The list of accepted talks for EuroPython was published with lots of great talks and topics. I got my tutorial on Cython with NumPy accepted, as well as my talk on Fast Async Code with Cython and AsyncIO that I'm giving together with a collegue from Skoobe. I guess both proposals colided too heavily with the current list of hype buzzwords to get rejected. ;-)

Thanks to everyone who voted for them! Hope to see you there!

And in case you're interested in learning Cython from a pro, there are still places left for the extensive Cython training that I'll be giving in Leipzig (DE) next June, 16-17th. It's part of a larger series of courses throughout that week on High-Performance Computing with Python.

It was really nice to hear Cython being mentioned in pretty much every second talk at this year's EuroSciPy in Cambridge, either directly or by promoting tools that were themselves written in Cython. It seems to have become a key cornerstone in Python's ecosystem.

For me, this has been an intense conference summer overall, so I only just noticed that I hadn't written up anything about the long list of changes in Cython 0.23 yet. Well, apart from the changelog :). So here it goes.

Cython 0.23 is a major feature release. It implements the new language features of the new Python 3.5, but with the usual backports to make them available in Python 2.6 and later. My personal feature favourites are PEP 492 (async/await) and inlined generator expressions, but also the new support for Cython code coverage analysis with the coverage.py tool, that I blogged about already. Note that some Py3.5 features, such as the PEP 465 matrix multiplication operator, were already released early this year in Cython 0.22.

One of the greatest changes in Python 3.5, from a Cython perspective, is the shift from generators and coroutines as builtin types to making them ABCs, i.e. abstract base classes in the collections.abc module. This enables full interoperability of Cython's own implementations with Python's builtin types. To make this available in older Python versions, I created the backports_abc package for applications to use. Basically, you would install it and then say

import backports_abc
backports_abc.patch()

to get the Coroutine and Generator classes added to the collections.abc module if they are missing (i.e. in CPython versions before 3.5). This allows Cython to register its internal types with them and from that point on, any code using isinstance(x, Generator) or isinstance(x, Coroutine) will be able to support Cython's fast builtin types. If you find software packages that do not support this yet because they do type checks instead of ABC instance checks, please file a bug report with their projects to get them adapted. Twisted comes to mind, for example, or Tornado. [Update 2015-09-15]: The next Tornado release will have support for Cython coroutines.

CPython 3.5 itself has already been fixed up, thanks to Yury Selivanov, to natively support Cython implemented generators and coroutines. This allows asynchronous code (currently, but not limited to, asyncio) to take advantage of Cython's speed, which can easily be several times faster than Python coroutines in practice.

What else is there in Cython 0.23? Well, inlined generator expressions are back. This means that when you call builtin functions like any() or sorted() on a generator expression, Cython will inline its own (partial) implementation of that function into the generator implementation and avoid calling back and forth between the generator and its consumer. Instead, it will calculate the final result inside of the generator and only return it in one step at the very end. This can speed up the overall execution considerably.

Another feature that I invested some time into is faster numeric operations on Python's long (Py3 int) and float types. Arithmetic or shifting operations that involves integer or float constants are now faster because they avoid double unpacking of the operands and inline their constant parts.

On the C++ integration front, several minor issues have seen improvements and new standard declarations were added, which makes Cython 0.23 an even nicer and rounder tool for wrapping C++ code bases, or for making use of STL containers in Cython code. More C++ improvements are planned for the upcoming 0.24 release.

As usual, the complete list of features and fixes is much longer for such a large release. Hope you like it.

The coverage analysis tool for Python has recently gained a plugin API that allows external tools to provide source code information. Cython has line tracing support since release 0.19, but the new API allows it to support coverage.py for line coverage reporting.

