def compute_c2(argv=None):
"""Function to compute the average angular intensity function, <C2(q,dPhi)>, from FXS images
extracted from xtc (smd,idx,xtc format) or h5 files.
Works for Single CPU, Multi-Processor interactive jobs and MPI batch jobs
For a definition of input arguments argv and batch processing instructions see *** mpi_fxs_launch.py ***
compute_c2 produces the following output files:
* Index file : Information about the events processed including time-stamps, beam center, total and peak intensities, streak locations, particle size etc
* I(q) : Average Azimuthal intensities (SAXS)
* C2(q,dPhi) : Average 2-point correlation functions (FXS)
* Bg_img : Average image in polar coordinates
* Bg_norm : Mean subtracted average image in polar coordinates
* Bg_msk : Average mask image in polar coordinates
The I and C2 output data are split in 2 ~equal bins [0 and 1] for statistical comparison to be made
example:
C2_run111_0_29793.dat is the average C2(q,dPhi) from run111 bin 0 where 29,793 images have been processed
C2_run111_1_29718.dat is the average C2(q,dPhi) from run111 bin 1 where 29,718 images have been processed
"""
if argv == None:
argv = sys.argv[1:]
try:
from mpi4py import MPI
except ImportError:
raise Sorry("MPI not found")
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
if argv.hit is None:
hit = -1.0e20 # Process everything
else:
hit = argv.hit # Process everything > hit
ftype = argv.ftype
if argv.param_path is not None:
if ftype == 'h5':
param_file = np.genfromtxt(argv.param_path, skiprows=1, dtype=None)
timestamps, filestamps = pnccd_tbx.get_h5_event(param_file)
elif ftype == 'xtc':
param_file = np.genfromtxt(argv.param_path, skiprows=1, dtype=None)
timestamps = pnccd_tbx.get_time(param_file)
else:
param_file = np.genfromtxt(argv.param_path, skiprows=1)
timestamps = pnccd_tbx.get_psana_event(param_file)
else:
timestamps = None
# The first and last events to processed
first = argv.first
last = argv.last
# Check data format
if ftype == 'h5':
import h5py
run = int(argv.run)
# Get time-stamps from all h5-files
if argv.param_path is None:
timestamps = []
filestamps = []
# Loop over all h5-files and store the time-stamps
for i in os.listdir(argv.xtc_dir):
if i.endswith(".h5"):
f = h5py.File(i, 'r')
filestamps.append(i[-7:-4])
timestamps.append(f.keys())
continue
else:
continue
dataset_name = "%s-r%s" % (argv.experiment, str(argv.run).zfill(4)
) # Ascert 4 digit run number
exprun = os.path.join(argv.xtc_dir, dataset_name)
if argv.first is None:
first = 0
if argv.last is None:
last = len(timestamps)
else:
last = min(last,
len(timestamps)) # Check that last time-stamp exists
timestamps = timestamps[first:last]
filestamps = filestamps[first:last]
evtgen = h5gen
else:
exprun = "exp=%s:run=%d" % (argv.experiment, argv.run)
if (ftype == 'xtc'):
dataset_name = exprun + ':xtc'
elif (ftype == 'idx'):
dataset_name = exprun + ':idx'
elif (ftype == 'idx_ffb'):
dataset_name = exprun + ':idx'
# as ffb is only at SLAC, ok to hardcode /reg/d here
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc" % (
argv.experiment[0:3], argv.experiment)
elif (ftype == 'smd'):
dataset_name = exprun + ':smd'
elif (ftype == 'smd_ffb'):
dataset_name = exprun + ':smd'
# as ffb is only at SLAC, ok to hardcode /reg/d here ADD live!
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc:live" % (
argv.experiment[0:3], argv.experiment)
exprun = dataset_name
ds = DataSource(dataset_name)
run = ds.runs().next()
# Select event generator
if (ftype == 'smd') or (ftype == 'smd_ffb') or (ftype == 'xtc'):
evtgen = smdgen
elif (ftype == 'idx') or (ftype == 'idx_ffb'):
evtgen = idxgen
if size == 1:
plot = argv.plot
else:
plot = 0
FXS = fxs.fluctuation_scattering(
dataset_name=exprun,
detector_address=argv.address,
data_type=argv.ftype,
mask_path=argv.mask_path,
mask_angles=
None, #np.array([88, 270]), # static masking at 88 and 270 deg
mask_widths=None, #np.array([6, 10]), # +/- degrees
backimg_path=argv.bg_img_path,
backmsk_path=argv.bg_msk_path,
geom_path=argv.geom_path,
det_dist=argv.det_distance,
det_pix=argv.det_pixel,
beam_l=argv.lambda_b,
mask_thr=argv.thr,
nQ=argv.nQ,
nPhi=argv.nPhi,
dQ=argv.dQ,
dPhi=argv.dP,
cent0=[argv.x, argv.y],
r_max=argv.r_max,
dr=argv.dr,
dx=argv.dx,
dy=argv.dy,
r_0=argv.r0,
q_bound=argv.q_bound,
peak=[0.037, 0.064], # Location in q for peaks to be integrated
dpeak=[0.002, 0.002]) # Width in q for peaks to be integrated
# Initialize iterator
FXS.cnt_0 = np.array([0.])
FXS.cnt_1 = np.array([0.])
# Initialize Index variables
if argv.param_path is None:
maxevents = 400000 # We don't always know the total nr of events. Therefore set to large value
else:
maxevents = min(len(timestamps), len(timestamps[first:last]))
FXS.get_index(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
if size > 1:
if rank > 0:
hd = pnccd_hit.hit()
# MPI process. Here we set rank 0 to work as a listening server only.
for j, evt in evtgen(run,
timestamps=timestamps,
first=first,
last=last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j % 10 == 0: print 'Rank', rank, 'processing event', j
if ftype == 'h5':
FXS.get_h5(filestamps[j], evt)
else:
FXS.get_image(evt)
# Process hits
if (FXS.img is not None) and (float(FXS.img.sum()) > hit):
FXS.get_beam(plot=plot) # Beam center refinement
FXS.get_polar(plot=plot) # Polar transform
FXS.get_streak_mask(plot=plot) # Mask out streaks
FXS.get_pixel_mask(plot=plot) # Mask out pixels
FXS.get_norm(plot=plot) # Normalize image, get SAXS
FXS.get_c2(plot=plot) # Compute C2
# Split upp into 2 half sets
if int(FXS.cnt_0 + FXS.cnt_1) % 2 == 0:
FXS.sum_c2(flag=0) # Accumulate C2 for Half I
else:
FXS.sum_c2(flag=1) # Accumulate C2 for Half II
if FXS.r_0 is not None:
FXS.get_size()
FXS.sum_bg2() # Sum Background
if ftype == 'h5':
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec << 32) | nsec), fid)
#######################################
FXS.store_index(et, j) # Store index
if int(FXS.cnt_0 + FXS.cnt_1) % 10 == 0:
print 'Rank', rank, 'processed events: ', int(
FXS.cnt_0 + FXS.cnt_1)
# Send partial results to master (rank 0)
if int(FXS.cnt_0 +
FXS.cnt_1) % 50 == 0: # Send every 50 events
# C2 and Saxs data
tmp_n = int(FXS.cnt_0 + FXS.cnt_1)
tmp_saxs = (FXS.Isaxs_0 + FXS.Isaxs_1) / tmp_n
tmp_c2 = (FXS.C2_0 + FXS.C2_1) / (FXS.C2m_0 +
FXS.C2m_1)
# Average image
tmp_im = FXS.ave / tmp_n
# Ascert no nan values
tmp_saxs[np.isnan(tmp_saxs)] = 0
tmp_c2[np.isnan(tmp_c2)] = 0
# Total intensity, Size and Score
tmp_ind = np.column_stack(
(FXS.tot_int, FXS.tot_size, FXS.tot_score))
hd.send(tmp_n,
image=tmp_im,
saxs=tmp_saxs,
c2=tmp_c2,
ind=tmp_ind)
hd.endrun()
print 'Rank', rank, 'total events: ', int(FXS.cnt_0 +
FXS.cnt_1), ' * '
else:
if ftype == 'h5':
FXS.run_nr = run
else:
FXS.run_nr = int(run.run())
hd = pnccd_hit.hit()
adim = FXS.ave.shape
sdim = len(FXS.q)
cdim = (len(FXS.q), len(FXS.phi))
idim = (maxevents, 3)
hd.total_ave = [np.zeros(adim)] * (size - 1)
hd.total_c2 = [np.zeros(cdim)] * (size - 1)
hd.total_ind = [np.zeros(idim)] * (size - 1)
hd.total_saxs = [np.zeros(sdim)] * (size - 1)
hd.total_ev_a = [0.0] * (size - 1)
hd.total_ev_c = [0.0] * (size - 1)
hd.total_ev_s = [0.0] * (size - 1)
hd.total_ev_i = [0.0] * (size - 1)
nClients = size - 1
while nClients > 0:
# Remove client if the run ended
if hd.recv():
nClients -= 1
else:
na = sum(hd.total_ev_a)
ns = sum(hd.total_ev_s)
nc = sum(hd.total_ev_c)
ni = sum(hd.total_ev_i)
if (na == ns == nc == ni) and (
ns % 100 == 0): # Publish every 100 events
AVE = np.zeros(adim)
C2ave = np.zeros(cdim)
SAXSave = np.zeros(sdim)
IND = np.zeros(idim)
for i in range(size - 1):
AVE = AVE + (hd.total_ave[i] *
(hd.total_ev_a[i] / na))
C2ave = C2ave + (hd.total_c2[i] *
(hd.total_ev_c[i] / nc))
SAXSave = SAXSave + (hd.total_saxs[i] *
(hd.total_ev_s[i] / ns))
IND = IND + hd.total_ind[i]
FXS.publish(image=AVE,
saxs=SAXSave,
c2=C2ave,
ind=IND,
n_a=na,
n_saxs=ns,
n_c2=nc,
n_i=ni)
else:
# Single CPU
for j, evt in evtgen(run,
timestamps=timestamps,
first=first,
last=last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j % 10 == 0: print 'Rank', rank, 'processing event', j
if ftype == 'h5':
FXS.get_h5(filestamps[j], evt)
else:
FXS.get_image(evt)
# Process hits
if (FXS.img is not None) and (float(FXS.img.sum()) > hit):
FXS.get_beam(plot=plot) # Beam center refinement
FXS.get_polar() # Polar transform
FXS.get_streak_mask(plot=plot) # Mask out streaks
FXS.get_pixel_mask(plot=plot) # Mask out pixels
FXS.get_norm(plot=plot) # Normalize image, get SAXS
FXS.get_c2(plot=plot) # Compute C2
# Split upp into 2 half sets
if int(FXS.cnt_0 + FXS.cnt_1) % 2 == 0:
FXS.sum_c2(flag=0) # Accumulate C2 for Half I
else:
FXS.sum_c2(flag=1) # Accumulate C2 for Half II
if FXS.r_0 is not None:
FXS.get_size()
FXS.sum_bg2() # Sum Background
if ftype == 'h5':
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec << 32) | nsec), fid)
#######################################
FXS.store_index(et, j) # Store index
print 'Rank', rank, 'total events: ', int(FXS.cnt_0 +
FXS.cnt_1), ' * '
#sum the images across mpi cores
if size > 1:
print "Synchronizing rank", rank
Tot_0 = np.zeros(FXS.cnt_0.shape)
Tot_1 = np.zeros(FXS.cnt_1.shape)
comm.Reduce(FXS.cnt_0, Tot_0)
comm.Reduce(FXS.cnt_1, Tot_1)
if rank == 0 and Tot_0[0] == 0 and Tot_1[0]:
raise Sorry("No events found in the run")
if not hasattr(FXS, 'Isaxs_0'):
FXS.Isaxs_0 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'Vsaxs_0'):
FXS.Vsaxs_0 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'C2_0'):
FXS.C2_0 = np.zeros((len(FXS.q), len(FXS.phi)))
if not hasattr(FXS, 'C2m_0'):
