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-rw-r--r--numpy/core/include/numpy/ndarrayobject.h2
-rw-r--r--numpy/core/src/common/lowlevel_strided_loops.h4
-rw-r--r--numpy/core/src/common/numpyos.c2
-rw-r--r--numpy/core/src/umath/ufunc_object.c2
-rw-r--r--numpy/doc/glossary.py2
-rw-r--r--numpy/doc/subclassing.py2
-rw-r--r--numpy/fft/_pocketfft.py8
-rw-r--r--numpy/lib/npyio.py2
-rw-r--r--numpy/ma/extras.py2
-rw-r--r--numpy/matrixlib/defmatrix.py2
-rw-r--r--numpy/polynomial/chebyshev.py4
-rw-r--r--numpy/polynomial/hermite.py8
-rw-r--r--numpy/polynomial/hermite_e.py8
-rw-r--r--numpy/polynomial/laguerre.py8
-rw-r--r--numpy/polynomial/legendre.py4
-rw-r--r--numpy/testing/_private/noseclasses.py2
-rw-r--r--numpy/testing/_private/utils.py4
17 files changed, 33 insertions, 33 deletions
diff --git a/numpy/core/include/numpy/ndarrayobject.h b/numpy/core/include/numpy/ndarrayobject.h
index b18d75f35..5ef1f10aa 100644
--- a/numpy/core/include/numpy/ndarrayobject.h
+++ b/numpy/core/include/numpy/ndarrayobject.h
@@ -214,7 +214,7 @@ PyArray_DiscardWritebackIfCopy(PyArrayObject *arr)
/*
Check to see if this key in the dictionary is the "title"
entry of the tuple (i.e. a duplicate dictionary entry in the fields
- dict.
+ dict).
*/
static NPY_INLINE int
diff --git a/numpy/core/src/common/lowlevel_strided_loops.h b/numpy/core/src/common/lowlevel_strided_loops.h
index 9208d5499..f2f12a55b 100644
--- a/numpy/core/src/common/lowlevel_strided_loops.h
+++ b/numpy/core/src/common/lowlevel_strided_loops.h
@@ -638,7 +638,7 @@ npy_bswap8_unaligned(char * x)
*
* Here is example code for a single array:
*
- * if (PyArray_TRIVIALLY_ITERABLE(self) {
+ * if (PyArray_TRIVIALLY_ITERABLE(self)) {
* char *data;
* npy_intp count, stride;
*
@@ -656,7 +656,7 @@ npy_bswap8_unaligned(char * x)
*
* Here is example code for a pair of arrays:
*
- * if (PyArray_TRIVIALLY_ITERABLE_PAIR(a1, a2) {
+ * if (PyArray_TRIVIALLY_ITERABLE_PAIR(a1, a2)) {
* char *data1, *data2;
* npy_intp count, stride1, stride2;
*
diff --git a/numpy/core/src/common/numpyos.c b/numpy/core/src/common/numpyos.c
index 7a629f46f..42a71777b 100644
--- a/numpy/core/src/common/numpyos.c
+++ b/numpy/core/src/common/numpyos.c
@@ -248,7 +248,7 @@ check_ascii_format(const char *format)
* Fix the generated string: make sure the decimal is ., that exponent has a
* minimal number of digits, and that it has a decimal + one digit after that
* decimal if decimal argument != 0 (Same effect that 'Z' format in
- * PyOS_ascii_formatd
+ * PyOS_ascii_formatd)
*/
static char*
fix_ascii_format(char* buf, size_t buflen, int decimal)
diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c
index f34fbaf7f..c57199c79 100644
--- a/numpy/core/src/umath/ufunc_object.c
+++ b/numpy/core/src/umath/ufunc_object.c
@@ -2926,7 +2926,7 @@ PyUFunc_GeneralizedFunction(PyUFuncObject *ufunc,
/*
* The first nop strides are for the inner loop (but only can
- * copy them after removing the core axes
+ * copy them after removing the core axes)
*/
memcpy(inner_strides, NpyIter_GetInnerStrideArray(iter),
NPY_SIZEOF_INTP * nop);
diff --git a/numpy/doc/glossary.py b/numpy/doc/glossary.py
index 6d2e0010f..16a3b9241 100644
--- a/numpy/doc/glossary.py
+++ b/numpy/doc/glossary.py
@@ -159,7 +159,7 @@ Glossary
field
In a :term:`structured data type`, each sub-type is called a `field`.
