Initial commit

src/algo1.py

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+# 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 time
+import pandas as pd
+from pathlib import Path
+import argparse
+from tqdm import tqdm
+
+parser = argparse.ArgumentParser()
+parser.add_argument("file", type=Path, help="Source data")
+
+args = parser.parse_args()
+
+run_data_path: Path = args.file
+
+df = pd.read_csv(run_data_path).set_index("time_us", drop=False)
+
+sync_ok = False
+cycle_ctr = 0
+cam_ctr = 0
+exhaust_timestamp = 0
+intake_timestamp = 0
+spark_release_time_1 = 0
+spark_release_time_2 = 0
+spark_release_time_3 = 0
+spark_adv = 25
+
+rows = []
+
+for _, row in tqdm(df.iterrows(), total=len(df)):
+    time_us: int = row["time_us"]
+    crank: int = row["crank"]
+    cam: int = row["cam"]
+
+    if cam == 1:
+        if cycle_ctr < 4:
+            sync_ok = False
+        if not sync_ok and cycle_ctr == 4:
+            cam_ctr += 1
+        if cam_ctr >= 2:
+            sync_ok = True
+        cycle_ctr = 0
+
+    if crank == 1:
+        cycle_ctr += 1
+        if cycle_ctr >= 5:
+            sync_ok = False
+
+        if cycle_ctr == 2:
+            exhaust_timestamp = time_us
+
+        if cycle_ctr == 3:
+            intake_timestamp = time_us
+
+        if cycle_ctr == 4 and sync_ok:
+            d1a = time_us - intake_timestamp
+            d1b = intake_timestamp - exhaust_timestamp
+            spark_release_time_1 = int(
+                time_us + (2 * d1a - d1b) * (45 - spark_adv) / 180
+            )
+
+            t_s = (
+                d1a * (135 + spark_adv) / 180 + (2 * d1a - d1b) * (45 - spark_adv) / 180
+            )
+
+            t_a = d1a / 2 + t_s / 2
+
+            spark_release_time_2 = int(t_a * (45 - spark_adv) / 180 + time_us)
+
+            f_a = (315 + spark_adv) / (d1a) + (45 - spark_adv) / (2 * d1a - d1b)
+
+            t_a = 1 / f_a
+
+            spark_release_time_3 = int(2 * (t_a * (45 - spark_adv)) + time_us)
+
+    spark_1 = 1 if spark_release_time_1 == time_us and sync_ok else 0
+    spark_2 = 1 if spark_release_time_2 == time_us and sync_ok else 0
+    spark_3 = 1 if spark_release_time_3 == time_us and sync_ok else 0
+    rows.append(
+        {
+            "time_us": time_us,
+            "crank": crank,
+            "cam": cam,
+            "spark_1": spark_1,
+            "spark_2": spark_2,
+            "spark_3": spark_3,
+        }
+    )
+
+out_df = pd.DataFrame(rows)
+base_name, _ = run_data_path.stem.rsplit("_", 1)
+output = run_data_path.parent / f"{base_name}_output.csv"
+out_df.to_csv(output)

src/plot.py

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+# 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 != "output":
+        print(f"Skipping unknown file: {path}")
+        continue
+
+    files.append(path)
+
+
+for file in files:
+    output_df = pd.read_csv(file).set_index("time_us", drop=False)
+    fig, ax = plt.subplots(figsize=(12, 6))
+
+    # RPM
+    ax.plot(output_df["time_us"] / 1_000_000, output_df["crank"], label="crank")
+    ax.plot(
+        output_df["time_us"] / 1_000_000, output_df["cam"], label="cam", color="red"
+    )
+    ax.plot(
+        output_df["time_us"] / 1_000_000,
+        output_df["spark_1"],
+        label="spark_1",
+        color="green",
+    )
+    ax.plot(
+        output_df["time_us"] / 1_000_000,
+        output_df["spark_2"],
+        label="spark_2",
+        color="yellow",
+    )
+    ax.plot(
+        output_df["time_us"] / 1_000_000,
+        output_df["spark_3"],
+        label="spark_3",
+        color="orange",
+    )
+    ax.set_xlabel("Time (s)")
+    ax.set_ylim(0, 1)
+    ax.grid(True)
+
+    fig.tight_layout()
+    plt.show()
+    plt.close(fig)