def run(file_names):
assert len(file_names) == 2
tabs = []
for file_name in file_names:
tabs.append(itvc_section61_io.read_table6111(file_name))
all_labels = {}
for tab in tabs:
for label in tab.elements: all_labels[label] = 1
all_labels = all_labels.keys()
for label in all_labels:
e0 = tabs[0].entries.get(label, None)
e1 = tabs[1].entries.get(label, None)
if ([e0,e1].count(None) == 0):
assert e0.atomic_number == e1.atomic_number, \
(label, e0.atomic_number, e1.atomic_number)
assert e0.method == e1.method, \
(label, e0.method, e1.method)
min_size = min(e0.table_y.size(), e1.table_y.size())
show_differences_if_any(
label=label,
y0=e0.table_y[:min_size],
y1=e1.table_y[:min_size],
stols=international_tables_stols[:min_size])
for tab in tabs:
ei = tab.entries.get(label, None)
if (ei is not None and ei.element != ei.atomic_symbol):
ee = tab.entries.get(ei.atomic_symbol, None)
if ([ee,ei].count(None) == 0):
assert ee.table_y.size() == 62
assert ei.table_y.size() == 62
show_differences_if_any(
label=label,
y0=ee.table_y[-6:],
y1=ei.table_y[-6:],
stols=international_tables_stols[-6:])
def run(file_names):
assert len(file_names) == 2
tabs = []
for file_name in file_names:
tabs.append(itvc_section61_io.read_table6111(file_name))
all_labels = {}
for tab in tabs:
for label in tab.elements:
all_labels[label] = 1
for label in all_labels:
e0 = tabs[0].entries.get(label, None)
e1 = tabs[1].entries.get(label, None)
if ([e0, e1].count(None) == 0):
assert e0.atomic_number == e1.atomic_number, \
(label, e0.atomic_number, e1.atomic_number)
assert e0.method == e1.method, \
(label, e0.method, e1.method)
min_size = min(e0.table_y.size(), e1.table_y.size())
show_differences_if_any(
label=label,
y0=e0.table_y[:min_size],
y1=e1.table_y[:min_size],
stols=international_tables_stols[:min_size])
for tab in tabs:
ei = tab.entries.get(label, None)
if (ei is not None and ei.element != ei.atomic_symbol):
ee = tab.entries.get(ei.atomic_symbol, None)
if ([ee, ei].count(None) == 0):
assert ee.table_y.size() == 62
assert ei.table_y.size() == 62
show_differences_if_any(
label=label,
y0=ee.table_y[-6:],
y1=ei.table_y[-6:],
stols=international_tables_stols[-6:])
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(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)
def run(file_name,
table_of_gaussians,
cutoff,
low_resolution_only=False,
high_resolution_only=False,
significant_errors_only=False,
plots_dir=None,
quiet=0,
verbose=0):
assert not (low_resolution_only and high_resolution_only)
tab = itvc_section61_io.read_table6111(file_name)
for wk in xray_scattering.wk1995_iterator():
label = wk.label()
if (not label in tab.entries):
print("Warning: missing scatterer:", label)
stols = cctbx.eltbx.gaussian_fit.international_tables_stols
sel = stols <= cutoff + 1.e-6
stols = stols.select(sel)
if (low_resolution_only):
sel = stols <= 2
stols = stols.select(sel)
assert stols.size() == 56
elif (high_resolution_only):
sel = stols > 2
stols = stols.select(sel)
assert stols.size() == 6
range_62 = flex.size_t(range(62))
labels = flex.std_string()
errors = []
correlations = flex.double()
max_errors = flex.double()
cmp_plots = flex.std_string()
for element in tab.elements:
entry = tab.entries[element]
wk = table_of_gaussians(element, 1)
assert entry.table_y.size() == 62
if (not flex.sort_permutation(data=entry.table_y,
reverse=True).all_eq(range_62)):
print("Increasing: %s (%d)" % (element, entry.atomic_number))
prev_y = entry.table_y[0]
for y in entry.table_y:
if (y > prev_y):
print("higher:", y, "before:", prev_y)
prev_y = y
raise RuntimeError("Data values are not increasing.")
