheHzzz/HL3/5SwDAkSNH0K9fP+Tm5mLkyJFYt24d7r33XpSWliIiIgIAsGjRIsybNw/nz5+HRqPBvHnzsGbNGhQUFEjnfuihh1BVVYX169ffcK0mkwl6vR5GoxE6ne7mvvhNKDc1YMSfc6BUAIf+cDe8vVSy1ULkSWw2gfc2HsM73x0DAKT2DMGCyUMR7KeRuTIiupaO+P3ulB6gY8eOITo6Gj179sTkyZNRXFwMAMjLy0NjYyPS0tKktomJiYiLi0Nubi4AIDc3FwMHDpTCDwCkp6fDZDLh4MGDUpvLz2FvYz/H1ZjNZphMJoeHMzh+vgYAEBvsy/BD1IWUSgVmpfXB3x9Nhp9GhdyTF3Df+9twsNQod2lE1Mk6PAClpKRgyZIlWL9+PRYuXIiioiKMHj0a1dXVMBgM0Gg0CAwMdHhPREQEDAYDAMBgMDiEH/tx+7FrtTGZTKivr79qbVlZWdDr9dIjNjb2Zr9uhzh5vnkCdK8wf5krIfJM6f0jsSpzFLqH+OJsVT0eXLgd3+wrlbssIupEHR6Axo8fj1/96lcYNGgQ0tPTsXbtWlRVVWHFihUd/VFtNn/+fBiNRulRUuIcEx9PtPQA9QrjHWBEcukTEYD/Zt6GMX3C0NBow3Nf7MUb646gifOCiNxSp98GHxgYiD59+uD48eOIjIyExWJBVVWVQ5uysjJERkYCACIjI6+4K8z+/HptdDodfHyuvo2EVquFTqdzeDiDE+wBInIKel8vLH58OKbf3gsAsGjLCTz2j12oqDHLXBkRdbROD0A1NTU4ceIEoqKikJycDC8vL+Tk5EjHCwsLUVxcjNTUVABAamoqDhw4gPLycqlNdnY2dDodkpKSpDaXn8Pexn4OV3OivKUHKJwBiEhuKqUCL41PxPuTboGvRoXtJy7g3ve2cTNVIjfT4QHof/7nf7BlyxacOnUK27dvxy9+8QuoVCpMmjQJer0e06ZNw+zZs7Fp0ybk5eVh6tSpSE1NxciRIwEA48aNQ1JSEh599FHs27cPGzZswMsvv4zMzExotVoAwPTp03Hy5EnMnTsXR44cwYcffogVK1bghRde6Oiv0+nqLVacrWqet8QeICLncd/gaPw3cxR6hfnBYGrAxL/nYun2U+jgG2eJSCYdHoDOnDmDSZMmoW/fvvj1r3+NkJAQ7NixA2FhYQCAv/3tb7j33nvx4IMPYsyYMYiMjMSXX34pvV+lUmH16tVQqVRITU3FI488gsceewx/+MMfpDY9evTAmjVrkJ2djcGDB+Ptt9/GJ598gvT09I7+Op3uZEVz70+QrxdvvSVyMr0jAvDfmbfhnoGRaLQKvPr1Qcxano86S9P130xETq3D1wFyJc6wDtDX+0rxmy/2Ylh8EP4941ZZaiCiaxNC4P+2FSFr3RFYbQJ9Ivyx6JFk9GSvLZEsnHYdILpx9vk/PXkHGJHTUigUeHJ0T3zx1EiEB2hxtKwGP//gB6wvOCd3aUTUTgxAMrt0Czz/S5LI2Y3oEYzVv7kNKT2CUWNuwvRP9+DPaw9zCw0iF8QAJLPjUg8QAxCRKwgP8MZnT6bg6TE9AQAfbT2JiX/PlW5mICLXwAAko0arTeoB6hsRIHM1RHSj1ColfntPPyx6JBkB3mrsKa7CPe9+j+xDZdd/MxE5BQYgGRVV1KLRKuCrUSEm6OoLOBKRc7p7QCTW/mY0BsfoYaxvxFP/3I0/rj4ESxOHxIicHQOQjI4YqgEAfSMDoFQqZK6GiNojNtgXK6ffimm39QAA/N+2Ivzq77koqayTuTIiuhYGIBkVGpp3o0+M5PAXkSvTqJX4/b1J+PixYdD7eGFfSRXuee973iVG5MQYgGRUaO8B4vwfIrfws6QIrH1+NIbGBaK6ofkusVf/WwBzk1Xu0ojoJxiAZHRpCMw5NmUlopvXLdAHy59JxTO3N98ltjT3NB5cuB2nKmplroyILscAJJMacxPOXGy+bZZDYETuxUulxPzx/bD48eEI8vVCwVkT7nnve6zcXcK9xIicBAOQTOzDX+EBWgRxDzAit3RnYjjWPj8aI3sGo85ixZx/78fML/bCWN8od2lEHo8BSCb2AJQYxeEvIncWpffBZ0+OxJz0vlArFViz/xzuefd77CqqlLs0Io/GACSTglIjAKBfFIe/iNydSqlA5p0J+PeMWxEf4ouzVfV46KNc/PXbQjRxGw0iWTAAyaTgbHMAGtQtUN5CiKjLDIkNxJrfjMaDQ2NgE8B7G4/j11wziEgWDEAyMDdZcfhc8xpAg2L0MldDRF3JX6vG278ejPcm3SJtozH+3e/x1d6zcpdG5FEYgGRw1FCDRqtAoK8Xt8Ag8lA/HxyNtb8ZjWHxQagxN2HW8nzMWrYXpgZOkCbqCgxAMth/tgoAMLCbHgoFt8Ag8lSxwb5Y9vRIzErrDaUC+Cq/FOl/24pNheVyl0bk9hiAZGCf/zOwG4e/iDydWqXErLQ+WDk9Fd1DfHHO2ICpi3/EnJX7YKxjbxBRZ2EAksH+My0ToDn/h4haJMcHY93zY/DEqB5QKICVeWcw/M/fYeriXfjk+5MoOGuE1cZFFIk6ilruAjxNnaVJWgNoYEygvMUQkVPx0ajwyn1JGD8wEi+vKkBhWTU2FZ7HpsLzAIAAbzVSegRjZM8QpPQIQVK0Diolh9GJ2oMBqIvll1ShySYQrfdGt0BOgCaiKw3vHoz1s0bjaFkNth49jx0nL2BXUSWqG5rw3eFyfHe4eY5QgFaN5O5BGNEjGCk9gjGwWyA0anbsE90IBqAu9mPRRQDAsO7BMldCRM5MoVCgb2QA+kYG4KkxPWG1CRwqNWHHyQuXApG5CZsLz2NzSw+RVq3E0LhLgeiWuCD4aFQyfxMi58QA1MV2n25e/n549yCZKyEiV6JSKjAwRo+BMXopEB0+Z8Kuosrmx6lKVNZakHvyAnJPXgAAqJUKDIrRY0SPEKT0CEZy9yDovL1k/iZEzkEhPHhrYpPJBL1eD6PRCJ2u8/fkarLaMPj1b1FrsWLd86PRj/uAEVEHEULgxPka7GwJRDtPVsJganBoo1AASVE6qYdoePdghPhrZaqYqP064vebPUBd6PC5atRarAjwVqNPBPcAI6KOo1AokBAegITwAExOiYcQAmcu1rcEouYhs1MX6nCw1ISDpSYs/uEUACAh3F8KRCN6BCNKz7mJ5BkYgLrQ98ebx+lHdA/mnRtE1KkUCgVig30RG+yLXybHAADKTA2XhsyKKlFYVo3j5TU4Xl6Dz3cWAwBig30wonuIFIjiQ3y5YCu5JQagLrSlZaLiHX3DZK6EiDxRhM4b9w2Oxn2DowEAF2st+PHUpTlEBWeNKKmsR0nlGfxnzxkAQHiAFsN7BGNoXBCGxOrRP1oPby9OrCbXxwDURaobGpF3uvkOsNv7hMtcDREREOSnwbj+kRjXPxLApX9P2XuI9p2pQnm1GWv2n8Oa/ecAAEoFEOKvRZi/FqEB9v/VIMxfi7AAx9f1Pl5QsrebnBQDUBfZfuICmmwCPUL9EBfiK3c5RERXCPD2wh19w3FH3+b/SGtotGJvcRV2n2oOQ/klVaioseB8tRnnq83AuWufT61UIPSygBTaEpJCW0JSqP+l1wN9vTjURl2KAaiLrC8wAODwFxG5Dm8vFVJ7hSC1VwiA5jvNzlebUV5txvma5hBUIf2vBeerG1BRY0FFjRlVdY1osgkYTA1X3I3WGrVSgRB/TXM4sj8uC06XHwv203AeJd00BqAu0NBoxbcHmwPQvYOiZa6GiKh9FAoFwnXeCNd5X7etpcmGC7WthaTm8FTR8npFjQXG+uawVGYyo8xkvu65lQog2O/ysKS5rFfp0vOwgOaw5KXi6th0JQagLpBzuBy1Fiu6BfpgaFyg3OUQEXU6jVqJKL3PDd1Wbw9LFdXNvUfna1rCUcvzSw8LLtZZYBNo6WmyAKi+7vmDfL0QcnlQkobiLgtRLc+1ak7w9hQMQF3gn7mnAAATbonmGDcR0U+0JSw1WW2orLO0Go4q7L1LLcNwlbUWWG0CF+sacbGuEcfLr19LgLdaGnYLDXAckrMPw9knfvtq+BPqyvin18kKzhqxs6gSKqUCj4yMl7scIiKXplYpER7gjfCA6w/D2WwCF+ssUiCyD8VdqLU4DMHZjzVaBaobmlDd0ISTFbXXPb+vRtXqEFxYK0Ny/lo1/wPYyTAAdSIhBN5YdwQAkDEwiiusEhF1IaVSgRB/LUL8teiLa6++L4SAqb7p0vCbNE/psvBUcyk4mZtsqLNYUVxZh+LKuuvWolUrHcNSKz1MYS3P9T68I64rMAB1on/8cArbjldAo1JiTnpfucshIqKrUCgU0Pt6Qe/rhYRw/2u2FUKg1mK9rBfJMRw59CxVm1FrscLcZMPZqnqcraq/bi1eKgVC/JoDUohf89BbWMsQXPPrWoS0TAIP9tNAo+Yk7/Zw+QC0YMECvPXWWzAYDBg8eDDef/99jBgxQrZ6hBB4Y/0RHDVUY1PLys9z7+6L2GCu/UNE5A4UCgX8tWr4a9XoHup33fb1Fuulyd2tDMGdv6zHydTQhEbrjS8fAAB6H6/Llgm4FJqk55fNYQrgUJzEpQPQ8uXLMXv2bCxatAgpKSl45513kJ6ejsLCQoSHy7PaskKhwKo9Z1Fe3Xwr57N39MK023rIUgsREcnPR6OS9mW7HnOTFRdaeo8uSMNvFlyoaQlOP3lutQkY6xthrG/EyfPXn7ekUSsR6qdpGRrUXJrc3UqPU5CbLyGgEEIIuYtor5SUFAwfPhwffPABAMBmsyE2NhbPPfccXnrppeu+32QyQa/Xw2g0QqfTdVhd/7etCACQ2jMESdEdd14iIiI7W0v4aV5vyYILtVeGpoqWoHShxoIac1ObPyPQ16s5JPlpHO6Esw/HhV0WmrpyondH/H67bA+QxWJBXl4e5s+fL72mVCqRlpaG3NzcVt9jNpthNl9aZMtkMnVKbezxISKizqZUKhDkp0GQnwYJNzDo0dBolXqWpHWXLgtNl4enylozbAKoqmtEVV0jjt9APfaJ3lJIaulpenpMTwT7aW76+3Y0lw1AFRUVsFqtiIiIcHg9IiICR44cafU9WVlZeP3117uiPCIiIqfi7aVCTJAvYoKuPxRnswlU1TdKk7ovSD1KLeHpstB0oebaE72fuK17J32jm+OyAag95s+fj9mzZ0vPTSYTYmNjZayIiIjI+SiVCgT7aRDsp0GfiGsvIQBcmuhtn+B9eUgK9nW+3h/AhQNQaGgoVCoVysrKHF4vKytDZGRkq+/RarXQarVdUR4REZHHaMtEb2fhstO7NRoNkpOTkZOTI71ms9mQk5OD1NRUGSsjIiIiZ+eyPUAAMHv2bEyZMgXDhg3DiBEj8M4776C2thZTp06VuzQiIiJyYi4dgCZOnIjz58/jlVdegcFgwJAhQ7B+/forJkYTERERXc6l1wG6WZ21DhARERF1no74/XbZOUBERERE7cUARERERB6HAYiIiIg8DgMQEREReRwGICIiIvI4DEBERETkcRiAiIiIyOMwABEREZHHYQAiIiIij+PSW2HcLPsi2CaTSeZKiIiI6EbZf7dvZjMLjw5A1dXVAIDY2FiZKyEiIqK2qq6uhl6vb9d7PXovMJvNhtLSUgQEBEChUHTYeU0mE2JjY1FSUsI9xtqA1619eN3ah9etfXjd2ofXrX2udt2EEKiurkZ0dDSUyvbN5vHoHiClUomYmJhOO79Op+Nf9HbgdWsfXrf24XVrH1639uF1a5/Wrlt7e37sOAmaiIiIPA4DEBEREXkcBqBOoNVq8eqrr0Kr1cpdikvhdWsfXrf24XVrH1639uF1a5/OvG4ePQmaiIiIPBN7gIiIiMjjMAARERGRx2EAIiIiIo/DAEREREQehwGoEyxYsADdu3eHt7c3UlJSsGvXLrlLks3WrVtx3333ITo6GgqFAl999ZXDcSEEXnnlFURFRcHHxwdpaWk4duyYQ5vKykpMnjwZOp0OgYGBmDZtGmpqarrwW3S9rKwsDB8+HAEBAQgPD8eECRNQWFjo0KahoQGZmZkICQmBv78/HnzwQZSVlTm0KS4uRkZGBnx9fREeHo45c+agqampK79Kl1q4cCEGDRokLZqWmpqKdevWScd5za7vjTfegEKhwKxZs6TXeN1a99prr0GhUDg8EhMTpeO8bq07e/YsHnnkEYSEhMDHxwcDBw7E7t27peNd9rsgqEMtW7ZMaDQa8Y9//EMcPHhQPPXUUyIwMFCUlZXJXZos1q5dK373u9+JL7/8UgAQq1atcjj+xhtvCL1eL7766iuxb98+8fOf/1z06NFD1NfXS23uvvtuMXjwYLFjxw7x/fffi4SEBDFp0qQu/iZdKz09XSxevFgUFBSI/Px8cc8994i4uDhRU1MjtZk+fbqIjY0VOTk5Yvfu3WLkyJHi1ltvlY43NTWJAQMGiLS0NLF3716xdu1aERoaKubPny/HV+oSX3/9tVizZo04evSoKCwsFL/97W+Fl5eXKCgoEELwml3Prl27RPfu3cWgQYPE888/L73O69a6V199VfTv31+cO3dOepw/f146zut2pcrKShEfHy8ef/xxsXPnTnHy5EmxYcMGcfz4calNV/0uMAB1sBEjRojMzEzpudVqFdHR0SIrK0vGqpzDTwOQzWYTkZGR4q233pJeq6qqElqtVnzxxRdCCCEOHTokAIgff/xRarNu3TqhUCjE2bNnu6x2uZWXlwsAYsuWLUKI5uvk5eUlVq5cKbU5fPiwACByc3OFEM3hU6lUCoPBILVZuHCh0Ol0wmw2d+0XkFFQUJD45JNPeM2uo7q6WvTu3VtkZ2eL22+/XQpAvG5X9+qrr4rBgwe3eozXrXXz5s0Tt91221WPd+XvAofAOpDFYkFeXh7S0tKk15RKJdLS0pCbmytjZc6pqKgIBoPB4Xrp9XqkpKRI1ys3NxeBgYEYNmyY1CYtLQ1KpRI7d+7s8prlYjQaAQDBwcEAgLy8PDQ2Njpcu8TERMTFxTlcu4EDByIiIkJqk56eDpPJhIMHD3Zh9fKwWq1YtmwZamtrkZqaymt2HZmZmcjIyHC4PgD/rl3PsWPHEB0djZ49e2Ly5MkoLi4GwOt2NV9//TWGDRuGX/3qVwgPD8ctt9yCjz/+WDrelb8LDEAdqKKiAlar1eEvMwBERETAYDDIVJXzsl+Ta10vg8GA8PBwh+NqtRrBwcEec01tNhtmzZqFUaNGYcCAAQCar4tGo0FgYKBD259eu9aurf2Yuzpw4AD8/f2h1Woxffp0rFq1CklJSbxm17Bs2TLs2bMHWVlZVxzjdbu6lJQULFmyBOvXr8fChQtRVFSE0aNHo7q6mtftKk6ePImFCxeid+/e2LBhA2bMmIHf/OY3WLp0KYCu/V3w6N3giVxBZmYmCgoKsG3bNrlLcQl9+/ZFfn4+jEYj/v3vf2PKlCnYsmWL3GU5rZKSEjz//PPIzs6Gt7e33OW4lPHjx0v/PGjQIKSkpCA+Ph4rVqyAj4+PjJU5L5vNhmHDhuHPf/4zAOCWW25BQUEBFi1ahClTpnRpLewB6kChoaFQqVRXzPIvKytDZGSkTFU5L/s1udb1ioyMRHl5ucPxpqYmVFZWesQ1nTlzJlavXo1NmzYhJiZGej0yMhIWiwVVVVUO7X967Vq7tvZj7kqj0SAhIQHJycnIysrC4MGD8e677/KaXUVeXh7Ky8sxdOhQqNVqqNVqbNmyBe+99x7UajUiIiJ43W5QYGAg+vTpg+PHj/Pv21VERUUhKSnJ4bV+/fpJQ4dd+bvAANSBNBoNkpOTkZOTI71ms9mQk5OD1NRUGStzTj169EBkZKTD9TKZTNi5c6d0vVJTU1FVVYW8vDypzcaNG2Gz2ZCSktLlNXcVIQRmzpyJVatWYePGjejRo4fD8eTkZHh5eTlcu8LCQhQXFztcuwMHDjj8iyI7Oxs6ne6KfwG5M5vNBrPZzGt2FWPHjsWBAweQn58vPYYNG4bJkydL/8zrdmNqampw4sQJREVF8e/bVYwaNeqKJT2OHj2K+Ph4AF38u9D2Odx0LcuWLRNarVYsWbJEHDp0SDz99NMiMDDQYZa/J6murhZ79+4Ve/fuFQDEX//6V7F3715x+vRpIUTz7Y6BgYHiv//9r9i/f7+4//77W73d8ZZbbhE7d+4U27ZtE71793b72+BnzJgh9Hq92Lx5s8MttnV1dVKb6dOni7i4OLFx40axe/dukZqaKlJTU6Xj9ltsx40bJ/Lz88X69etFWFiYW99i+9JLL4ktW7aIoqIisX//fvHSSy8JhUIhvv32WyEEr9mNuvwuMCF43a7mxRdfFJs3bxZFRUXihx9+EGlpaSI0NFSUl5cLIXjdWrNr1y6hVqvF//7v/4pjx46Jzz77TPj6+opPP/1UatNVvwsMQJ3g/fffF3FxcUKj0YgRI0aIHTt2yF2SbDZt2iQAXPGYMmWKEKL5lsff//73IiIiQmi1WjF27FhRWFjocI4LFy6ISZMmCX9/f6HT6cTUqVNFdXW1DN+m67R2zQCIxYsXS23q6+vFs88+K4KCgoSvr6/4xS9+Ic6dO+dwnlOnTonx48cLHx8fERoaKl588UXR2NjYxd+m6zzxxBMiPj5eaDQaERYWJsaOHSuFHyF4zW7UTwMQr1vrJk6cKKKiooRGoxHdunUTEydOdFjPhtetdd98840YMGCA0Gq1IjExUXz00UcOx7vqd0EhhBBt7MEiIiIicmmcA0REREQehwGIiIiIPA4DEBEREXkcBiAiIiLyOAxARERE5HEYgIiIiMjjMAARERGRx2EAIiIiIo/DAEREREQehwGIiIiIPA4DEBEREXkcBiAiIiLyOP8fO9OiBfJr0sUAAAAASUVORK5CYII=", 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" ] @@ -543,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 505, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -552,7 +574,7 @@ "318.99999999906896" ] }, - "execution_count": 505, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -563,100 +585,9 @@ }, { "cell_type": "code", - "execution_count": 506, + "execution_count": 63, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", 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NC5j97ps5ykyKtrgyAOg+hBHAIgfLqvXdv2zRZyVVkqS7pl6iB68ZrHCWcwcQZAgjgAXe3VmiB1/+VNWOBvWMidBv5ozR1MG9rC4LACxBGAG6kctt9MR7e/R/Hx2QJE3sn6TffytHKfGRFlcGANYhjADd5HRNve5fulUf7zspSZp3aX89fN0wLssACHqEEaAbbD9Sobte3Kyj5bWKCg/VL28apRvH9La6LADwCYQRoIu9se2YHnz5Uzka3OrfM1qL/22chqbFW10WAPgMwgjQRYwx+v0H+/XEyr2SpCuG9NJT38xRQlS4xZUBgG8hjABdoM7p0o9e26YVBcckSbd/JUsPXzdModxpFwC+hDACeFlZlUP/8edN2lJUrrAQmx67caS+Namv1WUBgM8ijABeVHiyRnOf26Di07WKjwzT4m+P05SByVaXBQA+jTACeMmnxeWat+SfOl1Tr349o/X8bRM0oFes1WUBgM8jjABesHpvme5+cbPO1rs0qneCnp83QcmxdqvLAgC/QBgBLtKyrUf0g1e2qcFtdNmgZC369jjF2vnTAoD24h0TuAgv5B/SIyt2SpJuHJOhx/81WxFhrKgKAB1BGAE66dmPD+q/3twtSfrOpVn6yaxhCmHqLgB0GGEE6ISnP9yvx9/dI0maf8Ul+v41Q2SzEUQAoDMII0AHGGP05Mq9+t0H+yVJC64erPuuGmRxVQDg3wgjQAf87wf7PUFk4cyh+o+pl1hcEQD4P8II0E7PfnxQv2m6z8xPZg3THZcNsLgiAAgMDPsH2uGlDUWewaoPXj2YIAIAXkQYAS5g2dYj+vHy7ZKku6ZeonuuHGhxRQAQWAgjwHl8tOeEvv/KNhkjzc3tpx9dy6wZAPA2wgjQhh1HKzT/L1vkcht9Lae3fnb9CIIIAHQBwgjQiqPltfrOkn+qpt6lKZf01P/cNJoFzQCgixBGgC+oqHVq3vMbdaLKoSGpcVr8b+NY4h0AuhDvsMA5XG6j+/66VXtLq5USZ9fz8yYoPjLc6rIAIKARRoBz/Pq9PVq9t0yR4SF67rYJykiMsrokAAh4HQojeXl5mjBhguLi4pSSkqLZs2drz549FzzvlVde0dChQxUZGalRo0bprbfe6nTBQFd5Y9sxLfrogCTpV/+arZG9EyyuCACCQ4fCyOrVqzV//nx98sknWrlypZxOp6655hrV1NS0ec769et188036/bbb9fWrVs1e/ZszZ49Wzt27Ljo4gFv+aykUj94ZZsk6T8uH6AbsjMsrggAgofNGGM6e3JZWZlSUlK0evVqXX755a0eM2fOHNXU1OiNN97wbJs8ebLGjBmjxYsXt+t5KisrlZCQoIqKCsXHx3e2XKBVZ+sb9NX/XauDZTW6bFCylsybqFBmzgDARWvv5/dFjRmpqKiQJCUlJbV5TH5+vqZPn95i24wZM5Sfn9/mOQ6HQ5WVlS0eQFd5dMVOHSyrUVp8pH77zRyCCAB0s06HEbfbrQceeECXXnqpRo4c2eZxJSUlSk1NbbEtNTVVJSUlbZ6Tl5enhIQEzyMzM7OzZQLntaLgqF7ZfEQhNumpb45RUkyE1SUBQNDpdBiZP3++duzYoaVLl3qzHknSwoULVVFR4XkUFxd7/TmAw6dq9ONljWOX7rlykCYP6GlxRQAQnMI6c9I999yjN954Q2vWrFGfPn3Oe2xaWppKS0tbbCstLVVaWlqb59jtdtnt9s6UBrSLy2304MufqtrRoIn9k3QfN78DAMt0qGfEGKN77rlHy5Yt0wcffKCsrKwLnpObm6tVq1a12LZy5Url5uZ2rFLAi/60/pA2HT6jmIhQPfGNbIWFsuQOAFilQz0j8+fP10svvaQVK1YoLi7OM+4jISFBUVGNi0PNnTtXvXv3Vl5eniTp/vvv19SpU/XEE09o1qxZWrp0qTZt2qRnnnnGyy8FaJ9DJ2v0q3c/kyQtvG6YMpOiLa4IAIJbh/53cNGiRaqoqNC0adOUnp7uefztb3/zHFNUVKTjx497vp8yZYpeeuklPfPMM8rOztarr76q5cuXn3fQK9BV3G6jH762TXVOt6Zc0lPfmtjX6pIAIOhd1Doj3YV1RuAtf/7ksH66fIeiI0L17gOX0ysCAF2oW9YZAfzJyWqHHn+n8fLMD2cMIYgAgI8gjCBo/M/bn6myrkEjMuL1b7n9rS4HANCEMIKgsPnwGb2y+Ygk6bEbR7LKKgD4EMIIAp7bbfTIisbFzb4xvo/G9ethcUUAgHMRRhDwlhcc1c5jlYqLDNOPrh1qdTkAgC8gjCCg1TldeuK9vZKku6ddop6xrOwLAL6GMIKA9kL+IR0tr1V6QqS+c+mFVwwGAHQ/wggCVsVZp57+8IAk6XtXD1ZkeKjFFQEAWkMYQcD648cHVVHr1JDUON009vw3dAQAWIcwgoBUcdapJesPSWrsFWEqLwD4LsIIAtJz6wpV7WjQ0LQ4XTM81epyAADnQRhBwKmsc+r5dYWSpHuvHKQQekUAwKcRRhBwXlh/SJV1DRqYEquZI9OsLgcAcAGEEQSUOqdL/29tc6/IQHpFAMAPEEYQUJZtPaozZ53qnRilWaPSrS4HANAOhBEEDGOMnmvqFZl3aX+FhfLrDQD+gHdrBIw1+05q34lqxUSE6hsTMq0uBwDQToQRBIzmsSLfmJCp+Mhwi6sBALQXYQQBYV9pldbsLZPNJs2bwj1oAMCfEEYQEP6yoUiSNH1Yqvr2jLa4GgBARxBG4PfqnC4t23pUkvStSX0trgYA0FGEEfi9d3aUqKK2cTrv5YN6WV0OAKCDCCPwe3/d2HiJ5uvj+3BDPADwQ4QR+LWDZdXaUHhaITbpG+OZzgsA/ogwAr/2t38WS5KmDu6ljMQoi6sBAHQGYQR+y+U2er1p4OqcCQxcBQB/RRiB3/rk4CmVVTmUGB2uK4emWF0OAKCTCCPwWysKGntFrhuVrogwfpUBwF/xDg6/VOd06e0dJZKkG7MzLK4GAHAxCCPwSx/tKVNVXYPSEyI1oX+S1eUAAC4CYQR+6e+fNl6iuSE7QyGsLQIAfo0wAr9TVefU+7tPSJJuGMMlGgDwd4QR+J33d5eqvsGtS3rFaHh6vNXlAAAuEmEEfue9naWSGmfR2GxcogEAf0cYgV+pc7r00Z4ySdKMEWkWVwMA8AbCCPzK2n0nVet0qXdilEZkcIkGAAIBYQR+5d2djWuLXD08lUs0ABAgCCPwGw0ut97f3The5JoRqRZXAwDwFsII/Mamw2d05qxTidHhmshCZwAQMAgj8BvNs2iuGpqqsFB+dQEgUPCODr/xwWeNYeTq4VyiAYBAQhiBXzh0skaHTp1VeKhNXxmUbHU5AAAvIozAL6zZ17i2yPh+SYq1h1lcDQDAmwgj8AvNC51NHdLL4koAAN5GGIHPq3O6lH/glCRp6mDCCAAEGsIIfN6mQ2dU63QpJc6uoWlxVpcDAPAywgh83uq9JyQ19oqw6ioABB7CCHze6r2MFwGAQNbhMLJmzRpdf/31ysjIkM1m0/Lly897/EcffSSbzfalR0lJSWdrRhA5XlGrvaXVCrFJXxnIlF4ACEQdDiM1NTXKzs7W008/3aHz9uzZo+PHj3seKSkpHX1qBKF1+xsHro7uk6jE6AiLqwEAdIUOL9gwc+ZMzZw5s8NPlJKSosTExA6fh+DWPIsm95KeFlcCAOgq3TZmZMyYMUpPT9fVV1+tdevWnfdYh8OhysrKFg8EH2OMPjnYFEYGEEYAIFB1eRhJT0/X4sWL9dprr+m1115TZmampk2bpi1btrR5Tl5enhISEjyPzMzMri4TPujImVodLa9VWIhN4/v3sLocAEAX6fJ1tYcMGaIhQ4Z4vp8yZYoOHDigJ598Un/+859bPWfhwoVasGCB5/vKykoCSRBqvkSTnZmo6AiWgAeAQGXJO/zEiRO1du3aNvfb7XbZ7fZurAi+KJ9LNAAQFCxZZ6SgoEDp6elWPDX8hDGGwasAECQ63DNSXV2t/fv3e74vLCxUQUGBkpKS1LdvXy1cuFBHjx7VCy+8IEl66qmnlJWVpREjRqiurk7PPvusPvjgA7333nveexUIOIdOnVVJZZ3CQ20a25fxIgAQyDocRjZt2qQrrrjC833z2I5bb71VS5Ys0fHjx1VUVOTZX19frwcffFBHjx5VdHS0Ro8erffff7/FzwC+qHkWTU5mD0VFhFpcDQCgK9mMMcbqIi6ksrJSCQkJqqioUHx8vNXloBvcv3SrVhQc031XDdKCqwdbXQ4AoBPa+/nNvWngkzYdOiNJmpSVZHElAICuRhiBzzle0bi+SIhNGpOZaHU5AIAuRhiBz2nuFRmWHq8YO+uLAECgI4zA52w+3BhGxvdjFg0ABAPCCHxOcxgZ15/xIgAQDAgj8Ck1jgbtOt54Y0R6RgAgOBBG4FM+PVIul9soPSFSGYlRVpcDAOgGhBH4lM1Ng1fH0SsCAEGDMAKfsonBqwAQdAgj8Blut9GWoqYwwuBVAAgahBH4jP1l1aqqa1B0RKiGpsVZXQ4AoJsQRuAzCorLJUkjeycoLJRfTQAIFrzjw2dsO1IuiSXgASDYEEbgMz4trpAkje6TYHElAIDuRBiBT3A0uPRZSeNiZ9l9Eq0tBgDQrQgj8Am7j1fJ6TJKiolQnx4sdgYAwYQwAp/wadPg1dF9EmSz2awtBgDQrQgj8AmfNg1e5RINAAQfwgh8wrYjjYNXszMZvAoAwYYwAstV1Tl1oKxakjSanhEACDqEEVhu+9EKGSP1ToxScqzd6nIAAN2MMALLcYkGAIIbYQSW+3wmTaKldQAArEEYgeU8PSOEEQAISoQRWOpktUNHy2tls0mjWAYeAIISYQSW2nWscQn4rJ4xirWHWVwNAMAKhBFYavfxxjAyLCPe4koAAFYhjMBSu5rCyPB0wggABCvCCCzl6RlJj7O4EgCAVQgjsEyd06UDZTWSpOHpDF4FgGBFGIFl9pVWy+U26hEdrtR4Vl4FgGBFGIFlmi/RDM+Il81ms7gaAIBVCCOwTPPg1WFpDF4FgGBGGIFlPGGEmTQAENQII7CEMabFZRoAQPAijMASR87UqqquQeGhNl3SK9bqcgAAFiKMwBLNvSIDU+IUEcavIQAEMz4FYAlWXgUANCOMwBKsvAoAaEYYgSV2H6+SxOBVAABhBBaoqnOq6PRZSVymAQAQRmCBz0oae0XSEyKVGB1hcTUAAKsRRtDtdh1j8CoA4HOEEXS73ay8CgA4B2EE3Y4wAgA4F2EE3arB5faMGWEmDQBAIoygmx06VSNHg1vREaHqlxRtdTkAAB9AGEG32tk0eHVIWpxCQmwWVwMA8AWEEXQrz2JnjBcBADTpcBhZs2aNrr/+emVkZMhms2n58uUXPOejjz7S2LFjZbfbNXDgQC1ZsqQTpSIQMHgVAPBFHQ4jNTU1ys7O1tNPP92u4wsLCzVr1ixdccUVKigo0AMPPKA77rhD7777boeLhf/z3CCPwasAgCZhHT1h5syZmjlzZruPX7x4sbKysvTEE09IkoYNG6a1a9fqySef1IwZMzr69PBjZVUOlVU5ZLNJQ9O4QR4AoFGXjxnJz8/X9OnTW2ybMWOG8vPz2zzH4XCosrKyxQP+r/kSTf+eMYqO6HAOBgAEqC4PIyUlJUpNTW2xLTU1VZWVlaqtrW31nLy8PCUkJHgemZmZXV0mukFzGGHwKgDgXD45m2bhwoWqqKjwPIqLi60uCV7w+eBVLtEAAD7X5X3laWlpKi0tbbGttLRU8fHxioqKavUcu90uu93e1aWhm+1iJg0AoBVd3jOSm5urVatWtdi2cuVK5ebmdvVTw4fUOV06UFYjiZk0AICWOhxGqqurVVBQoIKCAkmNU3cLCgpUVFQkqfESy9y5cz3H33XXXTp48KB++MMf6rPPPtP//d//6eWXX9b3vvc977wC+IV9pdVyuY0So8OVFh9pdTkAAB/S4TCyadMm5eTkKCcnR5K0YMEC5eTk6JFHHpEkHT9+3BNMJCkrK0tvvvmmVq5cqezsbD3xxBN69tlnmdYbZM4dvGqzsQw8AOBzHR4zMm3aNBlj2tzf2uqq06ZN09atWzv6VAggjBcBALTFJ2fTIPAQRgAAbSGMoMsZY1hjBADQJsIIutzR8lpV1TUoPNSmgSmxVpcDAPAxhBF0uV3HGntFLukVq4gwfuUAAC3xyYAut/t4lSTWFwEAtI4wgi6363iFJMaLAABaRxhBl2vuGWEmDQCgNYQRdKmqOqeKTp+VRBgBALSOMIIu9VlJY69IWnykkmIiLK4GAOCLCCPoUp71RRi8CgBoA2EEXap5Wu+w9DiLKwEA+CrCCLrUbpaBBwBcAGEEXabB5faMGWFaLwCgLYQRdJlDp2rkaHArKjxU/XrGWF0OAMBHEUbQZXY1rS8yJC1OoSE2i6sBAPgqwgi6TPPgVWbSAADOhzCCLsPgVQBAexBG0GU8a4wQRgAA50EYQZcoq3LoRJVDNps0NI01RgAAbSOMoEvsauoVyeoZoxh7mMXVAAB8GWEEXcKz8iqDVwEAF0AYQZdo7hkZQRgBAFwAYQRdYuexCkkMXgUAXBhhBF53tr5BhSdrJEkjMhIsrgYA4OsII/C63cerZIzUK86uXnF2q8sBAPg4wgi8jvEiAICOIIzA63YxXgQA0AGEEXhd87RexosAANqDMAKvanC59VlJ4916uUEeAKA9CCPwqoMna+RocCsmIlT9kqKtLgcA4AcII/Aqz8qr6fEKCbFZXA0AwB8QRuBVzYudMZMGANBehBF4VfO0XsaLAADaizACrzHGaGfTZZrh6cykAQC0D2EEXnPkTK3KzzoVHmrT4LRYq8sBAPgJwgi8ZtuRxvEiw9LjZQ8LtbgaAIC/IIzAa7YdKZckjerNJRoAQPsRRuA1zT0j2X0SrS0EAOBXCCPwCrfbaMfRxjAyqg89IwCA9iOMwCsKT9WoytGgyPAQDUph8CoAoP0II/CK5vEiIzISFBbKrxUAoP341IBXNI8XGc0lGgBABxFG4BXbCSMAgE4ijOCiNbjc2tF0T5pRvROtLQYA4HcII7ho+8uqVed0K9YepgHJMVaXAwDwM4QRXLRtxY29IiN7xyskxGZxNQAAf0MYwUXbUnRGkpTTt4fFlQAA/BFhBBdt8+HGMDKOMAIA6IROhZGnn35a/fv3V2RkpCZNmqSNGze2eeySJUtks9laPCIjIztdMHxLxVmn9p2oliTl9E20thgAgF/qcBj529/+pgULFujRRx/Vli1blJ2drRkzZujEiRNtnhMfH6/jx497HocPH76oouE7thY39opkJceoZ6zd4moAAP6ow2HkN7/5je68807NmzdPw4cP1+LFixUdHa3nnnuuzXNsNpvS0tI8j9TU1IsqGr5jS9MlmrFcogEAdFKHwkh9fb02b96s6dOnf/4DQkI0ffp05efnt3ledXW1+vXrp8zMTN14443auXPneZ/H4XCosrKyxQO+aXPT4NVx/QgjAIDO6VAYOXnypFwu15d6NlJTU1VSUtLqOUOGDNFzzz2nFStW6MUXX5Tb7daUKVN05MiRNp8nLy9PCQkJnkdmZmZHykQ3aXC5VVBULokwAgDovC6fTZObm6u5c+dqzJgxmjp1ql5//XX16tVLf/jDH9o8Z+HChaqoqPA8iouLu7pMdMKe0irV1LsUZw/jTr0AgE4L68jBycnJCg0NVWlpaYvtpaWlSktLa9fPCA8PV05Ojvbv39/mMXa7XXY7gyF9XfOU3jF9E1nsDADQaR3qGYmIiNC4ceO0atUqzza3261Vq1YpNze3XT/D5XJp+/btSk9P71il8Dn5B05JkiYP6GlxJQAAf9ahnhFJWrBggW699VaNHz9eEydO1FNPPaWamhrNmzdPkjR37lz17t1beXl5kqTHHntMkydP1sCBA1VeXq7HH39chw8f1h133OHdV4Ju5XYbbSg8LYkwAgC4OB0OI3PmzFFZWZkeeeQRlZSUaMyYMXrnnXc8g1qLiooUEvJ5h8uZM2d05513qqSkRD169NC4ceO0fv16DR8+3HuvAt1u74kqna6pV1R4qEb3SbC6HACAH7MZY4zVRVxIZWWlEhISVFFRofj4eKvLgaQl6wr1s3/s0mWDkvXn2ydZXQ4AwAe19/Obe9OgU/IPNo4Xyb2ESzQAgItDGEGHMV4EAOBNhBF02GclVSo/61RMRKhG9Wa8CADg4hBG0GHr9p+UJI3vn6TwUH6FAAAXh08SdNiHexrv0Dx1cC+LKwEABALCCDqk2tGgfx5qHC9yxdAUi6sBAAQCwgg6ZP3+k3K6jPr1jFZWcozV5QAAAgBhBB3y4Z4ySdI0LtEAALyEMIJ2M8ZoddN4kWlcogEAeAlhBO2270S1jlXUyR4WolzWFwEAeAlhBO22cleppMZVVyPDQy2uBgAQKAgjaLe3th+XJM0cmWZxJQCAQEIYQbscPlWjnccqFRpi09XDCSMAAO8hjKBd3t5RIkmaPCBJSTERFlcDAAgkhBG0y5vbmi/RpFtcCQAg0BBGcEH7Squ0/WiFwkJsupbxIgAALyOM4IJe23JUkjRtSC8lx9otrgYAEGgIIzgvl9to2dYjkqSbxvaxuBoAQCAijOC81uwrU2mlQwlR4bpyGKuuAgC8jzCC8/pz/mFJ0r+M7S17GAudAQC8jzCCNh0+VaMPm+5FMze3v7XFAAACFmEEbXoh/7CMkaYO7qWs5BirywEABCjCCFp1pqZeSzcWSZJum9Lf2mIAAAGNMIJWPbeuUDX1Lg1Pj9e0Ib2sLgcAEMAII/iSilqnlqw7JEm698qBstls1hYEAAhohBF8ydMf7leVo0GDU2M1YwQrrgIAuhZhBC0cOlmj59cVSpIWXjdMISH0igAAuhZhBB7GGD32xi45XUaXD+6lK4awyBkAoOsRRuDx+paj+uCzE4oIDdFPZw2zuhwAQJAgjECSdLS8Vj//x05J0v3TB2lQapzFFQEAggVhBKpzunT3i5tVWdeg7D4J+o/LB1hdEgAgiIRZXUAwM8ao2tGg8rNOVdQ6PV8rap2qrHPK5TZyu42MpBCbFGsPU2xkuGLtYeoRHa70hCilJtgv6p4xbrfRw69v17YjFUqMDtfvvzVWYaFkVABA9yGMdIEzNfX648cHtfnwGcVFhinWHqYGt5HT5Va1o0Gnqut15my9ztQ4Ve9yX/TzJcdG6JJesRqRkaCRveM1oX+SMpOiL3ieMUb//dZuvb71qEJDbPrtN3PadR4AAN5EGPGiilqn/t/aQj23tlDVjoZ2n2cPC1FidLgSoyKUEBWu+KhwxUeFKTwkRCEhkmST221UXd+g6roGVdU5deasU8fKa+VocOtkdb1OVp/WhsLTnp/ZNylalw7sqcsH9dLUIb0UHdHyP7XbbfTzf+zUn5ruyvurm0Zr6mBWWgUAdD/CiBecqKzTixuK9Kf1h1RR65QkDU+P1y2T+yrEZlN1XYPCQm2KCAtRTESYkmIilBQToR4xEUqKjlBUROcusxhjVH7WqaPltdp9vFI7j1Vq25FyfXqkQkWnz6po41n9dWOxIsNDNG1wimaOStNVw1Llchl9/9VPtXJXqWw26bEbR+qmcX282SQAALSbzRhjrC7iQiorK5WQkKCKigrFx8dbXY7HrmOV+uPHB/XGtmNyuhqbcVBKrBZcPVgzRqRZtmBYVZ1TGwtPa+3+k3p/d6mKT9d69kWFhyouMkwnqhyKCA3R418frRvH9LakTgBAYGvv5zdhpJOeWXNAeW9/pubWG9+vh267tL9mjkxXqA+tWmqM0c5jlXpnR4ne3H5chSdrJEmZSVH6v2+N06g+CRZXCAAIVISRLlR8+qyuemK16l1uzRiRqvlXDNToPolWl3VBxhgVFJdr25EKzc7prYSocKtLAgAEsPZ+fjNmpBOeeG+P6l1uXTqwpxZ/e5zf3NXWZrMpp28P5fTtYXUpAAB4sKBEB+04WqHlBcckSQ9dO8xvgggAAL6KMNJBv3p3jyTphuwMxlsAAOAFhJEOWLf/pNbsLVN4qE3fv2aI1eUAABAQCCPt5HS59V9v7pYk3TKpn/r2ZKVSAAC8gTDSTs9+XKjdxyuVGB2ue68caHU5AAAEDMJIO+wrrdJT7++VJP1k1nD1jLVbXBEAAIGDMHIBNY4G3fXiZjka3Lp8cC/dNJbVSgEA8CbCyHm43Ebff+VTHSirUWq8Xb/5RjZTeQEA8DLCSBuMMXr07zv09o4ShYfa9PtvjVUyl2cAAPC6ToWRp59+Wv3791dkZKQmTZqkjRs3nvf4V155RUOHDlVkZKRGjRqlt956q1PFdheny60fvrpNL35SJJtNenLOGE3on2R1WQAABKQOh5G//e1vWrBggR599FFt2bJF2dnZmjFjhk6cONHq8evXr9fNN9+s22+/XVu3btXs2bM1e/Zs7dix46KL7woVtU59Z8k/9crmIwqxSf/zL6P11dEZVpcFAEDA6vCN8iZNmqQJEybo97//vSTJ7XYrMzNT9957rx566KEvHT9nzhzV1NTojTfe8GybPHmyxowZo8WLF7frObvjRnnGGL23q1Q/+/tOHa+oU1R4qJ6+JUdXDk3tkucDACDQdcmN8urr67V582YtXLjQsy0kJETTp09Xfn5+q+fk5+drwYIFLbbNmDFDy5cvb/N5HA6HHA6H5/vKysqOlNluz60tVOHJGlXUOlVQXK6i02clSf17Ruv33xqrkb1Z7h0AgK7WoTBy8uRJuVwupaa27C1ITU3VZ5991uo5JSUlrR5fUlLS5vPk5eXp5z//eUdK65R/bDumrUXlnu9jIkJ165T+mn/FQMXYuaExAADdwSc/cRcuXNiiN6WyslKZmZlef56bxvbRZQOTFRcZrr49o/WVgcmEEAAAulmHPnmTk5MVGhqq0tLSFttLS0uVlpbW6jlpaWkdOl6S7Ha77Paun0b77cn9uvw5AADA+XVoNk1ERITGjRunVatWeba53W6tWrVKubm5rZ6Tm5vb4nhJWrlyZZvHAwCA4NLhaxILFizQrbfeqvHjx2vixIl66qmnVFNTo3nz5kmS5s6dq969eysvL0+SdP/992vq1Kl64oknNGvWLC1dulSbNm3SM888491XAgAA/FKHw8icOXNUVlamRx55RCUlJRozZozeeecdzyDVoqIihYR83uEyZcoUvfTSS/rJT36ihx9+WIMGDdLy5cs1cuRI770KAADgtzq8zogVumOdEQAA4F3t/fzm3jQAAMBShBEAAGApwggAALAUYQQAAFiKMAIAACxFGAEAAJYijAAAAEsRRgAAgKUIIwAAwFIdXg7eCs2LxFZWVlpcCQAAaK/mz+0LLfbuF2GkqqpKkpSZmWlxJQAAoKOqqqqUkJDQ5n6/uDeN2+3WsWPHFBcXJ5vN5rWfW1lZqczMTBUXF3PPmy+gbVpHu7SNtmkd7dI22qZtgdI2xhhVVVUpIyOjxU10v8gvekZCQkLUp0+fLvv58fHxfv0fuyvRNq2jXdpG27SOdmkbbdO2QGib8/WINGMAKwAAsBRhBAAAWCqow4jdbtejjz4qu91udSk+h7ZpHe3SNtqmdbRL22ibtgVb2/jFAFYAABC4grpnBAAAWI8wAgAALEUYAQAAliKMAAAASwV1GHn66afVv39/RUZGatKkSdq4caPVJXWpNWvW6Prrr1dGRoZsNpuWL1/eYr8xRo888ojS09MVFRWl6dOna9++fS2OOX36tG655RbFx8crMTFRt99+u6qrq7vxVXhfXl6eJkyYoLi4OKWkpGj27Nnas2dPi2Pq6uo0f/589ezZU7GxsbrppptUWlra4piioiLNmjVL0dHRSklJ0Q9+8AM1NDR050vxukWLFmn06NGehZdyc3P19ttve/YHa7t80S9/+UvZbDY98MADnm3B2jY/+9nPZLPZWjyGDh3q2R+s7SJJR48e1be//W317NlTUVFRGjVqlDZt2uTZH6zvwZIkE6SWLl1qIiIizHPPPWd27txp7rzzTpOYmGhKS0utLq3LvPXWW+bHP/6xef31140ks2zZshb7f/nLX5qEhASzfPly8+mnn5obbrjBZGVlmdraWs8x1157rcnOzjaffPKJ+fjjj83AgQPNzTff3M2vxLtmzJhhnn/+ebNjxw5TUFBgrrvuOtO3b19TXV3tOeauu+4ymZmZZtWqVWbTpk1m8uTJZsqUKZ79DQ0NZuTIkWb69Olm69at5q233jLJyclm4cKFVrwkr/n73/9u3nzzTbN3716zZ88e8/DDD5vw8HCzY8cOY0zwtsu5Nm7caPr3729Gjx5t7r//fs/2YG2bRx991IwYMcIcP37c8ygrK/PsD9Z2OX36tOnXr5+57bbbzIYNG8zBgwfNu+++a/bv3+85Jljfg40xJmjDyMSJE838+fM937tcLpORkWHy8vIsrKr7fDGMuN1uk5aWZh5//HHPtvLycmO3281f//pXY4wxu3btMpLMP//5T88xb7/9trHZbObo0aPdVntXO3HihJFkVq9ebYxpbIfw8HDzyiuveI7ZvXu3kWTy8/ONMY1BLyQkxJSUlHiOWbRokYmPjzcOh6N7X0AX69Gjh3n22WdpF2NMVVWVGTRokFm5cqWZOnWqJ4wEc9s8+uijJjs7u9V9wdwuP/rRj8xXvvKVNvcH+3twUF6mqa+v1+bNmzV9+nTPtpCQEE2fPl35+fkWVmadwsJClZSUtGiThIQETZo0ydMm+fn5SkxM1Pjx4z3HTJ8+XSEhIdqwYUO319xVKioqJElJSUmSpM2bN8vpdLZom6FDh6pv374t2mbUqFFKTU31HDNjxgxVVlZq586d3Vh913G5XFq6dKlqamqUm5tLu0iaP3++Zs2a1aINJH5n9u3bp4yMDA0YMEC33HKLioqKJAV3u/z973/X+PHj9fWvf10pKSnKycnRH//4R8/+YH8PDsowcvLkSblcrha/7JKUmpqqkpISi6qyVvPrPl+blJSUKCUlpcX+sLAwJSUlBUy7ud1uPfDAA7r00ks1cuRISY2vOyIiQomJiS2O/WLbtNZ2zfv82fbt2xUbGyu73a677rpLy5Yt0/Dhw4O+XZYuXaotW7YoLy/vS/uCuW0mTZqkJUuW6J133tGiRYtUWFioyy67TFVVVUHdLgcPHtSiRYs0aNAgvfvuu7r77rt133336U9/+pMk3oP94q69QHeZP3++duzYobVr11pdis8YMmSICgoKVFFRoVdffVW33nqrVq9ebXVZliouLtb999+vlStXKjIy0upyfMrMmTM9/x49erQmTZqkfv366eWXX1ZUVJSFlVnL7XZr/Pjx+sUvfiFJysnJ0Y4dO7R48WLdeuutFldnvaDsGUlOTlZoaOiXRnCXlpYqLS3Noqqs1fy6z9cmaWlpOnHiRIv9DQ0NOn36dEC02z333KM33nhDH374ofr06ePZnpaWpvr6epWXl7c4/ott01rbNe/zZxERERo4cKDGjRunvLw8ZWdn67e//W1Qt8vmzZt14sQJjR07VmFhYQoLC9Pq1av1u9/9TmFhYUpNTQ3atvmixMREDR48WPv37w/q35n09HQNHz68xbZhw4Z5LmEF+3twUIaRiIgIjRs3TqtWrfJsc7vdWrVqlXJzcy2szDpZWVlKS0tr0SaVlZXasGGDp01yc3NVXl6uzZs3e4754IMP5Ha7NWnSpG6v2VuMMbrnnnu0bNkyffDBB8rKymqxf9y4cQoPD2/RNnv27FFRUVGLttm+fXuLN4qVK1cqPj7+S29A/s7tdsvhcAR1u1x11VXavn27CgoKPI/x48frlltu8fw7WNvmi6qrq3XgwAGlp6cH9e/MpZde+qUlA/bu3at+/fpJCu73YEnBPbXXbrebJUuWmF27dpl///d/N4mJiS1GcAeaqqoqs3XrVrN161YjyfzmN78xW7duNYcPHzbGNE4rS0xMNCtWrDDbtm0zN954Y6vTynJycsyGDRvM2rVrzaBBg/x+Wtndd99tEhISzEcffdRiOuLZs2c9x9x1112mb9++5oMPPjCbNm0yubm5Jjc317O/eTriNddcYwoKCsw777xjevXq5ffTER966CGzevVqU1hYaLZt22YeeughY7PZzHvvvWeMCd52ac25s2mMCd62efDBB81HH31kCgsLzbp168z06dNNcnKyOXHihDEmeNtl48aNJiwszPz3f/+32bdvn/nLX/5ioqOjzYsvvug5Jljfg40J4qm9xhjzv//7v6Zv374mIiLCTJw40XzyySdWl9SlPvzwQyPpS49bb73VGNM4teynP/2pSU1NNXa73Vx11VVmz549LX7GqVOnzM0332xiY2NNfHy8mTdvnqmqqrLg1XhPa20iyTz//POeY2pra813v/td06NHDxMdHW2+9rWvmePHj7f4OYcOHTIzZ840UVFRJjk52Tz44IPG6XR286vxru985zumX79+JiIiwvTq1ctcddVVniBiTPC2S2u+GEaCtW3mzJlj0tPTTUREhOndu7eZM2dOi7U0grVdjDHmH//4hxk5cqSx2+1m6NCh5plnnmmxP1jfg40xxmaMMdb0yQAAAATpmBEAAOA7CCMAAMBShBEAAGApwggAALAUYQQAAFiKMAIAACxFGAEAAJYijAAAAEsRRgAAgKUIIwAAwFKEEQAAYCnCCAAAsNT/B8VNWxQIotAJAAAAAElFTkSuQmCC", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -694,8 +625,8 @@ "pos = np.array([318.99999999906896, 0, 0], dtype='float64')\n", "time = t0\n", "\n", - "init_latitude = df.at[2, 'latitude']\n", - "init_longitude = df.at[2, 'longitude']\n", + "init_latitude = df.at[2, 'latitude'] * pi / 180\n", + "init_longitude = df.at[2, 'longitude'] * pi / 180\n", "\n", "t0 = df.at[2, 'Time']\n", "omega_E = (2*pi) / (24*60*60)\n", @@ -705,15 +636,14 @@ "acc_total = [0]\n", "\n", "pitch, yaw, roll = df.at[2, 'pitch_l'] * pi / 180, df.at[2, 'yaw_l'] * pi / 180, df.at[2, 'roll_l'] * pi / 180\n", - "decl = df.at[2, 'declination']\n", + "decl = df.at[2, 'declination'] * pi / 180\n", "\n", "B_to_L = T1(yaw) @ T2(pi / 2 - pitch) @ T1(-roll)\n", - "L_to_G = np.linalg.inv(T2(-decl) @ T3(init_longitude + omega_E * t0))\n", "G_to_LF = T2(-pi/2 - init_latitude) @ T3(init_longitude)\n", - "L_to_LF = G_to_LF @ L_to_G\n", "\n", "x_FL, y_FL, z_FL = [8.20214666930979e-13], [-1.51393643837011e-12], [-2.40063988328838e-12]\n", "vx_FL, vy_FL, vz_FL = [0], [0], [0]\n", + "ax_FL, ay_FL, az_FL = [0], [0], [0]\n", "\n", "for i in range(3, len(df)+2):\n", " dt = df.at[i, 'Time'] - df.at[i-1, 'Time']\n", @@ -721,8 +651,8 @@ " # Fetch values for the current time step.