def scale_frame_by_mean_I(self, frame_no, pickle_filename, iph, mean_of_mean_I):
observations_pickle = pickle.load(open(pickle_filename,"rb"))
observations_original, alpha_angle_obs = self.organize_input(observations_pickle, iph)
wavelength = observations_pickle["wavelength"]
crystal_init_orientation = observations_pickle["current_orientation"][0]
crystal_pointgroup = observations_pickle["pointgroup"]
if observations_original is None:
return None
if iph.target_pointgroup != '' and crystal_pointgroup != iph.target_pointgroup:
print 'frame %6.0f'%frame_no, ' - wrong pointgroup', crystal_pointgroup
return None
polar_hkl, cc_iso_raw_asu, cc_iso_raw_rev = self.determine_polar(observations_original, iph, pickle_filename)
observations_non_polar = self.get_observations_non_polar(observations_original, polar_hkl)
uc_params = crystal_init_orientation.unit_cell().parameters()
G = np.mean(observations_non_polar.data())/mean_of_mean_I
refined_params = np.array([G,0,0,0,0,0,0,uc_params[0],uc_params[1],uc_params[2],uc_params[3],uc_params[4],uc_params[5]])
se_params = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0])
stats = (0,0,0)
partiality_sel = flex.double([1]*len(observations_non_polar.data()))
SE_I = flex.double([1]*len(observations_non_polar.data()))
var_I_p = flex.double([0]*len(observations_non_polar.data()))
var_k = flex.double([0]*len(observations_non_polar.data()))
var_p = flex.double([0]*len(observations_non_polar.data()))
pres = postref_results()
pres.set_params(observations = observations_non_polar,
refined_params=refined_params,
se_params=se_params,
stats=stats,
partiality=partiality_sel,
frame_no=frame_no,
pickle_filename=pickle_filename,
wavelength=wavelength,
SE_I=SE_I,
var_I_p=var_I_p,
var_k=var_k,
var_p=var_p)
print 'frame %6.0f'%frame_no, '<I>=%9.2f <G>=%9.2f G=%9.2f'%(np.mean(observations_non_polar.data()), mean_of_mean_I, G), polar_hkl
return pres
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]
inputs, txt_organize_input = self.organize_input(
observations_pickle,
iparams,
avg_mode,
pickle_filename=pickle_filename)
txt_exception = ' {0:40} ==> '.format(img_filename_only)
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 postrefine_by_frame(self, frame_no, pickle_filename, iparams,
miller_array_ref, pres_in, avg_mode):
#1. Prepare data
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)
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
#2. Select data for post-refinement (only select indices that are common with the reference set
observations_non_polar, index_basis_name = self.get_observations_non_polar(
observations_original, pickle_filename, iparams)
matches = miller.match_multi_indices(
miller_indices_unique=miller_array_ref.indices(),
miller_indices=observations_non_polar.indices())
pair_0 = flex.size_t([pair[0] for pair in matches.pairs()])
pair_1 = flex.size_t([pair[1] for pair in matches.pairs()])
references_sel = miller_array_ref.select(pair_0)
observations_original_sel = observations_original.select(pair_1)
observations_non_polar_sel = observations_non_polar.select(pair_1)
alpha_angle_set = alpha_angle.select(pair_1)
spot_pred_x_mm_set = spot_pred_x_mm.select(pair_1)
spot_pred_y_mm_set = spot_pred_y_mm.select(pair_1)
#4. Do least-squares refinement
lsqrh = leastsqr_handler()
try:
refined_params, stats, n_refl_postrefined = lsqrh.optimize(
references_sel.data(), observations_original_sel, wavelength,
crystal_init_orientation, alpha_angle_set, spot_pred_x_mm_set,
spot_pred_y_mm_set, iparams, pres_in,
observations_non_polar_sel, detector_distance_mm)
except Exception:
txt_exception += 'optimization failed.\n'
return None, txt_exception
#caculate partiality for output (with target_anomalous check)
