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megbedell/barycentric.ipynb

Created January 14, 2019 04:55
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barycentric.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
}
],
"source": [
"import wobble\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get the Gaia coordinates:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created TAP+ (v1.0.1) - Connection:\n",
"\tHost: gea.esac.esa.int\n",
"\tUse HTTPS: False\n",
"\tPort: 80\n",
"\tSSL Port: 443\n",
"Downloading http://naif.jpl.nasa.gov/pub/naif/generic_kernels/spk/planets/de430.bsp [Done]\n"
]
}
],
"source": [
"from astropy.time import Time\n",
"from astroquery.gaia import Gaia\n",
"import astropy.units as u\n",
"from astropy import coordinates\n",
"coordinates.solar_system_ephemeris.set('jpl')\n",
"from astropy.coordinates import SkyCoord, EarthLocation"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: W35: None:5:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
"WARNING: W35: None:6:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
"WARNING: W35: None:7:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
"WARNING: W35: None:8:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
"WARNING: W35: None:10:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n",
"WARNING: W27: None:11:0: W27: COOSYS deprecated in VOTable 1.2 [astropy.io.votable.tree]\n",
"WARNING: W06: None:48:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n",
"WARNING: W50: None:51:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n",
"WARNING: W50: None:54:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n",
"WARNING: W50: None:57:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n",
"WARNING: W50: None:60:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n",
"WARNING: W50: None:123:0: W50: Invalid unit string 'mas**-2' [astropy.io.votable.tree]\n",
"WARNING: W50: None:126:0: W50: Invalid unit string 'um**-1' [astropy.io.votable.tree]\n",
"WARNING: W06: None:129:0: W06: Invalid UCD 'em.wavenumber;stat.error': Primary word 'stat.error' is not valid as a secondary word [astropy.io.votable.tree]\n",
"WARNING: W50: None:129:0: W50: Invalid unit string 'um**-1' [astropy.io.votable.tree]\n",
"WARNING: W06: None:141:0: W06: Invalid UCD 'pos.errorEllipse;stat.max': Secondary word 'pos.errorEllipse' is not valid as a primary word [astropy.io.votable.tree]\n",
"WARNING: W50: None:156:0: W50: Invalid unit string ''electron'.s**-1' [astropy.io.votable.tree]\n",
"WARNING: W50: None:159:0: W50: Invalid unit string ''electron'.s**-1' [astropy.io.votable.tree]\n",
"WARNING: W06: None:162:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n",
"WARNING: W50: None:171:0: W50: Invalid unit string ''electron'.s**-1' (suppressing further warnings of this type...) [astropy.io.votable.tree]\n",
"WARNING: W06: None:177:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n",
"WARNING: W06: None:192:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n",
"WARNING: W06: None:204:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n",
"WARNING: W06: None:207:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n",
"WARNING: W06: None:210:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n",
"WARNING: W06: None:282:0: W06: Invalid UCD 'phys.size.radius;stat.error': Primary word 'stat.error' is not valid as a secondary word (suppressing further warnings of this type...) [astropy.io.votable.tree]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Query finished.\n"
]
}
],
"source": [
"coord = SkyCoord(ra=269.4486, dec=4.7379807, unit=(u.degree, u.degree), frame='icrs')\n",
"width = u.Quantity(0.01, u.degree)\n",
"height = u.Quantity(0.01, u.degree)\n",
"r = Gaia.query_object_async(coordinate=coord, width=width, height=height);"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"star = r[r['source_id'] == 4472832130942575872]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"sc = SkyCoord(ra=star['ra'][0]*u.deg, dec=star['dec'][0]*u.deg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Try a naive BERV calculation neglecting proper motion effects:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"loc = EarthLocation.of_site('lasilla')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"data = wobble.Data('barnards_e2ds.hdf5', filepath='/Users/mbedell/python/wobble/data/', orders=[60])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"dates = Time(data.dates, format='jd')\n",
