diff options
| -rw-r--r-- | numpy/core/fromnumeric.py | 30 | ||||
| -rw-r--r-- | numpy/core/tests/test_numeric.py | 28 |
2 files changed, 46 insertions, 12 deletions
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index f09f2a465..d63e410f6 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -1377,10 +1377,17 @@ def resize(a, new_shape): See Also -------- + np.reshape : Reshape an array without changing the total size. + np.pad : Enlarge and pad an array. + np.repeat: Repeat elements of an array. ndarray.resize : resize an array in-place. Notes ----- + When the total size of the array does not change `~numpy.reshape` should + be used. In most other cases either indexing (to reduce the size) + or padding (to increase the size) may be a more appropriate solution. + Warning: This functionality does **not** consider axes separately, i.e. it does not apply interpolation/extrapolation. It fills the return array with the required number of elements, taken @@ -1404,22 +1411,21 @@ def resize(a, new_shape): """ if isinstance(new_shape, (int, nt.integer)): new_shape = (new_shape,) + a = ravel(a) - Na = len(a) - total_size = um.multiply.reduce(new_shape) - if Na == 0 or total_size == 0: - return mu.zeros(new_shape, a.dtype) - n_copies = int(total_size / Na) - extra = total_size % Na + new_size = 1 + for dim_length in new_shape: + new_size *= dim_length + if dim_length < 0: + raise ValueError('all elements of `new_shape` must be non-negative') - if extra != 0: - n_copies = n_copies + 1 - extra = Na - extra + if a.size == 0 or new_size == 0: + # First case must zero fill. The second would have repeats == 0. + return np.zeros_like(a, shape=new_shape) - a = concatenate((a,) * n_copies) - if extra > 0: - a = a[:-extra] + repeats = -(-new_size // a.size) # ceil division + a = concatenate((a,) * repeats)[:new_size] return reshape(a, new_shape) diff --git a/numpy/core/tests/test_numeric.py b/numpy/core/tests/test_numeric.py index ffebdf648..22706a812 100644 --- a/numpy/core/tests/test_numeric.py +++ b/numpy/core/tests/test_numeric.py @@ -29,6 +29,17 @@ class TestResize(object): Ar3 = np.array([[1, 2, 3], [4, 1, 2], [3, 4, 1], [2, 3, 4]]) assert_equal(np.resize(A, (4, 3)), Ar3) + def test_repeats(self): + A = np.array([1, 2, 3]) + Ar1 = np.array([[1, 2, 3, 1], [2, 3, 1, 2]]) + assert_equal(np.resize(A, (2, 4)), Ar1) + + Ar2 = np.array([[1, 2], [3, 1], [2, 3], [1, 2]]) + assert_equal(np.resize(A, (4, 2)), Ar2) + + Ar3 = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]]) + assert_equal(np.resize(A, (4, 3)), Ar3) + def test_zeroresize(self): A = np.array([[1, 2], [3, 4]]) Ar = np.resize(A, (0,)) @@ -48,6 +59,23 @@ class TestResize(object): assert_array_equal(Ar, np.zeros((2, 1), Ar.dtype)) assert_equal(A.dtype, Ar.dtype) + def test_negative_resize(self): + A = np.arange(0, 10, dtype=np.float32) + new_shape = (-10, -1) + with pytest.raises(ValueError, match=r"negative"): + np.resize(A, new_shape=new_shape) + + def test_subclass(self): + class MyArray(np.ndarray): + __array_priority__ = 1. + + my_arr = np.array([1]).view(MyArray) + assert type(np.resize(my_arr, 5)) is MyArray + assert type(np.resize(my_arr, 0)) is MyArray + + my_arr = np.array([]).view(MyArray) + assert type(np.resize(my_arr, 5)) is MyArray + class TestNonarrayArgs(object): # check that non-array arguments to functions wrap them in arrays |