\n", " acc = np.array([df.at[i, 'ax_B'], df.at[i, 'ay_B'], df.at[i, 'az_B']])\n", " pitch, yaw, roll = df.at[i, 'pitch_l'] * pi / 180, df.at[i, 'yaw_l'] * pi / 180, df.at[i, 'roll_l'] * pi / 180\n", - " decl = df.at[i, 'declination']\n", - " long = df.at[i, 'longitude']\n", + " decl = df.at[i, 'declination'] * pi / 180\n", + " long = df.at[i, 'longitude'] * pi / 180\n", "\n", " B_to_L = T1(yaw) @ T2(pi / 2 - pitch) @ T1(-roll)\n", " L_to_G = np.linalg.inv(T2(-decl) @ T3(long + omega_E * t0))\n", @@ -730,11 +660,12 @@ " L_to_LF = G_to_LF @ L_to_G\n", "\n", " acc_L = B_to_L @ acc + np.array([-g, 0, 0])\n", - " pos += dt * vel + 1/2*dt**2 * acc_L\n", + " pos += dt * vel + 1/2*dt**2*acc_L\n", " vel += dt * acc_L\n", "\n", " pos_LF = -(L_to_LF @ pos)\n", " vel_LF = -(L_to_LF @ vel)\n", + " acc_LF = -(L_to_LF @ acc_L)\n", "\n", " x_FL.append(pos_LF[0])\n", " y_FL.append(pos_LF[1])\n", @@ -744,6 +675,10 @@ " vy_FL.append(vel_LF[1])\n", " vz_FL.append(vel_LF[2])\n", "\n", + " ax_FL.append(acc_LF[0])\n", + " ay_FL.append(acc_LF[1])\n", + " az_FL.append(acc_LF[2])\n", + "\n", " altitudes.append(pos[0])\n", " velocities.append(np.sqrt(vel[0]**2 + vel[1]**2 + vel[2]**2))\n", " acc_total.append(np.sqrt(acc[0]**2 + acc[1]**2 + acc[2]**2))\n", @@ -762,37 +697,50 @@ "plt.legend()\n", "plt.show()\n", "\n", + "print('x_FL')\n", "plt.plot(df['Time'], x_FL)\n", "plt.show()\n", "\n", + "print('y_FL')\n", "plt.plot(df['Time'], y_FL)\n", "plt.show()\n", "\n", + "print('z_FL')\n", "plt.plot(df['Time'], z_FL)\n", "plt.show()\n", "\n", + "print('vx_FL')\n", "plt.plot(df['Time'], vx_FL)\n", "plt.show()\n", "\n", + "print('vy_FL')\n", "plt.plot(df['Time'], vy_FL)\n", "plt.show()\n", "\n", + "print('vz_FL')\n", "plt.plot(df['Time'], vz_FL)\n", + "plt.show()\n", + "\n", + "print('acc')\n", + "plt.plot(df['Time'], ax_FL, label='ax')\n", + "plt.plot(df['Time'], ay_FL, label='ay')\n", + "plt.plot(df['Time'], az_FL, label='az')\n", + "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 507, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "7.766996294563251" + "5.7964371935639445" ] }, - "execution_count": 507, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -803,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": 508, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -815,15 +763,15 @@ "5 319.000000\n", "6 319.000000\n", " ... \n", - "1715 9.558576\n", - "1716 6.360338\n", - "1717 3.162590\n", - "1718 -0.034668\n", - "1719 -0.034668\n", - "Name: altitude, Length: 1718, dtype: float64" + "1723 9.596509\n", + "1724 6.411547\n", + "1725 3.227070\n", + "1726 0.043079\n", + "1727 0.043079\n", + "Name: altitude, Length: 1726, dtype: float64" ] }, - "execution_count": 508, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -834,16 +782,16 @@ }, { "cell_type": "code", - "execution_count": 509, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "28850.044718887697" + "35196.311584186704" ] }, - "execution_count": 509, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -854,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 510, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -866,15 +814,15 @@ "5 0.050000\n", "6 0.050000\n", " ... \n", - "1715 637.534465\n", - "1716 638.073041\n", - "1717 638.611618\n", - "1718 639.150194\n", - "1719 639.150194\n", - "Name: Time, Length: 1718, dtype: float64" + "1723 577.348034\n", + "1724 577.800240\n", + "1725 578.252446\n", + "1726 578.704652\n", + "1727 578.704652\n", + "Name: Time, Length: 1726, dtype: float64" ] }, - "execution_count": 510, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -885,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 511, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -958,7 +906,7 @@ "}, dtype=str)\n", "\n", "df_new = pd.concat([descriptions, df_new], axis=0)\n", - "df_new.to_csv('data/simulations/28km.txt', sep='\\t')" + "df_new.to_csv('data/simulations/predicted_flight.csv', sep='\\t')" ] }, { diff --git a/data/balloon_data.csv b/data/balloon_data.csv new file mode 100644 index 0000000..2b481a3 --- /dev/null +++ b/data/balloon_data.csv @@ -0,0 +1,1416 @@ +Time,Relative Humidity (%),Exterior Temperature (Celsius) ,Exterior Temperature (Farenheit),Barometric Pressure (mBar),Interior Temperature (Celsius),Raw Pressure Data (D1),Raw Temperature Data (D2),Altitude (Meters),Altitude (Feet),Ascent/Descent Rate (mts/s),Ascent/Descent Rate (km/h),,,, +8:08,97.6,23.8,74.84,1021.52,27.57,5882058,8329892,10.40, 34 ,0.10,0.36,,,, +8:08,97.6,23.9,75.02,1021.54,27.58,5882060,8330040,10.24, 34 ,-0.03,-0.12,,,, +8:08,97.6,23.9,75.02,1021.56,27.59,5882058,8330340,10.07, 33 ,-0.03,-0.12,,,, +8:08,97.6,23.9,75.02,1021.57,27.59,5882060,8330522,9.99, 33 ,-0.02,-0.06,,,, +8:08,97.7,23.9,75.02,1021.57,27.6,5882024,8330730,9.99, 33 ,0.00,0.00,,,, +8:08,97.7,23.9,75.02,1021.53,27.6,5881902,8330894,10.32, 34 ,0.07,0.24,,,, +8:08,97.7,23.9,75.02,1021.57,27.61,5881950,8331164,9.99, 33 ,-0.07,-0.24,,,, +8:08,97.7,23.9,75.02,1021.59,27.62,5881956,8331400,9.82, 32 ,-0.03,-0.12,,,, +8:09,97.8,23.9,75.02,1021.57,27.63,5881870,8331616,9.99, 33 ,0.03,0.12,,,, +8:09,97.8,24,75.2,1021.57,27.63,5881836,8331810,9.99, 33 ,0.00,0.00,,,, +8:09,97.9,24,75.2,1021.54,27.64,5881714,8332152,10.24, 34 ,0.05,0.18,,,, +8:09,97.9,24,75.2,1021.54,27.65,5881692,8332348,10.24, 34 ,0.00,0.00,,,, +8:09,97.9,24,75.2,1021.47,27.66,5881508,8332538,10.82, 35 ,0.12,0.42,,,, 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9,882 ",4.49,16.18,,,, +8:19,42.9,13.1,55.58,704.82,28.09,5184780,8345618,3036.31," 9,962 ",4.84,17.42,,,, +8:19,42.4,13,55.4,702.3,28.08,5179290,8345274,3064.49," 10,054 ",5.64,20.29,,,, +8:19,42.1,13,55.4,700.22,28.07,5174758,8344920,3087.81," 10,131 ",4.66,16.79,,,, +8:19,41.9,12.8,55.04,697.86,28.06,5169594,8344882,3114.34," 10,218 ",5.31,19.10,,,, +8:19,41.7,12.6,54.68,695.9,28.06,5165306,8344710,3136.42," 10,290 ",4.42,15.90,,,, +8:19,41.5,12.5,54.5,693.36,28.06,5159744,8344596,3165.12," 10,384 ",5.74,20.66,,,, +8:20,41.2,12.3,54.14,691.13,28.05,5154872,8344478,3190.38," 10,467 ",5.05,18.19,,,, +8:20,40.7,12.2,53.96,688.88,28.05,5149944,8344350,3215.94," 10,551 ",5.11,18.40,,,, +8:20,40.3,12.1,53.78,686.31,28.04,5144338,8344180,3245.21," 10,647 ",5.85,21.08,,,, +8:20,40.2,12,53.6,683.74,28.04,5138706,8344076,3274.58," 10,743 ",5.87,21.14,,,, +8:20,40,11.8,53.24,681.31,28.03,5133394,8343816,3302.42," 10,835 ",5.57,20.05,,,, 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14,192 ",6.22,22.41,,,, +8:23,34.1,5.7,42.26,594.45,27.61,4944088,8331030,4355.03," 14,288 ",5.84,21.01,,,, +8:23,34.3,5.3,41.54,592.5,27.59,4939856,8330456,4380.05," 14,370 ",5.00,18.02,,,, +8:23,34.6,5.2,41.36,589.88,27.58,4934134,8330060,4413.78," 14,481 ",6.75,24.28,,,, +8:23,34.9,5.1,41.18,587.85,27.57,4929700,8329818,4440.00," 14,567 ",5.24,18.88,,,, +8:23,35.1,4.9,40.82,585.71,27.55,4925056,8329402,4467.71," 14,658 ",5.54,19.96,,,, +8:23,35,4.8,40.64,582.67,27.54,4918410,8329040,4507.23," 14,787 ",7.90,28.45,,,, +8:23,34.5,4.4,39.92,580.41,27.53,4913490,8328576,4536.71," 14,884 ",5.90,21.23,,,, +8:24,34.3,4.2,39.56,578.71,27.5,4909828,8327866,4558.95," 14,957 ",4.45,16.01,,,, +8:24,34.1,4.2,39.56,576.76,27.49,4905578,8327486,4584.52," 15,041 ",5.11,18.41,,,, +8:24,34.1,4.1,39.38,575.19,27.46,4902194,8326674,4605.17," 15,109 ",4.13,14.86,,,, +8:24,34,4,39.2,573.59,27.44,4898750,8326082,4626.25," 15,178 ",4.22,15.18,,,, +8:24,34,3.9,39.02,571.96,27.42,4895218,8325386,4647.78," 15,249 ",4.31,15.50,,,, +8:24,33.7,3.7,38.66,570.31,27.41,4891648,8324900,4669.62," 15,320 ",4.37,15.73,,,, +8:24,33.3,3.5,38.3,568.66,27.38,4888086,8324190,4691.51," 15,392 ",4.38,15.76,,,, +8:24,33,3.4,38.12,566.88,27.36,4884228,8323522,4715.19," 15,470 ",4.74,17.05,,,, +8:24,32.8,3.3,37.94,564.8,27.33,4879746,8322722,4742.93," 15,561 ",5.55,19.98,,,, +8:24,32.6,3.2,37.76,562.08,27.31,4873830,8322058,4779.34," 15,680 ",7.28,26.21,,,, +8:24,32.4,3,37.4,560.27,27.3,4869894,8321686,4803.64," 15,760 ",4.86,17.50,,,, +8:24,32.6,2.7,36.86,558.49,27.29,4866002,8321394,4827.61," 15,839 ",4.79,17.25,,,, +8:25,32.6,2.6,36.68,556.02,27.28,4860600,8321052,4860.96," 15,948 ",6.67,24.02,,,, +8:25,32.9,2.5,36.5,554.31,27.26,4856890,8320606,4884.13," 16,024 ",4.63,16.68,,,, +8:25,33,2.3,36.14,552.28,27.26,4852458,8320326,4911.70," 16,114 ",5.51,19.85,,,, +8:25,33.2,2.2,35.96,549.7,27.24,4846822,8319812,4946.86," 16,230 ",7.03,25.32,,,, +8:25,33.5,2,35.6,547.17,27.22,4841316,8319170,4981.47," 16,343 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+8:27,32.5,-1.7,28.94,499.72,26.75,4738196,8305022,5655.90," 18,556 ",4.07,14.64,,,, +8:27,32.4,-1.8,28.76,497.86,26.73,4734152,8304480,5683.38," 18,646 ",5.50,19.78,,,, +8:27,32.2,-2.1,28.22,495.79,26.7,4729698,8303402,5714.06," 18,747 ",6.14,22.09,,,, +8:27,31.9,-2.2,28.04,494.26,26.68,4726370,8302868,5736.80," 18,822 ",4.55,16.37,,,, +8:27,31.6,-2.4,27.68,492.75,26.66,4723102,8302158,5759.30," 18,895 ",4.50,16.20,,,, +8:27,31.5,-2.4,27.68,491.29,26.63,4719966,8301260,5781.10," 18,967 ",4.36,15.70,,,, +8:27,31.5,-2.5,27.5,489.75,26.61,4716598,8300898,5804.16," 19,043 ",4.61,16.60,,,, +8:27,31.4,-2.6,27.32,488.1,26.59,4713020,8300214,5828.94," 19,124 ",4.95,17.84,,,, +8:28,31.4,-2.6,27.32,486.07,26.57,4708614,8299590,5859.51," 19,224 ",6.11,22.01,,,, +8:28,31.2,-2.8,26.96,484.58,26.55,4705374,8298878,5882.01," 19,298 ",4.50,16.20,,,, +8:28,31.1,-3.1,26.42,482.85,26.52,4701654,8297896,5908.21," 19,384 ",5.24,18.86,,,, +8:28,31.3,-3.1,26.42,481.17,26.49,4698020,8297096,5933.72," 19,468 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+8:29,30.4,-4.6,23.72,461.09,26.09,4654724,8285094,6244.41," 20,487 ",7.45,26.81,,,, +8:29,30.3,-4.6,23.72,458.47,26.07,4649008,8284330,6285.76," 20,623 ",8.27,29.77,,,, +8:29,30.2,-4.8,23.36,456.16,26.04,4643980,8283550,6322.36," 20,743 ",7.32,26.36,,,, +8:29,30.1,-5,23,454.56,26.01,4640540,8282582,6347.81," 20,826 ",5.09,18.32,,,, +8:29,30,-5,23,452.63,25.98,4636358,8281562,6378.60," 20,927 ",6.16,22.17,,,, +8:29,29.8,-5.2,22.64,450.82,25.95,4632420,8280786,6407.57," 21,022 ",5.79,20.86,,,, +8:29,29.6,-5.5,22.1,449.05,25.92,4628616,8279728,6436.00," 21,115 ",5.68,20.46,,,, +8:29,29.5,-5.5,22.1,446.94,25.88,4624034,8278730,6470.00," 21,227 ",6.80,24.48,,,, +8:29,29.3,-5.6,21.92,445.35,25.86,4620592,8277856,6495.71," 21,311 ",5.14,18.51,,,, +8:30,29.2,-5.8,21.56,443.82,25.82,4617300,8276760,6520.51," 21,393 ",4.96,17.86,,,, +8:30,29.1,-6,21.2,441.95,25.79,4613248,8275762,6550.93," 21,493 ",6.08,21.90,,,, +8:30,29.1,-6.2,20.84,440.35,25.76,4609776,8275024,6577.04," 21,578 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25,217 ",6.04,21.73,,,, +8:34,21.1,-13,8.6,375.17,24.35,4468750,8232114,7712.33," 25,303 ",5.24,18.86,,,, +8:34,21,-13.1,8.42,373.74,24.3,4465696,8230638,7738.97," 25,390 ",5.33,19.18,,,, +8:34,20.8,-13.2,8.24,372.1,24.25,4462156,8229270,7769.63," 25,491 ",6.13,22.07,,,, +8:34,20.6,-13.4,7.88,370.12,24.2,4457864,8227730,7806.80," 25,613 ",7.43,26.76,,,, +8:34,20.4,-13.5,7.7,368.76,24.16,4454918,8226520,7832.41," 25,697 ",5.12,18.45,,,, +8:34,20.2,-13.6,7.52,367.61,24.11,4452484,8224986,7854.14," 25,768 ",4.34,15.64,,,, +8:34,20.1,-13.7,7.34,366.08,24.06,4449186,8223426,7883.12," 25,863 ",5.80,20.87,,,, +8:34,19.9,-13.8,7.16,364.95,24.02,4446768,8222166,7904.59," 25,934 ",4.29,15.46,,,, +8:34,19.7,-14,6.8,363.59,23.96,4443860,8220456,7930.51," 26,019 ",5.18,18.66,,,, +8:34,19.6,-14,6.8,362.19,23.91,4440858,8218942,7957.26," 26,107 ",5.35,19.27,,,, +8:34,19.4,-14.3,6.26,361.1,23.9,4438480,8218402,7978.15," 26,175 ",4.18,15.04,,,, 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28,163 ",4.94,17.78,,,, +8:37,17.2,-18.2,-0.76,329.56,22.97,4370214,8190368,8605.98," 28,235 ",4.37,15.75,,,, +8:37,17.1,-18.4,-1.12,328.2,22.95,4367218,8189800,8634.12," 28,327 ",5.63,20.27,,,, +8:37,17.1,-18.4,-1.12,326.88,22.93,4364336,8189184,8661.54," 28,417 ",5.48,19.74,,,, +8:37,17,-18.6,-1.48,325.39,22.91,4361072,8188440,8692.59," 28,519 ",6.21,22.36,,,, +8:37,16.9,-18.8,-1.84,324.11,22.89,4358250,8187814,8719.35," 28,607 ",5.35,19.27,,,, +8:37,16.8,-19,-2.2,322.83,22.85,4355478,8186668,8746.20," 28,695 ",5.37,19.33,,,, +8:37,16.7,-19.2,-2.56,321.74,22.81,4353136,8185424,8769.13," 28,770 ",4.59,16.51,,,, +8:37,16.6,-19.7,-3.46,320.37,22.74,4350202,8183496,8798.05," 28,865 ",5.78,20.82,,,, +8:37,16.5,-19.8,-3.64,319.39,22.68,4348132,8181564,8818.79," 28,933 ",4.15,14.94,,,, +8:37,16.4,-19.8,-3.64,317.67,22.65,4344344,8180782,8855.33," 29,053 ",7.31,26.30,,,, +8:37,16.4,-20,-4,316.3,22.6,4341388,8179258,8884.54," 29,149 ",5.84,21.03,,,, 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30,454 ",5.24,18.85,,,, +8:38,15.2,-23.1,-9.58,296.79,22.12,4298824,8164422,9312.12," 30,552 ",5.93,21.34,,,, +8:39,15.1,-23.2,-9.76,295.61,22.09,4296260,8163530,9338.70," 30,639 ",5.32,19.14,,,, +8:39,15,-23.4,-10.12,294.67,22.06,4294192,8162804,9359.94," 30,708 ",4.25,15.29,,,, +8:39,15,-23.5,-10.3,293,22.03,4290536,8161952,9397.81," 30,833 ",7.57,27.26,,,, +8:39,14.9,-23.7,-10.66,291.59,22.01,4287432,8161138,9429.91," 30,938 ",6.42,23.12,,,, +8:39,14.7,-24,-11.2,289.87,21.98,4283666,8160188,9469.25," 31,067 ",7.87,28.32,,,, +8:39,14.7,-24.1,-11.38,288.48,21.94,4280610,8159220,9501.18," 31,172 ",6.39,22.99,,,, +8:39,14.5,-24.4,-11.92,287.64,21.92,4278796,8158362,9520.53," 31,235 ",3.87,13.94,,,, +8:39,14.5,-24.4,-11.92,286.33,21.88,4275920,8157274,9550.81," 31,335 ",6.06,21.80,,,, +8:39,14.4,-24.6,-12.28,284.88,21.85,4272750,8156290,9584.45," 31,445 ",6.73,24.22,,,, +8:39,14.3,-24.7,-12.46,282.92,21.81,4268452,8155204,9630.15," 31,595 ",9.14,32.90,,,, +8:39,14.2,-24.9,-12.82,281.12,21.77,4264502,8154076,9672.35," 31,733 ",8.44,30.38,,,, +8:39,14.1,-25.2,-13.36,279.5,21.74,4260948,8152934,9710.51," 31,859 ",7.63,27.48,,,, +8:40,14,-25.5,-13.9,278.26,21.69,4258274,8151402,9739.84," 31,955 ",5.87,21.12,,,, +8:40,13.9,-25.8,-14.44,276.99,21.63,4255522,8149828,9769.99," 32,054 ",6.03,21.71,,,, +8:40,13.8,-25.8,-14.44,276.1,21.6,4253578,8148762,9791.19," 32,123 ",4.24,15.26,,,, +8:40,13.8,-25.8,-14.44,274.53,21.54,4250182,8146818,9828.71," 32,246 ",7.51,27.02,,,, +8:40,13.7,-26.2,-15.16,272.66,21.48,4246112,8145028,9873.64," 32,394 ",8.98,32.35,,,, +8:40,13.5,-26.6,-15.88,271.11,21.42,4242740,8143258,9911.06," 32,517 ",7.49,26.95,,,, +8:40,13.4,-26.9,-16.42,269.83,21.34,4239996,8140980,9942.10," 32,618 ",6.21,22.35,,,, +8:40,13.4,-26.9,-16.42,268.21,21.29,4236448,8139516,9981.55," 32,748 ",7.89,28.41,,,, +8:40,13.2,-27.4,-17.32,266.65,21.24,4233054,8137802,9979.67," 32,742 ",-0.38,-1.36,,,, 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69,396 ",5.23,18.84,,,, +9:05,6.5,-50.8,-59.44,45.92,1.86,3746752,7586560,21183.72," 69,500 ",6.36,22.91,,,, +9:05,6.6,-50.6,-59.08,45.67,1.8,3746194,7585066,21218.49," 69,614 ",6.95,25.04,,,, +9:05,6.7,-50.3,-58.54,45.5,1.73,3745800,7583448,21242.25," 69,692 ",4.75,17.10,,,, +9:05,6.7,-50.1,-58.18,45.26,1.67,3745248,7581910,21275.93," 69,803 ",6.74,24.25,,,, +9:06,6.7,-49.9,-57.82,45,1.62,3744662,7580364,21312.63," 69,923 ",7.34,26.42,,,, +9:06,6.8,-49.6,-57.28,44.86,1.56,3744358,7578878,21332.47," 69,988 ",3.97,14.29,,,, +9:06,6.8,-49.4,-56.92,44.59,1.5,3743738,7577362,21370.93," 70,115 ",7.69,27.69,,,, +9:06,6.9,-49.1,-56.38,44.42,1.45,3743360,7575918,21395.26," 70,194 ",4.87,17.52,,,, +9:06,7,-48.9,-56.02,44.18,1.39,3742802,7574336,21429.76," 70,308 ",6.90,24.85,,,, +9:06,7,-48.6,-55.48,44.02,1.34,3742462,7572890,21452.87," 70,383 ",4.62,16.64,,,, +9:06,7.1,-48.4,-55.12,43.8,1.28,3741940,7571466,21484.78," 70,488 ",6.38,22.98,,,, 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72,866 ",6.83,24.57,,,, +9:08,8.6,-41.9,-43.42,38.98,0.03,3730988,7538342,22227.36," 72,924 ",3.59,12.92,,,, +9:08,8.6,-41.6,-42.88,38.78,-0.02,3730516,7537200,22260.13," 73,032 ",6.55,23.59,,,, +9:08,8.6,-41.4,-42.52,38.57,-0.07,3730028,7536104,22294.71," 73,145 ",6.92,24.90,,,, +9:08,8.7,-41.1,-41.98,38.4,-0.11,3729646,7534872,22322.85," 73,238 ",5.63,20.26,,,, +9:08,8.8,-40.9,-41.62,38.19,-0.16,3729166,7533732,22357.78," 73,352 ",6.99,25.15,,,, +9:09,8.8,-40.6,-41.08,38.03,-0.19,3728790,7532638,22384.52," 73,440 ",5.35,19.25,,,, +9:09,8.9,-40.4,-40.72,37.82,-0.24,3728318,7531440,22419.79," 73,556 ",7.05,25.39,,,, +9:09,9,-40.1,-40.18,37.66,-0.29,3727962,7530358,22446.79," 73,644 ",5.40,19.44,,,, +9:09,9,-39.9,-39.82,37.46,-0.32,3727484,7529260,22480.71," 73,756 ",6.78,24.42,,,, +9:09,9,-39.6,-39.28,37.31,-0.37,3727150,7528112,22506.26," 73,839 ",5.11,18.40,,,, +9:09,9.1,-39.4,-38.92,37.1,-0.41,3726656,7527054,22542.22," 73,957 ",7.19,25.89,,,, 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75,157 ",5.81,20.90,,,, +9:10,9.8,-36.1,-32.98,34.92,-0.91,3721658,7513976,22927.93," 75,223 ",4.01,14.42,,,, +9:10,9.8,-35.8,-32.44,34.72,-0.95,3721198,7513016,22964.52," 75,343 ",7.32,26.34,,,, +9:10,9.8,-35.6,-32.08,34.61,-0.98,3720952,7512178,22984.73," 75,409 ",4.04,14.55,,,, +9:10,9.8,-35.4,-31.72,34.4,-1.02,3720468,7511226,23023.49," 75,536 ",7.75,27.91,,,, +9:10,9.9,-35.1,-31.18,34.25,-1.05,3720106,7510314,23051.33," 75,628 ",5.57,20.04,,,, +9:10,9.9,-34.9,-30.82,34.11,-1.09,3719782,7509414,23077.42," 75,713 ",5.22,18.78,,,, +9:11,10,-34.7,-30.46,33.95,-1.12,3719434,7508514,23107.36," 75,812 ",5.99,21.56,,,, +9:11,10,-34.4,-29.92,33.79,-1.15,3719060,7507688,23137.45," 75,910 ",6.02,21.66,,,, +9:11,10,-34.2,-29.56,33.62,-1.18,3718664,7506842,23169.58," 76,016 ",6.43,23.13,,,, +9:11,10.1,-34,-29.2,33.44,-1.22,3718240,7505888,23203.77," 76,128 ",6.84,24.62,,,, +9:11,10.1,-33.7,-28.66,33.3,-1.25,3717932,7505158,23230.49," 76,216 ",5.34,19.24,,,, 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83,068 ",7.94,28.59,,,, +9:17,10.6,-20.8,-5.44,23.89,-2.61,3696034,7469846,25345.76," 83,155 ",5.32,19.16,,,, +9:17,10.6,-20.6,-5.08,23.8,-2.63,3695822,7469476,25369.80," 83,234 ",4.81,17.31,,,, +9:17,10.6,-20.5,-4.9,23.71,-2.64,3695608,7469320,25393.94," 83,313 ",4.83,17.37,,,, +9:17,10.6,-20.3,-4.54,23.57,-2.65,3695284,7468960,25431.66," 83,437 ",7.54,27.16,,,, +9:17,10.5,-20.1,-4.18,23.49,-2.65,3695112,7468852,25453.31," 83,508 ",4.33,15.59,,,, +9:17,10.5,-19.9,-3.82,23.37,-2.66,3694818,7468568,25485.94," 83,615 ",6.52,23.49,,,, +9:17,10.5,-19.7,-3.46,23.23,-2.67,3694496,7468386,25524.21," 83,741 ",7.65,27.56,,,, +9:17,10.4,-19.6,-3.28,23.14,-2.68,3694290,7468130,25548.93," 83,822 ",4.95,17.80,,,, +9:17,10.5,-19.1,-2.38,23.05,-2.7,3694070,7467854,25573.75," 83,903 ",4.96,17.87,,,, +9:17,10.5,-19,-2.2,22.93,-2.71,3693786,7467604,25607.00," 84,012 ",6.65,23.94,,,, +9:17,10.5,-18.8,-1.84,22.78,-2.71,3693434,7467536,25648.80," 84,150 ",8.36,30.10,,,, 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88,932 ",12.59,45.33,,,, +9:21,8.4,-11.4,11.48,18.1,-2.81,3682454,7464796,27113.59," 88,955 ",1.41,5.06,,,, +9:22,8.4,-11.2,11.84,18.03,-2.8,3682302,7464954,27138.27," 89,036 ",4.94,17.77,,,, +9:22,8.4,-11.1,12.02,17.94,-2.8,3682082,7464942,27170.14," 89,141 ",6.37,22.95,,,, +9:22,8.3,-10.9,12.38,17.84,-2.8,3681854,7465010,27205.74," 89,258 ",7.12,25.63,,,, +9:22,8.2,-10.8,12.56,17.74,-2.79,3681610,7465218,27241.55," 89,375 ",7.16,25.78,,,, +9:22,8.2,-10.6,12.92,17.7,-2.79,3681504,7465206,27255.93," 89,422 ",2.88,10.35,,,, +9:22,8.2,-10.5,13.1,17.59,-2.79,3681266,7465338,27295.63," 89,553 ",7.94,28.59,,,, +9:22,8.1,-10.3,13.46,17.54,-2.79,3681148,7465482,27313.76," 89,612 ",3.63,13.05,,,, +9:22,8.1,-10.2,13.64,17.44,-2.78,3680910,7465574,27350.18," 89,732 ",7.28,26.22,,,, +9:22,8,-10.1,13.82,17.4,-2.78,3680808,7465652,27364.81," 89,780 ",2.93,10.53,,,, +9:22,8,-9.9,14.18,17.26,-2.77,3680478,7465814,27416.26," 89,948 ",10.29,37.05,,,, 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93,432 ",6.96,25.04,,,, +9:25,5.9,-5,23,14.53,-2.48,3674048,7473326,28512.93," 93,546 ",6.99,25.18,,,, +9:25,5.9,-4.9,23.18,14.46,-2.47,3673874,7473584,28543.69," 93,647 ",6.15,22.15,,,, +9:26,5.8,-4.8,23.36,14.39,-2.46,3673728,7473892,28574.60," 93,749 ",6.18,22.25,,,, +9:26,5.8,-4.6,23.72,14.31,-2.45,3673528,7474234,28610.10," 93,865 ",7.10,25.57,,,, +9:26,5.8,-4.5,23.9,14.22,-2.44,3673326,7474432,28650.29," 93,997 ",8.04,28.93,,,, +9:26,5.7,-4.4,24.08,14.22,-2.43,3673310,7474758,28650.29," 93,997 ",0.00,0.00,,,, +9:26,5.6,-4.3,24.26,14.1,-2.41,3673034,7474930,28704.27," 94,174 ",10.80,38.86,,,, +9:26,5.7,-4.1,24.62,14.06,-2.4,3672946,7475300,28722.36," 94,233 ",3.62,13.03,,,, +9:26,5.6,-4,24.8,13.99,-2.39,3672772,7475564,28754.15," 94,338 ",6.36,22.89,,,, +9:26,5.6,-3.8,25.16,13.92,-2.38,3672604,7475836,28786.10," 94,443 ",6.39,23.00,,,, +9:26,5.6,-3.7,25.34,13.86,-2.37,3672476,7476174,28813.62," 94,533 ",5.50,19.81,,,, +9:26,5.5,-3.6,25.52,13.77,-2.36,3672260,7476502,28855.11," 94,669 ",8.30,29.88,,,, +9:26,5.5,-3.4,25.88,13.72,-2.34,3672128,7476816,28878.28," 94,745 ",4.63,16.68,,,, +9:26,5.4,-3.3,26.06,13.67,-2.33,3672026,7477110,28901.54," 94,821 ",4.65,16.74,,,, +9:27,5.4,-3.1,26.42,13.6,-2.32,3671852,7477376,28934.24," 94,929 ",6.54,23.54,,,, +9:27,5.4,-3,26.6,13.53,-2.31,3671692,7477662,28967.11," 95,036 ",6.57,23.67,,,, +9:27,5.3,-2.9,26.78,13.45,-2.3,3671498,7478042,29004.88," 95,160 ",7.55,27.20,,,, +9:27,5.3,-2.7,27.14,13.42,-2.29,3671428,7478366,29019.10," 95,207 ",2.84,10.24,,,, +9:27,5.2,-2.6,27.32,13.33,-2.27,3671212,7478656,29061.96," 95,348 ",8.57,30.86,,,, +9:27,5.2,-2.5,27.5,13.31,-2.26,3671164,7478978,29071.52," 95,379 ",1.91,6.89,,,, +9:27,5.1,-2.3,27.86,13.23,-2.25,3670986,7479226,29109.92," 95,505 ",7.68,27.65,,,, +9:27,5,-2.2,28.04,13.13,-2.24,3670756,7479640,29158.25," 95,664 ",9.67,34.80,,,, +9:27,5,-2.1,28.22,13.07,-2.23,3670602,7480030,29187.42," 95,759 ",5.83,21.00,,,, +9:27,5,-1.9,28.58,13.01,-2.21,3670474,7480340,29216.73," 95,855 ",5.86,21.10,,,, +9:27,4.9,-1.8,28.76,12.97,-2.19,3670362,7480702,29236.34," 95,920 ",3.92,14.12,,,, +9:27,4.9,-1.7,28.94,12.94,-2.19,3670310,7480936,29251.09," 95,968 ",2.95,10.62,,,, +9:28,4.9,-1.5,29.3,12.84,-2.17,3670060,7481398,29300.51," 96,130 ",9.88,35.58,,,, +9:28,4.8,-1.4,29.48,12.78,-2.16,3669928,7481734,29330.34," 96,228 ",5.97,21.48,,,, +9:28,4.8,-1.3,29.66,12.72,-2.14,3669790,7482056,29360.31," 96,326 ",5.99,21.58,,,, +9:28,4.7,-1.2,29.84,12.69,-2.13,3669712,7482452,29375.35," 96,376 ",3.01,10.83,,,, +9:28,4.7,-1,30.2,12.6,-2.12,3669492,7482756,29420.69," 96,525 ",9.07,32.64,,,, +9:28,4.7,-0.9,30.38,12.52,-2.1,3669310,7483148,29461.26," 96,658 ",8.11,29.21,,,, +9:28,4.6,-0.8,30.56,12.47,-2.09,3669200,7483496,29486.75," 96,741 ",5.10,18.35,,,, +9:28,4.6,-0.6,30.92,12.44,-2.07,3669118,7483854,29502.09," 96,792 ",3.07,11.05,,,, +9:28,4.5,-0.5,31.1,12.34,-2.06,3668894,7484224,29553.50," 96,960 ",10.28,37.01,,,, 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108,957 ",0.00,0.00,,,, +9:38,1.6,12.4,54.32,6.94,0.03,3656158,7538576,33219.38," 108,987 ",1.83,6.60,,,, +9:38,1.6,12.5,54.5,6.97,0.05,3656238,7539126,33191.91," 108,897 ",-5.49,-19.78,,,, +9:38,1.6,12.6,54.68,6.87,0.07,3656008,7539614,33283.95," 109,199 ",18.41,66.27,,,, +9:39,1.6,12.7,54.86,6.87,0.08,3656010,7540112,33283.95," 109,199 ",0.00,0.00,,,, +9:39,1.7,12.9,55.22,6.79,0.11,3655826,7540674,33358.56," 109,444 ",14.92,53.72,,,, +9:39,1.6,13,55.4,6.8,0.13,3655832,7541140,33349.19," 109,413 ",-1.87,-6.75,,,, +9:39,1.6,13.1,55.58,6.79,0.15,3655818,7541672,33358.56," 109,444 ",1.87,6.75,,,, +9:39,1.6,13.2,55.76,6.79,0.17,3655804,7542138,33358.56," 109,444 ",0.00,0.00,,,, +9:39,1.6,13.3,55.94,6.7,0.19,3655600,7542654,33443.55," 109,723 ",17.00,61.19,,,, +9:39,1.6,13.4,56.12,6.62,0.2,3655410,7543160,33520.06," 109,974 ",15.30,55.09,,,, +9:39,1.6,13.5,56.3,6.63,0.22,3655442,7543678,33510.45," 109,942 ",-1.92,-6.92,,,, +9:39,1.6,13.5,56.3,6.58,0.25,3655322,7544234,33558.66," 110,101 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104,145 ",-112.56,-405.23,,,, +9:45,1,20,68,9.47,1.72,3661994,7582968,31239.59," 102,492 ",-100.73,-362.64,,,, +9:45,1,19.9,67.82,10.27,1.74,3663860,7583628,30723.04," 100,797 ",-103.31,-371.91,,,, +9:45,1,19.8,67.64,11.08,1.77,3665732,7584214,30239.51," 99,211 ",-96.71,-348.14,,,, +9:46,1,19.7,67.46,11.79,1.79,3667394,7584886,29843.91," 97,913 ",-79.12,-284.84,,,, +9:46,1,19.6,67.28,12.72,1.81,3669546,7585522,29360.31," 96,326 ",-96.72,-348.19,,,, +9:46,1,19.5,67.1,13.58,1.83,3671560,7586144,28943.61," 94,959 ",-83.34,-300.03,,,, +9:46,1,19.4,66.92,14.51,1.86,3673716,7586722,28521.70," 93,575 ",-84.38,-303.78,,,, +9:46,1,19.3,66.74,15.51,1.88,3676046,7587442,28097.20," 92,182 ",-84.90,-305.64,,,, +9:46,1,19.2,66.56,16.48,1.9,3678290,7588028,27710.81," 90,915 ",-77.28,-278.20,,,, +9:46,1,19,66.2,17.5,1.93,3680666,7588658,27328.31," 89,660 ",-76.50,-275.40,,,, +9:46,1,18.8,65.84,18.64,1.96,3683302,7589276,26926.34," 88,341 ",-80.39,-289.42,,,, 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+10:00,46.8,-9.5,14.9,615.32,2.63,5068334,7607210,4091.28," 13,423 ",-13.46,-48.45,,,, +10:00,48.9,-8.9,15.98,620.53,2.63,5080408,7607194,4026.57," 13,211 ",-12.94,-46.59,,,, +10:00,50.9,-8.5,16.7,625.7,2.58,5092580,7605820,3962.79," 13,001 ",-12.76,-45.92,,,, +10:01,52.9,-8.1,17.42,631.27,2.53,5105682,7604526,3894.56," 12,777 ",-13.65,-49.13,,,, +10:01,55,-7.5,18.5,636.84,2.54,5118562,7604896,3826.80," 12,555 ",-13.55,-48.78,,,, +10:01,57,-7,19.4,642.33,2.53,5131330,7604682,3760.49," 12,338 ",-13.26,-47.74,,,, +10:01,59,-6.6,20.12,647.91,2.53,5144298,7604734,3693.56," 12,118 ",-13.39,-48.19,,,, +10:01,61,-6.2,20.84,653.51,2.53,5157330,7604464,3626.86," 11,899 ",-13.34,-48.03,,,, +10:01,63.1,-5.8,21.56,659.25,2.53,5170656,7604498,3558.97," 11,676 ",-13.58,-48.88,,,, +10:01,65,-5.6,21.92,664.91,2.53,5183760,7604658,3492.49," 11,458 ",-13.30,-47.86,,,, +10:01,67.1,-5.1,22.82,670.59,2.54,5196920,7604992,3426.24," 11,241 ",-13.25,-47.70,,,, +10:01,68.9,-4.8,23.36,676.08,2.58,5209526,7605934,3362.63," 11,032 ",-12.72,-45.80,,,, +10:01,70.8,-4.5,23.9,681.86,2.59,5222946,7605972,3296.11," 10,814 ",-13.30,-47.89,,,, +10:01,72.5,-4.2,24.44,687.56,2.61,5236100,7606654,3230.96," 10,600 ",-13.03,-46.91,,,, +10:01,74.5,-3.6,25.52,693.38,2.62,5249546,7607100,3164.89," 10,383 ",-13.21,-47.57,,,, +10:02,76.4,-3.1,26.42,699.07,2.63,5262728,7607240,3100.73," 10,173 ",-12.83,-46.20,,,, +10:02,78.2,-2.7,27.14,704.9,2.66,5276180,7607826,3035.42," 9,959 ",-13.06,-47.02,,,, +10:02,80.1,-2.1,28.22,710.79,2.67,5289804,7608154,2969.89," 9,744 ",-13.11,-47.18,,,, +10:02,81.9,-1.7,28.94,716.58,2.68,5303184,7608610,2905.89," 9,534 ",-12.80,-46.08,,,, +10:02,83.5,-1.2,29.84,722.6,2.7,5317098,7609012,2839.80," 9,317 ",-13.22,-47.59,,,, +10:02,85.2,-0.8,30.56,728.5,2.7,5330782,7609094,2775.45," 9,106 ",-12.87,-46.33,,,, +10:02,86.8,-0.4,31.28,734.37,2.7,5344382,7609206,2711.85," 8,897 ",-12.72,-45.79,,,, +10:02,88.4,-0.1,31.82,740.28,2.69,5358160,7608762,2648.23," 8,688 ",-12.72,-45.81,,,, +10:02,90,0.2,32.36,746.37,2.69,5372316,7608704,2583.10," 8,475 ",-13.03,-46.89,,,, +10:02,91.6,0.7,33.26,752.32,2.7,5386048,7609180,2519.88," 8,267 ",-12.64,-45.52,,,, +10:02,93,1.1,33.98,758.45,2.72,5400220,7609658,2455.18," 8,055 ",-12.94,-46.59,,,, +10:02,94.3,1.7,35.06,764.61,2.75,5414410,7610248,2390.58," 7,843 ",-12.92,-46.51,,,, +10:03,95.5,2.2,35.96,770.52,2.76,5428088,7610628,2328.99," 7,641 ",-12.32,-44.34,,,, +10:03,96.7,2.6,36.68,776.73,2.79,5442362,7611440,2264.70," 7,430 ",-12.86,-46.29,,,, +10:03,97.5,2.7,36.86,783.08,2.81,5457026,7612018,2199.38," 7,216 ",-13.06,-47.03,,,, +10:03,98.6,2.9,37.22,789.37,2.84,5471496,7612746,2135.10," 7,005 ",-12.86,-46.28,,,, +10:03,99.3,3.1,37.58,795.65,2.87,5485932,7613570,2071.33," 6,796 ",-12.75,-45.91,,,, +10:03,99.7,3.2,37.76,801.9,2.92,5500256,7614816,2008.28," 6,589 ",-12.61,-45.40,,,, +10:03,99.9,3.5,38.3,808.34,2.93,5515120,7615198,1943.72," 6,377 ",-12.91,-46.48,,,, +10:03,99.9,3.8,38.84,814.71,2.98,5529712,7616422,1880.27," 6,169 ",-12.69,-45.68,,,, +10:03,99.9,4.4,39.92,821.05,3,5544312,7617138,1817.52," 5,963 ",-12.55,-45.18,,,, +10:03,99.9,4.9,40.82,827.29,3.03,5558664,7617820,1756.13," 5,762 ",-12.28,-44.19,,,, +10:03,99.9,5.3,41.54,833.98,3.07,5574036,7618724,1690.74," 5,547 ",-13.08,-47.08,,,, +10:03,99.9,5.7,42.26,840.44,3.11,5588816,7619964,1628.00," 5,341 ",-12.55,-45.17,,,, +10:04,99.9,6.3,43.34,846.69,3.12,5603226,7620458,1567.67," 5,143 ",-12.07,-43.44,,,, +10:04,99.9,6.8,44.24,853.06,3.14,5617956,7620798,1506.55," 4,943 ",-12.22,-44.01,,,, +10:04,99.9,7.5,45.5,859.52,3.16,5632832,7621298,1444.94," 4,741 ",-12.32,-44.36,,,, +10:04,99.9,8,46.4,866.02,3.19,5647742,7622284,1383.33," 4,538 ",-12.32,-44.36,,,, +10:04,99.9,8.6,47.48,872.46,3.24,5662500,7623312,1322.65," 4,339 ",-12.14,-43.69,,,, +10:04,99.9,9,48.2,878.9,3.26,5677308,7624072,1262.34," 4,142 ",-12.06,-43.43,,,, +10:04,99.9,9.6,49.28,885.49,3.28,5692488,7624652,1200.99," 3,940 ",-12.27,-44.17,,,, +10:04,99.9,10.4,50.72,892.17,3.31,5707844,7625366,1139.18," 3,737 ",-12.36,-44.50,,,, +10:04,99.9,10.8,51.44,898.86,3.34,5723192,7626212,1077.65," 3,536 ",-12.31,-44.30,,,, +10:04,99.9,11.4,52.52,905.73,3.37,5738984,7626942,1014.85," 3,330 ",-12.56,-45.22,,,, +10:04,99.9,12.2,53.96,912.51,3.4,5754564,7627786,953.25," 3,127 ",-12.32,-44.35,,,, +10:04,99.9,12.8,55.04,919.34,3.43,5770218,7628666,891.57," 2,925 ",-12.34,-44.41,,,, +10:05,99.9,13.3,55.94,926.02,3.47,5785516,7629738,831.60," 2,728 ",-11.99,-43.18,,,, +10:05,99.9,13.7,56.66,932.91,3.5,5801382,7630288,770.11," 2,527 ",-12.30,-44.27,,,, +10:05,99.9,14.3,57.74,939.74,3.53,5817032,7631286,709.52," 2,328 ",-12.12,-43.62,,,, +10:05,99.9,14.6,58.28,946.57,3.57,5832676,7632186,649.29," 2,130 ",-12.05,-43.37,,,, +10:05,99.9,14.9,58.82,953.35,3.6,5848196,7633196,589.84," 1,935 ",-11.89,-42.80,,,, +10:05,99.9,15.3,59.54,960.29,3.68,5863910,7635158,529.35," 1,737 ",-12.10,-43.56,,,, +10:05,99.9,16.3,61.34,967.28,3.71,5879928,7636030,468.77," 1,538 ",-12.11,-43.61,,,, +10:05,99.9,16.8,62.24,973.96,3.77,5895120,7637600,411.21," 1,349 ",-11.51,-41.44,,,, +10:05,99.9,17.3,63.14,980.18,3.81,5909270,7638954,357.91," 1,174 ",-10.66,-38.38,,,, +10:05,99.9,18,64.4,986.57,3.86,5923816,7640186,303.43, 995 ,-10.90,-39.23,,,, +10:05,99.9,18.4,65.12,992.77,3.9,5937984,7641206,250.84, 823 ,-10.52,-37.86,,,, +10:05,99.9,18.9,66.02,998.69,3.96,5951364,7642830,200.87, 659 ,-9.99,-35.98,,,, +10:06,99.9,19.7,67.46,1005.86,4.02,5967670,7644340,140.68, 462 ,-12.04,-43.34,,,, +10:06,99.9,20,68,1013.15,4.07,5984240,7645920,79.83, 262 ,-12.17,-43.81,,,, +10:06,99.9,20.5,68.9,1020.1,4.13,6000028,7647412,22.15, 73 ,-11.54,-41.53,,,, \ No newline at end of file diff --git a/data/simulations/40km.txt b/data/simulations/40km.txt index 6198c06..f938e95 100644 --- a/data/simulations/40km.txt +++ b/data/simulations/40km.txt @@ -1,1819 +1,1728 @@ -Phase acceleration_without_gravity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acceleration_without_gravity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acceleration_without_gravity_radial~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket acceleration_without_gravity_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket acceleration_without_gravity_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket acceleration_without_gravity~STAHR_Rocket acceleration_x~STAHR_Rocket#J2000@Earth acceleration_y~STAHR_Rocket#J2000@Earth acceleration_z~STAHR_Rocket#J2000@Earth acceleration~STAHR_Rocket#J2000@Earth acc_aero_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_aero_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_aero_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_aero~STAHR_Rocket acc_normal~STAHR_Rocket acc_thrust_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_thrust_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_thrust_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_thrust~STAHR_Rocket aero_area_ref~STAHR_Rocket aero_bank_angle~STAHR_Rocket aero_roll_angle~STAHR_Rocket airpath_angle~STAHR_Rocket#LD~STAHR_Rocket:Earth airpath_angle~STAHR_Rocket#L~STAHR_Rocket:Earth AIRPATH_BANK_ANGLE~Esrange#A~Esrange:Earth AIRPATH_BANK_ANGLE~STAHR_Rocket#A~STAHR_Rocket:Earth airpath_heading~STAHR_Rocket#LD~STAHR_Rocket:Earth airpath_heading~STAHR_Rocket#L~STAHR_Rocket:Earth airpath_speed~STAHR_Rocket altitude_apoapsis~STAHR_Rocket@Earth altitude_periapsis~STAHR_Rocket@Earth altitude~STAHR_Rocket@Earth angle1~IB3:STAHR_Rocket#B~STAHR_Rocket angle1~IB3:STAHR_Rocket#J2000 angle1~STAHR:STAHR_Rocket#B~STAHR_Rocket angle1~STAHR:STAHR_Rocket#J2000 angle2~IB3:STAHR_Rocket#B~STAHR_Rocket angle2~IB3:STAHR_Rocket#J2000 angle2~STAHR:STAHR_Rocket#B~STAHR_Rocket angle2~STAHR:STAHR_Rocket#J2000 angle3~IB3:STAHR_Rocket#B~STAHR_Rocket angle3~IB3:STAHR_Rocket#J2000 angle3~STAHR:STAHR_Rocket#B~STAHR_Rocket angle3~STAHR:STAHR_Rocket#J2000 angle_body_and_local_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth angle_of_attack_rate~STAHR_Rocket#A~STAHR_Rocket:Earth ANGLE_OF_ATTACK~Esrange#A~Esrange:Earth ANGLE_OF_ATTACK~STAHR_Rocket#A~STAHR_Rocket:Earth angular_velocity~STAHR_Rocket#B~STAHR_Rocket arc_length~STAHR_Rocket ascending_node_rel~STAHR_Rocket#TOD~Earth ascending_node~STAHR_Rocket#J2000 ascending_node~STAHR_Rocket#TOD~Earth asymptote_decl~STAHR_Rocket#J2000 asymptote_decl~STAHR_Rocket#TOD~Earth asymptote_right_ascension~STAHR_Rocket#J2000 asymptote_right_ascension~STAHR_Rocket#TOD~Earth atmos_density~STAHR_Rocket atmos_pressure~STAHR_Rocket atmos_temperature~STAHR_Rocket bank_angle_rate~STAHR_Rocket#A~STAHR_Rocket:Earth bank_angle~STAHR_Rocket#A~STAHR_Rocket:Earth bending_moment~STAHR_Rocket burn_time~IB3:STAHR_Rocket cross_range~STAHR_Rocket declination~STAHR_Rocket#J2000@Earth declination~STAHR_Rocket#PCPF~Earth@Earth declination~STAHR_Rocket#TOD~Earth@Earth DECL~STAHR_Rocket dimension_x~STAHR_Rocket dimension_y~STAHR_Rocket dimension_z~STAHR_Rocket down_range~STAHR_Rocket drag_acc~STAHR_Rocket drag_coeff~STAHR_Rocket drag~STAHR_Rocket dummy_altitude dummy_declination dummy_latitude dummy_longitude dummy_radius dynamic_pressure~STAHR_Rocket dyn_viscosity~STAHR_Rocket eccentricity~STAHR_Rocket@Earth eccentric_anomaly~STAHR_Rocket@Earth energy_total~STAHR_Rocket@Earth equinoctial_f~STAHR_Rocket#TOD~Earth equinoctial_g~STAHR_Rocket#TOD~Earth equinoctial_h~STAHR_Rocket#TOD~Earth equinoctial_k~STAHR_Rocket#TOD~Earth equinoctial_l~STAHR_Rocket#TOD~Earth equinoctial_p~STAHR_Rocket@Earth excess_velocity~STAHR_Rocket@Earth flightpath_angle~STAHR_Rocket#LD~STAHR_Rocket:Earth flightpath_angle~STAHR_Rocket#L~STAHR_Rocket:Earth flightpath_heading~STAHR_Rocket#LD~STAHR_Rocket:Earth