notebooks/plot_submissions_with_date.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot submissions per month versus time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme(style='whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = '2025-01-09'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('submissions_with_date_{}.csv'.format(date), index_col='last_updated',\n",
    "                 usecols=['collaboration', 'last_updated'], parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop all rows with collaboration of `IPPP Durham`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.collaboration == 'IPPP Durham'].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.collaboration != 'IPPP Durham']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop all rows with zero microseconds, because these are mostly records migrated from the old site."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.index.microsecond != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First group by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>collaboration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_updated</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              collaboration\n",
       "last_updated               \n",
       "2016                      1\n",
       "2017                    125\n",
       "2018                    172\n",
       "2019                    202\n",
       "2020                    274\n",
       "2021                    348\n",
       "2022                    264\n",
       "2023                    336\n",
       "2024                    273\n",
       "2025                      3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_year = df.groupby(by=df.index.year).count()\n",
    "df_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then also by month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df.index.strftime('%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>collaboration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_updated</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01</th>\n",
       "      <td>A2 at MAMI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01</th>\n",
       "      <td>A2 at MAMI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01</th>\n",
       "      <td>A2 at MAMI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01</th>\n",
       "      <td>A2 at MAMI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02</th>\n",
       "      <td>A2 at MAMI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             collaboration\n",
       "last_updated              \n",
       "2022-01         A2 at MAMI\n",
       "2022-01         A2 at MAMI\n",
       "2022-01         A2 at MAMI\n",
       "2022-01         A2 at MAMI\n",
       "2022-02         A2 at MAMI"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_count = df.groupby(by=df.index).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_count = df_count.rename(columns={'collaboration': 'Number of papers'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_count.index.names = ['Month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Number of papers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Number of papers\n",
       "Month                    \n",
       "2016-12                 1\n",
       "2017-01                 7\n",
       "2017-02                 7\n",
       "2017-03                13\n",
       "2017-04                18"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Number of papers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-09</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Number of papers\n",
       "Month                    \n",
       "2024-09                18\n",
       "2024-10                25\n",
       "2024-11                20\n",
       "2024-12                24\n",
       "2025-01                 3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_count.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the first and last month since likely to be incomplete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_count = df_count[1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Number of papers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Number of papers\n",
       "Month                    \n",
       "2017-01                 7\n",
       "2017-02                 7\n",
       "2017-03                13\n",
       "2017-04                18\n",
       "2017-05                 7"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Number of papers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-08</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Number of papers\n",
       "Month                    \n",
       "2024-08                17\n",
       "2024-09                18\n",
       "2024-10                25\n",
       "2024-11                20\n",
       "2024-12                24"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_count.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a bar plot of 'Number of papers' against 'Month' with a linear fit overlaid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(facecolor='white', figsize=(15,10))\n",
    "ax = plt.subplot()\n",
    "\n",
    "# Bar plot of 'Number of papers' against 'Month'.\n",
    "df_count.plot(kind='bar', width=0.8, legend=False, fontsize='medium', ax=ax)\n",
    "\n",
    "# Add a red line with a linear regression fit.\n",
    "sns.regplot(x=np.arange(df_count.shape[0]), y='Number of papers', data=df_count,\n",
    "            scatter=False, ci=None, color='red')\n",
    "\n",
    "plt.title('hepdata.net submissions by experiments ({})'.format(date), fontsize='xx-large')\n",
    "plt.xlabel('Month', fontsize='x-large')\n",
    "plt.ylabel('Number of papers', fontsize='xx-large')\n",
    "plt.tight_layout()\n",
    "plt.savefig('submissions_with_date_{}.png'.format(date))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also make a bar plot of 'Number of papers' against 'Year'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Number of papers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Number of papers\n",
       "Year                  \n",
       "2017               125\n",
       "2018               172\n",
       "2019               202\n",
       "2020               274\n",
       "2021               348\n",
       "2022               264\n",
       "2023               336\n",
       "2024               273"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_year = df_year.rename(columns={'collaboration': 'Number of papers'})\n",
    "df_year.index.names = ['Year']\n",
    "df_year = df_year[1:-1]  # drop the first and last year\n",
    "df_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(facecolor='white', figsize=(15,10))\n",
    "ax = plt.subplot()\n",
    "\n",
    "# Bar plot of 'Number of papers' against 'Year'.\n",
    "df_year.plot(kind='bar', rot=0, width=0.8, legend=False, fontsize='xx-large', ax=ax)\n",
    "\n",
    "plt.title('hepdata.net submissions by experiments ({})'.format(date), fontsize='xx-large')\n",
    "plt.xlabel('Year', fontsize='xx-large')\n",
    "plt.ylabel('Number of papers', fontsize='xx-large')\n",
    "plt.tight_layout()\n",
    "plt.savefig('submissions_per_year_{}.png'.format(date))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.21"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}