Building and installing SciPy
+++++++++++++++++++++++++++++

See http://www.scipy.org/Installing_SciPy/
for more extensive (and possibly more up-to-date) build instructions.

.. Contents::

INTRODUCTION
============

It is *strongly* recommended that you use either a complete scientific Python
distribution or binary packages on your platform if they are available, in
particular on Windows and Mac OS X. You should not attempt to build SciPy if
you are not familiar with compiling software from sources.

Recommended distributions are:

  - Enthought Canopy (https://www.enthought.com/products/canopy/)
  - Anaconda (https://store.continuum.io/cshop/anaconda)
  - Python(x,y) (http://code.google.com/p/pythonxy/)
  - WinPython (http://code.google.com/p/winpython/)


PREREQUISITES
=============

SciPy requires the following software installed for your platform:

1) Python__ 2.6.x or newer

__ http://www.python.org

2) NumPy__ 1.6.2 or newer (note: SciPy trunk at times requires latest NumPy
   trunk).

__ http://www.numpy.org/

3) If you want to build the documentation: Sphinx__ 1.1.0 or newer

__ http://sphinx-doc.org/

4) If you want to build SciPy master or other unreleased version from source
   (Cython-generated C sources are included in official releases):
   Cython__ 0.22 or newer

__ http://cython.org/

Windows
-------

Compilers
~~~~~~~~~

It is recommended to use the mingw__ compilers on Windows: you will need gcc
(C), g++ (C++) and g77 (Fortran) compilers.

__ http://www.mingw.org

Blas/Lapack
~~~~~~~~~~~

Blas/Lapack are core routines for linear algebra (vector/matrix operations).
You should use ATLAS__ with a full LAPACK, or simple BLAS/LAPACK built with g77
from netlib__ sources. Building those libraries on windows may be difficult, as
they assume a unix-style environment. Please use the binaries if you don't feel
comfortable with cygwin, make and similar tools.

__ http://math-atlas.sourceforge.net/
__ http://www.netlib.org/lapack/

Mac OS X
--------

Compilers
~~~~~~~~~

It is recommended to use gcc. gcc is available for free when installing
`Xcode`_, the developer toolsuite on Mac OS X. You also need a fortran compiler,
which is not included with Xcode: you should use gfortran from this page:

   http://r.research.att.com/tools/

Please do NOT use gfortran from `hpc.sourceforge.net <http://hpc.sourceforge.net>`_, it is known to generate
buggy scipy binaries.

.. _Xcode: http://developer.apple.com/TOOLS/xcode

Blas/Lapack
~~~~~~~~~~~

Mac OS X includes the Accelerate framework: it should be detected without any
intervention when building SciPy.

Linux
-----

Most common distributions include all the dependencies. Here are some
instructions for the most common ones:

Ubuntu >= 8.10
~~~~~~~~~~~~~~

You can get all the dependencies as follows::

        sudo apt-get install python python-dev libatlas-base-dev gcc gfortran g++

Ubuntu < 8.10, Debian
~~~~~~~~~~~~~~~~~~~~~

You can get all the dependencies as follows::

