ddr-scores/summary.ipynb

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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from datetime import datetime, timezone, timedelta\n",
"import pytz\n",
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"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import sqlite3\n",
"\n",
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"plt.rcParams[\"font.family\"] = \"Helvetica Neue\"\n",
"\n",
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"db = sqlite3.connect(\"./scores.db\")\n",
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"\n",
"with open(\"data/output.csv\", \"rb\") as f:\n",
" data = pd.read_csv(f, delimiter=\"\\t\")"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
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"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x13f6d8550>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
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},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x500 with 1 Axes>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from datetime import date, datetime\n",
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"import july\n",
"from july.utils import date_range\n",
"from utils import CST\n",
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"c = db.cursor()\n",
"result = c.execute(\"\"\"\n",
" SELECT COUNT(*), UNIXEPOCH(DATE(scores.\"Time Played\")) as date FROM scores\n",
" GROUP BY date\n",
"\"\"\")\n",
"result = list(result)\n",
"result.sort(key=lambda x: x[1])\n",
"y, x = list(zip(*result))\n",
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"dates = list(map(lambda x: datetime.utcfromtimestamp(x).astimezone(tz=None), x))\n",
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"# dates = matplotlib.dates.date2num(x)\n",
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"# plt.title(\"Amount of maps played per session over time\")\n",
"# plt.xticks(rotation=45, ha=\"right\")\n",
"# plt.plot(dates, y)\n",
"# plt.gcf().autofmt_xdate()\n",
"# plt.show()\n",
"july.heatmap(dates, y, title='# of scores per session over time',\n",
" # cmap='Pastel1',\n",
" # date_label=True,\n",
" value_label=True,\n",
" month_grid=True,\n",
")"
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]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from statistics import mean\n",
"from datetime import datetime\n",
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"from matplotlib.scale import ScaleBase, register_scale\n",
"from matplotlib.transforms import Transform\n",
"from matplotlib.axis import YAxis\n",
"\n",
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"\n",
"c = db.cursor()\n",
"ratings = list(map(lambda r: r[0], c.execute(\"select distinct Rating from scores\")))\n",
"ratings.sort()\n",
"\n",
"for rating in ratings:\n",
" result = c.execute('select \"Song ID\", UNIXEPOCH(\"Time Played\"), \"Song Name\", Score from scores where Rating = ? order by \"Time Played\"', (rating,))\n",
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" result = list(result)\n",
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" scores = dict()\n",
" xs = []\n",
" ys = []\n",
" for record in result:\n",
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" song_id = record[0]\n",
" scores[song_id] = 0\n",
" for record in result:\n",
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" song_id, time_played, song_name, score = record\n",
" date = datetime.utcfromtimestamp(time_played)\n",
" scores[song_id] = score\n",
" values = list(scores.values())\n",
" avg = mean(values)\n",
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" xs.append(date)\n",
" ys.append(avg)\n",
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"\n",
" if xs and ys:\n",
" plt.plot(xs, ys, label=f\"Rating {rating}\")\n",
"\n",
"plt.legend()\n",
"plt.xticks(rotation=45, ha=\"right\")\n",
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"plt.yscale('log')\n",
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"plt.title(\"Average score for each level\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time Played\n",
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"2024-03-04 29\n",
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"2024-05-04 28\n",
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"2024-04-14 24\n",
