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''' Classes for read / write of matlab (TM) 5 files
'''
# Small fragments of current code adapted from matfile.py by Heiko
# Henkelmann
## Notice in matfile.py file
# Copyright (c) 2003 Heiko Henkelmann
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
import zlib
from copy import copy as pycopy
from cStringIO import StringIO
import numpy as N
from scipy.io.miobase import *
try: # Python 2.3 support
from sets import Set as set
except:
pass
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
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
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')],
'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',
mxSINGLE_CLASS: 'f4',
mxDOUBLE_CLASS: 'f8',
}
''' 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},
}
miUINT16_codec = sys.getdefaultencoding()
mx_numbers = (
mxDOUBLE_CLASS,
mxSINGLE_CLASS,
mxINT8_CLASS,
mxUINT8_CLASS,
mxINT16_CLASS,
mxUINT16_CLASS,
mxINT32_CLASS,
mxUINT32_CLASS,
)
class mat_struct(object):
''' Placeholder for holding read data from structs '''
pass
class mat_obj(object):
''' Placeholder for holding read data from objects '''
pass
class Mat5ArrayReader(MatArrayReader):
''' Class to get Mat5 arrays
Provides element reader functions, header reader, matrix reader
factory function
'''
def __init__(self, mat_stream, dtypes, processor_func, codecs, class_dtypes):
super(Mat5ArrayReader, self).__init__(mat_stream,
dtypes,
processor_func,
)
self.codecs = codecs
self.class_dtypes = class_dtypes
def read_element(self, copy=True):
raw_tag = self.mat_stream.read(8)
tag = N.ndarray(shape=(),
dtype=self.dtypes['tag_full'],
buffer = raw_tag)
mdtype = tag['mdtype'].item()
byte_count = mdtype >> 16
if byte_count: # small data element format
if byte_count > 4:
raise ValueError, 'Too many bytes for sde format'
mdtype = mdtype & 0xFFFF
dt = self.dtypes[mdtype]
el_count = byte_count / dt.itemsize
return N.ndarray(shape=(el_count,),
dtype=dt,
buffer=raw_tag[4:])
byte_count = tag['byte_count'].item()
if mdtype == miMATRIX:
return self.current_getter(byte_count).get_array()
if mdtype in self.codecs: # encoded char data
raw_str = self.mat_stream.read(byte_count)
codec = self.codecs[mdtype]
if not codec:
raise TypeError, 'Do not support encoding %d' % mdtype
el = raw_str.decode(codec)
else: # numeric data
dt = self.dtypes[mdtype]
el_count = byte_count / dt.itemsize
el = N.ndarray(shape=(el_count,),
dtype=dt,
buffer=self.mat_stream.read(byte_count))
if copy:
el = el.copy()
mod8 = byte_count % 8
if mod8:
self.mat_stream.seek(8 - mod8, 1)
return el
def matrix_getter_factory(self):
''' Returns reader for next matrix at top level '''
tag = self.read_dtype(self.dtypes['tag_full'])
mdtype = tag['mdtype'].item()
byte_count = tag['byte_count'].item()
next_pos = self.mat_stream.tell() + byte_count
if mdtype == miCOMPRESSED:
getter = Mat5ZArrayReader(self, byte_count).matrix_getter_factory()
elif not mdtype == miMATRIX:
raise TypeError, \
'Expecting miMATRIX type here, got %d' % mdtype
else:
getter = self.current_getter(byte_count)
getter.next_position = next_pos
return getter
def current_getter(self, byte_count):
''' Return matrix getter for current stream position
Returns matrix getters at top level and sub levels
'''
if not byte_count: # an empty miMATRIX can contain no bytes
return Mat5EmptyMatrixGetter(self)
af = self.read_dtype(self.dtypes['array_flags'])
header = {}
flags_class = af['flags_class']
mc = flags_class & 0xFF
header['mclass'] = mc
header['is_logical'] = flags_class >> 9 & 1
header['is_global'] = flags_class >> 10 & 1
header['is_complex'] = flags_class >> 11 & 1
header['nzmax'] = af['nzmax']
header['dims'] = self.read_element()
header['name'] = self.read_element().tostring()
if mc in mx_numbers:
return Mat5NumericMatrixGetter(self, header)
if mc == mxSPARSE_CLASS:
return Mat5SparseMatrixGetter(self, header)
if mc == mxCHAR_CLASS:
return Mat5CharMatrixGetter(self, header)
if mc == mxCELL_CLASS:
return Mat5CellMatrixGetter(self, header)
if mc == mxSTRUCT_CLASS:
return Mat5StructMatrixGetter(self, header)
if mc == mxOBJECT_CLASS:
return Mat5ObjectMatrixGetter(self, header)
raise TypeError, 'No reader for class code %s' % mc
class Mat5ZArrayReader(Mat5ArrayReader):
''' Getter for compressed arrays
Reads and uncompresses gzipped stream on init, providing wrapper
for this new sub-stream.
