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Fixed database typo and removed unnecessary class identifier.

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Batuhan Berk Başoğlu 2020-10-14 10:10:37 -04:00
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venv/Lib/site-packages/scipy/io/matlab/mio4.py Normal file
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''' Classes for read / write of matlab (TM) 4 files
'''
import sys
import warnings
import numpy as np
from numpy.compat import asbytes, asstr
import scipy.sparse
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 given by `hdr` ``dtype`` and shape given 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.tobytes().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 are 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=(), dtype=dt,
buffer=self.mat_stream.read(dt.itemsize))
self.mat_stream.seek(dt.itemsize * (dims[0] - 1), 1)
cols = np.ndarray(shape=(), 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, str):
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 1-D vectors as row vectors or column vectors.
See documentation for ``matdims`` for more detail
Returns
-------
arr2d : array
2-D 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.tobytes(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.prod(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 2-D
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)