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Khamisi Kibet

Khamisi Kibet

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I am a computer scientist, software developer, and YouTuber, as well as the developer of this website, spinncode.com. I create content to help others learn and grow in the field of software development.

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7 Months ago | 44 views

**Course Title:** MATLAB Programming: Applications in Engineering, Data Science, and Simulation **Section Title:** Packaging, Deployment, and Version Control **Topic:** Package a MATLAB project and deploy it as a standalone application or share it as a toolbox.(Lab topic) **Objective:** In this lab, you will learn how to package a MATLAB project and deploy it as a standalone application or share it as a toolbox. You will use MATLAB's packaging and deployment tools to create a deployable application that can be shared with others. **Materials Needed:** * A MATLAB project you want to package and deploy (e.g., a GUI application, a script, or a toolbox) * MATLAB Release 2019b or later * MATLAB Compiler (included with MATLAB) * MATLAB Runtime (optional, but recommended for distributing standalone applications) **Key Concepts:** * Packaging MATLAB projects * Creating standalone applications * Deploying toolboxes * Using MATLAB Compiler and MATLAB Runtime **Step 1: Prepare Your Project** Before packaging your project, make sure you have the following: 1. A well-organized folder structure for your project 2. All necessary files (e.g., .m files, .mat files, images) 3. Any required third-party libraries or dependencies Review your project's content and structure to ensure it is self-contained and ready for packaging. **Step 2: Create a Toolbox** To create a toolbox, you need to package your project into a single folder with the following structure: ``` +mytoolbox |-+toolbox | |-+translations | | |- default.po | | |- locale.po (optional) | |-math_functions | |- utilities | |- toolbox.m |-README.md |- Changelog.md ``` This structure includes: * `+mytoolbox`: The root folder for your toolbox * `+toolbox`: A subfolder containing your toolbox files * `translations`: A subfolder for storing translation files * `math_functions`, `utilities`, etc.: Subfolders for your toolbox functions and utilities * `toolbox.m`: A file that defines your toolbox's name, version, and dependencies * `README.md` and `Changelog.md`: Files for describing your toolbox and tracking changes To create a toolbox, you can use the ` matlab.addons.toolbox` function. Here's an example: ```matlab %% Create a toolbox folder toolbox_folder = '+mytoolbox'; %% Add toolbox files and folders matlab.addons.toolbox ... ('ToolboxName', 'My Toolbox', ... 'ToolboxVersion', '1.0', ... 'ToolboxDescription', 'My Toolbox Description', ... 'ToolboxIcon', [], ... 'ToolboxLicense', [], ... 'ToolboxURL', [], ... 'ToolboxFileNames', {'+mytoolbox/toolbox.m'}); %% Save the toolbox matlab.addons.toolbox('ToolboxSave', true); ``` **Step 3: Compile and Package Your Project** To deploy your project as a standalone application or toolbox, you need to use the MATLAB Compiler. Here's an example: ```matlab %% Compile the project project = {[pwd ','filesep,'myGUI.m']}; compile_command = {[project],'-nodisplay', ... '-nojvm', ... '-a','mylib.lib', ... '-W', 'compopt', '-a', ... 'my_app.prj'}; compile_command = sprintf('%s %s', 'mcc', compile_command{:}); system(compile_command); ``` This code compiles the `myGUI.m` file and creates a standalone application. **Step 4: Create a Standalone Application** To create a standalone application, you need to create a `.prj` file that defines the application's settings. Here's an example: ```matlab %% Create a project file project = {[pwd filesep 'my_app.prj']}; %% Define the application settings options = {'-nodisplay', ... '-nojvm', ... '-a', 'mylib.lib', ... '-W', 'compopt', '-a'}; options = sprintf('%s %s', options{:}); %% Save the project file fid = fopen(project{:}, 'w'); fprintf(fid, 'version 8.7\n'); fprintf(fid, 'type = application\n'); fprintf(fid, 'requirelib = ""\n'); fprintf(fid, 'filename = "my_app.exe"\n'); fprintf(fid, 'output = ""\n'); fprintf(fid, 'title = "My Application"\n'); fprintf(fid, 'icon = ""\n'); fprintf(fid, 'version = "1.0"\n'); fprintf(fid, 'author = "Your Name"\n'); fprintf(fid, 'toolchain = ""\n'); fclose(fid); ``` **Step 5: Deploy Your Application** To deploy your standalone application, you can use the ` mcc` command. Here's an example: ```matlab %% Deploy the application project = {[pwd filesep 'my_app.prj']}; deploy_command = {[project],'-nodisplay', ... '-nojvm', ... '-a','mylib.lib', ... '-W', 'compopt', '-a'}; deploy_command = sprintf('%s %s', 'mcc', deploy_command{:}); system(deploy_command); ``` **Troubleshooting:** * If you encounter issues with the compilation or deployment process, review the MATLAB Compiler documentation and the ` mcc` command documentation for more information. * Make sure you have the necessary dependencies and libraries installed on your system. **Conclusion:** In this lab, you learned how to package a MATLAB project and deploy it as a standalone application or share it as a toolbox. You used MATLAB's packaging and deployment tools to create a deployable application that can be shared with others. **Additional Resources:** * MATLAB Compiler Documentation: [www.mathworks.com/help/compiler/index.html](http://www.mathworks.com/help/compiler/index.html) * MATLAB Runtime Documentation: [www.mathworks.com/help/compiler/deploying-to-standalone-targets.html](http://www.mathworks.com/help/compiler/deploying-to-standalone-targets.html) * MATLAB Add-Ons Documentation: [www.mathworks.com/help/matlab/add-ons/index.html](http://www.mathworks.com/help/matlab/add-ons/index.html) **Questions or Comments?** If you have any questions or need help with this lab, please leave a comment below. [ // You can leave a comment below Disclaimer: The content of this answer is based on the latest MATLAB documentation available at the time of writing.
Course

Deploying MATLAB Projects as Standalone Applications or Toolboxes

**Course Title:** MATLAB Programming: Applications in Engineering, Data Science, and Simulation **Section Title:** Packaging, Deployment, and Version Control **Topic:** Package a MATLAB project and deploy it as a standalone application or share it as a toolbox.(Lab topic) **Objective:** In this lab, you will learn how to package a MATLAB project and deploy it as a standalone application or share it as a toolbox. You will use MATLAB's packaging and deployment tools to create a deployable application that can be shared with others. **Materials Needed:** * A MATLAB project you want to package and deploy (e.g., a GUI application, a script, or a toolbox) * MATLAB Release 2019b or later * MATLAB Compiler (included with MATLAB) * MATLAB Runtime (optional, but recommended for distributing standalone applications) **Key Concepts:** * Packaging MATLAB projects * Creating standalone applications * Deploying toolboxes * Using MATLAB Compiler and MATLAB Runtime **Step 1: Prepare Your Project** Before packaging your project, make sure you have the following: 1. A well-organized folder structure for your project 2. All necessary files (e.g., .m files, .mat files, images) 3. Any required third-party libraries or dependencies Review your project's content and structure to ensure it is self-contained and ready for packaging. **Step 2: Create a Toolbox** To create a toolbox, you need to package your project into a single folder with the following structure: ``` +mytoolbox |-+toolbox | |-+translations | | |- default.po | | |- locale.po (optional) | |-math_functions | |- utilities | |- toolbox.m |-README.md |- Changelog.md ``` This structure includes: * `+mytoolbox`: The root folder for your toolbox * `+toolbox`: A subfolder containing your toolbox files * `translations`: A subfolder for storing translation files * `math_functions`, `utilities`, etc.: Subfolders for your toolbox functions and utilities * `toolbox.m`: A file that defines your toolbox's name, version, and dependencies * `README.md` and `Changelog.md`: Files for describing your toolbox and tracking changes To create a toolbox, you can use the ` matlab.addons.toolbox` function. Here's an example: ```matlab %% Create a toolbox folder toolbox_folder = '+mytoolbox'; %% Add toolbox files and folders matlab.addons.toolbox ... ('ToolboxName', 'My Toolbox', ... 'ToolboxVersion', '1.0', ... 'ToolboxDescription', 'My Toolbox Description', ... 'ToolboxIcon', [], ... 'ToolboxLicense', [], ... 'ToolboxURL', [], ... 'ToolboxFileNames', {'+mytoolbox/toolbox.m'}); %% Save the toolbox matlab.addons.toolbox('ToolboxSave', true); ``` **Step 3: Compile and Package Your Project** To deploy your project as a standalone application or toolbox, you need to use the MATLAB Compiler. Here's an example: ```matlab %% Compile the project project = {[pwd ','filesep,'myGUI.m']}; compile_command = {[project],'-nodisplay', ... '-nojvm', ... '-a','mylib.lib', ... '-W', 'compopt', '-a', ... 'my_app.prj'}; compile_command = sprintf('%s %s', 'mcc', compile_command{:}); system(compile_command); ``` This code compiles the `myGUI.m` file and creates a standalone application. **Step 4: Create a Standalone Application** To create a standalone application, you need to create a `.prj` file that defines the application's settings. Here's an example: ```matlab %% Create a project file project = {[pwd filesep 'my_app.prj']}; %% Define the application settings options = {'-nodisplay', ... '-nojvm', ... '-a', 'mylib.lib', ... '-W', 'compopt', '-a'}; options = sprintf('%s %s', options{:}); %% Save the project file fid = fopen(project{:}, 'w'); fprintf(fid, 'version 8.7\n'); fprintf(fid, 'type = application\n'); fprintf(fid, 'requirelib = ""\n'); fprintf(fid, 'filename = "my_app.exe"\n'); fprintf(fid, 'output = ""\n'); fprintf(fid, 'title = "My Application"\n'); fprintf(fid, 'icon = ""\n'); fprintf(fid, 'version = "1.0"\n'); fprintf(fid, 'author = "Your Name"\n'); fprintf(fid, 'toolchain = ""\n'); fclose(fid); ``` **Step 5: Deploy Your Application** To deploy your standalone application, you can use the ` mcc` command. Here's an example: ```matlab %% Deploy the application project = {[pwd filesep 'my_app.prj']}; deploy_command = {[project],'-nodisplay', ... '-nojvm', ... '-a','mylib.lib', ... '-W', 'compopt', '-a'}; deploy_command = sprintf('%s %s', 'mcc', deploy_command{:}); system(deploy_command); ``` **Troubleshooting:** * If you encounter issues with the compilation or deployment process, review the MATLAB Compiler documentation and the ` mcc` command documentation for more information. * Make sure you have the necessary dependencies and libraries installed on your system. **Conclusion:** In this lab, you learned how to package a MATLAB project and deploy it as a standalone application or share it as a toolbox. You used MATLAB's packaging and deployment tools to create a deployable application that can be shared with others. **Additional Resources:** * MATLAB Compiler Documentation: [www.mathworks.com/help/compiler/index.html](http://www.mathworks.com/help/compiler/index.html) * MATLAB Runtime Documentation: [www.mathworks.com/help/compiler/deploying-to-standalone-targets.html](http://www.mathworks.com/help/compiler/deploying-to-standalone-targets.html) * MATLAB Add-Ons Documentation: [www.mathworks.com/help/matlab/add-ons/index.html](http://www.mathworks.com/help/matlab/add-ons/index.html) **Questions or Comments?** If you have any questions or need help with this lab, please leave a comment below. [ // You can leave a comment below Disclaimer: The content of this answer is based on the latest MATLAB documentation available at the time of writing.

