0bb490a Jan 4, 2016
@bgrant @WarrenWeckesser @fuglede @rgommers @juliantaylor
2532 lines (2005 sloc) 105 KB
Weave Documentation
By Eric Jones
.. contents::
The ``weave`` package provides tools for including C/C++ code within Python
code. This offers both another level of optimization to those who need it, and
an easy way to modify and extend any supported extension libraries such as
wxPython and hopefully VTK soon. Inlining C/C++ code within Python generally
results in speedups of 1.5x to 30x over algorithms written in pure Python
(however, it is also possible to slow things down...). Generally algorithms
that require a large number of calls to the Python API don't benefit as much
from the conversion to C/C++ as algorithms that have inner loops completely
convertible to C.
There are three basic ways to use ``weave``. The ``weave.inline()`` function
executes C code directly within Python, and ``weave.blitz()`` translates Python
NumPy expressions to C++ for fast execution. ``blitz()`` was the original
reason ``weave`` was built. For those interested in building extension
libraries, the ``ext_tools`` module provides classes for building extension
modules within Python.
Most of ``weave's`` functionality should work on Windows and Unix, although
some of its functionality requires ``gcc`` or a similarly modern C++ compiler
that handles templates well. Up to now, most testing has been done on Windows
2000 with Microsoft's C++ compiler (MSVC) and with gcc (mingw32 2.95.2 and
2.95.3-6). All tests also pass on Linux (RH 7.1 with gcc 2.96), and I've had
reports that it works on Debian also (thanks Pearu).
The ``inline`` and ``blitz`` functions provide new functionality to Python
(although I've recently learned about the `PyInline`_ project which may offer
similar functionality to ``inline``). On the other hand, tools for building
Python extension modules already exist (SWIG, SIP, pycpp, CXX, and others). As
of yet, I'm not sure where ``weave`` fits in this spectrum. It is closest in
flavor to CXX in that it makes creating new C/C++ extension modules pretty
easy. However, if you're wrapping a gaggle of legacy functions or classes, SWIG
and friends are definitely the better choice. ``weave`` is set up so that you
can customize how Python types are converted to C types in ``weave``. This is
great for ``inline()``, but, for wrapping legacy code, it is more flexible to
specify things the other way around -- that is, how C types map to Python
types. This ``weave`` does not do. I guess it would be possible to build such a
tool on top of ``weave``, but with good tools like SWIG around, I'm not sure
the effort produces any new capabilities. Things like function overloading are
probably easily implemented in ``weave`` and it might be easier to mix Python/C
code in function calls, but nothing beyond this comes to mind. So, if you're
developing new extension modules or optimizing Python functions in C,
``weave.ext_tools()`` might be the tool for you. If you're wrapping legacy
code, stick with SWIG.
The next several sections give the basics of how to use ``weave``. We'll
discuss what's happening under the covers in more detail later on. Serious
users will need to at least look at the type conversion section to understand
how Python variables map to C/C++ types and how to customize this behavior.
One other note. If you don't know C or C++ then these docs are probably of
very little help to you. Further, it'd be helpful if you know something about
writing Python extensions. ``weave`` does quite a bit for you, but for
anything complex, you'll need to do some conversions, reference counting,
.. note::
``weave`` is actually part of the `SciPy`_ package. However, it
also works fine as a standalone package (you can install with
``python install`` in the scipy/weave directory). The examples here
are given as if it is used as a stand alone package. If you are using from
within scipy, you can use ``from scipy import weave`` and the examples will
work identically.
- Python
I use 2.1.1. Probably 2.0 or higher should work.
- C++ compiler
``weave`` uses ``distutils`` to actually build extension modules, so
it uses whatever compiler was originally used to build Python. ``weave``
itself requires a C++ compiler. If you used a C++ compiler to build
Python, your probably fine.
On Unix gcc is the preferred choice because I've done a little
testing with it. All testing has been done with gcc, but I expect the
majority of compilers should work for ``inline`` and ``ext_tools``. The
one issue I'm not sure about is that I've hard coded things so that
compilations are linked with the ``stdc++`` library. *Is this standard
across Unix compilers, or is this a gcc-ism?*
For ``blitz()``, you'll need a reasonably recent version of gcc.
2.95.2 works on windows and 2.96 looks fine on Linux. Other versions are
likely to work. Its likely that KAI's C++ compiler and maybe some others
will work, but I haven't tried. My advice is to use gcc for now unless
your willing to tinker with the code some.
On Windows, either MSVC or gcc (`mingw32`_) should work. Again,
you'll need gcc for ``blitz()`` as the MSVC compiler doesn't handle
templates well.
I have not tried Cygwin, so please report success if it works for
- NumPy
The python `NumPy`_ module is required for ``blitz()`` to
work and for numpy.distutils, which is used by weave.
There are currently two ways to get ``weave``. First, ``weave`` is part of
SciPy and is installed automatically (as a sub-package) whenever SciPy is
installed. Second, since ``weave`` is useful outside of the scientific
community, it has been set up so that it can be used as a stand-alone module.
The stand-alone version can be downloaded from `here`_. Instructions for
installing should be found there as well. file to simplify
Once ``weave`` is installed, fire up python and run its unit tests.
>>> import weave
>>> weave.test()
runs long time... spews tons of output and a few warnings
Ran 184 tests in 158.418s
This takes a while -- usually several minutes. On Unix with remote file
systems, I've had it take 15 or so minutes. In the end, it should run about
180 tests and spew some speed results along the way. If you get errors,
they'll be reported at the end of the output. Please report errors that you
find. Some tests are known to fail at this point.
If you only want to test a single module of the package, you can do this by
running test() for that specific module.
>>> import weave.scalar_spec
>>> weave.scalar_spec.test()
Ran 7 tests in 23.284s
Testing Notes:
- Windows 1
I've had some test fail on windows machines where I have msvc,
gcc-2.95.2 (in c:\gcc-2.95.2), and gcc-2.95.3-6 (in c:\gcc) all
installed. My environment has c:\gcc in the path and does not have
c:\gcc-2.95.2 in the path. The test process runs very smoothly until the
end where several test using gcc fail with cpp0 not found by g++. If I
check os.system('gcc -v') before running tests, I get gcc-2.95.3-6. If I
check after running tests (and after failure), I get gcc-2.95.2. ??huh??.
The os.environ['PATH'] still has c:\gcc first in it and is not corrupted
(msvc/distutils messes with the environment variables, so we have to undo
its work in some places). If anyone else sees this, let me know - - it
may just be an quirk on my machine (unlikely). Testing with the gcc-
2.95.2 installation always works.
- Windows 2
If you run the tests from PythonWin or some other GUI tool, you'll
get a ton of DOS windows popping up periodically as ``weave`` spawns the
compiler multiple times. Very annoying. Anyone know how to fix this?
- wxPython
wxPython tests are not enabled by default because importing wxPython
on a Unix machine without access to a X-term will cause the program to
exit. Anyone know of a safe way to detect whether wxPython can be
imported and whether a display exists on a machine?
This section has not been updated from old scipy weave and Numeric....
This section has a few benchmarks -- thats all people want to see anyway
right? These are mostly taken from running files in the ``weave/example``
directory and also from the test scripts. Without more information about what
the test actually do, their value is limited. Still, their here for the
curious. Look at the example scripts for more specifics about what problem
was actually solved by each run. These examples are run under windows 2000
using Microsoft Visual C++ and python2.1 on a 850 MHz PIII laptop with 320 MB
of RAM. Speed up is the improvement (degradation) factor of ``weave``
compared to conventional Python functions. ``The blitz()`` comparisons are
shown compared to NumPy.
inline and ext_tools
Speed up
binary search 1.50
fibonacci (recursive) 82.10
fibonacci (loop) 9.17
return None 0.14
map 1.20
dictionary sort 2.54
vector quantization 37.40
blitz -- double precision
Speed up
a = b + c 512x512 3.05
a = b + c + d 512x512 4.59
5 pt avg. filter, 2D Image 512x512 9.01
Electromagnetics (FDTD) 100x100x100 8.61
These benchmarks show ``blitz`` in the best possible light. NumPy (at least on
my machine) is significantly worse for double-precision than it is for
single-precision calculations. If you're interested in single-precision results, you
can pretty much divide the double-precision speed up by 3 and you'll be
``inline()`` compiles and executes C/C++ code on the fly. Variables in the
local and global Python scopes are also available in the C/C++ code. Values
are passed to the C/C++ code by assignment much like variables are passed
into a standard Python function. Values are returned from the C/C++ code
through a special argument called return_val. Also, the contents of mutable
objects can be changed within the C/C++ code and the changes remain after the
C code exits and returns to Python. (more on this later)
Here's a trivial ``printf`` example using ``inline()``::
>>> import weave
>>> a = 1
>>> weave.inline('printf("%d\\n",a);',['a'])
In this, its most basic form, ``inline(c_code, var_list)`` requires two
arguments. ``c_code`` is a string of valid C/C++ code. ``var_list`` is a list
of variable names that are passed from Python into C/C++. Here we have a
simple ``printf`` statement that writes the Python variable ``a`` to the
screen. The first time you run this, there will be a pause while the code is
written to a .cpp file, compiled into an extension module, loaded into
Python, cataloged for future use, and executed. On windows (850 MHz PIII),
this takes about 1.5 seconds when using Microsoft's C++ compiler (MSVC) and
6-12 seconds using gcc (mingw32 2.95.2). All subsequent executions of the
code will happen very quickly because the code only needs to be compiled
once. If you kill and restart the interpreter and then execute the same code
fragment again, there will be a much shorter delay in the fractions of
seconds range. This is because ``weave`` stores a catalog of all previously
compiled functions in an on disk cache. When it sees a string that has been
compiled, it loads the already compiled module and executes the appropriate
.. note::
If you try the ``printf`` example in a GUI shell such as IDLE,
PythonWin, PyShell, etc., you're unlikely to see the output. This is because
the C code is writing to stdout, instead of to the GUI window. This doesn't
mean that inline doesn't work in these environments -- it only means that
standard out in C is not the same as the standard out for Python in these
cases. Non input/output functions will work as expected.