- The `field` has a name (a string), a type (any valid dtype, and
+ The `field` has a name (a string), a type (any valid dtype), and
an optional `title`. See :ref:`arrays.dtypes`
Fortran order
diff --git a/numpy/doc/subclassing.py b/numpy/doc/subclassing.py
index 5a54ddd90..7dc10e1c8 100644
--- a/numpy/doc/subclassing.py
+++ b/numpy/doc/subclassing.py
@@ -72,7 +72,7 @@ class, that points to the data in the original.
There are other points in the use of ndarrays where we need such views,
such as copying arrays (``c_arr.copy()``), creating ufunc output arrays
(see also :ref:`array-wrap`), and reducing methods (like
-``c_arr.mean()``.
+``c_arr.mean()``).
Relationship of view casting and new-from-template
--------------------------------------------------
diff --git a/numpy/fft/_pocketfft.py b/numpy/fft/_pocketfft.py
index 3eab242e5..e9f554fe7 100644
--- a/numpy/fft/_pocketfft.py
+++ b/numpy/fft/_pocketfft.py
@@ -524,8 +524,8 @@ def hfft(a, n=None, axis=-1, norm=None):
domain and is real in the frequency domain. So here it's `hfft` for
which you must supply the length of the result if it is to be odd.
- * even: ``ihfft(hfft(a, 2*len(a) - 2) == a``, within roundoff error,
- * odd: ``ihfft(hfft(a, 2*len(a) - 1) == a``, within roundoff error.
+ * even: ``ihfft(hfft(a, 2*len(a) - 2)) == a``, within roundoff error,
+ * odd: ``ihfft(hfft(a, 2*len(a) - 1)) == a``, within roundoff error.
The correct interpretation of the hermitian input depends on the length of
the original data, as given by `n`. This is because each input shape could
@@ -604,8 +604,8 @@ def ihfft(a, n=None, axis=-1, norm=None):
domain and is real in the frequency domain. So here it's `hfft` for
which you must supply the length of the result if it is to be odd:
- * even: ``ihfft(hfft(a, 2*len(a) - 2) == a``, within roundoff error,
- * odd: ``ihfft(hfft(a, 2*len(a) - 1) == a``, within roundoff error.
+ * even: ``ihfft(hfft(a, 2*len(a) - 2)) == a``, within roundoff error,
+ * odd: ``ihfft(hfft(a, 2*len(a) - 1)) == a``, within roundoff error.
Examples
--------
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index 1d62fc6e4..f5a548433 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -1655,7 +1655,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
-----
* When spaces are used as delimiters, or when no delimiter has been given
as input, there should not be any missing data between two fields.
- * When the variables are named (either by a flexible dtype or with `names`,
+ * When the variables are named (either by a flexible dtype or with `names`),
there must not be any header in the file (else a ValueError
exception is raised).
* Individual values are not stripped of spaces by default.
diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py
index 31648fb2e..f86ebf551 100644
--- a/numpy/ma/extras.py
+++ b/numpy/ma/extras.py
@@ -811,7 +811,7 @@ def compress_nd(x, axis=None):
----------
x : array_like, MaskedArray
The array to operate on. If not a MaskedArray instance (or if no array
- elements are masked, `x` is interpreted as a MaskedArray with `mask`
+ elements are masked), `x` is interpreted as a MaskedArray with `mask`
set to `nomask`.
axis : tuple of ints or int, optional
Which dimensions to suppress slices from can be configured with this
diff --git a/numpy/matrixlib/defmatrix.py b/numpy/matrixlib/defmatrix.py
index 12ac74cb2..d1a1211aa 100644
--- a/numpy/matrixlib/defmatrix.py
+++ b/numpy/matrixlib/defmatrix.py
@@ -802,7 +802,7 @@ class matrix(N.ndarray):
-------
ret : matrix object
If `self` is non-singular, `ret` is such that ``ret * self`` ==
- ``self * ret`` == ``np.matrix(np.eye(self[0,:].size)`` all return
+ ``self * ret`` == ``np.matrix(np.eye(self[0,:].size))`` all return
``True``.