G_fin, B_fin, rotx_fin, roty_fin, ry_fin, rz_fin, r0_fin, re_fin, voigt_nu_fin, \
a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin = refined_params
inputs, txt_organize_input = self.organize_input(
observations_pickle,
iparams,
avg_mode,
pickle_filename=pickle_filename)
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, detector_distance_mm, wavelength, crystal_init_orientation = inputs
observations_non_polar, index_basis_name = self.get_observations_non_polar(
observations_original, pickle_filename, iparams)
from cctbx.uctbx import unit_cell
uc_fin = unit_cell(
(a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin))
if pres_in is not None:
crystal_init_orientation = pres_in.crystal_orientation
two_theta = observations_original.two_theta(
wavelength=wavelength).data()
ph = partiality_handler()
partiality_fin, dummy, rs_fin, rh_fin = ph.calc_partiality_anisotropy_set(
uc_fin, rotx_fin, roty_fin, observations_original.indices(),
ry_fin, rz_fin, r0_fin, re_fin, voigt_nu_fin, 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)
#calculate the new crystal orientation
O = sqr(uc_fin.orthogonalization_matrix()).transpose()
R = sqr(crystal_init_orientation.crystal_rotation_matrix()).transpose()
from cctbx.crystal_orientation import crystal_orientation, basis_type
CO = crystal_orientation(O * R, basis_type.direct)
crystal_fin_orientation = CO.rotate_thru(
(1, 0, 0), rotx_fin).rotate_thru((0, 1, 0), roty_fin)
#remove reflections with partiality below threshold
i_sel = partiality_fin > iparams.merge.partiality_min
partiality_fin_sel = partiality_fin.select(i_sel)
rs_fin_sel = rs_fin.select(i_sel)
rh_fin_sel = rh_fin.select(i_sel)
observations_non_polar_sel = observations_non_polar.customized_copy(\
indices=observations_non_polar.indices().select(i_sel),
data=observations_non_polar.data().select(i_sel),
sigmas=observations_non_polar.sigmas().select(i_sel))
observations_original_sel = observations_original.customized_copy(\
indices=observations_original.indices().select(i_sel),
data=observations_original.data().select(i_sel),
sigmas=observations_original.sigmas().select(i_sel))
pres = postref_results()
pres.set_params(observations=observations_non_polar_sel,
observations_original=observations_original_sel,
refined_params=refined_params,
stats=stats,
partiality=partiality_fin_sel,
rs_set=rs_fin_sel,
rh_set=rh_fin_sel,
frame_no=frame_no,
pickle_filename=pickle_filename,
wavelength=wavelength,
crystal_orientation=crystal_fin_orientation,
detector_distance_mm=detector_distance_mm)
r_change = ((pres.R_final - pres.R_init) / pres.R_init) * 100
r_xy_change = (
(pres.R_xy_final - pres.R_xy_init) / pres.R_xy_init) * 100
cc_change = ((pres.CC_final - pres.CC_init) / pres.CC_init) * 100
txt_postref = '{0:40} => RES:{1:5.2f} NREFL:{2:5d} R:{3:6.1f}% RXY:{4:5.1f}% CC:{5:5.1f}% G:{6:6.4f} B:{7:5.1f} CELL:{8:6.1f}{9:6.1f} {10:6.1f} {11:5.1f} {12:5.1f} {13:5.1f}'.format(
img_filename_only + ' (' + index_basis_name + ')',
observations_original_sel.d_min(),
len(observations_original_sel.data()), r_change, r_xy_change,
cc_change, pres.G, pres.B, a_fin, b_fin, c_fin, alpha_fin,
beta_fin, gamma_fin)
print txt_postref
txt_postref += '\n'
return pres, txt_postref
def scale_frame_by_mean_I(self, frame_no, pickle_filename, iparams, mean_of_mean_I, avg_mode):
observations_pickle = pickle.load(open(pickle_filename, "rb"))
pickle_filepaths = pickle_filename.split("/")
img_filename_only = pickle_filepaths[len(pickle_filepaths) - 1]
inputs, txt_organize_input = self.organize_input(
observations_pickle, iparams, avg_mode, pickle_filename=pickle_filename
)
txt_exception = " {0:40} ==> ".format(img_filename_only)
if inputs is not None:
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, detector_distance_mm = inputs
