""" Module for reading and writing matlab (TM) .mat files """ # Authors: Travis Oliphant, Matthew Brett import os import warnings from contextlib import contextmanager from ._miobase import _get_matfile_version, docfiller from ._mio4 import MatFile4Reader, MatFile4Writer from ._mio5 import MatFile5Reader, MatFile5Writer from scipy._lib.deprecation import _NoValue __all__ = ['loadmat', 'savemat', 'whosmat'] @contextmanager def _open_file_context(file_like, appendmat, mode='rb'): f, opened = _open_file(file_like, appendmat, mode) try: yield f finally: if opened: f.close() def _open_file(file_like, appendmat, mode='rb'): """ Open `file_like` and return as file-like object. First, check if object is already file-like; if so, return it as-is. Otherwise, try to pass it to open(). If that fails, and `file_like` is a string, and `appendmat` is true, append '.mat' and try again. """ reqs = {'read'} if set(mode) & set('r+') else set() if set(mode) & set('wax+'): reqs.add('write') if reqs.issubset(dir(file_like)): return file_like, False try: return open(file_like, mode), True except OSError as e: # Probably "not found" if isinstance(file_like, str): if appendmat and not file_like.endswith('.mat'): file_like += '.mat' return open(file_like, mode), True else: raise OSError( 'Reader needs file name or open file-like object' ) from e def mat_reader_factory(file_name, appendmat=True, **kwargs): """ Create reader for matlab .mat format files. Returns ------- matreader : MatFileReader object Initialized instance of MatFileReader class matching the mat file type detected in `filename`. file_opened : bool Whether the file was opened by this routine. """ byte_stream, file_opened = _open_file(file_name, appendmat) mjv, mnv = _get_matfile_version(byte_stream) if mjv == 0: return MatFile4Reader(byte_stream, **kwargs), file_opened elif mjv == 1: return MatFile5Reader(byte_stream, **kwargs), file_opened elif mjv == 2: raise NotImplementedError('Please use HDF reader for matlab v7.3 ' 'files, e.g. h5py') else: raise TypeError(f'Did not recognize version {mjv}') def loadmat(file_name, mdict=None, appendmat=True, *, spmatrix=_NoValue, **kwargs): """ Load MATLAB file. Parameters ---------- file_name : str Name of the mat file (do not need .mat extension if appendmat==True). Can also pass open file-like object. mdict : dict, optional Dictionary in which to insert matfile variables. appendmat : bool, optional True to append the .mat extension to the end of the given filename, if not already present. Default is True. spmatrix : bool, optional (default: True) If ``True``, return sparse matrix. Otherwise return sparse array. Format is `COO` for MatFile 4 and `CSC` for MatFile 5. Only relevant for sparse variables. .. deprecated:: 1.18.0 The default value for `spmatrix` is changing to False in v1.20. That means the default return value will be a sparse array. Unless you use * instead of @, ** for matrix power, or you depend on 2D shapes from e.g. ``A.sum(axis=0)``, it may not matter to you. See :ref:`Migration from spmatrix to sparray `. **kwargs The following aditional keyword arguments can be passed: byte_order : str or None, optional None by default, implying byte order guessed from mat file. Otherwise can be one of ('native', '=', 'little', '<', 'BIG', '>'). mat_dtype : bool, optional If True, return arrays in same dtype as would be loaded into MATLAB (instead of the dtype with which they are saved). squeeze_me : bool, optional Whether to squeeze unit matrix dimensions or not. chars_as_strings : bool, optional Whether to convert char arrays to string arrays. matlab_compatible : bool, 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_record : bool, 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. verify_compressed_data_integrity : bool, optional Whether 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_names : None or sequence If 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_cells : False, optional If 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. uint16_codec : str, optional The codec to use for decoding characters, which are stored as uint16 values. The default uses the system encoding, but this can be manually set to other values such as 'ascii', 'latin1', and 'utf-8'. This parameter is relevant only for files stored as v6 and above, and not for files stored as v4. Returns ------- mat_dict : dict dictionary 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, spmatrix=False) 