# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Archival NOAA NWP forecasting data covering most of 2016-2022. """ import numpy as np import xarray as xr import json import datasets # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{ocf:gfs, title = {GFS Forecast Dataset}, author={Jacob Bieker}, year={2022} } """ # You can copy an official description _DESCRIPTION = """\ This dataset consists of various NOAA datasets related to operational forecasts, including FNL Analysis files, GFS operational forecasts, and the raw observations used to initialize the grid. """ _HOMEPAGE = "https://mtarchive.geol.iastate.edu/" _LICENSE = "US Government data, Open license, no restrictions" # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "gfs_v16": "gfs_v16.json", "raw": "raw.json", "analysis": "analysis.json", } class GFEReforecastDataset(datasets.GeneratorBasedBuilder): """Archival MRMS Precipitation Rate Radar data for the continental US, covering most of 2016-2022.""" VERSION = datasets.Version("1.0.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [ datasets.BuilderConfig(name="analysis", version=VERSION, description="FNL 0.25 degree Analysis files"), datasets.BuilderConfig(name="raw_analysis", version=VERSION, description="FNL 0.25 degree Analysis files coupled with raw observations"), datasets.BuilderConfig(name="gfs_v16", version=VERSION, description="GFS v16 Forecasts from April 2021 through 2022, returned as a 696 channel image"), datasets.BuilderConfig(name="raw_gfs_v16", version=VERSION, description="GFS v16 Forecasts from April 2021 through 2022, returned as a 696 channel image, coupled with raw observations"), datasets.BuilderConfig(name="gfs_v16_variables", version=VERSION, description="GFS v16 Forecasts from April 2021 through 2022 with one returned array per variable"), ] DEFAULT_CONFIG_NAME = "gfs_v16" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): features = {} if "v16" in self.config.name: # TODO Add the variables one with all 696 variables, potentially combined by level features = { "current_state": datasets.Array3D((721,1440,696), dtype="float32"), "next_state": datasets.Array3D((721,1440,696), dtype="float32"), "timestamp": datasets.Sequence(datasets.Value("timestamp[ns]")), "latitude": datasets.Sequence(datasets.Value("float32")), "longitude": datasets.Sequence(datasets.Value("float32")) # These are the features of your dataset like images, labels ... } elif "analysis" in self.config.name: # TODO Add the variables one with all 322 variables, potentially combined by level features = { "current_state": datasets.Array3D((721,1440,322), dtype="float32"), "next_state": datasets.Array3D((721,1440,322), dtype="float32"), "timestamp": datasets.Sequence(datasets.Value("timestamp[ns]")), "latitude": datasets.Sequence(datasets.Value("float32")), "longitude": datasets.Sequence(datasets.Value("float32")) # These are the features of your dataset like images, labels ... } if "raw" in self.config.name: # Add the raw observation features, capping at 256,000 observations, padding if not enough raw_features = {"observations": datasets.Array2D((256000,1), dtype="float32"), "observation_type": datasets.Array2D((256000,1), dtype="string"), "observation_lat": datasets.Array2D((256000,1), dtype="float32"), "observation_lon": datasets.Array2D((256000,1), dtype="float32"), } features = features.update(raw_features) features = datasets.Features(features) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive urls = _URLS[self.config.name] streaming = dl_manager.is_streaming if streaming: urls = dl_manager.download_and_extract(urls) else: with open(filepath, "r") as f: filepaths = json.load(f) data_dir = dl_manager.download_and_extract(filepaths) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": urls if streaming else data_dir, "split": "train", "streaming": streaming, }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": urls if streaming else data_dir, "split": "test", "streaming": streaming, }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": urls if streaming else data_dir, "split": "valid", "streaming": streaming }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split, streaming): # Load the list of files for the type of data if streaming: with open(filepath, "r") as f: filepaths = json.load(f) filepaths = ['zip:///::https://huggingface.co/datasets/openclimatefix/gfs-reforecast/resolve/main/' + f for f in filepaths] else: filepaths = filepath if "v16" in self.config.name: idx = 0 for f in filepaths: dataset = xr.open_dataset(f, engine='zarr', chunks={}) try: for t in range(len(dataset["time"].values)-1): data_t = dataset.isel(time=t) data_t1 = dataset.isel(time=(t+1)) value = {"current_state": np.stack([data_t[v].values for v in sorted(data_t.data_vars)], axis=2), "next_state": np.stack([data_t1[v].values for v in sorted(data_t.data_vars)], axis=2), "timestamp": data_t["time"].values, "latitude": data_t["latitude"].values, "longitude": data_t["longitude"].values} idx += 1 yield idx, value except: # Some of the zarrs potentially have corrupted data at the end, and might fail, so this avoids that continue