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Python: An Ecosystem for Scientific Computing

Fernando Perez, Brian E. Granger, John D. Hunter
2011 Computing in science & engineering (Print)  
The identification program was written in scientific python ecosystem by implementing Ibrahim Time Domain (ITD) and Eigensystem Realization Algorithm (ERA) method.  ...  This research presented a study for verification the performance and the effectivity of identification program for modal parameters identification.  ...  There are lot scientific modules in python, but the main modules for numerical computation are NumPy (numerical python) and SciPy (scientific python).  ... 
doi:10.1109/mcse.2010.119 fatcat:xsmsxftkgvdknkwlom3h4x42bm

AiiDAlab – an ecosystem for developing, executing, and sharing scientific workflows

Aliaksandr V. Yakutovich, Kristjan Eimre, Ole Schütt, Leopold Talirz, Carl S. Adorf, Casper W. Andersen, Edward Ditler, Dou Du, Daniele Passerone, Berend Smit, Nicola Marzari, Giovanni Pizzi (+1 others)
2021 Computational materials science  
AiiDAlab is a web platform that enables computational scientists to package scientific workflows and computational environments and share them with their collaborators and peers.  ...  By leveraging the AiiDA workflow manager and its plugin ecosystem, developers get access to a growing range of simulation codes through a python API, coupled with automatic provenance tracking of simulations  ...  Computing PASC.  ... 
doi:10.1016/j.commatsci.2020.110165 fatcat:yj5fdrpjtbc3vholgf2t6eoiqu

Python in neuroscience

Eilif Muller, James A. Bednar, Markus Diesmann, Marc-Oliver Gewaltig, Michael Hines, Andrew P. Davison
2015 Frontiers in Neuroinformatics  
It became clear to us in 2007 that we were on the cusp of an emerging Python in neuroscience ecosystem, particularly in computational neuroscience and neuroimaging, but also in electrophysiological data  ...  The use of Python as a scientific programming language began to increase with the development of numerical libraries for optimized operations on large arrays in the late 1990s, in which an important development  ...  in neuroscience research, and by the particular strengths of the Python language and the broader Python scientific computing ecosystem.  ... 
doi:10.3389/fninf.2015.00011 pmid:25926788 pmcid:PMC4396193 fatcat:kexvxwhuajaexpdthguc5j4qrq

zfit: scalable pythonic fitting [article]

Jonas Eschle, Albert Puig Navarro, Rafael Silva Coutinho, Nicola Serra
2019 arXiv   pre-print
The standard framework to perform this task is the C++ ROOT/RooFit toolkit; with Python bindings that are only loosely integrated into the scientific Python ecosystem.  ...  It is designed to be extendable in a very simple fashion, allowing the usage of cutting-edge developments from the scientific Python ecosystem in a transparent way.  ...  Acknowledgements We are grateful to Anton Poluektov, Chris Burr and Igor Babuschkin for demonstrating the potential of unbinned model fitting within the context of TensorFlow which inspired this work.  ... 
arXiv:1910.13429v1 fatcat:kjvdzofiqjdkfeavgbovgns7dm

zfit: Scalable pythonic fitting

Jonas Eschle, Albert Puig Navarro, Rafael Silva Coutinho, Nicola Serra
2020 SoftwareX  
The standard framework to perform this task in HEP is the C++ ROOT/RooFit toolkit; with Python bindings that are only loosely integrated into the scientific Python ecosystem.  ...  It is designed to be extendable in a very simple fashion, allowing the usage of cutting-edge developments from the scientific Python ecosystem in a transparent way.  ...  We also thank the Zurich LHCb Group, Matthieu Marinangeli, Josh Bendavid, Lukas Heinrich and the HSF community, especially Scikit-HEP project members, for useful discussions. A. Puig, R.  ... 
doi:10.1016/j.softx.2020.100508 fatcat:sfjhakuypvct7fukaneguyxjti

PolyBench/Python: benchmarking Python environments with polyhedral optimizations

Miguel Á. Abella-González, Pedro Carollo-Fernández, Louis-Noël Pouchet, Fabrice Rastello, Gabriel Rodríguez
2021 Proceedings of the 30th ACM SIGPLAN International Conference on Compiler Construction  
In this work, we aim to enable the evaluation of current and upcoming Python ecosystems for scientific programming, focusing specifically on numerical kernels that are typically implemented using dense  ...  Implementing Numerical Kernels in Python The Python Ecosystem.  ... 
doi:10.1145/3446804.3446842 fatcat:nb5t5lox3fhfjbgbrmh6t7usri

OpenML-Python: an extensible Python API for OpenML [article]

Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
2021 arXiv   pre-print
In this paper we introduce OpenML-Python, a client API for Python, opening up the OpenML platform for a wide range of Python-based tools.  ...  OpenML is an online platform for open science collaboration in machine learning, used to share datasets and results of machine learning experiments.  ...  The authors also thank Bilge Celik, Victor Gal and everyone listed at for their contributions.  ... 
arXiv:1911.02490v2 fatcat:uzpfeu2tp5dbtfu5yfcamoj6ci

