DOC: Remove empty notes (numpy#27706)

* DOC: remove empty notes

* DOC: remove empty line

Author: linus-md

numpy/_core/_add_newdocs.py

@@ -3246,9 +3246,6 @@
         is a new array of the same shape as the input array, with dtype, order
         given by `dtype`, `order`.
 
-    Notes
-    -----
-
     Raises
     ------
     ComplexWarning

numpy/_core/code_generators/ufunc_docstrings.py

@@ -836,9 +836,6 @@ def add_newdoc(place, name, doc):
     --------
     ceil, floor, rint, fix
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np
@@ -1028,7 +1025,6 @@ def add_newdoc(place, name, doc):
 
     Notes
     -----
-
     rad2deg(x) is ``180 * x / pi``.
 
     Examples
@@ -1066,9 +1062,6 @@ def add_newdoc(place, name, doc):
         The output array, element-wise Heaviside step function of `x1`.
         $OUT_SCALAR_2
 
-    Notes
-    -----
-
     References
     ----------
     .. [1] Wikipedia, "Heaviside step function",
@@ -1269,9 +1262,6 @@ def add_newdoc(place, name, doc):
     --------
     power
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np
@@ -2219,9 +2209,6 @@ def add_newdoc(place, name, doc):
     --------
     logaddexp2: Logarithm of the sum of exponentiations of inputs in base 2.
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np
@@ -2262,9 +2249,6 @@ def add_newdoc(place, name, doc):
     --------
     logaddexp: Logarithm of the sum of exponentiations of the inputs.
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np
@@ -2662,7 +2646,6 @@ def add_newdoc(place, name, doc):
 
     Notes
     -----
-
     The fmax is equivalent to ``np.where(x1 >= x2, x1, x2)`` when neither
     x1 nor x2 are NaNs, but it is faster and does proper broadcasting.
 
@@ -2720,7 +2703,6 @@ def add_newdoc(place, name, doc):
 
     Notes
     -----
-
     The fmin is equivalent to ``np.where(x1 <= x2, x1, x2)`` when neither
     x1 nor x2 are NaNs, but it is faster and does proper broadcasting.
 
@@ -2818,7 +2800,6 @@ def add_newdoc(place, name, doc):
 
     Notes
     -----
-
     The behavior depends on the arguments in the following way.
 
     - If both arguments are 2-D they are multiplied like conventional

numpy/_core/fromnumeric.py

@@ -166,7 +166,6 @@ def take(a, indices, axis=None, out=None, mode='raise'):
 
     Notes
     -----
-
     By eliminating the inner loop in the description above, and using `s_` to
     build simple slice objects, `take` can be expressed  in terms of applying
     fancy indexing to each 1-d slice::

numpy/_core/multiarray.py

@@ -632,9 +632,6 @@ def min_scalar_type(a):
     out : dtype
         The minimal data type.
 
-    Notes
-    -----
-
     See Also
     --------
     result_type, promote_types, dtype, can_cast
@@ -697,7 +694,6 @@ def result_type(*arrays_and_dtypes):
 
     Notes
     -----
-
     The specific algorithm used is as follows.
 
     Categories are determined by first checking which of boolean,
@@ -1006,9 +1002,6 @@ def ravel_multi_index(multi_index, dims, mode=None, order=None):
     --------
     unravel_index
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np

numpy/_core/shape_base.py

@@ -839,7 +839,6 @@ def block(arrays):
 
     Notes
     -----
-
     When called with only scalars, ``np.block`` is equivalent to an ndarray
     call. So ``np.block([[1, 2], [3, 4]])`` is equivalent to
     ``np.array([[1, 2], [3, 4]])``.

numpy/lib/_arraysetops_impl.py

@@ -1066,7 +1066,6 @@ def isin(element, test_elements, assume_unique=False, invert=False, *,
 
     Notes
     -----
-
     `isin` is an element-wise function version of the python keyword `in`.
     ``isin(a, b)`` is roughly equivalent to
     ``np.array([item in b for item in a])`` if `a` and `b` are 1-D sequences.

numpy/lib/_index_tricks_impl.py

@@ -968,9 +968,6 @@ def diag_indices(n, ndim=2):
     --------
     diag_indices_from
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np
@@ -1033,9 +1030,6 @@ def diag_indices_from(arr):
     --------
     diag_indices
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np

numpy/lib/_stride_tricks_impl.py

@@ -404,9 +404,6 @@ def broadcast_to(array, shape, subok=False):
     broadcast_arrays
     broadcast_shapes
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np

numpy/lib/_twodim_base_impl.py

@@ -856,9 +856,6 @@ def mask_indices(n, mask_func, k=0):
     --------
     triu, tril, triu_indices, tril_indices
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np
@@ -925,9 +922,6 @@ def tril_indices(n, k=0, m=None):
     mask_indices : generic function accepting an arbitrary mask function.
     tril, triu
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np
@@ -1038,10 +1032,6 @@ def tril_indices_from(arr, k=0):
     See Also
     --------
     tril_indices, tril, triu_indices_from
-
-    Notes
-    -----
-
     """
     if arr.ndim != 2:
         raise ValueError("input array must be 2-d")
@@ -1079,9 +1069,6 @@ def triu_indices(n, k=0, m=None):
     mask_indices : generic function accepting an arbitrary mask function.
     triu, tril
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np
@@ -1195,10 +1182,6 @@ def triu_indices_from(arr, k=0):
     See Also
     --------
     triu_indices, triu, tril_indices_from
-
-    Notes
-    -----
-
     """
     if arr.ndim != 2:
         raise ValueError("input array must be 2-d")

numpy/ma/extras.py

@@ -1435,9 +1435,6 @@ def in1d(ar1, ar2, assume_unique=False, invert=False):
     isin       : Version of this function that preserves the shape of ar1.
     numpy.in1d : Equivalent function for ndarrays.
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np
@@ -1485,9 +1482,6 @@ def isin(element, test_elements, assume_unique=False, invert=False):
     in1d       : Flattened version of this function.
     numpy.isin : Equivalent function for ndarrays.
 
-    Notes
-    -----
-
     Examples
     --------
     >>> import numpy as np
@@ -2214,9 +2208,6 @@ def clump_unmasked(a):
         The list of slices, one for each continuous region of unmasked
         elements in `a`.
 
-    Notes
-    -----
-
     See Also
     --------
     flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges
@@ -2253,9 +2244,6 @@ def clump_masked(a):
         The list of slices, one for each continuous region of masked elements
         in `a`.
 
-    Notes
-    -----
-
     See Also
     --------
     flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges