There is a nice package called mat4py
which can easily be installed using
pip install mat4py
It is straightforward to use [from the website]:
Load data from a MAT-file
The function loadmat
loads all variables stored in the MAT-file into a simple Python data structure, using only Python’s dict
and list
objects. Numeric and cell arrays are converted to row-ordered nested lists. Arrays are squeezed to eliminate arrays with only one element. The resulting
data structure is composed of simple types that are compatible with the JSON format.
Example: Load a MAT-file into a Python data structure:
from mat4py import loadmat
data = loadmat['datafile.mat']
The variable data
is a dict
with the variables and values contained in the MAT-file.
Save a Python data structure to a MAT-file
Python data can be saved to a MAT-file, with the function savemat
. Data has to be structured in the same way as for loadmat
, i.e. it should be composed of simple data types,
like dict
, list
, str
, int
, and float
.
Example: Save a Python data structure to a MAT-file:
from mat4py import savemat
savemat['datafile.mat', data]
The parameter data
shall be a dict
with the variables.
Load MATLAB file.
Parametersfile_namestrName of the mat file [do not need .mat extension if appendmat==True]. Can also pass open file-like object.
mdictdict, optionalDictionary in which to insert matfile variables.
appendmatbool, optionalTrue to append the .mat extension to the end of the given filename, if not already present. Default is True.
byte_orderstr or None, optionalNone by default, implying byte order guessed from mat file. Otherwise can be one of [‘native’, ‘=’, ‘little’, ‘’].
mat_dtypebool, optionalIf True, return arrays in same dtype as would be loaded into MATLAB [instead of the dtype with which they are saved].
squeeze_mebool, optionalWhether to squeeze unit matrix dimensions or not.
chars_as_stringsbool, optionalWhether to convert char arrays to string arrays.
Returns matrices as would be loaded by MATLAB [implies squeeze_me=False, chars_as_strings=False, mat_dtype=True, struct_as_record=True].
struct_as_recordbool, optionalWhether to load MATLAB structs as NumPy record arrays, or as old-style NumPy arrays with dtype=object. Setting this flag to False replicates the behavior of scipy version 0.7.x [returning NumPy object arrays]. The default setting is True, because it allows easier round-trip load and save of MATLAB files.
verify_compressed_data_integritybool, optionalWhether the length of compressed sequences in the MATLAB file should be checked, to ensure that they are not longer than we expect. It is advisable to enable this [the default] because overlong compressed sequences in MATLAB files generally indicate that the files have experienced some sort of corruption.
variable_namesNone or sequenceIf None [the default] - read all variables in file. Otherwise, variable_names should be a sequence of strings, giving names of the MATLAB variables to read from the file. The reader will skip any variable with a name not in this sequence, possibly saving some read processing.
simplify_cellsFalse, optionalIf True, return a simplified dict structure [which is useful if the mat file contains cell arrays]. Note that this only affects the structure of the result and not its contents [which is identical for both output structures]. If True, this automatically sets struct_as_record to False and squeeze_me to True, which is required to simplify cells.
Returnsmat_dictdictdictionary with variable names as keys, and loaded matrices as values.
Notes
v4 [Level 1.0], v6 and v7 to 7.2 matfiles are supported.
You will need an HDF5 Python library to read MATLAB 7.3 format mat files. Because SciPy does not supply one, we do not implement the HDF5 / 7.3 interface here.
Examples
>>> from os.path import dirname, join as pjoin >>> import scipy.io as sio
Get the filename for an example .mat file from the tests/data directory.
>>> data_dir = pjoin[dirname[sio.__file__], 'matlab', 'tests', 'data'] >>> mat_fname = pjoin[data_dir, 'testdouble_7.4_GLNX86.mat']
Load the .mat file contents.
>>> mat_contents = sio.loadmat[mat_fname]
The result is a dictionary, one key/value pair for each variable:
>>> sorted[mat_contents.keys[]] ['__globals__', '__header__', '__version__', 'testdouble'] >>> mat_contents['testdouble'] array[[[0. , 0.78539816, 1.57079633, 2.35619449, 3.14159265, 3.92699082, 4.71238898, 5.49778714, 6.28318531]]]
By default SciPy reads MATLAB structs as structured NumPy arrays where the dtype fields are of type object and the names correspond to the MATLAB struct field names. This can be disabled by setting the optional argument struct_as_record=False.
Get the filename for an example .mat file that contains a MATLAB struct called teststruct and load the contents.
>>> matstruct_fname = pjoin[data_dir, 'teststruct_7.4_GLNX86.mat'] >>> matstruct_contents = sio.loadmat[matstruct_fname] >>> teststruct = matstruct_contents['teststruct'] >>> teststruct.dtype dtype[[['stringfield', 'O'], ['doublefield', 'O'], ['complexfield', 'O']]]
The size of the structured array is the size of the MATLAB struct, not the number of elements in any particular field. The shape defaults to 2-D unless the optional argument squeeze_me=True, in which case all length 1 dimensions are removed.
>>> teststruct.size 1 >>> teststruct.shape [1, 1]
Get the ‘stringfield’ of the first element in the MATLAB struct.
>>> teststruct[0, 0]['stringfield'] array[['Rats live on no evil star.'], dtype='