XML to MATLAB Converter

About XML to MATLAB Converter

Convert XML data to MATLAB code with support for multiple data structures: cell arrays, matrices, struct arrays, and tables. Perfect for data analysis, scientific computing, and importing XML data into MATLAB.

Key Features

• Automatic Parsing: Intelligently extracts tabular data from XML structure
• Multiple Formats: Cell array, matrix, struct array, and table formats
• Type Detection: Automatically detects numeric vs. string data
• Header Support: Optional header row handling
• String Escaping: Properly escapes single quotes in strings
• Attribute Support: Includes XML attributes as columns (prefixed with @)

How to Use

1. Input XML Data: Paste your XML data or upload an .xml file
2. Select Format: Choose the desired MATLAB data structure
3. Configure Options: Toggle header inclusion
4. Copy or Download: Copy the code or download as .m file
5. Run in MATLAB: Execute the code in MATLAB or Octave

Output Formats

1. Cell Array

Flexible format that can hold mixed data types:

data = {
  'id', 'name', 'age'
  '1', 'John Doe', '28'
  '2', 'Jane Smith', '34'
};

2. Matrix (Numeric)

Efficient format for numeric data only:

data = [
  1, 28, 75000
  2, 34, 82000
  3, 45, 95000
];

3. Struct Array

Named fields for structured data access:

data(1) = struct(...
  'id', 1, ...
  'name', 'John Doe', ...
  'age', 28);
data(2) = struct(...
  'id', 2, ...
  'name', 'Jane Smith', ...
  'age', 34);

4. Table (R2013b+)

Modern table format with named columns:

id = [1; 2; 3];
name = {'John Doe'; 'Jane Smith'; 'Bob Johnson'};
age = [28; 34; 45];

data = table(id, name, age);

Example Conversion

XML Input:

<?xml version="1.0"?>
<employees>
  <employee>
    <id>1</id>
    <name>John Doe</name>
    <age>28</age>
  </employee>
  <employee>
    <id>2</id>
    <name>Jane Smith</name>
    <age>34</age>
  </employee>
</employees>

MATLAB Output (Cell Array):

% Cell array with headers
data = {
  'id', 'name', 'age'
  '1', 'John Doe', '28'
  '2', 'Jane Smith', '34'
};

% Access headers: data(1,:)
% Access data: data(2:end,:)

Format Comparison

• Cell Array: Most flexible, handles mixed types, easy to use
• Matrix: Fastest for numeric operations, requires all numeric data
• Struct Array: Named field access, good for complex data
• Table: Modern, supports mixed types, best for data analysis

Supported XML Structures

• Repeating Elements: Automatically detects common record names (row, record, item, entry, employee, product, user)
• Nested Elements: Extracts child element values as columns
• Attributes: Includes XML attributes as columns (prefixed with @)
• Mixed Content: Handles various XML structures intelligently

Common Use Cases

• Data Analysis: Import XML data for statistical analysis
• Scientific Computing: Process experimental data from XML
• Simulation: Load configuration and parameters from XML
• Visualization: Import data for plotting and graphing
• Machine Learning: Prepare datasets from XML sources
• Signal Processing: Import time-series data

MATLAB Tips

• Cell Array Access: Use data{row,col} for cell contents
• Matrix Operations: Use standard matrix operations (+, *, etc.)
• Struct Access: Use data(idx).fieldname or {data.fieldname}
• Table Operations: Use data.VariableName or data{:,col}
• Type Conversion: Use cell2mat(), str2double(), etc. as needed

Compatibility

• MATLAB: All versions (Table format requires R2013b+)
• GNU Octave: Compatible with cell, matrix, and struct formats
• Scilab: Cell and matrix formats with minor syntax adjustments

FAQ

• Which output format should I choose for my MATLAB workflow?

  If you need maximum flexibility and have mixed types, the cell array format is usually the best choice and works in both MATLAB and Octave. For purely numeric data and heavy numerical computations, the matrix format is more efficient. Struct arrays are convenient when you want named fields and record-style access (for example, data(1).salary). Tables are ideal for modern data analysis in MATLAB (R2013b+) where you want column labels, heterogeneous types, and advanced indexing capabilities.

• Why does the tool sometimes fall back from matrix to cell array?

  The matrix format requires that all values in all columns be numeric or empty. If the tool detects any non-numeric value in any cell, it emits a warning comment and switches to a cell array representation instead. This prevents invalid MATLAB syntax and ensures that you still get a correct representation of your data, even if some fields are textual.

• How are missing or empty values represented in the generated MATLAB code?

  For numeric matrices and table columns, empty strings are converted to NaN so that they remain compatible with numeric operations. In cell arrays and struct arrays, empty or missing values remain empty strings unless they are numeric by context. You can always post-process the data in MATLAB to convert these placeholders to more appropriate representations for your specific domain.

• Can I use this tool with very large XML datasets?

  The generator builds MATLAB code by enumerating every value in the XML as a literal in the source. This is ideal for small to medium-sized datasets and for creating reproducible examples or test fixtures. For very large datasets, however, it is more efficient to parse the XML directly in MATLAB using functions like readtable (for compatible formats) or xmlread combined with custom parsing logic. You can still use the generated code as a template for structure and field naming.

• Is my data sent anywhere while converting XML to MATLAB code?

  No. All XML parsing and MATLAB code generation are executed entirely in your browser using client-side JavaScript. Nothing is transmitted to external servers as part of this tool, so it is safe to use with private, proprietary, or unpublished data.

Privacy & Security

All conversions happen locally in your browser. Your XML data is never uploaded to any server, ensuring complete privacy and security.