--- /dev/null
+.. highlight:: cython
+
+.. _numpy_tutorial:
+
+**************************
+Cython for NumPy users
+**************************
+
+This tutorial is aimed at NumPy users who have no experience with Cython at
+all. If you have some knowledge of Cython you may want to skip to the
+''Efficient indexing'' section which explains the new improvements made in
+summer 2008.
+
+The main scenario considered is NumPy end-use rather than NumPy/SciPy
+development. The reason is that Cython is not (yet) able to support functions
+that are generic with respect to datatype and the number of dimensions in a
+high-level fashion. This restriction is much more severe for SciPy development
+than more specific, "end-user" functions. See the last section for more
+information on this.
+
+The style of this tutorial will not fit everybody, so you can also consider:
+
+* Robert Bradshaw's `slides on cython for SciPy2008
+ <http://wiki.sagemath.org/scipy08?action=AttachFile&do=get&target=scipy-cython.tgz>`_
+ (a higher-level and quicker introduction)
+* Basic Cython documentation (see `Cython front page <http://cython.org>`_).
+* ``[:enhancements/buffer:Spec for the efficient indexing]``
+
+.. Note::
+ The fast array access documented below is a completely new feature, and
+ there may be bugs waiting to be discovered. It might be a good idea to do
+ a manual sanity check on the C code Cython generates before using this for
+ serious purposes, at least until some months have passed.
+
+Cython at a glance
+====================
+
+Cython is a compiler which compiles Python-like code files to C code. Still,
+''Cython is not a Python to C translator''. That is, it doesn't take your full
+program and "turns it into C" -- rather, the result makes full use of the
+Python runtime environment. A way of looking at it may be that your code is
+still Python in that it runs within the Python runtime environment, but rather
+than compiling to interpreted Python bytecode one compiles to native machine
+code (but with the addition of extra syntax for easy embedding of faster
+C-like code).
+
+This has two important consequences:
+
+* Speed. How much depends very much on the program involved though. Typical Python numerical programs would tend to gain very little as most time is spent in lower-level C that is used in a high-level fashion. However for-loop-style programs can gain many orders of magnitude, when typing information is added (and is so made possible as a realistic alternative).
+* Easy calling into C code. One of Cython's purposes is to allow easy wrapping
+ of C libraries. When writing code in Cython you can call into C code as
+ easily as into Python code.
+
+Some Python constructs are not yet supported, though making Cython compile all
+Python code is a stated goal (among the more important omissions are inner
+functions and generator functions).
+
+Your Cython environment
+========================
+
+Using Cython consists of these steps:
+
+1. Write a :file:`.pyx` source file
+2. Run the Cython compiler to generate a C file
+3. Run a C compiler to generate a compiled library
+4. Run the Python interpreter and ask it to import the module
+
+However there are several options to automate these steps:
+
+1. The `SAGE <http://sagemath.org>`_ mathematics software system provides
+ excellent support for using Cython and NumPy from an interactive command
+ line (like IPython) or through a notebook interface (like
+ Maple/Mathematica). See `this documentation
+ <http://www.sagemath.org/doc/prog/node40.html>`_.
+2. A version of `pyximport <http://www.prescod.net/pyximport/>`_ is shipped
+ with Cython, so that you can import pyx-files dynamically into Python and
+ have them compiled automatically (See :ref:`pyximport`).
+3. Cython supports distutils so that you can very easily create build scripts
+ which automate the process, this is the preferred method for full programs.
+4. Manual compilation (see below)
+
+.. Note::
+ If using another interactive command line environment than SAGE, like
+ IPython or Python itself, it is important that you restart the process
+ when you recompile the module. It is not enough to issue an "import"
+ statement again.
+
+Installation
+=============
+
+Unless you are used to some other automatic method:
+`download Cython <http://cython.org/#download>`_ (0.9.8.1.1 or later), unpack it,
+and run the usual ```python setup.py install``. This will install a
+``cython`` executable on your system. It is also possible to use Cython from
+the source directory without installing (simply launch :file:`cython.py` in the
+root directory).
+
+As of this writing SAGE comes with an older release of Cython than required
+for this tutorial. So if using SAGE you should download the newest Cython and
+then execute ::
+
+ $ cd path/to/cython-distro
+ $ path-to-sage/sage -python setup.py install
+
+This will install the newest Cython into SAGE.