"pipeline_bervs = data.bervs * u.m / u.s"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"naive_bervs = sc.radial_velocity_correction(obstime=dates, location=loc)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot_date(dates.plot_date, naive_bervs - pipeline_bervs, 'r.')\n",
"plt.ylabel('Resids w.r.t \\n HARPS pipeline BERVs (m/s)', fontsize=12);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Try it using coordinates calculated with Gaia proper motions:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"sc = SkyCoord(ra=star['ra'][0]*u.deg, dec=star['dec'][0]*u.deg,\n",
" pm_ra_cosdec=star['pmra'][0]*u.mas/u.year,\n",
" pm_dec=star['pmdec'][0]*u.mas/u.year,\n",
" obstime=Time(2015.5, format='decimalyear'))\n",
"#bervs = sc.radial_velocity_correction(obstime=dates, location=loc)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def calc_new_coords(sc, date):\n",
" new_sc = SkyCoord(ra=sc.ra + sc.pm_ra_cosdec / np.cos(sc.dec.radian) * (date - sc.obstime),\n",
" dec=sc.dec + sc.pm_dec * (date - sc.obstime),\n",
" obstime=date)\n",
" return new_sc"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<Longitude 269.44861436 deg>, <Latitude 4.73798077 deg>)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sc.ra, sc.dec"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: ErfaWarning: ERFA function \"starpm\" yielded 1 of \"binary logical OR of the above warnings\" [astropy._erfa.core]\n"
]
},
{
"data": {
"text/plain": [
"(<Longitude 269.44861436 deg>, <Latitude 4.73798077 deg>)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_sc = sc.apply_space_motion(new_obstime=Time(2005., format='decimalyear'))\n",
"new_sc.ra, new_sc.dec"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<Longitude 269.45096351 deg>, <Latitude 4.70776161 deg>)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_sc = calc_new_coords(sc, Time(2005., format='decimalyear'))\n",
"new_sc.ra, new_sc.dec"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"scs = [calc_new_coords(sc, Time(d, format='jd')) for d in dates]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"bervs = [s.radial_velocity_correction(location=loc).value for s in scs]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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wMfAV4CDgZuC6CoEVcfX2wvz5MHMmnHUW3HCD0vY0G0W1STtIaPpIXQ2Qu580zMu7uPtd+f1uBTa7+6b89jPADvWU3QCfBX7g7t9x9//n7qcBTwB/39BSVq4svZXt6xt63/7xgjVrwk81Ps1HUW3SDnp64NZbw/SRW2+N1hNTVQNkZqeb2QF1ltU0neVmNpawYN6vy176NXBEQwtLKJ5eElJ8lzp/fuX/mBozkla0bFnphfOyZVGKqfbb8BDgc2a2I2Fc54b84w53HyqjwQ75lVD77Vi0bQzffRfbzoTw8SfLnn+SCl2D06dPH/DcwoUL6enpGfyA8i+crVurraM0o1qj2pQNW2RIVTVA7j4XwMz+FHh3/tG/mNzkIQ77VNl2eXLS71ZVwyazfv16pk2bVv0B5Xc8Y2qOaJdWNtiYkRogaXZz58LixYV5QHPnRimm6v6g/HIK7wY6gXcCvyfcBQ3K3ZfWW7mInga2AbuWPb8rsLGhJSU0o1ialJLRSivq6YEbbywk0Y0U7VntGNCTwC+BfYBlwAx3P8zdvxClVpG5+2vA7cAxZS8dQ4iGa5w5c0oHrY+uEPynMYNsqXXMSKQZJJTDsto7oF8ARwJ/BewEvMHMbnD39VFqlYwLgSvM7BbgJuAUYBphnlLj1JqKRWMG2dM/TDrkcKlIkxksG3aE76Fqx4BOAjCzXYGjCF1xl5nZ0xXWBGpa7v5jM/sT4GxgN+A+4C+ipBCq5QtIYwbZogsKaUUJRe/WMgZ0EKHhmU24G9oM3BKlVgnJT4q9LGohtX4BacwgW5YvL72guP56NUDS/BIau652DOhZ4GfAwYTuuMPcfbq7f7SKY8flc8A9ZGbP55871sxOrafiLaPWmfMaM8iW9WW91Bs2pFMPkVrMnl24Cxo9OvVs2Ae5+7oRlnERMB34GHBd/rn7889/c4Tv2TpqvaNR9uRsef750u3nnkunHiK1qjaLfx2qHQNaV0cZfw3s6+6bzWx7/v3Wm9nAGZ1ZpPVg2tv++8Pq1YXtA+pNKCKSgFpSiNUhibwwr5WXY2ZTgGcSKDt9uqNpb3194cLj7rtD46PkpdIKmmxJ7nr8G7DUzPYGMLPdCF1vVydQtkh6crlwB7xtG5x+eshuPn582rUSqazJluSuxwLga8C9hAzYDwDfAXoTKFskPQrBllaUy4U7ILMwdWT8+MoT6Eeo2ii42cV3MGa21My+b2ZTKx3r7q+5+3x3n0RIdbNjfvvV+qou0uS0dpC0ou7u0GXsHhqhgw9OdzkGwlyZ/nuyxcAYYDtweTUHm9nrzewwYD9gtpl1mVmcuD6RZqG1g6QVFWdBcC8kJI2g2ned7u6PmlkHYXXTvQjBBRUnNZjZicClwEvAy0UvOSG3nEg2KQJSWlGCa5hV+84v5NPwzAB+6+4v5Rd1q2ZtgfOBD7r7dRX3FMkSRUBKK0owg3+1XXDfAG4Ffki4m4GwJMPvqji2g4Erj4qISDNKKAsCVNkAufvXCCuFvtPd+8On1wOfruLwrwFnm1kSId8iIlKvBLIgQA1h2O7+++G2hzEfmAqcYWYlk0/dfc9qyxcRkQQklAUBhmmAzOwxQqDAsKpoRE6otVIiIpKSJglCKG44DgXmAZcAjxCi4E4lrI46LHcfctluERFpMgkGIQzZABU3HGZ2KfCe4hVQzew64L8I84JKmNmX3P38/O9DZjxw9+4R1ltERGKYMwfuvbeQwT9SFgSofgxoGmEeT7GXCMssDGb3ot/3qLVSIiKSkgTnr5lXsUy0mf0A2Bs4D3ic0KicBTzq7vOi1a6JmNk0YP369euZNm1a2tWRfrlcSB3S1xfCRXt7o/ZZi0j1NmzYwPTp0yEkMxiQuKDa/6mnAD3Atwl3Q08APwEGbRrNrKoMB+7+UJXliwxOCT9FWla184Becfcz3f2N7j7B3ffJb28Z4pAHCVmvHxzm8UD91Ze2l1bCz1wOFiyAww8PPyMO1IokKsHP9nBh2Ee5+43534ecCuvuA4LE3V2TTiUZtS553ii685KsSvCzPeQYkJnd5+4z8r8/PMTx7u5VdbeZ2R6EfsA1I6ppyjQG1KRyOVi4sHTANPYYUC4He+4JTzxReG7mTFjTkh9tkVIzZ8IttxS2DzsM1q4d0VuNeAyov/HJ/773iEoHzGxP4EfAgYSJrZPM7IPAce5eTSofkaGlkfCzuxueeqqwPWpUyJ+VRQryaB/9f+v77y99vgmyYZcws9nAtv4uugr+BfglcCTQn4rnegaZPyTSEvr6Sifrbd+eXl1iU1dj++juhiVLQnd2sbSzYZvZDWb2zvzvXwSuBn5kZguqOPww4Kvuvp18ah93fx54/ciqLJKyrq6BSRpXrkynLrFpVdf20dc3sPEB6OyMVmS1wQIzgP4O7pOA2cDhhPDsSp4E9i1+wszeBjxaZdkizaW3N/ST9xs9OrtdcJ2dpV0wZor4y6rBLqwAfvObaEVW2wCNAtzM3kgIXPituz8G7FTFsRcA/2lmnwA6zOwjwI8JyzSItK6EUtanrri78dZbQ9CHZE9vbwi9LnfPPdGKrLYB+m/gm4TG5GcA+cbo6UoHuvv3gC8AHwIeIyQ1PcfdfziSCoukrrs7RAX1R5Dmctntglu1qnCeEBojdcNlU0dH6IYbU7bQ9eTJ8Yqscr8Tgc8Bm4Cv5597C3BxNQe7+8+Bn9daOZGmtGJF6ZcyJDf/KGmdnQNDcLPa3SjhLmjr1tLnIk47qaoBcvdngAVlz/2y2kLM7JPARwhpfDYQghi+59UkopNsyUJYb/l6KZMmwTnnpFOXJIwaVYj0y3p3Y7srX3zODI45Jlpx1UbBjTOz883sITN7Pv/csWZ2ahXH/jPwReAaQlfcNcDn0RhQe+oP6127NoR8tuJ4Qvkg/EsvteZ5VGPlytIwc/fsdjdKuCgcPz78bgazZkXNhl3tGNBFhEi4j1FYJfV+4O+rOPZEYI67f8vdr3X3bwHHAp+osa6SBVkI6x3sju3SS5OvRxJGDfIVoS647Orthc9+NkR5nnUW3HBD1B6KahugvwY+6u6rge0A+cXphloPqNiL+Uf5cy9UW0nJkK6ukLMNks3d1kjlK0YCbN5cup2VZKXr1g18rlXPRSrrzyyyZk34Gbl7vNp3f618XzObQiGzwXCWANeY2VcprCX0BeCi4mUbtDRDm0hwsato5swpzZUFA8dGspxB4Nvfhq9/vfJ+IhVUuyDdBYTJpPOB24G3ExqWB939SxWOrSZPibv76Mq7pUfJSKuUhSCDSnI52GMP2Lix8Fx5MtLDDy+NHmvVZKWzZg2sd0fHwEgpkUFUSkZabRfcAuBh4F5gMmEtnw1Ab6UD3X1UFY+mbnxaVhrdQFkIMqikowM+9rHSu54jjyzdJwtdjTB4d+MuuyRfD8mkaheke83d57v7JGBXYMf89qtxqyd1SaMxyEKQQTWuuqp0LtBVV5W+3tsL8+eHO5/581uzqxEGhpxDtpOvtruEL1pr7htx900AZrYf0O3uHyrfx8z+y92Py//+GwqRc+XvdVSt5UsNBmsMYo9DpLVAXLNJY5mIGB55ZOBzz1Qz9CstKeGxy2EbIDPbATiLsJbPA0APsDNhKYVjgKVDHLqs6Pfv1l1LGZk0GoMsBBlUY+5cWLw4XCF2dITtdqHxn+xK+KK10h3QpcBBwK+APwf2I6TgWQqc5O6D5oJz96uKfh+qkZJiMQbv02gMsnLlX8l554Xuqaw3tHvtVbryq2RbZyfcdlsY+0sgy3ulb7j3AAe6+1Nm9g3CEgrvdvea8nMrFU8VYtz6tktjkIZK/7ZZiQbUeE/7STDLe6UghEnu/hSAuz8OvDSCxqc8Fc9PSTEVj5mtMjMve1ydRl1KtMvgfbvISjTgu9+ddg0kSStXFgIPcrmBueEarNIlWUd++e0/NoXl2+5eqYYnAgfnG7D+9/glcAdwRq0VbpDvU5pcdZBlABOmwftsSSMAJIaIi5FJEypPvTRYFGQDVWqAngK+V7T9TNm2A/swvGZMxfOyu2+svFuCsjJ4n5Wup3pl5YJisMXIlBE7u8qjHgdLxdRAVWVCqKsAs9OAvwLKU/H8HLi2f7+kUvGY2SpCYlUIy4VfB3zZ3csbyfLjpgHrB3tt4cKF9PT0NLCWLWzBgsJY1oQJYQ5MK1751yuXC91uxRcUrdgQT5o0MM8dDFwPSbJh2rTSoJPddoMNAxIYVK1SJoQkGqCmSsVjZicDjxCCId4OLAIecPdjKxwXNxVPVu4cspKCRoKOjsGzIagByqby1EuzZsHNN4/47So1QNG/4dy92nQ/I2Zm5wHD5qQDZrv7Kne/vOi5e83sIWCtmR3s7nfEq2UFWUlemZWuJwkUBddeyv/eg118NFALXmIPaglwZYV9Hh3i+duAbcCfEQIj0pGVQeusjGVJMHq0ll9oF7lc+HubhTvcCRPg6KOjFpmJBig/IXbQSbFV2A8YDaQ72y4rdw6ae5Qt48apAWoX3d1w112h8TGDgw6KfgGZiQaoWmb2RsKqrtcSGqy3EdIK3QnclFrFcrlw67vTTjB5MsybpzsHaQ5b0p+hIAnp6yv8vd1D91vkcejo4zNN5jVgDiG10P8AlwC/Bo5297idncPp7oZvfCNEmzz3XIjFb8UABMmewZbklmxKYQmRRL/lzOzNhLuOO919XZJlA7j7Y0DzTe1esaJ0/Gf5cnVhtYKsRC6KQCrjt9H+t5jZhcAd7n5lfnsuYRLrs8AkM/uAu18Xq/yWUj7bWF9ireHsswsZsW+7LXRbLFqUdq0aS+HW7aOjIzQ67qERco9+URXz/vqvgBuLtr8CnO7uU4BTgBZNjhVBeahjKw/6prEKa1qWLSuc37ZtYRuy9W8wblzaNZAkJZzDMGYDtLO7PwpgZjOAPwH+Nf/alcCbIpbdWubMKe17jRz6GFVWknDWo7s7nPvatfDVr8JRR7VuI6QghPaScFLkmA3Q82a2a/73I4HbipbwHkNRQtO2l5Xlm6G9snrPnVvonihelK48mmjNmtZtiNUF114SDkSIOdjwE+BqM/sZ8DlCLrh+M4H/jVh28xluwDpLc2eyMp+pGkMtStfVBbfcUvjy7u9Tz8LfV7It4UCEmA3QmYQlD44BLge+XfTagcC/RCy7+WQl1U4l7ZQJYagLh95eWLUq3Pn0zyjPckMs2VB+kZxAAt0kkpG+wd3/ELWQBNSdjFRJOttLVrJhD7X0grrmsidCJvtKyUijjQGZ2eFmth7YZGbrzOygWGW1hBQmeUmK+u+O1qwJP1ux8ZH2snx56fjt9ddHLzLm/4oLCNFuS4FPAF8HWji8q07t1DUlIq1nfdlyZ3WsA1StaF1wZvYHYIq7bzOzccAj7j41SmEJiL4ekLSeoQJLspQhQV1w7aN88cGJE+Gll+p6yzTXA+roz6/m7q+a2diIZYkkb6hMCFkKODnttJCnULJvxozScer99oteZMx5QOPNbFn/A5hYvJ1/TqR1LV1amglh6dLwe5bmQl14Ydo1kAyLeQdUfsn3lYhliSTvxRcH387SXKiODthlF3jqqcJzu+469P7Suu67r3T73nujFxmtAXJ3jbJLtr3udaV95jvuGH5mLeBkjz1KG6Ddd0+vLhLPfvuVTg3Zf//oRaay2IeZHW9mt6RRtkjDlKfimTev8HuWQrCfKFsseOPGdOohcc2aNfx2BDHnAU01syvM7F4zu8bMds/PDbqDkBXh32OVLZKI886DM84Ik4rPOAPOPTc8n6Vs2FB69zPYtrS+XA6++c3S5y6/PHqxMS/NLgUmAZcBHwB+AexEGBta6u5bI5YtEt9QqXiyFAUHMHZsaSM6VgGtmdPdDVvLvpJffXXwfRsoZhfckcCH3P1bwIcJ+d/e5+7fVeMjmZalKDgI4bnDbUvr6+sb+Nwhh0QvNmoYtru/AODuzwDPu/v9EcsTaQ5ZS7v02GOl248+mk49JJ6urtLFB6dPH7xRarCYXXBjzOwTFNb9GWNmnyzewd2/F7F8kXRkLQru2WdLt597Lp16SDy9vWEi9dKl4bN7wgmJBM/ETMWzChjuzd3du6IUHkG0VDxZStvSjPTvW7/B0vEoFU+25HJh5d61a2FVlr4cAAAL6klEQVT79sSyYcecB9QZ670zJWsD1s0m6/++amClEbq7C+tXQWHsMvL/lcTnAZnZG8zsHzUPKC9rA9bNJuv/vv0N7Nq1sGRJ6y79Lenq6yu9qzVLZOwykQbIzDrM7C/N7BpgA/BZ4N+SKLvpZW3Autkk/e+b9BygJBrY8tQ7U1s2qb0MpasLxo8Pv5uFSagJjF1GvVc3s3cA84CP5Mu6BngFmOXums0G2RuwbjZJ//sm3eWXRN65Bx+Et7wlZETYbTf43e8aX4akq7s7LCN/zz0hBc+KFS0fhHAfsA9wLXAV8Mv8sgxPAAe0WgOk9YCkKrGXXi8f8+nuDhkYWn3pb0lXhOW4Id31gHYAtgFbgJeB1yKWJdIcYt+RDHWHlaXACkneihWlXbnLlyfymYoZBbePmR1F6IL7MfCKmf0EGM/w4dkirSt2l1/5mM/3vx/KUgSc1GP06NLthD5HUUtx9xuBG83sVOBvgLnAjsAqM7vU3S+LWb5I4obKD9coxXdYo0fDpk1hbCaLIeaSnG3bSrcTSqCbSBScu29x9yvd/VhgL+BK4LQkyhbJlN7e0D8/c2aITuv/oshiiLkkZ/bs0qVFupLJEZD4PCB3X+/ui9z9rUmXLdLyitcamjdPIfzS0qJ1wZnZFVQY63H3ubHKF8k8hfBLo6xaVbibzuVg5cpEio05BvRg2fYXga9FLE+kvcQeb5L28a53lU4fOPLIRIqNNg