def expected_labels(kissel_dir):
result = []
if (kissel_dir is None):
for wk in xray_scattering.wk1995_iterator():
result.append(wk.label())
else:
for atomic_number in xrange(1,100):
result.append(tiny_pse.table(atomic_number).symbol())
return result
def expected_labels(kissel_dir):
result = []
if (kissel_dir is None):
for wk in xray_scattering.wk1995_iterator():
result.append(wk.label())
else:
for atomic_number in xrange(1, 100):
result.append(tiny_pse.table(atomic_number).symbol())
return result
def exercise_wk1995():
e = xray_scattering.wk1995("c1")
assert e.table() == "WK1995"
assert e.label() == "C"
g = e.fetch()
assert approx_equal(g.array_of_a(),
(2.657506, 1.078079, 1.490909, -4.241070, 0.713791))
assert approx_equal(g.array_of_b(),
(14.780758, 0.776775, 42.086842, -0.000294, 0.239535))
assert approx_equal(g.c(), 4.297983)
assert approx_equal(g.at_stol_sq(0), 5.99719834328)
assert approx_equal(g.at_stol_sq(1. / 9), 2.26895371584)
assert approx_equal(g.at_stol(1. / 9), 4.93735084739)
assert approx_equal(g.at_d_star_sq(1. / 9), 4.04679561237)
e = xray_scattering.wk1995("yb2+", True)
assert e.label() == "Yb2+"
g = e.fetch()
assert approx_equal(g.array_of_a()[0], 28.443794)
assert approx_equal(g.array_of_b()[4], 0.001463)
assert approx_equal(g.c(), -23.214935)
e = xray_scattering.wk1995(" yB3+")
assert e.label() == "Yb3+"
g = e.fetch()
assert approx_equal(g.array_of_a()[0], 28.191629)
n = 0
for e in xray_scattering.wk1995_iterator():
n += 1
if (n == 213):
assert e.label() == "Pu6+"
else:
assert e.label() != "Pu6+"
d = xray_scattering.wk1995(e.label(), True)
assert d.label() == e.label()
assert n == 213
i = xray_scattering.wk1995_iterator()
j = iter(i)
assert i is j
def exercise_wk1995():
e = xray_scattering.wk1995("c1")
assert e.table() == "WK1995"
assert e.label() == "C"
g = e.fetch()
assert approx_equal(g.array_of_a(),
(2.657506,1.078079,1.490909,-4.241070,0.713791))
assert approx_equal(g.array_of_b(),
(14.780758,0.776775,42.086842,-0.000294,0.239535))
assert approx_equal(g.c(), 4.297983)
assert approx_equal(g.at_stol_sq(0), 5.99719834328)
assert approx_equal(g.at_stol_sq(1./9), 2.26895371584)
assert approx_equal(g.at_stol(1./9), 4.93735084739)
assert approx_equal(g.at_d_star_sq(1./9), 4.04679561237)
e = xray_scattering.wk1995("yb2+", True)
assert e.label() == "Yb2+"
g = e.fetch()
assert approx_equal(g.array_of_a()[0], 28.443794)
assert approx_equal(g.array_of_b()[4], 0.001463)
assert approx_equal(g.c(), -23.214935)
e = xray_scattering.wk1995(" yB3+")
assert e.label() == "Yb3+"
g = e.fetch()
assert approx_equal(g.array_of_a()[0], 28.191629)
n = 0
for e in xray_scattering.wk1995_iterator():
n += 1
if (n == 213):
assert e.label() == "Pu6+"
else:
assert e.label() != "Pu6+"
d = xray_scattering.wk1995(e.label(), True)
assert d.label() == e.label()
assert n == 213
i = xray_scattering.wk1995_iterator()
j = iter(i)
assert i is j
def ensure_common_symbols():
lbl_it = []
for e in xray_scattering.it1992_iterator(): lbl_it.append(e.label())
lbl_it.sort()
lbl_wk = []
for e in xray_scattering.wk1995_iterator(): lbl_wk.append(e.label())
lbl_wk.sort()
assert lbl_wk == lbl_it
lbl_ng = []
for i_entry in xrange(xray_scattering.n_gaussian_table_size()):
lbl_ng.append(xray_scattering.n_gaussian_table_entry(i_entry, 6).label())
lbl_ng.sort()
assert lbl_ng == lbl_it
