[Python-modules-commits] [python-numpy] 01/15: Import python-numpy_1.11.0~b2.orig.tar.gz
Sandro Tosi
morph at moszumanska.debian.org
Sun Jan 31 02:06:07 UTC 2016
This is an automated email from the git hooks/post-receive script.
morph pushed a commit to branch master
in repository python-numpy.
commit b5aed68ccb4df09efbda67bf005f88e0fc0d400b
Author: Sandro Tosi <morph at debian.org>
Date: Sat Jan 30 13:12:34 2016 +0000
Import python-numpy_1.11.0~b2.orig.tar.gz
---
BENTO_BUILD.txt | 22 -
COMPATIBILITY | 59 -
DEV_README.txt | 18 -
INSTALL.rst.txt | 155 +
INSTALL.txt | 168 -
LICENSE.txt | 2 +-
MANIFEST.in | 3 +-
PKG-INFO | 2 +-
README.txt | 22 -
doc/Makefile | 2 +-
doc/release/1.10.0-notes.rst | 16 +-
doc/release/1.10.4-notes.rst | 2 +-
doc/release/1.11.0-notes.rst | 343 +
doc/source/dev/development_environment.rst | 24 +-
doc/source/dev/governance/governance.rst | 400 +
doc/source/dev/governance/index.rst | 9 +
doc/source/dev/governance/people.rst | 55 +
doc/source/dev/index.rst | 1 +
doc/source/reference/arrays.classes.rst | 18 +-
doc/source/reference/arrays.datetime.rst | 62 +-
doc/source/reference/arrays.dtypes.rst | 2 +
doc/source/reference/arrays.indexing.rst | 2 +-
doc/source/reference/arrays.interface.rst | 4 +-
doc/source/reference/arrays.ndarray.rst | 4 +-
doc/source/reference/c-api.array.rst | 18 +-
.../reference/c-api.types-and-structures.rst | 7 +-
doc/source/reference/c-api.ufunc.rst | 11 +-
.../reference/routines.array-manipulation.rst | 1 +
doc/source/reference/routines.indexing.rst | 1 +
doc/source/reference/routines.other.rst | 16 +
doc/source/reference/routines.rst | 2 +
doc/source/reference/swig.interface-file.rst | 2 +-
doc/source/reference/ufuncs.rst | 4 +-
doc/source/release.rst | 1 +
doc/source/user/basics.io.genfromtxt.rst | 22 +-
doc/source/user/basics.rst | 2 +-
doc/source/user/{install.rst => building.rst} | 71 +-
doc/source/user/c-info.ufunc-tutorial.rst | 7 +-
doc/source/user/howtofind.rst | 7 -
doc/source/user/index.rst | 22 +-
doc/source/user/install.rst | 198 +-
doc/source/user/introduction.rst | 10 -
doc/source/user/misc.rst | 2 -
doc/source/user/numpy-for-matlab-users.rst | 746 +
doc/source/user/performance.rst | 5 -
doc/source/user/quickstart.rst | 1414 +
doc/source/user/setting-up.rst | 9 +
numpy/__init__.py | 6 +-
numpy/_build_utils/waf.py | 531 -
numpy/add_newdocs.py | 107 +-
numpy/core/__init__.py | 6 +-
numpy/core/_internal.py | 8 +
numpy/core/arrayprint.py | 22 +-
numpy/core/code_generators/cversions.txt | 1 +
numpy/core/code_generators/genapi.py | 16 +-
numpy/core/code_generators/generate_numpy_api.py | 10 +-
numpy/core/code_generators/generate_ufunc_api.py | 4 -
numpy/core/code_generators/generate_umath.py | 2 +-
numpy/core/code_generators/ufunc_docstrings.py | 16 +-
numpy/core/fromnumeric.py | 105 +-
numpy/core/function_base.py | 2 +-
numpy/core/include/numpy/_numpyconfig.h.in | 1 -
numpy/core/include/numpy/ndarraytypes.h | 6 +-
numpy/core/include/numpy/npy_3kcompat.h | 60 +-
numpy/core/include/numpy/npy_common.h | 18 +
numpy/core/include/numpy/npy_math.h | 4 +-
numpy/core/include/numpy/numpyconfig.h | 1 +
numpy/core/include/numpy/ufuncobject.h | 30 +-
numpy/core/numeric.py | 136 +-
numpy/core/numerictypes.py | 2 +-
numpy/core/records.py | 8 +-
numpy/core/setup.py | 43 +-
numpy/core/setup_common.py | 8 +-
numpy/core/shape_base.py | 2 +-
numpy/core/src/multiarray/_datetime.h | 5 -
numpy/core/src/multiarray/array_assign.c | 27 +-
numpy/core/src/multiarray/arrayobject.c | 1 +
numpy/core/src/multiarray/arraytypes.c.src | 165 +-
numpy/core/src/multiarray/arraytypes.h | 2 -
numpy/core/src/multiarray/buffer.h | 4 -
numpy/core/src/multiarray/cblasfuncs.c | 254 +-
numpy/core/src/multiarray/cblasfuncs.h | 3 -
numpy/core/src/multiarray/common.c | 33 -
numpy/core/src/multiarray/common.h | 5 -
numpy/core/src/multiarray/conversion_utils.c | 115 +-
numpy/core/src/multiarray/conversion_utils.h | 4 -
