!20031 fix numpy docstrings

Merge pull request !20031 from wangrao124/code_docs_numpy_0712
2021-07-12 06:59:11 +00:00 · 2021-07-12 06:59:11 +00:00 · 8b56ccadeb
parent 4902090b65 44bb28ff3c
commit 8b56ccadeb
3 changed files with 49 additions and 52 deletions
--- a/mindspore/numpy/array_creations.py
+++ b/mindspore/numpy/array_creations.py
@ -1116,26 +1116,26 @@ def diagonal(a, offset=0, axis1=0, axis2=1):

    Examples:
        >>> import mindspore.numpy as np
-        >>> a = np.arange(4).reshape(2,2)
+        >>> a = np.arange(4).reshape(2,2).astype(np.float32)
        >>> print(a)
-        [[0 1]
-        [2 3]]
+        [[0. 1.]
+        [2. 3.]]
        >>> output = np.diagonal(a)
        >>> print(output)
-        [0 3]
+        [0. 3.]
        >>> output = np.diagonal(a, 1)
        >>> print(output)
-        [1]
-        >>> a = np.arange(8).reshape(2, 2, 2)
+        [1.]
+        >>> a = np.arange(8).reshape(2, 2, 2).astype(np.float32)
        >>> print(a)
-        [[[0 1]
-        [2 3]]
-        [[4 5]
-        [6 7]]]
+        [[[0. 1.]
+        [2. 3.]]
+        [[4. 5.]
+        [6. 7.]]]
        >>> output = np.diagonal(a, 0, 0, 1)
        >>> print(output)
-        [[0 6]
-        [1 7]]
+        [[0. 6.]
+        [1. 7.]]
    """
    return a.diagonal(offset=offset, axis1=axis1, axis2=axis2)

--- a/mindspore/numpy/array_ops.py
+++ b/mindspore/numpy/array_ops.py
@ -1653,16 +1653,16 @@ def flip(m, axis=None):
        >>> A = np.arange(8.0).reshape((2,2,2))
        >>> output = np.flip(A)
        >>> print(output)
-        [[[7, 6],
-        [5, 4]],
-        [[3, 2],
-        [1, 0]]]
+        [[[7. 6]
+        [5. 4]]
+        [[3. 2]
+        [1. 0]]]
        >>> output = np.flip(A, (0, 2))
        >>> print(output)
-        [[[5, 4],
-        [7, 6]],
-        [[1, 0],
-        [3, 2]]]
+        [[[5. 4]
+        [7. 6]]
+        [[1. 0]
+        [3. 2]]]
    """
    _check_input_tensor(m)
    ndim = F.rank(m)
@ -1707,10 +1707,10 @@ def flipud(m):
        >>> A = np.arange(8.0).reshape((2,2,2))
        >>> output = np.flipud(A)
        >>> print(output)
-        [[[4., 5.],
-        [6., 7.]],
-        [[0., 1.],
-        [2., 3.]]]
+        [[[4. 5.]
+        [6. 7.]]
+        [[0. 1.]
+        [2. 3.]]]
    """
    return flip(m, 0)

@ -1740,10 +1740,10 @@ def fliplr(m):
        >>> A = np.arange(8.0).reshape((2,2,2))
        >>> output = np.fliplr(A)
        >>> print(output)
-        [[[2., 3.],
-        [0., 1.]],
-        [[6., 7.],
-        [4., 5.]]]
+        [[[2. 3.]
+        [0. 1.]]
+        [[6. 7.]
+        [4. 5.]]]
    """
    return flip(m, 1)

--- a/mindspore/numpy/math_ops.py
+++ b/mindspore/numpy/math_ops.py
@ -2213,7 +2213,7 @@ def convolve(a, v, mode='full'):
        >>> import mindspore.numpy as np
        >>> output = np.convolve([1., 2., 3., 4., 5.], [2., 3.], mode="valid")
        >>> print(output)
-        [ 3.  6.  9. 12.]
+        [ 7. 12. 17. 22.]
    """
    if not isinstance(a, Tensor):
        a = asarray_const(a)
@ -2926,6 +2926,7 @@ def cross(a, b, axisa=- 1, axisb=- 1, axisc=- 1, axis=None):
        [[-3  6 -3]
        [ 3 -6  3]]
        >>> output = np.cross(x, y, axisc=0)
+        >>> print(output)
        [[-3  3]
        [ 6 -6]
        [-3  3]]
@ -3785,7 +3786,7 @@ def arctan2(x1, x2, dtype=None):
        if both `x1` and `x2` are scalars.

