Merge pull request #12253 from tylerjereddy/enable_doctests

DOC, TST: enable doctests

1 files changed, 82 insertions, 88 deletions

diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py
index f56c4f4db..e088a6c4a 100644
--- a/numpy/lib/shape_base.py
+++ b/numpy/lib/shape_base.py
@@ -129,7 +129,7 @@ def take_along_axis(arr, indices, axis):
            [40, 50, 60]])
     >>> ai = np.argsort(a, axis=1); ai
     array([[0, 2, 1],
-           [1, 2, 0]], dtype=int64)
+           [1, 2, 0]])
     >>> np.take_along_axis(a, ai, axis=1)
     array([[10, 20, 30],
            [40, 50, 60]])
@@ -142,7 +142,7 @@ def take_along_axis(arr, indices, axis):
     >>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
     >>> ai
     array([[1],
-           [0], dtype=int64)
+           [0]])
     >>> np.take_along_axis(a, ai, axis=1)
     array([[30],
            [60]])
@@ -152,10 +152,10 @@ def take_along_axis(arr, indices, axis):
 
     >>> ai_min = np.expand_dims(np.argmin(a, axis=1), axis=1)
     >>> ai_max = np.expand_dims(np.argmax(a, axis=1), axis=1)
-    >>> ai = np.concatenate([ai_min, ai_max], axis=axis)
-    >> ai
+    >>> ai = np.concatenate([ai_min, ai_max], axis=1)
+    >>> ai
     array([[0, 1],
-           [1, 0]], dtype=int64)
+           [1, 0]])
     >>> np.take_along_axis(a, ai, axis=1)
     array([[10, 30],
            [40, 60]])
@@ -243,7 +243,7 @@ def put_along_axis(arr, indices, values, axis):
     >>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
     >>> ai
     array([[1],
-           [0]], dtype=int64)
+           [0]])
     >>> np.put_along_axis(a, ai, 99, axis=1)
     >>> a
     array([[10, 99, 20],
@@ -330,9 +330,9 @@ def apply_along_axis(func1d, axis, arr, *args, **kwargs):
     ...     return (a[0] + a[-1]) * 0.5
     >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
     >>> np.apply_along_axis(my_func, 0, b)
-    array([ 4.,  5.,  6.])
+    array([4., 5., 6.])
     >>> np.apply_along_axis(my_func, 1, b)
-    array([ 2.,  5.,  8.])
+    array([2.,  5.,  8.])
 
     For a function that returns a 1D array, the number of dimensions in
     `outarr` is the same as `arr`.
@@ -732,11 +732,11 @@ def array_split(ary, indices_or_sections, axis=0):
     --------
     >>> x = np.arange(8.0)
     >>> np.array_split(x, 3)
-        [array([ 0.,  1.,  2.]), array([ 3.,  4.,  5.]), array([ 6.,  7.])]
+        [array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.])]
 
     >>> x = np.arange(7.0)
     >>> np.array_split(x, 3)
-        [array([ 0.,  1.,  2.]), array([ 3.,  4.]), array([ 5.,  6.])]
+        [array([0.,  1.,  2.]), array([3.,  4.]), array([5.,  6.])]
 
     """
     try:
@@ -828,14 +828,14 @@ def split(ary, indices_or_sections, axis=0):
     --------
     >>> x = np.arange(9.0)
     >>> np.split(x, 3)
-    [array([ 0.,  1.,  2.]), array([ 3.,  4.,  5.]), array([ 6.,  7.,  8.])]
+    [array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.,  8.])]
 