To test this, you need the latest developer versions of both coverage.py (pre-4.0) and Cython (post-0.22.0):

https://bitbucket.org/ned/coveragepy

https://github.com/cython/cython

Then, enable the Cython plugin in the .coveragerc config file of your project:

[run]
plugins = Cython.Coverage

And compile your Cython modules with line tracing support. This can be done by putting the following two comment lines at the top of the modules that you want to trace:

# cython: linetrace=True
# distutils: define_macros=CYTHON_TRACE=1

That's a double opt-in. The first line instructs Cython to generate verbose line tracing code (and thus increase the size of the resulting C/C++ file), and the second line enables this code at C compile time, which will most likely slow down your program. You can also configure both settings globally in your setup.py script, see the Cython documentation.

Then make sure you build your project in place (python setup.py build_ext --inplace) so that the generated C/C++ code files can be found right next to the binary modules and their sources.

That's it. Now your Cython modules should show up in your coverage reports. Any questions, bug reports or suggestions regarding this new feature can be discussed on the Cython-users mailing list.

Update: Coverage reporting is now also available for code that frees the GIL if the C macro CYTHON_TRACE_NOGIL=1 is set, i.e.:

# cython: linetrace=True
# distutils: define_macros=CYTHON_TRACE_NOGIL=1

All reporting formats of coverage.py are supported: plain text, XML and annotated HTML sources.

Cython 0.22 is nearing completion and it's a major feature release, so here's a list of major improvements to Cython's type system.

C arrays have become first class citizens in Cython's type system. Previously, they behaved mostly just like pointers, as they do in C. The new release allows them to coerce to Python lists (by default) and tuples (when requested by the context). They can also be assigned by value, without the need for an explicit copy loop.

cdef int[3] x = [0, 1, 2]
cdef int[3] y = x
y[2] = 5
print(y)          # -> [0, 1, 5]
print(<tuple>x)   # -> (0, 1, 2)

cdef int[2][3] z = [x, y]
print(z)          # -> [[0, 1, 2], [0, 1, 5]]

Typed tuples were added. They behave like Python tuples and coerce from and to them at need, but support any C types for their arguments.

cdef int x = 1
cdef double y = 2

cdef (int*, double*) xy = (&x, &y)
ptr_a, ptr_b = xy
print(ptr_a[0], ptr_b[0])    # ->  1 2.0

cdef (int, double) ab = (ptr_a[0], ptr_b[0])
print(ab)                    # ->  (1, 2.0)

C/C++ functions have learned to auto-coerce to callable Python objects when used in an object context. Similarly, in order to provide a flat wrapper to an external C function, it is now sufficient to declare it as an external cpdef function in the module that should export it (or in the corresponding .pxd file).

cdef extern from "someheader.h":
    cpdef int cpfunc(int x, double y)
    cdef int cfunc(int x)

def use_cpfunc_from_cython():
    return cpfunc(1, 3.0)

def pass_cfunc_to_python():
    return cfunc

def use_from_python():
    """
    >>> cpfunc(1, 3.0)
    4.0
    >>> cfunc = pass_cfunc_to_python()
    >>> cfunc(4)
    123
    """

My bicycle was recently stolen and since I now have to get a new one, here's a proposal.

From today on until December 24th, I will divert all donations that I receive for my work on lxml to help in restoring my local mobility.

If you do not like this 'misuse', do not donate in this time frame. I do hope, however, that some of you like the idea that the money they give for something they value is used for something that is of value to the receiver.

All the best -- Stefan

I spent some time optimising Python's "fractions" module. Fractions (i.e. rational numbers) are great for all sorts of exact computations, especially money calculations. You never have to care about loss of precision, you can freely mix very large and very small numbers any way you like in your computations - the result is always exact as it's all done in integers internally.

But the performance used to suck. Totally. The main problem was the type instantiation, which is really expensive. For example, simply changing this code

f = n * Fraction(x ,y)

to this

f = Fraction(n * x, y)

(which avoids intermediate Fraction operations) could speed it up by factors. I provided some patches that streamline common cases (numerator and denominator will usually be Python ints), and this made the implementation in CPython 3.5 twice as fast as before. It actually starts being usable. :)

For those who can't wait for Python 3.5 to come out (in about a year's time), and also for those who want even better performance (like me), I dropped the implementation into Cython and optimised it further at the C level. That gave me another factor of 5, so the result is currently about 10x faster than what's in the standard library.

