r25337 - /trunk/specific_analyses/relax_disp/estimate_r2eff.py -- August 27, 2014 - 14:23

Author: tlinnet
Date: Wed Aug 27 14:23:41 2014
New Revision: 25337

URL: http://svn.gna.org/viewcvs/relax?rev=25337&view=rev
Log:
By using minfx, and the reported Jacobian, it is now possible to get the 
exact same error estimation as scipy.optimize.leastsq.

The fatal error was to set the weighting matrix with diagonal elements as the 
error.
There weights are 1/errors**2.

There is though some un-answered questions left.

The Jacobian used, is the direct derivative of the function.

It is not the chi2 derivative Jacobian.

task #7822(https://gna.org/task/index.php?7822): Implement user function to 
estimate R2eff and associated errors for exponential curve fitting.

Modified:
    trunk/specific_analyses/relax_disp/estimate_r2eff.py

Modified: trunk/specific_analyses/relax_disp/estimate_r2eff.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/estimate_r2eff.py?rev=25337&r1=25336&r2=25337&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/estimate_r2eff.py        (original)
+++ trunk/specific_analyses/relax_disp/estimate_r2eff.py        Wed Aug 27 
14:23:41 2014
@@ -390,8 +390,7 @@
         eye_mat = eye(self.errors.shape[0])
 
         # Now form the error matrix, with errors down the diagonal.
-        #weights = self.errors**2
-        weights = self.errors
+        weights = 1. / self.errors**2
 
         W = multiply(weights, eye_mat)
 
@@ -411,7 +410,7 @@
 
 # 'minfx'
 # 'scipy.optimize.leastsq'
-def estimate_r2eff(spin_id=None, ftol=1e-15, xtol=1e-15, maxfev=10000000, 
factor=100.0, method='minfx', verbosity=1):
+def estimate_r2eff(method='scipy.optimize.leastsq', spin_id=None, 
ftol=1e-15, xtol=1e-15, maxfev=10000000, factor=100.0, verbosity=1):
     """Estimate r2eff and errors by exponential curve fitting with 
scipy.optimize.leastsq.
 
     scipy.optimize.leastsq is a wrapper around MINPACK's lmdif and lmder 
algorithms.
@@ -427,6 +426,8 @@
     Then solving initial guess by linear least squares of: ln(Intensity[j]) 
= ln(i0) - time[j]* r2eff.
 
 
+    @keyword method:            The method to minimise and estimate errors.  
Options are: 'minfx' or 'scipy.optimize.leastsq'.
+    @type method:               string
     @keyword spin_id:           The spin identification string.
     @type spin_id:              str
     @keyword ftol:              The function tolerance for the relative 
error desired in the sum of squares, parsed to leastsq.
@@ -437,8 +438,6 @@
     @type maxfev:               int
     @keyword factor:            The initial step bound, parsed to leastsq.  
It determines the initial step bound (''factor * || diag * x||'').  Should be 
in the interval (0.1, 100).
     @type factor:               float
-    @keyword method:            The method to minimise and estimate errors.  
Options are: 'scipy.optimize.leastsq' or 'minfx'.
-    @type method:               string
     @keyword verbosity:         The amount of information to print.  The 
higher the value, the greater the verbosity.
     @type verbosity:            int
     """