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 605 articles )

Showing results 1 to 20 of 605.
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  1. Albuquerque-Ferreira, A. C.; Ureña, Miguel; Ramos, Higinio: The generalized finite difference method with third- and fourth-order approximations and treatment of ill-conditioned stars (2021)
  2. Alessandro Sebastianelli, Maria Pia Del Rosso, Silvia Liberata Ullo: Automatic dataset builder for Machine Learning applications to satellite imagery (2021) not zbMATH
  3. Alfredo Mejia-Narvaez, Gustavo Bruzual, Sebastian F. Sanchez, Leticia Carigi, Jorge Barrera-Ballesteros, Mabel Valerdi, Renbin Yan, Niv Drory: CoSHA: Code for Stellar properties Heuristic Assignment - for the MaStar stellar library (2021) arXiv
  4. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  5. Arthur A. B. Pessa, Haroldo V. Ribeiro: ordpy: A Python package for data analysis with permutation entropy and ordinal network methods (2021) arXiv
  6. Barnafi, Nicolás; Zunino, Paolo; Dedè, Luca; Quarteroni, Alfio: Mathematical analysis and numerical approximation of a general linearized poro-hyperelastic model (2021)
  7. B. Boys, T. J. Dodwell, M. Hobbs, M. Girolami: PeriPy - A High Performance OpenCL Peridynamics Package (2021) arXiv
  8. Bender, Jason D.; Schilling, Oleg; Raman, Kumar S.; Managan, Robert A.; Olson, Britton J.; Copeland, Sean R.; Ellison, C. Leland; Erskine, David J.; Huntington, Channing M.; Morgan, Brandon E.; Nagel, Sabrina R.; Prisbrey, Shon T.; Pudliner, Brian S.; Sterne, Philip A.; Wehrenberg, Christopher E.; Zhou, Ye: Simulation and flow physics of a shocked and reshocked high-energy-density mixing layer (2021)
  9. Benjamin F. Maier: epipack: An infectious disease modeling package for Python (2021) not zbMATH
  10. Changjie Chen, Jasmeet Judge, David Hulse: PyLUSAT: An open-source Python toolkit for GIS-based land use suitability analysis (2021) arXiv
  11. Chaudhry, Jehanzeb H.; Collins, J. B.: \textitAposteriori error estimation for the spectral deferred correction method (2021)
  12. Christophe Besse, Romain Duboscq, Stefan Le Coz: Numerical Simulations on Nonlinear Quantum Graphs with the GraFiDi Library (2021) arXiv
  13. Christopher M J Osborne, Ivan Milić: The Lightweaver Framework for NLTE Radiative Transfer in Python (2021) arXiv
  14. Christopher P. Bridge, Chris Gorman, Steven Pieper, Sean W. Doyle, Jochen K. Lennerz, Jayashree Kalpathy-Cramer, David A. Clunie, Andriy Y. Fedorov, Markus D. Herrmann: Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology (2021) arXiv
  15. Civitelli, Enrico; Lapucci, Matteo; Schoen, Fabio; Sortino, Alessio: An effective procedure for feature subset selection in logistic regression based on information criteria (2021)
  16. Derryn Knife: SurPyval: Survival Analysis with Python (2021) not zbMATH
  17. Dong, Zhaonan; Georgoulis, Emmanuil H.; Kappas, Thomas: GPU-accelerated discontinuous Galerkin methods on polytopic meshes (2021)
  18. Eike Caldeweyher: kallisto: A command-line interface to simplify computational modelling and the generation of atomic features (2021) not zbMATH
  19. Erdem Bıyık, Aditi Talati, Dorsa Sadigh: APReL: A Library for Active Preference-based Reward Learning Algorithms (2021) arXiv
  20. Ermakov, S. M.; Smilovitskiy, M. G.: The Monte Carlo method for solving large systems of linear ordinary differential equations (2021)

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