Storage Format

This package adopts certain conventions for the conversion and storage of Python datatypes and the metadata that is written with them. Then, to make the data MATLAB MAT file compatible, additional metadata must be written. This page assumes that one has imported collections and numpy as

import collections as cl
import numpy as np

Also, pickling is not used at all in this format and should not be added. It is a security risk since pickled data is read through the interpreter allowing arbitrary code (which could be malicious) to be executed in the interpreter. One wants to be able to read possibly HDF5 and MAT files from untrusted sources, so pickling is avoided in this package.

MATLAB File Header

In order for a file to be MATLAB v7.3 MAT file compatible, it must have a properly formatted file header, or userblock in HDF5 terms. The file must have a 512 byte userblock, of which 128 bytes are used. The 128 bytes consists of a 116 byte string (spaces pad the end) followed by a specific 12 byte sequence (magic number). On MATLAB, the 116 byte string, depending on the computer system and the date, looks like

b'MATLAB 7.3 MAT-file, Platform: GLNXA64, Created on: Fri Feb 07 02:29:00 2014 HDF5 schema 1.00 .'

This package just changes the Platform part to

b'CPython A.B.C'

Where A, B, and C are the major, minor, and micro version numbers of the Python interpreter (e.g. 3.3.0).

The 12 byte sequence, in hexidecimal is

00000000 00000000 0002494D

How Data Is Stored

All data is stored either as a Dataset or as a Group. Most non-Numpy types must be converted to a Numpy type before they are written, and some Numpy types must be converted to other ones before being written. The table below lists how every supported Python datatype is stored (Group or Dataset), what type/s it is converted to (no conversion if none are listed), as well as the first version of this package to support the datatype.

Type	Version	Converted to	Group or Dataset
bool	0.1	np.bool_ or np.uint8 [1]	Dataset
None	0.1	np.float64([])	Dataset
int [2] [3]	0.1	np.int64 [2]	Dataset
long [3] [4]	0.1	np.int64	Dataset
float	0.1	np.float64	Dataset
complex	0.1	np.complex128	Dataset
str	0.1	np.uint32/16 [5]	Dataset
bytes	0.1	np.bytes_ or np.uint16 [6]	Dataset
bytearray	0.1	np.bytes_ or np.uint16 [6]	Dataset
list	0.1	np.object_	Dataset
tuple	0.1	np.object_	Dataset
set	0.1	np.object_	Dataset
frozenset	0.1	np.object_	Dataset
cl.deque	0.1	np.object_	Dataset
dict [7]	0.1		Group
np.bool_	0.1	not or np.uint8 [1]	Dataset
np.void	0.1		Dataset
np.uint8	0.1		Dataset
np.uint16	0.1		Dataset
np.uint32	0.1		Dataset
np.uint64	0.1		Dataset
np.uint8	0.1		Dataset
np.int16	0.1		Dataset
np.int32	0.1		Dataset
np.int64	0.1		Dataset
np.float16 [8]	0.1		Dataset
np.float32	0.1		Dataset
np.float64	0.1		Dataset
np.complex64	0.1		Dataset
np.complex128	0.1		Dataset
np.str_	0.1	np.uint32/16 [5]	Dataset
np.bytes_	0.1	np.bytes_ or np.uint16 [6]	Dataset
np.object_	0.1		Dataset
np.ndarray	0.1	not or Group of contents [9]	Dataset or Group [9]
np.matrix	0.1	np.ndarray	Dataset
np.chararray	0.1	np.bytes_ or np.uint16/32 [5] [6]	Dataset
np.recarray	0.1	structured np.ndarray [9]	Dataset or Group [9]

[1]	(1, 2) Depends on the selected options. Always np.uint8 when convert_bools_to_uint8 == True (set implicitly when matlab_compatible == True).

[2]	(1, 2) In Python 2.x, it may be read back as a long if it can’t fit in the size of an int.

[3]	(1, 2) Must be small enough to fit into an np.int64.

[4]	Type only found in Python 2.x. Python 2.x’s long and int are unified into a single int type in Python 3.x. Read as an int in Python 3.x.

[5]

(1, 2, 3) Depends on the selected options and whether it can be converted to UTF-16 without using doublets. If convert_numpy_str_to_utf16 == True (set implicitly when matlab_compatible == True) and it can be converted to UTF-16 without losing any characters that can’t be represented in UTF-16 or using UTF-16 doublets (MATLAB doesn’t support them), then it is written as np.uint16 in UTF-16 encoding. Otherwise, it is stored at np.uint32 in UTF-32 encoding.

[6]

(1, 2, 3, 4) Depends on the selected options. If convert_numpy_bytes_to_utf16 == True (set implicitly when matlab_compatible == True), it will be stored as np.uint16 in UTF-16 encoding unless it contains non-ASCII characters in which case a NotImplementedError is raised. Otherwise, it is just written as np.bytes_.