'''
def __init__(self, array_reader, byte_count):
'''Reads and uncompresses gzipped stream'''
data = array_reader.mat_stream.read(byte_count)
super(Mat5ZArrayReader, self).__init__(
StringIO(zlib.decompress(data)),
array_reader.dtypes,
array_reader.processor_func,
array_reader.codecs,
array_reader.class_dtypes)
class Mat5MatrixGetter(MatMatrixGetter):
''' Base class for getting Mat5 matrices
Gets current read information from passed array_reader
'''
def __init__(self, array_reader, header):
super(Mat5MatrixGetter, self).__init__(array_reader, header)
self.class_dtypes = array_reader.class_dtypes
self.codecs = array_reader.codecs
self.is_global = header['is_global']
self.mat_dtype = None
def read_element(self, *args, **kwargs):
return self.array_reader.read_element(*args, **kwargs)
class Mat5EmptyMatrixGetter(Mat5MatrixGetter):
''' Dummy class to return empty array for empty matrix
'''
def __init__(self, array_reader):
self.array_reader = array_reader
self.mat_stream = array_reader.mat_stream
self.data_position = self.mat_stream.tell()
self.header = {}
self.is_global = False
self.mat_dtype = 'f8'
def get_raw_array(self):
return N.array([[]])
class Mat5NumericMatrixGetter(Mat5MatrixGetter):
def __init__(self, array_reader, header):
super(Mat5NumericMatrixGetter, self).__init__(array_reader, header)
if header['is_logical']:
self.mat_dtype = N.dtype('bool')
else:
self.mat_dtype = self.class_dtypes[header['mclass']]
def get_raw_array(self):
if self.header['is_complex']:
# avoid array copy to save memory
res = self.read_element(copy=False)
res_j = self.read_element(copy=False)
res = res + (res_j * 1j)
else:
res = self.read_element()
return N.ndarray(shape=self.header['dims'],
dtype=res.dtype,
buffer=res,
order='F')
class Mat5SparseMatrixGetter(Mat5MatrixGetter):
def get_raw_array(self):
rowind = self.read_element()
colind = self.read_element()
if self.header['is_complex']:
# avoid array copy to save memory
res = self.read_element(copy=False)
res_j = self.read_element(copy=False)
res = res + (res_j * 1j)
else:
res = self.read_element()
''' From the matlab (TM) API documentation, last found here:
http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_external/
rowind are simply the row indices for all the (res) non-zero
entries in the sparse array. rowind has nzmax entries, so
may well have more entries than len(res), the actual number
of non-zero entries, but rowind[len(res):] can be discarded
and should be 0. colind has length (number of columns + 1),
and is such that, if D = diff(colind), D[j] gives the number
of non-zero entries in column j. Because rowind values are
stored in column order, this gives the column corresponding to
each rowind
'''
cols = N.empty((len(res)), dtype=rowind.dtype)
col_counts = N.diff(colind)
start_row = 0
for i in N.where(col_counts)[0]:
end_row = start_row + col_counts[i]
cols[start_row:end_row] = i
start_row = end_row
ij = N.vstack((rowind[:len(res)], cols))
if have_sparse:
result = scipy.sparse.csc_matrix((res,ij),
self.header['dims'])
else:
result = (dims, ij, res)
return result
class Mat5CharMatrixGetter(Mat5MatrixGetter):
def get_raw_array(self):
res = self.read_element()
# Convert non-string types to unicode
if isinstance(res, N.ndarray):
if res.dtype.type == N.uint16:
codec = miUINT16_codec