FXS.C2m_0 = np.zeros((len(FXS.q), len(FXS.phi)))
if not hasattr(FXS, 'Isaxs_1'):
FXS.Isaxs_1 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'Vsaxs_1'):
FXS.Vsaxs_1 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'C2_1'):
FXS.C2_1 = np.zeros((len(FXS.q), len(FXS.phi)))
if not hasattr(FXS, 'C2m_1'):
FXS.C2m_1 = np.zeros((len(FXS.q), len(FXS.phi)))
if not hasattr(FXS, 'Back_img'):
FXS.Back_img = np.zeros((len(FXS.q), len(FXS.phi)))
if not hasattr(FXS, 'Back_norm'):
FXS.Back_norm = np.zeros((len(FXS.q), len(FXS.phi)))
if not hasattr(FXS, 'Back_msk'):
FXS.Back_msk = np.zeros((len(FXS.q), len(FXS.phi)))
# Collect Variables
SAXS_0_all = np.zeros(FXS.Isaxs_0.shape)
comm.Reduce(FXS.Isaxs_0, SAXS_0_all)
VAR_0_all = np.zeros(FXS.Vsaxs_0.shape)
comm.Reduce(FXS.Vsaxs_0, VAR_0_all)
C2_0_all = np.zeros(FXS.C2_0.shape)
comm.Reduce(FXS.C2_0, C2_0_all)
C2m_0_all = np.zeros(FXS.C2m_0.shape)
comm.Reduce(FXS.C2m_0, C2m_0_all)
SAXS_1_all = np.zeros(FXS.Isaxs_1.shape)
comm.Reduce(FXS.Isaxs_1, SAXS_1_all)
VAR_1_all = np.zeros(FXS.Vsaxs_1.shape)
comm.Reduce(FXS.Vsaxs_1, VAR_1_all)
C2_1_all = np.zeros(FXS.C2_1.shape)
comm.Reduce(FXS.C2_1, C2_1_all)
C2m_1_all = np.zeros(FXS.C2m_1.shape)
comm.Reduce(FXS.C2m_1, C2m_1_all)
# Collect Indexing variables
Tot_t = np.zeros(FXS.tot_t.shape)
comm.Reduce(FXS.tot_t, Tot_t)
Tot_s = np.zeros(FXS.tot_s.shape)
comm.Reduce(FXS.tot_s, Tot_s)
Tot_ns = np.zeros(FXS.tot_ns.shape)
comm.Reduce(FXS.tot_ns, Tot_ns)
Tot_fd = np.zeros(FXS.tot_fd.shape)
comm.Reduce(FXS.tot_fd, Tot_fd)
Tot_int = np.zeros(FXS.tot_int.shape)
comm.Reduce(FXS.tot_int, Tot_int)
Tot_peak1 = np.zeros(FXS.tot_peak1_int.shape)
comm.Reduce(FXS.tot_peak1_int, Tot_peak1)
Tot_peak2 = np.zeros(FXS.tot_peak2_int.shape)
comm.Reduce(FXS.tot_peak2_int, Tot_peak2)
Tot_s_m = np.zeros(FXS.tot_streak_m.shape)
comm.Reduce(FXS.tot_streak_m, Tot_s_m)
Tot_s_s = np.zeros(FXS.tot_streak_s.shape)
comm.Reduce(FXS.tot_streak_s, Tot_s_s)
Tot_cx = np.zeros(FXS.tot_cx.shape)
comm.Reduce(FXS.tot_cx, Tot_cx)
Tot_cy = np.zeros(FXS.tot_cy.shape)
comm.Reduce(FXS.tot_cy, Tot_cy)
Tot_size = np.zeros(FXS.tot_size.shape)
comm.Reduce(FXS.tot_size, Tot_size)
Tot_score = np.zeros(FXS.tot_score.shape)
comm.Reduce(FXS.tot_score, Tot_score)
# Collect background variables
BG_img_all = np.zeros(FXS.Back_img.shape)
comm.Reduce(FXS.Back_img, BG_img_all)
BG_norm_all = np.zeros(FXS.Back_norm.shape)
comm.Reduce(FXS.Back_norm, BG_norm_all)
BG_msk_all = np.zeros(FXS.Back_msk.shape)
comm.Reduce(FXS.Back_msk, BG_msk_all)
# Reduce results
if rank == 0:
if size > 1:
print "Synchronized"
# Write out data
if argv.outputdir is None:
opath = os.getcwd()
else:
opath = argv.outputdir
f_saxs0 = os.path.join(
opath,
'Saxs_run' + str(argv.run) + '_0_' + str(int(Tot_0)) + '.dat')
f_saxs1 = os.path.join(
opath,
'Saxs_run' + str(argv.run) + '_1_' + str(int(Tot_1)) + '.dat')
stamps_s = ['q', 'Mean', 'Std']
head_s = " ".join(stamps_s)
f_c0 = os.path.join(
opath, 'C2_run' + str(argv.run) + '_0_' + str(int(Tot_0)) + '.dat')
f_c1 = os.path.join(
opath, 'C2_run' + str(argv.run) + '_1_' + str(int(Tot_1)) + '.dat')
stamps_c = ['C2']
head_c = " ".join(stamps_c)
f_index = os.path.join(opath, 'Index_run' + str(argv.run) + '.dat')
stamps = [
'Time', 'Seconds', 'Nanoseconds', 'Fiducial', 'Total Intensity',
'Peak1, q=' + str(FXS.peak[0]) + '+/-' + str(FXS.dpeak[0]),
'Peak2, q=' + str(FXS.peak[1]) + '+/-' + str(FXS.dpeak[1]),
'Mean streak angle', 'Std streak angle', 'Beam X', 'Beam Y',
'Radius [Ang]', 'Score'
]
head = " ".join(stamps)
Tot_0 = int(Tot_0)
Isaxs_ave_0 = SAXS_0_all / Tot_0
Isaxs_std_0 = np.sqrt(VAR_0_all / Tot_0)
C2_ave_0 = (C2_0_all / Tot_0) / (C2m_0_all / Tot_0)
Tot_1 = int(Tot_1)
Isaxs_ave_1 = SAXS_1_all / Tot_1
Isaxs_std_1 = np.sqrt(VAR_1_all / Tot_1)
C2_ave_1 = (C2_1_all / Tot_1) / (C2m_1_all / Tot_1)
# Prepare background
tmp2 = np.copy(BG_msk_all)
ind = tmp2 == 0
tmp2[ind] = 1.0
Bg_img = BG_img_all / tmp2
Bg_norm = BG_norm_all / tmp2
Bg_msk = np.ones(tmp2.shape)
Bg_msk[ind] = 0.0
f = open(f_saxs0, 'w')
np.savetxt(f,
np.c_[FXS.q, Isaxs_ave_0, Isaxs_std_0],
header=head_s,
comments='')
f.close()
f = open(f_c0, 'w')
np.savetxt(f, C2_ave_0, header=head_c, comments='')
f.close()
f = open(f_saxs1, 'w')
np.savetxt(f,
np.c_[FXS.q, Isaxs_ave_1, Isaxs_std_1],
header=head_s,
comments='')
f.close()
f = open(f_c1, 'w')
np.savetxt(f, C2_ave_1, header=head_c, comments='')
f.close()
f_bg_im = os.path.join(
opath,
'Bg_img_' + str(argv.run) + '_' + str(Tot_0 + Tot_1) + '.dat')
f_bg_norm = os.path.join(
opath,
'Bg_norm_' + str(argv.run) + '_' + str(Tot_0 + Tot_1) + '.dat')
f_bg_ms = os.path.join(
opath,
'Bg_msk_' + str(argv.run) + '_' + str(Tot_0 + Tot_1) + '.dat')
stamps_s = ['q', 'Mean', 'Std']
head_s = " ".join(stamps_s)
# Get rid of zero lines at the end
# Last non-zero intensity
nz = np.nonzero(Tot_t)
fend = nz[0][-1] + 1
f = open(f_index, 'w')
np.savetxt(f,
np.c_[Tot_t[:fend], Tot_s[:fend], Tot_ns[:fend],
Tot_fd[:fend], Tot_int[:fend], Tot_peak1[:fend],
Tot_peak2[:fend], Tot_s_m[:fend], Tot_s_s[:fend],
Tot_cx[:fend], Tot_cy[:fend], Tot_size[:fend],
Tot_score[:fend]],
header=head,
comments='')
f.close()
f = open(f_bg_im, 'w')
np.savetxt(f, Bg_img)
f.close()
f = open(f_bg_norm, 'w')
np.savetxt(f, Bg_norm)
f.close()
f = open(f_bg_ms, 'w')
np.savetxt(f, Bg_msk)
f.close()
def compute_bg(argv=None):
"""Function to compute the average background images and mask from FXS images
extracted from xtc (smd,idx,xtc format) or h5 files.
Works for Single CPU, Multi-Processor interactive jobs and MPI batch jobs
For a definition of input arguments argv and batch processing instructions see *** mpi_fxs_launch.py ***
compute_bg produces the following output files:
* Index file : Information about the events processed including time-stamps, beam center, total intensity particle size etc
* I(q) : Average Azimuthal intensities (SAXS)
* Bg_img : Average image in polar coordinates
* Bg_norm : Mean subtracted average image in polar coordinates
* Bg_msk : Average mask image in polar coordinates
* Average : Average image in cartesian coordinates
"""
if argv == None:
argv = sys.argv[1:]
try:
from mpi4py import MPI
except ImportError:
raise Sorry("MPI not found")
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
if argv.hit is None:
hit = -1.0e20 # Process everything
else:
hit = argv.hit # Process everything > hit
ftype = argv.ftype
if argv.param_path is not None:
if ftype == 'h5':
param_file = np.genfromtxt(argv.param_path, skiprows=1, dtype=None)
timestamps, filestamps = pnccd_tbx.get_h5_event(param_file)
elif ftype == 'xtc':
param_file = np.genfromtxt(argv.param_path, skiprows=1, dtype=None)
timestamps = pnccd_tbx.get_time(param_file)
else:
param_file = np.genfromtxt(argv.param_path, skiprows=1)
timestamps = pnccd_tbx.get_psana_event(param_file)
else:
timestamps = None
# The first and last events to processed
first = argv.first
last = argv.last
# Check data format
if ftype == 'h5':
import h5py
run = int(argv.run)
# Get time-stamps from all h5-files
if argv.param_path is None:
timestamps = []
filestamps = []
# Loop over all h5-files and store the time-stamps
for i in os.listdir(argv.xtc_dir):
if i.endswith(".h5"):
f = h5py.File(i, 'r')
filestamps.append(i[-7:-4])
timestamps.append(f.keys())
continue
else:
continue
dataset_name = "%s-r%s" % (argv.experiment, str(argv.run).zfill(4)
) # Ascert 4 digit run number
exprun = os.path.join(argv.xtc_dir, dataset_name)
if argv.first is None:
first = 0
if argv.last is None:
last = len(timestamps)
else:
last = min(last,
len(timestamps)) # Check that last time-stamp exists
timestamps = timestamps[first:last]
filestamps = filestamps[first:last]
evtgen = h5gen
else:
exprun = "exp=%s:run=%d" % (argv.experiment, argv.run)
if (ftype == 'xtc'):
dataset_name = exprun + ':xtc'
elif (ftype == 'idx'):
dataset_name = exprun + ':idx'
elif (ftype == 'idx_ffb'):
dataset_name = exprun + ':idx'
# as ffb is only at SLAC, ok to hardcode /reg/d here
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc" % (
argv.experiment[0:3], argv.experiment)
elif (ftype == 'smd'):
dataset_name = exprun + ':smd'
elif (ftype == 'smd_ffb'):
dataset_name = exprun + ':smd'
# as ffb is only at SLAC, ok to hardcode /reg/d here ADD live!
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc:live" % (
argv.experiment[0:3], argv.experiment)
exprun = dataset_name
ds = DataSource(dataset_name)
run = ds.runs().next()
# Select event generator
if (ftype == 'smd') or (ftype == 'smd_ffb') or (ftype == 'xtc'):
evtgen = smdgen
elif (ftype == 'idx') or (ftype == 'idx_ffb'):
evtgen = idxgen
if size == 1:
plot = argv.plot
else:
plot = 0
FXS = fxs.fluctuation_scattering(
dataset_name=exprun,
detector_address=argv.address,
data_type=argv.ftype,
mask_path=argv.mask_path,
mask_angles=
None, #np.array([88, 270]), # static masking at 88 and 270 deg
mask_widths=None, #np.array([6, 10]), # +/- degrees
backimg_path=argv.bg_img_path,
backmsk_path=argv.bg_msk_path,
geom_path=argv.geom_path,
det_dist=argv.det_distance,
det_pix=argv.det_pixel,
beam_l=argv.lambda_b,
mask_thr=argv.thr,
nQ=argv.nQ,
nPhi=argv.nPhi,
dQ=argv.dQ,
dPhi=argv.dP,
cent0=[argv.x, argv.y],
r_max=argv.r_max,
dr=argv.dr,
dx=argv.dx,
dy=argv.dy,
r_0=argv.r0,
q_bound=argv.q_bound)
# Initialize iterator
FXS.cnt = np.array([0.])
# Initialize Index variables
if argv.param_path is None:
maxevents = 400000 # We don't always know the total nr of events. Therefore set to large value
else:
maxevents = min(len(timestamps), len(timestamps[first:last]))
FXS.get_index(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
if size > 1:
if rank > 0:
hd = pnccd_hit.hit()
