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authorPierre de Buyl <pdebuyl@pdebuyl.be>2016-09-06 14:42:08 +0200
committerPierre de Buyl <pdebuyl@pdebuyl.be>2016-09-06 14:42:08 +0200
commit2a55233b81a6ea18a57d1dd4f7bc5fff9f2fb681 (patch)
tree1a4a81faf9e59d0cabf9cbace9dc967bad60caa1
parent773e3cad9a71cb9a7849d8e251fb8a99ab35d06b (diff)
downloadnumpy-2a55233b81a6ea18a57d1dd4f7bc5fff9f2fb681.tar.gz
DOC: change Numpy to NumPy in dosctrings and comments
The strings in error messages were left untouched
-rw-r--r--numpy/__init__.py4
-rw-r--r--numpy/add_newdocs.py20
-rw-r--r--numpy/core/_internal.py2
-rw-r--r--numpy/core/code_generators/cversions.txt2
-rw-r--r--numpy/core/code_generators/generate_numpy_api.py2
-rw-r--r--numpy/core/code_generators/generate_ufunc_api.py2
-rw-r--r--numpy/core/code_generators/ufunc_docstrings.py6
-rw-r--r--numpy/core/defchararray.py14
-rw-r--r--numpy/core/fromnumeric.py10
-rw-r--r--numpy/core/include/numpy/npy_common.h6
-rw-r--r--numpy/core/memmap.py2
-rw-r--r--numpy/core/numeric.py22
-rw-r--r--numpy/core/src/multiarray/buffer.c2
-rw-r--r--numpy/core/src/multiarray/methods.c2
-rw-r--r--numpy/core/src/multiarray/multiarraymodule.c2
-rw-r--r--numpy/core/src/umath/struct_ufunc_test.c.src2
-rw-r--r--numpy/core/src/umath/test_rational.c.src2
-rw-r--r--numpy/core/src/umath/ufunc_object.c2
-rw-r--r--numpy/core/tests/test_indexing.py4
-rw-r--r--numpy/core/tests/test_regression.py4
-rw-r--r--numpy/ctypeslib.py2
-rw-r--r--numpy/doc/basics.py10
-rw-r--r--numpy/doc/constants.py12
-rw-r--r--numpy/doc/creation.py6
-rw-r--r--numpy/doc/glossary.py2
-rw-r--r--numpy/doc/indexing.py4
-rw-r--r--numpy/doc/internals.py8
-rw-r--r--numpy/doc/structured_arrays.py4
-rw-r--r--numpy/doc/subclassing.py4
-rw-r--r--numpy/doc/ufuncs.py2
-rw-r--r--numpy/dual.py2
-rw-r--r--numpy/lib/_version.py6
-rw-r--r--numpy/lib/function_base.py6
-rw-r--r--numpy/lib/nanfunctions.py6
-rw-r--r--numpy/lib/npyio.py10
-rw-r--r--numpy/lib/type_check.py2
-rw-r--r--numpy/lib/ufunclike.py4
-rw-r--r--numpy/lib/utils.py6
-rw-r--r--numpy/linalg/linalg.py2
-rw-r--r--numpy/polynomial/_polybase.py2
-rw-r--r--numpy/polynomial/chebyshev.py2
-rw-r--r--numpy/polynomial/hermite.py2
-rw-r--r--numpy/polynomial/hermite_e.py2
-rw-r--r--numpy/polynomial/laguerre.py2
-rw-r--r--numpy/polynomial/legendre.py2
-rw-r--r--numpy/polynomial/polynomial.py2
-rw-r--r--numpy/random/mtrand/mtrand.pyx2
47 files changed, 113 insertions, 113 deletions
diff --git a/numpy/__init__.py b/numpy/__init__.py
index 5384d61ab..0f1bcf766 100644
--- a/numpy/__init__.py
+++ b/numpy/__init__.py
@@ -65,7 +65,7 @@ fft
polynomial
Polynomial tools
testing
- Numpy testing tools
+ NumPy testing tools
f2py
Fortran to Python Interface Generator.
distutils
@@ -83,7 +83,7 @@ dual
matlib
Make everything matrices.
__version__
- Numpy version string
+ NumPy version string
Viewing documentation using IPython
-----------------------------------
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py
index 41267b797..adeaff90c 100644
--- a/numpy/add_newdocs.py
+++ b/numpy/add_newdocs.py
@@ -296,7 +296,7 @@ add_newdoc('numpy.core', 'nditer',
Notes
-----
`nditer` supersedes `flatiter`. The iterator implementation behind
- `nditer` is also exposed by the Numpy C API.
+ `nditer` is also exposed by the NumPy C API.
The Python exposure supplies two iteration interfaces, one which follows
the Python iterator protocol, and another which mirrors the C-style
@@ -570,7 +570,7 @@ add_newdoc('numpy.core', 'broadcast', ('ndim',
add_newdoc('numpy.core', 'broadcast', ('nd',
"""
- Number of dimensions of broadcasted result. For code intended for Numpy
+ Number of dimensions of broadcasted result. For code intended for NumPy
1.12.0 and later the more consistent `ndim` is preferred.
Examples
@@ -2014,7 +2014,7 @@ add_newdoc('numpy.core', 'matmul',
were elements.
.. warning::
- This function is preliminary and included in Numpy 1.10 for testing
+ This function is preliminary and included in NumPy 1.10 for testing
and documentation. Its semantics will not change, but the number and
order of the optional arguments will.
@@ -4841,7 +4841,7 @@ add_newdoc('numpy.core.umath', 'frompyfunc',
"""
frompyfunc(func, nin, nout)
- Takes an arbitrary Python function and returns a Numpy ufunc.
+ Takes an arbitrary Python function and returns a NumPy ufunc.
Can be used, for example, to add broadcasting to a built-in Python
function (see Examples section).
@@ -4858,7 +4858,7 @@ add_newdoc('numpy.core.umath', 'frompyfunc',
Returns
-------
out : ufunc
- Returns a Numpy universal function (``ufunc``) object.
+ Returns a NumPy universal function (``ufunc``) object.
