lightkurve-stats

Publication stats on the use of Lightkurve.
https://github.com/lightkurve/lightkurve-stats
Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:
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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
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Academic email domains
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (2.4%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·
Repository

Publication stats on the use of Lightkurve.
Basic Info

Host: GitHub
Owner: lightkurve
Language: Jupyter Notebook
Default Branch: main
Size: 3.12 MB
Statistics

Stars: 1
Watchers: 2
Forks: 1
Open Issues: 0
Releases: 0
Created over 5 years ago · Last pushed 6 months ago
Metadata Files

Readme Citation
README.md

Lightkurve statistics

publications
| | Date | Title | Author | |---:|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | 0 | 2025-12-01 | mml:math mml:mrow mml:miu/mml:mi mml:miv/mml:mi mml:mib/mml:mi mml:miy/mml:mi mml:mo‑/mml:mo mml:msub mml:mrow mml:miH/mml:mi /mml:mrow mml:mrow mml:miβ/mml:mi /mml:mrow /mml:msub mml:mspace /mml:mspace /mml:mrow /mml:math Photoelectric photometry of 65 A/F-type candidate stars in the Kepler field: Observations, stellar parameters and variability analysis | Fox-Machado, L | | 1 | 2025-11-01 | Diagnozing star formation in the Chamaeleon–Musca dark cloud complex | Chen, H | | 2 | 2025-10-01 | SAS in ESA Datalabs: A new platform for XMM-Newton analysis | Gulbahar, E | | 4 | 2025-09-01 | Probing Periodic Trends in the TESS Light Curves of the Seventeen Known Double White Dwarf Systems | Sajadian, S | | 5 | 2025-09-01 | Discovery of Faint Nebulosity around a Z Camelopardalis–type Cataclysmic Variable in Antlia: Nova Shell or Ancient Planetary Nebula? | Bond, H |
Owner

Name: Lightkurve
Login: lightkurve
Kind: organization
Website: https://docs.lightkurve.org
Repositories: 1
Profile: https://github.com/lightkurve
A friendly Python package for making discoveries with data from NASA's Kepler & TESS telescopes.
Citation (citations/lightkurve-citation-stats.ipynb)

