Stochastic Weather Generator

This folder was originally conceived to address training Variational Autoencoder (VAE). It later evolved in a general folder for Stochastic Weather Generator (SWG).

Folder structure

• vae_learn2.py is the main script which is responsible for training the VAE. It includes a range of different routines for training, testing, and analyzing the VAE. It overloads many of the classes and methods defined in Learn2_new.py, ERA_Fields_New.py and TF_Fields.py. Note, you may use vae_learn2.py to also train PCA autoencoder and also generate folder structure without any dimensionality reduction necessary to work with the routines we will discuss below. For detailed description of use consult the doc file provided in vae_learn2.py
• history.py and history_training.py are routines which allow plotting the history of the training of the VAE. It is used for the outputs of vae_learn2.py
• reconstruction.py and reconstruction_zg.py are routines which allow to reconstruct the input data from the latent space of the VAE. They are used for the outputs of vae_learn2.py and can be used to visualize the method.
• analogue_dario.py routine finds the analogs for SWG. It should be applied to the output of vae_learn2.py without the use of VAE or PCA autoencoder.
• analogue_george.py routine finds the analogs for SWG. It should be applied to the output of vae_learn2.py with the use of VAE or PCA autoencoder.
• committor_analogue.py computes committor using the Matrix of analogs created by analogue_dario.py or analogue_george.py.
• trajectory_analogue.ipynb samples synthetic trajectories using the Matrix of analogs created by analogue_dario.py or analogue_george.py.
• test_committor_dario.ipynb defines and uses a routine which compares the skill of the Learn2_new.py trained CNN with of SWG based on the output of committor_analogue.py.

Usage:

If you would like to work with SWG you must run vae_learn2.py even if you do not intend to train VAE because vae_learn2.py generates the necessary folder structure consistent with k-fold cross validation. The first step is to call:

python vae_learn2.py <folder_name>

*Don't forget to set up the correct *kwargs

After the routine has been called you may inspect the quality of reconstruction (if VAE was used) via the following script

python reconstruction.py <folder_name> <checkpoint_number> <random_seed>

To inspect the loss during training use may use

python history.py <folder_name> <number_of_folds>
python histroy_training.py <folder_name> <number_of_folds>

Direct input

If you are don't want to use dimensinoality reduction, to compute the matrix of analogs for the SWG you may use

python analogue_dario.py <folder> <coefficients> <NN>

where typical usage is to set <coefficients>=1,5,10,50,100,500 and <NN>=100

Dimansionality reduction

In this case you run the following

analogue_george.py <folder> <coefficients> <NN>

To use the matrix of analogs and compute the committor function (independent of whether or not you use dimesnionality reduction) you should use

python committor_analogue.py <committor_file>

To investigate the resulting skill you should look at the notebook test_committor_dario.ipynb

To use the analogs and compute the long trajectories you may inspect the notebook trajectory_analogue.ipynb