Skip to content

Repository files navigation

mmwcore

mmwcore decodes captured and live mmWave data for offline training and real-time inference. Its boundary starts at caller-owned bytes plus explicit physical contracts, then produces range-Doppler cubes, detections, calibrated point clouds, clusters, tracks, and vital-sign products.

PyPI crates.io docs.rs CI License

Install

CPython 3.12–3.14:

pip install mmwcore

Rust 1.85 or newer:

cargo add mmwcore

Input boundary

Maintained inputs are:

  • archived DCA1000 datagrams;
  • headerless int16 ADC files with an explicit frame contract;
  • completed versioned capture directories opened with open_capture;
  • finite mmwcli.capture_stream.v1 data supplied through a caller-owned BinaryIO;
  • published radar-plus-camera sessions opened with open_multisensor_capture;
  • finite mmwcli.multisensor_stream.v1 data opened with open_multisensor_stream.
mmwcli output mmwcore entry point Typical use
radar capture-session directory open_capture reproducible offline radar processing
radar --stream stdout open_capture_stream pull-driven real-time radar inference
radar-plus-camera session directory open_multisensor_capture training data, indexed source access, and causal joins
aggregate --stream stdout open_multisensor_stream provisional multi-sensor inference followed by COMMIT/EOF validation

The matching acquisition commands and camera-producer workflow are documented by mmwcli and its multi-sensor guide.

The library does not configure devices, render firmware commands, or manage live acquisition. Callers must supply ADC layout, frame geometry, timing, antenna geometry, and trusted packet/frame origins when the stored format does not prove them.

TI capture contracts

  • group2_i_then_q: complex16 two-lane layout documented by the TI mmWave Studio reader for xWR16xx, xWR18xx, and xWR68xx captures.
  • group4_i_then_q: complex16 four-lane, channel-interleaved layout documented by the TI mmWave Studio reader for xWR12xx and xWR14xx captures.
  • Source-backed antenna geometries: XWR1642, standard XWR1843 EVM, IWR6843ISK, IWR6843 AOP, and AWR1843 AOP.

These explicit decoders and geometry presets are usable only when the caller proves the actual layout and board. Versioned mmwcli directory and stream v1 readers accept the three closed family tuples family=xwr16xx, family=xwr18xx, and family=xwr68xx; each also requires vendor=ti, empty model/revision, identity_source=route_declaration, config_format=ti_mmwave_legacy_cli.v1, dtype=int16, byte_order=little, lane_count=2, and layout=group2_i_then_q.

The embedded legacy CFG must match the declared family: xWR16xx uses the 76–81 GHz range and up to two TX identifiers, xWR18xx uses 76–81 GHz and up to three, and xWR68xx uses 57–64 GHz and up to three. Chirp order determines the explicit tx_order; no family selects antenna geometry or a preset. The standalone TI CLI parser likewise requires an explicit family keyword and has no default. ADCFileCapture.raw_capture and CaptureStreamContract.raw_capture expose the declared tuple, but decoder acceptance does not claim that an mmwcli acquisition route has been validated. route_declaration is not an observed device identity. See the mmwcli hardware-support matrix.

Python examples

The repository includes complete command-line examples that consume files or binary stdin and never start hardware:

Workflow Example
capture directory or explicit raw frames capture_or_raw.py
explicit xWR18xx/XWR1843 EVM geometry and recipe xwr18_range_doppler.py
finite radar live stream radar_live_stream.py
multi-sensor offline training pairs multisensor_offline_training.py
multi-sensor provisional live inference multisensor_live_inference.py

See the example index for copyable commands and finalization semantics.

Open a versioned capture directory

open_capture validates the manifest schema, required regular files, hashes, byte count, and finite physical contract. The directory must remain unchanged while it is open.

from mmwcore import open_capture

capture = open_capture("capture-session")
raw = capture.frame(0)
print(capture.raw_capture.family, raw.samples.shape)

The hash proves internal consistency, not provenance. A supplied preset is the caller's explicit declaration of a processing contract; the route-declared manifest does not identify or guess a board. Pass an exact RangeDopplerRecipe or callable preset to open_capture(..., range_doppler=...) when Range-Doppler processing is wanted. Built-in board presets are conveniences, not family defaults.

Process a finite capture stream

import mmwcore

stream = mmwcore.open_capture_stream(source)  # caller-owned BinaryIO
for item in stream.frames():
    infer(item.frame, stream.contract.radar_capture)
commit = stream.require_commit()

Frames remain provisional until require_commit validates COMMIT and terminal EOF. mmwcore neither closes the source nor opens a process, socket, or device. The source must make read honor any required deadline or cancellation. Processing is synchronous and pull-driven, without prefetch or worker threads.

