def zig_zag_fits(label, null_fit, null_fit_more, params): six_term_best_min = scitbx.math.gaussian_fit.fit_with_golay_starts( label=label, null_fit=null_fit, null_fit_more=null_fit_more, params=params) results = [] n_term_best_min = six_term_best_min have_all_points_in_previous = True while 1: while 1: if (n_term_best_min.final_gaussian_fit.n_terms() == 1): existing_gaussian = null_fit else: decr_best_min = scitbx.math.gaussian_fit.decremental_fit( existing_gaussian=n_term_best_min.final_gaussian_fit, params=params) assert decr_best_min is not None print "Decremental:", decr_best_min.show_summary() existing_gaussian = decr_best_min.final_gaussian_fit if (n_term_best_min.max_error <= params.negligible_max_error and have_all_points_in_previous): break incr_best_min = find_max_x_multi( null_fit=null_fit, existing_gaussian=existing_gaussian, target_powers=params.target_powers, minimize_using_sigmas=params.minimize_using_sigmas, n_repeats_minimization=params.n_repeats_minimization, shift_sqrt_b_mod_n=params.shift_sqrt_b_mod_n, b_min=params.b_min, max_max_error=params.max_max_error, n_start_fractions=params.n_start_fractions) assert incr_best_min is not None print "Incremental:", incr_best_min.show_summary() if (existing_gaussian is null_fit): break if (not incr_best_min.is_better_than(n_term_best_min)): break n_term_best_min = incr_best_min print " Settled on:", n_term_best_min.show_summary() results.append( xray_scattering.fitted_gaussian( stol=n_term_best_min.final_gaussian_fit.table_x()[-1], gaussian_sum=n_term_best_min.final_gaussian_fit, max_error=n_term_best_min.max_error)) if (existing_gaussian is null_fit): break have_all_points_in_previous = ( n_term_best_min.final_gaussian_fit.table_x().size() == decr_best_min.final_gaussian_fit.table_x().size()) n_term_best_min = decr_best_min print return results
def zig_zag_fits(label, null_fit, null_fit_more, params): six_term_best_min = scitbx.math.gaussian_fit.fit_with_golay_starts( label=label, null_fit=null_fit, null_fit_more=null_fit_more, params=params) results = [] n_term_best_min = six_term_best_min have_all_points_in_previous = True while 1: while 1: if (n_term_best_min.final_gaussian_fit.n_terms() == 1): existing_gaussian = null_fit else: decr_best_min = scitbx.math.gaussian_fit.decremental_fit( existing_gaussian=n_term_best_min.final_gaussian_fit, params=params) assert decr_best_min is not None print "Decremental:", decr_best_min.show_summary() existing_gaussian = decr_best_min.final_gaussian_fit if (n_term_best_min.max_error <= params.negligible_max_error and have_all_points_in_previous): break incr_best_min = find_max_x_multi( null_fit=null_fit, existing_gaussian=existing_gaussian, target_powers=params.target_powers, minimize_using_sigmas=params.minimize_using_sigmas, n_repeats_minimization=params.n_repeats_minimization, shift_sqrt_b_mod_n=params.shift_sqrt_b_mod_n, b_min=params.b_min, max_max_error=params.max_max_error, n_start_fractions=params.n_start_fractions) assert incr_best_min is not None print "Incremental:", incr_best_min.show_summary() if (existing_gaussian is null_fit): break if (not incr_best_min.is_better_than(n_term_best_min)): break n_term_best_min = incr_best_min print " Settled on:", n_term_best_min.show_summary() results.append(xray_scattering.fitted_gaussian( stol=n_term_best_min.final_gaussian_fit.table_x()[-1], gaussian_sum=n_term_best_min.final_gaussian_fit, max_error=n_term_best_min.max_error)) if (existing_gaussian is null_fit): break have_all_points_in_previous = ( n_term_best_min.final_gaussian_fit.table_x().size() == decr_best_min.final_gaussian_fit.table_x().size()) n_term_best_min = decr_best_min print return results