if (low_resolution_only):
gaussian_fit = scitbx.math.gaussian.fit(stols, entry.table_y[:-6],
entry.table_sigmas[:-6],
wk.fetch())
elif (high_resolution_only):
gaussian_fit = scitbx.math.gaussian.fit(stols, entry.table_y[-6:],
entry.table_sigmas[-6:],
wk.fetch())
elif (entry.element != entry.atomic_symbol
and entry.table_y[-6:].all_eq(0)):
atom_entry = tab.entries[entry.atomic_symbol]
patched_table_y = entry.table_y[:-6]
patched_table_y.append(atom_entry.table_y[-6:])
patched_table_sigmas = entry.table_sigmas[:-6]
patched_table_sigmas.append(atom_entry.table_sigmas[-6:])
gaussian_fit = scitbx.math.gaussian.fit(stols, patched_table_y,
patched_table_sigmas,
wk.fetch())
else:
gaussian_fit = scitbx.math.gaussian.fit(
stols, entry.table_y[:stols.size()],
entry.table_sigmas[:stols.size()], wk.fetch())
labels.append(element)
errors.append(gaussian_fit.significant_relative_errors())
max_errors.append(flex.max(errors[-1]))
correlations.append(
flex.linear_correlation(
gaussian_fit.table_y(),
gaussian_fit.fitted_values()).coefficient())
if (plots_dir is not None):
if (not os.path.isdir(plots_dir)):
print("No plots because the directory %s does not exist." %
plots_dir)
plots_dir = None
else:
cmp_plots.append(
cctbx.eltbx.gaussian_fit.write_plots(
plots_dir=plots_dir,
label=element,
gaussian_fit=gaussian_fit))
perm = flex.sort_permutation(data=max_errors, reverse=True)
labels = labels.select(perm)
errors = flex.select(errors, perm)
correlations = correlations.select(perm)
if (plots_dir is None):
cmp_plots = [None] * len(labels)
else:
cmp_plots = cmp_plots.select(perm)
for l, e, cc, p in zip(labels, errors, correlations, cmp_plots):
entry = tab.entries[l]
y = entry.table_y
perm = flex.sort_permutation(data=e, reverse=True)[:3]
high = []
for i in perm:
if (significant_errors_only and e[i] < 0.01): break
s = stols[i]
a = ""
if (not quiet and s < 2.1): a = "@%.3f" % y[i]
high.append("%7.4f %4.2f%s" % (e[i], s, a))
if (high_resolution_only): break
if (verbose or len(high) > 0):
print("Element %-5s(%2d) cc=%.4f:" % (l, entry.atomic_number, cc),
", ".join(high))
if (verbose and p is not None):
print(p)
sys.stdout.write(open(p).read())
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 main():
parser = OptionParser(
usage="usage: python %prog [options]" +
" itvc_table all_fits_six_terms.pickle" +
" all_fits_decremental.pickle all_fits_incremental.pickle")
(options, args) = parser.parse_args()
if (len(args) != 4):
parser.print_help()
return
itvc_tab = itvc_section61_io.read_table6111(args[0])
six_term_fits = easy_pickle.load(args[1])
fits = []
for file_name in args[2:]:
fits.append(easy_pickle.load(file_name))
for label, fit_group in fits[-1].all.items():
for fit in fit_group:
reset_max_error(itvc_tab.entries[label], fit)
best_fits = {}
n_less = 0
n_greater = 0
n_equal = 0
n_less_list = [0] * 10
n_greater_list = [0] * 10
n_equal_list = [0] * 10
for label in expected_labels(kissel_dir=None):
fit_group_0 = fits[0].all.get(label, None)
fit_group_1 = fits[1].all.get(label, None)
if (fit_group_0 is None and fit_group_1 is None):
best_fits[label] = None
continue
if (fit_group_0 is None):
best_fits[label] = fit_group_0
continue
if (fit_group_1 is None):
best_fits[label] = fit_group_1
continue
best_group = []
all_n_terms = {}
n_terms_dicts = []
for fit_group in [fit_group_0, fit_group_1]:
n_terms_dict = {}
for fit in fit_group:
n_terms_dict[fit.n_terms()] = fit
n_terms_dicts.append(n_terms_dict)
all_n_terms.update(n_terms_dicts[-1])