else:
txt_exception += txt_organize_input + "\n"
return None, txt_exception
wavelength = observations_pickle["wavelength"]
crystal_init_orientation = observations_pickle["current_orientation"][0]
# 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)
polar_hkl, cc_iso_raw_asu, cc_iso_raw_rev = self.determine_polar(
observations_original, iparams, pickle_filename
)
observations_non_polar_sel = self.get_observations_non_polar(observations_original_sel, polar_hkl)
observations_non_polar = self.get_observations_non_polar(observations_original, polar_hkl)
uc_params = observations_original.unit_cell().parameters()
from mod_leastsqr import calc_spot_radius
r0 = 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:
from mod_util import mx_handler
mxh = mx_handler()
asu_contents = mxh.get_asu_contents(iparams.n_residues)
observations_as_f = observations_non_polar.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 * 1e3
B = wp.wilson_b
except Exception:
txt_exception += "warning B-factor calculation failed.\n"
return None, txt_exception
from mod_leastsqr import calc_partiality_anisotropy_set
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, rotx, roty = (0, 0, iparams.gamma_e, 0, 0)
partiality_init, delta_xy_init, rs_init, rh_init = calc_partiality_anisotropy_set(
crystal_init_orientation.unit_cell(),
rotx,
roty,
observations_original.indices(),
ry,
rz,
r0,
re,
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))
# save results
refined_params = flex.double(
[
G,
B,
rotx,
roty,
ry,
rz,
r0,
re,
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:{3:10.3e} B:{4:7.1f} CELL:{5:6.2f} {6:6.2f} {7:6.2f} {8:6.2f} {9:6.2f} {10:6.2f}".format(
img_filename_only + " (" + polar_hkl + ")",
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 postrefine_by_frame(self, frame_no, pickle_filename, iparams, miller_array_ref, pres_in, avg_mode):
# 1. Prepare data
observations_pickle = pickle.load(open(pickle_filename, "rb"))
crystal_init_orientation = observations_pickle["current_orientation"][0]
wavelength = observations_pickle["wavelength"]
pickle_filepaths = pickle_filename.split("/")
img_filename_only = pickle_filepaths[len(pickle_filepaths) - 1]
txt_exception = " {0:40} ==> ".format(img_filename_only)
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 = inputs
else:
txt_exception += txt_organize_input + "\n"
return None, txt_exception
# 2. Determine polarity - always do this even if flag_polar = False
# the function will take care of it.
polar_hkl, cc_iso_raw_asu, cc_iso_raw_rev = self.determine_polar(
observations_original, iparams, pickle_filename, pres=pres_in
)
# 3. Select data for post-refinement (only select indices that are common with the reference set
observations_non_polar = self.get_observations_non_polar(observations_original, polar_hkl)
matches = miller.match_multi_indices(
miller_indices_unique=miller_array_ref.indices(), miller_indices=observations_non_polar.indices()
)
I_ref_match = flex.double([miller_array_ref.data()[pair[0]] for pair in matches.pairs()])
miller_indices_ref_match = flex.miller_index((miller_array_ref.indices()[pair[0]] for pair in matches.pairs()))
I_obs_match = flex.double([observations_non_polar.data()[pair[1]] for pair in matches.pairs()])
sigI_obs_match = flex.double([observations_non_polar.sigmas()[pair[1]] for pair in matches.pairs()])
miller_indices_original_obs_match = flex.miller_index(
(observations_original.indices()[pair[1]] for pair in matches.pairs())
)
miller_indices_non_polar_obs_match = flex.miller_index(
(observations_non_polar.indices()[pair[1]] for pair in matches.pairs())
)
alpha_angle_set = flex.double([alpha_angle[pair[1]] for pair in matches.pairs()])
spot_pred_x_mm_set = flex.double([spot_pred_x_mm[pair[1]] for pair in matches.pairs()])
