citation-explorer

Python file to calculate a cumulative citation numbers using InspireHEP API
https://github.com/suzuki-r/citation-explorer
Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:
✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
○
DOI references
○
Academic publication links
○
Academic email domains
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (2.6%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·
Repository

Python file to calculate a cumulative citation numbers using InspireHEP API
Basic Info

Host: GitHub
Owner: suzuki-r
Language: Jupyter Notebook
Default Branch: main
Size: 130 KB
Statistics

Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 0
Created 11 months ago · Last pushed 10 months ago
Metadata Files

Readme Citation
README.md

Citation-Explorer

Python file to calculate a cumulative citation numbers using InspireHEP API.
Usage

Launch jupyter notebook, change the author_id as you like. The output file will be generated.
Owner

Name: Ryo Suzuki
Login: suzuki-r
Kind: user
Location: China
Company: Southeast University
Repositories: 2
Profile: https://github.com/suzuki-r
Interested in mathematical physics
Citation (Citation_plotter.ipynb)

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8213416f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from datetime import datetime\n",
    "import re\n",
    "from time import sleep  # optional: to be nice to the API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f55a7b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ----- Run for a specific author -----\n",
    "author_id = \"R.Suzuki.2\"\n",
    "#author_id = \"Y.Tachikawa.1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b7b6de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- GET AUTHOR'S PAPERS ---\n",
    "def fetch_author_recids(author_id):\n",
    "    base_url = \"https://inspirehep.net/api/literature\"\n",
    "    params = {\n",
    "        \"q\": f\"a {author_id}\",\n",
    "        \"size\": 1000,\n",
    "        \"fields\": \"control_number\"\n",
    "    }\n",
    "    response = requests.get(base_url, params=params)\n",
    "    response.raise_for_status()\n",
    "    data = response.json()\n",
    "    recids = []\n",
    "    for hit in data.get(\"hits\", {}).get(\"hits\", []):\n",
    "        recid = hit.get(\"metadata\", {}).get(\"control_number\")\n",
    "        if recid:\n",
    "            recids.append(recid)\n",
    "    return recids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "618aaf9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 149 papers. Getting citations...\n"
     ]
    }
   ],
   "source": [
    "# --- fetch paper IDs ---\n",
    "recids = fetch_author_recids(author_id)\n",
    "print(f\"Found {len(recids)} papers. Getting citations...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7447fc8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- GET CITING DATES FOR A SINGLE PAPER ---\n",
    "def get_citing_dates(recid):\n",
    "    all_dates = []\n",
    "    url    = \"https://inspirehep.net/api/literature\"\n",
    "    params = {\n",
    "        \"q\":    f\"refersto:recid:{recid}\",  \n",
    "        \"size\": 1000,                        \n",
    "        \"sort\": \"mostrecent\"                \n",
    "    }\n",
    "\n",
    "    while url:\n",
    "        resp = requests.get(url, params=params)\n",
    "        resp.raise_for_status()\n",
    "        data = resp.json()\n",
    "        raw  = resp.text\n",
    "\n",
    "        # 1) full dates: YYYY-MM-DD\n",
    "        for d in re.findall(r'\"earliest_date\":\"(\\d{4}-\\d{2}-\\d{2})\"', raw):\n",
    "            all_dates.append(datetime.strptime(d, \"%Y-%m-%d\"))\n",
    "\n",
    "        # 2) year‑month only: YYYY-MM  (but not YYYY-MM-DD -> Day = 01)\n",
    "        for ym in re.findall(r'\"earliest_date\":\"(\\d{4}-\\d{2})\"(?!-)', raw):\n",
    "            all_dates.append(datetime.strptime(f\"{ym}-01\", \"%Y-%m-%d\"))\n",
    "\n",
    "        # 3) year only: YYYY (but not YYYY- or YYYY-MM -> Month,Day = July,01)\n",
    "        for y in re.findall(r'\"earliest_date\":\"(\\d{4})\"(?![-\\d])', raw):\n",
    "            all_dates.append(datetime.strptime(f\"{y}-07-01\", \"%Y-%m-%d\"))\n",
    "\n",
    "        # follow pagination\n",
    "        url    = data.get(\"links\", {}).get(\"next\")\n",
    "        params = {}  # next links already include all query parameters\n",
    "\n",
    "    return sorted(all_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f25568b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check behavior\n",
