def _start(self):
import os
if isinstance(self._image_file, basestring) and os.path.isfile(self._image_file):
stream = FormatPYunspecified.open_file(self._image_file, "rb")
import cPickle as pickle
data = pickle.load(stream)
else:
data = self._image_file
if not "DETECTOR_ADDRESS" in data:
# legacy format; try to guess the address
self.LCLS_detector_address = "CxiDs1-0|Cspad-0"
if "DISTANCE" in data and data["DISTANCE"] > 1000:
# downstream CS-PAD detector station of CXI instrument
self.LCLS_detector_address = "CxiDsd-0|Cspad-0"
else:
self.LCLS_detector_address = data["DETECTOR_ADDRESS"]
from xfel.detector_formats import reverse_timestamp
self._timesec = reverse_timestamp(data["TIMESTAMP"])[0]
from xfel.detector_formats import detector_format_version as detector_format_function
version_lookup = detector_format_function(self.LCLS_detector_address, self._timesec)
self.start_helper(version_token="distl.detector_format_version=%s" % version_lookup)
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
def integrate_one_image(data, **kwargs):
from xfel.cxi.display_spots import run_one_index_core
from labelit.dptbx.error import NoAutoIndex
from libtbx.utils import Sorry
from spotfinder.exception import SpotfinderError
from labelit.exception import AutoIndexError
from xfel.detector_formats import detector_format_version as detector_format_function
from xfel.detector_formats import reverse_timestamp
basename = kwargs.get("integration_basename")
if (basename is None):
basename = ""
dirname = kwargs.get("integration_dirname")
if (dirname is None):
dirname = "integration"
if (not os.path.isdir(dirname)):
import errno
try:
os.makedirs(dirname)
except OSError as exc:
if exc.errno==errno.EEXIST: pass
path = os.path.join(dirname, basename \
+ data['TIMESTAMP'] \
+ ("_%05d.pickle" % data['SEQUENCE_NUMBER']))
args = ["indexing.data=dummy",
"beam_search_scope=0.5",
"lepage_max_delta = 3.0",
"spots_pickle = None",
"subgroups_pickle = None",
"refinements_pickle = None",
"rmsd_tolerance = 5.0",
"mosflm_rmsd_tolerance = 5.0",
"indexing.completeness_pickle=%s"%path,
"difflimit_sigma_cutoff=2.0",
#"indexing.open_wx_viewer=True"
]
detector_format_version = detector_format_function(
data['DETECTOR_ADDRESS'], reverse_timestamp(data['TIMESTAMP'])[0])
args += ["distl.detector_format_version=%s" % detector_format_version]
from xfel.phil_preferences import load_cxi_phil
horizons_phil = load_cxi_phil(data["xtal_target"], args)
horizons_phil.indexing.data = data
print "XFEL processing: %s"%path
try:
return run_one_index_core(horizons_phil)
except NoAutoIndex,e:
print "NoAutoIndex", data['TIMESTAMP'], e
def event(self, evt, env):
"""The event() function is called for every L1Accept transition.
@param evt Event data object, a configure object
@param env Environment object
"""
super(common_mode_correction, self).event(evt, env)
if (evt.get("skip_event")):
return
if not hasattr(self, 'active_areas') or self.active_areas is None or \
not hasattr(self, 'beam_center') or self.beam_center is None:
if self.address == 'XppGon-0|marccd-0':
# The mod_mar module needs to have been called before this one
# to set this up. The MAR does not have a configure object.
self.beam_center = evt.get("marccd_beam_center")
self.active_areas = evt.get("marccd_active_areas")
elif self.address == 'XppEndstation-0|Rayonix-0':
pass # bc and aa set in the beginjob function
elif self.address == 'XppGon-0|Cspad-0':
# Load the active areas as determined from the optical metrology
from xfel.detector_formats import detector_format_version, reverse_timestamp
from xfel.cxi.cspad_ana.cspad_tbx import xpp_active_areas
version_lookup = detector_format_version(self.address, reverse_timestamp(self.timestamp)[0])
assert version_lookup is not None
self.active_areas = xpp_active_areas[version_lookup]['active_areas']
self.beam_center = [1765 // 2, 1765 // 2]
else:
(self.beam_center, self.active_areas) = \
cspad_tbx.cbcaa(cspad_tbx.getConfig(self.address, env), self.sections)
if self.filter_laser_1_status is not None:
if (self.laser_1_status.status != self.filter_laser_1_status or
(self.laser_1_ms_since_change is not None and
self.laser_1_ms_since_change < self.filter_laser_wait_time)):
evt.put(skip_event_flag(), "skip_event")
return
if self.filter_laser_4_status is not None:
if (self.laser_4_status.status != self.filter_laser_4_status or
(self.laser_4_ms_since_change is not None and
self.laser_4_ms_since_change < self.filter_laser_wait_time)):
evt.put(skip_event_flag(), "skip_event")
return
# Early return if the full detector image is already stored in the
# event. Otherwise, get it from the stream as a double-precision
# floating-point flex array. XXX It is probably not safe to key
# the image on self.address, so we should come up with our own
# namespace. XXX Misnomer--could be CAMP, too
self.cspad_img = evt.get(self.address)
if self.cspad_img is not None:
return
if self.address == 'XppGon-0|Cspad-0':
