def exercise_sort() :
expected_unsorted_data=""" 4 I 4
1 0 0 0 1 0 0 0 1
0 0 0
0 -1 0 1 0 0 0 0 1
0 0 0
0 1 0 -1 0 0 0 0 1
0 0 0
-1 0 0 0 -1 0 0 0 1
0 0 0
0 0 4 0 0 4 0 1 0 1 80331.8 8648.0
0 0 12 0 0 12 0 1 0 1104526.7 11623.3
0 0 -4 0 0 4 0 2 0 1 44883.6 4527.6
0 0 -8 0 0 8 0 2 0 1 41134.1 4431.9
0 0 8 0 0 8 0 1 0 1 50401.8 5464.6
0 0 8 0 0 8 0 1 0 1 53386.0 5564.1
0 0 -8 0 0 8 0 2 0 1119801.4 12231.2
0 0 12 0 0 12 0 1 0 1105312.9 12602.2
0 0 16 0 0 16 0 1 0 1 14877.6 2161.5
"""
expected_sorted_data=""" 4 I 4
1 0 0 0 1 0 0 0 1
0 0 0
0 -1 0 1 0 0 0 0 1
0 0 0
0 1 0 -1 0 0 0 0 1
0 0 0
-1 0 0 0 -1 0 0 0 1
0 0 0
0 0 4 0 0 4 0 1 0 1 80331.8 8648.0
0 0 -4 0 0 4 0 2 0 1 44883.6 4527.6
0 0 8 0 0 8 0 1 0 1 50401.8 5464.6
0 0 8 0 0 8 0 1 0 1 53386.0 5564.1
0 0 -8 0 0 8 0 2 0 1 41134.1 4431.9
0 0 -8 0 0 8 0 2 0 1119801.4 12231.2
0 0 12 0 0 12 0 1 0 1104526.7 11623.3
0 0 12 0 0 12 0 1 0 1105312.9 12602.2
0 0 16 0 0 16 0 1 0 1 14877.6 2161.5
"""
from cctbx.xray import observation_types
from cctbx.array_family import flex
xs = crystal.symmetry((113.949,113.949,32.474,90.000,90.000,90.000), "I4")
mi = flex.miller_index((
(0,0,4),
(0,0,12),
(0,0,-4),
(0,0,-8),
(0,0,8),
(0,0,8),
(0,0,-8),
(0,0,12),
(0,0,16),
))
data = flex.double(( 80331.8, 104526.7, 44883.6, 41134.1, 50401.8, 53386.0, 119801.4, 105312.9, 14877.6,))
sigmas = flex.double((8648.0, 11623.3, 4527.6, 4431.9, 5464.6, 5564.1, 12231.2, 12602.2, 2161.5,))
ms = miller.set(xs, mi,anomalous_flag=True)
i_obs = miller.array(ms, data=data, sigmas=sigmas).set_observation_type(
observation_types.intensity() )
i_obs.export_as_scalepack_unmerged(file_name='unsorted.sca',
scale_intensities_for_scalepack_merge=True)
i_obs=i_obs.sort(by_value="asu_indices")
i_obs.export_as_scalepack_unmerged(file_name='sorted.sca',
scale_intensities_for_scalepack_merge=True)
unsorted=open('unsorted.sca').read()
sorted=open('sorted.sca').read()
assert unsorted==expected_unsorted_data
assert sorted==expected_sorted_data
def exercise_sort():
expected_unsorted_data = """ 4 I 4
1 0 0 0 1 0 0 0 1
0 0 0
0 -1 0 1 0 0 0 0 1
0 0 0
0 1 0 -1 0 0 0 0 1
0 0 0
-1 0 0 0 -1 0 0 0 1
0 0 0
0 0 4 0 0 4 0 1 0 1 80331.8 8648.0
0 0 12 0 0 12 0 1 0 1104526.7 11623.3
0 0 -4 0 0 4 0 2 0 1 44883.6 4527.6
0 0 -8 0 0 8 0 2 0 1 41134.1 4431.9
0 0 8 0 0 8 0 1 0 1 50401.8 5464.6
0 0 8 0 0 8 0 1 0 1 53386.0 5564.1
0 0 -8 0 0 8 0 2 0 1119801.4 12231.2
0 0 12 0 0 12 0 1 0 1105312.9 12602.2
0 0 16 0 0 16 0 1 0 1 14877.6 2161.5
"""
expected_sorted_data = """ 4 I 4
1 0 0 0 1 0 0 0 1
0 0 0
0 -1 0 1 0 0 0 0 1
0 0 0
0 1 0 -1 0 0 0 0 1
0 0 0
-1 0 0 0 -1 0 0 0 1
0 0 0
0 0 4 0 0 4 0 1 0 1 80331.8 8648.0
0 0 -4 0 0 4 0 2 0 1 44883.6 4527.6
0 0 8 0 0 8 0 1 0 1 50401.8 5464.6
0 0 8 0 0 8 0 1 0 1 53386.0 5564.1
0 0 -8 0 0 8 0 2 0 1 41134.1 4431.9
0 0 -8 0 0 8 0 2 0 1119801.4 12231.2
0 0 12 0 0 12 0 1 0 1104526.7 11623.3
0 0 12 0 0 12 0 1 0 1105312.9 12602.2
0 0 16 0 0 16 0 1 0 1 14877.6 2161.5
"""
from cctbx.xray import observation_types
from cctbx.array_family import flex
xs = crystal.symmetry((113.949, 113.949, 32.474, 90.000, 90.000, 90.000),
"I4")
mi = flex.miller_index((
(0, 0, 4),
(0, 0, 12),
(0, 0, -4),
(0, 0, -8),
(0, 0, 8),
