White Paper MATLAB® to Python - Enthought
White Paper
MATLAB? to Python:
A Migration Guide
Copyright ? Enthought, Inc.
-
All Rights Reserved. Use only permitted under license. Copying, sharing, redistributing or other unauthorized use strictly prohibited. All trademarks and registered trademarks are the property of their respective owners. and Simulink are registered trademark of The MathWorks, Inc.
Enthought, Inc. W Cesar Chavez St Suite
Austin TX United States
Version . , September
Contents
Introduction
Why Python Getting Started
Differences Between Python and MATLAB?
Fundamental Data Types Organizing Code in Packages, not Toolboxes Syntax Indexing and Slicing: Why Zero-Based Indexing NumPy Arrays Are Not Matrices Programming Paradigm: Object-Oriented vs. Procedural
How Do I?
Load Data Signal processing Linear algebra Machine learning Statistical Analysis Image Processing and Computer Vision Optimization Natural Language Processing Data Visualization Save Data What Else?
Strategies for Converting to Python
From the Bottom Up: Converting One Function at a Time From the Top Down: Calling Python from MATLAB?
What Next?
Appendix
Code Example: Pro ling Contiguous Array Operations Complete Version of main.py, in Chapter Anti-Patterns
References
Introduction
This document will guide you through your transition from
? to
Python. The first section presents some reasons why you would want to to
do so as well as how to get started quickly by installing Enthought Canopy.
The second section highlights some of the most important di erences
between the two languages, including the fundamental data types; how
code is organized in packages; an overview of the syntax di erences; how
indexing and slicing work; NumPy arrays; and how Python mainly uses an
object-oriented programming paradigm.
The third section is structured around vignettes for common tasks when
doing data analysis or running simulations. The vignettes highlight the
most common packages used for each task, such as loading data, cleaning
and reformatting data, performing analysis or simulation, plotting, and
saving data.
The fourth section introduces two strategies to gradually transition to
Python. Both rely on testing to validate that the new Python code works
the same way (or is broken in the same way!) as your
? code. They
approach the problem by either converting all function directly to Python
or by calling Python from
?. You should use the one that is most
convenient for your project.
Who Is This Guide For This guide is for long time
? users who want
to migrate to Python, either partially or completely. I once was such a
person.
Who Is This Document Not For If you rely heavily on the Simulink? graphical programming environment, you're out of luck. There is no good Simulink? equivalent in the Python ecosystem. If you have very special hardware integration needs, you might be able to find a package that works for you on the Python Package Index, but there is no guarantee that it will be actively maintained or that it will support all the features you need. However, there is nothing stopping you from implementing the features you need and sharing them with the world. Someone, somewhere,
Download Enthought Canopy at https: //store.downloads/. For a good discussion about Simulink? alternatives, I recommend the article Free and commercial alternatives to Simulink on Mike Croucher's blog, Walking Randomly.
? Enthought, Inc.
Accelerate your Python migration with Enthought's Python for Scientists and Engineers training course!
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