numpy · charris · Nov 19, 2019 · Sep 21, 2019 · Sep 25, 2019 · Sep 25, 2019
diff --git a/doc/release/1.17.3-notes.rst b/doc/release/1.17.3-notes.rst
@@ -23,6 +23,10 @@ Compatibility notes
 ===================
 
 - The seldom used ``PyArray_DescrCheck`` macro has been changed/fixed.
+- The use of the new ``numpy.random`` features from Cython and Numba
+  was not well documented and parts have been removed or refactored.
+  We plan to finish the refactor and fully document it in 1.18.0
+
 
 
 Contributors

diff --git a/doc/source/reference/random/extending.rst b/doc/source/reference/random/extending.rst
diff --git a/doc/source/reference/random/index.rst b/doc/source/reference/random/index.rst
@@ -152,10 +152,6 @@ What's New or Different
 * Optional ``out`` argument that allows existing arrays to be filled for
   select distributions
 * All BitGenerators can produce doubles, uint64s and uint32s via CTypes
-  (`~.PCG64.ctypes`) and CFFI (`~.PCG64.cffi`). This allows the bit generators
-  to be used in numba.
-* The bit generators can be used in downstream projects via
-  :ref:`Cython <randomgen_cython>`.
 * `~.Generator.integers` is now the canonical way to generate integer
   random numbers from a discrete uniform distribution. The ``rand`` and
   ``randn`` methods are only available through the legacy `~.RandomState`.
@@ -199,7 +195,6 @@ Features
    Multithreaded Generation <multithreading>
    new-or-different
    Comparing Performance <performance>
-   extending
 
 Original Source
 ~~~~~~~~~~~~~~~

diff --git a/doc/source/reference/random/new-or-different.rst b/doc/source/reference/random/new-or-different.rst
@@ -56,13 +56,12 @@ And in more detail:
   ``randn`` methods are only available through the legacy `~.RandomState`.
   This replaces both ``randint`` and the deprecated ``random_integers``.
 * The Box-Muller method used to produce NumPy's normals is no longer available.
-* All bit generators can produce doubles, uint64s and
-  uint32s via CTypes (`~PCG64.ctypes`) and CFFI (`~PCG64.cffi`).
-  This allows these bit generators to be used in numba.
+* All bit generators can produce doubles, uint64s and uint32s via CTypes
+  (`~PCG64.ctypes`) and CFFI (`~PCG64.cffi`).  This allows these bit generators
+  to be used in numba.
 * The bit generators can be used in downstream projects via
   Cython.
 
-
 .. ipython:: python

   from  numpy.random import Generator, PCG64

diff --git a/numpy/random/common.pxd b/numpy/random/common.pxd
@@ -5,7 +5,7 @@ from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t,
                           uintptr_t)
 from libc.math cimport sqrt
 
-cdef extern from "numpy/random/bitgen.h":
+cdef extern from "src/bitgen.h":
     struct bitgen:
         void *state
         uint64_t (*next_uint64)(void *st) nogil

diff --git a/numpy/random/setup.py b/numpy/random/setup.py
@@ -34,8 +34,6 @@ def generate_libraries(ext, build_dir):
 
     defs.append(('NPY_NO_DEPRECATED_API', 0))
     config.add_data_dir('tests')
-    config.add_d
F438
ata_files('common.pxd')
-    config.add_data_files('bit_generator.pxd')
 
     EXTRA_LINK_ARGS = []
     # Math lib

diff --git a/numpy/core/include/numpy/random/bitgen.h → numpy/random/src/bitgen.h b/numpy/core/include/numpy/random/bitgen.h → numpy/random/src/bitgen.h
diff --git a/numpy/random/src/distributions/distributions.h b/numpy/random/src/distributions/distributions.h
@@ -9,7 +9,7 @@
 #include "Python.h"
 #include "numpy/npy_common.h"
 #include "numpy/npy_math.h"
-#include "numpy/random/bitgen.h"
+#include "src/bitgen.h"
 
 /*
  * RAND_INT_TYPE is used to share integer generators with RandomState which