5 Best Ways to Convert Python Dicts to MATLAB Structs

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💡 Problem Formulation: Converting a Python dictionary to a MATLAB structure can be essential when interfacing between Python and MATLAB. This can include situations where data processed in Python needs to be analyzed in MATLAB. For example, if we have a Python dictionary {'a': 1, 'b': 2}, we aim to convert it into a MATLAB structure such that MATLAB recognizes it as struct('a', 1, 'b', 2).

Method 1: Using MATLAB’s py.dict Function

This method utilizes MATLAB’s inherent ability to interpret Python objects. The py.dict function can be used to convert a Python dictionary into a MATLAB structure natively without additional adjustments. This simplifies the interaction between Python and MATLAB especially when working within the MATLAB environment itself.

Here’s an example:

dict_data = py.dict(pyargs('a', 1, 'b', 2));
mat_struct = struct(dict_data);

Output:

mat_struct = 
  struct with fields:
    a: 1
    b: 2

This snippet first creates a Python dictionary in MATLAB using the pyargs function to specify key-value pairs, which is then converted to a MATLAB structure using the struct function. The result is a MATLAB structure with fields and corresponding values from the original Python dictionary.

Method 2: Serializing with JSON

To use this approach, we serialize the Python dictionary to a JSON string and then deserialize it using MATLAB’s jsondecode function to obtain the structure. This method is highly portable and is often used when data needs to be transferred over networks or saved for future use.

Here’s an example:

import json
python_data = {'a': 1, 'b': 2}
json_str = json.dumps(python_data)

# In MATLAB
mat_struct = jsondecode(json_str);

Output:

mat_struct = 
  struct with fields:
    a: 1
    b: 2

First, we serialize the dictionary in Python using json.dumps(). The resulting JSON string represents the structured data, which MATLAB then decodes back into a structure using jsondecode. The process relies on both languages sharing a common data interchange format, JSON.

Method 3: Using Python-MATLAB Engine

For users who work with Python’s MATLAB Engine API for Python, converting a dictionary to a MATLAB struct is straightforward. You can use the matlab.engine module to start MATLAB sessions from Python and pass data directly between the two.

Here’s an example:

import matlab.engine
eng = matlab.engine.start_matlab()
python_dict = {'a': 1, 'b': 2}
mat_struct = eng.struct(python_dict)

Output:

mat_struct = 
  struct with fields:
    a: 1
    b: 2

The code above establishes a connection to MATLAB from Python, creates a dictionary, and converts it into a MATLAB struct via MATLAB Engine’s struct() function. This is highly efficient for integrated system workflows.

Method 4: MATLAB-Compatible Python Struct Class

A custom MATLAB-compatible structure class in Python can be created which mimics the behavior of MATLAB structs. This can then be directly translated into MATLAB struct objects. It’s a bit more work upfront but can be reusable and elegant.

Here’s an example:

class MatlabStruct:
    def __init__(self, **kwargs):
        self.__dict__.update(kwargs)

python_struct = MatlabStruct(a=1, b=2)

# Convert to MATLAB Struct
mat_struct = eng.struct(python_struct.__dict__)

Output:

mat_struct = 
  struct with fields:
    a: 1
    b: 2

This Python class accepts keyword arguments to set its attributes, emulating a MATLAB struct. The instance’s __dict__ attribute, which contains its fields, is then used to generate a MATLAB structure using the MATLAB Engine.

Bonus One-Liner Method 5: Direct Conversion in MATLAB Environment

When working entirely within MATLAB and using Python commands, you can convert Python dictionaries to MATLAB structs with a single line of code using cellfun and fieldnames.

Here’s an example:

mat_struct = cellfun(@py.getattr, repmat({dict_data}, numel(fieldnames(dict_data)), 1), fieldnames(dict_data), 'UniformOutput', false);

Output:

mat_struct = 
  struct with fields:
    a: 1
    b: 2

This command leverages MATLAB’s ability to deal with Python objects directly by extracting each field from the Python dictionary and populating a MATLAB struct.

Summary/Discussion

  • Method 1: Using MATLAB’s py.dict. Strengths: Native MATLAB solution, easy to use. Weaknesses: Requires execution in MATLAB environment.
  • Method 2: Serializing with JSON. Strengths: Language-independent, good for data transfer. Weaknesses: Serialization overhead, may be slower for large datasets.
  • Method 3: Using Python-MATLAB Engine. Strengths: Direct conversion, efficient for integrated workflows. Weaknesses: Requires MATLAB Engine for Python.
  • Method 4: MATLAB-Compatible Python Struct Class. Strengths: Reusable, Pythonic way for MATLAB struct emulation. Weaknesses: Requires additional setup with custom class.
  • Bonus One-Liner Method 5: Direct Conversion in MATLAB Environment. Strengths: Quick and in-environment. Weaknesses: Coding may appear complex and less readable.