solar_system.ipynb

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  {
   "source": [
    "### Solar system"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "\n",
    "from datetime import datetime\n",
    "from astropy.time import Time \n",
    "from astropy.coordinates import solar_system_ephemeris, EarthLocation \n",
    "from astropy.coordinates import get_body_barycentric, get_body, get_moon\n",
    "from astroquery.jplhorizons import Horizons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = Time.now()\n",
    "loc = EarthLocation.of_site('greenwich')\n",
    "with solar_system_ephemeris.set('builtin'):\n",
    "    jupiter = get_body('jupiter', t, loc)\n",
    "cart_earth = get_body_barycentric('sun', t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "      name         x         y\n",
       "0      sun -0.007217  0.004969\n",
       "1    earth -0.996137 -0.119545\n",
       "2  jupiter  3.524075 -3.303216\n",
       "3     mars -0.522909  1.387997\n",
       "4    venus  0.708314  0.127155\n",
       "5  mercury  0.204107 -0.323857\n",
       "6   saturn  5.853473 -7.360630"
      ],
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     },
     "metadata": {},
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   ],
   "source": [
    "objects = ['sun', 'earth', 'jupiter', 'mars', 'venus', 'mercury', 'saturn']\n",
    "\n",
    "objs = []\n",
    "for obj in objects:\n",
    "    coords = get_body_barycentric(obj, t)\n",
    "    objs.append({'name':obj, \"x\":coords.x.value, \"y\":coords.y.value })\n",
    "objs\n",
    "df_objs = pd.DataFrame(objs)\n",
    "df_objs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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0du3axVVXXUVycjIJCQmsWLEi6PqCpc8BiEjIO7R4MbtmPIQ70aSttKCAXTMeAqDZyJFBzb1161beeOMNZs+eTWpqKq+++ioff/wxb7/9NrNmzeLll1/mo48+IiIigoyMDO6//34WLlwIwMqVK8nJyaFly5Y89dRTDB8+nAceeICysjKOHj0a3DddCxQAIhLy9jz928qD/0muuJg9T/826ACIi4sjMTERgPj4eIYOHYqZkZiYSF5eHocOHeLmm28mNzcXM6OkpKTyucOGDaNly5YApKamMnHiREpKSvjhD39IcnJyUHXVBi0BiUjIKz1xR62ajgciKiqq8t9hYWGVj8PCwigtLWXGjBkMHjyY9evXs3jx4iqtok9t7XzVVVfx0Ucf0aFDB37yk59U3i/YSwoAEQl5EafcUKUm47Xp0KFDdOjQAaDyNpHV2b59O61bt+a2225j0qRJrFmzps5rOxcFgIiEvNb3Tsaio6uMWXQ0re+dXOf7vu+++5g+fToDBw6krKzsjNt98MEHJCcnk5KSwsKFC7nnnuBbVQdL7aBFpF4KtB30ocWL2fP0byndtYuIdu1ofe/koNf/Q5HaQYuI7zQbOdKXB/xgaAlIRMSnFAAiIj6lABAR8SkFgIiIT3kSAGb2hJltMrMcM/uzmTX3og4RET/z6gzgb0CCcy4J2AJM96gOERHf8iQAnHNLnXMn+6P+L9DRizpERPysPlwDmAj89UxfNLOfmVmmmWXu3bv3ApYlIqFky2df8af7P+H525fxp/s/YctnXwU957Rp0/jv//7vysczZ87kqaee4oknniA1NZWkpCQefvhhoKJ1dPfu3bntttuIj48nLS2NoqIiAAYNGsTJD7Hu27eP2NhYADZs2EDfvn1JTk4mKSmJ3NzcoGsORJ0FgJllmNn6av5cd8o2DwClwLwzzeOcm+2c6+Oc69OqVau6KldEQtiWz75i+bxNFB44BkDhgWMsn7cp6BAYN24cCxYsqHz8+uuv06pVK3Jzc1m1ahXZ2dlkZWXx0UcfAZCbm8udd97Jhg0baN68eWVb6DN58cUXueeee8jOziYzM5OOHS/sYkidfRLYOXf12b5uZjcD1wJDXSj1oxCRemflW3+n9Hh5lbHS4+WsfOvvdO3X9rznTUlJYc+ePRQUFLB3715atGhBTk4OS5cuJSUlBYDCwkJyc3P5zne+Q1xcXGWb5969e5OXl3fW+fv3789jjz1Gfn4+o0ePpkuXLudd6/nw6l1A1wDTgFHOOe/viiAiIe3kK/+ajgdizJgxpKens2DBAsaNG4dzjunTp5OdnU12djZbt25l0qRJQNXW0eHh4ZW3goyIiKC8vCKgTm0XfeONN/L222/ToEEDhg8fzrJly4KuNxBeXQN4DmgC/M3Mss3sRY/qEJGLQOOWUQGNB2LcuHG89tprpKenM2bMGIYPH86cOXMoLCwEYOfOnezZs+esc8TGxpKVlQVAenp65fi2bdvo3Lkzd999N6NGjSInJyfoegPhSTM459zlXuxXRC5O/a+7jOXzNlVZBoq4JIz+110W9Nzx8fEcPnyYDh060K5dO9q1a8fGjRvp378/AI0bN+aVV14hPDz8jHNMmTKFsWPH8j//8z8MGTKkcnzBggW88sorREZG0rZtWx566KGg6w2E2kGLSL0UaDvoLZ99xcq3/k7hgWM0bhlF/+suC2r9P1SpHbSI+E7Xfm19ecAPRn34HICIiHhAASAi4lMKABERn1IAiIj4lAJARMSnFAAiIj6lABARqWMnW0J49fwzUQCIyEVh44rlzL7zFp4aN5LZd97CxhXLg54zLy+PK664gltvvZWEhARuuukmMjIyGDhwIF26dGHVqlUcOXKEiRMnkpqaSkpKCm+99RYAc+fO5frrr2fkyJGkpaVRWFjILbfcQmJiIklJSZWdQhs3bly5v/T0dCZMmADAhAkT+I//+A8GDx7M1KlT6dKlCydb4peXl3P55Zezb9++oL4/fRBMRELexhXLWTr7OUqPVzR/O7xvL0tnPwdA9+8PDmrurVu38sYbbzB79mxSU1N59dVX+fjjj3n77beZNWsWPXr0YMiQIcyZM4eDBw/St29frr66ohnyypUrycnJoWXLlkybNo1mzZqxbt06AL7++utz7nvLli1kZGQQHh5O8+bNmTdvHpMnTyYjI4OePXsSExMT1PemMwARCXkrXnu58uB/UunxY6x47eWg546LiyMxMZGwsDDi4+MZOnQoZkZiYiJ5eXksXbqUxx9/nOTkZAYNGkRxcTE7duwAYNiwYbRs2RKAjIwM7rzzzsp5W7Rocc59X3/99ZU9hiZOnMjLL1d8P3PmzOGWW24J+nvTGYCIhLzD+6tfCjnTeCBObfEcFhZW+TgsLIzS0lLCw8NZuHAh3bp1q/K8zz77jEaNGlU+ds5hZqfNf+rYqa2igSrP79SpE23atGHZsmV89tlnzJt3xvto1ZjOAEQk5DW5tPqlkDON16bhw4fz7LPPcrKx5ueff17tdmlpaTz33HOVj08uAbVp04aNGzdSXl7On//857Pu69Zbb2X8+PGMHTv2rN1Ha0oBICIh7/vjfkrEJVV7/0dcEsX3x/20zvc9Y8YMSkpKSEpKIiEhgRkzZlS73YMPPsjXX39NQkICPXv2ZPnyiovUjz/+ONdeey1DhgyhXbt2Z93XqFGjKi8m14ZztoM2