demo + utils venv

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/__init__.py

@@ -0,0 +1,20 @@
+"""
+Utilities for dealing with MATLAB(R) files
+
+Notes
+-----
+MATLAB(R) is a registered trademark of The MathWorks, Inc., 3 Apple Hill
+Drive, Natick, MA 01760-2098, USA.
+
+"""
+from __future__ import division, print_function, absolute_import
+
+# Matlab file read and write utilities
+from .mio import loadmat, savemat, whosmat
+from . import byteordercodes
+
+__all__ = ['loadmat', 'savemat', 'whosmat', 'byteordercodes']
+
+from scipy._lib._testutils import PytestTester
+test = PytestTester(__name__)
+del PytestTester

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/byteordercodes.py

@@ -0,0 +1,70 @@
+''' Byteorder utilities for system - numpy byteorder encoding
+
+Converts a variety of string codes for little endian, big endian,
+native byte order and swapped byte order to explicit numpy endian
+codes - one of '<' (little endian) or '>' (big endian)
+
+'''
+from __future__ import division, print_function, absolute_import
+
+import sys
+
+sys_is_le = sys.byteorder == 'little'
+native_code = sys_is_le and '<' or '>'
+swapped_code = sys_is_le and '>' or '<'
+
+aliases = {'little': ('little', '<', 'l', 'le'),
+           'big': ('big', '>', 'b', 'be'),
+           'native': ('native', '='),
+           'swapped': ('swapped', 'S')}
+
+
+def to_numpy_code(code):
+    """
+    Convert various order codings to numpy format.
+
+    Parameters
+    ----------
+    code : str
+        The code to convert. It is converted to lower case before parsing.
+        Legal values are:
+        'little', 'big', 'l', 'b', 'le', 'be', '<', '>', 'native', '=',
+        'swapped', 's'.
+
+    Returns
+    -------
+    out_code : {'<', '>'}
+        Here '<' is the numpy dtype code for little endian,
+        and '>' is the code for big endian.
+
+    Examples
+    --------
+    >>> import sys
+    >>> sys_is_le == (sys.byteorder == 'little')
+    True
+    >>> to_numpy_code('big')
+    '>'
+    >>> to_numpy_code('little')
+    '<'
+    >>> nc = to_numpy_code('native')
+    >>> nc == '<' if sys_is_le else nc == '>'
+    True
+    >>> sc = to_numpy_code('swapped')
+    >>> sc == '>' if sys_is_le else sc == '<'
+    True
+
+    """
+    code = code.lower()
+    if code is None:
+        return native_code
+    if code in aliases['little']:
+        return '<'
+    elif code in aliases['big']:
+        return '>'
+    elif code in aliases['native']:
+        return native_code
+    elif code in aliases['swapped']:
+        return swapped_code
+    else:
+        raise ValueError(
+            'We cannot handle byte order %s' % code)

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/mio.py

@@ -0,0 +1,326 @@
+"""
+Module for reading and writing matlab (TM) .mat files
+"""
+# Authors: Travis Oliphant, Matthew Brett
+
+from __future__ import division, print_function, absolute_import
+
+from scipy._lib.six import string_types
+
+from .miobase import get_matfile_version, docfiller
+from .mio4 import MatFile4Reader, MatFile4Writer
+from .mio5 import MatFile5Reader, MatFile5Writer
+
+__all__ = ['mat_reader_factory', 'loadmat', 'savemat', 'whosmat']
+
+
+def _open_file(file_like, appendmat):
+    """
+    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.
+    """
+    try:
+        file_like.read(0)
+        return file_like, False
+    except AttributeError:
+        pass
+
+    try:
+        return open(file_like, 'rb'), True
+    except IOError:
+        # Probably "not found"
+        if isinstance(file_like, string_types):
+            if appendmat and not file_like.endswith('.mat'):
+                file_like += '.mat'
+            return open(file_like, 'rb'), True
+        else:
+            raise IOError('Reader needs file name or open file-like object')
+
+@docfiller
+def mat_reader_factory(file_name, appendmat=True, **kwargs):
+    """
+    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.
+
+    """
+    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')
+    else:
+        raise TypeError('Did not recognize version %s' % mjv)
+
+
+@docfiller
+def loadmat(file_name, mdict=None, appendmat=True, **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.
+    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.
+
+    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.
+
+    >>> teststruct[0, 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])
+    """
+    variable_names = kwargs.pop('variable_names', None)
+    MR, file_opened = mat_reader_factory(file_name, appendmat, **kwargs)
+    matfile_dict = MR.get_variables(variable_names)
+    if mdict is not None:
+        mdict.update(matfile_dict)
+    else:
+        mdict = matfile_dict
+    if file_opened:
+        MR.mat_stream.close()
+    return mdict
+
+
+@docfiller
+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.
+    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.
+
+    See also
+    --------
+    mio4.MatFile4Writer
+    mio5.MatFile5Writer
+    """
+    file_opened = False
+    if hasattr(file_name, 'write'):
+        # File-like object already; use as-is
+        file_stream = file_name
+    else:
+        if isinstance(file_name, string_types):
+            if appendmat and not file_name.endswith('.mat'):
+                file_name = file_name + ".mat"
+
+        file_stream = open(file_name, 'wb')
+        file_opened = True
+
+    if format == '4':
+        if long_field_names:
+            raise ValueError("Long field names are not available for version 4 files")
+        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)
+    if file_opened:
+        file_stream.close()
+
+
+@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.  Because scipy does not supply one, we do not implement the
+    HDF5 / 7.3 interface here.
+
+    .. versionadded:: 0.12.0
+
+    """
+    ML, file_opened = mat_reader_factory(file_name, **kwargs)
+    variables = ML.list_variables()
+    if file_opened:
+        ML.mat_stream.close()
+    return variables

