معرفی چند منبع در زمینه آموزش برنامه نویسی MATLABیا متلب
کتاب های به زبان فارسی
کتاب های به زبان انگلیسی
عنوانMatlab, Third Edition: A Practical Introduction to :
Programming and Problem Solving
ترجمه عنوان :متلب :مقدمه ای عملی بر برنامه نويسی و حل مساله ،چاپ سوم
مولفینStormy Attaway :
عنوان :اصول و مبانی متلب برای علوم مهندسی
مولفین :برايان هان ،دانیل تی ،والنتین
مترجمین :رامین موالنا پور ،سارا موالناپور ،نینا اسدی پور
انتشارات :سها دانش
سال چاپ2013 :
لینک دسترسی :لینک
انتشاراتButterworth-Heinemann :
لینک دسترسی :لینک
عنوانMATLAB For Dummies :
عنوان :کاربرد MATLABدر علوم مهندسی
ترجمه عنوان :تلب به زبان ساده
مولفین :حیدرعلی شايانفر ،حسین شايقی
مولفینJim Sizemore, John Paul Mueller :
انتشارات :ياوريان
سال چاپ2014 :
لینک دسترسی :لینک
انتشاراتFor Dummies :
لینک دسترسی :لینک
عنوانEssential MATLAB for Engineers and Scientists :
عنوان :برنامه نويسی MATLABبرای مهندسان
ترجمه عنوان :آنچه بايد مهندسین و دانشمندان از متلب بدانند
مولفین :محمود کشاورز مهر ،بهزاد عبدی
مولفینBrian Hahn, Daniel Valentine:
انتشارات :نوپردازان
سال چاپ2013 :
لینک دسترسی :لینک
انتشاراتAcademic Press :
لینک دسترسی :لینک
عنوانMATLAB: An Introduction with Applications :
عنوان :آموزش کاربردی مباحث پیشرفته با MATLAB
ترجمه عنوان :مقدمه ای بر متلب و کاربردهای آن
مولفین :نیما جمشیدی ،علی ابويی مهريزی ،رسول مواليی
مولفAmos Gilat :
انتشارات :عابد
سال چاپ2014 :
لینک دسترسی :لینک
انتشاراتWiley :
لینک دسترسی :لینک
عنوانMATLAB For Beginners: A Gentle Approach:
عنوان :کاملترين مرجع آموزشی و کاربردی MATLAB
ترجمه عنوان :متلب برای افراد مبتدی با يک رويکرد تدريجی
مولفین :علی اکبر علمداری ،نسرين علمداری
مولفPeter I. Kattan:
انتشارات :نگارنده دانش
سال چاپ2008 :
انتشاراتCreateSpace Independent Publishing Platform :
لینک دسترسی :لینک
لینک دسترسی :لینک
عنوانMATLAB for Engineers :
عنوان :برنامه نويسی MATLABبرای مهندسین
ترجمه عنوان :متلب برای مهندسین
مولف :استفن چاپمن
مولفHolly Moore :
مترجم :سعدان زکائی
سال چاپ2011 :
انتشارات :دانشگاه صنعتی خواجه نصیرالدين طوسی
انتشاراتPrentice Hall :
لینک دسترسی :لینک
لینک دسترسی :لینک
عنوانMastering MATLAB :
عنوان :آموزش گام به گام محاسبات عددی با متلب
ترجمه عنوان :تسلط بر متلب
مولفینDuane C. Hanselman, Bruce L. Littlefield :
سال چاپ2011 :
مولف :کلیو مولر
مترجم :رسول نصیری
انتشارات :نشر گستر
انتشاراتPrentice Hall :
لینک دسترسی :لینک
لینک دسترسی :لینک
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منابع آموزشی آنالین
عنوان :مجموعه فرادرسهای برنامهنويسی متلب
مدرس :دکتر سید مصطفی کالمی هريس
مدت زمان ۹ :ساعت و ۳دقیقه
زبان :فارسی
ارائه دهنده :فرادرس
لینک دسترسی :لینک
عنوان :مجموعه فرادرسهای متلب برای علوم و مهندسی
مدرس :دکتر سید مصطفی کالمی هريس
مدت زمان 14 :ساعت و 2۲دقیقه
زبان :فارسی
ارائه دهنده :فرادرس
لینک دسترسی :لینک
عنوان :مجموعه فرادرسهای برنامه نويسی متلب پیشرفته
مدرس :دکتر سید مصطفی کالمی هريس
مدت زمان ۲ :ساعت و 12دقیقه
زبان :فارسی
ارائه دهنده :فرادرس
لینک دسترسی :لینک
عنوانIntroduction to Programming with MATLAB :
ترجمه عنوان :آشنايی با برنامهنويسی متلب
مدرسینAkos Ledeczi, Michael Fitzpatrick, Robert Tairas :
زبان :انگلیسی
ارائه دهندهVanderbilt University :
لینک دسترسی :لینک
عنوانIntroduction to MATLAB :
ترجمه عنوان :مقدمهای بر متلب
مدرسDanilo Šćepanović :
زبان :انگلیسی
ارائه دهندهMIT OCW :
لینک دسترسی :لینک
عنوانUp and Running with MATLAB :
ترجمه عنوان :شروع سريع کار با متلب
مدرسPatrick Royal :
زبان :انگلیسی
ارائه دهندهlynda.com :
لینک دسترسی :لینک
عنوانModelling and Simulation using MATLAB :
ترجمه عنوان :مدلسازی و شبیهسازی با استفاده از متلب
مدرسین Prof. Dr.-Ing. Georg Fries :و دیگران
زبان :انگلیسی
ارائه دهندهiversity.org :
لینک دسترسی :لینک
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MATLAB® Coder™
User's Guide
R2014b
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How to Contact MathWorks
Latest news:
www.mathworks.com
Sales and services:
www.mathworks.com/sales_and_services
User community:
www.mathworks.com/matlabcentral
Technical support:
www.mathworks.com/support/contact_us
Phone:
508-647-7000
The MathWorks, Inc.
3 Apple Hill Drive
Natick, MA 01760-2098
MATLAB® Coder™ User's Guide
© COPYRIGHT 2011–2014 by The MathWorks, Inc.
The software described in this document is furnished under a license agreement. The software may be used
or copied only under the terms of the license agreement. No part of this manual may be photocopied or
reproduced in any form without prior written consent from The MathWorks, Inc.
FEDERAL ACQUISITION: This provision applies to all acquisitions of the Program and Documentation
by, for, or through the federal government of the United States. By accepting delivery of the Program
or Documentation, the government hereby agrees that this software or documentation qualifies as
commercial computer software or commercial computer software documentation as such terms are used
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and Documentation by the federal government (or other entity acquiring for or through the federal
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to meet the government's needs or is inconsistent in any respect with federal procurement law, the
government agrees to return the Program and Documentation, unused, to The MathWorks, Inc.
Trademarks
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Patents
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Revision History
April 2011
September 2011
March 2012
September 2012
March 2013
September 2013
March 2014
October 2014
Online only
Online only
Online only
Online only
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Online only
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New for Version 2 (R2011a)
Revised for Version 2.1 (Release 2011b)
Revised for Version 2.2 (Release 2012a)
Revised for Version 2.3 (Release 2012b)
Revised for Version 2.4 (Release 2013a)
Revised for Version 2.5 (Release 2013b)
Revised for Version 2.6 (Release 2014a)
Revised for Version 2.7 (Release 2014b)
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Check Bug Reports for Issues and Fixes
Software is inherently complex and is not free of errors. The output of a code generator
might contain bugs, some of which are not detected by a compiler. MathWorks
reports critical known bugs brought to its attention on its Bug Report system at
www.mathworks.com/support/bugreports/. Use the Saved Searches and Watched Bugs
tool with the search phrase "Incorrect Code Generation" to obtain a report of known bugs
that produce code that might compile and execute, but still produce wrong answers.
The bug reports are an integral part of the documentation for each release. Examine
periodically all bug reports for a release, as such reports may identify inconsistencies
between the actual behavior of a release you are using and the behavior described in this
documentation.
In addition to reviewing bug reports, you should implement a verification and validation
strategy to identify potential bugs in your design, code, and tools.
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Contents
About MATLAB Coder
1
2
MATLAB Coder Product Description . . . . . . . . . . . . . . . . . . .
Key Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1-2
1-2
Product Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
When to Use MATLAB Coder . . . . . . . . . . . . . . . . . . . . . . . .
Code Generation for Embedded Software Applications . . . . .
Code Generation for Fixed-Point Algorithms . . . . . . . . . . . . .
1-3
1-3
1-3
1-3
Code Generation Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1-5
1-5
Design Considerations for C/C++ Code Generation
When to Generate Code from MATLAB Algorithms . . . . . . .
When Not to Generate Code from MATLAB Algorithms . . . .
2-2
2-2
Which Code Generation Feature to Use . . . . . . . . . . . . . . . . .
2-4
Prerequisites for C/C++ Code Generation from MATLAB . . .
2-5
MATLAB Code Design Considerations for Code Generation
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2-6
2-7
Differences in Behavior After Compiling MATLAB Code . . .
Why Are There Differences? . . . . . . . . . . . . . . . . . . . . . . . . .
Character Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Order of Evaluation in Expressions . . . . . . . . . . . . . . . . . . . .
Termination Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2-8
2-8
2-8
2-8
2-9
v
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Size of Variable-Size N-D Arrays . . . . . . . . . . . . . . . . . . . . .
Size of Empty Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Floating-Point Numerical Results . . . . . . . . . . . . . . . . . . . .
NaN and Infinity Patterns . . . . . . . . . . . . . . . . . . . . . . . . .
Code Generation Target . . . . . . . . . . . . . . . . . . . . . . . . . . .
MATLAB Class Initial Values . . . . . . . . . . . . . . . . . . . . . . .
Variable-Size Support for Code Generation . . . . . . . . . . . . .
MATLAB Language Features Supported for C/C++ Code
Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MATLAB Language Features Not Supported for C/C++ Code
Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
2-12
3-2
Functions, Classes, and System Objects Supported
for Code Generation
Functions and Objects Supported for C and C++ Code
Generation — Alphabetical List . . . . . . . . . . . . . . . . . . . . . .
Functions and Objects Supported for C and C++ Code
Generation — Category List . . . . . . . . . . . . . . . . . . . . . . .
Aerospace Toolbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Arithmetic Operations in MATLAB . . . . . . . . . . . . . . . . . .
Bit-Wise Operations MATLAB . . . . . . . . . . . . . . . . . . . . .
Casting in MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Communications System Toolbox . . . . . . . . . . . . . . . . . . .
Complex Numbers in MATLAB . . . . . . . . . . . . . . . . . . . . .
Computer Vision System Toolbox . . . . . . . . . . . . . . . . . . .
Control Flow in MATLAB . . . . . . . . . . . . . . . . . . . . . . . . .
Data and File Management in MATLAB . . . . . . . . . . . . . .
Data Types in MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . .
vi
2-12
System Objects Supported for Code Generation
Code Generation for System Objects . . . . . . . . . . . . . . . . . . .
4
2-9
2-9
2-10
2-10
2-11
2-11
2-11
Contents
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4-2
4-134
4-136
4-136
4-137
4-138
4-138
4-144
4-144
4-153
4-154
4-157
Desktop Environment in MATLAB . . . . . . . . . . . . . . . . . .
Discrete Math in MATLAB . . . . . . . . . . . . . . . . . . . . . . . .
DSP System Toolbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Error Handling in MATLAB . . . . . . . . . . . . . . . . . . . . . . .
Exponents in MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . .
Filtering and Convolution in MATLAB . . . . . . . . . . . . . . .
Fixed-Point Designer . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HDL Coder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Histograms in MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . .
Image Acquisition Toolbox . . . . . . . . . . . . . . . . . . . . . . . . .
Image Processing in MATLAB . . . . . . . . . . . . . . . . . . . . . .
Image Processing Toolbox . . . . . . . . . . . . . . . . . . . . . . . . .
Input and Output Arguments in MATLAB . . . . . . . . . . . .
Interpolation and Computational Geometry in MATLAB . .
Linear Algebra in MATLAB . . . . . . . . . . . . . . . . . . . . . . .
Logical and Bit-Wise Operations in MATLAB . . . . . . . . . .
MATLAB Compiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Matrices and Arrays in MATLAB . . . . . . . . . . . . . . . . . . .
Neural Network Toolbox . . . . . . . . . . . . . . . . . . . . . . . . . .
Nonlinear Numerical Methods in MATLAB . . . . . . . . . . . .
Numerical Integration and Differentiation in MATLAB . . .
Optimization Functions in MATLAB . . . . . . . . . . . . . . . . .
Phased Array System Toolbox . . . . . . . . . . . . . . . . . . . . . .
Polynomials in MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . .
Programming Utilities in MATLAB . . . . . . . . . . . . . . . . . .
Relational Operators in MATLAB . . . . . . . . . . . . . . . . . . .
Rounding and Remainder Functions in MATLAB . . . . . . .
Set Operations in MATLAB . . . . . . . . . . . . . . . . . . . . . . .
Signal Processing in MATLAB . . . . . . . . . . . . . . . . . . . . .
Signal Processing Toolbox . . . . . . . . . . . . . . . . . . . . . . . . .
Special Values in MATLAB . . . . . . . . . . . . . . . . . . . . . . . .
Specialized Math in MATLAB . . . . . . . . . . . . . . . . . . . . . .
Statistics in MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Statistics Toolbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
String Functions in MATLAB . . . . . . . . . . . . . . . . . . . . . .
Structures in MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . .
Trigonometry in MATLAB . . . . . . . . . . . . . . . . . . . . . . . . .
4-158
4-158
4-159
4-166
4-167
4-167
4-168
4-178
4-178
4-178
4-178
4-179
4-186
4-187
4-190
4-191
4-191
4-192
4-199
4-199
4-199
4-200
4-201
4-209
4-209
4-209
4-210
4-210
4-215
4-216
4-221
4-221
4-222
4-222
4-231
4-233
4-233
vii
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5
6
Defining MATLAB Variables for C/C++ Code
Generation
Variables Definition for Code Generation . . . . . . . . . . . . . . .
5-2
Best Practices for Defining Variables for C/C++ Code
Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Define Variables By Assignment Before Using Them . . . . . .
Use Caution When Reassigning Variables . . . . . . . . . . . . . . .
Use Type Cast Operators in Variable Definitions . . . . . . . . .
Define Matrices Before Assigning Indexed Variables . . . . . . .
5-3
5-3
5-5
5-5
5-6
Eliminate Redundant Copies of Variables in Generated
Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
When Redundant Copies Occur . . . . . . . . . . . . . . . . . . . . . . .
How to Eliminate Redundant Copies by Defining Uninitialized
Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Defining Uninitialized Variables . . . . . . . . . . . . . . . . . . . . . .
5-7
5-8
Reassignment of Variable Properties . . . . . . . . . . . . . . . . . . .
5-9
Define and Initialize Persistent Variables . . . . . . . . . . . . . .
5-10
Reuse the Same Variable with Different Properties . . . . . .
When You Can Reuse the Same Variable with Different
Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
When You Cannot Reuse Variables . . . . . . . . . . . . . . . . . . .
Limitations of Variable Reuse . . . . . . . . . . . . . . . . . . . . . . .
5-11
Avoid Overflows in for-Loops . . . . . . . . . . . . . . . . . . . . . . . . .
5-15
Supported Variable Types . . . . . . . . . . . . . . . . . . . . . . . . . . .
5-17
5-11
5-11
5-14
Defining Data for Code Generation
Data Definition for Code Generation . . . . . . . . . . . . . . . . . . .
viii
5-7
5-7
Contents
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6-2
7
Code Generation for Complex Data . . . . . . . . . . . . . . . . . . . .
Restrictions When Defining Complex Variables . . . . . . . . . . .
Expressions With Complex Operands Yield Complex Results .
6-4
6-4
6-4
Code Generation for Characters . . . . . . . . . . . . . . . . . . . . . . .
6-6
Array Size Restrictions for Code Generation . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6-7
6-7
Code Generation for Variable-Size Data
What Is Variable-Size Data? . . . . . . . . . . . . . . . . . . . . . . . . . . .
7-2
Variable-Size Data Definition for Code Generation . . . . . . .
7-3
Bounded Versus Unbounded Variable-Size Data . . . . . . . . . .
7-4
Control Memory Allocation of Variable-Size Data . . . . . . . . .
7-5
Specify Variable-Size Data Without Dynamic Memory
Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fixing Upper Bounds Errors . . . . . . . . . . . . . . . . . . . . . . . . .
Specifying Upper Bounds for Variable-Size Data . . . . . . . . . .
7-6
7-6
7-6
Variable-Size Data in Code Generation Reports . . . . . . . . . .
What Reports Tell You About Size . . . . . . . . . . . . . . . . . . . .
How Size Appears in Code Generation Reports . . . . . . . . . .
How to Generate a Code Generation Report . . . . . . . . . . . .
7-9
7-9
7-10
7-10
Define Variable-Size Data for Code Generation . . . . . . . . .
When to Define Variable-Size Data Explicitly . . . . . . . . . . .
Using a Matrix Constructor with Nonconstant Dimensions .
Inferring Variable Size from Multiple Assignments . . . . . . .
Defining Variable-Size Data Explicitly Using coder.varsize .
7-11
7-11
7-11
7-12
7-13
C Code Interface for Arrays . . . . . . . . . . . . . . . . . . . . . . . . . .
C Code Interface for Statically Allocated Arrays . . . . . . . . .
C Code Interface for Dynamically Allocated Arrays . . . . . . .
Utility Functions for Creating emxArray Data Structures . .
7-17
7-17
7-18
7-19
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Diagnose and Fix Variable-Size Data Errors . . . . . . . . . . . .
Diagnosing and Fixing Size Mismatch Errors . . . . . . . . . . .
Diagnosing and Fixing Errors in Detecting Upper Bounds . .
Incompatibilities with MATLAB in Variable-Size Support for
Code Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Incompatibility with MATLAB for Scalar Expansion . . . . . .
Incompatibility with MATLAB in Determining Size of VariableSize N-D Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Incompatibility with MATLAB in Determining Size of Empty
Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Incompatibility with MATLAB in Determining Class of Empty
Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Incompatibility with MATLAB in Vector-Vector Indexing . .
Incompatibility with MATLAB in Matrix Indexing Operations
for Code Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Incompatibility with MATLAB in Concatenating Variable-Size
Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dynamic Memory Allocation Not Supported for MATLAB
Function Blocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Variable-Sizing Restrictions for Code Generation of Toolbox
Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Common Restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Toolbox Functions with Variable Sizing Restrictions . . . . . .
8
x
7-21
7-21
7-23
7-25
7-25
7-27
7-28
7-29
7-30
7-30
7-31
7-32
7-33
7-33
7-34
Code Generation for MATLAB Structures
Structure Definition for Code Generation . . . . . . . . . . . . . . .
8-2
Structure Operations Allowed for Code Generation . . . . . . .
8-3
Define Scalar Structures for Code Generation . . . . . . . . . . .
Restriction When Using struct . . . . . . . . . . . . . . . . . . . . . . .
Restrictions When Defining Scalar Structures by
Assignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Adding Fields in Consistent Order on Each Control Flow
Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Restriction on Adding New Fields After First Use . . . . . . . . .
8-4
8-4
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8-4
8-4
8-5
9
Define Arrays of Structures for Code Generation . . . . . . . . .
Ensuring Consistency of Fields . . . . . . . . . . . . . . . . . . . . . . .
Using repmat to Define an Array of Structures with Consistent
Field Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Defining an Array of Structures Using Concatenation . . . . . .
8-7
8-7
Make Structures Persistent . . . . . . . . . . . . . . . . . . . . . . . . . . .
8-9
Index Substructures and Fields . . . . . . . . . . . . . . . . . . . . . . .
8-10
Assign Values to Structures and Fields . . . . . . . . . . . . . . . .
8-12
Pass Structure Arguments by Reference or by Value . . . . .
Specify Pass by Reference or by Value Using a Project . . . .
Specify Pass by Reference or by Value Using the CommandLine Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pass Input Structure Argument by Reference . . . . . . . . . . .
Pass Input Structure Argument by Value . . . . . . . . . . . . . .
Pass Output Structure Argument by Reference . . . . . . . . . .
Pass Output Structure Argument by Value . . . . . . . . . . . . .
Pass Input and Output Structure Argument by Reference . .
8-14
8-14
8-7
8-8
8-15
8-15
8-16
8-16
8-17
8-18
Code Generation for Enumerated Data
Enumerated Data Definition for Code Generation . . . . . . . .
9-2
Enumerated Types Supported for Code Generation . . . . . . .
Enumeration Class Base Types for Code Generation . . . . . . .
C Code Representation for Base Type int32 . . . . . . . . . . . .
C Code Representation for Base Type Other Than int32 . . .
9-3
9-3
9-4
9-4
When to Use Enumerated Data for Code Generation . . . . . .
9-6
Generate Code for Enumerated Data from MATLAB
Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9-7
9-7
Define Enumerated Data for Code Generation . . . . . . . . . . .
Naming Enumerated Types for Code Generation . . . . . . . . . .
9-8
9-9
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Operations on Enumerated Data for Code Generation . . . .
Assignment Operator, = . . . . . . . . . . . . . . . . . . . . . . . . . . .
Relational Operators, < > <= >= == ~= . . . . . . . . . . . . . . . .
Cast Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Indexing Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Control Flow Statements: if, switch, while . . . . . . . . . . . . .
9-10
9-10
9-10
9-10
9-11
9-11
Include Enumerated Data in Control Flow Statements . . .
if Statement with Enumerated Data Types . . . . . . . . . . . .
switch Statement with Enumerated Data Types . . . . . . . .
while Statement with Enumerated Data Types . . . . . . . . .
9-13
9-13
9-14
9-16
Customize Enumerated Types for Code Generation . . . . . .
Customizing Enumerated Types . . . . . . . . . . . . . . . . . . . . .
Specify a Default Enumerated Value . . . . . . . . . . . . . . . . . .
Specify a Header File . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9-19
9-19
9-20
9-21
Use Enumerated Types in LED Control Function . . . . . . . .
9-23
Control Names of Enumerated Type Values in Generated
Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9-26
Change and Reload Enumerated Data Types . . . . . . . . . . . .
9-29
Restrictions on Use of Enumerated Data in for-Loops . . . .
9-30
Toolbox Functions That Support Enumerated Types for Code
Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9-31
10
xii
Code Generation for MATLAB Classes
MATLAB Classes Definition for Code Generation . . . . . . . .
Language Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Code Generation Features Not Compatible with Classes . . .
Defining Class Properties for Code Generation . . . . . . . . . .
Calls to Base Class Constructor . . . . . . . . . . . . . . . . . . . . .
Inheritance from Built-In MATLAB Classes Not Supported .
10-2
10-2
10-3
10-4
10-5
10-6
Classes That Support Code Generation . . . . . . . . . . . . . . . .
10-7
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11
12
Generate Code for MATLAB Value Classes . . . . . . . . . . . . .
10-8
Generate Code for MATLAB Handle Classes and System
Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10-13
MATLAB Classes in Code Generation Reports . . . . . . . . . .
What Reports Tell You About Classes . . . . . . . . . . . . . . . .
How Classes Appear in Code Generation Reports . . . . . . .
How to Generate a Code Generation Report . . . . . . . . . . .
10-15
10-15
10-15
10-17
Troubleshooting Issues with MATLAB Classes . . . . . . . . .
Class class does not have a property with name name . . . .
10-18
10-18
Code Generation for Function Handles
Function Handle Definition for Code Generation . . . . . . . .
11-2
Define and Pass Function Handles for Code Generation . .
11-3
Function Handle Limitations for Code Generation . . . . . . .
11-5
Defining Functions for Code Generation
Specify Variable Numbers of Arguments . . . . . . . . . . . . . . .
12-2
Supported Index Expressions . . . . . . . . . . . . . . . . . . . . . . . .
12-3
Apply Operations to a Variable Number of Arguments . . . .
When to Force Loop Unrolling . . . . . . . . . . . . . . . . . . . . . .
Using Variable Numbers of Arguments in a for-Loop . . . . . .
12-4
12-4
12-5
Implement Wrapper Functions . . . . . . . . . . . . . . . . . . . . . . .
12-6
Passing Variable Numbers of Arguments from One Function to
Another . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12-6
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13
Pass Property/Value Pairs . . . . . . . . . . . . . . . . . . . . . . . . . . .
12-7
Variable Length Argument Lists for Code Generation . . . .
12-9
Calling Functions for Code Generation
Resolution of Function Calls for Code Generation . . . . . . .
Key Points About Resolving Function Calls . . . . . . . . . . . . .
Compile Path Search Order . . . . . . . . . . . . . . . . . . . . . . . .
When to Use the Code Generation Path . . . . . . . . . . . . . . .
13-2
13-4
13-4
13-5
Resolution of File Types on Code Generation Path . . . . . . .
13-6
Compilation Directive %#codegen . . . . . . . . . . . . . . . . . . . . .
13-8
Call Local Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13-9
Call Supported Toolbox Functions . . . . . . . . . . . . . . . . . . .
13-10
Call MATLAB Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Declaring MATLAB Functions as Extrinsic Functions . . . .
Calling MATLAB Functions Using feval . . . . . . . . . . . . . .
How MATLAB Resolves Extrinsic Functions During
Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Working with mxArrays . . . . . . . . . . . . . . . . . . . . . . . . . .
Restrictions on Extrinsic Functions for Code Generation . .
Limit on Function Arguments . . . . . . . . . . . . . . . . . . . . . .
13-11
13-12
13-16
Fixed-Point Conversion
14
xiv
13-16
13-17
13-19
13-19
Convert MATLAB Code to Fixed-Point C Code . . . . . . . . . .
14-3
Propose Fixed-Point Data Types Based on Simulation
Ranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14-4
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Propose Fixed-Point Data Types Based on Derived
Ranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14-18
Type Proposal Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14-34
Detect Overflows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14-38
Replace the exp Function with a Lookup Table . . . . . . . .
14-45
Replace a Custom Function with a Lookup Table . . . . . . .
14-51
Enable Plotting Using the Simulation Data Inspector . . .
14-58
Log Data for Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14-59
View and Modify Variable Information . . . . . . . . . . . . . . .
View Variable Information . . . . . . . . . . . . . . . . . . . . . . . .
Modify Variable Information . . . . . . . . . . . . . . . . . . . . . . .
Revert Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Promote Sim Min and Sim Max Values . . . . . . . . . . . . . . .
14-62
14-62
14-62
14-64
14-65
Build Instrumented MEX Function . . . . . . . . . . . . . . . . . . .
14-66
Propose Fixed-Point Data Types . . . . . . . . . . . . . . . . . . . . .
14-67
Apply Fixed-Point Data Types . . . . . . . . . . . . . . . . . . . . . . .
14-77
Modify Data Type Proposal Settings . . . . . . . . . . . . . . . . . .
14-82
Modify Instrumentation Report Settings . . . . . . . . . . . . . .
14-85
Automated Fixed-Point Conversion . . . . . . . . . . . . . . . . . .
License Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Automated Fixed-Point Conversion Capabilities . . . . . . . .
Code Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Proposing Data Types . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Locking Proposed Data Types . . . . . . . . . . . . . . . . . . . . . .
Viewing Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Viewing Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Function Replacements . . . . . . . . . . . . . . . . . . . . . . . . . .
Validating Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Testing Numerics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14-86
14-86
14-86
14-88
14-91
14-93
14-93
14-94
14-100
14-102
14-103
14-103
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Detecting Overflows . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xvi
14-104
Instrumented MEX Functions . . . . . . . . . . . . . . . . . . . . . .
Generating Instrumented MEX Functions . . . . . . . . . . . .
Merging Instrumentation Results . . . . . . . . . . . . . . . . . .
Clearing Instrumentation Results . . . . . . . . . . . . . . . . . .
Redirecting Entry-Point Calls to MEX Function . . . . . . .
Proposing Fraction Lengths . . . . . . . . . . . . . . . . . . . . . . .
Proposing Word Lengths . . . . . . . . . . . . . . . . . . . . . . . . .
14-105
14-105
14-105
14-106
14-106
14-106
14-106
Convert Fixed-Point Conversion Project to MATLAB
Scripts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14-107
Generated Fixed-Point Code . . . . . . . . . . . . . . . . . . . . . . .
Location of Generated Fixed-Point Files . . . . . . . . . . . . .
Minimizing fi-casts to Improve Code Readability . . . . . .
Avoiding Overflows in the Generated Fixed-Point Code . .
Controlling Bit Growth . . . . . . . . . . . . . . . . . . . . . . . . . .
Avoiding Loss of Range or Precision . . . . . . . . . . . . . . . .
Handling Non-Constant mpower Exponents . . . . . . . . . .
14-110
14-110
14-111
14-111
14-112
14-112
14-114
Fixed-Point Code for MATLAB Classes . . . . . . . . . . . . . . .
Automated Conversion Support for MATLAB Classes . . .
Unsupported Constructs . . . . . . . . . . . . . . . . . . . . . . . . .
Coding Style Best Practices . . . . . . . . . . . . . . . . . . . . . . .
14-116
14-116
14-116
14-117
Automated Fixed-Point Conversion Best Practices . . . . .
Create a Test File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Prepare Your Algorithm for Code Acceleration or Code
Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Check for Fixed-Point Support for Functions Used in Your
Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Manage Data Types and Control Bit Growth . . . . . . . . . .
Convert to Fixed Point . . . . . . . . . . . . . . . . . . . . . . . . . .
Use the Histogram to Fine-Tune Data Type Settings . . . .
Optimize Your Algorithm . . . . . . . . . . . . . . . . . . . . . . . .
Avoid Explicit Double and Single Casts . . . . . . . . . . . . . .
14-119
14-119
14-120
14-121
14-121
14-122
14-122
14-124
14-126
Replacing Functions Using Lookup Table
Approximations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14-128
MATLAB Language Features Supported for Automated
Fixed-Point Conversion . . . . . . . . . . . . . . . . . . . . . . . . . .
14-129
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15
Inspecting Data Using the Simulation Data Inspector . .
What Is the Simulation Data Inspector? . . . . . . . . . . . . .
Import Logged Data . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Export Logged Data . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Group Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Run Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Create Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Comparison Options . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Enabling Plotting Using the Simulation Data Inspector . .
Save and Load Simulation Data Inspector Sessions . . . . .
14-131
14-131
14-131
14-131
14-131
14-132
14-132
14-132
14-132
14-132
Custom Plot Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14-134
Data Type Issues in Generated Code . . . . . . . . . . . . . . . .
Enable the Highlight Option in a MATLAB Coder Project
Enable the Highlight Option at the Command Line . . . . .
Stowaway Doubles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Stowaway Singles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Expensive Fixed-Point Operations . . . . . . . . . . . . . . . . . .
14-136
14-136
14-136
14-136
14-136
14-136
Automated Fixed-Point Conversion Using
Programmatic Workflow
Convert MATLAB Code to Fixed-Point C Code . . . . . . . . . .
15-2
Propose Fixed-Point Data Types Based on Simulation
Ranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15-5
Propose Fixed-Point Data Types Based on Derived
Ranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15-11
Detect Overflows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15-19
Replace the exp Function with a Lookup Table . . . . . . . .
15-23
Replace a Custom Function with a Lookup Table . . . . . . .
15-25
Enable Plotting Using the Simulation Data Inspector . . .
15-28
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Visualize Differences Between Floating-Point and FixedPoint Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16
xviii
15-29
Setting Up a MATLAB Coder Project
MATLAB Coder Project Set Up Workflow . . . . . . . . . . . . . .
16-2
Creating a New Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
From the MATLAB APPS Tab . . . . . . . . . . . . . . . . . . . . . .
At the Command Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
From a MATLAB Coder Project . . . . . . . . . . . . . . . . . . . . .
16-3
16-3
16-3
16-4
Opening an Existing Project . . . . . . . . . . . . . . . . . . . . . . . . .
From the MATLAB APPS Tab . . . . . . . . . . . . . . . . . . . . . .
At the Command Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
From a MATLAB Coder Project . . . . . . . . . . . . . . . . . . . . .
16-5
16-5
16-5
16-5
Adding Files to the Project . . . . . . . . . . . . . . . . . . . . . . . . . .
16-6
Specifying Properties of Primary Function Inputs in a
Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Why You Must Specify Input Properties . . . . . . . . . . . . . . .
How to Specify an Input Definition in a Project . . . . . . . . . .
16-7
16-7
16-7
Autodefine Input Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
How MATLAB Coder Autodefines Input Types . . . . . . . . . .
Prerequisites for Autodefining Input Types . . . . . . . . . . . . .
How to Autodefine Input Types . . . . . . . . . . . . . . . . . . . . . .
16-8
16-8
16-8
16-8
Define Input Parameters by Example in a Project . . . . . .
How to Define an Input Parameter by Example . . . . . . . .
Specifying Input Parameters by Example . . . . . . . . . . . . .
Specifying an Enumerated Type Input Parameter by
Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Specifying a Fixed-Point Input Parameter by Example . . .
16-15
16-16
Define or Edit Input Parameter Type in a Project . . . . . .
How to Define or Edit an Input Parameter Type . . . . . . . .
Specifying an Enumerated Type Input Parameter by Type
16-19
16-19
16-21
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16-12
16-12
16-13
17
Specifying a Fixed-Point Input Parameter by Type . . . . . .
Specifying Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16-22
16-23
Define Constant Input Parameters in a Project . . . . . . . .
16-29
Define Inputs Programmatically in the MATLAB File . . .
16-30
Adding Global Variables in a Project . . . . . . . . . . . . . . . . .
16-31
Specifying Global Variable Type and Initial Value in a
Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Why Specify a Type Definition for Global Variables? . . . . .
How to Specify a Global Variable Type . . . . . . . . . . . . . . .
Defining a Global Variable by Example . . . . . . . . . . . . . . .
Defining or Editing Global Variable Type . . . . . . . . . . . . .
Defining Global Variable Initial Value . . . . . . . . . . . . . . .
Defining Global Variable Constant Value . . . . . . . . . . . . .
Removing Global Variables . . . . . . . . . . . . . . . . . . . . . . . .
16-32
16-32
16-32
16-33
16-34
16-36
16-38
16-39
Specify Output File Name . . . . . . . . . . . . . . . . . . . . . . . . . .
Command Line Alternative . . . . . . . . . . . . . . . . . . . . . . . .
16-40
16-40
Specify Output File Locations . . . . . . . . . . . . . . . . . . . . . . .
Command Line Alternative . . . . . . . . . . . . . . . . . . . . . . . .
16-41
16-41
Selecting Output Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Command Line Alternative . . . . . . . . . . . . . . . . . . . . . . . .
Changing Output Type . . . . . . . . . . . . . . . . . . . . . . . . . . .
16-42
16-42
16-42
Preparing MATLAB Code for C/C++ Code Generation
Workflow for Preparing MATLAB Code for Code
Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17-2
17-3
Fixing Errors Detected at Design Time . . . . . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17-4
17-4
Using the Code Analyzer . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17-5
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Check Code With the Code Analyzer . . . . . . . . . . . . . . . . . .
17-6
Check Code Using the Code Generation Readiness Tool . .
Run Code Generation Readiness Tool at the Command Line
Run Code Generation Readiness Tool from the Current Folder
Browser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Run the Code Generation Readiness Tool in a Project . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17-8
17-8
17-8
17-8
17-9
Code Generation Readiness Tool . . . . . . . . . . . . . . . . . . . . .
What Information Does the Code Generation Readiness Tool
Provide? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Code Structure Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17-10
Unable to Determine Code Generation Readiness . . . . . . .
17-17
Generate MEX Functions Using the MATLAB Coder Project
Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Project Workflow for Generating MEX Functions . . . . . . .
Generate MEX Functions Using the Project Interface . . . .
Configure Project Settings . . . . . . . . . . . . . . . . . . . . . . . . .
Build a MATLAB Coder Project . . . . . . . . . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17-18
17-18
17-18
17-23
17-24
17-25
17-10
17-11
17-13
17-16
Generate MEX Functions at the Command Line . . . . . . . .
Command-line Workflow for Generating MEX Functions . .
Generate MEX Functions at the Command Line . . . . . . . .
Generating MEX Functions at the Command Line Using
codegen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17-26
17-26
17-26
Fix Errors Detected at Code Generation Time . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17-28
17-28
17-27
17-27
Design Considerations When Writing MATLAB Code for Code
Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17-29
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17-30
Running MEX Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Debugging MEX Functions . . . . . . . . . . . . . . . . . . . . . . . .
xx
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17-31
17-31
Debugging Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18
19
17-32
Testing MEX Functions in MATLAB
Workflow for Testing MEX Functions in MATLAB . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18-2
18-2
Why Test MEX Functions in MATLAB? . . . . . . . . . . . . . . . . .
18-4
Running MEX Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Debugging MEX Functions . . . . . . . . . . . . . . . . . . . . . . . . .
18-5
18-5
Verify MEX Functions in a Project . . . . . . . . . . . . . . . . . . . .
Using Test Files That Call Only MATLAB Functions . . . . .
Using Test Files That Call MEX Functions . . . . . . . . . . . . .
18-6
18-6
18-7
Verify MEX Functions at the Command Line . . . . . . . . . . .
18-8
Debug Run-Time Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Viewing Errors in the Run-Time Stack . . . . . . . . . . . . . . . .
Handling Run-Time Errors . . . . . . . . . . . . . . . . . . . . . . . .
18-9
18-9
18-10
Generating C/C++ Code from MATLAB Code
Code Generation Workflow . . . . . . . . . . . . . . . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-3
19-3
C/C++ Code Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Specify Custom Files to Build . . . . . . . . . . . . . . . . . . . . . . .
19-5
19-5
Generating C/C++ Static Libraries from MATLAB Code . . .
Generate a C Static Library Using the Project Interface . . .
Generate a C Static Library at the Command Line . . . . . . .
19-6
19-6
19-9
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xxii
Generating C/C++ Dynamically Linked Libraries from
MATLAB Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dynamic Libraries Generated by MATLAB Coder . . . . . . .
Generate a C Dynamically Linked Library (DLL) Using the
Project Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Generate a C Dynamic Library at the Command Line . . . .
19-10
19-12
Generating Standalone C/C++ Executables from MATLAB
Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Generate a C Executable Using the Project Interface . . . . .
Generate a C Executable at the Command Line . . . . . . . .
Specifying main Functions for C/C++ Executables . . . . . . .
Specify main Functions . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-13
19-13
19-15
19-16
19-17
Build Setting Configuration . . . . . . . . . . . . . . . . . . . . . . . . .
Specify Output Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Specify a Language for Code Generation . . . . . . . . . . . . . .
Specify Data Type Used in Generated Code . . . . . . . . . . . .
Specify Output File Name . . . . . . . . . . . . . . . . . . . . . . . . .
Specify Output File Locations . . . . . . . . . . . . . . . . . . . . . .
Parameter Specification Methods . . . . . . . . . . . . . . . . . . .
Specify Build Configuration Parameters . . . . . . . . . . . . . .
19-19
19-19
19-21
19-22
19-23
19-24
19-25
19-25
Standard Math Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-32
Change the Standard Math Library . . . . . . . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-33
19-33
Share Build Configuration Settings . . . . . . . . . . . . . . . . . .
Export Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Import Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-34
19-34
19-35
19-36
Convert MATLAB Coder Project to MATLAB Script . . . . .
19-37
Primary Function Input Specification . . . . . . . . . . . . . . . .
Why You Must Specify Input Properties . . . . . . . . . . . . . .
Properties to Specify . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rules for Specifying Properties of Primary Inputs . . . . . . .
Methods for Defining Properties of Primary Inputs . . . . . .
Define Input Properties by Example at the Command Line
Specify Constant Inputs at the Command Line . . . . . . . . .
Specify Variable-Size Inputs at the Command Line . . . . . .
19-39
19-39
19-39
19-42
19-43
19-44
19-46
19-47
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19-10
19-10
Control Constant Inputs in MEX Function Signatures . . .
Control MEX Function Signature Using the Project
Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Control MEX Function Signature at the Command-Line
Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Options for Controlling Constant Inputs in MEX Function
Signatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Call MEX Function with a Constant Input . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-49
19-49
19-49
19-49
19-51
19-52
Define Input Properties Programmatically in the MATLAB
File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
How to Use assert with MATLAB Coder . . . . . . . . . . . . . .
Rules for Using assert Function . . . . . . . . . . . . . . . . . . . .
Specifying General Properties of Primary Inputs . . . . . . . .
Specifying Properties of Primary Fixed-Point Inputs . . . . .
Specifying Class and Size of Scalar Structure . . . . . . . . . .
Specifying Class and Size of Structure Array . . . . . . . . . .
19-53
19-53
19-59
19-59
19-60
19-61
19-62
Speed Up Compilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Generate Code Only . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Disable Compiler Optimization . . . . . . . . . . . . . . . . . . . . .
19-63
19-63
19-63
Paths and File Infrastructure Setup . . . . . . . . . . . . . . . . . .
Compile Path Search Order . . . . . . . . . . . . . . . . . . . . . . . .
Specifying Folders to Search for Custom Code . . . . . . . . . .
Naming Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-65
19-65
19-65
19-66
Generate Code for Multiple Entry-Point Functions . . . . .
19-70
Advantages of Generating Code for More Than One Entry-Point
Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-70
Generating Code for More Than One Entry-Point Function
Using the Project Interface . . . . . . . . . . . . . . . . . . . . . .
19-70
Generating Code for More Than One Entry-Point Function at
the Command Line . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-72
How to Call an Entry-Point Function in a MEX Function .
19-74
How to Call an Entry-Point Function in a C/C++ Library
Function from C/C++ Code . . . . . . . . . . . . . . . . . . . . . .
19-74
Generate Code for Global Data . . . . . . . . . . . . . . . . . . . . . .
Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Declare Global Variables . . . . . . . . . . . . . . . . . . . . . . . . . .
Define Global Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-75
19-75
19-75
19-76
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Synchronizing Global Data with MATLAB . . . . . . . . . . . .
Define Constant Global Data . . . . . . . . . . . . . . . . . . . . . . .
Limitations of Using Global Data . . . . . . . . . . . . . . . . . . .
19-77
19-80
19-83
Generation of Traceable Code . . . . . . . . . . . . . . . . . . . . . . .
About Code Traceability . . . . . . . . . . . . . . . . . . . . . . . . . .
Generate Traceable Code . . . . . . . . . . . . . . . . . . . . . . . . . .
Format of Traceability Tags . . . . . . . . . . . . . . . . . . . . . . .
Location of Comments in Generated Code . . . . . . . . . . . . .
Traceability Limitations . . . . . . . . . . . . . . . . . . . . . . . . . .
19-84
19-84
19-84
19-87
19-87
19-91
Generate Code for Enumerated Types . . . . . . . . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-93
19-93
Generate Code for Variable-Size Data . . . . . . . . . . . . . . . .
19-94
Disable Support for Variable-Size Data . . . . . . . . . . . . . . .
19-94
Control Dynamic Memory Allocation . . . . . . . . . . . . . . . . .
19-95
Generating Code for MATLAB Functions with Variable-Size
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-97
Generate Code for a MATLAB Function That Expands a Vector
in a Loop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-98
Using Dynamic Memory Allocation for an "Atoms"
Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-104
xxiv
Code Generation for MATLAB Classes . . . . . . . . . . . . . . .
19-113
How MATLAB Coder Partitions Generated Code . . . . . .
Partitioning Generated Files . . . . . . . . . . . . . . . . . . . . . .
How to Select the File Partitioning Method . . . . . . . . . . .
Partitioning Generated Files with One C/C++ File Per
MATLAB File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Generated Files and Locations . . . . . . . . . . . . . . . . . . . .
File Partitioning and Inlining . . . . . . . . . . . . . . . . . . . . .
19-114
19-114
19-114
Requirements for Signed Integer Representation . . . . . .
19-126
Customize the Post-Code-Generation Build Process . . . .
Customize Build Using coder.updateBuildInfo . . . . . . . . .
Customize Build Using Post-Code-Generation Command .
Build Information Object . . . . . . . . . . . . . . . . . . . . . . . . .
Build Information Methods . . . . . . . . . . . . . . . . . . . . . . .
Write Post-Code-Generation Command . . . . . . . . . . . . . .
Use Post-Code-Generation Command to Customize Build
19-127
19-127
19-127
19-128
19-128
19-164
19-165
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19-115
19-120
19-122
Write and Use Post-Code-Generation Command at the
Command Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
20
19-165
Code Generation Reports . . . . . . . . . . . . . . . . . . . . . . . . . .
About Code Generation Reports . . . . . . . . . . . . . . . . . . .
Enable Code Generation Reports . . . . . . . . . . . . . . . . . . .
View Your MATLAB Code in a Report . . . . . . . . . . . . . .
Viewing Call Stack Information . . . . . . . . . . . . . . . . . . .
View Generated C and C++ Code in a Report . . . . . . . . .
View the Build Summary Information . . . . . . . . . . . . . . .
View Errors and Warnings in a Report . . . . . . . . . . . . . .
Viewing Variables in Your MATLAB Code . . . . . . . . . . .
View Target Build Information . . . . . . . . . . . . . . . . . . . .
Keyboard Shortcuts for the Code Generation Report . . . .
Report Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19-167
19-167
19-170
19-170
19-172
19-174
19-174
19-175
19-176
19-182
19-183
19-184
Troubleshooting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Run-time Stack Overflow . . . . . . . . . . . . . . . . . . . . . . . .
19-186
19-186
Package Code For Other Development Environments . .
When to Package Code . . . . . . . . . . . . . . . . . . . . . . . . . .
Package Generated Code in a Project . . . . . . . . . . . . . . .
Package Generated Code at the Command Line . . . . . . .
Specify packNGo options . . . . . . . . . . . . . . . . . . . . . . . . .
19-187
19-187
19-187
19-188
19-189
Code Replacement for MATLAB Code
What Is Code Replacement? . . . . . . . . . . . . . . . . . . . . . . . . . .
20-2
Code Replacement Libraries . . . . . . . . . . . . . . . . . . . . . . . . .
20-4
Code Replacement Terminology . . . . . . . . . . . . . . . . . . . . . .
20-6
Code Replacement Limitations . . . . . . . . . . . . . . . . . . . . . . .
20-9
Replace Code Generated from MATLAB Code . . . . . . . . . .
20-10
Choose a Code Replacement Library . . . . . . . . . . . . . . . . .
About Choosing a Code Replacement Library . . . . . . . . . .
20-12
20-12
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Explore Available Code Replacement Libraries . . . . . . . . .
Explore Code Replacement Library Contents . . . . . . . . . .
21
xxvi
20-12
20-12
Custom Toolchain Registration
Custom Toolchain Registration . . . . . . . . . . . . . . . . . . . . . . .
What Is a Custom Toolchain? . . . . . . . . . . . . . . . . . . . . . . .
What Is a Factory Toolchain? . . . . . . . . . . . . . . . . . . . . . . .
What is a Toolchain Definition? . . . . . . . . . . . . . . . . . . . . .
Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Typical Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21-2
21-2
21-2
21-3
21-4
21-4
About coder.make.ToolchainInfo . . . . . . . . . . . . . . . . . . . . . .
21-6
Create and Edit Toolchain Definition File . . . . . . . . . . . . . .
21-8
Toolchain Definition File with Commentary . . . . . . . . . . .
Steps Involved in Writing a Toolchain Definition File . . . .
Write a Function That Creates a ToolchainInfo Object . . .
Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Macros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C Compiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C++ Compiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Linker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Archiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Build Configurations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21-10
21-10
21-10
21-11
21-11
21-12
21-12
21-13
21-13
21-14
21-14
Create and Validate ToolchainInfo Object . . . . . . . . . . . . .
21-16
Register the Custom Toolchain . . . . . . . . . . . . . . . . . . . . . .
21-17
Use the Custom Toolchain . . . . . . . . . . . . . . . . . . . . . . . . . .
21-19
Troubleshooting Custom Toolchain Validation . . . . . . . . .
Build Tool Command Path Incorrect . . . . . . . . . . . . . . . . .
Build Tool Not in System Path . . . . . . . . . . . . . . . . . . . . .
Tool Path Does Not Exist . . . . . . . . . . . . . . . . . . . . . . . . .
Unsupported Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21-20
21-20
21-20
21-21
21-21
Contents
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22
Toolchain is Not installed . . . . . . . . . . . . . . . . . . . . . . . . .
Project or Configuration is Using the Template Makefile . .
Skipped Validation of Build Tool “Download” or “Execute” .
21-22
21-22
21-23
Prevent Circular Data Dependencies with One-Pass or
Single-Pass Linkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21-24
Deploying Generated Code
Call a C Static Library Function from C Code . . . . . . . . . . .
22-2
Call a C/C++ Static Library Function from MATLAB Code .
22-4
Call Generated C/C++ Functions . . . . . . . . . . . . . . . . . . . . . .
Conventions for Calling Functions in Generated Code . . . . .
How to Call C/C++ Functions from MATLAB Code . . . . . . .
Calling Initialize and Terminate Functions . . . . . . . . . . . . .
Calling C/C++ Functions with Multiple Outputs . . . . . . . . .
Calling C/C++ Functions that Return Arrays . . . . . . . . . . .
22-6
22-6
22-6
22-7
22-8
22-8
Use a MATLAB Coder Dynamic Library in a Simple Microsoft
Visual Studio Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22-9
Specify External File Locations . . . . . . . . . . . . . . . . . . . . . .
External File Locations for External Code Integration . . . .
Specify External Files in a Class Derived from
coder.ExternalDependency . . . . . . . . . . . . . . . . . . . . . . .
Specify External Files in MATLAB Code Using
coder.updateBuildInfo . . . . . . . . . . . . . . . . . . . . . . . . . .
Specify External Files in the Project Settings Dialog Box . .
Specify External Files at the Command Line . . . . . . . . . . .
Specify External Files with Configuration Objects . . . . . . .
22-12
22-12
22-12
22-12
22-13
22-13
22-14
xxvii
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23
Accelerating MATLAB Algorithms
Workflow for Accelerating MATLAB Algorithms . . . . . . . . .
See Also . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Best Practices for Using MEX Functions to Accelerate
MATLAB Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Accelerate Code That Dominates Execution Time . . . . . . . .
Include Loops Inside MEX Function . . . . . . . . . . . . . . . . . .
Avoid Generating MEX Functions from Unsupported
Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Avoid Generating MEX Functions if Built-In MATLAB
Functions Dominate Run Time . . . . . . . . . . . . . . . . . . . .
Minimize MEX Function Calls . . . . . . . . . . . . . . . . . . . . . .
Edge Detection on Images . . . . . . . . . . . . . . . . . . . . . . . . . . .
xxviii
23-2
23-3
23-4
23-4
23-4
23-5
23-6
23-6
23-7
Accelerate MATLAB Algorithms . . . . . . . . . . . . . . . . . . . . .
23-14
Modifying MATLAB Code for Acceleration . . . . . . . . . . . .
How to Modify Your MATLAB Code for Acceleration . . . . .
23-15
23-15
Control Run-Time Checks . . . . . . . . . . . . . . . . . . . . . . . . . . .
Types of Run-Time Checks . . . . . . . . . . . . . . . . . . . . . . . .
When to Disable Run-Time Checks . . . . . . . . . . . . . . . . . .
How to Disable Run-Time Checks . . . . . . . . . . . . . . . . . . .
23-16
23-16
23-16
23-17
Algorithm Acceleration Using Parallel for-Loops (parfor)
Parallel for-Loops (parfor) in Generated Code . . . . . . . . . .
How parfor-Loops Improve Execution Speed . . . . . . . . . . .
When to Use parfor-Loops . . . . . . . . . . . . . . . . . . . . . . . . .
When Not to Use parfor-Loops . . . . . . . . . . . . . . . . . . . . .
parfor-Loop Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
parfor Restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23-18
23-18
23-19
23-19
23-19
23-20
23-20
Control Compilation of parfor-Loops . . . . . . . . . . . . . . . . .
When to Disable parfor . . . . . . . . . . . . . . . . . . . . . . . . . . .
23-24
23-24
Reduction Assignments in parfor-Loops . . . . . . . . . . . . . . .
What are Reduction Assignments? . . . . . . . . . . . . . . . . . .
Multiple Reductions in a parfor-Loop . . . . . . . . . . . . . . . .
23-25
23-25
23-25
Contents
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Classification of Variables in parfor-Loops . . . . . . . . . . . .
Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Sliced Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Broadcast Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Reduction Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Temporary Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23-26
23-26
23-27
23-28
23-28
23-33
Accelerate MATLAB Algorithms That Use Parallel for-Loops
(parfor) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23-35
24
Specify Maximum Number of Threads in parfor-Loops . .
23-36
Troubleshooting parfor-Loops . . . . . . . . . . . . . . . . . . . . . . .
What Causes Errors With Global Structures in Parallel
Regions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Compiler Does Not Support OpenMP . . . . . . . . . . . . . . . .
23-37
Accelerating Simulation of Bouncing Balls . . . . . . . . . . . .
23-38
23-37
23-37
Calling C/C++ Functions from Generated Code
External Function Calls from Generated Code . . . . . . . . . .
Calling External Functions from Generated Code . . . . . . . .
Why Call External Functions from Generated Code? . . . . . .
How To Call External Functions . . . . . . . . . . . . . . . . . . . . .
Pass Arguments by Reference to External Functions . . . . . .
Manipulate C Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24-2
24-2
24-2
24-2
24-3
24-4
Call External Functions Using coder.ceval . . . . . . . . . . . . .
Workflow for Calling External Functions . . . . . . . . . . . . . .
Best Practices for Calling External Code from Generated
Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24-6
24-6
Return Multiple Values from C Functions . . . . . . . . . . . . . .
24-8
How MATLAB Coder Infers C/C++ Data Types . . . . . . . . . .
Mapping MATLAB Types to C/C++ Types . . . . . . . . . . . . . .
Mapping 64-Bit Integer Types to C/C++ . . . . . . . . . . . . . .
Mapping Fixed-Point Types to C/C++ . . . . . . . . . . . . . . . .
24-9
24-9
24-10
24-11
24-7
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Mapping
Mapping
Mapping
Mapping
Mapping
25
26
xxx
Arrays to C/C++ . . . . . . . . . . . . . . . . . . . . . . . . .
Complex Values to C/C++ . . . . . . . . . . . . . . . . . .
Structures to C/C++ Structures . . . . . . . . . . . . .
Strings to C/C++ . . . . . . . . . . . . . . . . . . . . . . . . .
Multiword Types to C/C++ . . . . . . . . . . . . . . . . .
24-11
24-12
24-13
24-13
24-14
External Code Integration
External Code Integration for Code Generation . . . . . . . . .
25-2
Encapsulating the Interface to External Code . . . . . . . . . . .
25-3
Best Practices for Using coder.ExternalDependency . . . . .
Terminate Code Generation for Unsupported External
Dependency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Parameterize Methods for MATLAB and Generated Code . .
Parameterize updateBuildInfo for Multiple Platforms . . . . .
25-4
25-4
25-4
25-5
Encapsulate Interface to an External C Library . . . . . . . . .
25-6
Update Build Information from MATLAB code . . . . . . . . . .
25-9
Call External Functions Encapsulated by
coder.ExternalDependency . . . . . . . . . . . . . . . . . . . . . . . .
25-10
Generate Efficient and Reusable Code
Optimization Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26-2
Modularize MATLAB Code . . . . . . . . . . . . . . . . . . . . . . . . . . .
26-5
Eliminate Redundant Copies of Function Inputs . . . . . . . .
26-6
Inline Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Prevent Function Inlining . . . . . . . . . . . . . . . . . . . . . . . . . .
26-8
26-8
Contents
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Use Inlining in Control Flow Statements . . . . . . . . . . . . . .
26-8
Control Inlining Using Configuration Object . . . . . . . . . . .
Control Size of Functions Inlined . . . . . . . . . . . . . . . . . . .
Control Size of Functions After Inlining . . . . . . . . . . . . . .
Control Stack Size Limit on Inlined Functions . . . . . . . . .
26-10
26-10
26-11
26-11
Fold Function Calls into Constants . . . . . . . . . . . . . . . . . . .
26-13
Control Stack Space Usage . . . . . . . . . . . . . . . . . . . . . . . . . .
26-15
Stack Allocation and Performance . . . . . . . . . . . . . . . . . . .
26-16
Rewrite Logical Array Indexing as a Loop . . . . . . . . . . . . .
26-17
Dynamic Memory Allocation and Performance . . . . . . . . .
When Dynamic Memory Allocation Occurs . . . . . . . . . . . .
26-18
26-18
Minimize Dynamic Memory Allocation . . . . . . . . . . . . . . . .
26-19
Provide Maximum Size for Variable-Size Arrays . . . . . . . .
26-20
Disable Dynamic Memory Allocation During Code
Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26-26
Set Dynamic Memory Allocation Threshold . . . . . . . . . . . .
Set Dynamic Memory Allocation Threshold Using Project
Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Set Dynamic Memory Allocation Threshold from Command
Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26-27
Excluding Unused Paths from Generated Code . . . . . . . . .
26-30
Prevent Code Generation for Unused Execution Paths . .
Prevent Code Generation When Local Variable Controls
Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Prevent Code Generation When Input Variable Controls
Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26-31
Generate Code with Parallel for-Loops (parfor) . . . . . . . .
26-33
Minimize Redundant Operations in Loops . . . . . . . . . . . . .
26-35
26-27
26-29
26-31
26-32
xxxi
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xxxii
Unroll for-Loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Limit Copying the for-loop Body in Generated Code . . . . .
26-37
26-37
Support for Integer Overflow and Non-Finites . . . . . . . . .
Disable Support for Integer Overflow . . . . . . . . . . . . . . . .
Disable Support for Non-Finites . . . . . . . . . . . . . . . . . . . .
26-40
26-40
26-41
Integrate Custom Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26-42
MATLAB Coder Optimizations in Generated Code . . . . . .
Constant Folding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Loop Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Successive Matrix Operations Combined . . . . . . . . . . . . . .
Unreachable Code Elimination . . . . . . . . . . . . . . . . . . . . .
26-48
26-48
26-49
26-49
26-50
Generate Reusable Code . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26-51
Contents
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1
About MATLAB Coder
• “MATLAB Coder Product Description” on page 1-2
• “Product Overview” on page 1-3
• “Code Generation Workflow” on page 1-5
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1
About MATLAB Coder
MATLAB Coder Product Description
Generate C and C++ code from MATLAB code
MATLAB® Coder™ generates standalone C and C++ code from MATLAB code. The
generated source code is portable and readable. MATLAB Coder supports a subset of
core MATLAB language features, including program control constructs, functions, and
matrix operations. It can generate MEX functions that let you accelerate computationally
intensive portions of MATLAB code and verify the behavior of the generated code.
Key Features
• ANSI®/ISO® compliant C and C++ code generation
• MEX function generation for fixed-point and floating-point math
• Project management tool for specifying entry points, input data properties, and other
code-generation configuration options
• Static or dynamic memory allocation for variable-size data
• Code generation support for many functions and System objects in Communications
System Toolbox™, DSP System Toolbox™, Computer Vision System Toolbox™, and
Phased Array System Toolbox™
• Support for common MATLAB language features, including matrix operations,
subscripting, program controls statements (if, switch, for, while), and structures
1-2
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Product Overview
Product Overview
In this section...
“When to Use MATLAB Coder” on page 1-3
“Code Generation for Embedded Software Applications” on page 1-3
“Code Generation for Fixed-Point Algorithms” on page 1-3
When to Use MATLAB Coder
Use MATLAB Coder to:
• Generate readable, efficient, standalone C/C++ code from MATLAB code.
• Generate MEX functions from MATLAB code to:
• Accelerate your MATLAB algorithms.
• Verify generated C code within MATLAB.
• Integrate custom C/C++ code into MATLAB.
Code Generation for Embedded Software Applications
The Embedded Coder® product extends the MATLAB Coder product with features that
are important for embedded software development. Using the Embedded Coder addon product, you can generate code that has the clarity and efficiency of professional
handwritten code. For example, you can:
• Generate code that is compact and fast, which is essential for real-time simulators,
on-target rapid prototyping boards, microprocessors used in mass production, and
embedded systems.
• Customize the appearance of the generated code.
• Optimize the generated code for a specific target environment.
• Enable tracing options that help you to verify the generated code.
• Generate reusable, reentrant code.
Code Generation for Fixed-Point Algorithms
Using the Fixed-Point Designer™ product, you can generate:
1-3
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1
About MATLAB Coder
• MEX functions to accelerate fixed-point algorithms.
• Fixed-point code that provides a bit-wise match to MEX function results.
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Code Generation Workflow
Code Generation Workflow
See Also
• “MATLAB Coder Project Set Up Workflow”
• “Workflow for Preparing MATLAB Code for Code Generation”
• “Workflow for Testing MEX Functions in MATLAB”
• “Code Generation Workflow”
• “Workflow for Accelerating MATLAB Algorithms”
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2
Design Considerations for C/C++
Code Generation
• “When to Generate Code from MATLAB Algorithms” on page 2-2
• “Which Code Generation Feature to Use” on page 2-4
• “Prerequisites for C/C++ Code Generation from MATLAB” on page 2-5
• “MATLAB Code Design Considerations for Code Generation” on page 2-6
• “Differences in Behavior After Compiling MATLAB Code” on page 2-8
• “MATLAB Language Features Supported for C/C++ Code Generation” on page
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2
Design Considerations for C/C++ Code Generation
When to Generate Code from MATLAB Algorithms
Generating code from MATLAB algorithms for desktop and embedded systems allows
you to perform your software design, implementation, and testing completely within the
MATLAB workspace. You can:
• Verify that your algorithms are suitable for code generation
• Generate efficient, readable, and compact C/C++ code automatically, which eliminates
the need to manually translate your MATLAB algorithms and minimizes the risk of
introducing errors in the code.
• Modify your design in MATLAB code to take into account the specific requirements of
desktop and embedded applications, such as data type management, memory use, and
speed.
• Test the generated code and easily verify that your modified algorithms are
functionally equivalent to your original MATLAB algorithms.
• Generate MEX functions to:
• Accelerate MATLAB algorithms in certain applications.
• Speed up fixed-point MATLAB code.
• Generate hardware description language (HDL) from MATLAB code.
When Not to Generate Code from MATLAB Algorithms
Do not generate code from MATLAB algorithms for the following applications. Use the
recommended MathWorks product instead.
To:
Use:
Deploy an application that uses handle
graphics
MATLAB Compiler™
Use Java®
MATLAB Builder™ JA
Use toolbox functions that do not support
code generation
Toolbox functions that you rewrite for
desktop and embedded applications
Deploy MATLAB based GUI applications
on a supported MATLAB host
MATLAB Compiler
Deploy web-based or Windows®
applications
• MATLAB Builder NE
• MATLAB Builder JA
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When to Generate Code from MATLAB Algorithms
To:
Use:
Interface C code with MATLAB
MATLAB mex function
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2
Design Considerations for C/C++ Code Generation
Which Code Generation Feature to Use
To...
Use...
Required Product
To Explore Further...
Generate MEX
codegen function
functions for verifying
generated code
MATLAB Coder
Try this in “MEX
Function Generation at
the Command Line”.
Produce readable,
MATLAB Coder user
efficient, and compact interface
code from MATLAB
algorithms for
codegen function
deployment to desktop
and embedded
systems.
MATLAB Coder
Try this in “C Code
Generation Using the
Project Interface”.
MATLAB Coder
Try this in “C Code
Generation at the
Command Line”.
Generate MEX
MATLAB Coder user
functions to accelerate interface
MATLAB algorithms codegen function
MATLAB Coder
See “Accelerate
MATLAB Algorithms”.
Integrate MATLAB
code into Simulink®
MATLAB Function
block
Simulink
Try this in “Track Object
Using MATLAB Code”.
Speed up fixed-point
MATLAB code
fiaccel function
Fixed-Point Designer
Learn more in “Code
Acceleration and
Code Generation from
MATLAB”.
MATLAB Coder
Learn more in “Specify
External File Locations”.
Integrate custom C
codegen function
code into MATLAB
and generate efficient,
readable code
MATLAB Coder
Integrate custom
C code into code
generated from
MATLAB
coder.ceval function MATLAB Coder
Learn more in
coder.ceval.
Generate HDL from
MATLAB code
MATLAB Function
block
Learn more at
www.mathworks.com/
products/
slhdlcoder.
Simulink and
HDL Coder™
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Prerequisites for C/C++ Code Generation from MATLAB
Prerequisites for C/C++ Code Generation from MATLAB
To generate C/C++ or MEX code from MATLAB algorithms, you must install the
following software:
• MATLAB Coder product
• C/C++ compiler
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2
Design Considerations for C/C++ Code Generation
MATLAB Code Design Considerations for Code Generation
When writing MATLAB code that you want to convert into efficient, standalone C/C++
code, you must consider the following:
• Data types
C and C++ use static typing. To determine the types of your variables before use,
MATLAB Coder requires a complete assignment to each variable.
• Array sizing
Variable-size arrays and matrices are supported for code generation. You can define
inputs, outputs, and local variables in MATLAB functions to represent data that
varies in size at run time.
• Memory
You can choose whether the generated code uses static or dynamic memory allocation.
With dynamic memory allocation, you potentially use less memory at the expense
of time to manage the memory. With static memory, you get better speed, but with
higher memory usage. Most MATLAB code takes advantage of the dynamic sizing
features in MATLAB, therefore dynamic memory allocation typically enables you
to generate code from existing MATLAB code without modifying it much. Dynamic
memory allocation also allows some programs to compile even when upper bounds
cannot be found.
Static allocation reduces the memory footprint of the generated code, and therefore is
suitable for applications where there is a limited amount of available memory, such as
embedded applications.
• Speed
Because embedded applications must run in real time, the code must be fast enough
to meet the required clock rate.
To improve the speed of the generated code:
• Choose a suitable C/C++ compiler. Do not use the default compiler that
MathWorks supplies with MATLAB for Windows 32-bit platforms.
• Consider disabling run-time checks.
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MATLAB Code Design Considerations for Code Generation
By default, for safety, the code generated for your MATLAB code contains memory
integrity checks and responsiveness checks. Generally, these checks result in more
generated code and slower simulation. Disabling run-time checks usually results
in streamlined generated code and faster simulation. Disable these checks only if
you have verified that array bounds and dimension checking is unnecessary.
See Also
• “Data Definition Basics”
• “Variable-Size Data”
• “Bounded Versus Unbounded Variable-Size Data”
• “Control Dynamic Memory Allocation”
• “Control Run-Time Checks”
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2
Design Considerations for C/C++ Code Generation
Differences in Behavior After Compiling MATLAB Code
In this section...
“Why Are There Differences?” on page 2-8
“Character Size” on page 2-8
“Order of Evaluation in Expressions” on page 2-8
“Termination Behavior” on page 2-9
“Size of Variable-Size N-D Arrays” on page 2-9
“Size of Empty Arrays” on page 2-9
“Floating-Point Numerical Results” on page 2-10
“NaN and Infinity Patterns” on page 2-10
“Code Generation Target” on page 2-11
“MATLAB Class Initial Values” on page 2-11
“Variable-Size Support for Code Generation” on page 2-11
Why Are There Differences?
To convert MATLAB code to C/C++ code that works efficiently, the code generation
process introduces optimizations that intentionally cause the generated code to behave
differently — and sometimes produce different results — from the original source code.
This section describes these differences.
Character Size
MATLAB supports 16-bit characters, but the generated code represents characters in 8
bits, the standard size for most embedded languages like C. See “Code Generation for
Characters” on page 6-6.
Order of Evaluation in Expressions
Generated code does not enforce order of evaluation in expressions. For most expressions,
order of evaluation is not significant. However, for expressions with side effects, the
generated code may produce the side effects in different order from the original MATLAB
code. Expressions that produce side effects include those that:
• Modify persistent or global variables
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Differences in Behavior After Compiling MATLAB Code
• Display data to the screen
• Write data to files
• Modify the properties of handle class objects
In addition, the generated code does not enforce order of evaluation of logical operators
that do not short circuit.
For more predictable results, it is good coding practice to split expressions that depend on
the order of evaluation into multiple statements. For example, rewrite:
A = f1() + f2();
as
A = f1();
A = A + f2();
so that the generated code calls f1 before f2.
Termination Behavior
Generated code does not match the termination behavior of MATLAB source code. For
example, optimizations remove infinite loops from generated code if they do not have side
effects. As a result, the generated code may terminate even though the corresponding
MATLAB code does not.
Size of Variable-Size N-D Arrays
For variable-size N-D arrays, the size function might return a different result in
generated code than in MATLAB source code. The size function sometimes returns
trailing ones (singleton dimensions) in generated code, but always drops trailing ones
in MATLAB. For example, for an N-D array X with dimensions [4 2 1 1], size(X)
might return [4 2 1 1] in generated code, but always returns [4 2] in MATLAB. See
“Incompatibility with MATLAB in Determining Size of Variable-Size N-D Arrays” on
page 7-27.
Size of Empty Arrays
The size of an empty array in generated code might be different from its size in MATLAB
source code. See “Incompatibility with MATLAB in Determining Size of Empty Arrays”
on page 7-28.
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2
Design Considerations for C/C++ Code Generation
Floating-Point Numerical Results
The generated code might not produce the same floating-point numerical results as
MATLAB in the following situations:
When computer hardware uses extended precision
registers
Results vary depending on how the C/C++ compiler allocates extended precision floatingpoint registers. Computation results might not match MATLAB calculations because of
different compiler optimization settings or different code surrounding the floating-point
calculations.
For certain advanced library functions
The generated code might use different algorithms to implement certain advanced
library functions, such as fft, svd, eig, mldivide, and mrdivide.
For example, the generated code uses a simpler algorithm to implement svd to
accommodate a smaller footprint. Results might also vary according to matrix properties.
For example, MATLAB might detect symmetric or Hermitian matrices at run time and
switch to specialized algorithms that perform computations faster than implementations
in the generated code.
For implementation of BLAS library functions
For implementations of BLAS library functions. Generated C/C++ code uses reference
implementations of BLAS functions, which may produce different results from platformspecific BLAS implementations in MATLAB.
NaN and Infinity Patterns
The generated code might not produce exactly the same pattern of NaN and inf values
as MATLAB code when these values are mathematically meaningless. For example, if
MATLAB output contains a NaN, output from the generated code should also contain a
NaN, but not necessarily in the same place.
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For implementation of BLAS library functions
Code Generation Target
The coder.target function returns different values in MATLAB than in the generated
code. The intent is to help you determine whether your function is executing in MATLAB
or has been compiled for a simulation or code generation target. See coder.target.
MATLAB Class Initial Values
MATLAB computes class initial values at class loading time before code generation. The
code generation software uses the value that MATLAB computed, it does not recompute
the initial value. If the initialization uses a function call to compute the initial value,
the code generation software does not execute this function. If the function modifies a
global state, for example, a persistent variable, code generation software might provide
a different initial value than MATLAB. For more information, see “Defining Class
Properties for Code Generation”.
Variable-Size Support for Code Generation
For incompatibilities with MATLAB in variable-size support for code generation, see:
• “Incompatibility with MATLAB for Scalar Expansion”
• “Incompatibility with MATLAB in Determining Size of Variable-Size N-D Arrays”
• “Incompatibility with MATLAB in Determining Size of Empty Arrays”
• “Incompatibility with MATLAB in Vector-Vector Indexing”
• “Incompatibility with MATLAB in Matrix Indexing Operations for Code Generation”
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2
Design Considerations for C/C++ Code Generation
MATLAB Language Features Supported for C/C++ Code
Generation
MATLAB supports the following language features in generated code:
• N-dimensional arrays (see “Array Size Restrictions for Code Generation” on page
6-7)
• Matrix operations, including deletion of rows and columns
• Variable-sized data (see “Variable-Size Data Definition for Code Generation” on page
7-3)
• Subscripting (see “Incompatibility with MATLAB in Matrix Indexing Operations for
Code Generation” on page 7-30)
• Complex numbers (see “Code Generation for Complex Data” on page 6-4)
• Numeric classes (see “Supported Variable Types” on page 5-17)
• Double-precision, single-precision, and integer math
• Fixed-point arithmetic (see “Code Acceleration and Code Generation from MATLAB”)
• Program control statements if, switch, for, while, and break
• Arithmetic, relational, and logical operators
• Local functions
• Persistent variables (see “Define and Initialize Persistent Variables” on page 5-10)
• Global variables (see “Specifying Global Variable Type and Initial Value in a Project”).
• Structures
• Characters (see “Code Generation for Characters” on page 6-6)
• Function handles
• Frames
• Variable length input and output argument lists
• Subset of MATLAB toolbox functions
• MATLAB classes
• Ability to call functions (see “Resolution of Function Calls for Code Generation” on
page 13-2)
MATLAB Language Features Not Supported for C/C++ Code Generation
MATLAB does not support the following features in generated code:
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MATLAB Language Features Supported for C/C++ Code Generation
• Anonymous functions
• Cell arrays
• Java
• Nested functions
• Recursion
• Sparse matrices
• try/catch statements
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3
System Objects Supported for Code
Generation
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3
System Objects Supported for Code Generation
Code Generation for System Objects
You can generate C and C++ code for a subset of System objects provided by the following
toolboxes.
Toolbox Name
See
Communications System Toolbox
“System Objects in MATLAB Code
Generation” in the DSP System Toolbox
documentation.
Computer Vision System Toolbox
“System Objects in MATLAB Code
Generation” in the Computer Vision
System Toolbox documentation.
DSP System Toolbox
“System Objects in MATLAB Code
Generation” in the DSP System Toolbox
documentation.
Image Acquisition Toolbox™
• imaq.VideoDevice.
• “Code Generation with VideoDevice
System Object” in the Image Acquisition
Toolbox documentation.
Phased Array System Toolbox
“Code Generation” in the Phased Array
System Toolbox documentation.
To use these System objects, you need to install the requisite toolbox. For a list of System
objects supported for C and C++ code generation, see “Functions and Objects Supported
for C and C++ Code Generation — Alphabetical List” and “Functions and Objects
Supported for C and C++ Code Generation — Category List”.
System objects are MATLAB object-oriented implementations of algorithms. They
extend MATLAB by enabling you to model dynamic systems represented by time-varying
algorithms. System objects are well integrated into the MATLAB language, regardless of
whether you are writing simple functions, working interactively in the command window,
or creating large applications.
In contrast to MATLAB functions, System objects automatically manage state
information, data indexing, and buffering, which is particularly useful for iterative
computations or stream data processing. This enables efficient processing of long data
sets. For general information about MATLAB objects, see “Begin Using Object-Oriented
Programming”.
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4
Functions, Classes, and System
Objects Supported for Code
Generation
• “Functions and Objects Supported for C and C++ Code Generation — Alphabetical
List” on page 4-2
• “Functions and Objects Supported for C and C++ Code Generation — Category List”
on page 4-134
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4
Functions, Classes, and System Objects Supported for Code Generation
Functions and Objects Supported for C and C++ Code Generation
— Alphabetical List
You can generate efficient C and C++ code for a subset of MATLAB built-in functions and
toolbox functions, classes, and System objects that you call from MATLAB code. These
function, classes, and System objects appear in alphabetical order in the following table.
To find supported functions, classes, and System objects by MATLAB category or toolbox,
see “Functions and Objects Supported for C and C++ Code Generation — Category List”.
Note: For more information on code generation for fixed-point algorithms, refer to “Code
Acceleration and Code Generation from MATLAB”.
Name
Product
Remarks and Limitations
abs
MATLAB
—
abs
Fixed-Point
Designer
—
accumneg
Fixed-Point
Designer
—
accumpos
Fixed-Point
Designer
—
acos
MATLAB
• Generates an error during simulation and
returns NaN in generated code when the
input value x is real, but the output should
be complex. To get the complex result,
make the input value complex by passing in
complex(x).
acosd
MATLAB
—
acosh
MATLAB
• Generates an error during simulation and
returns NaN in generated code when the
input value x is real, but the output should
be complex. To get the complex result,
make the input value complex by passing in
complex(x).
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Functions and Objects Supported for C and C++ Code Generation — Alphabetical List
Name
Product
Remarks and Limitations
acot
MATLAB
—
acotd
MATLAB
—
acoth
MATLAB
—
acsc
MATLAB
—
acscd
MATLAB
—
acsch
MATLAB
—
add
Fixed-Point
Designer
Code generation in MATLAB does not support
the syntax F.add(a,b). You must use the
syntax add(F,a,b).
affine2d
Image Processing When generating code, you can only specify
Toolbox™
single objects—arrays of objects are not
supported.
aictest
Phased Array
System Toolbox
Does not support variable-size inputs.
albersheim
Phased Array
System Toolbox
Does not support variable-size inputs.
all
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
all
Fixed-Point
Designer
—
ambgfun
Phased Array
System Toolbox
Does not support variable-size inputs.
and
MATLAB
—
any
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
any
Fixed-Point
Designer
—
aperture2gain
Phased Array
System Toolbox
Does not support variable-size inputs.
asec
MATLAB
—
asecd
MATLAB
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
asech
MATLAB
—
asin
MATLAB
• Generates an error during simulation and
returns NaN in generated code when the
input value x is real, but the output should
be complex. To get the complex result,
make the input value complex by passing in
complex(x).
asind
MATLAB
—
asinh
MATLAB
—
assert
MATLAB
• Generates specified error messages at
compile time only if all input arguments are
constants or depend on constants. Otherwise,
generates specified error messages at run
time.
• For standalone code generation, excluded
from the generated code.
• See “Rules for Using assert Function”.
assignDetectionsToTracks
Computer Vision Compile-time constant input: No restriction.
System Toolbox
Supports MATLAB Function block: Yes
atan
MATLAB
—
atan2
MATLAB
—
atan2
Fixed-Point
Designer
—
atan2d
MATLAB
—
atand
MATLAB
—
atanh
MATLAB
• Generates an error during simulation and
returns NaN in generated code when the
input value x is real, but the output should
be complex. To get the complex result,
make the input value complex by passing in
complex(x).
az2broadside
Phased Array
System Toolbox
Does not support variable-size inputs.
4-4
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Specifying constants
Name
Product
Remarks and Limitations
azel2phitheta
Phased Array
System Toolbox
Does not support variable-size inputs.
azel2phithetapat
Phased Array
System Toolbox
Does not support variable-size inputs.
azel2uv
Phased Array
System Toolbox
Does not support variable-size inputs.
azel2uvpat
Phased Array
System Toolbox
Does not support variable-size inputs.
azelaxes
Phased Array
System Toolbox
Does not support variable-size inputs.
barthannwin
Signal Processing Window length must be a constant. Expressions
Toolbox™
or variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
bartlett
Signal Processing Window length must be a constant. Expressions
Toolbox
or variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
bboxOverlapRatio
Computer Vision Compile-time constant input: No restriction
System Toolbox
Supports MATLAB Function block: No
beat2range
Phased Array
System Toolbox
Does not support variable-size inputs.
4-5
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
besselap
Signal Processing Filter order must be a constant. Expressions
Toolbox
or variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
beta
MATLAB
—
betacdf
Statistics
Toolbox™
—
betainc
MATLAB
Always returns a complex result.
betaincinv
MATLAB
Always returns a complex result.
betainv
Statistics Toolbox —
betaln
MATLAB
betapdf
Statistics Toolbox —
betarnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
—
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
betastat
Statistics Toolbox —
bi2de
Communications —
System Toolbox
billingsleyicm
Phased Array
System Toolbox
Does not support variable-size inputs.
bin2dec
MATLAB
• Does not match MATLAB when the input is
empty.
binaryFeatures
Computer Vision —
System Toolbox
4-6
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Specifying constants
Name
Product
Remarks and Limitations
binocdf
Statistics Toolbox —
binoinv
Statistics Toolbox —
binopdf
Statistics Toolbox —
binornd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
binostat
Statistics Toolbox —
bitand
MATLAB
—
bitand
Fixed-Point
Designer
• Not supported for slope-bias scaled fi
objects.
bitandreduce
Fixed-Point
Designer
—
bitcmp
MATLAB
—
bitcmp
Fixed-Point
Designer
—
bitconcat
Fixed-Point
Designer
—
bitget
MATLAB
—
bitget
Fixed-Point
Designer
—
bitmax
MATLAB
—
bitor
MATLAB
—
bitor
Fixed-Point
Designer
• Not supported for slope-bias scaled fi
objects.
bitorreduce
Fixed-Point
Designer
—
bitreplicate
Fixed-Point
Designer
—
4-7
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
bitrevorder
Signal Processing —
Toolbox
bitrol
Fixed-Point
Designer
—
bitror
Fixed-Point
Designer
—
bitset
MATLAB
—
bitset
Fixed-Point
Designer
—
bitshift
MATLAB
—
bitshift
Fixed-Point
Designer
—
bitsliceget
Fixed-Point
Designer
—
bitsll
Fixed-Point
Designer
• Generated code may not handle out of range
shifting.
bitsra
Fixed-Point
Designer
• Generated code may not handle out of range
shifting.
bitsrl
Fixed-Point
Designer
• Generated code may not handle out of range
shifting.
bitxor
MATLAB
—
bitxor
Fixed-Point
Designer
• Not supported for slope-bias scaled fi
objects.
bitxorreduce
Fixed-Point
Designer
—
4-8
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Specifying constants
Name
Product
Remarks and Limitations
blackman
Signal Processing Window length must be a constant. Expressions
Toolbox
or variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
blackmanharris
Signal Processing Window length must be a constant. Expressions
Toolbox
or variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
blanks
MATLAB
—
blkdiag
MATLAB
—
bohmanwin
Signal Processing Window length must be a constant. Expressions
Toolbox
or variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
break
MATLAB
—
4-9
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
BRISKPoints
Computer Vision Compile-time constant inputs: No restriction
System Toolbox
Supports MATLAB Function block: No
To index locations with this object, use
the syntax: points.Location(idx,:),
for points object. See
visionRecovertformCodeGeneration_kernel.m,
which is used in the “Introduction to Code
Generation with Feature Matching and
Registration” example.
broadside2az
Phased Array
System Toolbox
Does not support variable-size inputs.
bsxfun
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
buttap
Signal Processing Filter order must be a constant. Expressions
Toolbox
or variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
butter
Signal Processing Filter coefficients must be constants.
Toolbox
Expressions or variables are allowed if their
values do not change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
4-10
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Specifying constants
Name
Product
Remarks and Limitations
buttord
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
bwdist
Image Processing The method argument must be a compile-time
Toolbox
constant. Input images must have fewer than 232
pixels.
Generated code for this function uses a
precompiled, “platform-specific shared library”.
bwlookup
Image Processing • For best results, specify an input image of
Toolbox
class logical.
bwmorph
Image Processing • The text string specifying the operation must
Toolbox
be a constant and, for best results, specify an
input image of class logical.
bwpack
Image Processing Generated code for this function uses a
Toolbox
precompiled platform-specific shared library.
bwselect
Image Processing Supports only the 3 and 4 input argument
Toolbox
syntaxes: BW2 = bwselect(BW,c,r) and
BW2 = bwselect(BW,c,r,n). The optional
fourth input argument, n, must be a compiletime constant. In addition, with code generation,
bwselect only supports only the 1 and 2 output
argument syntaxes: BW2 = bwselect(___) or
[BW2, idx] = bwselect(___).
Generated code for this function uses a
precompiled platform-specific shared library.
4-11
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
bwtraceboundary
Image Processing The dir, fstep, and conn arguments must be
Toolbox
compile-time constants.
bwunpack
Image Processing Generated code for this function uses a
Toolbox
precompiled platform-specific shared library.
ca2tf
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
cart2pol
MATLAB
—
cart2sph
MATLAB
—
cart2sphvec
Phased Array
System Toolbox
Does not support variable-size inputs.
cast
MATLAB
—
cat
MATLAB
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
cbfweights
Phased Array
System Toolbox
Does not support variable-size inputs.
cdf
Statistics Toolbox —
ceil
MATLAB
—
ceil
Fixed-Point
Designer
—
cfirpm
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
char
MATLAB
—
4-12
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Specifying constants
Name
Product
Remarks and Limitations
cheb1ap
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
cheb1ord
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
cheb2ap
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
4-13
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
cheb2ord
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
chebwin
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
cheby1
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
4-14
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Specifying constants
Name
Product
Remarks and Limitations
cheby2
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
chi2cdf
Statistics Toolbox —
chi2inv
Statistics Toolbox —
chi2pdf
Statistics Toolbox —
chi2rnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
chi2stat
Statistics Toolbox —
chol
MATLAB
—
circpol2pol
Phased Array
System Toolbox
Does not support variable-size inputs.
circshift
MATLAB
—
cl2tf
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
class
MATLAB
—
4-15
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
colon
MATLAB
• Does not accept complex inputs.
• The input i cannot have a logical value.
• Does not accept vector inputs.
• Inputs must be constants.
• Uses single-precision arithmetic to produce
single-precision results.
comm.ACPR
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.AGC
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.AlgebraicDeinterleaver
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.APPDecoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.AWGNChannel
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.BarkerCode
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.BCHDecoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.BCHEncoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.BinarySymmetricChannel
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.BlockDeinterleaver Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.BlockInterleaver
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.BPSKDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.BPSKModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
4-16
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Specifying constants
Name
Product
Remarks and Limitations
comm.CCDF
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.ConstellationDiagram
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.ConvolutionalDeinterleaver
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.ConvolutionalEncoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.ConvolutionalInterleaver
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.CPFSKDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.CPFSKModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.CPMCarrierPhaseSynchronizer
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.CPMDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.CPMModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.CRCDetector
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.CRCGenerator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.DBPSKDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.DBPSKModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.Descrambler
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.DifferentialDecoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
4-17
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
comm.DifferentialEncoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.DiscreteTimeVCO
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.DPSKDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.DPSKModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.DQPSKDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.DQPSKModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.EarlyLateGateTimingSynchronizer
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.ErrorRate
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.EVM
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.FSKDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.FSKModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.GardnerTimingSynchronizer
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.GeneralQAMDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.GeneralQAMModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.GeneralQAMTCMDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.GeneralQAMTCMModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
4-18
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Specifying constants
Name
Product
Remarks and Limitations
comm.GMSKDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.GMSKModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.GMSKTimingSynchronizer
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.GoldSequence
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.HadamardCode
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.HDLCRCDetector
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.HDLCRCGenerator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.HDLRSDecoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.HDLRSEncoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.HelicalDeinterleaver
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.HelicalInterleaver Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.IntegrateAndDumpFilter
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.IQImbalanceCompensator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.KasamiSequence
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.LDPCDecoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.LDPCEncoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
4-19
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
comm.LTEMIMOChannel
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MatrixDeinterleaver
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MatrixHelicalScanDeinterleaver
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MatrixHelicalScanInterLeaver
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MatrixInterleaver
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MemorylessNonlinearity
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MER
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MIMOChannel
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MLSEEqualizer
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MSKDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MSKModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MSKTimingSynchronizer
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MuellerMullerTimingSynchronizer
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MultiplexedDeinterleaver
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.MultiplexedInterleaver
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.OFDMDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
4-20
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Specifying constants
Name
Product
Remarks and Limitations
comm.OFDMModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.OSTBCCombiner
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.OSTBCEncoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.OQPSKDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.OQPSKModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.PAMDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.PAMModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.PhaseRequencyOffset
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.PhaseNoise
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.PNSequence
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.PSKCoarseFrequency-Communications “System Objects in MATLAB Code Generation”
System Toolbox
Estimator
comm.PSKCoarseFrequence-Communications “System Objects in MATLAB Code Generation”
System Toolbox
Estimator
comm.PSKDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.PSKModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.PSKTCMDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.PSKTCMModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
4-21
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
comm.QAMCoarseFrequency-Communications “System Objects in MATLAB Code Generation”
System Toolbox
Estimator
comm.QPSKDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.QPSKModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.RaisedCosineReceiveFilter
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.RaisedCosineTransmitFilter
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.RayleighChannel
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.RectangularQAMDemodulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.RectangularModulator
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.RectangularQAMTCM- Communications “System Objects in MATLAB Code Generation”
System Toolbox
Demodulator
comm.RectangularQAMTCM- Communications “System Objects in MATLAB Code Generation”
System Toolbox
Modulator
comm.RicianChannel
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.RSDecoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.RSEncoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.Scrambler
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.SphereDecoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.ThermalNoise
Communications “System Objects in MATLAB Code Generation”
System Toolbox
4-22
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Specifying constants
Name
Product
Remarks and Limitations
comm.TurboDecoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.TurboEncoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.ViterbiDecoder
Communications “System Objects in MATLAB Code Generation”
System Toolbox
comm.WalshCode
Communications “System Objects in MATLAB Code Generation”
System Toolbox
compan
MATLAB
—
complex
MATLAB
—
complex
Fixed-Point
Designer
—
computer
MATLAB
• Information about the computer on which the
code generation software is running.
• Use only when the code generation target is
S-function (Simulation) or MEX-function.
cond
MATLAB
—
conj
MATLAB
—
conj
Fixed-Point
Designer
—
conndef
Image Processing All input arguments must be compile-time
Toolbox
constants.
continue
MATLAB
—
conv
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
4-23
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
conv
Fixed-Point
Designer
• Variable-sized inputs are only supported
when the SumMode property of the governing
fimath is set to Specify precision or
Keep LSB.
• For variable-sized signals, you may see
different results between MATLAB and the
generated code.
• In generated code, the output for variablesized signals is computed using the
SumMode property of the governing
fimath.
• In MATLAB, the output for variablesized signals is computed using the
SumMode property of the governing
fimath when both inputs are nonscalar.
However, if either input is a scalar,
MATLAB computes the output using the
ProductMode of the governing fimath.
conv2
MATLAB
—
convergent
Fixed-Point
Designer
—
convn
MATLAB
—
cordicabs
Fixed-Point
Designer
• Variable-size signals are not supported.
cordicangle
Fixed-Point
Designer
• Variable-size signals are not supported.
cordicatan2
Fixed-Point
Designer
• Variable-size signals are not supported.
cordiccart2pol
Fixed-Point
Designer
• Variable-size signals are not supported.
cordiccexp
Fixed-Point
Designer
• Variable-size signals are not supported.
