def get_raw_data(self, index=None): if index is None: index = 0 assert len(self.params.detector_address) == 1 d = self.get_detector(index) event = self._get_event(index) run_number = event.run() run = self._psana_runs[run_number] det = self._get_psana_detector(run) data = cspad_cbf_tbx.get_psana_corrected_data( det, event, use_default=self.params.cspad.use_psana_calib, dark=self._pedestals[run_number], common_mode=self.params.cspad.common_mode, apply_gain_mask=self.params.cspad.apply_gain_mask, gain_mask_value=None, per_pixel_gain=False, gain_mask=self._get_psana_gain_map(run), ) data = data.astype(np.float64) self._raw_data = [] for quad_count, quad in enumerate(d.hierarchy()): for sensor_count, sensor in enumerate(quad): for asic_count, asic in enumerate(sensor): fdim, sdim = asic.get_image_size() asic_data = data[ sensor_count + quad_count * 8, :, asic_count * fdim : (asic_count + 1) * fdim, ] # 8 sensors per quad self._raw_data.append(flex.double(np.array(asic_data))) assert len(d) == len(self._raw_data) return tuple(self._raw_data)
def get_raw_data(self, index): import psana from scitbx.array_family import flex import numpy as np det = psana.Detector(self._src, self._env) d = self.get_detector() data = cspad_cbf_tbx.get_psana_corrected_data(det, self._get_event(index), use_default=False, dark=True, common_mode=None, apply_gain_mask=False, gain_mask_value=None, per_pixel_gain=False) data = data.astype(np.float64) self._raw_data = [] for quad_count, quad in enumerate(d.hierarchy()): for sensor_count, sensor in enumerate(quad): for asic_count, asic in enumerate(sensor): fdim, sdim = asic.get_image_size() asic_data = data[sensor_count, :, asic_count * fdim:(asic_count + 1) * fdim] self._raw_data.append( flex.double(np.ascontiguousarray(asic_data))) asic_count += 1 sensor_count += 1 assert len(d) == len(self._raw_data) return tuple(self._raw_data)
def get_raw_data(self, index): import psana from scitbx.array_family import flex import numpy as np assert len(self.params.detector_address) == 1 det = psana.Detector(self.params.detector_address[0], self._env) d = FormatXTCCspad.get_detector(self, index) data = cspad_cbf_tbx.get_psana_corrected_data( det, self._get_event(index), use_default=False, dark=self.params.cspad.dark_correction, common_mode=None, apply_gain_mask=self.params.cspad.apply_gain_mask, gain_mask_value=None, per_pixel_gain=False) data = data.astype(np.float64) self._raw_data = [] for quad_count, quad in enumerate(d.hierarchy()): for sensor_count, sensor in enumerate(quad): for asic_count, asic in enumerate(sensor): fdim, sdim = asic.get_image_size() asic_data = data[sensor_count + quad_count * 8, :, asic_count * fdim:(asic_count + 1) * fdim] # 8 sensors per quad self._raw_data.append(flex.double(np.array(asic_data))) assert len(d) == len(self._raw_data) return tuple(self._raw_data)
def run(self): """ Process all images assigned to this thread """ params, options = self.parser.parse_args(show_diff_phil=True) if params.input.experiment is None or \ params.input.run_num is None or \ params.input.address is None: raise Usage(self.usage) if params.format.file_format == "cbf": if params.format.cbf.detz_offset is None: raise Usage(self.usage) elif params.format.file_format == "pickle": if params.format.pickle.cfg is None: raise Usage(self.usage) else: raise Usage(self.usage) if not os.path.exists(params.output.output_dir): raise Sorry("Output path not found:" + params.output.output_dir) # Save the paramters self.params = params self.options = options from mpi4py import MPI comm = MPI.COMM_WORLD rank = comm.Get_rank( ) # each process in MPI has a unique id, 0-indexed size = comm.Get_size() # size: number of processes running in this job # set up psana if params.format.file_format == "pickle": psana.setConfigFile(params.format.pickle.cfg) dataset_name = "exp=%s:run=%s:idx" % (params.input.experiment, params.input.run_num) ds = psana.DataSource(dataset_name) if params.format.file_format == "cbf": src = psana.Source('DetInfo(%s)' % params.input.address) psana_det = psana.Detector(params.input.address, ds.env()) # set this to sys.maxint to analyze all events if params.dispatch.max_events is None: max_events = sys.maxint else: max_events = params.dispatch.max_events for run in ds.runs(): if params.format.file_format == "cbf": # load a header only cspad cbf from the slac metrology base_dxtbx = cspad_cbf_tbx.env_dxtbx_from_slac_metrology( run, params.input.address) if base_dxtbx is None: raise Sorry("Couldn't load calibration file for run %d" % run.run()) # list of all events times = run.times() nevents = min(len(times), max_events) # chop the list into pieces, depending on rank. This assigns each process # events such that the get every Nth event where N is the number of processes mytimes = [ times[i] for i in xrange(nevents) if (i + rank) % size == 0 ] for i in xrange(len(mytimes)): evt = run.event(mytimes[i]) id = evt.get(psana.EventId) print "Event #", i, " has id:", id timestamp = cspad_tbx.evt_timestamp( cspad_tbx.evt_time(evt)) # human readable format if timestamp is None: print "No timestamp, skipping shot" continue t = timestamp s = t[0:4] + t[5:7] + t[8:10] + t[11:13] + t[14:16] + t[ 17:19] + t[20:23] print "Processing shot", s if params.format.file_format == "pickle": if evt.get("skip_event"): print "Skipping event", id continue # the data needs to have already been processed and put into the event by psana data = evt.get(params.format.pickle.out_key) if data is None: print "No data" continue # set output paths according to the templates path = os.path.join(params.output.output_dir, "shot-" + s + ".pickle") print "Saving", path easy_pickle.dump(path, data) elif params.format.file_format == "cbf": # get numpy array, 32x185x388 data = cspad_cbf_tbx.get_psana_corrected_data( psana_det, evt, use_default=False, dark=True, common_mode=None, apply_gain_mask=params.format.cbf.gain_mask_value is not None, gain_mask_value=params.format.cbf.gain_mask_value, per_pixel_gain=False) distance = cspad_tbx.env_distance( params.input.address, run.env(), params.format.cbf.detz_offset) if distance is None: print "No distance, skipping shot" continue if self.params.format.cbf.override_energy is None: wavelength = cspad_tbx.evt_wavelength(evt) if wavelength is None: print "No wavelength, skipping shot" continue else: wavelength = 12398.4187 / self.params.format.cbf.override_energy # stitch together the header, data and metadata into the final dxtbx format object cspad_img = cspad_cbf_tbx.format_object_from_data( base_dxtbx, data, distance, wavelength, timestamp, params.input.address) path = os.path.join(params.output.output_dir, "shot-" + s + ".cbf") print "Saving", path # write the file import pycbf cspad_img._cbf_handle.write_widefile(path, pycbf.CBF,\ pycbf.MIME_HEADERS|pycbf.MSG_DIGEST|pycbf.PAD_4K, 0) run.end() ds.end()
