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Introduction to Eclipse and Pydev Fall 2021 Page 1 of 6
Introduction to Eclipse and PyDev Fall 2021 Page 1 of 6 Introduction to Eclipse and PyDev We will be using Eclipse PyDev plugin in this course to create and run Python programs. Eclipse is a platform that can be used for application development tasks using different languages, including Java and Python. A separate handout describes how to install Eclipse and its Python plugin - PyDev. This handout will take you through the process of creating and running a Python program. Specifying the Eclipse workspace When you start Eclipse, you will be asked to specify the directory in which to store your projects. By default, on Windows, it creates a folder called eclipse-workspace in your personal folder Use this directory or another of your choice. To switch the workspace to a different folder, after starting Eclipse, go to File > Switch Workspace and select the desired folder. Creating a new PyDev project To create and run a Python program in Eclipse, you must form a project for it: 1. Create a new PyDev Project by selecting File > New > Project>PyDev > PyDev Project. This will open the PyDev Project dialog window (see Error! Reference source not found.) 2. In the dialog window: • Enter a project name of your choice into the Project name field. 3. In the PyDev Project dialog box ( Figure 1), make sure the right interpreter is selected (To make sure python version 3 is selected, click on Configure the interpreter not listed link verifying that the selected interpreter’s location should be within anaconda3 installation, i.e. mention anaconda3 in the path (Figure 2) ) Click on Finish. -
Mysql NDB Cluster 7.5.16 (And Later)
Licensing Information User Manual MySQL NDB Cluster 7.5.16 (and later) Table of Contents Licensing Information .......................................................................................................................... 2 Licenses for Third-Party Components .................................................................................................. 3 ANTLR 3 .................................................................................................................................... 3 argparse .................................................................................................................................... 4 AWS SDK for C++ ..................................................................................................................... 5 Boost Library ............................................................................................................................ 10 Corosync .................................................................................................................................. 11 Cyrus SASL ............................................................................................................................. 11 dtoa.c ....................................................................................................................................... 12 Editline Library (libedit) ............................................................................................................. 12 Facebook Fast Checksum Patch .............................................................................................. -
Introduction to Python for IBM I
Introduction to Python for IBM i Mike Pavlak – IT Strategist [email protected] Agenda ■ A little about Python ■ Why use Python ■ How to install/determine if installed ▶IDE ■ Syntax 101 ▶Variables ▶Strings ▶Functions 2 Acknowledgements ■ Kevin Adler ■ Tony Cairns ■ Jesse Gorzinski ■ Google ■ Memegenerator ■ Corn chips and salsa ■ Parrots ■ And, of course, spam 3 A little about Python What is it, really? ■ General purpose language ■ Easy to get started ■ Simple syntax ■ Great for integrations (glue between systems) ■ Access to C and other APIs ■ Infrastructure first, but applications, too 5 Historically… ■ Python was conceptualized by Guido Van Rossum in the late 1980’s ■ Rossum published the first version of Python code (0.9.0) in February of 1991 at the CWI(Centrum Wiskunde & Informatica) in the Netherlands, Amsterdam ■ Python is derived from the ABC programming language, which is a general purpose language that was also developed at CWI. ■ Rossum chose the name “Python” since he was a fan of Monty Python’s Flying Circus. ■ Python is now maintained by a core development team at the institute, although Rossum still holds a vital role in directing its progress and as leading “commitor”. 6 Python lineage ■ Python 1 – 1994 ■ Python 2 – 2000 (Not dead yet…) ▶2.7 – 2010 ■ Python 3 – 2008 ▶3.5 – 2015 ▶3.6.2 – July 2017 ▶3.7 ➔ ETA July 2018 7 Python 2 or 3? 8 What’s the diff? ■ Example: ▶Python 2 print statement replaced by function ● Python 2 – print “Hello World!” ● Python 3 – print(“Hello World!”) ■ Many more differences, tho… -