    "# tmp1=get_citing_dates(836695)\n",
    "# len(tmp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d7c8294",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Works with a paper with more than 1000 citations!\n",
    "# tmp2=get_citing_dates(823411)\n",
    "# print(len(tmp2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03c7a556",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- ACCUMULATE AND PLOT CITATIONS ---\n",
    "def build_citation_df(dates):\n",
    "    df = pd.DataFrame(dates, columns=[\"date\"])\n",
    "    df[\"count\"] = 1\n",
    "    df = df.groupby(\"date\").sum().sort_index()\n",
    "    df[\"cumulative\"] = df[\"count\"].cumsum()\n",
    "    return df\n",
    "\n",
    "def plot_citations(df):\n",
    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
    "    ax.plot(df.index, df[\"cumulative\"], marker=\"o\")\n",
    "    ax.set_title(f\"Total Cumulative Citation of {author_id}\")\n",
    "    ax.set_xlabel(\"Year\")\n",
    "    ax.set_ylabel(\"Citations\")\n",
    "    ax.grid(True)\n",
    "\n",
    "    import matplotlib.dates as mdates\n",
    "    ax.xaxis.set_major_locator(mdates.YearLocator(2))\n",
    "    ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y\"))\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9bfc1572",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1/149] Processing recid 1246028\n",
      "[2/149] Processing recid 900954\n",
      "[3/149] Processing recid 1290551\n",
      "[4/149] Processing recid 1494100\n",
      "[5/149] Processing recid 633720\n",
      "[6/149] Processing recid 1333422\n",
      "[7/149] Processing recid 699333\n",
      "[8/149] Processing recid 1335337\n",
      "[9/149] Processing recid 1853033\n",
      "[10/149] Processing recid 1590920\n",
      "[11/149] Processing recid 1231757\n",
      "[12/149] Processing recid 1093702\n",
      "[13/149] Processing recid 686825\n",
      "[14/149] Processing recid 1818484\n",
      "[15/149] Processing recid 1940068\n",
      "[16/149] Processing recid 1607283\n",
      "[17/149] Processing recid 2848292\n",
      "[18/149] Processing recid 2658442\n",
      "[19/149] Processing recid 1252583\n",
      "[20/149] Processing recid 1357442\n",
      "[21/149] Processing recid 1904054\n",
      "[22/149] Processing recid 1672182\n",
      "[23/149] Processing recid 1672158\n",
      "[24/149] Processing recid 1669864\n",
      "[25/149] Processing recid 1462050\n",
      "[26/149] Processing recid 856211\n",
      "[27/149] Processing recid 866876\n",
      "[28/149] Processing recid 897777\n",
      "[29/149] Processing recid 821279\n",
      "[30/149] Processing recid 837844\n",
      "[31/149] Processing recid 2753912\n",
      "[32/149] Processing recid 1122701\n",
      "[33/149] Processing recid 1450013\n",
      "[34/149] Processing recid 708310\n",
      "[35/149] Processing recid 822014\n",
      "[36/149] Processing recid 759753\n",
      "[37/149] Processing recid 799708\n",
      "[38/149] Processing recid 693772\n",
      "[39/149] Processing recid 1419681\n",
      "[40/149] Processing recid 1222525\n",
      "[41/149] Processing recid 1645293\n",
      "[42/149] Processing recid 1645295\n",
      "[43/149] Processing recid 611107\n",
      "[44/149] Processing recid 731773\n",
      "[45/149] Processing recid 939589\n",
      "[46/149] Processing recid 922970\n",
      "[47/149] Processing recid 855154\n",
      "[48/149] Processing recid 830390\n",
      "[49/149] Processing recid 878396\n",
      "[50/149] Processing recid 1205326\n",
      "[51/149] Processing recid 1324421\n",
      "[52/149] Processing recid 1782669\n",
      "[53/149] Processing recid 1424872\n",
      "[54/149] Processing recid 2060745\n",
      "[55/149] Processing recid 643344\n",
      "[56/149] Processing recid 603275\n",
      "[57/149] Processing recid 602575\n",
      "[58/149] Processing recid 797023\n",
      "[59/149] Processing recid 830538\n",
      "[60/149] Processing recid 783281\n",
      "[61/149] Processing recid 916045\n",
      "[62/149] Processing recid 955223\n",
      "[63/149] Processing recid 851143\n",
      "[64/149] Processing recid 1359716\n",
      "[65/149] Processing recid 1355239\n",
      "[66/149] Processing recid 886330\n",
      "[67/149] Processing recid 1767446\n",
      "[68/149] Processing recid 1837830\n",
      "[69/149] Processing recid 1736243\n",
      "[70/149] Processing recid 1306081\n",
      "[71/149] Processing recid 1386699\n",
      "[72/149] Processing recid 790120\n",
