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gfs-reforecast

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gfs-reforecast / gfs-reforecast.py

jacobbieker

Update for some streaming things

01e4e87 almost 2 years ago

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	# 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