[pull] main from numpy:main

doc/Makefile

@@ -39,7 +39,7 @@ help:
 	@echo "  clean     to remove generated doc files and start fresh"
 	@echo "  docenv    make a virtual environment in which to build docs"
 	@echo "  html      to make standalone HTML files"
-	@echo "  htmlhelp  to make HTML files and a HTML help project"
+	@echo "  htmlhelp  to make HTML files and an HTML help project"
 	@echo "  latex     to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
 	@echo "  changes   to make an overview over all changed/added/deprecated items"
 	@echo "  linkcheck to check all external links for integrity"

numpy/_core/code_generators/ufunc_docstrings.py

@@ -1619,7 +1619,7 @@ def add_newdoc(place, name, doc):
     returned. In a two's-complement system, this operation effectively flips
     all the bits, resulting in a representation that corresponds to the
     negative of the input plus one. This is the most common method of
-    representing signed integers on computers [1]_. A N-bit two's-complement
+    representing signed integers on computers [1]_. An N-bit two's-complement
     system can represent every integer in the range :math:`-2^{N-1}` to
     :math:`+2^{N-1}-1`.
 

numpy/_core/fromnumeric.py

@@ -1188,7 +1188,7 @@ def argsort(a, axis=-1, kind=None, order=None, *, stable=None):
     array([[0, 3],
            [2, 2]])
 
-    Indices of the sorted elements of a N-dimensional array:
+    Indices of the sorted elements of an N-dimensional array:
 
     >>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape)
     >>> ind
@@ -1275,7 +1275,7 @@ def argmax(a, axis=None, out=None, *, keepdims=np._NoValue):
     >>> np.argmax(a, axis=1)
     array([2, 2])
 
-    Indexes of the maximal elements of a N-dimensional array:
+    Indexes of the maximal elements of an N-dimensional array:
 
     >>> a.flat[np.argmax(a)]
     15
@@ -1375,7 +1375,7 @@ def argmin(a, axis=None, out=None, *, keepdims=np._NoValue):
     >>> np.argmin(a, axis=1)
     array([0, 0])
 
-    Indices of the minimum elements of a N-dimensional array:
+    Indices of the minimum elements of an N-dimensional array:
 
     >>> a.flat[np.argmin(a)]
     10

numpy/_core/numeric.py

@@ -2010,7 +2010,7 @@ def binary_repr(num, width=None):
 
     In a two's-complement system negative numbers are represented by the two's
     complement of the absolute value. This is the most common method of
-    representing signed integers on computers [1]_. A N-bit two's-complement
+    representing signed integers on computers [1]_. An N-bit two's-complement
     system can represent every integer in the range
     :math:`-2^{N-1}` to :math:`+2^{N-1}-1`.
 

numpy/lib/_datasource.py

@@ -155,7 +155,7 @@ def open(path, mode='r', destpath=os.curdir, encoding=None, newline=None):
     """
     Open `path` with `mode` and return the file object.
 
-    If ``path`` is an URL, it will be downloaded, stored in the
+    If ``path`` is a URL, it will be downloaded, stored in the
     `DataSource` `destpath` directory and opened from there.
 
     Parameters
@@ -340,7 +340,7 @@ def _cache(self, path):
     def _findfile(self, path):
         """Searches for ``path`` and returns full path if found.
 
-        If path is an URL, _findfile will cache a local copy and return the
+        If path is a URL, _findfile will cache a local copy and return the
         path to the cached file.  If path is a local file, _findfile will
         return a path to that local file.
 
@@ -372,7 +372,7 @@ def abspath(self, path):
         """
         Return absolute path of file in the DataSource directory.
 
-        If `path` is an URL, then `abspath` will return either the location
+        If `path` is a URL, then `abspath` will return either the location
         the file exists locally or the location it would exist when opened
         using the `open` method.
 
@@ -448,7 +448,7 @@ def exists(self, path):
 
         Notes
         -----
-        When `path` is an URL, `exists` will return True if it's either
+        When `path` is a URL, `exists` will return True if it's either
         stored locally in the `DataSource` directory, or is a valid remote
         URL.  `DataSource` does not discriminate between the two, the file
         is accessible if it exists in either location.
@@ -484,7 +484,7 @@ def open(self, path, mode='r', encoding=None, newline=None):
         """
         Open and return file-like object.
 
-        If `path` is an URL, it will be downloaded, stored in the
+        If `path` is a URL, it will be downloaded, stored in the
         `DataSource` directory and opened from there.
 
         Parameters
@@ -594,7 +594,7 @@ def abspath(self, path):
         """
         Return absolute path of file in the Repository directory.
 
-        If `path` is an URL, then `abspath` will return either the location
+        If `path` is a URL, then `abspath` will return either the location
         the file exists locally or the location it would exist when opened
         using the `open` method.
 
