@contextmanager
def timing(label: str):
t0 = perf_counter()
yield lambda: (label, t1 - t0)
t1 = perf_counter()
with timing('Array tests') as total:
with timing('Array creation innermul') as inner:
x = array('d', [0] * 1000000)
with timing('Array creation outermul') as outer:
x = array('d', [0]) * 1000000
print('Total [%s]: %.6f s' % total())
print(' Timing [%s]: %.6f s' % inner())
print(' Timing [%s]: %.6f s' % outer())
The array module in the standard library has an unusual
approach to initialization: you pass it an existing sequence, such
as a large list, and it converts the data into the datatype of your
array if possible; however, you can also create an array from a
short sequence, after which you expand it to its full size. Have
you ever wondered which is faster? In a moment, we’ll create a
timing context manager to measure this and know for sure!
The key step you need to do to make your own context manager
is to use the @contextmanager decorator.
The section before the yield is where you can write code that
must execute before the body of your context manager will run.
Here we record the timestamp before the body will run.
The yield is where execution is transferred to the body of your
context manager; in our case, this is where our arrays get cre‐
ated. You can also return data: here I return a closure that will
calculate the elapsed time when called. It’s a little clever but
hopefully not excessively so: the
nal time t1 is captured within
the closure even though it will only be determined on the next
line.
After the yield, we write the code that will be executed when
the context manager finishes. For scenarios like file handling,
this would be where you close them. In this example, this is
where we record the final time t1.
The Standard Library | 7
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