[AstroPy] consistency of reference frames between SkyCoord and JPL Horizons
Paolo Tanga
Paolo.Tanga at oca.eu
Sat May 9 11:27:27 EDT 2020
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{
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"from astropy.table import Table\n",
"import numpy as np\n",
"from astroquery.jplhorizons import Horizons\n",
"from astropy import coordinates as coord\n",
"from astropy.coordinates import GCRS, ICRS, Distance, solar_system_ephemeris\n",
"from astropy.time import Time, TimeDelta\n",
"import astropy.units as u"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ScienceState solar_system_ephemeris: 'de430'>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from astropy.coordinates.solar_system import get_body_barycentric\n",
"from astropy.coordinates.solar_system import _apparent_position_in_true_coordinates\n",
"\n",
"solar_system_ephemeris.set('de430') "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"time = Time('2020-04-09T00:00:00', scale='utc')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: ErfaWarning: ERFA function \"taiutc\" yielded 1 of \"dubious year (Note 4)\" [astropy._erfa.core]\n"
]
}
],
"source": [
"dt = TimeDelta(1000.0,format='jd')\n",
"time_start = time - dt\n",
"time_end = time + dt"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: ErfaWarning: ERFA function \"d2dtf\" yielded 1 of \"dubious year (Note 5)\" [astropy._erfa.core]\n"
]
}
],
"source": [
"asteroid = Horizons(id='Eris', location='500', epochs={'start':time_start.iso, 'stop':time_end.iso,\n",
"... 'step':'1d'}) # geocentric ICRS\n",
"ast_eph = asteroid.ephemerides(quantities='1,2,19,20,21,23',extra_precision=True)\n",
"\n",
"times = Time(ast_eph['datetime_jd'],format='jd',scale='utc')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: ErfaWarning: ERFA function \"utctai\" yielded 370 of \"dubious year (Note 3)\" [astropy._erfa.core]\n",
"WARNING: ErfaWarning: ERFA function \"taiutc\" yielded 370 of \"dubious year (Note 4)\" [astropy._erfa.core]\n"
]
}
],
"source": [
"# Build SkyCoord object in ICRS - this will be the geocentric direction to the asteroid, shifted to ICRS barycenter\n",
"ast_coord_ICRS = coord.SkyCoord(ra=ast_eph['RA'],dec=ast_eph['DEC'],distance=ast_eph['delta'],frame='icrs')\n",
"# Move origin to geocenter\n",
"ast_ICRS_geoc = coord.SkyCoord(ast_coord_ICRS.cartesian + get_body_barycentric('earth', times))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"my_object_apparent = _apparent_position_in_true_coordinates(ast_ICRS_geoc.transform_to(GCRS(obstime=times)))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"d_ra= (my_object_apparent.ra - ast_eph['RA_app']).to(u.mas)*np.cos(my_object_apparent.dec.to(u.rad))\n",
