Python for Scientists and Engineers

By K. Jarrod Millman, M. Aivazis. 2011

855 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems and Management

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Citations in the corpus

Array programming with NumPy (2020, neutral): “[4] K.”
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].”
HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations (2013, background): “Over the past decade, Python has become a popular scripting language for scientific computing in general [12] and the molecular simulation community in particular.”
QuSpin: a Python Package for Dynamics and Exact Diagonalisation of Quantum Many Body Systems part I: spin chains (2016, methodology): “These packages provide reliable state-of-the-art tools for scientific computation as well as support from the Python community to regularly improve and update them [54, 55, 56, 24].”
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).”
OpenPNM: A Pore Network Modeling Package (2016, methodology): “OpenPNM was coded in Python, which is a powerful, easy to use, and free objectoriented programming (OOP) language that is being increasingly used for engineering calculations [35], [36].”
Parallel distributed computing using Python (2011, neutral): “For a throughout discussion about the role of Python in scientiﬁc computing and additional information about selected Python packages, see [5–9].”
Developing Open-Source Scientific Practice * (2018, neutral): “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.”
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 .”