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 resultdims = ds.domain_dimensions*refthis 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 ZingaleAssociate ProfessorDept. of Physics & Astronomy • Stony Brook University • Stony Brook, NY 11794-3800phone: 631-632-8225e-mail: Michael.Zingale@stonybrook.edugithub: http://github.com/zingale
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