4-24
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Specifying constants
Name
Product
Remarks and Limitations
cordiccos
Fixed-Point
Designer
• Variable-size signals are not supported.
cordicpol2cart
Fixed-Point
Designer
• Variable-size signals are not supported.
cordicrotate
Fixed-Point
Designer
• Variable-size signals are not supported.
cordicsin
Fixed-Point
Designer
• Variable-size signals are not supported.
cordicsincos
Fixed-Point
Designer
• Variable-size signals are not supported.
cornerPoints
Computer Vision Compile-time constant input: No restriction
System Toolbox
Supports MATLAB Function block: No
To index locations with this object, use
the syntax: points.Location(idx,:),
for points object. See
visionRecovertformCodeGeneration_kernel.m,
which is used in the “Introduction to Code
Generation with Feature Matching and
Registration” example.
corrcoef
MATLAB
• Row-vector input is only supported when the
first two inputs are vectors and nonscalar.
cos
MATLAB
—
cos
Fixed-Point
Designer
—
cosd
MATLAB
—
cosh
MATLAB
—
cot
MATLAB
—
cotd
MATLAB
• In some cases, returns -Inf when MATLAB
returns Inf.
• In some cases, returns Inf when MATLAB
returns -Inf.
coth
MATLAB
—
4-25
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
cov
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
cross
MATLAB
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
csc
MATLAB
—
cscd
MATLAB
• In some cases, returns -Inf when MATLAB
returns Inf.
• In some cases, returns Inf when MATLAB
returns -Inf.
csch
MATLAB
—
ctranspose
MATLAB
—
ctranspose
Fixed-Point
Designer
—
cumprod
MATLAB
• Does not support logical inputs. Cast input to
double first.
• Does not support the direction argument.
cumsum
MATLAB
• Does not support logical inputs. Cast input to
double first.
• Does not support the direction argument.
cumtrapz
MATLAB
—
db2pow
Signal Processing —
Toolbox
4-26
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Specifying constants
Name
Product
Remarks and Limitations
dct
Signal Processing • Code generation for this function requires the
Toolbox
DSP System Toolbox software.
• Length of transform dimension must
be a power of two. If specified, the pad
or truncation value must be constant.
Expressions or variables are allowed if their
values do not change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command
Line”.
de2bi
Communications —
System Toolbox
deal
MATLAB
—
deblank
MATLAB
• Supports only inputs from the char class.
• Input values must be in the range 0-127.
dec2bin
MATLAB
• If input d is double, d must be less than
2^52.
• If input d is single, d must be less than
2^23.
• Unless you specify input n to be constant and
n is large enough that the output has a fixed
number of columns regardless of the input
values, this function requires variable-sizing
support. Without variable-sizing support,
n must be at least 52 for double, 23 for
single, 16 for char, 32 for int32, 16 for
int16, and so on.
4-27
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
dec2hex
MATLAB
• If input d is double, d must be less than
2^52.
• If input d is single, d must be less than
2^23.
• Unless you specify input n to be constant
and n is large enough that the output has a
fixed number of columns regardless of the
input values, this function requires variablesizing support. Without variable-sizing
support, n must be at least 13 for double,
6 for single, 4 for char, 8 for int32, 4 for
int16, and so on.
dechirp
Phased Array
System Toolbox
Does not support variable-size inputs.
deconv
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
del2
MATLAB
—
delayseq
Phased Array
System Toolbox
Does not support variable-size inputs.
depressionang
Phased Array
System Toolbox
Does not support variable-size inputs.
det
MATLAB
—
detectBRISKFeatures
Computer Vision Supports MATLAB Function block: No
System Toolbox
Generated code for this function uses a
precompiled platform-specific shared library.
detectFASTFeatures
Computer Vision Supports MATLAB Function block: No
System Toolbox
Generated code for this function uses a
precompiled platform-specific shared library.
detectHarrisFeatures
Computer Vision Compile-time constant input: FilterSize
System Toolbox
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
4-28
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Specifying constants
Name
Product
Remarks and Limitations
detectMinEigenFeatures
Computer Vision Compile-time constant input: FilterSize
System Toolbox
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
detectMSERFeatures
Computer Vision Compile-time constant input: No restriction
System Toolbox
Supports MATLAB Function block: No
For code generation, the function outputs
regions.PixelList as an array. The region sizes
are defined in regions.Lengths.
detectSURFFeatures
Computer Vision Compile-time constant input: No restrictions
System Toolbox
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
detrend
MATLAB
• If supplied and not empty, the input
argument bp must satisfy the following
requirements:
• Be real.
• Be sorted in ascending order.
• Restrict elements to integers in the
interval [1, n-2]. n is the number of
elements in a column of input argument X
, or the number of elements in X when X is
a row vector.
• Contain all unique values.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
4-29
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
diag
MATLAB
• If supplied, the argument representing the
order of the diagonal matrix must be a real
and scalar integer value.
• For variable-size inputs that are variablelength vectors (1-by-: or :-by-1), diag:
• Treats the input as a vector input.
• Returns a matrix with the given vector
along the specified diagonal.
• For variable-size inputs that are not variablelength vectors, diag:
• Treats the input as a matrix.
• Does not support inputs that are vectors
at run time.
• Returns a variable-length vector.
If the input is variable-size (:m-by-:n) and
has shape 0-by-0 at run time, the output is
0-by-1 not 0-by-0. However, if the input is a
constant size 0-by-0, the output is [].
• For variable-size inputs that are not variablelength vectors (1-by-: or :-by-1), diag treats
the input as a matrix from which to extract
a diagonal vector. This behavior occurs even
if the input array is a vector at run time. To
force diag to build a matrix from variablesize inputs that are not 1-by-: or :-by-1, use:
• diag(x(:)) instead of diag(x)
• diag(x(:),k) instead of diag(x,k)
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
diag
Fixed-Point
Designer
• If supplied, the index, k, must be a real and
scalar integer value that is not a fi object.
4-30
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Specifying constants
Name
Product
Remarks and Limitations
diff
MATLAB
• If supplied, the arguments representing
the number of times to apply diff and
the dimension along which to calculate the
difference must be constants.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
disparity
Computer Vision Compile-time constant input: Method.
System Toolbox
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
divide
Fixed-Point
Designer
• Any non-fi input must be constant. Its value
must be known at compile time so that it can
be cast to a fi object.
• Complex and imaginary divisors are not
supported.
• The syntax T.divide(a,b) is not
supported.
dop2speed
Phased Array
System Toolbox
Does not support variable-size inputs.
dopsteeringvec
Phased Array
System Toolbox
Does not support variable-size inputs.
dot
MATLAB
—
double
MATLAB
—
double
Fixed-Point
Designer
—
downsample
Signal Processing —
Toolbox
4-31
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
dpss
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
dsp.AdaptiveLatticeFilter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.AffineProjectionFilter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.AllpoleFilter
DSP System
Toolbox
• “System Objects in MATLAB Code
Generation”
• Only the Denominator property is tunable
for code generation.
dsp.AnalyticSignal
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.ArrayPlot
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.ArrayVectorAdder
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.ArrayVectorDivider
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.ArrayVectorMultiplier
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.ArrayVectorSubtractor
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.AudioFileReader
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
4-32
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Specifying constants
Name
Product
Remarks and Limitations
dsp.AudioRecorder
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.AudioFileWriter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.AudioPlayer
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Autocorrelator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.BiquadFilter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.BurgAREstimator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.BurgSpectrumEstimator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.CepstralToLPC
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.CICCompensationDecimator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.CICCompensationInterpolator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.CICDecimator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.CICInterpolator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Convolver
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Counter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Crosscorrelator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.CrossSpectrumEstimator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
4-33
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
dsp.CumulativeProduct
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.CumulativeSum
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.DCBlocker
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.DCT
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Delay
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.DelayLine
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.DigitalDownConverter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.DigitalUpConverter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.DigitalFilter
DSP System
Toolbox
• “System Objects in MATLAB Code
Generation”
• The SOSMatrix and Scalevalues
properties are not supported for code
generation.
dsp.FarrowRateConverter DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.FastTransversalFilter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.FFT
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
4-34
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Specifying constants
Name
Product
Remarks and Limitations
dsp.FilterCascade
DSP System
Toolbox
• You cannot generate code directly from
dsp.FilterCascade. You can use the
generateFilteringCode method to
generate a MATLAB function. You can
generate C/C++ code from this MATLAB
function.
“System Objects in MATLAB Code Generation”
dsp.FilteredXLMSFilter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.FIRDecimator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.FIRFilter
DSP System
Toolbox
• “System Objects in MATLAB Code
Generation”
• Only the Numerator property is tunable for
code generation.
dsp.FIRHalfbandDecimator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.FIRHalfbandInterpolator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.FIRInterpolator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.FIRRateConverter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.FrequencyDomainAdaptiveFilter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Histogram
DSP System
Toolbox
• This object has no tunable properties for code
generation.
• “System Objects in MATLAB Code
Generation”
dsp.IDCT
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
4-35
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
dsp.IFFT
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.IIRFilter
DSP System
Toolbox
• Only the Numerator and Denominator
properties are tunable for code generation.
• “System Objects in MATLAB Code
Generation”
dsp.Interpolator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.KalmanFilter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.LDLFactor
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.LevinsonSolver
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.LMSFilter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.LowerTriangularSolver
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.LPCToAutocorrelation
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.LPCToCepstral
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.LPCToLSF
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.LPCToLSP
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.LPCToRC
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.LSFToLPC
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.LSPToLPC
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
4-36
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Specifying constants
Name
Product
Remarks and Limitations
dsp.LUFactor
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Maximum
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Mean
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Median
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Minimum
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.NCO
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Normalizer
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.PeakFinder
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.PeakToPeak
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.PeakToRMS
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.PhaseExtractor
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.PhaseUnwrapper
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.RCToAutocorrelation DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.RCToLPC
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.RMS
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.RLSFilter
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
dsp.SampleRateConverter DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.ScalarQuantizerDecoder
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.ScalarQuantizerEncoder
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.SignalSource
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.SineWave
DSP System
Toolbox
• This object has no tunable properties for code
generation.
• “System Objects in MATLAB Code
Generation”
dsp.SpectrumAnalyzer
DSP System
Toolbox
This System object™ does not generate code. It
is automatically declared as an extrinsic variable
using the coder.extrinsic function.
dsp.SpectrumEstimator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.StandardDeviation
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.StateLevels
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.TimeScope
DSP System
Toolbox
This System object does not generate code. It is
automatically declared as an extrinsic variable
using the coder.extrinsic function.
dsp.TransferFunctionEstimator
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.UDPReceiver
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.UDPSender
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.UpperTriangularSolver
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
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Specifying constants
Name
Product
Remarks and Limitations
dsp.VariableFractionDelay
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.VariableIntegerDelay
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Variance
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.VectorQuantizerDecoder
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.VectorQuantizerEncoder
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
dsp.Window
DSP System
Toolbox
• This object has no tunable properties for code
generation.
• “System Objects in MATLAB Code
Generation”
dsp.ZeroCrossingDetector
DSP System
Toolbox
“System Objects in MATLAB Code Generation”
edge
Image Processing The method, direction, and sigma arguments
Toolbox
must be a compile-time constants. In addition,
nonprogrammatic syntaxes are not supported.
For example, the syntax edge(im), where edge
does not return a value but displays an image
instead, is not supported.
Generated code for this function uses a
precompiled platform-specific shared library.
effearthradius
Phased Array
System Toolbox
Does not support variable-size inputs.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
eig
MATLAB
• For code generation,QZ algorithm is
used in all cases. MATLAB can use
different algorithms for different inputs.
Consequently, V might represent a different
basis of eigenvectors. The eigenvalues in
D might not be in the same order as in
MATLAB.
• With one input, [V,D] = eig(A), the
results are similar to those obtained using
[V,D] = eig(A,eye(size(A)),'qz') in
MATLAB, except that for code generation,
the columns of V are normalized.
• Options 'balance', and 'nobalance' are
not supported for the standard eigenvalue
problem. 'chol' is not supported for the
symmetric generalized eigenvalue problem.
• Outputs are of complex type.
• Does not support the option to calculate left
eigenvectors.
ellip
Signal Processing Inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
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Specifying constants
Name
Product
Remarks and Limitations
ellipap
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
ellipke
MATLAB
—
ellipord
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
end
MATLAB
—
end
Fixed-Point
Designer
—
epipolarLine
Computer Vision Compile-time constant input: No restrictions.
System Toolbox
Supports MATLAB Function block: Yes
eps
MATLAB
—
eps
Fixed-Point
Designer
• Supported for scalar fixed-point signals only.
eq
MATLAB
—
eq
Fixed-Point
Designer
Not supported for fixed-point signals with
different biases.
• Supported for scalar, vector, and matrix, fi
single and fi double signals.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
erf
MATLAB
—
erfc
MATLAB
—
erfcinv
MATLAB
—
erfcx
MATLAB
—
erfinv
MATLAB
—
error
MATLAB
For standalone code generation, excluded from
the generated code.
espritdoa
Phased Array
System Toolbox
Does not support variable-size inputs.
estimateFundamentalMatrix
Computer Vision Compile-time constant input: Method,
System Toolbox
OutputClass, DistanceType, and
ReportRuntimeError.
Supports MATLAB Function block: Yes
estimateGeometricTransform
Computer Vision Compile-time constant input: transformType
System Toolbox
Supports MATLAB Function block: No
estimateUncalibratedRectification
Computer Vision Compile-time constant input: transformType
System Toolbox
Supports MATLAB Function block: No
extractFeatures
Computer Vision Generates platform-dependent library: Yes for
System Toolbox
BRISK, FREAK, and SURF methods only.
Compile-time constant input: Method
Supports MATLAB Function block: Yes for Block
method only.
Generated code for this function uses a
precompiled platform-specific shared library.
extractHOGFeatures
Computer Vision Compile-time constant input: No
System Toolbox
Supports MATLAB Function block: No
evcdf
Statistics Toolbox —
evinv
Statistics Toolbox —
evpdf
Statistics Toolbox —
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Specifying constants
Name
Product
Remarks and Limitations
evrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
evstat
Statistics Toolbox —
exp
MATLAB
expcdf
Statistics Toolbox —
expint
MATLAB
expinv
Statistics Toolbox —
expm
MATLAB
—
expm1
MATLAB
—
exppdf
Statistics Toolbox —
exprnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
—
—
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
expstat
Statistics Toolbox —
eye
MATLAB
classname must be a built-in MATLAB
numeric type. Does not invoke the static
eye method for other classes. For example,
eye(m, n, 'myclass’) does not invoke
myclass.eye(m,n).
factor
MATLAB
• The maximum double precision input is
2^33.
• The maximum single precision input is 2^25.
• The input n cannot have type int64 or
uint64.
factorial
MATLAB
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
false
MATLAB
• Dimensions must be real, nonnegative,
integers.
fcdf
Statistics Toolbox —
fclose
MATLAB
—
feof
MATLAB
—
fft
MATLAB
• Length of input vector must be a power of 2.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
fft2
MATLAB
• Length of input matrix dimensions must each
be a power of 2.
fftn
MATLAB
• Length of input matrix dimensions must each
be a power of 2.
fftshift
MATLAB
—
fi
Fixed-Point
Designer
• Use to create a fixed-point constant or
variable.
• The default constructor syntax without input
arguments is not supported.
• The rand
fi('PropertyName',PropertyValue...)
is not supported. To use property name/
property value pairs, you must first
specify the value v of the fi object as in
fi(v,'PropertyName',PropertyValue...).
• If the input value is not known at
compile time, you must provide complete
numerictype information.
• All properties related to data type must be
constant for code generation.
• numerictype object information must be
available for non-fixed-point Simulink inputs.
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Specifying constants
Name
Product
Remarks and Limitations
filter
MATLAB
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
filter
Fixed-Point
Designer
• Variable-sized inputs are only supported
when the SumMode property of the governing
fimath is set to Specify precision or
Keep LSB.
filter2
MATLAB
—
filtfilt
Signal Processing Filter coefficients must be constants.
Toolbox
Expressions or variables are allowed if their
values do not change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
fimath
Fixed-Point
Designer
• Fixed-point signals coming in to a MATLAB
Function block from Simulink are assigned
the fimath object defined in the MATLAB
Function dialog in the Model Explorer.
• Use to create fimath objects in generated
code.
• If the ProductMode property of the
fimath object is set to anything other than
FullPrecision, the ProductWordLength
and ProductFractionLength properties
must be constant.
• If the SumMode property of the fimath
object is set to anything other than
FullPrecision, the SumWordLength and
SumFractionLength properties must be
constant.
4-45
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
find
MATLAB
• Issues an error if a variable-sized input
becomes a row vector at run time.
Note: This limitation does not apply when
the input is scalar or a variable-length row
vector.
• For variable-sized inputs, the shape of empty
outputs, 0-by-0, 0-by-1, or 1-by-0, depends on
the upper bounds of the size of the input. The
output might not match MATLAB when the
input array is a scalar or [] at run time. If the
input is a variable-length row vector, the size
of an empty output is 1-by-0, otherwise it is
0-by-1.
findpeaks
Signal Processing —
Toolbox
finv
Statistics Toolbox —
fir1
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
4-46
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Specifying constants
Name
Product
Remarks and Limitations
fir2
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
firceqrip
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
fircls
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
fircls1
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
4-47
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
fireqint
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
firgr
DSP System
Toolbox
• All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
• Does not support syntaxes that have cell
array input.
firhalfband
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
firlpnorm
DSP System
Toolbox
• All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
• Does not support syntaxes that have cell
array input.
firls
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
firminphase
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
firnyquist
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
4-48
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Specifying constants
Name
Product
Remarks and Limitations
firpr2chfb
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
firpm
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
firpmord
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
fitgeotrans
Image Processing The transformtype argument must be
Toolbox
a compile-time constant. The function
supports the following transformation
types: 'nonreflectivesimilarity',
'similarity', 'affine', or 'projective'.
fix
MATLAB
—
fix
Fixed-Point
Designer
—
fixed.Quantizer
Fixed-Point
Designer
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
flattopwin
Signal Processing All inputs must be constants. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
flintmax
MATLAB
—
flip
MATLAB
—
flip
Fixed-Point
Designer
The dimensions argument must be a built-in
type; it cannot be a fi object.
flipdim
MATLAB
Note: flipdim will be removed in a future
release. Use flip instead.
fliplr
MATLAB
—
fliplr
Fixed-Point
Designer
—
flipud
MATLAB
—
flipud
Fixed-Point
Designer
—
floor
MATLAB
—
floor
Fixed-Point
Designer
—
fminsearch
MATLAB
• Ignores the Display option. Does not print
status information during execution. Test the
exitflag output for the exit condition.
• The output structure does not include the
algorithm or message fields.
• Ignores the OutputFcn and PlotFcns
options.
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Specifying constants
Name
Product
Remarks and Limitations
fopen
MATLAB
• Does not support:
• machineformat, encoding, or fileID
inputs
• message output
• fopen('all')
• If you disable extrinsic calls, you cannot
return fileIDs created with fopen to
MATLAB or extrinsic functions. You can use
such fileIDs only internally.
• When generating C/C++ executables, static
libraries, or dynamic libraries, you can open
up to 20 files.
• The generated code does not report errors
from invalid file identifiers. Write your own
file open error handling in your MATLAB
code. Test whether fopen returns -1,
which indicates that the file open failed. For
example:
...
fid = fopen(filename, 'r');
if fid == -1
% fopen failed
else
% fopen successful, okay to call fread
A = fread(fid);
...
• The behavior of the generated code for fread
is compiler-dependent if you:
1
Open a file using fopen with a
permission of a+.
2
Read the file using fread before calling
an I/O function, such as fseek or
4-51
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
frewind, that sets the file position
indicator.
for
MATLAB
—
for
Fixed-Point
Designer
—
fpdf
Statistics Toolbox —
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Specifying constants
Name
Product
Remarks and Limitations
fprintf
MATLAB
• Does not support:
• b and t subtypes on %u, %o %x, and %X
formats.
• $ flag for reusing input arguments.
• printing arrays.
• There is no automatic casting. Input
arguments must match their format types for
predictable results.
• Escaped characters are limited to the decimal
range of 0–127.
• A call to fprintf with fileID equal to 1 or
2 becomes printf in the generated C/C++
code in the following cases:
• The fprintf call is inside a parfor loop.
• Extrinsic calls are disabled.
• When the MATLAB behavior differs from the
C compiler behavior, fprintf matches the C
compiler behavior in the following cases:
• The format specifier has a corresponding
C format specifier, for example, %e or %E.
• The fprintf call is inside a parfor loop.
• Extrinsic calls are disabled.
• When you call fprintf with the format
specifier %s, do not put a null character
in the middle of the input string. Use
fprintf(fid, '%c', char(0)) to write a
null character.
• When you call fprintf with an integer
format specifier, the type of the integer
argument must be a type that the target
hardware can represent as a native C type.
For example, if you call fprintf('%d',
4-53
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
int64(n)), the target hardware must
have a native C type that supports a 64-bit
integer.
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Specifying constants
Name
Product
Remarks and Limitations
fread
MATLAB
• precision must be a constant.
• The source and output that precision
specifies cannot have values long, ulong,
unsigned long, bitN, or ubitN.
• You cannot use the machineformat input.
• If the source or output that precision
specifies is a C type, for example, int, the
target and production sizes for that type
must:
• Match.
• Map directly to a MATLAB type.
• The source type that precision specifies
must map directly to a C type on the target
hardware.
• If the fread call reads the entire file, all
of the data must fit in the largest array
available for code generation.
• If sizeA is not constant or contains a
nonfinite element, then dynamic memory
allocation is required.
• Treats a char value for source or output
as a signed 8-bit integer. Use values between
0 and 127 only.
• The generated code does not report file
read errors. Write your own file read error
handling in your MATLAB code. Test that
the number of bytes read matches the
number of bytes that you requested. For
example:
...
N = 100;
[vals, numRead] = fread(fid, N, '*double');
if numRead ~= N
% fewer elements read than expected
4-55
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
end
...
freqspace
MATLAB
—
freqz
Signal Processing When called with no output arguments, and
Toolbox
without a semicolon at the end, freqz returns
the complex frequency response of the input
filter, evaluated at 512 points.
If the semicolon is added, the function produces
a plot of the magnitude and phase response of
the filter.
See “freqz With No Output Arguments”.
frewind
MATLAB
—
frnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
fspecial
Image Processing All inputs must be constants at compilation
Toolbox
time. Expressions or variables are allowed if
their values do not change.
fspl
Phased Array
System Toolbox
fstat
Statistics Toolbox —
full
MATLAB
—
fzero
MATLAB
• The first argument must be a function
handle. Does not support structure, inline
function, or string inputs for the first
argument.
Does not support variable-size inputs.
• Supports up to three output arguments. Does
not support the fourth output argument (the
output structure).
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Specifying constants
Name
Product
Remarks and Limitations
gain2aperture
Phased Array
System Toolbox
Does not support variable-size inputs.
gamcdf
Statistics Toolbox —
gaminv
Statistics Toolbox —
gamma
MATLAB
—
gammainc
MATLAB
Output is always complex.
gammaincinv
MATLAB
Output is always complex.
gammaln
MATLAB
—
gampdf
Statistics Toolbox —
gamrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
gamstat
Statistics Toolbox —
gausswin
Signal Processing All inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
gcd
MATLAB
—
ge
MATLAB
—
ge
Fixed-Point
Designer
• Not supported for fixed-point signals with
different biases.
geocdf
Statistics Toolbox —
geoinv
Statistics Toolbox —
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
geomean
Statistics Toolbox —
geopdf
Statistics Toolbox —
geornd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
geostat
Statistics Toolbox —
get
Fixed-Point
Designer
• The syntax structure = get(o) is not
supported.
getlsb
Fixed-Point
Designer
—
getmsb
Fixed-Point
Designer
—
getrangefromclass
Image Processing —
Toolbox
gevcdf
Statistics Toolbox —
gevinv
Statistics Toolbox —
gevpdf
Statistics Toolbox —
gevrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
gevstat
Statistics Toolbox —
global2localcoord
Phased Array
System Toolbox
gpcdf
Statistics Toolbox —
gpinv
Statistics Toolbox —
gppdf
Statistics Toolbox —
Does not support variable-size inputs.
4-58
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Specifying constants
Name
Product
Remarks and Limitations
gprnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
gpstat
Statistics Toolbox —
gradient
MATLAB
—
grazingang
Phased Array
System Toolbox
Does not support variable-size inputs.
gt
MATLAB
—
gt
Fixed-Point
Designer
• Not supported for fixed-point signals with
different biases.
hadamard
MATLAB
—
hamming
Signal Processing All inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
hankel
MATLAB
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
hann
Signal Processing All inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
harmmean
Statistics Toolbox —
hdl.RAM
MATLAB
—
hex2dec
MATLAB
—
hex2num
MATLAB
• For n = hex2num(S), size(S,2) <=
length(num2hex(0))
hilb
MATLAB
—
hist
MATLAB
• Histogram bar plotting not supported; call
with at least one output argument.
• If supplied, the second argument x must be a
scalar constant.
• Inputs must be real.
histc
MATLAB
• The output of a variable-size array that
becomes a column vector at run time is a
column-vector, not a row-vector.
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
histeq
Image Processing All the syntaxes that include indexed images are
Toolbox
not supported. This includes all syntaxes that
accept map as input and return newmap.
Generated code for this function uses a
precompiled “platform-specific shared library”.
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Specifying constants
Name
Product
Remarks and Limitations
horizonrange
Phased Array
System Toolbox
Does not support variable-size inputs.
horzcat
Fixed-Point
Designer
—
hygecdf
Statistics Toolbox —
hygeinv
Statistics Toolbox —
hygepdf
Statistics Toolbox —
hygernd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
hygestat
Statistics Toolbox —
hypot
MATLAB
icdf
Statistics Toolbox —
idct
Signal Processing • Code generation for this function requires the
Toolbox
DSP System Toolbox software.
—
• Length of transform dimension must
be a power of two. If specified, the pad
or truncation value must be constant.
Expressions or variables are allowed if their
values do not change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command
Line”.
if, elseif, else
MATLAB
—
4-61
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
idivide
MATLAB
• For efficient generated code, MATLAB rules
for divide by zero are supported only for the
'round' option.
ifft
MATLAB
• Length of input vector must be a power of 2.
• Output of ifft block is complex.
• Does not support the 'symmetric' option.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
ifft2
MATLAB
• Length of input matrix dimensions must each
be a power of 2.
• Does not support the 'symmetric' option.
ifftn
MATLAB
• Length of input matrix dimensions must each
be a power of 2.
• Does not support the 'symmetric' option.
ifftshift
MATLAB
—
ifir
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
iircomb
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
iirgrpdelay
DSP System
Toolbox
• All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
• Does not support syntaxes that have cell
array input.
iirlpnorm
DSP System
Toolbox
• All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
• Does not support syntaxes that have cell
array input.
4-62
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Specifying constants
Name
Product
Remarks and Limitations
iirlpnormc
DSP System
Toolbox
• All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
• Does not support syntaxes that have cell
array input.
iirnotch
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
iirpeak
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
im2double
MATLAB
—
im2int16
Image Processing Generated code for this function uses a
Toolbox
precompiled platform-specific shared library.
im2single
Image Processing —
Toolbox
im2uint8
Image Processing Generated code for this function uses a
Toolbox
precompiled platform-specific shared library.
im2uint16
Image Processing Generated code for this function uses a
Toolbox
precompiled platform-specific shared library.
imadjust
Image Processing Does not support syntaxes that include indexed
Toolbox
images. This includes all syntaxes that accept
map as input and return newmap.
Generated code for this function uses a
precompiled “platform-specific shared library”.
imag
MATLAB
—
imag
Fixed-Point
Designer
—
imaq.VideoDevice
Image
Acquisition
Toolbox
“Code Generation with VideoDevice System
Object”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
imbothat
Image Processing The input image IM must be either 2-D or 3-D
Toolbox
image. The structuring element input argument
SE must be a compile-time constant.
Generated code for this function uses a
precompiled platform-specific shared library.
imclearborder
Image Processing The optional second input argument, conn, must
Toolbox
be a compile-time constant. Supports only up to
3-D inputs.
Generated code for this function uses a
precompiled “platform-specific shared library”.
imclose
Image Processing The input image IM must be either 2-D or 3-D
Toolbox
image. The structuring element input argument
SE must be a compile-time constant.
Generated code for this function uses a
precompiled platform-specific shared library.
imcomplement
Image Processing Does not support int64 and uint64 data types.
Toolbox
imdilate
Image Processing The input image IM must be either 2-D or 3Toolbox
D image. The SE, PACKOPT, and SHAPE input
arguments must be a compile-time constant.
The structuring element argument SE must be a
single element—arrays of structuring elements
are not supported. To obtain the same result
as that obtained using an array of structuring
elements, call the function sequentially.
Generated code for this function uses a
precompiled platform-specific shared library.
4-64
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Specifying constants
Name
Product
Remarks and Limitations
imerode
Image Processing The input image IM must be either 2-D or 3Toolbox
D image. The SE, PACKOPT, and SHAPE input
arguments must be a compile-time constant.
The structuring element argument SE must be a
single element—arrays of structuring elements
are not supported. To obtain the same result
as that obtained using an array of structuring
elements, call the function sequentially.
Generated code for this function uses a
precompiled platform-specific shared library.
imextendedmax
Image Processing The optional third input argument, conn, must
Toolbox
be a compile-time constant.
Generated code for this function uses a
precompiled platform-specific shared library.
imextendedmin
Image Processing The optional third input argument, conn, must
Toolbox
be a compile-time constant.
Generated code for this function uses a
precompiled platform-specific shared library.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
imfill
Image Processing The optional input connectivity, conn and the
Toolbox
string 'holes' must be compile time constants.
Supports only up to 3-D inputs. (No N-D
support.)
The interactive mode to select points,
imfill(BW,0,CONN) is not supported in code
generation.
locations can be a P-by-1 vector, in which
case it contains the linear indices of the
starting locations. locations can also be a Pby-ndims(I) matrix, in which case each row
contains the array indices of one of the starting
locations. Once you select a format at compiletime, you cannot change it at run time. However,
the number of points in locations can be varied
at run time.
Generated code for this function uses a
precompiled platform-specific shared library.
imfilter
Image Processing The input image can be either 2-D or 3-D. The
Toolbox
value of the input argument, options, must be
a compile-time constant.
Generated code for this function uses a
precompiled platform-specific shared library.
imhist
Image Processing The optional second input argument, n, must
Toolbox
be a compile-time constant. In addition,
nonprogrammatic syntaxes are not supported.
For example, the syntaxes where imhist
displays the histogram are not supported.
Generated code for this function uses a
precompiled platform-specific shared library.
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Specifying constants
Name
Product
Remarks and Limitations
imhmax
Image Processing The optional third input argument, conn, must
Toolbox
be a compile-time constant
Generated code for this function uses a
precompiled platform-specific shared library.
imhmin
Image Processing The optional third input argument, conn, must
Toolbox
be a compile-time constant
Generated code for this function uses a
precompiled platform-specific shared library.
imlincomb
Image Processing The output_class argument must be a
Toolbox
compile-time constant.
Generated code for this function uses a
precompiled “platform-specific shared library”.
imopen
Image Processing The input image IM must be either 2-D or 3-D
Toolbox
image. The structuring element input argument
SE must be a compile-time constant.
Generated code for this function uses a
precompiled platform-specific shared library.
imquantize
Image Processing —
Toolbox
imreconstruct
Image Processing The optional third input argument, conn, must
Toolbox
be a compile-time constant.
Generated code for this function uses a
precompiled platform-specific shared library.
imref2d
Image Processing The XWorldLimits, YWorldLimits and
Toolbox
ImageSize properties can be set only during
object construction. When generating code, you
can only specify single objects—arrays of objects
are not supported.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
imref3d
Image Processing The XWorldLimits, YWorldLimits,
Toolbox
ZWorldLimits and ImageSize properties can
be set only during object construction. When
generating code, you can only specify single
objects—arrays of objects are not supported.
imregionalmax
Image Processing The optional second input argument, conn, must
Toolbox
be a compile-time constant.
Generated code for this function uses a
precompiled platform-specific shared library.
imregionalmin
Image Processing The optional second input argument, conn, must
Toolbox
be a compile-time constant.
Generated code for this function uses a
precompiled platform-specific shared library.
imtophat
Image Processing The input image IM must be either 2-D or 3-D
Toolbox
image. The structuring element input argument
SE must be a compile-time constant.
Generated code for this function uses a
precompiled platform-specific shared library.
imwarp
Image Processing Geometric transformation object input, tform,
Toolbox
must be either affine2d or projective2d.
Additionally, the interpolation method and
optional parameter names must be string
constants.
Generated code for this function uses a
precompiled platform-specific shared library.
ind2sub
MATLAB
• The first argument should be a valid size
vector. Size vectors for arrays with more than
intmax elements are not supported.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
inf
MATLAB
• Dimensions must be real, nonnegative,
integers.
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Specifying constants
Name
Product
Remarks and Limitations
insertMarker
Computer Vision Compile-time constant input: marker
System Toolbox
Supports MATLAB Function block: Yes
insertShape
Computer Vision Compile-time constant input: shape and
System Toolbox
SmoothEdges
Supports MATLAB Function block: Yes
int8, int16, int32, int64
MATLAB
No integer overflow detection for int64 in
MEX or MATLAB Function block simulation on
Windows 32-bit platforms.
int8, int16, int32, int64
Fixed-Point
Designer
—
integralImage
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
interp1
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
interp2
MATLAB
• Xq and Yq must be the same size. Use
meshgrid to evaluate on a grid.
• For best results, provide X and Y as vectors.
• For the 'cubic' method, reports an error if
the grid does not have uniform spacing. In
this case, use the 'spline' method.
• For best results when you use the 'spline'
method:
• Use meshgrid to create the inputs Xq
and Yq.
• Use a small number of interpolation
points relative to the dimensions of V.
Interpolating over a large set of scattered
points can be inefficient.
4-69
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
interp3
MATLAB
• Xq, Yq, and Zq must be the same size. Use
meshgrid to evaluate on a grid.
• For best results, provide X, Y, and Z as
vectors.
• For the 'cubic' method, reports an error if
the grid does not have uniform spacing. In
this case, use the 'spline' method.
• For best results when you use the 'spline'
method:
• Use meshgrid to create the inputs Xq,
Yq, and Zq.
• Use a small number of interpolation
points relative to the dimensions of V.
Interpolating over a large set of scattered
points can be inefficient.
4-70
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Specifying constants
Name
Product
Remarks and Limitations
intersect
MATLAB
• When you do not specify the 'rows' option:
• Inputs A and B must be vectors. If you
specify the 'legacy' option, inputs A and
B must be row vectors.
• The first dimension of a variable-size
row vector must have fixed length 1.
The second dimension of a variable-size
column vector must have fixed length 1.
• The input [] is not supported. Use a
1-by-0 or 0-by-1 input, for example,
zeros(1,0), to represent the empty set.
• If you specify the 'legacy' option, empty
outputs are row vectors, 1-by-0, never 0by-0.
• When you specify both the 'legacy' option
and the 'rows' option, the outputs ia and
ib are column vectors. If these outputs are
empty, they are 0-by-1, never 0-by-0, even if
the output C is 0-by-0.
• When the setOrder is 'sorted' or when
you specify the 'legacy' option, the inputs
must already be sorted in ascending order.
The first output, C, is sorted in ascending
order.
• Complex inputs must be single or double.
• When one input is complex and the other
input is real, do one of the following:
• Set setOrder to 'stable'.
• Sort the real input in complex
ascending order (by absolute
value). Suppose the real input
is x. Use sort(complex(x))or
sortrows(complex(x)).
4-71
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
intfilt
Signal Processing All inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
intlut
Image Processing Generated code for this function uses a
Toolbox
precompiled “platform-specific shared library”.
intmax
MATLAB
—
intmin
MATLAB
—
inv
MATLAB
Singular matrix inputs can produce nonfinite
values that differ from MATLAB results.
invhilb
MATLAB
—
ipermute
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
ipermute
Fixed-Point
Designer
—
iptcheckconn
Image Processing All input arguments must be compile-time
Toolbox
constants.
iptcheckmap
Image Processing —
Toolbox
iqcoef2imbal
Communications —
System Toolbox
iqimbal2coef
Communications —
System Toolbox
iqr
Statistics Toolbox —
isa
MATLAB
—
iscell
MATLAB
—
4-72
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Specifying constants
Name
Product
Remarks and Limitations
ischar
MATLAB
—
iscolumn
MATLAB
—
iscolumn
Fixed-Point
Designer
—
isdeployed
MATLAB
Compiler
• Returns true and false as appropriate for
MEX and SIM targets
• Returns false for other targets
isempty
MATLAB
—
isempty
Fixed-Point
Designer
—
isEpipoleInImage
Computer Vision Compile-time constant input: No restrictions.
System Toolbox
Supports MATLAB Function block: Yes
isequal
MATLAB
—
isequal
Fixed-Point
Designer
—
isequaln
MATLAB
—
isfi
Fixed-Point
Designer
—
isfield
MATLAB
• Does not support cell input for second
argument
isfimath
Fixed-Point
Designer
—
isfimathlocal
Fixed-Point
Designer
—
isfinite
MATLAB
—
isfinite
Fixed-Point
Designer
—
isfloat
MATLAB
—
ishermitian
MATLAB
—
isinf
MATLAB
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
isinf
Fixed-Point
Designer
—
isinteger
MATLAB
—
isletter
MATLAB
• Input values from the char class must be in
the range 0-127
islogical
MATLAB
—
ismac
MATLAB
• Returns true or false based on the MATLAB
version used for code generation.
• Use only when the code generation target is
S-function (Simulation) or MEX-function.
ismatrix
MATLAB
—
ismcc
MATLAB
Compiler
• Returns true and false as appropriate for
MEX and SIM targets.
• Returns false for other targets.
ismember
MATLAB
• The second input, B, must be sorted in
ascending order.
• Complex inputs must be single or double.
isnan
MATLAB
—
isnan
Fixed-Point
Designer
—
isnumeric
MATLAB
—
isnumeric
Fixed-Point
Designer
—
isnumerictype
Fixed-Point
Designer
—
isobject
MATLAB
—
ispc
MATLAB
• Returns true or false based on the MATLAB
version you use for code generation.
• Use only when the code generation target is
S-function (Simulation) or MEX-function.
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Specifying constants
Name
Product
Remarks and Limitations
isprime
MATLAB
• The maximum double precision input is
2^33.
• The maximum single precision input is 2^25.
• The input X cannot have type int64 or
uint64.
isreal
MATLAB
—
isreal
Fixed-Point
Designer
—
isrow
MATLAB
—
isrow
Fixed-Point
Designer
—
isscalar
MATLAB
—
isscalar
Fixed-Point
Designer
—
issigned
Fixed-Point
Designer
—
issorted
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
isspace
MATLAB
• Input values from the char class must be in
the range 0–127.
issparse
MATLAB
—
isstrprop
MATLAB
• Supports only inputs from char and
integer classes.
• Input values must be in the range 0-127.
isstruct
MATLAB
—
issymmetric
MATLAB
—
istrellis
Communications —
System Toolbox
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
isunix
MATLAB
• Returns true or false based on the MATLAB
version used for code generation.
• Use only when the code generation target is
S-function (Simulation) or MEX-function.
isvector
MATLAB
—
isvector
Fixed-Point
Designer
—
kaiser
Signal Processing All inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
kaiserord
Signal Processing —
Toolbox
kron
MATLAB
kurtosis
Statistics Toolbox —
—
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Specifying constants
Name
Product
Remarks and Limitations
label2rgb
Image Processing Referring to the standard syntax:
Toolbox
RGB = label2rgb(L, map, zerocolor, order)
• Submit at least two input arguments: the
label matrix, L, and the colormap matrix,
map.
• map must be an n-by-3, double, colormap
matrix. You cannot use a string containing
the name of a MATLAB colormap function or
a function handle of a colormap function.
• If you set the boundary color zerocolor
to the same color as one of the regions,
label2rgb will not issue a warning.
• If you supply a value for order, it must be
'noshuffle'.
lcm
MATLAB
—
lcmvweights
Phased Array
System Toolbox
Does not support variable-size inputs.
ldivide
MATLAB
—
le
MATLAB
—
le
Fixed-Point
Designer
• Not supported for fixed-point signals with
different biases.
length
MATLAB
—
length
Fixed-Point
Designer
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
levinson
Signal Processing • Code generation for this function requires the
Toolbox
DSP System Toolbox software.
• If specified, the order of recursion must be
a constant. Expressions or variables are
allowed if their values do not change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command
Line”.
lineToBorderPoints
Computer Vision Compile-time constant input: No restrictions.
System Toolbox
Supports MATLAB Function block: Yes
linsolve
MATLAB
• The option structure must be a constant.
• Supports only a scalar option structure
input. It does not support arrays of option
structures.
• Only optimizes these cases:
• UT
• LT
• UHESS = true (the TRANSA can be either
true or false)
• SYM = true and POSDEF = true
Other options are equivalent to using
mldivide.
linspace
MATLAB
—
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Specifying constants
Name
Product
Remarks and Limitations
load
MATLAB
• Use only when generating MEX or code for
Simulink simulation. To load compile-time
constants, use coder.load.
• Does not support use of the function without
assignment to a structure or array. For
example, use S = load(filename), not
load(filename).
• The output S must be the name of a structure
or array without any subscripting. For
example, S[i] = load('myFile.mat') is
not allowed.
• Arguments to load must be compile-time
constant strings.
• Does not support loading objects.
• If the MAT-file contains
unsupported constructs, use
load(filename,variables) to load only
the supported constructs.
• You cannot use save in a function intended
for code generation. The code generation
software does not support the save
function. Furthermore, you cannot use
coder.extrinsic with save. Prior to
generating code, you can use save to save
the workspace data to a MAT-file.
You must use coder.varsize to explicitly
declare variable-size data loaded using the
load function.
local2globalcoord
Phased Array
System Toolbox
Does not support variable-size inputs.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
log
MATLAB
• Generates an error during simulation and
returns NaN in generated code when the
input value x is real, but the output should
be complex. To get the complex result,
make the input value complex by passing in
complex(x).
log2
MATLAB
—
log10
MATLAB
—
log1p
MATLAB
—
logical
MATLAB
—
logical
Fixed-Point
Designer
—
logncdf
Statistics Toolbox —
logninv
Statistics Toolbox —
lognpdf
Statistics Toolbox —
lognrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
lognstat
Statistics Toolbox —
logspace
MATLAB
—
lower
MATLAB
• Supports only char inputs.
• Input values must be in the range 0-127.
lowerbound
Fixed-Point
Designer
—
lsb
Fixed-Point
Designer
• Supported for scalar fixed-point signals only.
MATLAB
—
lt
• Supported for scalar, vector, and matrix, fi
single and double signals.
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Specifying constants
Name
Product
Remarks and Limitations
lteZadoffChuSeq
Communications —
System Toolbox
lt
Fixed-Point
Designer
• Not supported for fixed-point signals with
different biases.
lu
MATLAB
—
mad
Statistics Toolbox Input dim cannot be empty.
magic
MATLAB
matchFeatures
Computer Vision Generates platform-dependent library: Yes for
System Toolbox
MATLAB host. The function generates portable
C code for non-host target.
Compile-time constant input: Method and
Metric.
Supports MATLAB Function block: Yes
Generated code for this function uses a
precompiled platform-specific shared library.
max
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
max
Fixed-Point
Designer
—
maxflat
Signal Processing Inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
mdltest
Phased Array
System Toolbox
Does not support variable-size inputs.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
mean
MATLAB
• Does not support the 'native' output class
option for integer types.
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
mean
Fixed-Point
Designer
N/A
mean2
Image Processing —
Toolbox
medfilt2
Image Processing The padopt argument must be a compile-time
Toolbox
constant.
Generated code for this function uses a
precompiled “platform-specific shared library”.
median
MATLAB
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
median
Fixed-Point
Designer
—
meshgrid
MATLAB
—
mfilename
MATLAB
—
min
MATLAB
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
min
Fixed-Point
Designer
—
minus
MATLAB
—
minus
Fixed-Point
Designer
• Any non-fi input must be constant. Its value
must be known at compile time so that it can
be cast to a fi object.
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Specifying constants
Name
Product
Remarks and Limitations
mkpp
MATLAB
• The output structure pp differs from the pp
structure in MATLAB. In MATLAB, ppval
cannot use the pp structure from the code
generation software. For code generation,
ppval cannot use a pp structure created by
MATLAB. unmkpp can use a MATLAB pp
structure for code generation.
To create a MATLAB pp structure from a
pp structure created by the code generation
software:
• In code generation, use unmkpp to return
the piecewise polynomial details to
MATLAB.
• In MATLAB, use mkpp to create the pp
structure.
• If you do not provide d, then coefs must be
two-dimensional and have a fixed number of
columns. In this case, the number of columns
is the order.
• To define a piecewise constant polynomial,
coefs must be a column vector or d must
have at least two elements.
• If you provide d and d is 1, d must be a
constant. Otherwise, if the input to ppval is
nonscalar, the shape of the output of ppval
can differ from ppval in MATLAB.
• If you provide d, it must have a fixed length.
One of the following sets of statements must
be true:
1
Suppose that m = length(d) and
npieces = length(breaks) - 1.
size(coefs,j) = d(j)
size(coefs,m+1) = npieces
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
size(coefs,m+2) = order
j = 1,2,...,m. The dimension m+2 must be
fixed length.
2
Suppose that m = length(d) and
npieces = length(breaks) - 1.
size(coefs,1) = prod(d)*npieces
size(coefs,2) = order
The second dimension must be fixed
length.
• If you do not provide d, the following
statements must be true:
Suppose that m = length(d) and npieces
= length(breaks) - 1.
size(coefs,1) = prod(d)*npieces
size(coefs,2) = order
The second dimension must be fixed length.
mldivide
MATLAB
—
mnpdf
Statistics Toolbox —
mod
MATLAB
• Performs the arithmetic using the output
class. Results might not match MATLAB due
to differences in rounding errors.
If one of the inputs has type int64 or
uint64, then both inputs must have the
same type.
mode
MATLAB
• Does not support third output argument C
(cell array).
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
moment
Statistics Toolbox If order is nonintegral and X is real, use
moment(complex(X), order).
mpower
MATLAB
—
4-84
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Specifying constants
Name
Product
Remarks and Limitations
mpower
Fixed-Point
Designer
• The exponent input, k, must be constant;
that is, its value must be known at compile
time.
• Variable-sized inputs are only supported
when the SumMode property of the governing
fimath is set to Specify precision or
Keep LSB.
• For variable-sized signals, you may see
different results between MATLAB and the
generated code.
• In generated code, the output for variablesized signals is computed using the
SumMode property of the governing
fimath.
• In MATLAB, the output for variablesized signals is computed using the
SumMode property of the governing
fimath when both inputs are nonscalar.
However, if either input is a scalar,
MATLAB computes the output using the
ProductMode of the governing fimath.
mpy
Fixed-Point
Designer
• Code generation in MATLAB does not
support the syntax F.mpy(a,b). You must
use the syntax mpy(F,a,b).
• When you provide complex inputs to the
mpy function inside a MATLAB Function
block, you must declare the input as complex
before running the simulation. To do so, go
to the Ports and data manager and set
the Complexity parameter for all known
complex inputs to On.
mrdivide
MATLAB
—
mrdivide
Fixed-Point
Designer
—
4-85
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
MSERRegions
Computer Vision Compile-time constant input: No restrictions.
System Toolbox
Supports MATLAB Function block: Yes
For code generation, you must specify both
the pixellist cell array and the length of each
array, as the second input. The object outputs,
regions.PixelList as an array. The region sizes
are defined in regions.Lengths.
Generated code for this function uses a
precompiled platform-specific shared library.
mtimes
MATLAB
• Multiplication of pure imaginary numbers
by non-finite numbers might not match
MATLAB. The code generation software
does not specialize multiplication by pure
imaginary numbers—it does not eliminate
calculations with the zero real part. For
example, (Inf + 1i)*1i = (Inf*0 –
1*1) + (Inf*1 + 1*0)i = NaN + Infi.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
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Specifying constants
Name
Product
Remarks and Limitations
mtimes
Fixed-Point
Designer
• Any non-fi input must be constant; that is,
its value must be known at compile time so
that it can be cast to a fi object.
• Variable-sized inputs are only supported
when the SumMode property of the governing
fimath is set to Specify precision or
Keep LSB.
• For variable-sized signals, you may see
different results between MATLAB and the
generated code.
• In generated code, the output for variablesized signals is computed using the
SumMode property of the governing
fimath.
• In MATLAB, the output for variablesized signals is computed using the
SumMode property of the governing
fimath when both inputs are nonscalar.
However, if either input is a scalar,
MATLAB computes the output using the
ProductMode of the governing fimath.
multithresh
Image Processing —
Toolbox
mvdrweights
Phased Array
System Toolbox
Does not support variable-size inputs.
NaN or nan
MATLAB
• Dimensions must be real, nonnegative,
integers.
nancov
Statistics Toolbox If the input is variable-size and is [] at run
time, returns [] not NaN.
nanmax
Statistics Toolbox —
nanmean
Statistics Toolbox —
nanmedian
Statistics Toolbox —
nanmin
Statistics Toolbox —
4-87
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
nanstd
Statistics Toolbox —
nansum
Statistics Toolbox —
nanvar
Statistics Toolbox —
nargchk
MATLAB
• Output structure does not include stack
information.
Note: nargchk will be removed in a future
release.
nargin
MATLAB
—
narginchk
MATLAB
—
nargout
MATLAB
• For a function with no output arguments,
returns 1 if called without a terminating
semicolon.
Note: This behavior also affects extrinsic calls
with no terminating semicolon. nargout is 1 for
the called function in MATLAB.
nargoutchk
MATLAB
—
nbincdf
Statistics Toolbox —
nbininv
Statistics Toolbox —
nbinpdf
Statistics Toolbox —
nbinrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
nbinstat
Statistics Toolbox —
ncfcdf
Statistics Toolbox —
ncfinv
Statistics Toolbox —
4-88
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Specifying constants
Name
Product
Remarks and Limitations
ncfpdf
Statistics Toolbox —
ncfrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
ncfstat
Statistics Toolbox —
nchoosek
MATLAB
• When the first input, x, is a scalar,
nchoosek returns a binomial coefficient. In
this case, x must be a nonnegative integer. It
cannot have type int64 or uint64.
• When the first input, x, is a vector,
nchoosek treats it as a set. In this case, x
can have type int64 or uint64.
• The second input, k, cannot have type int64
or uint64.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
nctcdf
Statistics Toolbox —
nctinv
Statistics Toolbox —
nctpdf
Statistics Toolbox —
nctrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
nctstat
Statistics Toolbox —
ncx2cdf
Statistics Toolbox —
4-89
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
ncx2rnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
ncx2stat
Statistics Toolbox —
ndgrid
MATLAB
—
ndims
MATLAB
—
ndims
Fixed-Point
Designer
—
ne
MATLAB
—
ne
Fixed-Point
Designer
• Not supported for fixed-point signals with
different biases.
nearest
Fixed-Point
Designer
—
nextpow2
MATLAB
—
nnz
MATLAB
—
noisepow
Phased Array
System Toolbox
Does not support variable-size inputs.
nonzeros
MATLAB
—
norm
MATLAB
—
normcdf
Statistics Toolbox —
normest
MATLAB
norminv
Statistics Toolbox —
normpdf
Statistics Toolbox —
—
4-90
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Specifying constants
Name
Product
Remarks and Limitations
normrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
normstat
Statistics Toolbox —
not
MATLAB
—
npwgnthresh
Phased Array
System Toolbox
Does not support variable-size inputs.
nthroot
MATLAB
—
null
MATLAB
• Might return a different basis than MATLAB
• Does not support rational basis option
(second input)
num2hex
MATLAB
—
numberofelements
Fixed-Point
Designer
numberofelements will be removed in a future
release. Use numel instead.
numel
MATLAB
—
numel
Fixed-Point
Designer
—
numerictype
Fixed-Point
Designer
• Fixed-point signals coming into a MATLAB
Function block from Simulink are assigned
a numerictype object that is populated
with the signal's data type and scaling
information.
• Returns the data type when the input is a
nonfixed-point signal.
• Use to create numerictype objects in the
generated code.
• All numerictype object properties related to
the data type must be constant.
4-91
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
nuttallwin
Signal Processing Inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
ocr
Computer Vision Compile-time constant input: TextLayout,
System Toolbox
Language, and CharacterSet.
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
ocrText
Computer Vision Compile-time constant input: No restrictions.
System Toolbox
Supports MATLAB Function block: No
ode23
MATLAB
• All odeset option arguments must be
constant.
• Does not support a constant mass matrix in
the options structure. Provide a mass matrix
as a function .
• You must provide at least the two output
arguments T and Y.
• Input types must be homogeneous—all
double or all single.
• Variable-sizing support must be enabled.
Requires dynamic memory allocation when
tspan has two elements or you use event
functions.
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Specifying constants
Name
Product
Remarks and Limitations
ode45
MATLAB
• All odeset option arguments must be
constant.
• Does not support a constant mass matrix in
the options structure. Provide a mass matrix
as a function .
• You must provide at least the two output
arguments T and Y.
• Input types must be homogeneous—all
double or all single.
• Variable-sizing support must be enabled.
Requires dynamic memory allocation when
tspan has two elements or you use event
functions.
odeget
MATLAB
The name argument must be constant.
odeset
MATLAB
All inputs must be constant.
ones
MATLAB
• Dimensions must be real, nonnegative
integers.
• The input optimfun must be a function
supported for code generation.
optimget
MATLAB
Input parameter names must be constant.
4-93
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
optimset
MATLAB
• Does not support the syntax that has no
input or output arguments:
optimset
• Functions specified in the options must be
supported for code generation.
• The fields of the options structure oldopts
must be fixed-size fields.
• For code generation, optimization functions
ignore the Display option.
• Does not support the additional options in an
options structure created by the Optimization
Toolbox™ optimset function. If an input
options structure includes the additional
Optimization Toolbox options, the output
structure does not include them.
ordfilt2
Image Processing The padopt argument must be a compile-time
Toolbox
constant.
Generated code for this function uses a
precompiled “platform-specific shared library”.
or
MATLAB
—
orth
MATLAB
• Can return a different basis than MATLAB
padarray
Image Processing • Support only up to 3-D inputs.
Toolbox
• Input arguments, padval and direction
are expected to be compile-time constants.
parfor
MATLAB
• Treated as a for-loop in a MATLAB
Function block or when used with fiaccel.
• See the parfor reference page in the
MATLAB Coder documentation.
• “Generate Code with Parallel for-Loops
(parfor)”.
4-94
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Specifying constants
Name
Product
Remarks and Limitations
parzenwin
Signal Processing Inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
pascal
MATLAB
—
pchip
MATLAB
• Input x must be strictly increasing.
• Does not remove y entries with NaN values.
• If you generate code for the pp =
pchip(x,y) syntax, you cannot input pp to
the ppval function in MATLAB. To create a
MATLAB pp structure from a pp structure
created by the code generation software:
• In code generation, use unmkpp to return
the piecewise polynomial details to
MATLAB.
• In MATLAB, use mkpp to create the pp
structure.
pdf
Statistics Toolbox —
permute
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
permute
Fixed-Point
Designer
The dimensions argument must be a built-in
type; it cannot be a fi object.
phased.ADPCACanceller
Phased Array
System Toolbox
“Code Generation”
phased.AngleDopplerResponse
Phased Array
System Toolbox
“Code Generation”
4-95
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
phased.ArrayGain
Phased Array
System Toolbox
• Does not support arrays containing
polarized antenna elements, that is, the
phased.ShortDipoleAntennaElement or
phased.CrossedDipoleAntennaElement
antennas.
• “Code Generation”.
phased.ArrayResponse
Phased Array
System Toolbox
“Code Generation”
phased.BarrageJammer
Phased Array
System Toolbox
“Code Generation”
phased.BeamscanEstimator
Phased Array
System Toolbox
“Code Generation”
phased.BeamscanEstimator2D
Phased Array
System Toolbox
“Code Generation”
phased.BeamspaceESPRITEstimator
Phased Array
System Toolbox
“Code Generation”.
phased.CFARDetector
Phased Array
System Toolbox
“Code Generation”
phased.Collector
Phased Array
System Toolbox
“Code Generation”
phased.ConformalArray
Phased Array
System Toolbox
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.ConstantGammaClutter
Phased Array
System Toolbox
“Code Generation”
phased.CosineAntennaElement
Phased Array
System Toolbox
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.CrossedDipoleAntennaElement
Phased Array
System Toolbox
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
4-96
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Specifying constants
Name
Product
Remarks and Limitations
phased.CustomAntennaElement
Phased Array
System Toolbox
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.CustomMicrophoneElement
Phased Array
System Toolbox
• plotResponse and viewArray methods are
not supported.
• “Code Generation”
phased.DPCACanceller
Phased Array
System Toolbox
“Code Generation”
phased.ElementDelay
Phased Array
System Toolbox
“Code Generation”
phased.ESPRITEstimator
Phased Array
System Toolbox
“Code Generation”
phased.FMCWWaveform
Phased Array
System Toolbox
“Code Generation”
phased.FreeSpace
Phased Array
System Toolbox
• Requires dynamic memory allocation. See
“Limitations for System Objects that Require
Dynamic Memory Allocation”.
• “Code Generation”.
phased.FrostBeamformer
Phased Array
System Toolbox
• Requires dynamic memory allocation. See
“Limitations for System Objects that Require
Dynamic Memory Allocation”.
• “Code Generation”.
phased.IsotropicAntennaElement
Phased Array
System Toolbox
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
Phased Array
System Toolbox
“Code Generation”
phased.LinearFMWaveform Phased Array
System Toolbox
“Code Generation”
phased.LCMVBeamformer
phased.MatchedFilter
Phased Array
System Toolbox
• The CustomSpectrumWindow property is
not supported.
• “Code Generation”.
4-97
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
phased.MVDRBeamformer
Phased Array
System Toolbox
“Code Generation”
phased.MVDREstimator
Phased Array
System Toolbox
“Code Generation”
phased.MVDREstimator2D
Phased Array
System Toolbox
“Code Generation”
phased.Omnidirectional- Phased Array
System Toolbox
MicrophoneElement
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.PartitionedArray Phased Array
System Toolbox
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.PhaseCodedWaveform
Phased Array
System Toolbox
“Code Generation”
phased.PhaseShiftBeamformer
Phased Array
System Toolbox
“Code Generation”
phased.Platform
Phased Array
System Toolbox
“Code Generation”
phased.RadarTarget
Phased Array
System Toolbox
“Code Generation”
phased.Radiator
Phased Array
System Toolbox
“Code Generation”
phased.RangeDopplerResponse
Phased Array
System Toolbox
• The CustomRangeWindow and the
CustomDopplerWindow properties are not
supported.
• “Code Generation”.
phased.RectangularWaveform
Phased Array
System Toolbox
“Code Generation”
phased.ReceiverPreamp
Phased Array
System Toolbox
“Code Generation”
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Specifying constants
Name
Product
Remarks and Limitations
phased.ReplicatedSubarray
Phased Array
System Toolbox
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
Phased Array
System Toolbox
“Code Generation”
phased.RootWSFEstimator Phased Array
System Toolbox
“Code Generation”
phased.RootMUSICEstimator
phased.ShortDipoleAntennaElement
Phased Array
System Toolbox
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
Phased Array
System Toolbox
“Code Generation”
phased.StretchProcessor Phased Array
System Toolbox
“Code Generation”
phased.SubbandPhaseShiftBeamformer
Phased Array
System Toolbox
“Code Generation”
phased.SteeringVector
Phased Array
System Toolbox
“Code Generation”
phased.SteppedFMWaveform
Phased Array
System Toolbox
“Code Generation”
phased.SumDifferenceMonopulseTracker
Phased Array
System Toolbox
“Code Generation”.
phased.SumDifferenceMonopulseTracker2D
Phased Array
System Toolbox
“Code Generation”.
phased.TimeDelayBeamformer
Phased Array
System Toolbox
• Requires dynamic memory allocation. See
“Limitations for System Objects that Require
Dynamic Memory Allocation”.
phased.STAPSMIBeamformer
• “Code Generation”.
phased.TimeDelayLCMVBeamformer
Phased Array
System Toolbox
• Requires dynamic memory allocation. See
“Limitations for System Objects that Require
Dynamic Memory Allocation”.
• “Code Generation”.
4-99
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
phased.TimeVaryingGain
Phased Array
System Toolbox
“Code Generation”.
phased.Transmitter
Phased Array
System Toolbox
“Code Generation”.
phased.ULA
Phased Array
System Toolbox
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.URA
Phased Array
System Toolbox
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.WidebandCollector
Phased Array
System Toolbox
• Requires dynamic memory allocation. See
“Limitations for System Objects that Require
Dynamic Memory Allocation”.
• “Code Generation”.
phitheta2azel
Phased Array
System Toolbox
Does not support variable-size inputs.
phitheta2azelpat
Phased Array
System Toolbox
Does not support variable-size inputs.
phitheta2uv
Phased Array
System Toolbox
Does not support variable-size inputs.
phitheta2uvpat
Phased Array
System Toolbox
Does not support variable-size inputs.
physconst
Phased Array
System Toolbox
Does not support variable-size inputs.
pi
MATLAB
—
pinv
MATLAB
—
planerot
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
plus
MATLAB
—
plus
Fixed-Point
Designer
• Any non-fi input must be constant; that is,
its value must be known at compile time so
that it can be cast to a fi object.
4-100
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Specifying constants
Name
Product
Remarks and Limitations
poisscdf
Statistics Toolbox —
poissinv
Statistics Toolbox —
poisspdf
Statistics Toolbox —
poissrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
poisstat
Statistics Toolbox —
pol2cart
MATLAB
—
pol2circpol
Phased Array
System Toolbox
Does not support variable-size inputs.
polellip
Phased Array
System Toolbox
Does not support variable-size inputs.
polloss
Phased Array
System Toolbox
Does not support variable-size inputs.
polratio
Phased Array
System Toolbox
Does not support variable-size inputs.
polsignature
Phased Array
System Toolbox
• Does not support variable-size inputs.
MATLAB
• Does not discard nonfinite input values
poly
• Supported only when output arguments are
specified.
• Complex input produces complex output
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
polyarea
MATLAB
—
poly2trellis
Communications —
System Toolbox
polyder
MATLAB
The output can contain fewer NaNs than the
MATLAB output. However, if the input contains
a NaN, the output contains at least one NaN.
4-101
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
polyfit
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
polyint
MATLAB
—
polyval
MATLAB
—
polyvalm
MATLAB
—
pow2
Fixed-Point
Designer
—
pow2db
Signal Processing —
Toolbox
power
MATLAB
• Generates an error during simulation. When
both X and Y are real, but power(X,Y) is
complex, returns NaN in the generated code.
To get the complex result, make the input
value X complex by passing in complex(X).
For example, power(complex(X),Y).
• Generates an error during simulation. When
both X and Y are real, but X .^ Y is complex,
returns NaN in generated code. To get the
complex result, make the input value X
complex by using complex(X). For example,
complex(X).^Y.
power
Fixed-Point
Designer
• The exponent input, k, must be constant. Its
value must be known at compile time.
4-102
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Specifying constants
Name
Product
Remarks and Limitations
ppval
MATLAB
The size of output v does not match MATLAB
when both of the following statements are true:
• The input x is a variable-size array that is
not a variable-length vector.
• x becomes a row vector at run time.
The code generation software does not remove
the singleton dimensions. However, MATLAB
might remove singleton dimensions.
For example, suppose that x is a :4-by-:5 array
(the first dimension is variable size with an
upper bound of 4 and the second dimension is
variable size with an upper bound of 5). Suppose
that ppval(pp,0) returns a 2-by-3 fixed-size
array. v has size 2-by-3-by-:4-by-:5. At run time,
suppose that, size(x,1) =1 and size (x,2) = 5. In
the generated code, the size(v) is [2,3,1,5]. In
MATLAB, the size is [2,3,5].
prctile
Statistics Toolbox • “Automatic dimension restriction”
• If the output Y is a vector, the orientation
of Y differs from MATLAB when all of the
following are true:
• You do not supply the dim input.
• X is a variable-size array.
• X is not a variable-length vector.
• X is a vector at run time.
• The orientation of the vector X does not
match the orientation of the vector p.
In this case, the output Y matches the
orientation of X not the orientation of p.
4-103
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
primes
MATLAB
• The maximum double precision input is
2^32.
• The maximum single precision input is 2^24.
• The input n cannot have type int64 or
uint64.
prod
MATLAB
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
projective2d
Image Processing When generating code, you can only specify
Toolbox
single objects—arrays of objects are not
supported.
psi
MATLAB
—
pulsint
Phased Array
System Toolbox
Does not support variable-size inputs.
qr
MATLAB
—
quad2d
MATLAB
• Generates a warning if the size of the
internal storage arrays is not large enough.
If a warning occurs, a possible workaround is
to divide the region of integration into pieces
and sum the integrals over each piece.
quadgk
MATLAB
—
quantile
Statistics Toolbox —
quantize
Fixed-Point
Designer
—
quatconj
Aerospace
Toolbox
Code generation for this function requires the
Aerospace Blockset™ software.
quatdivide
Aerospace
Toolbox
Code generation for this function requires the
Aerospace Blockset software.
quatinv
Aerospace
Toolbox
Code generation for this function requires the
Aerospace Blockset software.
quatmod
Aerospace
Toolbox
Code generation for this function requires the
Aerospace Blockset software.
4-104
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Specifying constants
Name
Product
Remarks and Limitations
quatmultiply
Aerospace
Toolbox
Code generation for this function requires the
Aerospace Blockset software.
quatnorm
Aerospace
Toolbox
Code generation for this function requires the
Aerospace Blockset software.
quatnormalize
Aerospace
Toolbox
Code generation for this function requires the
Aerospace Blockset software.
radareqpow
Phased Array
System Toolbox
Does not support variable-size inputs.
radareqrng
Phased Array
System Toolbox
Does not support variable-size inputs.
radareqsnr
Phased Array
System Toolbox
Does not support variable-size inputs.
radarvcd
Phased Array
System Toolbox
Does not support variable-size inputs.
radialspeed
Phased Array
System Toolbox
Does not support variable-size inputs.
rand
MATLAB
• classname must be a built-in MATLAB
numeric type. Does not invoke the static
rand method for other classes. For example,
rand(sz,'myclass’) does not invoke
myclass.rand(sz).
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
randg
Statistics Toolbox —
randi
MATLAB
• classname must be a built-in MATLAB
numeric type. Does not invoke the static
randi method for other classes. For example,
randi(imax,sz,'myclass’) does not
invoke myclass.randi(imax,sz).
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
randn
MATLAB
• classname must be a built-in MATLAB
numeric type. Does not invoke the static
randn method for other classes. For example,
randn(sz,'myclass’) does not invoke
myclass.randn(sz).
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
random
Statistics Toolbox —
randperm
MATLAB
—
range
Fixed-Point
Designer
—
range2beat
Phased Array
System Toolbox
Does not support variable-size inputs.
range2bw
Phased Array
System Toolbox
Does not support variable-size inputs.
range2time
Phased Array
System Toolbox
Does not support variable-size inputs.
rangeangle
Phased Array
System Toolbox
Does not support variable-size inputs.
rank
MATLAB
—
raylcdf
Statistics Toolbox —
raylinv
Statistics Toolbox —
raylpdf
Statistics Toolbox —
raylrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
raylstat
Statistics Toolbox —
rcond
MATLAB
—
4-106
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Specifying constants
Name
Product
Remarks and Limitations
rcosdesign
Signal Processing All inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
rdcoupling
Phased Array
System Toolbox
Does not support variable-size inputs.
rdivide
MATLAB
—
rdivide
Fixed-Point
Designer
—
real
MATLAB
—
real
Fixed-Point
Designer
—
reallog
MATLAB
—
realmax
MATLAB
—
realmax
Fixed-Point
Designer
—
realmin
MATLAB
—
realmin
Fixed-Point
Designer
—
realpow
MATLAB
—
realsqrt
MATLAB
—
rectint
MATLAB
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
rectwin
Signal Processing All inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
reinterpretcast
Fixed-Point
Designer
—
rem
MATLAB
• Performs the arithmetic using the output
class. Results might not match MATLAB due
to differences in rounding errors.
• If one of the inputs has type int64 or
uint64, then both inputs must have the
same type.
removefimath
Fixed-Point
Designer
—
repmat
MATLAB
—
repmat
Fixed-Point
Designer
The dimensions argument must be a built-in
type; it cannot be a fi object.
resample
Signal Processing All inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
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Specifying constants
Name
Product
Remarks and Limitations
rescale
Fixed-Point
Designer
—
reshape
MATLAB
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
reshape
Fixed-Point
Designer
—
return
MATLAB
—
rgb2ycbcr
Image Processing —
Toolbox
rng
MATLAB
• For library code generation targets,
executable code generation targets, and MEX
targets with extrinsic calls disabled:
• Does not support the 'shuffle' input.
• For the generator input, supports
'twister', 'v4', and 'v5normal'.
For these targets, the output of s=rng in the
generated code differs from the MATLAB
output. You cannot return the output of
s=rng from the generated code and pass it to
rng in MATLAB.
• For MEX targets, if extrinsic calls are
enabled, you cannot access the data in the
structure returned by rng.
rocpfa
rocsnr
rootmusicdoa
Phased Array
System Toolbox
• Does not support variable-size inputs.
Phased Array
System Toolbox
• Does not support variable-size inputs.
Phased Array
System Toolbox
Does not support variable-size inputs.
• The NonfluctuatingNoncoherent signal
type is not supported.
• Does not support the
NonfluctuatingNoncoherent signal type.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
roots
MATLAB
• Output is variable size.
• Output is complex.
• Roots are not always in the same order as
MATLAB.
• Roots of poorly conditioned polynomials do
not always match MATLAB.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
rosser
MATLAB
—
rot90
MATLAB
—
rot90
Fixed-Point
Designer
In the syntax rot90(A,k), the argument k
must be a built-in type; it cannot be a fi object.
rotx
Phased Array
System Toolbox
Does not support variable-size inputs.
roty
Phased Array
System Toolbox
Does not support variable-size inputs.
rotz
Phased Array
System Toolbox
Does not support variable-size inputs.
round
MATLAB
—
round
Fixed-Point
Designer
—
rsf2csf
MATLAB
—
schur
MATLAB
Can return a different Schur decomposition in
generated code than in MATLAB.
sec
MATLAB
—
secd
MATLAB
• In some cases, returns -Inf when MATLAB
returns Inf.
• In some cases, returns Inf when MATLAB
returns -Inf.
sech
MATLAB
—
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Specifying constants
Name
Product
Remarks and Limitations
selectStrongestBbox
Computer Vision Compile-time constant input: No restriction
System Toolbox
Supports MATLAB Function block: No
sensorcov
Phased Array
System Toolbox
Does not support variable-size inputs.
sensorsig
Phased Array
System Toolbox
Does not support variable-size inputs.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
setdiff
MATLAB
• When you do not specify the 'rows' option:
• Inputs A and B must be vectors. If you
specify the 'legacy' option, inputs A and
B must be row vectors.
• The first dimension of a variable-size
row vector must have fixed length 1.
The second dimension of a variable-size
column vector must have fixed length 1.
• Do not use [] to represent the empty set.
Use a 1-by-0 or 0-by-1 input, for example,
zeros(1,0), to represent the empty set.
• If you specify the 'legacy' option, empty
outputs are row vectors, 1-by-0, never 0by-0.
• When you specify both the 'legacy' and
'rows' options, the output ia is a column
vector. If ia is empty, it is 0-by-1, never 0by-0, even if the output C is 0-by-0.
• When the setOrder is 'sorted' or when
you specify the 'legacy' option, the inputs
must already be sorted in ascending order.
The first output, C, is sorted in ascending
order.
• Complex inputs must be single or double.
• When one input is complex and the other
input is real, do one of the following:
• Set setOrder to 'stable'.
• Sort the real input in complex
ascending order (by absolute
value). Suppose the real input
is x. Use sort(complex(x))or
sortrows(complex(x)).
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Specifying constants
Name
Product
Remarks and Limitations
setfimath
Fixed-Point
Designer
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
setxor
MATLAB
• When you do not specify the 'rows' option:
• Inputs A and B must be vectors with
the same orientation. If you specify the
'legacy' option, inputs A and B must be
row vectors.
• The first dimension of a variable-size
row vector must have fixed length 1.
The second dimension of a variable-size
column vector must have fixed length 1.
• The input [] is not supported. Use a
1-by-0 or 0-by-1 input, for example ,
zeros(1,0), to represent the empty set.
• If you specify the 'legacy' option, empty
outputs are row vectors, 1-by-0, never 0by-0.
• When you specify both the 'legacy' option
and the 'rows' option, the outputs ia and
ib are column vectors. If these outputs are
empty, they are 0-by-1, never 0-by-0, even if
the output C is 0-by-0.
• When the setOrder is 'sorted' or when
you specify the 'legacy' flag, the inputs
must already be sorted in ascending order.
The first output, C, is sorted in ascending
order.
• Complex inputs must be single or double.
• When one input is complex and the other
input is real, do one of the following:
• Set setOrder to 'stable'.
• Sort the real input in complex
ascending order (by absolute
value). Suppose the real input
is x. Use sort(complex(x))or
sortrows(complex(x)).
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Specifying constants
Name
Product
Remarks and Limitations
sfi
Fixed-Point
Designer
• All properties related to data type must be
constant for code generation.
sgolay
Signal Processing All inputs must be constant. Expressions or
Toolbox
variables are allowed if their values do not
change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
shiftdim
MATLAB
• Second argument must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
shiftdim
Fixed-Point
Designer
The dimensions argument must be a built-in
type; it cannot be a fi object.
shnidman
Phased Array
System Toolbox
Does not support variable-size inputs.
sign
MATLAB
—
sign
Fixed-Point
Designer
—
sin
MATLAB
—
sin
Fixed-Point
Designer
—
sind
MATLAB
—
single
MATLAB
—
single
Fixed-Point
Designer
—
sinh
MATLAB
—
size
MATLAB
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
size
Fixed-Point
Designer
—
skewness
Statistics Toolbox —
sort
MATLAB
If the input is a complex type, sort orders the
output according to absolute value. When x is a
complex type that has all zero imaginary parts,
use sort(real(x)) to compute the sort order
for real types. See “Code Generation for Complex
Data”.
sort
Fixed-Point
Designer
The dimensions argument must be a built-in
type; it cannot be a fi object.
sortrows
MATLAB
If the input is a complex type, sortrows orders
the output according to absolute value. When
x is a complex type that has all zero imaginary
parts, use sortrows(real(x)) to compute the
sort order for real types. See “Code Generation
for Complex Data”.
sosfilt
Signal Processing —
Toolbox
speed2dop
Phased Array
System Toolbox
Does not support variable-size inputs.
sph2cart
MATLAB
—
sph2cartvec
Phased Array
System Toolbox
Does not support variable-size inputs.
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Specifying constants
Name
Product
Remarks and Limitations
spline
MATLAB
• Input x must be strictly increasing.
• Does not remove Y entries with NaN values.
• Does not report an error for infinite
endslopes in Y.
• If you generate code for the pp =
spline(x,Y) syntax, you cannot input pp
to the ppval function in MATLAB. To create
a MATLAB pp structure from a pp structure
created by the code generation software:
• In code generation, use unmkpp to return
the piecewise polynomial details to
MATLAB.
• In MATLAB, use mkpp to create the pp
structure.
spsmooth
Phased Array
System Toolbox
Does not support variable-size inputs.
squeeze
MATLAB
—
squeeze
Fixed-Point
Designer
—
sqrt
MATLAB
• Generates an error during simulation and
returns NaN in generated code when the
input value x is real, but the output should
be complex. To get the complex result,
make the input value complex by passing in
complex(x).
sqrt
Fixed-Point
Designer
• Complex and [Slope Bias] inputs error out.
sqrtm
MATLAB
—
std
MATLAB
“Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
steervec
Phased Array
System Toolbox
Does not support variable-size inputs.
• Negative inputs yield a 0 result.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
stokes
Phased Array
System Toolbox
• Does not support variable-size inputs.
storedInteger
Fixed-Point
Designer
—
storedIntegerToDouble
Fixed-Point
Designer
—
str2double
MATLAB
• Does not support cell arrays.
• Supported only when output arguments are
specified.
• Always returns a complex result.
str2func
MATLAB
String must be constant/known at compile time.
strcmp
MATLAB
—
strcmpi
MATLAB
• Input values from the char class must be in
the range 0-127.
strel
Image Processing Input arguments must be compile-time
Toolbox
constants. The following methods are not
supported for code generation: getsequence,
reflect, translate, disp, display,
loadobj. When generating code, you can only
specify single objects—arrays of objects are not
supported.
stretchfreq2rng
Phased Array
System Toolbox
stretchlim
Image Processing Generated code for this function uses a
Toolbox
precompiled “platform-specific shared library”.
strfind
MATLAB
Does not support variable-size inputs.
• Does not support cell arrays.
• If pattern does not exist in str, returns
zeros(1,0) not []. To check for an empty
return, use isempty.
• Inputs must be character row vectors.
strjust
MATLAB
—
strncmp
MATLAB
—
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Specifying constants
Name
Product
Remarks and Limitations
strncmpi
MATLAB
• Input values from the char class must be in
the range 0-127.
strrep
MATLAB
• Does not support cell arrays.
• If oldSubstr does not exist in origStr,
returns blanks(0). To check for an empty
return, use isempty.
• Inputs must be character row vectors.
strtok
MATLAB
—
strtrim
MATLAB
• Supports only inputs from the char class.
• Input values must be in the range 0-127.
struct
MATLAB
—
structfun
MATLAB
• Does not support the ErrorHandler option.
• The number of outputs must be less than or
equal to three.
sub
Fixed-Point
Designer
Code generation in MATLAB does not support
the syntax F.sub(a,b). You must use the
syntax sub(F,a,b).
sub2ind
MATLAB
• The first argument should be a valid size
vector. Size vectors for arrays with more than
intmax elements are not supported.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
subsasgn
Fixed-Point
Designer
—
subspace
MATLAB
—
subsref
Fixed-Point
Designer
—
sum
MATLAB
• Specify dim as a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
sum
Fixed-Point
Designer
• Variable-sized inputs are only supported
when the SumMode property of the governing
fimath is set to Specify precision or
Keep LSB.
surfacegamma
Phased Array
System Toolbox
Does not support variable-size inputs.
surfclutterrcs
Phased Array
System Toolbox
Does not support variable-size inputs.
SURFPoints
Computer Vision Compile-time constant input: No restrictions.
System Toolbox
Supports MATLAB Function block: No
To index locations with this object, use
the syntax: points.Location(idx,:),
for points object. See
visionRecovertformCodeGeneration_kernel.m,
which is used in the “Introduction to Code
Generation with Feature Matching and
Registration” example.
svd
MATLAB
Uses a different SVD implementation than
MATLAB. Because the singular value
decomposition is not unique, left and right
singular vectors might differ from those
computed by MATLAB.
swapbytes
MATLAB
Inheritance of the class of the input to
swapbytes in a MATLAB Function block is
supported only when the class of the input is
double. For non-double inputs, the input port
data types must be specified, not inherited.
switch, case, otherwise MATLAB
• If all case expressions are scalar integer
values, generates a C switch statement.
At run time, if the switch value is not an
integer, generates an error.
• When the case expressions contain
noninteger or nonscalar values, the code
generation software generates C if
statements in place of a C switch statement.
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Specifying constants
Name
Product
Remarks and Limitations
systemp
Phased Array
System Toolbox
Does not support variable-size inputs.
tan
MATLAB
—
tand
MATLAB
• In some cases, returns -Inf when MATLAB
returns Inf.
• In some cases, returns Inf when MATLAB
returns -Inf.
tanh
MATLAB
—
taylorwin
Signal Processing Inputs must be constant
Toolbox
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
tcdf
Statistics Toolbox —
tf2ca
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
tf2cl
DSP System
Toolbox
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
time2range
Phased Array
System Toolbox
Does not support variable-size inputs.
times
MATLAB
Multiplication of pure imaginary numbers by
non-finite numbers might not match MATLAB.
The code generation software does not specialize
multiplication by pure imaginary numbers—
it does not eliminate calculations with the zero
real part. For example, (Inf + 1i)*1i =
(Inf*0 – 1*1) + (Inf*1 + 1*0)i = NaN
+ Infi.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
times
Fixed-Point
Designer
• Any non-fi input must be constant; that is,
its value must be known at compile time so
that it can be cast to a fi object.
• When you provide complex inputs to the
times function inside a MATLAB Function
block, you must declare the input as complex
before running the simulation. To do so, go
to the Ports and data manager and set
the Complexity parameter for all known
complex inputs to On.
tinv
Statistics Toolbox —
toeplitz
MATLAB
tpdf
Statistics Toolbox —
trace
MATLAB
—
transpose
MATLAB
—
transpose
Fixed-Point
Designer
—
trapz
MATLAB
• If supplied, dim must be a constant.
—
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
triang
Signal Processing Inputs must be constant
Toolbox
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
tril
MATLAB
• If supplied, the argument representing the
order of the diagonal matrix must be a real
and scalar integer value.
tril
Fixed-Point
Designer
• If supplied, the index, k, must be a real and
scalar integer value that is not a fi object.
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Specifying constants
Name
Product
Remarks and Limitations
triu
MATLAB
• If supplied, the argument representing the
order of the diagonal matrix must be a real
and scalar integer value.
triu
Fixed-Point
Designer
• If supplied, the index, k, must be a real and
scalar integer value that is not a fi object.
trnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
true
MATLAB
• Dimensions must be real, nonnegative,
integers.
tstat
Statistics Toolbox —
tukeywin
Signal Processing Inputs must be constant.
Toolbox
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
typecast
MATLAB
• Value of string input argument type must be
lowercase.
• When you use typecast with inheritance of
input port data types in MATLAB Function
blocks, you can receive a size error. To avoid
this error, specify the block's input port data
types explicitly.
• Integer input or result classes must map
directly to a C type on the target hardware.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
ufi
Fixed-Point
Designer
• All properties related to data type must be
constant for code generation.
uint8, uint16, uint32,
uint64
MATLAB
No integer overflow detection for int64 in
MEX or MATLAB Function block simulation on
Windows 32-bit platforms.
uint8, uint16, uint32,
uint64
Fixed-Point
Designer
—
uminus
MATLAB
—
uminus
Fixed-Point
Designer
—
unidcdf
Statistics Toolbox —
unidinv
Statistics Toolbox —
unidpdf
Statistics Toolbox —
unidrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
unidstat
Statistics Toolbox —
unifcdf
Statistics Toolbox —
unifinv
Statistics Toolbox —
unifpdf
Statistics Toolbox —
unifrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
unifstat
Statistics Toolbox —
unigrid
Phased Array
System Toolbox
Does not support variable-size inputs.
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Specifying constants
Name
Product
Remarks and Limitations
union
MATLAB
• When you do not specify the 'rows' option:
• Inputs A and B must be vectors with
the same orientation. If you specify the
'legacy' option, inputs A and B must be
row vectors.
• The first dimension of a variable-size
row vector must have fixed length 1.
The second dimension of a variable-size
column vector must have fixed length 1.
• The input [] is not supported. Use a
1-by-0 or 0-by-1 input, for example ,
zeros(1,0), to represent the empty set.
• If you specify the 'legacy' option, empty
outputs are row vectors, 1-by-0, never 0by-0.
• When you specify both the 'legacy' option
and the 'rows' option, the outputs ia and
ib are column vectors. If these outputs are
empty, they are 0-by-1, never 0-by-0, even if
the output C is 0-by-0.
• When the setOrder is 'sorted' or when
you specify the 'legacy' option, the inputs
must already be sorted in ascending order.
The first output, C, is sorted in ascending
order.
• Complex inputs must be single or double.
• When one input is complex and the other
input is real, do one of the following:
• Set setOrder to 'stable'.
• Sort the real input in complex
ascending order (by absolute
value). Suppose the real input
is x. Use sort(complex(x))or
sortrows(complex(x)).
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
unique
MATLAB
• When you do not specify the'rows' option:
• The input A must be a vector. If you
specify the 'legacy' option, the input A
must be a row vector.
• The first dimension of a variable-size
row vector must have fixed length 1.
The second dimension of a variable-size
column vector must have fixed length 1.
• The input [] is not supported. Use a
1-by-0 or 0-by-1 input, for example,
zeros(1,0), to represent the empty set.
• If you specify the 'legacy' option, empty
outputs are row vectors, 1-by-0, never 0by-0.
• When you specify both the 'rows' option
and the 'legacy'option, outputs ia and
ic are column vectors. If these outputs are
empty, they are 0-by-1, even if the output C is
0-by-0.
• When the setOrder is 'sorted' or when
you specify the 'legacy' option, the input
A must already be sorted in ascending order.
The first output, C, is sorted in ascending
order.
• Complex inputs must be single or double.
unmkpp
MATLAB
• pp must be a valid piecewise polynomial
structure created by mkpp, spline, or
pchip in MATLAB or by the code generation
software.
• Does not support pp structures created by
interp1 in MATLAB.
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Specifying constants
Name
Product
Remarks and Limitations
unwrap
MATLAB
• Row vector input is only supported when the
first two inputs are vectors and nonscalar
• Performs arithmetic in the output class.
Hence, results might not match MATLAB
due to different rounding errors
upfirdn
Signal Processing • Code generation for this function requires the
Toolbox
DSP System Toolbox software.
• Filter coefficients, upsampling factor, and
downsampling factor must be constants.
Expressions or variables are allowed if their
values do not change.
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command
Line”.
uplus
MATLAB
—
uplus
Fixed-Point
Designer
—
upper
MATLAB
• Supports only char inputs.
• Input values must be in the range 0-127.
upperbound
Fixed-Point
Designer
—
upsample
Signal Processing Either declare input n as constant, or use the
Toolbox
assert function in the calling function to set
upper bounds for n. For example,
assert(n<10)
uv2azel
Phased Array
System Toolbox
Does not support variable-size inputs.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
uv2azelpat
Phased Array
System Toolbox
Does not support variable-size inputs.
uv2phitheta
Phased Array
System Toolbox
Does not support variable-size inputs.
uv2phithetapat
Phased Array
System Toolbox
Does not support variable-size inputs.
val2ind
Phased Array
System Toolbox
Does not support variable-size inputs.
vander
MATLAB
—
var
MATLAB
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code
Generation of Toolbox Functions”
vertcat
Fixed-Point
Designer
—
vision.AlphaBlender
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Autocorrelator
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Autothresholder
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.BlobAnalysis
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.BoundaryTracer
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.CascadeObjectDetector
Computer Vision Supports MATLAB Function block: No
System Toolbox
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
vision.ChromaResampler
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.ColorSpaceConverter
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
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Specifying constants
Name
Product
Remarks and Limitations
vision.ConnectedComponentLabeler
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Convolver
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.ContrastAdjuster Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Crosscorrelator
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.DemosaicInterpolator
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.DCT
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Deinterlacer
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Deployable
Computer Vision Generates code on Windows host only.
System Toolbox
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
vision.DeployableVideo- Computer Vision Supports MATLAB Function block: Yes
System Toolbox
Player
Generates code on Linux® and Windows
platforms
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
vision.EdgeDetector
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.FFT
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.ForegroundDetector
Computer Vision Supports MATLAB Function block: No
System Toolbox
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
vision.GammaCorrector
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.GeometricRotator Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.GeometricScaler
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.GeometricShearer Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.GeometricTransformer
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.GeometricTranslator
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Histogram
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.HistogramBasedTracker
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.HistogramEqualizer
vision.HoughLines
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.HoughTransform
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.IDCT
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.IFFT
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.ImageComplementer
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.ImageFilter
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.ImageDataTypeConverter
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
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Specifying constants
Name
Product
Remarks and Limitations
vision.ImagePadder
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.KalmanFilter
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.LocalMaximaFinder
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.MarkerInserter
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Maximum
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Median
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.MedianFilter
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Mean
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Minimum
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.MorphologicalClose
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.MorphologicalDilate
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.MorphologicalErode
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.MorphologicalOpen
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.PeopleDetector
Computer Vision Supports MATLAB Function block: No
System Toolbox
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
vision.PointTracker
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Product
Remarks and Limitations
vision.PSNR
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Pyramid
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.ShapeInserter
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.StandardDeviation
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.TemplateMatcher
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.TextInserter
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.Variance
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
“System Objects in MATLAB Code Generation”
vision.VideoFileReader
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
vision.VideoFileWriter
Computer Vision Supports MATLAB Function block: Yes
System Toolbox
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
wblcdf
Statistics Toolbox —
wblinv
Statistics Toolbox —
wblpdf
Statistics Toolbox —
wblrnd
Statistics Toolbox Can return a different sequence of numbers than
MATLAB if either of the following is true:
• The output is nonscalar.
• An input parameter is invalid for the
distribution.
wblstat
Statistics Toolbox —
while
MATLAB
—
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Specifying constants
Name
Product
Remarks and Limitations
wilkinson
MATLAB
—
xcorr
Signal Processing —
Toolbox
xor
MATLAB
ycbcr2rgb
Image Processing —
Toolbox
yulewalk
Signal Processing If specified, the order of recursion must be a
Toolbox
constant. Expressions or variables are allowed if
their values do not change.
—
Specifying constants
To specify a constant input for codegen, use
coder.Constant. For more information, see
“Specify Constant Inputs at the Command Line”.
zeros
MATLAB
• Dimensions must be real, nonnegative,
integers.
zp2tf
MATLAB
—
zscore
Statistics Toolbox —
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4
Functions, Classes, and System Objects Supported for Code Generation
Functions and Objects Supported for C and C++ Code Generation
— Category List
You can generate efficient C and C++ code for a subset of MATLAB built-in functions and
toolbox functions, classes, and System objects that you call from MATLAB code. These
functions, classes, and System objects are listed by MATLAB category or toolbox category
in the following tables.
For an alphabetical list of supported functions, classes, and System objects, see
“Functions and Objects Supported for C and C++ Code Generation — Alphabetical List”.
Note: For more information on code generation for fixed-point algorithms, refer to “Code
Acceleration and Code Generation from MATLAB”.
