From b246f6b19c49751c3419c329832def45cc16d9ad Mon Sep 17 00:00:00 2001
From: Hector van der Aa <hector@h3cx.dev>
Date: Wed, 27 May 2026 14:30:13 +0200
Subject: [PATCH] rpm_graph v1

---
 src/rpm_graph.py | 75 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 src/rpm_graph.py

diff --git a/src/rpm_graph.py b/src/rpm_graph.py
new file mode 100644
index 0000000..c751259
--- /dev/null
+++ b/src/rpm_graph.py
@@ -0,0 +1,75 @@
+# Copyright (C) 2026 Hector van der Aa <hector@h3cx.dev>
+# 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
+import matplotlib.pyplot as plt
+
+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}")
+
+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
+
+    files.append(path)
+
+
+for file in files:
+    print(f"Processing {file.name}")
+    df = pd.read_csv(file).set_index("time_us", drop=False)
+    rows = []
+    current_time_us: int = 0
+    current_crank: int = 0
+    last_crank: int = -1
+    for _, row in tqdm(df.iterrows(), total=len(df)):
+        current_time_us = row["time_us"]
+        current_crank = row["crank"]
+
+        if current_crank == 1:
+            if last_crank != -1:
+                rpm = 30_000_000 / (current_time_us - last_crank)
+                rows.append({"time_us": current_time_us, "rpm": rpm})
+            last_crank = current_time_us
+
+    out_df = pd.DataFrame(rows)
+    base_name, _ = file.stem.rsplit("_", 1)
+    output = file.parent / f"{base_name}_rpm.csv"
+    image_output = file.parent / f"{base_name}_rpm.png"
+    out_df.to_csv(output)
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+
+    ax.plot(out_df["time_us"] / 1_000_000, out_df["rpm"])
+    ax.set_xlabel("Time (s)")
+    ax.set_ylabel("RPM")
+    ax.set_title(f"{base_name} RPM over Time")
+    ax.grid = True
+
+    fig.tight_layout()
+    fig.savefig(image_output, dpi=300)
+
+    plt.close(fig)