[yt-dev] Issues with 3D convolution and reshaping data?

[Rasmi Elasmar]

That's exactly what I need, thanks so much Nathan!

On Mon, Aug 8, 2016 at 3:58 PM, Nathan Goldbaum <nathan12343 at gmail.com>
wrote:

>
>
> On Mon, Aug 8, 2016 at 2:49 PM, Rasmi Elasmar <re2300 at columbia.edu> wrote:
>
>> Hi all,
>>
>> I'm trying to gaussian smooth a 3D field of gas. I'm convolving the field
>> with a gaussian kernel, but my results look odd. I think this might be
>> because of how I'm reshaping my data. You can see my scripts here
>> <https://github.com/rasmi/deviant-cosmology/blob/master/scripts/convolve.py>
>> and here
>> <https://github.com/rasmi/deviant-cosmology/blob/4f6b4886c01c92cfe3987d4a66e25ba84ecebf86/scripts/convolve.py>,
>> and a more detailed explanation with plots here
>> <https://github.com/astropy/astropy/issues/5169#issuecomment-238304706>.
>>
>> My suspicion (and someone from astropy's) is that I'm reshaping data
>> improperly. I access the fields like this:
>> ad = ds.all_data()
>> density = ad[fields['density']]
>> Which gives me a 1D array of length (1024^3) = 1073741824. To convolve
>> this with a 3D gaussian kernel, I attempt to reshape the 1D array into its
>> original 3D shape:
>> density = density.reshape(ds.domain_dimensions)
>> Which gives me a 3D 1024^3 matrix, but I'm not sure if this is correct.
>> Does this correctly transform the 1D data from ds.all_data()[field] back
>> into its 3D grid, or are things being misaligned somehow? Is there a better
>> way to access a certain field's data in 3D without getting it in 1D first
>> and reshaping?
>>
>
> HI Ramsi,
>
> As you've discovered, yt data objects return data as a flattened,
> unordered 1D array. Simply reshaping the data as you've done will not
> produce what you're expect them to produce, since the data are unordered.
>
> It sounds like you're dealing with a uniform resolution dataset, and what
> I'm saying to you probably doesn't make much sense in that context, but
> keep in mind that yt can deal with multiresolution data natively, where
> this flattened, unordered representation is the most convenient way to deal
> with the data.
>
> For this particular use case, I'd bet that what you really want is either
> a covering_grid data object or an arbitrary_grid data object:
>
> http://yt-project.org/doc/examining/low_level_inspection.html#examining-
> grid-data-in-a-fixed-resolution-array
> http://yt-project.org/doc/analyzing/objects.html#arbitrary-grid
>
> http://yt-project.org/doc/reference/api/generated/yt.
> data_objects.construction_data_containers.YTArbitraryGrid.html
> http://yt-project.org/doc/reference/api/generated/yt.
> data_objects.construction_data_containers.YTCoveringGrid.html
>
> A covering grid is a uniform resolution 3D version of the data object. For
> multiresolution data, this will simply sample the data at a single
> resolution level, using nearest neighbor interpolation where the data are
> too coarse, and sampling when the data are too finely resolved. Covering
> grids are somewhat restrictive, as the resolution of the grid much be a
> power-of-two multiple of the coarsest resolution of your data.
>
> Arbitrary_grid lets you create uniform resolution data at an arbitrary
> position or resolution, so it's a bit more flexible, although it's not
> necessarily as "honest" a representation of the original dataset.
>
> Hope that helped clear things up, please let us know if you have
> additional questions.
>
> -Nathan
>
>
>
>>
>>
>> Thanks,
>>
>> Rasmi
>>
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>>
>
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