Create processing_utils_lecroy and add first parser

packs/proc/processing_utils_lecroy.py

@@ -0,0 +1,56 @@
+import numpy as np
+import pandas as pd
+import os
+from tqdm import tqdm
+import csv
+import re
+
+"""
+Processing utilities for the Lecroy oscilloscope
+
+This file holds all the relevant functions for the processing of data from csv files to h5.
+"""
+
+def parse_lecroy_segmented(lines):
+    # Line 1 has to have: Segments,1000,SegmentSize,5002
+    segments = int(lines[1][1])
+    seg_size = int(lines[1][3])
+
+    # Line 2, header is: Segment,TrigTime,TimeSinceSegment1
+    # Lines 3 to 3 + segments - 1 are header lines
+    header_start = 3
+    header_end = header_start + segments
+    header_lines = lines[header_start:header_end]
+
+    header_df = pd.DataFrame(header_lines, columns=["Segment", "TrigTime", "TimeSinceSegment1"])
+
+    # Find the "Time,Ampl" line
+    for i, line in enumerate(lines):
+        if line[0].strip() == "Time":
+            data_start = i + 1
+            break
+    else:
+        raise ValueError("Time,Ampl line not found")
+
+    # Read the data block (segments × segment size)
+    raw_data = lines[data_start:]
+    if len(raw_data) < segments * seg_size:
+        print(f"Warning: expected {segments * seg_size} rows, got {len(raw_data)}")
+
+    value_list = []
+    for j in range(segments):
+        segment_data = []
+        for k in range(seg_size):
+            x = j * seg_size + k
+            if x >= len(raw_data): # x = line in the file
+                segment_data.append(None)
+            else:
+                try:
+                    value = float(raw_data[x][1])  # column 1 = Amplitude of signal
+                    segment_data.append(value)
+                except (ValueError, IndexError):
+                    segment_data.append(None)
+        value_list.append(segment_data)
+
+    value_df = pd.DataFrame(value_list)
+    return value_df, header_df