spot_pred_y_mm_set = flex.double([spot_pred_y_mm[pair[1]] for pair in matches.pairs()])
references_sel = miller_array_ref.customized_copy(data=I_ref_match, indices=miller_indices_ref_match)
observations_original_sel = observations_original.customized_copy(
data=I_obs_match, sigmas=sigI_obs_match, indices=miller_indices_original_obs_match
)
observations_non_polar_sel = observations_non_polar.customized_copy(
data=I_obs_match, sigmas=sigI_obs_match, indices=miller_indices_non_polar_obs_match
)
# 4. Do least-squares refinement
lsqrh = leastsqr_handler()
try:
refined_params, stats, n_refl_postrefined = lsqrh.optimize(
I_ref_match,
observations_original_sel,
wavelength,
crystal_init_orientation,
alpha_angle_set,
spot_pred_x_mm_set,
spot_pred_y_mm_set,
iparams,
pres_in,
observations_non_polar_sel,
detector_distance_mm,
)
except Exception:
txt_exception += "optimization failed.\n"
return None, txt_exception
# caculate partiality for output (with target_anomalous check)
G_fin, B_fin, rotx_fin, roty_fin, ry_fin, rz_fin, r0_fin, re_fin, a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin = (
refined_params
)
inputs, txt_organize_input = self.organize_input(
observations_pickle, iparams, avg_mode, pickle_filename=pickle_filename
)
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, detector_distance_mm = inputs
observations_non_polar = self.get_observations_non_polar(observations_original, polar_hkl)
from cctbx.uctbx import unit_cell
uc_fin = unit_cell((a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin))
if pres_in is not None:
crystal_init_orientation = pres_in.crystal_orientation
two_theta = observations_original.two_theta(wavelength=wavelength).data()
from mod_leastsqr import calc_partiality_anisotropy_set
partiality_fin, dummy, rs_fin, rh_fin = calc_partiality_anisotropy_set(
uc_fin,
rotx_fin,
roty_fin,
observations_original.indices(),
ry_fin,
rz_fin,
r0_fin,
re_fin,
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,
)
# calculate the new crystal orientation
O = sqr(uc_fin.orthogonalization_matrix()).transpose()
R = sqr(crystal_init_orientation.crystal_rotation_matrix()).transpose()
from cctbx.crystal_orientation import crystal_orientation, basis_type
CO = crystal_orientation(O * R, basis_type.direct)
crystal_fin_orientation = CO.rotate_thru((1, 0, 0), rotx_fin).rotate_thru((0, 1, 0), roty_fin)
# remove reflections with partiality below threshold
i_sel = partiality_fin > iparams.merge.partiality_min
partiality_fin_sel = partiality_fin.select(i_sel)
rs_fin_sel = rs_fin.select(i_sel)
rh_fin_sel = rh_fin.select(i_sel)
observations_non_polar_sel = observations_non_polar.customized_copy(
indices=observations_non_polar.indices().select(i_sel),
data=observations_non_polar.data().select(i_sel),
sigmas=observations_non_polar.sigmas().select(i_sel),
)
observations_original_sel = observations_original.customized_copy(
indices=observations_original.indices().select(i_sel),
data=observations_original.data().select(i_sel),
sigmas=observations_original.sigmas().select(i_sel),
)
pres = postref_results()
pres.set_params(
observations=observations_non_polar_sel,
observations_original=observations_original_sel,
refined_params=refined_params,
stats=stats,
partiality=partiality_fin_sel,
rs_set=rs_fin_sel,
rh_set=rh_fin_sel,
frame_no=frame_no,
pickle_filename=pickle_filename,
wavelength=wavelength,
crystal_orientation=crystal_fin_orientation,
detector_distance_mm=detector_distance_mm,
)
r_change, r_xy_change, cc_change, cc_iso_change = (0, 0, 0, 0)
try:
r_change = ((pres.R_final - pres.R_init) / pres.R_init) * 100
r_xy_change = ((pres.R_xy_final - pres.R_xy_init) / pres.R_xy_init) * 100
cc_change = ((pres.CC_final - pres.CC_init) / pres.CC_init) * 100
