def find_merge_common_images(args):
phil = iotbx.phil.process_command_line(args = args,
master_string = master_phil).show()
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")) :
raise Usage("cxi.merge "
"d_min=4.0 "
"data=~/scratch/r0220/006/strong/ "
"model=3bz1_3bz2_core.pdb")
if ((work_params.rescale_with_average_cell) and
(not work_params.set_average_unit_cell)) :
raise Usage(
"If rescale_with_average_cell=True, you must also specify "+
"set_average_unit_cell=True.")
# Read Nat's reference model from an MTZ file. XXX The observation
# type is given as F, not I--should they be squared? Check with Nat!
log = open("%s_%s_scale.log" % (work_params.output.prefix,
work_params.scaling.algorithm), "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print >> out, "Target unit cell and space group:"
print >> out, " ", work_params.target_unit_cell
print >> out, " ", work_params.target_space_group
uc = work_params.target_unit_cell
miller_set = symmetry(
unit_cell=work_params.target_unit_cell,
space_group_info=work_params.target_space_group
).build_miller_set(
anomalous_flag=not work_params.merge_anomalous,
d_min=work_params.d_min)
print 'Miller set size: %d' % len(miller_set.indices())
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.read_all()
print "finished reading"
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
miller_set.show_summary()
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
def nint(a):
return int(round(a))
from collections import defaultdict
i_scale = 0.1
i_hist = defaultdict(int)
for j, se in enumerate(zip(starts, ends)):
s, e = se
for i in intensi[s:e]:
i_hist[nint(i_scale * i)] += 1
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
fout = open('i_hist.dat', 'w')
for i in i_hist:
fout.write('%.2f %d\n' % (i / i_scale, i_hist[i]))
fout.close()
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
frames = []
odd = 0
even = 0
for s, e in zip(starts, ends):
for x in range(s, e):
hkl = lookup[hkl_asu[x]]
if (hkl[0] + hkl[1] + hkl[2]) % 2 == 1:
odd += 1
else:
even += 1
indices = [tuple(lookup[hkl_asu[x]]) for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(uc, indices, intensities, sigmas))
# pre-scale the data - first determine average ln(k), B; then apply
kbs = [f.kb() for f in frames]
mn_k = sum([kb[0] for kb in kbs]) / len(kbs)
mn_B = sum([kb[1] for kb in kbs]) / len(kbs)
n_lt_500 = 0
n_gt_500 = 0
for j, f in enumerate(frames):
s_i = f.scale_to_kb(mn_k, mn_B)
fout = open('frame-s-i-%05d.dat' % j, 'w')
for s, i, si in s_i:
fout.write('%f %f %f\n' % (s, i, si))
if i < 500:
n_lt_500 += 1
else:
n_gt_500 += 1
fout.close()
from collections import defaultdict
hist = defaultdict(int)
fout = open('kb.dat', 'w')
for j, f in enumerate(frames):
kb = f.kb()
fout.write('%4d %6.3f %6.3f\n' % (j, kb[0], kb[1]))
hist[int(round(kb[1]))] += 1
fout.close()
for b in sorted(hist):
print b, hist[b]
print odd, even
print n_lt_500, n_gt_500
return
def run(args):
phil = iotbx.phil.process_command_line(
args = args, master_string = master_phil)
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")):
raise Usage("cxi.merge "
"d_min=4.0 "
"data=~/scratch/r0220/006/strong/ "
"model=3bz1_3bz2_core.pdb")
if ((work_params.rescale_with_average_cell) and
(not work_params.set_average_unit_cell)) :
raise Usage("If rescale_with_average_cell=True, you must also specify "+
"set_average_unit_cell=True.")
