From c98bd3bddab78cb9d172f4ec4b10bc7d5fecb0e3 Mon Sep 17 00:00:00 2001
From: Mika <kontakt@donau2space.de>
Date: Sun, 22 Feb 2026 03:07:04 +0000
Subject: [PATCH] Add data_analysis/src/data_analysis/core.py

---
 data_analysis/src/data_analysis/core.py | 102 ++++++++++++++++++++++++
 1 file changed, 102 insertions(+)
 create mode 100644 data_analysis/src/data_analysis/core.py

diff --git a/data_analysis/src/data_analysis/core.py b/data_analysis/src/data_analysis/core.py
new file mode 100644
index 0000000..fe934f2
--- /dev/null
+++ b/data_analysis/src/data_analysis/core.py
@@ -0,0 +1,102 @@
+import json
+import argparse
+import logging
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from typing import List, Dict, Any
+
+
+class DataValidationError(Exception):
+    """Custom exception for invalid input data."""
+    pass
+
+
+def _validate_data_points(data: List[Dict[str, Any]], required_fields=("intensity", "background_noise")) -> None:
+    if not isinstance(data, list):
+        raise DataValidationError("Input data must be a list of dictionaries.")
+    for i, item in enumerate(data):
+        if not isinstance(item, dict):
+            raise DataValidationError(f"Data item at index {i} is not a dictionary.")
+        for field in required_fields:
+            if field not in item:
+                raise DataValidationError(f"Missing field '{field}' in data item at index {i}.")
+            if not isinstance(item[field], (int, float)):
+                raise DataValidationError(f"Field '{field}' in item {i} must be numeric.")
+
+
+def correct_flourescence(raw_data: List[Dict[str, float]]) -> List[Dict[str, float]]:
+    """Korrigiert Roh-Fluoreszenzdaten durch Glättung und Basislinienabzug."""
+    _validate_data_points(raw_data)
+    df = pd.DataFrame(raw_data)
+    # Glättung (moving average filter)
+    df['intensity'] = df['intensity'].rolling(window=3, min_periods=1, center=True).mean()
+    # Basislinienabzug anhand des Medianwerts
+    baseline = df['background_noise'].median()
+    df['corrected_intensity'] = np.maximum(df['intensity'] - baseline, 0)
+    corrected = [
+        {
+            'intensity': float(row['corrected_intensity']),
+            'background_noise': float(row['background_noise'])
+        }
+        for _, row in df.iterrows()
+    ]
+    return corrected
+
+
+def substract_reference(data: List[Dict[str, float]], reference: List[Dict[str, float]]) -> List[Dict[str, float]]:
+    """Subtrahiert Referenzwerte (z. B. Fremdlicht) aus den Daten."""
+    _validate_data_points(data)
+    _validate_data_points(reference)
+    df_data = pd.DataFrame(data)
+    df_ref = pd.DataFrame(reference)
+    # Falls Längen unterschiedlich, mit Minimalgröße arbeiten
+    min_len = min(len(df_data), len(df_ref))
+    df_data = df_data.iloc[:min_len].reset_index(drop=True)
+    df_ref = df_ref.iloc[:min_len].reset_index(drop=True)
+    df_result = df_data.copy()
+    df_result['intensity'] = np.maximum(df_data['intensity'] - df_ref['intensity'], 0)
+    return df_result.to_dict(orient='records')
+
+
+def _load_json(path: Path) -> List[Dict[str, Any]]:
+    with path.open('r', encoding='utf-8') as f:
+        return json.load(f)
+
+
+def _save_json(data: List[Dict[str, Any]], path: Path) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open('w', encoding='utf-8') as f:
+        json.dump(data, f, indent=2)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Fluoreszenzdaten-Korrektur")
+    parser.add_argument('--input', required=True, help='Pfad zur Eingabedatei mit Rohdaten (JSON).')
+    parser.add_argument('--reference', required=True, help='Pfad zur Referenzdatei (JSON).')
+    parser.add_argument('--output', required=True, help='Pfad zur Ausgabedatei (JSON).')
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO, format='[%(levelname)s] %(message)s')
+    logger = logging.getLogger(__name__)
+
+    in_path = Path(args.input)
+    ref_path = Path(args.reference)
+    out_path = Path(args.output)
+
+    logger.info(f"Lade Rohdaten aus {in_path}...")
+    raw_data = _load_json(in_path)
+    logger.info(f"Lade Referenzdaten aus {ref_path}...")
+    reference_data = _load_json(ref_path)
+
+    logger.info("Korrigiere Fluoreszenzdaten...")
+    corrected = correct_flourescence(raw_data)
+    logger.info("Subtrahiere Referenzsignal...")
+    result = substract_reference(corrected, reference_data)
+
+    _save_json(result, out_path)
+    logger.info(f"Korrigierte Daten wurden nach {out_path} geschrieben.")
+
+
+if __name__ == '__main__':
+    main()