Fellow yt users:
A few weeks ago, we asked yt users and developers to fill out a brief
survey (http://goo.gl/forms/hRNryOWTPO) to provide us with feedback on how
well yt is meeting the needs of the community and how we can improve.
Thank you to all 39 people who responded, as it has given us a great deal
to consider as we move forward with the code. We summarize the results of
the survey below, but I start with the basic takeaway from the survey:
Overall Survey Takeaway:
The survey respondents are generally pleased with yt. It meets their
needs, has a wonderful community, is relatively easy to install, and has
fair documentation. Major short-term requests were for improvements in
documentation, particularly in API docs and source code commenting, as well
as more cross-linking in the existing documentation and making sure docs
were up to date. Furthermore, people wanted more attention to making sure
existing code in 3.0 works and for resurrecting all 2.x functionality in
3.0.
The single biggest takeaway from the survey is that the transition to yt
3.0 has been fraught with difficulties. Many submitters expressed
satisfaction with the new functionality in 3.0, but the overall process of
transition through documentation, analysis modules and community response
has been found to be lacking.
Background:
There were 39 unique responses to our survey. 75% of the respondents were
grads and postdocs with a smattering of faculty, undergrads, and
researchers. Nearly everyone is at 4-year universities. 50% of the
respondents consider themselves intermediate users, 20% novice, 20%
advanced, and 10% gurus.
Installation:
90% of the respondents use the standalone install script, with a several
users employing other methods (potentially in addition to the standalone
script). 95% of the respondents rated installation as a 3 or better (out
of 5) with most people settling on a 4 out of 5. Installation comments
were aimed at having better means of installing on remote supercomputing
systems and/or making pip installs work more frequently.
Community Responsiveness:
72% of respondents gave yt 5 out of 5 and 97% were 3 or greater for
community responsiveness. Clearly this is our strong point. There was a
very wide distribution of ways in which people contacted the community for
help with the most popular means being the mailing lists, the irc channel,
mailing developers directly, and searching google. Comments in this
section were mostly positive, but one user wished for more concrete action
to be taken after bugs were reported.
Documentation:
77% of respondents gave 4 or 5 out of 5 for the overall rating of the
documentation. Individual docs components were more of a mix. Cookbooks
were ranked very highly, and quickstart notebooks and narrative docs were
generally ranked well. The two documentation components that seemed be
ranked lower (although still fair) were API docs and comments in the source
code with 15% of respondents noting that they were “mostly not useful” (ie
2 / 5). There were a lot of comments regarding ways to improve the docs,
which I bullet point here:
-
Organization of docs is difficult to parse; Hard to find what you’re
looking for.
-
Hard to know what to search for, so make command list (ie API docs) more
prominent
-
Docs not always up to date (even between 3.0 and dev)
-
Discrepancies between API docs and narrative docs
-
Examples are either too simple or too advanced--need more intermediate
examples
-
Units docs need more explanation
-
Not enough source code commenting or API docs
-
Not enough cross-linking between docs
-
More FAQ / Gotchas for common mistakes
-
API docs should include more examples and also note how to use all of
the options, not just the most common.
Functionality:
88% of respondents found yt to meet their research needs (4 or 5 out of
5). Respondents are generally using yt on a variety of datasets including
grid data, octree data, particle data, and MHD with only a handful of users
dealing with spherical or cylindrical data at present. Nearly all of the
frontends are being used by respondents, with a few exceptions: Chombo,
Moab, Nyx, Pluto, and non-astro data. Visualization remains the main use
of yt with 97% of respondents, but simple analysis received 82% and
advanced analysis received 62%. Interestingly, 31% of respondents use halo
analysis tools, with only 15% using synthetic observation analysis.
Big Picture:
51% of respondents gave yt 5 out of 5 for general satisfaction, with 28% 4
out of 5 and 15% 3 out of 5. Overall, this is pretty good but probably
biased by the fact that people filled out this survey. Comments on the
greatest strengths of yt include:
-
visualization capabilities
-
community support
-
flexibility
Comments on the biggest shortcomings of yt include:
-
documentation (see above)
-
learning to “think in yt”
-
making new functionality when there is existing broken functionality (or
missing documentation)
-
making sure 3.0 matches all functionality from 2.x
-
keeping the documentation up to date
-
making the transition from 2.x to 3.0 easier (how to update scripts)
Things to focus on in the next year:
-
documentation (almost unanimously)
-
making sure 3.0 can do all functionality from 2.x
Thank you for all of the valuable feedback. We sincerely appreciate the
constructive criticism in making for a better code and community! We will
put together a blueprint of how to address these shortcomings soon. Look
for it after the holiday break. Have a wonderful holiday!
