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SciPy Matlab Arrays

SciPy provides utilities to read and write MATLAB files via the scipy.io module. This allows researchers and developers to seamlessly share data between Python and MATLAB environments, making SciPy a versatile tool for cross-platform scientific computing.

Key Topics

Loading MATLAB Data

The loadmat function reads MATLAB .mat files and returns a dictionary-like object, where the keys are variable names, and the values are the corresponding arrays or objects.

Example

from scipy.io import loadmat

# Load a MATLAB file
data = loadmat('example.mat')
print(data)

Output

{'__header__': b'MATLAB 5.0 MAT-file, Platform: ...', 'variable1': array(...), ...}

Explanation: The returned dictionary includes metadata (e.g., __header__) along with variables saved in the MATLAB file. You can access each variable by its dictionary key.

Saving MATLAB Data

Use savemat to write dictionaries or NumPy arrays to a .mat file. Specify the filename and a dictionary containing your data.

Example

import numpy as np
from scipy.io import savemat

arr = np.array([[1, 2], [3, 4]])
savemat('output.mat', {'myarray': arr})
print("Data saved to output.mat")

Output

Data saved to output.mat

Data Conversions

Be mindful of data types and structures (e.g., MATLAB uses 1-based indexing, while Python uses 0-based). NumPy arrays often map directly to MATLAB matrices, but nested cells or structures may require custom handling.

Example: Handling Nested Structures

from scipy.io import loadmat

# Load a MATLAB file with nested structures
data = loadmat('nested_example.mat')

# Access nested structure
nested_data = data['nested_structure'][0,0]
print(nested_data['field1'])
print(nested_data['field2'])

Output

[1 2 3]
[4 5 6]

Explanation: Accessing nested structures in MATLAB files requires indexing into the array and then accessing the fields.

Key Takeaways

  • Interoperability: Easily transfer data between Python and MATLAB.
  • Metadata Preservation: MATLAB headers and variable names are retained.
  • Simple API: loadmat and savemat for convenient file operations.
  • Type Handling: Pay attention to indexing and variable types across platforms.
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