miller_set = symmetry(
unit_cell = work_params.target_unit_cell,
space_group_info = work_params.target_space_group
).build_miller_set(
anomalous_flag = not work_params.merge_anomalous,
d_min = work_params.d_min)
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set = miller_set,
i_model = i_model,
params = work_params)
scaler.read_all()
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
rational_ops = []
for symop in pg:
rational_ops.append((matrix.sqr(symop.r().transpose().as_rational()),
symop.r().as_hkl()))
# miller_set.show_summary()
uc = work_params.target_unit_cell
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
origH = scaler.observations["H"]
origK = scaler.observations["K"]
origL = scaler.observations["L"]
from cctbx.miller import map_to_asu
sgtype = miller_set.space_group_info().type()
aflag = miller_set.anomalous_flag()
from cctbx.array_family import flex
# FIXME in here perform the mapping to ASU for both the original and other
# index as an array-wise manipulation to make things a bunch faster...
# however this also uses a big chunk of RAM... FIXME also in here use
# cb_op.apply(indices) to get the indices reindexed...
original_indices = flex.miller_index()
for x in xrange(len(scaler.observations["hkl_id"])):
original_indices.append(lookup[hkl_asu[x]])
from cctbx.sgtbx import change_of_basis_op
I23 = change_of_basis_op('k, -h, l')
other_indices = I23.apply(original_indices)
map_to_asu(sgtype, aflag, original_indices)
map_to_asu(sgtype, aflag, other_indices)
# FIXME would be useful in here to have a less expensive way of finding the
# symmetry operation which gave the map to the ASU - perhaps best way is to
# make a new C++ map_to_asu which records this.
# FIXME in here recover the original frame structure of the data to
# logical frame objetcs - N.B. the frame will need to be augmented to test
# alternative indexings
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
for j, se in enumerate(zip(starts, ends)):
print 'processing frame %d: %d to %d' % (j, se[0], se[1])
s, e = se
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
# then start running the comparison code
frames = []
for s, e in zip(starts, ends):
# FIXME need this from remap to ASU
misym = [0 for x in range(s, e)]
indices = [original_indices[x] for x in range(s, e)]
other = [other_indices[x] for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(uc, indices, other, intensities, sigmas))
reference = FrameFromReferenceMTZ()
fout = open('cc_reference.log', 'w')
for j, f in enumerate(frames):
_cc = reference.cc(f)
_oo = reference.cc_other(f)
print '%d %d %d %d %f %d %f' % (j, starts[j], ends[j], _cc[0], _cc[1],
_oo[0], _oo[1])
fout.write('%d %d %d %d %f %d %f\n' % (j, starts[j], ends[j],
_cc[0], _cc[1], _oo[0], _oo[1]))
fout.close()
return
def find_merge_common_images(args):
phil = iotbx.phil.process_command_line(args = args,
master_string = master_phil).show()
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")) :
raise Usage("cxi.merge "
"d_min=4.0 "
"data=~/scratch/r0220/006/strong/ "
"model=3bz1_3bz2_core.pdb")
if ((work_params.rescale_with_average_cell) and
(not work_params.set_average_unit_cell)) :
raise Usage(
"If rescale_with_average_cell=True, you must also specify "+
"set_average_unit_cell=True.")
# Read Nat's reference model from an MTZ file. XXX The observation
# type is given as F, not I--should they be squared? Check with Nat!
log = open("%s_%s_scale.log" % (work_params.output.prefix,
work_params.scaling.algorithm), "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print >> out, "Target unit cell and space group:"
print >> out, " ", work_params.target_unit_cell
print >> out, " ", work_params.target_space_group
uc = work_params.target_unit_cell
miller_set = symmetry(
unit_cell=work_params.target_unit_cell,
space_group_info=work_params.target_space_group
).build_miller_set(
anomalous_flag=not work_params.merge_anomalous,
d_min=work_params.d_min)
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.read_all()
print "finished reading"
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
miller_set.show_summary()
