I'd like to nominate Austin Gilbert to be a yt project member.
So far he has merged two quite substantial pull requests to the main yt
repository and has demonstrated an eagerness to improve the plotting code.
He has also started contributing substantial improvements to the plotting
code and is also working on experimental GUI support (
As per YTEP-1776, if we get three more project members to second the
nomination, I'll go ahead and add Austin to the website and YTEP.
I'm trying to gaussian smooth a 3D field of gas. I'm convolving the field
with a gaussian kernel, but my results look odd. I think this might be
because of how I'm reshaping my data. You can see my scripts here
and a more detailed explanation with plots here
My suspicion (and someone from astropy's) is that I'm reshaping data
improperly. I access the fields like this:
ad = ds.all_data()
density = ad[fields['density']]
Which gives me a 1D array of length (1024^3) = 1073741824. To convolve this
with a 3D gaussian kernel, I attempt to reshape the 1D array into its
original 3D shape:
density = density.reshape(ds.domain_dimensions)
Which gives me a 3D 1024^3 matrix, but I'm not sure if this is correct.
Does this correctly transform the 1D data from ds.all_data()[field] back
into its 3D grid, or are things being misaligned somehow? Is there a better
way to access a certain field's data in 3D without getting it in 1D first
New issue 1256: OffAxisProjection Fails to find proper dataset limits
In trying to generate some OffAxisProjections from outside of the simulation domain, I've stumbled on some problems with auto-determination of field extrema. This simple script demonstrates:
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
yt.OffAxisProjectionPlot(ds, [1., 1., 0.5], 'density', [0.5, 0.5, 0.5], 1.5, north_vector=[0., 0., 1.]).save()
yields this STDERR:
yt : [INFO ] 2016-08-05 16:38:13,256 Parameters: current_time = 0.00600002000283
yt : [INFO ] 2016-08-05 16:38:13,256 Parameters: domain_dimensions = [32 32 32]
yt : [INFO ] 2016-08-05 16:38:13,256 Parameters: domain_left_edge = [ 0. 0. 0.]
yt : [INFO ] 2016-08-05 16:38:13,257 Parameters: domain_right_edge = [ 1. 1. 1.]
yt : [INFO ] 2016-08-05 16:38:13,257 Parameters: cosmological_simulation = 0.0
^MParsing Hierarchy : 0%| | 0/173 [00:00<?, ?it/s]^MParsing Hierarchy : 100%|##########| 173/173 [00:00<00:00, 20876.19it/s]
yt : [INFO ] 2016-08-05 16:38:13,287 Gathering a field list (this may take a moment.)
yt : [INFO ] 2016-08-05 16:38:15,246 xlim = -0.750000 0.750000
yt : [INFO ] 2016-08-05 16:38:15,246 ylim = -0.750000 0.750000
yt : [INFO ] 2016-08-05 16:38:15,246 zlim = -0.500000 0.500000
yt : [INFO ] 2016-08-05 16:38:15,250 Making a fixed resolution buffer of (('gas', 'density')) 800 by 800
/Users/chummels/src/yt-x86_64/src/yt-hg/yt/units/yt_array.py:964: RuntimeWarning: overflow encountered in multiply
np.multiply(self, oth, out=self)
yt : [WARNING ] 2016-08-05 16:38:16,264 Plot image for field ('gas', 'density') has both positive and negative values. Min = -inf, Max = 54704375419815931049795757413255569402681538393870135161391039774901627123390825374482250621889063954180467028564003619350195202205284149300884842122024669609440680126995877926691701507872243776797761225104772892079277541233926298501475834441217774443211945332828680339300087327793097590112761046546513920.000000.
yt : [WARNING ] 2016-08-05 16:38:16,264 Switching to symlog colorbar scaling unless linear scaling is specified later
which results in this image:
Interestingly, I don't get this problem with different datasets, or with different views of *this* dataset, so I'm not entirely sure what is going on. Here is the output of the same script using the `enzo_cosmology_plus/RD0009/RD0009` dataset:
I was wondering if there is currently an effort to implement data access along streamlines in yt 3.x. The streamline documentation (http://yt-project.org/doc/visualizing/streamlines.html <http://yt-project.org/doc/visualizing/streamlines.html>) indicates that this functionality is not yet available in 3.x, but seems to imply that it was available in previous versions (possibly 2.x?).
If no one else is working on this currently, I would be interested in contributing to this feature. However, I am relatively new to the yt codebase and don’t know what level of expertise would be needed. Any comments/feedback would be appreciated.
I recently submitted a pull request that enables all yt plots to utilize the various interactive backends matplotlib(mpl) has to offer. The primary benefit of this change is that it enables a few more features from mpl in jupyter notebooks, but it also creates the possibility for user created applications using python gui packages (pyqt, tk, cairo, etc.). However, enabling this feature outside of a notebook requires the user to configure matplotlib for the backend they are using, as well as implementing their own event loop for the application. I would like to hear from others if they feel this is to complex a task to put on the user, and also if they would utilize the feature.