16 files changed, 58 insertions, 37 deletions
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 455e9748b..072a3b44e 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -39,7 +39,7 @@ source_suffix = '.rst'
 
 # General substitutions.
 project = 'NumPy'
-copyright = '2008-2018, The SciPy community'
+copyright = '2008-2019, The SciPy community'
 
 # The default replacements for |version| and |release|, also used in various
 # other places throughout the built documents.
diff --git a/doc/source/dev/development_environment.rst b/doc/source/dev/development_environment.rst
index aa4326f63..f9b438bfd 100644
--- a/doc/source/dev/development_environment.rst
+++ b/doc/source/dev/development_environment.rst
@@ -8,7 +8,9 @@ Recommended development setup
 
 Since NumPy contains parts written in C and Cython that need to be
 compiled before use, make sure you have the necessary compilers and Python
-development headers installed - see :ref:`building-from-source`.
+development headers installed - see :ref:`building-from-source`. Building
+NumPy as of version ``1.17`` requires a C99 compliant compiler. For
+some older compilers this may require ``export CFLAGS='-std=c99'``.
 
 Having compiled code also means that importing NumPy from the development
 sources needs some additional steps, which are explained below.  For the rest
diff --git a/doc/source/reference/alignment.rst b/doc/source/reference/alignment.rst
index c749972b4..ebc8f353c 100644
--- a/doc/source/reference/alignment.rst
+++ b/doc/source/reference/alignment.rst
@@ -34,6 +34,14 @@ datatype is implemented as ``struct { float real, imag; }``. This has "true"
 alignment of 4 and "uint" alignment of 8 (equal to the true alignment of
 ``uint64``).
 
+Some cases where uint and true alignment are different (default gcc linux):
+   arch     type        true-aln    uint-aln
+   ----     ----        --------    --------
+   x86_64   complex64          4           8
+   x86_64   float128          16           8
+   x86      float96            4           -
+
+
 Variables in Numpy which control and describe alignment
 -------------------------------------------------------
 
@@ -82,17 +90,15 @@ Here is how the variables above are used:
     appropriate N. Otherwise numpy copies by doing ``memcpy(dst, src, N)``.
  5. Nditer code: Since this often calls the strided copy code, it must
     check for "uint alignment".
- 6. Cast code: if the array is "uint aligned" this will essentially do
-    ``*dst = CASTFUNC(*src)``. If not, it does
+ 6. Cast code: This checks for "true" alignment, as it does
+    ``*dst = CASTFUNC(*src)`` if aligned. Otherwise, it does
     ``memmove(srcval, src); dstval = CASTFUNC(srcval); memmove(dst, dstval)``
     where dstval/srcval are aligned.
 
-Note that in principle, only "true alignment" is required for casting code.
-However, because the casting code and copy code are deeply intertwined they
-both use "uint" alignment. This should be safe assuming uint alignment is
-always larger than true alignment, though it can cause unnecessary buffering if
-an array is "true aligned" but not "uint aligned". If there is ever a big
-rewrite of this code it would be good to allow them to use different
-alignments.
+Note that the strided-copy and strided-cast code are deeply intertwined and so
+any arrays being processed by them must be both uint and true aligned, even
+though the copy-code only needs uint alignment and the cast code only true
+alignment.  If there is ever a big rewrite of this code it would be good to
+allow them to use different alignments.
 
 
diff --git a/doc/source/reference/arrays.indexing.rst b/doc/source/reference/arrays.indexing.rst
index 62d36e28c..3a319ecca 100644
--- a/doc/source/reference/arrays.indexing.rst
+++ b/doc/source/reference/arrays.indexing.rst
@@ -111,9 +111,10 @@ concepts to remember include:
               [5],
               [6]]])
 
-- :const:`Ellipsis` expand to the number of ``:`` objects needed to
-  make a selection tuple of the same length as ``x.ndim``. There may
-  only be a single ellipsis present.
+- :const:`Ellipsis` expands to the number of ``:`` objects needed for the
+  selection tuple to index all dimensions. In most cases, this means that
+  length of the expanded selection tuple is ``x.ndim``. There may only be a
+  single ellipsis present.
 
   .. admonition:: Example
 
@@ -513,14 +514,10 @@ only the part of the data in the specified field. Also
 :ref:`record array <arrays.classes.rec>` scalars can be "indexed" this way.
 