In this section...
“Aerospace Toolbox” on page 4-136
“Arithmetic Operations in MATLAB” on page 4-136
“Bit-Wise Operations MATLAB” on page 4-137
“Casting in MATLAB” on page 4-138
“Communications System Toolbox” on page 4-138
“Complex Numbers in MATLAB” on page 4-144
“Computer Vision System Toolbox” on page 4-144
“Control Flow in MATLAB” on page 4-153
“Data and File Management in MATLAB” on page 4-154
“Data Types in MATLAB” on page 4-157
“Desktop Environment in MATLAB” on page 4-158
“Discrete Math in MATLAB” on page 4-158
“DSP System Toolbox” on page 4-159
“Error Handling in MATLAB” on page 4-166
“Exponents in MATLAB” on page 4-167
“Filtering and Convolution in MATLAB” on page 4-167
“Fixed-Point Designer” on page 4-168
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Functions and Objects Supported for C and C++ Code Generation — Category List
In this section...
“HDL Coder” on page 4-178
“Histograms in MATLAB” on page 4-178
“Image Acquisition Toolbox” on page 4-178
“Image Processing in MATLAB” on page 4-178
“Image Processing Toolbox” on page 4-179
“Input and Output Arguments in MATLAB” on page 4-186
“Interpolation and Computational Geometry in MATLAB” on page 4-187
“Linear Algebra in MATLAB” on page 4-190
“Logical and Bit-Wise Operations in MATLAB” on page 4-191
“MATLAB Compiler” on page 4-191
“Matrices and Arrays in MATLAB” on page 4-192
“Neural Network Toolbox” on page 4-199
“Nonlinear Numerical Methods in MATLAB” on page 4-199
“Numerical Integration and Differentiation in MATLAB” on page 4-199
“Optimization Functions in MATLAB” on page 4-200
“Phased Array System Toolbox” on page 4-201
“Polynomials in MATLAB” on page 4-209
“Programming Utilities in MATLAB” on page 4-209
“Relational Operators in MATLAB” on page 4-209
“Rounding and Remainder Functions in MATLAB” on page 4-210
“Set Operations in MATLAB” on page 4-210
“Signal Processing in MATLAB” on page 4-215
“Signal Processing Toolbox” on page 4-216
“Special Values in MATLAB” on page 4-221
“Specialized Math in MATLAB” on page 4-221
“Statistics in MATLAB” on page 4-222
“Statistics Toolbox” on page 4-222
“String Functions in MATLAB” on page 4-231
“Structures in MATLAB” on page 4-233
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4
Functions, Classes, and System Objects Supported for Code Generation
In this section...
“Trigonometry in MATLAB” on page 4-233
Aerospace Toolbox
C and C++ code generation for the following Aerospace Toolbox quaternion functions
requires the Aerospace Blockset software.
Function
Remarks and Limitations
quatconj
—
quatdivide
—
quatinv
—
quatmod
—
quatmultiply
—
quatnorm
—
quatnormalize
—
Arithmetic Operations in MATLAB
See “Array vs. Matrix Operations” for detailed descriptions of the following operator
equivalent functions.
Function
Remarks and Limitations
ctranspose
—
idivide
• For efficient generated code, MATLAB rules for divide by zero are
supported only for the 'round' option.
isa
—
ldivide
—
minus
—
mldivide
—
mpower
—
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
mrdivide
—
mtimes
• Multiplication of pure imaginary numbers by non-finite numbers might
not match MATLAB. The code generation software does not specialize
multiplication by pure imaginary numbers—it does not eliminate
calculations with the zero real part. For example, (Inf + 1i)*1i =
(Inf*0 – 1*1) + (Inf*1 + 1*0)i = NaN + Infi.
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
plus
—
power
• Generates an error during simulation. When both X and Y are real,
but power(X,Y) is complex, returns NaN in the generated code. To
get the complex result, make the input value X complex by passing in
complex(X). For example, power(complex(X),Y).
• Generates an error during simulation. When both X and Y are real, but
X .^ Y is complex, returns NaN in generated code. To get the complex
result, make the input value X complex by using complex(X). For
example, complex(X).^Y.
rdivide
—
times
Multiplication of pure imaginary numbers by non-finite numbers might
not match MATLAB. The code generation software does not specialize
multiplication by pure imaginary numbers—it does not eliminate
calculations with the zero real part. For example, (Inf + 1i)*1i =
(Inf*0 – 1*1) + (Inf*1 + 1*0)i = NaN + Infi.
transpose
—
uminus
—
uplus
—
Bit-Wise Operations MATLAB
Function
Remarks and Limitations
flintmax
—
swapbytes
Inheritance of the class of the input to swapbytes in a MATLAB Function
block is supported only when the class of the input is double. For nondouble inputs, the input port data types must be specified, not inherited.
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4
Functions, Classes, and System Objects Supported for Code Generation
Casting in MATLAB
Function
Remarks and Limitations
cast
—
char
—
class
—
double
—
int8, int16, int32, No integer overflow detection for int64 in MEX or MATLAB Function
int64
block simulation on Windows 32-bit platforms.
logical
—
single
—
typecast
• Value of string input argument type must be lowercase.
• When you use typecast with inheritance of input port data types in
MATLAB Function blocks, you can receive a size error. To avoid this
error, specify the block's input port data types explicitly.
• Integer input or result classes must map directly to a C type on the
target hardware.
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
uint8, uint16,
uint32, uint64
No integer overflow detection for int64 in MEX or MATLAB Function
block simulation on Windows 32-bit platforms.
Communications System Toolbox
C and C++ code generation for the following functions and System objects requires the
Communications System Toolbox software.
Name
Remarks and Limitations
Input and Output
comm.BarkerCode
“System Objects in MATLAB Code Generation”
comm.GoldSequence
“System Objects in MATLAB Code Generation”
comm.HadamardCode
“System Objects in MATLAB Code Generation”
comm.KasamiSequence
“System Objects in MATLAB Code Generation”
comm.WalshCode
“System Objects in MATLAB Code Generation”
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
comm.PNSequence
“System Objects in MATLAB Code Generation”
lteZadoffChuSeq
—
Signal and Delay Management
bi2de
—
de2bi
—
Display and Visual Analysis
comm.ConstellationDiagram
“System Objects in MATLAB Code Generation”
dsp.ArrayPlot
“System Objects in MATLAB Code Generation”
dsp.SpectrumAnalyzer
“System Objects in MATLAB Code Generation”
dsp.TimeScope
“System Objects in MATLAB Code Generation”
Source Coding
comm.DifferentialDecoder
“System Objects in MATLAB Code Generation”
comm.DifferentialEncoder
“System Objects in MATLAB Code Generation”
Cyclic Redundancy Check Coding
comm.CRCDetector
“System Objects in MATLAB Code Generation”
comm.CRCGenerator
“System Objects in MATLAB Code Generation”
comm.HDLCRCDetector
“System Objects in MATLAB Code Generation”
comm.HDLCRCGenerator
“System Objects in MATLAB Code Generation”
BCH Codes
comm.BCHDecoder
“System Objects in MATLAB Code Generation”
comm.BCHEncoder
“System Objects in MATLAB Code Generation”
Reed-Solomon Codes
comm.RSDecoder
“System Objects in MATLAB Code Generation”
comm.RSEncoder
“System Objects in MATLAB Code Generation”
comm.HDLRSDecoder
“System Objects in MATLAB Code Generation”
comm.HDLRSEncoder
“System Objects in MATLAB Code Generation”
LDPC Codes
comm.LDPCDecoder
“System Objects in MATLAB Code Generation”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
comm.LDPCEncoder
“System Objects in MATLAB Code Generation”
Convolutional Coding
comm.APPDecoder
“System Objects in MATLAB Code Generation”
comm.ConvolutionalEncoder
“System Objects in MATLAB Code Generation”
comm.TurboDecoder
“System Objects in MATLAB Code Generation”
comm.TurboEncoder
“System Objects in MATLAB Code Generation”
comm.ViterbiDecoder
“System Objects in MATLAB Code Generation”
istrellis
—
poly2trellis
—
Signal Operations
comm.Descrambler
“System Objects in MATLAB Code Generation”
comm.Scrambler
“System Objects in MATLAB Code Generation”
Interleaving
comm.AlgebraicDeinterleaver
“System Objects in MATLAB Code Generation”
comm.AlgebraicInterleaver
“System Objects in MATLAB Code Generation”
comm.BlockDeinterleaver
“System Objects in MATLAB Code Generation”
comm.BlockInterleaver
“System Objects in MATLAB Code Generation”
comm.ConvolutionalDeinterleaver
“System Objects in MATLAB Code Generation”
comm.ConvolutionalInterleaver
“System Objects in MATLAB Code Generation”
comm.HelicalDeinterleaver
“System Objects in MATLAB Code Generation”
comm.HelicalInterleaver
“System Objects in MATLAB Code Generation”
comm.MatrixDeinterleaver
“System Objects in MATLAB Code Generation”
comm.MatrixInterleaver
“System Objects in MATLAB Code Generation”
comm.MatrixHelicalScanDeinterleaver
“System Objects in MATLAB Code Generation”
comm.MatrixHelicalScanInterleaver
“System Objects in MATLAB Code Generation”
comm.MultiplexedDeinterleaver
“System Objects in MATLAB Code Generation”
comm.MultiplexedInterleaver
“System Objects in MATLAB Code Generation”
Frequency Modulation
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
comm.FSKDemodulator
“System Objects in MATLAB Code Generation”
comm.FSKModulator
“System Objects in MATLAB Code Generation”
Phase Modulation
comm.BPSKDemodulator
“System Objects in MATLAB Code Generation”
comm.BPSKModulator
“System Objects in MATLAB Code Generation”
comm.DBPSKDemodulator
“System Objects in MATLAB Code Generation”
comm.DBPSKModulator
“System Objects in MATLAB Code Generation”
comm.DPSKDemodulator
“System Objects in MATLAB Code Generation”
comm.DPSKModulator
“System Objects in MATLAB Code Generation”
comm.DQPSKDemodulator
“System Objects in MATLAB Code Generation”
comm.DQPSKModulator
“System Objects in MATLAB Code Generation”
comm.OQPSKDemodulator
“System Objects in MATLAB Code Generation”
comm.OQPSKModulator
“System Objects in MATLAB Code Generation”
comm.PSKDemodulator
“System Objects in MATLAB Code Generation”
comm.PSKModulator
“System Objects in MATLAB Code Generation”
comm.QPSKDemodulator
“System Objects in MATLAB Code Generation”
comm.QPSKModulator
“System Objects in MATLAB Code Generation”
Amplitude Modulation
comm.GeneralQAMDemodulator
“System Objects in MATLAB Code Generation”
comm.GeneralQAMModulator
“System Objects in MATLAB Code Generation”
comm.PAMDemodulator
“System Objects in MATLAB Code Generation”
comm.PAMModulator
“System Objects in MATLAB Code Generation”
comm.RectangularQAMDemodulator
“System Objects in MATLAB Code Generation”
comm.RectangularQAMModulator
“System Objects in MATLAB Code Generation”
Continuous Phase Modulation
comm.CPFSKDemodulator
“System Objects in MATLAB Code Generation”
comm.CPFSKModulator
“System Objects in MATLAB Code Generation”
comm.CPMDemodulator
“System Objects in MATLAB Code Generation”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
comm.CPMModulator
“System Objects in MATLAB Code Generation”
comm.GMSKDemodulator
“System Objects in MATLAB Code Generation”
comm.GMSKModulator
“System Objects in MATLAB Code Generation”
comm.MSKDemodulator
“System Objects in MATLAB Code Generation”
comm.MSKModulator
“System Objects in MATLAB Code Generation”
Trellis Coded Modulation
comm.GeneralQAMTCMDemodulator
“System Objects in MATLAB Code Generation”
comm.GeneralQAMTCMModulator
“System Objects in MATLAB Code Generation”
comm.PSKTCMDemodulator
“System Objects in MATLAB Code Generation”
comm.PSKTCMModulator
“System Objects in MATLAB Code Generation”
comm.RectangularQAMTCMDemodulator
“System Objects in MATLAB Code Generation”
comm.RectangularQAMTCMModulator
“System Objects in MATLAB Code Generation”
Orthogonal Frequency-Division Modulation
comm.OFDMDemodulator
“System Objects in MATLAB Code Generation”
comm.OFDMModulator
“System Objects in MATLAB Code Generation”
Filtering
comm.IntegrateAndDumpFilter
“System Objects in MATLAB Code Generation”
comm.RaisedCosineReceiveFilter
“System Objects in MATLAB Code Generation”
comm.RaisedCosineTransmitFilter
“System Objects in MATLAB Code Generation”
Carrier Phase Synchronization
comm.CPMCarrierPhaseSynchronizer
“System Objects in MATLAB Code Generation”
comm.PSKCarrierPhaseSynchronizer
“System Objects in MATLAB Code Generation”
Timing Phase Synchronization
comm.EarlyLateGateTimingSynchronizer
“System Objects in MATLAB Code Generation”
comm.GardnerTimingSynchronizer
“System Objects in MATLAB Code Generation”
comm.GMSKTimingSynchronizer
“System Objects in MATLAB Code Generation”
comm.MSKTimingSynchronizer
“System Objects in MATLAB Code Generation”
comm.MuellerMullerTimingSynchronizer
“System Objects in MATLAB Code Generation”
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
Synchronization Utilities
“System Objects in MATLAB Code Generation”
comm.DiscreteTimeVCO
Equalization
“System Objects in MATLAB Code Generation”
comm.MLSEEqualizer
MIMO
comm.LTEMIMOChannel
“System Objects in MATLAB Code Generation”
comm.MIMOChannel
“System Objects in MATLAB Code Generation”
comm.OSTBCCombiner
“System Objects in MATLAB Code Generation”
comm.OSTBCEncoder
“System Objects in MATLAB Code Generation”
comm.SphereDecoder
“System Objects in MATLAB Code Generation”
Channel Modeling and RF Impairments
comm.AGC
“System Objects in MATLAB Code Generation”
comm.AWGNChannel
“System Objects in MATLAB Code Generation”
comm.BinarySymmetricChannel
“System Objects in MATLAB Code Generation”
comm.IQImbalanceCompensator
“System Objects in MATLAB Code Generation”
comm.LTEMIMOChannel
“System Objects in MATLAB Code Generation”
comm.MemorylessNonlinearity
“System Objects in MATLAB Code Generation”
comm.MIMOChannel
“System Objects in MATLAB Code Generation”
comm.PhaseFrequencyOffset
“System Objects in MATLAB Code Generation”
comm.PhaseNoise
“System Objects in MATLAB Code Generation”
comm.RayleighChannel
“System Objects in MATLAB Code Generation”
comm.RicianChannel
“System Objects in MATLAB Code Generation”
comm.ThermalNoise
“System Objects in MATLAB Code Generation”
comm.PSKCoarseFrequencyEstimator
“System Objects in MATLAB Code Generation”
comm.QAMCoarseFrequencyEstimator
“System Objects in MATLAB Code Generation”
iqcoef2imbal
—
iqimbal2coef
—
Measurements and Analysis
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
comm.ACPR
“System Objects in MATLAB Code Generation”
comm.CCDF
“System Objects in MATLAB Code Generation”
comm.ErrorRate
“System Objects in MATLAB Code Generation”
comm.EVM
“System Objects in MATLAB Code Generation”
comm.MER
“System Objects in MATLAB Code Generation”
Complex Numbers in MATLAB
Function
Remarks and Limitations
complex
—
conj
—
imag
—
isnumeric
—
isreal
—
isscalar
—
real
—
unwrap
• Row vector input is only supported when the first two inputs are vectors
and nonscalar
• Performs arithmetic in the output class. Hence, results might not match
MATLAB due to different rounding errors
Computer Vision System Toolbox
C and C++ code generation for the following functions and System objects requires the
Computer Vision System Toolbox software.
Name
Remarks and Limitations
Feature Detection, Extraction, and Matching
BRISKPoints
Compile-time constant inputs: No restriction
Supports MATLAB Function block: No
To index locations with this object, use
the syntax: points.Location(idx,:),
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
for points object. See
visionRecovertformCodeGeneration_kernel.m,
which is used in the “Introduction to Code
Generation with Feature Matching and
Registration” example.
cornerPoints
Compile-time constant input: No restriction
Supports MATLAB Function block: No
To index locations with this object, use
the syntax: points.Location(idx,:),
for points object. See
visionRecovertformCodeGeneration_kernel.m,
which is used in the “Introduction to Code
Generation with Feature Matching and
Registration” example.
detectBRISKFeatures
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
detectFASTFeatures
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
detectHarrisFeatures
Compile-time constant input: FilterSize
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
detectMinEigenFeatures
Compile-time constant input: FilterSize
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
detectMSERFeatures
Compile-time constant input: No restriction
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
For code generation, the function outputs
regions.PixelList as an array. The region sizes
are defined in regions.Lengths.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
detectSURFFeatures
Compile-time constant input: No restrictions
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
extractFeatures
Generates platform-dependent library: Yes for
BRISK, FREAK, and SURF methods only.
Compile-time constant input: Method
Supports MATLAB Function block: Yes for Block
method only.
Generated code for this function uses a
precompiled platform-specific shared library.
extractHOGFeatures
Compile-time constant input: No
Supports MATLAB Function block: No
matchFeatures
Generates platform-dependent library: Yes for
MATLAB host.
Generates portable C code for non-host target.
Compile-time constant input: Method and
Metric.
Supports MATLAB Function block: Yes
MSERRegions
Compile-time constant input: No restrictions.
Supports MATLAB Function block: Yes
For code generation, you must specify both
the pixellist cell array and the length of each
array, as the second input. The object outputs,
regions.PixelList as an array. The region sizes
are defined in regions.Lengths.
Generated code for this function uses a
precompiled platform-specific shared library.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
SURFPoints
Compile-time constant input: No restrictions.
Supports MATLAB Function block: No
To index locations with this object, use
the syntax: points.Location(idx,:),
for points object. See
visionRecovertformCodeGeneration_kernel.m,
which is used in the “Introduction to Code
Generation with Feature Matching and
Registration” example.
vision.BoundaryTracer
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.EdgeDetector
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
Image Registration and Geometric Transformations
estimateGeometricTransform
Compile-time constant input: transformType
Supports MATLAB Function block: No
vision.GeometricRotator
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.GeometricScaler
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.GeometricShearer
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.GeometricTransformer
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.GeometricTranslator
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
Object Detection and Recognition
ocr
Compile-time constant input: TextLayout,
Language, and CharacterSet.
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
ocrText
Compile-time constant input: No restrictions.
Supports MATLAB Function block: No
vision.PeopleDetector
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
vision.CascadeObjectDetector
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
Tracking and Motion Estimation
assignDetectionsToTracks
Compile-time constant input: No restriction.
Supports MATLAB Function block: Yes
vision.BlockMatcher
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.ForegroundDetector
Supports MATLAB Function block: No
Generates platform-dependent library: Yes for
MATLAB host.
Generates portable C code for non-host target.
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
vision.HistogramBasedTracker
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.KalmanFilter
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.OpticalFlow
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.PointTracker
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.TemplateMatcher
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
Camera Calibration and Stereo Vision
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
bboxOverlapRatio
Compile-time constant input: No restriction
Supports MATLAB Function block: No
disparity
Compile-time constant input: Method.
Supports MATLAB Function block: No
Generated code for this function uses a
precompiled platform-specific shared library.
epipolarline
Compile-time constant input: No restrictions.
Supports MATLAB Function block: Yes
estimateFundamentalMatrix
Compile-time constant input: Method,
OutputClass, DistanceType, and
ReportRuntimeError.
Supports MATLAB Function block: Yes
estimateUncalibratedRectification
Compile-time constant input: transformType
Supports MATLAB Function block: No
isEpipoleInImage
Compile-time constant input: No restrictions.
Supports MATLAB Function block: Yes
lineToBorderPoints
Compile-time constant input: No restrictions.
Supports MATLAB Function block: Yes
selectStrongestBbox
Compile-time constant input: No restriction
Supports MATLAB Function block: No
Statistics
vision.Autocorrelator
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.BlobAnalysis
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.Crosscorrelator
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.Histogram
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.LocalMaximaFinder
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.Maximum
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
vision.Mean
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.Median
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.Minimum
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.PSNR
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.StandardDeviation
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.Variance
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
Morphological Operations
vision.ConnectedComponentLabeler
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.MorphologicalBottomHat
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.MorphologicalClose
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.MorphologicalDilate
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.MorphologicalErode
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.MorphologicalOpen
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.MorphologicalTopHat
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
Filters, Transforms, and Enhancements
integralImage
Supports MATLAB Function block: Yes
vision.Convolver
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
vision.ContrastAdjuster
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.DCT
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.Deinterlacer
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.EdgeDetector
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.FFT
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.HistogramEqualizer
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.HoughLines
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.HoughTransform
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.IDCT
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.IFFT
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.ImageFilter
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.MedianFilter
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.Pyramid
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
Video Loading, Saving, and Streaming
vision.DeployableVideoPlayer
Supports MATLAB Function block: Yes
Generates code on Linux and Windows
platforms
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
vision.VideoFileReader
Supports MATLAB Function block: Yes
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
vision.VideoFileWriter
Supports MATLAB Function block: Yes
Generated code for this function uses a
precompiled platform-specific shared library.
“System Objects in MATLAB Code Generation”
Color Space Formatting and Conversions
vision.Autothresholder
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.ChromaResampler
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.ColorSpaceConverter
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.DemosaicInterpolator
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.GammaCorrector
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.ImageComplementer
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.ImageDataTypeConverter
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.ImagePadder
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
Graphics
insertMarker
Compile-time constant input: marker
Supports MATLAB Function block: Yes
insertShape
Compile-time constant input: shape and
SmoothEdges
Supports MATLAB Function block: Yes
vision.AlphaBlender
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
vision.MarkerInserter
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.ShapeInserter
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
vision.TextInserter
Supports MATLAB Function block: Yes
“System Objects in MATLAB Code Generation”
Control Flow in MATLAB
Function
Remarks and Limitations
break
—
continue
—
end
—
for
—
if, elseif, else —
parfor
• Treated as a for-loop in a MATLAB Function block or when used with
fiaccel.
• See the parfor reference page in the MATLAB Coder documentation.
• “Generate Code with Parallel for-Loops (parfor)”.
return
—
switch, case,
otherwise
• If all case expressions are scalar integer values, generates a C switch
statement. At run time, if the switch value is not an integer, generates
an error.
• When the case expressions contain noninteger or nonscalar values, the
code generation software generates C if statements in place of a C
switch statement.
while
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Data and File Management in MATLAB
Function
Remarks and Limitations
computer
• Information about the computer on which the code generation software
is running.
• Use only when the code generation target is S-function (Simulation) or
MEX-function.
fclose
—
feof
—
fopen
• Does not support:
• machineformat, encoding, or fileID inputs
• message output
• fopen('all')
• If you disable extrinsic calls, you cannot return fileIDs created with
fopen to MATLAB or extrinsic functions. You can use such fileIDs
only internally.
• When generating C/C++ executables, static libraries, or dynamic
libraries, you can open up to 20 files.
• The generated code does not report errors from invalid file identifiers.
Write your own file open error handling in your MATLAB code. Test
whether fopen returns -1, which indicates that the file open failed. For
example:
...
fid = fopen(filename, 'r');
if fid == -1
% fopen failed
else
% fopen successful, okay to call fread
A = fread(fid);
...
• The behavior of the generated code for fread is compiler-dependent if
you:
1
Open a file using fopen with a permission of a+.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
2 Read the file using fread before calling an I/O function, such as
fseek or frewind, that sets the file position indicator.
fprintf
• Does not support:
• b and t subtypes on %u, %o %x, and %X formats.
• $ flag for reusing input arguments.
• printing arrays.
• There is no automatic casting. Input arguments must match their
format types for predictable results.
• Escaped characters are limited to the decimal range of 0–127.
• A call to fprintf with fileID equal to 1 or 2 becomes printf in the
generated C/C++ code in the following cases:
• The fprintf call is inside a parfor loop.
• Extrinsic calls are disabled.
• When the MATLAB behavior differs from the C compiler behavior,
fprintf matches the C compiler behavior in the following cases:
• The format specifier has a corresponding C format specifier, for
example, %e or %E.
• The fprintf call is inside a parfor loop.
• Extrinsic calls are disabled.
• When you call fprintf with the format specifier %s, do not put a null
character in the middle of the input string. Use fprintf(fid, '%c',
char(0)) to write a null character.
• When you call fprintf with an integer format specifier, the type of
the integer argument must be a type that the target hardware can
represent as a native C type. For example, if you call fprintf('%d',
int64(n)), the target hardware must have a native C type that
supports a 64-bit integer.
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
fread
• precision must be a constant.
• The source and output that precision specifies cannot have values
long, ulong, unsigned long, bitN, or ubitN.
• You cannot use the machineformat input.
• If the source or output that precision specifies is a C type, for
example, int, the target and production sizes for that type must:
• Match.
• Map directly to a MATLAB type.
• The source type that precision specifies must map directly to a C
type on the target hardware.
• If the fread call reads the entire file, all of the data must fit in the
largest array available for code generation.
• If sizeA is not constant or contains a nonfinite element, then dynamic
memory allocation is required.
• Treats a char value for source or output as a signed 8-bit integer. Use
values between 0 and 127 only.
• The generated code does not report file read errors. Write your own
file read error handling in your MATLAB code. Test that the number
of bytes read matches the number of bytes that you requested. For
example:
...
N = 100;
[vals, numRead] = fread(fid, N, '*double');
if numRead ~= N
% fewer elements read than expected
end
...
frewind
—
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
load
• Use only when generating MEX or code for Simulink simulation. To load
compile-time constants, use coder.load.
• Does not support use of the function without assignment to a
structure or array. For example, use S = load(filename), not
load(filename).
• The output S must be the name of a structure or array without any
subscripting. For example, S[i] = load('myFile.mat') is not
allowed.
• Arguments to load must be compile-time constant strings.
• Does not support loading objects.
• If the MAT-file contains unsupported constructs, use
load(filename,variables) to load only the supported constructs.
• You cannot use save in a function intended for code generation.
The code generation software does not support the save function.
Furthermore, you cannot use coder.extrinsic with save. Prior to
generating code, you can use save to save the workspace data to a MATfile.
You must use coder.varsize to explicitly declare variable-size data
loaded using the load function.
Data Types in MATLAB
Function
Remarks and Limitations
deal
—
iscell
—
isobject
—
nargchk
• Output structure does not include stack information.
Note: nargchk will be removed in a future release.
narginchk
—
nargoutchk
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
str2func
• String must be constant/known at compile time
structfun
• Does not support the ErrorHandler option.
• The number of outputs must be less than or equal to three.
Desktop Environment in MATLAB
Function
Remarks and Limitations
ismac
• Returns true or false based on the MATLAB version used for code
generation.
• Use only when the code generation target is S-function (Simulation)
or MEX-function.
• Returns true or false based on the MATLAB version you use for code
generation.
ispc
• Use only when the code generation target is S-function (Simulation)
or MEX-function.
• Returns true or false based on the MATLAB version used for code
generation.
isunix
• Use only when the code generation target is S-function (Simulation)
or MEX-function.
Discrete Math in MATLAB
Function
Remarks and Limitations
factor
• The maximum double precision input is 2^33.
• The maximum single precision input is 2^25.
• The input n cannot have type int64 or uint64.
gcd
—
isprime
• The maximum double precision input is 2^33.
• The maximum single precision input is 2^25.
• The input X cannot have type int64 or uint64.
lcm
—
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
nchoosek
• When the first input, x, is a scalar, nchoosek returns a binomial
coefficient. In this case, x must be a nonnegative integer. It cannot have
type int64 or uint64.
• When the first input, x, is a vector, nchoosek treats it as a set. In this
case, x can have type int64 or uint64.
• The second input, k, cannot have type int64 or uint64.
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
• The maximum double precision input is 2^32.
primes
• The maximum single precision input is 2^24.
• The input n cannot have type int64 or uint64.
DSP System Toolbox
C code generation for the following functions and System objects requires the DSP
System Toolbox license. Many DSP System Toolbox functions require constant inputs
for code generation. See “Define Constant Input Parameters in a Project” and“Specify
Constant Inputs at the Command Line”.
Name
Remarks and Limitations
Estimation
dsp.BurgAREstimator
“System Objects in MATLAB Code Generation”
dsp.BurgSpectrumEstimator
“System Objects in MATLAB Code Generation”
dsp.CepstralToLPC
“System Objects in MATLAB Code Generation”
dsp.CrossSpectrumEstimator
“System Objects in MATLAB Code Generation”
dsp.LevinsonSolver
“System Objects in MATLAB Code Generation”
dsp.LPCToAutocorrelation
“System Objects in MATLAB Code Generation”
dsp.LPCToCepstral
“System Objects in MATLAB Code Generation”
dsp.LPCToLSF
“System Objects in MATLAB Code Generation”
dsp.LPCToLSP
“System Objects in MATLAB Code Generation”
dsp.LPCToRC
“System Objects in MATLAB Code Generation”
dsp.LSFToLPC
“System Objects in MATLAB Code Generation”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
dsp.LSPToLPC
“System Objects in MATLAB Code Generation”
dsp.RCToAutocorrelation
“System Objects in MATLAB Code Generation”
dsp.RCToLPC
“System Objects in MATLAB Code Generation”
dsp.SpectrumEstimator
“System Objects in MATLAB Code Generation”
dsp.TransferFunctionEstimator
“System Objects in MATLAB Code Generation”
Filters
ca2tf
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
cl2tf
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
dsp.AdaptiveLatticeFilter
“System Objects in MATLAB Code Generation”
dsp.AffineProjectionFilter
“System Objects in MATLAB Code Generation”
dsp.AllpoleFilter
• “System Objects in MATLAB Code
Generation”
• Only the Denominator property is tunable
for code generation.
dsp.BiquadFilter
“System Objects in MATLAB Code Generation”
dsp.CICCompensationDecimator
“System Objects in MATLAB Code Generation”
dsp.CICCompensationInterpolator
“System Objects in MATLAB Code Generation”
dsp.CICDecimator
“System Objects in MATLAB Code Generation”
dsp.CICInterpolator
“System Objects in MATLAB Code Generation”
dsp.FarrowRateConverter
“System Objects in MATLAB Code Generation”
dsp.FastTransversalFilter
“System Objects in MATLAB Code Generation”
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
dsp.FilterCascade
• You cannot generate code directly from
dsp.FilterCascade. You can use the
generateFilteringCode method to
generate a MATLAB function. You can
generate C/C++ code from this MATLAB
function.
• “System Objects in MATLAB Code
Generation”
dsp.FilteredXLMSFilter
“System Objects in MATLAB Code Generation”
dsp.FIRDecimator
“System Objects in MATLAB Code Generation”
dsp.FIRFilter
• “System Objects in MATLAB Code
Generation”
• Only the Numerator property is tunable for
code generation.
dsp.FIRHalfbandDecimator
“System Objects in MATLAB Code Generation”
dsp.FIRHalfbandInterpolator
“System Objects in MATLAB Code Generation”
dsp.FIRInterpolator
“System Objects in MATLAB Code Generation”
dsp.FIRRateConverter
“System Objects in MATLAB Code Generation”
dsp.FrequencyDomainAdaptiveFilter
“System Objects in MATLAB Code Generation”
dsp.IIRFilter
• Only the Numerator and Denominator
properties are tunable for code generation.
• “System Objects in MATLAB Code
Generation”
dsp.KalmanFilter
“System Objects in MATLAB Code Generation”
dsp.LMSFilter
“System Objects in MATLAB Code Generation”
dsp.RLSFilter
“System Objects in MATLAB Code Generation”
dsp.SampleRateConverter
“System Objects in MATLAB Code Generation”
firceqrip
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
fireqint
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
firgr
• All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
• Does not support syntaxes that have cell
array input.
firhalfband
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
firlpnorm
• All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
• Does not support syntaxes that have cell
array input.
firminphase
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
firnyquist
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
firpr2chfb
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
ifir
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
iircomb
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
iirgrpdelay
• All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
• Does not support syntaxes that have cell
array input.
iirlpnorm
• All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
• Does not support syntaxes that have cell
array input.
iirlpnormc
• All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
• Does not support syntaxes that have cell
array input.
iirnotch
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
iirpeak
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
tf2ca
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
tf2cl
All inputs must be constant. Expressions or
variables are allowed if their values do not
change.
Math Operations
dsp.ArrayVectorAdder
“System Objects in MATLAB Code Generation”
dsp.ArrayVectorDivider
“System Objects in MATLAB Code Generation”
dsp.ArrayVectorMultiplier
“System Objects in MATLAB Code Generation”
dsp.ArrayVectorSubtractor
“System Objects in MATLAB Code Generation”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
dsp.CumulativeProduct
“System Objects in MATLAB Code Generation”
dsp.CumulativeSum
“System Objects in MATLAB Code Generation”
dsp.LDLFactor
“System Objects in MATLAB Code Generation”
dsp.LevinsonSolver
“System Objects in MATLAB Code Generation”
dsp.LowerTriangularSolver
“System Objects in MATLAB Code Generation”
dsp.LUFactor
“System Objects in MATLAB Code Generation”
dsp.Normalizer
“System Objects in MATLAB Code Generation”
dsp.UpperTriangularSolver
“System Objects in MATLAB Code Generation”
Quantizers
dsp.ScalarQuantizerDecoder
“System Objects in MATLAB Code Generation”
dsp.ScalarQuantizerEncoder
“System Objects in MATLAB Code Generation”
dsp.VectorQuantizerDecoder
“System Objects in MATLAB Code Generation”
dsp.VectorQuantizerEncoder
“System Objects in MATLAB Code Generation”
Scopes
dsp.SpectrumAnalyzer
This System object does not generate code. It is
automatically declared as an extrinsic variable
using the coder.extrinsic function.
dsp.TimeScope
This System object does not generate code. It is
automatically declared as an extrinsic variable
using the coder.extrinsic function.
Signal Management
dsp.Counter
“System Objects in MATLAB Code Generation”
dsp.DelayLine
“System Objects in MATLAB Code Generation”
Signal Operations
dsp.Convolver
“System Objects in MATLAB Code Generation”
dsp.DCBlocker
“System Objects in MATLAB Code Generation”
dsp.Delay
“System Objects in MATLAB Code Generation”
dsp.DigitalDownConverter
“System Objects in MATLAB Code Generation”
dsp.DigitalUpConverter
“System Objects in MATLAB Code Generation”
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
dsp.Interpolator
“System Objects in MATLAB Code Generation”
dsp.NCO
“System Objects in MATLAB Code Generation”
dsp.PeakFinder
“System Objects in MATLAB Code Generation”
dsp.PhaseExtractor
“System Objects in MATLAB Code Generation”
dsp.PhaseUnwrapper
“System Objects in MATLAB Code Generation”
dsp.VariableFractionalDelay
“System Objects in MATLAB Code Generation”
dsp.VariableIntegerDelay
“System Objects in MATLAB Code Generation”
dsp.Window
• This object has no tunable properties for code
generation.
• “System Objects in MATLAB Code
Generation”
dsp.ZeroCrossingDetector
“System Objects in MATLAB Code Generation”
Sinks
dsp.AudioPlayer
“System Objects in MATLAB Code Generation”
dsp.AudioFileWriter
“System Objects in MATLAB Code Generation”
dsp.UDPSender
“System Objects in MATLAB Code Generation”
Sources
dsp.AudioFileReader
“System Objects in MATLAB Code Generation”
dsp.AudioRecorder
“System Objects in MATLAB Code Generation”
dsp.SignalSource
“System Objects in MATLAB Code Generation”
dsp.SineWave
• This object has no tunable properties for code
generation.
• “System Objects in MATLAB Code
Generation”
dsp.UDPReceiver
“System Objects in MATLAB Code Generation”
Statistics
dsp.Autocorrelator
“System Objects in MATLAB Code Generation”
dsp.Crosscorrelator
“System Objects in MATLAB Code Generation”
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
dsp.Histogram
• This object has no tunable properties for code
generation.
• “System Objects in MATLAB Code
Generation”
dsp.Maximum
“System Objects in MATLAB Code Generation”
dsp.Mean
“System Objects in MATLAB Code Generation”
dsp.Median
“System Objects in MATLAB Code Generation”
dsp.Minimum
“System Objects in MATLAB Code Generation”
dsp.PeakToPeak
“System Objects in MATLAB Code Generation”
dsp.PeakToRMS
“System Objects in MATLAB Code Generation”
dsp.RMS
“System Objects in MATLAB Code Generation”
dsp.StandardDeviation
“System Objects in MATLAB Code Generation”
dsp.StateLevels
“System Objects in MATLAB Code Generation”
dsp.Variance
“System Objects in MATLAB Code Generation”
Transforms
dsp.AnalyticSignal
“System Objects in MATLAB Code Generation”
dsp.DCT
“System Objects in MATLAB Code Generation”
dsp.FFT
“System Objects in MATLAB Code Generation”
dsp.IDCT
“System Objects in MATLAB Code Generation”
dsp.IFFT
“System Objects in MATLAB Code Generation”
Error Handling in MATLAB
Function
Remarks and Limitations
assert
• Generates specified error messages at compile time only if all input
arguments are constants or depend on constants. Otherwise, generates
specified error messages at run time.
• For standalone code generation, excluded from the generated code.
• See “Rules for Using assert Function”.
error
For standalone code generation, excluded from the generated code.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Exponents in MATLAB
Function
Remarks and Limitations
exp
—
expm
—
expm1
—
factorial
—
log
• Generates an error during simulation and returns NaN in generated
code when the input value x is real, but the output should be complex.
To get the complex result, make the input value complex by passing in
complex(x).
log2
—
log10
—
log1p
—
nextpow2
—
nthroot
—
reallog
—
realpow
—
realsqrt
—
sqrt
• Generates an error during simulation and returns NaN in generated
code when the input value x is real, but the output should be complex.
To get the complex result, make the input value complex by passing in
complex(x).
Filtering and Convolution in MATLAB
Function
Remarks and Limitations
conv
—
conv2
—
convn
—
deconv
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
detrend
• If supplied and not empty, the input argument bp must satisfy the
following requirements:
• Be real.
• Be sorted in ascending order.
• Restrict elements to integers in the interval [1, n-2]. n is the
number of elements in a column of input argument X , or the number
of elements in X when X is a row vector.
• Contain all unique values.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
filter
—
filter2
—
Fixed-Point Designer
In addition to function-specific limitations listed in the table, the following general
limitations apply to the use of Fixed-Point Designer functions in generated code, with
fiaccel:
• fipref and quantizer objects are not supported.
• Word lengths greater than 128 bits are not supported.
• You cannot change the fimath or numerictype of a given fi variable after that
variable has been created.
• The boolean value of the DataTypeMode and DataType properties are not
supported.
• For all SumMode property settings other than FullPrecision, the CastBeforeSum
property must be set to true.
• You can use parallel for (parfor) loops in code compiled with fiaccel, but those
loops are treated like regular for loops.
• When you compile code containing fi objects with nontrivial slope and bias scaling,
you may see different results in generated code than you achieve by running the same
code in MATLAB.
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Functions and Objects Supported for C and C++ Code Generation — Category List
• The general limitations of C/C++ code generated from MATLAB apply. For
more information, see “MATLAB Language Features Supported for C/C++ Code
Generation”.
Function
Remarks/Limitations
abs
N/A
accumneg
N/A
accumpos
N/A
add
• Code generation in MATLAB does not support the syntax
F.add(a,b). You must use the syntax add(F,a,b).
all
N/A
any
N/A
atan2
N/A
bitand
Not supported for slope-bias scaled fi objects.
bitandreduce
N/A
bitcmp
N/A
bitconcat
N/A
bitget
N/A
bitor
Not supported for slope-bias scaled fi objects.
bitorreduce
N/A
bitreplicate
N/A
bitrol
N/A
bitror
N/A
bitset
N/A
bitshift
N/A
bitsliceget
N/A
bitsll
Generated code may not handle out of range shifting.
bitsra
Generated code may not handle out of range shifting.
bitsrl
Generated code may not handle out of range shifting.
bitxor
Not supported for slope-bias scaled fi objects.
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks/Limitations
bitxorreduce
N/A
ceil
N/A
complex
N/A
conj
N/A
conv
• Variable-sized inputs are only supported when the SumMode
property of the governing fimath is set to Specify precision or
Keep LSB.
• For variable-sized signals, you may see different results between
generated code and MATLAB.
• In the generated code, the output for variable-sized signals is
computed using the SumMode property of the governing fimath.
• In MATLAB, the output for variable-sized signals is computed
using the SumMode property of the governing fimath when
both inputs are nonscalar. However, if either input is a scalar,
MATLAB computes the output using the ProductMode of the
governing fimath.
convergent
N/A
cordicabs
Variable-size signals are not supported.
cordicangle
Variable-size signals are not supported.
cordicatan2
Variable-size signals are not supported.
cordiccart2pol
Variable-size signals are not supported.
cordiccexp
Variable-size signals are not supported.
cordiccos
Variable-size signals are not supported.
cordicpol2cart
Variable-size signals are not supported.
cordicrotate
Variable-size signals are not supported.
cordicsin
Variable-size signals are not supported.
cordicsincos
Variable-size signals are not supported.
cos
N/A
ctranspose
N/A
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks/Limitations
diag
If supplied, the index, k, must be a real and scalar integer value that is
not a fi object.
divide
• Any non-fi input must be constant; that is, its value must be
known at compile time so that it can be cast to a fi object.
• Complex and imaginary divisors are not supported.
• Code generation in MATLAB does not support the syntax
T.divide(a,b).
double
For the automated workflow, do not use explicit double or single casts
in your MATLAB algorithm to insulate functions that do not support
fixed-point data types. The automated conversion tool does not support
these casts. Instead of using casts, supply a replacement function. For
more information, see “Function Replacements”.
end
N/A
eps
• Supported for scalar fixed-point signals only.
• Supported for scalar, vector, and matrix, fi single and fi double
signals.
eq
Not supported for fixed-point signals with different biases.
fi
• The default constructor syntax without any input arguments is not
supported.
• If the numerictype is not fully specified, the input to fi must be a
constant, a fi, a single, or a built-in integer value. If the input is a
built-in double value, it must be a constant. This limitation allows
fi to autoscale its fraction length based on the known data type of
the input.
• All properties related to data type must be constant for code
generation.
• numerictype object information must be available for nonfixedpoint Simulink inputs.
filter
• Variable-sized inputs are only supported when the SumMode
property of the governing fimath is set to Specify precision or
Keep LSB.
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks/Limitations
fimath
• Fixed-point signals coming in to a MATLAB Function block from
Simulink are assigned a fimath object. You define this object in
the MATLAB Function block dialog in the Model Explorer.
• Use to create fimath objects in the generated code.
• If the ProductMode property of the fimath object is set to
anything other than FullPrecision, the ProductWordLength
and ProductFractionLength properties must be constant.
• If the SumMode property of the fimath object is set to anything
other than FullPrecision, the SumWordLength and
SumFractionLength properties must be constant.
fix
N/A
fixed.Quantizer
N/A
flip
The dimensions argument must be a built-in type; it cannot be a fi
object.
fliplr
N/A
flipud
N/A
floor
N/A
for
N/A
ge
Not supported for fixed-point signals with different biases.
get
The syntax structure = get(o) is not supported.
getlsb
N/A
getmsb
N/A
gt
Not supported for fixed-point signals with different biases.
horzcat
N/A
imag
N/A
int8, int16, int32,
int64
N/A
ipermute
N/A
iscolumn
N/A
isempty
N/A
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks/Limitations
isequal
N/A
isfi
Avoid using the isfi function in code that you intend to convert
using the automated workflow. The value returned by isfi in the
fixed-point code might differ from the value returned in the original
MATLAB algorithm. The behavior of the fixed-point code might differ
from the behavior of the original algorithm.
isfimath
N/A
isfimathlocal
N/A
isfinite
N/A
isinf
N/A
isnan
N/A
isnumeric
N/A
isnumerictype
N/A
isreal
N/A
isrow
N/A
isscalar
N/A
issigned
N/A
isvector
N/A
le
Not supported for fixed-point signals with different biases.
length
N/A
logical
N/A
lowerbound
N/A
lsb
• Supported for scalar fixed-point signals only.
• Supported for scalar, vector, and matrix, fi single and double
signals.
lt
Not supported for fixed-point signals with different biases.
max
N/A
mean
N/A
median
N/A
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks/Limitations
min
N/A
minus
Any non-fi input must be constant; that is, its value must be known
at compile time so that it can be cast to a fi object.
mpower
• When the exponent k is a variable and the input is a scalar,
the ProductMode property of the governing fimath must be
SpecifyPrecision.
• When the exponent k is a variable and the input is not scalar,
the SumMode property of the governing fimath must be
SpecifyPrecision.
• Variable-sized inputs are only supported when the SumMode
property of the governing fimath is set to SpecifyPrecision or
Keep LSB.
• For variable-sized signals, you may see different results between
the generated code and MATLAB.
• In the generated code, the output for variable-sized signals is
computed using the SumMode property of the governing fimath.
• In MATLAB, the output for variable-sized signals is computed
using the SumMode property of the governing fimath when
the first input, a, is nonscalar. However, when a is a scalar,
MATLAB computes the output using the ProductMode of the
governing fimath.
mpy
• Code generation in MATLAB does not support the syntax
F.mpy(a,b). You must use the syntax mpy(F,a,b).
• When you provide complex inputs to the mpy function inside of a
MATLAB Function block, you must declare the input as complex
before running the simulation. To do so, go to the Ports and
data manager and set the Complexity parameter for all known
complex inputs to On.
mrdivide
N/A
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks/Limitations
mtimes
• Any non-fi input must be constant; that is, its value must be
known at compile time so that it can be cast to a fi object.
• Variable-sized inputs are only supported when the SumMode
property of the governing fimath is set to SpecifyPrecision or
KeepLSB.
• For variable-sized signals, you may see different results between
the generated code and MATLAB.
• In the generated code, the output for variable-sized signals is
computed using the SumMode property of the governing fimath.
• In MATLAB, the output for variable-sized signals is computed
using the SumMode property of the governing fimath when
both inputs are nonscalar. However, if either input is a scalar,
MATLAB computes the output using the ProductMode of the
governing fimath.
ndims
N/A
ne
Not supported for fixed-point signals with different biases.
nearest
N/A
numberofelements
numberofelements will be removed in a future release. Use numel
instead.
numel
N/A
numerictype
• Fixed-point signals coming in to a MATLAB Function block from
Simulink are assigned a numerictype object that is populated
with the signal's data type and scaling information.
• Returns the data type when the input is a nonfixed-point signal.
• Use to create numerictype objects in generated code.
• All numerictype object properties related to the data type must be
constant.
permute
The dimensions argument must be a built-in type; it cannot be a fi
object.
plus
Any non-fi inputs must be constant; that is, its value must be known
at compile time so that it can be cast to a fi object.
pow2
N/A
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks/Limitations
power
When the exponent k is a variable, the ProductMode property of the
governing fimath must be SpecifyPrecision.
qr
N/A
quantize
N/A
range
N/A
rdivide
N/A
real
N/A
realmax
N/A
realmin
N/A
reinterpretcast
N/A
removefimath
N/A
repmat
The dimensions argument must be a built-in type; it cannot be a fi
object.
rescale
N/A
reshape
N/A
rot90
In the syntax rot90(A,k), the argument k must be a built-in type; it
cannot be a fi object.
round
N/A
setfimath
N/A
sfi
• All properties related to data type must be constant for code
generation.
shiftdim
The dimensions argument must be a built-in type; it cannot be a fi
object.
sign
N/A
sin
N/A
single
For the automated workflow, do not use explicit double or single casts
in your MATLAB algorithm to insulate functions that do not support
fixed-point data types. The automated conversion tool does not support
these casts. Instead of using casts, supply a replacement function. For
more information, see “Function Replacements”.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks/Limitations
size
N/A
sort
The dimensions argument must be a built-in type; it cannot be a fi
object.
squeeze
N/A
sqrt
• Complex and [Slope Bias] inputs error out.
• Negative inputs yield a 0 result.
storedInteger
N/A
storedIntegerToDouble N/A
sub
• Code generation in MATLAB does not support the syntax
F.sub(a,b). You must use the syntax sub(F,a,b).
subsasgn
N/A
subsref
N/A
sum
Variable-sized inputs are only supported when the SumMode property
of the governing fimath is set to Specify precision or Keep LSB.
times
• Any non-fi input must be constant; that is, its value must be
known at compile time so that it can be cast to a fi object.
• When you provide complex inputs to the times function inside of
a MATLAB Function block, you must declare the input as complex
before running the simulation. To do so, go to the Ports and
data manager and set the Complexity parameter for all known
complex inputs to On.
transpose
N/A
tril
If supplied, the index, k, must be a real and scalar integer value that is
not a fi object.
triu
If supplied, the index, k, must be a real and scalar integer value that is
not a fi object.
ufi
• All properties related to data type must be constant for code
generation.
uint8, uint16, uint32,
uint64
N/A
uminus
N/A
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks/Limitations
uplus
N/A
upperbound
N/A
vertcat
N/A
HDL Coder
Function
Remarks and Limitations
hdl.RAM
This System object is available with MATLAB.
Histograms in MATLAB
Function
Remarks and Limitations
hist
• Histogram bar plotting not supported; call with at least one output
argument.
• If supplied, the second argument x must be a scalar constant.
• Inputs must be real.
• The output of a variable-size array that becomes a column vector at run
time is a column-vector, not a row-vector.
histc
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
Image Acquisition Toolbox
If you install Image Acquisition Toolbox software, you can generate C and C++ code for
the VideoDevice System object. See imaq.VideoDevice and “Code Generation with
VideoDevice System Object”.
Image Processing in MATLAB
Function
Remarks and Limitations
im2double
—
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Functions and Objects Supported for C and C++ Code Generation — Category List
Image Processing Toolbox
The following table lists the Image Processing Toolbox functions that have been enabled
for code generation. You must have the MATLAB Coder and Image Processing Toolbox
software installed to generate C code from MATLAB for these functions.
Image Processing Toolbox provides three types of code generation support:
• Functions that generate C code.
• Functions that generate C code that depends on a platform-specific shared library
(.dll, .so, or .dylib). Use of a shared library preserves performance optimizations
in these functions, but this limits the target platforms for which you can generate
code. For more information, see “Code Generation for Image Processing”.
• Functions that generate C code or C code that depends on a shared library, depending
on which target platform you specify in MATLAB Coder. If you specify the generic
MATLAB Host Computer target platform, these functions generate C code that
depends on a shared library. If you specify any other target platform, these functions
generate C code.
In generated code, each supported toolbox function has the same name, arguments, and
functionality as its Image Processing Toolbox counterpart. However, some functions have
limitations. The following table includes information about code generation limitations
that might exist for each function. In the following table, all the functions generate C
code. The table identifies those functions that generate C code that depends on a shared
library, and those functions that can do both, depending on which target platform you
choose.
Function
Remarks/Limitations
affine2d
When generating code, you can only specify single objects—arrays of
objects are not supported.
bwdist
The method argument must be a compile-time constant. Input images
must have fewer than 232 pixels.
Generated code for this function uses a precompiled, “platform-specific
shared library”.
bwlookup
For best results, specify an input image of class logical.
If you choose the generic MATLAB Host Computer target platform,
generated code uses a precompiled, “platform-specific shared library”.
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks/Limitations
bwmorph
The text string specifying the operation must be a constant and, for
best results, specify an input image of class logical.
If you choose the generic MATLAB Host Computer target platform,
generated code uses a precompiled, “platform-specific shared library”.
bwpack
Generated code for this function uses a precompiled “platform-specific
shared library”.
bwselect
Supports only the 3 and 4 input argument syntaxes: BW2 =
bwselect(BW,c,r) and BW2 = bwselect(BW,c,r,n). The optional
fourth input argument, n, must be a compile-time constant. In addition,
with code generation, bwselect only supports only the 1 and 2 output
argument syntaxes: BW2 = bwselect(___) or [BW2, idx] =
bwselect(___).
Generated code for this function uses a precompiled “platform-specific
shared library”.
bwtraceboundary
The dir, fstep, and conn arguments must be compile-time constants.
bwunpack
Generated code for this function uses a precompiled “platform-specific
shared library”.
conndef
Input arguments must be compile-time constants.
edge
The method, direction, and sigma arguments must be a compiletime constants. In addition, nonprogrammatic syntaxes are not
supported. For example, the syntax edge(im), where edge does not
return a value but displays an image instead, is not supported.
Generated code for this function uses a precompiled “platform-specific
shared library”.
fitgeotrans
The transformtype argument must be a compile-time constant.
The function supports the following transformation types:
'nonreflectivesimilarity', 'similarity', 'affine', or
'projective'.
fspecial
Inputs must be compile-time constants. Expressions or variables are
allowed if their values do not change.
getrangefromclass
—
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks/Limitations
histeq
All the syntaxes that include indexed images are not supported. This
includes all syntaxes that accept map as input and return newmap.
Generated code for this function uses a precompiled “platform-specific
shared library”.
im2uint8
Generated code for this function uses a precompiled “platform-specific
shared library”.
im2uint16
Generated code for this function uses a precompiled “platform-specific
shared library”.
im2int16
Generated code for this function uses a precompiled “platform-specific
shared library”.
im2single
—
im2double
—
imadjust
Does not support syntaxes that include indexed images. This includes
all syntaxes that accept map as input and return newmap.
Generated code for this function uses a precompiled “platform-specific
shared library”.
imbothat
The input image IM must be either 2-D or 3-D image. The structuring
element input argument SE must be a compile-time constant.
If you choose the generic MATLAB Host Computer target platform,
generated code uses a precompiled, “platform-specific shared library”.
imclearborder
The optional second input argument, conn, must be a compile-time
constant. Supports only up to 3-D inputs.
Generated code for this function uses a precompiled “platform-specific
shared library”.
imclose
The input image IM must be either 2-D or 3-D image. The structuring
element input argument SE must be a compile-time constant.
If you choose the generic MATLAB Host Computer target platform,
generated code uses a precompiled, “platform-specific shared library”.
imcomplement
Does not support int64 and uint64 data types.
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks/Limitations
imdilate
The input image IM must be either 2-D or 3-D image. The SE, PACKOPT,
and SHAPE input arguments must be a compile-time constant. The
structuring element argument SE must be a single element—arrays of
structuring elements are not supported. To obtain the same result as
that obtained using an array of structuring elements, call the function
sequentially.
If you choose the generic MATLAB Host Computer target platform,
generated code uses a precompiled, “platform-specific shared library”.
imerode
The input image IM must be either 2-D or 3-D image. The SE, PACKOPT,
and SHAPE input arguments must be a compile-time constant. The
structuring element argument SE must be a single element—arrays of
structuring elements are not supported. To obtain the same result as
that obtained using an array of structuring elements, call the function
sequentially.
If you choose the generic MATLAB Host Computer target platform,
generated code uses a precompiled, “platform-specific shared library”.
imextendedmax
The optional third input argument, conn, must be a compile-time
constant.
Generated code for this function uses a precompiled “platform-specific
shared library”.
imextendedmin
The optional third input argument, conn, must be a compile-time
constant.
Generated code for this function uses a precompiled “platform-specific
shared library”.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks/Limitations
imfill
The optional input connectivity, conn and the string 'holes' must be
compile-time constants.
Supports only up to 3-D inputs.
The interactive mode to select points, imfill(BW,0,CONN) is not
supported in code generation.
locations can be a P-by-1 vector, in which case it contains the
linear indices of the starting locations. locations can also be a Pby-ndims(I) matrix, in which case each row contains the array indices
of one of the starting locations. Once you select a format at compiletime, you cannot change it at run time. However, the number of points
in locations can be varied at run time.
Generated code for this function uses a precompiled “platform-specific
shared library”.
imfilter
The input image can be either 2-D or 3-D. The value of the input
argument, options, must be a compile-time constant.
If you choose the generic MATLAB Host Computer target platform,
generated code uses a precompiled, “platform-specific shared library”.
imhist
The optional second input argument, n, must be a compile-time
constant. In addition, nonprogrammatic syntaxes are not supported.
For example, the syntaxes where imhist displays the histogram are
not supported.
Generated code for this function uses a precompiled “platform-specific
shared library”.
imhmax
The optional third input argument, conn, must be a compile-time
constant
Generated code for this function uses a precompiled “platform-specific
shared library”.
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks/Limitations
imhmin
The optional third input argument, conn, must be a compile-time
constant
Generated code for this function uses a precompiled “platform-specific
shared library”.
imlincomb
The output_class argument must be a compile-time constant.
Generated code for this function uses a precompiled “platform-specific
shared library”.
imopen
The input image IM must be either 2-D or 3-D image. The structuring
element input argument SE must be a compile-time constant.
If you choose the generic MATLAB Host Computer target platform,
generated code uses a precompiled, “platform-specific shared library”.
imquantize
—
imreconstruct
The optional third input argument, conn, must be a compile-time
constant.
Generated code for this function uses a precompiled “platform-specific
shared library”.
imref2d
The XWorldLimits, YWorldLimits and ImageSize properties can be
set only during object construction. When generating code, you can only
specify single objects—arrays of objects are not supported.
imref3d
The XWorldLimits, YWorldLimits, ZWorldLimits and ImageSize
properties can be set only during object construction. When generating
code, you can only specify single objects—arrays of objects are not
supported.
imregionalmax
The optional second input argument, conn, must be a compile-time
constant.
Generated code for this function uses a precompiled “platform-specific
shared library”.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks/Limitations
imregionalmin
The optional second input argument, conn, must be a compile-time
constant.
Generated code for this function uses a precompiled “platform-specific
shared library”.
imtophat
The input image IM must be either 2-D or 3-D image. The structuring
element input argument SE must be a compile-time constant.
If you choose the generic MATLAB Host Computer target platform,
generated code uses a precompiled, “platform-specific shared library”.
imwarp
The geometric transformation object input, tform, must be either
affine2d or projective2d. Additionally, the interpolation method
and optional parameter names must be string constants.
Generated code for this function uses a precompiled “platform-specific
shared library”.
intlut
Generated code for this function uses a precompiled “platform-specific
shared library”.
iptcheckconn
Input arguments must be compile-time constants.
iptcheckmap
—
label2rgb
Referring to the standard syntax:
RGB = label2rgb(L, map, zerocolor, order)
• Submit at least two input arguments: the label matrix, L, and the
colormap matrix, map.
• map must be an n-by-3, double, colormap matrix. You cannot use
a string containing the name of a MATLAB colormap function or a
function handle of a colormap function.
• If you set the boundary color zerocolor to the same color as one of
the regions, label2rgb will not issue a warning.
• If you supply a value for order, it must be 'noshuffle'.
mean2
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks/Limitations
medfilt2
The padopt argument must be a compile-time constant.
Generated code for this function uses a precompiled “platform-specific
shared library”.
multithresh
—
ordfilt2
The padopt argument must be a compile-time constant.
Generated code for this function uses a precompiled “platform-specific
shared library”.
padarray
Support only up to 3-D inputs.
Input arguments, padval and direction are expected to be compiletime constants.
projective2d
When generating code, you can only specify single objects—arrays of
objects are not supported.
rgb2ycbcr
—
strel
Input arguments must be compile-time constants. The following
methods are not supported for code generation: getsequence,
reflect, translate, disp, display, loadobj. When generating
code, you can only specify single objects—arrays of objects are not
supported.
stretchlim
Generated code for this function uses a precompiled “platform-specific
shared library”.
ycbcr2rgb
—
Input and Output Arguments in MATLAB
Function
Remarks and Limitations
nargin
—
nargout
• For a function with no output arguments,
returns 1 if called without a terminating
semicolon.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
Note: This behavior also affects extrinsic calls
with no terminating semicolon. nargout is 1 for
the called function in MATLAB.
Interpolation and Computational Geometry in MATLAB
Function
Remarks and Limitations
cart2pol
—
cart2sph
—
interp1
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
interp2
• Xq and Yq must be the same size. Use meshgrid to evaluate on a grid.
• For best results, provide X and Y as vectors.
• For the 'cubic' method, reports an error if the grid does not have
uniform spacing. In this case, use the 'spline' method.
• For best results when you use the 'spline' method:
• Use meshgrid to create the inputs Xq and Yq.
• Use a small number of interpolation points relative to the dimensions
of V. Interpolating over a large set of scattered points can be
inefficient.
interp3
• Xq, Yq, and Zq must be the same size. Use meshgrid to evaluate on a
grid.
• For best results, provide X, Y, and Z as vectors.
• For the 'cubic' method, reports an error if the grid does not have
uniform spacing. In this case, use the 'spline' method.
• For best results when you use the 'spline' method:
• Use meshgrid to create the inputs Xq, Yq, and Zq.
• Use a small number of interpolation points relative to the dimensions
of V. Interpolating over a large set of scattered points can be
inefficient.
meshgrid
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
mkpp
• The output structure pp differs from the pp structure in MATLAB. In
MATLAB, ppval cannot use the pp structure from the code generation
software. For code generation, ppval cannot use a pp structure
created by MATLAB. unmkpp can use a MATLAB pp structure for code
generation.
To create a MATLAB pp structure from a pp structure created by the
code generation software:
• In code generation, use unmkpp to return the piecewise polynomial
details to MATLAB.
• In MATLAB, use mkpp to create the pp structure.
• If you do not provide d, then coefs must be two-dimensional and have
a fixed number of columns. In this case, the number of columns is the
order.
• To define a piecewise constant polynomial, coefs must be a column
vector or d must have at least two elements.
• If you provide d and d is 1, d must be a constant. Otherwise, if the input
to ppval is nonscalar, the shape of the output of ppval can differ from
ppval in MATLAB.
• If you provide d, it must have a fixed length. One of the following sets of
statements must be true:
1
Suppose that m = length(d) and npieces = length(breaks)
- 1.
size(coefs,j) = d(j)
size(coefs,m+1) = npieces
size(coefs,m+2) = order
j = 1,2,...,m. The dimension m+2 must be fixed length.
2
Suppose that m = length(d) and npieces = length(breaks)
- 1.
size(coefs,1) = prod(d)*npieces
size(coefs,2) = order
The second dimension must be fixed length.
• If you do not provide d, the following statements must be true:
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
Suppose that m = length(d) and npieces = length(breaks) - 1.
size(coefs,1) = prod(d)*npieces
size(coefs,2) = order
The second dimension must be fixed length.
pchip
• Input x must be strictly increasing.
• Does not remove y entries with NaN values.
• If you generate code for the pp = pchip(x,y) syntax, you cannot
input pp to the ppval function in MATLAB. To create a MATLAB pp
structure from a pp structure created by the code generation software:
• In code generation, use unmkpp to return the piecewise polynomial
details to MATLAB.
• In MATLAB, use mkpp to create the pp structure.
pol2cart
—
polyarea
—
ppval
The size of output v does not match MATLAB when both of the following
statements are true:
• The input x is a variable-size array that is not a variable-length vector.
• x becomes a row vector at run time.
The code generation software does not remove the singleton dimensions.
However, MATLAB might remove singleton dimensions.
For example, suppose that x is a :4-by-:5 array (the first dimension is
variable size with an upper bound of 4 and the second dimension is variable
size with an upper bound of 5). Suppose that ppval(pp,0) returns a 2by-3 fixed-size array. v has size 2-by-3-by-:4-by-:5. At run time, suppose
that, size(x,1) =1 and size (x,2) = 5. In the generated code, the size(v) is
[2,3,1,5]. In MATLAB, the size is [2,3,5].
rectint
—
sph2cart
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
spline
• Input x must be strictly increasing.
• Does not remove Y entries with NaN values.
• Does not report an error for infinite endslopes in Y.
• If you generate code for the pp = spline(x,Y) syntax, you cannot
input pp to the ppval function in MATLAB. To create a MATLAB pp
structure from a pp structure created by the code generation software:
• In code generation, use unmkpp to return the piecewise polynomial
details to MATLAB.
• In MATLAB, use mkpp to create the pp structure.
• pp must be a valid piecewise polynomial structure created by mkpp,
spline, or pchip in MATLAB or by the code generation software.
unmkpp
• Does not support pp structures created by interp1 in MATLAB.
Linear Algebra in MATLAB
Function
Remarks and Limitations
ishermitian
—
issymmetric
—
linsolve
• The option structure must be a constant.
• Supports only a scalar option structure input. It does not support arrays
of option structures.
• Only optimizes these cases:
• UT
• LT
• UHESS = true (the TRANSA can be either true or false)
• SYM = true and POSDEF = true
Other options are equivalent to using mldivide.
null
• Might return a different basis than MATLAB
• Does not support rational basis option (second input)
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
orth
• Can return a different basis than MATLAB
rsf2csf
—
schur
Can return a different Schur decomposition in generated code than in
MATLAB.
sqrtm
—
Logical and Bit-Wise Operations in MATLAB
Function
Remarks and Limitations
and
—
bitand
—
bitcmp
—
bitget
—
bitor
—
bitset
—
bitshift
—
bitxor
—
not
—
or
—
xor
—
MATLAB Compiler
C and C++ code generation for the following functions requires the MATLAB Compiler
software.
Function
Remarks and Limitations
isdeployed
• Returns true and false as appropriate for MEX and SIM targets
• Returns false for other targets
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
ismcc
• Returns true and false as appropriate for MEX and SIM targets.
• Returns false for other targets.
Matrices and Arrays in MATLAB
Function
Remarks and Limitations
abs
—
all
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
angle
—
any
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
blkdiag
—
bsxfun
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
cat
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
circshift
—
colon
• Does not accept complex inputs.
• The input i cannot have a logical value.
• Does not accept vector inputs.
• Inputs must be constants.
• Uses single-precision arithmetic to produce single-precision results.
compan
—
cond
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
cov
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
cross
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
cumprod
• Does not support logical inputs. Cast input to double first.
• Does not support the direction argument.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
cumsum
• Does not support logical inputs. Cast input to double first.
• Does not support the direction argument.
det
—
diag
• If supplied, the argument representing the order of the diagonal
matrix must be a real and scalar integer value.
• For variable-size inputs that are variable-length vectors (1-by-: or :by-1), diag:
• Treats the input as a vector input.
• Returns a matrix with the given vector along the specified
diagonal.
• For variable-size inputs that are not variable-length vectors, diag:
• Treats the input as a matrix.
• Does not support inputs that are vectors at run time.
• Returns a variable-length vector.
If the input is variable-size (:m-by-:n) and has shape 0-by-0 at run
time, the output is 0-by-1 not 0-by-0. However, if the input is a
constant size 0-by-0, the output is [].
• For variable-size inputs that are not variable-length vectors (1-by-:
or :-by-1), diag treats the input as a matrix from which to extract
a diagonal vector. This behavior occurs even if the input array is a
vector at run time. To force diag to build a matrix from variablesize inputs that are not 1-by-: or :-by-1, use:
• diag(x(:)) instead of diag(x)
• diag(x(:),k) instead of diag(x,k)
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
diff
• If supplied, the arguments representing the number of times
to apply diff and the dimension along which to calculate the
difference must be constants.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
dot
—
eig
• For code generation,QZ algorithm is used in all cases. MATLAB can
use different algorithms for different inputs. Consequently, V might
represent a different basis of eigenvectors. The eigenvalues in D
might not be in the same order as in MATLAB.
• With one input, [V,D] = eig(A), the results are similar to
those obtained using [V,D] = eig(A,eye(size(A)),'qz') in
MATLAB, except that for code generation, the columns of V are
normalized.
• Options 'balance', and 'nobalance' are not supported for the
standard eigenvalue problem. 'chol' is not supported for the
symmetric generalized eigenvalue problem.
• Outputs are of complex type.
• Does not support the option to calculate left eigenvectors.
eye
classname must be a built-in MATLAB numeric type. Does not invoke
the static eye method for other classes. For example, eye(m, n,
'myclass’) does not invoke myclass.eye(m,n).
false
• Dimensions must be real, nonnegative, integers.
find
• Issues an error if a variable-sized input becomes a row vector at run
time.
Note: This limitation does not apply when the input is scalar or a
variable-length row vector.
• For variable-sized inputs, the shape of empty outputs, 0-by-0, 0by-1, or 1-by-0, depends on the upper bounds of the size of the input.
The output might not match MATLAB when the input array is a
scalar or [] at run time. If the input is a variable-length row vector,
the size of an empty output is 1-by-0, otherwise it is 0-by-1.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
flip
—
flipdim
Note: flipdim will be removed in a future release. Use flip instead.
fliplr
—
flipud
—
full
—
hadamard
—
hankel
—
hilb
—
ind2sub
• The first argument should be a valid size vector. Size vectors for
arrays with more than intmax elements are not supported.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
inv
Singular matrix inputs can produce nonfinite values that differ from
MATLAB results.
invhilb
—
ipermute
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
iscolumn
—
isempty
—
isequal
—
isequaln
—
isfinite
—
isfloat
—
isinf
—
isinteger
—
islogical
—
ismatrix
—
isnan
—
isrow
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
issparse
—
isvector
—
kron
—
length
—
linspace
—
logspace
—
lu
—
magic
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
max
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
min
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
ndgrid
—
ndims
—
nnz
—
nonzeros
—
norm
—
normest
—
numel
—
ones
• Dimensions must be real, nonnegative integers.
• The input optimfun must be a function supported for code
generation.
pascal
—
permute
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
pinv
—
planerot
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
prod
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
qr
—
rand
• classname must be a built-in MATLAB numeric type. Does not
invoke the static rand method for other classes. For example,
rand(sz,'myclass’) does not invoke myclass.rand(sz).
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
randi
• classname must be a built-in MATLAB numeric type. Does
not invoke the static randi method for other classes. For
example, randi(imax,sz,'myclass’) does not invoke
myclass.randi(imax,sz).
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
randn
• classname must be a built-in MATLAB numeric type. Does not
invoke the static randn method for other classes. For example,
randn(sz,'myclass’) does not invoke myclass.randn(sz).
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
randperm
—
rank
—
rcond
—
repmat
—
reshape
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
rng
• For library code generation targets, executable code generation
targets, and MEX targets with extrinsic calls disabled:
• Does not support the 'shuffle' input.
• For the generator input, supports 'twister', 'v4', and
'v5normal'.
For these targets, the output of s=rng in the generated code differs
from the MATLAB output. You cannot return the output of s=rng
from the generated code and pass it to rng in MATLAB.
• For MEX targets, if extrinsic calls are enabled, you cannot access
the data in the structure returned by rng.
rosser
—
rot90
—
shiftdim
• Second argument must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
sign
—
size
—
sort
If the input is a complex type, sort orders the output according to
absolute value. When x is a complex type that has all zero imaginary
parts, use sort(real(x)) to compute the sort order for real types. See
“Code Generation for Complex Data”.
sortrows
If the input is a complex type, sortrows orders the output according
to absolute value. When x is a complex type that has all zero imaginary
parts, use sortrows(real(x)) to compute the sort order for real
types. See “Code Generation for Complex Data”.
squeeze
—
sub2ind
• The first argument should be a valid size vector. Size vectors for
arrays with more than intmax elements are not supported.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
subspace
—
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
sum
• Specify dim as a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
toeplitz
—
trace
—
tril
• If supplied, the argument representing the order of the diagonal
matrix must be a real and scalar integer value.
triu
• If supplied, the argument representing the order of the diagonal
matrix must be a real and scalar integer value.
true
• Dimensions must be real, nonnegative, integers.
vander
—
wilkinson
—
zeros
• Dimensions must be real, nonnegative, integers.
Neural Network Toolbox
You can use genFunction in the Neural Network Toolbox™ to generate a standalone
MATLAB function for a trained neural network. You can generate C/C++ code from this
standalone MATLAB function. To generate Simulink blocks, use thegenSim function. See
“Deploy Neural Network Functions”.
Nonlinear Numerical Methods in MATLAB
Function
Remarks and Limitations
quad2d
• Generates a warning if the size of the internal storage arrays is not
large enough. If a warning occurs, a possible workaround is to divide the
region of integration into pieces and sum the integrals over each piece.
quadgk
—
Numerical Integration and Differentiation in MATLAB
Function
Remarks and Limitations
cumtrapz
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
del2
—
diff
• If supplied, the arguments representing the number of times to apply
diff and the dimension along which to calculate the difference must be
constants.
gradient
—
ode23
• All odeset option arguments must be constant.
• Does not support a constant mass matrix in the options structure.
Provide a mass matrix as a function .
• You must provide at least the two output arguments T and Y.
• Input types must be homogeneous—all double or all single.
• Variable-sizing support must be enabled. Requires dynamic memory
allocation when tspan has two elements or you use event functions.
• All odeset option arguments must be constant.
ode45
• Does not support a constant mass matrix in the options structure.
Provide a mass matrix as a function .
• You must provide at least the two output arguments T and Y.
• Input types must be homogeneous—all double or all single.
• Variable-sizing support must be enabled. Requires dynamic memory
allocation when tspan has two elements or you use event functions.
odeget
The name argument must be constant.
odeset
All inputs must be constant.
trapz
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
Optimization Functions in MATLAB
Function
Remarks and Limitations
fminsearch
• Ignores the Display option. Does not print status information during
execution. Test the exitflag output for the exit condition.
• The output structure does not include the algorithm or message
fields.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
• Ignores the OutputFcn and PlotFcns options.
fzero
• The first argument must be a function handle. Does not support
structure, inline function, or string inputs for the first argument.
• Supports up to three output arguments. Does not support the fourth
output argument (the output structure).
optimget
Input parameter names must be constant.
optimset
• Does not support the syntax that has no input or output arguments:
optimset
• Functions specified in the options must be supported for code
generation.
• The fields of the options structure oldopts must be fixed-size fields.
• For code generation, optimization functions ignore the Display option.
• Does not support the additional options in an options structure created
by the Optimization Toolbox optimset function. If an input options
structure includes the additional Optimization Toolbox options, the
output structure does not include them.
Phased Array System Toolbox
C and C++ code generation for the following functions requires the Phased Array System
Toolbox software.
Name
Remarks and Limitations
Antenna and Microphone Elements
aperture2gain
Does not support variable-size inputs.
azel2phithetapat
Does not support variable-size inputs.
azel2uvpat
Does not support variable-size inputs.
circpol2pol
Does not support variable-size inputs.
gain2aperture
Does not support variable-size inputs.
phased.CosineAntennaElement
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
4-201
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
phased.CrossedDipoleAntennaElement
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
• plotResponse and viewArray methods are
not supported.
phased.CustomAntennaElement
• “Code Generation”.
phased.CustomMicrophoneElement
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.IsotropicAntennaElement
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.OmnidirectionalMicrophoneElement• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.ShortDipoleAntennaElement
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phitheta2azelpat
Does not support variable-size inputs.
phitheta2uvpat
Does not support variable-size inputs.
pol2circpol
Does not support variable-size inputs.
polellip
Does not support variable-size inputs.
polloss
Does not support variable-size inputs.
polratio
Does not support variable-size inputs.
polsignature
• Does not support variable-size inputs.
• Supported only when output arguments are
specified.
stokes
• Does not support variable-size inputs.
• Supported only when output arguments are
specified.
4-202
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
uv2azelpat
Does not support variable-size inputs.
uv2phithetapat
Does not support variable-size inputs.
Array Geometries and Analysis
az2broadside
Does not support variable-size inputs.
broadside2az
Does not support variable-size inputs.
phased.ArrayGain
• Does not support arrays containing
polarized antenna elements, that is, the
phased.ShortDipoleAntennaElement or
phased.CrossedDipoleAntennaElement
antennas.
• “Code Generation”.
phased.ArrayResponse
“Code Generation”.
phased.ConformalArray
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.ElementDelay
“Code Generation”.
phased.PartitionedArray
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.ReplicatedSubarray
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.SteeringVector
See “Code Generation”.
phased.ULA
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
phased.URA
• plotResponse and viewArray methods are
not supported.
• “Code Generation”.
Signal Radiation and Collection
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
phased.Collector
“Code Generation”.
phased.Radiator
“Code Generation”.
phased.WidebandCollector
• Requires dynamic memory allocation. See
“Limitations for System Objects that Require
Dynamic Memory Allocation”.
• “Code Generation”.
sensorsig
Does not support variable-size inputs.
Waveforms
ambgfun
Does not support variable-size inputs.
phased.FMCWWaveform
“Code Generation”.
phased.LinearFMWaveform
“Code Generation”.
phased.PhaseCodedWaveform
“Code Generation”.
phased.RectangularWaveform
“Code Generation”.
phased.SteppedFMWaveform
“Code Generation”.
range2bw
Does not support variable-size inputs.
range2time
Does not support variable-size inputs.
time2range
Does not support variable-size inputs.
unigrid
Does not support variable-size inputs.
Transmitters and Receivers
delayseq
Does not support variable-size inputs.
noisepow
Does not support variable-size inputs.
phased.ReceiverPreamp
“Code Generation”.
phased.Transmitter
“Code Generation”.
systemp
Does not support variable-size inputs.
Beamforming
cbfweights
Does not support variable-size inputs.
lcmvweights
Does not support variable-size inputs.
mvdrweights
Does not support variable-size inputs.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
phased.FrostBeamformer
• Requires dynamic memory allocation. See
“Limitations for System Objects that Require
Dynamic Memory Allocation”.
• “Code Generation”.
phased.LCMVBeamformer
“Code Generation”.
phased.MVDRBeamformer
“Code Generation”.
phased.PhaseShiftBeamformer
“Code Generation”.
phased.SteeringVector
“Code Generation”.
phased.SubbandPhaseShiftBeamformer
“Code Generation”.
phased.TimeDelayBeamformer
• Requires dynamic memory allocation. See
“Limitations for System Objects that Require
Dynamic Memory Allocation”.
• “Code Generation”.
phased.TimeDelayLCMVBeamformer
• Requires dynamic memory allocation. See
“Limitations for System Objects that Require
Dynamic Memory Allocation”.
• “Code Generation”.
sensorcov
Does not support variable-size inputs.
steervec
Does not support variable-size inputs.
Direction of Arrival (DOA) Estimation
aictest
Does not support variable-size inputs.
espritdoa
Does not support variable-size inputs.
mdltest
Does not support variable-size inputs.
phased.BeamscanEstimator
“Code Generation”.
phased.BeamscanEstimator2D
“Code Generation”.
phased.BeamspaceESPRITEstimator
“Code Generation”.
phased.ESPRITEstimator
“Code Generation”.
phased.MVDREstimator
“Code Generation”.
phased.MVDREstimator2D
“Code Generation”.
4-205
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
phased.RootMUSICEstimator
“Code Generation”.
phased.RootWSFEstimator
“Code Generation”.
phased.SumDifferenceMonopulseTracker
“Code Generation”.
phased.SumDifferenceMonopulseTracker2D “Code Generation”.
rootmusicdoa
Does not support variable-size inputs.
spsmooth
Does not support variable-size inputs.
Space-Time Adaptive Processing (STAP)
dopsteeringvec
Does not support variable-size inputs.
phased.ADPCACanceller
“Code Generation”.
phased.AngleDopplerResponse
“Code Generation”.
phased.DPCACanceller
“Code Generation”.
phased.STAPSMIBeamformer
“Code Generation”.
val2ind
Does not support variable-size inputs.
Signal Propagation and Environment
billingsleyicm
Does not support variable-size inputs.
depressionang
Does not support variable-size inputs.
effearthradius
Does not support variable-size inputs.
fspl
Does not support variable-size inputs.
grazingang
Does not support variable-size inputs.
horizonrange
Does not support variable-size inputs.
phased.BarrageJammer
“Code Generation”.
phased.ConstantGammaClutter
“Code Generation”.
phased.FreeSpace
• Requires dynamic memory allocation. See
“Limitations for System Objects that Require
Dynamic Memory Allocation”.
• “Code Generation”.
phased.RadarTarget
“Code Generation”.
physconst
Does not support variable-size inputs.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Name
Remarks and Limitations
surfacegamma
Does not support variable-size inputs.
surfclutterrcs
Does not support variable-size inputs.
Detection and System Analysis
albersheim
Does not support variable-size inputs.
beat2range
Does not support variable-size inputs.
dechirp
Does not support variable-size inputs.
npwgnthresh
Does not support variable-size inputs.
phased.CFARDetector
“Code Generation”.
phased.MatchedFilter
• The CustomSpectrumWindow property is
not supported.
• “Code Generation”.
phased.RangeDopplerResponse
• The CustomRangeWindow and the
CustomDopplerWindow properties are not
supported.
• “Code Generation”.
phased.StretchProcessor
“Code Generation”.
phased.TimeVaryingGain
“Code Generation”.
pulsint
Does not support variable-size inputs.
radareqpow
Does not support variable-size inputs.
radareqrng
Does not support variable-size inputs.
radareqsnr
Does not support variable-size inputs.
radarvcd
Does not support variable-size inputs.
range2beat
Does not support variable-size inputs.
rdcoupling
Does not support variable-size inputs.
rocpfa
• Does not support variable-size inputs.
• The NonfluctuatingNoncoherent signal
type is not supported.
4-207
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4
Functions, Classes, and System Objects Supported for Code Generation
Name
Remarks and Limitations
rocsnr
• Does not support variable-size inputs.
• The NonfluctuatingNoncoherent signal
type is not supported.
shnidman
Does not support variable-size inputs.
stretchfreq2rng
Does not support variable-size inputs.
Motion Modeling and Coordinate Systems
azel2phitheta
Does not support variable-size inputs.
azel2uv
Does not support variable-size inputs.
azelaxes
Does not support variable-size inputs.
cart2sphvec
Does not support variable-size inputs.
dop2speed
Does not support variable-size inputs.
global2localcoord
Does not support variable-size inputs.
local2globalcoord
Does not support variable-size inputs.
phased.Platform
“Code Generation”.
phitheta2azel
Does not support variable-size inputs.
phitheta2uv
Does not support variable-size inputs.
radialspeed
Does not support variable-size inputs.
rangeangle
Does not support variable-size inputs.
rotx
Does not support variable-size inputs.
roty
Does not support variable-size inputs
rotz
Does not support variable-size inputs.
speed2dop
Does not support variable-size inputs.
sph2cartvec
Does not support variable-size inputs.
uv2azel
Does not support variable-size inputs.
uv2phitheta
Does not support variable-size inputs.
4-208
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Functions and Objects Supported for C and C++ Code Generation — Category List
Polynomials in MATLAB
Function
Remarks and Limitations
poly
• Does not discard nonfinite input values
• Complex input produces complex output
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
polyder
The output can contain fewer NaNs than the MATLAB output. However, if
the input contains a NaN, the output contains at least one NaN.
polyfit
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
polyint
—
polyval
—
polyvalm
—
roots
• Output is variable size.
• Output is complex.
• Roots are not always in the same order as MATLAB.
• Roots of poorly conditioned polynomials do not always match MATLAB.
Programming Utilities in MATLAB
Function
Remarks and Limitations
mfilename
—
Relational Operators in MATLAB
Function
Remarks and Limitations
eq
—
ge
—
gt
—
le
—
lt
—
ne
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Rounding and Remainder Functions in MATLAB
Function
Remarks and Limitations
ceil
—
fix
—
floor
—
mod
• Performs the arithmetic using the output class. Results might not match
MATLAB due to differences in rounding errors.
If one of the inputs has type int64 or uint64, then both inputs must
have the same type.
• Performs the arithmetic using the output class. Results might not match
MATLAB due to differences in rounding errors.
rem
• If one of the inputs has type int64 or uint64, then both inputs must
have the same type.
—
round
Set Operations in MATLAB
Function
Remarks and Limitations
intersect
• When you do not specify the 'rows' option:
• Inputs A and B must be vectors. If you specify the 'legacy' option,
inputs A and B must be row vectors.
• The first dimension of a variable-size row vector must have fixed
length 1. The second dimension of a variable-size column vector must
have fixed length 1.
• The input [] is not supported. Use a 1-by-0 or 0-by-1 input, for
example, zeros(1,0), to represent the empty set.
• If you specify the 'legacy' option, empty outputs are row vectors,
1-by-0, never 0-by-0.
• When you specify both the 'legacy' option and the 'rows' option, the
outputs ia and ib are column vectors. If these outputs are empty, they
are 0-by-1, never 0-by-0, even if the output C is 0-by-0.
4-210
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
• When the setOrder is 'sorted' or when you specify the 'legacy'
option, the inputs must already be sorted in ascending order. The first
output, C, is sorted in ascending order.
• Complex inputs must be single or double.
• When one input is complex and the other input is real, do one of the
following:
• Set setOrder to 'stable'.
• Sort the real input in complex ascending order (by absolute
value). Suppose the real input is x. Use sort(complex(x))or
sortrows(complex(x)).
ismember
• The second input, B, must be sorted in ascending order.
• Complex inputs must be single or double.
issorted
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
setdiff
• When you do not specify the 'rows' option:
• Inputs A and B must be vectors. If you specify the 'legacy' option,
inputs A and B must be row vectors.
• The first dimension of a variable-size row vector must have fixed
length 1. The second dimension of a variable-size column vector must
have fixed length 1.
• Do not use [] to represent the empty set. Use a 1-by-0 or 0-by-1
input, for example, zeros(1,0), to represent the empty set.
• If you specify the 'legacy' option, empty outputs are row vectors,
1-by-0, never 0-by-0.
• When you specify both the 'legacy' and 'rows' options, the output
ia is a column vector. If ia is empty, it is 0-by-1, never 0-by-0, even if
the output C is 0-by-0.
• When the setOrder is 'sorted' or when you specify the 'legacy'
option, the inputs must already be sorted in ascending order. The first
output, C, is sorted in ascending order.
• Complex inputs must be single or double.
• When one input is complex and the other input is real, do one of the
following:
• Set setOrder to 'stable'.
• Sort the real input in complex ascending order (by absolute
value). Suppose the real input is x. Use sort(complex(x))or
sortrows(complex(x)).
4-212
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
setxor
• When you do not specify the 'rows' option:
• Inputs A and B must be vectors with the same orientation. If you
specify the 'legacy' option, inputs A and B must be row vectors.
• The first dimension of a variable-size row vector must have fixed
length 1. The second dimension of a variable-size column vector must
have fixed length 1.
• The input [] is not supported. Use a 1-by-0 or 0-by-1 input, for
example , zeros(1,0), to represent the empty set.
• If you specify the 'legacy' option, empty outputs are row vectors,
1-by-0, never 0-by-0.
• When you specify both the 'legacy' option and the 'rows' option, the
outputs ia and ib are column vectors. If these outputs are empty, they
are 0-by-1, never 0-by-0, even if the output C is 0-by-0.
• When the setOrder is 'sorted' or when you specify the 'legacy'
flag, the inputs must already be sorted in ascending order. The first
output, C, is sorted in ascending order.
• Complex inputs must be single or double.
• When one input is complex and the other input is real, do one of the
following:
• Set setOrder to 'stable'.
• Sort the real input in complex ascending order (by absolute
value). Suppose the real input is x. Use sort(complex(x))or
sortrows(complex(x)).
4-213
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
union
• When you do not specify the 'rows' option:
• Inputs A and B must be vectors with the same orientation. If you
specify the 'legacy' option, inputs A and B must be row vectors.
• The first dimension of a variable-size row vector must have fixed
length 1. The second dimension of a variable-size column vector must
have fixed length 1.
• The input [] is not supported. Use a 1-by-0 or 0-by-1 input, for
example , zeros(1,0), to represent the empty set.
• If you specify the 'legacy' option, empty outputs are row vectors,
1-by-0, never 0-by-0.
• When you specify both the 'legacy' option and the 'rows' option, the
outputs ia and ib are column vectors. If these outputs are empty, they
are 0-by-1, never 0-by-0, even if the output C is 0-by-0.
• When the setOrder is 'sorted' or when you specify the 'legacy'
option, the inputs must already be sorted in ascending order. The first
output, C, is sorted in ascending order.
• Complex inputs must be single or double.
• When one input is complex and the other input is real, do one of the
following:
• Set setOrder to 'stable'.
• Sort the real input in complex ascending order (by absolute
value). Suppose the real input is x. Use sort(complex(x))or
sortrows(complex(x)).
4-214
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
unique
• When you do not specify the'rows' option:
• The input A must be a vector. If you specify the 'legacy' option, the
input A must be a row vector.
• The first dimension of a variable-size row vector must have fixed
length 1. The second dimension of a variable-size column vector must
have fixed length 1.
• The input [] is not supported. Use a 1-by-0 or 0-by-1 input, for
example, zeros(1,0), to represent the empty set.
• If you specify the 'legacy' option, empty outputs are row vectors,
1-by-0, never 0-by-0.
• When you specify both the 'rows' option and the 'legacy'option,
outputs ia and ic are column vectors. If these outputs are empty, they
are 0-by-1, even if the output C is 0-by-0.
• When the setOrder is 'sorted' or when you specify the 'legacy'
option, the input A must already be sorted in ascending order. The first
output, C, is sorted in ascending order.
• Complex inputs must be single or double.
Signal Processing in MATLAB
Function
Remarks and Limitations
chol
—
conv
—
fft
• Length of input vector must be a power of 2.
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
fft2
• Length of input matrix dimensions must each be a power of 2.
fftn
• Length of input matrix dimensions must each be a power of 2.
fftshift
—
filter
• If supplied, dim must be a constant.
• v
freqspace
—
4-215
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
ifft
• Length of input vector must be a power of 2.
• Output of ifft block is complex.
• Does not support the 'symmetric' option.
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
• Length of input matrix dimensions must each be a power of 2.
ifft2
• Does not support the 'symmetric' option.
• Length of input matrix dimensions must each be a power of 2.
ifftn
• Does not support the 'symmetric' option.
ifftshift
—
svd
Uses a different SVD implementation than MATLAB. Because the singular
value decomposition is not unique, left and right singular vectors might
differ from those computed by MATLAB.
zp2tf
—
Signal Processing Toolbox
C and C++ code generation for the following functions requires the Signal Processing
Toolbox software. These functions do not support variable-size inputs, you must define
the size and type of the function inputs. For more information, see “Specifying Inputs in
Code Generation from MATLAB ”.
