Python for Software Development
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A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya 1 Introduction
A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya Sai K. Popuri and Matthias K. Gobbert* Department of Mathematics and Statistics, University of Maryland, Baltimore County *Corresponding author: [email protected], www.umbc.edu/~gobbert Technical Report HPCF{2017{3, hpcf.umbc.edu > Publications Abstract Matlab is the most popular commercial package for numerical computations in mathematics, statistics, the sciences, engineering, and other fields. Octave is a freely available software used for numerical computing. R is a popular open source freely available software often used for statistical analysis and computing. Julia is a recent open source freely available high-level programming language with a sophisticated com- piler for high-performance numerical and statistical computing. They are all available to download on the Linux, Windows, and Mac OS X operating systems. We investigate whether the three freely available software are viable alternatives to Matlab for uses in research and teaching. We compare the results on part of the equipment of the cluster maya in the UMBC High Performance Computing Facility. The equipment has 72 nodes, each with two Intel E5-2650v2 Ivy Bridge (2.6 GHz, 20 MB cache) proces- sors with 8 cores per CPU, for a total of 16 cores per node. All nodes have 64 GB of main memory and are connected by a quad-data rate InfiniBand interconnect. The tests focused on usability lead us to conclude that Octave is the most compatible with Matlab, since it uses the same syntax and has the native capability of running m-files. R was hampered by somewhat different syntax or function names and some missing functions. -
Ironpython in Action
IronPytho IN ACTION Michael J. Foord Christian Muirhead FOREWORD BY JIM HUGUNIN MANNING IronPython in Action Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> IronPython in Action MICHAEL J. FOORD CHRISTIAN MUIRHEAD MANNING Greenwich (74° w. long.) Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> For online information and ordering of this and other Manning books, please visit www.manning.com. The publisher offers discounts on this book when ordered in quantity. For more information, please contact Special Sales Department Manning Publications Co. Sound View Court 3B fax: (609) 877-8256 Greenwich, CT 06830 email: [email protected] ©2009 by Manning Publications Co. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps. Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Recognizing also our responsibility to conserve the resources of our planet, Manning books are printed on paper that is at least 15% recycled and processed without the use of elemental chlorine. -
Data Visualization in Python
Data visualization in python Day 2 A variety of packages and philosophies • (today) matplotlib: http://matplotlib.org/ – Gallery: http://matplotlib.org/gallery.html – Frequently used commands: http://matplotlib.org/api/pyplot_summary.html • Seaborn: http://stanford.edu/~mwaskom/software/seaborn/ • ggplot: – R version: http://docs.ggplot2.org/current/ – Python port: http://ggplot.yhathq.com/ • Bokeh (live plots in your browser) – http://bokeh.pydata.org/en/latest/ Biocomputing Bootcamp 2017 Matplotlib • Gallery: http://matplotlib.org/gallery.html • Top commands: http://matplotlib.org/api/pyplot_summary.html • Provides "pylab" API, a mimic of matlab • Many different graph types and options, some obscure Biocomputing Bootcamp 2017 Matplotlib • Resulting plots represented by python objects, from entire figure down to individual points/lines. • Large API allows any aspect to be tweaked • Lengthy coding sometimes required to make a plot "just so" Biocomputing Bootcamp 2017 Seaborn • https://stanford.edu/~mwaskom/software/seaborn/ • Implements more complex plot types – Joint points, clustergrams, fitted linear models • Uses matplotlib "under the hood" Biocomputing Bootcamp 2017 Others • ggplot: – (Original) R version: http://docs.ggplot2.org/current/ – A recent python port: http://ggplot.yhathq.com/ – Elegant syntax for compactly specifying plots – but, they can be hard to tweak – We'll discuss this on the R side tomorrow, both the basics of both work similarly. • Bokeh – Live, clickable plots in your browser! – http://bokeh.pydata.org/en/latest/ -
Mastering Machine Learning with Scikit-Learn
