Processors

NumpyDataReader

class dabstract.dataprocessor.processors.processors.NumpyDatareader(read_range: (<class 'int'>, <class 'int'>) = None, **kwargs)

Bases: dabstract.dataprocessor.processing_chain.Processor

Processor to read numpy data

process(file: str, **kwargs) -> (<class 'numpy.ndarray'>, typing.Dict)

WavDataReader

class dabstract.dataprocessor.processors.processors.WavDatareader(select_channel: int = None, fs: float = None, read_range: (<class 'int'>, <class 'int'>) = None, dtype: Any = None, resample: bool = False, resample_axis: int = 0, resample_window: str = 'hann', **kwargs)

Bases: dabstract.dataprocessor.processing_chain.Processor

Processor to read wav data

process(file: str, **kwargs) -> (<class 'numpy.ndarray'>, typing.Dict)

Framing

class dabstract.dataprocessor.processors.processors.Framing(windowsize: float = None, stepsize: float = None, window_func: str = 'hamming', axis: int = - 1, **kwargs)

Bases: dabstract.dataprocessor.processing_chain.Processor

Processor to frame data

process(data: numpy.ndarray, **kwargs) -> (<class 'numpy.ndarray'>, typing.Dict)

Windowing

class dabstract.dataprocessor.processors.processors.Windowing(axis=- 1, window_func='hamming', symmetry=True, **kwargs)

Bases: dabstract.dataprocessor.processing_chain.Processor

process(data, **kwargs)

FFT

class dabstract.dataprocessor.processors.processors.FFT(type: str = 'real', nfft: str = 'nextpow2', format: str = 'magnitude', dc_reset: bool = False, norm: str = None, axis: int = - 1, **kwargs)

Bases: dabstract.dataprocessor.processing_chain.Processor

Processor to apply a FFT

process(data: numpy.ndarray, **kwargs) -> (<class 'numpy.ndarray'>, typing.Dict)

Filterbank

class dabstract.dataprocessor.processors.processors.Filterbank(n_bands: int = None, scale: str = 'linear', nfft: int = None, fmin: int = 0, norm: str = None, fmax: int = inf, axis: int = - 1, **kwargs)

Bases: dabstract.dataprocessor.processing_chain.Processor

process(data: numpy.ndarray, **kwargs)

Aggregation

class dabstract.dataprocessor.processors.processors.Aggregation(methods: List[str] = ['mean', 'std'], axis: int = 0, combine: str = None, combine_axis: int = None)

Bases: dabstract.dataprocessor.processing_chain.Processor

Processor to aggregate data

process(data: numpy.ndarray, **kwargs) -> (<class 'numpy.ndarray'>, typing.Dict)

FIRFilter

class dabstract.dataprocessor.processors.processors.FIRFilter(type: str = <class 'type'>, f: float = None, taps: int = None, axis: int = 1, fs: float = None, window: str = 'hamming')

Bases: dabstract.dataprocessor.processing_chain.Processor

Processor to apply a FIR filter

get_filter(fs: int)

process(data: numpy.ndarray, **kwargs) -> (<class 'numpy.ndarray'>, typing.Dict)

Logarithm

class dabstract.dataprocessor.processors.processors.Logarithm(type: str = 'base10', **kwargs)

Bases: dabstract.dataprocessor.processing_chain.Processor

Processor to apply a logarithm

inv_process(data: numpy.ndarray, **kwargs) -> (<class 'numpy.ndarray'>, typing.Dict)

process(data: numpy.ndarray, **kwargs) -> (<class 'numpy.ndarray'>, typing.Dict)

Normalizer

class dabstract.dataprocessor.processors.processors.Normalizer(type: str = None, feature_range: (<class 'int'>, <class 'int'>) = [0, 1], **kwargs)

Bases: dabstract.dataprocessor.processing_chain.Processor

Processor to normalize data based on fitted parameters

fit(data: numpy.ndarray, **kwargs) → None

inv_process(data: numpy.ndarray, **kwargs)

process(data: numpy.ndarray, **kwargs) -> (<class 'numpy.ndarray'>, typing.Dict)

Scaler

class dabstract.dataprocessor.processors.processors.Scaler(**kwargs)

Bases: dabstract.dataprocessor.processing_chain.Processor

Processor to scale data

inv_process(data: numpy.ndarray) → numpy.ndarray

process(data: numpy.ndarray, **kwargs) -> (<class 'numpy.ndarray'>, typing.Dict)

Resample

class dabstract.dataprocessor.processors.processors.Resample(target_fs: float = None, fs: float = None, axis: int = 0, window: str = 'hann')

Bases: dabstract.dataprocessor.processing_chain.Processor

Processor to resample data

process(data, **kwargs)