Note: Many Signal Processing Toolbox functions require constant inputs in generated
code. To specify a constant input for codegen, use coder.Constant.
Function
Remarks/Limitations
barthannwin
Window length must be a constant. Expressions or variables are allowed
if their values do not change.
bartlett
Window length must be a constant. Expressions or variables are allowed
if their values do not change.
besselap
Filter order must be a constant. Expressions or variables are allowed if
their values do not change.
4-216
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks/Limitations
bitrevorder
—
blackman
Window length must be a constant. Expressions or variables are allowed
if their values do not change.
blackmanharris
Window length must be a constant. Expressions or variables are allowed
if their values do not change.
bohmanwin
Window length must be a constant. Expressions or variables are allowed
if their values do not change.
buttap
Filter order must be a constant. Expressions or variables are allowed if
their values do not change.
butter
Filter coefficients must be constants. Expressions or variables are allowed
if their values do not change.
buttord
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
cfirpm
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
cheb1ap
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
cheb2ap
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
cheb1ord
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
cheb2ord
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
chebwin
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
cheby1
All Inputs must be constants. Expressions or variables are allowed if
their values do not change.
cheby2
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
db2pow
—
4-217
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks/Limitations
dct
C and C++ code generation for dct requires DSP System Toolbox
software.
Length of transform dimension must be a power of two. If specified, the
pad or truncation value must be constant. Expressions or variables are
allowed if their values do not change.
downsample
—
dpss
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
ellip
Inputs must be constant. Expressions or variables are allowed if their
values do not change.
ellipap
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
ellipord
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
filtfilt
Filter coefficients must be constants. Expressions or variables are allowed
if their values do not change.
findpeaks
—
fir1
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
fir2
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
fircls
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
fircls1
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
firls
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
firpm
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
firpmord
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks/Limitations
flattopwin
All inputs must be constants. Expressions or variables are allowed if their
values do not change.
freqz
When called with no output arguments, and without a semicolon at the
end, freqz returns the complex frequency response of the input filter,
evaluated at 512 points.
If the semicolon is added, the function produces a plot of the magnitude
and phase response of the filter.
See “freqz With No Output Arguments”.
gausswin
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
hamming
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
hann
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
idct
C and C++ code generation for idct requires DSP System Toolbox
software.
Length of transform dimension must be a power of two. If specified, the
pad or truncation value must be constant. Expressions or variables are
allowed if their values do not change.
intfilt
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
kaiser
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
kaiserord
—
levinson
C and C++ code generation for levinson requires DSP System Toolbox
software.
If specified, the order of recursion must be a constant. Expressions or
variables are allowed if their values do not change.
maxflat
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
4-219
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks/Limitations
nuttallwin
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
parzenwin
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
pow2db
—
rcosdesign
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
rectwin
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
resample
The upsampling and downsampling factors must be specified as
constants. Expressions or variables are allowed if their values do not
change.
sgolay
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
sosfilt
—
taylorwin
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
triang
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
tukeywin
All inputs must be constant. Expressions or variables are allowed if their
values do not change.
upfirdn
C and C++ code generation for upfirdn requires DSP System Toolbox
software.
Filter coefficients, upsampling factor, and downsampling factor must
be constants. Expressions or variables are allowed if their values do not
change.
Variable-size inputs are not supported.
upsample
Either declare input n as constant, or use the assert function in the
calling function to set upper bounds for n. For example,
assert(n<10)
xcorr
—
4-220
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks/Limitations
yulewalk
If specified, the order of recursion must be a constant. Expressions or
variables are allowed if their values do not change.
Special Values in MATLAB
Function
Remarks and Limitations
eps
• Supported for scalar fixed-point signals only.
• Supported for scalar, vector, and matrix, fi single and fi double
signals.
inf
• Dimensions must be real, nonnegative, integers.
intmax
—
intmin
—
NaN or nan
• Dimensions must be real, nonnegative, integers.
pi
—
realmax
—
realmin
—
Specialized Math in MATLAB
Function
Remarks and Limitations
beta
—
betainc
Always returns a complex result.
betaincinv
Always returns a complex result.
betaln
—
ellipke
—
erf
—
erfc
—
erfcinv
—
erfcx
—
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
erfinv
—
expint
—
gamma
—
gammainc
Output is always complex.
gammaincinv
Output is always complex.
gammaln
—
psi
—
Statistics in MATLAB
Function
Remarks and Limitations
corrcoef
• Row-vector input is only supported when the first two inputs are vectors
and nonscalar.
mean
• Does not support the 'native' output class option for integer types.
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
median
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”“Variable-Sizing Restrictions for Code Generation of Toolbox
Functions”
mode
• Does not support third output argument C (cell array).
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
std
“Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
var
• If supplied, dim must be a constant.
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions”
Statistics Toolbox
C and C++ code generation for the following functions requires the Statistics Toolbox
software.
4-222
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
betacdf
—
betainv
—
betapdf
—
betarnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
betastat
—
binocdf
—
binoinv
—
binopdf
—
binornd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
binostat
—
cdf
—
chi2cdf
—
chi2inv
—
chi2pdf
—
chi2rnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
chi2stat
—
evcdf
—
evinv
—
evpdf
—
4-223
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
evrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
evstat
—
expcdf
—
expinv
—
exppdf
—
exprnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
expstat
—
fcdf
—
finv
—
fpdf
—
frnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
fstat
—
gamcdf
—
gaminv
—
gampdf
—
gamrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
4-224
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
gamstat
—
geocdf
—
geoinv
—
geomean
—
geopdf
—
geornd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
geostat
—
gevcdf
—
gevinv
—
gevpdf
—
gevrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
gevstat
—
gpcdf
—
gpinv
—
gppdf
—
gprnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
gpstat
—
harmmean
—
hygecdf
—
4-225
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
hygeinv
—
hygepdf
—
hygernd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
hygestat
—
icdf
—
iqr
—
kurtosis
—
logncdf
—
logninv
—
lognpdf
—
lognrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
lognstat
—
mad
Input dim cannot be empty.
mnpdf
Input dim cannot be empty.
moment
If order is nonintegral and X is real, use moment(complex(X),
order).
nancov
If the input is variable-size and is [] at run time, returns [] not NaN.
nanmax
—
nanmean
—
nanmedian
—
nanmin
—
nanstd
—
4-226
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
nansum
—
nanvar
—
nbincdf
—
nbininv
—
nbinpdf
—
nbinrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
nbinstat
—
ncfcdf
—
ncfinv
—
ncfpdf
—
ncfrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
ncfstat
—
nctcdf
—
nctinv
—
nctpdf
—
nctrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
nctstat
—
ncx2cdf
—
4-227
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
ncx2rnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
ncx2stat
—
normcdf
—
norminv
—
normpdf
—
normrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
normstat
—
pdf
—
poisscdf
—
poissinv
—
poisspdf
—
poissrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
poisstat
—
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
prctile
• “Automatic dimension restriction”
• If the output Y is a vector, the orientation of Y differs from MATLAB
when all of the following are true:
• You do not supply the dim input.
• X is a variable-size array.
• X is not a variable-length vector.
• X is a vector at run time.
• The orientation of the vector X does not match the orientation of
the vector p.
In this case, the output Y matches the orientation of X not the
orientation of p.
quantile
—
randg
—
random
—
raylcdf
—
raylinv
—
raylpdf
—
raylrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
raylstat
—
skewness
—
tcdf
—
tinv
—
tpdf
—
4-229
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
trnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
tstat
—
unidcdf
—
unidinv
—
unidpdf
—
unidrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
unidstat
—
unifcdf
—
unifinv
—
unifpdf
—
unifrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
unifstat
—
wblcdf
—
wblinv
—
wblpdf
—
wblrnd
Can return a different sequence of numbers than MATLAB if either of
the following is true:
• The output is nonscalar.
• An input parameter is invalid for the distribution.
4-230
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
wblstat
—
zscore
—
String Functions in MATLAB
Function
Remarks and Limitations
bin2dec
• Does not match MATLAB when the input is empty.
bitmax
—
blanks
—
char
—
deblank
• Supports only inputs from the char class.
• Input values must be in the range 0-127.
dec2bin
• If input d is double, d must be less than 2^52.
• If input d is single, d must be less than 2^23.
• Unless you specify input n to be constant and n is large enough that the
output has a fixed number of columns regardless of the input values,
this function requires variable-sizing support. Without variable-sizing
support, n must be at least 52 for double, 23 for single, 16 for char,
32 for int32, 16 for int16, and so on.
dec2hex
• If input d is double, d must be less than 2^52.
• If input d is single, d must be less than 2^23.
• Unless you specify input n to be constant and n is large enough that the
output has a fixed number of columns regardless of the input values,
this function requires variable-sizing support. Without variable-sizing
support, n must be at least 13 for double, 6 for single, 4 for char, 8
for int32, 4 for int16, and so on.
hex2dec
—
hex2num
• For n = hex2num(S), size(S,2) <= length(num2hex(0))
ischar
—
isletter
• Input values from the char class must be in the range 0-127
isspace
• Input values from the char class must be in the range 0–127.
4-231
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
isstrprop
• Supports only inputs from char and integer classes.
• Input values must be in the range 0-127.
lower
• Supports only char inputs.
• Input values must be in the range 0-127.
num2hex
—
str2double
• Does not support cell arrays.
• Always returns a complex result.
strcmp
—
strcmpi
Input values from the char class must be in the range 0-127.
strfind
• Does not support cell arrays.
• If pattern does not exist in str, returns zeros(1,0) not []. To check
for an empty return, use isempty.
• Inputs must be character row vectors.
strjust
—
strncmp
—
strncmpi
• Input values from the char class must be in the range 0-127.
strrep
• Does not support cell arrays.
• If oldSubstr does not exist in origStr, returns blanks(0). To check
for an empty return, use isempty.
• Inputs must be character row vectors.
strtok
—
strtrim
• Supports only inputs from the char class.
• Input values must be in the range 0-127.
upper
• Supports only char inputs.
• Input values must be in the range 0-127.
4-232
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Functions and Objects Supported for C and C++ Code Generation — Category List
Structures in MATLAB
Function
Remarks and Limitations
isfield
• Does not support cell input for second argument
isstruct
—
struct
—
Trigonometry in MATLAB
Function
Remarks and Limitations
acos
• Generates an error during simulation and returns NaN in generated
code when the input value x is real, but the output should be complex.
To get the complex result, make the input value complex by passing in
complex(x).
acosd
—
acosh
• Generates an error during simulation and returns NaN in generated
code when the input value x is real, but the output should be complex.
To get the complex result, make the input value complex by passing in
complex(x).
acot
—
acotd
—
acoth
—
acsc
—
acscd
—
acsch
—
asec
—
asecd
—
asech
—
asin
• Generates an error during simulation and returns NaN in generated
code when the input value x is real, but the output should be complex.
To get the complex result, make the input value complex by passing in
complex(x).
asind
—
4-233
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4
Functions, Classes, and System Objects Supported for Code Generation
Function
Remarks and Limitations
asinh
—
atan
—
atan2
—
atan2d
—
atand
—
atanh
Generates an error during simulation and returns NaN in generated code
when the input value x is real, but the output should be complex. To get the
complex result, make the input value complex by passing in complex(x).
cos
—
cosd
—
cosh
—
cot
—
cotd
• In some cases, returns -Inf when MATLAB returns Inf.
• In some cases, returns Inf when MATLAB returns -Inf.
coth
—
csc
—
cscd
• In some cases, returns -Inf when MATLAB returns Inf.
• In some cases, returns Inf when MATLAB returns -Inf.
csch
—
hypot
—
sec
—
secd
• In some cases, returns -Inf when MATLAB returns Inf.
• In some cases, returns Inf when MATLAB returns -Inf.
sech
—
sin
—
sind
—
sinh
—
tan
—
4-234
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Functions and Objects Supported for C and C++ Code Generation — Category List
Function
Remarks and Limitations
tand
• In some cases, returns -Inf when MATLAB returns Inf.
• In some cases, returns Inf when MATLAB returns -Inf.
tanh
—
4-235
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5
Defining MATLAB Variables for C/C++
Code Generation
• “Variables Definition for Code Generation” on page 5-2
• “Best Practices for Defining Variables for C/C++ Code Generation” on page 5-3
• “Eliminate Redundant Copies of Variables in Generated Code” on page 5-7
• “Reassignment of Variable Properties” on page 5-9
• “Define and Initialize Persistent Variables” on page 5-10
• “Reuse the Same Variable with Different Properties” on page 5-11
• “Avoid Overflows in for-Loops” on page 5-15
• “Supported Variable Types” on page 5-17
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5
Defining MATLAB Variables for C/C++ Code Generation
Variables Definition for Code Generation
In the MATLAB language, variables can change their properties dynamically at run time
so you can use the same variable to hold a value of any class, size, or complexity. For
example, the following code works in MATLAB:
function x = foo(c) %#codegen
if(c>0)
x = 0;
else
x = [1 2 3];
end
disp(x);
end
However, statically-typed languages like C must be able to determine variable properties
at compile time. Therefore, for C/C++ code generation, you must explicitly define the
class, size, and complexity of variables in MATLAB source code before using them. For
example, rewrite the above source code with a definition for x:
function x = foo(c) %#codegen
x = zeros(1,3);
if(c>0)
x = 0;
else
x = [1 2 3];
end
disp(x);
end
For more information, see “Best Practices for Defining Variables for C/C++ Code
Generation” on page 5-3.
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Best Practices for Defining Variables for C/C++ Code Generation
Best Practices for Defining Variables for C/C++ Code Generation
In this section...
“Define Variables By Assignment Before Using Them” on page 5-3
“Use Caution When Reassigning Variables” on page 5-5
“Use Type Cast Operators in Variable Definitions” on page 5-5
“Define Matrices Before Assigning Indexed Variables” on page 5-6
Define Variables By Assignment Before Using Them
For C/C++ code generation, you should explicitly and unambiguously define the class,
size, and complexity of variables before using them in operations or returning them as
outputs. Define variables by assignment, but note that the assignment copies not only
the value, but also the size, class, and complexity represented by that value to the new
variable. For example:
Assignment:
Defines:
a = 14.7;
a as a real double scalar.
b = a;
b with properties of a (real double scalar).
c = zeros(5,2);
c as a real 5-by-2 array of doubles.
d = [1 2 3 4 5; 6 7 8 9 0];
d as a real 5-by-2 array of doubles.
y = int16(3);
y as a real 16-bit integer scalar.
Define properties this way so that the variable is defined on the required execution paths
during C/C++ code generation (see Defining a Variable for Multiple Execution Paths).
The data that you assign to a variable can be a scalar, matrix, or structure. If your
variable is a structure, define the properties of each field explicitly (see Defining Fields in
a Structure).
Initializing the new variable to the value of the assigned data sometimes results in
redundant copies in the generated code. To avoid redundant copies, you can define
variables without initializing their values by using the coder.nullcopy construct as
described in “Eliminate Redundant Copies of Variables in Generated Code” on page
5-7.
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5
Defining MATLAB Variables for C/C++ Code Generation
When you define variables, they are local by default; they do not persist between function
calls. To make variables persistent, see “Define and Initialize Persistent Variables” on
page 5-10.
Defining a Variable for Multiple Execution Paths
Consider the following MATLAB code:
...
if c > 0
x = 11;
end
% Later in your code ...
if c > 0
use(x);
end
...
Here, x is assigned only if c > 0 and used only when c > 0. This code works in
MATLAB, but generates a compilation error during code generation because it detects
that x is undefined on some execution paths (when c <= 0),.
To make this code suitable for code generation, define x before using it:
x = 0;
...
if c > 0
x = 11;
end
% Later in your code ...
if c > 0
use(x);
end
...
Defining Fields in a Structure
Consider the following MATLAB code:
...
if c > 0
s.a = 11;
disp(s);
else
s.a = 12;
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Best Practices for Defining Variables for C/C++ Code Generation
s.b = 12;
end
% Try to use s
use(s);
...
Here, the first part of the if statement uses only the field a, and the else clause uses
fields a and b. This code works in MATLAB, but generates a compilation error during C/
C++ code generation because it detects a structure type mismatch. To prevent this error,
do not add fields to a structure after you perform certain operations on the structure. For
more information, see “Structure Definition for Code Generation”.
To make this code suitable for C/C++ code generation, define all fields of s before using it.
...
% Define all fields in structure s
s = struct(‘a’,0, ‘b’, 0);
if c > 0
s.a = 11;
disp(s);
else
s.a = 12;
s.b = 12;
end
% Use s
use(s);
...
Use Caution When Reassigning Variables
In general, you should adhere to the "one variable/one type" rule for C/C++ code
generation; that is, each variable must have a specific class, size and complexity.
Generally, if you reassign variable properties after the initial assignment, you get a
compilation error during code generation, but there are exceptions, as described in
“Reassignment of Variable Properties” on page 5-9.
Use Type Cast Operators in Variable Definitions
By default, constants are of type double. To define variables of other types, you can
use type cast operators in variable definitions. For example, the following code defines
variable y as an integer:
...
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5
Defining MATLAB Variables for C/C++ Code Generation
x = 15; % x is of type double by default.
y = uint8(x); % y has the value of x, but cast to uint8.
...
Define Matrices Before Assigning Indexed Variables
When generating C/C++ code from MATLAB, you cannot grow a variable by writing into
an element beyond its current size. Such indexing operations produce run-time errors.
You must define the matrix first before assigning values to its elements.
For example, the following initial assignment is not allowed for code generation:
g(3,2) = 14.6; % Not allowed for creating g
% OK for assigning value once created
For more information about indexing matrices, see “Incompatibility with MATLAB in
Matrix Indexing Operations for Code Generation” on page 7-30.
5-6
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Eliminate Redundant Copies of Variables in Generated Code
Eliminate Redundant Copies of Variables in Generated Code
In this section...
“When Redundant Copies Occur” on page 5-7
“How to Eliminate Redundant Copies by Defining Uninitialized Variables” on page
5-7
“Defining Uninitialized Variables” on page 5-8
When Redundant Copies Occur
During C/C++ code generation, MATLAB checks for statements that attempt to access
uninitialized memory. If it detects execution paths where a variable is used but is
potentially not defined, it generates a compile-time error. To prevent these errors, define
variables by assignment before using them in operations or returning them as function
outputs.
Note, however, that variable assignments not only copy the properties of the assigned
data to the new variable, but also initialize the new variable to the assigned value.
This forced initialization sometimes results in redundant copies in C/C++ code. To
eliminate redundant copies, define uninitialized variables by using the coder.nullcopy
function, as described in “How to Eliminate Redundant Copies by Defining Uninitialized
Variables” on page 5-7.
How to Eliminate Redundant Copies by Defining Uninitialized Variables
1
Define the variable with coder.nullcopy.
2
Initialize the variable before reading it.
When the uninitialized variable is an array, you must initialize all of its elements
before passing the array as an input to a function or operator — even if the function
or operator does not read from the uninitialized portion of the array.
What happens if you access uninitialized data?
Uninitialized memory contains arbitrary values. Therefore, accessing uninitialized
data may lead to segmentation violations or nondeterministic program behavior
(different runs of the same program may yield inconsistent results).
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5
Defining MATLAB Variables for C/C++ Code Generation
Defining Uninitialized Variables
In the following code, the assignment statement X = zeros(1,N) not only defines X to
be a 1-by-5 vector of real doubles, but also initializes each element of X to zero.
function X = fcn %#codegen
N = 5;
X = zeros(1,N);
for i = 1:N
if mod(i,2) == 0
X(i) = i;
else
X(i) = 0;
end
end
This forced initialization creates an extra copy in the generated code. To eliminate this
overhead, use coder.nullcopy in the definition of X:
function X = fcn2 %#codegen
N = 5;
X = coder.nullcopy(zeros(1,N));
for i = 1:N
if mod(i,2) == 0
X(i) = i;
else
X(i) = 0;
end
end
5-8
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Reassignment of Variable Properties
Reassignment of Variable Properties
For C/C++ code generation, there are certain variables that you can reassign after the
initial assignment with a value of different class, size, or complexity:
Dynamically sized variables
A variable can hold values that have the same class and complexity but different sizes.
If the size of the initial assignment is not constant, the variable is dynamically sized in
generated code. For more information, see “Variable-Size Data”.
Variables reused in the code for different purposes
You can reassign the type (class, size, and complexity) of a variable after the initial
assignment if each occurrence of the variable can have only one type. In this case, the
variable is renamed in the generated code to create multiple independent variables.
For more information, see “Reuse the Same Variable with Different Properties” on page
5-11.
5-9
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5
Defining MATLAB Variables for C/C++ Code Generation
Define and Initialize Persistent Variables
Persistent variables are local to the function in which they are defined, but they retain
their values in memory between calls to the function. To define persistent variables for C/
C++ code generation, use the persistent statement, as in this example:
persistent PROD_X;
The definition should appear at the top of the function body, after the header and
comments, but before the first use of the variable. During code generation, the value of
the persistent variable is initialized to an empty matrix by default. You can assign your
own value after the definition by using the isempty statement, as in this example:
function findProduct(inputvalue) %#codegen
persistent PROD_X
if isempty(PROD_X)
PROD_X = 1;
end
PROD_X = PROD_X * inputvalue;
end
5-10
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Reuse the Same Variable with Different Properties
Reuse the Same Variable with Different Properties
In this section...
“When You Can Reuse the Same Variable with Different Properties” on page 5-11
“When You Cannot Reuse Variables” on page 5-11
“Limitations of Variable Reuse” on page 5-14
When You Can Reuse the Same Variable with Different Properties
You can reuse (reassign) an input, output, or local variable with different class, size, or
complexity if MATLAB can unambiguously determine the properties of each occurrence
of this variable during C/C++ code generation. If so, MATLAB creates separate uniquely
named local variables in the generated code. You can view these renamed variables in
the code generation report (see “Viewing Variables in Your MATLAB Code”).
A common example of variable reuse is in if-elseif-else or switch-case
statements. For example, the following function example1 first uses the variable t in an
if statement, where it holds a scalar double, then reuses t outside the if statement to
hold a vector of doubles.
function y = example1(u) %#codegen
if all(all(u>0))
% First, t is used to hold a scalar double value
t = mean(mean(u)) / numel(u);
u = u - t;
end
% t is reused to hold a vector of doubles
t = find(u > 0);
y = sum(u(t(2:end-1)));
To compile this example and see how MATLAB renames the reused variable t, see
Variable Reuse in an if Statement.
When You Cannot Reuse Variables
You cannot reuse (reassign) variables if it is not possible to determine the class, size, and
complexity of an occurrence of a variable unambiguously during code generation. In this
case, variables cannot be renamed and a compilation error occurs.
For example, the following example2 function assigns a fixed-point value to x in the if
statement and reuses x to store a matrix of doubles in the else clause. It then uses x
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5
Defining MATLAB Variables for C/C++ Code Generation
after the if-else statement. This function generates a compilation error because after
the if-else statement, variable x can have different properties depending on which ifelse clause executes.
function y = example2(use_fixpoint, data) %#codegen
if use_fixpoint
% x is fixed-point
x = fi(data, 1, 12, 3);
else
% x is a matrix of doubles
x = data;
end
% When x is reused here, it is not possible to determine its
% class, size, and complexity
t = sum(sum(x));
y = t > 0;
end
Variable Reuse in an if Statement
To see how MATLAB renames a reused variable t:
1
Create a MATLAB file example1.m containing the following code.
function y = example1(u) %#codegen
if all(all(u>0))
% First, t is used to hold a scalar double value
t = mean(mean(u)) / numel(u);
u = u - t;
end
% t is reused to hold a vector of doubles
t = find(u > 0);
y = sum(u(t(2:end-1)));
end
2
Compile example1.
For example, to generate a MEX function, enter:
codegen -o example1x -report example1.m -args {ones(5,5)}
Note: codegen requires a MATLAB Coder license.
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Reuse the Same Variable with Different Properties
When the compilation is complete, codegen generates a MEX function, example1x
in the current folder, and provides a link to the code generation report.
3
Open the code generation report.
4
In the MATLAB code pane of the code generation report, place your pointer over the
variable t inside the if statement.
The code generation report highlights both instances of t in the if statement
because they share the same class, size, and complexity. It displays the data type
information for t at this point in the code. Here, t is a scalar double.
5
In the MATLAB code pane of the report, place your pointer over the variable t
outside the for-loop.
This time, the report highlights both instances of t outside the if statement. The
report indicates that t might hold up to 25 doubles. The size of t is :25, that is, a
column vector containing a maximum of 25 doubles.
6
Click the Variables tab to view the list of variables used in example1.
5-13
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5
Defining MATLAB Variables for C/C++ Code Generation
The report displays a list of the variables in example1. There are two uniquely
named local variables t>1 and t>2.
7
In the list of variables, place your pointer over t>1.
The code generation report highlights both instances of t in the if statement.
8
In the list of variables, place your pointer over t>2
The code generation report highlights both instances of t outside the if statement.
Limitations of Variable Reuse
The following variables cannot be renamed in generated code:
• Persistent variables.
• Global variables.
• Variables passed to C code using coder.ref, coder.rref, coder.wref.
• Variables whose size is set using coder.varsize.
• Variables whose names are controlled using coder.cstructname.
• The index variable of a for-loop when it is used inside the loop body.
• The block outputs of a MATLAB Function block in a Simulink model.
• Chart-owned variables of a MATLAB function in a Stateflow® chart.
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Avoid Overflows in for-Loops
Avoid Overflows in for-Loops
When memory integrity checks are enabled, if the code generation software detects that a
loop variable might overflow on the last iteration of the for-loop, it reports an error.
To avoid this error, use the workarounds provided in the following table.
Loop conditions causing the error
Workaround
• The loop counter increments by 1
Rewrite the loop so that the end value is
not equal to the maximum value of the
integer type. For example, replace:
• The end value equals the maximum
value of the integer type
• The loop is not covering the full range of N=intmax('int16')
for k=N-10:N
the integer type
with:
for k=1:10
• The loop counter decrements by 1
• The end value equals the minimum
value of the integer type
Rewrite the loop so that the end value is
not equal to the minimum value of the
integer type. For example, replace:
• The loop is not covering the full range of N=intmin('int32')
for k=N+10:-1:N
the integer type
with:
for k=10:-1:1
• The loop counter increments or
decrements by 1
• The start value equals the minimum or
maximum value of the integer type
Rewrite the loop casting the type of the
loop counter start, step, and end values to
a bigger integer or to double For example,
rewrite:
• The end value equals the maximum or
minimum value of the integer type
M= intmin('int16');
N= intmax('int16');
for k=M:N
The loop covers the full range of the integer % Loop body
end
type.
to
M= intmin('int16');
N= intmax('int16');
for k=int32(M):int32(N)
% Loop body
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5
Defining MATLAB Variables for C/C++ Code Generation
Loop conditions causing the error
Workaround
end
• The loop counter increments or
decrements by a value not equal to 1
• On last loop iteration, the loop variable
value is not equal to the end value
Rewrite the loop so that the loop variable
on the last loop iteration is equal to the end
value.
Note: The software error checking is
conservative. It may incorrectly report a
loop as being potentially infinite.
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Supported Variable Types
Supported Variable Types
You can use the following data types for C/C++ code generation from MATLAB:
Type
Description
char
Character array (string)
complex
Complex data. Cast function takes real and imaginary
components
double
Double-precision floating point
int8, int16, int32,
int64
Signed integer
logical
Boolean true or false
single
Single-precision floating point
struct
Structure
uint8, uint16,
uint32, uint64
Unsigned integer
Fixed-point
See “Fixed-Point Data Types”.
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6
Defining Data for Code Generation
• “Data Definition for Code Generation” on page 6-2
• “Code Generation for Complex Data” on page 6-4
• “Code Generation for Characters” on page 6-6
• “Array Size Restrictions for Code Generation” on page 6-7
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6
Defining Data for Code Generation
Data Definition for Code Generation
To generate efficient standalone code, you must define the following types and classes
of data differently than you normally would when running your code in the MATLAB
environment:
Data
What's Different
More Information
Arrays
Maximum number of
elements is restricted
“Array Size Restrictions for
Code Generation” on page
6-7
Complex numbers
• Complexity of variables
must be set at time of
assignment and before
first use
“Code Generation for
Complex Data” on page
6-4
• Expressions containing
a complex number or
variable evaluate to a
complex result, even if
the result is zero
Note: Because MATLAB
does not support complex
integer arithmetic, you
cannot generate code for
functions that use complex
integer arithmetic
Characters
Restricted to 8 bits of
precision
“Code Generation for
Characters” on page 6-6
Enumerated data
• Supports integer-based
enumerated types only
“Enumerated Data”
• Restricted use in
switch statements and
for-loops
Function handles
• Same bound variable
cannot reference
6-2
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“Function Handles”
Data Definition for Code Generation
Data
What's Different
different function
handles
More Information
• Cannot pass function
handles to or from
primary or extrinsic
functions
• Cannot view function
handles from the
debugger
6-3
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6
Defining Data for Code Generation
Code Generation for Complex Data
In this section...
“Restrictions When Defining Complex Variables” on page 6-4
“Expressions With Complex Operands Yield Complex Results” on page 6-4
Restrictions When Defining Complex Variables
For code generation, you must set the complexity of variables at the time of assignment,
either by assigning a complex constant or using the complex function, as in these
examples:
x = 5 + 6i; % x is a complex number by assignment.
y = 7 + 8j; % y is a complex number by assignment.
x = complex(5,6); % x is the complex number 5 + 6i.
Once you set the type and size of a variable, you cannot cast it to another type or size. In
the following example, the variable x is defined as complex and stays complex:
x = 1 + 2i; % Defines x as a complex variable.
y = int16(x); % Real and imaginary parts of y are int16.
x = 3; % x now has the value 3 + 0i.
Mismatches can also occur when you assign a real operand the complex result of an
operation:
z = 3; % Sets type of z to double (real)
z = 3 + 2i; % ERROR: cannot recast z to complex
As a workaround, set the complexity of the operand to match the result of the operation:
m = complex(3); % Sets m to complex variable of value 3 + 0i
m = 5 + 6.7i; % Assigns a complex result to a complex number
Expressions With Complex Operands Yield Complex Results
In general, expressions that contain one or more complex operands produce a complex
result in generated code, even if the value of the result is zero. Consider the following
example:
x = 2 + 3i;
6-4
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Code Generation for Complex Data
y = 2 - 3i;
z = x + y; % z is 4 + 0i.
In MATLAB, this code generates the real result z = 4. During code generation, the
types for x and y are known, but their values are not. Because either or both operands in
this expression are complex, z is defined as a complex variable requiring storage for both
a real and an imaginary part. z equals the complex result 4 + 0i in generated code, not
4 as in MATLAB code.
Exceptions to this behavior are:
• Values returned by MEX functions are real when the imaginary part of the value is
zero.
function y = foo()
y = 1 + 0i;
% y is complex with imaginary part equal to zero
end
The MEX function foo_mex returns the real value 1.
z = foo_mex
• Complex arguments to extrinsic functions are real when the imaginary part of the
argument is zero.
function y = foo()
coder.extrinsic('sqrt')
x = 1 + 0i;
% x is complex
y = sqrt(x); % x is real, y is real
end
• Functions that take complex arguments but produce real results return real values.
y = real(x); % y is the real part of the complex number x.
y = imag(x); % y is the real-valued imaginary part of x.
y = isreal(x); % y is false (0) for a complex number x.
• Functions that take real arguments but produce complex results return complex
values.
z = complex(x,y); % z is a complex number for a real x and y.
6-5
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6
Defining Data for Code Generation
Code Generation for Characters
The complete set of Unicode® characters is not supported for code generation. Characters
are restricted to 8 bits of precision in generated code. Because many mathematical
operations require more than 8 bits of precision, it is recommended that you do not
perform arithmetic with characters if you intend to generate code from your MATLAB
algorithm.
6-6
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Array Size Restrictions for Code Generation
Array Size Restrictions for Code Generation
For code generation, the maximum number of elements of an array is constrained by the
code generation software and the target hardware.
For fixed-size arrays and variable-size arrays that use static memory allocation, the
maximum number of elements is the smaller of:
• intmax('int32').
• The largest integer that fits in the C int data type on the target hardware.
For variable-size arrays that use dynamic memory allocation, the maximum number of
elements is the smaller of:
• intmax('int32').
• The largest power of 2 that fits in the C int data type on the target hardware.
These restrictions apply even on a 64-bit platform.
For a fixed-size array, if the number of elements exceeds the maximum, the code
generation software reports an error at compile time. For a variable-size array, if the
number of elements exceeds the maximum during execution of the generated MEX in
MATLAB, the MEX code reports an error. Generated standalone code cannot report
array size violations.
See Also
• “Variable-Size Data”
• coder.HardwareImplementation
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6-8
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7
Code Generation for Variable-Size
Data
• “What Is Variable-Size Data?” on page 7-2
• “Variable-Size Data Definition for Code Generation” on page 7-3
• “Bounded Versus Unbounded Variable-Size Data” on page 7-4
• “Control Memory Allocation of Variable-Size Data” on page 7-5
• “Specify Variable-Size Data Without Dynamic Memory Allocation” on page 7-6
• “Variable-Size Data in Code Generation Reports” on page 7-9
• “Define Variable-Size Data for Code Generation” on page 7-11
• “C Code Interface for Arrays” on page 7-17
• “Diagnose and Fix Variable-Size Data Errors” on page 7-21
• “Incompatibilities with MATLAB in Variable-Size Support for Code Generation” on
page 7-25
• “Variable-Sizing Restrictions for Code Generation of Toolbox Functions” on page
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7
Code Generation for Variable-Size Data
What Is Variable-Size Data?
Variable-size data is data whose size can change at run time. By contrast, fixed-size data
is data whose size is known and locked at compile time and, therefore, cannot change at
run time.
For example, in the following MATLAB function nway, B is a variable-size array; its
length is not known at compile time.
function B = nway(A,n)
% Compute average of every N elements of A and put them in B.
if ((mod(numel(A),n) == 0) && (n>=1 && n<=numel(A)))
B = ones(1,numel(A)/n);
k = 1;
for i = 1 : numel(A)/n
B(i) = mean(A(k + (0:n-1)));
k = k + n;
end
else
error('n <= 0 or does not divide number of elements evenly');
end
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Variable-Size Data Definition for Code Generation
Variable-Size Data Definition for Code Generation
In the MATLAB language, data can vary in size. By contrast, the semantics of generated
code constrains the class, complexity, and shape of every expression, variable, and
structure field. Therefore, for code generation, you must use each variable consistently.
Each variable must:
• Be either complex or real (determined at first assignment)
• Have a consistent shape
For fixed-size data, the shape is the same as the size returned in MATLAB. For
example, if size(A) == [4 5], the shape of variable A is 4 x 5. For variable-size
data, the shape can be abstract. That is, one or more dimensions can be unknown
(such as 4x? or ?x?).
By default, the compiler detects code logic that attempts to change these fixed attributes
after initial assignments, and flags these occurrences as errors during code generation.
However, you can override this behavior by defining variables or structure fields as
variable-size data.
For more information, see “Bounded Versus Unbounded Variable-Size Data” on page
7-4
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7
Code Generation for Variable-Size Data
Bounded Versus Unbounded Variable-Size Data
You can generate code for bounded and unbounded variable-size data. Bounded variablesize data has fixed upper bounds; this data can be allocated statically on the stack
or dynamically on the heap. Unbounded variable-size data does not have fixed upper
bounds; this data must be allocated on the heap. If you use unbounded data, you must
use dynamic memory allocation so that the compiler:
• Does not check for upper bounds
• Allocates memory on the heap instead of the stack
You can control the memory allocation of variable-size data. For more information, see
“Control Memory Allocation of Variable-Size Data” on page 7-5.
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Control Memory Allocation of Variable-Size Data
Control Memory Allocation of Variable-Size Data
Data whose size (in bytes) is greater than or equal to the dynamic memory allocation
threshold is allocated on the heap. The default dynamic memory allocation threshold is
64 kilobytes. Data whose size is less than this threshold is allocated on the stack.
Dynamic memory allocation is an expensive operation; the performance cost might be
too high for small data sets. If you use small variable-size data sets or data that does
not change size at run time, disable dynamic memory allocation. See “Control Dynamic
Memory Allocation”.
You can control memory allocation globally for your application by modifying the
dynamic memory allocation threshold. See “Generate Code for a MATLAB Function That
Expands a Vector in a Loop”. You can control memory allocation for individual variables
by specifying upper bounds. See “Specifying Upper Bounds for Variable-Size Data” on
page 7-6.
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7
Code Generation for Variable-Size Data
Specify Variable-Size Data Without Dynamic Memory Allocation
In this section...
“Fixing Upper Bounds Errors” on page 7-6
“Specifying Upper Bounds for Variable-Size Data” on page 7-6
Fixing Upper Bounds Errors
If MATLAB cannot determine or compute the upper bound, you must specify an upper
bound. See “Specifying Upper Bounds for Variable-Size Data” on page 7-6 and
“Diagnosing and Fixing Errors in Detecting Upper Bounds” on page 7-23
Specifying Upper Bounds for Variable-Size Data
• “When to Specify Upper Bounds for Variable-Size Data” on page 7-6
• “Specifying Upper Bounds on the Command Line for Variable-Size Inputs” on page
7-6
• “Specifying Unknown Upper Bounds for Variable-Size Inputs” on page 7-7
• “Specifying Upper Bounds for Local Variable-Size Data” on page 7-7
• “Using a Matrix Constructor with Nonconstant Dimensions” on page 7-8
When to Specify Upper Bounds for Variable-Size Data
When using static allocation on the stack during code generation, MATLAB must be able
to determine upper bounds for variable-size data. Specify the upper bounds explicitly for
variable-size data from external sources, such as inputs and outputs.
Specifying Upper Bounds on the Command Line for Variable-Size Inputs
Use the coder.typeof construct with the -args option on the codegen command line
(requires a MATLAB Coder license). For example:
codegen foo -args {coder.typeof(double(0),[3 100],1)}
This command specifies that the input to function foo is a matrix of real doubles
with two variable dimensions. The upper bound for the first dimension is 3; the upper
bound for the second dimension is 100. For a detailed explanation of this syntax, see
coder.typeof.
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Specify Variable-Size Data Without Dynamic Memory Allocation
Specifying Unknown Upper Bounds for Variable-Size Inputs
If you use dynamic memory allocation, you can specify that you don't know the upper
bounds of inputs. To specify an unknown upper bound, use the infinity constant Inf in
place of a numeric value. For example:
codegen foo -args {coder.typeof(double(0), [1 Inf])}
In this example, the input to function foo is a vector of real doubles without an upper
bound.
Specifying Upper Bounds for Local Variable-Size Data
When using static allocation, MATLAB uses a sophisticated analysis to calculate the
upper bounds of local data at compile time. However, when the analysis fails to detect an
upper bound or calculates an upper bound that is not precise enough for your application,
you need to specify upper bounds explicitly for local variables.
You do not need to specify upper bounds when using dynamic allocation on the heap. In
this case, MATLAB assumes variable-size data is unbounded and does not attempt to
determine upper bounds.
Constraining the Value of a Variable That Specifies Dimensions of Variable-Size Data
Use the assert function with relational operators to constrain the value of variables
that specify the dimensions of variable-size data. For example:
function y = dim_need_bound(n) %#codegen
assert (n <= 5);
L= ones(n,n);
M = zeros(n,n);
M = [L; M];
y = M;
This assert statement constrains input n to a maximum size of 5, defining L and M as
variable-sized matrices with upper bounds of 5 for each dimension.
Specifying the Upper Bounds for All Instances of a Local Variable
Use the coder.varsize function to specify the upper bounds for all instances of a local
variable in a function. For example:
function Y = example_bounds1(u) %#codegen
Y = [1 2 3 4 5];
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7
Code Generation for Variable-Size Data
coder.varsize('Y', [1 10]);
if (u > 0)
Y = [Y Y+u];
else
Y = [Y Y*u];
end
The second argument of coder.varsize specifies the upper bound for each instance
of the variable specified in the first argument. In this example, the argument [1 10]
indicates that for every instance of Y:
• First dimension is fixed at size 1
• Second dimension can grow to an upper bound of 10
By default, coder.varsize assumes dimensions of 1 are fixed size. For more
information, see the coder.varsize reference page.
Using a Matrix Constructor with Nonconstant Dimensions
You can define a variable-size matrix by using a constructor with nonconstant
dimensions. For example:
function y = var_by_assign(u) %#codegen
if (u > 0)
y = ones(3,u);
else
y = zeros(3,1);
end
If you are not using dynamic memory allocation, you must also add an assert statement
to provide upper bounds for the dimensions. For example:
function y = var_by_assign(u) %#codegen
assert (u < 20);
if (u > 0)
y = ones(3,u);
else
y = zeros(3,1);
end
7-8
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Variable-Size Data in Code Generation Reports
Variable-Size Data in Code Generation Reports
In this section...
“What Reports Tell You About Size” on page 7-9
“How Size Appears in Code Generation Reports” on page 7-10
“How to Generate a Code Generation Report” on page 7-10
What Reports Tell You About Size
Code generation reports:
• Differentiate fixed-size from variable-size data
• Identify variable-size data with unknown upper bounds
• Identify variable-size data with fixed dimensions
If you define a variable-size array and then subsequently fix the dimensions of this
array in the code, the report appends * to the size of the variable. In the generated C
code, this variable appears as a variable-size array, but the size of its dimensions does
not change during execution.
• Provide guidance on how to fix size mismatch and upper bounds errors.
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7
Code Generation for Variable-Size Data
How Size Appears in Code Generation Reports
:? means variable size,
unknown upper bound
No colon prefix (:)
means fixed size
:100 means variable size,
upper bound = 100
* means that you declared y as variable size,
but subsequently fixed its dimensions
How to Generate a Code Generation Report
Add the -report option to your codegen command.
7-10
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Define Variable-Size Data for Code Generation
Define Variable-Size Data for Code Generation
In this section...
“When to Define Variable-Size Data Explicitly” on page 7-11
“Using a Matrix Constructor with Nonconstant Dimensions” on page 7-11
“Inferring Variable Size from Multiple Assignments” on page 7-12
“Defining Variable-Size Data Explicitly Using coder.varsize” on page 7-13
When to Define Variable-Size Data Explicitly
For code generation, you must assign variables to have a specific class, size, and
complexity before using them in operations or returning them as outputs. Generally, you
cannot reassign variable properties after the initial assignment. Therefore, attempts to
grow a variable or structure field after assigning it a fixed size might cause a compilation
error. In these cases, you must explicitly define the data as variable sized using one of
these methods:
Method
See
Assign the data from a variable-size matrix “Using a Matrix Constructor with
constructor such as
Nonconstant Dimensions” on page 7-11
• ones
• zeros
• repmat
Assign multiple, constant sizes to the
same variable before using (reading) the
variable.
“Inferring Variable Size from Multiple
Assignments” on page 7-12
Define all instances of a variable to be
variable sized
“Defining Variable-Size Data Explicitly
Using coder.varsize” on page 7-13
Using a Matrix Constructor with Nonconstant Dimensions
You can define a variable-size matrix by using a constructor with nonconstant
dimensions. For example:
7-11
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7
Code Generation for Variable-Size Data
function y = var_by_assign(u) %#codegen
if (u > 0)
y = ones(3,u);
else
y = zeros(3,1);
end
If you are not using dynamic memory allocation, you must also add an assert statement
to provide upper bounds for the dimensions. For example:
function y = var_by_assign(u) %#codegen
assert (u < 20);
if (u > 0)
y = ones(3,u);
else
y = zeros(3,1);
end
Inferring Variable Size from Multiple Assignments
You can define variable-size data by assigning multiple, constant sizes to the same
variable before you use (read) the variable in your code. When MATLAB uses static
allocation on the stack for code generation, it infers the upper bounds from the largest
size specified for each dimension. When you assign the same size to a given dimension
across all assignments, MATLAB assumes that the dimension is fixed at that size. The
assignments can specify different shapes as well as sizes.
When dynamic memory allocation is used, MATLAB does not check for upper bounds; it
assumes variable-size data is unbounded.
Inferring Upper Bounds from Multiple Definitions with Different Shapes
function y = var_by_multiassign(u) %#codegen
if (u > 0)
y = ones(3,4,5);
else
y = zeros(3,1);
end
When static allocation is used, this function infers that y is a matrix with three
dimensions, where:
• First dimension is fixed at size 3
7-12
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Define Variable-Size Data for Code Generation
• Second dimension is variable with an upper bound of 4
• Third dimension is variable with an upper bound of 5
The code generation report represents the size of matrix y like this:
When dynamic allocation is used, the function analyzes the dimensions of y differently:
• First dimension is fixed at size 3
• Second and third dimensions are unbounded
In this case, the code generation report represents the size of matrix y like this:
Defining Variable-Size Data Explicitly Using coder.varsize
Use the function coder.varsize to define one or more variables or structure fields as
variable-size data. Optionally, you can also specify which dimensions vary along with
their upper bounds (see “Specifying Which Dimensions Vary” on page 7-14). For
example:
• Define B as a variable-size 2-by-2 matrix, where each dimension has an upper bound
of 64:
coder.varsize('B', [64 64]);
• Define B as a variable-size matrix:
coder.varsize('B');
When you supply only the first argument, coder.varsize assumes all dimensions of
B can vary and that the upper bound is size(B).
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7
Code Generation for Variable-Size Data
For more information, see the coder.varsize reference page.
Specifying Which Dimensions Vary
You can use the function coder.varsize to specify which dimensions vary. For
example, the following statement defines B as a row vector whose first dimension is fixed
at 2, but whose second dimension can grow to an upper bound of 16:
coder.varsize('B', [2, 16], [0 1])
The third argument specifies which dimensions vary. This argument must be a logical
vector or a double vector containing only zeros and ones. Dimensions that correspond to
zeros or false have fixed size; dimensions that correspond to ones or true vary in size.
coder.varsize usually treats dimensions of size 1 as fixed (see “Defining Variable-Size
Matrices with Singleton Dimensions” on page 7-14).
For more information about the syntax, see the coder.varsize reference page.
Allowing a Variable to Grow After Defining Fixed Dimensions
Function var_by_if defines matrix Y with fixed 2-by-2 dimensions before first use
(where the statement Y = Y + u reads from Y). However, coder.varsize defines Y
as a variable-size matrix, allowing it to change size based on decision logic in the else
clause:
function Y = var_by_if(u) %#codegen
if (u > 0)
Y = zeros(2,2);
coder.varsize('Y');
if (u < 10)
Y = Y + u;
end
else
Y = zeros(5,5);
end
Without coder.varsize, MATLAB infers Y to be a fixed-size, 2-by-2 matrix and
generates a size mismatch error during code generation.
Defining Variable-Size Matrices with Singleton Dimensions
A singleton dimension is a dimension for which size(A,dim) = 1. Singleton dimensions
are fixed in size when:
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Define Variable-Size Data for Code Generation
• You specify a dimension with an upper bound of 1 in coder.varsize expressions.
For example, in this function, Y behaves like a vector with one variable-size
dimension:
function Y = dim_singleton(u) %#codegen
Y = [1 2];
coder.varsize('Y', [1 10]);
if (u > 0)
Y = [Y 3];
else
Y = [Y u];
end
• You initialize variable-size data with singleton dimensions using matrix constructor
expressions or matrix functions.
For example, in this function, both X and Y behave like vectors where only their
second dimensions are variable sized:
function [X,Y] = dim_singleton_vects(u) %#codegen
Y = ones(1,3);
X = [1 4];
coder.varsize('Y','X');
if (u > 0)
Y = [Y u];
else
X = [X u];
end
You can override this behavior by using coder.varsize to specify explicitly that
singleton dimensions vary. For example:
function Y = dim_singleton_vary(u) %#codegen
Y = [1 2];
coder.varsize('Y', [1 10], [1 1]);
if (u > 0)
Y = [Y Y+u];
else
Y = [Y Y*u];
end
In this example, the third argument of coder.varsize is a vector of ones, indicating
that each dimension of Y varies in size. For more information, see the coder.varsize
reference page.
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7
Code Generation for Variable-Size Data
Defining Variable-Size Structure Fields
To define structure fields as variable-size arrays, use colon (:) as the index expression.
The colon (:) indicates that all elements of the array are variable sized. For example:
function y=struct_example() %#codegen
d = struct('values', zeros(1,0), 'color', 0);
data = repmat(d, [3 3]);
coder.varsize('data(:).values');
for i = 1:numel(data)
data(i).color = rand-0.5;
data(i).values = 1:i;
end
y = 0;
for i = 1:numel(data)
if data(i).color > 0
y = y + sum(data(i).values);
end;
end
The expression coder.varsize('data(:).values') defines the field values inside
each element of matrix data to be variable sized.
Here are other examples:
• coder.varsize('data.A(:).B')
In this example, data is a scalar variable that contains matrix A. Each element of
matrix A contains a variable-size field B.
• coder.varsize('data(:).A(:).B')
This expression defines field B inside each element of matrix A inside each element of
matrix data to be variable sized.
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C Code Interface for Arrays
C Code Interface for Arrays
In this section...
“C Code Interface for Statically Allocated Arrays” on page 7-17
“C Code Interface for Dynamically Allocated Arrays” on page 7-18
“Utility Functions for Creating emxArray Data Structures” on page 7-19
C Code Interface for Statically Allocated Arrays
In generated code, MATLAB contains two pieces of information about statically allocated
arrays: the maximum size of the array and its actual size.
For example, consider the MATLAB function uniquetol:
function B = uniquetol(A, tol) %#codegen
A = sort(A);
coder.varsize('B');
B = A(1);
k = 1;
for i = 2:length(A)
if abs(A(k) - A(i)) > tol
B = [B A(i)];
k = i;
end
end
Generate code for uniquetol specifying that input A is a variable-size real double vector
whose first dimension is fixed at 1 and second dimension can vary up to 100 elements.
codegen -config:lib -report uniquetol -args {coder.typeof(0,[1 100],1),coder.typeof(0)}
In the generated code, the function declaration is:
extern void uniquetol(const double A_data[100], const int A_size[2],...
double tol, emxArray_real_T *B);
There are two pieces of information about A:
• double A_data[100]: the maximum size of input A (where 100 is the maximum
size specified using coder.typeof).
• int A_size[2]: the actual size of the input.
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7
Code Generation for Variable-Size Data
C Code Interface for Dynamically Allocated Arrays
In generated code, MATLAB represents dynamically allocated data as a structure type
called emxArray. An embeddable version of the MATLAB mxArray, the emxArray is a
family of data types, specialized for all base types.
emxArray Structure Definition
typedef struct emxArray_<baseTypedef>
{
<baseType> *data;
int *size;
int allocatedSize;
int numDimensions;
boolean_T canFreeData;
} emxArray_<baseTypedef>;
where baseTypedef is the predefined type in rtwtypes.h corresponding to baseType.
For example, here's the definition for an emxArray of base type double with unknown
upper bounds:
typedef struct emxArray_real_T
{
double *data;
int *size;
int allocatedSize;
int numDimensions;
boolean_T canFreeData;
} emxArray_real_T;
Note that the predefined type corresponding to double is real_T. For more information
on the correspondence between built-in data types and predefined types in rtwtypes.h,
see “How MATLAB Coder Infers C/C++ Data Types”.
To define two variables, in1 and in2, of this type, use this statement:
emxArray_real_T *in1, *in2;
C Code Interface for Structure Fields
Field
Description
*data
Pointer to data of type <baseType>
*size
Pointer to first element of size vector. Length of
the vector equals the number of dimensions.
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C Code Interface for Arrays
Field
Description
allocatedSize
Number of elements currently allocated for the
array. If the size changes, MATLAB reallocates
memory based on the new size.
numDimensions
Number of dimensions of the size vector, that
is, the number of dimensions you can access
without crossing into unallocated or unused
memory
canFreeData
Boolean flag indicating how to deallocate
memory:
• true – MATLAB deallocates memory
automatically
• false – Calling program determines when to
deallocate memory
Utility Functions for Creating emxArray Data Structures
When you generate code that uses variable-size data, the code generation software
exports a set of utility functions that you can use to create and interact with emxArrays
in your generated code. To call these functions in your main C function, include the
generated header file. For example, when you generate code for function foo, include
foo_emxAPI.h in your main C function. For more information, see the “Write a C Main
Function” section in “Using Dynamic Memory Allocation for an "Atoms" Simulation”.
Note: The code generation software exports emxArray utility functions only for variablesize arrays that are entry-point function inputs or outputs.
Function
Arguments
Description
emxArray_<baseType>
*emxCreateWrapper_<baseType>
(...)
*data
num_rows
num_cols
Creates a new 2dimensional emxArray,
but does not allocate
it on the heap. Instead
uses memory provided
by the user and sets
canFreeData to
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7
Code Generation for Variable-Size Data
Function
Arguments
Description
false so it does not
inadvertently free user
memory, such as the
stack.
emxArray_<baseType>
*emxCreateWrapperND_<baseType>
(...)
*data
numDimensions
*size
Same as
emxCreateWrapper,
except it creates a
new N-dimensional
emxArray.
emxArray_<baseType>
*emxCreate_<baseType> (...)
num_rows
num_cols
Creates a new twodimensional emxArray
on the heap, initialized
to zero. All data
elements have the
data type specified by
baseTypeName.
emxArray_<baseType>
*emxCreateND_<baseType> (...)
numDimensions
*size
Same as emxCreate,
except it creates a
new N-dimensional
emxArray on the heap.
emxArray_<baseType>
*emxArray
*emxDestroyArray_<baseType> (...)
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Frees dynamic
memory allocated
by *emxCreate
and *emxCreateND
functions.
Diagnose and Fix Variable-Size Data Errors
Diagnose and Fix Variable-Size Data Errors
In this section...
“Diagnosing and Fixing Size Mismatch Errors” on page 7-21
“Diagnosing and Fixing Errors in Detecting Upper Bounds” on page 7-23
Diagnosing and Fixing Size Mismatch Errors
Check your code for these issues:
Assigning Variable-Size Matrices to Fixed-Size Matrices
You cannot assign variable-size matrices to fixed-size matrices in generated code.
Consider this example:
function Y = example_mismatch1(n) %#codegen
assert(n<10);
B = ones(n,n);
A = magic(3);
A(1) = mean(A(:));
if (n == 3)
A = B;
end
Y = A;
Compiling this function produces this error:
??? Dimension 1 is fixed on the left-hand side
but varies on the right ...
There are several ways to fix this error:
• Allow matrix A to grow by adding the coder.varsize construct:
function Y = example_mismatch1_fix1(n) %#codegen
coder.varsize('A');
assert(n<10);
B = ones(n,n);
A = magic(3);
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7
Code Generation for Variable-Size Data
A(1) = mean(A(:));
if (n == 3)
A = B;
end
Y = A;
• Explicitly restrict the size of matrix B to 3-by-3 by modifying the assert statement:
function Y = example_mismatch1_fix2(n) %#codegen
coder.varsize('A');
assert(n==3)
B = ones(n,n);
A = magic(3);
A(1) = mean(A(:));
if (n == 3)
A = B;
end
Y = A;
• Use explicit indexing to make B the same size as A:
function Y = example_mismatch1_fix3(n) %#codegen
assert(n<10);
B = ones(n,n);
A = magic(3);
A(1) = mean(A(:));
if (n == 3)
A = B(1:3, 1:3);
end
Y = A;
Empty Matrix Reshaped to Match Variable-Size
Specification
If you assign an empty matrix [] to variable-size data, MATLAB might silently reshape
the data in generated code to match a coder.varsize specification. For example:
function Y = test(u) %#codegen
Y = [];
coder.varsize(‘Y’, [1 10]);
If u < 0
Y = [Y u];
end
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Performing Binary Operations on Fixed and Variable-Size Operands
In this example, coder.varsize defines Y as a column vector of up to 10 elements,
so its first dimension is fixed at size 1. The statement Y = [] designates the first
dimension of Y as 0, creating a mismatch. The right hand side of the assignment is an
empty matrix and the left hand side is a variable-size vector. In this case, MATLAB
reshapes the empty matrix Y = [] in generated code to Y = zeros(1,0) so it matches
the coder.varsize specification.
Performing Binary Operations on Fixed and Variable-Size
Operands
You cannot perform binary operations on operands of different sizes. Operands have
different sizes if one has fixed dimensions and the other has variable dimensions. For
example:
function z = mismatch_operands(n) %#codegen
assert(n>=3 && n<10);
x = ones(n,n);
y = magic(3);
z = x + y;
When you compile this function, you get an error because y has fixed dimensions (3 x 3),
but x has variable dimensions. Fix this problem by using explicit indexing to make x the
same size as y:
function z = mismatch_operands_fix(n) %#codegen
assert(n>=3 && n<10);
x = ones(n,n);
y = magic(3);
z = x(1:3,1:3) + y;
Diagnosing and Fixing Errors in Detecting Upper Bounds
Check your code for these issues:
Using Nonconstant Dimensions in a Matrix Constructor
You can define variable-size data by assigning a variable to a matrix with nonconstant
dimensions. For example:
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7
Code Generation for Variable-Size Data
function y = dims_vary(u) %#codegen
if (u > 0)
y = ones(3,u);
else
y = zeros(3,1);
end
However, compiling this function generates an error because you did not specify an upper
bound for u.
There are several ways to fix the problem:
• Enable dynamic memory allocation and recompile. During code generation, MATLAB
does not check for upper bounds when it uses dynamic memory allocation for variablesize data.
• If you do not want to use dynamic memory allocation, add an assert statement
before the first use of u:
function y = dims_vary_fix(u) %#codegen
assert (u < 20);
if (u > 0)
y = ones(3,u);
else
y = zeros(3,1);
end
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Incompatibilities with MATLAB in Variable-Size Support for Code Generation
Incompatibilities with MATLAB in Variable-Size Support for Code
Generation
In this section...
“Incompatibility with MATLAB for Scalar Expansion” on page 7-25
“Incompatibility with MATLAB in Determining Size of Variable-Size N-D Arrays” on
page 7-27
“Incompatibility with MATLAB in Determining Size of Empty Arrays” on page 7-28
“Incompatibility with MATLAB in Determining Class of Empty Arrays” on page 7-29
“Incompatibility with MATLAB in Vector-Vector Indexing” on page 7-30
“Incompatibility with MATLAB in Matrix Indexing Operations for Code Generation” on
page 7-30
“Incompatibility with MATLAB in Concatenating Variable-Size Matrices” on page
7-31
“Dynamic Memory Allocation Not Supported for MATLAB Function Blocks” on page
7-32
Incompatibility with MATLAB for Scalar Expansion
Scalar expansion is a method of converting scalar data to match the dimensions of vector
or matrix data. Except for some matrix operators, MATLAB arithmetic operators work
on corresponding elements of arrays with equal dimensions. For vectors and rectangular
arrays, both operands must be the same size unless one is a scalar. If one operand is a
scalar and the other is not, MATLAB applies the scalar to every element of the other
operand—this property is known as scalar expansion.
During code generation, the standard MATLAB scalar expansion rules apply except
when operating on two variable-size expressions. In this case, both operands must be
the same size. The generated code does not perform scalar expansion even if one of the
variable-size expressions turns out to be scalar at run time. Instead, it generates a size
mismatch error at run time for MEX functions. Run-time error checking does not occur
for non-MEX builds; the generated code will have unspecified behavior.
For example, in the following function, z is scalar for the switch statement case 0 and
case 1. MATLAB applies scalar expansion when evaluating y(:) = z; for these two
cases.
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7
Code Generation for Variable-Size Data
function y = scalar_exp_test_err1(u) %#codegen
y = ones(3);
switch u
case 0
z = 0;
case 1
z = 1;
otherwise
z = zeros(3);
end
y(:) = z;
When you generate code for this function, the code generation software determines that z
is variable size with an upper bound of 3.
If you run the MEX function with u equal to zero or one, even though z is scalar at run
time, the generated code does not perform scalar expansion and a run-time error occurs.
scalar_exp_test_err1_mex(0)
Sizes mismatch: 9 ~= 1.
Error in scalar_exp_test_err1 (line 11)
y(:) = z;
Workaround
Use indexing to force z to be a scalar value:
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Incompatibilities with MATLAB in Variable-Size Support for Code Generation
function y = scalar_exp_test_err1(u) %#codegen
y = ones(3);
switch u
case 0
z = 0;
case 1
z = 1;
otherwise
z = zeros(3);
end
y(:) = z(1);
Incompatibility with MATLAB in Determining Size of Variable-Size N-D
Arrays
For variable-size N-D arrays, the size function can return a different result in generated
code than in MATLAB. In generated code, size(A) returns a fixed-length output
because it does not drop trailing singleton dimensions of variable-size N-D arrays. By
contrast, size(A) in MATLAB returns a variable-length output because it drops trailing
singleton dimensions.
For example, if the shape of array A is :?x:?x:? and size(A,3)==1, size(A) returns:
• Three-element vector in generated code
• Two-element vector in MATLAB code
Workarounds
If your application requires generated code to return the same size of variable-size N-D
arrays as MATLAB code, consider one of these workarounds:
• Use the two-argument form of size.
For example, size(A,n) returns the same answer in generated code and MATLAB
code.
• Rewrite size(A):
B = size(A);
X = B(1:ndims(A));
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7
Code Generation for Variable-Size Data
This version returns X with a variable-length output. However, you cannot pass
a variable-size X to matrix constructors such as zeros that require a fixed-size
argument.
Incompatibility with MATLAB in Determining Size of Empty Arrays
The size of an empty array in generated code might be different from its size in MATLAB
source code. The size might be 1x0 or 0x1 in generated code, but 0x0 in MATLAB.
Therefore, you should not write code that relies on the specific size of empty matrices.
For example, consider the following code:
function y = foo(n) %#codegen
x = [];
i=0;
while (i<10)
x = [5, x];
i=i+1;
end
if n > 0
x = [];
end
y=size(x);
end
Concatenation requires its operands to match on the size of the dimension that is not
being concatenated. In the preceding concatenation the scalar value has size 1x1 and x
has size 0x0. To support this use case, the code generation software determines the size
for x as [1 x :?]. Because there is another assignment x = [] after the concatenation,
the size of x in the generated code is 1x0 instead of 0x0.
Workaround
If your application checks whether a matrix is empty, use one of these workarounds:
• Rewrite your code to use the isempty function instead of the size function.
• Instead of using x=[] to create empty arrays, create empty arrays of a specific size
using zeros. For example:
function y = test_empty(n) %#codegen
x = zeros(1,0);
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Incompatibilities with MATLAB in Variable-Size Support for Code Generation
i=0;
while (i<10)
x = [5, x];
i=i+1;
end
if n > 0
x = zeros(1,0);
end
y=size(x);
end
Incompatibility with MATLAB in Determining Class of Empty Arrays
The class of an empty array in generated code can be different from its class in MATLAB
source code. Therefore, do not write code that relies on the class of empty matrices.
For example, consider the following code:
function y = fun(n)
x = [];
if n > 1
x = ['a', x];
end
y=class(x);
end
fun(0) returns double in MATLAB, but char in the generated code. When the
statement n > 1 is false, MATLAB does not execute x = ['a', x]. The class of x is
double, the class of the empty array. However, the code generation software considers
all execution paths. It determines that based on the statement x = ['a', x], the class
of x is char.
Workaround
Instead of using x=[] to create an empty array, create an empty array of a specific class.
For example, use blanks(0) to create an empty array of characters.
function y = fun(n)
x = blanks(0);
if n > 1
x = ['a', x];
end
y=class(x);
end
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7
Code Generation for Variable-Size Data
Incompatibility with MATLAB in Vector-Vector Indexing
In vector-vector indexing, you use one vector as an index into another vector. When
either vector is variable sized, you might get a run-time error during code generation.
Consider the index expression A(B). The general rule for indexing is that size(A(B))
== size(B). However, when both A and B are vectors, MATLAB applies a special rule:
use the orientation of A as the orientation of the output. For example, if size(A) == [1
5] and size(B) == [3 1], then size(A(B)) == [1 3].
In this situation, if the code generation software detects that both A and B are vectors
at compile time, it applies the special rule and gives the same result as MATLAB.
However, if either A or B is a variable-size matrix (has shape ?x?) at compile time, the
code generation software applies only the general indexing rule. Then, if both A and B
become vectors at run time, the code generation software reports a run-time error when
you run the MEX function. Run-time error checking does not occur for non-MEX builds;
the generated code will have unspecified behavior. It is best practice to generate and test
a MEX function before generating C code.
Workaround
Force your data to be a vector by using the colon operator for indexing: A(B(:)). For
example, suppose your code intentionally toggles between vectors and regular matrices at
run time. You can do an explicit check for vector-vector indexing:
...
if isvector(A) && isvector(B)
C = A(:);
D = C(B(:));
else
D = A(B);
end
...
The indexing in the first branch specifies that C and B(:) are compile-time vectors. As a
result, the code generation software applies the standard vector-vector indexing rule.
Incompatibility with MATLAB in Matrix Indexing Operations for Code
Generation
The following limitation applies to matrix indexing operations for code generation:
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Incompatibilities with MATLAB in Variable-Size Support for Code Generation
• Initialization of the following style:
for i = 1:10
M(i) = 5;
end
In this case, the size of M changes as the loop is executed. Code generation does not
support increasing the size of an array over time.
For code generation, preallocate M as highlighted in the following code.
M=zeros(1,10);
for i = 1:10
M(i) = 5;
end
The following limitation applies to matrix indexing operations for code generation when
dynamic memory allocation is disabled:
• M(i:j) where i and j change in a loop
During code generation, memory is not dynamically allocated for the size of the
expressions that change as the program executes. To implement this behavior, use
for-loops as shown in the following example:
...
M = ones(10,10);
for i=1:10
for j = i:10
M(i,j) = 2 * M(i,j);
end
end
...
Note: The matrix M must be defined before entering the loop, as shown in the
highlighted code.
Incompatibility with MATLAB in Concatenating Variable-Size Matrices
For code generation, when you concatenate variable-sized arrays, the dimensions that
are not being concatenated must match exactly.
7-31
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7
Code Generation for Variable-Size Data
Dynamic Memory Allocation Not Supported for MATLAB Function Blocks
You cannot use dynamic memory allocation for variable-size data in MATLAB Function
blocks. Use bounded instead of unbounded variable-size data.
7-32
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Variable-Sizing Restrictions for Code Generation of Toolbox Functions
Variable-Sizing Restrictions for Code Generation of Toolbox
Functions
In this section...
“Common Restrictions” on page 7-33
“Toolbox Functions with Variable Sizing Restrictions” on page 7-34
Common Restrictions
The following common restrictions apply to multiple toolbox functions, but only for code
generation. To determine which of these restrictions apply to specific library functions,
see the table in “Toolbox Functions with Variable Sizing Restrictions” on page 7-34.
Variable-length vector restriction
Inputs to the library function must be variable-length vectors or fixed-size vectors.
A variable-length vector is a variable-size array that has the shape 1x:n or :nx1
(one dimension is variable sized and the other is fixed at size 1). Other shapes are not
permitted, even if they are vectors at run time.
Automatic dimension restriction
When the function selects the working dimension automatically, it bases the selection
on the upper bounds for the dimension sizes. In the case of the sum function, sum(X)
selects its working dimension automatically, while sum(X, dim) uses dim as the explicit
working dimension.
For example, if X is a variable-size matrix with dimensions 1x:3x:5, sum(x) behaves
like sum(X,2) in generated code. In MATLAB, it behaves like sum(X,2) provided
size(X,2) is not 1. In MATLAB, when size(X,2) is 1, sum(X) behaves like
sum(X,3). Consequently, you get a run-time error if an automatically selected working
dimension assumes a length of 1 at run time.
To avoid the issue, specify the intended working dimension explicitly as a constant value.
Array-to-vector restriction
The function issues an error when a variable-size array that is not a variable-length
vector assumes the shape of a vector at run time. To avoid the issue, specify the input
explicitly as a variable-length vector instead of a variable-size array.
7-33
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7
Code Generation for Variable-Size Data
Array-to-scalar restriction
The function issues an error if a variable-size array assumes a scalar value at run time.
To avoid this issue, specify scalars as fixed size.
Toolbox Functions with Variable Sizing Restrictions
The following restrictions apply to specific toolbox functions, but only for code generation.
Function
Restrictions with Variable-Size Data
all
• See “Automatic dimension restriction” on page 7-33.
• An error occurs if you pass the first argument a
variable-size matrix that is 0-by-0 at run time.
any
• See “Automatic dimension restriction” on page 7-33.
• An error occurs if you pass the first argument a
variable-size matrix that is 0-by-0 at run time.
bsxfun
• Dimensions expand only where one input array or the
other has a fixed length of 1.
cat
• Dimension argument must be a constant.
• An error occurs if variable-size inputs are empty at run
time.
conv
• See “Variable-length vector restriction” on page
7-33.
• Input vectors must have the same orientation, either
both row vectors or both column vectors.
cov
• For cov(X), see“Array-to-vector restriction” on page
7-33.
cross
• Variable-size array inputs that become vectors at run
time must have the same orientation.
deconv
• For both arguments, see“Variable-length vector
restriction” on page 7-33.
detrend
• For first argument for row vectors only, see “Array-tovector restriction” on page 7-33 .
diag
• See “Array-to-vector restriction” on page 7-33 .
diff
• See “Automatic dimension restriction” on page 7-33.
7-34
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Variable-Sizing Restrictions for Code Generation of Toolbox Functions
Function
Restrictions with Variable-Size Data
• Length of the working dimension must be greater than
the difference order input when the input is variable
sized. For example, if the input is a variable-size matrix
that is 3-by-5 at run time, diff(x,2,1) works but
diff(x,5,1) generates a run-time error.
fft
• See “Automatic dimension restriction” on page 7-33.
filter
• For first and second arguments, see “Variable-length
vector restriction” on page 7-33.
• See “Automatic dimension restriction” on page 7-33.
hist
• For second argument, see “Variable-length vector
restriction” on page 7-33.
• For second input argument, see“Array-to-scalar
restriction” on page 7-34.
histc
• See “Automatic dimension restriction” on page 7-33.
ifft
• See “Automatic dimension restriction” on page 7-33.
ind2sub
• First input (the size vector input) must be fixed size.
interp1
• For the Y input and xi input, see“Array-to-vector
restriction” on page 7-33.
• Y input can become a column vector dynamically.
• A run-time error occurs if Y input is not a variablelength vector and becomes a row vector at run time.
ipermute
• Order input must be fixed size.
issorted
• For optional rows input, see “Variable-length vector
restriction” on page 7-33.
magic
• Argument must be a constant.
• Output can be fixed-size matrices only.
max
• See “Automatic dimension restriction” on page 7-33.
mean
• See “Automatic dimension restriction” on page 7-33.
• An error occurs if you pass as the first argument a
variable-size matrix that is 0-by-0 at run time.
7-35
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7
Code Generation for Variable-Size Data
Function
Restrictions with Variable-Size Data
median
• See “Automatic dimension restriction” on page 7-33.
• An error occurs if you pass as the first argument a
variable-size matrix that is 0-by-0 at run time.
min
• See “Automatic dimension restriction” on page 7-33.
mode
• See “Automatic dimension restriction” on page 7-33.
• An error occurs if you pass as the first argument a
variable-size matrix that is 0-by-0 at run time.
mtimes
• When an input is variable-size, MATLAB determines
whether to generate code for a general matrix*matrix
multiplication or a scalar*matrix multiplication, based
on whether one of the arguments is a fixed-size scalar.
If neither argument is a fixed-size scalar, the inner
dimensions of the two arguments must agree even if a
variable-size matrix input is a scalar at run time.
nchoosek
• The second input, k, must be a fixed-size scalar.
• The second input, k, must be a constant for static
allocation. If you enable dynamic allocation, the second
input can be a variable.
• You cannot create a variable-size array by passing in a
variable, k, unless you enable dynamic allocation.
permute
• Order input must be fixed-size.
planerot
• Input must be a fixed-size, two-element column vector.
It cannot be a variable-size array that takes on the size
2-by-1 at run time.
poly
• See “Variable-length vector restriction” on page
7-33.
polyfit
• For first and second arguments, see “Variable-length
vector restriction” on page 7-33.
prod
• See “Automatic dimension restriction” on page 7-33.
• An error occurs if you pass as the first argument a
variable-size matrix that is 0-by-0 at run time.
7-36
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Variable-Sizing Restrictions for Code Generation of Toolbox Functions
Function
Restrictions with Variable-Size Data
rand
• For an upper-bounded variable N, rand(1,N) produces
a variable-length vector of 1x:M where M is the upper
bound on N.
• For an upper-bounded variable N, rand([1,N]) may
produce a variable-length vector of :1x:M where M is the
upper bound on N.
Generated fixed-point code • For an upper-bounded variable N, randn(1,N) produces
enhancements
a variable-length vector of 1x:M where M is the upper
bound on N.
• For an upper-bounded variable N, randn([1,N]) may
produce a variable-length vector of :1x:M where M is the
upper bound on N.
Generated fixed-point code • For an upper-bounded variable N, randn(1,N) produces
enhancements
a variable-length vector of 1x:M where M is the upper
bound on N.
• For an upper-bounded variable N, randn([1,N]) may
produce a variable-length vector of :1x:M where M is the
upper bound on N.
reshape
• If the input is a variable-size array and the output
array has at least one fixed-length dimension, do not
specify the output dimension sizes in a size vector sz.
Instead, specify the output dimension sizes as scalar
values, sz1,...,szN. Specify fixed-size dimensions as
constants.
• When the input is a variable-size empty array, the
maximum dimension size of the output array (also
empty) cannot be larger than that of the input.
roots
• See “Variable-length vector restriction” on page
7-33.
7-37
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7
Code Generation for Variable-Size Data
Function
Restrictions with Variable-Size Data
shiftdim
• If you do not supply the second argument, the number
of shifts is determined at compilation time by the upper
bounds of the dimension sizes. Consequently, at run
time the number of shifts is constant.
• An error occurs if the dimension that is shifted to the
first dimension has length 1 at run time. To avoid the
error, supply the number of shifts as the second input
argument (must be a constant).
• First input argument must have the same number of
dimensions when you supply a positive number of shifts.
std
• See “Automatic dimension restriction” on page 7-33.
• An error occurs if you pass a variable-size matrix with
0-by-0 dimensions at run time.
sub2ind
• First input (the size vector input) must be fixed size.
sum
• See “Automatic dimension restriction” on page 7-33.
• An error occurs if you pass as the first argument a
variable-size matrix that is 0-by-0 at run time.
trapz
• See “Automatic dimension restriction” on page 7-33.
• An error occurs if you pass as the first argument a
variable-size matrix that is 0-by-0 at run time.
typecast
• See “Variable-length vector restriction” on page 7-33
on first argument.
var
• See “Automatic dimension restriction” on page 7-33.
• An error occurs if you pass a variable-size matrix with
0-by-0 dimensions at run time.
7-38
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8
Code Generation for MATLAB
Structures
• “Structure Definition for Code Generation” on page 8-2
• “Structure Operations Allowed for Code Generation” on page 8-3
• “Define Scalar Structures for Code Generation” on page 8-4
• “Define Arrays of Structures for Code Generation” on page 8-7
• “Make Structures Persistent” on page 8-9
• “Index Substructures and Fields” on page 8-10
• “Assign Values to Structures and Fields” on page 8-12
• “Pass Structure Arguments by Reference or by Value” on page 8-14
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8
Code Generation for MATLAB Structures
Structure Definition for Code Generation
To generate efficient standalone code for structures, you must define and use structures
differently than you normally would when running your code in the MATLAB
environment:
What's Different
More Information
Use a restricted set of operations.
“Structure Operations Allowed for Code
Generation” on page 8-3
Observe restrictions on properties and
values of scalar structures.
“Define Scalar Structures for Code
Generation” on page 8-4
Make structures uniform in arrays.
“Define Arrays of Structures for Code
Generation” on page 8-7
Reference structure fields individually
during indexing.
“Index Substructures and Fields” on page
8-10
Avoid type mismatch when assigning
values to structures and fields.
“Assign Values to Structures and Fields” on
page 8-12
8-2
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Structure Operations Allowed for Code Generation
Structure Operations Allowed for Code Generation
To generate efficient standalone code for MATLAB structures, you are restricted to the
following operations:
• Define structures as local and persistent variables by assignment and using the
struct function
• Index structure fields using dot notation
• Define primary function inputs as structures
• Pass structures to local functions
8-3
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8
Code Generation for MATLAB Structures
Define Scalar Structures for Code Generation
In this section...
“Restriction When Using struct” on page 8-4
“Restrictions When Defining Scalar Structures by Assignment” on page 8-4
“Adding Fields in Consistent Order on Each Control Flow Path” on page 8-4
“Restriction on Adding New Fields After First Use” on page 8-5
Restriction When Using struct
When you use the struct function to create scalar structures for code generation, you
cannot create structures of cell arrays.
Restrictions When Defining Scalar Structures by Assignment
When you define a scalar structure by assigning a variable to a preexisting structure,
you do not need to define the variable before the assignment. However, if you already
defined that variable, it must have the same class, size, and complexity as the structure
you assign to it. In the following example, p is defined as a structure that has the same
properties as the predefined structure S:
...
S = struct('a',
p = S;
...
0, 'b',
1, 'c',
2);
Adding Fields in Consistent Order on Each Control Flow Path
When you create a structure, you must add fields in the same order on each control flow
path. For example, the following code generates a compiler error because it adds the
fields of structure x in a different order in each if statement clause:
function
if u > 0
x.a =
x.b =
else
x.b =
y = fcn(u) %#codegen
10;
20;
30;
% Generates an error (on variable x)
8-4
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Define Scalar Structures for Code Generation
x.a = 40;
end
y = x.a + x.b;
In this example, the assignment to x.a comes before x.b in the first if statement
clause, but the assignments appear in reverse order in the else clause. Here is the
corrected code:
function y = fcn(u) %#codegen
if u > 0
x.a = 10;
x.b = 20;
else
x.a = 40;
x.b = 30;
end
y = x.a + x.b;
Restriction on Adding New Fields After First Use
You cannot add fields to a structure after you perform the following operations on the
structure:
• Reading from the structure
• Indexing into the structure array
• Passing the structure to a function
For example, consider this code:
...
x.c = 10; % Defines structure and creates field c
y = x; % Reads from structure
x.d = 20; % Generates an error
...
In this example, the attempt to add a new field d after reading from structure x
generates an error.
This restriction extends across the structure hierarchy. For example, you cannot add
a field to a structure after operating on one of its fields or nested structures, as in this
example:
function y = fcn(u) %#codegen
8-5
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8
Code Generation for MATLAB Structures
x.c = 10;
y = x.c;
x.d = 20; % Generates an error
In this example, the attempt to add a new field d to structure x after reading from the
structure's field c generates an error.
8-6
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Define Arrays of Structures for Code Generation
Define Arrays of Structures for Code Generation
In this section...
“Ensuring Consistency of Fields” on page 8-7
“Using repmat to Define an Array of Structures with Consistent Field Properties” on
page 8-7
“Defining an Array of Structures Using Concatenation” on page 8-8
Ensuring Consistency of Fields
When you create an array of MATLAB structures with the intent of generating code,
you must be sure that each structure field in the array has the same size, type, and
complexity.
Once you have created the array of structures, you can make the structure fields
variable-size using coder.varsize. For more information, see “Declare a variable-size
structure field.”.
Using repmat to Define an Array of Structures with Consistent Field
Properties
You can create an array of structures from a scalar structure by using the MATLAB
repmat function, which replicates and tiles an existing scalar structure:
1
Create a scalar structure, as described in “Define Scalar Structures for Code
Generation” on page 8-4.
2
Call repmat, passing the scalar structure and the dimensions of the array.
3
Assign values to each structure using standard array indexing and structure dot
notation.
For example, the following code creates X, a 1-by-3 array of scalar structures. Each
element of the array is defined by the structure s, which has two fields, a and b:
...
s.a = 0;
s.b = 0;
X = repmat(s,1,3);
X(1).a = 1;
8-7
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8
Code Generation for MATLAB Structures
X(2).a
X(3).a
X(1).b
X(2).b
X(3).b
...
=
=
=
=
=
2;
3;
4;
5;
6;
Defining an Array of Structures Using Concatenation
To create a small array of structures, you can use the concatenation operator, square
brackets ( [ ] ), to join one or more structures into an array (see “Concatenating
Matrices”). For code generation, the structures that you concatenate must have the same
size, class, and complexity.
For example, the following code uses concatenation and a local function to create the
elements of a 1-by-3 structure array:
...
W = [ sab(1,2) sab(2,3) sab(4,5) ];
function s = sab(a,b)
s.a = a;
s.b = b;
...
8-8
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Make Structures Persistent
Make Structures Persistent
To make structures persist, you define them to be persistent variables and initialize
them with the isempty statement, as described in “Define and Initialize Persistent
Variables” on page 5-10.
For example, the following function defines structure X to be persistent and initializes its
fields a and b:
function f(u)
persistent X
%#codegen
if isempty(X)
X.a = 1;
X.b = 2;
end
8-9
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8
Code Generation for MATLAB Structures
Index Substructures and Fields
Use these guidelines when indexing substructures and fields for code generation:
Reference substructure field values individually using dot
notation
For example, the following MATLAB code uses dot notation to index fields and
substructures:
...
substruct1.a1 = 15.2;
substruct1.a2 = int8([1 2;3 4]);
mystruct = struct('ele1',20.5,'ele2',single(100),
'ele3',substruct1);
substruct2 = mystruct;
substruct2.ele3.a2 = 2*(substruct1.a2);
...
The generated code indexes elements of the structures in this example by resolving
symbols as follows:
Dot Notation
Symbol Resolution
substruct1.a1
Field a1 of local structure substruct1
substruct2.ele3.a1
Value of field a1 of field ele3, a substructure of local structure
substruct2
substruct2.ele3.a2(1,1)
Value in row 1, column 1 of field a2 of field ele3, a substructure
of local structure substruct2
Reference field values individually in structure arrays
To reference the value of a field in a structure array, you must index into the array to
the structure of interest and then reference that structure's field individually using dot
notation, as in this example:
8-10
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Do not reference fields dynamically
...
y = X(1).a % Extracts the value of field a
% of the first structure in array X
...
To reference all the values of a particular field for each structure in an array, use this
notation in a for loop, as in this example:
...
s.a
s.b
X =
for
= 0;
= 0;
repmat(s,1,5);
i = 1:5
X(i).a = i;
X(i).b = i+1;
end
This example uses the repmat function to define an array of structures, each with two
fields a and b as defined by s. See “Define Arrays of Structures for Code Generation” on
page 8-7 for more information.
Do not reference fields dynamically
You cannot reference fields in a structure by using dynamic names, which express the
field as a variable expression that MATLAB evaluates at run time (see “Generate Field
Names from Variables”).
8-11
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8
Code Generation for MATLAB Structures
Assign Values to Structures and Fields
Use these guidelines when assigning values to a structure, substructure, or field for code
generation:
Field properties must be consistent across structure-tostructure assignments
If:
Then:
Assigning one structure to another
structure.
Define each structure with the same
number, type, and size of fields.
Assigning one structure to a substructure
of a different structure and vice versa.
Define the structure with the same
number, type, and size of fields as the
substructure.
Assigning an element of one structure to an The elements must have the same type and
element of another structure.
size.
Do not use field values as constants
The values stored in the fields of a structure are not treated as constant values in
generated code. Therefore, you cannot use field values to set the size or class of other
data. For example, the following code generates a compiler error if variable-sizing is
disabled:
...
Y.a = 3;
Y.b = 5;
X = zeros(Y.a,Y.b); % Generates an error
In this example, even though you set fields a and b of structure Y to the values 3 and 5
respectively, Y.a and Y.b are not constants in generated code. Therefore, they are not
valid arguments to pass to the function zeros.
Note: An exception to this behavior occurs if the structure is declared completely using
the struct function
8-12
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Do not assign mxArrays to structures
...
Y = struct('a',3,'b',5);
X = zeros(Y.a,Y.b); % Generates a fixed-size 3 X 5 matrix
Do not assign mxArrays to structures
You cannot assign mxArrays to structure elements; convert mxArrays to known types
before code generation (see “Working with mxArrays” on page 13-17).
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8
Code Generation for MATLAB Structures
Pass Structure Arguments by Reference or by Value
In this section...
“Specify Pass by Reference or by Value Using a Project” on page 8-14
“Specify Pass by Reference or by Value Using the Command-Line Interface” on page
8-15
“Pass Input Structure Argument by Reference” on page 8-15
“Pass Input Structure Argument by Value” on page 8-16
“Pass Output Structure Argument by Reference” on page 8-16
“Pass Output Structure Argument by Value” on page 8-17
“Pass Input and Output Structure Argument by Reference” on page 8-18
For standalone C code generation, you can control whether a generated entry-point
function passes structure arguments by reference or by value. Passing by reference uses
a pointer to access the structure arguments. If the function writes to an element of the
input structure, it overwrites the input value. Passing by value makes a copy of the input
or output structure argument. To reduce memory usage and execution time, use pass by
reference.
If a structure argument is both an input and output, the generated entry-point function
passes the argument by reference.
Generated MEX functions pass structure arguments by reference. For MEX function
output, you cannot specify that you want to pass structure arguments by value.
Specify Pass by Reference or by Value Using a Project
1
On the Build tab, set the Output Type to one of the following:
• C/C++ Static Library
• C/C++ Dynamic Library
2
3
• C/C++ Executable
To open the Project Settings dialog box, click the More settings link.
On the All Settings tab, set the Pass structures by reference to entry-point
functions option.
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Pass Structure Arguments by Reference or by Value
Set To
For
Yes
Pass by reference (default)
No
Pass by value
Specify Pass by Reference or by Value Using the Command-Line Interface
1
Create a code configuration object for 'lib', 'dll', or 'exe':
cfg = coder.config('lib'); % or dll or exe
2
Set the PassStructByReference property.
Set To
For
true
Pass by reference
false
Pass by value (default)
For example,
cfg.PassStructByReference = true;
Pass Input Structure Argument by Reference
This example shows how to generate an entry-point function that passes an input
structure argument by reference.
1
Write the function my_struct_in.
function y = my_struct_in(s)
y = s.f;
2
Declare a structure variable str in the MATLAB workspace.
3
Create a code configuration object to generate a C static library.
str = struct('f', 1:4);
cfg = coder.config('lib');
4
Specify that you want to pass structure arguments by reference.
cfg.PassStructByReference = true;
5
Generate code. Specify that the input argument has the type of the variable str.
codegen -config cfg -args {str} my_struct_in -report
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8
Code Generation for MATLAB Structures
6
To view the generated C code, click View report.
The generated function signature for my_struct_in is:
void my_struct_in(const struct0_T *s, double y[4])
my_struct_in passes the input structure s by reference.
Pass Input Structure Argument by Value
This example shows how to generate an entry-point function that passes an input
structure argument by value.
1
Write the function my_struct_in.
function y = my_struct_in(s)
y = s.f;
2
Declare a structure variable str in the MATLAB workspace.
3
Create a code configuration object to generate a C static library.
str = struct('f', 1:4);
cfg = coder.config('lib');
4
Specify that you want to pass structure arguments by value.
cfg.PassStructByReference = false;
5
By default, the PassStructByReference parameter is false.
Generate code. Specify that the input argument has the type of the variable str.
6
To view the generated C code, click View report.
codegen -config cfg -args {str} my_struct_in -report
The generated function signature for my_struct_in is:
void my_struct_in(const struct0_T s, double y[4])
my_struct_in passes the input structure s by value.
Pass Output Structure Argument by Reference
This example shows how to generate an entry-point function that passes an output
structure argument by reference.
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Pass Structure Arguments by Reference or by Value
1
Write the function my_struct_out.
function s = my_struct_out(x)
s.f = x;
2
Declare a variable a in the MATLAB workspace.
a = 1:4;
3
Create a code configuration object to generate a C static library.
4
Specify that you want to pass structure arguments by reference.
cfg = coder.config('lib');
cfg.PassStructByReference = true;
5
Generate code. Specify that the input argument has the type of the variable a.
codegen -config cfg -args {a} my_struct_out -report
6
To view the generated C code, click View report.
The generated function signature for my_struct_out is:
void my_struct_out(const double x[4], struct0_T *s)
my_struct_out passes the output structure s by reference.
Pass Output Structure Argument by Value
This example shows how to generate an entry-point function that passes an output
structure argument by value.
1
Write the function my_struct_out.
function s = my_struct_out(x)
s.f = x;
2
Declare a variable a in the MATLAB workspace.
a = 1:4;
3
Create a code configuration object to generate a C static library.
4
Specify that you want to pass structure arguments by value.
cfg = coder.config('lib');
cfg.PassStructByReference = false;
By default, the PassStructByReference parameter is false
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8
Code Generation for MATLAB Structures
5
Generate code. Specify that the input argument has the type of the variable a.
codegen -config cfg -args {a} my_struct_out -report
6
To view the generated C code, click View report.
The generated function signature for my_struct_out is:
struct0_T my_struct_out(const double x[4])
my_struct_out returns an output structure.
Pass Input and Output Structure Argument by Reference
This example shows how a generated entry-point function passes a structure argument
by reference when the structure argument is both an input and an output. In this
case, the function passes the structure argument by reference even though you set
PassStructByReference to false.
1
Write the function my_struct_inout.
function [y,s] = my_struct_inout(x,s)
y = x + sum(s.f);
2
Define the variable a and structure str in the MATLAB workspace.
a = 1:4;
str = struct('f',a);
3
Create a code configuration object to generate a C static library.
cfg = coder.config('lib');
4
Specify that you want to pass structure arguments by value.
cfg.PassStructByReference = false;
5
By default, the PassStructByReference parameter is false.
Generate code. Specify that the first input has the type of a and the second input has
the type of str.
codegen -config cfg -args {a, str} my_struct_inout -report
6
To view the generated C code, click View report.
The generated function signature for my_struct_inout is:
void my_struct_inout(const double x[4], const struct0_T *s, double y[4])
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Pass Structure Arguments by Reference or by Value
my_struct_inout passes the structure s by reference even though
PassStructByReference is false.
