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epassaro/tess_lightkurve.ipynb

Created October 25, 2019 22:20
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tess_lightkurve.ipynb
{
"cells": [
{
"cell_type": "markdown",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Downloading TESS data with LightKurve\n",
"\n",
"`planets_2019-10-11.csv` is a copy-pasted table from: https://tess.mit.edu/publications/#tess_discovered_exoplanets\n",
"\n",
"`exo_CTL_08.01.csv` is the _Candidate Target List_ and can be downloaded from: [https://archive.stsci.edu/tess/tic_ctl.html](https://archive.stsci.edu/tess/tic_ctl.html)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"planets = pd.read_csv('./planets_2019-10-11.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Planet Name</th>\n <th>TOI ID</th>\n <th>TIC ID</th>\n <th>RA (deg)</th>\n <th>Dec (deg)</th>\n <th>TESS magnitude</th>\n <th>Period (Days)</th>\n <th>Period Err (avg)</th>\n <th>Rs</th>\n <th>Rs Err (avg)</th>\n <th>Rp (Rearth)</th>\n <th>Rp Err (avg)</th>\n <th>Mp (Mearth)</th>\n <th>Mp Err (avg)</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>0</td>\n <td>HATS-71 b1</td>\n <td>127.01</td>\n <td>234523599</td>\n <td>15.551099</td>\n <td>-61.756001</td>\n <td>14.097</td>\n <td>3.79561</td>\n <td>6.9E-05</td>\n <td>0.5161</td>\n <td>0.0076</td>\n <td>12.106</td>\n <td>0.179</td>\n <td>143</td>\n <td>76.3</td>\n </tr>\n <tr>\n <td>1</td>\n <td>TOI 125 b1</td>\n <td>125.01</td>\n <td>52368076</td>\n <td>23.594697</td>\n <td>-66.675830</td>\n <td>10.138</td>\n <td>4.65382</td>\n <td>0.000315</td>\n <td>0.852</td>\n <td>0.0165</td>\n <td>2.755</td>\n <td>0.085</td>\n <td>8.5</td>\n <td>2.3</td>\n </tr>\n <tr>\n <td>2</td>\n <td>TOI 125 c1</td>\n <td>125.02</td>\n <td>52368076</td>\n <td>23.594697</td>\n <td>-66.675830</td>\n <td>10.138</td>\n <td>9.15067</td>\n <td>0.000655</td>\n <td>0.852</td>\n <td>0.0165</td>\n <td>2.79</td>\n <td>0.1</td>\n <td>8.6</td>\n <td>2.35</td>\n </tr>\n <tr>\n <td>3</td>\n <td>HD 2685 b</td>\n <td>135.01</td>\n <td>267263253</td>\n <td>7.328937</td>\n <td>-76.304075</td>\n <td>9.213</td>\n <td>4.1269</td>\n <td>5.7E-05</td>\n <td>1.57</td>\n <td>0.01</td>\n <td>16.14</td>\n <td>0.112</td>\n <td>375</td>\n <td>28.6</td>\n </tr>\n <tr>\n <td>4</td>\n <td>HD 1397 b</td>\n <td>120.01</td>\n <td>394137592</td>\n <td>4.446408</td>\n <td>-66.358985</td>\n <td>7.144</td>\n <td>11.53533</td>\n <td>0.000795</td>\n <td>2.336</td>\n <td>0.0555</td>\n <td>11.5</td>\n <td>0.29</td>\n <td>132</td>\n <td>6</td>\n </tr>\n <tr>\n <td>5</td>\n <td>TOI 172</td>\n <td>172.01</td>\n <td>29857954</td>\n <td>316.631883</td>\n <td>-26.692620</td>\n <td>10.711</td>\n <td>9.47725</td>\n <td>0.000715</td>\n <td>1.777</td>\n <td>0.0455</td>\n <td>10.8</td>\n <td>0.35</td>\n <td>1723</td>\n <td>67</td>\n </tr>\n <tr>\n <td>6</td>\n <td>HD 202772 A b</td>\n <td>123.01</td>\n <td>290131778</td>\n <td>319.699400</td>\n <td>-26.616077</td>\n <td>8.803</td>\n <td>3.30896</td>\n <td>8.3E-05</td>\n <td>2.591</td>\n <td>0.0855</td>\n <td>17.32</td>\n <td>0.625</td>\n <td>323.2</td>\n <td>21.9</td>\n </tr>\n <tr>\n <td>7</td>\n <td>TOI 216 c</td>\n <td>216.01</td>\n <td>55652896</td>\n <td>73.980231</td>\n <td>-63.260063</td>\n <td>11.504</td>\n <td>34.53934</td>\n <td>0.00115</td>\n <td>0.747</td>\n <td>0.0145</td>\n <td>8.6</td>\n <td>2.4</td>\n <td>15.90-31.80</td>\n <td>0.025-0.027</td>\n </tr>\n <tr>\n <td>8</td>\n <td>TOI 216 b</td>\n <td>216.02</td>\n <td>55652896</td>\n <td>73.980231</td>\n <td>-63.260063</td>\n <td>11.504</td>\n <td>17.09914</td>\n <td>0.001504</td>\n <td>0.747</td>\n <td>0.0145</td>\n <td>10.2</td>\n <td>0.2</td>\n <td>82.60-181.0</td>\n <td>0.155-0.185</td>\n </tr>\n <tr>\n <td>9</td>\n <td>HD 219666 b</td>\n <td>118.01</td>\n <td>266980320</td>\n <td>349.556843</td>\n <td>-56.903885</td>\n <td>9.154</td>\n <td>6.03607</td>\n <td>0.000635</td>\n <td>1.03</td>\n <td>0.03</td>\n <td>4.71</td>\n <td>0.17</td>\n <td>16.6</td>\n <td>1.3</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Planet Name TOI ID TIC ID RA (deg) Dec (deg) TESS magnitude \\\n0 HATS-71 b1 127.01 234523599 15.551099 -61.756001 14.097 \n1 TOI 125 b1 125.01 52368076 23.594697 -66.675830 10.138 \n2 TOI 125 c1 125.02 52368076 23.594697 -66.675830 10.138 \n3 HD 2685 b 135.01 267263253 7.328937 -76.304075 9.213 \n4 HD 1397 b 120.01 394137592 4.446408 -66.358985 7.144 \n5 