# MPI process. Here we set rank 0 to work as a listening server only.
for j, evt in evtgen(run,
timestamps=timestamps,
first=first,
last=last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j % 10 == 0: print 'Rank', rank, 'processing event', j
if ftype == 'h5':
FXS.get_h5(filestamps[j], evt)
else:
FXS.get_image(evt)
# Process hits
if (FXS.img is not None) and (float(FXS.img.sum()) > hit):
FXS.get_beam(plot=plot) # Beam center refinement
FXS.get_polar(plot=plot) # Polar transform
FXS.get_streak_mask(plot=plot) # Mask out streaks
FXS.get_pixel_mask(plot=plot) # Mask out pixels
FXS.get_norm(plot=plot) # Normalize image, get SAXS
if FXS.r_0 is not None:
FXS.get_size()
if ftype == 'h5':
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec << 32) | nsec), fid)
#######################################
FXS.store_index(et, j) # Store index
FXS.sum_bg() # Sum Background
if int(FXS.cnt) % 10 == 0:
print 'Rank', rank, 'processed events: ', int(FXS.cnt)
# Send partial results to master (rank 0)
if (int(FXS.cnt) > 0) and (int(FXS.cnt) % 100
== 0): # Send every 100 events
tmp_n = int(FXS.cnt)
# Average image
tmp_im = FXS.ave / tmp_n
# Total intensity, Size and Score
tmp_ind = np.column_stack(
(FXS.tot_int, FXS.tot_size, FXS.tot_score))
hd.send(tmp_n, image=tmp_im, ind=tmp_ind)
FXS.cnt += 1
hd.endrun()
else:
if ftype == 'h5':
FXS.run_nr = run
else:
FXS.run_nr = int(run.run())
hd = pnccd_hit.hit()
adim = FXS.ave.shape
idim = (maxevents, 3)
hd.total_ave = [np.zeros(adim)] * (size - 1)
hd.total_ind = [np.zeros(idim)] * (size - 1)
hd.total_ev_a = [0.0] * (size - 1)
hd.total_ev_i = [0.0] * (size - 1)
nClients = size - 1
while nClients > 0:
# Remove client if the run ended
if hd.recv():
nClients -= 1
else:
na = sum(hd.total_ev_a)
ni = sum(hd.total_ev_i)
if (na == ni) and (na % 100
== 0): # Publish every 100 events
AVE = np.zeros(adim)
IND = np.zeros(idim)
for i in range(size - 1):
AVE = AVE + (hd.total_ave[i] *
(hd.total_ev_a[i] / na))
IND = IND + hd.total_ind[i]
FXS.publish(image=AVE, ind=IND, n_a=na, n_i=ni)
else:
# Single CPU
for j, evt in evtgen(run,
timestamps=timestamps,
first=first,
last=last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j % 10 == 0: print 'Rank', rank, 'processing event', j
if ftype == 'h5':
FXS.get_h5(filestamps[j], evt)
else:
FXS.get_image(evt)
# Process hits
if (FXS.img is not None) and (float(FXS.img.sum()) > hit):
FXS.get_beam(plot=plot) # Beam center refinement
FXS.get_polar(plot=plot) # Polar transform
FXS.get_streak_mask(plot=plot) # Mask out streaks
FXS.get_pixel_mask(plot=plot) # Mask out pixels
FXS.get_norm(plot=plot) # Normalize image, get SAXS
if FXS.r_0 is not None:
FXS.get_size()
if ftype == 'h5':
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec << 32) | nsec), fid)
#######################################
FXS.store_index(et, j) # Store index
FXS.sum_bg() # Sum Background
FXS.cnt += 1
print 'Rank', rank, 'total events: ', int(FXS.cnt), ' * '
#sum the images across mpi cores
if size > 1:
print "Synchronizing rank", rank
Tot = np.zeros(FXS.cnt.shape)
comm.Reduce(FXS.cnt, Tot)
if rank == 0 and Tot[0] == 0:
raise Sorry("No events found in the run")
# Collect Background variables
if not hasattr(FXS, 'ave'):
FXS.ave = np.zeros(FXS.msk.shape)
if not hasattr(FXS, 'Isaxs'):
FXS.Isaxs = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'Vsaxs'):
FXS.Vsaxs = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'Back_img'):
FXS.Back_img = np.zeros((len(FXS.q), len(FXS.phi)))
if not hasattr(FXS, 'Back_norm'):
FXS.Back_norm = np.zeros((len(FXS.q), len(FXS.phi)))
if not hasattr(FXS, 'Back_msk_0'):
FXS.Back_msk = np.zeros((len(FXS.q), len(FXS.phi)))
AVE_all = np.zeros(FXS.ave.shape)
comm.Reduce(FXS.ave, AVE_all)
BG_img_all = np.zeros(FXS.Back_img.shape)
comm.Reduce(FXS.Back_img, BG_img_all)
BG_msk_all = np.zeros(FXS.Back_msk.shape)
comm.Reduce(FXS.Back_msk, BG_msk_all)
BG_norm_all = np.zeros(FXS.Back_norm.shape)
comm.Reduce(FXS.Back_norm, BG_norm_all)
SAXS_all = np.zeros(FXS.Isaxs.shape)
comm.Reduce(FXS.Isaxs, SAXS_all)
VAR_all = np.zeros(FXS.Vsaxs.shape)
comm.Reduce(FXS.Vsaxs, VAR_all)
# Collect Indexing variables
Tot_t = np.zeros(FXS.tot_t.shape)
comm.Reduce(FXS.tot_t, Tot_t)
Tot_s = np.zeros(FXS.tot_s.shape)
comm.Reduce(FXS.tot_s, Tot_s)
Tot_ns = np.zeros(FXS.tot_ns.shape)
comm.Reduce(FXS.tot_ns, Tot_ns)
Tot_fd = np.zeros(FXS.tot_fd.shape)
comm.Reduce(FXS.tot_fd, Tot_fd)
Tot_int = np.zeros(FXS.tot_int.shape)
comm.Reduce(FXS.tot_int, Tot_int)
Tot_cx = np.zeros(FXS.tot_cx.shape)
comm.Reduce(FXS.tot_cx, Tot_cx)
Tot_cy = np.zeros(FXS.tot_cy.shape)
comm.Reduce(FXS.tot_cy, Tot_cy)
Tot_size = np.zeros(FXS.tot_size.shape)
comm.Reduce(FXS.tot_size, Tot_size)
Tot_score = np.zeros(FXS.tot_score.shape)
comm.Reduce(FXS.tot_score, Tot_score)
# Reduce results
if rank == 0:
if size > 1:
print "Synchronized"
# Write out data
if argv.outputdir is None:
opath = os.getcwd()
else:
opath = argv.outputdir
Tot = int(Tot)
Isaxs_ave = SAXS_all / Tot
Isaxs_std = np.sqrt(VAR_all / Tot)
Ave = AVE_all / Tot
tmp = np.copy(BG_msk_all)
ind = tmp == 0
tmp[ind] = 1.0
Bg_img = BG_img_all / tmp
Bg_norm = BG_norm_all / tmp
Bg_msk = np.ones(tmp.shape)
Bg_msk[ind] = 0.0
f_index = os.path.join(opath, 'Index_run' + str(argv.run) + '.dat')
stamps = [
'Time', 'Seconds', 'Nanoseconds', 'Fiducial', 'Total Intensity',
'Beam X', 'Beam Y', 'Radius [Ang]', 'Score'
]
head = " ".join(stamps)
f_ave = os.path.join(
opath, 'Average_run' + str(argv.run) + '_' + str(Tot) + '.dat')
f_saxs = os.path.join(
opath, 'Saxs_run' + str(argv.run) + '_' + str(Tot) + '.dat')
f_bg_im = os.path.join(
opath, 'Bg_img_' + str(argv.run) + '_' + str(Tot) + '.dat')
f_bg_norm = os.path.join(
opath, 'Bg_norm_' + str(argv.run) + '_' + str(Tot) + '.dat')
f_bg_ms = os.path.join(
opath, 'Bg_msk_' + str(argv.run) + '_' + str(Tot) + '.dat')
stamps_s = ['q', 'Mean', 'Std']
head_s = " ".join(stamps_s)
# Get rid of zero lines add the end
# Last non-zero intensity
nz = np.nonzero(Tot_t)
fend = nz[0][-1] + 1
f = open(f_index, 'w')
np.savetxt(f,
np.c_[Tot_t[:fend], Tot_s[:fend], Tot_ns[:fend],
Tot_fd[:fend], Tot_int[:fend], Tot_cx[:fend],
Tot_cy[:fend], Tot_size[:fend], Tot_score[:fend]],
header=head,
comments='')
f.close()
f = open(f_ave, 'w')
np.savetxt(f, Ave)
f.close()
f = open(f_saxs, 'w')
np.savetxt(f,
np.c_[FXS.q, Isaxs_ave, Isaxs_std],
header=head_s,
comments='')
f.close()
f = open(f_bg_im, 'w')
np.savetxt(f, Bg_img)
f.close()
f = open(f_bg_norm, 'w')
np.savetxt(f, Bg_norm)
f.close()
f = open(f_bg_ms, 'w')
np.savetxt(f, Bg_msk)
f.close()
def compute_c2(argv=None) :
"""Function to compute the average angular intensity function, <C2(q,dPhi)>, from FXS images
extracted from xtc (smd,idx,xtc format) or h5 files.
Works for Single CPU, Multi-Processor interactive jobs and MPI batch jobs
For a definition of input arguments argv and batch processing instructions see *** mpi_fxs_launch.py ***
compute_c2 produces the following output files:
* Index file : Information about the events processed including time-stamps, beam center, total and peak intensities, streak locations, particle size etc
* I(q) : Average Azimuthal intensities (SAXS)
* C2(q,dPhi) : Average 2-point correlation functions (FXS)
* Bg_img : Average image in polar coordinates
* Bg_norm : Mean subtracted average image in polar coordinates
* Bg_msk : Average mask image in polar coordinates
The I and C2 output data are split in 2 ~equal bins [0 and 1] for statistical comparison to be made
example:
C2_run111_0_29793.dat is the average C2(q,dPhi) from run111 bin 0 where 29,793 images have been processed
C2_run111_1_29718.dat is the average C2(q,dPhi) from run111 bin 1 where 29,718 images have been processed
"""
if argv == None:
argv = sys.argv[1:]
try:
from mpi4py import MPI
except ImportError:
raise Sorry("MPI not found")
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
if argv.hit is None :
hit = -1.0e20 # Process everything
else:
hit = argv.hit # Process everything > hit
ftype = argv.ftype
if argv.param_path is not None :
if ftype == 'h5' :
param_file = np.genfromtxt(argv.param_path,skiprows=1,dtype=None)
timestamps,filestamps = pnccd_tbx.get_h5_event(param_file)
elif ftype == 'xtc' :
param_file = np.genfromtxt(argv.param_path,skiprows=1,dtype=None)
timestamps = pnccd_tbx.get_time(param_file)
else :
param_file = np.genfromtxt(argv.param_path,skiprows=1)
timestamps = pnccd_tbx.get_psana_event(param_file)
else:
timestamps = None
# The first and last events to processed
first = argv.first
last = argv.last
# Check data format
if ftype == 'h5' :
import h5py
run = int(argv.run)
# Get time-stamps from all h5-files
if argv.param_path is None :
timestamps = []
filestamps = []
# Loop over all h5-files and store the time-stamps
for i in os.listdir(argv.xtc_dir):
if i.endswith(".h5"):
f = h5py.File(i,'r')
filestamps.append(i[-7:-4])
timestamps.append(f.keys())
continue
else:
continue
dataset_name = "%s-r%s"%(argv.experiment, str(argv.run).zfill(4)) # Ascert 4 digit run number
exprun = os.path.join(argv.xtc_dir,dataset_name)
if argv.first is None :
first = 0
if argv.last is None :
last = len(timestamps)
else:
last = min(last,len(timestamps)) # Check that last time-stamp exists
timestamps = timestamps[first:last]
filestamps = filestamps[first:last]
evtgen = h5gen
else :
exprun = "exp=%s:run=%d"%(argv.experiment, argv.run)
if (ftype == 'xtc') :
dataset_name = exprun+':xtc'
elif (ftype == 'idx') :
dataset_name = exprun+':idx'
elif(ftype == 'idx_ffb') :
dataset_name = exprun+':idx'
# as ffb is only at SLAC, ok to hardcode /reg/d here
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc"%(argv.experiment[0:3],argv.experiment)
elif(ftype == 'smd') :
dataset_name = exprun+':smd'
elif(ftype == 'smd_ffb') :
dataset_name = exprun+':smd'
# as ffb is only at SLAC, ok to hardcode /reg/d here ADD live!
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc:live"%(argv.experiment[0:3],argv.experiment)
exprun = dataset_name
ds = DataSource(dataset_name)
run = ds.runs().next()
# Select event generator
if (ftype=='smd') or (ftype == 'smd_ffb') or (ftype == 'xtc'):
evtgen = smdgen
elif (ftype=='idx') or (ftype == 'idx_ffb'):
evtgen = idxgen
if size == 1:
plot = argv.plot
else:
plot = 0
FXS = fxs.fluctuation_scattering(dataset_name = exprun,
detector_address = argv.address,
data_type = argv.ftype,
mask_path = argv.mask_path,
mask_angles = None,#np.array([88, 270]), # static masking at 88 and 270 deg
mask_widths = None,#np.array([6, 10]), # +/- degrees
backimg_path = argv.bg_img_path,
backmsk_path = argv.bg_msk_path,
geom_path = argv.geom_path,
det_dist = argv.det_distance,
det_pix = argv.det_pixel,
beam_l = argv.lambda_b,
mask_thr = argv.thr,
nQ = argv.nQ,
nPhi = argv.nPhi,
dQ = argv.dQ,
dPhi = argv.dP,
cent0 = [argv.x,argv.y],
r_max = argv.r_max,
dr = argv.dr,
dx = argv.dx,
dy = argv.dy,
r_0 = argv.r0,
q_bound = argv.q_bound,
peak = [0.037, 0.064], # Location in q for peaks to be integrated
dpeak = [0.002, 0.002]) # Width in q for peaks to be integrated
# Initialize iterator
FXS.cnt_0 = np.array([0.])
FXS.cnt_1 = np.array([0.])
