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 271 articles )

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  1. C. M. Biwer; Collin D. Capano; Soumi De; Miriam Cabero; Duncan A. Brown; Alexander H. Nitz; V. Raymond: PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals (2018) arXiv
  2. Collin J. Wilkinson, Yihong Z. Mauro, John C. Mauro: RelaxPy: Python code for modeling of glass relaxation behavior (2018) not zbMATH
  3. Czuppon, Peter; Gokhale, Chaitanya S.: Disentangling eco-evolutionary effects on trait fixation (2018)
  4. Czuppon, Peter; Traulsen, Arne: Fixation probabilities in populations under demographic fluctuations (2018)
  5. Daniel M. Faes: Use of Python programming language in astronomy and science (2018) arXiv
  6. Dechristé, Guillaume; Fehrenbach, Jérôme; Griseti, Elena; Lobjois, Valérie; Poignard, Clair: Viscoelastic modeling of the fusion of multicellular tumor spheroids in growth phase (2018)
  7. Edward Higson: dyPolyChord: dynamic nested sampling with PolyChord (2018) not zbMATH
  8. Endres, Stefan C.; Sandrock, Carl; Focke, Walter W.: A simplicial homology algorithm for Lipschitz optimisation (2018)
  9. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  10. Giordano, Ryan; Broderick, Tamara; Jordan, Michael I.: Covariances, robustness, and variational Bayes (2018)
  11. Gubbiotti, G.; Latini, D.: A multiple scales approach to maximal superintegrability (2018)
  12. Guedj, Benjamin; Desikan, Bhargav Srinivasa: Pycobra: a Python toolbox for ensemble learning and visualisation (2018)
  13. Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Einevoll, Gaute T.; Plesser, Hans E.: Firing-rate models for neurons with a broad repertoire of spiking behaviors (2018)
  14. Héléna Alexandra Gaspar: ugtm: A Python Package for Data Modeling and Visualization Using Generative Topographic Mapping (2018) not zbMATH
  15. Himpe, Christian; Leibner, Tobias; Rave, Stephan: Hierarchical approximate proper orthogonal decomposition (2018)
  16. Ignatiev, Alexey; Morgado, Antonio; Marques-Silva, Joao: PySAT: A Python toolkit for prototyping with SAT oracles (2018)
  17. Johannes Hauschild, Frank Pollmann: Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy) (2018) arXiv
  18. K.T. Schütt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, K.-R. Müller: SchNetPack: A Deep Learning Toolbox For Atomistic Systems (2018) arXiv
  19. Lakatos, Dóra; Somfai, Ellák; Méhes, Elod; Czirók, András: Soluble VEGFR1 signaling guides vascular patterns into dense branching morphologies (2018)
  20. Liew, A.; Pagonakis, D.; Van Mele, T.; Block, P.: Load-path optimisation of funicular networks (2018)

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