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 492 articles )

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  1. Chaudhry, Jehanzeb H.; Collins, J. B.: \textitAposteriori error estimation for the spectral deferred correction method (2021)
  2. Karban, Pavel; Pánek, David; Orosz, Tamás; Petrášová, Iveta; Doležel, Ivo: FEM based robust design optimization with Agros and Ārtap (2021)
  3. Aguirre-Mesa, Andres M.; Garcia, Manuel J.; Millwater, Harry: MultiZ: a library for computation of high-order derivatives using multicomplex or multidual numbers (2020)
  4. Alain Jungo, Olivier Scheidegger, Mauricio Reyes, Fabian Balsiger: pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis (2020) arXiv
  5. Allen, Henry R.; Ptashnyk, Mariya: Mathematical modelling of auxin transport in plant tissues: flux meets signalling and growth (2020)
  6. Andò, Alessia; Breda, Dimitri; Scarabel, Francesca: Numerical continuation and delay equations: a novel approach for complex models of structured populations (2020)
  7. Andrew R. McCluskey; Tim Snow: uravu: Making Bayesian modelling easy(er) (2020) not zbMATH
  8. Andrieux, Stephane; Baranger, Thouraya N.: Nonlinear Cauchy problem and identification in contact mechanics: a solving method based on Bregman-gap (2020)
  9. Archis S. Joglekar; Matthew C. Levy: VlaPy: A Python package for Eulerian Vlasov-Poisson-Fokker-Planck Simulations (2020) not zbMATH
  10. Arora, Rajat; Zhang, Xiaohan; Acharya, Amit: Finite element approximation of finite deformation dislocation mechanics (2020)
  11. Baines, W.; Kuchment, P.; Ragusa, J.: Deep learning for 2D passive source detection in presence of complex cargo (2020)
  12. Balm, Floris; Krikun, Alexander; Romero-Bermúdez, Aurelio; Schalm, Koenraad; Zaanen, Jan: Isolated zeros destroy Fermi surface in holographic models with a lattice (2020)
  13. Belton, Robin Lynne; Fasy, Brittany Terese; Mertz, Rostik; Micka, Samuel; Millman, David L.; Salinas, Daniel; Schenfisch, Anna; Schupbach, Jordan; Williams, Lucia: Reconstructing embedded graphs from persistence diagrams (2020)
  14. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  15. Betcke, Timo; Scroggs, Matthew W.; Śmigaj, Wojciech: Product algebras for Galerkin discretisations of boundary integral operators and their applications (2020)
  16. Burt, David R.; Rasmussen, Carl Edward; van der Wilk, Mark: Convergence of sparse variational inference in Gaussian processes regression (2020)
  17. Canlı, Özge; Günel, Serkan: Can we detect clusters of chaotic dynamical networks via causation entropy? (2020)
  18. Challa, Aditya; Danda, Sravan; Sagar, B. S. Daya; Najman, Laurent: Power spectral clustering (2020)
  19. Cohen, William; Yang, Fan; Mazaitis, Kathryn Rivard: TensorLog: a probabilistic database implemented using deep-learning infrastructure (2020)
  20. de Amorim, Renato Cordeiro; Makarenkov, Vladimir; Mirkin, Boris: Core clustering as a tool for tackling noise in cluster labels (2020)

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