#
10.7 Calibration

The profiler subtracts a constant from each event handling time to compensate for the overhead of calling the time function, and socking away the results. By default, the constant is 0. The following procedure can be used to obtain a better constant for a given platform (see discussion in section Limitations above).

    import profile
    pr = profile.Profile()
    for i in range(5):
        print pr.calibrate(10000)

The method executes the number of Python calls given by the argument, directly and again under the profiler, measuring the time for both. It then computes the hidden overhead per profiler event, and returns that as a float. For example, on an 800 MHz Pentium running Windows 2000, and using Python's time.clock() as the timer, the magical number is about 12.5e-6.

The object of this exercise is to get a fairly consistent result. If your computer is very fast, or your timer function has poor resolution, you might have to pass 100000, or even 1000000, to get consistent results.

When you have a consistent answer, there are three ways you can use it:10.3

    import profile

    # 1. Apply computed bias to all Profile instances created hereafter.
    profile.Profile.bias = your_computed_bias

    # 2. Apply computed bias to a specific Profile instance.
    pr = profile.Profile()
    pr.bias = your_computed_bias

    # 3. Specify computed bias in instance constructor.
    pr = profile.Profile(bias=your_computed_bias)

If you have a choice, you are better off choosing a smaller constant, and then your results will ``less often'' show up as negative in profile statistics.

#### Footnotes

... it:10.3
  Prior to Python 2.2, it was necessary to edit the profiler source code to embed the bias as a literal number. You still can, but that method is no longer described, because no longer needed.