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/mio4.py

@@ -0,0 +1,618 @@
+''' Classes for read / write of matlab (TM) 4 files
+'''
+from __future__ import division, print_function, absolute_import
+
+import sys
+import warnings
+
+import numpy as np
+from numpy.compat import asbytes, asstr
+
+import scipy.sparse
+
+from scipy._lib.six import string_types
+
+from .miobase import (MatFileReader, docfiller, matdims, read_dtype,
+                      convert_dtypes, arr_to_chars, arr_dtype_number)
+
+from .mio_utils import squeeze_element, chars_to_strings
+from functools import reduce
+
+
+SYS_LITTLE_ENDIAN = sys.byteorder == 'little'
+
+miDOUBLE = 0
+miSINGLE = 1
+miINT32 = 2
+miINT16 = 3
+miUINT16 = 4
+miUINT8 = 5
+
+mdtypes_template = {
+    miDOUBLE: 'f8',
+    miSINGLE: 'f4',
+    miINT32: 'i4',
+    miINT16: 'i2',
+    miUINT16: 'u2',
+    miUINT8: 'u1',
+    'header': [('mopt', 'i4'),
+               ('mrows', 'i4'),
+               ('ncols', 'i4'),
+               ('imagf', 'i4'),
+               ('namlen', 'i4')],
+    'U1': 'U1',
+    }
+
+np_to_mtypes = {
+    'f8': miDOUBLE,
+    'c32': miDOUBLE,
+    'c24': miDOUBLE,
+    'c16': miDOUBLE,
+    'f4': miSINGLE,
+    'c8': miSINGLE,
+    'i4': miINT32,
+    'i2': miINT16,
+    'u2': miUINT16,
+    'u1': miUINT8,
+    'S1': miUINT8,
+    }
+
+# matrix classes
+mxFULL_CLASS = 0
+mxCHAR_CLASS = 1
+mxSPARSE_CLASS = 2
+
+order_codes = {
+    0: '<',
+    1: '>',
+    2: 'VAX D-float',  # !
+    3: 'VAX G-float',
+    4: 'Cray',  # !!
+    }
+
+mclass_info = {
+    mxFULL_CLASS: 'double',
+    mxCHAR_CLASS: 'char',
+    mxSPARSE_CLASS: 'sparse',
+    }
+
+
+class VarHeader4(object):
+    # Mat4 variables never logical or global
+    is_logical = False
+    is_global = False
+
+    def __init__(self,
+                 name,
+                 dtype,
+                 mclass,
+                 dims,
+                 is_complex):
+        self.name = name
+        self.dtype = dtype
+        self.mclass = mclass
+        self.dims = dims
+        self.is_complex = is_complex
+
+
+class VarReader4(object):
+    ''' Class to read matlab 4 variables '''
+
+    def __init__(self, file_reader):
+        self.file_reader = file_reader
+        self.mat_stream = file_reader.mat_stream
+        self.dtypes = file_reader.dtypes
+        self.chars_as_strings = file_reader.chars_as_strings
+        self.squeeze_me = file_reader.squeeze_me
+
+    def read_header(self):
+        ''' Read and return header for variable '''
+        data = read_dtype(self.mat_stream, self.dtypes['header'])
+        name = self.mat_stream.read(int(data['namlen'])).strip(b'\x00')
+        if data['mopt'] < 0 or data['mopt'] > 5000:
+            raise ValueError('Mat 4 mopt wrong format, byteswapping problem?')
+        M, rest = divmod(data['mopt'], 1000)  # order code
+        if M not in (0, 1):
+            warnings.warn("We do not support byte ordering '%s'; returned "
+                          "data may be corrupt" % order_codes[M],
+                          UserWarning)
+        O, rest = divmod(rest, 100)  # unused, should be 0
+        if O != 0:
+            raise ValueError('O in MOPT integer should be 0, wrong format?')
+        P, rest = divmod(rest, 10)  # data type code e.g miDOUBLE (see above)
+        T = rest  # matrix type code e.g. mxFULL_CLASS (see above)
+        dims = (data['mrows'], data['ncols'])
+        is_complex = data['imagf'] == 1
+        dtype = self.dtypes[P]
+        return VarHeader4(
+            name,
+            dtype,
+            T,
+            dims,
+            is_complex)
+
+    def array_from_header(self, hdr, process=True):
+        mclass = hdr.mclass
+        if mclass == mxFULL_CLASS:
+            arr = self.read_full_array(hdr)
+        elif mclass == mxCHAR_CLASS:
+            arr = self.read_char_array(hdr)
+            if process and self.chars_as_strings:
+                arr = chars_to_strings(arr)
+        elif mclass == mxSPARSE_CLASS:
+            # no current processing (below) makes sense for sparse
+            return self.read_sparse_array(hdr)
+        else:
+            raise TypeError('No reader for class code %s' % mclass)
+        if process and self.squeeze_me:
+            return squeeze_element(arr)
+        return arr
+
+    def read_sub_array(self, hdr, copy=True):
+        ''' Mat4 read using header `hdr` dtype and dims
+
+        Parameters
+        ----------
+        hdr : object
+           object with attributes ``dtype``, ``dims``.  dtype is assumed to be
+           the correct endianness
+        copy : bool, optional
+           copies array before return if True (default True)
+           (buffer is usually read only)
+
+        Returns
+        -------
+        arr : ndarray
+            of dtype givem by `hdr` ``dtype`` and shape givem by `hdr` ``dims``
+        '''
+        dt = hdr.dtype
+        dims = hdr.dims
+        num_bytes = dt.itemsize
+        for d in dims:
+            num_bytes *= d
+        buffer = self.mat_stream.read(int(num_bytes))
+        if len(buffer) != num_bytes:
+            raise ValueError("Not enough bytes to read matrix '%s'; is this "
+                             "a badly-formed file? Consider listing matrices "
+                             "with `whosmat` and loading named matrices with "
+                             "`variable_names` kwarg to `loadmat`" % hdr.name)
+        arr = np.ndarray(shape=dims,
+                         dtype=dt,
+                         buffer=buffer,
+                         order='F')
+        if copy:
+            arr = arr.copy()
+        return arr
+
+    def read_full_array(self, hdr):
+        ''' Full (rather than sparse) matrix getter
+
+        Read matrix (array) can be real or complex
+
+        Parameters
+        ----------
+        hdr : ``VarHeader4`` instance
+
+        Returns
+        -------
+        arr : ndarray
+            complex array if ``hdr.is_complex`` is True, otherwise a real
+            numeric array
+        '''
+        if hdr.is_complex:
+            # avoid array copy to save memory
+            res = self.read_sub_array(hdr, copy=False)
+            res_j = self.read_sub_array(hdr, copy=False)
+            return res + (res_j * 1j)
+        return self.read_sub_array(hdr)
+
+    def read_char_array(self, hdr):
+        ''' latin-1 text matrix (char matrix) reader
+
+        Parameters
+        ----------
+        hdr : ``VarHeader4`` instance
+
+        Returns
+        -------
+        arr : ndarray
+            with dtype 'U1', shape given by `hdr` ``dims``
+        '''
+        arr = self.read_sub_array(hdr).astype(np.uint8)
+        S = arr.tostring().decode('latin-1')
+        return np.ndarray(shape=hdr.dims,
+                          dtype=np.dtype('U1'),
+                          buffer=np.array(S)).copy()
+
+    def read_sparse_array(self, hdr):
+        ''' Read and return sparse matrix type
+
+        Parameters
+        ----------
+        hdr : ``VarHeader4`` instance
+
+        Returns
+        -------
+        arr : ``scipy.sparse.coo_matrix``
+            with dtype ``float`` and shape read from the sparse matrix data
+
+        Notes
+        -----
+        MATLAB 4 real sparse arrays are saved in a N+1 by 3 array format, where
+        N is the number of non-zero values.  Column 1 values [0:N] are the
+        (1-based) row indices of the each non-zero value, column 2 [0:N] are the
+        column indices, column 3 [0:N] are the (real) values.  The last values
+        [-1,0:2] of the rows, column indices are shape[0] and shape[1]
+        respectively of the output matrix. The last value for the values column
+        is a padding 0. mrows and ncols values from the header give the shape of
+        the stored matrix, here [N+1, 3].  Complex data is saved as a 4 column
+        matrix, where the fourth column contains the imaginary component; the
+        last value is again 0.  Complex sparse data do *not* have the header
+        ``imagf`` field set to True; the fact that the data are complex is only
+        detectable because there are 4 storage columns
+        '''
+        res = self.read_sub_array(hdr)
+        tmp = res[:-1,:]
+        # All numbers are float64 in Matlab, but Scipy sparse expects int shape
+        dims = (int(res[-1,0]), int(res[-1,1]))
+        I = np.ascontiguousarray(tmp[:,0],dtype='intc')  # fixes byte order also
+        J = np.ascontiguousarray(tmp[:,1],dtype='intc')
+        I -= 1  # for 1-based indexing
+        J -= 1
+        if res.shape[1] == 3:
+            V = np.ascontiguousarray(tmp[:,2],dtype='float')
+        else:
+            V = np.ascontiguousarray(tmp[:,2],dtype='complex')
+            V.imag = tmp[:,3]
+        return scipy.sparse.coo_matrix((V,(I,J)), dims)
+
+    def shape_from_header(self, hdr):
+        '''Read the shape of the array described by the header.
+        The file position after this call is unspecified.
+        '''
+        mclass = hdr.mclass
+        if mclass == mxFULL_CLASS:
+            shape = tuple(map(int, hdr.dims))
+        elif mclass == mxCHAR_CLASS:
+            shape = tuple(map(int, hdr.dims))
+            if self.chars_as_strings:
+                shape = shape[:-1]
+        elif mclass == mxSPARSE_CLASS:
+            dt = hdr.dtype
+            dims = hdr.dims
+
+            if not (len(dims) == 2 and dims[0] >= 1 and dims[1] >= 1):
+                return ()
+
+            # Read only the row and column counts
+            self.mat_stream.seek(dt.itemsize * (dims[0] - 1), 1)
+            rows = np.ndarray(shape=(1,), dtype=dt,
+                              buffer=self.mat_stream.read(dt.itemsize))
+            self.mat_stream.seek(dt.itemsize * (dims[0] - 1), 1)
+            cols = np.ndarray(shape=(1,), dtype=dt,
+                              buffer=self.mat_stream.read(dt.itemsize))
+
+            shape = (int(rows), int(cols))
+        else:
+            raise TypeError('No reader for class code %s' % mclass)
+
+        if self.squeeze_me:
+            shape = tuple([x for x in shape if x != 1])
+        return shape
+
+
+class MatFile4Reader(MatFileReader):
+    ''' Reader for Mat4 files '''
+    @docfiller
+    def __init__(self, mat_stream, *args, **kwargs):
+        ''' Initialize matlab 4 file reader
+
+    %(matstream_arg)s
+    %(load_args)s
+        '''
+        super(MatFile4Reader, self).__init__(mat_stream, *args, **kwargs)
+        self._matrix_reader = None
+
+    def guess_byte_order(self):
+        self.mat_stream.seek(0)
+        mopt = read_dtype(self.mat_stream, np.dtype('i4'))
+        self.mat_stream.seek(0)
+        if mopt == 0:
+            return '<'
+        if mopt < 0 or mopt > 5000:
+            # Number must have been byteswapped
+            return SYS_LITTLE_ENDIAN and '>' or '<'
+        # Not byteswapped
+        return SYS_LITTLE_ENDIAN and '<' or '>'
+
+    def initialize_read(self):
+        ''' Run when beginning read of variables
+
+        Sets up readers from parameters in `self`
+        '''
+        self.dtypes = convert_dtypes(mdtypes_template, self.byte_order)
+        self._matrix_reader = VarReader4(self)
+
+    def read_var_header(self):
+        ''' Read and return header, next position
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        header : object
+           object that can be passed to self.read_var_array, and that
+           has attributes ``name`` and ``is_global``
+        next_position : int
+           position in stream of next variable
+        '''
+        hdr = self._matrix_reader.read_header()
+        n = reduce(lambda x, y: x*y, hdr.dims, 1)  # fast product
+        remaining_bytes = hdr.dtype.itemsize * n
+        if hdr.is_complex and not hdr.mclass == mxSPARSE_CLASS:
+            remaining_bytes *= 2
+        next_position = self.mat_stream.tell() + remaining_bytes
+        return hdr, next_position
+
+    def read_var_array(self, header, process=True):
+        ''' Read array, given `header`
+
+        Parameters
+        ----------
+        header : header object
+           object with fields defining variable header
+        process : {True, False}, optional
+           If True, apply recursive post-processing during loading of array.
+
+        Returns
+        -------
+        arr : array
+           array with post-processing applied or not according to
+           `process`.
+        '''
+        return self._matrix_reader.array_from_header(header, process)
+
+    def get_variables(self, variable_names=None):
+        ''' get variables from stream as dictionary
+
+        Parameters
+        ----------
+        variable_names : None or str or sequence of str, optional
+            variable name, or sequence of variable names to get from Mat file /
+            file stream.  If None, then get all variables in file
+        '''
+        if isinstance(variable_names, string_types):
+            variable_names = [variable_names]
+        elif variable_names is not None:
+            variable_names = list(variable_names)
+        self.mat_stream.seek(0)
+        # set up variable reader
+        self.initialize_read()
+        mdict = {}
+        while not self.end_of_stream():
+            hdr, next_position = self.read_var_header()
+            name = asstr(hdr.name)
+            if variable_names is not None and name not in variable_names:
+                self.mat_stream.seek(next_position)
+                continue
+            mdict[name] = self.read_var_array(hdr)
+            self.mat_stream.seek(next_position)
+            if variable_names is not None:
+                variable_names.remove(name)
+                if len(variable_names) == 0:
+                    break
+        return mdict
+
+    def list_variables(self):
+        ''' list variables from stream '''
+        self.mat_stream.seek(0)
+        # set up variable reader
+        self.initialize_read()
+        vars = []
+        while not self.end_of_stream():
+            hdr, next_position = self.read_var_header()
+            name = asstr(hdr.name)
+            shape = self._matrix_reader.shape_from_header(hdr)
+            info = mclass_info.get(hdr.mclass, 'unknown')
+            vars.append((name, shape, info))
+
+            self.mat_stream.seek(next_position)
+        return vars
+
+
+def arr_to_2d(arr, oned_as='row'):
+    ''' Make ``arr`` exactly two dimensional
+
+    If `arr` has more than 2 dimensions, raise a ValueError
+
+    Parameters
+    ----------
+    arr : array
+    oned_as : {'row', 'column'}, optional
+       Whether to reshape 1D vectors as row vectors or column vectors.
+       See documentation for ``matdims`` for more detail
+
+    Returns
+    -------
+    arr2d : array
+       2D version of the array
+    '''
+    dims = matdims(arr, oned_as)
+    if len(dims) > 2:
+        raise ValueError('Matlab 4 files cannot save arrays with more than '
+                         '2 dimensions')
+    return arr.reshape(dims)
+
+
+class VarWriter4(object):
+    def __init__(self, file_writer):
+        self.file_stream = file_writer.file_stream
+        self.oned_as = file_writer.oned_as
+
+    def write_bytes(self, arr):
+        self.file_stream.write(arr.tostring(order='F'))
+
+    def write_string(self, s):
+        self.file_stream.write(s)
+
+    def write_header(self, name, shape, P=miDOUBLE, T=mxFULL_CLASS, imagf=0):
+        ''' Write header for given data options
+
+        Parameters
+        ----------
+        name : str
+            name of variable
+        shape : sequence
+           Shape of array as it will be read in matlab
+        P : int, optional
+            code for mat4 data type, one of ``miDOUBLE, miSINGLE, miINT32,
+            miINT16, miUINT16, miUINT8``
+        T : int, optional
+            code for mat4 matrix class, one of ``mxFULL_CLASS, mxCHAR_CLASS,
+            mxSPARSE_CLASS``
+        imagf : int, optional
+            flag indicating complex
+        '''
+        header = np.empty((), mdtypes_template['header'])
+        M = not SYS_LITTLE_ENDIAN
+        O = 0
+        header['mopt'] = (M * 1000 +
+                          O * 100 +
+                          P * 10 +
+                          T)
+        header['mrows'] = shape[0]
+        header['ncols'] = shape[1]
+        header['imagf'] = imagf
+        header['namlen'] = len(name) + 1
+        self.write_bytes(header)
+        self.write_string(asbytes(name + '\0'))
+
+    def write(self, arr, name):
+        ''' Write matrix `arr`, with name `name`
+
+        Parameters
+        ----------
+        arr : array_like
+           array to write
+        name : str
+           name in matlab workspace
+        '''
+        # we need to catch sparse first, because np.asarray returns an
+        # an object array for scipy.sparse
+        if scipy.sparse.issparse(arr):
+            self.write_sparse(arr, name)
+            return
+        arr = np.asarray(arr)
+        dt = arr.dtype
+        if not dt.isnative:
+            arr = arr.astype(dt.newbyteorder('='))
+        dtt = dt.type
+        if dtt is np.object_:
+            raise TypeError('Cannot save object arrays in Mat4')
+        elif dtt is np.void:
+            raise TypeError('Cannot save void type arrays')
+        elif dtt in (np.unicode_, np.string_):
+            self.write_char(arr, name)
+            return
+        self.write_numeric(arr, name)
+
+    def write_numeric(self, arr, name):
+        arr = arr_to_2d(arr, self.oned_as)
+        imagf = arr.dtype.kind == 'c'
+        try:
+            P = np_to_mtypes[arr.dtype.str[1:]]
+        except KeyError:
+            if imagf:
+                arr = arr.astype('c128')
+            else:
+                arr = arr.astype('f8')
+            P = miDOUBLE
+        self.write_header(name,
+                          arr.shape,
+                          P=P,
+                          T=mxFULL_CLASS,
+                          imagf=imagf)
+        if imagf:
+            self.write_bytes(arr.real)
+            self.write_bytes(arr.imag)
+        else:
+            self.write_bytes(arr)
+
+    def write_char(self, arr, name):
+        arr = arr_to_chars(arr)
+        arr = arr_to_2d(arr, self.oned_as)
+        dims = arr.shape
+        self.write_header(
+            name,
+            dims,
+            P=miUINT8,
+            T=mxCHAR_CLASS)
+        if arr.dtype.kind == 'U':
+            # Recode unicode to latin1
+            n_chars = np.product(dims)
+            st_arr = np.ndarray(shape=(),
+                                dtype=arr_dtype_number(arr, n_chars),
+                                buffer=arr)
+            st = st_arr.item().encode('latin-1')
+            arr = np.ndarray(shape=dims, dtype='S1', buffer=st)
+        self.write_bytes(arr)
+
+    def write_sparse(self, arr, name):
+        ''' Sparse matrices are 2D
+
+        See docstring for VarReader4.read_sparse_array
+        '''
+        A = arr.tocoo()  # convert to sparse COO format (ijv)
+        imagf = A.dtype.kind == 'c'
+        ijv = np.zeros((A.nnz + 1, 3+imagf), dtype='f8')
+        ijv[:-1,0] = A.row
+        ijv[:-1,1] = A.col
+        ijv[:-1,0:2] += 1  # 1 based indexing
+        if imagf:
+            ijv[:-1,2] = A.data.real
+            ijv[:-1,3] = A.data.imag
+        else:
+            ijv[:-1,2] = A.data
+        ijv[-1,0:2] = A.shape
+        self.write_header(
+            name,
+            ijv.shape,
+            P=miDOUBLE,
+            T=mxSPARSE_CLASS)
+        self.write_bytes(ijv)
+
+
+class MatFile4Writer(object):
+    ''' Class for writing matlab 4 format files '''
+    def __init__(self, file_stream, oned_as=None):
+        self.file_stream = file_stream
+        if oned_as is None:
+            oned_as = 'row'
+        self.oned_as = oned_as
+        self._matrix_writer = None
+
+    def put_variables(self, mdict, write_header=None):
+        ''' Write variables in `mdict` to stream
+
+        Parameters
+        ----------
+        mdict : mapping
+           mapping with method ``items`` return name, contents pairs
+           where ``name`` which will appeak in the matlab workspace in
+           file load, and ``contents`` is something writeable to a
+           matlab file, such as a numpy array.
+        write_header : {None, True, False}
+           If True, then write the matlab file header before writing the
+           variables.  If None (the default) then write the file header
+           if we are at position 0 in the stream.  By setting False
+           here, and setting the stream position to the end of the file,
+           you can append variables to a matlab file
+        '''
+        # there is no header for a matlab 4 mat file, so we ignore the
+        # ``write_header`` input argument.  It's there for compatibility
+        # with the matlab 5 version of this method
+        self._matrix_writer = VarWriter4(self)
+        for name, var in mdict.items():
+            self._matrix_writer.write(var, name)

