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