On behalf of the yt development team,
Cameron
--
Cameron Hummels
Postdoctoral Researcher
Steward Observatory
University of Arizona
http://chummels.org
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New issue 963: RAMSES particle depositition onto mesh does not work with filtered datasets
https://bitbucket.org/yt_analysis/yt/issue/963/ramses-particle-deposititi...
Benjamin Thompson:
Hello.
With a RAMSES dataset. The deposition of particles onto a mesh works fine for unfilltered cases.
For example, should I call
```
#!python
print data["deposit,"io_density"]
```
I get answers which are sensible.
However, I wish to filter my particle data for stars/dark matter and treat the particles seperatly. If for example I do this,
```
#!python
def star_filter(pfilter,data):
filter = np.logical_and(data.particles.source["particle_age"] != 0, data.particles.source["particle_age"] != None)
return filter
add_particle_filter("stars", function=star_filter, filtered_type="all", requires=["particle_age"])
data.ds.add_particle_filter("stars")
```
Which filters the particles as expected, which also creates the fields
('deposit', 'io_cic')
('deposit', 'io_cic_velocity_x')
('deposit', 'io_cic_velocity_y')
('deposit', 'io_cic_velocity_z')
('deposit', 'io_count')
('deposit', 'io_density')
('deposit', 'io_mass')
('deposit', 'io_nn_velocity_x')
('deposit', 'io_nn_velocity_y')
('deposit', 'io_nn_velocity_z')
('deposit', 'stars_cic')
('deposit', 'stars_cic_velocity_x')
('deposit', 'stars_cic_velocity_y')
('deposit', 'stars_cic_velocity_z')
('deposit', 'stars_count')
('deposit', 'stars_density')
('deposit', 'stars_mass')
('deposit', 'stars_nn_velocity_x')
('deposit', 'stars_nn_velocity_y')
('deposit', 'stars_nn_velocity_z')
As you would expect
However, upon trying to call
print data["deposit,"stars_density"]
I get the error below
```
#!shell
Traceback (most recent call last):
File "selene_gradients.py", line 46, in <module>
print cylinder[("deposit", "stars_density")]
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 240, in __getitem__
self.get_data(f)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 673, in get_data
self._generate_fields(fields_to_generate)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 693, in _generate_fields
fd = self._generate_field(field)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 277, in _generate_field
tr = self._generate_fluid_field(field)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 295, in _generate_fluid_field
rv = self._generate_spatial_fluid(field, ngt_exception.ghost_zones)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 311, in _generate_spatial_fluid
ind += o.select(self.selector, self[field], rv, ind)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 240, in __getitem__
self.get_data(f)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 673, in get_data
self._generate_fields(fields_to_generate)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 693, in _generate_fields
fd = self._generate_field(field)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 277, in _generate_field
tr = self._generate_fluid_field(field)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 297, in _generate_fluid_field
rv = finfo(gen_obj)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/fields/derived_field.py", line 184, in __call__
dd = self._function(self, data)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/fields/particle_fields.py", line 106, in particle_density
pos = data[ptype, coord_name]
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/octree_subset.py", line 76, in __getitem__
tr = super(OctreeSubset, self).__getitem__(key)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 240, in __getitem__
self.get_data(f)
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.py", line 615, in get_data
with f.apply(self):
File "/home/bthompson1/python/yt/yt-x86_64/lib/python2.7/contextlib.py", line 17, in __enter__
return self.gen.next()
File "/gpfs/home/bthompson1/python/yt/yt-x86_64/src/yt-hg/yt/data_objects/particle_filters.py", line 46, in apply
filter = self.function(self, dobj)
File "/gpfs/home/bthompson1/ramses_pp/ramses_pp/ben/real_selene_analysis/ramses_pp/modules/yt/YT.py", line 59, in star_filter
filter = np.logical_and(data.particles.source["particle_age"] != 0, data.particles.source["particle_age"] != None)
AttributeError: 'RAMSESDomainSubset' object has no attribute 'particles'
```
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New issue 962: Unit warnings and errors should reference locations higher on the stack
https://bitbucket.org/yt_analysis/yt/issue/962/unit-warnings-and-errors-s...
Nathan Goldbaum:
Warnings like these aren't useful:
These warnings are happening when we invoke numpy on the array data - normally these warnings would be issued wherever we are manipulating numpy arrays elsewhere, but since we access numpy indireactly via YTArray, the warnings show up as function calls in YTArray.
We should be doing these superclass calls in try/except blocks to catch the warnings and if they happen manipulate them so the warning corresponds to a useful place in the call stack - probably a call or two higher.
Responsible: ngoldbaum
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New issue 960: Bad sub-volume plots for BoxLib/Maestro spherical data
https://bitbucket.org/yt_analysis/yt/issue/960/bad-sub-volume-plots-for-b...