if self.codecs['uint16_len'] == 1:
res = res.astype(N.uint8)
elif res.dtype.type in (N.uint8, N.int8):
codec = 'ascii'
else:
raise TypeError, 'Did not expect type %s' % res.dtype
res = res.tostring().decode(codec)
return N.ndarray(shape=self.header['dims'],
dtype=N.dtype('U1'),
buffer=N.array(res),
order='F').copy()
class Mat5CellMatrixGetter(Mat5MatrixGetter):
def get_raw_array(self):
# Account for fortran indexing of cells
tupdims = tuple(self.header['dims'][::-1])
length = N.product(tupdims)
result = N.empty(length, dtype=object)
for i in range(length):
result[i] = self.get_item()
return result.reshape(tupdims).T
def get_item(self):
return self.read_element()
class Mat5StructMatrixGetter(Mat5CellMatrixGetter):
def __init__(self, *args, **kwargs):
super(Mat5StructMatrixGetter, self).__init__(*args, **kwargs)
self.obj_template = mat_struct()
def get_raw_array(self):
namelength = self.read_element()[0]
# get field names
names = self.read_element()
splitnames = [names[i:i+namelength] for i in \
xrange(0,len(names),namelength)]
self.obj_template._fieldnames = [x.tostring().strip('\x00')
for x in splitnames]
return super(Mat5StructMatrixGetter, self).get_raw_array()
def get_item(self):
item = pycopy(self.obj_template)
for element in item._fieldnames:
item.__dict__[element] = self.read_element()
return item
class Mat5ObjectMatrixGetter(Mat5StructMatrixGetter):
def __init__(self, *args, **kwargs):
super(Mat5StructMatrixGetter, self).__init__(*args, **kwargs)
self.obj_template = mat_obj()
def get_raw_array(self):
self.obj_template._classname = self.read_element().tostring()
return super(Mat5ObjectMatrixGetter, self).get_raw_array()
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)
'''
def __init__(self,
mat_stream,
byte_order=None,
mat_dtype=False,
squeeze_me=True,
chars_as_strings=True,
matlab_compatible=False,
uint16_codec=None
):
self.codecs = {}
self._array_reader = Mat5ArrayReader(
mat_stream,
None,
None,
None,
None,
)
super(MatFile5Reader, self).__init__(
mat_stream,
byte_order,
mat_dtype,
squeeze_me,
chars_as_strings,
matlab_compatible,
)
self._array_reader.processor_func = self.processor_func
self.uint16_codec = uint16_codec
def get_uint16_codec(self):
return self._uint16_codec
def set_uint16_codec(self, uint16_codec):
if not uint16_codec:
uint16_codec = sys.getdefaultencoding()
# Set length of miUINT16 char encoding
self.codecs['uint16_len'] = len(" ".encode(uint16_codec)) \
- len(" ".encode(uint16_codec))
self.codecs['uint16_codec'] = uint16_codec
self._array_reader.codecs = self.codecs
self._uint16_codec = uint16_codec
uint16_codec = property(get_uint16_codec,
set_uint16_codec,
None,
'get/set uint16_codec')
def set_dtypes(self):
''' Set dtypes and codecs '''
self.dtypes = self.convert_dtypes(mdtypes_template)
self.class_dtypes = self.convert_dtypes(mclass_dtypes_template)
codecs = {}
postfix = self.order_code == '<' and '_le' or '_be'
for k, v in codecs_template.items():
codec = v['codec']
try:
" ".encode(codec)
except LookupError:
codecs[k] = None
continue
if v['width'] > 1:
codec += postfix
codecs[k] = codec
self.codecs.update(codecs)
self.update_array_reader()
def update_array_reader(self):
self._array_reader.codecs = self.codecs
self._array_reader.dtypes = self.dtypes
self._array_reader.class_dtypes = self.class_dtypes