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9
Code Generation for Enumerated
Data
• “Enumerated Data Definition for Code Generation” on page 9-2
• “Enumerated Types Supported for Code Generation” on page 9-3
• “When to Use Enumerated Data for Code Generation” on page 9-6
• “Generate Code for Enumerated Data from MATLAB Algorithms” on page 9-7
• “Define Enumerated Data for Code Generation” on page 9-8
• “Operations on Enumerated Data for Code Generation” on page 9-10
• “Include Enumerated Data in Control Flow Statements” on page 9-13
• “Customize Enumerated Types for Code Generation” on page 9-19
• “Use Enumerated Types in LED Control Function” on page 9-23
• “Control Names of Enumerated Type Values in Generated Code” on page 9-26
• “Change and Reload Enumerated Data Types” on page 9-29
• “Restrictions on Use of Enumerated Data in for-Loops” on page 9-30
• “Toolbox Functions That Support Enumerated Types for Code Generation” on page
9-31
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9
Code Generation for Enumerated Data
Enumerated Data Definition for Code Generation
To generate efficient standalone code for enumerated data, you must define and use
enumerated types differently than you do in the MATLAB environment:
Difference
More Information
Supports integer-based enumerated types
only
“Enumerated Types Supported for Code
Generation” on page 9-3
Name of each enumerated data type must
be unique
“Naming Enumerated Types for Code
Generation” on page 9-9
Each enumerated data type must be
defined in a separate file on the MATLAB
path
“Define Enumerated Data for Code
Generation” on page 9-8 and
“Generate Code for Enumerated Data from
MATLAB Algorithms” on page 9-7
Restricted set of operations
“Operations on Enumerated Data for Code
Generation” on page 9-10
Restricted use in for-loops
“Restrictions on Use of Enumerated Data
in for-Loops” on page 9-30
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Enumerated Types Supported for Code Generation
Enumerated Types Supported for Code Generation
An enumerated type is a user-defined type whose values belong to a predefined set
of enumerated values. Each enumerated value consists of a name and an underlying
numeric value.
You define an enumerated data type in an enumeration class definition file. For code
generation, you must base the class on int8, uint8, int16, uint16, or int32. For
example:
classdef(Enumeration) PrimaryColors < int32
enumeration
Red(1),
Blue(2),
Yellow(4)
end
end
In this example, the statement classdef(Enumeration) PrimaryColors < int32
means that the enumerated type PrimaryColors is based on the built-in type int32.
PrimaryColors inherits the characteristics of the int32 type. It also defines its own
unique characteristics. For example, PrimaryColors is restricted to three enumerated
values:
Enumerated Value
Enumerated Name
Underlying Numeric Value
Red(1)
Red
1
Blue(2)
Blue
2
Yellow(4)
Yellow
4
Enumeration Class Base Types for Code Generation
For code generation, you must base an enumerated type on one of the following built-in
MATLAB integer data types:
• int8
• uint8
• int16
• uint16
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9
Code Generation for Enumerated Data
• int32
You can use the base type to control the size of an enumerated type in generated C/C++
code. You can:
• Represent an enumerated type as a fixed-size integer that is portable to different
targets.
• Reduce memory usage.
• Interface to legacy code.
• Match company standards.
The base type determines the representation of the enumerated type in generated C/C++
code.
C Code Representation for Base Type int32
If the base type is int32, the code generation software generates a C enumeration type.
Consider the following MATLAB enumerated type definition:
classdef(Enumeration) LEDcolor < int32
enumeration
GREEN(1),
RED(2)
end
end
This enumerated type definition results in the following C code:
enum LEDcolor
{
GREEN = 1,
RED
};
typedef enum LEDcolor LEDcolor;
C Code Representation for Base Type Other Than int32
For built-in integer base types other than int32, the code generation software generates
a typedef statement for the enumerated type and #define statements for the
enumerated values. Consider the following MATLAB enumerated type definition:
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Enumerated Types Supported for Code Generation
classdef(Enumeration) LEDcolor < int16
enumeration
GREEN(1),
RED(2)
end
end
This enumerated type definition results in the following C code:
typedef short LEDcolor;
#define GREEN ((LEDcolor)1)
#define RED ((LEDcolor)2)
The C type in the typedef statement depends on:
• The integer sizes defined for the production hardware in the
Hardware Implementation object or the project settings. See
coder.HardwareImplementation.
• The setting that determines use of built-in C types or MathWorks typedefs in the
generated code. See “Specify Data Type Used in Generated Code” and “How MATLAB
Coder Infers C/C++ Data Types” .
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9
Code Generation for Enumerated Data
When to Use Enumerated Data for Code Generation
You can use enumerated types to represent program states and to control program logic,
especially when you restrict data to a predetermined set of values and refer to these
values by name. You can sometimes achieve these goals by using integers or strings,
however, enumerated types offer the following advantages:
• More readable code than integers.
• More robust error checking than integers or strings.
For example, if you mistype the name of an element in the enumerated type, you get a
compile-time error that the element does not belong to the set of allowable values.
• More efficient code than strings.
For example, comparisons of enumerated values execute faster than comparisons of
strings.
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Generate Code for Enumerated Data from MATLAB Algorithms
Generate Code for Enumerated Data from MATLAB Algorithms
The basic workflow for generating code for enumerated types in MATLAB code is:
1
Define an enumerated data type that inherits from a base type that code generation
supports. See “Enumerated Types Supported for Code Generation”.
2
Save the enumerated data type in a file on the MATLAB path.
3
Write a MATLAB function that uses the enumerated type.
4
Specify enumerated type inputs using the project or the command-line interface.
5
Generate code.
See Also
• “Use Enumerated Types in LED Control Function”
• “Define Enumerated Data for Code Generation”
• “Specifying an Enumerated Type Input Parameter by Example”
• “Specifying an Enumerated Type Input Parameter by Type”
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9
Code Generation for Enumerated Data
Define Enumerated Data for Code Generation
To define enumerated data for code generation from MATLAB algorithms:
1
Create a class definition file.
In the Command Window, select File > New > Class.
2
Enter the class definition:
classdef(Enumeration) EnumTypeName < BaseType
EnumTypeName is a case-sensitive string that must be unique among data type
names and workspace variable names. BaseType must be int8, uint8, int16,
uint16, or int32.
For example, the following code defines an enumerated type called sysMode that
inherits from the built-in type int32:
classdef(Enumeration) sysMode < int32
...
end
3
Define enumerated values in an enumeration section:
classdef(Enumeration) EnumTypeName < BaseType
enumeration
EnumName(N)
...
end
end
For example, the following code defines a set of two values for enumerated type
sysMode:
classdef(Enumeration) sysMode < int32
enumeration
OFF(0),
ON(1)
end
end
Each enumerated value consists of a string EnumName and an underlying integer
N. Each EnumName must be unique within its type. If the enumerated value name
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Define Enumerated Data for Code Generation
does not include the class name prefix, EnumName must be unique across enumerated
types. See “Control Names of Enumerated Type Values in Generated Code” on page
9-26.
The underlying integers do not have to be consecutive or ordered, or unique within or
across types.
4
Save the file on the MATLAB path.
The name of the file must match the name of the enumerated data type. The match
is case sensitive.
For examples, see “Include Enumerated Data in Control Flow Statements” on page
9-13.
Naming Enumerated Types for Code Generation
You must use a unique name for each enumerated data type. Do not use the name of:
• A toolbox function supported for code generation.
• Another data type.
• A variable in the MATLAB base workspace.
For example, you cannot name an enumerated data type mode because MATLAB for code
generation provides a toolbox function of the same name.
For a list of toolbox functions supported for code generation, see “Functions and Objects
Supported for C and C++ Code Generation — Alphabetical List”.
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9
Code Generation for Enumerated Data
Operations on Enumerated Data for Code Generation
To generate efficient standalone code for enumerated data, you are restricted to the
following operations. The examples use the following enumerated class definition:
classdef(Enumeration) LEDcolor < int32
enumeration
GREEN(1),
RED(2)
end
end
.
Assignment Operator, =
Example
Result
xon = LEDcolor.GREEN
xoff = LEDcolor.RED
xon =
GREEN
xoff =
RED
Relational Operators, < > <= >= == ~=
Example
Result
xon == xoff
ans =
xon <= xoff
ans =
0
1
xon > xoff
ans =
0
Cast Operation
Example
Result
double(LEDcolor.RED)
ans =
9-10
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Operations on Enumerated Data for Code Generation
Example
Result
2
z = 2
y = LEDcolor(z)
z =
2
y =
RED
Indexing Operation
Example
Result
m = [1 2]
n = LEDcolor(m)
p = n(LEDcolor.GREEN)
m =
1
2
n =
GREEN
RED
p =
GREEN
Control Flow Statements: if, switch, while
Statement
Example
Executable Example
if
if state == sysMode.ON
led = LEDcolor.GREEN;
else
led = LEDcolor.RED;
end
switch
switch button
case VCRButton.Stop
state = VCRState.Stop;
“if Statement with
Enumerated Data
Types” on page 9-13
“switch Statement
with Enumerated Data
Types” on page 9-14
9-11
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9
Code Generation for Enumerated Data
Statement
Example
Executable Example
case VCRButton.PlayOrPause
state = VCRState.Play;
case VCRButton.Next
state = VCRState.Forward;
case VCRButton.Previous
state = VCRState.Rewind;
otherwise
state = VCRState.Stop;
end
while
while state ~= State.Ready
switch state
case State.Standby
initialize();
state = State.Boot;
case State.Boot
boot();
state = State.Ready;
end
end
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“while Statement
with Enumerated Data
Types” on page 9-16
Include Enumerated Data in Control Flow Statements
Include Enumerated Data in Control Flow Statements
The following examples define enumerated types that use the base type int32. You can
base an enumerated type on one of these built-in integer types:
• int8
• uint8
• int16
• uint16
• int32
The base type determines the representation of the enumerated type in the generated C/
C++ code. See “Enumerated Types Supported for Code Generation” on page 9-3.
if Statement with Enumerated Data Types
This example defines the enumeration types LEDcolor and sysMode. The function
displayState uses these enumerated data types to activate an LED display.
Class Definition: sysMode
classdef(Enumeration) sysMode < int32
enumeration
OFF(0),
ON(1)
end
end
This definition must reside on the MATLAB path in a file with the same name as the
class, sysMode.m.
Class Definition: LEDcolor
classdef(Enumeration) LEDcolor < int32
enumeration
GREEN(1),
RED(2)
end
end
This definition must reside on the MATLAB path in a file called LEDcolor.m.
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9
Code Generation for Enumerated Data
MATLAB Function: displayState
This function uses enumerated data to activate an LED display, based on the state of
a device. It lights a green LED display to indicate the ON state and lights a red LED
display to indicate the OFF state.
function led = displayState(state)
%#codegen
if state == sysMode.ON
led = LEDcolor.GREEN;
else
led = LEDcolor.RED;
end
Build and Test a MEX Function for displayState
1
Generate a MEX function for displayState. Use the -args option to pass one of
the allowable values for the enumerated data input as a sample value.
codegen displayState -args {sysMode.ON}
2
Test the function. For example,
displayState(sysMode.OFF)
ans =
RED
switch Statement with Enumerated Data Types
This example is based on the definition of the enumeration types VCRState and
VCRButton. The function VCR uses these enumerated data types to set the state of the
VCR.
Class Definition: VCRState
classdef(Enumeration) VCRState < int32
enumeration
Stop(0),
Pause(1),
Play(2),
Forward(3),
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Include Enumerated Data in Control Flow Statements
Rewind(4)
end
end
This definition must reside on the MATLAB path in a file with the same name as the
class, VCRState.m.
Class Definition: VCRButton
classdef(Enumeration) VCRButton < int32
enumeration
Stop(1),
PlayOrPause(2),
Next(3),
Previous(4)
end
end
This definition must reside on the MATLAB path in a file with the same name as the
class, VCRButton.m.
MATLAB Function: VCR
This function uses enumerated data to set the state of a VCR, based on the initial state of
the VCR and the state of the VCR button.
function s = VCR(button)
%#codegen
persistent state
if isempty(state)
state = VCRState.Stop;
end
switch state
case {VCRState.Stop, VCRState.Forward, VCRState.Rewind}
state = handleDefault(button);
case VCRState.Play
switch button
case VCRButton.PlayOrPause, state = VCRState.Pause;
otherwise, state = handleDefault(button);
end
case VCRState.Pause
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9
Code Generation for Enumerated Data
switch button
case VCRButton.PlayOrPause, state = VCRState.Play;
otherwise, state = handleDefault(button);
end
end
s = state;
function state = handleDefault(button)
switch button
case VCRButton.Stop, state = VCRState.Stop;
case VCRButton.PlayOrPause, state = VCRState.Play;
case VCRButton.Next, state = VCRState.Forward;
case VCRButton.Previous, state = VCRState.Rewind;
otherwise, state = VCRState.Stop;
end
Build and Test a MEX Function for VCR
1
Generate a MEX function for VCR. Use the -args option to pass one of the allowable
values for the enumerated data input as a sample value.
codegen -args {VCRButton.Stop}
2
VCR
Test the function. For example,
s = VCR(VCRButton.Stop)
s =
Stop
while Statement with Enumerated Data Types
This example is based on the definition of the enumeration type State. The function
Setup uses this enumerated data type to set the state of a device.
Class Definition: State
classdef(Enumeration) State < int32
enumeration
Standby(0),
Boot(1),
Ready(2)
end
end
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Include Enumerated Data in Control Flow Statements
This definition must reside on the MATLAB path in a file with the same name as the
class, State.m.
MATLAB Function: Setup
The following function Setup uses enumerated data to set the state of a device.
function s = Setup(initState)
%#codegen
state = initState;
if isempty(state)
state = State.Standby;
end
while state ~= State.Ready
switch state
case State.Standby
initialize();
state = State.Boot;
case State.Boot
boot();
state = State.Ready;
end
end
s = state;
function initialize()
% Perform initialization.
function boot()
% Boot the device.
Build and Test a MEX Executable for Setup
1
Generate a MEX executable for Setup. Use the -args option to pass one of the
allowable values for the enumerated data input as a sample value.
codegen Setup -args {State.Standby}
2
Test the function. For example,
s = Setup(State.Standby)
s =
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9
Code Generation for Enumerated Data
Ready
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Customize Enumerated Types for Code Generation
Customize Enumerated Types for Code Generation
Customizing Enumerated Types
For code generation, you can customize an enumerated type by using the same
techniques that work with MATLAB classes, as described in “Modifying Superclass
Methods and Properties”. You can customize an enumerated type by including a methods
section in the enumerated class definition. You can override the following methods
to customize the behavior of an enumerated type. To override a method, include a
customized version of the method in the methods section in the enumerated class
definition. If you do not want to override the inherited methods, omit the methods
section.
Method
Description
Default Value
When to Use
Returned or Specified
addClassNameToEnumNames Specifies whether
the class name
becomes a prefix in
the generated code.
false — prefix is
not used.
If you want the class
name to become
a prefix in the
generated code,
override this method
to set the return
value to true. See
“Control Names of
Enumerated Type
Values in Generated
Code” on page
9-26.
Returns the default
enumerated value.
First value in the
enumerated class
definition.
If you want the
default value for the
enumerated type
to be something
other than the first
value listed in the
enumerated class
definition, override
this method to specify
a default value. See
“Specify a Default
getDefaultValue
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9
Code Generation for Enumerated Data
Method
Description
Default Value
When to Use
Returned or Specified
Enumerated Value”
on page 9-20.
getHeaderFile
Specifies the
file in which the
enumerated class
is defined for code
generation.
''
If you want to use
an enumerated
class definition that
is specified in a
custom header file,
override this method
to return the path to
this header file. In
this case, the code
generation software
does not generate the
class definition. See
“Specify a Header
File” on page 9-21
Specify a Default Enumerated Value
If the value of a variable that is cast to an enumerated type does not match one of the
enumerated type values:
• Generated MEX reports an error.
• Generated C/C++ code replaces the value of the variable with the enumerated type
default value.
Unless you specify otherwise, the default value for an enumerated type is the first value
in the enumerated class definition. To specify a different default value, add your own
getDefaultValue method to the methods section. The following code shows a shell for
the getDefaultValue method:
function retVal = getDefaultValue()
% GETDEFAULTVALUE Returns the default enumerated value.
%
This value must be an instance of the enumerated class.
%
If this method is not defined, the first enumerated value is used.
retVal = ThisClass.EnumName;
end
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Customize Enumerated Types for Code Generation
To customize this method, provide a value for ThisClass.EnumName that specifies
the default that you want. ThisClass must be the name of the class within which the
method exists. EnumName must be the name of an enumerated value defined in that
class. For example:
classdef(Enumeration) LEDcolor < int32
enumeration
GREEN(1),
RED(2)
end
methods (Static)
function y = getDefaultValue()
y = LEDcolor.RED;
end
end
end
This example defines the default as LEDcolor.RED. If this method does not appear,
the default value is LEDcolor.GREEN, because that value is the first value listed in the
enumerated class definition.
Specify a Header File
To prevent the declaration of an enumerated type from being embedded in the generated
code, allowing you to provide the declaration in an external file, include the following
method in the enumerated class methods section:
function y = getHeaderFile()
% GETHEADERFILE File where type is defined for generated code.
%
If specified, this file is #included where required in the code.
%
Otherwise, the type is written out in the generated code.
y = 'filename';
end
Substitute a legal filename for filename. Be sure to provide a filename suffix, typically
.h. Providing the method replaces the declaration that appears in the generated code
with an #include statement like:
#include "imported_enum_type.h"
The getHeaderFile method does not create the declaration file itself. You must provide
a file of the specified name that declares the enumerated data type. The file can also
contain definitions of enumerated types that you do not use in your MATLAB code.
For example, to use the definition of LEDcolor in my_LEDcolor.h:
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9
Code Generation for Enumerated Data
1
Modify the definition of LEDcolor to override the getHeaderFile method to return
the name of the external header file:
classdef(Enumeration) LEDcolor < int32
enumeration
GREEN(1),
RED(2)
end
methods(Static)
function y=getHeaderFile()
y='my_LEDcolor.h';
end
end
end
2
In the current folder, provide a header file, my_LEDcolor.h, that contains the
definition:
enum LEDcolor
{
GREEN = 1,
RED
};
typedef enum LEDcolor LEDcolor;
3
Generate a library for the function displayState that takes one input of
enumerated data type sysMode.
codegen -config:lib -report
displayState -args {sysMode.ON}
codegen generates a C static library with the default name, displayState, and
supporting files in the default folder, codegen/lib/displayState.
4
Click the View Report link.
5
In the report, on the C Code tab, click the link to the displayState_types.h file.
The header file contains a #include statement for the external header file.
#include "my_LEDcolor.h"
It does not include a declaration for the enumerated class.
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Use Enumerated Types in LED Control Function
Use Enumerated Types in LED Control Function
This example shows how to define, use, and generate code for enumerated types in a
function that controls an LED. In this example, the enumerated types inherit from base
type int32. The base type can be int8, uint8, int16, uint16, or int32.
1
Define the enumerated type sysMode. Store it in sysMode.m on the MATLAB path.
classdef(Enumeration) sysMode < int32
enumeration
OFF(0),
ON(1)
end
end
2
Define the enumerated type LEDcolor. Store it in LEDcolor.m on the MATLAB
path.
classdef(Enumeration) LEDcolor < int32
enumeration
GREEN(1),
RED(2)
end
end
3
Define the function displayState, which uses enumerated data to activate an LED
display, based on the state of a device. displayState lights a green LED display
to indicate the ON state. It lights a red LED display to indicate the OFF state. Store
displayState in displayState.m on the MATLAB path.
function led = displayState(state)
%#codegen
if state == sysMode.ON
led = LEDcolor.GREEN;
else
led = LEDcolor.RED;
end
4
Generate the MEX function displayState_mex. Specify that displayState has
one input that is an enumerated data type sysMode.
codegen -report displayState -args {sysMode.ON}
5
Test the MEX function.
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9
Code Generation for Enumerated Data
displayState_mex(sysMode.OFF)
ans =
RED
6
Generate a static library for the function displayState that takes one input of
enumerated data type sysMode.
codegen -config:lib -report
displayState -args {sysMode.ON}
codegen generates a C static library with the default name, displayState. It
generates supporting files in the default folder, codegen/lib/displayState.
7
Click the View Report link.
8
In the report, on the C Code tab, click the link to the displayState_types.h file.
The report displays the header file containing the enumerated data type definition.
enum LEDcolor
{
GREEN = 1,
RED
};
typedef enum LEDcolor LEDcolor;
The enumerated type LEDcolor is represented as a C enumerated type because the
base type in the class definition for LEDcolor is int32. When the base type is int8,
uint8, int16, or uint16, the code generation software generates a typedef for the
enumerated type. It generates #define statements for the enumerated type values.
For example:
typedef short LEDcolor;
#define GREEN ((LEDcolor)1)
#define RED ((LEDcolor)2)
Related Examples
•
“Generate Code for Enumerated Data from MATLAB Algorithms” on page 9-7
•
“Customize Enumerated Types for Code Generation” on page 9-19
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Use Enumerated Types in LED Control Function
More About
•
“Enumerated Data Definition for Code Generation” on page 9-2
•
“Enumerated Types Supported for Code Generation” on page 9-3
9-25
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9
Code Generation for Enumerated Data
Control Names of Enumerated Type Values in Generated Code
This example shows how to control whether generated enumerated type value names
include the class name prefix from the enumerated type definition. By default, the
generated enumerated type value name does not include the class name prefix.
1
Define the enumerated type sysMode. Store it in sysMode.m on the MATLAB path.
classdef(Enumeration) sysMode < int32
enumeration
OFF(0),
ON(1)
end
end
2
Define the enumerated type LEDcolor. Store it in LEDcolor.m on the MATLAB
path.
classdef(Enumeration) LEDcolor < int32
enumeration
GREEN(1),
RED(2)
end
end
3
Define the function displayState, which uses enumerated data to activate an LED
display, based on the state of a device. displayState lights a green LED display to
indicate the ON state and lights a red LED display to indicate the OFF state. Store
in displayState.m on the MATLAB path.
function led = displayState(state)
%#codegen
if state == sysMode.ON
led = LEDcolor.GREEN;
else
led = LEDcolor.RED;
end
4
Generate a library for the function displayState that takes one input of
enumerated data type sysMode.
codegen -config:lib -report
displayState -args {sysMode.ON}
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Control Names of Enumerated Type Values in Generated Code
codegen generates a C static library with the default name, displayState, and
supporting files in the default folder, codegen/lib/displayState.
5
Click the View Report link.
6
In the report, on the C Code tab, click the link to the displayState_types.h file.
The report displays the header file containing the enumerated data type definition.
enum LEDcolor
{
GREEN = 1,
RED
};
typedef enum LEDcolor LEDcolor;
The enumerated value names do not include the class name prefix LEDcolor_.
7
Modify the definition of LEDcolor to add the addClassNameToEnumNames method.
Set the return value to true so that the enumerated value names in the generated
code contain the class prefix.
classdef(Enumeration) LEDcolor < int32
enumeration
GREEN(1),
RED(2)
end
methods(Static)
function y=addClassNameToEnumNames()
y=true;
end
end
end
8
Clear existing class instances.
clear classes
9
Generate code.
codegen -config:lib -report
displayState -args {sysMode.ON}
10 Open the code generation report and look at the enumerated type definition in
displayState_types.h.
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9
Code Generation for Enumerated Data
enum LEDcolor
{
LEDcolor_GREEN = 1,
LEDcolor_RED
};
typedef enum LEDcolor LEDcolor;
The enumerated value names include the class name prefix.
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Change and Reload Enumerated Data Types
Change and Reload Enumerated Data Types
You can change the definition of an enumerated data type by editing and saving the file
that contains the definition. You do not need to inform MATLAB that a class definition
has changed. MATLAB automatically reads the modified definition when you save
the file. However, the class definition changes do not take full effect if class instances
(enumerated values) exist that reflect the previous class definition. Such instances might
exist in the base workspace or might be cached. The following table explains options for
removing instances of an enumerated data type from the base workspace and cache.
If In Base Workspace...
If In Cache...
Do one of the following:
• Clear MEX functions that are caching
instances of the class.
• Locate and delete specific obsolete
instances.
• Delete the classes from the workspace
by using the clear classes
command. For more information, see
clear.
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9
Code Generation for Enumerated Data
Restrictions on Use of Enumerated Data in for-Loops
Do not use enumerated data as the loop counter variable in
for- loops
To iterate over a range of enumerated data with consecutive values, in the loop counter,
cast the enumerated data to a built-in integer type. The size of the built-in integer type
must be big enough to contain the enumerated value.
For example, suppose you define an enumerated type ColorCodes as follows:
classdef(Enumeration) ColorCodes < int32
enumeration
Red(1),
Blue(2),
Green(3),
Yellow(4),
Purple(5)
end
end
Because the enumerated values are consecutive, you can use ColorCodes data in a
for-loop like this:
...
for i = int32(ColorCodes.Red):int32(ColorCodes.Purple)
c = ColorCodes(i);
...
end
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Toolbox Functions That Support Enumerated Types for Code Generation
Toolbox Functions That Support Enumerated Types for Code
Generation
The following MATLAB toolbox functions support enumerated types for code generation:
• cast
• cat
• circshift
• flipdim
• fliplr
• flipud
• histc
• intersect
• ipermute
• isequal
• isequaln
• isfinite
• isinf
• ismember
• isnan
• issorted
• length
• permute
• repmat
• reshape
• rot90
• setdiff
• setxor
• shiftdim
• sort
• sortrows
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9
Code Generation for Enumerated Data
• squeeze
• union
• unique
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10
Code Generation for MATLAB Classes
• “MATLAB Classes Definition for Code Generation” on page 10-2
• “Classes That Support Code Generation” on page 10-7
• “Generate Code for MATLAB Value Classes” on page 10-8
• “Generate Code for MATLAB Handle Classes and System Objects” on page 10-13
• “MATLAB Classes in Code Generation Reports” on page 10-15
• “Troubleshooting Issues with MATLAB Classes” on page 10-18
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10
Code Generation for MATLAB Classes
MATLAB Classes Definition for Code Generation
To generate efficient standalone code for MATLAB classes, you must use classes
differently than when running your code in the MATLAB environment.
What’s Different
More Information
Class must be in a single file. Because
“Creating a Single, Self-Contained Class
of this limitation, code generation is not
Definition File”
supported for a class definition that uses an
@-folder.
Restricted set of language features.
“Language Limitations” on page 10-2
Restricted set of code generation features.
“Code Generation Features Not Compatible
with Classes” on page 10-3
Definition of class properties.
“Defining Class Properties for Code
Generation” on page 10-4
Use of handle classes.
“Generate Code for MATLAB Handle
Classes and System Objects” on page
10-13
Calls to base class constructor.
“Calls to Base Class Constructor” on page
10-5
Global variables containing MATLAB
objects are not supported for code
generation.
N/A
Inheritance from built-in MATLAB classes “Inheritance from Built-In MATLAB
is not supported.
Classes Not Supported” on page 10-6
Language Limitations
Although code generation support is provided for common features of classes such
as properties and methods, there are a number of advanced features which are not
supported, such as:
• Events
• Listeners
• Arrays of objects
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MATLAB Classes Definition for Code Generation
• Recursive data structures
• Linked lists
• Trees
• Graphs
• Overloadable operators subsref, subsassign, and subsindex
In MATLAB, classes can define their own versions of the subsref, subsassign, and
subsindex methods. Code generation does not support classes that have their own
definitions of these methods.
• The empty method
In MATLAB, classes have a built-in static method, empty, which creates an empty
array of the class. Code generation does not support this method.
• The following MATLAB handle class methods:
• addlistener
• delete
• eq
• findobj
• findpro
• The AbortSet property attribute
Code Generation Features Not Compatible with Classes
• You can generate code for entry-point MATLAB functions that use classes, but you
cannot generate code directly for a MATLAB class.
For example, if ClassNameA is a class definition, you cannot generate code by
executing:
codegen ClassNameA
• If an entry-point MATLAB function has an input or output that is a MATLAB class,
you cannot generate code for this function.
For example, if function foo takes one input, a, that is a MATLAB object, you cannot
generate code for foo by executing:
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10
Code Generation for MATLAB Classes
codegen foo -args {a}
• Code generation does not support assigning an object of a value class into a
nontunable property. For example, obj.prop=v; is invalid when prop is a
nontunable property and v is an object based on a value class.
• You cannot use coder.extrinsic to declare a class or method as extrinsic.
• You cannot pass a MATLAB class to the coder.ceval function.
• If you use classes in code in the MATLAB Function block, you cannot use the
debugger to view class information.
• The coder.nullcopy function does not support MATLAB classes as inputs.
Defining Class Properties for Code Generation
For code generation, you must define class properties differently than you normally
would when running your code in the MATLAB environment:
• After defining a property, do not assign it an incompatible type. Do not use a property
before attempting to grow it.
When you define class properties for code generation, consider the same factors that
you take into account when defining variables. In the MATLAB language, variables
can change their class, size, or complexity dynamically at run time so you can use
the same variable to hold a value of varying class, size, or complexity. C and C++ use
static typing. Before using variables, to determine their type, the code generation
software requires a complete assignment to each variable. Similarly, before using
properties, you must explicitly define their class, size, and complexity.
• Initial values:
• If the property does not have an explicit initial value, the code generation software
assumes that it is undefined at the beginning of the constructor. The code
generation software does not assign an empty matrix as the default.
• If the property does not have an initial value and the code generation software
cannot determine that the property is assigned prior to first use, the software
generates a compilation error.
• For System objects, if a nontunable property is a structure, you must completely
assign the structure. You cannot do partial assignment using subscripting.
For example, for a nontunable property, you can use the following assignment:
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MATLAB Classes Definition for Code Generation
mySystemObject.nonTunableProperty=struct('fieldA','a','fieldB','b');
You cannot use the following partial assignments:
mySystemObject.nonTunableProperty.fieldA = a;
mySystemObject.nonTunableProperty.fieldB = b;
• If dynamic memory allocation is enabled, code generation supports variable-size
properties for handle classes. Without dynamic memory allocation, you cannot
generate code for handle classes that have variable-size properties.
• coder.varsize is not supported for class properties.
• MATLAB computes class initial values at class loading time before code generation.
If you use persistent variables in MATLAB class property initialization, the value of
the persistent variable computed when the class loads belongs to MATLAB; it is not
the value used at code generation time. If you use coder.target in MATLAB class
property initialization, coder.target('MATLAB') returns true (1).
Calls to Base Class Constructor
If a class constructor contains a call to the constructor of the base class, the call to the
base class constructor must come before for, if, return, switch or while statements.
For example, if you define a class B based on class A:
classdef B < A
methods
function obj = B(varargin)
if nargin == 0
a = 1;
b = 2;
elseif nargin == 1
a = varargin{1};
b = 1;
elseif nargin == 2
a = varargin{1};
b = varargin{2};
end
obj = [email protected](a,b);
end
end
end
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10
Code Generation for MATLAB Classes
Because the class definition for B uses an if statement before calling the base class
constructor for A, you cannot generate code for function callB:
function [y1,y2] = callB
x = B;
y1 = x.p1;
y2 = x.p2;
end
However, you can generate code for callB if you define class B as:
classdef B < A
methods
function obj = NewB(varargin)
[a,b] = getaandb(varargin{:});
obj = [email protected](a,b);
end
end
end
function [a,b] = getaandb(varargin)
if nargin == 0
a = 1;
b = 2;
elseif nargin == 1
a = varargin{1};
b = 1;
elseif nargin == 2
a = varargin{1};
b = varargin{2};
end
end
Inheritance from Built-In MATLAB Classes Not Supported
You cannot generate code for classes that inherit from built-in MATLAB classes. For
example, you cannot generate code for the following class:
classdef myclass < double
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Classes That Support Code Generation
Classes That Support Code Generation
You can generate code for MATLAB value and handle classes and user-defined System
objects. Your class can have multiple methods and properties and can inherit from
multiple classes.
To generate code for:
Example:
Value classes
“Generate Code for MATLAB Value
Classes” on page 10-8
Handle classes including user-defined
System objects
“Generate Code for MATLAB Handle
Classes and System Objects” on page
10-13
For more information, see:
• “Classes in the MATLAB Language”
• “MATLAB Classes Definition for Code Generation” on page 10-2
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10
Code Generation for MATLAB Classes
Generate Code for MATLAB Value Classes
This example shows how to generate code for a MATLAB value class and then view the
generated code in the code generation report.
1
In a writable folder, create a MATLAB value class, Shape. Save the code as
Shape.m.
classdef Shape
% SHAPE Create a shape at coordinates
% centerX and centerY
properties
centerX;
centerY;
end
properties (Dependent = true)
area;
end
methods
function out = get.area(obj)
out = obj.getarea();
end
function obj = Shape(centerX,centerY)
obj.centerX = centerX;
obj.centerY = centerY;
end
end
methods(Abstract = true)
getarea(obj);
end
methods(Static)
function d = distanceBetweenShapes(shape1,shape2)
xDist = abs(shape1.centerX - shape2.centerX);
yDist = abs(shape1.centerY - shape2.centerY);
d = sqrt(xDist^2 + yDist^2);
end
end
end
2
In the same folder, create a class, Square, that is a subclass of Shape. Save the code
as Square.m.
classdef Square < Shape
% Create a Square at coordinates center X and center Y
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Generate Code for MATLAB Value Classes
% with sides of length of side
properties
side;
end
methods
function obj = Square(side,centerX,centerY)
[email protected](centerX,centerY);
obj.side = side;
end
function Area = getarea(obj)
Area = obj.side^2;
end
end
end
3
In the same folder, create a class, Rhombus, that is a subclass of Shape. Save the
code as Rhombus.m.
classdef Rhombus < Shape
properties
diag1;
diag2;
end
methods
function obj = Rhombus(diag1,diag2,centerX,centerY)
[email protected](centerX,centerY);
obj.diag1 = diag1;
obj.diag2 = diag2;
end
function Area = getarea(obj)
Area = 0.5*obj.diag1*obj.diag2;
end
end
end
4
Write a function that uses this class.
use_shape
function [TotalArea, Distance] =
%#codegen
s = Square(2,1,2);
r = Rhombus(3,4,7,10);
TotalArea = s.area + r.area;
Distance = Shape.distanceBetweenShapes(s,r);
5
Generate a static library for use_shape and generate a code generation report.
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10
Code Generation for MATLAB Classes
codegen -config:lib -report use_shape
codegen generates a C static library with the default name, use_shape, and
supporting files in the default folder, codegen/lib/use_shape.
6
Click the View report link.
7
In the report, on the MATLAB code tab, click the link to the Rhombus class.
The report displays the class definition of the Rhombus class and highlights the
class constructor. On the Variables tab, it provides details of the variables used
in the class. If a variable is a MATLAB object, by default, the report displays the
object without displaying its properties. To view the list of properties, expand the
list. Within the list of properties, the list of inherited properties is collapsed. In the
following report, the lists of properties and inherited properties are expanded.
8
At the top right side of the report, expand the Calls list.
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Generate Code for MATLAB Value Classes
The Calls list shows that there is a call to the Rhombus constructor from use_shape
and that this constructor calls the Shape constructor.
9
The constructor for the Rhombus class calls the Shape method of the base Shape
class: [email protected] In the report, click the Shape link in this call.
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10
Code Generation for MATLAB Classes
The link takes you to the Shape method in the Shape class definition.
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Generate Code for MATLAB Handle Classes and System Objects
Generate Code for MATLAB Handle Classes and System Objects
This example shows how to generate code for a user-defined System object and then view
the generated code in the code generation report.
1
In a writable folder, create a System object, AddOne, which subclasses from
matlab.System. Save the code as AddOne.m.
classdef AddOne < matlab.System
% ADDONE Compute an output value that increments the input by one
methods (Access=protected)
% stepImpl method is called by the step method
function y = stepImpl(~,x)
y = x+1;
end
end
end
2
Write a function that uses this System object.
function y = testAddOne(x)
%#codegen
p = AddOne();
y = p.step(x);
end
3
Generate a MEX function for this code.
codegen -report testAddOne -args {0}
The -report option instructs codegen to generate a code generation report, even
if no errors or warnings occur. The -args option specifies that the testAddOne
function takes one scalar double input.
>> codegen -report testAddOne -args {0}
Code generation successful: View report
4
Click the View report link.
5
In the report, on the MATLAB Code tab Functions panel, click testAddOne, then
click the Variables tab. You can view information about the variable p on this tab.
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10
Code Generation for MATLAB Classes
6
To view the class definition, on the Classes panel, click AddOne.
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MATLAB Classes in Code Generation Reports
MATLAB Classes in Code Generation Reports
What Reports Tell You About Classes
Code generation reports:
• Provide a hierarchical tree of the classes used in your MATLAB code.
• Display a list of methods for each class in the MATLAB code tab.
• Display the objects used in your MATLAB code together with their properties on the
Variables tab.
• Provide a filter so that you can sort methods by class, size, and complexity.
• List the set of calls from and to the selected method in the Calls list.
How Classes Appear in Code Generation Reports
In the MATLAB Code Tab
The report displays an alphabetical hierarchical list of the classes used in the your
MATLAB code. For each class, you can:
• Expand the class information to view the class methods.
• View a class method by clicking its name. The report displays the methods in the
context of the full class definition.
• Filter the methods by size, complexity, and class by using the Filter functions and
methods option.
Default Constructors
If a class has a default constructor, the report displays the constructor in italics.
Specializations
If the same class is specialized into multiple different classes, the report differentiates
the specializations by grouping each one under a single node in the tree. The report
associates the class definition functions and static methods with the primary node. It
associates the instance-specific methods with the corresponding specialized node.
For example, consider a base class, Shape that has two specialized subclasses,
Rhombus and Square. The Shape class has an abstract method, getarea, and a
static method, distanceBetweenShapes. The code generation report, displays a
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10
Code Generation for MATLAB Classes
node for the specialized Rhombus and Square classes with their constructors and
getarea method. It displays a node for the Shape class and its associated static method,
distanceBetweenShapes, and two instances of the Shape class, Shape1 and Shape2.
Packages
If you define classes as part of a package, the report displays the package in the list
of classes. You can expand the package to view the classes that it contains. For more
information about packages, see “Packages Create Namespaces”.
In the Variables Tab
The report displays the objects in the selected function or class. By default, for classes
that have properties, the list of properties is collapsed. To expand the list, click the
+ symbol next to the object name. Within the list of properties, the list of inherited
properties is collapsed. To expand the list of inherited properties, click the + symbol next
to Inherited.
The report displays the properties using just the base property name, not the fully
qualified name. For example, if your code uses variable obj1 that is a MATLAB object
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MATLAB Classes in Code Generation Reports
with property prop1, then the report displays the property as prop1 not obj1.prop1.
When you sort the Variables column, the sort order is based on the fully qualified
property name.
In the Call Stack
The call stack lists the functions and methods in the order that the top-level function
calls them. It also lists the local functions that each function calls.
How to Generate a Code Generation Report
Add the -report option to your codegen command (requires a MATLAB Coder license)
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10
Code Generation for MATLAB Classes
Troubleshooting Issues with MATLAB Classes
Class class does not have a property with name name
If a MATLAB class has a method, mymethod, that returns a handle class with a property,
myprop, you cannot generate code for the following type of assignment:
obj.mymethod().myprop=...
For example, consider the following classes:
classdef MyClass < handle
properties
myprop
end
methods
function this = MyClass
this.myprop = MyClass2;
end
function y = mymethod(this)
y = this.myprop;
end
end
end
classdef MyClass2 < handle
properties
aa
end
end
You cannot generate code for function foo.
function foo
h = MyClass;
h.mymethod().aa = 12;
In this function, h.mymethod() returns a handle object of type MyClass2. In MATLAB,
the assignment h.mymethod().aa = 12; changes the property of that object. Code
generation does not support this assignment.
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Troubleshooting Issues with MATLAB Classes
Workaround
Rewrite the code to return the object and then assign a value to a property of the object.
function foo
h = MyClass;
b=h.mymethod();
b.aa=12;
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11
Code Generation for Function Handles
• “Function Handle Definition for Code Generation” on page 11-2
• “Define and Pass Function Handles for Code Generation” on page 11-3
• “Function Handle Limitations for Code Generation” on page 11-5
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11
Code Generation for Function Handles
Function Handle Definition for Code Generation
You can use function handles to invoke functions indirectly and parameterize operations
that you repeat frequently. You can perform the following operations with function
handles:
• Define handles that reference user-defined functions and built-in functions supported
for code generation (see “Functions and Objects Supported for C and C++ Code
Generation — Alphabetical List”)
Note: You cannot define handles that reference extrinsic MATLAB functions.
• Define function handles as scalar values
• Define structures that contain function handles
• Pass function handles as arguments to other functions (excluding extrinsic functions)
To generate efficient standalone code for enumerated data, you are restricted to using a
subset of the operations you can perform with function handles in MATLAB, as described
in “Function Handle Limitations for Code Generation” on page 11-5
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Define and Pass Function Handles for Code Generation
Define and Pass Function Handles for Code Generation
The following code example shows how to define and call function handles for code
generation. You can copy the example to a MATLAB Function block in Simulink or
MATLAB function in Stateflow. To convert this function to a MEX function using
codegen, uncomment the two calls to the assert function, highlighted below:
function addval(m)
%#codegen
%
%
%
%
Define class and size of primary input m
Uncomment next two lines to build MEX function with codegen
assert(isa(m,'double'));
assert(all (size(m) == [3 3]));
% Pass function handle to addone
% to add one to each element of m
m = map(@addone, m);
disp(m);
% Pass function handle to addtwo
% to add two to each element of m
m = map(@addtwo, m);
disp(m);
function y = map(f,m)
y = m;
for i = 1:numel(y)
y(i) = f(y(i));
end
function y = addone(u)
y = u + 1;
function y = addtwo(u)
y = u + 2;
This code passes function handles @addone and @addtwo to the function map which
increments each element of the matrix m by the amount prescribed by the referenced
function. Note that map stores the function handle in the input variable f and then uses
f to invoke the function — in this case addone first and then addtwo.
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11
Code Generation for Function Handles
If you have MATLAB Coder, you can use the function codegen to convert the function
addval to a MEX executable that you can run in MATLAB. Follow these steps:
1
At the MATLAB command prompt, issue this command:
codegen addval
2
Define and initialize a 3-by-3 matrix by typing a command like this at the MATLAB
prompt:
m = zeros(3)
3
Execute the function by typing this command:
addval(m)
You should see the following result:
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
3
3
3
3
3
3
3
3
3
For more information, see “MEX Function Generation at the Command Line”.
11-4
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Function Handle Limitations for Code Generation
Function Handle Limitations for Code Generation
You cannot use the same bound variable to reference
different function handles.
After you bind a variable to a specific function, you cannot use the same variable to
reference two different function handles, as in this example:
%Incorrect code
...
x = @plus;
x = @minus;
...
This code produces a compilation error.
You cannot pass function handles to or from
coder.ceval.
You cannot pass function handles as inputs to or outputs from coder.ceval. For
example, suppose that f and str.f are function handles:
f = @sin;
str.x = pi;
str.f = f;
The following statements result in compilation errors:
coder.ceval('foo', @sin);
coder.ceval('foo', f);
coder.ceval('foo', str);
You cannot pass function handles to or from extrinsic
functions.
You cannot pass function handles to or from feval and other extrinsic MATLAB
functions. For more information, see “Declaring MATLAB Functions as Extrinsic
Functions” on page 13-12.
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11
Code Generation for Function Handles
You cannot pass function handles to or from primary
functions.
You cannot pass function handles as inputs to or outputs from primary functions. For
example, consider this function:
function x = plotFcn(fhandle, data)
assert(isa(fhandle,'function_handle') && isa(data,'double'));
plot(data, fhandle(data));
x = fhandle(data);
In this example, the function plotFcn receives a function handle and its data as primary
inputs. plotFcn attempts to call the function referenced by the fhandle with the input
data and plot the results. However, this code generates a compilation error. The error
indicates that the function isa does not recognize 'function_handle' as a class name
when called inside a MATLAB function to specify properties of primary inputs.
11-6
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12
Defining Functions for Code
Generation
• “Specify Variable Numbers of Arguments” on page 12-2
• “Supported Index Expressions” on page 12-3
• “Apply Operations to a Variable Number of Arguments” on page 12-4
• “Implement Wrapper Functions” on page 12-6
• “Pass Property/Value Pairs” on page 12-7
• “Variable Length Argument Lists for Code Generation” on page 12-9
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12
Defining Functions for Code Generation
Specify Variable Numbers of Arguments
You can use varargin in a function definition to specify that the function accepts a
variable number of input arguments for a given input argument. You can use varargout
in a function definition to specify that the function returns a variable number of
arguments for a given output argument.
When you use varargin and varargout for code generation, there are the following
limitations:
• You cannot use varargout in the function definition for a top-level function.
• You cannot use varargin in the function definition for a top-level function in
a MATLAB Function block in a Simulink model, or in a MATLAB function in a
Stateflow diagram.
• If you use varargin to define an argument to a top-level function, the code
generation software generates the function with a fixed number of arguments. This
fixed number of arguments is based on the number of example arguments that you
provide on the command line or in a MATLAB Coder project test file.
Common applications of varargin and varargout for code generation are to:
• “Apply Operations to a Variable Number of Arguments” on page 12-4
• “Implement Wrapper Functions” on page 12-6
• “Pass Property/Value Pairs” on page 12-7
Code generation relies on loop unrolling to produce simple and efficient code for
varargin and varargout. This technique permits most common uses of varargin and
varargout, but some uses are not allowed (see “Variable Length Argument Lists for
Code Generation” on page 12-9).
For more information about using varargin and varargout in MATLAB functions, see
“Passing Variable Numbers of Arguments”.
12-2
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Supported Index Expressions
Supported Index Expressions
In MATLAB, varargin and varargout are cell arrays. Generated code does not support
cell arrays, but does allow you to use the most common syntax — curly braces {} — for
indexing into varargin and varargout arrays, as in this example:
%#codegen
function [x,y,z] = fcn(a,b,c)
[x,y,z] = subfcn(a,b,c);
function varargout = subfcn(varargin)
for i = 1:length(varargin)
varargout{i} = varargin{i};
end
You can use the following index expressions. The exp arguments must be constant
expressions or depend on a loop index variable.
Expression
varargin
(read only)
varargout
(read and write)
Description
varargin{exp}
Read the value of element exp
varargin{exp1: exp2}
Read the values of elements
exp1 through exp2
varargin{:}
Read the values of all
elements
varargout{exp}
Read or write the value of
element exp
Note: The use of () is not supported for indexing into varargin and varargout arrays.
12-3
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12
Defining Functions for Code Generation
Apply Operations to a Variable Number of Arguments
You can use varargin and varargout in for-loops to apply operations to a variable
number of arguments. To index into varargin and varargout arrays in generated
code, the value of the loop index variable must be known at compile time. Therefore,
during code generation, the compiler attempts to automatically unroll these for-loops.
Unrolling eliminates the loop logic by creating a separate copy of the loop body in the
generated code for each iteration. Within each iteration, the loop index variable becomes
a constant. For example, the following function automatically unrolls its for-loop in the
generated code:
%#codegen
function [cmlen,cmwth,cmhgt] = conv_2_metric(inlen,inwth,inhgt)
[cmlen,cmwth,cmhgt] = inch_2_cm(inlen,inwth,inhgt);
function varargout = inch_2_cm(varargin)
for i = 1:length(varargin)
varargout{i} = varargin{i} * 2.54;
end
When to Force Loop Unrolling
To automatically unroll for-loops containing varargin and varargout expressions,
the relationship between the loop index expression and the index variable must be
determined at compile time.
In the following example, the function fcn cannot detect a logical relationship between
the index expression j and the index variable i:
%#codegen
function [x,y,z] = fcn(a,b,c)
[x,y,z] = subfcn(a,b,c);
function varargout = subfcn(varargin)
j = 0;
for i = 1:length(varargin)
j = j+1;
varargout{j} = varargin{j};
end
As a result, the function does not unroll the loop and generates a compilation error:
12-4
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Apply Operations to a Variable Number of Arguments
Nonconstant expression or empty matrix.
This expression must be constant because
its value determines the size or class of some expression.
To fix the problem, you can force loop unrolling by wrapping the loop header in the
function coder.unroll, as follows:
%#codegen
function [x,y,z] = fcn(a,b,c)
[x,y,z] = subfcn(a,b,c);
function varargout = subfcn(varargin)
j = 0;
for i = coder.unroll(1:length(varargin))
j = j + 1;
varargout{j} = varargin{j};
end;
Using Variable Numbers of Arguments in a for-Loop
The following example multiplies a variable number of input dimensions in inches by
2.54 to convert them to centimeters:
%#codegen
function [cmlen,cmwth,cmhgt] = conv_2_metric(inlen,inwth,inhgt)
[cmlen,cmwth,cmhgt] = inch_2_cm(inlen,inwth,inhgt);
function varargout = inch_2_cm(varargin)
for i = 1:length(varargin)
varargout{i} = varargin{i} * 2.54;
end
Key Points About the Example
• varargin and varargout appear in the local function inch_2_cm, not in the toplevel function conv_2_metric.
• The index into varargin and varargout is a for-loop variable
For more information, see “Variable Length Argument Lists for Code Generation” on
page 12-9.
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12
Defining Functions for Code Generation
Implement Wrapper Functions
You can use varargin and varargout to write wrapper functions that accept up to 64
inputs and pass them directly to another function.
Passing Variable Numbers of Arguments from One Function to Another
The following example passes a variable number of inputs to different optimization
functions, based on a specified input method:
%#codegen
function answer = fcn(method,a,b,c)
answer = optimize(method,a,b,c);
function answer = optimize(method,varargin)
if strcmp(method,'simple')
answer = simple_optimization(varargin{:});
else
answer = complex_optimization(varargin{:});
end
...
Key Points About the Example
• You can use {:} to read all elements of varargin and pass them to another function.
• You can mix variable and fixed numbers of arguments.
For more information, see “Variable Length Argument Lists for Code Generation” on
page 12-9.
12-6
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Pass Property/Value Pairs
Pass Property/Value Pairs
You can use varargin to pass property/value pairs in functions. However, for code
generation, you must take precautions to avoid type mismatch errors when evaluating
varargin array elements in a for-loop:
If
Do This:
You assign varargin array elements to
local variables in the for-loop
Verify that for all pairs, the size, type, and
complexity are the same for each property
and the same for each value
Properties or values have different sizes,
types, or complexity
Do not assign varargin array elements to
local variables in a for-loop; reference the
elements directly
For example, in the following function test1, the sizes of the property strings and
numeric values are not the same in each pair:
%#codegen
function test1
v = create_value('size', 18, 'rgb', [240 9 44]);
end
function v = create_value(varargin)
v = new_value();
for i = 1 : 2 : length(varargin)
name = varargin{i};
value = varargin{i+1};
switch name
case 'size'
v = set_size(v, value);
case 'rgb'
v = set_color(v, value);
otherwise
end
end
end
...
Generated code determines the size, type, and complexity of a local variable based on its
first assignment. In this example, the first assignments occur in the first iteration of the
for-loop:
12-7
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12
Defining Functions for Code Generation
• Defines local variable name with size equal to 4
• Defines local variable value with a size of scalar
However, in the second iteration, the size of the property string changes to 3 and the size
of the numeric value changes to a vector, resulting in a type mismatch error. To avoid
such errors, reference varargin array values directly, not through local variables, as
highlighted in this code:
%#codegen
function test1
v = create_value('size', 18, 'rgb', [240 9 44]);
end
function v = create_value(varargin)
v = new_value();
for i = 1 : 2 : length(varargin)
switch varargin{i}
case 'size'
v = set_size(v, varargin{i+1});
case 'rgb'
v = set_color(v, varargin{i+1});
otherwise
end
end
end
...
12-8
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Variable Length Argument Lists for Code Generation
Variable Length Argument Lists for Code Generation
Use variable length argument lists in top-level functions
according to guidelines
When you use varargin and varargout for code generation, there are the following
limitations:
• You cannot use varargout in the function definition for a top-level function.
• You cannot use varargin in the function definition for a top-level function in
a MATLAB Function block in a Simulink model, or in a MATLAB function in a
Stateflow diagram.
• If you use varargin to define an argument to a top-level function, the code
generation software generates the function with a fixed number of arguments. This
fixed number of arguments is based on the number of example arguments that you
provide on the command line or in a MATLAB Coder project test file.
A top-level function is:
• The function called by Simulink in a MATLAB Function block or by Stateflow in a
MATLAB function.
• The function that you provide on the command line to codegen or fiaccel.
For example, the following code generates compilation errors:
%#codegen
function varargout = inch_2_cm(varargin)
for i = 1:length(varargin)
varargout{i} = varargin{i} * 2.54;
end
To fix the problem, write a top-level function that specifies a fixed number of inputs
and outputs. Then call inch_2_cm as an external function or local function, as in this
example:
%#codegen
function [cmL, cmW, cmH] = conv_2_metric(inL, inW, inH)
[cmL, cmW, cmH] = inch_2_cm(inL, inW, inH);
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12
Defining Functions for Code Generation
function varargout = inch_2_cm(varargin)
for i = 1:length(varargin)
varargout{i} = varargin{i} * 2.54;
end
Use curly braces {} to index into the argument list
For code generation, you can use curly braces {}, but not parentheses (), to index
into varargin and varargout arrays. For more information, see “Supported Index
Expressions” on page 12-3.
Verify that indices can be computed at compile time
If you use an expression to index into varargin or varargout, make sure that the value
of the expression can be computed at compile time. For examples, see “Apply Operations
to a Variable Number of Arguments” on page 12-4.
Do not write to varargin
Generated code treats varargin as a read-only variable. If you want to write to input
arguments, copy the values into a local variable.
12-10
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13
Calling Functions for Code Generation
• “Resolution of Function Calls for Code Generation” on page 13-2
• “Resolution of File Types on Code Generation Path” on page 13-6
• “Compilation Directive %#codegen” on page 13-8
• “Call Local Functions” on page 13-9
• “Call Supported Toolbox Functions” on page 13-10
• “Call MATLAB Functions” on page 13-11
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13
Calling Functions for Code Generation
Resolution of Function Calls for Code Generation
From a MATLAB function, you can call local functions, supported toolbox functions,
and other MATLAB functions. MATLAB resolves function names for code generation as
follows:
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Resolution of Function Calls for Code Generation
Start
Dispatch to
MATLAB
for execution
at runtime
Yes
Function
on
MATLAB
path?
No
Yes
Extrinsic
function?
No
Yes
Subfunction?
No
Function
on the code
generation
path?
Yes
No
Suitable
for code
generation?
Yes
No
Generate
C code
Function
on
MATLAB
path?
Yes
No
Generate error
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13
Calling Functions for Code Generation
Key Points About Resolving Function Calls
The diagram illustrates key points about how MATLAB resolves function calls for code
generation:
• Searches two paths, the code generation path and the MATLAB path
See “Compile Path Search Order” on page 13-4.
• Attempts to compile functions unless the code generation software determines that it
should not compile them or you explicitly declare them to be extrinsic.
If a MATLAB function is not supported for code generation, you can declare it to
be extrinsic by using the construct coder.extrinsic, as described in “Declaring
MATLAB Functions as Extrinsic Functions”. During simulation, the code generation
software generates code for the call to an extrinsic function, but does not generate
the function's internal code. Therefore, simulation can run only on platforms where
MATLAB software is installed. During standalone code generation, MATLAB
attempts to determine whether the extrinsic function affects the output of the
function in which it is called — for example by returning mxArrays to an output
variable. Provided that the output does not change, MATLAB proceeds with code
generation, but excludes the extrinsic function from the generated code. Otherwise,
compilation errors occur.
The code generation software detects calls to many common visualization functions,
such as plot, disp, and figure. The software treats these functions like extrinsic
functions but you do not have to declare them extrinsic using the coder.extrinsic
function.
• Resolves file type based on precedence rules described in “Resolution of File Types on
Code Generation Path” on page 13-6
Compile Path Search Order
During code generation, function calls are resolved on two paths:
1
Code generation path
MATLAB searches this path first during code generation. The code generation path
contains the toolbox functions supported for code generation.
2
MATLAB path
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Resolution of Function Calls for Code Generation
If the function is not on the code generation path, MATLAB searches this path.
MATLAB applies the same dispatcher rules when searching each path (see “Function
Precedence Order”).
When to Use the Code Generation Path
Use the code generation path to override a MATLAB function with a customized version.
A file on the code generation path shadows a file of the same name on the MATLAB path.
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13
Calling Functions for Code Generation
Resolution of File Types on Code Generation Path
MATLAB uses the following precedence rules for code generation:
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Resolution of File Types on Code Generation Path
Start
M-file and
MEX-file in same
directory?
Yes
No
Yes
MEX-file?
No
Yes
Generate
error
MDL-file?
Compile
M-file
No
Yes
P-file?
No
No
Yes
M-file?
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13-7
13
Calling Functions for Code Generation
Compilation Directive %#codegen
Add the %#codegen directive (or pragma) to your function after the function signature
to indicate that you intend to generate code for the MATLAB algorithm. Adding this
directive instructs the MATLAB code analyzer to help you diagnose and fix violations
that would result in errors during code generation.
function y = my_fcn(x) %#codegen
....
13-8
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Call Local Functions
Call Local Functions
Local functions are functions defined in the body of MATLAB function. They work the
same way for code generation as they do when executing your algorithm in the MATLAB
environment.
The following example illustrates how to define and call a local function mean:
function [mean, stdev] = stats(vals)
%#codegen
% Calculates a statistical mean and a standard
% deviation for the values in vals.
len = length(vals);
mean = avg(vals, len);
stdev = sqrt(sum(((vals-avg(vals,len)).^2))/len);
plot(vals,'-+');
function mean = avg(array,size)
mean = sum(array)/size;
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13
Calling Functions for Code Generation
Call Supported Toolbox Functions
You can call toolbox functions directly if they are supported for code generation. For a
list of supported functions, see “Functions and Objects Supported for C and C++ Code
Generation — Alphabetical List” on page 4-2.
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Call MATLAB Functions
Call MATLAB Functions
The code generation software attempts to generate code for functions, even if they
are not supported for C code generation. The software detects calls to many common
visualization functions, such as plot, disp, and figure. The software treats these
functions like extrinsic functions but you do not have to declare them extrinsic using
coder.extrinsic. During simulation, the code generation software generates code
for these functions, but does not generate their internal code. During standalone code
generation, MATLAB attempts to determine whether the visualization function affects
the output of the function in which it is called. Provided that the output does not change,
MATLAB proceeds with code generation, but excludes the visualization function from the
generated code. Otherwise, compilation errors occur.
For example, you might want to call plot to visualize your results in the MATLAB
environment. If you generate a MEX function from a function that calls plot and then
run the generated MEX function, the code generation software dispatches calls to the
plot function to MATLAB. If you generate a library or executable, the generated code
does not contain calls to the plot function. The code generation report highlights calls
from your MATLAB code to extrinsic functions so that it is easy to determine which
functions are supported only in the MATLAB environment.
For unsupported functions other than common visualization functions, you must declare
the functions (like pause) to be extrinsic (see “Resolution of Function Calls for Code
Generation” on page 13-2). Extrinsic functions are not compiled, but instead executed in
MATLAB during simulation (see “How MATLAB Resolves Extrinsic Functions During
Simulation” on page 13-16).
There are two ways to declare a function to be extrinsic:
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13
Calling Functions for Code Generation
• Use the coder.extrinsic construct in main functions or local functions (see
“Declaring MATLAB Functions as Extrinsic Functions” on page 13-12).
• Call the function indirectly using feval (see “Calling MATLAB Functions Using
feval” on page 13-16).
Declaring MATLAB Functions as Extrinsic Functions
To declare a MATLAB function to be extrinsic, add the coder.extrinsic construct at
the top of the main function or a local function:
coder.extrinsic('function_name_1', ... , 'function_name_n');
Declaring Extrinsic Functions
The following code declares the MATLAB patch function extrinsic in the local function
create_plot:
function c = pythagoras(a,b,color) %#codegen
% Calculates the hypotenuse of a right triangle
% and displays the triangle.
c = sqrt(a^2 + b^2);
create_plot(a, b, color);
function create_plot(a, b, color)
%Declare patch and axis as extrinsic
coder.extrinsic('patch');
x = [0;a;a];
y = [0;0;b];
patch(x, y, color);
axis('equal');
The code generation software detects that axis is not supported for code generation and
automatically treats it as an extrinsic function. The compiler does not generate code for
patch and axis, but instead dispatches them to MATLAB for execution.
To test the function, follow these steps:
1
Convert pythagoras to a MEX function by executing this command at the MATLAB
prompt:
13-12
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Call MATLAB Functions
codegen -report pythagoras -args {1, 1, [.3 .3 .3]}
2
Click the link to the code generation report and then, in the report, view the
MATLAB code for create_plot.
The report highlights the patch and axis functions to indicate that they are
supported only within the MATLAB environment.
3
Run the MEX function by executing this command:
pythagoras_mex(3, 4, [1.0 0.0 0.0]);
MATLAB displays a plot of the right triangle as a red patch object:
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13
Calling Functions for Code Generation
When to Use the coder.extrinsic Construct
Use the coder.extrinsic construct to:
• Call MATLAB functions that do not produce output — such as pause — during
simulation, without generating unnecessary code (see “How MATLAB Resolves
Extrinsic Functions During Simulation” on page 13-16).
• Make your code self-documenting and easier to debug. You can scan the source code
for coder.extrinsic statements to isolate calls to MATLAB functions, which can
potentially create and propagate mxArrays (see “Working with mxArrays” on page
13-17).
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Call MATLAB Functions
• Save typing. With one coder.extrinsic statement, each subsequent function call
is extrinsic, as long as the call and the statement are in the same scope (see “Scope of
Extrinsic Function Declarations” on page 13-15).
• Declare the MATLAB function(s) extrinsic throughout the calling function scope (see
“Scope of Extrinsic Function Declarations” on page 13-15). To narrow the scope,
use feval (see “Calling MATLAB Functions Using feval” on page 13-16).
Rules for Extrinsic Function Declarations
Observe the following rules when declaring functions extrinsic for code generation:
• Declare the function extrinsic before you call it.
• Do not use the extrinsic declaration in conditional statements.
Scope of Extrinsic Function Declarations
The coder.extrinsic construct has function scope. For example, consider the following
code:
function y = foo %#codegen
coder.extrinsic('rat','min');
[N D] = rat(pi);
y = 0;
y = min(N, D);
In this example, rat and min as treated as extrinsic every time they are called in the
main function foo. There are two ways to narrow the scope of an extrinsic declaration
inside the main function:
• Declare the MATLAB function extrinsic in a local function, as in this example:
function y = foo %#codegen
coder.extrinsic('rat');
[N D] = rat(pi);
y = 0;
y = mymin(N, D);
function y = mymin(a,b)
coder.extrinsic('min');
y = min(a,b);
Here, the function rat is extrinsic every time it is called inside the main function
foo, but the function min is extrinsic only when called inside the local function
mymin.
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13
Calling Functions for Code Generation
• Call the MATLAB function using feval, as described in “Calling MATLAB Functions
Using feval” on page 13-16.
Calling MATLAB Functions Using feval
The function feval is automatically interpreted as an extrinsic function during code
generation. Therefore, you can use feval to conveniently call functions that you want to
execute in the MATLAB environment, rather than compiled to generated code.
Consider the following example:
function y = foo
coder.extrinsic('rat');
[N D] = rat(pi);
y = 0;
y = feval('min', N, D);
Because feval is extrinsic, the statement feval('min', N, D) is evaluated by
MATLAB — not compiled — which has the same result as declaring the function min
extrinsic for just this one call. By contrast, the function rat is extrinsic throughout the
function foo.
How MATLAB Resolves Extrinsic Functions During Simulation
MATLAB resolves calls to extrinsic functions — functions that do not support code
generation — as follows:
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Call MATLAB Functions
During simulation, MATLAB generates code for the call to an extrinsic function, but does
not generate the function's internal code. Therefore, you can run the simulation only on
platforms where you install MATLAB software.
During code generation, MATLAB attempts to determine whether the extrinsic function
affects the output of the function in which it is called — for example by returning
mxArrays to an output variable (see “Working with mxArrays” on page 13-17).
Provided that the output does not change, MATLAB proceeds with code generation, but
excludes the extrinsic function from the generated code. Otherwise, MATLAB issues a
compiler error.
Working with mxArrays
The output of an extrinsic function is an mxArray — also called a MATLAB array. The
only valid operations for mxArrays are:
• Storing mxArrays in variables
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13
Calling Functions for Code Generation
• Passing mxArrays to functions and returning them from functions
• Converting mxArrays to known types at run time
To use mxArrays returned by extrinsic functions in other operations, you must first
convert them to known types, as described in “Converting mxArrays to Known Types” on
page 13-18.
Converting mxArrays to Known Types
To convert an mxArray to a known type, assign the mxArray to a variable whose type is
defined. At run time, the mxArray is converted to the type of the variable assigned to it.
However, if the data in the mxArray is not consistent with the type of the variable, you
get a run-time error.
For example, consider this code:
function y = foo %#codegen
coder.extrinsic('rat');
[N D] = rat(pi);
y = min(N, D);
Here, the top-level function foo calls the extrinsic MATLAB function rat, which returns
two mxArrays representing the numerator N and denominator D of the rational fraction
approximation of pi. Although you can pass these mxArrays to another MATLAB
function — in this case, min — you cannot assign the mxArray returned by min to the
output y.
If you run this function foo in a MATLAB Function block in a Simulink model, the code
generates the following error during simulation:
Function output 'y' cannot be of MATLAB type.
To fix this problem, define y to be the type and size of the value that you expect min to
return — in this case, a scalar double — as follows:
function y = foo %#codegen
coder.extrinsic('rat');
[N D] = rat(pi);
y = 0; % Define y as a scalar of type double
y = min(N,D);
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Call MATLAB Functions
Restrictions on Extrinsic Functions for Code Generation
The full MATLAB run-time environment is not supported during code generation.
Therefore, the following restrictions apply when calling MATLAB functions extrinsically:
• MATLAB functions that inspect the caller, or read or write to the caller's workspace
do not work during code generation. Such functions include:
• dbstack
• evalin
• assignin
• save
• The MATLAB debugger cannot inspect variables defined in extrinsic functions.
• Functions in generated code may produce unpredictable results if your extrinsic
function performs the following actions at run time:
• Change folders
• Change the MATLAB path
• Delete or add MATLAB files
• Change warning states
• Change MATLAB preferences
• Change Simulink parameters
Limit on Function Arguments
You can call functions with up to 64 inputs and 64 outputs.
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14
Fixed-Point Conversion
• “Convert MATLAB Code to Fixed-Point C Code” on page 14-3
• “Propose Fixed-Point Data Types Based on Simulation Ranges” on page 14-4
• “Propose Fixed-Point Data Types Based on Derived Ranges” on page 14-18
• “Type Proposal Settings” on page 14-34
• “Detect Overflows” on page 14-38
• “Replace the exp Function with a Lookup Table” on page 14-45
• “Replace a Custom Function with a Lookup Table” on page 14-51
• “Enable Plotting Using the Simulation Data Inspector” on page 14-58
• “Log Data for Histogram” on page 14-59
• “View and Modify Variable Information” on page 14-62
• “Build Instrumented MEX Function” on page 14-66
• “Propose Fixed-Point Data Types” on page 14-67
• “Apply Fixed-Point Data Types” on page 14-77
• “Modify Data Type Proposal Settings” on page 14-82
• “Modify Instrumentation Report Settings” on page 14-85
• “Automated Fixed-Point Conversion” on page 14-86
• “Instrumented MEX Functions” on page 14-105
• “Convert Fixed-Point Conversion Project to MATLAB Scripts” on page 14-107
• “Generated Fixed-Point Code” on page 14-110
• “Fixed-Point Code for MATLAB Classes” on page 14-116
• “Automated Fixed-Point Conversion Best Practices” on page 14-119
• “Replacing Functions Using Lookup Table Approximations” on page 14-128
• “MATLAB Language Features Supported for Automated Fixed-Point Conversion” on
page 14-129
• “Inspecting Data Using the Simulation Data Inspector” on page 14-131
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14
Fixed-Point Conversion
• “Custom Plot Functions” on page 14-134
• “Data Type Issues in Generated Code” on page 14-136
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Convert MATLAB Code to Fixed-Point C Code
Convert MATLAB Code to Fixed-Point C Code
1
Create a MATLAB Coder project, add the entry-point function from which you want
to generate code, and then define entry-point input types.
2
On the project Overview tab Fixed-Point Conversion pane, select Convert to
fixed-point at build time. Then on the project Fixed-Point Conversion pane,
click Define and validate fixed-point types to open the Fixed-Point Conversion
tool.
3
Compute ranges by either simulating using a test file, using static analysis to derive
ranges from design ranges, or both.
4
Validate the proposed data types. See “Validating Types” on page 14-103.
5
Test numerics. See “Testing Numerics” on page 14-103.
6
In the MATLAB Coder project, select the Build tab, set the Output type to build a
static or dynamic library, or executable, and then click Build.
MATLAB Coder generates fixed-point C code for your entry-point MATLAB function.
For more information, see “Propose Fixed-Point Data Types Based on Simulation
Ranges” on page 14-4 and “Propose Fixed-Point Data Types Based on Derived
Ranges” on page 14-18.
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14
Fixed-Point Conversion
Propose Fixed-Point Data Types Based on Simulation Ranges
This example shows how to propose fixed-point data types based on simulation range
data.
Prerequisites
To complete this example, you must install the following products:
• MATLAB
• MATLAB Coder
• Fixed-Point Designer
• C compiler (for most platforms, a default C compiler is supplied with MATLAB)
For a list of supported compilers, see http://www.mathworks.com/support/
compilers/current_release/
You can use mex -setup to change the default compiler. See “Changing Default
Compiler”.
Create a New Folder and Copy Relevant Files
1
Create a local working folder, for example, c:\coder\fun_with_matlab.
2
Change to the docroot\toolbox\coder\examples folder. At the MATLAB
command line, enter:
cd(fullfile(docroot, 'toolbox', 'coder', 'examples'))
3
Copy the fun_with_matlab.m and fun_with_matlab_test.m files to your local
working folder.
Type
Name
Description
Function code
fun_with_matlab.m
Entry-point MATLAB function
Test file
fun_with_matlab_test.m
MATLAB script that tests
fun_with_matlab.m
The fun_with_matlab Function
function y = fun_with_matlab(x) %#codegen
persistent z
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The fun_with_matlab_test Script
if isempty(z)
z = zeros(2,1);
end
% [b,a] = butter(2, 0.25)
b = [0.0976310729378175, 0.195262145875635, 0.0976310729378175];
a = [1, -0.942809041582063, 0.3333333333333333];
y = zeros(size(x));
for i = 1:length(x)
y(i) = b(1)*x(i) + z(1);
z(1) = b(2)*x(i) + z(2) - a(2) * y(i);
z(2) = b(3)*x(i)
- a(3) * y(i);
end
end
The fun_with_matlab_test Script
The test script runs the fun_with_matlab function with three input signals: chirp, step,
and impulse to cover the full intended operating range of the system. The script then
plots the outputs.
% fun_with_matlab_test
%
% Define representative inputs
N = 256;
% Number of points
t = linspace(0,1,N);
% Time vector from 0 to 1 second
f1 = N/2;
% Target frequency of chirp set to Nyquist
x_chirp = sin(pi*f1*t.^2); % Linear chirp from 0 to Fs/2 Hz in 1 second
x_step = ones(1,N);
% Step
x_impulse = zeros(1,N);
% Impulse
x_impulse(1) = 1;
% Run the function under test
x = [x_chirp;x_step;x_impulse];
y = zeros(size(x));
for i = 1:size(x,1)
y(i,:) = fun_with_matlab(x(i,:));
end
% Plot the results
titles = {'Chirp','Step','Impulse'}
clf
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14
Fixed-Point Conversion
for i = 1:size(x,1)
subplot(size(x,1),1,i)
plot(t,x(i,:),t,y(i,:))
title(titles{i})
legend('Input','Output')
end
xlabel('Time (s)')
figure(gcf)
disp('Test complete.')
Check Code Generation Readiness
In the current working folder, right-click the fun_with_matlab.m function. From the
context menu, select Check Code Generation Readiness.
The code generation readiness tool screens the code for features and functions that
are not supported for code generation. The tool reports that the fun_with_matlab.m
function is already suitable for code generation.
If your entry-point function is not suitable for code generation, the tool provides a report
that lists the source files that contain unsupported features and functions. The report
also provides an indication of how much work you must do to make the MATLAB code
ready for code generation. Before proposing data types, you must fix these issues. For
more information, see “MATLAB Code Analysis”.
Create and set up a MATLAB Coder Project
1
Navigate to the work folder that contains the file for this example.
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The fun_with_matlab_test Script
2
On the MATLAB Apps tab, select MATLAB Coder and then, in the MATLAB
Coder Project dialog box, set Name to fun_with_matlab_project.prj.
Alternatively, at the MATLAB command line, enter
coder -new fun_with_matlab_project.prj
By default, the project opens in the MATLAB workspace.
3
On the project Overview tab, click the Add files link. Browse to the file
fun_with_matlab.m and then click OK to add the file to the project.
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14
Fixed-Point Conversion
Define Input Types
1
On the project Overview tab, click the Autodefine types link.
2
In the Autodefine Input Types dialog box, add fun_with_matlab_test as a test
file and then click Run.
The test file runs and displays the outputs of the filter for each of the input signals.
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The fun_with_matlab_test Script
MATLAB Coder determines the input types from the test file and then displays
them.
3
In the Autodefine Input Types dialog box, click Use These Types.
MATLAB Coder sets the type of x to double(1x256).
Fixed-Point Conversion
1
On the project Overview tab Fixed-Point Conversion pane, select Convert to
fixed-point at build time.
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14
Fixed-Point Conversion
The project indicates that you must first define the fixed-point data types.
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The fun_with_matlab_test Script
2
In the Fixed-Point Conversion pane, click Define and validate fixed-point
types.
The Fixed-Point Conversion window opens and the tool generates an instrumented
MEX function for your entry-point MATLAB function. After generating the MEX
function, the tool displays compiled information — type, size, and complexity —
for variables in your code. For more information, see “View and Modify Variable
Information”.
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14
Fixed-Point Conversion
If the MEX function generation fails, the tool provides error message links to help
you navigate to the code that caused the build issues. If your code contains functions
that are not supported for fixed-point conversion, the tool displays these on the
Function Replacements tab. For more information, see “Running a Simulation”.
3
Click Run Simulation and verify that the fun_with_matlab_test file is selected
as a test file to run. You can add test files and select to run more than one test file
during the simulation. If you run multiple test files, the conversion tool merges the
simulation results. To clear results, right-click the Variables tab and select Reset
entire table.
4
Click Run Simulation and select Log data for histogram.
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The fun_with_matlab_test Script
By default, the Show code coverage option is selected. This option provides
code coverage information that helps you verify that your test file is testing your
algorithm over the intended operating range.
5
Click the Run Simulation button.
The simulation runs and the conversion tool displays a color-coded code coverage bar
to the left of the MATLAB code. Review this information to verify that the test file
is testing the algorithm adequately. Here, the dark green line to the left of the code
indicates that the code is run every time the algorithm is executed. The orange bar
indicates that the code next to it is executed only once. In this example, this is the
expected behavior because the code is initializing a persistent variable. If your test
file is not covering all your code, update the test or add more test files.
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14
Fixed-Point Conversion
If a value has ... next to it, the value is rounded. Place your cursor over the ... to
view the actual value.
The tool displays simulation minimum and maximum ranges on the Variables
tab. Using the simulation range data, the software proposes fixed-point types for
each variable based on the default type proposal settings, and displays them in the
Proposed Type column. The Validate Types option is now enabled.
Note: If you manually enter static ranges, these manually-entered ranges take
precedence over simulation ranges and the tool uses them to propose data types. In
addition, you can also modify and lock the proposed type.
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The fun_with_matlab_test Script
6
Examine the proposed types and verify that they cover the full simulation range. To
view logged histogram data for a variable, click its Proposed Type field.
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14
Fixed-Point Conversion
To modify the proposed data types, either enter the required type into the
ProposedType field or use the histogram controls. For more information about the
histogram, see “Histogram”.
7
To validate the build using the proposed types, click Validate Types.
The software validates the proposed types, displays a Validation succeeded
message, and enables the Test Numerics option. The project indicates that you
have validated the fixed-point data types.
If the errors or warnings occur during validation, they are displayed on the Type
Validation Output tab. For more information, see “Validating Types”.
8
Click Test Numerics, select Log inputs and outputs for comparison
plots, and then click the Test Numerics button.
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The fun_with_matlab_test Script
The tool runs the test file that you used to define input types to test the fixedpoint MATLAB code. Optionally, you can add test files and select to run more than
one test file to test numerics. The software runs both a floating-point and a fixedpoint simulation and then calculates the errors for the output variable y. Because
you selected to log inputs and outputs for comparison plots, the tool generates an
additional plot for each scalar output.
The maximum error is less than 0.03%. For the purpose of this example, this margin
of error is acceptable, so you are ready to generate fixed-point C code.
If the difference is not acceptable, modify the fixed-point data types or your original
algorithm. For more information, see “Testing Numerics”.
9
Return to the MATLAB Coder project.
Generate Fixed-Point C Code
1
In the MATLAB Coder project, verify that the Fixed-Point Conversion pane
displays Ready for conversion, and then select the Build tab.
2
On this tab, set the Output type to C/C++ Static library.
The default output file name is fun_with_matlab.
3
Click Build to generate a library using the default project settings.
MATLAB Coder builds the project and generates a C static library and supporting
files in the default subfolder, codegen/lib/fun_with_matlab_fixpt.
4
To view the generated code, click View report.
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14
Fixed-Point Conversion
Propose Fixed-Point Data Types Based on Derived Ranges
This example shows how to propose fixed-point data types based on static ranges that
you specify. The advantage of proposing data types based on derived ranges is that you
do not have to provide test files that exercise your algorithm over its full operating range.
Running such test files often takes a very long time so you can save time by deriving
ranges instead.
Prerequisites
To complete this example, you must install the following products:
• MATLAB
• MATLAB Coder
• Fixed-Point Designer
• C compiler (for most platforms, a default C compiler is supplied with MATLAB)
For a list of supported compilers, see http://www.mathworks.com/support/
compilers/current_release/
You can use mex -setup to change the default compiler. See “Changing Default
Compiler”.
Create a New Folder and Copy Relevant Files
1
Create a local working folder, for example, c:\coder\dti.
2
Change to the docroot\toolbox\coder\examples folder. At the MATLAB
command line, enter:
cd(fullfile(docroot, 'toolbox', 'coder', 'examples'))
3
Copy the dti.m and dti_test.m files to your local working folder.
Type
Name
Description
Function code
dti.m
Entry-point MATLAB function
Test file
dti_test.m
MATLAB script that tests
dti.m
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The dti Function
The dti Function
The dti function implements a Discrete Time Integrator in MATLAB.
function [y, clip_status] = dti(u_in) %#codegen
% Discrete Time Integrator in MATLAB
%
% Forward Euler method, also known as Forward Rectangular, or left-hand
% approximation. The resulting expression for the output of the block at
% step 'n' is y(n) = y(n-1) + K * u(n-1)
%
init_val = 1;
gain_val = 1;
limit_upper = 500;
limit_lower = -500;
% variable to hold state between consecutive calls to this block
persistent u_state
if isempty(u_state)
u_state = init_val+1;
end
% Compute Output
if (u_state > limit_upper)
y = limit_upper;
clip_status = -2;
elseif (u_state >= limit_upper)
y = limit_upper;
clip_status = -1;
elseif (u_state < limit_lower)
y = limit_lower;
clip_status = 2;
elseif (u_state <= limit_lower)
y = limit_lower;
clip_status = 1;
else
y = u_state;
clip_status = 0;
end
% Update State
tprod = gain_val * u_in;
u_state = y + tprod;
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14
Fixed-Point Conversion
function b = subFunction(a)
b = a*a;
The dti_test Function
The test script runs the dti function with a sine wave input. The script then plots the
input and output signals.
% dti_test
% cleanup
clear dti
% input signal
x_in = sin(2.*pi.*(0:0.001:2)).';
pause(10)
len = length(x_in);
y_out = zeros(1,len);
is_clipped_out = zeros(1,len);
for ii = 1:len
data = x_in(ii);
% call to the dti function
init_val = 0;
gain_val = 1;
upper_limit = 500;
lower_limit = -500;
% call to the design that does DTI
[y_out(ii), is_clipped_out(ii)] = dti(data);
end
figure('Name', [mfilename, '_plot'])
subplot(2,1,1)
plot(1:len,x_in)
xlabel('Time')
ylabel('Amplitude')
title('Input Signal (Sin)')
subplot(2,1,2); plot(1:len,y_out)
xlabel('Time')
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The dti_test Function
ylabel('Amplitude')
title('Output Signal (DTI)')
disp('Test complete.')
Check Code Generation Readiness
In the current working folder, right-click the dti.m function. From the context menu,
select Check Code Generation Readiness.
The code generation readiness tool screens the code for features and functions that are
not supported for code generation. The tool reports that the dti.m function is already
suitable for code generation.
If your entry-point function is not suitable for code generation, the tool provides a report
that lists the source files that contain unsupported features and functions. The report
also provides an indication of how much work you must do to make the MATLAB code
ready for code generation. Before proposing data types, you must fix these issues. For
more information, see “MATLAB Code Analysis”.
Create and set up a MATLAB Coder Project
1
Navigate to the work folder that contains the file for this example.
2
On the MATLAB Apps tab, select MATLAB Coder and then, in the MATLAB
Coder Project dialog box, set Name to dti.prj.
Alternatively, at the MATLAB command line, enter
coder -new dti.prj
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14
Fixed-Point Conversion
By default, the project opens in the MATLAB workspace.
3
On the project Overview tab, click the Add files link. Browse to the file dti.m and
then click OK to add the file to the project.
Define Input Types
1
On the project Overview tab, click the Autodefine types link.
2
In the Autodefine Input Types dialog box, add dti_test as a test file and then click
Run.
The test file runs and displays the outputs of the filter for each of the input signals.
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The dti_test Function
MATLAB Coder determines the input types from the test file and then displays
them.
3
In the Autodefine Input Types dialog box, click Use These Types.
MATLAB Coder sets the type of x to double(1x1).
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14
Fixed-Point Conversion
Fixed-Point Conversion
1
On the project Overview tab Fixed-Point Conversion pane, select Convert to
fixed-point at build time.
The project indicates that you must first define the fixed-point data types.
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The dti_test Function
2
In the Fixed-Point Conversion pane, click Define and validate fixed-point
types.
The Fixed-Point Conversion window opens and the tool generates an instrumented
MEX function for your entry-point MATLAB function. After generating the MEX
function, the tool displays compiled information — type, size, and complexity —
for variables in your code. For more information, see “View and Modify Variable
Information”.
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14
Fixed-Point Conversion
If the MEX function generation fails, the tool provides error message links to help
you navigate to the code that caused the build issues. If your code contains functions
that are not supported for fixed-point conversion, the tool displays these on the
Function Replacements tab. For more information, see “Running a Simulation”.
3
In the Fixed-Point Conversion window, on the Variables tab, for input u_in, select
Static Min and set it to -1. Then set Static Max to 1.
To compute derived range information, at a minimum you must specify static
minimum and maximum values or proposed data types for all input variables.
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The dti_test Function
Note: If you manually enter static ranges, these manually-entered ranges take
precedence over simulation ranges and the tool uses them to propose data types. In
addition, you can also modify and lock the proposed type.
4
Click the Compute Derived Ranges button.
Range analysis computes the derived ranges and displays them in the Variables
tab. Using these derived ranges, the analysis proposes fixed-point types for each
variable based on the default type proposal settings, and displays them in the
Proposed Type column. The Validate Types option is now enabled.
In the dti function, the clip_status output has a minimum value of -2 and a
maximum of 2.
% Compute Output
if (u_state > limit_upper)
y = limit_upper;
clip_status = -2;
elseif (u_state >= limit_upper)
y = limit_upper;
clip_status = -1;
elseif (u_state < limit_lower)
y = limit_lower;
clip_status = 2;
elseif (u_state <= limit_lower)
y = limit_lower;
clip_status = 1;
else
y = u_state;
clip_status = 0;
end
When you derive ranges, the Fixed-Point Conversion tool analyses the function and
computes these minimum and maximum values for clip_status.
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14
Fixed-Point Conversion
The tool provides a Quick derived range analysis option and the option to specify
a timeout in case the analysis takes a very long time. For more information, see
“Computing Derived Ranges”
5
To validate the build using the proposed types, click Validate Types.
The software validates the proposed types, displays a Validation succeeded
message, and enables the Test Numerics option. The project indicates that you
have validated the fixed-point data types.
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The dti_test Function
If the errors or warnings occur during validation, they are displayed on the Type
Validation Output tab. For more information, see “Validating Types”.
6
Use the Simulation Data Inspector to plot the floating-point and fixed-point results.
a
Click
b
Expand the Plotting and Reporting settings and set Plot with Simulation
Data Inspector to Yes.
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14
Fixed-Point Conversion
7
Click Test Numerics, select Log inputs and outputs for comparison
plots, and then click the Test Numerics button.
The tool runs the test file that you used to define input types to test the fixed-point
MATLAB code. Optionally, you can add test files and select to run more than one
test file to test numerics. The software runs both a floating-point and a fixed-point
simulation and then calculates the errors for the output variable y. Because you
selected to log inputs and outputs for comparison plots and to use the Simulation
Data Inspector for these plots, the Simulation Data Inspector opens.
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The dti_test Function
On the Verification Output tab, the tool provides a link to a type proposal report.
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14
Fixed-Point Conversion
8
You can use the Simulation Data Inspector to view floating-point and fixed-point
run information and compare results. For example, to compare the floating-point
and fixed-point values for the output y, on the Compare tab, select y, and then click
Compare Runs.
The Simulation Data Inspector displays a plot of the baseline floating-point run
against the fixed-point run and the difference between them.
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The dti_test Function
Generate Fixed-Point C Code
1
In the MATLAB Coder project, select the Build tab.
2
On this tab, set the Output type to C/C++ Static library.
The default output file name is dti.
3
Click Build to generate a library using the default project settings.
MATLAB Coder builds the project and generates a C static library and supporting
files in the default subfolder, codegen/lib/dti_fixpt.
4
To view the generated code, click View report.
The code generation report opens and displays the generated code for dti_fixpt.c.
In the generated C code, variables are assigned fixed-point data types.
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14
Fixed-Point Conversion
Type Proposal Settings
Basic Type Proposal Settings
Values
Description
Fixed-point type proposal
mode
Propose fraction lengths for Use the specified word
specified word length
length for data type
proposals and propose the
minimum fraction lengths to
avoid overflows.
Propose word lengths for
specified fraction length
(default)
Use the specified fraction
length for data type
proposals and propose the
minimum word lengths to
avoid overflows.
Default word length
16 (default)
Default word length to
use when Fixed point
type proposal mode is
set to Propose fraction
lengths for specified
word lengths
Default fraction length
4 (default)
Default fraction length to use
when Fixed point type
proposal mode is set to
Propose word lengths
for specified fraction
lengths
Advanced Type Proposal Settings
Values
Description
When proposing types
ignore simulation
ranges
Propose data types based on
derived ranges.
ignore derived
Note: Manually-entered static ranges
ranges
always take precedence over simulation
use all collected data
ranges.
(default)
Propose data types based on
simulation ranges.
Propose target container types
Propose data type with the smallest
word length that can represent the
range and is suitable for C code
generation ( 8,16,32, 64 … ). For
Yes
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Propose data types based on both
simulation and derived ranges.
Type Proposal Settings
Advanced Type Proposal Settings
Optimize whole numbers
Signedness
Safety margin for sim min/max (%)
Values
Description
example, for a variable with range
[0..7], propose a word length of 8
rather than 3.
No (default)
Propose data types with the
minimum word length needed to
represent the value.
No
Do not use integer scaling for
variables that were whole numbers
during simulation.
Yes (default)
Use integer scaling for variables
that were whole numbers during
simulation.
Automatic (default)
Proposes signed and unsigned
data types depending on the range
information for each variable.
Signed
Propose signed data types.
Unsigned
Propose unsigned data types.
0 (default)
Specify safety factor for simulation
minimum and maximum values.
The simulation minimum and
maximum values are adjusted
by the percentage designated by
this parameter, allowing you to
specify a range different from that
obtained from the simulation run.
For example, a value of 55 specifies
that you want a range at least
55 percent larger. A value of -15
specifies that a range up to 15
percent smaller is acceptable.
fimath Settings
Values
Description
Rounding method
Ceiling
Specify the fimath
properties for the generated
fixed-point data types.
Convergent
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14
Fixed-Point Conversion
fimath Settings
Values
Floor (default)
Nearest
Round
Zero
Overflow action
Saturate
Wrap (default)
Product mode
FullPrecision (default)
KeepLSB
KeepMSB
SpecifyPrecision
Sum mode
FullPrecision (default)
KeepLSB
KeepMSB
Description
The default fixed-point
math properties use the
Floor rounding and Wrap
overflow because they are
the default actions in C.
These settings generate
the most efficient code but
might cause problems with
overflow.
After code generation, if
required, modify these
settings to optimize the
generated code, or example,
avoid overflow or eliminate
bias, and then rerun the
verification.
SpecifyPrecision
Product word length
32 (default)|any positive
integer
Word length, in bits, of the
product data type
Sum word length
32 (default)|any positive
integer
Word length, in bits, of the
sum data type
Generated File Settings
Value
Description
Generated fixed-point file
name suffix
_fixpt (default)
Specify the suffix to add to
the generated fixed-point
file names. For example, by
default, if you generate a
static library for a project
named test, the generated
files are in the subfolder
codegen\lib\test_fixpt.
The generated static library
is named test.lib, but
the generated C code files
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Type Proposal Settings
Generated File Settings
Value
Description
use the suffix, for example,
test_fixpt.c.
Plotting and Reporting
Settings
Values
Description
Custom plot function
Empty string
Specify the name of a custom
plot function to use for
comparison plots.