Although effort has been made to reduce the overhead associated with calling
inline, it is still less efficient for simple code snippets than using
equivalent Python code. The simple ``printf`` example is actually slower by
30% or so than using Python ``print`` statement. And, it is not difficult to
create code fragments that are 8-10 times slower using inline than equivalent
Python. However, for more complicated algorithms, the speed up can be worth
while -- anywhere from 1.5-30 times faster. Algorithms that have to
manipulate Python objects (sorting a list) usually only see a factor of 2 or
so improvement. Algorithms that are highly computational or manipulate NumPy
arrays can see much larger improvements. The examples/ file shows a
factor of 30 or more improvement on the vector quantization algorithm that is
used heavily in information theory and classification problems.
More with printf
MSVC users will actually see a bit of compiler output that distutils does not
supress the first time the code executes::
>>> weave.inline(r'printf("%d\n",a);',['a'])
Creating library C:\DOCUME~1\eric\LOCALS~1\Temp\python21_compiled\temp
\Release\sc_e013937dbc8c647ac62438874e5795131.lib and
object C:\DOCUME~1\eric\LOCALS~1\Temp\python21_compiled\temp\Release\sc_e013937dbc8c647ac62438874e5795131.exp
Nothing bad is happening, its just a bit annoying. * Anyone know how to turn
this off?*
This example also demonstrates using 'raw strings'. The ``r`` preceding the
code string in the last example denotes that this is a 'raw string'. In raw
strings, the backslash character is not interpreted as an escape character,
and so it isn't necessary to use a double backslash to indicate that the '\n'
is meant to be interpreted in the C ``printf`` statement instead of by
Python. If your C code contains a lot of strings and control characters, raw
strings might make things easier. Most of the time, however, standard strings
work just as well.
The ``printf`` statement in these examples is formatted to print out
integers. What happens if ``a`` is a string? ``inline`` will happily, compile
a new version of the code to accept strings as input, and execute the code.
The result?
>>> a = 'string'
>>> weave.inline(r'printf("%d\n",a);',['a'])
In this case, the result is nonsensical, but also non-fatal. In other
situations, it might produce a compile time error because ``a`` is required
to be an integer at some point in the code, or it could produce a
segmentation fault. Its possible to protect against passing ``inline``
arguments of the wrong data type by using asserts in Python.
>>> a = 'string'
>>> def protected_printf(a):
... assert(type(a) == type(1))
... weave.inline(r'printf("%d\n",a);',['a'])
>>> protected_printf(1)
>>> protected_printf('string')
For printing strings, the format statement needs to be changed. Also, weave
doesn't convert strings to char*. Instead it uses CXX Py::String type, so you
have to do a little more work. Here we convert it to a C++ std::string and
then ask cor the char* version.
>>> a = 'string'
>>> weave.inline(r'printf("%s\n",std::string(a).c_str());',['a'])
.. admonition:: XXX
This is a little convoluted. Perhaps strings should convert to ``std::string``
objects instead of CXX objects. Or maybe to ``char*``.
As in this case, C/C++ code fragments often have to change to accept
different types. For the given printing task, however, C++ streams provide a
way of a single statement that works for integers and strings. By default,
the stream objects live in the std (standard) namespace and thus require the
use of ``std::``.
>>> weave.inline('std::cout << a << std::endl;',['a'])
>>> a = 'string'
>>> weave.inline('std::cout << a << std::endl;',['a'])
Examples using ``printf`` and ``cout`` are included in
More examples
This section shows several more advanced uses of ``inline``. It includes a
few algorithms from the `Python Cookbook`_ that have been re-written in
inline C to improve speed as well as a couple examples using NumPy and
Binary search
Lets look at the example of searching a sorted list of integers for a value.
For inspiration, we'll use Kalle Svensson's `binary_search()`_ algorithm
from the Python Cookbook. His recipe follows::
def binary_search(seq, t):
min = 0; max = len(seq) - 1
while 1:
if max < min:
return -1
m = (min + max) / 2
if seq[m] < t:
min = m + 1
elif seq[m] > t:
max = m - 1
return m
This Python version works for arbitrary Python data types. The C version
below is specialized to handle integer values. There is a little type
checking done in Python to assure that we're working with the correct data
types before heading into C. The variables ``seq`` and ``t`` don't need to be
declared because ``weave`` handles converting and declaring them in the C
code. All other temporary variables such as ``min, max``, etc. must be
declared -- it is C after all. Here's the new mixed Python/C function::
def c_int_binary_search(seq,t):
# do a little type checking in Python
assert(type(t) == type(1))
assert(type(seq) == type([]))
# now the C code
code = """
#line 29 ""
int val, m, min = 0;
int max = seq.length() - 1;
PyObject *py_val;
if (max < min )
return_val = Py::new_reference_to(Py::Int(-1));
m = (min + max) /2;
val = py_to_int(PyList_GetItem(seq.ptr(),m),"val");
if (val < t)
min = m + 1;
else if (val > t)
max = m - 1;
return_val = Py::new_reference_to(Py::Int(m));
return inline(code,['seq','t'])
We have two variables ``seq`` and ``t`` passed in. ``t`` is guaranteed (by
the ``assert``) to be an integer. Python integers are converted to C int
types in the transition from Python to C. ``seq`` is a Python list. By
default, it is translated to a CXX list object. Full documentation for the
CXX library can be found at its `website`_. The basics are that the CXX
provides C++ class equivalents for Python objects that simplify, or at least
object orientify, working with Python objects in C/C++. For example,
``seq.length()`` returns the length of the list. A little more about CXX and
its class methods, etc. is in the ** type conversions ** section.
.. note::
CXX uses templates and therefore may be a little less portable than
another alternative by Gordan McMillan called SCXX which was
inspired by CXX. It doesn't use templates so it should compile
faster and be more portable. SCXX has a few less features, but it
appears to me that it would mesh with the needs of weave quite well.
Hopefully xxx_spec files will be written for SCXX in the future, and
we'll be able to compare on a more empirical basis. Both sets of
spec files will probably stick around, it just a question of which
becomes the default.
Most of the algorithm above looks similar in C to the original Python code.
There are two main differences. The first is the setting of ``return_val``
instead of directly returning from the C code with a ``return`` statement.
``return_val`` is an automatically defined variable of type ``PyObject*``
that is returned from the C code back to Python. You'll have to handle
reference counting issues when setting this variable. In this example, CXX
classes and functions handle the dirty work. All CXX functions and classes
live in the namespace ``Py::``. The following code converts the integer ``m``
to a CXX ``Int()`` object and then to a ``PyObject*`` with an incremented
reference count using ``Py::new_reference_to()``.
return_val = Py::new_reference_to(Py::Int(m));
The second big differences shows up in the retrieval of integer values from
the Python list. The simple Python ``seq[i]`` call balloons into a C Python
API call to grab the value out of the list and then a separate call to
``py_to_int()`` that converts the PyObject* to an integer. ``py_to_int()``
includes both a NULL check and a ``PyInt_Check()`` call as well as the
conversion call. If either of the checks fail, an exception is raised. The
entire C++ code block is executed with in a ``try/catch`` block that handles
exceptions much like Python does. This removes the need for most error
checking code.
It is worth note that CXX lists do have indexing operators that result in
code that looks much like Python. However, the overhead in using them appears
to be relatively high, so the standard Python API was used on the
``seq.ptr()`` which is the underlying ``PyObject*`` of the List object.
The ``#line`` directive that is the first line of the C code block isn't
necessary, but it's nice for debugging. If the compilation fails because of
the syntax error in the code, the error will be reported as an error in the
Python file "" with an offset from the given line number (29
So what was all our effort worth in terms of efficiency? Well not a lot in
this case. The examples/ file runs both Python and C versions
of the functions As well as using the standard ``bisect`` module. If we run
it on a 1 million element list and run the search 3000 times (for 0- 2999),
here are the results we get::
C:\home\ej\wrk\scipy\weave\examples> python
Binary search for 3000 items in 1000000 length list of integers:
speed in python: 0.159999966621
speed of bisect: 0.121000051498
speed up: 1.32
speed in c: 0.110000014305
speed up: 1.45
speed in c(no asserts): 0.0900000333786
speed up: 1.78
So, we get roughly a 50-75% improvement depending on whether we use the
Python asserts in our C version. If we move down to searching a 10000 element
list, the advantage evaporates. Even smaller lists might result in the Python
version being faster. I'd like to say that moving to NumPy lists (and getting
rid of the GetItem() call) offers a substantial speed up, but my preliminary
efforts didn't produce one. I think the log(N) algorithm is to blame. Because
the algorithm is nice, there just isn't much time spent computing things, so
moving to C isn't that big of a win. If there are ways to reduce conversion
overhead of values, this may improve the C/Python speed up. Anyone have other
explanations or faster code, please let me know.
Dictionary Sort
The demo in examples/ is another example from the Python
CookBook. `This submission`_, by Alex Martelli, demonstrates how to return
the values from a dictionary sorted by their keys:
def sortedDictValues3(adict):
keys = adict.keys()
return map(adict.get, keys)
Alex provides 3 algorithms and this is the 3rd and fastest of the set. The C
version of this same algorithm follows::
def c_sort(adict):
assert(type(adict) == type({}))
code = """
#line 21 ""
Py::List keys = adict.keys();
Py::List items(keys.length()); keys.sort();
PyObject* item = NULL;
for(int i = 0; i < keys.length();i++)
item = PyList_GET_ITEM(keys.ptr(),i);
item = PyDict_GetItem(adict.ptr(),item);
return_val = Py::new_reference_to(items);
return inline_tools.inline(code,['adict'],verbose=1)
Like the original Python function, the C++ version can handle any Python
dictionary regardless of the key/value pair types. It uses CXX objects for
the most part to declare python types in C++, but uses Python API calls to
manipulate their contents. Again, this choice is made for speed. The C++
version, while more complicated, is about a factor of 2 faster than Python.
C:\home\ej\wrk\scipy\weave\examples> python
Dict sort of 1000 items for 300 iterations:
speed in python: 0.319999933243
[0, 1, 2, 3, 4]
speed in c: 0.151000022888
speed up: 2.12
[0, 1, 2, 3, 4]
NumPy -- cast/copy/transpose
CastCopyTranspose is a function called quite heavily by Linear Algebra
routines in the NumPy library. Its needed in part because of the row-major
memory layout of multi-dimensional Python (and C) arrays vs. the col-major
order of the underlying Fortran algorithms. For small matrices (say 100x100
or less), a significant portion of the common routines such as LU
decomposition or singular value decomposition are spent in this setup routine.
