New issue 1087: python seg faults when trying to load face-centered Enzo MHDCT fields
Dataset is here: http://use.yt/upload/58d7f69e
ds = yt.load('DD0004/DD0004')
ad = ds.all_data()
The traceback the Apple crash reporter generates doesn't have anything particularly illuminating in it. The error message that gets printed to my console is:
Python(56127,0x7fff742f5300) malloc: *** error for object 0x7ffb01f54608: incorrect checksum for freed object - object was probably modified after being freed.
*** set a breakpoint in malloc_error_break to debug
Before I get too far into writing up a YTEP, I wanted to know if
anyone would object to disabling "caching" of datasets in-memory.
Right now, if you do:
ds1 = yt.load("something")
ds2 = yt.load("something")
"ds1 is ds2" will return True; this was done so that you could load a
dataset, then unpickle an object with a reference to that dataset, and
get the dataset back rather than a newly-loaded one. But I think a)
folks don't pickle much b) it causes a lot of problems. So I'm
wondering if it would be okay to just turn this off.
New issue 1086: derived_field_list not updated after add_volume_weighted_smoothed_field
When ``add_volume_weighted_smoothed_field`` is used to add a smoothed field to a dataset (``ds`` thereafter) as shown below, ``ds.derived_field_list`` remains the same before and after the operation. The issue is that ``ds.field_info`` is supposed to be passed to ``add_volume_weighted_smoothed_field``. Then ``ds.field_info.add_field`` is called to add the field, which updates ``ds.field_info`` but not ``ds.derived_field_list``. One way to fix it is to call ``registry.ds.add_field`` instead of ``registry.add_field`` in ``add_volume_weighted_smoothed_field`` (here ``registry`` is supposed to be ``ds.field_info``. But I'm not sure if this is the best way to do it.
ds = yt.load('GadgetDiskGalaxy/snapshot_200.hdf5')
from yt.fields.particle_fields import add_volume_weighted_smoothed_field
add_volume_weighted_smoothed_field('PartType0', 'Coordinates', 'Masses',
ad = ds.all_data()
field = ('deposit', 'PartType0_smoothed_particle_ones')
print(field in ds.derived_field_list)
BTW, ``('deposit', 'PartType0_smoothed_particle_ones')`` can be accessed with no problem.
Today I issued a pull request with some updates for YTEP-1776:
This reminded me that we should be on the lookout for new contributors who
have made or are making substantial contributions to the codebase. To that
end, I'd like to nominate Allyson Julian as a new member.
Allyson has been working for the past few months on a frontend for the
Exodus II unstructured mesh data format. We are getting ready to merge the
frontend into the experimental bookmark soon and I think her work will be
extremely useful for expanding yt's ability to load unstructured mesh data
Once I've heard back from enough of you in support of Allyson's nomination,
I'll add Allyson to the member list.
---------- Forwarded message ----------
From: Thomas Caswell <tcaswell(a)gmail.com>
Date: Wed, Sep 23, 2015 at 10:51 PM
Subject: [Matplotlib-devel] Fwd: [NumFOCUS Projects] grant opportunity
To: matplotlib development list <matplotlib-devel(a)python.org>
If anyone has some downtime and would like to be paid to hack on open
Submissions are due Sept 30
---------- Forwarded message ---------
From: Gina Helfrich <gina(a)numfocus.org>
Date: Fri, Sep 18, 2015 at 12:10 PM
Subject: [NumFOCUS Projects] grant opportunity
Wanted to bring this opportunity to your attention:
Stripe is looking to host three to four developers at their San Francisco
office for three months to work full-time on an open-source project. The
grant is no-strings-attached: $7,500 per month in addition to desk space at
our office and meals during the week. "We want to provide everything needed
to focus and have a substantial impact on an open-source project."
More info: https://stripe.com/blog/open-source-retreat-2016
The program will run from January 15th until April 15th, 2016 at Stripe HQ
in the Mission District of San Francisco.
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Matplotlib-devel mailing list
This might be useful for us. Both numtraits and traitlets are pure python
packages that would not be difficult to add as dependencies.
---------- Forwarded message ----------
From: Thomas Robitaille <thomas.robitaille(a)gmail.com>
Date: Wed, Sep 23, 2015 at 11:39 AM
Subject: [Numpy-discussion] ANN: numtraits v0.2
To: "Discussions about using IPython. http://ipython.org" <
ipython-user(a)scipy.org>, Discussion of Numerical Python <
numpy-discussion(a)scipy.org>, SciPy Users List <scipy-user(a)scipy.org>
We have released a small experimental package called numtraits that
builds on top of the traitlets package and provides a NumericalTrait
class that can be used to validate properties such as:
* number of dimension (for arrays)
* shape (for arrays)
* domain (e.g. positive, negative, range of values)
* units (with support for astropy.units, pint, and quantities)
The idea is to be able to write a class like:
radius = NumericalTrait(domain='strictly-positive', ndim=0)
position = NumericalTrait(shape=(3,))
and all the validation will then be done automatically when the user
sets 'radius' or 'position'.
In addition, tuples and lists can get automatically converted to
arrays, and default values can be specified. You can read more about
the package and see examples of it in use here:
and it can be easily installed with
pip install numtraits
The package supports both Python 3.3+ and Legacy Python (2.7) :)
At this point, we would be very interested in feedback - the package
is still very young and we can still change the API if needed. Please
open issues with suggestions!
Tom and Francesco
NumPy-Discussion mailing list
I make an internship at a German reasearcher institute. My job is to write a
frontend for yt to load a special kind of HDF5 files. Most parts of the code
are are working now. So the yt.load() function create a dataset without
But I have still some erros when I wand to make a plot.
The reason is I have problems with the code in the io.py.
Can you please help me with further explanation of the class in io.py.
Thank you for helping.