Plot with Simulation Data
Inspector
No (default)
Specify whether to use the
Simulation Data Inspector
for comparison plots.
Highlight potential data
type issues
No (default)
Yes
Yes
Specify whether to highlight
potential data types in the
generated html report. If
this option is turned on, the
report highlights singleprecision, double-precision,
and expensive fixed-point
operation usage in your
MATLAB code.
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14
Fixed-Point Conversion
Detect Overflows
This example shows how to detect overflows using the Fixed-Point Conversion tool.
At the numerical testing stage in the conversion process, the tool simulates the fixedpoint code using scaled doubles. It then reports which expressions in the generated code
produce values that would overflow the fixed-point data type.
Prerequisites
To complete this example, you must install the following products:
• MATLAB
• MATLAB Coder
• Fixed-Point Designer
Create a Project
In a local writable folder, create a MATLAB Coder project named overflow.
At the MATLAB command line, enter
coder -new overflow.prj
By default, the project opens in the MATLAB workspace.
Add File
1
In the same folder, create a function, overflow.
function y = overflow(b,x,reset)
if nargin<3, reset = true; end
persistent z p
if isempty(z) || reset
p = 0;
z = zeros(size(b));
end
[y,z,p] = fir_filter(b,x,z,p);
end
function [y,z,p] = fir_filter(b,x,z,p)
y = zeros(size(x));
nx = length(x);
nb = length(b);
for n = 1:nx
p=p+1; if p>nb, p=1; end
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Detect Overflows
z(p) = x(n);
acc = 0;
k = p;
for j=1:nb
acc = acc + b(j)*z(k);
k=k-1; if k<1, k=nb; end
end
y(n) = acc;
end
end
2
On the project Overview tab, click the Add files link. Browse to the file
overflow.m and then click OK to add the file to the project.
Define Input Types
1
Create a test file, overflow_test.m to exercise the overflow algorithm. You use
this test file to define input types for b, x, and reset, and, later, to verify the fixedpoint version of the algorithm.
function overflow_test
% The filter coefficients were computed using the FIR1 function from
% Signal Processing Toolbox.
%
b = fir1(11,0.25);
b = [-0.004465461051254
-0.004324228005260
+0.012676739550326
+0.074351188907780
+0.172173206073645
+0.249588554524763
+0.249588554524763
+0.172173206073645
+0.074351188907780
+0.012676739550326
-0.004324228005260
-0.004465461051254]';
% Input signal
nx = 256;
t = linspace(0,10*pi,nx)';
% Impulse
x_impulse = zeros(nx,1); x_impulse(1) = 1;
% Max Gain
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14
Fixed-Point Conversion
% The maximum gain of a filter will occur when the inputs line up with the
% signs of the filter's impulse response.
x_max_gain = sign(b)';
x_max_gain = repmat(x_max_gain,ceil(nx/length(b)),1);
x_max_gain = x_max_gain(1:nx);
% Sums of sines
f0=0.1; f1=2;
x_sines = sin(2*pi*t*f0) + 0.1*sin(2*pi*t*f1);
% Chirp
f_chirp = 1/16;
x_chirp = sin(pi*f_chirp*t.^2);
% Target frequency
% Linear chirp
x = [x_impulse, x_max_gain, x_sines, x_chirp];
titles = {'Impulse', 'Max gain', 'Sum of sines', 'Chirp'};
y = zeros(size(x));
for i=1:size(x,2)
reset = true;
y(:,i) = overflow(b,x(:,i),reset);
end
test_plot(1,titles,t,x,y)
end
function test_plot(fig,titles,t,x,y1)
figure(fig)
clf
sub_plot = 1;
font_size = 10;
for i=1:size(x,2)
subplot(4,1,sub_plot)
sub_plot = sub_plot+1;
plot(t,x(:,i),'c',t,y1(:,i),'k')
axis('tight')
xlabel('t','FontSize',font_size);
title(titles{i},'FontSize',font_size);
ax = gca;
ax.FontSize = 10;
end
figure(gcf)
end
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Detect Overflows
2
3
On the project Overview tab, click the Autodefine types link.
In the Autodefine Input Types dialog box, add overflow_test as a test file and
then click Run.
The test file runs and displays the outputs of the filter for each of the input
signals.MATLAB Coder determines the input types from the test file and then
displays them.
4
In the Autodefine Input Types dialog box, click Use These Types.
MATLAB Coder sets the types of b to double(1x12), x to double(256x1), and
reset to logical(1x1).
Fixed-Point Conversion
1
On the project Overview tab Fixed-Point Conversion pane, select Convert to
fixed-point at build time.
The project indicates that you must first define the fixed-point data types.
2
In the Fixed-Point Conversion pane, click Define and validate fixed-point
types.
The Fixed-Point Conversion tool opens and generates an instrumented MEX function
for your entry-point MATLAB function. After generating the MEX function, the tool
displays compiled information — type, size, and complexity — for variables in your
code. For more information, see “View and Modify Variable Information”.
3
In the Fixed-Point Conversion tool, click Advanced to view the advanced settings.
Set the fimath Product mode and Sum mode to KeepLSB. These settings models
the behavior of integer operations in the C language.
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14
Fixed-Point Conversion
4
In the Fixed-Point Conversion tool, click Run Simulation and verify that the
overflow_test file is selected as the test file to run and then click the Run
Simulation button.
The simulation runs and the conversion tool displays simulation minimum and
maximum ranges on the Variables tab. Using the simulation range data, the
software proposes fixed-point types for each variable based on the default type
proposal settings, and displays them in the Proposed Type column. The Validate
Types option is now enabled.
5
To validate the build using the proposed types, click Validate Types.
The software validates the proposed types, displays a Validation succeeded
message, and enables the Test Numerics option.
If the errors or warnings occur during validation, they are displayed on the Type
Validation Output tab. For more information, see “Validating Types”.
Test Numerics and Check for Overflows
1
Click Test Numerics, select Use scaled doubles to detect overflows, and
then click the Test Numerics button.
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Detect Overflows
The tool runs the test file that you used to define input types to test the fixed-point
MATLAB code. Because you selected to detect overflows, it also runs the simulation
using scaled double versions of the proposed fixed-point types. Scaled doubles store
their data in double-precision floating-point, so they carry out arithmetic in full
range. They also retain their fixed-point settings, so they are able to report when a
computation goes out of the range of the fixed-point type.
The simulation runs and the tool detects an overflow. The tool reports on the
Overflow tab and highlights the expression that overflowed in the code window.
2
Determine whether it was the sum or the multiplication that overflowed.
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14
Fixed-Point Conversion
In the Advanced settings, set Product mode to FullPrecision, and then click
Test Numerics.
The overflow still occurs, indicating that it is the addition in the expression that is
overflowing.
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Replace the exp Function with a Lookup Table
Replace the exp Function with a Lookup Table
This example shows how to replace the exp function with a lookup table approximation
in fixed-point code generated using the Fixed-Point Conversion tool.
Create Algorithm and Test Files
1
Create a MATLAB function, my_fcn, that calls the exp function.
function y = my_fcn(x)
y = exp(x);
end
2
Create a test file, my_fcn_test, that uses my_fcn.
close all
x = linspace(-10,10,1e3);
for itr = 1e3:-1:1
y(itr) = my_fcn( x(itr) );
end
plot( x, y );
Create and Set Up a MATLAB Coder Project
1
Navigate to the work folder that contains the file for this example.
2
On the MATLAB Apps tab, select MATLAB Coder and then, in the MATLAB
Coder Project dialog box, set Name to fun_with_matlab_project.prj.
Alternatively, at the MATLAB command line, enter
coder -new my_fcn_project.prj
By default, the project opens in the MATLAB workspace.
3
On the project Overview tab, click the Add files link. Browse to the file my_fcn.m
and then click OK to add the file to the project.
Define Input Types
1
On the project Overview tab, click the Autodefine types link.
2
In the Autodefine Input Types dialog box, add my_fcn_test as a test file and then
click Run.
The test file runs and plots the output.
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14
Fixed-Point Conversion
3
MATLAB Coder determines from the test file that x is a scalar double.
4
In the Autodefine Input Types dialog box, click Use These Types.
MATLAB Coder sets the type of x to double(1x1).
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Replace the exp Function with a Lookup Table
Fixed-Point Conversion
1
On the project Overview tab Fixed-Point Conversion pane, select Convert to
fixed-point at build time.
The project indicates that you must first define the fixed-point data types.
2
In the Fixed-Point Conversion pane, click Define and validate fixed-point
types.
The Fixed-Point Conversion window opens and the tool generates an instrumented
MEX function for your entry-point MATLAB function. After generating the MEX
function, the tool displays compiled information — type, size, and complexity — for
variables in your code.
The tool displays functions in your original algorithm that are not supported for
fixed-point conversion on the Function Replacements tab.
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14
Fixed-Point Conversion
3
On the Function Replacements tab, right-click the exp function and select
Lookup Table.
The tool moves the exp function to the list of functions that it will replace with a
Lookup Table. By default, the lookup table uses linear interpolation, 1000 points,
and the design minimum and maximum values that the tool detects by either
running a simulation or computing derived ranges.
4
Click Run Simulation, select Log data for histogram, and verify that the
my_fcn_test file is selected as a test file to run.
5
Click the Run Simulation button,
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Replace the exp Function with a Lookup Table
The simulation runs and the tool displays simulation minimum and maximum
ranges on the Variables tab. Using the simulation range data, the software
proposes fixed-point types for each variable based on the default type proposal
settings, and displays them in the Proposed Type column. The Validate Types
option is now enabled.
6
Examine the proposed types and verify that they cover the full simulation range.
To view logged histogram data for a variable, click its Proposed Type field. The
histogram provides range information and the percentage of simulation range
covered by the proposed data type.
7
To validate the build using the proposed types, click Validate Types.
The software validates the proposed types and generates a fixed-point function,
my_fcn_fixpt.
8
On the Type Validation Output tab, click the my_fcn_fixpt link to view the
generated fixed-point code.
The conversion process generates a lookup table approximation, exp1, for the exp
function.
The generated fixed-point function, my_fcn_fixpt, calls this approximation instead
of calling exp.
function y = my_fcn_fixpt(x)
fm = fimath('RoundingMethod', 'Floor', 'OverflowAction', 'Wrap',...
'ProductMode', 'FullPrecision', 'MaxProductWordLength', 128, ...
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14
Fixed-Point Conversion
'SumMode', 'FullPrecision', 'MaxSumWordLength', 128);
y = fi(exp1(x), 0, 16, 1, fm);
end
You can now test the generated fixed-point code and compare the results against the
original MATLAB function. If the behavior of the generated fixed-point code does
not match the behavior of the original code closely enough, modify the interpolation
method or number of points used in the lookup table and then regenerate code.
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Replace a Custom Function with a Lookup Table
Replace a Custom Function with a Lookup Table
This example shows how to replace a custom function with a lookup table approximation
function using the Fixed-Point Conversion tool.
Create Algorithm and Test Files
In a local, writable folder:
1
Create a MATLAB function, custom_fcn which is the function that you want to
replace.
function y = custom_fcn(x)
y = 1./(1+exp(-x));
end
2
Create a wrapper function that calls custom_fcn.
function y = call_custom_fcn(x)
y = custom_fcn(x);
end
3
Create a test file, custom_test, that uses call_custom_fcn.
close all
x = linspace(-10,10,1e3);
for itr = 1e3:-1:1
y(itr) = call_custom_fcn( x(itr) );
end
plot( x, y );
Create and Set Up a MATLAB Coder Project
1
Navigate to the work folder that contains the file for this example.
2
On the MATLAB Apps tab, select MATLAB Coder and then, in the MATLAB
Coder Project dialog box, set Name to custom_project.prj.
Alternatively, at the MATLAB command line, enter
coder -new custom_project.prj
By default, the project opens in the MATLAB workspace.
3
On the project Overview tab, click the Add files link. Browse to the file
call_custom_fcn.m and then click OK to add the file to the project.
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14
Fixed-Point Conversion
Define Input Types
1
On the project Overview tab, click the Autodefine types link.
2
In the Autodefine Input Types dialog box, add custom_test as a test file and then
click Run.
The test file runs and plots the output.
3
MATLAB Coder determines from the test file that x is a scalar double.
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Replace a Custom Function with a Lookup Table
4
In the Autodefine Input Types dialog box, click Use These Types.
MATLAB Coder sets the type of x to double(1x1).
Fixed-Point Conversion
1
On the project Overview tab Fixed-Point Conversion pane, select Convert to
fixed-point at build time.
The project indicates that you must first define the fixed-point data types.
2
In the Fixed-Point Conversion pane, click Define and validate fixed-point
types.
The Fixed-Point Conversion window opens and the tool generates an instrumented
MEX function for your entry-point MATLAB function. After generating the MEX
function, the tool displays compiled information — type, size, and complexity — for
variables in your code.
3
Select the Function Replacements tab.
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14
Fixed-Point Conversion
4
Enter the name of the function to replace, custom_fcn, select Lookup Table, and
then click +.
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Replace a Custom Function with a Lookup Table
The tool adds custom_fcn to the list of functions that it will replace with a Lookup
Table. By default, the lookup table uses linear interpolation, 1000 points, and the
design minimum and maximum values that the app detects by either running a
simulation or computing derived ranges.
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14
Fixed-Point Conversion
5
Click Run Simulation, select Log data for histogram and verify that the
custom_test file is selected as a test file to run.
6
Click the Run Simulation button.
The simulation runs and the tool displays simulation minimum and maximum
ranges on the Variables tab. Using the simulation range data, the software
proposes fixed-point types for each variable based on the default type proposal
settings, and displays them in the Proposed Type column. The Validate Types
option is now enabled.
7
Examine the proposed types and verify that they cover the full simulation range.
To view logged histogram data for a variable, click its Proposed Type field. The
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Replace a Custom Function with a Lookup Table
histogram provides range information and the percentage of simulation range
covered by the proposed data type.
8
To validate the build using the proposed types, click Validate Types.
The software validates the proposed types and generates a fixed-point code,
call_custom_fcn_fixpt.
9
On the Type Validation Output tab, click the call_custom_fcn_fixpt link to view
the generated fixed-point code.
The conversion process generates a lookup table approximation, custom_fcn1, for
the custom_fcn function.
The generated fixed-point function, call_custom_fcn_fixpt.m, calls this
approximation instead of calling custom_fcn.
function y = call_custom_fcn_fixpt(x)
fm = fimath('RoundingMethod', 'Floor', 'OverflowAction', 'Wrap',...
'ProductMode', 'FullPrecision', 'MaxProductWordLength', 128,...
'SumMode', 'FullPrecision', 'MaxSumWordLength', 128);
y = fi(custom_fcn1(x), 0, 16, 16, fm);
end
You can now test the generated fixed-point code and compare the results against the
original MATLAB function.
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14
Fixed-Point Conversion
Enable Plotting Using the Simulation Data Inspector
You can use the Simulation Data Inspector to inspect and compare floating-point and
fixed-point logged input and output data. In the Fixed-Point Conversion tool:
1
Click Advanced.
2
In the Advanced Settings dialog box, set Plot with Simulation Data Inspector to
Yes.
3
At the Test Numerics stage in the conversion process, click Test Numerics, select
Log inputs and outputs for comparison plots, and then click
.
For an example, see “Propose Fixed-Point Data Types Based on Derived Ranges”.
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Log Data for Histogram
Log Data for Histogram
To log data for histograms:
1
In the Fixed-Point Conversion window, click Run Simulation and select Log data
for histogram, and then click the Run Simulation button.
The simulation runs and the simulation minimum and maximum ranges are
displayed on the Variables tab. Using the simulation range data, the software
proposes fixed-point types for each variable based on the default type proposal
settings, and displays them in the Proposed Type column.
2
To view a histogram for a variable, click the variable’s Proposed Type field.
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14
Fixed-Point Conversion
3
You can view the effect of changing the proposed data types by:
• Selecting and dragging the white bounding box in the histogram window. This
action does not change the word length of the proposed data type, but modifies
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Log Data for Histogram
the position of the binary point within the word so that the fraction length of the
proposed data type changes.
• Selecting and dragging the left edge of the bounding box to increase or decrease
the word length. This action does not change the fraction length or the position of
the binary point.
• Selecting and dragging the right edge to increase or decrease the fraction length
of the proposed data type. This action does not change the position of the binary
point. The word length changes to accommodate the fraction length.
• Selecting or clearing Signed. Clear Signed to ignore negative values.
Before committing changes, you can revert to the types proposed by the automatic
conversion by clicking
.
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14
Fixed-Point Conversion
View and Modify Variable Information
View Variable Information
To view information about the variables in the MATLAB function selected in the
Navigation pane, use the Variables tab or place your cursor over a variable in the code
window. For more information, see “Viewing Variables” on page 14-94.
You can view the variable information:
• Variable
Variable name. Variables are classified and sorted as inputs, outputs, persistent, or
local variables.
• Type
The original size, type, and complexity of each variable.
• Sim Min
The minimum value assigned to the variable during simulation.
• Sim Max
The maximum value assigned to the variable during simulation.
To search for variables in the MATLAB code pane and on the Variables tab, use Ctrl
+F. The tool highlights occurrences in the code and displays only the variable with the
specified name on the Variables tab.
Modify Variable Information
If you modify variable information, the tool highlights the values in bold. You can modify
the following fields:
• Static Min
You can enter a value for Static Min into the field or promote Sim Min information.
See “Promote Sim Min and Sim Max Values” on page 14-65.
Editing this field does not trigger static range analysis, but the tool uses the edited
values in subsequent analyses.
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View and Modify Variable Information
• Static Max
You can enter a value for Static Max into the field or promote Sim Max information.
See “Promote Sim Min and Sim Max Values” on page 14-65.
Editing this field does not trigger static range analysis, but the tool uses the edited
values in subsequent analyses.
• Whole Number
The Fixed-Point Conversion tool uses simulation data to determine whether the
values assigned to a variable during simulation were always integers. You can
manually override this field.
Editing this field does not trigger static range analysis, but the tool uses the edited
value in subsequent analyses.
• Proposed Type
You can modify the signedness, word length, and fraction length settings individually
by:
• On the Variables tab, by modifying the value in the ProposedType field.
• In the code window, by selecting a variable and then modifying the
ProposedType field.
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14
Fixed-Point Conversion
If you selected to log data for a histogram, the histogram dynamically updates to
reflect the modifications to the proposed type. You can also modify the proposed type
in the histogram, see “Histogram” on page 14-100.
Revert Changes
• To clear results and revert edited values, right-click the Variables tab and select
Reset entire table.
• To revert the type of a selected variable to the type computed by the tool, right-click
the field and select Undo changes.
• To revert changes to variables, right-click the field and select Undo changes for
all variables.
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View and Modify Variable Information
• To clear a static range value, right-click an edited field and select Clear static
range.
• To clear manually-entered static range values, right-click anywhere on the Variables
tab and select Clear all manually entered static ranges.
Promote Sim Min and Sim Max Values
The Fixed-Point Conversion tool allows you to promote simulation minimum and
maximum values to static minimum and maximum values. This capability is useful if
you have not specified static ranges and you have simulated the model with inputs that
cover the full intended operating range.
To copy:
• A simulation range for a selected variable, select a variable, right-click and then select
Copy sim range.
• Simulation ranges for top-level inputs, right-click the Static Min or Static Max
column and then select Copy sim ranges for all top-level inputs.
• Simulation ranges for persistent variables, right-click the Static Min or Static Max
column and then select Copy sim ranges for all persistent inputs.
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14
Fixed-Point Conversion
Build Instrumented MEX Function
Note: This capability is not compatible with automatic fixed-point conversion. If you
select Convert to fixed point at build time, you cannot build instrumented
MEX functions.
1
In the project, click the Build tab.
2
On the Build tab, set the Output type to Instrumented MEX Function.
3
Click the Build button.
The Build progress dialog box opens. When the build is complete, MATLAB Coder
generates an instrumented MEX function in the current folder. It also provides a link to
the report on the Show Instrumentation Results pane. In this report, you can view
the types of variables in your MATLAB code.
After you run the instrumented MEX function, the instrumentation report provides
fixed-point data type proposals based on the simulation range data. You can use this
information to convert your MATLAB code to fixed point by hand. For more information,
see “Propose Fixed-Point Data Types” on page 14-67
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Propose Fixed-Point Data Types
Propose Fixed-Point Data Types
This example shows how to propose fixed-point data types using an instrumented MEX
function.
This capability is not compatible with automatic fixed-point conversion. If you select
Convert to fixed point at build time, you cannot build instrumented MEX
functions.
Prerequisites
To complete this example, you must install the following products:
• MATLAB
• MATLAB Coder
• Fixed-Point Designer
• C compiler (for most platforms, a default C compiler is supplied with MATLAB)
For a list of supported compilers, see http://www.mathworks.com/support/
compilers/current_release/
You can use mex -setup to change the default compiler. See “Changing Default
Compiler”.
Create a New Folder and Copy Relevant Files
1
Create a local working folder, for example, c:\coder\fun_with_matlab.
2
Change to the docroot\toolbox\coder\examples folder. At the MATLAB
command line, enter:
cd(fullfile(docroot, 'toolbox', 'coder', 'examples'))
3
Copy the fun_with_matlab.m and fun_with_matlab_test.m files to your local
working folder.
Type
Name
Description
Function code
fun_with_matlab.m
Entry-point MATLAB function
Test file
fun_with_matlab_test.m
MATLAB script that tests
fun_with_matlab.m
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Fixed-Point Conversion
The fun_with_matlab Function
function y = fun_with_matlab(x) %#codegen
persistent z
if isempty(z)
z = zeros(2,1);
end
% [b,a] = butter(2, 0.25)
b = [0.0976310729378175, 0.195262145875635, 0.0976310729378175];
a = [1, -0.942809041582063, 0.3333333333333333];
y = zeros(size(x));
for i = 1:length(x)
y(i) = b(1)*x(i) + z(1);
z(1) = b(2)*x(i) + z(2) - a(2) * y(i);
z(2) = b(3)*x(i)
- a(3) * y(i);
end
end
Check Code Generation Readiness
In the current working folder, right-click the fun_with_matlab.m function. From the
context menu, select Check Code Generation Readiness.
The code generation readiness tool screens the code for features and functions that
are not supported for code generation. The tool reports that the fun_with_matlab.m
function is already suitable for code generation.
If your entry-point function is not suitable for code generation, the tool provides a report
that lists the source files that contain unsupported features and functions. The report
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Propose Fixed-Point Data Types
also provides an indication of how much work you must do to make the MATLAB code
ready for code generation. Before proposing data types, you must fix these issues. For
more information, see “MATLAB Code Analysis”.
Create and set up a MATLAB Coder Project
1
Navigate to the work folder that contains the file for this tutorial.
2
On the MATLAB Apps tab, select MATLAB Coder and then, in the MATLAB
Coder Project dialog box, set Name to fun_with_matlab_project.prj.
Alternatively, at the MATLAB command line, enter
coder -new fun_with_matlab_project.prj
By default, the project opens in the MATLAB workspace.
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Fixed-Point Conversion
3
On the project Overview tab, click the Add files link. Browse to the file
fun_with_matlab.m and then click OK to add the file to the project.
About the fun_with_matlab_test Script
The test script runs the fun_with_matlab function with three input signals: chirp, step,
and impulse. The script then plots the results.
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Contents of fun_with_matlab_test
Contents of fun_with_matlab_test
% fun_with_matlab_test
%
% Define representative inputs
% Number of points
N = 256;
t = linspace(0,1,N);
% Time vector from 0 to 1 second
f1 = N/2;
% Target frequency of chirp set to Nyquist
x_chirp = sin(pi*f1*t.^2); % Linear chirp from 0 to Fs/2 Hz in 1 second
% Step
x_step = ones(1,N);
x_impulse = zeros(1,N);
% Impulse
x_impulse(1) = 1;
% Run the function under test
x = [x_chirp;x_step;x_impulse];
y = zeros(size(x));
for i = 1:size(x,1)
y(i,:) = fun_with_matlab(x(i,:));
end
% Plot the results
titles = {'Chirp','Step','Impulse'};
clf
for i = 1:size(x,1)
subplot(size(x,1),1,i)
plot(t,x(i,:),t,y(i,:))
title(titles{i})
legend('Input','Output')
end
xlabel('Time (s)')
figure(gcf)
disp('Test complete.')
Define Input Types
1
On the project Overview tab, click the Autodefine types link.
2
In the Autodefine Input Types dialog box, add fun_with_matlab_test as a test
file and then click Run.
The test file runs and displays the outputs of the filter for each of the input signals.
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Fixed-Point Conversion
MATLAB Coder determines the input types from the test file and then displays them
in the Autodefine Input Types dialog box.
3
In this dialog box, click Use These Types.
MATLAB Coder sets the type of x to double(1x256).
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Contents of fun_with_matlab_test
Build Instrumented MEX Function
1
In the project, click the Build tab.
2
On the Build tab, set the Output type to Instrumented MEX Function.
3
Click the Build button.
The Build progress dialog box opens. When the build is complete, MATLAB Coder
generates an instrumented MEX function fun_with_matlab_mex in the current folder.
It also provides a link to the report on the Show Instrumentation Results pane. In
this report, you can view the types of variables in your MATLAB code.
View Data Type Proposal Settings
1
On the Show Instrumentation Results pane, click the Data type proposal and
report settings link.
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14
Fixed-Point Conversion
This example uses the default data type proposal settings which propose fraction
lengths for the specified word lengths. Because the MATLAB code is floatingpoint, the word length is specified by the Default data type of all floating-point
expressions field. You can specify the numerictype signedness, word length and
fraction length. Specifying [] for signedness instructs MATLAB Coder to choose the
signedness based on simulation values. The default word length is 16. The default
fraction length is 12.
For more information, see “Modify Data Type Proposal Settings”.
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Contents of fun_with_matlab_test
2
Close the dialog box.
Run Simulation
1
On the Run Simulation pane, verify that the test file is set to
fun_with_matlab_test and that Redirect entry-point calls to MEX function
is selected. That way, each call to fun_with_matlab is replaced with a call to the
instrumented MEX function fun_with_matlab_mex.
2
On the Run Simulation pane, click Run.
The fun_with_matlab_test file runs and calls fun_with_matlab_mex. The
outputs of the filters are displayed as before.
View Code Generation Report
1
On the Show Instrumentation Results pane, click View Report.
2
In the Code Generation Report, click the Variables tab.
The report displays the simulation minimum and maximum values and the proposed
data types.
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14
Fixed-Point Conversion
MATLAB Coder proposes data types with word length of 16 and fraction length
optimized to avoid overflows.
Next Steps
To learn how to apply the proposed data types to your entry-point MATLAB function and
verify that the fixed-point version of your algorithm is functionally equivalent to your
original MATLAB algorithm, see “Apply Fixed-Point Data Types”.
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Apply Fixed-Point Data Types
Apply Fixed-Point Data Types
This example shows how to write a fixed-point version of your entry-point function using
the data types proposed in “Propose Fixed-Point Data Types”.
This capability is not compatible with automatic fixed-point conversion. If you select
Convert to fixed point at build time, you cannot build instrumented MEX
functions.
You will learn how to:
• Use the proposed data types to create a fixed-point version of your entry-point
function.
• Update your test file to call the fixed-point entry-point function.
• Verify that the fixed-point function is functionally equivalent to the original MATLAB
algorithm.
Prerequisites
To complete this example, you must install the following products:
• MATLAB
• MATLAB Coder
• Fixed-Point Designer
• C compiler (for most platforms, a default C compiler is supplied with MATLAB)
For a list of supported compilers, see http://www.mathworks.com/support/
compilers/current_release/
You can use mex -setup to change the default compiler. See “Changing Default
Compiler”.
Create a New Folder and Copy Relevant Files
1
Create a local working folder, for example, c:\coder\fun_with_matlab.
2
Change to the docroot\toolbox\coder\examples folder. At the MATLAB
command line, enter:
cd(fullfile(docroot, 'toolbox', 'coder', 'examples'))
3
Copy the following files to your local working folder.
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14
Fixed-Point Conversion
Type
Name
Description
Function
code
fun_with_matlab.m
Entry-point MATLAB function
Test file
fun_with_matlab_test.m
MATLAB script that tests
fun_with_matlab.m
Function
code
fun_with_fi.m
Entry-point MATLAB function
— fixed-point version of
fun_with_matlab that uses data
types proposed in “Propose FixedPoint Data Types”
Test file
fun_with_fi_test.m
MATLAB script that runs
both fun_with_matlab and
fun_with_fi and compares the
results
The fun_with_fi Function
The fun_with_fi is a fixed-point version of the fun_with_matlab function that uses
the data types proposed in “Propose Fixed-Point Data Types”.
Variable
Proposed Signedness
Proposed Word Length
Proposed Fraction Length
y
Signed
16
14
x
Signed
16
14
z
Signed
16
15
a
Unsigned
16
18
b
Signed
16
14
i
Unsigned
16
0
For example, in fun_with_matlab, variable y is defined as y = zeros(size(x));. In
fun_with_fi, to specify that it is a signed fixed-point data type with a word length of 16
and a fraction length of 14:
y = fi(zeros(size(x)),1,16,14,'OverflowAction','Wrap','RoundingMethod','Floor');
For more information, see fi.
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Apply Fixed-Point Data Types
Create and set up a MATLAB Coder Project
1
Navigate to the work folder that contains the file for this tutorial.
2
On the MATLAB Apps tab, select MATLAB Coder and then, in the MATLAB
Coder Project dialog box, set Name to fun_with_fi_project.prj.
Alternatively, at the MATLAB command line, enter
coder -new fun_with_fi_project.prj
By default, the project opens in the MATLAB workspace.
3
On the project Overview tab, click the Add files link. Browse to the file
fun_with_fi.m, and then click OK to add the file to the project.
Define Input Types
1
On the project Overview tab, click the Autodefine types link.
2
In the Autodefine Input Types dialog box, add fun_with_fi_test as a test file, and
then click Run.
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14
Fixed-Point Conversion
The test file runs and plots the outputs of the filter. MATLAB Coder determines the
input types from the test file and then displays them.
3
In the Autodefine Input Types dialog box, click Use These Types to accept the
autodefined input type.
MATLAB Coder sets the type of x to double(1x256).
The fun_with_fi_test Script
The fun_with_fi_test script runs the original floating-point MATLAB algorithm,
fun_with_matlab, then runs the fixed-point version of the algorithm, fun_with_fi.
The script then plots the outputs for the floating-point and fixed-point algorithms and
the difference in results.
Run Simulation
1
In the project, click the Build tab.
2
On the Verification pane, verify that the test file is set to fun_with_fi_test.
Clear Redirect entry-point calls to MEX function so that the test file calls the
MATLAB versions of the original and fixed-point algorithms.
3
On Verification pane, click Run.
The fun_with_fi_test file runs. The test file runs the original MATLAB
algorithm and the fixed-point version, and plots the difference in their outputs.
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Apply Fixed-Point Data Types
4
Optionally, zoom in on each plot in turn to view the error (difference between the two
versions of the algorithm). In this example, the errors are very small, on the order of
-3
10 . If the error is unacceptably large, refine the fixed-point data types.
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14
Fixed-Point Conversion
Modify Data Type Proposal Settings
When generating instrumented MEX functions, to modify data type proposal settings, on
the project Build tab, on the Show Instrumentation Results pane, click Data type
proposal and report settings.
Type Proposal
Setting
Description
Propose data
types
Specify whether to propose data types based on simulation minimum and
maximum values. You can view the proposed data types in the code generation
report.
Dependencies:
• This parameter enables:
• Propose fraction lengths for specified word lengths
• Propose word lengths for specified fraction lengths
• Default data type of all floating-point expressions
• Safety margin for min/max values
• Optimize whole numbers
Propose fraction Select to propose fraction lengths for the word lengths specified in the code.
lengths for
Use simulation minimum and maximum information to propose fraction
specified word
lengths for variables in your entry-point MATLAB function. MATLAB Coder
lengths
proposes data types for variables that are scaled doubles and built-in data
types only. For floating-point data types in your entry-point function, uses the
word length and signedness specified in Default data type of all floatingpoint expressions to determine the optimal fraction lengths.
Dependency:
• Clearing Propose data types disables this parameter.
Propose word
Select to propose word lengths for the fraction lengths specified in the code.
lengths for
Use simulation minimum and maximum information to propose word lengths
specified
fraction lengths for variables in your entry-point MATLAB function. MATLAB Coder proposes
data types for variables that are scaled doubles and built-in data types only.
For floating-point data types in your entry-point function, uses the fraction
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Modify Data Type Proposal Settings
Type Proposal
Setting
Description
length and signedness specified in Default data type of all floating-point
expressions to determine the optimal word lengths.
Dependency:
• Clearing Propose data types disables this parameter.
Default data
type of all
floating-point
expressions
Specify the default data type to use for floating-point expressions in your
entry-point MATLAB function.
MATLAB Coder uses this default data type to change the floating-point data
types in the code to fixed point.
Dependency:
• Clearing Propose data types disables this parameter.
numerictype([],16,12) Set the default data type for floating-point
signals to the fixed-point data type specified by
(Default)
numerictype. You can modify the parameters
provided to numerictype to specify signedness,
word length, and fraction length.
Specifying [] for signedness instructs MATLAB
Coder to choose the appropriate signedness.
Remain floatingpoint
Do not change the data type of floating-point
signals.
int8
Set the default data type for floating-point signals
to int8.
int16
Set the default data type for floating-point signals
to int16.
int32
Set the default data type for floating-point signals
to int32.
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14
Fixed-Point Conversion
Type Proposal
Setting
Description
Safety margin
for min/max
values
Specify safety factor for simulation minimum and maximum values.
The simulation minimum and maximum values are adjusted by the
percentage designated by this parameter, allowing you to specify a range
different from that obtained from the simulation run. For example, a value of
55 specifies that you want a range at least 55 percent larger. A value of -15
specifies that a range up to 15 percent smaller is acceptable.
Dependency:
• Clearing Propose data types disables this parameter.
Optimize whole
numbers
Specify to use integer scaling for variables that were whole numbers during
simulation.
Dependency:
• Clearing Propose data types disables this parameter.
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Modify Instrumentation Report Settings
Modify Instrumentation Report Settings
When generating instrumented MEX functions, to modify instrumentation report
settings, on the project Build tab, on the Show Instrumentation Results pane, click
Data type proposal and report settings.
Report Setting
Description
Automatically launch report Specify whether to automatically display the report after running
the test file.
after running test file
Open report in a web
browser
Specify whether to open the report in a Web browser. Enabling
this option allows you to open multiple reports simultaneously.
Create printable report
Specify whether to create a printable report.
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14
Fixed-Point Conversion
Automated Fixed-Point Conversion
In this section...
“License Requirements” on page 14-86
“Automated Fixed-Point Conversion Capabilities” on page 14-86
“Code Coverage” on page 14-88
“Proposing Data Types” on page 14-91
“Locking Proposed Data Types” on page 14-93
“Viewing Functions” on page 14-93
“Viewing Variables” on page 14-94
“Histogram” on page 14-100
“Function Replacements” on page 14-102
“Validating Types” on page 14-103
“Testing Numerics” on page 14-103
“Detecting Overflows” on page 14-104
License Requirements
Fixed-point conversion requires the following licenses:
• Fixed-Point Designer
• MATLAB Coder
Automated Fixed-Point Conversion Capabilities
You can convert floating-point MATLAB code to fixed-point code using the Fixed-Point
Conversion tool in MATLAB Coder projects. You can choose to propose data types based
on simulation range data, derived (also known as static) range data, or both.
You can manually enter static ranges. These manually-entered ranges take precedence
over simulation ranges and the tool uses them when proposing data types. In addition,
you can modify and lock the proposed type so that the tool cannot change it. For more
information, see “Locking Proposed Data Types” on page 14-93.
For a list of supported MATLAB features and functions, see “MATLAB Language
Features Supported for Automated Fixed-Point Conversion”.
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Automated Fixed-Point Conversion
During fixed-point conversion, you can:
• Verify that your test files cover the full intended operating range of your algorithm
using code coverage results.
• Propose fraction lengths based on default word lengths.
• Propose word lengths based on default fraction lengths.
• Optimize whole numbers.
• Specify safety margins for simulation min/max data.
• Validate that you can build your project with the proposed data types.
• Test numerics by running the test file with the fixed-point types applied.
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14
Fixed-Point Conversion
• View a histogram of bits used by each variable.
• Detect overflows.
Code Coverage
By default, the Fixed-Point Conversion tool shows code coverage results. Your test files
should exercise the algorithm over its full operating range so that the simulation ranges
are accurate. The quality of the proposed fixed-point data types depends on how well the
test files cover the operating range of the algorithm with the accuracy that you want.
Reviewing code coverage results helps you verify that your test file is exercising the
algorithm adequately. If the code coverage is inadequate, modify the test file or add
more test files to increase coverage. If you simulate multiple test files in one run, the tool
displays cumulative coverage. However, if you specify multiple test files but run them
one at a time, the tool displays the coverage of the file that ran last.
Code coverage is on by default. Turn it off only after you have verified that you have
adequate test file coverage. Turning off code coverage might speed up simulation. To turn
off code coverage, in the Fixed-Point Conversion tool:
1
Click Run Simulation.
2
Clear Show code coverage.
The tool covers basic MATLAB control constructs and shows statement coverage for basic
blocks of code. The tool displays a color-coded coverage bar to the left of the code.
Coverage Bar
Color
How Often Code is Executed During Test File Simulation
Dark green
Always
Light green
Sometimes
Orange
Once
Red
Never
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Automated Fixed-Point Conversion
When you position your cursor over the coverage bar, the color highlighting extends over
the code and the tool displays more information about how often the code is executed.
For MATLAB constructs that affect control flow (if-elseif-else, switch-case, for-continuebreak, return), it displays statement coverage as a percentage coverage for basic blocks
inside these constructs.
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Fixed-Point Conversion
To verify that your test file is testing your algorithm over the intended operating range,
review the code coverage results and take action as described in the following table.
Coverage Bar
Color
Action Required
Dark green
None
Light green
Review percentage coverage and verify that it is reasonable based
on your algorithm. If there are areas of code that you expect to be
executed more frequently, modify your test file or add more test files
to increase coverage.
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Automated Fixed-Point Conversion
Coverage Bar
Color
Action Required
Orange
This is expected behavior for initialization code, for example, the
initialization of persistent variables. For other cases, verify that this
behavior is reasonable for your algorithm. If there are areas of code
that you expect to be executed more frequently, modify your test file or
add more test files to increase coverage.
Red
If the code that is not executed is an error condition, this is acceptable
behavior. If the code should be executed, modify the test file or
add another test file to extend coverage. If the code is written
conservatively and has upper and lower boundary limits and you
cannot modify the test file to reach this code, add static minimum and
maximum values (see “Computing Derived Ranges”).
Proposing Data Types
The Fixed-Point Conversion tool proposes fixed-point data types based on computed
ranges and the word length or fraction length setting. The computed ranges are based on
simulation range data, derived range data, or both. If you run a simulation and compute
derived ranges, the conversion tool merges the simulation and derived ranges.
Note: You cannot propose data types based on derived ranges for MATLAB classes.
You can manually enter static ranges. These manually-entered ranges take precedence
over simulation ranges and the tool uses them when proposing data types. In addition,
you can modify and lock the proposed type so that the tool cannot change it. For more
information, see “Locking Proposed Data Types” on page 14-93.
Running a Simulation
When you open the Fixed-Point Conversion tool, the tool generates an instrumented
MEX function for your entry-point MATLAB file. If the build completes without errors,
the tool displays compiled information (type, size, complexity) for functions and variables
in your code. To navigate to local functions, click the Functions tab. If build errors
occur, the tool provides error messages that link to the line of code that caused the
build issues. You must address these errors before running a simulation. Use the
link to navigate to the offending line of code in the MATLAB editor and modify the
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14
Fixed-Point Conversion
code to fix the issue. If your code uses functions that are not supported for fixed-point
conversion, the tool displays them on the Function Replacements tab. See “Function
Replacements” on page 14-102.
Before running a simulation, specify the test file or files that you want to run. When you
run a simulation, the tool runs the test file, calling the instrumented MEX function. If
you modify the MATLAB design code, the tool automatically generates an updated MEX
function before running a test file.
If the test file runs successfully, the simulation minimum and maximum values and the
proposed types are displayed on the Variables tab. If you manually enter static ranges
for a variable, the manually-entered ranges take precedence over the simulation ranges.
If you manually modify the proposed types by typing or using the histogram, the data
types are locked so that the tool cannot modify them.
If the test file fails, the errors are displayed on the Simulation Output tab.
Test files should exercise your algorithm over its full operating range. The quality of the
proposed fixed-point data types depends on how well the test file covers the operating
range of the algorithm with the desired accuracy. You can add test files and select to
run more than one test file during the simulation. If you run multiple test files, the
conversion tool merges the simulation results.
Optionally, you can select to log data for histograms. After running a simulation, you
can view the histogram for each variable. For more information, see “Histogram” on page
14-100.
Computing Derived Ranges
The advantage of proposing data types based on derived ranges is that you do not have to
provide test files that exercise your algorithm over its full operating range. Running such
test files often takes a very long time.
To compute derived ranges and propose data types based on these ranges, provide
static minimum and maximum values or proposed data types for all input variables.
To improve the analysis, enter as much static range information as possible for other
variables. You can manually enter ranges or promote simulation ranges to use as static
ranges. Manually-entered static ranges always take precedence over simulation ranges.
If you know what data type your hardware target uses, set the proposed data types to
match this type. Manually-entered data types are locked so that the tool cannot modify
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Automated Fixed-Point Conversion
them. The tool uses these data types to calculate the input minimum and maximum
values and to derive ranges for other variables. For more information, see “Locking
Proposed Data Types” on page 14-93.
When you select Compute Derived Ranges, the tool runs a derived range analysis to
compute static ranges for variables in your MATLAB algorithm. When the analysis is
complete, the static ranges are displayed on the Variables tab. If the run produces +/Inf derived ranges, consider defining ranges for all persistent variables.
Optionally, you can select Quick derived range analysis. With this option, the
conversion tool performs faster static analysis. The computed ranges might be larger
than necessary. Select this option in cases where the static analysis takes more time than
you can afford.
If the derived range analysis for your project is taking a long time, you can optionally set
a timeout. The tool aborts the analysis when the timeout is reached.
Locking Proposed Data Types
You can lock proposed data types against changes by the Fixed-Point Conversion tool
using one of the following methods:
• Manually setting a proposed data type in the Fixed-Point Conversion tool.
• Right-clicking a type proposed by the tool and selecting Lock computed value.
The tool displays locked data types in bold so that they are easy to identify. You can
unlock a type using one of the following methods:
• Manually overwriting it.
• Right-clicking it and selecting Undo changes. This action unlocks only the selected
type.
• Right-clicking and selecting Undo changes for all variables. This action
unlocks all locked proposed types.
Viewing Functions
You can view a list of functions in your project on the Navigation pane. This list also
includes function specializations and class methods. When you select a function from the
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14
Fixed-Point Conversion
list, the MATLAB code for that function or class method is displayed in the Fixed-Point
Conversion tool code window.
After conversion, the left pane also displays a list of output files including the fixedpoint version of the original algorithm. If your function is not specialized, the conversion
retains the original function name in the fixed-point filename and appends the
fixed-point suffix. For example, the fixed-point version of fun_with_matlab.m is
fun_with_matlab_fixpt.m.
Viewing Variables
The Variables tab provides the following information for each variable in the function
selected in the Navigation pane:
• Type — The original data type of the variable in the MATLAB algorithm.
• Sim Min and Sim Max — The minimum and maximum values assigned to the
variable during simulation.
You can edit the simulation minimum and maximum values. Edited fields are shown
in bold. Editing these fields does not trigger static range analysis, but the tool uses
the edited values in subsequent analyses. You can revert to the types proposed by the
tool.
• Static Min and Static Max — The static minimum and maximum values.
To compute derived ranges and propose data types based on these ranges, provide
static minimum and maximum values for all input variables. To improve the analysis,
enter as much static range information as possible for other variables.
When you compute derived ranges, the Fixed-Point Conversion tool runs a static
analysis to compute static ranges for variables in your code. When the analysis is
complete, the static ranges are displayed. You can edit the computed results. Edited
fields are shown in bold. Editing these fields does not trigger static range analysis,
but the tool uses the edited values in subsequent analyses. You can revert to the types
proposed by the tool.
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Automated Fixed-Point Conversion
• Whole Number — Whether all values assigned to the variable during simulation are
integers.
The Fixed-Point Conversion tool determines whether a variable is always a whole
number. You can modify this field. Edited fields are shown in bold. Editing these
fields does not trigger static range analysis, but the tool uses the edited values in
subsequent analyses. You can revert to the types proposed by the tool.
• The proposed fixed-point data type for the specified word (or fraction)
length. Proposed data types use the numerictype notation. For example,
numerictype(1,16,12) denotes a signed fixed-point type with a word length of 16
and a fraction length of 12. numerictype(0,16,12) denotes an unsigned fixed-point
type with a word length of 16 and a fraction length of 12.
Because the tool does not apply data types to expressions, it does not display proposed
types for them. Instead, it displays their original data types.
You can also view and edit variable information in the code pane by placing your cursor
over a variable name.
You can use Ctrl+F to search for variables in the MATLAB code and on the Variables
tab. The tool highlights occurrences in the code and displays only the variable with the
specified name on the Variables tab.
Viewing Information for MATLAB Classes
The tool displays:
• Code for MATLAB classes and code coverage for class methods in the code window.
Use the Function list in the Navigation bar to select which class or class method to
view.
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14
Fixed-Point Conversion
• Information about MATLAB classes on the Variables tab.
Specializations
If a function is specialized, the tool lists each specialization and numbers them
sequentially. For example, consider a function, dut, that calls subfunctions, foo and
bar, multiple times with different input types.
function y = dut(u, v)
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Automated Fixed-Point Conversion
tt1 = foo(u);
tt2 = foo([u v]);
tt3 = foo(complex(u,v));
ss1 = bar(u);
ss2 = bar([u v]);
ss3 = bar(complex(u,v));
y = (tt1 + ss1) + sum(tt2 + ss2) + real(tt3) + real(ss3);
end
function y = foo(u)
y = u * 2;
end
function y = bar(u)
y = u * 4;
end
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Fixed-Point Conversion
If you select a specialization, the app displays only the variables used by the
specialization.
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Automated Fixed-Point Conversion
In the generated fixed-point code, the number of each fixed-point specialization matches
the number in the Source Code list which makes it easy to trace between the floatingpoint and fixed-point versions of your code. For example, the generated fixed-point
function for foo > 1 is named foo_s1.
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Fixed-Point Conversion
Histogram
To log data for histograms, in the Fixed-Point Conversion window, click Run
Simulation and select Log data for histogram, and then click the Run Simulation
button.
After simulation, to view the histogram for a variable, on the Variables tab, click the
Proposed Type field for that variable.
The histogram provides the range of the proposed data type and the percentage of
simulation values that the proposed data type covers. The bit weights are displayed along
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Automated Fixed-Point Conversion
the X-axis, and the percentage of occurrences along the Y-axis. Each bin in the histogram
corresponds to a bit in the binary word. For example, this histogram displays the range
for a variable of type numerictype(1,16,14).
You can view the effect of changing the proposed data types by:
• Dragging the edges of the bounding box in the histogram window to change the
proposed data type.
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14
Fixed-Point Conversion
• Selecting or clearing Signed.
To revert to the types proposed by the automatic conversion, in the histogram window,
click
.
Function Replacements
If your MATLAB code uses functions that do not have fixed-point support, the tool
lists these functions on the Function Replacements tab. You can choose to replace
unsupported functions with a custom function replacement or with a lookup table.
You can add and remove function replacements from this list. If you enter a function
replacements for a function, the replacement function is used when you build the
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Automated Fixed-Point Conversion
project. If you do not enter a replacement, the tool uses the type specified in the original
MATLAB code for the function.
Note: Using this table, you can replace the names of the functions but you cannot replace
argument patterns.
Validating Types
Selecting Validate Types validates the build using the proposed fixed-point data types.
If the validation is successful, you are ready to test the numerical behavior of the fixedpoint MATLAB algorithm.
If the errors or warnings occur during validation, they are displayed on the Type
Validation Output tab. If errors or warning occur:
• On the Variables tab, inspect the proposed types and manually modified types to
verify that they are valid.
• On the Function Replacements tab, verify that you have provided function
replacements for unsupported functions.
Testing Numerics
After validating the proposed fixed-point data types, select Test Numerics to verify the
behavior of the fixed-point MATLAB algorithm. By default, if you added a test file to
define inputs or run a simulation, the tool uses this test file to test numerics. Optionally,
you can add test files and select to run more than one test file. The tool compares the
numerical behavior of the generated fixed-point MATLAB code with the original floatingpoint MATLAB code. If you select to log inputs and outputs for comparison plots, the tool
generates an additional plot for each scalar output. This plot shows the floating-point
and fixed-point results and the difference between them. For non-scalar outputs, only the
error information is shown.
By default, the Fixed-Point Conversion tool runs the test files that you added and
selected for running the simulation. You can add test files and select to run more than
one test file to test numerics.
If the numerical results do not meet your desired accuracy after fixed-point simulation,
modify fixed-point data type settings and repeat the type validation and numerical
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14
Fixed-Point Conversion
testing steps. You might have to iterate through these steps multiple times to achieve the
desired results.
Detecting Overflows
When testing numerics, selecting Use scaled doubles to detect overflows enables
overflow detection. When this option is selected, the conversion tool runs the simulation
using scaled double versions of the proposed fixed-point types. Because scaled doubles
store their data in double-precision floating-point, they carry out arithmetic in full range.
They also retain their fixed-point settings, so they are able to report when a computation
goes out of the range of the fixed-point type. .
If the tool detects overflows, on its Overflow tab, it provides:
• A list of variables and expressions that overflowed
• Information on how much each variable overflowed
• A link to the variables or expressions in the code window
If your original algorithm uses scaled doubles, the tool also provides overflow information
for these expressions.
See Also
“Detect Overflows”
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Instrumented MEX Functions
Instrumented MEX Functions
In this section...
“Generating Instrumented MEX Functions” on page 14-105
“Merging Instrumentation Results” on page 14-105
“Clearing Instrumentation Results” on page 14-106
“Redirecting Entry-Point Calls to MEX Function” on page 14-106
“Proposing Fraction Lengths” on page 14-106
“Proposing Word Lengths” on page 14-106
Generating Instrumented MEX Functions
Note: This capability is not compatible with automatic fixed-point conversion. If you
select Convert to fixed point at build time, you cannot build instrumented
MEX functions.
Generating an instrumented MEX function for your MATLAB function enables
instrumentation for logging minimum and maximum values of named and intermediate
variables in your algorithm. It also enables instrumentation for log2 histograms of
named, intermediate and expression values.
When you run the instrumented MEX function, the instrumentation report provides
fixed-point data type proposals based on the simulation range data. You can use this
information to convert your MATLAB code to fixed point by hand. For more information,
see “Propose Fixed-Point Data Types” on page 14-67
Merging Instrumentation Results
When generating instrumented MEX functions, use the Merge instrumentation
results from multiple simulations option to specify whether to merge new
simulation minimum and maximum results with existing simulation results. Merging
instrumentation results allows you to collect complete range information from multiple
test files.
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Fixed-Point Conversion
Clearing Instrumentation Results
When generating instrumented MEX functions, click the Clear instrumentation
results button to clear instrumentation results from previous runs.
Redirecting Entry-Point Calls to MEX Function
By default, with the Redirect entry-point calls to MEX function option selected, the
MATLAB Coder software automatically redirects calls to your MATLAB algorithm in the
test file to calls to the generated MEX function. The generated MEX function must be in
the same folder as the entry-point functions.
If your test file already calls the MEX function, or you want to run the test file to test the
original MATLAB algorithm, clear this option.
Proposing Fraction Lengths
When you simulate an instrumented MEX function, if you select to propose fraction
lengths for the word lengths specified in the code, MATLAB Coder uses simulation
minimum and maximum information and proposes fraction lengths for variables in your
entry-point MATLAB function. For floating-point data types in your entry-point function,
MATLAB Coder uses the word length and signedness specified in Default data type of
all floating-point expressions to determine the optimal fraction lengths.
Optionally, specify a safety margin to use when proposing fraction lengths. For more
information, see “Modify Data Type Proposal Settings” on page 14-82.
Proposing Word Lengths
When you simulate an instrumented MEX function, if you select to propose word lengths
for the fraction lengths specified in the code, MATLAB Coder uses simulation minimum
and maximum information and proposes word lengths for variables in your entry-point
MATLAB function. For floating-point data types in your entry-point function, MATLAB
Coder uses the fraction length and signedness specified in Default data type of all
floating-point expressions to determine the optimal word lengths.
Optionally, specify a safety margin to use when proposing word lengths. For more
information, see “Modify Data Type Proposal Settings” on page 14-82.
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Convert Fixed-Point Conversion Project to MATLAB Scripts
Convert Fixed-Point Conversion Project to MATLAB Scripts
This example shows how to convert a MATLAB Coder project to MATLAB scripts when
the project includes automated fixed-point conversion. You can use the -tocode option of
the coder command to create a pair of scripts for fixed-point conversion and fixed-point
code generation. You can use the scripts to repeat the project workflow in a commandline workflow. Before you convert the project to the scripts, you must complete the Test
Numerics step of the fixed-point conversion process.
Prerequisites
This example uses the following files:
• Project file fun_with_matlab.prj
• Entry-point file fun_with_matlab.m
• Test bench file fun_with_matlab_test.m
• Generated fixed-point MATLAB file fun_with_matlab_fixpt.m
To obtain these files, complete the example “Propose Fixed-Point Data Types Based on
Simulation Ranges” on page 14-4, including these steps:
1
Complete the Test Numerics step of the fixed-point conversion process.
2
Configure the project to build a C/C++ static library.
Generate the Scripts
1
Change to the folder that contains the project file fun_with_matlab.prj and the
entry-point function file fun_with_matlab.m.
2
At the command line, use the -tocode option of the coder command to convert the
project to the scripts. Use the -script option to specify the file name for the scripts.
coder -tocode fun_with_matlab_project -script fun_with_matlab_script.m
The coder command generates two scripts in the current folder:
fun_with_matlab_script.m contains the MATLAB commands to:
• Create a code configuration object that has the same settings as the project.
• Run the codegen command to convert the fixed-point MATLAB function
fun_with_matlab_fixpt to a fixed-point C function.
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14
Fixed-Point Conversion
fun_with_matlab_script_fixpt.m contains the MATLAB commands to:
• Create a floating-point to fixed-point conversion configuration object that has the
same fixed-point conversion settings as the project.
• Run the codegen command to convert the MATLAB function fun_with_matlab
to the fixed-point MATLAB function fun_with_matlab_fixpt.
The suffix in the script file name is the generated fixed-point file name suffix
specified by the project file. In this example, the suffix is the default value
_fixpt.
The coder command overwrites existing files that have the same names as the
generated scripts. If you omit the -script option, the coder command writes the
scripts to the Command Window.
Run Script That Generates Fixed-Point C Code
To run the script that generates fixed-point C code from fixed-point MATLAB code, the
fixed-point MATLAB function specified in the script must be available.
1
Make sure that the fixed-point MATLAB function fun_with_matlab_fixpt.m is
on the search path.
addpath c:\coder\fun_with_matlab\codegen\fun_with_matlab\fixpt
2
Run the script:
fun_with_matlab_script
The code generation software generates a C static library with the
name fun_with_matlab_fixpt in the folder codegen\lib
\fun_with_matlab_fixpt. The variables cfg and ARGS appear in the base
workspace.
Run Script That Generates Fixed-Point MATLAB Code
If you do not have the fixed-point MATLAB function, or if you want to regenerate it,
use the script that generates the fixed-point MATLAB function from the floating-point
MATLAB function.
1
Make sure that the current folder contains the entry-point function
fun_with_matlab.m, and the test bench file fun_with_matlab_test.m.
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Convert Fixed-Point Conversion Project to MATLAB Scripts
2
Run the script:
fun_with_matlab_script_fixpt
The code generation software generates fun_with_matlab_fixpt.m in the folder
codegen\fun_with_matlab\fixpt. The variables cfg and ARGS appear in the
base workspace.
See Also
coder.FixptConfig | codegen | coder
Related Examples
•
“Convert MATLAB Code to Fixed-Point C Code”
•
“Propose Fixed-Point Data Types Based on Simulation Ranges”
•
“Convert MATLAB Coder Project to MATLAB Script”
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14
Fixed-Point Conversion
Generated Fixed-Point Code
In this section...
“Location of Generated Fixed-Point Files” on page 14-110
“Minimizing fi-casts to Improve Code Readability” on page 14-111
“Avoiding Overflows in the Generated Fixed-Point Code” on page 14-111
“Controlling Bit Growth” on page 14-112
“Avoiding Loss of Range or Precision” on page 14-112
“Handling Non-Constant mpower Exponents” on page 14-114
Location of Generated Fixed-Point Files
By default, the fixed-point conversion process generates files in a folder named
codegen/fcn_name/fixpt in your local working folder. fcn_name is the name of the
MATLAB function that you are converting to fixed point.
Filename
Description
fcn_name_fixpt.m
Generated fixed-point MATLAB code.
To integrate this fixed-point code into a
larger application, consider generating a
MEX-function for the function and calling
this MEX-function in place of the original
MATLAB code.
fcn_name_fixpt_exVal.mat
MAT-file containing:
• A structure for the input arguments.
• The name of the fixed-point file.
fcn_name_fixpt_report.html
Link to the type proposal report that
displays the generated fixed-point code and
the proposed type information.
fcn_name_report.html
Link to the type proposal report that
displays the original MATLAB code and the
proposed type information.
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Generated Fixed-Point Code
Filename
Description
fcn_name_wrapper_fixpt.m
File that converts the floating-point data
values supplied by the test file to the fixedpoint types determined for the inputs
during the conversion step. These fixedpoint values are fed into the converted
fixed-point function, fcn_name_fixpt.
Minimizing fi-casts to Improve Code Readability
The conversion process tries to reduce the number of fi-casts by analyzing the floatingpoint code. If an arithmetic operation is comprised of only compile-time constants, the
conversion process does not cast the operands to fixed point individually. Instead, it casts
the entire expression to fixed point.
For example, here is the fixed-point code generated for the constant expression x = 1/
sqrt(2) when the selected word length is 14.
Original MATLAB Code
Generated Fixed-Point Code
x = 1/sqrt(2);
x = fi(1/sqrt(2), 0, 14, 14, fm);
fm is the local fimath.
Avoiding Overflows in the Generated Fixed-Point Code
The conversion process avoids overflows by:
• Using full-precision arithmetic unless you specify otherwise.
• Avoiding arithmetic operations that involve double and fi data types. Otherwise, if
the word length of the fi data type is not able to represent the value in the double
constant expression, overflows occur.
• Avoiding overflows when adding and subtracting non fixed-point variables and fixedpoint variables.
The fixed-point conversion process casts non-fi expressions to the corresponding fi
type.
For example, consider the following MATLAB algorithm.
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Fixed-Point Conversion
% A = 5;
% B = ones(300, 1)
function y = fi_plus_non_fi(A, B)
% '1024' is non-fi, cast it
y = A + 1024;
% 'size(B, 1)*length(A)' is a non-fi, cast it
y = A + size(B, 1)*length(A);
end
The generated fixed-point code is:
%#codegen
% A = 5;
% B = ones(300, 1)
function y = fi_plus_non_fi_fixpt(A, B)
% '1024' is non-fi, cast it
fm = fimath('RoundingMethod', 'Floor', 'OverflowAction', 'Wrap',...
'ProductMode', 'FullPrecision', 'MaxProductWordLength', 128,...
'SumMode', 'FullPrecision', 'MaxSumWordLength', 128);
y = fi(A + fi(1024, 0, 11, 0, fm), 0, 11, 0, fm);
% 'size(B, 1)*length(A)' is a non-fi, cast it
y(:) = A + fi(size(B, fi(1, 0, 1, 0, fm))*length(A), 0, 9, 0, fm);
end
Controlling Bit Growth
The conversion process controls bit growth by using subscripted assignments, that
is, assignments that use the colon (:) operator, in the generated code. When you use
subscripted assignments, MATLAB overwrites the value of the left-hand side argument
but retains the existing data type and array size. Using subscripted assignment keeps
fixed-point variables fixed point rather than inadvertently turning them into doubles.
Maintaining the fixed-point type reduces the number of type declarations in the
generated code. Subscripted assignment also prevents bit growth which is useful when
you want to maintain a particular data type for the output.
Avoiding Loss of Range or Precision
Avoiding Loss of Range or Precision in Unsigned Subtraction Operations
When the result of the subtraction is negative, the conversion process promotes the left
operand to a signed type.
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Generated Fixed-Point Code
For example, consider the following MATLAB algorithm.
% A = 1;
% B = 5
function [y,z] = unsigned_subtraction(A,B)
y = A - B;
C = -20;
z = C - B;
end
In the original code, both A and B are unsigned and the result of A-B can be negative. In
the generated fixed-point code, A is promoted to signed. In the original code, C is signed,
so does not require promotion in the generated code.
%#codegen
% A = 1;
% B = 5
function [y,z] = unsigned_subtraction_fixpt(A,B)
fm = fimath('RoundingMethod', 'Floor', 'OverflowAction', 'Wrap',...
'ProductMode', 'FullPrecision', 'MaxProductWordLength', 128,...
'SumMode', 'FullPrecision', 'MaxSumWordLength', 128);
y = fi(fi_signed(A) - B, 1, 3, 0, fm);
C = fi(-20, 1, 6, 0, fm);
z = fi(C - B, 1, 6, 0, fm);
end
function y = fi_signed(a)
coder.inline( 'always' );
if isfi( a ) && ~(issigned( a ))
nt = numerictype( a );
new_nt = numerictype( 1, nt.WordLength + 1, nt.FractionLength );
y = fi( a, new_nt, fimath( a ) );
else
y = a;
end
end
Avoiding Loss of Range When Concatenating Arrays of Fixed-Point Numbers
If you concatenate matrices using vertcat and horzcat, the conversion process uses
the largest numerictype among the expressions of a row and casts the leftmost element to
that type. This type is then used for the concatenated matrix to avoid loss of range.
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14
Fixed-Point Conversion
For example, consider the following MATLAB algorithm.
% A = 1, B = 100, C = 1000
function [y, z] = lb_node(A, B, C)
%% single rows
y = [A B C];
%% multiple rows
z = [A 5; A B; A C];
end
In the generated fixed-point code:
• For the expression y = [A B C], the leftmost element, A, is cast to the type of C
because C has the largest type in the row.
• For the expression [A 5; A B; A C]:
• In the first row, A is cast to the type of C because C has the largest type of the
whole expression.
• In the second row, A is cast to the type of B because B has the larger type in the
row.
• In the third row, A is cast to the type of C because C has the larger type in the row.
%#codegen
% A = 1, B = 100, C = 1000
function [y, z] = lb_node_fixpt(A, B, C)
%% single rows
fm = fimath('RoundingMethod', 'Floor', 'OverflowAction', 'Wrap',...
'ProductMode', 'FullPrecision', 'MaxProductWordLength', 128, ...
'SumMode', 'FullPrecision', 'MaxSumWordLength', 128);
y = fi([fi(A, 0, 10, 0, fm) B C], 0, 10, 0, fm);
%% multiple rows
z = fi([fi(A, 0, 10, 0, fm) 5; fi(A, 0, 7, 0, fm) B;...
fi(A, 0, 10, 0, fm) C], 0, 10, 0, fm);
end
Handling Non-Constant mpower Exponents
If the function that you are converting has a scalar input, and the mpower exponent
input is not constant, the conversion process sets the fimath ProductMode to
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Generated Fixed-Point Code
SpecifyPrecision in the generated code. With this setting , the output data type can
be determined at compile time.
For example, consider the following MATLAB algorithm.
% a = 1
% b = 3
function y = exp_operator(a, b)
% exponent is a constant so no need to specify precision
y = a^3;
% exponent is not a constant, use 'SpecifyPrecision' for 'ProductMode'
y = b^a;
end
In the generated fixed-point code, for the expression y = a^3 , the exponent is a
constant, so there is no need to specify precision. For the expression, y = b^a, the
exponent is not constant, so the ProductMode is set to SpecifyPrecision.
%#codegen
% a = 1
% b = 3
function y = exp_operator_fixpt(a, b)
% exponent is a constant so no need to specify precision
fm = fimath('RoundingMethod', 'Floor', 'OverflowAction', 'Wrap',...
'ProductMode', 'FullPrecision', 'MaxProductWordLength', 128,...
'SumMode', 'FullPrecision', 'MaxSumWordLength', 128);
y = fi(a^3, 0, 2, 0, fm);
% exponent is not a constant, use 'SpecifyPrecision' for 'ProductMode'
y(:) = fi(b, 'ProductMode', 'SpecifyPrecision',...
'ProductWordLength', 2, 'ProductFractionLength', 0 )^a;
end
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14
Fixed-Point Conversion
Fixed-Point Code for MATLAB Classes
In this section...
“Automated Conversion Support for MATLAB Classes” on page 14-116
“Unsupported Constructs” on page 14-116
“Coding Style Best Practices” on page 14-117
Automated Conversion Support for MATLAB Classes
The automated conversion process:
• Proposes fixed-point data types based on simulation ranges for MATLAB classes. It
does not propose data types based on derived ranges for MATLAB classes.
After simulation, the Fixed-Point Conversion tool:
• Function list contains class constructors, methods, and specializations.
• Code window displays the objects used in each function.
• Provides code coverage for methods.
For more information, see “Viewing Information for MATLAB Classes”.
• Supports class methods, properties, and specializations. For each specialization of a
class, class_name, the conversion generates a separate class_name_fixpt.m file.
For every instantiation of a class, the generated fixed-point code contains a call to the
constructor of the appropriate specialization.
• Supports classes that have get and set methods such as get.PropertyName,
set.PropertyName. These methods are called when properties are read or assigned.
The set methods can be specialized. Sometimes, in the generated fixed-point code,
assignment statements are transformed to function calls.
Unsupported Constructs
The automated conversion process does not support:
• Class inheritance.
• Packages.
• Constructors that use nargin and varargin.
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Fixed-Point Code for MATLAB Classes
Coding Style Best Practices
When you write MATLAB code that uses MATLAB classes:
• Initialize properties in the class constructor.
• Replace constant properties with static methods.
For example, consider the counter class.
classdef Counter < handle
properties
Value = 0;
end
properties(Constant)
MAX_VALUE = 128
end
methods
function out = next(this)
out = this.Count;
if this.Value == this.MAX_VALUE
this.Value = 0;
else
this.Value = this.Value + 1;
end
end
end
end
To use the automated fixed-point conversion process, rewrite the class to have a static
class that initializes the constant property MAX_VALUE and a constructor that initializes
the property Value.
classdef Counter < handle
properties
Value;
end
methods(Static)
function t = MAX_VALUE()
t = 128;
end
end
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14
Fixed-Point Conversion
methods
function this = Counter()
this.Value = 0;
end
function out = next(this)
out = this.Value;
if this.Value == this.MAX_VALUE
this.Value = 0;
else
this.Value = this.Value + 1;
end
end
end
end
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Automated Fixed-Point Conversion Best Practices
Automated Fixed-Point Conversion Best Practices
In this section...
“Create a Test File” on page 14-119
“Prepare Your Algorithm for Code Acceleration or Code Generation” on page 14-120
“Check for Fixed-Point Support for Functions Used in Your Algorithm” on page
14-121
“Manage Data Types and Control Bit Growth” on page 14-121
“Convert to Fixed Point” on page 14-122
“Use the Histogram to Fine-Tune Data Type Settings” on page 14-122
“Optimize Your Algorithm” on page 14-124
“Avoid Explicit Double and Single Casts” on page 14-126
Create a Test File
A best practice for structuring your code is to separate your core algorithm from other
code that you use to test and verify the results. Create a test file to call your original
MATLAB algorithm and fixed-point versions of the algorithm. For example, as shown in
the following table, you might set up some input data to feed into your algorithm, and
then, after you process that data, create some plots to verify the results. Since you need
to convert only the algorithmic portion to fixed-point, it is more efficient to structure your
code so that you have a test file, in which you create your inputs, call your algorithm, and
plot the results, and one (or more) algorithmic files, in which you do the core processing.
Original code
Best Practice
Modified code
% TEST INPUT
x = randn(100,1);
Issue
Test file
Generation of test input
and verification of results
are intermingled with the
algorithm code.
% TEST INPUT
x = randn(100,1);
% ALGORITHM
y = zeros(size(x));
y(1) = x(1);
for n=2:length(x)
y(n)=y(n-1) + x(n);
end
% VERIFY RESULTS
yExpected=cumsum(x);
Fix
Create a test file that is
separate from your algorithm.
% ALGORITHM
y = cumulative_sum(x);
% VERIFY RESULTS
yExpected = cumsum(x);
plot(y-yExpected)
title('Error')
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14
Fixed-Point Conversion
Original code
plot(y-yExpected)
title('Error')
Best Practice
Put the algorithm in its own
function.
Modified code
Algorithm in its own function
function y = cumulative_sum(x)
y = zeros(size(x));
y(1) = x(1);
for n=2:length(x)
y(n) = y(n-1) + x(n);
end
end
You can use the test file to:
• Verify that your floating-point algorithm behaves as you expect before you convert it
to fixed point. The floating-point algorithm behavior is the baseline against which you
compare the behavior of the fixed-point versions of your algorithm.
• Propose fixed-point data types.
• Compare the behavior of the fixed-point versions of your algorithm to the floatingpoint baseline.
• Help you determine initial values for static ranges.
By default, the Fixed-Point Conversion tool shows code coverage results. Your test files
should exercise the algorithm over its full operating range so that the simulation ranges
are accurate. For example, for a filter, realistic inputs are impulses, sums of sinusoids,
and chirp signals. With these inputs, using linear theory, you can verify that the outputs
are correct. Signals that produce maximum output are useful for verifying that your
system does not overflow. The quality of the proposed fixed-point data types depends on
how well the test files cover the operating range of the algorithm with the accuracy that
you want. Reviewing code coverage results help you verify that your test file is exercising
the algorithm adequately. Review code flagged with a red code coverage bar because this
code is not executed. If the code coverage is inadequate, modify the test file or add more
test files to increase coverage. For more information see “Code Coverage”.
Prepare Your Algorithm for Code Acceleration or Code Generation
The automated conversion process instruments your code and provides data type
proposals to help you convert your algorithm to fixed point.
MATLAB algorithms that you want to convert to fixed point automatically must comply
with code generation requirements and rules. To view the subset of the MATLAB
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Automated Fixed-Point Conversion Best Practices
language that is supported for code generation, see “Functions and Objects Supported for
C and C++ Code Generation — Alphabetical List”.
To help you identify unsupported functions or constructs in your MATLAB code, add the
%#codegen pragma to the top of your MATLAB file. The MATLAB code analyzer flags
functions and constructs that are not available in the subset of the MATLAB language
supported for code generation. This advice appears in real-time as you edit your code in
the MATLAB editor. For more information, see “Check Code With the Code Analyzer”.
The software provides a link to a report that identifies calls to functions and the use of
data types that are not supported for code generation. For more information, see “Check
Code Using the Code Generation Readiness Tool”.
Check for Fixed-Point Support for Functions Used in Your Algorithm
The Fixed-Point Conversion tool flags unsupported function calls found in your algorithm
on the Function Replacements tab. For example, if you use the fft, which is not
supported for fixed point. The tool adds an entry to the table on this tab and indicates
that you need to specify a replacement function to use for fixed-point operations.
You can specify additional replacement functions. For example, functions like sin, cos,
and sqrt may support fixed point, but for better efficiency, you may want to consider an
alternative implementation like a lookup table or CORDIC-based algorithm.
Manage Data Types and Control Bit Growth
The automated fixed-point conversion process automatically manages data types and
controls bit growth. It controls bit growth by using subscripted assignments, that
is, assignments that use the colon (:) operator, in the generated code. When you use
subscripted assignments, MATLAB overwrites the value of the left-hand side argument
but retains the existing data type and array size. In addition to preventing bit growth,
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14
Fixed-Point Conversion
subscripted assignment reduces the number of casts in the generated fixed-point code
and makes the code more readable.
Convert to Fixed Point
What Are Your Goals for Converting to Fixed Point?
Before you start the conversion, consider your goals for converting to fixed point. Are
you implementing your algorithm in C or HDL? What are your target constraints? The
answers to these questions determine many fixed-point properties such as the available
word length, fraction length, and math modes, as well as available math libraries.
To set up these properties, use the Advanced settings.
For more information, see “Type Proposal Settings”.
Run With Fixed-Point Types and Compare Results
Create a test file to validate that the floating-point algorithm works as expected
before converting it to fixed point. You can use the same test file to propose fixedpoint data types, and to compare fixed-point results to the floating-point baseline after
the conversion. For more information, see “Running a Simulation” on page 14-91 and
“Histogram” on page 14-100 .
Use the Histogram to Fine-Tune Data Type Settings
To fine-tune fixed-point type settings, use the histogram. To log data for histograms, in
the Fixed-Point Conversion window, click Run Simulation and select Log data for
histogram, and then click the Run Simulation button.
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Automated Fixed-Point Conversion Best Practices
After simulation and static analysis:
• To view the histogram for a variable, on the Variables tab, click the Proposed Type
field for that variable.
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Fixed-Point Conversion
You can view the effect of changing the proposed data types by dragging the edges
of the bounding box in the histogram window to change the proposed data type and
selecting or clearing the Signed option.
• If the values overflow and the range cannot fit the proposed type, the table shows
proposed types in red.
When the tool applies data types, it generates an html report that provides overflow
information and highlights overflows in red. Review the proposed data types.
Optimize Your Algorithm
Use fimath to Get Optimal Types for C or HDL
fimath properties define the rules for performing arithmetic operations on fi
objects, including math, rounding, and overflow properties. You can use the fimath
ProductMode and SumMode properties to retain optimal data types for C and HDL. HDL
can have arbitrary word length types in the generated HDL code whereas C requires
container types (uint8, uint16, uint32). Use the Advanced settings, see “Type
Proposal Settings”.
C
The KeepLSB setting for ProductMode and SumMode models the behavior of integer
operations in the C language, while KeepMSB models the behavior of many DSP devices.
Different rounding methods require different amounts of overhead code. Setting
the RoundingMethod property to Floor, which is equivalent to two's complement
truncation, provides the most efficient rounding implementation. Similarly, the standard
method for handling overflows is to wrap using modulo arithmetic. Other overflow
handling methods create costly logic. Whenever possible, set OverflowAction to Wrap.
MATLAB Code
Best Practice
Generated C Code
Code being compiled
Issue
int adder(short a, short b)
{
int y;
int i0;
int i1;
int i2;
int i3;
i0 = a;
i1 = b;
if ((i0 & 65536) != 0) {
function y = adder(a,b) With the default word length set to
y = a + b;
16 and the default fimath settings,
end
Note: In the Fixed-Point
Conversion tool, set
additional code is generated to
implement saturation overflow, nearest
rounding, and full-precision arithmetic.
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Automated Fixed-Point Conversion Best Practices
MATLAB Code
Default word length to
16.
Best Practice
Generated C Code
i2 = i0 | -65536;
} else {
i2 = i0 & 65535;
}
if ((i1 & 65536) != 0) {
i3 = i1 | -65536;
} else {
i3 = i1 & 65535;
}
i0 = i2 + i3;
if ((i0 & 65536) != 0) {
y = i0 | -65536;
} else {
y = i0 & 65535;
}
return y;
}
Fix
To make the generated C code more
efficient, choose fixed-point math
settings that match your processor
types.
int adder(short a, short b)
{
return a + b;
}
To customize fixed-point type proposals,
use the Fixed-Point Conversion tool
Advanced settings. Select fimath and
then set:
Rounding method
Floor
Overflow action
Wrap
Product mode
KeepLSB
Sum mode
KeepLSB
Product word
length
32
Sum word length
32
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14
Fixed-Point Conversion
HDL
For HDL code generation, set:
• ProductMode and SumMode to FullPrecision
• Overflow action to Wrap
• Rounding method to Floor
Replace Built-in Functions With More Efficient Fixed-Point Implementations
Some MATLAB built-in functions can be made more efficient for fixed-point
implementation. For example, you can replace a built-in function with a Lookup table
implementation, or a CORDIC implementation, which requires only iterative shift-add
operations. For more information, see “Function Replacements” on page 14-102.
Re-implement Division Operations Where Possible
Often, division is not fully supported by hardware and can result in slow processing.
When your algorithm requires a division, consider replacing it with one of the following
options:
• Use bit shifting when the denominator is a power of two. For example, bitsra(x,3)
instead of x/8.
• Multiply by the inverse when the denominator is constant. For example, x*0.2
instead of x/5.
• If the divisor is not constant, use a temporary variable for the division. Doing so
results in a more efficient data type proposal and, if overflows occur, makes it easier
to see which expression is overflowing.
Eliminate Floating-Point Variables
For more efficient code, the automated fixed-point conversion process eliminates floatingpoint variables. The one exception to this is loop indices because they usually become
integer types. It is good practice to inspect the fixed-point code after conversion to verify
that there are no floating-point variables in the generated fixed-point code.
Avoid Explicit Double and Single Casts
For the automated workflow, do not use explicit double or single casts in your MATLAB
algorithm to insulate functions that do not support fixed-point data types. The automated
conversion tool does not support these casts.
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Automated Fixed-Point Conversion Best Practices
Instead of using casts, supply a replacement function. For more information, see
“Function Replacements” on page 14-102.
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Fixed-Point Conversion
Replacing Functions Using Lookup Table Approximations
The Fixed-Point Designer software provides an option to generate lookup table
approximations for continuous and stateless single-input, single-output functions in your
original MATLAB code. These functions must be on the MATLAB path.
You can use this capability to handle functions that are not supported for fixed point
and to replace your own custom functions. The fixed-point conversion process infers
the ranges for the function and then uses an interpolated lookup table to replace the
function. You can control the interpolation method and number of points in the lookup
table. By adjusting these settings, you can tune the behavior of replacement function to
match the behavior of the original function as closely as possible.
The fixed-point conversion process generates one lookup table approximation per call site
of the function that needs replacement.
To use lookup table approximations in a MATLAB Coder project, see “Replace the exp
Function with a Lookup Table” and “Replace a Custom Function with a Lookup Table”.
To use lookup table approximations in the programmatic workflow, see
coder.approximation, “Replace the exp Function with a Lookup Table”, and “Replace
a Custom Function with a Lookup Table”.
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MATLAB Language Features Supported for Automated Fixed-Point Conversion
MATLAB Language Features Supported for Automated Fixed-Point
Conversion
Fixed-Point Designer supports the following MATLAB language features in automated
fixed-point conversion:
• N-dimensional arrays
• Matrix operations, including deletion of rows and columns
• Variable-sized data (see “Generate Code for Variable-Size Data”). Range computation
for variable–sized data is supported via simulation mode only. Variable-sized data is
not supported for comparison plotting.
• Subscripting (see “Incompatibility with MATLAB in Matrix Indexing Operations for
Code Generation”)
• Complex numbers (see “Code Generation for Complex Data”)
• Numeric classes (see “Supported Variable Types”)
• Double-precision, single-precision, and integer math
• Fixed-point arithmetic (see “Code Acceleration and Code Generation from MATLAB”)
• Program control statements if, switch, for, while, and break
• Arithmetic, relational, and logical operators
• Local functions
• Persistent variables (see “Define and Initialize Persistent Variables”)
• Structures. Range computation for structures is supported via simulation mode only.
• Characters
The complete set of Unicode characters is not supported for code generation.
Characters are restricted to 8 bits of precision in generated code. Because many
mathematical operations require more than 8 bits of precision, it is recommended that
you do not perform arithmetic with characters if you intend to convert your MATLAB
algorithm to fixed point.
• MATLAB classes. Range computation for MATLAB classes is supported via
simulation mode only.
Automated conversion supports:
• Class properties
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Fixed-Point Conversion
• Constructors
• Methods
• Specializations
It does not support class inheritance or packages. For more information, see “FixedPoint Code for MATLAB Classes”.
• Ability to call functions (see “Resolution of Function Calls for Code Generation” on
page 13-2)
• Subset of MATLAB toolbox functions (see “Functions Supported for Code Acceleration
or C Code Generation”).
• Subset of DSP System Toolbox System objects.
The DSP System Toolbox System objects supported for automated conversion are:
• dsp.BiquadFilter
• dsp.FIRFilter, Direct Form only
• dsp.FIRRateConverter
• dsp.LowerTriangularSolver
• dsp.UpperTriangularSolver
• dsp.ArrayVectorAdder
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Inspecting Data Using the Simulation Data Inspector
Inspecting Data Using the Simulation Data Inspector
In this section...
“What Is the Simulation Data Inspector?” on page 14-131
“Import Logged Data” on page 14-131
“Export Logged Data” on page 14-131
“Group Signals” on page 14-131
“Run Options” on page 14-132
“Create Report” on page 14-132
“Comparison Options” on page 14-132
“Enabling Plotting Using the Simulation Data Inspector” on page 14-132
“Save and Load Simulation Data Inspector Sessions” on page 14-132
What Is the Simulation Data Inspector?
The Simulation Data Inspector allows you to view data logged during the fixed-point
conversion process. You can use it to inspect and compare the inputs and outputs to the
floating-point and fixed-point versions of your algorithm.
For fixed-point conversion, there is no programmatic interface for the Simulation Data
Inspector.
Import Logged Data
Before importing data into the Simulation Data Inspector, you must have previously
logged data to the base workspace or to a MAT-file.
Export Logged Data
The Simulation Data Inspector provides the capability to save data collected by the fixedpoint conversion process to a MAT-file that you can later reload. The format of the MATfile is different from the format of a MAT-file created from the base workspace.
Group Signals
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Fixed-Point Conversion
You can customize the organization of your logged data in the Simulation Data Inspector
Runs pane. By default, data is first organized by run. You can then organize your data
by logged variable or no hierarchy.
Run Options
You can configure the Simulation Data Inspector to:
• Append New Runs
In the Run Options dialog box, the default is set to add new runs to the bottom of the
run list. To append new runs to the top of the list, select Add new runs to top.
• Specify a Run Naming Rule
To specify run naming rules, in the Simulation Data Inspector toolbar, click Run
Configuration.
Create Report
You can create a report of the runs or comparison plots. Specify the name and location
of the report file. By default, the Simulation Data Inspector overwrites existing files. To
preserve existing reports, select If report exists, increment file name to prevent
overwriting.
Comparison Options
To change how signals are matched when runs are compared, specify the Align by and
Then by parameters and then click OK.
Enabling Plotting Using the Simulation Data Inspector
To enable the Simulation Data Inspector in the Fixed-Point Conversion tool, see “Enable
Plotting Using the Simulation Data Inspector”.
To enable the Simulation Data Inspector in the programmatic workflow, see “Enable
Plotting Using the Simulation Data Inspector”.
Save and Load Simulation Data Inspector Sessions
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Inspecting Data Using the Simulation Data Inspector
If you have data in the Simulation Data Inspector and you want to archive or share the
data to view in the Simulation Data Inspector later, save the Simulation Data Inspector
session. When you save a Simulation Data Inspector session, the MAT-file contains:
• All runs, data, and properties from the Runs and Comparisons panes.
• Check box selection state for data in the Runs pane.
Save a Session to a MAT-File
1
On the Visualize tab, click Save.
2
Browse to where you want to save the MAT-file to, name the file, and click Save.
Load a Saved Simulation Data Inspector Simulation
1
On the Visualize tab, click Open.
2
Browse, select the MAT-file saved from the Simulation Data Inspector, and click
Open.
3
If data in the session is plotted on multiple subplots, on the Format tab, click
Subplots and select the subplot layout.
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Fixed-Point Conversion
Custom Plot Functions
The Fixed-Point Conversion tool provides a default time series based plotting function.
The conversion process uses this function at the test numerics step to show the floatingpoint and fixed-point results and the difference between them. However, during fixedpoint conversion you might want to visualize the numerical differences in a view that is
more suitable for your application domain. For example, plots that show eye diagrams
and bit error differences are more suitable in the communications domain and histogram
difference plots are more suitable in image processing designs.
You can choose to use a custom plot function at the test numerics step. The Fixed-Point
Conversion tool facilitates custom plotting by providing access to the raw logged input
and output data before and after fixed-point conversion. You supply a custom plotting
function to visualize the differences between the floating-point and fixed-point results. If
you specify a custom plot function, the fixed-point conversion process calls the function
for each input and output variable, passes in the name of the variable and the function
that uses it, and the results of the floating-point and fixed-point simulations.
Your function should accept three inputs:
• A structure that holds the name of the variable and the function that uses it.
Use this information to:
• Customize plot headings and axes.
• Choose which variables to plot.
• Generate different error metrics for different output variables.
• A cell array to hold the logged floating-point values for the variable.
This cell array contains values observed during floating-point simulation of the
algorithm during the test numerics phase. You might need to reformat this raw data.
• A cell array to hold the logged values for the variable after fixed-point conversion.
This cell array contains values observed during fixed-point simulation of the
converted design.
For example, function customComparisonPlot(varInfo, floatVarVals,
fixedPtVarVals).
To use a custom plot function, in the Fixed-Point Conversion tool, select Advanced, and
then set Custom plot function to the name of your plot function.
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Custom Plot Functions
In the programmatic workflow, set the coder.FixptConfig configuration object
PlotFunction property to the name of your plot function. See “Visualize Differences
Between Floating-Point and Fixed-Point Results”.
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14
Fixed-Point Conversion
Data Type Issues in Generated Code
Within the fixed-point conversion HTML report you have the option to highlight
MATLAB code that results in double, single, or expensive fixed-point operations.
Consider enabling these checks when trying to achieve a strict single, or fixed-point
design.
These checks are disabled by default.
Enable the Highlight Option in a MATLAB Coder Project
1
2
Open the Settings menu.
Under Plotting and Reporting, set Highlight potential data type issues to
Yes.
Enable the Highlight Option at the Command Line
1
Create a fixed-point code configuration object:
cfg = coder.config('fixpt');
2
Set the HighlightPotentialDataTypeIssues property of the configuration object
to true.
cfg.HighlightPotentialDataTypeIssues = true;
Stowaway Doubles
When trying to achieve a strict-single or fixed-point design, manual inspection of code
can be time-consuming and error prone. This check highlights all expressions that result
in a double operation.
Stowaway Singles
This check highlights all expressions that result in a single operation.
Expensive Fixed-Point Operations
The expensive fixed-point operations check identifies optimization opportunities
by highlighting expressions in the MATLAB code which result in cumbersome
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Data Type Issues in Generated Code
multiplication or division, or expensive rounding in generated code. For more information
on optimizing generated fixed-point code, see “Tips for Making Generated Code More
Efficient”.
Cumbersome Operations
Cumbersome operations most often occur due to insufficient range of output. Avoid
inputs to a multiply or divide operation that have word lengths larger than the base
integer type of your processor. Operations with larger word lengths can be handled in
software, but this approach requires much more code and is much slower.
Expensive Rounding
Traditional handwritten code, especially for control applications, almost always uses
"no effort" rounding. For example, for unsigned integers and two's complement signed
integers, shifting right and dropping the bits is equivalent to rounding to floor. To get
results comparable to, or better than, what you expect from traditional handwritten code,
use the floor rounding method. This check identifies expensive rounding operations in
multiplication and division.
Expensive Comparison Operations
Comparison operations generate extra code when a casting operation is required to do
the comparison. For example, when comparing an unsigned integer to a signed integer,
one of the inputs must first be cast to the signedness of the other before the comparison
operation can be performed. Consider optimizing the data types of the input arguments
so that a cast is not required in the generated code.
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15
Automated Fixed-Point Conversion
Using Programmatic Workflow
• “Convert MATLAB Code to Fixed-Point C Code” on page 15-2
• “Propose Fixed-Point Data Types Based on Simulation Ranges” on page 15-5
• “Propose Fixed-Point Data Types Based on Derived Ranges” on page 15-11
• “Detect Overflows” on page 15-19
• “Replace the exp Function with a Lookup Table” on page 15-23
• “Replace a Custom Function with a Lookup Table” on page 15-25
• “Enable Plotting Using the Simulation Data Inspector” on page 15-28
• “Visualize Differences Between Floating-Point and Fixed-Point Results” on page
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
Convert MATLAB Code to Fixed-Point C Code
This example shows how to generate fixed-point C code from floating-point MATLAB
code using the programmatic workflow.
Set Up the Fixed-Point Configuration Object
Create a fixed-point configuration object and configure the test file name. For example:
fixptcfg = coder.config('fixpt');
fixptcfg.TestBenchName = 'fun_with_matlab_test';
Configure the Fixed-Point Configuration Object for Type Proposal
The fixed-point conversion software can propose types based on simulation ranges,
derived ranges, or both.
• For type proposal using only simulation ranges, enable the collection and reporting of
simulation range data. By default, derived range analysis is disabled.
fixptcfg.ComputeSimulationRanges = true;
• For type proposal using only derived ranges:
1
Specify the design range for input parameters. For example:
fixptcfg.addDesignRangeSpecification('dti', 'u_in', -1.0, 1.0);
2
Enable derived range analysis. Disable collection and reporting of simulation
range data.
fixptcfg.ComputeDerivedRanges = true;
fixptcfg.ComputeSimulationRanges = false;
Enable Numerics Testing
Select to run the test file to verify the generated fixed-point MATLAB code.
fixptcfg.TestNumerics = true;
Enable Plotting
Log inputs and outputs for comparison plotting. Select to plot using a custom function or
Simulation Data Inspector. For example, to plot using Simulation Data Inspector:
fixptcfg.LogIOForComparisonPlotting = true;
fixptcfg.PlotWithSimulationDataInspector = true;
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Convert MATLAB Code to Fixed-Point C Code
Configure Additional Fixed-Point Configuration Object Properties
Configure additional fixed-point configuration object properties as necessary. For
example, define the default fixed-point word length:
fixptcfg.DefaultWordLength = 16;
Set Up the C Code Generation Configuration Object
Create a code configuration object for generation of a C static library, dynamic library, or
executable. Enable the code generation report. For example:
cfg = coder.config('lib');
cfg.GenerateReport = true;
Generate Fixed-Point C Code
Use the codegen function to convert the floating-point MATLAB function to fixed-point
C code. For example:
codegen -float2fixed fixptcfg -config cfg fun_with_matlab
View the Type Proposal Report
Click the link to the type proposal report for the entry-point function.
