def sample_e_pot(id_code, f_obs, xray_structure, edge_list, params):
if (edge_list is None):
print("NO_TARDY: no edge_list")
return
#
xs = xray_structure
if (xs.special_position_indices().size() != 0):
print("NO_TARDY: special positions")
return
sites_cart = xs.sites_cart()
labels = xs.scatterers().extract_labels()
pat = xs.pair_asu_table(distance_cutoff=2.5)
if (0):
pat.show_distances(sites_cart=sites_cart, site_labels=labels)
pst = pat.extract_pair_sym_table()
for i, j in edge_list:
assert i <= j
sym_dict = pst[i].get(j)
if (sym_dict is None):
print("NO_TARDY: large distance for edge:", labels[i], labels[j])
return
#
import scitbx.graph.tardy_tree
tt = scitbx.graph.tardy_tree.construct(sites=sites_cart,
edge_list=edge_list)
import scitbx.rigid_body
tardy_model = scitbx.rigid_body.tardy_model(
labels=labels,
sites=sites_cart,
masses=xs.atomic_weights(),
tardy_tree=tt,
potential_obj=potential_object(f_obs, xs),
near_singular_hinges_angular_tolerance_deg=5)
if (tardy_model.number_of_trees != 1):
print("NO_TARDY: multiple trees")
return
#
print("Single tardy tree:", \
id_code, xs.scatterers().size(), xs.space_group_info())
tt.show_summary(vertex_labels=labels, prefix=" ")
print("dof each joint:", list(tardy_model.degrees_of_freedom_each_joint()))
print("q_size each joint:", list(tardy_model.q_size_each_joint()))
q_packed = tardy_model.pack_q()
print("q_packed.size():", q_packed.size())
print("q_packed:", numstr(q_packed))
if (params.iq < 0):
return
assert params.iq < q_packed.size()
#
xy = []
from libtbx.utils import xsamples
from math import pi
for q_deg in xsamples(params.qmin, params.qmax, params.qstep):
q_packed[params.iq] = q_deg * pi / 180
tardy_model.unpack_q(q_packed=q_packed)
e_pot = tardy_model.e_pot()
xy.append((q_deg, e_pot))
from libtbx import pyplot
pyplot.plot_pairs(xy, "r-")
pyplot.show()
def sample_e_pot(id_code, f_obs, xray_structure, edge_list, params):
if (edge_list is None):
print "NO_TARDY: no edge_list"
return
#
xs = xray_structure
if (xs.special_position_indices().size() != 0):
print "NO_TARDY: special positions"
return
sites_cart = xs.sites_cart()
labels = xs.scatterers().extract_labels()
pat = xs.pair_asu_table(distance_cutoff=2.5)
if (0):
pat.show_distances(sites_cart=sites_cart, site_labels=labels)
pst = pat.extract_pair_sym_table()
for i,j in edge_list:
assert i <= j
sym_dict = pst[i].get(j)
if (sym_dict is None):
print "NO_TARDY: large distance for edge:", labels[i], labels[j]
return
#
import scitbx.graph.tardy_tree
tt = scitbx.graph.tardy_tree.construct(
sites=sites_cart, edge_list=edge_list)
import scitbx.rigid_body
tardy_model = scitbx.rigid_body.tardy_model(
labels=labels,
sites=sites_cart,
masses=xs.atomic_weights(),
tardy_tree=tt,
potential_obj=potential_object(f_obs, xs),
near_singular_hinges_angular_tolerance_deg=5)
if (tardy_model.number_of_trees != 1):
print "NO_TARDY: multiple trees"
return
#
print "Single tardy tree:", \
id_code, xs.scatterers().size(), xs.space_group_info()
tt.show_summary(vertex_labels=labels, prefix=" ")
print "dof each joint:", list(tardy_model.degrees_of_freedom_each_joint())
print "q_size each joint:", list(tardy_model.q_size_each_joint())
q_packed = tardy_model.pack_q()
print "q_packed.size():", q_packed.size()
print "q_packed:", numstr(q_packed)
if (params.iq < 0):
return
assert params.iq < q_packed.size()
#
xy = []
from libtbx.utils import xsamples
from math import pi
for q_deg in xsamples(params.qmin, params.qmax, params.qstep):
q_packed[params.iq] = q_deg*pi/180
tardy_model.unpack_q(q_packed=q_packed)
e_pot = tardy_model.e_pot()
xy.append((q_deg,e_pot))
from libtbx import pyplot
pyplot.plot_pairs(xy, "r-")
pyplot.show()
def f_obs_and_f_calc_agree_well(O, co):
if (O.c_obs.indices().size() == 0): return False
from cctbx.array_family import flex
f_obs = O.c_obs.as_amplitude_array(algorithm="xtal_3_7")
f_calc = f_obs.structure_factors_from_scatterers(
