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))