libxrk
A Python library for reading AIM XRK and XRZ files from AIM automotive data loggers.
Features
- Read AIM XRK files (raw data logs)
- Read AIM XRZ files (zlib-compressed XRK files)
- Parse track data and telemetry channels
- GPS coordinate conversion and lap detection
- High-performance Cython implementation
- Supports Python 3.10 - 3.14
Installation
Install from PyPI
pip install libxrk
Install from Source
Prerequisites
On Ubuntu/Debian:
sudo apt install build-essential python3-dev
Install with uv
uv sync
The Cython extension will be automatically compiled during installation.
Usage
from libxrk import aim_xrk
# Read an XRK file
log = aim_xrk('path/to/file.xrk')
# Read an XRZ file (automatically decompressed)
log = aim_xrk('path/to/file.xrz')
# Access channels (each channel is a PyArrow table with 'timecodes' and value columns)
for channel_name, channel_table in log.channels.items():
print(f"{channel_name}: {channel_table.num_rows} samples")
# Get all channels merged into a single PyArrow table
# (handles different sample rates with interpolation/forward-fill)
merged_table = log.get_channels_as_table()
print(merged_table.column_names)
# Convert to pandas DataFrame
df = merged_table.to_pandas()
# Access laps (PyArrow table with 'num', 'start_time', 'end_time' columns)
print(f"Laps: {log.laps.num_rows}")
for i in range(log.laps.num_rows):
lap_num = log.laps.column("num")[i].as_py()
start = log.laps.column("start_time")[i].as_py()
end = log.laps.column("end_time")[i].as_py()
print(f"Lap {lap_num}: {start} - {end}")
# Access metadata
print(log.metadata)
# Includes: Driver, Vehicle, Venue, Log Date/Time, Logger ID, Logger Model, Device Name, etc.
Filtering and Resampling
from libxrk import aim_xrk
log = aim_xrk('session.xrk')
# Select specific channels
gps_log = log.select_channels(['GPS Latitude', 'GPS Longitude', 'GPS Speed'])
# Filter to a time range (milliseconds, inclusive start, exclusive end)
segment = log.filter_by_time_range(60000, 120000)
# Filter to a specific lap
lap5 = log.filter_by_lap(5)
# Combine filtering and channel selection
lap5_gps = log.filter_by_lap(5, channel_names=['GPS Latitude', 'GPS Longitude'])
# Resample all channels to match a reference channel's timebase
aligned = log.resample_to_channel('GPS Speed')
# Resample to a custom timebase
import pyarrow as pa
target = pa.array(range(0, 100000, 100), type=pa.int64()) # 10 Hz
resampled = log.resample_to_timecodes(target)
# Chain operations for analysis workflows
df = (log
.filter_by_lap(5)
.select_channels(['Engine RPM', 'GPS Speed'])
.resample_to_channel('GPS Speed')
.get_channels_as_table()
.to_pandas())
All filtering and resampling methods return new LogFile instances (immutable pattern), enabling method chaining for complex analysis workflows.
Channel Metadata
Each channel carries typed metadata accessible via ChannelMetadata:
from libxrk import aim_xrk, ChannelMetadata
log = aim_xrk('session.xrk')
# Extract typed metadata from a channel
meta = ChannelMetadata.from_channel_table(log.channels['Engine RPM'])
print(meta.units) # "rpm"
print(meta.dec_pts) # 0
print(meta.interpolate) # True
print(meta.function) # "Engine RPM"
# Or from a PyArrow field directly
field = log.channels['Engine RPM'].schema.field('Engine RPM')
meta = ChannelMetadata.from_field(field)
# Create metadata for custom channels
meta = ChannelMetadata(units="m/s", dec_pts=1, interpolate=True)
field = pa.field("speed", pa.float32(), metadata=meta.to_field_metadata())
Available fields: units, dec_pts, interpolate, function, source_type, source_channel_id, device_tag, cal_value_1, cal_value_2, display_range_min, display_range_max.
Development
Quick Check
# Run all quality checks (format check, type check, tests)
just check
Code Formatting
This project uses Black for code formatting.
# Format all Python files
just format
Type Checking
This project uses mypy for static type checking.
# Run type checker on all Python files
just typecheck
Running Tests
This project uses pytest for testing.
# Run all tests
just test
# Run specific test file
uv run pytest tests/test_xrk_loading.py
# Run tests with coverage
uv run pytest --cov=libxrk
Testing with Pyodide (WebAssembly)
You can test the library in a WebAssembly environment using Pyodide. This requires Node.js to be installed.
# Build and run tests in Pyodide 0.29.x (Python 3.13, requires pyenv)
just pyodide-test
Note: Pyodide tests run automatically in CI via GitHub Actions.
Building
# Build CPython wheel and sdist
uv build
# Build all wheels (CPython, Pyodide/WebAssembly, and sdist)
just build-all
Clean Build
# Clean all build artifacts and rebuild
rm -rf build/ dist/ src/libxrk/*.so && uv sync
Testing
The project includes end-to-end tests that validate XRK and XRZ file loading and parsing.
Test files are located in tests/test_data/ and include real XRK and XRZ files for validation.
Credits
This project incorporates code from TrackDataAnalysis by Scott Smith, used under the MIT License.
License
MIT License - See LICENSE file for details.
1 maintainer
Release files for libxrk 0.13.0
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