cc_iso_change = ((pres.CC_iso_final - pres.CC_iso_init) / pres.CC_iso_init) * 100
except Exception:
pass
txt_postref = " {0:40} ==> RES:{1:5.2f} NREFL:{2:5d} R:{3:8.2f}% RXY:{4:8.2f}% CC:{5:6.2f}% CCISO:{6:6.2f}% G:{7:10.3e} B:{8:7.1f} CELL:{9:6.2f} {10:6.2f} {11:6.2f} {12:6.2f} {13:6.2f} {14:6.2f}".format(
img_filename_only + " (" + polar_hkl + ")",
observations_original_sel.d_min(),
len(observations_original_sel.data()),
r_change,
r_xy_change,
cc_change,
cc_iso_change,
pres.G,
pres.B,
a_fin,
b_fin,
c_fin,
alpha_fin,
beta_fin,
gamma_fin,
)
print txt_postref
txt_postref += "\n"
return pres, txt_postref
def postrefine_by_frame(self, frame_no, pres_in, iparams, miller_array_ref, avg_mode):
#Prepare data
if pres_in is None:
return None, 'Found empty pickle file'
observations_pickle = pickle.load(open(pres_in.pickle_filename,"rb"))
wavelength = observations_pickle["wavelength"]
crystal_init_orientation = observations_pickle["current_orientation"][0]
pickle_filename = pres_in.pickle_filename
pickle_filepaths = pickle_filename.split('/')
img_filename_only = pickle_filepaths[len(pickle_filepaths)-1]
txt_exception = ' {0:40} ==> '.format(img_filename_only)
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, identified_isoform, mapped_predictions, xbeam, ybeam = inputs
else:
txt_exception += txt_organize_input + '\n'
return None, txt_exception
#Select data for post-refinement (only select indices that are common with the reference set
observations_non_polar, index_basis_name = self.get_observations_non_polar(observations_original, pickle_filename, iparams)
matches = miller.match_multi_indices(
miller_indices_unique=miller_array_ref.indices(),
miller_indices=observations_non_polar.indices())
I_ref_match = flex.double([miller_array_ref.data()[pair[0]] for pair in matches.pairs()])
miller_indices_ref_match = flex.miller_index((miller_array_ref.indices()[pair[0]] for pair in matches.pairs()))
I_obs_match = flex.double([observations_non_polar.data()[pair[1]] for pair in matches.pairs()])
sigI_obs_match = flex.double([observations_non_polar.sigmas()[pair[1]] for pair in matches.pairs()])
miller_indices_original_obs_match = flex.miller_index((observations_original.indices()[pair[1]] \
for pair in matches.pairs()))
miller_indices_non_polar_obs_match = flex.miller_index((observations_non_polar.indices()[pair[1]] \
for pair in matches.pairs()))
alpha_angle_set = flex.double([alpha_angle[pair[1]] for pair in matches.pairs()])
spot_pred_x_mm_set = flex.double([spot_pred_x_mm[pair[1]] for pair in matches.pairs()])
spot_pred_y_mm_set = flex.double([spot_pred_y_mm[pair[1]] for pair in matches.pairs()])
references_sel = miller_array_ref.customized_copy(data=I_ref_match, indices=miller_indices_ref_match)
observations_original_sel = observations_original.customized_copy(data=I_obs_match,
sigmas=sigI_obs_match,
indices=miller_indices_original_obs_match)
observations_non_polar_sel = observations_non_polar.customized_copy(data=I_obs_match,
sigmas=sigI_obs_match,
indices=miller_indices_non_polar_obs_match)
#Do least-squares refinement
lsqrh = leastsqr_handler()
try:
refined_params, stats, n_refl_postrefined = lsqrh.optimize(I_ref_match,
observations_original_sel, wavelength,
crystal_init_orientation, alpha_angle_set,
spot_pred_x_mm_set, spot_pred_y_mm_set,
iparams,
pres_in,
observations_non_polar_sel,
detector_distance_mm)
except Exception:
txt_exception += 'optimization failed.\n'
return None, txt_exception
#caculate partiality for output (with target_anomalous check)
G_fin, B_fin, rotx_fin, roty_fin, ry_fin, rz_fin, r0_fin, re_fin, voigt_nu_fin, \
a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin = refined_params