# Initialize Index variables
if argv.param_path is None :
maxevents = 400000 # We don't always know the total nr of events. Therefore set to large value
else:
maxevents = min(len(timestamps),len(timestamps[first:last]))
FXS.get_index(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
if size > 1 :
if rank > 0 :
hd=pnccd_hit.hit()
# MPI process. Here we set rank 0 to work as a listening server only.
for j,evt in evtgen(run,timestamps = timestamps, first = first, last = last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j%10==0: print 'Rank',rank,'processing event',j
if ftype == 'h5' :
FXS.get_h5(filestamps[j],evt)
else :
FXS.get_image(evt)
# Process hits
if (FXS.img is not None) and (float(FXS.img.sum()) > hit) :
FXS.get_beam(plot = plot) # Beam center refinement
FXS.get_polar(plot = plot) # Polar transform
FXS.get_streak_mask(plot = plot) # Mask out streaks
FXS.get_pixel_mask(plot = plot) # Mask out pixels
FXS.get_norm(plot = plot) # Normalize image, get SAXS
FXS.get_c2(plot = plot) # Compute C2
# Split upp into 2 half sets
if int(FXS.cnt_0 + FXS.cnt_1) % 2 == 0:
FXS.sum_c2(flag = 0) # Accumulate C2 for Half I
else:
FXS.sum_c2(flag = 1) # Accumulate C2 for Half II
if FXS.r_0 is not None :
FXS.get_size()
FXS.sum_bg2() # Sum Background
if ftype == 'h5' :
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec<<32)|nsec),fid)
#######################################
FXS.store_index(et, j) # Store index
if int(FXS.cnt_0 + FXS.cnt_1)%10==0: print 'Rank',rank,'processed events: ', int(FXS.cnt_0 + FXS.cnt_1)
# Send partial results to master (rank 0)
if int(FXS.cnt_0 + FXS.cnt_1) % 50 == 0: # Send every 50 events
# C2 and Saxs data
tmp_n = int(FXS.cnt_0 + FXS.cnt_1)
tmp_saxs = (FXS.Isaxs_0 + FXS.Isaxs_1) / tmp_n
tmp_c2 = (FXS.C2_0 + FXS.C2_1) / (FXS.C2m_0 + FXS.C2m_1)
# Average image
tmp_im = FXS.ave / tmp_n
# Ascert no nan values
tmp_saxs[np.isnan(tmp_saxs)] = 0
tmp_c2[np.isnan(tmp_c2)] = 0
# Total intensity, Size and Score
tmp_ind = np.column_stack((FXS.tot_int,FXS.tot_size,FXS.tot_score))
hd.send(tmp_n,image = tmp_im, saxs=tmp_saxs,c2=tmp_c2,ind=tmp_ind)
hd.endrun()
print 'Rank',rank,'total events: ', int(FXS.cnt_0 + FXS.cnt_1),' * '
else:
if ftype == 'h5' :
FXS.run_nr = run
else:
FXS.run_nr = int(run.run())
hd = pnccd_hit.hit()
adim = FXS.ave.shape
sdim = len(FXS.q)
cdim = (len(FXS.q),len(FXS.phi))
idim = (maxevents,3)
hd.total_ave = [np.zeros(adim)]*(size-1)
hd.total_c2 = [np.zeros(cdim)]*(size-1)
hd.total_ind = [np.zeros(idim)]*(size-1)
hd.total_saxs = [np.zeros(sdim)]*(size-1)
hd.total_ev_a = [0.0]*(size-1)
hd.total_ev_c = [0.0]*(size-1)
hd.total_ev_s = [0.0]*(size-1)
hd.total_ev_i = [0.0]*(size-1)
nClients = size - 1
while nClients > 0:
# Remove client if the run ended
if hd.recv():
nClients -= 1
else:
na = sum(hd.total_ev_a)
ns = sum(hd.total_ev_s)
nc = sum(hd.total_ev_c)
ni = sum(hd.total_ev_i)
if (na == ns == nc == ni) and (ns % 100 == 0) : # Publish every 100 events
AVE = np.zeros(adim)
C2ave = np.zeros(cdim)
SAXSave = np.zeros(sdim)
IND = np.zeros(idim)
for i in range(size-1) :
AVE = AVE + (hd.total_ave[i] * (hd.total_ev_a[i] /na))
C2ave = C2ave + (hd.total_c2[i] * (hd.total_ev_c[i] /nc))
SAXSave = SAXSave + (hd.total_saxs[i] * (hd.total_ev_s[i] /ns))
IND = IND + hd.total_ind[i]
FXS.publish(image = AVE, saxs=SAXSave, c2=C2ave, ind=IND, n_a=na, n_saxs=ns, n_c2=nc, n_i=ni)
else :
# Single CPU
for j,evt in evtgen(run,timestamps = timestamps, first = first, last = last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j%10==0: print 'Rank',rank,'processing event',j
if ftype == 'h5' :
FXS.get_h5(filestamps[j],evt)
else :
FXS.get_image(evt)
# Process hits
if (FXS.img is not None) and (float(FXS.img.sum()) > hit) :
FXS.get_beam(plot=plot) # Beam center refinement
FXS.get_polar() # Polar transform
FXS.get_streak_mask(plot=plot) # Mask out streaks
FXS.get_pixel_mask(plot=plot) # Mask out pixels
FXS.get_norm(plot=plot) # Normalize image, get SAXS
FXS.get_c2(plot=plot) # Compute C2
# Split upp into 2 half sets
if int(FXS.cnt_0 + FXS.cnt_1) % 2 == 0:
FXS.sum_c2(flag = 0) # Accumulate C2 for Half I
else:
FXS.sum_c2(flag = 1) # Accumulate C2 for Half II
if FXS.r_0 is not None :
FXS.get_size()
FXS.sum_bg2() # Sum Background
if ftype == 'h5' :
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec<<32)|nsec),fid)
#######################################
FXS.store_index(et, j) # Store index
print 'Rank',rank,'total events: ', int(FXS.cnt_0 + FXS.cnt_1),' * '
#sum the images across mpi cores
if size > 1:
print "Synchronizing rank", rank
Tot_0 = np.zeros(FXS.cnt_0.shape)
Tot_1 = np.zeros(FXS.cnt_1.shape)
comm.Reduce(FXS.cnt_0,Tot_0)
comm.Reduce(FXS.cnt_1,Tot_1)
if rank == 0 and Tot_0[0] == 0 and Tot_1[0]:
raise Sorry("No events found in the run")
if not hasattr(FXS, 'Isaxs_0'):
FXS.Isaxs_0 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'Vsaxs_0'):
FXS.Vsaxs_0 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'C2_0'):
FXS.C2_0 = np.zeros((len(FXS.q),len(FXS.phi)))
if not hasattr(FXS, 'C2m_0'):
FXS.C2m_0 = np.zeros((len(FXS.q),len(FXS.phi)))
if not hasattr(FXS, 'Isaxs_1'):
FXS.Isaxs_1 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'Vsaxs_1'):
FXS.Vsaxs_1 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'C2_1'):
FXS.C2_1 = np.zeros((len(FXS.q),len(FXS.phi)))
if not hasattr(FXS, 'C2m_1'):
FXS.C2m_1 = np.zeros((len(FXS.q),len(FXS.phi)))
if not hasattr(FXS, 'Back_img'):
FXS.Back_img = np.zeros((len(FXS.q),len(FXS.phi)))
if not hasattr(FXS, 'Back_norm'):
FXS.Back_norm = np.zeros((len(FXS.q),len(FXS.phi)))
if not hasattr(FXS, 'Back_msk'):
FXS.Back_msk = np.zeros((len(FXS.q),len(FXS.phi)))
# Collect Variables
SAXS_0_all = np.zeros(FXS.Isaxs_0.shape)
comm.Reduce(FXS.Isaxs_0,SAXS_0_all)
VAR_0_all = np.zeros(FXS.Vsaxs_0.shape)
comm.Reduce(FXS.Vsaxs_0,VAR_0_all)
C2_0_all = np.zeros(FXS.C2_0.shape)
comm.Reduce(FXS.C2_0,C2_0_all)
C2m_0_all = np.zeros(FXS.C2m_0.shape)
comm.Reduce(FXS.C2m_0,C2m_0_all)
SAXS_1_all = np.zeros(FXS.Isaxs_1.shape)
comm.Reduce(FXS.Isaxs_1,SAXS_1_all)
VAR_1_all = np.zeros(FXS.Vsaxs_1.shape)
comm.Reduce(FXS.Vsaxs_1,VAR_1_all)
C2_1_all = np.zeros(FXS.C2_1.shape)
comm.Reduce(FXS.C2_1,C2_1_all)
C2m_1_all = np.zeros(FXS.C2m_1.shape)
comm.Reduce(FXS.C2m_1,C2m_1_all)
# Collect Indexing variables
Tot_t = np.zeros(FXS.tot_t.shape)
comm.Reduce(FXS.tot_t,Tot_t)
Tot_s = np.zeros(FXS.tot_s.shape)
comm.Reduce(FXS.tot_s,Tot_s)
Tot_ns = np.zeros(FXS.tot_ns.shape)
comm.Reduce(FXS.tot_ns,Tot_ns)
Tot_fd = np.zeros(FXS.tot_fd.shape)
comm.Reduce(FXS.tot_fd,Tot_fd)
Tot_int = np.zeros(FXS.tot_int.shape)
comm.Reduce(FXS.tot_int,Tot_int)
Tot_peak1 = np.zeros(FXS.tot_peak1_int.shape)
comm.Reduce(FXS.tot_peak1_int,Tot_peak1)
Tot_peak2 = np.zeros(FXS.tot_peak2_int.shape)
comm.Reduce(FXS.tot_peak2_int,Tot_peak2)
Tot_s_m = np.zeros(FXS.tot_streak_m.shape)
comm.Reduce(FXS.tot_streak_m,Tot_s_m)
Tot_s_s = np.zeros(FXS.tot_streak_s.shape)
comm.Reduce(FXS.tot_streak_s,Tot_s_s)
Tot_cx = np.zeros(FXS.tot_cx.shape)
comm.Reduce(FXS.tot_cx,Tot_cx)
Tot_cy = np.zeros(FXS.tot_cy.shape)
comm.Reduce(FXS.tot_cy,Tot_cy)
Tot_size = np.zeros(FXS.tot_size.shape)
comm.Reduce(FXS.tot_size,Tot_size)
Tot_score = np.zeros(FXS.tot_score.shape)
comm.Reduce(FXS.tot_score,Tot_score)
# Collect background variables
BG_img_all = np.zeros(FXS.Back_img.shape)
comm.Reduce(FXS.Back_img,BG_img_all)
BG_norm_all = np.zeros(FXS.Back_norm.shape)
comm.Reduce(FXS.Back_norm,BG_norm_all)
BG_msk_all = np.zeros(FXS.Back_msk.shape)
comm.Reduce(FXS.Back_msk,BG_msk_all)
# Reduce results
if rank==0:
if size > 1:
print "Synchronized"
# Write out data
if argv.outputdir is None:
opath = os.getcwd()
else:
opath = argv.outputdir
f_saxs0 = os.path.join(opath,'Saxs_run' + str(argv.run) + '_0_'+ str(int(Tot_0)) + '.dat')
f_saxs1 = os.path.join(opath,'Saxs_run' + str(argv.run) + '_1_'+ str(int(Tot_1)) + '.dat')
stamps_s = ['q','Mean','Std']
head_s =" ".join(stamps_s)
f_c0 = os.path.join(opath,'C2_run' + str(argv.run) + '_0_'+ str(int(Tot_0)) + '.dat')
f_c1 = os.path.join(opath,'C2_run' + str(argv.run) + '_1_'+ str(int(Tot_1)) + '.dat')
stamps_c = ['C2']
head_c =" ".join(stamps_c)
f_index = os.path.join(opath,'Index_run' + str(argv.run) + '.dat')
stamps = ['Time','Seconds','Nanoseconds','Fiducial','Total Intensity','Peak1, q='+str(FXS.peak[0])+'+/-'+str(FXS.dpeak[0]),'Peak2, q='+str(FXS.peak[1])+'+/-'+str(FXS.dpeak[1]),'Mean streak angle','Std streak angle','Beam X','Beam Y','Radius [Ang]','Score']
head =" ".join(stamps)
Tot_0 = int(Tot_0)
Isaxs_ave_0 = SAXS_0_all / Tot_0
Isaxs_std_0 = np.sqrt( VAR_0_all / Tot_0 )
C2_ave_0 = ( C2_0_all / Tot_0 ) / ( C2m_0_all / Tot_0 )
Tot_1 = int(Tot_1)
Isaxs_ave_1 = SAXS_1_all / Tot_1
Isaxs_std_1 = np.sqrt( VAR_1_all / Tot_1 )
C2_ave_1 = ( C2_1_all / Tot_1 ) / ( C2m_1_all / Tot_1 )
# Prepare background
tmp2 = np.copy(BG_msk_all)
ind = tmp2 == 0
tmp2[ind] = 1.0
Bg_img = BG_img_all / tmp2
Bg_norm = BG_norm_all / tmp2
Bg_msk = np.ones(tmp2.shape)
Bg_msk[ind] = 0.0
f = open(f_saxs0,'w')
np.savetxt(f,np.c_[FXS.q,Isaxs_ave_0,Isaxs_std_0],header = head_s, comments='')
f.close()
f = open(f_c0,'w')
np.savetxt(f,C2_ave_0,header = head_c, comments='')
f.close()
f = open(f_saxs1,'w')
np.savetxt(f,np.c_[FXS.q,Isaxs_ave_1,Isaxs_std_1],header = head_s, comments='')
f.close()
f = open(f_c1,'w')
np.savetxt(f,C2_ave_1,header = head_c, comments='')
f.close()
f_bg_im = os.path.join(opath,'Bg_img_' + str(argv.run) + '_'+ str(Tot_0+Tot_1) + '.dat')
f_bg_norm = os.path.join(opath,'Bg_norm_' + str(argv.run) + '_'+ str(Tot_0+Tot_1) + '.dat')
f_bg_ms = os.path.join(opath,'Bg_msk_' + str(argv.run) + '_'+ str(Tot_0+Tot_1) + '.dat')
stamps_s = ['q','Mean','Std']
head_s =" ".join(stamps_s)
# Get rid of zero lines at the end
# Last non-zero intensity
nz = np.nonzero(Tot_t)
fend = nz[0][-1]+1
f = open(f_index,'w')
np.savetxt(f,np.c_[Tot_t[:fend],Tot_s[:fend],Tot_ns[:fend],Tot_fd[:fend],Tot_int[:fend],Tot_peak1[:fend],Tot_peak2[:fend],Tot_s_m[:fend],Tot_s_s[:fend],Tot_cx[:fend],Tot_cy[:fend],Tot_size[:fend],Tot_score[:fend]],header = head, comments='' )
f.close()
f = open(f_bg_im,'w')
np.savetxt(f,Bg_img)
f.close()
f = open(f_bg_norm,'w')
np.savetxt(f,Bg_norm)
f.close()
f = open(f_bg_ms,'w')
np.savetxt(f,Bg_msk)
f.close()
def compute_c2(argv=None) :
if argv == None:
argv = sys.argv[1:]
try:
from mpi4py import MPI
except ImportError:
raise Sorry("MPI not found")
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
if argv.hit is None :
hit = -1.0e6 # Process everything
else:
hit = argv.hit # Process everything > hit
if argv.param_path is not None :
param_file = np.genfromtxt(argv.param_path,skiprows=1)
if (argv.ftype == 'xtc') or (argv.ftype == 'ffb') or (argv.ftype == 'smd') :
if (argv.ftype == 'xtc') :
dataset_name = "exp=%s:run=%d:idx"%(argv.experiment, argv.run)
elif(argv.ftype == 'ffb') :
dataset_name = "exp=%s:run=%d:idx"%(argv.experiment, argv.run)
# as ffb is only at SLAC, ok to hardcode /reg/d here
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc"%(argv.experiment[0:3],argv.experiment)
elif(argv.ftype == 'smd') :
dataset_name = "exp=%s:run=%d:smd"%(argv.experiment, argv.run)
