def log_frame(self, experiments, reflections, run, n_strong, timestamp = None):
if self.params.experiment_tag is None:
return
try:
from xfel.ui.db.dxtbx_db import log_frame
log_frame(experiments, reflections, self.params, run, n_strong, timestamp)
except Exception, e:
import traceback; traceback.print_exc()
print str(e), "event", timestamp
self.debug_write("db_logging_failed_%d" % len(integrated), "fail")
def log_batched_frames(self):
db_app = dxtbx_xfel_db_application(self.params, cache_connection=True)
for q in self.queries:
experiments, reflections, run, n_strong, timestamp, two_theta_low, two_theta_high, db_event = q
log_frame(experiments,
reflections,
self.params,
run,
n_strong,
timestamp=timestamp,
two_theta_low=two_theta_low,
two_theta_high=two_theta_high,
db_event=db_event,
app=db_app)
self.queries = []
def log_frame(self,
experiments,
reflections,
run,
n_strong,
timestamp=None,
two_theta_low=None,
two_theta_high=None,
db_event=None):
# update an existing db_event if db_event is not None
if self.params.experiment_tag is None:
return
if self.params.db.logging_batch_size:
self.queries.append(
(experiments, reflections, run, n_strong, timestamp,
two_theta_low, two_theta_high, db_event))
if len(self.queries) >= self.params.db.logging_batch_size:
self.log_batched_frames()
else:
db_event = log_frame(experiments,
reflections,
self.params,
run,
n_strong,
timestamp=timestamp,
two_theta_low=two_theta_low,
two_theta_high=two_theta_high,
db_event=db_event,
app=self.db_app)
return db_event
def log_frame(self,
experiments,
reflections,
run,
n_strong,
timestamp=None):
if self.params.experiment_tag is None:
return
try:
from xfel.ui.db.dxtbx_db import log_frame
log_frame(experiments, reflections, self.params, run, n_strong,
timestamp)
except Exception, e:
import traceback
traceback.print_exc()
print str(e), "event", timestamp
self.debug_write("db_logging_failed_%d" % len(integrated), "fail")
def log_frame(self, experiments, reflections, run, n_strong, timestamp = None,
two_theta_low = None, two_theta_high = None, db_event = None):
# update an existing db_event if db_event is not None
if self.params.experiment_tag is None:
return
db_event = log_frame(experiments, reflections, self.params, run, n_strong, timestamp = timestamp,
two_theta_low = two_theta_low, two_theta_high = two_theta_high,
db_event = db_event, app = self.db_app)
return db_event
def log_batched_frames(self):
current_run = self.params.input.run_num
current_dbrun = self.run
inserts = "BEGIN;\n" # start a transaction
for q in self.queries:
experiments, reflections, run, n_strong, timestamp, two_theta_low, two_theta_high, db_event = q
if run != current_run:
current_run = run
current_dbrun = self.db_app.get_run(run_number=run)
inserts += log_frame(experiments,
reflections,
self.params,
current_dbrun,
n_strong,
timestamp=timestamp,
two_theta_low=two_theta_low,
two_theta_high=two_theta_high,
db_event=db_event,
app=self.db_app,
trial=self.trial)
inserts += "COMMIT;\n"
# patch up query so for example '@row_id' becomes @row_id
newinserts = []
for line in inserts.split('\n'):
if '@' in line:
newline = []
for word in line.split(' '):
if '@' in word:
word = word.replace("'", "")
newline.append(word)
line = ' '.join(newline)
newinserts.append(line)
inserts = '\n'.join(newinserts)
self.db_app.execute_query(
inserts, commit=False) # transaction, so don't commit twice
self.queries = []
def log_frame(self,
experiments,
reflections,
run,
n_strong,
timestamp=None,
two_theta_low=None,
two_theta_high=None,
db_event=None):
# update an existing db_event if db_event is not None
if self.params.experiment_tag is None:
return
from xfel.ui.db.dxtbx_db import log_frame
db_event = log_frame(experiments,
reflections,
self.params,
run,
n_strong,
timestamp=timestamp,
two_theta_low=two_theta_low,
two_theta_high=two_theta_high,
db_event=db_event)
return db_event
def event(self, evt, env):
"""The event() function is called for every L1Accept transition.
