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.
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