flightpath_heading~STAHR_Rocket#L~STAHR_Rocket:Earth flightpath_speed~STAHR_Rocket flightpath_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth flight_time flight_time~STAHR_Rocket force_aero_axial~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket force_aero_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket force_aero_lateral~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket force_aero_normal~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket force_aero_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket force_aero_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket gps_time gravity_force_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket gravity_force_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket gravity_force_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket gravity_potential~Esrange gravity_potential~STAHR_Rocket@Earth gravity~STAHR_Rocket great_circle_distance~STAHR_Rocket heat_flux_density~STAHR_Rocket hload_lift~STAHR_Rocket inclination~STAHR_Rocket#J2000 inclination~STAHR_Rocket#TOD~Earth inertial_flightpath_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth inertial_flightpath~STAHR_Rocket#L~STAHR_Rocket:Earth inertial_heading~STAHR_Rocket#L~STAHR_Rocket:Earth inertial_speed~STAHR_Rocket inertia_xx~STAHR_Rocket inertia_xy~STAHR_Rocket inertia_xz~STAHR_Rocket inertia_yy~STAHR_Rocket inertia_yz~STAHR_Rocket inertia_zz~STAHR_Rocket isp_vacuum~IB3:STAHR_Rocket isp~IB3:STAHR_Rocket julian_date_tt julian_date_utc knudsen_number~STAHR_Rocket latitude~STAHR_Rocket#PCPF~Earth@Earth lift_acc~STAHR_Rocket lift_coeff~STAHR_Rocket lift_drag_ratio~STAHR_Rocket lift~STAHR_Rocket load_factor_axial~STAHR_Rocket load_factor_cross~STAHR_Rocket load_factor_normal~STAHR_Rocket load_factor~STAHR_Rocket local_central_body_radius~STAHR_Rocket longitude~STAHR_Rocket#PCPF~Earth@Earth LONG~STAHR_Rocket mach~STAHR_Rocket magnetic_flux_density_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket magnetic_flux_density_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket magnetic_flux_density_radial~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket magnetic_flux_density_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket magnetic_flux_density_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket magnetic_flux_density_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket magnetic_flux_density~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket mass_flow~IB3:STAHR_Rocket mass_total~STAHR_Rocket mean_anomaly~STAHR_Rocket@Earth mean_local_time_of_the_ascending_node~STAHR_Rocket mean_motion~STAHR_Rocket@Earth mission_time moment_aero_x~STAHR_Rocket#B~STAHR_Rocket moment_aero_y~STAHR_Rocket#B~STAHR_Rocket moment_aero_z~STAHR_Rocket#B~STAHR_Rocket Norm_Indep_Var nozzle_area~IB3:STAHR_Rocket OMEGA_X~STAHR_Rocket omega_x~STAHR_Rocket#B~STAHR_Rocket OMEGA_Y~STAHR_Rocket omega_y~STAHR_Rocket#B~STAHR_Rocket OMEGA_Z~STAHR_Rocket omega_z~STAHR_Rocket#B~STAHR_Rocket orbital_period~STAHR_Rocket@Earth periapsis_argument~STAHR_Rocket#J2000 periapsis_argument~STAHR_Rocket#TOD~Earth pitch_rate~STAHR_Rocket#J2000 pitch_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth pitch_rate~STAHR_Rocket#L~STAHR_Rocket:Earth PITCH~Esrange#L~Esrange:Earth PITCH~STAHR_Rocket pitch~STAHR_Rocket#J2000 pitch~STAHR_Rocket#LD~STAHR_Rocket:Earth pitch~STAHR_Rocket#LO~STAHR_Rocket:Earth pitch~STAHR_Rocket#L~STAHR_Rocket:Earth pitch~STAHR_Rocket#N~STAHR_Rocket pitch~STAHR_Rocket#TOD~Earth PROP_MASS~STAHR:STAHR_Rocket quaternion_w~Earth#J2000 quaternion_w~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_w~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_w~STAHR_Rocket#J2000 quaternion_w~STAHR_Rocket#L~STAHR_Rocket:Earth quaternion_x~Earth#J2000 quaternion_x~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_x~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_x~STAHR_Rocket#J2000 quaternion_x~STAHR_Rocket#L~STAHR_Rocket:Earth quaternion_y~Earth#J2000 quaternion_y~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_y~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_y~STAHR_Rocket#J2000 quaternion_y~STAHR_Rocket#L~STAHR_Rocket:Earth quaternion_z~Earth#J2000 quaternion_z~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_z~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_z~STAHR_Rocket#J2000 quaternion_z~STAHR_Rocket#L~STAHR_Rocket:Earth radius_apoapsis~STAHR_Rocket@Earth radius_periapsis~STAHR_Rocket@Earth radius~STAHR_Rocket@Earth reynolds_number~STAHR_Rocket right_ascension~STAHR_Rocket#J2000@Earth right_ascension~STAHR_Rocket#TOD~Earth@Earth roll_rate~STAHR_Rocket#J2000 roll_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth roll_rate~STAHR_Rocket#L~STAHR_Rocket:Earth ROLL~Esrange#L~Esrange:Earth ROLL~STAHR_Rocket roll~STAHR_Rocket#J2000 roll~STAHR_Rocket#LD~STAHR_Rocket:Earth roll~STAHR_Rocket#LO~STAHR_Rocket:Earth roll~STAHR_Rocket#L~STAHR_Rocket:Earth roll~STAHR_Rocket#N~STAHR_Rocket roll~STAHR_Rocket#TOD~Earth R~STAHR_Rocket semimajor~STAHR_Rocket@Earth sideslip_rate~STAHR_Rocket#A~STAHR_Rocket:Earth sideslip~STAHR_Rocket#A~STAHR_Rocket:Earth side_force~STAHR_Rocket SIDE_SLIP_ANGLE~Esrange#A~Esrange:Earth SIDE_SLIP_ANGLE~STAHR_Rocket#A~STAHR_Rocket:Earth solar_beta_angle~STAHR_Rocket#PF~STAHR_Rocket:Earth sonic_velocity~STAHR_Rocket static_stability_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket thrust_to_ew~STAHR_Rocket thrust_vacuum~IB3:STAHR_Rocket thrust_x~STAHR_Rocket#J2000 thrust_y~STAHR_Rocket#J2000 thrust_z~STAHR_Rocket#J2000 thrust~IB3:STAHR_Rocket thrust~STAHR_Rocket Time torque_x~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket torque_x~STAHR_Rocket#B~STAHR_Rocket torque_y~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket torque_y~STAHR_Rocket#B~STAHR_Rocket torque_z~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket torque_z~STAHR_Rocket#B~STAHR_Rocket total_angle_of_attack_slope_normal_coefficient~STAHR_Rocket#TA~STAHR_Rocket@STAHR_Rocket total_angle_of_attack~STAHR_Rocket#A~STAHR_Rocket:Earth total_lift~STAHR_Rocket total_solar_irradiance_at_earth~STAHR_Rocket TRAJ_SMOOTHNESS~STAHR_Rocket true_anomaly~STAHR_Rocket@Earth u_length~STAHR_Rocket#LO~STAHR_Rocket:Earth u_length~STAHR_Rocket#L~STAHR_Rocket:Earth u_n~STAHR_Rocket#LO~STAHR_Rocket:Earth u_r~STAHR_Rocket#LO~STAHR_Rocket:Earth u_t~STAHR_Rocket#LO~STAHR_Rocket:Earth u_x~STAHR_Rocket#L~STAHR_Rocket:Earth u_y~STAHR_Rocket#L~STAHR_Rocket:Earth u_z~STAHR_Rocket#L~STAHR_Rocket:Earth velocity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket velocity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket velocity_rel_apoapsis~STAHR_Rocket@Earth velocity_rel_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket velocity_rel_periapsis~STAHR_Rocket@Earth velocity_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket VI_EAST~STAHR_Rocket VI_NORTH~STAHR_Rocket VI_RADIAL~STAHR_Rocket vload_lift~STAHR_Rocket vx~STAHR_Rocket#J2000@Earth vx~STAHR_Rocket#TOD~Earth@Earth vy~STAHR_Rocket#J2000@Earth vy~STAHR_Rocket#TOD~Earth@Earth vz~STAHR_Rocket#J2000@Earth vz~STAHR_Rocket#TOD~Earth@Earth wind_gust_p~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_gust_q~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_gust_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_velocity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_velocity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_velocity_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket x_offset_CoM~Esrange x_offset_CoM~Main_Stage:STAHR_Rocket x_offset_CoM~STAHR_Rocket x_offset_CoP~STAHR_Rocket x_offset~IB3:STAHR_Rocket x_offset~STAHR:STAHR_Rocket x~Earth#J2000@SSB x~STAHR_Rocket#J2000@Earth x~STAHR_Rocket#TOD~Earth@Earth x~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket yaw_rate~STAHR_Rocket#J2000 yaw_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth yaw_rate~STAHR_Rocket#L~STAHR_Rocket:Earth YAW~Esrange#L~Esrange:Earth YAW~STAHR_Rocket yaw~STAHR_Rocket#J2000 yaw~STAHR_Rocket#LD~STAHR_Rocket:Earth yaw~STAHR_Rocket#LO~STAHR_Rocket:Earth yaw~STAHR_Rocket#L~STAHR_Rocket:Earth yaw~STAHR_Rocket#N~STAHR_Rocket yaw~STAHR_Rocket#TOD~Earth y_offset_CoM~Esrange y_offset_CoM~Main_Stage:STAHR_Rocket y_offset_CoM~STAHR_Rocket y_offset_CoP~STAHR_Rocket y_offset~IB3:STAHR_Rocket y_offset~STAHR:STAHR_Rocket y~Earth#J2000@SSB y~STAHR_Rocket#J2000@Earth y~STAHR_Rocket#TOD~Earth@Earth y~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket z_offset_CoM~Esrange z_offset_CoM~Main_Stage:STAHR_Rocket z_offset_CoM~STAHR_Rocket z_offset_CoP~STAHR_Rocket z_offset~IB3:STAHR_Rocket z_offset~STAHR:STAHR_Rocket z~Earth#J2000@SSB z~STAHR_Rocket#J2000@Earth z~STAHR_Rocket#TOD~Earth@Earth z~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket z~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket -Phase Index East Acceleration w/o Gravity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Acceleration w/o Gravity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Acceleration w/o Gravity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket X-Acceleration w/o Gravity of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Y-Acceleration w/o Gravity of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Z-Acceleration w/o Gravity of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Norm of the Total Acceleration w/o Gravity of STAHR_Rocket Acceleration X of STAHR_Rocket in J2000 at Earth Acceleration Y of STAHR_Rocket in J2000 at Earth Acceleration Z of STAHR_Rocket in J2000 at Earth Acceleration of STAHR_Rocket in J2000 at Earth East Aerodynamic Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Aerodynamic Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Aerodynamic Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Aerodynamic Acceleration of STAHR_Rocket Total Normal Acceleration w/o Gravity of STAHR_Rocket East Thrust Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Thrust Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Thrust Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Thrust Acceleration of STAHR_Rocket Aerodynamic Reference Area of STAHR_Rocket Aerodynamic Bank Angle of STAHR_Rocket Aerodynamic Roll Angle of STAHR_Rocket Air Path Angle of STAHR_Rocket in LD of STAHR_Rocket of Earth Air Path Angle of STAHR_Rocket in L of STAHR_Rocket of Earth Airpath Bank Angle of Esrange in A of Esrange of Earth Airpath Bank Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Air Path Heading of STAHR_Rocket in LD of STAHR_Rocket of Earth Air Path Heading of STAHR_Rocket in L of STAHR_Rocket of Earth Air Path Speed of STAHR_Rocket Apoapsis Altitude of STAHR_Rocket at Earth Periapsis Altitude of STAHR_Rocket at Earth Altitude of STAHR_Rocket at Earth Angle 1 of IB3 of STAHR_Rocket in B of STAHR_Rocket Angle 1 of IB3 of STAHR_Rocket in J2000 Angle 1 of STAHR of STAHR_Rocket in B of STAHR_Rocket Angle 1 of STAHR of STAHR_Rocket in J2000 Angle 2 of IB3 of STAHR_Rocket in B of STAHR_Rocket Angle 2 of IB3 of STAHR_Rocket in J2000 Angle 2 of STAHR of STAHR_Rocket in B of STAHR_Rocket Angle 2 of STAHR of STAHR_Rocket in J2000 Angle 3 of IB3 of STAHR_Rocket in B of STAHR_Rocket Angle 3 of IB3 of STAHR_Rocket in J2000 Angle 3 of STAHR of STAHR_Rocket in B of STAHR_Rocket Angle 3 of STAHR of STAHR_Rocket in J2000 Angle Body Axis and Local Vertical Axis of STAHR_Rocket in L of STAHR_Rocket of Earth Angle of Attack Rate of STAHR_Rocket in A of STAHR_Rocket of Earth Angle of Attack of Esrange in A of Esrange of Earth Angle of Attack of STAHR_Rocket in A of STAHR_Rocket of Earth Angular velocity of STAHR_Rocket in B of STAHR_Rocket Distance along Trajectory of STAHR_Rocket Relative Ascending Node of STAHR_Rocket in TOD of Earth Osculating Ascending Node of STAHR_Rocket in J2000 Osculating Ascending Node of STAHR_Rocket in TOD of Earth Asymptote Declination of STAHR_Rocket in J2000 Asymptote Declination of STAHR_Rocket in TOD of Earth Asymptote Right_Ascension of STAHR_Rocket in J2000 Asymptote Right Ascension of STAHR_Rocket in TOD of Earth Atmospheric Density of STAHR_Rocket Atmospheric Pressure of STAHR_Rocket Atmospheric Temperature of STAHR_Rocket Airpath Bank Angle Rate of STAHR_Rocket in A of STAHR_Rocket of Earth Airpath Bank Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Bending Moment of STAHR_Rocket Burn Time of IB3 of STAHR_Rocket Cross Range of STAHR_Rocket Declination of STAHR_Rocket in J2000 at Earth Declination of STAHR_Rocket in PCPF~Earth at Earth Declination of STAHR_Rocket in TOD~Earth at Earth Declination of STAHR_Rocket X Dimension of STAHR_Rocket Y Dimension of STAHR_Rocket Z Dimension of STAHR_Rocket Down Range of STAHR_Rocket Drag Acceleration of STAHR_Rocket Drag Coefficient of STAHR_Rocket Drag Force of STAHR_Rocket Dummy Altitude Dummy Declination Dummy Latitude Dummy Longitude Dummy Radius Dynamic Pressure of STAHR_Rocket Dynamic Viscosity of STAHR_Rocket Osculating Eccentricity of STAHR_Rocket at Earth Osculating Eccentric Anomaly of STAHR_Rocket at Earth Total Energy of STAHR_Rocket at Earth Equinoctial Element f of STAHR_Rocket in TOD of Earth Equinoctial Element g of STAHR_Rocket in TOD of Earth Equinoctial Element h of STAHR_Rocket in TOD of Earth Equinoctial Element k of STAHR_Rocket in TOD of Earth Equinoctial Element L of STAHR_Rocket in TOD of Earth Equinoctial Element p of STAHR_Rocket at Earth Excess Velocity of STAHR_Rocket at Earth Flight Path Angle of STAHR_Rocket in LD of STAHR_Rocket of Earth Flight Path Angle of STAHR_Rocket in L of STAHR_Rocket of Earth Flight Path Heading of STAHR_Rocket in LD of STAHR_Rocket of Earth Flight Path Heading of STAHR_Rocket in L of STAHR_Rocket of Earth Flight Path Speed of STAHR_Rocket Angle Relative Velocity and Local Vertical of STAHR_Rocket in L of STAHR_Rocket of Earth Flight Time Flight time of STAHR_Rocket Axial Aerodynamic Force of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket East Aerodynamic Force of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Lateral Aerodynamic Force of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Normal Aerodynamic Force of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket North Aerodynamic Force of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Aerodynamic Force of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket GPS Time Gravity Force x-Component of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Gravity Force y-Component of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Gravity Force z-Component of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Gravity Potential Energy of Esrange Gravity Potential Energy of STAHR_Rocket at Earth Gravity Acceleration of STAHR_Rocket Distance along Great Circle of STAHR_Rocket Heat Flux Density of STAHR_Rocket Horizontal Lift Load Factor of STAHR_Rocket Osculating Inclination of STAHR_Rocket in J2000 Osculating Inclination of STAHR_Rocket in TOD of Earth Angle Inertial Velocity and Local Vertical of STAHR_Rocket in L of STAHR_Rocket of Earth Inertial Flight Path Angle of STAHR_Rocket in L of STAHR_Rocket of Earth Inertial Heading of STAHR_Rocket in L of STAHR_Rocket of Earth Inertial Speed of STAHR_Rocket Moment of Inertia Ixx of STAHR_Rocket Moment of Inertia Ixy of STAHR_Rocket Moment of Inertia Ixz of STAHR_Rocket Moment of Inertia Iyy of STAHR_Rocket Moment of Inertia Iyz of STAHR_Rocket Moment of Inertia Izz of STAHR_Rocket Vacuum Specific Impulse of IB3 of STAHR_Rocket Specific Impulse of IB3 of STAHR_Rocket Julian Date (TT) Julian Date (UTC) Knudsen Number of STAHR_Rocket Geodetic Latitude of STAHR_Rocket in PCPF~Earth at Earth Lift Acceleration of STAHR_Rocket Lift Coefficient of STAHR_Rocket Lift to Drag Ratio of STAHR_Rocket Lift Force of STAHR_Rocket Axial Load Factor of STAHR_Rocket Side Load Factor of STAHR_Rocket Normal Load Factor of STAHR_Rocket Total Load Factor of STAHR_Rocket Local Central Body Radius of STAHR_Rocket Longitude of STAHR_Rocket in PCPF~Earth at Earth Longitude of STAHR_Rocket Mach Number of STAHR_Rocket Magnetic Flux Density East of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Magnetic Flux Density North of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Magnetic Flux Density Radial of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Magnetic Flux Density X of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Magnetic Flux Density Y of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Magnetic Flux Density Z of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Magnetic Flux Density of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Total Mass Flow of IB3 of STAHR_Rocket Total Mass of STAHR_Rocket Osculating Mean Anomaly of STAHR_Rocket at Earth Mean Local Time of the Ascending Node of STAHR_Rocket Mean Motion of STAHR_Rocket at Earth Mission Time Aerodynamic Moment x of STAHR_Rocket in B of STAHR_Rocket Aerodynamic Moment y of STAHR_Rocket in B of STAHR_Rocket Aerodynamic Moment z of STAHR_Rocket in B of STAHR_Rocket Normalized Independent Variable Total Exit Area of IB3 of STAHR_Rocket Angular Velocity about Body x-axis of STAHR_Rocket Angular Velocity x of STAHR_Rocket in B of STAHR_Rocket Angular Velocity about Body y-axis of STAHR_Rocket Angular Velocity y of STAHR_Rocket in B of STAHR_Rocket Angular Velocity about Body z-axis of STAHR_Rocket Angular Velocity z of STAHR_Rocket in B of STAHR_Rocket Orbital Period of STAHR_Rocket at Earth Osculating Periapsis Argument of STAHR_Rocket in J2000 Osculating Periapsis Argument of STAHR_Rocket in TOD of Earth Pitch Rate of STAHR_Rocket in J2000 Pitch Rate of STAHR_Rocket in LO of STAHR_Rocket of Earth Pitch Rate of STAHR_Rocket in L of STAHR_Rocket of Earth Pitch Angle of Esrange in L of Esrange of Earth Pitch Angle of STAHR_Rocket Pitch of STAHR_Rocket in J2000 Pitch of STAHR_Rocket in LD of STAHR_Rocket of Earth Pitch of STAHR_Rocket in LO of STAHR_Rocket of Earth Pitch of STAHR_Rocket in L of STAHR_Rocket of Earth Pitch of STAHR_Rocket in N of STAHR_Rocket Pitch of STAHR_Rocket in TOD of Earth Propellant Mass of STAHR of STAHR_Rocket Quaternion w of Earth in J2000 Quaternion w of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion w of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion w of STAHR_Rocket in J2000 Quaternion w of STAHR_Rocket in L of STAHR_Rocket of Earth Quaternion x of Earth in J2000 Quaternion x of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion x of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion x of STAHR_Rocket in J2000 Quaternion x of STAHR_Rocket in L of STAHR_Rocket of Earth Quaternion y of Earth in J2000 Quaternion y of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion y of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion y of STAHR_Rocket in J2000 Quaternion y of STAHR_Rocket in L of STAHR_Rocket of Earth Quaternion z of Earth in J2000 Quaternion z of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion z of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion z of STAHR_Rocket in J2000 Quaternion z of STAHR_Rocket in L of STAHR_Rocket of Earth Apoapsis Radius of STAHR_Rocket at Earth Periapsis Radius of STAHR_Rocket at Earth Radial Position of STAHR_Rocket at Earth Reynolds Number of STAHR_Rocket Right Ascension of STAHR_Rocket in J2000 at Earth Right Ascension of STAHR_Rocket in TOD~Earth at Earth Roll Rate of STAHR_Rocket in J2000 Roll Rate of STAHR_Rocket in LO of STAHR_Rocket of Earth Roll Rate of STAHR_Rocket in L of STAHR_Rocket of Earth Roll Angle of Esrange in L of Esrange of Earth Roll Angle of STAHR_Rocket Roll of STAHR_Rocket in J2000 Roll of STAHR_Rocket in LD of STAHR_Rocket of Earth Roll of STAHR_Rocket in LO of STAHR_Rocket of Earth Roll of STAHR_Rocket in L of STAHR_Rocket of Earth Roll of STAHR_Rocket in N of STAHR_Rocket Roll of STAHR_Rocket in TOD of Earth Radial Position of STAHR_Rocket Osculating Semimajor of STAHR_Rocket at Earth Sideslip Angle Rate of STAHR_Rocket in A of STAHR_Rocket of Earth Sideslip Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Side Force of STAHR_Rocket Side Slip Angle of Esrange in A of Esrange of Earth Side Slip Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Solar beta angle of STAHR_Rocket in PF of STAHR_Rocket of Earth Speed of Sound of STAHR_Rocket Static Stability along x-axis of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Thrust to Effective Weight Ratio of STAHR_Rocket Vacuum Thrust of IB3 of STAHR_Rocket Total Thrust x of STAHR_Rocket in J2000 Total Thrust y of STAHR_Rocket in J2000 Total Thrust z of STAHR_Rocket in J2000 Thrust of IB3 of STAHR_Rocket Total Thrust of STAHR_Rocket Time Torque x of IB3 of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Torque x of STAHR_Rocket in B of STAHR_Rocket Torque y of IB3 of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Torque y of STAHR_Rocket in B of STAHR_Rocket Torque z of IB3 of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Torque z of STAHR_Rocket in B of STAHR_Rocket Total Angle of Attack Slope Normal Coefficient of STAHR_Rocket in TA~STAHR_Rocket at STAHR_Rocket Total Angle of Attack of STAHR_Rocket in A of STAHR_Rocket of Earth Total Lift Force of STAHR_Rocket Total Solar Irradiance At Earth of STAHR_Rocket Integral of Gamma-Dot Squared of STAHR_Rocket Osculating True Anomaly of STAHR_Rocket at Earth Control Vector Length of STAHR_Rocket in LO of STAHR_Rocket of Earth Control Vector Length of STAHR_Rocket in L of STAHR_Rocket of Earth Normal Control Portion of STAHR_Rocket in LO of STAHR_Rocket of Earth Radial Control Portion of STAHR_Rocket in LO of STAHR_Rocket of Earth Transverse Control Portion of STAHR_Rocket in LO of STAHR_Rocket of Earth Control Vector x of STAHR_Rocket in L of STAHR_Rocket of Earth Control Vector y of STAHR_Rocket in L of STAHR_Rocket of Earth Control Vector z of STAHR_Rocket in L of STAHR_Rocket of Earth East Inertial Velocity Component of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Velocity Component of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Relative Velocity at Apoapsis of STAHR_Rocket at Earth East Relative Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Relative Velocity at Periapsis of STAHR_Rocket at Earth Radial Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Inertial Longitudinal Velocity of STAHR_Rocket Inertial Latitudinal Velocity of STAHR_Rocket Inertial Radial Velocity of STAHR_Rocket Vertical Lift Load Factor of STAHR_Rocket vx of STAHR_Rocket in J2000 at Earth vx of STAHR_Rocket in TOD~Earth at Earth vy of STAHR_Rocket in J2000 at Earth vy of STAHR_Rocket in TOD~Earth at Earth vz of STAHR_Rocket in J2000 at Earth vz of STAHR_Rocket in TOD~Earth at Earth Wind turbolence p of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Wind turbolence q of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Wind turbolence r of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket East Wind Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Wind Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Wind Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket x-Offset Center of Mass wrt Global Node of Esrange x-Offset Center of Mass wrt Global Node of Main_Stage of STAHR_Rocket x-Offset Center of Mass wrt Global Node of STAHR_Rocket x-Offset of Static Center of Pressure of STAHR_Rocket x-Offset of Bottom Node wrt Global Node of IB3 of STAHR_Rocket x-Offset of Bottom Node wrt Global Node of STAHR of STAHR_Rocket x-Position of Earth in J2000 at SSB x of STAHR_Rocket in J2000 at Earth x of STAHR_Rocket in TOD~Earth at Earth x of Sun in L~STAHR_Rocket:Earth at STAHR_Rocket Yaw Rate of STAHR_Rocket in J2000 Yaw Rate of STAHR_Rocket in LO of STAHR_Rocket of Earth Yaw Rate of STAHR_Rocket in L of STAHR_Rocket of Earth Yaw Angle of Esrange in L of Esrange of Earth Yaw Angle of STAHR_Rocket Yaw of STAHR_Rocket in J2000 Yaw of STAHR_Rocket in LD of STAHR_Rocket of Earth Yaw of STAHR_Rocket in LO of STAHR_Rocket of Earth Yaw of STAHR_Rocket in L of STAHR_Rocket of Earth Yaw of STAHR_Rocket in N of STAHR_Rocket Yaw of STAHR_Rocket in TOD of Earth y-Offset Center of Mass wrt Global Node of Esrange y-Offset Center of Mass wrt Global Node of Main_Stage of STAHR_Rocket y-Offset Center of Mass wrt Global Node of STAHR_Rocket y-Offset of Static Center of Pressure of STAHR_Rocket y-Offset of Bottom Node wrt Global Node of IB3 of STAHR_Rocket y-Offset of Bottom Node wrt Global Node of STAHR of STAHR_Rocket y-Position of Earth in J2000 at SSB y of STAHR_Rocket in J2000 at Earth y of STAHR_Rocket in TOD~Earth at Earth y of Sun in L~STAHR_Rocket:Earth at STAHR_Rocket z-Offset Center of Mass wrt Global Node of Esrange z-Offset Center of Mass wrt Global Node of Main_Stage of STAHR_Rocket z-Offset Center of Mass wrt Global Node of STAHR_Rocket z-Offset of Static Center of Pressure of STAHR_Rocket z-Offset of Bottom Node wrt Global Node of IB3 of STAHR_Rocket z-Offset of Bottom Node wrt Global Node of STAHR of STAHR_Rocket z-Position of Earth in J2000 at SSB z of STAHR_Rocket in J2000 at Earth z of STAHR_Rocket in TOD~Earth at Earth z of Sun in L~STAHR_Rocket:Earth at STAHR_Rocket z of Sun in L~STAHR_Rocket:Earth at 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-0.0392850771601222 -0.0161938118353275 0.0495000114103828 0.00704692610094306 9.827236994211862 0.486448343346023 +1721 576.443622102058 7.0 67.9989700277821 21.0392192692845 68.1323231415289 0.0159678895976394 -32.6301481231519 3.2015459389987617 -4.753464703129173 -0.015620120493065196 0.0036496128548032963 -0.015808419663472274 -87.7191149114784 1196.10344659034 -160.508118456985 101133.322992541 288.046208978333 340.232815657511 0.0207089110598809 0.0 -0.0392850771601222 -0.0161938118353275 0.0495000114103828 0.00704585111910426 9.827245926225629 0.486448785480806 +1722 576.895828130633 7.0 67.9989699607986 21.0392192722696 68.1323231413497 0.0127819562274963 -32.6372139896236 3.2031963556790526 -4.760611570757392 -0.015630506961728255 0.0036497812312157067 -0.015800649603206506 -86.8131241474099 1209.44292083153 -173.831191007427 101171.543086884 288.066917451581 340.245045597208 0.0207050084428624 0.0 -0.0392850771601222 -0.0161938118353275 0.0495000114103828 0.0070447765417323 9.827254857294147 0.4864492275688 +1723 577.348034159208 7.0 67.9989698938252 21.0392192752538 68.1323231411707 0.00959650869946927 -32.64428455292981 3.204846848494415 -4.767754923801935 -0.015640893444895027 0.003649949582268962 -0.015792875504677686 -85.8125589570996 1216.66788787756 -181.03973686619 101209.769083504 288.087622787621 340.257273244661 0.0207011074338532 0.0 -0.0392850771601222 -0.0161938118353275 0.0495000114103828 0.00704370236858767 9.82726378742439 0.486449669610349 +1724 577.800240187784 7.0 67.9989698268621 21.0392192782371 68.1323231409916 0.00641154682077467 -32.65135981307708 3.2064974174333876 -4.774894760436862 -0.01565127994256436 0.0036501179079728354 -0.01578509736828867 -84.7714352881877 1221.06879619991 -185.424201779614 101248.00098212 288.108324987699 340.269498600851 0.0206972080318687 0.0 -0.0392850771601222 -0.0161938118353275 0.0495000114103828 0.00704262859944146 9.827272716663874 0.486450111607805 +1725 578.252446216359 7.0 67.9989697599092 21.0392192812196 68.1323231408127 0.00322707042004913 -32.658439770071915 3.208148062484551 -4.78203107883629 -0.015661666454734754 0.0036502862083371817 -0.015777315194466304 -83.7098605299198 1224.00082653543 -188.339763746944 101286.238782193 288.129024052918 340.281721666677 0.0206933102358452 0.0 -0.0392850771601222 -0.0161938118353275 0.0495000114103828 0.00704155523403609 9.82728164495947 0.486450553558539 +1726 578.704652244934 7.0 67.9989696929664 21.039219284201 68.1323231406339 4.30793119594455e-05 -32.665524423920935 3.209798783636286 -4.789163877174405 -0.015672052981405907 0.0036504544833718855 -0.01576952898334364 -82.6366748494293 1226.08572927553 -190.408170412013 101324.482483355 288.149719984474 340.293942443092 0.0206894140454426 0.0 -0.0392850771601222 -0.0161938118353275 0.0495000114103828 0.00704048227236115 9.82729057296076 0.486450995494705 +1727 578.704652244934 7.0 67.9989696929664 21.039219284201 68.1323231406339 4.30793119594455e-05 -32.665524423920935 3.209798783636286 -4.789163877174405 -0.015672052981405907 0.0036504544833718855 -0.01576952898334364 -82.6366748494293 1226.08572927553 -190.408170412013 101324.482483355 288.149719984474 340.293942443092 0.0206894140454426 0.0 -0.0392850771601222 -0.0161938118353275 0.0495000114103828 0.00704048227236115 9.82729057296076 0.486450995494705 diff --git a/data/simulations/raw/40km.txt b/data/simulations/raw/40km.txt new file mode 100644 index 0000000..d0215c5 --- /dev/null +++ b/data/simulations/raw/40km.txt @@ -0,0 +1,1724 @@ +Phase acceleration_without_gravity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acceleration_without_gravity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acceleration_without_gravity_radial~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket acceleration_without_gravity_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket acceleration_without_gravity_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket acceleration_without_gravity~STAHR_Rocket acceleration_x~STAHR_Rocket#J2000@Earth acceleration_y~STAHR_Rocket#J2000@Earth acceleration_z~STAHR_Rocket#J2000@Earth acceleration~STAHR_Rocket#J2000@Earth acc_aero_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_aero_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_aero_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_aero~STAHR_Rocket acc_normal~STAHR_Rocket acc_thrust_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_thrust_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_thrust_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_thrust~STAHR_Rocket aero_area_ref~STAHR_Rocket aero_bank_angle~STAHR_Rocket aero_roll_angle~STAHR_Rocket