def average(argv=None): if argv == None: argv = sys.argv[1:] try: from mpi4py import MPI except ImportError: raise Sorry("MPI not found") command_line = (libtbx.option_parser.option_parser(usage=""" %s [-p] -c config -x experiment -a address -r run -d detz_offset [-o outputdir] [-A averagepath] [-S stddevpath] [-M maxpath] [-n numevents] [-s skipnevents] [-v] [-m] [-b bin_size] [-X override_beam_x] [-Y override_beam_y] [-D xtc_dir] [-f] [-g gain_mask_value] [--min] [--minpath minpath] To write image pickles use -p, otherwise the program writes CSPAD CBFs. Writing CBFs requires the geometry to be already deployed. Examples: cxi.mpi_average -c cxi49812/average.cfg -x cxi49812 -a CxiDs1.0:Cspad.0 -r 25 -d 571 Use one process on the current node to process all the events from run 25 of experiment cxi49812, using a detz_offset of 571. mpirun -n 16 cxi.mpi_average -c cxi49812/average.cfg -x cxi49812 -a CxiDs1.0:Cspad.0 -r 25 -d 571 As above, using 16 cores on the current node. bsub -a mympi -n 100 -o average.out -q psanaq cxi.mpi_average -c cxi49812/average.cfg -x cxi49812 -a CxiDs1.0:Cspad.0 -r 25 -d 571 -o cxi49812 As above, using the psanaq and 100 cores, putting the log in average.out and the output images in the folder cxi49812. """ % libtbx.env.dispatcher_name).option( None, "--as_pickle", "-p", action="store_true", default=False, dest="as_pickle", help="Write results as image pickle files instead of cbf files" ).option( None, "--raw_data", "-R", action="store_true", default=False, dest="raw_data", help= "Disable psana corrections such as dark pedestal subtraction or common mode (cbf only)" ).option( None, "--background_pickle", "-B", default=None, dest="background_pickle", help="" ).option( None, "--config", "-c", type="string", default=None, dest="config", metavar="PATH", help="psana config file" ).option( None, "--experiment", "-x", type="string", default=None, dest="experiment", help="experiment name (eg cxi84914)" ).option( None, "--run", "-r", type="int", default=None, dest="run", help="run number" ).option( None, "--address", "-a", type="string", default="CxiDs2.0:Cspad.0", dest="address", help="detector address name (eg CxiDs2.0:Cspad.0)" ).option( None, "--detz_offset", "-d", type="float", default=None, dest="detz_offset", help= "offset (in mm) from sample interaction region to back of CSPAD detector rail (CXI), or detector distance (XPP)" ).option( None, "--outputdir", "-o", type="string", default=".", dest="outputdir", metavar="PATH", help="Optional path to output directory for output files" ).option( None, "--averagebase", "-A", type="string", default="{experiment!l}_avg-r{run:04d}", dest="averagepath", metavar="PATH", help= "Path to output average image without extension. String substitution allowed" ).option( None, "--stddevbase", "-S", type="string", default="{experiment!l}_stddev-r{run:04d}", dest="stddevpath", metavar="PATH", help= "Path to output standard deviation image without extension. String substitution allowed" ).option( None, "--maxbase", "-M", type="string", default="{experiment!l}_max-r{run:04d}", dest="maxpath", metavar="PATH", help= "Path to output maximum projection image without extension. String substitution allowed" ).option( None, "--numevents", "-n", type="int", default=None, dest="numevents", help="Maximum number of events to process. Default: all" ).option( None, "--skipevents", "-s", type="int", default=0, dest="skipevents", help="Number of events in the beginning of the run to skip. Default: 0" ).option( None, "--verbose", "-v", action="store_true", default=False, dest="verbose", help="Print more information about progress" ).option( None, "--pickle-optical-metrology", "-m", action="store_true", default=False, dest="pickle_optical_metrology", help= "If writing pickle files, use the optical metrology in the experiment's calib directory" ).option( None, "--bin_size", "-b", type="int", default=None, dest="bin_size", help="Rayonix detector bin size" ).option( None, "--override_beam_x", "-X", type="float", default=None, dest="override_beam_x", help="Rayonix detector beam center x coordinate" ).option( None, "--override_beam_y", "-Y", type="float", default=None, dest="override_beam_y", help="Rayonix detector beam center y coordinate" ).option( None, "--calib_dir", "-C", type="string", default=None, dest="calib_dir", metavar="PATH", help="calibration directory" ).option( None, "--pickle_calib_dir", "-P", type="string", default=None, dest="pickle_calib_dir", metavar="PATH", help= "pickle calibration directory specification. Replaces --calib_dir functionality." ).option( None, "--xtc_dir", "-D", type="string", default=None, dest="xtc_dir", metavar="PATH", help="xtc stream directory" ).option( None, "--use_ffb", "-f", action="store_true", default=False, dest="use_ffb", help= "Use the fast feedback filesystem at LCLS. Only for the active experiment!" ).option( None, "--gain_mask_value", "-g", type="float", default=None, dest="gain_mask_value", help= "Ratio between low and high gain pixels, if CSPAD in mixed-gain mode. Only used in CBF averaging mode." ).option( None, "--min", None, action="store_true", default=False, dest="do_minimum_projection", help="Output a minimum projection" ).option( None, "--minpath", None, type="string", default="{experiment!l}_min-r{run:04d}", dest="minpath", metavar="PATH", help= "Path to output minimum image without extension. String substitution allowed" )).process(args=argv) if len(command_line.args) > 0 or \ command_line.options.as_pickle is None or \ command_line.options.experiment is None or \ command_line.options.run is None or \ command_line.options.address is None or \ command_line.options.detz_offset is None or \ command_line.options.averagepath is None or \ command_line.options.stddevpath is None or \ command_line.options.maxpath is None or \ command_line.options.pickle_optical_metrology is None: command_line.parser.show_help() return # set this to sys.maxint to analyze all events if command_line.options.numevents is None: maxevents = sys.maxsize else: maxevents = command_line.options.numevents comm = MPI.COMM_WORLD rank = comm.Get_rank() size = comm.Get_size() if command_line.options.config is not None: psana.setConfigFile(command_line.options.config) dataset_name = "exp=%s:run=%d:smd" % (command_line.options.experiment, command_line.options.run) if command_line.options.xtc_dir is not None: if command_line.options.use_ffb: raise Sorry("Cannot specify the xtc_dir and use SLAC's ffb system") dataset_name += ":dir=%s" % command_line.options.xtc_dir elif command_line.options.use_ffb: # as ffb is only at SLAC, ok to hardcode /reg/d here dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc" % ( command_line.options.experiment[0:3], command_line.options.experiment) if command_line.options.calib_dir is not None: psana.setOption('psana.calib-dir', command_line.options.calib_dir) ds = psana.DataSource(dataset_name) address = command_line.options.address src = psana.Source('DetInfo(%s)' % address) nevent = np.array([0.]) if command_line.options.background_pickle is not None: background = easy_pickle.load( command_line.options.background_pickle)['DATA'].as_numpy_array() for run in ds.runs(): runnumber = run.run() if not command_line.options.as_pickle: psana_det = psana.Detector(address, ds.env()) # list of all events if command_line.options.skipevents > 0: print("Skipping first %d events" % command_line.options.skipevents) elif "Rayonix" in command_line.options.address: print("Skipping first image in the Rayonix detector" ) # Shuttering issue command_line.options.skipevents = 1 for i, evt in enumerate(run.events()): if i % size != rank: continue if i < command_line.options.skipevents: continue if i >= maxevents: break if i % 10 == 0: print('Rank', rank, 'processing event', i) #print "Event #",rank*mylength+i," has id:",evt.get(EventId) if 'Rayonix' in command_line.options.address or 'FeeHxSpectrometer' in command_line.options.address or 'XrayTransportDiagnostic' in command_line.options.address: data = evt.get(psana.Camera.FrameV1, src) if data is None: print("No data") continue data = data.data16().astype(np.float64) elif command_line.options.as_pickle: data = evt.get(psana.ndarray_float64_3, src, 'image0') else: # get numpy array, 32x185x388 from xfel.cftbx.detector.cspad_cbf_tbx import get_psana_corrected_data if command_line.options.raw_data: data = get_psana_corrected_data(psana_det, evt, use_default=False, dark=False, common_mode=None, apply_gain_mask=False, per_pixel_gain=False) else: if command_line.options.gain_mask_value is None: data = get_psana_corrected_data(psana_det, evt, use_default=True) else: data = get_psana_corrected_data( psana_det, evt, use_default=False, dark=True, common_mode=None, apply_gain_mask=True, gain_mask_value=command_line.options. gain_mask_value, per_pixel_gain=False) if data is None: print("No data") continue if command_line.options.background_pickle is not None: data -= background if 'FeeHxSpectrometer' in command_line.options.address or 'XrayTransportDiagnostic' in command_line.options.address: distance = np.array([0.0]) wavelength = np.array([1.0]) else: d = cspad_tbx.env_distance(address, run.env(), command_line.options.detz_offset) if d is None: print("No distance, using distance", command_line.options.detz_offset) assert command_line.options.detz_offset is not None if 'distance' not in locals(): distance = np.array([command_line.options.detz_offset]) else: distance += command_line.options.detz_offset else: if 'distance' in locals(): distance += d else: distance = np.array([float(d)]) w = cspad_tbx.evt_wavelength(evt) if w is None: print("No wavelength") if 'wavelength' not in locals(): wavelength = np.array([1.0]) else: if 'wavelength' in locals(): wavelength += w else: wavelength = np.array([w]) t = cspad_tbx.evt_time(evt) if t is None: print("No timestamp, skipping shot") continue if 'timestamp' in locals(): timestamp += t[0] + (t[1] / 1000) else: timestamp = np.array([t[0] + (t[1] / 1000)]) if 'sum' in locals(): sum += data else: sum = np.array(data, copy=True) if 'sumsq' in locals(): sumsq += data * data else: sumsq = data * data if 'maximum' in locals(): maximum = np.maximum(maximum, data) else: maximum = np.array(data, copy=True) if command_line.options.do_minimum_projection: if 'minimum' in locals(): minimum = np.minimum(minimum, data) else: minimum = np.array(data, copy=True) nevent += 1 #sum the images across mpi cores if size > 1: print("Synchronizing rank", rank) totevent = np.zeros(nevent.shape) comm.Reduce(nevent, totevent) if rank == 0 and totevent[0] == 0: raise Sorry("No events found in the run") sumall = np.zeros(sum.shape).astype(sum.dtype) comm.Reduce(sum, sumall) sumsqall = np.zeros(sumsq.shape).astype(sumsq.dtype) comm.Reduce(sumsq, sumsqall) maxall = np.zeros(maximum.shape).astype(maximum.dtype) comm.Reduce(maximum, maxall, op=MPI.MAX) if command_line.options.do_minimum_projection: minall = np.zeros(maximum.shape).astype(minimum.dtype) comm.Reduce(minimum, minall, op=MPI.MIN) waveall = np.zeros(wavelength.shape).astype(wavelength.dtype) comm.Reduce(wavelength, waveall) distall = np.zeros(distance.shape).astype(distance.dtype) comm.Reduce(distance, distall) timeall = np.zeros(timestamp.shape).astype(timestamp.dtype) comm.Reduce(timestamp, timeall) if rank == 0: if size > 1: print("Synchronized") # Accumulating floating-point numbers introduces errors, # which may cause negative variances. Since a two-pass # approach is unacceptable, the standard deviation is # clamped at zero. mean = sumall / float(totevent[0]) variance = (sumsqall / float(totevent[0])) - (mean**2) variance[variance < 0] = 0 stddev = np.sqrt(variance) wavelength = waveall[0] / totevent[0] distance = distall[0] / totevent[0] pixel_size = cspad_tbx.pixel_size saturated_value = cspad_tbx.cspad_saturated_value timestamp = timeall[0] / totevent[0] timestamp = (int(timestamp), timestamp % int(timestamp) * 1000) timestamp = cspad_tbx.evt_timestamp(timestamp) if command_line.options.as_pickle: extension = ".pickle" else: extension = ".cbf" dest_paths = [ cspad_tbx.pathsubst(command_line.options.averagepath + extension, evt, ds.env()), cspad_tbx.pathsubst(command_line.options.stddevpath + extension, evt, ds.env()), cspad_tbx.pathsubst(command_line.options.maxpath + extension, evt, ds.env()) ] if command_line.options.do_minimum_projection: dest_paths.append( cspad_tbx.pathsubst(command_line.options.minpath + extension, evt, ds.env())) dest_paths = [ os.path.join(command_line.options.outputdir, path) for path in dest_paths ] if 'Rayonix' in command_line.options.address: all_data = [mean, stddev, maxall] if command_line.options.do_minimum_projection: all_data.append(minall) from xfel.cxi.cspad_ana import rayonix_tbx pixel_size = rayonix_tbx.get_rayonix_pixel_size( command_line.options.bin_size) beam_center = [ command_line.options.override_beam_x, command_line.options.override_beam_y ] active_areas = flex.int([0, 0, mean.shape[1], mean.shape[0]]) split_address = cspad_tbx.address_split(address) old_style_address = split_address[0] + "-" + split_address[ 1] + "|" + split_address[2] + "-" + split_address[3] for data, path in zip(all_data, dest_paths): print("Saving", path) d = cspad_tbx.dpack( active_areas=active_areas, address=old_style_address, beam_center_x=pixel_size * beam_center[0], beam_center_y=pixel_size * beam_center[1], data=flex.double(data), distance=distance, pixel_size=pixel_size, saturated_value=rayonix_tbx.rayonix_saturated_value, timestamp=timestamp, wavelength=wavelength) easy_pickle.dump(path, d) elif 'FeeHxSpectrometer' in command_line.options.address or 'XrayTransportDiagnostic' in command_line.options.address: all_data = [mean, stddev, maxall] split_address = cspad_tbx.address_split(address) old_style_address = split_address[0] + "-" + split_address[ 1] + "|" + split_address[2] + "-" + split_address[3] if command_line.options.do_minimum_projection: all_data.append(minall) for data, path in zip(all_data, dest_paths): d = cspad_tbx.dpack(address=old_style_address, data=flex.double(data), distance=distance, pixel_size=0.1, timestamp=timestamp, wavelength=wavelength) print("Saving", path) easy_pickle.dump(path, d) elif command_line.options.as_pickle: split_address = cspad_tbx.address_split(address) old_style_address = split_address[0] + "-" + split_address[ 1] + "|" + split_address[2] + "-" + split_address[3] xpp = 'xpp' in address.lower() if xpp: evt_time = cspad_tbx.evt_time( evt) # tuple of seconds, milliseconds timestamp = cspad_tbx.evt_timestamp( evt_time) # human readable format from iotbx.detectors.cspad_detector_formats import detector_format_version, reverse_timestamp from xfel.cxi.cspad_ana.cspad_tbx import xpp_active_areas version_lookup = detector_format_version( old_style_address, reverse_timestamp(timestamp)[0]) assert version_lookup is not None active_areas = xpp_active_areas[version_lookup]['active_areas'] beam_center = [1765 // 2, 1765 // 2] else: if command_line.options.pickle_calib_dir is not None: metro_path = command_line.options.pickle_calib_dir elif command_line.options.pickle_optical_metrology: from xfel.cftbx.detector.cspad_cbf_tbx import get_calib_file_path metro_path = get_calib_file_path(run.env(), address, run) else: metro_path = libtbx.env.find_in_repositories( "xfel/metrology/CSPad/run4/CxiDs1.0_Cspad.0") sections = parse_calib.calib2sections(metro_path) beam_center, active_areas = cspad_tbx.cbcaa( cspad_tbx.getConfig(address, ds.env()), sections) class fake_quad(object): def __init__(self, q, d): self.q = q self.d = d def quad(self): return self.q def data(self): return self.d if xpp: quads = [ fake_quad(i, mean[i * 8:(i + 1) * 8, :, :]) for i in range(4) ] mean = cspad_tbx.image_xpp(old_style_address, None, ds.env(), active_areas, quads=quads) mean = flex.double(mean.astype(np.float64)) quads = [ fake_quad(i, stddev[i * 8:(i + 1) * 8, :, :]) for i in range(4) ] stddev = cspad_tbx.image_xpp(old_style_address, None, ds.env(), active_areas, quads=quads) stddev = flex.double(stddev.astype(np.float64)) quads = [ fake_quad(i, maxall[i * 8:(i + 1) * 8, :, :]) for i in range(4) ] maxall = cspad_tbx.image_xpp(old_style_address, None, ds.env(), active_areas, quads=quads) maxall = flex.double(maxall.astype(np.float64)) if command_line.options.do_minimum_projection: quads = [ fake_quad(i, minall[i * 8:(i + 1) * 8, :, :]) for i in range(4) ] minall = cspad_tbx.image_xpp(old_style_address, None, ds.env(), active_areas, quads=quads) minall = flex.double(minall.astype(np.float64)) else: quads = [ fake_quad(i, mean[i * 8:(i + 1) * 8, :, :]) for i in range(4) ] mean = cspad_tbx.CsPadDetector(address, evt, ds.env(), sections, quads=quads) mean = flex.double(mean.astype(np.float64)) quads = [ fake_quad(i, stddev[i * 8:(i + 1) * 8, :, :]) for i in range(4) ] stddev = cspad_tbx.CsPadDetector(address, evt, ds.env(), sections, quads=quads) stddev = flex.double(stddev.astype(np.float64)) quads = [ fake_quad(i, maxall[i * 8:(i + 1) * 8, :, :]) for i in range(4) ] maxall = cspad_tbx.CsPadDetector(address, evt, ds.env(), sections, quads=quads) maxall = flex.double(maxall.astype(np.float64)) if command_line.options.do_minimum_projection: quads = [ fake_quad(i, minall[i * 8:(i + 1) * 8, :, :]) for i in range(4) ] minall = cspad_tbx.CsPadDetector(address, evt, ds.env(), sections, quads=quads) minall = flex.double(minall.astype(np.float64)) all_data = [mean, stddev, maxall] if command_line.options.do_minimum_projection: all_data.append(minall) for data, path in zip(all_data, dest_paths): print("Saving", path) d = cspad_tbx.dpack(active_areas=active_areas, address=old_style_address, beam_center_x=pixel_size * beam_center[0], beam_center_y=pixel_size * beam_center[1], data=data, distance=distance, pixel_size=pixel_size, saturated_value=saturated_value, timestamp=timestamp, wavelength=wavelength) easy_pickle.dump(path, d) else: # load a header only cspad cbf from the slac metrology from xfel.cftbx.detector import cspad_cbf_tbx import pycbf base_dxtbx = cspad_cbf_tbx.env_dxtbx_from_slac_metrology( run, address) if base_dxtbx is None: raise Sorry("Couldn't load calibration file for run %d" % run.run()) all_data = [mean, stddev, maxall] if command_line.options.do_minimum_projection: all_data.append(minall) for data, path in zip(all_data, dest_paths): print("Saving", path) cspad_img = cspad_cbf_tbx.format_object_from_data( base_dxtbx, data, distance, wavelength, timestamp, address, round_to_int=False) cspad_img._cbf_handle.write_widefile(path, pycbf.CBF,\ pycbf.MIME_HEADERS|pycbf.MSG_DIGEST|pycbf.PAD_4K, 0)
def process_event(self, run, timestamp): """ Process a single event from a run @param run psana run object @param timestamp psana timestamp object """ ts = cspad_tbx.evt_timestamp((timestamp.seconds(),timestamp.nanoseconds()/1e6)) if ts is None: print "No timestamp, skipping shot" return if len(self.params_cache.debug.event_timestamp) > 0 and ts not in self.params_cache.debug.event_timestamp: return if self.params_cache.debug.skip_processed_events or self.params_cache.debug.skip_unprocessed_events or self.params_cache.debug.skip_bad_events: if ts in self.known_events: if self.known_events[ts] not in ["stop", "done", "fail"]: if self.params_cache.debug.skip_bad_events: print "Skipping event %s: possibly caused an unknown exception previously"%ts return elif self.params_cache.debug.skip_processed_events: print "Skipping event %s: processed successfully previously"%ts return else: if self.params_cache.debug.skip_unprocessed_events: print "Skipping event %s: not processed previously"%ts return self.debug_start(ts) evt = run.event(timestamp) if evt.get("skip_event") or "skip_event" in [key.key() for key in evt.keys()]: print "Skipping event",ts self.debug_write("psana_skip", "skip") return print "Accepted", ts self.params = copy.deepcopy(self.params_cache) # the data needs to have already been processed and put into the event by psana if self.params.format.file_format == 'cbf': # get numpy array, 32x185x388 data = cspad_cbf_tbx.get_psana_corrected_data(self.psana_det, evt, use_default=False, dark=True, common_mode=self.common_mode, apply_gain_mask=self.params.format.cbf.gain_mask_value is not None, gain_mask_value=self.params.format.cbf.gain_mask_value, per_pixel_gain=False) if data is None: print "No data" self.debug_write("no_data", "skip") return if self.params.format.cbf.override_distance is None: distance = cspad_tbx.env_distance(self.params.input.address, run.env(), self.params.format.cbf.detz_offset) if distance is None: print "No distance, skipping shot" self.debug_write("no_distance", "skip") return else: distance = self.params.format.cbf.override_distance if self.params.format.cbf.override_energy is None: wavelength = cspad_tbx.evt_wavelength(evt) if wavelength is None: print "No wavelength, skipping shot" self.debug_write("no_wavelength", "skip") return else: wavelength = 12398.4187/self.params.format.cbf.override_energy if self.params.format.file_format == 'pickle': image_dict = evt.get(self.params.format.pickle.out_key) data = image_dict['DATA'] timestamp = t = ts s = t[0:4] + t[5:7] + t[8:10] + t[11:13] + t[14:16] + t[17:19] + t[20:23] print "Processing shot", s if self.params.format.file_format == 'cbf': # stitch together the header, data and metadata into the final dxtbx format object cspad_img = cspad_cbf_tbx.format_object_from_data(self.base_dxtbx, data, distance, wavelength, timestamp, self.params.input.address) if self.params.input.reference_geometry is not None: from dxtbx.model import Detector # copy.deep_copy(self.reference_detctor) seems unsafe based on tests. Use from_dict(to_dict()) instead. cspad_img._detector_instance = Detector.from_dict(self.reference_detector.to_dict()) cspad_img.sync_detector_to_cbf() elif self.params.format.file_format == 'pickle': from dxtbx.format.FormatPYunspecifiedStill import FormatPYunspecifiedStillInMemory cspad_img = FormatPYunspecifiedStillInMemory(image_dict) cspad_img.timestamp = s if self.params.dispatch.dump_all: self.save_image(cspad_img, self.params, os.path.join(self.params.output.output_dir, "shot-" + s)) self.cache_ranges(cspad_img, self.params) imgset = MemImageSet([cspad_img]) if self.params.dispatch.estimate_gain_only: from dials.command_line.estimate_gain import estimate_gain estimate_gain(imgset) return if not self.params.dispatch.find_spots: self.debug_write("data_loaded", "done") return datablock = DataBlockFactory.from_imageset(imgset)[0] # before calling DIALS for processing, set output paths according to the templates if self.indexed_filename_template is not None and "%s" in self.indexed_filename_template: self.params.output.indexed_filename = os.path.join(self.params.output.output_dir, self.indexed_filename_template%("idx-" + s)) if "%s" in self.refined_experiments_filename_template: self.params.output.refined_experiments_filename = os.path.join(self.params.output.output_dir, self.refined_experiments_filename_template%("idx-" + s)) if "%s" in self.integrated_filename_template: self.params.output.integrated_filename = os.path.join(self.params.output.output_dir, self.integrated_filename_template%("idx-" + s)) if "%s" in self.reindexedstrong_filename_template: self.params.output.reindexedstrong_filename = os.path.join(self.params.output.output_dir, self.reindexedstrong_filename_template%("idx-" + s)) # Load a dials mask from the trusted range and psana mask from dials.util.masking import MaskGenerator generator = MaskGenerator(self.params.border_mask) mask = generator.generate(imgset) if self.params.format.file_format == "cbf": mask = tuple([a&b for a, b in zip(mask,self.dials_mask)]) if self.spotfinder_mask is None: self.params.spotfinder.lookup.mask = mask else: self.params.spotfinder.lookup.mask = tuple([a&b for a, b in zip(mask,self.spotfinder_mask)]) if self.integration_mask is None: self.params.integration.lookup.mask = mask else: self.params.integration.lookup.mask = tuple([a&b for a, b in zip(mask,self.integration_mask)]) self.debug_write("spotfind_start") try: observed = self.find_spots(datablock) except Exception, e: import traceback; traceback.print_exc() print str(e), "event", timestamp self.debug_write("spotfinding_exception", "fail") return