See Also
--------
@@ -4888,7 +4888,7 @@ add_newdoc('numpy.core.umath', 'geterrobj',
Return the current object that defines floating-point error handling.
The error object contains all information that defines the error handling
- behavior in Numpy. `geterrobj` is used internally by the other
+ behavior in NumPy. `geterrobj` is used internally by the other
functions that get and set error handling behavior (`geterr`, `seterr`,
`geterrcall`, `seterrcall`).
@@ -4952,7 +4952,7 @@ add_newdoc('numpy.core.umath', 'seterrobj',
Set the object that defines floating-point error handling.
The error object contains all information that defines the error handling
- behavior in Numpy. `seterrobj` is used internally by the other
+ behavior in NumPy. `seterrobj` is used internally by the other
functions that set error handling behavior (`seterr`, `seterrcall`).
Parameters
@@ -5028,7 +5028,7 @@ add_newdoc('numpy.core.multiarray', 'digitize',
Parameters
----------
x : array_like
- Input array to be binned. Prior to Numpy 1.10.0, this array had to
+ Input array to be binned. Prior to NumPy 1.10.0, this array had to
be 1-dimensional, but can now have any shape.
bins : array_like
Array of bins. It has to be 1-dimensional and monotonic.
@@ -6235,7 +6235,7 @@ add_newdoc('numpy.core.multiarray', 'dtype', ('isbuiltin',
2 if the dtype is for a user-defined numpy type
A user-defined type uses the numpy C-API machinery to extend
numpy to handle a new array type. See
- :ref:`user.user-defined-data-types` in the Numpy manual.
+ :ref:`user.user-defined-data-types` in the NumPy manual.
= ========================================================================
Examples
@@ -7623,7 +7623,7 @@ add_newdoc('numpy.core.numerictypes', 'generic', ('view',
##############################################################################
add_newdoc('numpy.core.numerictypes', 'bool_',
- """Numpy's Boolean type. Character code: ``?``. Alias: bool8""")
+ """NumPy's Boolean type. Character code: ``?``. Alias: bool8""")
add_newdoc('numpy.core.numerictypes', 'complex64',
"""
diff --git a/numpy/core/_internal.py b/numpy/core/_internal.py
index 85be66d06..741c8bb5f 100644
--- a/numpy/core/_internal.py
+++ b/numpy/core/_internal.py
@@ -366,7 +366,7 @@ def _view_is_safe(oldtype, newtype):
return
# Given a string containing a PEP 3118 format specifier,
-# construct a Numpy dtype
+# construct a NumPy dtype
_pep3118_native_map = {
'?': '?',
diff --git a/numpy/core/code_generators/cversions.txt b/numpy/core/code_generators/cversions.txt
index c66947faf..0726fc85e 100644
--- a/numpy/core/code_generators/cversions.txt
+++ b/numpy/core/code_generators/cversions.txt
@@ -22,7 +22,7 @@
# Version 9 (NumPy 1.8) Added interface for partition functions,
# PyArray_NEW_ZEROED, commented out as the hash changed in
-# Numpy 1.9 due to annotation.
+# NumPy 1.9 due to annotation.
#0x00000009 = 327bd114df09c2eb7a0bcc6901e2a3ed
# Version 9 (NumPy 1.9) Added function annotations.
diff --git a/numpy/core/code_generators/generate_numpy_api.py b/numpy/core/code_generators/generate_numpy_api.py
index d376ffd29..d6d6ab21f 100644
--- a/numpy/core/code_generators/generate_numpy_api.py
+++ b/numpy/core/code_generators/generate_numpy_api.py
@@ -151,7 +151,7 @@ void *PyArray_API[] = {
c_api_header = """
===========
-Numpy C-API
+NumPy C-API
===========
"""
diff --git a/numpy/core/code_generators/generate_ufunc_api.py b/numpy/core/code_generators/generate_ufunc_api.py
index bb2ad7854..b6034bbe7 100644
--- a/numpy/core/code_generators/generate_ufunc_api.py
+++ b/numpy/core/code_generators/generate_ufunc_api.py
@@ -204,7 +204,7 @@ def do_generate_api(targets, sources):
fid = open(doc_file, 'w')
fid.write('''
=================
-Numpy Ufunc C-API
+NumPy Ufunc C-API
=================
''')
for func in ufunc_api_list:
diff --git a/numpy/core/code_generators/ufunc_docstrings.py b/numpy/core/code_generators/ufunc_docstrings.py
index a017ca26d..0d506102f 100644
--- a/numpy/core/code_generators/ufunc_docstrings.py
+++ b/numpy/core/code_generators/ufunc_docstrings.py
@@ -1530,7 +1530,7 @@ add_newdoc('numpy.core.umath', 'isfinite',
Not a Number, positive infinity and negative infinity are considered
to be non-finite.
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Also that positive infinity is not equivalent to negative infinity. But
infinity is equivalent to positive infinity. Errors result if the
@@ -1597,7 +1597,7 @@ add_newdoc('numpy.core.umath', 'isinf',
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754).
Errors result if the second argument is supplied when the first
@@ -1650,7 +1650,7 @@ add_newdoc('numpy.core.umath', 'isnan',
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Examples
diff --git a/numpy/core/defchararray.py b/numpy/core/defchararray.py
index e18f912d6..3e01aaa8e 100644
--- a/numpy/core/defchararray.py
+++ b/numpy/core/defchararray.py
@@ -1680,7 +1680,7 @@ class chararray(ndarray):
`dtype` `object_`, `string_` or `unicode_`, and use the free functions
in the `numpy.char` module for fast vectorized string operations.
- Versus a regular Numpy array of type `str` or `unicode`, this
+ Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
@@ -1817,7 +1817,7 @@ class chararray(ndarray):
else:
dtype = string_
- # force itemsize to be a Python long, since using Numpy integer
+ # force itemsize to be a Python long, since using NumPy integer
# types results in itemsize.itemsize being used as the size of
# strings in the new array.
itemsize = long(itemsize)
@@ -2486,7 +2486,7 @@ def array(obj, itemsize=None, copy=True, unicode=None, order=None):
in :mod:`numpy.char <numpy.core.defchararray>` for fast
vectorized string operations instead.