This shouldn't happen. Here is the Python version of the function using
standard NumPy operations.
def _castCopyAndTranspose(type, array):
if a.typecode() == type:
cast_array = copy.copy(NumPy.transpose(a))
cast_array = copy.copy(NumPy.transpose(a).astype(type))
return cast_array
And the following is a inline C version of the same function::
from weave.blitz_tools import blitz_type_factories
from weave import scalar_spec
from weave import inline
def _cast_copy_transpose(type,a_2d):
assert(len(shape(a_2d)) == 2)
new_array = zeros(shape(a_2d),type)
NumPy_type = scalar_spec.NumPy_to_blitz_type_mapping[type]
code = \
for(int i = 0;i < _Na_2d[0]; i++)
for(int j = 0; j < _Na_2d[1]; j++)
new_array(i,j) = (%s) a_2d(j,i);
""" % NumPy_type
type_factories = blitz_type_factories,compiler='gcc')
return new_array
This example uses blitz++ arrays instead of the standard representation of
NumPy arrays so that indexing is simpler to write. This is accomplished by
passing in the blitz++ "type factories" to override the standard Python to
C++ type conversions. Blitz++ arrays allow you to write clean, fast code, but
they also are sloooow to compile (20 seconds or more for this snippet). This
is why they aren't the default type used for Numeric arrays (and also because
most compilers can't compile blitz arrays...). ``inline()`` is also forced to
use 'gcc' as the compiler because the default compiler on Windows (MSVC) will
not compile blitz code. ('gcc' I think will use the standard compiler on
Unix machine instead of explicitly forcing gcc (check this)) Comparisons of
the Python vs inline C++ code show a factor of 3 speed up. Also shown are the
results of an "inplace" transpose routine that can be used if the output of
the linear algebra routine can overwrite the original matrix (this is often
appropriate). This provides another factor of 2 improvement.
#C:\home\ej\wrk\scipy\weave\examples> python
# Cast/Copy/Transposing (150,150)array 1 times
# speed in python: 0.870999932289
# speed in c: 0.25
# speed up: 3.48
# inplace transpose c: 0.129999995232
# speed up: 6.70
``inline`` knows how to handle wxPython objects. Thats nice in and of itself,
but it also demonstrates that the type conversion mechanism is reasonably
flexible. Chances are, it won't take a ton of effort to support special types
you might have. The examples/ borrows the scrolled window
example from the wxPython demo, accept that it mixes inline C code in the
middle of the drawing function.
def DoDrawing(self, dc):
red = wxNamedColour("RED");
blue = wxNamedColour("BLUE");
grey_brush = wxLIGHT_GREY_BRUSH;
code = \
#line 108 ""
dc->DrawRectangle(15, 15, 50, 50);
dc.SetFont(wxFont(14, wxSWISS, wxNORMAL, wxNORMAL))
dc.SetTextForeground(wxColour(0xFF, 0x20, 0xFF))
te = dc.GetTextExtent("Hello World")
dc.DrawText("Hello World", 60, 65)
dc.SetPen(wxPen(wxNamedColour('VIOLET'), 4))
dc.DrawLine(5, 65+te[1], 60+te[0], 65+te[1])
Here, some of the Python calls to wx objects were just converted to C++
calls. There isn't any benefit, it just demonstrates the capabilities. You
might want to use this if you have a computationally intensive loop in your
drawing code that you want to speed up. On windows, you'll have to use the
MSVC compiler if you use the standard wxPython DLLs distributed by Robin
Dunn. Thats because MSVC and gcc, while binary compatible in C, are not
binary compatible for C++. In fact, its probably best, no matter what
platform you're on, to specify that ``inline`` use the same compiler that was
used to build wxPython to be on the safe side. There isn't currently a way to
learn this info from the library -- you just have to know. Also, at least on
the windows platform, you'll need to install the wxWindows libraries and link
to them. I think there is a way around this, but I haven't found it yet -- I
get some linking errors dealing with wxString. One final note. You'll
probably have to tweak weave/ or weave/ for your
machine's configuration to point at the correct directories etc. There. That
should sufficiently scare people into not even looking at this... :)
Keyword Option
The basic definition of the ``inline()`` function has a slew of optional
variables. It also takes keyword arguments that are passed to ``distutils``
as compiler options. The following is a formatted cut/paste of the argument
section of ``inline's`` doc-string. It explains all of the variables. Some
examples using various options will follow.
def inline(code,arg_names,local_dict = None, global_dict = None,
force = 0,
verbose = 0,
support_code = None,
type_factories = None,
``inline`` has quite a few options as listed below. Also, the keyword
arguments for distutils extension modules are accepted to specify extra
information needed for compiling.
Inline Arguments
code string. A string of valid C++ code. It should not specify a return
statement. Instead it should assign results that need to be returned to
Python in the return_val. arg_names list of strings. A list of Python
variable names that should be transferred from Python into the C/C++ code.
local_dict optional. dictionary. If specified, it is a dictionary of values
that should be used as the local scope for the C/C++ code. If local_dict is
not specified the local dictionary of the calling function is used.
global_dict optional. dictionary. If specified, it is a dictionary of values
that should be used as the global scope for the C/C++ code. If global_dict is
not specified the global dictionary of the calling function is used. force
optional. 0 or 1. default 0. If 1, the C++ code is compiled every time inline
is called. This is really only useful for debugging, and probably only useful
if you're editing support_code a lot. compiler optional. string. The name
of compiler to use when compiling. On windows, it understands 'msvc' and
'gcc' as well as all the compiler names understood by distutils. On Unix,
it'll only understand the values understood by distutils. (I should add 'gcc'
though to this).
On windows, the compiler defaults to the Microsoft C++ compiler. If this
isn't available, it looks for mingw32 (the gcc compiler).
On Unix, it'll probably use the same compiler that was used when compiling
Python. Cygwin's behavior should be similar.
verbose optional. 0,1, or 2. default 0. Specifies how much much
information is printed during the compile phase of inlining code. 0 is silent
(except on windows with msvc where it still prints some garbage). 1 informs
you when compiling starts, finishes, and how long it took. 2 prints out the
command lines for the compilation process and can be useful if you're having
problems getting code to work. Its handy for finding the name of the .cpp
file if you need to examine it. verbose has no affect if the compilation
isn't necessary. support_code optional. string. A string of valid C++ code
declaring extra code that might be needed by your compiled function. This
could be declarations of functions, classes, or structures. customize
optional. base_info.custom_info object. An alternative way to specify
support_code, headers, etc. needed by the function see the weave.base_info
module for more details. (not sure this'll be used much). type_factories
optional. list of type specification factories. These guys are what convert
Python data types to C/C++ data types. If you'd like to use a different set
of type conversions than the default, specify them here. Look in the type
conversions section of the main documentation for examples. auto_downcast
optional. 0 or 1. default 1. This only affects functions that have Numeric
arrays as input variables. Setting this to 1 will cause all floating point
values to be cast as float instead of double if all the NumPy arrays are of
type float. If even one of the arrays has type double or double complex, all
variables maintain there standard types.
Distutils keywords
``inline()`` also accepts a number of ``distutils`` keywords for
controlling how the code is compiled. The following descriptions have been
copied from Greg Ward's ``distutils.extension.Extension`` class doc- strings
for convenience: sources [string] list of source filenames, relative to the
distribution root (where the setup script lives), in Unix form (slash-
separated) for portability. Source files may be C, C++, SWIG (.i), platform-
specific resource files, or whatever else is recognized by the "build_ext"
command as source for a Python extension. Note: The module_path file is
always appended to the front of this list include_dirs [string] list of
directories to search for C/C++ header files (in Unix form for portability)
define_macros [(name : string, value : string|None)] list of macros to
define; each macro is defined using a 2-tuple, where 'value' is either the
string to define it to or None to define it without a particular value
(equivalent of "#define FOO" in source or -DFOO on Unix C compiler command
line) undef_macros [string] list of macros to undefine explicitly
library_dirs [string] list of directories to search for C/C++ libraries at
link time libraries [string] list of library names (not filenames or paths)
to link against runtime_library_dirs [string] list of directories to search
for C/C++ libraries at run time (for shared extensions, this is when the
extension is loaded) extra_objects [string] list of extra files to link
with (eg. object files not implied by 'sources', static library that must be
explicitly specified, binary resource files, etc.) extra_compile_args
[string] any extra platform- and compiler-specific information to use when
compiling the source files in 'sources'. For platforms and compilers where
"command line" makes sense, this is typically a list of command-line
arguments, but for other platforms it could be anything. extra_link_args
[string] any extra platform- and compiler-specific information to use when
linking object files together to create the extension (or to create a new
static Python interpreter). Similar interpretation as for
'extra_compile_args'. export_symbols [string] list of symbols to be
exported from a shared extension. Not used on all platforms, and not
generally necessary for Python extensions, which typically export exactly one
symbol: "init" + extension_name.
Keyword Option Examples
We'll walk through several examples here to demonstrate the behavior of
``inline`` and also how the various arguments are used. In the simplest
(most) cases, ``code`` and ``arg_names`` are the only arguments that need to
be specified. Here's a simple example run on Windows machine that has
Microsoft VC++ installed.
>>> from weave import inline
>>> a = 'string'
>>> code = """
... int l = a.length();
... return_val = Py::new_reference_to(Py::Int(l));
... """
>>> inline(code,['a'])
library C:\DOCUME~1\eric\LOCALS~1\Temp\python21_compiled\temp\Release\sc_86e98826b65b047ffd2cd5f479c627f12.lib
and object C:\DOCUME~1\eric\LOCALS~1\Temp\python21_compiled\temp\Release\sc_86e98826b65b047ff
>>> inline(code,['a'])
When ``inline`` is first run, you'll notice that pause and some trash printed
to the screen. The "trash" is actually part of the compilers output that
distutils does not suppress. The name of the extension file,
``sc_bighonkingnumber.cpp``, is generated from the md5 check sum of the C/C++
code fragment. On Unix or windows machines with only gcc installed, the trash
will not appear. On the second call, the code fragment is not compiled since
it already exists, and only the answer is returned. Now kill the interpreter
and restart, and run the same code with a different string.
>>> from weave import inline
>>> a = 'a longer string'
>>> code = """
... int l = a.length();
... return_val = Py::new_reference_to(Py::Int(l));
... """
>>> inline(code,['a'])
Notice this time, ``inline()`` did not recompile the code because it found
the compiled function in the persistent catalog of functions. There is a
short pause as it looks up and loads the function, but it is much shorter
than compiling would require.