Adam Jacobs:
When I try to plot an off-axis slice of a subset of BoxLib/Maestro spherical data (yt test dataset maestro_subCh_plt00248) I find that my data gets junked if the plot includes any regions outside of the subset. My understanding is that in yt the regions outside of the sub-volume should be white and the other data should be plotted as it is when the full dataset is plotted.
This issue feels similar to a previously resolved issue dealing with off-axis slices of spherical Maestro data (https://bitbucket.org/yt_analysis/yt/issue/955/off-axis-slices-returning-...). I've attached code that produces the error as well as images of the output.
Responsible: ngoldbaum
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New issue 958: Document detailed cuts on particle chunks
https://bitbucket.org/yt_analysis/yt/issue/958/document-detailed-cuts-on-...
Cameron Hummels:
This is a followup on a conversation from the yt-users email list:
---
*Hi, Everybody-*
*I have some old code in which I pulled particle ids from grid objects, and I'm porting it to yt3.0.*
*My old code used a "cut mask" defined like the following:*
```
#!python
for grid in data._grids:
cut_mask= data._get_cut_mask(grid)
```
*I see that grids is now handled through chunks, is there a modern equivalent of the cut_mask? Or, is there a new-and-improved method to get particle ids from complex objects?*
---
*The yt-3.0 equivalent using the chunking system looks like this:*
http://nbviewer.ipython.org/gist/ngoldbaum/700d2235d24a5faf9d58
---
Perhaps a note about this functionality should go in the "Particle Filters" section of the docs?
http://yt-project.org/docs/dev/analyzing/filtering.html#filtering-particl...
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Today was the day we were nominally supposed to release 3.1. I know we have Cameron's docs PR.
Anything else?
John ZuHone
Kavli Center for Astrophysics and Space Research
Massachusetts Institute of Technology
77 Massachusetts Ave., 37-582G
Cambridge, MA 02139
(w) 617-253-2354
(m) 781-708-5004
jzuhone(a)space.mit.edu
jzuhone(a)gmail.com
http://www.jzuhone.com
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New issue 957: Ragged array tests fail on numpy 1.9
https://bitbucket.org/yt_analysis/yt/issue/957/ragged-array-tests-fail-on...
Nathan Goldbaum:
Running the following command with numpy 1.9 installed generates the following:
```
#!base
$nosetests utilities.lib.tests.test_ragged_arrays
FFFFFF..FFF.................................FFFF.F...F..........................................................................................................................
```
See http://paste.yt-project.org/show/5263/ for the tracebacks returned by nose.
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New issue 956: enable_parallelism dies due to missing rootLogger
https://bitbucket.org/yt_analysis/yt/issue/956/enable_parallelism-dies-du...
Nathan Goldbaum:
This script:
```
#!python
import yt
yt.enable_parallelism()
```
Will fail with the following (somewhat garbled) traceback when run in parallel:
```
#!python
Traceback (most recent call last):
File "test.py", line 2, in <module>
yt.enable_parallelism()
File "/Users/goldbaum/Documents/yt-hg/yt/utilities/parallel_tools/parallel_analysis_interface.py", line 116, in enable_parallelism
Traceback (most recent call last):
File "test.py", line 2, in <module>
if len(yt.utilities.logger.rootLogger.handlers) > 0:
yt.enable_parallelism()
AttributeError: 'module' object has no attribute 'rootLogger'
File "/Users/goldbaum/Documents/yt-hg/yt/utilities/parallel_tools/parallel_analysis_interface.py", line 116, in enable_parallelism
if len(yt.utilities.logger.rootLogger.handlers) > 0:
AttributeError: 'module' object has no attribute 'rootLogger'
Traceback (most recent call last):
File "test.py", line 2, in <module>
yt.enable_parallelism()
File "/Users/goldbaum/Documents/yt-hg/yt/utilities/parallel_tools/parallel_analysis_interface.py", line 116, in enable_parallelism
if len(yt.utilities.logger.rootLogger.handlers) > 0:
AttributeError: 'module' object has no attribute 'rootLogger'
yt : [INFO ] 2014-12-13 12:04:00,092 Global parallel computation enabled: 0 / 4
Traceback (most recent call last):
File "test.py", line 2, in <module>
yt.enable_parallelism()
File "/Users/goldbaum/Documents/yt-hg/yt/utilities/parallel_tools/parallel_analysis_interface.py", line 116, in enable_parallelism
if len(yt.utilities.logger.rootLogger.handlers) > 0:
AttributeError: 'module' object has no attribute 'rootLogger'
```
This was caused by PR 1355. I've provisionally assigned this issue to John ZuHone because he's been thinking about loggers a lot lately. I'm also not sure what the parallel analysis interface is doing with the loggers here, so I can't fix this myself immediately. If anyone else knows how to handle this, feel free to jump in with a patch :)
Responsible: jzuhone
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