all_n_terms = all_n_terms.keys()
all_n_terms.sort()
for n_terms in all_n_terms:
fit_0 = n_terms_dicts[0].get(n_terms, None)
fit_1 = n_terms_dicts[1].get(n_terms, None)
if (fit_0 is None):
best_group.append(fit_1)
continue
if (fit_1 is None):
best_group.append(fit_0)
continue
if (fit_0.stol < fit_1.stol):
best_group.append(fit_1)
status = "less"
n_less += 1
n_less_list[n_terms] += 1
elif (fit_0.stol > fit_1.stol):
best_group.append(fit_0)
status = "greater"
n_greater += 1
n_greater_list[n_terms] += 1
else:
best_group.append(pick_nicest_fit(fit_0, fit_1))
status = "equal"
n_equal += 1
n_equal_list[n_terms] += 1
print "%-4s n_terms=%d %4.2f %4.2f %s" % (
label, n_terms, fit_0.stol, fit_1.stol, status)
best_fits[label] = best_group
print "n_less:", n_less
print "n_greater:", n_greater
print "n_equal:", n_equal
print "total:", n_less + n_greater + n_equal
n_terms = -1
for n_less, n_greater, n_equal in zip(n_less_list, n_greater_list,
n_equal_list):
n_terms += 1
if (n_less == 0 and n_greater == 0 and n_equal == 0): continue
print "n_terms:", n_terms
print " n_less:", n_less
print " n_greater:", n_greater
print " n_equal:", n_equal
print " total:", n_less + n_greater + n_equal
print
for label in expected_labels(kissel_dir=None):
if (best_fits[label] is None):
print "# Warning: Missing scattering_type:", label
print
print "Best fits:"
print
for label in expected_labels(kissel_dir=None):
fit_group = best_fits[label]
if (fit_group is None): continue
print "scattering_type:", label
assert len(six_term_fits.all[label]) == 1
assert six_term_fits.all[label][0].n_terms() == 6
fit_group.append(six_term_fits.all[label][0])
reset_max_error(itvc_tab.entries[label], fit_group[-1])
trimmed_fit_group = []
prev_fit = None
for fit in fit_group:
if (prev_fit is None or fit.stol > prev_fit.stol
or (fit.stol == prev_fit.stol
and fit.max_error < prev_fit.max_error)):
trimmed_fit_group.append(fit)
fit.show()
prev_fit = fit
else:
print "# skipped: %s, n_terms: %d, stol: %.2f, max_error: %.4f" % (
label, fit.n_terms(), fit.stol, fit.max_error)
best_fits[label] = trimmed_fit_group
print
easy_pickle.dump("best_fits.pickle", best_fits)
def run(
file_name,
table_of_gaussians,
cutoff,
low_resolution_only=False,
high_resolution_only=False,
significant_errors_only=False,
plots_dir=None,
quiet=0,
verbose=0,
):
assert not (low_resolution_only and high_resolution_only)
tab = itvc_section61_io.read_table6111(file_name)
for wk in xray_scattering.wk1995_iterator():
label = wk.label()
if not label in tab.entries:
print "Warning: missing scatterer:", label
stols = cctbx.eltbx.gaussian_fit.international_tables_stols
sel = stols <= cutoff + 1.0e-6
stols = stols.select(sel)
if low_resolution_only:
sel = stols <= 2
stols = stols.select(sel)
assert stols.size() == 56
elif high_resolution_only:
sel = stols > 2
stols = stols.select(sel)
assert stols.size() == 6
range_62 = flex.size_t(xrange(62))
labels = flex.std_string()
errors = []
correlations = flex.double()
max_errors = flex.double()
cmp_plots = flex.std_string()
for element in tab.elements:
entry = tab.entries[element]
wk = table_of_gaussians(element, 1)
assert entry.table_y.size() == 62
if not flex.sort_permutation(data=entry.table_y, reverse=True).all_eq(range_62):
print "Increasing: %s (%d)" % (element, entry.atomic_number)
prev_y = entry.table_y[0]
for y in entry.table_y:
if y > prev_y:
print "higher:", y, "before:", prev_y
prev_y = y
raise RuntimeError("Data values are not increasing.")