You can specify the local and global dictionaries if you'd like (much like
``exec`` or ``eval()`` in Python), but if they aren't specified, the
"expected" ones are used -- i.e. the ones from the function that called
``inline()``. This is accomplished through a little call frame trickery.
Here is an example where the local_dict is specified using the same code
example from above::
>>> a = 'a longer string'
>>> b = 'an even longer string'
>>> my_dict = {'a':b}
>>> inline(code,['a'])
>>> inline(code,['a'],my_dict)
Every time the ``code`` is changed, ``inline`` does a recompile. However,
changing any of the other options in inline does not force a recompile. The
``force`` option was added so that one could force a recompile when tinkering
with other variables. In practice, it is just as easy to change the ``code``
by a single character (like adding a space some place) to force the
.. note::
It also might be nice to add some methods for purging the
cache and on disk catalogs.
I use ``verbose`` sometimes for debugging. When set to 2, it'll output all
the information (including the name of the .cpp file) that you'd expect from
running a make file. This is nice if you need to examine the generated code
to see where things are going haywire. Note that error messages from failed
compiles are printed to the screen even if ``verbose`` is set to 0.
The following example demonstrates using gcc instead of the standard msvc
compiler on windows using same code fragment as above. Because the example
has already been compiled, the ``force=1`` flag is needed to make
``inline()`` ignore the previously compiled version and recompile using gcc.
The verbose flag is added to show what is printed out::
running build_ext
building 'sc_86e98826b65b047ffd2cd5f479c627f13' extension
c:\gcc-2.95.2\bin\g++.exe -mno-cygwin -mdll -O2 -w -Wstrict-prototypes -IC:
\home\ej\wrk\scipy\weave -IC:\Python21\Include -c C:\DOCUME~1\eric\LOCAL
-o C:\DOCUME~1\eric\LOCALS~1\Temp\python21_compiled\temp\Release\sc_86e98826b65b04ffd2cd5f479c627f13.o
skipping C:\home\ej\wrk\scipy\weave\CXX\cxxextensions.c
(C:\DOCUME~1\eric\LOCALS~1\Temp\python21_compiled\temp\Release\cxxextensions.o up-to-date)
skipping C:\home\ej\wrk\scipy\weave\CXX\cxxsupport.cxx
(C:\DOCUME~1\eric\LOCALS~1\Temp\python21_compiled\temp\Release\cxxsupport.o up-to-date)
skipping C:\home\ej\wrk\scipy\weave\CXX\IndirectPythonInterface.cxx
(C:\DOCUME~1\eric\LOCALS~1\Temp\python21_compiled\temp\Release\indirectpythoninterface.o up-to-date)
skipping C:\home\ej\wrk\scipy\weave\CXX\cxx_extensions.cxx
writing C:\DOCUME~1\eric\LOCALS~1\Temp\python21_compiled\temp\Release\sc_86e98826b65b047ffd2cd5f479c627f13.def
c:\gcc-2.95.2\bin\dllwrap.exe --driver-name g++ -mno-cygwin
-mdll -static --output-lib
C:\DOCUME~1\eric\LOCALS~1\Temp\python21_compiled\temp\Release\libsc_86e98826b65b047ffd2cd5f479c627f13.a --def
C:\DOCUME~1\eric\LOCALS~1\Temp\python21_compiled\temp\Release\cxx_extensions.o -LC:\Python21\libs
-lpython21 -o
That's quite a bit of output. ``verbose=1`` just prints the compile time.
Compiling code...
finished compiling (sec): 6.00800001621
.. note::
I've only used the ``compiler`` option for switching between 'msvc'
and 'gcc' on windows. It may have use on Unix also, but I don't know yet.
The ``support_code`` argument is likely to be used a lot. It allows you to
specify extra code fragments such as function, structure or class definitions
that you want to use in the ``code`` string. Note that changes to
``support_code`` do *not* force a recompile. The catalog only relies on
``code`` (for performance reasons) to determine whether recompiling is
necessary. So, if you make a change to support_code, you'll need to alter
``code`` in some way or use the ``force`` argument to get the code to
recompile. I usually just add some innocuous whitespace to the end of one of
the lines in ``code`` somewhere. Here's an example of defining a separate
method for calculating the string length:
>>> from weave import inline
>>> a = 'a longer string'
>>> support_code = """
... PyObject* length(Py::String a)
... {
... int l = a.length();
... return Py::new_reference_to(Py::Int(l));
... }
... """
>>> inline("return_val = length(a);",['a'],
... support_code = support_code)
``customize`` is a left over from a previous way of specifying compiler
options. It is a ``custom_info`` object that can specify quite a bit of
information about how a file is compiled. These ``info`` objects are the
standard way of defining compile information for type conversion classes.
However, I don't think they are as handy here, especially since we've exposed
all the keyword arguments that distutils can handle. Between these keywords,
and the ``support_code`` option, I think ``customize`` may be obsolete. We'll
see if anyone cares to use it. If not, it'll get axed in the next version.
The ``type_factories`` variable is important to people who want to customize
the way arguments are converted from Python to C. We'll talk about this in
the next chapter **xx** of this document when we discuss type conversions.
``auto_downcast`` handles one of the big type conversion issues that is
common when using NumPy arrays in conjunction with Python scalar values. If
you have an array of single precision values and multiply that array by a
Python scalar, the result is upcast to a double precision array because the
scalar value is double precision. This is not usually the desired behavior
because it can double your memory usage. ``auto_downcast`` goes some distance
towards changing the casting precedence of arrays and scalars. If your only
using single precision arrays, it will automatically downcast all scalar
values from double to single precision when they are passed into the C++
code. This is the default behavior. If you want all values to keep there
default type, set ``auto_downcast`` to 0.
Returning Values
Python variables in the local and global scope transfer seamlessly from
Python into the C++ snippets. And, if ``inline`` were to completely live up
to its name, any modifications to variables in the C++ code would be
reflected in the Python variables when control was passed back to Python. For
example, the desired behavior would be something like::
>>> a = 1
>>> weave.inline("a++;",['a'])
>>> a
Instead you get::
>>> a = 1
>>> weave.inline("a++;",['a'])
>>> a
Variables are passed into C++ as if you are calling a Python function.
Python's calling convention is sometimes called "pass by assignment". This
means its as if a ``c_a = a`` assignment is made right before ``inline`` call
is made and the ``c_a`` variable is used within the C++ code. Thus, any
changes made to ``c_a`` are not reflected in Python's ``a`` variable. Things
do get a little more confusing, however, when looking at variables with
mutable types. Changes made in C++ to the contents of mutable types *are*
reflected in the Python variables.
>>> a= [1,2]
>>> weave.inline("PyList_SetItem(a.ptr(),0,PyInt_FromLong(3));",['a'])
>>> print a
[3, 2]
So modifications to the contents of mutable types in C++ are seen when
control is returned to Python. Modifications to immutable types such as
tuples, strings, and numbers do not alter the Python variables. If you need
to make changes to an immutable variable, you'll need to assign the new value
to the "magic" variable ``return_val`` in C++. This value is returned by the
``inline()`` function::
>>> a = 1
>>> a = weave.inline("return_val = Py::new_reference_to(Py::Int(a+1));",['a'])
>>> a
The ``return_val`` variable can also be used to return newly created values.
This is possible by returning a tuple. The following trivial example
illustrates how this can be done::
# python version
def multi_return():
return 1, '2nd'
# C version.
def c_multi_return():
code = """
py::tuple results(2);
results[0] = 1;
results[1] = "2nd";
return_val = results;
return inline_tools.inline(code)
The example is available in ``examples/``. It also has the
dubious honor of demonstrating how much ``inline()`` can slow things down.
The C version here is about 7-10 times slower than the Python version. Of
course, something so trivial has no reason to be written in C anyway.
The issue with ``locals()``
``inline`` passes the ``locals()`` and ``globals()`` dictionaries from Python
into the C++ function from the calling function. It extracts the variables
that are used in the C++ code from these dictionaries, converts then to C++
variables, and then calculates using them. It seems like it would be trivial,
then, after the calculations were finished to then insert the new values back
into the ``locals()`` and ``globals()`` dictionaries so that the modified
values were reflected in Python. Unfortunately, as pointed out by the Python
manual, the locals() dictionary is not writable.
I suspect ``locals()`` is not writable because there are some optimizations
done to speed lookups of the local namespace. I'm guessing local lookups
don't always look at a dictionary to find values. Can someone "in the know"
confirm or correct this? Another thing I'd like to know is whether there is a
way to write to the local namespace of another stack frame from C/C++. If so,
it would be possible to have some clean up code in compiled functions that
wrote final values of variables in C++ back to the correct Python stack
frame. I think this goes a long way toward making ``inline`` truly live up
to its name. I don't think we'll get to the point of creating variables in
Python for variables created in C -- although I suppose with a C/C++ parser
you could do that also.
A quick look at the code
``weave`` generates a C++ file holding an extension function for each
``inline`` code snippet. These file names are generated using from the md5
signature of the code snippet and saved to a location specified by the
PYTHONCOMPILED environment variable (discussed later). The cpp files are
generally about 200-400 lines long and include quite a few functions to
support type conversions, etc. However, the actual compiled function is
pretty simple. Below is the familiar ``printf`` example:
>>> import weave
>>> a = 1
>>> weave.inline('printf("%d\\n",a);',['a'])
And here is the extension function generated by ``inline``::
static PyObject* compiled_func(PyObject*self, PyObject* args)
py::object return_val;
int exception_occurred = 0;
PyObject *py__locals = NULL;
PyObject *py__globals = NULL;
PyObject *py_a;
py_a = NULL;
return NULL;
PyObject* raw_locals = py_to_raw_dict(py__locals,"_locals");
PyObject* raw_globals = py_to_raw_dict(py__globals,"_globals");
/* argument conversion code */
py_a = get_variable("a",raw_locals,raw_globals);
int a = convert_to_int(py_a,"a");
/* inline code */
printf("%d\n",a); /*I would like to fill in changed locals and globals here...*/
return_val = py::object();
exception_occurred = 1;
/* cleanup code */
if(!(PyObject*)return_val && !exception_occurred)
return_val = Py_None;
return return_val.disown();
Every inline function takes exactly two arguments -- the local and global
dictionaries for the current scope. All variable values are looked up out of
these dictionaries. The lookups, along with all ``inline`` code execution,
are done within a C++ ``try`` block. If the variables aren't found, or there
is an error converting a Python variable to the appropriate type in C++, an
exception is raised. The C++ exception is automatically converted to a Python
exception by SCXX and returned to Python. The ``py_to_int()`` function
illustrates how the conversions and exception handling works. py_to_int first
checks that the given PyObject* pointer is not NULL and is a Python integer.