airpath_angle~STAHR_Rocket#LD~STAHR_Rocket:Earth airpath_angle~STAHR_Rocket#L~STAHR_Rocket:Earth AIRPATH_BANK_ANGLE~Esrange#A~Esrange:Earth AIRPATH_BANK_ANGLE~STAHR_Rocket#A~STAHR_Rocket:Earth airpath_heading~STAHR_Rocket#LD~STAHR_Rocket:Earth airpath_heading~STAHR_Rocket#L~STAHR_Rocket:Earth airpath_speed~STAHR_Rocket altitude_apoapsis~STAHR_Rocket@Earth altitude_periapsis~STAHR_Rocket@Earth altitude~STAHR_Rocket@Earth angle1~IB3:STAHR_Rocket#B~STAHR_Rocket angle1~IB3:STAHR_Rocket#J2000 angle1~STAHR:STAHR_Rocket#B~STAHR_Rocket angle1~STAHR:STAHR_Rocket#J2000 angle2~IB3:STAHR_Rocket#B~STAHR_Rocket angle2~IB3:STAHR_Rocket#J2000 angle2~STAHR:STAHR_Rocket#B~STAHR_Rocket angle2~STAHR:STAHR_Rocket#J2000 angle3~IB3:STAHR_Rocket#B~STAHR_Rocket angle3~IB3:STAHR_Rocket#J2000 angle3~STAHR:STAHR_Rocket#B~STAHR_Rocket angle3~STAHR:STAHR_Rocket#J2000 angle_body_and_local_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth angle_of_attack_rate~STAHR_Rocket#A~STAHR_Rocket:Earth ANGLE_OF_ATTACK~Esrange#A~Esrange:Earth angle_of_attack~STAHR_Rocket#A~STAHR_Rocket:Earth angular_velocity~STAHR_Rocket#B~STAHR_Rocket arc_length~STAHR_Rocket ascending_node_rel~STAHR_Rocket#TOD~Earth ascending_node~STAHR_Rocket#J2000 ascending_node~STAHR_Rocket#TOD~Earth asymptote_decl~STAHR_Rocket#J2000 asymptote_decl~STAHR_Rocket#TOD~Earth asymptote_right_ascension~STAHR_Rocket#J2000 asymptote_right_ascension~STAHR_Rocket#TOD~Earth atmos_density~STAHR_Rocket atmos_pressure~STAHR_Rocket atmos_temperature~STAHR_Rocket bank_angle_rate~STAHR_Rocket#A~STAHR_Rocket:Earth bank_angle~STAHR_Rocket#A~STAHR_Rocket:Earth bending_moment~STAHR_Rocket burn_time~IB3:STAHR_Rocket cross_range~STAHR_Rocket declination~STAHR_Rocket#J2000@Earth declination~STAHR_Rocket#PCPF~Earth@Earth declination~STAHR_Rocket#TOD~Earth@Earth DECL~STAHR_Rocket dimension_x~STAHR_Rocket dimension_y~STAHR_Rocket dimension_z~STAHR_Rocket down_range~STAHR_Rocket drag_acc~STAHR_Rocket drag_coeff~STAHR_Rocket drag~STAHR_Rocket dummy_altitude dummy_declination dummy_latitude dummy_longitude dummy_radius dynamic_pressure~STAHR_Rocket dyn_viscosity~STAHR_Rocket eccentricity~STAHR_Rocket@Earth eccentric_anomaly~STAHR_Rocket@Earth energy_total~STAHR_Rocket@Earth equinoctial_f~STAHR_Rocket#TOD~Earth equinoctial_g~STAHR_Rocket#TOD~Earth equinoctial_h~STAHR_Rocket#TOD~Earth equinoctial_k~STAHR_Rocket#TOD~Earth equinoctial_l~STAHR_Rocket#TOD~Earth equinoctial_p~STAHR_Rocket@Earth excess_velocity~STAHR_Rocket@Earth flightpath_angle~STAHR_Rocket#LD~STAHR_Rocket:Earth flightpath_angle~STAHR_Rocket#L~STAHR_Rocket:Earth flightpath_heading~STAHR_Rocket#LD~STAHR_Rocket:Earth flightpath_heading~STAHR_Rocket#L~STAHR_Rocket:Earth flightpath_speed~STAHR_Rocket flightpath_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth flight_time flight_time~STAHR_Rocket force_aero_axial~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket force_aero_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket force_aero_lateral~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket force_aero_normal~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket force_aero_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket force_aero_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket gps_time gravity_force_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket gravity_force_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket gravity_force_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket gravity_potential~Esrange gravity_potential~STAHR_Rocket@Earth gravity~STAHR_Rocket great_circle_distance~STAHR_Rocket heat_flux_density~STAHR_Rocket hload_lift~STAHR_Rocket inclination~STAHR_Rocket#J2000 inclination~STAHR_Rocket#TOD~Earth inertial_flightpath_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth inertial_flightpath~STAHR_Rocket#L~STAHR_Rocket:Earth inertial_heading~STAHR_Rocket#L~STAHR_Rocket:Earth inertial_speed~STAHR_Rocket inertia_xx~STAHR_Rocket inertia_xy~STAHR_Rocket inertia_xz~STAHR_Rocket inertia_yy~STAHR_Rocket inertia_yz~STAHR_Rocket inertia_zz~STAHR_Rocket isp_vacuum~IB3:STAHR_Rocket isp~IB3:STAHR_Rocket julian_date_tt julian_date_utc knudsen_number~STAHR_Rocket latitude~STAHR_Rocket#PCPF~Earth@Earth lift_acc~STAHR_Rocket lift_coeff~STAHR_Rocket lift_drag_ratio~STAHR_Rocket lift~STAHR_Rocket load_factor_axial~STAHR_Rocket load_factor_cross~STAHR_Rocket load_factor_normal~STAHR_Rocket load_factor~STAHR_Rocket local_central_body_radius~STAHR_Rocket longitude~STAHR_Rocket#PCPF~Earth@Earth LONG~STAHR_Rocket mach~STAHR_Rocket magnetic_flux_density_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket magnetic_flux_density_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket magnetic_flux_density_radial~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket magnetic_flux_density_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket magnetic_flux_density_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket magnetic_flux_density_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket magnetic_flux_density~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket mass_flow~IB3:STAHR_Rocket mass_total~STAHR_Rocket mean_anomaly~STAHR_Rocket@Earth mean_local_time_of_the_ascending_node~STAHR_Rocket mean_motion~STAHR_Rocket@Earth mission_time moment_aero_x~STAHR_Rocket#B~STAHR_Rocket moment_aero_y~STAHR_Rocket#B~STAHR_Rocket moment_aero_z~STAHR_Rocket#B~STAHR_Rocket Norm_Indep_Var nozzle_area~IB3:STAHR_Rocket OMEGA_X~STAHR_Rocket omega_x~STAHR_Rocket#B~STAHR_Rocket OMEGA_Y~STAHR_Rocket omega_y~STAHR_Rocket#B~STAHR_Rocket OMEGA_Z~STAHR_Rocket omega_z~STAHR_Rocket#B~STAHR_Rocket orbital_period~STAHR_Rocket@Earth periapsis_argument~STAHR_Rocket#J2000 periapsis_argument~STAHR_Rocket#TOD~Earth pitch_rate~STAHR_Rocket#J2000 pitch_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth pitch_rate~STAHR_Rocket#L~STAHR_Rocket:Earth PITCH~Esrange#L~Esrange:Earth PITCH~STAHR_Rocket pitch~STAHR_Rocket#J2000 pitch~STAHR_Rocket#LD~STAHR_Rocket:Earth pitch~STAHR_Rocket#LO~STAHR_Rocket:Earth pitch~STAHR_Rocket#L~STAHR_Rocket:Earth pitch~STAHR_Rocket#N~STAHR_Rocket pitch~STAHR_Rocket#TOD~Earth PROP_MASS~STAHR:STAHR_Rocket quaternion_w~Earth#J2000 quaternion_w~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_w~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_w~STAHR_Rocket#J2000 quaternion_w~STAHR_Rocket#L~STAHR_Rocket:Earth quaternion_x~Earth#J2000 quaternion_x~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_x~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_x~STAHR_Rocket#J2000 quaternion_x~STAHR_Rocket#L~STAHR_Rocket:Earth quaternion_y~Earth#J2000 quaternion_y~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_y~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_y~STAHR_Rocket#J2000 quaternion_y~STAHR_Rocket#L~STAHR_Rocket:Earth quaternion_z~Earth#J2000 quaternion_z~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_z~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_z~STAHR_Rocket#J2000 quaternion_z~STAHR_Rocket#L~STAHR_Rocket:Earth radius_apoapsis~STAHR_Rocket@Earth radius_periapsis~STAHR_Rocket@Earth radius~STAHR_Rocket@Earth reynolds_number~STAHR_Rocket right_ascension~STAHR_Rocket#J2000@Earth right_ascension~STAHR_Rocket#TOD~Earth@Earth roll_rate~STAHR_Rocket#J2000 roll_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth roll_rate~STAHR_Rocket#L~STAHR_Rocket:Earth ROLL~Esrange#L~Esrange:Earth ROLL~STAHR_Rocket roll~STAHR_Rocket#J2000 roll~STAHR_Rocket#LD~STAHR_Rocket:Earth roll~STAHR_Rocket#LO~STAHR_Rocket:Earth roll~STAHR_Rocket#L~STAHR_Rocket:Earth roll~STAHR_Rocket#N~STAHR_Rocket roll~STAHR_Rocket#TOD~Earth R~STAHR_Rocket semimajor~STAHR_Rocket@Earth sideslip_rate~STAHR_Rocket#A~STAHR_Rocket:Earth sideslip~STAHR_Rocket#A~STAHR_Rocket:Earth side_force~STAHR_Rocket SIDE_SLIP_ANGLE~Esrange#A~Esrange:Earth SIDE_SLIP_ANGLE~STAHR_Rocket#A~STAHR_Rocket:Earth solar_beta_angle~STAHR_Rocket#PF~STAHR_Rocket:Earth sonic_velocity~STAHR_Rocket static_stability_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket thrust_to_ew~STAHR_Rocket thrust_vacuum~IB3:STAHR_Rocket thrust_x~STAHR_Rocket#J2000 thrust_y~STAHR_Rocket#J2000 thrust_z~STAHR_Rocket#J2000 thrust~IB3:STAHR_Rocket thrust~STAHR_Rocket Time torque_x~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket torque_x~STAHR_Rocket#B~STAHR_Rocket torque_y~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket torque_y~STAHR_Rocket#B~STAHR_Rocket torque_z~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket torque_z~STAHR_Rocket#B~STAHR_Rocket total_angle_of_attack_slope_normal_coefficient~STAHR_Rocket#TA~STAHR_Rocket@STAHR_Rocket total_angle_of_attack~STAHR_Rocket#A~STAHR_Rocket:Earth total_lift~STAHR_Rocket total_solar_irradiance_at_earth~STAHR_Rocket TRAJ_SMOOTHNESS~STAHR_Rocket true_anomaly~STAHR_Rocket@Earth u_length~STAHR_Rocket#LO~STAHR_Rocket:Earth u_length~STAHR_Rocket#L~STAHR_Rocket:Earth u_n~STAHR_Rocket#LO~STAHR_Rocket:Earth u_r~STAHR_Rocket#LO~STAHR_Rocket:Earth u_t~STAHR_Rocket#LO~STAHR_Rocket:Earth u_x~STAHR_Rocket#L~STAHR_Rocket:Earth u_y~STAHR_Rocket#L~STAHR_Rocket:Earth u_z~STAHR_Rocket#L~STAHR_Rocket:Earth velocity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket velocity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket velocity_rel_apoapsis~STAHR_Rocket@Earth velocity_rel_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket velocity_rel_periapsis~STAHR_Rocket@Earth velocity_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket VI_EAST~STAHR_Rocket VI_NORTH~STAHR_Rocket VI_RADIAL~STAHR_Rocket vload_lift~STAHR_Rocket vx~STAHR_Rocket#J2000@Earth vx~STAHR_Rocket#TOD~Earth@Earth vy~STAHR_Rocket#J2000@Earth vy~STAHR_Rocket#TOD~Earth@Earth vz~STAHR_Rocket#J2000@Earth vz~STAHR_Rocket#TOD~Earth@Earth wind_gust_p~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_gust_q~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_gust_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_velocity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_velocity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_velocity_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket x_offset_CoM~Esrange x_offset_CoM~Main_Stage:STAHR_Rocket x_offset_CoM~STAHR_Rocket x_offset_CoP~STAHR_Rocket x_offset~IB3:STAHR_Rocket x_offset~STAHR:STAHR_Rocket x~Earth#J2000@SSB x~STAHR_Rocket#J2000@Earth x~STAHR_Rocket#TOD~Earth@Earth x~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket yaw_rate~STAHR_Rocket#J2000 yaw_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth yaw_rate~STAHR_Rocket#L~STAHR_Rocket:Earth YAW~Esrange#L~Esrange:Earth YAW~STAHR_Rocket yaw~STAHR_Rocket#J2000 yaw~STAHR_Rocket#LD~STAHR_Rocket:Earth yaw~STAHR_Rocket#LO~STAHR_Rocket:Earth yaw~STAHR_Rocket#L~STAHR_Rocket:Earth yaw~STAHR_Rocket#N~STAHR_Rocket yaw~STAHR_Rocket#TOD~Earth y_offset_CoM~Esrange y_offset_CoM~Main_Stage:STAHR_Rocket y_offset_CoM~STAHR_Rocket y_offset_CoP~STAHR_Rocket y_offset~IB3:STAHR_Rocket y_offset~STAHR:STAHR_Rocket y~Earth#J2000@SSB y~STAHR_Rocket#J2000@Earth y~STAHR_Rocket#TOD~Earth@Earth y~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket z_offset_CoM~Esrange z_offset_CoM~Main_Stage:STAHR_Rocket z_offset_CoM~STAHR_Rocket z_offset_CoP~STAHR_Rocket z_offset~IB3:STAHR_Rocket z_offset~STAHR:STAHR_Rocket z~Earth#J2000@SSB z~STAHR_Rocket#J2000@Earth z~STAHR_Rocket#TOD~Earth@Earth z~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket z~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket +Phase Index East Acceleration w/o Gravity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Acceleration w/o Gravity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Acceleration w/o Gravity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket X-Acceleration w/o Gravity of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Y-Acceleration w/o Gravity of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Z-Acceleration w/o Gravity of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Norm of the Total Acceleration w/o Gravity of STAHR_Rocket Acceleration X of STAHR_Rocket in J2000 at Earth Acceleration Y of STAHR_Rocket in J2000 at Earth Acceleration Z of STAHR_Rocket in J2000 at Earth Acceleration of STAHR_Rocket in J2000 at Earth East Aerodynamic Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Aerodynamic Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Aerodynamic Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Aerodynamic Acceleration of STAHR_Rocket Total Normal Acceleration w/o Gravity of STAHR_Rocket East Thrust Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Thrust Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Thrust Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Thrust Acceleration of STAHR_Rocket Aerodynamic Reference Area of STAHR_Rocket Aerodynamic Bank Angle of STAHR_Rocket Aerodynamic Roll Angle of STAHR_Rocket Air Path Angle of STAHR_Rocket in LD of STAHR_Rocket of Earth Air Path Angle of STAHR_Rocket in L of STAHR_Rocket of Earth Airpath Bank Angle of Esrange in A of Esrange of Earth Airpath Bank Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Air Path Heading of STAHR_Rocket in LD of STAHR_Rocket of Earth Air Path Heading of STAHR_Rocket in L of STAHR_Rocket of Earth Air Path Speed of STAHR_Rocket Apoapsis Altitude of STAHR_Rocket at Earth Periapsis Altitude of STAHR_Rocket at Earth Altitude of STAHR_Rocket at Earth Angle 1 of IB3 of STAHR_Rocket in B of STAHR_Rocket Angle 1 of IB3 of STAHR_Rocket in J2000 Angle 1 of STAHR of STAHR_Rocket in B of STAHR_Rocket Angle 1 of STAHR of STAHR_Rocket in J2000 Angle 2 of IB3 of STAHR_Rocket in B of STAHR_Rocket Angle 2 of IB3 of STAHR_Rocket in J2000 Angle 2 of STAHR of STAHR_Rocket in B of STAHR_Rocket Angle 2 of STAHR of STAHR_Rocket in J2000 Angle 3 of IB3 of STAHR_Rocket in B of STAHR_Rocket Angle 3 of IB3 of STAHR_Rocket in J2000 Angle 3 of STAHR of STAHR_Rocket in B of STAHR_Rocket Angle 3 of STAHR of STAHR_Rocket in J2000 Angle Body Axis and Local Vertical Axis of STAHR_Rocket in L of STAHR_Rocket of Earth Angle of Attack Rate of STAHR_Rocket in A of STAHR_Rocket of Earth Angle of Attack of Esrange in A of Esrange of Earth Angle of Attack of STAHR_Rocket in A of STAHR_Rocket of Earth Angular velocity of STAHR_Rocket in B of STAHR_Rocket Distance along Trajectory of STAHR_Rocket Relative Ascending Node of STAHR_Rocket in TOD of Earth Osculating Ascending Node of STAHR_Rocket in J2000 Osculating Ascending Node of STAHR_Rocket in TOD of Earth Asymptote Declination of STAHR_Rocket in J2000 Asymptote Declination of STAHR_Rocket in TOD of Earth Asymptote Right_Ascension of STAHR_Rocket in J2000 Asymptote Right Ascension of STAHR_Rocket in TOD of Earth Atmospheric Density of STAHR_Rocket Atmospheric Pressure of STAHR_Rocket Atmospheric Temperature of STAHR_Rocket Airpath Bank Angle Rate of STAHR_Rocket in A of STAHR_Rocket of Earth Airpath Bank Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Bending Moment of STAHR_Rocket Burn Time of IB3 of STAHR_Rocket Cross Range of STAHR_Rocket Declination of STAHR_Rocket in J2000 at Earth Declination of STAHR_Rocket in PCPF~Earth at Earth Declination of STAHR_Rocket in TOD~Earth at Earth Declination of STAHR_Rocket X Dimension of STAHR_Rocket Y Dimension of STAHR_Rocket Z Dimension of STAHR_Rocket Down Range of STAHR_Rocket Drag Acceleration of STAHR_Rocket Drag Coefficient of STAHR_Rocket Drag Force of STAHR_Rocket Dummy Altitude Dummy Declination Dummy Latitude Dummy Longitude Dummy Radius Dynamic Pressure of STAHR_Rocket Dynamic Viscosity of STAHR_Rocket Osculating Eccentricity of STAHR_Rocket at Earth Osculating Eccentric Anomaly of STAHR_Rocket at Earth Total Energy of STAHR_Rocket at Earth Equinoctial Element f of STAHR_Rocket in TOD of Earth Equinoctial Element g of STAHR_Rocket in TOD of Earth Equinoctial Element h of STAHR_Rocket in TOD of Earth Equinoctial Element k of STAHR_Rocket in TOD of Earth Equinoctial Element L of STAHR_Rocket in TOD of Earth Equinoctial Element p of STAHR_Rocket at Earth Excess Velocity of STAHR_Rocket at Earth Flight Path Angle of STAHR_Rocket in LD of STAHR_Rocket of Earth Flight Path Angle of STAHR_Rocket in L of STAHR_Rocket of Earth Flight Path Heading of STAHR_Rocket in LD of STAHR_Rocket of Earth Flight Path Heading of STAHR_Rocket in L of STAHR_Rocket of Earth Flight Path Speed of STAHR_Rocket Angle Relative Velocity and Local Vertical of STAHR_Rocket in L of STAHR_Rocket of Earth Flight Time Flight time of STAHR_Rocket Axial Aerodynamic Force of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket East Aerodynamic Force of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Lateral Aerodynamic Force of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Normal Aerodynamic Force of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket North Aerodynamic Force of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Aerodynamic Force of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket GPS Time Gravity Force x-Component of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Gravity Force y-Component of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Gravity Force z-Component of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Gravity Potential Energy of Esrange Gravity Potential Energy of STAHR_Rocket at Earth Gravity Acceleration of STAHR_Rocket Distance along Great Circle of STAHR_Rocket Heat Flux Density of STAHR_Rocket Horizontal Lift Load Factor of STAHR_Rocket Osculating Inclination of STAHR_Rocket in J2000 Osculating Inclination of STAHR_Rocket in TOD of Earth Angle Inertial Velocity and Local Vertical of STAHR_Rocket in L of STAHR_Rocket of Earth Inertial Flight Path Angle of STAHR_Rocket in L of STAHR_Rocket of Earth Inertial Heading of STAHR_Rocket in L of STAHR_Rocket of Earth Inertial Speed of STAHR_Rocket Moment of Inertia Ixx of STAHR_Rocket Moment of Inertia Ixy of STAHR_Rocket Moment of Inertia Ixz of STAHR_Rocket Moment of Inertia Iyy of STAHR_Rocket Moment of Inertia Iyz of STAHR_Rocket Moment of Inertia Izz of STAHR_Rocket Vacuum Specific Impulse of IB3 of STAHR_Rocket Specific Impulse of IB3 of STAHR_Rocket Julian Date (TT) Julian Date (UTC) Knudsen Number of STAHR_Rocket Geodetic Latitude of STAHR_Rocket in PCPF~Earth at Earth Lift Acceleration of STAHR_Rocket Lift Coefficient of STAHR_Rocket Lift to Drag Ratio of STAHR_Rocket Lift Force of STAHR_Rocket Axial Load Factor of STAHR_Rocket Side Load Factor of STAHR_Rocket Normal Load Factor of STAHR_Rocket Total Load Factor of STAHR_Rocket Local Central Body Radius of STAHR_Rocket Longitude of STAHR_Rocket in PCPF~Earth at Earth Longitude of STAHR_Rocket Mach Number of STAHR_Rocket Magnetic Flux Density East of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Magnetic Flux Density North of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Magnetic Flux Density Radial of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Magnetic Flux Density X of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Magnetic Flux Density Y of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Magnetic Flux Density Z of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Magnetic Flux Density of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Total Mass Flow of IB3 of STAHR_Rocket Total Mass of STAHR_Rocket Osculating Mean Anomaly of STAHR_Rocket at Earth Mean Local Time of the Ascending Node of STAHR_Rocket Mean Motion of STAHR_Rocket at Earth Mission Time Aerodynamic Moment x of STAHR_Rocket in B of STAHR_Rocket Aerodynamic Moment y of STAHR_Rocket in B of STAHR_Rocket Aerodynamic Moment z of STAHR_Rocket in B of STAHR_Rocket Normalized Independent Variable Total Exit Area of IB3 of STAHR_Rocket Angular Velocity about Body x-axis of STAHR_Rocket Angular Velocity x of STAHR_Rocket in B of STAHR_Rocket Angular Velocity about Body y-axis of STAHR_Rocket Angular Velocity y of STAHR_Rocket in B of STAHR_Rocket Angular Velocity about Body z-axis of STAHR_Rocket Angular Velocity z of STAHR_Rocket in B of STAHR_Rocket Orbital Period of STAHR_Rocket at Earth Osculating Periapsis Argument of STAHR_Rocket in J2000 Osculating Periapsis Argument of STAHR_Rocket in TOD of Earth Pitch Rate of STAHR_Rocket in J2000 Pitch Rate of STAHR_Rocket in LO of STAHR_Rocket of Earth Pitch Rate of STAHR_Rocket in L of STAHR_Rocket of Earth Pitch Angle of Esrange in L of Esrange of Earth Pitch Angle of STAHR_Rocket Pitch of STAHR_Rocket in J2000 Pitch of STAHR_Rocket in LD of STAHR_Rocket of Earth Pitch of STAHR_Rocket in LO of STAHR_Rocket of Earth Pitch of STAHR_Rocket in L of STAHR_Rocket of Earth Pitch of STAHR_Rocket in N of STAHR_Rocket Pitch of STAHR_Rocket in TOD of Earth Propellant Mass of STAHR of STAHR_Rocket Quaternion w of Earth in J2000 Quaternion w of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion w of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion w of STAHR_Rocket in J2000 Quaternion w of STAHR_Rocket in L of STAHR_Rocket of Earth Quaternion x of Earth in J2000 Quaternion x of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion x of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion x of STAHR_Rocket in J2000 Quaternion x of STAHR_Rocket in L of STAHR_Rocket of Earth Quaternion y of Earth in J2000 Quaternion y of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion y of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion y of STAHR_Rocket in J2000 Quaternion y of STAHR_Rocket in L of STAHR_Rocket of Earth Quaternion z of Earth in J2000 Quaternion z of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion z of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion z of STAHR_Rocket in J2000 Quaternion z of STAHR_Rocket in L of STAHR_Rocket of Earth Apoapsis Radius of STAHR_Rocket at Earth Periapsis Radius of STAHR_Rocket at Earth Radial Position of STAHR_Rocket at Earth Reynolds Number of STAHR_Rocket Right Ascension of STAHR_Rocket in J2000 at Earth Right Ascension of STAHR_Rocket in TOD~Earth at Earth Roll Rate of STAHR_Rocket in J2000 Roll Rate of STAHR_Rocket in LO of STAHR_Rocket of Earth Roll Rate of STAHR_Rocket in L of STAHR_Rocket of Earth Roll Angle of Esrange in L of Esrange of Earth Roll Angle of STAHR_Rocket Roll of STAHR_Rocket in J2000 Roll of STAHR_Rocket in LD of STAHR_Rocket of Earth Roll of STAHR_Rocket in LO of STAHR_Rocket of Earth Roll of STAHR_Rocket in L of STAHR_Rocket of Earth Roll of STAHR_Rocket in N of STAHR_Rocket Roll of STAHR_Rocket in TOD of Earth Radial Position of STAHR_Rocket Osculating Semimajor of STAHR_Rocket at Earth Sideslip Angle Rate of STAHR_Rocket in A of STAHR_Rocket of Earth Sideslip Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Side Force of STAHR_Rocket Side Slip Angle of Esrange in A of Esrange of Earth Side Slip Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Solar beta angle of STAHR_Rocket in PF of STAHR_Rocket of Earth Speed of Sound of STAHR_Rocket Static Stability along x-axis of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Thrust to Effective Weight Ratio of STAHR_Rocket Vacuum Thrust of IB3 of STAHR_Rocket Total Thrust x of STAHR_Rocket in J2000 Total Thrust y of STAHR_Rocket in J2000 Total Thrust z of STAHR_Rocket in J2000 Thrust of IB3 of STAHR_Rocket Total Thrust of STAHR_Rocket Time Torque x of IB3 of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Torque x of STAHR_Rocket in B of STAHR_Rocket Torque y of IB3 of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Torque y of STAHR_Rocket in B of STAHR_Rocket Torque z of IB3 of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Torque z of STAHR_Rocket in B of STAHR_Rocket Total Angle of Attack Slope Normal Coefficient of STAHR_Rocket in TA~STAHR_Rocket at STAHR_Rocket Total Angle of Attack of STAHR_Rocket in A of STAHR_Rocket of Earth Total Lift Force of STAHR_Rocket Total Solar Irradiance At Earth of STAHR_Rocket Integral of Gamma-Dot Squared of STAHR_Rocket Osculating True Anomaly of STAHR_Rocket at Earth Control Vector Length of STAHR_Rocket in LO of STAHR_Rocket of Earth Control Vector Length of STAHR_Rocket in L of STAHR_Rocket of Earth Normal Control Portion of STAHR_Rocket in LO of STAHR_Rocket of Earth Radial Control Portion of STAHR_Rocket in LO of STAHR_Rocket of Earth Transverse Control Portion of STAHR_Rocket in LO of STAHR_Rocket of Earth Control Vector x of STAHR_Rocket in L of STAHR_Rocket of Earth Control Vector y of STAHR_Rocket in L of STAHR_Rocket of Earth Control Vector z of STAHR_Rocket in L of STAHR_Rocket of Earth East Inertial Velocity Component of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Velocity Component of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Relative Velocity at Apoapsis of STAHR_Rocket at Earth East Relative Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Relative Velocity at Periapsis of STAHR_Rocket at Earth Radial Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Inertial Longitudinal Velocity of STAHR_Rocket Inertial Latitudinal Velocity of STAHR_Rocket Inertial Radial Velocity of STAHR_Rocket Vertical Lift Load Factor of STAHR_Rocket vx of STAHR_Rocket in J2000 at Earth vx of STAHR_Rocket in TOD~Earth at Earth vy of STAHR_Rocket in J2000 at Earth vy of STAHR_Rocket in TOD~Earth at Earth vz of STAHR_Rocket in J2000 at Earth vz of STAHR_Rocket in TOD~Earth at Earth Wind turbolence p of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Wind turbolence q of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Wind turbolence r of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket East Wind Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Wind Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Wind Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket x-Offset Center of Mass wrt Global Node of Esrange x-Offset Center of Mass wrt Global Node of Main_Stage of STAHR_Rocket x-Offset Center of Mass wrt Global Node of STAHR_Rocket x-Offset of Static Center of Pressure of STAHR_Rocket x-Offset of Bottom Node wrt Global Node of IB3 of STAHR_Rocket x-Offset of Bottom Node wrt Global Node of STAHR of STAHR_Rocket x-Position of Earth in J2000 at SSB x of STAHR_Rocket in J2000 at Earth x of STAHR_Rocket in TOD~Earth at Earth x of Sun in L~STAHR_Rocket:Earth at STAHR_Rocket Yaw Rate of STAHR_Rocket in J2000 Yaw Rate of STAHR_Rocket in LO of STAHR_Rocket of Earth Yaw Rate of STAHR_Rocket in L of STAHR_Rocket of Earth Yaw Angle of Esrange in L of Esrange of Earth Yaw Angle of STAHR_Rocket Yaw of STAHR_Rocket in J2000 Yaw of STAHR_Rocket in LD of STAHR_Rocket of Earth Yaw of STAHR_Rocket in LO of STAHR_Rocket of Earth Yaw of STAHR_Rocket in L of STAHR_Rocket of Earth Yaw of STAHR_Rocket in N of STAHR_Rocket Yaw of STAHR_Rocket in TOD of Earth y-Offset Center of Mass wrt Global Node of Esrange y-Offset Center of Mass wrt Global Node of Main_Stage of STAHR_Rocket y-Offset Center of Mass wrt Global Node of STAHR_Rocket y-Offset of Static Center of Pressure of STAHR_Rocket y-Offset of Bottom Node wrt Global Node of IB3 of STAHR_Rocket y-Offset of Bottom Node wrt Global Node of STAHR of STAHR_Rocket y-Position of Earth in J2000 at SSB y of STAHR_Rocket in J2000 at Earth y of STAHR_Rocket in TOD~Earth at Earth y of Sun in L~STAHR_Rocket:Earth at STAHR_Rocket z-Offset Center of Mass wrt Global Node of Esrange z-Offset Center of Mass wrt Global Node of Main_Stage of STAHR_Rocket z-Offset Center of Mass wrt Global Node of STAHR_Rocket z-Offset of Static Center of Pressure of STAHR_Rocket z-Offset of Bottom Node wrt Global Node of IB3 of STAHR_Rocket z-Offset of Bottom Node wrt Global Node of STAHR of STAHR_Rocket z-Position of Earth in J2000 at SSB z of STAHR_Rocket in J2000 at Earth z of STAHR_Rocket in TOD~Earth at Earth z of Sun in L~STAHR_Rocket:Earth at STAHR_Rocket z of Sun in L~STAHR_Rocket:Earth at STAHR_Rocket +None Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter^2 Degree Degree Degree Degree Radian Radian Degree Degree Kilo-Meter/Second Kilo-Meter Kilo-Meter Kilo-Meter Radian Radian Radian Radian Radian Radian Radian Radian Radian Radian Radian Radian Degree Degree/Second Radian Degree Degree/Second Kilo-Meter Degree Degree Degree Degree Degree Degree Degree Kilogram/Meter**3 Pascal Kelvin Degree/Second Degree Pascal*Radian Second Kilo-Meter Degree Degree Degree Radian Meter Meter Meter Kilo-Meter Meter/Second**2 None Kilo-Newton Meter Degree Degree Degree Meter Pascal Pascal*Second None Degree Kilo-Joule/Kilogram None None None None Degree Kilo-Meter Kilo-Meter/Second Degree Degree Degree Degree Kilo-Meter/Second Degree Second Second Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Second Newton Newton Newton Kilo-Joule/Kilogram Kilo-Joule/Kilogram Meter/Second**2 Kilo-Meter Kilo-Watt/Meter**2 None Degree Degree Degree Degree Degree Kilo-Meter/Second Kilogram*Meter**2 Kilogram*Meter**2 Kilogram*Meter**2 Kilogram*Meter**2 Kilogram*Meter**2 Kilogram*Meter**2 Second Second Day Day None Degree Meter/Second**2 None None Kilo-Newton None None None None Kilo-Meter Degree Radian None Nano-Tesla Nano-Tesla Nano-Tesla Nano-Tesla Nano-Tesla Nano-Tesla Nano-Tesla Kilogram/Second Mega-Gram Degree Hour Radian/Second Second Newton*Meter Newton*Meter Newton*Meter None Meter^2 Radian/Second Degree/Second Radian/Second Degree/Second Radian/Second Degree/Second Second Degree Degree Degree/Second Degree/Second Degree/Second Radian Radian Degree Degree Degree Degree Degree Degree Kilogram None None None None None None None None None None None None None None None None None None None None Kilo-Meter Kilo-Meter Kilo-Meter None Degree Degree Degree/Second Degree/Second Degree/Second Radian Radian Degree Degree Degree Degree Degree Degree Meter Kilo-Meter Degree/Second Degree Kilo-Newton Radian Radian Degree Meter/Second Meter None Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Second Newton*Meter Newton*Meter Newton*Meter Newton*Meter Newton*Meter Newton*Meter None/Degree