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MATLAB TIPS
2026年10月17日 00:38
Matlab的一些现成脚本、函数，前人写的技巧、教程、文档。 Just tricks that finally work or what have learnt through search engines.受限于个人领域和Matlab水平，诚邀频道管理者；只要在使用Matlab过程中顺手把用上的网页、帖子发上来就好，十分简单。请联系 @MatLabTipsBot。#MATLAB
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Oct 19, 2026, 1:04 PM
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| Channel Name | MATLAB TIPS |
|---|---|
| Username | @matlab_tips |
| Category | 编码与编程 |
| Language | 中文 |
| Country | 中国 |
| 成员 | 2,639 |
| Channel Type | Public Channel |
| Created | 2025年10月8日 |
| Last Updated | 2026年10月19日 • 前天 |
| Status | Active |
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Top 10 posts
Most viewed posts from this channel
- #1https://www.youtube.com/watch?v=83lV4S2mLlA312
- #2JOSS： https://joss.theoj.org/papers/10.21105/joss.09147258
- #3https://www.youtube.com/watch?v=OgSTjyUhez4257
- #4https://www.youtube.com/watch?v=FBRqqmTx2yI251
- #5JOSS： https://joss.theoj.org/papers/10.21105/joss.09176226
- #6https://mres.uni-potsdam.de/2026/03/18/63rd-online-shortcourse-on-matl220
- #7https://www.youtube.com/watch?v=UP_XEu2Ls_0219
- #8https://www.youtube.com/watch?v=xAbjya_eJNc219
- #9https://www.youtube.com/watch?v=gFX9bFTuuSE214
- #10https://blogs.mathworks.com/finance/?p=2639&s_tid=feedtopost213
最新帖子
MATLAB TIPS
2026年10月16日 19:04
MATLAB TIPS
2026年10月16日 08:37
📡 https://www.mathworks.com/matlabcentral/fileexchange/49762-gplot3-graphs-in-three-dimensions
gplot3 draws every nonzero entry of an adjacency matrix as a 3D line segment. It accepts a line specification or line properties and returns one line handle.``matlabA = [0 1 0; 1 0 1; 0 1 0];xyz = [0 0 0; 1 0 1; 1 1 0];h = gplot3(A, xyz, 'LineWidth', 2);axis equal``A must be square, and xyz must have one row and three coordinates per node. A symmetric adjacency matrix draws each undirected edge twice. Call gplot3 without arguments for MATLAB's buckyball example. For newer MATLAB versions, plot(graph(A), ...) is a native alternative. See the GitHub README and tests for details.
gplot3 draws every nonzero entry of an adjacency matrix as a 3D line segment. It accepts a line specification or line properties and returns one line handle.``matlabA = [0 1 0; 1 0 1; 0 1 0];xyz = [0 0 0; 1 0 1; 1 1 0];h = gplot3(A, xyz, 'LineWidth', 2);axis equal``A must be square, and xyz must have one row and three coordinates per node. A symmetric adjacency matrix draws each undirected edge twice. Call gplot3 without arguments for MATLAB's buckyball example. For newer MATLAB versions, plot(graph(A), ...) is a native alternative. See the GitHub README and tests for details.
MATLAB TIPS
2026年10月16日 04:59
MATLAB TIPS
2026年10月16日 04:34
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MATLAB TIPS
2026年10月16日 00:36
📡 https://www.mathworks.com/matlabcentral/fileexchange/183126-matlab-docmaker
With DocMaker, you can:* write documentation in Markdown and convert to HTML for viewing in MATLAB* run MATLAB scripts and capture output as images for inclusion in documentation* create MATLAB documentation index files from a Markdown table of contents
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With DocMaker, you can:* write documentation in Markdown and convert to HTML for viewing in MATLAB* run MATLAB scripts and capture output as images for inclusion in documentation* create MATLAB documentation index files from a Markdown table of contents
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MATLAB TIPS
2026年10月15日 19:03
MATLAB TIPS
2026年10月14日 19:02
MATLAB TIPS
2026年10月14日 08:32
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MATLAB TIPS
2026年10月14日 08:32
📡 https://www.mathworks.com/matlabcentral/fileexchange/63721-csv-files-processing-for-mocap-with-optitrack
This could be considered as a toolbox for extracting and animating MoCap data obtained from Optitrack's Motive. The file named "csv2struc.m" reads a ".csv" file exported from Motive, for extracting its basic information. This toolbox processes data exported only in Markersets and not in Rigid Bodies or Skeletons. The file named "plotmarkers.m" is a function for animating that MoCap data from "csv2strc". Example files included.
