:new: Added working script and README draft

README.md

@@ -9,11 +9,11 @@ pretty_name: QuakeSet
 size_categories:
 - 1K<n<10K
 ---
-# Dataset Card for Dataset Name
+# Dataset Card for QuakeSet
 
-<!-- Provide a quick summary of the dataset. -->
+QuakeSet is a dataset to analyze different attributes of earthquakes. It contains bi-temporal timeseries of images and ground truth annotations for magnitudes, hypocenters and affected areas.
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+The dataset is divided in three folds with equal distribution of magnitudes and balanced in positive and negative examples.
 
 ## Dataset Details
 
@@ -24,10 +24,7 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 
 - **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
+- **License:** OPENRAIL
 
 ### Dataset Sources [optional]
 
@@ -35,7 +32,6 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
@@ -47,12 +43,6 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 [More Information Needed]
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
-
 ## Dataset Structure
 
 <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
@@ -83,66 +73,18 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 [More Information Needed]
 
-### Annotations [optional]
+### Annotations
 
 <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
 
-#### Annotation process
-
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
-
-#### Personal and Sensitive Information
-
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Dataset Card Authors [optional]
-
-[More Information Needed]
-
-## Dataset Card Contact
-
-[More Information Needed]
+```
+@article{
+    
+}
+```

quakeset.py

@@ -0,0 +1,181 @@
+# 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.
+
+
+import json
+import os
+
+import datasets
+import h5py
+import numpy as np
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+WIP
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+QuakeSet is a dataset of earthquake images from the Copernicus Sentinel-1 satellites. 
+It contains images from before, after an earthquake, and a sample before the "before" sample.
+Ground truth contains magnitudes and locations of earthquakes provided by ISC.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/DarthReca/quakeset"
+
+_LICENSE = "OPENRAIL"
+
+# 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 = "earthquakes.h5"
+
+
+class QuakeSet(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    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="default",
+            version=VERSION,
+            description="Default configuration. No other configuration is available",
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "pre_post_image": datasets.Array3D(
+                    shape=(512, 513, 2), dtype="float32"
+                ),
+                "affected": datasets.ClassLabel(num_classes=2),
+                "magnitude": datasets.Value("float32"),
+                "hypocenter": datasets.Sequence(datasets.Value("float32"), length=3),
+                "epsg": datasets.Value("int32"),
+                "x": datasets.Sequence(datasets.Value("float32"), length=512),
+                "y": datasets.Sequence(datasets.Value("float32"), length=512),
+            }
+        )
+
+        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):
+        # 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
+        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, "earthquakes.h5"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "earthquakes.h5"),
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "earthquakes.h5"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        sample_ids = []
+        with h5py.File(filepath) as f:
+            for key, patches in f.items():
+                attributes = dict(f[key].attrs)
+                if attributes["split"] != split:
+                    continue
+                sample_ids += [(f"{key}/{p}", 1, attributes) for p in patches.keys()]
+                sample_ids += [
+                    (f"{key}/{p}", 0, attributes)
+                    for p, v in patches.items()
+                    if "before" in v
+                ]
+
+            for sample_id, label, attributes in sample_ids:
+                if "x" in sample_id or "y" in sample_id:
+                    continue
+
+                resource_id, patch_id = sample_id.split("/")
+                x = f[resource_id]["x"][...]
+                y = f[resource_id]["y"][...]
+                x_start = int(patch_id.split("_")[1]) % (x.shape[0] // 512)
+                y_start = int(patch_id.split("_")[1]) // (x.shape[0] // 512)
+                x = x[x_start * 512 : (x_start + 1) * 512]
+                y = y[y_start * 512 : (y_start + 1) * 512]
+
+                pre_key = "pre" if label == 1 else "before"
+                post_key = "post" if label == 1 else "pre"
+                pre_sample = f[sample_id][pre_key][...]
+                post_sample = f[sample_id][post_key][...]
+                pre_sample = np.nan_to_num(pre_sample, nan=0).transpose(2, 0, 1)
+                post_sample = np.nan_to_num(post_sample, nan=0).transpose(2, 0, 1)
+                sample = np.concatenate(
+                    [pre_sample, post_sample], axis=0, dtype=np.float32
+                )
+
+                yield f"{sample_id}/{post_key}", {
+                    "pre_post_image": sample,
+                    "affected": label,
+                    "magnitude": np.float32(attributes["magnitude"]),
+                    "hypocenter": attributes["hypocenter"],
+                    "epsg": attributes["epsg"],
+                    "x": x.flatten(),
+                    "y": y.flatten(),
+                }