def test_remove_millers():
"""
Confirming that remove_millers function yields the desired P1 completeness.
"""
base_path = "./reference/pdb_files/"
pdb_ids = ['6d6g', '4bfh', '2id8']
c_vals = np.random.rand(3)
for pdb, c in zip(pdb_ids, c_vals):
# generate reference structure factors
pdb_path = os.path.join(base_path, "%s.pdb" % pdb)
refI, refp = cctbx_utils.reference_sf(pdb_path,
4.0,
expand_to_p1=True,
table='electron')
# extract crystal symmetry object
pdb_input = iotbx.pdb.input(file_name=pdb_path)
cs = pdb_input.crystal_symmetry()
# test that remove millers yields correct completeness
refI_sel, refp_sel = mock_data.remove_millers(refI, refp, c)
ma = cctbx_utils.convert_to_sf(refI_sel.keys(),
np.array(refI_sel.values()),
np.deg2rad(np.array(refp_sel.values())),
cs.cell_equivalent_p1())
# ascertain within 2 percent of expected completeness
np.testing.assert_allclose(ma.completeness(), c, atol=0.02)
return
def test_find_origin():
import ProcessCrystals as proc
# set up paths, default values, reference structure factors
res, n_processes, grid_spacing = 3.0, 8, 1.0
pdb_path = "./reference/pdb_files/4bfh.pdb"
sg_symbol, sg_no, cell, cs = cctbx_utils.unit_cell_info(pdb_path)
refI, refp = cctbx_utils.reference_sf(pdb_path,
res,
expand_to_p1=True,
table='electron')
# confirm phase origin at (0,0,0) for reference structure
# equivalent origins should be ranked lower due to interpolation errors
fo = proc.FindOrigin(sg_symbol, cell, cs, refp, refI)
dmetrics, shifts = fo.scan_candidates(grid_spacing, n_processes)
np.testing.assert_array_equal(shifts, np.zeros(3))
# confirm that correct origin is identified if phases are shifted
t_shifts = np.random.random(3)
p_shifted = fo.shift_phases(t_shifts)
refp_s = OrderedDict((key, val) for key, val in zip(fo.hkl, p_shifted))
fo_s = proc.FindOrigin(sg_symbol, cell, cs, refp_s, refI)
data, shifts = fo_s.scan_candidates(grid_spacing, n_processes)
# compare identified shifts to permitted fractional shifts (pf_origins) for P 21 21 21
pf_origins = np.array([0.0, 0.5, 1.0])
tol = grid_spacing / np.array(cell)[:3]
residual = np.abs(1.0 - np.array(shifts) - t_shifts)
assert all(
np.min(np.abs(pf_origins - residual.reshape(3, 1)), axis=1) < tol)
return
def test_compare_crystals():
import ProcessCrystals as proc
# set up paths and default values
res, n_processes, grid_spacing = 3.0, 8, 1.0
# make sure correct shifts are found for two different PDB files
for pdb_id in ['4bfh', '6d6g']:
pdb_path = "./reference/pdb_files/%s.pdb" % pdb_id
sg_symbol, sg_no, cell, cs = cctbx_utils.unit_cell_info(pdb_path)
# compute reference and randomly-shifted data
tarI, tarp, eshifts = mock_data.generate_mock_data(pdb_path,
res,
completeness=1.0,
hkl_sel=None,
sigma=0.0)
refI, refp = cctbx_utils.reference_sf(pdb_path,
res,
expand_to_p1=True,
table='electron')
# use CompareCrystals class to calculate shifts that relate data
comp = proc.CompareCrystals(cell, tarp, hklI=tarI)
mgrid, hklp_shifted, fshifts = comp.grid_shift(refp,
grid_spacing,
n_processes,
hklI_ref=refI)
# ensure computed shifts are below tolerance
tol = grid_spacing / np.array(cell)[:3]
assert all(np.abs(1.0 - fshifts - eshifts) < tol)
return
def simulate_damage(specs_path, pdb_path, resolution, tilt_order, bfactor):
"""
Simulate damage according to the model: I(q,n) = I(q,0)*exp(-n*B*q^2/(16*pi^2)), where
n is proportional to the amount of dose received, B is the initial B factor, and I(q,0)
is the initial intensity of the reflection under consideration. The B-factor increases
linearly with dose, which itself increases linearly with the number of tilts collected.
