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@max-sixty the issue you have mentioned does not include an implementation of a convolution operator, or did I miss something? Could you please reopen this issue?

I think this is closed by #4219, please reopen if not

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Or maybe we can convolve over the shared dimensions. python da = xr.DataArray(np.random.randn(15, 30), dims=['x', 'y']) kernel = xr.DataArray(np.random.randn(3, 3), dims=['x', 'y']) da.convolve(kernel, mode='same') Other dimensions maybe broadcasted.

Maybe we can keep this issue open.

python da.convolve(kernel, x='kx', y='ky', mode='same') would be a possible API?

The contribution will be very much appreciated ;)

Maybe we can have a simpler API for convolution operation, though.

python In [1]: import numpy as np ...: import xarray as xr ...: ...: da = xr.DataArray(np.random.randn(15, 30), dims=['x', 'y']) ...: kernel = xr.DataArray(np.random.randn(3, 3), dims=['kx', 'ky']) ...: ...: da.rolling(x=3, y=3).construct(x='kx', y='ky').dot(kernel) Out[1]: <xarray.DataArray (x: 15, y: 30)> array([[ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], [ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], ... [ nan, nan, -2.30319699e-01, 3.98542408e-01, 7.65734275e+00, -3.78602564e-01, -3.79670552e+00, -4.63870114e+00, 3.34264622e-02, -3.12097772e+00, -5.76697267e+00, 1.19804861e+00, -8.94696248e-01, 2.29308845e+00, -6.39524525e-01, 4.63574750e+00, 9.72065650e-01, -2.79080617e-01, -4.08284408e-01, 4.09498738e+00, 2.21513156e+00, 2.46188185e-01, -1.30140822e+00, -4.70525588e+00, -4.60012056e+00, 2.33333189e-01, -2.86204413e-01, -5.63190762e-01, 9.31915537e-01, 7.84188609e-01], [ nan, nan, 1.04286238e+00, -1.51693719e+00, 2.49199283e+00, 1.74931359e-01, -4.26361392e+00, -1.85066273e-01, -2.45780660e+00, -3.20920459e+00, -4.13765502e+00, -3.64119127e+00, 1.13819179e-01, -2.10588083e-01, -2.58307399e-02, -6.73602885e-01, 1.51186293e+00, 2.22395020e+00, 3.59169613e+00, 4.44203028e+00, 3.15528384e-01, -2.30913656e+00, 3.07864240e+00, -9.21743416e-01, -2.87995499e+00, -1.92025700e+00, -3.95047208e-01, 4.60378793e+00, 1.11828099e+00, 4.29419626e-01]]) Dimensions without coordinates: x, y

Could you please give an example on how to perform a 2d convolution with a 3x3 kernel?

One thing I would like to implement in somday is multi-dimensional rolling operation. The 1-dimensional convolution can be done with rolling -> construct -> dot, as can be seen in the doc page (see the last paragraph of http://xarray.pydata.org/en/stable/computation.html#rolling-window-operations)

This is can be extended to multiple dimensions, but it may not be straightforward.