def skewness_calculation(space_group_info, n_test_points=10,
n_sites=20, d_min=3, volume_per_atom=200):
structure = random_structure.xray_structure(
space_group_info=space_group_info,
elements=["Se"]*n_sites,
volume_per_atom=volume_per_atom,
random_u_iso=True)
structure.show_summary()
print
f_calc = structure.structure_factors(
d_min=d_min, anomalous_flag=False).f_calc()
f_calc.show_summary()
print
for i_fudge_factor in xrange(n_test_points+1):
fudge_factor = i_fudge_factor/float(n_test_points)
randomized_f_calc = randomize_phases(f_calc, fudge_factor)
mwpe = f_calc.mean_weighted_phase_error(randomized_f_calc)
rho = randomized_f_calc.fft_map().real_map_unpadded()
# <(rho-rho_bar)**3>/<(rho-rho_bar)**2>**3/2
rho_rho_bar = rho - flex.mean(rho)
num = flex.mean(flex.pow(rho_rho_bar, 3))
den = flex.mean(flex.pow(rho_rho_bar, 2))**(3/2.)
assert den != 0
skewness = num / den
print "fudge factor, phase difference, map skewness:",
print "%4.2f, %5.2f, %.4g" % (fudge_factor, mwpe, skewness)
print
def exercise():
if (not libtbx.env.has_module("mmtbx")):
print "Skipping exercise(): mmtbx module not available"
return
if (libtbx.env.find_in_repositories(relative_path="chem_data") is None):
print "Skipping exercise(): chem_data directory not available"
return
from mmtbx.monomer_library import pdb_interpretation
file_name = "phe_tst_adp_aniso_restraints.pdb"
open(file_name, "w").write(phe_pdb)
out = StringIO()
processed_pdb_file = pdb_interpretation.run(
args = [file_name],
strict_conflict_handling = False,
log = out)
geo = processed_pdb_file.geometry_restraints_manager()
xray_structure = processed_pdb_file.xray_structure()
xray_structure.scatterers().flags_set_grads(state=False)
xray_structure.scatterers().flags_set_grad_u_iso(
iselection=xray_structure.use_u_iso().iselection())
xray_structure.scatterers().flags_set_grad_u_aniso(
iselection=xray_structure.use_u_aniso().iselection())
adp_rm = cctbx.adp_restraints.adp_aniso_restraints(
xray_structure = xray_structure,
restraints_manager = geo,
use_hd = False)
assert approx_equal(flex.mean(adp_rm.gradients_iso), 0.713756592583)
assert approx_equal(flex.mean(adp_rm.gradients_aniso_cart.as_double()), -0.118959432097)
assert approx_equal(adp_rm.target, 8.97112989232)
fd(xray_structure = xray_structure, restraints_manager = geo, eps=1.e-4)
def calculate_solvent_mask(self):
# calculate mask
lsd = local_standard_deviation_map(
self.map_coeffs,
self.radius,
mean_solvent_density=self.mean_solvent_density,
symmetry_flags=maptbx.use_space_group_symmetry,
resolution_factor=self.params.grid_resolution_factor,
method=2)
self.rms_map = lsd.map
self.mask = lsd.mask(self.params.solvent_fraction)
# setup solvent/protein selections
self.solvent_selection = (self.mask == 1)
self.protein_selection = (self.mask == 0)
self.solvent_iselection = self.solvent_selection.iselection()
self.protein_iselection = self.protein_selection.iselection()
self.n_solvent_grid_points = self.mask.count(1)
self.n_protein_grid_points = self.mask.count(0)
# map statistics
self.mean_protein_density = self.mean_protein_density_start = flex.mean(
self.map.select(self.protein_iselection))
self.mean_solvent_density = self.mean_solvent_density_start = flex.mean(
self.map.select(self.solvent_iselection))
self.mask_percent = self.n_solvent_grid_points/(self.mask.size()) * 100
self.f000_over_v = ((
(1/self.params.protein_solvent_ratio) * self.mean_protein_density)
- self.mean_solvent_density) \
* (self.params.protein_solvent_ratio/(self.params.protein_solvent_ratio-1))
self.rms_protein_density = rms(self.map.select(self.protein_iselection))
self.rms_solvent_density = rms(self.map.select(self.solvent_iselection))
self.standard_deviation_local_rms = flex.mean_and_variance(
lsd.map.as_1d()).unweighted_sample_standard_deviation()
def exercise_f_model_no_scales(symbol = "C 2"):
random.seed(0)
flex.set_random_seed(0)
x = random_structure.xray_structure(
space_group_info = sgtbx.space_group_info(symbol=symbol),
elements =(("O","N","C")*5+("H",)*10),
volume_per_atom = 200,
min_distance = 1.5,
general_positions_only = True,
random_u_iso = True,
random_occupancy = False)
f_obs = abs(x.structure_factors(d_min = 1.5, algorithm="fft").f_calc())
x.shake_sites_in_place(mean_distance=1)
k_iso = flex.double(f_obs.data().size(), 2)
k_aniso = flex.double(f_obs.data().size(), 3)
fmodel = mmtbx.f_model.manager(
xray_structure = x,
k_isotropic = k_iso,
k_anisotropic = k_aniso,
f_obs = f_obs)
fc = abs(fmodel.f_calc()).data()
fm = abs(fmodel.f_model()).data()
fmns = abs(fmodel.f_model_no_scales()).data()
assert approx_equal(flex.mean(fm/fc), 6)
assert approx_equal(flex.mean(fmns/fc), 1)
def exercise_SFweight_spline_core(structure, d_min, verbose=0):
structure.scattering_type_registry(d_min=d_min)
f_obs = abs(structure.structure_factors(
d_min=d_min, anomalous_flag=False).f_calc())
if (0 or verbose):
f_obs.show_summary()
f_obs = miller.array(
miller_set=f_obs,
data=f_obs.data(),
sigmas=flex.sqrt(f_obs.data()))
partial_structure = xray.structure(
crystal_symmetry=structure,
scatterers=structure.scatterers()[:-2])
f_calc = f_obs.structure_factors_from_scatterers(
xray_structure=partial_structure).f_calc()
test_set_flags = (flex.random_double(size=f_obs.indices().size()) < 0.1)
sfweight = clipper.SFweight_spline_interface(
unit_cell=f_obs.unit_cell(),
space_group=f_obs.space_group(),
miller_indices=f_obs.indices(),
anomalous_flag=f_obs.anomalous_flag(),
f_obs_data=f_obs.data(),
f_obs_sigmas=f_obs.sigmas(),
f_calc=f_calc.data(),
test_set_flags=test_set_flags,
n_refln=f_obs.indices().size()//10,
n_param=20)
if (0 or verbose):
print "number_of_spline_parameters:",sfweight.number_of_spline_parameters()
print "mean fb: %.8g" % flex.mean(flex.abs(sfweight.fb()))
print "mean fd: %.8g" % flex.mean(flex.abs(sfweight.fd()))
print "mean phi: %.8g" % flex.mean(sfweight.centroid_phases())
print "mean fom: %.8g" % flex.mean(sfweight.figures_of_merit())
return sfweight
def set_chunk_stats(chunk, stats, stat_choice, n_residues=None, ref_cell=None, space_group=None, d_min=None, ref_data=None):
if "reslimit" in stat_choice: stats["reslimit"].append(chunk.res_lim)
else: stats["reslimit"].append(float("nan"))
if "pr" in stat_choice: stats["pr"].append(chunk.profile_radius)
else: stats["pr"].append(float("nan"))
stats["ccref"].append(float("nan"))
if set(["ioversigma","resnatsnr1","ccref"]).intersection(stat_choice):
iobs = chunk.data_array(space_group, False)
iobs = iobs.select(iobs.sigmas()>0).merge_equivalents(use_internal_variance=False).array()
binner = iobs.setup_binner(auto_binning=True)
if "resnatsnr1" in stat_choice:
res = float("nan")
for i_bin in binner.range_used():
sel = binner.selection(i_bin)
tmp = iobs.select(sel)
if tmp.size() == 0: continue
sn = flex.mean(tmp.data()/tmp.sigmas())
if sn <= 1:
res = binner.bin_d_range(i_bin)[1]
break
stats["resnatsnr1"].append(res)
else:
stats["resnatsnr1"].append(float("nan"))
if d_min: iobs = iobs.resolution_filter(d_min=d_min)
if "ccref" in stat_choice:
corr = iobs.correlation(ref_data, assert_is_similar_symmetry=False)
if corr.is_well_defined(): stats["ccref"][-1] = corr.coefficient()
if "ioversigma" in stat_choice:
stats["ioversigma"].append(flex.mean(iobs.data()/iobs.sigmas()))
else:
stats["ioversigma"].append(float("nan"))
else:
stats["ioversigma"].append(float("nan"))
stats["resnatsnr1"].append(float("nan"))
if "abdist" in stat_choice:
from cctbx.uctbx.determine_unit_cell import NCDist
G6a, G6b = make_G6(ref_cell), make_G6(chunk.cell)
abdist = NCDist(G6a, G6b)
stats["abdist"].append(abdist)
else:
stats["abdist"].append(float("nan"))
if "wilsonb" in stat_choice:
iso_scale_and_b = ml_iso_absolute_scaling(iobs, n_residues, 0)
stats["wilsonb"].append(iso_scale_and_b.b_wilson)
else:
stats["wilsonb"].append(float("nan"))
def print_table(self):
from libtbx import table_utils
from libtbx.str_utils import format_value
table_header = ["Tile","Dist","Nobs","aRmsd","Rmsd","delx","dely","disp","rotdeg","Rsigma","Tsigma"]
table_data = []
table_data.append(table_header)
sort_radii = flex.sort_permutation(flex.double(self.radii))
tile_rmsds = flex.double()
radial_sigmas = flex.double(len(self.tiles) // 4)
tangen_sigmas = flex.double(len(self.tiles) // 4)
for idx in range(len(self.tiles) // 4):
x = sort_radii[idx]
if self.tilecounts[x] < 3:
wtaveg = 0.0
radial = (0,0)
tangential = (0,0)
rmean,tmean,rsigma,tsigma=(0,0,1,1)
else:
wtaveg = self.weighted_average_angle_deg_from_tile(x)
radial,tangential,rmean,tmean,rsigma,tsigma = get_radial_tangential_vectors(self,x)
radial_sigmas[x]=rsigma
tangen_sigmas[x]=tsigma
table_data.append( [
format_value("%3d", x),
format_value("%7.2f", self.radii[x]),
format_value("%6d", self.tilecounts[x]),
format_value("%5.2f", self.asymmetric_tile_rmsd[x]),
format_value("%5.2f", self.tile_rmsd[x]),
format_value("%5.2f", self.mean_cv[x][0]),
format_value("%5.2f", self.mean_cv[x][1]),
format_value("%5.2f", matrix.col(self.mean_cv[x]).length()),
format_value("%6.2f", wtaveg),
format_value("%6.2f", rsigma),
format_value("%6.2f", tsigma),
])
table_data.append([""]*len(table_header))
rstats = flex.mean_and_variance(radial_sigmas,self.tilecounts.as_double())
tstats = flex.mean_and_variance(tangen_sigmas,self.tilecounts.as_double())
table_data.append( [
format_value("%3s", "ALL"),
format_value("%s", ""),
format_value("%6d", self.overall_N),
format_value("%5.2f", math.sqrt(flex.mean(self.delrsq))),
format_value("%5.2f", self.overall_rmsd),
format_value("%5.2f", self.overall_cv[0]),
format_value("%5.2f", self.overall_cv[1]),
format_value("%5.2f", flex.mean(flex.double([matrix.col(cv).length() for cv in self.mean_cv]))),
format_value("%s", ""),
format_value("%6.2f", rstats.mean()),
format_value("%6.2f", tstats.mean()),
])
print
print table_utils.format(table_data,has_header=1,justify='center',delim=" ")
def get_rmsds_obs_pred(self, observations, experiment):
reflections = observations.select(observations.get_flags(
observations.flags.used_in_refinement))
assert len(reflections) > 0
obs_x, obs_y, obs_z = reflections['xyzobs.mm.value'].parts()
calc_x, calc_y, calc_z = reflections['xyzcal.mm'].parts()
rmsd_x = flex.mean(flex.pow2(obs_x-calc_x))**0.5
rmsd_y = flex.mean(flex.pow2(obs_y-calc_y))**0.5
rmsd_z = flex.mean(flex.pow2(obs_z-calc_z))**0.5
return (rmsd_x, rmsd_y, rmsd_z)
def prepare_simulation_with_noise(sim, transmittance,
apply_noise,
ordered_intensities=None,
half_data_flag = 0):
result = intensity_data()
result.frame = sim["frame_lookup"]
result.miller= sim['miller_lookup']
raw_obs_no_noise = transmittance * sim['observed_intensity']
if apply_noise:
import scitbx.random
from scitbx.random import variate, normal_distribution
# bernoulli_distribution, gamma_distribution, poisson_distribution
scitbx.random.set_random_seed(321)
g = variate(normal_distribution())
noise = flex.sqrt(raw_obs_no_noise) * g(len(raw_obs_no_noise))
# adds in Gauss noise to signal
else:
noise = flex.double(len(raw_obs_no_noise),0.)
raw_obs = raw_obs_no_noise + noise
if half_data_flag in [1,2]: # apply selection after random numbers have been applied
half_data_selection = (sim["frame_lookup"]%2)==(half_data_flag%2)
result.frame = sim["frame_lookup"].select(half_data_selection)
result.miller = sim['miller_lookup'].select(half_data_selection)
raw_obs = raw_obs.select(half_data_selection)
mean_signal = flex.mean(raw_obs)
sigma_obs = flex.sqrt(flex.abs(raw_obs))
mean_sigma = flex.mean(sigma_obs)
print "<I> / <sigma>", (mean_signal/ mean_sigma)
scale_factor = mean_signal/10.
print "Mean signal is",mean_signal,"Applying a constant scale factor of ",scale_factor
#most important line; puts input data on a numerically reasonable scale
result.raw_obs = raw_obs / scale_factor
scaled_sigma = sigma_obs / scale_factor
result.exp_var = scaled_sigma * scaled_sigma
#ordered intensities gets us the unit cell & miller indices to
# gain a static array of (sin theta over lambda)**2
if ordered_intensities is not None:
uc = ordered_intensities.unit_cell()
stol_sq = flex.double()
for i in xrange(len(result.miller)):
this_hkl = ordered_intensities.indices()[result.miller[i]]
stol_sq_item = uc.stol_sq(this_hkl)
stol_sq.append(stol_sq_item)
result.stol_sq = stol_sq
return result
def prepare_observations_for_scaling(work_params,obs,reference_intensities=None,
half_data_flag = 0,files = None):
result = intensity_data()
result.frame = obs["frame_lookup"]
result.miller= obs['miller_lookup']
result.origHKL = flex.miller_index(obs["original_H"],obs["original_K"],obs["original_L"])
raw_obs = obs["observed_intensity"]
sigma_obs = obs["observed_sigI"]
if half_data_flag in [1,2]: # apply selection after random numbers have been applied
if files==None:
half_data_selection = (obs["frame_lookup"]%2)==(half_data_flag%2)
else:
# if file names are available, base half data selection on the last digit in filename.
extension = work_params.filename_extension
frame_selection = flex.bool([
(half_data_flag==1 and (int(item.split("."+extension)[0][-1])%2==1)) or \
(half_data_flag==2 and (int(item.split("."+extension)[0][-1])%2==0))
for item in files])
half_data_selection = frame_selection.select(obs["frame_lookup"])
result.frame = obs["frame_lookup"].select(half_data_selection)
result.miller = obs['miller_lookup'].select(half_data_selection)
result.origHKL = result.origHKL.select(half_data_selection)
raw_obs = raw_obs.select(half_data_selection)
sigma_obs = sigma_obs.select(half_data_selection)
mean_signal = flex.mean(raw_obs)
mean_sigma = flex.mean(sigma_obs)
print "<I> / <sigma>", (mean_signal/ mean_sigma)
scale_factor = mean_signal/10.
print "Mean signal is",mean_signal,"Applying a constant scale factor of ",scale_factor
SDFAC_FROM_CHISQ = work_params.levmar.sdfac_value
#most important line; puts input data on a numerically reasonable scale
# XXX
result.raw_obs = raw_obs / scale_factor
scaled_sigma = SDFAC_FROM_CHISQ * sigma_obs / scale_factor
result.exp_var = scaled_sigma * scaled_sigma
#reference intensities gets us the unit cell & miller indices to
# gain a static array of (sin theta over lambda)**2
if reference_intensities is not None:
uc = reference_intensities.unit_cell()
stol_sq = flex.double()
for i in xrange(len(result.miller)):
this_hkl = reference_intensities.indices()[result.miller[i]]
stol_sq_item = uc.stol_sq(this_hkl)
stol_sq.append(stol_sq_item)
result.stol_sq = stol_sq
return result
def get_phase_scores(miller_arrays):
result = []
for miller_array in miller_arrays:
score = 0
if ( miller_array.is_complex_array()
or miller_array.is_hendrickson_lattman_array()):
score = 4
elif (miller_array.is_real_array()):
if (miller_array.is_xray_reconstructed_amplitude_array()):
pass
elif (miller_array.is_xray_amplitude_array()):
pass
elif (miller_array.is_xray_intensity_array()):
pass
elif (miller_array.data().size() == 0):
pass
else:
m = flex.mean(flex.abs(miller_array.data()))
if (m < 5):
score = 2
elif (m < 500):
score = 3
else:
score = 1
result.append(score)
return result
def quick_test(file_name): from libtbx.utils import user_plus_sys_time t = user_plus_sys_time() s = reader(file_name) print "Time read:", t.delta() s.show_summary() print tuple(s.original_indices[:3]) print tuple(s.unique_indices[:3]) print tuple(s.batch_numbers[:3]) print tuple(s.centric_tags[:3]) print tuple(s.spindle_flags[:3]) print tuple(s.asymmetric_unit_indices[:3]) print tuple(s.i_obs[:3]) print tuple(s.sigmas[:3]) print tuple(s.original_indices[-3:]) print tuple(s.unique_indices[-3:]) print tuple(s.batch_numbers[-3:]) print tuple(s.centric_tags[-3:]) print tuple(s.spindle_flags[-3:]) print tuple(s.asymmetric_unit_indices[-3:]) print tuple(s.i_obs[-3:]) print tuple(s.sigmas[-3:]) m = s.as_miller_array(merge_equivalents=False).merge_equivalents() print "min redundancies:", flex.min(m.redundancies().data()) print "max redundancies:", flex.max(m.redundancies().data()) print "mean redundancies:", flex.mean(m.redundancies().data().as_double()) s.as_miller_arrays()[0].show_summary() print
def unit_cell_bases_mean_square_difference(self, other):
diff_sqs = flex.double()
for basis_vector in [(1,0,0),(0,1,0),(0,0,1)]:
self_v = matrix.col(self.orthogonalize(basis_vector))
other_v = matrix.col(other.orthogonalize(basis_vector))
diff_sqs.append((self_v - other_v).norm_sq())
return flex.mean(diff_sqs)
def dump_R_in_bins(obs, calc, scale_B=True, log_out=sys.stdout, n_bins=20):
#obs, calc = obs.common_sets(calc, assert_is_similar_symmetry=False)
if scale_B:
scale, B = kBdecider(obs, calc).run()
d_star_sq = calc.d_star_sq().data()
calc = calc.customized_copy(data = scale * flex.exp(-B*d_star_sq) * calc.data())
binner = obs.setup_binner(n_bins=n_bins)
count=0
log_out.write("dmax - dmin: R (nref) <I1> <I2> scale\n")
for i_bin in binner.range_used():
tmp_obs = obs.select(binner.bin_indices() == i_bin)
tmp_calc = calc.select(binner.bin_indices() == i_bin)
low = binner.bin_d_range(i_bin)[0]
high = binner.bin_d_range(i_bin)[1]
if scale_B:
scale = 1.
