def __init__(self,datadir,n_frame,transmittance,apply_noise,plot=False,esd_plot=False,half_data_flag=0):
# read the ground truth values back in
import cPickle as pickle
ordered_intensities = pickle.load(open(os.path.join(datadir,"intensities.pickle"),"rb"))
frames = pickle.load(open(os.path.join(datadir,"frames.pickle"),"rb"))
sim = pickle.load(open(os.path.join(datadir,"simulated%05d_0.pickle"%n_frame),"rb"))
print "accepted obs %d"%(len(sim["observed_intensity"]))
FSIM = prepare_simulation_with_noise(sim, transmittance=transmittance,
apply_noise=apply_noise,
ordered_intensities=ordered_intensities,
half_data_flag=half_data_flag)
I,I_visited,G,G_visited = I_and_G_base_estimate(FSIM)
model_I = ordered_intensities.data()[0:len(I)]
model_G = frames["scale_factors"][0:len(G)]
model_B = frames["B_factors"][0:len(G)]
T = Timer("%d frames, %f transmittance, %s noise"%(
n_frame, transmittance, {False:"NO", True:"YES"}[apply_noise]))
mapper = mapper_factory(xscale6e)
minimizer = mapper(I,G,I_visited,G_visited,FSIM)
del T
minimizer.show_summary()
Fit = minimizer.e_unpack()
show_correlation(Fit["G"],model_G,G_visited,"Correlation of G:")
show_correlation(Fit["B"],model_B,G_visited,"Correlation of B:")
show_correlation(Fit["I"],model_I,I_visited,"Correlation of I:")
Fit_stddev = minimizer.e_unpack_stddev()
if plot:
plot_it(Fit["G"], model_G, mode="G")
plot_it(Fit["B"], model_B, mode="B")
plot_it(Fit["I"], model_I, mode="I")
print
if esd_plot:
minimizer.esd_plot()
from cctbx.examples.merging.show_results import show_overall_observations
table1,self.n_bins,self.d_min = show_overall_observations(
Fit["I"],Fit_stddev["I"],I_visited,
ordered_intensities,FSIM,title="Statistics for all reflections")
self.FSIM=FSIM
self.ordered_intensities=ordered_intensities
self.Fit_I=Fit["I"]
self.Fit_I_stddev=Fit_stddev["I"]
self.I_visited=I_visited
def __init__(self,datadir,work_params,plot=False,esd_plot=False,half_data_flag=0):
casetag = work_params.output.prefix
# read the ground truth values back in
import cPickle as pickle
# it is assumed (for now) that the reference millers contain a complete asymmetric unit
# of indices, within the (d_max,d_min) region of interest and possibly outside the region.
reference_millers = pickle.load(open(os.path.join(datadir,casetag+"_miller.pickle"),"rb"))
experiment_manager = read_experiments(work_params)
obs = pickle.load(open(os.path.join(datadir,casetag+"_observation.pickle"),"rb"))
print "Read in %d observations"%(len(obs["observed_intensity"]))
reference_millers.show_summary(prefix="Miller index file ")
print len(obs["frame_lookup"]),len(obs["observed_intensity"]), flex.max(obs['miller_lookup']),flex.max(obs['frame_lookup'])
max_frameno = flex.max(obs["frame_lookup"])
from iotbx import mtz
mtz_object = mtz.object(file_name=work_params.scaling.mtz_file)
#for array in mtz_object.as_miller_arrays():
# this_label = array.info().label_string()
# print this_label, array.observation_type()
I_sim = mtz_object.as_miller_arrays()[0].as_intensity_array()
I_sim.show_summary()
MODEL_REINDEX_OP = work_params.model_reindex_op
I_sim = I_sim.change_basis(MODEL_REINDEX_OP).map_to_asu()
#match up isomorphous (the simulated fake F's) with experimental unique set
matches = miller.match_multi_indices(
miller_indices_unique=reference_millers.indices(),
miller_indices=I_sim.indices())
print "original unique",len(reference_millers.indices())
print "isomorphous set",len(I_sim.indices())
print "pairs",len(matches.pairs())
iso_data = flex.double(len(reference_millers.indices()))
for pair in matches.pairs():
iso_data[pair[0]] = I_sim.data()[pair[1]]
reference_data = miller.array(miller_set = reference_millers,
data = iso_data)
reference_data.set_observation_type_xray_intensity()
FOBS = prepare_observations_for_scaling(work_params,obs=obs,
reference_intensities=reference_data,
files = experiment_manager.get_files(),
half_data_flag=half_data_flag)
I,I_visited,G,G_visited = I_and_G_base_estimate(FOBS,params=work_params)
print "I length",len(I), "G length",len(G), "(Reference set; entire asymmetric unit)"
assert len(reference_data.data()) == len(I)
#presumably these assertions fail when half data are taken for CC1/2 or d_min is cut
model_I = reference_data.data()[0:len(I)]
T = Timer("%d frames"%(len(G), ))
mapper = mapper_factory(xscale6e)
minimizer = mapper(I,G,I_visited,G_visited,FOBS,params=work_params,
experiments=experiment_manager.get_experiments())
del T
minimizer.show_summary()
Fit = minimizer.e_unpack()
Gstats=flex.mean_and_variance(Fit["G"].select(G_visited==1))
print "G mean and standard deviation:",Gstats.mean(),Gstats.unweighted_sample_standard_deviation()
if "Bfactor" in work_params.levmar.parameter_flags:
Bstats=flex.mean_and_variance(Fit["B"].select(G_visited==1))
print "B mean and standard deviation:",Bstats.mean(),Bstats.unweighted_sample_standard_deviation()
show_correlation(Fit["I"],model_I,I_visited,"Correlation of I:")
Fit_stddev = minimizer.e_unpack_stddev()