If all is well, it calls the Python API to convert the value to an ``int``.
Otherwise, it calls ``handle_bad_type()`` which gathers information about
what went wrong and then raises a SCXX TypeError which returns to Python as a
int py_to_int(PyObject* py_obj,char* name)
if (!py_obj || !PyInt_Check(py_obj))
handle_bad_type(py_obj,"int", name);
return (int) PyInt_AsLong(py_obj);
void handle_bad_type(PyObject* py_obj, char* good_type, char* var_name)
char msg[500];
sprintf(msg,"received '%s' type instead of '%s' for variable '%s'",
throw Py::TypeError(msg);
char* find_type(PyObject* py_obj)
if(py_obj == NULL) return "C NULL value";
if(PyCallable_Check(py_obj)) return "callable";
if(PyString_Check(py_obj)) return "string";
if(PyInt_Check(py_obj)) return "int";
if(PyFloat_Check(py_obj)) return "float";
if(PyDict_Check(py_obj)) return "dict";
if(PyList_Check(py_obj)) return "list";
if(PyTuple_Check(py_obj)) return "tuple";
if(PyFile_Check(py_obj)) return "file";
if(PyModule_Check(py_obj)) return "module";
//should probably do more integration (and thinking) on these.
if(PyCallable_Check(py_obj) && PyInstance_Check(py_obj)) return "callable";
if(PyInstance_Check(py_obj)) return "instance";
if(PyCallable_Check(py_obj)) return "callable";
return "unknown type";
Since the ``inline`` is also executed within the ``try/catch`` block, you can
use CXX exceptions within your code. It is usually a bad idea to directly
``return`` from your code, even if an error occurs. This skips the clean up
section of the extension function. In this simple example, there isn't any
clean up code, but in more complicated examples, there may be some reference
counting that needs to be taken care of here on converted variables. To avoid
this, either uses exceptions or set ``return_val`` to NULL and use
``if/then's`` to skip code after errors.
Technical Details
There are several main steps to using C/C++ code withing Python:
1. Type conversion
2. Generating C/C++ code
3. Compile the code to an extension module
4. Catalog (and cache) the function for future use
Items 1 and 2 above are related, but most easily discussed separately. Type
conversions are customizable by the user if needed. Understanding them is
pretty important for anything beyond trivial uses of ``inline``. Generating
the C/C++ code is handled by ``ext_function`` and ``ext_module`` classes and
. For the most part, compiling the code is handled by distutils. Some
customizations were needed, but they were relatively minor and do not require
changes to distutils itself. Cataloging is pretty simple in concept, but
surprisingly required the most code to implement (and still likely needs some
work). So, this section covers items 1 and 4 from the list. Item 2 is covered
later in the chapter covering the ``ext_tools`` module, and distutils is
covered by a completely separate document xxx.
Passing Variables in/out of the C/C++ code
.. note::
Passing variables into the C code is pretty straight forward, but
there are subtleties to how variable modifications in C are returned to
Python. see `Returning Values`_ for a more thorough discussion of this issue.
Type Conversions
.. note::
Maybe ``xxx_converter`` instead of ``xxx_specification`` is a more
descriptive name. Might change in future version?
By default, ``inline()`` makes the following type conversions between Python
and C++ types.
Default Data Type Conversions
int int
float double
complex std::complex
string py::string
list py::list
dict py::dict
tuple py::tuple
file FILE*
callable py::object
instance py::object
numpy.ndarray PyArrayObject*
wxXXX wxXXX*
The ``Py::`` namespace is defined by the SCXX library which has C++ class
equivalents for many Python types. ``std::`` is the namespace of the standard
library in C++.
.. note::
- I haven't figured out how to handle ``long int`` yet (I think they
are currenlty converted to int - - check this).
- Hopefully VTK will be added to the list soon
Python to C++ conversions fill in code in several locations in the generated
``inline`` extension function. Below is the basic template for the function.
This is actually the exact code that is generated by calling
The ``/* inline code */`` section is filled with the code passed to the
``inline()`` function call. The ``/*argument conversion code*/`` and ``/*
cleanup code */`` sections are filled with code that handles conversion from
Python to C++ types and code that deallocates memory or manipulates reference
counts before the function returns. The following sections demonstrate how
these two areas are filled in by the default conversion methods. * Note: I'm
not sure I have reference counting correct on a few of these. The only thing
I increase/decrease the ref count on is NumPy arrays. If you see an issue,
please let me know.
NumPy Argument Conversion
Integer, floating point, and complex arguments are handled in a very similar
fashion. Consider the following inline function that has a single integer
variable passed in::
>>> a = 1
>>> inline("",['a'])
The argument conversion code inserted for ``a`` is::
/* argument conversion code */
int a = py_to_int (get_variable("a",raw_locals,raw_globals),"a");
``get_variable()`` reads the variable ``a`` from the local and global
namespaces. ``py_to_int()`` has the following form::
static int py_to_int(PyObject* py_obj,char* name)
if (!py_obj || !PyInt_Check(py_obj))
handle_bad_type(py_obj,"int", name);
return (int) PyInt_AsLong(py_obj);
Similarly, the float and complex conversion routines look like::
static double py_to_float(PyObject* py_obj,char* name)
if (!py_obj || !PyFloat_Check(py_obj))
handle_bad_type(py_obj,"float", name);
return PyFloat_AsDouble(py_obj);
static std::complex py_to_complex(PyObject* py_obj,char* name)
if (!py_obj || !PyComplex_Check(py_obj))
handle_bad_type(py_obj,"complex", name);
return std::complex(PyComplex_RealAsDouble(py_obj),
NumPy conversions do not require any clean up code.
String, List, Tuple, and Dictionary Conversion
Strings, Lists, Tuples and Dictionary conversions are all converted to SCXX
types by default. For the following code,
>>> a = [1]
>>> inline("",['a'])
The argument conversion code inserted for ``a`` is::
/* argument conversion code */
Py::List a = py_to_list(get_variable("a",raw_locals,raw_globals),"a");
``get_variable()`` reads the variable ``a`` from the local and global
namespaces. ``py_to_list()`` and its friends has the following form::
static Py::List py_to_list(PyObject* py_obj,char* name)
if (!py_obj || !PyList_Check(py_obj))
handle_bad_type(py_obj,"list", name);
return Py::List(py_obj);
static Py::String py_to_string(PyObject* py_obj,char* name)
if (!PyString_Check(py_obj))
handle_bad_type(py_obj,"string", name);
return Py::String(py_obj);
static Py::Dict py_to_dict(PyObject* py_obj,char* name)
if (!py_obj || !PyDict_Check(py_obj))
handle_bad_type(py_obj,"dict", name);
return Py::Dict(py_obj);
static Py::Tuple py_to_tuple(PyObject* py_obj,char* name)
if (!py_obj || !PyTuple_Check(py_obj))
handle_bad_type(py_obj,"tuple", name);
return Py::Tuple(py_obj);
SCXX handles reference counts on for strings, lists, tuples, and
dictionaries, so clean up code isn't necessary.
File Conversion
For the following code,
>>> a = open("bob",'w')
>>> inline("",['a'])
The argument conversion code is::
/* argument conversion code */
PyObject* py_a = get_variable("a",raw_locals,raw_globals);
FILE* a = py_to_file(py_a,"a");
``get_variable()`` reads the variable ``a`` from the local and global
namespaces. ``py_to_file()`` converts PyObject* to a FILE* and increments the
reference count of the PyObject*::
FILE* py_to_file(PyObject* py_obj, char* name)
if (!py_obj || !PyFile_Check(py_obj))
handle_bad_type(py_obj,"file", name);
return PyFile_AsFile(py_obj);
Because the PyObject* was incremented, the clean up code needs to decrement
the counter
/* cleanup code */
Its important to understand that file conversion only works on actual files
-- i.e. ones created using the ``open()`` command in Python. It does not
support converting arbitrary objects that support the file interface into C
``FILE*`` pointers. This can affect many things. For example, in initial
``printf()`` examples, one might be tempted to solve the problem of C and
Python IDE's (PythonWin, PyCrust, etc.) writing to different stdout and
stderr by using ``fprintf()`` and passing in ``sys.stdout`` and
``sys.stderr``. For example, instead of
>>> weave.inline('printf("hello\\n");')
You might try:
>>> buf = sys.stdout
>>> weave.inline('fprintf(buf,"hello\\n");',['buf'])
This will work as expected from a standard python interpreter, but in
PythonWin, the following occurs:
>>> buf = sys.stdout
>>> weave.inline('fprintf(buf,"hello\\n");',['buf'])
Traceback (most recent call last):
File "", line 1, in ?
File "C:\Python21\weave\", line 315, in inline
auto_downcast = auto_downcast,
File "C:\Python21\weave\", line 386, in compile_function
type_factories = type_factories)
File "C:\Python21\weave\", line 197, in __init__
auto_downcast, type_factories)
File "C:\Python21\weave\", line 390, in assign_variable_types
raise TypeError, format_error_msg(errors)
TypeError: {'buf': "Unable to convert variable 'buf' to a C++ type."}
The traceback tells us that ``inline()`` was unable to convert 'buf' to a C++
type (If instance conversion was implemented, the error would have occurred
at runtime instead). Why is this? Let's look at what the ``buf`` object
really is::
>>> buf
pywin.framework.interact.InteractiveView instance at 00EAD014
PythonWin has reassigned ``sys.stdout`` to a special object that implements
the Python file interface. This works great in Python, but since the special
object doesn't have a FILE* pointer underlying it, fprintf doesn't know what
to do with it (well this will be the problem when instance conversion is
Callable, Instance, and Module Conversion
.. note::
Need to look into how ref counts should be handled. Also, Instance and
Module conversion are not currently implemented.
>>> def a():
>>> inline("",['a'])
Callable and instance variables are converted to PyObject*. Nothing is done
to there reference counts.
/* argument conversion code */
PyObject* a = py_to_callable(get_variable("a",raw_locals,raw_globals),"a");
``get_variable()`` reads the variable ``a`` from the local and global
namespaces. The ``py_to_callable()`` and ``py_to_instance()`` don't currently
increment the ref count.
