Publications

Oliphant, Margaret

doi:10.1017/cbo9780511711312.001

Cited by 109 publications

(122 citation statements)

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“…The project has grown rapidly, and to date, over 200 individuals are signed up to the development mailing list for the Astropy project 2 . One of the primary aims of the Astropy project is to develop a core astropy package that covers much of the astronomyspecific functionality needed by researchers, complementing more general scientific packages such as NumPy (Oliphant 2006;Van Der Walt et al 2011) and SciPy (Jones et al 2001), which are invaluable for numerical array-based calculations and more general scientific algorithms (e.g. interpolation, integration, clustering).…”

Section: Introductionmentioning

confidence: 99%

Astropy: A community Python package for astronomy

Robitaille

Tollerud

Greenfield

et al. 2013

A&A

10,094

549

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We present the first public version (v0.2) of the open-source and community-developed Python package, Astropy. This package provides core astronomy-related functionality to the community, including support for domain-specific file formats such as flexible image transport system (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions. Significant functionality is under active development, such as a model fitting framework, VO client and server tools, and aperture and point spread function (PSF) photometry tools. The core development team is actively making additions and enhancements to the current code base, and we encourage anyone interested to participate in the development of future Astropy versions.

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Section: Introductionmentioning

confidence: 99%

Astropy: A community Python package for astronomy

Robitaille

Tollerud

Greenfield

et al. 2013

A&A

10,094

549

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“…Correlation networks were generated with the rcorr function from the Hmisc R library (version 4.2-0) [23]. Analysis of simulated data sets were carried out in Python using NetworkX (version 2.1) [24], numpy (version 1.15.4) [25], pandas (version 0.21.0) [26] and scipy (version 1.2.0) [27]. Additional analyses for case studies were carried out in R using igraph (version 1.2.4.1) [28], phyloseq (version 1.26.1) [29] and vegan (version 2.5-5) [30].…”

Section: Methodsmentioning

confidence: 99%

manta- a clustering algorithm for weighted ecological networks

Röttjers

Faust

2019

Preprint

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Microbial network inference and analysis has become a successful approach to generate biological hypotheses from microbial sequencing data.Network clustering is a crucial step in this analysis. Here, we present a novel heuristic flow-based network clustering algorithm, which equals or outperforms existing algorithms on noise-free synthetic data. manta comes with unique strengths such as the ability to identify nodes that represent an intermediate between clusters, to exploit negative edges and to assess the robustness of cluster membership. manta does not require parameter tuning, is straightforward to install and run, and can easily be combined with existing microbial network inference tools.

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“…This feature makes cottoncandy an ideal solution for data science workflows that rely on cloud-based storage. cottoncandy is optimized for accessing and storing numpy (T. E. Oliphant, 2006) array data and provides support for other data formats widely used in data science (e.g. json, pickle, sparse arrays (Jones, Oliphant, & Peterson, 2014)).…”

Section: Long Descriptionmentioning

confidence: 99%