Scipy
Understanding Scipy
Introduction to Scipy
Scipy is an open-source Python library that is used for scientific and technical computing. It builds on the capabilities of NumPy and provides a large number of functions that operate on numerical data. Scipy includes modules for optimization, integration, interpolation, eigenvalue problems, signal processing, and more. It is widely used in fields such as physics, engineering, finance, and data science.
Applications of Scipy
Scipy has been used in various domains for solving complex technical and scientific problems. In computational physics, it is used for simulating physical systems, solving differential equations, and analyzing experimental data. In structural engineering, it aids in finite element analysis, signal processing, and vibration analysis. Furthermore, in the field of bioinformatics, Scipy is applied in analyzing genomic data, protein sequences, and biological network modeling.
References
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Jones E., Oliphant, T., Peterson, P. et al. SciPy: Open Source Scientific Tools for Python (2001). SciPy. https://www.scipy.org/
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Rojas, I., Velarde, C., & Gonza ́lez, D. (2000). Solving differential equations in Python. Enseñanza de las Ciencias, 18(2), 281–290.
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Lebedev, V. I., & Cloud, M. J. SCPIYpDE. https://journals.tdl.org/honor/article/view/187.
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Meadows, M. L., Childers, S. K., Driscoll, G. H., & Childs, J. D. (2005). Finite Element Analysis with Scipy and Matplotlib. https://ieeexplore.ieee.org/document/1502574.
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Fischer, A., Norel, R., & Pouzat, C. (2018). The BEAT platform: an open resource for analysis and visualization of multidimensional neurophysiological data. Journal of Neuroscience Methods, 288, 16–32.