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-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
-https://scipy.org/install.html.
-
-
-.. Contents::
-
-Prerequisites
-=============
-
-Building NumPy requires the following installed software:
-
-1) Python__ 3.8.x or newer.
-
-   Please note that the Python development headers also need to be installed,
-   e.g., on Debian/Ubuntu one needs to install both `python3` and
-   `python3-dev`. On Windows and macOS this is normally not an issue.
-
-2) Cython >= 0.29.30 but < 3.0
-
-3) pytest__ (optional)
-
-   This is required for testing NumPy, but not for using it.
-
-4) Hypothesis__ (optional) 5.3.0 or later
-
-   This is required for testing NumPy, but not for using it.
-
-Python__ https://www.python.org/
-pytest__ https://docs.pytest.org/en/stable/
-Hypothesis__ https://hypothesis.readthedocs.io/en/latest/
-
-
-.. note::
-
-   If you want to build NumPy in order to work on NumPy itself, use
-   ``runtests.py``.  For more details, see
-   https://numpy.org/devdocs/dev/development_environment.html
-
-.. note::
-
-   More extensive information on building NumPy is maintained at
-   https://numpy.org/devdocs/user/building.html#building-from-source
-
-
-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
-
-See `Requirements for Installing Packages <https://packaging.python.org/tutorials/installing-packages/>`_
-for more details.
-
-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 needs 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.
-
-For Fortran, ``gfortran`` works, ``g77`` does not.  In case ``g77`` is
-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 (in particular if you need to
-build SciPy as well, because that requires a Fortran compiler). Currently, the
-most robust option is to use MSVC (for NumPy only). If you also need SciPy,
-you can either use MSVC + Intel Fortran or the Intel compiler suite.
-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, our current recommendation is to
-use Docker or Windows subsystem for Linux (WSL).  See
-https://scipy.github.io/devdocs/dev/contributor/contributor_toc.html#development-environment
-for more details.
-
-
-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.
-
-For an overview of the state of BLAS/LAPACK libraries on Windows, see
-`here <https://mingwpy.github.io/blas_lapack.html>`_.
-
-macOS
------
-
-You will need to install a BLAS/LAPACK library. We recommend using OpenBLAS or
-Intel MKL. Apple's Accelerate also still works, however it has bugs and we are
-likely to drop support for it in the near future.
-
-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 and SciPy
-`mailing list <https://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.