def event(self, evt, env):
"""The event() function is called for every L1Accept transition. It
outputs the detector image associated with the event @p evt to the
file system.
@param evt Event data object, a configure object
@param env Environment object
"""
super(mod_dump_bitmap, self).event(evt, env)
if (evt.get('skip_event')):
return
# Where the sample-detector distance is not available, set it to
# zero.
distance = cspad_tbx.env_distance(self.address, env, self._detz_offset)
if distance is None:
distance = 0
# See r17537 of mod_average.py.
device = cspad_tbx.address_split(self.address)[2]
if device == 'Cspad':
pixel_size = cspad_tbx.pixel_size
saturated_value = cspad_tbx.cspad_saturated_value
elif device == 'marccd':
pixel_size = 0.079346
saturated_value = 2**16 - 1
from iotbx.detectors import FlexImage_d as FlexImage
vendortype = device
saturation = 65535
flex_img = FlexImage(rawdata=self.cspad_img,
binning=self._binning,
vendortype=vendortype,
brightness=self._brightness,
saturation=saturated_value)
flex_img.setWindow(0, 0, 1)
flex_img.adjust(color_scheme=self._color_scheme)
flex_img.prep_string()
import Image
# XXX is size//self._binning safe here?
pil_img = Image.fromstring('RGB', (flex_img.size2() // self._binning,
flex_img.size1() // self._binning),
flex_img.export_string)
# The output path should not contain any funny characters which may
# not work in all environments. This constructs a sequence number a
# la evt_seqno() from the dictionary's timestamp.
t = self.timestamp
s = t[0:4] + t[5:7] + t[8:10] + t[11:13] + t[14:16] + t[17:19] + t[
20:23]
path = os.path.join(self._dirname,
self._basename + s + '.' + self._ext)
self._logger.info("Exporting %s" % path)
tmp_stream = open(path, 'wb')
pil_img.save(tmp_stream, format=self._format)
tmp_stream.close()
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(mod_radial_average, 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
# See r17537 of mod_average.py.
device = cspad_tbx.address_split(self.address)[2]
if device == 'Cspad':
pixel_size = cspad_tbx.pixel_size
saturated_value = cspad_tbx.cspad_saturated_value
elif device == 'marccd':
pixel_size = 0.079346
saturated_value = 2**16 - 1
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)
from xfel.command_line.radial_average import run
args = [
"file_path=XTC stream",
"xfel_target=%s"%self.m_xtal_target,
"verbose=False"
]
t = self.timestamp
s = t[0:4] + t[5:7] + t[8:10] + t[11:13] + t[14:16] + t[17:19] + t[20:23]
if self._dirname is not None:
dest_path = os.path.join(self._dirname, self._basename + s + ".txt")
args.append("output_file=%s"%dest_path)
self.logger.info("Calculating radial average for image %s"%s)
xvals, results = run(args, d)
evt.put(xvals, "cctbx.xfel.radial_average.xvals")
evt.put(results, "cctbx.xfel.radial_average.results")
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(mod_image_dict, 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]
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 == 'Rayonix':
pixel_size = rayonix_tbx.get_rayonix_pixel_size(self.bin_size)
saturated_value = rayonix_tbx.rayonix_saturated_value
elif device == 'marccd':
pixel_size = evt.get("marccd_pixel_size")
saturated_value = evt.get("marccd_saturated_value")
if distance == 0:
distance = evt.get("marccd_distance")
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)
evt.put(d, self.m_out_key)
# 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))
def event(self, evt, env):
"""The event() function is called for every L1Accept transition. It
outputs the detector image associated with the event @p evt to the
file system.
@param evt Event data object, a configure object
@param env Environment object
"""
super(mod_dump, self).event(evt, env)
if (evt.get('skip_event')):
return
if self.cspad_img is None:
print("No image to save for %s"%self.timestamp)
return
# Where the sample-detector distance is not available, set it to
# zero.
distance = cspad_tbx.env_distance(self.address, env, self._detz_offset)
if distance is None:
distance = 0
# See r17537 of mod_average.py.
device = cspad_tbx.address_split(self.address)[2]
if device == 'Cspad':
pixel_size = cspad_tbx.pixel_size
saturated_value = cspad_tbx.cspad_saturated_value
output_filename = self._basename
elif device == 'Rayonix':
pixel_size = rayonix_tbx.get_rayonix_pixel_size(self.bin_size)
saturated_value = rayonix_tbx.rayonix_saturated_value
output_filename = self._basename
elif device == 'marccd':
if distance == 0:
distance = evt.get('marccd_distance')
pixel_size = 0.079346
saturated_value = 2**16 - 1
output_filename = self._basename + evt.get(str, 'mccd_name') + "_"
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)
if self._format == "pickle":
cspad_tbx.dwritef(d, self._dirname, output_filename)
elif self._format == "tiff":
cspad_tbx.write_tiff(d, self._dirname, output_filename)
output_filename = None
def event(self, evt, env):
"""The event() function is called for every L1Accept transition. It
outputs the detector image associated with the event @p evt to the
file system.
@param evt Event data object, a configure object
@param env Environment object
"""
super(mod_dump, self).event(evt, env)
if (evt.get('skip_event')):
return
if self.cspad_img is None:
print "No image to save for %s"%self.timestamp
return
# Where the sample-detector distance is not available, set it to
# zero.
distance = cspad_tbx.env_distance(self.address, env, self._detz_offset)
if distance is None:
distance = 0
# See r17537 of mod_average.py.
device = cspad_tbx.address_split(self.address)[2]
if device == 'Cspad':
pixel_size = cspad_tbx.pixel_size
saturated_value = cspad_tbx.cspad_saturated_value
output_filename = self._basename
elif device == 'Rayonix':
pixel_size = rayonix_tbx.get_rayonix_pixel_size(self.bin_size)
saturated_value = rayonix_tbx.rayonix_saturated_value
output_filename = self._basename
elif device == 'marccd':
if distance == 0:
distance = evt.get('marccd_distance')
pixel_size = 0.079346
saturated_value = 2**16 - 1
output_filename = self._basename + evt.get(str, 'mccd_name') + "_"
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)
if self._format == "pickle":
cspad_tbx.dwritef(d, self._dirname, output_filename)
elif self._format == "tiff":
cspad_tbx.write_tiff(d, self._dirname, output_filename)
output_filename = None
def __init__(self,
address,
n_collate = None,
n_update = 120,
common_mode_correction = "none",
wait=None,
photon_counting=False,
sigma_scaling=False,
**kwds):
"""The mod_view class constructor XXX.
@param address Full data source address of the DAQ device
@param calib_dir Directory with calibration information
@param common_mode_correction The type of common mode correction to apply
@param dark_path Path to input average dark image
@param dark_stddev Path to input standard deviation dark
image, required if @p dark_path is given
@param wait Minimum time (in seconds) to wait on the current
image before moving on to the next
@param n_collate Number of shots to average, or <= 0 to
average all shots
@param n_update Number of shots between updates
"""
super(mod_view, self).__init__(
address=address,
common_mode_correction=common_mode_correction,
**kwds)
self.detector = cspad_tbx.address_split(address)[0]
self.nvalid = 0
self.ncollate = cspad_tbx.getOptInteger(n_collate)
self.nupdate = cspad_tbx.getOptInteger(n_update)
self.photon_counting = cspad_tbx.getOptBool(photon_counting)
self.sigma_scaling = cspad_tbx.getOptBool(sigma_scaling)
if (self.ncollate is None):
self.ncollate = self.nupdate
if (self.ncollate > self.nupdate):
self.ncollate = self.nupdate
self.logger.warning("n_collate capped to %d" % self.nupdate)
linger = True # XXX Make configurable
wait = cspad_tbx.getOptFloat(wait)
# Create a managed FIFO queue shared between the viewer and the
# current process. The current process will produce images, while
# the viewer process will consume them.
manager = multiprocessing.Manager()
self._queue = manager.Queue()
self._proc = multiprocessing.Process(
target=_xray_frame_process, args=(self._queue, linger, wait))
self._proc.start()
self.n_shots = 0
def __init__(self,
address,
n_collate=None,
n_update=120,
common_mode_correction="none",
wait=None,
photon_counting=False,
sigma_scaling=False,
**kwds):
"""The mod_view class constructor XXX.
