**π‘ Problem Formulation:** When working with Python and MATLAB simultaneously, one may need to convert data structures that are natively handled in Python, such as dictionaries, to MATLAB structs for compatibility and interoperability. Consider a Python dictionary with nested structures, and the aim is to convert this into a MATLAB struct that preserves the hierarchy and data types, making it easy to work with in MATLAB. This article discusses the best methods to achieve this conversion effectively.

## Method 1: Using MATLAB’s `py.dict`

Function

This method involves MATLAB’s built-in capability to recognize Python objects. Python dictionaries can be passed directly to MATLAB using the `py.dict`

function, which MATLAB inherently understands and can convert to MATLAB structs using `struct`

function.

Here’s an example:

pyDict = py.dict(pyargs('a', 1, 'b', 2, 'c', py.list({3, 4}))); matlabStruct = struct(pyDict);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

The `cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

This custom MATLAB function `dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

Use MATLAB’s `cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

The `cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

The Python dictionary is first converted to a JSON string using Python’s `json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Creating a MATLAB function that specifically handles the conversion process can be useful for repeated operations. This function would recursively process Python dictionaries and convert them to MATLAB structs, handling the nesting properly.

Here’s an example:

function s = dict2struct(pyDict) s = struct(); keys = fieldnames(pyDict); for i = 1:length(keys) key = keys{i}; val = pyDict.(key); if isa(val, 'py.dict') s.(char(key)) = dict2struct(val); else s.(char(key)) = val; end end end % Use the function pyDict = py.dict(pyargs('e', 5, 'f', py.dict(pyargs('g', 6)))); matlabStruct = dict2struct(pyDict);

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

This custom MATLAB function `dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

Use MATLAB’s `cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

The `cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: x: 10 y: 20

The code iterates through the keys of the Python dictionary, converting each key-value pair to a field in a MATLAB struct. The `fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Serialize the Python dictionary to JSON format, and deserialize it in MATLAB to achieve a struct. Both Python and MATLAB have strong JSON support, and this can be a robust method for complex nested dictionaries.

Here’s an example:

% Python side import json pyDict = {'alpha': 1, 'beta': {'gamma': 3, 'delta': 4}} jsonStr = json.dumps(pyDict)

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

The Python dictionary is first converted to a JSON string using Python’s `json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Creating a MATLAB function that specifically handles the conversion process can be useful for repeated operations. This function would recursively process Python dictionaries and convert them to MATLAB structs, handling the nesting properly.

Here’s an example:

function s = dict2struct(pyDict) s = struct(); keys = fieldnames(pyDict); for i = 1:length(keys) key = keys{i}; val = pyDict.(key); if isa(val, 'py.dict') s.(char(key)) = dict2struct(val); else s.(char(key)) = val; end end end % Use the function pyDict = py.dict(pyargs('e', 5, 'f', py.dict(pyargs('g', 6)))); matlabStruct = dict2struct(pyDict);

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

This custom MATLAB function `dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

Use MATLAB’s `cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: a: 1 b: 2 c: [3, 4]

This code snippet creates a Python dictionary in MATLAB with numerical and list values and converts it into a MATLAB struct. The `pyargs`

function is used to mimic the Python syntax for dictionary creation, allowing seamless creation and conversion. The resulting MATLAB struct has fields corresponding to the keys in the original Python dictionary, with values matching the dictionary values.

## Method 2: Manually Constructing Structs in MATLAB

If automated importing is not possible, one can iterate over the Python dictionary entries in MATLAB and manually construct the MATLAB struct by dynamically assigning field names and values.

Here’s an example:

pyDict = py.dict(pyargs('x', 10, 'y', 20)); matlabStruct = struct(); fields = fieldnames(pyDict); for i = 1:length(fields) fld = fields{i}; matlabStruct.(char(fld)) = pyDict{fld}; end

Output:

matlabStruct = struct with fields: x: 10 y: 20

The code iterates through the keys of the Python dictionary, converting each key-value pair to a field in a MATLAB struct. The `fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Serialize the Python dictionary to JSON format, and deserialize it in MATLAB to achieve a struct. Both Python and MATLAB have strong JSON support, and this can be a robust method for complex nested dictionaries.

