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 run(self):
""" Process all images assigned to this thread """
params, options = self.parser.parse_args(show_diff_phil=True)
if params.input.experiment is None or \
params.input.run_num is None or \
params.input.address is None:
raise Usage(self.usage)
if params.format.file_format == "cbf":
if params.format.cbf.detz_offset is None:
raise Usage(self.usage)
elif params.format.file_format == "pickle":
if params.format.pickle.cfg is None:
raise Usage(self.usage)
else:
raise Usage(self.usage)
if not os.path.exists(params.output.output_dir):
raise Sorry("Output path not found:" + params.output.output_dir)
# Save the paramters
self.params = params
self.options = options
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank(
) # each process in MPI has a unique id, 0-indexed
size = comm.Get_size() # size: number of processes running in this job
# set up psana
if params.format.file_format == "pickle":
psana.setConfigFile(params.format.pickle.cfg)
dataset_name = "exp=%s:run=%s:idx" % (params.input.experiment,
params.input.run_num)
ds = psana.DataSource(dataset_name)
if params.format.file_format == "cbf":
src = psana.Source('DetInfo(%s)' % params.input.address)
psana_det = psana.Detector(params.input.address, ds.env())
# set this to sys.maxint to analyze all events
if params.dispatch.max_events is None:
max_events = sys.maxint
else:
max_events = params.dispatch.max_events
for run in ds.runs():
if params.format.file_format == "cbf":
# load a header only cspad cbf from the slac metrology
base_dxtbx = cspad_cbf_tbx.env_dxtbx_from_slac_metrology(
run, params.input.address)
if base_dxtbx is None:
raise Sorry("Couldn't load calibration file for run %d" %
run.run())
# list of all events
times = run.times()
nevents = min(len(times), max_events)
# chop the list into pieces, depending on rank. This assigns each process
# events such that the get every Nth event where N is the number of processes
mytimes = [
times[i] for i in xrange(nevents) if (i + rank) % size == 0
]
for i in xrange(len(mytimes)):
evt = run.event(mytimes[i])
id = evt.get(psana.EventId)
print "Event #", i, " has id:", id
timestamp = cspad_tbx.evt_timestamp(
cspad_tbx.evt_time(evt)) # human readable format
if timestamp is None:
print "No timestamp, skipping shot"
continue
t = timestamp
s = t[0:4] + t[5:7] + t[8:10] + t[11:13] + t[14:16] + t[
17:19] + t[20:23]
print "Processing shot", s
if params.format.file_format == "pickle":
if evt.get("skip_event"):
print "Skipping event", id
continue
# the data needs to have already been processed and put into the event by psana
data = evt.get(params.format.pickle.out_key)
if data is None:
print "No data"
continue
# set output paths according to the templates
path = os.path.join(params.output.output_dir,
"shot-" + s + ".pickle")
print "Saving", path
easy_pickle.dump(path, data)
elif params.format.file_format == "cbf":
# get numpy array, 32x185x388
data = cspad_cbf_tbx.get_psana_corrected_data(
psana_det,
evt,
use_default=False,
dark=True,
common_mode=None,
apply_gain_mask=params.format.cbf.gain_mask_value
is not None,
gain_mask_value=params.format.cbf.gain_mask_value,
per_pixel_gain=False)
distance = cspad_tbx.env_distance(
params.input.address, run.env(),
params.format.cbf.detz_offset)
if distance is None:
print "No distance, skipping shot"
continue
if self.params.format.cbf.override_energy is None:
wavelength = cspad_tbx.evt_wavelength(evt)
if wavelength is None:
print "No wavelength, skipping shot"
continue
else:
wavelength = 12398.4187 / self.params.format.cbf.override_energy
# stitch together the header, data and metadata into the final dxtbx format object
cspad_img = cspad_cbf_tbx.format_object_from_data(
base_dxtbx, data, distance, wavelength, timestamp,
params.input.address)
path = os.path.join(params.output.output_dir,
"shot-" + s + ".cbf")
print "Saving", path
# write the file
import pycbf
cspad_img._cbf_handle.write_widefile(path, pycbf.CBF,\
pycbf.MIME_HEADERS|pycbf.MSG_DIGEST|pycbf.PAD_4K, 0)
run.end()
ds.end()
def run(self):
""" Process all images assigned to this thread """
params, options = self.parser.parse_args(show_diff_phil=True)
if params.input.experiment is None or \
params.input.run_num is None or \
params.input.address is None:
raise Usage(self.usage)
if params.format.file_format == "cbf":
if params.format.cbf.detz_offset is None:
raise Usage(self.usage)
elif params.format.file_format == "pickle":
if params.input.cfg is None:
raise Usage(self.usage)
else:
raise Usage(self.usage)
if not os.path.exists(params.output.output_dir):
raise Sorry("Output path not found:" + params.output.output_dir)
#Environment variable redirect for CBFLib temporary CBF_TMP_XYZ file output
if params.format.file_format == "cbf":
if params.output.tmp_output_dir is None:
tmp_dir = os.path.join(params.output.output_dir, '.tmp')
else:
tmp_dir = os.path.join(params.output.tmp_output_dir, '.tmp')
if not os.path.exists(tmp_dir):
with show_mail_on_error():
try:
os.makedirs(tmp_dir)
# Can fail if running multiprocessed - that's OK if the folder was created
except OSError as e: # In Python 2, a FileExistsError is just an OSError
if e.errno != errno.EEXIST: # If this OSError is not a FileExistsError
raise
os.environ['CBF_TMP_DIR'] = tmp_dir
# Save the paramters
self.params = params
self.options = options
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank(
) # each process in MPI has a unique id, 0-indexed
size = comm.Get_size() # size: number of processes running in this job
# set up psana
if params.input.cfg is not None:
psana.setConfigFile(params.input.cfg)
if params.input.calib_dir is not None:
psana.setOption('psana.calib-dir', params.input.calib_dir)
dataset_name = "exp=%s:run=%s:idx" % (params.input.experiment,
params.input.run_num)
if params.input.xtc_dir is not None:
dataset_name = "exp=%s:run=%s:idx:dir=%s" % (
params.input.experiment, params.input.run_num,
params.input.xtc_dir)
ds = psana.DataSource(dataset_name)
if params.format.file_format == "cbf":
src = psana.Source('DetInfo(%s)' % params.input.address)
psana_det = psana.Detector(params.input.address, ds.env())
# set this to sys.maxint to analyze all events
if params.dispatch.max_events is None:
max_events = sys.maxsize
else:
max_events = params.dispatch.max_events
for run in ds.runs():
if params.format.file_format == "cbf":
if params.format.cbf.mode == "cspad":
# load a header only cspad cbf from the slac metrology
base_dxtbx = cspad_cbf_tbx.env_dxtbx_from_slac_metrology(
run, params.input.address)
if base_dxtbx is None:
raise Sorry(
"Couldn't load calibration file for run %d" %
run.run())
elif params.format.cbf.mode == "rayonix":
# load a header only rayonix cbf from the input parameters
detector_size = rayonix_tbx.get_rayonix_detector_dimensions(
ds.env())
base_dxtbx = rayonix_tbx.get_dxtbx_from_params(
params.format.cbf.rayonix, detector_size)
# list of all events
times = run.times()
if params.dispatch.selected_events:
times = [
t for t in times
if cspad_tbx.evt_timestamp((t.seconds(), t.nanoseconds() /
1e6)) in params.input.timestamp
]
nevents = min(len(times), max_events)
# chop the list into pieces, depending on rank. This assigns each process
# events such that the get every Nth event where N is the number of processes
mytimes = [
times[i] for i in range(nevents) if (i + rank) % size == 0
]
for i in range(len(mytimes)):
evt = run.event(mytimes[i])
id = evt.get(psana.EventId)
print("Event #", i, " has id:", id)
timestamp = cspad_tbx.evt_timestamp(
cspad_tbx.evt_time(evt)) # human readable format
if timestamp is None:
print("No timestamp, skipping shot")
continue
if evt.get("skip_event") or "skip_event" in [
key.key() for key in evt.keys()
]:
print("Skipping event", timestamp)
continue
t = timestamp
s = t[0:4] + t[5:7] + t[8:10] + t[11:13] + t[14:16] + t[
17:19] + t[20:23]
print("Processing shot", s)
if params.format.file_format == "pickle":
if evt.get("skip_event"):
print("Skipping event", id)
continue
# the data needs to have already been processed and put into the event by psana
data = evt.get(params.format.pickle.out_key)
if data is None:
print("No data")
continue
# set output paths according to the templates
path = os.path.join(params.output.output_dir,
"shot-" + s + ".pickle")
print("Saving", path)
easy_pickle.dump(path, data)
elif params.format.file_format == "cbf":
if params.format.cbf.mode == "cspad":
# get numpy array, 32x185x388
data = cspad_cbf_tbx.get_psana_corrected_data(
psana_det,
evt,
use_default=False,
dark=True,
common_mode=None,
apply_gain_mask=params.format.cbf.cspad.
