コード例 #1
0
def reset_max_error(itvc_entry, fit):
  sel = international_tables_stols <= fit.stol + 1.e-6
  gaussian_fit = scitbx.math.gaussian.fit(
    international_tables_stols.select(sel),
    itvc_entry.table_y.select(sel),
    itvc_entry.table_sigmas.select(sel),
    fit)
  fit.max_error = flex.max(gaussian_fit.significant_relative_errors())
コード例 #2
0
def run(gaussian_fit_pickle_file_names, itvc_file_name, kissel_dir):
    itvc_tab = None
    if (itvc_file_name is not None):
        itvc_tab = itvc_section61_io.read_table6111(itvc_file_name)
    fits = read_pickled_fits(gaussian_fit_pickle_file_names)
    #easy_pickle.dump("all_fits.pickle", fits)
    for k, v in fits.parameters.items():
        print "# %s:" % k, v
    print
    max_errors = flex.double()
    labeled_fits = []
    n_processed = 0
    for label in expected_labels(kissel_dir):
        try:
            fit_group = fits.all[label]
        except Exception:
            print "# Warning: Missing scattering_type:", label
        else:
            print "scattering_type:", label
            prev_fit = None
            for fit in fit_group:
                if (prev_fit is not None):
                    if (fit.stol > prev_fit.stol):
                        print "# Warning: decreasing stol"
                    elif (fit.stol == prev_fit.stol):
                        if (fit.max_error < prev_fit.max_error):
                            print "# Warning: same stol but previous has larger error"
                prev_fit = fit
                fit.sort().show()
                gaussian_fit = None
                if (itvc_tab is not None and label != "O2-"):
                    entry = itvc_tab.entries[label]
                    sel = international_tables_stols <= fit.stol + 1.e-6
                    gaussian_fit = scitbx.math.gaussian.fit(
                        international_tables_stols.select(sel),
                        entry.table_y.select(sel),
                        entry.table_sigmas.select(sel), fit)
                elif (kissel_dir is not None):
                    file_name = os.path.join(
                        kissel_dir, "%02d_%s_rf" %
                        (tiny_pse.table(label).atomic_number(), label))
                    tab = kissel_io.read_table(file_name)
                    sel = tab.itvc_sampling_selection() & (tab.x <=
                                                           fit.stol + 1.e-6)
                    gaussian_fit = scitbx.math.gaussian.fit(
                        tab.x.select(sel), tab.y.select(sel),
                        tab.sigmas.select(sel), fit)
                if (gaussian_fit is not None):
                    max_errors.append(
                        flex.max(gaussian_fit.significant_relative_errors()))
                    labeled_fits.append(labeled_fit(label, gaussian_fit))
            n_processed += 1
    print
    if (n_processed != len(fits.all)):
        print "# Warning: %d fits were not processed." % (len(fits.all) -
                                                          n_processed)
        print
    if (max_errors.size() > 0):
        print "Summary:"
        perm = flex.sort_permutation(data=max_errors, reverse=True)
        max_errors = max_errors.select(perm)
        labeled_fits = flex.select(labeled_fits, perm)
        quick_summary = {}
        for me, lf in zip(max_errors, labeled_fits):
            print lf.label, "n_terms=%d max_error: %.4f" % (
                lf.gaussian_fit.n_terms(), me)
            quick_summary[lf.label + "_" + str(lf.gaussian_fit.n_terms())] = me
            if (me > 0.01):
                fit = lf.gaussian_fit
                re = fit.significant_relative_errors()
                for s, y, a, r in zip(fit.table_x(), fit.table_y(),
                                      fit.fitted_values(), re):
                    comment = ""
                    if (r > 0.01): comment = " large error"
                    print "%4.2f %7.4f %7.4f %7.4f %7.4f%s" % (s, y, a, a - y,
                                                               r, comment)
                print
        print
コード例 #3
0
def run(gaussian_fit_pickle_file_names, itvc_file_name, kissel_dir):
  itvc_tab = None
  if (itvc_file_name is not None):
    itvc_tab = itvc_section61_io.read_table6111(itvc_file_name)
  fits = read_pickled_fits(gaussian_fit_pickle_file_names)
  #easy_pickle.dump("all_fits.pickle", fits)
  for k,v in fits.parameters.items():
    print "# %s:" % k, v
  print
  max_errors = flex.double()
  labeled_fits = []
  n_processed = 0
  for label in expected_labels(kissel_dir):
    try:
      fit_group = fits.all[label]
    except Exception:
      print "# Warning: Missing scattering_type:", label
    else:
      print "scattering_type:", label
      prev_fit = None
      for fit in fit_group:
        if (prev_fit is not None):
          if (fit.stol > prev_fit.stol):
            print "# Warning: decreasing stol"
          elif (fit.stol == prev_fit.stol):
            if (fit.max_error < prev_fit.max_error):
              print "# Warning: same stol but previous has larger error"
        prev_fit = fit
        fit.sort().show()
        gaussian_fit = None
        if (itvc_tab is not None and label != "O2-"):
          entry = itvc_tab.entries[label]
          sel = international_tables_stols <= fit.stol + 1.e-6
          gaussian_fit = scitbx.math.gaussian.fit(
            international_tables_stols.select(sel),
            entry.table_y.select(sel),
            entry.table_sigmas.select(sel),
            fit)
        elif (kissel_dir is not None):
          file_name = os.path.join(kissel_dir, "%02d_%s_rf" % (
            tiny_pse.table(label).atomic_number(), label))
          tab = kissel_io.read_table(file_name)
          sel = tab.itvc_sampling_selection() & (tab.x <= fit.stol + 1.e-6)
          gaussian_fit = scitbx.math.gaussian.fit(
            tab.x.select(sel),
            tab.y.select(sel),
            tab.sigmas.select(sel),
            fit)
        if (gaussian_fit is not None):
          max_errors.append(
            flex.max(gaussian_fit.significant_relative_errors()))
          labeled_fits.append(labeled_fit(label, gaussian_fit))
      n_processed += 1
  print
  if (n_processed != len(fits.all)):
    print "# Warning: %d fits were not processed." % (
      len(fits.all) - n_processed)
    print
  if (max_errors.size() > 0):
    print "Summary:"
    perm = flex.sort_permutation(data=max_errors, reverse=True)
    max_errors = max_errors.select(perm)
    labeled_fits = flex.select(labeled_fits, perm)
    quick_summary = {}
    for me,lf in zip(max_errors, labeled_fits):
      print lf.label, "n_terms=%d max_error: %.4f" % (
        lf.gaussian_fit.n_terms(), me)
      quick_summary[lf.label + "_" + str(lf.gaussian_fit.n_terms())] = me
      if (me > 0.01):
        fit = lf.gaussian_fit
        re = fit.significant_relative_errors()
        for s,y,a,r in zip(fit.table_x(),fit.table_y(),fit.fitted_values(),re):
          comment = ""
          if (r > 0.01): comment = " large error"
          print "%4.2f %7.4f %7.4f %7.4f %7.4f%s" % (s,y,a,a-y,r,comment)
        print
    print
コード例 #4
0
ファイル: combine_fits.py プロジェクト: zhuligs/cctbx_project
def reset_max_error(itvc_entry, fit):
    sel = international_tables_stols <= fit.stol + 1.e-6
    gaussian_fit = scitbx.math.gaussian.fit(
        international_tables_stols.select(sel), itvc_entry.table_y.select(sel),
        itvc_entry.table_sigmas.select(sel), fit)
    fit.max_error = flex.max(gaussian_fit.significant_relative_errors())