9oQEFmz7r7TokUFoHmAbUg5UkTqSyXg8mTYfPmwnPTp8Pjj9f91mnOAyqn0GtJVtYTkYo0Qnd3aeMDIbIyAYkHIYgkJuuJSEUaYbCF53bbLZGiozVAZrZP8QMYZWZ7lz0nEk9nZ2lo6ezZqVZHpCl1dcGosqbgIx9JpOiYd0APAg/kfz4IvA7436LtByKWLZJduRyceSZMmxYeZ56Z2MRByaDu7kI4f7/f/CaRomOm4lH3nqQrpdDS6Lq7YfHiwrktXhyyImh8S0aitxdefbWw3dGR2JwyNRJpSnrtmHaT1bWW+vpKPyu5nMa3ZOTKP0+77JLYnLLMxaSa2cmE9YcOAl4P7O3u68r22Qm4BHh//qlfAKe5+3MJVlVRWrFldaJmVxfcfnvhSyPBK1bJoOL8gmaw116JFZ3YPKCkmNk/ARMIy0BcxOAN0HXAnsCn8099F3jI3Y8f5n0bPw8o9toxkk25HJxzDixbFrbnzg1rAmmOk4xELgdHHRW+e9zDyqif/Wwi6wFlrgvO3Ze4+yLgvwd73czeChwHnOzuq919NfAZ4H1m9uYEq5rdLqJ2kkY3akcHLFoE69eHx6JFanxk5Do6YPv20PgAvPJKYl267fipnQW8BNxc9NxNwGbgCOB/EqtJVruI2kl5N6p7+JsmkX1BmR6kUZJY2n0Q7fhpnQps8qK+R3d3M3sq/9qw8reTJRYuXEhPT0/tNVEur9ZXPtl16VJ4/vm443r9Dc8PfhDWA8rlNIYo9UnpYrgluuDM7Dwz8wqPziTqsn79ety95DGixkeyobwb1Sx+9oXubliyJKRL0XpA0gjFy3ycf342VkRtoCWEReyG82iV77URmGJm1n8XZGYG7JJ/TaR65VeO27bBJZfE7cro6wvvX0yRcNKCWqIBcvengacb9HargUmEsaD+caBZwERKx4VEKivvRs3lwqTQmF0ZXV2l0ZMQGkGNIUqLyWIY9lTCWM7bgB8C7wU2AI+6+x/y+1wH7A6cnD/scmBd4mHYIiORy8HYsYWoJYAxY+C119Krk8gg2i4MGzgFuJPQ+AD8Mr/9/qJ9PgrcDfwq/7gb+HiCdRQZuY6Ogbm7xo5Npy7S+lLMyNISXXC1cPceoKfCPs8CJyRRH5EoyseA+gMfRGp19tmF3IK33RburBctSqToLN4BiWSf2fDbItVaurRw17NtW9hOiBogkVZ06KGl24cdlk49pPU980zp9tONiveqTA2QSCtatQqOOAImTgw/s7LUhCQvxUC0zI0BibSF8ePhppvSroVkwSGHlCZBPuSQxIrWHZCISDtbubL0brqvL7GidQckItLOUryb1h1Qg7RLPjidZ7boPLOl1c4zc5kQYqmUCcHMaId/S51ntug8s6XZzrMdMyGIiEgLUAMkIiKpUAMkIiKpUBRc9UYBbNw49JJBGzYM6OLMJJ1ntug8s6WZzrPo+3LQmx0FIVTJzA4C7ki7HiIiLehgd7+z/Ek1QFUyszHADGATsD3l6oiItIJRwBTgPnffWv6iGiAREUmFghBERCQVaoBERCQVaoBERCQVaoBERCQVaoBERCQVaoDyzOwsM7vVzF4ws01m9h9mNqNsHzOzHjPbYGZbzGyVmb29bJ+dzOwKM3s+/7jCzCYXvd5jZj7EY5esnGd+n781s7vM7GUze8TMvhD7/IrKbtR5fsnMbjKzzWY2aMiomV1sZreZ2Stmti7iaQ1WdiLnaWZTzOxX+fd41cweM7NLzez1sc8xX36Sf8/B/m+eEvP8ispO6u954jDfQ4eW7x+LGqCCTuAy4AigC8gBy83sDUX7nAF8DjgNOBR4CrjezHYs2ucq4GDguPzjYOCKotcvAHYre9wArHL3pxp+VgN1ksB5mtmf5/e5nDB/6h+A+WZ2apSzGqiTxpznOOAaYMkwZY0ClgLLGlX5GnSSzHluB34GHA+8CTgRmAN8p0HnUUknyf09AU6i9P/o0vpPoSqdJHOeP2bg99CVwEPAbQ06l8rcXY9BHsAkYBtwfH7bgCeALxXtMwF4EfhMfvutgAPvLNrnXfnn3jxEOXvky/lols6T0Pj8rKys04DHyM8/a/bzLDv+g+G/y7BlfB5Y12qf21rPs2jf04Ensnae+c/xB9P8Oyb99wR2AJ4DFiR5froDGtqOhCvbZ/PbewNTgV/37+DuW4AbCVcrALOAl4Cbi97nJmBz0T7lPpUv46eNqniNYp3nOOCVsrK2ALsDezWu+lUbyXm2okTO08L6WB8g3L2nIfZ5XmxmT+e7w04xs7S+K5P63P4tMBH4Xh3vUTM1QEO7GLgLWJ3fnpr/+WTZfk8WvTYV2OT5SwrIX3qEW+SpZcdhZqOBTwJXuPurjat6TWKd56+AvzSzY81slJm9idBtAOF2P2kjOc9WFPU8zexHZvYysJ5w1f2JEdazXjHPsxv4O+Bo4GpgMbBgZNWsW1Kf25OB/3T3obMtR6Bs2IMwswsJXUrvcvdtEYs6jtAFl1Q/eonI5/kd4I3Az4ExwAuE/0w9JJxLL8G/Z6oSOs/5wJcJ40CLCGMMn4lU1qBin6e7n1u0eVf+QvFLwHmNLms4SX1u8wEMs4D3xipjKLoDKmNmFwEfAbrc/aGil/qvDHYtO2TXotc2AlPMzIrez4BdivYpdjJws7v/thF1r0Xs8/Tgi4Q+7L0IV2e35HcvLi+qOs+zZSR1nu6+0d1/5+6/IDQ8J5vZHiOp80ik9PdcC7zOzMrfO5qEz/Nkwtjsf43w+BFTA1TEzC6m8Ef/XdnLDxP+wMcU7T8eOJLCWMhqwhfurKLjZhH6VovHS/r70N9LCnc/SZ6nu29z9/Xu/lq+zNXuvqmBpzOkBpxnS0jxPPu/P8bV+T5VSfE8DySMZz5X5/tUJcnzzB/7ceB77p58lv+0ozya5QFcSugm6iJcrfc/JhXt80XgecLg6wxC//AGYMeifa4D7iV8Ic/K//4fg5R3dv69dsjieQI7A39PiJg7kND9tgU4rMXOc898/T9PiI46MP8ofp99889dmD++f5+xWTlP4H3AvPzxf0q4ePot4YIiM39PQpj5Sfnj3wh8Ov+eF2fpPIv2O4EQZbdnEuc34HzTKLQZH/k/0mCPnqJ9jDCG8QThiugGYEbZ++xEiKd/If+4Ephcto8RrmQuy+p5Ehqg1YRouc3AcmBmC57nD4Z4n86ifVYNsc+fZuU8CQPyqwl3AVuA3wNfA3bK0t+TMC57JyHAYjPhwur/AB1ZOs+i/W4Ark3q/2X5Q+sBiYhIKjQGJCIiqVADJCIiqVADJCIiqVADJCIiqVADJCIiqVADJCIiqVADJCIiqVADJCIiqfj/mWVcPLhBqdUAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot_date(dates.plot_date, bervs - data.bervs, 'r.')\n",
"plt.ylabel('Resids w.r.t \\n HARPS pipeline BERVs (m/s)', fontsize=12);"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x113536828>]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(data.bervs, bervs - data.bervs, 'r.')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1135416d8>]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot_date(dates.plot_date, bervs - naive_bervs.value, 'r.')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1136306d8>]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bervs, bervs - naive_bervs.value, 'r.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Try to replicate pipeline with HARPS header coords:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 306/306 [01:37<00:00, 3.15it/s]\n"
]
}
],
"source": [
"from astropy.io import fits\n",
"from tqdm import tqdm\n",
"scs = []\n",
"harps_bervs = []\n",
"for f in tqdm(data.filelist):\n",
" sp = fits.open(f)\n",
" sc = SkyCoord(ra=sp[0].header['RA'] * u.deg,\n",
" dec=sp[0].header['DEC'] * u.deg,\n",
" pm_ra_cosdec = sp[0].header['HIERARCH ESO TEL TARG PMA'] * u.arcsec / u.year,\n",