gain_mask_value is not None,
gain_mask_value=params.format.cbf.cspad.
gain_mask_value,
per_pixel_gain=False)
distance = cspad_tbx.env_distance(
params.input.address, run.env(),
params.format.cbf.detz_offset)
elif params.format.cbf.mode == "rayonix":
data = rayonix_tbx.get_data_from_psana_event(
evt, params.input.address)
distance = params.format.cbf.detz_offset
if distance is None:
print("No distance, skipping shot")
continue
if self.params.format.cbf.override_energy is None:
wavelength = cspad_tbx.evt_wavelength(evt)
if wavelength is None:
print("No wavelength, skipping shot")
continue
else:
wavelength = 12398.4187 / self.params.format.cbf.override_energy
# stitch together the header, data and metadata into the final dxtbx format object
if params.format.cbf.mode == "cspad":
image = cspad_cbf_tbx.format_object_from_data(
base_dxtbx,
data,
distance,
wavelength,
timestamp,
params.input.address,
round_to_int=False)
elif params.format.cbf.mode == "rayonix":
image = rayonix_tbx.format_object_from_data(
base_dxtbx, data, distance, wavelength, timestamp,
params.input.address)
path = os.path.join(params.output.output_dir,
"shot-" + s + ".cbf")
print("Saving", path)
# write the file
import pycbf
image._cbf_handle.write_widefile(path.encode(), pycbf.CBF,\
pycbf.MIME_HEADERS|pycbf.MSG_DIGEST|pycbf.PAD_4K, 0)
run.end()
ds.end()
class InMemScript(DialsProcessScript):
""" Script to process XFEL data at LCLS """
def __init__(self):
""" Set up the option parser. Arguments come from the command line or a phil file """
self.usage = """
%s input.experiment=experimentname input.run_num=N input.address=address
format.file_format=cbf format.cbf.detz_offset=N
%s input.experiment=experimentname input.run_num=N input.address=address
format.file_format=pickle input.cfg=filename
""" % (libtbx.env.dispatcher_name, libtbx.env.dispatcher_name)
self.parser = OptionParser(usage=self.usage, phil=phil_scope)
self.debug_file_path = None
self.debug_str = None
self.mpi_log_file_path = None
self.reference_detector = None
def debug_start(self, ts):
self.debug_str = "%s,%s" % (socket.gethostname(), ts)
self.debug_str += ",%s,%s,%s\n"
self.debug_write("start")
def debug_write(self, string, state=None):
ts = cspad_tbx.evt_timestamp() # Now
debug_file_handle = open(self.debug_file_path, 'a')
if string == "":
debug_file_handle.write("\n")
else:
if state is None:
state = " "
debug_file_handle.write(self.debug_str % (ts, state, string))
debug_file_handle.close()
def mpi_log_write(self, string):
mpi_log_file_handle = open(self.mpi_log_file_path, 'a')
mpi_log_file_handle.write(string)
mpi_log_file_handle.close()
def psana_mask_to_dials_mask(self, psana_mask):
if psana_mask.dtype == np.bool:
psana_mask = flex.bool(psana_mask)
else:
psana_mask = flex.bool(psana_mask == 1)
assert psana_mask.focus() == (32, 185, 388)
dials_mask = []
for i in xrange(32):
dials_mask.append(psana_mask[i:i + 1, :, :194])
dials_mask[-1].reshape(flex.grid(185, 194))
dials_mask.append(psana_mask[i:i + 1, :, 194:])
dials_mask[-1].reshape(flex.grid(185, 194))
return dials_mask
def run(self):
""" Process all images assigned to this thread """
