def run(args):
from libtbx.utils import show_times_at_exit
show_times_at_exit()
from rstbx.simage import create
work_params = create.process_args(args=args,
extra_phil_str="""\
use_symmetry = False
.type = bool
number_of_shots = None
.type = int
min_count_target = None
.type = int
usable_partiality_threshold = 0.1
.type = float
kirian_delta_vs_ewald_proximity = False
.type = bool
multiprocessing = False
.type = bool
xy_prefix = None
.type = str
plot = completeness redundancy
.type = choice
""")
i_calc = create.build_i_calc(work_params)
i_calc.p1_anom.show_comprehensive_summary()
print
sys.stdout.flush()
stats = simulate(work_params, i_calc)
stats.report(plot=work_params.plot, xy_prefix=work_params.xy_prefix)
def process_args(args, extra_phil_str=""):
import libtbx.load_env
libtbx.env.require_module("spotfinder")
import spotfinder
from rstbx.simage import create
return create.process_args(
args=args, extra_phil_str=spotfinder.phil_str + extra_phil_str)
def run(args):
from libtbx.utils import show_times_at_exit
show_times_at_exit()
from rstbx.simage import create
work_params = create.process_args(
args=args,
extra_phil_str="""\
use_symmetry = False
.type = bool
number_of_shots = None
.type = int
min_count_target = None
.type = int
usable_partiality_threshold = 0.1
.type = float
kirian_delta_vs_ewald_proximity = False
.type = bool
multiprocessing = False
.type = bool
xy_prefix = None
.type = str
plot = completeness redundancy
.type = choice
""")
i_calc = create.build_i_calc(work_params)
i_calc.p1_anom.show_comprehensive_summary()
print
sys.stdout.flush()
stats = simulate(work_params, i_calc)
stats.report(plot=work_params.plot, xy_prefix=work_params.xy_prefix)
def process_args(args, extra_phil_str=""):
import libtbx.load_env
libtbx.env.require_module("spotfinder")
import spotfinder
from rstbx.simage import create
return create.process_args(args=args,
extra_phil_str=spotfinder.phil_str +
extra_phil_str)
def check(args, expected_block):
sio = StringIO()
work_params = create.process_args(args=args, out=sio)
assert not block_show_diff(sio.getvalue(), expected_block)
pixels = create.compute_image(work_params)
assert pixels.all() == tuple(work_params.detector.pixels)
def run(args):
from rstbx.simage import create
from scitbx.array_family import flex
work_params = create.process_args(args=args)
i_calc, image_info = create.compute(
work_params=work_params,
store_miller_index_i_seqs=True,
store_spots=True,
store_signals=True)
if (work_params.wavelength_2 is None):
for i_seq,x,s in zip(
image_info.miller_index_i_seqs,
image_info.spots,
image_info.signals):
h = i_calc.p1_anom.indices()[i_seq]
print("%3d %3d %3d" % h, "(%8.2f, %8.2f)" % tuple(x[:2]), "%.6g" % s)
print()
else:
i_calc_2, image_info_2 = create.compute(
work_params=work_params,
use_wavelength_2=True,
store_miller_index_i_seqs=True,
store_spots=True,
store_signals=True)
assert i_calc.p1_anom.indices().all_eq(i_calc_2.p1_anom.indices())
lookup_dict = {}
for ii,i_seq in enumerate(image_info_2.miller_index_i_seqs):
lookup_dict[i_seq] = ii
d_spacings = i_calc.p1_anom.d_spacings().data()
d_array = flex.double()
streak_array = flex.double()
i_mean_array = flex.double()
for i_seq,x,s in zip(
image_info.miller_index_i_seqs,
image_info.spots,
image_info.signals):
ii = lookup_dict.get(i_seq)
if (ii is not None):
x = tuple(x[:2])
x2 = tuple(image_info_2.spots[ii][:2])
s2 = image_info_2.signals[ii]
dx = tuple([b-a for a,b in zip(x,x2)])
streak = (dx[0]**2 + dx[1]**2)**0.5
h = i_calc.p1_anom.indices()[i_seq]
print("%3d %3d %3d" % h, " %8.5f" % d_spacings[i_seq], \
"(%7.1f, %7.1f)" % x, "%.1f" % s)
print(" ", \
"(%7.1f, %7.1f)" % x2, "%.1f" % s2, " %.1f" % (s2-s))
print(" (%7.1f, %7.1f)" % dx, " %.1f" % streak)
d_array.append(d_spacings[i_seq])
streak_array.append(streak)
i_mean_array.append((s+s2)*0.5)
print()
print("Writing file: d_vs_streak.xy")
f = open("d_vs_streak.xy", "w")