Train from synchronized radar and camera data

from mmwcore import open_multisensor_capture

session = open_multisensor_capture("training-session")
radar = session.source("radar-0")
radar_capture = radar.open_radar_capture()
for camera_item, radar_item in session.causal_pairs(
    "camera-0", "radar-0", lag_min_ns=0, lag_max_ns=50_000_000
):
    train(
        camera_item.payload,
        radar_capture.frame(radar_item.item_index),
    )

The join uses conservative mapped time intervals, not equal frame numbers or nearest arrival time. If training needs Range-Doppler cubes instead of raw frames, bind the exact recipe or preset through open_radar_capture(range_doppler=...) and call range_doppler. Choose processing geometry from the actual board, never from the family string alone.

Consume a live aggregate stream

from mmwcore import open_multisensor_stream

stream = open_multisensor_stream(source)  # caller-owned BinaryIO
provisional = list(stream.items())
commit = stream.require_commit()
accepted = [item for item in provisional if commit.accepts(item)]

Radar and delivery_observed camera items expose mapped_time on the same host-relative axis. A camera exposure_midpoint remains unmapped in the live stream unless the producer supplies a live mapping; mmwcore never substitutes delivery time for exposure time. Items and derived results remain provisional until global COMMIT and EOF.

Read a headerless ADC file

from mmwcore.core import ADCComplexLayout, ADCFrameSpec
from mmwcore.io import ADCFileFrameReader

spec = ADCFrameSpec(
    num_chirps=2,
    num_rx=4,
    num_samples=128,
    layout=ADCComplexLayout.GROUP2_I_THEN_Q,
)
reader = ADCFileFrameReader("adc.bin", spec, frame_periodicity_s=0.01)
raw = reader.read_frame(0)
print(raw.samples.shape)

ADCFileFrameReader rejects incomplete files by default and reads frames without loading the full capture.

Assemble archived datagrams

from mmwcore.core import ADCFrameSpec
from mmwcore.io import assemble_dca1000_frame_bytes

spec = ADCFrameSpec(num_chirps=1, num_rx=1, num_samples=2)
raw, stats = assemble_dca1000_frame_bytes(
    [datagram_0, datagram_1],
    spec,
    frame_start_byte_count=trusted_frame_origin,
    payload_values_per_packet=2,
)

Stateless assembly requires exactly one complete frame: fixed payload lengths, contiguous u48 byte slots, and contiguous wrapping u32 packet numbers. frame_start_byte_count must come from the capture lifecycle; it is never inferred from the first packet or a modulo guess.

Continue to point clouds and tracks

Use an explicit PointCloudRecipe with process_adc_to_calibrated_point_cloud, then cluster_point_cloud and ClusterTracker2D. Source-backed geometry helpers cover XWR1642, standard XWR1843 EVM, IWR6843ISK, IWR6843 AOP, and AWR1843 AOP; the IWR6843 processing recipes are one optional preset family, not the mmwcore input boundary. Thresholds, calibration, Tx order, and tracker timing remain explicit. Keep one tracker instance for a sequence; recreating it discards temporal state.

Rust example

use mmwcore::{AdcComplexLayout, AdcFrameSpec, decode_adc_i16};

let spec = AdcFrameSpec::new(1, 1, 2, AdcComplexLayout::Group2IThenQ).expect("valid spec");
let cube = decode_adc_i16(&[1, 2, 3, 4], spec, false).expect("valid captured payload");
assert_eq!(cube.shape(), [1, 1, 1, 2]);

Package map

  • mmwcore.core: axes, units, ADC, cube, detection, point-cloud, and tracking contracts.
  • mmwcore.config: finite radar profiles, capture contracts, antenna geometries, and recipes.
  • mmwcore.io: packet, ADC-file, radar/multi-sensor directory, and finite live-stream readers.
  • mmwcore.dsp: FFT, clutter removal, CFAR, calibration, AoA, projection, and clustering.
  • mmwcore.tracking: assignment, stateful trackers, runners, metrics, and validation artifacts.
  • mmwcore.plot: optional research visualizations outside the Rust compute core.

Validation boundaries

The retained laboratory capture used for the figures below has 5000 frames, 2 chirps, 4 receivers, 128 complex samples, group2_i_then_q, and a 10 ms period. It lacks slope and sample-rate metadata, so range remains in bins. The source capture is not distributed; the figures are evidence, not a runnable fixture.

Range-Time magnitude before and after temporal-background suppression

Raw ADC I/Q and four-receiver range spectra

Synthetic tests verify shapes, axes, finite values, wrap behavior, and deterministic transforms. They do not prove a board configuration, capture provenance, universal thresholds, or performance superiority. Device documentation and redistributable reference vectors remain authoritative.

Benchmarks and development

The reproducible synthetic pipeline, workload contract, and comparison rules are documented in docs/benchmarking.md.

uv sync --extra dev --locked
uv run pytest --cov=mmwcore
cargo test --workspace --locked
uv run python benchmarks/pipeline.py --warmups 0 --samples 1 --stream-frames 2

See CONTRIBUTING.md and docs/architecture.md.

License

Apache-2.0. See LICENSE.

About

Decode mmWave radar and synchronized multi-sensor sessions or live streams into Python training and real-time inference pipelines backed by Rust.

Topics

Resources

Contributing

Security policy

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages