gaussian_filter

function of dascore.proc.filter

source

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

Applies a Gaussian filter along specified dimensions.

Parameters

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 dimensions.

Examples

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)

Note

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