def test_04(self):
img = np.random.randn(64, 64)
blk = util.imageblocks(img, (8, 8))
from sporco import plot
"""
Load training images.
"""
exim = util.ExampleImages(scaled=True, zoom=0.25, gray=True)
S1 = exim.image('barbara.png', idxexp=np.s_[10:522, 100:612])
S2 = exim.image('kodim23.png', idxexp=np.s_[:, 60:572])
S3 = exim.image('monarch.png', idxexp=np.s_[:, 160:672])
S4 = exim.image('sail.png', idxexp=np.s_[:, 210:722])
S5 = exim.image('tulips.png', idxexp=np.s_[:, 30:542])
"""
Extract all 8x8 image blocks, reshape, and subtract block means.
"""
S = util.imageblocks((S1, S2, S3, S4, S5), (8, 8))
S = np.reshape(S, (np.prod(S.shape[0:2]), S.shape[2]))
S -= np.mean(S, axis=0)
"""
Load initial dictionary.
"""
D0 = util.convdicts()['G:8x8x64']
D0 = np.reshape(D0, (np.prod(D0.shape[0:2]), D0.shape[2]))
"""
Compute sparse representation on current dictionary.
"""
lmbda = 0.1
opt = bpdn.BPDN.Options({
'Verbose': True,
