Hi Johann,
I believe the scipy optimization package gives a variety of
optimization algorithms but you would have to implement your own
codes to fix/thaw parameters. That being said, there is a lot there
and I haven't dug into all of it into detail. I would suggest posting
a message to the
hi jessica, thanks So scipy.optmizer as it stands cannot do that? I
gues I should move the issue to the scipy list then. Yes there is mpfit,
there is also pyminuit in google.code that is wrapper of the high energy
physics standard package MINUIT, etc but I would think that fitting
data,
Is there a new version of this which uses numpy instead of Numeric? I found
the old Numeric version to work very well.
cheers,
William
On Dec 26, 2007 12:58 PM, Jessica Lu <[EMAIL PROTECTED]> wrote:
> Hi Johann,
>
> I would recommend using the python mpfit module:
>
> http://cars9.uchicago.edu/
Hi Johann,
I would recommend using the python mpfit module:
http://cars9.uchicago.edu/software/python/mpfit.html
Cheers,
Jessica
On Dec 22, 2007, at 8:57 PM, Johann Cohen-Tanugi wrote:
> hi jessica,
> This FittingData tutorial is very nice. Could you illustrate how to
> fix/thaw parameters?
>
hi jessica,
This FittingData tutorial is very nice. Could you illustrate how to
fix/thaw parameters?
I did not find any such attribute and when I try some kludges they fail
with a msg saying
: shape mismatch: objects cannot be
broadcast to a single shape
thanks,
Johann
---
Hi,
I just happened to do the same thing two days ago. If you want
uncertainties as well, here is some code that uses scipy.optimize. I
just put up a preliminary example on the scipy wiki:
http://www.scipy.org/Cookbook/FittingData?action=show
Not the cleanest or most sophisticated code in th
Ping Yeh wrote:
> Hi,
>
> I have (x,y) data that I want to fit to the formula
> y = a * x^b
> to determine a and b. How can I do it? The current
> manual only lists linear fit and polynomial fit.
>
If you just want quick power law fit without turning to the other
solutions, you can just transfor
Ahhh... Yes, I should turn to scipy for this. Great suggestion!
I'll look for least square fit and maximum likelihood fit.
My next question is about plotting any function f(x) on top of data.
I know I could just produce enough (x,y) points and plot(x,y).
But a convenience function like plot(f, mi
Hi,
You could use another package, like openopt and the generic optimizers that
give you what you want provided that you create at least the gradient of
the function (I didn't create a class that can numerically derive a fit
function).
For instance
http://projects.scipy.org/scipy/scikits/wiki/Opt
Ping,
You should investigate scipy.optimize.lsqFit for using least squares
to fit an arbitrary function and scipy.odr for regular or orthogonal
least squares fitting.
barry
On Dec 5, 2007 9:45 PM, Ping Yeh <[EMAIL PROTECTED]> wrote:
> Hi,
>
> I have (x,y) data that I want to fit to the formula
>
Hi,
I have (x,y) data that I want to fit to the formula
y = a * x^b
to determine a and b. How can I do it? The current
manual only lists linear fit and polynomial fit.
Or, putting it in a more general setting, is there a
module to do fitting to an arbitrary function?
It would be something like
p
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