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smooth

Functions

signal_smooth_boxcar(data, size=10)

Smooth signal using boxcar method.

Parameters:

  • data (NDArray) –

    Signal

  • size (int, default: 10 ) –

    Kernel size. Defaults to 10.

Returns:

  • NDArray –

    npt.NDArray: Smoothed signal

Source code in physiokit/signal/smooth.py 
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def signal_smooth_boxcar(data: npt.NDArray, size: int = 10) -> npt.NDArray:
    """Smooth signal using boxcar method.

    Args:
        data (npt.NDArray): Signal
        size (int, optional): Kernel size. Defaults to 10.

    Returns:
        npt.NDArray: Smoothed signal
    """

    return scipy.ndimage.uniform_filter1d(data, size, mode="nearest")

signal_smooth_boxzen(data, size=10)

Smooth signal using boxzen method.

Parameters:

  • data (NDArray) –

    Signal

  • size (int, default: 10 ) –

    Kernel size. Defaults to 10.

Returns:

  • NDArray –

    npt.NDArray: Smoothed signal

Source code in physiokit/signal/smooth.py 
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def signal_smooth_boxzen(data: npt.NDArray, size: int = 10) -> npt.NDArray:
    """Smooth signal using boxzen method.

    Args:
        data (npt.NDArray): Signal
        size (int, optional): Kernel size. Defaults to 10.

    Returns:
        npt.NDArray: Smoothed signal
    """

    # 1st pass - boxcar kernel
    smoothed = scipy.ndimage.uniform_filter1d(data, size, mode="nearest")
    # 2nd pass - parzen kernel
    smoothed = signal_smooth_conv(smoothed, kernel="parzen", size=size)
    return smoothed

signal_smooth_conv(data, kernel, size=5)

Smooth signal using convolution.

Parameters:

  • data (NDArray) –

    Signal

  • kernel (str) –

    Kernel type

  • size (int, default: 5 ) –

    Kernel size. Defaults to 10.

Returns:

  • NDArray –

    npt.NDArray: Smoothed signal

Source code in physiokit/signal/smooth.py 
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def signal_smooth_conv(data: npt.NDArray, kernel: str, size: int = 5) -> npt.NDArray:
    """Smooth signal using convolution.

    Args:
        data (npt.NDArray): Signal
        kernel (str): Kernel type
        size (int, optional): Kernel size. Defaults to 10.

    Returns:
        npt.NDArray: Smoothed signal
    """

    # Get window
    window = scipy.signal.get_window(kernel, size)
    w = window / window.sum()

    # Extend signal edges to avoid boundary effects
    x = np.concatenate((data[0] * np.ones(size), data, data[-1] * np.ones(size)))

    # Compute moving average
    smoothed = np.convolve(w, x, mode="same")
    smoothed = smoothed[size:-size]
    return smoothed

signal_smooth_median(data, size=5)

Smooth signal using median filter.

Parameters:

  • data (NDArray) –

    Signal

  • size (int, default: 5 ) –

    Kernel size. Defaults to 5.

Returns:

  • NDArray –

    npt.NDArray: Smoothed signal

Source code in physiokit/signal/smooth.py 
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def signal_smooth_median(data: npt.NDArray, size: int = 5) -> npt.NDArray:
    """Smooth signal using median filter.

    Args:
        data (npt.NDArray): Signal
        size (int, optional): Kernel size. Defaults to 5.

    Returns:
        npt.NDArray: Smoothed signal
    """
    # Enforce odd kernel size.
    if size % 2 == 0:
        size += 1
    smoothed = scipy.signal.medfilt(data, kernel_size=size)
    return smoothed

signal_smooth_savgol(data, window_length=None, polyorder=3, sample_rate=1000, axis=-1)

Smooths signal using savitzky-golay filter

Parameters:

  • data (NDArray) –

    Signal

  • window_length (int | None, default: None ) –

    Filter window length. Defaults to None.

  • polyorder (int, default: 3 ) –

    Poly fit order. Defaults to 3.

  • sample_rate (float, default: 1000 ) –

    Sampling rate in Hz. Defaults to 1000 Hz.

  • axis (int, default: -1 ) –

    Axis to filter along. Defaults to -1.

Returns:

  • NDArray –

    npt.NDArray: Smoothed signal

Source code in physiokit/signal/smooth.py 
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def signal_smooth_savgol(
    data: npt.NDArray,
    window_length: int | None = None,
    polyorder: int = 3,
    sample_rate: float = 1000,
    axis: int = -1,
) -> npt.NDArray:
    """Smooths signal using savitzky-golay filter

    Args:
        data (npt.NDArray): Signal
        window_length (int | None, optional): Filter window length. Defaults to None.
        polyorder (int, optional): Poly fit order. Defaults to 3.
        sample_rate (float, optional): Sampling rate in Hz. Defaults to 1000 Hz.
        axis (int, optional): Axis to filter along. Defaults to -1.

    Returns:
        npt.NDArray: Smoothed signal

    """

    if window_length is None:
        window_length = sample_rate // 10

    if window_length % 2 == 0 or window_length == 0:
        window_length += 1

    return scipy.signal.savgol_filter(data, window_length=window_length, polyorder=polyorder, axis=axis)