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astropy.stats.TransitPeriodogram tutorial
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline\n", | |
"%config IPython.matplotlib.backend = \"retina\"" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"In this tutorial, we will demonstrate the usage patterns for the `transit_periodogram` package that is to be submitted to `atropy.stats`.\n", | |
"To run this tutorial, you need to instal `transit_periodogram` following the instructions in [the README](https://github.com/dfm/astropy-transit-periodogram).\n", | |
"You will also need `numpy`, `scipy`, `matplotlib`, and `astropy`.\n", | |
"\n", | |
"The transit periodogram is a modern implementation of the \"box least-squares\" (BLS) method developed [by Kovács et al. (2002)](https://arxiv.org/abs/astro-ph/0206099).\n", | |
"This is the standard algorithm used to detect transiting exoplanets in time series datasets.\n", | |
"We won't go into the details here, but the basic idea is that the transit is modeled as a top hat and, under the assumption of known independent Gaussian uncertainties, this leads to several simplifications that make evaluating the model likelihood (relatively) computationally efficient.\n", | |
"The transit periodogram computes the log likelihood of the \"box\" fit (maximized over transit depth, duration, and phase) for a list of periods.\n", | |
"Peaks in the periodogram generally indicate transiting planets or other unmodeled noise.\n", | |
"Methods for determining the false alarm rates with these methods is an active area of research so we won't go into that here, but users should always be cautious with their interpretation of the results from a method like this.\n", | |
"\n", | |
"To demonstrate the code, we start by downloading the K2 light curve for `K2-3` a system with 3 known transiting planets:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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x4gSio6MxYsQITfs6efIkMjIysHXrVgwcOBCZmZnljtvorl+/joceesiufPbs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\nmYwAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<Figure size 432x216 with 1 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"\n", | |
"from astropy.io import fits\n", | |
"from astropy import units as u\n", | |
"\n", | |
"url = \"https://archive.stsci.edu/hlsps/everest/v2/c01/201300000/67065/hlsp_everest_k2_llc_201367065-c01_kepler_v2.0_lc.fits\"\n", | |
"with fits.open(url) as hdus:\n", | |
" data = hdus[1].data\n", | |
" t = data[\"TIME\"]\n", | |
" y = data[\"FLUX\"]\n", | |
" q = data[\"QUALITY\"]\n", | |
" \n", | |
"# This is from the EVEREST source. These are the flagged data points\n", | |
"# that should be removed. Ref: https://github.com/rodluger/everest\n", | |
"m = np.isfinite(t) & np.isfinite(y)\n", | |
"for b in [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17]:\n", | |
" m &= (q & (2 ** (b - 1))) == 0\n", | |
"\n", | |
"t = np.ascontiguousarray(t[m], dtype=np.float64) * u.day\n", | |
"y = np.ascontiguousarray(y[m], dtype=np.float64)\n", | |
"y = (y / np.median(y) - 1)*1e3\n", | |
"\n", | |
"fig, ax = plt.subplots(1, 1, sharex=True, figsize=(6, 3))\n", | |
"ax.plot(t, y, \"k\")\n", | |
"ax.set_xlim(t.min().value, t.max().value)\n", | |
"ax.set_xlabel(\"time [days]\")\n", | |
"ax.set_ylabel(\"relative flux [ppt]\");" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Then we'll fit for the long-term trends using a running windowed median filter." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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yhbCb7z55jStrP8DVJ5A6I2ZQ59GZBHR+CLeA2sybN6/0AoSwI2WLzjSl1Bit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\nT/4Pk6fFYeqkv88AAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<Figure size 432x432 with 2 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"from scipy.signal import medfilt\n", | |
"trend = medfilt(y, 45)\n", | |
"mu = np.median(y)\n", | |
"y_filt =(y - trend)\n", | |
"\n", | |
"fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 6))\n", | |
"ax = axes[0]\n", | |
"ax.plot(t, y, \"k\")\n", | |
"ax.plot(t, trend)\n", | |
"ax.set_ylabel(\"relative flux [ppt]\")\n", | |
"\n", | |
"ax = axes[1]\n", | |
"ax.plot(t, y_filt, \"k\")\n", | |
"ax.set_xlim(t.min().value, t.max().value)\n", | |
"ax.set_xlabel(\"time [days]\")\n", | |
"ax.set_ylabel(\"de-trended flux [ppt]\");" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Now, to find a transiting planet in this light curve, we use the `TransitPeriodogram` class.\n", | |
"The interface was designed to follow the conventions of `astropy.stats.LombScargle` so this might seem familiar to those of you who are familiar with that.\n", | |
"\n", | |
"First, the user must select a set of durations to test (in the same units as the time variable above) and then a search can be run using the following commands (see the docstrings for all the options):" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
" depth: array([0.01184199, 0.0125384 , 0.01319945, ..., 0.68477102, 0.61095512,\n", | |
" 0.68057992])\n", | |
" depth_err: array([0.04153426, 0.04059689, 0.04059689, ..., 0.3337471 , 0.316664 ,\n", | |
" 0.3337471 ])\n", | |
" depth_snr: array([0.28511392, 0.30885138, 0.32513453, ..., 2.05176621, 1.92934819,\n", | |
" 2.03920851])\n", | |
" duration: <Quantity [0.085, 0.085, 0.085, ..., 0.085, 0.085, 0.085] d>\n", | |
" log_likelihood: array([0.05076428, 0.06052646, 0.06707681, ..., 2.11010106, 1.8663308 ,\n", | |
" 2.08435064])\n", | |
" objective: 'likelihood'\n", | |
" period: <Quantity [ 0.4 , 0.40000125, 0.4000025 , ..., 40.01389036,\n", | |
" 40.02638037, 40.03887817] d>\n", | |
" power: array([0.05076428, 0.06052646, 0.06707681, ..., 2.11010106, 1.8663308 ,\n", | |
" 2.08435064])\n", | |
" transit_time: <Quantity [7.50000e-03, 3.97500e-01, 3.92500e-01, ..., 2.99975e+01,\n", | |
" 2.93725e+01, 2.87475e+01] d>\n" | |
] | |
} | |
], | |
"source": [ | |
"from astropy.stats.transit_periodogram import TransitPeriodogram\n", | |
"durations = np.linspace(0.05, 0.2, 10) * u.day\n", | |
"model = TransitPeriodogram(t, y_filt)\n", | |
"results = model.autopower(durations)\n", | |
"print(results)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The output from this method has several useful columns, but the most useful ones are probably `period` and `power`.\n", | |