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
for j, se in enumerate(zip(starts, ends)):
s, e = se
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
frames = []
for s, e in zip(starts, ends):
indices = [tuple(lookup[hkl_asu[x]]) for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(uc, indices, intensities, sigmas))
cycle = 0
total_nref = sum([len(f.get_indices()) for f in frames])
# pre-scale the data - first determine average ln(k), B; then apply
kbs = [f.kb() for f in frames]
mn_k = sum([kb[0] for kb in kbs]) / len(kbs)
mn_B = sum([kb[1] for kb in kbs]) / len(kbs)
for f in frames:
f.scale_to_kb(mn_k, mn_B)
while True:
print 'Analysing %d frames' % len(frames)
print 'Cycle %d' % cycle
cycle += 1
print 'Power spectrum'
fn = frame_numbers(frames)
for j in sorted(fn):
print '%4d %4d' % (j, fn[j])
nref_cycle = sum([len(f.get_indices()) for f in frames])
assert(nref_cycle == total_nref)
common_reflections = numpy.zeros((len(frames), len(frames)),
dtype = numpy.short)
obs = { }
from cctbx.sgtbx import rt_mx, change_of_basis_op
oh = change_of_basis_op(rt_mx('h,l,k'))
for j, f in enumerate(frames):
indices = set(f.get_indices())
for i in indices:
_i = tuple(i)
if not _i in obs:
obs[_i] = []
obs[_i].append(j)
# work through unique observations ignoring those which include no
# hand information
for hkl in obs:
if hkl == oh.apply(hkl):
continue
obs[hkl].sort()
for j, f1 in enumerate(obs[hkl][:-1]):
for f2 in obs[hkl][j + 1:]:
common_reflections[(f1, f2)] += 1
cmn_rfl_list = []
for f1 in range(len(frames)):
for f2 in range(f1 + 1, len(frames)):
if common_reflections[(f1, f2)] > 20:
cmn_rfl_list.append((common_reflections[(f1, f2)], f1, f2))
cmn_rfl_list.sort()
cmn_rfl_list.reverse()
joins = []
used = []
for n, f1, f2 in cmn_rfl_list:
if f1 in used or f2 in used:
continue
_cc = frames[f1].cc(frames[f2])
# really only need to worry about f2 which will get merged...
# merging multiple files together should be OK provided they are
# correctly sorted (though the order should not matter anyhow?)
# anyhow they are sorted anyway... ah as f2 > f1 then just sorting
# the list by f2 will make sure the data cascase correctly.
# p-value very small for cc > 0.75 for > 20 observations - necessary
# as will be correlated due to Wilson curves
if _cc[0] > 20 and _cc[1] > 0.75:
print '%4d %.3f' % _cc, f1, f2
joins.append((f2, f1))
# used.append(f1)
used.append(f2)
if not joins:
print 'No pairs found'
break
joins.sort()
joins.reverse()
for j2, j1 in joins:
rmerge = frames[j1].merge(frames[j2])
if rmerge:
print 'R: %4d %4d %6.3f' % (j1, j2, rmerge)
else:
print 'R: %4d %4d ------' % (j1, j2)
continue
frames.sort()
print 'Biggest few: #frames; #unique refl'
j = -1
while frames[j].get_frames() > 1:
print frames[j].get_frames(), frames[j].get_unique_indices()
j -= 1
return
def run(args):
phil = iotbx.phil.process_command_line(
args = args, master_string = master_phil)
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")):
raise Usage("cxi.merge "
"d_min=4.0 "
"data=~/scratch/r0220/006/strong/ "
"model=3bz1_3bz2_core.pdb")
if ((work_params.rescale_with_average_cell) and
(not work_params.set_average_unit_cell)) :
raise Usage("If rescale_with_average_cell=True, you must also specify "+
"set_average_unit_cell=True.")
miller_set = symmetry(
unit_cell = work_params.target_unit_cell,
space_group_info = work_params.target_space_group
).build_miller_set(
anomalous_flag = not work_params.merge_anomalous,
d_min = work_params.d_min)
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set = miller_set,
i_model = i_model,
params = work_params)
scaler.read_all()
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
rational_ops = []
for symop in pg:
rational_ops.append((matrix.sqr(symop.r().transpose().as_rational()),
symop.r().as_hkl()))
# miller_set.show_summary()
uc = work_params.target_unit_cell
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
origH = scaler.observations["H"]
origK = scaler.observations["K"]
origL = scaler.observations["L"]
from cctbx.miller import map_to_asu
sgtype = miller_set.space_group_info().type()
aflag = miller_set.anomalous_flag()
