def to_ewald_sphere(list_of_images):
from dxtbx.sweep import SweepFactory
from dxtbx import ImageToEwaldSphere
sweep = SweepFactory.sweep(list_of_images)
beam = sweep.get_beam()
detector = sweep.get_detector()
gonio = sweep.get_goniometer()
scan = sweep.get_scan()
start, end = sweep.get_array_range()
image_to_ewald_sphere = ImageToEwaldSphere(beam, detector, gonio, scan)
for frame in range(start, end):
intensity = sweep[frame]
x_list = image_to_ewald_sphere(frame)
for k, x in enumerate(x_list):
i = intensity[k]
print("%f %f %f %d" % (x[0], x[1], x[2], i))
def to_ewald_sphere(list_of_images):
from dxtbx.sweep import SweepFactory
from dxtbx import ImageToEwaldSphere
sweep = SweepFactory.sweep(list_of_images)
beam = sweep.get_beam()
detector = sweep.get_detector()
gonio = sweep.get_goniometer()
scan = sweep.get_scan()
start, end = sweep.get_array_range()
image_to_ewald_sphere = ImageToEwaldSphere(beam, detector, gonio, scan)
for frame in xrange(start, end):
intensity = sweep[frame]
x_list = image_to_ewald_sphere(frame)
for k, x in enumerate(x_list):
i = intensity[k]
print '%f %f %f %d' % (x[0], x[1], x[2], i)
handle = NexusFile(filename, 'r')
# Get the reflection list
print 'Reading reflections.'
reflections = handle.get_reflections()
print 'Read {0} reflections.'.format(len(reflections))
# Read images
template = os.path.join(dials_regression,
'centroid_test_data', 'centroid_*.cbf')
#template = os.path.join('/home/upc86896/Data/X1_strong_M1S1_1_', 'X1_strong_M1S1_1_*.cbf')
filenames = glob(template)
# Load the sweep
print 'Loading sweep'
sweep = SweepFactory.sweep(filenames)[0:10]
print 'Loaded sweep of {0} images.'.format(len(sweep))
# Select the strong pixels to use in the divergence calculation
print 'Select the strong pixels from the images.'
trusted_range = (0, 20000)
image, mask = select_strong_pixels(sweep, trusted_range)
# Putting pixels into groups
print 'Putting pixels into groups'
objects = group_pixels(mask)
print 'Found {0} objects'.format(len(objects))
print 'Filtering objects'
min_pixels = 6
objects = filter_objects(mask, objects, min_pixels)
if __name__ == "__main__":
from dxtbx.sweep import SweepFactory
from glob import glob
from scipy.ndimage.measurements import histogram
import numpy
from scitbx.array_family import flex
from thresholding import maximum_deviation
from thresholding import percentage_threshold
from thresholding import normal_threshold
# filenames = glob("/home/upc86896/Data/X1_strong_M1S1_1_/X1_strong_M1S1_1_*.cbf")
filenames = glob(
"/home/upc86896/Projects/cctbx/sources/dials_regression/centroid_test_data/centroid_000*.cbf"
)
sweep = SweepFactory.sweep(filenames)
trusted_range = (0, 20000)
histo = []
threshold_list = []
start = 0
stop = 3
for i, flex_image in enumerate(sweep[start:stop]):
image = flex_image.as_numpy_array()
image.shape = -1
ind = numpy.where(image < trusted_range[0])
image[ind] = trusted_range[0]
ind = numpy.where(image > trusted_range[1])
image[ind] = trusted_range[1]