Start tutorial

You can copy this tutorial in a Jupyter notebook or run it directly on .

1. Install packages

The DIRESA package depends on the tensorflow package. This tutorial also uses numpy and matplotlib.

# Install needed packages
!pip install numpy
!pip install matplotlib
!pip install tensorflow
!pip install diresa

2. Load the dataset

In this tutorial, we are going to compress the 3D lorenz ‘63 butterfly into a 2D latent space. The lorenz.csv:_ contains a list of butterfly points, with three colums for the X, Y and Z coordinate. The DIRESA model has 2 inputs: the original dataset and a shuffled version of this dataset for the twin encoder.

!wget https://gitlab.com/etrovub/ai4wcm/public/diresa/-/raw/master/docs/lorenz.csv

import numpy as np
data_file = "lorenz.csv"
data = np.loadtxt(data_file, delimiter=",")
print("Shape", data_file, ":", data.shape)
train = data[:30000]
val = data[30000:]
id_train = np.argsort((np.random.random(train.shape[0])))
id_val = np.argsort((np.random.random(val.shape[0])))
train_twin = train[id_train]
val_twin = val[id_val]