def test_denoise_tv_chambolle_1d():
 x = (125 + (100 * np.sin(np.linspace(0, (8 * np.pi), 1000))))
 x += (20 * np.random.rand(x.size))
 x = np.clip(x, 0, 255)
 res = restoration.denoise_tv_chambolle(x.astype(np.uint8), weight=0.1)
 assert_((res.dtype == np.float))
 assert_(((res.std() * 255) < x.std()))

def test_denoise_tv_chambolle_1d ( ) : x = ( 125 + ( 100 * np . sin ( np . linspace ( 0 , ( 8 * np . pi ) , 1000 ) ) ) ) x += ( 20 * np . random . rand ( x . size ) ) x = np . clip ( x , 0 , 255 ) res = restoration . denoise_tv_chambolle ( x . astype ( np . uint8 ) , weight = 0.1 ) assert_ ( ( res . dtype == np . float ) ) assert_ ( ( ( res . std ( ) * 255 ) < x . std ( ) ) )
apply the tv denoising algorithm on a 1d sinusoid .