Degree Kilo-Newton Watt/Meter**2 Radian^2/Second Degree None None None None None None None None Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Meter/Second Meter/Second Meter/Second None Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Degree/Second Degree/Second Degree/Second Meter/Second Meter/Second Meter/Second Meter Meter Meter Meter Meter Meter Kilo-Meter Kilo-Meter Kilo-Meter Kilo-Meter Degree/Second Degree/Second Degree/Second Radian Radian Degree Degree Degree Degree Degree Degree Meter Meter Meter Meter Meter Meter Kilo-Meter Kilo-Meter Kilo-Meter Kilo-Meter Meter Meter Meter Meter Meter Meter Kilo-Meter Kilo-Meter Kilo-Meter Kilo-Meter Kilo-Meter + 1.00000000000000E+00 2.32452945780892E-16 2.31312154733763E-02 9.82375317258386E+00 9.67852498145304E+00 2.32452945780892E-16 -1.68309703593414E+00 9.82378040516892E+00 5.78199798200330E-03 1.14256473865165E-02 -1.38389956524775E-05 1.28053547542698E-02 0.00000000000000E+00 0.00000000000000E+00 -0.00000000000000E+00 0.00000000000000E+00 1.68309703593414E+00 0.00000000000000E+00 0.00000000000000E+00 0.00000000000000E+00 0.00000000000000E+00 2.60000000000000E-02 1.80000000000000E+02 1.80000000000000E+02 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acceleration_without_gravity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acceleration_without_gravity_radial~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket acceleration_without_gravity_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket acceleration_without_gravity_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket acceleration_without_gravity~STAHR_Rocket acceleration_x~STAHR_Rocket#J2000@Earth acceleration_y~STAHR_Rocket#J2000@Earth acceleration_z~STAHR_Rocket#J2000@Earth acceleration~STAHR_Rocket#J2000@Earth acc_aero_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_aero_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_aero_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_aero~STAHR_Rocket acc_normal~STAHR_Rocket acc_thrust_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_thrust_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_thrust_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket acc_thrust~STAHR_Rocket aero_area_ref~STAHR_Rocket aero_bank_angle~STAHR_Rocket aero_roll_angle~STAHR_Rocket airpath_angle~STAHR_Rocket#LD~STAHR_Rocket:Earth airpath_angle~STAHR_Rocket#L~STAHR_Rocket:Earth AIRPATH_BANK_ANGLE~Esrange#A~Esrange:Earth AIRPATH_BANK_ANGLE~STAHR_Rocket#A~STAHR_Rocket:Earth airpath_heading~STAHR_Rocket#LD~STAHR_Rocket:Earth airpath_heading~STAHR_Rocket#L~STAHR_Rocket:Earth airpath_speed~STAHR_Rocket altitude_apoapsis~STAHR_Rocket@Earth altitude_periapsis~STAHR_Rocket@Earth altitude~STAHR_Rocket@Earth angle1~IB3:STAHR_Rocket#B~STAHR_Rocket angle1~IB3:STAHR_Rocket#J2000 angle1~STAHR:STAHR_Rocket#B~STAHR_Rocket angle1~STAHR:STAHR_Rocket#J2000 angle2~IB3:STAHR_Rocket#B~STAHR_Rocket angle2~IB3:STAHR_Rocket#J2000 angle2~STAHR:STAHR_Rocket#B~STAHR_Rocket angle2~STAHR:STAHR_Rocket#J2000 angle3~IB3:STAHR_Rocket#B~STAHR_Rocket angle3~IB3:STAHR_Rocket#J2000 angle3~STAHR:STAHR_Rocket#B~STAHR_Rocket angle3~STAHR:STAHR_Rocket#J2000 angle_body_and_local_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth angle_of_attack_rate~STAHR_Rocket#A~STAHR_Rocket:Earth ANGLE_OF_ATTACK~Esrange#A~Esrange:Earth ANGLE_OF_ATTACK~STAHR_Rocket#A~STAHR_Rocket:Earth angular_velocity~STAHR_Rocket#B~STAHR_Rocket arc_length~STAHR_Rocket ascending_node_rel~STAHR_Rocket#TOD~Earth ascending_node~STAHR_Rocket#J2000 ascending_node~STAHR_Rocket#TOD~Earth asymptote_decl~STAHR_Rocket#J2000 asymptote_decl~STAHR_Rocket#TOD~Earth asymptote_right_ascension~STAHR_Rocket#J2000 asymptote_right_ascension~STAHR_Rocket#TOD~Earth atmos_density~STAHR_Rocket atmos_pressure~STAHR_Rocket atmos_temperature~STAHR_Rocket bank_angle_rate~STAHR_Rocket#A~STAHR_Rocket:Earth bank_angle~STAHR_Rocket#A~STAHR_Rocket:Earth bending_moment~STAHR_Rocket burn_time~IB3:STAHR_Rocket cross_range~STAHR_Rocket declination~STAHR_Rocket#J2000@Earth declination~STAHR_Rocket#PCPF~Earth@Earth declination~STAHR_Rocket#TOD~Earth@Earth DECL~STAHR_Rocket dimension_x~STAHR_Rocket dimension_y~STAHR_Rocket dimension_z~STAHR_Rocket down_range~STAHR_Rocket drag_acc~STAHR_Rocket drag_coeff~STAHR_Rocket drag~STAHR_Rocket dummy_altitude dummy_declination dummy_latitude dummy_longitude dummy_radius dynamic_pressure~STAHR_Rocket dyn_viscosity~STAHR_Rocket eccentricity~STAHR_Rocket@Earth eccentric_anomaly~STAHR_Rocket@Earth energy_total~STAHR_Rocket@Earth equinoctial_f~STAHR_Rocket#TOD~Earth equinoctial_g~STAHR_Rocket#TOD~Earth equinoctial_h~STAHR_Rocket#TOD~Earth equinoctial_k~STAHR_Rocket#TOD~Earth equinoctial_l~STAHR_Rocket#TOD~Earth equinoctial_p~STAHR_Rocket@Earth excess_velocity~STAHR_Rocket@Earth flightpath_angle~STAHR_Rocket#LD~STAHR_Rocket:Earth flightpath_angle~STAHR_Rocket#L~STAHR_Rocket:Earth flightpath_heading~STAHR_Rocket#LD~STAHR_Rocket:Earth flightpath_heading~STAHR_Rocket#L~STAHR_Rocket:Earth flightpath_speed~STAHR_Rocket flightpath_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth flight_time flight_time~STAHR_Rocket force_aero_axial~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket force_aero_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket force_aero_lateral~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket force_aero_normal~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket force_aero_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket force_aero_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket gps_time gravity_force_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket gravity_force_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket gravity_force_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket gravity_potential~Esrange gravity_potential~STAHR_Rocket@Earth gravity~STAHR_Rocket great_circle_distance~STAHR_Rocket heat_flux_density~STAHR_Rocket hload_lift~STAHR_Rocket inclination~STAHR_Rocket#J2000 inclination~STAHR_Rocket#TOD~Earth inertial_flightpath_vertical~STAHR_Rocket#L~STAHR_Rocket:Earth inertial_flightpath~STAHR_Rocket#L~STAHR_Rocket:Earth inertial_heading~STAHR_Rocket#L~STAHR_Rocket:Earth inertial_speed~STAHR_Rocket inertia_xx~STAHR_Rocket inertia_xy~STAHR_Rocket inertia_xz~STAHR_Rocket inertia_yy~STAHR_Rocket inertia_yz~STAHR_Rocket inertia_zz~STAHR_Rocket isp_vacuum~IB3:STAHR_Rocket isp~IB3:STAHR_Rocket julian_date_tt julian_date_utc knudsen_number~STAHR_Rocket latitude~STAHR_Rocket#PCPF~Earth@Earth lift_acc~STAHR_Rocket lift_coeff~STAHR_Rocket lift_drag_ratio~STAHR_Rocket lift~STAHR_Rocket load_factor_axial~STAHR_Rocket load_factor_cross~STAHR_Rocket load_factor_normal~STAHR_Rocket load_factor~STAHR_Rocket local_central_body_radius~STAHR_Rocket longitude~STAHR_Rocket#PCPF~Earth@Earth LONG~STAHR_Rocket mach~STAHR_Rocket magnetic_flux_density_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket magnetic_flux_density_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket magnetic_flux_density_radial~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket magnetic_flux_density_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket magnetic_flux_density_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket magnetic_flux_density_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket magnetic_flux_density~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket mass_flow~IB3:STAHR_Rocket mass_total~STAHR_Rocket mean_anomaly~STAHR_Rocket@Earth mean_local_time_of_the_ascending_node~STAHR_Rocket mean_motion~STAHR_Rocket@Earth mission_time moment_aero_x~STAHR_Rocket#B~STAHR_Rocket moment_aero_y~STAHR_Rocket#B~STAHR_Rocket moment_aero_z~STAHR_Rocket#B~STAHR_Rocket Norm_Indep_Var nozzle_area~IB3:STAHR_Rocket OMEGA_X~STAHR_Rocket omega_x~STAHR_Rocket#B~STAHR_Rocket OMEGA_Y~STAHR_Rocket omega_y~STAHR_Rocket#B~STAHR_Rocket OMEGA_Z~STAHR_Rocket omega_z~STAHR_Rocket#B~STAHR_Rocket orbital_period~STAHR_Rocket@Earth periapsis_argument~STAHR_Rocket#J2000 periapsis_argument~STAHR_Rocket#TOD~Earth pitch_rate~STAHR_Rocket#J2000 pitch_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth pitch_rate~STAHR_Rocket#L~STAHR_Rocket:Earth PITCH~Esrange#L~Esrange:Earth PITCH~STAHR_Rocket pitch~STAHR_Rocket#J2000 pitch~STAHR_Rocket#LD~STAHR_Rocket:Earth pitch~STAHR_Rocket#LO~STAHR_Rocket:Earth pitch~STAHR_Rocket#L~STAHR_Rocket:Earth pitch~STAHR_Rocket#N~STAHR_Rocket pitch~STAHR_Rocket#TOD~Earth PROP_MASS~STAHR:STAHR_Rocket quaternion_w~Earth#J2000 quaternion_w~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_w~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_w~STAHR_Rocket#J2000 quaternion_w~STAHR_Rocket#L~STAHR_Rocket:Earth quaternion_x~Earth#J2000 quaternion_x~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_x~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_x~STAHR_Rocket#J2000 quaternion_x~STAHR_Rocket#L~STAHR_Rocket:Earth quaternion_y~Earth#J2000 quaternion_y~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_y~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_y~STAHR_Rocket#J2000 quaternion_y~STAHR_Rocket#L~STAHR_Rocket:Earth quaternion_z~Earth#J2000 quaternion_z~IB3:STAHR_Rocket#B~STAHR_Rocket quaternion_z~STAHR:STAHR_Rocket#B~STAHR_Rocket quaternion_z~STAHR_Rocket#J2000 quaternion_z~STAHR_Rocket#L~STAHR_Rocket:Earth radius_apoapsis~STAHR_Rocket@Earth radius_periapsis~STAHR_Rocket@Earth radius~STAHR_Rocket@Earth reynolds_number~STAHR_Rocket right_ascension~STAHR_Rocket#J2000@Earth right_ascension~STAHR_Rocket#TOD~Earth@Earth roll_rate~STAHR_Rocket#J2000 roll_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth roll_rate~STAHR_Rocket#L~STAHR_Rocket:Earth ROLL~Esrange#L~Esrange:Earth ROLL~STAHR_Rocket roll~STAHR_Rocket#J2000 roll~STAHR_Rocket#LD~STAHR_Rocket:Earth roll~STAHR_Rocket#LO~STAHR_Rocket:Earth roll~STAHR_Rocket#L~STAHR_Rocket:Earth roll~STAHR_Rocket#N~STAHR_Rocket roll~STAHR_Rocket#TOD~Earth R~STAHR_Rocket semimajor~STAHR_Rocket@Earth sideslip_rate~STAHR_Rocket#A~STAHR_Rocket:Earth sideslip~STAHR_Rocket#A~STAHR_Rocket:Earth side_force~STAHR_Rocket SIDE_SLIP_ANGLE~Esrange#A~Esrange:Earth SIDE_SLIP_ANGLE~STAHR_Rocket#A~STAHR_Rocket:Earth solar_beta_angle~STAHR_Rocket#PF~STAHR_Rocket:Earth sonic_velocity~STAHR_Rocket static_stability_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket thrust_to_ew~STAHR_Rocket thrust_vacuum~IB3:STAHR_Rocket thrust_x~STAHR_Rocket#J2000 thrust_y~STAHR_Rocket#J2000 thrust_z~STAHR_Rocket#J2000 thrust~IB3:STAHR_Rocket thrust~STAHR_Rocket Time torque_x~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket torque_x~STAHR_Rocket#B~STAHR_Rocket torque_y~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket torque_y~STAHR_Rocket#B~STAHR_Rocket torque_z~IB3:STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket torque_z~STAHR_Rocket#B~STAHR_Rocket total_angle_of_attack_slope_normal_coefficient~STAHR_Rocket#TA~STAHR_Rocket@STAHR_Rocket total_angle_of_attack~STAHR_Rocket#A~STAHR_Rocket:Earth total_lift~STAHR_Rocket total_solar_irradiance_at_earth~STAHR_Rocket TRAJ_SMOOTHNESS~STAHR_Rocket true_anomaly~STAHR_Rocket@Earth u_length~STAHR_Rocket#LO~STAHR_Rocket:Earth u_length~STAHR_Rocket#L~STAHR_Rocket:Earth u_n~STAHR_Rocket#LO~STAHR_Rocket:Earth u_r~STAHR_Rocket#LO~STAHR_Rocket:Earth u_t~STAHR_Rocket#LO~STAHR_Rocket:Earth u_x~STAHR_Rocket#L~STAHR_Rocket:Earth u_y~STAHR_Rocket#L~STAHR_Rocket:Earth u_z~STAHR_Rocket#L~STAHR_Rocket:Earth velocity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket velocity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket velocity_rel_apoapsis~STAHR_Rocket@Earth velocity_rel_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket velocity_rel_periapsis~STAHR_Rocket@Earth velocity_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket VI_EAST~STAHR_Rocket VI_NORTH~STAHR_Rocket VI_RADIAL~STAHR_Rocket vload_lift~STAHR_Rocket vx~STAHR_Rocket#J2000@Earth vx~STAHR_Rocket#TOD~Earth@Earth vy~STAHR_Rocket#J2000@Earth vy~STAHR_Rocket#TOD~Earth@Earth vz~STAHR_Rocket#J2000@Earth vz~STAHR_Rocket#TOD~Earth@Earth V~STAHR_Rocket wind_gust_p~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_gust_q~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_gust_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_velocity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_velocity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket wind_velocity_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket x_offset_CoM~Esrange x_offset_CoM~Main_Stage:STAHR_Rocket x_offset_CoM~STAHR_Rocket x_offset_CoP~STAHR_Rocket x_offset~IB3:STAHR_Rocket x_offset~STAHR:STAHR_Rocket x~Earth#J2000@SSB x~STAHR_Rocket#J2000@Earth x~STAHR_Rocket#TOD~Earth@Earth x~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket yaw_rate~STAHR_Rocket#J2000 yaw_rate~STAHR_Rocket#LO~STAHR_Rocket:Earth yaw_rate~STAHR_Rocket#L~STAHR_Rocket:Earth YAW~Esrange#L~Esrange:Earth YAW~STAHR_Rocket yaw~STAHR_Rocket#J2000 yaw~STAHR_Rocket#LD~STAHR_Rocket:Earth yaw~STAHR_Rocket#LO~STAHR_Rocket:Earth yaw~STAHR_Rocket#L~STAHR_Rocket:Earth yaw~STAHR_Rocket#N~STAHR_Rocket yaw~STAHR_Rocket#TOD~Earth y_offset_CoM~Esrange y_offset_CoM~Main_Stage:STAHR_Rocket y_offset_CoM~STAHR_Rocket y_offset_CoP~STAHR_Rocket y_offset~IB3:STAHR_Rocket y_offset~STAHR:STAHR_Rocket y~Earth#J2000@SSB y~STAHR_Rocket#J2000@Earth y~STAHR_Rocket#TOD~Earth@Earth y~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket z_offset_CoM~Esrange z_offset_CoM~Main_Stage:STAHR_Rocket z_offset_CoM~STAHR_Rocket z_offset_CoP~STAHR_Rocket z_offset~IB3:STAHR_Rocket z_offset~STAHR:STAHR_Rocket z~Earth#J2000@SSB z~STAHR_Rocket#J2000@Earth z~STAHR_Rocket#TOD~Earth@Earth z~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket z~Sun#L~STAHR_Rocket:Earth@STAHR_Rocket +Phase Index East Acceleration w/o Gravity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Acceleration w/o Gravity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Acceleration w/o Gravity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket X-Acceleration w/o Gravity of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Y-Acceleration w/o Gravity of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Z-Acceleration w/o Gravity of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Norm of the Total Acceleration w/o Gravity of STAHR_Rocket Acceleration X of STAHR_Rocket in J2000 at Earth Acceleration Y of STAHR_Rocket in J2000 at Earth Acceleration Z of STAHR_Rocket in J2000 at Earth Acceleration of STAHR_Rocket in J2000 at Earth East Aerodynamic Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Aerodynamic Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Aerodynamic Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Aerodynamic Acceleration of STAHR_Rocket Total Normal Acceleration w/o Gravity of STAHR_Rocket East Thrust Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Thrust Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Thrust Acceleration of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Thrust Acceleration of STAHR_Rocket Aerodynamic Reference Area of STAHR_Rocket Aerodynamic Bank Angle of STAHR_Rocket Aerodynamic Roll Angle of STAHR_Rocket Air Path Angle of STAHR_Rocket in LD of STAHR_Rocket of Earth Air Path Angle of STAHR_Rocket in L of STAHR_Rocket of Earth Airpath Bank Angle of Esrange in A of Esrange of Earth Airpath Bank Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Air Path Heading of STAHR_Rocket in LD of STAHR_Rocket of Earth Air Path Heading of STAHR_Rocket in L of STAHR_Rocket of Earth Air Path Speed of STAHR_Rocket Apoapsis Altitude of STAHR_Rocket at Earth Periapsis Altitude of STAHR_Rocket at Earth Altitude of STAHR_Rocket at Earth Angle 1 of IB3 of STAHR_Rocket in B of STAHR_Rocket Angle 1 of IB3 of STAHR_Rocket in J2000 Angle 1 of STAHR of STAHR_Rocket in B of STAHR_Rocket Angle 1 of STAHR of STAHR_Rocket in J2000 Angle 2 of IB3 of STAHR_Rocket in B of STAHR_Rocket Angle 2 of IB3 of STAHR_Rocket in J2000 Angle 2 of STAHR of STAHR_Rocket in B of STAHR_Rocket Angle 2 of STAHR of STAHR_Rocket in J2000 Angle 3 of IB3 of STAHR_Rocket in B of STAHR_Rocket Angle 3 of IB3 of STAHR_Rocket in J2000 Angle 3 of STAHR of STAHR_Rocket in B of STAHR_Rocket Angle 3 of STAHR of STAHR_Rocket in J2000 Angle Body Axis and Local Vertical Axis of STAHR_Rocket in L of STAHR_Rocket of Earth Angle of Attack Rate of STAHR_Rocket in A of STAHR_Rocket of Earth Angle of Attack of Esrange in A of Esrange of Earth Angle of Attack of STAHR_Rocket in A of STAHR_Rocket of Earth Angular velocity of STAHR_Rocket in B of STAHR_Rocket Distance along Trajectory of STAHR_Rocket Relative Ascending Node of STAHR_Rocket in TOD of Earth Osculating Ascending Node of STAHR_Rocket in J2000 Osculating Ascending Node of STAHR_Rocket in TOD of Earth Asymptote Declination of STAHR_Rocket in J2000 Asymptote Declination of STAHR_Rocket in TOD of Earth Asymptote Right_Ascension of STAHR_Rocket in J2000 Asymptote Right Ascension of STAHR_Rocket in TOD of Earth Atmospheric Density of STAHR_Rocket Atmospheric Pressure of STAHR_Rocket Atmospheric Temperature of STAHR_Rocket Airpath Bank Angle Rate of STAHR_Rocket in A of STAHR_Rocket of Earth Airpath Bank Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Bending Moment of STAHR_Rocket Burn Time of IB3 of STAHR_Rocket Cross Range of STAHR_Rocket Declination of STAHR_Rocket in J2000 at Earth Declination of STAHR_Rocket in PCPF~Earth at Earth Declination of STAHR_Rocket in TOD~Earth at Earth Declination of STAHR_Rocket X Dimension of STAHR_Rocket Y Dimension of STAHR_Rocket Z Dimension of STAHR_Rocket Down Range of STAHR_Rocket Drag Acceleration of STAHR_Rocket Drag Coefficient of STAHR_Rocket Drag Force of STAHR_Rocket Dummy Altitude Dummy Declination Dummy Latitude Dummy Longitude Dummy Radius Dynamic Pressure of STAHR_Rocket Dynamic Viscosity of STAHR_Rocket Osculating Eccentricity of STAHR_Rocket at Earth Osculating Eccentric Anomaly of STAHR_Rocket at Earth Total Energy of STAHR_Rocket at Earth Equinoctial Element f of STAHR_Rocket in TOD of Earth Equinoctial Element g of STAHR_Rocket in TOD of Earth Equinoctial Element h of STAHR_Rocket in TOD of Earth Equinoctial Element k of STAHR_Rocket in TOD of Earth Equinoctial Element L of STAHR_Rocket in TOD of Earth Equinoctial Element p of STAHR_Rocket at Earth Excess Velocity of STAHR_Rocket at Earth Flight Path Angle of STAHR_Rocket in LD of STAHR_Rocket of Earth Flight Path Angle of STAHR_Rocket in L of STAHR_Rocket of Earth Flight Path Heading of STAHR_Rocket in LD of STAHR_Rocket of Earth Flight Path Heading of STAHR_Rocket in L of STAHR_Rocket of Earth Flight Path Speed of STAHR_Rocket Angle Relative Velocity and Local Vertical of STAHR_Rocket in L of STAHR_Rocket of Earth Flight Time Flight time of STAHR_Rocket Axial Aerodynamic Force of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket East Aerodynamic Force of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Lateral Aerodynamic Force of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Normal Aerodynamic Force of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket North Aerodynamic Force of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Aerodynamic Force of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket GPS Time Gravity Force x-Component of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Gravity Force y-Component of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Gravity Force z-Component of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Gravity Potential Energy of Esrange Gravity Potential Energy of STAHR_Rocket at Earth Gravity Acceleration of STAHR_Rocket Distance along Great Circle of STAHR_Rocket Heat Flux Density of STAHR_Rocket Horizontal Lift Load Factor of STAHR_Rocket Osculating Inclination of STAHR_Rocket in J2000 Osculating Inclination of STAHR_Rocket in TOD of Earth Angle Inertial Velocity and Local Vertical of STAHR_Rocket in L of STAHR_Rocket of Earth Inertial Flight Path Angle of STAHR_Rocket in L of STAHR_Rocket of Earth Inertial Heading of STAHR_Rocket in L of STAHR_Rocket of Earth Inertial Speed of STAHR_Rocket Moment of Inertia Ixx of STAHR_Rocket Moment of Inertia Ixy of STAHR_Rocket Moment of Inertia Ixz of STAHR_Rocket Moment of Inertia Iyy of STAHR_Rocket Moment of Inertia Iyz of STAHR_Rocket Moment of Inertia Izz of STAHR_Rocket Vacuum Specific Impulse of IB3 of STAHR_Rocket Specific Impulse of IB3 of STAHR_Rocket Julian Date (TT) Julian Date (UTC) Knudsen Number of STAHR_Rocket Geodetic Latitude of STAHR_Rocket in PCPF~Earth at Earth Lift Acceleration of STAHR_Rocket Lift Coefficient of STAHR_Rocket Lift to Drag Ratio of STAHR_Rocket Lift Force of STAHR_Rocket Axial Load Factor of STAHR_Rocket Side Load Factor of STAHR_Rocket Normal Load Factor of STAHR_Rocket Total Load Factor of STAHR_Rocket Local Central Body Radius of STAHR_Rocket Longitude of STAHR_Rocket in PCPF~Earth at Earth Longitude of STAHR_Rocket Mach Number of STAHR_Rocket Magnetic Flux Density East of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Magnetic Flux Density North of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Magnetic Flux Density Radial of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Magnetic Flux Density X of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Magnetic Flux Density Y of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Magnetic Flux Density Z of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Magnetic Flux Density of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Total Mass Flow of IB3 of STAHR_Rocket Total Mass of STAHR_Rocket Osculating Mean Anomaly of STAHR_Rocket at Earth Mean Local Time of the Ascending Node of STAHR_Rocket Mean Motion of STAHR_Rocket at Earth Mission Time Aerodynamic Moment x of STAHR_Rocket in B of STAHR_Rocket Aerodynamic Moment y of STAHR_Rocket in B of STAHR_Rocket Aerodynamic Moment z of STAHR_Rocket in B of STAHR_Rocket Normalized Independent Variable Total Exit Area of IB3 of STAHR_Rocket Angular Velocity about Body x-axis of STAHR_Rocket Angular Velocity x of STAHR_Rocket in B of STAHR_Rocket Angular Velocity about Body y-axis of STAHR_Rocket Angular Velocity y of STAHR_Rocket in B of STAHR_Rocket Angular Velocity about Body z-axis of STAHR_Rocket Angular Velocity z of STAHR_Rocket in B of STAHR_Rocket Orbital Period of STAHR_Rocket at Earth Osculating Periapsis Argument of STAHR_Rocket in J2000 Osculating Periapsis Argument of STAHR_Rocket in TOD of Earth Pitch Rate of STAHR_Rocket in J2000 Pitch Rate of STAHR_Rocket in LO of STAHR_Rocket of Earth Pitch Rate of STAHR_Rocket in L of STAHR_Rocket of Earth Pitch Angle of Esrange in L of Esrange of Earth Pitch Angle of STAHR_Rocket Pitch of STAHR_Rocket in J2000 Pitch of STAHR_Rocket in LD of STAHR_Rocket of Earth Pitch of STAHR_Rocket in LO of STAHR_Rocket of Earth Pitch of STAHR_Rocket in L of STAHR_Rocket of Earth Pitch of STAHR_Rocket in N of STAHR_Rocket Pitch of STAHR_Rocket in TOD of Earth Propellant Mass of STAHR of STAHR_Rocket Quaternion w of Earth in J2000 Quaternion w of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion w of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion w of STAHR_Rocket in J2000 Quaternion w of STAHR_Rocket in L of STAHR_Rocket of Earth Quaternion x of Earth in J2000 Quaternion x of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion x of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion x of STAHR_Rocket in J2000 Quaternion x of STAHR_Rocket in L of STAHR_Rocket of Earth Quaternion y of Earth in J2000 Quaternion y of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion y of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion y of STAHR_Rocket in J2000 Quaternion y of STAHR_Rocket in L of STAHR_Rocket of Earth Quaternion z of Earth in J2000 Quaternion z of IB3 of STAHR_Rocket in B of STAHR_Rocket Quaternion z of STAHR of STAHR_Rocket in B of STAHR_Rocket Quaternion z of STAHR_Rocket in J2000 Quaternion z of STAHR_Rocket in L of STAHR_Rocket of Earth Apoapsis Radius of STAHR_Rocket at Earth Periapsis Radius of STAHR_Rocket at Earth Radial Position of STAHR_Rocket at Earth Reynolds Number of STAHR_Rocket Right Ascension of STAHR_Rocket in J2000 at Earth Right Ascension of STAHR_Rocket in TOD~Earth at Earth Roll Rate of STAHR_Rocket in J2000 Roll Rate of STAHR_Rocket in LO of STAHR_Rocket of Earth Roll Rate of STAHR_Rocket in L of STAHR_Rocket of Earth Roll Angle of Esrange in L of Esrange of Earth Roll Angle of STAHR_Rocket Roll of STAHR_Rocket in J2000 Roll of STAHR_Rocket in LD of STAHR_Rocket of Earth Roll of STAHR_Rocket in LO of STAHR_Rocket of Earth Roll of STAHR_Rocket in L of STAHR_Rocket of Earth Roll of STAHR_Rocket in N of STAHR_Rocket Roll of STAHR_Rocket in TOD of Earth Radial Position of STAHR_Rocket Osculating Semimajor of STAHR_Rocket at Earth Sideslip Angle Rate of STAHR_Rocket in A of STAHR_Rocket of Earth Sideslip Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Side Force of STAHR_Rocket Side Slip Angle of Esrange in A of Esrange of Earth Side Slip Angle of STAHR_Rocket in A of STAHR_Rocket of Earth Solar beta angle of STAHR_Rocket in PF of STAHR_Rocket of Earth Speed of Sound of STAHR_Rocket Static Stability along x-axis of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Thrust to Effective Weight Ratio of STAHR_Rocket Vacuum Thrust of IB3 of STAHR_Rocket Total Thrust x of STAHR_Rocket in J2000 Total Thrust y of STAHR_Rocket in J2000 Total Thrust z of STAHR_Rocket in J2000 Thrust of IB3 of STAHR_Rocket Total Thrust of STAHR_Rocket Time Torque x of IB3 of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Torque x of STAHR_Rocket in B of STAHR_Rocket Torque y of IB3 of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Torque y of STAHR_Rocket in B of STAHR_Rocket Torque z of IB3 of STAHR_Rocket in B~STAHR_Rocket at STAHR_Rocket Torque z of STAHR_Rocket in B of STAHR_Rocket Total Angle of Attack Slope Normal Coefficient of STAHR_Rocket in TA~STAHR_Rocket at STAHR_Rocket Total Angle of Attack of STAHR_Rocket in A of STAHR_Rocket of Earth Total Lift Force of STAHR_Rocket Total Solar Irradiance At Earth of STAHR_Rocket Integral of Gamma-Dot Squared of STAHR_Rocket Osculating True Anomaly of STAHR_Rocket at Earth Control Vector Length of STAHR_Rocket in LO of STAHR_Rocket of Earth Control Vector Length of STAHR_Rocket in L of STAHR_Rocket of Earth Normal Control Portion of STAHR_Rocket in LO of STAHR_Rocket of Earth Radial Control Portion of STAHR_Rocket in LO of STAHR_Rocket of Earth Transverse Control Portion of STAHR_Rocket in LO of STAHR_Rocket of Earth Control Vector x of STAHR_Rocket in L of STAHR_Rocket of Earth Control Vector y of STAHR_Rocket in L of STAHR_Rocket of Earth Control Vector z of STAHR_Rocket in L of STAHR_Rocket of Earth East Inertial Velocity Component of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Velocity Component of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Relative Velocity at Apoapsis of STAHR_Rocket at Earth East Relative Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Relative Velocity at Periapsis of STAHR_Rocket at Earth Radial Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Inertial Longitudinal Velocity of STAHR_Rocket Inertial Latitudinal Velocity of STAHR_Rocket Inertial Radial Velocity of STAHR_Rocket Vertical Lift Load Factor of STAHR_Rocket vx of STAHR_Rocket in J2000 at Earth vx of STAHR_Rocket in TOD~Earth at Earth vy of STAHR_Rocket in J2000 at Earth vy of STAHR_Rocket in TOD~Earth at Earth vz of STAHR_Rocket in J2000 at Earth vz of STAHR_Rocket in TOD~Earth at Earth Flight-Path Velocity of STAHR_Rocket Wind turbolence p of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Wind turbolence q of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Wind turbolence r of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket East Wind Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket North Wind Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket Radial Wind Velocity of STAHR_Rocket in L~STAHR_Rocket:Earth at STAHR_Rocket x-Offset Center of Mass wrt Global Node of Esrange x-Offset Center of Mass wrt Global Node of Main_Stage of STAHR_Rocket x-Offset Center of Mass wrt Global Node of STAHR_Rocket x-Offset of Static Center of Pressure of STAHR_Rocket x-Offset of Bottom Node wrt Global Node of IB3 of STAHR_Rocket x-Offset of Bottom Node wrt Global Node of STAHR of STAHR_Rocket x-Position of Earth in J2000 at SSB x of STAHR_Rocket in J2000 at Earth x of STAHR_Rocket in TOD~Earth at Earth x of Sun in L~STAHR_Rocket:Earth at STAHR_Rocket Yaw Rate of STAHR_Rocket in J2000 Yaw Rate of STAHR_Rocket in LO of STAHR_Rocket of Earth Yaw Rate of STAHR_Rocket in L of STAHR_Rocket of Earth Yaw Angle of Esrange in L of Esrange of Earth Yaw Angle of STAHR_Rocket Yaw of STAHR_Rocket in J2000 Yaw of STAHR_Rocket in LD of STAHR_Rocket of Earth Yaw of STAHR_Rocket in LO of STAHR_Rocket of Earth Yaw of STAHR_Rocket in L of STAHR_Rocket of Earth Yaw of STAHR_Rocket in N of STAHR_Rocket Yaw of STAHR_Rocket in TOD of Earth y-Offset Center of Mass wrt Global Node of Esrange y-Offset Center of Mass wrt Global Node of Main_Stage of STAHR_Rocket y-Offset Center of Mass wrt Global Node of STAHR_Rocket y-Offset of Static Center of Pressure of STAHR_Rocket y-Offset of Bottom Node wrt Global Node of IB3 of STAHR_Rocket y-Offset of Bottom Node wrt Global Node of STAHR of STAHR_Rocket y-Position of Earth in J2000 at SSB y of STAHR_Rocket in J2000 at Earth y of STAHR_Rocket in TOD~Earth at Earth y of Sun in L~STAHR_Rocket:Earth at STAHR_Rocket z-Offset Center of Mass wrt Global Node of Esrange z-Offset Center of Mass wrt Global Node of Main_Stage of STAHR_Rocket z-Offset Center of Mass wrt Global Node of STAHR_Rocket z-Offset of Static Center of Pressure of STAHR_Rocket z-Offset of Bottom Node wrt Global Node of IB3 of STAHR_Rocket z-Offset of Bottom Node wrt Global Node of STAHR of STAHR_Rocket z-Position of Earth in J2000 at SSB z of STAHR_Rocket in J2000 at Earth z of STAHR_Rocket in TOD~Earth at Earth z of Sun in L~STAHR_Rocket:Earth at STAHR_Rocket z of Sun in L~STAHR_Rocket:Earth at STAHR_Rocket +None Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter/Second**2 Meter^2 Degree Degree Degree Degree Radian Radian Degree Degree Kilo-Meter/Second Kilo-Meter Kilo-Meter Kilo-Meter Radian Radian Radian Radian Radian Radian Radian Radian Radian Radian Radian Radian Degree Degree/Second Radian Radian Degree/Second Kilo-Meter Degree Degree Degree Degree Degree Degree Degree Kilogram/Meter**3 Pascal Kelvin Degree/Second Degree Pascal*Radian Second Kilo-Meter Degree Degree Degree Radian Meter Meter Meter Kilo-Meter Meter/Second**2 None Kilo-Newton Meter Degree Degree Degree Meter Pascal Pascal*Second None Degree Kilo-Joule/Kilogram None None None None Degree Kilo-Meter Kilo-Meter/Second Degree Degree Degree Degree Kilo-Meter/Second Degree Second Second Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Second Newton Newton Newton Kilo-Joule/Kilogram Kilo-Joule/Kilogram Meter/Second**2 Kilo-Meter Kilo-Watt/Meter**2 None Degree Degree Degree Degree Degree Kilo-Meter/Second Kilogram*Meter**2 Kilogram*Meter**2 Kilogram*Meter**2 Kilogram*Meter**2 Kilogram*Meter**2 Kilogram*Meter**2 Second Second Day Day None Degree Meter/Second**2 None None Kilo-Newton None None None None Kilo-Meter Degree Radian None Nano-Tesla Nano-Tesla Nano-Tesla Nano-Tesla Nano-Tesla Nano-Tesla Nano-Tesla Kilogram/Second Mega-Gram Degree Hour Radian/Second Second Newton*Meter Newton*Meter Newton*Meter None Meter^2 Radian/Second Degree/Second Radian/Second Degree/Second Radian/Second Degree/Second Second Degree Degree Degree/Second Degree/Second Degree/Second Radian Radian Degree Degree Degree Degree Degree Degree Kilogram None None None None None None None None None None None None None None None None None None None None Kilo-Meter Kilo-Meter Kilo-Meter None Degree Degree Degree/Second Degree/Second Degree/Second Radian Radian Degree Degree Degree Degree Degree Degree Meter Kilo-Meter Degree/Second Degree Kilo-Newton Radian Radian Degree Meter/Second Meter None Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Kilo-Newton Second Newton*Meter Newton*Meter Newton*Meter Newton*Meter Newton*Meter Newton*Meter None/Degree Degree Kilo-Newton Watt/Meter**2 Radian^2/Second Degree None None None None None None None None Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Meter/Second Meter/Second Meter/Second None Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Kilo-Meter/Second Meter/Second Degree/Second Degree/Second Degree/Second Meter/Second Meter/Second Meter/Second Meter Meter Meter Meter Meter Meter Kilo-Meter Kilo-Meter Kilo-Meter Kilo-Meter Degree/Second Degree/Second Degree/Second Radian Radian Degree Degree Degree Degree Degree Degree Meter Meter Meter Meter Meter Meter Kilo-Meter Kilo-Meter Kilo-Meter Kilo-Meter Meter Meter Meter Meter Meter Meter Kilo-Meter Kilo-Meter 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1.06205092781350E+07 5.89894871429063E+03 5.89660568170522E+03 -8.08584347592153E+07 -8.08584347592153E+07 diff --git a/demo.ipynb b/demo.ipynb index febee06..e716235 100644 --- a/demo.ipynb +++ b/demo.ipynb @@ -140,7 +140,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -170,7 +170,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████▉| 344.9000000000099/345.0 [00:33<00:00, 10.36it/s] \n" + " 1%| | 3.3000000000000016/345.0 [00:00<00:31, 10.80it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████▉| 344.9000000000099/345.0 [00:47<00:00, 7.33it/s] \n" ] } ], @@ -218,7 +225,6 @@ " \n", " \n", " time\n", - " 0\n", " MS5611_01BA03/ts_effects\n", " mach/mach_no\n", " mach/speedofsound\n", @@ -227,16 +233,17 @@ " WSEN_ISDS_ACC/FL_x\n", " WSEN_ISDS_ACC/FL_y\n", " WSEN_ISDS_ACC/FL_z\n", + " WSEN_ISDS_ACC/B_x\n", " ...