Here’s an example:

% Python side import json pyDict = {'alpha': 1, 'beta': {'gamma': 3, 'delta': 4}} jsonStr = json.dumps(pyDict)

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

The Python dictionary is first converted to a JSON string using Python’s `json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Creating a MATLAB function that specifically handles the conversion process can be useful for repeated operations. This function would recursively process Python dictionaries and convert them to MATLAB structs, handling the nesting properly.

Here’s an example:

function s = dict2struct(pyDict) s = struct(); keys = fieldnames(pyDict); for i = 1:length(keys) key = keys{i}; val = pyDict.(key); if isa(val, 'py.dict') s.(char(key)) = dict2struct(val); else s.(char(key)) = val; end end end % Use the function pyDict = py.dict(pyargs('e', 5, 'f', py.dict(pyargs('g', 6)))); matlabStruct = dict2struct(pyDict);

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: a: 1 b: 2 c: [3, 4]

This code snippet creates a Python dictionary in MATLAB with numerical and list values and converts it into a MATLAB struct. The `pyargs`

function is used to mimic the Python syntax for dictionary creation, allowing seamless creation and conversion. The resulting MATLAB struct has fields corresponding to the keys in the original Python dictionary, with values matching the dictionary values.

## Method 2: Manually Constructing Structs in MATLAB

If automated importing is not possible, one can iterate over the Python dictionary entries in MATLAB and manually construct the MATLAB struct by dynamically assigning field names and values.

Here’s an example:

pyDict = py.dict(pyargs('x', 10, 'y', 20)); matlabStruct = struct(); fields = fieldnames(pyDict); for i = 1:length(fields) fld = fields{i}; matlabStruct.(char(fld)) = pyDict{fld}; end

Output:

matlabStruct = struct with fields: x: 10 y: 20

The code iterates through the keys of the Python dictionary, converting each key-value pair to a field in a MATLAB struct. The `fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Serialize the Python dictionary to JSON format, and deserialize it in MATLAB to achieve a struct. Both Python and MATLAB have strong JSON support, and this can be a robust method for complex nested dictionaries.

Here’s an example:

% Python side import json pyDict = {'alpha': 1, 'beta': {'gamma': 3, 'delta': 4}} jsonStr = json.dumps(pyDict)

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: a: 1 b: 2 c: [3, 4]

This code snippet creates a Python dictionary in MATLAB with numerical and list values and converts it into a MATLAB struct. The `pyargs`

function is used to mimic the Python syntax for dictionary creation, allowing seamless creation and conversion. The resulting MATLAB struct has fields corresponding to the keys in the original Python dictionary, with values matching the dictionary values.

## Method 2: Manually Constructing Structs in MATLAB

If automated importing is not possible, one can iterate over the Python dictionary entries in MATLAB and manually construct the MATLAB struct by dynamically assigning field names and values.

Here’s an example:

pyDict = py.dict(pyargs('x', 10, 'y', 20)); matlabStruct = struct(); fields = fieldnames(pyDict); for i = 1:length(fields) fld = fields{i}; matlabStruct.(char(fld)) = pyDict{fld}; end

Output:

matlabStruct = struct with fields: x: 10 y: 20

`fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Here’s an example:

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: x: 10 y: 20

`fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Here’s an example:

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: a: 1 b: 2 c: [3, 4]

`pyargs`

function is used to mimic the Python syntax for dictionary creation, allowing seamless creation and conversion. The resulting MATLAB struct has fields corresponding to the keys in the original Python dictionary, with values matching the dictionary values.

