359 lines
12 KiB
Python
359 lines
12 KiB
Python
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"""
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Module for reading and writing matlab (TM) .mat files
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"""
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# Authors: Travis Oliphant, Matthew Brett
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from contextlib import contextmanager
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from ._miobase import _get_matfile_version, docfiller
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from ._mio4 import MatFile4Reader, MatFile4Writer
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from ._mio5 import MatFile5Reader, MatFile5Writer
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__all__ = ['mat_reader_factory', 'loadmat', 'savemat', 'whosmat']
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@contextmanager
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def _open_file_context(file_like, appendmat, mode='rb'):
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f, opened = _open_file(file_like, appendmat, mode)
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try:
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yield f
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finally:
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if opened:
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f.close()
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def _open_file(file_like, appendmat, mode='rb'):
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"""
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Open `file_like` and return as file-like object. First, check if object is
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already file-like; if so, return it as-is. Otherwise, try to pass it
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to open(). If that fails, and `file_like` is a string, and `appendmat` is true,
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append '.mat' and try again.
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"""
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reqs = {'read'} if set(mode) & set('r+') else set()
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if set(mode) & set('wax+'):
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reqs.add('write')
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if reqs.issubset(dir(file_like)):
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return file_like, False
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try:
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return open(file_like, mode), True
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except OSError as e:
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# Probably "not found"
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if isinstance(file_like, str):
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if appendmat and not file_like.endswith('.mat'):
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file_like += '.mat'
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return open(file_like, mode), True
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else:
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raise OSError(
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'Reader needs file name or open file-like object'
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) from e
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@docfiller
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def mat_reader_factory(file_name, appendmat=True, **kwargs):
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"""
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Create reader for matlab .mat format files.
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Parameters
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----------
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%(file_arg)s
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%(append_arg)s
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%(load_args)s
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%(struct_arg)s
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Returns
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-------
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matreader : MatFileReader object
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Initialized instance of MatFileReader class matching the mat file
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type detected in `filename`.
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file_opened : bool
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Whether the file was opened by this routine.
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"""
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byte_stream, file_opened = _open_file(file_name, appendmat)
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mjv, mnv = _get_matfile_version(byte_stream)
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if mjv == 0:
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return MatFile4Reader(byte_stream, **kwargs), file_opened
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elif mjv == 1:
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return MatFile5Reader(byte_stream, **kwargs), file_opened
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elif mjv == 2:
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raise NotImplementedError('Please use HDF reader for matlab v7.3 '
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'files, e.g. h5py')
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else:
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raise TypeError('Did not recognize version %s' % mjv)
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@docfiller
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def loadmat(file_name, mdict=None, appendmat=True, **kwargs):
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"""
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Load MATLAB file.
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Parameters
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----------
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file_name : str
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Name of the mat file (do not need .mat extension if
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appendmat==True). Can also pass open file-like object.
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mdict : dict, optional
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Dictionary in which to insert matfile variables.
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appendmat : bool, optional
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True to append the .mat extension to the end of the given
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filename, if not already present. Default is True.
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byte_order : str or None, optional
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None by default, implying byte order guessed from mat
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file. Otherwise can be one of ('native', '=', 'little', '<',
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'BIG', '>').
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mat_dtype : bool, optional
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If True, return arrays in same dtype as would be loaded into
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MATLAB (instead of the dtype with which they are saved).
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squeeze_me : bool, optional
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Whether to squeeze unit matrix dimensions or not.
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chars_as_strings : bool, optional
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Whether to convert char arrays to string arrays.
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matlab_compatible : bool, optional
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Returns matrices as would be loaded by MATLAB (implies
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squeeze_me=False, chars_as_strings=False, mat_dtype=True,
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struct_as_record=True).
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struct_as_record : bool, optional
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Whether to load MATLAB structs as NumPy record arrays, or as
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old-style NumPy arrays with dtype=object. Setting this flag to
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False replicates the behavior of scipy version 0.7.x (returning
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NumPy object arrays). The default setting is True, because it
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allows easier round-trip load and save of MATLAB files.
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verify_compressed_data_integrity : bool, optional
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Whether the length of compressed sequences in the MATLAB file
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should be checked, to ensure that they are not longer than we expect.