numpy/core/src/multiarray/convert.c | 45 +-
numpy/core/src/multiarray/ctors.c | 51 +-
numpy/core/src/multiarray/datetime.c | 54 +-
numpy/core/src/multiarray/datetime_busday.c | 1 +
numpy/core/src/multiarray/datetime_busdaycal.h | 4 -
numpy/core/src/multiarray/datetime_strings.c | 228 +-
numpy/core/src/multiarray/datetime_strings.h | 7 +-
numpy/core/src/multiarray/descriptor.c | 10 +-
numpy/core/src/multiarray/descriptor.h | 2 -
numpy/core/src/multiarray/dtype_transfer.c | 6 +-
numpy/core/src/multiarray/getset.c | 1 +
numpy/core/src/multiarray/getset.h | 2 -
numpy/core/src/multiarray/item_selection.c | 31 +-
numpy/core/src/multiarray/iterators.c | 16 +-
numpy/core/src/multiarray/mapping.c | 130 +-
numpy/core/src/multiarray/mapping.h | 4 -
numpy/core/src/multiarray/methods.h | 2 -
numpy/core/src/multiarray/multiarray_tests.c.src | 651 +-
numpy/core/src/multiarray/multiarraymodule.c | 270 +-
numpy/core/src/multiarray/number.c | 13 +-
numpy/core/src/multiarray/number.h | 5 -
numpy/core/src/multiarray/numpyos.c | 152 +-
numpy/core/src/multiarray/numpyos.h | 6 +
numpy/core/src/multiarray/scalartypes.c.src | 24 +-
numpy/core/src/multiarray/scalartypes.h | 13 -
numpy/core/src/multiarray/sequence.h | 4 -
numpy/core/src/multiarray/usertypes.h | 4 -
numpy/core/src/npymath/npy_math.c.src | 15 +-
numpy/core/src/private/mem_overlap.c | 918 +
numpy/core/src/private/mem_overlap.h | 50 +
numpy/core/src/private/npy_config.h | 25 +-
numpy/core/src/private/npy_extint128.h | 317 +
numpy/core/src/private/ufunc_override.h | 47 +-
numpy/core/src/umath/loops.c.src | 148 +-
numpy/core/src/umath/reduction.c | 2 -
numpy/core/src/umath/scalarmath.c.src | 58 +-
numpy/core/src/umath/simd.inc.src | 166 +-
numpy/core/src/umath/ufunc_object.c | 33 +-
numpy/core/src/umath/ufunc_type_resolution.c | 2 -
numpy/core/src/umath/umathmodule.c | 3 -
numpy/core/tests/test_datetime.py | 431 +-
numpy/core/tests/test_defchararray.py | 10 +-
numpy/core/tests/test_deprecations.py | 327 +-
numpy/core/tests/test_dtype.py | 15 +-
numpy/core/tests/test_extint128.py | 225 +
numpy/core/tests/test_indexing.py | 248 +-
numpy/core/tests/test_item_selection.py | 7 +-
numpy/core/tests/test_longdouble.py | 209 +
numpy/core/tests/test_mem_overlap.py | 522 +
numpy/core/tests/test_memmap.py | 12 +-
numpy/core/tests/test_multiarray.py | 710 +-
numpy/core/tests/test_multiarray_assignment.py | 84 -
numpy/core/tests/test_numeric.py | 444 +-
numpy/core/tests/test_print.py | 8 +-
numpy/core/tests/test_scalarinherit.py | 12 +-
numpy/core/tests/test_scalarmath.py | 94 +-
numpy/core/tests/test_shape_base.py | 4 +-
numpy/core/tests/test_ufunc.py | 6 +-
numpy/core/tests/test_umath.py | 37 +-
numpy/ctypeslib.py | 27 +
numpy/distutils/__init__.py | 14 +-
numpy/distutils/ccompiler.py | 8 +-
numpy/distutils/command/build_src.py | 47 +-
numpy/distutils/command/egg_info.py | 8 +
numpy/distutils/fcompiler/intel.py | 8 +-
numpy/distutils/mingw32ccompiler.py | 4 +-
numpy/distutils/misc_util.py | 3 +-
numpy/distutils/npy_pkg_config.py | 5 +-
numpy/distutils/system_info.py | 53 +-
numpy/distutils/tests/f2py_ext/__init__.py | 1 -
numpy/distutils/tests/f2py_ext/setup.py | 13 -
numpy/distutils/tests/f2py_ext/src/fib1.f | 18 -
numpy/distutils/tests/f2py_ext/src/fib2.pyf | 9 -
numpy/distutils/tests/f2py_ext/tests/test_fib2.py | 12 -
numpy/distutils/tests/f2py_f90_ext/__init__.py | 1 -
.../distutils/tests/f2py_f90_ext/include/body.f90 | 5 -
numpy/distutils/tests/f2py_f90_ext/setup.py | 18 -
.../distutils/tests/f2py_f90_ext/src/foo_free.f90 | 6 -
.../distutils/tests/f2py_f90_ext/tests/test_foo.py | 11 -
numpy/distutils/tests/gen_ext/__init__.py | 1 -
numpy/distutils/tests/gen_ext/setup.py | 48 -
numpy/distutils/tests/gen_ext/tests/test_fib3.py | 11 -
numpy/distutils/tests/pyrex_ext/__init__.py | 1 -
numpy/distutils/tests/pyrex_ext/primes.pyx | 22 -
numpy/distutils/tests/pyrex_ext/setup.py | 14 -
