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 492 articles )

Showing results 21 to 40 of 492.
Sorted by year (citations)

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  1. Drzisga, Daniel; Keith, Brendan; Wohlmuth, Barbara: The surrogate matrix methodology: low-cost assembly for isogeometric analysis (2020)
  2. Duncan N. Johnstone, Ben H. Martineau, Phillip Crout, Paul A. Midgley, Alexander S. Eggeman: Density-based clustering of crystal orientations and misorientations and the orix python library (2020) arXiv
  3. Dutta, Supriyo: Constructing non-isomorphic signless Laplacian cospectral graphs (2020)
  4. Dutta, Supriyo; Adhikari, Bibhas: Construction of cospectral graphs (2020)
  5. Emerson Boeira; Diego Eckhard: pyvrft: A Python package for the Virtual Reference Feedback Tuning, a direct data-driven control method (2020) not zbMATH
  6. Emmanuel Jordy Menvouta, Sven Serneels, Tim Verdonck: direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods (2020) arXiv
  7. Faouzi, Johann; Janati, Hicham: pyts: a Python package for time series classification (2020)
  8. Fernando Pérez-García, Rachel Sparks, Sebastien Ourselin: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020) arXiv
  9. Fjordholm, U. S.; Lye, K.; Mishra, S.; Weber, F.: Statistical solutions of hyperbolic systems of conservation laws: numerical approximation (2020)
  10. Francesco Biscani; Dario Izzo: A parallel global multiobjective framework for optimization: pagmo (2020) not zbMATH
  11. Francesco Witte; Ilja Tuschy: TESPy: Thermal Engineering Systems in Python (2020) not zbMATH
  12. Frison, Gianluca; Sartor, Tommaso; Zanelli, Andrea; Diehl, Moritz: The BLAS API of BLASFEO: optimizing performance for small matrices (2020)
  13. Gavrilov, Serge N.; Krivtsov, Anton M.: Steady-state kinetic temperature distribution in a two-dimensional square harmonic scalar lattice lying in a viscous environment and subjected to a point heat source (2020)
  14. Golman, Boris; Andreev, Vsevolod V.; Skrzypacz, Piotr: Dead-core solutions for slightly non-isothermal diffusion-reaction problems with power-law kinetics (2020)
  15. Gómez-Larrañaga, J. C.; González-Acuña, F.; Heil, Wolfgang: Models of simply-connected trivalent 2-dimensional stratifolds (2020)
  16. González-González, José M.; Vázquez-Méndez, Miguel E.; Diéguez-Aranda, Ulises: A note on the regularity of a new metric for measuring even-flow in forest planning (2020)
  17. Guioth, Jules; Jack, Robert L.: Dynamical phase transitions for the activity biased Ising model in a magnetic field (2020)
  18. Hamedmohseni, Bardia; Rahmati, Zahed; Mondal, Debajyoti: Simplified emanation graphs: a sparse plane spanner with Steiner points (2020)
  19. Han, Weimin; Jureczka, Michal; Ochal, Anna: Numerical studies of a hemivariational inequality for a viscoelastic contact problem with damage (2020)
  20. Heider, Yousef; Wang, Kun; Sun, WaiChing: (\mathrmSO(3))-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials (2020)

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