Add artifact.1/src/artifact_1/core.py

artifact.1/src/artifact_1/core.py

@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+import statistics
+from dataclasses import dataclass
+from typing import List
+
+
+@dataclass
+class RunData:
+    """Repräsentiert die Daten eines einzelnen Runs."""
+
+    run_id: str
+    healing_rate: float
+    overhead_ms: float
+    warn_rate: float
+    unknown_rate: float
+
+
+@dataclass
+class AggregatedResults:
+    """Aggregierte Zusammenfassung über mehrere Runs."""
+
+    total_runs: int
+    avg_healing_rate: float
+    peak_overhead: float
+    overall_warn_rate: float
+
+
+def analyze_runs(run_data_list: List[RunData]) -> AggregatedResults:
+    """Analysiert eine Liste von RunData und gibt aggregierte Ergebnisse zurück.
+
+    Parameters
+    ----------
+    run_data_list : list[RunData]
+        Liste der zu analysierenden Laufdaten.
+
+    Returns
+    -------
+    AggregatedResults
+        Aggregierte Kennwerte über alle übergebenen Runs.
+    """
+
+    if not isinstance(run_data_list, list):
+        raise TypeError("run_data_list muss eine Liste von RunData sein.")
+
+    if not run_data_list:
+        raise ValueError("run_data_list darf nicht leer sein.")
+
+    for item in run_data_list:
+        if not isinstance(item, RunData):
+            raise TypeError(f"Alle Elemente in run_data_list müssen vom Typ RunData sein, nicht {type(item)}.")
+
+    total_runs = len(run_data_list)
+    
+    healing_rates = [r.healing_rate for r in run_data_list]
+    overheads = [r.overhead_ms for r in run_data_list]
+    warn_rates = [r.warn_rate for r in run_data_list]
+
+    # Validiere Zahlenbereiche
+    for r in run_data_list:
+        if not (0.0 <= r.healing_rate <= 1.0):
+            raise ValueError(f"Ungültige healing_rate in {r.run_id}: {r.healing_rate}")
+        if r.overhead_ms < 0:
+            raise ValueError(f"Negativer overhead_ms in {r.run_id}: {r.overhead_ms}")
+        if not (0.0 <= r.warn_rate <= 1.0):
+            raise ValueError(f"Ungültige warn_rate in {r.run_id}: {r.warn_rate}")
+        if not (0.0 <= r.unknown_rate <= 1.0):
+            raise ValueError(f"Ungültige unknown_rate in {r.run_id}: {r.unknown_rate}")
+
+    avg_healing_rate = statistics.mean(healing_rates)
+    peak_overhead = max(overheads)
+    overall_warn_rate = statistics.mean(warn_rates)
+
+    return AggregatedResults(
+        total_runs=total_runs,
+        avg_healing_rate=avg_healing_rate,
+        peak_overhead=peak_overhead,
+        overall_warn_rate=overall_warn_rate,
+    )