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 103 articles )

Showing results 1 to 20 of 103.
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  1. Al-Hinai, Omar; Wheeler, Mary F.; Yotov, Ivan: A generalized mimetic finite difference method and two-point flux schemes over Voronoi diagrams (2017)
  2. Kirby, Robert C.: Fast inversion of the simplicial Bernstein mass matrix (2017)
  3. Myers, A.; Colella, P.; Straalen, B.van: A 4th-order particle-in-cell method with phase-space remapping for the Vlasov-Poisson equation (2017)
  4. Piotr Szymanski: A scikit-based Python environment for performing multi-label classification (2017) arXiv
  5. 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
  6. Arthur, Robert; Dorey, Patrick; Parini, Robert: Breaking integrability at the boundary: the sine-Gordon model with Robin boundary conditions (2016)
  7. 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)
  8. Davis, Jon H.: Methods of applied mathematics with a software overview (2016)
  9. Doran, Gary; Ray, Soumya: Multiple-instance learning from distributions (2016)
  10. Elfverson, Daniel; Hellman, Fredrik; Målqvist, Axel: A multilevel Monte Carlo method for computing failure probabilities (2016)
  11. Ferreira, Vanderley jun.; Gazzola, Filippo; Moreira dos Santos, Ederson: Instability of modes in a partially hinged rectangular plate (2016)
  12. Garrido, José M.: Introduction to computational models with Python (2016)
  13. Gorodetsky, Alex; Marzouk, Youssef: Mercer kernels and integrated variance experimental design: connections between Gaussian process regression and polynomial approximation (2016)
  14. Kraus, Michael; Tassi, Emanuele; Grasso, Daniela: Variational integrators for reduced magnetohydrodynamics (2016)
  15. Linge, Svein; Langtangen, Hans Petter: Programming for computations -- Python. A gentle introduction to numerical simulations with Python (2016)
  16. Milk, René; Rave, Stephan; Schindler, Felix: PyMOR -- generic algorithms and interfaces for model order reduction (2016)
  17. Mudunuru, M.K.; Nakshatrala, K.B.: On enforcing maximum principles and achieving element-wise species balance for advection-diffusion-reaction equations under the finite element method (2016)
  18. Navas-Palencia, Guillermo; Arratia, Argimiro: On the computation of confluent hypergeometric functions for large imaginary part of parameters $b$ and $z$ (2016)
  19. Pahikkala, Tapio; Airola, Antti: RLScore: regularized least-squares learners (2016)
  20. Pearce, David J.: A space-efficient algorithm for finding strongly connected components (2016)

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