def background(imageset, indx, n_bins):
from dials.array_family import flex
from libtbx.phil import parse
from scitbx import matrix
import math
detector = imageset.get_detector()
beam = imageset.get_beam()
assert(len(detector) == 1)
detector = detector[0]
trusted = detector.get_trusted_range()
n = matrix.col(detector.get_normal()).normalize()
b = matrix.col(beam.get_s0()).normalize()
wavelength = beam.get_wavelength()
if math.fabs(b.dot(n)) < 0.95:
from libtbx.utils import Sorry
raise Sorry('Detector not perpendicular to beam')
data = imageset.get_raw_data(indx)
assert(len(data) == 1)
data = data[0]
negative = (data < 0)
hot = (data > int(round(trusted[1])))
bad = negative | hot
from dials.algorithms.spot_finding.factory import SpotFinderFactory
from dials.algorithms.spot_finding.factory import phil_scope
data = data.as_double()
from dxtbx import datablock
spot_params = phil_scope.fetch(source=parse("")).extract()
threshold_function = SpotFinderFactory.configure_threshold(
spot_params, datablock.DataBlock([imageset]))
peak_pixels = threshold_function.compute_threshold(data, ~bad)
signal = data.select(peak_pixels.iselection())
background = data.select((~bad & ~peak_pixels).iselection())
# print some summary information
print 'Mean background: %.3f' % (flex.sum(background) / background.size())
print 'Max/total signal pixels: %.0f / %.0f' % (flex.max(signal),
flex.sum(signal))
print 'Peak/background/hot pixels: %d / %d / %d' % (peak_pixels.count(True),
background.size(),
hot.count(True))
# compute histogram of two-theta values, then same weighted
# by pixel values, finally divide latter by former to get
# the radial profile out, need to set the number of bins
# sensibly; flex.histogram does not allow weights so use
# numpy.histogram to get the same effect... inspired by
# method in PyFAI
data = data.as_1d()
two_theta_array = detector.get_two_theta_array(beam.get_s0())
two_theta_array.set_selected((bad | peak_pixels).iselection(), 0.0)
data.set_selected((bad | peak_pixels).iselection(), 0.0)
# new fangled flex.weighted_histogram :-)
h0 = flex.weighted_histogram(two_theta_array, n_slots=n_bins)
h1 = flex.weighted_histogram(two_theta_array, data, n_slots=n_bins)
h2 = flex.weighted_histogram(two_theta_array, data * data, n_slots=n_bins)
d0 = h0.slots()
d1 = h1.slots()
d2 = h2.slots()
I = d1 / d0
I2 = d2 / d0
sig = flex.sqrt(I2 - flex.pow2(I))
tt = h0.slot_centers()
d_spacings = wavelength / (2.0 * flex.sin(0.5 * tt))
return d_spacings, I, sig
def background(imageset, indx, n_bins, mask_params=None):
from dials.array_family import flex
from libtbx.phil import parse
from scitbx import matrix
if mask_params is None:
# Default mask params for trusted range
mask_params = phil_scope.fetch(parse("")).extract().masking
from dials.util.masking import MaskGenerator
mask_generator = MaskGenerator(mask_params)
mask = mask_generator.generate(imageset)
detector = imageset.get_detector()
beam = imageset.get_beam()
# Only working with single panel detector for now
assert len(detector) == 1
panel = detector[0]
mask = mask[0]
n = matrix.col(panel.get_normal()).normalize()
b = matrix.col(beam.get_s0()).normalize()
wavelength = beam.get_wavelength()
if math.fabs(b.dot(n)) < 0.95:
raise Sorry("Detector not perpendicular to beam")
data = imageset.get_raw_data(indx)
assert len(data) == 1
data = data[0]
data = data.as_double()
spot_params = spot_phil.fetch(source=parse("")).extract()
threshold_function = SpotFinderFactory.configure_threshold(spot_params)
peak_pixels = threshold_function.compute_threshold(data, mask)
signal = data.select(peak_pixels.iselection())
background_pixels = mask & ~peak_pixels
background = data.select(background_pixels.iselection())
