# 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).

Warning

The shape that is stored in the Dataset if `Options.store_shape_for_empty` is set is the wrong order (reverse) compared to the non 0.1.x versions of this package after 2021-07-11 and Matlab (see Issue #114). This bug cannot be fixed in the 0.1.x series without breaking compatibility and thus will not be fixed in the 0.1.x series.

### 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.