The Hitchhiker's Guide to Python
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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. -
SPLLIFT — Statically Analyzing Software Product Lines in Minutes Instead of Years
SPLLIFT — Statically Analyzing Software Product Lines in Minutes Instead of Years Eric Bodden1 Tarsis´ Toledoˆ 3 Marcio´ Ribeiro3;4 Claus Brabrand2 Paulo Borba3 Mira Mezini1 1 EC SPRIDE, Technische Universitat¨ Darmstadt, Darmstadt, Germany 2 IT University of Copenhagen, Copenhagen, Denmark 3 Federal University of Pernambuco, Recife, Brazil 4 Federal University of Alagoas, Maceio,´ Brazil [email protected], ftwt, [email protected], [email protected], [email protected], [email protected] Abstract A software product line (SPL) encodes a potentially large variety v o i d main () { of software products as variants of some common code base. Up i n t x = secret(); i n t y = 0; until now, re-using traditional static analyses for SPLs was virtu- # i f d e f F ally intractable, as it required programmers to generate and analyze x = 0; all products individually. In this work, however, we show how an # e n d i f important class of existing inter-procedural static analyses can be # i f d e f G transparently lifted to SPLs. Without requiring programmers to y = foo (x); LIFT # e n d i f v o i d main () { change a single line of code, our approach SPL automatically i n t x = secret(); converts any analysis formulated for traditional programs within the print (y); } i n t y = 0; popular IFDS framework for inter-procedural, finite, distributive, y = foo (x); subset problems to an SPL-aware analysis formulated in the IDE i n t foo ( i n t p) { print (y); framework, a well-known extension to IFDS. -
Python on Gpus (Work in Progress!)
Python on GPUs (work in progress!) Laurie Stephey GPUs for Science Day, July 3, 2019 Rollin Thomas, NERSC Lawrence Berkeley National Laboratory Python is friendly and popular Screenshots from: https://www.tiobe.com/tiobe-index/ So you want to run Python on a GPU? You have some Python code you like. Can you just run it on a GPU? import numpy as np from scipy import special import gpu ? Unfortunately no. What are your options? Right now, there is no “right” answer ● CuPy ● Numba ● pyCUDA (https://mathema.tician.de/software/pycuda/) ● pyOpenCL (https://mathema.tician.de/software/pyopencl/) ● Rewrite kernels in C, Fortran, CUDA... DESI: Our case study Now Perlmutter 2020 Goal: High quality output spectra Spectral Extraction CuPy (https://cupy.chainer.org/) ● Developed by Chainer, supported in RAPIDS ● Meant to be a drop-in replacement for NumPy ● Some, but not all, NumPy coverage import numpy as np import cupy as cp cpu_ans = np.abs(data) #same thing on gpu gpu_data = cp.asarray(data) gpu_temp = cp.abs(gpu_data) gpu_ans = cp.asnumpy(gpu_temp) Screenshot from: https://docs-cupy.chainer.org/en/stable/reference/comparison.html eigh in CuPy ● Important function for DESI ● Compared CuPy eigh on Cori Volta GPU to Cori Haswell and Cori KNL ● Tried “divide-and-conquer” approach on both CPU and GPU (1, 2, 5, 10 divisions) ● Volta wins only at very large matrix sizes ● Major pro: eigh really easy to use in CuPy! legval in CuPy ● Easy to convert from NumPy arrays to CuPy arrays ● This function is ~150x slower than the cpu version! ● This implies there -
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. -
Introduction Shrinkage Factor Reference
Comparison study for implementation efficiency of CUDA GPU parallel computation with the fast iterative shrinkage-thresholding algorithm Younsang Cho, Donghyeon Yu Department of Statistics, Inha university 4. TensorFlow functions in Python (TF-F) Introduction There are some functions executed on GPU in TensorFlow. So, we implemented our algorithm • Parallel computation using graphics processing units (GPUs) gets much attention and is just using that functions. efficient for single-instruction multiple-data (SIMD) processing. 5. Neural network with TensorFlow in Python (TF-NN) • Theoretical computation capacity of the GPU device has been growing fast and is much higher Neural network model is flexible, and the LASSO problem can be represented as a simple than that of the CPU nowadays (Figure 1). neural network with an ℓ1-regularized loss function • There are several platforms for conducting parallel computation on GPUs using compute 6. Using dynamic link library in Python (P-DLL) unified device architecture (CUDA) developed by NVIDIA. (Python, PyCUDA, Tensorflow, etc. ) As mentioned before, we can load DLL files, which are written in CUDA C, using "ctypes.CDLL" • However, it is unclear what platform is the most efficient for CUDA. that is a built-in function in Python. 7. Using dynamic link library in R (R-DLL) We can also load DLL files, which are written in CUDA C, using "dyn.load" in R. FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) We consider FISTA (Beck and Teboulle, 2009) with backtracking as the following: " Step 0. Take �! > 0, some � > 1, and �! ∈ ℝ . Set �# = �!, �# = 1. %! Step k. � ≥ 1 Find the smallest nonnegative integers �$ such that with �g = � �$&# � �(' �$ ≤ �(' �(' �$ , �$ . -
Vxworks Third Party Software Notices