params, options = self.parser.parse_args(show_diff_phil=True)
# Check inputs
if params.input.experiment is None or \
params.input.run_num is None or \
params.input.address is None:
raise Usage(self.usage)
if params.format.file_format == "cbf":
if params.format.cbf.detz_offset is None:
raise Usage(self.usage)
elif params.format.file_format == "pickle":
if params.input.cfg is None:
raise Usage(self.usage)
else:
raise Usage(self.usage)
if not os.path.exists(params.output.output_dir):
raise Sorry("Output path not found:" + params.output.output_dir)
self.params = params
self.load_reference_geometry()
# The convention is to put %s in the phil parameter to add a time stamp to
# each output datafile. Save the initial templates here.
self.strong_filename_template = params.output.strong_filename
self.indexed_filename_template = params.output.indexed_filename
self.refined_experiments_filename_template = params.output.refined_experiments_filename
self.integrated_filename_template = params.output.integrated_filename
self.reindexedstrong_filename_template = params.output.reindexedstrong_filename
# Don't allow the strong reflections to be written unless there are enough to
# process
params.output.strong_filename = None
# Save the paramters
self.params_cache = copy.deepcopy(params)
self.options = options
if params.mp.method == "mpi":
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank(
) # each process in MPI has a unique id, 0-indexed
size = comm.Get_size(
) # size: number of processes running in this job
elif params.mp.method == "sge" and \
'SGE_TASK_ID' in os.environ and \
'SGE_TASK_FIRST' in os.environ and \
'SGE_TASK_LAST' in os.environ:
if 'SGE_STEP_SIZE' in os.environ:
assert int(os.environ['SGE_STEP_SIZE']) == 1
if os.environ['SGE_TASK_ID'] == 'undefined' or os.environ[
'SGE_TASK_ID'] == 'undefined' or os.environ[
'SGE_TASK_ID'] == 'undefined':
rank = 0
size = 1
else:
rank = int(os.environ['SGE_TASK_ID']) - int(
os.environ['SGE_TASK_FIRST'])
size = int(os.environ['SGE_TASK_LAST']) - int(
os.environ['SGE_TASK_FIRST']) + 1
else:
rank = 0
size = 1
# Configure the logging
if params.output.logging_dir is None:
info_path = ''
debug_path = ''
else:
log_path = os.path.join(params.output.logging_dir,
"log_rank%04d.out" % rank)
error_path = os.path.join(params.output.logging_dir,
"error_rank%04d.out" % rank)
print "Redirecting stdout to %s" % log_path
print "Redirecting stderr to %s" % error_path
sys.stdout = open(log_path, 'a', buffering=0)
sys.stderr = open(error_path, 'a', buffering=0)
print "Should be redirected now"
info_path = os.path.join(params.output.logging_dir,
"info_rank%04d.out" % rank)
debug_path = os.path.join(params.output.logging_dir,
"debug_rank%04d.out" % rank)
from dials.util import log
log.config(params.verbosity, info=info_path, debug=debug_path)
debug_dir = os.path.join(params.output.output_dir, "debug")
if not os.path.exists(debug_dir):
try:
os.makedirs(debug_dir)
except OSError, e:
pass # due to multiprocessing, makedirs can sometimes fail
assert os.path.exists(debug_dir)
if params.debug.skip_processed_events or params.debug.skip_unprocessed_events or params.debug.skip_bad_events:
print "Reading debug files..."