Efficient Parallel I/O on Multi-Core Architectures
Lecture series title/ lecture title Efficient parallel I/O on multi-core architectures Adrien Devresse CERN IT-SDC-ID Thematic CERN School of Computing 2014 1 Author(s) names – Affiliation Lecture series title/ lecture title How to make I/O bound application scale with multi-core ? What is an IO bound application ? → A server application → A job that accesses big number of files → An application that uses intensively network 2 Author(s) names – Affiliation Lecture series title/ lecture title Stupid example: Simple server monothreaded // create socket socket_desc = socket(AF_INET , SOCK_STREAM , 0); // bind the socket bind(socket_desc,(struct sockaddr *)&server , sizeof(server)); listen(socket_desc , 100); //accept connection from an incoming client while(1){ // declarations client_sock = accept(socket_desc, (struct sockaddr *)&client, &c); //Receive a message from client while( (read_size = recv(client_sock , client_message , 2000 , 0)) > 0{ // Wonderful, we have a client, do some useful work std::string msg("hello bob"); write(client_sock, msg.c_str(), msg.size()); } } 3 Author(s) names – Affiliation Lecture series title/ lecture title Stupid example: Let's make it parallel ! int main(int argc, char** argv){ // creat socket void do_work(int socket){ socket_desc = socket(AF_INET , SOCK_STREAM , 0); //Receive a message while( (read_size = // bind the socket recv(client_sock , bind(socket_desc, server , sizeof(server)); client_message , 2000 , 0)) > 0{ listen(socket_desc , 100); // Wonderful, we have a client // useful works //accept connection -
Message Passing and Network Programming
Message Passing and Network Programming Advanced Operating Systems Lecture 13 Colin Perkins | https://csperkins.org/ | Copyright © 2017 | This work is licensed under the Creative Commons Attribution-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nd/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. Lecture Outline • Actors, sockets, and network protocols • Asynchronous I/O frameworks • Higher level abstractions Colin Perkins | https://csperkins.org/ | Copyright © 2017 2 Message Passing and Network Protocols • Recap: • Actor-based framework for message passing Send to • Each actor has a receive loop other actors Mailbox Actor Calls to one function per state Queue • Receive Message • Messages delivered by runtime system; Receiver processed sequentially Message Done Message Process • Actor can send messages in reply; Message Dispatcher return identity of next state Dequeue • Can we write network code this way? Request next • Send data by sending a message to an actor representing a socket • Receive messages representing data received on a socket Colin Perkins | https://csperkins.org/ | Copyright © 2017 3 Integrating Actors and Sockets Sending Thread Send to other actors Encoder Network Socket Mailbox Actor Queue Parser Receive Message Receiver Message Done Receiving Thread Message Process Message Dispatcher • Conceptually straightforward to integrate Dequeue actors with network code Request next • Runtime system maintains sending and -
Python-101.Pdf
What is Python? I Object-oriented, rapid prototyping language I Multi-purpose: Web, GUI, scripting, science, etc. I Interpreted and interactive; very easy to learn I Strongly typed and dynamically typed I Focus on readability and productivity I Extensive standard library and community I CPython, Jython, IronPython, PyPy, etc. I Home page: http://www.python.org/ Python 101 1 Who uses Python? I Google (various projects) I NASA (several projects) I NYSE (one of only three languages \on the floor") I Industrial Light & Magic (everything) I Yahoo! (Yahoo mail & groups) I RealNetworks (function and load testing) I RedHat and Ubuntu (Linux installation tools) I LLNL, Fermilab (steering scientific applications) More success stories at http://www.pythonology.com/ Python 101 2 Python vs Java Python Java I Dynamically typed I Statically typed (assign names to values) (must declare variables) I Concise: \expressing much I Verbose: \abounding in in a few words; implies words; using or containing clean-cut brevity, attained by more words than are excision of the superfluous” necessary" import java.io.*; ... # open the filed.txt // open the filed.txt myFile = open("d.txt") BufferedReader myFile = new BufferedReader( new FileReader("d.txt")); http://pythonconquerstheuniverse.wordpress.com/category/java-and-python/ Python 101 3 Some Basics I Indentation matters (no braces!) I Type matters (must be consistent) I Comments start with a # I Strings use 's or "s (or """s) I print (vs System.out.println) I None ≈ null, self ≈ this I and, or, not instead of &&, -
