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1. SciPy 1.0: fundamental algorithms for scientific computing in Python: 24 February 2020 : An amendment to this paper has been published and can be accessed via a link at the top of the paper

Author

Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., Walt, S.J., Brett, M., Wilson, J., Millman, K.J., Mayorov, N., Nelson, A.R.J., Jones, E., Kern, R., Larson, E., Carey, C.J., Polat, I., Feng, Y., Moore, E.W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E.A., Harris, C.R., Archibald, A.M., Ribeiro, A.H., Pedregosa, F., Mulbregt, P., Vijaykumar, A., Bardelli, A.P., Rothberg, A., Hilboll, A., Kloeckner, A., Scopatz, A., Lee, A., Rokem, A., Woods, C.N., Fulton, C., Masson, C., Häggström, C., Fitzgerald, C., Nicholson, D.A., Hagen, D.R., Pasechnik, D.V., Olivetti, E., Martin, E., Wieser, E., Lenders, F., Silva, Fabrice, Wilhelm, F., Young, G., Price, G.A., Ingold, G.-L., Allen, G.E., Lee, G.R., Audren, H., Probst, Irvin, Dietrich, J.P., Silterra, J., Webber, J.T., Slavič, J., Nothman, J., Buchner, J., Kulick, J., Schönberger, J.L., Miranda Cardoso, J.V., Reimer, J., Harrington, J., Rodríguez, J.L.C., Nunez-Iglesias, J., Kuczynski, J., Tritz, K., Thoma, M., Newville, M., Kümmerer, M., Bolingbroke, M., Tartre, M., Pak, M., Smith, N.J., Nowaczyk, N., Shebanov, N., Pavlyk, O., Brodtkorb, P.A., Lee, P., McGibbon, R.T., Feldbauer, R., Lewis, S., Tygier, S., Sievert, S., Vigna, S., Peterson, S., More, S., Pudlik, T., Oshima, T., Pingel, T.J., Robitaille, T.P., Spura, T., Jones, T.R., Cera, T., Leslie, T., Zito, T., Krauss, T., Upadhyay, U., Halchenko, Y.O., Vázquez-Baeza, Y., SciPy 1.0, Contributors, Low Temperature Laboratory, TKK Helsinki University of Technology (TKK), Centre Interdisciplinaire de Nanoscience de Marseille (CINaM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Tribologie et Dynamique des Systèmes (LTDS), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-École Nationale des Travaux Publics de l'État (ENTPE)-Ecole Nationale d'Ingénieurs de Saint Etienne-Centre National de la Recherche Scientifique (CNRS), SNECMA Villaroche [Moissy-Cramayel], Safran Group, New Technologies Research Centre [Plzeň] (NTC), University of West Bohemia [Plzeň ], Institute of Environmental Physics [Bremen] (IUP), University of Bremen, Istituto Nazionale di Ricerca Metrologica (INRiM), Laboratoire Chrono-environnement - CNRS - UBFC (UMR 6249) (LCE), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Laboratoire de Mécanique et d'Acoustique [Marseille] (LMA ), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Sons, Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), European Synchrotron Radiation Facility (ESRF), Lab-STICC_ENSTAB_CID_PRASYS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Max Planck Institute for Solar System Research (MPS), Max-Planck-Gesellschaft, University of Chicago, Consortium of Advanced Radiation Sciences, Advanced Photon Source, GSECARS, University of Chicago, Laboratoire de physique des gaz et des plasmas (LPGP), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), The Open University [Milton Keynes] (OU), Nobeyama Radio Observatory, Gommers, Ralf [0000-0002-0300-3333], Haberland, Matt [0000-0003-4806-3601], Reddy, Tyler [0000-0003-2364-6157], van der Walt, Stéfan J. [0000-0001-9276-1891], Millman, K. Jarrod [0000-0002-5263-5070], Nelson, Andrew R. J. [0000-0002-4548-3558], Laxalde, Denis [0000-0002-5540-4825], Ribeiro, Antônio H. [0000-0003-3632-8529], van Mulbregt, Paul [0000-0002-2382-8308], Apollo - University of Cambridge Repository, van der Walt, Stéfan J [0000-0001-9276-1891], Millman, K Jarrod [0000-0002-5263-5070], Nelson, Andrew RJ [0000-0002-4548-3558], Ribeiro, Antônio H [0000-0003-3632-8529], Quansight LLC, California Polytechnic State University [San Luis Obispo] (CAL POLY), Los Alamos National Laboratory (LANL), AUTRES, National Research University Higher School of Economics [St. Petersburg], Tallinn Technical University, University of California [Berkeley] (UC Berkeley), University of California (UC), University of Birmingham [Birmingham], Skolkovo Innovation Center, Australian Nuclear Science and Technology Organisation [Australie] (ANSTO), Enthought Inc, University of Washington [Seattle], University of Massachusetts [Amherst] (UMass Amherst), University of Massachusetts System (UMASS), Bruker BioSpin Corporation, Université de Lyon-Université de Lyon-École Nationale des Travaux Publics de l'État (ENTPE)-Ecole Nationale d'Ingénieurs de Saint Etienne (ENISE)-Centre National de la Recherche Scientifique (CNRS), University of Texas at Austin [Austin], Astronomical Institute Anton Pannekoek (AI PANNEKOEK), University of Amsterdam [Amsterdam] (UvA), Universidade Federal de Minas Gerais [Belo Horizonte] (UFMG), Google LLC, Laboratoire Chrono-environnement (UMR 6249) (LCE), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), and Max-Planck-Institut für Sonnensystemforschung = Max Planck Institute for Solar System Research (MPS)