[7]	All keys must be str in Python 3 or unicode in Python 2. They cannot have null characters ('\x00') or forward slashes ('/') in them.

[8]	np.float16 are not supported for h5py versions before 2.2.

[9]

(1, 2, 3, 4) If it doesn’t have any fields in its dtype or if Options.structured_numpy_ndarray_as_struct is not set and none of its fields are of dtype 'object', it is not converted and is written as is as a Dataset. Otherwise, it is written as a Group with its the contents of its individual fields written as Datasets within the Group having the fields as names. Field names cannot have null characters ('\x00') and, when writing as an GROUP, forward slashes ('/') in them.

Attributes

Many different HDF5 Attributes are set for each object written if the Options.store_python_metadata and/or Options.matlab_compatible options are set. The attributes associated with each will be referred to as “Python Attributes” and “MATLAB Attributes” respectively. If neither of them are set, then no Attributes are used. The table below lists the Attributes that have definite values depending only on the particular Python datatype being stored. Then, the other attributes are detailed individually.

Note

‘Python.Type’, ‘Python.numpy.UnderlyingType’, and ‘MATLAB_class’ are all np.bytes_. ‘MATLAB_int_decode’ is a np.int64. ‘Python.Fields’ is a np.object_ array of str.

Type	Python.Type	Python.numpy.UnderlyingType	MATLAB_class	MATLAB_int_decode
bool	‘bool’	‘bool’	‘logical’	1
None	‘builtins.NoneType’	‘float64’	‘double’
int	‘int’	‘int64’	‘int64’
long	‘long’	‘int64’	‘int64’
float	‘float’	‘float64’	‘double’
complex	‘complex’	‘complex128’	‘double’
str	‘str’	‘str#’ [10]	‘char’	2
bytes	‘bytes’	‘bytes#’ [10]	‘char’	2
bytearray	‘bytearray’	‘bytes#’ [10]	‘char’	2
list	‘list’	‘object’	‘cell’
tuple	‘tuple’	‘object’	‘cell’
set	‘set’	‘object’	‘cell’
frozenset	‘frozenset’	‘object’	‘cell’
cl.deque	‘collections.deque’	‘object’	‘cell’
dict	‘dict’		‘struct’
np.bool_	‘numpy.bool’	‘bool’	‘logical’	1
np.void	‘numpy.void’	‘void#’ [10]
np.uint8	‘numpy.uint8’	‘uint8’	‘uint8’
np.uint16	‘numpy.uint16’	‘uint16’	‘uint16’
np.uint32	‘numpy.uint32’	‘uint32’	‘uint32’
np.uint64	‘numpy.uint64’	‘uint64’	‘uint64’
np.uint8	‘numpy.int8’	‘int8’	‘int8’
np.int16	‘numpy.int16’	‘int16’	‘int16’
np.int32	‘numpy.int32’	‘int32’	‘int32’
np.int64	‘numpy.int64’	‘int64’	‘int64’
np.float16	‘numpy.float16’	‘float16’
np.float32	‘numpy.float32’	‘float32’	‘single’
np.float64	‘numpy.float64’	‘float64’	‘double’
np.complex64	‘numpy.complex64’	‘complex64’	‘single’
np.complex128	‘numpy.complex128’	‘complex128’	‘double’
np.str_	‘numpy.str_’	‘str#’ [10]	‘char’ or ‘uint32’	2 or 4 [11]
np.bytes_	‘numpy.bytes_’	‘bytes#’ [10]	‘char’	2
np.object_	‘numpy.object_’	‘object’	‘cell’
np.ndarray	‘numpy.ndarray’	[12]	[12] [13]
np.matrix	‘numpy.matrix’	[12]	[12]
np.chararray	‘numpy.chararray’	[12]	‘char’ [12]
np.recarray	‘numpy.recarray’	[12]	[12] [13]

[10]	(1, 2, 3, 4, 5, 6) ‘#’ is replaced by the number of bits taken up by the string, or each string in the case that it is an array of strings. This is 8 and 32 bits per character for np.bytes_ and np.str_ respectively.

[11]	2 if it is stored as np.uint16 or 4 if np.uint32.

[12]	(1, 2, 3, 4, 5, 6, 7, 8) The value that would be put in for a scalar of the same dtype is used.

[13]	(1, 2) If it is structured (its dtype has fields), Options.structured_numpy_ndarray_as_struct is set, and none of its fields are of dtype 'object'; it is set to 'struct' overriding anything else.

Python.Shape

Python Attribute

np.ndarray(dtype='uint64')

Every Python datatype that is or ends up being converted to a Numpy datatype has a shape attribute, which is stored in this Attribute. This holds the shape before any conversions of arrays to at least 2D, array transposes, or conversions of strings to unsigned integer types.

