Weave Documentation

By Eric Jones eric@enthought.com

Outline

Introduction

Requirements

Installation

Testing

Benchmarks

Inline

More with printf

More examples

Binary search

Dictionary sort

Numeric -- cast/copy/transpose

wxPython

Keyword options

Returning values

The issue with locals()

A quick look at the code

Technical Details

Converting Types

Numeric Argument Conversion

String, List, Tuple, and Dictionary Conversion

File Conversion

Callable, Instance, and Module Conversion

Customizing Conversions

Compiling Code

"Cataloging" functions

Function Storage

The PYTHONCOMPILED evnironment variable

Blitz

Requirements

Limitations

Numeric Efficiency Issues

The Tools

Parser

Blitz and Numeric

Type defintions and coersion

Cataloging Compiled Functions

Checking Array Sizes

Creating the Extension Module

Extension Modules

A Simple Example

Fibonacci Example

Customizing Type Conversions -- Type Factories (not written)

Type Specifications

Type Information

The Conversion Process

Introduction

The weave package provides tools for including C/C++ code within in 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 speed ups of 1.5x to 30x speed-up 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 convertable 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 Numeric 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 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 exists (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, etc.

Note: weave is actually part of the SciPy package. However, it works fine as a standalone package. 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.

Requirements

• 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 you.

• Numeric (optional)

  The python Numeric module from here. is required for blitz() to work. Be sure and get NumPy, not NumArray which is the "next generation" implementation. This is not required for using inline() or ext_tools.

• scipy_distutils and scipy_test (packaged with weave)

  These two modules are packaged with weave in both the windows installer and the source distributions. If you are using CVS, however, you'll need to download these separately (also available through CVS at SciPy).

Installation

There are currently two ways to get weave. Fist, weave is part of SciPy and installed automatically (as a sub- package) whenever SciPy is installed (although the latest version isn't in SciPy yet, so use this one for now). Second, since weave is useful outside of the scientific community, it has been setup so that it can be used as a stand-alone module.

The stand-alone version can be downloaded from here. Unix users should grab the tar ball (.tgz file) and install it using the following commands.

    tar -xzvf weave-0.2.tar.gz
    cd weave-0.2
    python setup.py install
    

For Windows users, it's even easier. You can download the click-install .exe file and run it for automatic installation. There is also a .zip file of the source for those interested. It also includes a setup.py file to simplify installation.

If you're using the CVS version, you'll need to install scipy_distutils and scipy_test packages (also available from CVS) on your own.

Note: The dependency issue here is a little sticky. I hate to make people download more than one file (and so I haven't), but distutils doesn't have a way to do conditional installation -- at least that I know about. This can lead to undesired clobbering of the scipy_test and scipy_distutils modules. What to do, what to do... Right now it is a very minor issue.

Testing

    >>> import weave
    >>> weave.test()
    runs long time... spews tons of output and a few warnings
    .
    .
    .
    ..............................................................
    ................................................................
    ..................................................
    ----------------------------------------------------------------------
    Ran 184 tests in 158.418s

    OK
    
    >>> 
    

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
    

• 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?

Benchmarks

The benchmarks shown blitz in the best possible light. Numeric (at least on my machine) is significantly worse for double precision than it is for single precision calculations. If your interested in single precision results, you can pretty much divide the double precision speed up by 3 and you'll be close.

Inline

inline() compiles and executes C/C++ code on the fly. Variables in the local and global Python scope 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'])
    1
    

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 function.

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 -- anywhwere 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 Numeric arrays can see much larger improvements. The examples/vq.py 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'])
    sc_e013937dbc8c647ac62438874e5795131.cpp
       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_e013937dbc8c64
       7ac62438874e5795131.exp
    1
    

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 preceeding 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'])
    32956972
    

In this case, the result is non-sensical, 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)
     1
    >>> protected_printf('string')
    AssertError...
    

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'])
    string
    

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'])
    1    
    >>> a = 'string'
    >>> weave.inline('std::cout << a << std::endl;',['a'])
    string
    

Examples using printf and cout are included in examples/print_example.py.

More examples

Binary search

    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 
            else:
                return m    
    

    
    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 "binary_search.py"
               int val, m, min = 0;  
               int max = seq.length() - 1;
               PyObject *py_val; 
               for(;;)
               {
                   if (max < min  ) 
                   { 
                       return_val =  Py::new_reference_to(Py::Int(-1)); 
                       break;
                   } 
                   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;
                   else
                   {
                       return_val = Py::new_reference_to(Py::Int(m));
                       break;
                   }
               }
               """
        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 cheack 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 "binary_search.py" with an offset from the given line number (29 here).

So what was all our effort worth in terms of efficiency? Well not a lot in this case. The examples/binary_search.py 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.py
    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 Numeric 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/dict_sort.py 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()
        keys.sort()
        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 "dict_sort.py"  
        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);
            Py_XINCREF(item);
            PyList_SetItem(items.ptr(),i,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.py
    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]
    

Numeric -- cast/copy/transpose

       
    def _castCopyAndTranspose(type, array):
        if a.typecode() == type:
            cast_array = copy.copy(Numeric.transpose(a))
        else:
            cast_array = copy.copy(Numeric.transpose(a).astype(type))
        return cast_array
    

    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)
        numeric_type = scalar_spec.numeric_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);
        """ % numeric_type
        inline(code,['new_array','a_2d'],
               type_factories = blitz_type_factories,compiler='gcc')
        return new_array
    

     #C:\home\ej\wrk\scipy\weave\examples> python cast_copy_transpose.py
    # 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
    

Keyword Options

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, 
               compiler='',
               verbose = 0, 
               support_code = None,
               customize=None, 
               type_factories = None, 
               auto_downcast=1,
               **kw):
    

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 understoof 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. defualt 0. Speficies 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 specifiy 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 Numeric 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 dles 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 "binary_search.py" with an offset from the given line number (29 here).

So what was all our effort worth in terms of efficiency? Well not a lot in this case. The examples/binary_search.py 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.py
    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 Numeric 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/dict_sort.py 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()
        keys.sort()
        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 "dict_sort.py"  
        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);
            Py_XINCREF(item);
            PyList_SetItem(items.ptr(),i,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.py
    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]
    

Numeric -- cast/copy/transpose

CastCopyTranspose is a function called quite heavily by Linear Algebra routines in the Numeric library. Its needed in part because of the row-major memory layout of multi-demensional 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 decompisition or singular value decompostion are spent in this setup routine. This shouldn't happen. Here is the Python version of the function using standard Numeric operations.

       
    def _castCopyAndTranspose(type, array):
        if a.typecode() == type:
            cast_array = copy.copy(Numeric.transpose(a))
        else:
            cast_array = copy.copy(Numeric.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)
        numeric_type = scalar_spec.numeric_to_blitz_type_mapping[type]
        code = \
        """  
        for(int i = 0;i < _Na_2d[0]; i++)  
            for(int j =