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 285 articles )

Showing results 221 to 240 of 285.
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  1. Sarra, Scott A.: Regularized symmetric positive definite matrix factorizations for linear systems arising from RBF interpolation and differentiation (2014)
  2. Sicsic, Paul; Marigo, Jean-Jacques; Maurini, Corrado: Initiation of a periodic array of cracks in the thermal shock problem: A gradient damage modeling (2014)
  3. Temple Lang, Duncan: Enhancing \textttRwith advanced compilation tools and methods (2014)
  4. Wang, Qiqi; Hu, Rui; Blonigan, Patrick: Least squares shadowing sensitivity analysis of chaotic limit cycle oscillations (2014)
  5. Alwan, Aravind; Aluru, N. R.: Improved statistical models for limited datasets in uncertainty quantification using stochastic collocation (2013)
  6. Barra, L. P. S.; Telles, J. C. F.: A geometric inverse problem identification procedure for detection of cavities (2013)
  7. Brian P. Kent, Alessandro Rinaldo, Timothy Verstynen: DeBaCl: A Python Package for Interactive DEnsity-BAsed CLustering (2013) arXiv
  8. Daniel Müllner: fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python (2013) not zbMATH
  9. de la Hoz, Francisco; Vadillo, Fernando: The solution of two-dimensional advection-diffusion equations via operational matrices (2013)
  10. Demšar, Janez; Curk, Tomaž; Erjavec, Aleš; Gorup, Črt; Hočevar, Tomaž; Milutinovič, Mitar; Možina, Martin; Polajnar, Matija; Toplak, Marko; Starič, Anže; Štajdohar, Miha; Umek, Lan; Žagar, Lan; Žbontar, Jure; Žitnik, Marinka; Zupan, Blaž: Orange: data mining toolbox in Python (2013)
  11. Dimitrov, Nedialko B.; Moffett, Alexander; Morton, David P.; Sarkar, Sahotra: Selecting malaria interventions: a top-down approach (2013)
  12. Downey, Allen B.: Think Bayes. Bayesian statistics in Python (2013)
  13. Eckstein, Jonathan; Silva, Paulo J. S.: A practical relative error criterion for augmented Lagrangians (2013)
  14. Emeneker, Wesley; Apon, Amy: On modeling contention for shared caches in multi-core processors with techniques from ecology (2013) ioport
  15. Finch, Craig; Clarke, Thomas; Hickman, James J.: A continuum hard-sphere model of protein adsorption (2013)
  16. Fischer, Christian; Fritz, Karl-Peter; Eberhard, Peter; Kück, Heinz: Investigation and design of an impact actuated micro shift valve (2013) ioport
  17. Lindblad, Thomas; Kinser, Jason M.: Image processing using pulse-coupled neural networks. Aplications in Python (2013)
  18. Merrison-Hort, Robert; Yousif, Nada; Njap, Felix; Hofmann, Ulrich G.; Burylko, Oleksandr; Borisyuk, Roman: An interactive channel model of the basal ganglia: bifurcation analysis under healthy and Parkinsonian conditions (2013)
  19. Miha Štajdohar; Janez Demšar: Interactive Network Exploration with Orange (2013) not zbMATH
  20. Rauch-Wojciechowski, Stefan; Rutstam, Nils: Dynamics of the Tippe Top-properties of numerical solutions versus the dynamical equations (2013)

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