from cctbx.array_family import flex
# FIXME in here perform the mapping to ASU for both the original and other
# index as an array-wise manipulation to make things a bunch faster...
# however this also uses a big chunk of RAM... FIXME also in here use
# cb_op.apply(indices) to get the indices reindexed...
original_indices = flex.miller_index()
for x in xrange(len(scaler.observations["hkl_id"])):
original_indices.append(lookup[hkl_asu[x]])
from cctbx.sgtbx import change_of_basis_op
I23 = change_of_basis_op('k, -h, l')
other_indices = I23.apply(original_indices)
map_to_asu(sgtype, aflag, original_indices)
map_to_asu(sgtype, aflag, other_indices)
# FIXME would be useful in here to have a less expensive way of finding the
# symmetry operation which gave the map to the ASU - perhaps best way is to
# make a new C++ map_to_asu which records this.
# FIXME in here recover the original frame structure of the data to
# logical frame objetcs - N.B. the frame will need to be augmented to test
# alternative indexings
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
for j, se in enumerate(zip(starts, ends)):
print 'processing frame %d: %d to %d' % (j, se[0], se[1])
s, e = se
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
# then start running the comparison code
frames = []
for s, e in zip(starts, ends):
# FIXME need this from remap to ASU
misym = [0 for x in range(s, e)]
indices = [original_indices[x] for x in range(s, e)]
other = [other_indices[x] for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(uc, indices, other, intensities, sigmas))
cycle = 0
total_nref = sum([len(f.get_indices()) for f in frames])
# pre-scale the data - first determine average ln(k), B; then apply
kbs = [f.kb() for f in frames]
mn_k = sum([kb[0] for kb in kbs]) / len(kbs)
mn_B = sum([kb[1] for kb in kbs]) / len(kbs)
for f in frames:
f.scale_to_kb(mn_k, mn_B)
while True:
print 'Analysing %d frames' % len(frames)
print 'Cycle %d' % cycle
cycle += 1
print 'Power spectrum'
fn = frame_numbers(frames)
for j in sorted(fn):
print '%4d %4d' % (j, fn[j])
nref_cycle = sum([len(f.get_indices()) for f in frames])
assert(nref_cycle == total_nref)
# first work on the original indices
import numpy
common_reflections = numpy.zeros((len(frames), len(frames)),
dtype = numpy.short)
obs = { }
# for other hand add -j
for j, f in enumerate(frames):
indices = set(f.get_indices())
for i in indices:
_i = tuple(i)
if not _i in obs:
obs[_i] = []
obs[_i].append(j)
for hkl in obs:
obs[hkl].sort()
for j, f1 in enumerate(obs[hkl][:-1]):
for f2 in obs[hkl][j + 1:]:
if f1 * f2 > 0:
common_reflections[(abs(f1), abs(f2))] += 1
cmn_rfl_list = []
for f1 in range(len(frames)):
for f2 in range(f1 + 1, len(frames)):
if common_reflections[(f1, f2)] > 10:
cmn_rfl_list.append((common_reflections[(f1, f2)], f1, f2))
cmn_rfl_list.sort()
cmn_rfl_list.reverse()
joins = []
used = []
for n, f1, f2 in cmn_rfl_list:
if f1 in used or f2 in used:
continue
_cc = frames[f1].cc(frames[f2])
# really only need to worry about f2 which will get merged...
# merging multiple files together should be OK provided they are
# correctly sorted (though the order should not matter anyhow?)
# anyhow they are sorted anyway... ah as f2 > f1 then just sorting
# the list by f2 will make sure the data cascase correctly.
# p-value small (3% ish) for cc > 0.6 for > 10 observations -
# necessary as will be correlated due to Wilson curves though
# with B factor < 10 this is less of an issue
if _cc[0] > 10 and _cc[1] > 0.6:
print '%4d %.3f' % _cc, f1, f2
joins.append((f2, f1))
used.append(f2)
if not joins:
print 'No pairs found'
break
joins.sort()
joins.reverse()
for j2, j1 in joins:
rmerge = frames[j1].merge(frames[j2])
if rmerge:
print 'R: %4d %4d %6.3f' % (j1, j2, rmerge)
else:
print 'R: %4d %4d ------' % (j1, j2)
all_joins = [j for j in joins]
# then do the same for the alternative indices
other_reflections = numpy.zeros((len(frames), len(frames)),
dtype = numpy.short)
obs = { }
# for other hand add -j
for j, f in enumerate(frames):
indices = set(f.get_indices())
for i in indices:
_i = tuple(i)
if not _i in obs:
obs[_i] = []
obs[_i].append(j)
indices = set(f.get_other())
for i in indices:
_i = tuple(i)
if not _i in obs:
obs[_i] = []
obs[_i].append(-j)
for hkl in obs:
obs[hkl].sort()
for j, f1 in enumerate(obs[hkl][:-1]):
for f2 in obs[hkl][j + 1:]:
if f1 * f2 < 0:
other_reflections[(abs(f1), abs(f2))] += 1
oth_rfl_list = []
for f1 in range(len(frames)):
for f2 in range(f1 + 1, len(frames)):
if other_reflections[(f1, f2)] > 10:
oth_rfl_list.append((other_reflections[(f1, f2)], f1, f2))
joins = []
oth_rfl_list.sort()
oth_rfl_list.reverse()
for n, f1, f2 in oth_rfl_list:
if f1 in used or f2 in used:
continue
_cc = frames[f1].cc_other(frames[f2])
