class FinalizeModel(object):
"""
A class to finalize the background model
"""
def __init__(self,
experiments,
filter_type="median",
kernel_size=10,
niter=100):
"""
Initialize the finalizer
:param experiments: The experiment list
:param kernel_size: The median filter kernel size
:param niter: The number of iterations for filling holes
"""
from dials.algorithms.background.gmodel import PolarTransform
# Set some parameters
self.filter_type = filter_type
self.kernel_size = kernel_size
self.niter = niter
# Check the input
assert len(experiments) == 1
experiment = experiments[0]
assert len(experiment.detector) == 1
# Save the experiment
self.experiment = experiment
# Create the transform object
self.transform = PolarTransform(experiment.beam,
experiment.detector[0],
experiment.goniometer)
def finalize(self, data, mask):
"""
Finalize the model
:param data: The data array
:param mask: The mask array
"""
from dials.algorithms.image.filter import median_filter, mean_filter
from dials.algorithms.image.fill_holes import diffusion_fill
from dials.algorithms.image.fill_holes import simple_fill
from dials.array_family import flex
# Print some image properties
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Raw image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
# Transform to polar
logger.info("Transforming image data to polar grid")
result = self.transform.to_polar(data, mask)
data = result.data()
mask = result.mask()
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Polar image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
# Filter the image to remove noise
if self.kernel_size > 0:
if self.filter_type == "median":
logger.info("Applying median filter")
data = median_filter(data,
mask, (self.kernel_size, 0),
periodic=True)
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Median polar image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
elif self.filter_type == "mean":
logger.info("Applying mean filter")
mask_as_int = mask.as_1d().as_int()
mask_as_int.reshape(mask.accessor())
data = mean_filter(data, mask_as_int, (self.kernel_size, 0), 1)
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Mean polar image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
else:
raise RuntimeError("Unknown filter_type: %s" %
self.filter_type)
# Fill any remaining holes
logger.info("Filling holes")
data = simple_fill(data, mask)
data = diffusion_fill(data, mask, self.niter)
mask = flex.bool(data.accessor(), True)
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Filled polar image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
# Transform back
logger.info("Transforming image data from polar grid")
result = self.transform.from_polar(data, mask)
data = result.data()
mask = result.mask()
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Final image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
# Fill in any discontinuities
mask = ~self.transform.discontinuity()[:-1, :-1]
data = diffusion_fill(data, mask, self.niter)
# Get and apply the mask
mask = self.experiment.imageset.get_mask(0)[0]
mask = mask.as_1d().as_int().as_double()
mask.reshape(data.accessor())
data *= mask
# Return the result
return data