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"2024-05-03 24\n",
"2024-05-01 23\n",
"2024-04-13 22\n",
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"2024-04-18 20\n",
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"2024-04-25 20\n",
"2024-04-20 18\n",
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"2024-03-10 17\n",
"Name: Time Played, dtype: int64\n"
]
}
],
"source": [
"JST = pytz.timezone(\"Asia/Tokyo\")\n",
"CST = pytz.timezone(\"America/Chicago\")\n",
"\n",
"def to_jst_timestamp(s: str, format_str=\"%Y-%m-%d %H:%M:%S\"):\n",
" if type(s) is not str: return None\n",
" naive_dt = datetime.strptime(s, format_str)\n",
" jst_dt = JST.localize(naive_dt)\n",
" cst_dt = jst_dt.astimezone(CST)\n",
" return cst_dt\n",
"\n",
"times = data[\"Time Played\"]\n",
"times_mapped = times.map(to_jst_timestamp)\n",
"days_played = times_mapped.groupby(times_mapped.dt.date).count()\n",
"print(days_played.sort_values(ascending=False).head(10))"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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" Clears Average Max Score Max Score Lamp Max Score Song\n",
"Rating \n",
"8 7 895073 996620 GFC 朧 (dj TAKA Remix)\n",
"9 25 918551 999650 PFC Why not\n",
"10 31 924433 992160 GFC 隅田川夏恋歌\n",
"11 44 899018 992520 GFC 朧\n",
"12 62 897328 987030 GFC MY SUMMER LOVE\n",
"13 74 843144 978430 GFC Struggle\n",
"14 125 801284 960440 FC FUNKY SUMMER BEACH\n",
"15 27 802587 917120 Clear Toy Box Factory\n",
"16 19 779613 862810 Clear Life is beautiful\n",
"17 3 724233 765430 Clear Elemental Creation\n"
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]
}
],
"source": [
"records_by_level = data.loc[data[\"Lamp\"] != \"Fail\"].groupby(data[\"Rating\"])\n",
"num_clears_by_level = records_by_level[\"Score\"].count().rename(\"Clears\")\n",
"average_scores_by_level = records_by_level[\"Score\"].mean().round().astype(\"int\").rename(\"Average\")\n",
"def max_info(group):\n",
" max_idx = group[\"Score\"].idxmax()\n",
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" return group.loc[max_idx][[\"Score\", \"Lamp\", \"Song Name\"]].rename({ \"Score\": \"Max Score\", \"Lamp\": \"Max Score Lamp\", \"Song Name\": \"Max Score Song\" })\n",
"max_score_by_level = records_by_level.apply(max_info)\n",
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"result = pd.merge(num_clears_by_level, average_scores_by_level, on=\"Rating\")\n",
"result = pd.merge(result, max_score_by_level, on=\"Rating\")\n",
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"with pd.option_context('expand_frame_repr', False):\n",
" print(result)"
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]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Song ID Song Name Difficulty Rating Score Grade Lamp Time Uploaded Time Played\n",
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"44 1idIoi66ll806D8ddldOQi8bdiDO0Oil Struggle ESP 13 978430 AA+ GFC 2024-04-18 18:14:49 2024-04-18 18:05:47\n",
"128 9i6dOd608qb0IlqoDIPb8q1o8q1ddQQd FUNKY SUMMER BEACH ESP 14 960440 AA+ FC 2024-05-05 05:34:41 2024-05-05 05:22:53\n",
"59 1qPIiqqQo0P9dD90I11q90b0ooIidbPO CyberConnect ESP 15 903280 AA Clear 2024-05-05 02:40:07 2024-05-05 02:32:47\n",
"112 90lolio9qd6qo6Pl8oo69iqi81oiiQib I'm so Happy CSP 16 839540 A Clear 2024-05-05 02:19:22 2024-05-05 02:14:35\n",
"347 OiIOPd80d9PQQIbidO6ObioboO88OD9l Elemental Creation ESP 17 765430 B+ Clear 2024-05-05 05:34:41 2024-05-05 05:12:27\n"
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]
}
],
"source": [
"# Gold 1\n",
"gold1scores = []\n",
"gold1scores.append(data[(data[\"Score\"] >= 975000) & (data[\"Rating\"] == 13)].head(1))\n",
"gold1scores.append(data[(data[\"Score\"] >= 925000) & (data[\"Rating\"] == 14)].head(1))\n",
"gold1scores.append(data[(data[\"Score\"] >= 875000) & (data[\"Rating\"] == 15)].head(1))\n",
"gold1scores.append(data[(data[\"Score\"] >= 825000) & (data[\"Rating\"] == 16)].head(1))\n",
"gold1scores.append(data[(data[\"Score\"] >= 750000) & (data[\"Rating\"] == 17)].head(1))\n",
"with pd.option_context('expand_frame_repr', False):\n",
" gold1scores = pd.concat(gold1scores)\n",
" print(gold1scores)"
]
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}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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