# really only need to worry about f2 which will get merged...
# merging multiple files together should be OK provided they are
# correctly sorted (though the order should not matter anyhow?)
# anyhow they are sorted anyway... ah as f2 > f1 then just sorting
# the list by f2 will make sure the data cascase correctly.
# p-value small (3% ish) for cc > 0.6 for > 10 observations -
# necessary as will be correlated due to Wilson curves though
# with B factor < 10 this is less of an issue
if _cc[0] > 10 and _cc[1] > 0.6:
print '%4d %.3f' % _cc, f1, f2
joins.append((f2, f1))
used.append(f2)
all_joins += joins
if not all_joins:
break
joins.sort()
joins.reverse()
for j2, j1 in joins:
frames[j2].reindex()
rmerge = frames[j1].merge(frames[j2])
if rmerge:
print 'R: %4d %4d %6.3f' % (j1, j2, rmerge)
else:
print 'R: %4d %4d ------' % (j1, j2)
continue
frames.sort()
print 'Biggest few: #frames; #unique refl'
j = -1
while frames[j].get_frames() > 1:
print frames[j].get_frames(), frames[j].get_unique_indices()
frames[j].output_as_scalepack(sg, 'scalepack-%d.sca' % j)
j -= 1
return
def find_merge_common_images(args):
phil = iotbx.phil.process_command_line(args = args,
master_string = master_phil).show()
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")) :
raise Usage("cxi.merge "
"d_min=4.0 "
"data=~/scratch/r0220/006/strong/ "
"model=3bz1_3bz2_core.pdb")
if ((work_params.rescale_with_average_cell) and
(not work_params.set_average_unit_cell)) :
raise Usage(
"If rescale_with_average_cell=True, you must also specify "+
"set_average_unit_cell=True.")
# Read Nat's reference model from an MTZ file. XXX The observation
# type is given as F, not I--should they be squared? Check with Nat!
log = open("%s_%s_scale.log" % (work_params.output.prefix,
work_params.scaling.algorithm), "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print >> out, "Target unit cell and space group:"
print >> out, " ", work_params.target_unit_cell
print >> out, " ", work_params.target_space_group
uc = work_params.target_unit_cell
miller_set = symmetry(
unit_cell=work_params.target_unit_cell,
space_group_info=work_params.target_space_group
).build_miller_set(
anomalous_flag=not work_params.merge_anomalous,
d_min=work_params.d_min)
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.read_all()
print "finished reading"
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
miller_set.show_summary()
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
for j, se in enumerate(zip(starts, ends)):
s, e = se
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
frames = []
for s, e in zip(starts, ends):
indices = [tuple(lookup[hkl_asu[x]]) for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(uc, indices, intensities, sigmas))
from collections import defaultdict
hist = defaultdict(int)
fout = open('common_oh.dat', 'w')
for j, f in enumerate(frames):
hp = f.hand_pairs()
fout.write('%4d %d\n' % (j, hp))
hist[int(hp)] += 1
fout.close()
for b in sorted(hist):
print b, hist[b]
return
def find_merge_common_images(args):
phil = iotbx.phil.process_command_line(args = args,
master_string = master_phil).show()
work_params = phil.work.extract()
if ("--help" in args) :
libtbx.phil.parse(master_phil.show())
return
if ((work_params.d_min is None) or
(work_params.data is None) or
((work_params.model is None) and
work_params.scaling.algorithm != "mark1")) :
raise Usage("cxi.merge "
"d_min=4.0 "
"data=~/scratch/r0220/006/strong/ "
"model=3bz1_3bz2_core.pdb")
if ((work_params.rescale_with_average_cell) and
(not work_params.set_average_unit_cell)) :
raise Usage(
"If rescale_with_average_cell=True, you must also specify "+
"set_average_unit_cell=True.")
