Voodoo method
Project description
VoodooNet
Predicting liquid droplets in mixed-phase clouds beyond lidar attenuation using artificial neural nets and Doppler cloud radar spectra
VOODOO is a machine learning approach based convolutional neural networks (CNN) to relate Doppler spectra morphologies to the presence of (supercooled) liquid cloud droplets in mixed-phase clouds.
Installation
Prerequisites
VoodooNet requires Python 3.10.
Before installing VoodooNet, install PyTorch according to your infrastructure. For example on a Linux machine without GPU you might run:
pip3 install torch --extra-index-url https://download.pytorch.org/whl/cpu
From PyPI
pip3 install voodoonet
Usage
Make predictions using the default model and settings
import glob
import voodoonet
rpg_files = glob.glob('/path/to/rpg/files/*.LV0')
probability_liquid = voodoonet.infer(rpg_files)
Generate a training data set
import glob
import voodoonet
rpg_files = glob.glob('/path/to/rpg/files/*.LV0')
classification_files = glob.glob('/path/to/classification/files/*.nc')
voodoonet.generate_training_data(rpg_files, classification_files, 'training-data-set.pt')
Train a VoodooNet model
import voodoonet
pre_computed_training_data_set = 'training-data-set.pt'
voodoonet.train(pre_computed_training_data_set, 'trained-model.pt')
Make predictions using the new model
import glob
import voodoonet
from voodoonet import VoodooOptions
rpg_files = glob.glob('/path/to/rpg/files/*.LV0')
options = VoodooOptions(trained_model='new_model.pt')
probability_liquid = voodoonet.infer(rpg_files, options=options)
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