Scipy 1.0: Fundamental Algorithms for Scientific Computing in Python
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Sagemath and Sagemathcloud
Viviane Pons Ma^ıtrede conf´erence,Universit´eParis-Sud Orsay [email protected] { @PyViv SageMath and SageMathCloud Introduction SageMath SageMath is a free open source mathematics software I Created in 2005 by William Stein. I http://www.sagemath.org/ I Mission: Creating a viable free open source alternative to Magma, Maple, Mathematica and Matlab. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 2 / 7 SageMath Source and language I the main language of Sage is python (but there are many other source languages: cython, C, C++, fortran) I the source is distributed under the GPL licence. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 3 / 7 SageMath Sage and libraries One of the original purpose of Sage was to put together the many existent open source mathematics software programs: Atlas, GAP, GMP, Linbox, Maxima, MPFR, PARI/GP, NetworkX, NTL, Numpy/Scipy, Singular, Symmetrica,... Sage is all-inclusive: it installs all those libraries and gives you a common python-based interface to work on them. On top of it is the python / cython Sage library it-self. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 4 / 7 SageMath Sage and libraries I You can use a library explicitly: sage: n = gap(20062006) sage: type(n) <c l a s s 'sage. interfaces .gap.GapElement'> sage: n.Factors() [ 2, 17, 59, 73, 137 ] I But also, many of Sage computation are done through those libraries without necessarily telling you: sage: G = PermutationGroup([[(1,2,3),(4,5)],[(3,4)]]) sage : G . g a p () Group( [ (3,4), (1,2,3)(4,5) ] ) Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 5 / 7 SageMath Development model Development model I Sage is developed by researchers for researchers: the original philosophy is to develop what you need for your research and share it with the community. -
Tuto Documentation Release 0.1.0
Tuto Documentation Release 0.1.0 DevOps people 2020-05-09 09H16 CONTENTS 1 Documentation news 3 1.1 Documentation news 2020........................................3 1.1.1 New features of sphinx.ext.autodoc (typing) in sphinx 2.4.0 (2020-02-09)..........3 1.1.2 Hypermodern Python Chapter 5: Documentation (2020-01-29) by https://twitter.com/cjolowicz/..................................3 1.2 Documentation news 2018........................................4 1.2.1 Pratical sphinx (2018-05-12, pycon2018)...........................4 1.2.2 Markdown Descriptions on PyPI (2018-03-16)........................4 1.2.3 Bringing interactive examples to MDN.............................5 1.3 Documentation news 2017........................................5 1.3.1 Autodoc-style extraction into Sphinx for your JS project...................5 1.4 Documentation news 2016........................................5 1.4.1 La documentation linux utilise sphinx.............................5 2 Documentation Advices 7 2.1 You are what you document (Monday, May 5, 2014)..........................8 2.2 Rédaction technique...........................................8 2.2.1 Libérez vos informations de leurs silos.............................8 2.2.2 Intégrer la documentation aux processus de développement..................8 2.3 13 Things People Hate about Your Open Source Docs.........................9 2.4 Beautiful docs.............................................. 10 2.5 Designing Great API Docs (11 Jan 2012)................................ 10 2.6 Docness................................................. -
Fortran Resources 1
Fortran Resources 1 Ian D Chivers Jane Sleightholme May 7, 2021 1The original basis for this document was Mike Metcalf’s Fortran Information File. The next input came from people on comp-fortran-90. Details of how to subscribe or browse this list can be found in this document. If you have any corrections, additions, suggestions etc to make please contact us and we will endeavor to include your comments in later versions. Thanks to all the people who have contributed. Revision history The most recent version can be found at https://www.fortranplus.co.uk/fortran-information/ and the files section of the comp-fortran-90 list. https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=comp-fortran-90 • May 2021. Major update to the Intel entry. Also changes to the editors and IDE section, the graphics section, and the parallel programming section. • October 2020. Added an entry for Nvidia to the compiler section. Nvidia has integrated the PGI compiler suite into their NVIDIA HPC SDK product. Nvidia are also contributing to the LLVM Flang project. Updated the ’Additional Compiler Information’ entry in the compiler section. The Polyhedron benchmarks discuss automatic parallelisation. The fortranplus entry covers the diagnostic capability of the Cray, gfortran, Intel, Nag, Oracle and Nvidia compilers. Updated one entry and removed three others from the software tools section. Added ’Fortran Discourse’ to the e-lists section. We have also made changes to the Latex style sheet. • September 2020. Added a computer arithmetic and IEEE formats section. • June 2020. Updated the compiler entry with details of standard conformance. -
