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 341 to 360 of 589.
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  1. Lynch, Stephen: Dynamical systems with applications using Python (2018)
  2. Mai, Vinh Q.; Vo, Tuoi T.; Meere, Martin: Modelling hyaluronan degradation by Streptococcus pneumoniae hyaluronate lyase (2018)
  3. Maximilian Christ, Nils Braun, Julius Neuffer, Andreas W. Kempa-Liehr: Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh - A Python package) (2018) not zbMATH
  4. McRae, Andrew T. T.; Cotter, Colin J.; Budd, Chris J.: Optimal-transport -- based mesh adaptivity on the plane and sphere using finite elements (2018)
  5. Michel, T.; Fehrenbach, J.; Lobjois, V.; Laurent, J.; Gomes, A.; Colin, T.; Poignard, Clair: Mathematical modeling of the proliferation gradient in multicellular tumor spheroids (2018)
  6. Minjie Zhu, Frank McKenna, Michael H. Scott: OpenSeesPy: Python library for the OpenSees finite element framework (2018) not zbMATH
  7. Müller, Fabian; Schötzau, Dominik; Schwab, Christoph: Discontinuous Galerkin methods for acoustic wave propagation in polygons (2018)
  8. Obayashi, Ippei; Hiraoka, Yasuaki; Kimura, Masao: Persistence diagrams with linear machine learning models (2018)
  9. Park, Youngmin; Ermentrout, G. Bard: A multiple timescales approach to bridging spiking- and population-level dynamics (2018)
  10. 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
  11. Pitton, Giuseppe; Heltai, Luca: NURBS-SEM: a hybrid spectral element method on NURBS maps for the solution of elliptic PDEs on surfaces (2018)
  12. Robert Sare; George E. Hilley: Scarplet: A Python package for topographic template matching and diffusion dating (2018) not zbMATH
  13. Römer, Ulrich; Narayanamurthi, Mahesh; Sandu, Adrian: Solving parameter estimation problems with discrete adjoint exponential integrators (2018)
  14. Rost, Thomas; Deger, Moritz; Nawrot, Martin P.: Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick (2018)
  15. Rubio Scola, Ignacio; Besançon, Gildas; Georges, Didier: Optimizing Kalman optimal observer for state affine systems by input selection (2018)
  16. Ruprecht, Daniel: Wave propagation characteristics of Parareal (2018)
  17. Salman, Sinan; Alaswad, Suzan: Alleviating road network congestion: traffic pattern optimization using Markov chain traffic assignment (2018)
  18. Sanderson, Conrad; Curtin, Ryan: A user-friendly hybrid sparse matrix class in C++ (2018)
  19. Sankaranarayanan, Sriram; Feijoo, Felipe; Siddiqui, Sauleh: Sensitivity and covariance in stochastic complementarity problems with an application to north American natural gas markets (2018)
  20. Sanyal, Amartya; Kumar, Pawan; Kar, Purushottam; Chawla, Sanjay; Sebastiani, Fabrizio: Optimizing non-decomposable measures with deep networks (2018)

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