.. -*- rest -*-

NumPy Distutils - Users Guide
=============================

.. contents::

SciPy structure
'''''''''''''''

Currently SciPy project consists of two packages:

- NumPy --- it provides packages like:

  + numpy.distutils - extension to Python distutils
  + numpy.f2py - a tool to bind Fortran/C codes to Python
  + numpy.core - future replacement of Numeric and numarray packages
  + numpy.lib - extra utility functions
  + numpy.testing - numpy-style tools for unit testing
  + etc

- SciPy --- a collection of scientific tools for Python.

The aim of this document is to describe how to add new tools to SciPy.


Requirements for SciPy packages
'''''''''''''''''''''''''''''''

SciPy consists of Python packages, called SciPy packages, that are
available to Python users via the ``scipy`` namespace. Each SciPy package
may contain other SciPy packages. And so on. Therefore, the SciPy
directory tree is a tree of packages with arbitrary depth and width.
Any SciPy package may depend on NumPy packages but the dependence on other
SciPy packages should be kept minimal or zero.

A SciPy package contains, in addition to its sources, the following
files and directories:

  + ``setup.py`` --- building script
  + ``__init__.py`` --- package initializer
  + ``tests/`` --- directory of unittests

Their contents are described below.

The ``setup.py`` file
'''''''''''''''''''''

In order to add a Python package to SciPy, its build script (``setup.py``)
must meet certain requirements. The most important requirement is that the
package define a ``configuration(parent_package='',top_path=None)`` function
which returns a dictionary suitable for passing to
``numpy.distutils.core.setup(..)``. To simplify the construction of
this dictionary, ``numpy.distutils.misc_util`` provides the
``Configuration`` class, described below.

SciPy pure Python package example
---------------------------------

Below is an example of a minimal ``setup.py`` file for a pure SciPy package::

  #!/usr/bin/env python
  def configuration(parent_package='',top_path=None):
      from numpy.distutils.misc_util import Configuration
      config = Configuration('mypackage',parent_package,top_path)
      return config

  if __name__ == "__main__":
      from numpy.distutils.core import setup
      #setup(**configuration(top_path='').todict())
      setup(configuration=configuration)

The arguments of the ``configuration`` function specify the name of
parent SciPy package (``parent_package``) and the directory location
of the main ``setup.py`` script (``top_path``).  These arguments,
along with the name of the current package, should be passed to the
``Configuration`` constructor.

The ``Configuration`` constructor has a fourth optional argument,
``package_path``, that can be used when package files are located in
a different location than the directory of the ``setup.py`` file.

Remaining ``Configuration`` arguments are all keyword arguments that will
be used to initialize attributes of ``Configuration``
instance. Usually, these keywords are the same as the ones that
``setup(..)`` function would expect, for example, ``packages``,
``ext_modules``, ``data_files``, ``include_dirs``, ``libraries``,
``headers``, ``scripts``, ``package_dir``, etc.  However, the direct
specification of these keywords is not recommended as the content of
these keyword arguments will not be processed or checked for the
consistency of SciPy building system.

Finally, ``Configuration`` has ``.todict()`` method that returns all
the configuration data as a dictionary suitable for passing on to the
``setup(..)`` function.

``Configuration`` instance attributes
-------------------------------------

In addition to attributes that can be specified via keyword arguments
to ``Configuration`` constructor, ``Configuration`` instance (let us
denote as ``config``) has the following attributes that can be useful
in writing setup scripts:

+ ``config.name`` - full name of the current package. The names of parent
  packages can be extracted as ``config.name.split('.')``.

+ ``config.local_path`` - path to the location of current ``setup.py`` file.

+ ``config.top_path`` - path to the location of main ``setup.py`` file.

``Configuration`` instance methods
----------------------------------

+ ``config.todict()`` --- returns configuration dictionary suitable for
  passing to ``numpy.distutils.core.setup(..)`` function.

+ ``config.paths(*paths) --- applies ``glob.glob(..)`` to items of
  ``paths`` if necessary. Fixes ``paths`` item that is relative to
  ``config.local_path``.

+ ``config.get_subpackage(subpackage_name,subpackage_path=None)`` ---
  returns a list of subpackage configurations. Subpackage is looked in the
  current directory under the name ``subpackage_name`` but the path
  can be specified also via optional ``subpackage_path`` argument.
  If ``subpackage_name`` is specified as ``None`` then the subpackage
  name will be taken the basename of ``subpackage_path``.
  Any ``*`` used for subpackage names are expanded as wildcards.

