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'hdf5storage VERSION'
Where VERSION is the hdf5storage version.
Changed in version 0.2: In the version 0.1.x series, the platform part was
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 |
|
Dataset |
Ellipsis |
0.2 |
|
Dataset |
NotImplemented |
0.2 |
|
Dataset |
0.1 |
Dataset |
||
0.1 |
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 |
cl.ChainMap |
0.2 |
np.object_ |
Dataset |
dict 7 |
0.1 |
Group |
|
cl.OrderedDict 7 |
0.2 |
Group |
|
cl.Counter 7 |
0.2 |
Group |
|
slice |
0.2 |
Group |
|
range |
0.2 |
Group |
|
datetime.timedelta |
0.2 |
Group |
|
datetime.timezone |
0.2 |
Group |
|
datetime.date |
0.2 |
Group |
|
datetime.time |
0.2 |
Group |
|
datetime.datetime |
0.2 |
Group |
|
fractions.Fraction |
0.2 |
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 |
Dataset |
|
np.recarray |
0.1 |
structured np.ndarray 9 |
Dataset or Group 9 |
np.dtype 10 |
0.2 |
np.bytes_ or np.uint16 6 |
Dataset |
- 1(1,2)
Depends on the selected options. Always
np.uint8
whenconvert_bools_to_uint8 == True
(set implicitly whenmatlab_compatible == True
).- 2(1,2)
In Python 2.x with version 0.1.x of this package, it may be read back as a
long
if it can’t fit in the size of anint
.- 3(1,2,3,4)
Stored as a
np.int64
if it is small enough to fit. Otherwise its decimal string representation is stored as annp.bytes_
for hdf5storage >= 0.2 (error in earlier versions).- 4(1,2)
Type only found in Python 2.x. Python 2.x’s
long
andint
are unified into a singleint
type in Python 3.x. Read as anint
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 whenmatlab_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 asnp.uint16
in UTF-16 encoding. Otherwise, it is stored atnp.uint32
in UTF-32 encoding.- 6(1,2,3,4,5)
Depends on the selected options. If
convert_numpy_bytes_to_utf16 == True
(set implicitly whenmatlab_compatible == True
), it will be stored asnp.uint16
in UTF-16 encoding unless it contains non-ASCII characters in which case aNotImplementedError
is raised. Otherwise, it is just written asnp.bytes_
.- 7(1,2,3)
Stored either as each key-value as their own Dataset or as two Datasets, one for keys and one for values. The former is used if all keys can be converted to
str
in Python 3 orunicode
in Python 2. Otherwise, the latter format is used.- 8
np.float16
are not supported for h5py versions before2.2
. Version2.3
or higher is required for this package since version0.2
.- 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.- 10
Stored as its string representation converted to
np.bytes_
.
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’ |
|
Ellipsis |
‘builtins.ellipsis’ |
‘float64’ |
‘double’ |
|
NotImplemented |
‘builtins.NotImplementedType’ |
‘float64’ |
‘double’ |
|
int |
‘int’ |
‘int64’ or ‘char’ |
not used or 2 11 |
|
long |
‘long’ |
‘int64’ or ‘char’ |
not used or 2 11 |
|
float |
‘float’ |
‘float64’ |
‘double’ |
|
complex |
‘complex’ |
‘complex128’ |
‘double’ |
|
str |
‘str’ |
‘str#’ 12 |
‘char’ |
2 |
bytes |
‘bytes’ |
‘bytes#’ 12 |
‘char’ |
2 |
bytearray |
‘bytearray’ |
‘bytes#’ 12 |
‘char’ |
2 |
list |
‘list’ |
‘object’ |
‘cell’ |
|
tuple |
‘tuple’ |
‘object’ |
‘cell’ |
|
set |
‘set’ |
‘object’ |
‘cell’ |
|
frozenset |
‘frozenset’ |
‘object’ |
‘cell’ |
|
cl.deque |
‘collections.deque’ |
‘object’ |
‘cell’ |
|
cl.ChainMap |
‘collections.ChainMap’ |
‘object’ |
