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410 lines
12 KiB
Python
410 lines
12 KiB
Python
# Authors: Travis Oliphant, Matthew Brett
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"""
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Base classes for MATLAB file stream reading.
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MATLAB is a registered trademark of the Mathworks inc.
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"""
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import operator
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import functools
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import numpy as np
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from scipy._lib import doccer
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from . import byteordercodes as boc
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class MatReadError(Exception):
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pass
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class MatWriteError(Exception):
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pass
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class MatReadWarning(UserWarning):
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pass
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doc_dict = \
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{'file_arg':
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'''file_name : str
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Name of the mat file (do not need .mat extension if
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appendmat==True) Can also pass open file-like object.''',
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'append_arg':
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'''appendmat : bool, optional
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True to append the .mat extension to the end of the given
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filename, if not already present.''',
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'load_args':
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'''byte_order : str or None, optional
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None by default, implying byte order guessed from mat
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file. Otherwise can be one of ('native', '=', 'little', '<',
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'BIG', '>').
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mat_dtype : bool, optional
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If True, return arrays in same dtype as would be loaded into
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MATLAB (instead of the dtype with which they are saved).
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squeeze_me : bool, optional
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Whether to squeeze unit matrix dimensions or not.
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chars_as_strings : bool, optional
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Whether to convert char arrays to string arrays.
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matlab_compatible : bool, optional
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Returns matrices as would be loaded by MATLAB (implies
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squeeze_me=False, chars_as_strings=False, mat_dtype=True,
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struct_as_record=True).''',
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'struct_arg':
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'''struct_as_record : bool, optional
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Whether to load MATLAB structs as NumPy record arrays, or as
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old-style NumPy arrays with dtype=object. Setting this flag to
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False replicates the behavior of SciPy version 0.7.x (returning
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numpy object arrays). The default setting is True, because it
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allows easier round-trip load and save of MATLAB files.''',
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'matstream_arg':
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'''mat_stream : file-like
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Object with file API, open for reading.''',
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'long_fields':
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'''long_field_names : bool, optional
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* False - maximum field name length in a structure is 31 characters
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which is the documented maximum length. This is the default.
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* True - maximum field name length in a structure is 63 characters
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which works for MATLAB 7.6''',
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'do_compression':
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'''do_compression : bool, optional
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Whether to compress matrices on write. Default is False.''',
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'oned_as':
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'''oned_as : {'row', 'column'}, optional
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If 'column', write 1-D NumPy arrays as column vectors.
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If 'row', write 1D NumPy arrays as row vectors.''',
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'unicode_strings':
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'''unicode_strings : bool, optional
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If True, write strings as Unicode, else MATLAB usual encoding.'''}
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docfiller = doccer.filldoc(doc_dict)
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'''
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Note on architecture
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======================
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There are three sets of parameters relevant for reading files. The
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first are *file read parameters* - containing options that are common
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for reading the whole file, and therefore every variable within that
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file. At the moment these are:
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* mat_stream
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* dtypes (derived from byte code)
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* byte_order
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* chars_as_strings
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* squeeze_me
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* struct_as_record (MATLAB 5 files)
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* class_dtypes (derived from order code, MATLAB 5 files)
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* codecs (MATLAB 5 files)
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* uint16_codec (MATLAB 5 files)
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Another set of parameters are those that apply only to the current
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variable being read - the *header*:
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* header related variables (different for v4 and v5 mat files)
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* is_complex
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* mclass
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* var_stream
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With the header, we need ``next_position`` to tell us where the next
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variable in the stream is.
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Then, for each element in a matrix, there can be *element read
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parameters*. An element is, for example, one element in a MATLAB cell
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array. At the moment, these are:
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* mat_dtype
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The file-reading object contains the *file read parameters*. The
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*header* is passed around as a data object, or may be read and discarded
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in a single function. The *element read parameters* - the mat_dtype in
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this instance, is passed into a general post-processing function - see
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``mio_utils`` for details.