else:
scale = flex.sum(tmp_obs.data()*tmp_calc.data()) / flex.sum(flex.pow2(tmp_calc.data()))
R = flex.sum(flex.abs(tmp_obs.data() - scale*tmp_calc.data())) / flex.sum(0.5 * tmp_obs.data() + 0.5 * scale*tmp_calc.data())
log_out.write("%5.2f - %5.2f: %.5f (%d) %.1f %.1f %.3e\n" % (low, high, R, len(tmp_obs.data()),
flex.mean(tmp_obs.data()), flex.mean(tmp_calc.data()),
scale))
log_out.write("Overall R = %.5f (scale=%.3e, %%comp=%.3f)\n\n" % (calc_R(obs, calc, do_scale=not scale_B) + (obs.completeness()*100.,)) )
def amplitude_quasi_normalisations(ma, d_star_power=1, set_to_minimum=None):
epsilons = ma.epsilons().data().as_double()
mean_f_sq_over_epsilon = flex.double()
for i_bin in ma.binner().range_used():
sel = ma.binner().selection(i_bin)
#sel_f_sq = flex.pow2(ma.data().select(sel))
sel_f_sq = ma.data().select(sel)
if (sel_f_sq.size() > 0):
sel_epsilons = epsilons.select(sel)
sel_f_sq_over_epsilon = sel_f_sq / sel_epsilons
mean_f_sq_over_epsilon.append(flex.mean(sel_f_sq_over_epsilon))
else:
mean_f_sq_over_epsilon.append(0)
mean_f_sq_over_epsilon_interp = ma.binner().interpolate(
mean_f_sq_over_epsilon, d_star_power)
if set_to_minimum and not mean_f_sq_over_epsilon_interp.all_gt(0):
# HACK NO REASON THIS SHOULD WORK
sel = (mean_f_sq_over_epsilon_interp <= set_to_minimum)
mean_f_sq_over_epsilon_interp.set_selected(sel,-mean_f_sq_over_epsilon_interp)
sel = (mean_f_sq_over_epsilon_interp <= set_to_minimum)
mean_f_sq_over_epsilon_interp.set_selected(sel,set_to_minimum)
assert mean_f_sq_over_epsilon_interp.all_gt(0)
from cctbx.miller import array
#return array(ma, flex.sqrt(mean_f_sq_over_epsilon_interp))
return array(ma, mean_f_sq_over_epsilon_interp)
def target_and_gradients(self, xray_structure, to_compute_weight=False):
if(to_compute_weight):
xrs = xray_structure.deep_copy_scatterers()
# This may be useful to explore:
#xrs.shake_adp_if_all_equal(b_iso_tolerance = 1.e-3)
#xrs.shake_adp(spread=10, keep_anisotropic= False)
else:
xrs = xray_structure
if(self.refine_adp): params = xrs.extract_u_iso_or_u_equiv()
if(self.refine_occ): params = xrs.scatterers().extract_occupancies()
if(to_compute_weight):
pmin = flex.min(params)
pmax = flex.max(params)
if(abs(pmin-pmax)/abs(pmin+pmax)*2*100<1.e-3):
pmean = flex.mean(params)
n_par = params.size()
params = flex.double()
for i in xrange(n_par):
params.append(pmean + 0.1 * pmean * random.choice([-1,0,1]))
return crystal.adp_iso_local_sphere_restraints_energies(
pair_sym_table = self.pair_sym_table,
orthogonalization_matrix = self.orthogonalization_matrix,
sites_frac = self.sites_frac,
u_isos = params,
selection = self.selection,
use_u_iso = self.selection,
grad_u_iso = self.selection,
sphere_radius = self.sphere_radius,
distance_power = 2,
average_power = 1,
min_u_sum = 1.e-6,
compute_gradients = True,
collect = False)
def create_da_xray_structures(xray_structure, params):
def grid(sphere, gap, overlap):
c = flex.double(sphere.center)
x_start, y_start, z_start = c - float(sphere.radius)
x_end, y_end, z_end = c + float(sphere.radius)
x_range = frange(c[0], c[0]+gap, overlap)
y_range = frange(c[1], c[1]+gap, overlap)
z_range = frange(c[2], c[2]+gap, overlap)
return group_args(x_range = x_range, y_range = y_range, z_range = z_range)
grids = []
for sphere in params.sphere:
grids.append(grid(sphere = sphere, gap = params.atom_gap,
overlap = params.overlap_interval))
initial_b_factor = params.initial_b_factor
if(initial_b_factor is None):
initial_b_factor = flex.mean(
xray_structure.extract_u_iso_or_u_equiv()*adptbx.u_as_b(1.))
da_xray_structures = []
counter = 0
for grid, sphere in zip(grids, params.sphere):
cntr_g = 0 # XXX
for x_start in grid.x_range:
for y_start in grid.y_range:
for z_start in grid.z_range:
cntr_g += 1
if(cntr_g>1): continue # XXX
counter += 1
new_center = flex.double([x_start, y_start, z_start])
atom_grid = make_grid(
center = new_center,
radius = sphere.radius,
gap = params.atom_gap,
occupancy = params.initial_occupancy,
b_factor = initial_b_factor,
atom_name = params.atom_name,
scattering_type = params.atom_type,
resname = params.residue_name)
da_xray_structure = pdb_atoms_as_xray_structure(pdb_atoms = atom_grid,
crystal_symmetry = xray_structure.crystal_symmetry())
closest_distances_result = xray_structure.closest_distances(
sites_frac = da_xray_structure.sites_frac(),
distance_cutoff = 5)
selection = closest_distances_result.smallest_distances > 0
selection &= closest_distances_result.smallest_distances < 1
da_xray_structure = da_xray_structure.select(~selection)
#print counter, da_xray_structure.scatterers().size()
if(cntr_g==1): # XXX
da_xray_structures.append(da_xray_structure)
###
result = []
for i, x1 in enumerate(da_xray_structures):
for j, x2 in enumerate(da_xray_structures):
if(x1 is not x2):
closest_distances_result = x1.closest_distances(
sites_frac = x2.sites_frac(),
distance_cutoff = 5) # XXX ???
selection = closest_distances_result.smallest_distances > 0
selection &= closest_distances_result.smallest_distances < params.atom_gap
da_xray_structures[j] = x2.select(~selection)
return da_xray_structures
def show(self, weight = None, prefix = "", show_neutron=True,
print_stats=True):
deltab = self.model.rms_b_iso_or_b_equiv_bonded()
r_work = self.fmodels.fmodel_xray().r_work()*100.
r_free = self.fmodels.fmodel_xray().r_free()*100.
mean_b = flex.mean(
self.model.xray_structure.extract_u_iso_or_u_equiv())*adptbx.u_as_b(1)
if(deltab is None):
print >> self.log, " r_work=%5.2f r_free=%5.2f"%(r_work, r_free)
return None
neutron_r_work = neutron_r_free = None
if (show_neutron) and (self.fmodels.fmodel_neutron() is not None) :
neutron_r_work = self.fmodels.fmodel_neutron().r_work()*100.
neutron_r_free = self.fmodels.fmodel_neutron().r_free()*100.
xrs = self.fmodels.fmodel_xray().xray_structure
result = weight_result(
r_work=r_work,
r_free=r_free,
delta_b=deltab,
mean_b=mean_b,
weight=weight,
xray_target=self.fmodels.fmodel_xray().target_w(),
neutron_r_work=neutron_r_work,
neutron_r_free=neutron_r_free,
u_star=xrs.scatterers().extract_u_star(),
u_iso=xrs.scatterers().extract_u_iso())
if (print_stats) :
result.show(out=self.log)
return result
def fvec_callable(pfh,current_values):
rotz = current_values[0]
indep = current_values[1:]
effective_orientation = OO.input_orientation.rotate_thru((0,0,1),rotz)
pfh.convert.set_orientation(effective_orientation)
pfh.convert.forward_independent_parameters()
effective_orientation = pfh.convert.backward_orientation(independent=indep)
OO.ucbp3.set_orientation(effective_orientation)
pfh.last_set_orientation = effective_orientation
OO.ucbp3.gaussian_fast_slow()
# note the reversal of x & y with obs vs. predicted
displacements = flex.double(
[(
col(
OO.ucbp3.simple_forward_calculation_spot_position(
wavelength = OO.central_wavelength_ang,
observation_no = obsno).position) -
col(
(OO.parent.spots[OO.parent.indexed_pairs[obsno]["spot"]].ctr_mass_y(),
OO.parent.spots[OO.parent.indexed_pairs[obsno]["spot"]].ctr_mass_x(),
0.0))
).length()
for obsno in xrange(len(OO.parent.indexed_pairs))])
rmsdexc = math.sqrt(flex.mean(displacements*displacements))
print "rotz %7.3f degrees, RMSD displacement %7.3f pixels"%(
(rotz * 180./math.pi), rmsdexc)
return list(displacements)
def run(fmodel, model, log, params = None):
print_statistics.make_header("Fit water hydrogens into residual map",
out = log)
if(params is None):
params = all_master_params().extract()
print_statistics.make_sub_header("find peak-candidates", out = log)
peaks = find_hydrogen_peaks(
fmodel = fmodel,
pdb_atoms = model.pdb_atoms,
params = params,
log = log)
waters_and_peaks = extract_hoh_peaks(
peaks = peaks,
pdb_hierarchy = model.pdb_hierarchy(),
pdb_atoms = model.pdb_atoms,
xray_structure = model.xray_structure)
print_statistics.make_sub_header("6D rigid body fit of HOH", out = log)
print >> log, "Fit quality:"
for water_and_peaks in waters_and_peaks:
fit_water(water_and_peaks = water_and_peaks,
xray_structure = model.xray_structure,
params = params,
log = log)
# adjust ADP for H
# TODO mrt: probably H bfactors should be equal to those
# of the bonded atom
u_isos = model.xray_structure.extract_u_iso_or_u_equiv()
u_iso_mean = flex.mean(u_isos)
sel_big = u_isos > u_iso_mean*2
hd_sel = model.xray_structure.hd_selection()
sel_big.set_selected(~hd_sel, False)
model.xray_structure.set_u_iso(value = u_iso_mean, selection = sel_big)
def map_stat(distances, map_values):
result = []
#
n_points_max = -1
nn=20
x = [[i/100,i/100+nn/100.] for i in range(0,800, nn)]
for x_ in x:
l,r = x_
sel = distances >= l
sel &= distances < r
mv = map_values.select(sel)
if(mv.size()>n_points_max): n_points_max = mv.size()
#
for x_ in x:
l,r = x_
sel = distances >= l
sel &= distances < r
mv = map_values.select(sel)
if(mv.size()>0):
sz = mv.size()
rms = math.sqrt( flex.sum(mv*mv)/sz )
#fr = sz*100./map_values.size()
fr = sz*1./n_points_max
result.append([l, r, flex.mean(mv), rms, sz, fr])
return result
def vectors(self):
self.database.initialize_tables_and_insert_command()
self.tile_rmsd = [0.]*64
for run,tokens in self.literals():
try:
itile = self.register_line( float(tokens[2]),float(tokens[3]),
float(tokens[5]),float(tokens[6]),
float(tokens[8]),float(tokens[9]),
float(tokens[11]),float(tokens[12]) )
if run is not None:
self.database.insert(run,itile,tokens)
yield "OK"
except ValueError:
print "Valueerror"
self.database.send_insert_command()
for x in xrange(64):
if self.tilecounts[x]==0: continue
self.radii[x]/=self.tilecounts[x]
sum_cv = matrix.col(self.mean_cv[x])
self.mean_cv[x] = sum_cv/self.tilecounts[x]
mean_cv = matrix.col(self.mean_cv[x])
selection = (self.master_tiles == x)
selected_cv = self.master_cv.select(selection)
if len(selected_cv)>0:
self.tile_rmsd[x] = math.sqrt(
flex.mean(flex.double([ (matrix.col(cv) - mean_cv).length_sq() for cv in selected_cv ]))
)
else: self.tile_rmsd[x]=0.
self.overall_N = flex.sum(flex.int( [int(t) for t in self.tilecounts] ))
self.overall_cv = matrix.col(self.overall_cv)/self.overall_N
self.overall_rmsd = math.sqrt( self.sum_sq_cv / self.overall_N )
def run(hklin, n_bins):
for array in iotbx.file_reader.any_file(hklin).file_server.miller_arrays:
# skip if not anomalous intensity data
if not (array.is_xray_intensity_array() and array.anomalous_flag()):
print "skipping", array.info()
continue
# We assume that data is already merged
assert array.is_unique_set_under_symmetry()
# take anomalous differences
dano = array.anomalous_differences()
# process with binning
dano.setup_binner(n_bins=n_bins)
binner = dano.binner()
print "Array:", array.info()
print " dmax dmin nrefs dano"
for i_bin in binner.range_used():
# selection for this bin. sel is flex.bool object (list of True of False)
sel = binner.selection(i_bin)
# take mean of absolute value of anomalous differences in a bin
bin_mean = flex.mean(flex.abs(dano.select(sel).data()))
d_max, d_min = binner.bin_d_range(i_bin)
print "%7.2f %7.2f %6d %.2f" % (d_max, d_min, binner.count(i_bin), bin_mean)
def nearest_rotamer_sites_cart(self, residue):
sites_cart_result = residue.atoms().extract_xyz()
get_class = iotbx.pdb.common_residue_names_get_class
if get_class(residue.resname) == "common_amino_acid":
sites_cart = residue.atoms().extract_xyz()
rotamer_iterator = self.mon_lib_srv.rotamer_iterator(
fine_sampling=True,
comp_id=residue.resname,
atom_names=residue.atoms().extract_name(),
sites_cart=sites_cart,
)
if (
rotamer_iterator is None
or rotamer_iterator.problem_message is not None
or rotamer_iterator.rotamer_info is None
):
rotamer_iterator = None
if rotamer_iterator is not None:
dist_min = 1.0e9
for r, rotamer_sites_cart in rotamer_iterator:
d = flex.mean(flex.sqrt((sites_cart - rotamer_sites_cart).dot()))
if d < dist_min:
dist_min = d
sites_cart_result = rotamer_sites_cart
return sites_cart_result
def fvec_callable_pvr(pfh,current_values):
rotx = current_values[0]
roty = current_values[1]
effective_orientation = OO.input_orientation.rotate_thru((1,0,0),rotx
).rotate_thru((0,1,0),roty
).rotate_thru((0,0,1),0.0)
OO.ucbp3.set_orientation(effective_orientation)
pfh.last_set_orientation = effective_orientation
OO.ucbp3.gaussian_fast_slow()
excursions = flex.double(
[OO.ucbp3.simple_forward_calculation_spot_position(
wavelength = OO.central_wavelength_ang,
observation_no = obsno).rotax_excursion_rad_pvr/(2.*math.pi)
for obsno in xrange(len(OO.parent.indexed_pairs))])
degrees = 360.*excursions
rmsdexc = math.sqrt(flex.mean(degrees*degrees))
#print "rotx %7.3f roty %7.3f degrees, -PVR excursion %7.3f degrees"%(
#(rotx * 180./math.pi),(roty * 180./math.pi), rmsdexc)
# Note. Luc Bourhis wants scale to be from 0 to 1. So instead of
# returning on scale of degrees, use radians/(2*pi)
# The parameters rotx roty are still expressed in radians
return excursions
def run(hklfiles, params):
arrays = map(lambda x: crystfel.hkl.HKLfile(symm_source=params.pdb, hklin=x), hklfiles)
for a in arrays:
a.set_resolution(d_min=params.dmin, d_max=params.dmax)
ofs = open(params.datout, "w")
ofs.write(" dmax dmin nref cmpl red1 red2 %s\n" % " ".join(map(lambda x: "%7s" % x, params.fom)))
a1, a2 = arrays[0].array.common_sets(arrays[1].array)
r1, r2 = arrays[0].redundancies.common_sets(arrays[1].redundancies)
r1, r2 = map(lambda x: x.as_double(), (r1, r2))
binner = a1.setup_binner(n_bins=params.nshells)
for i_bin in binner.range_used():
sel = binner.selection(i_bin)
d_max, d_min = binner.bin_d_range(i_bin)
r1s, r2s = r1.select(sel), r2.select(sel)
r1sm = flex.mean(r1s.data()) if r1s.size() > 0 else float("nan")
r2sm = flex.mean(r2s.data()) if r2s.size() > 0 else float("nan")
a1s, a2s = a1.select(sel), a2.select(sel)
ofs.write(
"%6.2f %6.2f %5d %5.1f %6.1f %6.1f "
% (d_max, d_min, a1s.size(), a1s.completeness(d_max=d_max) * 100.0, r1sm, r2sm)
)
if "cc" in params.fom:
ofs.write("% 7.4f " % calc_cc(a1s, a2s))
if "ccano" in params.fom:
ofs.write("% 7.4f " % calc_ccano(a1s, a2s))
if "rsplit" in params.fom:
ofs.write("% 7.4f " % calc_rsplit(a1s, a2s))
ofs.write("\n")
ofs.write(
"# overall %5d %5.1f %6.1f %6.1f "
% (a1.size(), a1.completeness(d_max=params.dmax) * 100.0, flex.mean(r1.data()), flex.mean(r2.data()))
)
if "cc" in params.fom:
ofs.write("% 7.4f " % calc_cc(a1, a2))
if "ccano" in params.fom:
ofs.write("% 7.4f " % calc_ccano(a1, a2))
if "rsplit" in params.fom:
ofs.write("% 7.4f " % calc_rsplit(a1, a2))
ofs.write("\n")
def show_xray_structure_statistics(xray_structure, atom_selections, hd_sel = None):
result = group_args(
all = None,
macromolecule = None,
sidechain = None,
solvent = None,
ligand = None,
backbone = None)
if(hd_sel is not None):
xray_structure = xray_structure.select(~hd_sel)
for key in atom_selections.__dict__.keys():
value = atom_selections.__dict__[key]
if(value.count(True) > 0):
if(hd_sel is not None):
value = value.select(~hd_sel)
xrs = xray_structure.select(value)
atom_counts = xrs.scattering_types_counts_and_occupancy_sums()
atom_counts_strs = []
for ac in atom_counts:
atom_counts_strs.append("%s:%s:%s"%(ac.scattering_type,str(ac.count),
str("%10.2f"%ac.occupancy_sum).strip()))
atom_counts_str = " ".join(atom_counts_strs)
b_isos = xrs.extract_u_iso_or_u_equiv()
n_aniso = xrs.use_u_aniso().count(True)
n_not_positive_definite = xrs.is_positive_definite_u().count(False)
b_mean = format_value("%-6.1f",adptbx.u_as_b(flex.mean(b_isos)))
b_min = format_value("%-6.1f",adptbx.u_as_b(flex.min(b_isos)))
b_max = format_value("%-6.1f",adptbx.u_as_b(flex.max(b_isos)))
n_atoms = format_value("%-8d",xrs.scatterers().size()).strip()
n_npd = format_value("%-8s",n_not_positive_definite).strip()
occ = xrs.scatterers().extract_occupancies()
o_mean = format_value("%-6.2f",flex.mean(occ)).strip()
o_min = format_value("%-6.2f",flex.min(occ)).strip()
o_max = format_value("%-6.2f",flex.max(occ)).strip()
tmp_result = group_args(
n_atoms = n_atoms,
atom_counts_str = atom_counts_str,
b_min = b_min,
b_max = b_max,
b_mean = b_mean,
o_min = o_min,
o_max = o_max,
o_mean = o_mean,
n_aniso = n_aniso,
n_npd = n_npd)
setattr(result,key,tmp_result)
return result
def run():
def compute_map(xray_structure, d_min=1.5, resolution_factor=1./4):
fc = xray_structure.structure_factors(d_min = d_min).f_calc()
fft_map = fc.fft_map(resolution_factor=resolution_factor)
fft_map.apply_sigma_scaling()
result = fft_map.real_map_unpadded()
return result, fc, fft_map
xrs = random_structure.xray_structure(
space_group_info = sgtbx.space_group_info("P212121"),
elements = ["N","C","O","S","P"]*10,
volume_per_atom = 50)
map_target,tmp,tmp = compute_map(xray_structure = xrs)
xrs_sh = xrs.deep_copy_scatterers()
xrs_sh.shake_sites_in_place(mean_distance=0.8)
start_error = flex.mean(xrs.distances(other = xrs_sh))
print "Start:", start_error
map_current, miller_array, crystal_gridding = compute_map(
xray_structure = xrs_sh)
for step in [miller_array.d_min()/4]*5:
if(1):
minimized = real_space_target_and_gradients.minimization(
xray_structure = xrs_sh,
miller_array = miller_array,
crystal_gridding = crystal_gridding,
map_target = map_target,
max_iterations = 500,
min_iterations = 25,
step = step,
geometry_restraints_manager = None,
target_type = "diff_map")
xrs_sh = minimized.xray_structure
map_current = minimized.map_current
final_error = flex.mean(xrs.distances(other = minimized.xray_structure))
if(0):
minimized = real_space_refinement_simple.lbfgs(
sites_cart=xrs_sh.sites_cart(),
density_map=map_target,
unit_cell=xrs_sh.unit_cell(),
geometry_restraints_manager=None,
real_space_gradients_delta=step)
xrs_sh = xrs_sh.replace_sites_cart(minimized.sites_cart)
final_error = flex.mean(xrs.distances(other = xrs_sh))
print "Final:", final_error
assert approx_equal(start_error, 0.8, 1.e-3)
assert final_error < 1.e-4
print "OK"
def exercise(pdb_poor_str, d_min = 1.0, resolution_factor = 0.25):
# Fit one residue in many-residues model
#
# answer
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_answer)
pdb_inp.write_pdb_file(file_name = "answer.pdb")
xrs_answer = pdb_inp.xray_structure_simple()
f_calc = xrs_answer.structure_factors(d_min = d_min).f_calc()
fft_map = f_calc.fft_map(resolution_factor=resolution_factor)
fft_map.apply_sigma_scaling()
target_map = fft_map.real_map_unpadded()
mtz_dataset = f_calc.as_mtz_dataset(column_root_label = "FCmap")
mtz_object = mtz_dataset.mtz_object()
mtz_object.write(file_name = "answer.mtz")
# take TYR9
sites_answer = list(
pdb_inp.construct_hierarchy().residue_groups())[1].atoms().extract_xyz()
# poor
mon_lib_srv = monomer_library.server.server()
master_params = iotbx.phil.parse(
input_string=mmtbx.monomer_library.pdb_interpretation.master_params_str,
process_includes=True).extract()
master_params.link_distance_cutoff=999
processed_pdb_file = monomer_library.pdb_interpretation.process(
mon_lib_srv = mon_lib_srv,
params = master_params,
ener_lib = monomer_library.server.ener_lib(),
raw_records = flex.std_string(pdb_poor_str.splitlines()),
strict_conflict_handling = True,
force_symmetry = True,
log = None)
pdb_hierarchy_poor = processed_pdb_file.all_chain_proxies.pdb_hierarchy
xrs_poor = processed_pdb_file.xray_structure()
sites_cart_poor = xrs_poor.sites_cart()
pdb_hierarchy_poor.write_pdb_file(file_name = "poor.pdb")
#
rotamer_manager = RotamerEval()
get_class = iotbx.pdb.common_residue_names_get_class
for model in pdb_hierarchy_poor.models():
for chain in model.chains():
for residue in chain.only_conformer().residues():
if(get_class(residue.resname) == "common_amino_acid" and
int(residue.resseq)==9): # take TYR9
t0 = time.time()
ro = mmtbx.refinement.real_space.fit_residue.run_with_minimization(
target_map = target_map,
residue = residue,
xray_structure = xrs_poor,
mon_lib_srv = mon_lib_srv,
rotamer_manager = rotamer_manager,
real_space_gradients_delta = d_min*resolution_factor,
geometry_restraints_manager = processed_pdb_file.geometry_restraints_manager(show_energies=False))
sites_final = residue.atoms().extract_xyz()
t1 = time.time()-t0
pdb_hierarchy_poor.adopt_xray_structure(ro.xray_structure)
pdb_hierarchy_poor.write_pdb_file(file_name = "refined.pdb")
dist = flex.mean(flex.sqrt((sites_answer - sites_final).dot()))
# Highly unstable test
assert dist < 0.9
def check_sites_rt(
cmd, xrsp_init, output, selection, selection_str, verbose,
tolerance=1.e-3):
remove_files(output)
run_command(command=cmd, verbose=verbose)
xrsp = xray_structure_plus(file_name = output)
assert approx_equal(xrsp.occ, xrsp_init.occ,tolerance)
assert approx_equal(xrsp.u_iso, xrsp_init.u_iso,tolerance)
assert approx_equal(xrsp.u_cart, xrsp_init.u_cart,tolerance)
if(selection_str is None):
diff = xrsp.sites_cart - xrsp_init.sites_cart
assert math.sqrt(flex.mean(diff.dot())) > 1.0
else:
diff = xrsp.sites_cart - xrsp_init.sites_cart
assert math.sqrt(flex.mean(diff.select(selection).dot())) > 1.0
assert approx_equal(
math.sqrt(flex.mean(diff.select(~selection).dot())),0.,tolerance)
def run(hklin, pdbin, wdir, anisotropy_correction=False):
arrays = iotbx.file_reader.any_file(hklin).file_server.miller_arrays
i_arrays = filter(
lambda x: x.is_xray_intensity_array() and x.anomalous_flag(), arrays)
f_arrays = filter(
lambda x: x.is_xray_amplitude_array() and x.anomalous_flag(), arrays)
if not i_arrays and not f_arrays:
print "No anomalous observation data"
return
if os.path.exists(wdir):
print "%s already exists. quiting." % wdir
return
os.mkdir(wdir)
xs = crystal_symmetry_from_any.extract_from(pdbin)
sh_out = open(os.path.join(wdir, "run_anode.sh"), "w")
sh_out.write("#!/bin/sh\n\n")
sh_out.write("shelxc anode <<+ > shelxc.log 2>&1\n")
sh_out.write("cell %s\n" % format_unit_cell(xs.unit_cell()))
sh_out.write("spag %s\n" % str(xs.space_group_info()).replace(" ", ""))
if i_arrays:
obs_array = i_arrays[0]
infile = "%s.hkl" % os.path.splitext(os.path.basename(hklin))[0]
in_opt = "%s" % infile
print "Using intensity array:", obs_array.info().label_string()
else:
obs_array = f_arrays[0]
infile = "%s_f.hkl" % os.path.splitext(os.path.basename(hklin))[0]
in_opt = "-f %s" % infile
print "No intensity arrays. Using amplitude arrays instead:", obs_array.info(
).label_string()
sh_out.write("! data from %s : %s\n" %
(os.path.abspath(hklin), obs_array.info().label_string()))
obs_array.crystal_symmetry().show_summary(sh_out, prefix="! ")
check_symm(obs_array.crystal_symmetry(), xs)
n_org = obs_array.size()
obs_array = obs_array.eliminate_sys_absent()
n_sys_abs = n_org - obs_array.size()
if n_sys_abs > 0:
print " %d systematic absences removed." % n_sys_abs
if anisotropy_correction:
print "Correcting anisotropy.."