def matrix_getter_factory(self):
return self._array_reader.matrix_getter_factory()
def guess_byte_order(self):
self.mat_stream.seek(126)
mi = self.mat_stream.read(2)
self.mat_stream.seek(0)
return mi == 'IM' and '<' or '>'
def file_header(self):
''' Read in mat 5 file header '''
hdict = {}
hdr = self.read_dtype(self.dtypes['file_header'])
hdict['__header__'] = hdr['description'].item().strip(' \t\n\000')
v_major = hdr['version'] >> 8
v_minor = hdr['version'] & 0xFF
hdict['__version__'] = '%d.%d' % (v_major, v_minor)
return hdict
def format_looks_right(self):
# Mat4 files have a zero somewhere in first 4 bytes
self.mat_stream.seek(0)
mopt_bytes = N.ndarray(shape=(4,),
dtype=N.uint8,
buffer = self.mat_stream.read(4))
self.mat_stream.seek(0)
return 0 not in mopt_bytes
class Mat5MatrixWriter(MatStreamWriter):
mat_tag = N.zeros((), mdtypes_template['tag_full'])
mat_tag['mdtype'] = miMATRIX
def __init__(self, file_stream, arr, name, is_global=False):
super(Mat5MatrixWriter, self).__init__(file_stream, arr, name)
self.is_global = is_global
def write_dtype(self, arr):
self.file_stream.write(arr.tostring)
def write_element(self, arr):
# check if small element works - do it
# write tag, data
pass
def write_header(self, mclass,
is_global=False,
is_complex=False,
is_logical=False,
nzmax=0):
''' Write header for given data options
mclass - mat5 matrix class
is_global - True if matrix is global
is_complex - True is matrix is complex
is_logical - True if matrix is logical
nzmax - max non zero elements for sparse arrays
'''
self._mat_tag_pos = self.file_stream.tell()
self.write_dtype(self.mat_tag)
# write array flags (complex, global, logical, class, nzmax)
af = N.zeros((), mdtypes_template['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_dtype(af)
self.write_element(N.array(self.arr.shape, dtype='i4'))
self.write_element(self.name)
def update_matrix_tag(self):
curr_pos = self.file_stream.tell()
self.file_stream.seek(self._mat_tag_pos)
self.mat_tag['byte_count'] = curr_pos - self._mat_tag_pos - 8
self.write_dtype(self.mat_tag)
self.file_stream.seek(curr_pos)
def write(self):
assert False, 'Not implemented'
class Mat5NumericWriter(Mat5MatrixWriter):
def write(self):
# identify matlab type for array
# make at least 2d
# maybe downcast array to smaller matlab type
# write real
# write imaginary
# put padded length in miMATRIX tag
pass
class Mat5CharWriter(Mat5MatrixWriter):
def write(self):
self.arr_to_chars()
self.arr_to_2d()
dims = self.arr.shape
self.write_header(P=miUINT8,
T=mxCHAR_CLASS)
if self.arr.dtype.kind == 'U':
# Recode unicode to ascii
n_chars = N.product(dims)
st_arr = N.ndarray(shape=(),
dtype=self.arr_dtype_number(n_chars),
buffer=self.arr)
st = st_arr.item().encode('ascii')
self.arr = N.ndarray(shape=dims, dtype='S1', buffer=st)
self.write_bytes(self.arr)
class Mat5SparseWriter(Mat5MatrixWriter):
def write(self):
''' Sparse matrices are 2D
See docstring for Mat5SparseGetter
'''
imagf = self.arr.dtype.kind == 'c'
N = self.arr.nnz
ijd = N.zeros((N+1, 3+imagf), dtype='f8')
for i in range(N):
ijd[i,0], ijd[i,1] = self.arr.rowcol(i)
ijd[:-1,0:2] += 1 # 1 based indexing
if imagf:
ijd[:-1,2] = self.arr.data.real
ijd[:-1,3] = self.arr.data.imag
else:
ijd[:-1,2] = self.arr.data
ijd[-1,0:2] = self.arr.shape
