r25341 - /trunk/user_functions/relax_disp.py -- August 27, 2014 - 17:40

Author: bugman
Date: Wed Aug 27 17:40:27 2014
New Revision: 25341

URL: http://svn.gna.org/viewcvs/relax?rev=25341&view=rev
Log:
Fixes for the relax_disp.r2eff_estimate user function documentation.

This is to allow the relax manual to compile again as the original 
documentation was causing LaTeX
failures.


Modified:
    trunk/user_functions/relax_disp.py

Modified: trunk/user_functions/relax_disp.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/user_functions/relax_disp.py?rev=25341&r1=25340&r2=25341&view=diff
==============================================================================
--- trunk/user_functions/relax_disp.py  (original)
+++ trunk/user_functions/relax_disp.py  Wed Aug 27 17:40:27 2014
@@ -686,12 +686,17 @@
 # Description.
 uf.desc.append(Desc_container())
 uf.desc[-1].add_paragraph("This is a new experimental feature from version 
3.3, and should only be tried out with big care.")
-uf.desc[-1].add_paragraph("This will estimate R2eff and the associated error 
by exponential curve fittting through scipy.optimize.leastsq.")
-uf.desc[-1].add_paragraph("scipy.optimize.leastsq is a wrapper around 
MINPACK's lmdif and lmder algorithms.")
-uf.desc[-1].add_paragraph("MINPACK is a FORTRAN90 library which solves 
systems of nonlinear equations, or carries out the least squares minimization 
of the residual of a set of linear or nonlinear equations.")
+uf.desc[-1].add_paragraph("This will estimate R2eff and the associated error 
by exponential curve fitting through scipy.optimize.leastsq, which is a 
wrapper around the MINPACK lmdif and lmder algorithms.  MINPACK is a 
FORTRAN90 library which solves systems of nonlinear equations, or carries out 
the least squares minimization of the residual of a set of linear or 
nonlinear equations.")
 uf.desc[-1].add_paragraph("Errors are calculated by taking the square root 
of the reported co-variance from leastsq.")
 uf.desc[-1].add_paragraph("This can be an huge time saving step, when 
performing model fitting in R1rho.  Errors of R2eff values, are normally 
estimated by time-consuming Monte-Carlo simulations.")
-uf.desc[-1].add_paragraph("Initial guess for the starting parameter x0 = 
[r2eff_est, i0_est], is by converting the exponential curve to a linear 
problem.  Then solving initial guess by linear least squares of: 
ln(Intensity[j]) = ln(i0) - time[j]* r2eff.")
+uf.desc[-1].add_paragraph("Initial guess for the starting parameter")
+uf.desc[-1].add_verbatim("""
+x0 = [r2eff_est, i0_est],
+""")
+uf.desc[-1].add_paragraph("is by converting the exponential curve to a 
linear problem.  Then solving initial guess by linear least squares of")
+uf.desc[-1].add_verbatim("""
+ln(Intensity[j]) = ln(i0) - time[j] * r2eff.
+""")
 uf.backend = estimate_r2eff
 uf.menu_text = "&r2eff_estimate"
 uf.gui_icon = "relax.relax_fit"