if low_resolution_only:
gaussian_fit = scitbx.math.gaussian.fit(stols, entry.table_y[:-6], entry.table_sigmas[:-6], wk.fetch())
elif high_resolution_only:
gaussian_fit = scitbx.math.gaussian.fit(stols, entry.table_y[-6:], entry.table_sigmas[-6:], wk.fetch())
elif entry.element != entry.atomic_symbol and entry.table_y[-6:].all_eq(0):
atom_entry = tab.entries[entry.atomic_symbol]
patched_table_y = entry.table_y[:-6]
patched_table_y.append(atom_entry.table_y[-6:])
patched_table_sigmas = entry.table_sigmas[:-6]
patched_table_sigmas.append(atom_entry.table_sigmas[-6:])
gaussian_fit = scitbx.math.gaussian.fit(stols, patched_table_y, patched_table_sigmas, wk.fetch())
else:
gaussian_fit = scitbx.math.gaussian.fit(
stols, entry.table_y[: stols.size()], entry.table_sigmas[: stols.size()], wk.fetch()
)
labels.append(element)
errors.append(gaussian_fit.significant_relative_errors())
max_errors.append(flex.max(errors[-1]))
correlations.append(flex.linear_correlation(gaussian_fit.table_y(), gaussian_fit.fitted_values()).coefficient())
if plots_dir is not None:
if not os.path.isdir(plots_dir):
print "No plots because the directory %s does not exist." % plots_dir
plots_dir = None
else:
cmp_plots.append(
cctbx.eltbx.gaussian_fit.write_plots(plots_dir=plots_dir, label=element, gaussian_fit=gaussian_fit)
)
perm = flex.sort_permutation(data=max_errors, reverse=True)
labels = labels.select(perm)
errors = flex.select(errors, perm)
correlations = correlations.select(perm)
if plots_dir is None:
cmp_plots = [None] * len(labels)
else:
cmp_plots = cmp_plots.select(perm)
for l, e, cc, p in zip(labels, errors, correlations, cmp_plots):
entry = tab.entries[l]
y = entry.table_y
perm = flex.sort_permutation(data=e, reverse=True)[:3]
high = []
for i in perm:
if significant_errors_only and e[i] < 0.01:
break
s = stols[i]
a = ""
if not quiet and s < 2.1:
a = "@%.3f" % y[i]
high.append("%7.4f %4.2f%s" % (e[i], s, a))
if high_resolution_only:
break
if verbose or len(high) > 0:
print "Element %-5s(%2d) cc=%.4f:" % (l, entry.atomic_number, cc), ", ".join(high)
if verbose and p is not None:
print p
sys.stdout.write(open(p).read())
print
def main():
parser = OptionParser(
usage="usage: python %prog [options]"
+" itvc_table all_fits_six_terms.pickle"
+" all_fits_decremental.pickle all_fits_incremental.pickle")
(options, args) = parser.parse_args()
if (len(args) != 4):
parser.print_help()
return
itvc_tab = itvc_section61_io.read_table6111(args[0])
six_term_fits = easy_pickle.load(args[1])
fits = []
for file_name in args[2:]:
fits.append(easy_pickle.load(file_name))
for label,fit_group in fits[-1].all.items():
for fit in fit_group:
reset_max_error(itvc_tab.entries[label], fit)
best_fits = {}
n_less = 0
n_greater = 0
n_equal = 0
n_less_list = [0] * 10