inputs, txt_organize_input = self.organize_input(observations_pickle, iparams, avg_mode, pickle_filename=pickle_filename)
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, \
detector_distance_mm, identified_isoform, mapped_predictions, xbeam, ybeam = inputs
observations_non_polar, index_basis_name = self.get_observations_non_polar(observations_original, pickle_filename, iparams)
from cctbx.uctbx import unit_cell
uc_fin = unit_cell((a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin))
crystal_init_orientation = pres_in.crystal_orientation
two_theta = observations_original.two_theta(wavelength=wavelength).data()
ph = partiality_handler()
partiality_fin, dummy, rs_fin, rh_fin = ph.calc_partiality_anisotropy_set(uc_fin, rotx_fin, roty_fin,
observations_original.indices(),
ry_fin, rz_fin, r0_fin, re_fin, voigt_nu_fin,
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)
#calculate the new crystal orientation
O = sqr(uc_fin.orthogonalization_matrix()).transpose()
R = sqr(crystal_init_orientation.crystal_rotation_matrix()).transpose()
from cctbx.crystal_orientation import crystal_orientation, basis_type
CO = crystal_orientation(O*R, basis_type.direct)
crystal_fin_orientation = CO.rotate_thru((1,0,0), rotx_fin
).rotate_thru((0,1,0), roty_fin)
#remove reflections with partiality below threshold
i_sel = partiality_fin > iparams.merge.partiality_min
partiality_fin_sel = partiality_fin.select(i_sel)
rs_fin_sel = rs_fin.select(i_sel)
rh_fin_sel = rh_fin.select(i_sel)
observations_non_polar_sel = observations_non_polar.select(i_sel)
observations_original_sel = observations_original.select(i_sel)
mapped_predictions = mapped_predictions.select(i_sel)
pres = postref_results()
pres.set_params(observations = observations_non_polar_sel,
observations_original = observations_original_sel,
refined_params=refined_params,
stats=stats,
partiality=partiality_fin_sel,
rs_set=rs_fin_sel,
rh_set=rh_fin_sel,
frame_no=frame_no,
pickle_filename=pickle_filename,
wavelength=wavelength,
crystal_orientation=crystal_init_orientation,
detector_distance_mm=detector_distance_mm,
identified_isoform=identified_isoform,
mapped_predictions=mapped_predictions,
xbeam=xbeam,
ybeam=ybeam)
r_change, r_xy_change, cc_change, cc_iso_change = (0,0,0,0)
try:
r_change = ((pres.R_final - pres.R_init)/pres.R_init)*100
r_xy_change = ((pres.R_xy_final - pres.R_xy_init)/pres.R_xy_init)*100
cc_change = ((pres.CC_final - pres.CC_init)/pres.CC_init)*100
cc_iso_change = ((pres.CC_iso_final - pres.CC_iso_init)/pres.CC_iso_init)*100
except Exception:
pass
txt_postref= ' {0:40} ==> RES:{1:5.2f} NREFL:{2:5d} R:{3:8.2f}% RXY:{4:8.2f}% CC:{5:6.2f}% CCISO:{6:6.2f}% G:{7:10.3e} B:{8:7.1f} CELL:{9:6.2f} {10:6.2f} {11:6.2f} {12:6.2f} {13:6.2f} {14:6.2f}'.format(img_filename_only+' ('+index_basis_name+')', observations_original_sel.d_min(), len(observations_original_sel.data()), r_change, r_xy_change, cc_change, cc_iso_change, pres.G, pres.B, a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin)
print txt_postref
txt_postref += '\n'
return pres, txt_postref
def postrefine_by_frame(self, frame_no, pickle_filename, iph, miller_array_ref):
#1. Prepare data
observations_pickle = pickle.load(open(pickle_filename,"rb"))
crystal_init_orientation = observations_pickle["current_orientation"][0]
wavelength = observations_pickle["wavelength"]
#grab img. name
imgname = pickle_filename
if iph.file_name_in_img != '':
fh = file_handler()
imgname = fh.get_imgname_from_pickle_filename(iph.file_name_in_img, pickle_filename)
observations_original, alpha_angle_obs = self.organize_input(observations_pickle, iph)
if observations_original is None:
print frame_no, '-fail obs is none'
return None
#2. Determine polarity - always do this even if flag_polar = False
#the function will take care of it.
