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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. -
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. -
Goless Documentation Release 0.6.0
goless Documentation Release 0.6.0 Rob Galanakis July 11, 2014 Contents 1 Intro 3 2 Goroutines 5 3 Channels 7 4 The select function 9 5 Exception Handling 11 6 Examples 13 7 Benchmarks 15 8 Backends 17 9 Compatibility Details 19 9.1 PyPy................................................... 19 9.2 Python 2 (CPython)........................................... 19 9.3 Python 3 (CPython)........................................... 19 9.4 Stackless Python............................................. 20 10 goless and the GIL 21 11 References 23 12 Contributing 25 13 Miscellany 27 14 Indices and tables 29 i ii goless Documentation, Release 0.6.0 • Intro • Goroutines • Channels • The select function • Exception Handling • Examples • Benchmarks • Backends • Compatibility Details • goless and the GIL • References • Contributing • Miscellany • Indices and tables Contents 1 goless Documentation, Release 0.6.0 2 Contents CHAPTER 1 Intro The goless library provides Go programming language semantics built on top of gevent, PyPy, or Stackless Python. For an example of what goless can do, here is the Go program at https://gobyexample.com/select reimplemented with goless: c1= goless.chan() c2= goless.chan() def func1(): time.sleep(1) c1.send(’one’) goless.go(func1) def func2(): time.sleep(2) c2.send(’two’) goless.go(func2) for i in range(2): case, val= goless.select([goless.rcase(c1), goless.rcase(c2)]) print(val) It is surely a testament to Go’s style that it isn’t much less Python code than Go code, but I quite like this. Don’t you? 3 goless Documentation, Release 0.6.0 4 Chapter 1. Intro CHAPTER 2 Goroutines The goless.go() function mimics Go’s goroutines by, unsurprisingly, running the routine in a tasklet/greenlet. -
Jupyter Tutorial Release 0.8.0
Jupyter Tutorial Release 0.8.0 Veit Schiele Oct 01, 2021 CONTENTS 1 Introduction 3 1.1 Status...................................................3 1.2 Target group...............................................3 1.3 Structure of the Jupyter tutorial.....................................3 1.4 Why Jupyter?...............................................4 1.5 Jupyter infrastructure...........................................4 2 First steps 5 2.1 Install Jupyter Notebook.........................................5 2.2 Create notebook.............................................7 2.3 Example................................................. 10 2.4 Installation................................................ 13 2.5 Follow us................................................. 15 2.6 Pull-Requests............................................... 15 3 Workspace 17 3.1 IPython.................................................. 17 3.2 Jupyter.................................................. 50 4 Read, persist and provide data 143 4.1 Open data................................................. 143 4.2 Serialisation formats........................................... 144 4.3 Requests................................................. 154 4.4 BeautifulSoup.............................................. 159 4.5 Intake................................................... 160 4.6 PostgreSQL................................................ 174 4.7 NoSQL databases............................................ 199 4.8 Application Programming Interface (API).............................. -
Pykota Documentation
PyKota Documentation A full featured Print Quota Solution for CUPS Jérôme Alet C@LL - Conseil Internet & Logiciels Libres 48 Avenue Vincent Arnaud 06300 Nice France Email : [email protected] Phone : +33 (0) 4 93 27 12 98 PyKota Documentation: A full featured Print Quota Solution for CUPS by Jérôme Alet Copyright © 2003, 2004, 2005, 2006 Jerome Alet Abstract This document will describe the setup of the PyKota Print Quota system for the Common UNIX Printing System, and its day-to-day use. Legal Notice This documentation is licensed under the terms of the GNU General Public License as described below. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. A copy of the entire license is included in the section entitled "GNU General Public License". In case you encounter any problem, please contact Jérôme Alet (mailto:[email protected]) , author of the present document. $Date: 2006-12-03 11:50:20 +0100 (dim, 03 déc 2006) $ $Revision: 2422 $ Table of Contents 1. -
Performance Research and Optimization on Cpython's Interpreter
Proceedings of the Federated Conference on DOI: 10.15439/2015F139 Computer Science and Information Systems pp. 435–441 ACSIS, Vol. 5 Performance Research and Optimization on CPython’s Interpreter Huaxiong Cao, Naijie Gu1, Kaixin Ren, and Yi Li 1) Department of Computer Science and Technology, University of Science and Technology of China 2) Anhui Province Key Laboratory of Computing and Communication Software 3) Institute of Advanced Technology, University of Science and Technology of China Hefei, China, 230027 Email: [email protected], [email protected] Abstract—In this paper, the performance research on monitoring programs’ behavior and using this information to CPython’s latest interpreter is presented, concluding that drive optimization decisions [1]. The dominant concepts that bytecode dispatching takes about 25 percent of total execution have influenced effective optimization technologies in today’s time on average. Based on this observation, a novel bytecode virtual machines include JIT compilers, interpreters, and their dispatching mechanism is proposed to reduce the time spent on this phase to a minimum. With this mechanism, the blocks integrations. associated with each kind of bytecodes are rewritten in JIT (just-in-time) techniques exploit the well-known fact that hand-tuned assembly, their opcodes are renumbered, and their large scale programs usually spend the majority of time on a memory spaces are rescheduled. With these preparations, this small fraction of the code [2]. During the execution of new bytecode dispatching mechanism replaces the interpreters, they record the bytecode blocks which have been time-consuming memory reading operations with rapid executed more than a specified number of times, and cache the operations on registers. -
