[yt-users] Santa Barbara Cluster Comparison

Britton Smith brittonsmith at gmail.com
Wed Jun 29 06:10:13 PDT 2011


Hi Casey,

That is a very good question.  I think we need a place user-submitted
recipes.  I know that one of the major long term goals of yt as a project is
to be a hub for astrophysicists running simulations and analyzing data.  I
think a page where people can upload recipes for doing various clever things
and browse what others have submitted would be very useful.  This doesn't
seem to exist yet, but maybe it's worth putting in the effort to set up.
Thoughts anyone?

Britton

On Tue, Jun 28, 2011 at 7:25 PM, Casey W. Stark <caseywstark at gmail.com>wrote:

> Ah thanks Britton.
>
> Didn't think to snoop around the scipy stuff. With that and a custom
> colormap, I should be able to do this just fine.
>
> What's the best place to post examples like this?
>
> Best,
> Casey
>
>
> On Tue, Jun 28, 2011 at 7:30 AM, Britton Smith <brittonsmith at gmail.com>wrote:
>
>> Hi Casey,
>>
>> You might try adding a gaussian filter from scipy:
>>
>> http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.filters.gaussian_filter.html
>> Once you make the projection, you can get N^2 image data by creating a
>> FixedResolutionBuffer like this:
>> frb = FixedResolutionBuffer(projection, (x_left, x_right, y_left,
>> y_right), (N, N), antialias=False)
>> where projection is your projection object (like pc.plots[-1])
>> The actual data for a field can be accessed by frb[field].
>>
>> You should be able to take that NxN array and apply whatever filter you
>> like.
>>
>> Britton
>>
>> On Mon, Jun 27, 2011 at 11:59 PM, Casey W. Stark <caseywstark at gmail.com>wrote:
>>
>>> Hello yt.
>>>
>>> I'm trying to generate plots like the ones in Frenk 1999, the Santa
>>> Barbara Cluster Comparison Project. I would like to reproduce the plots of
>>> projections of the dark matter density, gas density, and temperature at z =
>>> 0.
>>>
>>> The first step is pretty easy - just make a plotcollection and add a
>>> projection. The part I'm not sure about is the smoothing. I need to apply
>>> Gaussian smoothing to this 256^3 data. I figure I do something like what is
>>> in AMRSmoothedCoveringGridBase, but I'm really not sure.
>>>
>>> Has anyone done anything similar with yt?
>>>
>>> _______________________________________________
>>> yt-users mailing list
>>> yt-users at lists.spacepope.org
>>> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
>>>
>>>
>>
>> _______________________________________________
>> yt-users mailing list
>> yt-users at lists.spacepope.org
>> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
>>
>>
>
> _______________________________________________
> yt-users mailing list
> yt-users at lists.spacepope.org
> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.spacepope.org/pipermail/yt-users-spacepope.org/attachments/20110629/7ac4c9aa/attachment.htm>


More information about the yt-users mailing list