import dascore
from dascore.units import m, s
pa = dascore.get_example_patch()
# 1. Apply median filter only over time dimension with 0.10 sec window
filtered_pa_1 = pa.median_filter(time=0.1)
# 2. Apply median filter over both time and distance
# using a 0.1 second time window and 2 m distance window
filtered_pa_2 = pa.median_filter(time=0.1 * s, distance=2 * m)
# 3. Apply median filter with 3 time samples and 4 distance samples
filtered_pa = pa.median_filter(
time=3, distance=4, samples=True,
)MedianFilter
MedianFilter(
*args ,
samples: Any = False,
mode: Any = reflect,
cval: Any = 0.0,
**kwargs ,
)-> None
Apply 2-D median filter.
Parameters
| Parameter | Description |
|---|---|
| patch | The patch to filter |
| samples |
If True, the values in kwargs and step represent samples along a dimension. Must be integers. Otherwise, values are assumed to have same units as the specified dimension, or have units attached. |
| mode | The mode for handling edges. |
| cval | The constant value for when mode == constant. |
| **kwargs |
Used to specify the shape of the median filter in each dimension. See examples for more info. |
Examples
Note
See scipy.ndimage.median_filter for more info on implementation and arguments.
Values specified with kwargs should be small, for example < 10 samples otherwise this can take a long time and use lots of memory.
Keywords
filtering, median, smoothing, denoising
Methods
| Name | Description |
|---|---|
| check | Refuse a patch which does not carry what the operation needs. |
| get_metadata | Return the window along every axis, and the axes it spans. |
| model_copy | Copy the model, dropping cached values the update invalidates. |
| new | Create new instance with some attributed updated. |
| numpy_kernel | Return the median of the window around each sample. |
| median_filter | Apply 2-D median filter. |
| reconcile | Return metadata or a patch holding the final data; default as is. |
| run | Run the operation: check, get_metadata, kernel, reconcile, record. |