.../distutils/tests/pyrex_ext/tests/test_primes.py | 13 -
numpy/distutils/tests/setup.py | 16 -
numpy/distutils/tests/swig_ext/__init__.py | 1 -
numpy/distutils/tests/swig_ext/setup.py | 20 -
numpy/distutils/tests/swig_ext/src/example.c | 14 -
numpy/distutils/tests/swig_ext/src/example.i | 14 -
numpy/distutils/tests/swig_ext/src/zoo.cc | 23 -
numpy/distutils/tests/swig_ext/src/zoo.h | 9 -
numpy/distutils/tests/swig_ext/src/zoo.i | 10 -
.../distutils/tests/swig_ext/tests/test_example.py | 17 -
.../tests/swig_ext/tests/test_example2.py | 15 -
numpy/distutils/tests/test_npy_pkg_config.py | 54 +-
numpy/distutils/tests/test_system_info.py | 17 +-
numpy/doc/basics.py | 39 +
numpy/doc/glossary.py | 6 +-
numpy/doc/howtofind.py | 10 -
numpy/doc/io.py | 10 -
numpy/doc/jargon.py | 10 -
numpy/doc/methods_vs_functions.py | 10 -
numpy/doc/misc.py | 10 +-
numpy/doc/performance.py | 10 -
numpy/doc/structured_arrays.py | 2 +-
numpy/doc/subclassing.py | 36 +-
numpy/f2py/__init__.py | 42 +-
numpy/f2py/auxfuncs.py | 4 -
numpy/f2py/capi_maps.py | 6 +-
numpy/f2py/f90mod_rules.py | 2 +-
numpy/f2py/tests/test_array_from_pyobj.py | 6 +-
numpy/f2py/tests/util.py | 39 +-
numpy/fft/__init__.py | 6 +-
numpy/fft/fftpack.py | 20 +-
numpy/fft/info.py | 2 +-
numpy/lib/__init__.py | 8 +-
numpy/lib/_iotools.py | 13 +
numpy/lib/arraypad.py | 5 +-
numpy/lib/arrayterator.py | 11 +-
numpy/lib/financial.py | 4 +-
numpy/lib/format.py | 3 +-
numpy/lib/function_base.py | 306 +-
numpy/lib/index_tricks.py | 6 +-
numpy/lib/info.py | 6 +
numpy/lib/nanfunctions.py | 11 +-
numpy/lib/npyio.py | 15 +-
numpy/lib/polynomial.py | 27 +-
numpy/lib/shape_base.py | 21 +-
numpy/lib/stride_tricks.py | 3 -
numpy/lib/tests/test__datasource.py | 8 +-
numpy/lib/tests/test_arraypad.py | 2 +-
numpy/lib/tests/test_format.py | 5 +-
numpy/lib/tests/test_function_base.py | 205 +-
numpy/lib/tests/test_io.py | 142 +-
numpy/lib/tests/test_nanfunctions.py | 33 +-
numpy/lib/tests/test_regression.py | 4 -
numpy/lib/tests/test_shape_base.py | 25 +-
numpy/lib/twodim_base.py | 2 +-
numpy/lib/type_check.py | 2 +-
numpy/linalg/__init__.py | 6 +-
numpy/linalg/linalg.py | 25 +-
numpy/linalg/setup.py | 41 +-
numpy/linalg/tests/test_linalg.py | 100 +-
numpy/ma/__init__.py | 6 +-
numpy/ma/core.py | 310 +-
numpy/ma/extras.py | 57 +-
numpy/ma/tests/test_core.py | 175 +-
numpy/ma/tests/test_extras.py | 20 +
numpy/ma/tests/test_mrecords.py | 16 +-
numpy/ma/tests/test_old_ma.py | 58 -
numpy/matrixlib/__init__.py | 6 +-
numpy/matrixlib/defmatrix.py | 28 +-
numpy/polynomial/__init__.py | 6 +-
numpy/polynomial/_polybase.py | 7 +-
numpy/polynomial/chebyshev.py | 34 +-
numpy/polynomial/hermite.py | 35 +-
numpy/polynomial/hermite_e.py | 35 +-
numpy/polynomial/laguerre.py | 34 +-
numpy/polynomial/legendre.py | 42 +-
numpy/polynomial/polynomial.py | 34 +-
numpy/polynomial/tests/test_chebyshev.py | 31 +
numpy/polynomial/tests/test_classes.py | 9 +
numpy/polynomial/tests/test_hermite.py | 31 +
numpy/polynomial/tests/test_hermite_e.py | 31 +
numpy/polynomial/tests/test_laguerre.py | 16 +
numpy/polynomial/tests/test_legendre.py | 31 +
numpy/polynomial/tests/test_polynomial.py | 27 +
numpy/random/__init__.py | 6 +-
numpy/random/mtrand/distributions.c | 5 +-
numpy/random/mtrand/mt_compat.h | 68 +
numpy/random/mtrand/mtrand.c | 42607 +++++++++++--------
numpy/random/mtrand/mtrand.pyx | 499 +-
numpy/random/mtrand/numpy.pxd | 16 +
numpy/random/mtrand/randomkit.c | 224 +-
numpy/random/mtrand/randomkit.h | 41 +-
numpy/random/tests/test_random.py | 159 +-
numpy/setup.py | 1 -
numpy/testing/__init__.py | 2 +-
numpy/testing/decorators.py | 21 +-
numpy/testing/noseclasses.py | 31 +-
numpy/testing/nosetester.py | 60 +-
numpy/testing/tests/test_decorators.py | 20 +-
numpy/testing/tests/test_utils.py | 52 +-
numpy/testing/utils.py | 140 +-
numpy/tests/test_numpy_version.py | 23 +
numpy/tests/test_scripts.py | 7 +-
numpy/version.py | 10 +-
setup.py | 175 +-
setupegg.py | 25 -
