[yt-users] Covering Grid Dimensions: multiple refinement levels

Carla Bernhardt carla.j.bernhardt at gmail.com
Thu Nov 19 08:00:20 PST 2015


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 <http://pastebin.com/JVhenc4z> 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,
Carla

2015-11-19 16:11 GMT+01:00 Nathan Goldbaum <nathan12343 at 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,
>
> Nathan
>
> On Thu, Nov 19, 2015 at 8:58 AM, Carla Bernhardt <
> carla.j.bernhardt at 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 at 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 at 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 at stonybrook.edu
>>> *web*: http://www.astro.sunysb.edu/mzingale
>>> github: http://github.com/zingale
>>>
>>>
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