Computational Physics With Python
a project are large sets of data. Rather than re-enter these large data sets Another is Computational Physics by Mark Newman.[9] The official Python.
Computational Physics with Maxima or R Project 1 Classical Particle
Maxima languages for computational physics projects of modest size. R language free and open-source software: http://www.r-project.org/. Maxima language free
Physics Simulations in Python
The projects in this manual touch on some fascinating fields of physics including • Mark Newman
Lecture 1 Laboratorio di Fisica Computazionale Computational
necessary executables to use the packages that a Python project would need Effective Computation in Physics: Field Guide to Research with Python. (A ...
Suggestions of Bachelor thesis projects in Theoretical Physics
Knowledge in theoretical biophysics and computational physics corresponding to FYTN05 and FYTN03 in Python
Computational Physics Education with Python
At the end of the course students give a short presentation on a small project of their choice. In this task
MPInterfaces: A Materials Project based Python tool for high
2016年5月16日 computational high throughput discovery projects. 480 run on differing job ... Molecular Dynamics Journal of Computational Physics. 705. 117 (1) ...
Tuesdays Lecture 1 Laboratorio di Fisica Computazionale
Why Python? (in a Computational Physics Laboratory). Page 7. • This course is about computational physics *not* a coding class. ->For a dedicated introduction
Integrating Python data analysis in an existing introductory
Due to these demands there is a need to teach 'computational skills' in the physics curriculum
The challenges of developing computational physics: the case of
computational physics research projects in various branches of physics. Very ... computational LAN working in an opensource environment and use the Python ...
Project-based introduction to scientific computing for physics majors
puting for physics majors using Python and the IPython Notebook that was to an independent research-level computational physics project.
Physics Simulations in Python
For Project 6 you may need to install a free version of the Python Nicholas J. Giordano and Hisao Nakanishi
Computational Physics
Since it seems somewhat premature to follow immediately with a Python version (it's not like This medium appears ideal for computational projects; the.
Computational Physics with Python
Computational Physics programs using Python programming engaged with projects as if each were an original scientific investigation and having.
Computational Physics Object Oriented Programming in Python
Define a function we'll call a lot. Initialize variables (data) we need to do the calculation. Set up a structure to hold the result.
Effective Computation in Physics (Python).pdf
16-Dec-2015 Anthony Scopatz a computational physicist and longtime Python developer
Project in Computational Physics PERCOLATION
17-Mar-2011 Project in Computational Physics. PERCOLATION ... In this project however
COMPUTATIONAL PHYSICS Morten Hjorth-Jensen
like Python can be used to solve computational problems computational speed and libraries suitable for computational science projects
“Computational Approach of Physics with the Study of Projectile
The project talks about the importance of computational physics depicting a project. They are simulation python
Snakes on a Spaceshipâ•?An Overview of Python in Heliophysics
14-Dec-2018 national survey designed to gage community involvement in collaborative space physics python projects. (Burrell et al. 2018).
SCIENCEPROGRAMMING
Effective Computation in Physics
ISBN: 978-1-491-90153-3
US $49.99
CAN $57.99
" - Fernando PerezStaff Scientist,
Lawrence Berkeley National Laboratory
Twitter: @oreillymedia
facebook.com/oreilly More physicists today are taking on the role of software developer as part of their research, but software development isn"t always easy or obvious, even for physicists. This practical book teaches essential software development skills to help you automate and accomplish nearly any aspect of research in a physics-based field. Written by two PhDs in nuclear engineering, this book includes practicalexamples drawn from a working knowledge of physics concepts. You"ll learn how to use the Python programming language to perform everything
from collecting and analyzing data to building software and publishing your results.In four parts, this book includes:
Getting Started: Jump into Python, the command line, data containers, functions, flow control and logic, and classes and objects Getting It Done: Learn about regular expressions, analysis and visualization, NumPy, storing data in files and HDF5, important data structures in physics, computing in parallel, and deploying software Getting It Right: Build pipelines and software, learn to use local and remote version control, and debug and test your code Getting It Out There: Document your code, process and publish your ndings, and collaborate eciently; dive into software licenses, ownership, and copyright procedures Kathryn Huff is a fellow with the Berkeley Institute for Data Science and a postdoctoral scholar with the Nuclear Science and Security Consortium at the University of California Berkeley. She received her Ph.D. in Nuclear Engineering from the University of Wisconsin-Madison. Anthony Scopatz, a computational physicist and longtime Python developer, holds a Ph.D. in Mechanical/Nuclear Engineering from the University of Texas at Austin. In August 2015, he'll start as a professor in Mechanical Engineering at theUniversity of South Carolina.