\n", - " WSEN_ISDS_ACC/acc_x_noise\n", - " WSEN_ISDS_ACC/acc_y_noise\n", - " WSEN_ISDS_ACC/acc_z_noise\n", " WSEN_ISDS_ACC/out_0\n", " WSEN_ISDS_ACC/out_1\n", " WSEN_ISDS_ACC/out_2\n", " WSEN_ISDS_GYRO/out_0\n", " WSEN_ISDS_GYRO/out_1\n", " WSEN_ISDS_GYRO/out_2\n", + " Erinome-I/out_0\n", + " Erinome-I/out_1\n", + " Erinome-I/out_2\n", " general/altitude\n", " \n", " \n", @@ -244,122 +251,122 @@ " \n", " 0\n", " 0.1\n", - " NaN\n", " 0.0\n", " 0.007016\n", " 339.067143\n", - " 0.842335\n", - " 976.340813\n", + " -0.018387\n", + " 975.480091\n", " -0.0\n", " 4.044397\n", - " 33.066113\n", + " 23.256113\n", + " -32.577163\n", " ...\n", + " 32.574601\n", + " -4.081343\n", + " -5.725432\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 32.623429\n", - " -4.020263\n", - " -5.677741\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.319118\n", + " 67.885478\n", + " 21.079909\n", + " 18284.099507\n", + " 319.117737\n", " \n", " \n", " 1\n", " 0.2\n", - " NaN\n", " 0.0\n", " 0.013913\n", " 339.065795\n", - " -0.998771\n", - " 974.458942\n", + " 0.512119\n", + " 975.969833\n", " -0.0\n", " 3.97431\n", - " 32.663091\n", + " 22.853091\n", + " -32.180101\n", " ...\n", + " 32.2092\n", + " -3.986376\n", + " -5.665523\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 32.142847\n", - " -3.994765\n", - " -5.614795\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.319468\n", + " 67.885479\n", + " 21.079909\n", + " 18304.151142\n", + " 319.467704\n", " \n", " \n", " 2\n", " 0.3\n", - " NaN\n", " 0.0\n", " 0.020692\n", " 339.063569\n", - " -0.363539\n", - " 975.026847\n", + " -2.260561\n", + " 973.129825\n", " -0.0\n", " 3.903998\n", - " 32.258775\n", + " 22.448775\n", + " -31.781763\n", " ...\n", + " 31.811645\n", + " -3.93001\n", + " -5.484266\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 31.770757\n", - " -3.886765\n", - " -5.512629\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.320046\n", + " 67.88548\n", + " 21.079909\n", + " 18337.270949\n", + " 320.045754\n", " \n", " \n", " 3\n", " 0.4\n", - " NaN\n", " 0.0\n", " 0.027351\n", " 339.060477\n", - " 0.700004\n", - " 975.996892\n", + " 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3449 rows × 23 columns

\n", + "

3449 rows × 25 columns

\n", "" ], "text/plain": [ - " time 0 MS5611_01BA03/ts_effects mach/mach_no mach/speedofsound \\\n", - "0 0.1 NaN 0.0 0.007016 339.067143 \n", - "1 0.2 NaN 0.0 0.013913 339.065795 \n", - "2 0.3 NaN 0.0 0.020692 339.063569 \n", - "3 0.4 NaN 0.0 0.027351 339.060477 \n", - "4 0.5 NaN 0.0 0.033927 339.056534 \n", - "... ... ... ... ... ... \n", - "3444 344.5 NaN 0.0 0.015286 339.111824 \n", - "3445 344.6 NaN 0.0 0.015286 339.11382 \n", - "3446 344.7 NaN 0.0 0.015285 339.115816 \n", - "3447 344.8 NaN 0.0 0.015285 339.117812 \n", - "3448 344.9 NaN 0.0 0.015284 339.119808 \n", + " time MS5611_01BA03/ts_effects mach/mach_no mach/speedofsound \\\n", + "0 0.1 0.0 0.007016 339.067143 \n", + "1 0.2 0.0 0.013913 339.065795 \n", + "2 0.3 0.0 0.020692 339.063569 \n", + "3 0.4 0.0 0.027351 339.060477 \n", + "4 0.5 0.0 0.033927 339.056534 \n", + "... ... ... ... ... \n", + "3444 344.5 0.0 0.015286 339.111824 \n", + "3445 344.6 0.0 0.015286 339.11382 \n", + "3446 344.7 0.0 0.015285 339.115816 \n", + "3447 344.8 0.0 0.015285 339.117812 \n", + "3448 344.9 0.0 0.015284 339.119808 \n", "\n", " MS5611_01BA03/noise MS5611_01BA03/out WSEN_ISDS_ACC/FL_x \\\n", - "0 0.842335 976.340813 -0.0 \n", - "1 -0.998771 974.458942 -0.0 \n", - "2 -0.363539 975.026847 -0.0 \n", - "3 0.700004 975.996892 0.0 \n", - "4 0.112829 975.290499 -0.0 \n", + "0 -0.018387 975.480091 -0.0 \n", + "1 0.512119 975.969833 -0.0 \n", + "2 -2.260561 973.129825 -0.0 \n", + "3 0.722061 976.018948 0.0 \n", + "4 0.400507 975.578178 -0.0 \n", "... ... ... ... \n", - "3444 -2.321929 974.528648 -0.0 \n", - "3445 1.090926 978.001954 -0.0 \n", - "3446 -1.034095 975.937385 -0.0 \n", - "3447 -2.728543 974.303388 -0.0 \n", - "3448 -1.735287 975.357095 -0.0 \n", + "3444 -0.531794 976.318784 -0.0 \n", + "3445 1.496568 978.407596 -0.0 \n", + "3446 -0.387131 976.584349 -0.0 \n", + "3447 0.392596 977.424527 -0.0 \n", + "3448 -0.013876 977.078506 -0.0 \n", "\n", - " WSEN_ISDS_ACC/FL_y WSEN_ISDS_ACC/FL_z ... WSEN_ISDS_ACC/acc_x_noise \\\n", - "0 4.044397 33.066113 ... 0.0 \n", - "1 3.97431 32.663091 ... 0.0 \n", - "2 3.903998 32.258775 ... 0.0 \n", - "3 3.83641 31.870123 ... 0.0 \n", - "4 3.808092 31.70728 ... 0.0 \n", - "... ... ... ... ... \n", - "3444 0.0 9.811295 ... 0.0 \n", - "3445 0.0 9.811295 ... 0.0 \n", - "3446 0.0 9.811295 ... 0.0 \n", - "3447 0.0 9.811295 ... 0.0 \n", - "3448 0.0 9.811295 ... 0.0 \n", + " WSEN_ISDS_ACC/FL_y WSEN_ISDS_ACC/FL_z WSEN_ISDS_ACC/B_x ... \\\n", + "0 4.044397 23.256113 -32.577163 ... \n", + "1 3.97431 22.853091 -32.180101 ... \n", + "2 3.903998 22.448775 -31.781763 ... \n", + "3 3.83641 22.060123 -31.398858 ... \n", + "4 3.808092 21.89728 -31.238423 ... \n", + "... ... ... ... ... \n", + "3444 0.0 0.001295 -0.012063 ... \n", + "3445 0.0 0.001295 -0.012063 ... \n", + "3446 0.0 0.001295 -0.012063 ... \n", + "3447 0.0 0.001295 -0.012063 ... \n", + "3448 0.0 0.001295 -0.012063 ... \n", "\n", - " WSEN_ISDS_ACC/acc_y_noise WSEN_ISDS_ACC/acc_z_noise WSEN_ISDS_ACC/out_0 \\\n", - "0 0.0 0.0 32.623429 \n", - "1 0.0 0.0 32.142847 \n", - "2 0.0 0.0 31.770757 \n", - "3 0.0 0.0 31.523523 \n", - "4 0.0 0.0 31.345873 \n", - "... ... ... ... \n", - "3444 0.0 0.0 0.076978 \n", - "3445 0.0 0.0 0.023716 \n", - "3446 0.0 0.0 0.065746 \n", - "3447 0.0 0.0 -0.032622 \n", - "3448 0.0 0.0 -0.02422 \n", + " WSEN_ISDS_ACC/out_0 WSEN_ISDS_ACC/out_1 WSEN_ISDS_ACC/out_2 \\\n", + "0 32.574601 -4.081343 -5.725432 \n", + "1 32.2092 -3.986376 -5.665523 \n", + "2 31.811645 -3.93001 -5.484266 \n", + "3 31.417342 -3.875468 -5.482977 \n", + "4 31.165447 -3.815917 -5.545503 \n", + "... ... ... ... \n", + "3444 0.048509 0.059759 -9.807724 \n", + "3445 0.0775 0.014265 -9.828498 \n", + "3446 -0.055745 -0.00512 -9.8085 \n", + "3447 -0.002507 -0.021518 -9.813728 \n", + "3448 -0.047244 0.022966 -9.780433 \n", "\n", - " WSEN_ISDS_ACC/out_1 WSEN_ISDS_ACC/out_2 WSEN_ISDS_GYRO/out_0 \\\n", - "0 -4.020263 -5.677741 0.0 \n", - "1 -3.994765 -5.614795 0.0 \n", - "2 -3.886765 -5.512629 0.0 \n", - "3 -3.759221 -5.532761 0.0 \n", - "4 -3.834753 -5.383155 0.0 \n", - "... ... ... ... \n", - "3444 0.017459 -9.739685 0.0 \n", - "3445 -0.065154 -9.798154 0.0 \n", - "3446 0.113411 -9.76346 0.0 \n", - "3447 0.033615 -9.912886 0.0 \n", - "3448 0.069812 -9.781453 0.0 \n", + " WSEN_ISDS_GYRO/out_0 WSEN_ISDS_GYRO/out_1 WSEN_ISDS_GYRO/out_2 \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "... ... ... ... \n", + "3444 0.0 0.0 0.0 \n", + "3445 0.0 0.0 0.0 \n", + "3446 0.0 0.0 0.0 \n", + "3447 0.0 0.0 0.0 \n", + "3448 0.0 0.0 0.0 \n", "\n", - " WSEN_ISDS_GYRO/out_1 WSEN_ISDS_GYRO/out_2 general/altitude \n", - "0 0.0 0.0 0.319118 \n", - "1 0.0 0.0 0.319468 \n", - "2 0.0 0.0 0.320046 \n", - "3 0.0 0.0 0.320849 \n", - "4 0.0 0.0 0.321872 \n", - "... ... ... ... \n", - "3444 0.0 0.0 0.307517 \n", - "3445 0.0 0.0 0.306998 \n", - "3446 0.0 0.0 0.30648 \n", - "3447 0.0 0.0 0.305962 \n", - "3448 0.0 0.0 0.305443 \n", + " Erinome-I/out_0 Erinome-I/out_1 Erinome-I/out_2 general/altitude \n", + "0 67.885478 21.079909 18284.099507 319.117737 \n", + "1 67.885479 21.079909 18304.151142 319.467704 \n", + "2 67.88548 21.079909 18337.270949 320.045754 \n", + "3 67.885481 21.079909 18383.266845 320.848534 \n", + "4 67.885482 21.079909 18441.920488 321.872233 \n", + "... ... ... ... ... \n", + "3444 67.950884 21.08013 17619.406241 307.516651 \n", + "3445 67.950884 21.08013 17589.706895 306.9983 \n", + "3446 67.950884 21.08013 17560.007549 306.479948 \n", + "3447 67.950884 21.08013 17530.308203 305.961597 \n", + "3448 67.950884 21.08013 17500.608856 305.443246 \n", "\n", - "[3449 rows x 23 columns]" + "[3449 rows x 25 columns]" ] }, "execution_count": 7, @@ -612,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -621,19 +628,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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", 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", 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", 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7hfi4CAxor8Ojd8YiROOLoV0auaFKcgcGGCdSqVRoF6PF9hOXcOS84YYBpmoPjO0WEsfAEBHZE0LgpRWH8P3vZx2eM6BdFKK0AZgyqA2CNb6YnNTGjRWSuzHAOFmryFBsP3EJGZeKHJ5jLOMtJCKimrBaBRZtPYV/rkuv9vgXo7uj720N3VwVeQIGGCcLCSi/pAVGk8Nzsg3li901qDINL9C//HN5C4mICCgqNWPYhztxIqew2uNfje2BhKvTnKnuYYBxspyr4eSrPVmY+1Cnas8pLC1fqC48yM/uea7ES0R13Wc7MqBSAXqDER9tPe3wvO0v90NsRJAbKyNPwwDjZDtPXZIeW62i2hHtN5pGfb0ZTERE3urslWLMWnX0uueserY3VCowvBCnUTvb6/e1lx7/nnml2nOkhex8HS1kx60EiKjuKCw14/NdZ9D7nd8cntOrZX0cmjkAHRqFoX1MmBurI0/FHhgnS2wXJT3e99cVdGsWcc05tq0Eqq7EW7GVgPWazyEi8iZHzucjJiwQ205cxHNfH3B43pdj4rn6LVWLAcYFfNUqmK0CS3eewdN3tbjmuKN1YIK4mSMR1QGHzubhgX/vvOF5H4zowvBCDvEWkgskx5fvnxER7F/t8RvdQuIsJCLyZou2nLru8UmJrXB0VhIXnaPrYg+MC9zVuiE+T/kLKgcrUhtN1x/Ey1lIRORtCowmbD9xCYfP5WPNH/pqz3lneEc8eue1GygSVYcBxgUiQwMAADkFpdUed9QDE8jNHInIC2UbjIifs8nh8XpBftj2cj+EBvg5PIeoKgYYF4jUli9Qd7GgFGaLFb4+9kHF4RgYv/L/HWargMlihZ8P7/ARkbKl/XUFwxftcnh8+dh49GzBcS5Uey75C1lQUIBJkyahadOmCAwMRM+ePbF3717puBACM2bMQHR0NAIDA5GYmIgTJ07YvUZubi6Sk5Oh1WoRHh6OMWPGoLCw+tUYPU394IoVdg+ezbvmuDQLqeoYGP+KNm8jEZGSFZeZcfc/f7tueHl1cBuGF7ppLgkwTz31FDZs2ID//e9/OHz4MAYMGIDExEScO3cOADBv3jwsWLAAixcvRmpqKoKDg5GUlASj0Si9RnJyMo4cOYINGzZg1apV2LZtG8aNG+eKcp2u8vbsqw5duOa4dAupyjRqfx+19LlczI6IlMhssWLD0Wy0m7EOZy4XV3vOmN5xODhjAMb1vXaWJlFNOT3AlJSU4Pvvv8e8efPQt29ftGzZEjNnzkTLli2xaNEiCCEwf/58TJs2DUOHDkWnTp3wxRdf4Pz581i5ciUA4NixY1i7di3++9//Ij4+Hr1798bChQvx9ddf4/z5884u2SU6NNICAH45eG29jlbiValUCOJ2AkSkQFm5xViw6QRavrYGY7/YV+05I7vHYv/0ezH9vnYIC+J4F7o1Tg8wZrMZFosFAQEBds8HBgZix44dyMjIgF6vR2JionQsLCwM8fHxSElJAQCkpKQgPDwc3bp1k85JTEyEWq1Gamqqs0t2if93R2MAqHZQmi2cVB0DAwABHMhLRAoz8+cj6DPvN7y34bjDcx7oHIO5D3VCPQfLSxDVltMH8YaGhiIhIQFvvvkm2rZti6ioKHz11VdISUlBy5YtodeXT5+Lioqy+7yoqCjpmF6vR2RkpH2hvr6IiIiQzqmqtLQUpaUVs34MBoMzv61a69KkHoBrg4gQQtrMURtw7eWvWI2Xi9kRkWczW6x45KMU/J6Z5/CcdZP6okXDYLtb60TO4JIxMP/73/8ghECjRo2g0WiwYMECjBw5Emq162bVzJ07F2FhYdJHbGysy75WTcSElfdA6Q1GFBhN0vNGkxUWqwAABGuuDTDckZqIPJ3VKjDrl6No+dqa64aXbZP7obUuFL4+aqgcLYxFdJNckihatGiBrVu3orCwEFlZWdizZw9MJhOaN28OnU4HAMjOzrb7nOzsbOmYTqdDTk6O3XGz2Yzc3FzpnKqmTp2K/Px86SMrK8sF31nNhQdVdJPO31gxw6qgtDzMqFQVvS2VcS0YIvJUQgjkl5jw1upj+GxnRrXntNGFYt2kvjg9ZzCa1OeO0eQ6Ll1oJDg4GNHR0bhy5QrWrVuHoUOHIi4uDjqdDps2VSxqZDAYkJqaioSEBABAQkIC8vLykJaWJp2zefNmWK1WxMfHV/u1NBoNtFqt3Yec/CtNkT58Ll96XGgsvzUUovGt9l8kFbeQGGCIyLO8vfZPdH5jPT7dcW14Gde3Obo3i8A3/0hAa10o1LxlRC7mkoXs1q1bByEEWrdujZMnT2Ly5Mlo06YN/v73v0OlUmHSpEmYPXs2WrVqhbi4OEyfPh0xMTEYNmwYAKBt27YYOHAgxo4di8WLF8NkMmHixIkYMWIEYmJiXFGyS0VU6o2xjX8Jreb2EVBxC4k9METkKb5PO4sXVxx0eDzAT41XB7d1Y0VELgow+fn5mDp1Ks6ePYuIiAgMHz4cb731Fvz8ymfkvPzyyygqKsK4ceOQl5eH3r17Y+3atXYzl5YtW4aJEyeif//+UKvVGD58OBYsWOCKcl3m9fvb4Y1fjiK/pGIMjNQDU80AXgAI9C9/nmNgiEhu647oMenrA9ftEe57W0O8MrC1G6siKueSAPPII4/gkUcecXhcpVJh1qxZmDVrlsNzIiIisHz5cleU5zatdaEAgOyCigX6CkorbiFVJ/Dq4na8hUREckr76wr+8b+0ao9Fhmqw7eV+1S4FQeQu3AvJhaK0Vzd1NFRM767ogal+Eaegqz0wvIVERO50+mIhCkvNaBQeiB0nL+G5rw9Ue94fbyQ5/AcYkTvxXehCkaHleyIVlpqhzzdCFxZwwzEwAZxGTURu9s3eTLzy/eEbnje6VxzDC3kMbnfsQpV/0OesPgagYhCvo18CnIVERO5Wk/Dy/fgETL+PA3XJczBKu1DladK23hjD1UXtHA3ilQJMGVfiJSLX23g02+GxEXfG4o2h7a/Zt43IEzDAuMm6o3pMu68dCozX74Gx3UJiDwwRudJ3aWfx0nWmRh95I6na1cKJPAXfnS7WMFSDiwWluJBXPhPJFmC0gY4G8XIMDBG5RmGpGSv2ZeGjraehNxirPadXy/pY/FhXhhfyeHyHutjkpNZ4+btDaGi7hXR1TZjqNnIEKhayM7IHhoic5EJ+Cb7bdxb/us5u0QBwas5gbrpIisEA42rl+zbiQr4RVquQNnZ01AMTyB4YInKi/+3+C9NX/nHdc47PHgQftYrhhRSFAcbF2sVU7Ml0qagUl4vKAADhjgIMx8AQkZMcPpt/3fDywYguGNqlkRsrInIeBhgX69AoTHqclVuCc1dKAACxEdXv0sqF7IjoVgghsPnPHPx3ewZSTl++5viz97TEhH4tkVtUhpjwQBkqJHIOBhg3CPBTw2iy4q1fj8JsFfBRq6DTBlR7Lm8hEdGtGPD+NpzIKaz22J7X+iMytPx3D8MLKR0DjBsYTVYAwO+ZeQDKB/A62mo+kAvZEdFNuNFqurOGtpfCC5E3YIBxg0e7xeKbfVlS+0qxyeG5QVfHwJSZrbBc7a0hInLk7JVivLTiIHafzq32+Jm3h7i5IiL3YIBxg+FdG9sFmOux9cAA5b0w3HeEiKoyW6zwUavw+s9H8EXKX9Wec3rOYIc9vUTegH8d3SAm3L7b1ratQHU0vmqoVIAQQHGZmQGGiCQWq8AvB89j0jcHHJ7TMjIEs4d1YHghr8e/jm7QuJ79jKNFj93h8FyVSoVAPx8Ul1lgLLO6ujQiUpC5q4/hvzsyHB7/ZlwPxDev78aKiOTDACOD2HrVT6G2CfIvDzDFJm7oSETlK3M//WUatqRfdHjOZ092Y3ihOoUBxk2Wj43H3z5JhcZXjUgHU6htOJWaiADgj3P5+GT7afx04LzDc7o2rYd3H+6MuAbBbqyMSH4MMG7Ss0UDHJ2VJC1Udz3SfkgMMER1ktlixYV8I+5buMPhObOHdcDI7k04U5HqLAYYN6pJeAGAwKvnVe2BEaJ8YyWVir+wiLzZM8t+x/qj2Q6PH5wxAGFB1W9HQlRXMMB4oEA/NQD7xeyEEIibuhotGgZjw/N3cYYBkRexWgUuGIzIMRix7kh2teHlqd5xeKxHU6hVKoYXIjDAeKTq9kNakXYWAHDqYhEuFZVyRU0iL/LMst+x9oje4fHHejTBtPvaubEiIs/HAOOBqttOYNnuisWqtqZfxMPdYt1eFxE5V4HRhOPZhQ7DyzfjeqBzbDgC/HyqPU5UlzHAeCDbIN7KY2AahgYAyAcA5BSUylEWETnJf7acxKqDF3D0gsHhOdwCgOj6GGA8UJCtB6asYh2YEE3Fv8CM3OiRSHGMJgvWH81GyqnL+GpPpsPzusdFYAZvFxHdEAOMB7L1wFS+haQ3GKXHF/KN13wOEXm20Uv3Ytepyw6P73m1/w3XiCKiCgwwHqi6hez0lUKLngGGSDFWHTqPicv3Ozz+xgPt8URCUy6PQFRLDDAeqLoemMrjXi7kl7i9JiKqvRyD0WF4+fGZnri9ST03V0TkPRhgPFDFGJjyAGO2WO16Y7INHMRL5ImEELhYUAqNnw86v7He4Xl33dYQnRuHu68wIi/EAOOBbCvx2npgiqqsyFtYakaB0YTQAC5mReRJnvhsD7afuOTw+Beju6PvbQ3dWBGR91LLXQBdq+o06qLS8tlIfj4qaAPKww0H8hJ5lrFf7HMYXrrEhuPbfySgT6sGbq6KyHuxB8YDVb2FZAswwRpfRAT7w2A0I7eoTLb6iKjCxYJS3PnWRofHn76rBaYMauPGiojqBgYYDxRQZRBvoS3A+PsiRFP+v8wWaojI/fT5RmRdKcaKfVn4dt/Zas/p3iwCX4/rwX3LiFyEAcYDXdsDU/7fEE1FgClkgCGSTY+5m657/OGujTHzgfYML0QuxADjgaruhST1wGh8EMwAQySLP/UGDJy/3eHxNrpQ/Pp/feDD0ELkFgwwHqhiEG95SCmsNAaGt5CI3MtosmDK94ew8sD5ao/f0SQc3z3dk70tRG7GAOOBbLeQjCYrrFYhhZXQgMq3kLgfEpGrZRuMiJ9T/e2i0b3i0DY6FA/d0ZjhhUgGDDAeyHYLCQCMZovUAxPk71txC8nIHhgiVzqXV4Jeb2+u9tie1/ojMpT7FhHJiQHGAwX4VgSYkjKLdCspROOL0ADeQiJypT/1Bjy8OAUFDv6R8OmobgwvRB6AAcYDqdUqBPipYTSVbyFgm4UUrPFB8NXemcIyBhgiZzFbrPh4+2nMW5te7fHhdzTG5KTW0IUxuBB5CgYYDxXk7wujqQwlpooeGN5CInK+E9kFuPf9bQ6Pz7y/HZ7sFefGioioJhhgPJS0I3WZRdoLKdjfh7eQiJxECIGnv0zDuiPZ1R7X+KpxeGYS/H254wqRJ3L6T6bFYsH06dMRFxeHwMBAtGjRAm+++SaEENI5QgjMmDED0dHRCAwMRGJiIk6cOGH3Orm5uUhOToZWq0V4eDjGjBmDwsJCZ5frsWwDeYvLLCi2DeLV+HIdGKJbVFhqRrMpvyJu6upqw8snT3TD/Ee7YMvkuxleiDyY038633nnHSxatAj//ve/cezYMbzzzjuYN28eFi5cKJ0zb948LFiwAIsXL0ZqaiqCg4ORlJQEo7Fig8Lk5GQcOXIEGzZswKpVq7Bt2zaMGzfO2eV6rIqp1JV7YLgSL9HNEkJg0ZZT6PD6OofnPNy1Me5tF4VhtzdCdFigG6sjotpy+i2kXbt2YejQoRgyZAgAoFmzZvjqq6+wZ88eAOW/RObPn49p06Zh6NChAIAvvvgCUVFRWLlyJUaMGIFjx45h7dq12Lt3L7p16wYAWLhwIQYPHox3330XMTExzi7b4wRU2pFaGgOj8eFCdkQ3qcusDcgvMVV7TK0CUqb2R5SWg3SJlMLpPTA9e/bEpk2bcPz4cQDAwYMHsWPHDgwaNAgAkJGRAb1ej8TEROlzwsLCEB8fj5SUFABASkoKwsPDpfACAImJiVCr1UhNTa3265aWlsJgMNh9KFlQpe0EKu+FFCwFGC5kR1QTY5buRbMpvzoML/e0icSJtwYzvBApjNN7YKZMmQKDwYA2bdrAx8cHFosFb731FpKTkwEAer0eABAVFWX3eVFRUdIxvV6PyMhI+0J9fRERESGdU9XcuXPxxhtvOPvbkU3FIF4zLheWAgDCA/2kAFNmsaLUbIGm0poxRFTOZLGi0GjG9J/+wKY/c645vv3lfmgYqpF6OolIeZweYL799lssW7YMy5cvR/v27XHgwAFMmjQJMTExGDVqlLO/nGTq1Kl44YUXpLbBYEBsbKzLvp6r2QbxGoxmGK5OmW4YqpHWgQGA4lIGGKKqzueVoKeDFXSb1g/C+uf78ueGyAs4PcBMnjwZU6ZMwYgRIwAAHTt2xF9//YW5c+di1KhR0Ol0AIDs7GxER0dLn5ednY0uXboAAHQ6HXJy7P/VZDabkZubK31+VRqNBhqNxtnfjmxsPTDZhoqBzaEBfvBRq6DxVaPUbEVRmRn1gv3lKpHIoxSWmrF0ZwbeXX+82uNP39UCrwxsDZWK+xYReQOnB5ji4mKo1fZDa3x8fGC1WgEAcXFx0Ol02LRpkxRYDAYDUlNTMX78eABAQkIC8vLykJaWhq5duwIANm/eDKvVivj4eGeX7JFsY2ByDOW3j4L9feBzdcO4EI0vSs1lHAdDBCBdX4Dxy9Jw+mJRtcfvbReFjx7ryg0XibyM0wPM/fffj7feegtNmjRB+/btsX//frz33nsYPXo0AEClUmHSpEmYPXs2WrVqhbi4OEyfPh0xMTEYNmwYAKBt27YYOHAgxo4di8WLF8NkMmHixIkYMWJEnZiBBACB/uX/a7ILyntgQgP8pGNBGh9cLgKKuJ0A1WEWq8CaPy5g4vL91R5/6PZG6NqsHh7uGsvwQuSFnB5gFi5ciOnTp+OZZ55BTk4OYmJi8I9//AMzZsyQznn55ZdRVFSEcePGIS8vD71798batWsREFAxC2DZsmWYOHEi+vfvD7VajeHDh2PBggXOLtdjSbeQ8m0BpuJ/VbA/p1JT3XapsBTdZm90ePyDEV0wtEsjN1ZERO6mEpWXyPUiBoMBYWFhyM/Ph1arlbucWvt81xm8/vMRqd2nVQP8b0z57bMH/7MT+zPz8PHjXTGgffVjgoi80Y2W/187qQ/a6JT3805EFWr695t7IXmowCrTO5tEBEmPK68RQ1QXWK0CHWeuk1alrurFe2/Ds/1bubkqIpITA4yHCvS3DzCN61UEmMobPRLVBQ8t2uUwvHwxujv63tbQzRURkdwYYDxUUJUA06hexb4stgG+7IEhbyWEwD/+l4btJy5V+z5/oHMMpt3XFv4+aoQHcSkBorqIAcZDVb2F1DoqtNKx8mnqxeyBIS9kNFnw5JI92H06t9rjf4tvgjkPdnRzVUTkaRhgPFTVW0jNGlx7C8nIHhjyIsVlZnyR8hfeXvNntcc1vmqMv7sFRveOc3NlROSJGGA8VOUAow3wtVv6XLqFxB4Y8gLn8krQy8HS/zZH3kiS9gEjIgIYYDxWkF/F/5oGIfZbJNh6YIrZA0MKdjKnEMM+3InC66xn9NAdjTBtSDuGFyK6Bn8reKgA/4rtGOqH2A9StA3wNbIHhhQs8b2tDo8tHxuPni0auLEaIlIaBhgPFeRf8b+m8jYCABBwNcBwEC8p0YGsPAz7cKfD4/un38tNSonohhhgPFTlWUgx4QHVHuM0alKaf61Px8LNJ6s9NmtoezyR0My9BRGRYjHAeCifSpvPjaryS50r8ZIS/ZaeU214eeOB9hjVs5n7CyIiRWOA8WAfP94VlwrL0KrSGjBARYApNHIzR1KG/BIT/r5kr91zt0WF4P1Hu6B9TJhMVRGRkjHAeDBHGzXaVh7NKy5zZzlEN+3BKmNeDs4YgLAgPwdnExHdmPrGp5Cn0QaU584C9sCQAvx66AJOXyqS2uuf78vwQkS3jAFGgQJsK/GaOQaGPFuB0YQJy3+X2j5qFW6rckuUiOhmMMAokC3AmCwCFquQuRoixzrOXG/XXvxYV5kqISJvwwCjQAF+Ff/buB8Seaoffj97zXP9WjeUoRIi8kYMMAoUUGlfJAYY8lQvfHvQrr3++b7w9eGvHCJyDs5CUiC1WgV/XzXKzFauBUMe6bs0+96XtGmJqF9lTy8iolvBfw4pVIBv+f86o8kqcyVE9owmC15aUdH78sGILgwvROR0DDAKJc1EYg8MeZg209dKj18d3AZDuzSSsRoi8la8haRQgVdX4y3lVGryEEII/H2p/Wq74/q2kKkaIvJ27IFRKNtA3pIy3kIiz7DmDz22pF+U2mN6x8lYDRF5OwYYhbJNpeYtJPIUzyz73a49bUhbmSohorqAAUahbGNgOAuJPMHmP7Pt2run9odKpXJwNhHRrWOAUSjbjtQlZQwwJC+zxYrRS/dJ7bWT+kAXFiBjRURUFzDAKFSQf/n46+IybuhI8pr+0xG7dhudVqZKiKguYYBRKNsspGLeQiIZbTiaja/2ZErt78cnyFgNEdUlDDAKxVtIJDd9vhFjv6i4dfRIt8bo2jRCxoqIqC5hgFEoqQeGAYZkkvzf3Xbttx/qJFMlRFQXMcAoVJCfbQwMAwy533+2nMSpi0VS+/jsQVCrOeuIiNyHAUahKm4hcRAvud+8tenS43vbRcHfl79KiMi9+FtHoWy3kLgODLnbR1tP2bU//NsdMlVCRHUZA4xCBXEMDMlAn2/E3DV/Su3V/9eHvS9EJAv+5lEozkIiOfSYu0l63KdVA7SL4ZovRCQPBhiFCvTnIF5yr/c2HLdrfzG6u0yVEBExwChWIPdCIjfaeyYXCzadkNrbJvfjXkdEJCsGGIWqGAPDWUjkWlarwMOLU6R216b10KR+kIwVERExwCgWF7Ijd/nu97N27aV/v1OmSoiIKjDAKBQH8ZI7CCHw8neHpPb8R7sgNMBPxoqIiMoxwCiUbSVes1WgzGyVuRryRmaLFXFTV0vtrk3rYdjtjWSsiIiogtMDTLNmzaBSqa75mDBhAgDAaDRiwoQJqF+/PkJCQjB8+HBkZ2fbvUZmZiaGDBmCoKAgREZGYvLkyTCbOdajMtstJIC9MOQaH1QatAsAbw7tIFMlRETXcnqA2bt3Ly5cuCB9bNiwAQDw8MMPAwCef/55/PLLL1ixYgW2bt2K8+fP46GHHpI+32KxYMiQISgrK8OuXbvw+eefY+nSpZgxY4azS1U0f181fK/uPVNsYrgj59p7JhcLN5+U2vMf7cI1X4jIozg9wDRs2BA6nU76WLVqFVq0aIG77roL+fn5+PTTT/Hee+/hnnvuQdeuXbFkyRLs2rULu3eX72y7fv16HD16FF9++SW6dOmCQYMG4c0338SHH36IsrIyZ5eraBzIS65SedbRkz2b8dYREXkcl46BKSsrw5dffonRo0dDpVIhLS0NJpMJiYmJ0jlt2rRBkyZNkJJS/gszJSUFHTt2RFRUlHROUlISDAYDjhw54vBrlZaWwmAw2H14Ow7kJVfYdeqSXXvGfe1kqoSIyDGXBpiVK1ciLy8PTz75JABAr9fD398f4eHhdudFRUVBr9dL51QOL7bjtmOOzJ07F2FhYdJHbGys874RDxV0dTVeLmZHzvS3T1Klx5+O6ga1mgvWEZHncWmA+fTTTzFo0CDExMS48ssAAKZOnYr8/HzpIysry+VfU2621Xh5C4mcpdmUX+3a/dtGOTiTiEhevq564b/++gsbN27EDz/8ID2n0+lQVlaGvLw8u16Y7Oxs6HQ66Zw9e/bYvZZtlpLtnOpoNBpoNBonfgeer+IWEgfx0q37Zm+mXTt99kCZKiEiujGX9cAsWbIEkZGRGDJkiPRc165d4efnh02bKna0TU9PR2ZmJhISEgAACQkJOHz4MHJycqRzNmzYAK1Wi3bteC++Mg7iJWcpNVvwyveHpXavlvWh8fW5zmcQEcnLJT0wVqsVS5YswahRo+DrW/ElwsLCMGbMGLzwwguIiIiAVqvFs88+i4SEBPTo0QMAMGDAALRr1w6PP/445s2bB71ej2nTpmHChAl1roflRngLiZxl6L932rWXPdVDpkqIiGrGJQFm48aNyMzMxOjRo6859v7770OtVmP48OEoLS1FUlIS/vOf/0jHfXx8sGrVKowfPx4JCQkIDg7GqFGjMGvWLFeUqmichUTO8NOBc/hTXyC15zzYUcZqiIhqRiWEEHIX4QoGgwFhYWHIz8+HVuudC3BN/eEwvtqTiecTb8Nzia3kLocU6Mj5fAxZsENqf/uPBHSPi5CxIiKq62r695t7ISmYrQeGK/HSzaocXgAwvBCRYjDAKBhvIdGt+HzXGbv28qfi5SmEiOgmuGwaNbkeZyHRzfp2bxZe/7liZeu1k/qgjc47b7USkXdiD4yCBV2dhcSVeKk2hBB4+ftDds8xvBCR0jDAKJi0lQB7YKgWKu8yDQDHZnHBOiJSHgYYBau4hcRBvFQzWbnFeG/Dcak9sV9L6X1ERKQkDDAKxkG8VFt95v1m135xwG0yVUJEdGsYYBSMg3ipNpbuzLBrn3hrEFQq7jRNRMrEAKNgtjEwDDBUEzN/OSo93vtaIvx8+ONPRMrF32AKFshZSFRD/f+1RXrcs0V9NAzlvmJEpGwMMAoWxEG8VAPZBiNOXSyS2svHcqNGIlI+BhgFs42BMZqssFq9cksrukWXCksRP2eT1P7kiW4yVkNE5DwMMAoWVGn6K28jUVVCCHSbvdHuuXvbRclUDRGRczHAKFiAb0WA4UBeqmr4ol127a2T75anECIiF2CAUTC1WiUN5DWyB4YqKS4z4/fMPKn97T8S0LR+sHwFERE5GQOMwgVxLRiqQgiBdjPWSe1RCU3RPS5CxoqIiJyPAUbhuJ0AVXX0gsGu/cbQDjJVQkTkOgwwCsftBKiyPRm5GLJgh9Te/OJdMlZDROQ6DDAKF8jVeOkqIQQe+ShFaodofNG8YYiMFRERuQ4DjMIFXR3EW8xBvHVey9fW2LX3vpYoUyVERK7HAKNwFbeQOAamLvt81xlYKi1m+OMzPaXxUURE3ogBRuG4IzUZTRa8/vMRqT38jsa4vUk9GSsiInI9BhiFs60DwwBTd/V7d4td+92HO8lTCBGRGzHAKBxnIdVtn+86gwv5Rql9dFYSVCqVjBUREbkHA4zCcRZS3fWn3mB362jJk3ci6Or7gYjI2zHAKJzUA8NZSHXOA//eadfu1yZSpkqIiNyPAUbhOAupbiowmlBmtkrtjLmDZayGiMj9GGAUjrOQ6p6s3GJ0nLleaq96tjfHvRBRncMAo3C8hVT39Jn3m127Q6MwmSohIpIPR/wpXKAfB/HWFUIIzN94wu65Ha/0k6kaIiJ5sQdG4YJ4C6nO2HHyEj7YVBFgvh/fE43rBclYERGRfBhgFI6DeOuOxz/dIz0ODfBF16ZcbZeI6i4GGIXjIN66YeiH9lOmD70+QKZKiIg8AwOMwtkWLuNKvN6rqNSMg1l5Unv5U/GcdUREdR4DjMJJeyGZLBBC3OBsUprj2QVo//o6qf3eI53Rs2UDGSsiIvIMDDAKZ7uFZLEKlFmsNziblGbA+9vs2g/d0VimSoiIPAsDjMLZBvECgLGMAcab5BQY7drbX+aUaSIiGwYYhfPzUcPPp3w8RLGJM5G8Sfe3NkmPvxwTj9gITpkmIrJhgPEC0jgYDuT1GmO/2GfX7t2K416IiCpjgPECnInkPUrNFrzy3SFsOJotPcdbR0RE12KA8QJcjdd7LNpyCt/sy5Lav/5fb946IiKqhksCzLlz5/DYY4+hfv36CAwMRMeOHbFvX0WXuBACM2bMQHR0NAIDA5GYmIgTJ+z3eMnNzUVycjK0Wi3Cw8MxZswYFBYWuqJcxatYzI5jYJTMYrXf62j+o13QPoYbNRIRVcfpAebKlSvo1asX/Pz8sGbNGhw9ehT/+te/UK9exbLn8+bNw4IFC7B48WKkpqYiODgYSUlJMBorZl0kJyfjyJEj2LBhA1atWoVt27Zh3Lhxzi7XK1RsJ8AeGCWbvOKg9LhJRBCG3d5IxmqIiDyb03ejfueddxAbG4slS5ZIz8XFxUmPhRCYP38+pk2bhqFDhwIAvvjiC0RFRWHlypUYMWIEjh07hrVr12Lv3r3o1q0bAGDhwoUYPHgw3n33XcTExDi7bEUL9OeO1Er30oqD+GH/Oam9blJfGashIvJ8Tu+B+fnnn9GtWzc8/PDDiIyMxO23345PPvlEOp6RkQG9Xo/ExETpubCwMMTHxyMlJQUAkJKSgvDwcCm8AEBiYiLUajVSU1Or/bqlpaUwGAx2H3VFUKXVeEl5hBD4Lu2s1H5neEfptiAREVXP6QHm9OnTWLRoEVq1aoV169Zh/Pjx+L//+z98/vnnAAC9Xg8AiIqKsvu8qKgo6Zher0dkZKTdcV9fX0REREjnVDV37lyEhYVJH7Gxsc7+1jwWd6RWtrfX/Ck9VqmAR7rVnfcuEdHNcnqAsVqtuOOOOzBnzhzcfvvtGDduHMaOHYvFixc7+0vZmTp1KvLz86WPrKysG3+Sl+CO1Mr1ybbT+Gjbaam945V7uFEjEVENOD3AREdHo127dnbPtW3bFpmZmQAAnU4HAMjOzrY7Jzs7Wzqm0+mQk5Njd9xsNiM3N1c6pyqNRgOtVmv3UVfYFrIr4S0kRRFC4K3Vx6T2+492RqPwQBkrIiJSDqcHmF69eiE9Pd3uuePHj6Np06YAygf06nQ6bNpUsUy6wWBAamoqEhISAAAJCQnIy8tDWlqadM7mzZthtVoRHx/v7JIVj7OQlEcIgbipq6X2hH4t8ODt3KiRiKimnD4L6fnnn0fPnj0xZ84cPPLII9izZw8+/vhjfPzxxwAAlUqFSZMmYfbs2WjVqhXi4uIwffp0xMTEYNiwYQDKe2wGDhwo3XoymUyYOHEiRowYwRlI1eAsJOWZ8v1hu/bTd7WQqRIiImVyeoC588478eOPP2Lq1KmYNWsW4uLiMH/+fCQnJ0vnvPzyyygqKsK4ceOQl5eH3r17Y+3atQgICJDOWbZsGSZOnIj+/ftDrVZj+PDhWLBggbPL9QrsgVEWg9Fkt9ruxhfuQmiAn4wVEREpj0oIIeQuwhUMBgPCwsKQn5/v9eNhvt2XhZe/O4R+rRtiyd+7y10O3UCzKb9Kj6cMasPeFyKiSmr695t7IXkB7oWkHP/ZctKu/ViPpjJVQkSkbAwwXkC6hcRZSB5tS3oO5q2tGOD+xgPtEaJx+l1cIqI6gQHGCwT6cRCvp7uQX4Inl+y1e25Uz2byFENE5AUYYLxAsKa8B6aolCvxeqqEuZvt2lsn3y1PIUREXoL9114g+OptiEIGGI/0UqVdpgHgzzcHIsCPex0REd0K9sB4gdCrAaao1AwvnVSmWGVmq91Gjd89ncDwQkTkBAwwXsDWA2MVgNFklbkashFC4LZpa6T2+492RrdmETJWRETkPRhgvECQvw9s+//xNpLnGPTBdulxp8Zh3CqAiMiJGGC8gEqlQrB/xW0kkl9WbjH+1BdI7a/H9ZCxGiIi78MA4yVsM5HYA+MZ+sz7TXr80O2NEOTP8fJERM7EAOMlOBPJc4xZar/ey3uPdpGnECIiL8YA4yVCNLyF5Ame+nwfNv2ZI7WPzkqSsRoiIu/FAOMlbGNg2AMjnyPn87HxWLbU/u8T3XjriIjIRfjb1UuEB/kBAPJLTDJXUncNWbBDevzd0wmcMk1E5ELsgfES9YL9AQCXC8tkrqRueuvXo9Lj2IhAhhciIhdjgPESoRzEK5sL+SX4ZHuG1P5pQm8ZqyEiqhsYYLxEMAfxyuLslWK7jRrXPNcHEVd7w4iIyHUYYLwEp1G7n9Uq0PudivVe/t6rGdpGa2WsiIio7mCA8RIhVxeyYw+M+9z+5ga79oz72slUCRFR3cMA4yXYA+NeZy4V2c34OvJGElS2DamIiMjlGGC8RERQ+biLK8WcRu1qVqvA3e9ukdoju8dKAZKIiNyDAcZL2KZR5xVzGrWrJby9ya4996FOMlVCRFR3McB4iYqtBCwyV+LdluzMQLahVGpnzB0sYzVERHUXA4yXCPIvH8RbYrLAYhUyV+OdMi8X441fKhase+OB9hz3QkQkEwYYL1F5DEZxGQfyOtvJnEL0/edvds+N6tlMnmKIiIgBxltofNXwVZf3BvA2kvNNXP67XfvUHN46IiKSEwOMl1CpVNJtJE6ldq60v3Lxp75Aah+dlQQfNW8dERHJiQHGi9gG8vIWkvMIITB8UYrU3vNafwT5c8o0EZHcGGC8SBAXs3O6xVtPS48XjLwdkaEBMlZDREQ2/KekF6kf7I+TAPT5RrlLUTyrVeC2aWtgvjqjq2n9IDzQOUbmqoiIyIY9MF6kcb0gAIDewABzqz787aQUXhqEaPDbi3fLWxAREdlhgPEiEcF+AIArRVyN91YYTRb8a8Nxqf3d0wlQc9AuEZFHYYDxIrbtBHKLuB/SzSosNaPN9LVSe3SvODRrECxjRUREVB0GGC9SsaEje2BuhhACHV5fZ/fca0PaylQNERFdDwOMF4mQemAYYG7Gk0v22rW/HteD670QEXkoBhgvYgsw7IGpve0nLmLr8YtSe2T3JujRvL6MFRER0fVwGrUXqccemJsihMDjn+6R2tOGtMVTfZrLWBEREd0Ie2C8iG0MTIHRDJPFKnM1yvHSikN2bYYXIiLPxwDjRbSBfrAN2eBtpJr5dm8Wvv/9rNQ+8kaSjNUQEVFNMcB4ER+1CuG2mUicSn1Df5zLx8vfV/S+/PhMTwRreFeViEgJGGC8TL2g8sXsOA7m+kwWK+5buENq39suCrc3qSdjRUREVBtODzAzZ86ESqWy+2jTpo103Gg0YsKECahfvz5CQkIwfPhwZGdn271GZmYmhgwZgqCgIERGRmLy5Mkwm7lBYU1wJlLNDHh/m137kye6yVQJERHdDJf0l7dv3x4bN26s+CK+FV/m+eefx6+//ooVK1YgLCwMEydOxEMPPYSdO3cCACwWC4YMGQKdToddu3bhwoULeOKJJ+Dn54c5c+a4olyvUi+IM5Fu5PfMK8i4VCS1T741SMZqiIjoZrgkwPj6+kKn013zfH5+Pj799FMsX74c99xzDwBgyZIlaNu2LXbv3o0ePXpg/fr1OHr0KDZu3IioqCh06dIFb775Jl555RXMnDkT/v7+rijZa0g9MAww1bpcWIqH/rNLav/4TE/4+vBOKhGR0rjkN/eJEycQExOD5s2bIzk5GZmZmQCAtLQ0mEwmJCYmSue2adMGTZo0QUpKCgAgJSUFHTt2RFRUlHROUlISDAYDjhw54vBrlpaWwmAw2H3URdJaMLyFdI3LhaXoOruiZ3Bk91iOeyEiUiinB5j4+HgsXboUa9euxaJFi5CRkYE+ffqgoKAAer0e/v7+CA8Pt/ucqKgo6PV6AIBer7cLL7bjtmOOzJ07F2FhYdJHbGysc78xhZD2Q2IPzDUqh5c7moRj7kOdZKyGiIhuhdNvIQ0aVDGeoFOnToiPj0fTpk3x7bffIjAw0NlfTjJ16lS88MILUttgMNTJEFPRA8Np1JUZjPbX44dneslUCREROYPLb/6Hh4fjtttuw8mTJ6HT6VBWVoa8vDy7c7Kzs6UxMzqd7ppZSbZ2deNqbDQaDbRard1HXRQRXD6Nmj0wFfZk5KLTzPVSe/nYeBmrISIiZ3B5gCksLMSpU6cQHR2Nrl27ws/PD5s2bZKOp6enIzMzEwkJCQCAhIQEHD58GDk5OdI5GzZsgFarRbt27VxdruJxFpK9YxcMeOSjFKndNlqLni0ayFgRERE5g9NvIb300ku4//770bRpU5w/fx6vv/46fHx8MHLkSISFhWHMmDF44YUXEBERAa1Wi2effRYJCQno0aMHAGDAgAFo164dHn/8ccybNw96vR7Tpk3DhAkToNFonF2u1+E6MPYGfbDdrr3i6QSZKiEiImdyeoA5e/YsRo4cicuXL6Nhw4bo3bs3du/ejYYNGwIA3n//fajVagwfPhylpaVISkrCf/7zH+nzfXx8sGrVKowfPx4JCQkIDg7GqFGjMGvWLGeX6pVsY2CKyywwmiwI8PORuSL59J33m117z2v9EcKtAoiIvIJKCCHkLsIVDAYDwsLCkJ+fX6fGwwgh0Oq1NTBbBVKm3oPoMNcNnPZk5/JK0OvtzVL7l4m90bFxmIwVERFRTdT07zdX8PIyKpWqYiZSHR0Hsz/zil14+XliL4YXIiIvwwDjhRqGlI8VOp9nlLkSeTxYaaVdAOjUOFyeQoiIyGUYYLxQq6gQAMDx7AKZK3G/e/61xa6945V+8hRCREQuxQDjhWLrBQEAsg11qwcm41IRTl+s2KRxxdMJaHz1WhARkXdhgPFCDUPLbyFdLCiVuRL3OXWxEP3e3SK1n+vfCnc2i5CvICIicikGGC9UFwNM/39tlR6P7B6L5++9TcZqiIjI1RhgvJAUYArrRoAZvsh+0O4rA9vIVAkREbkLA4wXss1CulQHemCOZxcg7a8rUnvLS3cj/Op2CkRE5L0YYLyQrQemqMyColKzzNW4TmGpGQPe3ya1lz0Vj2YNgmWsiIiI3IUBxgsFa3wR5F++hYC3zkSyWAU6vL5OakeHBaBXS27SSERUVzDAeKm4qz0RJ3MKZa7ENVq8utquvfOVe2SqhIiI5MAA46WaRJSvf3Iur0TmSpxvw9Fsu/bx2YOgVqtkqoaIiOTAAOOlGoWXb+J47op3BZjzeSUY+8U+qb3h+b7w9+XbmIioruFvfi/VqF55gNl56rLMlTjPH+fy0bPSJo0z7muHVlGhMlZERERyYYDxUjptAADg2AWDzJU4R0mZBfct3CG1h3aJwejecTJWREREcmKA8VJ3xlUso28wmmSsxDnazlhr1/5gxO0yVUJERJ6AAcZLNQjRoF6QHwDlj4NpO90+vOyffq9MlRARkadggPFitp2Yzyo4wOzJyEWJySK1X7j3NtQL5kq7RER1HQOMF2t8dSDv2SvFMldyc0wWKx75KEVqP9mzGf6vfysZKyIiIk/hK3cB5DpN6pf3wJy5VCRzJbV3ubAUXWdvlNrvDO+IR+9sImNFRETkSdgD48VaNgwBAJy8qLzVeCuHl86x4QwvRERkhwHGi7WILA8w6fpCCCFkrqbmcqrs37TymZ4yVUJERJ6KAcaL3RYVCh+1CpcKS3HmsjLGwVwqLEX3OZuk9obn+0Kl4jYBRERkjwHGi4VofNFGV75S7YnsApmruTGjyYJulW4dPdmzGVfaJSKiajHAeLl20VoAQGpGrsyV3FibSuu9hAb4YuYD7WWshoiIPBkDjJfr0bw+AODoec/dUiC/xIRmU361e27ftESZqiEiIiVggPFycQ2DAQAZHjyV+pllaXbt9NkDofH1kakaIiJSAgYYL9fi6lRqvcGI7CqzezzBwaw87DxZsWP2lpfuZnghIqIbYoDxcmGBfvBVl8/i2XHikszV2Dt2wYChH+6U2gtH3o5mDYJlrIiIiJSCAaYOuLddFADguAfNRLJaBQZ9sN3uufs6RctUDRERKQ0DTB3Qp1VDAMBH207LXEmF5q+utmuveDqB670QEVGNcS+kOqBLbLj0+MylItlv03y7N8uufWrOYPioGV6IiKjm2ANTB7SNrlgMbvfpy9c50/Xe23AcL39/SGpvnXw3wwsREdUaA0wdoFKp8MzdLQAAO0/JF2AuFpRiwaYTUntM7zg0rc9Bu0REVHsMMHWEbUG7Xw6eR0mZRZYa7nyrYpuAnyb0wvT72slSBxERKR8DTB3Ro3l9BPqVr6+y/qje7V//hW8PSI/VKqBzpXE5REREtcUAU0f4+6rRv20kAGDdEfcGmJEf78YPv58DADQI0eDUnMFu/fpEROR9GGDqkIQW5beRVh/WI6fAPavybjqWjZRKA4d3TbmH06WJiOiWMcDUIR1iwqTHD364y+Vf76UVBzHm831S+8sx8fD35VuOiIhuHdeBqUMqjzs5l1cCIYTLekMmrziI79LOSu20aYmoH6JxydciIqK6h/8crmM+eaKb9HjjsRyXfI3lqZlYUSm8NK4XyPBCRERO5fIA8/bbb0OlUmHSpEnSc0ajERMmTED9+vUREhKC4cOHIzs72+7zMjMzMWTIEAQFBSEyMhKTJ0+G2Wx2dblez7YvEgBsPe78AGO1Crz642Gp/eWYeOx45R6nfx0iIqrbXBpg9u7di48++gidOnWye/7555/HL7/8ghUrVmDr1q04f/48HnroIem4xWLBkCFDUFZWhl27duHzzz/H0qVLMWPGDFeWW2csSr4DAPDl7kyccOIGj+fySuz2OGpWPwi9WzVw2usTERHZuCzAFBYWIjk5GZ988gnq1asnPZ+fn49PP/0U7733Hu655x507doVS5Yswa5du7B7924AwPr163H06FF8+eWX6NKlCwYNGoQ333wTH374IcrKylxVcp2R1F6HDo20AIB7398GIcQtv2a6vgC93t5s99yGF+665dclIiKqjssCzIQJEzBkyBAkJibaPZ+WlgaTyWT3fJs2bdCkSROkpKQAAFJSUtCxY0dERVXc7khKSoLBYMCRI0eq/XqlpaUwGAx2H1Q9tVqFRcldpfZ/t2fc0utlXi5G0vxtds+lzx4IPx8OsSIiItdwyV+Yr7/+Gr///jvmzp17zTG9Xg9/f3+Eh4fbPR8VFQW9Xi+dUzm82I7bjlVn7ty5CAsLkz5iY2Od8J14r9iIIOnxW6uP4WRO4U29jtFkQd9//ia1G9cLxJm3h0Dj63PLNRIRETni9ACTlZWF5557DsuWLUNAQICzX96hqVOnIj8/X/rIyspy29dWqp1TKgbXJr63Fen62o2HWbTlFNpMX2v3HAfsEhGROzg9wKSlpSEnJwd33HEHfH194evri61bt2LBggXw9fVFVFQUysrKkJeXZ/d52dnZ0Ol0AACdTnfNrCRb23ZOVRqNBlqt1u6Drq9ReCCeT7xNaifN3waTxXrDzyszW9Fsyq94Z+2f0nOdGodxiwAiInIbpweY/v374/Dhwzhw4ID00a1bNyQnJ0uP/fz8sGnTJulz0tPTkZmZiYSEBABAQkICDh8+jJycimm+GzZsgFarRbt23MHYmZ5LbGXXbvXaGvxz3Z8wVxNkcgqM6PfuFtw2bY3d84uS78DPE3vDR80tAoiIyD1UwhlTUG7g7rvvRpcuXTB//nwAwPjx47F69WosXboUWq0Wzz77LABg167y5e0tFgu6dOmCmJgYzJs3D3q9Ho8//jieeuopzJkzp0Zf02AwICwsDPn5+eyNuYGiUjPav76u2mN/79UMS3aecfi5+6YlogEXqSMiIiep6d9vWaaJvP/++7jvvvswfPhw9O3bFzqdDj/88IN03MfHB6tWrYKPjw8SEhLw2GOP4YknnsCsWbPkKNfrBWt8kT57YLXHrhdeDs8cwPBCRESycEsPjBzYA3NzDp/Nx/3/3uHweNtoLVY9y9tFRETkGjX9+83NHMlOx8ZhOPP2EKTrCzDqsz0YdnsjCAjotAHo1DgcXZvWu/GLEBERuRgDDFWrtS4Uu1/tL3cZRERE1eJSqURERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4XrsbtRACAGAwGGSuhIiIiGrK9nfb9nfcEa8NMAUFBQCA2NhYmSshIiKi2iooKEBYWJjD4ypxo4ijUFarFefPn0doaChUKpXTXtdgMCA2NhZZWVnQarVOe12l4XXgNQB4DWx4HXgNbHgdbv0aCCFQUFCAmJgYqNWOR7p4bQ+MWq1G48aNXfb6Wq22zr45K+N14DUAeA1seB14DWx4HW7tGlyv58WGg3iJiIhIcRhgiIiISHEYYGpJo9Hg9ddfh0ajkbsUWfE68BoAvAY2vA68Bja8Du67Bl47iJeIiIi8F3tgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYGrpww8/RLNmzRAQEID4+Hjs2bNH7pJcZubMmVCpVHYfbdq0kY4bjUZMmDAB9evXR0hICIYPH47s7GwZK75127Ztw/3334+YmBioVCqsXLnS7rgQAjNmzEB0dDQCAwORmJiIEydO2J2Tm5uL5ORkaLVahIeHY8yYMSgsLHTjd3HrbnQdnnzyyWveGwMHDrQ7R+nXYe7cubjzzjsRGhqKyMhIDBs2DOnp6Xbn1ORnIDMzE0OGDEFQUBAiIyMxefJkmM1md34rN60m1+Duu+++5r3w9NNP252j5GsAAIsWLUKnTp2khdkSEhKwZs0a6bi3vw+AG18DWd4Hgmrs66+/Fv7+/uKzzz4TR44cEWPHjhXh4eEiOztb7tJc4vXXXxft27cXFy5ckD4uXrwoHX/66adFbGys2LRpk9i3b5/o0aOH6Nmzp4wV37rVq1eL1157Tfzwww8CgPjxxx/tjr/99tsiLCxMrFy5Uhw8eFA88MADIi4uTpSUlEjnDBw4UHTu3Fns3r1bbN++XbRs2VKMHDnSzd/JrbnRdRg1apQYOHCg3XsjNzfX7hylX4ekpCSxZMkS8ccff4gDBw6IwYMHiyZNmojCwkLpnBv9DJjNZtGhQweRmJgo9u/fL1avXi0aNGggpk6dKse3VGs1uQZ33XWXGDt2rN17IT8/Xzqu9GsghBA///yz+PXXX8Xx48dFenq6ePXVV4Wfn5/4448/hBDe/z4Q4sbXQI73AQNMLXTv3l1MmDBBalssFhETEyPmzp0rY1Wu8/rrr4vOnTtXeywvL0/4+fmJFStWSM8dO3ZMABApKSluqtC1qv7htlqtQqfTiX/+85/Sc3l5eUKj0YivvvpKCCHE0aNHBQCxd+9e6Zw1a9YIlUolzp0757banclRgBk6dKjDz/HG65CTkyMAiK1btwohavYzsHr1aqFWq4Ver5fOWbRokdBqtaK0tNS934ATVL0GQpT/4Xruueccfo63XQObevXqif/+97918n1gY7sGQsjzPuAtpBoqKytDWloaEhMTpefUajUSExORkpIiY2WudeLECcTExKB58+ZITk5GZmYmACAtLQ0mk8nuerRp0wZNmjTx2uuRkZEBvV5v9z2HhYUhPj5e+p5TUlIQHh6Obt26SeckJiZCrVYjNTXV7TW70pYtWxAZGYnWrVtj/PjxuHz5snTMG69Dfn4+ACAiIgJAzX4GUlJS0LFjR0RFRUnnJCUlwWAw4MiRI26s3jmqXgObZcuWoUGDBujQoQOmTp2K4uJi6Zi3XQOLxYKvv/4aRUVFSEhIqJPvg6rXwMbd7wOv3czR2S5dugSLxWJ38QEgKioKf/75p0xVuVZ8fDyWLl2K1q1b48KFC3jjjTfQp08f/PHHH9Dr9fD390d4eLjd50RFRUGv18tTsIvZvq/q3gO2Y3q9HpGRkXbHfX19ERER4VXXZeDAgXjooYcQFxeHU6dO4dVXX8WgQYOQkpICHx8fr7sOVqsVkyZNQq9evdChQwcAqNHPgF6vr/b9YjumJNVdAwD429/+hqZNmyImJgaHDh3CK6+8gvT0dPzwww8AvOcaHD58GAkJCTAajQgJCcGPP/6Idu3a4cCBA3XmfeDoGgDyvA8YYMihQYMGSY87deqE+Ph4NG3aFN9++y0CAwNlrIzkNmLECOlxx44d0alTJ7Ro0QJbtmxB//79ZazMNSZMmIA//vgDO3bskLsU2Ti6BuPGjZMed+zYEdHR0ejfvz9OnTqFFi1auLtMl2ndujUOHDiA/Px8fPfddxg1ahS2bt0qd1lu5egatGvXTpb3AW8h1VCDBg3g4+Nzzcjy7Oxs6HQ6mapyr/DwcNx22204efIkdDodysrKkJeXZ3eON18P2/d1vfeATqdDTk6O3XGz2Yzc3FyvvS4A0Lx5czRo0AAnT54E4F3XYeLEiVi1ahV+++03NG7cWHq+Jj8DOp2u2veL7ZhSOLoG1YmPjwcAu/eCN1wDf39/tGzZEl27dsXcuXPRuXNnfPDBB3XqfeDoGlTHHe8DBpga8vf3R9euXbFp0ybpOavVik2bNtndA/RmhYWFOHXqFKKjo9G1a1f4+fnZXY/09HRkZmZ67fWIi4uDTqez+54NBgNSU1Ol7zkhIQF5eXlIS0uTztm8eTOsVqv0A+2Nzp49i8uXLyM6OhqAd1wHIQQmTpyIH3/8EZs3b0ZcXJzd8Zr8DCQkJODw4cN2YW7Dhg3QarVS17snu9E1qM6BAwcAwO69oORr4IjVakVpaWmdeB84YrsG1XHL++Cmhv7WUV9//bXQaDRi6dKl4ujRo2LcuHEiPDzcblS1N3nxxRfFli1bREZGhti5c6dITEwUDRo0EDk5OUKI8qmDTZo0EZs3bxb79u0TCQkJIiEhQeaqb01BQYHYv3+/2L9/vwAg3nvvPbF//37x119/CSHKp1GHh4eLn376SRw6dEgMHTq02mnUt99+u0hNTRU7duwQrVq1UtT0YSGufx0KCgrESy+9JFJSUkRGRobYuHGjuOOOO0SrVq2E0WiUXkPp12H8+PEiLCxMbNmyxW5qaHFxsXTOjX4GbFNHBwwYIA4cOCDWrl0rGjZsqJjpsze6BidPnhSzZs0S+/btExkZGeKnn34SzZs3F3379pVeQ+nXQAghpkyZIrZu3SoyMjLEoUOHxJQpU4RKpRLr168XQnj/+0CI618Dud4HDDC1tHDhQtGkSRPh7+8vunfvLnbv3i13SS7z6KOPiujoaOHv7y8aNWokHn30UXHy5EnpeElJiXjmmWdEvXr1RFBQkHjwwQfFhQsXZKz41v32228CwDUfo0aNEkKUT6WePn26iIqKEhqNRvTv31+kp6fbvcbly5fFyJEjRUhIiNBqteLvf/+7KCgokOG7uXnXuw7FxcViwIABomHDhsLPz080bdpUjB079pogr/TrUN33D0AsWbJEOqcmPwNnzpwRgwYNEoGBgaJBgwbixRdfFCaTyc3fzc250TXIzMwUffv2FREREUKj0YiWLVuKyZMn263/IYSyr4EQQowePVo0bdpU+Pv7i4YNG4r+/ftL4UUI738fCHH9ayDX+0AlhBA313dDREREJA+OgSEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsX5/3zqd8Jo1bbqAAAAAElFTkSuQmCC", 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", 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", 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rocket. +orientation = np.array([ + [ 0, 1, 0], + [ 0, 0, 1], + [-1, 0, 0] +]) +accelerometer = simulation.add_sensor(WSEN_ISDS_ACC, orientation=orientation) +gyroscope = simulation.add_sensor(WSEN_ISDS_GYRO) + +orientation = np.array([ + [0, 1, 0], + [0, 0, -1], + [1, 0, 0] +]) +high_g = simulation.add_sensor(H3LIS100DL) +erinome = simulation.add_sensor(Erinome_I) + + +for i in tqdm.tqdm(range(5000)): + simulation.advance(0.1) + pressure = barometer() + acc = accelerometer() + omegas = gyroscope() + g = high_g() + gps = erinome() + +df = simulation.get_logger().get_dataframe() +df.to_csv('dummy2.csv') \ No newline at end of file diff --git a/siemens_parsed.csv b/siemens_parsed.csv new file mode 100644 index 0000000..8e60f6c --- /dev/null +++ b/siemens_parsed.csv @@ -0,0 +1,91 @@ +time,DB5 Objekte_-ST3_Isttemperatur [°C],DB5 Objekte_-V2_Rückmeldung Arbeitsstellung für Trend,DB5 Objekte_-SV1_Istdruck Visu [mBar abs],DB5 Objekte_-SV1_Istdruck Visu Mantisse [mBar abs],DB5 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self.get_mach_number(t) > 1 - def get_velocity(self, t: float | None = None) -> float: + def get_total_velocity(self, t: float | None = None) -> float: """ Args: t (float): Allows specification of a different time instead of the current time. None for current time. diff --git a/spatz/logger.py b/spatz/logger.py index 07862fa..8bd69d4 100644 --- a/spatz/logger.py +++ b/spatz/logger.py @@ -1,51 +1,45 @@ import numpy as np import pandas as pd +from abc import abstractmethod from typing import Any, Tuple, List from numpy.typing import ArrayLike -from abc import abstractmethod - - -class Advanceable: - def __init__(self) -> None: - self.reset() - - def step(self, dt: float): - """Advances the simulation data in time. - - Args: - dt (float): The step in time to make. - """ - self.__t += dt - self._on_step(dt) - - def reset(self): - """ - Reset the Avanceable object to its initial state. - """ - self.__t = 0 - self._on_reset() - - @abstractmethod - def _on_step(self, dt: float): - pass - - @abstractmethod - def _on_reset(self): - pass - - def get_time(self) -> float: - """ - Returns: - float: Returns the current time of the Advanceable. - """ - return self.__t +from spatz.simulations.advanceable import Advanceable class Logger(Advanceable): def __init__(self) -> None: super().__init__() + + @abstractmethod + def write(self, attrib: str | List[str], value: Any | List[Any] | List[ArrayLike], domain: str = 'all'): + pass + + +class EmptyLogger(Logger): + def __init__(self) -> None: + super().__init__() + + self.__idx = -1 + + def _on_step(self, _: float): + pass + + def _on_reset(self): + pass + + def write(self, attrib: str | List[str], value: Any | List[Any] | List[ArrayLike], domain: str = 'all'): + pass + + def get_dataframe(self) -> pd.DataFrame: + pass + + +class CSVLogger(Logger): + def __init__(self) -> None: + super().__init__() + self.__idx = -1 def _on_step(self, _: float): diff --git a/spatz/models/pressure.py b/spatz/models/pressure.py index a7ddb73..893797f 100644 --- a/spatz/models/pressure.py +++ b/spatz/models/pressure.py @@ -2,6 +2,7 @@ from typing import Any import numpy as np from enum import Enum +from ambiance import Atmosphere class PressUnit: @@ -42,3 +43,11 @@ class AltitudeModel: press = to_hpa[unit] return 44330 * (1 - (press / self.__press_0)**(1 / 5.255)) + + +class StandardAtmosphere: + def __init__(self) -> None: + pass + + def predict(self, press: float, unit: PressUnit = PressUnit.Pa) -> float: + return Atmosphere.from_pressure(press).h[0] diff --git a/spatz/sensors/__init__.py b/spatz/sensors/__init__.py index fa85c4b..520cbc9 100644 --- a/spatz/sensors/__init__.py +++ b/spatz/sensors/__init__.py @@ -1,5 +1,6 @@ from spatz.sensors.sensor import Sensor from spatz.sensors.gps import GPS -from spatz.sensors.imu import Accelerometer, Gyroscope, IMU +from spatz.sensors.imu import Accelerometer, Gyroscope, IMU, CoordSystem from spatz.sensors.pressure import PressureSensor +from spatz.sensors.temperature import TemperatureSensor from spatz.sensors.compound import CompoundSensor \ No newline at end of file diff --git a/spatz/sensors/gps/erinome1.py b/spatz/sensors/gps/erinome1.py index 0b1d48a..9c4d193 100644 --- a/spatz/sensors/gps/erinome1.py +++ b/spatz/sensors/gps/erinome1.py @@ -1,16 +1,20 @@ from typing import List from numpy.typing import ArrayLike -from sensors.gps import GPS +from spatz.sensors.gps import GPS from spatz.dataset import ArrayLike, Dataset +from spatz.simulations.data_source import DataSource from spatz.logger import ArrayLike, Logger from spatz.transforms import Transform class Erinome_I(GPS): - def __init__(self, dataset: Dataset, logger: Logger, transforms: List[Transform] = []): + def __init__(self, dataset: DataSource, logger: Logger, transforms: List[Transform] = []): super().__init__(dataset, logger, transforms) + def _get_name(self): + return "Erinome-I" + def _sensor_specific_effects(self, x: ArrayLike) -> ArrayLike: # TODO: What's the GPS module's behavior? diff --git a/spatz/sensors/gps/gps.py b/spatz/sensors/gps/gps.py index 07160d0..7762621 100644 --- a/spatz/sensors/gps/gps.py +++ b/spatz/sensors/gps/gps.py @@ -1,3 +1,6 @@ +import math +import numpy as np + from typing import Any, List from numpy.linalg import norm @@ -5,6 +8,7 @@ from numpy.typing import ArrayLike from pandas import NA from spatz.dataset import ArrayLike, Dataset +from spatz.simulations.data_source import DataSource from spatz.logger import ArrayLike, Logger from spatz.sensors import Sensor from spatz.transforms import Transform @@ -14,22 +18,24 @@ from spatz.transforms import Transform class GPS(Sensor): - def __init__(self, dataset: Dataset, logger: Logger, transforms: List[Transform] = []): + def __init__(self, dataset: DataSource, logger: Logger, transforms: List[Transform] = []): """GPS Module which provides the following information: - Longitude (in °) - Latitiude (in °) - Altitude (in m) + - Lock (1 or 0) """ super().__init__(dataset, logger, transforms) def _get_data(self) -> ArrayLike: - vel = norm(self._dataset.get_velocity()) + long = self._dataset.get_longitude() + lat = self._dataset.get_latitude() + alt = self._dataset.get_altitude() + lock = 1 - # TODO: At which speed do we assume that GPS becomes unreliable? - if vel / self._dataset.get_mach_number() > 1: - return NA - - x = self._dataset.fetch_values(['latitude', 'longitude', 'altitude']) - x = self._sensor_specific_effects(x) + self._log('longitude', long) + self._log('latitude', lat) + self._log('altitude', alt) + self._log('lock', lock) - return x + return np.array([long, lat, alt, lock]) diff --git a/spatz/sensors/imu/__init__.py b/spatz/sensors/imu/__init__.py index a7d5d19..0a7d9b2 100644 --- a/spatz/sensors/imu/__init__.py +++ b/spatz/sensors/imu/__init__.py @@ -1,3 +1,3 @@ -from spatz.sensors.imu.accelerometer import Accelerometer +from spatz.sensors.imu.accelerometer import Accelerometer, CoordSystem from spatz.sensors.imu.gyroscope import Gyroscope from spatz.sensors.imu.imu import IMU \ No newline at end of file diff --git a/spatz/sensors/imu/accelerometer.py b/spatz/sensors/imu/accelerometer.py index fdd2848..91f382f 100644 --- a/spatz/sensors/imu/accelerometer.py +++ b/spatz/sensors/imu/accelerometer.py @@ -1,11 +1,13 @@ import numpy as np from typing import List -from numpy.typing import ArrayLike +from enum import Enum +from numpy.typing import ArrayLike, NDArray from spatz.sensors import Sensor from spatz.transforms import Transform from spatz.dataset import Dataset +from spatz.simulations.data_source import DataSource from spatz.logger import Logger @@ -18,32 +20,55 @@ __all__=[ g = 9.81 +class CoordSystem: + LEFT_HANDED = 0, + RIGHT_HANDED = 0, + + class Accelerometer(Sensor): - def __init__(self, dataset: Dataset, logger: Logger, offset: float = 0, transforms: List[Transform] = []): + def __init__( + self, dataset: DataSource, + logger: Logger, + orientation: NDArray = np.identity(3), + offset: float = 0, + transforms: List[Transform] = []): + """_summary_ + + Args: + dataset (Dataset): A dataset object to fetch data from. + logger (Logger): A logger object to write data to. + coord_system (CoordSystem, optional): The type of coordinate system to use. Defaults to CoordSystem.RIGHT_HANDED. + orientation (NDArray, optional): The orientation of the sensor inside the spacecraft. Defaults to np.identity(3). + offset (float, optional): The offset of the sensor from the origin around which the object rotates. Defaults to 0. + transforms (List[Transform], optional): A list of transformations applied to the sensor measurements. Defaults to []. + """ super().__init__(dataset, logger, transforms) + assert orientation.shape == (3, 3), 'Orientation has to be a 3x3 matrix.' + self._offset = np.array([offset, 0, 0]) + self._orientation = orientation def _get_data(self) -> ArrayLike | float: - acc = self._dataset.get_acceleration(frame='FL') - acc += np.array([0, 0, g]) + acc = self._dataset.get_acceleration('global') - self._logger.write('FL_x', acc[0], self._get_name()) - self._logger.write('FL_y', acc[1], self._get_name()) - self._logger.write('FL_z', acc[2], self._get_name()) + self._logger.write('global_ax', acc[0], self._get_name()) + self._logger.write('global_ay', acc[1], self._get_name()) + self._logger.write('global_az', acc[2], self._get_name()) # Convert FL to body - acc = self._dataset.launch_rail_to_body() @ acc + acc = self._dataset.global_to_local() @ acc + acc += self._dataset.global_to_local() @ np.array([0, 0, g]) - self._logger.write('B_x', acc[0], self._get_name()) - self._logger.write('B_y', acc[1], self._get_name()) - self._logger.write('B_z', acc[2], self._get_name()) + self._logger.write('local_x', acc[0], self._get_name()) + self._logger.write('local_y', acc[1], self._get_name()) + self._logger.write('local_z', acc[2], self._get_name()) - # Flip axes to sensor's perspective. - acc *= -1 + # Rotate the acceleration vector to accomodate the accelerometer's orientation on the rocket. + acc = self._orientation @ acc # Add the effects of the imu's offset. - omega = self._dataset.get_angular_velocities() + omega = self._dataset.get_angular_velocity() acc += (np.cross(omega, self._offset) + np.cross(omega, np.cross(omega, self._offset))) return acc diff --git a/spatz/sensors/imu/bmi088.py b/spatz/sensors/imu/bmi088.py new file mode 100644 index 0000000..a71bcde --- /dev/null +++ b/spatz/sensors/imu/bmi088.py @@ -0,0 +1,41 @@ +import numpy as np + +from typing import List, AnyStr + +from numpy.typing import ArrayLike +from spatz.dataset import ArrayLike, Dataset +from spatz.logger import ArrayLike, Logger +from spatz.sensors import IMU, Accelerometer, Gyroscope, CoordSystem +from spatz.transforms import Transform, GaussianNoise + + +class BMI088(IMU): + def __init__(self, dataset: Dataset, logger: Logger, orientation = np.identity(3), offset=0, transforms: List[Transform] = []): + acc = BMI088Acc(dataset, logger, orientation, offset, transforms) + gyro = BMI088Gyro(dataset, logger, offset, transforms) + + super().__init__(dataset, logger, acc, gyro, transforms) + + +class BMI088Gyro(Gyroscope): + def __init__(self, dataset: Dataset, logger: Logger, orientation=np.identity(3), transforms: List[Transform] = []): + super().__init__(dataset, logger, orientation, transforms) + + def _get_name(self) -> AnyStr: + return 'BMI088' + + def _sensor_specific_effects(self, x: ArrayLike) -> ArrayLike: + return x + + +class BMI088Acc(Accelerometer): + def __init__(self, dataset: Dataset, logger: Logger, orientation = np.identity(3), offset: float = 0, transforms: List[Transform] = []): + super().__init__(dataset, logger, CoordSystem.RIGHT_HANDED, orientation, offset, transforms) + + self.__noise = GaussianNoise(0, 0.05) + + def _get_name(self) -> AnyStr: + return 'BMI088' + + def _sensor_specific_effects(self, x: ArrayLike) -> ArrayLike: + return self.__noise(0, x) \ No newline at end of file diff --git a/spatz/sensors/imu/gyroscope.py b/spatz/sensors/imu/gyroscope.py index ce6df4f..d3982a6 100644 --- a/spatz/sensors/imu/gyroscope.py +++ b/spatz/sensors/imu/gyroscope.py @@ -1,20 +1,30 @@ +import numpy as np + from numpy.typing import ArrayLike from typing import List from spatz.sensors import Sensor from spatz.transforms import Transform -from spatz.dataset import Dataset +from spatz.simulations.data_source import DataSource from spatz.logger import Logger class Gyroscope(Sensor): - def __init__(self, dataset: Dataset, logger: Logger, offset: float = 0, transforms: List[Transform] = []): + def __init__(self, dataset: DataSource, logger: Logger, orientation=np.identity(3), transforms: List[Transform] = []): super().__init__(dataset, logger, transforms) - self._offset = offset + self._orientation = orientation + + def calibrate(self, n_samples): + return np.sum(self() for i in range(n_samples)) / n_samples def _get_data(self) -> ArrayLike | float: # Rotation in rad/sec - x = self._dataset.get_rotation_rates() + omegas = self._dataset.get_angular_velocity() + omegas = self._orientation @ omegas - return x + self._log('ox', omegas[0]) + self._log('oy', omegas[1]) + self._log('oz', omegas[2]) + + return omegas diff --git a/spatz/sensors/imu/h3lis100dl.py b/spatz/sensors/imu/h3lis100dl.py new file mode 100644 index 0000000..0e23e1b --- /dev/null +++ b/spatz/sensors/imu/h3lis100dl.py @@ -0,0 +1,38 @@ +import numpy as np + +from typing import List, AnyStr +from numpy.core.numeric import identity as identity +from numpy.typing import ArrayLike, NDArray + +from spatz.dataset import Dataset +from spatz.simulations.data_source import DataSource +from spatz.logger import Logger +from spatz.sensors import Accelerometer, CoordSystem +from spatz.transforms import Transform + + +class H3LIS100DL(Accelerometer): + def __init__(self, dataset: DataSource, logger: Logger, orientation=np.identity(3), offset: float = 0, transforms: List[Transform] = []): + super().__init__(dataset, logger, orientation, offset, transforms) + + def _get_name(self) -> AnyStr: + return "H3LIS100DL" + + def _sensor_specific_effects(self, x: ArrayLike) -> ArrayLike: + # The sensor only measures values between -100g and +100g and in g/LSB. + g = 9.81 + x = np.floor(np.clip(x, -100*g, +100*g) / g) + + for i in range(3): + value = np.random.random() + if (value < 0.1): + x[i] += 1 + + if (value > 0.9): + x[i] -= 1 + + return x + + + + diff --git a/spatz/sensors/imu/imu.py b/spatz/sensors/imu/imu.py index 3af4da0..663d3b3 100644 --- a/spatz/sensors/imu/imu.py +++ b/spatz/sensors/imu/imu.py @@ -12,7 +12,7 @@ class IMU(CompoundSensor): dataset: Dataset, logger: Logger, acc: Accelerometer, - gyro: Gyroscope, + gyro: Gyroscope, transforms: List[Transform] = []): """_summary_ diff --git a/spatz/sensors/imu/wsen_isds.py b/spatz/sensors/imu/wsen_isds.py index 013f8a2..fcb0343 100644 --- a/spatz/sensors/imu/wsen_isds.py +++ b/spatz/sensors/imu/wsen_isds.py @@ -3,8 +3,9 @@ import numpy as np from typing import AnyStr, List from numpy.typing import ArrayLike -from spatz.sensors import Accelerometer, Gyroscope, IMU -from spatz.transforms import Transform, GaussianNoise +from spatz.sensors import Accelerometer, Gyroscope, IMU, CoordSystem +from spatz.transforms import Transform, GaussianNoise, DriftingBias +from spatz.simulations.data_source import DataSource from spatz.dataset import Dataset from spatz.logger import Logger @@ -13,21 +14,37 @@ class WSEN_ISDS(IMU): pass -class WSEN_ISDS_ACC(Accelerometer): - def __init__(self, dataset: Dataset, logger: Logger, offset: float, transforms: List[Transform] = []): - super().__init__(dataset, logger, offset, transforms) +g = 9.81 - self.__variance = 0.05 - self.__noise = GaussianNoise(np.zeros(3), np.identity(3) * self.__variance) + +class WSEN_ISDS_ACC(Accelerometer): + def __init__(self, dataset: DataSource, logger: Logger, orientation=np.identity(3), offset=0, transforms: List[Transform] = []): + super().__init__(dataset, logger, orientation, offset, transforms) + + self.__bias = DriftingBias(np.zeros(3), np.array([ + 0.00113044 / g * 1000, + 0.00108539 / g * 1000, + 0.00127884 / g * 1000 + ]), 400) + + self.__constant_bias = np.random.normal(0, 0.81423, 3) + + self.__normal = GaussianNoise(0, np.array([ + 0.0003330315865455515 / g * 1000, + 0.00016874534484267122 / g * 1000, + 0.0003885568325537318 / g * 100 + ])) def _get_name(self) -> AnyStr: return 'WSEN_ISDS_ACC' def _sensor_specific_effects(self, x: ArrayLike) -> ArrayLike: t = self._dataset.get_time() + # Convert to milli-g. + x = x / g * 1000 # Apply noise to the true values. - y = self.__noise(t, x) + y = self.__constant_bias + self.__normal(t, self.__bias(t, x)) noise = y - x # Log the chosen noise values. @@ -35,20 +52,34 @@ class WSEN_ISDS_ACC(Accelerometer): self._logger.write('acc_y_noise', noise[1], self._get_name()) self._logger.write('acc_z_noise', noise[2], self._get_name()) + # The WSEN-ISDS accelerometer only measures acceleration between -16g and 16g. + y = np.clip(y, -16000, +16000) + return y class WSEN_ISDS_GYRO(Gyroscope): - def __init__(self, dataset: Dataset, logger: Logger, offset: float, transforms: List[Transform] = []): - super().__init__(dataset, logger, offset, transforms) + def __init__(self, dataset: Dataset, logger: Logger, orientation = np.identity(3), transforms: List[Transform] = []): + super().__init__(dataset, logger, orientation, transforms) + + self.__bias = DriftingBias(np.zeros(3), np.array([0.00218 * 1000, 0.00105 * 1000, 0.00203 * 1000]), 400) + self.__constant_bias = np.random.normal(0, 2*2000, 3) + self.__normal = GaussianNoise(0, np.array([0.0049272 * 1000, 0.00557833 * 1000, 0.00407826 * 1000])) def _get_name(self) -> AnyStr: return 'WSEN_ISDS_GYRO' def _sensor_specific_effects(self, x: ArrayLike) -> ArrayLike: - # Convert to degrees per second. - x = (x / np.pi) * 180 + # Convert to milli-degrees per second. + x = (x / np.pi) * 180 * 1000 + + t = self._dataset.get_time() + x = self.__constant_bias + self.__normal(t, self.__bias(t, x)) # TODO: Noise model. + self._log('ox', x[0]) + self._log('oy', x[1]) + self._log('oz', x[2]) + return x diff --git a/spatz/sensors/pressure/ms5611.py b/spatz/sensors/pressure/ms5611.py new file mode 100644 index 0000000..c61ecd2 --- /dev/null +++ b/spatz/sensors/pressure/ms5611.py @@ -0,0 +1,31 @@ +from typing import List, AnyStr +from numpy.typing import ArrayLike + +from spatz.sensors import PressureSensor +from spatz.dataset import Dataset, Phase +from spatz.simulations.data_source import DataSource +from spatz.logger import Logger +from spatz.transforms import GaussianNoise, Transform, ProportionalGaussian + + +class MS5611(PressureSensor): + def __init__(self, dataset: DataSource, logger: Logger, transforms: List[Transform] = [], ts_effects=True): + super().__init__(dataset, logger, transforms, ts_effects) + + # Noise model obtained by a test flight using this sensor. + # self.__pad_noise = GaussianNoise(0, 0.03) + # self.__flight_noise = GaussianNoise(0, 1.5) + self.__noise = GaussianNoise(0, 0.00043300242654881085) + + def _get_name(self) -> AnyStr: + return 'MS5611' + + def _sensor_specific_effects(self, x: ArrayLike | float) -> ArrayLike | float: + t = self._dataset.get_time() + + noisy = self.__noise(t, x) # self.__pad_noise(t, x) if self._dataset.get_phase() == Phase.ONPAD else self.__flight_noise(t, x) + + # Log the noise added to the pressure measurements. + self._logger.write('noise', noisy - x, domain=self._get_name()) + + return noisy \ No newline at end of file diff --git a/spatz/sensors/pressure/ms5611_01ba03.py b/spatz/sensors/pressure/ms5611_01ba03.py deleted file mode 100644 index 108a2e5..0000000 --- a/spatz/sensors/pressure/ms5611_01ba03.py +++ /dev/null @@ -1,32 +0,0 @@ -from typing import List, AnyStr -from numpy.typing import ArrayLike - -from spatz.sensors import PressureSensor -from spatz.dataset import Dataset, Phase -from spatz.logger import Logger -from spatz.transforms import GaussianNoise, Transform - - -class MS5611_01BA03(PressureSensor): - def __init__(self, dataset: Dataset, logger: Logger, transforms: List[Transform] = [], ts_effects=True): - super().__init__(dataset, logger, transforms, ts_effects) - - # Noise model obtained by a test flight using this sensor. - self.__pad_noise = GaussianNoise(0, 0.03) - self.__flight_noise = GaussianNoise(0, 1.5) - - def _get_name(self) -> AnyStr: - return 'MS5611_01BA03' - - def _sensor_specific_effects(self, x: ArrayLike | float) -> ArrayLike | float: - t = self._dataset.get_time() - - # Transform from Pa to hPa - x /= 1e2 - - noisy = self.__pad_noise(t, x) if self._dataset.get_phase() == Phase.ONPAD else self.__flight_noise(t, x) - - # Log the noise added to the pressure measurements. - self._logger.write('noise', noisy - x, domain=self._get_name()) - - return noisy diff --git a/spatz/sensors/pressure/pressure.py b/spatz/sensors/pressure/pressure.py index 1e811c4..cac1aaa 100644 --- a/spatz/sensors/pressure/pressure.py +++ b/spatz/sensors/pressure/pressure.py @@ -6,11 +6,12 @@ from typing import List from spatz.sensors import Sensor from spatz.logger import Logger from spatz.dataset import Dataset +from spatz.simulations.data_source import DataSource from spatz.transforms import Transform class PressureSensor(Sensor): - def __init__(self, dataset: Dataset, logger: Logger, transforms: List[Transform] = [], ts_effects=True, delay=0.0): + def __init__(self, dataset: DataSource, logger: Logger, transforms: List[Transform] = [], ts_effects=True, delay=0.0): """_summary_ Args: @@ -28,7 +29,7 @@ class PressureSensor(Sensor): self._ts_effects = active def _get_data(self) -> float: - x = self._dataset.get_pressure() + x = self._dataset.get_static_pressure() if self._ts_effects: # Pre-defined constants. @@ -36,16 +37,16 @@ class PressureSensor(Sensor): sigma = 40 # How far away from transsonic speed (mach 1) are we? - vvec = self._dataset.get_velocity() + vvec = self._dataset.get_velocity('global') dv = np.abs(np.linalg.norm(vvec) - self._dataset.get_speed_of_sound()) # Model transsonic effects by a peak at mach 1 which decays the further we are away from it. ts_eff = _p * math.exp(-0.5* (dv / sigma)**2 ) / (sigma * math.sqrt(2*math.pi)) # Log the values for the transsonic effect. - self._logger.write('ts_effects', ts_eff, domain=self._get_name()) - self._logger.write('mach_no', self._dataset.get_mach_number(), domain='mach') - self._logger.write('speedofsound', self._dataset.get_speed_of_sound(), domain='mach') + self._log('ts_effects', ts_eff) + self._log('mach_no', self._dataset.get_mach_number()) + self._log('speedofsound', self._dataset.get_speed_of_sound()) x = x + ts_eff diff --git a/spatz/sensors/sensor.py b/spatz/sensors/sensor.py index 1773a64..9cd135d 100644 --- a/spatz/sensors/sensor.py +++ b/spatz/sensors/sensor.py @@ -4,21 +4,29 @@ import numpy as np from abc import abstractmethod from typing import List, AnyStr from numpy.typing import ArrayLike +from pandas import NA from spatz.transforms import * from spatz.logger import * from spatz.dataset import * +from spatz.simulations.data_source import DataSource class Sensor: - def __init__(self, dataset: Dataset, logger: Logger, transforms: List[Transform] = [], min_value=-np.inf, max_value=np.inf): + def __init__( + self, + dataset: DataSource, + logger: Logger, + transforms: List[Transform] = [], + min_value=-np.inf, + max_value=np.inf): self._dataset = dataset self._logger = logger self._transforms = transforms self._min_value = min_value self._max_value = max_value - def set_dataset(self, dataset: Dataset): + def set_dataset(self, dataset: DataSource): self._dataset = dataset def set_logger(self, logger: Logger): @@ -37,12 +45,12 @@ class Sensor: @abstractmethod def _sensor_specific_effects(self, x: ArrayLike | float) -> ArrayLike | float: raise NotImplementedError() - + @abstractmethod def _get_data(self) -> ArrayLike | float: raise NotImplementedError() - - def get_init_data() -> ArrayLike: + + def get_init_data(self) -> ArrayLike: pass def __call__(self) -> ArrayLike | float: diff --git a/spatz/sensors/temperature/__init__.py b/spatz/sensors/temperature/__init__.py index b5ff6cd..f927231 100644 --- a/spatz/sensors/temperature/__init__.py +++ b/spatz/sensors/temperature/__init__.py @@ -1 +1 @@ -from temperature import TemperatureSensor \ No newline at end of file +from spatz.sensors.temperature.temperature import TemperatureSensor \ No newline at end of file diff --git a/spatz/sensors/temperature/ms5611.py b/spatz/sensors/temperature/ms5611.py new file mode 100644 index 0000000..4b0f77f --- /dev/null +++ b/spatz/sensors/temperature/ms5611.py @@ -0,0 +1,26 @@ +from numpy.typing import ArrayLike +from typing import List, AnyStr + +from spatz.transforms.noise import GaussianNoise +from spatz.dataset import Dataset +from spatz.simulations.data_source import DataSource +from spatz.sensors import TemperatureSensor +from spatz.transforms import Transform + + +class MS5611Temperature(TemperatureSensor): + def __init__(self, dataset: DataSource, transforms: List[Transform] = []): + super().