Drop spatial elements from sklearn-style class

[tsalo]

Currently we pass around shape_3d (a three-element tuple with the shape of the image) and mask_vec (a 1D array indicating whether a given voxel falls within the mask) throughout the MovingAveragePCA class, and I would love to support 2D data, if that's possible.

[tsalo]

If it isn't possible to drop the spatial elements, then we should probably at least try to find the most general set of arguments to use (e.g., a 3D array for the mask instead of both a shape tuple and a mask vector, or possibly something else entirely).

[tsalo]

Per Gitter conversations, our ability to drop the spatial elements of the algorithm will determine if we end up proposing adding it to scikit-learn or nilearn. If the algorithm requires spatial information, then it's probably specific to neuroimaging data, in which case we will ask the nilearn devs if they'd be interested in incorporating it. If it doesn't, then we'll open an issue in the scikit-learn repo. If the package we open an issue for isn't interested in this algorithm, then we will move forward with this repo. At least that's where I think we stand.

[eurunuela]

Thank you @tsalo for bringing the Gitter conversation over here. That's right, that's where we stand.

[tsalo]

It doesn't seem like it's possible to remove the spatial elements. I think we can close this.