Nathan,Dims*2**3 is 256*2^3 or 2048. With the data I am working with, I think this should be on the order of 1 GB. I don't think that should overload the memory... Is it possible there is something incorrectly defined?Thanks,Carla
Am Donnerstag, 19. November 2015 schrieb Nathan Goldbaum :What is dims*2**3? Depending on your data this might be a very large array that won't fit in memory on your machine.You will need to find a machine that has more RAM to do this or create a covering grid that only covers a subset of the domain.
On Thursday, November 19, 2015, Carla Bernhardt <carla.j.bernhardt@gmail.com> wrote:Thanks again for the help. Once I implemented that, I have a follow-up problem: Once I have this much larger grid covering, it seems possibly too large for yt/python to handle. When I run:
cg=ds.smoothed_covering_grid(3,[0,0,0], dims*2**3)
data=cg['density'](where before I had dims*2**1), the variable data does not load and gives a few errors which lead me to believe that python or yt will not let me use that much space. Can I force yt to let me use more space?Thanks,Carla2015-11-19 16:11 GMT+01:00 Nathan Goldbaum <nathan12343@gmail.com>:Hi Carla,So it looks like ref_factors is only defined in the boxlib frontend, which is why it doesn't work for your Enzo data. Boxlib needs a concept of ref_factors because it allows AMR refinement jumps larger than a factor of two on a single level (i.e. level 2 might be four times higher resolution as level 1). Enzo and many other AMR codes do not allow this, instead only allowing a factor of two increase in linear resolution per AMR level.The dimensions you want to use depend on what fraction of the full domain you want to interpolate to uniform resolution. It looks like you want to create a covering grid covering your full domain, so to create a covering grid at the same resolution as AMR level 3 you would do:all_data_level_3 = ds.covering_grid(level=2, left_edge=[0,0.0,0.0],dims=ds.domain_dimensions * 2**3)This is because each refinement level jump in an Enzo simulation corresponds to a factor of two increase in spatial resolution.Hope that makes sense,NathanOn Thu, Nov 19, 2015 at 8:58 AM, Carla Bernhardt <carla.j.bernhardt@gmail.com> wrote: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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