An Introduction to Numpy and Scipy
An introduction to Numpy and Scipy Table of contents Table of contents ............................................................................................................................ 1 Overview ......................................................................................................................................... 2 Installation ...................................................................................................................................... 2 Other resources .............................................................................................................................. 2 Importing the NumPy module ........................................................................................................ 2 Arrays .............................................................................................................................................. 3 Other ways to create arrays............................................................................................................ 7 Array mathematics .......................................................................................................................... 8 Array iteration ............................................................................................................................... 10 Basic array operations .................................................................................................................. 11 Comparison operators and value testing .................................................................................... -
Bioimage Analysis Fundamentals in Python with Scikit-Image, Napari, & Friends
Bioimage analysis fundamentals in Python with scikit-image, napari, & friends Tutors: Juan Nunez-Iglesias ([email protected]) Nicholas Sofroniew ([email protected]) Session 1: 2020-11-30 17:30 UTC – 2020-11-30 22:00 UTC Session 2: 2020-12-01 07:30 UTC – 2020-12-01 12:00 UTC ## Information about the tutors Juan Nunez-Iglesias is a Senior Research Fellow at Monash Micro Imaging, Monash University, Australia. His work on image segmentation in connectomics led him to contribute to the scikit-image library, of which he is now a core maintainer. He has since co-authored the book Elegant SciPy and co-created napari, an n-dimensional image viewer in Python. He has taught image analysis and scientific Python at conferences, university courses, summer schools, and at private companies. Nicholas Sofroniew leads the Imaging Tech Team at the Chan Zuckerberg Initiative. There he's focused on building tools that provide easy access to reproducible, quantitative bioimage analysis for the research community. He has a background in mathematics and systems neuroscience research, with a focus on microscopy and image analysis, and co-created napari, an n-dimensional image viewer in Python. ## Title and abstract of the tutorial. Title: **Bioimage analysis fundamentals in Python** **Abstract** The use of Python in science has exploded in the past decade, driven by excellent scientific computing libraries such as NumPy, SciPy, and pandas. In this tutorial, we will explore some of the most critical Python libraries for scientific computing on images, by walking through fundamental bioimage analysis applications of linear filtering (aka convolutions), segmentation, and object measurement, leveraging the napari viewer for interactive visualisation and processing. -
Pandas: Powerful Python Data Analysis Toolkit Release 0.25.3
pandas: powerful Python data analysis toolkit Release 0.25.3 Wes McKinney& PyData Development Team Nov 02, 2019 CONTENTS i ii pandas: powerful Python data analysis toolkit, Release 0.25.3 Date: Nov 02, 2019 Version: 0.25.3 Download documentation: PDF Version | Zipped HTML Useful links: Binary Installers | Source Repository | Issues & Ideas | Q&A Support | Mailing List pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. See the overview for more detail about whats in the library. CONTENTS 1 pandas: powerful Python data analysis toolkit, Release 0.25.3 2 CONTENTS CHAPTER ONE WHATS NEW IN 0.25.2 (OCTOBER 15, 2019) These are the changes in pandas 0.25.2. See release for a full changelog including other versions of pandas. Note: Pandas 0.25.2 adds compatibility for Python 3.8 (GH28147). 1.1 Bug fixes 1.1.1 Indexing • Fix regression in DataFrame.reindex() not following the limit argument (GH28631). • Fix regression in RangeIndex.get_indexer() for decreasing RangeIndex where target values may be improperly identified as missing/present (GH28678) 1.1.2 I/O • Fix regression in notebook display where <th> tags were missing for DataFrame.index values (GH28204). • Regression in to_csv() where writing a Series or DataFrame indexed by an IntervalIndex would incorrectly raise a TypeError (GH28210) • Fix to_csv() with ExtensionArray with list-like values (GH28840). 1.1.3 Groupby/resample/rolling • Bug incorrectly raising an IndexError when passing a list of quantiles to pandas.core.groupby. DataFrameGroupBy.quantile() (GH28113). -
Scipy and Numpy
