Data processing chain V1

src/concat.py

@@ -1,3 +1,7 @@
+# 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

src/filter_1.py

@@ -1,4 +1,8 @@
-from threading import Thread
+# 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
@@ -44,9 +48,11 @@ def handle_dedupe(df: pd.DataFrame, time_a: int, time_b: int) -> int:
 
     if delta_a_diff < delta_b_diff:
         df.loc[time_b, "crank"] = 0
+        df.loc[time_b - 1000 : time_b + 1000, "cam"] = 0
         return time_a
     else:
         df.loc[time_a, "crank"] = 0
+        df.loc[time_a - 1000 : time_a + 1000, "cam"] = 0
         return time_b
 
 
@@ -57,13 +63,16 @@ def filter_data(file: Path) -> pd.DataFrame:
     last_crank_delta = -1
     previous_crank = -1
     last_cam = -1
-    last_cam_delta = -1
+    cam_flag = 0
+    crank_flag = 0
 
-    for _, row in tqdm(df.iterrows(), total=len(df)):
+    for _, row in tqdm(df.iterrows(), total=len(df), desc="Filter pass 1"):
         time_us: int = row["time_us"]
         crank: int = row["crank"]
         cam: int = row["cam"]
         if crank == 1:
+            crank_flag = 1
+            cam_flag = 0
             if last_crank != -1:
                 delta = time_us - last_crank
                 if (
@@ -88,8 +97,11 @@ def filter_data(file: Path) -> pd.DataFrame:
                 last_crank = time_us
 
         if cam == 1:
-            if last_cam != -1:
-                last_cam_delta = time_us - last_cam
+            if crank_flag == 1:
+                if cam_flag == 0:
+                    cam_flag = 1
+                else:
+                    df.loc[time_us, "cam"] = 0
 
             last_cam = time_us
 
@@ -125,8 +137,6 @@ for path in directory.glob("*.csv"):
 
     concat_files.append(path)
 
-threads: list[Thread] = []
-
 for file in concat_files:
     base_name, _ = file.stem.rsplit("_", 1)
     output = file.parent / f"{base_name}_dedupe.csv"

src/filter_2.py

@@ -0,0 +1,97 @@
+# 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 filter_data(file: Path) -> pd.DataFrame:
+    df = pd.read_csv(file).set_index("time_us", drop=False)
+
+    last_crank = -1
+    last_crank_delta = -1
+    last_cam = -1
+    cam_flag = 0
+    crank_flag = 0
+    cam_proportion = 0
+    crank_ctr = 0
+    insert_active = False
+
+    for _, row in tqdm(df.iterrows(), total=len(df), desc="Filter pass 2"):
+        # for _, row in df.iterrows():
+        time_us: int = row["time_us"]
+        crank: int = row["crank"]
+        cam: int = row["cam"]
+        if crank == 1:
+            crank_flag = 1
+            last_crank_delta = time_us - last_crank
+            if cam_flag == 1:
+                cam_proportion = (last_cam - last_crank) / last_crank_delta
+            if insert_active:
+                crank_ctr += 1
+                if crank_ctr >= 5:
+                    insert_time = last_crank + int(
+                        round(cam_proportion * last_crank_delta)
+                    )
+                    df.loc[
+                        insert_time,
+                        "cam",
+                    ] = 1
+                    print(
+                        f"Inserted cam at: {insert_time}\nLast cam at: {last_cam}\nLast crank at: {last_crank}\nCurrent time: {time_us}\n"
+                    )
+                    cam = 1
+                    last_cam = insert_time
+                    crank_ctr = 0
+            cam_flag = 0
+            last_crank = time_us
+
+        if cam == 1:
+            insert_active = True
+            crank_ctr = 0
+            if crank_flag == 1 and cam_flag == 0:
+                cam_flag = 1
+
+            last_cam = time_us
+
+    return df
+
+
+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 != "dedupe":
+        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}_rebuilt.csv"
+    out_df = filter_data(file)
+    out_df.to_csv(output)

src/plot.py

@@ -1,3 +1,7 @@
+# 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
 import matplotlib.pyplot as plt
 from pathlib import Path

src/trim.py

@@ -0,0 +1,58 @@
+# 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 subprocess
+import sys
+import pandas as pd
+from pathlib import Path
+import argparse
+
+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 != "rebuilt":
+        print(f"Skipping unknown file: {path}")
+        continue
+
+    files.append(path)
+
+
+for file in files:
+    print(f"Processing {file.name}")
+    subprocess.run([sys.executable, "src/plot.py", f"{file}"], check=True)
+    start_s = float(input("Start time:"))
+    end_s = float(input("End time:"))
+
+    start_us = int(start_s * 1_000_000)
+    end_us = int(end_s * 1_000_000)
+
+    df = pd.read_csv(file).set_index("time_us", drop=False)
+    df_trimmed: pd.DataFrame = df.loc[start_us:end_us].copy()
+    df_trimmed["time_us"] = df_trimmed["time_us"] - start_us
+    df_trimmed.index = df_trimmed.index - start_us
+
+    base_name, _ = file.stem.rsplit("_", 1)
+    output = file.parent / f"{base_name}_trimmed.csv"
+    df_trimmed.to_csv(output)