# as ffb is only at SLAC, ok to hardcode /reg/d here ADD live!
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc:live"%(argv.experiment[0:3],argv.experiment)
ds = DataSource(dataset_name)
# Get run
for run in ds.runs():
if rank == 0:
print "Processing run ", run.run()
# Get timestamps
if argv.param_path is not None :
timestamps = pnccd_tbx.get_psana_event(param_file)
else:
timestamps = run.times()
elif argv.ftype == 'h5' :
import h5py
dataset_name = "%s_run_%d.h5"%(argv.experiment, argv.run)
dataset_name = os.path.join(argv.xtc_dir,dataset_name)
run = int(argv.run)
f = h5py.File(dataset_name,'r')
timestamps = f.keys()
if rank == 0:
print "Processing run ", argv.run
if argv.first is None :
first = 0
else:
first = argv.first
if argv.last is None :
last = len(timestamps)
else:
last = min(argv.last,len(timestamps))
times = timestamps[first:last]
nevents = len(times)
if rank == 0 :
print "Processing events " +str(first)+ " to " +str(last)
if size == 1:
plot = argv.plot
else:
plot = 0
FXS = fxs.fluctuation_scattering(dataset_name = dataset_name,
detector_address = argv.address,
data_type = argv.ftype,
mask_path = argv.mask_path,
mask_angles = None,#np.array([90, 270]) # static masking at 90 and 270
mask_widths = None,#np.array([10, 10]) # +/- degrees
backimg_path = argv.bg_img_path,
backmsk_path = argv.bg_msk_path,
param_path = argv.param_path,
det_dist = argv.det_distance,
det_pix = argv.det_pixel,
beam_l = argv.lambda_b,
mask_thr = argv.thr,
nQ = argv.nQ,
nPhi = argv.nPhi,
dQ = argv.dQ,
dPhi = argv.dP,
cent0 = [argv.x,argv.y],
r_max = argv.r_max,
dr = argv.dr,
dx = argv.dx,
dy = argv.dy,
r_0 = argv.r0,
q_bound = argv.q_bound)
# Initialize iterator
FXS.cnt_0 = np.array([0.])
FXS.cnt_1 = np.array([0.])
# Initialize Index variables
FXS.get_index(nevents)
# 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
if size > 1 :
if rank > 0 :
hd=pnccd_hit.hit()
# MPI process. Here we set rank 0 to work as a listening server only.
mytimes,myevents = zip(*[(times[i],i) for i in xrange(nevents) if (i+rank)%(size-1) == 0])
for j in xrange(len(mytimes)):
if j%10==0: print 'Rank',rank,'processing event',rank*len(mytimes)+j,', ',j,'of',len(mytimes)
FXS.get_image(run,mytimes[j])
# Process hits
if float(FXS.img.sum()) >= hit :
FXS.get_beam(plot = plot) # Beam center refinement
FXS.get_polar(plot = plot) # Polar transform
FXS.get_streak_mask(plot = plot) # Mask out streaks
FXS.get_pixel_mask(plot = plot) # Mask out pixels
FXS.get_norm(plot = plot) # Normalize image, get SAXS
FXS.get_c2(plot = plot) # Compute C2
# Split upp into 2 half sets
if int(FXS.cnt_0 + FXS.cnt_1) % 2 == 0:
FXS.sum_c2(flag = 0) # Accumulate C2 for Half I
else:
FXS.sum_c2(flag = 1) # Accumulate C2 for Half II
if FXS.r_0 is not None :
FXS.get_size()
FXS.store_index(mytimes[j], myevents[j])
# Send partial results to master (rank 0)
if int(FXS.cnt_0 + FXS.cnt_1) % 100 == 0: # Send every 100 events
# C2 and Saxs data
tmp_n = int(FXS.cnt_0 + FXS.cnt_1)
tmp_saxs = (FXS.Isaxs_0 + FXS.Isaxs_1) / tmp_n
tmp_c2 = (FXS.C2_0 + FXS.C2_1) / (FXS.C2m_0 + FXS.C2m_1)
# Average image
tmp_im = FXS.ave / tmp_n
# Ascert no nan values
tmp_saxs[np.isnan(tmp_saxs)] = 0
tmp_c2[np.isnan(tmp_c2)] = 0
# Total intensity, Size and Score
tmp_ind = np.column_stack((FXS.tot_int,FXS.tot_size,FXS.tot_score))
hd.send(tmp_n,image = tmp_im, saxs=tmp_saxs,c2=tmp_c2,ind=tmp_ind)
hd.endrun()
else:
FXS.run_nr = int(run.run())
hd = pnccd_hit.hit()
adim = FXS.ave.shape
sdim = len(FXS.q)
cdim = (len(FXS.q),len(FXS.phi))
idim = (nevents,3)
hd.total_ave = [np.zeros(adim)]*(size-1)
hd.total_c2 = [np.zeros(cdim)]*(size-1)
hd.total_ind = [np.zeros(idim)]*(size-1)
hd.total_saxs = [np.zeros(sdim)]*(size-1)
hd.total_ev_a = [0.0]*(size-1)
hd.total_ev_c = [0.0]*(size-1)
hd.total_ev_s = [0.0]*(size-1)
hd.total_ev_i = [0.0]*(size-1)
nClients = size - 1
while nClients > 0:
# Remove client if the run ended
if hd.recv():
nClients -= 1
else:
na = sum(hd.total_ev_a)
ns = sum(hd.total_ev_s)
nc = sum(hd.total_ev_c)
ni = sum(hd.total_ev_i)
if (na == ns == nc == ni) and (ns % 100 == 0) : # Publish every 100 events
AVE = np.zeros(adim)
C2ave = np.zeros(cdim)
SAXSave = np.zeros(sdim)
IND = np.zeros(idim)
for i in range(size-1) :
AVE = AVE + (hd.total_ave[i] * (hd.total_ev_a[i] /na))
C2ave = C2ave + (hd.total_c2[i] * (hd.total_ev_c[i] /nc))
SAXSave = SAXSave + (hd.total_saxs[i] * (hd.total_ev_s[i] /ns))
IND = IND + hd.total_ind[i]
FXS.publish(image = AVE, saxs=SAXSave, c2=C2ave, ind=IND, n_a=na, n_saxs=ns, n_c2=nc, n_i=ni)
else :
# Single CPU
mytimes,myevents = zip(*[(times[i],i) for i in xrange(nevents) if (i+rank)%size == 0])
for j in xrange(len(mytimes)):
if j%10==0: print 'Rank',rank,'processing event',rank*len(mytimes)+j,', ',j,'of',len(mytimes)
FXS.get_image(run,mytimes[j])
# Process hits
if float(FXS.img.sum()) >= hit :
FXS.get_beam(plot = plot) # Beam center refinement
FXS.get_polar(plot = plot) # Polar transform
FXS.get_streak_mask(plot = plot) # Mask out streaks
FXS.get_pixel_mask(plot = plot) # Mask out pixels
FXS.get_norm(plot = plot) # Normalize image, get SAXS
FXS.get_c2(plot = plot) # Compute C2
# Split upp into 2 half sets
if int(FXS.cnt_0 + FXS.cnt_1) % 2 == 0:
FXS.sum_c2(flag = 0) # Accumulate C2 for Half I
else:
FXS.sum_c2(flag = 1) # Accumulate C2 for Half II
if FXS.r_0 is not None :
FXS.get_size()
FXS.store_index(mytimes[j], myevents[j])
#sum the images across mpi cores
if size > 1:
print "Synchronizing rank", rank
Tot_0 = np.zeros(FXS.cnt_0.shape)
Tot_1 = np.zeros(FXS.cnt_1.shape)
comm.Reduce(FXS.cnt_0,Tot_0)
comm.Reduce(FXS.cnt_1,Tot_1)
if rank == 0 and Tot_0[0] == 0 and Tot_1[0]:
raise Sorry("No events found in the run")
if not hasattr(FXS, 'Isaxs_0'):
FXS.Isaxs_0 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'Vsaxs_0'):
FXS.Vsaxs_0 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'C2_0'):
FXS.C2_0 = np.zeros((len(FXS.q),len(FXS.phi)))
if not hasattr(FXS, 'C2m_0'):
FXS.C2m_0 = np.zeros((len(FXS.q),len(FXS.phi)))
if not hasattr(FXS, 'Isaxs_1'):
FXS.Isaxs_1 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'Vsaxs_1'):
FXS.Vsaxs_1 = np.zeros(FXS.q.shape)
if not hasattr(FXS, 'C2_1'):
FXS.C2_1 = np.zeros((len(FXS.q),len(FXS.phi)))
if not hasattr(FXS, 'C2m_1'):
FXS.C2m_1 = np.zeros((len(FXS.q),len(FXS.phi)))
# Collect Variables
SAXS_0_all = np.zeros(FXS.Isaxs_0.shape)
comm.Reduce(FXS.Isaxs_0,SAXS_0_all)
VAR_0_all = np.zeros(FXS.Vsaxs_0.shape)
comm.Reduce(FXS.Vsaxs_0,VAR_0_all)
C2_0_all = np.zeros(FXS.C2_0.shape)
comm.Reduce(FXS.C2_0,C2_0_all)
C2m_0_all = np.zeros(FXS.C2m_0.shape)
comm.Reduce(FXS.C2m_0,C2m_0_all)
SAXS_1_all = np.zeros(FXS.Isaxs_1.shape)
comm.Reduce(FXS.Isaxs_1,SAXS_1_all)
VAR_1_all = np.zeros(FXS.Vsaxs_1.shape)
comm.Reduce(FXS.Vsaxs_1,VAR_1_all)
C2_1_all = np.zeros(FXS.C2_1.shape)
comm.Reduce(FXS.C2_1,C2_1_all)
C2m_1_all = np.zeros(FXS.C2m_1.shape)
comm.Reduce(FXS.C2m_1,C2m_1_all)
# Collect Indexing variables
Tot_t = np.zeros(FXS.tot_t.shape)
comm.Reduce(FXS.tot_t,Tot_t)
Tot_s = np.zeros(FXS.tot_s.shape)
comm.Reduce(FXS.tot_s,Tot_s)
Tot_ns = np.zeros(FXS.tot_ns.shape)
comm.Reduce(FXS.tot_ns,Tot_ns)
Tot_fd = np.zeros(FXS.tot_fd.shape)
comm.Reduce(FXS.tot_fd,Tot_fd)
Tot_int = np.zeros(FXS.tot_int.shape)
comm.Reduce(FXS.tot_int,Tot_int)
Tot_cx = np.zeros(FXS.tot_cx.shape)
comm.Reduce(FXS.tot_cx,Tot_cx)
Tot_cy = np.zeros(FXS.tot_cy.shape)
comm.Reduce(FXS.tot_cy,Tot_cy)
Tot_size = np.zeros(FXS.tot_size.shape)
comm.Reduce(FXS.tot_size,Tot_size)
Tot_score = np.zeros(FXS.tot_score.shape)
comm.Reduce(FXS.tot_score,Tot_score)
# Reduce results
if rank==0:
if size > 1:
print "Synchronized"
# Write out data
if argv.outputdir is None:
opath = os.getcwd()
else:
opath = argv.outputdir
f_saxs0 = os.path.join(opath,'Saxs_run' + str(argv.run) + '_0_'+ str(int(Tot_0)) + '.dat')
f_saxs1 = os.path.join(opath,'Saxs_run' + str(argv.run) + '_1_'+ str(int(Tot_1)) + '.dat')
stamps_s = ['q','Mean','Std']
head_s =" ".join(stamps_s)
f_c0 = os.path.join(opath,'C2_run' + str(argv.run) + '_0_'+ str(int(Tot_0)) + '.dat')
f_c1 = os.path.join(opath,'C2_run' + str(argv.run) + '_1_'+ str(int(Tot_1)) + '.dat')
stamps_c = ['C2']
head_c =" ".join(stamps_c)
f_index = os.path.join(opath,'Index_run' + str(argv.run) + '.dat')
stamps = ['Time','Seconds','Nanoseconds','Fiducial','Total Intensity','Beam X','Beam Y','Radius [Ang]','Score']
head =" ".join(stamps)
Tot_0 = int(Tot_0)
Isaxs_ave_0 = SAXS_0_all / Tot_0
Isaxs_std_0 = np.sqrt( VAR_0_all / Tot_0 )
C2_ave_0 = ( C2_0_all / Tot_0 ) / ( C2m_0_all / Tot_0 )
f = open(f_saxs0,'w')
np.savetxt(f,np.c_[FXS.q,Isaxs_ave_0,Isaxs_std_0],header = head_s, comments='')
f.close()
f = open(f_c0,'w')
np.savetxt(f,C2_ave_0,header = head_c, comments='')
f.close()
Tot_1 = int(Tot_1)
Isaxs_ave_1 = SAXS_1_all / Tot_1
Isaxs_std_1 = np.sqrt( VAR_1_all / Tot_1 )
C2_ave_1 = ( C2_1_all / Tot_1 ) / ( C2m_1_all / Tot_1 )
f = open(f_saxs1,'w')
np.savetxt(f,np.c_[FXS.q,Isaxs_ave_1,Isaxs_std_1],header = head_s, comments='')
f.close()
f = open(f_c1,'w')
np.savetxt(f,C2_ave_1,header = head_c, comments='')
f.close()
f = open(f_index,'w')
np.savetxt(f,np.c_[Tot_t,Tot_s,Tot_ns,Tot_fd,Tot_int,Tot_cx,Tot_cy,Tot_size,Tot_score],header = head, comments='' )
f.close()
def compute_calib(argv=None) :
"""Function to compute the average image without applied geometry from FXS images
extracted from xtc (smd,idx,xtc format) or h5 files.
