Hi Michael,

It makes a lot of sense that this would fail, but skimage.filter.rank.mean has a `mask` keyword argument that can do what you want. However, it only works for uint8 or uint16 images, and if I remember correctly, it will be much slower if values exceed 12 bits, that is, your image intensity values should be in [0, 4096).

>>> from skimage.filter import rank
>>> arr = array([[np.nan, 1, 2, 3, 4, 5, np.nan]]) # make sure this is 2D
>>> selem = np.ones((3, 3))
>>> arr_mean1 = rank.mean(arr.astype(np.uint8), selem)
>>> arr_mean1
array([[0, 1, 2, 3, 4, 3, 2]], dtype=uint8)

>>> arr.astype(np.uint8) # nan's are actually converted to 0... Not good!
array([[0, 1, 2, 3, 4, 5, 0]], dtype=uint8)

>>> nan_mask = True - np.isnan(arr)
>>> arr_mean2 = rank.mean(arr.astype(np.uint8), selem, mask=nan_mask)
>>> arr_mean2 # using the nan mask produces the correct output
array([[1, 1, 2, 3, 4, 4, 5]], dtype=uint8)

Hope this helps!

Juan.

On Mon, Sep 2, 2013 at 10:12 PM, Michael Aye <kmicha...@gmail.com> wrote:

Hi!

I am trying to use ndimage's uniform_filter (for a simple local mean filtering) on a map-projected image that has NAN's at the border (basically the corners where the rotated map projected image does not fit into the rectangular grid).

Is there a way, maybe in skimage, to use a uniform_filter on an array that contains NANs? ndimage' version does not cope with it correctly:

> arr = array([np.nan, 1,2,3,4,5,np.nan])

> arr

array([ nan,   1.,   2.,   3.,   4.,   5.,  nan])

> nd.filters.uniform_filter(arr, 3)

array([ nan,  nan,  nan,  nan,  nan,  nan,  nan])

Cheers,

Michael

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