File size: 9,382 Bytes
61cc671
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# 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 xarray as xr

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 = {
    "2021": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2021/2021.zarr.zip",
    "2017": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2017/2017.zarr.zip",
    "2016": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016/2016.zarr.zip",
    "2018": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2018/2018.zarr.zip",
    "2019": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2019/2019.zarr.zip",
    "2022": "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2022/2022.zarr.zip",
}
# Add default training one, train on all before 2020, validate on 2021, test on 2022
_URLS["default"] = {"train": [_URLS["2016"], _URLS["2017"], _URLS["2018"], _URLS["2019"]], "valid": [_URLS["2021"]], "test": [_URLS["2022"]]}
_URLS["default_sequence"] = _URLS["default"]

class MRMS(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="default_sequence", version=VERSION, description="Train on 2016-2020, validate on 2021, test on 2022, with 24 timesteps per example"),
    ]

    DEFAULT_CONFIG_NAME = "analysis"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        if "sequence" in self.config.name:
            features = datasets.Features(
                {
                    "precipitation_rate": datasets.Array4D((3500,7000,2), dtype="float16"),
                    "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 ...
                }
            )
        else:
            features = datasets.Features(
                {
                    "precipitation_rate": datasets.Array3D((3500,7000,1), dtype="float16"),
                    "timestamp": 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 ...
                }
            )
        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]
        #data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": urls,
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": urls,
                    "split": "test"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": urls,
                    "split": "valid",
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        if isinstance(filepath, dict):
            # Select the correct set of filepaths
            filepaths = filepath[split]
        else:
            filepaths = [filepath]
        if "sequence" in self.config.name:
            for f in filepaths:
                dataset = xr.open_dataset('zip:///::'+f, engine='zarr', chunks={}).sortby("time").drop_duplicates("time")
                for idx in range(0, len(dataset["time"].values), 24):
                    try:
                        data = dataset.isel(time=slice(idx, idx+24))
                        value = {"precipitation_rate": data["unknown"].values,
                                 "timestamp": data["time"].values,
                                 "latitude": data["latitude"].values,
                                 "longitude": data["longitude"].values}
                        yield idx, value
                    except:
                        # Some of the zarrs potentially have corrupted data at the end, and might fail, so this avoids that
                        continue
        else:
            for f in filepaths:
                dataset = xr.open_dataset('zip:///::'+f, engine='zarr', chunks={}).sortby("time").drop_duplicates("time")
                for key, row in enumerate(dataset["time"].values):
                    try:
                        data = dataset.sel(time=row)
                        value = {"precipitation_rate": data["unknown"].values,
                                 "timestamp": data["time"].values,
                                 "latitude": data["latitude"].values,
                                 "longitude": data["longitude"].values}
                        yield key, value
                    except:
                        # Some of the zarrs potentially have corrupted data at the end, and might fail, so this avoids that
                        continue