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 173 articles )

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  1. Ansmann, Gerrit: Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE (2018)
  2. Bahsoun, Wael; Galatolo, Stefano; Nisoli, Isaia; Niu, Xiaolong: A rigorous computational approach to linear response (2018)
  3. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  4. Liew, A.; Pagonakis, D.; Van Mele, T.; Block, P.: Load-path optimisation of funicular networks (2018)
  5. McRae, Andrew T. T.; Cotter, Colin J.; Budd, Chris J.: Optimal-transport -- based mesh adaptivity on the plane and sphere using finite elements (2018)
  6. Minjie Zhu, Frank McKenna, Michael H. Scott: OpenSeesPy: Python library for the OpenSees finite element framework (2018)
  7. Phillip Weinberg, Marin Bukov: QuSpin: a Python Package for Dynamics and Exact Diagonalisation of Quantum Many Body Systems. Part II: bosons, fermions and higher spins (2018) arXiv
  8. Sankaranarayanan, Sriram; Feijoo, Felipe; Siddiqui, Sauleh: Sensitivity and covariance in stochastic complementarity problems with an application to north American natural gas markets (2018)
  9. Al-Hinai, Omar; Wheeler, Mary F.; Yotov, Ivan: A generalized mimetic finite difference method and two-point flux schemes over Voronoi diagrams (2017)
  10. Allen, Henry R.; Ptashnyk, Mariya: Mathematical modelling and analysis of the brassinosteroid and gibberellin signalling pathways and their interactions (2017)
  11. Benjamin Guedj, Bhargav Srinivasa Desikan: Pycobra: A Python Toolbox for Ensemble Learning and Visualisation (2017) arXiv
  12. Boyle, Michael: The integration of angular velocity (2017)
  13. Bryan W. Weber, Chih-Jen Sung: UConnRCMPy: Python-based data analysis for rapid compression machines (2017) arXiv
  14. Budanur, Nazmi Burak; Cvitanović, Predrag: Unstable manifolds of relative periodic orbits in the symmetry-reduced state space of the Kuramoto-Sivashinsky system (2017)
  15. Carmen Moret-Tatay, Daniel Gamermann, Esperanza Navarro-Pardo, Pedro Fernandez de Cordoba: ExGUtils: A python package for statistical analysis with the ex-gaussian probability density (2017) arXiv
  16. Carr, Hamish (ed.); Garth, Christoph (ed.); Weinkauf, Tino (ed.): Topological methods in data analysis and visualization IV. Theory, algorithms, and applications. Selected papers based on the presentations at the TopoInVis workshop, Annweiler, Germany, 2015 (2017)
  17. Chapman, Harrison: Asymptotic laws for random knot diagrams (2017)
  18. Chrétien, Stéphane; Darses, Sébastien; Guyeux, Christophe; Clarkson, Paul: On the pinning controllability of complex networks using perturbation theory of extreme singular values. Application to synchronisation in power grids (2017)
  19. Daniel Johnson, E. A. Huerta, Roland Haas: Python Open Source Waveform Extractor (POWER): An open source, Python package to monitor and post-process numerical relativity simulations (2017) arXiv
  20. DeBlasio, Dan; Kececioglu, John: Parameter advising for multiple sequence alignment (2017)

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