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How to Access Python for Doing Scientific Computing
How to access Python for doing scientific computing1 Hans Petter Langtangen1,2 1Center for Biomedical Computing, Simula Research Laboratory 2Department of Informatics, University of Oslo Mar 23, 2015 A comprehensive eco system for scientific computing with Python used to be quite a challenge to install on a computer, especially for newcomers. This problem is more or less solved today. There are several options for getting easy access to Python and the most important packages for scientific computations, so the biggest issue for a newcomer is to make a proper choice. An overview of the possibilities together with my own recommendations appears next. Contents 1 Required software2 2 Installing software on your laptop: Mac OS X and Windows3 3 Anaconda and Spyder4 3.1 Spyder on Mac............................4 3.2 Installation of additional packages.................5 3.3 Installing SciTools on Mac......................5 3.4 Installing SciTools on Windows...................5 4 VMWare Fusion virtual machine5 4.1 Installing Ubuntu...........................6 4.2 Installing software on Ubuntu....................7 4.3 File sharing..............................7 5 Dual boot on Windows8 6 Vagrant virtual machine9 1The material in this document is taken from a chapter in the book A Primer on Scientific Programming with Python, 4th edition, by the same author, published by Springer, 2014. 7 How to write and run a Python program9 7.1 The need for a text editor......................9 7.2 Spyder................................. 10 7.3 Text editors.............................. 10 7.4 Terminal windows.......................... 11 7.5 Using a plain text editor and a terminal window......... 12 8 The SageMathCloud and Wakari web services 12 8.1 Basic intro to SageMathCloud................... -
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. -
IMPLEMENTING OPTION PRICING MODELS USING PYTHON and CYTHON Sanjiv Dasa and Brian Grangerb
JOURNAL OF INVESTMENT MANAGEMENT, Vol. 8, No. 4, (2010), pp. 1–12 © JOIM 2010 JOIM www.joim.com IMPLEMENTING OPTION PRICING MODELS USING PYTHON AND CYTHON Sanjiv Dasa and Brian Grangerb In this article we propose a new approach for implementing option pricing models in finance. Financial engineers typically prototype such models in an interactive language (such as Matlab) and then use a compiled language such as C/C++ for production systems. Code is therefore written twice. In this article we show that the Python programming language and the Cython compiler allows prototyping in a Matlab-like manner, followed by direct generation of optimized C code with very minor code modifications. The approach is able to call upon powerful scientific libraries, uses only open source tools, and is free of any licensing costs. We provide examples where Cython speeds up a prototype version by over 500 times. These performance gains in conjunction with vast savings in programmer time make the approach very promising. 1 Introduction production version in a compiled language like C, C++, or Fortran. This duplication of effort slows Computing in financial engineering needs to be fast the deployment of new algorithms and creates ongo- in two critical ways. First, the software development ing software maintenance problems, not to mention process must be fast for human developers; pro- added development costs. grams must be easy and time efficient to write and maintain. Second, the programs themselves must In this paper we describe an approach to tech- be fast when they are run. Traditionally, to meet nical software development that enables a single both of these requirements, at least two versions of version of a program to be written that is both easy a program have to be developed and maintained; a to develop and maintain and achieves high levels prototype in a high-level interactive language like of performance. -
Micropython for Satlink 3 Documentation Release 1.8.4
MicroPython for Satlink 3 Documentation Release 1.8.4 Damien P. George, contributors, and Sutron Corporation Jul 28, 2017 CONTENTS 1 Python first time setup & configuration1 1.1 1. Download & Install LinkComm....................................1 1.2 2. Download & Install Python......................................1 1.3 3. Download & Install Pyinstaller....................................3 1.4 4. Download & Install PyCharm.....................................3 1.5 5. Testing out .py to .exe converter....................................5 1.6 6. Python PyQt5 GUI..........................................6 1.7 7. Connect PyCharm into external programs like linkcomm or micropython..............6 1.8 8. Configure PyCharm for program development using LinkComm..................9 1.9 9. Configure PyCharm with SL3 API for auto completion....................... 11 1.10 10. Setting docstring stub in PyCharm.................................. 13 2 MicroPython libraries 15 2.1 Python standard libraries and micro-libraries.............................. 15 2.2 MicroPython-specific libraries...................................... 16 2.3 Libraries specific to the Satlink 3.................................... 19 3 The MicroPython language 39 3.1 Overview of MicroPython Differences from Standard Python..................... 39 3.2 Code examples of how MicroPython differs from Standard Python with work-arounds........ 41 3.3 The MicroPython Interactive Interpreter Mode (aka REPL)....................... 56 3.4 Maximising Python Speed....................................... -
High-Performance Computation with Python | Python Academy | Training IT
