Method repeat

tools/python-3.11.9-amd64/Lib/timeit.py:186–210

Call timeit() a few times. This is a convenience function that calls the timeit() repeatedly, returning a list of results. The first argument specifies how many times to call timeit(), defaulting to 5; the second argument specifies the timer argument, defaultin

(self, repeat=default_repeat, number=default_number)

        return timing

    def repeat(self, repeat=default_repeat, number=default_number):
        """Call timeit() a few times.

        This is a convenience function that calls the timeit()
        repeatedly, returning a list of results.  The first argument
        specifies how many times to call timeit(), defaulting to 5;
        the second argument specifies the timer argument, defaulting
        to one million.

        Note: it's tempting to calculate mean and standard deviation
        from the result vector and report these.  However, this is not
        very useful.  In a typical case, the lowest value gives a
        lower bound for how fast your machine can run the given code
        snippet; higher values in the result vector are typically not
        caused by variability in Python's speed, but by other
        processes interfering with your timing accuracy.  So the min()
        of the result is probably the only number you should be
        interested in.  After that, you should look at the entire
        vector and apply common sense rather than statistics.
        """
        r = []
        for i in range(repeat):
            t = self.timeit(number)
            r.append(t)
        return r

    def autorange(self, callback=None):
        """Return the number of loops and time taken so that total time >= 0.2.