Gramian Angular Field

This example shows how you can transform a time series into a Gramian Angular Field using pyts.image.GASF for Gramian Angular Summation Field and pyts.image.GADF for Gramian Angular Difference Field.

import numpy as np
import matplotlib.pyplot as plt
from pyts.image import GASF, GADF

# Parameters
n_samples, n_features = 100, 144

# Toy dataset
rng = np.random.RandomState(41)
X = rng.randn(n_samples, n_features)

# GAF transformations
image_size = 24
gasf = GASF(image_size)
X_gasf = gasf.fit_transform(X)
gadf = GADF(image_size)
X_gadf = gadf.fit_transform(X)

# Show the results for the first time series
plt.figure(figsize=(16, 8))
plt.subplot(121)
plt.imshow(X_gasf[0], cmap='rainbow', origin='lower')
plt.title("GASF", fontsize=16)
plt.subplot(122)
plt.imshow(X_gadf[0], cmap='rainbow', origin='lower')
plt.title("GADF", fontsize=16)
plt.show()

Total running time of the script: ( 0 minutes 0.120 seconds)