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# Python-generated files
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may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

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README.md Normal file
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# Engine Data Processing
A set of python scripts used for processing recorded hall effect signals recorded using [Engine-Recorder](https://git.h3cx.dev/Exergie/Engine-Recorder)

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pyproject.toml Normal file
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[project]
name = "engine-data-processing"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.14"
dependencies = [
"matplotlib>=3.10.9",
"pandas>=3.0.3",
"tqdm>=4.67.3",
]

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src/concat.py Normal file
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import pandas as pd
from pathlib import Path
import argparse
def concat_pair(r_file: Path, s_file: Path) -> pd.DataFrame:
crank_df = pd.read_csv(r_file)
cam_df = pd.read_csv(s_file)
crank_values = crank_df.iloc[:, 0]
cam_values = cam_df.iloc[:, 0]
start_time = min(crank_values.iloc[0], cam_values.iloc[0])
crank_values = crank_values - start_time
cam_values = cam_values - start_time
rows = max(crank_values.max(), cam_values.max()) + 1
final_df = pd.DataFrame(index=range(rows), columns=["crank", "cam"])
final_df["crank"] = final_df.index.isin(crank_values).astype(int)
final_df["cam"] = final_df.index.isin(cam_values).astype(int)
final_df.index.name = "time_us"
return final_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}")
file_groups: dict[str, dict[str, 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 not in ("R", "S"):
print(f"Skipping unknown file: {path}")
continue
file_groups.setdefault(base_name, {})[channel] = path
file_pairs: list[tuple[Path, Path]] = []
for base_name, files in sorted(file_groups.items()):
if "R" not in files:
print(f"Missing R file for {base_name}")
continue
if "S" not in files:
print(f"Missing S file for {base_name}")
continue
file_pairs.append((files["R"], files["S"]))
for r_file, s_file in file_pairs:
df = concat_pair(r_file, s_file)
base_name, _ = r_file.stem.rsplit("_", 1)
output = r_file.parent / f"{base_name}_concat.csv"
df.to_csv(output)
exit(0)

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from threading import Thread
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 handle_dedupe(df: pd.DataFrame, time_a: int, time_b: int) -> int:
prev_hit = find_last_crank(df, time_a)
next_hit = find_next_crank(df, time_b)
if prev_hit is None or next_hit is None:
df.loc[time_b, "crank"] = 0
return time_a
delta_a_1 = abs(time_a - prev_hit)
delta_a_2 = abs(time_a - next_hit)
delta_a_diff = abs(delta_a_1 - delta_a_2)
delta_b_1 = abs(time_b - prev_hit)
delta_b_2 = abs(time_b - next_hit)
delta_b_diff = abs(delta_b_1 - delta_b_2)
if delta_a_diff < delta_b_diff:
df.loc[time_b, "crank"] = 0
return time_a
else:
df.loc[time_a, "crank"] = 0
return time_b
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
previous_crank = -1
last_cam = -1
last_cam_delta = -1
for _, row in tqdm(df.iterrows(), total=len(df)):
time_us: int = row["time_us"]
crank: int = row["crank"]
cam: int = row["cam"]
if crank == 1:
if last_crank != -1:
delta = time_us - last_crank
if (
last_crank_delta != -1
and abs(delta - last_crank_delta) / last_crank_delta > 0.5
):
valid_time = handle_dedupe(df, last_crank, time_us)
if valid_time == last_crank:
last_crank_delta = last_crank - previous_crank
else:
last_crank_delta = (
valid_time - previous_crank
if previous_crank != -1
else delta
)
last_crank = valid_time
else:
last_crank_delta = delta
previous_crank = last_crank
last_crank = time_us
else:
last_crank = time_us
if cam == 1:
if last_cam != -1:
last_cam_delta = time_us - last_cam
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 != "concat":
print(f"Skipping unknown file: {path}")
continue
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"
out_df = filter_data(file)
out_df.to_csv(output)

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src/plot.py Normal file
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import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("directory", type=Path, help="Source data directory")
args = parser.parse_args()
INPUT_CSV: Path = args.directory
# Width of the artificial pulse, in microseconds.
# Increase this if the pulses are still hard to see.
PULSE_WIDTH_US = 500 # 5 ms
# Optional: plot only a smaller time window.
# Use None to plot the whole recording.
START_US = None
END_US = None
def build_pulse_trace(edge_times_us, pulse_width_us):
"""
Convert falling-edge timestamps into a drawable square pulse trace.
Each edge becomes:
low before edge
high from edge to edge + pulse_width_us
low after that
"""
x = []
y = []
for t in edge_times_us:
x.extend([t, t, t + pulse_width_us, t + pulse_width_us])
y.extend([0, 1, 1, 0])
return x, y
def main():
df = pd.read_csv(INPUT_CSV)
# If the CSV has an index/time column from df_final.to_csv(),
# use the first column as time in microseconds.
time_col = df.columns[0]
# If your CSV columns are: index, crank, cam
time_us = df[time_col]
crank_edges = time_us[df["crank"] == 1].to_numpy()
cam_edges = time_us[df["cam"] == 1].to_numpy()
if START_US is not None:
crank_edges = crank_edges[crank_edges >= START_US]
cam_edges = cam_edges[cam_edges >= START_US]
if END_US is not None:
crank_edges = crank_edges[crank_edges <= END_US]
cam_edges = cam_edges[cam_edges <= END_US]
crank_x, crank_y = build_pulse_trace(crank_edges, PULSE_WIDTH_US)
cam_x, cam_y = build_pulse_trace(cam_edges, PULSE_WIDTH_US)
# Convert microseconds to seconds for a more readable x-axis
crank_x = [x / 1_000_000 for x in crank_x]
cam_x = [x / 1_000_000 for x in cam_x]
# Offset cam vertically so both traces are readable
crank_y = [y * 0.8 + 0 for y in crank_y]
cam_y = [y * 0.8 + 1.2 for y in cam_y]
plt.figure(figsize=(14, 5))
plt.plot(crank_x, crank_y, label="Crank")
plt.plot(cam_x, cam_y, label="Cam")
plt.yticks([0.4, 1.6], ["Crank", "Cam"])
plt.xlabel("Time [s]")
plt.title(f"Crank and Cam Falling Edges, pulse width = {PULSE_WIDTH_US} µs")
plt.grid(True)
plt.legend()
if START_US is not None or END_US is not None:
start_s = START_US / 1_000_000 if START_US is not None else None
end_s = END_US / 1_000_000 if END_US is not None else None
plt.xlim(start_s, end_s)
plt.tight_layout()
plt.show()
if __name__ == "__main__":
main()

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