commit 5dab9594dd549cdff3ca7962504aac896074ae42
Author: Mika <kontakt@donau2space.de>
Date:   Thu Feb 12 11:16:20 2026 +0000

    Add unknown_analysis/src/unknown_analysis/core.py

diff --git a/unknown_analysis/src/unknown_analysis/core.py b/unknown_analysis/src/unknown_analysis/core.py
new file mode 100644
index 0000000..5fe7be3
--- /dev/null
+++ b/unknown_analysis/src/unknown_analysis/core.py
@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+import pandas as pd
+from typing import List, Dict
+
+
+class InputValidationError(ValueError):
+    """Exception raised when input log_data is invalid."""
+    pass
+
+
+def _validate_log_data(log_data: list[dict]) -> None:
+    if not isinstance(log_data, list):
+        raise InputValidationError("log_data must be a list of dictionaries")
+    required_fields = {"artifact_key", "status", "cause", "path", "error"}
+    for i, entry in enumerate(log_data):
+        if not isinstance(entry, dict):
+            raise InputValidationError(f"Entry {i} is not a dictionary")
+        missing = required_fields - set(entry.keys())
+        if missing:
+            raise InputValidationError(f"Entry {i} missing required keys: {missing}")
+
+
+def calculate_unknown_rates(log_data: List[Dict]) -> Dict[str, float]:
+    """Berechnet die Unknown-Artifakt- und Unknown-Schema-Quoten aus Logdaten.
+
+    Args:
+        log_data: Liste von Dictionaries mit Artefaktinformationen.
+
+    Returns:
+        Dictionary mit den Raten der Unknown-Kategorien.
+    """
+    _validate_log_data(log_data)
+    if not log_data:
+        return {"unknown_artifact_missing_rate": 0.0, "unknown_schema_rate": 0.0}
+
+    df = pd.DataFrame(log_data)
+    total_count = max(len(df), 1)
+
+    unknown_mask = df["status"].str.upper() == "UNKNOWN"
+    unknown_df = df[unknown_mask]
+
+    missing_rate = (unknown_df["cause"].str.contains("missing", case=False, na=False).sum()) / total_count
+    schema_rate = (unknown_df["cause"].str.contains("schema", case=False, na=False).sum()) / total_count
+
+    return {
+        "unknown_artifact_missing_rate": round(missing_rate, 4),
+        "unknown_schema_rate": round(schema_rate, 4),
+    }
+
+
+def get_top_pass_unknown_switches(log_data: List[Dict]) -> List[Dict]:
+    """Analysiert die häufigsten PASS→Unknown-Umschaltungen.
+
+    Args:
+        log_data: Liste der Logeinträge mit Statusänderungen und Fehlern.
+
+    Returns:
+        Liste der Top-Umschaltungen mit 'cause', 'path' und 'error'.
+    """
+    _validate_log_data(log_data)
+    if not log_data:
+        return []
+
+    df = pd.DataFrame(log_data)
+    df = df.sort_values(by=["artifact_key"])  # Ensure grouping order is stable
+
+    pass_unknown = df.query('status.str.upper() == "UNKNOWN"', engine="python")
+    # For simplicity, treat any UNKNOWN with a previous PASS artifact as a switch
+    known_pass_artifacts = set(df.loc[df["status"].str.upper() == "PASS", "artifact_key"])
+    switches = pass_unknown[pass_unknown["artifact_key"].isin(known_pass_artifacts)]
+
+    if switches.empty:
+        return []
+
+    grouped = (
+        switches.groupby(["cause", "path", "error"])
+        .size()
+        .reset_index(name="count")
+        .sort_values(by="count", ascending=False)
+    )
+
+    top = grouped.head(10)[["cause", "path", "error"]]
+    return top.to_dict(orient="records")