Python: An Ecosystem for Scientific Computing

By Fernando Pérez, Brian E. Granger, John D. Hunter. 2011

207 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems and Management

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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Citations in the corpus

Array programming with NumPy (2020, neutral): “[5] F.”
FireWorks: a dynamic workflow system designed for high‐throughput applications (2015, methodology): “FireWorks is implemented in the Python language, which is widely recognized as one of the major tools in modern scientific computing, with extensive support for scientific computing in almost every domain.[7]–[9] As a high-level language, Python allows scientists (whose primary background is not in ”
Teaching Computational Reproducibility for Neuroimaging (2018, methodology): “Rather than focusing on a specific neuroimaging analysis package, we taught scientific coding with Python and its associated scientific libraries [7, 12], and then showed the students how to run standard statistical procedures on imaging data using these tools [8].”
Developing Open-Source Scientific Practice * (2018, unknown): “To illustrate our point, we briefly describe the scientific Python ecosystem [25, 22, 29] and introduce a few core projects, which serve as examples throughout the chapter.”
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python (2011, methodology): “It has extensive scientific computing and visualization support through packages such as SciPy, NumPy, Matplotlib, and Mayavi (Pérez et al., 2010; Millman and Aivazis, 2011).”
SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python (2019, background): “The follow-up March/April 2011 Python for Scientists and Engineers special issue 38 focused more on the core parts of the scientific Python eco-system 39 including NumPy 2 , Cython 40 and Mayavi 41 .”
pyGIMLi: An open-source library for modelling and inversion in geophysics (2017, background): “…nature, which therefore makes it widely used in the (geo)scienti ﬁ c community (e.g., Guyer et al., 2009; Logg and Wells, 2010; P (cid:1) erez et al., 2011; Wellmann et al., 2012; Uieda et al., 2013; Cockett et al., 2015; Weigand and Kemna, 2016; Hector and Hinderer, 2016; Schaa et…”
Scripting MODFLOW Model Development Using Python and FloPy (2016, background): “ularity in science and engineering (Pérez et al. 2011);”