diff --git a/tjmonopix2/analysis/corryvreckan_converter.py b/tjmonopix2/analysis/corryvreckan_converter.py
new file mode 100644
index 0000000..c15675f
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+++ b/tjmonopix2/analysis/corryvreckan_converter.py
@@ -0,0 +1,94 @@
+#
+# ------------------------------------------------------------
+# Copyright (c) All rights reserved
+# SiLab, Institute of Physics, University of Bonn
+# ------------------------------------------------------------
+#
+
+from pathlib import Path
+
+import numpy as np
+import tables as tb
+
+from tjmonopix2.analysis import analysis
+
+
+def format_dut(input_filename: str | Path, output_filename: str | Path = None, trigger_mode: str = "AIDA") -> None:
+    """Format hit table to be compatible with corryvreckan EventLoaderHDF5 as of commit 149e937f
+
+    Parameters
+    ----------
+    input_filename : str | Path
+        Interpreted hit file with Hit and/or Event data depending on trigger_mode
+    output_filename : str | Path, optional
+         Output file name. If None, defaults to input_filename with suffix '_converted', by default None
+    trigger_mode : str, optional
+        Trigger mode during data taking. EUDET mode (with trigger handshake) and AIDA, mode (without
+        handshake only) are supported. In general, use `DATA_FORMAT=1` in `testbench.yaml` for data
+        taking. By default "AIDA"
+
+    Raises
+    ------
+    RuntimeError
+        Invalid trigger mode selected
+    """
+    hit_dtype_converted = np.dtype(
+        [("column", "<u2"), ("row", "<u2"), ("charge", "<u2"), ("timestamp", "<u8"), ("trigger_number", "<u4")]
+    )
+
+    if type(input_filename) is str:
+        input_filename = Path(input_filename)
+    if output_filename is None:
+        output_filename = Path(input_filename.parent) / Path(input_filename.stem + "_converted" + input_filename.suffix)
+    with tb.open_file(input_filename, "r") as in_file:
+        if trigger_mode.lower() == "aida":
+            hit_table_in = in_file.root.Dut[:]
+            sel = hit_table_in["col"] <= 512  # Select only DUT words
+            hits_selected = hit_table_in[sel]
+
+            hit_table_converted = np.zeros(len(hits_selected), dtype=hit_dtype_converted)
+            with tb.open_file(output_filename, "w") as out_file:
+                hit_table_out = out_file.create_table(out_file.root, name="Hits", description=hit_dtype_converted)
+                hit_table_converted["column"] = hits_selected["col"]
+                hit_table_converted["row"] = hits_selected["row"]
+                hit_table_converted["charge"] = (hits_selected["te"] - hits_selected["le"]) & 0x7F  # calculate TOT
+                hit_table_converted["timestamp"] = 25 * hits_selected["timestamp"].astype(np.uint64)  # convert to ns
+                hit_table_converted["trigger_number"] = 0
+                hit_table_out.append(hit_table_converted)
+                hit_table_out.flush()
+        elif trigger_mode.lower() == "eudet":
+            hit_table_in = in_file.root.Hits[:]
+            sel = hit_table_in["column"] <= 512
+            hits_selected = hit_table_in[sel]
+
+            hit_table_converted = np.zeros(len(hits_selected), dtype=hit_dtype_converted)
+            with tb.open_file(output_filename, "w") as out_file:
+                hit_table_out = out_file.create_table(out_file.root, name="Hits", description=hit_dtype_converted)
+                # Hits are already assiged to TLU number in event builder and data has been pre-formatted
+                # Only convert column names and types
+                hit_table_converted["column"] = hits_selected["column"]
+                hit_table_converted["row"] = hits_selected["row"]
+                hit_table_converted["charge"] = hits_selected["charge"]
+                hit_table_converted["timestamp"] = 0
+                hit_table_converted["trigger_number"] = hits_selected["event_number"].astype(np.uint64)
+                hit_table_out.append(hit_table_converted)
+                hit_table_out.flush()
+        else:
+            raise RuntimeError("Invalid trigger mode selected. Accepted options are 'AIDA' or 'EUDET'")
+
+
+if __name__ == "__main__":
+    input_file = "/path/to/file.h5"
+    trigger_mode = "aida"
+
+    if "_interpreted.h5" not in input_file:
+        with analysis.Analysis(
+            raw_data_file=input_file,
+            store_hits=True,
+            create_pdf=False,
+            build_events=True if trigger_mode == "eudet" else False,
+        ) as a:
+            a.analyze_data()
+            input_file = a.analyzed_data_file
+
+    format_dut(input_filename=input_file, trigger_mode=trigger_mode)