XXX more?
Previously, common-mode correction was applied only after initial
threshold filtering. Since the common_mode class applies the
(lengthy) common-mode correction immediately after reading the
image from the stream, this optimisation is currently not
(elegantly) doable.
@param evt Event data object, a configure object
@param env Environment object
"""
super(mod_hitfind, self).event(evt, env)
if (evt.get("skip_event")):
return
# This module only applies to detectors for which a distance is
# available.
distance = cspad_tbx.env_distance(self.address, env, self._detz_offset)
if distance is None:
self.nfail += 1
self.logger.warning("event(): no distance, shot skipped")
evt.put(skip_event_flag(), "skip_event")
return
device = cspad_tbx.address_split(self.address)[2]
# ***** HITFINDING ***** XXX For hitfinding it may be interesting
# to look at the fraction of subzero pixels in the dark-corrected
# image.
if (self.m_threshold is not None):
# If a threshold value is given it can be applied in one of three ways:
# 1. Apply it over the whole image
if (self.m_roi is None and self.m_distl_min_peaks is None):
vmax = flex.max(self.cspad_img)
if (vmax < self.m_threshold):
if not self.m_negate_hits:
# Tell downstream modules to skip this event if the threshold was not met.
evt.put(skip_event_flag(), "skip_event")
return
elif self.m_negate_hits:
evt.put(skip_event_flag(), "skip_event")
return
# 2. Apply threshold over a rectangular region of interest.
elif (self.m_roi is not None):
vmax = flex.max(self.cspad_img[self.m_roi[2]:self.m_roi[3],
self.m_roi[0]:self.m_roi[1]])
if (vmax < self.m_threshold):
if not self.m_negate_hits:
evt.put(skip_event_flag(), "skip_event")
return
elif self.m_negate_hits:
evt.put(skip_event_flag(), "skip_event")
return
# 3. Determine the spotfinder spots within the central ASICS, and accept the
# image as a hit if there are m_distl_min_peaks exceeding m_threshold.
# As a further requirement, the peaks must exceed 2.5 * the 90-percentile
# pixel value of the central ASICS. This filter was added to avoid high-background
# false positives.
elif (self.m_distl_min_peaks is not None):
if device == 'marccd':
self.hitfinder_d['BEAM_CENTER_X'] = self.beam_center[0]
self.hitfinder_d['BEAM_CENTER_Y'] = self.beam_center[1]
elif device == 'Rayonix':
self.hitfinder_d['BEAM_CENTER_X'] = self.beam_center[0]
self.hitfinder_d['BEAM_CENTER_Y'] = self.beam_center[1]
peak_heights, outvalue = self.distl_filter(
self.address,
self.cspad_img.iround(), # XXX correct?
distance,
self.timestamp,
self.wavelength)
if ('permissive' in self.m_distl_flags):
number_of_accepted_peaks = (peak_heights >
self.m_threshold).count(True)
else:
number_of_accepted_peaks = ((
peak_heights > self.m_threshold).__and__(
outvalue == 0)).count(True)
sec, ms = cspad_tbx.evt_time(evt)
evt_time = sec + ms / 1000
self.stats_logger.info("BRAGG %.3f %d" %
(evt_time, number_of_accepted_peaks))
skip_event = False
if number_of_accepted_peaks < self.m_distl_min_peaks:
self.logger.info(
"Subprocess %02d: Spotfinder NO HIT image #%05d @ %s; %d spots > %d"
% (env.subprocess(), self.nshots, self.timestamp,
number_of_accepted_peaks, self.m_threshold))
if not self.m_negate_hits:
skip_event = True
else:
self.logger.info(
"Subprocess %02d: Spotfinder YES HIT image #%05d @ %s; %d spots > %d"
% (env.subprocess(), self.nshots, self.timestamp,
number_of_accepted_peaks, self.m_threshold))
if self.m_negate_hits:
skip_event = True
if skip_event:
if self.m_db_logging:
# log misses to the database
self.queue_entry(
(self.trial, evt.run(), "%.3f" % evt_time,
number_of_accepted_peaks, distance, self.sifoil,
self.wavelength, False, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, self.m_db_tags))
evt.put(skip_event_flag(), "skip_event")
