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        • navis.smooth_voxels
          • smooth_voxels()

    navis.smooth_voxels¶

    navis.smooth_voxels(x, sigma=1, inplace=False)[source]¶

    Smooth voxel(s) using a Gaussian filter.

    Parameters:
    • x (TreeNeuron | NeuronList) – Neuron(s) to be processed.

    • sigma (int | (3, ) ints, optional) – Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.

    • inplace (bool, optional) – If False, will use and return copy of original neuron(s).

    • parallel (bool) – If True and input is NeuronList, use parallel processing. Requires pathos.

    • n_cores (int, optional) – Numbers of cores to use if parallel=True. Defaults to half the available cores.

    • progress (bool) – Whether to show a progress bar. Overruled by navis.set_pbars.

    • omit_failures (bool) – If True will omit failures instead of raising an exception. Ignored if input is single neuron.

    Returns:

    Smoothed neuron(s).

    Return type:

    VoxelNeuron/List

    Examples

    >>> import navis
    >>> n = navis.example_neurons(1, kind='mesh')
    >>> vx = navis.voxelize(n, pitch='1 micron')
    >>> smoothed = navis.smooth_voxels(vx, sigma=2)
    

    See also

    navis.smooth_mesh()

    For smoothing MeshNeurons and other mesh-likes.

    navis.smooth_skeleton()

    For smoothing TreeNeurons.

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    Source

    © Copyright 2018, Philipp Schlegel.
    Created using Sphinx 5.3.0.