The simulated structure factors are treated as though they belong to a crystal randomly
oriented relative to the missing wedge.
Inputs:
-------
pdb_path: path to reference PDB file
specs_path: path to file indicating details of data collection strategy
resolution: high-resolution limit of structure factors
tilt_order: tilt angles in order of image collected, first to last
bfactor: initial B factor in Angstrom squared at image 0
Outputs:
--------
hklI: dict whose keys are Millers and values are 'damaged' intensities
hklp: dict whose keys are Millers in hklI and values are reference phases
A: crystal setting matrix, randomly oriented
"""
# compute reference (undamaged) structure factors
refI, refp = cctbx_utils.reference_sf(pdb_path,
resolution,
expand_to_p1=True)
# compute tilt angles for structure factors in a random orientation
A = generate_random_A(pdb_path)
hklt = predict_angles(specs_path, pdb_path, resolution, A)
tilts = np.array(hklt.values())
# convert tilts to n, proportional to dose, by interpolating to nearest tilt
tilt_order = OrderedDict((v, c) for c, v in enumerate(tilt_order))
n = np.zeros_like(tilts)
for i in range(len(tilts)):
nearest_tilt = min(tilt_order, key=lambda x: abs(x - tilts[i]))
n[i] = tilt_order[nearest_tilt]
n += 1 # this way, first image effectively receives some dose
# compute qmags and initial intensity
qvecs = np.inner(A, np.squeeze(np.array(hklt.keys()))).T
qmags = np.linalg.norm(qvecs, axis=1)
I_0 = np.array([refI[key] for key in hklt.keys()])
# compute damaged intensities
I_d = I_0 * np.exp(-(bfactor /
(16 * np.square(np.pi))) * n * np.square(qmags))
hklI = OrderedDict((key, val) for key, val in zip(hklt.keys(), I_d))
hklp = OrderedDict((key, refp[key]) for key in hklI.keys())
return hklI, hklp, hklt, A
def test_merge_crystals():
import ProcessCrystals as proc
res, grid_spacing, n_processes = 3.3, 0.5, 8
for pdb_id in ['4bfh', '6d6g']:
# generate reference information and structure factors
pdb_path = "./reference/pdb_files/%s.pdb" % pdb_id
sg_symbol, sg_no, cell, cs = cctbx_utils.unit_cell_info(pdb_path)
refI, refp = cctbx_utils.reference_sf(pdb_path,
res,
expand_to_p1=True,
table='electron')
# generate randomly-shifted, half-complete (in P1) mock data
hklI1, hklp1, eshifts1 = mock_data.generate_mock_data(pdb_path,
res,
completeness=0.5,
sigma=0.0)
hklI2, hklp2, eshifts2 = mock_data.generate_mock_data(pdb_path,
res,
completeness=0.5,
sigma=0.0)
# merge crystals using MergeCrystals class
mc = proc.MergeCrystals(space_group=sg_no, grid_spacing=grid_spacing)
mc.add_crystal(hklI1,
hklp1,
np.array(cell),
n_processes=n_processes,
weighted=True)
mc.add_crystal(hklI2,
hklp2,
np.array(cell),
n_processes=n_processes,
weighted=True)
# check that fractional shift for merge matches expected value
tol = np.max(grid_spacing / np.array(cell)[:3])
p_origins = np.array([0.0, 1.0])
c_origin = eshifts2 - eshifts1 + mc.fshifts[1]
assert all(
np.min(np.abs(p_origins - c_origin.reshape(3, 1)), axis=1) < tol)
# check that intensities of merged data match reference
Imerge, pmerge = mc.merge_values()
assert all(
np.array([(Imerge[hkl] - refI[hkl]) / Imerge[hkl]
for hkl in Imerge.keys()]) < 1e-6)
return
def test_reduce_crystals():
import ProcessCrystals as proc
# set up paths and default values
pdb_path, res = "./reference/pdb_files/4bfh.pdb", 3.0
sg_symbol, sg_no, cell, cs = cctbx_utils.unit_cell_info(pdb_path)
refI, refp = cctbx_utils.reference_sf(pdb_path,
res,
expand_to_p1=True,
table='electron')
refI_mod = OrderedDict((key, np.array([val])) for key, val in refI.items())