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This could be considered as a toolbox for extracting and animating MoCap data obtained from Optitrack's Motive. The file named "csv2struc.m" reads a ".csv" file exported from Motive, for extracting its basic information. This toolbox processes data exported only in Markersets and not in Rigid Bodies or Skeletons. The file named "plotmarkers.m" is a function for animating that MoCap data from "csv2strc". Example files included.
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MATLAB TIPS
2026年10月12日 19:03
MATLAB TIPS
2026年10月12日 00:24
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MATLAB TIPS
2026年10月11日 16:23
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MATLAB TIPS
2026年10月11日 16:23
📡 https://www.mathworks.com/matlabcentral/fileexchange/80137-simple-robotics-toolbox
This is a simple robotics toolbox designed to study and understand the forward kinematics of robot manipulators. Unlike Peter Corke's toolbox, the representation is not done automatically through the Denavit Hartenberg representation, but the homogeneous transformations are at the user's discretion and therefore the representation is built by the user. This feature enables to represent the kinematic diagram of parallel robots, such as the paralelogram configuration in the Fanuc Robots.All the elements of any kinematic diagram can be represented with solid parts (3D frames, joints and links). The solid links need STL files. If you don't need the representation of solid links, these can be represented with simple lines.The toolbox needs the next included complementary toolboxes/functions:\* FreezeColors toolbox by John Iversen (2005)\* Stlread and patchslim functions by Francis Esmonde-White (2010).A
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This is a simple robotics toolbox designed to study and understand the forward kinematics of robot manipulators. Unlike Peter Corke's toolbox, the representation is not done automatically through the Denavit Hartenberg representation, but the homogeneous transformations are at the user's discretion and therefore the representation is built by the user. This feature enables to represent the kinematic diagram of parallel robots, such as the paralelogram configuration in the Fanuc Robots.All the elements of any kinematic diagram can be represented with solid parts (3D frames, joints and links). The solid links need STL files. If you don't need the representation of solid links, these can be represented with simple lines.The toolbox needs the next included complementary toolboxes/functions:\* FreezeColors toolbox by John Iversen (2005)\* Stlread and patchslim functions by Francis Esmonde-White (2010).A
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MATLAB TIPS
2026年10月10日 12:33
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MATLAB TIPS
2026年10月09日 00:17
📡 https://www.mathworks.com/matlabcentral/fileexchange/89679-fluorescence-tracker-app
# Fluorescence Tracker App https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg">!Open in MATLAB OnlineThis app uses computer vision point tracking to quantify near infrared signals emitted by (ICG) Indocyanine Green during fluorescence angiography. Short overview videos of how to use this app can be found in the https://www.mathworks.com/videos/series/how-to-video-series-for-biomedical-and-pharmaceutical-applications.html">"How To" Video Series for Biomedical and Pharmaceutical Applications.!Fluorescence Tracker AppThe following research in colorectal cancer was carried out using this software: https://www.nature.com/articles/s41598-021-90089-7 by J. Dalli et al., Scientific Reports 11, Article 11349
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# Fluorescence Tracker App https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg">!Open in MATLAB OnlineThis app uses computer vision point tracking to quantify near infrared signals emitted by (ICG) Indocyanine Green during fluorescence angiography. Short overview videos of how to use this app can be found in the https://www.mathworks.com/videos/series/how-to-video-series-for-biomedical-and-pharmaceutical-applications.html">"How To" Video Series for Biomedical and Pharmaceutical Applications.!Fluorescence Tracker AppThe following research in colorectal cancer was carried out using this software: https://www.nature.com/articles/s41598-021-90089-7 by J. Dalli et al., Scientific Reports 11, Article 11349
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MATLAB TIPS
2026年10月08日 00:15
📡 https://www.mathworks.com/matlabcentral/fileexchange/118460-http-progress-bar
Download files from the web and upload files to the web while showing live progress, using MATLAB's HTTP interface. Progress is shown in a waitbar, in a progress dialog attached to an app figure, or in the Command Window. It includes the transferred size, the percentage and an estimate of the remaining time.