(0, 0, 8),
(0, 0, -8),
(0, 0, 12),
(0, 0, 16),
))
data = flex.double((
80331.8,
104526.7,
44883.6,
41134.1,
50401.8,
53386.0,
119801.4,
105312.9,
14877.6,
))
sigmas = flex.double((
8648.0,
11623.3,
4527.6,
4431.9,
5464.6,
5564.1,
12231.2,
12602.2,
2161.5,
))
ms = miller.set(xs, mi, anomalous_flag=True)
i_obs = miller.array(ms, data=data, sigmas=sigmas).set_observation_type(
observation_types.intensity())
i_obs.export_as_scalepack_unmerged(
file_name='unsorted.sca', scale_intensities_for_scalepack_merge=True)
i_obs = i_obs.sort(by_value="asu_indices")
i_obs.export_as_scalepack_unmerged(
file_name='sorted.sca', scale_intensities_for_scalepack_merge=True)
unsorted = open('unsorted.sca').read()
sorted = open('sorted.sca').read()
assert unsorted == expected_unsorted_data
assert sorted == expected_sorted_data
tagbase = "PSIfulls"
tag = "%s_N%04d_tE%1d" % (tagbase, N, -math.log10(trans))
for half_data_flag in [1, 2, 0]:
case = test.execute_case(datadir,
n_frame=N,
transmittance=trans,
apply_noise=True,
plot=plot_flag,
esd_plot=esd_plot_flag,
half_data_flag=half_data_flag)
model_subset = case.ordered_intensities[0:len(case.Fit_I)]
from cctbx.xray import observation_types
fitted_miller_array = model_subset.customized_copy(
data=case.Fit_I,
sigmas=case.Fit_I_stddev,
observation_type=observation_types.intensity())
output_result = fitted_miller_array.select(case.I_visited == 1)
outfile = "%s_s%1d_levmar.mtz" % (tag, half_data_flag)
output_result.show_summary(prefix="%s: " % outfile)
mtz_out = output_result.as_mtz_dataset(
column_root_label="Iobs", title=outfile, wavelength=None)
mtz_obj = mtz_out.mtz_object()
mtz_obj.write(outfile)
written_files.append(outfile)
print "OK"
#raw_input("OK")
sys.stdout.flush()
from xfel.command_line.cxi_merge import master_phil
import iotbx.phil
args = [
esd_plot_flag=False
written_files = []
#for N in [25, 200, 300, 400, 500, 800, 1000, 2000, 5000]:
for N in [400]:
#for trans in [1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001]:
for trans in [0.001,]:
tagbase = "PSIfulls"
tag = "%s_N%04d_tE%1d"%(tagbase,N,-math.log10(trans))
for half_data_flag in [1,2,0]:
case = test.execute_case(datadir, n_frame=N, transmittance=trans, apply_noise=True,
plot=plot_flag, esd_plot = esd_plot_flag,half_data_flag = half_data_flag)
model_subset = case.ordered_intensities[0:len(case.Fit_I)]
from cctbx.xray import observation_types
fitted_miller_array = model_subset.customized_copy(data = case.Fit_I, sigmas = case.Fit_I_stddev,
observation_type=observation_types.intensity())
output_result = fitted_miller_array.select(case.I_visited==1)
outfile = "%s_s%1d_levmar.mtz"%(tag,half_data_flag)
output_result.show_summary(prefix="%s: "%outfile)
mtz_out = output_result.as_mtz_dataset(column_root_label="Iobs",title=outfile,wavelength=None)
mtz_obj = mtz_out.mtz_object()
mtz_obj.write(outfile)
written_files.append(outfile)
print "OK"
#raw_input("OK")
sys.stdout.flush()
from xfel.command_line.cxi_merge import master_phil
import iotbx.phil
args = ["output.prefix=%s"%tag,"scaling.algorithm=levmar",
"d_min=%f"%(case.d_min),"output.n_bins=%d"%(case.n_bins),"model=%s"%(os.path.join(datadir,"not_used.pdb")),