+ ``config.add_subpackage(subpackage_name,subpackage_path=None)`` ---
  add SciPy subpackage configuration to the current one. The meaning
  and usage of arguments is explained above, see
  ``config.get_subpackage()`` method.

+ ``config.add_data_files(*files)`` --- prepend ``files`` to ``data_files``
  list. If ``files`` item is a tuple then its first element defines
  the suffix of where data files are copied relative to package installation
  directory and the second element specifies the path to data
  files. By default data files are copied under package installation
  directory. For example,

  ::

    config.add_data_files('foo.dat',
	                  ('fun',['gun.dat','nun/pun.dat','/tmp/sun.dat']),
                          'bar/car.dat'.
                          '/full/path/to/can.dat',
                          )

  will install data files to the following locations

  ::

    <installation path of config.name package>/
      foo.dat
      fun/
        gun.dat
	pun.dat
        sun.dat
      bar/
        car.dat
      can.dat

  Path to data files can be a function taking no arguments and
  returning path(s) to data files -- this is a useful when data files
  are generated while building the package. (XXX: explain the step
  when this function are called exactly)

+ ``config.add_data_dir(data_path)`` --- add directory ``data_path``
  recursively to ``data_files``. The whole directory tree starting at
  ``data_path`` will be copied under package installation directory.
  If ``data_path`` is a tuple then its first element defines
  the suffix of where data files are copied relative to package installation
  directory and the second element specifies the path to data directory.
  By default, data directory are copied under package installation
  directory under the basename of ``data_path``. For example,

  ::

    config.add_data_dir('fun')  # fun/ contains foo.dat bar/car.dat
    config.add_data_dir(('sun','fun'))
    config.add_data_dir(('gun','/full/path/to/fun'))

  will install data files to the following locations

  ::

    <installation path of config.name package>/
      fun/
         foo.dat
         bar/
            car.dat
      sun/
         foo.dat
         bar/
            car.dat
      gun/
         foo.dat
         bar/
            car.dat

+ ``config.add_include_dirs(*paths)`` --- prepend ``paths`` to
  ``include_dirs`` list. This list will be visible to all extension
  modules of the current package.

+ ``config.add_headers(*files)`` --- prepend ``files`` to ``headers``
  list. By default, headers will be installed under
  ``<prefix>/include/pythonX.X/<config.name.replace('.','/')>/``
  directory. If ``files`` item is a tuple then it's first argument
  specifies the installation suffix relative to
  ``<prefix>/include/pythonX.X/`` path.  This is a Python distutils
  method; its use is discouraged for NumPy and SciPy in favour of
  ``config.add_data_files(*files)``.

+ ``config.add_scripts(*files)`` --- prepend ``files`` to ``scripts``
  list. Scripts will be installed under ``<prefix>/bin/`` directory.

+ ``config.add_extension(name,sources,**kw)`` --- create and add an
  ``Extension`` instance to ``ext_modules`` list. The first argument
  ``name`` defines the name of the extension module that will be
  installed under ``config.name`` package. The second argument is
  a list of sources. ``add_extension`` method takes also keyword
  arguments that are passed on to the ``Extension`` constructor.
  The list of allowed keywords is the following: ``include_dirs``,
  ``define_macros``, ``undef_macros``, ``library_dirs``, ``libraries``,
  ``runtime_library_dirs``, ``extra_objects``, ``extra_compile_args``,
  ``extra_link_args``, ``export_symbols``, ``swig_opts``, ``depends``,
  ``language``, ``f2py_options``, ``module_dirs``, ``extra_info``,
  ``extra_f77_compile_args``, ``extra_f90_compile_args``.

  Note that ``config.paths`` method is applied to all lists that
  may contain paths. ``extra_info`` is a dictionary or a list
  of dictionaries that content will be appended to keyword arguments.
  The list ``depends`` contains paths to files or directories
  that the sources of the extension module depend on. If any path
  in the ``depends`` list is newer than the extension module, then
  the module will be rebuilt.

  The list of sources may contain functions ('source generators')
  with a pattern ``def <funcname>(ext, build_dir): return
  <source(s) or None>``. If ``funcname`` returns ``None``, no sources
  are generated. And if the ``Extension`` instance has no sources
  after processing all source generators, no extension module will
  be built. This is the recommended way to conditionally define
  extension modules. Source generator functions are called by the
  ``build_src`` command of ``numpy.distutils``.