PyObject* py_to_callable(PyObject* py_obj, char* name)
if (!py_obj || !PyCallable_Check(py_obj))
handle_bad_type(py_obj,"callable", name);
return py_obj;
PyObject* py_to_instance(PyObject* py_obj, char* name)
if (!py_obj || !PyFile_Check(py_obj))
handle_bad_type(py_obj,"instance", name);
return py_obj;
There is no cleanup code for callables, modules, or instances.
Customizing Conversions
Converting from Python to C++ types is handled by xxx_specification classes.
A type specification class actually serve in two related but different roles.
The first is in determining whether a Python variable that needs to be
converted should be represented by the given class. The second is as a code
generator that generate C++ code needed to convert from Python to C++ types
for a specific variable.
>>> a = 1
>>> weave.inline('printf("%d",a);',['a'])
is called for the first time, the code snippet has to be compiled. In this
process, the variable 'a' is tested against a list of type specifications
(the default list is stored in weave/ The *first* specification
in the list is used to represent the variable.
Examples of ``xxx_specification`` are scattered throughout numerous
"" files in the ``weave`` package. Closely related to the
``xxx_specification`` classes are ``yyy_info`` classes. These classes contain
compiler, header, and support code information necessary for including a
certain set of capabilities (such as blitz++ or CXX support) in a compiled
module. ``xxx_specification`` classes have one or more ``yyy_info`` classes
associated with them. If you'd like to define your own set of type
specifications, the current best route is to examine some of the existing
spec and info files. Maybe looking over and are
a good place to start. After defining specification classes, you'll need to
pass them into ``inline`` using the ``type_factories`` argument. A lot of
times you may just want to change how a specific variable type is
represented. Say you'd rather have Python strings converted to
``std::string`` or maybe ``char*`` instead of using the CXX string object,
but would like all other type conversions to have default behavior. This
requires that a new specification class that handles strings is written and
then prepended to a list of the default type specifications. Since it is
closer to the front of the list, it effectively overrides the default string
specification. The following code demonstrates how this is done: ...
The Catalog
```` has a class called ``catalog`` that helps keep track of
previously compiled functions. This prevents ``inline()`` and related
functions from having to compile functions every time they are called.
Instead, catalog will check an in memory cache to see if the function has
already been loaded into python. If it hasn't, then it starts searching
through persistent catalogs on disk to see if it finds an entry for the given
function. By saving information about compiled functions to disk, it isn't
necessary to re-compile functions every time you stop and restart the
interpreter. Functions are compiled once and stored for future use.
When ``inline(cpp_code)`` is called the following things happen:
1. A fast local cache of functions is checked for the last function
called for ``cpp_code``. If an entry for ``cpp_code`` doesn't exist in
the cache or the cached function call fails (perhaps because the function
doesn't have compatible types) then the next step is to check the
2. The catalog class also keeps an in-memory cache with a list of all
the functions compiled for ``cpp_code``. If ``cpp_code`` has ever been
called, then this cache will be present (loaded from disk). If the cache
isn't present, then it is loaded from disk.
If the cache is present, each function in the cache is called until
one is found that was compiled for the correct argument types. If none of
the functions work, a new function is compiled with the given argument
types. This function is written to the on-disk catalog as well as into
the in-memory cache.
3. When a lookup for ``cpp_code`` fails, the catalog looks through the
on-disk function catalogs for the entries. The PYTHONCOMPILED variable
determines where to search for these catalogs and in what order. If
PYTHONCOMPILED is not present several platform dependent locations are
searched. All functions found for ``cpp_code`` in the path are loaded
into the in-memory cache with functions found earlier in the search path
closer to the front of the call list.
If the function isn't found in the on-disk catalog, then the function
is compiled, written to the first writable directory in the
PYTHONCOMPILED path, and also loaded into the in-memory cache.
Function Storage
Function caches are stored as dictionaries where the key is the entire C++
code string and the value is either a single function (as in the "level 1"
cache) or a list of functions (as in the main catalog cache). On disk
catalogs are stored in the same manor using standard Python shelves.
Early on, there was a question as to whether md5 check sums of the C++ code
strings should be used instead of the actual code strings. I think this is
the route inline Perl took. Some (admittedly quick) tests of the md5 vs. the
entire string showed that using the entire string was at least a factor of 3
or 4 faster for Python. I think this is because it is more time consuming to
compute the md5 value than it is to do look-ups of long strings in the
dictionary. Look at the examples/ file for the test run.
Catalog search paths and the PYTHONCOMPILED variable
The default location for catalog files on Unix is is ~/.pythonXX_compiled
where XX is version of Python being used. If this directory doesn't exist, it
is created the first time a catalog is used. The directory must be writable.
If, for any reason it isn't, then the catalog attempts to create a directory
based on your user id in the /tmp directory. The directory permissions are
set so that only you have access to the directory. If this fails, I think
you're out of luck. I don't think either of these should ever fail though. On
Windows, a directory called pythonXX_compiled is created in the user's
temporary directory.
The actual catalog file that lives in this directory is a Python shelve with
a platform specific name such as "nt21compiled_catalog" so that multiple OSes
can share the same file systems without trampling on each other. Along with
the catalog file, the .cpp and .so or .pyd files created by inline will live
in this directory. The catalog file simply contains keys which are the C++
code strings with values that are lists of functions. The function lists
point at functions within these compiled modules. Each function in the lists
executes the same C++ code string, but compiled for different input
You can use the PYTHONCOMPILED environment variable to specify alternative
locations for compiled functions. On Unix this is a colon (':') separated
list of directories. On windows, it is a (';') separated list of directories.
These directories will be searched prior to the default directory for a
compiled function catalog. Also, the first writable directory in the list is
where all new compiled function catalogs, .cpp and .so or .pyd files are
written. Relative directory paths ('.' and '..') should work fine in the
PYTHONCOMPILED variable as should environment variables.
There is a "special" path variable called MODULE that can be placed in the
PYTHONCOMPILED variable. It specifies that the compiled catalog should reside
in the same directory as the module that called it. This is useful if an
admin wants to build a lot of compiled functions during the build of a
package and then install them in site-packages along with the package. User's
who specify MODULE in their PYTHONCOMPILED variable will have access to these
compiled functions. Note, however, that if they call the function with a set
of argument types that it hasn't previously been built for, the new function
will be stored in their default directory (or some other writable directory
in the PYTHONCOMPILED path) because the user will not have write access to
the site-packages directory.
An example of using the PYTHONCOMPILED path on bash follows::
If you are using python21 on linux, and the module in site-packages
has a compiled function in it, then the catalog search order when calling
that function for the first time in a python session would be::
The default location is always included in the search path.
.. note::
hmmm. see a possible problem here. I should probably make a sub-
directory such as /usr/lib/python21/site-
packages/python21_compiled/linuxpython_compiled so that library files
compiled with python21 are tried to link with python22 files in some strange
scenarios. Need to check this.
The in-module cache (in ``weave.inline_tools`` reduces the overhead of
calling inline functions by about a factor of 2. It can be reduced a little
more for type loop calls where the same function is called over and over
again if the cache was a single value instead of a dictionary, but the
benefit is very small (less than 5%) and the utility is quite a bit less. So,
we'll stick with a dictionary as the cache.
.. note::
most of this section is lifted from old documentation. It should be
pretty accurate, but there may be a few discrepancies.
``weave.blitz()`` compiles NumPy Python expressions for fast execution. For
most applications, compiled expressions should provide a factor of 2-10
speed-up over NumPy arrays. Using compiled expressions is meant to be as
unobtrusive as possible and works much like pythons exec statement. As an
example, the following code fragment takes a 5 point average of the 512x512
2d image, b, and stores it in array, a::
from scipy import * # or from NumPy import *
a = ones((512,512), Float64)
b = ones((512,512), Float64)
# some stuff to fill in b...
# now average
a[1:-1,1:-1] = (b[1:-1,1:-1] + b[2:,1:-1] + b[:-2,1:-1] \
+ b[1:-1,2:] + b[1:-1,:-2]) / 5.
To compile the expression, convert the expression to a string by putting
quotes around it and then use ``weave.blitz``::
import weave
expr = "a[1:-1,1:-1] = (b[1:-1,1:-1] + b[2:,1:-1] + b[:-2,1:-1]" \
"+ b[1:-1,2:] + b[1:-1,:-2]) / 5."
The first time ``weave.blitz`` is run for a given expression and set of
arguments, C++ code that accomplishes the exact same task as the Python
expression is generated and compiled to an extension module. This can take up
to a couple of minutes depending on the complexity of the function.
Subsequent calls to the function are very fast. Further, the generated module
is saved between program executions so that the compilation is only done once
for a given expression and associated set of array types. If the given
expression is executed with a new set of array types, the code most be
compiled again. This does not overwrite the previously compiled function --
both of them are saved and available for execution.
The following table compares the run times for standard NumPy code and
compiled code for the 5 point averaging.
Method Run Time (seconds)
Standard NumPy 0.46349
blitz (1st time compiling) 78.95526
blitz (subsequent calls) 0.05843 (factor of 8 speedup)
These numbers are for a 512x512 double precision image run on a 400 MHz
Celeron processor under RedHat Linux 6.2.
Because of the slow compile times, its probably most effective to develop
algorithms as you usually do using the capabilities of scipy or the NumPy
module. Once the algorithm is perfected, put quotes around it and execute it
using ``weave.blitz``. This provides the standard rapid prototyping strengths
of Python and results in algorithms that run close to that of hand coded C or
Currently, the ``weave.blitz`` has only been tested under Linux with
gcc-2.95-3 and on Windows with Mingw32 (2.95.2). Its compiler requirements
are pretty heavy duty (see the `blitz++ home page`_), so it won't work with
just any compiler. Particularly MSVC++ isn't up to snuff. A number of other
compilers such as KAI++ will also work, but my suspicions are that gcc will
get the most use.
1. Currently, ``weave.blitz`` handles all standard mathematic operators
except for the ** power operator. The built-in trigonometric, log,
floor/ceil, and fabs functions might work (but haven't been tested). It
also handles all types of array indexing supported by the NumPy module.
numarray's NumPy compatible array indexing modes are likewise supported,
but numarray's enhanced (array based) indexing modes are not supported.
``weave.blitz`` does not currently support operations that use array
broadcasting, nor have any of the special purpose functions in NumPy such
as take, compress, etc. been implemented. Note that there are no obvious
reasons why most of this functionality cannot be added to scipy.weave, so
it will likely trickle into future versions. Using ``slice()`` objects
directly instead of ``start:stop:step`` is also not supported.