‘cell’ |
|
dict |
‘dict’ |
‘struct’ |
||
cl.OrderedDict |
‘collections.OrderedDict’ |
‘struct’ |
||
cl.Counter |
‘collections.Counter’ |
‘struct’ |
||
slice |
‘slice’ |
‘struct’ |
||
range |
‘range’ |
‘struct’ |
||
datetime.timedelta |
‘datetime.timedelta’ |
‘struct’ |
||
datetime.timezone |
‘datetime.timezone’ |
‘struct’ |
||
datetime.date |
‘datetime.date’ |
‘struct’ |
||
datetime.time |
‘datetime.time’ |
‘struct’ |
||
datetime.datetime |
‘datetime.datetime’ |
‘struct’ |
||
fractions.Fraction |
‘fractions.Fraction’ |
‘struct’ |
||
np.bool_ |
‘numpy.bool’ |
‘bool’ |
‘logical’ |
1 |
np.void |
‘numpy.void’ |
‘void#’ 12 |
||
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#’ 12 |
‘char’ or ‘uint32’ |
2 or 4 13 |
np.bytes_ |
‘numpy.bytes_’ |
‘bytes#’ 12 |
‘char’ |
2 |
np.object_ |
‘numpy.object_’ |
‘object’ |
‘cell’ |
|
np.ndarray |
‘numpy.ndarray’ |
|||
np.matrix |
‘numpy.matrix’ |
|||
np.chararray |
‘numpy.chararray’ |
‘char’ 14 |
||
np.recarray |
‘numpy.recarray’ |
- 11(1,2,3,4)
The former if it can fit in a
np.int64
and the latter if not.- 12(1,2,3,4,5,6,7,8)
‘#’ 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_
andnp.str_
respectively.- 13
2
if it is stored asnp.uint16
or4
ifnp.uint32
.- 14(1,2,3,4,5,6,7,8)
The value that would be put in for a scalar of the same dtype is used.
- 15(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
, cl.OrderedDict
, 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.
Changed in version 0.2: The field names are escaped as described in Paths.
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
. Version 0.2
of this package drops support for
h5py 2.1.x
and 2.2.x
.
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)
Changed in version 0.2: The field names are escaped as described in Paths.
Python.dict.StoredAs¶
Python Attribute
np.bytes_
: {b'individual', b'keys_values'}
New in version 0.2.
dict
like data (includes cl.OrderedDict
) can be stored in two
ways. This Attribute specifies which one. It is b'individual'
if
each key-value pair is stored as its own Dataset with the key as the
name. It is set to b'key_values'
if the keys are all stored in their
own Dataset (as a tuple
) and the values in another Dataset (as a
tuple
).
Python.dict.key_str_types¶
Python Attribute
np.bytes_
New in version 0.2.
A dict
like object (includes cl.OrderedDict
) is stored with
each key-value pair as its own Dataset if all the keys are string like
and either are Python 3.x str
or Python 2.x unicode
or can be
converted to those. A key is string like if it is a Python 3.x 'str
or 'bytes'
, a Python 2.x 'unicode'
or 'str'
, a
np.unicode_
, or a np.bytes_
.
This Attribute stores what their original types are. Otherwise, the
exact type would be lost in the conversion to Dataset names. The
Attribute has one character corresponding to each field in the
'Python.Fields'
Attribute. The character designates the type. The
characters are
Character |
Type |
---|---|
|
Python 3.x |
|
Python 3.x |
|
|
|
|
Python.dict.keys_values_names¶
Python Attribute
np.object_
array of str
New in version 0.2.
If dict
like data (includes cl.OrderedDict
) is stored with the
keys in their own Dataset and the values in another Dataset (the
'Python.dict.StoredAs'
Attribute is b'keys_values'
), it stores
the names of the Datasets holding the keys and values respectively (in
that order). It has exactly two elements.
