Author: tlinnet Date: Wed Aug 27 21:08:45 2014 New Revision: 25355 URL: http://svn.gna.org/viewcvs/relax?rev=25355&view=rev Log: Inserted checks for C module is available in module for estimateing R2eff error. 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=25355&r1=25354&r2=25355&view=diff ============================================================================== --- trunk/specific_analyses/relax_disp/estimate_r2eff.py (original) +++ trunk/specific_analyses/relax_disp/estimate_r2eff.py Wed Aug 27 21:08:45 2014 @@ -69,6 +69,10 @@ @keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity. @type verbosity: int """ + + # Check that the C modules have been compiled. + if not C_module_exp_fn: + raise RelaxError("Relaxation curve fitting is not available. Try compiling the C modules on your platform.") # Perform checks. check_model_type(model=MODEL_R2EFF) @@ -606,6 +610,10 @@ # Perform checks. check_model_type(model=MODEL_R2EFF) + # Check that the C modules have been compiled. + if not C_module_exp_fn and method == 'minfx': + raise RelaxError("Relaxation curve fitting is not available. Try compiling the C modules on your platform.") + # Set class scipy setting. E = Exp(verbosity=verbosity) E.set_settings_leastsq(ftol=ftol, xtol=xtol, maxfev=maxfev, factor=factor) @@ -870,6 +878,10 @@ @return: Packed list with optimised parameter, parameter error set to 'inf', chi2, iter_count, f_count, g_count, h_count, warning @rtype: list """ + + # Check that the C modules have been compiled. + if not C_module_exp_fn: + raise RelaxError("Relaxation curve fitting is not available. Try compiling the C modules on your platform.") # Initial guess for minimisation. Solved by linear least squares. x0 = asarray( E.estimate_x0_exp() )