Python.numpy.Container

Python Attribute

{‘scalar’, ‘ndarray’, ‘matrix’, ‘chararray’, ‘recarray’}

For Numpy types (or types converted to them), whether the type is a scalar (its type is something such as np.uint16, np.str_, etc.), some form of array (its type is np.ndarray), a matrix (type is np.matrix), is a np.chararray, or is a np.recarray is stored in this Attribute.

Python.Fields

Python Attribute

np.object_ array of str

For dict and structured np.ndarray types (and those converted to them), an array of the field names of the array is stored in this Attribute in the proper order. In the HDF5 file, they are variable length strings.

Python.Empty and MATLAB_empty

Python and MATLAB Attributes respectively

np.uint8

If the datatype being stored has zero elements, then this Attribute is set to 1. Otherwise, the Attribute is deleted. For Numpy types (or those converted to them), the shape after conversions to at least 2D, array transposes, and conversions of strings to unsigned integer types is stored in place of the data as an array of np.uint64 if Options.store_shape_for_empty is set (set implicitly if the matlab_compatible option is set).

H5PATH

MATLAB Attribute

np.str_

For every object that is stored inside a Group other than the root of the HDF5 file ('/'), the path to the object is stored in this Attribute. MATLAB does not seem to require this Attribute to be there, though it does set it in the files it produces.

MATLAB_fields

MATLAB Attribute

numpy array of vlen numpy arrays of 'S1'

Changed in version 0.1.2: Support for this Attribute added. Was deleted upon writing and ignored when reading before.

For MATLAB structures, MATLAB sets this field to all of the field names of the structure. If this Attribute is missing, MATLAB does not seem to care. Can only be set or read properly for h5py version 2.3 and newer. Trying to set it to a differently formatted array of strings that older versions of h5py can handle causes an error in MATLAB when the file is imported, so this package does not set this Attribute at all for h5py version before 2.3.

The Attribute is an array of variable length arrays of single character ASCII numpy strings (vlen of 'S1'). If there are two fields named 'a' and 'cd', it is created like so:

fields = ['a', 'cd']
dt = h5py.special_dtype(vlen=np.dtype('S1'))
fs = np.empty(shape=(len(fields),), dtype=dt)
for i, s in enumerate(fields):
    fs[i] = np.array([c.encode('ascii') for c in s],
                     dtype='S1')

Then fs looks like:

array([array([b'a'], dtype='|S1'),
       array([b'c', b'd'], dtype='|S1']), dtype=object)

Storage of Special Types

int and long

Python 2.x has two integer types: a fixed-width int corresponding to a C int type, and a variable-width long for holding arbitrarily large values. An int is thus 32 or 64 bits depending on whether the python interpreter was is a 32 or 64 bit executable. In Python 3.x, both types are both unified into a single int type.

Both an int and a long written in Python 2.x will be read as a int in Python 3.x. Python 3.x always writes as int. Due to this and the fact that the interpreter in Python 2.x could be using 32-bits int, it is possible that a value could be read that is too large to fit into int. When that happens, it read as a long instead.

Warning

Writing Python 2.x long and Python 3.x int too big to fit into an np.int64 is not supported. A NotImplementedError is raised if attempted.

Complex Numbers

Complex numbers and np.object_ arrays (and things converted to them) have to be stored in a special fashion.

Since HDF5 has no builtin complex type, complex numbers are stored as an HDF5 COMPOUND type with different fieldnames for the real and imaginary partd like many other pieces of software (including MATLAB) do. Unfortunately, there is not a standardized pair of field names. h5py by default uses ‘r’ and ‘i’ for the real and imaginary parts. MATLAB uses ‘real’ and ‘imag’ instead. The Options.complex_names option controls the field names (given as a tuple in real, imaginary order) that are used for complex numbers as they are written. It is set automatically to ('real', 'imag') when matlab_compatible == True. When reading data, this package automatically checks numeric types for many combinations of reasonably expected field names to find complex types.

np.object_

When storing np.object_ arrays, the individual elements are stored elsewhere and then an array of HDF5 Object References to their storage locations is written as the data object. The elements are all written to the Group path set by Options.group_for_references with a randomized name (this package keeps generating randomized names till an available one is found). It must be '/#refs#' for MATLAB (setting matlab_compatible sets this automatically). Those elements that can’t be written (doing MATLAB compatibility and we are set to discard MATLAB incompatible types Options.action_for_matlab_incompatible) will instead end up being a reference to the canonical empty inside the group. The canonical empty has the same format as in MATLAB and is a Dataset named ‘a’ of np.uint32/64([0, 0]) with the Attribute ‘MATLAB_class’ set to ‘canonical empty’ and the Attribute ‘MATLAB_empty’ set to np.uint8(1).

