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SciPy (pronounced 'saɪpaɪ "sigh pie") is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and , ODE solvers and other tasks common in science and engineering. SciPy is also a family of conferences for users and developers of these tools: SciPy (in the United States), EuroSciPy (in Europe) and SciPy.in (in India). Enthought originated the SciPy conference in the United States and continues to sponsor many of the international conferences as well as host the SciPy website. The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers. It is also supported by NumFOCUS, a community foundation for supporting reproducible and accessible science. The SciPy package is at the core of Python's scientific computing capabilities. Available sub-packages include: cluster: hierarchical clustering, vector quantization, K-means constants: physical constants and conversion factors fft: Discrete Fourier Transform algorithms fftpack: Legacy interface for Discrete Fourier Transforms integrate: numerical integration routines interpolate: interpolation tools io: data input and output linalg: linear algebra routines misc: miscellaneous utilities (e.g. example images) ndimage: various functions for multi-dimensional ODR: orthogonal distance regression classes and algorithms optimize: optimization algorithms including linear programming signal: signal processing tools sparse: sparse matrices and related algorithms spatial: algorithms for spatial structures such as k-d trees, nearest neighbors, Convex hulls, etc. special: special functions stats: statistical functions weave: tool for writing C/C++ code as Python multiline strings (now deprecated in favor of Cython) The basic data structure used by SciPy is a multidimensional array provided by the NumPy module.

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pyFFS: A Python Library for Fast Fourier Series Computation and Interpolation with GPU Acceleration

Martin Vetterli, Paul Hurley, Eric Bezzam, Sepand Kashani, Matthieu Martin Jean-André Simeoni

Fourier transforms are an often necessary component in many computational tasks, and can be computed efficiently through the fast Fourier transform (FFT) algorithm. However, many applications involve an underlying continuous signal, and a more natural choi ...

2022

Random time step probabilistic methods for uncertainty quantification in chaotic and geometric numerical integration

Assyr Abdulle, Giacomo Garegnani

A novel probabilistic numerical method for quantifying the uncertainty induced by the time integration of ordinary differential equations (ODEs) is introduced. Departing from the classical strategy to randomize ODE solvers by adding a random forcing term, ...

2020

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Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. In this paper we consider the problem of efficiently learning set functions that are defined over a ground ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

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