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rehearsal and the second for the true results, as in some situations CPU caching, memory caching, and other factors can taint the results. Therefore, repeating the test can lead to more accurate figures. Replacing the bm method with bmbm in the preceding example gives results like these:

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A function is something you can call (possibly with some parameters, the things you put in the parentheses), which performs an action and returns a value. In general, you can tell whether something is callable or not with the built-in function callable: >>> >>> >>> >>> 0 >>> 1 import math x = 1 y = math.sqrt callable(x) callable(y)

When you reach the rst Key Point slide, write the word Focus in the blank space above the photo of the hurdle as you relate the topic to what your audience told you on the Point A slide and as you convey the message of the headline, here shortened on the slide from the original, Overcome the focus hurdle by reviewing the plan each morning (upper right). Then write Communication on the second Key Point slide (lower left) as you relate the headline, shortened on the slide from the original, Overcome the communication hurdle by talking to someone directly. Last, write Duplicated Effort on the third Key Point slide (lower right) as you relate the headline shortened from the original, Overcome the duplicated effort hurdle by checking off items on the intranet.

Rehearsal ---------------------------------------for: 0.780000 0.000001 0.780001 ( 0.958378) times: 0.100000 0.010000 0.110000 ( 1.342837) ------------------------------- total: 0.890001sec user system for: 0.850000 0.000000 times: 0.970000 0.010000 total 0.850000 ( 0.980000 ( real 0.967980) 1.301703)

bmbm runs the tests twice and gives both sets of results, where the latter set should be the most accurate.

The headlines were shortened on the Key Point slides to leave empty space on the screen where you could write with a Tablet PC stylus to increase engagement if you don t have a Tablet PC, insert text boxes on the screen instead.

Where benchmarking is the process of measuring the total time it takes to achieve something and comparing those results between different versions of code, profiling tells you what code is taking what amount of time. For example, you might have a single line in your code that s causing the program to run slowly, so by profiling your code you can immediately see where you should focus your optimization efforts.

As you know from the previous section, creating functions is central to structured programming. So how do you define a function With the def (or function definition ) statement: def hello(name): return 'Hello, ' + name + '!' After running this, you have a new function available, called hello, which returns a string with a greeting for the name given as the only parameter. You can use this function just like you used the built-in ones: >>> print hello('world') Hello, world! >>> print hello('Gumby') Hello, Gumby! Pretty neat, huh Consider how you would write a function that returned a list of Fibonacci numbers. Easy! You just use the code from before, and instead of reading in a number from the user, you receive it as a parameter: def fibs(num): result = [0, 1] for i in range(num-2): result.append(result[-2] + result[-1]) return result After running this statement, you ve basically told the interpreter how to calculate Fibonacci numbers so now you don t have to worry about the details anymore. You simply use the function fibs: >>> [0, >>> [0, fibs(10) 1, 1, 2, 3, 5, 8, 13, 21, 34] fibs(15) 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377]

Note Some people consider profiling to be the holy grail of optimization. Rather than thinking of efficient

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ways to write your application ahead of time, some developers suggest writing your application, profiling it, and then fixing the slowest areas. This is to prevent premature optimization. After all, you might prematurely optimize something that didn t actually warrant it, but miss out on an area of code that could do with significant optimization.

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