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SciPy (pronounced / ˈ s aɪ p aɪ / "sigh pie" [2]) is a free and open-source Python library used for scientific computing and technical computing. [3]SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
It is best known for the early development and maintenance of the SciPy library of mathematics, science, and engineering algorithms [3] and for its Python for scientific computing distribution Enthought Canopy (formerly EPD). [4] The company was founded in 2001 by Travis Vaught and Eric Jones. [1] [2]
SciPy, [17] [18] [19] a large BSD-licensed library of scientific tools. De facto standard for scientific computations in Python. ScientificPython, a library with a different set of scientific tools; SymPy, a library based on New BSD license for symbolic computation. Features of Sympy range from basic symbolic arithmetic to calculus, algebra ...
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3]
scikit-image (formerly scikits.image) is an open-source image processing library for the Python programming language. [2] It includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. [3]
It is an open-source cross-platform integrated development environment (IDE) for scientific programming in the Python language.Spyder integrates with a number of prominent packages in the scientific Python stack, including NumPy, SciPy, Matplotlib, pandas, IPython, SymPy and Cython, as well as other open-source software.
Variants of this algorithm are available in MATLAB as the routine lsqnonneg [8] [1] and in SciPy as optimize.nnls. [9] Many improved algorithms have been suggested since 1974. [1] Fast NNLS (FNNLS) is an optimized version of the Lawson–Hanson algorithm. [2]
IPython frequently draws from SciPy stack [6] libraries like NumPy and SciPy, often installed alongside one of many Scientific Python distributions. [6] IPython provides integration with some libraries of the SciPy stack, notably matplotlib, producing inline graphs when used with the Jupyter notebook. Python libraries can implement IPython ...