www.it-ebooks.info SciPy and NumPy Eli Bressert Beijing • Cambridge • Farnham • Koln¨ • Sebastopol • Tokyo www.it-ebooks.info 9781449305468_text.pdf 1 10/31/12 2:35 PM SciPy and NumPy by Eli Bressert Copyright © 2013 Eli Bressert. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://my.safaribooksonline.com). For more information, contact our corporate/institutional sales department: (800) 998-9938 or [email protected]. Interior Designer: David Futato Project Manager: Paul C. Anagnostopoulos Cover Designer: Randy Comer Copyeditor: MaryEllen N. Oliver Editors: Rachel Roumeliotis, Proofreader: Richard Camp Meghan Blanchette Illustrators: EliBressert,LaurelMuller Production Editor: Holly Bauer November 2012: First edition Revision History for the First Edition: 2012-10-31 First release See http://oreilly.com/catalog/errata.csp?isbn=0636920020219 for release details. Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trademarks of O’Reilly Media, Inc. SciPy and NumPy, the image of a three-spined stickleback, and related trade dress are trademarks of O’Reilly Media, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O’Reilly Media, Inc., was aware of a trademark claim, the designations have been printed in caps or initial caps. While every precaution has been taken in the preparation of this book, the publisher and authors assume no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. -
Scikit-Learn
Scikit-Learn i Scikit-Learn About the Tutorial Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib. Audience This tutorial will be useful for graduates, postgraduates, and research students who either have an interest in this Machine Learning subject or have this subject as a part of their curriculum. The reader can be a beginner or an advanced learner. Prerequisites The reader must have basic knowledge about Machine Learning. He/she should also be aware about Python, NumPy, Scipy, Matplotlib. If you are new to any of these concepts, we recommend you take up tutorials concerning these topics, before you dig further into this tutorial. Copyright & Disclaimer Copyright 2019 by Tutorials Point (I) Pvt. Ltd. All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any contents or a part of contents of this e-book in any manner without written consent of the publisher. We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. Tutorials Point (I) Pvt. Ltd. provides no guarantee regarding the accuracy, timeliness or completeness of our website or its contents including this tutorial. -
Image Processing with Scikit-Image Emmanuelle Gouillart
Image processing with scikit-image Emmanuelle Gouillart Surface, Glass and Interfaces, CNRS/Saint-Gobain Paris-Saclay Center for Data Science Image processing Manipulating images in order to retrieve new images or image characteristics (features, measurements, ...) Often combined with machine learning Principle Some features The world is getting more and more visual Image processing Manipulating images in order to retrieve new images or image characteristics (features, measurements, ...) Often combined with machine learning Principle Some features The world is getting more and more visual Image processing Manipulating images in order to retrieve new images or image characteristics (features, measurements, ...) Often combined with machine learning Principle Some features The world is getting more and more visual Image processing Manipulating images in order to retrieve new images or image characteristics (features, measurements, ...) Often combined with machine learning Principle Some features The world is getting more and more visual Image processing Manipulating images in order to retrieve new images or image characteristics (features, measurements, ...) Often combined with machine learning Principle Some features The world is getting more and more visual Principle Some features The world is getting more and more visual Image processing Manipulating images in order to retrieve new images or image characteristics (features, measurements, ...) Often combined with machine learning Principle Some features scikit-image http://scikit-image.org/ A module of the Scientific Python stack Language: Python Core modules: NumPy, SciPy, matplotlib Application modules: scikit-learn, scikit-image, pandas, ... A general-purpose image processing library open-source (BSD) not an application (ImageJ) less specialized than other libraries (e.g. OpenCV for computer vision) 1 Principle Principle Some features 1 First steps from skimage import data , io , filter image = data . -
Sphinx As a Tool for Documenting Technical Projects 1