"Using these, we can find plot the periodogram:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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CwexzyFw8wfyaU4noU58egcCFSWMRJ719vG7aV6aXF4xMDyGEqfMiDZGb1w4KA23i2LCG\nGjs8Ty2VYH7N6lVEcwHsUX22hI17TyuHnFbLtMoHupYZ3t97ytR5kYSI2B0UYtxuN8pqOTAcasw+\nh8zFE0wLMVYIMQMX9hFQ7ylga37605+aOs8uwVYiHh0UatxuNwQbVqYFEZZVRO3Cxx9/jF8ssFqL\n8OF28+igUON2uxHDcRamBdGsEZBWDI0qeme01037yvTygpEFomfHdqbOiyjEgeFQ43a7kdiWJ+CF\nGrPPIXPx2HKYw4he6bppX5leXjAyPYQQuLJn9I8OchO7LkINEcHBQcyQY+Y5ZEKDLVuIjw/9oBxy\nWi3TKh/oWmb45LC58yIJzxgOPZ7AsNNqNVocZp9D5uKxZU+gtMapm/aV6eUFIwuoQ7UNGgLimECo\ncbvdcBHXaagx+xwyF4+RTWVu0cje2hqXjxBC2GITd3YHhR43965CTkNDAxrq66xWo9VipIWYCiAd\nnoli6dKxQtpbwA8hxGIhhEOVniiE2CLtP3xRJLaNUQ45rZZplQ90LaMIIZDQJvobAnLzPIFQ43a7\nERfHgeFQ8uqrr+Ify56zWo1WixF3UAoRrZATQojriWiKtNGM18xhqaEfA2CRlO4FYAoRjRVCzBVC\nTCSiDWaVvnlwlm7aV6aXF4xMDyEEJlzRzfB5kYbIDd5UJrS43W5kpiRarUaLIiEhAV2T+GXFKozU\n/AkhxENCiEHS5LHzQoieuLDdpAIRLYH3/IIxAJZJn5cDGGtWYQA4eKpCOeS0WqZVPtC1zHDoVLmp\n8yIJzxgOPUSE6oYmq9VoUcTHx+NsGe8nYBVBtxBEdB+AvfA06LlEtA9Aqbp3EAAHpOUniKgcQJpe\nQSHELCFErhAit6SkRLPMwdMVyiGn1TKt8noEkgXQEYdMnBdpeHRQ6HG73ahudFutRotCCIGyGt63\n2SqMviYOAdAHwBRpp7FgW0Lf1+YCzVIAiGg5EQ0jomEZGRkG1YsM9gkM8+igUMOB4dAjLUtvtRqt\nFiOjgx4CsJyIKoQQKQAWA5gT5Om5AHoBKJCCxecNaxpFCCEAO/xoiXh0UIjhtYOYloaRwHBH+c1f\nMgS6vQApMDwMwHwhxLtElCe5eRwAJsMTFzDNuJwuumlfmV5eMDI9hBC4vl8nw+dFGs9bKzdYoYQD\nw+Ghq4Pr1CqMGIHz0lyBrQCGByooBYaX+OQtF0I4iOiiDEA0YBd3AJEbMTH20NUuuFwutGnTxmo1\nWhTsDrIWI4Hh5+AZbzgfQE8iesTol0lB4Yvm40NnlUNOq2Va5QNdywxbjxSZOi+SkJs3lQk1LpcL\nRXaYLW4jiAhnKxusVqPVYnSPYQFPUFcIIWa01j2G7fLmQgR2B4UYl8uF2FieLBZKuE6tpVXvJ2AW\nu4wO8riD2AiEEpfLhZhYWy65FbVwnVqLLfcTGNAtRTftK9PLC0amhxAC/bt0MHxepGF3UOhxuVzI\nSLbBXhI2wul0cp1aiC3N74CsFN20r6yxsdEvL9C1gkEIgf6ZNjACRIhhIxBSXC4XOju4wQolLpcL\nXVJ5UxmrsKWvYOPeU8ohp9UyNRkZGX55vtcyihAC/+/gGcPnRRriVURDjsvlwnelvOJlKHG5XDjB\ndWoZtuwJ1PlM21enfWWVlZV+eYGuFbQONlg/xu0mCF5ALqS4XC64wUHMUOJyuUBcp5bBr4kmsM2M\nYRsEr+2GJ4jJDVYocTqdiI2z5ftoi8CWRiCtfRvlkNNqmVb5QNcyihACKe2i/0dLbjc+2fCG1Wq0\nKFwuF5IS2lqtRovC5XIhKZHr1CpsaQTG5WQqh5xWy7TKB7qWUYQQuO4y48tGHDx4EOfPR27ZJLfb\njZ/eNiNi39caaGpqQt8uyVar0eLo0yn6B1oAwIsvvmi1CiHHlkZgV8F55ZDTaplW+UDXMooQAnkn\nSw2f9+STT2L79u2GzzMLEa94GWqEEDhdzkHMUHOqrNZqFYKisLDQahVCji2NwPGSGuWQ02qZVvlA\n1zLDd+eM/2jd7shO3uIZw+GhLEo3RbfDLHY9ympdVqsQFDU15tqLaKbFtRALZt4S9u8wu2xEpNei\n5xnD4SFaZ4sPGqS53bc9sIkBq621R4/FCC2uhcg/uCfs32F22YhIb/fIM4bDQ6SH3TY2Brfr1oED\nB8KsCdMSewLRP8RFg5sHdwuYbq68kXO1EEJg3OWdDZ8X+Z6AfWYM//DDD8jMNB6kt4J+EZ4t3qlT\nJ5SXR/+e1hdDpOvULNXV1VarEHJs2RM4X9OgHHK6ufJmZHoIIVBaa/y8SPts7TRjuHfv3larEDR/\nfuH5iH5fRYW5/azvueeeEGsSPuqd9ti3uSX2BOzRQvjw2bfnlENON1fejEwPIQR2FRgfHSSfGync\nbvtsL1lXZ58RN/U20fW1116zWoWg+e6cPRpXNgJRyo7N70f8O8281VOkN34ney0bYZfRLbfc+1ur\nVQgau9SpXeDAcJTytycfiOj3mV02IuJGwEbExsbC7baHSyAaRwdpNfaxsbFwuewx9NIuAxhqa2tb\nnGG1ZWD4yp6pIStv9FqAxwgMzDI3a7SlG4GGhgbEx8cbPk9usOyww1Q3R4LVKvjR1NTkV3dt27aF\n0+k0vCfy2bNn0aVLl1Cq1yxZqdFXp1o0NjbC6XSibdsLy1y4XC7E2Xjto4hpLoRwAFgBz/aUALCM\niAoCnKJJVVWV4Snmgcqbma4uhECPdF7/XIvOnTubGskSFxcHl8tlyoBEmvSk6GuwGhoavBomAGjT\npg0aGxvRrl3w+x8QETIzMyP6tktESG1nj7WDnE6nX11nZWXh7Flze5VHA5F2BxUQ0TzpMGwAACA1\nNRVrdhUqRzAEKhfsNdQIIfDB3tOGz2tp3UgtzI5kkY2AHTh4ytz/GE60emBt2rSB02lsdnN5eTkS\nExNDqVpQRGOdahETE+M3b6OoqMgibUJDxGMCQoiJQoheAeSzhBC5QojckpISP3lTk/46/pFqZC/G\npdPS3UFmadOmjW2MQCQJNk4SKiNQV1cHh8Nh6JyLRQhhi5gAEaFt27ZeRkCvzXnttdds89IXaSPQ\nC0AegIlCiDFaBYhoORENI6JhGRkZAS+27Z/vhEHF5ikoKDA9OsguRHq1xLi4OMMNVmvA1/+sR2Nj\no6Y7yGidal2H8VBdXY20tDQvI+B0OjXjAbNnz1bKVVVVYezYsX5liouL0dBgfL5RqImYESCiciKa\nREQFRLQEwCSz1+rqSEBXRwJWPDPP9zt0ywe6llF69uyJTh2MPyh2Gh304IMP+uWFU3c7uYM6JEQu\nCBhso9zQ0ICEhASvZ8D3rTXY70tISAjY4w419fX1SI5gnZqlvr4eycnJXnVaX1+v6T5LSkpSZhcf\nP35cM2Ywfvx45Obmhk/hIImYEVC7gKTPx81eqzp/N/q01/5xazVUowOs/R9IpkdsbCyu7Jlm+DyA\n3UF6hMMInD59OiyT0LI7Rm5QgBEj0L59e683f7M9AYfDEdHJewkJCRGtU7M0NDSgQ4cOfkYgIcH/\nRbJDhw6oqqoC4Jlg1qGD/wCULl26oLi42Cuvvr4ex44dC7HmgYmkO6hUCLFMCLEYwDwAy81eaOGS\nv2D1ps/88vV6AtuOFmvmNyfTQwiBnceNzzS2kzvIl3CP4Q+HEZg1axY+/fTTkF4TiOzs1sbGRmWI\nZ2NjI+rr63XLJSUlXbQRaGhoiLgRAIDjRRVRP09Ezwho9QTat2+v9ARqa2u9Rmjt378fJ06cQNeu\nXXHmzBmv83bt2oW77747TP+BNpF2B82WRgbNJiLTK2I1uNwordHp5mq8aZ8p135wmpPpIYRAcaW5\n8/Rwu91RbSTq6uoMjzc3wvHjx00ZgWeffdbvQZIxu+R3c1TVuyJ2r9Q9gb/97W947LHHNMs1NDR4\nGQGtIGaw3+dwOCI2M7a2ttbjF09Ijuiue2ZoaGhAcnKylx+/rq5OsyeQmJioGOza2lq0b3+hp7No\n0SJ88cUXcDgcfsOpjx8/HvF1tGw7Y9jKBtOsSycmJkb3beepp57CihUrLkatsOJyucI+dNCMEVi7\ndi20RpEBnvsUjrfLOBNv2GZRB4YrKyt1DbFvA6XVMwiGSBuB0tJS9O7dG+2SkqN+hU4j7qDExESl\nN1VVVYXU1FTlvLS0NJSXl2sa6cLCQlxyySVh/C/8sY0RcLvdmDTJE0v2vOH5l4n0BBejBDICe/fu\nxYkTJy5WLT+IQtMI7ty5E5WVlc18l6dOzAQVr7jiClNGoLnd2sIRg4mTJmFFAnVPwO12606ma2xs\nREpKivL2WV9fj5SUFMUvHSyyOyjcRuCll14C4Blxk5SUhPYdUrx6dFVVVZaPFvP9fiPuILURKCsr\nQ8+ePTFt2jQAQEZGBvLz8zVjPZHecwSwkRF47rnnsGHDBgBAt9R2GH2Z9vBRrfXzbx/RXfe6gWR6\nCCHws4HmNqjXMwLHjh3DuXPG4wzNsXHV/4XkOidPnlQ+6w1rkxsgMw2k2ZhAc3s0hOPFYESfzhE3\nAvLIMr3/R+4JyPegoaEBaWlphuu0trYWHTt2DLsRuP/++wFcMAJTrh3g5Rp54IEHsG7dOq9zsrOz\ng76fzz777EXr+Pjjj3t9n5YRqKur0zTMaiNQXl6O7OxsvPPOhSHtjY2NSE01vmQNADz88MOmztPD\nNkZA/RZa0+DCaY2NqfV+IMeK9d+GfGVE5Pfj80UIgZPnjHddAz3E1113HQYOHGj4ms1hZsVLrYZV\nHZDs1El7RJXL5UK3bt1MBRXNGoFAjUK4RmKV1TdF1AgkJiYqvatARsC3J5CYmGjYHVZbW4vLLrsM\n+/btM61zaWnwy6zLRqC0Mcar11JcXOz3pnzy5MmgX5Tmz58fVDki0nUnlpSUeN1nOe6izvMN+sok\nJCQoz4HL5ULHjh295GVlZcqkvE8++SQoXWWefz60+1nYxgiof8zldU7kF+k1wgL19fU4evSokrP7\nRJnudX1lDQ0NuOuuuwLqIoTAgQDT3Ovq6vDzn//cLz/QSpnhmkNgZsVLrQkwaiOg5xZyOp2mGh7A\nvBEINNM4XIHh0xXOiBqB9u3bw+VyweVy6boKtNxBiYmJQf3/U6dORWGhZ/mU2tpa9OvXT1k3f/Pm\nzTh92rNESkVFBcrKypSG+MyZM34b1+zbtw/p6enNLqUQExOD6upqxQgcK3P7/a5837AvvfRSzRcQ\n3xFTRu55UVERRowYoSmrqqryMkxyb8vXCKiDvnI59b0AoDT4souprKxM6Qn85Cc/CVrfcMS4ot4I\nyDfUq3tHpDvNvKnJhcOHD+O2224z9X319fXNzuJrrrFuamrCf/7zH7/8QDEBPV/gxd50M/sJuFwu\nvwBkMA200+lEfHx8RI1AoBEwavdbfn6+4WvrERvXfGC4ubjI+fPnsWDBAgDwGyuuRl4Azul0Qgih\nXNe3rnzdQbIR8HXr7N692+878vLylPNqamqQnp6upMeNG4epU6eiuLgYDocDaWlpuOWWWwAABw8e\n9Nu4ZvDgwQCArl27Bvz/MzIyUFxcrBiBhPbtvYxAu3bt/HqU3bppbwXbpUsXr/qQXwCDmY2bn5+v\nu61pZWWlV7C6rq4OKSkp+PLLL5XfVU1NjZ8RqK6uRqdOnRT9a2pq0L17d1x11VUoLS1FXFyclxHQ\nQs+QBYob/t//mXP9RrURKCoqwj333OP3VuFxV/iXlxcvu5g3wGBdGc25IbTkgYyAnm+7Vy/dZZaC\nxmhdqJfGraiowFtvvYWRI0di5MiRARt4p9Nperap2WUjAo2Fj4uLU3RZtmwZamtrsXbtWmzdujXg\nNWV/tRZEhPiERKXBuuOOO5Ceno6FCxd61fOAAQNQVqbfA92wYQP++Mc/AvCsvKp3j+SewL59+7Bw\n4UI8/fTTXufIrgwtI/Ddd9/5vQxpvfW2a9dOMRZab7aHDx/G1VdfraRLSkpQXl6uTGrSemYC/U7c\nbje6du2Ks2fPXjACie2xZcsWpYzap37y5Ek0NDRg6NChmvpXVFQo8YS3334bhYWFmDBhgmJc5V6O\n7zlz5szBqFGjkJaWhgEDBgAAVqxYofxfVVVVqK6uhtPpRGVlpWIEXnzxRRw8eFCpL9lIy41wVVUV\nOnfurNRpUlISLrnkEsycORMFBQVIT09HZmYmOnf23qf82LFjcLlcSE1NRXl5ueYuZqdPn/a6F2oe\neOABUy9gUW0E3G43Xn/9dazTwSLgAAAaK0lEQVRcudIr35EYhwFZKX7lZcvv2wiPutTjj9Pyc8oy\nmWACYkIIDO3h//0yTqdT88ForicgGwF1AEkdkDVDTGwM3AYbZbUROHfuHBYsWACn04mkpKSA+wU0\nNjZiz549fusOnT59Gr/61a8CfqfZBeTkNfMBTx2q77u8lLLT6cSxY8fQvn17HDp0CDt27MC7776r\njEt/9dVXva4pj1zRY1jfTKWxXbNmDUpLS7Fnzx7MmDED27ZtAwB8/fXXug8rAMydO9crHWgS2Dvv\nvIPHH3/cK7+0tBRVVVXKS4KvC6KhocHQEtJyT1teJlnt17/uuuuQn5+PRx99FAAwcOBAdO3aVflf\nZ8yYoXtd+cVMdikBnp5PTk4Ozp49i5KSEiQlJWF0v0745JNPlLd4tRHIzs7GFVdcgR07dmDs2LFe\ndeV2uzFq1ChlwbY777wTL7zwAhwOB7p39wz66NGjBzZt2oQpU6bA7XbjxIkTcDgcyn1PS0vDoUOH\nsGfPHsyaNQtffvklAM9zcOjQIbzxxhtISUnBjBkzFLeO2gh07doVbdu2xcaNGwF4egKdO3dGTU0N\ncnNzlXpKSUnBsWPHkJGRgQ8++AA9e/ZUXGtDhw5F3759UVJSgqysLHz77bdISkpCYWEhduzYgS1b\ntuCxxx5DUVERsrKyUFVVhQ8//FDp7e7duxcATO3HEdVGQOYPf/iDVzpOAMkJ/sOrZLeA7xt1entP\noyV3VbVkMsGMVY6JiUFygvZ47fz8fBQUeFbJ9h2eF6w7yKwrS4u4uLZwuYy9YauNQGxsLJqampSZ\nq7W1tZrjooEL/s4lS5Z45dfV1eHjjz/W/T4i0nQHBdOj2L59u3LfH3jgAYwfP16RyQaitrYWR44c\nwaBBgxAfHw+Xy6W4OABgzpw5ADwN4MyZM5v9zk6ODqivr0djYyPi4uIwZcoUTJo0CeXl5V6ByyNH\njuheY+rUqXj44YeVRlJvqYDGxkb84Q9/8Ho5kQOUP/zwg9LQ19fXw+FwePUE5Dd62TCq/6p7Ty6X\nC++++y4A4IcffoAQAm3btsWIESOQmpqK9957D4DHjz1u3DisW7cOdXV12LBhAyZNmoQ1a9ZACIE3\n3ngDTzzxhHJdIQQcDgd69OiBrKwsCCEghEBmZiYGDhyIW2+9FY8++iiSkpKU57Bfv34QQiAmJgYf\nffSR8iwfPXoUf/3rX/GnP/0JiYmJEEKgW7duiI2NRU5ODlasWKE8P5988oniOpLPX7JkCdatW4fY\n2Fiv3vXIkSPxzDPPAACGDRsGALjrrrvwj3/8AyUlJbjtttuU34TL5UKnTp0wc+ZM/OIXv8CTTz6J\nV155BSNHjgQAbNu2DUIIDBgwAP/zP/+DNWvW4Cc/+Qm2b98OwLPvwF133YXOnTsreskB47y8PABA\n//790b9/f4wbNw6Ax4BdffXVmD17Np555hlMnjwZ2dnZSE5OxoQJEwB4ntEhQ4Zo/n6CIaqNgN5M\n0KLKOnypsdG7Xk9gY4C1/31l6i7Yz372M81zDhw4gNc/8l+2AgBuvPFGHDp0CACQnp7uJfMdIvr4\n448rrolAgWGXy+XVCIwePVrnv/Enrk0bNDVjBHwNk3qHL7m7KxsBeYExLZxOJ66//nr87ne/85MF\niqPIfm+1EWhqavIKTj/xxBMoLy/H5s2bFf++fI/lBq1nz57o0aMHPv/8c6xevVrRt6amBvn5+UhJ\nSfEyxOpRIRUVFVi1apVfr1OLnSerUF9fr7gy2rVrh/r6esTHxyu/wX79+gW8xpEjRxAfH4/q6mqM\nHz9ecR0REVJTUxUd9+7di/Hjx3u5RHfs2IHq6moUFRWhXbt2ICLMnz9f6QnIjfpll10GwPPysWrV\nKsyePRuAJ9g7a9Ys5XrZ2dkAgGnTpmH5cs9qLi+88AJ2794Nl8uFmpoa1NXVYenSpX7GXF1f06dP\nx9q1a7Fr1y5ce+21Sr6vO+Y3v/kNrr/+eiUdFxeHjXtP46233lLyli5dik2bNgHwGHfA0yD27dtX\nKSO3D5WVlcpv4te//jUA4KGHHlKM4PTp05WGGPAEmN1uN9asWYO5c+eiW7duyjMr/7YnTpyI+Ph4\nPPnkk1i7di369+8PAMjMzFTq6KmnnsKJEycwfPhwrFy5Ei+88ILyHTk5OTh69Ci+/vprfPaZp60Y\nPHgwdu3ahauuukop98c//hFFRUWor6+H2+3GnDlzcPnll6O8vByvvPKKUk4dC5D/l+rqauUF+aab\nbsLzzz/f7O9OE7kLHY0HANI6+g/5X5r317fIoz4pf/fv308AaPfu3V6yt3ee9Cono5aVlZXRf//7\nX9q8ebPfddW43W4CQFf/9BY/GRFR37596eWXX1Z0JSL697//TUREt99+O7311lte3/+rX/2KiIju\nvfdeWr58ud/3xsTE0JIlS2jy5MlUVVVFZ86cIQDkdDo1v1/NoKt+TCOu/zm9+N4XumVKSkr8/s/v\nvvuOLrnkElq6dCktWLCAANCbb75JU6dOVWRERBUVFeR2u5Xz8vLyaM2aNfTQQw95Xe/o0aPUt29f\nr7JqKioq6O6776Z169YRkaeOKyoqCIByTlpaGh05coRGjhxJ69evJyJPvQKgxx57jIiIrrnmGpo8\nebJS9927d6dXXnmFvv32WwJA99xzDz399NM0f/58pUxTUxMBoNzcXPrXv/7ldd98aWxspIULF9Iz\nb/2L/vKXv9DJkyfpkksuod/85jf04osv0vTp02nlypXkdrtp5syZ9Mgjj+jWe1xcHD333HO0dOlS\n6tOnD02dOpWIiJKTkwkAvfrqq3TXXXcRAPrwww8pJiaGJk+eTC+88AL17dvX63n40Y9+RACoqKjI\nK//IkSOaz498zJw5k4YPH+6XT0R+aTnvhhtuoLy8PLr11lupoKCA3G43ff/995SXl0cAKCUlRSn/\n+uuvEwB69NFH6aabbqL9+/dTfn6+Ip85cybV1NQQEdHbO0/SuXPn6Mknn6Q77rhD+e5FixbRmTNn\n6ODBg8p5ZWVl1K1bN3ruuefI6XSS2+2mV155hfbv309ERC6XSyl777330pYtW+hvf/sb1dXV0dmz\nZ6mxsdHvftTW1tLhw4fJ7XbTvHnzlPoMhNZv5eTJk7R06VIiItq+fXvA8wOxbNkyIvI8C+vXr6ce\nPXoo6fLycvrqq6+UstXV1XTixAk6d+6crFcuGWhno7onoAcR+Y16uf/++3XdQUDzo1tefPFF/OhH\nP9IMxqiR33LrarzdRmoXiOyLlLun8ltPfHy8n+9Xfksin8Cg2+3G9u3bER8fj1WrVmHdunVYvXq1\nsrhUc6MvAKBXv4EYOOIaxATwEy5duhQAvEZ5yO6g/fv3K13lvXv3ok2bNsjOzlZGDt1www3405/+\nBCEEvvjiCzidTiQnJ+P5558HEWHs2LFwOp3KkNOYmBgsWrQIRITp06cD8LjPKisrkZqaioqKCnz8\n8ce4//77lZ6PPHKjtLQUH374oVcQ9cMPP/T8n1L3/vPPP/ea4zFhwgSlJwB4ut6+vbE9e/YA8Ph4\nm3MFFhcXK0MUH3zwQVRVVaFDhw7KipHyEOCioiIcP35cc0ao7CJs06YNUlJSsGXLFrz00ktKHEgO\nON9333148803AXiCuW63G++88w6+//57v5FO//3vfwHAb7SJ/NYs+6p9WbFiBb766itNGRFh7dq1\nWLVqlZLndruxefNmDB48GBs2bEDPnj0hhEBWVhYGDx6M0tJSr/V/pk2bBiLC008/jY0bN2LgwIHo\n06ePIl++fLlX3CI9PR0LFizA6tWrQUQoLi7GI488gszMTOTk5CjlHA4HTp06hYceeghxcXEQQuCX\nv/ylMs9G7RdfuXIlxowZgzlz5iAhIQGdO3fWXHojMTERl19+OYQQePbZZ+F2u3Xnw6jryPe57d69\nO+677z4AwKhRowKeHwi5pyaEwMSJE/Hdd98p6ZSUFMV1BXgWq8vOzvbzPASLLY1AfKzA2aN5Xnkv\nvfSS0hVPTvbeBL53RnulMRk6dChuuOEGLxkAJXgjNwTffPMNAE9gNj8/H6+99hq+/vprnDp1CgBA\nDd7GYt48z94G8gPaqVMnJCYmYteuXQCAO++802sCiRxMk0c1rFq1Ck6nU/lRlZSUYPTo0UhISFAe\nrDfffBObN29W5IDHT1tQUKC5LjmB0DYhEZVl5/HVV19h4cKFikxesO7w4cMAgHvuuQeFhYUQQihG\nQAihxFH+8pe/KMG9kpISZGZmYteuXYoP+JprrkFtba0yhT4mJgZbt27F8ePHkZOTg6+//hoA8Oij\nj2LTpk2KD3rMmDHYtm0bMjMzUVlZicrKSpw9e1apJ9kNMm7cOAwYMADJyclYt24dPvvsM1xxxRV4\n5ZVXUFVVpTm6Jj4+XokJAJ5hib5lr7zySgDaRmD16tVeQeOioiJ06dIFP/7RcNxxxx2oqqpCUlKS\nMkIpKSkJNTU1OHPmjOKWICLF1ZORkYHk5GSsX78eQ4YMQUJCAt577z1lnPgjjzwCwHs464oVK5CR\nkYHRo0dDCKEY5d27d+P5559XGuNz586hTZs2qK+vR+/evZGVlYW4uDgUFhbipptugsvlgtPpmd/g\nG2SWg4qAJ1ah/qxe0bK5odGpqammApPAhedQTXObSoWTVrXku5FuQ6QP6HRjR44cqXyW3TMA6NNP\nPyUAlJ+fTwAUN4DURdLs8jY1NXnJFy1aRADo8ccfJwB08803EwCaMGECbdy4kW699VYCQIMGDSIi\nok2bNinnX3fddQSA1q5dS2lpaZSRkaFcd8iQIfTggw96fXdOTg6tXLlSSf/973+nxsZGio2Npc8/\n/5wAUJcuXUgI4XGD9e/v9T8cOHCAhg8fruhIRJSVlaV0E2++9wH6/fOrvM55/fXXadasWfTMM8/Q\nyy+/THPnzlVka9asIQB06NAhJS8nJ0f5PG7cuIDuhY8++oh27twZsAwA6tSpEwFQXD4A6K23PO69\nYcOGKXkTJ05UdL755ptpw4YNNH/+fMrJyVFkx44do5/97GfUp08f5bwf//jHyueBAwfSrFmzKCcn\nh37729/S5ZdfHlC39PR0AkDbt2+nSy+9VNH3xIkTBIB27Nih3K+0tDQaNWoUPfHEEzRjxgy6//77\n6cYbbyQA9MUXX3jdf9+jR48eVFhYqNy30aNHa7oXtNBzqxlB7TIhItq3bx/t2bPnoq/LWA8MuoMs\nb+gDKtdMY+J7yP78o0ePEgBauHAhAaAbJ/3Cr6wcP/jlo0/Tq6++6icfMGCAV4Myfvx4+uCDD2jS\npEkEgDp3u4QOHDhAAKiqqsrr3FWrVlFKSgp1797dK/+RRx7xM0obNmygXr16EQCaP3++n482NTW1\n2f/bt4zM4KuupweeWap5zvjx4wkA/e53v1Pynn76aQJA+/btU/Kys7OVz6NHj6aBAwfqNm7r1q2j\ngwcP0uDBg4O6X2lpacrnfv36+cnVBmHChAl077330i9+4X0v1S8BAGjw4MH0v//7v37Xys3NVV4O\nAh3//Oc/A8rPnj1L/zp4ptnrNDQ00JdffumVd//999MPP/xAAOjll1/2enDV/0dr5F8Hz1itQouh\nVRuBDz74gIALb+SZmZmGztc6unbtqnx+//33acqUKQSARt4wgTZt2hTw3DZt2nil5YCX/LZp5JDf\nMIM5iIiuvvpq6tazL72wbptmmT//+c80ZcoU6t+/P/3973+nadOmKbI333xT85z4+Hj64IMPvAKr\nvkdBQQEReYKCsjGRexgAqLq6mgDQqFGjvM7Ly8ujbds8usqN5w033BDU//rrX/+asrKyqE+fPtTU\n1OT1pjxw4EClnNzQ/uc//6GGhgZau3YtPfzww1RUVETDhw+ntWvXEpEnUJ2WlkZ33nknNTY2Kj3K\n66+/nog8Qcz77ruPrrnmGnrmmWdo/Pjx9NRTT9G4ceOIiGjRokVeD2VhYWFQD295eXnQZVsa8gAN\n5uJp1UYgEkfv3r0JAHXO6hHR71W7bZo71G/3y7ccUD47HA7dhtQ3r2PHjspneWTPvn37qLGxkWbO\nnEkA6Nprr6WPPvqI6urqlLLFxcWaP0x1w9zU1ESfffYZAaDCwkJd90ZNTQ2tXr2a3njjDQJA06dP\np4qKCiLyjDgK9B3h5O2dJyP2Xa0FNgKhI6qNAICJALYAmBtkecsb/XAe8vBMraNDhw7Nnq/2g2sd\n1/58Er298yTNfHRxwHJEF96SR4wYQQDom2++obKyMkXuS2VlpVfa6XTSZ599ZvwXa0Pey/veahVa\nHFynocOoEYj0RvNTiGislJ4Yqe+2gt///vd+k6pqamqUkRl33nkn0tPTkZGRASLyGqGRl5eHyspK\nJCcnY86cOTh58iSmTZuGP//5zyAiFBQU4LnnnkN+fj6ICHfffTc2bdqEw4cPo66uDvfeey8OHTqE\nGY96hq2OnjAVp06dwoEDB1BRUaFlbJUJdjt37gQR4bLLLoPD4VDkvvhunB0XF4drrrkmZPUXzdw8\nOMtqFVocXKfWIfQe8pB/kRCzABQQ0VYhhAPAYiKa3cw5YVXun//8pzL1WmbVqlXo3bs3rr32Wjz+\n+ONISEhAWloahBCoq6vDgw8+iJdeegmbtm7H/q/+iwULFiA2NhbXXXcd8vLyMGDAAPTt21cZYrZh\nwwbceuutlgw5W7PrwkxNM5vnMNocPFWhuXYVYx6u09AhhNhDRMOaL+khrvkiIcMBoAAAiKhcCJGm\nVUgyFrO0ZL7kXHkNpk2agDPkwEN33IiqqirsOheHyUO74ZtvvsGgQYPgdrvx5hfHMH3Upcp5TU1N\niImJgRACb+88qTSQRBeWbtAzjr/9rWeTlrThE7DJp2Ht2bOnX/mJE1t0h6dVcvA0N1ihhuvUOiJp\nBMp90gVahYhoOYDlADBs2DDSmgTl+4a7ZlehMoN2T3khEhISMGjQIACeSUtt473dMnoTWlrVBBGG\nYRhEdsZwLoBeACC5g84HLs4wDMOEm4j1BIgoTwgxSzIAkyG97ZthXE4X3bSvTC8vGJkRHZjWA9/7\n0MN1ah0RXTtIcvWAiJYTka97iGEYhokwEV9ALhSN/8eHziqHnFbLtMoHupZZHZjWCd/70MN1ah22\nXEWUYRiGCQ1sBBiGYVoxEZssZgYhRAmAi9tpPXx0BHCu2VLWw3qGFtYz9NhFV7voeRkRdWi+mIdI\nzhMwDBFZt6tEMwghco3MyrMK1jO0sJ6hxy662klPI+XZHcQwDNOKYSPAMAzTimEjYB7Tk90iDOsZ\nWljP0GMXXVuknlEdGGYYhmHCC/cEGIZhWjFsBBiGYVoxbARaGEKIxdIifXJ6ohBiixBirpV62RUh\nxFwhxDIhxDJVHtepSaTf52KpTh1SHtfnRSKE6GX2N8pGwABCCIcQYr3qh9zLap3USDd8jCodtVt6\nqo1VtNarVF9bpR3w1ksGISrr1NdYRWOdCiGGAFhGRPMArAcwOVrrE/A3WNFYpyrmyR8M16mRDYlb\n+wHP7miLrdajGR2XAXBIn2cBGKPSfZnV+km6zAWwR6VnVNarrJ9P3UZdnQKYCGCI9HmMVL9RWacq\nnRcDGBKN9SnpMgRAL1WdzorWOpXudy+57ozWKfcETCB1taLpLUAPry09AWhu6RlpiGgJPJsMeRFt\n9UqqFW+lbU+XITrrdCsR5QEAEW0F0FsWRFudSm6L9fA0sHmIzvoEEeURkbz74Viofq/RVKdS76pc\npStgsE7ZCBinF4A8ABOFEGOaK2wxQW3pGSVEbb1K3ekCqdGKujrVMVZAFNYpERUQ0SQA84QQixGF\n9SmjYbCA6KvTKQB6S3U5RnIJG6pTNgIGIKJyIpok/ZCXAJhktU7NYIstPaO5XqVGtUB6wwaiuE7V\nxioa61R6a5Uphaceo7Y+fQ1WNNYpEc2TDwB5ql520HXKRsAA6i6g9Pm4her4Ib0FDAMwXwgxRHp7\n6RWKLT3DSbTWq9SoTgIwWwoOzorWOvU1VlFapwVSQHUuPDGBeVFcn34GK0rr1A+jdcozhg0gVarc\nhXXA8yOO+m0yhRCOaNJTagSmANgK4F14uqu2qtdoqlPJWM3GhW7/HgDrYKM6jab6BJRnfT48b9G9\n4anLUrTAOmUjwDAM04phdxDDMEwrho0AwzBMK4aNAMMwTCuGjQDDMEwrho0AwzBMK4aNANOqkWaF\nNjvzUwgxRm8hLmkOwcRgywep1yxpFijDhBU2AkyrRpr9ubX5ks1eZ0Mo9FFdbzk8Y9EZJqzEWa0A\nw4QKaZbnYnimzJcDuJ6IyqU38rEA1hPRVqncGADpAHYA6EJEy6VJbOkAzkvT7+WJbZDyvwpCB6/y\nqgmGxwHP4nlCiGXkWZ5anumbC88iX5OkcnmhMEwMEwzcE2BaGgVE1BvSss9Sg99LanSHiAsb7oyV\n1ls5BXituzMPwHJpff4x8KzQuATBGQC/8tJ6M7OlvOFS0fUqV9FQaZr/JHiWKV7CBoCJJGwEmJaG\nvJ7LOnim+08BlDduQFpYC8AWn/OGw7OMhbwqZ2941pQ30iBrlhdCDJEafYc0lX8rgLGSQZL1nQfP\nQmVbfNatYZiwwu4gpqWSBk8DWw4gV7UUsO/iYDLH4Vl8b6vUOJfDsxZPLwS/vLFfeal34CCiDUKI\nsT7fNx/AIkAxPLKLaJn8mWHCDRsBpqUxXBUDkLfcWyE1rL2g82YvxQQWSw21sjiYtJ3gEHh6Br69\nB99rbNAoXwBgsRAiDRd6IYBnZcf18gJfks7Dpe9eb+o/ZxgT8AJyTItBFfDd4LPTkvxGXuCbH6Lv\nVQK9Bs4ZAyg7gYXsugxjFO4JMC0OrYY+moKt0lt/mjQMlGEshY0A02KQ/P55zRYMA0KIicHOFQim\nnBTIjtq16pmWA7uDGIZhWjE8RJRhGKYVw0aAYRimFcNGgGEYphXDRoBhGKYVw0aAYRimFfP/AeOw\nEeBSp2C0AAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<Figure size 432x216 with 1 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Find the period of the peak\n", | |
"period = results.period[np.argmax(results.power)]\n", | |
"\n", | |
"fig, ax = plt.subplots(1, 1, figsize=(6, 3))\n", | |
"\n", | |
"# Highlight the harmonics of the peak period\n", | |
"ax.axvline(period.value, alpha=0.4, lw=3)\n", | |
"for n in range(2, 10):\n", | |
" ax.axvline(n*period.value, alpha=0.4, lw=1, linestyle=\"dashed\")\n", | |
" ax.axvline(period.value / n, alpha=0.4, lw=1, linestyle=\"dashed\")\n", | |
"\n", | |
"# Plot the periodogram\n", | |
"ax.plot(results.period, results.power, \"k\", lw=0.5)\n", | |
"\n", | |
"ax.set_xlim(results.period.min().value, results.period.max().value)\n", | |
"ax.set_xlabel(\"period [days]\")\n", | |
"ax.set_ylabel(\"log likelihood\");" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The structure that you can see in this periodogram is pretty typical for systems with transiting planets.\n", | |
"The peak period is highlighted with a thick blue line and the integer harmonics of this period are indicated with dashed blue lines.\n", | |
"The code can compute some descriptive stats at the maximum peak that are useful for vetting our transit candidate:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"{'depth': (1.21022444799407, 0.1630767640938403),\n", | |
" 'depth_even': (1.1873669172062102, 0.23002134783754802),\n", | |
" 'depth_half': (0.652497403957128, 0.12069157154651625),\n", | |
" 'depth_odd': (1.23308197878193, 0.23002134783754802),\n", | |
" 'depth_phased': (-0.009700891587897999, 0.17756897577182104),\n", | |
" 'harmonic_amplitude': 0.024330400161685493,\n", | |
" 'harmonic_delta_log_likelihood': -27.001926578770068,\n", | |
" 'per_transit_count': array([5, 4, 4, 5, 5, 5, 5, 5]),\n", | |
" 'per_transit_log_likelihood': array([3.52260421, 3.17307127, 2.96919614, 3.23568475, 3.60852427,\n", | |
" 4.09542079, 3.28819381, 3.93552583]),\n", | |
" 'transit_times': <Quantity [1980.41719885, 1990.47088353, 2000.52456822, 2010.57825291,\n", | |
" 2020.6319376 , 2030.68562229, 2040.73930697, 2050.79299166] d>}" | |
] | |
}, | |
"execution_count": 6, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"index = np.argmax(results.power)\n", | |
"period = results.period[index]\n", | |
"t0 = results.transit_time[index]\n", | |
"duration = results.duration[index]\n", | |
"\n", | |
"model.compute_stats(period, duration, t0)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"These statistics are documented in the docstring for the `compute_stats` method:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"\u001b[0;31mSignature:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_stats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransit_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;31mDocstring:\u001b[0m\n", | |
"Compute descriptive statistics for a given transit model\n", | |
"\n", | |
"These statistics are commonly used for vetting of transit candidates.\n", | |
"\n", | |
"Parameters\n", | |
"----------\n", | |
"period : float or Quantity\n", | |
" The period of the transits.\n", | |
"duration : float or Quantity\n", | |
" The duration of the transit.\n", | |
"transit_time : float or Quantity\n", | |
" The mid-transit time of a reference transit.\n", | |
"\n", | |
"Returns\n", | |
"-------\n", | |
"stats : dict\n", | |
" A dictionary containing several descriptive statistics:\n", | |
"\n", | |
" - ``depth``: The depth and uncertainty (as a tuple with two\n", | |
" values) on the depth for the fiducial model.\n", | |
" - ``depth_odd``: The depth and uncertainty on the depth for a\n", | |
" model where the period is twice the fiducial period.\n", | |
" - ``depth_even``: The depth and uncertainty on the depth for a\n", | |
" model where the period is twice the fiducial period and the\n", | |
" phase is offset by one orbital period.\n", | |
" - ``harmonic_amplitude``: The amplitude of the best fit sinusoidal\n", | |
" model.\n", | |
" - ``harmonic_delta_log_likelihood``: The difference in log\n", | |
" likelihood between a sinusoidal model and the transit model.\n", | |
" If ``harmonic_delta_log_likelihood`` is greater than zero, the\n", | |
" sinusoidal model is preferred.\n", | |
" - ``transit_times``: The mid-transit time for each transit in the\n", | |
" baseline.\n", | |
" - ``per_transit_count``: An array with a count of the number of\n", | |
" data points in each unique transit included in the baseline.\n", | |
" - ``per_transit_log_likelihood``: An array with the value of the\n", | |
" log likelihood for each unique transit included in the\n", | |
" baseline.\n", | |
"\u001b[0;31mFile:\u001b[0m ~/research/projects/astropy/astropy/stats/transit_periodogram/core.py\n", | |
