A Crash Course on Python Programming
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Fortran Resources 1
Fortran Resources 1 Ian D Chivers Jane Sleightholme May 7, 2021 1The original basis for this document was Mike Metcalf’s Fortran Information File. The next input came from people on comp-fortran-90. Details of how to subscribe or browse this list can be found in this document. If you have any corrections, additions, suggestions etc to make please contact us and we will endeavor to include your comments in later versions. Thanks to all the people who have contributed. Revision history The most recent version can be found at https://www.fortranplus.co.uk/fortran-information/ and the files section of the comp-fortran-90 list. https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=comp-fortran-90 • May 2021. Major update to the Intel entry. Also changes to the editors and IDE section, the graphics section, and the parallel programming section. • October 2020. Added an entry for Nvidia to the compiler section. Nvidia has integrated the PGI compiler suite into their NVIDIA HPC SDK product. Nvidia are also contributing to the LLVM Flang project. Updated the ’Additional Compiler Information’ entry in the compiler section. The Polyhedron benchmarks discuss automatic parallelisation. The fortranplus entry covers the diagnostic capability of the Cray, gfortran, Intel, Nag, Oracle and Nvidia compilers. Updated one entry and removed three others from the software tools section. Added ’Fortran Discourse’ to the e-lists section. We have also made changes to the Latex style sheet. • September 2020. Added a computer arithmetic and IEEE formats section. • June 2020. Updated the compiler entry with details of standard conformance. -
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................... -
Ginga Documentation Release 2.5.20160420043855
Ginga Documentation Release 2.5.20160420043855 Eric Jeschke May 05, 2016 Contents 1 About Ginga 1 2 Copyright and License 3 3 Requirements and Supported Platforms5 4 Getting the source 7 5 Building and Installation 9 5.1 Detailed Installation Instructions for Ginga...............................9 6 Documentation 15 6.1 What’s New in Ginga?.......................................... 15 6.2 Ginga Quick Reference......................................... 19 6.3 The Ginga FAQ.............................................. 22 6.4 The Ginga Viewer and Toolkit Manual................................. 25 6.5 Reference/API.............................................. 87 7 Bug reports 107 8 Developer Info 109 9 Etymology 111 10 Pronunciation 113 11 Indices and tables 115 Python Module Index 117 i ii CHAPTER 1 About Ginga Ginga is a toolkit designed for building viewers for scientific image data in Python, visualizing 2D pixel data in numpy arrays. It can view astronomical data such as contained in files based on the FITS (Flexible Image Transport System) file format. It is written and is maintained by software engineers at the Subaru Telescope, National Astronomical Observatory of Japan. The Ginga toolkit centers around an image display class which supports zooming and panning, color and intensity mapping, a choice of several automatic cut levels algorithms and canvases for plotting scalable geometric forms. In addition to this widget, a general purpose “reference” FITS viewer is provided, based on a plugin framework. A fairly complete set of “standard” plugins are provided for features that we expect from a modern FITS viewer: panning and zooming windows, star catalog access, cuts, star pick/fwhm, thumbnails, etc. 1 Ginga Documentation, Release 2.5.20160420043855 2 Chapter 1. -
Pyconfr 2014 1 / 105 Table Des Matières Bienvenue À Pyconfr 2014
PyconFR 2014 1 / 105 Table des matières Bienvenue à PyconFR 2014...........................................................................................................................5 Venir à PyconFR............................................................................................................................................6 Plan du campus..........................................................................................................................................7 Bâtiment Thémis...................................................................................................................................7 Bâtiment Nautibus................................................................................................................................8 Accès en train............................................................................................................................................9 Accès en avion........................................................................................................................................10 Accès en voiture......................................................................................................................................10 Accès en vélo...........................................................................................................................................11 Plan de Lyon................................................................................................................................................12 -
Python Guide Documentation Release 0.0.1