self.known_events = {}
for filename in os.listdir(debug_dir):
# format: hostname,timestamp_event,timestamp_now,status,detail
for line in open(os.path.join(debug_dir, filename)):
vals = line.strip().split(',')
if len(vals) != 5:
continue
_, ts, _, status, detail = vals
if status in ["done", "stop", "fail"]:
self.known_events[ts] = status
else:
self.known_events[ts] = "unknown"
self.debug_file_path = os.path.join(debug_dir, "debug_%d.txt" % rank)
write_newline = os.path.exists(self.debug_file_path)
if write_newline: # needed if the there was a crash
self.debug_write("")
if params.mp.method != 'mpi' or params.mp.mpi.method == 'client_server':
if rank == 0:
self.mpi_log_file_path = os.path.join(debug_dir, "mpilog.out")
write_newline = os.path.exists(self.mpi_log_file_path)
if write_newline: # needed if the there was a crash
self.mpi_log_write("\n")
# set up psana
if params.input.cfg is not None:
psana.setConfigFile(params.input.cfg)
dataset_name = "exp=%s:run=%s:idx" % (params.input.experiment,
params.input.run_num)
if params.input.xtc_dir is not None:
if params.input.use_ffb:
raise Sorry(
"Cannot specify the xtc_dir and use SLAC's ffb system")
dataset_name += ":dir=%s" % params.input.xtc_dir
elif params.input.use_ffb:
# as ffb is only at SLAC, ok to hardcode /reg/d here
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc" % (
params.input.experiment[0:3], params.input.experiment)
if params.input.stream is not None:
dataset_name += ":stream=%d" % params.input.stream
ds = psana.DataSource(dataset_name)
if params.format.file_format == "cbf":
self.psana_det = psana.Detector(params.input.address, ds.env())
# set this to sys.maxint to analyze all events
if params.dispatch.max_events is None:
max_events = sys.maxint
else:
max_events = params.dispatch.max_events
for run in ds.runs():
if params.format.file_format == "cbf":
# load a header only cspad cbf from the slac metrology
try:
self.base_dxtbx = cspad_cbf_tbx.env_dxtbx_from_slac_metrology(
run, params.input.address)
except Exception, e:
raise Sorry(
"Couldn't load calibration file for run %d, %s" %
(run.run(), str(e)))
if self.base_dxtbx is None:
raise Sorry("Couldn't load calibration file for run %d" %
run.run())
if params.format.file_format == 'cbf':
if params.format.cbf.common_mode.algorithm == "custom":
self.common_mode = params.format.cbf.common_mode.custom_parameterization
assert self.common_mode is not None
else:
self.common_mode = params.format.cbf.common_mode.algorithm # could be None or default
if params.format.cbf.invalid_pixel_mask is not None:
self.dials_mask = easy_pickle.load(
params.format.cbf.invalid_pixel_mask)
assert len(self.dials_mask) == 64
if self.params.format.cbf.mask_nonbonded_pixels:
psana_mask = self.psana_det.mask(run,
calib=False,
status=False,
edges=False,
central=False,
unbond=True,
unbondnbrs=True)
dials_mask = self.psana_mask_to_dials_mask(psana_mask)
self.dials_mask = [
self.dials_mask[i] & dials_mask[i]
for i in xrange(len(dials_mask))
]
else:
psana_mask = self.psana_det.mask(run,
calib=True,
status=True,
edges=True,
central=True,
unbond=True,
unbondnbrs=True)
self.dials_mask = self.psana_mask_to_dials_mask(psana_mask)
if self.params.spotfinder.lookup.mask is not None:
self.spotfinder_mask = easy_pickle.load(
self.params.spotfinder.lookup.mask)
else:
self.spotfinder_mask = None
if self.params.integration.lookup.mask is not None:
self.integration_mask = easy_pickle.load(
self.params.integration.lookup.mask)
else:
self.integration_mask = None
# list of all events
times = run.times()
nevents = min(len(times), max_events)
times = times[:nevents]
if params.dispatch.process_percent is not None:
import fractions
percent = params.dispatch.process_percent / 100
f = fractions.Fraction(percent).limit_denominator(100)
times = [
times[i] for i in xrange(len(times))
if i % f.denominator < f.numerator
]
print "Dividing %d of %d events (%4.1f%%) between all processes" % (
len(times), nevents, 100 * len(times) / nevents)
nevents = len(times)
else:
print "Dividing %d events between all processes" % nevents