Sage Tutorial (Pdf)
Sage Tutorial Release 9.4 The Sage Development Team Aug 24, 2021 CONTENTS 1 Introduction 3 1.1 Installation................................................4 1.2 Ways to Use Sage.............................................4 1.3 Longterm Goals for Sage.........................................5 2 A Guided Tour 7 2.1 Assignment, Equality, and Arithmetic..................................7 2.2 Getting Help...............................................9 2.3 Functions, Indentation, and Counting.................................. 10 2.4 Basic Algebra and Calculus....................................... 14 2.5 Plotting.................................................. 20 2.6 Some Common Issues with Functions.................................. 23 2.7 Basic Rings................................................ 26 2.8 Linear Algebra.............................................. 28 2.9 Polynomials............................................... 32 2.10 Parents, Conversion and Coercion.................................... 36 2.11 Finite Groups, Abelian Groups...................................... 42 2.12 Number Theory............................................. 43 2.13 Some More Advanced Mathematics................................... 46 3 The Interactive Shell 55 3.1 Your Sage Session............................................ 55 3.2 Logging Input and Output........................................ 57 3.3 Paste Ignores Prompts.......................................... 58 3.4 Timing Commands............................................ 58 3.5 Other IPython -
Introduction to Asynchronous Programming
Introduction to Asynchronous Programming In this document we introduce an asynchronous model for concurrent programming. For certain appli- cations, an asynchronous model may yield performance benefits over traditional multithreading. Much of the material presented in this document is taken from Dave Peticola’s excellent introduction to Twisted1, a Python framework for asynchronous programming. 1 The Models We will start by reviewing two (hopefully) familiar models in order to contrast them with the asynchronous model. By way of illustration we will imagine a program that consists of three conceptually distinct tasks which must be performed to complete the program. Note I am using task in the non-technical sense of something that needs to be done. The first model we will look at is the single-threaded synchronous model, in Figure 1 below: Figure 1: The single-threaded synchronous model This is the simplest style of programming. Each task is performed one at a time, with one finishing completely before another is started. And if the tasks are always performed in a definite order, the imple- mentation of a later task can assume that all earlier tasks have finished without errors, with all their output available for use — a definite simplification in logic. We can contrast the single-threaded synchronous model with the multi-threaded synchronous model illustrated in Figure 2. In this model, each task is performed in a separate thread of control. The threads are managed by the operating system and may, on a system with multiple processors or multiple cores, run truly concurrently, 1http://krondo.com/?page_id=1327 1 CS168 Async Programming Figure 2: The threaded model or may be interleaved together on a single processor. -
Memc3: Compact and Concurrent Memcache with Dumber Caching and Smarter Hashing
MemC3: Compact and Concurrent MemCache with Dumber Caching and Smarter Hashing Bin Fan, David G. Andersen, Michael Kaminsky∗ Carnegie Mellon University, ∗Intel Labs Abstract Standard Memcached, at its core, uses a typical hash table design, with linked-list-based chaining to handle This paper presents a set of architecturally and workload- collisions. Its cache replacement algorithm is strict LRU, inspired algorithmic and engineering improvements also based on linked lists. This design relies on locking to the popular Memcached system that substantially to ensure consistency among multiple threads, and leads improve both its memory efficiency and throughput. to poor scalability on multi-core CPUs [11]. These techniques—optimistic cuckoo hashing, a com- This paper presents MemC3 (Memcached with pact LRU-approximating eviction algorithm based upon CLOCK and Concurrent Cuckoo Hashing), a complete CLOCK, and comprehensive implementation of opti- redesign of the Memcached internals. This re-design mistic locking—enable the resulting system to use 30% is informed by and takes advantage of several observa- less memory for small key-value pairs, and serve up to tions. First, architectural features can hide memory access 3x as many queries per second over the network. We latencies and provide performance improvements. In par- have implemented these modifications in a system we ticular, our new hash table design exploits CPU cache call MemC3—Memcached with CLOCK and Concur- locality to minimize the number of memory fetches re- rent Cuckoo hashing—but believe that they also apply quired to complete any given operation; and it exploits more generally to many of today’s read-intensive, highly instruction-level and memory-level parallelism to overlap concurrent networked storage and caching systems. -