Compared to the Decimal type in Python 2.7, it's about 15x faster. The hugely improved C reimplementation of the "decimal" module in Python 3.3 is still about 5-6x faster - or less, if you often need to rescale your values along the way. Plus, with decimal, you always have to take care of using the right precision scale for your code to prevent rounding errors, and playing it safe will slow it down.

I released the module to PyPI, it's called quicktions. Hope you like it.

Quelle: https://www.deutschepost.de/de/b/brief_postkarte.html

Ist das Problem da jetzt die Gummierung der Briefmarke oder sind es die scharfen Kanten der Postkarten?

I'm giving an in-depth learn-from-a-core-dev Cython training at the Python Academy in Leipzig (Germany) next month, October 16-17. In two days, the course will cover everything from a Cython intro up to the point where you bring Python code to C speed and use C/C++ libraries and data types in your code.

Read more on it here:

http://www.python-academy.com/courses/specialtopics/python_course_cython.html

I keep seeing a lot of code around that does this:

import re

_MATCH_DIGITS_RE = re.compile('[0-9]+')

and then uses this regular expression only in one place, e.g. like this:

def func():
    numbers = _MATCH_DIGITS_RE.findall(input)
    ...

Python's re module actually uses expression caching internally, so it's very unlikely that this is any faster in practice than just writing this:

def func():
    numbers = re.findall('[0-9]+', input)
    ...

Which is a shorter and much more straight forward way to write what's going on. Now, for longer and more complex regular expressions, this can actually get out of hand and it does help to give them a readable name. However, all-upper-case constant names tend to be pretty far from readable. So, I always wonder why people don't just write this using a bound method:

_find_numbers = re.compile('[0-9]+').findall

def func():
    numbers = _find_numbers(input)
    ...

I find this much clearer to read. And it nicely abstracts the code that uses the function-like callable _find_numbers() from the underlying implementation, which (in case you really want to know) happens to be a method of a compiled regular expression object.

I spent some time during the last two weeks reducing the call overhead for Python functions and methods in Cython. It was already quite low compared to CPython before, about 30-40% faster, but profiling then made me stumble over the fact that method calls in CPython really just do one thing: they repack the argument tuple and prepend the 'self' object to it. However, that is done right after Cython has carefully packed up exactly that argument tuple in the first place, so by simply inlining what PyMethodObject does, we can avoid packing tuples twice.

Avoiding to create a PyMethodObject at all may also appear as an interesting goal, but doing that is totally not easy (it happens during attribute lookup) and it's also most likely not worth it as method objects are created from a freelist, which makes their instantiation very fast. Method objects also hold actual state that the caller must receive: the underlying function and the self object. So getting rid of them will severly complicate things without a major gain to expect.

Another obvious optimisation, however, is that Python code calls into C implemented functions quite often, and if those are implemented as specialised functions that take exactly one or no argument (METH_O/METH_NOARGS), then the tuple packing and unpacking can be avoided completely. Together with the method call optimisation, this means that Cython can now call very simple methods without creating an argument tuple, and less simple ones without redundantly creating a second argument tuple.

I implemented these optimisations and they immediately blew up the method call micro benchmarks in Python's benchmark suite from about 1/3 to 2-3 times faster than CPython 3.5 (pre). Those are only simple micro benchmarks, so any real world code will benefit substantially less overall. However, it turned out that a couple of benchmarks in the suite that are based on real production code ended up loosing 5-15% of their total runtime. That's quite remarkable, given that the code they call actually does something (much) more heavy weight than the call overhead itself. I'm still tuning it a bit, but so far am really happy with this result.