Person:

Ellis, Jon Robert

We have assembled a tiled array ((220 cm^2)) of fine pixel (0.6 mm) imaging CZT detectors for a balloon borne widefield hard X-ray telescope, ProtoEXIST2. ProtoEXIST2 is a prototype experiment for a next generation hard X-ray imager MlRAX-HXI on board Lattes, a spacecraft from the Agencia Espacial Brasilieira. MlRAX will survey the 5 to 200 keV sky of Galactic bulge, adjoining southern Galactic plane and the extragalactic sky with 6' angular resolution. This survey will open a vast discovery space in timing studies of accretion neutron stars and black holes. The ProtoEXIST2 CZT detector plane consists of 64 of 5 mm thick 2cm × 2cm CZT crystals tiled with a minimal gap. MIRAX will consist of 4 such detector planes, each of which will be imaged with its own coded-aperture mask. We present the packaging architecture and assembly procedure of the ProtoEXIST2 detector. On 2012, Oct 10, we conducted a successful high altitude balloon experiment of the ProtoEXISTl and 2 telescopes, which demonstrates their technology readiness for space application. Both telescopes performed flawlessly during the flight as well as on the ground. We report the results of pre-flight ground calibration and the preliminary results for the detector performance in the balloon flight.

PyBio is an easy-to-install, open-source library for working with bioinformatics data in Python, designed to encourage interactive data exploration and scripting.

The Pybio API is designed to be explorable in an IPython session or in the Jupyter Notebook. The modules are laid out with hierarchical structure. All interface classes, functions, and modules are documented. Function hiding and module interface declarations are used to present users with a simple, powerful interface.

PyBio's modules are integrated with each other to encourage smooth workflows and simple, direct code. The same class abstractions are used throughout the library. The output of one function can often be used as the input for another. Opinionated, higher-level APIs abstract away complexity, lower the barrier to entry, and encourage expressive, functional code.

Bioinformatics data from NCBI's Entrez service is available from within Python through the pybio.entrez module. The module parses sequences directly from Entrez into PyBio's sequence representation, so acquiring and using new sequence data is fast and seamless.

PyBio is built for speed; Numpy and Cython are used extensively to achieve fast execution speeds not normally associated with Python, while maintaining Python's simplicity and clarity. Execution speed is critical for productive interactive data science. Performant tools written mostly in Python make bioinformatics code more accessible to less advanced programmers, fostering a closer connection between developers and biologists.

PyBio is a tool for bioinformatics developers. Classes like Sequence and Alignment, and modules like pybio.parse, provide a shared environment for new code, reducing the need to write parsers and data abstraction classes, and making it easier to write code that interoperates with existing code. PyBio could potentially provide a platform for new useful bioinformatics algorithms and implementations to become quickly available to the community.

PyBio has the potential to become a powerful tool for bioinformatics, encouraging a data science approach to bioinformatics data and stimulating innovation in the implementations of bioinformatics algorithms.