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 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
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 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)
asic_end = asic_start + 185
a = raw_data[asic_start:asic_end, :]
asic_start = asic_end
asic_end = asic_start + 185
b = raw_data[asic_start:asic_end, :]
asic_start = asic_end
quad_asics.append(numpy.concatenate((a, b), axis=1))
quad_data = numpy.dstack(quad_asics)
quad_data = numpy.rollaxis(quad_data, 2, 0)
data3d.append(fake_cspad_ElementV2(quad_data, i_quad))
env = fake_env(fake_config())
evt = fake_evt(data3d)
beam_center, active_areas = cbcaa(fake_config(), sections)
data = flex.int(
CsPadDetector(address, evt, env, sections).astype(numpy.float64))
img_dict = dpack(active_areas=active_areas,
address=address,
beam_center_x=beam_center[0] * pixel_size,
beam_center_y=beam_center[1] * pixel_size,
data=data,
distance=params.distance,
pixel_size=pixel_size,
timestamp=timestamp,
wavelength=params.wavelength)
img = NpyImage("", source_data=img_dict)
def convert_detector(raw_data, detector_format_version, address, optical_metrology_path=None):
# https://confluence.slac.stanford.edu/display/PCDS/CSPad+metrology+and+calibration+files%2C+links
data3d = []
if raw_data.shape == (5920,388):
asic_start = 0
if optical_metrology_path is None:
calib_dir = libtbx.env.find_in_repositories("xfel/metrology/CSPad/run4/CxiDs1.0_Cspad.0")
sections = parse_calib.calib2sections(calib_dir)
else:
sections = parse_calib.calib2sections(optical_metrology_path)
for i_quad in range(4):
asic_size = 185 * 388
section_size = asic_size * 2
quad_start = i_quad * section_size * 4
quad_asics = []
for i_2x2 in range(4):
for i_asic in range(2):
asic_end = asic_start + 185
quad_asics.append(raw_data[asic_start:asic_end, :])
asic_start = asic_end
quad_data = numpy.dstack(quad_asics)
quad_data = numpy.rollaxis(quad_data, 2,0)
data3d.append(fake_cspad_ElementV2(quad_data, i_quad))
env = fake_env(fake_config())
evt = fake_evt(data3d)
return flex.double(cspad_tbx.CsPadDetector(address, evt, env, sections).astype(numpy.float64)), None
else:
asic_start = 0
if detector_format_version is not None and 'XPP' in detector_format_version:
from xfel.cxi.cspad_ana.cspad_tbx import xpp_active_areas
rotations = xpp_active_areas[detector_format_version]['rotations']
active_areas = xpp_active_areas[detector_format_version]['active_areas']
det = flex.double([0]*(1765*1765))
det.reshape(flex.grid((1765,1765)))
for i in xrange(64):
row = active_areas[i*4]
col = active_areas[i*4 + 1]
block = flex.double(raw_data[i * 185:(i+1)*185, :])
det.matrix_paste_block_in_place(block.matrix_rot90(rotations[i]), row, col)
return det, active_areas
else:
if optical_metrology_path is None:
calib_dir = libtbx.env.find_in_repositories("xfel/metrology/CSPad/run4/CxiDs1.0_Cspad.0")
sections = parse_calib.calib2sections(calib_dir)
else:
sections = parse_calib.calib2sections(optical_metrology_path)
for i_quad in range(4):
asic_size = 185 * 194
section_size = asic_size * 4
quad_start = i_quad * section_size * 4
quad_asics = []
for i_2x2 in range(4):
for i_asic in range(2):
asic_end = asic_start + 185
a = raw_data[asic_start:asic_end, :]
asic_start = asic_end
asic_end = asic_start + 185
b = raw_data[asic_start:asic_end, :]
asic_start = asic_end
quad_asics.append(numpy.concatenate((a,b),axis=1))
quad_data = numpy.dstack(quad_asics)
quad_data = numpy.rollaxis(quad_data, 2,0)
data3d.append(fake_cspad_ElementV2(quad_data, i_quad))
env = fake_env(fake_config())
evt = fake_evt(data3d)
beam_center, active_areas = cspad_tbx.cbcaa(fake_config(),sections)
