IPython: A System for Interactive Scientific Computing

By Fernando Pérez, Brian E. Granger. 2007

2,904 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture, Evaluation & Benchmarks, Numerical Methods & Scientific Computing

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Citations in the corpus

Array programming with NumPy (2020, background): “The combination of NumPy, SciPy, and Matplotlib, together with an advanced interactive environment like IPython [21], or Jupyter [22], provides a solid foundation for array programming in Python.”
Behaviour Suite for Reinforcement Learning (2019, methodology): “Since loading the environment via bsuite handles the logging automatically, any agent interacting with that environment will generate the data required for required for analysis through the Jupyter notebook we provide (P´erez & Granger, 2007).”
Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? (2019, neutral): “Analysis was conducted in Jupyter Notebooks [30] using the IPython [45] kernels.”
SymPy: Symbolic computing in Python (2017, methodology): “However, SymPy exposes a rich interactive display system, and supports registering printers with Jupyter [30] frontends, including the Notebook and Qt Console, which will render SymPy expressions using MathJax [9] or L A TEX.”
Jupyter Notebooks - a publishing format for reproducible computational workflows (2016, background): “Jupyter grew out of the IPython project (Pérez & Granger, 2007), which initially provided this interface only for the Python language.”
The second data release of the INT Photometric Hα Survey of the Northern Galactic Plane (IPHAS DR2) (2014, methodology): “Preparation of the catalogue was eased greatly by a number of software packages, including the POSTGRESQL data base software, the TOPCAT and STILTS packages (Taylor 2005, 2006), and the PYTHON modules ASTROPY (Astropy Collaboration et al. 2013), NUMPY and SCIPY (Oliphant 2007), MATPLOTLIB (Hunter 20”
SOK: (State of) The Art of War: Offensive Techniques in Binary Analysis (2016, background): “angr is almost completely implemented in Python, with a concise, simple API that is easily usable from the IPython interactive shell [44].”
DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia (2017, methodology): “Additionally, all of the DifferentialEquations.jl functions are thoroughly tested and documented with the Jupyter notebook system [19], allowing for reproducible exploration.”
Mayavi: 3D Visualization of Scientific Data (2010, methodology): “In this article, we present an overview of the various features of Mayavi, we then provide insight on the design and engineering decisions made in implementing Mayavi, and finally discuss a few novel applications.”