Пример #1
0
  def distl_filter(self,
                   address,
                   cspad_img,
                   distance,
                   timestamp,
                   wavelength):
    self.hitfinder_d["DATA"] = cspad_img
    self.hitfinder_d["DISTANCE"] = distance
    self.hitfinder_d["TIMESTAMP"] = timestamp
    self.hitfinder_d["WAVELENGTH"] = wavelength
    self.hitfinder_d["DETECTOR_ADDRESS"] = address

    args = ["indexing.data=dummy",
            "distl.bins.verbose=False",
            self.asic_filter,
            ]

    detector_format_version = detector_format_function(
      address, reverse_timestamp(timestamp)[0])
    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 = self.hitfinder_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)

    from spotfinder.applications import signal_strength
    info = signal_strength.run_signal_strength_core(horizons_phil,imagefile_arguments)

    imgdata = info.Files.images[0].linearintdata

    active_data = self.get_active_data(info.Files.images[0],horizons_phil)

    peak_heights = flex.int( [
      imgdata[ spot.max_pxl_x(), spot.max_pxl_y() ]
      for spot in info.S.images[info.frames[0]]["spots_total"]
    ])

    outscale = 256
    corrected = peak_heights.as_double() * self.correction
    outvalue = outscale *(1.0-corrected)
    outvalue.set_selected(outvalue<0.0,0.)
    outvalue.set_selected(outvalue>=outscale,int(outscale)-1)
    outvalue = flex.int(outvalue.as_numpy_array().astype(numpy.int32))
    # essentially, select a peak if the peak's ADU value is > 2.5 * the 90-percentile pixel value

    #work = display_spots.wrapper_of_callback(info)
    #work.display_with_callback(horizons_phil.indexing.data)
    return peak_heights,outvalue
Пример #2
0
  def distl_filter(self,
                   address,
                   cspad_img,
                   distance,
                   timestamp,
                   wavelength):
    self.hitfinder_d["DATA"] = cspad_img
    self.hitfinder_d["DISTANCE"] = distance
    self.hitfinder_d["TIMESTAMP"] = timestamp
    self.hitfinder_d["WAVELENGTH"] = wavelength
    self.hitfinder_d["DETECTOR_ADDRESS"] = address

    args = ["indexing.data=dummy",
            "distl.bins.verbose=False",
            self.asic_filter,
            ]

    detector_format_version = detector_format_function(
      address, reverse_timestamp(timestamp)[0])
    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 = self.hitfinder_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)

    from spotfinder.applications import signal_strength
    info = signal_strength.run_signal_strength_core(horizons_phil,imagefile_arguments)

    imgdata = info.Files.images[0].linearintdata

    active_data = self.get_active_data(info.Files.images[0],horizons_phil)

    peak_heights = flex.int( [
      imgdata[ spot.max_pxl_x(), spot.max_pxl_y() ]
      for spot in info.S.images[info.frames[0]]["spots_total"]
    ])

    outscale = 256
    corrected = peak_heights.as_double() * self.correction
    outvalue = outscale *(1.0-corrected)
    outvalue.set_selected(outvalue<0.0,0.)
    outvalue.set_selected(outvalue>=outscale,int(outscale)-1)
    outvalue = flex.int(outvalue.as_numpy_array().astype(numpy.int32))
    # essentially, select a peak if the peak's ADU value is > 2.5 * the 90-percentile pixel value

    #work = display_spots.wrapper_of_callback(info)
    #work.display_with_callback(horizons_phil.indexing.data)
    return peak_heights,outvalue
Пример #3
0
def run_one_index_core(horizons_phil):
  global parameters
  parameters.horizons_phil = horizons_phil

  from rstbx.new_horizons.index import pre_indexing_validation,pack_names,new_horizons_state
  pre_indexing_validation(horizons_phil)
  imagefile_arguments = pack_names(horizons_phil)
  info = new_horizons_state(horizons_phil,imagefile_arguments)

  info.process()

  info.S = info.spotfinder_results
  return info
Пример #4
0
def run_one_index_core(horizons_phil):
  global parameters
  parameters.horizons_phil = horizons_phil

  from rstbx.new_horizons.index import pre_indexing_validation,pack_names,new_horizons_state
  pre_indexing_validation(horizons_phil)
  imagefile_arguments = pack_names(horizons_phil)
  info = new_horizons_state(horizons_phil,imagefile_arguments)

  try:
    info.process()
  except Exception, e:
    e.info = info
    raise e
Пример #5
0
    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))
Пример #6
0
  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
      if indexed and self.m_progress_logging:
        # 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()

      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))