Temporal Filters¶
- class myoverse.datasets.filters.temporal.ARFilter(n_coefficients=4, input_is_chunked=True, representations_to_filter=(0,))[source]¶
Bases:
FilterBaseClass
Filter that computes n autoregressive coefficients for each window of the input array.
- class myoverse.datasets.filters.temporal.GaileyFeature2(window_size, shift=1, input_is_chunked=True, is_output=False, name=None)[source]¶
Bases:
FilterBaseClass
Computes the second EMG feature from the Gailey et al. paper with given window length and window shift over the input signal. See formula in the following paper: https://doi.org/10.3389/fneur.2017.00007.
- class myoverse.datasets.filters.temporal.GaileyFeature3(window_size, shift=1, input_is_chunked=True, is_output=False, name=None)[source]¶
Bases:
FilterBaseClass
Computes the third EMG feature from the Gailey et al. paper with given window length and window shift over the input signal. See formula in the following paper: https://doi.org/10.3389/fneur.2017.00007.
- class myoverse.datasets.filters.temporal.HISTFilter(window_size, shift=1, bins=10, input_is_chunked=True, is_output=False, name=None)[source]¶
Bases:
FilterBaseClass
Computes the Histogram with given window length and window shift over the input signal.
- class myoverse.datasets.filters.temporal.IAVFilter(window_size, shift=1, input_is_chunked=True, is_output=False, name=None)[source]¶
Bases:
FilterBaseClass
Computes the Integrated Absolute Value with given window length and window shift over the input signal. See formula in the following paper: https://doi.org/10.1080/10255842.2023.2165068.
- class myoverse.datasets.filters.temporal.MAVFilter(window_size, shift=1, input_is_chunked=True, is_output=False, name=None)[source]¶
Bases:
FilterBaseClass
Computes the Mean Absolute Value with given window length and window shift over the input signal. See formula in the following paper: https://doi.org/10.1080/10255842.2023.2165068.
- class myoverse.datasets.filters.temporal.RMSFilter(window_size, shift=1, input_is_chunked=None, is_output=False, name=None)[source]¶
Bases:
FilterBaseClass
Filter that computes the root mean squared value [1] of the input array.
- Parameters:
- __call__(input_array: np.ndarray) np.ndarray ¶
Filters the input array. Input shape is determined by whether the allowed_input_type is “both”, “chunked” or “not chunked”.
References
- class myoverse.datasets.filters.temporal.RectifyFilter(input_is_chunked=None, is_output=False)[source]¶
Bases:
ApplyFunctionFilter
Filter that rectifies the input array.
- class myoverse.datasets.filters.temporal.SOSFrequencyFilter(sos_filter_coefficients, forwards_and_backwards=True, input_is_chunked=None, is_output=False, name=None)[source]¶
Bases:
FilterBaseClass
Filter that applies a second-order-section filter to the input array.
- Parameters:
sos_filter_coefficients (tuple[np.ndarray, np.ndarray | float, np.ndarray]) – The second-order-section filter coefficients. This is a tuple of the form (sos, gain, delay).
forwards_and_backwards (bool) – Whether to apply the filter forwards and backwards or only forwards.
input_is_chunked (bool) – Whether the input is chunked or not.
is_output (bool) – Whether the filter is an output filter. If True, the resulting signal will be outputted by and dataset pipeline.
name (str)
- __call__(input_array: np.ndarray) np.ndarray ¶
Filters the input array. Input shape is determined by whether the allowed_input_type is “both”, “chunked” or “not chunked”.
- class myoverse.datasets.filters.temporal.SSCFilter(window_size, shift=1, input_is_chunked=True, is_output=False, name=None)[source]¶
Bases:
FilterBaseClass
Computes the Slope Sign Change with given window length and window shift over the input signal. See formula in the following paper: https://doi.org/10.1080/10255842.2023.2165068.
- class myoverse.datasets.filters.temporal.SpectralInterpolationFilter(bandwidth=(47.5, 50.75), number_of_harmonics=5, emg_frequency=2044, input_is_chunked=True, representations_to_filter='all')[source]¶
Bases:
FilterBaseClass
- class myoverse.datasets.filters.temporal.VARFilter(window_size, shift=1, input_is_chunked=True, is_output=False, name=None)[source]¶
Bases:
FilterBaseClass
Computes the Variance with given window length and window shift over the input signal.
- class myoverse.datasets.filters.temporal.WFLFilter(window_size, shift=1, input_is_chunked=True, is_output=False, name=None)[source]¶
Bases:
FilterBaseClass
Computes the Waveform Length with given window length and window shift over the input signal. See formula in the following paper: https://doi.org/10.1080/10255842.2023.2165068.
- class myoverse.datasets.filters.temporal.ZCFilter(window_size, shift=1, input_is_chunked=True, is_output=False, name=None)[source]¶
Bases:
FilterBaseClass
Computes the Zero Crossings with given window length and window shift over the input signal. See formula in the following paper: https://doi.org/10.1080/10255842.2023.2165068.