"\u001b[0;31mType:\u001b[0m method\n" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"model.compute_stats?" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"We can look more closely at this specific candidate transit using the `TransitPeriodogram.model` method:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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AceiJiO5FKYw4mfYzMy+vp2CN4KqrrsJtf7GqaVbX9vf3I5/Py3oRQRACxS3M\n+BWNEiQMnnrqKeTz+cgaih//+N8AACNPPIEPnOn+/kAtceaDQA11jYyM4OLuWL/nIDQxcfF8oiin\n49CTsV6i/LdZUOVw0UUXRbr3fdNN6wAAt2+6PVxBXFBDXYObNoUsiTei2BAtCVlOr3NecclOoXbc\n5ij2EtHtAJYR0T1EdK+x3TPRhIloKRFtIaI1FsdSRLSZiNYb25yJpmfFP//v/x1ZbwIAbr55HQDg\ncyuj/f0oZWxXrFwZsiRCkMicl6BwNBTMvMFYkd3HzMuZ+Qpjm9D8hKH4lzPzIuP3UovTRpm5z9hG\nJ5JeXMldUjJi8+fPD1kSZ5Sxnf/BD4YsiRAkUY6RVtOHi2Li+kTxJQavQQG/536WLxYCUOMUg7BZ\nwGd4HYF7E3GJ9eSXsF+PjQtxKfaw5fQaoTSMdiTeTmMJ68NFKQCjAMDMYwBOtDhnDoBtAJYSUVVY\ncyJaQUQjRDSye/fuugobF6TxeOMLX/iCGFMPRLnjEWVvpxmpm6EwFLl5U0NMY6bTK4aWmHmMmZcx\n8ygzDwBYZr4/Mw8y8zxmnjdz5sw6PUW4+PV8wm48cfHQfvnLX4ox9UCUOx61fI8hikM6VkSxHdXN\nUBiK3LzdZxweQcljABGlAOzRr9WHm4z/dwYpG/le8xwP5Ktr3njf+97ny5iG1bMOO4id945Hc7Uj\noRq3BXeX2x1j5vtrTZSZtxkeRgrAFSjNU4CINjPzMpTettqEkueRAtBXa1pxJmxF4Ze49Nj+/u//\nHvNnW412WqP3rFvJCIe1LqdexKU5RVFMN4/iTWObj/HosYQAoscy86D6a8xTwDASauhppfHG00p1\nXHAm7DHlz17zWd9phy2zF8Ie0hOEsHF7PbbIzEUAe5j5e2pDQENBYRmAZn3rKewx5V/8/Oe+0w5D\nZr/lHtaQXlyqZ7O1I6Eaz3MURHQtEc0iohxKHobQIOIymf1bv/VbvtMOW2ZBiBpRNLxe11FsAPAi\nSm8fTW+WGFARLI8JEfZk9j/8wz/4TjsMmeNS7lFUGFY0Wsw4DFc2G37eenoCwBZmvp+IZtVHHEEQ\nBGfCHmJtRbx+uOgSACtRWlENAIvEWDSOuLxNJAiNoL+/H5lMBmNjYz5DeMSjHUWxvXv1KJYZw09v\nGr9HYayDiCPjk9nRKxCh/sSl2KOoMKwIox3t2rULzzzzjHgVDcLzF+6I6HoA04noIgArmfnf6iiX\nUAMbNmzAtGnTsGHDhrBFEYS6sWrVKuzfvx+JREJegmgQfoICFgHMADAPQG89hao3P/7xjwEA/3d4\nOGRJvOG1w3bLLbdg//79uOXaNIZUAAAgAElEQVSWW+orkA1x6akLzcHs2bN9hvCICREU1O3DRZer\nDcBslCa09wGI9XLN/ptvBgB84xvfCFmSYFm7di2SySTWrl0btigRJ4It0YK4GN5Gi7lx40Zks1ls\n3LixwSm3Ln5XZiti/b3Lm266CQDwpS9/KWRJvOFVYfT09OD9738/enp66iuQDXEZUxe8EdWhzFpf\nqW4mw9voV4RDXZkdFh/72CUAgOxvZ0OWJFhWrVqF4eFhrFq1KmxRhCYg7KFMwZ5Gt/WWXJndrCE8\nBG/EpdzDFvPKK69EW1sbrrzySsfz4pKfzcQ777xT8bfe+F2ZvRRNtDI7Lngd0gl77FYURnOxY8cO\nHDt2DDt27AhblApqHXaJy9Col9eNp06dWvG33nhdcDcNpXDfL5Z+2ocfjyKtsuQ/7BAetdAqZRNH\nohqHS1ZmN75T6HXo6UkArP2Oh2k2MFes8c8WxeoxmpIw5lViU+ohCxrVb2ZH1YA1km3btuHpp5/G\ntm3bGpKeV0OxnZnvN01ox4a4V6y4DOnERc64sOqLq0L1tKLq7clbT41/0cDPZPY9RHS92uopVNDc\nc889eOyxx3DPPfeELYpgIux5lSjzyx3hfttbhnjCgd1PafiaKa+G4jYAX0VpdbbaYsOdd96JY8eO\n4c477wQwPlkUlx6GECxxifH1vgsvDNUL9uqJN3oIN6qeTiNZvXo13nrrLaxevboh6Xl962k7Smsn\n2Ph/X12lCphrrrkGbW1tuOaaa8IWpSbiodZqUxiy9sOev/mbvw31xYSovhxRq6cTn3bkTqQW3Cni\nHmZ8cHAQR48exeDgYNiiCCbefvvtir+C4Ebc5xzd8DL01OhhwZYIM262vnHpWbQCxx9/fMXfRhCX\n8o/JCFnDMzSqnk4jabSxrDXM+Io4hRmP+6RcXBRGLXLKZHZ0aba5gLi0Iy802lj6WUehhxlfUTeJ\n6kCzu6pxRnqH0SXuHSwhONo9nreSmW8AsL2ewtSLXC5XoYjiF+spHoLGQ8r4lHvYYvb39yOfz3t4\n6ykuxEPSKErp1aPYS0QPx3UdRbO50ILQCMTbCwcvk9mNxqtHUTC2WKK70LlcTltHEUXbLdSbuIRu\niUv9jIucQu149Sj2MfN2tSGaRs+WuM9RxKUdxkVOoTWJS/2Mopie5ygA3KD9Xg7gqeDFqQ/mOQpB\nEISoEsVeuKOhMD5StAzAPCJKoWTsTkTMv3CniKLlFupPbHqWcZEzbAGEuuNoKIzPoBaJqJeZ7wg6\ncSJaD+A2Zh6zOLYUJU9mCzMPBJ12nIhPQ4yPpELrEZfaGUU5vcZ6ugMAgnzbiYjWYDwkiPnYHADL\nmXmR8XtpUOkKgiBEmSgOPXkOM24wI6iEDS9hxObwQgCbjP8HASwKKl2duLj2QmsSl+op7ShYbr31\n1si9yu/XUOypixTVpFCKJwVjWOpE8wlEtIKIRohoZPfu3Q0SKxzi8vphTMQUWpS4tKMXfvWryK2G\n9/rWk/pu9hbj/1nM/JLL+VZhPvYy830ekjPPWYyaT2DmQZS8DcybNy+K3pogCIJvzj7nHPT/6bKw\nxajAk6EwwozPRWn47CmUwoxvcTIWhiKvlRGUotOOGm9b1cWTicvCKyFYYtKxjI+cMWlHfX19aPvy\nH0X+Vfkbb7wRublnhC1GBaGFGTcms+cBuIGIeox9mwGAmbcBmGMYiStgeA6tSjyaYXzkFILlS1/+\nUuTG1K147tlnIzekY0UU21GtYcZXTjTMODMPMPNcZu4zDAOYeZl2fFD9tXp9VhCEaPDMjh2xUMDn\nX3B+bKMzhI3X12O/h8ow4731FEpLt64GIjaufWzkjIegcRkqiWbfsppMyN/2FuqP28rsy027njD+\n5gDcXxeJBEGIFRu/8Q0sODuwN+frhhp6ivocRRRx8yjeNLb5qOze1GVdgyAIQr04/4ILxPOpES8h\nPEBEPcbwE4zfs+stWCOIh2Mfn6GSeEgZp6G8sCXwRkzExNc2DOCS808OW4xY4nnBHRFdS0SzjECB\n8+sokxBTli9fHou3X4TWZPX1q6V+1ojXyewNAF5EKZLsdGa+oq5SCbFk27ZtsXj7RWhNno3J67G3\n3npL5AyaZ4+CmYvMvIGZm2cSOyY+c1yGIHp6Lo7FGHBc8jMmYsZG0AtiMkfxq1+9EDmD5jfWkxAC\nS5cujVwPQ0fJ9uUvfznSb5QoOR9/7PGQJRHCYMPXNkS6firOOefsyBk0MRQxIOpDOkq2r3/96yFL\n4oyS8+/+7u9ClqS5WPXFVZHuyMSNG29cGzmD1tKGIi5vE/X09ESuh6GjZPuzL385ZEmcUXJ+4U+/\nELIk3ljR2xsLBfzLX/4y0h0ZYeK0tKGIC/fdd1/kehg6SrZsdkHIkjij5Pzwhz4csiTe+MXTT8dC\nAb/vfbIyO0iiOIfmOcx4M1EsFjHjqX/Ezx/vxMKFlh/Ziwz3fz6L01KJsMVw5QdfWIDTT4i+nA/+\n6X/DnJlTwxbDlfyHOvCtR09G/803hS2KIw+t+gjmzLwMUyZPClsUR/71yx/BmSccF7YYrmz5s/+O\nU6ZPCVuMKlrSo8jn83jy4c246aZoN8JisYgv/sHv4Sc/nlD8xYbwgTNTmNQWwa6QifefMR2TJ0W/\n2n/2U4sw/Oj/ibQnCQCZ06ZF3kgAwKs7nsCll3w08kN555ycRHLK5LDFqCL6LaYO9Pf3I5vNRt5d\nzufzGB4ejsXwgxAsxWIRCxYsiLxiiwvSliZGSxqKXC6HrVu3Rr63FheDBohiCxpRbMGyZMkSJJNJ\nLFmyJGxR4gkzx36bO3cuNyuFQoGz2SwXCoWwRXEkm80yAM5ms2GL0hTEpdzjQlzqZ6PLHcAIe9Cx\noSv5ILZaDEVcGqJU8GCJi5xCsMSl3Bvd3sVQuCAKOFjiImdcyl1oTcSjiJihiItiiwtxUcBS7oIw\njldDQaVz4828efN4ZGQkbDFammKxiHw+j/7+/si/JCAIQgkiepKZ57md15JvPQnBE5c3yeKCvEUm\nRImWNRRxaYhxkTMuxCU/5fVYIVJ4GZ+K+uZ3jqJQKHAymYzFmHpcxv7jQiaTYQCcyWTCFsWRuMyl\nxEVOwRp4nKNoSY8in89j//79SCaTkV/MFqdFd0JwxGUob9WqVRgeHsaqVavCFkWoIy1pKJTyfeCB\nByLfEOOiMOLCxo0bkc1msXHjxrBFaQrefvvtir9Cc9KShkKUb+siZR8sxx9/fMVfYWJEdQ6tJQ2F\nIESdqCoMM+KhBUtUX2KQdRSCEEEWLFiA4eFhZLNZbN26NWxxhAbR6PVIso5CEGKMvMQgRAnxKARB\nECJCoz3JWHgURLSeiFIW+1NEtNk4vp6I5oQhnyAIQiOJqicZ2jeziWgNgIUAbrM5ZZSZ+xookiAI\nQqjkcrlIvpEXmkfBzAMAHMeLiGipnTdBRCuIaISIRnbv3l0XGQVBEIQQPQoPzAGwCcBSItrGzAX9\nIDMPAhgEACLaTUS/9nHvGQDeCEzS+hEHOeMgIyByBo3IGRxhypj2clLdDAURrbDYvZeZ73O7lpnH\nACwzfg4Q0SYABYfzZ/qUbcTLBE7YxEHOOMgIiJxBI3IGRxxkrJuhMHr8NUFEc5h5VP0PYGdgggmC\nIAi+CHsyex6AG4joHmbeRkSbmXkZgL2GFzEGIAVAJrUFQRBCIjRDYUxmD5j2LTP+jgFYWcfka/Z2\nGkwc5IyDjIDIGTQiZ3BEXsamWHAnCIIg1A8J4SEIgiA4IoZCEARBcKSpDIU5JIixYG+h/qquFhak\nHBrEOG+LMcEeFTnt9jVSzjVEtMl4scBWBq/7wpbT2G+V95GS08/zhCijakObVH5GMS+N/XPCyEuv\nctqFLGp0e3fEy/dS47ABWAPgSQAp43cPgIXG/3MALDU2tS8FYL1xbLN2j6URkNNqX6PlXAqgx/h/\noZFmlQxe94Utp03eR05OP88Toow9AOZo562IYl5q528CsCnCZZ4CsN50bUPbu+uzhJl4HQpmk6YE\nVqhC0o7NUQViFJCq5Lrx2BQBOe32NUxOJZ+bDF73hS2nQ95HSk6/zxNmXhr71qNkOCKXl8b/Sjmr\n35GTE+Od1qUYN8AN10tOW1MNPZkYQckQgIgWAjiRxxfxrQfwQeOcFIBRoPxa7olhy2mzr6FyGmnA\nkGEFxiu0WQav+8KW04rIyTnB52mIjMbxOUS0GSXFtq1RMvqRk4h6AIypdm8QOTmNU+YA2IZSyKKF\njZTTC01rKIzKq4xCCsCoUVhbmLmPS5Fp16O0qE9nFA3ESk6rfWHJSURLNZmsZPC6r654kNOKyMpZ\n4/M0TEZmHuXSuqc+o55GMS+XAzjLkG+hMdYfOTmZeYyZlxl5OoBS+KJQ9ZKZpjUUQGlRn2EQFqFk\nzctoE5ojKFlztW9PQ4WEtZwW+xoup2FYR3k8IKOVDF73hS2nFZGUcwLP0xAZjZ66Yq9xPHJ5qTqE\nRjvaxuMRqyMlJ2kRsmk8ZFHoekknytFjfUGmkCAoWeAbjMObDPdz0HirYBGM0CDMPEalkOUpAFeg\nzqskvchpyGKWHQ2WcylKPZu5RAQATzLzoFkGq/xrZJ56ldM41ypsTKTk9PM8YclonLseJeV1Fkrt\naDRqeWl1bRTL3Di3ImRRo/WSG7Iy24CIUvqYYlSJgpxWMnjd10i8pi9yuhMHGf2kL3L6QwyFIAiC\n4EhTz1EIgiAIE0cMhSAIguCIGApBEATBETEUgiAIgiNiKARBEARHxFAIgoEROkH9P0f/PcH7bjLe\nqa9Iy7zP5z1XGGsZBKHuiKEQhHGWqX+McAoFp5P9wMz3BXUv436DKC3OEoS60zQrswVhIhihFuYZ\nvXQVvG0eSqEUVqIUnnwZxqOlftCIdaRW4C5CKSy0o3Gh8W8LnATgCWPl7XqUwjaAmQeIaBMzr9Tk\nGkEpKNwy47xtQRoxQXBDPApBQLmHPmLEBqoKwGYc34RS+PcBlJR8jxH3aI6h2Hu0GGJVGENZY+p6\n475jzLzS2PdB49TN2rDUXCOY3DKUQuQPiJEQGo0YCkFwZ6fxdwzjUT3V3+VAuecPGIHcbOgBUKXk\nDYOzFEDKCNlQALDIMDoq7T6UIrVuMQXlE4S6I0NPgjCOl5j/e02/d6LkiWzzcO0oSoZEDye+EKUP\n3NxnBKvU73sDgNuA8jcJ1HDUJvW/IDQCMRSCMM6oMUdxj49r7gVwh6G85wAoWA1dAaUJbSp9G7kH\npairW1AyGuuJ6ERUeiODKM15jAHleZAPojR3stnncwnChJCggIIQAIZnMGplJPTJaZ/3g9N8RC33\nFYRaEI9CEAIgyAlmw3s40ZhAF4TQEUMhCA2AiJZ6XUvh5Txj8jz07xQIrYEMPQmCIAiOyOuxgiAI\ngiNiKARBEARHxFAIgiAIjoihEARBEBwRQyEIgiA4IoZCEARBcEQMhSAIguCIGApBEATBETEUgiAI\ngiNiKARBEARHmiLW04wZM3jWrFlhiyEIghArnnzyyTeYeabbeU1hKGbNmoWRkZGwxRAEQYgVRPRr\nL+fJ0JMgCILgiBgKQRAEwRExFIIgCIIjYigEoYkoFotYsGABisVi2KIITYQYCkFoIvL5PIaHh5HP\n58MWRWgixFAIQhPR39+PbDaL/v7+sEURmoimeD1WEIQSuVwOuVwubDGEJkM8CkEQYkeQczEyr+MO\nMXPYMkyYefPmsSy4E4TWYcGCBRgeHkY2m8XWrVsjc6+4QURPMvM8t/PEoxAEIXZYzcXU6hnIvI47\n4lEIgtAUtLJnUCviUQSMjGM2J81arq04hh8Hz8BLXtqd43Tthg0bMG3aNGzYsCFwmQEAzBz7be7c\nuVxvstksA+BsNlv3tITG0azlGuRzxSGPCoUCZ7NZLhQKYYviiJe8tDvH6dpkMskAOJlM+pIHwAh7\n0LGhK/kgNr+GopZKFZeK2OwEXQ5xL1c7+YN8rjjkURyMGbO3vKylTAcGBjiZTPLAwIAvecRQ2FAo\nFMrWN+qVKuqEoUDsFEIclFk9CEpB1jP/gjZmVtfFqfyjJKsYChtUw0omkw3rlfqtGPWoSEHLyBxO\nL85OTitZCoUCZzIZzmQykWiUQRPk89WzLP0OpbjVRbs23CgFPBGvgDla3k/LG4pGuOQKt4J3Om4l\nTyaTYQCcyWQ8ne8Fp3s6HXdKL0rDQFbXqnz3o4yi1Ntzw6/CUc82MDBQU4+81s6Gn/1uHr8yjolE\nouqcRilglU4mk7F9LrdnaES78ZJOyxsKt8K0otYemluBOI0fWlVuJ6Vea2NwMwTd3d1Vx70M01k9\ne63jpbWUmRNO5anyI51OV6QVpd6eG34Vt94Tr0WJTaRD5PUZ3Dx+dTyRSFSVq1N5OxlJv6h7qTrU\n1tZWUddrGbVQsnd3dzvK76dcnORQ9wPwPEfZUABYCmALgDW1HNc3s6HQK4wqTJWJXjJcVcSgeptO\nDchKqdbai/fSYzQfs1OYutxWjdLp2dzewHDrAZnLzO25nc6xUx7KMHZ0dHiuH1HDTVZz50Cd39vb\n66vjoq5Np9OcSCRsOwC15J2djE7Gz8nQ2Q1B6nUyiI7AwMBA2asx1/Va8kHXPU7ye9Uhbtdp6b3N\nUTUUAOYA2Gz8vwbAUj/HzZvZUOiW1KwY3ZQ2EdkOV7gN39hhp8DslKLbtXbneWkM5mdQv50MY1dX\nV9mYmLGqpKoRdXd3OxoXvaejP6ff4Tjz81sNR5ivdTKQcTEWbj14uzyz62k6GQO7Ybxa8kq/xk+b\ncut1M1vXRyd9UGunTNW1zs5OW6NrlsUtrXQ6zR0dHVXtxquX5acjqnWio+tRAFgBYKHxfwrAJj/H\nzZuVR6GsvVkBOvW6VYZ3dnZaNha3Su1nyMVJUemyeu0B6b1/J/dV5Yveg3NLQ11DRFUNTD2HuRK7\nzbOY03RrDF7vZ6X8zM+s9tsNU0R9+MmLwlTn2XlYTmP8du3GKk234UKrfHZS3E7y2xkr/Vy7IVQ7\nA1jrMK+Xtm72rGs17F68LD/DUvo+eJyjmLRu3To0mptvvjkHYOu6dev2rVu37tDNN9/8x+vWrdvs\n9biZP1t9w7ppZ12MIx3T8fLeA2iffgr+/clf4h1MAaaehKd2vooL538UL+89gKdH/xP//uQODP3k\nMbw6dghbf/48Lvpvi/Dy3gOYkT4Prx04hqld3Rh7rx2vHThWvu7lvQdwsG0qfvWbfVjyh9cieers\n8v6X9x7AD4qP4q+++nUcnZLCYzt2YsGlv4+v3f4d/OHnvoRXxw7jhf/ah8+vyePdyUkc6ZiOHxQe\nxTuYgiOd07HqhptxxvkXV93v83034XD78UieNgdf/It15ef7QfFR/PHn/xx/+527wVNPwpGO6WXZ\nj5t5Jv7zzcM49dzfwu8t/58V9/zcV/4KY++147iT01hz83oc6ZiO9umnIDP/I3jtwDFctfJL5TTK\nMqzJ44O/8wm8tOdtTJrWVc7Lr9x0G36x81Xse68dU2acgfc6p5eP/aD4KL5f3Ao6fgamnTannL9q\ns0pzRvo8bP3Ff+BIxzTc9/C/4/iT02VZvnb7d/DT7c/ihDPOQceJp5afWd3vKzfdhtfefg+TTzwN\nn/3iGnwgmysfe3r0P/Efr+7BjPR5+PzqtRV1pPDYU/iPV/dU1A+9HvzR576EnvNno62NGtEsbPmn\nH2zBZ78yXne+ctNt+I9X9+AdSliW89du/w6u7P1TPPzoCFb8+V9W1K3PfeWv8NLu/Th5TgZnfeBD\n5fz/QfFRPLz1SRxuPx7tqVMs283jO3bijUPAGwe5fGzfu5Pw3CtvYN+7bfjPNw9XtKev3f4drL55\nPfa924Z9RyaVr5mRPg+P7diJw+3H47UDx/C/bv9HtE8/pareffdfHsEL/zVWvueM9HkYff0tzEif\nh89+cXVVHfjFzldxZEqqqt61Tz8F3/neEI50TMNPntyBaafOLl9794NbcHhyEjx1Br774JZyHuv1\n4IzzLsJ1q9fi1bHD5XZ/5vkX4w9Xfglnmtqtvk0+4VT86jf7cPV1f47kqbPL9/vQJb+LG9d/vSKt\nl/ceKOsEc5tpn34KFi35g4o80vPp9HN/C3903Z9XHVfPMPr6WzjYdlyFrlD79vz6+bfXrVv3t251\nMKzvUYyZfo/6PA4iWoGS54FJU0/A4P0FrDhpdvn4ZVd9Hj/84RAA4CO/+3vY/vI+AMDg/QX815EE\nJs08C5MAHD1hJra/vA87R0dRLBaRW3I1gNJy+Y/kchXHDh06iGOpbvz46Zdwzof3VcgzeH8BHaec\nCyLCoksvxfaX9+HuwuM4lurGPz30U8yY+Sx27ztalvOyqz6Pu+66C+8ePlwlu7rfsVQ3kid34jOf\n+Qxw0uyqZ1D/rzhpNnDSbPzPP1+HRx99FHt/8hOcPf93yucrjp7Qjc4jCczsmllxP3XtztFRDK4d\nQC6Xw1lz5mDw/gJ27TuKd5NHsOKG2yry5CNLrsbo2HfAzGif3I5Zp55WPjZ4fwF00mwkOztx2Wc+\nUyWHniaA0vGTZqPrgvnY/fruyucCcHfhcdBJs3GICAePcFV+fWTJ1dh1oJSXetnsHB0t5fGRBM7s\n7q58ZuO6d5Pjz2SWbctzr+Pjb7yDc05OVsvfQG596Hm8Nnas/NwfWXI13ppcXbcVKr/eBKryStUB\nTk3Hu8np2PkmA0aZjU06AZ2nnYD2ye044YQTLNuNKtePLCmV64+ffgl00my0o6RMVHtScnScci5A\nwMyZM8fvd9JsXP1nN1XUJ52Ndz2I3fuOYlLnTHSeduL4PU+ajS/kv1Y+z6osMxdcgGeefRY5030X\nLe/Fw488DHDp/quMPOk47Tx0tp+IgwTs3He04piqB7fceiuOpbpxd+FxnPPhRa7lVdYluRxu/pt/\nHJfVuN/gHXdgl6YLFJdd9fnydZZtxoRqn7d885/xmc8wzpozp1qWNxl721KVeuak2Zh86nnY9fLL\nAHC6a0JAaENPPagcWlrj57h5mzp16oTfLtDdMTuXV58gNw/BWLnQAwMD3NbWZuuaOw0hOA2NeBk2\ncXrTwS6vzG6q2/lOk2hub5j4mXhW6fT29noe3nDLCy95cuMDT/Pz//WW5XWN5ONffYh/20c+Os0R\n2c2NOdUpp+NOw7xuc1V2ee9lWLYWrIZ29GFL8zH1DJ2dndzR0WFbz/0OXfqd1/EyWW039Oc0H2Xk\nx0GO6hwFj89DpNRfY99mp+N2WxCxnvTCc1KY5sqmziUix4bpppytKpZThbO7r26c/I6x+5lj8VLZ\n7d5AsRsjrxWnN3VqkVGx9vtP83O/Cd9QLN80zMeOHbM85nc+xYsR94ObgXGSbyLlVqusfjoazM5v\n8FnJ72Yca0HJkEgkPL8U46WclfwcZUPBhrcwkeNqO/fccwOtWE4V1ep1PqWY/So+O4/CrpCdjJk6\nbjWp67Xh+VE6Xs61eutD770FtTrey/M5KQm7a/Pff5qf/c2bE5ZvoizfNGx7zK9SDXqi3kvv2W1t\ngx+Psx4GxAmnzpOVPLUG53N6NiWDmqj30nH0Us6FQoERxOuxAL4K4JsW2+3a39u8JFTPberUqQ3r\nmfh19Z3ksCtML/ut7mnnZnpVDn7yy8u5fr0vL2naDf3VYgTdFFXmqpv4Hx54xJeM9cDJUPgl6Dbh\nxQDXYpyshomCNnITwcmjn8hiUzfF7tV46m3FyatAECuzAfS63sDDOfXe7DyKWoZu6oFfF9ut8P2u\n3pzIs07kWivvayILnqyG6SZiBN2GRU7IreAPLlriW86gCdJQNAq3To1bvbKbT2i0R2FHrUYr6Dbq\nZWjPqc0FYijKJwGzTL8v8nJdoza7OQqnzG9k7yTICm6lLOvJRPIp6OGDiXgUXuXT0zlrWR9/OwIe\nxRW3x89QuJWLlyGrqBgFK9zk8zuKUGt6Xobv6u5RlE8yDS9FYbhJ32qZzI56RbTDacx3Ivf0M1bc\n7KjGnLkqz0+/Mha2OLE0FG7Uox7r9w67zvodRaj1frWiDQlOfGU2gJwxDzGizUnc2wyGQhgnSmO/\nUUA1omv/7kdiKLh+irde9c583zAMR6PmgmpFG5kILtZTFOYhnLYoG4paC7iRFS0KPbAo0v/gDv7F\nLjEU9VLo9ap35vtKR6iaQD2K8knANADXG17F9QCmebmuUVuUDUWtlTToyi2NxT+3PLiDf75rX9hi\nhG4o4t6RiKL8UZEp6DmKJwBcC2A6gNkAvunlukZtUTYUcfAo4kw9n+vWoR381MtiKITgiUrHLWhD\n8YLp96fNb0KFuUXZUAj1pZ4N7v/74TO8XQyFUAei0nHzaii8BgUcJaJvAtgJgADMAzCbiMDMX3O+\nVBDqR39/P/L5PPr7+wO/d7gxY4VmJpfLIZfLhS2GZ7waij7jL6PUfgr1EUcQ/FGvBlcsFvHP//xj\nzPj0R3HRme4RQ+sJicUSQqbN43k7ASwE8DmUXpndyczbmXl73SQThBDJ5/N49dVXMDg4GLYoghA6\nXg1FEcA+lDyL7wFYXzeJBCEC9Pf34/TTT8e1vSvCFkUQQsfr0NMJzPwt4/83iahARLOY+aU6ySUI\noZLL5XD1kdMx74KusEURhNCRyWxBsIEIKL3kJwitjd/JbIVMZgtND4EQBTshxkoIG0dDQUSXM/P9\nTpPW6pzgRROE8BElLQjuHsWlROT0biCh9MqsGAqh6SgNPYmlEARHQ8HMn2uUIIIQNVQvSBBaHa+v\nxwpC60EkQ0+CADEUgmBLyaMQSyEIYigEwQaSsSdBAODRUBDRNNPvi+ojjiBEh6i8HisIYePVoxgg\noksAgIg+jVLcJ0FoaiQYnyCU8GQojLef5hLRwwD2yWpsoRUgRGMdhRgsIWy8Dj3NAnAWgL9AaW3F\nNMcLBKEJIJLJbEEAvIfwWKmtqdhORLcBuKFOMglCJCB5PVYQAHg3FCNEdLn2+4l6CCMIUSMKdkKM\nlRA2fl6PJWM7C0C4n1q6+bkAABBdSURBVPwShAYRhRAeY2P7sGDBAhSLxbBFEVoUr5PZ39O2DfUW\nShCiwE9/+n/w6U9/Ghs2hFvlX3zxJQwPDyOfz4cqh9C6eBp6IqLVGPfClVchCE3NvxWLOHDwEG65\n5RasXr06NDlmz56Fmdks+vv7Q5NBaG28zlEUMG4oxoLwKohoKYCVALYw84DpWArAHQBGjV2bmHkU\ngtBAcrkcHtn8Cv5i7dpQ5UilTsDDW7eGKoPQ2rh9j+JelAwEmfYzMy+vNVEimgNgOTMvIqI1RLSU\nme8znTbKzOYPJglCw/idj34U1y77XeQuODlsUQQhVNzCjF9Rp3QXAthk/D8IYD0As6FQXsc28SaE\nMJBPoQpCCcfJbGO9RPlvgKRgDCsx8xiAEy3OmQNgG4ClRFQVMoSIVhDRCBGN7N69O2DxBCEasZ6K\nxSK2b98ubzwJoeI2R7GXiG4HsNAYLlJDUK5DT0S0wup+xhDTmGl/hcdgGI9lxs8BItoE03e6mXkQ\nJW8E8+bNC7s9C01IFL5wl8/n8Vb6fyCfzyOXy4Uqi9C6uA09bQBKgQCZ+Xt+bmwocjtGUPIYRo2J\n6z36QSKao4abDAO100/aghAUYfdA+vv7cd3m5+WNJyFUPL315NdIeLjfNmPoKAXgChieARFtZuZl\nKHkym1DyPFIAZFJbaDgUgWh8uVwOF79wHHK53w5bFKGFCe3DRcrjYOZBY6gJhpEAM48x80pm7jP+\nmoeqBKHuRCV67NjYmKzMFkIl1C/ciQEQokzJoQjfUrz44ouyMlsIFa8L7gSh5YiKRzF7tqzMFsLF\nbcHd5XbHmPn+4MURhGgRATshK7OF0HEbenrT2OZjPHosQaLHCi2AfI9CEEq4vR5bBAAi6tHffCKi\n2fUWTBDCRr5wJwglPM9RENG1KC16OwslD0MQmpqozFEIQth4/R7FBgAvorRaenodY0AJQnSg8EN4\nCEIU8PPW0xMA9jDzU0Q0i5lfqpNMghAJSh6FmApB8ORRENElKH07QgXnW0REs+okkyBEgueffw7r\n1q2ThW5Cy+N1wd0yY/jpTeP3KEqxmgShaXnwX/4Fv/rVC7LQTWh5PH/hjoiuBwAiugjASpmnEJqd\nT37yk3jw6BH0f7Hmb3QJQlPgOSggEV0MYDmAeQB66yqVIESAzAUX4KL355HrOSNsUQQhVPyuzH7C\n+JsDICuzhaZGvnAnCCX8rsxWyMpsoekhhB9mXBCigKzMFgQ7KBqxngQhbGRltiA4IOsoBMH/yuyl\nkJXZQotACN+jKBaL2L59u6zlEELF64K7aSh9kvTF0k/78OOC0CwQhT/2lM/n8dZbb8laDiFUvC64\nexKVTUZm+YSmp+RRhGsp+vv7MW3aNPlokRAqXucotsuHioRWIwqvx+ZyOVz8wnHI5X47XEGElsbP\nZPY9GF9HAWb+Wl0kEoSIEIGRJ0GIBF4NxW11lUIQIghBvnAnCID3t562A9hZ+pe3A9hXV6kEIQLI\nF+4EoYSEGRcEB8SjEAQJMy4ItpB84U4QAHg3FCrM+HQjzPgKZv63OsolCKFDgLgUggB/6yiKAGag\nFGZ8Rd0kEoSIEJW3nqIgg9DaeH3raSUz3wBgez2FEQRBEKKHV0Oxl4geBrBF7ZB1FEKzE5XXYyUM\nghA2nj+FamyC0DKUVmZHwFIIQsh4naPYx8