- Versus a regular Numpy array of type `str` or `unicode`, this
+ Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
@@ -2552,12 +2552,12 @@ def array(obj, itemsize=None, copy=True, unicode=None, order=None):
if sys.maxunicode == 0xffff:
# On a narrow Python build, the buffer for Unicode
# strings is UCS2, which doesn't match the buffer for
- # Numpy Unicode types, which is ALWAYS UCS4.
+ # NumPy Unicode types, which is ALWAYS UCS4.
# Therefore, we need to convert the buffer. On Python
# 2.6 and later, we can use the utf_32 codec. Earlier
# versions don't have that codec, so we convert to a
# numerical array that matches the input buffer, and
- # then use Numpy to convert it to UCS4. All of this
+ # then use NumPy to convert it to UCS4. All of this
# should happen in native endianness.
if sys.hexversion >= 0x2060000:
obj = obj.encode('utf_32')
@@ -2593,7 +2593,7 @@ def array(obj, itemsize=None, copy=True, unicode=None, order=None):
itemsize = obj.itemsize
# itemsize is in 8-bit chars, so for Unicode, we need
# to divide by the size of a single Unicode character,
- # which for Numpy is always 4
+ # which for NumPy is always 4
if issubclass(obj.dtype.type, unicode_):
itemsize //= 4
@@ -2642,7 +2642,7 @@ def asarray(obj, itemsize=None, unicode=None, order=None):
Convert the input to a `chararray`, copying the data only if
necessary.
- Versus a regular Numpy array of type `str` or `unicode`, this
+ Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py
index 99173d105..8a56a6e1f 100644
--- a/numpy/core/fromnumeric.py
+++ b/numpy/core/fromnumeric.py
@@ -471,8 +471,8 @@ def swapaxes(a, axis1, axis2):
Returns
-------
a_swapped : ndarray
- For Numpy >= 1.10, if `a` is an ndarray, then a view of `a` is
- returned; otherwise a new array is created. For earlier Numpy
+ For NumPy >= 1.10, if `a` is an ndarray, then a view of `a` is
+ returned; otherwise a new array is created. For earlier NumPy
versions a view of `a` is returned only if the order of the
axes is changed, otherwise the input array is returned.
@@ -1059,7 +1059,7 @@ def searchsorted(a, v, side='left', sorter=None):
-----
Binary search is used to find the required insertion points.
- As of Numpy 1.4.0 `searchsorted` works with real/complex arrays containing
+ As of NumPy 1.4.0 `searchsorted` works with real/complex arrays containing
`nan` values. The enhanced sort order is documented in `sort`.
Examples
@@ -2631,7 +2631,7 @@ def rank(a):
Notes
-----
In the old Numeric package, `rank` was the term used for the number of
- dimensions, but in Numpy `ndim` is used instead.
+ dimensions, but in NumPy `ndim` is used instead.
Examples
--------
@@ -2737,7 +2737,7 @@ def around(a, decimals=0, out=None):
Notes
-----
- For values exactly halfway between rounded decimal values, Numpy
+ For values exactly halfway between rounded decimal values, NumPy
rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
-0.5 and 0.5 round to 0.0, etc. Results may also be surprising due
to the inexact representation of decimal fractions in the IEEE
diff --git a/numpy/core/include/numpy/npy_common.h b/numpy/core/include/numpy/npy_common.h
index baf5549d9..4509f2164 100644
--- a/numpy/core/include/numpy/npy_common.h
+++ b/numpy/core/include/numpy/npy_common.h
@@ -372,21 +372,21 @@ typedef struct {npy_longdouble real, imag;} npy_clongdouble;
#endif
#if NPY_SIZEOF_COMPLEX_DOUBLE != 2 * NPY_SIZEOF_DOUBLE
#error npy_cdouble definition is not compatible with C99 complex definition ! \
- Please contact Numpy maintainers and give detailed information about your \
+ Please contact NumPy maintainers and give detailed information about your \
compiler and platform
#endif
typedef struct { double real, imag; } npy_cdouble;
#if NPY_SIZEOF_COMPLEX_FLOAT != 2 * NPY_SIZEOF_FLOAT
#error npy_cfloat definition is not compatible with C99 complex definition ! \
- Please contact Numpy maintainers and give detailed information about your \
+ Please contact NumPy maintainers and give detailed information about your \
compiler and platform
#endif
typedef struct { float real, imag; } npy_cfloat;
#if NPY_SIZEOF_COMPLEX_LONGDOUBLE != 2 * NPY_SIZEOF_LONGDOUBLE
#error npy_clongdouble definition is not compatible with C99 complex definition ! \
- Please contact Numpy maintainers and give detailed information about your \
+ Please contact NumPy maintainers and give detailed information about your \
compiler and platform
#endif
typedef struct { npy_longdouble real, imag; } npy_clongdouble;
diff --git a/numpy/core/memmap.py b/numpy/core/memmap.py
index 6fa845277..2d15f0a74 100644
--- a/numpy/core/memmap.py
+++ b/numpy/core/memmap.py
@@ -21,7 +21,7 @@ class memmap(ndarray):
"""Create a memory-map to an array stored in a *binary* file on disk.
Memory-mapped files are used for accessing small segments of large files
- on disk, without reading the entire file into memory. Numpy's
+ on disk, without reading the entire file into memory. NumPy's
memmap's are array-like objects. This differs from Python's ``mmap``
module, which uses file-like objects.
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index 81ed0178a..51f8dd034 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -759,7 +759,7 @@ def isfortran(a):
This function is obsolete and, because of changes due to relaxed stride
checking, its return value for the same array may differ for versions
- of Numpy >= 1.10 and previous versions. If you only want to check if an
+ of NumPy >= 1.10 and previous versions. If you only want to check if an
array is Fortran contiguous use ``a.flags.f_contiguous`` instead.
Parameters
@@ -1158,11 +1158,11 @@ def alterdot():
"""
Change `dot`, `vdot`, and `inner` to use accelerated BLAS functions.
