[yt-users] plotting average quantaties

[John Forbes]

One issue I've run into in the past is that binning by 'x' with
BinnedProfile1D may not produce exactly what one would like. In particular,
if your data has non-uniform cell sizes, then in the derefined regions the
1D profile will have peaks at the positions of cell centers. For instance
if my coarsest grid extends from x=0 to x=4 with 8 cells, and much of my
grid is not refined, my 1D profile will have features at x=0.25, 0.75, etc.
I'm not sure if any built-in yt functionality which gets around this issue
(if you run into it). Maybe you could create a FixedResolutionBuffer and do
whatever sum you want manually over one dimension of the corresponding
numpy array?

Good luck,
John


On Tue, Oct 22, 2013 at 5:45 AM, j s oishi <jsoishi at gmail.com> wrote:

> I think Sasha may want a simpler, unweighted average. You can do this by
> setting the weight field to Ones. If you set the weight to None, you'll get
> a sum across x.
>
> You'll probably want to set n_bins to the number of x cells to get what
> you are looking for.
>
> Hope this is helpful,
>
> Jeff
> On Oct 22, 2013 8:25 AM, "Matthew Turk" <matthewturk at gmail.com> wrote:
>
>> Hi Sasha,
>>
>> I'm not sure I understand what you're looking for.  The functionality to
>> take averages of one field wrt another field might work for what you're
>> looking for, using the BinnedProfile1D object.  If so, you could bin by the
>> "x" field and then take a weighted average of Density.  This might look
>> something like this:
>>
>> dd = pf.h.all_data()
>> prof = BinnedProfile1D(dd, n_bins, "x", pf.domain_left_edge[0],
>> pf.domain_right_edge[0], log_space = False)
>> prof.add_fields(["Density"], weight="CellMassMsun")
>>
>> Does that help?
>>
>> Best,
>>
>> Matt
>>
>>
>> On Mon, Oct 21, 2013 at 11:29 PM, Alexander Philippov <
>> philippo at astro.princeton.edu> wrote:
>>
>>> Hi all,
>>>
>>> I would like to plot 1D profile of quantity (say, density) averaged over
>>> 1 axis
>>> (say, x) from two-dimensional data. Is there any cheap way to do it?
>>>
>>> Thanks in advance,
>>> Sasha
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>>
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