def run(args, cutoff, max_n_terms, six_term=False, params=None, plots_dir="kissel_fits_plots", verbose=0): if (params is None): params = cctbx.eltbx.gaussian_fit.fit_parameters( max_n_terms=max_n_terms) chunk_n = 1 chunk_i = 0 if (len(args) > 0 and len(args[0].split(",")) == 2): chunk_n, chunk_i = [int(i) for i in args[0].split(",")] args = args[1:] if (not six_term): if (not os.path.isdir(plots_dir)): print "No plots because target directory does not exist (mkdir %s)." % \ plots_dir plots_dir = None if (chunk_n > 1): assert plots_dir is not None i_chunk = 0 for file_name in args: flag = i_chunk % chunk_n == chunk_i i_chunk += 1 if (not flag): continue results = {} results["fit_parameters"] = params tab = kissel_io.read_table(file_name) more_selection = tab.itvc_sampling_selection() fit_selection = more_selection & (tab.x <= cutoff + 1.e-6) null_fit = scitbx.math.gaussian.fit( tab.x.select(fit_selection), tab.y.select(fit_selection), tab.sigmas.select(fit_selection), xray_scattering.gaussian(0, False)) null_fit_more = scitbx.math.gaussian.fit( tab.x.select(more_selection), tab.y.select(more_selection), tab.sigmas.select(more_selection), xray_scattering.gaussian(0, False)) if (not six_term): results[tab.element] = cctbx.eltbx.gaussian_fit.incremental_fits( label=tab.element, null_fit=null_fit, params=params, plots_dir=plots_dir, verbose=verbose) else: best_min = scitbx.math.gaussian_fit.fit_with_golay_starts( label=tab.element, null_fit=null_fit, null_fit_more=null_fit_more, params=params) g = best_min.final_gaussian_fit results[tab.element] = [xray_scattering.fitted_gaussian( stol=g.table_x()[-1], gaussian_sum=g)] sys.stdout.flush() pickle_file_name = "%s_fits.pickle" % identifier(tab.element) easy_pickle.dump(pickle_file_name, 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 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
def decremental_fits(label, null_fit, full_fit=None, params=None, plots_dir=None, verbose=0): if (params is None): params = fit_parameters() results = [] last_fit = scitbx.math.gaussian.fit(null_fit.table_x(), null_fit.table_y(), null_fit.table_sigmas(), full_fit) while (last_fit.n_terms() > 1): good_min = scitbx.math.gaussian_fit.decremental_fit( existing_gaussian=last_fit, params=params) if (good_min is None): print "%s n_terms=%d: No successful minimization. Aborting." % ( label, last_fit.n_terms() - 1) break show_fit_summary("Best fit", label, good_min.final_gaussian_fit, good_min.max_error) show_literature_fits( label=label, n_terms=good_min.final_gaussian_fit.n_terms(), null_fit=null_fit, n_points=good_min.final_gaussian_fit.table_x().size(), e_other=good_min.max_error) good_min.final_gaussian_fit.show() good_min.show_minimization_parameters() last_fit = good_min.final_gaussian_fit print show_minimize_multi_histogram() sys.stdout.flush() if (plots_dir): write_plots(plots_dir=plots_dir, label=label + "_%d" % good_min.final_gaussian_fit.n_terms(), gaussian_fit=good_min.final_gaussian_fit) g = good_min.final_gaussian_fit results.append( xray_scattering.fitted_gaussian(stol=g.table_x()[-1], gaussian_sum=g)) return results