 Indexing into a structured array can also be done with a list of field names,
-*e.g.* ``x[['field-name1','field-name2']]``. Currently this returns a new
-array containing a copy of the values in the fields specified in the list.
-As of NumPy 1.7, returning a copy is being deprecated in favor of returning
-a view. A copy will continue to be returned for now, but a FutureWarning
-will be issued when writing to the copy. If you depend on the current
-behavior, then we suggest copying the returned array explicitly, i.e. use
-x[['field-name1','field-name2']].copy(). This will work with both past and
-future versions of NumPy.
+*e.g.* ``x[['field-name1','field-name2']]``. As of NumPy 1.16 this returns a
+view containing only those fields. In older versions of numpy it returned a
+copy. See the user guide section on :ref:`structured_arrays` for more
+information on multifield indexing.
 
 If the accessed field is a sub-array, the dimensions of the sub-array
 are appended to the shape of the result.
diff --git a/doc/source/reference/c-api.array.rst b/doc/source/reference/c-api.array.rst
index 76aa680ae..7c298e118 100644
--- a/doc/source/reference/c-api.array.rst
+++ b/doc/source/reference/c-api.array.rst
@@ -307,10 +307,10 @@ From scratch
 .. c:function:: PyObject* PyArray_SimpleNewFromDescr( \
         int nd, npy_intp* dims, PyArray_Descr* descr)
 
-    This function steals a reference to *descr* if it is not NULL.
+    This function steals a reference to *descr*.
 
-    Create a new array with the provided data-type descriptor, *descr*
-    , of the shape determined by *nd* and *dims*.
+    Create a new array with the provided data-type descriptor, *descr*,
+    of the shape determined by *nd* and *dims*.
 
 .. c:function:: PyArray_FILLWBYTE(PyObject* obj, int val)
 
@@ -1904,10 +1904,10 @@ Item selection and manipulation
         all values are clipped to the region [0, len(*op*) ).
 
 
-.. c:function:: PyObject* PyArray_Sort(PyArrayObject* self, int axis)
+.. c:function:: PyObject* PyArray_Sort(PyArrayObject* self, int axis, NPY_SORTKIND kind)
 
-    Equivalent to :meth:`ndarray.sort<numpy.ndarray.sort>` (*self*, *axis*). Return an array with
-    the items of *self* sorted along *axis*.
+    Equivalent to :meth:`ndarray.sort<numpy.ndarray.sort>` (*self*, *axis*, *kind*). Return an array with
+    the items of *self* sorted along *axis*.Array is sorted according to *kind* which is an integer/enum pointing to the type of sorting algorithms used.
 
 .. c:function:: PyObject* PyArray_ArgSort(PyArrayObject* self, int axis)
 
