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 397 articles )

Showing results 61 to 80 of 397.
Sorted by year (citations)

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  1. Chua, Lynn; Kummer, Mario; Sturmfels, Bernd: Schottky algorithms: classical meets tropical (2019)
  2. Cimrman, Robert; Lukeš, Vladimír; Rohan, Eduard: Multiscale finite element calculations in Python using sfepy (2019)
  3. Clerx, M., Robinson, M., Lambert, B., Lei, C.L., Ghosh, S., Mirams, G.R. and Gavaghan, D.J.: Probabilistic Inference on Noisy Time Series (PINTS) (2019) not zbMATH
  4. Climaco, Joyce S.; Saa, Alberto: Optimal global synchronization of partially forced Kuramoto oscillators (2019)
  5. Cole, S., Donoghue, T., Gao, R., Voytek, B.: NeuroDSP: A package for neural digital signal processing (2019) not zbMATH
  6. Davide Micieli, Triestino Minniti, Giuseppe Gorini: NeuTomPy toolbox, a Python package for tomographic data processing and reconstruction (2019) not zbMATH
  7. D. Huppenkothen, M. Bachetti, A. L. Stevens, S. Migliari, P. Balm, O. Hammad, U. M. Khan, H. Mishra, H. Rashid, S. Sharma, R. V. Blanco, E. M. Ribeiro: Stingray: A Modern Python Library For Spectral Timing (2019) arXiv
  8. Dudziuk, Grzegorz; Lachowicz, Mirosław; Leszczyński, Henryk; Szymańska, Zuzanna: A simple model of collagen remodeling (2019)
  9. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  10. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  11. Frérot, Lucas; Bonnet, Marc; Molinari, Jean-François; Anciaux, Guillaume: A Fourier-accelerated volume integral method for elastoplastic contact (2019)
  12. Georgios Exarchakis, Jörg Bornschein, Abdul-Saboor Sheikh, Zhenwen Dai, Marc Henniges, Jakob Drefs, Jörg Lücke: ProSper - A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions (2019) arXiv
  13. Gevorkyan, Migran N.; Korolkova, Anna V.; Kulyabov, Dmitry S.; Lovetskiy, Konstantin P.: Statistically significant comparative performance testing of Julia and Fortran languages in case of Runge-Kutta methods (2019)
  14. Ghosh, Souvik; Loiseau, Jean-Christophe; Breugem, Wim-Paul; Brandt, Luca: Modal and non-modal linear stability of Poiseuille flow through a channel with a porous substrate (2019)
  15. Grosheintz-Laval, L.; Käppeli, R.: High-order well-balanced finite volume schemes for the Euler equations with gravitation (2019)
  16. Hirschler, T.; Bouclier, R.; Duval, A.; Elguedj, T.; Morlier, J.: The embedded isogeometric Kirchhoff-Love shell: from design to shape optimization of non-conforming stiffened multipatch structures (2019)
  17. Hough, Robert D.; Jerison, Daniel C.; Levine, Lionel: Sandpiles on the square lattice (2019)
  18. Hsieh-Fu Tsai, Joanna Gajda, Tyler F.W. Sloan, Andrei Rares, Jason Ting-Chun Chou, Amy Q. Shen: Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning (2019) not zbMATH
  19. Johansson, Fredrik: Computing hypergeometric functions rigorously (2019)
  20. Johansson, Robert: Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib (2019)

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