f



Best fit line for sinusoid

Hi all,

I was wondering if there was a 'simple' way to get IDL to plot a best fit l=
ine for a sinusoidally-varying data set. For example, say temperatures were=
 recorded each hour for 5 days at a certain location, with each measurement=
 having an error of 2 deg C. Then we would have something like:

day =3D findgen(121)/24
temp =3D 15 + 10*sin(10*!pi*findgen(121)/120) + 5*randomu(seed, 121)
error =3D fltarr(121)+2

Is it possible from these three arrays for IDL to work out and plot a best =
fit line? I have searched online, and can't really find what I'm looking fo=
r. (surprisingly I have never had to plot a best fit line to anything befor=
e!)

Cheers,

Liam
0
liam
9/30/2015 2:22:57 PM
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liam.steele@gmx.co.uk writes: 

> I was wondering if there was a 'simple' way to get IDL to plot a best fit line for a sinusoidally-varying data set. For example, say temperatures were recorded each hour for 5 days at a certain location, with each measurement having an error of 2 deg C. Then we would have something like:
> 
> day = findgen(121)/24
> temp = 15 + 10*sin(10*!pi*findgen(121)/120) + 5*randomu(seed, 121)
> error = fltarr(121)+2
> 
> Is it possible from these three arrays for IDL to work out and plot a best fit line? I have searched online, and can't really find what I'm looking for. (surprisingly I have never had to plot a best fit line to anything before!)

You will do well to start here:

   http://cow.physics.wisc.edu/~craigm/idl/fitting.html

Cheers,

David
-- 
David Fanning, Ph.D.
Fanning Software Consulting, Inc.
Coyote's Guide to IDL Programming: http://www.idlcoyote.com/
Sepore ma de ni thue. ("Perhaps thou speakest truth.")
0
David
9/30/2015 2:37:45 PM
On Wednesday, 30 September 2015 15:37:47 UTC+1, David Fanning  wrote:
> liam.steele@gmx.co.uk writes:=20
>=20
> > I was wondering if there was a 'simple' way to get IDL to plot a best f=
it line for a sinusoidally-varying data set. For example, say temperatures =
were recorded each hour for 5 days at a certain location, with each measure=
ment having an error of 2 deg C. Then we would have something like:
> >=20
> > day =3D findgen(121)/24
> > temp =3D 15 + 10*sin(10*!pi*findgen(121)/120) + 5*randomu(seed, 121)
> > error =3D fltarr(121)+2
> >=20
> > Is it possible from these three arrays for IDL to work out and plot a b=
est fit line? I have searched online, and can't really find what I'm lookin=
g for. (surprisingly I have never had to plot a best fit line to anything b=
efore!)
>=20
> You will do well to start here:
>=20
>    http://cow.physics.wisc.edu/~craigm/idl/fitting.html
>=20
> Cheers,
>=20
> David
> --=20
> David Fanning, Ph.D.
> Fanning Software Consulting, Inc.
> Coyote's Guide to IDL Programming: http://www.idlcoyote.com/
> Sepore ma de ni thue. ("Perhaps thou speakest truth.")

Aha! That looks far more useful than anything I found. Thanks very much.
0
liam
9/30/2015 2:51:31 PM
On Wednesday, September 30, 2015 at 8:51:33 AM UTC-6, liam....@gmx.co.uk wr=
ote:
> On Wednesday, 30 September 2015 15:37:47 UTC+1, David Fanning  wrote:
> > liam.steele@gmx.co.uk writes:=20
> >=20
> > > I was wondering if there was a 'simple' way to get IDL to plot a best=
 fit line for a sinusoidally-varying data set. For example, say temperature=
s were recorded each hour for 5 days at a certain location, with each measu=
rement having an error of 2 deg C. Then we would have something like:
> > >=20
> > > day =3D findgen(121)/24
> > > temp =3D 15 + 10*sin(10*!pi*findgen(121)/120) + 5*randomu(seed, 121)
> > > error =3D fltarr(121)+2
> > >=20
> > > Is it possible from these three arrays for IDL to work out and plot a=
 best fit line? I have searched online, and can't really find what I'm look=
ing for. (surprisingly I have never had to plot a best fit line to anything=
 before!)
> >=20
> > You will do well to start here:
> >=20
> >    http://cow.physics.wisc.edu/~craigm/idl/fitting.html
> >=20
> > Cheers,
> >=20
> > David
> > --=20
> > David Fanning, Ph.D.
> > Fanning Software Consulting, Inc.
> > Coyote's Guide to IDL Programming: http://www.idlcoyote.com/
> > Sepore ma de ni thue. ("Perhaps thou speakest truth.")
>=20
> Aha! That looks far more useful than anything I found. Thanks very much.

Hi Liam,

Once you've done your fit, you can do an error bar plot like this:

day =3D dindgen(121)/24
temp =3D 15 + 10*sin(10*!pi*findgen(121)/120) + 5*randomu(seed, 121)
error =3D fltarr(121)+2
p =3D errorplot(day, temp, error, linestyle=3D'none')
yfit =3D ...
p1 =3D plot(day, yfit, '2', /overplot)

Cheers,
Chris
0
Chris
9/30/2015 3:19:43 PM
On Wednesday, 30 September 2015 15:23:01 UTC+1, liam....@gmx.co.uk  wrote:
> Hi all,
>=20
> I was wondering if there was a 'simple' way to get IDL to plot a best fit=
 line for a sinusoidally-varying data set. For example, say temperatures we=
re recorded each hour for 5 days at a certain location, with each measureme=
nt having an error of 2 deg C. Then we would have something like:
>=20
> day =3D findgen(121)/24
> temp =3D 15 + 10*sin(10*!pi*findgen(121)/120) + 5*randomu(seed, 121)
> error =3D fltarr(121)+2
>=20
> Is it possible from these three arrays for IDL to work out and plot a bes=
t fit line? I have searched online, and can't really find what I'm looking =
for. (surprisingly I have never had to plot a best fit line to anything bef=
ore!)
>=20
> Cheers,
>=20
> Liam

Thanks for the tips everyone. I've managed to get something which I think l=
ooks correct, so that's good! I'll add the error bar plot when I work on th=
e real data. The code I used was this:

day =3D findgen(121)/24
temp =3D 15 + 10*sin(10*!pi*findgen(121)/120)+5*randomu(seed, 121)
error =3D fltarr(121)+2
meanval =3D mean(temp)

expr =3D 'P[0] + P[1]*sin(P[2]*2*!pi*findgen(P[3])/(P[3]-1) + P[4])'
start =3D [meanval, (max(temp)-min(temp))/2, 5., 121, 0.]

result =3D MPFITEXPR(expr, day, temp, error, start)
fitline =3D result[0] + result[1]*sin(result[2]*2*!pi*findgen(result[3])/(r=
esult[3]-1) + result[4])

plot, day, temp, psym=3Dsym(2)
oplot, day, fitline         
0
liam
9/30/2015 4:07:24 PM
Reply:

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