def run(self): """ Process all images assigned to this thread """ params, options = self.parser.parse_args(show_diff_phil=True) if params.input.experiment is None or \ params.input.run_num is None or \ params.input.address is None: raise Usage(self.usage) if params.format.file_format == "cbf": if params.format.cbf.detz_offset is None: raise Usage(self.usage) elif params.format.file_format == "pickle": if params.input.cfg is None: raise Usage(self.usage) else: raise Usage(self.usage) if not os.path.exists(params.output.output_dir): raise Sorry("Output path not found:" + params.output.output_dir) #Environment variable redirect for CBFLib temporary CBF_TMP_XYZ file output if params.format.file_format == "cbf": if params.output.tmp_output_dir is None: tmp_dir = os.path.join(params.output.output_dir, '.tmp') else: tmp_dir = os.path.join(params.output.tmp_output_dir, '.tmp') if not os.path.exists(tmp_dir): with show_mail_on_error(): try: os.makedirs(tmp_dir) # Can fail if running multiprocessed - that's OK if the folder was created except OSError as e: # In Python 2, a FileExistsError is just an OSError if e.errno != errno.EEXIST: # If this OSError is not a FileExistsError raise os.environ['CBF_TMP_DIR'] = tmp_dir # Save the paramters self.params = params self.options = options from mpi4py import MPI comm = MPI.COMM_WORLD rank = comm.Get_rank( ) # each process in MPI has a unique id, 0-indexed size = comm.Get_size() # size: number of processes running in this job # set up psana if params.input.cfg is not None: psana.setConfigFile(params.input.cfg) if params.input.calib_dir is not None: psana.setOption('psana.calib-dir', params.input.calib_dir) dataset_name = "exp=%s:run=%s:idx" % (params.input.experiment, params.input.run_num) if params.input.xtc_dir is not None: dataset_name = "exp=%s:run=%s:idx:dir=%s" % ( params.input.experiment, params.input.run_num, params.input.xtc_dir) ds = psana.DataSource(dataset_name) if params.format.file_format == "cbf": src = psana.Source('DetInfo(%s)' % params.input.address) psana_det = psana.Detector(params.input.address, ds.env()) # set this to sys.maxint to analyze all events if params.dispatch.max_events is None: max_events = sys.maxsize else: max_events = params.dispatch.max_events for run in ds.runs(): if params.format.file_format == "cbf": if params.format.cbf.mode == "cspad": # load a header only cspad cbf from the slac metrology base_dxtbx = cspad_cbf_tbx.env_dxtbx_from_slac_metrology( run, params.input.address) if base_dxtbx is None: raise Sorry( "Couldn't load calibration file for run %d" % run.run()) elif params.format.cbf.mode == "rayonix": # load a header only rayonix cbf from the input parameters detector_size = rayonix_tbx.get_rayonix_detector_dimensions( ds.env()) base_dxtbx = rayonix_tbx.get_dxtbx_from_params( params.format.cbf.rayonix, detector_size) # list of all events times = run.times() if params.dispatch.selected_events: times = [ t for t in times if cspad_tbx.evt_timestamp((t.seconds(), t.nanoseconds() / 1e6)) in params.input.timestamp ] nevents = min(len(times), max_events) # chop the list into pieces, depending on rank. This assigns each process # events such that the get every Nth event where N is the number of processes mytimes = [ times[i] for i in range(nevents) if (i + rank) % size == 0 ] for i in range(len(mytimes)): evt = run.event(mytimes[i]) id = evt.get(psana.EventId) print("Event #", i, " has id:", id) timestamp = cspad_tbx.evt_timestamp( cspad_tbx.evt_time(evt)) # human readable format if timestamp is None: print("No timestamp, skipping shot") continue if evt.get("skip_event") or "skip_event" in [ key.key() for key in evt.keys() ]: print("Skipping event", timestamp) continue t = timestamp s = t[0:4] + t[5:7] + t[8:10] + t[11:13] + t[14:16] + t[ 17:19] + t[20:23] print("Processing shot", s) if params.format.file_format == "pickle": if evt.get("skip_event"): print("Skipping event", id) continue # the data needs to have already been processed and put into the event by psana data = evt.get(params.format.pickle.out_key) if data is None: print("No data") continue # set output paths according to the templates path = os.path.join(params.output.output_dir, "shot-" + s + ".pickle") print("Saving", path) easy_pickle.dump(path, data) elif params.format.file_format == "cbf": if params.format.cbf.mode == "cspad": # get numpy array, 32x185x388 data = cspad_cbf_tbx.get_psana_corrected_data( psana_det, evt, use_default=False, dark=True, common_mode=None, apply_gain_mask=params.format.cbf.cspad. gain_mask_value is not None, gain_mask_value=params.format.cbf.cspad. gain_mask_value, per_pixel_gain=False) distance = cspad_tbx.env_distance( params.input.address, run.env(), params.format.cbf.detz_offset) elif params.format.cbf.mode == "rayonix": data = rayonix_tbx.get_data_from_psana_event( evt, params.input.address) distance = params.format.cbf.detz_offset if distance is None: print("No distance, skipping shot") continue if self.params.format.cbf.override_energy is None: wavelength = cspad_tbx.evt_wavelength(evt) if wavelength is None: print("No wavelength, skipping shot") continue else: wavelength = 12398.4187 / self.params.format.cbf.override_energy # stitch together the header, data and metadata into the final dxtbx format object if params.format.cbf.mode == "cspad": image = cspad_cbf_tbx.format_object_from_data( base_dxtbx, data, distance, wavelength, timestamp, params.input.address, round_to_int=False) elif params.format.cbf.mode == "rayonix": image = rayonix_tbx.format_object_from_data( base_dxtbx, data, distance, wavelength, timestamp, params.input.address) path = os.path.join(params.output.output_dir, "shot-" + s + ".cbf") print("Saving", path) # write the file import pycbf image._cbf_handle.write_widefile(path.encode(), pycbf.CBF,\ pycbf.MIME_HEADERS|pycbf.MSG_DIGEST|pycbf.PAD_4K, 0) run.end() ds.end()
def process_event(self, run, timestamp): """ Process a single event from a run @param run psana run object @param timestamp psana timestamp object """ ts = cspad_tbx.evt_timestamp( (timestamp.seconds(), timestamp.nanoseconds() / 1e6)) if ts is None: print "No timestamp, skipping shot" return if len(self.params_cache.debug.event_timestamp ) > 0 and ts not in self.params_cache.debug.event_timestamp: return if self.params_cache.debug.skip_processed_events or self.params_cache.debug.skip_unprocessed_events or self.params_cache.debug.skip_bad_events: if ts in self.known_events: if self.known_events[ts] not in ["stop", "done", "fail"]: if self.params_cache.debug.skip_bad_events: print "Skipping event %s: possibly caused an unknown exception previously" % ts return elif self.params_cache.debug.skip_processed_events: print "Skipping event %s: processed successfully previously" % ts return else: if self.params_cache.debug.skip_unprocessed_events: print "Skipping event %s: not processed previously" % ts return self.debug_start(ts) evt = run.event(timestamp) if evt.get("skip_event") or "skip_event" in [ key.key() for key in evt.keys() ]: print "Skipping event", ts self.debug_write("psana_skip", "skip") return print "Accepted", ts self.params = copy.deepcopy(self.params_cache) # the data needs to have already been processed and put into the event by psana if self.params.format.file_format == 'cbf': # get numpy array, 32x185x388 data = cspad_cbf_tbx.get_psana_corrected_data( self.psana_det, evt, use_default=False, dark=True, common_mode=self.common_mode, apply_gain_mask=self.params.format.cbf.gain_mask_value is not None, gain_mask_value=self.params.format.cbf.gain_mask_value, per_pixel_gain=False) if data is None: print "No data" self.debug_write("no_data", "skip") return if self.params.format.cbf.override_distance is None: distance = cspad_tbx.env_distance( self.params.input.address, run.env(), self.params.format.cbf.detz_offset) if distance is None: print "No distance, skipping shot" self.debug_write("no_distance", "skip") return else: distance = self.params.format.cbf.override_distance if self.params.format.cbf.override_energy is None: wavelength = cspad_tbx.evt_wavelength(evt) if wavelength is None: print "No wavelength, skipping shot" self.debug_write("no_wavelength", "skip") return else: wavelength = 12398.4187 / self.params.format.cbf.override_energy if self.params.format.file_format == 'pickle': image_dict = evt.get(self.params.format.pickle.out_key) data = image_dict['DATA'] timestamp = t = ts s = t[0:4] + t[5:7] + t[8:10] + t[11:13] + t[14:16] + t[17:19] + t[ 20:23] print "Processing shot", s if self.params.format.file_format == 'cbf': # stitch together the header, data and metadata into the final dxtbx format object cspad_img = cspad_cbf_tbx.format_object_from_data( self.base_dxtbx, data, distance, wavelength, timestamp, self.params.input.address) if self.params.input.reference_geometry is not None: from dxtbx.model import Detector # copy.deep_copy(self.reference_detctor) seems unsafe based on tests. Use from_dict(to_dict()) instead. cspad_img._detector_instance = Detector.from_dict( self.reference_detector.to_dict()) cspad_img.sync_detector_to_cbf() elif self.params.format.file_format == 'pickle': from dxtbx.format.FormatPYunspecifiedStill import FormatPYunspecifiedStillInMemory cspad_img = FormatPYunspecifiedStillInMemory(image_dict) cspad_img.timestamp = s if self.params.dispatch.dump_all: self.save_image( cspad_img, self.params, os.path.join(self.params.output.output_dir, "shot-" + s)) self.cache_ranges(cspad_img, self.params) imgset = MemImageSet([cspad_img]) if self.params.dispatch.estimate_gain_only: from dials.command_line.estimate_gain import estimate_gain estimate_gain(imgset) return if not self.params.dispatch.find_spots: self.debug_write("data_loaded", "done") return datablock = DataBlockFactory.from_imageset(imgset)[0] # before calling DIALS for processing, set output paths according to the templates if self.indexed_filename_template is not None and "%s" in self.indexed_filename_template: self.params.output.indexed_filename = os.path.join( self.params.output.output_dir, self.indexed_filename_template % ("idx-" + s)) if "%s" in self.refined_experiments_filename_template: self.params.output.refined_experiments_filename = os.path.join( self.params.output.output_dir, self.refined_experiments_filename_template % ("idx-" + s)) if "%s" in self.integrated_filename_template: self.params.output.integrated_filename = os.path.join( self.params.output.output_dir, self.integrated_filename_template % ("idx-" + s)) if "%s" in self.reindexedstrong_filename_template: self.params.output.reindexedstrong_filename = os.path.join( self.params.output.output_dir, self.reindexedstrong_filename_template % ("idx-" + s)) # Load a dials mask from the trusted range and psana mask from dials.util.masking import MaskGenerator generator = MaskGenerator(self.params.border_mask) mask = generator.generate(imgset) if self.params.format.file_format == "cbf": mask = tuple([a & b for a, b in zip(mask, self.dials_mask)]) if self.spotfinder_mask is None: self.params.spotfinder.lookup.mask = mask else: self.params.spotfinder.lookup.mask = tuple( [a & b for a, b in zip(mask, self.spotfinder_mask)]) if self.integration_mask is None: self.params.integration.lookup.mask = mask else: self.params.integration.lookup.mask = tuple( [a & b for a, b in zip(mask, self.integration_mask)]) self.debug_write("spotfind_start") try: observed = self.find_spots(datablock) except Exception, e: import traceback traceback.print_exc() print str(e), "event", timestamp self.debug_write("spotfinding_exception", "fail") return
def average(argv=None): if argv == None: argv = sys.argv[1:] try: from mpi4py import MPI except ImportError: raise Sorry("MPI not found") command_line = (libtbx.option_parser.option_parser( usage=""" %s [-p] -c config -x experiment -a address -r run -d detz_offset [-o outputdir] [-A averagepath] [-S stddevpath] [-M maxpath] [-n numevents] [-s skipnevents] [-v] [-m] [-b bin_size] [-X override_beam_x] [-Y override_beam_y] [-D xtc_dir] [-f] [-g gain_mask_value] [--min] [--minpath minpath] To write image pickles use -p, otherwise the program writes CSPAD CBFs. Writing CBFs requires the geometry to be already deployed. Examples: cxi.mpi_average -c cxi49812/average.cfg -x cxi49812 -a CxiDs1.0:Cspad.0 -r 25 -d 571 Use one process on the current node to process all the events from run 25 of experiment cxi49812, using a detz_offset of 571. mpirun -n 16 cxi.mpi_average -c cxi49812/average.cfg -x cxi49812 -a CxiDs1.0:Cspad.0 -r 25 -d 571 As above, using 16 cores on the current node. bsub -a mympi -n 100 -o average.out -q psanaq cxi.mpi_average -c cxi49812/average.cfg -x cxi49812 -a CxiDs1.0:Cspad.0 -r 25 -d 571 -o cxi49812 As above, using the psanaq and 100 cores, putting the log in average.out and the output images in the folder cxi49812. """ % libtbx.env.dispatcher_name) .option(None, "--as_pickle", "-p", action="store_true", default=False, dest="as_pickle", help="Write results as image pickle files instead of cbf files") .option(None, "--raw_data", "-R", action="store_true", default=False, dest="raw_data", help="Disable psana corrections such as dark pedestal subtraction or common mode (cbf only)") .option(None, "--background_pickle", "-B", default=None, dest="background_pickle", help="") .option(None, "--config", "-c", type="string", default=None, dest="config", metavar="PATH", help="psana config file") .option(None, "--experiment", "-x", type="string", default=None, dest="experiment", help="experiment name (eg cxi84914)") .option(None, "--run", "-r", type="int", default=None, dest="run", help="run number") .option(None, "--address", "-a", type="string", default="CxiDs2.0:Cspad.0", dest="address", help="detector address name (eg CxiDs2.0:Cspad.0)") .option(None, "--detz_offset", "-d", type="float", default=None, dest="detz_offset", help="offset (in mm) from sample interaction region to back of CSPAD detector rail (CXI), or detector distance (XPP)") .option(None, "--outputdir", "-o", type="string", default=".", dest="outputdir", metavar="PATH", help="Optional path to output directory for output files") .option(None, "--averagebase", "-A", type="string", default="{experiment!l}_avg-r{run:04d}", dest="averagepath", metavar="PATH", help="Path to output average image without extension. String substitution allowed") .option(None, "--stddevbase", "-S", type="string", default="{experiment!l}_stddev-r{run:04d}", dest="stddevpath", metavar="PATH", help="Path to output standard deviation image without extension. String substitution allowed") .option(None, "--maxbase", "-M", type="string", default="{experiment!l}_max-r{run:04d}", dest="maxpath", metavar="PATH", help="Path to output maximum projection image without extension. String substitution