tools/swig/numpy.i | 2 +-
tools/swig/test/testFortran.py | 10 -
279 files changed, 37021 insertions(+), 22833 deletions(-)
diff --git a/BENTO_BUILD.txt b/BENTO_BUILD.txt
deleted file mode 100644
index ac11b04..0000000
--- a/BENTO_BUILD.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-No-frill version:
-
- * Clone bento::
-
- git clone git://github.com/cournape/Bento.git bento-git
-
- * Bootstrap bento::
-
- cd bento-git && python bootstrap.py
-
- * Download waf >= 1.6.5 from a release (or from git)::
-
- git clone https://code.google.com/p/waf/
-
- * Build numpy with bento:
-
- export WAFDIR=ROOT_OF_WAF_CHECKOUT # WAFDIR should be such as $WAFDIR/waflib exists
- $BENTO_ROOT/bentomaker build -j 4 # 4 threads in parallel
- # or with progress bar
- $BENTO_ROOT/bentomaker build -p
- # or with verbose output
- $BENTO_ROOT/bentomaker build -v
diff --git a/COMPATIBILITY b/COMPATIBILITY
deleted file mode 100644
index d2cd3cd..0000000
--- a/COMPATIBILITY
+++ /dev/null
@@ -1,59 +0,0 @@
-
-
-X.flat returns an indexable 1-D iterator (mostly similar to an array
-but always 1-d) --- only has .copy and .__array__ attributes of an array!!!
-
-.typecode() --> .dtype.char
-
-.iscontiguous() --> .flags['CONTIGUOUS'] or .flags.contiguous
-
-.byteswapped() -> .byteswap()
-
-.itemsize() -> .itemsize
-
-.toscalar() -> .item()
-
-If you used typecode characters:
-
-'c' -> 'S1' or 'c'
-'b' -> 'B'
-'1' -> 'b'
-'s' -> 'h'
-'w' -> 'H'
-'u' -> 'I'
-
-
-C -level
-
-some API calls that used to take PyObject * now take PyArrayObject *
-(this should only cause warnings during compile and not actual problems).
- PyArray_Take
-
-These commands now return a buffer that must be freed once it is used
-using PyMemData_FREE(ptr);
-
-a->descr->zero --> PyArray_Zero(a)
-a->descr->one --> PyArray_One(a)
-
-Numeric/arrayobject.h --> numpy/oldnumeric.h
-
-
-# These will actually work and are defines for PyArray_BYTE,
-# but you really should change it in your code
-PyArray_CHAR --> PyArray_CHAR
- (or PyArray_STRING which is more flexible)
-PyArray_SBYTE --> PyArray_BYTE
-
-Any uses of character codes will need adjusting....
-use PyArray_XXXLTR where XXX is the name of the type.
-
-
-If you used function pointers directly (why did you do that?),
-the arguments have changed. Everything that was an int is now an intp.
-Also, arrayobjects should be passed in at the end.
-
-a->descr->cast[i](fromdata, fromstep, todata, tostep, n)
-a->descr->cast[i](fromdata, todata, n, PyArrayObject *in, PyArrayObject *out)
- anything but single-stepping is not supported by this function
- use the PyArray_CastXXXX functions.
-
diff --git a/DEV_README.txt b/DEV_README.txt
deleted file mode 100644
index 7dc8bce..0000000
--- a/DEV_README.txt
+++ /dev/null
@@ -1,18 +0,0 @@
-Thank you for your willingness to help make NumPy the best array system
-available.
-
-We have a few simple rules:
-
- * try hard to keep the Git repository in a buildable state and to not
- indiscriminately muck with what others have contributed.
-
- * Simple changes (including bug fixes) and obvious improvements are
- always welcome. Changes that fundamentally change behavior need
- discussion on numpy-discussions at scipy.org before anything is
- done.
-
- * Please add meaningful comments when you check changes in. These
- comments form the basis of the change-log.
-
- * Add unit tests to exercise new code, and regression tests
- whenever you fix a bug.
diff --git a/INSTALL.rst.txt b/INSTALL.rst.txt
new file mode 100644
index 0000000..0b778d9
--- /dev/null
+++ b/INSTALL.rst.txt
@@ -0,0 +1,155 @@
+Building and installing NumPy
++++++++++++++++++++++++++++++
+
+**IMPORTANT**: the below notes are about building Numpy, which for most users
+is *not* the recommended way to install Numpy. Instead, use either a complete
+scientific Python distribution (recommended) or a binary installer - see
+http://scipy.org/install.html.