2. Currently Python only works on expressions that include assignment
such as
>>> result = b + c + d
This means that the result array must exist before calling
``weave.blitz``. Future versions will allow the following::
>>> result = weave.blitz_eval("b + c + d")
3. ``weave.blitz`` works best when algorithms can be expressed in a
"vectorized" form. Algorithms that have a large number of if/thens and
other conditions are better hand written in C or Fortran. Further, the
restrictions imposed by requiring vectorized expressions sometimes
preclude the use of more efficient data structures or algorithms. For
maximum speed in these cases, hand-coded C or Fortran code is the only
way to go.
4. ``weave.blitz`` can produce different results than NumPy in certain
situations. It can happen when the array receiving the results of a
calculation is also used during the calculation. The NumPy behavior is to
carry out the entire calculation on the right hand side of an equation
and store it in a temporary array. This temporary array is assigned to
the array on the left hand side of the equation. blitz, on the other
hand, does a "running" calculation of the array elements assigning values
from the right hand side to the elements on the left hand side
immediately after they are calculated. Here is an example, provided by
Prabhu Ramachandran, where this happens::
# 4 point average.
>>> expr = "u[1:-1, 1:-1] = (u[0:-2, 1:-1] + u[2:, 1:-1] + \
... "u[1:-1,0:-2] + u[1:-1, 2:])*0.25"
>>> u = zeros((5, 5), 'd'); u[0,:] = 100
>>> exec (expr)
>>> u
array([[ 100., 100., 100., 100., 100.],
[ 0., 25., 25., 25., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
>>> u = zeros((5, 5), 'd'); u[0,:] = 100
>>> weave.blitz (expr)
>>> u
array([[ 100. , 100. , 100. , 100. , 100. ],
[ 0. , 25. , 31.25 , 32.8125 , 0. ],
[ 0. , 6.25 , 9.375 , 10.546875 , 0. ],
[ 0. , 1.5625 , 2.734375 , 3.3203125, 0. ],
[ 0. , 0. , 0. , 0. , 0. ]])
You can prevent this behavior by using a temporary array.
>>> u = zeros((5, 5), 'd'); u[0,:] = 100
>>> temp = zeros((4, 4), 'd');
>>> expr = "temp = (u[0:-2, 1:-1] + u[2:, 1:-1] + "\
... "u[1:-1,0:-2] + u[1:-1, 2:])*0.25;"\
... "u[1:-1,1:-1] = temp"
>>> weave.blitz (expr)
>>> u
array([[ 100., 100., 100., 100., 100.],
[ 0., 25., 25., 25., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
5. One other point deserves mention lest people be confused.
``weave.blitz`` is not a general purpose Python->C compiler. It only
works for expressions that contain NumPy arrays and/or Python scalar
values. This focused scope concentrates effort on the computationally
intensive regions of the program and sidesteps the difficult issues
associated with a general purpose Python->C compiler.
NumPy efficiency issues: What compilation buys you
Some might wonder why compiling NumPy expressions to C++ is beneficial since
operations on NumPy array operations are already executed within C loops. The
problem is that anything other than the simplest expression are executed in
less than optimal fashion. Consider the following NumPy expression::
a = 1.2 * b + c * d
When NumPy calculates the value for the 2d array, ``a``, it does the
following steps::
temp1 = 1.2 * b
temp2 = c * d
a = temp1 + temp2
Two things to note. Since ``c`` is an (perhaps large) array, a large
temporary array must be created to store the results of ``1.2 * b``. The same
is true for ``temp2``. Allocation is slow. The second thing is that we have 3
loops executing, one to calculate ``temp1``, one for ``temp2`` and one for
adding them up. A C loop for the same problem might look like::
for(int i = 0; i < M; i++)
for(int j = 0; j < N; j++)
a[i,j] = 1.2 * b[i,j] + c[i,j] * d[i,j]
Here, the 3 loops have been fused into a single loop and there is no longer a
need for a temporary array. This provides a significant speed improvement
over the above example (write me and tell me what you get).
So, converting NumPy expressions into C/C++ loops that fuse the loops and
eliminate temporary arrays can provide big gains. The goal then,is to convert
NumPy expression to C/C++ loops, compile them in an extension module, and
then call the compiled extension function. The good news is that there is an
obvious correspondence between the NumPy expression above and the C loop. The
bad news is that NumPy is generally much more powerful than this simple
example illustrates and handling all possible indexing possibilities results
in loops that are less than straight forward to write. (take a peak in NumPy
for confirmation). Luckily, there are several available tools that simplify
the process.
The Tools
``weave.blitz`` relies heavily on several remarkable tools. On the Python
side, the main facilitators are Jermey Hylton's parser module and Travis
Oliphant's NumPy module. On the compiled language side, Todd Veldhuizen's
blitz++ array library, written in C++ (shhhh. don't tell David Beazley), does
the heavy lifting. Don't assume that, because it's C++, it's much slower than
C or Fortran. Blitz++ uses a jaw dropping array of template techniques
(metaprogramming, template expression, etc) to convert innocent looking and
readable C++ expressions into to code that usually executes within a few
percentage points of Fortran code for the same problem. This is good.
Unfortunately all the template raz-ma-taz is very expensive to compile, so
the 200 line extension modules often take 2 or more minutes to compile. This
isn't so good. ``weave.blitz`` works to minimize this issue by remembering
where compiled modules live and reusing them instead of re-compiling every
time a program is re-run.
Tearing NumPy expressions apart, examining the pieces, and then rebuilding
them as C++ (blitz) expressions requires a parser of some sort. I can imagine
someone attacking this problem with regular expressions, but it'd likely be
ugly and fragile. Amazingly, Python solves this problem for us. It actually
exposes its parsing engine to the world through the ``parser`` module. The
following fragment creates an Abstract Syntax Tree (AST) object for the
expression and then converts to a (rather unpleasant looking) deeply nested
list representation of the tree.
>>> import parser
>>> import scipy.weave.misc
>>> ast = parser.suite("a = b * c + d")
>>> ast_list = ast.tolist()
>>> sym_list = scipy.weave.misc.translate_symbols(ast_list)
>>> pprint.pprint(sym_list)
['factor', ['power', ['atom', ['NAME', 'a']]]]]]]]]]]]]]],
['EQUAL', '='],
['factor', ['power', ['atom', ['NAME', 'b']]]],
['STAR', '*'],
['factor', ['power', ['atom', ['NAME', 'c']]]]],
['PLUS', '+'],
['factor', ['power', ['atom', ['NAME', 'd']]]]]]]]]]]]]]]]],
['NEWLINE', '']]],
['ENDMARKER', '']]
Despite its looks, with some tools developed by Jermey H., its possible to
search these trees for specific patterns (sub-trees), extract the sub-tree,
manipulate them converting python specific code fragments to blitz code
fragments, and then re-insert it in the parse tree. The parser module
documentation has some details on how to do this. Traversing the new
blitzified tree, writing out the terminal symbols as you go, creates our new
blitz++ expression string.
Blitz and NumPy
The other nice discovery in the project is that the data structure used for
NumPy arrays and blitz arrays is nearly identical. NumPy stores "strides" as
byte offsets and blitz stores them as element offsets, but other than that,
they are the same. Further, most of the concept and capabilities of the two
libraries are remarkably similar. It is satisfying that two completely
different implementations solved the problem with similar basic
architectures. It is also fortuitous. The work involved in converting NumPy
expressions to blitz expressions was greatly diminished. As an example,
consider the code for slicing an array in Python with a stride::
>>> a = b[0:4:2] + c
>>> a
In Blitz it is as follows::
Array<2,int> b(10);
Array<2,int> c(3);
// ...
Array<2,int> a = b(Range(0,3,2)) + c;
Here the range object works exactly like Python slice objects with the
exception that the top index (3) is inclusive where as Python's (4) is
exclusive. Other differences include the type declarations in C++ and
parentheses instead of brackets for indexing arrays. Currently,
``weave.blitz`` handles the inclusive/exclusive issue by subtracting one from
upper indices during the translation. An alternative that is likely more
robust/maintainable in the long run, is to write a PyRange class that behaves
like Python's range. This is likely very easy.
The stock blitz also doesn't handle negative indices in ranges. The current
implementation of the ``blitz()`` has a partial solution to this problem. It
calculates and index that starts with a '-' sign by subtracting it from the
maximum index in the array so that::
upper index limit
b[:-1] -> b(Range(0,Nb[0]-1-1))
This approach fails, however, when the top index is calculated from other
values. In the following scenario, if ``i+j`` evaluates to a negative value,
the compiled code will produce incorrect results and could even core- dump.
Right now, all calculated indices are assumed to be positive.
b[:i-j] -> b(Range(0,i+j))
A solution is to calculate all indices up front using if/then to handle the
+/- cases. This is a little work and results in more code, so it hasn't been
done. I'm holding out to see if blitz++ can be modified to handle negative
indexing, but haven't looked into how much effort is involved yet. While it
needs fixin', I don't think there is a ton of code where this is an issue.
The actual translation of the Python expressions to blitz expressions is
currently a two part process. First, all x:y:z slicing expression are removed
from the AST, converted to slice(x,y,z) and re-inserted into the tree. Any
math needed on these expressions (subtracting from the maximum index, etc.)
are also preformed here. _beg and _end are used as special variables that are
defined as blitz::fromBegin and blitz::toEnd.
a[i+j:i+j+1,:] = b[2:3,:]
becomes a more verbose::
a[slice(i+j,i+j+1),slice(_beg,_end)] = b[slice(2,3),slice(_beg,_end)]
The second part does a simple string search/replace to convert to a blitz
expression with the following translations::
slice(_beg,_end) -> _all # not strictly needed, but cuts down on code.
slice -> blitz::Range
[ -> (
] -> )
_stp -> 1
``_all`` is defined in the compiled function as ``blitz::Range.all()``. These
translations could of course happen directly in the syntax tree. But the
string replacement is slightly easier. Note that name spaces are maintained
in the C++ code to lessen the likelihood of name clashes. Currently no effort
is made to detect name clashes. A good rule of thumb is don't use values that
start with '_' or 'py\_' in compiled expressions and you'll be fine.
Type definitions and coersion
So far we've glossed over the dynamic vs. static typing issue between Python
and C++. In Python, the type of value that a variable holds can change
through the course of program execution. C/C++, on the other hand, forces you
to declare the type of value a variables will hold prior at compile time.