     >>> x = np.arange(8.0)
     >>> np.split(x, [3, 5, 6, 10])
-    [array([ 0.,  1.,  2.]),
-     array([ 3.,  4.]),
-     array([ 5.]),
-     array([ 6.,  7.]),
+    [array([0.,  1.,  2.]),
+     array([3.,  4.]),
+     array([5.]),
+     array([6.,  7.]),
      array([], dtype=float64)]
 
     """
@@ -872,43 +872,43 @@ def hsplit(ary, indices_or_sections):
     --------
     >>> x = np.arange(16.0).reshape(4, 4)
     >>> x
-    array([[  0.,   1.,   2.,   3.],
-           [  4.,   5.,   6.,   7.],
-           [  8.,   9.,  10.,  11.],
-           [ 12.,  13.,  14.,  15.]])
+    array([[ 0.,   1.,   2.,   3.],
+           [ 4.,   5.,   6.,   7.],
+           [ 8.,   9.,  10.,  11.],
+           [12.,  13.,  14.,  15.]])
     >>> np.hsplit(x, 2)
     [array([[  0.,   1.],
            [  4.,   5.],
            [  8.,   9.],
-           [ 12.,  13.]]),
+           [12.,  13.]]),
      array([[  2.,   3.],
            [  6.,   7.],
-           [ 10.,  11.],
-           [ 14.,  15.]])]
+           [10.,  11.],
+           [14.,  15.]])]
     >>> np.hsplit(x, np.array([3, 6]))
-    [array([[  0.,   1.,   2.],
-           [  4.,   5.,   6.],
-           [  8.,   9.,  10.],
-           [ 12.,  13.,  14.]]),
-     array([[  3.],
-           [  7.],
-           [ 11.],
-           [ 15.]]),
-     array([], dtype=float64)]
+    [array([[ 0.,   1.,   2.],
+           [ 4.,   5.,   6.],
+           [ 8.,   9.,  10.],
+           [12.,  13.,  14.]]),
+     array([[ 3.],
+           [ 7.],
+           [11.],
+           [15.]]),
+     array([], shape=(4, 0), dtype=float64)]
 
     With a higher dimensional array the split is still along the second axis.
 
     >>> x = np.arange(8.0).reshape(2, 2, 2)
     >>> x
-    array([[[ 0.,  1.],
-            [ 2.,  3.]],
-           [[ 4.,  5.],
-            [ 6.,  7.]]])
+    array([[[0.,  1.],
+            [2.,  3.]],
+           [[4.,  5.],
+            [6.,  7.]]])
     >>> np.hsplit(x, 2)
-    [array([[[ 0.,  1.]],
-           [[ 4.,  5.]]]),
-     array([[[ 2.,  3.]],
-           [[ 6.,  7.]]])]
+    [array([[[0.,  1.]],
+           [[4.,  5.]]]),
+     array([[[2.,  3.]],
+           [[6.,  7.]]])]
 
     """
     if _nx.ndim(ary) == 0:
@@ -936,35 +936,31 @@ def vsplit(ary, indices_or_sections):
     --------
     >>> x = np.arange(16.0).reshape(4, 4)
     >>> x
-    array([[  0.,   1.,   2.,   3.],
-           [  4.,   5.,   6.,   7.],
-           [  8.,   9.,  10.,  11.],
-           [ 12.,  13.,  14.,  15.]])
+    array([[ 0.,   1.,   2.,   3.],
+           [ 4.,   5.,   6.,   7.],
+           [ 8.,   9.,  10.,  11.],
+           [12.,  13.,  14.,  15.]])
     >>> np.vsplit(x, 2)
-    [array([[ 0.,  1.,  2.,  3.],
-           [ 4.,  5.,  6.,  7.]]),
-     array([[  8.,   9.,  10.,  11.],
-           [ 12.,  13.,  14.,  15.]])]
+    [array([[0., 1., 2., 3.],
+           [4., 5., 6., 7.]]), array([[ 8.,  9., 10., 11.],
+           [12., 13., 14., 15.]])]
     >>> np.vsplit(x, np.array([3, 6]))
-    [array([[  0.,   1.,   2.,   3.],
-           [  4.,   5.,   6.,   7.],
-           [  8.,   9.,  10.,  11.]]),
-     array([[ 12.,  13.,  14.,  15.]]),
-     array([], dtype=float64)]
+    [array([[ 0.,  1.,  2.,  3.],
+           [ 4.,  5.,  6.,  7.],
+           [ 8.,  9., 10., 11.]]), array([[12., 13., 14., 15.]]), array([], shape=(0, 4), dtype=float64)]
 
     With a higher dimensional array the split is still along the first axis.
 
