Re: Using ndimage.gaussian_filter for 3d array

Hello Juan,
                        Thanks for your prompt response. You  are right - I
only want to smooth along 2d slices along the first axis.
 args = []
#abc shape is (10,100,100)
  for abc2d in abc:
           ndimage.gaussian_filter(abc2d*1e2,sigma=2,output=abc2d,order=0)
            b = abc2d[newaxis,:,:]
            args.append(b)
    smoothedABC = np.concatenate(args,axis=0)

Is this the best vectorized approach that I can take forward ?

On Tue, Oct 26, 2021 at 3:43 PM Juan Nunez-Iglesias <[email protected]>
wrote:

> A 3D gaussian filter is smoothing across all axes, so in abc[k, :, :] you
> are getting some of abc[k-1, :, :] and abc[k+1, :, :], and so on, according
> to the kernel weights in 3D. To smooth in 2D across all slices of a 3D
> array, specify a different sigma per axis, like so:
>
> abc = ndimage.gaussian_filter(abc * 1e2, sigma=(0, 2, 2), output=abc,
> order=0)
>
> Note that you don't need the [:, :, :]: using output=array is how to
> ensure that the array is modified in-place (if you are trying to avoid an
> extra allocation).
>
> Juan.
>
> On Tue, 26 Oct 2021, at 4:59 AM, ashwin .D wrote:
>
> Hello,
>              I am wanting to use ndimage.gaussian_filter for a 3d array in
> order to smooth the data .
>
> When I do this -
>
> abc[:,:,:] = ndimage.gaussian_filter(abc[:,:,:]*1e2,sigma=2,order=0) I get
> unreal values.
>
> However the function call when done within a loop like this
>
> for k in range(0,N):
>      abc[k,:,:] = ndimage.gaussian_filter(abc[k,:,:]*1e2,sigma=2,order=0)
> gives reasonable smoothed values.
>
> From the docs -
> https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html
>
> the input array does not seem to have any restriction on the
> dimensionality . So where am I going wrong ?
>
> Best regards,
> Ashwin.
>
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