mat-io 0.4.2

A package for reading MATLAB .mat files, with support for MATLAB datatypes like table and string

Mat-IO Module

The mat-io module provides tools for load & save with .mat files, particularly perfomring read-write operations on user-defined objects or MATLAB datatypes such as datetime, table and string. It uses a wrapper built around scipy.io to read-write subsystem data in MAT-files, which is where MATLAB stores serialized object data. MAT-file versions v7 to v7.3 are supported.

mat-io can load and save almost all types of objects from MAT-files, including user-defined objects and handle class objects. Additionally, it includes utilities to convert the following MATLAB datatypes into their respective Pythonic objects, and vice versa:

• string
• datetime, duration and calendarDuration
• table and timetable
• containers.Map and dictionary
• categorical
• Enumeration Instance Arrays

Note: load_from_mat() uses a modified version of scipy. The modifications include a few minor changes to scipy.io to process mxOPAQUE_CLASS variables. You can view the changes under patches/ and apply it manually. Note that you might need to rebuild as parts of the Cython code was modified. Follow the instructions on the official SciPy documentation.

Usage

Install using pip

pip install mat-io

Available Commands

To read MATLAB objects from a .mat file:

from matio import load_from_mat

file_path = "path/to/your/file.mat"
data = load_from_mat(
    file_path,
    raw_data=False,
    add_table_attrs=False,
    mdict=None,
    variable_names=None,
    **kwargs
)
print(data)

MATLAB Opaque objects are returned as an instance of class MatioOpaque with the following attributes:

• classname: The class name, including namespace qualifiers (if any).
• type_system: An interal MATLAB type identifier. Usually MCOS, but could also be java or handle.
• properties: A dictionary containing the property names and property values.

These objects are contained within numpy.ndarray in case of object arrays. If the raw_data parameter is set to False, then load_from_mat converts these objects into a corresponding Pythonic datatype.

---

To write Python objects to a .mat file:

from matio import save_to_mat

file_path = "path/to/your/file.mat"
mdict = {"var1": data1, "var2": data2}
save_to_mat(
    file_path,
    mdict=mdict,
    version="v7",
    do_compression=True,
    global_vars=None,
    oned_as="row",
)

When writing objects, matio tries to guess the class name of the object based on its datatype. For example, pandas.DataFrames could be read in as table or timetable. You can also explicitly mention the object by wrapping your data around an instance of MatioOpaque as follows:

from matio import MatioOpaque, save_to_mat

df = some_dataframe
mat_df = MatioOpaque(properties=df, classname="table")
mdict = {"table1": mat_df}
data = save_to_mat(file_path="temp.mat", mdict=mdict)

For user-defined classes, a dictionary of property name-value pairs must be wrapped around a MatioOpaque instance. In case of arrays, these objects should be contained within a numpy.ndarray with dtype=object and each MatioOpaque instance should be flagged with the is_array attribute. All user-defined classes default to the MCOS type system.

Notes

• Extra keyword arguments (**kwargs) are passed directly to scipy.io.loadmat or scipy.io.savemat.

• For conversion rules between MATLAB and Python datatypes, see the documentation.

• To differentiate between MATLAB character and string arrays, it would be ideal to wrap a numpy string array around a MatioOpaque instance. However, this is not feasible all the time, especially as MATLAB seems to be using strings in most of their new datatypes. Hence, this module uses Scipy's convention of converting Python str, list(str) or numpy.array(dtype=<U1/S1) to a character array. Numpy string arrays with multiple strings is converted to a MATLAB string

Contribution

Feel free to create a PR if you'd like to add something, or open up an issue if you'd like to discuss! I've also opened an issue with scipy.io detailing some of the workflow, as well as a PR to develop this iteratively. Please feel free to contribute there as well!

Acknowledgement

Big thanks to mahalex for their detailed breakdown of MAT-files. A lot of this wouldn't be possible without it.