n_residues = p_vm_calculator(obs_array, 1, 0).best_guess
abss = ml_aniso_absolute_scaling(obs_array, n_residues=n_residues)
abss.show()
tmp = -2. if i_arrays else -1.
b_cart = map(lambda x: x * tmp, abss.b_cart)
obs_array = obs_array.apply_debye_waller_factors(b_cart=b_cart)
sh_out.write("sad %s\n" % in_opt)
iotbx.shelx.hklf.miller_array_export_as_shelx_hklf(
obs_array,
open(os.path.join(wdir, infile), "w"),
normalise_if_format_overflow=True)
sh_out.write("+\n\n")
sh_out.write('ln -s "%s" anode.pdb\n\n' % os.path.relpath(pdbin, wdir))
sh_out.write("anode anode\n")
sh_out.close()
call(cmd="sh", arg="./run_anode.sh", wdir=wdir)
pha_file = os.path.join(wdir, "anode.pha")
if os.path.isfile(pha_file):
pha2mtz(pha_file, xs, os.path.join(wdir, "anode.pha.mtz"))
print "Done. See %s/" % wdir
fa_file = os.path.join(wdir, "anode_fa.hkl")
if os.path.isfile(fa_file):
r = iotbx.shelx.hklf.reader(open(fa_file))
fa_array = r.as_miller_arrays(crystal_symmetry=xs)[0]
print "\nData stats:"
print " # Cmpl.o = Anomalous completeness in original data"
print " # Cmpl.c = Anomalous completeness in shelxc result (rejections)"
print " # SigAno = <d''/sigma> in shelxc result"
print " d_max d_min Cmpl.o Cmpl.c SigAno"
binner = obs_array.setup_binner(n_bins=12)
for i_bin in binner.range_used():
d_max_bin, d_min_bin = binner.bin_d_range(i_bin)
obs_sel = obs_array.resolution_filter(d_max_bin, d_min_bin)
obs_sel_ano = obs_sel.anomalous_differences()
fa_sel = fa_array.resolution_filter(d_max_bin, d_min_bin)
cmplset = obs_sel_ano.complete_set(
d_max=d_max_bin, d_min=d_min_bin).select_acentric()
n_acentric = cmplset.size()
sigano = flex.mean(
fa_sel.data() /
fa_sel.sigmas()) if fa_sel.size() else float("nan")
print " %5.2f %5.2f %6.2f %6.2f %6.2f" % (
d_max_bin, d_min_bin, 100. * obs_sel_ano.size() / n_acentric,
100. * fa_sel.size() / n_acentric, sigano)
lsa_file = os.path.join(wdir, "anode.lsa")
if os.path.isfile(lsa_file):
print ""
flag = False
for l in open(lsa_file):
if "Strongest unique anomalous peaks" in l:
flag = True
elif "Reflections written to" in l:
flag = False
if flag:
print l.rstrip()
if os.path.isfile(("anode_fa.res")):
x = iotbx.shelx.cctbx_xray_structure_from(file=open("anode_fa.res"))
open("anode_fa.pdb", "w").write(x.as_pdb_file())
def exercise_3():
#test torsion restraints
for use_reference in ['True', 'False', 'top_out', 'None']:
pdb_inp = iotbx.pdb.input(
lines=flex.std_string(pdb_str_2.splitlines()), source_info=None)
model = manager(
model_input=pdb_inp,
log=null_out())
grm = model.get_restraints_manager().geometry
xrs2 = model.get_xray_structure()
awl2 = model.get_hierarchy().atoms_with_labels()
pdb2 = model.get_hierarchy()
pdb_inp3 = iotbx.pdb.input(source_info=None, lines=pdb_str_3)
xrs3 = pdb_inp3.xray_structure_simple()
ph3 = pdb_inp3.construct_hierarchy()
ph3.atoms().reset_i_seq()
awl3 = ph3.atoms_with_labels()
sites_cart_reference = flex.vec3_double()
selection = flex.size_t()
min_selection = flex.size_t()
reference_names = ["N", "CA", "CB", "CG", "CD", "NE", "CZ", "NH1", "NH2"]
minimize_names = ["CG", "CD", "NE", "CZ", "NH1", "NH2"]
for a2,a3 in zip(tuple(awl2), tuple(awl3)):
assert a2.resname == a3.resname
assert a2.name == a3.name
assert a2.i_seq == a3.i_seq
if(a2.resname == "ARG" and a2.name.strip() in reference_names):
selection.append(a2.i_seq)
sites_cart_reference.append(a3.xyz)
if a2.name.strip() in minimize_names:
min_selection.append(a2.i_seq)
assert selection.size() == len(reference_names)
selection_bool = flex.bool(xrs2.scatterers().size(), min_selection)
if(use_reference == 'True'):
grm.add_chi_torsion_restraints_in_place(
pdb_hierarchy = pdb2,
sites_cart = sites_cart_reference,
selection = selection,
sigma = 2.5)
elif(use_reference == 'top_out'):
grm.add_chi_torsion_restraints_in_place(
pdb_hierarchy = pdb2,
sites_cart = sites_cart_reference,
selection = selection,
sigma = 2.5,
limit = 180.0,
top_out_potential=True)
elif(use_reference == 'None'):
grm.add_chi_torsion_restraints_in_place(
pdb_hierarchy = pdb2,
sites_cart = sites_cart_reference,
selection = selection,
sigma = 2.5)
grm.remove_chi_torsion_restraints_in_place(
selection = selection)
d1 = flex.mean(flex.sqrt((xrs2.sites_cart().select(min_selection) -
xrs3.sites_cart().select(min_selection)).dot()))
print("distance start (use_reference: %s): %6.4f"%(str(use_reference), d1))
assert d1>4.0
assert approx_equal(
flex.max(flex.sqrt((xrs2.sites_cart().select(~selection_bool) -
xrs3.sites_cart().select(~selection_bool)).dot())), 0)
from cctbx import geometry_restraints
import mmtbx.refinement.geometry_minimization
import scitbx.lbfgs
grf = geometry_restraints.flags.flags(default=True)
grf.nonbonded = False
sites_cart = xrs2.sites_cart()
minimized = mmtbx.refinement.geometry_minimization.lbfgs(
sites_cart = sites_cart,
correct_special_position_tolerance=1.0,
geometry_restraints_manager = grm,
sites_cart_selection = flex.bool(sites_cart.size(), min_selection),
geometry_restraints_flags = grf,
lbfgs_termination_params = scitbx.lbfgs.termination_parameters(
max_iterations=5000))
xrs2.set_sites_cart(sites_cart = sites_cart)
d2 = flex.mean(flex.sqrt((xrs2.sites_cart().select(min_selection) -
xrs3.sites_cart().select(min_selection)).dot()))
print("distance final (use_reference: %s): %6.4f"%(str(use_reference), d2))
if (use_reference in ['True', 'top_out']):
assert d2<0.3, "%s, %f" % (use_reference, d2)
else:
assert d2>4.0, d2
assert approx_equal(
flex.max(flex.sqrt((xrs2.sites_cart().select(~selection_bool) -
xrs3.sites_cart().select(~selection_bool)).dot())), 0)
#test torsion manipulation
grm.remove_chi_torsion_restraints_in_place()
grm.remove_chi_torsion_restraints_in_place()
sites_cart_reference = []
selections_reference = []
for model in pdb2.models():
for chain in model.chains():
for residue in chain.residues():
sites_cart_reference.append(residue.atoms().extract_xyz())
selections_reference.append(residue.atoms().extract_i_seq())
#one residue at a time (effectively chi angles only)
for sites_cart, selection in zip(sites_cart_reference, selections_reference):
grm.add_chi_torsion_restraints_in_place(
pdb_hierarchy = pdb2,
sites_cart = sites_cart,
selection = selection)
assert grm.get_n_chi_torsion_proixes() == 6
grm.remove_chi_torsion_restraints_in_place()
#all sites at once, chi angles only
sites_cart = xrs2.sites_cart()
grm.add_chi_torsion_restraints_in_place(
pdb_hierarchy = pdb2,
sites_cart = sites_cart,
selection = None,
chi_angles_only = True)
assert grm.get_n_chi_torsion_proixes() == 6
#all sites at once, all torsions
grm.add_chi_torsion_restraints_in_place(
pdb_hierarchy = pdb2,
sites_cart = sites_cart,
selection = None,
chi_angles_only = False)
# grm.get_chi_torsion_proxies().show_sorted(
# by_value='residual',
# sites_cart=sites_cart,
# site_labels=[atom.id_str() for atom in pdb2.atoms()])
assert grm.get_n_chi_torsion_proixes() == 12, grm.get_n_chi_torsion_proixes()
def get_map_values_and_grid_sites_frac(fmodel, map_type, grid_step, d_min,
apply_sigma_scaling,
apply_volume_scaling, include_f000,
sel_bb, use_exact_phases):
#
resolution_factor = grid_step / d_min
mp = mmtbx.masks.mask_master_params.extract()
mp.grid_step_factor = 1. / resolution_factor
mmtbx_masks_asu_mask_obj = mmtbx.masks.asu_mask(
xray_structure=fmodel.xray_structure, d_min=d_min, mask_params=mp)
bulk_solvent_mask = mmtbx_masks_asu_mask_obj.mask_data_whole_uc()
sel = bulk_solvent_mask > 0
bulk_solvent_mask = bulk_solvent_mask.set_selected(sel, 1)
cr_gr = maptbx.crystal_gridding(
unit_cell=fmodel.xray_structure.unit_cell(),
space_group_info=fmodel.f_obs().space_group_info(),
pre_determined_n_real=bulk_solvent_mask.focus())
from mmtbx import map_tools
from cctbx import miller
#
#mc = map_tools.electron_density_map(fmodel = fmodel).map_coefficients(
# map_type = map_type,
# acentrics_scale = 1.0,
# centrics_pre_scale = 1.0)
if not use_exact_phases:
k = fmodel.k_isotropic() * fmodel.k_anisotropic()
print("flex.mean(k):", flex.mean(k))
f_model = fmodel.f_model()
mc_data = abs(fmodel.f_obs()).data() / k - abs(f_model).data() / k
tmp = miller.array(miller_set=f_model,
data=flex.double(
f_model.indices().size(),
1)).phase_transfer(phase_source=f_model)
mc = miller.array(miller_set=tmp, data=mc_data * tmp.data())
else:
fmodel.update_all_scales(fast=True, remove_outliers=False)
k = fmodel.k_isotropic() * fmodel.k_anisotropic()
fo = fmodel.f_obs().customized_copy(data=fmodel.f_obs().data() / k)
fo = fo.phase_transfer(phase_source=fmodel.f_model())
fc = fmodel.f_calc().customized_copy(data=fmodel.f_calc().data())
mc = miller.array(miller_set=fo, data=fo.data() - fc.data())
######## XXX
fft_map = miller.fft_map(crystal_gridding=cr_gr, fourier_coefficients=mc)
fft_map.apply_volume_scaling()
map_data = fft_map.real_map_unpadded()
xrs = fmodel.xray_structure
sites_cart = xrs.sites_cart().select(sel_bb)
sel = maptbx.grid_indices_around_sites(unit_cell=xrs.unit_cell(),
fft_n_real=map_data.focus(),
fft_m_real=map_data.all(),
sites_cart=sites_cart,
site_radii=flex.double(
sites_cart.size(), 0.5))
map_in = map_data.select(sel)
mm = flex.mean(map_in)
print("mean in (1):", mm)
#
#sites_frac = xrs.sites_frac().select(sel_bb)
#mm = 0
#for sf in sites_frac:
# mm += map_data.eight_point_interpolation(sf)
#mm = mm/sites_frac.size()
#print "mean in (2):", mm
########
#
# Add F000
#reg = fmodel.xray_structure.scattering_type_registry(table = "wk1995")
#f_000 = reg.sum_of_scattering_factors_at_diffraction_angle_0() +\
# 0.4*fmodel.xray_structure.unit_cell().volume()
if (include_f000):
#f_000 = include_f000*fmodel.xray_structure.unit_cell().volume()*0.3
#f_000 = None # XXX
f_000 = abs(mm * xrs.unit_cell().volume())
#f_000 = 0.626*fmodel.xray_structure.unit_cell().volume()*0.35
else:
f_000 = None
print("f_000:", f_000)
#print "XXX", include_f000*fmodel.xray_structure.unit_cell().volume()*0.3
#
fft_map = miller.fft_map(crystal_gridding=cr_gr,
fourier_coefficients=mc,
f_000=f_000)
#
assert [apply_sigma_scaling, apply_volume_scaling].count(True) == 1
if (apply_sigma_scaling): fft_map.apply_sigma_scaling()
elif (apply_volume_scaling): fft_map.apply_volume_scaling()
else: assert RuntimeError
nx, ny, nz = fft_map.n_real()
map_data = fft_map.real_map_unpadded()
#map_data = map_data * bulk_solvent_mask
print("n_real:", nx, ny, nz, map_data.size())
grid_sites_frac = flex.vec3_double()
map_values = flex.double()
for ix in range(nx):
for iy in range(ny):
for iz in range(nz):
mv = map_data[(ix, iy, iz)]
if 1: #if(mv != 0):
xf, yf, zf = ix / float(nx), iy / float(ny), iz / float(nz)
grid_sites_frac.append([xf, yf, zf])
map_at_ixiyiz = map_data[(ix, iy, iz)]
map_values.append(map_at_ixiyiz)
return map_values, grid_sites_frac
def __init__(self,
map_data,
xray_structure,
pdb_hierarchy,
geometry_restraints_manager,
gradients_method="fd",
ncs_groups=None,
rms_bonds_limit=0.015,
rms_angles_limit=2.0,
real_space_gradients_delta=1. / 4,
max_iterations=100,
range_size=10,
n_ranges=10,
default_weight=50):
"""
Fast determination of optimal data/restraints weight for real-space refinement
of individual sites.
"""
self.msg_strings = []
# split chains into chunks
result = []
for model in pdb_hierarchy.models():
for chain in model.chains():
if (chain.is_protein() or chain.is_na()):
residue_range_sel = flex.size_t()
cntr = 0
for rg in chain.residue_groups():
i_seqs = rg.atoms().extract_i_seq()
cntr += 1
if (cntr < 10):
residue_range_sel.extend(i_seqs)
else:
result.append(residue_range_sel)
residue_range_sel = flex.size_t()
residue_range_sel.extend(i_seqs)
cntr = 0
if (len(result) == 0):
assert residue_range_sel.size() > 0
result.append(residue_range_sel)
self.msg_strings.append("number of chunks: %d" % len(result))
# randomly pick chunks
random_chunks = []
if (len(result) > 0):
for i in xrange(n_ranges):
random_chunks.append(random.choice(xrange(len(result))))
self.msg_strings.append("random chunks:" % random_chunks)
# setup refinery
xrs_dc = xray_structure.deep_copy_scatterers()
sel_all = flex.bool(xrs_dc.scatterers().size(), True)
grm_dc = geometry_restraints_manager.select(sel_all)
ro = mmtbx.refinement.real_space.individual_sites.box_refinement_manager(
xray_structure=xrs_dc,
target_map=map_data,
geometry_restraints_manager=grm_dc.geometry,
real_space_gradients_delta=real_space_gradients_delta,
max_iterations=max_iterations,
ncs_groups=ncs_groups,
gradients_method=gradients_method)
optimal_weights = flex.double()
# loop over chunks: determine best weight for each chunk
if (len(result) == 0):
random_chunks = [None]
for chunk in random_chunks:
if (chunk is None):
sel = flex.bool(xrs_dc.scatterers().size(), True)
else:
sel = result[chunk]
sel = flex.bool(xrs_dc.scatterers().size(), sel)
ro.refine(selection=sel,
rms_bonds_limit=rms_bonds_limit,
rms_angles_limit=rms_angles_limit)
self.msg_strings.append("chunk %s optimal weight: %9.4f" %
(str(chunk), ro.weight_optimal))
if (ro.weight_optimal is not None):
optimal_weights.append(ro.weight_optimal)
# select overall best weight
mean = flex.mean(optimal_weights)
sel = optimal_weights < mean * 3
sel &= optimal_weights > mean / 3
if (sel.count(True) > 0):
optimal_weights = optimal_weights.select(sel)
self.weight = flex.mean_default(optimal_weights, default_weight)
self.msg_strings.append("overall best weight: %9.4f" % self.weight)
def __init__(self, **kwargs):
group_args.__init__(self, **kwargs)
# require Dij, d_c
P = Profiler("2. calculate rho density")
print("finished Dij, now calculating rho_i, the density")
from xfel.clustering import Rodriguez_Laio_clustering_2014
# alternative clustering algorithms: see http://scikit-learn.org/stable/modules/clustering.html
R = Rodriguez_Laio_clustering_2014(distance_matrix = self.Dij, d_c = self.d_c)
self.rho = rho = R.get_rho()
ave_rho = flex.mean(rho.as_double())
NN = self.Dij.focus()[0]
print("The average rho_i is %5.2f, or %4.1f%%"%(ave_rho, 100*ave_rho/NN))
i_max = flex.max_index(rho)
P = Profiler("3.transition")
print("the index with the highest density is %d"%(i_max))
delta_i_max = flex.max(flex.double([self.Dij[i_max,j] for j in range(NN)]))
print("delta_i_max",delta_i_max)
rho_order = flex.sort_permutation(rho,reverse=True)
rho_order_list = list(rho_order)
P = Profiler("4. delta")
self.delta = delta = R.get_delta(rho_order=rho_order, delta_i_max=delta_i_max)
P = Profiler("5. find cluster maxima")
#---- Now hunting for clusters ---Lot's of room for improvement (or simplification) here!!!
cluster_id = flex.int(NN,-1) # default -1 means no cluster
delta_order = flex.sort_permutation(delta,reverse=True)
N_CLUST = 10 # maximum of 10 points to be considered as possible clusters
#MAX_PERCENTILE_DELTA = 0.99 # cluster centers have to be in the top 10% percentile delta
MAX_PERCENTILE_RHO = 0.99 # cluster centers have to be in the top 75% percentile rho
n_cluster = 0
#max_n_delta = min(N_CLUST, int(MAX_PERCENTILE_DELTA*NN))
for ic in range(NN):
# test the density, rho
item_idx = delta_order[ic]
if delta[item_idx]>100: print("A: iteration", ic, "delta", delta[item_idx], delta[item_idx] < 0.25 * delta[delta_order[0]])
if delta[item_idx] < 0.25 * delta[delta_order[0]]: # too low (another heuristic!)
continue
item_rho_order = rho_order_list.index(item_idx)
if delta[item_idx]>100: print("B: iteration", ic, item_rho_order,item_rho_order/NN,MAX_PERCENTILE_RHO)
if item_rho_order/NN < MAX_PERCENTILE_RHO :
cluster_id[item_idx] = n_cluster
print(ic,item_idx,item_rho_order,cluster_id[item_idx])
n_cluster += 1
print("Found %d clusters"%n_cluster)
for x in range(NN):
if cluster_id[x]>=0:
print("XC",x,cluster_id[x],rho[x],delta[x])
self.cluster_id_maxima = cluster_id.deep_copy()
P = Profiler("6. assign all points")
R.cluster_assignment(rho_order,cluster_id)
self.cluster_id_full = cluster_id.deep_copy()
# assign the halos
P = Profiler("7. assign halos")
halo = flex.bool(NN,False)
border = R.get_border( cluster_id = cluster_id )
for ic in range(n_cluster): #loop thru all border regions; find highest density
print("cluster",ic, "in border",border.count(True))
this_border = (cluster_id == ic) & (border==True)
print(len(this_border), this_border.count(True))
if this_border.count(True)>0:
highest_density = flex.max(rho.select(this_border))
halo_selection = (rho < highest_density) & (this_border==True)
if halo_selection.count(True)>0:
cluster_id.set_selected(halo_selection,-1)
core_selection = (cluster_id == ic) & ~halo_selection
highest_density = flex.max(rho.select(core_selection))
too_sparse = core_selection & (rho.as_double() < highest_density/10.) # another heuristic
if too_sparse.count(True)>0:
cluster_id.set_selected(too_sparse,-1)
self.cluster_id_final = cluster_id.deep_copy()
print("%d in the excluded halo"%((cluster_id==-1).count(True)))
def scale_frame_by_mean_I(self, frame_no, pickle_filename, iparams,
mean_of_mean_I, avg_mode):
observations_pickle = read_frame(pickle_filename)
pickle_filepaths = pickle_filename.split('/')
img_filename_only = pickle_filepaths[len(pickle_filepaths) - 1]
txt_exception = ' {0:40} ==> '.format(img_filename_only)
if observations_pickle is None:
txt_exception += 'empty or bad input file\n'
return None, txt_exception
inputs, txt_organize_input = self.organize_input(
observations_pickle,
iparams,
avg_mode,
pickle_filename=pickle_filename)
if inputs is not None:
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, detector_distance_mm, wavelength, crystal_init_orientation = inputs
else:
txt_exception += txt_organize_input + '\n'
return None, txt_exception
#select only reflections matched with scale input params.