"d_dec=(my_object_apparent.dec - ast_eph['DEC_app']).to(u.mas)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-10.0, 10.0)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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iaM2q7R8YFX2Uf+wx2H//lN7esmV2gscPZtzFH/g9a//xeMaGk6kpDx7O1VMrV8KPeZxv6cbZPBXbcffd1lSoVy/42c8si3DnnZbDSDRzZuUfsGWL3bmTCPuRREu/MmXRIujB3Io77r47fv3YYys/yZIlsVp3VXj77eTj1wdatMjOfCWy2YJcRxbTkUWsX5c7za48eDhXT61cCX/iOrqFE3U+8ADcdRcMG2a15wUFcO+9cMIJtn/kyPgT3HZb/PrcuTBxYmz9mmugU6eknSwyXVn+2muxhmKLF0NP5pTv+/wHIy0AHHVU/JtuuQUmTYKf/9zWE8crCYuu7r/fmjM/9RSVadQIli+PvxyZsG2LVXr8lptYRGeatSioesCyLPLg4Vw9tXIlFLMxtuGkk2DEiKrHJTnggNjy4YfH32B79rQJwN9914q57r3XtieZFCTTleXHHmuZpgvO3sLeNw6jO/Nin92inS1EO3dcfrmNPz9gQKz3YOLUiXvtZZ0lH37Y1n/yE7j5ZlueNy/u0HPOsdePP07XN0queFOGJiZJAw8eztVTS5ZAM9bHNlSnp9ybb1pfkPPOs5vp/PkVjznsMCsS2rbN1r/+usIh4a5MK3tqFMN4DoC1RRY0tEHQ0aR1a3jlFStfuvvuWNAMGwWEEWBQZHLTvfaKz05cey3ccQd07w7PPFO+eeBAe830PO3NtywvX36XIQzffxJbtWFmP7SaPHg4Vw+pwntjttKYSBagOpN3N2tmxT3//KdVXLRoAQ89VPV7Ro2Cb7+tXYJraHe+AuD1pqcwo8MhAJQWNYkdcPzxFceeP/poS+/VV8M998ATT1iuKirs6Qjwy1/a6/vvl28K63T+8AebAjcTtm0po3vprPL1zxjEQ+MG5Exvcw8eztVDX34JZfMizU8b1KJLV9jM5667ku9/4w3o0aPm56+hHszh1/yVrTTkvObP8/S+tzKWQ5g36Ifbf3O3bjZg1GWX2Rj1s2dbjioUFldFVVLX8OyzNfwC26EnnMi+TCpf30gxkHyiq7rgwcO5euiCc0t5m8Nt5aabalc4f8opNpdtsl7n0XqDbDStChzH/3gTqxBvxDaeeQY6DN6VQxlLx91rOMtVWIcD8OMfx4a0DSXUe4Qy8rXXrqXRmNFxmxqQvetbHRkLHiKys4i8IyJficiXInJlkmMOFZE1IjI5+LkhU+lxbkcxdixsm/h5rBL5ggtihfQ1UVAAhxySvNjrkENiy++9V/PPSNH/OIFeWJHOrHte5eCDbe6LDz6AIUNqeNLoJO9t28Y/4nfsaMVWN95YYZTCjOQERo0qX3y7sTUxbrxTK/71rwx8Vg1lMudRAvxKVXcHDgAuE5Hdkxz3vqoOCH7+mMH0OLdD+OADOJCPYhs6dkzfyd98E37xC1seMgTatYvtu/NOa+K6cWPcWzJReb4tMrJSwTFHA1YfftBBtThptBdeYqAMK9VvuAFmWdAKL0MmmiVveOUdFhV0plfDb/n74P9wNTfT4roR5XX8uSBjwUNVF6nqxGB5HTAN2IGni3cuOyZPhiObfWpPz6tXp3cyiCOPhNtvt1ZML70E110Xa7X08svWue7TT+PekhBLaq2kBGbSq3y9Zes03cYaVtGKKdoiK2hi9be/2aVNZ7PkSy6Bnj2Uza+/y1tlh/GtdmNTYTNu5WqatMqRyo5AVuo8RKQHsA/waZLdg0Vkioi8KiJ7VHGO4SIyXkTGL8t0+zjn8lRJiTXR7SfTYZ99bByNTDj+eDt3+/bw6KPx+5YsiVtNd/DYsAHasoJZ7MLxvJLebg9Dh8JvfhNbD08eLQtbsACwwFFUlN7g8fQDqzno2ydpu20JH3BwXH1Ks2bp+5x0yHjwEJFmwHPAz1U1cTSYiUB3Ve0P3A28WNl5VPVBVR2oqgPbh2PzOOfi9OgB297/mN3WjavxuFQpSyziWbiQP/85thpOFZIuq1eW0ZYVjOIsRnN8lRmGlL34IvzpT7H1KVPgo4/iO08GwQPSHzwe4GKewHJy8+gWt69Jk2TvqDsZDR4i0hALHE+q6vOJ+1V1raquD5ZHAw1FpF3icc656lmwAD7mQFvp1Cn