# Kludge until cspad_tbx.image() can be rewritten to handle the
# XPP metrology.
self.cspad_img = cspad_tbx.image_xpp(
self.address, evt, env, self.active_areas)
elif self.address == 'XppEndstation-0|Rayonix-0':
from psana import Source, Camera
import numpy as np
address = cspad_tbx.old_address_to_new_address(self.address)
src=Source('DetInfo(%s)'%address)
self.cspad_img = evt.get(Camera.FrameV1,src)
if self.cspad_img is not None:
self.cspad_img = self.cspad_img.data16().astype(np.float64)
elif self.address=='CxiDg3-0|Opal1000-0':
if evt.getFrameValue(self.address) is not None:
self.cspad_img = evt.getFrameValue(self.address).data()
elif self.address=='CxiEndstation-0|Opal1000-2':
if evt.getFrameValue(self.address) is not None:
self.cspad_img = evt.getFrameValue(self.address).data()
elif self.address=='FeeHxSpectrometer-0|Opal1000-1':
if evt.getFrameValue(self.address) is not None:
self.cspad_img = evt.getFrameValue(self.address).data()
elif self.address=='NoDetector-0|Cspad2x2-0':
import numpy as np
from pypdsdata import xtc
test=[]
self.cspad_img = evt.get(xtc.TypeId.Type.Id_Cspad2x2Element,self.address).data()
self.cspad_img=np.reshape(self.cspad_img,(370, 388))
else:
try:
self.cspad_img = cspad_tbx.image(
self.address, cspad_tbx.getConfig(self.address, env),
evt, env, self.sections)
except Exception, e:
self.logger.error("Error reading image data: " + str(e))
evt.put(skip_event_flag(), "skip_event")
return
def beginjob(self, evt, env):
"""The beginjob() function does one-time initialisation from
event- or environment data. It is called at an XTC configure
transition.
@param evt Event data object, a configure object
@param env Environment object
"""
super(common_mode_correction, self).beginjob(evt, env)
# Load the dark image and ensure it is signed and at least 32 bits
# wide, since it will be used for differencing. If a dark image
# is provided, a standard deviation image is required, and all the
# ADU scales must match up.
#
# XXX Can we zap all the ADU_SCALE stuff?
#
# XXX Do we really need to store the substituted values in the
# instance here? At least self._dark_path is referenced later on.
#
# Note that this will load the dark, standard deviation and gain
# once (SEVERAL TIMES) for each process, but the gain is that we
# can do substitutions. But it is only done at the beginning of
# the job.
self.dark_img = None
if self._dark_path is not None:
self._dark_path = cspad_tbx.getOptEvalOrString(
cspad_tbx.pathsubst(self._dark_path, evt, env))
assert self._dark_stddev_path is not None
dark_dict = easy_pickle.load(self._dark_path)
#assert "ADU_SCALE" not in dark_dict # force use of recalculated dark
self.dark_img = dark_dict['DATA']
assert isinstance(self.dark_img, flex.double)
self._dark_stddev_path = cspad_tbx.getOptEvalOrString(
cspad_tbx.pathsubst(self._dark_stddev_path, evt, env))
self.dark_stddev = easy_pickle.load(self._dark_stddev_path)['DATA']
assert isinstance(self.dark_stddev, flex.double)
self.dark_mask = (self.dark_stddev > 0)