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lightkurve citation stats\n",
    "\n",
    "This notebook uses NASA's ADS service to identify publications that mention the string \"Lightkurve\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ads\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "FIELDS = ['date', 'pub', 'id', 'volume', 'links_data', 'citation', 'doi',       \n",
    "          'eid', 'keyword_schema', 'citation_count', 'data', 'data_facet',      \n",
    "          'year', 'identifier', 'keyword_norm', 'reference', 'abstract', 'recid',\n",
    "          'alternate_bibcode', 'arxiv_class', 'bibcode', 'first_author_norm',   \n",
    "          'pubdate', 'reader', 'doctype', 'doctype_facet_hier', 'title', 'pub_raw', 'property',\n",
    "          'author', 'email', 'orcid', 'keyword', 'author_norm',                 \n",
    "          'cite_read_boost', 'database', 'classic_factor', 'ack', 'page',       \n",
    "          'first_author', 'reader', 'read_count', 'indexstamp', 'issue', 'keyword_facet',\n",
    "          'aff', 'facility', 'simbid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "qry = ads.SearchQuery(q='full:\"lightkurve\" AND year:2017-2050', rows=999999, fl=FIELDS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gb/bin/anaconda/lib/python3.8/site-packages/ads-0.12.3-py3.8.egg/ads/search.py:543: UserWarning: Response rows did not match input rows. Setting this query's rows to 2000\n"
     ]
    }
   ],
   "source": [
    "papers = [q for q in qry ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = [p.date for p in papers[::-1]]\n",
    "titles = [p.title[0] for p in papers[::-1]]\n",
    "years = [p.year for p in papers[::-1]]\n",
    "authors = [p.first_author_norm for p in papers[::-1]]\n",
    "bibcodes = [p.bibcode for p in papers[::-1]]\n",
    "pubs = [p.pub for p in papers[::-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gb/bin/anaconda/lib/python3.8/site-packages/pandas/core/strings.py:1954: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n",
      "  return func(self, *args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'year': years,\n",
    "                   'date': pd.to_datetime(dates),\n",
    "                   'title': titles,\n",
    "                   'author': authors,\n",
    "                   'bibcode': bibcodes,\n",
    "                   'pub': pubs})\n",
    "# Filter out Zenodo entries and AAS Abstracts\n",
    "mask = ~df.pub.str.contains(\"(Zenodo)|(Abstracts)\")\n",
    "# Sort by date and reset index\n",
    "df = df[mask].sort_values('date', ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-06-01 00:00:00+00:00</td>\n",
       "      <td>TESS photometry of extreme helium stars PV Tel...</td>\n",
       "      <td>Jeffery, C</td>\n",
       "      <td>2020arXiv200600946J</td>\n",
       "      <td>arXiv e-prints</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-06-01 00:00:00+00:00</td>\n",
       "      <td>The one that got away: A unique eclipse in the...</td>\n",
       "      <td>Scholz, A</td>\n",
       "      <td>2020arXiv200603582S</td>\n",
       "      <td>arXiv e-prints</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-06-01 00:00:00+00:00</td>\n",
       "      <td>A search for young exoplanets in Sectors 1-5 o...</td>\n",
       "      <td>Battley, M</td>\n",
       "      <td>2020arXiv200601721B</td>\n",
       "      <td>arXiv e-prints</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-06-01 00:00:00+00:00</td>\n",
       "      <td>TOI-1338: TESS' First Transiting Circumbinary ...</td>\n",
       "      <td>Kostov, V</td>\n",
       "      <td>2020AJ....159..253K</td>\n",
       "      <td>The Astronomical Journal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-05-01 00:00:00+00:00</td>\n",
       "      <td>K2 Ultracool Dwarfs Survey - VI. White light s...</td>\n",
       "      <td>Paudel, R</td>\n",
       "      <td>2020MNRAS.494.5751P</td>\n",
       "      <td>Monthly Notices of the Royal Astronomical Society</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>79</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-02-01 00:00:00+00:00</td>\n",
       "      <td>Flares in open clusters with K2 . I. M 45 (Ple...</td>\n",
       "      <td>Ilin, E</td>\n",
       "      <td>2019A&amp;A...622A.133I</td>\n",
       "      <td>Astronomy and Astrophysics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-01 00:00:00+00:00</td>\n",
       "      <td>A Transiting Hot Jupiter Candidate toward the ...</td>\n",
       "      <td>Hedges, C</td>\n",
       "      <td>2019RNAAS...3...18H</td>\n",
       "      <td>Research Notes of the American Astronomical So...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2018</td>\n",
       "      <td>2018-12-01 00:00:00+00:00</td>\n",
       "      <td>Lightkurve: Kepler and TESS time series analys...</td>\n",
       "      <td>Lightkurve Collaboration</td>\n",
       "      <td>2018ascl.soft12013L</td>\n",
       "      <td>Astrophysics Source Code Library</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2018</td>\n",
       "      <td>2018-10-01 00:00:00+00:00</td>\n",
       "      <td>No Optimal SFF Motion Detrending Parameters fo...</td>\n",
       "      <td>Zhang, J</td>\n",
       "      <td>2018RNAAS...2..182Z</td>\n",
       "      <td>Research Notes of the American Astronomical So...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2018</td>\n",
       "      <td>2018-10-01 00:00:00+00:00</td>\n",
       "      <td>Kepler's Discoveries Will Continue: 21 Importa...</td>\n",
       "      <td>Barentsen, G</td>\n",
       "      <td>2018arXiv181012554B</td>\n",
       "      <td>arXiv e-prints</td>\n",
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      ],
      "text/plain": [
       "    year                      date  \\\n",
       "0   2020 2020-06-01 00:00:00+00:00   \n",
       "1   2020 2020-06-01 00:00:00+00:00   \n",
       "2   2020 2020-06-01 00:00:00+00:00   \n",
       "3   2020 2020-06-01 00:00:00+00:00   \n",
       "4   2020 2020-05-01 00:00:00+00:00   \n",