## Method 2: Manually Constructing Structs in MATLAB

Here’s an example:

Output:

matlabStruct = struct with fields: x: 10 y: 20

`fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Here’s an example:

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: x: 10 y: 20

`fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Here’s an example:

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: a: 1 b: 2 c: [3, 4]

`pyargs`

function is used to mimic the Python syntax for dictionary creation, allowing seamless creation and conversion. The resulting MATLAB struct has fields corresponding to the keys in the original Python dictionary, with values matching the dictionary values.

## Method 2: Manually Constructing Structs in MATLAB

Here’s an example:

Output:

matlabStruct = struct with fields: x: 10 y: 20

`fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Here’s an example:

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: x: 10 y: 20

`fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Here’s an example:

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: a: 1 b: 2 c: [3, 4]

`pyargs`

function is used to mimic the Python syntax for dictionary creation, allowing seamless creation and conversion. The resulting MATLAB struct has fields corresponding to the keys in the original Python dictionary, with values matching the dictionary values.

## Method 2: Manually Constructing Structs in MATLAB

Here’s an example:

Output:

matlabStruct = struct with fields: x: 10 y: 20

`fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Here’s an example:

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: x: 10 y: 20

`fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Here’s an example:

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.matlabStruct = struct with fields: a: 1 b: 2 c: [3, 4]

`pyargs`

function is used to mimic the Python syntax for dictionary creation, allowing seamless creation and conversion. The resulting MATLAB struct has fields corresponding to the keys in the original Python dictionary, with values matching the dictionary values.

## Method 2: Manually Constructing Structs in MATLAB

Here’s an example:

Output:

matlabStruct = struct with fields: x: 10 y: 20

`fieldnames`

function retrieves the keys from the Python dictionary, which are then used to dynamically create struct fields using MATLAB’s field access syntax.

## Method 3: Serializing and Deserializing with JSON

Here’s an example:

% MATLAB side jsonStr = py.json.dumps(pyDict); matlabStruct = jsondecode(jsonStr);

Output:

matlabStruct = struct with fields: alpha: 1 beta: [1Γ1 struct]

`json.dumps()`

method. MATLAB then decodes the JSON string back into a MATLAB struct using the `jsondecode`

function. This process handles nested dictionaries well and translates them into nested structs.

## Method 4: Using a Dedicated MATLAB Function

Here’s an example:

Output:

matlabStruct = struct with fields: e: 5 f: [1Γ1 struct]

`dict2struct()`

takes a Python dictionary as an argument and recursively converts it to a MATLAB struct. The function handles both simple key-value pairs and nested dictionaries, ensuring a comprehensive translation from Python to MATLAB data structures.

## Bonus One-Liner Method 5: Using MATLAB’s `cell2struct`

Function

`cell2struct`

function for a quick one-liner conversion. This method assumes the dictionary is already represented as cell arrays of keys and values in MATLAB. It’s less dynamic but works for simple cases.

Here’s an example:

keys = {'h', 'i'}; values = {7, [8, 9]}; matlabStruct = cell2struct(values, keys, 2);

Output:

matlabStruct = struct with fields: h: 7 i: [8, 9]

`cell2struct`

function takes three arguments: a cell array of values, a cell array of keys, and the dimension along which to structure the fields. This method is very concise but only works when the keys and values have been separated into cell arrays.

## Summary/Discussion

**Method 1:**Direct conversion with

`py.dict`

. Strengths: Straightforward and uses built-in functions. Weaknesses: May require manual management of data types. **Method 2:**Manual Struct Construction. Strengths: Full control over the conversion process. Weaknesses: Potentially time-consuming and verbose.

**Method 3:**JSON Serialization. Strengths: Great for nested dictionaries and standardized transfer format. Weaknesses: Requires serialization and deserialization steps.

**Method 4:**Dedicated MATLAB Function. Strengths: Ideal for complex and repeated conversions, handles nesting. Weaknesses: Requires initial setup of a custom function.

**Bonus Method 5:**

`cell2struct`

Function. Strengths: Quick one-liner for simple scenarios. Weaknesses: Not suitable for nested or complex dictionaries.