OCF Datapipes

OCF's DataPipes for training and inference in Pytorch.

Usage

These datapipes are designed to be composable and modular, and follow the same setup as for the in-built Pytorch Datapipes. There are some great docs on how they can be composed and used here.

End to end examples are given in ocf_datapipes.training and ocf_datapipes.production.

Organization

This repo is organized as follows. The general flow of data loading and processing goes from the ocf_datapipes.load -> .select -> .transform.xarray -> .convert and then optionally .transform.numpy.

training and production contain datapipes that go through all the steps of loading the config file, data, selecting and transforming data, and returning the numpy data to the PyTorch dataloader.

Modules have their own README's as well to go into further detail.

.
└── ocf_datapipes/
    ├── batch/
    │   └── fake
    ├── config/
    │   └── convert/
    │       └── numpy/
    │           └── batch
    ├── experimental
    ├── fake
    ├── load/
    │   ├── gsp
    │   ├── nwp
    │   └── pv
    ├── production
    ├── select
    ├── training
    │   ├── datamodules
    ├── transform/
    │   ├── numpy/
    │   │   └── batch
    │   └── xarray/
    │       └── pv
    ├── utils/
    │   └── split
    └── validation

Adding a new DataPipe

A general outline for a new DataPipe should go something like this:

from torchdata.datapipes.iter import IterDataPipe
from torchdata.datapipes import functional_datapipe

@functional_datapipe("<pipelet_name>")
class <PipeletName>IterDataPipe(IterDataPipe):
    def __init__(self):
        pass

    def __iter__(self):
        pass

Below is a little more detailed example on how to create and join multiple datapipes.

## The below code snippets have been picked from ocf_datapipes\training\pv_satellite_nwp.py file


# 1. read the configuration model for the dataset, detailing what kind of data is the dataset holding, e.g., pv, pv+satellite, pv+satellite+nwp, etc

    config_datapipe = OpenConfiguration(configuration)

# 2. create respective data pipes for pv, nwp and satellite

    pv_datapipe, pv_location_datapipe = (OpenPVFromNetCDF(pv=configuration.input_data.pv).pv_fill_night_nans().fork(2))

    nwp_datapipe = OpenNWP(configuration.input_data.nwp.nwp_zarr_path)

    satellite_datapipe = OpenSatellite(zarr_path=configuration.input_data.satellite.satellite_zarr_path)

# 3. pick all or random location data based on pv data pipeline

    location_datapipes = pv_location_datapipe.location_picker().fork(4, buffer_size=BUFFER_SIZE)

# 4. for the above picked locations get their respective spatial space slices from all the data pipes

    pv_datapipe, pv_time_periods_datapipe, pv_t0_datapipe = pv_datapipe.select_spatial_slice_meters(...)

    nwp_datapipe, nwp_time_periods_datapipe = nwp_datapipe.select_spatial_slice_pixels(...)

    satellite_datapipe, satellite_time_periods_datapipe = satellite_datapipe.select_spatial_slice_pixels(...)

# 5. get contiguous time period data for the above picked locations

    pv_time_periods_datapipe = pv_time_periods_datapipe.get_contiguous_time_periods(...)

    nwp_time_periods_datapipe = nwp_time_periods_datapipe.get_contiguous_time_periods(...)

    satellite_time_periods_datapipe = satellite_time_periods_datapipe.get_contiguous_time_periods(...)

# 6. since all the datapipes have different sampling period for their data, lets find the time that is common between all the data pipes

    overlapping_datapipe = pv_time_periods_datapipe.select_overlapping_time_slice(secondary_datapipes=[nwp_time_periods_datapipe, satellite_time_periods_datapipe])

# 7. take time slices for the above overlapping time from all the data pipes

    pv_datapipe = pv_datapipe.select_time_slice(...)

    nwp_datapipe = nwp_datapipe.convert_to_nwp_target_time(...)

    satellite_datapipe = satellite_datapipe.select_time_slice(...)

# 8. Finally join all the data pipes together

    combined_datapipe = MergeNumpyModalities([nwp_datapipe, pv_datapipe, satellite_datapipe])

Experimental DataPipes

For new datapipes being developed for new models or input modalities, to somewhat separate the more experimental and in development datapipes from the ones better tested for production purposes, there is an ocf_datapipes.experimental namespace for developing these more research-y datapipes. These datapipes might not, and probably are not, tested. Once the model(s) using them are in production, they should be upgraded to one of the other namespaces and have tests added.

Citation

If you find this code useful, please cite the following:

@misc{ocf_datapipes,
  author = {Bieker, Jacob, and Dudfield, Peter, and Kelly, Jack},
  title = {OCF Datapipes},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/openclimatefix/ocf_datapipes}},
}

Contributors ✨

Thanks goes to these wonderful people (emoji key):

_{Jacob Bieker}
💻

_Raj
💻

_{James Fulton}
💻

This project follows the all-contributors specification. Contributions of any kind welcome!

rjmcoder/ocf_datapipes