@param address Full data source address of the DAQ device
@param calib_dir Directory with calibration information
@param common_mode_correction The type of common mode correction to apply
@param dark_path Path to input average dark image
@param dark_stddev Path to input standard deviation dark
image, required if @p dark_path is given
@param wait Minimum time (in seconds) to wait on the current
image before moving on to the next
@param n_collate Number of shots to average, or <= 0 to
average all shots
@param n_update Number of shots between updates
"""
super(mod_view,
self).__init__(address=address,
common_mode_correction=common_mode_correction,
**kwds)
self.detector = cspad_tbx.address_split(address)[0]
self.nvalid = 0
self.ncollate = cspad_tbx.getOptInteger(n_collate)
self.nupdate = cspad_tbx.getOptInteger(n_update)
self.photon_counting = cspad_tbx.getOptBool(photon_counting)
self.sigma_scaling = cspad_tbx.getOptBool(sigma_scaling)
if (self.ncollate is None):
self.ncollate = self.nupdate
if (self.ncollate > self.nupdate):
self.ncollate = self.nupdate
self.logger.warning("n_collate capped to %d" % self.nupdate)
linger = True # XXX Make configurable
wait = cspad_tbx.getOptFloat(wait)
# Create a managed FIFO queue shared between the viewer and the
# current process. The current process will produce images, while
# the viewer process will consume them.
manager = multiprocessing.Manager()
self._queue = manager.Queue()
self._proc = multiprocessing.Process(target=_xray_frame_process,
args=(self._queue, linger, wait))
self._proc.start()
self.n_shots = 0
def set_up_hitfinder(self):
# See r17537 of mod_average.py.
device = cspad_tbx.address_split(self.address)[2]
if device == 'Cspad':
img_dim = (1765, 1765)
pixel_size = cspad_tbx.pixel_size
elif device == 'marccd':
img_dim = (4500, 4500)
pixel_size = 0.079346
elif device == 'Rayonix':
img_dim = rayonix_tbx.get_rayonix_detector_dimensions(
self.bin_size)
pixel_size = rayonix_tbx.get_rayonix_pixel_size(self.bin_size)
else:
raise RuntimeError("Unsupported device %s" % self.address)
if self.beam_center is None:
self.beam_center = [0, 0]
self.hitfinder_d = cspad_tbx.dpack(
active_areas=self.active_areas,
beam_center_x=pixel_size * self.beam_center[0],
beam_center_y=pixel_size * self.beam_center[1],
data=flex.int(flex.grid(img_dim[0], img_dim[1]), 0),
xtal_target=self.m_xtal_target)
if device == 'Cspad':
# Figure out which ASIC:s are on the central four sensors. This
# only applies to the CSPAD.
assert len(self.active_areas) % 4 == 0
distances = flex.double()
for i in range(0, len(self.active_areas), 4):
cenasic = (
(self.active_areas[i + 0] + self.active_areas[i + 2]) / 2,
(self.active_areas[i + 1] + self.active_areas[i + 3]) / 2)
distances.append(
math.hypot(cenasic[0] - self.beam_center[0],
cenasic[1] - self.beam_center[1]))
orders = flex.sort_permutation(distances)
# Use the central 8 ASIC:s (central 4 sensors).
flags = flex.int(len(self.active_areas) // 4, 0)
for i in range(8):
flags[orders[i]] = 1
self.asic_filter = "distl.tile_flags=" + ",".join(
["%1d" % b for b in flags])
elif device == 'marccd':
# There is only one active area for the MAR CCD, so use it.
self.asic_filter = "distl.tile_flags=1"
elif device == 'Rayonix':
# There is only one active area for the Rayonix, so use it.
self.asic_filter = "distl.tile_flags=1"
def set_up_hitfinder(self):
# See r17537 of mod_average.py.
device = cspad_tbx.address_split(self.address)[2]
if device == 'Cspad':
img_dim = (1765, 1765)
pixel_size = cspad_tbx.pixel_size
elif device == 'marccd':
img_dim = (4500, 4500)
pixel_size = 0.079346
elif device == 'Rayonix':
img_dim = rayonix_tbx.get_rayonix_detector_dimensions(self.bin_size)
pixel_size = rayonix_tbx.get_rayonix_pixel_size(self.bin_size)
else:
raise RuntimeError("Unsupported device %s" % self.address)
if self.beam_center is None:
self.beam_center = [0,0]
self.hitfinder_d = cspad_tbx.dpack(
active_areas=self.active_areas,
beam_center_x=pixel_size * self.beam_center[0],
beam_center_y=pixel_size * self.beam_center[1],
data=flex.int(flex.grid(img_dim[0], img_dim[1]), 0),
xtal_target=self.m_xtal_target)
if device == 'Cspad':
# Figure out which ASIC:s are on the central four sensors. This
# only applies to the CSPAD.
assert len(self.active_areas) % 4 == 0
distances = flex.double()
for i in range(0, len(self.active_areas), 4):
cenasic = ((self.active_areas[i + 0] + self.active_areas[i + 2]) / 2,
(self.active_areas[i + 1] + self.active_areas[i + 3]) / 2)
distances.append(math.hypot(cenasic[0] - self.beam_center[0],
cenasic[1] - self.beam_center[1]))
orders = flex.sort_permutation(distances)
# Use the central 8 ASIC:s (central 4 sensors).
flags = flex.int(len(self.active_areas) // 4, 0)
for i in range(8):
flags[orders[i]] = 1
self.asic_filter = "distl.tile_flags=" + ",".join(
["%1d" % b for b in flags])
elif device == 'marccd':
# There is only one active area for the MAR CCD, so use it.
self.asic_filter = "distl.tile_flags=1"
elif device == 'Rayonix':
# There is only one active area for the Rayonix, so use it.
self.asic_filter = "distl.tile_flags=1"
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)
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(mod_radial_average, 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
