Re: Programmatically finding "significant" data points

erikcw wrote:
Hi all,

I have a collection of ordered numerical data in a list. The numbers
when plotted on a line chart make a low-high-low-high-high-low (random)
pattern. I need an algorithm to extract the "significant" high and low
points from this data.

Here is some sample data:
data = [0.10, 0.50, 0.60, 0.40, 0.39, 0.50, 1.00, 0.80, 0.60, 1.20,
1.10, 1.30, 1.40, 1.50, 1.05, 1.20, 0.90, 0.70, 0.80, 0.40, 0.45, 0.35,
0.10]

In this data, some of the significant points include:
data[0]
data[2]
data[4]
data[6]
data[8]
data[9]
data[13]
data[14]
....

How do I sort through this data and pull out these points of
significance?

Its obviously a kind of time series and you are search for a "moving_max(data,t,window)>data(t)" / "moving_min(data,t,window)<data(t)": an extremum within a certain (time) window. And obviously your time window is as low as 2 or 3 or so.

Unfortunately a moving_max func is not yet in numpy and probably not achievable from other existing array functions. You have to create slow looping code.


Robert

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