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## Re: Smooth line approximating minima of a data series

 From: Matthias Brennwald Subject: Re: Smooth line approximating minima of a data series Date: Wed, 24 Feb 2010 15:10:11 +0100

```On Feb 24, 2010, at 3:03 PM, Ben Abbott wrote:

```
On Wednesday, February 24, 2010, at 03:04AM, "Matthias Brennwald" <address@hidden > wrote:
```Dear all

Consider a series of data values that reflect a smooth function (e.g.
a low-degree polynomial), but there might be additional features in
```
the data (e.g. narrow peaks or noise). I'd like to fit a polynomial to
```this data, whereby this polynomial reflects a smooth approximation of
the minima of the raw data (I call this the "base line"). The
following might help to illustrate what I'm trying to accomplish:

x = [-1:0.01:1]; % x-axis values
p = [-3 2 1 0]; yp = polyval (p,x); % make up a polynomial
reflecting the "base line" for illustration
y = yp + rand(size(x)); % this would be the raw data
plot (x,y,x,yp); legend ('raw data','base line') % plot the raw
data and the polynomial for illustration

Has anyone an idea of how to accomplish this? Are there standard
methods? I'd appreciate any hints.

Thanks
Matthias

```
```
```
Have you considered using convhull() in combination with polyfit(), or interp1() ?
```
Ah, I see, this should do the trick. Thanks!

Matthias

```