medfilt1 calculates median for dim1 only, then copies this to all other dims
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Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 35/100
Research direction
Start with the Python binding entry point af.medfilt1 and reproduce the reported 10×10 example using a window size of 3. Compare the output across dimensions; the work is done when filtering preserves each dimension's results instead of copying dim1 across the array.
Written by the indexing model from the issue text.
Description
In arrayfire previously, I've used medfilt1 to apply a median filter to multiple dimensions. However, in the python binding the output from dim1 is copied to all other dims.
For example:
import arrayfire as af
data = af.randn(10, 10)
filt_data = af.medfilt1(data, 3)
filt_data.to_ndarray()
array([[ 0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 ,
0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 ],
[ 0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 ,
0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 ],
[ 0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 ,
0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 , 0.3926208 ],
[-0.2309244 , -0.2309244 , -0.2309244 , -0.2309244 , -0.2309244 ,
-0.2309244 , -0.2309244 , -0.2309244 , -0.2309244 , -0.2309244 ],
[ 0.43086785, 0.43086785, 0.43086785, 0.43086785, 0.43086785,
0.43086785, 0.43086785, 0.43086785, 0.43086785, 0.43086785],
[-0.24984777, -0.24984777, -0.24984777, -0.24984777, -0.24984777,
-0.24984777, -0.24984777, -0.24984777, -0.24984777, -0.24984777],
[ 0.43086785, 0.43086785, 0.43086785, 0.43086785, 0.43086785,
0.43086785, 0.43086785, 0.43086785, 0.43086785, 0.43086785],
[-0.82585084, -0.82585084, -0.82585084, -0.82585084, -0.82585084,
-0.82585084, -0.82585084, -0.82585084, -0.82585084, -0.82585084],
[ 0.26141813, 0.26141813, 0.26141813, 0.26141813, 0.26141813,
0.26141813, 0.26141813, 0.26141813, 0.26141813, 0.26141813],
[ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]],
dtype=float32)
- Dominant language
- Python
- Stars
- 422
- Forks
- 63
- PR merge metrics
- No merged PRs in 30d
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