  For example, here is a typical source generator function::

    def generate_source(ext,build_dir):
        import os
        from distutils.dep_util import newer
        target = os.path.join(build_dir,'somesource.c')
        if newer(target,__file__):
            # create target file
        return target

  The first argument contains the Extension instance that can be
  useful to access its attributes like ``depends``, ``sources``,
  etc. lists and modify them during the building process.
  The second argument gives a path to a build directory that must
  be used when creating files to a disk.

+ ``config.add_library(name, sources, **build_info)`` --- add a
  library to ``libraries`` list. Allowed keywords arguments are
  ``depends``, ``macros``, ``include_dirs``, ``extra_compiler_args``,
  ``f2py_options``, ``extra_f77_compile_args``,
  ``extra_f90_compile_args``.  See ``.add_extension()`` method for
  more information on arguments.

+ ``config.have_f77c()`` --- return True if Fortran 77 compiler is
  available (read: a simple Fortran 77 code compiled successfully).

+ ``config.have_f90c()`` --- return True if Fortran 90 compiler is
  available (read: a simple Fortran 90 code compiled successfully).

+ ``config.get_version()`` --- return version string of the current package,
  ``None`` if version information could not be detected. This methods
  scans files ``__version__.py``, ``<packagename>_version.py``,
  ``version.py``, ``__svn_version__.py`` for string variables
  ``version``, ``__version__``, ``<packagename>_version``.

+ ``config.make_svn_version_py()`` --- appends a data function to
  ``data_files`` list that will generate ``__svn_version__.py`` file
  to the current package directory. The file will be removed from
  the source directory when Python exits.

+ ``config.get_build_temp_dir()`` --- return a path to a temporary
  directory. This is the place where one should build temporary
  files.

+ ``config.get_distribution()`` --- return distutils ``Distribution``
  instance.

+ ``config.get_config_cmd()`` --- returns ``numpy.distutils`` config
  command instance.

+ ``config.get_info(*names)`` ---


.. _templating:

Conversion of ``.src`` files using Templates
--------------------------------------------

NumPy distutils supports automatic conversion of source files named
<somefile>.src. This facility can be used to maintain very similar
code blocks requiring only simple changes between blocks. During the
build phase of setup, if a template file named <somefile>.src is
encountered, a new file named <somefile> is constructed from the
template and placed in the build directory to be used instead. Two
forms of template conversion are supported. The first form occurs for
files named <file>.ext.src where ext is a recognized Fortran
extension (f, f90, f95, f77, for, ftn, pyf). The second form is used
for all other cases.

.. index::
   single: code generation

Fortran files
-------------

This template converter will replicate all **function** and
**subroutine** blocks in the file with names that contain '<...>'
according to the rules in '<...>'. The number of comma-separated words
in '<...>' determines the number of times the block is repeated. What
these words are indicates what that repeat rule, '<...>', should be
replaced with in each block. All of the repeat rules in a block must
contain the same number of comma-separated words indicating the number
of times that block should be repeated. If the word in the repeat rule
needs a comma, leftarrow, or rightarrow, then prepend it with a
backslash ' \'. If a word in the repeat rule matches ' \\<index>' then
it will be replaced with the <index>-th word in the same repeat
specification. There are two forms for the repeat rule: named and
short.

Named repeat rule
^^^^^^^^^^^^^^^^^

A named repeat rule is useful when the same set of repeats must be
used several times in a block. It is specified using <rule1=item1,
item2, item3,..., itemN>, where N is the number of times the block
should be repeated. On each repeat of the block, the entire
expression, '<...>' will be replaced first with item1, and then with
item2, and so forth until N repeats are accomplished. Once a named
repeat specification has been introduced, the same repeat rule may be
used **in the current block** by referring only to the name
(i.e. <rule1>.


Short repeat rule
^^^^^^^^^^^^^^^^^

A short repeat rule looks like <item1, item2, item3, ..., itemN>. The
rule specifies that the entire expression, '<...>' should be replaced
first with item1, and then with item2, and so forth until N repeats
are accomplished.