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Download files from the web and upload files to the web while showing live progress, using MATLAB's HTTP interface. Progress is shown in a waitbar, in a progress dialog attached to an app figure, or in the Command Window. It includes the transferred size, the percentage and an estimate of the remaining time.
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MATLAB TIPS
2026年10月07日 19:01
MATLAB TIPS
2026年10月05日 19:04
MATLAB TIPS
2026年10月04日 12:08
📡 https://www.mathworks.com/matlabcentral/fileexchange/48-locally-optimal-block-preconditioned-conjugate-gradient
### Titlelobpcg — Locally Optimal Block Preconditioned Conjugate Gradient eigensolver### Short Summarylobpcg computes a few extreme eigenpairs of large Hermitian standard and generalized eigenproblems using a block preconditioned conjugate gradient method. It supports matrix and function-handle operators, optional constraints, optional preconditioning, and iteration histories for convergence analysis. Tested in GNU Octave Version: 11.3.0 and MATLAB with Parallel Computing Toolbox 26.2 (R2026b), but is expected to work on any post 2008 MATLAB and Octave.### Descriptionlobpcg is an iterative eigensolver for large-scale Hermitian problems where forming a full factorization is impractical. It is designed to compute partial spectra efficiently, especially for sparse matrices and operators supplied as functions or function handles.The solver accepts:- a full or sparse initial block of vectors,- a Hermitian operator A,- optional generalized operator B,- optional preconditioner T,- optional constraint vectors Y,- tolerance, iteration limit, and verbosity settings.It returns orthonormalized eigenvectors, the corresponding eigenvalues, a convergence flag, and optional histories of eigenvalue estimates and residual norms.### Features- Standard and generalized Hermitian eigenproblems- Block iterations for clustered and repeated eigenvalues- Optional preconditioning- Optional orthogonality constraints- Supports matrix, function-name, and function-handle operators- Works with sparse and full inputs- Residual and eigenvalue history output for diagnostics- Pure MATLAB (GNU Octave compatible) code- Support for MATLAB distributed or codistributed arrays- Support for double. single, and complex data types - Suitable for MATLAB and GNU Octave workflows### Typical Use CasesThis solver is useful for:- structural mechanics and vibration modes,- graph and network spectral problems,- repeated or clustered eigenvalue problems,- efficient preconditioning available,- large problems where MATLAB's built-in eig is too expensive and eigs is not the desired workflow.### Notes- A does not need to be positive definite- The code computes the algebraically smallest eigenvalues but can trivially be used for the largest simply applied to -A.- B and T should be positive definite when used. There is no check, and the code may break otherwise.- The code is intended for partial eigensolutions, not high-precision full diagonalization.- Performance and convergence depend on block size, preconditioner quality, and the spectral distribution.### Other implementations- A simplified C-version of this code is a part of thehttps://github.com/lobpcg/blopexpackage and is directly available, e.g., in SLEPc and HYPRE.- A Python version LOBPCG is inhttps://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.lobpcg.html- LOBPCG is implemented in many open-source packages in various languages for a great variety of applications; see https://en.wikipedia.org/wiki/LOBPCG- https://mfem.org/examples/ Maxwell Eigenproblem LOBPCG run generates the image used to identify this package.### ReferenceA. V. Knyazev, “Toward the Optimal Preconditioned Eigensolver: Locally Optimal Block Preconditioned Conjugate Gradient Method,” SIAM Journal on Scientific Computing, 23(2), 517–541, 2001.