ojstat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import psycopg2\n",
    "import psycopg2.extras\n",
    "import re\n",
    "import datetime\n",
    "import heapq\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import MONDAY, MonthLocator, WeekdayLocator, DateFormatter\n",
    "from tqdm import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = psycopg2.connect('dbname=online_judge user=abcdabcd987', cursor_factory=psycopg2.extras.DictCursor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weekly Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48053a0633b84f55a780e5406235a702",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "with conn:\n",
    "    with conn.cursor('cur') as cur:\n",
    "        xs, ys = [], []\n",
    "        cur.execute('SELECT * FROM records ORDER BY submit_datetime ASC')\n",
    "        x, y = datetime.date.min, 0\n",
    "        x_week = x.isocalendar()[1]\n",
    "        for row in tqdm_notebook(cur):\n",
    "            date = row['submit_datetime'].date()\n",
    "            week = date.isocalendar()[1]\n",
    "            if x_week == week:\n",
    "                y += 1\n",
    "            else:\n",
    "                xs.append(x)\n",
    "                ys.append(y)\n",
    "                x, y, x_week = date, 1, week\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "        xs, ys = xs[1:], ys[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xSHwOR1QdBsdjhKNzI+QmC5jpRiNxoonSf/MJS41wMCxOkprDWR2NXLi2dUGu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\n/A+XfWL1KTdPoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1122f3eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minor = MonthLocator(range(1,13), bymonthday=1, interval=1)\n",
    "months = MonthLocator(range(1,13), bymonthday=1, interval=6)\n",
    "monthsFmt = DateFormatter(\"%b '%y\")\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title('Weekly Submission')\n",
    "ax.plot_date(xs, ys, '-', linewidth=0.5)\n",
    "ax.xaxis.set_major_locator(months)\n",
    "ax.xaxis.set_major_formatter(monthsFmt)\n",
    "ax.xaxis.set_minor_locator(minor)\n",
    "ax.autoscale_view()\n",
    "ax.grid(True)\n",
    "fig.autofmt_xdate()\n",
    "plt.savefig('weekly-submit.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Maximum Exam Wait Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_time(judge_message):\n",
    "    return sum(map(int, re.findall('(\\d+)ms', judge_message))) / 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exam_wait_times(num_judge):\n",
    "    wait_times = []\n",
    "    with conn:\n",
    "        with conn.cursor('report') as report_cur:\n",
    "            report_cur.execute('SELECT * FROM reports WHERE visible=1')\n",
    "            for report in tqdm_notebook(report_cur):\n",
    "                title = report['title']\n",
    "                st, ed = report['start_datetime'], report['end_datetime']\n",
    "                wait_time = 0\n",
    "                queue = [0] * num_judge\n",
    "                with conn.cursor('submit') as submit_cur:\n",
    "                    submit_cur.execute('SELECT * FROM records WHERE submit_datetime BETWEEN %s AND %s ORDER BY submit_datetime ASC', (st, ed))\n",
    "                    for submit in submit_cur:\n",
    "                        timestamp = submit['submit_datetime'].timestamp()\n",
    "                        runtime = run_time(submit['judge_message'])\n",
    "                        first_judge = heapq.heappop(queue)\n",
    "                        wait_time = max(wait_time, first_judge - timestamp)\n",
    "                        heapq.heappush(queue, max(first_judge, timestamp) + runtime + 4)\n",
    "                wait_times.append((wait_time, title))\n",
    "    wait_times.sort(reverse=True)\n",
    "    return wait_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a24d343aaa744cf8641b2dfba0cfb37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
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