refp_mod = OrderedDict((key, np.array([val])) for key, val in refp.items())
# check that reduced phases are internally consistent
rc = proc.ReduceCrystals(refI_mod, refp_mod, cell, sg_symbol)
p_asu = rc.reduce_phases(weighted=True)
rc.reduce_intensities()
eq = np.array([np.allclose(v, v[0]) for v in p_asu.values()])
assert all([
np.allclose(np.around(p_asu.values()[i]) % 180, 0)
for i in np.where(eq == False)[0]
])
# check that reduced phases and intensities match reference
hkl_asu = list(
cs.build_miller_set(anomalous_flag=False, d_min=res).indices())
assert np.allclose(
utils.wraptopi(
np.array([rc.data['PHIB'][hkl] - refp[hkl] for hkl in hkl_asu])),
0)
assert np.allclose(
np.array([rc.data['IMEAN'][hkl] - refI[hkl] for hkl in hkl_asu]), 0)
# check that shifting phases from origin leads to loss of symmetry-expected relationships
rc.shift_phases(np.random.random(3))
p_asu = rc.reduce_phases(weighted=True)
eq = np.array([np.allclose(v, v[0]) for v in p_asu.values()])
assert not all([
np.allclose(np.around(p_asu.values()[i]) % 180, 0)
for i in np.where(eq == False)[0]
])
return
def test_residual_phases():
"""
Validating residual_phases function with a different (slightly slower) implementation.
"""
pdb_path, res = "./reference/pdb_files/4bfh.pdb", 3.0
refI, refp = cctbx_utils.reference_sf(pdb_path,
res,
expand_to_p1=True,
table='electron')
refp_err = mock_data.add_phase_errors(refp, 10.0)
p_shared = utils.shared_dict(refp, refp_err)
p_vals = np.deg2rad(np.array(p_shared.values()))
p1, p2 = p_vals[:, 0], p_vals[:, 1]
pr_ref = np.rad2deg(np.arccos(np.cos(p1 - p2)))
pr_est = utils.residual_phases(refp, refp_err)
np.testing.assert_allclose(pr_ref, pr_est)
return
def simulate_one_tilt(pdb_path, resolution, angle, A=np.eye(3), ang_width=1.0):
"""
Generate reflection data within +/- ang_width of input angle, and subject to a
random, global phase shift.
Inputs:
-------
pdb_path: path to coordinates file
resolution: maximum resolution to which to compute reflection data
angle: tilt angle reflection data are centered on
A: orientation matrix by which to rotate crystal, optional
ang_width: half of increment within angle for which reflections will be kept
Outputs:
--------
hklI_sel: OrderedDict of retained Miller indices and corresponding intensities
hklp_sel: OrderedDict of retained Miller indices and corresponding phases
shifts: global phase shift reflection data were subjected to
"""
# compute reference information
sg_symbol, sg_no, cell, cs = cctbx_utils.unit_cell_info(pdb_path)
refI, refp = cctbx_utils.reference_sf(pdb_path,
resolution,
expand_to_p1=True)
# rotate reflection data and compute tilt angles
hklt = predict_angles(pdb_path, resolution, A)
hkl, tilts = np.array(hklt.keys()), np.array(hklt.values())
# retain reflection data in observed angular range; add random phase shift
hkl_sel = hkl[np.where((tilts > angle - ang_width)
& (tilts < angle + ang_width))[0]]
hkl_sel = [tuple(h) for h in hkl_sel]
hkl_sel = utils.remove_Friedels(hkl_sel)
hklI_sel = OrderedDict(
(key, val) for key, val in refI.items() if key in hkl_sel)
hklp_sel = OrderedDict(
(key, val) for key, val in refp.items() if key in hkl_sel)
hklp_sel, shifts = mock_data.add_random_phase_shift(hklp_sel)
return hklI_sel, hklp_sel, shifts
def generate_mock_data(ref_path,
resolution,
completeness=1.0,
hkl_sel=None,
sigma=0.0):
"""
Generate a modified hklp dictionary in which phases have been shifted from
the reference origin and phase errors have been introduced. Remove Millers
either not listed in hkl_sel or to achieve specified completeness.
