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

Thu Nov 19 09:48:37 PST 2015

If I understand this, you are saying it will be a 2048**3 uniform grid.  At
8 bytes per #, this is 64 GB of memory to hold the data.

On Thu, Nov 19, 2015 at 12:45 PM, Carla Bernhardt <
carla.j.bernhardt at gmail.com> wrote:

> 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 at 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 <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
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> yt-users mailing list
>>>>>>> yt-users at lists.spacepope.org
>>>>>>> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> 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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-- 
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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