def incremental_fits(label, null_fit, params=None, plots_dir=None, verbose=0): if (params is None): params = fit_parameters() f0 = null_fit.table_y()[0] results = [] previous_n_points = 0 existing_gaussian = xray_scattering.gaussian([], []) while (existing_gaussian.n_terms() < params.max_n_terms): if (previous_n_points == null_fit.table_x().size()): print "%s: Full fit with %d terms. Search stopped." % ( label, existing_gaussian.n_terms()) print break n_terms = existing_gaussian.n_terms() + 1 best_min = find_max_x_multi( null_fit=null_fit, existing_gaussian=existing_gaussian, target_powers=params.target_powers, minimize_using_sigmas=params.minimize_using_sigmas, n_repeats_minimization=params.n_repeats_minimization, shift_sqrt_b_mod_n=params.shift_sqrt_b_mod_n, b_min=params.b_min, max_max_error=params.max_max_error, n_start_fractions=params.n_start_fractions) if (best_min is None): print "Warning: No fit: %s n_terms=%d" % (label, n_terms) print break if (previous_n_points > best_min.final_gaussian_fit.table_x().size()): print "Warning: previous fit included more sampling points." previous_n_points = best_min.final_gaussian_fit.table_x().size() show_fit_summary("Best fit", label, best_min.final_gaussian_fit, best_min.max_error) show_literature_fits( label=label, n_terms=n_terms, null_fit=null_fit, n_points=best_min.final_gaussian_fit.table_x().size(), e_other=best_min.max_error) best_min.final_gaussian_fit.show() best_min.show_minimization_parameters() existing_gaussian = best_min.final_gaussian_fit print show_minimize_multi_histogram() sys.stdout.flush() if (plots_dir): write_plots(plots_dir=plots_dir, label=label + "_%d" % n_terms, gaussian_fit=best_min.final_gaussian_fit) g = best_min.final_gaussian_fit results.append( xray_scattering.fitted_gaussian(stol=g.table_x()[-1], gaussian_sum=g)) return results
def incremental_fits(label, null_fit, params=None, plots_dir=None, verbose=0): if (params is None): params = fit_parameters() f0 = null_fit.table_y()[0] results = [] previous_n_points = 0 existing_gaussian = xray_scattering.gaussian([],[]) while (existing_gaussian.n_terms() < params.max_n_terms): if (previous_n_points == null_fit.table_x().size()): print "%s: Full fit with %d terms. Search stopped." % ( label, existing_gaussian.n_terms()) print break n_terms = existing_gaussian.n_terms() + 1 best_min = find_max_x_multi( null_fit=null_fit, existing_gaussian=existing_gaussian, target_powers=params.target_powers, minimize_using_sigmas=params.minimize_using_sigmas, n_repeats_minimization=params.n_repeats_minimization, shift_sqrt_b_mod_n=params.shift_sqrt_b_mod_n, b_min=params.b_min, max_max_error=params.max_max_error, n_start_fractions=params.n_start_fractions) if (best_min is None): print "Warning: No fit: %s n_terms=%d" % (label, n_terms) print break if (previous_n_points > best_min.final_gaussian_fit.table_x().size()): print "Warning: previous fit included more sampling points." previous_n_points = best_min.final_gaussian_fit.table_x().size() show_fit_summary( "Best fit", label, best_min.final_gaussian_fit, best_min.max_error) show_literature_fits( label=label, n_terms=n_terms, null_fit=null_fit, n_points=best_min.final_gaussian_fit.table_x().size(), e_other=best_min.max_error) best_min.final_gaussian_fit.show() best_min.show_minimization_parameters() existing_gaussian = best_min.final_gaussian_fit print show_minimize_multi_histogram() sys.stdout.flush() if (plots_dir): write_plots( plots_dir=plots_dir, label=label+"_%d"%n_terms, gaussian_fit=best_min.final_gaussian_fit) g = best_min.final_gaussian_fit results.append(xray_scattering.fitted_gaussian( stol=g.table_x()[-1], gaussian_sum=g)) return results