Python Guide Documentation Release 0.0.1 Kenneth Reitz February 19, 2018 Contents 1 Getting Started with Python 3 1.1 Picking an Python Interpreter (3 vs. 2).................................3 1.2 Properly Installing Python........................................5 1.3 Installing Python 3 on Mac OS X....................................6 1.4 Installing Python 3 on Windows.....................................8 1.5 Installing Python 3 on Linux.......................................9 1.6 Installing Python 2 on Mac OS X.................................... 10 1.7 Installing Python 2 on Windows..................................... 12 1.8 Installing Python 2 on Linux....................................... 13 1.9 Pipenv & Virtual Environments..................................... 14 1.10 Lower level: virtualenv.......................................... 17 2 Python Development Environments 21 2.1 Your Development Environment..................................... 21 2.2 Further Configuration of Pip and Virtualenv............................... 26 3 Writing Great Python Code 29 3.1 Structuring Your Project......................................... 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.................................................. 58 3.7 Common Gotchas............................................ 60 3.8 Choosing -
Prof. Jim Crutchfield Nonlinear Physics— Modeling Chaos
Nonlinear Physics— Modeling Chaos & Complexity Prof. Jim Crutchfield Physics Department & Complexity Sciences Center University of California, Davis cse.ucdavis.edu/~chaos Tuesday, March 30, 2010 1 Mechanism Revived Deterministic chaos Nature actively produces unpredictability What is randomness? Where does it come from? Specific mechanisms: Exponential divergence of states Sensitive dependence on parameters Sensitive dependence on initial state ... Tuesday, March 30, 2010 2 Brief History of Chaos Discovery of Chaos: 1890s, Henri Poincaré Invents Qualitative Dynamics Dynamics in 20th Century Develops in Mathematics (Russian & Europe) Exiled from Physics Re-enters Science in 1970s Experimental tests Simulation Flourishes in Mathematics: Ergodic theory & foundations of Statistical Mechanics Topological dynamics Catastrophe/Singularity theory Pattern formation/center manifold theory ... Tuesday, March 30, 2010 3 Discovering Order in Chaos Problem: No “closed-form” analytical solution for predicting future of nonlinear, chaotic systems One can prove this! Consequence: Each nonlinear system requires its own representation Pattern recognition: Detecting what we know Ultimate goal: Causal explanation What are the hidden mechanisms? Pattern discovery: Finding what’s truly new Tuesday, March 30, 2010 4 Major Roadblock to the Scientific Algorithm No “closed-form” analytical solutions Baconian cycle of successively refining models broken Solution: Qualitative dynamics: “Shape” of chaos Computing Tuesday, March 30, 2010 5 Logic of the Course Two -
Introduction to Python ______
Center for Teaching, Research and Learning Research Support Group American University, Washington, D.C. Hurst Hall 203 [email protected] (202) 885-3862 Introduction to Python ___________________________________________________________________________________________________________________ Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. It is used for its readability and productivity. WORKSHOP OBJECTIVE: This workshop is designed to give a basic understanding of Python, including object types, Python operations, Python module, basic looping, function, and control flows. By the end of this workshop you will: • Know how to install Python on your own computer • Understand the interface and language of Python • Be familiar with Python object types, operations, and modules • Create loops and functions • Load and use Python packages (libraries) • Use Python both as a powerful calculator and to write software that accept inputs and produce conditional outputs. NOTE: Many of our examples are from the Python website’s tutorial, http://docs.python.org/py3k/contents.html. We often include references to the relevant chapter section, which you could consult to get greater depth on that topic. I. Installation and starting Python Python is distributed free from the website http://www.python.org/. There are distributions for Windows, Mac, Unix, and Linux operating systems. Use http://python.org/download to find these distribution files for easy installation. The latest version of Python is version 3.1. Once installed, you can find the Python3.1 folder in “Programs” of the Windows “start” menu. For computers on campus, look for the “Math and Stats Applications” folder. -
Evolving Software Repositories
1 Evolving Software Rep ositories http://www.netli b.org/utk/pro ject s/esr/ Jack Dongarra UniversityofTennessee and Oak Ridge National Lab oratory Ron Boisvert National Institute of Standards and Technology Eric Grosse AT&T Bell Lab oratories 2 Pro ject Fo cus Areas NHSE Overview Resource Cataloging and Distribution System RCDS Safe execution environments for mobile co de Application-l evel and content-oriented to ols Rep ository interop erabili ty Distributed, semantic-based searching 3 NHSE National HPCC Software Exchange NASA plus other agencies funded CRPC pro ject Center for ResearchonParallel Computation CRPC { Argonne National Lab oratory { California Institute of Technology { Rice University { Syracuse University { UniversityofTennessee Uniform interface to distributed HPCC software rep ositories Facilitation of cross-agency and interdisciplinary software reuse Material from ASTA, HPCS, and I ITA comp onents of the HPCC program http://www.netlib.org/nhse/ 4 Goals: Capture, preserve and makeavailable all software and software- related artifacts pro duced by the federal HPCC program. Soft- ware related artifacts include algorithms, sp eci cations, designs, do cumentation, rep ort, ... Promote formation, growth, and interop eration of discipline-oriented rep ositories that organize, evaluate, and add value to individual contributions. Employ and develop where necessary state-of-the-art technologies for assisting users in nding, understanding, and using HPCC software and technologies. 5 Bene ts: 1. Faster development of high-quality software so that scientists can sp end less time writing and debugging programs and more time on research problems. 2. Less duplication of software development e ort by sharing of soft- ware mo dules. -
