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{
"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": {
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xSHwOR1QdBsdjhKNzI+QmC5jpRiNxoonSf/MJS41wMCxOkprDWR2NXLi2dUGu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\n93mEe5+e2sz3nceP8r9/+FzB8xK28Va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"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": [
"<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",
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