``weave.blitz`` handles this issue by examining the types of the variables in
the expression being executed, and compiling a function for those explicit
types. For example::
a = ones((5,5),Float32)
b = ones((5,5),Float32)
weave.blitz("a = a + b")
When compiling this expression to C++, ``weave.blitz`` sees that the values
for a and b in the local scope have type ``Float32``, or 'float' on a 32 bit
architecture. As a result, it compiles the function using the float type (no
attempt has been made to deal with 64 bit issues).
What happens if you call a compiled function with array types that are
different than the ones for which it was originally compiled? No biggie,
you'll just have to wait on it to compile a new version for your new types.
This doesn't overwrite the old functions, as they are still accessible. See
the catalog section in the inline() documentation to see how this is handled.
Suffice to say, the mechanism is transparent to the user and behaves like
dynamic typing with the occasional wait for compiling newly typed functions.
When working with combined scalar/array operations, the type of the array is
*always* used. This is similar to the savespace flag that was recently added
to NumPy. This prevents issues with the following expression perhaps
unexpectedly being calculated at a higher (more expensive) precision that can
occur in Python::
>>> a = array((1,2,3),typecode = Float32)
>>> b = a * 2.1 # results in b being a Float64 array.
In this example,
>>> a = ones((5,5),Float32)
>>> b = ones((5,5),Float32)
>>> weave.blitz("b = a * 2.1")
the ``2.1`` is cast down to a ``float`` before carrying out the operation. If
you really want to force the calculation to be a ``double``, define ``a`` and
``b`` as ``double`` arrays.
One other point of note. Currently, you must include both the right hand side
and left hand side (assignment side) of your equation in the compiled
expression. Also, the array being assigned to must be created prior to
calling ``weave.blitz``. I'm pretty sure this is easily changed so that a
compiled_eval expression can be defined, but no effort has been made to
allocate new arrays (and discern their type) on the fly.
Cataloging Compiled Functions
See `The Catalog`_ section in the ``weave.inline()``
Checking Array Sizes
Surprisingly, one of the big initial problems with compiled code was making
sure all the arrays in an operation were of compatible type. The following
case is trivially easy::
a = b + c
It only requires that arrays ``a``, ``b``, and ``c`` have the same shape.
However, expressions like::
a[i+j:i+j+1,:] = b[2:3,:] + c
are not so trivial. Since slicing is involved, the size of the slices, not
the input arrays must be checked. Broadcasting complicates things further
because arrays and slices with different dimensions and shapes may be
compatible for math operations (broadcasting isn't yet supported by
``weave.blitz``). Reductions have a similar effect as their results are
different shapes than their input operand. The binary operators in NumPy
compare the shapes of their two operands just before they operate on them.
This is possible because NumPy treats each operation independently. The
intermediate (temporary) arrays created during sub-operations in an
expression are tested for the correct shape before they are combined by
another operation. Because ``weave.blitz`` fuses all operations into a single
loop, this isn't possible. The shape comparisons must be done and guaranteed
compatible before evaluating the expression.
The solution chosen converts input arrays to "dummy arrays" that only
represent the dimensions of the arrays, not the data. Binary operations on
dummy arrays check that input array sizes are compatible and return a dummy
array with the size correct size. Evaluating an expression of dummy arrays
traces the changing array sizes through all operations and fails if
incompatible array sizes are ever found.
The machinery for this is housed in ``weave.size_check``. It basically
involves writing a new class (dummy array) and overloading it math operators
to calculate the new sizes correctly. All the code is in Python and there is
a fair amount of logic (mainly to handle indexing and slicing) so the
operation does impose some overhead. For large arrays (ie. 50x50x50), the
overhead is negligible compared to evaluating the actual expression. For
small arrays (ie. 16x16), the overhead imposed for checking the shapes with
this method can cause the ``weave.blitz`` to be slower than evaluating the
expression in Python.
What can be done to reduce the overhead? (1) The size checking code could be
moved into C. This would likely remove most of the overhead penalty compared
to NumPy (although there is also some calling overhead), but no effort has
been made to do this. (2) You can also call ``weave.blitz`` with
``check_size=0`` and the size checking isn't done. However, if the sizes
aren't compatible, it can cause a core-dump. So, foregoing size_checking
isn't advisable until your code is well debugged.
Creating the Extension Module
``weave.blitz`` uses the same machinery as ``weave.inline`` to build the
extension module. The only difference is the code included in the function is
automatically generated from the NumPy array expression instead of supplied
by the user.
Extension Modules
``weave.inline`` and ``weave.blitz`` are high level tools that generate
extension modules automatically. Under the covers, they use several classes
from ``weave.ext_tools`` to help generate the extension module. The main two
classes are ``ext_module`` and ``ext_function`` (I'd like to add
``ext_class`` and ``ext_method`` also). These classes simplify the process of
generating extension modules by handling most of the "boiler plate" code
.. note::
``inline`` actually sub-classes ``weave.ext_tools.ext_function`` to
generate slightly different code than the standard ``ext_function``.
The main difference is that the standard class converts function
arguments to C types, while inline always has two arguments, the
local and global dicts, and the grabs the variables that need to be
converted to C from these.
A Simple Example
The following simple example demonstrates how to build an extension module
within a Python function::
# examples/
from weave import ext_tools
def build_increment_ext():
""" Build a simple extension with functions that increment numbers.
The extension will be built in the local directory.
mod = ext_tools.ext_module('increment_ext')
a = 1 # effectively a type declaration for 'a' in the
# following functions.
ext_code = "return_val = Py::new_reference_to(Py::Int(a+1));"
func = ext_tools.ext_function('increment',ext_code,['a'])
ext_code = "return_val = Py::new_reference_to(Py::Int(a+2));"
func = ext_tools.ext_function('increment_by_2',ext_code,['a'])
The function ``build_increment_ext()`` creates an extension module named
``increment_ext`` and compiles it to a shared library (.so or .pyd) that can
be loaded into Python.. ``increment_ext`` contains two functions,
``increment`` and ``increment_by_2``. The first line of
mod = ext_tools.ext_module('increment_ext')
creates an ``ext_module`` instance that is ready to have ``ext_function``
instances added to it. ``ext_function`` instances are created much with a
calling convention similar to ``weave.inline()``. The most common call
includes a C/C++ code snippet and a list of the arguments for the function.
The following
ext_code = "return_val = Py::new_reference_to(Py::Int(a+1));"
func = ext_tools.ext_function('increment',ext_code,['a'])
creates a C/C++ extension function that is equivalent to the following Python
def increment(a):
return a + 1
A second method is also added to the module and then,
is called to build the extension module. By default, the module is created in
the current working directory. This example is available in the
``examples/`` file found in the ``weave`` directory. At
the bottom of the file in the module's "main" program, an attempt to import
``increment_ext`` without building it is made. If this fails (the module
doesn't exist in the PYTHONPATH), the module is built by calling
``build_increment_ext()``. This approach only takes the time consuming ( a
few seconds for this example) process of building the module if it hasn't
been built before.
if __name__ == "__main__":
import increment_ext
except ImportError:
import increment_ext
a = 1
print 'a, a+1:', a, increment_ext.increment(a)
print 'a, a+2:', a, increment_ext.increment_by_2(a)
.. note::
If we were willing to always pay the penalty of building the C++
code for a module, we could store the md5 checksum of the C++ code
along with some information about the compiler, platform, etc. Then,
``ext_module.compile()`` could try importing the module before it
actually compiles it, check the md5 checksum and other meta-data in
the imported module with the meta-data of the code it just produced
and only compile the code if the module didn't exist or the
meta-data didn't match. This would reduce the above code to::
if __name__ == "__main__":
a = 1
print 'a, a+1:', a, increment_ext.increment(a)
print 'a, a+2:', a, increment_ext.increment_by_2(a)
.. note::
There would always be the overhead of building the C++ code, but it
would only actually compile the code once. You pay a little in overhead and
get cleaner "import" code. Needs some thought.
If you run ```` from the command line, you get the
[eric@n0]$ python
a, a+1: 1 2
a, a+2: 1 3
If the module didn't exist before it was run, the module is created. If it
did exist, it is just imported and used.
Fibonacci Example
``examples/`` provides a little more complex example of how to
use ``ext_tools``. Fibonacci numbers are a series of numbers where each
number in the series is the sum of the previous two: 1, 1, 2, 3, 5, 8, etc.
Here, the first two numbers in the series are taken to be 1. One approach to
calculating Fibonacci numbers uses recursive function calls. In Python, it
might be written as::
def fib(a):
if a <= 2:
return 1
return fib(a-2) + fib(a-1)
In C, the same function would look something like this::
int fib(int a)
if(a <= 2)
return 1;
return fib(a-2) + fib(a-1);
Recursion is much faster in C than in Python, so it would be beneficial to
use the C version for fibonacci number calculations instead of the Python
version. We need an extension function that calls this C function to do this.
This is possible by including the above code snippet as "support code" and
then calling it from the extension function. Support code snippets (usually
structure definitions, helper functions and the like) are inserted into the
extension module C/C++ file before the extension function code. Here is how
to build the C version of the fibonacci number generator::
def build_fibonacci():
""" Builds an extension module with fibonacci calculators.
mod = ext_tools.ext_module('fibonacci_ext')
a = 1 # this is effectively a type declaration
# recursive fibonacci in C
fib_code = """
int fib1(int a)
if(a <= 2)
return 1;
return fib1(a-2) + fib1(a-1);
ext_code = """
int val = fib1(a);
return_val = Py::new_reference_to(Py::Int(val));
fib = ext_tools.ext_function('fib',ext_code,['a'])
XXX More about custom_info, and what xxx_info instances are good for.
.. note::
recursion is not the fastest way to calculate fibonacci numbers, but
this approach serves nicely for this example.
Customizing Type Conversions -- Type Factories
not written
Things I wish ``weave`` did
It is possible to get name clashes if you use a variable name that is
already defined in a header automatically included (such as ``stdio.h``). For
instance, if you try to pass in a variable named ``stdout``, you'll get a
cryptic error report due to the fact that ``stdio.h`` also defines the name.
``weave`` should probably try and handle this in some way. Other things...
.. _PyInline:
.. _SciPy:
.. _mingw32:
.. _NumPy:
.. _here:
.. _Python Cookbook:
.. _binary_search():
.. _website:
.. _This submission:
.. _blitz++ home page:
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