# See r17537 of mod_average.py.
device = cspad_tbx.address_split(self.address)[2]
if device == 'Cspad':
pixel_size = cspad_tbx.pixel_size
saturated_value = cspad_tbx.cspad_saturated_value
elif device == 'marccd':
pixel_size = 0.079346
saturated_value = 2**16 - 1
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)
from xfel.command_line.radial_average import run
args = [
"file_path=XTC stream",
"xfel_target=%s" % self.m_xtal_target, "verbose=False"
]
t = self.timestamp
s = t[0:4] + t[5:7] + t[8:10] + t[11:13] + t[14:16] + t[17:19] + t[
20:23]
if self._dirname is not None:
dest_path = os.path.join(self._dirname,
self._basename + s + ".txt")
args.append("output_file=%s" % dest_path)
self.logger.info("Calculating radial average for image %s" % s)
xvals, results = run(args, d)
evt.put(xvals, "cctbx.xfel.radial_average.xvals")
evt.put(results, "cctbx.xfel.radial_average.results")
def get_closest_idx(data, val):
from scitbx.array_family import flex
deltas = flex.abs(data - val)
return flex.first_index(deltas, flex.min(deltas))
if self._two_theta_low is not None:
i_low = results[get_closest_idx(xvals, self._two_theta_low)]
evt.put(i_low, "cctbx.xfel.radial_average.two_theta_low")
if self._two_theta_high is not None:
i_high = results[get_closest_idx(xvals, self._two_theta_high)]
evt.put(i_high, "cctbx.xfel.radial_average.two_theta_high")
def __init__(self,
address,
avg_dirname=None,
avg_basename=None,
stddev_dirname=None,
stddev_basename=None,
max_dirname=None,
max_basename=None,
background_path=None,
flags=None,
hot_threshold=None,
gain_threshold=None,
noise_threshold=7,
elastic_threshold=9,
symnoise_threshold=4,
**kwds):
"""
@param address Full data source address of the DAQ device
@param avg_dirname Directory portion of output average image
XXX mean
@param avg_basename Filename prefix of output average image XXX
mean
@param flags inactive: Eliminate the inactive pixels
noelastic: Eliminate elastic scattering
nohot: Eliminate the hot pixels
nonoise: Eliminate noisy pixels
symnoise: Symmetrically eliminate noisy pixels
@param stddev_dirname Directory portion of output standard
deviation image XXX std
@param stddev_basename Filename prefix of output standard
deviation image XXX std
@param max_dirname Directory portion of output maximum
projection image
@param max_basename Filename prefix of output maximum
projection image
"""
super(average_mixin, self).__init__(
address=address,
**kwds
)
self.roi = None
self.avg_basename = cspad_tbx.getOptString(avg_basename)
self.avg_dirname = cspad_tbx.getOptString(avg_dirname)
self.detector = cspad_tbx.address_split(address)[0]
self.flags = cspad_tbx.getOptStrings(flags, default = [])
self.stddev_basename = cspad_tbx.getOptString(stddev_basename)
self.stddev_dirname = cspad_tbx.getOptString(stddev_dirname)
self.max_basename = cspad_tbx.getOptString(max_basename)
self.max_dirname = cspad_tbx.getOptString(max_dirname)
self.background_path = cspad_tbx.getOptString(background_path)
self.hot_threshold = cspad_tbx.getOptFloat(hot_threshold)
self.gain_threshold = cspad_tbx.getOptFloat(gain_threshold)
self.noise_threshold = cspad_tbx.getOptFloat(noise_threshold)
self.elastic_threshold = cspad_tbx.getOptFloat(elastic_threshold)
self.symnoise_threshold = cspad_tbx.getOptFloat(symnoise_threshold)
if background_path is not None:
background_dict = easy_pickle.load(background_path)
self.background_img = background_dict['DATA']
self._have_max = self.max_basename is not None or \
self.max_dirname is not None
self._have_mean = self.avg_basename is not None or \
self.avg_dirname is not None
self._have_std = self.stddev_basename is not None or \
self.stddev_dirname is not None
# Start a server process which holds a set of Python objects that
# other processes can manipulate using proxies. The queues will
# be used in endjob() to pass images between the worker processes,
# and the lock will ensure the transfer is treated as a critical
# section. There is therefore the risk of a hang if the queues
# cannot hold all the data one process will supply before another
# empties it.
#
# In an attempt to alleviate this issue, separate queues are used
# for the potentially big images. The hope is to prevent
# producers from blocking while consumers are locked out by using
# more buffers.
mgr = multiprocessing.Manager()
self._lock = mgr.Lock()
self._metadata = mgr.dict()
self._queue_max = mgr.Queue()
self._queue_sum = mgr.Queue()
self._queue_ssq = mgr.Queue()
def event(self, evt, env):
"""The event() function is called for every L1Accept transition.
XXX Since the viewer is now running in a parallel process, the
averaging here is now the bottleneck.
@param evt Event data object, a configure object
@param env Environment object
"""
from pyana.event import Event
self.n_shots += 1
super(mod_view, self).event(evt, env)
if evt.status() != Event.Normal or evt.get(
'skip_event'): # XXX transition
return
# Get the distance for the detectors that should have it, and set
# it to NaN for those that should not.
if self.detector == 'CxiDs1' or \
self.detector == 'CxiDsd' or \
self.detector == 'XppGon':
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
else:
distance = float('nan')
if not self._proc.is_alive():
evt.setStatus(Event.Stop)
# Early return if the next update to the viewer is more than
# self.ncollate shots away. XXX Since the common_mode.event()
# function does quite a bit of processing, the savings are
# probably not so big.
next_update = (self.nupdate - 1) - (self.nshots - 1) % self.nupdate
if (self.ncollate > 0 and next_update >= self.ncollate):
return
if self.sigma_scaling:
self.do_sigma_scaling()
if self.photon_counting:
self.do_photon_counting()
# Trim the disabled section from the Sc1 detector image. XXX This
# is a bit of a kludge, really.
# if (self.address == "CxiSc1-0|Cspad2x2-0"):
# self.cspad_img = self.cspad_img[185:2 * 185, :]
# Update the sum of the valid images, starting a new collation if
# appropriate. This guarantees self.nvalid > 0.
if (self.nvalid == 0
or self.ncollate > 0 and self.nvalid >= self.ncollate):
self.img_sum = self.cspad_img
self.nvalid = 1
else:
self.img_sum += self.cspad_img
self.nvalid += 1
# Update the viewer to display the current average image, and
# start a new collation, if appropriate.
if (next_update == 0):
from time import localtime, strftime
time_str = strftime("%H:%M:%S", localtime(evt.getTime().seconds()))
title = "r%04d@%s: average of %d last images on %s" \
% (evt.run(), time_str, self.nvalid, self.address)
# See also mod_average.py.
device = cspad_tbx.address_split(self.address)[2]
if device == 'Cspad':
beam_center = self.beam_center
pixel_size = cspad_tbx.pixel_size
saturated_value = cspad_tbx.cspad_saturated_value
elif device == 'marccd':
beam_center = tuple(t // 2 for t in self.img_sum.focus())
pixel_size = 0.079346
saturated_value = 2**16 - 1
# Wait for the viewer process to empty the queue before feeding
# it a new image, and ensure not to hang if the viewer process
# exits. Because of multithreading/multiprocessing semantics,
# self._queue.empty() is unreliable.
fmt = _Format(BEAM_CENTER=beam_center,
DATA=self.img_sum / self.nvalid,
DETECTOR_ADDRESS=self.address,
DISTANCE=distance,
PIXEL_SIZE=pixel_size,
SATURATED_VALUE=saturated_value,
TIME_TUPLE=cspad_tbx.evt_time(evt),
WAVELENGTH=self.wavelength)
while not self._queue.empty():
if not self._proc.is_alive():
evt.setStatus(Event.Stop)
return
while True:
try:
self._queue.put((fmt, title), timeout=1)
break
except Exception:
pass
if (self.ncollate > 0):
self.nvalid = 0
def event(self, evt, env):
"""The event() function is called for every L1Accept transition.
XXX Since the viewer is now running in a parallel process, the
averaging here is now the bottleneck.
