68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
from flask import Flask, render_template, request
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import sqlite3
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import pandas as pd
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from config import DB_PATH
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import math
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app = Flask(__name__)
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def load_data():
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# 1) grab raw
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conn = sqlite3.connect(DB_PATH)
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df = pd.read_sql("SELECT * FROM speed_tests", conn)
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conn.close()
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# 2) parse epoch→UTC datetimes, then shift to Europe/Berlin
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df['datetime'] = (
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pd.to_datetime(df['timestamp'], unit='s', utc=True)
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.dt.tz_convert('Europe/Berlin')
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)
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# 3) for your table display, format as naive strings
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df['recorded_at'] = df['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S')
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return df
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def get_aggregated_data(df: pd.DataFrame, interval: str = '5min'):
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# ensure 'datetime' is the index
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df = df.set_index('datetime')
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# 1) resample into N-minute bins, mean
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agg = (
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df
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.resample(interval)
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.agg({
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'down_90th': 'mean',
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'up_90th': 'mean'
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})
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.reset_index()
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)
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# 3) output for Chart.js
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return {
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"times": agg['recorded_at'].tolist(),
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"down_90th": agg['down_90th'].round(2).tolist(),
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"up_90th": agg['up_90th'].round(2).tolist()
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}
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@app.route('/')
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def index():
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df = load_data()
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agg = request.args.get("agg", "5min")
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chart_data = get_aggregated_data(df, agg)
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print(f"down last 10: {chart_data['down_90th'][-10:]}")
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print(f"down last 10: {chart_data['up_90th'][-10:]}")
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print(math.isnan(chart_data['down_90th'][-1]) or math.isnan(chart_data['up_90th'][-1]))
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#if math.isnan(chart_data['down_90th'][-1]) or math.isnan(chart_data['up_90th'][-1]):
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#return render_template('local.html', data=df.to_dict(orient='records'), chart_data=chart_data)
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return render_template('index.html', aggregation=agg, data=df.to_dict(orient='records'), chart_data=chart_data)
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=5001)
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