How icenet-ai/icenet works
IceNet is a Python library for sea-ice forecasting. Its principal workflow acquires climate and sea-ice inputs, processes them into model-ready datasets, trains a model, generates forecasts, and then supports forecast analysis and delivery. Separate capabilities include plotting, threshold detection, and uploading results to local or Azure destinations. The graph reflects documented commands and sampled code; some subsystem internals were not sampled, so uncertain connections are omitted.
Main entry points: interfaces Data interfaces · train.py Model training · predict.py Forecast processing · process.py Data processing
Components 21
Data acquisition
- Data interfaces
icenet/data/interfaces - Sea-ice download
icenet/data/sic/osisaf.py - Download framework
icenet/data/interfaces/downloader.py
Data preparation
- Data processing
icenet/data/process.py - Variable processors
icenet/data/processors - Sea-ice masks
icenet/data/sic/mask.py - Dataset definition
icenet/data/dataset.py - Dataset generation
icenet/data/loader.py - Data loaders
icenet/data/loaders
Model forecasting
- Model training
icenet/model/train.py - Forecast models
icenet/model/models.py - Model prediction
icenet/model/predict.py
Forecast outputs
- Forecast processing
icenet/process/predict.py - Forecast utilities
icenet/process/forecasts.py - Result uploads
icenet/process - Forecast plotting
icenet/plotting - Forecast metrics
icenet/plotting/forecast.py - Threshold detection
icenet/results/threshold.py - Result metrics
icenet/results/metrics.py
Other components
- Operator
- External data APIs
Connections 24
- Operator → Data interfaces: requests data
- Operator → Sea-ice download: requests sea ice
- Data interfaces → Download framework: uses framework
- Data interfaces → External data APIs: requests data
- Sea-ice download → External data APIs: downloads data
- Operator → Data processing: processes inputs
- Variable processors → Data processing: specializes processing
- Data processing → Sea-ice masks: uses masks
- Operator → Dataset definition: checks dataset
- Operator → Dataset generation: creates dataset
- Dataset generation → Data loaders: uses loaders
- Operator → Model training: trains model
- Model training → Dataset definition: loads dataset
- Model training → Forecast models: builds model
- Operator → Model prediction: runs prediction
- Operator → Forecast processing: prepares forecast
- Operator → Forecast utilities: processes forecast
- Operator → Result uploads: uploads results
- Operator → Forecast plotting: plots outputs
- Operator → Forecast metrics: computes metrics
- Operator → Threshold detection: checks threshold
- Operator → Result metrics: uses metrics
- Forecast processing → Forecast plotting: writes forecast data
- Forecast processing → Threshold detection: writes forecast data