Pre-defined names
^^^^^^^^^^^^^^^^^

The following predefined named repeat rules are available:

- <prefix=s,d,c,z>

- <_c=s,d,c,z>

- <_t=real, double precision, complex, double complex>

- <ftype=real, double precision, complex, double complex>

- <ctype=float, double, complex_float, complex_double>

- <ftypereal=float, double precision, \\0, \\1>

- <ctypereal=float, double, \\0, \\1>


Other files
------------

Non-Fortran files use a separate syntax for defining template blocks
that should be repeated using a variable expansion similar to the
named repeat rules of the Fortran-specific repeats.

NumPy Distutils preprocesses C source files (extension: :file:`.c.src`) written
in a custom templating language to generate C code. The :c:data:`@` symbol is
used to wrap macro-style variables to empower a string substitution mechanism
that might describe (for instance) a set of data types.

The template language blocks are delimited by :c:data:`/**begin repeat`
and :c:data:`/**end repeat**/` lines, which may also be nested using
consecutively numbered delimiting lines such as :c:data:`/**begin repeat1`
and :c:data:`/**end repeat1**/`:

1. "/\**begin repeat "on a line by itself marks the beginning of
a segment that should be repeated.

2. Named variable expansions are defined using ``#name=item1, item2, item3,
..., itemN#`` and placed on successive lines. These variables are
replaced in each repeat block with corresponding word. All named
variables in the same repeat block must define the same number of
words.

3. In specifying the repeat rule for a named variable, ``item*N`` is short-
hand for ``item, item, ..., item`` repeated N times. In addition,
parenthesis in combination with \*N can be used for grouping several
items that should be repeated. Thus, #name=(item1, item2)*4# is
equivalent to #name=item1, item2, item1, item2, item1, item2, item1,
item2#

4. "\*/ "on a line by itself marks the end of the variable expansion
naming. The next line is the first line that will be repeated using
the named rules.

5. Inside the block to be repeated, the variables that should be expanded
are specified as ``@name@``

6. "/\**end repeat**/ "on a line by itself marks the previous line
as the last line of the block to be repeated.

7. A loop in the NumPy C source code may have a ``@TYPE@`` variable, targeted
for string substitution, which is preprocessed to a number of otherwise
identical loops with several strings such as INT, LONG, UINT, ULONG. The
``@TYPE@`` style syntax thus reduces code duplication and maintenance burden by
mimicking languages that have generic type support.

The above rules may be clearer in the following template source example:

.. code-block:: NumPyC
   :linenos:
   :emphasize-lines: 3, 13, 29, 31

    /* TIMEDELTA to non-float types */

    /**begin repeat
     *
     * #TOTYPE = BYTE, UBYTE, SHORT, USHORT, INT, UINT, LONG, ULONG,
     *           LONGLONG, ULONGLONG, DATETIME,
     *           TIMEDELTA#
     * #totype = npy_byte, npy_ubyte, npy_short, npy_ushort, npy_int, npy_uint,
     *           npy_long, npy_ulong, npy_longlong, npy_ulonglong,
     *           npy_datetime, npy_timedelta#
     */

    /**begin repeat1
     *
     * #FROMTYPE = TIMEDELTA#
     * #fromtype = npy_timedelta#
     */
    static void
    @FROMTYPE@_to_@TOTYPE@(void *input, void *output, npy_intp n,
            void *NPY_UNUSED(aip), void *NPY_UNUSED(aop))
    {
        const @fromtype@ *ip = input;
        @totype@ *op = output;

        while (n--) {
            *op++ = (@totype@)*ip++;
        }
    }
    /**end repeat1**/

    /**end repeat**/

The preprocessing of generically typed C source files (whether in NumPy
proper or in any third party package using NumPy Distutils) is performed
by `conv_template.py`_.
The type specific C files generated (extension: .c)
by these modules during the build process are ready to be compiled. This
form of generic typing is also supported for C header files (preprocessed
to produce .h files).

.. _conv_template.py: https://github.com/numpy/numpy/blob/master/numpy/distutils/conv_template.py

Useful functions in ``numpy.distutils.misc_util``
-------------------------------------------------

+ ``get_numpy_include_dirs()`` --- return a list of NumPy base
  include directories. NumPy base include directories contain
  header files such as ``numpy/arrayobject.h``, ``numpy/funcobject.h``
  etc. For installed NumPy the returned list has length 1
  but when building NumPy the list may contain more directories,
  for example, a path to ``config.h`` file that
  ``numpy/base/setup.py`` file generates and is used by ``numpy``
  header files.

