def run():
wk = xray_scattering.wk1995("O2-")
gaussian_fit = scitbx.math.gaussian.fit(table_2_stol, table_2_o2minus,
table_2_sigmas, wk.fetch())
print("max error:", flex.max(gaussian_fit.significant_relative_errors()))
cctbx.eltbx.gaussian_fit.write_plots(plots_dir="rez_plots",
label=wk.label(),
gaussian_fit=gaussian_fit)
def run():
wk = xray_scattering.wk1995("O2-")
gaussian_fit = scitbx.math.gaussian.fit(
table_2_stol,
table_2_o2minus,
table_2_sigmas,
wk.fetch())
print "max error:", flex.max(gaussian_fit.significant_relative_errors())
cctbx.eltbx.gaussian_fit.write_plots(
plots_dir="rez_plots",
label=wk.label(),
gaussian_fit=gaussian_fit)
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(
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
