def run(self):
'''Execute the script.'''
from dials.array_family import flex
from dials.util.options import flatten_datablocks
from time import time
from dials.util import log
from libtbx.utils import Sorry
start_time = time()
# Parse the command line
params, options = self.parser.parse_args(show_diff_phil=False)
# Configure the logging
log.config(params.verbosity,
info=params.output.log,
debug=params.output.debug_log)
from dials.util.version import dials_version
logger.info(dials_version())
# Log the diff phil
diff_phil = self.parser.diff_phil.as_str()
if diff_phil is not '':
logger.info('The following parameters have been modified:\n')
logger.info(diff_phil)
# Ensure we have a data block
datablocks = flatten_datablocks(params.input.datablock)
if len(datablocks) == 0:
self.parser.print_help()
return
elif len(datablocks) != 1:
raise Sorry('only 1 datablock can be processed at a time')
# Loop through all the imagesets and find the strong spots
reflections = flex.reflection_table.from_observations(
datablocks[0], params)
# Delete the shoeboxes
if not params.output.shoeboxes:
del reflections['shoebox']
# ascii spot count per image plot
from dials.util.ascii_art import spot_counts_per_image_plot
for i, imageset in enumerate(datablocks[0].extract_imagesets()):
ascii_plot = spot_counts_per_image_plot(
reflections.select(reflections['id'] == i))
if len(ascii_plot):
logger.info(
'\nHistogram of per-image spot count for imageset %i:' % i)
logger.info(ascii_plot)
# Save the reflections to file
logger.info('\n' + '-' * 80)
reflections.as_pickle(params.output.reflections)
logger.info('Saved {0} reflections to {1}'.format(
len(reflections), params.output.reflections))
# Save the datablock
if params.output.datablock:
from dxtbx.datablock import DataBlockDumper
logger.info('Saving datablocks to {0}'.format(
params.output.datablock))
dump = DataBlockDumper(datablocks)
dump.as_file(params.output.datablock)
# Print some per image statistics
if params.per_image_statistics:
from dials.algorithms.spot_finding import per_image_analysis
from cStringIO import StringIO
s = StringIO()
for i, imageset in enumerate(datablocks[0].extract_imagesets()):
print >> s, "Number of centroids per image for imageset %i:" % i
stats = per_image_analysis.stats_imageset(
imageset,
reflections.select(reflections['id'] == i),
resolution_analysis=False)
per_image_analysis.print_table(stats, out=s)
logger.info(s.getvalue())
# Print the time
logger.info("Time Taken: %f" % (time() - start_time))
def run(self, args=None):
"""Execute the script."""
from dials.array_family import flex
from dials.util.options import flatten_experiments
from time import time
from dials.util import log
start_time = time()
# Parse the command line
params, options = self.parser.parse_args(args=args,
show_diff_phil=False)
if __name__ == "__main__":
# Configure the logging
log.config(verbosity=options.verbose, logfile=params.output.log)
from dials.util.version import dials_version
logger.info(dials_version())
# Log the diff phil
diff_phil = self.parser.diff_phil.as_str()
if diff_phil != "":
logger.info("The following parameters have been modified:\n")
logger.info(diff_phil)
# Ensure we have a data block
experiments = flatten_experiments(params.input.experiments)
if len(experiments) == 0:
self.parser.print_help()
return
# Loop through all the imagesets and find the strong spots
reflections = flex.reflection_table.from_observations(
experiments, params)
# Add n_signal column - before deleting shoeboxes
from dials.algorithms.shoebox import MaskCode
good = MaskCode.Foreground | MaskCode.Valid
reflections["n_signal"] = reflections["shoebox"].count_mask_values(
good)
# Delete the shoeboxes
if not params.output.shoeboxes:
del reflections["shoebox"]
# ascii spot count per image plot
from dials.util.ascii_art import spot_counts_per_image_plot
for i, experiment in enumerate(experiments):
ascii_plot = spot_counts_per_image_plot(
reflections.select(reflections["id"] == i))
if len(ascii_plot):
logger.info(
"\nHistogram of per-image spot count for imageset %i:" % i)
logger.info(ascii_plot)
# Save the reflections to file
logger.info("\n" + "-" * 80)
reflections.as_file(params.output.reflections)
logger.info("Saved {} reflections to {}".format(
len(reflections), params.output.reflections))
# Save the experiments
if params.output.experiments:
logger.info("Saving experiments to {}".format(
params.output.experiments))
experiments.as_file(params.output.experiments)
# Print some per image statistics
if params.per_image_statistics:
from dials.algorithms.spot_finding import per_image_analysis
from six.moves import cStringIO as StringIO
s = StringIO()
for i, experiment in enumerate(experiments):
print("Number of centroids per image for imageset %i:" % i,
file=s)
imageset = experiment.imageset
stats = per_image_analysis.stats_imageset(
imageset,
reflections.select(reflections["id"] == i),
resolution_analysis=False,
)
per_image_analysis.print_table(stats, out=s)
logger.info(s.getvalue())
# Print the time
logger.info("Time Taken: %f" % (time() - start_time))
if params.output.experiments:
return experiments, reflections
else:
return reflections
def run(args):
usage = "dials.spot_counts_per_image [options] imported.expt strong.refl"
parser = OptionParser(
usage=usage,
read_reflections=True,
read_experiments=True,
phil=phil_scope,
check_format=False,
epilog=help_message,
)
params, options = parser.parse_args(show_diff_phil=False)
reflections = flatten_reflections(params.input.reflections)
experiments = flatten_experiments(params.input.experiments)
if not any([reflections, experiments]):
parser.print_help()