### Docstring%LOBPCG solves Hermitian partial eigenproblems using preconditioning%% [blockVectorX,lambda]=lobpcg(blockVectorX,operatorA)%% outputs the array of algebraic smallest eigenvalues lambda and% corresponding matrix of ortho-normalized eigenvectors blockVectorX of the% Hermitian (full or sparse) operator operatorA using input matrix% blockVectorX as an initial guess, without preconditioning, somewhat% similar to%% opts.issym=1;opts.isreal=1;K=size(blockVectorX,2);% [blockVectorX,lambda]=eigs(operatorA,K,'SR',opts);%% for real symmetric operator operatorA, or%% K=size(blockVectorX,2);[blockVectorX,lambda]=eigs(operatorA,K,'SR');% for
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### Titlelobpcg — Locally Optimal Block Preconditioned Conjugate Gradient eigensolver### Short Summarylobpcg computes a few extreme eigenpairs of large Hermitian standard and generalized eigenproblems using a block preconditioned conjugate gradient method. It supports matrix and function-handle operators, optional constraints, optional preconditioning, and iteration histories for convergence analysis. Tested in GNU Octave Version: 11.3.0 and MATLAB with Parallel Computing Toolbox 26.2 (R2026b), but is expected to work on any post 2008 MATLAB and Octave.### Descriptionlobpcg is an iterative eigensolver for large-scale Hermitian problems where forming a full factorization is impractical. It is designed to compute partial spectra efficiently, especially for sparse matrices and operators supplied as functions or function handles.The solver accepts:- a full or sparse initial block of vectors,- a Hermitian operator A,- optional generalized operator B,- optional preconditioner T,- optional constraint vectors Y,- tolerance, iteration limit, and verbosity settings.It returns orthonormalized eigenvectors, the corresponding eigenvalues, a convergence flag, and optional histories of eigenvalue estimates and residual norms.### Features- Standard and generalized Hermitian eigenproblems- Block iterations for clustered and repeated eigenvalues- Optional preconditioning- Optional orthogonality constraints- Supports matrix, function-name, and function-handle operators- Works with sparse and full inputs- Residual and eigenvalue history output for diagnostics- Pure MATLAB (GNU Octave compatible) code- Support for MATLAB distributed or codistributed arrays- Support for double. single, and complex data types - Suitable for MATLAB and GNU Octave workflows### Typical Use CasesThis solver is useful for:- structural mechanics and vibration modes,- graph and network spectral problems,- repeated or clustered eigenvalue problems,- efficient preconditioning available,- large problems where MATLAB's built-in eig is too expensive and eigs is not the desired workflow.### Notes- A does not need to be positive definite- The code computes the algebraically smallest eigenvalues but can trivially be used for the largest simply applied to -A.- B and T should be positive definite when used. There is no check, and the code may break otherwise.- The code is intended for partial eigensolutions, not high-precision full diagonalization.- Performance and convergence depend on block size, preconditioner quality, and the spectral distribution.### Other implementations- A simplified C-version of this code is a part of thehttps://github.com/lobpcg/blopexpackage and is directly available, e.g., in SLEPc and HYPRE.- A Python version LOBPCG is inhttps://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.lobpcg.html- LOBPCG is implemented in many open-source packages in various languages for a great variety of applications; see https://en.wikipedia.org/wiki/LOBPCG- https://mfem.org/examples/ Maxwell Eigenproblem LOBPCG run generates the image used to identify this package.