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

[Nathan Goldbaum]

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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>>> yt-users mailing list
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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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>>
>
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