Wind River® VxWorks® 7 Third Party License Notices This document contains third party intellectual property (IP) notices for the BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY Wind River® VxWorks® 7 distribution. Certain licenses and license notices THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, may appear in other parts of the product distribution in accordance with the OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN license requirements. ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Trademarks All company, product and service names used in this software are for ACPICA identification purposes only. Version: 20170303 Component(s): Runtime Wind River and VxWorks are registered trademarks of Wind River Systems. Description: Provides code to implement ACPI specification in VxWorks. UNIX is a registered trademark of The Open Group. IBM and Bluemix are registered trademarks of the IBM Corporation. NOTICES: All other third-party trademarks are the property of their respective owners. 1. Copyright Notice Some or all of this work - Copyright (c) 1999 - 2016, Intel Corp. All rights reserved. Third Party Notices 2. License 2.1. This is your license from Intel Corp. under its intellectual property rights. You may have additional license terms from the party that provided you this software, covering your right to use that party's intellectual property rights. 64-Bit Dynamic Linker Version: 2.2. Intel grants, free of charge, to any person ("Licensee") obtaining a copy Component(s): Runtime of the source code appearing in this file ("Covered Code") an irrevocable, Description: The dynamic linker is used to load shared libraries. -
Simple Plotter Documentation Release 0.0.0
simple_plotter Documentation Release 0.0.0 Thies Hecker Mar 30, 2020 Contents: 1 Getting started 3 1.1 Desktop..................................................3 1.2 Android..................................................3 1.3 Configuration options..........................................4 1.4 Requirements...............................................4 1.5 Source code...............................................4 2 User guide 7 2.1 Overview.................................................7 2.2 Defining an equation...........................................7 2.3 Creating a plot..............................................8 2.4 Adjust the plot appearance........................................8 2.5 Constants.................................................9 2.6 Plotting curve sets............................................ 10 2.7 Plot labels................................................ 11 2.8 Load, save and export.......................................... 12 2.9 Advanced usage............................................. 12 3 Licenses 17 3.1 simple-plotter............................................... 17 3.2 simple-plotter-qt............................................. 18 3.3 simple-plotter4a............................................. 18 3.4 simple-plotter4a binary releases (Android)............................... 18 4 Contributing 43 4.1 Concept.................................................. 43 4.2 Package versions............................................. 43 4.3 Building conda packages........................................ -
Implementation of Calfem for Python
IMPLEMENTATION OF CALFEM FOR PYTHON ANDREAS OTTOSSON Structural Master’s Dissertation Mechanics Detta är en tom sida! Department of Construction Sciences Structural Mechanics ISRN LUTVDG/TVSM--10/5167--SE (1-47) ISSN 0281-6679 IMPLEMENTATION OF CALFEM FOR PYTHON Master’s Dissertation by ANDREAS OTTOSSON Supervisors: Jonas Lindemann, PhD, Div. of Structural Mechanics Examiner: Ola Dahlblom, Professor, Div. of Structural Mechanics Copyright © 2010 by Structural Mechanics, LTH, Sweden. Printed by Wallin & Dalholm Digital AB, Lund, Sweden, August, 2010 (Pl). For information, address: Division of Structural Mechanics, LTH, Lund University, Box 118, SE-221 00 Lund, Sweden. Homepage: http://www.byggmek.lth.se Detta är en tom sida! Preface The work presented in this masters’s thesis was carried out during the period June 2009 to August 2010 at the Division of Structural Mechanics at the Faculty of Engineering, Lund University, Sweden. I would like to thank the staff of the Department of Structural Mechanics, es- pecially my supervisor Jonas Lindemann, for help during this work. I would also like to thank my Jennie, and both our families, for their support throughout my education. Lund, August 2010 Andreas Ottosson i Contents 1 Introduction 1 1.1Background.............................. 1 1.2WhyCALFEMforPython?..................... 1 1.3 Objective ............................... 1 2MATLAB 3 2.1Background.............................. 3 2.2 Objects ................................ 3 3 Python and NumPy 5 3.1Python................................ 5 3.1.1 Background.......................... 5 3.1.2 Influences ........................... 5 3.1.3 Objects ............................ 6 3.2NumPy................................ 6 3.2.1 Objects ............................ 7 3.2.2 Commonmatrixoperations................. 8 4 Integrated Development Environments 11 4.1MATLAB............................... 11 4.2PythonIDLE............................. 12 4.3IPython............................... -
Eric Franklin Enslen
