package scipy

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

Read HB-format file.

Parameters ---------- path_or_open_file : path-like or file-like If a file-like object, it is used as-is. Otherwise, it is opened before reading.

Returns ------- data : scipy.sparse.csc_matrix instance The data read from the HB file as a sparse matrix.

Notes ----- At the moment not the full Harwell-Boeing format is supported. Supported features are:

• assembled, non-symmetric, real matrices
• integer for pointer/indices
• exponential format for float values, and int format

Examples -------- We can read and write a harwell-boeing format file:

>>> from scipy.io.harwell_boeing import hb_read, hb_write >>> from scipy.sparse import csr_matrix, eye >>> data = csr_matrix(eye(3)) # create a sparse matrix >>> hb_write('data.hb', data) # write a hb file >>> print(hb_read('data.hb')) # read a hb file (0, 0) 1.0 (1, 1) 1.0 (2, 2) 1.0

Write HB-format file.

Parameters ---------- path_or_open_file : path-like or file-like If a file-like object, it is used as-is. Otherwise, it is opened before writing. m : sparse-matrix the sparse matrix to write hb_info : HBInfo contains the meta-data for write

Returns ------- None

Notes ----- At the moment not the full Harwell-Boeing format is supported. Supported features are:

• assembled, non-symmetric, real matrices
• integer for pointer/indices
• exponential format for float values, and int format

Examples -------- We can read and write a harwell-boeing format file:

>>> from scipy.io.harwell_boeing import hb_read, hb_write >>> from scipy.sparse import csr_matrix, eye >>> data = csr_matrix(eye(3)) # create a sparse matrix >>> hb_write('data.hb', data) # write a hb file >>> print(hb_read('data.hb')) # read a hb file (0, 0) 1.0 (1, 1) 1.0 (2, 2) 1.0

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. 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.

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)

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.

>>> teststruct0, 0'stringfield' array('Rats live on no evil star.', dtype='<U26')

Get the first element of the 'doublefield'.

>>> 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)

Return size and storage parameters from Matrix Market file-like 'source'.

Parameters ---------- source : str or file-like Matrix Market filename (extension .mtx) or open file-like object

Returns ------- rows : int Number of matrix rows. cols : int Number of matrix columns. entries : int Number of non-zero entries of a sparse matrix or rows*cols for a dense matrix. format : str Either 'coordinate' or 'array'. field : str Either 'real', 'complex', 'pattern', or 'integer'. symmetry : str Either 'general', 'symmetric', 'skew-symmetric', or 'hermitian'.

Reads the contents of a Matrix Market file-like 'source' into a matrix.

Parameters ---------- source : str or file-like Matrix Market filename (extensions .mtx, .mtz.gz) or open file-like object.

Returns ------- a : ndarray or coo_matrix Dense or sparse matrix depending on the matrix format in the Matrix Market file.

Writes the sparse or dense array `a` to Matrix Market file-like `target`.

Parameters ---------- target : str or file-like Matrix Market filename (extension .mtx) or open file-like object. a : array like Sparse or dense 2-D array. comment : str, optional Comments to be prepended to the Matrix Market file. field : None or str, optional Either 'real', 'complex', 'pattern', or 'integer'. precision : None or int, optional Number of digits to display for real or complex values. symmetry : None or str, optional Either 'general', 'symmetric', 'skew-symmetric', or 'hermitian'. If symmetry is None the symmetry type of 'a' is determined by its values.

Read an IDL .sav file.

Parameters ---------- file_name : str Name of the IDL save file. idict : dict, optional Dictionary in which to insert .sav file variables. python_dict : bool, optional By default, the object return is not a Python dictionary, but a case-insensitive dictionary with item, attribute, and call access to variables. To get a standard Python dictionary, set this option to True. uncompressed_file_name : str, optional This option only has an effect for .sav files written with the /compress option. If a file name is specified, compressed .sav files are uncompressed to this file. Otherwise, readsav will use the `tempfile` module to determine a temporary filename automatically, and will remove the temporary file upon successfully reading it in. verbose : bool, optional Whether to print out information about the save file, including the records read, and available variables.

Returns ------- idl_dict : AttrDict or dict If `python_dict` is set to False (default), this function returns a case-insensitive dictionary with item, attribute, and call access to variables. If `python_dict` is set to True, this function returns a Python dictionary with all variable names in lowercase. If `idict` was specified, then variables are written to the dictionary specified, and the updated dictionary is returned.

Examples -------- >>> from os.path import dirname, join as pjoin >>> import scipy.io as sio >>> from scipy.io import readsav

Get the filename for an example .sav file from the tests/data directory.

>>> data_dir = pjoin(dirname(sio.__file__), 'tests', 'data') >>> sav_fname = pjoin(data_dir, 'array_float32_1d.sav')

Load the .sav file contents.

>>> sav_data = readsav(sav_fname)

Get keys of the .sav file contents.

>>> print(sav_data.keys()) dict_keys('array1d')

Access a content with a key.

>>> print(sav_data'array1d') 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.

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 >>> 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)

List variables inside a 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. appendmat : bool, optional True to append the .mat extension to the end of the given filename, if not already present. 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.

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. Because SciPy does not supply one, we do not implement the HDF5 / 7.3 interface here.

.. versionadded:: 0.12.0