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 run(args,
cutoff,
high_resolution_only,
plots_dir="itvc_kissel_plots",
verbose=0):
itab = itvc_section61_io.read_table6111(args[0])
itab_x = cctbx.eltbx.gaussian_fit.international_tables_stols
isel = itab_x <= cutoff + 1.e-6
if (high_resolution_only):
isel &= itab_x > 2 + 1.e-6
itab_x = itab_x.select(isel)
for file_name in args[1:]:
ktab = kissel_io.read_table(file_name)
if (ktab.element == "Es"): continue
sel = ktab.x <= cutoff + 1
ktab_x = ktab.x.select(sel)
ktab_y = ktab.y.select(sel)
ktabs_sigmas = ktab.sigmas.select(sel)
itab_entry = itab.entries[ktab.element]
itab_y = itab_entry.table_y.select(isel)
itab_sigmas = itab_entry.table_sigmas.select(isel)
f = open(os.path.join(plots_dir, ktab.element + ".xy"), "w")
cctbx.eltbx.gaussian_fit.write_plot(f, ktab_x, ktab_y)
cctbx.eltbx.gaussian_fit.write_plot(f, itab_x, itab_y)
f.close()
ktab_y_i = flex.linear_interpolation(ktab.x, ktab.y, itab_x)
ktab_sigmas_i = flex.linear_interpolation(ktab.x, ktab.sigmas, itab_x)
assert ktab_y_i.all_gt(0)
s = StringIO.StringIO()
print >> s, "stol kissel itvc delta sig_itvc rel_sig rel_del tol_del"
max_delta = 0
max_tol_del = 0
for x, ky, ksig, iy, isig in zip(itab_x, ktab_y_i, ktab_sigmas_i,
itab_y, itab_sigmas):
if (iy > 0): ie = "%7.4f" % abs(isig / iy)
else: ie = " ******"
delta = iy - ky
rel_del = abs(delta) / ky
tol_del = max(0, abs(delta) - ksig - isig) / ky
print >> s, "%4.2f %7.4f %7.4f %7.4f %8.5f %-s %7.4f %7.4f" % (
x, ky, iy, delta, isig, ie, rel_del, tol_del)
max_delta = max(max_delta, abs(delta))
max_tol_del = max(max_tol_del, tol_del)
print "Element:", ktab.element, "max_delta=%.4f, max_tol_del=%.4f" % (
max_delta, max_tol_del)
sys.stdout.write(s.getvalue())
print
def run(args, cutoff, high_resolution_only,
plots_dir="itvc_kissel_plots", verbose=0):
itab = itvc_section61_io.read_table6111(args[0])
itab_x = cctbx.eltbx.gaussian_fit.international_tables_stols
isel = itab_x <= cutoff + 1.e-6
if (high_resolution_only):
isel &= itab_x > 2 + 1.e-6
itab_x = itab_x.select(isel)
for file_name in args[1:]:
ktab = kissel_io.read_table(file_name)
if (ktab.element == "Es"): continue
sel = ktab.x <= cutoff + 1
ktab_x = ktab.x.select(sel)
ktab_y = ktab.y.select(sel)
ktabs_sigmas = ktab.sigmas.select(sel)
itab_entry = itab.entries[ktab.element]
itab_y = itab_entry.table_y.select(isel)
itab_sigmas = itab_entry.table_sigmas.select(isel)
f = open(os.path.join(plots_dir, ktab.element + ".xy"), "w")
cctbx.eltbx.gaussian_fit.write_plot(f, ktab_x, ktab_y)
cctbx.eltbx.gaussian_fit.write_plot(f, itab_x, itab_y)
f.close()
ktab_y_i = flex.linear_interpolation(ktab.x, ktab.y, itab_x)
ktab_sigmas_i = flex.linear_interpolation(ktab.x, ktab.sigmas, itab_x)
assert ktab_y_i.all_gt(0)
s = StringIO.StringIO()
print >> s, "stol kissel itvc delta sig_itvc rel_sig rel_del tol_del"
max_delta = 0
max_tol_del = 0
for x,ky,ksig,iy,isig in zip(itab_x, ktab_y_i, ktab_sigmas_i,
itab_y, itab_sigmas):
if (iy > 0): ie = "%7.4f" % abs(isig/iy)
else: ie = " ******"
delta = iy - ky
rel_del = abs(delta) / ky
tol_del = max(0, abs(delta)-ksig-isig) / ky
print >> s, "%4.2f %7.4f %7.4f %7.4f %8.5f %-s %7.4f %7.4f" % (
x,ky,iy,delta,isig,ie,rel_del,tol_del)
max_delta = max(max_delta, abs(delta))
max_tol_del = max(max_tol_del, tol_del)
print "Element:", ktab.element, "max_delta=%.4f, max_tol_del=%.4f" % (
max_delta, max_tol_del)
sys.stdout.write(s.getvalue())
print
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
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