allowed") .option(None, "--numevents", "-n", type="int", default=None, dest="numevents", help="Maximum number of events to process. Default: all") .option(None, "--skipevents", "-s", type="int", default=0, dest="skipevents", help="Number of events in the beginning of the run to skip. Default: 0") .option(None, "--verbose", "-v", action="store_true", default=False, dest="verbose", help="Print more information about progress") .option(None, "--pickle-optical-metrology", "-m", action="store_true", default=False, dest="pickle_optical_metrology", help="If writing pickle files, use the optical metrology in the experiment's calib directory") .option(None, "--bin_size", "-b", type="int", default=None, dest="bin_size", help="Rayonix detector bin size") .option(None, "--override_beam_x", "-X", type="float", default=None, dest="override_beam_x", help="Rayonix detector beam center x coordinate") .option(None, "--override_beam_y", "-Y", type="float", default=None, dest="override_beam_y", help="Rayonix detector beam center y coordinate") .option(None, "--calib_dir", "-C", type="string", default=None, dest="calib_dir", metavar="PATH", help="calibration directory") .option(None, "--xtc_dir", "-D", type="string", default=None, dest="xtc_dir", metavar="PATH", help="xtc stream directory") .option(None, "--use_ffb", "-f", action="store_true", default=False, dest="use_ffb", help="Use the fast feedback filesystem at LCLS. Only for the active experiment!") .option(None, "--gain_mask_value", "-g", type="float", default=None, dest="gain_mask_value", help="Ratio between low and high gain pixels, if CSPAD in mixed-gain mode. Only used in CBF averaging mode.") .option(None, "--min", None, action="store_true", default=False, dest="do_minimum_projection", help="Output a minimum projection") .option(None, "--minpath", None, type="string", default="{experiment!l}_min-r{run:04d}", dest="minpath", metavar="PATH", help="Path to output minimum image without extension. String substitution allowed") ).process(args=argv) if len(command_line.args) > 0 or \ command_line.options.as_pickle is None or \ command_line.options.experiment is None or \ command_line.options.run is None or \ command_line.options.address is None or \ command_line.options.detz_offset is None or \ command_line.options.averagepath is None or \ command_line.options.stddevpath is None or \ command_line.options.maxpath is None or \ command_line.options.pickle_optical_metrology is None: command_line.parser.show_help() return # set this to sys.maxint to analyze all events if command_line.options.numevents is None: maxevents = sys.maxint else: maxevents = command_line.options.numevents comm = MPI.COMM_WORLD rank = comm.Get_rank() size = comm.Get_size() if command_line.options.config is not None: psana.setConfigFile(command_line.options.config) dataset_name = "exp=%s:run=%d:idx"%(command_line.options.experiment, command_line.options.run) if command_line.options.xtc_dir is not None: if command_line.options.use_ffb: raise Sorry("Cannot specify the xtc_dir and use SLAC's ffb system") dataset_name += ":dir=%s"%command_line.options.xtc_dir elif command_line.options.use_ffb: # as ffb is only at SLAC, ok to hardcode /reg/d here dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc"%(command_line.options.experiment[0:3],command_line.options.experiment) ds = psana.DataSource(dataset_name) address = command_line.options.address src = psana.Source('DetInfo(%s)'%address) nevent = np.array([0.]) if command_line.options.background_pickle is not None: background = easy_pickle.load(command_line.options.background_pickle)['DATA'].as_numpy_array() for run in ds.runs(): runnumber = run.run() if not command_line.options.as_pickle: psana_det = psana.Detector(address, ds.env()) # list of all events if command_line.options.skipevents > 0: print "Skipping first %d events"%command_line.options.skipevents elif "Rayonix" in command_line.options.address: print "Skipping first image in the Rayonix detector" # Shuttering issue command_line.options.skipevents = 1 times = run.times()[command_line.options.skipevents:] nevents = min(len(times),maxevents) # chop the list into pieces, depending on rank. This assigns each process # events such that the get every Nth event where N is the number of processes mytimes = [times[i] for i in xrange(nevents) if (i+rank)%size == 0] for i in xrange(len(mytimes)): if i%10==0: print 'Rank',rank,'processing event',rank*len(mytimes)+i,', ',i,'of',len(mytimes) evt = run.event(mytimes[i]) #print "Event #",rank*mylength+i," has id:",evt.get(EventId) if 'Rayonix' in command_line.options.address or 'FeeHxSpectrometer' in command_line.options.address or 'XrayTransportDiagnostic' in command_line.options.address: data = evt.get(psana.Camera.FrameV1,src) if data is None: print "No data" continue data=data.data16().astype(np.float64) elif command_line.options.as_pickle: data = evt.get(psana.ndarray_float64_3, src, 'image0') else: # get numpy array, 32x185x388 from xfel.cftbx.detector.cspad_cbf_tbx import get_psana_corrected_data if command_line.options.raw_data: data = get_psana_corrected_data(psana_det, evt, use_default=False, dark=False, common_mode=None, apply_gain_mask=False, per_pixel_gain=False) else: if command_line.options.gain_mask_value is None: data = get_psana_corrected_data(psana_det, evt, use_default=True) else: data = get_psana_corrected_data(psana_det, evt, use_default=False, dark=True, common_mode=None, apply_gain_mask=True, gain_mask_value=command_line.options.gain_mask_value, per_pixel_gain=False) if data is None: print "No data" continue if command_line.options.background_pickle is not None: data -= background if 'FeeHxSpectrometer' in command_line.options.address or 'XrayTransportDiagnostic' in command_line.options.address: distance = np.array([0.0]) wavelength = np.array([1.0]) else: d = cspad_tbx.env_distance(address, run.env(), command_line.options.detz_offset) if d is None: print "No distance, using distance", command_line.options.detz_offset assert command_line.options.detz_offset is not None if 'distance' not in locals(): distance = np.array([command_line.options.detz_offset]) else: distance += command_line.options.detz_offset else: if 'distance' in locals(): distance += d else: distance = np.array([float(d)]) w = cspad_tbx.evt_wavelength(evt) if w is None: print "No wavelength" if 'wavelength' not in locals(): wavelength = np.array([1.0]) else: if 'wavelength' in locals(): wavelength += w else: wavelength = np.array([w]) t = cspad_tbx.evt_time(evt) if t is None: print "No timestamp, skipping shot" continue if 'timestamp' in locals(): timestamp += t[0] + (t[1]/1000) else: timestamp = np.array([t[0] + (t[1]/1000)]) if 'sum' in locals(): sum+=data else: sum=np.array(data, copy=True) if 'sumsq' in locals(): sumsq+=data*data else: sumsq=data*data if 'maximum' in locals(): maximum=np.maximum(maximum,data) else: maximum=np.array(data, copy=True) if command_line.options.do_minimum_projection: if 'minimum' in locals(): minimum=np.minimum(minimum,data) else: minimum=np.array(data, copy=True) nevent += 1 #sum the images across mpi cores if size > 1: print "Synchronizing rank", rank totevent = np.zeros(nevent.shape) comm.Reduce(nevent,totevent) if rank == 0 and totevent[0] == 0: raise Sorry("No events found in the run") sumall = np.zeros(sum.shape).astype(sum.dtype) comm.Reduce(sum,sumall) sumsqall = np.zeros(sumsq.shape).astype(sumsq.dtype) comm.Reduce(sumsq,sumsqall) maxall = np.zeros(maximum.shape).astype(maximum.dtype) comm.Reduce(maximum,maxall, op=MPI.MAX) if command_line.options.do_minimum_projection: minall = np.zeros(maximum.shape).astype(minimum.dtype) comm.Reduce(minimum,minall, op=MPI.MIN) waveall = np.zeros(wavelength.shape).astype(wavelength.dtype) comm.Reduce(wavelength,waveall) distall = np.zeros(distance.shape).astype(distance.dtype) comm.Reduce(distance,distall) timeall = np.zeros(timestamp.shape).astype(timestamp.dtype) comm.Reduce(timestamp,timeall) if rank==0: if size > 1: print "Synchronized" # Accumulating floating-point numbers introduces errors, # which may cause negative variances. Since a two-pass # approach is unacceptable, the standard deviation is # clamped at zero. mean = sumall / float(totevent[0]) variance = (sumsqall / float(totevent[0])) - (mean**2) variance[variance < 0] = 0 stddev = np.sqrt(variance) wavelength = waveall[0] / totevent[0] distance = distall[0] / totevent[0] pixel_size = cspad_tbx.pixel_size saturated_value = cspad_tbx.cspad_saturated_value timestamp = timeall[0] / totevent[0] timestamp = (int(timestamp), timestamp % int(timestamp) * 1000) timestamp = cspad_tbx.evt_timestamp(timestamp) if command_line.options.as_pickle: extension = ".pickle" else: extension = ".cbf" dest_paths = [cspad_tbx.pathsubst(command_line.options.averagepath + extension, evt, ds.env()), cspad_tbx.pathsubst(command_line.options.stddevpath + extension, evt, ds.env()), cspad_tbx.pathsubst(command_line.options.maxpath + extension, evt, ds.env())] if command_line.options.do_minimum_projection: dest_paths.append(cspad_tbx.pathsubst(command_line.options.minpath + extension, evt, ds.env())) dest_paths = [os.path.join(command_line.options.outputdir, path) for path in dest_paths] if 'Rayonix' in command_line.options.address: all_data = [mean, stddev, maxall] if command_line.options.do_minimum_projection: all_data.append(minall) from xfel.cxi.cspad_ana import rayonix_tbx pixel_size = rayonix_tbx.get_rayonix_pixel_size(command_line.options.bin_size) beam_center = [command_line.options.override_beam_x,command_line.options.override_beam_y] detector_dimensions = rayonix_tbx.get_rayonix_detector_dimensions(command_line.options.bin_size) active_areas = flex.int([0,0,detector_dimensions[0],detector_dimensions[1]]) split_address = cspad_tbx.address_split(address) old_style_address = split_address[0] + "-" + split_address[1] + "|" + split_address[2] + "-" + split_address[3] for data, path in zip(all_data, dest_paths): print "Saving", path d = cspad_tbx.dpack( active_areas=active_areas, address=old_style_address, beam_center_x=pixel_size * beam_center[0], beam_center_y=pixel_size * beam_center[1], data=flex.double(data), distance=distance, pixel_size=pixel_size, saturated_value=rayonix_tbx.rayonix_saturated_value, timestamp=timestamp, wavelength=wavelength) easy_pickle.dump(path, d) elif 'FeeHxSpectrometer' in command_line.options.address or 'XrayTransportDiagnostic' in command_line.options.address: all_data = [mean, stddev, maxall] split_address = cspad_tbx.address_split(address) old_style_address = split_address[0] + "-" + split_address[1] + "|" + split_address[2] + "-" + split_address[3] if command_line.options.do_minimum_projection: all_data.append(minall) for data, path in zip(all_data, dest_paths): d = cspad_tbx.dpack( address = old_style_address, data = flex.double(data), distance = distance, pixel_size = 0.1, timestamp=timestamp, wavelength=wavelength ) print "Saving", path easy_pickle.dump(path, d) elif command_line.options.as_pickle: split_address = cspad_tbx.address_split(address) old_style_address = split_address[0] + "-" + split_address[1] + "|" + split_address[2] + "-" + split_address[3] xpp = 'xpp' in address.lower() if xpp: evt_time = cspad_tbx.evt_time(evt) # tuple of seconds, milliseconds timestamp = cspad_tbx.evt_timestamp(evt_time) # human readable format from iotbx.detectors.cspad_detector_formats import detector_format_version, reverse_timestamp from xfel.cxi.cspad_ana.cspad_tbx import xpp_active_areas version_lookup = detector_format_version(old_style_address, reverse_timestamp(timestamp)[0]) assert version_lookup is not None active_areas = xpp_active_areas[version_lookup]['active_areas'] beam_center = [1765 // 2, 1765 // 2] else: if command_line.options.calib_dir is not None: metro_path = command_line.options.calib_dir elif command_line.options.pickle_optical_metrology: from xfel.cftbx.detector.cspad_cbf_tbx import get_calib_file_path metro_path = get_calib_file_path(run.env(), address, run) else: metro_path = libtbx.env.find_in_repositories("xfel/metrology/CSPad/run4/CxiDs1.0_Cspad.0") sections = parse_calib.calib2sections(metro_path) beam_center, active_areas = cspad_tbx.cbcaa( cspad_tbx.getConfig(address, ds.env()), sections) class fake_quad(object): def __init__(self, q, d): self.q = q self.d = d def quad(self): return self.q def data(self): return self.d if xpp: quads = [fake_quad(i, mean[i*8:(i+1)*8,:,:]) for i in xrange(4)] mean = cspad_tbx.image_xpp(old_style_address, None, ds.env(), active_areas, quads = quads) mean = flex.double(mean.astype(np.float64)) quads = [fake_quad(i, stddev[i*8:(i+1)*8,:,:]) for i in xrange(4)] stddev = cspad_tbx.image_xpp(old_style_address, None, ds.env(), active_areas, quads = quads) stddev = flex.double(stddev.astype(np.float64)) quads = [fake_quad(i, maxall[i*8:(i+1)*8,:,:]) for i in xrange(4)] maxall = cspad_tbx.image_xpp(old_style_address, None, ds.env(), active_areas, quads = quads) maxall = flex.double(maxall.astype(np.float64)) if command_line.options.do_minimum_projection: quads = [fake_quad(i, minall[i*8:(i+1)*8,:,:]) for i in xrange(4)] minall = cspad_tbx.image_xpp(old_style_address, None, ds.env(), active_areas, quads = quads) minall = flex.double(minall.astype(np.float64)) else: quads = [fake_quad(i, mean[i*8:(i+1)*8,:,:]) for i in xrange(4)] mean = cspad_tbx.CsPadDetector( address, evt, ds.env(), sections, quads=quads) mean = flex.double(mean.astype(np.float64)) quads = [fake_quad(i, stddev[i*8:(i+1)*8,:,:]) for i in xrange(4)] stddev = cspad_tbx.CsPadDetector( address, evt, ds.env(), sections, quads=quads) stddev = flex.double(stddev.astype(np.float64)) quads = [fake_quad(i, maxall[i*8:(i+1)*8,:,:]) for i in xrange(4)] maxall = cspad_tbx.CsPadDetector( address, evt, ds.env(), sections, quads=quads) maxall = flex.double(maxall.astype(np.float64)) if command_line.options.do_minimum_projection: quads = [fake_quad(i, minall[i*8:(i+1)*8,:,:]) for i in xrange(4)] minall = cspad_tbx.CsPadDetector( address, evt, ds.env(), sections, quads=quads) minall = flex.double(minall.astype(np.float64)) all_data = [mean, stddev, maxall] if command_line.options.do_minimum_projection: all_data.append(minall) for data, path in zip(all_data, dest_paths): print "Saving", path d = cspad_tbx.dpack( active_areas=active_areas, address=old_style_address, beam_center_x=pixel_size * beam_center[0], beam_center_y=pixel_size * beam_center[1], data=data, distance=distance, pixel_size=pixel_size, saturated_value=saturated_value, timestamp=timestamp, wavelength=wavelength) easy_pickle.dump(path, d) else: # load a header only cspad cbf from the slac metrology from xfel.cftbx.detector import cspad_cbf_tbx import pycbf base_dxtbx = cspad_cbf_tbx.env_dxtbx_from_slac_metrology(run, address) if base_dxtbx is None: raise Sorry("Couldn't load calibration file for run %d"%run.run()) all_data = [mean, stddev, maxall] if command_line.options.do_minimum_projection: all_data.append(minall) for data, path in zip(all_data, dest_paths): print "Saving", path cspad_img = cspad_cbf_tbx.format_object_from_data(base_dxtbx, data, distance, wavelength, timestamp, address, round_to_int=False) cspad_img._cbf_handle.write_widefile(path, pycbf.CBF,\ pycbf.MIME_HEADERS|pycbf.MSG_DIGEST|pycbf.PAD_4K, 0)