:hammer: Added citation

quakeset.py

@@ -22,8 +22,15 @@ import numpy as np
 import pandas as pd
 
 # Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-WIP
+_CITATION = """
+@misc{cambrin2024quakeset,
+      title={QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1}, 
+      author={Daniele Rege Cambrin and Paolo Garza},
+      year={2024},
+      eprint={2403.18116},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
 """
 
 # You can copy an official description
@@ -39,7 +46,7 @@ _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", "epicenters.parquet"]
+_URLS = ["earthquakes.h5"]
 
 
 class QuakeSet(datasets.GeneratorBasedBuilder):
@@ -56,19 +63,12 @@ class QuakeSet(datasets.GeneratorBasedBuilder):
     # 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",
-        ),
-        datasets.BuilderConfig(
-            name="epicenter",
-            version=VERSION,
-            description="Epicenter configuration",
-        ),
+        )
     ]
 
     DEFAULT_CONFIG_NAME = "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
@@ -91,21 +91,6 @@ class QuakeSet(datasets.GeneratorBasedBuilder):
                     "y": datasets.Sequence(datasets.Value("float32"), length=512),
                 }
             )
-        elif self.config.name == "epicenter":
-            features = datasets.Features(
-                {
-                    "sample_key": datasets.Value("string"),  # sample_id
-                    "pre_post_image": datasets.Array3D(
-                        shape=(4, 512, 512), dtype="float32"
-                    ),
-                    "contains_epicenter": datasets.ClassLabel(num_classes=2),
-                    "epsg": datasets.Value("int32"),
-                    "epicenter": datasets.Sequence(datasets.Value("float32"), length=2),
-                    "lon": datasets.Sequence(datasets.Value("float32"), length=512),
-                    "lat": datasets.Sequence(datasets.Value("float32"), length=512),
-                    "affected": datasets.ClassLabel(num_classes=2),
-                }
-            )
 
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
@@ -160,7 +145,6 @@ class QuakeSet(datasets.GeneratorBasedBuilder):
     # 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.
-        df = pd.read_parquet(filepath[1])
         sample_ids = []
         with h5py.File(filepath[0]) as f:
             for key, patches in f.items():
@@ -194,30 +178,19 @@ class QuakeSet(datasets.GeneratorBasedBuilder):
                     "epsg": attributes["epsg"],
                 }
 
-                if self.config.name == "default":
-                    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]
-                    item |= {
-                        "affected": label,
-                        "magnitude": np.float32(attributes["magnitude"]),
-                        "hypocenter": attributes["hypocenter"],
-                        "x": x.flatten(),
-                        "y": y.flatten(),
-                    }
-                elif self.config.name == "epicenter":
-                    selected_infos = df[df["sample_id"] == sample_key]
-                    item |= {
-                        "affected": label,
-                        "contains_epicenter": label == 1
-                        and selected_infos["contains_epicenter"].item(),
-                        "epicenter": selected_infos["epicenter"].item(),
-                        "lon": selected_infos["lon"].item(),
-                        "lat": selected_infos["lat"].item(),
-                    }
+                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]
+                item |= {
+                    "affected": label,
+                    "magnitude": np.float32(attributes["magnitude"]),
+                    "hypocenter": attributes["hypocenter"],
+                    "x": x.flatten(),
+                    "y": y.flatten(),
+                }
 
                 yield sample_key, item