neuralop.data.datasets.darcy.DarcyDataset

class neuralop.data.datasets.darcy.DarcyDataset(root_dir: Path | str, n_train: int, n_tests: List[int], batch_size: int, test_batch_sizes: List[int], train_resolution: int, test_resolutions: int = [16, 32], encode_input: bool = False, encode_output: bool = True, encoding='channel-wise', channel_dim=1, subsampling_rate=None, download: bool = True)[source]

DarcyDataset stores data generated according to Darcy’s Law. Input is a coefficient function and outputs describe flow.

Data source: https://zenodo.org/records/12784353

Parameters:

root_dirUnion[Path, str]

root at which to download data files

n_trainint

number of train instances

n_testsList[int]

number of test instances per test dataset

batch_sizeint

batch size of training set

test_batch_sizesList[int]

batch size of test sets

train_resolutionint

resolution of data for training set

test_resolutionsList[int], optional

resolution of data for testing sets, by default [16,32]

encode_inputbool, optional

whether to normalize inputs in provided DataProcessor, by default False

encode_outputbool, optional

whether to normalize outputs in provided DataProcessor, by default True

encodingstr, optional

parameter for input/output normalization. Whether to normalize by channel (“channel-wise”) or by pixel (“pixel-wise”), default “channel-wise”

channel_dimint, optional

dimension of saved tensors to index data channels, by default 1

subsampling_rateint or List[int], optional

rate at which to subsample each input dimension, by default None

downloadbool, optional

whether to download data if not present, by default True

Attributes:

train_db: torch.utils.data.Dataset of training examples

test_db: “” of test examples

data_processor: neuralop.data.transforms.DataProcessor to process data examples

optional, default is None