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/mio5.py

@@ -0,0 +1,849 @@
+''' Classes for read / write of matlab (TM) 5 files
+
+The matfile specification last found here:
+
+https://www.mathworks.com/access/helpdesk/help/pdf_doc/matlab/matfile_format.pdf
+
+(as of December 5 2008)
+'''
+from __future__ import division, print_function, absolute_import
+
+'''
+=================================
+ Note on functions and mat files
+=================================
+
+The document above does not give any hints as to the storage of matlab
+function handles, or anonymous function handles.  I had therefore to
+guess the format of matlab arrays of ``mxFUNCTION_CLASS`` and
+``mxOPAQUE_CLASS`` by looking at example mat files.
+
+``mxFUNCTION_CLASS`` stores all types of matlab functions.  It seems to
+contain a struct matrix with a set pattern of fields.  For anonymous
+functions, a sub-fields of one of these fields seems to contain the
+well-named ``mxOPAQUE_CLASS``. This seems to contain:
+
+* array flags as for any matlab matrix
+* 3 int8 strings
+* a matrix
+
+It seems that, whenever the mat file contains a ``mxOPAQUE_CLASS``
+instance, there is also an un-named matrix (name == '') at the end of
+the mat file.  I'll call this the ``__function_workspace__`` matrix.
+
+When I saved two anonymous functions in a mat file, or appended another
+anonymous function to the mat file, there was still only one
+``__function_workspace__`` un-named matrix at the end, but larger than
+that for a mat file with a single anonymous function, suggesting that
+the workspaces for the two functions had been merged.
+
+The ``__function_workspace__`` matrix appears to be of double class
+(``mxCLASS_DOUBLE``), but stored as uint8, the memory for which is in
+the format of a mini .mat file, without the first 124 bytes of the file
+header (the description and the subsystem_offset), but with the version
+U2 bytes, and the S2 endian test bytes.  There follow 4 zero bytes,
+presumably for 8 byte padding, and then a series of ``miMATRIX``
+entries, as in a standard mat file. The ``miMATRIX`` entries appear to
+be series of un-named (name == '') matrices, and may also contain arrays
+of this same mini-mat format.
+
+I guess that:
+
+* saving an anonymous function back to a mat file will need the
+  associated ``__function_workspace__`` matrix saved as well for the
+  anonymous function to work correctly.
+* appending to a mat file that has a ``__function_workspace__`` would
+  involve first pulling off this workspace, appending, checking whether
+  there were any more anonymous functions appended, and then somehow
+  merging the relevant workspaces, and saving at the end of the mat
+  file.
+
+The mat files I was playing with are in ``tests/data``:
+
+* sqr.mat
+* parabola.mat
+* some_functions.mat
+
+See ``tests/test_mio.py:test_mio_funcs.py`` for a debugging
+script I was working with.
+
+'''
+
+# Small fragments of current code adapted from matfile.py by Heiko
+# Henkelmann
+
+import os
+import time
+import sys
+import zlib
+
+from io import BytesIO
+
+import warnings
+
+import numpy as np
+from numpy.compat import asbytes, asstr
+
+import scipy.sparse
+
+from scipy._lib.six import string_types
+
+from .byteordercodes import native_code, swapped_code
+
+from .miobase import (MatFileReader, docfiller, matdims, read_dtype,
+                      arr_to_chars, arr_dtype_number, MatWriteError,
+                      MatReadError, MatReadWarning)
+
+# Reader object for matlab 5 format variables
+from .mio5_utils import VarReader5
+
+# Constants and helper objects
+from .mio5_params import (MatlabObject, MatlabFunction, MDTYPES, NP_TO_MTYPES,
+                          NP_TO_MXTYPES, miCOMPRESSED, miMATRIX, miINT8,
+                          miUTF8, miUINT32, mxCELL_CLASS, mxSTRUCT_CLASS,
+                          mxOBJECT_CLASS, mxCHAR_CLASS, mxSPARSE_CLASS,
+                          mxDOUBLE_CLASS, mclass_info)
+
+from .streams import ZlibInputStream
+
+
+class MatFile5Reader(MatFileReader):
+    ''' Reader for Mat 5 mat files
+    Adds the following attribute to base class
+
+    uint16_codec - char codec to use for uint16 char arrays
+        (defaults to system default codec)
+
+    Uses variable reader that has the following stardard interface (see
+    abstract class in ``miobase``::
+
+       __init__(self, file_reader)
+       read_header(self)
+       array_from_header(self)
+
+    and added interface::
+
+       set_stream(self, stream)
+       read_full_tag(self)
+
+    '''
+    @docfiller
+    def __init__(self,
+                 mat_stream,
+                 byte_order=None,
+                 mat_dtype=False,
+                 squeeze_me=False,
+                 chars_as_strings=True,
+                 matlab_compatible=False,
+                 struct_as_record=True,
+                 verify_compressed_data_integrity=True,
+                 uint16_codec=None
+                 ):
+        '''Initializer for matlab 5 file format reader
+
+    %(matstream_arg)s
+    %(load_args)s
+    %(struct_arg)s
+    uint16_codec : {None, string}
+        Set codec to use for uint16 char arrays (e.g. 'utf-8').
+        Use system default codec if None
+        '''
+        super(MatFile5Reader, self).__init__(
+            mat_stream,
+            byte_order,
+            mat_dtype,
+            squeeze_me,
+            chars_as_strings,
+            matlab_compatible,
+            struct_as_record,
+            verify_compressed_data_integrity
+            )
+        # Set uint16 codec
+        if not uint16_codec:
+            uint16_codec = sys.getdefaultencoding()
+        self.uint16_codec = uint16_codec
+        # placeholders for readers - see initialize_read method
+        self._file_reader = None
+        self._matrix_reader = None
+
+    def guess_byte_order(self):
+        ''' Guess byte order.
+        Sets stream pointer to 0 '''
+        self.mat_stream.seek(126)
+        mi = self.mat_stream.read(2)
+        self.mat_stream.seek(0)
+        return mi == b'IM' and '<' or '>'
+
+    def read_file_header(self):
+        ''' Read in mat 5 file header '''
+        hdict = {}
+        hdr_dtype = MDTYPES[self.byte_order]['dtypes']['file_header']
+        hdr = read_dtype(self.mat_stream, hdr_dtype)
+        hdict['__header__'] = hdr['description'].item().strip(b' \t\n\000')
+        v_major = hdr['version'] >> 8
+        v_minor = hdr['version'] & 0xFF
+        hdict['__version__'] = '%d.%d' % (v_major, v_minor)
+        return hdict
+
+    def initialize_read(self):
+        ''' Run when beginning read of variables
+
+        Sets up readers from parameters in `self`
+        '''
+        # reader for top level stream.  We need this extra top-level
+        # reader because we use the matrix_reader object to contain
+        # compressed matrices (so they have their own stream)
+        self._file_reader = VarReader5(self)
+        # reader for matrix streams
+        self._matrix_reader = VarReader5(self)
+
+    def read_var_header(self):
+        ''' Read header, return header, next position
+
+        Header has to define at least .name and .is_global
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        header : object
+           object that can be passed to self.read_var_array, and that
+           has attributes .name and .is_global
+        next_position : int
+           position in stream of next variable
+        '''
+        mdtype, byte_count = self._file_reader.read_full_tag()
+        if not byte_count > 0:
+            raise ValueError("Did not read any bytes")
+        next_pos = self.mat_stream.tell() + byte_count
+        if mdtype == miCOMPRESSED:
+            # Make new stream from compressed data
+            stream = ZlibInputStream(self.mat_stream, byte_count)
+            self._matrix_reader.set_stream(stream)
+            check_stream_limit = self.verify_compressed_data_integrity
+            mdtype, byte_count = self._matrix_reader.read_full_tag()
+        else:
+            check_stream_limit = False
+            self._matrix_reader.set_stream(self.mat_stream)
+        if not mdtype == miMATRIX:
+            raise TypeError('Expecting miMATRIX type here, got %d' % mdtype)
+        header = self._matrix_reader.read_header(check_stream_limit)
+        return header, next_pos
+
+    def read_var_array(self, header, process=True):
+        ''' Read array, given `header`
+
+        Parameters
+        ----------
+        header : header object
+           object with fields defining variable header
+        process : {True, False} bool, optional
+           If True, apply recursive post-processing during loading of
+           array.
+
+        Returns
+        -------
+        arr : array
+           array with post-processing applied or not according to
+           `process`.
+        '''
+        return self._matrix_reader.array_from_header(header, process)
+
+    def get_variables(self, variable_names=None):
+        ''' get variables from stream as dictionary
+
+        variable_names   - optional list of variable names to get
+
+        If variable_names is None, then get all variables in file
+        '''
+        if isinstance(variable_names, string_types):
+            variable_names = [variable_names]
+        elif variable_names is not None:
+            variable_names = list(variable_names)
+
+        self.mat_stream.seek(0)
+        # Here we pass all the parameters in self to the reading objects
+        self.initialize_read()
+        mdict = self.read_file_header()
+        mdict['__globals__'] = []
+        while not self.end_of_stream():
+            hdr, next_position = self.read_var_header()
+            name = asstr(hdr.name)
+            if name in mdict:
+                warnings.warn('Duplicate variable name "%s" in stream'
+                              ' - replacing previous with new\n'
+                              'Consider mio5.varmats_from_mat to split '
+                              'file into single variable files' % name,
+                              MatReadWarning, stacklevel=2)
+            if name == '':
+                # can only be a matlab 7 function workspace
+                name = '__function_workspace__'
+                # We want to keep this raw because mat_dtype processing
+                # will break the format (uint8 as mxDOUBLE_CLASS)
+                process = False
+            else:
+                process = True
+            if variable_names is not None and name not in variable_names:
+                self.mat_stream.seek(next_position)
+                continue
+            try:
+                res = self.read_var_array(hdr, process)
+            except MatReadError as err:
+                warnings.warn(
+                    'Unreadable variable "%s", because "%s"' %
+                    (name, err),
+                    Warning, stacklevel=2)
+                res = "Read error: %s" % err
+            self.mat_stream.seek(next_position)
+            mdict[name] = res
+            if hdr.is_global:
+                mdict['__globals__'].append(name)
+            if variable_names is not None:
+                variable_names.remove(name)
+                if len(variable_names) == 0:
+                    break
+        return mdict
+
+    def list_variables(self):
+        ''' list variables from stream '''
+        self.mat_stream.seek(0)
+        # Here we pass all the parameters in self to the reading objects
+        self.initialize_read()
+        self.read_file_header()
+        vars = []
+        while not self.end_of_stream():
+            hdr, next_position = self.read_var_header()
+            name = asstr(hdr.name)
+            if name == '':
+                # can only be a matlab 7 function workspace
+                name = '__function_workspace__'
+
+            shape = self._matrix_reader.shape_from_header(hdr)
+            if hdr.is_logical:
+                info = 'logical'
+            else:
+                info = mclass_info.get(hdr.mclass, 'unknown')
+            vars.append((name, shape, info))
+
+            self.mat_stream.seek(next_position)
+        return vars
+
+
+def varmats_from_mat(file_obj):
+    """ Pull variables out of mat 5 file as a sequence of mat file objects
+
+    This can be useful with a difficult mat file, containing unreadable
+    variables.  This routine pulls the variables out in raw form and puts them,
+    unread, back into a file stream for saving or reading.  Another use is the
+    pathological case where there is more than one variable of the same name in
+    the file; this routine returns the duplicates, whereas the standard reader
+    will overwrite duplicates in the returned dictionary.
+
+    The file pointer in `file_obj` will be undefined.  File pointers for the
+    returned file-like objects are set at 0.
+
+    Parameters
+    ----------
+    file_obj : file-like
+        file object containing mat file
+
+    Returns
+    -------
+    named_mats : list
+        list contains tuples of (name, BytesIO) where BytesIO is a file-like
+        object containing mat file contents as for a single variable.  The
+        BytesIO contains a string with the original header and a single var. If
+        ``var_file_obj`` is an individual BytesIO instance, then save as a mat
+        file with something like ``open('test.mat',
+        'wb').write(var_file_obj.read())``
+
+    Examples
+    --------
+    >>> import scipy.io
+
+    BytesIO is from the ``io`` module in python 3, and is ``cStringIO`` for
+    python < 3.
+
+    >>> mat_fileobj = BytesIO()
+    >>> scipy.io.savemat(mat_fileobj, {'b': np.arange(10), 'a': 'a string'})
+    >>> varmats = varmats_from_mat(mat_fileobj)
+    >>> sorted([name for name, str_obj in varmats])
+    ['a', 'b']
+    """
+    rdr = MatFile5Reader(file_obj)
+    file_obj.seek(0)
+    # Raw read of top-level file header
+    hdr_len = MDTYPES[native_code]['dtypes']['file_header'].itemsize
+    raw_hdr = file_obj.read(hdr_len)
+    # Initialize variable reading
+    file_obj.seek(0)
+    rdr.initialize_read()
+    mdict = rdr.read_file_header()
+    next_position = file_obj.tell()
+    named_mats = []
+    while not rdr.end_of_stream():
+        start_position = next_position
+        hdr, next_position = rdr.read_var_header()
+        name = asstr(hdr.name)
+        # Read raw variable string
+        file_obj.seek(start_position)
+        byte_count = next_position - start_position
+        var_str = file_obj.read(byte_count)
+        # write to stringio object
+        out_obj = BytesIO()
+        out_obj.write(raw_hdr)
+        out_obj.write(var_str)
+        out_obj.seek(0)
+        named_mats.append((name, out_obj))
+    return named_mats
+
+
+class EmptyStructMarker(object):
+    """ Class to indicate presence of empty matlab struct on output """
+
+
+def to_writeable(source):
+    ''' Convert input object ``source`` to something we can write
+
+    Parameters
+    ----------
+    source : object
+
+    Returns
+    -------
+    arr : None or ndarray or EmptyStructMarker
+        If `source` cannot be converted to something we can write to a matfile,
+        return None.  If `source` is equivalent to an empty dictionary, return
+        ``EmptyStructMarker``.  Otherwise return `source` converted to an
+        ndarray with contents for writing to matfile.
+    '''
+    if isinstance(source, np.ndarray):
+        return source
+    if source is None:
+        return None
+    # Objects that implement mappings
+    is_mapping = (hasattr(source, 'keys') and hasattr(source, 'values') and
+                  hasattr(source, 'items'))
+    # Objects that don't implement mappings, but do have dicts
+    if isinstance(source, np.generic):
+        # Numpy scalars are never mappings (pypy issue workaround)
+        pass
+    elif not is_mapping and hasattr(source, '__dict__'):
+        source = dict((key, value) for key, value in source.__dict__.items()
+                      if not key.startswith('_'))
+        is_mapping = True
+    if is_mapping:
+        dtype = []
+        values = []
+        for field, value in source.items():
+            if (isinstance(field, string_types) and
+                    field[0] not in '_0123456789'):
+                dtype.append((str(field), object))
+                values.append(value)
