(ph5SrSSKJr SSKJrJr SSKJrJr SSK J r J r /SQr \SSj5r SS jr\SS j5r\SS S .Sjj5r\SSj5r\SSj5rg )z7 Module for reading and writing matlab (TM) .mat files )contextmanager)_get_matfile_version docfiller)MatFile4ReaderMatFile4Writer)MatFile5ReaderMatFile5Writer)loadmatsavematwhosmatc## [XU5up4Uv U(aUR5 gg!U(aUR5 ff=f7f)N) _open_fileclose) file_like appendmatmodefopeneds G/var/www/html/venv/lib/python3.13/site-packages/scipy/io/matlab/_mio.py_open_file_contextrs?96IA  GGI 6 GGI sA /A A  A c[U5[S5-(aS1O [5n[U5[S5-(aURS5 UR[U55(aUS4$[ X5S4$![ aZn[ U[5(a4U(aURS5(dUS- n[ X5S4sSnA$[ S 5UeSnAff=f) z 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. zr+readzwax+writeFTz.matNz/Reader needs file name or open file-like object) setaddissubsetdiropenOSError isinstancestrendswith)rrrreqses rrrs4y3t9,F8#%D 4y3v;  }}S^$$% I$d**   i % %!3!3F!;!;V#  ($. .A  s%; B C,AC'C, C''C,Tc [X5up4[U5upVUS:Xa[U40UD6U4$US:Xa[U40UD6U4$US:Xa [ S5e[ SU35e)aX Create reader for matlab .mat format files. Parameters ---------- %(file_arg)s %(append_arg)s %(load_args)s %(struct_arg)s 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. rrz6Please use HDF reader for matlab v7.3 files, e.g. h5pyzDid not recognize version )rrrr NotImplementedError TypeError) file_namerkwargs byte_stream file_openedmjvmnvs rmat_reader_factoryr04s* *)?K#K0HC axk4V4kAA k4V4kAA !#56 64SE:;;N)spmatrixc vURSS5n[X5n[U40UD6upxURU5n SSS5 U(aCSSKJn Jn [W R55HupU "U 5(dMU "U 5X'M UbURW 5 U$W nU$!,(df  Nr=f)a\ 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 ``coo_matrix``. Otherwise return ``coo_array``. Only relevant for sparse variables. 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_namesNr)issparse coo_matrix) poprr0 get_variables scipy.sparser5r6listitemsupdate)r*mdictrr2r+r4rMR_ matfile_dictr5r6namevars rr r VsdZZ 0$7N I 1Q"1//''7  25l0023ID}}%/_ "4  \" L L 2 1s B** B8c [XS5nUS:Xa U(a Sn[U5e[Xv5n OUS:Xa[UUSUUS9n O [S5eU R U5 SSS5 g!,(df  g=f) a Save a dictionary of names and arrays into a MATLAB-style .mat file. This saves the array objects in the given dictionary to a MATLAB- style .mat file. Parameters ---------- file_name : str or file-like object Name of the .mat file (.mat extension not needed if ``appendmat == True``). Can also pass open file_like object. mdict : dict Dictionary from which to save matfile variables. appendmat : bool, optional True (the default) to append the .mat extension to the end of the given filename, if not already present. format : {'5', '4'}, string, optional '5' (the default) for MATLAB 5 and up (to 7.2), '4' for MATLAB 4 .mat files. long_field_names : bool, optional False (the default) - maximum field name length in a structure is 31 characters which is the documented maximum length. True - maximum field name length in a structure is 63 characters which works for MATLAB 7.6+. do_compression : bool, optional Whether or not to compress matrices on write. Default is False. oned_as : {'row', 'column'}, optional If 'column', write 1-D NumPy arrays as column vectors. If 'row', write 1-D NumPy arrays as row vectors. Examples -------- >>> from scipy.io import savemat >>> import numpy as np >>> a = np.arange(20) >>> mdic = {"a": a, "label": "experiment"} >>> mdic {'a': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]), 'label': 'experiment'} >>> savemat("matlab_matrix.mat", mdic) wb4z6Long field names are not available for version 4 files5T)do_compressionunicode_stringslong_field_namesoned_aszFormat should be '4' or '5'N)r ValueErrorrr put_variables) r*r=rformatrIrGrJ file_streammessageMWs rr r s|d I$ 7; S=R )) 5B s] /=041A(/ 1B :; ;  8 7 7s AA-- A;c [X5n[U40UD6upEUR5nSSS5 U$!,(df  W$=f)a 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')] N)rr0list_variables)r*rr+rMLr- variabless rr r =sLh I 1Q,Q9&9%%'  2  2 1 s 5 A)rb)T)NT)TrFFFrow)__doc__ contextlibr_miobaserr_mio4rr_mio5r r __all__rrr0r r r r1rr^s &511 +6 <