### ReferenceA. V. Knyazev, “Toward the Optimal Preconditioned Eigensolver: Locally Optimal Block Preconditioned Conjugate Gradient Method,” SIAM Journal on Scientific Computing, 23(2), 517–541, 2001.### Docstring%LOBPCG solves Hermitian partial eigenproblems using preconditioning%% [blockVectorX,lambda]=lobpcg(blockVectorX,operatorA)%% outputs the array of algebraic smallest eigenvalues lambda and% corresponding matrix of ortho-normalized eigenvectors blockVectorX of the% Hermitian (full or sparse) operator operatorA using input matrix% blockVectorX as an initial guess, without preconditioning, somewhat% similar to%% opts.issym=1;opts.isreal=1;K=size(blockVectorX,2);% [blockVectorX,lambda]=eigs(operatorA,K,'SR',opts);%% for real symmetric operator operatorA, or%% K=size(blockVectorX,2);[blockVectorX,lambda]=eigs(operatorA,K,'SR');% for
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MATLAB TIPS
2026年9月13日 22:13
MATLAB TIPS
2026年9月13日 22:13
📡 https://www.mathworks.com/matlabcentral/fileexchange/67060-systems-level-microgrid-simulation-from-one-line-diagram
Simscape Power Systems can be used to schematically represent a one-line microgrid diagram using blocks that represent different distributed energy resources (DERs). The DERs in this example include renewables, such as solar, a diesel GenSet, and an energy storage system (ESS). Using the simple microgrid, you see how desktop simulation can be used to subject the distribution system with residential load changes or unintentional islanding of the microgrid. The included slides detail other common workflows for systems-level microgrid simulation. Using Simulink Real-time, this simple microgrid can quickly be migrated to a real-time machine for hardware-in-the-loop testing.
Simscape Power Systems can be used to schematically represent a one-line microgrid diagram using blocks that represent different distributed energy resources (DERs). The DERs in this example include renewables, such as solar, a diesel GenSet, and an energy storage system (ESS). Using the simple microgrid, you see how desktop simulation can be used to subject the distribution system with residential load changes or unintentional islanding of the microgrid. The included slides detail other common workflows for systems-level microgrid simulation. Using Simulink Real-time, this simple microgrid can quickly be migrated to a real-time machine for hardware-in-the-loop testing.
MATLAB TIPS
2026年9月13日 22:13
📡 https://www.mathworks.com/matlabcentral/fileexchange/169026-dec2hex_ex
Matlab's dec2hex converts a vector of decimals into a charachter array - which is cumbersom to manipulatedec2hex_ex gives a solution for this...while... out = dec2hex([1 10 20]) out = 3×2 char array '01' '0A' '14' Syntax: [out,outCell] = dec2hex_ex(dec_vector,numDigits,addHexPrefix) Inputs: dec_vector: hold 1 or more decimals numDigits : optional, integer, can be given as empty braces ([]) addHexPrefix: optional, default = false, if true: adds '0x' so output could be sent to 'eval' function Outputs: out : The trivial output as char array outCell : Cell arrayoutput Examples: out = dec2hex_ex([1 10 20]) % out = '[1,A,14]' out = dec2hex_ex([1 10 20],3) % out = '[001, 00A, 014]' out = dec2hex_ex([1 10 20],[], 1) % out = '[0x1,0xA,0x14]' [out, outCell] = dec2hex_ex([1 10 20]) % outCell = {'1','A','14'} [out, outCell] = dec2hex_ex([1 10 20],[], 1) % outCell = {'0x1','0xA','0x14'} [out, outCell] = dec2hex_ex([1 10 20],4 , 1) % out = '[0x0001, 0x000A, 0x0014]'