+
+
+.. Contents::
+
+Prerequisites
+=============
+
+Building NumPy requires the following software installed:
+
+1) For Python 2, Python__ 2.6.x or newer.
+ For Python 3, Python__ 3.2.x or newer.
+
+ On Debian and derivative (Ubuntu): python python-dev
+
+ On Windows: the official python installer on Python__ is enough
+
+ Make sure that the Python package distutils is installed before
+ continuing. For example, in Debian GNU/Linux, distutils is included
+ in the python-dev package.
+
+ Python must also be compiled with the zlib module enabled.
+
+2) Cython >= 0.19 (for development versions of numpy, not for released
+ versions)
+3) nose__ (optional) 1.0 or later
+
+ This is required for testing numpy, but not for using it.
+
+Python__ http://www.python.org
+nose__ http://somethingaboutorange.com/mrl/projects/nose/
+
+
+.. note::
+
+ If you want to build Numpy in order to work on Numpy itself, use
+ ``runtests.py``. For more details, see
+ http://docs.scipy.org/doc/numpy-dev/dev/development_environment.html
+
+.. note::
+
+ More extensive information on building Numpy (and Scipy) is maintained at
+ http://scipy.org/scipylib/building/index.html
+
+
+Basic Installation
+==================
+
+To install numpy run::
+
+ python setup.py build -j 4 install --prefix $HOME/.local
+
+This will compile numpy on 4 CPUs and install it into the specified prefix.
+To perform an inplace build that can be run from the source folder run::
+
+ python setup.py build_ext --inplace -j 4
+
+The number of build jobs can also be specified via the environment variable
+NPY_NUM_BUILD_JOBS.
+
+
+Choosing compilers
+==================
+
+Numpy needs a C compiler, and for development versions also Cython. A Fortran
+compiler isn't needed to build Numpy itself; the ``numpy.f2py`` tests will be
+skipped when running the test suite if no Fortran compiler is available. For
+building Scipy a Fortran compiler is needed though, so we include some details
+on Fortran compilers in the rest of this section.
+
+On OS X and Linux, all common compilers will work. Note that for Fortran,
+``gfortran`` is strongly preferred over ``g77``, but if you happen to have both
+installed then ``g77`` will be detected and used first. To explicitly select
+``gfortran`` in that case, do::
+
+ python setup.py build --fcompiler=gnu95
+
+Windows
+-------
+
+On Windows, building from source can be difficult. Currently the most robust
+option is to use the Intel compilers, or alternatively MSVC (the same version
+as used to build Python itself) with Intel ifort. Intel itself maintains a
+good `application note <https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl>`_
+on this.
+
+If you want to use a free compiler toolchain, the recommended compiler is MingwPy__.
+The older MinGW32 compiler set used to produce older .exe installers for Numpy
+itself is still available at https://github.com/numpy/numpy-vendor, but not
+recommended for use anymore.
+
+MingwPy__ http://mingwpy.github.io
+
+
+Building with optimized BLAS support
+====================================
+
+Configuring which BLAS/LAPACK is used if you have multiple libraries installed,
+or you have only one installed but in a non-standard location, is done via a
+``site.cfg`` file. See the ``site.cfg.example`` shipped with Numpy for more
+details.
+
+Windows
+-------
+
+The Intel compilers work with Intel MKL, see the application note linked above.
+MingwPy__ works with OpenBLAS.
+For an overview of the state of BLAS/LAPACK libraries on Windows, see
+`here <http://mingwpy.github.io/blas_lapack.html>`_.
+
+OS X
+----
+
+OS X ships the Accelerate framework, which Numpy can build against without any
+manual configuration. Other BLAS/LAPACK implementations (OpenBLAS, Intel MKL,
+ATLAS) will also work.
+
+Ubuntu/Debian
+-------------
+
+For best performance a development package providing BLAS and CBLAS should be
+installed. Some of the options available are:
+
+- ``libblas-dev``: reference BLAS (not very optimized)
+- ``libatlas-base-dev``: generic tuned ATLAS, it is recommended to tune it to
+ the available hardware, see /usr/share/doc/libatlas3-base/README.Debian for
+ instructions
+- ``libopenblas-base``: fast and runtime detected so no tuning required but a
+ very recent version is needed (>=0.2.15 is recommended). Older versions of
+ OpenBLAS suffered from correctness issues on some CPUs.
+
+The package linked to when numpy is loaded can be chosen after installation via
+the alternatives mechanism::
+
+ update-alternatives --config libblas.so.3
+ update-alternatives --config liblapack.so.3
+
+Or by preloading a specific BLAS library with::
+
+ LD_PRELOAD=/usr/lib/atlas-base/atlas/libblas.so.3 python ...
+
+
+Build issues
+============
+
+If you run into build issues and need help, the Numpy
+`mailing list <http://scipy.org/scipylib/mailing-lists.html>`_ is the best
+place to ask. If the issue is clearly a bug in Numpy, please file an issue (or
+even better, a pull request) at https://github.com/numpy/numpy.
diff --git a/INSTALL.txt b/INSTALL.txt
deleted file mode 100644
index 6339cbb..0000000
--- a/INSTALL.txt
+++ /dev/null
@@ -1,168 +0,0 @@
-.. -*- rest -*-
-.. vim:syntax=rest
-.. NB! Keep this document a valid restructured document.