        sudo apt-get install python python-dev atlas3-base-dev gcc g77 g++

OpenSuse >= 10
~~~~~~~~~~~~~~

RHEL 
~~~~

Fedora Core 
~~~~~~~~~~~

GETTING SCIPY
=============

For the latest information, see the web site:

  http://www.scipy.org


Development version from Git
----------------------------
Use the command::

  git clone https://github.com/scipy/scipy.git

Before building and installing from git, remove the old installation
(e.g. in /usr/lib/python3.4/site-packages/scipy or
$HOME/lib/python3.4/site-packages/scipy).  Then type::

  cd scipy
  git clean -xdf
  python setup.py install
 
Documentation
-------------
Type::

  cd scipy
  python setup.py build_sphinx

INSTALLATION
============

First make sure that all SciPy prerequisites are installed and working
properly.  Then be sure to remove any old SciPy installations (e.g.
/usr/lib/python3.4/site-packages/scipy or $HOME/lib/python3.4/
site-packages/scipy). On windows, if you installed scipy previously from a
binary, use the remove facility from the add/remove software panel, or remote
the scipy directory by hand if you installed from sources (e.g.
C:\Python34\Lib\site-packages\scipy for python 3.4).

From tarballs
-------------
Unpack ``SciPy-<version>.tar.gz``, change to the ``SciPy-<version>/``
directory, and run
::

  python setup.py install

This may take several minutes to an hour depending on the speed of your
computer.  To install to a user-specific location instead, run::

  python setup.py install --prefix=$MYDIR

where $MYDIR is, for example, $HOME or $HOME/usr.

        ** Note 1: On Unix, you should avoid installing in /usr, but rather in
        /usr/local or somewhere else. /usr is generally 'owned' by your package
        manager, and you may overwrite a packaged scipy this way.

TESTING
=======

To test SciPy after installation (highly recommended), execute in Python

   >>> import scipy
   >>> scipy.test()

To run the full test suite use

   >>> scipy.test('full')

Please note that you must have version 0.10 or later of the 'nose' test
framework installed in order to run the tests.  More information about nose is
available on the website__.

__ http://somethingaboutorange.com/mrl/projects/nose/

COMPILER NOTES
==============

Note that SciPy is developed mainly using GNU compilers. Compilers from
other vendors such as Intel, Absoft, Sun, NAG, Compaq, Vast, Portland,
Lahey, HP, IBM are supported in the form of community feedback.

gcc__ compiler is recommended.  gcc 3.x and 4.x are known to work. 
If building on OS X, you should use the provided gcc by xcode tools, and the
gfortran compiler available here:

http://r.research.att.com/tools/

You can specify which Fortran compiler to use by using the following
install command::

  python setup.py config_fc --fcompiler=<Vendor> install

To see a valid list of <Vendor> names, run::

  python setup.py config_fc --help-fcompiler

IMPORTANT: It is highly recommended that all libraries that scipy uses (e.g.
blas and atlas libraries) are built with the same Fortran compiler. In most
cases, if you mix compilers, you will not be able to import scipy at best, have
crashes and random results at worse.

__ http://gcc.gnu.org/

Using non-GNU Fortran compiler with gcc/g77 compiled Atlas/Lapack libraries
---------------------------------------------------------------------------

When Atlas/Lapack libraries are compiled with GNU compilers but
one wishes to build scipy with some non-GNU Fortran compiler then
linking extension modules may require -lg2c. You can specify it
in installation command line as follows::

  python setup.py build build_ext -lg2c install

If using non-GNU C compiler or linker, the location of g2c library can
be specified in a similar manner using -L/path/to/libg2c.a after
build_ext command.

Intel Fortran Compiler
----------------------

Note that code compiled by the Intel Fortran Compiler (IFC) is not
binary compatible with code compiled by g77. Therefore, when using IFC,
all Fortran codes used in SciPy must be compiled with IFC. This also
includes the LAPACK, BLAS, and ATLAS libraries. Using GCC for compiling
C code is OK. IFC version 5.0 is not supported (because it has bugs that
cause SciPy's tests to segfault).

Minimum IFC flags for building LAPACK and ATLAS are
::

  -FI -w90 -w95 -cm -O3 -unroll

Also consult 'ifc -help' for additional optimization flags suitable
for your computers CPU.

If you want to have the LAPACK tests pass the 'ieee' compliancy test, you have to
use the -mp (='more precise') compiler option. Note that there's a significant hit
on the resulting performance though, reducing it to almost GNU level, but not quite.

When finishing LAPACK build, you must recompile ?lamch.f, xerbla.f
with optimization disabled (otherwise infinite loops occur when using
these routines)::

  make lapacklib   # in /path/to/src/LAPACK/
  cd SRC
  ifc -FI -w90 -w95 -cm -O0 -c ?lamch.f xerbla.f
  cd ..
  make lapacklib


KNOWN INSTALLATION PROBLEMS
===========================

BLAS sources shipped with LAPACK are incomplete
-----------------------------------------------
Some distributions (e.g. Redhat Linux 7.1) provide BLAS libraries that
are built from such incomplete sources and therefore cause import
errors like
::

  ImportError: .../fblas.so: undefined symbol: srotmg_

Fix:
  Use ATLAS or the official release of BLAS libraries.

LAPACK library provided by ATLAS is incomplete
----------------------------------------------
You will notice it when getting import errors like
::

  ImportError: .../flapack.so : undefined symbol: sgesdd_

To be sure that SciPy is built against a complete LAPACK, check the
size of the file liblapack.a -- it should be about 6MB. The location
of liblapack.a is shown by executing
::

    python /lib/python3.4/site-packages/numpy/distutils/system_info.py

(or the appropriate installation directory).

To fix: follow the instructions in

    http://math-atlas.sourceforge.net/errata.html#completelp

  to create a complete liblapack.a. Then copy liblapack.a to the same
  location where libatlas.a is installed and retry with scipy build.

Using non-GNU Fortran Compiler
------------------------------
If import scipy shows a message
::

  ImportError: undefined symbol: s_wsfe

and you are using non-GNU Fortran compiler, then it means that any of
the (may be system provided) Fortran libraries such as LAPACK or BLAS
were compiled with g77. See also compilers notes above.

Recommended fix: Recompile all Fortran libraries with the same Fortran
compiler and rebuild/reinstall scipy.

Another fix: See `Using non-GNU Fortran compiler with gcc/g77 compiled
Atlas/Lapack libraries` section above.


TROUBLESHOOTING
===============

If you experience problems when building/installing/testing SciPy, you
can ask help from scipy-user@scipy.org or scipy-dev@scipy.org mailing
lists. Please include the following information in your message:

NOTE: You can generate some of the following information (items 1-5,7)
in one command:: 

  python -c 'from numpy.f2py.diagnose import run; run()'

1) Platform information::

     python -c 'import os, sys; print(os.name, sys.platform)'
     uname -a
     OS, its distribution name and version information
     etc.

2) Information about C,C++,Fortran compilers/linkers as reported by
   the compilers when requesting their version information, e.g.,
   the output of
   ::

     gcc -v
     g77 --version

3) Python version::

     python -c 'import sys; print(sys.version)'

4) NumPy version::

     python -c 'import numpy; print(numpy.__version__)'

5) ATLAS version, the locations of atlas and lapack libraries, building
   information if any. If you have ATLAS version 3.3.6 or newer, then
   give the output of the last command in
   ::

     cd scipy/Lib/linalg
     python setup_atlas_version.py build_ext --inplace --force
     python -c 'import atlas_version'

7) The output of the following commands
   ::

     python INSTALLDIR/numpy/distutils/system_info.py

where INSTALLDIR is, for example, /usr/lib/python3.4/site-packages/.

8) Feel free to add any other relevant information.
   For example, the full output (both stdout and stderr) of the SciPy
   installation command can be very helpful. Since this output can be
   rather large, ask before sending it into the mailing list (or
   better yet, to one of the developers, if asked).

9) In case of failing to import extension modules, the output of
   ::

     ldd /path/to/ext_module.so

   can be useful.

You may find the following notes useful:

  http://www.tuxedo.org/~esr/faqs/smart-questions.html

  http://www.chiark.greenend.org.uk/~sgtatham/bugs.html