www.it-ebooks.info Mastering Machine Learning with scikit-learn Apply effective learning algorithms to real-world problems using scikit-learn Gavin Hackeling BIRMINGHAM - MUMBAI www.it-ebooks.info Mastering Machine Learning with scikit-learn Copyright © 2014 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: October 2014 Production reference: 1221014 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78398-836-5 www.packtpub.com Cover image by Amy-Lee Winfield [email protected]( ) www.it-ebooks.info Credits Author Project Coordinator Gavin Hackeling Danuta Jones Reviewers Proofreaders Fahad Arshad Simran Bhogal Sarah -
Python Qt Tutorial Documentation Release 0.0
Python Qt tutorial Documentation Release 0.0 Thomas P. Robitaille Jun 11, 2018 Contents 1 Installing 3 2 Part 1 - Hello, World! 5 3 Part 2 - Buttons and events 7 4 Part 3 - Laying out widgets 9 5 Part 4 - Dynamically updating widgets 13 i ii Python Qt tutorial Documentation, Release 0.0 This is a short tutorial on using Qt from Python. There are two main versions of Qt in use (Qt4 and Qt5) and several Python libraries to use Qt from Python (PyQt and PySide), but rather than picking one of these, this tutorial makes use of the QtPy package which provides a way to use whatever Python Qt package is available. This is not meant to be a completely exhaustive tutorial but just a place to start if you’ve never done Qt development before, and it will be expanded over time. Contents 1 Python Qt tutorial Documentation, Release 0.0 2 Contents CHAPTER 1 Installing 1.1 conda If you use conda to manage your Python environment (for example as part of Anaconda or Miniconda), you can easily install Qt, PyQt5, and QtPy (a common interface to all Python Qt bindings) using: conda install pyqt qtpy 1.2 pip If you don’t make use of conda, an easy way to install Qt, PyQt5, and QtPy is to do: pip install pyqt5 qtpy 3 Python Qt tutorial Documentation, Release 0.0 4 Chapter 1. Installing CHAPTER 2 Part 1 - Hello, World! To start off, we need to create a QApplication object, which represents the overall application: from qtpy.QtWidgets import QApplication app= QApplication([]) You will always need to ensure that a QApplication object exists, otherwise your Python script will terminate with an error if you try and use any other Qt objects. -
Testing Pyside/Pyqt Code Using the Pytest Framework and Pytest-Qt
Testing PySide/PyQt Code Using the pytest framework and pytest-qt Florian Bruhin “The Compiler” Bruhin Software 06. November 2019 Qt World Summit, Berlin About me • 2011: Started using Python • 2013: Started using PyQt and developing qutebrowser • 2015: Switched to pytest, ended up as a maintainer • 2017: qutebrowser v1.0.0, QtWebEngine by default • 2019: 40% employed, 60% open-source and freelancing (Bruhin Software) Giving trainings and talks at various conferences and companies! Relevant Python features Decorators registered_functions: List[Callable] = [] def register(f: Callable) -> Callable: registered_functions.append(f) return f @register def func() -> None: .... Relevant Python features Context Managers def write_file() -> None: with open("test.txt", "w") as f: f.write("Hello World") Defining your own: Object with special __enter__ and __exit__ methods. Relevant Python features Generators/yield def gen_values() -> Iterable[int] for i in range(4): yield i print(gen_values()) # <generator object gen_values at 0x...> print(list(gen_values())) # [0, 1, 2, 3] PyQt • Started in 1998 (!) by Riverbank Computing • GPL/commercial • Qt4 $ PyQt4 Qt5 $ PyQt5 PySide / Qt for Python • Started in 2009 by Nokia • Unmaintained for a long time • Since 2016: Officially maintained by the Qt Company again • LGPL/commercial • Qt4 $ PySide Qt5 $ PySide2 (Qt for Python) Qt and Python import sys from PySide2.QtWidgets import QApplication, QWidget, QPushButton if __name__ == "__main__": app = QApplication(sys.argv) window = QWidget() button = QPushButton("Don't -
The Essentials of Stackless Python Tuesday, 10 July 2007 10:00 (30 Minutes)
EuroPython 2007 Contribution ID: 62 Type: not specified The Essentials of Stackless Python Tuesday, 10 July 2007 10:00 (30 minutes) This is a re-worked, actualized and improved version of my talk at PyCon 2007. Repeating the abstract: As a surprise for people who think they know Stackless, we present the new Stackless implementation For PyPy, which has led to a significant amount of new insight about parallel programming and its possible implementations. We will isolate the known Stackless as a special case of a general concept. This is a Stackless, not a PyPy talk. But the insights presented here would not exist without PyPy’s existance. Summary Stackless has been around for a long time now. After several versions with different goals in mind, the basic concepts of channels and tasklets turned out to be useful abstractions, and since many versions, Stackless is only ported from version to version, without fundamental changes to the principles. As some spin-off, Armin Rigo invented Greenlets at a Stackless sprint. They are some kind of coroutines and a bit of special semantics. The major benefit is that Greenlets can runon unmodified CPython. In parallel to that, the PyPy project is in its fourth year now, and one of its goals was Stackless integration as an option. And of course, Stackless has been integrated into PyPy in a very nice and elegant way, much nicer than expected. During the design of the Stackless extension to PyPy, it turned out, that tasklets, greenlets and coroutines are not that different in principle, and it was possible to base all known parallel paradigms on one simple coroutine layout, which is as minimalistic as possible. -