Subjects
FOS: Computer and information sciences, Computer science, 01 natural sciences, Biochemistry, Computer Science - Software Engineering, 631/45/56, Data Structures and Algorithms (cs.DS), 010303 astronomy & astrophysics, computer.programming_language, 0303 health sciences, Signal processing, Signal Processing, Computer-Assisted, Computational Physics (physics.comp-ph), ddc, Linear algebra, Perspective, Minification, Physics - Computational Physics, Algorithm, Algorithms, Biotechnology, De facto standard, FOS: Physical sciences, Image processing, History, 21st Century, Models, Biological, 706/703/559, Python (Computer program language), Computational science, 03 medical and health sciences, Computer Science - Data Structures and Algorithms, 0103 physical sciences, Computer Simulation, [INFO]Computer Science [cs], ddc:530, Cluster analysis, Molecular Biology, Scientific computing, 030304 developmental biology, Sparse matrix, software, Computational Biology, Cell Biology, Python (programming language), History, 20th Century, [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA], Software Engineering (cs.SE), Nonlinear Dynamics, Linear Models, Computer Science - Mathematical Software, Programming Languages, 631/114, computer, Mathematical Software (cs.MS), Python
Abstract

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments., Comment: Article source data is available here: https://github.com/scipy/scipy-articles

Published
2020
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2. The Astropy Project: building an open-science project and status of the v2.0 core package

Author

Price-Whelan, A.M., Sip?cz, B.M., Lim, P.L., Crawford, S.M., Conseil, S., Shupe, D.L., Craig, M.W., Dencheva, N., Ginsburg, A., VanderPlas, J.T., Bradley, L.D., de Val-Borro, M., Aldcroft, T.L., Cruz, K.L., Robitaille, T.P., Tollerud, E.J., and Bamford, S.P.

Subjects
data analysis – methods: miscellaneous, methods: statistical – reference systems
Abstract

The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package, as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of interoperable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy Project.

Published
2018

3. TRUST: I. A 3D externally illuminated slab benchmark for dust radiative transfer

Author

Gordon, K.D, Baes, M, Bianchi, S, Camps, P, Juvela, M, Kuiper, R, Lunttila, T, Misselt, K.A, Natale, Giovanni, Robitaille, T.P, Steinacker, J, Gordon, K.D, Baes, M, Bianchi, S, Camps, P, Juvela, M, Kuiper, R, Lunttila, T, Misselt, K.A, Natale, Giovanni, Robitaille, T.P, and Steinacker, J

Abstract

Context. The radiative transport of photons through arbitrary three-dimensional (3D) structures of dust is a challenging problem due to the anisotropic scattering of dust grains and strong coupling between different spatial regions. The radiative transfer problem in 3D is solved using Monte Carlo or Ray Tracing techniques as no full analytic solution exists for the true 3D structures. Aims. We provide the first 3D dust radiative transfer benchmark composed of a slab of dust with uniform density externally illuminated by a star. This simple 3D benchmark is explicitly formulated to provide tests of the different components of the radiative transfer problem including dust absorption, scattering, and emission. Methods. The details of the external star, the slab itself, and the dust properties are provided. This benchmark includes models with a range of dust optical depths fully probing cases that are optically thin at all wavelengths to optically thick at most wavelengths. The dust properties adopted are characteristic of the diffuse Milky Way interstellar medium. This benchmark includes solutions for the full dust emission including single photon (stochastic) heating as well as two simplifying approximations: One where all grains are considered in equilibrium with the radiation field and one where the emission is from a single effective grain with size-distribution-averaged properties. A total of six Monte Carlo codes and one Ray Tracing code provide solutions to this benchmark. Results. The solution to this benchmark is given as global spectral energy distributions (SEDs) and images at select diagnostic wavelengths from the ultraviolet through the infrared. Comparison of the results revealed that the global SEDs are consistent on average to a few percent for all but the scattered stellar flux at very high optical depths. The image results are consistent within 10%, again except for the stellar scattered flux at very high optical depths. The lack of agreement between d

Published
2017

4. The Red MSX Source survey: The bolometric fluxes and luminosity distributions of young massive stars

Author

Mottram, Joseph C., Hoare, Melvin G., Urquhart, J.S., Lumsden, Stuart L., Oudmaijer, Rene D., Robitaille, T.P., Moore, Toby J.T., Davies, Ben, Stead, Joseph J., Mottram, Joseph C., Hoare, Melvin G., Urquhart, J.S., Lumsden, Stuart L., Oudmaijer, Rene D., Robitaille, T.P., Moore, Toby J.T., Davies, Ben, and Stead, Joseph J.

Abstract

Context. The Red MSX Source (RMS) survey is returning a large sample of massive young stellar objects (MYSOs) and ultra-compact (UC) H?ii regions using follow-up observations of colour-selected candidates from the MSX point source catalogue. Aims. We obtain the bolometric fluxes and, using kinematic distance information, the luminosities for young RMS sources with far-infrared fluxes. Methods. We use a model spectral energy distribution (SED) fitter to obtain the bolometric flux for our sources, given flux data from our work and the literature. The inputs to the model fitter were optimised by a series of investigations designed to reveal the effect varying these inputs had on the resulting bolometric flux. Kinematic distances derived from molecular line observations were then used to calculate the luminosity of each source. Results. Bolometric fluxes are obtained for 1173 young RMS sources, of which 1069 have uniquely constrained kinematic distances and good SED fits. A comparison of the bolometric fluxes obtained using SED fitting with trapezium rule integration and two component greybody fits was also undertaken, and showed that both produce considerable scatter compared to the method used here. Conclusions. The bolometric flux results allowed us to obtain the luminosity distributions of YSOs and UCH?ii regions in the RMS sample, which we find to be different. We also find that there are few MYSOs with L ? 105 L?, despite finding many MYSOs with 104 L? ? L ? 105 L?.

Published
2011

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