Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/scipy/io/matlab/mio5_params.py

252 lines
6.8 KiB
Python

''' 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
'''
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 'saving/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')])