polar_hkl, cc_iso_raw_asu, cc_iso_raw_rev = self.determine_polar(observations_original, iph, pickle_filename)
#3. Select data for post-refinement (only select indices that are common with the reference set
observations_non_polar = self.get_observations_non_polar(observations_original, polar_hkl)
matches = miller.match_multi_indices(
miller_indices_unique=miller_array_ref.indices(),
miller_indices=observations_non_polar.indices())
I_ref_match = flex.double([miller_array_ref.data()[pair[0]] for pair in matches.pairs()])
miller_indices_ref_match = flex.miller_index((miller_array_ref.indices()[pair[0]] for pair in matches.pairs()))
I_obs_match = flex.double([observations_non_polar.data()[pair[1]] for pair in matches.pairs()])
sigI_obs_match = flex.double([observations_non_polar.sigmas()[pair[1]] for pair in matches.pairs()])
miller_indices_original_obs_match = flex.miller_index((observations_original.indices()[pair[1]] for pair in matches.pairs()))
alpha_angle_set = flex.double([alpha_angle_obs[pair[1]] for pair in matches.pairs()])
references_sel = miller_array_ref.customized_copy(data=I_ref_match, indices=miller_indices_ref_match)
observations_original_sel = observations_original.customized_copy(data=I_obs_match,
sigmas=sigI_obs_match,
indices=miller_indices_original_obs_match)
#4. Do least-squares refinement
lsqrh = leastsqr_handler()
refined_params, se_params, stats, partiality_sel, SE_I, var_I_p, var_k, var_p = lsqrh.optimize(I_ref_match, observations_original_sel,
wavelength, crystal_init_orientation, alpha_angle_set, iph)
if SE_I is None:
print 'frame', frame_no, ' - failed'
return None
else:
pres = postref_results()
observations_non_polar_sel = self.get_observations_non_polar(observations_original_sel, polar_hkl)
pres.set_params(observations = observations_non_polar_sel,
refined_params=refined_params,
se_params=se_params,
stats=stats,
partiality=partiality_sel,
frame_no=frame_no,
pickle_filename=pickle_filename,
wavelength=wavelength,
SE_I=SE_I,
var_I_p=var_I_p,
var_k=var_k,
var_p=var_p)
print 'frame %6.0f'%pres.frame_no, ' SE=%7.2f R-sq=%7.2f CC=%7.2f'%pres.stats, polar_hkl
return pres
def postrefine_by_frame(self, frame_no, pres_in, iparams, miller_array_ref,
avg_mode):
#Prepare data
if pres_in is None:
return None, 'Found empty pickle file'
observations_pickle = pickle.load(open(pres_in.pickle_filename, "rb"))
wavelength = observations_pickle["wavelength"]
crystal_init_orientation = observations_pickle["current_orientation"][
0]
pickle_filename = pres_in.pickle_filename
pickle_filepaths = pickle_filename.split('/')
img_filename_only = pickle_filepaths[len(pickle_filepaths) - 1]
txt_exception = ' {0:40} ==> '.format(img_filename_only)
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, identified_isoform, mapped_predictions, xbeam, ybeam = inputs
else:
txt_exception += txt_organize_input + '\n'
return None, txt_exception
#Select data for post-refinement (only select indices that are common with the reference set
observations_non_polar, index_basis_name = self.get_observations_non_polar(
observations_original, pickle_filename, iparams)
matches = miller.match_multi_indices(
miller_indices_unique=miller_array_ref.indices(),
miller_indices=observations_non_polar.indices())
I_ref_match = flex.double(
[miller_array_ref.data()[pair[0]] for pair in matches.pairs()])
miller_indices_ref_match = flex.miller_index(
(miller_array_ref.indices()[pair[0]] for pair in matches.pairs()))
I_obs_match = flex.double([
observations_non_polar.data()[pair[1]] for pair in matches.pairs()
])
sigI_obs_match = flex.double([
observations_non_polar.sigmas()[pair[1]]
for pair in matches.pairs()
])
miller_indices_original_obs_match = flex.miller_index((observations_original.indices()[pair[1]] \
for pair in matches.pairs()))
miller_indices_non_polar_obs_match = flex.miller_index((observations_non_polar.indices()[pair[1]] \
for pair in matches.pairs()))
alpha_angle_set = flex.double(
[alpha_angle[pair[1]] for pair in matches.pairs()])
spot_pred_x_mm_set = flex.double(
[spot_pred_x_mm[pair[1]] for pair in matches.pairs()])
spot_pred_y_mm_set = flex.double(
[spot_pred_y_mm[pair[1]] for pair in matches.pairs()])