def decremental_fits(label, null_fit, full_fit=None, params=None, plots_dir=None, verbose=0): if (params is None): params = fit_parameters() results = [] last_fit = scitbx.math.gaussian.fit( null_fit.table_x(), null_fit.table_y(), null_fit.table_sigmas(), full_fit) while (last_fit.n_terms() > 1): good_min = scitbx.math.gaussian_fit.decremental_fit( existing_gaussian=last_fit, params=params) if (good_min is None): print "%s n_terms=%d: No successful minimization. Aborting." % ( label, last_fit.n_terms()-1) break show_fit_summary( "Best fit", label, good_min.final_gaussian_fit, good_min.max_error) show_literature_fits( label=label, n_terms=good_min.final_gaussian_fit.n_terms(), null_fit=null_fit, n_points=good_min.final_gaussian_fit.table_x().size(), e_other=good_min.max_error) good_min.final_gaussian_fit.show() good_min.show_minimization_parameters() last_fit = good_min.final_gaussian_fit print show_minimize_multi_histogram() sys.stdout.flush() if (plots_dir): write_plots( plots_dir=plots_dir, label=label+"_%d"%good_min.final_gaussian_fit.n_terms(), gaussian_fit=good_min.final_gaussian_fit) g = good_min.final_gaussian_fit results.append(xray_scattering.fitted_gaussian( stol=g.table_x()[-1], gaussian_sum=g)) return results
def run(file_name, args, cutoff, params, zig_zag=False, six_term=False, full_fits=None, plots_dir="itvc_fits_plots", verbose=0): tab = itvc_section61_io.read_table6111(file_name) chunk_n = 1 chunk_i = 0 if (len(args) > 0 and len(args[0].split(",")) == 2): chunk_n, chunk_i = [int(i) for i in args[0].split(",")] args = args[1:] if (not six_term and not zig_zag): if (not os.path.isdir(plots_dir)): print("No plots because target directory does not exist (mkdir %s)." % \ plots_dir) plots_dir = None if (chunk_n > 1): assert plots_dir is not None stols_more = cctbx.eltbx.gaussian_fit.international_tables_stols sel = stols_more <= cutoff + 1.e-6 stols = stols_more.select(sel) i_chunk = 0 for element in tab.elements + ["O2-", "SDS"]: if (len(args) > 0 and element not in args): continue flag = i_chunk % chunk_n == chunk_i i_chunk += 1 if (not flag): continue results = {} results["fit_parameters"] = params if (element == "SDS"): wrk_lbl = element from cctbx.eltbx.development.hydrogen_plots import fit_input fi = fit_input() sel = fi.stols <= cutoff + 1.e-6 null_fit = scitbx.math.gaussian.fit( fi.stols.select(sel), fi.data.select(sel), fi.sigmas.select(sel), xray_scattering.gaussian(0, False)) null_fit_more = scitbx.math.gaussian.fit( fi.stols, fi.data, fi.sigmas, xray_scattering.gaussian(0, False)) else: wrk_lbl = xray_scattering.wk1995(element, True) if (element != "O2-"): entry = tab.entries[element] null_fit = scitbx.math.gaussian.fit( stols, entry.table_y[:stols.size()], entry.table_sigmas[:stols.size()], xray_scattering.gaussian(0, False)) null_fit_more = scitbx.math.gaussian.fit( stols_more, entry.table_y[:stols_more.size()], entry.table_sigmas[:stols_more.size()], xray_scattering.gaussian(0, False)) else: rrg_stols_more = rez_rez_grant.table_2_stol sel = rrg_stols_more <= cutoff + 1.e-6 rrg_stols = rrg_stols_more.select(sel) null_fit = scitbx.math.gaussian.fit( rrg_stols, rez_rez_grant.table_2_o2minus[:rrg_stols.size()], rez_rez_grant.table_2_sigmas[:rrg_stols.size()], xray_scattering.gaussian(0, False)) null_fit_more = scitbx.math.gaussian.fit( rrg_stols_more, rez_rez_grant.table_2_o2minus[:rrg_stols_more.size()], rez_rez_grant.table_2_sigmas[:rrg_stols_more.size()], xray_scattering.gaussian(0, False)) if (zig_zag): results[wrk_lbl] = cctbx.eltbx.gaussian_fit.zig_zag_fits( label=wrk_lbl, null_fit=null_fit, null_fit_more=null_fit_more, params=params) elif (full_fits is not None): assert len(full_fits.all[wrk_lbl]) == 1 results[wrk_lbl] = cctbx.eltbx.gaussian_fit.decremental_fits( label=wrk_lbl, null_fit=null_fit, full_fit=full_fits.all[wrk_lbl][0], params=params, plots_dir=plots_dir, verbose=verbose) elif (not six_term): results[wrk_lbl] = cctbx.eltbx.gaussian_fit.incremental_fits( label=wrk_lbl, null_fit=null_fit, params=params, plots_dir=plots_dir, verbose=verbose) else: best_min = scitbx.math.gaussian_fit.fit_with_golay_starts( label=wrk_lbl, null_fit=null_fit, null_fit_more=null_fit_more, params=params) g = best_min.final_gaussian_fit results[wrk_lbl] = [ xray_scattering.fitted_gaussian(stol=g.table_x()[-1], gaussian_sum=g) ] sys.stdout.flush() pickle_file_name = "%s_fits.pickle" % identifier(wrk_lbl) easy_pickle.dump(pickle_file_name, results)