# Load the mask image and ensure it is signed and at least 32 bits
# wide, since it will be used for differencing.
self.gain_map = None
if self._gain_map_path is not None:
self._gain_map_path = cspad_tbx.getOptEvalOrString(
cspad_tbx.pathsubst(self._gain_map_path, evt, env))
self.gain_map = easy_pickle.load(self._gain_map_path)['DATA']
if self.gain_map_level is not None:
sel = flex.bool([bool(f) for f in self.gain_map])
sel.reshape(flex.grid(self.gain_map.focus()))
self.gain_map = self.gain_map.set_selected(~sel, self.gain_map_level)
self.gain_map = self.gain_map.set_selected(sel, 1)
assert isinstance(self.gain_map, flex.double)
self.mask_img = None
if self._mask_path is not None:
self._mask_path = cspad_tbx.getOptEvalOrString(
cspad_tbx.pathsubst(self._mask_path, evt, env))
self.mask_img = easy_pickle.load(self._mask_path)['DATA']
assert isinstance(self.mask_img, flex.double) \
or isinstance(self.mask_img, flex.int)
if self.address == 'XppGon-0|marccd-0':
#mod_mar.py will set these during its event function
self.active_areas = None
self.beam_center = None
elif self.address == 'XppEndstation-0|Rayonix-0':
assert self.override_beam_x is not None
assert self.override_beam_y is not None
from xfel.cxi.cspad_ana import rayonix_tbx
maxx, maxy = rayonix_tbx.get_rayonix_detector_dimensions(self.bin_size)
if self.crop_rayonix:
bx = int(round(self.override_beam_x))
by = int(round(self.override_beam_y))
minsize = min([bx,by,maxx-bx,maxy-by])
self.beam_center = minsize,minsize
self.active_areas = flex.int([0,0,2*minsize,2*minsize])
self.rayonix_crop_slice = slice(by-minsize,by+minsize), slice(bx-minsize,bx+minsize)
else:
self.beam_center = self.override_beam_x,self.override_beam_y
self.active_areas = flex.int([0,0,maxx,maxy])
elif self.address == 'XppGon-0|Cspad-0':
evt_time = cspad_tbx.evt_time(evt) # tuple of seconds, milliseconds
timestamp = cspad_tbx.evt_timestamp(evt_time) # human readable format
from xfel.detector_formats import detector_format_version, reverse_timestamp
from xfel.cxi.cspad_ana.cspad_tbx import xpp_active_areas
version_lookup = detector_format_version(self.address, reverse_timestamp(timestamp)[0])
assert version_lookup is not None
self.active_areas = xpp_active_areas[version_lookup]['active_areas']
self.beam_center = [1765 // 2, 1765 // 2]
else:
(self.beam_center, self.active_areas) = cspad_tbx.cbcaa(
cspad_tbx.getConfig(self.address, env), self.sections)
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]
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, "--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!")
).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)
if not command_line.options.as_pickle:
psana_det = psana.Detector(address, ds.env())
nevent = np.array([0.])
for run in ds.runs():
runnumber = run.run()
# list of all events
if command_line.options.skipevents > 0:
print "Skipping first %d events"%command_line.options.skipevents
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:
data = evt.get(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
data = psana_det.calib(evt) # applies psana's complex run-dependent calibrations
if data is None:
print "No data"
continue
d = cspad_tbx.env_distance(address, run.env(), command_line.options.detz_offset)
if d is None:
print "No distance, skipping shot"
continue
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, skipping shot"
continue
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)
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)
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())]
dest_paths = [os.path.join(command_line.options.outputdir, path) for path in dest_paths]
if 'Rayonix' in command_line.options.address:
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([mean, stddev, maxall], 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 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 xfel.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))
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))
for data, path in zip([mean, stddev, maxall], 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())
for data, path in zip([mean, stddev, maxall], dest_paths):
print "Saving", path
cspad_img = cspad_cbf_tbx.format_object_from_data(base_dxtbx, data, distance, wavelength, timestamp, address)
cspad_img._cbf_handle.write_widefile(path, pycbf.CBF,\
pycbf.MIME_HEADERS|pycbf.MSG_DIGEST|pycbf.PAD_4K, 0)
print "Printing contents of", path
if data.has_key('TIMESTAMP'):
# this is how FormatPYunspecified guesses the address
if not "DETECTOR_ADDRESS" in data:
# legacy format; try to guess the address
LCLS_detector_address = 'CxiDs1-0|Cspad-0'
if "DISTANCE" in data and data["DISTANCE"] > 1000:
# downstream CS-PAD detector station of CXI instrument
LCLS_detector_address = 'CxiDsd-0|Cspad-0'
else:
LCLS_detector_address = data["DETECTOR_ADDRESS"]
detector_format_version = detector_format_function(
LCLS_detector_address, reverse_timestamp(data['TIMESTAMP'])[0])
print "Detector format version:", detector_format_version
image_pickle = True
else:
print "Not an image pickle"
image_pickle = False
for key in data:
if key == 'ACTIVE_AREAS':
print int(len(data[key])/4), "active areas, first one: ", list(data[key][0:4])
elif key == 'observations':
print key, data[key], "Showing unit cell/spacegroup:"
obs = data[key][0]
uc = obs.unit_cell()
uc.show_parameters()
obs.space_group().info().show_summary()
if params.output_file is None:
logger = sys.stdout
else:
logger = open(params.output_file, 'w')
logger.write("%s "%params.output_file)
if not "DETECTOR_ADDRESS" in source_data:
# legacy format; try to guess the address
LCLS_detector_address = 'CxiDs1-0|Cspad-0'
if "DISTANCE" in source_data and source_data["DISTANCE"] > 1000:
# downstream CS-PAD detector station of CXI instrument
LCLS_detector_address = 'CxiDsd-0|Cspad-0'
else:
LCLS_detector_address = source_data["DETECTOR_ADDRESS"]
timesec = reverse_timestamp( source_data["TIMESTAMP"] )[0]
version_lookup = detector_format_function(LCLS_detector_address,timesec)
args = [
"distl.detector_format_version=%s"%version_lookup,
"viewer.powder_arcs.show=False",
"viewer.powder_arcs.code=3n9c",
]
horizons_phil = cxi_phil.cxi_versioned_extract(args).persist.commands
img = NpyImage(params.file_path, source_data)
img.readHeader(horizons_phil)
img.translate_tiles(horizons_phil)
if params.verbose:
img.show_header()