__init__(dataset, transforms) + + self.__noise = GaussianNoise(0, 0.5) + + def _get_name(self) -> AnyStr: + return 'MS5611' + + def _sensor_specific_effects(self, x: ArrayLike | float) -> ArrayLike | float: + t = self._dataset.get_time() + noisy = self.__noise(t, x) + + self._log('temperature', noisy) + + return noisy \ No newline at end of file diff --git a/spatz/simulation.py b/spatz/simulation.py index 53715fd..4b69bb5 100644 --- a/spatz/simulation.py +++ b/spatz/simulation.py @@ -2,11 +2,13 @@ from typing import List from numpy.random import normal from tqdm import tqdm +from spatz.simulations.advanceable import Advanceable +from spatz.simulations.data_source import DataSource from spatz.dataset import Dataset from spatz.logger import Logger from spatz.sensors import Sensor from spatz.dataset import Dataset, Phase -from spatz.logger import Logger +from spatz.logger import Logger, EmptyLogger from spatz.sensors import Sensor from spatz.observer import Observer @@ -35,9 +37,11 @@ class UniformTimeSteps: return self.__dt + noise -class Simulation: +class Simulation(Advanceable): def __init__(self, time_steps=UniformTimeSteps(0.01)): - self.__dataset = None + super().__init__() + + self.__data_source = None self.__logger = None self.__sensors: List[Sensor] = [] self.__time_steps = time_steps @@ -46,26 +50,25 @@ class Simulation: idx = 0 # Clear all logs and reset the dataset to the first time step. - self.__dataset.reset() + self.__data_source.reset() self.__logger.reset() if verbose: - pbar = tqdm(total=self.__dataset.get_length()) + pbar = tqdm(total=self.__data_source.get_length()) while True: - t = self.__dataset.get_time() + t = self.get_time() dt = self.__time_steps(t) t_ = t + dt idx += 1 - if t_ > self.__dataset.get_length(): + if t_ > self.__data_source.get_length(): break - if until is not None and self.__dataset.get_phase() == until: + if until is not None and self.__data_source.get_phase() == until: break - self.__dataset.step(dt) - self.__logger.step(dt) + self.advance(dt) if verbose: pbar.update(dt) @@ -75,22 +78,26 @@ class Simulation: if verbose: pbar.close() - def get_dataset(self) -> Dataset: - return self.__dataset - - def get_logger(self) -> Logger: - return self.__logger + def _on_step(self, dt: float): + self.__data_source.advance(dt) + self.__logger.advance(dt) - def load(self, path: str): - self.__dataset = Dataset(path) - self.__logger = Logger() + def load(self, source: DataSource): + self.__data_source = source + self.__logger = EmptyLogger() for sensor in self.__sensors: - sensor.set_dataset(self.__dataset) + sensor.set_dataset(self.__data_source) sensor.set_logger(self.__logger) return self + def get_data_source(self) -> DataSource: + return self.__data_source + + def get_logger(self) -> Logger: + return self.__logger + def add_sensor(self, sensor, *args, **kwargs) -> Sensor: """Register a new sensor for this simulation. A registered sensor can be called like a function and returns the current measurements. The class' constructor arguments have to be given aswell. @@ -103,7 +110,7 @@ class Simulation: """ assert issubclass(sensor, Sensor), "Expected a subclass of Sensor." - self.__sensors.append(sensor(self.__dataset, self.__logger, *args, **kwargs)) + self.__sensors.append(sensor(self.__data_source, self.__logger, *args, **kwargs)) return self.__sensors[-1] @@ -123,10 +130,10 @@ class Simulation: attributes = observer_or_attributes assert len(attributes) != 0, "Observed attributes list must be nonempty." - self.__sensors.append(Observer(self.__dataset, self.__logger, attributes)) + self.__sensors.append(Observer(self.__data_source, self.__logger, attributes)) else: observer = observer_or_attributes - self.__sensors.append(observer(self.__dataset, self.__logger)) + self.__sensors.append(observer(self.__data_source, self.__logger)) return self.__sensors[-1] diff --git a/spatz/simulations/__init__.py b/spatz/simulations/__init__.py index e69de29..be2aea9 100644 --- a/spatz/simulations/__init__.py +++ b/spatz/simulations/__init__.py @@ -0,0 +1 @@ +from spatz.simulations.rocketpy import RocketPyCSV \ No newline at end of file diff --git a/spatz/simulations/advanceable.py b/spatz/simulations/advanceable.py new file mode 100644 index 0000000..ba9d7f8 --- /dev/null +++ b/spatz/simulations/advanceable.py @@ -0,0 +1,47 @@ +from abc import abstractmethod + + +class Advanceable: + def __init__(self) -> None: + self.reset() + + def advance(self, dt: float): + """Advances the simulation data in time. + + Args: + dt (float): The step in time to make. + """ + self.__t += dt + self._on_step(dt) + + def advance_to(self, t: float): + """Advances the simulation data to a new point in time. + + Args: + t (float): The target point in time. + """ + assert t > self.__t, 'Advanceable can only move forward in time.' + + self.advance(t - self.__t) + + def reset(self): + """ + Reset the Avanceable object to its initial state. + """ + self.__t = 0 + self._on_reset() + + @abstractmethod + def _on_step(self, dt: float): + pass + + @abstractmethod + def _on_reset(self): + pass + + def get_time(self) -> float: + """ + Returns: + float: Returns the current time of the Advanceable. + """ + return self.__t \ No newline at end of file diff --git a/spatz/simulations/astos_source.py b/spatz/simulations/astos_source.py new file mode 100644 index 0000000..f7a640a --- /dev/null +++ b/spatz/simulations/astos_source.py @@ -0,0 +1,53 @@ +import numpy as np + +from typing import Literal +from numpy.typing import NDArray + +from spatz.simulations.csv_source import CSVSource + + +class ASTOSSource(CSVSource): + def __init__(self, path: str, interpolation: Literal['linear'] = 'linear') -> None: + super().__init__(path, 'time', interpolation) + + def get_length(self) -> float: + pass + + def get_position(self) -> NDArray: + return self.fetch_values(['x', 'y', 'z']) + + def get_velocity(self, frame: Literal['global', 'local']) -> NDArray: + if frame == 'local': + pass + + return self.fetch_values(['vx', 'vy', 'vz']) + + def get_acceleration(self, frame: Literal['global', 'local']) -> NDArray: + if frame == 'local': + pass + + return self.fetch_values(['ax', 'ay', 'az']) + + def get_attitude(self) -> NDArray: + pass + + def local_to_global(self) -> NDArray: + pass + + def global_to_local(self) -> NDArray: + pass + + def get_angular_velocity(self) -> NDArray: + pass + + def get_static_pressure(self) -> float: + pass + + def get_longitude(self) -> float: + pass + + def get_latitude(self) -> float: + pass + + def get_altitude(self) -> float: + pass \ No newline at end of file diff --git a/spatz/simulations/csv_source.py b/spatz/simulations/csv_source.py new file mode 100644 index 0000000..d9a032b --- /dev/null +++ b/spatz/simulations/csv_source.py @@ -0,0 +1,91 @@ +import pandas as pd +import numpy as np + +from typing import Literal, List +from numpy.typing import NDArray + +from spatz.simulations.data_source import DataSource + + + +class CSVSource(DataSource): + def __init__(self, path: str, time_col: str, interpolation: Literal['linear']='linear') -> None: + """A data source that extracts all its data from a csv file. + + Args: + time_col (str): The name of the column that contains time data. + """ + super().__init__() + + self._df = pd.read_csv(path) + self._time_col = time_col + self._idx = 0 + self._interpolation = interpolation + + def get_length(self) -> float: + return max(self._df[self._time_col]) + + def _on_reset(self): + pass + + def _get_closest_idx(self, t: float) -> int: + """Gets an index _idx_ for the dataframe _df_ such that the values at the given time _t_ are somewhere between + _idx_ and _idx+1_. + + Args: + t (float): The requested time. + + Returns: + int: The computed index. + """ + idx = (self._df[self._time_col] - t).abs().idxmin() + idx = idx if self._df[self._time_col].loc[idx] <= t else idx - 1 + + return idx + + def _on_step(self, _: float): + self._idx = self._get_closest_idx(self.get_time()) + + def fetch_value(self, name: str, t: float | None = None, custom_interpolation=None) -> float: + """Get a specific value from the dataframe. + + Args: + name (str): The name of the value to fetch. + t (float): Allows specification of a different time instead of the current time. None for current time. + + Returns: + float: Returns the requested value. + """ + idx = self._idx if t is None else self._get_closest_idx(t) + + if self._interpolation == 'linear': + t_min = self._df.at[idx, self._time_col] + t_max = self._df.at[idx + 1, self._time_col] + + # Sometimes no time passes in-between two samples. + if t_max == t_min: + return self._df.at[name, idx] + + # Compute the weight for interpolation. + alpha = (self.get_time() - t_min) / (t_max - t_min) + + if custom_interpolation is not None: + a = self._df.at[idx, name] + b = self._df.at[idx + 1, name] + + return custom_interpolation(a, b, alpha) + + # Interpolate linearly between the two data points. + return (1 - alpha) * self._df.at[idx, name] + alpha * self._df.at[idx + 1, name] + + def fetch_values(self, names: List[str], t: float | None = None, custom_interpolation=None) -> NDArray: + """Get specific values from the dataframe. + + Args: + names (List[str]): Names of the values to get. + t (float): Allows specification of a different time instead of the current time. None for current time. + + Returns: + np.array: Returns a numpy array containing the requested values in the same order as in the input list. + """ + return np.asarray([self.fetch_value(name, t, custom_interpolation) for name in names]) \ No newline at end of file diff --git a/spatz/simulations/data_source.py b/spatz/simulations/data_source.py new file mode 100644 index 0000000..45e0195 --- /dev/null +++ b/spatz/simulations/data_source.py @@ -0,0 +1,73 @@ +import numpy as np + +from spatz.simulations.advanceable import Advanceable + +from numpy.typing import NDArray +from abc import abstractmethod +from typing import Literal + +from ambiance import Atmosphere + + +class DataSource(Advanceable): + def __init__(self) -> None: + super().__init__() + + def get_speed_of_sound(self) -> float: + return Atmosphere(self.get_altitude()).speed_of_sound + + def get_mach_number(self) -> float: + speed = np.linalg.norm(self.get_velocity('global')) + + return speed / self.get_speed_of_sound() + + def get_temperature(self) -> float: + return Atmosphere(self.get_altitude()).temperature + + @abstractmethod + def get_length(self) -> float: + pass + + @abstractmethod + def get_position(self) -> NDArray: + pass + + @abstractmethod + def get_velocity(self, frame: Literal['global', 'local']) -> NDArray: + pass + + @abstractmethod + def get_acceleration(self, frame: Literal['global', 'local']) -> NDArray: + pass + + @abstractmethod + def get_attitude(self) -> NDArray: + pass + + @abstractmethod + def local_to_global(self) -> NDArray: + pass + + @abstractmethod + def global_to_local(self) -> NDArray: + pass + + @abstractmethod + def get_angular_velocity(self) -> NDArray: + pass + + @abstractmethod + def get_static_pressure(self) -> float: + pass + + @abstractmethod + def get_longitude(self) -> float: + pass + + @abstractmethod + def get_latitude(self) -> float: + pass + + @abstractmethod + def get_altitude(self) -> float: + pass \ No newline at end of file diff --git a/spatz/simulations/rocketpy.py b/spatz/simulations/rocketpy.py new file mode 100644 index 0000000..8bc49ee --- /dev/null +++ b/spatz/simulations/rocketpy.py @@ -0,0 +1,84 @@ +import numpy as np + +from typing import Literal +from numpy.typing import NDArray + +from spatz.simulations.csv_source import CSVSource + + +class RocketPyCSV(CSVSource): + def __init__(self, path: str, interpolation: Literal['linear'] = 'linear') -> None: + super().__init__(path, ' Time (s)', interpolation) + + def get_position(self) -> NDArray: + return self.fetch_values([' X (m)', ' Y (m)', ' Z (m)']) + + def get_velocity(self, frame: Literal['global', 'local']) -> NDArray: + vel_global = self.fetch_values([' Vx (m/s)', ' Vy (m/s)', ' Vz (m/s)']) + + if frame == 'global': + return vel_global + + return self.global_to_local() @ vel_global + + def get_acceleration(self, frame: Literal['global', 'local']) -> NDArray: + acc_global = self.fetch_values([' Ax (m/s²)', ' Ay (m/s²)', ' Az (m/s²)']) + + if frame == 'global': + return acc_global + + return self.global_to_local() @ acc_global + + def get_attitude(self) -> NDArray: + t_min = self._df.at[self._idx, self._time_col] + t_max = self._df.at[self._idx + 1, self._time_col] + + def slerp(a, b, alpha): + theta = np.arccos(np.clip(np.dot(a, b), -1, 1)) + + if np.isclose(theta, 0): + return a + + return (a * np.sin((1-alpha) * theta) + b * np.sin(alpha * theta)) / np.sin(theta) + + qa = np.array([self._df.at[self._idx, ' e0'], self._df.at[self._idx, ' e1'], self._df.at[self._idx, ' e2'], self._df.at[self._idx, ' e3']]) + qb = np.array([self._df.at[self._idx+1, ' e0'], self._df.at[self._idx+1, ' e1'], self._df.at[self._idx+1, ' e2'], self._df.at[self._idx+1, ' e3']]) + + alpha = (self.get_time() - t_min) / (t_max - t_min) + + return slerp(qa, qb, alpha) + + def global_to_local(self) -> NDArray: + quat = self.get_attitude() + + e0, e1, e2, e3 = quat[0], quat[1], quat[2], quat[3] + + # Taken from: + # https://docs.rocketpy.org/en/latest/technical/equations_of_motion.html + mat = np.array([ + [e0**2 + e1**2 - e2**2 - e3**2, 2*(e1*e2+e0*e3), 2*(e1*e3 - e0*e2)], + [2*(e1*e2 - e0*e3), e0**2 - e1**2 + e2**2 - e3**2, 2*(e2*e3 + e0*e1)], + [2*(e1*e3 + e0*e2), 2*(e2*e3 - e0*e1), e0**2 - e1**2 - e2**2 + e3**2] + ]) + + return mat + + def local_to_global(self) -> NDArray: + return self.global_to_local().T + + def get_angular_velocity(self) -> NDArray: + return self.fetch_values([' ω1 (rad/s)',' ω2 (rad/s)',' ω3 (rad/s)']) + + def get_static_pressure(self) -> float: + return self.fetch_value(' Pressure (Pa)') + + def get_longitude(self) -> float: + return self.fetch_value(' Longitude (°)') + + def get_latitude(self) -> float: + return self.fetch_value(' Latitude (°)') + + def get_altitude(self) -> float: + return self.fetch_value(' Z (m)') + + \ No newline at end of file diff --git a/spatz/simulations/rocketpy/__init__.py b/spatz/simulations/rocketpy/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/spatz/transforms/__init__.py b/spatz/transforms/__init__.py index 82d111b..2d6932c 100644 --- a/spatz/transforms/__init__.py +++ b/spatz/transforms/__init__.py @@ -1,3 +1,3 @@ from spatz.transforms.transform import Transform -from spatz.transforms.noise import GaussianNoise +from spatz.transforms.noise import GaussianNoise, ProportionalGaussian, DriftingBias from spatz.transforms.failures import Downtime \ No newline at end of file diff --git a/spatz/transforms/noise.py b/spatz/transforms/noise.py index 9425745..f7583a2 100644 --- a/spatz/transforms/noise.py +++ b/spatz/transforms/noise.py @@ -7,7 +7,7 @@ from spatz.transforms import Transform class GaussianNoise(Transform): - def __init__(self, mu: ArrayLike, sigma: ArrayLike) -> None: + def __init__(self, mu: ArrayLike = None, sigma: ArrayLike = None) -> None: super().__init__() self.__mu = mu @@ -15,7 +15,8 @@ class GaussianNoise(Transform): def __call__(self, _: float, x: ArrayLike) -> ArrayLike: if np.isscalar(x): - noise = np.random.normal(0, 1) + noise = np.random.normal(0, 1) + x += self.__sigma * noise + self.__mu else: dim = len(x) @@ -26,7 +27,7 @@ class GaussianNoise(Transform): sigma = self.__sigma if np.isscalar(self.__mu): - mu = np.ones(dim) + mu = np.ones(dim) * self.__mu else: mu = self.__mu @@ -36,6 +37,50 @@ class GaussianNoise(Transform): return x +class DriftingBias(Transform): + def __init__(self, init: ArrayLike, covariance: ArrayLike, Tc: float) -> None: + """First order Gauss-Markov (GM) model used to model drift. + + Args: + init (ArrayLike): The initial bias. + covariance (ArrayLike): Covariance matrix of the process. + Tc (float): Correlation time of the process. + """ + super().__init__() + + self.__t = 0 + self.__beta = 1 / Tc + self.__covariance = covariance + self.__Tc = Tc + self.__x_old = np.copy(init) + + def __call__(self, t: float, x: ArrayLike) -> ArrayLike: + dt = t - self.__t + self.__t = t + + w = np.random.normal(np.zeros_like(x), self.__covariance*(1-np.exp(-2 * dt / self.__Tc))) + drift = (1 - self.__beta * dt) * self.__x_old + w + + self.__x_old = drift + + return x + drift + + +class ProportionalGaussian(Transform): + def __init__(self, mu, sigma) -> None: + super().__init__() + + self.__mu = mu + self.__sigma = sigma + + def __call__(self, _: float, x: ArrayLike) -> ArrayLike: + noise = np.random.normal(0, 1) + + x += (self.__sigma * x) * noise + (self.__mu * x) + + return x + + class PinkNoise(Transform): def __init__(self) -> None: super().__init__() diff --git a/test.csv b/test.csv new file mode 100644 index 0000000..a45db15 --- /dev/null +++ b/test.csv @@ -0,0 +1,3450 @@ +,Erinome-I/out_0 +0,67.88547818356959 +1,67.8854787292197 +2,67.8854796304842 +3,67.88548088213591 +4,67.88548247823311 +5,67.8854844153236 +6,67.8854866918731 +7,67.88548930466278 +8,67.8854922518091 +9,67.88549553158987 +10,67.88549982683277 +11,67.88550439041819 +12,67.88550895400361 +13,67.88551351758903 +14,67.88551920942774 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a/testing.ipynb b/testing.ipynb new file mode 100644 index 0000000..8f2a7f0 --- /dev/null +++ b/testing.ipynb @@ -0,0 +1,141 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from spatz.transforms.noise import DriftingBias\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "start = 0\n", + "covariance = 0.005\n", + "\n", + "bias = DriftingBias(start, covariance, 400)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5Umlpadq+fbtyc3PV0NCgoqKiFvdJSkrS/v37/daVlpZq2bJlmjhxoiSpublZkyZNUnx8vD744APt379f06ZNU7du3bRkyZJAXU6beL1eHW9qDuocAAA4V0R0C5XD4QjKuR1er9fbWSdbtmyZSkpKtHv37jbvM2TIEA0dOlSrVq2SJL3++uu67rrrVFNTo759+0qSnnjiCd199906ePCgunfv/pXHdLvdio6OVn19vaKior7exbTg2MlTGnTPXzrseAAAdGX/vH+CLujece+ltOf1u1Pvwamvr1dMTEybx2/dulWVlZXKycnxrauoqNCVV17pixtJmjBhgtxut/7xj3+0eJzGxka53W6/BQAA2BWwj6i+zOl0qri4+KwfT7Vk1apVGjhwoDIyMnzramtr/eJGku9xbW1ti8cpLCzUfffd9zVm3T4R3UL1z/snBPw8AAB0BRHdQoN27nYHTn5+vpYuXdrqmB07dig9Pd332OVyKTs7W1OmTFFubm6bznP8+HGtWbNGixcvbu8Uz7Bo0SLNnz/f99jtdispKekbH/fLHA5Hh74VBwAAvp52vxovWLBAM2bMaHVMSkqK7881NTXKyspSRkaGSktL23yeF198UceOHdO0adP81sfHx2vz5s1+6w4cOODb1pLw8HCFh4e3+dwAAKBra3fgxMbGKjY2tk1jXS6XsrKyNGzYMJWVlSkkpO23/KxatUo33HDDGecaPXq0HnroIdXV1SkuLk6SVF5erqioKA0aNKjtFwIAAMwK2E3GLpdLmZmZ6t+/v4qKinTw4EHV1tb63SfjcrmUnp5+xjsyTqdTGzZs0O23337GccePH69Bgwbp1ltv1bZt2/SXv/xFv/zlLzV79mzepQEAAJICeJNxeXm5nE6nnE6nEhMT/bad/mZ6U1OTqqurdezYMb/tq1evVmJiosaPH3/GcUNDQ/Xqq6/qJz/5iUaPHq2ePXtq+vTpuv/++wN1KQAAoIvp1N/BOVcE6ndwAABA4Jyzv4MDAADQGQgcAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMCVjg7NmzRzk5OUpOTlZERIRSU1NVUFCgkydPtrqPw+FocXnhhRd841ravnbt2kBdCgAA6GLCAnXgqqoqeTwerVy5Umlpadq+fbtyc3PV0NCgoqKiFvdJSkrS/v37/daVlpZq2bJlmjhxot/6srIyZWdn+x737t27w68BAAB0TQELnOzsbL8ASUlJUXV1tUpKSs4aOKGhoYqPj/dbt27dOt10003q1auX3/revXufMRYAAEDq5Htw6uvrFRMT0+bxW7duVWVlpXJycs7YNnv2bF100UUaMWKEVq9eLa/Xe9bjNDY2yu12+y0AAMCugL2D82VOp1PFxcVnffemJatWrdLAgQOVkZHht/7+++/XNddcowsuuEBvvvmmfvrTn+ro0aOaO3dui8cpLCzUfffd943mDwAAug6Ht7W3PlqQn5+vpUuXtjpmx44dSk9P9z12uVwaM2aMMjMz9eSTT7bpPMePH1e/fv20ePFiLViwoNWx99xzj8rKyrRv374Wtzc2NqqxsdH32O12KykpSfX19YqKimrTfAAAQHC53W5FR0e36fW73YFz8OBB/ec//2l1TEpKirp37y5JqqmpUWZmpkaNGqWnnnpKISFt+1Tst7/9rXJycuRyuRQbG9vq2D//+c+67rrrdOLECYWHh3/lsdvzBAEAgHNDe16/2/0RVWxs7FcGx2kul0tZWVkaNmyYysrK2hw30hcfT91www1tOldlZaUuvPDCNsUNAACwL2D34LhcLmVmZuriiy9WUVGRDh486Nt2+ttPLpdLY8eO1TPPPKMRI0b4tjudTm3YsEGvvfbaGcd95ZVXdODAAY0aNUo9evRQeXm5lixZorvuuitQlwIAALqYgAVOeXm5nE6nnE6nEhMT/bad/lSsqalJ1dXVOnbsmN/21atXKzExUePHjz/juN26ddOKFSuUl5cnr9ertLQ0PfbYY8rNzQ3UpQAAgC6m3ffgWMA9OAAAdD3tef3m76ICAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMCEjh79uxRTk6OkpOTFRERodTUVBUUFOjkyZOt7ldbW6tbb71V8fHx6tmzp4YOHao//OEPfmMOHTqkqVOnKioqSr1791ZOTo6OHj0aiMsAAABdVFggDlpVVSWPx6OVK1cqLS1N27dvV25urhoaGlRUVHTW/aZNm6bDhw/r5Zdf1kUXXaQ1a9bopptu0kcffaQhQ4ZIkqZOnar9+/ervLxcTU1Nmjlzpu644w6tWbMmEJcCAAC6IIfX6/V2xomWLVumkpIS7d69+6xjevXqpZKSEt16662+dX369NHSpUt1++23a8eOHRo0aJC2bNmi4cOHS5LeeOMNXXvttfr888+VkJDQprm43W5FR0ervr5eUVFR3+zCAABAp2jP63en3YNTX1+vmJiYVsdkZGToueee06FDh+TxeLR27VqdOHFCmZmZkqSKigr17t3bFzeSNG7cOIWEhGjTpk1nPW5jY6PcbrffAgAA7OqUwHE6nSouLtasWbNaHff888+rqalJffr0UXh4uGbNmqV169YpLS1N0hf36MTFxfntExYWppiYGNXW1p71uIWFhYqOjvYtSUlJ3/yiAADAOatdgZOfny+Hw9HqUlVV5bePy+VSdna2pkyZotzc3FaPv3jxYh0+fFhvvfWWPvroI82fP1833XSTPvnkk/Zf2f+zaNEi1dfX+5Z9+/Z9o+MBAIBzW7tuMl6wYIFmzJjR6piUlBTfn2tqapSVlaWMjAyVlpa2ut+nn36qxx9/XNu3b9fll18uSRo8eLD++te/asWKFXriiScUHx+vuro6v/1OnTqlQ4cOKT4+/qzHDg8PV3h4+FdcHQAAsKJdgRMbG6vY2Ng2jXW5XMrKytKwYcNUVlamkJDW3yw6duyYJJ0xLjQ0VB6PR5I0evRoHT58WFu3btWwYcMkSW+//bY8Ho9GjhzZnksBAACGBeQeHJfLpczMTPXv319FRUU6ePCgamtr/e6TcblcSk9P1+bNmyVJ6enpSktL06xZs7R582Z9+umnevTRR1VeXq7JkydLkgYOHKjs7Gzl5uZq8+bNev/99zVnzhzdfPPNbf4GFQAAsC8gv4NTXl4up9Mpp9OpxMREv22nv5Xe1NSk6upq3zs33bp102uvvab8/Hxdf/31Onr0qNLS0vT000/r2muv9e3/7LPPas6cORo7dqxCQkJ04403avny5YG4DAAA0EV12u/gnEv4HRwAALqec/J3cAAAADoLgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDlhwZ5AMHi9XkmS2+0O8kwAAEBbnX7dPv063przMnCOHDkiSUpKSgryTAAAQHsdOXJE0dHRrY5xeNuSQcZ4PB7V1NQoMjJSDoejQ4/tdruVlJSkffv2KSoqqkOPjf/hee4cPM+dg+e58/Bcd45APc9er1dHjhxRQkKCQkJav8vmvHwHJyQkRImJiQE9R1RUFP/xdAKe587B89w5eJ47D8915wjE8/xV79ycxk3GAADAHAIHAACYQ+B0sPDwcBUUFCg8PDzYUzGN57lz8Dx3Dp7nzsNz3TnOhef5vLzJGAAA2MY7OAAAwBwCBwAAmEPgAAAAcwgcAABgDoHTgVasWKEBAwaoR48eGjlypDZv3hzsKZlTWFio73znO4qMjFRcXJwmT56s6urqYE/LtIcfflgOh0Pz5s0L9lRMcrlcuuWWW9SnTx9FREToyiuv1EcffRTsaZnS3NysxYsXKzk5WREREUpNTdUDDzzQpr/PCK3bsGGDrr/+eiUkJMjhcOill17y2+71enXPPfeoX79+ioiI0Lhx47Rr165OmRuB00Gee+45zZ8/XwUFBfr44481ePBgTZgwQXV1dcGeminvvfeeZs+erQ8//FDl5eVqamrS+PHj1dDQEOypmbRlyxatXLlSV111VbCnYtJ///tfXX311erWrZtef/11/fOf/9Sjjz6qCy+8MNhTM2Xp0qUqKSnR448/rh07dmjp0qV65JFHVFxcHOypdXkNDQ0aPHiwVqxY0eL2Rx55RMuXL9cTTzyhTZs2qWfPnpowYYJOnDgR+Ml50SFGjBjhnT17tu9xc3OzNyEhwVtYWBjEWdlXV1fnleR97733gj0Vc44cOeK95JJLvOXl5d4xY8Z477zzzmBPyZy7777b+93vfjfY0zBv0qRJ3ttuu81v3Y9+9CPv1KlTgzQjmyR5161b53vs8Xi88fHx3mXLlvnWHT582BseHu79/e9/H/D58A5OBzh58qS2bt2qcePG+daFhIRo3LhxqqioCOLM7Kuvr5ckxcTEBHkm9syePVuTJk3y+/caHevll1/W8OHDNWXKFMXFxWnIkCH6zW9+E+xpmZORkaH169dr586dkqRt27Zp48aNmjhxYpBnZttnn32m2tpav/+HREdHa+TIkZ3y2nhe/mWbHe3f//63mpub1bdvX7/1ffv2VVVVVZBmZZ/H49G8efN09dVX64orrgj2dExZu3atPv74Y23ZsiXYUzFt9+7dKikp0fz58/Xzn/9cW7Zs0dy5c9W9e3dNnz492NMzIz8/X263W+np6QoNDVVzc7MeeughTZ06NdhTM622tlaSWnxtPL0tkAgcdFmzZ8/W9u3btXHjxmBPxZR9+/bpzjvvVHl5uXr06BHs6Zjm8Xg0fPhwLVmyRJI0ZMgQbd++XU888QSB04Gef/55Pfvss1qzZo0uv/xyVVZWat68eUpISOB5NoyPqDrARRddpNDQUB04cMBv/YEDBxQfHx+kWdk2Z84cvfrqq3rnnXeUmJgY7OmYsnXrVtXV1Wno0KEKCwtTWFiY3nvvPS1fvlxhYWFqbm4O9hTN6NevnwYNGuS3buDAgdq7d2+QZmTTwoULlZ+fr5tvvllXXnmlbr31VuXl5amwsDDYUzPt9OtfsF4bCZwO0L17dw0bNkzr16/3rfN4PFq/fr1Gjx4dxJnZ4/V6NWfOHK1bt05vv/22kpOTgz0lc8aOHatPPvlElZWVvmX48OGaOnWqKisrFRoaGuwpmnH11Vef8TMHO3fu1MUXXxykGdl07NgxhYT4v9yFhobK4/EEaUbnh+TkZMXHx/u9Nrrdbm3atKlTXhv5iKqDzJ8/X9OnT9fw4cM1YsQI/frXv1ZDQ4NmzpwZ7KmZMnv2bK1Zs0Z/+tOfFBkZ6fscNzo6WhEREUGenQ2RkZFn3NPUs2dP9enTh3udOlheXp4yMjK0ZMkS3XTTTdq8ebNKS0tVWloa7KmZcv311+uhhx5S//79dfnll+tvf/ubHnvsMd12223BnlqXd/ToUTmdTt/jzz77TJWVlYqJiVH//v01b948Pfjgg7rkkkuUnJysxYsXKyEhQZMnTw785AL+Pa3zSHFxsbd///7e7t27e0eMGOH98MMPgz0lcyS1uJSVlQV7aqbxNfHAeeWVV7xXXHGFNzw83Juenu4tLS0N9pTMcbvd3jvvvNPbv39/b48ePbwpKSneX/ziF97GxsZgT63Le+edd1r8f/L06dO9Xu8XXxVfvHixt2/fvt7w8HDv2LFjvdXV1Z0yN4fXy085AgAAW7gHBwAAmEPgAAAAcwgcAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADM+T8kHwyfnu8E6AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dt = 0.01\n", + "ts = np.arange(0, 1000, 1) * dt\n", + "x = [bias(dt, 0) for _ in ts]\n", + "\n", + "plt.plot(ts, x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "measured = pd.read_csv('druckkammer.csv')\n", + "truth = pd.read_csv('siemens_parsed.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00043300242654881085" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.var(measured['pressure'][55753:])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(measured['time'], measured['pressure'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/testing.py b/testing.py index 4c524d9..21cb0a1 100644 --- a/testing.py +++ b/testing.py @@ -1,10 +1,148 @@ -from spatz.sensors.antenna.tx_gain import GainPattern -import math +import pygame +import serial +import re +import numpy as np +import time -pattern = GainPattern("data/gain_pattern/farfield_all.txt") +from numpy import array +from pyquaternion import Quaternion +from math import cos, sin, pi -# pattern.get_gain(41,66) -# pattern.get_gain(40,100) -# pattern.get_gain(10,180) -# pattern.get_gain(0,95) -# pattern.get_gain(21,100) \ No newline at end of file +from spatz.sensors.imu.wsen_isds import WSEN_ISDS_GYRO +from spatz.simulation import Simulation +from spatz.simulations.rocketpy import RocketPyCSV + +# Blatantly stolen from: https://stackoverflow.com/questions/21019471/how-can-i-draw-a-3d-shape-using-pygame-no-other-modules + +X, Y, Z = 0, 1, 2 + + +def rotation_matrix(a, b, by): + """ + rotation matrix of a, b, by radians around x, y, z axes (respectively) + """ + sa, ca = sin(a), cos(a) + sb, cb = sin(b), cos(b) + sby, cby = sin(by), cos(by) + return ( + (cb*cby, -cb*sby, sb), + (ca*sby + sa*sb*cby, ca*cby - sby*sa*sb, -cb*sa), + (sby*sa - ca*sb*cby, ca*sby*sb + sa*cby, ca*cb) + ) + + +class Physical: + def __init__(self, vertices, edges, colors): + """ + a 3D object that can rotate around the three axes + :param vertices: a tuple of points (each has 3 coordinates) + :param edges: a tuple of pairs (each pair is a set containing 2 vertices' indexes) + """ + self.__vertices = array(vertices) + self.__edges = tuple(edges) + self.__colors = tuple(colors) + self.__rotation = Quaternion(axis=(1, 0, 0), angle=0) # radians around each axis + + def rotate(self, quaternion): + self.__rotation = quaternion + + @property + def lines(self): + location = array([self.__rotation.rotate(vertex) for vertex in self.__vertices]) # an index->location mapping + return (((location[v1], location[v2]), color) for (v1, v2), color in zip(self.__edges, self.__colors)) + + +BLACK, RED, GREEN, BLUE = (0, 0, 0), (255, 128, 128), (128, 255, 128), (128, 128, 255) + +LIGHTRED, LIGHTGREEN, LIGHTBLUE = (128, 64, 64), (64, 128, 64), (64, 64, 128) + + +class Paint: + def __init__(self, shape, shape2): + self.__shape = shape + self.__shape2 = shape2 + self.__size = 900, 450 + self.__clock = pygame.time.Clock() + self.__screen = pygame.display.set_mode(self.__size) + + def __fit(self, vec): + """ + ignore the z-element (creating a very cheap projection), and scale x, y to the coordinates of the screen + """ + # notice that len(self.__size) is 2, hence zip(vec, self.__size) ignores the vector's last coordinate + return [round(70 * coordinate + frame / 2) for coordinate, frame in zip(vec, self.__size)] + + def __draw_shape(self, thickness=4): + for (start, end), color in self.__shape.lines: + pygame.draw.line(self.__screen, color, self.__fit((start[0]-2, start[1], start[2])), self.__fit((end[0]-2, end[1], end[2])), thickness) + + for (start, end), color in self.__shape2.lines: + pygame.draw.line(self.__screen, color, self.__fit((start[0]+2, start[1], start[2])), self.__fit((end[0]+2, end[1], end[2])), thickness) + + def draw(self): + self.__screen.fill(BLACK) + self.__draw_shape() + pygame.display.flip() + self.__clock.tick(40) + + +def main(): + from pygame import K_q, K_w, K_a, K_s, K_z, K_x + + rotation = Quaternion(axis=[1, 0, 0], angle=pi/2) + + axes = Physical( + vertices=((0, 0, 0), (0, 0, 2), (0, 2, 0), (2, 0, 0)), + edges=({0, 1}, {0, 2}, {0, 3}), + colors=(BLUE, GREEN, RED) + ) + + truth = Physical( + vertices=((0, 0, 0), (0, 0, 2), (0, 2, 0), (2, 0, 0)), + edges=({0, 1}, {0, 2}, {0, 3}), + colors=(LIGHTBLUE, LIGHTGREEN, LIGHTRED) + ) + + pygame.init() + pygame.display.set_caption("Simulation") + renderer = Paint(axes, truth) + + simulation = Simulation().load(RocketPyCSV('nominal_wind.csv')) + + gyro = simulation.add_sensor(WSEN_ISDS_GYRO) + offset = gyro.calibrate(100) + att = simulation.add_observer([' e0', ' e1', ' e2', ' e3']) + dt = 0.01 + + quat = np.array([-0.706864,0.018510,0.018510,-0.706864]) + + while True: + time.sleep(0.01) + simulation.advance(dt) + omegas = gyro() - offset + true = att() + + omegas = (omegas / 1000) * np.pi / 180 + + matrix = np.array([ + [1, -dt/2*omegas[0], -dt/2*omegas[1], -dt/2*omegas[2]], + [dt/2*omegas[0], 1, dt/2*omegas[2], -dt/2*omegas[1]], + [dt/2*omegas[1], -dt/2*omegas[2], 1, dt/2*omegas[0]], + [dt/2*omegas[2], dt/2*omegas[1], -dt/2*omegas[0], 1] + ]) + + quat = matrix @ quat + quat /= np.linalg.norm(quat) + + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + exit() + + axes.rotate(Quaternion(x=quat[0], y=quat[1], z=quat[2], w=quat[3])) + truth.rotate(Quaternion(x=true[0], y=true[1], z=true[2], w=true[3])) + + renderer.draw() + +if __name__ == '__main__': + main() \ No newline at end of file