r25293 - /trunk/specific_analyses/relax_disp/estimate_r2eff.py -- August 26, 2014 - 15:56

Author: tlinnet
Date: Tue Aug 26 15:56:24 2014
New Revision: 25293

URL: http://svn.gna.org/viewcvs/relax?rev=25293&view=rev
Log:
More removal of code.

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=25293&r1=25292&r2=25293&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/estimate_r2eff.py        (original)
+++ trunk/specific_analyses/relax_disp/estimate_r2eff.py        Tue Aug 26 
15:56:24 2014
@@ -255,16 +255,13 @@
         # See: 
http://wiki.nmr-relax.com/Calculate_jacobian_hessian_matrix_in_sympy_exponential_decay
 
         # Make partial derivative, with respect to r2eff.
-        # d_chi2_d_r2eff = 2.0*i0*times*(-i0*exp(-r2eff*times) + 
values)*exp(-r2eff*times)/errors**2
         d_chi2_d_r2eff = sum( 2.0 * i0 * self.times * ( -i0 * exp( -r2eff * 
self.times) + self.values) * exp( -r2eff * self.times ) / self.errors**2 )
 
         # Make partial derivative, with respect to i0.
-        # d_chi2_d_i0 = -2.0*(-i0*exp(-r2eff*times) + 
values)*exp(-r2eff*times)/errors**2
         d_chi2_d_i0 = sum ( - 2.0 * ( -i0 * exp( -r2eff * self.times) + 
self.values) * exp( -r2eff * self.times) / self.errors**2 )
 
         # Define Jacobian as m rows with function derivatives and n columns 
of parameters.
-        #jacobian_matrix = transpose(array( [d_chi2_d_r2eff , d_chi2_d_i0] ) 
)
-        jacobian_matrix = array( [d_chi2_d_r2eff , d_chi2_d_i0] ) 
+        jacobian_matrix = transpose(array( [d_chi2_d_r2eff , d_chi2_d_i0] ) )
 
         # Return Jacobian matrix.
         return jacobian_matrix
@@ -384,7 +381,7 @@
 
 # '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(spin_id=None, ftol=1e-15, xtol=1e-15, maxfev=10000000, 
factor=100.0, method='scipy.optimize.leastsq', 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.