From 7c6880e2adfb2c38e31a8698d5ee88acb67b2df9 Mon Sep 17 00:00:00 2001
From: Mika <kontakt@donau2space.de>
Date: Wed, 17 Dec 2025 12:03:31 +0000
Subject: [PATCH] Add trace_analysis_script/src/trace_analysis_script/core.py

---
 .../src/trace_analysis_script/core.py         | 61 +++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 trace_analysis_script/src/trace_analysis_script/core.py

diff --git a/trace_analysis_script/src/trace_analysis_script/core.py b/trace_analysis_script/src/trace_analysis_script/core.py
new file mode 100644
index 0000000..1397c19
--- /dev/null
+++ b/trace_analysis_script/src/trace_analysis_script/core.py
@@ -0,0 +1,61 @@
+import json
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from typing import Dict
+
+def analyze_trace(trace_file: str) -> Dict[str, float]:
+    """Analysiert eine Kernel-Trace-Datei und erstellt Statistiken zu Signal- und Zeitoffset-Eigenschaften.
+
+    Parameters
+    ----------
+    trace_file : str
+        Pfad zur Kernel-Trace-Datei im CSV- oder JSON-Format.
+
+    Returns
+    -------
+    dict
+        Dictionary mit statistischen Kennwerten: peak_amplitude, median_bandpower, crosscorr_with_clockevents.
+    """
+    path = Path(trace_file)
+    if not path.exists():
+        raise FileNotFoundError(f"Trace file not found: {trace_file}")
+
+    # Laden der Daten je nach Format
+    if path.suffix.lower() == '.csv':
+        data = pd.read_csv(trace_file)
+    elif path.suffix.lower() == '.json':
+        with open(trace_file, 'r', encoding='utf-8') as f:
+            json_data = json.load(f)
+        data = pd.DataFrame(json_data)
+    else:
+        raise ValueError("Unsupported file format. Use CSV or JSON.")
+
+    # Eingabevalidierung
+    if 'signal' not in data.columns or 'timestamp' not in data.columns:
+        raise ValueError("Input data must contain 'signal' and 'timestamp' columns.")
+
+    # Berechnung der Kennwerte
+    signal = data['signal'].astype(float)
+
+    peak_amplitude = float(np.max(np.abs(signal)))
+    median_bandpower = float(np.median(signal ** 2))
+
+    # Kreuzkorrelation zwischen Signal und Timestamps
+    ts = data['timestamp'].astype(float)
+    if len(signal) != len(ts) or len(signal) < 2:
+        crosscorr_with_clockevents = 0.0
+    else:
+        signal_std = np.std(signal)
+        ts_std = np.std(ts)
+        if signal_std == 0 or ts_std == 0:
+            crosscorr_with_clockevents = 0.0
+        else:
+            corr_matrix = np.corrcoef(signal, ts)
+            crosscorr_with_clockevents = float(corr_matrix[0, 1])
+
+    return {
+        "peak_amplitude": peak_amplitude,
+        "median_bandpower": median_bandpower,
+        "crosscorr_with_clockevents": crosscorr_with_clockevents,
+    }