s7uAec65c1+xqGNqBy3iH4G2g964YjkrXnuZw/v30eTSGL4/7qdBXwCubzIzM7n33ntZsWLFGbep7XbQbYHVZrYGmAMs0T18RaS+6f79wRfdAf9Ujz/+OC+88EKtrP2fdM4lIOfcg0AX4CVgApBrZrPMLPhb7YiISI388pe/ZPv27Xzve9+rtTlrdA3gxCv+r078KQVaAOlm9l+1VomIiFxQ51wCMrO7gZuBfcAfganOuRIzCwNygfvqtkQREakLNbkGEAOMds5tP3XQOVduZtfWTVkicqF8efBLNn69Eecc3Vp24/Lml3tdklwg5wwA59wZb1PvnNtYu+WIyIW0+cBmJi2dxKFjhwBoHNmYl4a/RI9Le3hcmVwI+hyAiI8tyVtSefAHKCwpZFHuIg8rCl1z586loKDA6zICogAQ8bG8b/JOG9t2aBt6p3fgzicA6qrNc02pF5CIj/1L3L/wt+1/qzI2usvoanvW1HdHPt/DN0vyKDt4jPDmUTQdHkujlNbBzXnkCGPHjiU/P5+ysjJmzJjB5s2bWbx4MUVFRQwYMIDf//73LFy4kMzMTG666SYaNGjAypUr6d69O5mZmcTExJCZmcmUKVP44IMPmDlzJgUFBeTl5RETE0PXrl3ZsWMH27ZtY8eOHUyePJm77767ln4qZ6czABEfS22byv397qd5VHOaXtKUKX2mMLDdQK/LCtiRz/dwcFEuZQcrOoKWHTzGwUW5HPl8T1Dzvvfee7Rv3561a9eyfv16rrnmGu666y5Wr17N+vXrKSoq4i9/+QtjxoyhT58+zJs3j+zsbBo0aHDWebOysnjrrbd49dVXAdi0aRNLlixh1apVPPLII5SUlARVd00pAER8rFlUM2644gYWjVrEolGLuDn+Zlo0OHeb4vrmmyV5uJLyKmOupJxvluQFNW9iYiIZGRlMmzaNFStW0KxZM5YvX06/fv1ITExk2bJlbNiwIeB5R40aVSUkRowYQVRUFDExMbRu3Zrdu3cHVXdNaQlIRGjVsJXXJQTl5Cv/mo7XVNeuXcnKyuLdd99l+vTppKWl8fzzz5OZmUmnTp2YOXPmaS2cT4qIiKC8vCKUztbmGaq2nA4PD79g1wZ0BiAiIS+8eVRA4zVVUFBAw4YNGT9+PFOmTGHNmjUAxMTEUFhYSHp6euW2TZo04fDhw5WPY2NjycrKAqi8/WN9ozMAEQl5TYfHcnBRbpVlIIsMo+nw2KDmXbduHVOnTiUsLIzIyEheeOEF3nzzTRITE4mNjSU1NbVy2wkTJnD77bdXXgR++OGHmTRpErNmzaJfv35B1VFXztkOuj5RO2gR/wi0HXRdvAsoFNV2O2gRkXqvUUprXx7wg6FrACIiPqUAEJF6K5SWqOuDQH9eCgARqZeio6PZv3+/QqCGnHPs37+f6OjoGj9H1wBEpF7q2LEj+fn57N271+tSQkZ0dDQdO3as8faeBoCZTQGeAFo55/Z5WYuI1C+RkZHExcV5XcZFzbMlIDPrBAwDdnhVg4iIn3l5DeBpKm4nqQU+EREPeBIAZjYK2OmcW1uDbX9mZplmlqm1QBGR2lNn1wDMLANoW82XHgDuB9JqMo9zbjYwGyo+CVxrBYqI+FydBYBz7urqxs0sEYgD1p646URHYI2Z9XXOfVVX9YiISFUX/F1Azrl1QOXntc0sD+ijdwGJiFxY+iCYiIhPef5BMOdcrNc1iIj4kc4ARER8SgEgIuJTCgAREZ9SAIiI+JQCQETEpxQAIiI+pQAQEfEpBYCIiE8pAEREfEoBICLiUwoAERGfUgCIiPiUAkBExKcUACIiPqUAEBHxKQWAiIhPKQBERHxKASAi4lMKABERn1IAiIj4lAJARMSnFAAiIj6lABAR8SkFgIiITykARER8SgEgIuJTCgAREZ9SAIiI+JQCQETEpxQAIiI+pQAQEfEpBYCIiE8pAEREfEoBICLiUwoAERGfUgCIiPiUZwFgZj83s81mtsHM/surOkRE/CrCi52a2WDgOiDJOXfMzFp7UYeIiJ95dQZwB/C4c+4YgHNuj0d1iIj4llcB0BX4vpl9ZmYfmlnqmTY0s5+ZWaaZZe7du/cCligicnGrsyUgM8sA2lbzpQdO7LcFcCWQCrxuZp2dc+7bGzvnZgOzAfr06XPa10VE5PzUWQA4564+09fM7A5g0YkD/iozKwdiAL3EFxG5QLxaAnoTGAJgZl2BS4B9