List of Applications Updated in ARL #2530
List of Applications Updated in ARL #2530 Application Name Publisher .NET Core SDK 2 Microsoft Acrobat Elements Adobe Acrobat Elements 10 Adobe Acrobat Elements 11.0 Adobe Acrobat Elements 15.1 Adobe Acrobat Elements 15.7 Adobe Acrobat Elements 15.9 Adobe Acrobat Elements 6.0 Adobe Acrobat Elements 7.0 Adobe Application Name Acrobat Elements 8 Adobe Acrobat Elements 9 Adobe Acrobat Reader DC Adobe Acrobat.com 1 Adobe Alchemy OpenText Alchemy 9.0 OpenText Amazon Drive 4.0 Amazon Amazon WorkSpaces 1.1 Amazon Amazon WorkSpaces 2.1 Amazon Amazon WorkSpaces 2.2 Amazon Amazon WorkSpaces 2.3 Amazon Ansys Ansys Archive Server 10.1 OpenText AutoIt 2.6 AutoIt Team AutoIt 3.0 AutoIt Team AutoIt 3.2 AutoIt Team Azure Data Studio 1.9 Microsoft Azure Information Protection 1.0 Microsoft Captiva Cloud Toolkit 3.0 OpenText Capture Document Extraction OpenText CloneDVD 2 Elaborate Bytes Cognos Business Intelligence Cube Designer 10.2 IBM Cognos Business Intelligence Cube Designer 11.0 IBM Cognos Business Intelligence Cube Designer for Non-Production environment 10.2 IBM Commons Daemon 1.0 Apache Software Foundation Crystal Reports 11.0 SAP Data Explorer 8.6 Informatica DemoCreator 3.5 Wondershare Software Deployment Wizard 9.3 SAS Institute Deployment Wizard 9.4 SAS Institute Desktop Link 9.7 OpenText Desktop Viewer Unspecified OpenText Document Pipeline DocTools 10.5 OpenText Dropbox 1 Dropbox Dropbox 73.4 Dropbox Dropbox 74.4 Dropbox Dropbox 75.4 Dropbox Dropbox 76.4 Dropbox Dropbox 77.4 Dropbox Dropbox 78.4 Dropbox Dropbox 79.4 Dropbox Dropbox 81.4 -
Key Benefits Key Features
With the MapleSim LabVIEW®/VeriStand™ Connector, you can • Includes a set of Maple language commands, which extend your LabVIEW and VeriStand applications by integrating provides programmatic access to all functionality as an MapleSim’s high-performance, multi-domain environment alternative to the interactive interface and supports into your existing toolchain. The MapleSim LabVIEW/ custom application development. VeriStand Connector accelerates any project that requires • Supports both the External Model Interface (EMI) and high-fidelity engineering models for hardware-in-the-loop the Simulation Interface Toolkit (SIT). applications, such as component testing and electronic • Allows generated block code to be viewed and modified. controller development and integration. • Automatically generates an HTML help page for each block for easy lookup of definitions and parameter Key Benefits defaults. • Complex engineering system models can be developed and optimized rapidly in the intuitive visual modeling environment of MapleSim. • The high-performance, high-fidelity MapleSim models are automatically converted to user-code blocks for easy inclusion in your LabVIEW VIs and VeriStand Applications. • The model code is fully optimized for high-speed real- time simulation, allowing you to get the performance you need for hardware-in-the-loop (HIL) testing without sacrificing fidelity. Key Features • Exports MapleSim models to LabVIEW and VeriStand, including rotational, translational, and multibody mechanical systems, thermal models, and electric circuits. • Creates ANSI C code blocks for fast execution within LabVIEW, VeriStand, and the corresponding real-time platforms. • Code blocks are created from the symbolically simplified system equations produced by MapleSim, resulting in compact, highly efficient models. • The resulting code is further optimized using the powerful optimization tools in Maple, ensuring fast execution. -
Gui Programming Using Tkinter
