Array

class Array(dtype: str, shape: list[int], stype: str, data: bytes)

class Array(ndarray: ndarray[Any, dtype[Any]])

class Array(torch_tensor: torch.Tensor)

Bases: object

Array type.

A dataclass containing serialized data from an array-like or tensor-like object along with metadata about it. The class can be initialized in one of three ways:

1. By specifying explicit values for dtype, shape, stype, and data.

2. By providing a NumPy ndarray (via the ndarray argument).

3. By providing a PyTorch tensor (via the torch_tensor argument).

In scenarios (2)-(3), the dtype, shape, stype, and data are automatically derived from the input. In scenario (1), these fields must be specified manually.

Parameters:

• dtype (Optional[str] (default: None)) – A string representing the data type of the serialized object (e.g. “float32”). Only required if you are not passing in a ndarray or a tensor.

• shape (Optional[list[int]] (default: None)) – A list representing the shape of the unserialized array-like object. Only required if you are not passing in a ndarray or a tensor.

• stype (Optional[str] (default: None)) – A string indicating the serialization mechanism used to generate the bytes in data from an array-like or tensor-like object. Only required if you are not passing in a ndarray or a tensor.

• data (Optional[bytes] (default: None)) – A buffer of bytes containing the data. Only required if you are not passing in a ndarray or a tensor.

• ndarray (Optional[NDArray] (default: None)) – A NumPy ndarray. If provided, the dtype, shape, stype, and data fields are derived automatically from it.

• torch_tensor (Optional[torch.Tensor] (default: None)) – A PyTorch tensor. If provided, it will be detached and moved to CPU before conversion, and the dtype, shape, stype, and data fields will be derived automatically from it.

Examples

Initializing by specifying all fields directly:

>>> arr1 = Array(
>>>     dtype="float32",
>>>     shape=[3, 3],
>>>     stype="numpy.ndarray",
>>>     data=b"serialized_data...",
>>> )

Initializing with a NumPy ndarray:

>>> import numpy as np
>>> arr2 = Array(np.random.randn(3, 3))

Initializing with a PyTorch tensor:

>>> import torch
>>> arr3 = Array(torch.randn(3, 3))

Methods

from_numpy_ndarray(ndarray)	Create Array from NumPy ndarray.
from_torch_tensor(tensor)	Create Array from PyTorch tensor.
numpy()	Return the array as a NumPy array.

Attributes

dtype
shape
stype
data

classmethod from_numpy_ndarray(ndarray: ndarray[Any, dtype[Any]]) → Array

Create Array from NumPy ndarray.

classmethod from_torch_tensor(tensor: torch.Tensor) → Array

Create Array from PyTorch tensor.

numpy() → ndarray[Any, dtype[Any]]

Return the array as a NumPy array.