Raises
diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py
index 1329ba07d..4ddb0c688 100644
--- a/numpy/polynomial/chebyshev.py
+++ b/numpy/polynomial/chebyshev.py
@@ -1471,7 +1471,7 @@ def chebvander2d(x, y, deg):
-------
vander2d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
- :math:`order = (deg[0]+1)*(deg([1]+1)`. The dtype will be the same
+ :math:`order = (deg[0]+1)*(deg[1]+1)`. The dtype will be the same
as the converted `x` and `y`.
See Also
@@ -1525,7 +1525,7 @@ def chebvander3d(x, y, z, deg):
-------
vander3d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
- :math:`order = (deg[0]+1)*(deg([1]+1)*(deg[2]+1)`. The dtype will
+ :math:`order = (deg[0]+1)*(deg[1]+1)*(deg[2]+1)`. The dtype will
be the same as the converted `x`, `y`, and `z`.
See Also
diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py
index 44b26f5ee..487d8dfdb 100644
--- a/numpy/polynomial/hermite.py
+++ b/numpy/polynomial/hermite.py
@@ -1194,7 +1194,7 @@ def hermvander2d(x, y, deg):
-------
vander2d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
- :math:`order = (deg[0]+1)*(deg([1]+1)`. The dtype will be the same
+ :math:`order = (deg[0]+1)*(deg[1]+1)`. The dtype will be the same
as the converted `x` and `y`.
See Also
@@ -1248,7 +1248,7 @@ def hermvander3d(x, y, z, deg):
-------
vander3d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
- :math:`order = (deg[0]+1)*(deg([1]+1)*(deg[2]+1)`. The dtype will
+ :math:`order = (deg[0]+1)*(deg[1]+1)*(deg[2]+1)`. The dtype will
be the same as the converted `x`, `y`, and `z`.
See Also
@@ -1369,8 +1369,8 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None):
Fits using Hermite series are probably most useful when the data can be
approximated by ``sqrt(w(x)) * p(x)``, where `w(x)` is the Hermite
- weight. In that case the weight ``sqrt(w(x[i])`` should be used
- together with data values ``y[i]/sqrt(w(x[i])``. The weight function is
+ weight. In that case the weight ``sqrt(w(x[i]))`` should be used
+ together with data values ``y[i]/sqrt(w(x[i]))``. The weight function is
available as `hermweight`.
References
diff --git a/numpy/polynomial/hermite_e.py b/numpy/polynomial/hermite_e.py
index 1a18843ec..cbec15184 100644
--- a/numpy/polynomial/hermite_e.py
+++ b/numpy/polynomial/hermite_e.py
@@ -1187,7 +1187,7 @@ def hermevander2d(x, y, deg):
-------
vander2d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
- :math:`order = (deg[0]+1)*(deg([1]+1)`. The dtype will be the same
+ :math:`order = (deg[0]+1)*(deg[1]+1)`. The dtype will be the same
as the converted `x` and `y`.
See Also
@@ -1241,7 +1241,7 @@ def hermevander3d(x, y, z, deg):
-------
vander3d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
- :math:`order = (deg[0]+1)*(deg([1]+1)*(deg[2]+1)`. The dtype will
+ :math:`order = (deg[0]+1)*(deg[1]+1)*(deg[2]+1)`. The dtype will
be the same as the converted `x`, `y`, and `z`.
See Also
@@ -1362,8 +1362,8 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None):
Fits using HermiteE series are probably most useful when the data can
be approximated by ``sqrt(w(x)) * p(x)``, where `w(x)` is the HermiteE
- weight. In that case the weight ``sqrt(w(x[i])`` should be used
- together with data values ``y[i]/sqrt(w(x[i])``. The weight function is
+ weight. In that case the weight ``sqrt(w(x[i]))`` should be used
+ together with data values ``y[i]/sqrt(w(x[i]))``. The weight function is
available as `hermeweight`.