+        if dtype:
+            return np.array([tuple(values)], dtype)
+        else:
+            return EmptyStructMarker
+    # Next try and convert to an array
+    narr = np.asanyarray(source)
+    if narr.dtype.type in (object, np.object_) and \
+       narr.shape == () and narr == source:
+        # No interesting conversion possible
+        return None
+    return narr
+
+
+# Native byte ordered dtypes for convenience for writers
+NDT_FILE_HDR = MDTYPES[native_code]['dtypes']['file_header']
+NDT_TAG_FULL = MDTYPES[native_code]['dtypes']['tag_full']
+NDT_TAG_SMALL = MDTYPES[native_code]['dtypes']['tag_smalldata']
+NDT_ARRAY_FLAGS = MDTYPES[native_code]['dtypes']['array_flags']
+
+
+class VarWriter5(object):
+    ''' Generic matlab matrix writing class '''
+    mat_tag = np.zeros((), NDT_TAG_FULL)
+    mat_tag['mdtype'] = miMATRIX
+
+    def __init__(self, file_writer):
+        self.file_stream = file_writer.file_stream
+        self.unicode_strings = file_writer.unicode_strings
+        self.long_field_names = file_writer.long_field_names
+        self.oned_as = file_writer.oned_as
+        # These are used for top level writes, and unset after
+        self._var_name = None
+        self._var_is_global = False
+
+    def write_bytes(self, arr):
+        self.file_stream.write(arr.tostring(order='F'))
+
+    def write_string(self, s):
+        self.file_stream.write(s)
+
+    def write_element(self, arr, mdtype=None):
+        ''' write tag and data '''
+        if mdtype is None:
+            mdtype = NP_TO_MTYPES[arr.dtype.str[1:]]
+        # Array needs to be in native byte order
+        if arr.dtype.byteorder == swapped_code:
+            arr = arr.byteswap().newbyteorder()
+        byte_count = arr.size*arr.itemsize
+        if byte_count <= 4:
+            self.write_smalldata_element(arr, mdtype, byte_count)
+        else:
+            self.write_regular_element(arr, mdtype, byte_count)
+
+    def write_smalldata_element(self, arr, mdtype, byte_count):
+        # write tag with embedded data
+        tag = np.zeros((), NDT_TAG_SMALL)
+        tag['byte_count_mdtype'] = (byte_count << 16) + mdtype
+        # if arr.tostring is < 4, the element will be zero-padded as needed.
+        tag['data'] = arr.tostring(order='F')
+        self.write_bytes(tag)
+
+    def write_regular_element(self, arr, mdtype, byte_count):
+        # write tag, data
+        tag = np.zeros((), NDT_TAG_FULL)
+        tag['mdtype'] = mdtype
+        tag['byte_count'] = byte_count
+        self.write_bytes(tag)
+        self.write_bytes(arr)
+        # pad to next 64-bit boundary
+        bc_mod_8 = byte_count % 8
+        if bc_mod_8:
+            self.file_stream.write(b'\x00' * (8-bc_mod_8))
+
+    def write_header(self,
+                     shape,
+                     mclass,
+                     is_complex=False,
+                     is_logical=False,
+                     nzmax=0):
+        ''' Write header for given data options
+        shape : sequence
+           array shape
+        mclass      - mat5 matrix class
+        is_complex  - True if matrix is complex
+        is_logical  - True if matrix is logical
+        nzmax        - max non zero elements for sparse arrays
+
+        We get the name and the global flag from the object, and reset
+        them to defaults after we've used them
+        '''
+        # get name and is_global from one-shot object store
+        name = self._var_name
+        is_global = self._var_is_global
+        # initialize the top-level matrix tag, store position
+        self._mat_tag_pos = self.file_stream.tell()
+        self.write_bytes(self.mat_tag)
+        # write array flags (complex, global, logical, class, nzmax)
+        af = np.zeros((), NDT_ARRAY_FLAGS)
+        af['data_type'] = miUINT32
+        af['byte_count'] = 8
+        flags = is_complex << 3 | is_global << 2 | is_logical << 1
+        af['flags_class'] = mclass | flags << 8
+        af['nzmax'] = nzmax
+        self.write_bytes(af)
+        # shape
+        self.write_element(np.array(shape, dtype='i4'))
+        # write name
+        name = np.asarray(name)
+        if name == '':  # empty string zero-terminated
+            self.write_smalldata_element(name, miINT8, 0)
+        else:
+            self.write_element(name, miINT8)
+        # reset the one-shot store to defaults
+        self._var_name = ''
+        self._var_is_global = False
+
+    def update_matrix_tag(self, start_pos):
+        curr_pos = self.file_stream.tell()
+        self.file_stream.seek(start_pos)
+        byte_count = curr_pos - start_pos - 8
+        if byte_count >= 2**32:
+            raise MatWriteError("Matrix too large to save with Matlab "
+                                "5 format")
+        self.mat_tag['byte_count'] = byte_count
+        self.write_bytes(self.mat_tag)
+        self.file_stream.seek(curr_pos)
+
+    def write_top(self, arr, name, is_global):
+        """ Write variable at top level of mat file
+
+        Parameters
+        ----------
+        arr : array_like
+            array-like object to create writer for
+        name : str, optional
+            name as it will appear in matlab workspace
+            default is empty string
+        is_global : {False, True}, optional
+            whether variable will be global on load into matlab
+        """
+        # these are set before the top-level header write, and unset at
+        # the end of the same write, because they do not apply for lower levels
+        self._var_is_global = is_global
+        self._var_name = name
+        # write the header and data
+        self.write(arr)
+
+    def write(self, arr):
+        ''' Write `arr` to stream at top and sub levels
+
+        Parameters
+        ----------
+        arr : array_like
+            array-like object to create writer for
+        '''
+        # store position, so we can update the matrix tag
+        mat_tag_pos = self.file_stream.tell()
+        # First check if these are sparse
+        if scipy.sparse.issparse(arr):
+            self.write_sparse(arr)
+            self.update_matrix_tag(mat_tag_pos)
+            return
+        # Try to convert things that aren't arrays
+        narr = to_writeable(arr)
+        if narr is None:
+            raise TypeError('Could not convert %s (type %s) to array'
+                            % (arr, type(arr)))
+        if isinstance(narr, MatlabObject):
+            self.write_object(narr)
+        elif isinstance(narr, MatlabFunction):
+            raise MatWriteError('Cannot write matlab functions')
+        elif narr is EmptyStructMarker:  # empty struct array
+            self.write_empty_struct()
+        elif narr.dtype.fields:  # struct array
+            self.write_struct(narr)
+        elif narr.dtype.hasobject:  # cell array
+            self.write_cells(narr)
+        elif narr.dtype.kind in ('U', 'S'):
+            if self.unicode_strings:
+                codec = 'UTF8'
+            else:
+                codec = 'ascii'
+            self.write_char(narr, codec)
+        else:
+            self.write_numeric(narr)
+        self.update_matrix_tag(mat_tag_pos)
+
+    def write_numeric(self, arr):
+        imagf = arr.dtype.kind == 'c'
+        logif = arr.dtype.kind == 'b'
+        try:
+            mclass = NP_TO_MXTYPES[arr.dtype.str[1:]]
+        except KeyError:
+            # No matching matlab type, probably complex256 / float128 / float96
+            # Cast data to complex128 / float64.
+            if imagf:
+                arr = arr.astype('c128')
+            elif logif:
+                arr = arr.astype('i1')  # Should only contain 0/1
+            else:
+                arr = arr.astype('f8')
+            mclass = mxDOUBLE_CLASS
+        self.write_header(matdims(arr, self.oned_as),
+                          mclass,
+                          is_complex=imagf,
+                          is_logical=logif)
+        if imagf:
+            self.write_element(arr.real)
+            self.write_element(arr.imag)
+        else:
+            self.write_element(arr)
+
+    def write_char(self, arr, codec='ascii'):
+        ''' Write string array `arr` with given `codec`
+        '''
+        if arr.size == 0 or np.all(arr == ''):
+            # This an empty string array or a string array containing
+            # only empty strings.  Matlab cannot distinguish between a
+            # string array that is empty, and a string array containing
+            # only empty strings, because it stores strings as arrays of
+            # char.  There is no way of having an array of char that is
+            # not empty, but contains an empty string. We have to
+            # special-case the array-with-empty-strings because even
+            # empty strings have zero padding, which would otherwise
+            # appear in matlab as a string with a space.
+            shape = (0,) * np.max([arr.ndim, 2])
+            self.write_header(shape, mxCHAR_CLASS)
+            self.write_smalldata_element(arr, miUTF8, 0)
+            return
+        # non-empty string.
+        #
+        # Convert to char array
+        arr = arr_to_chars(arr)
+        # We have to write the shape directly, because we are going
+        # recode the characters, and the resulting stream of chars
+        # may have a different length
+        shape = arr.shape
+        self.write_header(shape, mxCHAR_CLASS)
+        if arr.dtype.kind == 'U' and arr.size:
+            # Make one long string from all the characters.  We need to
+            # transpose here, because we're flattening the array, before
+            # we write the bytes.  The bytes have to be written in
+            # Fortran order.
+            n_chars = np.product(shape)
+            st_arr = np.ndarray(shape=(),
+                                dtype=arr_dtype_number(arr, n_chars),
+                                buffer=arr.T.copy())  # Fortran order
+            # Recode with codec to give byte string
+            st = st_arr.item().encode(codec)
+            # Reconstruct as one-dimensional byte array
+            arr = np.ndarray(shape=(len(st),),
+                             dtype='S1',
+                             buffer=st)
+        self.write_element(arr, mdtype=miUTF8)
+
+    def write_sparse(self, arr):
+        ''' Sparse matrices are 2D
+        '''
+        A = arr.tocsc()  # convert to sparse CSC format
+        A.sort_indices()     # MATLAB expects sorted row indices
+        is_complex = (A.dtype.kind == 'c')
+        is_logical = (A.dtype.kind == 'b')
+        nz = A.nnz
+        self.write_header(matdims(arr, self.oned_as),
+                          mxSPARSE_CLASS,
+                          is_complex=is_complex,
+                          is_logical=is_logical,
+                          # matlab won't load file with 0 nzmax
+                          nzmax=1 if nz == 0 else nz)
+        self.write_element(A.indices.astype('i4'))
+        self.write_element(A.indptr.astype('i4'))
+        self.write_element(A.data.real)
+        if is_complex:
+            self.write_element(A.data.imag)
+
+    def write_cells(self, arr):
+        self.write_header(matdims(arr, self.oned_as),
+                          mxCELL_CLASS)
+        # loop over data, column major
+        A = np.atleast_2d(arr).flatten('F')
+        for el in A:
+            self.write(el)
+
+    def write_empty_struct(self):
+        self.write_header((1, 1), mxSTRUCT_CLASS)
+        # max field name length set to 1 in an example matlab struct
+        self.write_element(np.array(1, dtype=np.int32))
+        # Field names element is empty
+        self.write_element(np.array([], dtype=np.int8))
+
+    def write_struct(self, arr):
+        self.write_header(matdims(arr, self.oned_as),
+                          mxSTRUCT_CLASS)
+        self._write_items(arr)
+
+    def _write_items(self, arr):
+        # write fieldnames
+        fieldnames = [f[0] for f in arr.dtype.descr]
+        length = max([len(fieldname) for fieldname in fieldnames])+1
+        max_length = (self.long_field_names and 64) or 32
+        if length > max_length:
+            raise ValueError("Field names are restricted to %d characters" %
+                             (max_length-1))
+        self.write_element(np.array([length], dtype='i4'))
+        self.write_element(
+            np.array(fieldnames, dtype='S%d' % (length)),
+            mdtype=miINT8)
+        A = np.atleast_2d(arr).flatten('F')
+        for el in A:
+            for f in fieldnames:
+                self.write(el[f])
+
+    def write_object(self, arr):
+        '''Same as writing structs, except different mx class, and extra
+        classname element after header
+        '''
+        self.write_header(matdims(arr, self.oned_as),
+                          mxOBJECT_CLASS)
+        self.write_element(np.array(arr.classname, dtype='S'),
+                           mdtype=miINT8)
+        self._write_items(arr)
+
+
+class MatFile5Writer(object):
+    ''' Class for writing mat5 files '''
+
+    @docfiller
+    def __init__(self, file_stream,
+                 do_compression=False,
+                 unicode_strings=False,
+                 global_vars=None,
+                 long_field_names=False,
+                 oned_as='row'):
+        ''' Initialize writer for matlab 5 format files
+
+        Parameters
+        ----------
+        %(do_compression)s
+        %(unicode_strings)s
+        global_vars : None or sequence of strings, optional
+            Names of variables to be marked as global for matlab
+        %(long_fields)s
+        %(oned_as)s
+        '''
+        self.file_stream = file_stream
+        self.do_compression = do_compression
+        self.unicode_strings = unicode_strings
+        if global_vars:
+            self.global_vars = global_vars
+        else:
+            self.global_vars = []
+        self.long_field_names = long_field_names
+        self.oned_as = oned_as
+        self._matrix_writer = None
+
+    def write_file_header(self):
+        # write header
+        hdr = np.zeros((), NDT_FILE_HDR)
+        hdr['description'] = 'MATLAB 5.0 MAT-file Platform: %s, Created on: %s' \
+            % (os.name,time.asctime())
+        hdr['version'] = 0x0100
+        hdr['endian_test'] = np.ndarray(shape=(),
+                                      dtype='S2',
+                                      buffer=np.uint16(0x4d49))
+        self.file_stream.write(hdr.tostring())
+
+    def put_variables(self, mdict, write_header=None):
+        ''' Write variables in `mdict` to stream
+
+        Parameters
+        ----------
+        mdict : mapping
+           mapping with method ``items`` returns name, contents pairs where
+           ``name`` which will appear in the matlab workspace in file load, and
+           ``contents`` is something writeable to a matlab file, such as a numpy
+           array.
+        write_header : {None, True, False}, optional
+           If True, then write the matlab file header before writing the
+           variables.  If None (the default) then write the file header
+           if we are at position 0 in the stream.  By setting False
+           here, and setting the stream position to the end of the file,
+           you can append variables to a matlab file
+        '''
+        # write header if requested, or None and start of file
+        if write_header is None:
+            write_header = self.file_stream.tell() == 0
+        if write_header:
+            self.write_file_header()
+        self._matrix_writer = VarWriter5(self)
+        for name, var in mdict.items():
+            if name[0] == '_':
+                continue
+            is_global = name in self.global_vars
+            if self.do_compression:
+                stream = BytesIO()
+                self._matrix_writer.file_stream = stream
+                self._matrix_writer.write_top(var, asbytes(name), is_global)
+                out_str = zlib.compress(stream.getvalue())
+                tag = np.empty((), NDT_TAG_FULL)
+                tag['mdtype'] = miCOMPRESSED
+                tag['byte_count'] = len(out_str)
+                self.file_stream.write(tag.tostring())
+                self.file_stream.write(out_str)
+            else:  # not compressing
+                self._matrix_writer.write_top(var, asbytes(name), is_global)

