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# %%
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",
download_mode="force_redownload",
)
# print the first sample of the iterable dataset
for example in quakeflow_nc:
print("\nIterable test\n")
print(example.keys())
for key in example.keys():
if key == "data":
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 test\n")
print(f"Batch size: {len(batch)}")
print(batch[0].keys())
for key in batch[0].keys():
if key == "data":
print(key, np.array(batch[0][key]).shape)
else:
print(key, batch[0][key])
break
# %%
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