Software projects

crystals is a fully-tested Python package containing data structures representing abstract crystals. Parsing from structure files (e.g. CIF, PDB) is made easy, as is symmetry-determination through the SPGLIB package.

Here’s a quick example:

>>> from crystals import Crystal
>>>
>>> vo2 = Crystal.from_database('vo2-m1')
>>> print(vo2)	   # Short string representation
< Crystal object with following unit cell:
    Atom O  @ (0.90, 0.79, 0.80)
    Atom O  @ (0.90, 0.71, 0.30)
    Atom O  @ (0.61, 0.31, 0.71)
    Atom O  @ (0.39, 0.69, 0.29)
    Atom O  @ (0.61, 0.19, 0.21)
    Atom O  @ (0.10, 0.29, 0.70)
    Atom O  @ (0.10, 0.21, 0.20)
    Atom O  @ (0.39, 0.81, 0.79)
    Atom V  @ (0.76, 0.03, 0.97)
    Atom V  @ (0.76, 0.48, 0.47)
    ... omitting 2 atoms ...
Lattice parameters:
    a=5.743Å, b=4.517Å, c=5.375Å
    α=90.000°, β=122.600°, γ=90.000°
Chemical composition:
    O: 66.667%
    V: 33.333%
Source:
    (...omitted...)\crystals\cifs\vo2-m1.cif >
>>>
>>> print(vo2.symmetry())
{'international_symbol': 'P2_1/c', 
'hall_symbol': '-P 2ybc', 
'hm_symbol': 'P121/c1',
'international_number': 14, 
'hall_number': 81, 
'international_full': 'P 1 2_1/c 1', 
'pointgroup': 'C2h'}

To install from PyPI:

> python -m pip install crystals

For Anaconda users, crystals is also available on the conda-forge channel:

> conda install -c conda-forge crystals

Ultrafast electron diffractometer generates huge amounts of data in the forms of image stacks. While processing this data is generally straightforward, the sheer size of datasets is always a problem. Even once the data is combined into a single time-series of images, we are still looking at multiple gigabytes of data.

Iris allows us to interactively look at this data by slicing diffraction patterns (and powder patterns) through time. Making use of our baseline-removal routine based on the dual-tree complex wavelet transform, we can look at publication-quality data minutes after data collection is complete. While each new experiment ultimately requires different tools, our investigations always start with Iris.

The latest version of Iris includes a plug-in manager. You can write your own plug-in to interact with raw, unprocessed ultrafast electron diffraction data and explore it.

To install from PyPI:

> python -m pip install iris-ued

For Anaconda users, iris-ued is also available on the conda-forge channel:

> conda install -c conda-forge iris-ued

npstreams is an open-source Python package for streaming NumPy array operations. The goal is to provide tested routines that operate on streams of arrays instead of dense arrays.

Streaming reduction operations (sums, averages, etc.) can be implemented in constant memory, which in turns allows for easy parallelization. Some routines in npstreams are parallelized in this way. In our experience, this approach has resulted in huge speedups when working with images; the images are read one-by-one from disk and combined/processed in a streaming fashion, in parallel.

To install from PyPI:

> python -m pip install npstreams

For Anaconda users, npstreams is also available on the conda-forge channel:

> conda install -c conda-forge npstreams

scikit-ued is a fully-tested Python package containing routines and algorithms related to (ultrafast) electron diffraction. The package aims to provide software to deal with simulation, structure manipulation, image-analysis, baseline-determination, and more.

To install from PyPI:

> python -m pip install scikit-ued

For Anaconda users, scikit-ued is also available on the conda-forge channel:

> conda install -c conda-forge scikit-ued

Beam is a type-safe Haskell interface to relational databases, such as PostgreSQL and SQLite.

Cloud Haskell is a framework based on the research article Toward Haskell in the Cloud. It allows to build distributed systems using the Actor model, popularized by Erlang, while taking advantage of Haskell’s type-safety.

hakyll-images is suite of utilities to deal with images in the context of Hakyll.

hakyll-images, at this time, allows to compress JPEG images by re-encoding them at a lower quality. Further features are planned, namely image-resizing and image-scaling.

Series implemented in the javelin package are a hybrid between arrays and associative maps.

Like containers’s Map, Series support efficient:

• random access by key ( $\mathcal{O}(\log n)$ );
• slice by key ( $\mathcal{O}(\log n)$ );

Like vectors’s Vector, Series support efficient:

• random access by integer index ( $\mathcal{O}(1)$ );
• slice by integer index ( $\mathcal{O}(1)$ );
• numerical operations as fast as contiguous arrays;

pandoc-plot is a Pandoc filter that allows to keep documents and figures always in sync. Write plotting code to make a figure in your Markdown , and pandoc-plot will generate the figure for you and embed it in the document. This way, your documents and figures are always up-to-date with each other.

pandoc-plot supports many plotting toolkits, including matplotlib, MATLAB, GNU Octave, ggplot2, Julia’s Plots.jl, and many more! See the website for examples and documentation.