Hi Troels, It's clear that the constraints are adding a lot of time. And for exponential curve-fitting, this is not needed at all. I think we should turn this off in the dispersion auto-analysis, for just the 'R2eff' model optimisation. What do you think?
Regards, Edward On 22 August 2014 19:17, <tlin...@nmr-relax.com> wrote: > Author: tlinnet > Date: Fri Aug 22 19:17:19 2014 > New Revision: 25227 > > URL: http://svn.gna.org/viewcvs/relax?rev=25227&view=rev > Log: > Further extended the profiling script for curve fitting. > > Now profiling is in place for the implemented C code method in relax. > > A similar code should now be devised for numpy array for comparing. > > But this profiling shows that when contraints=True, is slowing down this > procedure by a factor 10 X ! > > Modified: > > trunk/test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py > > Modified: > trunk/test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py > URL: > http://svn.gna.org/viewcvs/relax/trunk/test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py?rev=25227&r1=25226&r2=25227&view=diff > ============================================================================== > --- > trunk/test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py > (original) > +++ > trunk/test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py > Fri Aug 22 19:17:19 2014 > @@ -24,10 +24,11 @@ > # Python module imports. > import cProfile > from os import getcwd, path, sep > -from numpy import array, arange, int32, float64, pi, load > +from numpy import array, arange, int32, float64, pi, load, sqrt, sum > import pstats > import sys > import tempfile > +from minfx.generic import generic_minimise > > # Python 3 support. > try: > @@ -52,46 +53,171 @@ > > # relax module imports. > from status import Status; status = Status() > +from target_functions.relax_fit import setup, func, dfunc, d2func, > back_calc_I > > > # Alter setup. > def main(): > - param_key_list = [ > - 'r1rho_799.77739910_118.078_431.000', > - 'r1rho_799.77739910_118.078_651.200', > - 'r1rho_799.77739910_118.078_800.500', > - 'r1rho_799.77739910_118.078_984.000', > - 'r1rho_799.77739910_118.078_1341.110', > - 'r1rho_799.77739910_118.078_1648.500', > - 'r1rho_799.77739910_124.247_1341.110', > - 'r1rho_799.77739910_130.416_800.500', > - 'r1rho_799.77739910_130.416_1341.110', > - 'r1rho_799.77739910_130.416_1648.500', > - 'r1rho_799.77739910_142.754_800.500', > - 'r1rho_799.77739910_142.754_1341.110', > - 'r1rho_799.77739910_179.768_1341.110', > - 'r1rho_799.77739910_241.459_1341.110' > - ] > - > - # Define maximum dimensions. > - NE, NS, NM, NO, ND, NT = 1, 1, 1, 6, 6, 5 > - > - # Define path to data > - data_path = status.install_path + > sep+'test_suite'+sep+'shared_data'+sep+'curve_fitting'+sep+'profiling'+sep > - > - values_arr = load(data_path + "values_arr.npy") > - errors_arr = load(data_path + "errors_arr.npy") > - times_arr = load(data_path + "times_arr.npy") > - struct_arr = load(data_path + "struct_arr.npy") > + v_cT_chi2_list = array(verify(constraints=True)) > + v_cF_chi2_list = array(verify(constraints=False)) > + > + sum_root_squared = sum( sqrt( (v_cT_chi2_list - v_cF_chi2_list)**2 ) ) > + print("The sum of the root squared differences are: %.3e" % > sum_root_squared) > + > + lines_report = 15 > + > + if True: > + ################# > + # Verify, with constraints > + constraints = True > + > + # Print statistics. > + verbose = True > + > + # Calc for verify with constraints. > + v_cT_filename = tempfile.NamedTemporaryFile(delete=False).name > + # Profile for a single spin. > + cProfile.run('verify(constraints=%s)'%constraints, v_cT_filename) > + > + # Read all stats files into a single object > + v_cT_stats = pstats.Stats(v_cT_filename) > + > + # Clean up filenames for the report > + v_cT_stats.strip_dirs() > + > + # Sort the statistics by the cumulative time spent in the function. > cumulative, time, calls > + v_cT_stats.sort_stats('cumulative') > + > + # Print report for single. > + if verbose: > + v_cT_stats.print_stats(lines_report) > + > + if True: > + ################# > + # Verify, without constraints > + constraints = False > + > + # Print statistics. > + verbose = True > + > + # Calc for verify with constraints. > + v_cF_filename = tempfile.NamedTemporaryFile(delete=False).name > + # Profile for a single spin. > + cProfile.run('verify(constraints=%s)'%constraints, v_cF_filename) > + > + # Read