Pdf for a Detailed Explanation, Along with Various Techniques for Debouncing
MicroPython Documentation Release 1.11 Damien P. George, Paul Sokolovsky, and contributors May 29, 2019 CONTENTS i ii CHAPTER ONE MICROPYTHON LIBRARIES Warning: Important summary of this section • MicroPython implements a subset of Python functionality for each module. • To ease extensibility, MicroPython versions of standard Python modules usually have u (“micro”) prefix. • Any particular MicroPython variant or port may miss any feature/function described in this general docu- mentation (due to resource constraints or other limitations). This chapter describes modules (function and class libraries) which are built into MicroPython. There are a few categories of such modules: • Modules which implement a subset of standard Python functionality and are not intended to be extended by the user. • Modules which implement a subset of Python functionality, with a provision for extension by the user (via Python code). • Modules which implement MicroPython extensions to the Python standard libraries. • Modules specific to a particular MicroPython port and thus not portable. Note about the availability of the modules and their contents: This documentation in general aspires to describe all modules and functions/classes which are implemented in MicroPython project. However, MicroPython is highly configurable, and each port to a particular board/embedded system makes available only a subset of MicroPython libraries. For officially supported ports, there is an effort to either filter out non-applicable items, or mark individual descriptions with “Availability:” clauses describing which ports provide a given feature. With that in mind, please still be warned that some functions/classes in a module (or even the entire module) described in this documentation may be unavailable in a particular build of MicroPython on a particular system. -
Writing a Custom Rpython-Based DSL Interpreter to Solve Pdes
Sean Fisk, Grand Valley State University Advisor: Davide Del Vento, NCAR SIParCS 2013 1 Huh? • DSL: domain-specific language – Often a programming language • PDE: partial differential equation – Relates rate of change to continuous variables – Can be solved analytically or numerically • RPython – A specialized version of Python used for writing interpreters SIParCS 2013 2 Goal Design and build a simple programming language to implement finite difference using stencils. SIParCS 2013 3 Finite Difference • A way to solve partial differential equations • Frequently used in weather simulations • Specify a grid, a transformation matrix (stencil), and time step SIParCS 2013 4 Stencils – A Simple Example Matrix to transform (M) Stencil (S) Transformed Matrix Other cells use a wrap-around. SIParCS 2013 5 Stencils – End Result Matrix to transform Stencil Transformed matrix SIParCS 2013 6 Stencil Language Instruction Action STO Rx V Store a value in register Rx ADD Rx V Add value V to register Rx PR Rx Print value in register Rx CMX Mx A B Create matrix Mx with dimension (A, B) SMX Mx … Set values of Mx with A × B arguments SMXF Mx “filename” Set values of Mx from file “filename” PMX Mx Print matrix Mx PDE Mx My Apply stencil Mx to My and store result in My BNE Rx V L Branch to relative location L if Rx ≠ V • Registers use integers • Matrices use real numbers (floats) • Matrices must have odd dimensions SIParCS 2013 7 Sample Program # Create matrices CMX 0 3 3 SMXF 0 "sample-programs/matrices/stencil" CMX 1 3 3 SMXF 1 "sample-programs/matrices/psi" -
Comparative Studies of Six Programming Languages
Comparative Studies of Six Programming Languages Zakaria Alomari Oualid El Halimi Kaushik Sivaprasad Chitrang Pandit Concordia University Concordia University Concordia University Concordia University Montreal, Canada Montreal, Canada Montreal, Canada Montreal, Canada [email protected] [email protected] [email protected] [email protected] Abstract Comparison of programming languages is a common topic of discussion among software engineers. Multiple programming languages are designed, specified, and implemented every year in order to keep up with the changing programming paradigms, hardware evolution, etc. In this paper we present a comparative study between six programming languages: C++, PHP, C#, Java, Python, VB ; These languages are compared under the characteristics of reusability, reliability, portability, availability of compilers and tools, readability, efficiency, familiarity and expressiveness. 1. Introduction: Programming languages are fascinating and interesting field of study. Computer scientists tend to create new programming language. Thousand different languages have been created in the last few years. Some languages enjoy wide popularity and others introduce new features. Each language has its advantages and drawbacks. The present work provides a comparison of various properties, paradigms, and features used by a couple of popular programming languages: C++, PHP, C#, Java, Python, VB. With these variety of languages and their widespread use, software designer and programmers should to be aware -
