SKIPP’D

class torchgeo.datasets.SKIPPD(root='data', split='trainval', task='nowcast', transforms=None, download=False, checksum=False)

Bases: NonGeoDataset

SKy Images and Photovoltaic Power Dataset (SKIPP’D).

The SKIPP’D dataset contains ground-based fish-eye photos of the sky for solar forecasting tasks.

Dataset Format:

• .hdf5 file containing images and labels

• .npy files with corresponding datetime timestamps

Dataset Features:

• fish-eye RGB images (64x64px)

• power output measurements from 30-kW rooftop PV array

• 1-min interval across 3 years (2017-2019)

Nowcast task:

• 349,372 images under the split key trainval

• 14,003 images under the split key test

Forecast task:

• 130,412 images under the split key trainval

• 2,462 images under the split key test

• consists of a concatenated RGB time-series of 16 time-steps

If you use this dataset in your research, please cite:

• https://doi.org/10.48550/arXiv.2207.00913

Note

This dataset requires the following additional library to be installed:

• https://pypi.org/project/h5py/ to load the dataset

Added in version 0.5.

__init__(root='data', split='trainval', task='nowcast', transforms=None, download=False, checksum=False)

Initialize a new Dataset instance.

Parameters:

• root (str | PathLike[str]) – root directory where dataset can be found

• split (str) – one of “trainval”, or “test”

• task (str) – one of “nowcast”, or “forecast”

• transforms (Callable[[dict[str, Any]], dict[str, Any]] | None) – a function/transform that takes an input sample and returns a transformed version

• download (bool) – if True, download dataset and store it in the root directory

• checksum (bool) – if True, check the MD5 after downloading files (may be slow)

Raises:

• AssertionError – if task or split is invalid

• DatasetNotFoundError – If dataset is not found and download is False.

• DependencyNotFoundError – If h5py is not installed.

__len__()

Return the number of data points in the dataset.

Returns:

length of the dataset

Return type:

int

__getitem__(index)

Return an index within the dataset.

Parameters:

index (int) – index to return

Returns:

data and label at that index

Return type:

dict[str, Any]

plot(sample, show_titles=True, suptitle=None)

Plot a sample from the dataset.

In the forecast task the latest image is plotted.

Parameters:

• sample (dict[str, Any]) – a sample return by __getitem__()

• show_titles (bool) – flag indicating whether to show titles above each panel

• suptitle (str | None) – optional suptitle to use for figure

Returns:

a matplotlib Figure with the rendered sample

Return type:

Figure

__annotate_func__()

The type of the None singleton.