def main(args):
from dials.algorithms.simulation.generate_test_reflections import \
simple_gaussian_spots
from dials.algorithms.simulation.generate_test_reflections import \
master_phil
from libtbx.phil import command_line
cmd = command_line.argument_interpreter(master_params=master_phil)
working_phil = cmd.process_and_fetch(args=sys.argv[1:])
params = working_phil.extract()
rlist = simple_gaussian_spots(params)
import cPickle as pickle
if params.output.all:
pickle.dump(rlist, open(params.output.all, 'w'))
def main(args):
from dials.algorithms.simulation.generate_test_reflections import \
simple_gaussian_spots
from dials.algorithms.simulation.generate_test_reflections import \
master_phil
from libtbx.phil import command_line
cmd = command_line.argument_interpreter(master_params = master_phil)
working_phil = cmd.process_and_fetch(args = sys.argv[1:])
params = working_phil.extract()
rlist = simple_gaussian_spots(params)
import cPickle as pickle
if params.output.all:
pickle.dump(rlist, open(params.output.all, 'w'))
def generate_reflections(num, min_cts, max_cts, bg):
from dials.algorithms.simulation.generate_test_reflections import (
simple_gaussian_spots, )
from random import randint
from dials.model.data import ReflectionList
from scitbx.array_family import flex
from dials.algorithms.shoebox import MaskCode
rlist = ReflectionList()
expected = []
for i in range(num):
cts = randint(min_cts, max_cts)
phil = create_phil(1, cts, bg)
expected.append(cts)
rlist.extend(simple_gaussian_spots(phil))
# background_inclined(rlist)
background_xds(rlist)
integrate_3d_summation(rlist)
import pickle
pickle.dump(rlist, open("test.pickle", "w"))
# for r in rlist:
# from math import sqrt, erf
# r.intensity *= 1.0 + (1.0 - erf(3.0 / sqrt(2.0)))
for r, e in zip(rlist, expected):
mask = r.shoebox_mask
background = flex.bool([bool(m & MaskCode.Background) for m in mask])
foreground = flex.bool([bool(m & MaskCode.Foreground) for m in mask])
pixels = r.shoebox.select(foreground)
print(e, r.intensity,
flex.sum(pixels) - len(pixels) * 0, e / r.intensity)
from math import sqrt
I = [r.intensity for r in rlist]
S = [sqrt(r.intensity_variance) for r in rlist]
Z = [(i - e) / s for i, e, s in zip(I, expected, S)]
meanz = sum(Z) / len(Z)
sdevz = sqrt(sum((z - meanz)**2 for z in Z) / len(Z))
print("MeanZ: %f, SDevZ: %f" % (meanz, sdevz))
from matplotlib import pylab
# pylab.ylim(0, 2)
pylab.scatter(expected, [e / r.intensity for r, e in zip(rlist, expected)])
pylab.show()
def main(args):
from dials.algorithms.simulation.generate_test_reflections import (
simple_gaussian_spots,
)
from dials.algorithms.simulation.generate_test_reflections import master_phil
from libtbx.phil import command_line
cmd = command_line.argument_interpreter(master_params=master_phil)
working_phil = cmd.process_and_fetch(args=sys.argv[1:])
params = working_phil.extract()
rlist = simple_gaussian_spots(params)
import six.moves.cPickle as pickle
if params.output.all:
with open(params.output.all, "wb") as fh:
pickle.dump(rlist, fh, pickle.HIGHEST_PROTOCOL)
def generate_reflections(num, min_cts, max_cts, bg):
from dials.algorithms.simulation.generate_test_reflections import simple_gaussian_spots
from random import randint
from dials.model.data import ReflectionList
from scitbx.array_family import flex
from dials.algorithms.shoebox import MaskCode
rlist = ReflectionList()
expected = []
for i in range(num):
cts = randint(min_cts, max_cts)
phil = create_phil(1, cts, bg)
expected.append(cts)
rlist.extend(simple_gaussian_spots(phil))
# background_inclined(rlist)
background_xds(rlist)
integrate_3d_summation(rlist)
import pickle
pickle.dump(rlist, open("test.pickle", "w"))
# for r in rlist:
# from math import sqrt, erf
# r.intensity *= 1.0 + (1.0 - erf(3.0 / sqrt(2.0)))
for r, e in zip(rlist, expected):
mask = r.shoebox_mask
background = flex.bool([bool(m & MaskCode.Background) for m in mask])
foreground = flex.bool([bool(m & MaskCode.Foreground) for m in mask])
pixels = r.shoebox.select(foreground)
print e, r.intensity, flex.sum(pixels) - len(pixels) * 0, e / r.intensity
from math import sqrt
I = [r.intensity for r in rlist]
S = [sqrt(r.intensity_variance) for r in rlist]
Z = [(i - e) / s for i, e, s in zip(I, expected, S)]
meanz =sum(Z) / len(Z)
sdevz =sqrt(sum((z - meanz)**2 for z in Z) / len(Z))
print "MeanZ: %f, SDevZ: %f" % (meanz, sdevz)
from matplotlib import pylab
#pylab.ylim(0, 2)
pylab.scatter(expected, [e / r.intensity for r, e in zip(rlist, expected)])
pylab.show()