# Read Nat's reference model from an MTZ file. XXX The observation
# type is given as F, not I--should they be squared? Check with Nat!
log = open("%s_%s_scale.log" % (work_params.output.prefix,
work_params.scaling.algorithm), "w")
out = multi_out()
out.register("log", log, atexit_send_to=None)
out.register("stdout", sys.stdout)
print >> out, "Target unit cell and space group:"
print >> out, " ", work_params.target_unit_cell
print >> out, " ", work_params.target_space_group
uc = work_params.target_unit_cell
miller_set = symmetry(
unit_cell=work_params.target_unit_cell,
space_group_info=work_params.target_space_group
).build_miller_set(
anomalous_flag=not work_params.merge_anomalous,
d_min=work_params.d_min)
from xfel.cxi.merging.general_fcalc import random_structure
i_model = random_structure(work_params)
# ---- Augment this code with any special procedures for x scaling
scaler = xscaling_manager(
miller_set=miller_set,
i_model=i_model,
params=work_params,
log=out)
scaler.read_all()
print "finished reading"
sg = miller_set.space_group()
pg = sg.build_derived_laue_group()
miller_set.show_summary()
hkl_asu = scaler.observations["hkl_id"]
imageno = scaler.observations["frame_id"]
intensi = scaler.observations["i"]
sigma_i = scaler.observations["sigi"]
lookup = scaler.millers["merged_asu_hkl"]
# construct table of start / end indices for frames: now using Python
# range indexing
starts = [0]
ends = []
for x in xrange(1, len(scaler.observations["hkl_id"])):
if imageno[x] != imageno[x - 1]:
ends.append(x)
starts.append(x)
ends.append(len(scaler.observations["hkl_id"]))
keep_start = []
keep_end = []
for j, se in enumerate(zip(starts, ends)):
s, e = se
isig = sum(i / s for i, s in zip(intensi[s:e], sigma_i[s:e])) / (e - s)
dmin = 100.0
for x in xrange(s, e):
d = uc.d(lookup[hkl_asu[x]])
if d < dmin:
dmin = d
if isig > 6.0 and dmin < 3.2:
keep_start.append(s)
keep_end.append(e)
starts = keep_start
ends = keep_end
print 'Keeping %d frames' % len(starts)
frames = []
for s, e in zip(starts, ends):
indices = [tuple(lookup[hkl_asu[x]]) for x in range(s, e)]
intensities = intensi[s:e]
sigmas = sigma_i[s:e]
frames.append(Frame(indices, intensities, sigmas))
while True:
print 'Analysing %d frames' % len(frames)
common_reflections = numpy.zeros((len(frames), len(frames)),
dtype = numpy.short)
obs = { }
for j, f in enumerate(frames):
indices = set(f.get_indices())
for i in indices:
_i = tuple(i)
if not _i in obs:
obs[_i] = []
obs[_i].append(j)
for hkl in obs:
obs[hkl].sort()
for j, f1 in enumerate(obs[hkl][:-1]):
for f2 in obs[hkl][j + 1:]:
common_reflections[(f1, f2)] += 1
cmn_rfl_list = []
for f1 in range(len(frames)):
for f2 in range(f1, len(frames)):
cmn_rfl_list.append((common_reflections[(f1, f2)], f1, f2))
cmn_rfl_list.sort()
cmn_rfl_list.reverse()
joins = []
for n, f1, f2 in cmn_rfl_list:
_cc = frames[f1].cc(frames[f2])
if _cc[0] > 20 and _cc[1] > 0.75:
# print '===> %d %d' % (n, frames[f1].common(frames[f2]))
print '%4d %.3f' % _cc, f1, f2
joins.append((f1, f2))
if not joins:
print 'No pairs found'
break
joins.sort()
joins.reverse()
for j1, j2 in joins:
frames[j1].merge(frames[j2])
frames.remove(frames[j2])
continue
return