On Wed, Nov 9, 2011 at 1:39 PM, Daniel Mader <danielstefanmader@googlemail.com> wrote:

Hi Josef,

thanks a lot for this quick answer, I'll definitely have a look at your suggestion!

However, I'd be very interested if this is possible with leastsq, too? Maybe I don't need it right now, but you never you what's next in life :)

there is also optimize.curve_fit as wrapper for least_sq your error function returns 2d instead of 1d def __residual(params, f, x, y): ''' Define fit function; Return residual error. ''' p0, p1, p2 = params #x,y,f = map(np.ravel, (x,y,f)) return np.ravel(p0 + p1*x + p2*y - f) you need one ravel I didn't look at the rest Josef

Thanks again, greetings,

Daniel

2011/11/9  <josef.pktd@gmail.com>:

...

On Wed, Nov 9, 2011 at 12:12 PM, Daniel Mader <danielstefanmader@googlemail.com> wrote:

...

Hi everyone,

I'd like to do some rather simple multidimensional curve fitting. Simple, because usually it's only a plane, or a weak 2nd order surface.

Here's the same question which I tried to follow, but I have no clue how to feed the 2D arrays into leastsq(): http://stackoverflow.com/questions/529184/simple-multidimensional-curve-fitt...

If you just want to estimate a multiple linear or polynomial function, then optimize.leastsq is overkill, linalg is enough.

The premade solution is to use:

statsmodels.sourceforge.net/generated/scikits.statsmodels.regression.linear_model.OLS.html

Josef

...

Likely I am just missing a small piece of information, and I'd be happy to get a clue :)

Thanks in advance, and here's some code to demonstrate what I want and to get started, Daniel

import pylab import scipy import scipy.optimize

from mpl_toolkits.mplot3d import Axes3D

#import sys,os,platform #if platform.system() == 'Windows': #    home = os.environ['HOMESHARE'] #elif platform.system() == 'Linux': #    home = os.environ['HOME'] #sys.path.append(home + '/python') #sys.path.append(home + '/11_PythonWork') #import pylabSettings

##****************************************************************************** ##******************************************************************************

''' f = p0 + p1*x + p2*y '''

##------------------------------------------------------------------------------ def __residual(params, f, x, y):    '''    Define fit function;    Return residual error.    '''    p0, p1, p2 = params    return p0 + p1*x + p2*y - f

## load raw data (=create some dummy data): dataX = scipy.arange(0,11,1) dataY = dataX/10. dataZ = 0.5 + 1.1*dataX + 1.5*dataY dataXX, dataYY = scipy.meshgrid(dataX,dataY) dataZZ = 0.5 + 1.1*dataXX + 1.5*dataYY

## plot data pylab.close('all') fig = pylab.figure() ax = Axes3D(fig) ax.plot_wireframe(dataXX, dataYY, dataZZ) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') pylab.show()

## guess initial values for parameters p0 = [0., 1., 1.] print __residual(p0, dataZZ, dataXX, dataYY)

## works but is not 2D! p1, p_cov = scipy.optimize.leastsq(__residual, x0=p0, args=(dataZ, dataX, dataY)) print p1

## doesn't work :() p1, p_cov = scipy.optimize.leastsq(__residual, x0=p0, args=(dataZZ, dataXX, dataYY)) print p1 _______________________________________________ SciPy-User mailing list SciPy-User@scipy.org http://mail.scipy.org/mailman/listinfo/scipy-user

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