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 add_bulk_solvent(self, reference_registry):
assert (self.interpreted_pdb is not None)
assert (self.xyz is not None)
# determine scattering types
self.scattering_types = self.interpreted_pdb.all_chain_proxies.\
nonbonded_energy_type_registry.symbols
self.element_types = self.interpreted_pdb.all_chain_proxies.\
scattering_type_registry.symbols
assert (len(self.scattering_types) == len(self.element_types))
for i in xrange(len(self.scattering_types)):
if (len(self.scattering_types[i]) == 0):
self.scattering_types[i] = self.element_types[i]
atom_library = self.interpreted_pdb.ener_lib.lib_atom
new_registry = scattering_type_registry()
for s_type, e_type in zip(self.scattering_types, self.element_types):
if (new_registry.has_key(s_type) is False):
new_registry.process(s_type)
# old parameters
g = reference_registry.gaussian(e_type)
a = list(g.array_of_a())
b = list(g.array_of_b())
c = g.c()
use_c = g.use_c()
# add new parameters for bulk solvent
if (s_type in atom_library.keys()):
if (self.explicit_h):
vdw_radius = atom_library[s_type].vdw_radius
else:
vdw_radius = atom_library[s_type].vdwh_radius
else:
vdw_radius = self.default_radius
if (vdw_radius is None):
vdw_radius = self.default_radius
v = 4.0 / 3.0 * math.pi * math.pow(vdw_radius,
3) * self.fudge_factor
a.append(-self.bulk_solvent_scale * v)
b.append(math.pi * math.pow(v, 2.0 / 3.0))
# finalize form factor
new_g = gaussian(a, b, c, use_c)
new_registry.assign(s_type, new_g)
else:
new_registry.process(s_type)
## # add O for boundary layer solvent
## o_g = reference_registry.gaussian('O')
## new_registry.process('HOH')
## new_registry.assign('HOH',o_g)
return new_registry
def incremental_fits(label, null_fit, params=None, plots_dir=None, verbose=0):
if (params is None): params = fit_parameters()
f0 = null_fit.table_y()[0]
results = []
previous_n_points = 0
existing_gaussian = xray_scattering.gaussian([], [])
while (existing_gaussian.n_terms() < params.max_n_terms):
if (previous_n_points == null_fit.table_x().size()):
print "%s: Full fit with %d terms. Search stopped." % (
label, existing_gaussian.n_terms())
print
break
n_terms = existing_gaussian.n_terms() + 1
best_min = find_max_x_multi(
null_fit=null_fit,
existing_gaussian=existing_gaussian,
target_powers=params.target_powers,
minimize_using_sigmas=params.minimize_using_sigmas,
n_repeats_minimization=params.n_repeats_minimization,
shift_sqrt_b_mod_n=params.shift_sqrt_b_mod_n,
b_min=params.b_min,
max_max_error=params.max_max_error,
n_start_fractions=params.n_start_fractions)
if (best_min is None):
print "Warning: No fit: %s n_terms=%d" % (label, n_terms)
print
break
if (previous_n_points > best_min.final_gaussian_fit.table_x().size()):
print "Warning: previous fit included more sampling points."