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/mio5_params.py

@@ -0,0 +1,254 @@
+''' Constants and classes for matlab 5 read and write
+
+See also mio5_utils.pyx where these same constants arise as c enums.
+
+If you make changes in this file, don't forget to change mio5_utils.pyx
+'''
+from __future__ import division, print_function, absolute_import
+
+import numpy as np
+
+from .miobase import convert_dtypes
+
+miINT8 = 1
+miUINT8 = 2
+miINT16 = 3
+miUINT16 = 4
+miINT32 = 5
+miUINT32 = 6
+miSINGLE = 7
+miDOUBLE = 9
+miINT64 = 12
+miUINT64 = 13
+miMATRIX = 14
+miCOMPRESSED = 15
+miUTF8 = 16
+miUTF16 = 17
+miUTF32 = 18
+
+mxCELL_CLASS = 1
+mxSTRUCT_CLASS = 2
+# The March 2008 edition of "Matlab 7 MAT-File Format" says that
+# mxOBJECT_CLASS = 3, whereas matrix.h says that mxLOGICAL = 3.
+# Matlab 2008a appears to save logicals as type 9, so we assume that
+# the document is correct.  See type 18, below.
+mxOBJECT_CLASS = 3
+mxCHAR_CLASS = 4
+mxSPARSE_CLASS = 5
+mxDOUBLE_CLASS = 6
+mxSINGLE_CLASS = 7
+mxINT8_CLASS = 8
+mxUINT8_CLASS = 9
+mxINT16_CLASS = 10
+mxUINT16_CLASS = 11
+mxINT32_CLASS = 12
+mxUINT32_CLASS = 13
+# The following are not in the March 2008 edition of "Matlab 7
+# MAT-File Format," but were guessed from matrix.h.
+mxINT64_CLASS = 14
+mxUINT64_CLASS = 15
+mxFUNCTION_CLASS = 16
+# Not doing anything with these at the moment.
+mxOPAQUE_CLASS = 17  # This appears to be a function workspace
+# Thread 'saveing/loading symbol table of annymous functions', octave-maintainers, April-May 2007
+# https://lists.gnu.org/archive/html/octave-maintainers/2007-04/msg00031.html
+# https://lists.gnu.org/archive/html/octave-maintainers/2007-05/msg00032.html
+# (Was/Deprecated: https://www-old.cae.wisc.edu/pipermail/octave-maintainers/2007-May/002824.html)
+mxOBJECT_CLASS_FROM_MATRIX_H = 18
+
+mdtypes_template = {
+    miINT8: 'i1',
+    miUINT8: 'u1',
+    miINT16: 'i2',
+    miUINT16: 'u2',
+    miINT32: 'i4',
+    miUINT32: 'u4',
+    miSINGLE: 'f4',
+    miDOUBLE: 'f8',
+    miINT64: 'i8',
+    miUINT64: 'u8',
+    miUTF8: 'u1',
+    miUTF16: 'u2',
+    miUTF32: 'u4',
+    'file_header': [('description', 'S116'),
+                    ('subsystem_offset', 'i8'),
+                    ('version', 'u2'),
+                    ('endian_test', 'S2')],
+    'tag_full': [('mdtype', 'u4'), ('byte_count', 'u4')],
+    'tag_smalldata':[('byte_count_mdtype', 'u4'), ('data', 'S4')],
+    'array_flags': [('data_type', 'u4'),
+                    ('byte_count', 'u4'),
+                    ('flags_class','u4'),
+                    ('nzmax', 'u4')],
+    'U1': 'U1',
+    }
+
+mclass_dtypes_template = {
+    mxINT8_CLASS: 'i1',
+    mxUINT8_CLASS: 'u1',
+    mxINT16_CLASS: 'i2',
+    mxUINT16_CLASS: 'u2',
+    mxINT32_CLASS: 'i4',
+    mxUINT32_CLASS: 'u4',
+    mxINT64_CLASS: 'i8',
+    mxUINT64_CLASS: 'u8',
+    mxSINGLE_CLASS: 'f4',
+    mxDOUBLE_CLASS: 'f8',
+    }
+
+mclass_info = {
+    mxINT8_CLASS: 'int8',
+    mxUINT8_CLASS: 'uint8',
+    mxINT16_CLASS: 'int16',
+    mxUINT16_CLASS: 'uint16',
+    mxINT32_CLASS: 'int32',
+    mxUINT32_CLASS: 'uint32',
+    mxINT64_CLASS: 'int64',
+    mxUINT64_CLASS: 'uint64',
+    mxSINGLE_CLASS: 'single',
+    mxDOUBLE_CLASS: 'double',
+    mxCELL_CLASS: 'cell',
+    mxSTRUCT_CLASS: 'struct',
+    mxOBJECT_CLASS: 'object',
+    mxCHAR_CLASS: 'char',
+    mxSPARSE_CLASS: 'sparse',
+    mxFUNCTION_CLASS: 'function',
+    mxOPAQUE_CLASS: 'opaque',
+    }
+
+NP_TO_MTYPES = {
+    'f8': miDOUBLE,
+    'c32': miDOUBLE,
+    'c24': miDOUBLE,
+    'c16': miDOUBLE,
+    'f4': miSINGLE,
+    'c8': miSINGLE,
+    'i8': miINT64,
+    'i4': miINT32,
+    'i2': miINT16,
+    'i1': miINT8,
+    'u8': miUINT64,
+    'u4': miUINT32,
+    'u2': miUINT16,
+    'u1': miUINT8,
+    'S1': miUINT8,
+    'U1': miUTF16,
+    'b1': miUINT8,  # not standard but seems MATLAB uses this (gh-4022)
+    }
+
+
+NP_TO_MXTYPES = {
+    'f8': mxDOUBLE_CLASS,
+    'c32': mxDOUBLE_CLASS,
+    'c24': mxDOUBLE_CLASS,
+    'c16': mxDOUBLE_CLASS,
+    'f4': mxSINGLE_CLASS,
+    'c8': mxSINGLE_CLASS,
+    'i8': mxINT64_CLASS,
+    'i4': mxINT32_CLASS,
+    'i2': mxINT16_CLASS,
+    'i1': mxINT8_CLASS,
+    'u8': mxUINT64_CLASS,
+    'u4': mxUINT32_CLASS,
+    'u2': mxUINT16_CLASS,
+    'u1': mxUINT8_CLASS,
+    'S1': mxUINT8_CLASS,
+    'b1': mxUINT8_CLASS,  # not standard but seems MATLAB uses this
+    }
+
+''' Before release v7.1 (release 14) matlab (TM) used the system
+default character encoding scheme padded out to 16-bits. Release 14
+and later use Unicode. When saving character data, R14 checks if it
+can be encoded in 7-bit ascii, and saves in that format if so.'''
+
+codecs_template = {
+    miUTF8: {'codec': 'utf_8', 'width': 1},
+    miUTF16: {'codec': 'utf_16', 'width': 2},
+    miUTF32: {'codec': 'utf_32','width': 4},
+    }
+
+
+def _convert_codecs(template, byte_order):
+    ''' Convert codec template mapping to byte order
+
+    Set codecs not on this system to None
+
+    Parameters
+    ----------
+    template : mapping
+       key, value are respectively codec name, and root name for codec
+       (without byte order suffix)
+    byte_order : {'<', '>'}
+       code for little or big endian
+
+    Returns
+    -------
+    codecs : dict
+       key, value are name, codec (as in .encode(codec))
+    '''
+    codecs = {}
+    postfix = byte_order == '<' and '_le' or '_be'
+    for k, v in template.items():
+        codec = v['codec']
+        try:
+            " ".encode(codec)
+        except LookupError:
+            codecs[k] = None
+            continue
+        if v['width'] > 1:
+            codec += postfix
+        codecs[k] = codec
+    return codecs.copy()
+
+
+MDTYPES = {}
+for _bytecode in '<>':
+    _def = {'dtypes': convert_dtypes(mdtypes_template, _bytecode),
+            'classes': convert_dtypes(mclass_dtypes_template, _bytecode),
+            'codecs': _convert_codecs(codecs_template, _bytecode)}
+    MDTYPES[_bytecode] = _def
+
+
+class mat_struct(object):
+    ''' Placeholder for holding read data from structs
+
+    We use instances of this class when the user passes False as a value to the
+    ``struct_as_record`` parameter of the :func:`scipy.io.matlab.loadmat`
+    function.
+    '''
+    pass
+
+
+class MatlabObject(np.ndarray):
+    ''' ndarray Subclass to contain matlab object '''
+    def __new__(cls, input_array, classname=None):
+        # Input array is an already formed ndarray instance
+        # We first cast to be our class type
+        obj = np.asarray(input_array).view(cls)
+        # add the new attribute to the created instance
+        obj.classname = classname
+        # Finally, we must return the newly created object:
+        return obj
+
+    def __array_finalize__(self,obj):
+        # reset the attribute from passed original object
+        self.classname = getattr(obj, 'classname', None)
+        # We do not need to return anything
+
+
+class MatlabFunction(np.ndarray):
+    ''' Subclass to signal this is a matlab function '''
+    def __new__(cls, input_array):
+        obj = np.asarray(input_array).view(cls)
+        return obj
+
+
+class MatlabOpaque(np.ndarray):
+    ''' Subclass to signal this is a matlab opaque matrix '''
+    def __new__(cls, input_array):
+        obj = np.asarray(input_array).view(cls)
+        return obj
+
+
+OPAQUE_DTYPE = np.dtype(
+    [('s0', 'O'), ('s1', 'O'), ('s2', 'O'), ('arr', 'O')])

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/miobase.py

@@ -0,0 +1,415 @@
+# Authors: Travis Oliphant, Matthew Brett
+
+"""
+Base classes for MATLAB file stream reading.
+
+MATLAB is a registered trademark of the Mathworks inc.
+"""
+from __future__ import division, print_function, absolute_import
+
+import sys
+import operator
+
+from scipy._lib.six import reduce
+
+import numpy as np
+
+if sys.version_info[0] >= 3:
+    byteord = int
+else:
+    byteord = ord
+
+from scipy.misc import doccer
+
+from . import byteordercodes as boc
+
+
+class MatReadError(Exception):
+    pass
+
+
+class MatWriteError(Exception):
+    pass
+
+
+class MatReadWarning(UserWarning):
+    pass
+
+
+doc_dict = \
+    {'file_arg':
+         '''file_name : str
+   Name of the mat file (do not need .mat extension if
+   appendmat==True) Can also pass open file-like object.''',
+     'append_arg':
+         '''appendmat : bool, optional
+   True to append the .mat extension to the end of the given
+   filename, if not already present.''',
+     'load_args':
+         '''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_arg':
+         '''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.''',
+     'matstream_arg':
+         '''mat_stream : file-like
+   Object with file API, open for reading.''',
+     'long_fields':
+         '''long_field_names : bool, optional
+   * False - maximum field name length in a structure is 31 characters
+     which is the documented maximum length. This is the default.
+   * True - maximum field name length in a structure is 63 characters
+     which works for MATLAB 7.6''',
+     'do_compression':
+         '''do_compression : bool, optional
+   Whether to compress matrices on write. Default is False.''',
+     'oned_as':
+         '''oned_as : {'row', 'column'}, optional
+   If 'column', write 1-D numpy arrays as column vectors.
+   If 'row', write 1D numpy arrays as row vectors.''',
+     'unicode_strings':
+         '''unicode_strings : bool, optional
+   If True, write strings as Unicode, else MATLAB usual encoding.'''}
+
+docfiller = doccer.filldoc(doc_dict)
+
+'''
+
+ Note on architecture
+======================
+
+There are three sets of parameters relevant for reading files.  The
+first are *file read parameters* - containing options that are common
+for reading the whole file, and therefore every variable within that
+file. At the moment these are:
+
+* mat_stream
+* dtypes (derived from byte code)
+* byte_order
+* chars_as_strings
+* squeeze_me
+* struct_as_record (MATLAB 5 files)
+* class_dtypes (derived from order code, MATLAB 5 files)
+* codecs (MATLAB 5 files)
+* uint16_codec (MATLAB 5 files)
+
+Another set of parameters are those that apply only to the current
+variable being read - the *header*:
+
+* header related variables (different for v4 and v5 mat files)
+* is_complex
+* mclass
+* var_stream
+
+With the header, we need ``next_position`` to tell us where the next
+variable in the stream is.
+
+Then, for each element in a matrix, there can be *element read
+parameters*.  An element is, for example, one element in a MATLAB cell
+array.  At the moment these are:
+
+* mat_dtype
+
+The file-reading object contains the *file read parameters*.  The
+*header* is passed around as a data object, or may be read and discarded
+in a single function.  The *element read parameters* - the mat_dtype in
+this instance, is passed into a general post-processing function - see
+``mio_utils`` for details.
+'''
+
+
+def convert_dtypes(dtype_template, order_code):
+    ''' Convert dtypes in mapping to given order
+
+    Parameters
+    ----------
+    dtype_template : mapping
+       mapping with values returning numpy dtype from ``np.dtype(val)``
+    order_code : str
+       an order code suitable for using in ``dtype.newbyteorder()``
+
+    Returns
+    -------
+    dtypes : mapping
+       mapping where values have been replaced by
+       ``np.dtype(val).newbyteorder(order_code)``
+
+    '''
+    dtypes = dtype_template.copy()
+    for k in dtypes:
+        dtypes[k] = np.dtype(dtypes[k]).newbyteorder(order_code)
+    return dtypes
+
+
+def read_dtype(mat_stream, a_dtype):
+    """
+    Generic get of byte stream data of known type
+
+    Parameters
+    ----------
+    mat_stream : file_like object
+        MATLAB (tm) mat file stream
+    a_dtype : dtype
+        dtype of array to read.  `a_dtype` is assumed to be correct
+        endianness.
+
+    Returns
+    -------
+    arr : ndarray
+        Array of dtype `a_dtype` read from stream.
+
+    """
+    num_bytes = a_dtype.itemsize
+    arr = np.ndarray(shape=(),
+                     dtype=a_dtype,
+                     buffer=mat_stream.read(num_bytes),
+                     order='F')
+    return arr
+
+
+def get_matfile_version(fileobj):
+    """
+    Return major, minor tuple depending on apparent mat file type
+
+    Where:
+
+     #. 0,x -> version 4 format mat files
+     #. 1,x -> version 5 format mat files
+     #. 2,x -> version 7.3 format mat files (HDF format)
+
+    Parameters
+    ----------
+    fileobj : file_like
+        object implementing seek() and read()
+
+    Returns
+    -------
+    major_version : {0, 1, 2}
+        major MATLAB File format version
+    minor_version : int
+        minor MATLAB file format version
+
+    Raises
+    ------
+    MatReadError
+        If the file is empty.
+    ValueError
+        The matfile version is unknown.
+
+    Notes
+    -----
+    Has the side effect of setting the file read pointer to 0
+    """
+    # Mat4 files have a zero somewhere in first 4 bytes
+    fileobj.seek(0)
+    mopt_bytes = fileobj.read(4)
+    if len(mopt_bytes) == 0:
+        raise MatReadError("Mat file appears to be empty")
+    mopt_ints = np.ndarray(shape=(4,), dtype=np.uint8, buffer=mopt_bytes)
+    if 0 in mopt_ints:
+        fileobj.seek(0)
+        return (0,0)
+    # For 5 format or 7.3 format we need to read an integer in the
+    # header. Bytes 124 through 128 contain a version integer and an
+    # endian test string
+    fileobj.seek(124)
+    tst_str = fileobj.read(4)
+    fileobj.seek(0)
+    maj_ind = int(tst_str[2] == b'I'[0])
+    maj_val = byteord(tst_str[maj_ind])
+    min_val = byteord(tst_str[1-maj_ind])
+    ret = (maj_val, min_val)
+    if maj_val in (1, 2):
+        return ret
+    raise ValueError('Unknown mat file type, version %s, %s' % ret)
+
+
+def matdims(arr, oned_as='column'):
+    """
+    Determine equivalent MATLAB dimensions for given array
+
+    Parameters
+    ----------
+    arr : ndarray
+        Input array
+    oned_as : {'column', 'row'}, optional
+        Whether 1-D arrays are returned as MATLAB row or column matrices.
+        Default is 'column'.
+
+    Returns
+    -------
+    dims : tuple
+        Shape tuple, in the form MATLAB expects it.
+
+    Notes
+    -----
+    We had to decide what shape a 1 dimensional array would be by
+    default.  ``np.atleast_2d`` thinks it is a row vector.  The
+    default for a vector in MATLAB (e.g. ``>> 1:12``) is a row vector.
+
+    Versions of scipy up to and including 0.11 resulted (accidentally)
+    in 1-D arrays being read as column vectors.  For the moment, we
+    maintain the same tradition here.
+
+    Examples
+    --------
+    >>> matdims(np.array(1)) # numpy scalar
+    (1, 1)
+    >>> matdims(np.array([1])) # 1d array, 1 element
+    (1, 1)
+    >>> matdims(np.array([1,2])) # 1d array, 2 elements
+    (2, 1)
+    >>> matdims(np.array([[2],[3]])) # 2d array, column vector
+    (2, 1)
+    >>> matdims(np.array([[2,3]])) # 2d array, row vector
+    (1, 2)
+    >>> matdims(np.array([[[2,3]]])) # 3d array, rowish vector
+    (1, 1, 2)
+    >>> matdims(np.array([])) # empty 1d array
+    (0, 0)
+    >>> matdims(np.array([[]])) # empty 2d
+    (0, 0)
+    >>> matdims(np.array([[[]]])) # empty 3d
+    (0, 0, 0)
+
+    Optional argument flips 1-D shape behavior.
+
+    >>> matdims(np.array([1,2]), 'row') # 1d array, 2 elements
+    (1, 2)
+
+    The argument has to make sense though
+
+    >>> matdims(np.array([1,2]), 'bizarre')
+    Traceback (most recent call last):
+       ...
+    ValueError: 1D option "bizarre" is strange
+
+    """
+    shape = arr.shape
+    if shape == ():  # scalar
+        return (1,1)
+    if reduce(operator.mul, shape) == 0:  # zero elememts
+        return (0,) * np.max([arr.ndim, 2])
+    if len(shape) == 1:  # 1D
+        if oned_as == 'column':
+            return shape + (1,)
+        elif oned_as == 'row':
+            return (1,) + shape
+        else:
+            raise ValueError('1D option "%s" is strange'
+                             % oned_as)
+    return shape
+
+
+class MatVarReader(object):
+    ''' Abstract class defining required interface for var readers'''
+    def __init__(self, file_reader):
+        pass
+
+    def read_header(self):
+        ''' Returns header '''
+        pass
+
+    def array_from_header(self, header):
+        ''' Reads array given header '''
+        pass
+
+
+class MatFileReader(object):
+    """ Base object for reading mat files
+
+    To make this class functional, you will need to override the
+    following methods:
+
+    matrix_getter_factory   - gives object to fetch next matrix from stream
+    guess_byte_order        - guesses file byte order from file
+    """
+
+    @docfiller
+    def __init__(self, mat_stream,
+                 byte_order=None,
+                 mat_dtype=False,
+                 squeeze_me=False,
+                 chars_as_strings=True,
+                 matlab_compatible=False,
+                 struct_as_record=True,
+                 verify_compressed_data_integrity=True
+                 ):
+        '''
+        Initializer for mat file reader
+
+        mat_stream : file-like
+            object with file API, open for reading
+    %(load_args)s
+        '''
+        # Initialize stream
+        self.mat_stream = mat_stream
+        self.dtypes = {}
+        if not byte_order:
+            byte_order = self.guess_byte_order()
+        else:
+            byte_order = boc.to_numpy_code(byte_order)
+        self.byte_order = byte_order
+        self.struct_as_record = struct_as_record
+        if matlab_compatible:
+            self.set_matlab_compatible()
+        else:
+            self.squeeze_me = squeeze_me
+            self.chars_as_strings = chars_as_strings
+            self.mat_dtype = mat_dtype
+        self.verify_compressed_data_integrity = verify_compressed_data_integrity
+
+    def set_matlab_compatible(self):
+        ''' Sets options to return arrays as MATLAB loads them '''
+        self.mat_dtype = True
+        self.squeeze_me = False
+        self.chars_as_strings = False
+
+    def guess_byte_order(self):
+        ''' As we do not know what file type we have, assume native '''
+        return boc.native_code
+
+    def end_of_stream(self):
+        b = self.mat_stream.read(1)
+        curpos = self.mat_stream.tell()
+        self.mat_stream.seek(curpos-1)
+        return len(b) == 0
+
+
+def arr_dtype_number(arr, num):
+    ''' Return dtype for given number of items per element'''
+    return np.dtype(arr.dtype.str[:2] + str(num))
+
+
+def arr_to_chars(arr):
+    ''' Convert string array to char array '''
+    dims = list(arr.shape)
+    if not dims:
+        dims = [1]
+    dims.append(int(arr.dtype.str[2:]))
+    arr = np.ndarray(shape=dims,
+                     dtype=arr_dtype_number(arr, 1),
+                     buffer=arr)
+    empties = [arr == '']
+    if not np.any(empties):
+        return arr
+    arr = arr.copy()
+    arr[tuple(empties)] = ' '
+    return arr

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/setup.py

@@ -0,0 +1,16 @@
+from __future__ import division, print_function, absolute_import
+
+
+def configuration(parent_package='io',top_path=None):
+    from numpy.distutils.misc_util import Configuration
+    config = Configuration('matlab', parent_package, top_path)
+    config.add_extension('streams', sources=['streams.c'])
+    config.add_extension('mio_utils', sources=['mio_utils.c'])
+    config.add_extension('mio5_utils', sources=['mio5_utils.c'])
+    config.add_data_dir('tests')
+    return config
+
+
+if __name__ == '__main__':
+    from numpy.distutils.core import setup
+    setup(**configuration(top_path='').todict())

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/afunc.m

@@ -0,0 +1,4 @@
+function [a, b] = afunc(c, d)
+% A function
+a = c + 1;
+b = d + 10;

