"In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable." from wikipedia.com
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fixed a issue when using float type observations. added a draw function to draw the KDE graph(you need to see all the bar history to see it, doesnt work for float observations)
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removed some redundant parameters, added bandwidth, nstep parameters, the graph looks stepd due to x axis havin interdigit floating numbers so it rounds to nearest causing that effect.
This is interesting, I'm having fun with it.
We're always going to lose some precision when plotting due to the discrete bar_index values, though scaling the data works.
Now programatically finding local max/mins is the next step!
Thanks again Mr Santos. You work hard and get good results.