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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................................................. -
The Types, Roles, and Practices of Documentation in Data Analytics Open Source Software Libraries
Computer Supported Cooperative Work (CSCW) https://doi.org/10.1007/s10606-018-9333-1 © The Author(s) 2018 The Types, Roles, and Practices of Documentation in Data Analytics Open Source Software Libraries A Collaborative Ethnography of Documentation Work R. Stuart Geiger1 , Nelle Varoquaux1,2 , Charlotte Mazel-Cabasse1 & Chris Holdgraf1,3 1Berkeley Institute for Data Science, University of California, Berkeley, 190 Doe Library, Berkeley, CA, 94730, USA (E-mail: [email protected]); 2Department of Statistics, Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA; 3Berkeley Institute for Data Science, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA Abstract. Computational research and data analytics increasingly relies on complex ecosystems of open source software (OSS) “libraries” – curated collections of reusable code that programmers import to perform a specific task. Software documentation for these libraries is crucial in helping programmers/analysts know what libraries are available and how to use them. Yet documentation for open source software libraries is widely considered low-quality. This article is a collaboration between CSCW researchers and contributors to data analytics OSS libraries, based on ethnographic fieldwork and qualitative interviews. We examine several issues around the formats, practices, and challenges around documentation in these largely volunteer-based projects. There are many dif- ferent kinds and formats of documentation that exist around such libraries, which play a variety of educational, promotional, and organizational roles. The work behind documentation is similarly multifaceted, including writing, reviewing, maintaining, and organizing documentation. Different aspects of documentation work require contributors to have different sets of skills and overcome various social and technical barriers. -
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 .................................................................................... -
Manticore Search Documentation Release 3.0.2
Manticore Search Documentation Release 3.0.2 The Manticore Search team Apr 01, 2021 Manticore Documentation 1 Introduction 1 2 Gettting Started 5 2.1 Getting started using Docker container.................................5 2.2 Getting Started using official packages................................. 10 2.3 Migrating from Manticore or Sphinx Search 2.x............................ 15 2.4 A guide on configuration file....................................... 17 2.5 A guide on connectivity......................................... 19 2.6 A guide on indexes............................................ 21 2.7 A guide on searching........................................... 24 3 Installation 31 3.1 Installing Manticore packages on Debian and Ubuntu.......................... 31 3.2 Installing Manticore packages on RedHat and CentOS......................... 32 3.3 Installing Manticore on Windows.................................... 33 3.4 Upgrading from Sphinx Search..................................... 34 3.5 Running Manticore Search in a Docker Container............................ 34 3.6 Compiling Manticore from source.................................... 35 3.7 Quick Manticore usage tour....................................... 38 4 Indexing 43 4.1 Indexes.................................................. 43 4.2 Data Types................................................ 47 4.3 Full-text fields.............................................. 49 4.4 Attributes................................................. 49 4.5 MVA (multi-valued attributes)..................................... -
Advanced Search Capabilities with Mysql and Sphinx
Advanced search capabilities with MySQL and Sphinx Vladimir Fedorkov, Blackbird Andrew Aksyonoff, Sphinx Percona Live MySQL UC, 2014 Knock knock who’s there • Vladimir – Used Sphinx in production since 2006 – Performance geek – Blog http://astellar.com, twitter @vfedorkov – Works for Blackbird • Andrew – Created Sphinx, http://sphinxsearch.com – Just some random guy Search is important • This is 2014, Google spoiled everyone! • Search needs to exist • Search needs to be fast • Search needs to be relevant • Today, we aim to show you how to start – With Sphinx, obviously Available solutions • Most databases have integrated FT engines – MySQL (My and Inno), Postgres, MS SQL, Oracle… • Standalone solutions – Sphinx – Lucene / Solr – Lucene / ElasticSearch • Hosted services – IndexDen, SearchBox, Flying Sphinx, WebSolr, … Why Sphinx? • Built-in DB search sucks • Sphinx works great with DBs and MySQL • Sphinx talks SQL => zero learning curive • Fast, scalable, relevant, and other buzzwords :P • You probably heard about Lucene anyway • NEED MOAR DIVERSITY What Sphinx is not • Not a plugin to MySQL • Does not require MySQL • Not SQL-based (but we talk SQL) – Non-SQL APIs are available • Not a complete database replacement – Yet? – Ever! OLAP vs OLTP vs Column vs FTS vs Webscale Quick overview • Sphinx = standalone, open-source search server • Supports Real-time indexes • Fast – 10+ MB/sec/core indexing, 700+ qps/core searching – And counting! • Scalable – Can do a lot even on 1 box – Lets you aggregate search results from N boxes – Auto-sharding, -
Sphinxql Query Builder Release 1.0.0
SphinxQL Query Builder Release 1.0.0 Oct 12, 2018 Contents 1 Introduction 1 1.1 Compatiblity...............................................1 2 CHANGELOG 3 2.1 What’s New in 1.0.0...........................................3 3 Configuration 5 3.1 Obtaining a Connection.........................................5 3.2 Connection Parameters..........................................5 4 SphinxQL Query Builder 7 4.1 Creating a Query Builder Instance....................................7 4.2 Building a Query.............................................7 4.3 COMPILE................................................ 10 4.4 EXECUTE................................................ 10 5 Multi-Query Builder 13 6 Facets 15 7 Contribute 17 7.1 Pull Requests............................................... 17 7.2 Coding Style............................................... 17 7.3 Testing.................................................. 17 7.4 Issue Tracker............................................... 17 i ii CHAPTER 1 Introduction The SphinxQL Query Builder provides a simple abstraction and access layer which allows developers to generate SphinxQL statements which can be used to query an instance of the Sphinx search engine for results. 1.1 Compatiblity SphinxQL Query Builder is tested against the following environments: • PHP 5.6 and later • Sphinx (Stable) • Sphinx (Development) Note: It is recommended that you always use the latest stable version of Sphinx with the query builder. 1 SphinxQL Query Builder, Release 1.0.0 2 Chapter 1. Introduction CHAPTER 2 CHANGELOG 2.1 What’s New in 1.0.0 3 SphinxQL Query Builder, Release 1.0.0 4 Chapter 2. CHANGELOG CHAPTER 3 Configuration 3.1 Obtaining a Connection You can obtain a SphinxQL Connection with the Foolz\SphinxQL\Drivers\Mysqli\Connection class. <?php use Foolz\SphinxQL\Drivers\Mysqli\Connection; $conn= new Connection(); $conn->setparams(array('host' => '127.0.0.1', 'port' => 9306)); Warning: The existing PDO driver written is considered experimental as the behaviour changes between certain PHP releases. -
{ Type = Xmlpipe Xmlpipe Command = Perl /Path/To/Bin/Sphinxpipe2.Pl } Index Xmlpipe Source
Setting up Sphinx By Brett Estrade <[email protected]> http://www.justanswer.com/computer/expertbestrade/ sphinx.conf: source example_xmlpipe_source { type = xmlpipe xmlpipe_command = perl /path/to/bin/sphinxpipe2.pl } index xmlpipe_source { src = example_xmlpipe_source path = /path/to/index_file_prefix docinfo = extern } command (assuming sphinxpipe2.pl outputs valid xmlpipe2 XML): indexer config /path/to/sphinx.conf all # creates indexes The above should just create the indexes. To set up the search server (searchd), the following needs to be added to the sphinx.conf: searchd { compat_sphinxql_magics = 0 listen = 192.168.0.2:9312 listen = 192.168.0.2:9306:mysql41 log = /path/to/searchd.log query_log = /path/to/query.log read_timeout = 30 max_children = 30 pid_file = /path/to/searchd.pid max_matches = 1000000 seamless_rotate = 1 preopen_indexes = 1 unlink_old = 1 workers = threads # for RT to work binlog_path = /path/to/sphinx_binlog } Assuming that searchd is running, the index command would require a “rotate” flag to read in the updated indexes whenever updated. indexer rotate config /path/to/sphinx.conf all Searching Note that there is a MySQL compatible listening interface that is defined above using the “listen = 192.168.0.2:9306:mysql41” line. This means you can point a mysql client to “192.168.0.2:9306” and issue SELECT statements as described here: http://sphinxsearch.com/docs/archives/1.10/sphinxql.html Using the PHP Sphinx Client is covered starting at listing 12 of this article http://www.ibm.com/developerworks/library/osphpsphinxsearch/#list12 Note the difference between fields and attributes. Fields provide the text that is subject to the full text searching and indexing. -
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.