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/data/japanese_utf8.txt

@@ -0,0 +1,5 @@
+Japanese: 
+すべての人間は、生まれながらにして自由であり、
+かつ、尊厳と権利と について平等である。
+人間は、理性と良心とを授けられており、
+互いに同胞の精神をもって行動しなければならない。

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/gen_mat4files.m

@@ -0,0 +1,50 @@
+% Generates mat files for loadmat unit tests
+% Uses save_matfile.m function
+% This is the version for matlab 4
+
+% work out matlab version and file suffix for test files
+global FILEPREFIX FILESUFFIX
+sepchar = '/';
+if strcmp(computer, 'PCWIN'), sepchar = '\'; end
+FILEPREFIX = [pwd sepchar 'data' sepchar];
+mlv = version;
+FILESUFFIX = ['_' mlv '_' computer '.mat'];
+
+% basic double array
+theta = 0:pi/4:2*pi;
+save_matfile('testdouble', theta);
+
+% string
+save_matfile('teststring', '"Do nine men interpret?" "Nine men," I nod.')
+
+% complex
+save_matfile('testcomplex', cos(theta) + 1j*sin(theta));
+
+% asymmetric array to check indexing
+a = zeros(3, 5);
+a(:,1) = [1:3]';
+a(1,:) = 1:5;
+
+% 2D matrix
+save_matfile('testmatrix', a);
+
+% minus number - tests signed int 
+save_matfile('testminus', -1);
+
+% single character
+save_matfile('testonechar', 'r');
+
+% string array
+save_matfile('teststringarray', ['one  '; 'two  '; 'three']);
+
+% sparse array
+save_matfile('testsparse', sparse(a));
+
+% sparse complex array
+b = sparse(a);
+b(1,1) = b(1,1) + j;
+save_matfile('testsparsecomplex', b);
+
+% Two variables in same file
+save([FILEPREFIX 'testmulti' FILESUFFIX], 'a', 'theta')
+

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/gen_mat5files.m

@@ -0,0 +1,100 @@
+% Generates mat files for loadmat unit tests
+% This is the version for matlab 5 and higher
+% Uses save_matfile.m function
+
+% work out matlab version and file suffix for test files
+global FILEPREFIX FILESUFFIX
+FILEPREFIX = [fullfile(pwd, 'data') filesep];
+temp = ver('MATLAB');
+mlv = temp.Version;
+FILESUFFIX = ['_' mlv '_' computer '.mat'];
+
+% basic double array
+theta = 0:pi/4:2*pi;
+save_matfile('testdouble', theta);
+
+% string
+save_matfile('teststring', '"Do nine men interpret?" "Nine men," I nod.')
+
+% complex
+save_matfile('testcomplex', cos(theta) + 1j*sin(theta));
+
+% asymmetric array to check indexing
+a = zeros(3, 5);
+a(:,1) = [1:3]';
+a(1,:) = 1:5;
+
+% 2D matrix
+save_matfile('testmatrix', a);
+
+% minus number - tests signed int 
+save_matfile('testminus', -1);
+
+% single character
+save_matfile('testonechar', 'r');
+
+% string array
+save_matfile('teststringarray', ['one  '; 'two  '; 'three']);
+
+% sparse array
+save_matfile('testsparse', sparse(a));
+
+% sparse complex array
+b = sparse(a);
+b(1,1) = b(1,1) + j;
+save_matfile('testsparsecomplex', b);
+
+% Two variables in same file
+save([FILEPREFIX 'testmulti' FILESUFFIX], 'a', 'theta')
+
+
+% struct
+save_matfile('teststruct', ...
+	  struct('stringfield','Rats live on no evil star.',...
+		 'doublefield',[sqrt(2) exp(1) pi],...
+		 'complexfield',(1+1j)*[sqrt(2) exp(1) pi]));
+
+% cell
+save_matfile('testcell', ...
+	  {['This cell contains this string and 3 arrays of increasing' ...
+	    ' length'], 1., 1.:2., 1.:3.});
+
+% scalar cell
+save_matfile('testscalarcell', {1})
+
+% Empty cells in two cell matrices
+save_matfile('testemptycell', {1, 2, [], [], 3});
+
+% 3D matrix
+save_matfile('test3dmatrix', reshape(1:24,[2 3 4]))
+
+% nested cell array
+save_matfile('testcellnest', {1, {2, 3, {4, 5}}});
+
+% nested struct
+save_matfile('teststructnest', struct('one', 1, 'two', ...
+				   struct('three', 'number 3')));
+
+% array of struct
+save_matfile('teststructarr', [struct('one', 1, 'two', 2) ...
+		    struct('one', 'number 1', 'two', 'number 2')]);
+
+% matlab object
+save_matfile('testobject', inline('x'))
+
+% array of matlab objects
+%save_matfile('testobjarr', [inline('x') inline('x')])
+
+% unicode test
+if str2num(mlv) > 7  % function added 7.0.1
+  fid = fopen([FILEPREFIX 'japanese_utf8.txt']);
+  from_japan = fread(fid, 'uint8')';
+  fclose(fid);
+  save_matfile('testunicode', native2unicode(from_japan, 'utf-8'));
+end
+  
+% func
+if str2num(mlv) > 7  % function pointers added recently
+  func = @afunc;
+  save_matfile('testfunc', func);
+end

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/save_matfile.m

@@ -0,0 +1,6 @@
+function save_matfile(test_name, v)
+% saves variable passed in m with filename from prefix
+  
+global FILEPREFIX FILESUFFIX
+eval([test_name ' = v;']);
+save([FILEPREFIX test_name FILESUFFIX], test_name)

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/test_byteordercodes.py

@@ -0,0 +1,31 @@
+''' Tests for byteorder module '''
+
+from __future__ import division, print_function, absolute_import
+
+import sys
+
+from numpy.testing import assert_
+from pytest import raises as assert_raises
+
+import scipy.io.matlab.byteordercodes as sibc
+
+
+def test_native():
+    native_is_le = sys.byteorder == 'little'
+    assert_(sibc.sys_is_le == native_is_le)
+
+
+def test_to_numpy():
+    if sys.byteorder == 'little':
+        assert_(sibc.to_numpy_code('native') == '<')
+        assert_(sibc.to_numpy_code('swapped') == '>')
+    else:
+        assert_(sibc.to_numpy_code('native') == '>')
+        assert_(sibc.to_numpy_code('swapped') == '<')
+    assert_(sibc.to_numpy_code('native') == sibc.to_numpy_code('='))
+    assert_(sibc.to_numpy_code('big') == '>')
+    for code in ('little', '<', 'l', 'L', 'le'):
+        assert_(sibc.to_numpy_code(code) == '<')
+    for code in ('big', '>', 'b', 'B', 'be'):
+        assert_(sibc.to_numpy_code(code) == '>')
+    assert_raises(ValueError, sibc.to_numpy_code, 'silly string')

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/test_mio5_utils.py

@@ -0,0 +1,185 @@
+""" Testing mio5_utils Cython module
+
+"""
+from __future__ import division, print_function, absolute_import
+
+import sys
+
+from io import BytesIO
+cStringIO = BytesIO
+
+import numpy as np
+
+from numpy.testing import assert_array_equal, assert_equal, assert_
+from pytest import raises as assert_raises
+
+from scipy._lib.six import u
+
+import scipy.io.matlab.byteordercodes as boc
+import scipy.io.matlab.streams as streams
+import scipy.io.matlab.mio5_params as mio5p
+import scipy.io.matlab.mio5_utils as m5u
+
+
+def test_byteswap():
+    for val in (
+        1,
+        0x100,
+        0x10000):
+        a = np.array(val, dtype=np.uint32)
+        b = a.byteswap()
+        c = m5u.byteswap_u4(a)
+        assert_equal(b.item(), c)
+        d = m5u.byteswap_u4(c)
+        assert_equal(a.item(), d)
+
+
+def _make_tag(base_dt, val, mdtype, sde=False):
+    ''' Makes a simple matlab tag, full or sde '''
+    base_dt = np.dtype(base_dt)
+    bo = boc.to_numpy_code(base_dt.byteorder)
+    byte_count = base_dt.itemsize
+    if not sde:
+        udt = bo + 'u4'
+        padding = 8 - (byte_count % 8)
+        all_dt = [('mdtype', udt),
+                  ('byte_count', udt),
+                  ('val', base_dt)]
+        if padding:
+            all_dt.append(('padding', 'u1', padding))
+    else:  # is sde
+        udt = bo + 'u2'
+        padding = 4-byte_count
+        if bo == '<':  # little endian
+            all_dt = [('mdtype', udt),
+                      ('byte_count', udt),
+                      ('val', base_dt)]
+        else:  # big endian
+            all_dt = [('byte_count', udt),
+                      ('mdtype', udt),
+                      ('val', base_dt)]
+        if padding:
+            all_dt.append(('padding', 'u1', padding))
+    tag = np.zeros((1,), dtype=all_dt)
+    tag['mdtype'] = mdtype
+    tag['byte_count'] = byte_count
+    tag['val'] = val
+    return tag
+
+
+def _write_stream(stream, *strings):
+    stream.truncate(0)
+    stream.seek(0)
+    for s in strings:
+        stream.write(s)
+    stream.seek(0)
+
+
+def _make_readerlike(stream, byte_order=boc.native_code):
+    class R(object):
+        pass
+    r = R()
+    r.mat_stream = stream
+    r.byte_order = byte_order
+    r.struct_as_record = True
+    r.uint16_codec = sys.getdefaultencoding()
+    r.chars_as_strings = False
+    r.mat_dtype = False
+    r.squeeze_me = False
+    return r
+
+
+def test_read_tag():
+    # mainly to test errors
+    # make reader-like thing
+    str_io = BytesIO()
+    r = _make_readerlike(str_io)
+    c_reader = m5u.VarReader5(r)
+    # This works for StringIO but _not_ cStringIO
+    assert_raises(IOError, c_reader.read_tag)
+    # bad SDE
+    tag = _make_tag('i4', 1, mio5p.miINT32, sde=True)
+    tag['byte_count'] = 5
+    _write_stream(str_io, tag.tostring())
+    assert_raises(ValueError, c_reader.read_tag)
+
+
+def test_read_stream():
+    tag = _make_tag('i4', 1, mio5p.miINT32, sde=True)
+    tag_str = tag.tostring()
+    str_io = cStringIO(tag_str)
+    st = streams.make_stream(str_io)
+    s = streams._read_into(st, tag.itemsize)
+    assert_equal(s, tag.tostring())
+
+
+def test_read_numeric():
+    # make reader-like thing
+    str_io = cStringIO()
+    r = _make_readerlike(str_io)
+    # check simplest of tags
+    for base_dt, val, mdtype in (('u2', 30, mio5p.miUINT16),
+                                 ('i4', 1, mio5p.miINT32),
+                                 ('i2', -1, mio5p.miINT16)):
+        for byte_code in ('<', '>'):
+            r.byte_order = byte_code
+            c_reader = m5u.VarReader5(r)
+            assert_equal(c_reader.little_endian, byte_code == '<')
+            assert_equal(c_reader.is_swapped, byte_code != boc.native_code)
+            for sde_f in (False, True):
+                dt = np.dtype(base_dt).newbyteorder(byte_code)
+                a = _make_tag(dt, val, mdtype, sde_f)
+                a_str = a.tostring()
+                _write_stream(str_io, a_str)
+                el = c_reader.read_numeric()
+                assert_equal(el, val)
+                # two sequential reads
+                _write_stream(str_io, a_str, a_str)
+                el = c_reader.read_numeric()
+                assert_equal(el, val)
+                el = c_reader.read_numeric()
+                assert_equal(el, val)
+
+
+def test_read_numeric_writeable():
+    # make reader-like thing
+    str_io = cStringIO()
+    r = _make_readerlike(str_io, '<')
+    c_reader = m5u.VarReader5(r)
+    dt = np.dtype('<u2')
+    a = _make_tag(dt, 30, mio5p.miUINT16, 0)
+    a_str = a.tostring()
+    _write_stream(str_io, a_str)
+    el = c_reader.read_numeric()
+    assert_(el.flags.writeable is True)
+
+
+def test_zero_byte_string():
+    # Tests hack to allow chars of non-zero length, but 0 bytes
+    # make reader-like thing
+    str_io = cStringIO()
+    r = _make_readerlike(str_io, boc.native_code)
+    c_reader = m5u.VarReader5(r)
+    tag_dt = np.dtype([('mdtype', 'u4'), ('byte_count', 'u4')])
+    tag = np.zeros((1,), dtype=tag_dt)
+    tag['mdtype'] = mio5p.miINT8
+    tag['byte_count'] = 1
+    hdr = m5u.VarHeader5()
+    # Try when string is 1 length
+    hdr.set_dims([1,])
+    _write_stream(str_io, tag.tostring() + b'        ')
+    str_io.seek(0)
+    val = c_reader.read_char(hdr)
+    assert_equal(val, u(' '))
+    # Now when string has 0 bytes 1 length
+    tag['byte_count'] = 0
+    _write_stream(str_io, tag.tostring())
+    str_io.seek(0)
+    val = c_reader.read_char(hdr)
+    assert_equal(val, u(' '))
+    # Now when string has 0 bytes 4 length
+    str_io.seek(0)
+    hdr.set_dims([4,])
+    val = c_reader.read_char(hdr)
+    assert_array_equal(val, [u(' ')] * 4)
+

