OK, solved both problems. The r matrix obtained from fjac must be post multiplied by the permutation matrix, not its transpose. Line 254 in minpack.py should be changed from: R = dot(r, transpose(perm)) to: R = dot(r, perm) That gets the ordering of the Covariance to match my test. As for the second issue, (the fact that giving Dfun=J in leastsq leads to the wrong minimum) I did some googling and found this post from John Hunter a year ago:

John Hunter scipy-user@scipy.net Mon, 21 Apr 2003 13:55:09 -0500

I am using leastsq to do a best fit to a simple exponential function. In my test script, I find that if I use col_deriv=0, I >get a different answer than I get if I do a col_deriv=1 with the err func returning the transposed jacobian. When I compare the true parameters with the best fit parameters, the correct answer is with col_deriv=1 but not with col_deriv=0. Am I misusing this parameter or is something amiss?

--example snipped-- Sure enough, providing the transpose of J to Dfun, and setting col_deriv = True gives the right answer! This is obviously a bug in how leastsq handles col_deriv, and needs to be looked at. The good news is I now have a covariance matrix! Thanks Travis! -Brendan On March 15 I wrote:

Here C has all the right outputs, but in the wrong order. Evidently we're applying the ipvt permutation matrix wrong somehow.

Here's something worse. If I include the jacobian in

the leastsq call as follows pMin, C, infodict, ier, mesg = leastsq(resid, p0, args=(y_meas, x), Dfun = J, full_output=True)

the resulting pMin is the same as p0, and C is way off of C_true. What gives?

-Brendan ______________________________________________________________________ Post your free ad now! http://personals.yahoo.ca