The two str
used are controlled by the Options
Options.dict_like_keys_name
and
Options.dict_like_values_name
respectively.
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 with version 0.1.x
of this package 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.
Changed in version 0.2: Added support for writing Python 2.x long
and Python 3.x int
too big to fit into an np.int64
. They are written as their
string base 10 representation (as a np.bytes_
) in such a case.
In earlier versions, 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.
dict and dict like¶
dict
like data (dict
, cl.OrderedDict
, and cl.Counter
)
are stored either with each key-value as their own Dataset or as two
Datasets, one for keys and one for values. The former is used if all
keys are string like. Keys are converted to str
. Otherwise, the
latter format is used. A key is string like if it is a 'str
, a
'bytes'
, a np.unicode_
, or a np.bytes_
and it an be
converted successfully to str
.
When they can’t be stored with each key-value pair as their own Dataset,
the keys and values are stored as tuple
in Datasets set by the the
Options
Options.dict_like_keys_name
and
Options.dict_like_values_name
respectively.
If Python metadata is being stored, Attributes are used to indicae how
the data is stored. The Attribute 'Python.dict.StoredAs'
is used to
store the method of storage (key-value pairs individually or as keys and
values).
When storing each key-value pair as its own Dataset, the Attribute
'Python.dict.key_str_types'
is used to store the type of each key so
that they can be converted back to the right string type.
When storing as keys and values in their own Datasets, the Dataset names
are stored in the Attribute 'Python.dict.keys_values_names'
.
Changed in version 0.2: Support added for storing the keys and values as their own Datasets
instead of each key-value pair as their own Dataset. This feature
adds the ability to store dict
like data with keys that are not
str
or can’t be converted to them. If writing the values to
individual datasets, the key names are escaped.
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 np.ndarray¶
When storing structured np.ndarray
(or data that is stored as it)
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, 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.
Changed in version 0.2: Support for field names with null characters and forward slashes in them added. The field names are escaped.
Warning
Field names cannot have null characters ('\x00'
) and, when
writing as an HDF5 GROUP, forward slashes ('/'
) in them are not
supported before version 0.2.
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.
Stored as dict (slice, range, fractions.Fraction, datetime objects)¶
Stored like a dict
of their relevant attributes or keyword arguments.
For slice
and range
; this is start, stop, and step. For example,
slice(3, None, 1)
is stored the same way as
{'start': 3, 'stop': None, 'step': 1}
For fractions.Fraction
, this is numerator and denominator. For
example, fractions.Fraction(1, 3)
is stored the same way as
{'numerator': 1, 'denominator': 3}
.
For datetime.timedelta
, datetime.timezone
,
datetime.date
, datetime.time
, and
datetime.datetime
; this is a the keyword arguments
required to build them.
collections.ChainMap¶
Stored by storings it collections.ChainMap.maps()
attribute as a
list
.
np.dtype¶
Stored in a string representation encoded as UTF-8 in np.bytes_
. It
is read back by converting to str
, passing through
ast.literal_eval()
, and then passing through the constructor of
np.dtype
. The conversion to the string representation is generally
just a matter of passing it through repr
and removing the leading
'dtype('
and trailing ')'
. However, aligned dtypes either have
to have align added to their dict representation as a string manually;
or if the representation is as a list, pass through str
instead.
New in version 0.2.
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, group_for_references, dict_like_keys_name and
dict_like_values_name, which were explained in the previous section.
attribute |
value |
---|---|
delete_unused_variables |
|
structured_numpy_ndarray_as_struct |
|
make_atleast_2d |
|
convert_numpy_bytes_to_utf16 |
|
convert_numpy_str_to_utf16 |
|
convert_bools_to_uint8 |
|
reverse_dimension_order |
|
store_shape_for_empty |
|
complex_names |
|
group_for_references |
|
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 16 |
double |
0.1 |
np.float64 or np.complex128 16 |
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 17 |
cell |
0.1 |
np.object_ |
canonical empty |
0.1 |
|