:new: Added epicenter mode

epicenters.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0632e0fb00f98c957531f9c5defee2cf41e960ae420f03f19a980d20254b613e
+size 15972926

quakeset.py

@@ -19,6 +19,7 @@ import os
 import datasets
 import h5py
 import numpy as np
+import pandas as pd
 
 # Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
@@ -38,7 +39,7 @@ _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"
+_URLS = ["earthquakes.h5", "epicenters.parquet"]
 
 
 class QuakeSet(datasets.GeneratorBasedBuilder):
@@ -61,27 +62,48 @@ class QuakeSet(datasets.GeneratorBasedBuilder):
         datasets.BuilderConfig(
             name="default",
             version=VERSION,
-            description="Default configuration. No other configuration is available",
-        )
+            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.
 
     def _info(self):
-        features = datasets.Features(
-            {
-                "sample_id": datasets.Value("string"),
-                "pre_post_image": datasets.Array3D(
-                    shape=(4, 512, 512), 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),
-            }
-        )
+        if self.config.name == "default":
+            features = datasets.Features(
+                {
+                    "pre_post_image": datasets.Array3D(
+                        shape=(4, 512, 512), 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),
+                }
+            )
+        elif self.config.name == "epicenter":
+            features = datasets.Features(
+                {
+                    "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.
@@ -105,13 +127,13 @@ class QuakeSet(datasets.GeneratorBasedBuilder):
         # 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
-        hdf5_file = dl_manager.download(urls)
+        files = dl_manager.download(urls)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": hdf5_file,
+                    "filepath": files,
                     "split": "train",
                 },
             ),
@@ -119,7 +141,7 @@ class QuakeSet(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": hdf5_file,
+                    "filepath": files,
                     "split": "validation",
                 },
             ),
@@ -127,7 +149,7 @@ class QuakeSet(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": hdf5_file,
+                    "filepath": files,
                     "split": "test",
                 },
             ),
@@ -136,8 +158,9 @@ 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) as f:
+        with h5py.File(filepath[0]) as f:
             for key, patches in f.items():
                 attributes = dict(f[key].attrs)
                 if attributes["split"] != split:
@@ -153,14 +176,6 @@ class QuakeSet(datasets.GeneratorBasedBuilder):
                 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][...]
@@ -170,14 +185,38 @@ class QuakeSet(datasets.GeneratorBasedBuilder):
                 sample = np.concatenate(
                     [pre_sample, post_sample], axis=0, dtype=np.float32
                 )
-
-                yield f"{sample_id}/{post_key}", {
-                    "sample_id": f"{sample_id}/{post_key}",
+                sample_key = f"{sample_id}/{post_key}"
+                item = {
                     "pre_post_image": sample,
-                    "affected": label,
-                    "magnitude": np.float32(attributes["magnitude"]),
-                    "hypocenter": attributes["hypocenter"],
                     "epsg": attributes["epsg"],
-                    "x": x.flatten(),
-                    "y": y.flatten(),
                 }
+
+                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]
+                    if len(selected_infos) > 1:
+                        print(selected_infos)
+                    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(),
+                    }
+
+                yield sample_key, item