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 91 articles )

Showing results 1 to 20 of 91.
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  1. Kirby, Robert C.: Fast inversion of the simplicial Bernstein mass matrix (2017)
  2. Piotr Szymanski: A scikit-based Python environment for performing multi-label classification (2017) arXiv
  3. Qiming Sun, Timothy C. Berkelbach, Nick S. Blunt, George H. Booth, Sheng Guo, Zhendong Li, Junzi Liu, James McClain, Sandeep Sharma, Sebastian Wouters, Garnet Kin-Lic Chan: The Python-based Simulations of Chemistry Framework (PySCF) (2017) arXiv
  4. Arthur, Robert; Dorey, Patrick; Parini, Robert: Breaking integrability at the boundary: the sine-Gordon model with Robin boundary conditions (2016)
  5. Blumentals, Alejandro; Brogliato, Bernard; Bertails-Descoubes, Florence: The contact problem in Lagrangian systems subject to bilateral and unilateral constraints, with or without sliding Coulomb’s friction: a tutorial (2016)
  6. Doran, Gary; Ray, Soumya: Multiple-instance learning from distributions (2016)
  7. Elfverson, Daniel; Hellman, Fredrik; Målqvist, Axel: A multilevel Monte Carlo method for computing failure probabilities (2016)
  8. Garrido, José M.: Introduction to computational models with Python (2016)
  9. Gorodetsky, Alex; Marzouk, Youssef: Mercer kernels and integrated variance experimental design: connections between Gaussian process regression and polynomial approximation (2016)
  10. Linge, Svein; Langtangen, Hans Petter: Programming for computations -- Python. A gentle introduction to numerical simulations with Python (2016)
  11. Milk, René; Rave, Stephan; Schindler, Felix: PyMOR -- generic algorithms and interfaces for model order reduction (2016)
  12. Navas-Palencia, Guillermo; Arratia, Argimiro: On the computation of confluent hypergeometric functions for large imaginary part of parameters $b$ and $z$ (2016)
  13. Pahikkala, Tapio; Airola, Antti: RLScore: regularized least-squares learners (2016)
  14. Pearce, David J.: A space-efficient algorithm for finding strongly connected components (2016)
  15. Pollock, Sara: Stabilized and inexact adaptive methods for capturing internal layers in quasilinear PDE (2016)
  16. Ramanantoanina, Andriamihaja; Hui, Cang: Formulating spread of species with habitat dependent growth and dispersal in heterogeneous landscapes (2016)
  17. Richardson, Casey L.; Younes, Laurent: Metamorphosis of images in reproducing kernel Hilbert spaces (2016)
  18. Rozada, Ignacio; Coombs, Daniel; Lima, Viviane D.: Conditions for eradicating hepatitis C in people who inject drugs: a fibrosis aware model of hepatitis C virus transmission (2016)
  19. Ruprecht, Daniel; Speck, Robert: Spectral deferred corrections with fast-wave slow-wave splitting (2016)
  20. Zhe, Sun; Micheletto, Ruggero: Noise influence on spike activation in a Hindmarsh-Rose small-world neural network (2016)

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