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# %%
import datasets
import numpy as np
from torch.utils.data import DataLoader
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 dataset\n")
print(example)
print(example.keys())
for key in example.keys():
if key == "waveform":
print(key, np.array(example[key]).shape)
else:
print(key, example[key])
break
# %%
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 dataset\n")
print(f"Batch size: {len(batch)}")
print(batch[0].keys())
for key in batch[0].keys():
if key == "waveform":
print(key, np.array(batch[0][key]).shape)
else:
print(key, batch[0][key])
break
# %%
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