8/24/2023 0 Comments Ema start time![]() The sensor is noisy, so we use exponential smoothing. """ Read from the temperature monitor - and smooth the value out. # and start calculating the exponentially smoothed averageĮma = (((yield ema) - ema) * multiplier) + emaĪnd I use it like this: def temp_monitor(pin): # Start by just returning the simple average until we have enough data. """Ĭum_temp = yield None # We are being primed Period: int - how many values to smooth over (default=100). Send in values - at first it'll return a simple average, but as soon as it's gahtered 'period' values, it'll start to use the Exponential Moving Averge to smooth the values. Smooths the values in v over ther period. ![]() I found the above code snippet by pretty useful - but I needed something that could continuously smooth a stream of values - so I refactored it to this: def exponential_moving_average(period=1000): You'd invoke the function something like this: result = movingAverageExponential(values, 0.75, 0.0001) The 'epsilon' argument lets you set a cutoff point, below which you will cease to care about old values (since their contribution to today's value will be insignificant). Of course, if you've got a huge array of values, the values from ten or fifteen days ago won't contribute very much to today's weighted average. ![]() For example, if you used an alpha of 0.5, then today's moving average value would be composed of the following weighted values: today: 1.0 The 'alpha' argument is the decay factor on each iteration. And in python lists, appending is much less expensive than prepending, which is why I built the list in reverse order. But I don't think you can declare a big empty array in python. (SIDE NOTE: if I was using a language other than python, I'd create a full-size empty array first and then fill it backwards-order, so that I wouldn't have to reverse it at the end. This function moves backward, from the end of the list to the beginning, calculating the exponential moving average for each value by working backward until the weight coefficient for an element is less than the given epsilon.Īt the end of the function, it reverses the values before returning the list (so that they're in the correct order for the caller). Raise ValueError("out of range, epsilon='%s'" % epsilon) Raise ValueError("out of range, alpha='%s'" % alpha) def movingAverageExponential(values, alpha, epsilon = 0): My python is a little bit rusty (anyone can feel free to edit this code to make corrections, if I've messed up the syntax somehow), but here goes. Return sum(alpha**((today - date).days) * iq for date, iq in iq_data)ĭays = The most recent data is the most valuable one. """Perform exponential smoothing with factor `alpha`.Įach time period the value of `iq` drops `alpha` times. The above is not pretty, so let's refactor it a bit: from collections import namedtupleĭef smooth(iq_data, alpha=1, today=None): sorted(zip(days, IQ), key=lambda p: p, reverse=True))) for n, iq in map(lambda (day, iq), today=max(days): (today-day, iq), To calculate an exponential smoothing of your data with a smoothing factor alpha (it is (1 - alpha) in Wikipedia's terms): > alpha = 0.5 It seems that mov_average_expw() function from _funcs submodule from SciKits (add-on toolkits that complement SciPy) better suits the wording of your question.
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