Structure Like Data

When storing data that is MATLAB struct like (dict or structured np.ndarray when Options.structured_numpy_ndarray_as_struct is set and none of its fields are of dtype 'object'), it is stored as an HDF5 Group with its contents of its fields written inside of the Group. For single element data (dict or structured np.ndarray with only a single element), the fields are written to Datasets inside the Group. For multi-element data, the elements for each field are written in Options.group_for_references and an HDF5 Reference array to all of those elements is written as a Dataset under the field name in the Groups. Othewise, it is written as is as a Dataset that is an HDF5 COMPOUND type.

Warning

Field names cannot have null characters ('\x00') and, when writing as an HDF5 GROUP, forward slashes ('/') in them.

Warning

If it has no elements and Options.structured_numpy_ndarray_as_struct is set, it can’t be read back from the file accurately. The dtype for all the fields will become ‘object’ instead of what they originally were.

Optional Data Transformations

Many different data conversions beyond turning most non-Numpy types into Numpy types, can be done and are controlled by individual settings in the Options class. Most are set to fixed values when matlab_compatible == True, which are shown in the table below. The transfomations are listed below by their option name, other than complex_names and group_for_references which were explained in the previous section.

attribute	value
delete_unused_variables	True
structured_numpy_ndarray_as_struct	True
make_atleast_2d	True
convert_numpy_bytes_to_utf16	True
convert_numpy_str_to_utf16	True
convert_bools_to_uint8	True
reverse_dimension_order	True
store_shape_for_empty	True
complex_names	('real', 'imag')
group_for_references	'/#refs#'

delete_unused_variables

bool

Whether any variable names in something that would be stored as an HDF5 Group (would end up a struct in MATLAB) that currently exist in the file but are not in the object being stored should be deleted on the file or not.

structured_numpy_ndarray_as_struct

bool

Whether np.ndarray types (or things converted to them) should be written as structures/Groups if their dtype has fields as long as none of the fields’ dtypes are 'object' in which case this option is treated as if it were True. A dtype with fields looks like np.dtype([('a', np.uint16), ('b': np.float32)]). If an array satisfies this criterion and the option is set, rather than writing the data as a single Dataset, it is written as a Group with the contents of the individual fields written as Datasets within it. This option is set to True implicitly by matlab_compatible.

make_at_least_2d

bool

Whether all Numpy types (or things converted to them) should be made into arrays of 2 dimensions if they have less than that or not. This option is set to True implicitly by matlab_compatible.

convert_numpy_bytes_to_utf16

bool

Whether all np.bytes_ strings (or things converted to it) should be converted to UTF-16 and written as an array of np.uint16 or not. This option is set to True implicitly by matlab_compatible.

Warning

Only ASCII characters are supported in np.bytes_ when this option is set. A NotImplementedError is raised if any non-ASCII characters are present.

convert_numpy_str_to_utf16

bool

Whether all np.str_ strings (or things converted to it) should be converted to UTF-16 and written as an array of np.uint16 if the strings use no characters outside of the UTF-16 set and the conversion does not result in any UTF-16 doublets or not. This option is set to True implicitly by matlab_compatible.

convert_bools_to_uint8

bool

Whether the np.bool_ type (or things converted to it) should be converted to np.uint8 (True becomes 1 and False becomes 0) or not. If not, then the h5py default of an enum type that is not MATLAB compatible is used. This option is set to True implicitly by matlab_compatible.

reverse_dimension_order

bool

Whether the dimension order of all arrays should be reversed (essentially a transpose) or not before writing to the file. This option is set to True implicitly by matlab_compatible. This option needs to be set if one wants an array to end up the same shape when imported into MATLAB. This option is necessary because Numpy and MATLAB use opposite dimension ordering schemes, which are C and Fortan schemes respectively. 2D arrays are stored by row in the C scheme and column in the Fortran scheme.

store_shape_for_empty

bool

Whether, for empty arrays, to store the shape of the array (after transformations) as the Dataset for the object. This option is set to True implicitly by matlab_compatible.

How Data Is Read from MATLAB MAT Files

This table gives the MATLAB classes that can be read from a MAT file, the first version of this package that can read them, and the Python type they are read as if there is no Python metadata attached to them.

MATLAB Class	Version	Python Type
logical	0.1	np.bool_
single	0.1	np.float32 or np.complex64 [14]
double	0.1	np.float64 or np.complex128 [14]
uint8	0.1	np.uint8
uint16	0.1	np.uint16
uint32	0.1	np.uint32
uint64	0.1	np.uint64
int8	0.1	np.int8
int16	0.1	np.int16
int32	0.1	np.int32
int64	0.1	np.int64
char	0.1	np.str_
struct	0.1	structured np.ndarray
cell	0.1	np.object_
canonical empty	0.1	np.float64([])

[14]	(1, 2) Depends on whether there is a complex part or not.