zb1YaAhk2JaD0RpWyOLSWiLUS0Joi0BMEvUYgeK4ZK\niAJeDcVK0+8Jf23eMAALbY7NAbCcmRcZv5dOND1B8EsUYj0xl+QQhDBxNBRElCOi21FaYPdNIrqd\niO4NImFmHgAwYnN4IYBNxv+DkE+vCqFQuY6iWCxiwYIFDf02BEM+ySqEj5dPoRaJqJeZ72iQTEDp\n2xejhgxjRHSi+QQiWgHjNd3u7u4Giia0CuY5inw+j+HhYeTzeeRyuYbIwMziUQih4zXW0x0AYCy6\n8wQRrbDYvA4hjZl+j1rINMjM85h53syZM72KJQieMevn/v5+ZLPZhn4bgiFDT0L4eA4zbjDD64nM\nPOjz3jojKIUIGTUmu/dM4F6CUBNEla/H5nK5hnkSCmYZehLCx+tktiIwhW1MZs8DcAMR9Rj7NgMA\nM28DMMcwElegNE8hCA3F6gt3jZ6nYMjQkxA+ng2F8d3sLcb/syaaMDMPMPNcZu4zDAOYeZl2fFD9\nZWbzUJQg1B2rt570eYp6UywW8bHf+Rj27t1b97QEwYlIhxkXAyGEiVWsp0bOU6xatQqPPf44nnv2\n2bqnJQhOeJ2jWMbM1xFRr/FbhRl/qS5SCUIEsArh0fh5iigs+xNanVrDjK+UMONCKxDmF+42btyI\nD334w7jggkxoMggC4P312O+hMsx4r/MVgtAEhLwyO5fLoVAoYMaMk8ITQhDgMvRERJebdj1h/M0B\nuL8uEglCRIjKy0ZRkUNoXdw8ijeNbT4q66uE1BCaHmrAe6nqddsNGzZYvnbLDZJDEJzwEsIDRNRj\nDD/B+D273oIJQtgQ6h+9Vb1u+/TTT2P//v0V4UGKxSJuvPlWdH1SAigL4eJnHcW1RDSLiHIoeRiC\n0NQ0Inqset127dq1Va/d5vN5PP74z/D888/VVwhBcMHrZPYGAC8CWAZgOjNfUVepBCECkCl6rGLD\nhg2YNm0aNmzYMOE0crkctm7dip6enqpj/f39+NCHPoTzzz9/wukIwkSgZvgwyrx583hkxC5iuSDU\nxtYX3sDIS/vwpYXnlPcVi0VceumlOHbsGJLJJN56661A0lqwYAGGh4eRzWaxdevW8v43DxzB9ff9\nHHdcPS+QdARBh4ieZGbXyuU31pMgtAxWsZ7y+TyOHTsGALjyyisDS8tuxTeD5a0nIXT8Ro8VhJaB\niDD8wh4cPfZ8ed/FV6/FC4nNOPzuu/i/b5+E//XI8w538MMZuPymb+Opo8BT2j0PHTmKNnnrSQgZ\nMRSCYENPOoXrP35exb6PnDMTF540Cd/4xtdxZNdeHPfWy5g7d25d5Th1+pS63l8Q3JA5CkGogQsv\nvBDPPPMMMpkMduzYgWKxiHw+j/7+/oZ/s0IQakXmKAShgUwk/HgY3+IWBD+IoRCEGti4cSOy2Sw2\nbtwIwDn8uNvq60Z+40IQaoKZY7/NnTuXBSEqFAoFzmazXCgUmJk5m80yAE4mkwyAs9lsxbmZTIYz\nmUz5fEFoFABG2IOOFY9CEALG7CG4rb5+5plnsGvXrrDEFQRXZDJbEAKkWCxi1apVAErDU24T28Vi\nEZ/61Kewf/9+ZDIZpFIpmRAXGoZMZgtCCCgPIZVKeVL2uVwODzzwALLZLADIXIUQScRQCEKA1PJN\nbRXvSU2QN+J73ILgBxl6EgRBaFFk6EkQGozbeoggo84KQiMRj0IQAkJFgE0mk3jggQeq5iimTZuG\n/fv3o62tDY888ohMWAuhIx6FIDSY/v5+JJNJ7N+/H5/61KeqPIu1a9eira0Nx44dq5iwlpXZQtQR\nQyEIAXLmmWcikUiUP2uqs3r1ajzyyCOWaynkbSchyoihEIQAUOshnnnmGcyePbvKGCivAQC2bt1a\nMexUy5tSgtBIZI5CEALAbX7CHG1WEKKAzFEIQgNRXoGVkRCEuCMfLhKEAMjlcrYGQk1SZzKZcrRZ\nQYgT4lEIQp3xG9ZDEKJGqIaCiNYTUcpif4qINhvH1xPRnDDkE4QgkMlqIe6ENvRERGsALARwm80p\no8zc10CRBKEuOA1LCUIcCM2jYOYBAI6vKhHRUvEmBEEQwiXKcxRzAGwDsJSIFpoPEtEKIhohopHd\nu3c3XjpBEIQWoW5DT0S0wmL3Xma+z+1aZh4DsMz4OUBEmwAUTOcMAhgESusoJiiuIAiCYEPdDIWh\nyGuCiOYw86j6H8DOwAQTBEEQfBHaymxjMns5Sp7CPcy8jYg2M/My402o9QDGAKQA9Bleht29dgP4\ndSPkdmEGgDfCFiIiSF6MI3kxjuTFOFHIizQzz3Q7qSlCeEQFIhrxshy+FZC8GEfyYhzJi3HilBdR\nnswWBEEQIoAYCkEQBMERMRTBUvMEfhMieTGO5MU4khfjxCYvZI5CEARBcEQ8CkEQBMERMRSCEBBG\nyJktxqvfvo83Ex7yYg0RbTIW0zY1XsqdiOZEOS/EUARAM1SEoGhVBWEsDF3OzIuM30v9HG8mPOTF\nUgAFZl4JYHMzG04f5R7pAKhiKCZIs1SEIGhxBbEQgDJ+gwAW+TzeTLg9a4GZtwEAMxcAnNVA2RqN\na7kb7WB9I4XyixiKidMUFSEgWllBpACMAuVYZSf6PN5MOD6rHmXBiAnXVN6lCce8IKIeAGMqZFFU\nEUMxcZqiIgREKysIc4gZc3m7HW8mPD2r4WGOqs5Dk+KWF8sBnEVE6wEsjKqXLd/M9oBLJFwvFQF6\nRTC+xRFLJpgX6h7NqCBGUAqNP2rEKtvj83gz4fqsRj0aabI6YIVjXugfZzOCoUZSN4ih8IBLJNym\nqAhemUheAM2rIIygliuM574CxmIqFejS7ngz4pYXRkdhGYC5RAQAT04k2nSUccuLcKXzjiy4CwBD\n+d2LUkW4l5nHrCpC3CpHLTjlhaEgVmLc02g6BUFEKZdIx47Hm4lWelY34p4XYigCIu4VIUgkLwSh\nuRBDIQiCIDgibz0JgiAIjoihEARBEBwRQyEIgiA4IoZCEARBcEQMhRBZiGih9v8c/XeAaQR+3zrc\nryyj272NgIvmGFsLaw1CaKwBaIXwM4IDYiiEKFNec8LMo0Z8qECp030DXStjktH13sYq+aDSHkQp\nNIvQwsjKbCGSGAv35hm92U0oKat5KK3+XgngSZSU5noAPQA+qBYzGr3nRQA2m42A0SNfBmAngG0A\n9mr3vcE4rQfAXGOx4BoAJxn7NzHzqMv9reReaNzjuwCuM9IGMw8YscAq0jXksZIR+r3d4odpcYNO\nAvCEsTp4vUofwFlGJN/yinmU4nOV066HcRbihxgKIZIw8yARzVUhUAyFaj6+F0CPoXDXaOfMYeaV\nxr4R0+K/ZQDWKyVrcV+1gnwhEY2hFNCxHHbFON/2/jZyL1Kh11EyciCizXbpomSEqmQ039sJwyCO\nGdcsNa4fM6W/iYiWGh7IXOPcTXraggDI0JMQT1SPeAzjgQjVXxWEUQUvnGO6tg9An/FxpR7TsSdM\nv3sAmHvUbve3Yov6h4h6DMWdMnr4Vuk6yegVK9kr0kfJg1hkyKHyNIi0hSZDPAohynj5ZsNe0++d\ncAg6aOpVb4JzqPNtKClcvXfteH8DS7mNXn6Kme8jItsPF7nI6PU7FqMwAjS6pL8TpaGv22zSXukx\nPaGJEUMhRJlRYzz+Hh/X3AvgDkPJzUHpY0m6slwK4IMo9ag3W9+iBDMXjLeI1Pnr3e7vIvcogPVE\ndCIcPBEXGcv3djJWhjHYbHgFZ6Hk1VilP4jSXMuYh7SFFkViPQlNidF7Hq3XWHu9718rRLRJTVB7\nPH8hUP7iYCD3FJoP8SiEpqTeb+s0w9tAhvdwYrOFeheCRwyFIDQZ2ptMjng5x5i0l5DxLY4MPQmC\nIAiOyOuxgiAIgiNiKARBEARHxFAIgiAIjoihEARBEBwRQyEIgiA4IoZCEARBcOT/Aarxx5FtcIDo\nAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<Figure size 432x432 with 2 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Extract the parameters of the best-fit model\n", | |
"index = np.argmax(results.power)\n", | |
"period = results.period[index]\n", | |
"t0 = results.transit_time[index]\n", | |
"duration = results.duration[index]\n", | |
"\n", | |
"fig, axes = plt.subplots(2, 1, figsize=(6, 6))\n", | |
"fig.subplots_adjust(hspace=0.3)\n", | |
"\n", | |
"# Plot the light curve and best-fit model\n", | |
"ax = axes[0]\n", | |
"ax.plot(t, y_filt, \".k\", ms=3)\n", | |
"x = np.linspace(t.min(), t.max(), 3*len(t))\n", | |
"f = model.model(x, period, duration, t0)\n", | |
"ax.plot(x, f, lw=0.75)\n", | |
"ax.set_xlim(t.min().value, t.max().value)\n", | |
"ax.set_ylim(-1.52, 0.4)\n", | |
"ax.set_xlabel(\"time [days]\")\n", | |
"ax.set_ylabel(\"de-trended flux [ppt]\");\n", | |
"\n", | |
"# Plot the folded data points within 0.5 days of the transit time\n", | |
"ax = axes[1]\n", | |
"x = (t - t0 + 0.5*period) % period - 0.5*period\n", | |
"m = np.abs(x) < 0.5 * u.day\n", | |
"ax.plot(x[m], y_filt[m], \".k\", ms=3)\n", | |
"\n", | |
"# Over-plot the best fit model\n", | |
"x = np.linspace(-0.5, 0.5, 1000) * u.day\n", | |
"f = model.model(x + t0, period, duration, t0)\n", | |
"ax.plot(x, f, lw=0.75)\n", | |
"ax.set_xlim(-0.5, 0.5)\n", | |
"ax.set_xlabel(\"time since transit [days]\")\n", | |
"ax.set_ylabel(\"de-trended flux [ppt]\");" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"That looks pretty good!\n", | |
"\n", | |
"The standard way to find more planets in a light curve where the highest signal-to-noise transit has been detected is to remove the in-transit data points and then run the algorithm again.\n", | |
"We can do that using the `TransitPeriodogram.transit_mask` method" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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qq6szsg5Fk1scEkKEz2i54447LLs7/+bEgQMH8PLLL4MONCLPH2m0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\nIkREFNv/ByP+aZqkCN9cAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<Figure size 432x648 with 3 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Find the in-transit points using a longer duration as a buffer to avoid ingress and egress\n", | |
"in_transit = model.transit_mask(t, period, 2*duration, t0)\n", | |
"\n", | |
"# Re-run the algorithm, and plot the results\n", | |
"model2 = TransitPeriodogram(t[~in_transit], y_filt[~in_transit])\n", | |
"results2 = model2.autopower(durations)\n", | |
"\n", | |
"# Extract the parameters of the best-fit model\n", | |
"index = np.argmax(results2.power)\n", | |
"period2 = results2.period[index]\n", | |
"t02 = results2.transit_time[index]\n", | |
"duration2 = results2.duration[index]\n", | |
"\n", | |
"fig, axes = plt.subplots(3, 1, figsize=(6, 9))\n", | |
"fig.subplots_adjust(hspace=0.3)\n", | |
"\n", | |