@param evt Event data object, a configure object
@param env Environment object
"""
from pyana.event import Event
self.n_shots += 1
super(mod_view, self).event(evt, env)
if evt.status() != Event.Normal or evt.get('skip_event'): # XXX transition
return
# Get the distance for the detectors that should have it, and set
# it to NaN for those that should not.
if self.detector == 'CxiDs1' or \
self.detector == 'CxiDsd' or \
self.detector == 'XppGon':
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
else:
distance = float('nan')
if not self._proc.is_alive():
evt.setStatus(Event.Stop)
# Early return if the next update to the viewer is more than
# self.ncollate shots away. XXX Since the common_mode.event()
# function does quite a bit of processing, the savings are
# probably not so big.
next_update = (self.nupdate - 1) - (self.nshots - 1) % self.nupdate
if (self.ncollate > 0 and next_update >= self.ncollate):
return
if self.sigma_scaling:
self.do_sigma_scaling()
if self.photon_counting:
self.do_photon_counting()
# Trim the disabled section from the Sc1 detector image. XXX This
# is a bit of a kludge, really.
# if (self.address == "CxiSc1-0|Cspad2x2-0"):
# self.cspad_img = self.cspad_img[185:2 * 185, :]
# Update the sum of the valid images, starting a new collation if
# appropriate. This guarantees self.nvalid > 0.
if (self.nvalid == 0 or self.ncollate > 0 and self.nvalid >= self.ncollate):
self.img_sum = self.cspad_img
self.nvalid = 1
else:
self.img_sum += self.cspad_img
self.nvalid += 1
# Update the viewer to display the current average image, and
# start a new collation, if appropriate.
if (next_update == 0):
from time import localtime, strftime
time_str = strftime("%H:%M:%S", localtime(evt.getTime().seconds()))
title = "r%04d@%s: average of %d last images on %s" \
% (evt.run(), time_str, self.nvalid, self.address)
# See also mod_average.py.
device = cspad_tbx.address_split(self.address)[2]
if device == 'Cspad':
beam_center = self.beam_center
pixel_size = cspad_tbx.pixel_size
saturated_value = cspad_tbx.cspad_saturated_value
elif device == 'marccd':
beam_center = tuple(t // 2 for t in self.img_sum.focus())
pixel_size = 0.079346
saturated_value = 2**16 - 1
# Wait for the viewer process to empty the queue before feeding
# it a new image, and ensure not to hang if the viewer process
# exits. Because of multithreading/multiprocessing semantics,
# self._queue.empty() is unreliable.
fmt = _Format(BEAM_CENTER=beam_center,
DATA=self.img_sum / self.nvalid,
DETECTOR_ADDRESS=self.address,
DISTANCE=distance,
PIXEL_SIZE=pixel_size,
SATURATED_VALUE=saturated_value,
TIME_TUPLE=cspad_tbx.evt_time(evt),
WAVELENGTH=self.wavelength)
while not self._queue.empty():
if not self._proc.is_alive():
evt.setStatus(Event.Stop)
return
while True:
try:
self._queue.put((fmt, title), timeout=1)
break
except Exception:
pass
if (self.ncollate > 0):
self.nvalid = 0
def endjob(self, obj1, obj2=None):
"""The endjob() function writes the mean and standard deviation images
to disk.
@param evt Event object (psana only)
@param env Environment object
"""
if obj2 is None:
env = obj1
else:
evt = obj1
env = obj2
stats = super(mod_average, self).endjob(env)
if stats is None:
return
device = cspad_tbx.address_split(self.address)[2]
if device == 'Andor':
beam_center = (0, 0) # XXX Fiction!
pixel_size = 13.5e-3 # XXX Should not be hardcoded here!
saturated_value = 10000
elif device == 'Cspad' or device == 'Cspad2x2':
beam_center = self.beam_center
pixel_size = cspad_tbx.pixel_size
saturated_value = cspad_tbx.cspad_saturated_value
elif device == 'marccd':
beam_center = tuple(t // 2 for t in d['mean_img'].focus())
pixel_size = 0.079346
saturated_value = 2**16 - 1
if stats['nmemb'] > 0:
if self.avg_dirname is not None or \
self.avg_basename is not None or \
self._mean_out is not None:
d = cspad_tbx.dpack(
active_areas=self.active_areas,
address=self.address,
beam_center_x=pixel_size * beam_center[0],
beam_center_y=pixel_size * beam_center[1],
data=stats['mean_img'],
distance=stats['distance'],
pixel_size=pixel_size,
saturated_value=saturated_value,
timestamp=cspad_tbx.evt_timestamp(stats['time']),
wavelength=stats['wavelength'])
if self._mean_out is not None:
p = cspad_tbx.dwritef2(d, self._mean_out)
else:
p = cspad_tbx.dwritef(d, self.avg_dirname, self.avg_basename)
self.logger.info("Average written to %s" % p)
if self.stddev_dirname is not None or \
self.stddev_basename is not None or \
self._std_out is not None:
d = cspad_tbx.dpack(
active_areas=self.active_areas,
address=self.address,
beam_center_x=pixel_size * beam_center[0],
beam_center_y=pixel_size * beam_center[1],
data=stats['std_img'],
distance=stats['distance'],
pixel_size=pixel_size,
saturated_value=saturated_value,
timestamp=cspad_tbx.evt_timestamp(stats['time']),
wavelength=stats['wavelength'])
if self._std_out is not None:
p = cspad_tbx.dwritef2(d, self._std_out)
else:
p = cspad_tbx.dwritef(d, self.stddev_dirname, self.stddev_basename)
self.logger.info("Standard deviation written to %s" % p)
if self.max_dirname is not None or \
self.max_basename is not None or \
self._max_out is not None:
d = cspad_tbx.dpack(
active_areas=self.active_areas,
address=self.address,
beam_center_x=pixel_size * beam_center[0],
beam_center_y=pixel_size * beam_center[1],
data=stats['max_img'],
distance=stats['distance'],
pixel_size=pixel_size,
saturated_value=saturated_value,
timestamp=cspad_tbx.evt_timestamp(stats['time']),
wavelength=stats['wavelength'])
if self._max_out is not None:
p = cspad_tbx.dwritef2(d, self._max_out)
else:
p = cspad_tbx.dwritef(d, self.max_dirname, self.max_basename)
self.logger.info("Max written to %s" % p)
if stats['nfail'] == 0:
self.logger.info("%d images processed" % stats['nmemb'])
else:
self.logger.warning(
"%d images processed, %d failed" % (stats['nmemb'], stats['nfail']))
def __init__(self,
address,
calib_dir=None,
common_mode_correction="none",
photon_threshold=None,
two_photon_threshold=None,
dark_path=None,
dark_stddev=None,
mask_path=None,
gain_map_path=None,
gain_map_level=None,
cache_image=True,
roi=None,
laser_1_status=None,
laser_4_status=None,
laser_wait_time=None,
override_beam_x=None,
override_beam_y=None,
bin_size=None,
crop_rayonix=False,
**kwds):
"""The common_mode_correction class constructor stores the
parameters passed from the pyana configuration file in instance
variables.
@param address Full data source address of the DAQ device
@param calib_dir Directory with calibration information
@param common_mode_correction The type of common mode correction to apply
@param dark_path Path to input average dark image
@param dark_stddev Path to input standard deviation dark
image, required if @p dark_path is given
@param mask_path Path to input mask. Pixels to mask out should be set to -2
@param gain_map_path Path to input gain map. Multiplied times the image.
@param gain_map_level If set, all the '1' pixels in the gain_map are set to this multiplier
and all the '0' pixels in the gain_map are set to '1'. If not set,
use the values in the gain_map directly
@param laser_1_status 0 or 1 to indicate that the laser should be off or on respectively
@param laser_4_status 0 or 1 to indicate that the laser should be off or on respectively
@param laser_wait_time Length of time in milliseconds to wait after a laser
change of status to begin accepting images again.
