Returns True if two arrays are element-wise equal within a tolerance. This function is equivalent to `allclose` except that masked values are treated as equal (default) or unequal, depending on the `masked_equal` argument. Parameters ---------- a, b : array_like
(a, b, masked_equal=True, rtol=1e-5, atol=1e-8)
| 8515 | |
| 8516 | |
| 8517 | def allclose(a, b, masked_equal=True, rtol=1e-5, atol=1e-8): |
| 8518 | """ |
| 8519 | Returns True if two arrays are element-wise equal within a tolerance. |
| 8520 | |
| 8521 | This function is equivalent to `allclose` except that masked values |
| 8522 | are treated as equal (default) or unequal, depending on the `masked_equal` |
| 8523 | argument. |
| 8524 | |
| 8525 | Parameters |
| 8526 | ---------- |
| 8527 | a, b : array_like |
| 8528 | Input arrays to compare. |
| 8529 | masked_equal : bool, optional |
| 8530 | Whether masked values in `a` and `b` are considered equal (True) or not |
| 8531 | (False). They are considered equal by default. |
| 8532 | rtol : float, optional |
| 8533 | Relative tolerance. The relative difference is equal to ``rtol * b``. |
| 8534 | Default is 1e-5. |
| 8535 | atol : float, optional |
| 8536 | Absolute tolerance. The absolute difference is equal to `atol`. |
| 8537 | Default is 1e-8. |
| 8538 | |
| 8539 | Returns |
| 8540 | ------- |
| 8541 | y : bool |
| 8542 | Returns True if the two arrays are equal within the given |
| 8543 | tolerance, False otherwise. If either array contains NaN, then |
| 8544 | False is returned. |
| 8545 | |
| 8546 | See Also |
| 8547 | -------- |
| 8548 | all, any |
| 8549 | numpy.allclose : the non-masked `allclose`. |
| 8550 | |
| 8551 | Notes |
| 8552 | ----- |
| 8553 | If the following equation is element-wise True, then `allclose` returns |
| 8554 | True:: |
| 8555 | |
| 8556 | absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`)) |
| 8557 | |
| 8558 | Return True if all elements of `a` and `b` are equal subject to |
| 8559 | given tolerances. |
| 8560 | |
| 8561 | Examples |
| 8562 | -------- |
| 8563 | >>> import numpy as np |
| 8564 | >>> a = np.ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1]) |
| 8565 | >>> a |
| 8566 | masked_array(data=[10000000000.0, 1e-07, --], |
| 8567 | mask=[False, False, True], |
| 8568 | fill_value=1e+20) |
| 8569 | >>> b = np.ma.array([1e10, 1e-8, -42.0], mask=[0, 0, 1]) |
| 8570 | >>> np.ma.allclose(a, b) |
| 8571 | False |
| 8572 | |
| 8573 | >>> a = np.ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1]) |
| 8574 | >>> b = np.ma.array([1.00001e10, 1e-9, -42.0], mask=[0, 0, 1]) |
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