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'''
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def convert_dtypes(dtype_template, order_code):
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''' Convert dtypes in mapping to given order
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Parameters
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----------
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dtype_template : mapping
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mapping with values returning numpy dtype from ``np.dtype(val)``
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order_code : str
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an order code suitable for using in ``dtype.newbyteorder()``
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Returns
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-------
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dtypes : mapping
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mapping where values have been replaced by
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``np.dtype(val).newbyteorder(order_code)``
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'''
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dtypes = dtype_template.copy()
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for k in dtypes:
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dtypes[k] = np.dtype(dtypes[k]).newbyteorder(order_code)
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return dtypes
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def read_dtype(mat_stream, a_dtype):
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"""
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Generic get of byte stream data of known type
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Parameters
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----------
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mat_stream : file_like object
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MATLAB (tm) mat file stream
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a_dtype : dtype
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dtype of array to read. `a_dtype` is assumed to be correct
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endianness.
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Returns
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-------
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arr : ndarray
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Array of dtype `a_dtype` read from stream.
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"""
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num_bytes = a_dtype.itemsize
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arr = np.ndarray(shape=(),
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dtype=a_dtype,
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buffer=mat_stream.read(num_bytes),
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order='F')
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return arr
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def get_matfile_version(fileobj):
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"""
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Return major, minor tuple depending on apparent mat file type
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Where:
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#. 0,x -> version 4 format mat files
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#. 1,x -> version 5 format mat files
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#. 2,x -> version 7.3 format mat files (HDF format)
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Parameters
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----------
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fileobj : file_like
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object implementing seek() and read()
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Returns
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-------
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major_version : {0, 1, 2}
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major MATLAB File format version
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minor_version : int
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minor MATLAB file format version
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Raises
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------
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MatReadError
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If the file is empty.
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ValueError
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The matfile version is unknown.
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Notes
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-----
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Has the side effect of setting the file read pointer to 0
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"""
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# Mat4 files have a zero somewhere in first 4 bytes
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fileobj.seek(0)
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mopt_bytes = fileobj.read(4)
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if len(mopt_bytes) == 0:
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raise MatReadError("Mat file appears to be empty")
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mopt_ints = np.ndarray(shape=(4,), dtype=np.uint8, buffer=mopt_bytes)
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if 0 in mopt_ints:
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fileobj.seek(0)
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return (0,0)
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# For 5 format or 7.3 format we need to read an integer in the
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# header. Bytes 124 through 128 contain a version integer and an
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# endian test string
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fileobj.seek(124)
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tst_str = fileobj.read(4)
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fileobj.seek(0)
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maj_ind = int(tst_str[2] == b'I'[0])
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maj_val = int(tst_str[maj_ind])
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min_val = int(tst_str[1 - maj_ind])
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ret = (maj_val, min_val)
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if maj_val in (1, 2):
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return ret
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raise ValueError('Unknown mat file type, version %s, %s' % ret)
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def matdims(arr, oned_as='column'):
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"""
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Determine equivalent MATLAB dimensions for given array
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Parameters
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----------
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arr : ndarray
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Input array
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oned_as : {'column', 'row'}, optional
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Whether 1-D arrays are returned as MATLAB row or column matrices.
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Default is 'column'.
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Returns
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-------
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dims : tuple
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Shape tuple, in the form MATLAB expects it.
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Notes
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-----
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We had to decide what shape a 1 dimensional array would be by
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default. ``np.atleast_2d`` thinks it is a row vector. The
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default for a vector in MATLAB (e.g., ``>> 1:12``) is a row vector.
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Versions of scipy up to and including 0.11 resulted (accidentally)
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in 1-D arrays being read as column vectors. For the moment, we
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maintain the same tradition here.
