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 146 articles )

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  1. Minjie Zhu, Frank McKenna, Michael H. Scott: OpenSeesPy: Python library for the OpenSees finite element framework (2018)
  2. Al-Hinai, Omar; Wheeler, Mary F.; Yotov, Ivan: A generalized mimetic finite difference method and two-point flux schemes over Voronoi diagrams (2017)
  3. Benjamin Guedj, Bhargav Srinivasa Desikan: Pycobra: A Python Toolbox for Ensemble Learning and Visualisation (2017) arXiv
  4. Boyle, Michael: The integration of angular velocity (2017)
  5. Bryan W. Weber, Chih-Jen Sung: UConnRCMPy: Python-based data analysis for rapid compression machines (2017) arXiv
  6. Budanur, Nazmi Burak; Cvitanović, Predrag: Unstable manifolds of relative periodic orbits in the symmetry-reduced state space of the Kuramoto-Sivashinsky system (2017)
  7. 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
  8. 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)
  9. Chapman, Harrison: Asymptotic laws for random knot diagrams (2017)
  10. 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)
  11. Douglas De Rizzo Meneghetti, Plinio Thomaz Aquino Junior: Computerized Adaptive Testing Simulation Through the Package catsim (2017) arXiv
  12. E. Bachelet, M. Norbury, V. Bozza, R. Street: pyLIMA : an open source package for microlensing modeling. I. presentation of the software and analysis on single lens models (2017) arXiv
  13. Giraldi, Loïc; Le Ma^ıtre, Olivier P.; Mandli, Kyle T.; Dawson, Clint N.; Hoteit, Ibrahim; Knio, Omar M.: Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate (2017)
  14. Gordon, Steven I.; Guilfoos, Brian: Introduction to modeling and simulation with MATLAB and Python (2017)
  15. Jackson, Haran: A fast numerical scheme for the Godunov-Peshkov-Romenski model of continuum mechanics (2017)
  16. Jerker Nordh: pyParticleEst: A Python Framework for Particle-Based Estimation Methods (2017)
  17. Josey, C.; Forget, B.; Smith, K.: High order methods for the integration of the Bateman equations and other problems of the form of $y^\prime=F(y,t)y$ (2017)
  18. Jyh-Miin Lin: Python Non-Uniform Fast Fourier Transform (PyNUFFT): multi-dimensional non-Cartesian image reconstruction package for heterogeneous platforms and applications to MRI (2017) arXiv
  19. Kirby, Robert C.: Fast inversion of the simplicial Bernstein mass matrix (2017)
  20. Lema^ıtre, Guillaume; Nogueira, Fernando; Aridas, Christos K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning (2017)

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