Benchmarking Python Interpreters
DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING 300 CREDITS, SECOND CYCLE STOCKHOLM, SWEDEN 2016 Benchmarking Python Interpreters MEASURING PERFORMANCE OF CPYTHON, CYTHON, JYTHON AND PYPY ALEXANDER ROGHULT KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF COMPUTER SCIENCE AND COMMUNICATION Benchmarking Python Interpreters Measuring Performance of CPython, Cython, Jython and PyPy ALEXANDER ROGHULT Master’s Thesis in Computer Science School of Computer Science and Communication(CSC) Royal Institute of Technology, Stockholm Supervisor: Douglas Wikström Examiner: Johan Håstad Abstract For the Python programming language there are several different interpreters and implementations. In this thesis project the performance regarding execution time is evalu- ated for four of these; CPython, Cython, Jython and PyPy. The performance was measured in a test suite, created dur- ing the project, comprised of tests for Python dictionaries, lists, tuples, generators and objects. Each test was run with both integers and objects as test data with varying prob- lem size. Each test was implemented using Python code. For Cython and Jython separate versions of the test were also implemented which contained syntax and data types specific for that interpreter. The results showed that Jython and PyPy were fastest for a majority of the tests when running code with only Python syntax and data types. Cython uses the Python C/API and is therefore dependent on CPython. The per- formance of Cython was therefore often similar to CPython. Cython did perform better on some of the tests when using Cython syntax and data types, as it could therefore de- crease its reliance to CPython. Each interpreter was able to perform fastest on at least one test, showing that there is not an interpreter that is best for all problems. -
Pypy's Approach to Virtual Machine Construction
PyPy’s Approach to Virtual Machine Construction Armin Rigo Samuele Pedroni Heinrich-Heine-Universitat¨ Dusseldorf¨ AB Strakt Institut fur¨ Informatik Norra Agatan˚ 10A Universitatsstraße¨ 1 416 64 Goteborg¨ D-40225 Dusseldorf¨ Sweden Deutschland [email protected] [email protected] Abstract in use by writing three versions of his virtual machine: for C/Posix, The PyPy project seeks to prove both on a research and a practical for Java, and for CLI/.NET. This is, at least, the current situation of level the feasibility of constructing a virtual machine (VM) for a the Python programming language, where independent volunteers dynamic language in a dynamic language – in this case, Python. have developed and are now maintaining Java and .NET versions The aim is to translate (i.e. compile) the VM to arbitrary target of Python, which follow the evolution of the “official” C version environments, ranging in level from C/Posix to Smalltalk/Squeak (CPython). via Java and CLI/.NET, while still being of reasonable efficiency However, we believe that platform standardization does not within these environments. have to be a necessary component of this equation. We are basically A key tool to achieve this goal is the systematic reuse of the using the standard “meta-programming” argument: if one could Python language as a system programming language at various write the VM at a sufficiently high level of abstraction in a language levels of our architecture and translation process. For each level, supporting such level, then the VM itself could be automatically we design a corresponding type system and apply a generic type translated to any lower-level platform. -
Python Development Documentation Release 1.0
Python Development Documentation Release 1.0 Victor Stinner Sep 24, 2021 Contents 1 Python 3 3 2 Python Packaging 9 3 Python Date and Time 13 4 Python Numbers 15 5 Python Continuous Integration: Travis CI, AppVeyor, VSTS, Buildbots 23 6 Python Debug Tools 29 7 Debug CPython with gdb 37 8 Unstable tests 41 9 External libraries and Generated files 47 10 Supported platforms and architectures 51 11 Downstream patches 57 12 Python on Android 59 13 Python Development Workflow 65 14 CPython infrastructure 67 15 Python Memory 71 16 Compile CPython on Windows 75 17 Price of the portability 79 18 C hacks in CPython source code 83 19 Bugs found by the Python project 87 20 Python Finalization 89 i 21 Python builds 95 22 Python Subinterpreters 99 23 Add a field to PyConfig 105 24 Embed Python in an application 109 25 Python C API 111 26 Python Thread State 113 27 Fork in Python 117 28 Test the next Python 121 29 Popular third party extension modules 123 30 Analysis of CPython binary assembly 125 31 Python garbage collector 129 32 Python performance 131 33 CPython implementation optimizations 133 34 Python Startup Time 137 35 PEP 393 performances 141 36 Python community 143 37 Diversity 147 38 Communication Channels 153 39 CPython mentoring 155 40 CPython Core Developers 157 41 CPython tasks 161 42 History of Python 163 43 Misc notes 167 44 See also 171 45 Indices and tables 173 Index 175 ii Python Development Documentation, Release 1.0 by Victor Stinner. Python: Contents 1 Python Development Documentation, Release 1.0 2 Contents CHAPTER 1 Python 3 Python 3.0 was released in December 2008.