#filter by resolution
i_sel_res = observations_original.resolution_filter_selection(
d_min=iparams.scale.d_min, d_max=iparams.scale.d_max)
observations_original_sel = observations_original.select(i_sel_res)
alpha_angle_sel = alpha_angle.select(i_sel_res)
spot_pred_x_mm_sel = spot_pred_x_mm.select(i_sel_res)
spot_pred_y_mm_sel = spot_pred_y_mm.select(i_sel_res)
#filter by sigma
i_sel_sigmas = (
observations_original_sel.data() /
observations_original_sel.sigmas()) > iparams.scale.sigma_min
observations_original_sel = observations_original_sel.select(
i_sel_sigmas)
alpha_angle_sel = alpha_angle_sel.select(i_sel_sigmas)
spot_pred_x_mm_sel = spot_pred_x_mm_sel.select(i_sel_sigmas)
spot_pred_y_mm_sel = spot_pred_y_mm_sel.select(i_sel_sigmas)
observations_non_polar_sel, index_basis_name = self.get_observations_non_polar(
observations_original_sel, pickle_filename, iparams)
observations_non_polar, index_basis_name = self.get_observations_non_polar(
observations_original, pickle_filename, iparams)
uc_params = observations_original.unit_cell().parameters()
ph = partiality_handler()
r0 = ph.calc_spot_radius(
sqr(crystal_init_orientation.reciprocal_matrix()),
observations_original_sel.indices(), wavelength)
#calculate first G
(G, B) = (1, 0)
stats = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
if mean_of_mean_I > 0:
G = flex.median(observations_original_sel.data()) / mean_of_mean_I
if iparams.flag_apply_b_by_frame:
try:
mxh = mx_handler()
asu_contents = mxh.get_asu_contents(iparams.n_residues)
observations_as_f = observations_non_polar_sel.as_amplitude_array(
)
binner_template_asu = observations_as_f.setup_binner(
auto_binning=True)
wp = statistics.wilson_plot(observations_as_f,
asu_contents,
e_statistics=True)
G = wp.wilson_intensity_scale_factor * 1e2
B = wp.wilson_b
except Exception:
txt_exception += 'warning B-factor calculation failed.\n'
return None, txt_exception
two_theta = observations_original.two_theta(
wavelength=wavelength).data()
sin_theta_over_lambda_sq = observations_original.two_theta(
wavelength=wavelength).sin_theta_over_lambda_sq().data()
ry, rz, re, voigt_nu, rotx, roty = (0, 0, iparams.gamma_e,
iparams.voigt_nu, 0, 0)
partiality_init, delta_xy_init, rs_init, rh_init = ph.calc_partiality_anisotropy_set(\
crystal_init_orientation.unit_cell(),
rotx, roty, observations_original.indices(),
ry, rz, r0, re, voigt_nu,
two_theta, alpha_angle, wavelength,
crystal_init_orientation, spot_pred_x_mm, spot_pred_y_mm,
detector_distance_mm, iparams.partiality_model,
iparams.flag_beam_divergence)
if iparams.flag_plot_expert:
n_bins = 20
binner = observations_original.setup_binner(n_bins=n_bins)
binner_indices = binner.bin_indices()
avg_partiality_init = flex.double()
avg_rs_init = flex.double()
avg_rh_init = flex.double()
one_dsqr_bin = flex.double()
for i in range(1, n_bins + 1):
i_binner = (binner_indices == i)
if len(observations_original.data().select(i_binner)) > 0:
print(
binner.bin_d_range(i)[1],
flex.mean(partiality_init.select(i_binner)),
flex.mean(rs_init.select(i_binner)),
flex.mean(rh_init.select(i_binner)),
len(partiality_init.select(i_binner)))
#monte-carlo merge
if iparams.flag_monte_carlo:
G = 1
B = 0
partiality_init = flex.double([1] * len(partiality_init))
#save results
refined_params = flex.double([
G, B, rotx, roty, ry, rz, r0, re, voigt_nu, uc_params[0],
uc_params[1], uc_params[2], uc_params[3], uc_params[4],
uc_params[5]
])
pres = postref_results()
pres.set_params(observations=observations_non_polar,
observations_original=observations_original,
refined_params=refined_params,
stats=stats,
partiality=partiality_init,
rs_set=rs_init,
rh_set=rh_init,
frame_no=frame_no,
pickle_filename=pickle_filename,
wavelength=wavelength,
crystal_orientation=crystal_init_orientation,
detector_distance_mm=detector_distance_mm)
txt_scale_frame_by_mean_I = ' {0:40} ==> RES:{1:5.2f} NREFL:{2:5d} G:{3:6.4f} B:{4:6.1f} CELL:{5:6.2f} {6:6.2f} {7:6.2f} {8:6.2f} {9:6.2f} {10:6.2f}'.format(
img_filename_only + ' (' + index_basis_name + ')',
observations_original.d_min(),
len(observations_original_sel.data()), G, B, uc_params[0],
uc_params[1], uc_params[2], uc_params[3], uc_params[4],
uc_params[5])
print(txt_scale_frame_by_mean_I)
txt_scale_frame_by_mean_I += '\n'
return pres, txt_scale_frame_by_mean_I
def refine_anomalous_substructure(fmodel,
pdb_hierarchy,
wavelength=None,
map_type="anom_residual",
exclude_waters=False,
exclude_non_water_light_elements=True,
n_cycles_max=None,
map_sigma_min=3.0,
refine=("f_prime", "f_double_prime"),
reset_water_u_iso=True,
use_all_anomalous=True,
verbose=True,
out=sys.stdout):
"""
Crude mimic of Phaser's substructure completion, with two essential
differences: only the existing real scatterers in the input model will be
used (with the assumption that the model is already more or less complete),
and the anomalous refinement will be performed in Phenix, yielding both
f-prime and f-double-prime. The refined f-prime provides us with an
orthogonal estimate of the number of electrons missing from an incorrectly
labeled scatterer.
:param wavelength: X-ray wavelenth in Angstroms
:param exclude_waters: Don't refine anomalous scattering for water oxygens
:param exclude_non_water_light_elements: Don't refine anomalous scattering
for light atoms other than water (CHNO).
:param n_cycles_max: Maximum number of refinement cycles
:param map_sigma_min: Sigma cutoff for identify anomalous scatterers
:param reset_water_u_iso: Reset B-factors for water atoms prior to f'
refinement
:param use_all_anomalous: include any scatterers which are already modeled
as anomalous in the refinement
"""
from cctbx import xray
assert (fmodel.f_obs().anomalous_flag())
assert (map_type in ["llg", "anom_residual"])
make_sub_header("Iterative anomalous substructure refinement", out=out)
fmodel.update(target_name="ls")
pdb_atoms = pdb_hierarchy.atoms()
non_water_non_hd_selection = pdb_hierarchy.atom_selection_cache(
).selection("(not element H and not element D and not resname HOH)")
sites_frac = fmodel.xray_structure.sites_frac()
scatterers = fmodel.xray_structure.scatterers()
u_iso_mean = flex.mean(
fmodel.xray_structure.extract_u_iso_or_u_equiv().select(
non_water_non_hd_selection))
anomalous_iselection = flex.size_t()
anomalous_groups = []
t_start = time.time()
n_cycle = 0
while ((n_cycles_max is None) or (n_cycle < n_cycles_max)):
n_cycle += 1
n_new_groups = 0
t_start_cycle = time.time()
print >> out, "Cycle %d" % n_cycle
anom_map = fmodel.map_coefficients(map_type=map_type).fft_map(
resolution_factor=0.25).apply_sigma_scaling().real_map_unpadded()
map_min = abs(flex.min(anom_map.as_1d()))
map_max = flex.max(anom_map.as_1d())
print >> out, " map range: -%.2f sigma to %.2f sigma" % (map_min,
map_max)
reset_u_iso_selection = flex.size_t()
for i_seq, atom in enumerate(pdb_atoms):
resname = atom.parent().resname
elem = atom.element.strip()
if ((i_seq in anomalous_iselection)
or ((exclude_waters) and (resname == "HOH")) or
((elem in ["H", "D", "N", "C", "O"]) and
(resname != "HOH") and exclude_non_water_light_elements)):
continue
scatterer = scatterers[i_seq]
site_frac = sites_frac[i_seq]
anom_map_value = anom_map.tricubic_interpolation(site_frac)
if ((anom_map_value >= map_sigma_min)
or ((scatterer.fdp != 0) and use_all_anomalous)):
if (verbose):
if (n_new_groups == 0):
print >> out, ""
print >> out, " new anomalous scatterers:"
print >> out, " %-34s map height: %6.2f sigma" % (
atom.id_str(), anom_map_value)
anomalous_iselection.append(i_seq)
selection_string = get_single_atom_selection_string(atom)
group = xray.anomalous_scatterer_group(
iselection=flex.size_t([i_seq]),
f_prime=0,
f_double_prime=0,
refine=list(refine),
selection_string=selection_string)
anomalous_groups.append(group)
n_new_groups += 1
if (resname == "HOH") and (reset_water_u_iso):
water_u_iso = scatterer.u_iso
if (water_u_iso < u_iso_mean):
reset_u_iso_selection.append(i_seq)
if (n_new_groups == 0):
print >> out, ""
print >> out, "No new groups - anomalous scatterer search terminated."
break
elif (not verbose):
print >> out, " %d new groups" % n_new_groups
for i_seq in anomalous_iselection:
sc = scatterers[i_seq]
sc.fp = 0
sc.fdp = 0
if (verbose):
print >> out, ""
print >> out, "Anomalous refinement:"
fmodel.info().show_targets(text="before minimization", out=out)
print >> out, ""
u_iso = fmodel.xray_structure.extract_u_iso_or_u_equiv()
u_iso.set_selected(reset_u_iso_selection, u_iso_mean)
fmodel.xray_structure.set_u_iso(values=u_iso)
fmodel.update_xray_structure(update_f_calc=True)
minimizer(fmodel=fmodel, groups=anomalous_groups)
if (verbose):
fmodel.info().show_targets(text="after minimization", out=out)
print >> out, ""
print >> out, " Refined sites:"
for i_seq, group in zip(anomalous_iselection, anomalous_groups):
print >> out, " %-34s f' = %6.3f f'' = %6.3f" % (
pdb_atoms[i_seq].id_str(), group.f_prime,
group.f_double_prime)
t_end_cycle = time.time()
print >> out, ""
if (verbose):
print >> out, " time for this cycle: %.1fs" % (t_end_cycle -
t_start_cycle)
fmodel.update(target_name="ml")
print >> out, "%d anomalous scatterer groups refined" % len(
anomalous_groups)
t_end = time.time()
print >> out, "overall time: %.1fs" % (t_end - t_start)
return anomalous_groups
def _index_prepare(self):
"""Prepare to do autoindexing - in XDS terms this will mean
calling xycorr, init and colspot on the input images."""
# decide on images to work with
logger.debug("XDS INDEX PREPARE:")
logger.debug("Wavelength: %.6f", self.get_wavelength())
logger.debug("Distance: %.2f", self.get_distance())
if self._indxr_images == []:
_select_images_function = getattr(
self, "_index_select_images_%s" % self._index_select_images
)
wedges = _select_images_function()
for wedge in wedges:
self.add_indexer_image_wedge(wedge)
self.set_indexer_prepare_done(True)
all_images = self.get_matching_images()
first = min(all_images)
last = max(all_images)
# next start to process these - first xycorr
xycorr = self.Xycorr()
xycorr.set_data_range(first, last)
xycorr.set_background_range(self._indxr_images[0][0], self._indxr_images[0][1])
converter = to_xds(self.get_imageset())
xds_beam_centre = converter.detector_origin
xycorr.set_beam_centre(xds_beam_centre[0], xds_beam_centre[1])
for block in self._indxr_images:
xycorr.add_spot_range(block[0], block[1])
# FIXME need to set the origin here
xycorr.run()
for file in ["X-CORRECTIONS.cbf", "Y-CORRECTIONS.cbf"]:
self._indxr_payload[file] = xycorr.get_output_data_file(file)
# next start to process these - then init
if PhilIndex.params.xia2.settings.input.format.dynamic_shadowing:
imageset = self._indxr_imagesets[0]
masker = (
imageset.get_format_class()
.get_instance(imageset.paths()[0])
.get_masker()
)
if masker is None:
# disable dynamic_shadowing
PhilIndex.params.xia2.settings.input.format.dynamic_shadowing = False
if PhilIndex.params.xia2.settings.input.format.dynamic_shadowing:
# find the region of the scan with the least predicted shadow
# to use for background determination in XDS INIT step
from dxtbx.model.experiment_list import ExperimentListFactory
imageset = self._indxr_imagesets[0]
xsweep = self._indxr_sweeps[0]
sweep_filename = os.path.join(
self.get_working_directory(), "%s_indexed.expt" % xsweep.get_name()
)
ExperimentListFactory.from_imageset_and_crystal(imageset, None).as_file(
sweep_filename
)
from xia2.Wrappers.Dials.ShadowPlot import ShadowPlot
shadow_plot = ShadowPlot()
shadow_plot.set_working_directory(self.get_working_directory())
auto_logfiler(shadow_plot)
shadow_plot.set_sweep_filename(sweep_filename)
shadow_plot.set_json_filename(
os.path.join(
self.get_working_directory(),
"%s_shadow_plot.json" % shadow_plot.get_xpid(),
)
)
shadow_plot.run()
results = shadow_plot.get_results()
fraction_shadowed = flex.double(results["fraction_shadowed"])
if flex.max(fraction_shadowed) == 0:
PhilIndex.params.xia2.settings.input.format.dynamic_shadowing = False
else:
scan_points = flex.double(results["scan_points"])
scan = imageset.get_scan()
oscillation = scan.get_oscillation()
if self._background_images is not None:
bg_images = self._background_images
bg_range_deg = (
scan.get_angle_from_image_index(bg_images[0]),
scan.get_angle_from_image_index(bg_images[1]),
)
bg_range_width = bg_range_deg[1] - bg_range_deg[0]
min_shadow = 100
best_bg_range = bg_range_deg
from libtbx.utils import frange
for bg_range_start in frange(
flex.min(scan_points),
flex.max(scan_points) - bg_range_width,
step=oscillation[1],
):
bg_range_deg = (bg_range_start, bg_range_start + bg_range_width)
sel = (scan_points >= bg_range_deg[0]) & (
scan_points <= bg_range_deg[1]
)
mean_shadow = flex.mean(fraction_shadowed.select(sel))
if mean_shadow < min_shadow:
min_shadow = mean_shadow
best_bg_range = bg_range_deg
self._background_images = (
scan.get_image_index_from_angle(best_bg_range[0]),
scan.get_image_index_from_angle(best_bg_range[1]),
)
logger.debug(
"Setting background images: %s -> %s" % self._background_images
)
init = self.Init()
for file in ["X-CORRECTIONS.cbf", "Y-CORRECTIONS.cbf"]:
init.set_input_data_file(file, self._indxr_payload[file])
init.set_data_range(first, last)
if self._background_images:
init.set_background_range(
self._background_images[0], self._background_images[1]
)
else:
init.set_background_range(
self._indxr_images[0][0], self._indxr_images[0][1]
)
for block in self._indxr_images:
init.add_spot_range(block[0], block[1])
init.run()
# at this stage, need to (perhaps) modify the BKGINIT.cbf image
# to mark out the back stop
if PhilIndex.params.xds.backstop_mask:
logger.debug("Applying mask to BKGINIT.pck")
# copy the original file
cbf_old = os.path.join(init.get_working_directory(), "BKGINIT.cbf")
cbf_save = os.path.join(init.get_working_directory(), "BKGINIT.sav")
shutil.copyfile(cbf_old, cbf_save)
# modify the file to give the new mask
from xia2.Toolkit.BackstopMask import BackstopMask
mask = BackstopMask(PhilIndex.params.xds.backstop_mask)
mask.apply_mask_xds(self.get_header(), cbf_save, cbf_old)
init.reload()
for file in ["BLANK.cbf", "BKGINIT.cbf", "GAIN.cbf"]:
self._indxr_payload[file] = init.get_output_data_file(file)
if PhilIndex.params.xia2.settings.developmental.use_dials_spotfinder:
spotfinder = self.DialsSpotfinder()
for block in self._indxr_images:
spotfinder.add_spot_range(block[0], block[1])
spotfinder.run()
export = self.DialsExportSpotXDS()
export.set_input_data_file(
"observations.refl",
spotfinder.get_output_data_file("observations.refl"),
)
export.run()
for file in ["SPOT.XDS"]:
self._indxr_payload[file] = export.get_output_data_file(file)
else:
# next start to process these - then colspot
colspot = self.Colspot()
for file in (
"X-CORRECTIONS.cbf",
"Y-CORRECTIONS.cbf",
"BLANK.cbf",
"BKGINIT.cbf",
"GAIN.cbf",
):
colspot.set_input_data_file(file, self._indxr_payload[file])
colspot.set_data_range(first, last)
colspot.set_background_range(
self._indxr_images[0][0], self._indxr_images[0][1]
)
for block in self._indxr_images:
colspot.add_spot_range(block[0], block[1])
colspot.run()
for file in ["SPOT.XDS"]:
self._indxr_payload[file] = colspot.get_output_data_file(file)
def run(params, mtzfiles):
arrays = get_arrays(mtzfiles, d_min=params.dmin, d_max=params.dmax)
if params.take_common:
arrays = commonalize(arrays)
maxlen_f = max(map(lambda x: len(x[0]), arrays))
ref_f_obs = arrays[0][1]
scales = []
for f, f_obs, f_model, flag in arrays:
if ref_f_obs == f_obs: k, B = 1., 0
else: k, B = kBdecider(ref_f_obs, f_obs).run()
scales.append((k, B))
if params.reference != "first":
if params.reference == "bmin": # scale to strongest
kref, bref = max(scales, key=lambda x:x[1])
elif params.reference == "bmax": # scale to most weak
kref, bref = min(scales, key=lambda x:x[1])
elif params.reference == "bmed": # scale to most weak
perm = range(len(scales))
perm.sort(key=lambda i:scales[i][1])
kref, bref = scales[perm[len(perm)//2]]
else:
raise "Never reaches here"
print "# Set K=%.2f B=%.2f as reference" % (kref,bref)
scales = map(lambda x: (x[0]/kref, x[1]-bref), scales) # not bref-x[1], because negated later
print ("%"+str(maxlen_f)+"s r_work r_free cc_work.E cc_free.E sigmaa fom k B") % "filename"
for (f, f_obs, f_model, flag), (k, B) in zip(arrays, scales):
d_star_sq = f_obs.d_star_sq().data()
scale = k * flex.exp(-B*d_star_sq)
# Normalized
#f_obs.setup_binner(auto_binning=True)
#f_model.setup_binner(auto_binning=True)
#e_obs, e_model = map(lambda x:x.quasi_normalize_structure_factors(), (f_obs, f_model))
e_obs = absolute_scaling.kernel_normalisation(f_obs.customized_copy(data=f_obs.data()*scale, sigmas=None), auto_kernel=True)
e_obs = e_obs.normalised_miller_dev_eps.f_sq_as_f()
e_model = absolute_scaling.kernel_normalisation(f_model.customized_copy(data=f_model.data()*scale, sigmas=None), auto_kernel=True)
e_model = e_model.normalised_miller_dev_eps.f_sq_as_f()
f_obs_w, f_obs_t = f_obs.select(~flag.data()), f_obs.select(flag.data())
f_model_w, f_model_t = f_model.select(~flag.data()), f_model.select(flag.data())
e_obs_w, e_obs_t = e_obs.select(~flag.data()), e_obs.select(flag.data())
e_model_w, e_model_t = e_model.select(~flag.data()), e_model.select(flag.data())
r_work = calc_r(f_obs_w, f_model_w, scale.select(~flag.data()))
r_free = calc_r(f_obs_t, f_model_t, scale.select(flag.data()))
cc_work_E = calc_cc(e_obs_w, e_model_w, False)
cc_free_E = calc_cc(e_obs_t, e_model_t, False)
#cc_work_E2 = calc_cc(e_obs_w, e_model_w, True)
#cc_free_E2 = calc_cc(e_obs_t, e_model_t, True)
se = calc_sigmaa(f_obs, f_model, flag)
sigmaa = flex.mean(se.sigmaa().data())
fom = flex.mean(se.fom().data())
print ("%"+str(maxlen_f)+"s %.4f %.4f % 7.4f % 7.4f %.4e %.4e %.3e %.3e") % (f, r_work, r_free, cc_work_E, cc_free_E, sigmaa, fom, k, B)
def __init__(
self,
model,
fmodels,
target_weights,
individual_adp_params,
adp_restraints_params,
h_params,
log,
all_params,
nproc=None):
adopt_init_args(self, locals())
d_min = fmodels.fmodel_xray().f_obs().d_min()
# initialize with defaults...
if(target_weights is not None):
import mmtbx.refinement.weights_params
wcp = mmtbx.refinement.weights_params.tw_customizations_params.extract()
for w_s_c in wcp.weight_selection_criteria:
if(d_min >= w_s_c.d_min and d_min < w_s_c.d_max):
r_free_range_width = w_s_c.r_free_range_width
r_free_r_work_gap = w_s_c.r_free_minus_r_work
mean_diff_b_iso_bonded_fraction = w_s_c.mean_diff_b_iso_bonded_fraction
min_diff_b_iso_bonded = w_s_c.min_diff_b_iso_bonded
break
# ...then customize
wsc = all_params.target_weights.weight_selection_criteria
if(wsc.r_free_minus_r_work is not None):
r_free_r_work_gap = wsc.r_free_minus_r_work
if(wsc.r_free_range_width is not None):
r_free_range_width = wsc.r_free_range_width
if(wsc.mean_diff_b_iso_bonded_fraction is not None):
mean_diff_b_iso_bonded_fraction = wsc.mean_diff_b_iso_bonded_fraction
if(wsc.min_diff_b_iso_bonded is not None):
min_diff_b_iso_bonded = wsc.min_diff_b_iso_bonded
#
print_statistics.make_sub_header(text="Individual ADP refinement", out = log)
assert fmodels.fmodel_xray().xray_structure is model.get_xray_structure()
#
fmodels.create_target_functors()
assert approx_equal(self.fmodels.fmodel_xray().target_w(),
self.fmodels.target_functor_result_xray(
compute_gradients=False).target_work())
rw = flex.double()
rf = flex.double()
rfrw = flex.double()
deltab = flex.double()
w = flex.double()
if(self.target_weights is not None):
fmth =" R-FACTORS <Bi-Bj> <B> WEIGHT TARGETS"
print(fmth, file=self.log)
print(" work free delta data restr", file=self.log)
else:
print("Unresrained refinement...", file=self.log)
self.save_scatterers = self.fmodels.fmodel_xray().xray_structure.\
deep_copy_scatterers().scatterers()
if(self.target_weights is not None):
default_weight = self.target_weights.adp_weights_result.wx*\
self.target_weights.adp_weights_result.wx_scale
if(self.target_weights.twp.optimize_adp_weight):
wx_scale = [0.03,0.125,0.5,1.,1.5,2.,2.5,3.,3.5,4.,4.5,5.]