TOI 172 172.01 29857954 316.631883 -26.692620 10.711 \n6 HD 202772 A b 123.01 290131778 319.699400 -26.616077 8.803 \n7 TOI 216 c 216.01 55652896 73.980231 -63.260063 11.504 \n8 TOI 216 b 216.02 55652896 73.980231 -63.260063 11.504 \n9 HD 219666 b 118.01 266980320 349.556843 -56.903885 9.154 \n\n Period (Days) Period Err (avg) Rs Rs Err (avg) Rp (Rearth) \\\n0 3.79561 6.9E-05 0.5161 0.0076 12.106 \n1 4.65382 0.000315 0.852 0.0165 2.755 \n2 9.15067 0.000655 0.852 0.0165 2.79 \n3 4.1269 5.7E-05 1.57 0.01 16.14 \n4 11.53533 0.000795 2.336 0.0555 11.5 \n5 9.47725 0.000715 1.777 0.0455 10.8 \n6 3.30896 8.3E-05 2.591 0.0855 17.32 \n7 34.53934 0.00115 0.747 0.0145 8.6 \n8 17.09914 0.001504 0.747 0.0145 10.2 \n9 6.03607 0.000635 1.03 0.03 4.71 \n\n Rp Err (avg) Mp (Mearth) Mp Err (avg) \n0 0.179 143 76.3 \n1 0.085 8.5 2.3 \n2 0.1 8.6 2.35 \n3 0.112 375 28.6 \n4 0.29 132 6 \n5 0.35 1723 67 \n6 0.625 323.2 21.9 \n7 2.4 15.90-31.80 0.025-0.027 \n8 0.2 82.60-181.0 0.155-0.185 \n9 0.17 16.6 1.3 "
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planets.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"ctl = pd.read_csv('./exo_CTL_08.01.csv', header=None)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>0</td>\n <td>80423805</td>\n <td>0.000192</td>\n <td>planetcandidate</td>\n <td>80</td>\n </tr>\n <tr>\n <td>1</td>\n <td>80423930</td>\n <td>0.000100</td>\n <td>cooldwarfs_v8</td>\n <td>92</td>\n </tr>\n <tr>\n <td>2</td>\n <td>80423934</td>\n <td>0.000116</td>\n <td>cooldwarfs_v8</td>\n <td>93</td>\n </tr>\n <tr>\n <td>3</td>\n <td>80424035</td>\n <td>0.000417</td>\n <td>planetcandidate</td>\n <td>98</td>\n </tr>\n <tr>\n <td>4</td>\n <td>80424100</td>\n <td>0.000096</td>\n <td>cooldwarfs_v8</td>\n <td>104</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " 0 1 2 3\n0 80423805 0.000192 planetcandidate 80\n1 80423930 0.000100 cooldwarfs_v8 92\n2 80423934 0.000116 cooldwarfs_v8 93\n3 80424035 0.000417 planetcandidate 98\n4 80424100 0.000096 cooldwarfs_v8 104"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ctl.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0 True\n1 True\n2 True\n3 True\n4 True\n5 True\n6 True\n7 True\n8 True\n9 True\n10 True\n11 True\n12 True\n13 True\n14 True\n15 True\n16 True\n17 True\n18 True\n19 True\n20 True\n21 False\n22 True\n23 True\n24 True\n25 True\n26 True\n27 True\n28 True\n29 True\n30 True\n31 True\nName: TIC ID, dtype: bool"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Seems one confirmed host (TIC ID: 441462736) isn't in the Candidate Target List! See: https://arxiv.org/pdf/1901.01643.pdf\n",
"planets['TIC ID'].isin(ctl[0])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import lightkurve as lk"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Add prefix 'TIC' for disambiguation (lightkurve search)\n",
"tic_id = [ 'TIC' + str(n) for n in planets['TIC ID'] ]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Downloading data for the first object in `tic_id`\n",
"# See: https://docs.lightkurve.org/tutorials/\n",
"\n",
"lcf = lk.search_lightcurvefile(tic_id[0], mission='TESS').download_all()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "lightkurve.collections.LightCurveFileCollection"
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(lcf)"
]
},
{
"cell_type": "markdown",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"The `LightCurveFileCollection` class have the following attributes: `data` (_list_), `PDCSAP_FLUX` (_LightCurveFileCollection_), `SAP_FLUX` (_LightCurveFileCollection_)\n",
"\n",
"I don't know why there are _LightCurveFileCollection_ objects inside another _LightCurveFileCollection_ object!\n",
"\n",
"Available methods are: `append`, `plot`, `stitch`.\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "[TessLightCurveFile(TICID: 234523599), TessLightCurveFile(TICID: 234523599)]"
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# In this case the attribute data stores two `LightCurveFile` objects:\n",
"lcf.data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([228.8698 , 228.47325, 234.6409 , ..., 229.18413, 225.92644,\n 224.98993], dtype=float32)"
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creo que es esto lo que andamos buscando, igual tiene mil cosas adentro para revisar!\n",
"lcf.data[0].SAP_FLUX.flux"
]
},
{
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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