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/test_mio_funcs.py

@@ -0,0 +1,57 @@
+''' Jottings to work out format for __function_workspace__ matrix at end
+of mat file.
+
+'''
+from __future__ import division, print_function, absolute_import
+
+import os.path
+import sys
+import io
+
+from numpy.compat import asstr
+
+from scipy.io.matlab.mio5 import (MatlabObject, MatFile5Writer,
+                                  MatFile5Reader, MatlabFunction)
+
+test_data_path = os.path.join(os.path.dirname(__file__), 'data')
+
+
+def read_minimat_vars(rdr):
+    rdr.initialize_read()
+    mdict = {'__globals__': []}
+    i = 0
+    while not rdr.end_of_stream():
+        hdr, next_position = rdr.read_var_header()
+        name = asstr(hdr.name)
+        if name == '':
+            name = 'var_%d' % i
+            i += 1
+        res = rdr.read_var_array(hdr, process=False)
+        rdr.mat_stream.seek(next_position)
+        mdict[name] = res
+        if hdr.is_global:
+            mdict['__globals__'].append(name)
+    return mdict
+
+
+def read_workspace_vars(fname):
+    fp = open(fname, 'rb')
+    rdr = MatFile5Reader(fp, struct_as_record=True)
+    vars = rdr.get_variables()
+    fws = vars['__function_workspace__']
+    ws_bs = io.BytesIO(fws.tostring())
+    ws_bs.seek(2)
+    rdr.mat_stream = ws_bs
+    # Guess byte order.
+    mi = rdr.mat_stream.read(2)
+    rdr.byte_order = mi == b'IM' and '<' or '>'
+    rdr.mat_stream.read(4)  # presumably byte padding
+    mdict = read_minimat_vars(rdr)
+    fp.close()
+    return mdict
+
+
+def test_jottings():
+    # example
+    fname = os.path.join(test_data_path, 'parabola.mat')
+    ws_vars = read_workspace_vars(fname)

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/test_mio_utils.py

@@ -0,0 +1,46 @@
+""" Testing
+
+"""
+
+from __future__ import division, print_function, absolute_import
+
+import numpy as np
+
+from numpy.testing import assert_array_equal, assert_array_almost_equal, \
+     assert_
+
+from scipy.io.matlab.mio_utils import squeeze_element, chars_to_strings
+
+
+def test_squeeze_element():
+    a = np.zeros((1,3))
+    assert_array_equal(np.squeeze(a), squeeze_element(a))
+    # 0d output from squeeze gives scalar
+    sq_int = squeeze_element(np.zeros((1,1), dtype=float))
+    assert_(isinstance(sq_int, float))
+    # Unless it's a structured array
+    sq_sa = squeeze_element(np.zeros((1,1),dtype=[('f1', 'f')]))
+    assert_(isinstance(sq_sa, np.ndarray))
+
+
+def test_chars_strings():
+    # chars as strings
+    strings = ['learn ', 'python', 'fast  ', 'here  ']
+    str_arr = np.array(strings, dtype='U6')  # shape (4,)
+    chars = [list(s) for s in strings]
+    char_arr = np.array(chars, dtype='U1')  # shape (4,6)
+    assert_array_equal(chars_to_strings(char_arr), str_arr)
+    ca2d = char_arr.reshape((2,2,6))
+    sa2d = str_arr.reshape((2,2))
+    assert_array_equal(chars_to_strings(ca2d), sa2d)
+    ca3d = char_arr.reshape((1,2,2,6))
+    sa3d = str_arr.reshape((1,2,2))
+    assert_array_equal(chars_to_strings(ca3d), sa3d)
+    # Fortran ordered arrays
+    char_arrf = np.array(chars, dtype='U1', order='F')  # shape (4,6)
+    assert_array_equal(chars_to_strings(char_arrf), str_arr)
+    # empty array
+    arr = np.array([['']], dtype='U1')
+    out_arr = np.array([''], dtype='U1')
+    assert_array_equal(chars_to_strings(arr), out_arr)
+

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/test_miobase.py

@@ -0,0 +1,31 @@
+""" Testing miobase module
+"""
+
+import numpy as np
+
+from numpy.testing import assert_equal
+from pytest import raises as assert_raises
+
+from scipy.io.matlab.miobase import matdims
+
+
+def test_matdims():
+    # Test matdims dimension finder
+    assert_equal(matdims(np.array(1)), (1, 1))  # numpy scalar
+    assert_equal(matdims(np.array([1])), (1, 1))  # 1d array, 1 element
+    assert_equal(matdims(np.array([1,2])), (2, 1))  # 1d array, 2 elements
+    assert_equal(matdims(np.array([[2],[3]])), (2, 1))  # 2d array, column vector
+    assert_equal(matdims(np.array([[2,3]])), (1, 2))  # 2d array, row vector
+    # 3d array, rowish vector
+    assert_equal(matdims(np.array([[[2,3]]])), (1, 1, 2))
+    assert_equal(matdims(np.array([])), (0, 0))  # empty 1d array
+    assert_equal(matdims(np.array([[]])), (0, 0))  # empty 2d
+    assert_equal(matdims(np.array([[[]]])), (0, 0, 0))  # empty 3d
+    # Optional argument flips 1-D shape behavior.
+    assert_equal(matdims(np.array([1,2]), 'row'), (1, 2))  # 1d array, 2 elements
+    # The argument has to make sense though
+    assert_raises(ValueError, matdims, np.array([1,2]), 'bizarre')
+    # Check empty sparse matrices get their own shape
+    from scipy.sparse import csr_matrix, csc_matrix
+    assert_equal(matdims(csr_matrix(np.zeros((3, 3)))), (3, 3))
+    assert_equal(matdims(csc_matrix(np.zeros((2, 2)))), (2, 2))

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/test_pathological.py

@@ -0,0 +1,35 @@
+""" Test reading of files not conforming to matlab specification
+
+We try and read any file that matlab reads, these files included
+"""
+from __future__ import division, print_function, absolute_import
+
+from os.path import dirname, join as pjoin
+
+from numpy.testing import assert_
+from pytest import raises as assert_raises
+
+from scipy.io.matlab.mio import loadmat
+
+TEST_DATA_PATH = pjoin(dirname(__file__), 'data')
+
+
+def test_multiple_fieldnames():
+    # Example provided by Dharhas Pothina
+    # Extracted using mio5.varmats_from_mat
+    multi_fname = pjoin(TEST_DATA_PATH, 'nasty_duplicate_fieldnames.mat')
+    vars = loadmat(multi_fname)
+    funny_names = vars['Summary'].dtype.names
+    assert_(set(['_1_Station_Q', '_2_Station_Q',
+                     '_3_Station_Q']).issubset(funny_names))
+
+
+def test_malformed1():
+    # Example from gh-6072
+    # Contains malformed header data, which previously resulted into a
+    # buffer overflow.
+    #
+    # Should raise an exception, not segfault
+    fname = pjoin(TEST_DATA_PATH, 'malformed1.mat')
+    with open(fname, 'rb') as f:
+        assert_raises(ValueError, loadmat, f)

utils/venv/lib/python3.6/site-packages/scipy/io/matlab/tests/test_streams.py

@@ -0,0 +1,184 @@
+""" Testing
+
+"""
+
+from __future__ import division, print_function, absolute_import
+
+import os
+import sys
+import zlib
+
+from io import BytesIO
+
+if sys.version_info[0] >= 3:
+    cStringIO = BytesIO
+else:
+    from cStringIO import StringIO as cStringIO
+
+from tempfile import mkstemp
+from contextlib import contextmanager
+
+import numpy as np
+
+from numpy.testing import assert_, assert_equal
+from pytest import raises as assert_raises
+
+from scipy.io.matlab.streams import (make_stream,
+    GenericStream, cStringStream, FileStream, ZlibInputStream,
+    _read_into, _read_string)
+
+IS_PYPY = ('__pypy__' in sys.modules)
+
+
+@contextmanager
+def setup_test_file():
+    val = b'a\x00string'
+    fd, fname = mkstemp()
+
+    with os.fdopen(fd, 'wb') as fs:
+        fs.write(val)
+    with open(fname, 'rb') as fs:
+        gs = BytesIO(val)
+        cs = cStringIO(val)
+        yield fs, gs, cs
+    os.unlink(fname)
+
+
+def test_make_stream():
+    with setup_test_file() as (fs, gs, cs):
+        # test stream initialization
+        assert_(isinstance(make_stream(gs), GenericStream))
+        if sys.version_info[0] < 3 and not IS_PYPY:
+            assert_(isinstance(make_stream(cs), cStringStream))
+            assert_(isinstance(make_stream(fs), FileStream))
+
+
+def test_tell_seek():
+    with setup_test_file() as (fs, gs, cs):
+        for s in (fs, gs, cs):
+            st = make_stream(s)
+            res = st.seek(0)
+            assert_equal(res, 0)
+            assert_equal(st.tell(), 0)
+            res = st.seek(5)
+            assert_equal(res, 0)
+            assert_equal(st.tell(), 5)
+            res = st.seek(2, 1)
+            assert_equal(res, 0)
+            assert_equal(st.tell(), 7)
+            res = st.seek(-2, 2)
+            assert_equal(res, 0)
+            assert_equal(st.tell(), 6)
+
+
+def test_read():
+    with setup_test_file() as (fs, gs, cs):
+        for s in (fs, gs, cs):
+            st = make_stream(s)
+            st.seek(0)
+            res = st.read(-1)
+            assert_equal(res, b'a\x00string')
+            st.seek(0)
+            res = st.read(4)
+            assert_equal(res, b'a\x00st')
+            # read into
+            st.seek(0)
+            res = _read_into(st, 4)
+            assert_equal(res, b'a\x00st')
+            res = _read_into(st, 4)
+            assert_equal(res, b'ring')
+            assert_raises(IOError, _read_into, st, 2)
+            # read alloc
+            st.seek(0)
+            res = _read_string(st, 4)
+            assert_equal(res, b'a\x00st')
+            res = _read_string(st, 4)
+            assert_equal(res, b'ring')
+            assert_raises(IOError, _read_string, st, 2)
+
+
+class TestZlibInputStream(object):
+    def _get_data(self, size):
+        data = np.random.randint(0, 256, size).astype(np.uint8).tostring()
+        compressed_data = zlib.compress(data)
+        stream = BytesIO(compressed_data)
+        return stream, len(compressed_data), data
+
+    def test_read(self):
+        block_size = 131072
+
+        SIZES = [0, 1, 10, block_size//2, block_size-1,
+                 block_size, block_size+1, 2*block_size-1]
+
+        READ_SIZES = [block_size//2, block_size-1,
+                      block_size, block_size+1]
+
+        def check(size, read_size):
+            compressed_stream, compressed_data_len, data = self._get_data(size)
+            stream = ZlibInputStream(compressed_stream, compressed_data_len)
+            data2 = b''
+            so_far = 0
+            while True:
+                block = stream.read(min(read_size,
+                                        size - so_far))
+                if not block:
+                    break
+                so_far += len(block)
+                data2 += block
+            assert_equal(data, data2)
+
+        for size in SIZES:
+            for read_size in READ_SIZES:
+                check(size, read_size)
+
+    def test_read_max_length(self):
+        size = 1234
+        data = np.random.randint(0, 256, size).astype(np.uint8).tostring()
+        compressed_data = zlib.compress(data)
+        compressed_stream = BytesIO(compressed_data + b"abbacaca")
+        stream = ZlibInputStream(compressed_stream, len(compressed_data))
+
+        stream.read(len(data))
+        assert_equal(compressed_stream.tell(), len(compressed_data))
+
+        assert_raises(IOError, stream.read, 1)
+
+    def test_seek(self):
+        compressed_stream, compressed_data_len, data = self._get_data(1024)
+
+        stream = ZlibInputStream(compressed_stream, compressed_data_len)
+
+        stream.seek(123)
+        p = 123
+        assert_equal(stream.tell(), p)
+        d1 = stream.read(11)
+        assert_equal(d1, data[p:p+11])
+
+        stream.seek(321, 1)
+        p = 123+11+321
+        assert_equal(stream.tell(), p)
+        d2 = stream.read(21)
+        assert_equal(d2, data[p:p+21])
+
+        stream.seek(641, 0)
+        p = 641
+        assert_equal(stream.tell(), p)
+        d3 = stream.read(11)
+        assert_equal(d3, data[p:p+11])
+
+        assert_raises(IOError, stream.seek, 10, 2)
+        assert_raises(IOError, stream.seek, -1, 1)
+        assert_raises(ValueError, stream.seek, 1, 123)
+
+        stream.seek(10000, 1)
+        assert_raises(IOError, stream.read, 12)
+
+    def test_all_data_read(self):
+        compressed_stream, compressed_data_len, data = self._get_data(1024)
+        stream = ZlibInputStream(compressed_stream, compressed_data_len)
+        assert_(not stream.all_data_read())
+        stream.seek(512)
+        assert_(not stream.all_data_read())
+        stream.seek(1024)
+        assert_(stream.all_data_read())
+