(rejection of images occurs immediately after status
change).
@param override_beam_x override value for x coordinate of beam center in pixels
@param override_beam_y override value for y coordinate of beam center in pixels
@param bin_size bin size for rayonix detector used to determin pixel size
@param crop_rayonix whether to crop rayonix images such that image center is the beam center
"""
# Cannot use the super().__init__() construct here, because
# common_mode_correction refers to the argument, and not the
# class.
mod_event_info.__init__(self, address=address, **kwds)
# The paths will be substituted in beginjob(), where evt and env
# are available.
self._dark_path = cspad_tbx.getOptString(dark_path)
self._dark_stddev_path = cspad_tbx.getOptString(dark_stddev)
self._gain_map_path = cspad_tbx.getOptString(gain_map_path)
self._mask_path = cspad_tbx.getOptString(mask_path)
self.gain_map_level = cspad_tbx.getOptFloat(gain_map_level)
self.common_mode_correction = cspad_tbx.getOptString(common_mode_correction)
self.photon_threshold = cspad_tbx.getOptFloat(photon_threshold)
self.two_photon_threshold = cspad_tbx.getOptFloat(two_photon_threshold)
self.cache_image = cspad_tbx.getOptBool(cache_image)
self.filter_laser_1_status = cspad_tbx.getOptInteger(laser_1_status)
self.filter_laser_4_status = cspad_tbx.getOptInteger(laser_4_status)
if self.filter_laser_1_status is not None:
self.filter_laser_1_status = bool(self.filter_laser_1_status)
if self.filter_laser_4_status is not None:
self.filter_laser_4_status = bool(self.filter_laser_4_status)
self.filter_laser_wait_time = cspad_tbx.getOptInteger(laser_wait_time)
self.override_beam_x = cspad_tbx.getOptFloat(override_beam_x)
self.override_beam_y = cspad_tbx.getOptFloat(override_beam_y)
self.bin_size = cspad_tbx.getOptInteger(bin_size)
self.crop_rayonix = cspad_tbx.getOptBool(crop_rayonix)
self.cspad_img = None # The current image - set by self.event()
self.sum_common_mode = 0
self.sumsq_common_mode = 0
self.roi = cspad_tbx.getOptROI(roi) # used to ignore the signal region in chebyshev fit
assert self.common_mode_correction in \
("gaussian", "mean", "median", "mode", "none", "chebyshev")
# Get and parse metrology.
self.sections = None
device = cspad_tbx.address_split(self.address)[2]
if device == 'Andor':
self.sections = [] # XXX FICTION
elif device == 'Cspad':
if self.address == 'XppGon-0|Cspad-0':
self.sections = [] # Not used for XPP
else:
self.sections = calib2sections(cspad_tbx.getOptString(calib_dir))
elif device == 'Cspad2x2':
# There is no metrology information for the Sc1 detector, so
# make it up. The sections are rotated by 90 degrees with
# respect to the "standing up" convention.
self.sections = [[Section(90, (185 / 2 + 0, (2 * 194 + 3) / 2)),
Section(90, (185 / 2 + 185, (2 * 194 + 3) / 2))]]
elif device == 'marccd':
self.sections = [] # XXX FICTION
elif device == 'pnCCD':
self.sections = [] # XXX FICTION
elif device == 'Rayonix':
self.sections = [] # XXX FICTION
elif device == 'Opal1000':
self.sections = [] # XXX FICTION
if self.sections is None:
raise RuntimeError("Failed to load metrology")
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 __init__(self,
address,
avg_dirname=None,
avg_basename=None,
stddev_dirname=None,
stddev_basename=None,
max_dirname=None,
max_basename=None,
background_path=None,
flags=None,
hot_threshold=None,
gain_threshold=None,
noise_threshold=7,
elastic_threshold=9,
symnoise_threshold=4,
**kwds):
"""
@param address Full data source address of the DAQ device
@param avg_dirname Directory portion of output average image
XXX mean
@param avg_basename Filename prefix of output average image XXX
mean
@param flags inactive: Eliminate the inactive pixels
noelastic: Eliminate elastic scattering
nohot: Eliminate the hot pixels
nonoise: Eliminate noisy pixels
symnoise: Symmetrically eliminate noisy pixels
@param stddev_dirname Directory portion of output standard
deviation image XXX std
@param stddev_basename Filename prefix of output standard
deviation image XXX std
@param max_dirname Directory portion of output maximum
projection image
@param max_basename Filename prefix of output maximum
projection image
"""
super(average_mixin, self).__init__(
address=address,
**kwds
)
self.roi = None
self.avg_basename = cspad_tbx.getOptString(avg_basename)
self.avg_dirname = cspad_tbx.getOptString(avg_dirname)
self.detector = cspad_tbx.address_split(address)[0]
self.flags = cspad_tbx.getOptStrings(flags, default = [])
self.stddev_basename = cspad_tbx.getOptString(stddev_basename)
self.stddev_dirname = cspad_tbx.getOptString(stddev_dirname)
self.max_basename = cspad_tbx.getOptString(max_basename)
self.max_dirname = cspad_tbx.getOptString(max_dirname)
self.background_path = cspad_tbx.getOptString(background_path)
self.hot_threshold = cspad_tbx.getOptFloat(hot_threshold)
self.gain_threshold = cspad_tbx.getOptFloat(gain_threshold)
self.noise_threshold = cspad_tbx.getOptFloat(noise_threshold)
self.elastic_threshold = cspad_tbx.getOptFloat(elastic_threshold)
self.symnoise_threshold = cspad_tbx.getOptFloat(symnoise_threshold)
if background_path is not None:
background_dict = easy_pickle.load(background_path)
self.background_img = background_dict['DATA']
self._have_max = self.max_basename is not None or \
self.max_dirname is not None
self._have_mean = self.avg_basename is not None or \
self.avg_dirname is not None
self._have_std = self.stddev_basename is not None or \
self.stddev_dirname is not None
# Start a server process which holds a set of Python objects that
# other processes can manipulate using proxies. The queues will
# be used in endjob() to pass images between the worker processes,
# and the lock will ensure the transfer is treated as a critical
# section. There is therefore the risk of a hang if the queues
# cannot hold all the data one process will supply before another
# empties it.
#
# In an attempt to alleviate this issue, separate queues are used
# for the potentially big images. The hope is to prevent
# producers from blocking while consumers are locked out by using
# more buffers.
mgr = multiprocessing.Manager()
self._lock = mgr.Lock()
self._metadata = mgr.dict()
self._queue_max = mgr.Queue()
self._queue_sum = mgr.Queue()
self._queue_ssq = mgr.Queue()
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))
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' or \
self.address == 'MfxEndstation-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 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(
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' or \
self.address == 'MfxEndstation-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 as e:
self.logger.error("Error reading image data: " + str(e))
evt.put(skip_event_flag(), "skip_event")
return
if self.cspad_img is None:
if cspad_tbx.address_split(self.address)[2] != 'Andor':
self.nfail += 1
self.logger.warning("event(): no image, shot skipped")
evt.put(skip_event_flag(), "skip_event")
return
self.cspad_img = flex.double(self.cspad_img.astype(numpy.float64))
# If a dark image was provided, subtract it from the image. There
# is no point in doing common-mode correction unless the dark
# image was subtracted.
if (self.dark_img is not None):
self.cspad_img -= self.dark_img
if (self.common_mode_correction != "none"):