       "..   ...                       ...   \n",
       "79  2019 2019-02-01 00:00:00+00:00   \n",
       "80  2019 2019-01-01 00:00:00+00:00   \n",
       "81  2018 2018-12-01 00:00:00+00:00   \n",
       "82  2018 2018-10-01 00:00:00+00:00   \n",
       "83  2018 2018-10-01 00:00:00+00:00   \n",
       "\n",
       "                                                title  \\\n",
       "0   TESS photometry of extreme helium stars PV Tel...   \n",
       "1   The one that got away: A unique eclipse in the...   \n",
       "2   A search for young exoplanets in Sectors 1-5 o...   \n",
       "3   TOI-1338: TESS' First Transiting Circumbinary ...   \n",
       "4   K2 Ultracool Dwarfs Survey - VI. White light s...   \n",
       "..                                                ...   \n",
       "79  Flares in open clusters with K2 . I. M 45 (Ple...   \n",
       "80  A Transiting Hot Jupiter Candidate toward the ...   \n",
       "81  Lightkurve: Kepler and TESS time series analys...   \n",
       "82  No Optimal SFF Motion Detrending Parameters fo...   \n",
       "83  Kepler's Discoveries Will Continue: 21 Importa...   \n",
       "\n",
       "                      author              bibcode  \\\n",
       "0                 Jeffery, C  2020arXiv200600946J   \n",
       "1                  Scholz, A  2020arXiv200603582S   \n",
       "2                 Battley, M  2020arXiv200601721B   \n",
       "3                  Kostov, V  2020AJ....159..253K   \n",
       "4                  Paudel, R  2020MNRAS.494.5751P   \n",
       "..                       ...                  ...   \n",
       "79                   Ilin, E  2019A&A...622A.133I   \n",
       "80                 Hedges, C  2019RNAAS...3...18H   \n",
       "81  Lightkurve Collaboration  2018ascl.soft12013L   \n",
       "82                  Zhang, J  2018RNAAS...2..182Z   \n",
       "83              Barentsen, G  2018arXiv181012554B   \n",
       "\n",
       "                                                  pub  \n",
       "0                                      arXiv e-prints  \n",
       "1                                      arXiv e-prints  \n",
       "2                                      arXiv e-prints  \n",
       "3                            The Astronomical Journal  \n",
       "4   Monthly Notices of the Royal Astronomical Society  \n",
       "..                                                ...  \n",
       "79                         Astronomy and Astrophysics  \n",
       "80  Research Notes of the American Astronomical So...  \n",
       "81                   Astrophysics Source Code Library  \n",
       "82  Research Notes of the American Astronomical So...  \n",
       "83                                     arXiv e-prints  \n",
       "\n",
       "[84 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"publications-using-lightkurve.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.date_range('2018-01-01T00:00:00Z', '2020-06-01T00:00:00Z', freq='1M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = [len(df[df.date < d]) for d in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load fancy style options\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as pl\n",
    "pl.style.use(\"fivethirtyeight\")\n",
    "style = {'font.family': 'Open Sans',\n",
    "         'font.weight': 300,\n",
    "         'figure.figsize': (6, 3),\n",
    "         'figure.dpi': 200,\n",
    "         'figure.facecolor': 'white',\n",
    "         'axes.facecolor': 'white',\n",
    "         'font.size': 9,\n",
    "         'axes.edgecolor': 'white',\n",
    "         'savefig.facecolor': 'white'}\n",
    "matplotlib.rcParams.update(style)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(x, y, marker='o')\n",
    "plt.ylabel(\"Publications\", fontsize=14)\n",
    "locator = mdates.AutoDateLocator(minticks=3, maxticks=7)\n",
    "formatter = mdates.ConciseDateFormatter(locator)\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "plt.savefig(\"lightkurve-publications.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File papers-using-lightkurve-annotated.csv does not exist: 'papers-using-lightkurve-annotated.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-fabfc55a15a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdfano\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"papers-using-lightkurve-annotated.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/bin/anaconda/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    674\u001b[0m         )\n\u001b[1;32m    675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\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    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/bin/anaconda/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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    449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/bin/anaconda/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 880\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\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    881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/bin/anaconda/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1112\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1113\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1114\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\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   1115\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1116\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/bin/anaconda/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1891\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File papers-using-lightkurve-annotated.csv does not exist: 'papers-using-lightkurve-annotated.csv'"
     ]
    }
   ],
   "source": [
    "dfano = pd.read_csv(\"papers-using-lightkurve-annotated.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "100* dfano.category.value_counts() / len(dfano)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
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