I've done this in the past:

ref = int(np.product(ds.ref_factors[0:max_level]))

# allocate for our uniformly-gridded result

dims = ds.domain_dimensions*ref

this will work for a more general case when the jump between levels can change as a function of level.

On Thu, Nov 19, 2015 at 9:10 AM, Carla Bernhardt <carla.j.bernhardt@gmail.com> wrote:

Dear YT Users,

To better understand covering_grid (or smoothed_covering_grid), can someone explain what dimensions I should use when I have multiple levels of refinement? If I have 1 level of refinement from AMR data, the dimensions should be the same, I believe, but what if I have 2 or 3 levels of refinement? Should the fixed resolution region then have dimensions of dims*2^2 and dims*3^2 respectively?

Here is one example from a tutorial if that helps isolate my question:
all_data_level_2 = ds.covering_grid(level=2, left_edge=[0,0.0,0.0],
dims=ds.domain_dimensions * 2**2)

Thanks in advance,
Carla

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Michael Zingale

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