previous_n_points = best_min.final_gaussian_fit.table_x().size()
show_fit_summary("Best fit", label, best_min.final_gaussian_fit,
best_min.max_error)
show_literature_fits(
label=label,
n_terms=n_terms,
null_fit=null_fit,
n_points=best_min.final_gaussian_fit.table_x().size(),
e_other=best_min.max_error)
best_min.final_gaussian_fit.show()
best_min.show_minimization_parameters()
existing_gaussian = best_min.final_gaussian_fit
print
show_minimize_multi_histogram()
sys.stdout.flush()
if (plots_dir):
write_plots(plots_dir=plots_dir,
label=label + "_%d" % n_terms,
gaussian_fit=best_min.final_gaussian_fit)
g = best_min.final_gaussian_fit
results.append(
xray_scattering.fitted_gaussian(stol=g.table_x()[-1],
gaussian_sum=g))
return results
def incremental_fits(label, null_fit, params=None, plots_dir=None, verbose=0):
if (params is None): params = fit_parameters()
f0 = null_fit.table_y()[0]
results = []
previous_n_points = 0
existing_gaussian = xray_scattering.gaussian([],[])
while (existing_gaussian.n_terms() < params.max_n_terms):
if (previous_n_points == null_fit.table_x().size()):
print "%s: Full fit with %d terms. Search stopped." % (
label, existing_gaussian.n_terms())
print
break
n_terms = existing_gaussian.n_terms() + 1
best_min = find_max_x_multi(
null_fit=null_fit,
existing_gaussian=existing_gaussian,
target_powers=params.target_powers,
minimize_using_sigmas=params.minimize_using_sigmas,
n_repeats_minimization=params.n_repeats_minimization,
shift_sqrt_b_mod_n=params.shift_sqrt_b_mod_n,
b_min=params.b_min,
max_max_error=params.max_max_error,
n_start_fractions=params.n_start_fractions)
if (best_min is None):
print "Warning: No fit: %s n_terms=%d" % (label, n_terms)
print
break
if (previous_n_points > best_min.final_gaussian_fit.table_x().size()):
print "Warning: previous fit included more sampling points."
previous_n_points = best_min.final_gaussian_fit.table_x().size()
show_fit_summary(
"Best fit", label, best_min.final_gaussian_fit, best_min.max_error)
show_literature_fits(
label=label,
n_terms=n_terms,
null_fit=null_fit,
n_points=best_min.final_gaussian_fit.table_x().size(),
e_other=best_min.max_error)
best_min.final_gaussian_fit.show()
best_min.show_minimization_parameters()
existing_gaussian = best_min.final_gaussian_fit
print
show_minimize_multi_histogram()
sys.stdout.flush()
if (plots_dir):
write_plots(
plots_dir=plots_dir,
label=label+"_%d"%n_terms,
gaussian_fit=best_min.final_gaussian_fit)
g = best_min.final_gaussian_fit
results.append(xray_scattering.fitted_gaussian(
stol=g.table_x()[-1], gaussian_sum=g))
return results
def __get_cache():
global __cache
if (__cache is None):
from cctbx.eltbx.xray_scattering import get_standard_label
from libtbx.containers import OrderedDict
__cache = OrderedDict()
assert len(ito_vol_c_2011_table_4_3_2_2) == 98
for line in ito_vol_c_2011_table_4_3_2_2:
flds = line.split()
assert len(flds) == 12
std_lbl = get_standard_label(flds[0], exact=True)
assert flds[0] == std_lbl
assert std_lbl not in __cache
assert flds[1] == str(len(__cache)+1)
def vals(i,j): return [float(s) for s in flds[i:j]]
array_of_a = vals(2,7)
array_of_b = vals(7,12)
__cache[std_lbl] = gaussian(array_of_a, array_of_b)
return __cache
def __get_cache():
global __cache
if (__cache is None):
from cctbx.eltbx.xray_scattering import get_standard_label