- Typically, as a user of Numpy, you do not explicitly call this
- function. If Numpy is built with an accelerated BLAS, this function is
- automatically called when Numpy is imported.
+ Typically, as a user of NumPy, you do not explicitly call this
+ function. If NumPy is built with an accelerated BLAS, this function is
+ automatically called when NumPy is imported.
- When Numpy is built with an accelerated BLAS like ATLAS, these
+ When NumPy is built with an accelerated BLAS like ATLAS, these
functions are replaced to make use of the faster implementations. The
faster implementations only affect float32, float64, complex64, and
complex128 arrays. Furthermore, the BLAS API only includes
@@ -1170,10 +1170,10 @@ def alterdot():
arrays with larger dimensionalities use the built in functions and are
not accelerated.
- .. note:: Deprecated in Numpy 1.10
+ .. note:: Deprecated in NumPy 1.10
The cblas functions have been integrated into the multarray
module and alterdot now longer does anything. It will be
- removed in Numpy 1.11.0.
+ removed in NumPy 1.11.0.
See Also
--------
@@ -1195,10 +1195,10 @@ def restoredot():
an accelerated BLAS, or when being very careful about benchmarking
linear algebra operations.
- .. note:: Deprecated in Numpy 1.10
+ .. note:: Deprecated in NumPy 1.10
The cblas functions have been integrated into the multarray
module and restoredot now longer does anything. It will be
- removed in Numpy 1.11.0.
+ removed in NumPy 1.11.0.
See Also
--------
@@ -1505,8 +1505,8 @@ def rollaxis(a, axis, start=0):
Returns
-------
res : ndarray
- For Numpy >= 1.10 a view of `a` is always returned. For earlier
- Numpy versions a view of `a` is returned only if the order of the
+ For NumPy >= 1.10 a view of `a` is always returned. For earlier
+ NumPy versions a view of `a` is returned only if the order of the
axes is changed, otherwise the input array is returned.
See Also
diff --git a/numpy/core/src/multiarray/buffer.c b/numpy/core/src/multiarray/buffer.c
index 5fa3ba95b..e76d406de 100644
--- a/numpy/core/src/multiarray/buffer.c
+++ b/numpy/core/src/multiarray/buffer.c
@@ -387,7 +387,7 @@ _buffer_format_string(PyArray_Descr *descr, _tmp_string_t *str,
break;
}
case NPY_UNICODE: {
- /* Numpy Unicode is always 4-byte */
+ /* NumPy Unicode is always 4-byte */
char buf[128];
assert(descr->elsize % 4 == 0);
PyOS_snprintf(buf, sizeof(buf), "%dw", descr->elsize / 4);
diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c
index 634690648..cb8fea213 100644
--- a/numpy/core/src/multiarray/methods.c
+++ b/numpy/core/src/multiarray/methods.c
@@ -1646,7 +1646,7 @@ array_setstate(PyArrayObject *self, PyObject *args)
Py_INCREF(rawdata);
#if defined(NPY_PY3K)
- /* Backward compatibility with Python 2 Numpy pickles */
+ /* Backward compatibility with Python 2 NumPy pickles */
if (PyUnicode_Check(rawdata)) {
PyObject *tmp;
tmp = PyUnicode_AsLatin1String(rawdata);
diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c
index 39acf87fa..7c3c95b24 100644
--- a/numpy/core/src/multiarray/multiarraymodule.c
+++ b/numpy/core/src/multiarray/multiarraymodule.c
@@ -1895,7 +1895,7 @@ array_scalar(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *kwds)
}
else {
#if defined(NPY_PY3K)
- /* Backward compatibility with Python 2 Numpy pickles */
+ /* Backward compatibility with Python 2 NumPy pickles */
if (PyUnicode_Check(obj)) {
tmpobj = PyUnicode_AsLatin1String(obj);
obj = tmpobj;
diff --git a/numpy/core/src/umath/struct_ufunc_test.c.src b/numpy/core/src/umath/struct_ufunc_test.c.src
index de8838b90..9a6318f47 100644
--- a/numpy/core/src/umath/struct_ufunc_test.c.src
+++ b/numpy/core/src/umath/struct_ufunc_test.c.src
@@ -10,7 +10,7 @@
/*
* struct_ufunc_test.c
* This is the C code for creating your own
- * Numpy ufunc for a structured array dtype.
+ * NumPy ufunc for a structured array dtype.
*
* Details explaining the Python-C API can be found under
* 'Extending and Embedding' and 'Python/C API' at
diff --git a/numpy/core/src/umath/test_rational.c.src b/numpy/core/src/umath/test_rational.c.src
index b0c06e1ac..01ded5bbd 100644
--- a/numpy/core/src/umath/test_rational.c.src
+++ b/numpy/core/src/umath/test_rational.c.src
@@ -731,7 +731,7 @@ static PyTypeObject PyRational_Type = {
0, /* tp_version_tag */
};
-/* Numpy support */
+/* NumPy support */
static PyObject*
npyrational_getitem(void* data, void* arr) {
diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c
index 28c59f890..ebe03b360 100644
--- a/numpy/core/src/umath/ufunc_object.c
+++ b/numpy/core/src/umath/ufunc_object.c
@@ -2047,7 +2047,7 @@ PyUFunc_GeneralizedFunction(PyUFuncObject *ufunc,
* Validate the core dimensions of all the operands, and collect all of
* the labelled core dimensions into 'core_dim_sizes'.