View the Comparison Plots
If you selected to log inputs and outputs for comparison plots, the conversion process
generates comparison plots.
• If you selected to use Simulation Data Inspector for these plots, the Simulation Data
Inspector opens. Use Simulation Data Inspector to view and compare the floatingpoint and fixed-point run information.
• If you selected to use a custom plotting function for these plots, the conversion process
uses the custom function to generate the plots.
View the Generated Fixed-Point MATLAB and Fixed-Point C Code
Click the View Report link that follows the type proposal report. To view the fixed-point
MATLAB code, click the MATLAB code tab. To view the fixed-point C code, click the C
code tab.
See Also
coder.FixptConfig
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Automated Fixed-Point Conversion Using Programmatic Workflow
Related Examples
•
“Propose Fixed-Point Data Types Based on Simulation Ranges”
•
“Propose Fixed-Point Data Types Based on Derived Ranges”
•
“Enable Plotting Using the Simulation Data Inspector”
More About
•
“Automated Fixed-Point Conversion”
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Propose Fixed-Point Data Types Based on Simulation Ranges
Propose Fixed-Point Data Types Based on Simulation Ranges
This example shows how to propose fixed-point data types based on simulation range
data.
Prerequisites
To complete this example, you must install the following products:
• MATLAB
• MATLAB Coder
• Fixed-Point Designer
• C compiler (for most platforms, a default C compiler is supplied with MATLAB)
For a list of supported compilers, see http://www.mathworks.com/support/
compilers/current_release/.
You can use mex -setup to change the default compiler. See “Changing Default
Compiler”.
Create a New Folder and Copy Relevant Files
1
Create a local working folder, for example, c:\fun_with_matlab.
2
Change to the docroot\toolbox\fixpoint\examples folder. At the MATLAB
command line, enter:
cd(fullfile(docroot, 'toolbox', 'fixpoint', 'examples'))
3
Copy the fun_with_matlab.m and fun_with_matlab_test.m files to your local
working folder.
Type
Name
Description
Function code
fun_with_matlab.m
Entry-point MATLAB function
Test file
fun_with_matlab_test.m
MATLAB script that tests
fun_with_matlab.m
The fun_with_matlab Function
function y = fun_with_matlab(x) %#codegen
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
persistent z
if isempty(z)
z = zeros(2,1);
end
% [b,a] = butter(2, 0.25)
b = [0.0976310729378175, 0.195262145875635, 0.0976310729378175];
a = [1, -0.942809041582063, 0.3333333333333333];
y = zeros(size(x));
for i = 1:length(x)
y(i) = b(1)*x(i) + z(1);
z(1) = b(2)*x(i) + z(2) - a(2) * y(i);
z(2) = b(3)*x(i)
- a(3) * y(i);
end
end
The fun_with_matlab_test Script
The test script runs the fun_with_matlab function with three input signals: chirp, step,
and impulse to cover the full intended operating range of the system. The script then
plots the outputs.
% fun_with_matlab_test
%
% Define representative inputs
N = 256;
% Number of points
t = linspace(0,1,N);
% Time vector from 0 to 1 second
f1 = N/2;
% Target frequency of chirp set to Nyquist
x_chirp = sin(pi*f1*t.^2); % Linear chirp from 0 to Fs/2 Hz in 1 second
x_step = ones(1,N);
% Step
x_impulse = zeros(1,N);
% Impulse
x_impulse(1) = 1;
% Run the function under test
x = [x_chirp;x_step;x_impulse];
y = zeros(size(x));
for i = 1:size(x,1)
y(i,:) = fun_with_matlab(x(i,:));
end
% Plot the results
titles = {'Chirp','Step','Impulse'}
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The fun_with_matlab_test Script
clf
for i = 1:size(x,1)
subplot(size(x,1),1,i)
plot(t,x(i,:),t,y(i,:))
title(titles{i})
legend('Input','Output')
end
xlabel('Time (s)')
figure(gcf)
disp('Test complete.')
Set Up the Fixed-Point Configuration Object
Create a fixed-point configuration object and configure the test file name.
fixptcfg = coder.config('fixpt');
fixptcfg.TestBenchName = 'fun_with_matlab_test';
Set Up the C Code Generation Configuration Object
Create a code configuration object to generate a C static library. Enable the code
generation report.
cfg = coder.config('lib');
cfg.GenerateReport = true;
Collect Simulation Ranges and Generate Fixed-Point Code
Use the codegen function to convert the floating-point MATLAB function,
fun_with_matlab, to fixed-point C code. Set the default word length for the fixed-point
data types to 16.
fixptcfg.ComputeSimulationRanges = true;
fixptcfg.DefaultWordLength = 16;
% Derive ranges and generate fixed-point code
codegen -float2fixed fixptcfg -config cfg fun_with_matlab
codegen analyzes the floating-point code. Because you did not specify the input types
for the fun_with_matlab function, the conversion process infers types by simulating
the test file. The conversion process then derives ranges for variables in the algorithm.
It uses these derived ranges to propose fixed-point types for these variables. When the
conversion is complete, it generates a type proposal report.
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
View Range Information
Click the link to the type proposal report for the fun_with_matlab function,
fun_with_matlab_report.html.
The report opens in a web browser.
View Generated Fixed-Point MATLAB Code
codegen generates a fixed-point version of the fun_with_matlab.m
function, fun_with_matlab_fixpt.m, and a wrapper function that calls
fun_with_matlab_fixpt. These files are generated in the codegen
\fun_with_matlab\fixpt folder in your local working folder.
function y = fun_with_matlab_fixpt(x)
fm = fimath('RoundingMethod', 'Floor', 'OverflowAction', 'Wrap', 'ProductMode',...
'FullPrecision', 'SumMode', 'FullPrecision');
%#codegen
persistent z
if isempty( z )
z = fi(zeros( 2, 1 ), 1, 16, 15, fm);
end
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The fun_with_matlab_test Script
% [b,a] = butter(2, 0.25)
b = fi([ 0.0976310729378175, 0.195262145875635, 0.0976310729378175 ], 0, 16, 18, fm);
a = fi([ 1, -0.942809041582063, 0.3333333333333333 ], 1, 16, 14, fm);
y = fi(zeros( size( x ) ), 1, 16, 14, fm);
for i = 1:length( x )
y( i ) = b( 1 )*x( i ) + z( 1 );
z( 1 ) = fi_signed(b( 2 )*x( i ) + z( 2 )) - a( 2 )*y( i );
z( 2 ) = fi_signed(b( 3 )*x( i )) - a( 3 )*y( i );
end
end
function y = fi_signed(a)
coder.inline( 'always' );
if isfi( a ) && ~(issigned( a ))
nt = numerictype( a );
new_nt = numerictype( 1, nt.WordLength + 1, nt.FractionLength );
y = fi( a, new_nt, fimath( a ) );
else
y = a;
end
end
View Generated Fixed-Point C Code
To view the code generation report for the C code generation, click the View Report link
that follows the type proposal report.
The code generation report opens and displays the generated code for
fun_with_matlab_fixpt.c.
See Also
coder.FixptConfig | codegen
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
Related Examples
•
“Convert MATLAB Code to Fixed-Point C Code”
•
“Propose Fixed-Point Data Types Based on Derived Ranges”
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Propose Fixed-Point Data Types Based on Derived Ranges
Propose Fixed-Point Data Types Based on Derived Ranges
This example shows how to propose fixed-point data types based on static ranges that
you specify. The advantage of proposing data types based on derived ranges is that you
do not have to provide test files that exercise your algorithm over its full operating range.
Running such test files often takes a very long time so you can save time by deriving
ranges instead.
Prerequisites
To complete this example, you must install the following products:
• MATLAB
• MATLAB Coder
• Fixed-Point Designer
• C compiler (for most platforms, a default C compiler is supplied with MATLAB)
For a list of supported compilers, see http://www.mathworks.com/support/
compilers/current_release/
You can use mex -setup to change the default compiler. See “Changing Default
Compiler”.
Create a New Folder and Copy Relevant Files
1
Create a local working folder, for example, c:\dti.
2
Change to the docroot\toolbox\fixpoint\examples folder. At the MATLAB
command line, enter:
cd(fullfile(docroot, 'toolbox', 'fixpoint', 'examples'))
3
Copy the dti.m and dti_test.m files to your local working folder.
Type
Name
Description
Function code
dti.m
Entry-point MATLAB function
Test file
dti_test.m
MATLAB script that tests
dti.m
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
The dti Function
The dti function implements a Discrete Time Integrator in MATLAB.
function [y, clip_status] = dti(u_in) %#codegen
% Discrete Time Integrator in MATLAB
%
% Forward Euler method, also known as Forward Rectangular, or left-hand
% approximation. The resulting expression for the output of the block at
% step 'n' is y(n) = y(n-1) + K * u(n-1)
%
init_val = 1;
gain_val = 1;
limit_upper = 500;
limit_lower = -500;
% variable to hold state between consecutive calls to this block
persistent u_state
if isempty(u_state)
u_state = init_val+1;
end
% Compute Output
if (u_state > limit_upper)
y = limit_upper;
clip_status = -2;
elseif (u_state >= limit_upper)
y = limit_upper;
clip_status = -1;
elseif (u_state < limit_lower)
y = limit_lower;
clip_status = 2;
elseif (u_state <= limit_lower)
y = limit_lower;
clip_status = 1;
else
y = u_state;
clip_status = 0;
end
% Update State
tprod = gain_val * u_in;
u_state = y + tprod;
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The dti_test Function
function b = subFunction(a)
b = a*a;
The dti_test Function
The test script runs the dti function with a sine wave input. The script then plots the
input and output signals.
% dti_test
% cleanup
clear dti
% input signal
x_in = sin(2.*pi.*(0:0.001:2)).';
pause(10)
len = length(x_in);
y_out = zeros(1,len);
is_clipped_out = zeros(1,len);
for ii=1:len
data = x_in(ii);
% call to the dti function
init_val = 0;
gain_val = 1;
upper_limit = 500;
lower_limit = -500;
% call to the design that does DTI
[y_out(ii), is_clipped_out(ii)] = dti(data);
end
figure('Name', [mfilename, '_plot'])
subplot(2,1,1)
plot(1:len,x_in)
xlabel('Time')
ylabel('Amplitude')
title('Input Signal (Sin)')
subplot(2,1,2)
plot(1:len,y_out)
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
xlabel('Time')
ylabel('Amplitude')
title('Output Signal (DTI)')
disp('Test complete.')
Set Up the Fixed-Point Configuration Object
Create a fixed-point configuration object and configure the test file name.
fixptcfg = coder.config('fixpt');
fixptcfg.TestBenchName = 'dti_test';
Specify Design Ranges
Specify design range information for the dti function input parameter u_in.
fixptcfg.addDesignRangeSpecification('dti', 'u_in', -1.0, 1.0)
Enable Plotting Using the Simulation Data Inspector
Select to run the test file to verify the generated fixed-point MATLAB code. Log inputs
and outputs for comparison plotting and select to use the Simulation Data Inspector to
plot the results.
fixptcfg.TestNumerics = true;
fixptcfg.LogIOForComparisonPlotting = true;
fixptcfg.PlotWithSimulationDataInspector = true;
Set Up the C Code Generation Configuration Object
Create a code configuration object to generate a C static library. Enable the code
generation report.
cfg = coder.config('lib');
cfg.GenerateReport = true;
Derive Ranges and Generate Fixed-Point Code
Use the codegen function to convert the floating-point MATLAB function, dti, to fixedpoint C code. Set the default word length for the fixed-point data types to 16.
fixptcfg.ComputeDerivedRanges = true;
fixptcfg.ComputeSimulationRanges = false;
fixptcfg.DefaultWordLength = 16;
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The dti_test Function
% Derive ranges and generate fixed-point code
codegen -float2fixed fixptcfg -config cfg dti
codegen analyzes the floating-point code. Because you did not specify the input types
for the dti function, the conversion process infers types by simulating the test file.
The conversion process then derives ranges for variables in the algorithm. It uses these
derived ranges to propose fixed-point types for these variables. When the conversion is
complete, it generates a type proposal report.
View Derived Range Information
Click the link to the type proposal report for the dti function, dti_report.html.
The report opens in a web browser.
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
View Generated Fixed-Point MATLAB Code
codegen generates a fixed-point version of the dti function, dti_fxpt.m, and a
wrapper function that calls dti_fxpt. These files are generated in the codegen\dti
\fixpt folder in your local working folder.
function [y,clip_status] = dti_fixpt(u_in)
fm = fimath( 'RoundingMethod', 'Floor', 'OverflowAction', 'Wrap', 'ProductMode',...
'FullPrecision', 'MaxProductWordLength', 128, 'SumMode', 'FullPrecision',.
'MaxSumWordLength', 128);
%#codegen
% Discrete Time Integrator in MATLAB
%
% Forward Euler method, also known as Forward Rectangular, or left-hand
% approximation. The resulting expression for the output of the block at
% step 'n' is y(n) = y(n-1) + K * u(n-1)
%
init_val = fi(1, 0, 1, 0, fm);
gain_val = fi(1, 0, 1, 0, fm);
limit_upper = fi(500, 0, 9, 0, fm);
limit_lower = fi(-500, 1, 10, 0, fm);
% variable to hold state between consecutive calls to this block
persistent u_state
if isempty( u_state )
u_state = fi(init_val + fi(1, 0, 1, 0, fm), 1, 16, 6, fm);
end
% Compute Output
if (u_state>limit_upper)
y = fi(limit_upper, 1, 16, 6, fm);
clip_status = fi(-2, 1, 16, 13, fm);
elseif (u_state>=limit_upper)
y = fi(limit_upper, 1, 16, 6, fm);
clip_status = fi(-1, 1, 16, 13, fm);
elseif (u_state
Compare Floating-Point and Fixed-Point Runs
Because you selected to log inputs and outputs for comparison plots and to use the
Simulation Data Inspector for these plots, the Simulation Data Inspector opens.
You can use the Simulation Data Inspector to view floating-point and fixed-point run
information and compare results. For example, to compare the floating-point and fixed-
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The dti_test Function
point values for the output y, on the Compare tab, select y, and then click Compare
Runs.
The Simulation Data Inspector displays a plot of the baseline floating-point run against
the fixed-point run and the difference between them.
View Generated Fixed-Point C Code
To view the code generation report for the C code generation, click the View Report link
that follows the type proposal report.
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
The code generation report opens and displays the generated code for dti_fixpt.c.
See Also
coder.FixptConfig | codegen
Related Examples
•
“Convert MATLAB Code to Fixed-Point C Code”
•
“Propose Fixed-Point Data Types Based on Simulation Ranges”
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Detect Overflows
Detect Overflows
This example shows how to detect overflows at the command line. At the numerical
testing stage in the conversion process, the tool simulates the fixed-point code using
scaled doubles. It then reports which expressions in the generated code produce values
that would overflow the fixed-point data type.
Prerequisites
To complete this example, you must install the following products:
• MATLAB
• MATLAB Coder
• Fixed-Point Designer
In a local, writable folder, create a function, overflow.
function y = overflow(b,x,reset)
if nargin<3, reset = true; end
persistent z p
if isempty(z) || reset
p = 0;
z = zeros(size(b));
end
[y,z,p] = fir_filter(b,x,z,p);
end
function [y,z,p] = fir_filter(b,x,z,p)
y = zeros(size(x));
nx = length(x);
nb = length(b);
for n = 1:nx
p=p+1; if p>nb, p=1; end
z(p) = x(n);
acc = 0;
k = p;
for j=1:nb
acc = acc + b(j)*z(k);
k=k-1; if k<1, k=nb; end
end
y(n) = acc;
end
end
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
Create a test file, overflow_test.m to exercise the overflow algorithm.
function overflow_test
% The filter coefficients were computed using the FIR1 function from
% Signal Processing Toolbox.
%
b = fir1(11,0.25);
b = [-0.004465461051254
-0.004324228005260
+0.012676739550326
+0.074351188907780
+0.172173206073645
+0.249588554524763
+0.249588554524763
+0.172173206073645
+0.074351188907780
+0.012676739550326
-0.004324228005260
-0.004465461051254]';
% Input signal
nx = 256;
t = linspace(0,10*pi,nx)';
% Impulse
x_impulse = zeros(nx,1); x_impulse(1) = 1;
% Max Gain
% The maximum gain of a filter will occur when the inputs line up with the
% signs of the filter's impulse response.
x_max_gain = sign(b)';
x_max_gain = repmat(x_max_gain,ceil(nx/length(b)),1);
x_max_gain = x_max_gain(1:nx);
% Sums of sines
f0=0.1; f1=2;
x_sines = sin(2*pi*t*f0) + 0.1*sin(2*pi*t*f1);
% Chirp
f_chirp = 1/16;
x_chirp = sin(pi*f_chirp*t.^2);
% Target frequency
% Linear chirp
x = [x_impulse, x_max_gain, x_sines, x_chirp];
titles = {'Impulse', 'Max gain', 'Sum of sines', 'Chirp'};
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Detect Overflows
y = zeros(size(x));
for i=1:size(x,2)
reset = true;
y(:,i) = overflow(b,x(:,i),reset);
end
test_plot(1,titles,t,x,y)
end
function test_plot(fig,titles,t,x,y1)
figure(fig)
clf
sub_plot = 1;
font_size = 10;
for i=1:size(x,2)
subplot(4,1,sub_plot)
sub_plot = sub_plot+1;
plot(t,x(:,i),'c',t,y1(:,i),'k')
axis('tight')
xlabel('t','FontSize',font_size);
title(titles{i},'FontSize',font_size);
ax = gca;
ax.FontSize = 10;
end
figure(gcf)
end
Create a coder.FixptConfig object, fixptcfg, with default settings.
fixptcfg = coder.config('fixpt');
Set the test bench name. In this example, the test bench function name is
overflow_test.
fixptcfg.TestBenchName = 'overflow_test';
Set the default word length to 16.
fixptcfg.DefaultWordLength = 16;
Enable overflow detection.
fixptcfg.TestNumerics = true;
fixptcfg.DetectFixptOverflows = true;
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
Set the fimath Product mode and Sum mode to KeepLSB. These settings models the
behavior of integer operations in the C language.
fixptcfg.fimath = ...
['fimath(''RoundingMethod'',''Floor'',''OverflowAction'',' ...
'''Wrap'',''ProductMode'',''KeepLSB'',''SumMode'',''KeepLSB'')'];
Create a code generation configuration object to generate a standalone C static library.
cfg = coder.config('lib');
Convert the floating-point MATLAB function, overflow, to fixed-point C code. You do
not need to specify input types for the codegen command because it infers the types
from the test file.
codegen -float2fixed fixptcfg -config cfg overflow
The numerics testing phase reports an overflow.
Overflow error in expression 'acc + b( j )*z( k )'. Percentage of Current Range = 104%.
Determine if the addition or the multiplication in this expression overflowed. Set the
fimath ProductMode to FullPrecision so that the multiplication will not overflow,
and then run the codegen command again.
fixptcfg.fimath = ['fimath(''RoundingMethod'',''Floor'',''OverflowAction'',' ...
'''Wrap'',''ProductMode'',''FullPrecision'',''SumMode'',''KeepLSB'')'];
codegen -float2fixed fixptcfg -config cfg overflow
The numerics testing phase still reports an overflow, indicating that it is the addition in
the expression that is overflowing.
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Replace the exp Function with a Lookup Table
Replace the exp Function with a Lookup Table
This example shows how to replace the exp function with a lookup table approximation
in the generated fixed-point code using the programmatic workflow.
Prerequisites
To complete this example, you must install the following products:
• MATLAB
• MATLAB Coder
• Fixed-Point Designer
• C compiler (for most platforms, a default C compiler is supplied with MATLAB).
For a list of supported compilers, see http://www.mathworks.com/support/
compilers/current_release/ .
You can use mex -setup to change the default compiler. See “Changing Default
Compiler”.
Create Algorithm and Test Files
1
Create a MATLAB function, my_fcn.m, that calls the exp function.
function y = my_fcn(x)
y = exp(x);
end
2
Create a test file, my_fcn_test.m, that uses my_fcn.m.
close all
x = linspace(-10,10,1e3);
for itr = 1e3:-1:1
y(itr) = my_fcn( x(itr) );
end
plot( x, y );
Configure Approximation
Create a function replacement configuration object to approximate the exp function,
using the default settings of linear interpolation and 1000 points in the lookup table.
q = coder.approximation('exp');
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
Set Up Configuration Object
Create a coder.FixptConfig object, fixptcfg. Specify the test file name and enable
numerics testing. Associate the function replacement configuration object with the fixedpoint configuration object.
fixptcfg = coder.config('fixpt');
fixptcfg.TestBenchName = 'my_fcn_test';
fixptcfg.TestNumerics = true;
fixptcfg.DefaultWordLength = 16;
fixptcfg.addApproximation(q);
Convert to Fixed Point
Generate fixed-point MATLAB code.
codegen -float2fixed fixptcfg my_fcn
View Generated Fixed-Point Code
To view the generated fixed-point code, click the link to my_fcn_fixpt.
The generated code contains a lookup table approximation, exp1, for the exp function.
The fixed-point conversion process infers the ranges for the function and then uses an
interpolated lookup table to replace the function. By default, the lookup table uses linear
interpolation, 1000 points, and the minimum and maximum values detected by running
the test file.
The generated fixed-point function, my_fcn_fixpt, calls this approximation instead of
calling exp.
function y = my_fcn_fixpt(x)
fm = fimath('RoundingMethod', 'Floor', 'OverflowAction', 'Wrap',...
'ProductMode', 'FullPrecision', 'MaxProductWordLength', 128, ...
'SumMode', 'FullPrecision', 'MaxSumWordLength', 128);
y = fi(exp1(x), 0, 16,1, fm);
end
You can now test the generated fixed-point code and compare the results against the
original MATLAB function. If the behavior of the generated fixed-point code does not
match the behavior of the original code closely enough, modify the interpolation method
or number of points used in the lookup table and then regenerate code.
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Replace a Custom Function with a Lookup Table
Replace a Custom Function with a Lookup Table
This example shows how to replace a custom function with a lookup table approximation
function using the programmatic workflow.
Prerequisites
To complete this example, you must install the following products:
• MATLAB
• MATLAB Coder
• Fixed-Point Designer
• C compiler (for most platforms, a default C compiler is supplied with MATLAB)
For a list of supported compilers, see http://www.mathworks.com/support/
compilers/current_release/
You can use mex -setup to change the default compiler. See “Changing Default
Compiler”.
Create a MATLAB function, custom_fcn.m. This is the function that you want to
replace.
function y = custom_fcn(x)
y = 1./(1+exp(-x));
end
Create a wrapper function that calls custom_fcn.m.
function y = call_custom_fcn(x)
y = custom_fcn(x);
end
Create a test file, custom_test.m, that uses call_custom_fcn.m.
close all
x = linspace(-10,10,1e3);
for itr = 1e3:-1:1
y(itr) = call_custom_fcn( x(itr) );
end
plot( x, y );
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
Create a function replacement configuration object to approximate custom_fcn. Specify
the function handle of the custom function and set the number of points to use in the
lookup table to 50.
q = coder.approximation('Function','custom_fcn',...
'CandidateFunction',@custom_fcn, 'NumberOfPoints',50);
Create a coder.FixptConfig object, fixptcfg. Specify the test file name and enable
numerics testing. Associate the function replacement configuration object with the fixedpoint configuration object.
fixptcfg = coder.config('fixpt');
fixptcfg.TestBenchName = 'custom_test';
fixptcfg.TestNumerics = true;
fixptcfg.addApproximation(q);
Generate fixed-point MATLAB code.
codegen -float2fixed fixptcfg call_custom_fcn
codegen generates fixed-point MATLAB code in call_custom_fcn_fixpt.m.
To view the generated fixed-point code, click the link to call_custom_fcn_fixpt.
The generated code contains a lookup table approximation, custom_fcn1, for the
custom_fcn function. The fixed-point conversion process infers the ranges for the
function and then uses an interpolated lookup table to replace the function. The
lookup table uses 50 points as specified. By default, it uses linear interpolation and the
minimum and maximum values detected by running the test file.
The generated fixed-point function, call_custom_fcn_fixpt, calls this approximation
instead of calling custom_fcn.
function y = call_custom_fcn_fixpt(x)
fm = fimath('RoundingMethod', 'Floor', 'OverflowAction', 'Wrap',...
'ProductMode', 'FullPrecision', 'MaxProductWordLength', 128, ...
'SumMode', 'FullPrecision', 'MaxSumWordLength', 128);
y = fi(custom_fcn1(x), 0, 14, 14, fm);
end
You can now test the generated fixed-point code and compare the results against the
original MATLAB function. If the behavior of the generated fixed-point code does not
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Replace a Custom Function with a Lookup Table
match the behavior of the original code closely enough, modify the interpolation method
or number of points used in the lookup table and then regenerate code.
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
Enable Plotting Using the Simulation Data Inspector
You can use the Simulation Data Inspector to inspect and compare floating-point and
fixed-point logged input and output data. At the MATLAB command line:
1
Create a fixed-point configuration object and configure the test file name.
fixptcfg = coder.config('fixpt');
fixptcfg.TestBenchName = 'dti_test';
2
Select to run the test file to verify the generated fixed-point MATLAB code. Log
inputs and outputs for comparison plotting and select to use the Simulation Data
Inspector to plot the results.
fixptcfg.TestNumerics = true;
fixptcfg.LogIOForComparisonPlotting = true;
fixptcfg.PlotWithSimulationDataInspector = true;
3
Generate fixed-point MATLAB code using codegen.
codegen -float2fixed fixptcfg -config cfg dti
For an example, see “Propose Fixed-Point Data Types Based on Derived Ranges”.
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Visualize Differences Between Floating-Point and Fixed-Point Results
Visualize Differences Between Floating-Point and Fixed-Point
Results
This example shows how to configure the codegen function to use a custom plot function
to compare the behavior of the generated fixed-point code against the behavior of the
original floating-point MATLAB code.
By default, when the LogIOForComparisonPlotting option is enabled, the conversion
process uses a time series based plotting function to show the floating-point and fixedpoint results and the difference between them. However, during fixed-point conversion
you might want to visualize the numerical differences in a view that is more suitable
for your application domain. This example shows how to customize plotting and produce
scatter plots at the test numerics step of the fixed-point conversion.
Prerequisites
To complete this example, you must install the following products:
• MATLAB
• MATLAB Coder
• Fixed-Point Designer
• C compiler (for most platforms, a default C compiler is supplied with MATLAB)
For a list of supported compilers, see http://www.mathworks.com/support/
compilers/current_release/
You can use mex -setup to change the default compiler. See “Changing Default
Compiler”.
Create a New Folder and Copy Relevant Files
1
Create a local working folder, for example, c:\custom_plot.
2
Change to the docroot\toolbox\fixpoint\examples folder. At the MATLAB
command line, enter:
cd(fullfile(docroot, 'toolbox', 'fixpoint', 'examples'))
3
Copy the myFilter.m, myFilterTest.m, plotDiff.m, and filterData.mat files
to your local working folder.
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
Type
Name
Description
Function code
myFilter.m
Entry-point MATLAB function
Test file
myFilterTest.m
MATLAB script that tests
myFilter.m
Plotting function plotDiff.m
Custom plot function
MAT-fiile
Data to filter.
filterData.mat
The myFilter Function
function [y, ho] = myFilter(in)
persistent b h;
if isempty(b)
b = complex(zeros(1,16));
h = complex(zeros(1,16));
h(8) = 1;
end
b = [in, b(1:end-1)];
y = b*h.';
errf = 1-sqrt(real(y)*real(y) + imag(y)*imag(y));
update = 0.001*conj(b)*y*errf;
h = h + update;
h(8) = 1;
ho = h;
end
The myFilterTest File
% load data
data = load('filterData.mat');
d = data.symbols;
for idx = 1:4000
y = myFilter(d(idx));
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The plotDiff Function
end
The plotDiff Function
% varInfo - structure with information about the variable. It has the following fields
%
i) name
%
ii) functionName
% floatVals - cell array of logged original values for the 'varInfo.name' variable
% fixedVals - cell array of logged values for the 'varInfo.name' variable after Fixed-P
function plotDiff(varInfo, floatVals, fixedVals)
varName = varInfo.name;
fcnName = varInfo.functionName;
% convert from cell to matrix
floatVals = cell2mat(floatVals);
fixedVals = cell2mat(fixedVals);
% escape the '_'s because plot titles treat these as subscripts
escapedVarName = regexprep(varName,'_','\\_');
escapedFcnName = regexprep(fcnName,'_','\\_');
% flatten the values
flatFloatVals = floatVals(1:end);
flatFixedVals = fixedVals(1:end);
% build Titles
floatTitle = [ escapedFcnName ' > ' 'float : ' escapedVarName ];
fixedTitle = [ escapedFcnName ' > ' 'fixed : ' escapedVarName ];
data = load('filterData.mat');
switch varName
case 'y'
x_vec = data.symbols;
figure('Name', 'Comparison plot', 'NumberTitle', 'off');
% plot floating point values
y_vec = flatFloatVals;
subplot(1, 2, 1);
plotScatter(x_vec, y_vec, 100, floatTitle);
% plot fixed point values
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
y_vec = flatFixedVals;
subplot(1, 2, 2);
plotScatter(x_vec, y_vec, 100, fixedTitle);
otherwise
% Plot only output 'y' for this example, skip the rest
end
end
function plotScatter(x_vec, y_vec, n, figTitle)
% plot the last n samples
x_plot = x_vec(end-n+1:end);
y_plot = y_vec(end-n+1:end);
hold on
scatter(real(x_plot),imag(x_plot), 'bo');
hold on
scatter(real(y_plot),imag(y_plot), 'rx');
title(figTitle);
end
Set Up Configuration Object
1
Create a coder.FixptConfig object.
fxptcfg = coder.config('fixpt');
2
Specify the test file name and custom plot function name. Enable logging and
numerics testing.
fxptcfg.TestBenchName = 'myFilterTest';
fxptcfg.PlotFunction = 'plotDiff';
fxptcfg.TestNumerics = true;
fxptcfg. LogIOForComparisonPlotting = true;
fxptcfg.DefaultWordLength = 16;
Convert to Fixed Point
Convert the floating-point MATLAB function, myFilter, to floating-point MATLAB
code. You do not need to specify input types for the codegen command because it infers
the types from the test file.
codegen -args {complex(0, 0)} -float2fixed fxptcfg myFilter
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The plotDiff Function
The conversion process generates fixed-point code using a default word length of 16 and
then runs a fixed-point simulation by running the myFilterTest.m function and calling
the fixed-point version of myFilter.m.
Because you selected to log inputs and outputs for comparison plots and to use the
custom plotting function, plotDiff.m, for these plots, the conversion process uses this
function to generate the comparison plot.
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15
Automated Fixed-Point Conversion Using Programmatic Workflow
The plot shows that the fixed-point results do not closely match the floating-point results.
Increase the word length to 24 and then convert to fixed point again.
fxptcfg.DefaultWordLength = 24;
codegen -args {complex(0, 0)} -float2fixed fxptcfg myFilter
The increased word length improved the results. This time, the plot shows that the fixedpoint results match the floating-point results.
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16
Setting Up a MATLAB Coder Project
• “MATLAB Coder Project Set Up Workflow” on page 16-2
• “Creating a New Project” on page 16-3
• “Opening an Existing Project” on page 16-5
• “Adding Files to the Project” on page 16-6
• “Specifying Properties of Primary Function Inputs in a Project” on page 16-7
• “Autodefine Input Types” on page 16-8
• “Define Input Parameters by Example in a Project” on page 16-12
• “Define or Edit Input Parameter Type in a Project” on page 16-19
• “Define Constant Input Parameters in a Project” on page 16-29
• “Define Inputs Programmatically in the MATLAB File” on page 16-30
• “Adding Global Variables in a Project” on page 16-31
• “Specifying Global Variable Type and Initial Value in a Project” on page 16-32
• “Specify Output File Name” on page 16-40
• “Specify Output File Locations” on page 16-41
• “Selecting Output Type” on page 16-42
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16
Setting Up a MATLAB Coder Project
MATLAB Coder Project Set Up Workflow
1
Create a new project or open an existing one.
2
Add the files from which you want to generate code.
3
Specify class, size, and complexity of all input parameters.
4
Optionally, add global variables.
5
Optionally, specify the output file name and output file locations.
6
Optionally, select the output type: MEX function (default), Instrumented MEX
function, C/C++ static library, C/C++ dynamic library or C/C++ executable.
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Creating a New Project
Creating a New Project
From the MATLAB APPS Tab
1
Select the MATLAB Apps tab.
2
In the Code Generation group, click MATLAB Coder.
3
In the MATLAB Coder Project dialog box, on the New tab, enter the name of your
project in the Name field.
4
Enter the location of the project in the Location field.
Alternatively, use the ... (browse) button to navigate to the location.
5
Click OK.
At the Command Line
1
At the MATLAB command line, enter:
coder
2
In the Name field, enter the project_name.
3
In the Location field, enter the location of the project.
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16
Setting Up a MATLAB Coder Project
Alternatively, use the ... (browse) button to navigate to the location.
Note: The path should not contain spaces, as this can lead to code generation failures
in certain operating system configurations. If the path contains non 7-bit ASCII
characters, such as Japanese characters, MATLAB Coder might not be able to find
files on this path.
4
Click OK.
MATLAB Coder creates a project named project_name.prj in the specified location
and marks it with the project icon:
.
From a MATLAB Coder Project
If you already have a MATLAB Coder project open, in the upper-right corner of the
project, click the Actions icon ( ) and select New Project.
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Opening an Existing Project
Opening an Existing Project
In this section...
“From the MATLAB APPS Tab” on page 16-5
“At the Command Line” on page 16-5
“From a MATLAB Coder Project” on page 16-5
From the MATLAB APPS Tab
1
Select the MATLAB Apps tab.
2
In the Code Generation group, click MATLAB Coder.
3
In the MATLAB Coder Project dialog box, click the Open tab.
4
From the drop-down list, select a previously opened project or use the Browse
button to find a project.
5
Click OK.
At the Command Line
1
At the MATLAB command line, enter coder.
2
In the Code Generation Project dialog box, click the Open tab.
3
From the drop-down list, select a previously opened project or click the Browse
button to find a project.
4
Click OK.
From a MATLAB Coder Project
If you already have a MATLAB Coder project open, in the upper-right corner of the
project, click the Actions icon ( ) and select Open Project.
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16
Setting Up a MATLAB Coder Project
Adding Files to the Project
First, you must add the MATLAB files from which you want to generate code to the
project.
• Add only the main (entry-point) files that you call from MATLAB.
• Do not add files that are called by these files.
• Do not add files that have spaces in their names. The path should not contain spaces,
as this can lead to code generation failures in certain operating system configurations.
• If the path contains non 7-bit ASCII characters, such as Japanese characters,
MATLAB Coder might not be able to find files on this path.
To add a file, do one of the following:
• In the project, in the Entry-Point Files pane on the Overview tab, click the Add
files link and browse to the file.
• Drag a file from the current folder and drop it in the Entry-Point Files pane on the
Overview tab.
If you add more than one entry-point file, MATLAB Coder lists them alphabetically.
If the functions that you added have inputs, you must define these inputs. See
“Specifying Properties of Primary Function Inputs in a Project” on page 16-7.
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Specifying Properties of Primary Function Inputs in a Project
Specifying Properties of Primary Function Inputs in a Project
Why You Must Specify Input Properties
Because C and C++ are statically typed languages, MATLAB Coder must determine the
properties of all variables in the MATLAB files at code generation time. To infer variable
properties in MATLAB files, MATLAB Coder must be able to identify the properties of
the inputs to the entry-point function. Therefore, if your entry-point function has inputs,
you must specify the properties of these inputs. If your primary function has no input
parameters, MATLAB Coder can compile your MATLAB file without modification. You
do not need to specify properties of inputs to local functions or external functions called
by the entry-point function.
You must specify the same number and order of inputs as the MATLAB function unless
you use the tilde (~) character to specify unused function inputs. If you use the tilde
character, the inputs default to real, scalar doubles.
See Also
• “Properties to Specify”
How to Specify an Input Definition in a Project
Specify an input definition in your MATLAB Coder project using one of the following
methods:
• Autodefine Input Types
• Define Type
• Define by Example
• Define Constant
• Define Programmatically in the MATLAB File
Alternatively, specify input definitions at the command line and then use the codegen
function to generate code. For more information, see “Primary Function Input
Specification” on page 19-39.
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16
Setting Up a MATLAB Coder Project
Autodefine Input Types
In this section...
“How MATLAB Coder Autodefines Input Types” on page 16-8
“Prerequisites for Autodefining Input Types” on page 16-8
“How to Autodefine Input Types” on page 16-8
How MATLAB Coder Autodefines Input Types
If you specify a test file that calls the project entry-point functions, the MATLAB Coder
software can infer the input parameter types by running the test file. If a test file calls
an entry-point function multiple times with different sized inputs, the MATLAB Coder
software takes the union of the inputs and infers that the inputs are variable size, with
an upper bound equal to the size of the largest input.
Prerequisites for Autodefining Input Types
Before using MATLAB Coder to autodefine function input parameter types, you must
add at least one entry-point file to your project. You must also specify a test file that calls
your entry-point functions with the expected input types. The test file can be either a
MATLAB function or a script. It should call the entry-point function at least once.
How to Autodefine Input Types
1
On the MATLAB Coder project Overview tab, click the Autodefine types link.
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Autodefine Input Types
2
In the Autodefine Input Types dialog box, click the
the project.
3
Browse to the folder that contains the test file and select the file.
button to add a test file to
Alternatively, if you have already added test files to the project, select one from the
list.
4
Click the Run button.
The software runs the test file and, if the file calls entry-point functions, infers input
types for these functions.
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16
Setting Up a MATLAB Coder Project
The dialog box displays a summary of the inferred types and provides the following
options:
• Make dimensions variable-sized if they are at least
If you want inputs above a specified size to be variable size with an upper bound,
select this option and specify the threshold. If the size, S, of any dimension of
an input is equal to or greater than this threshold, the software makes this
dimension variable size with an upper bound of S.
• Make dimensions unbounded if they are at least
If you want inputs above a specified size to be variable size with no upper bounds
(unbounded), select this option and specify the threshold. If the size of any
dimension of an input is equal to or greater than this threshold, the software
makes this dimension unbounded.
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Autodefine Input Types
5
Review the inferred types. If the types are acceptable, click Use These Types.
Otherwise, modify your test file, use a different test file to autodefine the types or
define them using an alternate method. For more information, see “How to Specify
an Input Definition in a Project” on page 16-7.
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16
Setting Up a MATLAB Coder Project
Define Input Parameters by Example in a Project
In this section...
“How to Define an Input Parameter by Example” on page 16-12
“Specifying Input Parameters by Example” on page 16-13
“Specifying an Enumerated Type Input Parameter by Example” on page 16-15
“Specifying a Fixed-Point Input Parameter by Example” on page 16-16
How to Define an Input Parameter by Example
1
On the MATLAB Coder project Overview tab, click the input parameter that you
want to define.
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Define Input Parameters by Example in a Project
2
From the list of input options, select Define by Example.
3
In the field to the right of the parameter, enter a MATLAB expression. MATLAB
Coder software uses the class, size, and complexity of the value of the specified
variable or MATLAB expression when compiling the code.
Specifying Input Parameters by Example
This example shows how too specify a 1-by-4 vector of unsigned 16-bit integers.
1
On the MATLAB Coder project Overview tab, click the input parameter that you
want to define.
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16
Setting Up a MATLAB Coder Project
2
From the list of input options, select Define by Example.
3
In the field to the right of the parameter, enter:
zeros(1,4,'uint16')
The input type is uint16(1x4).
4
Optionally, after specifying the input type, you can specify that the input is variable
size.
Select the second dimension.
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Define Input Parameters by Example in a Project
5
From the list of size options, select :4 to specify that the second dimension is
variable size with an upper bound of 4. Alternatively, select :Inf to specify that the
second dimension is unbounded.
Alternatively, you can specify that the input is variable size by using the
coder.newtype function. Enter the following MATLAB expression:
coder.newtype('uint16',[1 4],[0 1])
Note: To specify that an input is a double-precision scalar, simply enter 0.
Specifying an Enumerated Type Input Parameter by Example
This example shows how to specify that an input uses the enumerated type MyColors.
1
Define an enumeration MyColors. On the MATLAB path, create a file named
'MyColors' containing:
classdef(Enumeration) MyColors < int32
enumeration
green(1),
red(2),
end
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16
Setting Up a MATLAB Coder Project
end
2
On the MATLAB Coder project Overview tab, click to the input parameter that you
want to define.
3
From the list of input options, select Define by Example.
4
In the field to the right of the parameter, enter the following MATLAB expression:
MyColors.red
Specifying a Fixed-Point Input Parameter by Example
To specify fixed-point inputs, you must install Fixed-Point Designer software.
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Define Input Parameters by Example in a Project
This example shows how to specify a signed fixed-point type with a word length of 8 bits,
and a fraction length of 3 bits.
1
On the MATLAB Coder project Overview tab, click the input parameter that you
want to define.
2
From the list of input options, select Define by Example.
3
In the field to the right of the parameter, enter:
fi(10, 1, 8, 3)
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16
Setting Up a MATLAB Coder Project
MATLAB Coder sets the type of input u to embedded.fi(1x1). By default, if you
have not specified a local fimath, MATLAB Coder uses the default fimath. For
more information, see “fimath for Sharing Arithmetic Rules”.
Optionally, modify the fixed-point properties of the input, see “Specifying a FixedPoint Input Parameter by Type” on page 16-22 or the size of the input, see
“Define or Edit Input Parameter Type in a Project” on page 16-19.
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Define or Edit Input Parameter Type in a Project
Define or Edit Input Parameter Type in a Project
In this section...
“How to Define or Edit an Input Parameter Type” on page 16-19
“Specifying an Enumerated Type Input Parameter by Type” on page 16-21
“Specifying a Fixed-Point Input Parameter by Type” on page 16-22
“Specifying Structures” on page 16-23
How to Define or Edit an Input Parameter Type
The following procedure is for input types double, single, int64, int32, int16, int8,
uint64, uint32, uint16, uint8, logical, and char.
For more information about defining other types, see the following table.
Input Type
Link
A structure (struct)
“Specifying Structures” on page 16-23
A fixed-point data type (embedded.fi)
“Specifying a Fixed-Point Input Parameter
by Type” on page 16-22
An input by example (Define by
Example)
“Define Input Parameters by Example in a
Project” on page 16-12
A constant (Define Constant)
“Define Constant Input Parameters in a
Project” on page 16-29
1
On the Overview tab Entry-Point Files pane, click the field to the right of the
input parameter name to view the input options.
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16
Setting Up a MATLAB Coder Project
2
Optionally, for numeric types, select Complex number to make the parameter a
complex type. By default, inputs are real.
3
Select the input type.
The selected type is displayed for the input parameter together with size options.
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Define or Edit Input Parameter Type in a Project
4
From the list, select whether your input is a scalar, a 1 x n vector, a m x 1 vector
or a m x n matrix. By default, if you do not select a size option, MATLAB Coder
defines inputs as scalars.
5
Optionally, if your input is not scalar, enter sizes m and n. You can specify:
• Fixed size, for example, 10.
• Variable size, up to a specified limit, by using the : prefix. For example, to specify
that your input can vary in size up to 10, enter :10.
• Unbounded variable size by entering :Inf.
You can edit the size of each dimension after specifying it.
Specifying an Enumerated Type Input Parameter by Type
To specify that an input uses the enumerated type MyColors:
1
Define an enumeration MyColors. On the MATLAB path, create a file named
'MyColors' containing:
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16
Setting Up a MATLAB Coder Project
classdef(Enumeration) MyColors < int32
enumeration
green(1),
red(2),
end
end
2
In the field to the right of the input parameter, enter MyColors.
Specifying a Fixed-Point Input Parameter by Type
To specify fixed-point inputs, you must install Fixed-Point Designer software.
1
On the Overview tab Entry-Point Files pane, click the box to the right of the
input parameter name to view the input options.
2
Select embedded.fi.
The Properties dialog box opens.
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Define or Edit Input Parameter Type in a Project
3
In this dialog box, set up the input parameter numerictype and fimath properties
and then close the dialog box.
If you do not specify a local fimath, MATLAB Coder uses the default fimath. For
more information, see “Default fimath Usage to Share Arithmetic Rules”.
4
The size of the input defaults to 1x1. Optionally, modify the size by selecting the
dimension that you want to change and entering a new size.
Specifying Structures
When a primary input is a structure, MATLAB Coder treats each field as a separate
input. Therefore, you must specify properties for all fields of a primary structure input in
the order that they appear in the structure definition, as follows:
• For each field of input structures, specify class, size, and complexity.
• For each field that is fixed-point class, also specify numerictype, and fimath.
Specifying Structures by Type
1
On the Overview tab Entry-Point Files pane, click the field to the right of the
input parameter name to view the input options.
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16
Setting Up a MATLAB Coder Project
2
From the list of input options, select struct.
The selected type, struct, is displayed for the input parameter together with size
options.
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Define or Edit Input Parameter Type in a Project
3
From the list, select whether your structure is a scalar, 1 x n vector, m x 1 vector
or m x n matrix. By default, if you do not select a size option, MATLAB Coder
defines inputs as scalars.
4
Optionally, if your input is not scalar, enter sizes m and n. You can specify:
• Fixed size, for example, 10.
• Variable size, up to a specified limit, by using the : prefix. For example, to specify
that your input can vary in size up to 10, enter :10.
• Unbounded variable size by entering :Inf.
5
Optionally, add fields to the structure as described in “How to Add a Field to a
Structure” on page 16-28 and then set their size and complexity.
6
Optionally, specify properties for the structure in the generated code as described in
“How to Set Structure Properties” on page 16-25.
How to Set Structure Properties
1
To the right of the structure definition, click the Actions icon, ( ).
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16
Setting Up a MATLAB Coder Project
The structure properties dialog box opens.
2
Specify properties for the structure in the generated code.
Property
Description
C type definition name
Name to use for the structure variable in
the generated code.
Type definition is externally defined
Default: No — type definition is not
externally defined
If you select ‘Yes’ to declare an externally
defined structure, MATLAB Coder
does not generate the definition of the
structure type; you must provide it in a
custom include file.
Dependency: This option is enabled by C
type definition name.
C type definition header file
Name of the header file that contains the
external definition of the structure, for
example, "mystruct.h". Specify the
path to the file using the Additional
include directories parameter on the
Project Settings dialog box Custom
Code tab.
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Define or Edit Input Parameter Type in a Project
Property
Description
By default, the generated code contains
#include statements for custom header
files after the standard header files. If a
standard header file refers to the custom
structure type, then the compilation
fails. By specifying the C type definition
header file option, MATLAB Coder
includes that header file exactly at the
point where it is required.
Must be a non-empty string.
Dependency: This option is enabled when
Type definition is externally
defined is set to Yes.
Data alignment boundary
The run-time memory alignment of
structures of this type in bytes. If
you have an Embedded Coder license
and use Code Replacement Libraries
(CRLs), the CRLs provide the ability
to align data objects passed into a
replacement function to a specified
boundary. This capability allows you to
take advantage of target-specific function
implementations that require data to
be aligned. By default, the structure is
not aligned on any specific boundary so
it will not be matched by CRL functions
that require alignment.
Alignment must be either -1 or a power
of 2 that is no more than 128.
Default: 0
Dependency: This option is enabled when
Type definition is externally
defined is set to Yes.
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16
Setting Up a MATLAB Coder Project
How to Rename a Field in a Structure
On the project Overview tab, select the name field of the structure that you want to
rename and enter the new name.
How to Add a Field to a Structure
1
On the project Overview tab, select the structure to which you want to add a field.
2
To the right of the structure, click the Actions icon ( ) to open the context menu.
3
From the menu, select Add Field.
If the structure already contains other fields, MATLAB Coder adds the field after the
existing fields.
4
Enter the field name and define its type.
How to Insert a Field into a Structure
1
On the project Overview tab, select the field under which you want to add another
field.
2
To the right of the structure, click the Actions icon ( ) to open the context menu.
3
From the menu, select Insert Field.
MATLAB Coder adds the field after the selected field.
4
Enter the field name and define its type.
How to Remove a Field from a Structure
1
In the project Overview tab, select the field that you want to remove.
2
To the right of the structure, click the Actions icon ( ) to open the context menu.
3
From the menu, select Remove Field.
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Define Constant Input Parameters in a Project
Define Constant Input Parameters in a Project
1
On the Overview tab Entry-Point Files pane, click the field to the right of the
input parameter name to view the input options.
2
Select Define Constant.
3
In the field to the right of the parameter name, enter the value of the constant or a
MATLAB expression that represents the constant.
MATLAB Coder software uses the value of the specified MATLAB expression as a
compile-time constant.
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16
Setting Up a MATLAB Coder Project
Define Inputs Programmatically in the MATLAB File
You can use the MATLAB assert function to define properties of primary function
inputs directly in your MATLAB entry-point files.
To enable this option, on the Project Settings dialog box All Settings tab, under
Advanced, select Determine input types from source code preconditions. If you
enable this option:
• MATLAB Coder labels all entry-point function inputs as Deferred and determines
the input types at compile time.
• You cannot specify input types in this project using any other input specification
method.
For more information, see “Define Input Properties Programmatically in the MATLAB
File”.
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Adding Global Variables in a Project
Adding Global Variables in a Project
To add global variables to the project:
1
On the project Overview tab, click Add global.
By default, MATLAB Coder names the first global variable in a project g, and
subsequent global variables g1, g2, etc.
2
Enter the name of the global variable.
3
After adding a global variable, before building the project, specify its type and initial
value. If you do not do this, you must create a variable with the same name in
the global workspace. See “Specifying Global Variable Type and Initial Value in a
Project” on page 16-32.
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16
Setting Up a MATLAB Coder Project
Specifying Global Variable Type and Initial Value in a Project
In this section...
“Why Specify a Type Definition for Global Variables?” on page 16-32
“How to Specify a Global Variable Type” on page 16-32
“Defining a Global Variable by Example” on page 16-33
“Defining or Editing Global Variable Type” on page 16-34
“Defining Global Variable Initial Value” on page 16-36
“Defining Global Variable Constant Value” on page 16-38
“Removing Global Variables” on page 16-39
Why Specify a Type Definition for Global Variables?
If you use global variables in your MATLAB algorithm, before building the project, you
must add a global type definition and initial value for each. If you do not initialize the
global data, MATLAB Coder looks for the variable in the MATLAB global workspace. If
the variable does not exist, MATLAB Coder generates an error.
C and C++ use static typing. To determine the types of your variables before use,
MATLAB Coder requires a complete assignment to each variable. At code generation
time, MATLAB Coder needs to have an initial value to determine the type of a global
variable. Otherwise, the global variable might be used before it is defined and then
MATLAB Coder cannot determine the type to use in the generated code.
For MEX functions, if you use global data, you must also specify whether to synchronize
this data between MATLAB and the MEX function. For more information, see
“Synchronizing Global Data with MATLAB”.
How to Specify a Global Variable Type
1
Specify the type of each global variable using one of the following methods:
• Define by example
• Define type
2
Define an initial value for each global variable.
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Specifying Global Variable Type and Initial Value in a Project
If you do not provide a type definition and initial value for a global variable, you must
create a variable with the same name and suitable class, size, complexity, and value in
the MATLAB workspace.
Defining a Global Variable by Example
1
On the project Overview tab, click the field to the right of the global variable that
you want to define.
2
Select Define by Example.
3
In the field to the right of the global name, enter a MATLAB expression that has the
required class, size, and complexity. MATLAB Coder software uses the class, size,
and complexity of the value of this expression as the type for the global variable.
4
Optionally, change the size of the global variable. Click the dimension that you want
to change and enter the size, for example, 10.
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Setting Up a MATLAB Coder Project
You can specify:
• Fixed size. In this example, select 10.
• Variable size, up to a specified limit, by using the : prefix. In this example, to
specify that your input can vary in size up to 10, select :10.
• Unbounded variable size by selecting :Inf.
Note: You define global variables in the same way that you define input parameters. For
more information, see “Define Input Parameters by Example in a Project” on page 16-12
Defining or Editing Global Variable Type
1
On the project Overview tab, click the field to the right of the global variable that
you want to define.
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Specifying Global Variable Type and Initial Value in a Project
2
Optionally, for numeric types, select Complex to make the parameter a complex
type. By default, inputs are real.
3
Select the type for the global variable. For example, double.
By default, the global variable is a scalar.
4
Optionally, change the size of the global variable. Click the dimension that you want
to change and enter the size, for example, 10.
You can specify:
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16
Setting Up a MATLAB Coder Project
• Fixed size. In this example, select 10.
• Variable size, up to a specified limit, by using the : prefix. In this example, to
specify that your input can vary in size up to 10, select :10.
• Unbounded variable size by selecting :Inf.
Defining Global Variable Initial Value
• “Define Initial Value Before Defining Type” on page 16-36
• “Define Initial Value After Defining Type” on page 16-37
Define Initial Value Before Defining Type
1
On the project Overview tab, click the field to the right of the global variable.
2
Select Define Initial Value.
3
Enter a MATLAB expression. MATLAB Coder software uses the value of the
specified MATLAB expression as the value of the global variable. Because you
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Specifying Global Variable Type and Initial Value in a Project
did not define the type of the global variable before you defined its initial value,
MATLAB Coder uses the type of the initial value as the global variable type.
The project displays that the global variable is initialized.
If you change the type of a global variable after defining its initial value, you must
redefine the initial value.
Define Initial Value After Defining Type
1
On the project Overview tab, click the type field of the global variable.
2
Select Define Initial Value.
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16
Setting Up a MATLAB Coder Project
3
Enter a MATLAB expression. MATLAB Coder software uses the value of the
specified MATLAB expression as the value of the global variable.
The project displays that the global variable is initialized.
Defining Global Variable Constant Value
1
On the project Overview tab, click the field to the right of the global variable.
2
3
Select Define Constant Value.
In the field to the right of the global variable, enter a MATLAB expression.
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Specifying Global Variable Type and Initial Value in a Project
Removing Global Variables
1
On the project Overview tab, select the global variable that you want to remove.
2
To the right of the variable, click the Actions icon (
3
From this menu, select Remove Global.
) to open the context menu.
MATLAB Coder removes the global variable.
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16
Setting Up a MATLAB Coder Project
Specify Output File Name
On the project Build tab, in the Output file field, enter the file name. The file name can
include an existing path.
Note: Do not put any spaces in the file name.
By default, if the name of the first entry-point MATLAB file is fcn1, the output file name
is:
• fcn1 for C/C++ libraries and executables.
• fcn1_mex for MEX functions.
By default, MATLAB Coder generates files in the folder project_folder/codegen/
target/fcn1:
• project_folder is your current project folder
• target is:
• mex for MEX functions
• lib for static C/C++ libraries
• dll for dynamic C/C++ libraries
• exe for C/C++ executables
To learn how to change the default output folder, see “Specify Output File Locations” on
page 16-41.
Command Line Alternative
Use the codegen function -o option.
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Specify Output File Locations
Specify Output File Locations
The path should not contain:
• Spaces, as this can lead to code generation failures in certain operating system
configurations.
• Non 7-bit ASCII characters, such as Japanese characters.
1
On the project Build tab, click More settings.
2
In the Project Settings dialog box, click the Paths tab.
The default setting for the Build Folder field is A subfolder of the project
folder. By default, MATLAB Coder generates files in the folder project_folder/
codegen/target/fcn1:
• fcn1 is the name of the first entry-point file
• target is:
• mex for MEX functions
• dll for dynamic C/C++ libraries
• lib for static C/C++ libraries
• exe for C/C++ executables
3
To change the output location, you can either:
• Set Build Folder to A subfolder of the current MATLAB working
folder
MATLAB Coder generates files in the MATLAB_working_folder/codegen/
target/fcn1 folder
• Set Build Folder to Specified folder. In the Build folder name field,
provide the path to the folder.
Command Line Alternative
Use the codegen function -d option.
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16
Setting Up a MATLAB Coder Project
Selecting Output Type
On the project Build tab, from the Output type drop-down list, select one of the
available output types:
• MEX Function (default)
• Instrumented MEX Function
Building an instrumented MEX function requires a Fixed-Point Designer license and
clears prior instrumentation results.
• C/C++ Static Library
• C/C++ Dynamic Library
• C/C++ Executable
Command Line Alternative
Use the codegen function -config option.
Changing Output Type
MEX functions use a different set of configuration parameters than C/C++ libraries
and executables use. When you switch the output type between MEX Function or
Instrumented MEX Function and C/C++ Static Library, C/C++ Dynamic
Library or C/C++ Executable, you should verify these settings.
If you enable any of the following parameters when the output type is MEX Function
or Instrumented MEX Function, and you want to use the same setting for C/C++
code generation as well, you must enable it again for C/C++ Static Library, C/C++
Dynamic Library, and C/C++ Executable.
Check These MATLAB Coder Project Parameters When Changing Output Type
Project Settings Dialog Box Tab
Parameter Name
Paths
Working folder
Build folder
Search paths
Speed
Saturate on integer overflow
Memory
Enable variable-sizing
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Selecting Output Type
Project Settings Dialog Box Tab
Parameter Name
Dynamic memory allocation
Stack usage max
Code Appearance
Generated file partitioning method
Include comments
MATLAB source code as comments
Reserved names
Debugging
Always create a code generation report
Automatically launch a report if one is generated
Custom Code
Source file
Header file
Initialize function
Terminate function
Additional include directories
Additional source files
Additional libraries
Post-code-generation command
Advanced
Constant folding timeout
Language
Inline threshold
Inline threshold max
Inline stack limit
Use memcpy for vector assignment
Memcpy threshold (bytes)
Use memset to initialize floats and doubles to 0.0
Check These Command-Line Parameters When Changing Output Type
When you switch between MEX and C output types, check these
coder.MexCodeConfig, coder.CodeConfig or coder.EmbeddedCodeConfig
configuration object parameters, as applicable.
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16
Setting Up a MATLAB Coder Project
• ConstantFoldingTimeout
• CustomHeaderCode
• CustomInclude
• CustomInitializer
• CustomLibrary
• CustomSource
• CustomSourceCode
• CustomTerminator
• DynamicMemoryAllocation
• EnableMemcpy
• EnableVariableSizing
• FilePartitionMethod
• GenCodeOnly
• GenerateComments
• GenerateReport
• InitFltsAndDblsToZero
• InlineStackLimit
• InlineThreshold
• InlineThresholdMax
• LaunchReport
• MATLABSourceComments
• MemcpyThreshold
• PostCodeGenCommand
• ReservedNameArray
• SaturateOnIntegerOverflow
• StackUsageMax
• TargetLang
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17
Preparing MATLAB Code for C/C++
Code Generation
• “Workflow for Preparing MATLAB Code for Code Generation” on page 17-2
• “Fixing Errors Detected at Design Time” on page 17-4
• “Using the Code Analyzer” on page 17-5
• “Check Code With the Code Analyzer” on page 17-6
• “Check Code Using the Code Generation Readiness Tool” on page 17-8
• “Code Generation Readiness Tool” on page 17-10
• “Unable to Determine Code Generation Readiness” on page 17-17
• “Generate MEX Functions Using the MATLAB Coder Project Interface” on page
17-18
• “Generate MEX Functions at the Command Line” on page 17-26
• “Fix Errors Detected at Code Generation Time” on page 17-28
• “Design Considerations When Writing MATLAB Code for Code Generation” on page
17-29
• “Running MEX Functions” on page 17-31
• “Debugging Strategies” on page 17-32
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17
Preparing MATLAB Code for C/C++ Code Generation
Workflow for Preparing MATLAB Code for Code Generation
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Workflow for Preparing MATLAB Code for Code Generation
See Also
• “MATLAB Coder Project Set Up Workflow”
• “Fixing Errors Detected at Design Time” on page 17-4
• “Generate MEX Functions Using the MATLAB Coder Project Interface”
• “Fix Errors Detected at Code Generation Time” on page 17-28
• “Workflow for Testing MEX Functions in MATLAB”
• “C/C++ Code Generation”
• “Accelerate MATLAB Algorithms”
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17
Preparing MATLAB Code for C/C++ Code Generation
Fixing Errors Detected at Design Time
Use the code analyzer and the code generation readiness tool to detect issues at design
time. Before generating code, you must fix these issues.
See Also
• “Check Code With the Code Analyzer” on page 17-6
• “Check Code Using the Code Generation Readiness Tool” on page 17-8
• “Design Considerations When Writing MATLAB Code for Code Generation” on page
17-29
• “Debugging Strategies” on page 17-32
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Using the Code Analyzer
Using the Code Analyzer
You use the code analyzer in the MATLAB Editor to check for code violations at design
time, minimizing compilation errors. The code analyzer continuously checks your code as
you enter it. It reports problems and recommends modifications.
To use the code analyzer to identify warnings and errors specific to MATLAB for code
generation, you must add the %#codegen directive (or pragma) to your MATLAB file.
A complete list of code generation analyzer messages is available in the MATLAB Code
Analyzer preferences. For more information, see “Running the Code Analyzer Report”.
Note: The code analyzer might not detect all MATLAB for code generation issues. After
eliminating the errors or warnings that the code analyzer detects, compile your code with
MATLAB Coder to determine if the code has other compliance issues.
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17
Preparing MATLAB Code for C/C++ Code Generation
Check Code With the Code Analyzer
The code analyzer checks your code for problems and recommends modifications. You can
use the code analyzer to check your code interactively in the MATLAB Editor while you
work.
To verify that continuous code checking is enabled:
1
In MATLAB, select the Home tab and then click Preferences.
2
In the Preferences dialog box, select Code Analyzer.
3
In the Code Analyzer Preferences pane, verify that Enable integrated warning
and error messages is selected.
The code analyzer provides an indicator in the top right of the editor window. If the
indicator is green, the analyzer did not detect code generation issues.
If the indicator is red, the analyzer has detected errors in your code. If it is orange, it has
detected warning. When the indicator is red or orange, a red or orange marker appears
to the right of the code where the error occurs. Place your pointer over the marker for
information about the error. Click the underlined text in the error message for a more
detailed explanation and suggested actions to fix the error.
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Check Code With the Code Analyzer
Before generating code from your MATLAB code, you must fix the errors detected by the
code analyzer.
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17
Preparing MATLAB Code for C/C++ Code Generation
Check Code Using the Code Generation Readiness Tool
In this section...
“Run Code Generation Readiness Tool at the Command Line” on page 17-8
“Run Code Generation Readiness Tool from the Current Folder Browser” on page
17-8
“Run the Code Generation Readiness Tool in a Project” on page 17-8
“See Also” on page 17-9
Run Code Generation Readiness Tool at the Command Line
1
Navigate to the folder that contains the file that you want to check for code
generation readiness.
2
At the MATLAB command prompt, enter:
coder.screener('filename')
The Code Generation Readiness tool opens for the file named filename, provides
a code generation readiness score, and lists issues that must be fixed prior to code
generation.
Run Code Generation Readiness Tool from the Current Folder Browser
1
In the current folder browser, right-click the file that you want to check for code
generation readiness.
2
From the context menu, select Check Code Generation Readiness.
The Code Generation Readiness tool opens for the selected file. It provides a
code generation readiness score and lists issues that must be fixed prior to code
generation.
Run the Code Generation Readiness Tool in a Project
1
After you have added entry-point files to the project, if MATLAB Coder detects code
generation issues, it displays a link at the top of the project window.
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Check Code Using the Code Generation Readiness Tool
2
Click the link to open the Code Generation Readiness tool.
The tool opens and provides a code generation readiness score and lists issues that
must be fixed prior to code generation.
See Also
• “Code Generation Readiness Tool” on page 17-10
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17
Preparing MATLAB Code for C/C++ Code Generation
Code Generation Readiness Tool
In this section...
“What Information Does the Code Generation Readiness Tool Provide?” on page
17-10
“Summary Tab” on page 17-11
“Code Structure Tab” on page 17-13
“See Also” on page 17-16
What Information Does the Code Generation Readiness Tool Provide?
The code generation readiness tool screens MATLAB code for features and functions
that are not supported for code generation. The tool provides a report that lists the
source files that contain unsupported features and functions. The report also provides
an indication of how much work you must do to make the MATLAB code suitable for
code generation. The tool might not detect all code generation issues. Under certain
circumstances, it might report false errors. Because the tool might not detect all issues,
or might report false errors, generate a MEX function to verify that your code is suitable
for code generation before generating C code.
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Code Generation Readiness Tool
Summary Tab
The Summary tab provides a Code Generation Readiness Score which ranges from
1 to 5. A score of 1 indicates that the tool has detected issues that require extensive
changes to the MATLAB code to make it suitable for code generation. A score of 5
indicates that the tool has not detected code generation issues; the code is ready to use
with no or minimal changes.
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17
Preparing MATLAB Code for C/C++ Code Generation
On this tab, the tool also provides information about:
• MATLAB syntax issues. These issues are reported in the MATLAB editor. Use the
code analyzer to learn more about the issues and how to fix them.
• Unsupported MATLAB function calls.
• Unsupported MATLAB language features, such as recursion, cell arrays, and nested
functions.
• Unsupported data types.
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Code Generation Readiness Tool
Code Structure Tab
If the code that you are checking calls other MATLAB functions, or you are checking
multiple entry-point functions, the tool displays the Code Structure Tab.
This tab provides information about the relative size of each file and how suitable each
file is for code generation.
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17
Preparing MATLAB Code for C/C++ Code Generation
Code Distribution
The Code Distribution pane provides a pie chart that shows the relative sizes of the
files and how suitable each file is for code generation. This information is useful during
the planning phase of a project for estimation and scheduling purposes. If the report
indicates that there are multiple files not yet suitable for code generation, consider fixing
files that require minor changes before addressing files with significant issues.
Call Tree
The Call Tree pane provides information on the nesting of function calls. For each called
function, the report provides a Code Generation Readiness score which ranges from
1 to 5. A score of 1 indicates that the tool has detected issues that require extensive
changes to the MATLAB code to make it suitable for code generation. A score of 5
indicates that the tool has not detected code generation issues; the code is ready to use
with no or minimal changes. The report also lists the number of lines of code in each file.
Show MATLAB Functions
If you select Show MATLAB Functions, the report also lists the MATLAB functions
called by your function code. For each of these MATLAB functions, if the function is
supported for code generation, the report sets Code Generation Readiness to Yes.
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Code Generation Readiness Tool
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Preparing MATLAB Code for C/C++ Code Generation
See Also
• “Check Code Using the Code Generation Readiness Tool”
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Unable to Determine Code Generation Readiness
Unable to Determine Code Generation Readiness
Sometimes the code generation readiness tool cannot determine whether the entry-point
functions in your project are suitable for code generation. The most likely reason is that
the tool is unable to find the entry-point files. Verify that your current working folder is
set to the folder that contains your entry-point files. If it is not, either make this folder
your current working folder or add the folder containing these files to the MATLAB path.
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Preparing MATLAB Code for C/C++ Code Generation
Generate MEX Functions Using the MATLAB Coder Project Interface
In this section...
“Project Workflow for Generating MEX Functions” on page 17-18
“Generate MEX Functions Using the Project Interface” on page 17-18
“Configure Project Settings” on page 17-23
“Build a MATLAB Coder Project” on page 17-24
“See Also” on page 17-25
Project Workflow for Generating MEX Functions
Step Action
Details
1
Set up your MATLAB Coder project.
“Creating a New Project”
2
Fix errors detected by the code analyzer.
“Fixing Errors Detected at Design Time” on
page 17-4
3
Specify build configuration parameters.
“Configure Project Settings” on page
17-23
4
Build the project.
“Build a MATLAB Coder Project” on page
17-24
Generate MEX Functions Using the Project Interface
In this example, you create a MATLAB function that adds two numbers, then create a
MATLAB Coder project for this file. Using the project user interface, you specify types for
the function input parameters, and then generate a MEX function for the MATLAB code.
1
In a local writable folder, create a MATLAB file, mcadd.m, that contains:
function y = mcadd(u,v) %#codegen
y = u + v;
2
In the same folder, set up a MATLAB Coder project.
a
At the MATLAB command line, enter
coder -new mcadd.prj
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Generate MEX Functions Using the MATLAB Coder Project Interface
By default, the project opens in the MATLAB workspace on the right side.
b
On the project Overview tab, click the Add files link, browse to the file
mcadd.m, and click Open to add the file to the project.
The file is displayed on the Overview tab, and both inputs are undefined.
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17
Preparing MATLAB Code for C/C++ Code Generation
c
On the Overview tab, click the field to the right of the input parameter u and,
from the list of input options, select int16.
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Generate MEX Functions Using the MATLAB Coder Project Interface
d
From the list of size options, select 1 x 1 to specify that the input is a scalar.
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17
Preparing MATLAB Code for C/C++ Code Generation
e
3
Repeat the previous two steps to specify the input v.
In the MATLAB Coder project, click the Build tab.
By default, the Output type is MEX function and the Output file is mcadd_mex.
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Generate MEX Functions Using the MATLAB Coder Project Interface
4
On this tab, click the Build button to generate a MEX function using the default
project settings.
MATLAB Coder builds the project and, by default, generates a MEX function,
mcadd_mex, in the current folder. MATLAB Coder also generates other supporting
files in a subfolder called codegen/mex/mcadd. MATLAB Coder uses the name
of the MATLAB function as the root name for the generated files and creates a
platform-specific extension for the MEX file, as described in “Naming Conventions”
on page 19-66.
You can now test your MEX function in MATLAB. For more information, see “Verify
MEX Functions in a Project”.
Configure Project Settings
1
On the project Build tab, click the More settings link to view the project settings
for the selected output type.
Note: MEX functions use a different set of configuration parameters than C/C++
libraries and executables. When you change the output type from MEX Function
or Instrumented MEX Function to C/C++ Static Library, C/C++ Dynamic
Libraryor C/C++ Executable, verify these settings. For more information, see
“Changing Output Type” on page 16-42.
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17
Preparing MATLAB Code for C/C++ Code Generation
2
In the Project Settings dialog box, select the settings that you want to apply.
Tip To learn more about the configuration parameters on the current tab of the
Project Settings dialog box, click the Help button.
See Also
• “How to Enable Code Generation Reports in the Project Settings Dialog Box” on page
19-170
• “In the Project Settings Dialog Box” on page 19-114
• “How to Disable Inlining Globally in the Project Settings Dialog Box” on page
19-124
• “Generate Traceable Code” on page 19-84
• “Disabling Run-Time Checks in the Project Settings Dialog Box” on page 23-17
Build a MATLAB Coder Project
On the project Build tab, click the Build button to build the project using the specified
settings. While MATLAB Coder builds a project, it displays the build progress in the
Build dialog box. When the build is complete, MATLAB Coder provides details in the
Build Results pane.
Viewing Build Results
The Build Results pane provides information about the most recent build. If the code
generation report is enabled or build errors occur, MATLAB Coder generates a report
that provides detailed information about the most recent build and provides a link to the
report.
To view the report, click the View report link. After a build completes, this report
provides links to your MATLAB code and generated C/C++ files as well as compile-time
type information for the variables in your MATLAB code. If build errors occur, it lists
errors and warnings.
Saving Build Results
When MATLAB Coder builds a project, it displays the build progress and results in the
Build dialog box. To save the build results, click the Save to log file link and specify the
log file location.
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Generate MEX Functions Using the MATLAB Coder Project Interface
See Also
• “Code Generation Reports”
• “Generate Code for Multiple Entry-Point Functions” on page 19-70
• “Generate Code for Global Data” on page 19-75
See Also
• “Generate Code for Multiple Entry-Point Functions” on page 19-70
• “Generate Code for Global Data” on page 19-75
• “Specify Output File Name”
• “Specify Output File Locations”
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17
Preparing MATLAB Code for C/C++ Code Generation
Generate MEX Functions at the Command Line
Command-line Workflow for Generating MEX Functions
Step Action
Details
1
Install prerequisite products.
“Installing Prerequisite Products”
2
Set up your file infrastructure.
“Paths and File Infrastructure Setup” on
page 19-65
3
Fix errors detected by the code analyzer.
“Fixing Errors Detected at Design Time” on
page 17-4
4
Specify build configuration parameters.
“Specify Build Configuration Parameters” on
page 19-25
5
Specify properties of primary function inputs. “Primary Function Input Specification” on
page 19-39
6
Generate the MEX function using codegen
with suitable command-line options.
“Generating MEX Functions at the Command
Line Using codegen” on page 17-27
Generate MEX Functions at the Command Line
In this example, you use the codegen function to generate a MEX function from a
MATLAB file that adds two inputs. You use the codegen -args option to specify that
both inputs are int16.
1
In a local writable folder, create a MATLAB file, mcadd.m, that contains:
function y = mcadd(u,v) %#codegen
y = u + v;
2
Generate a platform-specific MEX function in the current folder. At the command
line, specify that the two input parameters are int16 using the -args option. By
default, if you do not use the -args option, codegen treats inputs as real, scalar
doubles.
codegen mcadd -args {int16(0), int16(0)}
codegen generates a MEX function, mcadd_mex, in the current folder. codegen also
generates other supporting files in a subfolder called codegen/mex/mcadd.codegen
uses the name of the MATLAB function as the root name for the generated files
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Generate MEX Functions at the Command Line
and creates a platform-specific extension for the MEX file, as described in “Naming
Conventions” on page 19-66.
Generating MEX Functions at the Command Line Using codegen
You generate a MEX function at the command line using the codegen function.
The basic command is:
codegen fcn
By default, codegen generates a MEX function in the current folder as described in
“Generate MEX Functions at the Command Line”.
You can modify this default behavior by specifying one or more compiler options with
codegen, separated by spaces on the command line. For more information, see codegen.
See Also
• “Primary Function Input Specification”
• “MEX Function Generation at the Command Line”
• “Generate Code for Multiple Entry-Point Functions” on page 19-70
• “Generate Code for Global Data” on page 19-75
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17
Preparing MATLAB Code for C/C++ Code Generation
Fix Errors Detected at Code Generation Time
When the code generation software detects errors or warnings, it automatically generates
an error report. The error report describes the issues and provides links to the MATLAB
code with errors.
To fix the errors, modify your MATLAB code to use only those MATLAB features that are
supported for code generation. For more information, see “MATLAB Algorithm Design
Basics”. Choose a debugging strategy for detecting and correcting code generation errors
in your MATLAB code. For more information, see “Debugging Strategies”.
When code generation is complete, the software generates a MEX function that you can
use to test your implementation in MATLAB.
If your MATLAB code calls functions on the MATLAB path, unless the code generation
software determines that these functions should be extrinsic or you declare them to be
extrinsic, it attempts to compile these functions. See “Resolution of Function Calls for
Code Generation”. To get detailed diagnostics, add the %#codegen directive to each
external function that you want codegen to compile.
See Also
• “Code Generation Reports”
• “Why Test MEX Functions in MATLAB?”
• “When to Generate Code from MATLAB Algorithms”
• “Debugging Strategies”
• “Declaring MATLAB Functions as Extrinsic Functions”
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Design Considerations When Writing MATLAB Code for Code Generation
Design Considerations When Writing MATLAB Code for Code
Generation
When writing MATLAB code that you want to convert into efficient, standalone C/C++
code, you must consider the following:
• Data types
C and C++ use static typing. To determine the types of your variables before use,
MATLAB Coder requires a complete assignment to each variable.
• Array sizing
Variable-size arrays and matrices are supported for code generation. You can define
inputs, outputs, and local variables in MATLAB functions to represent data that
varies in size at run time.
• Memory
You can choose whether the generated code uses static or dynamic memory allocation.
With dynamic memory allocation, you potentially use less memory at the expense
of time to manage the memory. With static memory, you get best speed, but with
higher memory usage. Most MATLAB code takes advantage of the dynamic sizing
features in MATLAB, therefore dynamic memory allocation typically enables you
to generate code from existing MATLAB code without modifying it much. Dynamic
memory allocation also allows some programs to compile even when upper bounds
cannot be found.
Static allocation reduces the memory footprint of the generated code, and therefore is
suitable for applications where there is a limited amount of available memory, such as
embedded applications.
• Speed
Because embedded applications must run in real time, the code must be fast enough
to meet the required clock rate.
To improve the speed of the generated code:
• Choose a suitable C or /C++ compiler. The default compiler that MathWorks
supplies with MATLAB for Windows 32-bit platforms is not a good compiler for
performance.
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17
Preparing MATLAB Code for C/C++ Code Generation
• Consider disabling run-time checks.
By default, the code generated for your MATLAB code contains memory integrity
checks and responsiveness checks. Generally, these checks result in more
generated code and slower MEX function execution. Disabling run-time checks
usually results in streamlined generated code and faster MEX function execution.
Disable these checks only if you have verified that array bounds and dimension
checking is unnecessary.
See Also
• “MATLAB Algorithm Design Basics”
• “Data Definition”
• “Variable-Size Data”
• “Bounded Versus Unbounded Variable-Size Data”
• “Control Dynamic Memory Allocation” on page 19-95
• “Control Run-Time Checks”
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Running MEX Functions
Running MEX Functions
When you call a MEX function, pass it the same inputs you use for the original MATLAB
algorithm. Do not pass coder.Constant or any of the coder.Type classes to a MEX
function; these classes are for use with the codegen function.
To run a MEX function generated by MATLAB Coder, you must have licenses for all the
toolboxes that the MEX function requires. For example, if you generate a MEX function
from a MATLAB algorithm that uses a Computer Vision System Toolbox function or
System object, to run the MEX function, you must have a Computer Vision System
Toolbox license.
When you upgrade MATLAB, before running MEX functions with the new version,
rebuild the MEX functions.
Debugging MEX Functions
You cannot use the disp and save functions during debugging to inspect the contents
of your MEX function variables. Because these functions are not supported for code
generation, you must declare them as extrinsic functions. For extrinsic functions, when
running the MEX function, MATLAB Coder calls out to MATLAB to run disp and save,
so they save and display the data found in the base workspace, not the MEX-function
workspace.
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17
Preparing MATLAB Code for C/C++ Code Generation
Debugging Strategies
Before you perform code verification, choose a debugging strategy for detecting and
correcting noncompliant code in your MATLAB applications, especially if they consist
of a large number of MATLAB files that call each other's functions. The following table
describes two general strategies, each of which has advantages and disadvantages.
Debugging
Strategy
What to Do
Bottom-up 1
verification
2
Top-down 1
verification
Pros
Cons
Verify that your lowestlevel (leaf) functions are
compliant.
• Efficient
Work your way up the
function hierarchy
incrementally to compile
and verify each function,
ending with the top-level
function.
• Easy to isolate
code generation
syntax violations
Declare functions called
by the top-level function
to be extrinsic so that
MATLAB Coder does
not compile them. See
“Declaring MATLAB
Functions as Extrinsic
Functions”.
You retain your top- Introduces extraneous code
level tests
that you must remove after
code verification, including:
2
Verify that your top-level
function is compliant.
3
Work your way down
the function hierarchy
incrementally by
removing extrinsic
declarations one by one to
compile and verify each
function, ending with the
leaf functions.
Requires application tests that
• Unlikely to cause work from the bottom up
errors
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• Extrinsic declarations
• Additional assignment
statements as required
to convert opaque values
returned by extrinsic
functions to nonopaque
values (see “Working with
mxArrays”).
18
Testing MEX Functions in MATLAB
• “Workflow for Testing MEX Functions in MATLAB” on page 18-2
• “Why Test MEX Functions in MATLAB?” on page 18-4
• “Running MEX Functions” on page 18-5
• “Verify MEX Functions in a Project” on page 18-6
• “Verify MEX Functions at the Command Line” on page 18-8
• “Debug Run-Time Errors” on page 18-9
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18
Testing MEX Functions in MATLAB
Workflow for Testing MEX Functions in MATLAB
See Also
• “MATLAB Coder Project Set Up Workflow”
• “Workflow for Preparing MATLAB Code for Code Generation”
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Workflow for Testing MEX Functions in MATLAB
• “Why Test MEX Functions in MATLAB?” on page 18-4
• “Debug Run-Time Errors” on page 18-9
• “C/C++ Code Generation”
• “Accelerate MATLAB Algorithms”
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18
Testing MEX Functions in MATLAB
Why Test MEX Functions in MATLAB?
Before generating C/C++ code for your MATLAB code, it is a best practice to test the
MEX function to verify that it provides the same functionality as the original MATLAB
code. To do this testing, run the MEX function using the same inputs as you used to run
the original MATLAB code and compare the results. For more information about how
to test a MEX function in a project, see “Verify MEX Functions in a Project” on page
18-6. For more information on how to test a MEX function at the command line, see
“Verify MEX Functions at the Command Line” on page 18-8.
In addition, running the MEX function in MATLAB before generating code enables you
to detect and fix run-time errors that are much harder to diagnose in the generated code.
If you encounter run-time errors in your MATLAB functions, fix them before generating
code. For more information, see “Debug Run-Time Errors” on page 18-9.
When you run your MEX function in MATLAB, by default, the following run-time checks
execute :
• Memory integrity checks. These checks perform array bounds checking, dimension
checking, and detect violations of memory integrity in code generated for MATLAB
functions. If a violation is detected, MATLAB stops execution and provides a
diagnostic message.
• Responsiveness checks in code generated for MATLAB functions. These checks enable
periodic checks for Ctrl+C breaks in code generated for MATLAB functions, allowing
you to terminate execution with Ctrl+C.
For more information, see “Control Run-Time Checks”.
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Running MEX Functions
Running MEX Functions
When you call a MEX function, pass it the same inputs you use for the original MATLAB
algorithm. Do not pass coder.Constant or any of the coder.Type classes to a MEX
function; these classes are for use with the codegen function.
To run a MEX function generated by MATLAB Coder, you must have licenses for all the
toolboxes that the MEX function requires. For example, if you generate a MEX function
from a MATLAB algorithm that uses a Computer Vision System Toolbox function or
System object, to run the MEX function, you must have a Computer Vision System
Toolbox license.
When you upgrade MATLAB, before running MEX functions with the new version,
rebuild the MEX functions.
Debugging MEX Functions
You cannot use the disp and save functions during debugging to inspect the contents
of your MEX function variables. Because these functions are not supported for code
generation, you must declare them as extrinsic functions. For extrinsic functions, when
running the MEX function, MATLAB Coder calls out to MATLAB to run disp and save,
so they save and display the data found in the base workspace, not the MEX-function
workspace.
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18
Testing MEX Functions in MATLAB
Verify MEX Functions in a Project
In this section...
“Using Test Files That Call Only MATLAB Functions” on page 18-6
“Using Test Files That Call MEX Functions” on page 18-7
Using Test Files That Call Only MATLAB Functions
If you have a test file that calls only your original entry-point MATLAB function, use the
following procedure. A test file can be either a MATLAB function or a script. To use this
procedure, you should verify that it calls at least one entry-point function. The generated
MEX function must be in the same folder as the entry-point functions.
Selecting the Redirect entry-point calls to MEX function option directs MATLAB
Coder software to replace calls to the MATLAB function with calls to the generated MEX
function. This capability allows you to compare the behavior of the MEX function with
that of the original function.
If your test file calls the generated MEX function, do not follow this procedure. Instead,
follow the procedure in “Using Test Files That Call MEX Functions” on page 18-7.
1
On the project Build tab Verification panel, click the
button to add a test file.
Alternatively, if you have already added test files to the project, select one from the
list.
2
Run the test file calling the original MATLAB algorithm.
a
Clear Redirect entry-point calls to MEX function.
b
Click the Run button.
The test file runs and calls your original MATLAB algorithm.
3
Verify that the test results are as expected.
4
Run the test file calling the MEX function instead of the original MATLAB
algorithm.
a
Select Redirect entry-point calls to MEX function.
b
Click the Run button.
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Verify MEX Functions in a Project
The project builds the MEX function. The test file runs and automatically replaces
calls to your original MATLAB algorithm with calls to the generated MEX function.
5
Compare the results of the two runs to verify that the MEX function provides the
same functionality as the original MATLAB algorithm.
Using Test Files That Call MEX Functions
If you have a test file that calls the generated MEX function, use the following procedure.
If your test file calls both the original MATLAB function and the generated MEX
function, you can also use this procedure.
A test file can be either a MATLAB function or a script. To use this procedure, you should
verify that it calls at least one MEX function. The MEX function must be in the same
folder as the entry-point functions.
1
On the project Build tab Verification panel, click the
button to add a test file.
Alternatively, if you have already added test files to the project, select one from the
list.
2
Run the test file.
a
Clear Redirect entry-point calls to MEX function.
Because the test file already calls the MEX function, you do not want MATLAB
Coder to redirect entry-point function calls.
b
Click the Run button.
The project builds the MEX function. The test file runs and calls the generated MEX
function. If applicable, it also calls the original MATLAB algorithm.
3
Use the results of this run to verify that the MEX function provides the same
functionality as the original MATLAB algorithm.
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18
Testing MEX Functions in MATLAB
Verify MEX Functions at the Command Line
If you have a test file that calls your original MATLAB function, use coder.runTest
to verify the MEX function at the command line. coder.runTest runs the test file
replacing calls to the original MATLAB function with calls to the generated MEX
function. If errors occur during the run with coder.runTest, call stack information
is available for debugging purposes. For more information, see the coder.runTest
function reference information and “Verifying the MEX Function” in the MATLAB Coder
“C Code Generation at the Command Line” tutorial.
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Debug Run-Time Errors
Debug Run-Time Errors
In this section...
“Viewing Errors in the Run-Time Stack” on page 18-9
“Handling Run-Time Errors” on page 18-10
If you encounter run-time errors in your MATLAB functions, the run-time stack appears
automatically in the MATLAB command window. Use the error message and stack
information to learn more about the source of the error and then either fix the issue or
add error-handling code. For more information, see “Viewing Errors in the Run-Time
Stack” on page 18-9“Handling Run-Time Errors” on page 18-10.
Viewing Errors in the Run-Time Stack
About the Run-Time Stack
The run-time stack is enabled by default for MEX code generation from MATLAB. Use
the error message and the following stack information to learn more about the source of
the error:
• The name of the function that generated the error
• The line number of the attempted operation
• The sequence of function calls that led up to the execution of the function and the line
at which each of these function calls occurred
Example Run-Time Stack Trace
This example shows the run-time stack trace for MEX function mlstack_mex:
mlstack_mex(-1)
Index exceeds matrix dimensions. Index
value -1 exceeds valid range [1-4] of
array x.
Error in mlstack>mayfail (line 31)
y = x(u);
Error in mlstack>subfcn1 (line 5)
switch (mayfail(u))
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18
Testing MEX Functions in MATLAB
Error in mlstack (line 2)
y = subfcn1(u);
The stack trace provides the following information:
• The type of error.
??? Index exceeds matrix dimensions.
Index value -1 exceeds valid range [1-4] of array x.
• Where the error occurred.
Error in ==>mlstack>mayfail at 31
y = x(u);
• The function call sequence prior to the failure.
Error in ==> mlstack>subfcn1 at 5
switch (mayfail(u))
Error in ==> mlstack at 2
y = subfcn1(u);
When to Use the Run-Time Stack
The run-time stack is useful during debugging to help you find the source of run-time
errors. However, when the stack is enabled, the generated code contains instructions for
maintaining the run-time stack, which might slow the run time. Consider disabling the
run-time stack for faster run time.
How to Disable the Run-Time Stack
You can disable the run-time stack by disabling the memory integrity checks as described
in “How to Disable Run-Time Checks”.
Caution Before disabling the memory integrity checks, you should verify that all array
bounds and dimension checking is unnecessary.
Handling Run-Time Errors
The code generation software propagates error ID's. If you throw an error or warning in
your MATLAB code, use the try-catch statement in your test bench code to examine
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Debug Run-Time Errors
the error information and attempt to recover, or clean up and abort. For example, for
the function in “Example Run-Time Stack Trace” on page 18-9, create a test script
containing:
try
mlstack_mex(u)
catch
% Add your error handling code here
end
For more information, see “ The try/catch Statement”.
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19
Generating C/C++ Code from
MATLAB Code
• “Code Generation Workflow” on page 19-3
• “C/C++ Code Generation” on page 19-5
• “Generating C/C++ Static Libraries from MATLAB Code” on page 19-6
• “Generating C/C++ Dynamically Linked Libraries from MATLAB Code” on page
19-10
• “Generating Standalone C/C++ Executables from MATLAB Code” on page 19-13
• “Build Setting Configuration” on page 19-19
• “Standard Math Libraries” on page 19-32
• “Change the Standard Math Library” on page 19-33
• “Share Build Configuration Settings” on page 19-34
• “Convert MATLAB Coder Project to MATLAB Script” on page 19-37
• “Primary Function Input Specification” on page 19-39
• “Control Constant Inputs in MEX Function Signatures” on page 19-49
• “Define Input Properties Programmatically in the MATLAB File” on page 19-53
• “Speed Up Compilation” on page 19-63
• “Paths and File Infrastructure Setup” on page 19-65
• “Generate Code for Multiple Entry-Point Functions” on page 19-70
• “Generate Code for Global Data” on page 19-75
• “Generation of Traceable Code” on page 19-84
• “Generate Code for Enumerated Types” on page 19-93
• “Generate Code for Variable-Size Data” on page 19-94
• “Code Generation for MATLAB Classes” on page 19-113
• “How MATLAB Coder Partitions Generated Code” on page 19-114
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19
Generating C/C++ Code from MATLAB Code
• “Requirements for Signed Integer Representation” on page 19-126
• “Customize the Post-Code-Generation Build Process” on page 19-127
• “Code Generation Reports” on page 19-167
• “Troubleshooting” on page 19-186
• “Package Code For Other Development Environments” on page 19-187
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Code Generation Workflow
Code Generation Workflow
See Also
• “MATLAB Coder Project Set Up Workflow”
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19
Generating C/C++ Code from MATLAB Code
• “Workflow for Preparing MATLAB Code for Code Generation”
• “Workflow for Testing MEX Functions in MATLAB”
• “Build Setting Configuration” on page 19-19
• “C/C++ Code Generation” on page 19-5
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C/C++ Code Generation
C/C++ Code Generation
Using MATLAB Coder, you can generate standalone C/C++ static and dynamic libraries
and C/C++ executables. If you specify C++, MATLAB Coder wraps the C code into .cpp
files so that you can use a C++ compiler and interface with external C++ applications. It
does not generate C++ classes. By default, if MATLAB Coder does not detect errors, it
generates a platform-specific MEX function in the current folder.