# Mask out inactive pixels prior to common mode correction.
# Pixels are marked as inactive either due to low ADU values
# or non-positive standard deviations in dark image. XXX Make
# the threshold tunable?
cspad_mask = self.dark_mask.deep_copy()
if self.roi is not None and self.common_mode_correction == "chebyshev":
roi_mask = cspad_mask[self.roi[2]:self.roi[3], :]
roi_mask = flex.bool(roi_mask.accessor(), False)
cspad_mask.matrix_paste_block_in_place(block=roi_mask,
i_row=self.roi[2],
i_column=0)
# Extract each active section from the assembled detector
# image and apply the common mode correction. XXX Make up a
# quadrant mask for the emission detector. Needs to be
# checked!
config = cspad_tbx.getConfig(self.address, env)
if len(self.sections) == 1:
q_mask = 1
else:
q_mask = config.quadMask()
for q in range(len(self.sections)):
if (not ((1 << q) & q_mask)):
continue
# XXX Make up section mask for the emission detector. Needs
# to be checked!
import _pdsdata
if len(self.sections) == 1 and type(config) in (
_pdsdata.cspad2x2.ConfigV1,
_pdsdata.cspad2x2.ConfigV2):
s_mask = config.roiMask()
else:
s_mask = config.roiMask(q)
for s in range(len(self.sections[q])):
# XXX DAQ misconfiguration? This mask appears not to work
# reliably for the Sc1 detector.
# if (not((1 << s) & s_mask)):
# continue
corners = self.sections[q][s].corners()
i_row = int(round(min(c[0] for c in corners)))
i_column = int(round(min(c[1] for c in corners)))
n_rows = int(round(max(c[0] for c in corners))) - i_row
n_columns = int(round(max(
c[1] for c in corners))) - i_column
section_img = self.cspad_img.matrix_copy_block(
i_row=i_row,
i_column=i_column,
n_rows=n_rows,
n_columns=n_columns)
section_mask = cspad_mask.matrix_copy_block(
i_row=i_row,
i_column=i_column,
n_rows=n_rows,
n_columns=n_columns)
section_stddev = self.dark_stddev.matrix_copy_block(
i_row=i_row,
i_column=i_column,
n_rows=n_rows,
n_columns=n_columns)
if section_mask.count(True) == 0: continue
if self.common_mode_correction == "chebyshev":
assert len(self.sections[q]) == 2
if s == 0:
section_imgs = [section_img]
section_masks = [section_mask]
i_rows = [i_row]
i_columns = [i_column]
continue
else:
section_imgs.append(section_img)
section_masks.append(section_mask)
i_rows.append(i_row)
i_columns.append(i_column)
chebyshev_corrected_imgs = self.chebyshev_common_mode(
section_imgs, section_masks)
for i in range(2):
section_imgs[i].as_1d().copy_selected(
section_masks[i].as_1d().iselection(),
chebyshev_corrected_imgs[i].as_1d())
self.cspad_img.matrix_paste_block_in_place(
block=section_imgs[i],
i_row=i_rows[i],
i_column=i_columns[i])
else:
common_mode = self.common_mode(
section_img, section_stddev, section_mask)
self.sum_common_mode += common_mode
self.sumsq_common_mode += common_mode**2
# Apply the common mode correction to the
# section, and paste it back into the image.
self.cspad_img.matrix_paste_block_in_place(
block=section_img - common_mode,
i_row=i_row,
i_column=i_column)
if self.gain_map is not None:
self.cspad_img *= self.gain_map
if (self.mask_img is not None):
sel = (self.mask_img == -2) | (self.mask_img
== cspad_tbx.cspad_mask_value)
self.cspad_img.set_selected(sel, cspad_tbx.cspad_mask_value)
if (self.address == 'XppEndstation-0|Rayonix-0' or \
self.address == 'MfxEndstation-0|Rayonix-0') and \
self.crop_rayonix:
# Crop the masked data so that the beam center is in the center of the image
self.cspad_img = self.cspad_img[self.rayonix_crop_slice[0],
self.rayonix_crop_slice[1]]
if self.cache_image:
# Store the image in the event.
evt.put(self.cspad_img, self.address)
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))
class common_mode_correction(mod_event_info):
"""Dark subtraction and alternate implementation of common mode
substituting for cspad_tbx.
Known problems: the algorithm relies on a substantial part of the
sensor having no signal, which is not the case if a water ring
crosses the sensor.
"""
def __init__(self,
address,
calib_dir=None,
common_mode_correction="none",
photon_threshold=None,
two_photon_threshold=None,
dark_path=None,
dark_stddev=None,
mask_path=None,
gain_map_path=None,
gain_map_level=None,
cache_image=True,
roi=None,
laser_1_status=None,
laser_4_status=None,
laser_wait_time=None,
override_beam_x=None,
override_beam_y=None,
bin_size=None,
crop_rayonix=False,
**kwds):
"""The common_mode_correction class constructor stores the
parameters passed from the pyana configuration file in instance
variables.
@param address Full data source address of the DAQ device
@param calib_dir Directory with calibration information
@param common_mode_correction The type of common mode correction to apply
@param dark_path Path to input average dark image
@param dark_stddev Path to input standard deviation dark
image, required if @p dark_path is given
@param mask_path Path to input mask. Pixels to mask out should be set to -2
@param gain_map_path Path to input gain map. Multiplied times the image.
@param gain_map_level If set, all the '1' pixels in the gain_map are set to this multiplier
and all the '0' pixels in the gain_map are set to '1'. If not set,
use the values in the gain_map directly
@param laser_1_status 0 or 1 to indicate that the laser should be off or on respectively
@param laser_4_status 0 or 1 to indicate that the laser should be off or on respectively
@param laser_wait_time Length of time in milliseconds to wait after a laser
change of status to begin accepting images again.
(rejection of images occurs immediately after status
change).
@param override_beam_x override value for x coordinate of beam center in pixels
@param override_beam_y override value for y coordinate of beam center in pixels
@param bin_size bin size for rayonix detector used to determin pixel size
@param crop_rayonix whether to crop rayonix images such that image center is the beam center
"""
# Cannot use the super().__init__() construct here, because
# common_mode_correction refers to the argument, and not the
# class.
mod_event_info.__init__(self, address=address, **kwds)
# The paths will be substituted in beginjob(), where evt and env
# are available.
self._dark_path = cspad_tbx.getOptString(dark_path)
self._dark_stddev_path = cspad_tbx.getOptString(dark_stddev)
self._gain_map_path = cspad_tbx.getOptString(gain_map_path)
self._mask_path = cspad_tbx.getOptString(mask_path)
self.gain_map_level = cspad_tbx.getOptFloat(gain_map_level)
self.common_mode_correction = cspad_tbx.getOptString(common_mode_correction)
self.photon_threshold = cspad_tbx.getOptFloat(photon_threshold)
self.two_photon_threshold = cspad_tbx.getOptFloat(two_photon_threshold)
self.cache_image = cspad_tbx.getOptBool(cache_image)
self.filter_laser_1_status = cspad_tbx.getOptInteger(laser_1_status)
self.filter_laser_4_status = cspad_tbx.getOptInteger(laser_4_status)
if self.filter_laser_1_status is not None:
self.filter_laser_1_status = bool(self.filter_laser_1_status)
if self.filter_laser_4_status is not None:
self.filter_laser_4_status = bool(self.filter_laser_4_status)
self.filter_laser_wait_time = cspad_tbx.getOptInteger(laser_wait_time)
self.override_beam_x = cspad_tbx.getOptFloat(override_beam_x)
self.override_beam_y = cspad_tbx.getOptFloat(override_beam_y)
self.bin_size = cspad_tbx.getOptInteger(bin_size)
self.crop_rayonix = cspad_tbx.getOptBool(crop_rayonix)
self.cspad_img = None # The current image - set by self.event()
self.sum_common_mode = 0
self.sumsq_common_mode = 0
self.roi = cspad_tbx.getOptROI(roi) # used to ignore the signal region in chebyshev fit
assert self.common_mode_correction in \
("gaussian", "mean", "median", "mode", "none", "chebyshev")