trial_weights = list( flex.double(wx_scale)*self.target_weights.adp_weights_result.wx )
self.wx_scale = 1
else:
trial_weights = [self.target_weights.adp_weights_result.wx]
self.wx_scale = self.target_weights.adp_weights_result.wx_scale
else:
default_weight = 1
trial_weights = [1]
self.wx_scale = 1
self.show(weight=default_weight)
trial_results = []
if nproc is None:
nproc = all_params.main.nproc
parallel = False
if (len(trial_weights) > 1) and ((nproc is Auto) or (nproc > 1)):
parallel = True
from libtbx import easy_mp
stdout_and_results = easy_mp.pool_map(
processes=nproc,
fixed_func=self.try_weight,
args=trial_weights,
func_wrapper="buffer_stdout_stderr") # XXX safer for phenix GUI
trial_results = [ r for so, r in stdout_and_results ]
else :
for weight in trial_weights:
result = self.try_weight(weight, print_stats=True)
trial_results.append(result)
for result in trial_results :
if(result is not None) and (result.r_work is not None):
if (parallel):
result.show(out=self.log)
rw .append(result.r_work)
rf .append(result.r_free)
rfrw .append(result.r_gap)
deltab .append(result.delta_b)
w .append(result.weight)
#
if(len(trial_weights)>1 and rw.size()>0):
# filter by rfree-rwork
rw,rf,rfrw,deltab,w = self.score(rw=rw,rf=rf,rfrw=rfrw,deltab=deltab,w=w,
score_target=rfrw,score_target_value=r_free_r_work_gap,
secondary_target=deltab)
# filter by rfree
rw,rf,rfrw,deltab,w = self.score(rw=rw,rf=rf,rfrw=rfrw,deltab=deltab,w=w,
score_target=rf,score_target_value=flex.min(rf)+r_free_range_width)
# filter by <Bi-Bj>
delta_b_target = max(min_diff_b_iso_bonded, flex.mean(self.fmodels.
fmodel_xray().xray_structure.extract_u_iso_or_u_equiv()*
adptbx.u_as_b(1))*mean_diff_b_iso_bonded_fraction)
print(" max suggested <Bi-Bj> for this run: %7.2f"%delta_b_target, file=log)
print(" max allowed Rfree-Rwork gap: %5.1f"%r_free_r_work_gap, file=log)
print(" range of equivalent Rfree: %5.1f"%r_free_range_width, file=log)
rw,rf,rfrw,deltab,w = self.score(rw=rw,rf=rf,rfrw=rfrw,deltab=deltab,w=w,
score_target=deltab,score_target_value=delta_b_target)
# select the result with lowest rfree
sel = flex.sort_permutation(rf)
rw,rf,rfrw,deltab,w= self.select(
rw=rw,rf=rf,rfrw=rfrw,deltab=deltab,w=w,sel=sel)
#
w_best = w[0]
rw_best = rw[0]
print("Best ADP weight: %8.3f"%w_best, file=self.log)
#
self.target_weights.adp_weights_result.wx = w_best
self.target_weights.adp_weights_result.wx_scale = 1
best_u_star = None
best_u_iso = None
for result in trial_results :
if(abs(result.weight-w_best)<=1.e-8):
best_u_star = result.u_star
best_u_iso = result.u_iso
break
if(best_u_iso is None) : # XXX this probably shouldn't happen...
self.fmodels.fmodel_xray().xray_structure.replace_scatterers(
self.save_scatterers.deep_copy())
else :
assert (best_u_star is not None)
xrs = self.fmodels.fmodel_xray().xray_structure
xrs.set_u_iso(values=best_u_iso)
xrs.scatterers().set_u_star(best_u_star)
new_u_iso = xrs.scatterers().extract_u_iso()
assert (new_u_iso.all_eq(best_u_iso))
self.fmodels.update_xray_structure(
xray_structure = self.fmodels.fmodel_xray().xray_structure,
update_f_calc = True)
print("Accepted refinement result:", file=self.log)
# reset alpha/beta parameters - if this is not done, the assertion
# below will fail
fmodels.create_target_functors()
if(self.fmodels.fmodel_neutron() is None):
assert approx_equal(self.fmodels.fmodel_xray().r_work()*100, rw_best,
eps=0.001)
# this needs to be done again again, just in case
fmodels.create_target_functors()
self.show(weight=w_best)
self.fmodels.fmodel_xray().xray_structure.tidy_us()
self.fmodels.update_xray_structure(
xray_structure = self.fmodels.fmodel_xray().xray_structure,
update_f_calc = True)
fmodels.create_target_functors()
assert approx_equal(self.fmodels.fmodel_xray().target_w(),
self.fmodels.target_functor_result_xray(
compute_gradients=False).target_work())
self.model.set_xray_structure(self.fmodels.fmodel_xray().xray_structure)
def __init__(self, obs, unobstructed, params, out=None, n_bins=12):
if out == None:
import sys
out = sys.stdout
from libtbx.str_utils import format_value
self.params = params
self.out = out
self.obs = obs
obs.setup_binner(n_bins=n_bins)
attenuated = obs.select(~unobstructed)
unattenuated = obs.select(unobstructed)
attenuated.use_binning_of(obs)
unattenuated.use_binning_of(obs)
self.result = []
counts_given = obs.binner().counts_given()
counts_complete = obs.binner().counts_complete()
counts_given_attenuated = attenuated.binner().counts_given()
counts_given_unattenuated = unattenuated.binner().counts_given()
for i_bin in obs.binner().range_used():
sel_w = obs.binner().selection(i_bin)
sel_fo_all = obs.select(sel_w)
d_max_, d_min_ = sel_fo_all.d_max_min()
d_range = obs.binner().bin_legend(i_bin=i_bin,
show_bin_number=False,
show_counts=True)
sel_data = obs.select(sel_w).data()
sel_sig = obs.select(sel_w).sigmas()
sel_unatten_w = unattenuated.binner().selection(i_bin)
sel_unatten_data = unattenuated.select(sel_unatten_w).data()
sel_unatten_sig = unattenuated.select(sel_unatten_w).sigmas()
sel_atten_w = attenuated.binner().selection(i_bin)
sel_atten_data = attenuated.select(sel_atten_w).data()
sel_atten_sig = attenuated.select(sel_atten_w).sigmas()
if len(sel_unatten_data) > 0:
unatten_mean_I = flex.mean(sel_unatten_data)
unatten_mean_I_sigI = flex.mean(sel_unatten_data /
sel_unatten_sig)
else:
unatten_mean_I = 0
unatten_mean_I_sigI = 0
if len(sel_atten_data) > 0:
atten_mean_I = flex.mean(sel_atten_data)
atten_mean_I_sigI = flex.mean(sel_atten_data / sel_atten_sig)
else:
atten_mean_I = 0
atten_mean_I_sigI = 0
if (sel_data.size() > 0):
bin = resolution_bin(
i_bin=i_bin,
d_range=d_range,
mean_I=flex.mean(sel_data),
n_work=sel_data.size(),
mean_I_sigI=flex.mean(sel_data / sel_sig),
d_max_min=(d_max_, d_min_),
completeness=(counts_given[i_bin], counts_complete[i_bin]),
given_unatten=counts_given_unattenuated[i_bin],
unatten_mean_I=unatten_mean_I,
unatten_mean_I_sigI=unatten_mean_I_sigI,
given_atten=counts_given_attenuated[i_bin],
atten_mean_I=atten_mean_I,
atten_mean_I_sigI=atten_mean_I_sigI,
)
self.result.append(bin)
self.set_limits(unobstructed)
print(
"\n Bin Resolution Range Compl. <I> <I/sig(I)> Unobstructed <I> <I/sig(I)> Obstructed <I> <I/sig(I)>",
file=out)
for bin in self.result:
fmt = " %s %s %s %s%s %s %s %s%s %s %s %s%s"
print(fmt % (
format_value("%3d", bin.i_bin),
format_value("%-17s", bin.d_range),
format_value("%8.1f", bin.mean_I),
format_value("%8.2f", bin.mean_I_sigI),
format_value("%1s", getattr(bin, "limit", " ")),
format_value("%6d", bin.given_unatten),
format_value("%8.1f", bin.unatten_mean_I),
format_value("%8.2f", bin.unatten_mean_I_sigI),
format_value("%1s", getattr(bin, "unatten_limit", " ")),
format_value("%6d", bin.given_atten),
format_value("%8.1f", bin.atten_mean_I),
format_value("%8.2f", bin.atten_mean_I_sigI),
format_value("%1s", getattr(bin, "atten_limit", " ")),
),
file=out)
def exercise(space_group_info,
n_elements = 10,
table = "wk1995",
d_min = 2.0,
k_sol = 0.35,
b_sol = 45.0,
b_cart = None,
quick=False,
verbose=0):
xray_structure = random_structure.xray_structure(
space_group_info = space_group_info,
elements =(("O","N","C")*(n_elements//3+1))[:n_elements],
volume_per_atom = 100,
min_distance = 1.5,
general_positions_only = True,
random_u_iso = False,
random_occupancy = False)
xray_structure.scattering_type_registry(table = table)
sg = xray_structure.space_group()
uc = xray_structure.unit_cell()
u_cart_1 = adptbx.random_u_cart(u_scale=5, u_min=5)
u_star_1 = adptbx.u_cart_as_u_star(uc, u_cart_1)
b_cart = adptbx.u_star_as_u_cart(uc, sg.average_u_star(u_star = u_star_1))
for anomalous_flag in [False, True]:
scatterers = xray_structure.scatterers()
if (anomalous_flag):
assert scatterers.size() >= 7
for i in [1,7]:
scatterers[i].fp = -0.2
scatterers[i].fdp = 5
have_non_zero_fdp = True
else:
for i in [1,7]:
scatterers[i].fp = 0
scatterers[i].fdp = 0
have_non_zero_fdp = False
f_obs = abs(xray_structure.structure_factors(
d_min = d_min,
anomalous_flag = anomalous_flag,
cos_sin_table = sfg_params.cos_sin_table,
algorithm = sfg_params.algorithm).f_calc())
f_obs_comp = f_obs.structure_factors_from_scatterers(
xray_structure = xray_structure,
algorithm = sfg_params.algorithm,
cos_sin_table = sfg_params.cos_sin_table).f_calc()
f_obs = abs(f_obs_comp)
flags = f_obs.generate_r_free_flags(fraction = 0.1,
max_free = 99999999)
#flags = flags.array(data = flex.bool(f_obs.data().size(), False))
xrs = xray_structure.deep_copy_scatterers()
xrs.shake_sites_in_place(rms_difference=0.3)
for target in mmtbx.refinement.targets.target_names:
if (quick):
if (target not in ["ls_wunit_k1", "ml", "mlhl", "ml_sad"]):
continue
if (target == "mlhl"):
if (have_non_zero_fdp): continue # XXX gradients not correct!
experimental_phases = generate_random_hl(miller_set=f_obs)
else:
experimental_phases = None
if (target == "ml_sad"
and (not anomalous_flag or mmtbx.refinement.targets.phaser is None)):
continue
print " ",target
xray.set_scatterer_grad_flags(
scatterers = xrs.scatterers(),
site = True)
ss = 1./flex.pow2(f_obs.d_spacings().data()) / 4.
u_star = adptbx.u_cart_as_u_star(
f_obs.unit_cell(), adptbx.b_as_u(b_cart))
k_anisotropic = mmtbx.f_model.ext.k_anisotropic(
f_obs.indices(), u_star)
k_mask = mmtbx.f_model.ext.k_mask(ss, k_sol, b_sol)
fmodel = mmtbx.f_model.manager(
xray_structure = xrs,
f_obs = f_obs,
r_free_flags = flags,
target_name = target,
abcd = experimental_phases,
sf_and_grads_accuracy_params = sfg_params,
k_mask = k_mask,
k_anisotropic = k_anisotropic,
mask_params = masks.mask_master_params.extract())
fmodel.update_xray_structure(
xray_structure=xrs,
update_f_calc=True,
update_f_mask=True)
xray.set_scatterer_grad_flags(
scatterers=fmodel.xray_structure.scatterers(),
site=True)
fmodel.update_xray_structure(update_f_calc=True)
t_f = fmodel.target_functor()
t_f.prepare_for_minimization()
gs = t_f(compute_gradients=True).d_target_d_site_cart().as_double()
gfd = finite_differences_site(target_functor=t_f)
cc = flex.linear_correlation(gs, gfd).coefficient()
if (0 or verbose):
print "ana:", list(gs)
print "fin:", list(gfd)
print "rat:", [f/a for a,f in zip(gs,gfd)]
print target, "corr:", cc, space_group_info
print
diff = gs - gfd
diff /= max(1, flex.max(flex.abs(gfd)))
tolerance = 1.2e-5
assert approx_equal(abs(flex.min(diff) ), 0.0, tolerance)
assert approx_equal(abs(flex.mean(diff)), 0.0, tolerance)
assert approx_equal(abs(flex.max(diff) ), 0.0, tolerance)
assert approx_equal(cc, 1.0, tolerance)
fmodel.model_error_ml()
def collect(self,
model,
fmodel,
step,
wilson_b=None,
rigid_body_shift_accumulator=None):
global time_collect_and_process
t1 = time.time()
if (self.sites_cart_start is None):
self.sites_cart_start = model.get_sites_cart()
sites_cart_curr = model.get_sites_cart()
if (sites_cart_curr.size() == self.sites_cart_start.size()):
self.shifts.append(
flex.mean(
flex.sqrt(
(self.sites_cart_start - sites_cart_curr).dot())))
else:
self.shifts.append("n/a")
if (wilson_b is not None): self.wilson_b = wilson_b
self.steps.append(step)
self.r_works.append(fmodel.r_work())
self.r_frees.append(fmodel.r_free())
use_amber = False
if hasattr(self.params, "amber"): # loaded amber scope
use_amber = self.params.amber.use_amber
self.is_amber_monitor = use_amber
use_afitt = False
if hasattr(self.params, "afitt"): # loaded amber scope
use_afitt = self.params.afitt.use_afitt
general_selection = None
if use_afitt:
from mmtbx.geometry_restraints import afitt
general_selection = afitt.get_non_afitt_selection(
model.restraints_manager, model.get_sites_cart(),
model.get_hd_selection(), None)
geom = model.geometry_statistics()
if (geom is not None):
self.geom.bonds.append(geom.bond().mean)
self.geom.angles.append(geom.angle().mean)
hd_sel = None
if (not self.neutron_refinement and not self.is_neutron_monitor):
hd_sel = model.get_hd_selection()
b_isos = model.get_xray_structure().extract_u_iso_or_u_equiv(
) * math.pi**2 * 8
if (hd_sel is not None): b_isos = b_isos.select(~hd_sel)
self.bs_iso_max_a.append(flex.max_default(b_isos, 0))
self.bs_iso_min_a.append(flex.min_default(b_isos, 0))
self.bs_iso_ave_a.append(flex.mean_default(b_isos, 0))
self.n_solv.append(model.number_of_ordered_solvent_molecules())
if (len(self.geom.bonds) > 0):
if ([self.bond_start, self.angle_start].count(None) == 2):
if (len(self.geom.bonds) > 0):
self.bond_start = self.geom.bonds[0]
self.angle_start = self.geom.angles[0]
if (len(self.geom.bonds) > 0):
self.bond_final = self.geom.bonds[len(self.geom.bonds) - 1]
self.angle_final = self.geom.angles[len(self.geom.angles) - 1]
elif (len(self.geom) == 1):
self.bond_final = self.geom.bonds[0]
self.angle_final = self.geom.angles[0]
if (rigid_body_shift_accumulator is not None):
self.rigid_body_shift_accumulator = rigid_body_shift_accumulator
t2 = time.time()
time_collect_and_process += (t2 - t1)
self.call_back(model, fmodel, method=step)
def __init__(self,
xray_structure,
step,
volume_cutoff=None,
mean_diff_map_threshold=None,
compute_whole=False,
largest_only=False,
wrapping=True,
f_obs=None,
r_sol=1.1,
r_shrink=0.9,
f_calc=None,
log=None,
write_masks=False):
adopt_init_args(self, locals())
#
self.d_spacings = f_obs.d_spacings().data()
self.sel_3inf = self.d_spacings >= 3
self.miller_array = f_obs.select(self.sel_3inf)
#
self.crystal_symmetry = self.xray_structure.crystal_symmetry()
# compute mask in p1 (via ASU)
self.crystal_gridding = maptbx.crystal_gridding(
unit_cell=xray_structure.unit_cell(),
space_group_info=xray_structure.space_group_info(),
symmetry_flags=maptbx.use_space_group_symmetry,
step=step)
self.n_real = self.crystal_gridding.n_real()
# XXX Where do we want to deal with H and occ==0?
mask_p1 = mmtbx.masks.mask_from_xray_structure(
xray_structure=xray_structure,
p1=True,
for_structure_factors=True,
solvent_radius=r_sol,
shrink_truncation_radius=r_shrink,
n_real=self.n_real,
in_asu=False).mask_data
maptbx.unpad_in_place(map=mask_p1)
self.f_mask_whole = None
if (compute_whole):
mask = asu_map_ext.asymmetric_map(
xray_structure.crystal_symmetry().space_group().type(),
mask_p1).data()
self.f_mask_whole = self._inflate(
self.miller_array.structure_factors_from_asu_map(
asu_map_data=mask, n_real=self.n_real))
self.solvent_content = 100. * mask_p1.count(1) / mask_p1.size()
if (write_masks):
write_map_file(crystal_symmetry=xray_structure.crystal_symmetry(),
map_data=mask_p1,
file_name="mask_whole.mrc")
# conn analysis
co = maptbx.connectivity(map_data=mask_p1,
threshold=0.01,
preprocess_against_shallow=False,
wrapping=wrapping)
co.merge_symmetry_related_regions(
space_group=xray_structure.space_group())
del mask_p1
self.conn = co.result().as_double()
z = zip(co.regions(), range(0, co.regions().size()))
sorted_by_volume = sorted(z, key=lambda x: x[0], reverse=True)
#
f_mask_data_0 = flex.complex_double(f_obs.data().size(), 0)
f_mask_data = flex.complex_double(f_obs.data().size(), 0)
self.FV = OrderedDict()
self.mc = None
diff_map = None
mean_diff_map = None
self.regions = OrderedDict()
self.f_mask_0 = None
self.f_mask = None
small_selection = None
weak_selection = None
#
if (log is not None):
print(" # volume_p1 uc(%) mFo-DFc: min,max,mean,sd",
file=log)
#
for i_seq, p in enumerate(sorted_by_volume):
v, i = p
# skip macromolecule
if (i == 0): continue
# skip small volume
volume = v * step**3
uc_fraction = v * 100. / self.conn.size()
if (volume_cutoff is not None):
if (volume < volume_cutoff and volume >= 10):
if (small_selection is None):
small_selection = self.conn == i
else:
small_selection = small_selection | (self.conn == i)
continue
if volume < volume_cutoff: continue
self.regions[i_seq] = group_args(id=i,
i_seq=i_seq,
volume=volume,
uc_fraction=uc_fraction)
selection = self.conn == i
mask_i_asu = self.compute_i_mask_asu(selection=selection,
volume=volume)
if (uc_fraction >= 1):
f_mask_i = self.compute_f_mask_i(mask_i_asu)
f_mask_data_0 += f_mask_i.data()
elif (largest_only):
break
if (uc_fraction < 1 and diff_map is None):
diff_map = self.compute_diff_map(f_mask_data=f_mask_data_0)
mi, ma, me, sd = None, None, None, None
if (diff_map is not None):
blob = diff_map.select(selection.iselection())
mean_diff_map = flex.mean(
diff_map.select(selection.iselection()))
mi, ma, me = flex.min(blob), flex.max(blob), flex.mean(blob)
sd = blob.sample_standard_deviation()
if (log is not None):
print("%3d" % i_seq,
"%12.3f" % volume,
"%8.4f" % round(uc_fraction, 4),
"%7s" % str(None) if diff_map is None else
"%7.3f %7.3f %7.3f %7.3f" % (mi, ma, me, sd),
file=log)
if (mean_diff_map_threshold is not None
and mean_diff_map is not None
and mean_diff_map <= mean_diff_map_threshold
and mean_diff_map > 0.1):
if (weak_selection is None): weak_selection = self.conn == i
else:
weak_selection = weak_selection | (self.conn == i)
if (mean_diff_map_threshold is not None
and mean_diff_map is not None
and mean_diff_map <= mean_diff_map_threshold):
continue
f_mask_i = self.compute_f_mask_i(mask_i_asu)
f_mask_data += f_mask_i.data()
self.FV[f_mask_i] = [round(volume, 3), round(uc_fraction, 1)]