# XXX FIXME known bug: the length of Fit["G"] could be smaller than the length of experiment_manager.get_files()
# Not sure if this has any operational drawbacks. It's a result of half-dataset selection.
if plot:
plot_it(Fit["I"], model_I, mode="I")
if "Rxy" in work_params.levmar.parameter_flags:
show_histogram(Fit["Ax"],"Histogram of x rotation (degrees)")
show_histogram(Fit["Ay"],"Histogram of y rotation (degrees)")
print
if esd_plot:
minimizer.esd_plot()
from cctbx.examples.merging.show_results import show_overall_observations
table1,self.n_bins,self.d_min = show_overall_observations(
Fit["I"],Fit_stddev["I"],I_visited,
reference_data,FOBS,title="Statistics for all reflections",
work_params = work_params)
self.FSIM=FOBS
self.ordered_intensities=reference_data
self.reference_millers=reference_millers
self.Fit_I=Fit["I"]
self.Fit_I_stddev=Fit_stddev["I"]
self.I_visited=I_visited
self.Fit = Fit
self.experiments = experiment_manager
def __init__(self,
datadir,
n_frame,
transmittance,
apply_noise,
plot=False,
esd_plot=False,
half_data_flag=0):
# read the ground truth values back in
import cPickle as pickle
ordered_intensities = pickle.load(
open(os.path.join(datadir, "intensities.pickle"), "rb"))
frames = pickle.load(open(os.path.join(datadir, "frames.pickle"),
"rb"))
sim = pickle.load(
open(os.path.join(datadir, "simulated%05d_0.pickle" % n_frame),
"rb"))
print "accepted obs %d" % (len(sim["observed_intensity"]))
FSIM = prepare_simulation_with_noise(
sim,
transmittance=transmittance,
apply_noise=apply_noise,
ordered_intensities=ordered_intensities,
half_data_flag=half_data_flag)
I, I_visited, G, G_visited = I_and_G_base_estimate(FSIM)
model_I = ordered_intensities.data()[0:len(I)]
model_G = frames["scale_factors"][0:len(G)]
model_B = frames["B_factors"][0:len(G)]
T = Timer("%d frames, %f transmittance, %s noise" %
(n_frame, transmittance, {
False: "NO",
True: "YES"
}[apply_noise]))
mapper = mapper_factory(xscale6e)
minimizer = mapper(I, G, I_visited, G_visited, FSIM)
del T
minimizer.show_summary()
Fit = minimizer.e_unpack()
show_correlation(Fit["G"], model_G, G_visited, "Correlation of G:")
show_correlation(Fit["B"], model_B, G_visited, "Correlation of B:")
show_correlation(Fit["I"], model_I, I_visited, "Correlation of I:")
Fit_stddev = minimizer.e_unpack_stddev()
if plot:
plot_it(Fit["G"], model_G, mode="G")
plot_it(Fit["B"], model_B, mode="B")
plot_it(Fit["I"], model_I, mode="I")
print
if esd_plot:
minimizer.esd_plot()
from cctbx.examples.merging.show_results import show_overall_observations
table1, self.n_bins, self.d_min = show_overall_observations(
Fit["I"],
Fit_stddev["I"],
I_visited,
ordered_intensities,
FSIM,
title="Statistics for all reflections")
self.FSIM = FSIM
self.ordered_intensities = ordered_intensities
self.Fit_I = Fit["I"]
self.Fit_I_stddev = Fit_stddev["I"]
self.I_visited = I_visited
def __init__(self,
datadir,
work_params,
plot=False,
esd_plot=False,
half_data_flag=0):