1 GUI PROGRAMMING USING TKINTER Cuauhtémoc Carbajal ITESM CEM April 17, 2013 2 Agenda • Introduction • Tkinter and Python Programming • Tkinter Examples 3 INTRODUCTION 4 Introduction • In this lecture, we will give you a brief introduction to the subject of graphical user interface (GUI) programming. • We cannot show you everything about GUI application development in just one lecture, but we will give you a very solid introduction to it. • The primary GUI toolkit we will be using is Tk, Python’s default GUI. We’ll access Tk from its Python interface called Tkinter (short for “Tk interface”). • Tk is not the latest and greatest, nor does it have the most robust set of GUI building blocks, but it is fairly simple to use, and with it, you can build GUIs that run on most platforms. • Once you have completed this lecture, you will have the skills to build more complex applications and/or move to a more advanced toolkit. Python has bindings or adapters to most of the current major toolkits, including commercial systems. 5 What Are Tcl, Tk, and Tkinter? • Tkinter is Python’s default GUI library. It is based on the Tk toolkit, originally designed for the Tool Command Language (Tcl). Due to Tk’s popularity, it has been ported to a variety of other scripting languages, including Perl (Perl/Tk), Ruby (Ruby/Tk), and Python (Tkinter). • The combination of Tk’s GUI development portability and flexibility along with the simplicity of a scripting language integrated with the power of systems language gives you the tools to rapidly design and implement a wide variety of commercial-quality GUI applications. -
KDE Free Qt Foundation Strengthens Qt
How the KDE Free Qt Foundation strengthens Qt by Olaf Schmidt-Wischhöfer (board member of the foundation)1, December 2019 Executive summary The development framework Qt is available both as Open Source and under paid license terms. Two decades ago, when Qt 2.0 was first released as Open Source, this was excep- tional. Today, most popular developing frameworks are Free/Open Source Software2. Without the dual licensing approach, Qt would not exist today as a popular high-quality framework. There is another aspect of Qt licensing which is still very exceptional today, and which is not as well-known as it ought to be. The Open Source availability of Qt is legally protected through the by-laws and contracts of a foundation. 1 I thank Eike Hein, board member of KDE e.V., for contributing. 2 I use the terms “Open Source” and “Free Software” interchangeably here. Both have a long history, and the exact differences between them do not matter for the purposes of this text. How the KDE Free Qt Foundation strengthens Qt 2 / 19 The KDE Free Qt Foundation was created in 1998 and guarantees the continued availabil- ity of Qt as Free/Open Source Software3. When it was set up, Qt was developed by Troll- tech, its original company. The foundation supported Qt through the transitions first to Nokia and then to Digia and to The Qt Company. In case The Qt Company would ever attempt to close down Open Source Qt, the founda- tion is entitled to publish Qt under the BSD license. This notable legal guarantee strengthens Qt. -
Installing Python Ø Basics of Programming • Data Types • Functions • List Ø Numpy Library Ø Pandas Library What Is Python?
Python in Data Science Programming Ha Khanh Nguyen Agenda Ø What is Python? Ø The Python Ecosystem • Installing Python Ø Basics of Programming • Data Types • Functions • List Ø NumPy Library Ø Pandas Library What is Python? • “Python is an interpreted high-level general-purpose programming language.” – Wikipedia • It supports multiple programming paradigms, including: • Structured/procedural • Object-oriented • Functional The Python Ecosystem Source: Fabien Maussion’s “Getting started with Python” Workshop Installing Python • Install Python through Anaconda/Miniconda. • This allows you to create and work in Python environments. • You can create multiple environments as needed. • Highly recommended: install Python through Miniconda. • Are we ready to “play” with Python yet? • Almost! • Most data scientists use Python through Jupyter Notebook, a web application that allows you to create a virtual notebook containing both text and code! • Python Installation tutorial: [Mac OS X] [Windows] For today’s seminar • Due to the time limitation, we will be using Google Colab instead. • Google Colab is a free Jupyter notebook environment that runs entirely in the cloud, so you can run Python code, write report in Jupyter Notebook even without installing the Python ecosystem. • It is NOT a complete alternative to installing Python on your local computer. • But it is a quick and easy way to get started/try out Python. • You will need to log in using your university account (possible for some schools) or your personal Google account. Basics of Programming Indentations – Not Braces • Other languages (R, C++, Java, etc.) use braces to structure code. # R code (not Python) a = 10 if (a < 5) { result = TRUE b = 0 } else { result = FALSE b = 100 } a = 10 if (a < 5) { result = TRUE; b = 0} else {result = FALSE; b = 100} Basics of Programming Indentations – Not Braces • Python uses whitespaces (tabs or spaces) to structure code instead.