#####
# Determine number of secondary regions. Must happen here!
self.n_regions = len(self.FV.values())
self.do_mosaic = False
if (self.n_regions > 1):
self.do_mosaic = True
# Handle accumulation of small
if (small_selection is not None and self.do_mosaic):
v = small_selection.count(True)
volume = v * step**3
uc_fraction = v * 100. / self.conn.size()
mask_i = flex.double(flex.grid(self.n_real), 0)
mask_i = mask_i.set_selected(small_selection, 1)
diff_map = diff_map.set_selected(diff_map < 0, 0)
#diff_map = diff_map.set_selected(diff_map>0,1)
mx = flex.mean(diff_map.select((diff_map > 0).iselection()))
diff_map = diff_map / mx
mask_i = mask_i * diff_map
mask_i_asu = asu_map_ext.asymmetric_map(
self.crystal_symmetry.space_group().type(), mask_i).data()
f_mask_i = self.compute_f_mask_i(mask_i_asu)
self.FV[f_mask_i] = [round(volume, 3), round(uc_fraction, 1)]
if (weak_selection is not None and self.do_mosaic):
v = weak_selection.count(True)
volume = v * step**3
uc_fraction = v * 100. / self.conn.size()
mask_i = flex.double(flex.grid(self.n_real), 0)
mask_i = mask_i.set_selected(weak_selection, 1)
diff_map = diff_map.set_selected(diff_map < 0, 0)
#diff_map = diff_map.set_selected(diff_map>0,1)
mx = flex.mean(diff_map.select((diff_map > 0).iselection()))
diff_map = diff_map / mx
mask_i = mask_i * diff_map
mask_i_asu = asu_map_ext.asymmetric_map(
self.crystal_symmetry.space_group().type(), mask_i).data()
f_mask_i = self.compute_f_mask_i(mask_i_asu)
self.FV[f_mask_i] = [round(volume, 3), round(uc_fraction, 1)]
#####
if (self.do_mosaic):
self.f_mask_0 = f_obs.customized_copy(data=f_mask_data_0)
self.f_mask = f_obs.customized_copy(data=f_mask_data)
def get_mean_sigI(self):
return flex.mean(self.sigI_merge) if self.get_size() else 0
def __init__(self, **kwargs):
group_args.__init__(self, **kwargs)
print('finished Dij, now calculating rho_i and density')
from xfel.clustering import Rodriguez_Laio_clustering_2014 as RL
R = RL(distance_matrix=self.Dij, d_c=self.d_c)
#from clustering.plot_with_dimensional_embedding import plot_with_dimensional_embedding
#plot_with_dimensional_embedding(1-self.Dij/flex.max(self.Dij), show_plot=True)
if hasattr(self, 'strategy') is False:
self.strategy = 'default'
self.rho = rho = R.get_rho()
ave_rho = flex.mean(rho.as_double())
NN = self.Dij.focus()[0]
i_max = flex.max_index(rho)
delta_i_max = flex.max(
flex.double([self.Dij[i_max, j] for j in range(NN)]))
rho_order = flex.sort_permutation(rho, reverse=True)
rho_order_list = list(rho_order)
self.delta = delta = R.get_delta(rho_order=rho_order,
delta_i_max=delta_i_max)
cluster_id = flex.int(NN, -1) # -1 means no cluster
delta_order = flex.sort_permutation(delta, reverse=True)
MAX_PERCENTILE_RHO = self.max_percentile_rho # cluster centers have to be in the top percentile
n_cluster = 0
#
#
print('Z_DELTA = ', self.Z_delta)
pick_top_solution = False
rho_stdev = flex.mean_and_variance(
rho.as_double()).unweighted_sample_standard_deviation()
delta_stdev = flex.mean_and_variance(
delta).unweighted_sample_standard_deviation()
if rho_stdev != 0.0 and delta_stdev != 0:
rho_z = (rho.as_double() -
flex.mean(rho.as_double())) / (rho_stdev)
delta_z = (delta - flex.mean(delta)) / (delta_stdev)
else:
pick_top_solution = True
if rho_stdev == 0.0:
centroids = [flex.first_index(delta, flex.max(delta))]
elif delta_stdev == 0.0:
centroids = [flex.first_index(rho, flex.max(rho))]
significant_delta = []
significant_rho = []
# Define strategy to decide cluster center here. Only one should be true
debug_fix_clustering = True
if self.strategy == 'one_cluster':
debug_fix_clustering = False
strategy2 = True
if self.strategy == 'strategy_3':
debug_fix_clustering = False
strategy3 = True
strategy2 = False
if debug_fix_clustering:
if not pick_top_solution:
delta_z_cutoff = min(1.0, max(delta_z))
rho_z_cutoff = min(1.0, max(rho_z))
for ic in range(NN):
# test the density & rho
if delta_z[ic] >= delta_z_cutoff or delta_z[
ic] <= -delta_z_cutoff:
significant_delta.append(ic)
if rho_z[ic] >= rho_z_cutoff or rho_z[ic] <= -rho_z_cutoff:
significant_rho.append(ic)
if True:
# Use idea quoted in Rodriguez Laio 2014 paper
# " Thus, cluster centers are recognized as points for which the value of delta is anomalously large."
centroid_candidates = list(significant_delta)
candidate_delta_z = flex.double()
for ic in centroid_candidates:
if ic == rho_order[0]:
delta_z_of_rho_order_0 = delta_z[ic]
candidate_delta_z.append(delta_z[ic])
i_sorted = flex.sort_permutation(candidate_delta_z,
reverse=True)
# Check that once sorted the top one is not equal to the 2nd or 3rd position
# If there is a tie, assign centroid to the first one in rho order
centroids = []
# rho_order[0] has to be a centroid
centroids.append(rho_order[0])
#centroids.append(centroid_candidates[i_sorted[0]])
for i in range(0, len(i_sorted[:])):
if centroid_candidates[i_sorted[i]] == rho_order[0]:
continue
if delta_z_of_rho_order_0 - candidate_delta_z[
i_sorted[i]] > 1.0:
if i > 1:
if -candidate_delta_z[i_sorted[
i - 1]] + candidate_delta_z[
i_sorted[0]] > 1.0:
centroids.append(
centroid_candidates[i_sorted[i]])
else:
centroids.append(
centroid_candidates[i_sorted[i]])
else:
break
if False:
centroid_candidates = list(
set(significant_delta).intersection(
set(significant_rho)))
# Now compare the relative orders of the max delta_z and max rho_z to make sure they are within 1 stdev
centroids = []
max_delta_z_candidates = -999.9
max_rho_z_candidates = -999.9
for ic in centroid_candidates:
if delta_z[ic] > max_delta_z_candidates:
max_delta_z_candidates = delta_z[ic]
if rho_z[ic] > max_rho_z_candidates:
max_rho_z_candidates = rho_z[ic]
for ic in centroid_candidates:
if max_delta_z_candidates - delta_z[
ic] < 1.0 and max_rho_z_candidates - rho_z[
ic] < 1.0:
centroids.append(ic)
#item_idxs = [delta_order[ic] for ic,centroid in enumerate(centroids)]
item_idxs = centroids
for item_idx in item_idxs:
cluster_id[item_idx] = n_cluster
print('CLUSTERING_STATS', item_idx, cluster_id[item_idx])
n_cluster += 1
####
elif strategy2:
# Go through list of clusters, see which one has highest joint rank in both rho and delta lists
# This will only assign one cluster center based on highest product of rho and delta ranks
product_list_of_ranks = []
for ic in range(NN):
rho_tmp = self.rho[ic]
delta_tmp = self.delta[ic]
product_list_of_ranks.append(rho_tmp * delta_tmp)
import numpy as np
item_idx = np.argmax(product_list_of_ranks)
cluster_id[item_idx] = n_cluster # Only cluster assigned
print('CLUSTERING_STATS', item_idx, cluster_id[item_idx])
n_cluster += 1
elif strategy3:
# use product of delta and rho and pick out top candidates
# have to use a significance z_score to filter out the very best
product_list_of_ranks = flex.double()
for ic in range(NN):
rho_tmp = self.rho[ic]
delta_tmp = self.delta[ic]
product_list_of_ranks.append(rho_tmp * delta_tmp)
import numpy as np
iid_sorted = flex.sort_permutation(product_list_of_ranks,
reverse=True)
cluster_id[
iid_sorted[0]] = n_cluster # first point always a cluster
n_cluster += 1
print('CLUSTERING_STATS S3', iid_sorted[0],
cluster_id[iid_sorted[0]])
#product_list_of_ranks[iid_sorted[0]]=0.0 # set this to 0.0 so that the mean/stdev does not get biased by one point
stdev = np.std(product_list_of_ranks)
mean = np.mean(product_list_of_ranks)
n_sorted = 3
#if stdev == 0.0:
# n_sorted=1
z_critical = 3.0 # 2 sigma significance ?
# Only go through say 3-4 datapoints
# basically there won't be more than 2-3 lattices on an image realistically
for iid in iid_sorted[1:n_sorted]:
z_score = (product_list_of_ranks[iid] - mean) / stdev
if z_score > z_critical:
cluster_id[iid] = n_cluster
n_cluster += 1
print('CLUSTERING_STATS S3', iid, cluster_id[iid])
else:
break # No point going over all points once below threshold z_score
else:
for ic in range(NN):
item_idx = delta_order[ic]
if ic != 0:
if delta[item_idx] <= 0.25 * delta[
delta_order[0]]: # too low to be a medoid
continue
item_rho_order = rho_order_list.index(item_idx)
if (item_rho_order) / NN < MAX_PERCENTILE_RHO:
cluster_id[item_idx] = n_cluster
print('CLUSTERING_STATS', ic, item_idx, item_rho_order,
cluster_id[item_idx])
n_cluster += 1
###
#
print('Found %d clusters' % n_cluster)
for x in range(NN):
if cluster_id[x] >= 0:
print("XC", x, cluster_id[x], rho[x], delta[x])
self.cluster_id_maxima = cluster_id.deep_copy()
R.cluster_assignment(rho_order, cluster_id, rho)
self.cluster_id_full = cluster_id.deep_copy()
#halo = flex.bool(NN,False)
#border = R.get_border( cluster_id = cluster_id )
#for ic in range(n_cluster): #loop thru all border regions; find highest density
# this_border = (cluster_id == ic) & (border==True)
# if this_border.count(True)>0:
# highest_density = flex.max(rho.select(this_border))
# halo_selection = (rho < highest_density) & (this_border==True)
# if halo_selection.count(True)>0:
# cluster_id.set_selected(halo_selection,-1)
# core_selection = (cluster_id == ic) & ~halo_selection
# highest_density = flex.max(rho.select(core_selection))
# too_sparse = core_selection & (rho.as_double() < highest_density/10.) # another heuristic
# if too_sparse.count(True)>0:
# cluster_id.set_selected(too_sparse,-1)
self.cluster_id_final = cluster_id.deep_copy()
def calculate_fsc(si=None,
f_array=None, # just used for binner
map_coeffs=None,
model_map_coeffs=None,
first_half_map_coeffs=None,
second_half_map_coeffs=None,
resolution=None,
fraction_complete=None,
min_fraction_complete=None,
is_model_based=None,
cc_cut=None,
scale_using_last=None,
max_cc_for_rescale=None,
pseudo_likelihood=False,
skip_scale_factor=False,
verbose=None,
out=sys.stdout):
# calculate anticipated fall-off of model data with resolution
if si.rmsd is None and is_model_based:
si.rmsd=resolution*si.rmsd_resolution_factor
print("Setting rmsd to %5.1f A based on resolution of %5.1f A" %(
si.rmsd,resolution), file=out)
# get f and model_f vs resolution and FSC vs resolution and apply
# scale to f_array and return sharpened map
dsd = f_array.d_spacings().data()
from cctbx.maptbx.segment_and_split_map import map_coeffs_to_fp
if is_model_based:
mc1=map_coeffs
mc2=model_map_coeffs
fo_map=map_coeffs # scale map_coeffs to model_map_coeffs*FSC
fc_map=model_map_coeffs
b_eff=get_b_eff(si=si,out=out)
else: # half_dataset
mc1=first_half_map_coeffs
mc2=second_half_map_coeffs
fo_map=map_coeffs # scale map_coeffs to cc*
fc_map=model_map_coeffs
b_eff=None
ratio_list=flex.double()
target_sthol2=flex.double()
cc_list=flex.double()
sthol_list=flex.double()
d_min_list=flex.double()
rms_fo_list=flex.double()
rms_fc_list=flex.double()
max_possible_cc=None
for i_bin in f_array.binner().range_used():
sel = f_array.binner().selection(i_bin)
d = dsd.select(sel)
if d.size()<1:
raise Sorry("Please reduce number of bins (no data in bin "+
"%s) from current value of %s" %(i_bin,f_array.binner().n_bins_used()))
d_min = flex.min(d)
d_max = flex.max(d)
d_avg = flex.mean(d)
n = d.size()
m1 = mc1.select(sel)
m2 = mc2.select(sel)
cc = m1.map_correlation(other = m2)
if fo_map:
fo = fo_map.select(sel)
f_array_fo=map_coeffs_to_fp(fo)
rms_fo=f_array_fo.data().norm()
else:
rms_fo=1.
if fc_map:
fc = fc_map.select(sel)
f_array_fc=map_coeffs_to_fp(fc)
rms_fc=f_array_fc.data().norm()
else:
rms_fc=1.
sthol2=0.25/d_avg**2
ratio_list.append(max(1.e-10,rms_fc)/max(1.e-10,rms_fo))
target_sthol2.append(sthol2)
if cc is None: cc=0.
cc_list.append(cc)
sthol_list.append(1/d_avg)
d_min_list.append(d_min)
rms_fo_list.append(rms_fo)
rms_fc_list.append(rms_fc)
if b_eff is not None:
max_cc_estimate=cc* math.exp(min(20.,sthol2*b_eff))
else:
max_cc_estimate=cc
max_cc_estimate=max(0.,min(1.,max_cc_estimate))
if max_possible_cc is None or (
max_cc_estimate > 0 and max_cc_estimate > max_possible_cc):
max_possible_cc=max_cc_estimate
if verbose:
print("d_min: %5.1f FC: %7.1f FOBS: %7.1f CC: %5.2f" %(
d_avg,rms_fc,rms_fo,cc), file=out)
if scale_using_last: # rescale to give final value average==0
cc_list,baseline=rescale_cc_list(
cc_list=cc_list,scale_using_last=scale_using_last,
max_cc_for_rescale=max_cc_for_rescale)
if baseline is None: # don't use it
scale_using_last=None
original_cc_list=deepcopy(cc_list)
if not is_model_based: # calculate cc* for half-dataset cc
cc_list=estimate_cc_star(cc_list=cc_list,sthol_list=sthol_list,
cc_cut=cc_cut,scale_using_last=scale_using_last)
if not max_possible_cc:
max_possible_cc=0.01
if si.target_scale_factors: # not using these
max_possible_cc=1.
fraction_complete=1.
elif (not is_model_based):
max_possible_cc=1.
fraction_complete=1.
else:
# Define overall CC based on model completeness (CC=sqrt(fraction_complete))
if fraction_complete is None:
fraction_complete=max_possible_cc**2
print("Estimated fraction complete is %5.2f based on low_res CC of %5.2f" %(
fraction_complete,max_possible_cc), file=out)
else:
print("Using fraction complete value of %5.2f " %(fraction_complete), file=out)
max_possible_cc=fraction_complete**0.5
target_scale_factors=flex.double()
for i_bin in f_array.binner().range_used():
index=i_bin-1
ratio=ratio_list[index]
cc=cc_list[index]
sthol2=target_sthol2[index]
d_min=d_min_list[index]
corrected_cc=max(0.00001,min(1.,cc/max_possible_cc))
if (not is_model_based): # cc is already cc*
scale_on_fo=ratio * corrected_cc
elif b_eff is not None:
if pseudo_likelihood:
scale_on_fo=(cc/max(0.001,1-cc**2))
else: # usual
scale_on_fo=ratio * min(1.,
max(0.00001,corrected_cc) * math.exp(min(20.,sthol2*b_eff)) )
else:
scale_on_fo=ratio * min(1.,max(0.00001,corrected_cc))
target_scale_factors.append(scale_on_fo)
if not pseudo_likelihood and not skip_scale_factor: # normalize
scale_factor=1./target_scale_factors.min_max_mean().max
target_scale_factors=\
target_scale_factors*scale_factor
print("Scale factor A: %.5f" %(scale_factor), file=out)
if fraction_complete < min_fraction_complete:
print("\nFraction complete (%5.2f) is less than minimum (%5.2f)..." %(
fraction_complete,min_fraction_complete) + "\nSkipping scaling", file=out)
target_scale_factors=flex.double(target_scale_factors.size()*(1.0,))
print ("\nAverage CC: %.3f" %(cc_list.min_max_mean().mean),file=out)
print("\nScale factors vs resolution:", file=out)
print("Note 1: CC* estimated from sqrt(2*CC/(1+CC))", file=out)
print("Note 2: CC estimated by fitting (smoothing) for values < %s" %(cc_cut), file=out)
print("Note 3: Scale = A CC* rmsFc/rmsFo (A is normalization)", file=out)
print(" d_min rmsFo rmsFc CC CC* Scale", file=out)
for sthol2,scale,rms_fo,cc,rms_fc,orig_cc in zip(
target_sthol2,target_scale_factors,rms_fo_list,cc_list,rms_fc_list,
original_cc_list):
print("%7.2f %9.1f %9.1f %7.3f %7.3f %5.2f" %(
0.5/sthol2**0.5,rms_fo,rms_fc,orig_cc,cc,scale), file=out)
si.target_scale_factors=target_scale_factors
si.target_sthol2=target_sthol2
si.d_min_list=d_min_list
si.cc_list=cc_list
return si
def run_correction_vector_plot(working_phil):
L = lines(working_phil)
for line in L.vectors():
pass # pull out the information, lines class does all the work
close_x = flex.double()
close_y = flex.double()
far_x = flex.double()
far_y = flex.double()
master_coords = L.master_coords
master_cv = L.master_cv
master_tiles = L.master_tiles
for idx in range(0, len(master_coords), 10):
if matrix.col(
master_cv[idx]).length() < L.tile_rmsd[master_tiles[idx]]:
pass
#close_x.append(master_coords[idx][0])
#close_y.append(master_coords[idx][1])
else:
far_x.append(master_coords[idx][0])
far_y.append(master_coords[idx][1])
close_x.append(master_coords[idx][0] + master_cv[idx][0])
close_y.append(master_coords[idx][1] + master_cv[idx][1])
if working_phil.show_plots is True:
from matplotlib import pyplot as plt
plt.plot(close_x, close_y, "r.")
plt.plot(far_x, far_y, "g.")
plt.axes().set_aspect("equal")
plt.show()
sort_radii = flex.sort_permutation(flex.double(L.radii))
tile_rmsds = flex.double()
radial_sigmas = flex.double(64)
tangen_sigmas = flex.double(64)
for idx in range(64):
x = sort_radii[idx]
print "Tile %2d: radius %7.2f, %6d observations, delx %5.2f dely %5.2f, rmsd = %5.2f" % (
x, L.radii[x], L.tilecounts[x], L.mean_cv[x][0], L.mean_cv[x][1],
L.tile_rmsd[x]),
if L.tilecounts[x] < 3:
print
radial = (0, 0)
tangential = (0, 0)
rmean, tmean, rsigma, tsigma = (0, 0, 1, 1)
else:
wtaveg = L.weighted_average_angle_deg_from_tile(x)
print "Tile rotation %6.2f deg" % wtaveg,
radial, tangential, rmean, tmean, rsigma, tsigma = get_radial_tangential_vectors(
L, x)
print "%6.2f %6.2f" % (rsigma, tsigma)
radial_sigmas[x] = rsigma
tangen_sigmas[x] = tsigma
rstats = flex.mean_and_variance(radial_sigmas, L.tilecounts.as_double())
tstats = flex.mean_and_variance(tangen_sigmas, L.tilecounts.as_double())
print "\nOverall %8d observations, delx %5.2f dely %5.2f, rmsd = %5.2f" % (
L.overall_N, L.overall_cv[0], L.overall_cv[1], L.overall_rmsd)
print "Average tile rmsd %5.2f" % flex.mean(flex.double(L.tile_rmsd))
print "Average tile displacement %5.2f" % (flex.mean(
flex.double([matrix.col(cv).length() for cv in L.mean_cv])))
print "Weighted average radial sigma %6.2f" % rstats.mean()
print "Weighted average tangential sigma %6.2f" % tstats.mean()
if working_phil.show_plots is True:
plt.plot([(L.tiles[4 * x + 0] + L.tiles[4 * x + 2]) / 2.