    Supported Platforms:
-        ``Ascend`` ``CPU``
+        ``Ascend`` ``CPU`` ``GPU``

    Examples:
        >>> import mindspore.numpy as np
@ -4430,6 +4431,7 @@ def sign(x, dtype=None):
        Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
        not supported.
        Complex inputs are not supported now.
+        On Ascend, integer inputs are not supported.

    Args:
        x (Union[int, float, list, tuple, Tensor]): Input values.
@ -4683,14 +4685,14 @@ def histogram(a, bins=10, range=None, weights=None, density=False): # pylint: di
    Examples:
        >>> from mindspore import numpy as np
        >>> print(np.histogram([1, 2, 1], bins=[0, 1, 2, 3]))
-        (Tensor(shape=[3], dtype=Float32, value= [0, 2, 1]),
+        (Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00,  2.00000000e+00,  1.00000000e+00]),
        Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 3]))
        >>> print(np.histogram(np.arange(4), bins=np.arange(5), density=True))
        (Tensor(shape=[4], dtype=Float32, value=
        [ 2.50000000e-01,  2.50000000e-01,  2.50000000e-01,  2.50000000e-01]),
        Tensor(shape=[5], dtype=Int32, value= [0, 1, 2, 3, 4]))
        >>> print(np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3]))
-        (Tensor(shape=[3], dtype=Float32, value= [1, 4, 1]),
+        (Tensor(shape=[3], dtype=Float32, value= [ 1.00000000e+00,  4.00000000e+00,  1.00000000e+00]),
        Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 3]))
    """
    a = _to_tensor(a)
@ -4779,14 +4781,13 @@ def histogramdd(sample, bins=10, range=None, weights=None, density=False): # pyl
        [12 13 14]]
        >>> print(np.histogramdd(sample, bins=(2, 3, 4)))
        (Tensor(shape=[2, 3, 4], dtype=Float32, value=
-        [[[1, 1, 0, 0],
-        [0, 0, 0, 0],
-        [0, 0, 0, 0]],
-        [[0, 0, 0, 0],
-        [0, 0, 1, 0],
-        [0, 0, 0, 2]]]),
-        [Tensor(shape=[3], dtype=Float32, value=
-        [ 0.00000000e+00,  6.00000000e+00,  1.20000000e+01]),
+        [[[ 1.00000000e+00,  1.00000000e+00,  0.00000000e+00,  0.00000000e+00],
+        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
+        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],
+        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
+        [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00,  0.00000000e+00],
+        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  2.00000000e+00]]]),
+        [Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00,  6.00000000e+00,  1.20000000e+01]),
        Tensor(shape=[4], dtype=Float32, value=
        [ 1.00000000e+00,  5.00000000e+00,  9.00000000e+00,  1.30000000e+01]),
        Tensor(shape=[5], dtype=Float32, value=
@ -4900,17 +4901,13 @@ def histogram2d(x, y, bins=10, range=None, weights=None, density=False): # pylin
        >>> from mindspore import numpy as np
        >>> x = np.arange(5)
        >>> y = np.arange(2, 7)
-        >>> print(np.histogram2d(x, y, bins=(4, 6)))
-        (Tensor(shape=[4, 6], dtype=Float32, value=
-        [[1, 0, 0, 0, 0, 0],
-        [0, 1, 0, 0, 0, 0],
-        [0, 0, 0, 1, 0, 0]
-        [0, 0, 0, 0, 1, 1]]),
-        Tensor(shape=[5], dtype=Float32, value=
-        [ 0.00000000e+00,  1.00000000e+00,  2.00000000e+00,  3.00000000e+00,  4.00000000e+00]),
-        Tensor(shape=[7], dtype=Float32, value=
-        [ 2.00000000e+00,  2.66666675e+00,  3.33333349e+00,  4.00000000e+00,  4.66666698e+00,
-        5.33333349e+00,  6.00000000e+00]))
+        >>> print(np.histogram2d(x, y, bins=(2, 3)))
+        (Tensor(shape=[2, 3], dtype=Float32, value=
+        [[ 2.00000000e+00,  0.00000000e+00,  0.00000000e+00],
+        [ 0.00000000e+00,  1.00000000e+00,  2.00000000e+00]]),
+        Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00,  2.00000000e+00,  4.00000000e+00]),
+        Tensor(shape=[4], dtype=Float32, value=
+        [ 2.00000000e+00,  3.33333349e+00,  4.66666698e+00,  6.00000000e+00]))
    """
    count, bin_edges = histogramdd((x, y), bins=bins, range=range, weights=weights, density=density)
    return count, bin_edges[0], bin_edges[1]
@ -5367,7 +5364,7 @@ def cumprod(a, axis=None, dtype=None):
        ValueError: If axis is out of range.

    Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``

    Examples:
        >>> import mindspore.numpy as np