# Get and parse metrology.
self.sections = None
device = cspad_tbx.address_split(self.address)[2]
if device == 'Andor':
self.sections = [] # XXX FICTION
elif device == 'Cspad':
if self.address == 'XppGon-0|Cspad-0':
self.sections = [] # Not used for XPP
else:
self.sections = calib2sections(cspad_tbx.getOptString(calib_dir))
elif device == 'Cspad2x2':
# There is no metrology information for the Sc1 detector, so
# make it up. The sections are rotated by 90 degrees with
# respect to the "standing up" convention.
self.sections = [[Section(90, (185 / 2 + 0, (2 * 194 + 3) / 2)),
Section(90, (185 / 2 + 185, (2 * 194 + 3) / 2))]]
elif device == 'marccd':
self.sections = [] # XXX FICTION
elif device == 'pnCCD':
self.sections = [] # XXX FICTION
elif device == 'Rayonix':
self.sections = [] # XXX FICTION
elif device == 'Opal1000':
self.sections = [] # XXX FICTION
if self.sections is None:
raise RuntimeError("Failed to load metrology")
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' or \
self.address == 'MfxEndstation-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 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(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 common_mode(self, img, stddev, mask):
"""The common_mode() function returns the mode of image stored in
the array pointed to by @p img. @p mask must be such that the @p
stddev at the selected pixels is greater than zero.
@param img 2D integer array of the image
@param stddev 2D integer array of the standard deviation of each
pixel in @p img
@param mask 2D Boolean array, @c True if the pixel is to be
included, @c False otherwise
@return Mode of the image, as a real number
"""
# Flatten the image and take out inactive pixels XXX because we
# cannot take means and medians of 2D arrays?
img_1d = img.as_1d().select(mask.as_1d()).as_double()
assert img_1d.size() > 0
if (self.common_mode_correction == "mean"):
# The common mode is approximated by the mean of the pixels with
# signal-to-noise ratio less than a given threshold. XXX Breaks
# if the selection is empty!
THRESHOLD_SNR = 2
img_snr = img_1d / stddev.as_double().as_1d().select(mask.as_1d())
return (flex.mean(img_1d.select(img_snr < THRESHOLD_SNR)))
elif (self.common_mode_correction == "median"):
return (flex.median(img_1d))
# Identify the common-mode correction as the peak histogram of the
# histogram of pixel values (the "standard" common-mode correction, as
# previously implemented in this class).
hist_min = -40
hist_max = 40
n_slots = 100
hist = flex.histogram(img_1d, hist_min, hist_max, n_slots=n_slots)
slots = hist.slots()
i = flex.max_index(slots)
common_mode = list(hist.slot_infos())[i].center()
if (self.common_mode_correction == "mode"):
return (common_mode)
# Determine the common-mode correction from the peak of a single
# Gaussian function fitted to the histogram.
from scitbx.math.curve_fitting import single_gaussian_fit
x = hist.slot_centers()
y = slots.as_double()
fit = single_gaussian_fit(x, y)
scale, mu, sigma = fit.a, fit.b, fit.c
self.logger.debug("fitted gaussian: mu=%.3f, sigma=%.3f" %(mu, sigma))
mode = common_mode
common_mode = mu
if abs(mode-common_mode) > 1000: common_mode = mode # XXX
self.logger.debug("delta common mode corrections: %.3f" %(mode-common_mode))
if 0 and abs(mode-common_mode) > 0:
#if 0 and skew > 0.5:
# view histogram and fitted gaussian
from numpy import exp
from matplotlib import pyplot
x_all = x
n, bins, patches = pyplot.hist(section_img.as_1d().as_numpy_array(), bins=n_slots, range=(hist_min, hist_max))
y_all = scale * flex.exp(-flex.pow2(x_all-mu) / (2 * sigma**2))
scale = slots[flex.max_index(slots)]
y_all *= scale/flex.max(y_all)
pyplot.plot(x_all, y_all)
pyplot.show()
return (common_mode)
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' or \
self.address == 'MfxEndstation-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 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(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' or \
self.address == 'MfxEndstation-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
if self.cspad_img is None:
if cspad_tbx.address_split(self.address)[2] != 'Andor':
self.nfail += 1
self.logger.warning("event(): no image, shot skipped")
evt.put(skip_event_flag(), "skip_event")
return
self.cspad_img = flex.double(self.cspad_img.astype(numpy.float64))
# If a dark image was provided, subtract it from the image. There
# is no point in doing common-mode correction unless the dark
# image was subtracted.
if (self.dark_img is not None):
self.cspad_img -= self.dark_img
if (self.common_mode_correction != "none"):
# Mask out inactive pixels prior to common mode correction.
# Pixels are marked as inactive either due to low ADU values
# or non-positive standard deviations in dark image. XXX Make
# the threshold tunable?
cspad_mask = self.dark_mask.deep_copy()
if self.roi is not None and self.common_mode_correction == "chebyshev":
roi_mask = cspad_mask[self.roi[2]:self.roi[3], :]
roi_mask = flex.bool(roi_mask.accessor(), False)
cspad_mask.matrix_paste_block_in_place(
block=roi_mask,
i_row=self.roi[2],
i_column=0)
# Extract each active section from the assembled detector
# image and apply the common mode correction. XXX Make up a
# quadrant mask for the emission detector. Needs to be
# checked!
config = cspad_tbx.getConfig(self.address, env)
if len(self.sections) == 1:
q_mask = 1
else:
q_mask = config.quadMask()
for q in xrange(len(self.sections)):
if (not((1 << q) & q_mask)):
continue
# XXX Make up section mask for the emission detector. Needs
# to be checked!
import _pdsdata
if len(self.sections) == 1 and type(config) in (
_pdsdata.cspad2x2.ConfigV1, _pdsdata.cspad2x2.ConfigV2):
s_mask = config.roiMask()
else:
s_mask = config.roiMask(q)
for s in xrange(len(self.sections[q])):