for x in range(64)],
[(L.tiles[4 * x + 1] + L.tiles[4 * x + 3]) / 2.
for x in range(64)], "go")
for x in range(64):
plt.text(10 + (L.tiles[4 * x + 0] + L.tiles[4 * x + 2]) / 2.,
10 + (L.tiles[4 * x + 1] + L.tiles[4 * x + 3]) / 2.,
"%d" % x)
plt.show()
for idx in range(64):
x = sort_radii[idx]
print "Tile %2d: radius %7.2f, %6d observations, delx %5.2f dely %5.2f, rmsd = %5.2f" % (
x, L.radii[x], L.tilecounts[x], L.mean_cv[x][0],
L.mean_cv[x][1], L.tile_rmsd[x]),
if L.tilecounts[x] < 3:
print
radial = (0, 0)
tangential = (0, 0)
rmean, tmean, rsigma, tsigma = (0, 0, 1, 1)
else:
wtaveg = L.weighted_average_angle_deg_from_tile(x)
print "Tile rotation %6.2f deg" % wtaveg,
radial, tangential, rmean, tmean, rsigma, tsigma = get_radial_tangential_vectors(
L, x)
print "%6.2f %6.2f" % (rsigma, tsigma)
if working_phil.colormap:
from pylab import imshow, axes, colorbar, show
import numpy
xcv, ycv = get_correction_vector_xy(L, x)
_min = min(min(xcv), min(ycv))
_max = max(max(xcv), max(ycv))
hist, xedges, yedges = numpy.histogram2d(xcv,
ycv,
bins=40,
range=[[_min, _max],
[_min, _max]])
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
imshow(hist.T,
extent=extent,
interpolation='nearest',
origin='lower')
from matplotlib.patches import Ellipse
ell = Ellipse(xy=(L.mean_cv[x][0], L.mean_cv[x][1]),
width=2. * rsigma,
height=2. * tsigma,
angle=math.atan2(-(radial[1]), -(radial[0])) *
180. / math.pi,
edgecolor="y",
linewidth=2,
fill=False,
zorder=100)
axes().add_artist(ell)
colorbar()
show()
else:
from matplotlib import pyplot as plt
xcv, ycv = get_correction_vector_xy(L, x)
if len(xcv) == 0 or len(ycv) == 0: continue
plt.plot(xcv, ycv, "r.")
plt.plot([L.mean_cv[x][0]], [L.mean_cv[x][1]], "go")
plt.plot([L.mean_cv[x][0] + radial[0]],
[L.mean_cv[x][1] + radial[1]], "yo")
plt.plot([L.mean_cv[x][0] + tangential[0]],
[L.mean_cv[x][1] + tangential[1]], "bo")
from matplotlib.patches import Ellipse
ell = Ellipse(xy=(L.mean_cv[x][0], L.mean_cv[x][1]),
width=2. * rsigma,
height=2. * tsigma,
angle=math.atan2(-(radial[1]), -(radial[0])) *
180. / math.pi,
edgecolor="y",
linewidth=2,
fill=False,
zorder=100)
plt.axes().add_artist(ell)
plt.axes().set_aspect("equal")
_min = min(min(xcv), min(ycv))
_max = max(max(xcv), max(ycv))
plt.axes().set_xlim(_min, _max)
plt.axes().set_ylim(_min, _max)
plt.show()
def set_chunk_stats(chunk,
stats,
stat_choice,
n_residues=None,
ref_cell=None,
space_group=None,
d_min=None,
ref_data=None):
if "reslimit" in stat_choice: stats["reslimit"].append(chunk.res_lim)
else: stats["reslimit"].append(float("nan"))
if "pr" in stat_choice: stats["pr"].append(chunk.profile_radius)
else: stats["pr"].append(float("nan"))
stats["ccref"].append(float("nan"))
if set(["ioversigma", "resnatsnr1", "ccref"]).intersection(stat_choice):
iobs = chunk.data_array(space_group, False)
iobs = iobs.select(iobs.sigmas() > 0).merge_equivalents(
use_internal_variance=False).array()
binner = iobs.setup_binner(auto_binning=True)
if "resnatsnr1" in stat_choice:
res = float("nan")
for i_bin in binner.range_used():
sel = binner.selection(i_bin)
tmp = iobs.select(sel)
if tmp.size() == 0: continue
sn = flex.mean(tmp.data() / tmp.sigmas())
if sn <= 1:
res = binner.bin_d_range(i_bin)[1]
break
stats["resnatsnr1"].append(res)
else:
stats["resnatsnr1"].append(float("nan"))
if d_min: iobs = iobs.resolution_filter(d_min=d_min)
if "ccref" in stat_choice:
corr = iobs.correlation(ref_data, assert_is_similar_symmetry=False)
if corr.is_well_defined(): stats["ccref"][-1] = corr.coefficient()
if "ioversigma" in stat_choice:
stats["ioversigma"].append(flex.mean(iobs.data() / iobs.sigmas()))
else:
stats["ioversigma"].append(float("nan"))
else:
stats["ioversigma"].append(float("nan"))
stats["resnatsnr1"].append(float("nan"))
if "abdist" in stat_choice:
from cctbx.uctbx.determine_unit_cell import NCDist
G6a, G6b = make_G6(ref_cell), make_G6(chunk.cell)
abdist = NCDist(G6a, G6b)
stats["abdist"].append(abdist)
else:
stats["abdist"].append(float("nan"))
if "wilsonb" in stat_choice:
iso_scale_and_b = ml_iso_absolute_scaling(iobs, n_residues, 0)
stats["wilsonb"].append(iso_scale_and_b.b_wilson)
else:
stats["wilsonb"].append(float("nan"))
def compute(pdb_hierarchy, unit_cell, fft_n_real, fft_m_real, map_1, map_2,
detail, atom_radius, use_hydrogens, hydrogen_atom_radius):
assert detail in ["atom", "residue"]
results = []
for chain in pdb_hierarchy.chains():
for residue_group in chain.residue_groups():
for conformer in residue_group.conformers():
for residue in conformer.residues():
r_id_str = "%2s %1s %3s %4s %1s" % (
chain.id, conformer.altloc, residue.resname,
residue.resseq, residue.icode)
r_sites_cart = flex.vec3_double()
r_b = flex.double()
r_occ = flex.double()
r_mv1 = flex.double()
r_mv2 = flex.double()
r_rad = flex.double()
for atom in residue.atoms():
a_id_str = "%s %4s" % (r_id_str, atom.name)
if (atom.element_is_hydrogen()):
rad = hydrogen_atom_radius
else:
rad = atom_radius
if (not (atom.element_is_hydrogen()
and not use_hydrogens)):
map_value_1 = map_1.eight_point_interpolation(
unit_cell.fractionalize(atom.xyz))
map_value_2 = map_2.eight_point_interpolation(
unit_cell.fractionalize(atom.xyz))
r_sites_cart.append(atom.xyz)
r_b.append(atom.b)
r_occ.append(atom.occ)
r_mv1.append(map_value_1)
r_mv2.append(map_value_2)
r_rad.append(rad)
if (detail == "atom"):
sel = maptbx.grid_indices_around_sites(
unit_cell=unit_cell,
fft_n_real=fft_n_real,
fft_m_real=fft_m_real,
sites_cart=flex.vec3_double([atom.xyz]),
site_radii=flex.double([rad]))
cc = flex.linear_correlation(
x=map_1.select(sel),
y=map_2.select(sel)).coefficient()
result = group_args(chain_id=chain.id,
atom=atom,
id_str=a_id_str,
cc=cc,
map_value_1=map_value_1,
map_value_2=map_value_2,
b=atom.b,
occupancy=atom.occ,
n_atoms=1)
results.append(result)
if (detail == "residue") and (len(r_mv1) > 0):
sel = maptbx.grid_indices_around_sites(
unit_cell=unit_cell,
fft_n_real=fft_n_real,
fft_m_real=fft_m_real,
sites_cart=r_sites_cart,
site_radii=r_rad)
cc = flex.linear_correlation(
x=map_1.select(sel),
y=map_2.select(sel)).coefficient()
result = group_args(residue=residue,
chain_id=chain.id,
id_str=r_id_str,
cc=cc,
map_value_1=flex.mean(r_mv1),
map_value_2=flex.mean(r_mv2),
b=flex.mean(r_b),
occupancy=flex.mean(r_occ),
n_atoms=r_sites_cart.size())
results.append(result)
return results
def __init__(self,
miller_array,
parameters,
out=None,
n_residues=100,
n_bases=0):
self.params=parameters
self.miller_array=miller_array.deep_copy().set_observation_type(
miller_array).merge_equivalents().array()
self.out = out
if self.out is None:
self.out = sys.stdout
if self.out == "silent":
self.out = null_out()
self.no_aniso_array = self.miller_array
if self.params.aniso.action == "remove_aniso":
# first perfom aniso scaling
aniso_scale_and_b = absolute_scaling.ml_aniso_absolute_scaling(
miller_array = self.miller_array,
n_residues = n_residues,
n_bases = n_bases)
aniso_scale_and_b.p_scale = 0 # set the p_scale back to 0!
aniso_scale_and_b.show(out=out)
# now do aniso correction please
self.aniso_p_scale = aniso_scale_and_b.p_scale
self.aniso_u_star = aniso_scale_and_b.u_star
self.aniso_b_cart = aniso_scale_and_b.b_cart
if self.params.aniso.final_b == "eigen_min":
b_use=aniso_scale_and_b.eigen_values[2]
elif self.params.aniso.final_b == "eigen_mean" :
b_use=flex.mean(aniso_scale_and_b.eigen_values)
elif self.params.aniso.final_b == "user_b_iso":
assert self.params.aniso.b_iso is not None
b_use=self.params.aniso.b_iso
else:
b_use = 30
b_cart_aniso_removed = [ -b_use,
-b_use,
-b_use,
0,
0,
0]
u_star_aniso_removed = adptbx.u_cart_as_u_star(
miller_array.unit_cell(),
adptbx.b_as_u( b_cart_aniso_removed ) )
## I do things in two steps, but can easely be done in 1 step
## just for clarity, thats all.
self.no_aniso_array = absolute_scaling.anisotropic_correction(
self.miller_array,0.0,aniso_scale_and_b.u_star )
self.no_aniso_array = absolute_scaling.anisotropic_correction(
self.no_aniso_array,0.0,u_star_aniso_removed)
self.no_aniso_array = self.no_aniso_array.set_observation_type(
miller_array )
# that is done now, now we can do outlier detection if desired
outlier_manager = outlier_rejection.outlier_manager(
self.no_aniso_array,
None,
out=self.out)
self.new_miller_array = self.no_aniso_array
if self.params.outlier.action == "basic":
print >> self.out, "Non-outliers found by the basic wilson statistics"
print >> self.out, "protocol will be written out."
basic_array = outlier_manager.basic_wilson_outliers(
p_basic_wilson = self.params.outlier.parameters.basic_wilson.level,
return_data = True)
self.new_miller_array = basic_array
if self.params.outlier.action == "extreme":
print >> self.out, "Non-outliers found by the extreme value wilson statistics"
print >> self.out, "protocol will be written out."
extreme_array = outlier_manager.extreme_wilson_outliers(
p_extreme_wilson = self.params.outlier.parameters.extreme_wilson.level,
return_data = True)
self.new_miller_array = extreme_array
if self.params.outlier.action == "beamstop":
print >> self.out, "Outliers found for the beamstop shadow"
print >> self.out, "problems detection protocol will be written out."
beamstop_array = outlier_manager.beamstop_shadow_outliers(
level = self.params.outlier.parameters.beamstop.level,
d_min = self.params.outlier.parameters.beamstop.d_min,
return_data=True)
self.new_miller_array = beamstop_array
if self.params.outlier.action == "None":
self.new_miller_array = self.no_aniso_array
# now we can twin or detwin the data if needed
self.final_array = self.new_miller_array
if self.params.symmetry.action == "twin":
alpha = self.params.symmetry.twinning_parameters.fraction
if (alpha is None):
raise Sorry("Twin fraction not specified, not twinning data")
elif not (0 <= alpha <= 0.5):
raise Sorry("Twin fraction must be between 0 and 0.5.")
print >> self.out
print >> self.out, "Twinning given data"
print >> self.out, "-------------------"
print >> self.out
print >> self.out, "Artifically twinning the data with fraction %3.2f" %\
alpha
self.final_array = self.new_miller_array.twin_data(
twin_law = self.params.symmetry.twinning_parameters.twin_law,
alpha=alpha).as_intensity_array()
elif (self.params.symmetry.action == "detwin"):
twin_law = self.params.symmetry.twinning_parameters.twin_law
alpha = self.params.symmetry.twinning_parameters.fraction
if (alpha is None):
raise Sorry("Twin fraction not specified, not detwinning data")
elif not (0 <= alpha <= 0.5):
raise Sorry("Twin fraction must be between 0 and 0.5.")
print >> self.out, """
Attempting to detwin data
-------------------------
Detwinning data with:
- twin law: %s
- twin fraciton: %.2f
BE WARNED! DETWINNING OF DATA DOES NOT SOLVE YOUR TWINNING PROBLEM!
PREFERABLY, REFINEMENT SHOULD BE CARRIED OUT AGAINST ORIGINAL DATA
ONLY USING A TWIN SPECIFIC TARGET FUNCTION!
""" % (twin_law, alpha)
self.final_array = self.new_miller_array.detwin_data(
twin_law=twin_law,
alpha=alpha).as_intensity_array()
assert self.final_array is not None
def run(hklin):
xscaled = xds_ascii.XDS_ASCII(hklin) # Must be XSCALE output
merged_iobs = xscaled.i_obs().merge_equivalents(
use_internal_variance=False).array()
binner = merged_iobs.setup_binner(n_bins=100)
isets = set(xscaled.iset)
for_plot = {}
cut_ios = (2, 1, 0.5, 0)
print "iset file",
for cut in cut_ios:
print "cut_ios_%.2f" % cut,
print
for iset in isets:
sel = (xscaled.iset == iset)
data_i = xscaled.i_obs().select(sel).merge_equivalents(
use_internal_variance=False).array()
cutoffs = eval_resolution(data_i, 100, cut_ios)
print "%3d %s %s" % (iset, xscaled.input_files[iset][0], " ".join(
map(lambda x: "%.2f" % x, cutoffs)))
for i_bin in binner.range_used():
dmax, dmin = binner.bin_d_range(i_bin)
Isel = data_i.resolution_filter(d_max=dmax, d_min=dmin)
for_plot.setdefault(
iset,
[]).append(flex.mean(Isel.data()) if Isel.size() > 0 else 0)
import matplotlib
matplotlib.use('Agg') # Allow to work without X
import pylab
import math
from matplotlib.ticker import FuncFormatter
s2_formatter = lambda x, pos: "inf" if x == 0 else "%.2f" % (1. / numpy.
sqrt(x))
exp_formatter = lambda x, pos: "%.1e" % x
plot_x = [binner.bin_d_range(i)[1]**(-2) for i in binner.range_used()]
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages("test.pdf")
keys = sorted(for_plot)
for names in (keys[i:i + 100] for i in xrange(0, len(keys), 100)):
ncols = 5
nrows = int(math.ceil(len(names) / float(ncols)))
fig, axes = pylab.plt.subplots(ncols=ncols,
nrows=nrows,
figsize=(5 * ncols, 5 * nrows),
sharex=False,
sharey=False)
axes = axes.flatten()
for name, ax in zip(names, axes):
ax.plot(
plot_x,
for_plot[name],
linewidth=1,
)
ax.axhline(y=0, color="red", linestyle="-")
ax.set_xlabel('(d^-2)')
ax.set_ylabel('<I>')
ax.xaxis.set_major_formatter(FuncFormatter(s2_formatter))
ax.yaxis.set_major_formatter(FuncFormatter(exp_formatter))
ax.set_title("data%.4d" % name)
ax.grid(True)
pylab.plt.tight_layout()
plot_title = ""
pylab.title(plot_title)
pp.savefig()
#pylab.savefig("test_%.3d.png"%i)
#pylab.show()
pp.close()
def scale_frame_detail(self, result, file_name, db_mgr, out):
# If the pickled integration file does not contain a wavelength,
# fall back on the value given on the command line. XXX The
# wavelength parameter should probably be removed from master_phil
# once all pickled integration files contain it.
if (result.has_key("wavelength")):
wavelength = result["wavelength"]
elif (self.params.wavelength is not None):
wavelength = self.params.wavelength
else:
# XXX Give error, or raise exception?
return None
assert (wavelength > 0)
observations = result["observations"][0]
cos_two_polar_angle = result["cos_two_polar_angle"]
assert observations.size() == cos_two_polar_angle.size()
tt_vec = observations.two_theta(wavelength)
#print "mean tt degrees",180.*flex.mean(tt_vec.data())/math.pi
cos_tt_vec = flex.cos(tt_vec.data())
sin_tt_vec = flex.sin(tt_vec.data())
cos_sq_tt_vec = cos_tt_vec * cos_tt_vec
sin_sq_tt_vec = sin_tt_vec * sin_tt_vec
P_nought_vec = 0.5 * (1. + cos_sq_tt_vec)
F_prime = -1.0 # Hard-coded value defines the incident polarization axis
P_prime = 0.5 * F_prime * cos_two_polar_angle * sin_sq_tt_vec
# XXX added as a diagnostic
prange = P_nought_vec - P_prime
other_F_prime = 1.0
otherP_prime = 0.5 * other_F_prime * cos_two_polar_angle * sin_sq_tt_vec
otherprange = P_nought_vec - otherP_prime
diff2 = flex.abs(prange - otherprange)
print "mean diff is", flex.mean(diff2), "range", flex.min(
diff2), flex.max(diff2)
# XXX done
observations = observations / (P_nought_vec - P_prime)
# This corrects observations for polarization assuming 100% polarization on
# one axis (thus the F_prime = -1.0 rather than the perpendicular axis, 1.0)
# Polarization model as described by Kahn, Fourme, Gadet, Janin, Dumas & Andre
# (1982) J. Appl. Cryst. 15, 330-337, equations 13 - 15.
print "Step 3. Correct for polarization."
indexed_cell = observations.unit_cell()
observations_original_index = observations.deep_copy()
if result.get(
"model_partialities", None
) is not None and result["model_partialities"][0] is not None:
# some recordkeeping useful for simulations
partialities_original_index = observations.customized_copy(
crystal_symmetry=self.miller_set.crystal_symmetry(),
data=result["model_partialities"][0]["data"],
sigmas=flex.double(result["model_partialities"][0]
["data"].size()), #dummy value for sigmas
indices=result["model_partialities"][0]["indices"],
).resolution_filter(d_min=self.params.d_min)
assert len(observations_original_index.indices()) == len(
observations.indices())
# Now manipulate the data to conform to unit cell, asu, and space group
# of reference. The resolution will be cut later.
# Only works if there is NOT an indexing ambiguity!
observations = observations.customized_copy(
anomalous_flag=not self.params.merge_anomalous,
crystal_symmetry=self.miller_set.crystal_symmetry()).map_to_asu()
observations_original_index = observations_original_index.customized_copy(
anomalous_flag=not self.params.merge_anomalous,
crystal_symmetry=self.miller_set.crystal_symmetry())
print "Step 4. Filter on global resolution and map to asu"
print >> out, "Data in reference setting:"
#observations.show_summary(f=out, prefix=" ")
show_observations(observations, out=out)
#if self.params.significance_filter.apply is True:
# raise Exception("significance filter not implemented in samosa")
if self.params.significance_filter.apply is True: #------------------------------------
# Apply an I/sigma filter ... accept resolution bins only if they
# have significant signal; tends to screen out higher resolution observations
# if the integration model doesn't quite fit
N_obs_pre_filter = observations.size()
N_bins_small_set = N_obs_pre_filter // self.params.significance_filter.min_ct
N_bins_large_set = N_obs_pre_filter // self.params.significance_filter.max_ct
# Ensure there is at least one bin.
N_bins = max([
min([self.params.significance_filter.n_bins,
N_bins_small_set]), N_bins_large_set, 1
])
print "Total obs %d Choose n bins = %d" % (N_obs_pre_filter,
N_bins)
bin_results = show_observations(observations,
out=out,
n_bins=N_bins)
#show_observations(observations, out=sys.stdout, n_bins=N_bins)
acceptable_resolution_bins = [
bin.mean_I_sigI > self.params.significance_filter.sigma
for bin in bin_results
]
acceptable_nested_bin_sequences = [
i for i in xrange(len(acceptable_resolution_bins))
if False not in acceptable_resolution_bins[:i + 1]
]
if len(acceptable_nested_bin_sequences) == 0:
return null_data(file_name=file_name,
log_out=out.getvalue(),
low_signal=True)
else:
N_acceptable_bins = max(acceptable_nested_bin_sequences) + 1
imposed_res_filter = float(bin_results[N_acceptable_bins -
1].d_range.split()[2])
imposed_res_sel = observations.resolution_filter_selection(
d_min=imposed_res_filter)
observations = observations.select(imposed_res_sel)
observations_original_index = observations_original_index.select(
imposed_res_sel)
print "New resolution filter at %7.2f" % imposed_res_filter, file_name
print "N acceptable bins", N_acceptable_bins
print "Old n_obs: %d, new n_obs: %d" % (N_obs_pre_filter,
observations.size())
print "Step 5. Frame by frame resolution filter"
# Finished applying the binwise I/sigma filter---------------------------------------
if self.params.raw_data.sdfac_auto is True:
raise Exception("sdfac auto not implemented in samosa.")
print "Step 6. Match to reference intensities, filter by correlation, filter out negative intensities."
