# 2D transform

Using the pylab environment (part of matplotlib) we can perform a simple example where we transform the standard ‘mandrill’ image and show the level 2 wavelet coefficients:

    # Load the mandrill image
    mandrill = datasets.mandrill()

    # Show mandrill
    figure(1)
    imshow(mandrill, cmap=cm.gray, clim=(0,1))

    import dtcwt
    transform = dtcwt.Transform2d()

    # Compute two levels of dtcwt with the defaul wavelet family
    mandrill_t = transform.forward(mandrill, nlevels=2)

    # Show the absolute images for each direction in level 2.
    # Note that the 2nd level has index 1 since the 1st has index 0.
    figure(2)
    for slice_idx in range(mandrill_t.highpasses[1].shape[2]):
        subplot(2, 3, slice_idx)
        imshow(np.abs(mandrill_t.highpasses[1][:,:,slice_idx]), cmap=cm.spectral, clim=(0, 1))

    # Show the phase images for each direction in level 2.
    figure(3)
    for slice_idx in range(mandrill_t.highpasses[1].shape[2]):
        subplot(2, 3, slice_idx)
        imshow(np.angle(mandrill_t.highpasses[1][:,:,slice_idx]), cmap=cm.hsv, clim=(-np.pi, np.pi))

(Source code)