*
- * The behavior has been changed in Numpy 1.10.0, and the following
+ * The behavior has been changed in NumPy 1.10.0, and the following
* requirements must be fulfilled or an error will be raised:
* * Arguments, both input and output, must have at least as many
* dimensions as the corresponding number of core dimensions. In
diff --git a/numpy/core/tests/test_indexing.py b/numpy/core/tests/test_indexing.py
index dcce2c300..f16756221 100644
--- a/numpy/core/tests/test_indexing.py
+++ b/numpy/core/tests/test_indexing.py
@@ -296,7 +296,7 @@ class TestIndexing(TestCase):
assert_raises(IndexError, a.__getitem__, (np.array([0]),) * 32)
def test_scalar_array_bool(self):
- # Numpy bools can be used as boolean index (python ones as of yet not)
+ # NumPy bools can be used as boolean index (python ones as of yet not)
a = np.array(1)
assert_equal(a[np.bool_(True)], a[np.array(True)])
assert_equal(a[np.bool_(False)], a[np.array(False)])
@@ -1133,7 +1133,7 @@ class TestFloatNonIntegerArgument(TestCase):
assert_raises(TypeError, np.take, a, [0], np.float64(1.))
def test_non_integer_sequence_multiplication(self):
- # Numpy scalar sequence multiply should not work with non-integers
+ # NumPy scalar sequence multiply should not work with non-integers
def mult(a, b):
return a * b
diff --git a/numpy/core/tests/test_regression.py b/numpy/core/tests/test_regression.py
index 5966ff688..51d0447e3 100644
--- a/numpy/core/tests/test_regression.py
+++ b/numpy/core/tests/test_regression.py
@@ -1734,7 +1734,7 @@ class TestRegression(TestCase):
b = np.array(a, dtype=object)
a[()] = b
assert_raises(TypeError, int, a)
- # Numpy has no tp_traverse currently, so circular references
+ # NumPy has no tp_traverse currently, so circular references
# cannot be detected. So resolve it:
a[()] = 0
@@ -1983,7 +1983,7 @@ class TestRegression(TestCase):
def test_string_truncation_ucs2(self):
# Ticket #2081. Python compiled with two byte unicode
# can lead to truncation if itemsize is not properly
- # adjusted for Numpy's four byte unicode.
+ # adjusted for NumPy's four byte unicode.
if sys.version_info[0] >= 3:
a = np.array(['abcd'])
else:
diff --git a/numpy/ctypeslib.py b/numpy/ctypeslib.py
index bf34ce38b..73328224e 100644
--- a/numpy/ctypeslib.py
+++ b/numpy/ctypeslib.py
@@ -95,7 +95,7 @@ else:
But there are cross-platform considerations, such as library file extensions,
plus the fact Windows will just load the first library it finds with that name.
- Numpy supplies the load_library function as a convenience.
+ NumPy supplies the load_library function as a convenience.
Parameters
----------
diff --git a/numpy/doc/basics.py b/numpy/doc/basics.py
index 745bff15a..dac236644 100644
--- a/numpy/doc/basics.py
+++ b/numpy/doc/basics.py
@@ -6,7 +6,7 @@ Array basics
Array types and conversions between types
=========================================
-Numpy supports a much greater variety of numerical types than Python does.
+NumPy supports a much greater variety of numerical types than Python does.
This section shows which are available, and how to modify an array's data-type.
========== ==========================================================
@@ -43,7 +43,7 @@ complex128 Complex number, represented by two 64-bit floats (real
Additionally to ``intc`` the platform dependent C integer types ``short``,
``long``, ``longlong`` and their unsigned versions are defined.
-Numpy numerical types are instances of ``dtype`` (data-type) objects, each
+NumPy numerical types are instances of ``dtype`` (data-type) objects, each
having unique characteristics. Once you have imported NumPy using
::
@@ -124,7 +124,7 @@ properties of the type, such as whether it is an integer::
Array Scalars
=============
-Numpy generally returns elements of arrays as array scalars (a scalar
+NumPy generally returns elements of arrays as array scalars (a scalar
with an associated dtype). Array scalars differ from Python scalars, but
for the most part they can be used interchangeably (the primary
exception is for versions of Python older than v2.x, where integer array
@@ -152,12 +152,12 @@ is possible in numpy depends on the hardware and on the development
environment: specifically, x86 machines provide hardware floating-point
with 80-bit precision, and while most C compilers provide this as their
``long double`` type, MSVC (standard for Windows builds) makes
-``long double`` identical to ``double`` (64 bits). Numpy makes the
+``long double`` identical to ``double`` (64 bits). NumPy makes the
compiler's ``long double`` available as ``np.longdouble`` (and
``np.clongdouble`` for the complex numbers). You can find out what your
numpy provides with``np.finfo(np.longdouble)``.
-Numpy does not provide a dtype with more precision than C
+NumPy does not provide a dtype with more precision than C
``long double``s; in particular, the 128-bit IEEE quad precision
data type (FORTRAN's ``REAL*16``) is not available.
diff --git a/numpy/doc/constants.py b/numpy/doc/constants.py
index 36f94d307..f9fccabfb 100644
--- a/numpy/doc/constants.py
+++ b/numpy/doc/constants.py
@@ -3,7 +3,7 @@
Constants
=========
-Numpy includes several constants:
+NumPy includes several constants:
%(constant_list)s
"""
@@ -82,7 +82,7 @@ add_newdoc('numpy', 'NINF',
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Also that positive infinity is not equivalent to negative infinity. But
infinity is equivalent to positive infinity.
@@ -122,7 +122,7 @@ add_newdoc('numpy', 'NZERO',
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). Negative zero is considered to be a finite number.
Examples
@@ -193,7 +193,7 @@ add_newdoc('numpy', 'PZERO',
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). Positive zero is considered to be a finite number.
Examples
@@ -253,7 +253,7 @@ add_newdoc('numpy', 'inf',
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Also that positive infinity is not equivalent to negative infinity. But
infinity is equivalent to positive infinity.
@@ -298,7 +298,7 @@ add_newdoc('numpy', 'nan',
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
`NaN` and `NAN` are aliases of `nan`.
diff --git a/numpy/doc/creation.py b/numpy/doc/creation.py
index b10d45d48..8480858d4 100644
--- a/numpy/doc/creation.py
+++ b/numpy/doc/creation.py
@@ -19,7 +19,7 @@ This section will not cover means of replicating, joining, or otherwise
expanding or mutating existing arrays. Nor will it cover creating object
arrays or structured arrays. Both of those are covered in their own sections.