xray_structure=O.xray_structure).f_calc().amplitudes()
fan_out_sel = f_obs.f_obs_f_calc_fan_outlier_selection(
f_calc=f_calc,
offset_low=co.fan_offset_low,
offset_high=co.fan_offset_high,
also_return_x_and_y=True)
if (fan_out_sel is None):
return False
if (co.i_obs_i_calc_plot and f_obs.indices().size() != 0):
from libtbx import pyplot
xs = O.c_obs.as_intensity_array(algorithm="simple").data()
ys = flex.pow2(f_calc.data())
pyplot.plot(xs.as_numpy_array(), ys.as_numpy_array(), "ro")
pyplot.show()
fan_out_sel, x, y = fan_out_sel
fan_in_sel = ~fan_out_sel
if (co.f_obs_f_calc_plot):
from libtbx import pyplot
xs = x.select(fan_out_sel)
ys = y.select(fan_out_sel)
if (xs.size() == 0):
pyplot.plot(x.as_numpy_array(), y.as_numpy_array(), "bo")
else:
pyplot.plot(xs.as_numpy_array(), ys.as_numpy_array(), "ro")
xs = x.select(fan_in_sel)
ys = y.select(fan_in_sel)
if (xs.size() != 0):
pyplot.plot(xs.as_numpy_array(), ys.as_numpy_array(), "bo")
pyplot.plot_pairs([(co.fan_offset_low, 0),
(1, 1 - co.fan_offset_high)], "r-")
pyplot.plot_pairs([(0, co.fan_offset_low),
(1 - co.fan_offset_high, 1)], "r-")
pyplot.plot_pairs([(0, 0), (1, 1)], "k--")
pyplot.show()
fan_outlier_fraction = fan_out_sel.count(True) / fan_out_sel.size()
def cc_r1(fo, fc):
lc = flex.linear_correlation(fo.data(), fc.data())
assert lc.is_well_defined()
cc = lc.coefficient()
from libtbx import Auto
r1 = f_obs.r1_factor(other=f_calc, scale_factor=Auto)
return cc, r1
cc_all, r1_all = cc_r1(f_obs, f_calc)
cc_in, r1_in = cc_r1(f_obs.select(fan_in_sel),
f_calc.select(fan_in_sel))
print "f_obs_f_calc %s" % O.cod_id, \
"| cc_all %.4f | r1_all %.4f | out %.4f | cc_in %.4f | r1_in %.4f |" % (
cc_all, r1_all, fan_outlier_fraction, cc_in, r1_in)
if (fan_outlier_fraction > co.max_fan_outlier_fraction):
return False
if (cc_all < co.min_f_obs_f_calc_correlation):
return False
return True
def f_obs_and_f_calc_agree_well(O, co):
if O.c_obs.indices().size() == 0:
return False
from cctbx.array_family import flex
f_obs = O.c_obs.as_amplitude_array(algorithm="xtal_3_7")
f_calc = f_obs.structure_factors_from_scatterers(xray_structure=O.xray_structure).f_calc().amplitudes()
fan_out_sel = f_obs.f_obs_f_calc_fan_outlier_selection(
f_calc=f_calc, offset_low=co.fan_offset_low, offset_high=co.fan_offset_high, also_return_x_and_y=True
)
if fan_out_sel is None:
return False
if co.i_obs_i_calc_plot and f_obs.indices().size() != 0:
from libtbx import pyplot
xs = O.c_obs.as_intensity_array(algorithm="simple").data()
ys = flex.pow2(f_calc.data())
pyplot.plot(xs.as_numpy_array(), ys.as_numpy_array(), "ro")
pyplot.show()
fan_out_sel, x, y = fan_out_sel
fan_in_sel = ~fan_out_sel
if co.f_obs_f_calc_plot:
from libtbx import pyplot
xs = x.select(fan_out_sel)
ys = y.select(fan_out_sel)
if xs.size() == 0:
pyplot.plot(x.as_numpy_array(), y.as_numpy_array(), "bo")
else:
pyplot.plot(xs.as_numpy_array(), ys.as_numpy_array(), "ro")
xs = x.select(fan_in_sel)
ys = y.select(fan_in_sel)
if xs.size() != 0:
pyplot.plot(xs.as_numpy_array(), ys.as_numpy_array(), "bo")
pyplot.plot_pairs([(co.fan_offset_low, 0), (1, 1 - co.fan_offset_high)], "r-")
pyplot.plot_pairs([(0, co.fan_offset_low), (1 - co.fan_offset_high, 1)], "r-")
pyplot.plot_pairs([(0, 0), (1, 1)], "k--")
pyplot.show()
fan_outlier_fraction = fan_out_sel.count(True) / fan_out_sel.size()
def cc_r1(fo, fc):
lc = flex.linear_correlation(fo.data(), fc.data())
assert lc.is_well_defined()
cc = lc.coefficient()
from libtbx import Auto
r1 = f_obs.r1_factor(other=f_calc, scale_factor=Auto)
return cc, r1
cc_all, r1_all = cc_r1(f_obs, f_calc)
cc_in, r1_in = cc_r1(f_obs.select(fan_in_sel), f_calc.select(fan_in_sel))
print "f_obs_f_calc %s" % O.cod_id, "| cc_all %.4f | r1_all %.4f | out %.4f | cc_in %.4f | r1_in %.4f |" % (
cc_all,
r1_all,
fan_outlier_fraction,
cc_in,
r1_in,
)
if fan_outlier_fraction > co.max_fan_outlier_fraction:
return False
if cc_all < co.min_f_obs_f_calc_correlation:
return False
return True