from libtbx.containers import OrderedDict
__cache = OrderedDict()
assert len(ito_vol_c_2011_table_4_3_2_2) == 98
for line in ito_vol_c_2011_table_4_3_2_2:
flds = line.split()
assert len(flds) == 12
std_lbl = get_standard_label(flds[0], exact=True)
assert flds[0] == std_lbl
assert std_lbl not in __cache
assert flds[1] == str(len(__cache)+1)
def vals(i,j): return [float(s) for s in flds[i:j]]
array_of_a = vals(2,7)
array_of_b = vals(7,12)
__cache[std_lbl] = gaussian(array_of_a, array_of_b)
return __cache
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 exercise_gaussian():
g = xray_scattering.gaussian(0)
assert g.n_terms() == 0
assert approx_equal(g.c(), 0)
assert g.use_c()
assert g.n_parameters() == 1
g = xray_scattering.gaussian(0, False)
assert g.n_terms() == 0
assert approx_equal(g.c(), 0)
assert not g.use_c()
assert g.n_parameters() == 0
g = xray_scattering.gaussian(1)
assert g.n_terms() == 0
assert g.array_of_a() == ()
assert g.array_of_b() == ()
assert approx_equal(g.c(), 1)
assert g.n_parameters() == 1
g = xray_scattering.gaussian((), ())
assert g.n_terms() == 0
assert g.array_of_a() == ()
assert g.array_of_b() == ()
assert g.c() == 0
g = xray_scattering.gaussian((), (), -2)
assert g.n_terms() == 0
assert g.array_of_a() == ()
assert g.array_of_b() == ()
assert approx_equal(g.c(), -2)
g = xray_scattering.gaussian(flex.double((1, 2, 3, 4)))
assert approx_equal(g.array_of_a(), (1, 3))
assert approx_equal(g.array_of_b(), (2, 4))
assert approx_equal(g.c(), 0)
assert not g.use_c()
g = xray_scattering.gaussian(flex.double((1, 2, 3, 4)), 0, True)
assert approx_equal(g.c(), 0)
assert g.use_c()
g = xray_scattering.gaussian(flex.double((1, 2, 3, 4)), 5)
assert approx_equal(g.c(), 5)
assert g.use_c()
g = xray_scattering.gaussian((1, -2, 3, -4, 5), (-.1, .2, -.3, .4, -.5), 6)
assert g.n_terms() == 5
assert approx_equal(g.array_of_a(), (1, -2, 3, -4, 5))
assert approx_equal(g.array_of_b(), (-.1, .2, -.3, .4, -.5))
assert approx_equal(g.c(), 6)
assert approx_equal(g.at_stol_sq(3 / 4.), 13.4251206)
assert approx_equal(g.at_stol(math.sqrt(3 / 4.)), 13.4251206)
assert approx_equal(g.at_d_star_sq(3), 13.4251206)
assert approx_equal(g.at_d_star_sq(flex.double([3, 4])),
[13.4251206, 15.079612])
assert approx_equal(g.at_d_star(math.sqrt(3)), 13.4251206)
s = pickle.dumps(g)
l = pickle.loads(s)
assert l.n_terms() == g.n_terms()
assert approx_equal(l.array_of_a(), g.array_of_a())
assert approx_equal(l.array_of_b(), g.array_of_b())
assert approx_equal(l.c(), g.c())
assert l.use_c()
g = xray_scattering.gaussian((1, -2, 3, -4, 5), (-.1, .2, -.3, .4, -.5))
s = pickle.dumps(g)
l = pickle.loads(s)
assert l.n_terms() == g.n_terms()
assert approx_equal(l.array_of_a(), g.array_of_a())
assert approx_equal(l.array_of_b(), g.array_of_b())
assert approx_equal(l.c(), g.c())
assert not l.use_c()
#
g = xray_scattering.gaussian(*list(
zip(*[(2.51340127252, 31.8053433708), (
1.74867019409, 0.445605499982), (1.72398202356,
10.5831679451)]))) # C 3-gaussian
assert not g.use_c()
e = g.electron_density
assert approx_equal(e(r=0, b_iso=0), 264.731533932)
assert approx_equal(e(r=0, b_iso=1), 47.3615000971)
assert approx_equal(e(r=1, b_iso=0), 0.233911429529)
assert approx_equal(e(r=1, b_iso=1), 0.243343387016)
g = xray_scattering.gaussian((2.31000, 1.02000, 1.58860, 0.865000),