" pm_dec = sp[0].header['HIERARCH ESO TEL TARG PMD'] * u.arcsec / u.year,\n",
" obstime=Time('J2000'))\n",
" scs.append(sc)\n",
" date = Time(sp[0].header['HIERARCH ESO DRS BJD'], format='jd')\n",
" new_sc = calc_new_coords(sc, date)\n",
" harps_bervs.append(new_sc.radial_velocity_correction(location=loc).value)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist([sc.pm_dec.value for sc in scs])\n",
"plt.axvline(star['pmdec'][0], c='k')\n",
"plt.xlabel('PM Dec (arcsec/yr)', fontsize=16);"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist([sc.pm_ra_cosdec.value for sc in scs])\n",
"plt.axvline(star['pmra'][0], c='k')\n",
"plt.xlabel('PM RA (arcsec/yr)', fontsize=16);"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot_date(dates.plot_date, harps_bervs - data.bervs, 'r.')\n",
"plt.ylabel('Resids w.r.t \\n HARPS pipeline BERVs (m/s)', fontsize=12);"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11d747e48>]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot_date(dates.plot_date, np.array(harps_bervs) - np.array(bervs), 'r.')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x119e17470>]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mots1I1hWNGofcM4HgcCFC9XxArPVHbaamF5EkhHu8h3oYLJH1Q9kRankq4J27/Z3As7BkiVw3XV+6sgzZ2r3D081Ga4SAl/MfOSR6qTz6kEkkg5B9e8TT/gJoH7xC7j6avjCFzrycgoAWTIy4i/2wZ3/ddfB3r1+fVA9VCr5kcKbNzeeatK5amNyQPMJiKRDuCNI8D397nfhN34DNm2KvV1PASBLwt3DnPMX+FKp+YQy0akmx8fhkktqL/6g+QRE0mbXrur3tFzu2FzgagPIkmhKiOCiHdw17N/vfwZ3CNH6xDNnfLVPsL1UgoEB37ag3EIi6bFoUf26DowJUAkgS8J3+hs2+BLAmjXNu3wG+4+PV9sIymXfW6i/3xcp779fXUVF0mZ6un5dB0rqKgFkSfhO3wwefrg+cVyj/W+/vbYkcOGCb1zau9ePL1AvIJF02bix9jvb2wt33BF7SV0BIKvaGS7eLMXE/v2tA4iIJGNsDN773urjYPxPzKV1BYCsatYe0EijkkB4EJnyCImkS6kEL79cfTwz43v0xVxSV+VvVjXq+dPO3/T0wHPPwfnz6gUkkkZvvFH7+OzZ2FNCKBVEUZXL/sMU7ToqIukwMADHj9euazMlhFJBSGPNuo6KSDrcdlttZtAOzAWuACAikkYPPAB33um7bQNcfjls3RrrSygAFI2SwIlkQ6kETz/tu22Drw76wAdifQkFgKJpNvF8oFsBQoFIpFaj78TRo7X7HDkS60uq4rdoGo0fCPcq6NZ8pJrPWKRWo+/EVVfVNvpefXWsL6kSQNHMNn4gGiDGxztzh675jKXI5jrH9803VxuCe3r84xipBFA0s40fGB72dx/ByMMzZ3xVUZCCOq7pJsOvo3EIUjThu/1Dh/wgL/AX+ZmZ6nweX/uafwz+5969sR6GAkDRBN0/m1W3NEogd+6c/yC+/np9lU257D/M7QaG+QxkE8mDctlPyBRO9xyUsM18//9gPo9Tp6p/19Pjg0KMFACkVnhCimCSmaVL4fTpxm0H863Lny0QieTVyEjthT3MOfj1X/ffi6Gh2qrXmZlqaSAmagOQxoIEckNDcM01PmVEIDwgRXX5InMT1PvPltMn+G41mhPgscdiPSQFAGksPFJ4ZqY2P/nll1fnIujpmXtSOpEiC0rL4fm7Fy3ypebA2rXV6tBGcwLEnLpHVUAyu2iD7erVfi6Cs2erE9NPT6suX6SVcGkZfHr2T33K/757d31Oro0b/XzA4Yv+6tWxHpICgMwu2mC7c2f1g3z+vL/4t5GgSqSQojdSd9zRug1sbMw3FocTwjUqFSyAAoDMLtpg6xw8/7y6cIq0o92eb6WS7w0U7ozx/vfHekhKBy3ta5VKer7dQkWk3gLTts+WDloBQOK1bVu1W+jSpb4nkbp6iiRC8wFIvGZL4tatVBIismAqm0t7Zhv41SyVhJK9iaSOSgDSnugd/o4dtaWBYABZb2/1b86d8z2HWpUclB5apOtUApD2hO/wSyU/pP348drSQKNUEosWtS45KD20SNepBCDtCaeIWLGieqceTQMR3m/LFt9/uVXbgFJKiHSdAoC0J5wiYtOm5mkgopPOb9xY3ReqbQPBjGTheQpKJXj1VVUFiXSYqoBk/toZ2NIszXSQVTTYvmOHr1aamlLjsUiHqQQg8xe9y281QCXY9/bbG5cagu2rVjWvVhLJm4Q7P6gEIN3VzoxkixZVq4I0oljyKOHODyoBSHeFSw333ee/AI26kfb3+y9DUBUUtBWIxCEt3Y4T7vygWypJTrO7n+3b/RchyIIYbisQiUNauh0nPDe2SgCSnF27au9+du6sbgv3ClLGUYlbWrodR7tLd3k+DZUAJDnRKe9KpWo20V27/FSU09M+Ba4mmpE4JXzn/UsJz42tACDJiU5uEVz8lU1UOq3d3Pw5pSogSU54cFgw2UVaiuaSL9FGX5h7F+YcK+ZZSzo0ugtzLh1Fc8mXtDT6powmhJF0WeAMSCINrVkDBw5UHw8NxT+PdQpnw5ttQhh9syRdEm4Uk5zqRqNvBksZsbcBmNknzGy3mf3CzJyZrW6wz2Vm9qiZvV5ZHjWzS+M+FhERoDvdLTPYftWJRuCLgceB0Rb7fAO4DvjXleU64NEOHItkRVpGZko+tZO3ar4yOHYl9nfBOfcQgJm9r9F2M3sP/qJ/k3NuX2XdHwFPmdm7nXM/iPuYJAMyWHyWlOt2nXwGu5Ym0QawFngTeCa07mngDLAOUAAookbFZwUAWYhu31RksP0qiXEAfcApF+p+VPn9p5VtLQ0ODmJmNcvo6Gjnjla6I4PFZ0m5OOrky2W4+24YGPDL3XfnqnpyTiUAM3sA+Pwsu21wzu1Z8BHNQt1AcyqDxWdJuTh6/oyMwBe/WL3of/GLPoXJ9u21VUzr1/vte/akpgvoXMz1CB8CHptln2NzfK4TwOVmZkEpwMwMWFHZJkWU5uJzCvt3yxzEcVMxMVF7x18u+2ol52BmBh5+2Jcynn3Wv1a5nKk2rDlVATnnXnPOvTjLcnaOr7kPWIZvCwisBXqpbRcQSYegLvnAAc1NkKRoT7Hz51v3HAtuKr7zHX/Bvumm5j3MGlX1nD/vL+RRZ87Agw/60kBQxTQ9ncmZ7GK/jTGzPnxd/r+srPqtSh//Y865nznnvm9m/wh81cw+Udnnq8D/VQ8gmZdGRfHdu31RfXra3/0t5K5dDdTpEG3U3bMHjhyZvZH3nnuq1TiHDvlg8OCD9c8drep59FE/IVEz4UBi5j9v5XK22rCcc7Eu+P7/rsGyObTPZfgqpdOV5THg0lmedwBwk5OTTqTG1q3OXXyxc+BcqeQX/zX3y5Ilzq1d69zQkN/3woX2n3/pUv9cS5c6t21bZ85DWrvxxtr/a29v7eOhocZ/199f/3fBZ+DCBf//jT5Xu0t/v3N33+2PYdu29j9jHTI5ORlcfwdcg+tqJ8YBjNJ6EBjOuZ8DH4v7taWgwnfojYr358/76puZmfnVz6qBOh2i80dceqn/n87WyHv6dO3jM2d8Nd727bWlirm8fl+fLxWEc6gtXQof/3gmS4VKBy3ZF+5CWirVV/WY+QsFzK9+thujSGX20eDRx/39c0vvsHx5/bpdu/zzPfLI3C7+y5bBZz8L//zPsHUr3HgjrFvnfyYwk1dc9EmW7AvfoW/Y4NdNTFRnGFu0CA4fVorptJtt4FY08F50kd/+hS/4m4Dxcbj6av+/X7LE73P+PLz2Wv1rbdzoX+/Uqdrnv+GG6melVIIVK2Dz5tqstGntrTYPCgCSfbN1IW2UYlrSJzpH9BNP+KqWoPvthQu1+wePh4dh3z7/+759/ibAzAeRt96qLzksWuQ/AzfdVLttxQr/WvffX5h05Pk9M5FAmscYSFVPpEb6Jz+Bv/gLf5F+9ln4lV+p3R5cmI8cqV0/W57/vj7/t9GBYps3+5JDgT4rCgAikg6vvFL7+Pjx6u/T0/V19S+/7H9eeunc6vEDQSYBNe6rEViksNKWgjvaW2c2b7zhf05Pt/d3t97qf6pxXyUAka5IYzqJtKXgbnd62mXLfOA6eXLuf1MqFfJOvxmVAES6YSHpJDp1p562GawuuaS9/a+4wrcRtOOii5IPvCmiACDSDQu52MaViygaSNavT1cK7tWr575vTw8cO5Z8tVXGKQCIdMNC5juI6049GkjMOj9PbjuCwXpzsWLF/F6jUXK3AlNZSKQbFtLjJI689lAfSHbvrjaApsHGjfD88/7YWimVfJdN52oTuM1Fu9VMOacAININCxmLEFd3xbgCSacE57lzp3+/Dh6sv7j39sIdd1Tfg0WLqvuXy/5vWtm0qTPHnlHm2m15T4iZDQCTmhFMZJ4ajYhOc4Po3XfDn/1Z7bpSqX5EcNjixa1LBBcupPucYzY1NcXg4CDAoHOuLre1AoCIpFO57HvthK9RixfD2283/5v+fjjRYmLBjFzv4jJbAFAjsEiWpG3wVieVSnDxxbXrLrqo9d+oiqctCgAiWVK06SnbvWN/4IHm2wpU9TNXCgAiWRJHl9B259bNkmYX+Z6e2txCAqgXkEi2NOrJ026aifnOrZsG4RJBO+f91lsqATSgd0QkSxp1CW03p0+0FHH0aHYmvQ8P5Gp23n19tQ3B/f26+DehKiCRNItW10B9Bst2q4Wio5KvvjpdKSHC3vGO+nVBFVWz8/7Rj2Bw0Ff7DA7CD3/YnWPNIAUAkTSbS6Nvu2kmxsZqU0BMTPjBVf39cNllPr1yt9sBmvVuuu222rv+s2f9FJDQ/LyXLfOTyUxP+5/LlnXvPLLGOZeJBRgA3OTkpBMpjKEh53zNt1+Ghur3uXDBuW3b/LZt2/zjdm3d6tzFF/vXWLrUP083NXv9Cxec6+2tfQ/6+6vbFnreOTc5OekABwy4BtdVlQBE0mwud/dxTGySdGroZq9fKtV3BX399eq2gk/oslB6x0TSrFvTFiadJ6jV60eTw50/391jyzGlghDJq3a6SSadJ6jV6zdK4ZyR61bSlAtIpKi2bat2k1y61Df4prV7ZyuDgzA1Vfv4Jz9J7ngyRLmARIoqWq8+Pp7Nkb4/+EFtt84XX0z6iHJDAUAkr8INyABnzmQzf5C6dXaMAoBIXgX9/Xt7q+vSMPm7pIYCgEheBd0kb789vSN9JVHqBiqSd93qSiqZo15AIiI5pV5AKTY6Opr0IXRcEc4RinGeRThHKM55gkoAiTIzsvL+z1cRzhGKcZ5FOEfI13mqBCAiIg0pAIiIFJQCgIhIQWWpG2gPwInwVG85MDVVVy2XO0U4RyjGeRbhHCE/5xm6Xja82c9SI/C1wD8lfRwiIhl0nXPuuejKLAWAxcCVwClgJuHDERHJgh7gcuAF59yF6MbMBAAREYmXGoFFRApKAUBEpKAUAERECkoBQESkoBQA5snM3mlmD5vZi2Z2zsxeNbP/Zma/GtnvMjN71MxeryyPmtmlkX2uMrO9leeZNLMRs9qZsM3sw2b2/8zsrcrPfx/ZbmY2amZTlefZY2a/HdO5fsLMdpvZL8zMmdnqBvtk/jznw8w+aWYvmdl5MztkZjcndSxRZnaLmf195b12ZrY5sn3W97Jb/9cFnONWM/uumZ02s1Nm9k0zuzJv59kxzjkt81jwXVL/N/DvgN8Efhf4HvB4ZL9vVdavrSzfA74Z2n4JcAL428pz/j7wBvCZ0D5rgTLweeA9lZ9lYCi0z+cqf/fhyvP8LTAFLI/hXD8NbK38dMDqBvtk/jzn8b58BLgA/KfK8T4MvAmsSvrzWTm+DwJ/UnmvzwKbI9tnfS+79X9dwDl+G/h45bWvAv5P5Xjemafz7NhnJOkDyNNS+cLNAJdUHr+ncsH8ndA+N1XWvbvy+I+B08DS0D73AJNUu+n+DfBE5LV2Av+j8rsBx4HPh7YvrXxA/yjG83sfDQJA3s6zjffjAPDXkXU/Ah5M+rPY4FjfJBQA5vJeduv/GvN5LgOmgX+b5/OMa1EVULwuAd7C322BvyN4E3gmtM/TwBlgXWifp5xz50L7fBsYAFaH9nk88lrfDj3Hu4C+8D6V53sytE8nFeU8f8nMLgKup/54H+/2sczTXN7Lbv1f47QcX7X988rjvJ5nLBQAYlKpL7wff0dYrqzuA065yq0AQOX3n1a2BfucjDzdydC2Vvv0RfZrtU8nFeU8w34NWJSSY5mPubyX3fq/xmkcOAzsixxD3s4zFgoAEWb2QKXBrNWyPvI3y4Bv4ouDdyVx3O2az3mKpJmZfQlfdfNh59x00seTBVnKBtotDwGPzbLPseCXysX/HyoP/41z7nxovxPA5WZmwd1FpdfAisq2YJ+VkedfGdrWap8Tkf1Who8tsk9UW+c5izSfZ6e8hq9rbnW8aTaX97Jb/9cFM7MvA38AbHDO/Ti0KVfnGTeVACKcc685516cZTkLYGbLgX/EVwV80Dn3ZuTp9uEbpdaG1q0FeqnWN+4DbjazJaF9bsX3Ung5tM+tkee+NfQcL+E/ZL/cp/J8N1Nbrzmv85yD1J5npzjn3gYO0fp402wu72W3/q8LYmbjwB8Cw865FyObc3OeHZF0K3RWF3xj0z58d7F/ga/nC5aLQvt9C3gpnZWXAAABMklEQVSeavey56ntXvYO/Af0f+K7l30I39sg3L1sHb472d3Av8J3ybxAfffI1yt/f2Xl+eLqBtoHXAN8FN8z4oOVx+/M03nO4335CPA28B/xPUnG8Y2JVyT9+awc37LK/+kafMeEkcrvq+b6Xnbr/7qAc/xK5fWGI9/BZe18ZtJ+nh37jCR9AFldgPWVi2GjZX1ov8vwVS2nK8tjwKWR57oK3yvhPL7L2r1UupaF9vl94MXKBef7wIci2w0Yrfz9eWAvcGVM5zra5Dw35+k85/nefBJ/B/gWvkRwS9KfzTl8Rh+Z63vZrf/rAs6x2XdwtJ3PTNrPs1OL0kGLiBSU2gBERApKAUBEpKAUAERECkoBQESkoBQAREQKSgFARKSgFABERApKAUBEpKAUAERECur/AzMTnN98Mye0AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(data.bervs, harps_bervs - data.bervs, 'r.')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"adc_ra = sp[0].header['HIERARCH ESO INS ADC1 RA']\n",
"adc_dec = sp[0].header['HIERARCH ESO INS ADC1 DEC']"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"57970.97671993"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp[0].header['MJD-OBS'] # MJD at observation start"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "\"Keyword 'MJD-END' not found.\"",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-30-6d1e522a78e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'MJD-END'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/astropy/io/fits/header.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0mkeyword\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m \u001b[0mcard\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cards\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cardindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 146\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfield_specifier\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mkeyword\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mcard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrawkeyword\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;31m# This is RVKC; if only the top-level keyword was specified return\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/astropy/io/fits/header.py\u001b[0m in \u001b[0;36m_cardindex\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1652\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1653\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1654\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Keyword {!r} not found.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1655\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1656\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: \"Keyword 'MJD-END' not found.\""
]
}
],
"source": [
"sp[0].header['MJD-END']"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"57970.98570562992"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp[0].header['HIERARCH ESO DRS BJD'] - 2400000.5"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12.939407892990857"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(sp[0].header['HIERARCH ESO DRS BJD'] - 2400000.5 - sp[0].header['MJD-OBS'])*24.*60."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.99997"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp[0].header['EXPTIME']/60."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### how much does flux weighting matter?"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"sc = SkyCoord(ra=star['ra'][0]*u.deg, dec=star['dec'][0]*u.deg,\n",
" pm_ra_cosdec=star['pmra'][0]*u.mas/u.year,\n",
" pm_dec=star['pmdec'][0]*u.mas/u.year,\n",
" obstime=Time(2015.5, format='decimalyear'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dates2 = dates + np.random.normal(0., 1./24./60., len(dates)) # 1-minute randomness in midpoint"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scs = [calc_new_coords(sc, Time(d, format='jd')) for d in dates2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bervs2 = [s.radial_velocity_correction(location=loc).value for s in scs]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.scatter(bervs, np.array(bervs2) - np.array(bervs))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.std(np.array(bervs2) - np.array(bervs))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mid = 56966.24165454\n",
"start = 56966.23644620\n",
"end = 56966.24686289"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(end - start)/2. + start # naively calculated midpoint time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"2456966.74612107 - 2400000.5 # BJD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"end - 22.6/60./60./24."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<SkyCoord (ICRS): (ra, dec) in deg\n",
" (269.44861436, 4.73798077)\n",
" (pm_ra_cosdec, pm_dec) in mas / yr\n",
" (-802.80264788, 10362.54216327)>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sc"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-5133.00971827538 m / s\n"
]
}
],
"source": [
"with coordinates.solar_system_ephemeris.set('jpl'):\n",
" print(sc.radial_velocity_correction(location=loc))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-5133.010115848567 m / s\n"
]
}
],
"source": [
"with coordinates.solar_system_ephemeris.set('builtin'):\n",
" print(sc.radial_velocity_correction(location=loc))"
]
},
{
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Raw
barycentric.py
# coding: utf-8
# In[1]:
import wobble
import numpy as np
import matplotlib.pyplot as plt
# #### Get the Gaia coordinates:
# In[2]:
from astropy.time import Time
from astroquery.gaia import Gaia
import astropy.units as u
from astropy import coordinates
coordinates.solar_system_ephemeris.set('jpl')
from astropy.coordinates import SkyCoord, EarthLocation
# In[3]:
coord = SkyCoord(ra=269.4486, dec=4.7379807, unit=(u.degree, u.degree), frame='icrs')
width = u.Quantity(0.01, u.degree)
height = u.Quantity(0.01, u.degree)
r = Gaia.query_object_async(coordinate=coord, width=width, height=height);
# In[4]:
star = r[r['source_id'] == 4472832130942575872]
# In[5]:
sc = SkyCoord(ra=star['ra'][0]*u.deg, dec=star['dec'][0]*u.deg)
# #### Try a naive BERV calculation neglecting proper motion effects:
# In[6]:
loc = EarthLocation.of_site('lasilla')
# In[7]:
data = wobble.Data('barnards_e2ds.hdf5', filepath='/Users/mbedell/python/wobble/data/', orders=[60])
# In[8]:
dates = Time(data.dates, format='jd')
pipeline_bervs = data.bervs * u.m / u.s
# In[9]:
naive_bervs = sc.radial_velocity_correction(obstime=dates, location=loc)
# In[10]:
plt.plot_date(dates.plot_date, naive_bervs - pipeline_bervs, 'r.')
