Programmaticall y finding "significan t" data points

[erikcw]

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 "significan t" 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?

Thanks for your help!

Erik

[robert]

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 "significan t" 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(dat a,t,window)>dat a(t)" / "moving_min(dat a,t,window)<dat a(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

[Paul McGuire]

"robert" <no*****@no-spam-no-spam.invalidwro te in message
news:ej******** **@news.albasan i.net...

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 "significan t" 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 ?

Using zip and map, it's easy to compute first and second derivatives of a
time series of values. The first lambda computes

[Paul McGuire]

.... dang touchy keyboard!

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]

Using the first derivative, and looking for sign changes, finds many of the
values you marked as "significan t".

-- Paul
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]

delta = lambda (x1,x2) : x2-x1
dy_dx =[0]+map(delta,zip( data,data[1:]))
d2y_dx2 = [0]+map(delta,zip( dy_dx,dy_dx[1:]))

sgnChange = lambda (x1,x2) : x1*x2<0
sigs = map(sgnChange,z ip(dy_dx,dy_dx[1:]))
print [i for i,v in enumerate(sigs) if v]
[2, 4, 6, 8, 9, 10, 13, 14, 15, 17, 18, 19, 20]