all stats files into a single object > + v_cF_stats = pstats.Stats(v_cF_filename) > + > + # Clean up filenames for the report > + v_cF_stats.strip_dirs() > + > + # Sort the statistics by the cumulative time spent in the function. > cumulative, time, calls > + v_cF_stats.sort_stats('cumulative') > + > + # Print report for single. > + if verbose: > + v_cF_stats.print_stats(lines_report) > + > + > +class Profile: > + """ > + Class Profile inherits the Dispersion container class object. > + """ > + > + def __init__(self): > + > + # Define parameters > + self.param_key_list = [ > + 'r1rho_799.77739910_118.078_431.000', > + 'r1rho_799.77739910_118.078_651.200', > + 'r1rho_799.77739910_118.078_800.500', > + 'r1rho_799.77739910_118.078_984.000', > + 'r1rho_799.77739910_118.078_1341.110', > + 'r1rho_799.77739910_118.078_1648.500', > + 'r1rho_799.77739910_124.247_1341.110', > + 'r1rho_799.77739910_130.416_800.500', > + 'r1rho_799.77739910_130.416_1341.110', > + 'r1rho_799.77739910_130.416_1648.500', > + 'r1rho_799.77739910_142.754_800.500', > + 'r1rho_799.77739910_142.754_1341.110', > + 'r1rho_799.77739910_179.768_1341.110', > + 'r1rho_799.77739910_241.459_1341.110' > + ] > + > + # Define maximum dimensions. > + self.NE, self.NS, self.NM, self.NO, self.ND, self.NT = 1, 1, 1, 6, > 6, 5 > + > + # Define path to data > + self.data_path = status.install_path + > sep+'test_suite'+sep+'shared_data'+sep+'curve_fitting'+sep+'profiling'+sep > + > + self.values_arr = load(self.data_path + "values_arr.npy") > + self.errors_arr = load(self.data_path + "errors_arr.npy") > + self.times_arr = load(self.data_path + "times_arr.npy") > + self.struct_arr = load(self.data_path + "struct_arr.npy") > + > + #param_vector = array([ 0., 0.]) > + self.param_vector = array([ 8.800000000000001e+00, > 2.000000000800000e+05]) > + self.scaling_list = [1.0, 1.0] > + self.func_tol = 1e-25 > + self.grad_tol = None > + self.max_iterations = 10000000 > + self.verbosity = 0 > + > + def set_options(self, constraints=None): > + # Define which constraints should be used. > + self.constraints = constraints > + > + if self.constraints: > + self.min_algor = 'Log barrier' > + self.min_options = ('simplex',) > + self.A = array([ [ 1., 0.], > + [-1., 0.], > + [ 0., 1.]] ) > + self.b = array([ 0., -200., 0.]) > + > + else: > + self.min_algor = 'simplex' > + self.min_options = () > + self.A = None > + self.b = None > + > + > +def verify(constraints=None): > + # Instantiate class. > + C = Profile() > + > + # Set the minimising options. > + C.set_options(constraints=constraints) > + > + chi2_list = [] > > # Print arrays. > - for ei in range(NE): > - for si in range(NS): > - for mi in range(NM): > - for oi in range(NO): > - for di in range(ND): > - print(ei, si, mi, oi, di, values_arr[ei, si, mi, oi, > di], struct_arr[ei, si, mi, oi, di]) > - > + for ei in range(C.NE): > + for si in range(C.NS): > + for mi in range(C.NM): > + for oi in range(C.NO): > + for di in range(C.ND): > + # Extract values > + values = C.values_arr[ei, si, mi, oi, di] > + errors = C.errors_arr[ei, si, mi, oi, di] > + times = C.times_arr[ei, si, mi, oi, di] > + struct = C.struct_arr[ei, si, mi, oi, di] > + num_times = int( sum(struct) ) > + > + if num_times == 0: > + continue > + > + # Initialise the function to minimise. > + setup(num_params=len(C.param_vector), > num_times=num_times, values=values, sd=errors, relax_times=times, > scaling_matrix=C.scaling_list) > + > + results = generic_minimise(func=func, dfunc=dfunc, > d2func=d2func, args=(), x0=C.param_vector, min_algor=C.min_algor, > min_options=C.min_options, func_tol=C.func_tol, grad_tol=C.grad_tol, > maxiter=C.max_iterations, A=C.A, b=C.b, full_output=True, > print_flag=C.verbosity) > + > + # Unpack > + param_vector, chi2, iter_count, f_count, g_count, > h_count, warning = results > + > + chi2_list.append(chi2) > + > + return chi2_list > > # Execute main function. > if __name__ == "__main__": > > > _______________________________________________ > relax (http://www.nmr-relax.com) > > This is the relax-commits mailing list > relax-comm...@gna.org > > To unsubscribe from this list, get a password > reminder, or change your subscription options, > visit the list information page at > https://mail.gna.org/listinfo/relax-commits _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-devel mailing list relax-devel@gna.org To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-devel
