block_from_array

metatensor.block_from_array(array) → TensorBlock

Creates a simple TensorBlock from an array.

The metadata in the resulting TensorBlock is filled with ranges of integers. This function should be seen as a quick way of creating a TensorBlock from arbitrary data. However, the metadata generated in this way has little meaning.

Parameters:

array – An array with two or more dimensions. This can either be a numpy.ndarray or a torch.Tensor.

Returns:

A TensorBlock whose values correspond to the provided array. The metadata names are set to "sample" for samples; "component_1", "component_2", … for components; and property for properties. The number of component labels is adapted to the dimensionality of the input array. The metadata associated with each label is a range of integers going from 0 to the size of the corresponding axis. The returned TensorBlock has no gradients.

Return type:

TensorBlock

>>> import numpy as np
>>> import metatensor
>>> # Construct a simple 4D array:
>>> array = np.linspace(0, 10, 42).reshape((7, 3, 1, 2))
>>> # Transform it into a TensorBlock:
>>> tensor_block = metatensor.block_from_array(array)
>>> print(tensor_block)
TensorBlock
    samples (7): ['sample']
    components (3, 1): ['component_1', 'component_2']
    properties (2): ['property']
    gradients: None
>>> # The data inside the TensorBlock will correspond to the provided array:
>>> print(np.all(array == tensor_block.values))
True