Inputs:
-------
ref_path: path to reference PDB file
resolution: high-resolution limit of structure factors
completeness: fraction of Millers to retain, default is 1.0
hkl_sel: list of Millers to retain; if None (default), retain all
sigma: standard deviation for error distribution, default is 0.0
Outputs:
--------
hklI: dict whose keys are Millers and values are intensities
hklp: dict whose keys are Millers and values are phases, ordered as hklI
shifts: fractional shifts by which phase origin has been translated
"""
hklI, hklp = cctbx_utils.reference_sf(ref_path,
resolution,
expand_to_p1=True,
table='electron')
if completeness != 1.0:
hklI, hklp = remove_millers(hklI, hklp, completeness)
if hkl_sel is not None:
hklI, hklp = retain_millers(hklI, hklp, hkl_sel)
hklp, shifts = add_random_phase_shift(hklp)
hklp = add_phase_errors(hklp, sigma, friedels_same=True)
return hklI, hklp, shifts
def assess_tr_data(args):
"""
Evaluate a series of mock crystals from randomly oriented tomograms.
Inputs:
-------
args: dict of pdb_path, n_processes, grid_spacing, resolution, n_cryst, data_paths
Outputs:
--------
results: dict of r_ref, r_sym, completeness, cc_map
shifts: dict of shifts for merging, finding origin, comparing to reference
"""
# compute reference information
pdb_input = iotbx.pdb.input(file_name=args['pdb_path'])
ref_sg_symbol, ref_sg_no, ref_cell, ref_cs = cctbx_utils.unit_cell_info(
args['pdb_path'])
refI, refp = cctbx_utils.reference_sf(args['pdb_path'],
args['resolution'],
expand_to_p1=True,
table='electron')
refp_mod = OrderedDict((key, np.array([val])) for key, val in refp.items())
# set up dictionary for storing results
results, shifts = dict(), dict()
for key in [
'r_ref', 'r_sym', 'completeness_p1', 'completeness_sg', 'cc_map',
'cc_I'
]:
results[key] = np.zeros(args['n_cryst'])
for key in ['reference', 'origin', 'merge']:
shifts[key] = np.zeros((args['n_cryst'], 3))
if ref_sg_no == 1: shifts.pop('origin')
# set up MergeCrystals class for scan over crystals
mc = proc.MergeCrystals(space_group=ref_sg_no,
grid_spacing=args['grid_spacing'])
for num in range(args['n_cryst']):
print "Intensites from: %s" % (args['data_paths']['intensities'][num])
print "Phases from: %s" % (args['data_paths']['phases'][num])
print "Indexing matrix from: %s" % (args['data_paths']['json'][num])
# load simulated data
hklI = pickle.load(open(args['data_paths']['intensities'][num]))
hklp = pickle.load(open(args['data_paths']['phases'][num]))
sg_symbol, sg_no, cell, cs = cctbx_utils.unit_cell_info(
args['data_paths']['json'][num])
assert sg_symbol == ref_sg_symbol
# filter Millers below resolution cutoff and with negative intensities
res = utils.compute_resolution(sg_no, cell, np.array(hklp.keys()))
indices = np.where((res < args['resolution'])
| (np.array(hklI.values()) < 0))[0]
hkl_to_remove = list()
for idx in indices:
hkl_to_remove.append(hklp.keys()[idx])
for htr in hkl_to_remove:
hklp.pop(htr)
hklI.pop(htr)
# merge crystal
mc.add_crystal(hklI,
hklp,
np.array(cell),
n_processes=args['n_processes'],
weighted=True)
hklI_avg, hklp_avg = mc.merge_values()
avg_cell = tuple(np.average(mc.cell_constants.values(), axis=0))
# locate crystallographic origin if not triclinic
if sg_no != 1:
fo = proc.FindOrigin(sg_symbol, avg_cell, cs, hklp_avg, hklI_avg)
