Shows how to develop tailored, flexible, and human-efficient working environments using the easy-to-learn, high-level Python language
Focuses on examples and applications of practical use to computational scientists
Compatible with the new NumPy implementation and features updated information, correction of errors, and improved associated software tools
All the tools and examples in the book are open source codes
With a primary focus on examples and applications of relevance to computational scientists, this brilliantly useful book shows computational scientists how to develop tailored, flexible, and human-efficient working environments built from smallillegalscripts written in the easy-to-learn, high-level Python language. All the tools and examples in this book are open source codes. This third edition features lots of new material. It is also released after a comprehensive reorganization of the text. The author has inserted improved examples and tools and updated information, as well as correcting any errors that crept in to the first imprint.
|Numerous readers of the second edition have noti?ed me about misprints and possible improvements of the text and the associated computer codes. The resulting modi?cations have been incorporated in this new edition and its accompanying software. The major change between the second and third editions, however, is caused by the new implementation of Numerical Python, now called numpy. The new numpy package encourages a slightly di?erent syntax compared to the old Numeric implementation, which was used in the previous editions. Since Numerical Python functionality appears in a lot of places in the book, there are hence a huge number of updates to the new suggested numpy syntax, especially in Chapters 4, 9, and 10. The second edition was based on Python version 2.3, while the third edition contains updates for version 2.5. Recent Python features, such as generator expressions (Chapter 8.9.4), Ctypes for interfacing shared libraries in C (Chapter 5.2.2), the with statement (Chapter 3.1.4), and the subprocess module for running external processes (Chapter 3.1.3) have been exempli?ed to make the reader aware of new tools. Chapter 4.4.4 is new and gives a taste of symbolic mathematics in Python.|
This book shows computational scientists how to develop tailored, flexible, and human-efficient working environments built from smallillegalscripts written in the easy-to-learn, high-level Python language. The focus is on examples and applications of relevance to computational scientists. All the tools and examples in this book are open source codes. The second edition features new material, reorganization of text, improved examples and tools, updated information, and correction of errors.
Getting Started with Pythonillegalscripting.- Basic Python.- Numerical Computing in Python.- Combining Python with Fortran, C, and C++.- to GUI Programming.- Web Interfaces and CGI Programming.- Advanced Python.- Fortran Programming with NumPy Arrays.- C and C++ Programming with NumPy Arrays.- More Advanced GUI Programming.- Tools and Examples.
From the reviews of the second edition:
"This book addresses primarily a CSE (computational science and engineering) audience. ... gives a clear and detailed account on the ways in which the surprisingly powerful Python language may aid the CSE community." (H. Muthsam, Monatshefte für Mathematik, Vol. 151 (4), 2007)
3rd edition
The goal of this book is to demonstrate how to develop tailored, flexible working environments built from small programs (illegalscripts) written in the easy-to-learn, high-level language Python. The focus is on applications relevant to computational scientists.
From the reviews of the second edition:
"This book addresses primarily a CSE (computational science and engineering) audience. ... gives a clear and detailed account on the ways in which the surprisingly powerful Python language may aid the CSE community." (H. Muthsam, Monatshefte für Mathematik, Vol. 151 (4), 2007)
Inhaltsverzeichnis
Getting Started with Pythonillegalscripting.- Basic Python.- Numerical Computing in Python.- Combining Python with Fortran, C, and C++.- to GUI Programming.- Web Interfaces and CGI Programming.- Advanced Python.- Fortran Programming with NumPy Arrays.- C and C++ Programming with NumPy Arrays.- More Advanced GUI Programming.- Tools and Examples.
Klappentext
Numerous readers of the second edition have noti?ed me about misprints and possible improvements of the text and the associated computer codes. The resulting modi?cations have been incorporated in this new edition and its accompanying software. The major change between the second and third editions, however, is caused by the new implementation of Numerical Python, now called numpy. The new numpy package encourages a slightly di?erent syntax compared to the old Numeric implementation, which was used in the previous editions. Since Numerical Python functionality appears in a lot of places in the book, there are hence a huge number of updates to the new suggested numpy syntax, especially in Chapters 4, 9, and 10. The second edition was based on Python version 2.3, while the third edition contains updates for version 2.5. Recent Python features, such as generator expressions (Chapter 8.9.4), Ctypes for interfacing shared libraries in C (Chapter 5.2.2), the with statement (Chapter 3.1.4), and the subprocess module for running external processes (Chapter 3.1.3) have been exempli?ed to make the reader aware of new tools. Chapter 4.4.4 is new and gives a taste of symbolic mathematics in Python.
This book shows computational scientists how to develop tailored, flexible, and human-efficient working environments built from smallillegalscripts written in the easy-to-learn, high-level Python language. The focus is on examples and applications of relevance to computational scientists. All the tools and examples in this book are open source codes. The second edition features new material, reorganization of text, improved examples and tools, updated information, and correction of errors.