SPHINX AS A TOOL FOR DOCUMENTING TECHNICAL PROJECTS 1 Sphinx as a Tool for Documenting Technical Projects Javier García-Tobar Abstract—Documentation is a vital part of a technical project, yet is sometimes overlooked because of its time-consuming nature. A successful documentation system can therefore benefit relevant organizations and individuals. The focus of this paper is to summarize the main features of Sphinx: an open-source tool created to generate Python documentation formatted into standard files (HTML, PDF and ePub). Based on its results, this research recommends the use of Sphinx for the more efficient writing and managing of a project’s technical documentation. Keywords—project, documentation, Python, Sphinx. —————————— —————————— 1 INTRODUCTION In a technologically advanced and fast-paced modern socie- tation is encouraged in the field of programming because it ty, computer-stored data is found in immense quantities. gives the programmer a copy of their previous work. Fur- Writers, researchers, journalists and teachers – to name but a thermore, it helps other programmers to modify their coding, few relevant fields – have acknowledged the issue of and it benefits the pooling of information within an organi- dealing with ever-increasing quantities of data. The data can zation. appear in many forms. Therefore, most computer-literate, Software documentation should be focused on transmit- modern professionals welcome software (or hardware) that ting information that is useful and meaningful rather than aids them in managing and collating substantial amounts of information that is precise and exact (Forward and Leth- data formats. bridge, 2002). Software tools are used to document a pro- All technical projects produce a large quantity of docu- gram’s source code. -
Evolving Software Repositories
1 Evolving Software Rep ositories http://www.netli b.org/utk/pro ject s/esr/ Jack Dongarra UniversityofTennessee and Oak Ridge National Lab oratory Ron Boisvert National Institute of Standards and Technology Eric Grosse AT&T Bell Lab oratories 2 Pro ject Fo cus Areas NHSE Overview Resource Cataloging and Distribution System RCDS Safe execution environments for mobile co de Application-l evel and content-oriented to ols Rep ository interop erabili ty Distributed, semantic-based searching 3 NHSE National HPCC Software Exchange NASA plus other agencies funded CRPC pro ject Center for ResearchonParallel Computation CRPC { Argonne National Lab oratory { California Institute of Technology { Rice University { Syracuse University { UniversityofTennessee Uniform interface to distributed HPCC software rep ositories Facilitation of cross-agency and interdisciplinary software reuse Material from ASTA, HPCS, and I ITA comp onents of the HPCC program http://www.netlib.org/nhse/ 4 Goals: Capture, preserve and makeavailable all software and software- related artifacts pro duced by the federal HPCC program. Soft- ware related artifacts include algorithms, sp eci cations, designs, do cumentation, rep ort, ... Promote formation, growth, and interop eration of discipline-oriented rep ositories that organize, evaluate, and add value to individual contributions. Employ and develop where necessary state-of-the-art technologies for assisting users in nding, understanding, and using HPCC software and technologies. 5 Bene ts: 1. Faster development of high-quality software so that scientists can sp end less time writing and debugging programs and more time on research problems. 2. Less duplication of software development e ort by sharing of soft- ware mo dules. -
Data Structures for Statistical Computing in Python
56 PROC. OF THE 9th PYTHON IN SCIENCE CONF. (SCIPY 2010) Data Structures for Statistical Computing in Python Wes McKinney‡∗ F Abstract—In this paper we are concerned with the practical issues of working in financial modeling. The package’s name derives from panel with data sets common to finance, statistics, and other related fields. pandas data, which is a term for 3-dimensional data sets encountered is a new library which aims to facilitate working with these data sets and to in statistics and econometrics. We hope that pandas will help provide a set of fundamental building blocks for implementing statistical models. make scientific Python a more attractive and practical statistical We will discuss specific design issues encountered in the course of developing computing environment for academic and industry practitioners pandas with relevant examples and some comparisons with the R language. alike. We conclude by discussing possible future directions for statistical computing and data analysis using Python. Statistical data sets Index Terms—data structure, statistics, R Statistical data sets commonly arrive in tabular format, i.e. as a two-dimensional list of observations and names for the fields Introduction of each observation. Usually an observation can be uniquely Python is being used increasingly in scientific applications tra- identified by one or more values or labels. We show an example ditionally dominated by [R], [MATLAB], [Stata], [SAS], other data set for a pair of stocks over the course of several days. The commercial or open-source research environments. The maturity NumPy ndarray with structured dtype can be used to hold this and stability of the fundamental numerical libraries ([NumPy], data: [SciPy], and others), quality of documentation, and availability of >>> data "kitchen-sink" distributions ([EPD], [Pythonxy]) have gone a long array([('GOOG', '2009-12-28', 622.87, 1697900.0), ('GOOG', '2009-12-29', 619.40, 1424800.0), way toward making Python accessible and convenient for a broad ('GOOG', '2009-12-30', 622.73, 1465600.0), audience.