mxtaltools.dataset_utils.utils

class mxtaltools.dataset_utils.utils.SimpleDataset(x, y)

Bases: Dataset

mxtaltools.dataset_utils.utils.basic_stats(values: tensor) → dict[str, Tensor]

mxtaltools.dataset_utils.utils.collate_data_list(data_list, exclude_unit_cell: bool = True, max_z_prime: int | None = None, exclude_keys: list | None = None, skip_default_exclusion: bool = False)

mxtaltools.dataset_utils.utils.filter_graph_nodewise(data, keep_index=None, delete_index=None)

# NOTE this does not work because of our custom data structure Function to get subgraph of data. Effectively filtering by nodes. :param data: pyg data batch :param keep_index: boolean or indexes of which nodes should be kept

Returns:

Parameters:

• data

• keep_index

• delete_index

mxtaltools.dataset_utils.utils.get_dataloaders(dataset_builder, machine, batch_size, test_fraction=0.2, shuffle=True, num_workers: int = 0)

mxtaltools.dataset_utils.utils.quick_combine_crystal_embedding_dataloaders(dataset, data_loader, batch_size, max_size)

mxtaltools.dataset_utils.utils.quick_combine_dataloaders(dataset, data_loader, batch_size, max_size)

mxtaltools.dataset_utils.utils.update_dataloader_batch_size(loader, new_batch_size)