Python for Scientific Computing

By T. Oliphant. 2007

2,995 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Evaluation & Benchmarks

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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Citations in the corpus

Pylearn2: a machine learning research library (2013, methodology): “If the data is alrea dy stored in NumPy [37] or pickle format, it is not even necessary to implement any new Python code to us e the dataset.”
Theano: new features and speed improvements (2012, methodology): “Table 1 provides a summary comparison of the features provided by Torch7 (including the ones inherited from Lua) and Theano (including the ones coming from Python and NumPy/SciPy).”
Array programming with NumPy (2020, neutral): “array([1, 2, 3])”
SciPy 1.0: fundamental algorithms for scientific computing in Python (2019, background): “For example, a three-part minisymposium organized for International Conferences on Computational Science and Engineering (CSE) 2009 was featured in SIAM News 36 .”
Convolutional Networks on Graphs for Learning Molecular Fingerprints (2015, methodology): “This package handles standard Numpy [18] code, and can differentiate code containing while loops, branches, and indexing.”
Gradient-based Hyperparameter Optimization through Reversible Learning (2015, background): “This package differentiates standard Numpy (Oliphant, 2007) code, and can differentiate code containing while loops, branches, and even gradient evaluations.”
Defeating Image Obfuscation with Deep Learning (2016, methodology): “For mosaicing (pixelation), we used a simple Python script with NumPy [34] to compute the average color of a block of pixels and then change the entire block to that color.”
CADD: predicting the deleteriousness of variants throughout the human genome (2018, positive): “4, a logistic regression model was fit using a fully open source pipeline based on SciPy (44) and scikit-learn (45).”
Photo-astrometric distances, extinctions, and astrophysical parameters for Gaia DR2 stars brighter than G = 18 (2019, methodology): “During the analysis, we have made extensive use of the astronomical java software TOPCAT and STILTS (Taylor 2005), as well as the python packages numpy and scipy (Oliphant 2007), astropy (Astropy Collaboration 2013), dask (Dask Development Team 2016), HoloViews, and matplotlib (Hunter 2007).”
Numba: a LLVM-based Python JIT compiler (2015, background): “Numba: A LLVM-based Python JIT Compiler Siu Kwan Lam Antoine Pitrou Stanley Seibert Continuum Analytics Continuum Analytics Continuum Analytics Austin, Texas Austin, Texas Austin, Texas slam@continuum.io apitrou@continuum.io sseibert@continuum.io ABSTRACT Dynamic, interpreted languages, like Pytho”
Constraining the Dense Matter Equation of State with Joint Analysis of NICER and LIGO/Virgo Measurements (2019, methodology): “Software: Python/C language (Oliphant 2007), GNU Scientific Library (GSL; Gough 2009), NumPy (van der Walt et al. 2011), Cython (Behnel et al. 2011), SciPy (Jones et al. 2001–), MPI (Forum 1994), MPI for Python (Dalćın et al. 2008), Matplotlib (Hunter 2007; Droettboometal.2018),…”
Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data (2009, methodology): “Our implementation of these methods, ∂a∂i, is written in cross-platform Python and C, making use of the NumPy [38], Scipy [39], and Matplotlib libraries [40].”