Quickstart

This guide will help you get started with PhenoNN quickly.

Predicting GCC with Pre-trained Models

The simplest way to use PhenoNN is to predict GCC using pre-trained models.

Prepare your data: Ensure your climate data is in the correct format.
Run prediction:
```
phenonn predict GR 4 ./example
```
This command: - Uses pre-trained models for Grassland (GR) PFT - Batch size of 4 - Reads data from the example directory
Output: The predictions will be saved to gcc_pred_test_GR_mfull.csv.

Training Your Own Models

To train a new model for a specific PFT:

phenonn train full DB 8 gcc_lowess 8 --epochs 100

Parameters: - full: No shuffling (use full dataset) - DB: Plant functional type (Deciduous Broadleaf) - 8: Number of features (dynamic + static climate) - gcc_lowess: Target variable - 8: Batch size - –epochs 100: Train for 100 epochs

Hyperparameter Tuning

Run hyperparameter tuning to find optimal settings:

phenonn hp-tuning full GR 8 gcc_lowess 4

Python API

You can also use PhenoNN programmatically:

from phenonn import LSTM, PhenoDataset
from phenonn import run_lstm_pred, run_lstm_train

# Make predictions
run_lstm_pred(m='full', pft='GR', batch_size=4, input_path='./example')

# Train a model
run_lstm_train(
    m='full',
    pft='DB',
    nr_features=8,
    target='gcc_lowess',
    batch_size=8
)