# XXX DAQ misconfiguration? This mask appears not to work
# reliably for the Sc1 detector.
# if (not((1 << s) & s_mask)):
# continue
corners = self.sections[q][s].corners()
i_row = int(round(min(c[0] for c in corners)))
i_column = int(round(min(c[1] for c in corners)))
n_rows = int(round(max(c[0] for c in corners))) - i_row
n_columns = int(round(max(c[1] for c in corners))) - i_column
section_img = self.cspad_img.matrix_copy_block(
i_row = i_row, i_column = i_column,
n_rows = n_rows, n_columns = n_columns)
section_mask = cspad_mask.matrix_copy_block(
i_row = i_row, i_column = i_column,
n_rows = n_rows, n_columns = n_columns)
section_stddev = self.dark_stddev.matrix_copy_block(
i_row = i_row, i_column = i_column,
n_rows = n_rows, n_columns = n_columns)
if section_mask.count(True) == 0: continue
if self.common_mode_correction == "chebyshev":
assert len(self.sections[q]) == 2
if s == 0:
section_imgs = [section_img]
section_masks = [section_mask]
i_rows = [i_row]
i_columns = [i_column]
continue
else:
section_imgs.append(section_img)
section_masks.append(section_mask)
i_rows.append(i_row)
i_columns.append(i_column)
chebyshev_corrected_imgs = self.chebyshev_common_mode(
section_imgs, section_masks)
for i in range(2):
section_imgs[i].as_1d().copy_selected(
section_masks[i].as_1d().iselection(),
chebyshev_corrected_imgs[i].as_1d())
self.cspad_img.matrix_paste_block_in_place(
block=section_imgs[i],
i_row=i_rows[i],
i_column=i_columns[i])
else:
common_mode = self.common_mode(
section_img, section_stddev, section_mask)
self.sum_common_mode += common_mode
self.sumsq_common_mode += common_mode**2
# Apply the common mode correction to the
# section, and paste it back into the image.
self.cspad_img.matrix_paste_block_in_place(
block = section_img - common_mode,
i_row = i_row,
i_column = i_column)
if self.gain_map is not None:
self.cspad_img *= self.gain_map
if (self.mask_img is not None):
sel = (self.mask_img == -2 )|(self.mask_img == cspad_tbx.cspad_mask_value)
self.cspad_img.set_selected(sel, cspad_tbx.cspad_mask_value)
if (self.address == 'XppEndstation-0|Rayonix-0' or \
self.address == 'MfxEndstation-0|Rayonix-0') and \
self.crop_rayonix:
# Crop the masked data so that the beam center is in the center of the image
self.cspad_img = self.cspad_img[self.rayonix_crop_slice[0], self.rayonix_crop_slice[1]]
if self.cache_image:
# Store the image in the event.
evt.put(self.cspad_img, self.address)
def event(self, evt, env):
"""The event() function is called for every L1Accept transition. It
outputs the detector image associated with the event @p evt to the
file system.
@param evt Event data object, a configure object
@param env Environment object
"""
super(mod_dump_bitmap, self).event(evt, env)
if (evt.get('skip_event')):
return
# Where the sample-detector distance is not available, set it to
# zero.
distance = cspad_tbx.env_distance(self.address, env, self._detz_offset)
if distance is None:
distance = 0
# See r17537 of mod_average.py.
device = cspad_tbx.address_split(self.address)[2]
if device == 'Cspad':
pixel_size = cspad_tbx.pixel_size
saturated_value = cspad_tbx.cspad_saturated_value
elif device == 'marccd':
pixel_size = 0.079346
saturated_value = 2**16 - 1
from iotbx.detectors import FlexImage_d as FlexImage
vendortype = device
saturation = 65535
flex_img = FlexImage(
rawdata=self.cspad_img,
binning=self._binning,
vendortype=vendortype,
brightness=self._brightness,
saturation=saturated_value)
flex_img.setWindow(0, 0, 1)
flex_img.adjust(color_scheme=self._color_scheme)
flex_img.prep_string()
import Image
# XXX is size//self._binning safe here?
pil_img = Image.fromstring(
'RGB', (flex_img.size2()//self._binning,
flex_img.size1()//self._binning),
flex_img.export_string)
# The output path should not contain any funny characters which may
# not work in all environments. This constructs a sequence number a
# la evt_seqno() from the dictionary's timestamp.
t = self.timestamp
s = t[0:4] + t[5:7] + t[8:10] + t[11:13] + t[14:16] + t[17:19] + t[20:23]
path = os.path.join(
self._dirname, self._basename + s + '.' + self._ext)
self._logger.info("Exporting %s" %path)
tmp_stream = open(path, 'wb')
pil_img.save(tmp_stream, format=self._format)
tmp_stream.close()
def endjob(self, obj1, obj2=None):
"""The endjob() function writes the mean and standard deviation images
to disk.
@param evt Event object (psana only)
@param env Environment object
"""
if obj2 is None:
env = obj1
else:
evt = obj1
env = obj2
stats = super(mod_average, self).endjob(env)
if stats is None:
return
device = cspad_tbx.address_split(self.address)[2]
if device == 'Andor':
beam_center = (0, 0) # XXX Fiction!
pixel_size = 13.5e-3 # XXX Should not be hardcoded here!
saturated_value = 10000
elif device == 'Cspad' or device == 'Cspad2x2':
beam_center = self.beam_center
pixel_size = cspad_tbx.pixel_size
saturated_value = cspad_tbx.cspad_saturated_value
elif device == 'marccd':
beam_center = tuple(t // 2 for t in d['mean_img'].focus())
pixel_size = 0.079346
saturated_value = 2**16 - 1
if stats['nmemb'] > 0:
if self.avg_dirname is not None or \
self.avg_basename is not None or \
self._mean_out is not None:
d = cspad_tbx.dpack(active_areas=self.active_areas,
address=self.address,
beam_center_x=pixel_size * beam_center[0],
beam_center_y=pixel_size * beam_center[1],
data=stats['mean_img'],
distance=stats['distance'],
pixel_size=pixel_size,
saturated_value=saturated_value,
timestamp=cspad_tbx.evt_timestamp(
stats['time']),
wavelength=stats['wavelength'])
if self._mean_out is not None:
p = cspad_tbx.dwritef2(d, self._mean_out)
else:
p = cspad_tbx.dwritef(d, self.avg_dirname,
self.avg_basename)
self.logger.info("Average written to %s" % p)
if self.stddev_dirname is not None or \
self.stddev_basename is not None or \
self._std_out is not None:
d = cspad_tbx.dpack(active_areas=self.active_areas,
address=self.address,
beam_center_x=pixel_size * beam_center[0],
beam_center_y=pixel_size * beam_center[1],
data=stats['std_img'],
distance=stats['distance'],
pixel_size=pixel_size,
saturated_value=saturated_value,
timestamp=cspad_tbx.evt_timestamp(
stats['time']),
wavelength=stats['wavelength'])
if self._std_out is not None:
p = cspad_tbx.dwritef2(d, self._std_out)
else:
p = cspad_tbx.dwritef(d, self.stddev_dirname,
self.stddev_basename)
self.logger.info("Standard deviation written to %s" % p)
if self.max_dirname is not None or \
self.max_basename is not None or \
self._max_out is not None:
d = cspad_tbx.dpack(active_areas=self.active_areas,
address=self.address,
beam_center_x=pixel_size * beam_center[0],
beam_center_y=pixel_size * beam_center[1],
data=stats['max_img'],
distance=stats['distance'],
pixel_size=pixel_size,
saturated_value=saturated_value,
timestamp=cspad_tbx.evt_timestamp(
stats['time']),
wavelength=stats['wavelength'])
if self._max_out is not None:
p = cspad_tbx.dwritef2(d, self._max_out)
else:
p = cspad_tbx.dwritef(d, self.max_dirname,
self.max_basename)
self.logger.info("Max written to %s" % p)
if stats['nfail'] == 0:
self.logger.info("%d images processed" % stats['nmemb'])
else:
self.logger.warning("%d images processed, %d failed" %
(stats['nmemb'], stats['nfail']))
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(mod_image_dict, self).event(evt, env)
if (evt.get("skip_event")):
return
if self.cspad_img is None:
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]
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 == 'Rayonix':
pixel_size = rayonix_tbx.get_rayonix_pixel_size(self.bin_size)
saturated_value = rayonix_tbx.rayonix_saturated_value
elif device == 'marccd':
pixel_size = evt.get("marccd_pixel_size")
saturated_value = evt.get("marccd_saturated_value")
if distance == 0:
distance = evt.get("marccd_distance")
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)
evt.put(d, self.m_out_key)
# 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))