Aiice
AIICE is an open-source Python framework designed as a standardized benchmark for spatio-temporal forecasting of Arctic sea ice concentration. It provides reproducible pipelines for loading, preprocessing, and evaluating satellite-derived OSI-SAF data, supporting both short- and long-term prediction horizons
Installation
The simplest way to install framework with pip:
pip install aiice-bench
Quickstart
The AIICE class provides a simple interface for loading Arctic ice data, preparing datasets, and benchmarking PyTorch models:
from aiice import AIICE
# Initialize AIICE with a sliding window
# of past 30 days and forecast of 7 days
aiice = AIICE(
pre_history_len=30,
forecast_len=7,
batch_size=32,
start="2022-01-01",
end="2022-12-31"
)
# Define your PyTorch model
model = MyModel()
# Run benchmarking to compute metrics on the dataset
report = aiice.bench(model)
print(report)
Check package doc and see more usage examples. You can also explore the raw dataset and work with it independently via Hugging Face
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Metadata
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Release files for aiice-bench 1.0.3
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| aiice_bench-1.0.3.tar.gz | 2.8 MB | Details |
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|---|---|---|---|---|
| aiice_bench-1.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.8 MB
Release files / aiice_bench-1.0.3.tar.gz
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Release files / aiice_bench-1.0.3-py3-none-any.whl
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