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MATLAB Programming: Applications in Engineering, Data Science, and Simulation

Course

Objectives

  • Gain a solid understanding of MATLAB's syntax and programming environment.
  • Learn how to perform mathematical computations and visualizations using MATLAB.
  • Develop skills in working with data, matrices, and arrays in MATLAB.
  • Master the creation of custom functions, scripts, and simulations in MATLAB.
  • Apply MATLAB to solve real-world problems in engineering, data analysis, and scientific research.

Introduction to MATLAB and Environment Setup

  • Overview of MATLAB: History, applications, and use cases in academia and industry.
  • Understanding the MATLAB interface: Command window, editor, workspace, and file structure.
  • Basic MATLAB syntax: Variables, data types, operators, and arrays.
  • Running scripts and creating basic MATLAB programs.
  • Lab: Set up MATLAB, explore the interface, and write a basic script that performs mathematical calculations.

Working with Arrays and Matrices

  • Introduction to arrays and matrices: Creation, indexing, and manipulation.
  • Matrix operations: Addition, subtraction, multiplication, and division.
  • Element-wise operations and the use of built-in matrix functions.
  • Reshaping and transposing matrices.
  • Lab: Create and manipulate arrays and matrices to solve a set of mathematical problems.

MATLAB Control Structures

  • Conditional statements: if-else, switch-case.
  • Looping structures: for, while, and nested loops.
  • Break and continue statements.
  • Best practices for writing clean and efficient control structures.
  • Lab: Write programs that use control structures to solve practical problems involving decision-making and repetition.

Functions and Scripts in MATLAB

  • Understanding MATLAB scripts and functions: Definitions and differences.
  • Creating and calling custom functions.
  • Function input/output arguments and variable scope.
  • Using anonymous and nested functions in MATLAB.
  • Lab: Write custom functions to modularize code, and use scripts to automate workflows.