References
diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py
index 89bb8e168..5b66d943e 100644
--- a/numpy/polynomial/laguerre.py
+++ b/numpy/polynomial/laguerre.py
@@ -1194,7 +1194,7 @@ def lagvander2d(x, y, deg):
-------
vander2d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
- :math:`order = (deg[0]+1)*(deg([1]+1)`. The dtype will be the same
+ :math:`order = (deg[0]+1)*(deg[1]+1)`. The dtype will be the same
as the converted `x` and `y`.
See Also
@@ -1248,7 +1248,7 @@ def lagvander3d(x, y, z, deg):
-------
vander3d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
- :math:`order = (deg[0]+1)*(deg([1]+1)*(deg[2]+1)`. The dtype will
+ :math:`order = (deg[0]+1)*(deg[1]+1)*(deg[2]+1)`. The dtype will
be the same as the converted `x`, `y`, and `z`.
See Also
@@ -1369,8 +1369,8 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None):
Fits using Laguerre series are probably most useful when the data can
be approximated by ``sqrt(w(x)) * p(x)``, where `w(x)` is the Laguerre
- weight. In that case the weight ``sqrt(w(x[i])`` should be used
- together with data values ``y[i]/sqrt(w(x[i])``. The weight function is
+ weight. In that case the weight ``sqrt(w(x[i]))`` should be used
+ together with data values ``y[i]/sqrt(w(x[i]))``. The weight function is
available as `lagweight`.
References
diff --git a/numpy/polynomial/legendre.py b/numpy/polynomial/legendre.py
index 85fd5b18b..47e47a7b6 100644
--- a/numpy/polynomial/legendre.py
+++ b/numpy/polynomial/legendre.py
@@ -1211,7 +1211,7 @@ def legvander2d(x, y, deg):
-------
vander2d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
- :math:`order = (deg[0]+1)*(deg([1]+1)`. The dtype will be the same
+ :math:`order = (deg[0]+1)*(deg[1]+1)`. The dtype will be the same
as the converted `x` and `y`.
See Also
@@ -1265,7 +1265,7 @@ def legvander3d(x, y, z, deg):
-------
vander3d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
- :math:`order = (deg[0]+1)*(deg([1]+1)*(deg[2]+1)`. The dtype will
+ :math:`order = (deg[0]+1)*(deg[1]+1)*(deg[2]+1)`. The dtype will
be the same as the converted `x`, `y`, and `z`.
See Also
diff --git a/numpy/testing/_private/noseclasses.py b/numpy/testing/_private/noseclasses.py
index 493bacfdd..69e19e959 100644
--- a/numpy/testing/_private/noseclasses.py
+++ b/numpy/testing/_private/noseclasses.py
@@ -210,7 +210,7 @@ class NumpyDoctest(npd.Doctest):
# starting Python and executing "import numpy as np", and,
# for SciPy packages, an additional import of the local
# package (so that scipy.linalg.basic.py's doctests have an
- # implicit "from scipy import linalg" as well.
+ # implicit "from scipy import linalg" as well).
#
# Note: __file__ allows the doctest in NoseTester to run
# without producing an error
diff --git a/numpy/testing/_private/utils.py b/numpy/testing/_private/utils.py
index 4569efa91..4097a6738 100644
--- a/numpy/testing/_private/utils.py
+++ b/numpy/testing/_private/utils.py
@@ -1620,7 +1620,7 @@ def assert_array_max_ulp(a, b, maxulp=1, dtype=None):
-----
For computing the ULP difference, this API does not differentiate between
various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000
- is zero.
+ is zero).
See Also
--------
@@ -1666,7 +1666,7 @@ def nulp_diff(x, y, dtype=None):
-----
For computing the ULP difference, this API does not differentiate between
various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000
- is zero.
+ is zero).
Examples
--------
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