diff --git a/src/derivatives.py b/src/derivatives.py
new file mode 100644
index 0000000..dd18ba2
--- /dev/null
+++ b/src/derivatives.py
@@ -0,0 +1,95 @@
+# Copyright (C) 2026 Hector van der Aa <hector@h3cx.dev>
+# Copyright (C) 2026 Pierre Barbier <pierrebarbier741@gmail.com>
+# Copyright (C) 2026 Association Exergie <association.exergie@gmail.com>
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import pandas as pd
+from pathlib import Path
+import argparse
+from tqdm import tqdm
+
+
+def find_last_crank(df: pd.DataFrame, time_us: int) -> int | None:
+    previous_crank_hits = df.loc[: time_us - 1]
+    previous_crank_hits = previous_crank_hits[previous_crank_hits["crank"] == 1]
+
+    if previous_crank_hits.empty:
+        return None
+
+    return previous_crank_hits.index[-1]
+
+
+def find_next_crank(df: pd.DataFrame, time_us: int) -> int | None:
+    next_crank_hits = df.loc[time_us + 1 :]
+    next_crank_hits = next_crank_hits[next_crank_hits["crank"] == 1]
+
+    if next_crank_hits.empty:
+        return None
+
+    return next_crank_hits.index[0]
+
+
+
+
+def filter_data(file: Path) -> pd.DataFrame:
+    df = pd.read_csv(file).set_index("time_us", drop=False)
+    rows = []
+    last_crank = -1
+    last_crank_delta = -1
+    previous_crank = -1
+    last_cam = -1
+    cam_flag = 0
+    crank_flag = 0
+
+    for _, row in tqdm(df.iterrows(), total=len(df), desc="Derivative"):
+        time_us: int = row["time_us"]
+        crank: int = row["crank"]
+        cam: int = row["cam"]
+        c1 = 0
+        c2 = 0
+        if crank==1:
+            rows.append({
+                "time_us": time_us,
+                "d1": time_us-c1,
+                "d2": (time_us-2*c1+c2)/(c1-c2),
+            })
+            c2=c1
+            c1=time_us
+        output = pd.DataFrame(rows)
+    return output
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("directory", type=Path, help="Source data directory")
+
+args = parser.parse_args()
+
+directory: Path = args.directory
+
+if not directory.is_dir():
+    parser.error(f"{directory} is not a valid directory")
+
+print(f"Processing data in: {directory}")
+
+concat_files: list[Path] = []
+
+for path in directory.glob("*.csv"):
+    stem = path.stem
+
+    try:
+        base_name, channel = stem.rsplit("_", 1)
+    except ValueError:
+        print(f"Skipping badly named file: {path}")
+        continue
+
+    if channel != "trimmed":
+        print(f"Skipping unknown file: {path}")
+        continue
+
+    concat_files.append(path)
+
+for file in concat_files:
+    base_name, _ = file.stem.rsplit("_", 1)
+    output = file.parent / f"{base_name}_derivative.csv"
+    out_df = filter_data(file)
+    out_df.to_csv(output)