if params.mp.method == "mpi" and params.mp.mpi.method == 'client_server' and size > 2:
print "Using MPI client server"
# use a client/server approach to be sure every process is busy as much as possible
# only do this if there are more than 2 processes, as one process will be a server
try:
if rank == 0:
# server process
self.mpi_log_write("MPI START\n")
for t in times:
# a client process will indicate it's ready by sending its rank
self.mpi_log_write(
"Getting next available process\n")
rankreq = comm.recv(source=MPI.ANY_SOURCE)
ts = cspad_tbx.evt_timestamp(
(t.seconds(), t.nanoseconds() / 1e6))
self.mpi_log_write(
"Process %s is ready, sending ts %s\n" %
(rankreq, ts))
comm.send(t, dest=rankreq)
# send a stop command to each process
self.mpi_log_write("MPI DONE, sending stops\n")
for rankreq in range(size - 1):
self.mpi_log_write(
"Getting next available process\n")
rankreq = comm.recv(source=MPI.ANY_SOURCE)
self.mpi_log_write("Sending stop to %d\n" %
rankreq)
comm.send('endrun', dest=rankreq)
else:
# client process
while True:
# inform the server this process is ready for an event
comm.send(rank, dest=0)
evttime = comm.recv(source=0)
if evttime == 'endrun': break
self.process_event(run, evttime)
except Exception, e:
print "Error caught in main loop"
print str(e)
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 run(self):
""" Process all images assigned to this thread """
params, options = self.parser.parse_args(show_diff_phil=True)
if params.input.experiment is None or params.input.run_num is None or params.input.address is None:
raise Usage(self.usage)
if params.format.file_format == "cbf":
if params.format.cbf.detz_offset is None:
raise Usage(self.usage)
elif params.format.file_format == "pickle":
if params.format.pickle.cfg is None:
raise Usage(self.usage)
else:
raise Usage(self.usage)
if not os.path.exists(params.output.output_dir):
raise Sorry("Output path not found:" + params.output.output_dir)
# Save the paramters
self.params = params
self.options = options
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank() # each process in MPI has a unique id, 0-indexed
size = comm.Get_size() # size: number of processes running in this job
# set up psana
if params.format.file_format == "pickle":
psana.setConfigFile(params.format.pickle.cfg)
dataset_name = "exp=%s:run=%s:idx" % (params.input.experiment, params.input.run_num)
ds = psana.DataSource(dataset_name)
if params.format.file_format == "cbf":
src = psana.Source("DetInfo(%s)" % params.input.address)
psana_det = psana.Detector(params.input.address, ds.env())
# set this to sys.maxint to analyze all events
if params.dispatch.max_events is None:
max_events = sys.maxint
else:
max_events = params.dispatch.max_events
for run in ds.runs():
if params.format.file_format == "cbf":
# load a header only cspad cbf from the slac metrology
base_dxtbx = cspad_cbf_tbx.env_dxtbx_from_slac_metrology(run, params.input.address)
if base_dxtbx is None:
raise Sorry("Couldn't load calibration file for run %d" % run.run())
if params.format.cbf.gain_mask_value is not None:
gain_mask = psana_det.gain_mask(gain=params.format.cbf.gain_mask_value)
# list of all events
times = run.times()
nevents = min(len(times), max_events)
# chop the list into pieces, depending on rank. This assigns each process
# events such that the get every Nth event where N is the number of processes
mytimes = [times[i] for i in xrange(nevents) if (i + rank) % size == 0]
for i in xrange(len(mytimes)):
evt = run.event(mytimes[i])
id = evt.get(psana.EventId)
print "Event #", i, " has id:", id
timestamp = cspad_tbx.evt_timestamp(cspad_tbx.evt_time(evt)) # human readable format
if timestamp is None:
print "No timestamp, skipping shot"
continue
t = timestamp
s = t[0:4] + t[5:7] + t[8:10] + t[11:13] + t[14:16] + t[17:19] + t[20:23]
print "Processing shot", s
if params.format.file_format == "pickle":
if evt.get("skip_event"):
print "Skipping event", id
continue
# the data needs to have already been processed and put into the event by psana
data = evt.get(params.format.pickle.out_key)
if data is None:
print "No data"