for x,y in zip(d_array, streak_array):
print(x, y, file=f)
del f
print("Writing file: i_vs_streak.xy")
perm = flex.sort_permutation(i_mean_array)
f = open("i_vs_streak.xy", "w")
for x,y in zip(i_mean_array.select(perm), streak_array.select(perm)):
print(x, y, file=f)
del f
_ = i_calc.p1_anom
d = _.d_spacings().data()
i = _.data()
print("Writing file: d_vs_i_calc.xy")
f = open("d_vs_i_calc.xy", "w")
for x,y in zip(d, i):
print(x, y, file=f)
del f
print("Writing file: i_calc_vs_d.xy")
perm = flex.sort_permutation(i)
f = open("i_calc_vs_d.xy", "w")
for x,y in zip(i.select(perm), d.select(perm)):
print(x, y, file=f)
del f
print()
_ = i_calc.p1_anom
for cutoff in [0.1, 0.05, 0.02, 0.01]:
print("Completeness with i_calc >= %.2f:" % cutoff)
visible = _.select(_.data() >= cutoff)
a, b = visible.indices().size(), _.indices().size()
print(" Overall: %d / %d = %.3f" % (a, b, a/max(1,b)))
visible.setup_binner(n_bins=10)
visible.completeness(use_binning=True).show(prefix=" ")
print()
def run(args):
from rstbx.simage import create
from scitbx.array_family import flex
work_params = create.process_args(args=args)
i_calc, image_info = create.compute(
work_params=work_params,
store_miller_index_i_seqs=True,
store_spots=True,
store_signals=True)
if (work_params.wavelength_2 is None):
for i_seq,x,s in zip(
image_info.miller_index_i_seqs,
image_info.spots,
image_info.signals):
h = i_calc.p1_anom.indices()[i_seq]
print "%3d %3d %3d" % h, "(%8.2f, %8.2f)" % tuple(x[:2]), "%.6g" % s
print
else:
i_calc_2, image_info_2 = create.compute(
work_params=work_params,
use_wavelength_2=True,
store_miller_index_i_seqs=True,
store_spots=True,
store_signals=True)
assert i_calc.p1_anom.indices().all_eq(i_calc_2.p1_anom.indices())
lookup_dict = {}
for ii,i_seq in enumerate(image_info_2.miller_index_i_seqs):
lookup_dict[i_seq] = ii
d_spacings = i_calc.p1_anom.d_spacings().data()
d_array = flex.double()
streak_array = flex.double()
i_mean_array = flex.double()
for i_seq,x,s in zip(
image_info.miller_index_i_seqs,
image_info.spots,
image_info.signals):
ii = lookup_dict.get(i_seq)
if (ii is not None):
x = tuple(x[:2])
x2 = tuple(image_info_2.spots[ii][:2])
s2 = image_info_2.signals[ii]
dx = tuple([b-a for a,b in zip(x,x2)])
streak = (dx[0]**2 + dx[1]**2)**0.5
h = i_calc.p1_anom.indices()[i_seq]
print "%3d %3d %3d" % h, " %8.5f" % d_spacings[i_seq], \
"(%7.1f, %7.1f)" % x, "%.1f" % s
print " ", \
"(%7.1f, %7.1f)" % x2, "%.1f" % s2, " %.1f" % (s2-s)
print " (%7.1f, %7.1f)" % dx, " %.1f" % streak
d_array.append(d_spacings[i_seq])
streak_array.append(streak)
i_mean_array.append((s+s2)*0.5)
print
print "Writing file: d_vs_streak.xy"
f = open("d_vs_streak.xy", "w")
for x,y in zip(d_array, streak_array):
print >> f, x, y
del f
print "Writing file: i_vs_streak.xy"
perm = flex.sort_permutation(i_mean_array)
f = open("i_vs_streak.xy", "w")
for x,y in zip(i_mean_array.select(perm), streak_array.select(perm)):
print >> f, x, y
del f
_ = i_calc.p1_anom
d = _.d_spacings().data()
i = _.data()
print "Writing file: d_vs_i_calc.xy"
f = open("d_vs_i_calc.xy", "w")
for x,y in zip(d, i):
print >> f, x, y
del f
print "Writing file: i_calc_vs_d.xy"
perm = flex.sort_permutation(i)
f = open("i_calc_vs_d.xy", "w")
for x,y in zip(i.select(perm), d.select(perm)):
print >> f, x, y
del f
print
_ = i_calc.p1_anom
for cutoff in [0.1, 0.05, 0.02, 0.01]:
print "Completeness with i_calc >= %.2f:" % cutoff
visible = _.select(_.data() >= cutoff)
a, b = visible.indices().size(), _.indices().size()
print " Overall: %d / %d = %.3f" % (a, b, a/max(1,b))
visible.setup_binner(n_bins=10)
visible.completeness(use_binning=True).show(prefix=" ")
print
def check(args, expected_block): sio = StringIO() work_params = create.process_args(args=args, out=sio) assert not block_show_diff(sio.getvalue(), expected_block) pixels = create.compute_image(work_params) assert pixels.all() == tuple(work_params.detector.pixels)