return
# the indexer will log this hit when it is ran. Bug: if the spotfinder is ran by itself, this
# hit will not be logged in the db.
evt.put(number_of_accepted_peaks, 'sfspots')
self.logger.info("Subprocess %02d: process image #%05d @ %s" %
(env.subprocess(), self.nshots, self.timestamp))
# See r17537 of mod_average.py.
if device == 'Cspad':
pixel_size = cspad_tbx.pixel_size
saturated_value = cspad_tbx.cspad_saturated_value
elif device == 'marccd':
pixel_size = evt.get("marccd_pixel_size")
saturated_value = evt.get("marccd_saturated_value")
elif device == 'Rayonix':
pixel_size = rayonix_tbx.get_rayonix_pixel_size(self.bin_size)
saturated_value = rayonix_tbx.rayonix_saturated_value
d = cspad_tbx.dpack(
active_areas=self.active_areas,
address=self.address,
beam_center_x=pixel_size * self.beam_center[0],
beam_center_y=pixel_size * self.beam_center[1],
data=self.cspad_img.iround(), # XXX ouch!
distance=distance,
pixel_size=pixel_size,
saturated_value=saturated_value,
timestamp=self.timestamp,
wavelength=self.wavelength,
xtal_target=self.m_xtal_target)
if (self.m_dispatch == "index"):
import sys
from xfel.cxi.integrate_image_api import integrate_one_image
info = integrate_one_image(
d,
integration_dirname=self.m_integration_dirname,
integration_basename=self.m_integration_basename)
sys.stdout = sys.__stdout__
sys.stderr = sys.__stderr__
indexed = info is not None and hasattr(info, 'spotfinder_results')
if self.m_progress_logging:
if self.m_db_version == 'v1':
if indexed:
# integration pickle dictionary is available here as info.last_saved_best
if info.last_saved_best[
"identified_isoform"] is not None:
#print info.last_saved_best.keys()
from cxi_xdr_xes.cftbx.cspad_ana import db
dbobj = db.dbconnect(self.m_db_host,
self.m_db_name,
self.m_db_user,
self.m_db_password)
cursor = dbobj.cursor()
if info.last_saved_best[
"identified_isoform"] in self.isoforms:
PM, indices, miller_id = self.isoforms[
info.last_saved_best["identified_isoform"]]
else:
from xfel.xpp.progress_support import progress_manager
PM = progress_manager(info.last_saved_best,
self.m_db_experiment_tag,
self.m_trial_id,
self.m_rungroup_id,
evt.run())
indices, miller_id = PM.get_HKL(cursor)
# cache these as they don't change for a given isoform
self.isoforms[info.last_saved_best[
"identified_isoform"]] = PM, indices, miller_id
if self.m_sql_buffer_size > 1:
self.queue_progress_entry(
PM.scale_frame_detail(self.timestamp,
cursor,
do_inserts=False))
else:
PM.scale_frame_detail(self.timestamp,
cursor,
do_inserts=True)
dbobj.commit()
cursor.close()
dbobj.close()
elif self.m_db_version == 'v2':
key_low = 'cctbx.xfel.radial_average.two_theta_low'
key_high = 'cctbx.xfel.radial_average.two_theta_high'
tt_low = evt.get(key_low)
tt_high = evt.get(key_high)
from xfel.ui.db.dxtbx_db import log_frame
if indexed:
n_spots = len(info.spotfinder_results.images[
info.frames[0]]['spots_total'])
else:
sfspots = evt.get('sfspots')
if sfspots is None:
if info is None or not isinstance(info, int):
n_spots = 0
else:
n_spots = info
else:
n_spots = sfspots
if indexed:
known_setting = info.horizons_phil.known_setting
indexed_setting = info.organizer.info[
'best_integration']['counter']
if known_setting is None or known_setting == indexed_setting:
from xfel.command_line.frame_unpickler import construct_reflection_table_and_experiment_list
c = construct_reflection_table_and_experiment_list(
info.last_saved_best,
None,
pixel_size,
proceed_without_image=True)
c.assemble_experiments()
c.assemble_reflections()
log_frame(c.experiment_list, c.reflections,
self.db_params, evt.run(), n_spots,