continue
# set output paths according to the templates
path = os.path.join(params.output.output_dir, "shot-" + s + ".pickle")
print "Saving", path
easy_pickle.dump(path, data)
elif params.format.file_format == "cbf":
# get numpy array, 32x185x388
data = psana_det.calib(evt) # applies psana's complex run-dependent calibrations
if params.format.cbf.gain_mask_value is not None:
# apply gain mask
data *= gain_mask
distance = cspad_tbx.env_distance(params.input.address, run.env(), params.format.cbf.detz_offset)
if distance is None:
print "No distance, skipping shot"
continue
if self.params.format.cbf.override_energy is None:
wavelength = cspad_tbx.evt_wavelength(evt)
if wavelength is None:
print "No wavelength, skipping shot"
continue
else:
wavelength = 12398.4187 / self.params.format.cbf.override_energy
# stitch together the header, data and metadata into the final dxtbx format object
cspad_img = cspad_cbf_tbx.format_object_from_data(
base_dxtbx, data, distance, wavelength, timestamp, params.input.address
)
path = os.path.join(params.output.output_dir, "shot-" + s + ".cbf")
print "Saving", path
# write the file
import pycbf
cspad_img._cbf_handle.write_widefile(
path, pycbf.CBF, pycbf.MIME_HEADERS | pycbf.MSG_DIGEST | pycbf.PAD_4K, 0
)
run.end()
ds.end()
def run(self):
""" Process all images assigned to this thread """
params, options = self.parser.parse_args(
show_diff_phil=True)
# Configure the logging
from dials.util import log
log.config(params.verbosity)
# Check inputs
if params.input.experiment is None or \
params.input.run_num is None or \
params.input.address is None:
raise Usage(self.usage)
if params.format.file_format == "cbf":
if params.format.cbf.detz_offset is None:
raise Usage(self.usage)
elif params.format.file_format == "pickle":
if params.format.pickle.cfg is None:
raise Usage(self.usage)
else:
raise Usage(self.usage)
if not os.path.exists(params.output.output_dir):
raise Sorry("Output path not found:" + params.output.output_dir)
# The convention is to put %s in the phil parameter to add a time stamp to
# each output datafile. Save the initial templates here.
self.strong_filename_template = params.output.strong_filename
self.indexed_filename_template = params.output.indexed_filename
self.refined_experiments_filename_template = params.output.refined_experiments_filename
self.integrated_filename_template = params.output.integrated_filename
# Don't allow the strong reflections to be written unless there are enough to
# process
params.output.strong_filename = None
# Save the paramters
self.params_cache = copy.deepcopy(params)
self.options = options
if params.mp.method == "mpi":
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank() # each process in MPI has a unique id, 0-indexed
size = comm.Get_size() # size: number of processes running in this job
elif params.mp.method == "sge" and \
'SGE_TASK_ID' in os.environ and \
'SGE_TASK_FIRST' in os.environ and \
'SGE_TASK_LAST' in os.environ:
if 'SGE_STEP_SIZE' in os.environ:
assert int(os.environ['SGE_STEP_SIZE']) == 1
if os.environ['SGE_TASK_ID'] == 'undefined' or os.environ['SGE_TASK_ID'] == 'undefined' or os.environ['SGE_TASK_ID'] == 'undefined':
rank = 0
size = 1
else:
rank = int(os.environ['SGE_TASK_ID']) - int(os.environ['SGE_TASK_FIRST'])
size = int(os.environ['SGE_TASK_LAST']) - int(os.environ['SGE_TASK_FIRST']) + 1
else:
rank = 0
size = 1
if params.output.logging_dir is not None:
log_path = os.path.join(params.output.logging_dir, "log_rank%04d.out"%rank)
error_path = os.path.join(params.output.logging_dir, "error_rank%04d.out"%rank)
print "Redirecting stdout to %s"%log_path
print "Redirecting stderr to %s"%error_path
assert os.path.exists(log_path)
sys.stdout = open(log_path,'a', buffering=0)
sys.stderr = open(error_path,'a',buffering=0)
print "Should be redirected now"
debug_dir = os.path.join(params.output.output_dir, "debug")
if not os.path.exists(debug_dir):
os.makedirs(debug_dir)
if params.debug.skip_processed_events or params.debug.skip_processed_events or params.debug.skip_bad_events:
print "Reading debug files..."
