rename data -> waveform

example.py

@@ -1,20 +1,33 @@
 # %%
+import datasets
 import numpy as np
-from datasets import load_dataset
 from torch.utils.data import DataLoader
 
-# quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test")
-quakeflow_nc = load_dataset(
-    "./quakeflow_nc.py",
-    name="station_test",
-    # name="event_test",
-    split="test",
+quakeflow_nc = datasets.load_dataset(
+    "AI4EPS/quakeflow_nc",
+    name="station",
+    split="train",
+    # name="station_test",
+    # split="test",
     download_mode="force_redownload",
+    trust_remote_code=True,
+    num_proc=36,
 )
+# quakeflow_nc = datasets.load_dataset(
+#     "./quakeflow_nc.py",
+#     name="station",
+#     split="train",
+#     # name="statoin_test",
+#     # split="test",
+#     num_proc=36,
+# )
+
+print(quakeflow_nc)
 
 # print the first sample of the iterable dataset
 for example in quakeflow_nc:
-    print("\nIterable test\n")
+    print("\nIterable dataset\n")
+    print(example)
     print(example.keys())
     for key in example.keys():
         if key == "data":
@@ -28,7 +41,7 @@ quakeflow_nc = quakeflow_nc.with_format("torch")
 dataloader = DataLoader(quakeflow_nc, batch_size=8, num_workers=0, collate_fn=lambda x: x)
 
 for batch in dataloader:
-    print("\nDataloader test\n")
+    print("\nDataloader dataset\n")
     print(f"Batch size: {len(batch)}")
     print(batch[0].keys())
     for key in batch[0].keys():

quakeflow_nc.py

@@ -167,7 +167,7 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
         ):
             features = datasets.Features(
                 {
-                    "data": datasets.Array2D(shape=(3, self.nt), dtype="float32"),
+                    "waveform": datasets.Array2D(shape=(3, self.nt), dtype="float32"),
                     "phase_time": datasets.Sequence(datasets.Value("string")),
                     "phase_index": datasets.Sequence(datasets.Value("int32")),
                     "phase_type": datasets.Sequence(datasets.Value("string")),
@@ -183,7 +183,7 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
         elif (self.config.name == "event") or (self.config.name == "event_train") or (self.config.name == "event_test"):
             features = datasets.Features(
                 {
-                    "data": datasets.Array3D(shape=(None, 3, self.nt), dtype="float32"),
+                    "waveform": datasets.Array3D(shape=(None, 3, self.nt), dtype="float32"),
                     "phase_time": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
                     "phase_index": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))),
                     "phase_type": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
@@ -224,19 +224,17 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
         # 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]
         # files = dl_manager.download(urls)
-        files = dl_manager.download_and_extract(urls)
-        # files = ["waveform_h5/1989.h5", "waveform_h5/1990.h5"]
-        print(files)
+        # files = dl_manager.download_and_extract(urls)
+        files = [f"waveform_h5/{x}" for x in _FILES]
+        for file in sorted(files):
+            print(file)
 
         if self.config.name == "station" or self.config.name == "event":
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
                     # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": files[:-1],
-                        "split": "train",
-                    },
+                    gen_kwargs={"filepath": files[:-1], "split": "train"},
                 ),
                 datasets.SplitGenerator(
                     name=datasets.Split.TEST,
@@ -247,10 +245,7 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepath": files,
-                        "split": "train",
-                    },
+                    gen_kwargs={"filepath": files, "split": "train"},
                 ),
             ]
         elif self.config.name == "station_test" or self.config.name == "event_test":
@@ -269,6 +264,7 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
 
         for file in filepath:
+            print(f"\nReading {file}")
             with fsspec.open(file, "rb") as fs:
                 with h5py.File(fs, "r") as fp:
                     event_ids = list(fp.keys())
@@ -292,10 +288,10 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
                             or (self.config.name == "station_train")
                             or (self.config.name == "station_test")
                         ):
-                            waveforms = np.zeros([3, self.nt], dtype="float32")
+                            waveform = np.zeros([3, self.nt], dtype="float32")
 
                             for i, sta_id in enumerate(station_ids):
-                                waveforms[:, : self.nt] = event[sta_id][:, : self.nt]
+                                waveform[:, : self.nt] = event[sta_id][:, : self.nt]
                                 attrs = event[sta_id].attrs
                                 phase_type = attrs["phase_type"]
                                 phase_time = attrs["phase_time"]
@@ -304,7 +300,7 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
                                 station_location = [attrs["longitude"], attrs["latitude"], -attrs["elevation_m"] / 1e3]
 
                                 yield f"{event_id}/{sta_id}", {
-                                    "data": waveforms,
+                                    "waveform": waveform,
                                     "phase_time": phase_time,
                                     "phase_index": phase_index,
                                     "phase_type": phase_type,
@@ -323,7 +319,7 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
                             or (self.config.name == "event_test")
                         ):
 
-                            waveforms = np.zeros([len(station_ids), 3, self.nt], dtype="float32")
+                            waveform = np.zeros([len(station_ids), 3, self.nt], dtype="float32")
                             phase_type = []
                             phase_time = []
                             phase_index = []
@@ -331,7 +327,7 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
                             station_location = []
 
                             for i, sta_id in enumerate(station_ids):
-                                waveforms[i, :, : self.nt] = event[sta_id][:, : self.nt]
+                                waveform[i, :, : self.nt] = event[sta_id][:, : self.nt]
                                 attrs = event[sta_id].attrs
                                 phase_type.append(list(attrs["phase_type"]))
                                 phase_time.append(list(attrs["phase_time"]))
@@ -341,7 +337,7 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
                                     [attrs["longitude"], attrs["latitude"], -attrs["elevation_m"] / 1e3]
                                 )
                             yield event_id, {
-                                "data": waveforms,
+                                "waveform": waveform,
                                 "phase_time": phase_time,
                                 "phase_index": phase_index,
                                 "phase_type": phase_type,