self.timestamp, tt_low, tt_high)
else:
print(
"Not logging %s, wrong bravais setting (expecting %d, got %d)"
% (self.timestamp, known_setting,
indexed_setting))
else:
log_frame(None, None, self.db_params, evt.run(),
n_spots, self.timestamp, tt_low, tt_high)
if self.m_db_logging:
sec, ms = cspad_tbx.evt_time(evt)
evt_time = sec + ms / 1000
sfspots = evt.get('sfspots')
if sfspots is None:
if indexed:
n_spots = len(info.spotfinder_results.images[
info.frames[0]]['spots_total'])
else:
n_spots = 0
else:
n_spots = sfspots
if indexed:
mosaic_bloc_rotation = info.last_saved_best.get(
'ML_half_mosaicity_deg', [0])[0]
mosaic_block_size = info.last_saved_best.get(
'ML_domain_size_ang', [0])[0]
ewald_proximal_volume = info.last_saved_best.get(
'ewald_proximal_volume', [0])[0]
obs = info.last_saved_best['observations'][0]
cell_a, cell_b, cell_c, cell_alpha, cell_beta, cell_gamma = obs.unit_cell(
).parameters()
pointgroup = info.last_saved_best['pointgroup']
resolution = obs.d_min()
else:
mosaic_bloc_rotation = mosaic_block_size = ewald_proximal_volume = cell_a = cell_b = cell_c = \
cell_alpha = cell_beta = cell_gamma = spacegroup = resolution = 0
self.queue_entry(
(self.trial, evt.run(), "%.3f" % evt_time, n_spots,
distance, self.sifoil, self.wavelength, indexed,
mosaic_bloc_rotation, mosaic_block_size,
ewald_proximal_volume, pointgroup, cell_a, cell_b, cell_c,
cell_alpha, cell_beta, cell_gamma, resolution,
self.m_db_tags))
if (not indexed):
evt.put(skip_event_flag(), "skip_event")
return
elif (self.m_dispatch == "nop"):
pass
elif (self.m_dispatch == "view"): #interactive image viewer
args = ["indexing.data=dummy"]
detector_format_version = detector_format_function(
self.address, evt.GetTime())
if detector_format_version is not None:
args += [
"distl.detector_format_version=%" % detector_format_version
]
from xfel.phil_preferences import load_cxi_phil
horizons_phil = load_cxi_phil(self.m_xtal_target, args)
horizons_phil.indexing.data = d
from xfel.cxi import display_spots
display_spots.parameters.horizons_phil = horizons_phil
display_spots.wrapper_of_callback().display(
horizons_phil.indexing.data)
elif (self.m_dispatch == "spots"): #interactive spotfinder viewer
args = ["indexing.data=dummy"]
detector_format_version = detector_format_function(
self.address, evt.GetTime())
if detector_format_version is not None:
args += [
"distl.detector_format_version=%s" %
detector_format_version
]
from xfel.phil_preferences import load_cxi_phil
horizons_phil = load_cxi_phil(self.m_xtal_target, args)
horizons_phil.indexing.data = d
from xfel.cxi import display_spots
display_spots.parameters.horizons_phil = horizons_phil
from rstbx.new_horizons.index import pre_indexing_validation, pack_names
pre_indexing_validation(horizons_phil)
imagefile_arguments = pack_names(horizons_phil)
horizons_phil.persist.show()
from spotfinder.applications import signal_strength
info = signal_strength.run_signal_strength_core(
horizons_phil, imagefile_arguments)
work = display_spots.wrapper_of_callback(info)
work.display_with_callback(horizons_phil.indexing.data)
elif (self.m_dispatch == "write_dict"):
self.logger.warning(
"event(): deprecated dispatch 'write_dict', use mod_dump instead"
)
if (self.m_out_dirname is not None
or self.m_out_basename is not None):
cspad_tbx.dwritef(d, self.m_out_dirname, self.m_out_basename)
# Diagnostic message emitted only when all the processing is done.
if (env.subprocess() >= 0):
self.logger.info("Subprocess %02d: accepted #%05d @ %s" %
(env.subprocess(), self.nshots, self.timestamp))
else:
self.logger.info("Accepted #%05d @ %s" %
(self.nshots, self.timestamp))
