Source code for pymatreader.pymatreader

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# Copyright (c) 2018, Dirk Gütlin & Thomas Hartmann
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import scipy.io
import os

from .utils import _import_h5py, _hdf5todict, _check_for_scipy_mat_struct

__all__ = 'read_mat'

"""
This is a small module intended to facilitate reading .mat files containing
large data structures into python, disregarding of the underlying .mat
file version.
"""


[docs]def read_mat(filename, variable_names=None, ignore_fields=None, uint16_codec=None): """This function reads .mat files of version <7.3 or 7.3 and returns the contained data structure as a dictionary of nested substructure similar to scipy.io.loadmat style. Parameters ---------- filename: str Path and filename of the .mat file containing the data. variable_names: list of strings, optional Reads only the data contained in the specified dict key or variable name. Default is None. ignore_fields: list of strings, optional Ignores every dict key/variable name specified in the list within the entire structure. Only works for .mat files v 7.3. Default is []. uint16_codec : str | None If your file contains non-ascii characters, sometimes reading it may fail and give rise to error message stating that "buffer is too small". ``uint16_codec`` allows to specify what codec (for example: 'latin1' or 'utf-8') should be used when reading character arrays and can therefore help you solve this problem. Returns ------- dict A structure of nested dictionaries, with variable names as keys and variable data as values. """ if not os.path.exists(filename): raise IOError('The file %s does not exist.' % (filename,)) if ignore_fields is None: ignore_fields = [] try: with open(filename, 'rb') as fid: # avoid open file warnings on error raw_data = scipy.io.loadmat(fid, struct_as_record=True, squeeze_me=True, mat_dtype=True, variable_names=variable_names, uint16_codec=uint16_codec) data = _check_for_scipy_mat_struct(raw_data) except NotImplementedError: ignore_fields.append('#refs#') h5py = _import_h5py() with h5py.File(filename, 'r') as hdf5_file: data = _hdf5todict(hdf5_file, variable_names=variable_names, ignore_fields=ignore_fields) return data