scipy.io.matlab.whosmat#

scipy.io.matlab.whosmat(file_name, appendmat=True, **kwargs)[source]#

List variables inside a MATLAB file.

Parameters:

file_namestr: Name of the mat file (do not need .mat extension if appendmat==True) Can also pass open file-like object.
appendmatbool, optional: True to append the .mat extension to the end of the given filename, if not already present. Default is True.
byte_orderstr or None, optional: None by default, implying byte order guessed from mat file. Otherwise can be one of (‘native’, ‘=’, ‘little’, ‘<’, ‘BIG’, ‘>’).
mat_dtypebool, optional: If True, return arrays in same dtype as would be loaded into MATLAB (instead of the dtype with which they are saved).
squeeze_mebool, optional: Whether to squeeze unit matrix dimensions or not.
chars_as_stringsbool, optional: Whether to convert char arrays to string arrays.
matlab_compatiblebool, optional: 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, optional: Whether 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.

Returns:

variableslist of tuples: A list of tuples, where each tuple holds the matrix name (a string), its shape (tuple of ints), and its data class (a string). Possible data classes are: int8, uint8, int16, uint16, int32, uint32, int64, uint64, single, double, cell, struct, object, char, sparse, function, opaque, logical, unknown.

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 (e.g. h5py). Because SciPy does not supply one, we do not implement the HDF5 / 7.3 interface here.

Added in version 0.12.0.

Examples

>>> from io import BytesIO
>>> import numpy as np
>>> from scipy.io import savemat, whosmat

Create some arrays, and use savemat to write them to a BytesIO instance.

>>> a = np.array([[10, 20, 30], [11, 21, 31]], dtype=np.int32)
>>> b = np.geomspace(1, 10, 5)
>>> f = BytesIO()
>>> savemat(f, {'a': a, 'b': b})

Use whosmat to inspect f. Each tuple in the output list gives the name, shape and data type of the array in f.

>>> whosmat(f)
[('a', (2, 3), 'int32'), ('b', (1, 5), 'double')]