Plotting and Data Visualization

  • Introduction to 2D plotting: Line plots, scatter plots, bar graphs, and histograms.
  • Customizing plots: Titles, labels, legends, and annotations.
  • Working with multiple plots and subplots.
  • Introduction to 3D plotting: Mesh, surface, and contour plots.
  • Lab: Create visualizations for a given dataset using different types of 2D and 3D plots.

Working with Data: Importing, Exporting, and Manipulating

  • Reading and writing data to/from files (text, CSV, Excel).
  • Working with tables and time series data in MATLAB.
  • Data preprocessing: Sorting, filtering, and handling missing values.
  • Introduction to MATLAB's `datastore` for large data sets.
  • Lab: Import data from external files, process it, and export the results to a different format.

Numerical Computation and Linear Algebra

  • Solving linear systems of equations using matrix methods.
  • Eigenvalues, eigenvectors, and singular value decomposition (SVD).
  • Numerical integration and differentiation.
  • Root-finding methods: Bisection, Newton's method, etc.
  • Lab: Solve real-world problems involving linear systems and numerical methods using MATLAB.

Polynomials, Curve Fitting, and Interpolation

  • Working with polynomials in MATLAB: Roots, derivatives, and integrals.
  • Curve fitting using polyfit and interpolation techniques (linear, spline, etc.).
  • Least squares fitting for data analysis.
  • Visualization of fitted curves and interpolated data.
  • Lab: Fit curves and interpolate data points to model relationships within a dataset.

Simulink and System Modeling

  • Introduction to Simulink for system modeling and simulation.
  • Building block diagrams for dynamic systems.
  • Simulating continuous-time and discrete-time systems.
  • Introduction to control system modeling with Simulink.
  • Lab: Design and simulate a dynamic system using Simulink, and analyze the results.

Solving Differential Equations with MATLAB

  • Introduction to differential equations and MATLAB's ODE solvers.
  • Solving ordinary differential equations (ODEs) using `ode45`, `ode23`, etc.
  • Systems of ODEs and initial value problems (IVPs).
  • Visualizing solutions of differential equations.
  • Lab: Solve a set of ODEs and visualize the results using MATLAB's built-in solvers.

Optimization and Nonlinear Systems

  • Introduction to optimization in MATLAB: `fminsearch`, `fmincon`, etc.
  • Solving unconstrained and constrained optimization problems.
  • Multi-variable and multi-objective optimization.
  • Applications of optimization in engineering and data science.
  • Lab: Solve real-world optimization problems using MATLAB's optimization toolbox.

Image Processing and Signal Processing

  • Introduction to digital image processing with MATLAB.
  • Working with image data: Reading, displaying, and manipulating images.
  • Basic signal processing: Fourier transforms, filtering, and spectral analysis.
  • Visualizing and interpreting image and signal processing results.
  • Lab: Process and analyze image and signal data using MATLAB's built-in functions.

Parallel Computing and Performance Optimization

  • Introduction to parallel computing in MATLAB.
  • Using `parfor`, `spmd`, and distributed arrays for parallel computations.
  • Improving MATLAB code performance: Vectorization and preallocation.
  • Profiling and debugging MATLAB code for performance issues.
  • Lab: Speed up a computationally intensive problem using parallel computing techniques in MATLAB.

Application Development with MATLAB

  • Introduction to MATLAB GUI development using App Designer.
  • Building interactive applications with buttons, sliders, and plots.
  • Event-driven programming and callback functions.
  • Packaging and deploying standalone MATLAB applications.
  • Lab: Develop a simple interactive GUI application using MATLAB's App Designer.

Machine Learning with MATLAB

  • Introduction to machine learning and MATLAB's Machine Learning Toolbox.
  • Supervised learning: Classification and regression.
  • Unsupervised learning: Clustering and dimensionality reduction.
  • Evaluating machine learning models and performance metrics.
  • Lab: Implement a machine learning model using MATLAB to analyze a dataset and make predictions.

Packaging, Deployment, and Version Control

  • Version control for MATLAB projects using Git.
  • MATLAB code packaging: Creating functions, toolboxes, and standalone applications.
  • Deploying MATLAB code to cloud platforms or integrating with other software.
  • Best practices for managing MATLAB projects and collaboration.
  • Lab: Package a MATLAB project and deploy it as a standalone application or share it as a toolbox.

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