Training: Python Academy High-Performance Computation with Python Training: Python Academy High-Performance Computation with Python TRAINING GOALS: The requirement for extensive and challenging computations is far older than the computing industry. Today, software is a mere means to a specific end, it needs to be newly developed or adapted in countless places in order to achieve the special goals of the users and to run their specific calculations efficiently. Nothing is of more avail to this need than a programming language that is easy for users to learn and that enables them to actively follow and form the realisation of their requirements. Python is this programming language. It is easy to learn, to use and to read. The language makes it easy to write maintainable code and to use it as a basis for communication with other people. Moreover, it makes it easy to optimise and specialise this code in order to use it for challenging and time critical computations. This is the main subject of this course. CONSPECT: Optimizing Python programs Guidelines for optimization. Optimization strategies - Pystone benchmarking concept, CPU usage profiling with cProfile, memory measuring with Guppy_PE Framework. Participants are encouraged to bring their own programs for profiling to the course. Algorithms and anti-patterns - examples of algorithms that are especially slow or fast in Python. The right data structure - comparation of built-in data structures: lists, sets, deque and defaulddict - big-O notation will be exemplified. Caching - deterministic and non-deterministic look on caching and developing decorates for these purposes. The example - we will use a computionally demanding example and implement it first in pure Python. -
Setting up Your Environment
APPENDIX A Setting Up Your Environment Choosing the correct tools to work with asyncio is a non-trivial choice, since it can significantly impact the availability and performance of asyncio. In this appendix, we discuss the interpreter and the packaging options that influence your asyncio experience. The Interpreter Depending on the API version of the interpreter, the syntax of declaring coroutines change and the suggestions considering API usage change. (Passing the loop parameter is considered deprecated for APIs newer than 3.6, instantiating your own loop should happen only in rare circumstances in Python 3.7, etc.) Availability Python interpreters adhere to the standard in varying degrees. This is because they are implementations/manifestations of the Python language specification, which is managed by the PSF. At the time of this writing, three relevant interpreters support at least parts of asyncio out of the box: CPython, MicroPython, and PyPy. © Mohamed Mustapha Tahrioui 2019 293 M. M. Tahrioui, asyncio Recipes, https://doi.org/10.1007/978-1-4842-4401-2 APPENDIX A SeTTinG Up YouR EnViROnMenT Since we are ideally interested in a complete or semi-complete implementation of asyncio, our choice is limited to CPython and PyPy. Both of these products have a great community. Since we are ideally using a lot powerful stdlib features, it is inevitable to pose the question of implementation completeness of a given interpreter with respect to the Python specification. The CPython interpreter is the reference implementation of the language specification and hence it adheres to the largest set of features in the language specification. At the point of this writing, CPython was targeting API version 3.7. -
Python Guide Documentation 0.0.1
Python Guide Documentation 0.0.1 Kenneth Reitz 2015 11 07 Contents 1 3 1.1......................................................3 1.2 Python..................................................5 1.3 Mac OS XPython.............................................5 1.4 WindowsPython.............................................6 1.5 LinuxPython...............................................8 2 9 2.1......................................................9 2.2...................................................... 15 2.3...................................................... 24 2.4...................................................... 25 2.5...................................................... 27 2.6 Logging.................................................. 31 2.7...................................................... 34 2.8...................................................... 37 3 / 39 3.1...................................................... 39 3.2 Web................................................... 40 3.3 HTML.................................................. 47 3.4...................................................... 48 3.5 GUI.................................................... 49 3.6...................................................... 51 3.7...................................................... 52 3.8...................................................... 53 3.9...................................................... 58 3.10...................................................... 59 3.11...................................................... 62 -
Python Crypto Misuses in the Wild
Python Crypto Misuses in the Wild Anna-Katharina Wickert Lars Baumgärtner [email protected] [email protected] Technische Universität Darmstadt Technische Universität Darmstadt Darmstadt, Germany Darmstadt, Germany Florian Breitfelder Mira Mezini [email protected] [email protected] Technische Universität Darmstadt Technische Universität Darmstadt Darmstadt, Germany Darmstadt, Germany ABSTRACT 1 INTRODUCTION Background: Previous studies have shown that up to 99.59 % of the Cryptography, hereafter crypto, is widely used nowadays to protect Java apps using crypto APIs misuse the API at least once. However, our data and ensure confidentiality. For example, without crypto, these studies have been conducted on Java and C, while empirical we would not be able to securely use online banking or do online studies for other languages are missing. For example, a controlled shopping. Unfortunately, previous research results show that crypto user study with crypto tasks in Python has shown that 68.5 % of the is often used in an insecure way [3, 4, 7, 9, 11]. One such problem is professional developers write a secure solution for a crypto task. the choice of an insecure parameter, like an insecure block mode, for Aims: To understand if this observation holds for real-world code, crypto primitives like encryption. Many static analysis tools exist we conducted a study of crypto misuses in Python. Method: We to identify these misuses such as CryptoREX [13], CryptoLint [4], developed a static analysis tool that covers common misuses of5 CogniCryptSAST [8], and Cryptoguard [12]. different Python crypto APIs. With this analysis, we analyzed 895 While these tools and the respective in-the-wild studies concen- popular Python projects from GitHub and 51 MicroPython projects trate on Java and C, user studies suggest that the existing Python for embedded devices. -
Circuitpython Documentation Release 0.0.0