Anthony Scopatz &
Kathryn D. Hu
Effective
FIELD GUIDE TO RESEARCH
WITH PYTHONEffective Computation
Scopatz & Hu
SCIENCEPROGRAMMING
Effective Computation in Physics
ISBN: 978-1-491-90153-3
US $49.99
CAN $57.99
" - Fernando PerezStaff Scientist,
Lawrence Berkeley National Laboratory
Twitter: @oreillymedia
facebook.com/oreilly More physicists today are taking on the role of software developer as part of their research, but software development isn"t always easy or obvious, even for physicists. This practical book teaches essential software development skills to help you automate and accomplish nearly any aspect of research in a physics-based field. Written by two PhDs in nuclear engineering, this book includes practicalexamples drawn from a working knowledge of physics concepts. You"ll learn how to use the Python programming language to perform everything
from collecting and analyzing data to building software and publishing your results.In four parts, this book includes:
Getting Started: Jump into Python, the command line, data containers, functions, flow control and logic, and classes and objects Getting It Done: Learn about regular expressions, analysis and visualization, NumPy, storing data in files and HDF5, important data structures in physics, computing in parallel, and deploying software Getting It Right: Build pipelines and software, learn to use local and remote version control, and debug and test your code Getting It Out There: Document your code, process and publish your ndings, and collaborate eciently; dive into software licenses, ownership, and copyright procedures Kathryn Huff is a fellow with the Berkeley Institute for Data Science and a postdoctoral scholar with the Nuclear Science and Security Consortium at the University of California Berkeley. She received her Ph.D. in Nuclear Engineering from the University of Wisconsin-Madison. Anthony Scopatz, a computational physicist and longtime Python developer, holds a Ph.D. in Mechanical/Nuclear Engineering from the University of Texas at Austin. In August 2015, he'll start as a professor in Mechanical Engineering at theUniversity of South Carolina.
Anthony Scopatz &
Kathryn D. Hu
Effective
FIELD GUIDE TO RESEARCH
WITH PYTHONEffective Computation
Scopatz & Hu
Anthony Scopatz and Kathryn D. Hufff
Boston
Efffective Computation in Physics
978-1-491-90153-3
[LSI]Efffective Computation in Physics by Anthony Scopatz and Kathryn D. Huff Copyright © 2015 Anthony Scopatz and Kathryn D. Huff. All rights reserved.Printed in the United States of America.
Published by O'Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.O'Reilly books may be purchased for educational, business, or sales promotional use. Online editions are
also available for most titles (http://safaribooksonline.com). For more information, contact our corporate/
institutional sales department: 800-998-9938 or corporate@oreilly.com.Editor: Meghan BlanchetteProduction Editor: Nicole Shelby
Copyeditor: Rachel Head
Proofreader: Rachel MonaghanIndexer: Judy McConvilleInterior Designer: David Futato
Cover Designer: Ellie Volckhausen
Illustrator: Rebecca DemarestJune 2015:
First Edition
Revision History for the First Edition
2015-06-09: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781491901533 for release details. The O'Reilly logo is a registered trademark of O'Reilly Media, Inc.Efffective Computation in Physics, the
cover image, and related trade dress are trademarks of O'Reilly Media, Inc. While the publisher and the authors have used good faith efforts to ensure that the information andinstructions contained in this work are accurate, the publisher and the authors disclaim all responsibility
for errors or omissions, including without limitation responsibility for damages resulting from the use of
or reliance on this work. Use of the information and instructions contained in this work is at your own
risk. If any code samples or other technology this work contains or describes is subject to open source
licenses or the intellectual property rights of others, it is your responsibility to ensure that your use
thereof complies with such licenses and ghts. To THW and friends: gonuke, animal1, kmo, redbeard, spidr, slayer, nicopresto, wolfman, blackbeard, johnnyb, jdangerx, punkish, radio, crbates, 3rdbit, fastmath, and others, this one is for you.Table of Contents
Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . xvPreface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . xviiPart I. Getting Started
1.Introduction to the Command Line. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Navigating the Shell 1
The Shell Is a Programming Language 2
Paths and pwd 3
Home Directory (~) 5
Listing the Contents (ls) 6
Changing Directories (cd) 7
File Inspection (head and tail) 10
Manipulating Files and Directories 11
Creating Files (nano, emacs, vi, cat, >, and touch) 11
Copying and Renaming Files (cp and mv) 17
Making Directories (mkdir) 18
Deleting Files and Directories (rm) 18
Flags and Wildcards 20
Getting Help 21
Reading the Manual (man) 21
Finding the Right Hammer (apropos) 24
Combining Utilities with Redirection and Pipes (>, >>, and |) 25Permissions and Sharing 26
Seeing Permissions (ls -l) 26
Setting Ownership (chown) 28
vSetting Permissions (chmod) 29
Creating Links (ln) 29
Connecting to Other Computers (ssh and scp) 30The Environment 31
Saving Environment Variables (.bashrc) 33
Running Programs (PATH) 34
Nicknaming Commands (alias) 36
Scripting with Bash 36
Command Line Wrap-up 382.