+
+Manual compilation
+====================
+
+As it is always important to know what is going on, I'll describe the manual
+method here. First Cython is run::
+
+ $ cython yourmod.pyx
+
+This creates :file:`yourmod.c` which is the C source for a Python extension
+module. A useful additional switch is ``-a`` which will generate a document
+:file:`yourmod.html`) that shows which Cython code translates to which C code
+line by line.
+
+Then we compile the C file. This may vary according to your system, but the C
+file should be built like Python was built. Python documentation for writing
+extensions should have some details. On Linux this often means something
+like::
+
+ $ gcc -shared -pthread -fPIC -fwrapv -O2 -Wall -fno-strict-aliasing -I/usr/include/python2.5 -o yourmod.so yourmod.c
+
+``gcc`` should have access to the NumPy C header files so if they are not
+installed at :file:`/usr/include/numpy` or similar you may need to pass another
+option for those.
+
+This creates :file:`yourmod.so` in the same directory, which is importable by
+Python by using a normal ``import yourmod`` statement.
+
+The first Cython program
+==========================
+
+The code below does 2D discrete convolution of an image with a filter (and I'm
+sure you can do better!, let it serve for demonstration purposes). It is both
+valid Python and valid Cython code. I'll refer to it as both
+:file:`convolve_py.py` for the Python version and :file:`convolve1.pyx` for the
+Cython version -- Cython uses ".pyx" as its file suffix.
+
+.. code-block:: python
+
+ from __future__ import division
+ import numpy as np
+ def naive_convolve(f, g):
+ # f is an image and is indexed by (v, w)
+ # g is a filter kernel and is indexed by (s, t),
+ # it needs odd dimensions
+ # h is the output image and is indexed by (x, y),
+ # it is not cropped
+ if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
+ raise ValueError("Only odd dimensions on filter supported")
+ # smid and tmid are number of pixels between the center pixel
+ # and the edge, ie for a 5x5 filter they will be 2.
+ #
+ # The output size is calculated by adding smid, tmid to each
+ # side of the dimensions of the input image.
+ vmax = f.shape[0]
+ wmax = f.shape[1]
+ smax = g.shape[0]
+ tmax = g.shape[1]
+ smid = smax // 2
+ tmid = tmax // 2
+ xmax = vmax + 2*smid
+ ymax = wmax + 2*tmid
+ # Allocate result image.
+ h = np.zeros([xmax, ymax], dtype=f.dtype)
+ # Do convolution
+ for x in range(xmax):
+ for y in range(ymax):
+ # Calculate pixel value for h at (x,y). Sum one component
+ # for each pixel (s, t) of the filter g.
+ s_from = max(smid - x, -smid)
+ s_to = min((xmax - x) - smid, smid + 1)
+ t_from = max(tmid - y, -tmid)
+ t_to = min((ymax - y) - tmid, tmid + 1)
+ value = 0
+ for s in range(s_from, s_to):
+ for t in range(t_from, t_to):
+ v = x - smid + s
+ w = y - tmid + t
+ value += g[smid - s, tmid - t] * f[v, w]
+ h[x, y] = value
+ return h
+
+This should be compiled to produce :file:`yourmod.so` (for Linux systems). We
+run a Python session to test both the Python version (imported from
+``.py``-file) and the compiled Cython module.
+
+.. sourcecode:: ipython
+
+ In [1]: import numpy as np
+ In [2]: import convolve_py
+ In [3]: convolve_py.naive_convolve(np.array([[1, 1, 1]], dtype=np.int),
+ ... np.array([[1],[2],[1]], dtype=np.int))
+ Out [3]:
+ array([[1, 1, 1],
+ [2, 2, 2],
+ [1, 1, 1]])
+ In [4]: import convolve1
+ In [4]: convolve1.naive_convolve(np.array([[1, 1, 1]], dtype=np.int),
+ ... np.array([[1],[2],[1]], dtype=np.int))
+ Out [4]:
+ array([[1, 1, 1],
+ [2, 2, 2],
+ [1, 1, 1]])
+ In [11]: N = 100
+ In [12]: f = np.arange(N*N, dtype=np.int).reshape((N,N))
+ In [13]: g = np.arange(81, dtype=np.int).reshape((9, 9))
+ In [19]: %timeit -n2 -r3 convolve_py.naive_convolve(f, g)
+ 2 loops, best of 3: 1.86 s per loop
+ In [20]: %timeit -n2 -r3 convolve1.naive_convolve(f, g)
+ 2 loops, best of 3: 1.41 s per loop
+
+There's not such a huge difference yet; because the C code still does exactly
+what the Python interpreter does (meaning, for instance, that a new object is
+allocated for each number used). Look at the generated html file and see what
+is needed for even the simplest statements you get the point quickly. We need
+to give Cython more information; we need to add types.
