function of dascore.proc.filter source

    patch: Patch ,
    samples = False,
    mode = reflect,
    cval = 0.0,
    truncate = 4.0,
    **kwargs ,
)-> ‘PatchType’

Applies a Gaussian filter along specified dimensions.


Parameter Description
patch The patch to filter
samples If True samples are specified
If False coordinate of dimension
mode The mode for handling edges.
cval The constant value for when mode == constant.
truncate Truncate the filter kernel length to this many standard deviations.
**kwargs Used to specify the sigma value (standard deviation) for desired


import dascore
from dascore.units import m, s
pa = dascore.get_example_patch()

# Apply Gaussian smoothing along time axis.
pa_1 = pa.gaussian_filter(time=0.1)

# Apply Gaussian filter over distance dimension
# using a 3 sample standard deviation.
pa_2 = pa.gaussian_filter(samples=True, distance=3)

# Apply filter to time and distance axis.
pa_3 = pa.gaussian_filter(time=0.1, distance=3)

See scipy.ndimage.gaussian_filter for more info on implementation and arguments.