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   https://doi.org/10.1007/978-3-030-61146-0_23  

   Scully-Allison, Connor (November 2020, BIS 2020: Business Information Systems Workshops)

   null (Ed.)

   Current solutions for data quality control (QC) in the environmental sciences are locked within propriety platforms or reliant on specialized software. This can pose a problem for data users when attempting to integrate QC into their existing workflows. To address this limitation, we developed an embedded domain specific language (EDSL), Materia, that provides functions, data structures, and a fluent syntax for defining and executing quality control tests on data. Materia enables developers to more easily integrate QC into complex data pipelines and makes QC more accessible for students and citizen scientists. We evaluate Materia via two metrics: productivity and a quantitative performance analysis. Our productivity examples show how Materia can simplify complex descriptions of tests in Pandas and mirror natural language descriptions of common QC tests. We also demonstrate that Materia achieves satisfactory performance with over 200,000 floating-point values processed in under three seconds. 

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2. pystablemotifs: Python library for attractor identification and control in Boolean networks

   https://doi.org/10.1093/bioinformatics/btab825  

   Rozum, Jordan C.; Deritei, Dávid; Park, Kyu Hyong; Gómez Tejeda Zañudo, Jorge; Albert, Réka; Cowen, ed., Lenore (December 2021, Bioinformatics)

   Abstract Summarypystablemotifs is a Python 3 library for analyzing Boolean networks. Its non-heuristic and exhaustive attractor identification algorithm was previously presented in Rozum et al. (2021). Here, we illustrate its performance improvements over similar methods and discuss how it uses outputs of the attractor identification process to drive a system to one of its attractors from any initial state. We implement six attractor control algorithms, five of which are new in this work. By design, these algorithms can return different control strategies, allowing for synergistic use. We also give a brief overview of the other tools implemented in pystablemotifs. Availability and implementationThe source code is on GitHub at https://github.com/jcrozum/pystablemotifs/. Supplementary informationSupplementary data are available at Bioinformatics online. 

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3. CANA: A Python Package for Quantifying Control and Canalization in Boolean Networks

   https://doi.org/10.3389/fphys.2018.01046  

   Correia, Rion B.; Gates, Alexander J.; Wang, Xuan; Rocha, Luis M. (August 2018, Frontiers in Physiology)
4. PolyBench/Python: benchmarking Python environments with polyhedral optimizations

   https://doi.org/10.1145/3446804.3446842  

   Abella-González, Miguel Á.; Carollo-Fernández, Pedro; Pouchet, Louis-Noël; Rastello, Fabrice; Rodríguez, Gabriel (February 2021, CC 2021: 30th ACM SIGPLAN International Conference on Compiler Construction)

   null (Ed.)

   Python has become one of the most used and taught languages nowadays. Its expressiveness, cross-compatibility and ease of use have made it popular in areas as diverse as finance, bioinformatics or machine learning. However, Python programs are often significantly slower to execute than an equivalent native C implementation, especially for computation-intensive numerical kernels. This work presents PolyBench/Python, implementing the 30 kernels in PolyBench/C, one of the standard benchmark suites for polyhedral optimization, in Python. In addition to the benchmark kernels, a functional wrapper including mechanisms for performance measurement, testing, and execution configuration has been developed. The framework includes support for different ways to translate C-array codes into Python, offering insight into the tradeoffs of Python lists and NumPy arrays. The benchmark performance is thoroughly evaluated on different Python interpreters, and compared against its PolyBench/C counterpart to highlight the profitability (or lack thereof) of using Python for regular numerical codes. 

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5. NURBS-Python: An open-source object-oriented NURBS modeling framework in Python

   https://doi.org/10.1016/j.softx.2018.12.005  

   Bingol, Onur Rauf; Krishnamurthy, Adarsh (January 2019, SoftwareX)