+
+Adding types
+=============
+
+To add types we use custom Cython syntax, so we are now breaking Python source
+compatibility. Here's :file:`convolve2.pyx`. *Read the comments!* ::
+
+ from __future__ import division
+ import numpy as np
+ # "cimport" is used to import special compile-time information
+ # about the numpy module (this is stored in a file numpy.pxd which is
+ # currently part of the Cython distribution).
+ cimport numpy as np
+ # We now need to fix a datatype for our arrays. I've used the variable
+ # DTYPE for this, which is assigned to the usual NumPy runtime
+ # type info object.
+ DTYPE = np.int
+ # "ctypedef" assigns a corresponding compile-time type to DTYPE_t. For
+ # every type in the numpy module there's a corresponding compile-time
+ # type with a _t-suffix.
+ ctypedef np.int_t DTYPE_t
+ # The builtin min and max functions works with Python objects, and are
+ # so very slow. So we create our own.
+ # - "cdef" declares a function which has much less overhead than a normal
+ # def function (but it is not Python-callable)
+ # - "inline" is passed on to the C compiler which may inline the functions
+ # - The C type "int" is chosen as return type and argument types
+ # - Cython allows some newer Python constructs like "a if x else b", but
+ # the resulting C file compiles with Python 2.3 through to Python 3.0 beta.
+ cdef inline int int_max(int a, int b): return a if a >= b else b
+ cdef inline int int_min(int a, int b): return a if a <= b else b
+ # "def" can type its arguments but not have a return type. The type of the
+ # arguments for a "def" function is checked at run-time when entering the
+ # function.
+ #
+ # The arrays f, g and h is typed as "np.ndarray" instances. The only effect
+ # this has is to a) insert checks that the function arguments really are
+ # NumPy arrays, and b) make some attribute access like f.shape[0] much
+ # more efficient. (In this example this doesn't matter though.)
+ def naive_convolve(np.ndarray f, np.ndarray g):
+ if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
+ raise ValueError("Only odd dimensions on filter supported")
+ assert f.dtype == DTYPE and g.dtype == DTYPE
+ # The "cdef" keyword is also used within functions to type variables. It
+ # can only be used at the top indendation level (there are non-trivial
+ # problems with allowing them in other places, though we'd love to see
+ # good and thought out proposals for it).
+ #
+ # For the indices, the "int" type is used. This corresponds to a C int,
+ # other C types (like "unsigned int") could have been used instead.
+ # Purists could use "Py_ssize_t" which is the proper Python type for
+ # array indices.
+ cdef int vmax = f.shape[0]
+ cdef int wmax = f.shape[1]
+ cdef int smax = g.shape[0]
+ cdef int tmax = g.shape[1]
+ cdef int smid = smax // 2
+ cdef int tmid = tmax // 2
+ cdef int xmax = vmax + 2*smid
+ cdef int ymax = wmax + 2*tmid
+ cdef np.ndarray h = np.zeros([xmax, ymax], dtype=DTYPE)
+ cdef int x, y, s, t, v, w
+ # It is very important to type ALL your variables. You do not get any
+ # warnings if not, only much slower code (they are implicitly typed as
+ # Python objects).
+ cdef int s_from, s_to, t_from, t_to
+ # For the value variable, we want to use the same data type as is
+ # stored in the array, so we use "DTYPE_t" as defined above.
+ # NB! An important side-effect of this is that if "value" overflows its
+ # datatype size, it will simply wrap around like in C, rather than raise
+ # an error like in Python.