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It is advisable to enable this (the default) because overlong
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compressed sequences in MATLAB files generally indicate that the
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files have experienced some sort of corruption.
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variable_names : None or sequence
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If None (the default) - read all variables in file. Otherwise,
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`variable_names` should be a sequence of strings, giving names of the
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MATLAB variables to read from the file. The reader will skip any
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variable with a name not in this sequence, possibly saving some read
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processing.
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simplify_cells : False, optional
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If True, return a simplified dict structure (which is useful if the mat
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file contains cell arrays). Note that this only affects the structure
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of the result and not its contents (which is identical for both output
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structures). If True, this automatically sets `struct_as_record` to
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False and `squeeze_me` to True, which is required to simplify cells.
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Returns
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-------
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mat_dict : dict
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dictionary with variable names as keys, and loaded matrices as
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values.
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Notes
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-----
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v4 (Level 1.0), v6 and v7 to 7.2 matfiles are supported.
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You will need an HDF5 Python library to read MATLAB 7.3 format mat
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files. Because SciPy does not supply one, we do not implement the
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HDF5 / 7.3 interface here.
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Examples
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--------
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>>> from os.path import dirname, join as pjoin
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>>> import scipy.io as sio
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Get the filename for an example .mat file from the tests/data directory.
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>>> data_dir = pjoin(dirname(sio.__file__), 'matlab', 'tests', 'data')
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>>> mat_fname = pjoin(data_dir, 'testdouble_7.4_GLNX86.mat')
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Load the .mat file contents.
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>>> mat_contents = sio.loadmat(mat_fname)
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The result is a dictionary, one key/value pair for each variable:
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>>> sorted(mat_contents.keys())
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['__globals__', '__header__', '__version__', 'testdouble']
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>>> mat_contents['testdouble']
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array([[0. , 0.78539816, 1.57079633, 2.35619449, 3.14159265,
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3.92699082, 4.71238898, 5.49778714, 6.28318531]])
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By default SciPy reads MATLAB structs as structured NumPy arrays where the
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dtype fields are of type `object` and the names correspond to the MATLAB
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struct field names. This can be disabled by setting the optional argument
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`struct_as_record=False`.
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Get the filename for an example .mat file that contains a MATLAB struct
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called `teststruct` and load the contents.
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>>> matstruct_fname = pjoin(data_dir, 'teststruct_7.4_GLNX86.mat')
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>>> matstruct_contents = sio.loadmat(matstruct_fname)
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>>> teststruct = matstruct_contents['teststruct']
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>>> teststruct.dtype
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dtype([('stringfield', 'O'), ('doublefield', 'O'), ('complexfield', 'O')])
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The size of the structured array is the size of the MATLAB struct, not the
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number of elements in any particular field. The shape defaults to 2-D
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unless the optional argument `squeeze_me=True`, in which case all length 1
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dimensions are removed.
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>>> teststruct.size
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1
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>>> teststruct.shape
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(1, 1)
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Get the 'stringfield' of the first element in the MATLAB struct.
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>>> teststruct[0, 0]['stringfield']
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array(['Rats live on no evil star.'],
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dtype='<U26')
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Get the first element of the 'doublefield'.
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>>> teststruct['doublefield'][0, 0]
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array([[ 1.41421356, 2.71828183, 3.14159265]])
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Load the MATLAB struct, squeezing out length 1 dimensions, and get the item
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from the 'complexfield'.
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>>> matstruct_squeezed = sio.loadmat(matstruct_fname, squeeze_me=True)
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>>> matstruct_squeezed['teststruct'].shape
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()
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>>> matstruct_squeezed['teststruct']['complexfield'].shape
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()
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>>> matstruct_squeezed['teststruct']['complexfield'].item()
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array([ 1.41421356+1.41421356j, 2.71828183+2.71828183j,
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3.14159265+3.14159265j])
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"""
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variable_names = kwargs.pop('variable_names', None)
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with _open_file_context(file_name, appendmat) as f:
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MR, _ = mat_reader_factory(f, **kwargs)
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matfile_dict = MR.get_variables(variable_names)
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if mdict is not None:
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mdict.update(matfile_dict)
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else:
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mdict = matfile_dict
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return mdict
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@docfiller
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def savemat(file_name, mdict,
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appendmat=True,
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format='5',
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long_field_names=False,
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do_compression=False,
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oned_as='row'):
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"""
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Save a dictionary of names and arrays into a MATLAB-style .mat file.