Python Guide Documentation 0.0.1
Python Guide Documentation 0.0.1 Kenneth Reitz 2015 09 13 Contents 1 Getting Started 3 1.1 Picking an Interpreter..........................................3 1.2 Installing Python on Mac OS X.....................................5 1.3 Installing Python on Windows......................................6 1.4 Installing Python on Linux........................................7 2 Writing Great Code 9 2.1 Structuring Your Project.........................................9 2.2 Code Style................................................ 15 2.3 Reading Great Code........................................... 24 2.4 Documentation.............................................. 24 2.5 Testing Your Code............................................ 26 2.6 Common Gotchas............................................ 30 2.7 Choosing a License............................................ 33 3 Scenario Guide 35 3.1 Network Applications.......................................... 35 3.2 Web Applications............................................ 36 3.3 HTML Scraping............................................. 41 3.4 Command Line Applications....................................... 42 3.5 GUI Applications............................................. 43 3.6 Databases................................................. 45 3.7 Networking................................................ 45 3.8 Systems Administration......................................... 46 3.9 Continuous Integration.......................................... 49 3.10 Speed.................................................. -
Learning to Program Using Python
Learning to Program Using Python Cody Jackson 2 Copyright © 2009-2013 Cody Jackson. This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View, California, 94041, USA. The source code within this text is licensed under the GNU General Public License (GPL). The following license information covers all code contained herein, except those that explicitly state otherwise: Copyright © 2006-2011 Cody Jackson. 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 3 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, see http://www.gnu.org/licenses/. Contents ITheCoreLanguage 1 1 Introduction 3 1.1 Why Python? . 4 1.2 Why Another Tutorial? . 5 1.3 Getting Python . 6 1.4 Conventions Used in this Book . 7 2 How is Python Different? 9 2.1 PythonConcepts ...................... 9 2.1.1 Dynamic vs. Static Types . 9 2.1.2 Interpreted vs. Compiled . 10 2.1.3 Prototyping . 11 2.1.4 Procedural vs. -
A Study on Performance Improvement of Tor Network with Active Circuit Switching
A Study on Performance Improvement of Tor Network with Active Circuit Switching Author Kale, Timothy Girry Doctoral Thesis in Engineering Graduate School of Information System Department of Information Network Systems The University of Electro-communications September 2015 A Study on Performance Improvement of Tor Network with Active Circuit Switching Author Kale, Timothy Girry Approved by supervisory committee: Chairperson: Assoc. Prof. Satoshi Ohzahata Member: Prof. Hiroyoshi Morita Member: Prof. Nagao Ogino Member: Assoc. Prof. Tomohiro Ogawa Member: Assoc. Prof. Shinobu Miwa © Copyright 2015 by Kale, Timothy Girry All Rights Reserved 動的経路切り替えを用いた Tor ネットワークの性 能改善に関する研究 カレ, ティモシー ギリ 概要 Tor ネットワークは分散サーキットスイッチによるアプリケーションレベルオーバ レイネットワークであり、世界中に配置されているオニオンルータによって、匿 名性のある通信を提供する。異なる転送速度を持つサーキットが TCP コネクショ ンに集約されてデータを転送するために競合が起き、Tor はネットワーク輻輳やパ フォーマンスに対して脆弱になっている。Tor では利用可能なネットワークの容量 の大部分はバルクのユーザのトラフィックによって消費されているため、対話性 のある軽量トラフィックの遅延が増大している。サーキットの不公平なトラフィ ックの分配によって、Tor ルータがボトルネックとなり、遅延が増加し、通信品質 も低下する。この問題のため、多くの Tor ユーザが Tor ネットワークを利用する 動機を低下させ、その結果、Tor ネットワークのパフォーマンスと匿名性が大幅に 低下している。 本論文では、まず、Tor ネットワークの遅延の問題を調査する。Tor サーキット のトラフィックと暗号科通信のための計算遅延の詳細の分析をするために測定分 析を行った。測定は研究室内に設置したテストベッドと実際の Tor ネットワーク で行い、遅延がどこで発生し、パケット損失がどのように遅延を引き起こすかを 分析した。Tor ネットワークで遅延増加の原因はサーキットの集約であることを明 らかにした。分析により、Tor ネットワークの設計は、低いネットワーク容量、大 きな遅延、TCP の送信バッファのキュー長の増大に対していくつか性能の問題が あることを明らかにした。 この性能低下の問題を解決するために、動的なサーキットスイッチ方式を用い て、限られたネットワーク容量の問題に対処する。トラフィックの輻輳の監視と より大きな帯域幅をもつ OR へのサーキットを動的に構築するのため、エントリ i OR に提案方式を実装した。提案方式では Tor の現在のアルゴリズムを修正し、バ ッファオーバーフローやソケット書き込み不可のイベントを考慮したメトリック