Eric Franklin Enslen email:[email protected]!403 Terra Drive phone:!(302) 827-ERIC (827-3742)!Newark, DE website:! www.cis.udel.edu/~enslen!19702 Education: Bachelor of Science in Computer and Information Sciences University of Delaware, Newark, DE!May 2011 Major GPA: 3.847/4.0 Academic Experience: CIS Study Abroad, London, UK!Summer 2008 Visited research labs at University College London, King"s College, and Brunel University, as well as IBM"s Hursley Park, and studied Software Testing and Tools for the Software Life Cycle Relevant Courses: Computer Ethics, Digital Intellectual Property, Computer Graphics, and Computational Photography Academic Honors: Alumni Enrichment Award, University of Delaware Alumni Association, Newark, DE!May 2009 Travel fund to present a first-author paper at the the 6th Working Conference on Mining Software Repositories (MSR), collocated with the 2009 International Conference on Software Engineering (ICSE) Hatem M. Khalil Memorial Award, College of Arts and Sciences, University of Delaware, Newark, DE!May 2009 Awarded to a CIS major in recognition of outstanding achievement in software engineering, selected by CIS faculty Deanʼs List, University of Delaware, Newark, DE!Fall 2007, Spring 2008, Fall 2008 and Fall 2009 Research Experience: Undergraduate Research, University of Delaware, Newark, DE!January 2009 - Present Developing, implementing, evaluating and documenting an algorithm to split Java identifiers into their component words in order to improve natural-language-based software maintenance tools. Co-advisors: Lori Pollock, K. Vijay-Shanker Research Publications: Eric Enslen, Emily Hill, Lori Pollock, K. Vijay-Shanker. “Mining Source Code to Automatically Split Identifiers for Software Analysis.” 6th Working Conference on Mining Software Repositories, May 2009. -
Python Programming
Python Programming Wikibooks.org June 22, 2012 On the 28th of April 2012 the contents of the English as well as German Wikibooks and Wikipedia projects were licensed under Creative Commons Attribution-ShareAlike 3.0 Unported license. An URI to this license is given in the list of figures on page 149. If this document is a derived work from the contents of one of these projects and the content was still licensed by the project under this license at the time of derivation this document has to be licensed under the same, a similar or a compatible license, as stated in section 4b of the license. The list of contributors is included in chapter Contributors on page 143. The licenses GPL, LGPL and GFDL are included in chapter Licenses on page 153, since this book and/or parts of it may or may not be licensed under one or more of these licenses, and thus require inclusion of these licenses. The licenses of the figures are given in the list of figures on page 149. This PDF was generated by the LATEX typesetting software. The LATEX source code is included as an attachment (source.7z.txt) in this PDF file. To extract the source from the PDF file, we recommend the use of http://www.pdflabs.com/tools/pdftk-the-pdf-toolkit/ utility or clicking the paper clip attachment symbol on the lower left of your PDF Viewer, selecting Save Attachment. After extracting it from the PDF file you have to rename it to source.7z. To uncompress the resulting archive we recommend the use of http://www.7-zip.org/. -
Xcode Package from App Store
KH Computational Physics- 2016 Introduction Setting up your computing environment Installation • MAC or Linux are the preferred operating system in this course on scientific computing. • Windows can be used, but the most important programs must be installed – python : There is a nice package ”Enthought Python Distribution” http://www.enthought.com/products/edudownload.php – C++ and Fortran compiler – BLAS&LAPACK for linear algebra – plotting program such as gnuplot Kristjan Haule, 2016 –1– KH Computational Physics- 2016 Introduction Software for this course: Essentials: • Python, and its packages in particular numpy, scipy, matplotlib • C++ compiler such as gcc • Text editor for coding (for example Emacs, Aquamacs, Enthought’s IDLE) • make to execute makefiles Highly Recommended: • Fortran compiler, such as gfortran or intel fortran • BLAS& LAPACK library for linear algebra (most likely provided by vendor) • open mp enabled fortran and C++ compiler Useful: • gnuplot for fast plotting. • gsl (Gnu scientific library) for implementation of various scientific algorithms. Kristjan Haule, 2016 –2– KH Computational Physics- 2016 Introduction Installation on MAC • Install Xcode package from App Store. • Install ‘‘Command Line Tools’’ from Apple’s software site. For Mavericks and lafter, open Xcode program, and choose from the menu Xcode -> Open Developer Tool -> More Developer Tools... You will be linked to the Apple page that allows you to access downloads for Xcode. You wil have to register as a developer (free). Search for the Xcode Command Line Tools in the search box in the upper left. Download and install the correct version of the Command Line Tools, for example for OS ”El Capitan” and Xcode 7.2, Kristjan Haule, 2016 –3– KH Computational Physics- 2016 Introduction you need Command Line Tools OS X 10.11 for Xcode 7.2 Apple’s Xcode contains many libraries and compilers for Mac systems. -
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...................