-Converting Python array_like Objects to Numpy Arrays
+Converting Python array_like Objects to NumPy Arrays
====================================================
In general, numerical data arranged in an array-like structure in Python can
@@ -38,10 +38,10 @@ Examples: ::
and types
>>> x = np.array([[ 1.+0.j, 2.+0.j], [ 0.+0.j, 0.+0.j], [ 1.+1.j, 3.+0.j]])
-Intrinsic Numpy Array Creation
+Intrinsic NumPy Array Creation
==============================
-Numpy has built-in functions for creating arrays from scratch:
+NumPy has built-in functions for creating arrays from scratch:
zeros(shape) will create an array filled with 0 values with the specified
shape. The default dtype is float64.
diff --git a/numpy/doc/glossary.py b/numpy/doc/glossary.py
index 91cd74651..97b7b3362 100644
--- a/numpy/doc/glossary.py
+++ b/numpy/doc/glossary.py
@@ -305,7 +305,7 @@ Glossary
[1, 2, 3, 4, 5, 6]
Row-major order is also known as the C order, as the C programming
- language uses it. New Numpy arrays are by default in row-major order.
+ language uses it. New NumPy arrays are by default in row-major order.
self
Often seen in method signatures, ``self`` refers to the instance
diff --git a/numpy/doc/indexing.py b/numpy/doc/indexing.py
index 33e9de3c4..3e3e95641 100644
--- a/numpy/doc/indexing.py
+++ b/numpy/doc/indexing.py
@@ -65,7 +65,7 @@ inefficient as a new temporary array is created after the first index
that is subsequently indexed by 2.
Note to those used to IDL or Fortran memory order as it relates to
-indexing. Numpy uses C-order indexing. That means that the last
+indexing. NumPy uses C-order indexing. That means that the last
index usually represents the most rapidly changing memory location,
unlike Fortran or IDL, where the first index represents the most
rapidly changing location in memory. This difference represents a
@@ -111,7 +111,7 @@ specific function.
Index arrays
============
-Numpy arrays may be indexed with other arrays (or any other sequence-
+NumPy arrays may be indexed with other arrays (or any other sequence-
like object that can be converted to an array, such as lists, with the
exception of tuples; see the end of this document for why this is). The
use of index arrays ranges from simple, straightforward cases to
diff --git a/numpy/doc/internals.py b/numpy/doc/internals.py
index c25872bc0..a14fee7c2 100644
--- a/numpy/doc/internals.py
+++ b/numpy/doc/internals.py
@@ -6,13 +6,13 @@ Array Internals
Internal organization of numpy arrays
=====================================
-It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book "Guide to Numpy".
+It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book "Guide to NumPy".
-Numpy arrays consist of two major components, the raw array data (from now on,
+NumPy arrays consist of two major components, the raw array data (from now on,
referred to as the data buffer), and the information about the raw array data.
The data buffer is typically what people think of as arrays in C or Fortran,
a contiguous (and fixed) block of memory containing fixed sized data items.
-Numpy also contains a significant set of data that describes how to interpret
+NumPy also contains a significant set of data that describes how to interpret
the data in the data buffer. This extra information contains (among other things):
1) The basic data element's size in bytes
@@ -109,7 +109,7 @@ The situation with
numpy makes this issue yet more complicated. The internal machinery of numpy
arrays is flexible enough to accept any ordering of indices. One can simply
reorder indices by manipulating the internal stride information for arrays
-without reordering the data at all. Numpy will know how to map the new index
+without reordering the data at all. NumPy will know how to map the new index
order to the data without moving the data.
So if this is true, why not choose
diff --git a/numpy/doc/structured_arrays.py b/numpy/doc/structured_arrays.py
index 1135c1395..5289e6d0b 100644
--- a/numpy/doc/structured_arrays.py
+++ b/numpy/doc/structured_arrays.py
@@ -6,7 +6,7 @@ Structured Arrays
Introduction
============
-Numpy provides powerful capabilities to create arrays of structured datatype.
+NumPy provides powerful capabilities to create arrays of structured datatype.
These arrays permit one to manipulate the data by named fields. A simple
example will show what is meant.: ::
@@ -79,7 +79,7 @@ The type specifiers can take 4 different forms: ::
Don't use these in new code!
d) Single character type specifiers (e.g H for unsigned short ints).
Avoid using these unless you must. Details can be found in the
- Numpy book
+ NumPy book
These different styles can be mixed within the same string (but why would you
want to do that?). Furthermore, each type specifier can be prefixed
diff --git a/numpy/doc/subclassing.py b/numpy/doc/subclassing.py
index 40522bb9c..9a339430b 100644
--- a/numpy/doc/subclassing.py
+++ b/numpy/doc/subclassing.py
@@ -97,7 +97,7 @@ Implications for subclassing
If we subclass ndarray, we need to deal not only with explicit
construction of our array type, but also :ref:`view-casting` or
-:ref:`new-from-template`. Numpy has the machinery to do this, and this
+:ref:`new-from-template`. NumPy has the machinery to do this, and this
machinery that makes subclassing slightly non-standard.
There are two aspects to the machinery that ndarray uses to support
@@ -523,7 +523,7 @@ Extra gotchas - custom ``__del__`` methods and ndarray.base
One of the problems that ndarray solves is keeping track of memory
ownership of ndarrays and their views. Consider the case where we have
created an ndarray, ``arr`` and have taken a slice with ``v = arr[1:]``.
-The two objects are looking at the same memory. Numpy keeps track of
+The two objects are looking at the same memory. NumPy keeps track of
where the data came from for a particular array or view, with the
``base`` attribute:
diff --git a/numpy/doc/ufuncs.py b/numpy/doc/ufuncs.py
index 0132202ad..a112e559c 100644
--- a/numpy/doc/ufuncs.py
+++ b/numpy/doc/ufuncs.py
@@ -6,7 +6,7 @@ Universal Functions
Ufuncs are, generally speaking, mathematical functions or operations that are
applied element-by-element to the contents of an array. That is, the result
in each output array element only depends on the value in the corresponding
-input array (or arrays) and on no other array elements. Numpy comes with a
+input array (or arrays) and on no other array elements. NumPy comes with a
large suite of ufuncs, and scipy extends that suite substantially. The simplest
example is the addition operator: ::
diff --git a/numpy/dual.py b/numpy/dual.py
index 1517d8421..8b91da262 100644
--- a/numpy/dual.py
+++ b/numpy/dual.py
@@ -5,7 +5,7 @@ Scipy_ can be built to use accelerated or otherwise improved libraries
for FFTs, linear algebra, and special functions. This module allows
developers to transparently support these accelerated functions when
scipy is available but still support users who have only installed
-Numpy.
