Programmatically finding "significant" data points

[Roberto Bonvallet]

erikcw wrote:
> 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.

In calculus, you identify high and low points by looking where the
derivative changes its sign.  When working with discrete samples, you can
look at the sign changes in finite differences:

>>> data = [...]
>>> diff = [data[i + 1] - data[i] for i in range(len(data))]
>>> map(str, diff)
['0.4', '0.1', '-0.2', '-0.01', '0.11', '0.5', '-0.2', '-0.2', '0.6',
'-0.1', '0.2', '0.1', '0.1', '-0.45', '0.15', '-0.3', '-0.2', '0.1',
'-0.4', '0.05', '-0.1', '-0.25']

The high points are those where diff changes from + to -, and the low
points are those where diff changes from - to +.

HTH,
-- 
Roberto Bonvallet