pyActigraphy.io.read_raw_bba

pyActigraphy.io.read_raw_bba(input_fname, name=None, uuid=None, frequency=None, start_time=None, period=None, engine='c', impute_missing=False, use_metadata_json=True, metadata_fname=None)[source]

Reader function for files produced by the biobankAccelerometerAnalysis package.

Parameters

  • input_fname (str) – Path to the BAA file.

  • name (str, optional) – Name of the recording. Default is None.

  • uuid (str, optional) – Device UUID. Default is None.

  • frequency (str, optional) – Sampling frequency. Cf. #timeseries-offset-aliases in <https://pandas.pydata.org/pandas-docs/stable/timeseries.html>. If None, the sampling frequency is inferred from the data. Otherwise, the data are resampled to the specified frequency. Default is None.

  • start_time (datetime-like, optional) – Read data from this time. Default is None.

  • period (str, optional) – Length of the read data. Cf. #timeseries-offset-aliases in <https://pandas.pydata.org/pandas-docs/stable/timeseries.html>. Default is None (i.e all the data).

  • engine (str, optional) – Parser engine to use. Argument passed to Pandas. Default is ‘c’.

  • impute_missing (bool, optional) – If set to True, use missing data imputation from the biobankanalysis package. Default is False.

  • use_metadata_json (bool, optional.) – If set to True, extract meta-data from summary json file. Default is True.

  • metadata_fname (str, optional) – Path to the summary json file. If None, the path to the summary json file is inferred from the input file (/path/to/XXX-timeSeries.csv.gz -> /path/to/XXX-summary.json). Default is None.

Returns

raw – An object containing preprocessed data from raw accelerometers.

Return type

Instance of RawBBA