self.write_header(P=miDOUBLE,
T=mxSPARSE_CLASS,
dims=ijd.shape)
self.write_bytes(ijd)
class Mat5WriterGetter(object):
''' Wraps stream and options, provides methods for getting Writer objects '''
def __init__(self, stream, unicode_strings):
self.stream = stream
self.unicode_strings = unicode_strings
def rewind(self):
self.stream.seek(0)
def matrix_writer_factory(self, arr, name, is_global=False):
''' Factory function to return matrix writer given variable to write
stream - file or file-like stream to write to
arr - array to write
name - name in matlab (TM) workspace
'''
if have_sparse:
if scipy.sparse.issparse(arr):
return Mat5SparseWriter(self.stream, arr, name, is_global)
arr = N.array(arr)
if arr.dtype.hasobject:
types, arr_type = classify_mobjects(arr)
if arr_type == 'c':
return Mat5CellWriter(self.stream, arr, name, is_global, types)
elif arr_type == 's':
return Mat5StructWriter(self.stream, arr, name, is_global)
elif arr_type == 'o':
return Mat5ObjectWriter(self.stream, arr, name, is_global)
if arr.dtype.kind in ('U', 'S'):
if self.unicode_strings:
return Mat5UniCharWriter(self.stream, arr, name, is_global)
else:
return Mat5IntCharWriter(self.stream, arr, name, is_global)
else:
return Mat5NumericWriter(self.stream, arr, name, is_global)
def classify_mobjects(self, objarr):
''' Function to classify objects passed for writing
returns
types - S1 array of same shape as objarr with codes for each object
i - invalid object
a - ndarray
s - matlab struct
o - matlab object
arr_type - one of
c - cell array
s - struct array
o - object array
'''
N = objarr.size
types = N.empty((N,), dtype='S1')
types[:] = 'i'
type_set = set()
flato = objarr.flat
for i in range(N):
obj = flato[i]
if isinstance(obj, N.ndarray):
types[i] = 'a'
continue
try:
fns = tuple(obj._fieldnames)
except AttributeError:
continue
try:
cn = obj._classname
except AttributeError:
types[i] = 's'
type_set.add(fns)
continue
types[i] = 'o'
type_set.add((cn, fns))
arr_type = 'c'
if len(set(types))==1 and len(type_set) == 1:
arr_type = types[0]
return types.reshape(objarr.shape), arr_type
class MatFile5Writer(MatFileWriter):
''' Class for writing mat5 files '''
def __init__(self, file_stream,
do_compression=False,
unicode_strings=False,
global_vars=None):
super(MatFile5Writer, self).__init__(file_stream)
self.do_compression = do_compression
if global_vars:
self.global_vars = global_vars
else:
self.global_vars = []
self.writer_getter = Mat5WriterGetter(
StringIO,
unicode_strings)
def get_unicode_strings(self):
return self.write_getter.unicode_strings
def set_unicode_strings(self, unicode_strings):
self.writer_getter.unicode_strings = unicode_strings
unicode_strings = property(get_unicode_strings,
set_unicode_strings,
None,
'get/set unicode strings property')
def put_variables(self, mdict):
for name, var in mdict.items():
is_global = name in self.global_vars
self.writer_getter.rewind()
self.writer_getter.matrix_writer_factory(
var,
name,
is_global,
).write()
if self.do_compression:
str = zlib.compress(stream.getvalue())
tag = N.empty((), mdtypes_template['tag_full'])
tag['mdtype'] = miCOMPRESSED
tag['byte_count'] = len(str)
self.file_stream.write(tag.tostring() + str)
else:
self.file_stream.write(stream.getvalue())
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