---------- Forwarded message ----------
From: Andrew Myers <atmyers2_at_gmail.com>
Date: Mon, Jul 25, 2016 at 4:46 PM
Subject: [yt-users] ANN: yt-3.3 released
To: Discussion of the yt analysis package <yt-users_at_lists.spacepope.org>
The yt community is proud to announce the release of yt 3.3!
yt (
http://yt-project.org) is an open source, community developed toolkit
for the analysis and visualization of volumetric data of all types, with a
particular emphasis on astrophysical and nuclear engineering simulations.
Since the yt 3.2 release, over 500 pull requests have been merged from 41
contributors, representing over 20% of the total pull requests merged to
date. Major new features in this release include:
* Raw and processed data from selections, projections, profiles and so
forth can now be saved in a ytdata format and loaded back in by yt. See
http://yt-project.org/docs/dev/analyzing/saving_data.html for more
information.
* Totally re-worked volume rendering API. The old API is still available
for users who prefer it, however. See
http://yt-project.org/docs/dev/quickstart/volume_rendering.html.
* Support for unstructured mesh visualization. See
http://yt-project.org/docs/dev/visualizing/plots.html?highlight=unstructured%20mesh%20slices
and
http://yt-project.org/docs/dev/visualizing/unstructured_mesh_rendering.html
for more information.
* Interactive Data Visualization for AMR and unstructured mesh datasets.
See
http://yt-project.org/docs/3.3.1/visualizing/interactive_data_visualization.html
.
* Several new colormaps, including a new default, 'arbre'. The other new
colormaps are named 'octarine', 'kelp', and 'dusk'. All these new colormaps
were generated using the viscm package (
https://github.com/matplotlib/viscm)
and should do a better job of representing the data for colorblind viewers
and when printed out in grayscale. See
http://yt-project.org/docs/dev/visualizing/colormaps/index.html for more
detail.
* New frontends for the Exodus II (
http://yt-project.org/docs/dev/examining/loading_data.html#exodus-ii-data),
GAMER (
http://yt-project.org/docs/dev/examining/loading_data.html#gamer-data),
and Gizmo (
http://yt-project.org/docs/dev/examining/loading_data.html#gizmo-data) data
formats.
* The unit system associated with a dataset is now customizable, defaulting
to CGS. See
http://yt-project.org/docs/dev/analyzing/units/unit_systems.html
.
* Enhancements and usability improvements for analysis modules, especially
the absorption_spectrum, photon_simulator, and light_ray modules. See
http://yt-project.org/docs/dev/analyzing/analysis_modules/synthetic_observation.html
.
* Data objects can now be created via an alternative Numpy-like API. See
http://yt-project.org/docs/dev/analyzing/objects.html?highlight=quickly%20selecting%20data#quickly-processing-data
.
* A line integral convolution plot modification. See
http://yt-project.org/docs/dev/visualizing/callbacks.html?highlight=line%20integral%20convolution#overplot-line-integral-convolution
.
* Many speed optimizations, including to the volume rendering, units,
tests, covering grids, the absorption spectrum and photon simulator
analysis modules, and ghost zone generation.
* Packaging and release-related improvements: better install and setup
scripts, automated PR backporting.
* Readability improvements to the codebase, including linting, removing
dead code, and refactoring much of the Cython.
* Improvements to the CI infrastructure, including more extensible answer
tests and automated testing for Python 3 and Windows.
* Numerous documentation improvements, including formatting tweaks,
bugfixes, and many new cookbook recipes.
* Support for geographic (lat/lon) coordinates.
* Several improvements for SPH codes, including alternative smoothing
kernels, an add_smoothed_particle_field function, and particle type-aware
octree construction for Gadget data.
* Roundtrip conversions between Pint and yt units.
* Added halo data containers for gadget_fof frontend.
* Enabled support for spherical datasets in the BoxLib frontend.
* Many new tests have been added.
* Better hashing for Selector objects.
For a more complete list of the changes in this release, see
http://yt-project.org/docs/dev/reference/changelog.html.
Standard Installation Methods
----------------------------------------
As with previous releases, you can install yt using one of the following
methods.
1. From the install script on OSX and Linux (
http://yt-project.org/#getyt):
As of this release, the install script has been updated to use conda by
default (see below). If you would like to retain the old behavior of
installing everything from source, you can edit the INST_CONDA flag in the
script manually.
# Installation
$ wget
http://bitbucket.org/yt_analysis/yt/raw/stable/doc/install_script.sh
$ bash install_script.sh
At the end of the installation process, the script will print a note about
how to update your PATH environment variable to activate your yt
installation. As of yt 3.3, you no longer need to use the “activate” script.
# Update
$ yt update
Note also that many of the dependencies have been updated since version
3.2. If you previously installed yt from the install script, it is advised
that you re-install yt from scratch.
2. From pip (source or binary wheel, see below for more details):
# Installation
$ pip install yt
# Update
$ pip install -U yt
3. From the Anaconda Python Distribution (
https://store.continuum.io/cshop/anaconda/). Because it may take several
weeks for the yt package on the official conda channel to be updated, we
recommend using the conda-forge instead:
# Installation
$ conda install -c conda-forge yt
# Update
$ conda update yt -c conda-forge yt
If you are on the “stable” branch, updating will bring you from yt 3.2 to
3.3, incorporating all
changes since 3.2, whereas if you are on the “dev” or “yt” branch, only the
changes since
your last update should be incorporated.
Installing Binary Packages via pip
---------------------------------------------
You can also install binary packages (“wheels”) using pip on Windows
(64-bit only), Mac OS X, and Linux (32 and 64-bit). This has the advantage
of not needing to install yt from source using a proper compiler setup,
which has caused occasional problems on both of these platforms in the past .
Binary wheels are available on the following platforms and python
installations (using either Python 2.7 or 3.4):
Linux x86_64:
Python.org Python
Windows x86_64:
Enthought Canopy Python (
https://www.enthought.com/products/canopy/)
WinPython (
http://winpython.sourceforge.net/)
Mac OS X x86_64:
Enthought Canopy Python (
https://www.enthought.com/products/canopy/)
Homebrew Python (
http://brew.sh/)
Python.org Python
Mac OS X’s system Python
MacPorts Python (
https://www.macports.org/)
The requirements for installing yt via this method are the same as from
source:
NumPy
h5py
HDF5
SymPy
Matplotlib
IPython (not required, but strongly recommended)
To install a new version of yt on one of these platforms, simply do
$ pip install yt
and you should get the binary distribution automatically. Also, if your
python installation is system-wide (e.g., the Mac system Python) you might
either need to run pip with administrator privileges or install to your
home directory:
$ pip install --user --prefix= yt
Ensure that the $HOME/.local/bin directory is in your PATH if you choose
this method.
For more information, including more installation instructions, links to
community resources, and information on contributing to yt’s development,
please see the yt homepage at
http://yt-project.org and the documentation
for yt-3.3 at
http://yt-project.org/docs/3.3.1.
yt is the product of a large community of developers and users and we are
extraordinarily grateful for and proud of their contributions. Please
forward this announcement on to any interested parties.
As always, if you have any questions, concerns, or run into any trouble
updating please don't hesitate to send a message to the mailing list or
stop by our IRC channel.
Best,
The yt development team
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Received on 2016-07-25 23:48:28