(20.8439, 10.2075, 0.568700, 51.6512),
0.215600) # C it1992
assert g.use_c()
e = g.electron_density
assert approx_equal(e(r=0, b_iso=0.1), 435.677592698)
assert approx_equal(e(r=0, b_iso=1), 47.9420591405)
assert approx_equal(e(r=1, b_iso=0.1), 0.241082494004)
assert approx_equal(e(r=1, b_iso=1), 0.248806720643)
def exercise_gaussian():
g = xray_scattering.gaussian(0)
assert g.n_terms() == 0
assert approx_equal(g.c(), 0)
assert g.use_c()
assert g.n_parameters() == 1
g = xray_scattering.gaussian(0, False)
assert g.n_terms() == 0
assert approx_equal(g.c(), 0)
assert not g.use_c()
assert g.n_parameters() == 0
g = xray_scattering.gaussian(1)
assert g.n_terms() == 0
assert g.array_of_a() == ()
assert g.array_of_b() == ()
assert approx_equal(g.c(), 1)
assert g.n_parameters() == 1
g = xray_scattering.gaussian((), ())
assert g.n_terms() == 0
assert g.array_of_a() == ()
assert g.array_of_b() == ()
assert g.c() == 0
g = xray_scattering.gaussian((), (), -2)
assert g.n_terms() == 0
assert g.array_of_a() == ()
assert g.array_of_b() == ()
assert approx_equal(g.c(), -2)
g = xray_scattering.gaussian(flex.double((1,2,3,4)))
assert approx_equal(g.array_of_a(), (1,3))
assert approx_equal(g.array_of_b(), (2,4))
assert approx_equal(g.c(), 0)
assert not g.use_c()
g = xray_scattering.gaussian(flex.double((1,2,3,4)), 0, True)
assert approx_equal(g.c(), 0)
assert g.use_c()
g = xray_scattering.gaussian(flex.double((1,2,3,4)), 5)
assert approx_equal(g.c(), 5)
assert g.use_c()
g = xray_scattering.gaussian((1,-2,3,-4,5), (-.1,.2,-.3,.4,-.5), 6)
assert g.n_terms() == 5
assert approx_equal(g.array_of_a(),(1,-2,3,-4,5))
assert approx_equal(g.array_of_b(),(-.1,.2,-.3,.4,-.5))
assert approx_equal(g.c(), 6)
assert approx_equal(g.at_stol_sq(3/4.), 13.4251206)
assert approx_equal(g.at_stol(math.sqrt(3/4.)), 13.4251206)
assert approx_equal(g.at_d_star_sq(3), 13.4251206)
assert approx_equal(g.at_d_star_sq(flex.double([3,4])),
[13.4251206, 15.079612])
assert approx_equal(g.at_d_star(math.sqrt(3)), 13.4251206)
s = pickle.dumps(g)
l = pickle.loads(s)
assert l.n_terms() == g.n_terms()
assert approx_equal(l.array_of_a(), g.array_of_a())
assert approx_equal(l.array_of_b(), g.array_of_b())
assert approx_equal(l.c(), g.c())
assert l.use_c()
g = xray_scattering.gaussian((1,-2,3,-4,5), (-.1,.2,-.3,.4,-.5))
s = pickle.dumps(g)
l = pickle.loads(s)
assert l.n_terms() == g.n_terms()
assert approx_equal(l.array_of_a(), g.array_of_a())
assert approx_equal(l.array_of_b(), g.array_of_b())
assert approx_equal(l.c(), g.c())
assert not l.use_c()
#
g = xray_scattering.gaussian(*zip(*[
(2.51340127252, 31.8053433708),
(1.74867019409, 0.445605499982),
(1.72398202356, 10.5831679451)])) # C 3-gaussian
assert not g.use_c()
e = g.electron_density
assert approx_equal(e(r=0, b_iso=0), 264.731533932)
assert approx_equal(e(r=0, b_iso=1), 47.3615000971)
assert approx_equal(e(r=1, b_iso=0), 0.233911429529)
assert approx_equal(e(r=1, b_iso=1), 0.243343387016)
g = xray_scattering.gaussian(
(2.31000, 1.02000, 1.58860, 0.865000),
(20.8439, 10.2075, 0.568700, 51.6512),
0.215600) # C it1992
assert g.use_c()
e = g.electron_density
assert approx_equal(e(r=0, b_iso=0.1), 435.677592698)
assert approx_equal(e(r=0, b_iso=1), 47.9420591405)
assert approx_equal(e(r=1, b_iso=0.1), 0.241082494004)
assert approx_equal(e(r=1, b_iso=1), 0.248806720643)
