Smoothing data from a sensor

The simplest is to do a moving average of your data. That is, to keep an array of sensor data readings and average them. Something like this (pseudocode): data_X = [0,0,0,0,0]; function read_X () { data_X.delete_first_element(); data_X.push(get_sensor_data_X()); return average(data_X); } There is a trade-off when doing this. The larger the array you use, the smoother … Read more

Smoothing a hand-drawn curve

You can reduce the number of points using the Ramer–Douglas–Peucker algorithm there is a C# implementation here. I gave this a try using WPFs PolyQuadraticBezierSegment and it showed a small amount of improvement depending on the tolerance. After a bit of searching sources (1,2) seem to indicate that using the curve fitting algorithm from Graphic … Read more

Python natural smoothing splines

After hours of investigation, I did not find any pip installable packages which could fit a natural cubic spline with user-controllable smoothness. However, after deciding to write one myself, while reading about the topic I stumbled upon a blog post by github user madrury. He has written python code capable of producing natural cubic spline … Read more

scrollintoview animation

In most modern browsers (Chrome and Firefox, but not Safari, UC, or IE) you can pass options in an object to .scrollIntoView(). Try this: elm.scrollIntoView({ behavior: ‘smooth’, block: ‘center’ }) Other values are behavior: ‘instant’ or block: ‘start’ or block: ‘end’. See https://developer.mozilla.org/en/docs/Web/API/Element/scrollIntoView

NumPy version of “Exponential weighted moving average”, equivalent to pandas.ewm().mean()

I think I have finally cracked it! Here’s a vectorized version of numpy_ewma function that’s claimed to be producing the correct results from @RaduS’s post – def numpy_ewma_vectorized(data, window): alpha = 2 /(window + 1.0) alpha_rev = 1-alpha scale = 1/alpha_rev n = data.shape[0] r = np.arange(n) scale_arr = scale**r offset = data[0]*alpha_rev**(r+1) pw0 = … Read more

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