     >>> x = np.arange(8.0).reshape(2, 2, 2)
     >>> x
-    array([[[ 0.,  1.],
-            [ 2.,  3.]],
-           [[ 4.,  5.],
-            [ 6.,  7.]]])
+    array([[[0.,  1.],
+            [2.,  3.]],
+           [[4.,  5.],
+            [6.,  7.]]])
     >>> np.vsplit(x, 2)
-    [array([[[ 0.,  1.],
-            [ 2.,  3.]]]),
-     array([[[ 4.,  5.],
-            [ 6.,  7.]]])]
+    [array([[[0., 1.],
+            [2., 3.]]]), array([[[4., 5.],
+            [6., 7.]]])]
 
     """
     if _nx.ndim(ary) < 2:
@@ -989,30 +985,28 @@ def dsplit(ary, indices_or_sections):
     --------
     >>> x = np.arange(16.0).reshape(2, 2, 4)
     >>> x
-    array([[[  0.,   1.,   2.,   3.],
-            [  4.,   5.,   6.,   7.]],
-           [[  8.,   9.,  10.,  11.],
-            [ 12.,  13.,  14.,  15.]]])
+    array([[[ 0.,   1.,   2.,   3.],
+            [ 4.,   5.,   6.,   7.]],
+           [[ 8.,   9.,  10.,  11.],
+            [12.,  13.,  14.,  15.]]])
     >>> np.dsplit(x, 2)
-    [array([[[  0.,   1.],
-            [  4.,   5.]],
-           [[  8.,   9.],
-            [ 12.,  13.]]]),
-     array([[[  2.,   3.],
-            [  6.,   7.]],
-           [[ 10.,  11.],
-            [ 14.,  15.]]])]
+    [array([[[ 0.,  1.],
+            [ 4.,  5.]],
+           [[ 8.,  9.],
+            [12., 13.]]]), array([[[ 2.,  3.],
+            [ 6.,  7.]],
+           [[10., 11.],
+            [14., 15.]]])]
     >>> np.dsplit(x, np.array([3, 6]))
-    [array([[[  0.,   1.,   2.],
-            [  4.,   5.,   6.]],
-           [[  8.,   9.,  10.],
-            [ 12.,  13.,  14.]]]),
-     array([[[  3.],
-            [  7.]],
-           [[ 11.],
-            [ 15.]]]),
-     array([], dtype=float64)]
-
+    [array([[[ 0.,   1.,   2.],
+            [ 4.,   5.,   6.]],
+           [[ 8.,   9.,  10.],
+            [12.,  13.,  14.]]]),
+     array([[[ 3.],
+            [ 7.]],
+           [[11.],
+            [15.]]]),
+    array([], shape=(2, 2, 0), dtype=float64)]
     """
     if _nx.ndim(ary) < 3:
         raise ValueError('dsplit only works on arrays of 3 or more dimensions')
@@ -1092,15 +1086,15 @@ def kron(a, b):
     Examples
     --------
     >>> np.kron([1,10,100], [5,6,7])
-    array([  5,   6,   7,  50,  60,  70, 500, 600, 700])
+    array([  5,   6,   7, ..., 500, 600, 700])
     >>> np.kron([5,6,7], [1,10,100])
-    array([  5,  50, 500,   6,  60, 600,   7,  70, 700])
+    array([  5,  50, 500, ...,   7,  70, 700])
 
     >>> np.kron(np.eye(2), np.ones((2,2)))
-    array([[ 1.,  1.,  0.,  0.],
-           [ 1.,  1.,  0.,  0.],
-           [ 0.,  0.,  1.,  1.],
-           [ 0.,  0.,  1.,  1.]])
+    array([[1.,  1.,  0.,  0.],
+           [1.,  1.,  0.,  0.],
+           [0.,  0.,  1.,  1.],
+           [0.,  0.,  1.,  1.]])
 
     >>> a = np.arange(100).reshape((2,5,2,5))
     >>> b = np.arange(24).reshape((2,3,4))