def run(file_name, args, cutoff, params, zig_zag=False, six_term=False, full_fits=None, plots_dir="itvc_fits_plots", verbose=0): tab = itvc_section61_io.read_table6111(file_name) chunk_n = 1 chunk_i = 0 if (len(args) > 0 and len(args[0].split(",")) == 2): chunk_n, chunk_i = [int(i) for i in args[0].split(",")] args = args[1:] if (not six_term and not zig_zag): if (not os.path.isdir(plots_dir)): print "No plots because target directory does not exist (mkdir %s)." % \ plots_dir plots_dir = None if (chunk_n > 1): assert plots_dir is not None stols_more = cctbx.eltbx.gaussian_fit.international_tables_stols sel = stols_more <= cutoff + 1.e-6 stols = stols_more.select(sel) i_chunk = 0 for element in tab.elements + ["O2-", "SDS"]: if (len(args) > 0 and element not in args): continue flag = i_chunk % chunk_n == chunk_i i_chunk += 1 if (not flag): continue results = {} results["fit_parameters"] = params if (element == "SDS"): wrk_lbl = element from cctbx.eltbx.development.hydrogen_plots import fit_input fi = fit_input() sel = fi.stols <= cutoff + 1.e-6 null_fit = scitbx.math.gaussian.fit( fi.stols.select(sel), fi.data.select(sel), fi.sigmas.select(sel), xray_scattering.gaussian(0, False)) null_fit_more = scitbx.math.gaussian.fit( fi.stols, fi.data, fi.sigmas, xray_scattering.gaussian(0, False)) else: wrk_lbl = xray_scattering.wk1995(element, True) if (element != "O2-"): entry = tab.entries[element] null_fit = scitbx.math.gaussian.fit( stols, entry.table_y[:stols.size()], entry.table_sigmas[:stols.size()], xray_scattering.gaussian(0, False)) null_fit_more = scitbx.math.gaussian.fit( stols_more, entry.table_y[:stols_more.size()], entry.table_sigmas[:stols_more.size()], xray_scattering.gaussian(0, False)) else: rrg_stols_more = rez_rez_grant.table_2_stol sel = rrg_stols_more <= cutoff + 1.e-6 rrg_stols = rrg_stols_more.select(sel) null_fit = scitbx.math.gaussian.fit( rrg_stols, rez_rez_grant.table_2_o2minus[:rrg_stols.size()], rez_rez_grant.table_2_sigmas[:rrg_stols.size()], xray_scattering.gaussian(0, False)) null_fit_more = scitbx.math.gaussian.fit( rrg_stols_more, rez_rez_grant.table_2_o2minus[:rrg_stols_more.size()], rez_rez_grant.table_2_sigmas[:rrg_stols_more.size()], xray_scattering.gaussian(0, False)) if (zig_zag): results[wrk_lbl] = cctbx.eltbx.gaussian_fit.zig_zag_fits( label=wrk_lbl, null_fit=null_fit, null_fit_more=null_fit_more, params=params) elif (full_fits is not None): assert len(full_fits.all[wrk_lbl]) == 1 results[wrk_lbl] = cctbx.eltbx.gaussian_fit.decremental_fits( label=wrk_lbl, null_fit=null_fit, full_fit=full_fits.all[wrk_lbl][0], params=params, plots_dir=plots_dir, verbose=verbose) elif (not six_term): results[wrk_lbl] = cctbx.eltbx.gaussian_fit.incremental_fits( label=wrk_lbl, null_fit=null_fit, params=params, plots_dir=plots_dir, verbose=verbose) else: best_min = scitbx.math.gaussian_fit.fit_with_golay_starts( label=wrk_lbl, null_fit=null_fit, null_fit_more=null_fit_more, params=params) g = best_min.final_gaussian_fit results[wrk_lbl] = [xray_scattering.fitted_gaussian( stol=g.table_x()[-1], gaussian_sum=g)] sys.stdout.flush() pickle_file_name = "%s_fits.pickle" % identifier(wrk_lbl) easy_pickle.dump(pickle_file_name, results)