#
for label in xray_scattering.standard_labels_list():
it = xray_scattering.it1992(label, True).fetch()
wk = xray_scattering.wk1995(label, True).fetch()
ng = xray_scattering.n_gaussian_table_entry(label, 0, 0).gaussian()
assert approx_equal(wk.at_stol(0)/it.at_stol(0), 1, 5.e-3)
def ensure_common_symbols():
lbl_it = []
for e in xray_scattering.it1992_iterator():
lbl_it.append(e.label())
lbl_it.sort()
lbl_wk = []
for e in xray_scattering.wk1995_iterator():
lbl_wk.append(e.label())
lbl_wk.sort()
assert lbl_wk == lbl_it
lbl_ng = []
for i_entry in range(xray_scattering.n_gaussian_table_size()):
lbl_ng.append(
xray_scattering.n_gaussian_table_entry(i_entry, 6).label())
lbl_ng.sort()
assert lbl_ng == lbl_it
#
for label in xray_scattering.standard_labels_list():
it = xray_scattering.it1992(label, True).fetch()
wk = xray_scattering.wk1995(label, True).fetch()
ng = xray_scattering.n_gaussian_table_entry(label, 0, 0).gaussian()
assert approx_equal(wk.at_stol(0) / it.at_stol(0), 1, 5.e-3)
def run(gaussian_fit_pickle_file_names):
localtime = time.localtime()
fits = read_pickled_fits(gaussian_fit_pickle_file_names)
f = sys.stdout
if (gaussian_fit_pickle_file_names[0].find("sds") >= 0): # retro-fitted
write_fit_group(f, "h_sds", fits.all["SDS"])
return
print >> f, "// This is an automatically generated file. DO NOT EDIT!"
print >> f
print >> f, "// Time %04d/%02d/%02d %02d:%02d:%02d" % localtime[:6]
print >> f, "// Time zone:", time.tzname
print >> f
write_module_info(f, cctbx.eltbx.gaussian_fit)
write_module_info(f, scitbx.math.gaussian_fit)
print >> f, "// Parameters:"
for k, v in fits.parameters.items():
print >> f, "// %s:" % k, v
print >> f
present = []
missing = []
for wk in xray_scattering.wk1995_iterator():
try:
fit_group = fits.all[wk.label()]
except Exception:
missing.append(wk.label())
else:
present.append(wk.label())
if (len(missing) > 0):
print >> f, "// Warning: Missing scattering labels:"
for label in missing:
print >> f, "// ", label
print >> f
write_header(f)
for label in present:
write_fit_group(f, label, fits.all[label])
write_labels(f, present)
write_table(f, present)
write_tail(f, localtime, len(present))
def run(gaussian_fit_pickle_file_names):
localtime = time.localtime()
fits = read_pickled_fits(gaussian_fit_pickle_file_names)
f = sys.stdout
if (gaussian_fit_pickle_file_names[0].find("sds") >= 0): # retro-fitted
write_fit_group(f, "h_sds", fits.all["SDS"])
return
print >> f, "// This is an automatically generated file. DO NOT EDIT!"
print >> f
print >> f, "// Time %04d/%02d/%02d %02d:%02d:%02d" % localtime[:6]
print >> f, "// Time zone:", time.tzname
print >> f
write_module_info(f, cctbx.eltbx.gaussian_fit)
write_module_info(f, scitbx.math.gaussian_fit)
print >> f, "// Parameters:"
for k,v in fits.parameters.items():
print >> f, "// %s:" % k, v
print >> f
present = []
missing = []
for wk in xray_scattering.wk1995_iterator():
try:
fit_group = fits.all[wk.label()]
except Exception:
missing.append(wk.label())
else:
present.append(wk.label())
if (len(missing) > 0):
print >> f, "// Warning: Missing scattering labels:"
for label in missing:
print >> f, "// ", label
print >> f
write_header(f)
for label in present:
write_fit_group(f, label, fits.all[label])
write_labels(f, present)
write_table(f, present)
write_tail(f, localtime, len(present))
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