      "[73/149] Processing recid 822281\n",
      "[74/149] Processing recid 799666\n",
      "[75/149] Processing recid 816700\n",
      "[76/149] Processing recid 699744\n",
      "[77/149] Processing recid 1468078\n",
      "[78/149] Processing recid 1496036\n",
      "[79/149] Processing recid 1482778\n",
      "[80/149] Processing recid 1480391\n",
      "[81/149] Processing recid 1610035\n",
      "[82/149] Processing recid 1636783\n",
      "[83/149] Processing recid 768911\n",
      "[84/149] Processing recid 1121028\n",
      "[85/149] Processing recid 1207635\n",
      "[86/149] Processing recid 1300836\n",
      "[87/149] Processing recid 1814341\n",
      "[88/149] Processing recid 900397\n",
      "[89/149] Processing recid 912982\n",
      "[90/149] Processing recid 860638\n",
      "[91/149] Processing recid 832218\n",
      "[92/149] Processing recid 1268680\n",
      "[93/149] Processing recid 2768776\n",
      "[94/149] Processing recid 2647750\n",
      "[95/149] Processing recid 2647275\n",
      "[96/149] Processing recid 823411\n",
      "[97/149] Processing recid 1119082\n",
      "[98/149] Processing recid 1327631\n",
      "[99/149] Processing recid 733047\n",
      "[100/149] Processing recid 1281712\n",
      "[101/149] Processing recid 1711145\n",
      "[102/149] Processing recid 1749549\n",
      "[103/149] Processing recid 1480379\n",
      "[104/149] Processing recid 816218\n",
      "[105/149] Processing recid 753124\n",
      "[106/149] Processing recid 866964\n",
      "[107/149] Processing recid 677790\n",
      "[108/149] Processing recid 694517\n",
      "[109/149] Processing recid 748604\n",
      "[110/149] Processing recid 809662\n",
      "[111/149] Processing recid 1225899\n",
      "[112/149] Processing recid 1674571\n",
      "[113/149] Processing recid 1606111\n",
      "[114/149] Processing recid 925588\n",
      "[115/149] Processing recid 930449\n",
      "[116/149] Processing recid 810047\n",
      "[117/149] Processing recid 1821986\n",
      "[118/149] Processing recid 2087968\n",
      "[119/149] Processing recid 1390138\n",
      "[120/149] Processing recid 1231763\n",
      "[121/149] Processing recid 1250337\n",
      "[122/149] Processing recid 1849337\n",
      "[123/149] Processing recid 790118\n",
      "[124/149] Processing recid 1663290\n",
      "[125/149] Processing recid 1235293\n",
      "[126/149] Processing recid 1630494\n",
      "[127/149] Processing recid 1782238\n",
      "[128/149] Processing recid 1513803\n",
      "[129/149] Processing recid 2134985\n",
      "[130/149] Processing recid 1336646\n",
      "[131/149] Processing recid 1708727\n",
      "[132/149] Processing recid 1726773\n",
      "[133/149] Processing recid 2632820\n",
      "[134/149] Processing recid 1914143\n",
      "[135/149] Processing recid 1629990\n",
      "[136/149] Processing recid 2157987\n",
      "[137/149] Processing recid 1254880\n",
      "[138/149] Processing recid 2772584\n",
      "[139/149] Processing recid 1748669\n",
      "[140/149] Processing recid 1870044\n",
      "[141/149] Processing recid 1801443\n",
      "[142/149] Processing recid 1790945\n",
      "[143/149] Processing recid 2846400\n",
      "[144/149] Processing recid 1788442\n",
      "[145/149] Processing recid 1312204\n",
      "[146/149] Processing recid 1358241\n",
      "[147/149] Processing recid 1253880\n",
      "[148/149] Processing recid 1516025\n",
      "[149/149] Processing recid 1734969\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- MAIN LOGIC ---\n",
    "\n",
    "all_dates = []\n",
    "for i, recid in enumerate(recids, 1):\n",
    "    print(f\"[{i}/{len(recids)}] Processing recid {recid}\")\n",
    "    dates = get_citing_dates(recid)\n",
    "    all_dates.extend(dates)\n",
    "    sleep(0.5)  # optional: prevent overwhelming the API\n",
    "\n",
    "if all_dates:\n",
    "    df = build_citation_df(all_dates)\n",
    "    plot_citations(df)\n",
    "else:\n",
    "    print(\"No citation dates found for any paper.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9142c3cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "fig = plot_citations(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ea9db62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to PDF:\n",
    "fig.savefig(\"cumulative_citations2.pdf\", format=\"pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75a01793",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3.10.10",
   "language": "python",
   "name": "python3.10.10"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Open Source Science
