''' 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 ''' from __future__ import division, print_function, absolute_import 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 'saveing/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')])