!16008 numpy-native fix docstrings

From: @jachua
Reviewed-by: @liangchenghui,@guoqi1024
Signed-off-by: @liangchenghui

mindspore/numpy/array_creations.py

@@ -1878,9 +1878,10 @@ def blackman(M):
 
     Examples:
         >>> import mindspore.numpy as np
-        >>> print(np.hamming(12))
-        [0.08000001 0.15302339 0.34890914 0.6054648  0.841236   0.9813669
-        0.9813668  0.8412359  0.6054647  0.34890908 0.15302327 0.08000001]
+        >>> print(np.blackman(12))
+        [-1.4901161e-08  3.2606430e-02  1.5990365e-01  4.1439798e-01
+        7.3604518e-01  9.6704674e-01  9.6704674e-01  7.3604518e-01
+        4.1439798e-01  1.5990365e-01  3.2606430e-02 -1.4901161e-08]
     """
     if not _check_window_size(M):
         return ones(_max(0, M))

mindspore/numpy/array_ops.py

@@ -1853,7 +1853,7 @@ def take(a, indices, axis=None, mode='clip'):
         mode (‘raise’, ‘wrap’, ‘clip’, optional): Specifies how out-of-bounds
             indices will behave.
 
-            ‘raise’ – raise an error (default);
+            ‘raise’ – raise an error;
 
             ‘wrap’ – wrap around;
 
@@ -2175,7 +2175,7 @@ def choose(a, choices, mode='clip'):
         mode (‘raise’, ‘wrap’, ‘clip’, optional): Specifies how indices outside
             ``[0, n-1]`` will be treated:
 
-            ‘raise’ – raise an error (default);
+            ‘raise’ – raise an error;
 
             ‘wrap’ – wrap around;
 
@@ -2420,7 +2420,7 @@ def piecewise(x, condlist, funclist, *args, **kw):
         >>> import mindspore.numpy as np
         >>> x = np.linspace(-2.5, 2.5, 6)
         >>> print(np.piecewise(x, [x < 0, x >= 0], [-1, 1]))
-        [2.5 1.5 0.5 0.5 1.5 2.5]
+        [-1 -1 -1  1  1  1]
     """
     x = _to_tensor(x)
     choicelist = funclist

mindspore/numpy/math_ops.py

@@ -2507,6 +2507,7 @@ def nanmax(a, axis=None, dtype=None, keepdims=False):
         ``GPU`` ``CPU``
 
     Examples:
+        >>> import mindspore.numpy as np
         >>> a = np.array([[1, 2], [3, np.nan]])
         >>> output = np.nanmax(a)
         >>> print(output)
@@ -2554,6 +2555,7 @@ def nanmin(a, axis=None, dtype=None, keepdims=False):
         ``GPU`` ``CPU``
 
     Examples:
+        >>> import mindspore.numpy as np
         >>> a = np.array([[1, 2], [3, np.nan]])
         >>> output = np.nanmin(a)
         >>> print(output)
@@ -4242,19 +4244,14 @@ def argmax(a, axis=None):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
+        >>> import mindspore.numpy as np
         >>> a = np.arange(10, 16).reshape(2, 3)
         >>> print(np.argmax(a))
         5
-        >>> a = np.arange(10, 16).reshape(2, 3)
-        >>> print(np.argmax(a), axis=0)
+        >>> print(np.argmax(a, axis=0))
         [1 1 1]
-        >>> a = np.arange(10, 16).reshape(2, 3)
-        >>> print(np.argmax(a), axis=0)
+        >>> print(np.argmax(a, axis=0))
         [2 2]
-        >>> b = np.array([0, 5, 2, 3, 4, 5])
-        >>> b[1] = 5
-        >>> print(np.argmax(b))
-        1
     """
     a = _to_tensor(a)
     return a.argmax(axis)
@@ -4283,19 +4280,14 @@ def argmin(a, axis=None):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
+        >>> import mindspore.numpy as np
         >>> a = np.arange(10, 16).reshape(2, 3)
         >>> print(np.argmin(a))
         0
-        >>> a = np.arange(10, 16).reshape(2, 3)
-        >>> print(np.argmin(a), axis=0)
+        >>> print(np.argmin(a, axis=0))
         [0 0 0]
-        >>> a = np.arange(10, 16).reshape(2, 3)
-        >>> print(np.argmin(a), axis=0)
+        >>> print(np.argmin(a, axis=0))
         [0 0]
-        >>> b = np.array([0, 5, 2, 3, 4, 5])
-        >>> b[1] = 5
-        >>> print(np.argmin(b))
-        0
     """
     a = _to_tensor(a)
     return a.argmin(axis)
@@ -4400,7 +4392,6 @@ def interp(x, xp, fp, left=None, right=None):
         >>> xp = [1, 2, 3]
         >>> fp = [3, 2, 0]
         >>> print(np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp))
-        >>> print(np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3]))
         [3.         3.         2.5        0.55999994 0.        ]
         >>> UNDEF = -99.0
         >>> print(np.interp(3.14, xp, fp, right=UNDEF))
@@ -4735,7 +4726,7 @@ def histogram(a, bins=10, range=None, weights=None, density=False): # pylint: di
     if density:
         count = F.cast(count, mstype.float32)
         count = count/diff(bin_edges)/F.reduce_sum(count)
-    return count, bin_edges
+    return count.astype(mstype.int32), bin_edges
 
 
 @constexpr
@@ -4874,7 +4865,7 @@ def histogramdd(sample, bins=10, range=None, weights=None, density=False): # pyl
             shape = _expanded_shape(ndim, dedges[i].size, i)
             count /= _to_tensor(dedges[i]).reshape(shape)
         count /= s
-    return count, bin_edges
+    return count.astype(mstype.int32), bin_edges
 
 
 def histogram2d(x, y, bins=10, range=None, weights=None, density=False): # pylint: disable=redefined-builtin
@@ -4938,7 +4929,7 @@ def histogram2d(x, y, bins=10, range=None, weights=None, density=False): # pylin
         5.33333349e+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]
+    return count.astype(mstype.int32), bin_edges[0], bin_edges[1]
 
 
 def matrix_power(a, n):

tests/st/numpy_native/test_math_ops.py

@@ -2176,15 +2176,15 @@ def test_histogram():
     for bins in [(1, 2, 3), [2], 1, 5, 10]:
         # pylint: disable=redefined-builtin
         for range in [None, (3, 3), (2, 20)]:
-            match_res(mnp.histogram, onp.histogram, x, bins=bins, range=range, error=3)
-            match_res(mnp.histogram, onp.histogram, x, bins=bins, range=range, density=True, error=3)
+            match_res(mnp.histogram, onp.histogram, x, bins=bins, range=range, error=1)
+            match_res(mnp.histogram, onp.histogram, x, bins=bins, range=range, density=True, error=1)
             mnp_res = mnp.histogram(to_tensor(x), bins=bins, range=range, weights=to_tensor(weights))
             onp_res = onp.histogram(x, bins=bins, range=range, weights=weights)
-            match_all_arrays(mnp_res, onp_res, error=3)
+            match_all_arrays(mnp_res, onp_res, error=1)
             mnp_res = mnp.histogram(to_tensor(x), bins=bins, range=range,
                                     weights=to_tensor(weights), density=True)
             onp_res = onp.histogram(x, bins=bins, range=range, weights=weights, density=True)
-            match_all_arrays(mnp_res, onp_res, error=3)
+            match_all_arrays(mnp_res, onp_res, error=1)
 
 
 @pytest.mark.level1