NdArray¶

class finalfusion.storage.ndarray.NdArray(array: numpy.ndarray)[source]¶

Bases: numpy.ndarray, finalfusion.storage.storage.Storage

Array storage.

Wraps an numpy matrix, either in-memory or memory-mapped.

Examples

>>> storage = NdArray(np.array([[1., 0.5], [0.5, 1.], [0.3, 0.4]],
...                            dtype=np.float32))
>>> # slicing an NdArray returns a storage backed by the same array
>>> storage[:2]
NdArray([[1. , 0.5],
         [0.5, 1. ]], dtype=float32)
>>> # NdArray storage can be treated as numpy arrays
>>> storage * 2
NdArray([[2. , 1. ],
         [1. , 2. ],
         [0.6, 0.8]], dtype=float32)
>>> # Indexing with arrays, lists or ints returns numpy.ndarray
>>> storage[0]
array([1. , 0.5], dtype=float32)

static __new__(cls, array: numpy.ndarray)[source]¶

Construct a new NdArray storage.

Parameters: array (np.ndarray) – The storage buffer.
Raises: TypeError – If the array is not a 2-dimensional float32 array.

classmethod load(file: BinaryIO, mmap: bool = False) → finalfusion.storage.ndarray.NdArray [source]¶

Load Storage from the given finalfusion file.

Parameters

file (IO[bytes]) – Finalfusion file with a storage chunk
mmap (bool)

Returns

storage (Storage) – The first storage in the input file
mmap (bool) – Toggles memory mapping the storage buffer as read-only.

Raises

ValueError – If the file did not contain a storage.

static chunk_identifier() → finalfusion.io.ChunkIdentifier [source]¶

Get the ChunkIdentifier for this Chunk.

Returns: chunk_identifier
Return type: ChunkIdentifier

static read_chunk(file: BinaryIO) → finalfusion.storage.ndarray.NdArray [source]¶

Read the Chunk and return it.

The file must be positioned before the contents of the Chunk but after its header.

Parameters: file (BinaryIO) – a finalfusion file containing the given Chunk
Returns: chunk – The chunk read from the file.
Return type: Chunk

property shape¶

Tuple of array dimensions.

The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.

Examples

>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
>>> np.zeros((4,2))[::2].shape = (-1,)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: Incompatible shape for in-place modification. Use
`.reshape()` to make a copy with the desired shape.