+NumPy.
.. _Scipy : http://www.scipy.org
diff --git a/numpy/lib/_version.py b/numpy/lib/_version.py
index 54b9c1dc7..0019c5607 100644
--- a/numpy/lib/_version.py
+++ b/numpy/lib/_version.py
@@ -1,4 +1,4 @@
-"""Utility to compare (Numpy) version strings.
+"""Utility to compare (NumPy) version strings.
The NumpyVersion class allows properly comparing numpy version strings.
The LooseVersion and StrictVersion classes that distutils provides don't
@@ -18,7 +18,7 @@ __all__ = ['NumpyVersion']
class NumpyVersion():
"""Parse and compare numpy version strings.
- Numpy has the following versioning scheme (numbers given are examples; they
+ NumPy has the following versioning scheme (numbers given are examples; they
can be > 9) in principle):
- Released version: '1.8.0', '1.8.1', etc.
@@ -40,7 +40,7 @@ class NumpyVersion():
Parameters
----------
vstring : str
- Numpy version string (``np.__version__``).
+ NumPy version string (``np.__version__``).
Examples
--------
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index b74e04028..11cfb2c99 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -489,8 +489,8 @@ def histogram(a, bins=10, range=None, normed=False, weights=None,
based on the actual data within `range`, the bin count will fill
the entire range including portions containing no data.
normed : bool, optional
- This keyword is deprecated in Numpy 1.6 due to confusing/buggy
- behavior. It will be removed in Numpy 2.0. Use the ``density``
+ This keyword is deprecated in NumPy 1.6 due to confusing/buggy
+ behavior. It will be removed in NumPy 2.0. Use the ``density``
keyword instead. If ``False``, the result will contain the
number of samples in each bin. If ``True``, the result is the
value of the probability *density* function at the bin,
@@ -809,7 +809,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None,
else:
return n, bins
else:
- # deprecated, buggy behavior. Remove for Numpy 2.0
+ # deprecated, buggy behavior. Remove for NumPy 2.0
if normed:
db = array(np.diff(bins), float)
return n/(n*db).sum(), bins
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index 7f7aea9bc..ab88c6a37 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -204,7 +204,7 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue):
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.
@@ -311,7 +311,7 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue):
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.
@@ -453,7 +453,7 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
Return the sum of array elements over a given axis treating Not a
Numbers (NaNs) as zero.
- In Numpy versions <= 1.8 Nan is returned for slices that are all-NaN or
+ In NumPy versions <= 1.8 Nan is returned for slices that are all-NaN or
empty. In later versions zero is returned.
Parameters
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index 0b2fdfaba..376d34b91 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -376,9 +376,9 @@ def load(file, mmap_mode=None, allow_pickle=True, fix_imports=True,
if encoding not in ('ASCII', 'latin1', 'bytes'):
# The 'encoding' value for pickle also affects what encoding
- # the serialized binary data of Numpy arrays is loaded
+ # the serialized binary data of NumPy arrays is loaded
# in. Pickle does not pass on the encoding information to
- # Numpy. The unpickling code in numpy.core.multiarray is
+ # NumPy. The unpickling code in numpy.core.multiarray is
# written to assume that unicode data appearing where binary
# should be is in 'latin1'. 'bytes' is also safe, as is 'ASCII'.
#
@@ -466,7 +466,7 @@ def save(file, arr, allow_pickle=True, fix_imports=True):
Notes
-----
For a description of the ``.npy`` format, see the module docstring
- of `numpy.lib.format` or the Numpy Enhancement Proposal
+ of `numpy.lib.format` or the NumPy Enhancement Proposal
http://docs.scipy.org/doc/numpy/neps/npy-format.html
Examples
@@ -552,7 +552,7 @@ def savez(file, *args, **kwds):
variables they contain. The archive is not compressed and each file
in the archive contains one variable in ``.npy`` format. For a
description of the ``.npy`` format, see `numpy.lib.format` or the
- Numpy Enhancement Proposal
+ NumPy Enhancement Proposal
http://docs.scipy.org/doc/numpy/neps/npy-format.html
When opening the saved ``.npz`` file with `load` a `NpzFile` object is
@@ -1428,7 +1428,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
References
----------
- .. [1] Numpy User Guide, section `I/O with Numpy
+ .. [1] NumPy User Guide, section `I/O with NumPy
<http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.
Examples
diff --git a/numpy/lib/type_check.py b/numpy/lib/type_check.py
index f620d49d5..07f4463c5 100644
--- a/numpy/lib/type_check.py
+++ b/numpy/lib/type_check.py
@@ -350,7 +350,7 @@ def nan_to_num(x):
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
diff --git a/numpy/lib/ufunclike.py b/numpy/lib/ufunclike.py
index e91f64d0e..b6c017b96 100644
--- a/numpy/lib/ufunclike.py
+++ b/numpy/lib/ufunclike.py
@@ -82,7 +82,7 @@ def isposinf(x, y=None):
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754).
Errors result if the second argument is also supplied when `x` is a
@@ -145,7 +145,7 @@ def isneginf(x, y=None):
Notes
-----
- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+ NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754).
Errors result if the second argument is also supplied when x is a scalar
diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py
index 133704d13..97b93cace 100644
--- a/numpy/lib/utils.py
+++ b/numpy/lib/utils.py
@@ -241,10 +241,10 @@ def byte_bounds(a):
def who(vardict=None):
"""
- Print the Numpy arrays in the given dictionary.
+ Print the NumPy arrays in the given dictionary.
If there is no dictionary passed in or `vardict` is None then returns
- Numpy arrays in the globals() dictionary (all Numpy arrays in the
+ NumPy arrays in the globals() dictionary (all NumPy arrays in the
namespace).