return flex.double(cspad_tbx.CsPadDetector(address, evt, env, sections).astype(numpy.float64)), active_areas
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 convert_detector(raw_data,
detector_format_version,
address,
optical_metrology_path=None):
# https://confluence.slac.stanford.edu/display/PCDS/CSPad+metrology+and+calibration+files%2C+links
data3d = []
if raw_data.shape == (5920, 388):
asic_start = 0
if optical_metrology_path is None:
calib_dir = libtbx.env.find_in_repositories(
"xfel/metrology/CSPad/run4/CxiDs1.0_Cspad.0")
sections = parse_calib.calib2sections(calib_dir)
else:
sections = parse_calib.calib2sections(optical_metrology_path)
for i_quad in range(4):
asic_size = 185 * 388
section_size = asic_size * 2
quad_start = i_quad * section_size * 4
quad_asics = []
for i_2x2 in range(4):
for i_asic in range(2):
asic_end = asic_start + 185
quad_asics.append(raw_data[asic_start:asic_end, :])
asic_start = asic_end
quad_data = numpy.dstack(quad_asics)
quad_data = numpy.rollaxis(quad_data, 2, 0)
data3d.append(fake_cspad_ElementV2(quad_data, i_quad))
env = fake_env(fake_config())
evt = fake_evt(data3d)
return flex.double(
cspad_tbx.CsPadDetector(address, evt, env,
sections).astype(numpy.float64)), None
else:
asic_start = 0
if detector_format_version is not None and 'XPP' in detector_format_version:
from xfel.cxi.cspad_ana.cspad_tbx import xpp_active_areas
rotations = xpp_active_areas[detector_format_version]['rotations']
active_areas = xpp_active_areas[detector_format_version][
'active_areas']
det = flex.double([0] * (1765 * 1765))
det.reshape(flex.grid((1765, 1765)))
for i in range(64):
row = active_areas[i * 4]
col = active_areas[i * 4 + 1]
block = flex.double(raw_data[i * 185:(i + 1) * 185, :])
det.matrix_paste_block_in_place(
block.matrix_rot90(rotations[i]), row, col)
return det, active_areas
else:
if optical_metrology_path is None:
calib_dir = libtbx.env.find_in_repositories(
"xfel/metrology/CSPad/run4/CxiDs1.0_Cspad.0")
sections = parse_calib.calib2sections(calib_dir)
else:
sections = parse_calib.calib2sections(optical_metrology_path)
for i_quad in range(4):
asic_size = 185 * 194
section_size = asic_size * 4
quad_start = i_quad * section_size * 4
quad_asics = []
for i_2x2 in range(4):
for i_asic in range(2):
asic_end = asic_start + 185
a = raw_data[asic_start:asic_end, :]
asic_start = asic_end
asic_end = asic_start + 185
b = raw_data[asic_start:asic_end, :]
asic_start = asic_end
quad_asics.append(numpy.concatenate((a, b), axis=1))
quad_data = numpy.dstack(quad_asics)
quad_data = numpy.rollaxis(quad_data, 2, 0)
data3d.append(fake_cspad_ElementV2(quad_data, i_quad))
env = fake_env(fake_config())
evt = fake_evt(data3d)
beam_center, active_areas = cspad_tbx.cbcaa(
fake_config(), sections)
return flex.double(
cspad_tbx.CsPadDetector(address, evt, env, sections).astype(
numpy.float64)), active_areas
asic_end = asic_start + 185
a = raw_data[asic_start:asic_end, :]
asic_start = asic_end
asic_end = asic_start + 185
b = raw_data[asic_start:asic_end, :]
asic_start = asic_end
quad_asics.append(numpy.concatenate((a,b),axis=1))
quad_data = numpy.dstack(quad_asics)
quad_data = numpy.rollaxis(quad_data, 2,0)
data3d.append(fake_cspad_ElementV2(quad_data, i_quad))
env = fake_env(fake_config())
evt = fake_evt(data3d)
beam_center, active_areas = cbcaa(fake_config(),sections)
data = flex.int(CsPadDetector(address, evt, env, sections).astype(numpy.float64))
img_dict = dpack(
active_areas=active_areas,
address=address,
beam_center_x=beam_center[0]*pixel_size,
beam_center_y=beam_center[1]*pixel_size,
data=data,
distance=params.distance,
pixel_size=pixel_size,
timestamp=timestamp,
wavelength=params.wavelength)
img = NpyImage("", source_data=img_dict)