(self)
| 414 | assert_array_equal(wsum, expected_wsum) |
| 415 | |
| 416 | def test_masked_weights(self): |
| 417 | # Test with masked weights. |
| 418 | # (Regression test for https://github.com/numpy/numpy/issues/10438) |
| 419 | a = np.ma.array(np.arange(9).reshape(3, 3), |
| 420 | mask=[[1, 0, 0], [1, 0, 0], [0, 0, 0]]) |
| 421 | weights_unmasked = masked_array([5, 28, 31], mask=False) |
| 422 | weights_masked = masked_array([5, 28, 31], mask=[1, 0, 0]) |
| 423 | |
| 424 | avg_unmasked = average(a, axis=0, |
| 425 | weights=weights_unmasked, returned=False) |
| 426 | expected_unmasked = np.array([6.0, 5.21875, 6.21875]) |
| 427 | assert_almost_equal(avg_unmasked, expected_unmasked) |
| 428 | |
| 429 | avg_masked = average(a, axis=0, weights=weights_masked, returned=False) |
| 430 | expected_masked = np.array([6.0, 5.576271186440678, 6.576271186440678]) |
| 431 | assert_almost_equal(avg_masked, expected_masked) |
| 432 | |
| 433 | # weights should be masked if needed |
| 434 | # depending on the array mask. This is to avoid summing |
| 435 | # masked nan or other values that are not cancelled by a zero |
| 436 | a = np.ma.array([1.0, 2.0, 3.0, 4.0], |
| 437 | mask=[False, False, True, True]) |
| 438 | avg_unmasked = average(a, weights=[1, 1, 1, np.nan]) |
| 439 | |
| 440 | assert_almost_equal(avg_unmasked, 1.5) |
| 441 | |
| 442 | a = np.ma.array([ |
| 443 | [1.0, 2.0, 3.0, 4.0], |
| 444 | [5.0, 6.0, 7.0, 8.0], |
| 445 | [9.0, 1.0, 2.0, 3.0], |
| 446 | ], mask=[ |
| 447 | [False, True, True, False], |
| 448 | [True, False, True, True], |
| 449 | [True, False, True, False], |
| 450 | ]) |
| 451 | |
| 452 | avg_masked = np.ma.average(a, weights=[1, np.nan, 1], axis=0) |
| 453 | avg_expected = np.ma.array([1.0, np.nan, np.nan, 3.5], |
| 454 | mask=[False, True, True, False]) |
| 455 | |
| 456 | assert_almost_equal(avg_masked, avg_expected) |
| 457 | assert_equal(avg_masked.mask, avg_expected.mask) |
| 458 | |
| 459 | |
| 460 | class TestConcatenator: |
nothing calls this directly
no test coverage detected