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 199 articles )

Showing results 1 to 20 of 199.
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  1. Ansmann, Gerrit: Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE (2018)
  2. Bahsoun, Wael; Galatolo, Stefano; Nisoli, Isaia; Niu, Xiaolong: A rigorous computational approach to linear response (2018)
  3. Benjamin J. Fulton; Erik A. Petigura; Sarah Blunt; Evan Sinukoff: RadVel: The Radial Velocity Modeling Toolkit (2018) arXiv
  4. Benyuan Liu; Bin Yang; Canhua Xu; Junying Xia; Meng Dai; Zhenyu Ji; Fusheng You; Xiuzhen Dong; Xuetao Shi; Feng Fu: pyEIT: A python based framework for Electrical Impedance Tomography (2018)
  5. Blais, Bruno; Ilinca, Florin: Development and validation of a stabilized immersed boundary CFD model for freezing and melting with natural convection (2018)
  6. C. M. Biwer; Collin D. Capano; Soumi De; Miriam Cabero; Duncan A. Brown; Alexander H. Nitz; V. Raymond: PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals (2018) arXiv
  7. Collin J. Wilkinson, Yihong Z. Mauro, John C. Mauro: RelaxPy: Python code for modeling of glass relaxation behavior (2018)
  8. Czuppon, Peter; Traulsen, Arne: Fixation probabilities in populations under demographic fluctuations (2018)
  9. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  10. Gubbiotti, G.; Latini, D.: A multiple scales approach to maximal superintegrability (2018)
  11. Himpe, Christian; Leibner, Tobias; Rave, Stephan: Hierarchical approximate proper orthogonal decomposition (2018)
  12. Ignatiev, Alexey; Morgado, Antonio; Marques-Silva, Joao: PySAT: A Python toolkit for prototyping with SAT oracles (2018)
  13. K.T. Schütt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, K.-R. Müller: SchNetPack: A Deep Learning Toolbox For Atomistic Systems (2018) arXiv
  14. 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)
  15. Liew, A.; Pagonakis, D.; Van Mele, T.; Block, P.: Load-path optimisation of funicular networks (2018)
  16. McRae, Andrew T. T.; Cotter, Colin J.; Budd, Chris J.: Optimal-transport -- based mesh adaptivity on the plane and sphere using finite elements (2018)
  17. Minjie Zhu, Frank McKenna, Michael H. Scott: OpenSeesPy: Python library for the OpenSees finite element framework (2018)
  18. Park, Youngmin; Ermentrout, G. Bard: A multiple timescales approach to bridging spiking- and population-level dynamics (2018)
  19. 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
  20. Römer, Ulrich; Narayanamurthi, Mahesh; Sandu, Adrian: Solving parameter estimation problems with discrete adjoint exponential integrators (2018)

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