On 5 April 2018 at 13:52, Peter O'Connor <peter.ed.ocon...@gmail.com> wrote:
> I was thinking it would be nice to be able to encapsulate this common type > of operation into a more compact comprehension. > > I propose a new "Reduce-Map" comprehension that allows us to write: > > signal = [math.sin(i*0.01) + random.normalvariate(0, 0.1) for i in > range(1000)] > smooth_signal = [average = (1-decay)*average + decay*x for x in signal from > average=0.] > > Instead of: > > def exponential_moving_average(signal: Iterable[float], decay: float, > initial_value: float=0.): > average = initial_value > for xt in signal: > average = (1-decay)*average + decay*xt > yield average > > signal = [math.sin(i*0.01) + random.normalvariate(0, 0.1) for i in > range(1000)] > smooth_signal = list(exponential_moving_average(signal, decay=0.05)) > > I wrote in this mail list the very same proposal some time ago. I was trying to let the scan higher order function (itertools.accumulate with a lambda, or what was done in the example above) fit into a simpler list comprehension. As a result, I wrote this project, that adds the "scan" feature to Python comprehensions using a decorator that performs bytecode manipulation (and it had to fit in with a valid Python syntax): https://github.com/danilobelli ni/pyscanprev In that GitHub page I've wrote several examples and a rationale on why this would be useful. -- Danilo J. S. Bellini --------------- "*It is not our business to set up prohibitions, but to arrive at conventions.*" (R. Carnap)
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