+ cdef DTYPE_t value
+ for x in range(xmax):
+ for y in range(ymax):
+ s_from = int_max(smid - x, -smid)
+ s_to = int_min((xmax - x) - smid, smid + 1)
+ t_from = int_max(tmid - y, -tmid)
+ t_to = int_min((ymax - y) - tmid, tmid + 1)
+ value = 0
+ for s in range(s_from, s_to):
+ for t in range(t_from, t_to):
+ v = x - smid + s
+ w = y - tmid + t
+ value += g[smid - s, tmid - t] * f[v, w]
+ h[x, y] = value
+ return h
+
+At this point, have a look at the generated C code for :file:`convolve1.pyx` and
+:file:`convolve2.pyx`. Click on the lines to expand them and see corresponding C.
+(Note that this code annotation is currently experimental and especially
+"trailing" cleanup code for a block may stick to the last expression in the
+block and make it look worse than it is -- use some common sense).
+
+* .. literalinclude: convolve1.html
+* .. literalinclude: convolve2.html
+
+Especially have a look at the for loops: In :file:`convolve1.c`, these are ~20 lines
+of C code to set up while in :file:`convolve2.c` a normal C for loop is used.
+
+After building this and continuing my (very informal) benchmarks, I get:
+
+.. sourcecode:: ipython
+
+ In [21]: import convolve2
+ In [22]: %timeit -n2 -r3 convolve2.naive_convolve(f, g)
+ 2 loops, best of 3: 828 ms per loop
+
+Efficient indexing
+====================
+
+There's still a bottleneck killing performance, and that is the array lookups
+and assignments. The ``[]``-operator still uses full Python operations --
+what we would like to do instead is to access the data buffer directly at C
+speed.
+
+What we need to do then is to type the contents of the :obj:`ndarray` objects.
+We do this with a special "buffer" syntax which must be told the datatype
+(first argument) and number of dimensions ("ndim" keyword-only argument, if
+not provided then one-dimensional is assumed).
+
+More information on this syntax [:enhancements/buffer:can be found here].
+
+Showing the changes needed to produce :file:`convolve3.pyx` only::
+
+ ...
+ def naive_convolve(np.ndarray[DTYPE_t, ndim=2] f, np.ndarray[DTYPE_t, ndim=2] g):
+ ...
+ cdef np.ndarray[DTYPE_t, ndim=2] h = ...
+
+Usage:
+
+.. sourcecode:: ipython
+
+ In [18]: import convolve3
+ In [19]: %timeit -n3 -r100 convolve3.naive_convolve(f, g)
+ 3 loops, best of 100: 11.6 ms per loop
+
+Note the importance of this change.
+
+*Gotcha*: This efficient indexing only affects certain index operations,
+namely those with exactly ``ndim`` number of typed integer indices. So if
+``v`` for instance isn't typed, then the lookup ``f[v, w]`` isn't
+optimized. On the other hand this means that you can continue using Python
+objects for sophisticated dynamic slicing etc. just as when the array is not
+typed.
+
+Tuning indexing further
+========================
+
+The array lookups are still slowed down by two factors:
+
+1. Bounds checking is performed.
+2. Negative indices are checked for and handled correctly. The code above is
+ explicitly coded so that it doesn't use negative indices, and it
+ (hopefully) always access within bounds. We can add a decorator to disable
+ bounds checking::
+
+ ...
+ cimport cython
+ @cython.boundscheck(False) # turn of bounds-checking for entire function
+ def naive_convolve(np.ndarray[DTYPE_t, ndim=2] f, np.ndarray[DTYPE_t, ndim=2] g):
+ ...
+
+Now bounds checking is not performed (and, as a side-effect, if you ''do''
+happen to access out of bounds you will in the best case crash your program
+and in the worst case corrupt data). It is possible to switch bounds-checking
+mode in many ways, see [:docs/compilerdirectives:compiler directives] for more
+information.
+
+Negative indices are dealt with by ensuring Cython that the indices will be
+positive, by casting the variables to unsigned integer types (if you do have
+negative values, then this casting will create a very large positive value
+instead and you will attempt to access out-of-bounds values). Casting is done
+with a special ``<>``-syntax. The code below is changed to use either
+unsigned ints or casting as appropriate::
+
+ ...