HtUiIuJLnrwLCJgDzDGz9cBx4Obqln9ERKTueBIAzrnjwHgv9i0iIhX0SWAREZ9SAIiI+JQCQETEpxQAIiI+pQAQEfEpBYCIiE8pAEREfEoBICLiUwoAERGfUgCIiPiUAkBExKcUACIiPqUAEBHxKQWAiIhPKQBERHxKASAi4lMKABERn1IAiIj4lAJARMSnFAAiIj6lABAR8SkFgIhIPeaco/xYKa7c1frcEbU+o4iI1IqSfUc5mrmboo0HiO7SnEZ92xLZulGtza8AEBGph8qPlnDgjS2UbD8MQOHuoxTnHiTm1gQimkTVyj60BCQiUg+V7CuqPPifVLr7KKV7i2ptHwoAEZF6yMKrPzyfafx8KABEROqhiFYNaNirVZWxqG4tiGjVoPb2UWsziYhIrQm7JJymw+OI6tKSY3mHiPpOU6I6NyO8YWSt7UMBICJST0U0iyIipTWNUlrXyfxaAhIR8SkFgIiITykARER8SgEgIuJTCgAREZ9SAIiI+JQ5V/sd5uqKme0Ftp/n02OAfbVYTl0LtXoh9GoOtXoh9GoOtXoh9GquSb3fdc61+vZgSAVAMMws0znXx+s6airU6oXQqznU6oXQqznU6oXQqzmYerUEJCLiUwoAERGf8lMAzPa6gACFWr0QejWHWr0QejWHWr0QejWfd72+uQYgIiJV+ekMQERETqEAEBHxKV8FgJldb2YbzKzczOrt27zM7Boz22xmW83sl17Xcy5mNsfM9pjZeq9rqQkz62Rmy81s44nfh3u8rulszCzazFaZ2doT9T7idU01YWbhZva5mf3F61pqwszyzGydmWWbWabX9dSEmTU3s3Qz23Ti97l/IM/3VQAA64HRwEdeF3ImZhYOPA/8P6AHcIOZ9fC2qnOaC1zjdREBKAV+4ZzrDlwJ3FnPf8bHgCHOuZ5AMnCNmV3pbUk1cg+w0esiAjTYOZccQp8DeAZ4zzl3BdCTAH/evgoA59xG59xmr+s4h77AVufcNufcceA14DqPazor59xHwAGv66gp59wu59yaE/8+TMX/NB28rerMXIXCEw8jT/yp1+/eMLOOwAjgj17XcrEys6bAVcBLAM654865g4HM4asACBEdgH+c8jifenxwCnVmFgukAJ95XMpZnVhOyQb2AH9zztXreoHfAvcB5R7XEQgHLDWzLDP7mdfF1EBnYC/w/08stf3RzBoFMsFFFwBmlmFm66v5U69fRZ/Cqhmr16/2QpWZNQYWApOdc994Xc/ZOOfKnHPJQEegr5kleFzSGZnZtcAe51yW17UEaKBzrhcVy693mtlVXhd0DhFAL+AF51wKcAQI6JrhRXdPYOfc1V7XEKR8oNMpjzsCBR7VctEys0gqDv7znHOLvK6nppxzB83sAyquudTXi+4DgVFm9i9ANNDUzF5xzo33uK6zcs4VnPh7j5n9mYrl2Hp7vZCKY0X+KWeD6QQYABfdGcBFYDXQxczizOwSYBzwtsc1XVTMzKhYN93onPuN1/Wci5m1MrPmJ/7dALga2ORpUWfhnJvunOvonIul4vd3WX0/+JtZIzNrcvLfQBr1N2ABcM59BfzDzLqdGBoKfBHIHL4KADP7VzPLB/oD75jZEq9r+jbnXClwF7CEiouTrzvnNnhb1dmZ2XxgJdDNzPLNbJLXNZ3DQOAnwJATb/nLPvFqtb5qByw3sxwqXiD8zTkXEm+tDCFtgI/NbC2wCnjHOfeexzXVxM+BeSd+N5KBWYE8Wa0gRER8yldnACIi8k8KABERn1IAiIj4lAJARMSnFAAiIj6lABAR8SkFgIiITykARIJgZqlmlnOiZ3+jE/36622fHpFT6YNgIkEys0ep6HnTgIreLL/2uCSRGlEAiATpRM+m1UAxMMA5V+ZxSSI1oiUgkeC1BBoDTag4ExAJCToDEAmSmb1NxZ3b4oB2zrm7PC5JpEYuuvsBiFxIZvZToNQ59+qJ+zl/amZDnHPLvK5N5Fx0BiAi4lO6BiAi4lMKABERn1IAiIj4lAJARMSnFAAiIj6lABAR8SkFgIiIT/0f3+UjBcix8poAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.scatterplot(data = df_objs, x = 'x', y='y', hue = 'name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}