def make_radial_average(self, num_bins=None, hires=None, lowres=None):
from dials.algorithms.background import RadialAverage
imageset = self.experiments.extract_imagesets()[0]
beam = imageset.get_beam()
detector = imageset.get_detector()
scan_range = (0, len(imageset))
summed_data = None
summed_mask = None
for i in range(*scan_range):
data = imageset.get_raw_data(i)
mask = imageset.get_mask(i)
assert isinstance(data, tuple)
assert isinstance(mask, tuple)
if summed_data is None:
summed_mask = mask
summed_data = data
else:
summed_data = [
sd + d for sd, d in list(zip(summed_data, data))
]
summed_mask = [
sm & m for sm, m in list(zip(summed_mask, mask))
]
if num_bins is None:
num_bins = int(sum(sum(p.get_image_size()) for p in detector) / 50)
if lowres is not None:
vmin = (1 / lowres)**2
else:
vmin = 0
if hires is not None:
vmax = (1 / hires)**2
else:
vmax = (1 / detector.get_max_resolution(beam.get_s0()))**2
# Compute the radial average
radial_average = RadialAverage(beam, detector, vmin, vmax, num_bins)
for d, m in list(zip(summed_data, summed_mask)):
radial_average.add(d.as_double() / (scan_range[1] - scan_range[0]),
m)
mean = radial_average.mean()
reso = radial_average.inv_d2()
return mean, reso
def run(self):
''' Perform the integration. '''
from dials.util.command_line import heading
from dials.util.options import flatten_datablocks, flatten_experiments
from dials.util import log
from time import time
from libtbx.utils import Sorry
from dials.array_family import flex
# Parse the command line
params, options = self.parser.parse_args(show_diff_phil=False)
experiments = flatten_experiments(params.input.experiments)
datablocks = flatten_datablocks(params.input.datablock)
if len(experiments) == 0 and len(datablocks) == 0:
self.parser.print_help()
return
if len(datablocks) > 0:
assert len(datablocks) == 1
imagesets = datablocks[0].extract_imagesets()
assert len(imagesets) == 1
imageset = imagesets[0]
beam = imageset.get_beam()
detector = imageset.get_detector()
else:
assert len(experiments) == 1
imageset = experiments[0].imageset
beam = experiments[0].beam
detector = experiments[0].detector
# Configure logging
log.config()
# Set the scan range
if params.scan_range is None:
scan_range = (0, len(imageset))
else:
scan_range = params.scan_range
i0, i1 = scan_range
if i0 < 0 or i1 > len(imageset):
raise RuntimeError('Scan range outside image range')
if i0 >= i1:
raise RuntimeError('Invalid scan range')
summed_data = None
summed_mask = None
# Loop through images
for i in range(*scan_range):
logger.info("Reading image %d" % i)
# Read image
data = imageset.get_raw_data(i)
mask = imageset.get_mask(i)
assert isinstance(data, tuple)
assert isinstance(mask, tuple)
if summed_data is None:
summed_mask = mask
summed_data = data
else:
summed_data = [ sd + d for sd, d in zip(summed_data, data) ]
summed_mask = [ sm & m for sm, m in zip(summed_mask, mask) ]
# Compute min and max and num
if params.num_bins is None:
num_bins = sum(sum(p.get_image_size()) for p in detector)
if params.d_max is None:
vmin = 0
else:
vmin = (1.0 / d_max)**2
if params.d_min is None:
params.d_min = detector.get_max_resolution(beam.get_s0())
vmax = (1.0 / params.d_min)**2
# Print some info
logger.info("Min 1/d^2: %f" % vmin)
logger.info("Max 1/d^2: %f" % vmax)
logger.info("Num bins: %d" % num_bins)
# Compute the radial average
from dials.algorithms.background import RadialAverage
radial_average = RadialAverage(beam, detector, vmin, vmax, num_bins)
for d, m in zip(summed_data, summed_mask):
radial_average.add(d.as_double(), m)
mean = radial_average.mean()
reso = radial_average.inv_d2()
logger.info("Writing to %s" % params.output.filename)
with open(params.output.filename, "w") as outfile:
for r, m in zip(reso, mean):
outfile.write("%f, %f\n" % (r, m))
def run(self):
''' Perform the integration. '''
from dials.util.options import flatten_datablocks, flatten_experiments
from dials.util import log
# Parse the command line
params, options = self.parser.parse_args(show_diff_phil=False)
experiments = flatten_experiments(params.input.experiments)
datablocks = flatten_datablocks(params.input.datablock)
if len(experiments) == 0 and len(datablocks) == 0:
self.parser.print_help()
return
if len(datablocks) > 0:
assert len(datablocks) == 1
imagesets = datablocks[0].extract_imagesets()
assert len(imagesets) == 1
imageset = imagesets[0]
beam = imageset.get_beam()
detector = imageset.get_detector()
else:
assert len(experiments) == 1
imageset = experiments[0].imageset
beam = experiments[0].beam
detector = experiments[0].detector
# Configure logging
log.config()
# Set the scan range
if params.scan_range is None:
scan_range = (0, len(imageset))
else:
scan_range = params.scan_range
i0, i1 = scan_range
if i0 < 0 or i1 > len(imageset):
raise RuntimeError('Scan range outside image range')
if i0 >= i1:
raise RuntimeError('Invalid scan range')
summed_data = None
summed_mask = None
# Loop through images
for i in range(*scan_range):
logger.info("Reading image %d" % i)
# Read image
data = imageset.get_raw_data(i)
mask = imageset.get_mask(i)
assert isinstance(data, tuple)
assert isinstance(mask, tuple)
if summed_data is None:
summed_mask = mask
summed_data = data
else:
summed_data = [sd + d for sd, d in zip(summed_data, data)]
summed_mask = [sm & m for sm, m in zip(summed_mask, mask)]
# Compute min and max and num
if params.num_bins is None:
num_bins = sum(sum(p.get_image_size()) for p in detector)
if params.d_max is None:
vmin = 0
else:
vmin = (1.0 / d_max)**2
if params.d_min is None:
params.d_min = detector.get_max_resolution(beam.get_s0())
vmax = (1.0 / params.d_min)**2
# Print some info
logger.info("Min 1/d^2: %f" % vmin)
logger.info("Max 1/d^2: %f" % vmax)
logger.info("Num bins: %d" % num_bins)
# Compute the radial average
from dials.algorithms.background import RadialAverage
radial_average = RadialAverage(beam, detector, vmin, vmax, num_bins)
for d, m in zip(summed_data, summed_mask):
radial_average.add(d.as_double() / (scan_range[1] - scan_range[0]),
m)
mean = radial_average.mean()
reso = radial_average.inv_d2()
logger.info("Writing to %s" % params.output.filename)
with open(params.output.filename, "w") as outfile:
for r, m in zip(reso, mean):
outfile.write("%f, %f\n" % (r, m))