dmetrics, shifts['origin'][num] = fo.scan_candidates(
args['grid_spacing'], args['n_processes'])
# shift phases to match reference for easier comparison
comp = proc.CompareCrystals(avg_cell, hklp_avg, hklI_avg)
m_grid, hklp_shifted, shifts['reference'][num] = comp.grid_shift(
refp, args['grid_spacing'], args['n_processes'], refI)
hklI_merge = OrderedDict((key, val) for key, val in mc.hklI.items())
hklp_merge = OrderedDict((key, val) for key, val in mc.hklp.items())
# reduce phases to asymmetric unit
rc = proc.ReduceCrystals(hklI_merge, hklp_merge, avg_cell, sg_symbol)
rc.shift_phases(shifts['reference'][num])
p_asu = rc.reduce_phases(weighted=True)
rc.reduce_intensities()
mtz_object = rc.generate_mtz()
# examine cross-correlation with reference map
savename = os.path.join(args['output'], "reduced%i.ccp4" % num)
ma = cctbx_utils.mtz_to_miller_array(mtz_object)
map_sim = cctbx_utils.compute_map(ma, save_name=savename)
results['cc_map'][num] = cctbx_utils.compare_maps(
map_sim, args['pdb_path'])
# compute completeness -- both (non-anomalous) P1 and space group
for cs_obj, tag in zip([cs, cs.cell_equivalent_p1()],
['completeness_sg', 'completeness_p1']):
ma_calc = miller.array(
miller_set=miller.set(cs_obj,
flex.miller_index(rc.hklI.keys()),
anomalous_flag=False),
data=flex.double(np.ones(len(rc.hklI.keys()))))
results[tag][num] = ma_calc.merge_equivalents().array(
).completeness()
# assess quality of phases
phib = OrderedDict(
(key, np.array([val])) for key, val in rc.data['PHIB'].items())
results['r_ref'][num] = np.mean(utils.residual_phases(phib, refp_mod))
results['r_sym'][num] = np.mean(utils.residual_to_avgphase(p_asu))
# assess accuracy of intensities: log-log cross-correlation
ivals = np.array(utils.shared_dict(refI, rc.data['IMEAN']).values())
results['cc_I'][num] = np.corrcoef(np.log10(ivals[:, 0]),
np.log10(ivals[:, 1]))[0, 1]
# examine errors in predicted fractional shifts
if num != 0:
shifts['merge'][num] = mc.fshifts[num]
print "\n"
return results, shifts
start_time = time.time()
# extract command line arguments
args = parse_commandline()
args['resolution'] = 3.3
args[
'grid_spacing'] = 0.5 # grid spacing used for crystallographic origin search
args[
'n_candidates'] = 150 # number of origin candidates to consider per tilt image
args['threshold'] = 4.0 # phase residual threshold for adding a tilt image
# reference information
args['sg_symbol'], args['sg_no'], args['cell'], args[
'cs'] = cctbx_utils.unit_cell_info(args['pdb_path'])
refI, refp = cctbx_utils.reference_sf(args['pdb_path'],
args['resolution'],
expand_to_p1=True)
refp_mod = OrderedDict((key, np.array([val])) for key, val in refp.items())
eq_origins = np.array([[0, 0, 0], [0, 0, 0.5], [0.5, 0.5, 0],
[0.5, 0.5, 0.5]])
# additional set-up information
pnames = os.path.join(args['dpath'], "pimage_*_p.pickle")
tilt_angles = np.loadtxt(args['tilt_angles'])
args['hkl_p1'], args['hkl_asu'] = retrieve_hkl(pnames, args['cs'],
tilt_angles)
remaining_angles = list(tilt_angles)
# information to track during merging
comp_sg, p_residuals = np.zeros(len(tilt_angles)), np.zeros(
len(tilt_angles))
def assess_mock_data(args, rep):
"""
Evaluate a series of mock crystals with specified completeness and phase errors.