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This saves the array objects in the given dictionary to a MATLAB-
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style .mat file.
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Parameters
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----------
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file_name : str or file-like object
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Name of the .mat file (.mat extension not needed if ``appendmat ==
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True``).
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Can also pass open file_like object.
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mdict : dict
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Dictionary from which to save matfile variables.
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appendmat : bool, optional
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True (the default) to append the .mat extension to the end of the
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given filename, if not already present.
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format : {'5', '4'}, string, optional
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'5' (the default) for MATLAB 5 and up (to 7.2),
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'4' for MATLAB 4 .mat files.
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long_field_names : bool, optional
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False (the default) - maximum field name length in a structure is
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31 characters which is the documented maximum length.
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True - maximum field name length in a structure is 63 characters
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which works for MATLAB 7.6+.
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do_compression : bool, optional
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Whether or not to compress matrices on write. Default is False.
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oned_as : {'row', 'column'}, optional
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If 'column', write 1-D NumPy arrays as column vectors.
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If 'row', write 1-D NumPy arrays as row vectors.
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Examples
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--------
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>>> from scipy.io import savemat
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>>> import numpy as np
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>>> a = np.arange(20)
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>>> mdic = {"a": a, "label": "experiment"}
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>>> mdic
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{'a': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
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17, 18, 19]),
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'label': 'experiment'}
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>>> savemat("matlab_matrix.mat", mdic)
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"""
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with _open_file_context(file_name, appendmat, 'wb') as file_stream:
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if format == '4':
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if long_field_names:
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raise ValueError("Long field names are not available for version 4 files")
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MW = MatFile4Writer(file_stream, oned_as)
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elif format == '5':
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MW = MatFile5Writer(file_stream,
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do_compression=do_compression,
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unicode_strings=True,
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long_field_names=long_field_names,
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oned_as=oned_as)
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else:
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raise ValueError("Format should be '4' or '5'")
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MW.put_variables(mdict)
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@docfiller
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def whosmat(file_name, appendmat=True, **kwargs):
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"""
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List variables inside a MATLAB file.
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Parameters
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----------
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%(file_arg)s
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%(append_arg)s
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%(load_args)s
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%(struct_arg)s
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Returns
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-------
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variables : list of tuples
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A list of tuples, where each tuple holds the matrix name (a string),
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its shape (tuple of ints), and its data class (a string).
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Possible data classes are: int8, uint8, int16, uint16, int32, uint32,
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int64, uint64, single, double, cell, struct, object, char, sparse,
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function, opaque, logical, unknown.
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Notes
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-----
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v4 (Level 1.0), v6 and v7 to 7.2 matfiles are supported.
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You will need an HDF5 python library to read matlab 7.3 format mat
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files (e.g. h5py). Because SciPy does not supply one, we do not implement the
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HDF5 / 7.3 interface here.
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.. versionadded:: 0.12.0
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Examples
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--------
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>>> from io import BytesIO
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>>> import numpy as np
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>>> from scipy.io import savemat, whosmat
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Create some arrays, and use `savemat` to write them to a ``BytesIO``
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instance.
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>>> a = np.array([[10, 20, 30], [11, 21, 31]], dtype=np.int32)
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>>> b = np.geomspace(1, 10, 5)
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>>> f = BytesIO()
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>>> savemat(f, {'a': a, 'b': b})
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Use `whosmat` to inspect ``f``. Each tuple in the output list gives
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the name, shape and data type of the array in ``f``.
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>>> whosmat(f)
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[('a', (2, 3), 'int32'), ('b', (1, 5), 'double')]
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"""
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with _open_file_context(file_name, appendmat) as f:
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ML, file_opened = mat_reader_factory(f, **kwargs)
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variables = ML.list_variables()
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return variables
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