What is the line? You can just have arguments on the next line without any problems:
a = dostuff(blahblah1, blahblah2, blahblah3, blahblah4, blahblah5,
blahblah6, blahblah7)
Otherwise you can do something like this:
if (a == True and
b == False):
or with explicit line break:
if a == True and \
b == False:
Check the style guide for more information.
Using parentheses, your example can be written over multiple lines:
a = ('1' + '2' + '3' +
'4' + '5')
The same effect can be obtained using explicit line break:
a = '1' + '2' + '3' + \
'4' + '5'
Note that the style guide says that using the implicit continuation with parentheses is preferred, but in this particular case just adding parentheses around your expression is probably the wrong way to go.
Since this question was asked in 2010, there has been real simplification in how to do simple multithreading with Python with map and pool.
The code below comes from an article/blog post that you should definitely check out (no affiliation) - Parallelism in one line: A Better Model for Day to Day Threading Tasks. I'll summarize below - it ends up being just a few lines of code:
from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(4)
results = pool.map(my_function, my_array)
Which is the multithreaded version of:
results = []
for item in my_array:
results.append(my_function(item))
Description
Map is a cool little function, and the key to easily injecting parallelism into your Python code. For those unfamiliar, map is something lifted from functional languages like Lisp. It is a function which maps another function over a sequence.
Map handles the iteration over the sequence for us, applies the function, and stores all of the results in a handy list at the end.
Implementation
Parallel versions of the map function are provided by two libraries:multiprocessing, and also its little known, but equally fantastic step child:multiprocessing.dummy.
multiprocessing.dummy
is exactly the same as multiprocessing module, but uses threads instead (an important distinction - use multiple processes for CPU-intensive tasks; threads for (and during) I/O):
multiprocessing.dummy replicates the API of multiprocessing, but is no more than a wrapper around the threading module.
import urllib2
from multiprocessing.dummy import Pool as ThreadPool
urls = [
'http://www.python.org',
'http://www.python.org/about/',
'http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html',
'http://www.python.org/doc/',
'http://www.python.org/download/',
'http://www.python.org/getit/',
'http://www.python.org/community/',
'https://wiki.python.org/moin/',
]
# Make the Pool of workers
pool = ThreadPool(4)
# Open the URLs in their own threads
# and return the results
results = pool.map(urllib2.urlopen, urls)
# Close the pool and wait for the work to finish
pool.close()
pool.join()
And the timing results:
Single thread: 14.4 seconds
4 Pool: 3.1 seconds
8 Pool: 1.4 seconds
13 Pool: 1.3 seconds
Passing multiple arguments (works like this only in Python 3.3 and later):
To pass multiple arrays:
results = pool.starmap(function, zip(list_a, list_b))
Or to pass a constant and an array:
results = pool.starmap(function, zip(itertools.repeat(constant), list_a))
If you are using an earlier version of Python, you can pass multiple arguments via this workaround).
(Thanks to user136036 for the helpful comment.)
Best Answer
I'm guessing you are getting a "Continuation line does not distinguish itself from next logical line". The solution is to move the second line another indent -
The reason is that
self.top_cheese(origin).size
is at the same indent level asraise IllegalMoveError
, which can be difficult for the reader, because it might is not obvious where the if statement ends and the actual block within the if statement begins. You can argue that this does not look much better, but that's the way it is.