diff --git a/doc/source/reference/c-api.coremath.rst b/doc/source/reference/c-api.coremath.rst
index 691f73287..bf08d4804 100644
--- a/doc/source/reference/c-api.coremath.rst
+++ b/doc/source/reference/c-api.coremath.rst
@@ -80,8 +80,9 @@ Floating point classification
 Useful math constants
 ~~~~~~~~~~~~~~~~~~~~~
 
-The following math constants are available in npy_math.h. Single and extended
-precision are also available by adding the F and L suffixes respectively.
+The following math constants are available in ``npy_math.h``. Single
+and extended precision are also available by adding the ``f`` and
+``l`` suffixes respectively.
 
 .. c:var:: NPY_E
 
diff --git a/doc/source/reference/maskedarray.generic.rst b/doc/source/reference/maskedarray.generic.rst
index 07ad6c292..7375d60fb 100644
--- a/doc/source/reference/maskedarray.generic.rst
+++ b/doc/source/reference/maskedarray.generic.rst
@@ -2,7 +2,7 @@
 
 .. _maskedarray.generic:
 
-
+.. module:: numpy.ma
 
 The :mod:`numpy.ma` module
 ==========================
diff --git a/doc/source/reference/routines.ctypeslib.rst b/doc/source/reference/routines.ctypeslib.rst
index b04713b61..71b944a63 100644
--- a/doc/source/reference/routines.ctypeslib.rst
+++ b/doc/source/reference/routines.ctypeslib.rst
@@ -1,3 +1,5 @@
+.. module:: numpy.ctypeslib
+
 ***********************************************************
 C-Types Foreign Function Interface (:mod:`numpy.ctypeslib`)
 ***********************************************************
diff --git a/doc/source/reference/routines.linalg.rst b/doc/source/reference/routines.linalg.rst
index 0520df413..c6bffc874 100644
--- a/doc/source/reference/routines.linalg.rst
+++ b/doc/source/reference/routines.linalg.rst
@@ -1,5 +1,7 @@
 .. _routines.linalg:
 
+.. module:: numpy.linalg
+
 Linear algebra (:mod:`numpy.linalg`)
 ************************************
 
diff --git a/doc/source/reference/routines.matlib.rst b/doc/source/reference/routines.matlib.rst
index a35eaec78..c7f675425 100644
--- a/doc/source/reference/routines.matlib.rst
+++ b/doc/source/reference/routines.matlib.rst
@@ -1,3 +1,5 @@
+.. module:: numpy.matlib
+
 Matrix library (:mod:`numpy.matlib`)
 ************************************
 
diff --git a/doc/source/reference/routines.polynomials.package.rst b/doc/source/reference/routines.polynomials.package.rst
index 61cb57fbb..7e40d9f00 100644
--- a/doc/source/reference/routines.polynomials.package.rst
+++ b/doc/source/reference/routines.polynomials.package.rst
@@ -1,3 +1,5 @@
+.. module:: numpy.polynomial
+
 Polynomial Package
 ==================
 
diff --git a/doc/source/reference/routines.polynomials.polynomial.rst b/doc/source/reference/routines.polynomials.polynomial.rst
index 8194ca867..365c8da98 100644
--- a/doc/source/reference/routines.polynomials.polynomial.rst
+++ b/doc/source/reference/routines.polynomials.polynomial.rst
@@ -1,3 +1,5 @@
+.. module:: numpy.polynomial.polynomial
+
 Polynomial Module (:mod:`numpy.polynomial.polynomial`)
 ======================================================
 
diff --git a/doc/source/reference/routines.random.rst b/doc/source/reference/routines.random.rst
index c8b097d7d..cda4e2b61 100644
--- a/doc/source/reference/routines.random.rst
+++ b/doc/source/reference/routines.random.rst
@@ -1,5 +1,7 @@
 .. _routines.random:
 
+.. module:: numpy.random
+
 Random sampling (:mod:`numpy.random`)
 *************************************
 
diff --git a/doc/source/reference/routines.testing.rst b/doc/source/reference/routines.testing.rst
index 5a52a40d6..77c046768 100644
--- a/doc/source/reference/routines.testing.rst
+++ b/doc/source/reference/routines.testing.rst
@@ -1,5 +1,7 @@
 .. _numpy-testing:
 
+.. module:: numpy.testing
+
 Test Support (:mod:`numpy.testing`)
 ===================================
 
diff --git a/doc/source/release.rst b/doc/source/release.rst
index 1cf215549..11a25d13e 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -2,6 +2,7 @@
 Release Notes
 *************
 
+.. include:: ../release/1.17.0-notes.rst
 .. include:: ../release/1.16.0-notes.rst
 .. include:: ../release/1.15.4-notes.rst
 .. include:: ../release/1.15.3-notes.rst
diff --git a/doc/source/user/numpy-for-matlab-users.rst b/doc/source/user/numpy-for-matlab-users.rst
index 399237c21..16ee48c5e 100644
--- a/doc/source/user/numpy-for-matlab-users.rst
+++ b/doc/source/user/numpy-for-matlab-users.rst
@@ -547,7 +547,7 @@ Linear Algebra Equivalents
      - eigenvalues and eigenvectors of ``a``
 
    * - ``[V,D]=eig(a,b)``
-     - ``V,D = np.linalg.eig(a,b)``
+     - ``D,V = scipy.linalg.eig(a,b)``
      - eigenvalues and eigenvectors of ``a``, ``b``
 
    * - ``[V,D]=eigs(a,k)``
@@ -693,19 +693,19 @@ this is just an example, not a statement of "best practices"):
 
 ::
 
-    # Make all numpy available via shorter 'num' prefix
-    import numpy as num
+    # Make all numpy available via shorter 'np' prefix
+    import numpy as np
     # Make all matlib functions accessible at the top level via M.func()
     import numpy.matlib as M
     # Make some matlib functions accessible directly at the top level via, e.g. rand(3,3)
     from numpy.matlib import rand,zeros,ones,empty,eye
     # Define a Hermitian function
     def hermitian(A, **kwargs):
-        return num.transpose(A,**kwargs).conj()
+        return np.transpose(A,**kwargs).conj()
     # Make some shortcuts for transpose,hermitian:
-    #    num.transpose(A) --> T(A)
+    #    np.transpose(A) --> T(A)
     #    hermitian(A) --> H(A)
-    T = num.transpose
+    T = np.transpose
     H = hermitian
 
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