File format reference - A111

This page explains the format of A111 data files saved from Exploration Tool using the GUI or recording module.

The data files store a Record, a simple key-value store containing the fields shown further down. Either HDF5 .h5 or NumPy .npz can be used to save it, although HDF5 is preferred due to its wider compatibility across systems and languages.

We recommend loading and saving records using the load and save functions in the acconeer.exptool.a111.recording module. These functions take care of packing and unpacking the Record fields on save and load respectively. See examples/a111/load_record.py for an example of loading data, and examples/a111/record_data/barebones.py for saving data.

For an example of how to load HDF5 records in Matlab, see examples/a111/load_record_h5.m.

Examples of loading

Some minimal examples of loading the session info from a data file:

Using acconeer.exptool.a111.recording.load (recommended):

import acconeer.exptool as et
record = et.a111.recording.load("data.h5")  # or .npz
print(record.session_info)
# {'data_length': 1238, 'range_length_m': 0.6, ...

Using h5py:

import json
import h5py
with h5py.File("data.h5") as f:
   print(json.loads(f["session_info"][()]))
# {'data_length': 1238, 'range_length_m': 0.6, ...

Using numpy:

import json
import numpy as np
with np.load("data.npz") as f:
   print(json.loads(str(f["session_info"])))
# {'data_length': 1238, 'range_length_m': 0.6, ...

Fields

The type here refers to the packed type - the type the value has in the stored file.

Sensor related

The following fields are used for storing the sensor session data itself. These fields are the only ones that are mandatory.

mode

Type: string

The mode (service) used in the recording. For example "sparse". Unpack the original enum member using acconeer.exptool.a111.get_mode on this field.

sensor_config_dump

Type: string

A JSON dump of the sensor configuration. Load the original sensor configuration using acconeer.exptool.configs.load on this field. It can also be loaded to a dict using json.loads.

Tip

With a record you can quickly access the sensor configuration with record.sensor_config.

session_info

Type: string

A JSON dump of the session information (metadata). Unpack the original dict using json.loads.

data

Type: ND array of numbers

An N-dimensional array containing all data from the sensor(s). The shape and data type depends on the mode used.

Power bins

Shape: (number of sweeps, number of sensors, number of distance bins)

Type: uint16 (previously float64)

Envelope

Shape: (number of sweeps, number of sensors, number of distances)

Type: uint16 (previously float64)

IQ

Shape: (number of sweeps, number of sensors, number of distances)

Type: complex128

Sparse

Shape: (number of frames, number of sensors, sweeps per frame, number of distances)

Type: uint16 (previously float64)

data_info

Type: string

A JSON dump of all data information (result_infos). Unpack with json.loads to a nested list of list of dicts.

The shape of the nested list is (number of frames/sweeps, number of sensors). The fields of the dicts depend on mode/service.

Processing related

The following fields are used for storing metadata for processing. These fields are optional.

module_key

Type: string

Key of the processing module used during the recording. For example sparse_presence. The keys are defined in respective _meta.py files; e.g. src/acconeer/exptool/a111/algo/presence_detection_sparse/_meta.py.

processing_config_dump

Type: string

A JSON dump of the processing configuration.

Load the original processing configuration using ProcessingConfig._load on this field. Here, ProcessingConfig refers to the subclassed acconeer.exptool.configbase.ProcessingConfig for the processing module used. For example, ProcessingConfiguration in Presence Detection.

This field can also be loaded to a dict using json.loads.

Other

The following optional fields are used for storing other metadata for the recording.

rss_version

Type: string

The server/RSS version used on the host (module, RPi, etc.).

lib_version

Type: string

The Exploration Tool library version used on the PC.

timestamp

Type: string

ISO formatted time at the start of recording. For example "2020-12-31T23:59:59".

sample_times

Type: 1D array of floats

The time in seconds for every return of a sweep/frame from get_next. Typically timed using Python’s time.time, meaning it will be the time since the epoch.

Caution

Since this is timed on the client side, the timing may significantly differ from the timing when the radar actually sampled the data. Thus, we recommend only using this for calculating average sampling rate.

note

Type: string

Free text field. Not used by Exploration Tool itself.

Legacy

The following optional fields are used by legacy components of Exploration Tool.

legacy_processing_config_dump

Type: string

A JSON dump of a legacy processing configuration.