"# Highlight the harmonics of the peak period\n", | |
"ax = axes[0]\n", | |
"ax.axvline(period2.value, alpha=0.4, lw=3)\n", | |
"for n in range(2, 15):\n", | |
" ax.axvline(n*period2.value, alpha=0.4, lw=1, linestyle=\"dashed\")\n", | |
" ax.axvline(period2.value / n, alpha=0.4, lw=1, linestyle=\"dashed\")\n", | |
"\n", | |
"# Plot the periodogram\n", | |
"ax.plot(results2.period, results2.power, \"k\", lw=0.5)\n", | |
"\n", | |
"ax.set_xlim(results2.period.min().value, results2.period.max().value)\n", | |
"ax.set_xlabel(\"period [days]\")\n", | |
"ax.set_ylabel(\"log likelihood\")\n", | |
"\n", | |
"# Plot the light curve and best-fit model\n", | |
"ax = axes[1]\n", | |
"ax.plot(t[~in_transit], y_filt[~in_transit], \".k\", ms=3)\n", | |
"x = np.linspace(t.min(), t.max(), 3*len(t))\n", | |
"f = model2.model(x, period2, duration2, t02)\n", | |
"ax.plot(x, f, lw=0.75)\n", | |
"ax.set_xlim(t.min().value, t.max().value)\n", | |
"ax.set_ylim(-0.9, 0.4)\n", | |
"ax.set_xlabel(\"time [days]\")\n", | |
"ax.set_ylabel(\"de-trended flux [ppt]\");\n", | |
"\n", | |
"ax = axes[2]\n", | |
"x = (t[~in_transit] - t02 + 0.5*period2) % period2 - 0.5*period2\n", | |
"m = np.abs(x) < 0.5 * u.day\n", | |
"ax.plot(x[m], y_filt[~in_transit][m], \".k\", ms=3)\n", | |
"x = np.linspace(-0.5, 0.5, 1000) * u.day\n", | |
"f = model2.model(x + t02, period2, duration2, t02)\n", | |
"ax.plot(x, f, lw=0.75)\n", | |
"ax.set_xlim(-0.5, 0.5)\n", | |
"ax.set_xlabel(\"time since transit [days]\")\n", | |
"ax.set_ylabel(\"de-trended flux [ppt]\");" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"As before, we can also compute some descriptive stats for this candidate." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"{'depth': (0.7500795973393076, 0.20070696579585914),\n", | |
" 'depth_even': (0.7438348111335513, 0.2778603300977977),\n", | |
" 'depth_half': (0.5065211961716971, 0.16526124604144968),\n", | |
" 'depth_odd': (0.7568447823955435, 0.28916538425668015),\n", | |
" 'depth_phased': (-0.0008858657573496174, 0.28916705509009644),\n", | |
" 'harmonic_amplitude': 0.006341693792687206,\n", | |
" 'harmonic_delta_log_likelihood': -6.947768314262834,\n", | |
" 'per_transit_count': array([6, 6, 7, 6]),\n", | |
" 'per_transit_log_likelihood': array([1.9920972 , 1.73864747, 1.60403579, 1.69796207]),\n", | |
" 'transit_times': <Quantity [1979.27543339, 2003.92265756, 2028.56988173, 2053.2171059 ] d>}" | |
] | |
}, | |
"execution_count": 10, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"model2.compute_stats(period2, duration2, t02)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Now let's do it one more time to find the third planet." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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Xf0b33K3sVxWeU1yJpC/VrN6W+KpeN2SbwOCLm4iePXta3FADBjSPX8hUVlZG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\nAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<Figure size 432x648 with 3 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"in_transit2 = in_transit | model2.transit_mask(t, period2, 2*duration2, t02)\n", | |
"\n", | |
"# Re-run the algorithm, and plot the results\n", | |
"model3 = TransitPeriodogram(t[~in_transit2], y_filt[~in_transit2])\n", | |
"results3 = model3.autopower(durations, maximum_period=50)\n", | |
"\n", | |
"# Extract the parameters of the best-fit model\n", | |
"index = np.argmax(results3.power)\n", | |
"period3 = results3.period[index]\n", | |
"t03 = results3.transit_time[index]\n", | |
"duration3 = results3.duration[index]\n", | |
"\n", | |
"fig, axes = plt.subplots(3, 1, figsize=(6, 9))\n", | |
"fig.subplots_adjust(hspace=0.3)\n", | |
"\n", | |
"# Highlight the harmonics of the peak period\n", | |
"ax = axes[0]\n", | |
"ax.axvline(period3.value, alpha=0.4, lw=3)\n", | |
"for n in range(2, 15):\n", | |
" ax.axvline(n*period3.value, alpha=0.4, lw=1, linestyle=\"dashed\")\n", | |
" ax.axvline(period3.value / n, alpha=0.4, lw=1, linestyle=\"dashed\")\n", | |
"\n", | |
"# Plot the periodogram\n", | |
"ax.plot(results3.period, results3.power, \"k\", lw=0.5)\n", | |
"\n", | |
"ax.set_xlim(results3.period.min().value, results3.period.max().value)\n", | |
"ax.set_xlabel(\"period [days]\")\n", | |
"ax.set_ylabel(\"log likelihood\")\n", | |
"\n", | |
"ax = axes[1]\n", | |
"ax.plot(t[~in_transit2], y_filt[~in_transit2], \".k\", ms=3)\n", | |
"x = np.linspace(t.min(), t.max(), 3*len(t))\n", | |
"f = model3.model(x, period3, duration3, t03)\n", | |
"ax.plot(x, f, lw=0.75)\n", | |
"ax.set_xlim(t.min().value, t.max().value)\n", | |
"ax.set_ylim(-0.8, 0.3)\n", | |
"ax.set_xlabel(\"time [days]\")\n", | |
"ax.set_ylabel(\"de-trended flux [ppt]\")\n", | |
"\n", | |
"ax = axes[2]\n", | |
"x = (t[~in_transit2] - t03 + 0.5*period3) % period3 - 0.5*period3\n", | |
"m = np.abs(x) < 0.5 * u.day\n", | |
"ax.plot(x[m], y_filt[~in_transit2][m], \".k\", ms=3)\n", | |
"x = np.linspace(-0.5, 0.5, 1000) * u.day\n", | |
"f = model3.model(x + t03, period3, duration3, t03)\n", | |
"ax.plot(x, f, lw=0.75)\n", | |
"ax.set_xlim(-0.5, 0.5)\n", | |
"ax.set_ylim(-0.8, 0.3)\n", | |
"ax.set_xlabel(\"time since transit [days]\")\n", | |
"ax.set_ylabel(\"de-trended flux [ppt]\");" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Interestingly, this time the detected period is actually half of the period reported in the literature.\n", | |
"Looking at the second plot above, it should be possible to figure out what is going on here.\n", | |
"In fact, the likelihood of the correct period is actually *identical* to the peak reported here because the middle transit falls in a data gap, but `np.argmax` returns the *first* index when multiple entries have the same value.\n", | |
"This issue can immediately be identified when we run `compute_stats`:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"{'depth': (0.6301247640709233, 0.26779675946482406),\n", | |
" 'depth_even': (0.0, inf),\n", | |
" 'depth_half': (0.19400073606454776, 0.15171120613953734),\n", | |
" 'depth_odd': (0.6301247640709233, 0.26779675946482406),\n", | |
" 'depth_phased': (-0.00948575858479225, 0.18336398412549232),\n", | |
" 'harmonic_amplitude': 0.005819085568224707,\n", | |
" 'harmonic_delta_log_likelihood': -2.739742041184561,\n", | |
" 'per_transit_count': array([7, 0, 7]),\n", | |
" 'per_transit_log_likelihood': array([1.27380904, 0. , 1.50559149]),\n", | |
" 'transit_times': <Quantity [1993.22909845, 2015.50436921, 2037.77963998] d>}" | |
] | |
}, | |
"execution_count": 12, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"model3.compute_stats(period3, duration3, t03)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"In this case, the `per_transit_count` statistic shows us that there are no data points in the middle transit because it falls in a gap." | |
] | |
}, | |
{ | |
"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": "3.6.1" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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