+ cdef int s, t # changed
+ cdef unsigned int x, y, v, w # changed
+ cdef int s_from, s_to, t_from, t_to
+ cdef DTYPE_t value
+ for x in range(xmax):
+ for y in range(ymax):
+ s_from = max(smid - x, -smid)
+ s_to = min((xmax - x) - smid, smid + 1)
+ t_from = max(tmid - y, -tmid)
+ t_to = min((ymax - y) - tmid, tmid + 1)
+ value = 0
+ for s in range(s_from, s_to):
+ for t in range(t_from, t_to):
+ v = <unsigned int>(x - smid + s) # changed
+ w = <unsigned int>(y - tmid + t) # changed
+ value += g[<unsigned int>(smid - s), <unsigned int>(tmid - t)] * f[v, w] # changed
+ h[x, y] = value
+ ...
+
+(In the next Cython release we will likely add a compiler directive or
+argument to the ``np.ndarray[]``-type specifier to disable negative indexing
+so that casting so much isn't necessary; feedback on this is welcome.)
+
+The function call overhead now starts to play a role, so we compare the latter
+two examples with larger N:
+
+.. sourcecode:: ipython
+
+ In [11]: %timeit -n3 -r100 convolve4.naive_convolve(f, g)
+ 3 loops, best of 100: 5.97 ms per loop
+ In [12]: N = 1000
+ In [13]: f = np.arange(N*N, dtype=np.int).reshape((N,N))
+ In [14]: g = np.arange(81, dtype=np.int).reshape((9, 9))
+ In [17]: %timeit -n1 -r10 convolve3.naive_convolve(f, g)
+ 1 loops, best of 10: 1.16 s per loop
+ In [18]: %timeit -n1 -r10 convolve4.naive_convolve(f, g)
+ 1 loops, best of 10: 597 ms per loop
+
+(Also this is a mixed benchmark as the result array is allocated within the
+function call.)
+
+.. Warning::
+
+ Speed comes with some cost. Especially it can be dangerous to set typed
+ objects (like ``f``, ``g`` and ``h`` in our sample code) to :keyword:`None`.
+ Setting such objects to :keyword:`None` is entirely legal, but all you can do with them
+ is check whether they are None. All other use (attribute lookup or indexing)
+ can potentially segfault or corrupt data (rather than raising exceptions as
+ they would in Python).
+
+ The actual rules are a bit more complicated but the main message is clear: Do
+ not use typed objects without knowing that they are not set to None.
+
+More generic code
+==================
+
+It would be possible to do::
+
+ def naive_convolve(object[DTYPE_t, ndim=2] f, ...):
+
+i.e. use :obj:`object` rather than :obj:`np.ndarray`. Under Python 3.0 this
+can allow your algorithm to work with any libraries supporting the buffer
+interface; and support for e.g. the Python Imaging Library may easily be added
+if someone is interested also under Python 2.x.
+
+There is some speed penalty to this though (as one makes more assumptions
+compile-time if the type is set to :obj:`np.ndarray`, specifically it is
+assumed that the data is stored in pure strided more and not in indirect
+mode).
+
+[:enhancements/buffer:More information]
+
+The future
+============
+
+These are some points to consider for further development. All points listed
+here has gone through a lot of thinking and planning already; still they may
+or may not happen depending on available developer time and resources for
+Cython.
+
+1. Support for efficient access to structs/records stored in arrays; currently
+ only primitive types are allowed.
+2. Support for efficient access to complex floating point types in arrays. The
+ main obstacle here is getting support for efficient complex datatypes in
+ Cython.
+3. Calling NumPy/SciPy functions currently has a Python call overhead; it
+ would be possible to take a short-cut from Cython directly to C. (This does
+ however require some isolated and incremental changes to those libraries;
+ mail the Cython mailing list for details).
+4. Efficient code that is generic with respect to the number of dimensions.
+ This can probably be done today by calling the NumPy C multi-dimensional
+ iterator API directly; however it would be nice to have for-loops over
+ :func:`enumerate` and :func:`ndenumerate` on NumPy arrays create efficient
+ code.
+5. A high-level construct for writing type-generic code, so that one can write
+ functions that work simultaneously with many datatypes. Note however that a
+ macro preprocessor language can help with doing this for now.
+
projects / cython.git / commitdiff