Release 2.7.1 Andrew Collette and Contributors
h5py Documentation Release 2.7.1 Andrew Collette and contributors Mar 08, 2018 Contents 1 Where to start 3 2 Other resources 5 3 Introductory info 7 4 High-level API reference 15 5 Advanced topics 35 6 Meta-info about the h5py project 51 i ii h5py Documentation, Release 2.7.1 The h5py package is a Pythonic interface to the HDF5 binary data format. HDF5 lets you store huge amounts of numerical data, and easily manipulate that data from NumPy. For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. Thousands of datasets can be stored in a single file, categorized and tagged however you want. Contents 1 h5py Documentation, Release 2.7.1 2 Contents CHAPTER 1 Where to start • Quick-start guide • Installation 3 h5py Documentation, Release 2.7.1 4 Chapter 1. Where to start CHAPTER 2 Other resources • Python and HDF5 O’Reilly book • Ask questions on the mailing list at Google Groups • GitHub project 5 h5py Documentation, Release 2.7.1 6 Chapter 2. Other resources CHAPTER 3 Introductory info 3.1 Quick Start Guide 3.1.1 Install With Anaconda or Miniconda: conda install h5py With Enthought Canopy, use the GUI package manager or: enpkg h5py With pip or setup.py, see Installation. 3.1.2 Core concepts An HDF5 file is a container for two kinds of objects: datasets, which are array-like collections of data, and groups, which are folder-like containers that hold datasets and other groups. The most fundamental thing to remember when using h5py is: Groups work like dictionaries, and datasets work like NumPy arrays The very first thing you’ll need to do is create a new file: >>> import h5py >>> import numpy as np >>> >>> f= h5py.File("mytestfile.hdf5","w") The File object is your starting point. -
The Journal of Computer Science and Its Applications Vol
The Journal of Computer Science and its Applications Vol. 25, No 1, June, 2018 THE JOURNAL OF COMPUTER SCIENCE AND ITS APPLICATIONS Vol. 25, No 1, June, 2018 PREDICTION OF PEDIATRIC HIV/AIDS SURVIVAL IN NIGERIA USING NAÏVE BAYES’ APPROACH P.A. Idowu1, T. A. Aladekomo2, O. Agbelusi3, and C. O. Akanbi4 1,2 Obafemi Awolowo University, Ile-Ife, Nigeria, 3 Rufus Giwa Polytechnic, Owo, Nigeria 4 Osun State University, Nigeria [email protected] , [email protected] ABSTRACT Epidemic diseases have highly destructive effects around the world and these diseases have affected both developed and developing nations. Disease epidemics are common in developing nations especially in Sub Saharan Africa in which Human Immunodeficiency Virus /Acquired Immunodeficiency Disease Syndrome (HIV/AIDS) is the most serious of all. This paper presents a prediction of pediatric HIV/AIDS survival in Nigeria. Data are collected from 216 pediatric HIV/AIDS patients who were receiving antiretroviral drug treatment in Nigeria was used to develop a predictive model for HIV/AIDS survival based on identified variables. Interviews were conducted with the virologists and pediatricians to identify the variables predictive for HIV/AIDS survival in pediatric patients. 10-fold cross validation method was used in performing the stratification of the datasets collected into training and testing datasets following data preprocessing of the collected datasets. The model was formulated using the naïve Bayes’ classifier – a supervised machine learning algorithm based on Bayes’ theory of conditional probability and simulated on the Waikato Environment for Knowledge Analysis [WEKA] using the identified variables, namely: CD4 count, viral load, opportunistic infection and the nutrition status of the pediatric patients involved in the study. -
Lfpy Homepage Release 2.1.1
LFPy Homepage Release 2.1.1 LFPy-team Oct 12, 2020 CONTENTS 1 Tutorial slides on LFPy 3 2 Related projects 5 3 Contents 7 3.1 Download LFPy.............................................7 3.2 Developing LFPy.............................................7 3.3 Getting started..............................................7 3.3.1 Dependencies..........................................7 3.3.2 Installing LFPy.........................................8 3.3.3 Uninstalling LFPy.......................................9 3.4 Documentation..............................................9 3.4.1 Installing NEURON with Python................................ 10 3.4.2 Installing NEURON with Python from source......................... 14 3.5 LFPy on the Neuroscience Gateway Portal............................... 16 3.6 LFPy Tutorial.............................................. 17 3.6.1 More examples......................................... 19 3.7 Notes on LFPy.............................................. 20 3.7.1 Morphology files........................................ 20 3.7.2 Using NEURON NMODL mechanisms............................ 22 3.7.3 Units.............................................. 22 3.7.4 Contributors........................................... 22 3.7.5 Contact............................................. 23 3.8 Module LFPy .............................................. 23 3.8.1 class Cell ........................................... 24 3.8.2 class TemplateCell ..................................... 32 3.8.3 class NetworkCell .....................................