SciPy

SciPy (pronounced ”Sigh Pie”) is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world’s leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try!


References in zbMATH (referenced in 287 articles )

Showing results 21 to 40 of 287.
Sorted by year (citations)

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  1. Jonas Fassbender: libconform v0.1.0: a Python library for conformal prediction (2019) arXiv
  2. Krishna Naidoo: MiSTree: a Python package for constructing andanalysing Minimum Spanning Trees (2019) arXiv
  3. Leo C. Stein: qnm: A Python package for calculating Kerr quasinormal modes, separation constants, and spherical-spheroidal mixing coefficients (2019) arXiv
  4. Linge, Svein; Langtangen, Hans Petter: Programming for computations -- Python. A gentle introduction to numerical simulations with Python 3.6 (2019)
  5. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
  6. Matthieu Ancellin; Frédéric Dias: Capytaine: a Python-based linear potential flow solver (2019) not zbMATH
  7. McClarren, Ryan G.: Uncertainty quantification and predictive computational science. A foundation for physical scientists and engineers (2019)
  8. Michael E.Rose; John R.Kitchin: pybliometrics: Scriptable bibliometrics using a Python interface to Scopus (2019) not zbMATH
  9. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv
  10. Michael Slugocki , Allison B. Sekuler, Patrick Bennett: BayesFit: A tool for modeling psychophysical data using Bayesian inference (2019) not zbMATH
  11. Miyaji, Tomoyuki; Okamoto, Hisashi: Existence proof of unimodal solutions of the Proudman-Johnson equation via interval analysis (2019)
  12. M. J. Townson, O. J. D. Farley, G. Orban de Xivry, J. Osborn, A. P. Reeves: AOtools - a Python package for adaptive optics modelling and analysis (2019) arXiv
  13. O. Melchert, B. Roth, U. Morgner, A. Demircan: OptFROG - Analytic signal spectrograms with optimized time–frequency resolution (2019) not zbMATH
  14. Paul Walker, Ulrich Krohn, David Carty: ARBTools: A Tricubic Spline Interpolator for Three-Dimensional Scalar or Vector Fields (2019) not zbMATH
  15. Philipp S. Sommer; Dilan Rech; Manuel Chevalier; Basil A. S.Davis: straditize: Digitizing stratigraphic diagrams (2019) not zbMATH
  16. R.D. Martin, Q. Cai, T. Garrow, C. Kapahi: QExpy: A python-3 module to support undergraduate physics laboratories (2019) not zbMATH
  17. Steven G. Murray, Francis J. Poulin: hankel: A Python library for performing simple and accurate Hankel transformations (2019) arXiv
  18. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  19. T.G. Tranter, M.D.R. Kok, M. Lam, J.T. Gostick: pytrax: A simple and efficient random walk implementation for calculating the directional tortuosity of images (2019) not zbMATH
  20. Toccaceli, Paolo; Gammerman, Alexander: Combination of inductive Mondrian conformal predictors (2019)

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