Inputs:
-------
args: dict specifying n_processes, grid_spacing, pdb_path, resolution, n_cryst,
completeness, sigma, specs_path, tilt_list, bfactor
rep: repetition number currently being computed
Outputs:
--------
results: dict of r_ref, r_sym, completeness, cc_map
shifts: dict of expected, merging, and origin shifts
hkl_tilts_Id: dict of retained hkl: np.array([tilt angle, intensity])
"""
# compute reference information
sg_symbol, sg_no, cell, cs = cctbx_utils.unit_cell_info(args['pdb_path'])
refI, refp = cctbx_utils.reference_sf(args['pdb_path'],
args['resolution'],
expand_to_p1=True,
table='electron')
refp_mod = OrderedDict((key, np.array([val])) for key, val in refp.items())
# set up dictionary for storing results
results, shifts, hkl_tilts_Id, A_matrices = dict(), dict(), dict(), dict()
for key in [
'sigma_i', 'r_ref_i', 'sigma', 'r_ref', 'r_sym', 'completeness_p1',
'completeness_sg', 'cc_map', 'cc_I'
]:
results[key] = np.zeros(args['n_cryst'])
for key in ['reference', 'expected', 'origin', 'merge']:
shifts[key] = np.zeros((args['n_cryst'], 3))
if sg_no == 1:
shifts.pop('origin')
# set up MergeCrystals class for scan over crystals
mc = proc.MergeCrystals(space_group=sg_no,
grid_spacing=args['grid_spacing'])
for num in range(args['n_cryst']):
# apply analytic model of damage to a randomly-oriented crystal
hklId, hklpd, hkltd, A_matrices[num] = mock_data.simulate_damage(
args['specs_path'], args['pdb_path'], args['resolution'],
args['tilt_list'], args['bfactor'])
# retain highest intensity reflections for specified completeness
hkl_sel = select_hkl(cs, args['resolution'], args['completeness'],
hklId)
# generate mock data based on above hkl list
hklI, hklp, shifts['expected'][num] = mock_data.generate_mock_data(
args['pdb_path'],
args['resolution'],
completeness=1.0,
hkl_sel=hkl_sel,
sigma=args['sigma'])
hklI_d = OrderedDict((key, hklId[key]) for key in hklI.keys())
hkl_tilts_Id[num] = OrderedDict(
(key, np.array([hkltd[key], hklI_d[key]])) for key in hklI.keys())
# compute starting phase errors and store
sigma_i, m_error = initial_errors(hklp, refp, shifts['expected'][num])
results['sigma_i'][num], results['r_ref_i'][num] = sigma_i, m_error
# add to MergeCrystals object
mc.add_crystal(hklI_d,
hklp,
np.array(cell),
n_processes=args['n_processes'],
weighted=True)
hklI_avg, hklp_avg = mc.merge_values()
# locate crystallographic origin if not triclinic
##if sg_no != 1:
## fo = proc.FindOrigin(sg_symbol, cell, cs, hklp_avg, hklI_avg)
## dmetrics, shifts['origin'][num] = fo.scan_candidates(args['grid_spacing'],
## args['n_processes'])
# shift phases to match reference for easier comparison
comp = proc.CompareCrystals(cell, hklp_avg, hklI_avg)
m_grid, hklp_shifted, shifts['reference'][num] = comp.grid_shift(
refp, args['grid_spacing'], args['n_processes'], refI)
hklI_merge = OrderedDict((key, val) for key, val in mc.hklI.items())
hklp_merge = OrderedDict((key, val) for key, val in mc.hklp.items())
# reduce phases to asymmetric unit
rc = proc.ReduceCrystals(hklI_merge, hklp_merge, cell, sg_symbol)
rc.shift_phases(shifts['reference'][num])
p_asu = rc.reduce_phases(weighted=True)
rc.reduce_intensities()
mtz_object = rc.generate_mtz()
# examine completeness and cross-correlation with reference map
#savename = "%s_r%in%i.ccp4" %(args['output'],rep,num)
ma = cctbx_utils.mtz_to_miller_array(mtz_object)
map_sim = cctbx_utils.compute_map(ma)
results['cc_map'][num] = cctbx_utils.compare_maps(
map_sim, args['pdb_path'])
# compute completeness -- both (non-anomalous) P1 and space group
for cs_obj, tag in zip([cs, cs.cell_equivalent_p1()],
['completeness_sg', 'completeness_p1']):
ma_calc = miller.array(
miller_set=miller.set(cs_obj,
flex.miller_index(rc.hklI.keys()),
anomalous_flag=False),
data=flex.double(np.ones(len(rc.hklI.keys()))))
results[tag][num] = ma_calc.merge_equivalents().array(
).completeness()
# assess quality of phases
results['sigma'][num], results['r_ref'][
num] = utils.residual_phase_distribution(refp, rc.data['PHIB'])
results['r_sym'][num] = np.mean(utils.residual_to_avgphase(p_asu))
# assess accuracy of intensities: log-log cross-correlation
ivals = np.array(utils.shared_dict(refI, rc.data['IMEAN']).values())
results['cc_I'][num] = np.corrcoef(np.log10(ivals[:, 0]),
np.log10(ivals[:, 1]))[0, 1]
# examine errors in predicted fractional shifts
if num != 0:
shifts['merge'][num] = mc.fshifts[num]
print "\n"
return results, shifts, hkl_tilts_Id, A_matrices