Works for Single CPU, Multi-Processor interactive jobs and MPI batch jobs
For a definition of input arguments argv and batch processing instructions see *** mpi_fxs_launch.py ***
compute_calib produces the following output files:
* Index file : Information about the events processed including time-stamps, beam center, total intensity, particle size etc
* Ave : Average image, as 1D array, in cartesian coordinates without geometry applied
"""
if argv == None:
argv = sys.argv[1:]
try:
from mpi4py import MPI
except ImportError:
raise Sorry("MPI not found")
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
if argv.hit is None :
hit = -1.0e20 # Process everything
else:
hit = argv.hit # Process everything > hit
ftype = argv.ftype
if argv.param_path is not None :
if ftype == 'h5' :
param_file = np.genfromtxt(argv.param_path,skiprows=1,dtype=None)
timestamps,filestamps = pnccd_tbx.get_h5_event(param_file)
elif ftype == 'xtc' :
param_file = np.genfromtxt(argv.param_path,skiprows=1,dtype=None)
timestamps = pnccd_tbx.get_time(param_file)
else :
param_file = np.genfromtxt(argv.param_path,skiprows=1)
timestamps = pnccd_tbx.get_psana_event(param_file)
else:
timestamps = None
# The first and last events to processed
first = argv.first
last = argv.last
# Check data format
if ftype == 'h5' :
import h5py
run = int(argv.run)
# Get time-stamps from all h5-files
if argv.param_path is None :
timestamps = []
filestamps = []
# Loop over all h5-files and store the time-stamps
for i in os.listdir(argv.xtc_dir):
if i.endswith(".h5"):
f = h5py.File(i,'r')
filestamps.append(i[-7:-4])
timestamps.append(f.keys())
continue
else:
continue
dataset_name = "%s-r%s"%(argv.experiment, str(argv.run).zfill(4)) # Ascert 4 digit run number
exprun = os.path.join(argv.xtc_dir,dataset_name)
if argv.first is None :
first = 0
if argv.last is None :
last = len(timestamps)
else:
last = min(last,len(timestamps)) # Check that last time-stamp exists
timestamps = timestamps[first:last]
filestamps = filestamps[first:last]
evtgen = h5gen
else :
exprun = "exp=%s:run=%d"%(argv.experiment, argv.run)
if (ftype == 'xtc') :
dataset_name = exprun+':xtc'
elif (ftype == 'idx') :
dataset_name = exprun+':idx'
elif(ftype == 'idx_ffb') :
dataset_name = exprun+':idx'
# as ffb is only at SLAC, ok to hardcode /reg/d here
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc"%(argv.experiment[0:3],argv.experiment)
elif(ftype == 'smd') :
dataset_name = exprun+':smd'
elif(ftype == 'smd_ffb') :
dataset_name = exprun+':smd'
# as ffb is only at SLAC, ok to hardcode /reg/d here ADD live!
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc:live"%(argv.experiment[0:3],argv.experiment)
exprun = dataset_name
ds = DataSource(dataset_name)
run = ds.runs().next()
# Select event generator
if (ftype=='smd') or (ftype == 'smd_ffb') or (ftype == 'xtc'):
evtgen = smdgen
elif (ftype=='idx') or (ftype == 'idx_ffb'):
evtgen = idxgen
if size == 1:
plot = argv.plot
else:
plot = 0
FXS = fxs.fluctuation_scattering(dataset_name = exprun,
detector_address = argv.address,
data_type = argv.ftype,
mask_path = argv.mask_path,
mask_angles = None, #np.array([88, 270]), # static masking at 88 and 270 deg
mask_widths = None, #np.array([6, 10]), # +/- degrees
backimg_path = argv.bg_img_path,
backmsk_path = argv.bg_msk_path,
geom_path = argv.geom_path,
det_dist = argv.det_distance,
det_pix = argv.det_pixel,
beam_l = argv.lambda_b,
mask_thr = argv.thr,
nQ = argv.nQ,
nPhi = argv.nPhi,
dQ = argv.dQ,
dPhi = argv.dP,
cent0 = [argv.x,argv.y],
r_max = argv.r_max,
dr = argv.dr,
dx = argv.dx,
dy = argv.dy,
r_0 = argv.r0,
q_bound = argv.q_bound)
# Initialize iterator
FXS.cnt = np.array([0.])
# Initialize Index variables
if argv.param_path is None :
maxevents = 400000 # We don't always know the total nr of events. Therefore set to large value
else:
maxevents = min(len(timestamps),len(timestamps[first:last]))
FXS.get_index2(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
if size > 1 :
if rank > 0 :
# MPI process. Here we set rank 0 to work as a listening server only.
for j,evt in evtgen(run,timestamps = timestamps, first = first, last = last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j%10==0: print 'Rank',rank,'processing event',j
if ftype == 'h5' :
FXS.get_h5(filestamps[j],evt)
else :
FXS.get_calib(evt)
# Process hits
if (FXS.image is not None) and (float(FXS.image.sum()) > hit) :
if ftype == 'h5' :
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec<<32)|nsec),fid)
#######################################
FXS.store_index2(et, j) # Store index
FXS.cnt += 1
else :
# Single CPU
for j,evt in evtgen(run,timestamps = timestamps, first = first, last = last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j%10==0: print 'Rank',rank,'processing event',j
if ftype == 'h5' :
FXS.get_h5(filestamps[j],evt)
else :
FXS.get_calib(evt)
# Process hits
if (FXS.image is not None) and (float(FXS.image.sum()) > hit) :
if ftype == 'h5' :
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec<<32)|nsec),fid)
#######################################
FXS.store_index2(et, j) # Store index
FXS.cnt += 1
print 'Rank',rank,'total events: ', int(FXS.cnt),' * '
#sum the images across mpi cores
if size > 1:
print "Synchronizing rank", rank
Tot = np.zeros(FXS.cnt.shape)
comm.Reduce(FXS.cnt,Tot)
# Collect Indexing variables
AVE_all = np.zeros(FXS.ave.shape)
comm.Reduce(FXS.ave,AVE_all)
Tot_t = np.zeros(FXS.tot_t.shape)
comm.Reduce(FXS.tot_t,Tot_t)
Tot_s = np.zeros(FXS.tot_s.shape)
comm.Reduce(FXS.tot_s,Tot_s)
Tot_ns = np.zeros(FXS.tot_ns.shape)
comm.Reduce(FXS.tot_ns,Tot_ns)
Tot_fd = np.zeros(FXS.tot_fd.shape)
comm.Reduce(FXS.tot_fd,Tot_fd)
Tot_int = np.zeros(FXS.tot_int.shape)
comm.Reduce(FXS.tot_int,Tot_int)
Tot_cx = np.zeros(FXS.tot_cx.shape)
comm.Reduce(FXS.tot_cx,Tot_cx)
Tot_cy = np.zeros(FXS.tot_cy.shape)
comm.Reduce(FXS.tot_cy,Tot_cy)
Tot_size = np.zeros(FXS.tot_size.shape)
comm.Reduce(FXS.tot_size,Tot_size)
Tot_score = np.zeros(FXS.tot_score.shape)
comm.Reduce(FXS.tot_score,Tot_score)
# Reduce results
if rank==0:
if size > 1:
print "Synchronized"
# Write out data
if argv.outputdir is None:
opath = os.getcwd()
else:
opath = argv.outputdir
f_ave = os.path.join(opath,'Average_run' + str(argv.run) + '_'+ str(int(Tot)) + '.dat')
f_index = os.path.join(opath,'Index_run' + str(argv.run) + '.dat')
stamps = ['Time','Seconds','Nanoseconds','Fiducial','Total Intensity','Beam X','Beam Y','Radius [Ang]','Score']
head =" ".join(stamps)
Ave = AVE_all / Tot
# Get rid of zero lines add the end
# Last non-zero intensity
nz = np.nonzero(Tot_t)
fend = nz[0][-1]+1
f = open(f_index,'w')
np.savetxt(f,np.c_[Tot_t[:fend],Tot_s[:fend],Tot_ns[:fend],Tot_fd[:fend],Tot_int[:fend],Tot_cx[:fend],Tot_cy[:fend],Tot_size[:fend],Tot_score[:fend]],header = head, comments='' )
f.close()
f = open(f_ave,'w')
np.savetxt(f_ave,Ave.reshape((-1)))
f.close()
def compute_calib(argv=None):
"""Function to compute the average image without applied geometry from FXS images
extracted from xtc (smd,idx,xtc format) or h5 files.
Works for Single CPU, Multi-Processor interactive jobs and MPI batch jobs
For a definition of input arguments argv and batch processing instructions see *** mpi_fxs_launch.py ***
compute_calib produces the following output files:
* Index file : Information about the events processed including time-stamps, beam center, total intensity, particle size etc
* Ave : Average image, as 1D array, in cartesian coordinates without geometry applied
"""
if argv == None:
argv = sys.argv[1:]
try:
from mpi4py import MPI
except ImportError:
raise Sorry("MPI not found")
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
if argv.hit is None:
hit = -1.0e20 # Process everything
else:
hit = argv.hit # Process everything > hit
ftype = argv.ftype
if argv.param_path is not None:
if ftype == 'h5':
param_file = np.genfromtxt(argv.param_path, skiprows=1, dtype=None)
timestamps, filestamps = pnccd_tbx.get_h5_event(param_file)
elif ftype == 'xtc':
param_file = np.genfromtxt(argv.param_path, skiprows=1, dtype=None)
timestamps = pnccd_tbx.get_time(param_file)
else:
param_file = np.genfromtxt(argv.param_path, skiprows=1)
timestamps = pnccd_tbx.get_psana_event(param_file)
else:
timestamps = None
# The first and last events to processed
first = argv.first
last = argv.last
# Check data format
if ftype == 'h5':
import h5py
run = int(argv.run)
# Get time-stamps from all h5-files
if argv.param_path is None:
timestamps = []
filestamps = []
# Loop over all h5-files and store the time-stamps
for i in os.listdir(argv.xtc_dir):
if i.endswith(".h5"):
f = h5py.File(i, 'r')
filestamps.append(i[-7:-4])
timestamps.append(f.keys())
continue
else:
continue
dataset_name = "%s-r%s" % (argv.experiment, str(argv.run).zfill(4)
) # Ascert 4 digit run number
exprun = os.path.join(argv.xtc_dir, dataset_name)
if argv.first is None:
first = 0
if argv.last is None:
last = len(timestamps)
else:
last = min(last,
len(timestamps)) # Check that last time-stamp exists
timestamps = timestamps[first:last]
filestamps = filestamps[first:last]
evtgen = h5gen
else:
exprun = "exp=%s:run=%d" % (argv.experiment, argv.run)
if (ftype == 'xtc'):
dataset_name = exprun + ':xtc'
elif (ftype == 'idx'):
dataset_name = exprun + ':idx'
elif (ftype == 'idx_ffb'):
dataset_name = exprun + ':idx'
# as ffb is only at SLAC, ok to hardcode /reg/d here
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc" % (
argv.experiment[0:3], argv.experiment)
elif (ftype == 'smd'):
dataset_name = exprun + ':smd'
elif (ftype == 'smd_ffb'):
dataset_name = exprun + ':smd'
# as ffb is only at SLAC, ok to hardcode /reg/d here ADD live!
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc:live" % (
argv.experiment[0:3], argv.experiment)
exprun = dataset_name
ds = DataSource(dataset_name)
run = ds.runs().next()
# Select event generator
if (ftype == 'smd') or (ftype == 'smd_ffb') or (ftype == 'xtc'):
evtgen = smdgen
elif (ftype == 'idx') or (ftype == 'idx_ffb'):
evtgen = idxgen
if size == 1:
plot = argv.plot
else:
plot = 0
FXS = fxs.fluctuation_scattering(
dataset_name=exprun,
detector_address=argv.address,
data_type=argv.ftype,
mask_path=argv.mask_path,
mask_angles=
None, #np.array([88, 270]), # static masking at 88 and 270 deg
mask_widths=None, #np.array([6, 10]), # +/- degrees
backimg_path=argv.bg_img_path,
backmsk_path=argv.bg_msk_path,
geom_path=argv.geom_path,
det_dist=argv.det_distance,
det_pix=argv.det_pixel,
beam_l=argv.lambda_b,
mask_thr=argv.thr,
nQ=argv.nQ,
nPhi=argv.nPhi,
dQ=argv.dQ,
dPhi=argv.dP,
cent0=[argv.x, argv.y],
r_max=argv.r_max,
dr=argv.dr,
dx=argv.dx,
dy=argv.dy,
r_0=argv.r0,
q_bound=argv.q_bound)
# Initialize iterator
FXS.cnt = np.array([0.])