-
-Building and installing NumPy
-+++++++++++++++++++++++++++++
-
-:Authors: Numpy Developers <numpy-discussion at scipy.org>
-:Discussions to: numpy-discussion at scipy.org
-
-**IMPORTANT**: the below notes are about building Numpy, which for most users
-is *not* the recommended way to install Numpy. Instead, use either a complete
-scientific Python distribution or a binary installer - see
-http://scipy.org/install.html.
-
-
-.. Contents::
-
-PREREQUISITES
-=============
-
-Building NumPy requires the following software installed:
-
-1) For Python 2, Python__ 2.6.x or newer.
- For Python 3, Python__ 3.2.x or newer.
-
- On Debian and derivative (Ubuntu): python python-dev
-
- On Windows: the official python installer on Python__ is enough
-
- Make sure that the Python package distutils is installed before
- continuing. For example, in Debian GNU/Linux, distutils is included
- in the python-dev package.
-
- Python must also be compiled with the zlib module enabled.
-
-2) nose__ (optional) 1.0 or later
-
- This is required for testing numpy, but not for using it.
-
-Python__ http://www.python.org
-nose__ http://somethingaboutorange.com/mrl/projects/nose/
-
-Basic Installation
-==================
-
-To install numpy run:
-
- python setup.py build -j 4 install --prefix $HOME/.local
-
-This will compile numpy on 4 CPUs and install it into the specified prefix.
-To perform an inplace build that can be run from the source folder run:
-
- python setup.py build_ext --inplace -j 4
-
-The number of build jobs can also be specified via the environment variable
-NPY_NUM_BUILD_JOBS.
-
-Fortran ABI mismatch
-====================
-
-The two most popular open source fortran compilers are g77 and gfortran.
-Unfortunately, they are not ABI compatible, which means that concretely you
-should avoid mixing libraries built with one with another. In particular,
-if your blas/lapack/atlas is built with g77, you *must* use g77 when
-building numpy and scipy; on the contrary, if your atlas is built with
-gfortran, you *must* build numpy/scipy with gfortran.
-
-Choosing the fortran compiler
------------------------------
-
-To build with g77:
-
- python setup.py build --fcompiler=gnu
-
-To build with gfortran:
-
- python setup.py build --fcompiler=gnu95
-
-How to check the ABI of blas/lapack/atlas
------------------------------------------
-
-One relatively simple and reliable way to check for the compiler used to
-build a library is to use ldd on the library. If libg2c.so is a dependency,
-this means that g77 has been used. If libgfortran.so is a dependency,
-gfortran has been used. If both are dependencies, this means both have been
-used, which is almost always a very bad idea.
-
-Building with optimized BLAS support
-====================================
-
-Ubuntu/Debian
--------------
-
-In order to build with optimized a BLAS providing development package must be installed.
-Options are for example:
-
- - libblas-dev
- reference BLAS not very optimized
- - libatlas-base-dev
- generic tuned ATLAS, it is recommended to tune it to the available hardware,
- see /usr/share/doc/libatlas3-base/README.Debian for instructions
- - libopenblas-base
- fast and runtime detected so no tuning required but as of version 2.11 still
- suffers from correctness issues on some CPUs, test your applications
- thoughly.
-
-The actual implementation can be exchanged also after installation via the
-alternatives mechanism:
-
- update-alternatives --config libblas.so.3
- update-alternatives --config liblapack.so.3
-
-Or by preloading a specific BLAS library with
- LD_PRELOAD=/usr/lib/atlas-base/atlas/libblas.so.3 python ...
-
-
-Windows 32 bits notes
-=====================
-
-The MinGW compilers used to build the official Numpy binary installers for
-32-bit Python on Windows can be found in https://github.com/numpy/numpy-vendor.
-That repo also contains pre-built ATLAS binarues. The command to build and
-install Numpy is:
-
- $ python setup.py config --compiler=mingw32 build --compiler=mingw32 install
-
-Typically, one needs to use a site.cfg file that looks like:
-
- [atlas]
- library_dirs = C:\local\lib\atlas
- include_dirs = C:\local\lib\atlas
-
-Windows 64 bits notes
-=====================
-
-Note: only AMD64 is supported (IA64 is not) - AMD64 is the version most
-people want.
-
-Free compilers (mingw-w64)
---------------------------
-
-http://mingw-w64.sourceforge.net/
-
-To use the free compilers (mingw-w64), you need to build your own
-toolchain, as the mingw project only distribute cross-compilers
-(cross-compilation is not supported by numpy). Since this toolchain is
-still being worked on, serious compiler bugs can be expected. binutil 2.19
-+ gcc 4.3.3 + mingw-w64 runtime gives you a working C compiler (but the C++
-is broken). gcc 4.4 will hopefully be able to run natively.
-
-This is the only tested way to get a numpy with a FULL blas/lapack (scipy
-does not work because of C++).
-
-MS compilers
-------------
-
-If you are familiar with MS tools, that's obviously the easiest path, and
-the compilers are hopefully more mature (although in my experience, they
-are quite fragile, and often segfault on invalid C code). The main drawback
-is that mingw-w64 gfortran + MSVC does not work at all (it is unclear
-whether it ever will). MSVC + ifort + MKL does work.
