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
I prefer a Savitzky-Golay filter. It uses least squares to regress a small window of your data onto a polynomial, then uses the polynomial to estimate the point in the center of the window. Finally the window is shifted forward by one data point and the process repeats. This continues until every point has been … Read more
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
A polynomial interpolation in the sense that you are using it is probably not the best idea, if you want it to go through all of your points. You have 24 points, which would need a polynomial of order 23, if it should go through all the points. I can’t seem to use poly with … Read more
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
A possible solution where the loess smoothed data is grabbed from the plot object and used for the geom_ribbon: # create plot object with loess regression lines g1 <- ggplot(df) + stat_smooth(aes(x = x, y = ymin, colour = “min”), method = “loess”, se = FALSE) + stat_smooth(aes(x = x, y = ymax, colour = … Read more
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
Here’s a simple Kalman filter that could be used for exactly this situation. It came from some work I did on Android devices. General Kalman filter theory is all about estimates for vectors, with the accuracy of the estimates represented by covariance matrices. However, for estimating location on Android devices the general theory reduces to … Read more
I prefer a Savitzky-Golay filter. It uses least squares to regress a small window of your data onto a polynomial, then uses the polynomial to estimate the point in the center of the window. Finally the window is shifted forward by one data point and the process repeats. This continues until every point has been … Read more
You could use scipy.interpolate.spline to smooth out your data yourself: from scipy.interpolate import spline # 300 represents number of points to make between T.min and T.max xnew = np.linspace(T.min(), T.max(), 300) power_smooth = spline(T, power, xnew) plt.plot(xnew,power_smooth) plt.show() spline is deprecated in scipy 0.19.0, use BSpline class instead. Switching from spline to BSpline isn’t a … Read more