diff options
| -rw-r--r-- | doc/source/about.rst | 5 | ||||
| -rw-r--r-- | doc/source/reference/routines.numarray.rst | 7 | ||||
| -rw-r--r-- | doc/source/reference/routines.oldnumeric.rst | 9 | ||||
| -rw-r--r-- | numpy/lib/function_base.py | 16 |
4 files changed, 9 insertions, 28 deletions
diff --git a/doc/source/about.rst b/doc/source/about.rst index cbddf9f33..0614c90af 100644 --- a/doc/source/about.rst +++ b/doc/source/about.rst @@ -18,10 +18,7 @@ data types can be defined. This allows *NumPy* to seamlessly and speedily integrate with a wide variety of databases. NumPy is a successor for two earlier scientific Python libraries: -NumPy derives from the :doc:`old Numerice <reference/routines.oldnumeric>` code -base and can be used as a replacement for *Numeric*. It also adds the features -introduced by :doc:`Numarray <reference/routines.numarray>` and can also be -used to replace *Numarray*. +Numeric and Numarray. NumPy community --------------- diff --git a/doc/source/reference/routines.numarray.rst b/doc/source/reference/routines.numarray.rst deleted file mode 100644 index 97251d293..000000000 --- a/doc/source/reference/routines.numarray.rst +++ /dev/null @@ -1,7 +0,0 @@ -:orphan: - -********************** -Numarray compatibility -********************** - -The numarray module was removed in NumPy 1.9.0. diff --git a/doc/source/reference/routines.oldnumeric.rst b/doc/source/reference/routines.oldnumeric.rst deleted file mode 100644 index 1a6218aac..000000000 --- a/doc/source/reference/routines.oldnumeric.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -************************* -Old Numeric compatibility -************************* - -.. currentmodule:: numpy - -The oldnumeric module was removed in NumPy 1.9.0. diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index c06065ba6..07c3f1478 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -3398,9 +3398,9 @@ def _median(a, axis=None, out=None, overwrite_input=False): def percentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False): """ - Compute the qth percentile of the data along the specified axis. + Compute the q-th percentile of the data along the specified axis. - Returns the qth percentile(s) of the array elements. + Returns the q-th percentile(s) of the array elements. Parameters ---------- @@ -3467,7 +3467,7 @@ def percentile(a, q, axis=None, out=None, Notes ----- - Given a vector ``V`` of length ``N``, the ``q``-th percentile of + Given a vector ``V`` of length ``N``, the q-th percentile of ``V`` is the value ``q/100`` of the way from the minimum to the maximum in a sorted copy of ``V``. The values and distances of the two nearest neighbors as well as the `interpolation` parameter @@ -3543,7 +3543,7 @@ def percentile(a, q, axis=None, out=None, def quantile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False): """ - Compute the qth quantile of the data along the specified axis. + Compute the q-th quantile of the data along the specified axis. ..versionadded:: 1.15.0 Parameters @@ -3603,7 +3603,7 @@ def quantile(a, q, axis=None, out=None, Notes ----- - Given a vector ``V`` of length ``N``, the ``q``-th quantile of + Given a vector ``V`` of length ``N``, the q-th quantile of ``V`` is the value ``q`` of the way from the minimum to the maximum in a sorted copy of ``V``. The values and distances of the two nearest neighbors as well as the `interpolation` parameter @@ -3721,7 +3721,7 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, indices = concatenate((indices, [-1])) ap.partition(indices, axis=axis) - # ensure axis with qth is first + # ensure axis with q-th is first ap = np.moveaxis(ap, axis, 0) axis = 0 @@ -3754,7 +3754,7 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, ap.partition(concatenate((indices_below, indices_above)), axis=axis) - # ensure axis with qth is first + # ensure axis with q-th is first ap = np.moveaxis(ap, axis, 0) weights_below = np.moveaxis(weights_below, axis, 0) weights_above = np.moveaxis(weights_above, axis, 0) @@ -3768,7 +3768,7 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, x1 = take(ap, indices_below, axis=axis) * weights_below x2 = take(ap, indices_above, axis=axis) * weights_above - # ensure axis with qth is first + # ensure axis with q-th is first x1 = np.moveaxis(x1, axis, 0) x2 = np.moveaxis(x2, axis, 0) |