manyi/venv/Lib/site-packages/scipy/io/matlab/mio4.py

''' 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)