Streamline Dockerfile, remove unused deps

* Dockerfile: Use non-root user, buildx cache, setup for readonly
   container, remove unused apt deps.
   For now don't install pip package and keep development flask server
   as this will be replaced in the future (fastapi). Then a proper
   webserver (e.g. nginx) should be used and the pip package can be
   created and deployed just to the run-stage (with the webserver).
 * docker-compose: Set to readonly (anonymous volumes declared in
   Dockerfile should maintain all writable data).
   Mount config.py for easier development. Should be replaced by
   environment support for all config file variables.
 * Remove unused runtime dependencies: mariadb, joblib, pytest,
   pytest-cov.
 * Move pytest-cov to dev dependencies.
 * Add output_dir to config.py.
 * Fix visualization_results.pdf endpoint.
 * Update docs.

src/akkudoktoreos/config.py

@@ -1,5 +1,7 @@
 from datetime import datetime, timedelta
 
+output_dir = "output"
+
 prediction_hours = 48
 optimization_hours = 24
 strafe = 10

src/akkudoktoreos/visualize.py

@@ -1,4 +1,5 @@
 import datetime
+import os
 
 # Set the backend for matplotlib to Agg
 import matplotlib
@@ -7,6 +8,7 @@ import numpy as np
 from matplotlib.backends.backend_pdf import PdfPages
 
 from akkudoktoreos.class_sommerzeit import ist_dst_wechsel
+from akkudoktoreos.config import output_dir
 
 matplotlib.use("Agg")
 
@@ -28,7 +30,10 @@ def visualisiere_ergebnisse(
     #####################
     # 24-hour visualization
     #####################
-    with PdfPages(filename) as pdf:
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    output_file = os.path.join(output_dir, filename)
+    with PdfPages(output_file) as pdf:
         # Load and PV generation
         plt.figure(figsize=(14, 14))
         plt.subplot(3, 3, 1)

src/akkudoktoreosserver/flask_server.py

@@ -18,7 +18,12 @@ from akkudoktoreos.class_load_corrector import LoadPredictionAdjuster
 from akkudoktoreos.class_optimize import optimization_problem
 from akkudoktoreos.class_pv_forecast import PVForecast
 from akkudoktoreos.class_strompreis import HourlyElectricityPriceForecast
-from akkudoktoreos.config import get_start_enddate, optimization_hours, prediction_hours
+from akkudoktoreos.config import (
+    get_start_enddate,
+    optimization_hours,
+    output_dir,
+    prediction_hours,
+)
 
 app = Flask(__name__)
 
@@ -262,11 +267,11 @@ def flask_optimize():
         return jsonify(result)
 
 
-@app.route("/visualisierungsergebnisse.pdf")
+@app.route("/visualization_results.pdf")
 def get_pdf():
     # Endpoint to serve the generated PDF with visualization results
     return send_from_directory(
-        "", "visualisierungsergebnisse.pdf"
+        os.path.abspath(output_dir), "visualization_results.pdf"
     )  # Adjust the directory if needed