return
# FIXME may want to change this to allow many to be passed i.e.
# from parallel runs
if len(reflections) != 1:
sys.exit("Only one reflection list may be passed")
reflections = reflections[0]
expts = set(reflections["id"])
if max(expts) >= len(experiments.imagesets()):
sys.exit("Unknown experiments in reflection list")
if params.id is not None:
reflections = reflections.select(reflections["id"] == params.id)
all_stats = []
for j, imageset in enumerate(experiments.imagesets()):
refl = reflections.select(reflections["id"] == j)
stats = per_image_analysis.stats_imageset(
imageset,
refl,
resolution_analysis=params.resolution_analysis,
plot=params.individual_plots,
)
all_stats.append(stats)
# transpose stats
class empty(object):
pass
e = empty()
for s in all_stats:
for k in dir(s):
if k.startswith("_") or k in ["merge", "next"]:
continue
if not hasattr(e, k):
setattr(e, k, [])
getattr(e, k).extend(getattr(s, k))
per_image_analysis.print_table(e)
from libtbx import table_utils
# FIXME this is now probably nonsense...
overall_stats = per_image_analysis.stats_single_image(
imageset, reflections, resolution_analysis=params.resolution_analysis
)
rows = [
("Overall statistics", ""),
("#spots", "%i" % overall_stats.n_spots_total),
("#spots_no_ice", "%i" % overall_stats.n_spots_no_ice),
("d_min", "%.2f" % overall_stats.estimated_d_min),
(
"d_min (distl method 1)",
"%.2f (%.2f)"
% (overall_stats.d_min_distl_method_1, overall_stats.noisiness_method_1),
),
(
"d_min (distl method 2)",
"%.2f (%.2f)"
% (overall_stats.d_min_distl_method_1, overall_stats.noisiness_method_1),
),
]
print(table_utils.format(rows, has_header=True, prefix="| ", postfix=" |"))
if params.json:
import json
if params.split_json:
for k in stats.__dict__:
start, end = params.json.split(".")
with open("%s_%s.%s" % (start, k, end), "wb") as fp:
json.dump(stats.__dict__[k], fp)
if params.joint_json:
with open(params.json, "wb") as fp:
json.dump(stats.__dict__, fp)
if params.plot:
import matplotlib
matplotlib.use("Agg")
per_image_analysis.plot_stats(stats, filename=params.plot)
def run(self):
'''Execute the script.'''
from dials.array_family import flex
from dials.util.options import flatten_datablocks
from time import time
from dials.util import log
from libtbx.utils import Sorry
start_time = time()
# Parse the command line
params, options = self.parser.parse_args(show_diff_phil=False)
# Configure the logging
log.config(
params.verbosity,
info=params.output.log,
debug=params.output.debug_log)
from dials.util.version import dials_version
logger.info(dials_version())
# Log the diff phil
diff_phil = self.parser.diff_phil.as_str()
if diff_phil is not '':
logger.info('The following parameters have been modified:\n')
logger.info(diff_phil)
# Ensure we have a data block
datablocks = flatten_datablocks(params.input.datablock)
if len(datablocks) == 0:
self.parser.print_help()
return
elif len(datablocks) != 1:
raise Sorry('only 1 datablock can be processed at a time')
# Loop through all the imagesets and find the strong spots
reflections = flex.reflection_table.from_observations(
datablocks[0], params)
# Delete the shoeboxes
if not params.output.shoeboxes:
del reflections['shoebox']
# ascii spot count per image plot
from dials.util.ascii_art import spot_counts_per_image_plot
for i, imageset in enumerate(datablocks[0].extract_imagesets()):
ascii_plot = spot_counts_per_image_plot(
reflections.select(reflections['id'] == i))
if len(ascii_plot):
logger.info('\nHistogram of per-image spot count for imageset %i:' %i)
logger.info(ascii_plot)
# Save the reflections to file
logger.info('\n' + '-' * 80)
reflections.as_pickle(params.output.reflections)
logger.info('Saved {0} reflections to {1}'.format(
len(reflections), params.output.reflections))
# Save the datablock
if params.output.datablock:
from dxtbx.datablock import DataBlockDumper
logger.info('Saving datablocks to {0}'.format(
params.output.datablock))
dump = DataBlockDumper(datablocks)
dump.as_file(params.output.datablock)
# Print some per image statistics
if params.per_image_statistics:
from dials.algorithms.spot_finding import per_image_analysis
from cStringIO import StringIO
s = StringIO()
for i, imageset in enumerate(datablocks[0].extract_imagesets()):
print >> s, "Number of centroids per image for imageset %i:" %i
stats = per_image_analysis.stats_imageset(
imageset, reflections.select(reflections['id'] == i),
resolution_analysis=False)
per_image_analysis.print_table(stats, out=s)
logger.info(s.getvalue())
