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README.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: irradiance
+    dtype: float32
+  splits:
+  - name: full
+    num_bytes: 13466250
+    num_examples: 1000
+  download_size: 14234112
+  dataset_size: 13466250
+---

solarirradiancedataset.py

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+# USAGE: this script is used to create an image dataset that is NOT hosted on HuggingFace but points to the original files
+#        to download and generate the dataset.
+
+import csv
+import os
+import datasets
+import io
+import tarfile
+
+# TODO: Add citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+# _CITATION = """\
+# @InProceedings{huggingface:dataset,
+# title = {A great new dataset},
+# author={huggingface, Inc.
+# },
+# year={2020}
+# }
+# """
+
+_DESCRIPTION = """\
+Images taken from the Sage Waggle Node's top camera and the solar irradiance values were taken from the Argonne National Laboratory 
+tower readings. We made sure to exclude night time photos since there is no sun and we exclusively used summer-time photos as we wanted 
+to stick to a seasonal model that would be able to make estimates more consistently. Furthermore we also eventually downsized the images 
+original 2000x2000 images to 500x500 images since the training was taking a bit too long when the images were larger.
+"""
+_HOMEPAGE = "https://sagecontinuum.org/"
+
+# TODO: Add the licence for the dataset here if you can find it
+# _LICENSE = ""
+
+# 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 = "https://web.lcrc.anl.gov/public/waggle/datasets/solar-irradiance-sample-224.tar"
+
+
+class SolarIrradianceDataset(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+
+        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= datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "irradiance": datasets.Value("float32")
+                }
+            ),
+            # 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):
+        data_dir = dl_manager.download(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name="full",
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "files": dl_manager.iter_archive(data_dir)
+                },
+            )
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, files):
+
+        for file_path, file_obj in files:
+            if ".csv" in file_path:
+                csv_bytes = file_obj.read()
+                csv_contents = str(csv_bytes,'UTF-8')
+                break
+
+        for file_path, file_obj in files:
+            filename = os.path.basename(file_path)
+            if ".jpg" in filename:
+                for row in csv.DictReader(csv_contents.strip().splitlines(),delimiter=','):
+                    if os.path.basename(row['image']) == filename:
+                        yield file_path,{
+                            "image": {"path": file_path, "bytes": file_obj.read()},
+                            "irradiance": row['irradiance']
+                        }