-
-For python 2.6, you need VS 2008. The freely available version does not
-contains 64 bits compilers (you also need the PSDK, v6.1).
-
-It is crucial to use the right MS compiler version. For python 2.6, you
-must use version 15. You can check the compiler version with cl.exe /?.
diff --git a/LICENSE.txt b/LICENSE.txt
index b4139af..9014534 100644
--- a/LICENSE.txt
+++ b/LICENSE.txt
@@ -1,4 +1,4 @@
-Copyright (c) 2005-2015, NumPy Developers.
+Copyright (c) 2005-2016, NumPy Developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
diff --git a/MANIFEST.in b/MANIFEST.in
index 6f48264..4e5206b 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -4,14 +4,13 @@
# data, etc files to distribution. Avoid using MANIFEST.in for that.
#
include MANIFEST.in
-include COMPATIBILITY
include *.txt
-include setupegg.py
include site.cfg.example
include numpy/random/mtrand/generate_mtrand_c.py
recursive-include numpy/random/mtrand *.pyx *.pxd
# Add build support that should go in sdist, but not go in bdist/be installed
recursive-include numpy/_build_utils *
+recursive-include numpy/linalg/lapack_lite *.c *.h
# Add sdist files whose use depends on local configuration.
include numpy/core/src/multiarray/cblasfuncs.c
include numpy/core/src/multiarray/python_xerbla.c
diff --git a/PKG-INFO b/PKG-INFO
index ee41683..9012076 100644
--- a/PKG-INFO
+++ b/PKG-INFO
@@ -1,6 +1,6 @@
Metadata-Version: 1.1
Name: numpy
-Version: 1.10.4
+Version: 1.11.0b2
Summary: NumPy: array processing for numbers, strings, records, and objects.
Home-page: http://www.numpy.org
Author: NumPy Developers
diff --git a/README.txt b/README.txt
deleted file mode 100644
index a20163a..0000000
--- a/README.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-NumPy is the fundamental package needed for scientific computing with Python.
-This package contains:
-
- * a powerful N-dimensional array object
- * sophisticated (broadcasting) functions
- * tools for integrating C/C++ and Fortran code
- * useful linear algebra, Fourier transform, and random number capabilities.
-
-It derives from the old Numeric code base and can be used as a replacement for Numeric. It also adds the features introduced by numarray and can be used to replace numarray.
-
-More information can be found at the website:
-
-http://www.numpy.org
-
-After installation, tests can be run with:
-
-python -c 'import numpy; numpy.test()'
-
-The most current development version is always available from our
-git repository:
-
-http://github.com/numpy/numpy
diff --git a/doc/Makefile b/doc/Makefile
index 063ab0d..52840be 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -82,7 +82,7 @@ real-dist: dist-build html html-scipyorg
dist-build:
rm -f ../dist/*.egg
- cd .. && $(PYTHON) setupegg.py bdist_egg
+ cd .. && $(PYTHON) setup.py bdist_egg
install -d $(subst :, ,$(INSTALL_PPH))
$(PYTHON) `which easy_install` --prefix=$(INSTALL_DIR) ../dist/*.egg
diff --git a/doc/release/1.10.0-notes.rst b/doc/release/1.10.0-notes.rst
index 38cdc1b..35e967f 100644
--- a/doc/release/1.10.0-notes.rst
+++ b/doc/release/1.10.0-notes.rst
@@ -20,7 +20,8 @@ Highlights
* Addition of `nanprod` to the set of nanfunctions.
* Support for the '@' operator in Python 3.5.
-Dropped Support:
+Dropped Support
+===============
* The _dotblas module has been removed. CBLAS Support is now in
Multiarray.
@@ -35,15 +36,22 @@ Dropped Support:
* Keywords ``skiprows`` and ``missing`` removed from np.genfromtxt.
* Keyword ``old_behavior`` removed from np.correlate.
-Future Changes:
+Future Changes
+==============
* In array comparisons like ``arr1 == arr2``, many corner cases
involving strings or structured dtypes that used to return scalars
now issue ``FutureWarning`` or ``DeprecationWarning``, and in the
future will be change to either perform elementwise comparisons or
raise an error.
-* The SafeEval class will be removed.
-* The alterdot and restoredot functions will be removed.
+* In ``np.lib.split`` an empty array in the result always had dimension
+ ``(0,)`` no matter the dimensions of the array being split. In Numpy 1.11
+ that behavior will be changed so that the dimensions will be preserved. A
+ ``FutureWarning`` for this change has been in place since Numpy 1.9 but,
+ due to a bug, sometimes no warning was raised and the dimensions were
+ already preserved.
+* The SafeEval class will be removed in Numpy 1.11.
+* The alterdot and restoredot functions will be removed in Numpy 1.11.
See below for more details on these changes.
diff --git a/doc/release/1.10.4-notes.rst b/doc/release/1.10.4-notes.rst
index 03eaf5e..7de732a 100644
--- a/doc/release/1.10.4-notes.rst
+++ b/doc/release/1.10.4-notes.rst
@@ -25,7 +25,7 @@ Issues Fixed
Merged PRs
==========
-The following PRs have been merged into 1.10.3. When the PR is a backport,
+The following PRs have been merged into 1.10.4. When the PR is a backport,
the PR number for the original PR against master is listed.