# Print the time
logger.info("Time Taken: %f" % (time() - start_time))
def run(args):
import libtbx.load_env
usage = "%s [options] datablock.json strong.pickle" % libtbx.env.dispatcher_name
parser = OptionParser(usage=usage,
read_reflections=True,
read_datablocks=True,
read_experiments=True,
phil=phil_scope,
check_format=False,
epilog=help_message)
from libtbx.utils import Sorry
params, options = parser.parse_args(show_diff_phil=False)
reflections = flatten_reflections(params.input.reflections)
datablocks = flatten_datablocks(params.input.datablock)
experiments = flatten_experiments(params.input.experiments)
if not any([reflections, experiments, datablocks]):
parser.print_help()
return
if len(reflections) != 1:
raise Sorry('exactly 1 reflection table must be specified')
if len(datablocks) != 1:
if experiments:
if len(experiments.imagesets()) != 1:
raise Sorry('exactly 1 datablock must be specified')
imageset = experiments.imagesets()[0]
else:
raise Sorry('exactly 1 datablock must be specified')
else:
imageset = datablocks[0].extract_imagesets()[0]
reflections = reflections[0]
if params.id is not None:
reflections = reflections.select(reflections['id'] == params.id)
stats = per_image_analysis.stats_imageset(
imageset,
reflections,
resolution_analysis=params.resolution_analysis,
plot=params.individual_plots)
per_image_analysis.print_table(stats)
from libtbx import table_utils
overall_stats = per_image_analysis.stats_single_image(
imageset, reflections, resolution_analysis=params.resolution_analysis)
rows = [
("Overall statistics", ""),
("#spots", "%i" % overall_stats.n_spots_total),
("#spots_no_ice", "%i" % overall_stats.n_spots_no_ice),
#("total_intensity", "%.0f" %overall_stats.total_intensity),
("d_min", "%.2f" % overall_stats.estimated_d_min),
("d_min (distl method 1)",
"%.2f (%.2f)" % (overall_stats.d_min_distl_method_1,
overall_stats.noisiness_method_1)),
("d_min (distl method 2)",
"%.2f (%.2f)" % (overall_stats.d_min_distl_method_1,
overall_stats.noisiness_method_1)),
]
print table_utils.format(rows, has_header=True, prefix="| ", postfix=" |")
if params.json is not None:
import json
with open(params.json, 'wb') as fp:
json.dump(stats.__dict__, fp)
if params.plot is not None:
per_image_analysis.plot_stats(stats, filename=params.plot)
def run(args):
import libtbx.load_env
usage = "%s [options] datablock.json strong.pickle" %libtbx.env.dispatcher_name
parser = OptionParser(
usage=usage,
read_reflections=True,
read_datablocks=True,
read_experiments=True,
phil=phil_scope,
check_format=False,
epilog=help_message)
from libtbx.utils import Sorry
params, options = parser.parse_args(show_diff_phil=False)
reflections = flatten_reflections(params.input.reflections)
datablocks = flatten_datablocks(params.input.datablock)
experiments = flatten_experiments(params.input.experiments)
if not any([reflections, experiments, datablocks]):
parser.print_help()
return
if len(reflections) != 1:
raise Sorry('exactly 1 reflection table must be specified')
if len(datablocks) != 1:
if experiments:
if len(experiments.imagesets()) != 1:
raise Sorry('exactly 1 datablock must be specified')
imageset = experiments.imagesets()[0]
else:
raise Sorry('exactly 1 datablock must be specified')
else:
imageset = datablocks[0].extract_imagesets()[0]
reflections = reflections[0]
if params.id is not None:
reflections = reflections.select(reflections['id'] == params.id)
stats = per_image_analysis.stats_imageset(
imageset, reflections, resolution_analysis=params.resolution_analysis,
plot=params.individual_plots)
per_image_analysis.print_table(stats)
from libtbx import table_utils
overall_stats = per_image_analysis.stats_single_image(
imageset, reflections, resolution_analysis=params.resolution_analysis)
rows = [
("Overall statistics", ""),
("#spots", "%i" %overall_stats.n_spots_total),
("#spots_no_ice", "%i" %overall_stats.n_spots_no_ice),
#("total_intensity", "%.0f" %overall_stats.total_intensity),
("d_min", "%.2f" %overall_stats.estimated_d_min),
("d_min (distl method 1)", "%.2f (%.2f)" %(
overall_stats.d_min_distl_method_1, overall_stats.noisiness_method_1)),
("d_min (distl method 2)", "%.2f (%.2f)" %(
overall_stats.d_min_distl_method_1, overall_stats.noisiness_method_1)),
]
print table_utils.format(rows, has_header=True, prefix="| ", postfix=" |")
if params.json is not None:
import json
with open(params.json, 'wb') as fp:
json.dump(stats.__dict__, fp)
if params.plot is not None:
per_image_analysis.plot_stats(stats, filename=params.plot)