plt.ylabel('Resids w.r.t \n HARPS pipeline BERVs (m/s)', fontsize=12);
# #### Try it using coordinates calculated with Gaia proper motions:
# In[11]:
sc = SkyCoord(ra=star['ra'][0]*u.deg, dec=star['dec'][0]*u.deg,
pm_ra_cosdec=star['pmra'][0]*u.mas/u.year,
pm_dec=star['pmdec'][0]*u.mas/u.year,
obstime=Time(2015.5, format='decimalyear'))
#bervs = sc.radial_velocity_correction(obstime=dates, location=loc)
# In[12]:
def calc_new_coords(sc, date):
new_sc = SkyCoord(ra=sc.ra + sc.pm_ra_cosdec / np.cos(sc.dec.radian) * (date - sc.obstime),
dec=sc.dec + sc.pm_dec * (date - sc.obstime),
obstime=date)
return new_sc
# In[13]:
sc.ra, sc.dec
# In[14]:
new_sc = sc.apply_space_motion(new_obstime=Time(2005., format='decimalyear'))
new_sc.ra, new_sc.dec
# In[15]:
new_sc = calc_new_coords(sc, Time(2005., format='decimalyear'))
new_sc.ra, new_sc.dec
# In[16]:
scs = [calc_new_coords(sc, Time(d, format='jd')) for d in dates]
# In[17]:
bervs = [s.radial_velocity_correction(location=loc).value for s in scs]
# In[18]:
plt.plot_date(dates.plot_date, bervs - data.bervs, 'r.')
plt.ylabel('Resids w.r.t \n HARPS pipeline BERVs (m/s)', fontsize=12);
# In[19]:
plt.plot(data.bervs, bervs - data.bervs, 'r.')
# In[20]:
plt.plot_date(dates.plot_date, bervs - naive_bervs.value, 'r.')
# In[21]:
plt.plot(bervs, bervs - naive_bervs.value, 'r.')
# #### Try to replicate pipeline with HARPS header coords:
# In[22]:
from astropy.io import fits
from tqdm import tqdm
scs = []
harps_bervs = []
for f in tqdm(data.filelist):
sp = fits.open(f)
sc = SkyCoord(ra=sp[0].header['RA'] * u.deg,
dec=sp[0].header['DEC'] * u.deg,
pm_ra_cosdec = sp[0].header['HIERARCH ESO TEL TARG PMA'] * u.arcsec / u.year,
pm_dec = sp[0].header['HIERARCH ESO TEL TARG PMD'] * u.arcsec / u.year,
obstime=Time('J2000'))
scs.append(sc)
date = Time(sp[0].header['HIERARCH ESO DRS BJD'], format='jd')
new_sc = calc_new_coords(sc, date)
harps_bervs.append(new_sc.radial_velocity_correction(location=loc).value)
# In[43]:
plt.hist([sc.pm_dec.value for sc in scs])
plt.axvline(star['pmdec'][0], c='k')
plt.xlabel('PM Dec (arcsec/yr)', fontsize=16);
# In[45]:
plt.hist([sc.pm_ra_cosdec.value for sc in scs])
plt.axvline(star['pmra'][0], c='k')
plt.xlabel('PM RA (arcsec/yr)', fontsize=16);
# In[25]:
plt.plot_date(dates.plot_date, harps_bervs - data.bervs, 'r.')
plt.ylabel('Resids w.r.t \n HARPS pipeline BERVs (m/s)', fontsize=12);
# In[26]:
plt.plot_date(dates.plot_date, np.array(harps_bervs) - np.array(bervs), 'r.')
# In[27]:
plt.plot(data.bervs, harps_bervs - data.bervs, 'r.')
# In[28]:
adc_ra = sp[0].header['HIERARCH ESO INS ADC1 RA']
adc_dec = sp[0].header['HIERARCH ESO INS ADC1 DEC']
# In[29]:
sp[0].header['MJD-OBS'] # MJD at observation start
# In[30]:
sp[0].header['MJD-END']
# In[31]:
sp[0].header['HIERARCH ESO DRS BJD'] - 2400000.5
# In[32]:
(sp[0].header['HIERARCH ESO DRS BJD'] - 2400000.5 - sp[0].header['MJD-OBS'])*24.*60.
# In[33]:
sp[0].header['EXPTIME']/60.
# ### how much does flux weighting matter?
# In[34]:
sc = SkyCoord(ra=star['ra'][0]*u.deg, dec=star['dec'][0]*u.deg,
pm_ra_cosdec=star['pmra'][0]*u.mas/u.year,
pm_dec=star['pmdec'][0]*u.mas/u.year,
obstime=Time(2015.5, format='decimalyear'))
# In[ ]:
dates2 = dates + np.random.normal(0., 1./24./60., len(dates)) # 1-minute randomness in midpoint
# In[ ]:
scs = [calc_new_coords(sc, Time(d, format='jd')) for d in dates2]
# In[ ]:
bervs2 = [s.radial_velocity_correction(location=loc).value for s in scs]
# In[ ]:
plt.scatter(bervs, np.array(bervs2) - np.array(bervs))
# In[ ]:
np.std(np.array(bervs2) - np.array(bervs))
# In[ ]:
mid = 56966.24165454
start = 56966.23644620
end = 56966.24686289
# In[ ]:
(end - start)/2. + start # naively calculated midpoint time
# In[ ]:
2456966.74612107 - 2400000.5 # BJD
# In[ ]:
end - 22.6/60./60./24.
# In[36]:
sc
# In[39]:
with coordinates.solar_system_ephemeris.set('jpl'):
print(sc.radial_velocity_correction(location=loc))
# In[40]:
with coordinates.solar_system_ephemeris.set('builtin'):
print(sc.radial_velocity_correction(location=loc))
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