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='>> teststruct['doublefield'][0, 0] array([[ 1.41421356, 2.71828183, 3.14159265]]) Load the MATLAB struct, squeezing out length 1 dimensions, and get the item from the 'complexfield'. >>> matstruct_squeezed = sio.loadmat(matstruct_fname, squeeze_me=True) >>> matstruct_squeezed['teststruct'].shape () >>> matstruct_squeezed['teststruct']['complexfield'].shape () >>> matstruct_squeezed['teststruct']['complexfield'].item() array([ 1.41421356+1.41421356j, 2.71828183+2.71828183j, 3.14159265+3.14159265j]) """ variable_names = kwargs.pop('variable_names', None) with _open_file_context(file_name, appendmat) as f: MR, _ = mat_reader_factory(f, **kwargs) matfile_dict = MR.get_variables(variable_names) warn_msg = """The default value for `spmatrix` is changing to `False` in v1.20. That means the default return type will be a sparse array. Unless you use * instead of @, ** for matrix power, or you depend on 2D shapes from e.g. `A.sum(axis=0)` it may not matter to you. See the spmatrix to sparray migration guide for details. https://docs.scipy.org/doc/scipy/reference/sparse.migration_to_sparray.html """ from scipy.sparse import issparse, coo_matrix, csc_matrix, coo_array, csc_array for name, var in list(matfile_dict.items()): if issparse(var): if spmatrix is _NoValue: prefixes = (os.path.dirname(__file__),) warnings.warn(warn_msg, DeprecationWarning, skip_file_prefixes=prefixes) spmatrix = True if spmatrix: fmt_matrix = coo_matrix if var.format == "coo" else csc_matrix else: fmt_matrix = coo_array if var.format == "coo" else csc_array matfile_dict[name] = fmt_matrix(var) if mdict is not None: mdict.update(matfile_dict) else: mdict = matfile_dict return mdict def savemat(file_name, mdict, appendmat=True, format='5', long_field_names=False, do_compression=False, oned_as='row'): """ Save a dictionary of names and arrays into a MATLAB-style .mat file. This saves the array objects in the given dictionary to a MATLAB- style .mat file. Parameters ---------- file_name : str or file-like object Name of the .mat file (.mat extension not needed if ``appendmat == True``). Can also pass open file_like object. mdict : dict Dictionary from which to save matfile variables. Note that if this dict has a key starting with ``_`` or a sub-dict has a key starting with ``_`` or a digit, these key's items will not be saved in the mat file and `MatWriteWarning` will be issued. appendmat : bool, optional True (the default) to append the .mat extension to the end of the given filename, if not already present. format : {'5', '4'}, string, optional '5' (the default) for MATLAB 5 and up (to 7.2), '4' for MATLAB 4 .mat files. long_field_names : bool, optional False (the default) - maximum field name length in a structure is 31 characters which is the documented maximum length. True - maximum field name length in a structure is 63 characters which works for MATLAB 7.6+. do_compression : bool, optional Whether or not to compress matrices on write. Default is False. oned_as : {'row', 'column'}, optional If 'column', write 1-D NumPy arrays as column vectors. If 'row', write 1-D NumPy arrays as row vectors. Examples -------- >>> from scipy.io import savemat >>> import numpy as np >>> a = np.arange(20) >>> mdic = {"a": a, "label": "experiment"} >>> mdic {'a': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]), 'label': 'experiment'} >>> savemat("matlab_matrix.mat", mdic) """ with _open_file_context(file_name, appendmat, 'wb') as file_stream: if format == '4': if long_field_names: message = "Long field names are not available for version 4 files" raise ValueError(message) MW = MatFile4Writer(file_stream, oned_as) elif format == '5': MW = MatFile5Writer(file_stream, do_compression=do_compression, unicode_strings=True, long_field_names=long_field_names, oned_as=oned_as) else: raise ValueError("Format should be '4' or '5'") MW.put_variables(mdict) @docfiller def whosmat(file_name, appendmat=True, **kwargs): """ List variables inside a MATLAB file. Parameters ---------- %(file_arg)s %(append_arg)s %(load_args)s %(struct_arg)s Returns ------- variables : list 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. .. versionadded:: 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')] """ # numpydoc ignore=PR01,PR02 with _open_file_context(file_name, appendmat) as f: ML, file_opened = mat_reader_factory(f, **kwargs) variables = ML.list_variables() return variables