To learn how to generate...
See...
C/C++ static libraries from your MATLAB
code
“Generating C/C++ Static Libraries from
MATLAB Code” on page 19-6
C/C++ dynamic libraries from your
MATLAB code
“Generating C/C++ Dynamically Linked
Libraries from MATLAB Code” on page
19-10
C/C++ executables from your MATLAB
code
“Generating Standalone C/C++ Executables
from MATLAB Code” on page 19-13
MEX functions from your MATLAB code
“Generate MEX Functions Using the
MATLAB Coder Project Interface”
If errors occur, MATLAB Coder does not generate code, but produces an error report and
provides a link to this report. For more information, see “Code Generation Reports” on
page 19-167.
Specify Custom Files to Build
In addition to your MATLAB file, you can specify the following types of custom files to
include in the build for standalone C/C++ code generation.
File Extension
Description
.c
Custom C file
.cpp
Custom C++ file
.h
Custom header file
.o , .obj
Custom object file
.a , .lib, .so
Library
.tmf
Template makefile for custom MATLAB
Coder builds
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19
Generating C/C++ Code from MATLAB Code
Generating C/C++ Static Libraries from MATLAB Code
In this section...
“Generate a C Static Library Using the Project Interface” on page 19-6
“Generate a C Static Library at the Command Line” on page 19-9
Generate a C Static Library Using the Project Interface
This example shows how to generate a C static library from MATLAB code using a
MATLAB Coder project.
In this example, you create a MATLAB function that adds two numbers. You then create
a MATLAB Coder project. Use the project user interface to generate a C static library for
the MATLAB code.
1
In a local writable folder, create a MATLAB file, mcadd.m, that contains:
function y = mcadd(u,v) %#codegen
y = u + v;
2
In the same folder, set up a MATLAB Coder project.
a
At the MATLAB command line, enter:
coder -new mcadd.prj
By default, the project opens in the MATLAB workspace on the right side.
b
On the project Overview tab, click the Add files link. Browse to the file
mcadd.m. Click OK to add the file to the project.
The file is displayed on the Overview tab. Both inputs are undefined.
3
Define the type of input u.
a
On the Overview tab, click the field to the right of the input parameter u and,
from the list of input options, select int16.
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Generating C/C++ Static Libraries from MATLAB Code
b
From the list of size options, select 1 x 1 to specify that the input is a scalar.
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19
Generating C/C++ Code from MATLAB Code
4
Repeat the previous step for input v.
5
In the MATLAB Coder project, click the Build tab.
6
On this tab, set the Output type to C/C++ Static library.
The default output file name is mcadd.
7
Click Build to generate a library using the default project settings.
MATLAB Coder builds the project and generates a C static library and supporting
files in the default folder, codegen/lib/mcadd. It generates the minimal set of
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Generating C/C++ Static Libraries from MATLAB Code
#include statements for header files required by the selected code replacement
library.
Generate a C Static Library at the Command Line
This example shows how to generate a C static library from MATLAB code at the
command line using the codegen function.
1
In a local writable folder, create a MATLAB file, mcadd.m, that contains:
function y = mcadd(u,v) %#codegen
y = u + v;
2
Using the config:lib option, generate C library files. Using the -args option,
specify that the first input is a 1-by-4 vector of unsigned 16-bit integers and that
the second input is a double-precision scalar.
codegen -config:lib mcadd -args {zeros(1,4,'uint16'),0}
MATLAB Coder generates a C static library with the default name, mcadd, and
supporting files in the default folder, codegen/lib/mcadd. It generates the
minimal set of #include statements for header files required by the selected code
replacement library.
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19
Generating C/C++ Code from MATLAB Code
Generating C/C++ Dynamically Linked Libraries from MATLAB
Code
In this section...
“Dynamic Libraries Generated by MATLAB Coder” on page 19-10
“Generate a C Dynamically Linked Library (DLL) Using the Project Interface” on page
19-10
“Generate a C Dynamic Library at the Command Line” on page 19-12
Dynamic Libraries Generated by MATLAB Coder
By default, when MATLAB Coder generates a dynamic library (DLL):
• The DLL is suitable for the platform that you are working on.
• The DLL uses the release version of the C run-time library.
• The DLL linkage conforms to the target language, by default, C. If you set the target
language to C++, the linkage conforms to C++.
• When the target language is C, the generated header files explicitly declare the
exported functions to be extern "C" to simplify integration of the DLL into C++
applications.
If you generate a DLL that uses dynamically allocated variable-size data, MATLAB
Coder automatically provides exported utility functions to interact with this data in the
generated code. For more information, see “Utility Functions for Creating emxArray
Data Structures”.
Generate a C Dynamically Linked Library (DLL) Using the Project Interface
This example shows how to generate a C DLL from MATLAB code using a MATLAB
Coder project.
In this example, you create a MATLAB function that generates a random scalar value.
You then create a MATLAB Coder project. Use the project user interface to generate a C
dynamic library for the MATLAB code.
1
Write two MATLAB functions, ep1 takes one input, a single scalar, and ep2 takes
two inputs, both double scalars. In a local writable folder, create a MATLAB file,
ep1.m, that contains:
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Generating C/C++ Dynamically Linked Libraries from MATLAB Code
function y = ep1(u) %#codegen
y = u;
In the same folder, create a MATLAB file, ep2.m, that contains:
function y = ep2(u, v) %#codegen
y = u + v;
2
In the same folder as the ep1 and ep2 files, set up a MATLAB Coder project. At the
MATLAB command line, enter:
coder -new ep.prj
By default, the project opens in the MATLAB workspace on the right side.
3
On the project Overview tab, click the Add files link and browse to the file ep1.m .
Click OK to add the file to the project.
The file is displayed on the Overview tab. MATLAB Coder indicates that input u is
undefined.
4
5
Define the type of input u.
a
On the Overview tab, click the field to the right of the input parameter u and
then, from the list of input options, select single.
b
From the list of size options, select 1 x 1 to specify that u is a scalar.
On the project Overview tab, click the Add files link and browse to the file ep2.m .
Click OK to add the file to the project.
The file is displayed on the Overview tab. MATLAB Coder indicates that inputs u
and v are undefined.
6
Define the type of input u.
a
On the Overview tab, click the field to the right of the input parameter u and
then, from the list of input options, select double.
b
From the list of size options, select 1 x 1 to specify that u is a scalar.
7
Repeat the previous step for input v.
8
In the MATLAB Coder project, click the Build tab.
9
On the Build tab, set the Output type to C/C++ Dynamic Library.
10 On the Build tab, click the Build button to generate a library using these project
settings.
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19
Generating C/C++ Code from MATLAB Code
On Microsoft® Windows systems, MATLAB Coder generates a C dynamic library,
ep1.dll, and supporting files, in the default folder, codegen/dll/ep1. It
generates the minimal set of #include statements for header files required by the
selected code replacement library. On Linux and Macintosh systems, it generates
a shared object (.so) file. The DLL linkage conforms to the target language, in this
example, C. If you set the target language to C++, the linkage conforms to C++.
Generate a C Dynamic Library at the Command Line
This example shows how to generate a C dynamic library from MATLAB code at the
command line using the codegen function.
1
Write two MATLAB functions, ep1 takes one input, a single scalar, and ep2 takes
two inputs, both double scalars. In a local writable folder, create a MATLAB file,
ep1.m, that contains:
function y = ep1(u) %#codegen
y = u;
In the same folder, create a MATLAB file, ep2.m, that contains:
function y = ep2(u, v) %#codegen
y = u + v;
2
Generate the C dynamic library.
codegen -config:dll ep1 -args single(0) ep2 -args {0,0}
On Microsoft Windows systems, codegen generates a C dynamic library, ep1.dll,
and supporting files, in the default folder, codegen/dll/ep1. It generates the
minimal set of #include statements for header files required by the selected code
replacement library. On Linux and Macintosh systems, it generates a shared object
(.so) file. The DLL linkage conforms to the target language, in this example, C. If you
set the target language to C++, the linkage conforms to C++.
Note: The default target language is C. To change the target language to C++, see
“Specify a Language for Code Generation” on page 19-21.
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Generating Standalone C/C++ Executables from MATLAB Code
Generating Standalone C/C++ Executables from MATLAB Code
In this section...
“Generate a C Executable Using the Project Interface” on page 19-13
“Generate a C Executable at the Command Line” on page 19-15
“Specifying main Functions for C/C++ Executables” on page 19-16
“Specify main Functions” on page 19-17
Generate a C Executable Using the Project Interface
In this example, you create a MATLAB function that generates a random scalar value
and a main C function that calls this MATLAB function. You then create a MATLAB
Coder project. Use the project user interface to specify types for the function input
parameters, specify the main function, and generate a C executable for the MATLAB
code.
1
Write a MATLAB function, coderand, that generates a random scalar value from
the standard uniform distribution on the open interval (0,1):
function r = coderand() %#codegen
r = rand();
2
Write a main C function, c:\myfiles\main.c, that calls coderand. For example:
/*
** main.c
*/
#include <stdio.h>
#include <stdlib.h>
#include "coderand.h"
#include "coderand_initialize.h"
#include "coderand_terminate.h"
int main()
{
coderand_initialize();
printf("coderand=%g\n", coderand());
coderand_terminate();
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19
Generating C/C++ Code from MATLAB Code
return 0;
}
Note: In this example, because the default file partitioning method is to generate
one file for each MATLAB file, you include coderand_initialize.h and
coderand_terminate.h. If your file partitioning method is set to generate
one file for all functions, do not include coderand_initialize.h and
coderand_terminate.h.
3
In the same folder as the coderand file, set up a MATLAB Coder project.
a
At the MATLAB command line, enter:
coder -new coderand.prj
By default, the project opens in the MATLAB workspace on the right side.
b
On the project Overview tab, click the Add files link and browse to the file
coderand.m . Click OK to add the file to the project.
The file is displayed on the Overview tab. MATLAB Coder indicates that the
coderand function has no inputs.
4
In the MATLAB Coder project, click the Build tab.
a
Set the Output type to C/C++ Executable.
b
Set the output file name to coderand_exe.
5
On the project Build tab, click the More settings link.
6
On the Project Settings dialog box Custom Code tab, under Additional files and
directories to be built, set:
a
Source files to main.c, which is the name of the C/C++ source file that
contains the main function.
b
Include directories to the location of main.c: c:\myfiles.
c
Close the dialog box.
Note: When you are building an executable, you must specify the main function. For
more information, see “Specifying main Functions for C/C++ Executables” on page
19-16.
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Generating Standalone C/C++ Executables from MATLAB Code
7
On the Build tab, click the Build button to generate a library using the default
project settings.
MATLAB Coder compiles and links the main function with the C code that it
generates for the project and, in the current folder, generates an executable,
coderand_exe. It generates supporting files in the default folder, codegen/exe/
coderand. MATLAB Coder generates the minimal set of #include statements for
header files required by the selected code replacement library.
See Also
• “MATLAB Coder Project Set Up Workflow”
• “Workflow for Preparing MATLAB Code for Code Generation”
• “Workflow for Testing MEX Functions in MATLAB”
• “Build Setting Configuration” on page 19-19
• “C/C++ Code Generation” on page 19-5
• “Optimization Strategies”
Generate a C Executable at the Command Line
In this example, you create a MATLAB function that generates a random scalar value
and a main C function that calls this MATLAB function. You then specify types for the
function input parameters, specify the main function, and generate a C executable for the
MATLAB code.
1
Write a MATLAB function, coderand, that generates a random scalar value from
the standard uniform distribution on the open interval (0,1):
function r = coderand() %#codegen
r = rand();
2
Write a main C function, c:\myfiles\main.c, that calls coderand. For example:
/*
** main.c
*/
#include <stdio.h>
#include <stdlib.h>
#include "coderand.h"
#include "coderand_initialize.h"
#include "coderand_terminate.h"
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19
Generating C/C++ Code from MATLAB Code
int main()
{
coderand_initialize();
printf("coderand=%g\n", coderand());
coderand_terminate();
return 0;
}
Note: In this example, because the default file partitioning method is to generate
one file for each MATLAB file, you include “coderand_initialize.h” and
“coderand_terminate.h”. If your file partitioning method is set to generate
one file for all functions, do not include “coderand_initialize.h” and
“coderand_terminate.h”.
3
Configure your code generation parameters to include the main C function and then
generate the C executable:
cfg = coder.config('exe');
cfg.CustomSource = 'main.c';
cfg.CustomInclude = 'c:\myfiles';
codegen -config cfg coderand
codegen generates a C executable, coderand.exe, in the current folder. It
generates supporting files in the default folder, codegen/exe/coderand. codegen
generates the minimal set of #include statements for header files required by the
selected code replacement library.
Specifying main Functions for C/C++ Executables
When you generate an executable, you must provide a main function. If you are
generating a C executable, provide a C file, main.c. If you are generating a C++
executable, provide a C++ file, main.cpp. Verify that the folder containing the main
function has only one main file. Otherwise, main.c takes precedence over main.cpp,
which causes an error when generating C++ code. You can specify the main file from the
project settings dialog box, the command line, or the Code Generation dialog box.
When you convert a MATLAB function to a C/C++ library function or a C/C++
executable, MATLAB Coder automatically generates an initialize function and a
terminate function.
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Generating Standalone C/C++ Executables from MATLAB Code
• If your file partitioning method is set to generate one file for each MATLAB file, you
must include the initialize and terminate header functions in main.c. Otherwise, do
not include them in main.c.
• You must call these functions along with the C/C++ function. For more information,
see “Calling Initialize and Terminate Functions” on page 22-7.
Specify main Functions
Specifying main Functions in the Project Settings Dialog Box
1
On the project Build tab, click the More settings link to open the Project Settings
dialog box.
2
On the Custom Code tab, set:
a
Additional source files to the name of the C/C++ source file that contains the
main function. For example, main.c. For more information, see “Specifying
main Functions for C/C++ Executables” on page 19-16.
b
Additional include directories to the location of main.c. For example, c:
\myfiles.
Specifying main Functions at the Command Line
Set the CustomSource and CustomInclude properties of the code generation
configuration object (see “Working with Configuration Objects” on page 19-27).
The CustomInclude property indicates the location of C/C++ files specified by
CustomSource.
1
Create a configuration object for an executable:
cfg = coder.config('exe');
2
Set the CustomSource property to the name of the C/C++ source file that contains
the main function. (For more information, see “Specifying main Functions for C/C++
Executables” on page 19-16.) For example:
cfg.CustomSource = 'main.c';
3
Set the CustomInclude property to the location of main.c. For example:
cfg.CustomInclude = 'c:\myfiles';
4
Generate the C/C++ executable using the command line options. For example, if
myFunction takes one input parameter of type double:
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19
Generating C/C++ Code from MATLAB Code
codegen -config cfg
myMFunction -args {0}
MATLAB Coder compiles and links the main function with the C/C++ code that it
generates from myMFunction.m.
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Build Setting Configuration
Build Setting Configuration
In this section...
“Specify Output Type” on page 19-19
“Specify a Language for Code Generation” on page 19-21
“Specify Data Type Used in Generated Code” on page 19-22
“Specify Output File Name” on page 19-23
“Specify Output File Locations” on page 19-24
“Parameter Specification Methods” on page 19-25
“Specify Build Configuration Parameters” on page 19-25
Specify Output Type
Output Types
MATLAB Coder can generate code for the following output types:
• MEX function
• Instrumented MEX function
• Standalone C/C++ code and compile it to a static library
• Standalone C/C++ code and compile it to a dynamically-linked library
• Standalone C/C++ code and compile it to an executable
Note: When you generate an executable, you must provide a C/C++ file that contains
the main function, as described in “Specifying main Functions for C/C++ Executables”
on page 19-16.
Location of Generated Files
By default, MATLAB Coder generates files in output folders based on your output type.
For more information, see “Generated Files and Locations” on page 19-120.
Note: Each time MATLAB Coder generates the same type of output for the same code or
project, it removes the files from the previous build. If you want to preserve files from a
build, copy them to a different location before starting another build.
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19
Generating C/C++ Code from MATLAB Code
Specifying the Output Type Using the MATLAB Coder Project Interface
On the MATLAB Coder project Build tab, set Output type to one of the available
output types:
• MEX Function (default)
• Instrumented MEX Function
• C/C++ Static Library
• C/C++ Dynamic Library
• C/C++ Executable
MEX functions use a different set of configuration parameters than C/C++ libraries
and executables. When you switch the output type between MEX Function or
Instrumented MEX Function and C/C++ Static Library, C/C++ Dynamic
Library or C/C++ Executable, verify these settings. For more information, see
“Changing Output Type” on page 16-42.
Specifying the Output Type at the Command Line
Call codegen with the -config option. For example, suppose you have a primary
function foo that takes no input parameters. The following table shows how to specify
different output types when compiling foo. If a primary function has input parameters,
you must specify these inputs. For more information, see “Primary Function Input
Specification” on page 19-39.
Note: C is the default language for code generation with MATLAB Coder. To generate C+
+ code, see “Specify a Language for Code Generation” on page 19-21.
To Generate:
MEX function using the default
code generation options
MEX function specifying code
generation options
Use This Command:
codegen foo
cfg = coder.config('mex');
% Set configuration parameters, for example,
% enable a code generation report
cfg.GenerateReport=true;
% Call codegen, passing the configuration
% object
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Build Setting Configuration
To Generate:
Use This Command:
codegen -config cfg foo
Standalone C/C++ code and
compile it to a library using the
default code generation options
Standalone C/C++ code and
compile it to a library specifying
code generation options
Standalone C/C++ code and
compile it to an executable using
the default code generation
options and specifying the
main.c file at the command line
Standalone C/C++ code and
compile it to an executable
specifying code generation
options
codegen -config:lib foo
cfg = coder.config('lib');
% Set configuration parameters, for example,
% enable a code generation report
cfg.GenerateReport=true;
% Call codegen, passing the configuration
% object
codegen -config cfg foo
codegen -config:exe main.c foo
Note: You must specify a main function for generating a C/
C++ executable. See “Specifying main Functions for C/C++
Executables” on page 19-16
cfg = coder.config('exe');
% Set configuration parameters, for example,
% specify main file
cfg.CustomSource = 'main.c';
cfg.CustomInclude = 'c:\myfiles';
codegen -config cfg foo
Note: You must specify a main function for generating a C/
C++ executable. See “Specifying main Functions for C/C++
Executables” on page 19-16
Specify a Language for Code Generation
• “Specifying a Language for Code Generation in the Project Settings Dialog Box” on
page 19-22
• “Specifying a Language for Code Generation at the Command Line” on page 19-22
MATLAB Coder can generate C or C++ libraries and executables. C is the default
language. You can specify a language explicitly from the project settings dialog box or at
the command line.
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19
Generating C/C++ Code from MATLAB Code
Specifying a Language for Code Generation in the Project Settings Dialog Box
1
Select a suitable compiler for your target language.
2
On the MATLAB Coder project Build tab, click the More settings link to open the
Project Settings dialog box.
3
On the All Settings tab, in the Advanced group, set Language to C or C++.
Note: If you specify C++, MATLAB Coder wraps the C code into .cpp files so that
you can use a C++ compiler and interface with external C++ applications. It does not
generate C++ classes.
Specifying a Language for Code Generation at the Command Line
1
Select a suitable compiler for your target language.
2
Create a configuration object for code generation. For example, for a library:
cfg = coder.config('lib');
3
Set the TargetLang property to 'C' or 'C++'. For example:
cfg.TargetLang = 'C++';
Note: If you specify C++, MATLAB Coder wraps the C code into .cpp files. You can
then use a C++ compiler and interface with external C++ applications. MATLAB
Coder does not generate C++ classes.
See Also
• “Working with Configuration Objects” on page 19-27
• “Setting Up the C or C++ Compiler”
Specify Data Type Used in Generated Code
• “Specify Data Type in the Project Settings Dialog Box” on page 19-23
• “Specify Data Type at the Command Line” on page 19-23
MATLAB Coder can use built-in C data types or predefined types from rtwtypes.h
in generated code. By default, the generated code uses built-in C types when declaring
variables.
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Build Setting Configuration
You can explicitly specify the data type used in generated code in the project settings
dialog box or at the command line.
Specify Data Type in the Project Settings Dialog Box
1
On the Build tab Settings pane, set the Output type to C/C++ Static
Library, C/C++ Dynamic Library, or C/C++ Executable (depending on your
requirements).
2
Click the More settings link to open the Project Settings dialog box.
3
To use built-in C types, on the Code Appearance tab, set Data Type
Replacement to Use built-in C data types in the generated code.
To use predefined types from rtwtypes.h, set Data Type Replacement to Use
MathWorks typedefs in the generated code.
Specify Data Type at the Command Line
1
Create a configuration object for code generation. Use coder.config with
arguments 'lib','dll' or 'exe' (depending on your requirements). For example:
cfg = coder.config('lib');
2
To use built-in C types, set the DataTypeReplacement property to 'CBuiltIn'.
cfg.DataTypeReplacement = 'CBuiltIn';
To use predefined types from rtwtypes.h, set the DataTypeReplacement property
to 'CoderTypedefs'.
Specify Output File Name
Specify Output File Name in a Project
On the project Build tab, in the Output file field, enter the file name. The file name can
include an existing path.
Note: Do not put spaces in the file name.
By default, if the name of the first entry-point MATLAB file is fcn1, the output file name
is:
• fcn1 for C/C++ libraries and executables.
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19
Generating C/C++ Code from MATLAB Code
• fcn1_mex for MEX functions.
By default, MATLAB Coder generates files in the folder project_folder/codegen/
target/fcn1:
• project_folder is your current project folder
• target is:
• mex for MEX functions
• lib for static C/C++ libraries
• dll for dynamic C/C++ libraries
• exe for C/C++ executables
Command Line Alternative
Use the codegen function -o option.
Specify Output File Locations
Specifying Output File Location in a Project
The output file location should not contain:
• Spaces, as this can lead to code generation failures in certain operating system
configurations.
• Non 7-bit ASCII characters, such as Japanese characters.
1
On the project Build tab, click More settings.
2
In the Project Settings dialog box, click the Paths tab.
The default setting for the Build Folder field is A subfolder of the project
folder. By default, MATLAB Coder generates files in the folder project_folder/
codegen/target/fcn1:
• fcn1 is the name of the first entry-point file
• target is:
• mex for MEX functions
• lib for static C/C++ libraries
• dll for dynamically-linked C/C++ libraries
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Build Setting Configuration
• exe for C/C++ executables
3
To change the output location, you can either:
• Set Build Folder to A subfolder of the current MATLAB working
folder
MATLAB Coder generates files in the MATLAB_working_folder/codegen/
target/fcn1 folder
• Set Build Folder to Specified folder. In the Build folder name field,
provide the path to the folder.
Command Line Alternative
Use the codegen function -d option.
Parameter Specification Methods
If you are using...
Use...
Details
A MATLAB Coder project
The Project Settings dialog box “Specifying Build Configuration
Parameters in the Project
Settings Dialog Box” on page
19-26
codegen at the command line
and want to specify a small
number of parameters
Configuration objects
“Specifying Build Configuration
Parameters at the Command
Line Using Configuration
Objects” on page 19-26
Configuration object dialog
boxes
“Specifying Build Configuration
Parameters at the Command
Line Using Dialog Boxes” on
page 19-30
codegen in build scripts
codegen at the command line
and want to specify a large
number of parameters
Specify Build Configuration Parameters
• “Specifying Build Configuration Parameters in the Project Settings Dialog Box” on
page 19-26
• “Specifying Build Configuration Parameters at the Command Line Using
Configuration Objects” on page 19-26
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19
Generating C/C++ Code from MATLAB Code
• “Specifying Build Configuration Parameters at the Command Line Using Dialog
Boxes” on page 19-30
You can specify build configuration parameters from the MATLAB Coder project settings
dialog box, the command line, or configuration object dialog boxes.
Specifying Build Configuration Parameters in the Project Settings Dialog Box
1
On the MATLAB Coder project Build tab, click More settings.
The Project Settings dialog box opens. This dialog box provides the set of
configuration parameters applicable to the output type that you select.
Note: MEX functions use a different set of configuration parameters than C/C++
libraries and executables. When you switch the output type between MEX Function
or Instrumented MEX Function and C/C++ Static Library , C/C++ Dynamic
Library or C/C++ Executable, verify these settings. For more information, see
“Changing Output Type” on page 16-42.
2
Modify the parameters as required. For more information about parameters on a tab,
click the Help button.
Changes to the parameter settings take place immediately.
3
After specifying the build parameters, you can generate code by clicking the Build
button on the same tab.
Specifying Build Configuration Parameters at the Command Line Using Configuration Objects
Types of Configuration Objects
The codegen function uses configuration objects to customize your environment for code
generation. The following table lists the available configuration objects.
Configuration Object
Description
coder.CodeConfig
If no Embedded Coder license is available or you disable
use of the Embedded Coder license, specifies parameters
for C/C++ library or executable generation.
For more information, see the class reference information
for coder.CodeConfig.
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Build Setting Configuration
Configuration Object
Description
coder.EmbeddedCodeConfig
If an Embedded Coder license is available, specifies
parameters for C/C++ library or executable generation.
For more information, see the class reference information
for coder.EmbeddedCodeConfig.
Specifies parameters of the target hardware
implementation. If not specified, codegen generates code
that is compatible with the MATLAB host computer.
coder.HardwareImplementation
For more information, see the class reference information
for coder.HardwareImplementation.
Specifies parameters for MEX code generation.
coder.MexCodeConfig
For more information, see the class reference information
for coder.MexCodeConfig.
Working with Configuration Objects
To use configuration objects to customize your environment for code generation:
1
In the MATLAB workspace, define configuration object variables, as described in
“Creating Configuration Objects” on page 19-28.
For example, to generate a configuration object for C static library generation:
cfg = coder.config('lib');
% Returns a coder.CodeConfig object if no
% Embedded Coder license available.
% Otherwise, returns a coder.EmbeddedCodeConfig object.
2
Modify the parameters of the configuration object as required, using one of these
methods:
• Interactive commands, as described in “Specifying Build Configuration
Parameters at the Command Line Using Configuration Objects” on page
19-26
• Dialog boxes, as described in “Specifying Build Configuration Parameters at the
Command Line Using Dialog Boxes” on page 19-30
3
Call the codegen function with the -config option. Specify the configuration object
as its argument.
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19
Generating C/C++ Code from MATLAB Code
The -config option instructs codegen to generate code for the target, based on
the configuration property values. In the following example, codegen generates a
C static library from a MATLAB function, foo, based on the parameters of a code
generation configuration object, cfg, defined in the first step:
codegen -config cfg foo
The -config option specifies the type of output that you want to build — in this
case, a C static library. For more information, see codegen.
Creating Configuration Objects
You can define a configuration object in the MATLAB workspace.
To Create...
Use a Command Such As...
MEX configuration object
coder.MexCodeConfig
cfg = coder.config('mex');
Code generation configuration
object for generating a standalone
C/C++ library or executable
coder.CodeConfig
% To generate a static library
cfg = coder.config('lib');
% To generate a dynamic library
cfg = coder.config('dll')
% To generate an executable
cfg = coder.config('exe');
Note: If an Embedded Coder license is available, creates a
coder.EmbeddedCodeConfig object.
If you use concurrent licenses, to disable check out of an
Embedded Coder license, use one of the following commands:
cfg = coder.config('lib', 'ecoder', false)
cfg = coder.config('dll', 'ecoder', false)
cfg = coder.config('exe', 'ecoder', false)
Code generation configuration
object for generating a standalone
C/C++ library or executable for an
embedded target
% To generate a static library
cfg = coder.config('lib');
% To generate a dynamic library
cfg = coder.config('dll')
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Build Setting Configuration
To Create...
coder.EmbeddedCodeConfig
Use a Command Such As...
% To generate an executable
cfg = coder.config('exe');
Note: Requires an Embedded Coder license; otherwise creates
a coder.CodeConfig object.
Hardware implementation
hwcfg = coder.HardwareImplementation
configuration object
coder.HardwareImplementation
Each configuration object comes with a set of parameters, initialized to default values.
You can change these settings, as described in “Modifying Configuration Objects at the
Command Line Using Dot Notation” on page 19-29.
Modifying Configuration Objects at the Command Line Using Dot Notation
You can use dot notation to modify the value of one configuration object parameter at a
time. Use this syntax:
configuration_object.property = value
Dot notation uses assignment statements to modify configuration object properties:
• To specify a main function during C/C++ code generation:
cfg = coder.config('exe');
cfg.CustomInclude = 'c:\myfiles';
cfg.CustomSource = 'main.c';
codegen -config cfg foo
• To automatically generate and launch code generation reports after generating a C/C+
+ static library:
cfg = coder.config('lib');
cfg.GenerateReport= true;
cfg.LaunchReport = true;
codegen -config cfg foo
Saving Configuration Objects
Configuration objects do not automatically persist between MATLAB sessions. Use one of
the following methods to preserve your settings:
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Generating C/C++ Code from MATLAB Code
Save a configuration object to a MAT-file and then load the
MAT-file at your next session
For example, assume you create and customize a MEX configuration object mexcfg in
the MATLAB workspace. To save the configuration object, at the MATLAB prompt,
enter:
save mexcfg.mat mexcfg
The save command saves mexcfg to the file mexcfg.mat in the current folder.
To restore mexcfg in a new MATLAB session, at the MATLAB prompt, enter:
load mexcfg.mat
The load command loads the objects defined in mexcfg.mat to the MATLAB workspace.
Write a script that creates the configuration object and sets
its properties.
You can rerun the script whenever you need to use the configuration object again.
Specifying Build Configuration Parameters at the Command Line Using Dialog Boxes
1
Create a configuration object as described in “Creating Configuration Objects” on
page 19-28.
For example, to create a coder.MexCodeConfig configuration object for MEX code
generation:
mexcfg = coder.config('mex');
2
Open the property dialog box using one of these methods:
• In the MATLAB workspace, double-click the configuration object variable.
• At the MATLAB prompt, issue the open command, passing it the configuration
object variable, as in this example:
open mexcfg
3
In the dialog box, modify configuration parameters as required, then click Apply.
4
Call the codegen function with the -config option. Specify the configuration object
as its argument:
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Write a script that creates the configuration object and sets its properties.
codegen -config mexcfg foo
The -config option specifies the type of output that you want to build. For more
information, see codegen.
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19
Generating C/C++ Code from MATLAB Code
Standard Math Libraries
By default, the MATLAB Coder software generates code that calls the C89/C90 (ANSI
C) library for math operations. Depending on your language choice, you have the option
of changing the standard math library that the code generation software uses. Available
libraries include:
Library Name
Language Support
Standard
C89/C90 (ANSI)
C, C++
ANSI C89/C90 (default)
C99 (ISO)
C, C++
ISO/IEC 9899:1990
C++03 (ISO)
C++
ISO/IEC 14882:2003
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Change the Standard Math Library
Change the Standard Math Library
By default, the MATLAB Coder software uses the ANSI C89/C90 C math library when
generating C or C++ code. If your compiler supports newer language standards, you can
specify a different supported library. To change the library:
• In a project, on the Hardware tab, set the Standard Math Library parameter.
• In a code configuration object, set the TargetLangStandard parameter.
See Also
• “Standard Math Libraries” on page 19-32
• “Specifying Build Configuration Parameters in the Project Settings Dialog Box” on
page 19-26
• “Specifying Build Configuration Parameters at the Command Line Using
Configuration Objects” on page 19-26
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19
Generating C/C++ Code from MATLAB Code
Share Build Configuration Settings
To share build configuration settings between multiple projects or between the project
and command-line workflow, use the project Export settings and Import settings
options.
Export Settings
To export the current project settings to a code generation configuration object stored in
the base workspace:
1
In the top right corner of the project, click the Actions icon ( ) and select Export
settings.
2
In the Export Project Settings dialog box, specify a name for the configuration
object.
MATLAB Coder saves the project settings information in a configuration object with
the specified name in the base workspace.
Project Output Type
Configuration Object
MEX Function
coder.MexCodeConfig
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Share Build Configuration Settings
Project Output Type
Configuration Object
Instrumented MEX Function
C/C++ Static Library
C/C++ Dynamic Library
C/C++ Executable
Without an Embedded Coder license:
coder.CodeConfig
With an Embedded Coder license:
coder.EmbeddedCodeConfig
You can then either import these settings into another project or use it with the
codegen function -config option to generate code at the command line.
Import Settings
To import the settings saved in a code generation configuration object stored in the base
workspace:
1
In the top right corner of the project, click the Actions icon ( ) and select Import
settings.
2
In the Import Project Settings dialog box, select the configuration object that you
want to use.
MATLAB Coder imports the settings saved in the configuration object and uses them
as the current project settings.
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19
Generating C/C++ Code from MATLAB Code
Note: When you import a coder.MexCodeConfig object, if the project output type
is not already set to Instrumented MEX Function, the output type is set to MEX
Function.
See Also
• “Build Setting Configuration” on page 19-19
• coder.config
• “Convert MATLAB Coder Project to MATLAB Script” on page 19-37
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Convert MATLAB Coder Project to MATLAB Script
Convert MATLAB Coder Project to MATLAB Script
You can convert a MATLAB Coder project to the equivalent script of MATLAB
commands. The script reproduces the project in a configuration object and runs the
codegen command. You can:
• Move from a project workflow to a command-line workflow.
• Save the project as a text file that you can share.
1
Suppose that the project file, myproject.prj, is on the search path. Convert
myproject to the script named myscript.m.
coder -tocode myproject -script myscript.m
2
3
myscript.m appears in the current working folder . If a file with the name
myscript.m exists, the coder command overwrites it. If you omit the -script
option, the coder command writes the script to the Command Window.
Make sure that the entry-point functions that are arguments to codegen in the
script are on the search path.
Run the script.
myscript
The following variables appear in the base workspace.
cfg
configuration object
ARGS
types of input arguments, if the project
has entry-point function inputs
GLOBALS
initial values of global data, if the project
has global data
cfg, ARGS, and GLOBALS appear in the workspace only after you run the script.
The type of configuration object depends on the project output type.
Project Output Type
Configuration Object
MEX Function
coder.MexCodeConfig
C/C++ Static Library
Without an Embedded Coder license:
coder.CodeConfig
C/C++ Dynamic Library
C/C++ Executable
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Generating C/C++ Code from MATLAB Code
Project Output Type
Configuration Object
With an Embedded Coder license:
coder.EmbeddedCodeConfig
You can import the settings from the configuration object cfg into a project. See “Share
Build Configuration Settings” on page 19-34.
If a project includes automated fixed-point conversion, the -tocode option of the coder
command generates a pair of scripts for fixed-point conversion and fixed-point code
generation. For an example, see “Convert Fixed-Point Conversion Project to MATLAB
Scripts”.
See Also
coder
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Primary Function Input Specification
Primary Function Input Specification
In this section...
“Why You Must Specify Input Properties” on page 19-39
“Properties to Specify” on page 19-39
“Rules for Specifying Properties of Primary Inputs” on page 19-42
“Methods for Defining Properties of Primary Inputs” on page 19-43
“Define Input Properties by Example at the Command Line” on page 19-44
“Specify Constant Inputs at the Command Line” on page 19-46
“Specify Variable-Size Inputs at the Command Line” on page 19-47
Why You Must Specify Input Properties
Because C and C++ are statically typed languages, MATLAB Coder must determine
the properties of all variables in the MATLAB files at compile time. To infer variable
properties in MATLAB files, MATLAB Coder must be able to identify the properties of
the inputs to the primary function, also known as the top-level or entry-point function.
Therefore, if your primary function has inputs, you must specify the properties of
these inputs, to MATLAB Coder. If your primary function has no input parameters,
MATLAB Coder can compile your MATLAB file without modification. You do not need to
specify properties of inputs to local functions or external functions called by the primary
function.
If you use the tilde (~) character to specify unused function inputs:
• In MATLAB Coder projects, if you want a different type to appear in the generated
code, specify the type. Otherwise, the inputs default to real, scalar doubles.
• When generating code with codegen, you must specify the type of these inputs using
the -args option.
Properties to Specify
If your primary function has inputs, you must specify the following properties for each
input.
For...
Specify properties...
Class
Size
Complexity
numerictype
fimath
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Generating C/C++ Code from MATLAB Code
For...
Specify properties...
Fixed-point
inputs
Each field in
a structure
input
Specify properties for each field according to its class
When a primary input is a structure, the code generation software treats each
field as a separate input. Therefore, you must specify properties for allfields of a
primary structure input in the order that they appear in the structure definition:
• For each field of input structures, specify class, size, and complexity.
• For each field that is fixed-point class, also specify numerictype, and fimath.
Other inputs
Default Property Values
MATLAB Coder assigns the following default values for properties of primary function
inputs.
Property
Default
class
double
size
scalar
complexity
real
numerictype
No default
fimath
MATLAB default fimath object
Specifying Default Values for Structure Fields
In most cases, when you don't explicitly specify values for properties, MATLAB Coder
uses defaults except for structure fields. The only way to name a field in a structure is
to set at least one of its properties. Therefore, you might need to specify default values
for properties of structure fields. For examples, see “Specifying Class and Size of Scalar
Structure” and “Specifying Class and Size of Structure Array”.
Specifying Default fimath Values for MEX Functions
MEX functions generated with MATLAB Coder use the default fimath value in effect
at compile time. If you do not specify a default fimath value, MATLAB Coder uses the
MATLAB default fimath. The MATLAB factory default has the following properties:
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Specify properties for each field according to its class
RoundingMethod: Nearest
OverflowAction: Saturate
ProductMode: FullPrecision
SumMode: FullPrecision
CastBeforeSum: true
For more information, see “fimath for Sharing Arithmetic Rules”.
When running MEX functions that depend on the default fimath value, do not change
this value during your MATLAB session. Otherwise, you receive a run-time warning,
alerting you to a mismatch between the compile-time and run-time fimath values.
For example, suppose you define the following MATLAB function test:
function y = test %#codegen
y = fi(0);
The function test constructs a fi object without explicitly specifying a fimath object.
Therefore, test relies on the default fimath object in effect at compile time. At the
MATLAB prompt, generate the MEX function text_mex to use the factory setting of the
MATLAB default fimath:
codegen test
% codegen generates a MEX function, test_mex,
% in the current folder
Next, run test_mex to display the MATLAB default fimath value:
test_mex
ans =
0
DataTypeMode:
Signedness:
WordLength:
FractionLength:
Fixed-point: binary point scaling
Signed
16
15
Now create a local MATLAB fimath value. so you no longer use the default setting:
F = fimath('RoundingMethod','Floor');
Finally, clear the MEX function from memory and rerun it:
clear test_mex
test_mex
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19
Generating C/C++ Code from MATLAB Code
The mismatch is detected and causes an error:
??? This function was generated with a different default
fimath than the current default.
Error in ==> test_mex
Supported Classes
The following table presents the class names supported by MATLAB Coder.
Class Name
Description
logical
Logical array of true and false values
char
Character array
int8
8-bit signed integer array
uint8
8-bit unsigned integer array
int16
16-bit signed integer array
uint16
16-bit unsigned integer array
int32
32-bit signed integer array
uint32
32-bit unsigned integer array
int64
64-bit signed integer array
uint64
64–bit unsigned integer array
single
Single-precision floating-point or fixed-point
number array
double
Double-precision floating-point or fixed-point
number array
struct
Structure array
embedded.fi
Fixed-point number array
Rules for Specifying Properties of Primary Inputs
When specifying the properties of primary inputs, follow these rules.
• You must specify the class of all primary inputs. If you do not specify the size or
complexity of primary inputs, they default to real scalars.
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Specify properties for each field according to its class
• For each primary function input whose class is fixed point (fi), you must specify the
input numerictype and fimath properties.
• For each primary function input whose class is struct, you must specify the
properties of each of its fields in the order that they appear in the structure definition.
Methods for Defining Properties of Primary Inputs
Method
Advantages
Disadvantages
“Specifying Properties • If you are working in a MATLAB • Not efficient for specifying
of Primary Function
Coder project, easy to use
memory-intensive inputs such as
Inputs in a Project”
large structures and arrays
• Does not alter original MATLAB
code
• MATLAB Coder saves the
definitions in the project file
“Define Input
• Easy to use
• Must be specified at the
Properties by Example • Does not alter original MATLAB
command line every time you
at the Command Line”
invoke codegen (unless you use
code
on page 19-44
a script)
• Designed for prototyping a
function that has a small number • Not efficient for specifying
memory-intensive inputs such as
of primary inputs
Note: If you define
large structures and arrays
input properties
programmatically
in the MATLAB file,
you cannot use this
method
“Define Input
Properties
Programmatically in
the MATLAB File”
• Integrated with MATLAB code;
no need to redefine properties
each time you invoke MATLAB
Coder
• Provides documentation of
property specifications in the
MATLAB code
• Uses complex syntax
• MATLAB Coder project
files do not currently
recognize properties defined
programmatically. If you are
using a project, you must reenter
the input types in the project.
• Efficient for specifying memoryintensive inputs such as large
structures
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Generating C/C++ Code from MATLAB Code
Define Input Properties by Example at the Command Line
• “Command Line Option -args” on page 19-44
• “Rules for Using the -args Option” on page 19-44
• “Specifying Properties of Primary Inputs by Example at the Command Line” on page
19-45
• “Specifying Properties of Primary Fixed-Point Inputs by Example at the Command
Line” on page 19-45
Command Line Option -args
The codegen function provides a command-line option -args for specifying the
properties of primary (entry-point) function inputs as a cell array of example values. The
cell array can be a variable or literal array of constant values. Using this option, you
specify the properties of inputs at the same time as you generate code for the MATLAB
function with codegen .
If you have a test function or script that calls the entry-point MATLAB function with the
required types, you can use coder.getArgTypes to determine the types of the function
inputs. coder.getArgTypes returns a cell array of coder.Type objects that you can
pass to codegen using the -args option. See “Specifying General Properties of Primary
Inputs” for codegen.
Rules for Using the -args Option
When using the -args command-line option to define properties by example, follow these
rules:
• The cell array of sample values must contain the same number of elements as
primary function inputs.
• The order of elements in the cell array must correspond to the order in which inputs
appear in the primary function signature — for example, the first element in the cell
array defines the properties of the first primary function input.
Note: If you specify an empty cell array with the -args option, codegen interprets this
to mean that the function takes no inputs; a compile-time error occurs if the function
does have inputs.
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Specify properties for each field according to its class
Specifying Properties of Primary Inputs by Example at the Command Line
Consider a MATLAB function that adds its two inputs:
function y = mcf(u,v)
%#codegen
y = u + v;
The following examples show how to specify different properties of the primary inputs u
and v by example at the command line:
• Use a literal cell array of constants to specify that both inputs are real scalar doubles:
codegen mcf -args {0,0}
• Use a literal cell array of constants to specify that input u is an unsigned 16-bit, 1by-4 vector and input v is a scalar double:
codegen
mcf -args {zeros(1,4,'uint16'),0}
• Assign sample values to a cell array variable to specify that both inputs are real,
unsigned 8-bit integer vectors:
a = uint8([1;2;3;4])
b = uint8([5;6;7;8])
ex = {a,b}
codegen mcf -args ex
Specifying Properties of Primary Fixed-Point Inputs by Example at the Command Line
To generate a MEX function or C/C++ code for fixed-point MATLAB code, you must
install Fixed-Point Designer software.
Consider a MATLAB function that calculates the square root of a fixed-point number:
%#codegen
function y = sqrtfi(x)
y = sqrt(x);
To specify the properties of the primary fixed-point input x by example, follow these
steps:
1
Define the numerictype properties for x, for example:
T = numerictype('WordLength',32,...
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Generating C/C++ Code from MATLAB Code
'FractionLength',23,...
'Signed',true);
2
Define the fimath properties for x, for example:
F = fimath('SumMode','SpecifyPrecision',...
'SumWordLength',32,...
'SumFractionLength',23,...
'ProductMode','SpecifyPrecision',...
'ProductWordLength',32,...
'ProductFractionLength',23);
3
Create a fixed-point variable with the numerictype and fimath properties that you
just defined, for example:
myeg = { fi(4.0,T,F) };
4
Compile the function sqrtfi using the codegen command, passing the variable
myeg as the argument to the -args option, for example:
codegen sqrtfi -args myeg;
Specify Constant Inputs at the Command Line
If you know that your primary inputs will not change at run time, you can reduce
overhead in the generated code by specifying that the primary inputs are constant
values. Constant inputs are commonly used for flags that control how an algorithm
executes and values that specify the sizes or types of data.
To specify that inputs are constants, use the -args command-line option with a
coder.Constant object. To specify that an input is a constant with the size, class,
complexity, and value of constant_input, use the following syntax:
-args {coder.Constant(constant_input)}
Calling Functions with Constant Inputs
The code generation software compiles constant function inputs into the generated code.
In the generated C or C++ code, function signatures do not contain the constant inputs.
By default, MEX function signatures contain the constant inputs. When you call a MEX
function, you must provide the compile-time constant values. The constant input values
must match the compile-time values. You can control whether a MEX function signature
includes constant inputs and whether the constant input values must match the compiletime values. See “Control Constant Inputs in MEX Function Signatures”.
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Specify properties for each field according to its class
Specifying a Structure as a Constant Input
Suppose you define a structure tmp in the MATLAB workspace to specify the dimensions
of a matrix:
tmp = struct('rows', 2, 'cols', 3);
The following MATLAB function rowcol accepts a structure input p to define matrix y:
function y = rowcol(u,p) %#codegen
y = zeros(p.rows,p.cols) + u;
The following example shows how to specify that primary input u is a double scalar
variable and primary input p is a constant structure:
codegen rowcol -args {0,coder.Constant(tmp)}
Specify Variable-Size Inputs at the Command Line
Variable-size data is data whose size might change at run time. MATLAB supports
bounded and unbounded variable-size data for code generation. Bounded variablesize data has fixed upper bounds. This data can be allocated statically on the stack
or dynamically on the heap. Unbounded variable-size data does not have fixed upper
bounds. This data must be allocated on the heap. You can define inputs to have one or
more variable-size dimensions — and specify their upper bounds — using the -args
option and coder.typeof function:
-args {coder.typeof(example_value, size_vector, variable_dims}
Specifies a variable-size input with:
• Same class and complexity as example_value
• Same size and upper bounds as size_vector
• Variable dimensions specified by variable_dims
When you enable dynamic memory allocation, you can specify Inf in the size vector for
dimensions with unknown upper bounds at compile time.
When variable_dims is a scalar, it is applied to all the dimensions, with the following
exceptions:
• If the dimension is 1 or 0, which are fixed.
• If the dimension is unbounded, which is always variable size.
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Generating C/C++ Code from MATLAB Code
For more information, see coder.typeof and “Generate Code for Variable-Size Data”.
Specifying a Variable-Size Vector Input
1
Write a function that computes the average of every n elements of a vector A and
stores them in a vector B:
function B = nway(A,n) %#codegen
% Compute average of every N elements of A and put them in B.
coder.extrinsic('error');
if ((mod(numel(A),n) == 0) && (n>=1 && n<=numel(A)))
B = ones(1,numel(A)/n);
k = 1;
for i = 1 : numel(A)/n
B(i) = mean(A(k + (0:n-1)));
k = k + n;
end
else
B = zeros(1,0);
error('n <= 0 or does not divide number of elements evenly');
end
2
Specify the first input A as a vector of double values. Its first dimension stays fixed in
size and its second dimension can grow to an upper bound of 100. Specify the second
input n as a double scalar.
codegen -report nway -args {coder.typeof(0,[1 100],1),1}
3
As an alternative, assign the coder.typeof expression to a MATLAB variable, then
pass the variable as an argument to -args:
vareg = coder.typeof(0,[1 100],1)
codegen -report nway -args {vareg, 0}
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Control Constant Inputs in MEX Function Signatures
Control Constant Inputs in MEX Function Signatures
In this section...
“Control MEX Function Signature Using the Project Interface” on page 19-49
“Control MEX Function Signature at the Command-Line Interface” on page 19-49
“Options for Controlling Constant Inputs in MEX Function Signatures” on page
19-49
“Call MEX Function with a Constant Input” on page 19-51
“See Also” on page 19-52
You can control whether a generated MEX function signature includes constant inputs.
If you want to use the same test file to run the original MATLAB function and the MEX
function, then the MEX function signature must contain the constant inputs. You can
also control whether the run-time values of the constant inputs must match the compiletime values. Checking that the values match can slow down execution speed.
Control MEX Function Signature Using the Project Interface
1
2
On the Build tab Settings pane, set Output type to MEX Function.
On the Project Settings dialog box All Settings tab, set Constant Inputs to one of
the menu options. See “Options for Controlling Constant Inputs in MEX Function
Signatures” on page 19-49.
Control MEX Function Signature at the Command-Line Interface
1
Create a code configuration object for MEX code generation.
2
Set the ConstantInputs parameter to 'CheckValues', 'IgnoreValues', or
'Remove' For example:
mexcfg = coder.config('mex');
mexcfg.ConstantInputs = 'IgnoreValues';
For a description of the options, see “Options for Controlling Constant Inputs in
MEX Function Signatures” on page 19-49
Options for Controlling Constant Inputs in MEX Function Signatures
The following table lists the options for the:
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19
Generating C/C++ Code from MATLAB Code
• Constant Inputs setting in a project with Output Type set to MEX.
• ConstantInputs property in a configuration object for MEX code generation.
Constant Inputs (Project)
ConstantInputs (Configuration
Object)
Check values at run time 'CheckValues'
(default)
Description
• The MEX function signature
includes the constant
inputs. When you call the
function, you must provide
the constant inputs.
• The run-time values of the
constant inputs must match
the compile-time values.
When you call the function,
you must provide the value
that was used at compiletime.
• Allows you to use the same
test file to run the original
MATLAB algorithm and the
MEX function.
• Slows down execution of the
MEX function.
• This setting is the default.
Ignore input value
'IgnoreValues'
• The MEX function signature
includes the constant
inputs. When you call the
function, you must provide
the constant inputs.
• The run-time values of the
constant inputs can differ
from the compile-time values.
• Allows you to use the same
test file to run the original
MATLAB algorithm and the
MEX function.
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Control Constant Inputs in MEX Function Signatures
Constant Inputs (Project)
ConstantInputs (Configuration
Object)
Description
Remove from MEX
signature
'Remove'
The MEX function signature
does not include the constant
inputs. When you call the
function, you do not provide the
constant inputs.
Call MEX Function with a Constant Input
This example shows how to call MEX functions that have constant inputs. It shows how
to use the ConstantInputs parameter to control whether the MEX function signature
includes constant inputs and whether the constant input values must match the compiletime values.
Write a function identity that copies its input to its output.
function y = identity(u) %#codegen
y = u;
Create a code configuration object for MEX code generation.
cfg = coder.config('mex');
Generate a MEX function identity_mex with the constant input 42.
codegen identity -config cfg -args {coder.Constant(42)}
Call identity_mex. You must provide the input 42.
identity_mex(42)
ans =
42
Configure ConstantInputs so that the MEX function does not check that the input
value matches the compile-time value.
cfg.ConstantInputs = 'IgnoreValues';
Generate identity_mex with the new configuration.
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Generating C/C++ Code from MATLAB Code
codegen identity -config cfg -args {coder.Constant(42)}
Call identity_mex with a constant input value other than 42 .
identity_mex(50)
ans =
42
The MEX function ignored the input value 50.
Configure ConstantInputs so that the MEX function does not include the constant
input.
cfg.ConstantInputs = 'Remove';
Generate identity_mex with the new configuration.
codegen identity -config cfg -args {coder.Constant(42)}
Call identity_mex. Do not provide the input value .
identity_mex()
ans =
42
See Also
• “Specify Constant Inputs at the Command Line”
• “Define Constant Input Parameters in a Project”
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Define Input Properties Programmatically in the MATLAB File
Define Input Properties Programmatically in the MATLAB File
With MATLAB Coder, you use the MATLAB assert function to define properties of
primary function inputs directly in your MATLAB file.
In this section...
“How to Use assert with MATLAB Coder” on page 19-53
“Rules for Using assert Function” on page 19-59
“Specifying General Properties of Primary Inputs” on page 19-59
“Specifying Properties of Primary Fixed-Point Inputs” on page 19-60
“Specifying Class and Size of Scalar Structure” on page 19-61
“Specifying Class and Size of Structure Array” on page 19-62
How to Use assert with MATLAB Coder
Use the assert function to invoke standard MATLAB functions for specifying the class,
size, and complexity of primary function inputs.
You must use one of the following methods when specifying input properties using the
assert function. Use the exact syntax that is provided; do not modify it.
• “Specify Any Class” on page 19-54
• “Specify fi Class” on page 19-54
• “Specify Structure Class” on page 19-55
• “Specify Fixed Size” on page 19-55
• “Specify Scalar Size” on page 19-55
• “Specify Upper Bounds for Variable-Size Inputs” on page 19-56
• “Specify Inputs with Fixed- and Variable-Size Dimensions” on page 19-56
• “Specify Size of Individual Dimensions” on page 19-56
• “Specify Real Input” on page 19-57
• “Specify Complex Input” on page 19-57
• “Specify numerictype of Fixed-Point Input” on page 19-57
• “Specify fimath of Fixed-Point Input” on page 19-58
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19
Generating C/C++ Code from MATLAB Code
• “Specify Multiple Properties of Input” on page 19-58
Specify Any Class
assert ( isa ( param, 'class_name') )
Sets the input parameter param to the MATLAB class class_name. For example, to set
the class of input U to a 32-bit signed integer, call:
...
assert(isa(U,'int32'));
...
If you set the class of an input parameter to fi, you must also set its numerictype,
see “Specify numerictype of Fixed-Point Input” on page 19-57. You can also set its
fimath properties, see “Specify fimath of Fixed-Point Input” on page 19-58. If you do
not set the fimath properties, codegen uses the MATLAB default fimath value.
If you set the class of an input parameter to struct, you must specify the properties of
all fields in the order that they appear in the structure definition.
Specify fi Class
assert ( isfi ( param ) )
assert ( isa ( param, 'embedded.fi' ) )
Sets the input parameter param to the MATLAB class fi (fixed-point numeric object).
For example, to set the class of input U to fi, call:
...
assert(isfi(U));
...
or
...
assert(isa(U,'embedded.fi'));
...
If you set the class of an input parameter to fi, you must also set its numerictype,
see “Specify numerictype of Fixed-Point Input” on page 19-57. You can also set its
fimath properties, see “Specify fimath of Fixed-Point Input” on page 19-58. If you do
not set the fimath properties, codegen uses the MATLAB default fimath value.
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Define Input Properties Programmatically in the MATLAB File
Specify Structure Class
assert ( isstruct ( param ) )
assert ( isa ( param, 'struct' ) )
Sets the input parameter param to the MATLAB class struct (structure). For example,
to set the class of input U to a struct, call:
...
assert(isstruct(U));
...
or
...
assert(isa(U, 'struct'));
...
If you set the class of an input parameter to struct, you must specify the properties of
all fields in the order they appear in the structure definition.
Specify Fixed Size
assert ( all ( size (param) == [dims ] ) )
Sets the input parameter param to the size specified by dimensions dims. For example,
to set the size of input U to a 3-by-2 matrix, call:
...
assert(all(size(U)== [3 2]));
...
Specify Scalar Size
assert ( isscalar (param ) )
assert ( all ( size (param) == [ 1 ] ) )
Sets the size of input parameter param to scalar. To set the size of input U to scalar, call:
...
assert(isscalar(U));
...
or
...
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19
Generating C/C++ Code from MATLAB Code
assert(all(size(U)== [1]));
...
Specify Upper Bounds for Variable-Size Inputs
assert ( all(size(param)<=[N0 N1 ...]));
assert ( all(size(param)<[N0 N1 ...]));
Sets the upper-bound size of each dimension of input parameter param. To set the upperbound size of input U to be less than or equal to a 3-by-2 matrix, call:
assert(all(size(U)<=[3 2]));
Note: You can also specify upper bounds for variable-size inputs using coder.varsize.
Specify Inputs with Fixed- and Variable-Size Dimensions
assert ( all(size(param)>=[M0 M1 ...]));
assert ( all(size(param)<=[N0 N1 ...]));
When you use assert(all(size(param)>=[M0 M1 ...])) to specify the lower-bound
size of each dimension of an input parameter:
• You must also specify an upper-bound size for each dimension of the input parameter.
• For each dimension, k, the lower-bound Mk must be less than or equal to the upperbound Nk.
• To specify a fixed-size dimension, set the lower and upper bound of a dimension to the
same value.
• Bounds must be non-negative.
To fix the size of the first dimension of input U to 3 and set the second dimension as
variable size with upper-bound of 2, call:
assert(all(size(U)>=[3 0]));
assert(all(size(U)<=[3 2]));
Specify Size of Individual Dimensions
assert (size(param, k)==Nk);
assert (size(param, k)<=Nk);
assert (size(param, k)<Nk);
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Define Input Properties Programmatically in the MATLAB File
You can specify individual dimensions as well as specifying all dimensions
simultaneously or instead of specifying all dimensions simultaneously. The following
rules apply:
• You must specify the size of each dimension at least once.
• The last dimension specification takes precedence over earlier specifications.
Sets the upper-bound size of dimension k of input parameter param. To set the upperbound size of the first dimension of input U to 3, call:
assert(size(U,1)<=3)
To fix the size of the second dimension of input U to 2, call:
assert(size(U,2)==2)
Specify Real Input
assert ( isreal (param ) )
Specifies that the input parameter param is real. To specify that input U is real, call:
...
assert(isreal(U));
...
Specify Complex Input
assert ( ~isreal (param ) )
Specifies that the input parameter param is complex. To specify that input U is complex,
call:
...
assert(~isreal(U));
...
Specify numerictype of Fixed-Point Input
assert ( isequal ( numerictype ( fiparam ), T ) )
Sets the numerictype properties of fi input parameter fiparam to the numerictype
object T. For example, to specify the numerictype property of fixed-point input U as a
signed numerictype object T with 32-bit word length and 30-bit fraction length, use the
following code:
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19
Generating C/C++ Code from MATLAB Code
%#codegen
...
% Define the numerictype object.
T = numerictype(1, 32, 30);
% Set the numerictype property of input U to T.
assert(isequal(numerictype(U),T));
...
Specify fimath of Fixed-Point Input
assert ( isequal ( fimath ( fiparam ), F ) )
Sets the fimath properties of fi input parameter fiparam to the fimath object F. For
example, to specify the fimath property of fixed-point input U so that it saturates on
integer overflow, use the following code:
%#codegen
...
% Define the fimath object.
F = fimath('OverflowMode','saturate');
% Set the fimath property of input U to F.
assert(isequal(fimath(U),F));
...
If you do not specify the fimath properties using assert, codegen uses the MATLAB
default fimath value.
Specify Multiple Properties of Input
assert ( function1 ( params ) &&
function2 ( params ) &&
function3 ( params ) && ... )
Specifies the class, size, and complexity of one or more inputs using a single assert
function call. For example, the following code specifies that input U is a double, complex,
3-by-3 matrix, and input V is a 16-bit unsigned integer:
%#codegen
...
assert(isa(U,'double') &&
~isreal(U) &&
all(size(U) == [3 3]) &&
isa(V,'uint16'));
...
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Define Input Properties Programmatically in the MATLAB File
Rules for Using assert Function
When using the assert function to specify the properties of primary function inputs,
follow these rules:
• Call assert functions at the beginning of the primary function, before control-flow
operations such as if statements or subroutine calls.
• Do not call assert functions inside conditional constructs, such as if, for, while,
and switch statements.
• Use the assert function with MATLAB Coder only for specifying properties of
primary function inputs before converting your MATLAB code to C/C++ code.
• If you set the class of an input parameter to fi, you must also set its numerictype.
See “Specify numerictype of Fixed-Point Input” on page 19-57. You can also set its
fimath properties. See “Specify fimath of Fixed-Point Input” on page 19-58. If you
do not set the fimath properties, codegen uses the MATLAB default fimath value.
• If you set the class of an input parameter to struct, you must specify the class, size,
and complexity of all fields in the order that they appear in the structure definition.
• When you use assert(all(size(param)>=[M0 M1 ...])) to specify the lowerbound size of each dimension of an input parameter:
• You must also specify an upper-bound size for each dimension of the input
parameter.
• For each dimension, k, the lower-bound Mk must be less than or equal to the
upper-bound Nk.
• To specify a fixed-size dimension, set the lower and upper bound of a dimension to
the same value.
• Bounds must be non-negative.
• If you specify individual dimensions, the following rules apply:
• You must specify the size of each dimension at least once.
• The last dimension specification takes precedence over earlier specifications.
Specifying General Properties of Primary Inputs
In the following code excerpt, a primary MATLAB function mcspecgram takes two
inputs: pennywhistle and win. The code specifies the following properties for these
inputs:
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19
Generating C/C++ Code from MATLAB Code
Input
Property
Value
pennywhistle
class
int16
size
220500-by-1 vector
complexity
real (by default)
class
double
size
1024-by-1 vector
complexity
real (by default)
win
%#codegen
function y = mcspecgram(pennywhistle,win)
nx = 220500;
nfft = 1024;
assert(isa(pennywhistle,'int16'));
assert(all(size(pennywhistle) == [nx 1]));
assert(isa(win, 'double'));
assert(all(size(win) == [nfft 1]));
...
Alternatively, you can combine property specifications for one or more inputs inside
assert commands:
%#codegen
function y = mcspecgram(pennywhistle,win)
nx = 220500;
nfft = 1024;
assert(isa(pennywhistle,'int16') && all(size(pennywhistle) == [nx 1]));
assert(isa(win, 'double') && all(size(win) == [nfft 1]));
...
Specifying Properties of Primary Fixed-Point Inputs
To specify fixed-point inputs, you must install Fixed-Point Designer software.
In the following example, the primary MATLAB function mcsqrtfi takes one fixed-point
input x. The code specifies the following properties for this input.
Property
Value
class
fi
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Define Input Properties Programmatically in the MATLAB File
Property
Value
numerictype
numerictype object T, as specified in the primary
function
fimath
fimath object F, as specified in the primary function
size
scalar
complexity
real (by default)
function y = mcsqrtfi(x) %#codegen
T = numerictype('WordLength',32,'FractionLength',23,...
'Signed',true);
F = fimath('SumMode','SpecifyPrecision',...
'SumWordLength',32,'SumFractionLength',23,...
'ProductMode','SpecifyPrecision',...
'ProductWordLength',32,'ProductFractionLength',23);
assert(isfi(x));
assert(isequal(numerictype(x),T));
assert(isequal(fimath(x),F));
y = sqrt(x);
Specifying Class and Size of Scalar Structure
Assume you have defined S as the following scalar MATLAB structure:
S = struct('r',double(1),'i',int8(4));
Here is code that specifies the class and size of S and its fields when passed as an input
to your MATLAB function:
function y = fcn(S)
%#codegen
% Specify the class of the input as struct.
assert(isstruct(S));
% Specify the class and size of the fields r and i
% in the order in which you defined them.
assert(isa(S.r,'double'));
assert(isa(S.i,'int8');
...
In most cases, when you don't explicitly specify values for properties, MATLAB Coder
uses defaults — except for structure fields. The only way to name a field in a structure
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19
Generating C/C++ Code from MATLAB Code
is to set at least one of its properties. As a minimum, you must specify the class of a
structure field
Specifying Class and Size of Structure Array
For structure arrays, you must choose a representative element of the array for
specifying the properties of each field. For example, assume you have defined S as the
following 1-by-2 array of MATLAB structures:
S = struct('r',{double(1), double(2)},'i',{int8(4), int8(5)});
The following code specifies the class and size of each field of structure input S using the
first element of the array:
%#codegen
function y = fcn(S)
% Specify the class of the input S as struct.
assert(isstruct(S));
% Specify the size of the fields r and i
% based on the first element of the array.
assert(all(size(S) == [1 2]));
assert(isa(S(1).r,'double'));
assert(isa(S(1).i,'int8'));
The only way to name a field in a structure is to set at least one of its properties. As a
minimum, you must specify the class of all fields.
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Speed Up Compilation
Speed Up Compilation
In this section...
“Generate Code Only” on page 19-63
“Disable Compiler Optimization” on page 19-63
Generate Code Only
If you select this option, MATLAB Coder does not invoke the make command or generate
compiled object code. When you want to iterate rapidly between modifying MATLAB code
and generating C/C++ code and you want to inspect the generated code, this option saves
you time during the development cycle .
In the Project Interface
On the project Build tab, select Generate code only.
At the Command Line
Use the codegen -c option to only generate code without invoking the make command.
For example, to generate code only for a function, foo, that takes one single, scalar
input:
codegen -c foo -args {single(0)}
For more information and a complete list of compilation options, see codegen.
Disable Compiler Optimization
Turning compiler optimizations off shortens compile time, but increases run time.
In the Project Interface
1
On the MATLAB Coder project Build tab, verify that the Output type is C/C++
Static Library, C/C++ Dynamic Library or C/C++ Executable.
2
On the Build tab, click the More settings link.
3
In the Project Settings dialog box All Settings tab, under Advanced, set
Compiler optimization level to Off.
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19
Generating C/C++ Code from MATLAB Code
At the Command Line
1
Create a code generation configuration object for C/C++ library or executable. For
example, for a static library:
cfg = coder.config('lib');
2
Set the CCompilerOptimization to Off.
cfg.CCompilerOptimization='Off';
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Paths and File Infrastructure Setup
Paths and File Infrastructure Setup
In this section...
“Compile Path Search Order” on page 19-65
“Specifying Folders to Search for Custom Code” on page 19-65
“Naming Conventions” on page 19-66
Compile Path Search Order
MATLAB Coder resolves MATLAB functions by searching first on the code generation
path and then on the MATLAB path. The code generation path contains the current
folder and the code generation libraries. By default, unless MATLAB Coder determines
that a function should be extrinsic or you explicitly declare the function to be extrinsic,
MATLAB Coder tries to compile and generate code for functions it finds on the path.
MATLAB Coder does not compile extrinsic functions, but rather dispatches them to
the MATLAB interpreter for execution. See “Resolution of Function Calls for Code
Generation”.
Specifying Folders to Search for Custom Code
If you want to integrate custom code — such as source, header, and library files — with
the generated code, you can specify additional folder to search. The following table
describes how to specify these search paths. The path should not contain spaces, as this
can lead to code generation failures in certain operating system configurations. If the
path contains non 7-bit ASCII characters, such as Japanese characters, MATLAB Coder
might not be able to find files on this path.
To specify additional
folders:
Do this:
Using the MATLAB
Coder interface
On the MATLAB Coder project Build tab:
At the command line
1
Click the More settings link.
2
In the Project Settings dialog box, click the Paths tab.
3
For the Search paths field, either browse to add a folder
to the search path or enter the full path. The search path
must not contain spaces.
Use the codegen function -I option.
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19
Generating C/C++ Code from MATLAB Code
Naming Conventions
MATLAB Coder enforces naming conventions for MATLAB functions and generated files.
• “Reserved Prefixes” on page 19-66
• “Reserved Keywords” on page 19-66
• “Conventions for Naming Generated files” on page 19-69
Reserved Prefixes
MATLAB Coder reserves the prefix eml for global C/C++ functions and variables in
generated code. For example, MATLAB for code generation run-time library function
names begin with the prefix emlrt, such as emlrtCallMATLAB. To avoid naming
conflicts, do not name C/C++ functions or primary MATLAB functions with the prefix
eml.
Reserved Keywords
• “C Reserved Keywords” on page 19-66
• “C++ Reserved Keywords” on page 19-67
• “Reserved Keywords for Code Generation” on page 19-67
• “MATLAB Coder Code Replacement Library Keywords” on page 19-68
MATLAB Coder software reserves certain words for its own use as keywords of the
generated code language. MATLAB Coder keywords are reserved for use internal to
MATLAB Coder software and should not be used in MATLAB code as identifiers or
function names. C reserved keywords should also not be used in MATLAB code as
identifiers or function names. If your MATLAB code contains reserved keywords, the
code generation build does not complete and an error message is displayed. To address
this error, modify your code to use identifiers or names that are not reserved.
If you are generating C++ code using the MATLAB Coder software, in addition, your
MATLAB code must not contain the “C++ Reserved Keywords” on page 19-67.
C Reserved Keywords
auto
double
int
struct
break
else
long
switch
case
enum
register
typedef
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Paths and File Infrastructure Setup
char
extern
return
union
const
float
short
unsigned
continue
for
signed
void
default
goto
sizeof
volatile
do
if
static
while
C++ Reserved Keywords
catch
friend
protected
try
class
inline
public
typeid
const_cast
mutable
reinterpret_cast
typename
delete
namespace
static_cast
using
dynamic_cast
new
template
virtual
explicit
operator
this
wchar_t
export
private
throw
Reserved Keywords for Code Generation
abs
fortran
localZCE
rtNaN
asm
HAVESTDIO
localZCSV
SeedFileBuffer
bool
id_t
matrix
SeedFileBufferLen
boolean_T
int_T
MODEL
single
byte_T
int8_T
MT
TID01EQ
char_T
int16_T
NCSTATES
time_T
cint8_T
int32_T
NULL
true
cint16_T
int64_T
NUMST
TRUE
cint32_T
INTEGER_CODE
pointer_T
uint_T
creal_T
LINK_DATA_BUFFER_SIZE PROFILING_ENABLED
creal32_T
LINK_DATA_STREAM
PROFILING_NUM_SAMPLES uint16_T
creal64_T
localB
real_T
uint32_T
cuint8_T
localC
real32_T
uint64_T
uint8_T
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19
Generating C/C++ Code from MATLAB Code
cuint16_T
localDWork
real64_T
UNUSED_PARAMETER
cuint32_T
localP
RT
USE_RTMODEL
ERT
localX
RT_MALLOC
VCAST_FLUSH_DATA
false
localXdis
rtInf
vector
FALSE
localXdot
rtMinusInf
MATLAB Coder Code Replacement Library Keywords
The list of code replacement library (CRL) reserved keywords for your development
environment varies depending on which CRLs currently are registered. Beyond the
default ANSI, ISO, and GNU® CRLs provided with MATLAB Coder software, additional
CRLs might be registered and available for use if you have installed other products that
provide CRLs (for example, a target product), or if you have used Embedded Coder APIs
to create and register custom CRLs.
To generate a list of reserved keywords for the CRLs currently registered in your
environment, use the following MATLAB function:
crl_ids = RTW.TargetRegistry.getInstance.getTflReservedIdentifiers()
This function returns an array of CRL keyword strings. Specifying the return argument
is optional.
Note: To list the CRLs currently registered in your environment, use the MATLAB
command RTW.viewTfl.
To generate a list of reserved keywords for the CRL that you are using to generate code,
call the function passing the name of the CRL as displayed in the Code replacement
library menu on the Code Generation > Interface pane of the Configuration
Parameters dialog box. For example,
crl_ids = RTW.TargetRegistry.getInstance.getTflReservedIdentifiers('GNU99 (GNU)')
Here is a partial example of the function output:
>> crl_ids = RTW.TargetRegistry.getInstance.getTflReservedIdentifiers('GNU99 (GNU)')
crl_ids =
'exp10'
'exp10f'
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Paths and File Infrastructure Setup
'acosf'
'acoshf'
'asinf'
'asinhf'
'atanf'
'atanhf'
...
'rt_lu_cplx'
'rt_lu_cplx_sgl'
'rt_lu_real'
'rt_lu_real_sgl'
'rt_mod_boolean'
'rt_rem_boolean'
'strcpy'
'utAssert'
Note: Some of the returned keyword strings appear with the suffix $N, for example,
'rt_atan2$N'. $N expands into the suffix _snf only if nonfinite numbers are
supported. For example, 'rt_atan2$N' represents 'rt_atan2_snf' if nonfinite
numbers are supported and 'rt_atan2' if nonfinite numbers are not supported. As a
precaution, you should treat both forms of the keyword as reserved.
Conventions for Naming Generated files
The following table describes how MATLAB Coder names generated files. MATLAB
Coder follows MATLAB conventions by providing platform-specific extensions for MEX
files.
Platform
MEX File Extension
MATLAB Coder Library MATLAB Coder
Extension
Executable Extension
Linus Torvalds'
Linux (32-bit)
.mexglx
.a
None
Linux x86-64
.mexa64
.a
None
Microsoft Windows
(32-bit)
.mexw32
.lib
.exe
Windows x64
.mexw64
.lib
.exe
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19
Generating C/C++ Code from MATLAB Code
Generate Code for Multiple Entry-Point Functions
In this section...
“Advantages of Generating Code for More Than One Entry-Point Function” on page
19-70
“Generating Code for More Than One Entry-Point Function Using the Project Interface”
on page 19-70
“Generating Code for More Than One Entry-Point Function at the Command Line” on
page 19-72
“How to Call an Entry-Point Function in a MEX Function” on page 19-74
“How to Call an Entry-Point Function in a C/C++ Library Function from C/C++ Code”
on page 19-74
Advantages of Generating Code for More Than One Entry-Point Function
Generating a single C/C++ library for more than one entry-point MATLAB function
allows you to:
• Create C/C++ libraries containing multiple, compiled MATLAB files to integrate with
larger C/C++ applications.
• Share code efficiently between library functions.
• Communicate between library functions using shared memory.
Generating a MEX function for more than one entry-point function allows you to validate
entry-point interactions in MATLAB before creating a C/C++ library.
Generating Code for More Than One Entry-Point Function Using the
Project Interface
In the project, in the Entry-Point Files pane on the Overview tab, click the Add files
link. Browse to the file that you want to add. Repeat this action for each entry-point file.
By default, MATLAB Coder:
• Lists the entry-point files alphabetically.
• Generates a MEX function in the current folder. MATLAB Coder names the MEX
function , fun_1_mex. fun_1 is the name of the first entry-point function.
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Generate Code for Multiple Entry-Point Functions
• Stores generated files in the subfolder codegen/mex/fun_1/.
Generating a MEX Function with Two Entry-Point Functions Using the Project Interface
Generate a MEX function with two entry-point functions, ep1 and ep2. Function ep1
takes one input, a single scalar, and ep2 takes two inputs, a double scalar and a double
vector.
1
In a local writable folder, create a MATLAB file, ep1.m, that contains:
function y = ep1(u) %#codegen
y = u;
2
In the same folder, create a MATLAB file, ep2.m, that contains:
function y = ep2(u, v) %#codegen
y = u + v;
3
In the same folder, set up a MATLAB Coder project.
a
At the MATLAB command line, enter:
coder -new ep.prj
By default, the project opens in the MATLAB workspace on the right side.
b
On the project Overview tab, click the Add files link. Browse to the file ep1.m.
Click OK to add the file to the project.
c
Define the type of input u.
The file is displayed on the Overview tab, and the input is undefined.
i
d
On the Overview tab, click the field to the right of the input parameter u
and then, from the list of input options, select single.
ii From the list of size options, select 1 x 1 to specify that u is a scalar.
On the project Overview tab, click the Add files link. Browse to the file ep2.m.
Click OK to add the file to the project.
The file is displayed on the Overview tab, and the inputs are undefined.
e
Define the type of input u.
i
On the Overview tab, click the field to the right of the input parameter u
and then, from the list of input options, select double.
ii
From the list of size options, select 1 x 1 to specify that u is a scalar.
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19
Generating C/C++ Code from MATLAB Code
f
4
Repeat the previous step for input v, setting the Size to 2x1.
In the MATLAB Coder project, click the Build tab.
By default, the Output type is MEX function and the Output file is ep1_mex.
5
On this tab, click the Build button to generate a MEX function using the default
project settings.
MATLAB Coder builds the project and, by default, generates a MEX function,
ep1_mex, in the current folder. MATLAB Coder also generates other supporting files
in a subfolder called codegen/mex/ep1_mex. MATLAB Coder uses the name of the
MATLAB function as the root name for the generated files and creates a platformspecific extension for the MEX file, as described in “Naming Conventions” on page
19-66.
You can now test your MEX function in MATLAB. For more information, see “How to
Call an Entry-Point Function in a MEX Function” on page 19-74.
Generating a C Static Library with Two Entry-Point Functions Using the Project Interface
You can generate a C static library with two entry-point functions, ep1 and ep2,
following the same project setup steps that you use to generate a MEX function.
(See Generating a MEX Function with Two Entry-Point Functions Using the Project
Interface.) When you build the project, set the Output type to C/C++ Static
Library.
MATLAB Coder builds the project and generates a C library, ep1, and supporting files in
the default folder, codegen/lib/ep1.
You can now test your library. For more information, see “How to Call an Entry-Point
Function in a C/C++ Library Function from C/C++ Code” on page 19-74.
Generating Code for More Than One Entry-Point Function at the
Command Line
To generate code for more than one entry-point function, use the following syntax, where
global_options applies to functions, fun_1 through fun_n, and options_n applies
only to the preceding function fun_n.
codegen -global_options fun_1 -options_1 ... fun_n -options_n
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Generate Code for Multiple Entry-Point Functions
By default, codegen:
• Generates a MEX function in the current folder. codegen names the MEX function,
fun_mex. fun is the name of the alphabetically first entry-point function.
Stores generated files in the subfolder codegen/mex/fun_1. fun_1 is the name of
the first entry-point function.
You can specify the output file name and subfolder name using the -o option.
codegen -o out_fun
In this case, codegen:
fun_1 -options_1 ... fun_n -options_n
• Generates a MEX function named out_fun_mex in the current folder.
• Stores generated files in the subfolder codegen/mex/out_fun.
For more information on setting build options at the command line, see codegen.
Generating a MEX Function with Two Entry-Point Functions at the Command Line
Generate a MEX function with two entry-point functions, ep1 and ep2. Function ep1
takes one input, a single scalar, and ep2 takes two inputs, a double scalar and a double
vector. Using the -o option, name the generated MEX function sharedmex.
codegen -o sharedmex ep1 -args single(0) ep2 -args { 0, zeros(1,1024) }
codegen generates a MEX function named sharedmex.mex in the current folder and
stores generated files in the subfolder codegen/mex/sharedmex.
Generating a C/C++ Static Library with Two Entry-Point Functions at the Command Line
Generate standalone C/C++ code and compile it to a library for two entry-point functions,
ep1 and ep2. Function ep1 takes one input, a single scalar, and ep2 takes two inputs, a
double scalar and a double vector. Use the -config:lib option to specify that the target
is a library. Using the -o option, name the generated library sharedlib.
codegen -config:lib -o sharedlib ep1 -args single(0) ep2 ...
-args { 0, zeros(1,1024) }
codegen generates C/C++ library code in the codegen/lib/sharedlib folder.
For information on viewing entry-point functions in the code generation report, see “Code
Generation Reports” on page 19-167.
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19
Generating C/C++ Code from MATLAB Code
How to Call an Entry-Point Function in a MEX Function
To call an entry-point function in a MEX function that has more than one entry point,
use this syntax:
MEX_Function('entry_point_function_name',
... entry_point_function_param1,
... , entry_point_function_paramn)
Calling an Entry-Point Function in a MEX Function
Consider a MEX function, sharedmex, that has entry-point functions ep1 and ep2.
Entry-point function ep1 takes one single scalar input and ep2 takes two inputs, a
double scalar and a double vector.
To call ep1 with an input parameter u, enter:
sharedmex('ep1', u)
To call ep2 with input parameters u and v, enter:
sharedmex('ep2', u, v)
How to Call an Entry-Point Function in a C/C++ Library Function from C/
C++ Code
To call an entry-point function in a C/C++ library function from C/C++ code, write a main
function in C/C++ that:
• Includes the generated header files, which contain the function prototypes for the
entry-point functions.
• Calls the initialize function before calling the entry-point functions for the first time.
• Calls the terminate function after calling the entry-point functions for the last time.
• Configures your target to integrate this custom C/C++ main function with your
generated code, as described in “Specify External File Locations” on page 22-12.
• Generates the C/C++ executable using codegen.
See the example, “Call a C Static Library Function from C Code” on page 22-2.
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Generate Code for Global Data
Generate Code for Global Data
In this section...
“Workflow” on page 19-75
“Declare Global Variables” on page 19-75
“Define Global Data” on page 19-76
“Synchronizing Global Data with MATLAB” on page 19-77
“Define Constant Global Data” on page 19-80
“Limitations of Using Global Data” on page 19-83
Workflow
To generate C/C++ code from MATLAB code that uses global data:
1
Declare the variables as global in your code.
2
Before using the global data, define and initialize it.
For more information, see “Define Global Data” on page 19-76.
3
Generate code from the MATLAB Coder project interface or using codegen.
If you use global data, you must also specify whether you want to synchronize this
data between MATLAB and the generated MEX function. For more information, see
“Synchronizing Global Data with MATLAB” on page 19-77.
Declare Global Variables
When using global data, you must first declare the global variables in your MATLAB
code. Consider the use_globals function that uses two global variables AR and B:
function y = use_globals(u)
%#codegen
% Turn off inlining to make
% generated code easier to read
coder.inline('never');
% Declare AR and B as global variables
global AR;
global B;
AR(1) = u + B(1);
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19
Generating C/C++ Code from MATLAB Code
y = AR * 2;
Define Global Data
You can define global data either in the MATLAB global workspace, in a MATLAB Coder
project, or at the command line. If you do not initialize global data in a project or at the
command line, MATLAB Coder looks for the variable in the MATLAB global workspace.
If the variable does not exist, MATLAB Coder generates an error.
Defining Global Data in the MATLAB Global Workspace
To generate a MEX function for the use_globals function described in “Declare Global
Variables” on page 19-75 using codegen:
1
In the MATLAB workspace, define and initialize the global data. At the MATLAB
prompt, enter:
global AR B;
AR = ones(4);
B=[1 2 3];
2
Generate a MEX file.
codegen use_globals -args {0}
% Use the -args option to specify that the input u
% is a real, scalar, double
% By default, codegen generates a MEX function,
% use_globals_mex, in the current folder
Defining Global Data in a MATLAB Coder Project
1
On the project Overview tab, click Add global and enter a name for the global
variable.
By default, MATLAB Coder names the first global variable in a project g, and
subsequent global variables g1, g2, etc.
2
After adding a global variable, before building the project, specify its type and initial
value. For more information, see “Specifying Global Variable Type and Initial Value
in a Project”.
Note: If you do not specify the type, you must create a variable with the same name
in the global workspace.
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Generate Code for Global Data
Defining Global Data at the Command Line
To define global data at the command line, use the codegen -globals option. For
example, to compile the use_globals function described in “Declare Global Variables”
on page 19-75, specify two global inputs AR and B at the command line. Use the
-args option to specify that the input u is a real, scalar double. By default, codegen
generates a MEX function, use_globals_mex, in the current folder.
codegen -globals {'AR',ones(4),'B',[1 2 3]} use_globals -args {0}
Alternatively, specify the type and initial value with the -globals flag using the format
-globals {'g', {type, initial_value}}.
Defining Variable-Size Global Data
To provide initial values for variable-size global data, specify the type and initial
value with the -globals flag using the format -globals {'g', {type,
initial_value}}. For example, to specify a global variable g1 that has an initial value
[1 1] and upper bound [2 2], enter:
codegen foo -globals {'g1', {coder.typeof(0, [2 2],1),[1 1]}}
For a detailed explanation of the syntax, see coder.typeof.
Synchronizing Global Data with MATLAB
Why Synchronize Global Data?
The generated MEX function and MATLAB each have their own copies of global data.
To make these copies consistent, you must synchronize their global data whenever the
two interact. If you do not synchronize the data, their global variables might differ. The
level of interaction determines when to synchronize global data. For more information,
see “When to Synchronize Global Data” on page 19-78.
When global data is constant, you cannot synchronize the global data with MATLAB. By
default, the MEX function tests for consistency between the compile-time constant global
values and the MATLAB values at function entry and after extrinsic function calls. If the
MATLAB values differ from the compile-time constant global values, the MEX function
ends with an error. For information about controlling when the MEX function tests for
consistency between the compile-time constant global values and the MATLAB values,
see “Consistency Between MATLAB and Constant Global Data” on page 19-82.
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19
Generating C/C++ Code from MATLAB Code
When to Synchronize Global Data
By default, synchronization between the MEX function's global data and MATLAB occurs
at MEX function entry and exit and for extrinsic calls. Use this synchronization method
for maximum consistency between the MEX function and MATLAB.
To improve performance, you can:
• Select to synchronize only at MEX function entry and exit points.
• Disable synchronization when the global data does not interact.
• Choose whether to synchronize before and after each extrinsic call.
The following table summarizes which global data synchronization options to use. To
learn how to set these options, see “How to Synchronize Global Data” on page 19-79.
Global Data Synchronization Options
If you want to...
Set the global data
synchronization mode
to:
Synchronize before and after
extrinsic calls?
Have maximum consistency
when all extrinsic calls modify
global data.
At MEX-function
entry, exit and
extrinsic calls
(default)
Yes. Default behavior.
Have maximum consistency
At MEX-function
when most extrinsic calls modify entry, exit and
global data, but a few do not.
extrinsic calls
(default)
Yes. Use the
coder.extrinsic sync:off option to turn
off synchronization for the
extrinsic calls that do not
change global data.
Have maximum consistency
At MEX-function
when most extrinsic calls do not entry and exit
modify global data, but a few do.
Yes. Use the
coder.extrinsic -sync:on
option to synchronize only the
calls that modify global data.
Maximize performance when
synchronizing global data, and
none of your extrinsic calls
modify global data.
No.
At MEX-function
entry and exit
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Generate Code for Global Data
If you want to...
Set the global data
synchronization mode
to:
Synchronize before and after
extrinsic calls?
Communicate between
generated MEX functions
only. No interaction between
MATLAB and MEX function
global data.
Disabled
No.
How to Synchronize Global Data
To control global data synchronization, set the global data synchronization mode and
select whether to synchronize extrinsic functions. For guidelines on which options to use,
see “When to Synchronize Global Data” on page 19-78.
You can control the global data synchronization mode from the project settings
dialog box, the command line, or a MEX configuration dialog box. You control the
synchronization of data with extrinsic functions using the coder.extrinsic -sync:on
and -sync:off options.
Controlling the Global Data Synchronization Mode in the Project Settings Dialog Box
1
On the MATLAB Coder project Build tab, verify that Output type is set to MEX
Function and then click the More settings link.
2
On the Project Settings dialog box Memory tab, set Global data
synchronization mode to At MEX-function entry and exit or Disabled, as
applicable.
Controlling the Global Data Synchronization Mode from the Command Line
1
In the MATLAB workspace, define the code generation configuration object. At the
MATLAB command line, enter:
mexcfg = coder.config('mex');
2
At the MATLAB command line, set the GlobalDataSyncMethod property to
SyncAtEntryAndExits or NoSync, as applicable. For example:
mexcfg.GlobalDataSyncMethod = 'SyncAtEntryAndExits';
3
When compiling your code, use the mexcfg configuration object. For example, to
generate a MEX function for function foo that has no inputs:
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19
Generating C/C++ Code from MATLAB Code
codegen -config mexcfg foo
Controlling Synchronization for Extrinsic Function Calls
To control whether synchronization between MATLAB and MEX function global data
occurs before and after you call an extrinsic function, use the coder.extrinsicsync:on and -sync:off options.
By default, global data is:
• Synchronized before and after each extrinsic call, if the global data synchronization
mode is At MEX-function entry, exit and extrinsic calls. If you are sure
that certain extrinsic calls do not change global data, turn off synchronization for
these calls using the -sync:off option. For example, if functions foo1 and foo2 do
not change global data, turn off synchronization for these functions:
coder.extrinsic('-sync:off', 'foo1', 'foo2');
• Not synchronized, if the global data synchronization mode is At MEX-function
entry and exit. If the code has a few extrinsic calls that change global data,
turn on synchronization for these calls using the -sync:on option. For example,
if functions foo1 and foo2 change global data, turn on synchronization for these
functions:
coder.extrinsic('-sync:on', 'foo1', 'foo2');
• Not synchronized, if the global data synchronization mode is Disabled. When
synchronization is disabled, you cannot use the -sync:on option to control the
synchronization for specific extrinsic calls.
Define Constant Global Data
If you know that the value of a global variable does not change at run time, you can
reduce overhead in the generated code by specifying that the global variable has a
constant value. You cannot write to the constant global variable.
Define Constant Global Data Using the Project Interface
1
On the project Overview tab, click the field to the right of the global variable.
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Generate Code for Global Data
2
3
Select Define Constant Value.
in the field to the right of the global variable, enter a MATLAB expression.
Define Constant Global Data at the Command-Line Interface
To specify that a global variable is constant using the codegen command, use the globals option with the coder.Constant class.
1
Define a configuration object for the code generation output type that you want. For
example, define a configuration object for MEX code generation:
2
Use coder.Constant to specify that a global variable has a constant value. For
example, the following code specifies that the global variable g has initial value 4
and that global variable gc has the constant value 42.
cfg = coder.config('mex');
global_values = {'g', 4, 'gc', coder.Constant(42)};
3
Generate the code using the -globals option. For example, generate code for
myfunction specifying that the global variables are defined in the cell array
global_values.
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19
Generating C/C++ Code from MATLAB Code
codegen -config cfg -globals global_values myfunction
Consistency Between MATLAB and Constant Global Data
By default, the generated MEX function verifies that the values of constant global data
in the MATLAB workspace are consistent with the compile-time values in the generated
MEX. It tests for consistency at function entry and after calls to extrinsic functions.
If the MEX function detects an inconsistency, it end