assert len(observations_original_index.indices()) \
== len(observations.indices())
data = frame_data(self.n_refl, file_name)
data.set_indexed_cell(indexed_cell)
data.d_min = observations.d_min()
# Ensure that match_multi_indices() will return identical results
# when a frame's observations are matched against the
# pre-generated Miller set, self.miller_set, and the reference
# data set, self.i_model. The implication is that the same match
# can be used to map Miller indices to array indices for intensity
# accumulation, and for determination of the correlation
# coefficient in the presence of a scaling reference.
if self.i_model is not None:
assert len(self.i_model.indices()) == len(self.miller_set.indices()) \
and (self.i_model.indices() ==
self.miller_set.indices()).count(False) == 0
matches = miller.match_multi_indices(
miller_indices_unique=self.miller_set.indices(),
miller_indices=observations.indices())
use_weights = False # New facility for getting variance-weighted correlation
if self.params.scaling.algorithm in ['mark1', 'levmar']:
# Because no correlation is computed, the correlation
# coefficient is fixed at zero. Setting slope = 1 means
# intensities are added without applying a scale factor.
sum_x = 0
sum_y = 0
for pair in matches.pairs():
data.n_obs += 1
if not self.params.include_negatives and observations.data()[
pair[1]] <= 0:
data.n_rejected += 1
else:
sum_y += observations.data()[pair[1]]
N = data.n_obs - data.n_rejected
# Early return if there are no positive reflections on the frame.
if data.n_obs <= data.n_rejected:
return null_data(file_name=file_name,
log_out=out.getvalue(),
low_signal=True)
# Update the count for each matched reflection. This counts
# reflections with non-positive intensities, too.
data.completeness += matches.number_of_matches(0).as_int()
data.wavelength = wavelength
if not self.params.scaling.enable: # Do not scale anything
print "Scale factor to an isomorphous reference PDB will NOT be applied."
slope = 1.0
offset = 0.0
observations_original_index_indices = observations_original_index.indices(
)
if db_mgr is None:
return unpack(MINI.x) # special exit for two-color indexing
kwargs = {
'wavelength': wavelength,
'beam_x': result['xbeam'],
'beam_y': result['ybeam'],
'distance': result['distance'],
'unique_file_name': data.file_name
}
ORI = result["current_orientation"][0]
Astar = matrix.sqr(ORI.reciprocal_matrix())
kwargs['res_ori_1'] = Astar[0]
kwargs['res_ori_2'] = Astar[1]
kwargs['res_ori_3'] = Astar[2]
kwargs['res_ori_4'] = Astar[3]
kwargs['res_ori_5'] = Astar[4]
kwargs['res_ori_6'] = Astar[5]
kwargs['res_ori_7'] = Astar[6]
kwargs['res_ori_8'] = Astar[7]
kwargs['res_ori_9'] = Astar[8]
assert self.params.scaling.report_ML is True
kwargs['half_mosaicity_deg'] = result["ML_half_mosaicity_deg"][0]
kwargs['domain_size_ang'] = result["ML_domain_size_ang"][0]
frame_id_0_base = db_mgr.insert_frame(**kwargs)
xypred = result["mapped_predictions"][0]
indices = flex.size_t([pair[1] for pair in matches.pairs()])
sel_observations = flex.intersection(size=observations.data().size(),
iselections=[indices])
set_original_hkl = observations_original_index_indices.select(
flex.intersection(size=observations_original_index_indices.size(),
iselections=[indices]))
set_xypred = xypred.select(
flex.intersection(size=xypred.size(), iselections=[indices]))
kwargs = {
'hkl_id_0_base': [pair[0] for pair in matches.pairs()],
'i': observations.data().select(sel_observations),
'sigi': observations.sigmas().select(sel_observations),
'detector_x': [xy[0] for xy in set_xypred],
'detector_y': [xy[1] for xy in set_xypred],
'frame_id_0_base': [frame_id_0_base] * len(matches.pairs()),
'overload_flag': [0] * len(matches.pairs()),
'original_h': [hkl[0] for hkl in set_original_hkl],
'original_k': [hkl[1] for hkl in set_original_hkl],
'original_l': [hkl[2] for hkl in set_original_hkl]
}
db_mgr.insert_observation(**kwargs)
print >> out, "Lattice: %d reflections" % (data.n_obs -
data.n_rejected)
print >> out, "average obs", sum_y / (data.n_obs - data.n_rejected), \
"average calc", sum_x / (data.n_obs - data.n_rejected)
print >> out, "Rejected %d reflections with negative intensities" % \
data.n_rejected
data.accept = True
for pair in matches.pairs():
if not self.params.include_negatives and (
observations.data()[pair[1]] <= 0):
continue
Intensity = observations.data()[pair[1]]
# Super-rare exception. If saved sigmas instead of I/sigmas in the ISIGI dict, this wouldn't be needed.
if Intensity == 0:
continue
# Add the reflection as a two-tuple of intensity and I/sig(I)
# to the dictionary of observations.
index = self.miller_set.indices()[pair[0]]
isigi = (Intensity, observations.data()[pair[1]] /
observations.sigmas()[pair[1]], 1.0)
if index in data.ISIGI:
data.ISIGI[index].append(isigi)
else:
data.ISIGI[index] = [isigi]
sigma = observations.sigmas()[pair[1]]
variance = sigma * sigma
data.summed_N[pair[0]] += 1
data.summed_wt_I[pair[0]] += Intensity / variance
data.summed_weight[pair[0]] += 1 / variance
data.set_log_out(out.getvalue())
return data
def run(x1, x2, n_bins):
hemispheres = []
merged_data = []
for x in (x1, x2):
print "Processing %s" % x
print "===================="
data = read_x(x, take_full=False) # if you need only full, change this to True.
data.crystal_symmetry().show_summary()
merge = data.merge_equivalents(use_internal_variance=False)
merge.show_summary()
print
array = merge.array()
array = array.select(array.sigmas() > 0)
merged_data.append(array)
# separate + and -
matches = array.match_bijvoet_mates()[1] # returns asu and matches
sel_p = matches.pairs_hemisphere_selection("+")
sel_p.extend(matches.singles("+"))
sel_m = matches.pairs_hemisphere_selection("-")
sel_m.extend(matches.singles("-"))
hemispheres.append([array.select(sel_p, anomalous_flag=False),
array.select(sel_m, anomalous_flag=False).map_to_asu()])
print "x1: merged=%d (+)=%d (-)=%d" % (merged_data[0].size(),
hemispheres[0][0].size(), hemispheres[0][1].size())
print "x2: merged=%d (+)=%d (-)=%d" % (merged_data[1].size(),
hemispheres[1][0].size(), hemispheres[1][1].size())
# for sigma calculation (new_sigma^2 = sigma1^2 + sigma2^2)
additive_sigmas = lambda x, y: flex.sqrt(flex.pow2(x.sigmas()) + flex.pow2(y.sigmas()))
# calculate data1(+) - data2(-)
""" # for debug
for i, x in enumerate(hemispheres[0][0]):
print x
if i > 10: break
print
for i, x in enumerate(hemispheres[1][0]):
print x
if i > 10: break
"""
h1p, h2m = hemispheres[0][0].common_sets(hemispheres[1][1])
h1p_h2m = h1p.customized_copy(data=h1p.data() - h2m.data(),
sigmas=additive_sigmas(h1p, h2m))
print h1p_h2m.size()
#for x in h1p_h2m: print x
# calculate data2(+) - data1(-)
h2p, h1m = hemispheres[1][0].common_sets(hemispheres[0][1])
h2p_h1m = h2p.customized_copy(data=h2p.data() - h1m.data(),
sigmas=additive_sigmas(h2p, h1m))
print h2p_h1m.size()
print
#for x in h2p_h1m: print x
# concatenate data1(+) - data2(-) and data2(+) - data1(-)
dano_tmp = h1p_h2m.concatenate(h2p_h1m)
merge = dano_tmp.merge_equivalents(use_internal_variance=False)
print "Merging stats of (+)-(-) data"
print "============================="
merge.show_summary()
dano = merge.array()
print "num_dano=", dano.size()
print
# process with binning
dano.setup_binner(n_bins=n_bins)
binner = dano.binner()
print "Result:"
print " dmax dmin nrefs dano"
for i_bin in binner.range_used():
# selection for this bin. sel is flex.bool object (list of True of False)
sel = binner.selection(i_bin)
count = binner.count(i_bin)
# take mean of absolute value of anomalous differences in a bin
if count > 0:
bin_mean = flex.mean(flex.abs(dano.select(sel).data()))
else:
bin_mean = float("nan")
d_max, d_min = binner.bin_d_range(i_bin)
print "%7.2f %7.2f %6d %.2f" % (d_max, d_min, count, bin_mean)
def exercise_2():
for use_reference in [True, False, None]:
pdb_inp = iotbx.pdb.input(
lines=flex.std_string(pdb_str_2.splitlines()), source_info=None)
model = manager(
model_input=pdb_inp,
log=null_out())
grm = model.get_restraints_manager().geometry
xrs2 = model.get_xray_structure()
awl2 = model.get_hierarchy().atoms_with_labels()
pdb_inp3 = iotbx.pdb.input(source_info=None, lines=pdb_str_3)
xrs3 = pdb_inp3.xray_structure_simple()
ph3 = pdb_inp3.construct_hierarchy()
ph3.atoms().reset_i_seq()
awl3 = ph3.atoms_with_labels()
sites_cart_reference = flex.vec3_double()
selection = flex.size_t()
reference_names = ["CG", "CD", "NE", "CZ", "NH1", "NH2"]
for a2,a3 in zip(tuple(awl2), tuple(awl3)):
assert a2.resname == a3.resname
assert a2.name == a3.name
assert a2.i_seq == a3.i_seq
if(a2.resname == "ARG" and a2.name.strip() in reference_names):
selection.append(a2.i_seq)
sites_cart_reference.append(a3.xyz)
assert selection.size() == len(reference_names)
selection_bool = flex.bool(xrs2.scatterers().size(), selection)
if(use_reference):
grm.adopt_reference_coordinate_restraints_in_place(
reference.add_coordinate_restraints(
sites_cart = sites_cart_reference,
selection = selection,
sigma = 0.01))
elif(use_reference is None):
grm.adopt_reference_coordinate_restraints_in_place(
reference.add_coordinate_restraints(
sites_cart = sites_cart_reference,
selection = selection,
sigma = 0.01))
grm.remove_reference_coordinate_restraints_in_place(
selection = selection)
d1 = flex.mean(flex.sqrt((xrs2.sites_cart().select(selection) -
xrs3.sites_cart().select(selection)).dot()))
print("distance start (use_reference: %s): %6.4f"%(str(use_reference), d1))
assert d1>4.0
assert approx_equal(
flex.max(flex.sqrt((xrs2.sites_cart().select(~selection_bool) -
xrs3.sites_cart().select(~selection_bool)).dot())), 0)
from cctbx import geometry_restraints
import mmtbx.refinement.geometry_minimization
import scitbx.lbfgs
grf = geometry_restraints.flags.flags(default=True)
sites_cart = xrs2.sites_cart()
minimized = mmtbx.refinement.geometry_minimization.lbfgs(
sites_cart = sites_cart,
correct_special_position_tolerance=1.0,
geometry_restraints_manager = grm,
sites_cart_selection = flex.bool(sites_cart.size(), selection),
geometry_restraints_flags = grf,
lbfgs_termination_params = scitbx.lbfgs.termination_parameters(
max_iterations=5000))
xrs2.set_sites_cart(sites_cart = sites_cart)
d2 = flex.mean(flex.sqrt((xrs2.sites_cart().select(selection) -
xrs3.sites_cart().select(selection)).dot()))
print("distance final (use_reference: %s): %6.4f"%(str(use_reference), d2))
if(use_reference): assert d2<0.005, "failed: %f<0.05" % d2
else: assert d2>4.0, d2
assert approx_equal(
flex.max(flex.sqrt((xrs2.sites_cart().select(~selection_bool) -
xrs3.sites_cart().select(~selection_bool)).dot())), 0)
def coefficient_of_determination(y, y_model):
mean_y = flex.mean(y)
r_sqr = flex.sum((y_model - mean_y)**2) / flex.sum((y - mean_y)**2)
return r_sqr
def __init__(self,
fmodel,
free_reflections_per_bin=140,
max_number_of_bins=30,
n_bins=None):
from cctbx.array_family import flex
mp = fmodel.mask_params
self.target_name = fmodel.target_name
if (self.target_name == "twin_lsq_f"):
self.twin_fraction = fmodel.twin_fraction
self.twin_law = fmodel.twin_law
else:
self.twin_fraction = None
self.twin_law = None
self.r_work = fmodel.r_work()
self.r_free = fmodel.r_free()
self.r_all = fmodel.r_all()
self.target_work = fmodel.target_w()
self.target_free = fmodel.target_t()
self.target_work_no_norm = fmodel.target_w()
if (self.target_work_no_norm is not None):
self.target_work_no_norm *= fmodel.f_calc_w().data().size()
self.target_free_no_norm = fmodel.target_t()
if (self.target_free_no_norm is not None):
self.target_free_no_norm *= fmodel.f_calc_t().data().size()
self.overall_scale_k1 = fmodel.scale_k1()
self.number_of_test_reflections = fmodel.f_calc_t().data().size()
self.number_of_work_reflections = fmodel.f_calc_w().data().size()
self.number_of_reflections = fmodel.f_obs().data().size()
self.number_of_reflections_merged = self.number_of_reflections
if (fmodel.f_obs().anomalous_flag() in (None, True)):
something, matches = fmodel.f_obs().match_bijvoet_mates()
self.number_of_reflections_merged = matches.pairs().size() + \
matches.n_singles()
self.mask_solvent_radius = mp.solvent_radius
self.mask_shrink_radius = mp.shrink_truncation_radius
self.mask_grid_step_factor = mp.grid_step_factor
self.ml_phase_error = flex.mean(fmodel.phase_errors())
self.ml_coordinate_error = fmodel.model_error_ml()
self.d_max, self.d_min = fmodel.f_obs().resolution_range()
self.completeness_in_range = fmodel.f_obs().completeness(
d_max=self.d_max)
self.completeness_d_min_inf = fmodel.f_obs().completeness()
f_obs_6 = fmodel.f_obs().resolution_filter(d_min=6)
self.completeness_6_inf = f_obs_6.completeness()
self.min_f_obs_over_sigma = fmodel.f_obs().min_f_over_sigma(
return_none_if_zero_sigmas=True)
self.sf_algorithm = fmodel.sfg_params.algorithm
self.alpha_w, self.beta_w = fmodel.alpha_beta_w()
self.alpha_work_min, self.alpha_work_max, self.alpha_work_mean = \
self.alpha_w.data().min_max_mean().as_tuple()
self.beta_work_min, self.beta_work_max, self.beta_work_mean = \
self.beta_w.data().min_max_mean().as_tuple()
self.fom = fmodel.figures_of_merit_work()
self.fom_work_min, self.fom_work_max, self.fom_work_mean = \
self.fom.min_max_mean().as_tuple()
self.pher_w = fmodel.phase_errors_work()
self.pher_work_min, self.pher_work_max, self.pher_work_mean = \
self.pher_w.min_max_mean().as_tuple()
self.pher_t = fmodel.phase_errors_test()
self.pher_free_min, self.pher_free_max, self.pher_free_mean = \
self.pher_t.min_max_mean().as_tuple()
self.bins = self.statistics_in_resolution_bins(
fmodel=fmodel,
free_reflections_per_bin=free_reflections_per_bin,
max_number_of_bins=max_number_of_bins,
n_bins=n_bins)
def run(args,
log=None,
ccp4_map=None,
return_as_miller_arrays=False,
nohl=False,
return_f_obs=False,
space_group_number=None,
out=sys.stdout):
if log is None: log = out
inputs = mmtbx.utils.process_command_line_args(
args=args, master_params=master_params())
got_map = False
if ccp4_map: got_map = True
broadcast(m="Parameters:", log=log)
inputs.params.show(prefix=" ", out=out)
params = inputs.params.extract()
if (ccp4_map is None and inputs.ccp4_map is not None):
broadcast(m="Processing input CCP4 map file: %s" %
inputs.ccp4_map_file_name,
log=log)
ccp4_map = inputs.ccp4_map
ccp4_map.show_summary(prefix=" ", out=out)
got_map = True
if (not got_map):
raise Sorry("Map file is needed.")
#
m = ccp4_map
if (m.space_group_number > 1):
raise Sorry("Input map space group: %d. Must be P1." %
m.space_group_number)
broadcast(m="Input map information:", log=log)
print >> out, "m.all() :", m.data.all()
print >> out, "m.focus() :", m.data.focus()
print >> out, "m.origin():", m.data.origin()
print >> out, "m.nd() :", m.data.nd()
print >> out, "m.size() :", m.data.size()
print >> out, "m.focus_size_1d():", m.data.focus_size_1d()
print >> out, "m.is_0_based() :", m.data.is_0_based()
print >> out, "map: min/max/mean:", flex.min(m.data), flex.max(
m.data), flex.mean(m.data)
print >> out, "unit cell:", m.unit_cell_parameters
#
if not space_group_number:
space_group_number = 1
if space_group_number <= 1:
symmetry_flags = None
else:
symmetry_flags = maptbx.use_space_group_symmetry,
cs = crystal.symmetry(m.unit_cell_parameters, space_group_number)
map_data = m.data
# Get origin in grid units and new position of origin in grid units
original_origin = map_data.origin()
print >> out, "Input map has origin at grid point (%s,%s,%s)" % (
tuple(original_origin))
if params.output_origin_grid_units is not None:
params.keep_origin = False
new_origin = tuple(params.output_origin_grid_units)
print >> out, "User-specified origin at grid point (%s,%s,%s)" % (
tuple(params.output_origin_grid_units))
if tuple(params.output_origin_grid_units) == tuple(original_origin):
print >> out, "This is the same as the input origin. No origin shift."
elif params.keep_origin:
new_origin = original_origin
print >> out, "Keeping origin at grid point (%s,%s,%s)" % (
tuple(original_origin))
else:
new_origin = (
0,
0,
0,
)
print >> out, "New origin at grid point (%s,%s,%s)" % (tuple((
0,
0,
0,
)))
# shift_cart is shift away from (0,0,0)
if new_origin != (
0,
0,
0,
):
shift_cart = get_shift_cart(map_data=map_data,
crystal_symmetry=cs,
origin=new_origin)
else:
shift_cart = (
0,
0,
0,
)
map_data = maptbx.shift_origin_if_needed(map_data=map_data).map_data
# generate complete set of Miller indices up to given high resolution d_min
n_real = map_data.focus()
crystal_gridding = maptbx.crystal_gridding(
unit_cell=cs.unit_cell(),
space_group_info=cs.space_group_info(),
symmetry_flags=symmetry_flags,
pre_determined_n_real=n_real)
#
d_min = params.d_min
if (d_min is None and not params.box):
d_min = maptbx.d_min_from_map(map_data=map_data,
unit_cell=cs.unit_cell())
if (d_min is None):
# box of reflections in |h|<N1/2, |k|<N2/2, 0<=|l|<N3/2
f_obs_cmpl = miller.structure_factor_box_from_map(
map=map_data.as_double(), crystal_symmetry=cs, include_000=True)
else:
complete_set = miller.build_set(crystal_symmetry=cs,
anomalous_flag=False,
d_min=d_min)
try:
f_obs_cmpl = complete_set.structure_factors_from_map(
map=map_data.as_double(),
use_scale=True,
anomalous_flag=False,
use_sg=False)
except Exception, e:
if (str(e) ==
"cctbx Error: Miller index not in structure factor map."):
msg = "Too high resolution requested. Try running with larger d_min."
raise Sorry(msg)
else:
raise Sorry(str(e))
def run(args):
centroids_filename = args[0]
hkl = flex.miller_index()
frame_obs = flex.double()
x_obs = flex.double()
y_obs = flex.double()
phi_obs = flex.double()
x_calc = flex.double()
y_calc = flex.double()
phi_calc = flex.double()
with open(centroids_filename, "rb") as f:
for i, line in enumerate(f.readlines()):
tokens = line.split()
if i == 0:
print(tokens)
assert tokens == [
"H",
"K",
"L",
"Frame_obs",
"X_obs",
"Y_obs",
"Phi_obs",
"X_calc",
"Y_calc",
"Phi_calc",
]
else:
hkl.append([int(t) for t in tokens[:3]])
frame_obs.append(float(tokens[3]))
x_obs.append(float(tokens[4]))
y_obs.append(float(tokens[5]))
phi_obs.append(float(tokens[6]))
x_calc.append(float(tokens[7]))
y_calc.append(float(tokens[8]))
phi_calc.append(float(tokens[9]))
phi_obs_deg = (180 / math.pi) * phi_obs
x_residuals = x_calc - x_obs
y_residuals = y_calc - y_obs
phi_residuals = phi_calc - phi_obs
mean_residuals_x = []
mean_residuals_y = []
mean_residuals_phi = []
phi = []
for i_phi in range(int(math.floor(flex.min(phi_obs_deg))),
int(math.ceil(flex.max(phi_obs_deg)))):
sel = (phi_obs_deg >= i_phi) & (phi_obs_deg < (i_phi + 1))
if sel.count(True) == 0:
continue
mean_residuals_x.append(flex.mean(x_residuals.select(sel)))
mean_residuals_y.append(flex.mean(y_residuals.select(sel)))
mean_residuals_phi.append(flex.mean(phi_residuals.select(sel)))
phi.append(i_phi)
from matplotlib import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(311)
pyplot.axhline(0, color="grey")
ax.scatter(phi, mean_residuals_x)
ax.set_xlabel("phi (deg)")
ax.set_ylabel("mean residual_x")
ax = fig.add_subplot(312)
pyplot.axhline(0, color="grey")
ax.scatter(phi, mean_residuals_y)
ax.set_xlabel("phi (deg)")
ax.set_ylabel("mean residual_y")
ax = fig.add_subplot(313)
pyplot.axhline(0, color="grey")
ax.scatter(phi, mean_residuals_phi)
ax.set_xlabel("phi (deg)")
ax.set_ylabel("mean residual_phi")
pyplot.show()
def get_average_cell_dimensions(self):
a = flex.mean(self.all_uc_a_values)
b = flex.mean(self.all_uc_b_values)
c = flex.mean(self.all_uc_c_values)
return a, b, c