+ ``append_path(prefix,path)`` --- smart append ``path`` to ``prefix``.

+ ``gpaths(paths, local_path='')`` --- apply glob to paths and prepend
  ``local_path`` if needed.

+ ``njoin(*path)`` --- join pathname components + convert ``/``-separated path
  to ``os.sep``-separated path and resolve ``..``, ``.`` from paths.
  Ex. ``njoin('a',['b','./c'],'..','g') -> os.path.join('a','b','g')``.

+ ``minrelpath(path)`` --- resolves dots in ``path``.

+ ``rel_path(path, parent_path)`` --- return ``path`` relative to ``parent_path``.

+ ``def get_cmd(cmdname,_cache={})`` --- returns ``numpy.distutils``
  command instance.

+ ``all_strings(lst)``

+ ``has_f_sources(sources)``

+ ``has_cxx_sources(sources)``

+ ``filter_sources(sources)`` --- return ``c_sources, cxx_sources,
  f_sources, fmodule_sources``

+ ``get_dependencies(sources)``

+ ``is_local_src_dir(directory)``

+ ``get_ext_source_files(ext)``

+ ``get_script_files(scripts)``

+ ``get_lib_source_files(lib)``

+ ``get_data_files(data)``

+ ``dot_join(*args)`` --- join non-zero arguments with a dot.

+ ``get_frame(level=0)`` --- return frame object from call stack with given level.

+ ``cyg2win32(path)``

+ ``mingw32()`` --- return ``True`` when using mingw32 environment.

+ ``terminal_has_colors()``, ``red_text(s)``, ``green_text(s)``,
  ``yellow_text(s)``, ``blue_text(s)``, ``cyan_text(s)``

+ ``get_path(mod_name,parent_path=None)`` --- return path of a module
  relative to parent_path when given. Handles also ``__main__`` and
  ``__builtin__`` modules.

+ ``allpath(name)`` --- replaces ``/`` with ``os.sep`` in ``name``.

+ ``cxx_ext_match``, ``fortran_ext_match``, ``f90_ext_match``,
  ``f90_module_name_match``

``numpy.distutils.system_info`` module
--------------------------------------

+ ``get_info(name,notfound_action=0)``
+ ``combine_paths(*args,**kws)``
+ ``show_all()``

``numpy.distutils.cpuinfo`` module
----------------------------------

+ ``cpuinfo``

``numpy.distutils.log`` module
------------------------------

+ ``set_verbosity(v)``


``numpy.distutils.exec_command`` module
---------------------------------------

+ ``get_pythonexe()``
+ ``find_executable(exe, path=None)``
+ ``exec_command( command, execute_in='', use_shell=None, use_tee=None, **env )``

The ``__init__.py`` file
''''''''''''''''''''''''

The header of a typical SciPy ``__init__.py`` is::

  """
  Package docstring, typically with a brief description and function listing.
  """

  # py3k related imports
  from __future__ import division, print_function, absolute_import

  # import functions into module namespace
  from .subpackage import *
  ...

  __all__ = [s for s in dir() if not s.startswith('_')]

  from numpy.testing import Tester
  test = Tester().test
  bench = Tester().bench

Note that NumPy submodules still use a file named ``info.py`` in which the
module docstring and ``__all__`` dict are defined.  These files will be removed
at some point.

Extra features in NumPy Distutils
'''''''''''''''''''''''''''''''''

Specifying config_fc options for libraries in setup.py script
-------------------------------------------------------------

It is possible to specify config_fc options in setup.py scripts.
For example, using

  config.add_library('library',
                     sources=[...],
                     config_fc={'noopt':(__file__,1)})

will compile the ``library`` sources without optimization flags.

It's recommended to specify only those config_fc options in such a way
that are compiler independent.

Getting extra Fortran 77 compiler options from source
-----------------------------------------------------

Some old Fortran codes need special compiler options in order to
work correctly. In order to specify compiler options per source
file, ``numpy.distutils`` Fortran compiler looks for the following
pattern::

  CF77FLAGS(<fcompiler type>) = <fcompiler f77flags>

in the first 20 lines of the source and use the ``f77flags`` for
specified type of the fcompiler (the first character ``C`` is optional).

TODO: This feature can be easily extended for Fortran 90 codes as
well. Let us know if you would need such a feature.