self.known_events = {}
for filename in os.listdir(debug_dir):
# format: hostname,timestamp,status
for line in open(os.path.join(debug_dir, filename)):
vals = line.strip().split(',')
if len(vals) == 2:
self.known_events[vals[1]] = "unknown"
elif len(vals) == 3:
self.known_events[vals[1]] = vals[2]
debug_file_path = os.path.join(debug_dir, "debug_%d.txt"%rank)
write_newline = os.path.exists(debug_file_path)
self.debug_file_handle = open(debug_file_path, 'a', 0) # 0 for unbuffered
if write_newline: # needed if the there was a crash
self.debug_file_handle.write("\n")
# set up psana
if params.format.file_format=="pickle":
psana.setConfigFile(params.format.pickle.cfg)
dataset_name = "exp=%s:run=%s:idx"%(params.input.experiment,params.input.run_num)
if params.input.xtc_dir is not None:
if params.input.use_ffb:
raise Sorry("Cannot specify the xtc_dir and use SLAC's ffb system")
dataset_name += ":dir=%s"%params.input.xtc_dir
elif params.input.use_ffb:
# as ffb is only at SLAC, ok to hardcode /reg/d here
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc"%(params.input.experiment[0:3],params.input.experiment)
ds = psana.DataSource(dataset_name)
if params.format.file_format == "cbf":
self.psana_det = psana.Detector(params.input.address, ds.env())
# set this to sys.maxint to analyze all events
if params.dispatch.max_events is None:
max_events = sys.maxint
else:
max_events = params.dispatch.max_events
for run in ds.runs():
if params.format.file_format == "cbf":
# load a header only cspad cbf from the slac metrology
self.base_dxtbx = cspad_cbf_tbx.env_dxtbx_from_slac_metrology(run, params.input.address)
if self.base_dxtbx is None:
raise Sorry("Couldn't load calibration file for run %d"%run.run())
if params.format.cbf.gain_mask_value is not None:
self.gain_mask = self.psana_det.gain_mask(gain=params.format.cbf.gain_mask_value)
# list of all events
times = run.times()
nevents = min(len(times),max_events)
if params.mp.method == "mpi" and size > 2:
# use a client/server approach to be sure every process is busy as much as possible
# only do this if there are more than 2 processes, as one process will be a server
if rank == 0:
# server process
for t in times[:nevents]:
# a client process will indicate it's ready by sending its rank
rankreq = comm.recv(source=MPI.ANY_SOURCE)
comm.send(t,dest=rankreq)
# send a stop command to each process
for rankreq in range(size-1):
rankreq = comm.recv(source=MPI.ANY_SOURCE)
comm.send('endrun',dest=rankreq)
else:
# client process
while True:
# inform the server this process is ready for an event
comm.send(rank,dest=0)
evttime = comm.recv(source=0)
if evttime == 'endrun': break
self.process_event(run, evttime)
else:
# 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)):
self.process_event(run, mytimes[i])
run.end()
ds.end()