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Examples
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--------
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>>> matdims(np.array(1)) # NumPy scalar
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(1, 1)
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>>> matdims(np.array([1])) # 1-D array, 1 element
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(1, 1)
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>>> matdims(np.array([1,2])) # 1-D array, 2 elements
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(2, 1)
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>>> matdims(np.array([[2],[3]])) # 2-D array, column vector
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(2, 1)
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>>> matdims(np.array([[2,3]])) # 2-D array, row vector
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(1, 2)
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>>> matdims(np.array([[[2,3]]])) # 3-D array, rowish vector
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(1, 1, 2)
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>>> matdims(np.array([])) # empty 1-D array
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(0, 0)
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>>> matdims(np.array([[]])) # empty 2-D array
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(0, 0)
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>>> matdims(np.array([[[]]])) # empty 3-D array
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(0, 0, 0)
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Optional argument flips 1-D shape behavior.
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>>> matdims(np.array([1,2]), 'row') # 1-D array, 2 elements
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(1, 2)
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The argument has to make sense though
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>>> matdims(np.array([1,2]), 'bizarre')
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Traceback (most recent call last):
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...
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ValueError: 1-D option "bizarre" is strange
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"""
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shape = arr.shape
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if shape == (): # scalar
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return (1,1)
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if functools.reduce(operator.mul, shape) == 0: # zero elememts
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return (0,) * np.max([arr.ndim, 2])
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if len(shape) == 1: # 1D
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if oned_as == 'column':
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return shape + (1,)
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elif oned_as == 'row':
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return (1,) + shape
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else:
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raise ValueError('1-D option "%s" is strange'
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% oned_as)
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return shape
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class MatVarReader(object):
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''' Abstract class defining required interface for var readers'''
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def __init__(self, file_reader):
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pass
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def read_header(self):
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''' Returns header '''
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pass
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def array_from_header(self, header):
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''' Reads array given header '''
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pass
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class MatFileReader(object):
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""" Base object for reading mat files
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To make this class functional, you will need to override the
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following methods:
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matrix_getter_factory - gives object to fetch next matrix from stream
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guess_byte_order - guesses file byte order from file
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"""
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@docfiller
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def __init__(self, mat_stream,
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byte_order=None,
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mat_dtype=False,
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squeeze_me=False,
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chars_as_strings=True,
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matlab_compatible=False,
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struct_as_record=True,
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verify_compressed_data_integrity=True,
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simplify_cells=False):
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'''
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Initializer for mat file reader
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mat_stream : file-like
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object with file API, open for reading
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%(load_args)s
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'''
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# Initialize stream
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self.mat_stream = mat_stream
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self.dtypes = {}
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if not byte_order:
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byte_order = self.guess_byte_order()
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else:
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byte_order = boc.to_numpy_code(byte_order)
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self.byte_order = byte_order
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self.struct_as_record = struct_as_record
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if matlab_compatible:
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self.set_matlab_compatible()
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else:
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self.squeeze_me = squeeze_me
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self.chars_as_strings = chars_as_strings
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self.mat_dtype = mat_dtype
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self.verify_compressed_data_integrity = verify_compressed_data_integrity
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self.simplify_cells = simplify_cells
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if simplify_cells:
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self.squeeze_me = True
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self.struct_as_record = False
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def set_matlab_compatible(self):
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''' Sets options to return arrays as MATLAB loads them '''
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self.mat_dtype = True
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self.squeeze_me = False
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self.chars_as_strings = False
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def guess_byte_order(self):
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''' As we do not know what file type we have, assume native '''
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return boc.native_code
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def end_of_stream(self):
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b = self.mat_stream.read(1)
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curpos = self.mat_stream.tell()
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self.mat_stream.seek(curpos-1)
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return len(b) == 0
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def arr_dtype_number(arr, num):
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''' Return dtype for given number of items per element'''
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return np.dtype(arr.dtype.str[:2] + str(num))
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def arr_to_chars(arr):
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''' Convert string array to char array '''
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dims = list(arr.shape)
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if not dims:
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dims = [1]
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dims.append(int(arr.dtype.str[2:]))
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arr = np.ndarray(shape=dims,
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dtype=arr_dtype_number(arr, 1),
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buffer=arr)
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empties = [arr == '']
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if not np.any(empties):
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return arr
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arr = arr.copy()
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arr[tuple(empties)] = ' '
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return arr
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