Parameters
@@ -646,7 +646,7 @@ def info(object=None, maxwidth=76, output=sys.stdout, toplevel='numpy'):
def source(object, output=sys.stdout):
"""
- Print or write to a file the source code for a Numpy object.
+ Print or write to a file the source code for a NumPy object.
The source code is only returned for objects written in Python. Many
functions and classes are defined in C and will therefore not return
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py
index a05b0ee8e..15c72fa02 100644
--- a/numpy/linalg/linalg.py
+++ b/numpy/linalg/linalg.py
@@ -677,7 +677,7 @@ def qr(a, mode='reduced'):
`a` is of type `matrix`, all the return values will be matrices too.
New 'reduced', 'complete', and 'raw' options for mode were added in
- Numpy 1.8 and the old option 'full' was made an alias of 'reduced'. In
+ NumPy 1.8 and the old option 'full' was made an alias of 'reduced'. In
addition the options 'full' and 'economic' were deprecated. Because
'full' was the previous default and 'reduced' is the new default,
backward compatibility can be maintained by letting `mode` default.
diff --git a/numpy/polynomial/_polybase.py b/numpy/polynomial/_polybase.py
index 09190ce4e..be5cd3888 100644
--- a/numpy/polynomial/_polybase.py
+++ b/numpy/polynomial/_polybase.py
@@ -742,7 +742,7 @@ class ABCPolyBase(object):
deg : int or 1-D array_like
Degree(s) of the fitting polynomials. If `deg` is a single integer
all terms up to and including the `deg`'th term are included in the
- fit. For Numpy versions >= 1.11 a list of integers specifying the
+ fit. For NumPy versions >= 1.11 a list of integers specifying the
degrees of the terms to include may be used instead.
domain : {None, [beg, end], []}, optional
Domain to use for the returned series. If ``None``,
diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py
index dd346fbda..f3f491c29 100644
--- a/numpy/polynomial/chebyshev.py
+++ b/numpy/polynomial/chebyshev.py
@@ -1620,7 +1620,7 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None):
deg : int or 1-D array_like
Degree(s) of the fitting polynomials. If `deg` is a single integer
all terms up to and including the `deg`'th term are included in the
- fit. For Numpy versions >= 1.11 a list of integers specifying the
+ fit. For NumPy versions >= 1.11 a list of integers specifying the
degrees of the terms to include may be used instead.
rcond : float, optional
Relative condition number of the fit. Singular values smaller than
diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py
index 06d68668f..3172b41d0 100644
--- a/numpy/polynomial/hermite.py
+++ b/numpy/polynomial/hermite.py
@@ -1391,7 +1391,7 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None):
deg : int or 1-D array_like
Degree(s) of the fitting polynomials. If `deg` is a single integer
all terms up to and including the `deg`'th term are included in the
- fit. For Numpy versions >= 1.11 a list of integers specifying the
+ fit. For NumPy versions >= 1.11 a list of integers specifying the
degrees of the terms to include may be used instead.
rcond : float, optional
Relative condition number of the fit. Singular values smaller than
diff --git a/numpy/polynomial/hermite_e.py b/numpy/polynomial/hermite_e.py
index a846b167c..7cc85de5d 100644
--- a/numpy/polynomial/hermite_e.py
+++ b/numpy/polynomial/hermite_e.py
@@ -1388,7 +1388,7 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None):
deg : int or 1-D array_like
Degree(s) of the fitting polynomials. If `deg` is a single integer
all terms up to and including the `deg`'th term are included in the
- fit. For Numpy versions >= 1.11 a list of integers specifying the
+ fit. For NumPy versions >= 1.11 a list of integers specifying the
degrees of the terms to include may be used instead.
rcond : float, optional
Relative condition number of the fit. Singular values smaller than
diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py
index 60728c5f9..6aa086356 100644
--- a/numpy/polynomial/laguerre.py
+++ b/numpy/polynomial/laguerre.py
@@ -1390,7 +1390,7 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None):
deg : int or 1-D array_like
Degree(s) of the fitting polynomials. If `deg` is a single integer
all terms up to and including the `deg`'th term are included in the
- fit. For Numpy versions >= 1.11 a list of integers specifying the
+ fit. For NumPy versions >= 1.11 a list of integers specifying the
degrees of the terms to include may be used instead.
rcond : float, optional
Relative condition number of the fit. Singular values smaller than
diff --git a/numpy/polynomial/legendre.py b/numpy/polynomial/legendre.py
index ec6c2f8bf..29e9a0897 100644
--- a/numpy/polynomial/legendre.py
+++ b/numpy/polynomial/legendre.py
@@ -1421,7 +1421,7 @@ def legfit(x, y, deg, rcond=None, full=False, w=None):
deg : int or 1-D array_like
Degree(s) of the fitting polynomials. If `deg` is a single integer
all terms up to and including the `deg`'th term are included in the
- fit. For Numpy versions >= 1.11 a list of integers specifying the
+ fit. For NumPy versions >= 1.11 a list of integers specifying the
degrees of the terms to include may be used instead.
rcond : float, optional
Relative condition number of the fit. Singular values smaller than
diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py
index 3ed6b67a4..edefeed6c 100644
--- a/numpy/polynomial/polynomial.py
+++ b/numpy/polynomial/polynomial.py
@@ -1309,7 +1309,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None):
deg : int or 1-D array_like
Degree(s) of the fitting polynomials. If `deg` is a single integer
all terms up to and including the `deg`'th term are included in the
- fit. For Numpy versions >= 1.11 a list of integers specifying the
+ fit. For NumPy versions >= 1.11 a list of integers specifying the
degrees of the terms to include may be used instead.
rcond : float, optional
Relative condition number of the fit. Singular values smaller
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index abf7a4102..24c8bd951 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -2547,7 +2547,7 @@ cdef class RandomState:
Parameters
----------
df : int or array_like of ints
- Degrees of freedom, should be > 0 as of Numpy 1.10,
+ Degrees of freedom, should be > 0 as of NumPy 1.10,
should be > 1 for earlier versions.
nonc : float or array_like of floats
Non-centrality, should be non-negative.