Fill out TODOs

eumetsat_uk_hrv.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+# Copyright 2020 The HuggingFace Datasets Authors and Open Climate Fix.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,14 +12,10 @@
 # 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.
-# TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
+"""This dataset consists of HRV channel imagery from the EUMETSAT SEVIRI RSS service covering the UK from 2020-2021"""
 
 
 import xarray
-import json
-import os
-
 import datasets
 
 
@@ -34,43 +30,29 @@ year={2022}
 }
 """
 
-# TODO: Add description of the dataset here
-# You can copy an official description
 _DESCRIPTION = """\
-This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+The EUMETSAT Spinning Enhanced Visible and InfraRed Imager (SEVIRI) rapid scanning service (RSS) takes an image of the northern third of the Meteosat disc every five minutes (see the EUMETSAT website for more information on SEVIRI RSS ). The original EUMETSAT dataset  contains data from 2008 to the present day from 12 channels, and for a wide geographical extent covering North Africa, Saudi Arabia, all of Europe, and Western Russia. In contrast, this dataset on Google Cloud is a small subset of the entire SEVIRI RSS dataset: This Google Cloud dataset is from a single channel: the "high resolution visible" (HRV) channel; and contains data from January 2020 to November 2021. The geographical extent of this dataset on Google Cloud is a small subset of the total SEVIRI RSS extent: This Google Cloud dataset includes data over the United Kingdom and over North Western Europe.
+
+This dataset is slightly transformed: It does not contain the original numerical values. 
+
+The original data is copyright EUMETSAT. EUMETSAT has given permission to redistribute this transformed data. The data was transformed by Open Climate Fix using satip.
+
+This public dataset is hosted in Google Cloud Storage and available free to use. 
 """
 
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
+_HOMEPAGE = "https://console.cloud.google.com/marketplace/product/bigquery-public-data/eumetsat-seviri-rss-hrv-uk?project=tactile-acrobat-249716"
 
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
+_LICENSE = "Cite EUMETSAT as the data source. This data is redistributed with permission from EUMETSAT under the terms of the EUMETSAT Data Policy for SEVIRI data with a latency of >3 hours . This redistributed dataset is released under the CC BY 4.0 open data license & is provided \"AS IS\" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset."
 
-# TODO: Add link to the official dataset URLs here
-# 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 = {
     "uk_hrv": "gs://public-datasets-eumetsat-solar-forecasting/satellite/EUMETSAT/SEVIRI_RSS/v3/eumetsat_seviri_hrv_uk.zarr",
 }
 
 
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
 class EumetsatUkHrvDataset(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
+    """This dataset consists of the HRV channel from the EUMETSAT SEVIRI RSS service covering the UK from 2020 to 2021."""
 
     VERSION = datasets.Version("1.1.0")
-
-    # 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="uk", version=VERSION, description="This part of the dataset covers the UK"),
     ]
@@ -82,7 +64,6 @@ class EumetsatUkHrvDataset(datasets.GeneratorBasedBuilder):
         features = datasets.Features(
             {
                 "timestamp": datasets.Value("time64[ns]"),
-                "channel": datasets.Value("string"),
                 "image": datasets.Array3D(shape=(1, ), dtype="int16"),
                 "x_coordinates": datasets.Sequence(datasets.Value("float64")),
                 "y_coordinates": datasets.Sequence(datasets.Value("float64"))
@@ -105,21 +86,15 @@ class EumetsatUkHrvDataset(datasets.GeneratorBasedBuilder):
         )
 
     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)
+        data_dir = dl_manager.download(urls)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train.jsonl"),
-                    "time_range": None,
+                    "filepath": data_dir,
+                    "time_range": slice("2020-01-01", "2020-12-31"),
                     "split": "train",
                 },
             ),
@@ -127,34 +102,20 @@ class EumetsatUkHrvDataset(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "test.jsonl"),
-                    "time_range": None, # TODO Decide on time range for train, test, val
+                    "filepath": data_dir,
+                    "time_range": slice("2021-01-01", "2021-12-31"),
                     "split": "test"
                 },
             ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.jsonl"),
-                    "time_range": None,
-                    "split": "dev",
-                },
-            ),
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, time_range, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        # TODO Here: Open Xarray, then iterate through the full images, returning them as np.ndarray
-        # TODO If streaming, then load from GCP directly
         sat_data = xarray.open_dataset(filepath, engine="zarr", chunks='auto')
         sat_data = sat_data.sel(time=time_range)
         for key, entry in enumerate(sat_data):
             yield key, {
                 "timestamp": entry.time.values,
-                "channel": entry["channels"].values,
                 "x_coordinates": entry.x_geospatial.values,
                 "y_coordinates": entry.y_geospatial.values,
                 "image": entry.values,