n_greater_list = [0] * 10
n_equal_list = [0] * 10
for label in expected_labels(kissel_dir=None):
fit_group_0 = fits[0].all.get(label, None)
fit_group_1 = fits[1].all.get(label, None)
if (fit_group_0 is None and fit_group_1 is None):
best_fits[label] = None
continue
if (fit_group_0 is None):
best_fits[label] = fit_group_0
continue
if (fit_group_1 is None):
best_fits[label] = fit_group_1
continue
best_group = []
all_n_terms = {}
n_terms_dicts = []
for fit_group in [fit_group_0, fit_group_1]:
n_terms_dict = {}
for fit in fit_group:
n_terms_dict[fit.n_terms()] = fit
n_terms_dicts.append(n_terms_dict)
all_n_terms.update(n_terms_dicts[-1])
all_n_terms = all_n_terms.keys()
all_n_terms.sort()
for n_terms in all_n_terms:
fit_0 = n_terms_dicts[0].get(n_terms, None)
fit_1 = n_terms_dicts[1].get(n_terms, None)
if (fit_0 is None):
best_group.append(fit_1)
continue
if (fit_1 is None):
best_group.append(fit_0)
continue
if (fit_0.stol < fit_1.stol):
best_group.append(fit_1)
status = "less"
n_less += 1
n_less_list[n_terms] += 1
elif (fit_0.stol > fit_1.stol):
best_group.append(fit_0)
status = "greater"
n_greater += 1
n_greater_list[n_terms] += 1
else:
best_group.append(pick_nicest_fit(fit_0, fit_1))
status = "equal"
n_equal += 1
n_equal_list[n_terms] += 1
print "%-4s n_terms=%d %4.2f %4.2f %s" % (
label, n_terms, fit_0.stol, fit_1.stol, status)
best_fits[label] = best_group
print "n_less:", n_less
print "n_greater:", n_greater
print "n_equal:", n_equal
print "total:", n_less + n_greater + n_equal
n_terms = -1
for n_less,n_greater,n_equal in zip(n_less_list,n_greater_list,n_equal_list):
n_terms += 1
if (n_less == 0 and n_greater == 0 and n_equal == 0): continue
print "n_terms:", n_terms
print " n_less:", n_less
print " n_greater:", n_greater
print " n_equal:", n_equal
print " total:", n_less + n_greater + n_equal
print
for label in expected_labels(kissel_dir=None):
if (best_fits[label] is None):
print "# Warning: Missing scattering_type:", label
print
print "Best fits:"
print
for label in expected_labels(kissel_dir=None):
fit_group = best_fits[label]
if (fit_group is None): continue
print "scattering_type:", label
assert len(six_term_fits.all[label]) == 1
assert six_term_fits.all[label][0].n_terms() == 6
fit_group.append(six_term_fits.all[label][0])
reset_max_error(itvc_tab.entries[label], fit_group[-1])
trimmed_fit_group =[]
prev_fit = None
for fit in fit_group:
if (prev_fit is None
or fit.stol > prev_fit.stol
or (fit.stol == prev_fit.stol
and fit.max_error < prev_fit.max_error)):
trimmed_fit_group.append(fit)
fit.show()
prev_fit = fit
else:
print "# skipped: %s, n_terms: %d, stol: %.2f, max_error: %.4f" % (
label, fit.n_terms(), fit.stol, fit.max_error)
best_fits[label] = trimmed_fit_group
print
easy_pickle.dump("best_fits.pickle", best_fits)
