File size: 10,078 Bytes
61cc671 5e9558f 61cc671 5e9558f 61cc671 c729c76 61cc671 01e4e87 5e9558f 61cc671 c729c76 61cc671 c729c76 61cc671 5e9558f 61cc671 4c9706a c729c76 4c9706a c729c76 61cc671 4c9706a 61cc671 5e9558f 4c9706a 61cc671 01e4e87 61cc671 01e4e87 61cc671 01e4e87 61cc671 01e4e87 10e2ca5 01e4e87 61cc671 01e4e87 61cc671 01e4e87 61cc671 01e4e87 5e9558f 01e4e87 5e9558f db8068a 5e9558f db8068a 4c9706a 5e9558f db8068a 61cc671 5e9558f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
# 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
|