Programming: Pick up Python

Jeffrey M. Perkel
2015 Nature  
It combines simple syntax, abundant online resources and a rich ecosystem of scientifically focused toolkits with a heavy emphasis on community.  ...  Among the host of computer-programming languages that scientists might choose to pick up, Python, first released in 1991 by Dutch programmer Guido van Rossum, is an increasingly popular (and free) recommendation  ... 
doi:10.1038/518125a pmid:25653001 fatcat:gx5qynkiqfcltnzrb4spvy7n7e

Want Drugs? Use Python [article]

Michał Nowotka, George Papadatos, Mark Davies, Nathan Dedman, Anne Hersey
2016 arXiv   pre-print
We describe how Python can be leveraged to streamline the curation, modelling and dissemination of drug discovery data as well as the development of innovative, freely available tools for the related scientific  ...  We look at various examples, such as chemistry toolkits, machine-learning applications and web frameworks and show how Python can glue it all together to create efficient data science pipelines.  ...  Django), provides a complete and versatile scientific computing ecosystem.  ... 
arXiv:1607.00378v1 fatcat:34cmu6a2bzfjxcvwlsmstio4uu

Python for Scientists and Engineers

K. Jarrod Millman, Michael Aivazis
2011 Computing in science & engineering (Print)  
in this issue We begin this issue with "Python: An Ecosystem for Scientific Computing," by Fernando Pérez, Brian E. Granger, and John D. Hunter.  ...  needed to provide an array type for numerical computing.  ...  Paul Dubois, "Python: Batteries Included," Computing in Science and Eng., vol. 9, no. 3, 2007 , pp. 7 -9. K. Jarrod Millman  ... 
doi:10.1109/mcse.2011.36 fatcat:b23hrx4o3zho7oazoumontv6tu

Sloppy Python

Philip J. Guo
2011 Proceedings of the Ninth International Workshop on Dynamic Analysis - WODA '11  
Thus, imperfect scripts will run to completion and produce partial results and an error log, which is more informative than simply crashing with no results.  ...  We implemented our technique as a "Sloppy" Python interpreter that automatically adds error tolerance to existing scripts without any programmer effort or run-time slowdown.  ...  Acknowledgments: Thanks to Dawson Engler, Kathleen Fisher, Robert Ikeda, and Jean Yang for feedback on drafts, to Martin Rinard and an anonymous reviewer for some of the future work ideas, and to the NSF  ... 
doi:10.1145/2002951.2002961 dblp:conf/issta/Guo11 fatcat:ifedcdrlwbbblguq6amrzjwhpi

Scikit-learn: Machine Learning in Python [article]

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Andreas Müller, Joel Nothman, Gilles Louppe, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas (+4 others)
2018 arXiv   pre-print
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.  ...  Introduction The Python programming language is establishing itself as one of the most popular languages for scientific computing.  ...  Thanks to its high-level interactive nature and its maturing ecosystem of scientific libraries, it is an appealing choice for algorithmic development and exploratory data analysis (Dubois, 2007; Milmann  ... 
arXiv:1201.0490v4 fatcat:rz47i2dguvbuvp2glinpp4fx24

Python - Based Image Processing

Mrs. Asha K H, Manjunathswamy B E, Mrs. Chaithra A S
2021 International Journal of Scientific Research and Management  
Python's growing popularity as a scientific programming language, along with the expanding availability of a large ecosystem of supporting tools, makes it an ideal environment for creating EC-2021-636  ...  It is completely integrated into the Scientific Python environment, making it compatible with perception libraries and other data preparation tools.  ... 
doi:10.18535/ijsrm/v9i11.ec03 fatcat:zlo6y3vjvjddrgigwzcvq5vldu

Momba: JANI Meets Python

Maximilian Köhl, Michaela Klauck, Holger Hermanns
2021 Zenodo  
Our vision is to harvest Python's ecosystem and the tools developed by the scientific community for dealing with formal models.  ...  Python is a popular high-level programming language, preferred by many for its ease of use and ecosystem.  ... 
doi:10.5281/zenodo.5750802 fatcat:nyql3snfgvgtdksttfiao5o7nm

PyNetLogo: Linking NetLogo with Python

Marc Jaxa-Rozen, Jan H. Kwakkel
2018 Journal of Artificial Societies and Social Simulation  
Given Python's increasing popularity for scientific computing, this provides additional flexibility for modellers and analysts.  ...  To extend these capabilities to an additional user base, this paper presents the pyNetLogo connector, which allows NetLogo to be controlled from the Python general-purpose programming language.  ...  Acknowledgements This research was supported by the Netherlands Organization for Scientific Research (NWO) under the project Aquifer Thermal Energy Storage Smart Grids (ATES-SG), grant number --.  ... 
doi:10.18564/jasss.3668 fatcat:bzat5axnebho5p6g3rnsandk2a
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