@@ -639,7 +639,7 @@ def exists(self, path):
 
         Notes
         -----
-        When `path` is an URL, `exists` will return True if it's either
+        When `path` is a URL, `exists` will return True if it's either
         stored locally in the `DataSource` directory, or is a valid remote
         URL.  `DataSource` does not discriminate between the two, the file
         is accessible if it exists in either location.
@@ -651,7 +651,7 @@ def open(self, path, mode='r', encoding=None, newline=None):
         """
         Open and return file-like object prepending Repository base URL.
 
-        If `path` is an URL, it will be downloaded, stored in the
+        If `path` is a URL, it will be downloaded, stored in the
         DataSource directory and opened from there.
 
         Parameters

numpy/lib/_function_base_impl.py

@@ -3073,15 +3073,14 @@ def blackman(M):
     "removing the foot", i.e. smoothing discontinuities at the beginning
     and end of the sampled signal) or tapering function. It is known as a
     "near optimal" tapering function, almost as good (by some measures)
-    as the kaiser window.
+    as the Kaiser window.
 
     References
     ----------
-    Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra,
-    Dover Publications, New York.
-
-    Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
-    Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
+    .. [1] Blackman, R.B. and Tukey, J.W., (1958)
+           The measurement of power spectra, Dover Publications, New York.
+    .. [2] Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
+           Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
 
     Examples
     --------

numpy/lib/tests/test__datasource.py

@@ -289,7 +289,7 @@ def test_RemoveHTTPFile(self, tmp_path):
 
     def test_CachedHTTPFile(self, tmp_path):
         localfile = valid_httpurl()
-        # Create a locally cached temp file with an URL based
+        # Create a locally cached temp file with a URL based
         # directory structure.  This is similar to what Repository.open
         # would do.
         repos = datasource.Repository(valid_baseurl(), tmp_path)

numpy/ma/core.pyi

@@ -618,7 +618,7 @@ def getmaskarray(arr: _ScalarLike_co) -> _MaskArray[tuple[()]]: ...
 def getmaskarray[ShapeT: _Shape](arr: np.ndarray[ShapeT, Any]) -> _MaskArray[ShapeT]: ...
 
 # It's sufficient for `m` to have dtype with type: `type[np.bool_]`,
-# which isn't necessarily a ndarray. Please open an issue if this causes issues.
+# which isn't necessarily an ndarray. Please open an issue if this causes issues.
 def is_mask(m: object) -> TypeIs[NDArray[bool_]]: ...
 
 #

numpy/polynomial/chebyshev.py

@@ -1420,7 +1420,7 @@ def chebvander2d(x, y, deg):
     correspond to the elements of a 2-D coefficient array `c` of shape
     (xdeg + 1, ydeg + 1) in the order
 
-    .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
+    .. math:: c_{00}, c_{01}, c_{02}, ... , c_{10}, c_{11}, c_{12}, ...
 
     and ``np.dot(V, c.flat)`` and ``chebval2d(x, y, c)`` will be the same
     up to roundoff. This equivalence is useful both for least squares

numpy/polynomial/hermite.py

@@ -1198,7 +1198,7 @@ def hermvander2d(x, y, deg):
     correspond to the elements of a 2-D coefficient array `c` of shape
     (xdeg + 1, ydeg + 1) in the order
 
-    .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
+    .. math:: c_{00}, c_{01}, c_{02}, ... , c_{10}, c_{11}, c_{12}, ...
 
     and ``np.dot(V, c.flat)`` and ``hermval2d(x, y, c)`` will be the same
     up to roundoff. This equivalence is useful both for least squares

numpy/polynomial/hermite_e.py

@@ -1147,7 +1147,7 @@ def hermevander2d(x, y, deg):
     correspond to the elements of a 2-D coefficient array `c` of shape
     (xdeg + 1, ydeg + 1) in the order
 
-    .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
+    .. math:: c_{00}, c_{01}, c_{02}, ... , c_{10}, c_{11}, c_{12}, ...
 
     and ``np.dot(V, c.flat)`` and ``hermeval2d(x, y, c)`` will be the same
     up to roundoff. This equivalence is useful both for least squares

numpy/polynomial/legendre.py

@@ -1372,7 +1372,7 @@ def legcompanion(c):
     """Return the scaled companion matrix of c.
 
     The basis polynomials are scaled so that the companion matrix is
-    symmetric when `c` is an Legendre basis polynomial. This provides
+    symmetric when `c` is a Legendre basis polynomial. This provides
     better eigenvalue estimates than the unscaled case and for basis
     polynomials the eigenvalues are guaranteed to be real if
     `numpy.linalg.eigvalsh` is used to obtain them.

numpy/polynomial/polynomial.py

@@ -1154,7 +1154,7 @@ def polyvander2d(x, y, deg):
     correspond to the elements of a 2-D coefficient array `c` of shape
     (xdeg + 1, ydeg + 1) in the order
 
-    .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
+    .. math:: c_{00}, c_{01}, c_{02}, ... , c_{10}, c_{11}, c_{12}, ...
 
     and ``np.dot(V, c.flat)`` and ``polyval2d(x, y, c)`` will be the same
     up to roundoff. This equivalence is useful both for least squares