(self)
| 1171 | assert_(dx.dtype == np.dtype('timedelta64[D]')) |
| 1172 | |
| 1173 | def test_masked(self): |
| 1174 | # Make sure that gradient supports subclasses like masked arrays |
| 1175 | x = np.ma.array([[1, 1], [3, 4]], |
| 1176 | mask=[[False, False], [False, False]]) |
| 1177 | out = gradient(x)[0] |
| 1178 | assert_equal(type(out), type(x)) |
| 1179 | # And make sure that the output and input don't have aliased mask |
| 1180 | # arrays |
| 1181 | assert_(x._mask is not out._mask) |
| 1182 | # Also check that edge_order=2 doesn't alter the original mask |
| 1183 | x2 = np.ma.arange(5) |
| 1184 | x2[2] = np.ma.masked |
| 1185 | np.gradient(x2, edge_order=2) |
| 1186 | assert_array_equal(x2.mask, [False, False, True, False, False]) |
| 1187 | |
| 1188 | def test_second_order_accurate(self): |
| 1189 | # Testing that the relative numerical error is less that 3% for |
nothing calls this directly
no test coverage detected