commit 75e4788dc38e39a817513b985727380420e99ba9
Author: Mika <kontakt@donau2space.de>
Date:   Sun Dec 14 16:47:08 2025 +0000

    Add measurements_analysis/src/measurements_analysis/core.py

diff --git a/measurements_analysis/src/measurements_analysis/core.py b/measurements_analysis/src/measurements_analysis/core.py
new file mode 100644
index 0000000..15d7662
--- /dev/null
+++ b/measurements_analysis/src/measurements_analysis/core.py
@@ -0,0 +1,67 @@
+from dataclasses import dataclass
+from typing import List, Dict
+import statistics
+from scipy.stats import levene
+
+
+@dataclass
+class MeasurementData:
+    spacing_mm: float
+    hf_amplitude: float
+    spike_outlier_rate: float
+    levenes_test_p: float
+
+
+def analyze_measurements(data: List[MeasurementData]) -> Dict[float, Dict[str, float]]:
+    """Analysiert HF-Messdaten und berechnet Kennzahlen pro Abstand.
+
+    Args:
+        data: Liste von MeasurementData-Objekten.
+
+    Returns:
+        Dictionary mit Abstand als Schlüssel und statistischen Kennzahlen
+        (Mittelwert, Varianz, Ausreißer-Rate, p-Wert) als Wert.
+    """
+    if not data:
+        return {}
+
+    # Gruppiere nach spacing_mm
+    grouped = {}
+    for d in data:
+        if not isinstance(d, MeasurementData):
+            raise TypeError("Each item must be a MeasurementData instance.")
+        grouped.setdefault(d.spacing_mm, []).append(d)
+
+    results = {}
+    all_amplitude_groups = [
+        [m.hf_amplitude for m in measurements]
+        for measurements in grouped.values()
+    ]
+
+    for spacing, measurements in grouped.items():
+        amplitudes = [m.hf_amplitude for m in measurements]
+        outliers = [m.spike_outlier_rate for m in measurements]
+        pre_pvals = [m.levenes_test_p for m in measurements]
+
+        mean_amp = statistics.mean(amplitudes)
+        var_amp = statistics.variance(amplitudes) if len(amplitudes) > 1 else 0.0
+        mean_outlier = statistics.mean(outliers)
+        pre_mean_p = statistics.mean(pre_pvals)
+
+        # Berechne Levene-Test über alle Gruppen, wenn sinnvoll
+        try:
+            if len(all_amplitude_groups) > 1:
+                p_val = levene(*all_amplitude_groups).pvalue
+            else:
+                p_val = pre_mean_p
+        except Exception:
+            p_val = pre_mean_p
+
+        results[spacing] = {
+            "mean_amplitude": mean_amp,
+            "variance_amplitude": var_amp,
+            "mean_outlier_rate": mean_outlier,
+            "levene_p": p_val,
+        }
+
+    return results