updated speed logger

app.py

@@ -1,38 +1,67 @@
-from flask import Flask, render_template
+from flask import Flask, render_template, request
 import sqlite3
 import pandas as pd
 from config import DB_PATH
-from datetime import datetime
+import math
 
 app = Flask(__name__)
 
 def load_data():
-    # Connect to SQLite database
+    # 1) grab raw
     conn = sqlite3.connect(DB_PATH)
-    query = "SELECT * FROM speed_tests"
-    df = pd.read_sql(query, conn)
+    df = pd.read_sql("SELECT * FROM speed_tests", conn)
     conn.close()
+
+    # 2) parse epoch→UTC datetimes, then shift to Europe/Berlin
+    df['datetime'] = (
+        pd.to_datetime(df['timestamp'], unit='s', utc=True)
+          .dt.tz_convert('Europe/Berlin')
+    )
+
+    # 3) for your table display, format as naive strings
+    df['recorded_at'] = df['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S')
+
     return df
 
+
+def get_aggregated_data(df: pd.DataFrame, interval: str = '5min'):
+    # ensure 'datetime' is the index
+    df = df.set_index('datetime')
+
+    # 1) resample into N-minute bins, mean
+    agg = (
+        df
+        .resample(interval)
+        .agg({
+            'down_90th': 'mean',
+            'up_90th':   'mean'
+        })
+        .reset_index()
+    )
+
+    # 3) output for Chart.js
+    return {
+        "times":     agg['recorded_at'].tolist(),
+        "down_90th": agg['down_90th'].round(2).tolist(),
+        "up_90th":   agg['up_90th'].round(2).tolist()
+    }
+
+
 @app.route('/')
 def index():
     df = load_data()
+    agg = request.args.get("agg", "5min")
+    chart_data = get_aggregated_data(df, agg)
 
+    print(f"down last 10: {chart_data['down_90th'][-10:]}")
+    print(f"down last 10: {chart_data['up_90th'][-10:]}")
 
+    print(math.isnan(chart_data['down_90th'][-1]) or math.isnan(chart_data['up_90th'][-1]))
 
-    # Convert timestamps to human-readable format
-    df['datetime'] = pd.to_datetime(df['timestamp'], unit='s')
-    # Suppose your DataFrame is called `df`
-    df["timestamp"] = df["timestamp"].apply(lambda ts: datetime.fromtimestamp(ts).strftime("%Y-%m-%d %H:%M:%S"))
+    #if math.isnan(chart_data['down_90th'][-1]) or math.isnan(chart_data['up_90th'][-1]):
+        #return render_template('local.html', data=df.to_dict(orient='records'), chart_data=chart_data)
 
-    # Collect the data for charts
-    chart_data = {
-        "times": df['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S').tolist(),
-        "down_90th": df['down_90th'].tolist(),
-        "up_90th": df['up_90th'].tolist()
-    }
-
-    return render_template('index.html', data=df.to_dict(orient='records'), chart_data=chart_data)
+    return render_template('index.html', aggregation=agg, data=df.to_dict(orient='records'), chart_data=chart_data)
 
 if __name__ == '__main__':
-    app.run(port=5001)
+    app.run(host='0.0.0.0', port=5001)