# Initialize Index variables
if argv.param_path is None:
maxevents = 400000 # We don't always know the total nr of events. Therefore set to large value
else:
maxevents = min(len(timestamps), len(timestamps[first:last]))
FXS.get_index2(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
if size > 1:
if rank > 0:
# MPI process. Here we set rank 0 to work as a listening server only.
for j, evt in evtgen(run,
timestamps=timestamps,
first=first,
last=last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j % 10 == 0: print 'Rank', rank, 'processing event', j
if ftype == 'h5':
FXS.get_h5(filestamps[j], evt)
else:
FXS.get_calib(evt)
# Process hits
if (FXS.image is not None) and (float(FXS.image.sum()) > hit):
if ftype == 'h5':
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec << 32) | nsec), fid)
#######################################
FXS.store_index2(et, j) # Store index
FXS.cnt += 1
else:
# Single CPU
for j, evt in evtgen(run,
timestamps=timestamps,
first=first,
last=last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j % 10 == 0: print 'Rank', rank, 'processing event', j
if ftype == 'h5':
FXS.get_h5(filestamps[j], evt)
else:
FXS.get_calib(evt)
# Process hits
if (FXS.image is not None) and (float(FXS.image.sum()) > hit):
if ftype == 'h5':
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec << 32) | nsec), fid)
#######################################
FXS.store_index2(et, j) # Store index
FXS.cnt += 1
print 'Rank', rank, 'total events: ', int(FXS.cnt), ' * '
#sum the images across mpi cores
if size > 1:
print "Synchronizing rank", rank
Tot = np.zeros(FXS.cnt.shape)
comm.Reduce(FXS.cnt, Tot)
# Collect Indexing variables
AVE_all = np.zeros(FXS.ave.shape)
comm.Reduce(FXS.ave, AVE_all)
Tot_t = np.zeros(FXS.tot_t.shape)
comm.Reduce(FXS.tot_t, Tot_t)
Tot_s = np.zeros(FXS.tot_s.shape)
comm.Reduce(FXS.tot_s, Tot_s)
Tot_ns = np.zeros(FXS.tot_ns.shape)
comm.Reduce(FXS.tot_ns, Tot_ns)
Tot_fd = np.zeros(FXS.tot_fd.shape)
comm.Reduce(FXS.tot_fd, Tot_fd)
Tot_int = np.zeros(FXS.tot_int.shape)
comm.Reduce(FXS.tot_int, Tot_int)
Tot_cx = np.zeros(FXS.tot_cx.shape)
comm.Reduce(FXS.tot_cx, Tot_cx)
Tot_cy = np.zeros(FXS.tot_cy.shape)
comm.Reduce(FXS.tot_cy, Tot_cy)
Tot_size = np.zeros(FXS.tot_size.shape)
comm.Reduce(FXS.tot_size, Tot_size)
Tot_score = np.zeros(FXS.tot_score.shape)
comm.Reduce(FXS.tot_score, Tot_score)
# Reduce results
if rank == 0:
if size > 1:
print "Synchronized"
# Write out data
if argv.outputdir is None:
opath = os.getcwd()
else:
opath = argv.outputdir
f_ave = os.path.join(
opath,
'Average_run' + str(argv.run) + '_' + str(int(Tot)) + '.dat')
f_index = os.path.join(opath, 'Index_run' + str(argv.run) + '.dat')
stamps = [
'Time', 'Seconds', 'Nanoseconds', 'Fiducial', 'Total Intensity',
'Beam X', 'Beam Y', 'Radius [Ang]', 'Score'
]
head = " ".join(stamps)
Ave = AVE_all / Tot
# Get rid of zero lines add the end
# Last non-zero intensity
nz = np.nonzero(Tot_t)
fend = nz[0][-1] + 1
f = open(f_index, 'w')
np.savetxt(f,
np.c_[Tot_t[:fend], Tot_s[:fend], Tot_ns[:fend],
Tot_fd[:fend], Tot_int[:fend], Tot_cx[:fend],
Tot_cy[:fend], Tot_size[:fend], Tot_score[:fend]],
header=head,
comments='')
f.close()
f = open(f_ave, 'w')
np.savetxt(f_ave, Ave.reshape((-1)))
f.close()
def compute_index(argv=None) :
"""Function to index FXS images
extracted from xtc (smd,idx,xtc format) or h5 files.
Works for Single CPU, Multi-Processor interactive jobs and MPI batch jobs
For a definition of input arguments argv and batch processing instructions see *** mpi_fxs_launch.py ***
compute_index produces the following output file:
* Index file : Information about the events processed including time-stamps, beam center, total and peak intensities, streak locations, particle size etc
"""
if argv == None:
argv = sys.argv[1:]
try:
from mpi4py import MPI
except ImportError:
raise Sorry("MPI not found")
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
if argv.hit is None :
hit = -1.0e20 # Process everything
else:
hit = argv.hit # Process everything > hit
ftype = argv.ftype
if argv.param_path is not None :
if ftype == 'h5' :
param_file = np.genfromtxt(argv.param_path,skiprows=1,dtype=None)
timestamps,filestamps = pnccd_tbx.get_h5_event(param_file)
elif ftype == 'xtc' :
param_file = np.genfromtxt(argv.param_path,skiprows=1,dtype=None)
timestamps = pnccd_tbx.get_time(param_file)
else :
param_file = np.genfromtxt(argv.param_path,skiprows=1)
timestamps = pnccd_tbx.get_psana_event(param_file)
else:
timestamps = None
# The first and last events to processed
first = argv.first
last = argv.last
# Check data format
if ftype == 'h5' :
import h5py
run = int(argv.run)
# Get time-stamps from all h5-files
if argv.param_path is None :
timestamps = []
filestamps = []
# Loop over all h5-files and store the time-stamps
for i in os.listdir(argv.xtc_dir):
if i.endswith(".h5"):
f = h5py.File(i,'r')
filestamps.append(i[-7:-4])
timestamps.append(f.keys())
continue
else:
continue
dataset_name = "%s-r%s"%(argv.experiment, str(argv.run).zfill(4)) # Ascert 4 digit run number
exprun = os.path.join(argv.xtc_dir,dataset_name)
if argv.first is None :
first = 0
if argv.last is None :
last = len(timestamps)
else:
last = min(last,len(timestamps)) # Check that last time-stamp exists
timestamps = timestamps[first:last]
filestamps = filestamps[first:last]
evtgen = h5gen
else :
exprun = "exp=%s:run=%d"%(argv.experiment, argv.run)
if (ftype == 'xtc') :
dataset_name = exprun+':xtc'
elif (ftype == 'idx') :
dataset_name = exprun+':idx'
elif(ftype == 'idx_ffb') :
dataset_name = exprun+':idx'
# as ffb is only at SLAC, ok to hardcode /reg/d here
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc"%(argv.experiment[0:3],argv.experiment)
elif(ftype == 'smd') :
dataset_name = exprun+':smd'
elif(ftype == 'smd_ffb') :
dataset_name = exprun+':smd'
# as ffb is only at SLAC, ok to hardcode /reg/d here ADD live!
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc:live"%(argv.experiment[0:3],argv.experiment)
exprun = dataset_name
ds = DataSource(dataset_name)
run = ds.runs().next()
# Select event generator
if (ftype=='smd') or (ftype == 'smd_ffb') or (ftype == 'xtc'):
evtgen = smdgen
elif (ftype=='idx') or (ftype == 'idx_ffb'):
evtgen = idxgen
if size == 1:
plot = argv.plot
else:
plot = 0
FXS = fxs.fluctuation_scattering(dataset_name = exprun,
detector_address = argv.address,
data_type = argv.ftype,
mask_path = argv.mask_path,
mask_angles = None,#np.array([88, 270]), # static masking at 88 and 270
mask_widths = None,#np.array([6, 10]), # +/- degrees
backimg_path = argv.bg_img_path,
backmsk_path = argv.bg_msk_path,
geom_path = argv.geom_path,
det_dist = argv.det_distance,
det_pix = argv.det_pixel,
beam_l = argv.lambda_b,
mask_thr = argv.thr,
nQ = argv.nQ,
nPhi = argv.nPhi,
dQ = argv.dQ,
dPhi = argv.dP,
cent0 = [argv.x,argv.y],
r_max = argv.r_max,
dr = argv.dr,
dx = argv.dx,
dy = argv.dy,
r_0 = argv.r0,
q_bound = argv.q_bound,
peak = [0.037, 0.064],
dpeak = [0.002, 0.002])
# Initialize iterator
FXS.cnt = np.array([0.])
# Initialize Index variables
if argv.param_path is None :
maxevents = 400000 # We don't always know the total nr of events. Therefore set to large value
else:
maxevents = min(len(timestamps),len(timestamps[first:last]))
FXS.get_index(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
if size > 1 :
if rank > 0 :
hd=pnccd_hit.hit()
# MPI process. Here we set rank 0 to work as a listening server only.
for j,evt in evtgen(run,timestamps = timestamps, first = first, last = last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j%10==0: print 'Rank',rank,'processing event',j
if ftype == 'h5' :
FXS.get_h5(filestamps[j],evt)
else :
FXS.get_image(evt)
# Process hits
if (FXS.img is not None) and (float(FXS.img.sum()) > hit) :
FXS.get_beam() # Beam center refinement
FXS.get_polar() # Polar transform
FXS.get_streak_mask(flag = 1) # Mask out streaks
FXS.get_norm(flag = 1) # Normalize image, get SAXS
if FXS.r_0 is not None :
FXS.get_size()
if ftype == 'h5' :
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec<<32)|nsec),fid)
#######################################
FXS.store_index(et, j) # Store index
if int(FXS.cnt)%10==0: print 'Rank',rank,'processed events: ', int(FXS.cnt)
# Send partial results to master (rank 0)
if (int(FXS.cnt) > 0) and (int(FXS.cnt) % 100 == 0): # Send every 100 events
tmp_n = int(FXS.cnt)
# Average image
tmp_im = FXS.ave / tmp_n
# Total intensity, Size and Score
tmp_ind = np.column_stack((FXS.tot_int,FXS.tot_size,FXS.tot_score))
hd.send(tmp_n, image = tmp_im, ind=tmp_ind)
FXS.cnt += 1
hd.endrun()
else:
if ftype == 'h5' :
FXS.run_nr = run
else:
FXS.run_nr = int(run.run())
hd = pnccd_hit.hit()
adim = FXS.ave.shape
idim = (maxevents,3)
hd.total_ave = [np.zeros(adim)]*(size-1)
hd.total_ind = [np.zeros(idim)]*(size-1)
hd.total_ev_a = [0.0]*(size-1)
hd.total_ev_i = [0.0]*(size-1)
nClients = size - 1
while nClients > 0:
# Remove client if the run ended
if hd.recv():
nClients -= 1
else:
na = sum(hd.total_ev_a)
ni = sum(hd.total_ev_i)
if (na == ni) and (na % 100 == 0) : # Publish every 100 events
AVE = np.zeros(adim)
IND = np.zeros(idim)
for i in range(size-1) :
AVE = AVE + (hd.total_ave[i] * (hd.total_ev_a[i] /na))
IND = IND + hd.total_ind[i]
FXS.publish(image = AVE, ind=IND, n_a=na, n_i=ni)
else :
# Single CPU
for j,evt in evtgen(run,timestamps = timestamps, first = first, last = last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j%10==0: print 'Rank',rank,'processing event',j
if ftype == 'h5' :
FXS.get_h5(filestamps[j],evt)
else :
FXS.get_image(evt)
# Process hits
if (FXS.img is not None) and (float(FXS.img.sum()) > hit) :
FXS.get_beam(plot = plot) # Beam center refinement
FXS.get_polar(plot = plot) # Polar transform
FXS.get_streak_mask(flag = 1, plot = plot) # Mask out streaks
FXS.get_norm(flag = 0, plot = plot) # Normalize image, get SAXS
if FXS.r_0 is not None :
FXS.get_size()
if ftype == 'h5' :
FXS.store_index_h5(evt, j)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec<<32)|nsec),fid)
#######################################
FXS.store_index(et, j) # Store index
FXS.cnt += 1
print 'Rank',rank,'total events: ', int(FXS.cnt),' * '
#sum the images across mpi cores
if size > 1:
print "Synchronizing rank", rank
Tot = np.zeros(FXS.cnt.shape)
comm.Reduce(FXS.cnt,Tot)
if rank == 0 and Tot[0] == 0 :
raise Sorry("No events found in the run")
# Collect Indexing variables
Tot_t = np.zeros(FXS.tot_t.shape)
comm.Reduce(FXS.tot_t,Tot_t)
Tot_s = np.zeros(FXS.tot_s.shape)
comm.Reduce(FXS.tot_s,Tot_s)
Tot_ns = np.zeros(FXS.tot_ns.shape)
comm.Reduce(FXS.tot_ns,Tot_ns)
Tot_fd = np.zeros(FXS.tot_fd.shape)
comm.Reduce(FXS.tot_fd,Tot_fd)
Tot_int = np.zeros(FXS.tot_int.shape)
comm.Reduce(FXS.tot_int,Tot_int)
Tot_peak1 = np.zeros(FXS.tot_peak1_int.shape)
comm.Reduce(FXS.tot_peak1_int,Tot_peak1)
Tot_peak2 = np.zeros(FXS.tot_peak2_int.shape)
comm.Reduce(FXS.tot_peak2_int,Tot_peak2)
Tot_s_m = np.zeros(FXS.tot_streak_m.shape)
comm.Reduce(FXS.tot_streak_m,Tot_s_m)
Tot_s_s = np.zeros(FXS.tot_streak_s.shape)
comm.Reduce(FXS.tot_streak_s,Tot_s_s)
Tot_cx = np.zeros(FXS.tot_cx.shape)
comm.Reduce(FXS.tot_cx,Tot_cx)
Tot_cy = np.zeros(FXS.tot_cy.shape)
comm.Reduce(FXS.tot_cy,Tot_cy)
Tot_size = np.zeros(FXS.tot_size.shape)
comm.Reduce(FXS.tot_size,Tot_size)
Tot_score = np.zeros(FXS.tot_score.shape)
comm.Reduce(FXS.tot_score,Tot_score)
# Reduce results
if rank==0:
if size > 1:
print "Synchronized"
# Write out data
if argv.outputdir is None:
opath = os.getcwd()
else:
opath = argv.outputdir
f_index = os.path.join(opath,'Index_run' + str(argv.run) + '.dat')
stamps = ['Time','Seconds','Nanoseconds','Fiducial','Total Intensity','Peak1, q='+str(FXS.peak[0])+'+/-'+str(FXS.dpeak[0]),'Peak2, q='+str(FXS.peak[1])+'+/-'+str(FXS.dpeak[1]),'Mean streak angle','Std streak angle','Beam X','Beam Y','Radius [Ang]','Score']
head =" ".join(stamps)
# Get rid of zero lines at the end
# Last non-zero intensity
nz = np.nonzero(Tot_t)
fend = nz[0][-1]+1
f = open(f_index,'w')
np.savetxt(f,np.c_[Tot_t[:fend],Tot_s[:fend],Tot_ns[:fend],Tot_fd[:fend],Tot_int[:fend],Tot_peak1[:fend],Tot_peak2[:fend],Tot_s_m[:fend],Tot_s_s[:fend],Tot_cx[:fend],Tot_cy[:fend],Tot_size[:fend],Tot_score[:fend]],header = head, comments='' )
f.close()
def compute_mask(argv=None):
"""Function to compute a mask of non-resposive pixels from FXS images
extracted from xtc (smd,idx,xtc format) or h5 files.
