libibt
A Python library for reading iRacing IBT telemetry files. Rust core with PyO3 bindings for fast parsing; returns data as PyArrow tables.
Installation
pip install libibt
or with uv:
uv add libibt
From source
uv sync
just build
Requires a Rust toolchain (for maturin) and uv.
Usage
from libibt import ibt
# Load from file path, bytes, PathLike, or file-like object
log = ibt('session.ibt')
print(log)
# LogFile(file_name='session.ibt', channels=142, laps=12)
# Access a single channel (PyArrow table with 'timecodes' + value columns)
speed = log.channels['Speed']
print(speed.column_names) # ['timecodes', 'Speed']
# Merge all channels into one table
table = log.get_channels_as_table()
df = table.to_pandas()
# Access laps (PyArrow table: num, start_time, end_time — all in ms)
for i in range(log.laps.num_rows):
lap = 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}: {start} - {end}")
# Session metadata
print(log.metadata['track_name'])
print(log.metadata['session_info_yaml']) # Full iRacing session YAML
Filtering and resampling
from libibt import ibt
log = ibt('session.ibt')
# Select specific channels
subset = log.select_channels(['Speed', 'Throttle', 'Brake'])
# Filter to a time range (ms, 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_speed = log.filter_by_lap(5, channel_names=['Speed', 'Throttle'])
# Resample all channels to match a reference channel's timebase
aligned = log.resample_to_channel('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
df = (log
.filter_by_lap(5)
.select_channels(['Speed', 'RPM', 'Gear'])
.get_channels_as_table()
.to_pandas())
All methods return new LogFile instances (immutable pattern).
Progress callback
def on_progress(current, total):
print(f"Channel {current}/{total}")
log = ibt('session.ibt', progress=on_progress)
Channel metadata
Each channel carries typed metadata via the ChannelMetadata dataclass:
from libibt import ibt, ChannelMetadata
log = ibt('session.ibt')
# Extract from a channel table
meta = ChannelMetadata.from_channel_table(log.channels['Speed'])
print(meta.units) # 'm/s'
print(meta.desc) # 'GPS based speed'
print(meta.interpolate) # True
# Or from a PyArrow field directly
field = log.channels['Speed'].schema.field('Speed')
meta = ChannelMetadata.from_field(field)
# Serialize back to PyArrow field metadata
raw = meta.to_field_metadata() # dict[bytes, bytes]
ChannelMetadata is a frozen (immutable) dataclass with fields:
| Field | Type | Description |
|---|---|---|
units |
str |
Unit string (e.g., "m/s", "revs/min") |
desc |
str |
Channel description |
interpolate |
bool |
Whether to use linear interpolation when resampling |
Metadata is preserved through all LogFile operations (filtering, resampling, merging).
Metadata fields
log.metadata contains fields from both the binary header and parsed session YAML:
| Key | Description |
|---|---|
session_info_yaml |
Full iRacing session info YAML |
session_info |
Parsed YAML as a dict |
track_name |
Track internal name |
track_display_name |
Track display name |
track_city |
Track city |
track_country |
Track country |
track_length |
Track length |
track_id |
Track ID |
track_type |
Track type |
series_id, season_id, session_id, sub_session_id |
iRacing IDs |
tick_rate |
Sample rate (typically 60 Hz) |
record_count |
Total number of records |
lap_count |
Number of laps |
session_start_date |
Session start date |
start_time, end_time |
Session time bounds |
event_type |
Event type (e.g., "Race") |
session_type, session_name |
Current session type and name |
driver_name |
Recording driver's username |
driver_user_id, driver_irating, driver_license |
Driver details |
car_name, car_id |
Car screen name and ID |
car_gear_count, car_redline_rpm, car_shift_rpm, car_idle_rpm |
Car specs |
num_drivers |
Number of drivers (excluding pace car) |
weather_temp, weather_surface_temp, weather_humidity |
Weather conditions |
weather_skies, weather_wind_speed, weather_wind_dir |
Weather conditions |
car_setup |
Full car setup dict |
sectors |
Sector split definitions |
Limitations
Array variables (count > 1, e.g. tire temperature arrays) are not yet supported. Only scalar variables are returned as channels.
Development
# Build Rust extension (release)
just build
# Build (debug, faster compile)
just build-debug
# Run tests
just test
# Run all checks (format, clippy, typecheck, tests)
just check
# Format code
just format
License
MIT
1 maintainer
Release files for libibt 0.0.5
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