references_sel = miller_array_ref.customized_copy(
data=I_ref_match, indices=miller_indices_ref_match)
observations_original_sel = observations_original.customized_copy(
data=I_obs_match,
sigmas=sigI_obs_match,
indices=miller_indices_original_obs_match)
observations_non_polar_sel = observations_non_polar.customized_copy(
data=I_obs_match,
sigmas=sigI_obs_match,
indices=miller_indices_non_polar_obs_match)
#Do least-squares refinement
lsqrh = leastsqr_handler()
try:
refined_params, stats, n_refl_postrefined = lsqrh.optimize(
I_ref_match, observations_original_sel, wavelength,
crystal_init_orientation, alpha_angle_set, spot_pred_x_mm_set,
spot_pred_y_mm_set, iparams, pres_in,
observations_non_polar_sel, detector_distance_mm)
except Exception:
txt_exception += 'optimization failed.\n'
return None, txt_exception
#caculate partiality for output (with target_anomalous check)
G_fin, B_fin, rotx_fin, roty_fin, ry_fin, rz_fin, r0_fin, re_fin, voigt_nu_fin, \
a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin = refined_params
inputs, txt_organize_input = self.organize_input(
observations_pickle,
iparams,
avg_mode,
pickle_filename=pickle_filename)
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, \
detector_distance_mm, identified_isoform, mapped_predictions, xbeam, ybeam = inputs
observations_non_polar, index_basis_name = self.get_observations_non_polar(
observations_original, pickle_filename, iparams)
from cctbx.uctbx import unit_cell
uc_fin = unit_cell(
(a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin))
crystal_init_orientation = pres_in.crystal_orientation
two_theta = observations_original.two_theta(
wavelength=wavelength).data()
ph = partiality_handler()
partiality_fin, dummy, rs_fin, rh_fin = ph.calc_partiality_anisotropy_set(
uc_fin, rotx_fin, roty_fin, observations_original.indices(),
ry_fin, rz_fin, r0_fin, re_fin, voigt_nu_fin, 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)
#calculate the new crystal orientation
O = sqr(uc_fin.orthogonalization_matrix()).transpose()
R = sqr(crystal_init_orientation.crystal_rotation_matrix()).transpose()
from cctbx.crystal_orientation import crystal_orientation, basis_type
CO = crystal_orientation(O * R, basis_type.direct)
crystal_fin_orientation = CO.rotate_thru(
(1, 0, 0), rotx_fin).rotate_thru((0, 1, 0), roty_fin)
#remove reflections with partiality below threshold
i_sel = partiality_fin > iparams.merge.partiality_min
partiality_fin_sel = partiality_fin.select(i_sel)
rs_fin_sel = rs_fin.select(i_sel)
rh_fin_sel = rh_fin.select(i_sel)
observations_non_polar_sel = observations_non_polar.select(i_sel)
observations_original_sel = observations_original.select(i_sel)
mapped_predictions = mapped_predictions.select(i_sel)
pres = postref_results()
pres.set_params(observations=observations_non_polar_sel,
observations_original=observations_original_sel,
refined_params=refined_params,
stats=stats,
partiality=partiality_fin_sel,
rs_set=rs_fin_sel,
rh_set=rh_fin_sel,
frame_no=frame_no,
pickle_filename=pickle_filename,
wavelength=wavelength,
crystal_orientation=crystal_init_orientation,
detector_distance_mm=detector_distance_mm,
identified_isoform=identified_isoform,
mapped_predictions=mapped_predictions,
xbeam=xbeam,
ybeam=ybeam)
r_change, r_xy_change, cc_change, cc_iso_change = (0, 0, 0, 0)
try:
r_change = ((pres.R_final - pres.R_init) / pres.R_init) * 100
r_xy_change = (
(pres.R_xy_final - pres.R_xy_init) / pres.R_xy_init) * 100
cc_change = ((pres.CC_final - pres.CC_init) / pres.CC_init) * 100
cc_iso_change = ((pres.CC_iso_final - pres.CC_iso_init) /
pres.CC_iso_init) * 100
except Exception:
pass
txt_postref = ' {0:40} ==> RES:{1:5.2f} NREFL:{2:5d} R:{3:8.2f}% RXY:{4:8.2f}% CC:{5:6.2f}% CCISO:{6:6.2f}% G:{7:10.3e} B:{8:7.1f} CELL:{9:6.2f} {10:6.2f} {11:6.2f} {12:6.2f} {13:6.2f} {14:6.2f}'.format(
img_filename_only + ' (' + index_basis_name + ')',
observations_original_sel.d_min(),
len(observations_original_sel.data()), r_change, r_xy_change,
cc_change, cc_iso_change, pres.G, pres.B, a_fin, b_fin, c_fin,
alpha_fin, beta_fin, gamma_fin)
print txt_postref
txt_postref += '\n'
return pres, txt_postref