Matlab's dec2hex converts a vector of decimals into a charachter array - which is cumbersom to manipulatedec2hex_ex gives a solution for this...while... out = dec2hex([1 10 20]) out = 3×2 char array '01' '0A' '14' Syntax: [out,outCell] = dec2hex_ex(dec_vector,numDigits,addHexPrefix) Inputs: dec_vector: hold 1 or more decimals numDigits : optional, integer, can be given as empty braces ([]) addHexPrefix: optional, default = false, if true: adds '0x' so output could be sent to 'eval' function Outputs: out : The trivial output as char array outCell : Cell arrayoutput Examples: out = dec2hex_ex([1 10 20]) % out = '[1,A,14]' out = dec2hex_ex([1 10 20],3) % out = '[001, 00A, 014]' out = dec2hex_ex([1 10 20],[], 1) % out = '[0x1,0xA,0x14]' [out, outCell] = dec2hex_ex([1 10 20]) % outCell = {'1','A','14'} [out, outCell] = dec2hex_ex([1 10 20],[], 1) % outCell = {'0x1','0xA','0x14'} [out, outCell] = dec2hex_ex([1 10 20],4 , 1) % out = '[0x0001, 0x000A, 0x0014]'
MATLAB TIPS
2026年9月13日 22:13
📡 https://www.mathworks.com/matlabcentral/fileexchange/62990-deep-learning-tutorial-series
The code provides hands-on examples to implement convolutional neural networks (CNNs) for object recognition. The three demos have associated instructional videos that will allow for a complete tutorial experience to understand and implement deep learning techniques. The demos include:- Training a neural network from scratch- Using a pre-trained model (transfer learning)- Using a neural network as a feature extractor The use of a GPU and Parallel Computing Toolbox™ is recommended when running the examples. Demo 3 requires Statistics and Machine Learning Toolbox™ in addition to the required products below.
The code provides hands-on examples to implement convolutional neural networks (CNNs) for object recognition. The three demos have associated instructional videos that will allow for a complete tutorial experience to understand and implement deep learning techniques. The demos include:- Training a neural network from scratch- Using a pre-trained model (transfer learning)- Using a neural network as a feature extractor The use of a GPU and Parallel Computing Toolbox™ is recommended when running the examples. Demo 3 requires Statistics and Machine Learning Toolbox™ in addition to the required products below.
MATLAB TIPS
2026年9月13日 22:13
MATLAB TIPS
2026年9月13日 22:13
MATLAB TIPS
2026年9月13日 22:13
MATLAB TIPS
2026年9月13日 22:13
📡 https://www.mathworks.com/matlabcentral/fileexchange/64398-catt2matlab
More info in GitHub, clic on 'Learn more'.I'm developing the information, sorry, soon it will be more complete.Work in Windows and OSX. Screenshoots in GitHub.
More info in GitHub, clic on 'Learn more'.I'm developing the information, sorry, soon it will be more complete.Work in Windows and OSX. Screenshoots in GitHub.
MATLAB TIPS
2026年9月13日 22:13
MATLAB TIPS
2026年9月13日 22:13
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MATLAB TIPS
2026年9月13日 22:13
📡 https://www.mathworks.com/matlabcentral/fileexchange/62990-deep-learning-tutorial-series
The code provides hands-on examples to implement convolutional neural networks (CNNs) for object recognition. The three demos have associated instructional videos that will allow for a complete tutorial experience to understand and implement deep learning techniques. The demos include:- Training a neural network from scratch- Using a pre-trained model (transfer learning)- Using a neural network as a feature extractor The use of a GPU and Parallel Computing Toolbox™ is recommended when running the examples. Demo 3 requires Statistics and Machine Learning Toolbox™ in addition to the required products below.
The code provides hands-on examples to implement convolutional neural networks (CNNs) for object recognition. The three demos have associated instructional videos that will allow for a complete tutorial experience to understand and implement deep learning techniques. The demos include:- Training a neural network from scratch- Using a pre-trained model (transfer learning)- Using a neural network as a feature extractor The use of a GPU and Parallel Computing Toolbox™ is recommended when running the examples. Demo 3 requires Statistics and Machine Learning Toolbox™ in addition to the required products below.
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