casetag = work_params.output.prefix
# read the ground truth values back in
import cPickle as pickle
# it is assumed (for now) that the reference millers contain a complete asymmetric unit
# of indices, within the (d_max,d_min) region of interest and possibly outside the region.
reference_millers = pickle.load(
open(os.path.join(datadir, casetag + "_miller.pickle"), "rb"))
experiment_manager = read_experiments(work_params)
obs = pickle.load(
open(os.path.join(datadir, casetag + "_observation.pickle"), "rb"))
print "Read in %d observations" % (len(obs["observed_intensity"]))
reference_millers.show_summary(prefix="Miller index file ")
print len(obs["frame_lookup"]), len(
obs["observed_intensity"]), flex.max(
obs['miller_lookup']), flex.max(obs['frame_lookup'])
max_frameno = flex.max(obs["frame_lookup"])
from iotbx import mtz
mtz_object = mtz.object(file_name=work_params.scaling.mtz_file)
#for array in mtz_object.as_miller_arrays():
# this_label = array.info().label_string()
# print this_label, array.observation_type()
I_sim = mtz_object.as_miller_arrays()[0].as_intensity_array()
I_sim.show_summary()
MODEL_REINDEX_OP = work_params.model_reindex_op
I_sim = I_sim.change_basis(MODEL_REINDEX_OP).map_to_asu()
#match up isomorphous (the simulated fake F's) with experimental unique set
matches = miller.match_multi_indices(
miller_indices_unique=reference_millers.indices(),
miller_indices=I_sim.indices())
print "original unique", len(reference_millers.indices())
print "isomorphous set", len(I_sim.indices())
print "pairs", len(matches.pairs())
iso_data = flex.double(len(reference_millers.indices()))
for pair in matches.pairs():
iso_data[pair[0]] = I_sim.data()[pair[1]]
reference_data = miller.array(miller_set=reference_millers,
data=iso_data)
reference_data.set_observation_type_xray_intensity()
FOBS = prepare_observations_for_scaling(
work_params,
obs=obs,
reference_intensities=reference_data,
files=experiment_manager.get_files(),
half_data_flag=half_data_flag)
I, I_visited, G, G_visited = I_and_G_base_estimate(FOBS,
params=work_params)
print "I length", len(I), "G length", len(
G), "(Reference set; entire asymmetric unit)"
assert len(reference_data.data()) == len(I)
#presumably these assertions fail when half data are taken for CC1/2 or d_min is cut
model_I = reference_data.data()[0:len(I)]
T = Timer("%d frames" % (len(G), ))
mapper = mapper_factory(xscale6e)
minimizer = mapper(I,
G,
I_visited,
G_visited,
FOBS,
params=work_params,
experiments=experiment_manager.get_experiments())
del T
minimizer.show_summary()
Fit = minimizer.e_unpack()
Gstats = flex.mean_and_variance(Fit["G"].select(G_visited == 1))
print "G mean and standard deviation:", Gstats.mean(
), Gstats.unweighted_sample_standard_deviation()
if "Bfactor" in work_params.levmar.parameter_flags:
Bstats = flex.mean_and_variance(Fit["B"].select(G_visited == 1))
print "B mean and standard deviation:", Bstats.mean(
), Bstats.unweighted_sample_standard_deviation()
show_correlation(Fit["I"], model_I, I_visited, "Correlation of I:")
Fit_stddev = minimizer.e_unpack_stddev()
# XXX FIXME known bug: the length of Fit["G"] could be smaller than the length of experiment_manager.get_files()
# Not sure if this has any operational drawbacks. It's a result of half-dataset selection.
if plot:
plot_it(Fit["I"], model_I, mode="I")
if "Rxy" in work_params.levmar.parameter_flags:
show_histogram(Fit["Ax"], "Histogram of x rotation (degrees)")
show_histogram(Fit["Ay"], "Histogram of y rotation (degrees)")
print
if esd_plot:
minimizer.esd_plot()
from cctbx.examples.merging.show_results import show_overall_observations
table1, self.n_bins, self.d_min = show_overall_observations(
Fit["I"],
Fit_stddev["I"],
I_visited,
reference_data,
FOBS,
title="Statistics for all reflections",
work_params=work_params)
self.FSIM = FOBS
self.ordered_intensities = reference_data
self.reference_millers = reference_millers
self.Fit_I = Fit["I"]
self.Fit_I_stddev = Fit_stddev["I"]
self.I_visited = I_visited
self.Fit = Fit
self.experiments = experiment_manager