def run(self):
from dials.algorithms.image.fill_holes import simple_fill
from scitbx.array_family import flex
from random import randint
from math import sqrt
import sys
mask = flex.bool(flex.grid(100, 100), True)
data = flex.double(flex.grid(100, 100), True)
for j in range(100):
for i in range(100):
data[j, i] = 10 + j * 0.01 + i * 0.01
if sqrt((j - 50)**2 + (i - 50)**2) <= 10.5:
mask[j, i] = False
data[j, i] = 0
result = simple_fill(data, mask)
known = data.as_1d().select(mask.as_1d())
filled = result.as_1d().select(mask.as_1d() == False)
assert flex.max(filled) <= flex.max(known)
assert flex.min(filled) >= flex.min(known)
# Test passed
print 'OK'
def run(self):
from dials.algorithms.image.fill_holes import simple_fill
from scitbx.array_family import flex
from random import randint
from math import sqrt
import sys
mask = flex.bool(flex.grid(100, 100), True)
data = flex.double(flex.grid(100, 100), True)
for j in range(100):
for i in range(100):
data[j,i] = 10 + j * 0.01 + i * 0.01
if sqrt((j - 50)**2 + (i - 50)**2) <= 10.5:
mask[j,i] = False
data[j,i] = 0
result = simple_fill(data, mask)
known = data.as_1d().select(mask.as_1d())
filled = result.as_1d().select(mask.as_1d() == False)
assert flex.max(filled) <= flex.max(known)
assert flex.min(filled) >= flex.min(known)
# Test passed
print 'OK'
def compute(self):
from dials.algorithms.background.gmodel import FillGaps
from dials.algorithms.image.fill_holes import simple_fill
result = self._filter.compute(self.min_count, self.nsigma)
data = result.data()
mask = result.mask()
data = simple_fill(data, mask)
fill = FillGaps(self.beam, self.detector[0])
mask = mask.as_1d().as_int()
mask = mask - (~self.detector_mask).as_1d().as_int()
mask.reshape(data.accessor())
fill(data, mask, self.sigma, self.kernel_size, self.niter)
return data
def test():
from dials.algorithms.image.fill_holes import simple_fill
from scitbx.array_family import flex
mask = flex.bool(flex.grid(100, 100), True)
data = flex.double(flex.grid(100, 100), True)
for j in range(100):
for i in range(100):
data[j, i] = 10 + j * 0.01 + i * 0.01
if math.sqrt((j - 50)**2 + (i - 50)**2) <= 10.5:
mask[j, i] = False
data[j, i] = 0
result = simple_fill(data, mask)
known = data.as_1d().select(mask.as_1d())
filled = result.as_1d().select(~mask.as_1d())
assert flex.max(filled) <= flex.max(known)
assert flex.min(filled) >= flex.min(known)
def compute(self):
from dials.algorithms.background.gmodel import FillGaps
from dials.algorithms.image.fill_holes import simple_fill
result = self._filter.compute(self.min_count, self.nsigma)
data = result.data()
mask = result.mask()
data = simple_fill(data, mask)
fill = FillGaps(self.beam, self.detector[0])
mask = mask.as_1d().as_int()
mask = mask - (~self.detector_mask).as_1d().as_int()
mask.reshape(data.accessor())
fill(data, mask, self.sigma, self.kernel_size, self.niter)
return data
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))
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
# FIXME NEED TO HANDLE DISCONTINUITY
# 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
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
