This example generates two 1D random walks and demonstrates reconstructing them using the forward and inverse 1D transforms. Note that :py:func`dtcwt.Transform1d.forward` and :pydtcwt.Transform1d.inverse will transform columns of an input array independently
from matplotlib.pylab import * import dtcwt
# Generate a 300x2 array of a random walk vecs = np.cumsum(np.random.rand(300,2) - 0.5, 0)
# Show input figure() plot(vecs) title('Input')
# 1D transform, 5 levels transform = dtcwt.Transform1d() vecs_t = transform.forward(vecs, nlevels=5)
# Show level 2 highpass coefficient magnitudes figure() plot(np.abs(vecs_t.highpasses[1])) title('Level 2 wavelet coefficient magnitudes')
# Show last level lowpass image figure() plot(vecs_t.lowpass) title('Lowpass signals')
# Inverse vecs_recon = transform.inverse(vecs_t)
# Show output figure() plot(vecs_recon) title('Output')
# Show error figure() plot(vecs_recon - vecs) title('Reconstruction error')
print('Maximum reconstruction error: {0}'.format(np.max(np.abs(vecs - vecs_recon))))