Release 0.17.0
30 Jan 2018With the new year comes also a new release of MDAnalysis. This release fixes bugs, removes deprecated code, and adds support for new file formats. The biggest changes however aim at making MDAnalysis more sustainable in the long run. The release note lists all the changes in this version.
This release of MDAnalysis comes with a long awaited change: MDAnalysis now officially supports Python 3.4 and newer! We cannot be your excuse to stick with Python 2 anymore. Nevertheless, Python 2.7 remains supported for the foreseeable future. The port to Python 3 was a long process that started in 2015 and was completed by Tyler Reddy and Richard Gowers thanks to a grant awarded by NumFOCUS. To complete the port, the handling of the DCD format, used by, e.g., CHARMM and NAMD, had to be completely rewritten, a big task carried out by Tyler Reddy and Max Linke during the last year. So this release brings a fresh DCD reader and a new low level library (libdcd) to manipulate DCD files.
An other change to stay up to date was supported by Google Summer of Code. GSoC student Utkarsh Bansal ported our tests from nose to pytest. Sadly, nose is now deprecated by lack of maintainers. While we liked nose and are grateful to its developers, we had to move away from it. We chose pytest as a replacement as it has a vibrant community and many great features. Utkarsh not only made the tests run under the new framework but he also applied the pytest recommended idioms wherever possible. As a result, our code coverage increased to 91% and the tests run significantly faster.
We added support for three new trajectory and topology formats.
The TXYZ and ARC files from Tinker and the GSD format of HOOMD.
Learn more about these file formats in our documentation.
If instead your prefer Amber’s netcdf4 trajectories, you should experience a performance gain when reading such trajectories. Gromacs users may notice that the parser for the TPR format now exposes molecule names under the moltype attribute of atoms and residues, it also makes it easier to split atom groups by molecules.
It is also now possible to create Universes withot existing topology and coordinate files via the Universe.empty class method. This should allow more possibilities in creating input files for simulations from a Python interface.
import MDAnalysis as mda
# `empty` classmethod does not require filenames
u = mda.Universe.empty(100, trajectory=True)
# can add the required attributes to the new Universe
u.add_TopologyAttr('names')
u.add_TopologyAttr('masses')
# ... position atoms, assign names etc
# then finally export to any supported format
u.atoms.write('system.gro')
A new tool makes an entrance in the analysis module tanks to Zhiyi Wu. It is now possible to follow water bridges between two selections using the WaterBridgeAnalysis module. Read the documentation of the module to learn more about it.
With this release, we introduced some deprecations. In HydrogenBondAnalysis, the keyword detect_hydrogen="heuristic" is deprecated in favor of detect_hydrogen="distance". Indeed, the former may not find all the hydrogens, making the latter safer to use. In the result of the same analysis, the column “donor_id”x and “acceptor_idx”, that start counting at 1, are deprecated in favor of “donor_index” and “acceptor_index, respectively, that start counting at 0. Finally, throughout the code base, the use of the format keyword of timeseries is deprecated and renamed order, which reflects better the use of the keyword to set the order of output columns. We plan on removing the deprecated code with the version 1.0 of MDAnalysis. For the list of deprecations we removed in this version, consult the release note.
We also have various speed improvements in the whole library. To ensure that MDAnalysis keeps getting faster Tyler Reddy has worked on developing asv benchmarks. The benchmarks can be found in Benchmark repo.
You should also experience a performance gain of about 20 % when running GNM analyses. For performance we also started experimenting with implementing multi core analysis in PMDA. This release contains fixes to enable the use of PMDA.
Fifteen people contributed to this release, among which five were first time contributors. Thank you to all contributors and welcome to Nestor Wendt, Micaela Mata, Sören von Bülow, Ruggero Cortini and Jose Borreguuero.
Upgrade
You can upgrade with pip install --upgrade MDAnalysis. If you use the conda package manager run conda update -c conda-forge mdanalysis.