Programming Blastofff with Python. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Running Python 40
Comments 41
Variables
42Special Variables 44
Boolean Values 45
None Is Not Zero! 45
NotImplemented Is Not None! 45
Operators 46
Strings
49String Indexing 50
String Concatenation 53
String Literals 54
String Methods 55
Modules
57Importing Modules 58
Importing Variables from a Module 58
Aliasing Imports 59
Aliasing Variables on Import 59
Packages 60
The Standard Library and the Python Ecosystem 62Python Wrap-up 63
3.Essential Containers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Lists 66Tuples
70Sets 71
Dictionaries
73Containers Wrap-up 75
4.Flow Control and Logic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Conditionals
77vi | Table of C ontents
if-else Statements 80
if-elif-else Statements 81
if-else Expression 82
Exceptions
82Raising Exceptions 84
Loops 85while Loops 86
for Loops 88Comprehensions 90
Flow Control and Logic Wrap-up 935.
Operating with Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
Functions in Python 96
Keyword Arguments 99
Variable Number of Arguments 101
Multiple Return Values 103
Scope 104Recursion
107Lambdas
108Generators 109
Decorators 112
Function Wrap-up 116
6.Classes and Objects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Object Orientation 118
Objects
119Classes
123Class Variables 124
Instance Variables 126
Constructors 127
Methods 129
Static Methods 132
Duck Typing 133
Polymorphism 135
Decorators and Metaclasses 139
Object Orientation Wrap-up 141
Part II. Getting It Done
7.Analysis and Visualization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Preparing Data 145
Table of Contents | vii
Experimental Data 149
Simulation Data 150
Metadata 151
Loading Data 151
NumPy 152
PyTables 153
Pandas 153
Blaze 155Cleaning and Munging Data 155
Missing Data 158
Analysis
159Model-Driven Analysis 160
Data-Driven Analysis 162
Visualization 162
Visualization Tools 164
Gnuplot 164
matplotlib 167
Bokeh 172
Inkscape 174
Analysis and Visualization Wrap-up 1758.
Regular Expressions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Messy Magnetism 178
Metacharacters on the Command Line 179
Listing Files with Simple Patterns 180
Globally Finding Filenames with Patterns (find) 182grep, sed, and awk 187
Finding Patterns in Files (grep) 188
Finding and Replacing Patterns in Files (sed) 190
Finding and Replacing a Complex Pattern 192
sed Extras 193
Manipulating Columns of Data (awk) 195
Python Regular Expressions 197
Regular Expressions Wrap-up 199
9.NumPy: Thinking in Arrays. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Arrays 202
dtypes 204Slicing and Views 208
Arithmetic and Broadcasting 211
Fancy Indexing 215
Masking
217viii | Table of C ontents
Structured Arrays 220
Universal Functions 223
Other Valuable Functions 226
NumPy Wrap-up 22710.
Storing Data: Files and HDF5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
Files in Python 230
An Aside About Computer Architecture 235
Big Ideas in HDF5 237
File Manipulations 239
Hierarchy Layout 242
Chunking 245
In-Core and Out-of-Core Operations 249
In-Core 249
Out-of-Core 250
Querying 252
Compression 252
HDF5 Utilities 254
Storing Data Wrap-up 255
11.Important Data Structures in Physics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257
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