I would like it if I were able to set some really basic curve fitting for a plot without having to rely on other packages and complex solutions to smooth out curve data. Its not the most useful feature data analysis wise, but its much more pleasant to look at to see a general smooth trend rather than always just having the straight lines between points.
Add an option to plotting to customize the amount of smoothness of the curve. 0 smoothness being what plot already does by just connecting the dots, and increasing values of smoothness increase the amount of smoothing done to the curve.
Additional context and prior art
There are some close solutions but nothing is perfect. The closest I’ve gotten is
x_np = np.array(x) y_np = np.array(y) y_smooth = np.linspace(x_np.min(), x_np.max(), 200) spl = make_interp_spline(x_np, y_np, k = 3) y_smooth = spl(x_smooth) plot(x_smooth, y_smooth)
where x and y are some lists of data. This solution comes close in some cases, but it is very finicky and doesn’t always work. Any proposed solution I have found, usually works about the same and isn’t a perfect fix.
I know that curve fitting is a very complex problem, and I’m not looking for a perfect interpolation of data, I just want to be able to smooth out the jaggedness of a curve to show a general trend in the data for visuals sake.