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 589 articles )

Showing results 321 to 340 of 589.
Sorted by year (citations)

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  1. Gubbiotti, G.; Latini, D.: A multiple scales approach to maximal superintegrability (2018)
  2. Guedj, Benjamin; Desikan, Bhargav Srinivasa: Pycobra: a Python toolbox for ensemble learning and visualisation (2018)
  3. 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)
  4. Héléna Alexandra Gaspar: ugtm: A Python Package for Data Modeling and Visualization Using Generative Topographic Mapping (2018) not zbMATH
  5. Himpe, Christian; Leibner, Tobias; Rave, Stephan: Hierarchical approximate proper orthogonal decomposition (2018)
  6. Hubara, Itay; Courbariaux, Matthieu; Soudry, Daniel; El-Yaniv, Ran; Bengio, Yoshua: Quantized neural networks: training neural networks with low precision weights and activations (2018)
  7. Hu, Zhan-Chao; Zhang, Xin-Rong: Onset of convection in a near-critical binary fluid mixture driven by concentration gradient (2018)
  8. Hyman, Jeffrey D.; Hagberg, Aric; Osthus, Dave; Srinivasan, Shriram; Viswanathan, Hari; Srinivasan, Gowri: Identifying backbones in three-dimensional discrete fracture networks: a bipartite graph-based approach (2018)
  9. Ignatiev, Alexey; Morgado, Antonio; Marques-Silva, Joao: PySAT: A Python toolkit for prototyping with SAT oracles (2018)
  10. Ish-Horowicz, Jonathan; Oksanen, Lauri: Fully discrete finite element data assimilation method for the heat equation (2018)
  11. Izaac, Joshua; Wang, Jingbo: Computational quantum mechanics (2018)
  12. Johannes Hauschild, Frank Pollmann: Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy) (2018) arXiv
  13. Johnnie Gray: quimb: A python package for quantum information and many-body calculations (2018) not zbMATH
  14. Kirby, Robert C.: A general approach to transforming finite elements (2018)
  15. K.T. Schütt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, K.-R. Müller: SchNetPack: A Deep Learning Toolbox For Atomistic Systems (2018) arXiv
  16. Kulshreshtha, K.; Narayanan, S. H. K.; Bessac, J.; MacIntyre, K.: Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C (2018)
  17. Lakatos, Dóra; Somfai, Ellák; Méhes, Elod; Czirók, András: Soluble VEGFR1 signaling guides vascular patterns into dense branching morphologies (2018)
  18. Liew, A.; Pagonakis, D.; Van Mele, T.; Block, P.: Load-path optimisation of funicular networks (2018)
  19. Lionel Roubeyrie; Sébastien Celles: Windrose: A Python Matplotlib, Numpy library to manage wind and pollution data, draw windrose (2018) not zbMATH
  20. Loiseau, Jean-Christophe; Brunton, Steven L.: Constrained sparse Galerkin regression (2018)

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