r25287 - /trunk/specific_analyses/relax_disp/estimate_r2eff.py -- August 26, 2014 - 13:23

Author: tlinnet
Date: Tue Aug 26 13:23:47 2014
New Revision: 25287

URL: http://svn.gna.org/viewcvs/relax?rev=25287&view=rev
Log:
Removed all code regarding scipy.optimize fmin_cg and fmin_ncg.

This problem should soon be able to be solved with minfx.

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=25287&r1=25286&r2=25287&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/estimate_r2eff.py        (original)
+++ trunk/specific_analyses/relax_disp/estimate_r2eff.py        Tue Aug 26 
13:23:47 2014
@@ -45,7 +45,7 @@
 # Scipy installed.
 if scipy_module:
     # Import leastsq.
-    from scipy.optimize import fmin_cg, fmin_ncg, leastsq
+    from scipy.optimize import leastsq
 
 
 class Exp:
@@ -424,7 +424,6 @@
 
 # 'minfx'
 # 'scipy.optimize.leastsq'
-# 'scipy.optimize.fmin_cg'
 def estimate_r2eff(spin_id=None, ftol=1e-15, xtol=1e-15, maxfev=10000000, 
factor=100.0, method='minfx', verbosity=1):
     """Estimate r2eff and errors by exponential curve fitting with 
scipy.optimize.leastsq.
 
@@ -540,10 +539,6 @@
                 # Acquire results.
                 results = minimise_leastsq(E=E)
 
-            elif method == 'scipy.optimize.fmin_cg':
-                # Acquire results.
-                results = minimise_fmin_cg(E=E)
-
             elif method == 'minfx':
                 # Acquire results.
                 results = minimise_minfx(E=E)
@@ -715,46 +710,6 @@
     return results
 
 
-def minimise_fmin_cg(E=None):
-    """Estimate r2eff and errors by exponential curve fitting with 
scipy.optimize.fmin_cg.
-
-    Unconstrained minimization of a function using the Newton-CG method.
-
-    @keyword E:     The Exponential function class, which contain data and 
functions.
-    @type E:        class
-    @return:        Packed list with optimised parameter, estimated 
parameter error, chi2, iter_count, f_count, g_count, h_count, warning
-    @rtype:         list
-    """
-
-    # Check that scipy.optimize.leastsq is available.
-    if not scipy_module:
-        raise RelaxError("scipy module is not available.")
-
-    # Initial guess for minimisation. Solved by linear least squares.
-    x0 = E.estimate_x0_exp()
-
-    # Define function to minimise. Use errors as weights in the minimisation.
-    use_weights = True
-
-    if use_weights:
-        func = E.func_exp_weighted_general
-        dfunc = E.func_exp_weighted_grad
-        d2func = E.func_exp_weighted_hess
-
-    # There are no args to the function, since values and times are stored 
in the class.
-    args=()
-
-    gfk = dfunc(x0)
-    deltak = numpy.dot(gfk, gfk)
-
-    # Cannot get this to work.
-
-    #xopt, fopt, fcalls, gcalls, hcalls, warnflag = fmin_ncg(f=func, x0=x0, 
fprime=dfunc, fhess=None, args=args, avextol=1e-05, 
epsilon=1.4901161193847656e-08, maxiter=maxfev, full_output=1, disp=1, 
retall=0, callback=None)
-    #test = fmin_ncg(f=func, x0=x0, fprime=dfunc, fhess=d2func, args=args, 
avextol=1e-05, epsilon=1.4901161193847656e-08, maxiter=maxfev)
-    #fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=Inf, 
epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0, 
callback=None):
-    #fmin_cg(f=func, x0=x0, fprime=dfunc, args=args, gtol=1e-5)
-
-
 def minimise_minfx(E=None):
     """Estimate r2eff and errors by minimising with minfx.