* gh-6840 TST: Update travis testing script in 1.10.x
diff --git a/doc/release/1.11.0-notes.rst b/doc/release/1.11.0-notes.rst
new file mode 100644
index 0000000..c9287ed
--- /dev/null
+++ b/doc/release/1.11.0-notes.rst
@@ -0,0 +1,343 @@
+NumPy 1.11.0 Release Notes
+**************************
+
+This release supports Python 2.6 - 2.7 and 3.2 - 3.5 and contains a number
+of enhancements and improvements. Note also the build system changes listed
+below as they may have subtle effects.
+
+No Windows (TM) binaries are provided for this release due to a broken
+toolchain. One of the providers of Python packages for Windows (TM) is your
+best bet.
+
+
+Highlights
+==========
+
+Details of these improvements can be found below.
+
+* The datetime64 type is now timezone naive.
+* A dtype parameter has been added to ``randint``.
+* Improved detection of two arrays possibly sharing memory.
+* Automatic bin size estimation for ``np.histogram``.
+* Speed optimization of A @ A.T and dot(A, A.T).
+* New function ``np.moveaxis`` for reordering array axes.
+
+
+Build System Changes
+====================
+
+* Numpy now uses ``setuptools`` for its builds instead of plain distutils.
+ This fixes usage of ``install_requires='numpy'`` in the ``setup.py`` files of
+ projects that depend on Numpy (see gh-6551). It potentially affects the way
+ that build/install methods for Numpy itself behave though. Please report any
+ unexpected behavior on the Numpy issue tracker.
+* Bento build support and related files have been removed.
+* Single file build support and related files have been removed.
+
+
+Future Changes
+==============
+
+The following changes are scheduled for Numpy 1.12.0.
+
+* Support for Python 2.6, 3.2, and 3.3 will be dropped.
+* Slicing a ``MaskedArray`` will return views of both data **and** mask.
+ Currently the mask is returned as a copy.
+* Relaxed stride checking will become the default. See the 1.8.0 release
+ notes for a more extended discussion of what this change implies.
+* The behavior of the datetime64 "not a time" (NaT) value will be changed
+ to match that of floating point "not a number" (NaN) values: all
+ comparisons involving NaT will return False, except for NaT != NaT which
+ will return True.
+
+In a future release the following changes will be made.
+
+* The ``rand`` function exposed in ``numpy.testing`` will be removed. That
+ function is left over from early Numpy and was implemented using the
+ Python random module. The random number generators from ``numpy.random``
+ should be used instead.
+* The ``ndarray.view`` method will only allow c_contiguous arrays to be
+ viewed using a dtype of different size causing the last dimension to
+ change. That differs from the current behavior where arrays that are
+ f_contiguous but not c_contiguous can be viewed as a dtype type of
+ different size causing the first dimension to change.
+
+
+Compatibility notes
+===================
+
+datetime64 changes
+~~~~~~~~~~~~~~~~~~
+In prior versions of NumPy the experimental datetime64 type always stored
+times in UTC. By default, creating a datetime64 object from a string or
+printing it would convert from or to local time::
+
+ # old behavior
+ >>>> np.datetime64('2000-01-01T00:00:00')
+ numpy.datetime64('2000-01-01T00:00:00-0800') # note the timezone offset -08:00
+
+A consensus of datetime64 users agreed that this behavior is undesirable
+and at odds with how datetime64 is usually used (e.g., by pandas_). For
+most use cases, a timezone naive datetime type is preferred, similar to the
+``datetime.datetime`` type in the Python standard library. Accordingly,
+datetime64 no longer assumes that input is in local time, nor does it print
+local times::
+
+ >>>> np.datetime64('2000-01-01T00:00:00')
+ numpy.datetime64('2000-01-01T00:00:00')
+
+For backwards compatibility, datetime64 still parses timezone offsets, which
+it handles by converting to UTC. However, the resulting datetime is timezone
+naive::
+
+ >>> np.datetime64('2000-01-01T00:00:00-08')
+ DeprecationWarning: parsing timezone aware datetimes is deprecated;
+ this will raise an error in the future
+ numpy.datetime64('2000-01-01T08:00:00')
+
+As a corollary to this change, we no longer prohibit casting between datetimes
+with date units and datetimes with time units. With timezone naive datetimes,
+the rule for casting from dates to times is no longer ambiguous.
+
+pandas_: http://pandas.pydata.org
+
+``linalg.norm`` return type changes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The return type of the ``linalg.norm`` function is now floating point without
+exception. Some of the norm types previously returned integers.
+
+and returns floating results.polynomial fit changes
+~~~~~~~~~~~~~~~~~~~~~~
+The various fit functions in the numpy polynomial package no longer accept
+non-integers for degree specification.
+
+*np.dot* now raises ``TypeError`` instead of ``ValueError``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+This behaviour mimics that of other functions such as ``np.inner``. If the two
+arguments cannot be cast to a common type, it could have raised a ``TypeError``
+or ``ValueError`` depending on their order. Now, ``np.dot`` will now always
+raise a ``TypeError``.
... 81923 lines suppressed ...
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