CircuitPython Documentation Release 0.0.0 Damien P. George, Paul Sokolovsky, and contributors Jun 26, 2020 API and Usage 1 Adafruit CircuitPython 3 1.1 Status...................................................3 1.2 Supported Boards............................................3 1.2.1 Designed for CircuitPython...................................3 1.2.2 Other..............................................4 1.3 Download.................................................4 1.4 Documentation..............................................4 1.5 Contributing...............................................4 1.6 Differences from MicroPython......................................4 1.6.1 Behavior.............................................5 1.6.2 API...............................................5 1.6.3 Modules.............................................5 1.6.4 atmel-samd21 features.....................................5 1.7 Project Structure.............................................5 1.7.1 Core...............................................6 1.7.2 Ports...............................................6 1.8 Full Table of Contents..........................................7 1.8.1 Core Modules..........................................7 1.8.2 Supported Ports......................................... 47 1.8.3 Troubleshooting......................................... 56 1.8.4 Additional Adafruit Libraries and Drivers on GitHub..................... 57 1.8.5 Design Guide.......................................... 59 1.8.6 Architecture.......................................... -
Python Guide Documentation Publicación 0.0.1
Python Guide Documentation Publicación 0.0.1 Kenneth Reitz 17 de May de 2018 Índice general 1. Empezando con Python 3 1.1. Eligiendo un Interprete Python (3 vs. 2).................................3 1.2. Instalando Python Correctamente....................................5 1.3. Instalando Python 3 en Mac OS X....................................6 1.4. Instalando Python 3 en Windows....................................8 1.5. Instalando Python 3 en Linux......................................9 1.6. Installing Python 2 on Mac OS X.................................... 10 1.7. Instalando Python 2 en Windows.................................... 12 1.8. Installing Python 2 on Linux....................................... 13 1.9. Pipenv & Ambientes Virtuales...................................... 14 1.10. Un nivel más bajo: virtualenv...................................... 17 2. Ambientes de Desarrollo de Python 21 2.1. Your Development Environment..................................... 21 2.2. Further Configuration of Pip and Virtualenv............................... 26 3. Escribiendo Buen Código Python 29 3.1. Estructurando tu Proyecto........................................ 29 3.2. Code Style................................................ 40 3.3. Reading Great Code........................................... 49 3.4. Documentation.............................................. 50 3.5. Testing Your Code............................................ 53 3.6. Logging.................................................. 57 3.7. Common Gotchas........................................... -
How Python Is Winning New Friends
How Python is Winning New Friends Steve Holden CTO, Global Stress Index Limited [email protected] IntroducFons • Programmer since 1967 • Computaonal scienFst by training • Engineer at heart • Python user since Python 1.4 (c. 1995) • Enjoy helping people to learn I’ve WriSen about Python Any Python users out there? Developments in CompuFng SOME HISTORY 1948 Programming Was Hard • No operang system • No libraries • No compilers • No assemblers • The painful process of abstracFon layering began 1977 Easier to Program • Assemblers/compilers available • UNIX starFng to emerge as a common base – Microprogramming handled hardware complexity • Storage flexibly handled by the OS • Networking heading to ubiquity 1984 2015 2016 2017 2020 ? Whatever it is, it will be complex! And so to Python “BUT IT’S [JUST] A SCRIPTING LANGUAGE …” What’s a “ScripFng Language”? • “First they ignore you; then they abuse you; then they crack down on you and then you win.” – not Mahatma Ghandi What’s a “ScripFng Language”? • “First they ignore you; then they abuse you; then they crack down on you and then you win.” – not Mahatma Ghandi • “Ridicule is like repression. Both give place to respect when they fail to produce the intended effect.” – Mahatma Ghandi Note to Purists • Learners do not have complex needs – Simplicity and consistency are important – ExecuFon speed mostly isn’t • Direct hands-on experience enables • Large resources not required – Wide availability and ease of access are criFcal The Programming Audience • Professional soiware engineers • ScienFsts • Lab -
Bfree: Enabling Battery-Free Sensor Prototyping with Python
BFree: Enabling Battery-free Sensor Prototyping with Python VITO KORTBEEK, Delft University of Technology, The Netherlands ABU BAKAR, Northwestern University, USA STEFANY CRUZ, Northwestern University, USA KASIM SINAN YILDIRIM, University of Trento, Italy PRZEMYSŁAW PAWEŁCZAK, Delft University of Technology, The Netherlands JOSIAH HESTER, Northwestern University, USA Building and programming tiny battery-free energy harvesting embedded computer systems is hard for the average maker because of the lack of tools, hard to comprehend programming models, and frequent power failures. With the high ecologic cost of equipping the next trillion embedded devices with batteries, it is critical to equip the makers, hobbyists, and novice embedded systems programmers with easy-to-use tools supporting battery-free energy harvesting application development. This way, makers can create untethered embedded systems that are not plugged into the wall, the desktop, or even a battery, providing numerous new applications and allowing for a more sustainable vision of ubiquitous computing. In this paper, we present BFree, a system that makes it possible for makers, hobbyists, and novice embedded programmers to develop battery-free applications using Python programming language and widely available hobbyist maker platforms. BFree provides energy harvesting hardware and a power failure resilient version of Python, with durable libraries that enable common coding practice and off the shelf sensors. We develop demonstration applications, benchmark BFree against battery-powered