def test_adjust_log():
 image = np.arange(0, 255, 4, np.uint8).reshape((8, 8))
 expected = np.array([[0, 5, 11, 16, 22, 27, 33, 38], [43, 48, 53, 58, 63, 68, 73, 77], [82, 86, 91, 95, 100, 104, 109, 113], [117, 121, 125, 129, 133, 137, 141, 145], [149, 153, 157, 160, 164, 168, 172, 175], [179, 182, 186, 189, 193, 196, 199, 203], [206, 209, 213, 216, 219, 222, 225, 228], [231, 234, 238, 241, 244, 246, 249, 252]], dtype=np.uint8)
 result = exposure.adjust_log(image, 1)
 assert_array_equal(result, expected)

def test_adjust_log ( ) : image = np . arange ( 0 , 255 , 4 , np . uint8 ) . reshape ( ( 8 , 8 ) ) expected = np . array ( [ [ 0 , 5 , 11 , 16 , 22 , 27 , 33 , 38 ] , [ 43 , 48 , 53 , 58 , 63 , 68 , 73 , 77 ] , [ 82 , 86 , 91 , 95 , 100 , 104 , 109 , 113 ] , [ 117 , 121 , 125 , 129 , 133 , 137 , 141 , 145 ] , [ 149 , 153 , 157 , 160 , 164 , 168 , 172 , 175 ] , [ 179 , 182 , 186 , 189 , 193 , 196 , 199 , 203 ] , [ 206 , 209 , 213 , 216 , 219 , 222 , 225 , 228 ] , [ 231 , 234 , 238 , 241 , 244 , 246 , 249 , 252 ] ] , dtype = np . uint8 ) result = exposure . adjust_log ( image , 1 ) assert_array_equal ( result , expected )
verifying the output with expected results for logarithmic correction with multiplier constant multiplier equal to unity .