# print some summary information
print("Mean background: %.3f" % (flex.sum(background) / background.size()))
if len(signal) > 0:
print("Max/total signal pixels: %.0f / %.0f" %
(flex.max(signal), flex.sum(signal)))
else:
print("No signal pixels on this image")
print("Peak/background/masked pixels: %d / %d / %d" %
(peak_pixels.count(True), background.size(), mask.count(False)))
# compute histogram of two-theta values, then same weighted
# by pixel values, finally divide latter by former to get
# the radial profile out, need to set the number of bins
# sensibly; inspired by method in PyFAI
two_theta_array = panel.get_two_theta_array(beam.get_s0())
two_theta_array = two_theta_array.as_1d().select(
background_pixels.iselection())
# Use flex.weighted_histogram
h0 = flex.weighted_histogram(two_theta_array, n_slots=n_bins)
h1 = flex.weighted_histogram(two_theta_array, background, n_slots=n_bins)
h2 = flex.weighted_histogram(two_theta_array,
background * background,
n_slots=n_bins)
d0 = h0.slots()
d1 = h1.slots()
d2 = h2.slots()
I = d1 / d0
I2 = d2 / d0
sig = flex.sqrt(I2 - flex.pow2(I))
tt = h0.slot_centers()
d_spacings = wavelength / (2.0 * flex.sin(0.5 * tt))
return d_spacings, I, sig
def background(imageset, indx, n_bins):
from dials.array_family import flex
from libtbx.phil import parse
from scitbx import matrix
import math
detector = imageset.get_detector()
beam = imageset.get_beam()
assert(len(detector) == 1)
detector = detector[0]
trusted = detector.get_trusted_range()
n = matrix.col(detector.get_normal()).normalize()
b = matrix.col(beam.get_s0()).normalize()
wavelength = beam.get_wavelength()
if math.fabs(b.dot(n)) < 0.95:
from libtbx.utils import Sorry
raise Sorry('Detector not perpendicular to beam')
data = imageset.get_raw_data(indx)
assert(len(data) == 1)
data = data[0]
negative = (data < 0)
hot = (data > int(round(trusted[1])))
bad = negative | hot
from dials.algorithms.spot_finding.factory import SpotFinderFactory
from dials.algorithms.spot_finding.factory import phil_scope
data = data.as_double()
from dxtbx import datablock
spot_params = phil_scope.fetch(source=parse("")).extract()
threshold_function = SpotFinderFactory.configure_threshold(
spot_params, datablock.DataBlock([imageset]))
peak_pixels = threshold_function.compute_threshold(data, ~bad)
signal = data.select(peak_pixels.iselection())
background = data.select((~bad & ~peak_pixels).iselection())
# print some summary information
print('Mean background: %.3f' % (flex.sum(background) / background.size()))
print('Max/total signal pixels: %.0f / %.0f' % (flex.max(signal),
flex.sum(signal)))
print('Peak/background/hot pixels: %d / %d / %d' % (peak_pixels.count(True),
background.size(),
hot.count(True)))
# compute histogram of two-theta values, then same weighted
# by pixel values, finally divide latter by former to get
# the radial profile out, need to set the number of bins
# sensibly; flex.histogram does not allow weights so use
# numpy.histogram to get the same effect... inspired by
# method in PyFAI
data = data.as_1d()
two_theta_array = detector.get_two_theta_array(beam.get_s0())
two_theta_array.set_selected((bad | peak_pixels).iselection(), 0.0)
data.set_selected((bad | peak_pixels).iselection(), 0.0)
# new fangled flex.weighted_histogram :-)
h0 = flex.weighted_histogram(two_theta_array, n_slots=n_bins)
h1 = flex.weighted_histogram(two_theta_array, data, n_slots=n_bins)
h2 = flex.weighted_histogram(two_theta_array, data * data, n_slots=n_bins)
d0 = h0.slots()
d1 = h1.slots()
d2 = h2.slots()
I = d1 / d0
I2 = d2 / d0
sig = flex.sqrt(I2 - flex.pow2(I))
tt = h0.slot_centers()
d_spacings = wavelength / (2.0 * flex.sin(0.5 * tt))
return d_spacings, I, sig