Thank you for your quick response. However, when I used: ref = int(np.product(ds.ref_factors[0:3])), I got this error: AttributeError: 'EnzoDataset' object has no attribute 'ref_factors' Did I misunderstand your suggestion? Or do I need to import something? Thanks, Carla 2015-11-19 15:39 GMT+01:00 Michael Zingale <michael.zingale@stonybrook.edu>:
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 <http://yt-project.org/doc/examining/low_level_inspection.html#examining-grid-data-in-a-fixed-resolution-array> 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 Associate Professor
Dept. of Physics & Astronomy • Stony Brook University • Stony Brook, NY 11794-3800 *phone*: 631-632-8225 *e-mail*: Michael.Zingale@stonybrook.edu *web*: http://www.astro.sunysb.edu/mzingale github: http://github.com/zingale
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