From d65baef617b16632ba5eb59d5bfe2b72f82a4cca Mon Sep 17 00:00:00 2001
From: Mika <kontakt@donau2space.de>
Date: Tue, 17 Mar 2026 11:07:04 +0000
Subject: [PATCH] Add data_analysis_script/src/data_analysis_script/core.py

---
 .../src/data_analysis_script/core.py          | 81 +++++++++++++++++++
 1 file changed, 81 insertions(+)
 create mode 100644 data_analysis_script/src/data_analysis_script/core.py

diff --git a/data_analysis_script/src/data_analysis_script/core.py b/data_analysis_script/src/data_analysis_script/core.py
new file mode 100644
index 0000000..ad525e3
--- /dev/null
+++ b/data_analysis_script/src/data_analysis_script/core.py
@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass, asdict
+from pathlib import Path
+from typing import Any, Dict
+
+import pandas as pd
+import statistics
+
+
+logger = logging.getLogger(__name__)
+logger.addHandler(logging.NullHandler())
+
+
+class LogAnalysisError(Exception):
+    """Custom exception for log analysis errors."""
+    pass
+
+
+@dataclass
+class AnalysisResults:
+    """Speichert Ergebnisse der Log-Analyse."""
+
+    max_outlier: float
+    band_center: float
+    band_width: float
+
+    def to_json(self) -> Dict[str, Any]:
+        """Konvertiert die Analyseergebnisse in ein JSON-kompatibles Dict."""
+        return asdict(self)
+
+
+def _validate_log_dataframe(df: pd.DataFrame) -> None:
+    required_cols = {"worker_start_offset", "expires_at_dist_hours", "retry_total_overhead_ms"}
+    missing = required_cols - set(df.columns)
+    if missing:
+        raise LogAnalysisError(f"Missing required columns: {', '.join(missing)}")
+    if df.empty:
+        raise LogAnalysisError("Input log file is empty.")
+
+
+def analyze_logs(log_file: str) -> AnalysisResults:
+    """Analysiert eine Logdatei, extrahiert relevante Metriken und gibt strukturierte Ergebnisse zurück."""
+
+    path = Path(log_file)
+    if not path.exists():
+        raise LogAnalysisError(f"Log file not found: {log_file}")
+
+    try:
+        df = pd.read_csv(path)
+    except Exception as exc:
+        logger.exception("Fehler beim Einlesen der CSV-Datei")
+        raise LogAnalysisError(f"Fehler beim Einlesen der CSV-Datei: {exc}")
+
+    _validate_log_dataframe(df)
+
+    # Berechnungen der Metriken
+    latencies = df["expires_at_dist_hours"].astype(float)
+    start_offsets = df["worker_start_offset"].astype(float)
+
+    max_outlier = float(latencies.max())
+    band_center = float(latencies.mean())
+    try:
+        band_width = float(statistics.stdev(latencies))
+    except statistics.StatisticsError:
+        band_width = 0.0
+
+    result = AnalysisResults(
+        max_outlier=max_outlier,
+        band_center=band_center,
+        band_width=band_width,
+    )
+
+    # Eingebaute Validierungen (CI-Ready)
+    assert result.max_outlier >= 0, "max_outlier muss nicht-negativ sein"
+    assert result.band_width >= 0, "band_width muss nicht-negativ sein"
+
+    logger.debug("Analyse abgeschlossen: %s", result)
+    return result