7CSgqgkWLuPbaWBPW9eurfkuq1rz5KYWUsZws3Cp69oTBg62p84QJtm1SrPlsuoPHzsSaVy+kM8OGxYJGrvTvCGVsPg8REeD/gGmqenslx3QElqiqisggLJhlYbgx5+qfsjIYSWRww+9/P3sf/tZb0LQpnH56eZ1Aq1a2K61Dic+axd4XW3CcTr80nrga9t3XauS//dYGtmrblkaNJK0V5htoWr586Nld+es/rRSwefPs/jqrI5OTQR0EnAN8ISKTg22/AcuLqer9wDDgUhEpATYBZ6rmWnx1Lj9s2gS3cjUAX3c7kj41brNaA4cHfUo6dIClS4FYx+5VKY6WXqVx48oXy/uxZFO7dtZQIKjrKSo6N605j27MYxntuJ9L2P2QdjRsCF27wpNPpu8z0iVjwUNVPwCqbOahqvcA92QqDc7tSDZsoPy5tbRJ0yqPzZgOHayO4LbbaH3qL4GC9AaPYHap3ZrM5cOxjaoaNT0zokOdvP12eoOHKt2Yx71cxg3cyH9ybP6ORN7D3Ll6Ilox3aA0SxOIJ+rTx8qpRo6k3RzLJaR1BPE5c1hdtBN0787++8fXY2dFNFqJpLXOQ5ctpwmbyyvKE4fkyjUePJyrJ9avivXGm7jPhXWTiJ/9rHyx6cJvEEk63UfNzZ7NvIa70LVrGs+Zimjt/5IlaQ0epU/9G4Bv6Q6QczMHJvLg4Vw9sXWxPeJfwZ3MGnBa3SSid2+49FIAZMVymjWzSQvTYswYePttZpbWYfAIe9Q3bZre4DFpEg1+MQKINdH14OGcy4oweBR27MB559VhQu65x/p+rFhBs2ZpbKobzAy4YlNxhXmcsua222yyqKBVWVFRmoYniQzN68VWzrmsKl1mwePS69vQuXMdJqSgwB6bV6ygefM05jyCp/5XOabuch7Nm8OFF1rDgMWLOeO722iwMbHvcw2880754kralH9ULvPg4Vx9sGwZA/5mPZMb7ZQDj6xt28Ly5ekLHrfdBsuXM6nZ93mBU9mj0oGMsqRDBygp4YJpI7n0u99s//jtmTePVSecQxM2EjZSrc7cXXUpx5PnnKuW116j2bI5ABR1zIHC8saN4ZlnOHbzC+kJHiNHArC8yLJUBx6YhnPWRmSIpGbbatmcrKQEFi1iQ7vubCbHxiCpggcP5+qD72LDWrTco67KdCKCaWkvnntteoJHx47QrBnXNbuLH/0ovQMF10hkPvg1WsvBJxctgrIy1rWyipyDDiKn5u2ojAcP5+qBbbNs4qczGUVx6xwYujsYkKnrhhlsWlvLCT1UYfVq9JJL+XxxB7rkwsQOgwaVz6LYYOvG2o0cPH8+AKubWdC/+25yat6OynjwcK4e2DJzHhPYl/1uPbOuk2LeeKO8udD+K16r3bnWr4fNm1le2IEtW6jbxgCh1q1h3jzmtx9A022r6NVr+2+pVDBK74rGFhVzvaI85MHDufpg3jzm0S1uzqI6tdde5UOJdFw7gwceqMW5grGyfnWLFRXttFNtE5c+m5u0pg0rWbSoFicJgseyRhY8cm3ejsp48HAu36lStPhb5tGt7vo/JNOyJWWt27Ars7jkEhu4sUaC4LEMq6Su88ryiC3FbWiNDd5VVlaDE0ydCj//OQAX/NrmUPfg4ZzLjjVraLh5Pd9Jt9yoD4go6LYzXbEy/b//vQYnWLsWnn4agKV04JRToHv3NCawlrY2tZwH1HDGxNujs1VYK4Di4tqnKxs8eDiXzy64oHwciy07dacoB+rK43TqRK+mVqZTo57Yt98Od90FWM/rcI6QXLG1eRs6sZhTeL5mMyYG870/yE/LN+V6/45QniTTOVdBaSn885/lqwU9ulVxcB3p1Ik+zS14NKjJBBAzZgCwjHYsp33OBY+SZha4n+e01IdhWbIEvvoKbrmFi3kQgHnz0pzADPLg4Vy+isylDVDSfdc6SkgVOnemYNkSCimt2QCCwTSEA7D55Fq0SGPa0qCkRaw3/6ZFKU6Z+M03AGzbYwAAN95IbtVZbYcHD+fyVaSJz7MFp9O0W9s6TEwlOnVCSkspoQF7f/Zw6u+fPZsNxw5jIVaZk2vzjG5rHGtX2/TZx1J7c9AQYFNza0WWLxXlIQ8ezuWrSNft4WX3RUfMyB2RntgHf3Z7FQcmUVoKc+eyqdMu5ZvSOV94OqzoGBtkq/HU8am9OQge64vtGjWto8kfayrjwUNEjhGRGSIyU0SuSbK/SET+Hez/VER6ZDpNztULQSH7olcmsIo2tM3BjAcHHwz9+gGwqGW/1N67cCFs3cr6nWLFcZdcks7E1d7SjnvTjHV8zAEULl9c/Tf+5jfl856sK7LRgj14RIhIIXAvcCywO3CWiOyecNiFwCpV7QXcAdySyTQ5V28EwWNViRWd5OT8D506wbRpfNboYBpvTnEy86CT4Zq2lvN4663caqYLsHkzbKAZi+hEoxXVDB7r18Nf/lK+umFbI8CDR6JBwExVna2qW4GngaEJxwwFwsLCZ4EjROp82DPncltJCYwaBcDSTTkcPAJrG7aheHMKo8++/jpcdx0AKzrsBuRm/4fNm+11MR1pvGoRvPde+aCQlVq+PG41bKXlwSNeF+C7yPr8YFvSY1S1BFgDVMiAi8hwERkvIuOXLVuWoeQ6lyeuuQZGjwbghLOspjWXg8f6hm0o3lLNnEdZGRxzDHz4IeyxB6uK7ZaRi8HjssvgiCOC4LFhBQwZAqecUvWbEoJH2D/Eg0eGqOqDqjpQVQe2z8maQeey6L//LV/ciN1Ve/asq8Rs34ZGrWm/6Tt46qntH7xiRWy5Z8/ym2suBo82beDVV2ERnWIbFy6s+k3R4LH//nkbPGrSbScVC4Boy+WuwbZkx8wXkQZAS2AFLrdt2GBNRd95B774Al57Dfr3h4YNrc3hsmUwcCD06gWHHML0NZ3o1q0aN4CtW2H6dGvQv24d7LlnDkzegLURLSmBd9+FffelzmunS0rKF5UCXnwxt28+DQuDgZ/OPht+9KOqD14cqTto2pSVQWlX6xyY4yqZhg1hUeHOUBps2N6wuGHwePNNtgw8iDXP2Wq+NdXNdPAYB/QWkZ5YkDgTSPzLeRk4F/gYGAa8rZprrbmradkyK+/s399uNg0b2vZcuPlVZeVKS2+bNsnTqmpjDG3dakNtP/ssvPhibH9hobWoGT/elr/7zv4TgmPKCgopKevH6QV/5eNWx3LPPXDWWcF7t26FMWPgT3+CKVOsELm0NHbuNm3snD16WEAZNgz2399qTrdutQrZMM2rVkHLlhZ8ROx3sfPO0K2bfYdUepipwgcfwM032z/7xImxG3br1rDLLjY2eMuWcMYZcMIJ2z9nWZldx7lzbdTZtWvt/Vu32sx7qVgR/3yVqzfW0PKmkZruFSuqDr5LlsSWr7yS5f+zITtyrXd51IrinSFsOb2qiuK5sjK7TwDstx+NW8dmDmxZyzmlsi2jwUNVS0TkcuB1oBB4RFW/FJE/AuNV9WXg/4DHRWQmsBILMNlTVmbtrRcvtgrITZtseZdd4LPP7OZVXAwDBsCkSXZ8mzb2Dz9/vt3wtm61G8vGjZR3o23QwNq4b9hghaKFhVauMG8e7LabHVtWBl9+CfvtZzffE09M32D+qvZHvHKlpXHlSqvMa9nSyso3b7Yn+9atLZtdWmo3sDB7sHq1La9ZYzV6s2bFzt24sbWZ7NQJTj2VTTv14LOvmnHIIbZ7xnTl+t8KPzx4EZ+8sIjd37uPU3iB/zQ4i+PbT+OqqzpxxhlQ8PV0exKdONEmDzrvPCgqslnjNmyAdu1gwgRL25QplqageWO5Tp2gUSP7LitX2nWOBh+wQNK4sQWdAw6AESPseyc+qm/aZIFszhxrDbN4saWna1cYPtzes/POlttauxYmT7bc1xNPwMUXw513UmFwqYkTLbcyeTL8+9+xjgoFBfb7b9IEtm2DU0+1v5k2beDMM206uWRWr7bvu2ZN3OZcvrECvLLrlZRt3sLl86+F99+Hk0+u/OAweEyfDn37Mv8Bi94c5TsAAB+aSURBVNOFhdlJa00sbdk7FjxWrIB777UKkURDh8Irr0BhIZuL4qNFrvWe3x7Jx4f8gQMH6vjxKXbIAbshFRTYzeHrr+HBB+2m+PXXtl/Ebrpt29ofQO/e9vTauLHdLBo1sn/+Ro1iN58ePWyCgTZt7NwdO9rNa9Uquylv3mz/BOvW2XqHDhasws+KOuAAuzElewpds8ZuHBs22E2uuNhu6lu22Hdo2NCKj7ZssRvg+vX2PZM57DBLa3Gx3bB697a0rV5tN7IGDezGuW4d9O1rgXHPPe34E0+0p+ZG1rxwxgz4xS+s3LdjR3uoit67mzSxTMgj13zNCdf1Z0G/I+g6+b9Mv+Yx+t75MzvnLbfwv9JjaNe/C/37W3xt2TJ2mcup2ndbtMhuxl26WG4nDNi7726/HxG7zn36wMyZ1uTzq6/sfd98Y9+nuNiK1UpK4Kij7HyTJ8daynz/+3D++XZTjzwSlpXFD1w38eMt7PbPX9PkobsY2+405v3tGX58jiBaZuNOXXGFPSi0aAGHHGJzX++1l12okhL729iyBT7/3K79ihX2eYsXV/w7WLTI7qI/+Un5PKWjOZbjGc3cubnXjDXq5JNh/qwtjP+6BVx5Jdx6a/IDH3sM/vAH+9tdtQpateKww+zSfPBBdtOciu99D3o2XsTTxz4G115rGxcvrjj5SJhLLixk+eKS8o6dxcXUbGDFFInIBFUdmJaTqWre/ey3335aIwcdpGq3IPvp0kV18GDVyy5Tvece1WXLVJcvVy0rU9240d5TUmKvc+aorlqlumJFbF8qSktV16615bVr7bxz59pnrV2r+sQTlqbDD1fdtMmOW71a9ayzVDt0UC0sjE97VT9Dhqh+//uqF12kesUVquPG2c/y5TVLe4KlS1Uff9ySFv3Yrl1Vf/IT1QMPVH32WdVbb1VdsiTyxr//XRX0LkZoCQU6u8ehesfIBdqwYewcRUWx5V69LMlp9fnnqn/4g+ppp6kOGqS66672Yd272zV75hnViRPt9xLYvFn1rbdU//IX1SZN7PWGG1QvuCCW1mv4syrocbyiDz+sqldeaTsGDFCdMkV127bqpe/ss2MnnTgxft9HH8Vd8A+uf7V8df36tF2hjDj9dNVGjVTX732AXefKhF+osLD8d9Czp/2t5bITT7RftT7ySOw7fPCB7fzuO9VDD1WdNi22r2VL/fbb2GqnTtlJJ1bik5b7cKbrPHJHWZk1oevRAwYPtsfkoUMrH+ozmIO5PK/co0ftPr+gIFYkFb6Gj4rNm1vxzbp1Vixz5532xHvhhfDRR1bBuNNO9kTatKkVfy1fbk+sxcVWxLFwoW0vLbUitwxRtaSFT4EXXQSnnw5HHlmNqp0RI+C//2XEW3ezktbsN/c5Vv3V2pe2bWvnmTzZHtjmzLFMQ7t2Vmp05ZVWYnTGGbX8AnvtZT+hkhLLcbVrV+HQCRNg5EjL3ERGAil/sAw1bAitbriKbf/3CLfPu4pnb1oEc++Eyy+34cRTqfM65xx48klb3nff+NxpQhPPNU06AlZdlMuV5WCleVu3wn2fH8RV/M1yfa+/Hl/3sToysGBpKYhQWmpVaLX+vWdYq1aW8Y9rLz1njn3x116zostwYvImTeCBB+Imx8r1Ysek0hWFsvlT45xHjvvmG9UZfU/QsoIC1ZYtVRs2VL3//rpOVrnRoy1XAarHHmu5j5QtWKB6zjm67fmX9emnVR9+WPXbb1XXrIkdUlZmGbWPP7aMYTR3M2KE6u23Wwbi44/t+BkzVBctstzO0qWqd96pOmGCPfDV1Nat8RnVX/7Svu+kSaq/+pWd/6WXVBcujGVO9YUXyt+wacABqlu26KxZlo4FC1T/9CfVa69VveQS+47Tptnr5s0JH752beyDDzvMPuT3v6+Qw3z8zhUKloHNdT/9qSX7DEbFvsNDD9nOsjLVe+9VffTR2L7x41XVvhuo3ndfHSa+Gq64wv5l9b33Yt/hqquSlwzMn6+qlrEMNx19dHbSSRpzHnUeCGryU5+Cx7Jl9g8yYID9NvoyTTc1aq7aurXdqXLA9ddbqQ6o9uihesstdnPPlq++sptHYjEZqLZtm/z/M/z58Y8jN/dqGjvWvidYoPr00+q/d+HL4/QX/E3POnp5eQlUq1ZW8hhNV8+eseUOHezB4eGHVadPVz3qKNU39r82dsC//530y91zd5lCQtFgjrr8ckv23kyOfYef/cyKdqZMSXpzVbWAnawEL9f87neWzpIpU2Pfo3fvir+3Nm3K3/PBB7HNF16YnXSmM3jsOMVWWfCf/1h9Zzh42+bNllWPNsFTtVKM1avhmWesEU9Yd15cDDM29qNf8XdM+7oBTdplpiyitNRK49autcrsv/0Ndt3VctMDBlid5fjxVi8/eXKsz1OnTlaUk+2ezLvtZj8XXGCN0jZvtoZLBxxQPkIHYEVMzzxj9cwNGtj3fOIJ+zn/fHjkkao/p6zM6q3POceuz8svW/uAVHQ6cSCzhw7kpZdi29atsxKoESOscV2PHjBuXKw9w9Kl1mYhai/aclS4EjboSPDoY1Yclmor37oQltx9TZ/Yxn/8w36GDIlta9vWGgUE/vUva/vQv3+WElpDYbHTiLv7cOPuB9N25mfl83XEiUzYEZ229qKLMpzATEhXFMrmT6ZyHhs2xK9Pm6a6bl1sfc2a+Hr0U06x+tZ+/VR32SX+AaNDB6tcbd7cntxvvln1/PPtiXbYMFWR+KfjSZMs9/7WW7Zt1Kj0fq81a1T/+Ed7iO3Y0dIFqi1aVP3kXlRkdbgrVuRmpeysWdaOYdkyW9+6NVbXXVpqxQHhdzn0UNWnn678XCNHxo4dPbrmaSottdzS2LFWklWZkhIrfhs71oo9hg+Pff55PBKf8OgvRUSfum1B+erWrTVPa7acf34s+VX+wR12WNz7WrdWvfjiuklzKqIlbqCVF1kdeWT5e156SaMldFmB5zzSJ2wdGdZnlZTYU8Tgwdb8tHVra3a/zz7w299aE/Q99rBWk2HP1y5d7MmoXz+rAy8osCf3YHpibrop/jPnz7eWoJs2weOPW2vY0JAh9uD1+OP2uTW1ZYt1U3nzTatQLSuL7Qu7GIBVtI4caf35Nmywvm577mn7i4tzv6IysW1A2C8T7Hu++qrl8o45xrq5jB1rLUAPOcRa9paVWdPgRx6xuu0ePeCGG+z4miooiOWWqlJYGKu7D/vIzJ5trZFXEsnevftu/BvLypj/19hqjaZ3zbJog4NJL8xlnwWvWIOCRPvuW75YVma/u8iUIDkr2kejRQsqbzcdGRkgzHnkemOHyuTBn11mbNgAd99tN4zIhGzlXn3ViptWrYL77ovft2iRTVMwcKAVpbRrV7FvmKp9RoMG1kVjzhy7kf/qV3bTSDw+VFhoxV433GCNbs4+O7Xv9eWXltV/6ikLUgCDBllx0267WZeGG26AadNs5JCwnfn116f2OfmioMCK2T77zH4Pu+4a62d4wAHWhy/st9e7twWYjh3rLr1hl5UVFccGjbNtW2w51wcwgPg+jcubdreOs8n06hX3HtXcHvAxdPDB1gd13Dh7+GKPYJKoK66wm0xon33KF8PgkYtjdlVLurIw2fypTbHVhg3WdyBanDFggLXb37Ah1vVi0yZ7ffdd1cWLrbhnxgzVTz6JddfIlM2bVQ8+2NL2059ay55It4M4JSXWBeWZZ+IrYaNFYtu2qa5cWfk5diTvvGNFKG3aWEV269Z2nV57LUmrpzowaJClpzPz43+R0V+uqv72t3GrOW/EiFh6n3su2LhkiZULhzuGDrU/1MCsWbb50UfrJs01ccwxqvvvH6xMn27/dF262D/yqFFW/hu4+277fmGRazbgxVY18+GHVhQUPpHffrs9CAwZEnt6C3ObYSVkWJd3+unZS2dRkXXAPfBAeOgh+wF7Iu7Rw/b36wf/+58VnUUr3sDq6ebMgUMPjRXj5PrYR9ly6KH2Ez7hL1sGCxZYD+FcEBZBLaQLM95eQN8PH7Hy0ttvt35KJ50ExIpM88Utt9jABqeeag01gFh51OrVFj4SOjuE3zEfch6hxo1jf1vl5dHhDSdB2M+jSZOku3PeDhM8tm2z/71ly6zT2U03wbnn1nWqKjd4sDVE6dvXysDDoZYWL7b0jx1rxxUX2xA6S5dacdcuu1iAieT+XRJhsWHXrvaTKx5+2PqGfvwxLJLO9L3++liZ4hdflJeprcizcaebNInV65QHj1AlIwLm+mi6yRQVRYLHdoQPfR48clzDhnbDnTzZch/5UE4cls0ffjj8+c/WDLVvX+tIPmOG1b316FG3ZfQuvXbbDR54APbeG156yXJJ5fbcs3wxOmp5vggHVohWnldmzJjY4LP5lPMoKorNLrg9mzbZ8dGx0vLJDhM8oHotYHLZD38YWx6YnqHNXA7qEsy1+fe/wx13VNyvClOn2nKk/jXnNWpkN8sKOY8EX31lDTtC+ZTziCu2qsS6dXDaaXYd8jXXATtY8HAuH7RpY7nLdu1inUqjNm60Ya7+8hf49a/rJo011bz59nMeicEln4JHdXIeM2day0uwjrf5Kk8zTM7Vb/vsY80+Cwpi4yFu3Gj1WlOm2Hq7dvlX5NGixfZzHtEGIN265UcP+lB1ch7RARETxrrMK57zcC4HRacknTLFukU8/bTVh4SjXuTTE3moOjmPaPDIt+LZsMI8WY4xFA0e0f46+SbPnluc2zFEn7bDJ9mwOOS77+w1H4NHqjmPXGoJVx1FRdYzPtKRvIJo8MhnHjycy0HREQjCJ/XE13wNHu++a/1qKpvqO5xRr2NHG0wyn4RBv6qiKw8eVRCRv4rIdBH5XEReEJGkU52IyFwR+UJEJotIDeaVda5+igaPtWvtRvvee7a+dKm95mPwCJvrfvYZPPdc8mPCnMeUKfnXXyn8vb3zTuXHePCo2pvAnqq6N/A1cG0Vxx6mqgM0XfPqOlcPJOY8jjwSRo+29XBQy3zq/xCKdiKfNi35Mfk8YGD4ezvpJKv3SCYaPKIDKuabjAQPVX1DVcNSv0+APCu5dK5uJeY8Jk6M319YGHuKzyfRpqlhDipRPve8jtZVVVYsFwaPZ5+1Pi35Kht1HhcAr1ayT4E3RGSCiAyv6iQiMlxExovI+GVh11Pn6qnoTWjBgor7W7XKj1ESEoUdIKHyIVY2bLDvn2/NkCE+6Fc2CkAYPI4/Pv565Jsa/3pEZIyITE3yMzRyzHVACfBkJac5WFX3BY4FLhORQyr7PFV9UFUHqurA9uE44s7VU9Gb0MMPV9yfb2NbhaLTXIQV44k2bszPIiuID/pLliQ/ZtMmC/yVTcuQL2rcz0NVj6xqv4icB5wAHBEMBZzsHAuC16Ui8gIwCHivpmlyrr7Y3o3lRz/KTjrS7cADbWy5996rOBp0aMOG/J3jIvp7qyp4NG6cnznHqEy1tjoG+DVwkqom/RMRkaYi0jxcBo4GpmYiPc7lm8LCyvetXGkTheWj4mKbd37w4MqDx/LlNpV5PooGj2R1Oi+/bIEzH+tzEmWqh/k9QBHwplh4/URVLxGRzsDDqnocsBPwQrC/AfCUqr6WofQ4l1eq6ieQj010ExUXV95kdf78/B0pOlpsldiTfv16GBoU6nfunL00ZUpGgoeqJm2draoLgeOC5dlA/0x8vnP5LrGyuE2b/JsAqipNmlgOY/Ro6N8fBgywSc/uvtumTfjlL+s6hTWTrHNnaNKk2PLChdlJTyblYXsG5+q/M86webGPP97W83n01WSKi+3mevzxNvT88uVWj/P227Y/X1shRRsBrFsHF18Md95p6/k4B0tVPHg4l4PatIH334/N4VLVWEn5KFoh/umn9hotxsrXYLnPPrDffrY8axY8+CD8/OdWOR5tch1tdZavPHg4l8P6BwW7BxxQt+lIt2jwmDmz4v6ddspeWtKpWTMYP94mffz88/h9H39sr++/HwuY+cyHZHcuhw0YYGM8tW0Ljz1W16lJn2jwWLQoft9NNyVMv5uHmjePzfYYmjfPXr/3PZsWO995zsO5HLf33tChA+yyi83pUR9U1lS1cWP4zW/ys3d5VLKhY2bOtAr1+hA4wHMezuWFhg2tDL2+qKwOp337/O88B8mDx/LlNvtjfZHn8d05V5/Uh85zUPmglfk4mGVlPHg457Lu/PPhyittjvKoqnrW5xMPHs45lwFNm1r/jj59bD2sQM/3uo5QGCT69YvfHp2bPt/Vk1+Vcy4fTZ9ur7vvbq+DB9ddWtKpZUt7TRyjy3MezjmXBmefba8vvQR33QW/+13dpiddLrgAjjkGrr46fnt07Kt858HDOVdn/vxnmymxc2cYMQK61pM5R9u1g1dfhX33reuUZI4HD+dcnSkoqF9FOYkSJ7WqbF7zfOTBwznnMqRlS7j0UmtZBh48nHPOVYMI/OMfcPjhdZ2S9PPg4ZxzGRYWX+2zT92mI518eBLnnMuwww+H//0PfvCDuk5J+njwcM65DBOB446r61SkV8aKrUTk9yKyQEQmBz9JL52IHCMiM0Rkpohck6n0OOecS59M5zzuUNXbKtspIoXAvcBRwHxgnIi8rKpfZThdzjnnaqGuK8wHATNVdbaqbgWeBobWcZqcc85tR6aDx+Ui8rmIPCIirZPs7wJ8F1mfH2yrQESGi8h4ERm/bNmyTKTVOedcNdUqeIjIGBGZmuRnKHAfsCswAFgE/K02n6WqD6rqQFUd2L59+9qcyjnnXC3Vqs5DVY+sznEi8hDwSpJdC4CdI+tdg23OOedyWCZbW3WKrJ4CTE1y2Digt4j0FJFGwJnAy5lKk3POufTIZGurW0VkAKDAXOBiABHpDDysqsepaomIXA68DhQCj6jqlxlMk3POuTTIWPBQ1XMq2b4QOC6yPhoYnal0OOecS7+6bqrrnHMuD3nwcM45lzIPHs4551LmwcM551zKPHg455xLmQcP55xzKfPg4ZxzLmUePJxzzqXMg4dzzrmUefBwzjmXMg8ezjnnUubBwznnXMo8eDjnnEuZBw/nnHMp8+DhnHMuZR48nHPOpcyDh3POuZR58HDOOZeyjExDKyL/BvoGq62A1ao6IMlxc4F1QClQoqoDM5Ee55xz6ZWR4KGqZ4TLIvI3YE0Vhx+mqsszkQ7nnHOZkZHgERIRAU4HDs/k5zjnnMuuTNd5fB9YoqrfVLJfgTdEZIKIDM9wWpxzzqVJjXMeIjIG6Jhk13Wq+lKwfBYwqorTHKyqC0SkA/CmiExX1fcq+bzhwHCAbt261TTZzjnn0kBUNTMnFmkALAD2U9X51Tj+98B6Vb1te8cOHDhQx48fX/tEOufcDkREJqSrYVImi62OBKZXFjhEpKmINA+XgaOBqRlMj3POuTTJZPA4k4QiKxHpLCKjg9WdgA9EZArwGfA/VX0tg+lxzjmXJhlrbaWq5yXZthA4LlieDfTP1Oc755zLHO9h7pxzLmUePJxzzqXMg4dzzrmUefBwzjmXMg8ezjnnUubBwznnXMo8eDjnnEuZBw/nnHMp8+DhnHMuZR48nHPOpcyDh3POuZR58HDOOZcyDx7OOedS5sHDOedcyjx4OOecS5kHD+eccynz4OGccy5lHjycc86lzIOHc865lNUqeIjID0XkSxEpE5GBCfuuFZGZIjJDRH5Qyft7isinwXH/FpFGtUmPc8657KhtzmMqcCrwXnSjiOwOnAnsARwD/ENECpO8/xbgDlXtBawCLqxlepxzzmVBrYKHqk5T1RlJdg0FnlbVLao6B5gJDIoeICICHA48G2x6DDi5NulxzjmXHQ0ydN4uwCeR9fnBtqi2wGpVLanimHIiMhwYHqyuF5FkQSsd2gHLM3Tu2vB0pcbTlRpPV2ryNV3d0/VB2w0eIjIG6Jhk13Wq+lK6ErI9qvog8GCmP0dExqvqwO0fmV2ertR4ulLj6UqNp6sawUNVj6zBeRcAO0fWuwbbolYArUSkQZD7SHaMc865HJSpprovA2eKSJGI9AR6A59FD1BVBd4BhgWbzgWylpNxzjlXc7VtqnuKiMwHBgP/E5HXAVT1S+A/wFfAa8BlqloavGe0iHQOTnE18EsRmYnVgfxfbdKTJhkvGqshT1dqPF2p8XSlZodPl1gGwDnnnKs+72HunHMuZR48nHPOpU5V8/oHa9X1Dla/8iVwZRXH7g+UAMMi20qBycHPy5HtRwATg+0fAL2C7UXAv7GOj58CPSLvuTbYPgM4O8vp+mXwWZ8DbwHdKznXG1lO13nAssh7Loq851zgm+DnF1lO1x2R47/G+hxl83odHqRrKtZBtkGwXYC7gr+jz4F9s3y9KkvX2UF6vgA+AvpH3jM32D4ZmJLldB0KrIm854bIe47B/hdnAn/JcrpGRo6fGry/TQauVzfsb3RacI4ewfae2P1pJna/apTi/esH2733bu+AXP8BOhH8gwHNsRvB7kmOKwTeBkYnXPz1lZz3a2C3YPlnwKOR5fuD5TOBfwfLuwd/CEXBL24uMDCL6ToMKA6WLw3TlXiuOrhe5wH3JDm+DTA7eG0NfAsMyVa6Eo4ZATySreuF5fi/A/oE638ELgyWjwNexYLIAcCn2bpe20nXgUDrYPnYMF3B+lygXR1dr0OBVyo5/yxgF6ARdmM+LVvpSjjuRODtDF2vd4GjguVmxO4B/wHODJbvBy6N/A9U5/41CyhM9j8V/uR9sZWqLlLVicHyOiwCJ+upPgJ4Dlha3VMDLYLllsDCYHko9oQBNrTKEcFQK4lDsswAGmYrXar6jqpuDLZ/gvWbqfjm7F+vyvwAeFNVV6rqKqxVXuc6StdZwKikb87M9WoLbFXVr4P1N4HTguWhwL/UfIL1hepEdq5XpelS1Y+Cz4Xs/31Vdb0qMwiYqaqzVXUr8ATQp47SlZG/r2AMwQaq+mbw/vWqunE7Qz9V9/5VYUipRHkfPKJEpAewD5Ydi27vApwC3JfkbY1FZLyIfCIi0bG1LgJGB02RzwFuDrZ3wZ42UOvcuAb7IyrfHigfbiVL6Yq6EHt6rfJcWUzXaSLyuYg8KyJh59GcuF4i0h170np7e+dKY7qWAw0iI1EPI9aptrLrko3rVVW6ohL/vhR4Q0QmBMMIhZ+frXQNFpEpIvKqiOwRbMuJ6yUixVjx2XORzem6Xn2A1SLyvIhMEpG/BgPQVjX0U8r3r8pkamyrrBORZtgv6OequjZh99+Bq1W1zIJsnO6qukBEdgHeFpEvVHUWVqZ8nKp+KiIjgduxG1FOp0tEfgwMBIZUdS5gSZbS9V9glKpuEZGLsaeew3PlemFZ92c16IeUreslImcCd4hIEVZmXZr4xupI9/XaXrpE5DAseBwc2XxwcK4OwJsiMh2rB8hGuiYG71kvIscBL2KdknPiemFFVh+q6soMXK8GwPexgDMPq8s4j2x1tq6qTCtffrDiodeBX1ayfw5WzjgXWI9l/U5Octyj2NNDe2BWZHs34Ktg+XVgcLDcAHv6EKyy6drIe17H/sGykq5g/Ugs29uhimv1KHBGNtMV2V4IrAmWzwIeiOx7APhxttMFTAIOzOb1SrL9aOA/ketwVmTfDKxcPOPXq6p0Bet7Y2Xhfaq4Xr8Hfp3NdCXsm4sNDjgYeD2y/VrgumynC3gB+FEmrhdWJzY2ctw5wL3Y/Wg5scr78mtBavevwZWlW1XzP3gEX/xfwN+reXz5Lx6reCwKltthrVh2j1zUsCLsQuC5YPky4iucwn/6PYivcJoNPJ7FdO2D/WP3TjhvsnO9lMV0dYqc6xTgk2C5TfBP0Tr4mQM8na10Bev9sH9Iyeb1CtY7BK9FWOu4w4P144mvMP8sW9drO+nqhpWDH5hw3qZA88jyR8CYLKarY/j7w8ro5wXXrgH2P9gTqzCfks3fY7CtJbASaJqh61UYfK/2wfo/sdE8AJ4hvsL8Z8FyKvevKivM60Ox1UFYxP1CRCYH236D/bGjqvdX8d7dgAdEpAyr/7lZVb8CEJGfAs8F+1YBFwTv+T/gcbEhVVZivwBU9UsRCYdkKcGeAG7LYrr+irW2eCbI2s5T1ZOSnOtZ4JospusKETkpuCYrsWw1qrpSRG4ExgXHjcKefrKVLrDf3dMa/PdUcq6MXC9gpIicEGy/T1XDOpfRWIurmcBG4PxsXq8q0nUDVjb+j+Dvq0Rt9NadgBeCbQ2AD4GfAh2ylK5hwKUiUgJswm6YCpSIyOXYE3QhdmP/aRavF9jD0huquiGyLW3XS1VLReQq4C2xE04AHgp2Xw08LSI3YbnrcOin6t6/yoeUqowPT+Kccy5l9aq1lXPOuezw4OGccy5lHjycc86lzIOHc865lHnwcM65HCcij4jIUhGZWs3jTxeRr0TkSxF5KiNp8tZWzjmX20TkEKyD4L9Udc/tHNsbGxjxcFVdJSIdVLW6Y8FVm+c8nHMux6nqe1i/jHIisquIvBaMkfW+iPQLdv0UuFeDgSwzETjAg4dzzuWrB4ERqrofcBXwj2B7H6CPiHwoNpDjMZn48PrQw9w553YowQCPBxIbUQJsaBGw+3pvbK6TrsB7IrKXqq5OZxo8eDjnXP4pwIZdH5Bk33xssq5twBwR+RoLJuOSHFurBDjnnMsjasO2zxGRHwKI6R/sfhHLdSAi7bBirNnpToMHD+ecy3EiMgr4GOgrIvNF5EJsXvkLRWQKNs3u0ODw14EVIvIVNj/6SFVdkfY0eVNd55xzqfKch3POuZR58HDOOZcyDx7OOedS5sHDOedcyjx4OOecS5kHD+eccynz4OGccy5l/w+iLmbwE6YaRAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(times.jd,d_ra-53.*u.mas,'b-',times.jd,d_dec,'r-')\n",
"plt.ylim([-10,10])\n",
"# plt.xlim([2459000,2459120])"
]
},
{
"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": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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