Works for Single CPU, Multi-Processor interactive jobs and MPI batch jobs
For a definition of input arguments argv and batch processing instructions see *** mpi_fxs_launch.py ***
compute_mask produces the following output files:
* Index file : Information about the events processed including time-stamps, beam center, total and peak intensities, streak locations, particle size etc
* Average : Average image in cartesian coordinates
* Variance : Variance map of intensities in cartesian coordinates
* Mask : Mask image in cartesian coordinates
"""
if argv == None:
argv = sys.argv[1:]
try:
from mpi4py import MPI
except ImportError:
raise Sorry("MPI not found")
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
if argv.hit is None:
hit = -1.0e20 # Process everything
else:
hit = argv.hit # Process everything > hit
ftype = argv.ftype
if argv.param_path is not None:
if ftype == 'h5':
param_file = np.genfromtxt(argv.param_path, skiprows=1, dtype=None)
timestamps, filestamps = pnccd_tbx.get_h5_event(param_file)
elif ftype == 'xtc':
param_file = np.genfromtxt(argv.param_path, skiprows=1, dtype=None)
timestamps = pnccd_tbx.get_time(param_file)
else:
param_file = np.genfromtxt(argv.param_path, skiprows=1)
timestamps = pnccd_tbx.get_psana_event(param_file)
else:
timestamps = None
# The first and last events to processed
first = argv.first
last = argv.last
# Check data format
if ftype == 'h5':
import h5py
run = int(argv.run)
# Get time-stamps from all h5-files
if argv.param_path is None:
timestamps = []
filestamps = []
# Loop over all h5-files and store the time-stamps
for i in os.listdir(argv.xtc_dir):
if i.endswith(".h5"):
f = h5py.File(i, 'r')
filestamps.append(i[-7:-4])
timestamps.append(f.keys())
continue
else:
continue
dataset_name = "%s-r%s" % (argv.experiment, str(argv.run).zfill(4)
) # Ascert 4 digit run number
exprun = os.path.join(argv.xtc_dir, dataset_name)
if argv.first is None:
first = 0
if argv.last is None:
last = len(timestamps)
else:
last = min(last,
len(timestamps)) # Check that last time-stamp exists
timestamps = timestamps[first:last]
filestamps = filestamps[first:last]
evtgen = h5gen
else:
exprun = "exp=%s:run=%d" % (argv.experiment, argv.run)
if (ftype == 'xtc'):
dataset_name = exprun + ':xtc'
elif (ftype == 'idx'):
dataset_name = exprun + ':idx'
elif (ftype == 'idx_ffb'):
dataset_name = exprun + ':idx'
# as ffb is only at SLAC, ok to hardcode /reg/d here
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc" % (
argv.experiment[0:3], argv.experiment)
elif (ftype == 'smd'):
dataset_name = exprun + ':smd'
elif (ftype == 'smd_ffb'):
dataset_name = exprun + ':smd'
# as ffb is only at SLAC, ok to hardcode /reg/d here ADD live!
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc:live" % (
argv.experiment[0:3], argv.experiment)
exprun = dataset_name
ds = DataSource(dataset_name)
run = ds.runs().next()
# Select event generator
if (ftype == 'smd') or (ftype == 'smd_ffb') or (ftype == 'xtc'):
evtgen = smdgen
elif (ftype == 'idx') or (ftype == 'idx_ffb'):
evtgen = idxgen
if size == 1:
plot = argv.plot
else:
plot = 0
FXS = fxs.fluctuation_scattering(
dataset_name=exprun,
detector_address=argv.address,
data_type=argv.ftype,
mask_path=argv.mask_path,
mask_angles=
None, #np.array([88, 270]), # static masking at 88 and 270 deg
mask_widths=None, #np.array([6, 10]), # +/- degree
backimg_path=argv.bg_img_path,
backmsk_path=argv.bg_msk_path,
geom_path=argv.geom_path,
det_dist=argv.det_distance,
det_pix=argv.det_pixel,
beam_l=argv.lambda_b,
mask_thr=argv.thr,
nQ=argv.nQ,
nPhi=argv.nPhi,
dQ=argv.dQ,
dPhi=argv.dP,
cent0=[argv.x, argv.y],
r_max=argv.r_max,
dr=argv.dr,
dx=argv.dx,
dy=argv.dy,
r_0=argv.r0,
q_bound=argv.q_bound,
peak=[0.037, 0.064],
dpeak=[0.002, 0.002])
# Initialize iterator
FXS.cnt = np.array([0.])
# Initialize Index variables
if argv.param_path is None:
maxevents = 400000 # We don't always know the total nr of events. Therefore set to large value
else:
maxevents = min(len(timestamps), len(timestamps[first:last]))
FXS.get_index(maxevents, flag=1)
# 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
if size > 1:
if rank > 0:
hd = pnccd_hit.hit()
# MPI process. Here we set rank 0 to work as a listening server only.
for j, evt in evtgen(run,
timestamps=timestamps,
first=first,
last=last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j % 10 == 0: print 'Rank', rank, 'processing event', j
if ftype == 'h5':
FXS.get_h5(filestamps[j], evt)
else:
FXS.get_image(evt) # Geometry applied image (FXS.img)
FXS.image = np.copy(FXS.img)
# Process hits
if (FXS.image is not None) and (float(FXS.image.sum()) > hit):
FXS.get_beam(plot=plot) # Beam center refinement
FXS.get_polar(plot=plot) # Polar transform
FXS.get_streak_mask(plot=plot) # Get info on streak
FXS.store_image(j) # Store raw images
if ftype == 'h5':
FXS.store_index_h5(evt, j, flag=0)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec << 32) | nsec), fid)
#######################################
FXS.store_index(et, j, flag=0) # Store index
if int(FXS.cnt) % 10 == 0:
print 'Rank', rank, 'processed events: ', int(FXS.cnt)
# Send partial results to master (rank 0)
if int(FXS.cnt) % 50 == 0: # Send every 50 events
# C2 and Saxs data
tmp_n = int(FXS.cnt)
# Total intensity, Size and Score
tmp_ind = np.column_stack(
(FXS.tot_int, FXS.tot_size, FXS.tot_score))
hd.send(tmp_n, ind=tmp_ind)
hd.endrun()
print 'Rank', rank, 'total events: ', int(FXS.cnt), ' * '
else:
if ftype == 'h5':
FXS.run_nr = run
else:
FXS.run_nr = int(run.run())
hd = pnccd_hit.hit()
idim = (maxevents, 3)
hd.total_ind = [np.zeros(idim)] * (size - 1)
hd.total_ev_i = [0.0] * (size - 1)
nClients = size - 1
while nClients > 0:
# Remove client if the run ended
if hd.recv():
nClients -= 1
else:
ns = sum(hd.total_ev_s)
ni = sum(hd.total_ev_i)
if (ns % 100 == 0): # Publish every 100 events
IND = np.zeros(idim)
for i in range(size - 1):
IND = IND + hd.total_ind[i]
FXS.publish(ind=IND, n_i=ni)
else:
# Single CPU
for j, evt in evtgen(run,
timestamps=timestamps,
first=first,
last=last):
#print '***',rank,j,evt.get(EventId).fiducials()
if j % 10 == 0: print 'Rank', rank, 'processing event', j
if ftype == 'h5':
FXS.get_h5(filestamps[j], evt)
else:
FXS.get_image(evt) # Geometry applied image (FXS.img)
FXS.image = np.copy(FXS.img)
# Process hits
if (FXS.image is not None) and (float(FXS.image.sum()) > hit):
FXS.get_beam(plot=plot) # Beam center refinement
FXS.get_polar() # Polar transform
FXS.get_streak_mask(plot=0) # Get info on streak
FXS.store_image(j) # Store raw images
if ftype == 'h5':
FXS.store_index_h5(evt, j, flag=0)
else:
######################################
# Ugly way to get the time-stamps. Fix!!
time = evt.get(EventId).time()
fid = evt.get(EventId).fiducials()
sec = time[0]
nsec = time[1]
et = EventTime(int((sec << 32) | nsec), fid)
#######################################
FXS.store_index(et, j, flag=0) # Store index
print 'Rank', rank, 'total events: ', int(FXS.cnt), ' * '
#sum the images across mpi cores
if size > 1:
print "Synchronizing rank", rank
Tot = np.zeros(FXS.cnt.shape)
comm.Reduce(FXS.cnt, Tot)
if rank == 0 and Tot[0] == 0:
raise Sorry("No events found in the run")
# Collect Variables
Images = np.zeros(FXS.images.shape)
comm.Reduce(FXS.images, Images)
# Collect Indexing variables
Tot_t = np.zeros(FXS.tot_t.shape)
comm.Reduce(FXS.tot_t, Tot_t)
Tot_s = np.zeros(FXS.tot_s.shape)
comm.Reduce(FXS.tot_s, Tot_s)
Tot_ns = np.zeros(FXS.tot_ns.shape)
comm.Reduce(FXS.tot_ns, Tot_ns)
Tot_fd = np.zeros(FXS.tot_fd.shape)
comm.Reduce(FXS.tot_fd, Tot_fd)
Tot_int = np.zeros(FXS.tot_int.shape)
comm.Reduce(FXS.tot_int, Tot_int)
Tot_peak1 = np.zeros(FXS.tot_peak1_int.shape)
comm.Reduce(FXS.tot_peak1_int, Tot_peak1)
Tot_peak2 = np.zeros(FXS.tot_peak2_int.shape)
comm.Reduce(FXS.tot_peak2_int, Tot_peak2)
Tot_s_m = np.zeros(FXS.tot_streak_m.shape)
comm.Reduce(FXS.tot_streak_m, Tot_s_m)
Tot_s_s = np.zeros(FXS.tot_streak_s.shape)
comm.Reduce(FXS.tot_streak_s, Tot_s_s)
Tot_cx = np.zeros(FXS.tot_cx.shape)
comm.Reduce(FXS.tot_cx, Tot_cx)
Tot_cy = np.zeros(FXS.tot_cy.shape)
comm.Reduce(FXS.tot_cy, Tot_cy)
Tot_size = np.zeros(FXS.tot_size.shape)
comm.Reduce(FXS.tot_size, Tot_size)
Tot_score = np.zeros(FXS.tot_score.shape)
comm.Reduce(FXS.tot_score, Tot_score)
# Reduce results
if rank == 0:
if size > 1:
print "Synchronized"
# Identify dead lines and pixels, get binary pixel mask
Ave, Var, Mask = pnccd_tbx.pixel_mask(Images, thr=0.12)
# Write out data
if argv.outputdir is None:
opath = os.getcwd()
else:
opath = argv.outputdir
f_index = os.path.join(opath, 'Index_run' + str(argv.run) + '.dat')
stamps = [
'Time', 'Seconds', 'Nanoseconds', 'Fiducial', 'Total Intensity',
'Peak1, q=' + str(FXS.peak[0]) + '+/-' + str(FXS.dpeak[0]),
'Peak2, q=' + str(FXS.peak[1]) + '+/-' + str(FXS.dpeak[1]),
'Mean streak angle', 'Std streak angle', 'Beam X', 'Beam Y',
'Radius [Ang]', 'Score'
]
head = " ".join(stamps)
f_ave = os.path.join(opath, 'Average_map_' + str(argv.run) + '.dat')
f_var = os.path.join(opath, 'Variance_map_' + str(argv.run) + '.dat')
f_mask = os.path.join(opath, 'Mask_map_' + str(argv.run) + '.dat')
# Get rid of zero lines at the end
# Last non-zero intensity
nz = np.nonzero(Tot_t)
fend = nz[0][-1] + 1
f = open(f_index, 'w')
np.savetxt(f,
np.c_[Tot_t[:fend], Tot_s[:fend], Tot_ns[:fend],
Tot_fd[:fend], Tot_int[:fend], Tot_peak1[:fend],
Tot_peak2[:fend], Tot_s_m[:fend], Tot_s_s[:fend],
Tot_cx[:fend], Tot_cy[:fend], Tot_size[:fend],
Tot_score[:fend]],
header=head,
comments='')
f.close()
f = open(f_ave, 'w')
np.savetxt(f, Ave)
f.close()
f = open(f_var, 'w')
np.savetxt(f, Var)
f.close()
f = open(f_mask, 'w')
np.savetxt(f, Mask)
f.close()
def compute_bg(argv=None):
if argv == None:
argv = sys.argv[1:]
try:
from mpi4py import MPI
except ImportError:
raise Sorry("MPI not found")
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
if argv.hit is None:
hit = -1.0e6 # Process everything
else:
hit = argv.hit # Process everything > hit
if argv.param_path is not None:
param_file = np.genfromtxt(argv.param_path, skiprows=1)
if (argv.ftype == "xtc") or (argv.ftype == "ffb") or (argv.ftype == "smd"):
if argv.ftype == "xtc":
dataset_name = "exp=%s:run=%d:idx" % (argv.experiment, argv.run)
elif argv.ftype == "ffb":
dataset_name = "exp=%s:run=%d:idx" % (argv.experiment, argv.run)
# as ffb is only at SLAC, ok to hardcode /reg/d here
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc" % (argv.experiment[0:3], argv.experiment)
elif argv.ftype == "smd":
dataset_name = "exp=%s:run=%d:smd" % (argv.experiment, argv.run)
# as ffb is only at SLAC, ok to hardcode /reg/d here ADD live!
dataset_name += ":dir=/reg/d/ffb/%s/%s/xtc:live" % (argv.experiment[0:3], argv.experiment)
ds = DataSource(dataset_name)
# Get run
for run in ds.runs():
if rank == 0:
print "Processing run ", run.run()
# Get timestamps
if argv.param_path is not None:
timestamps = pnccd_tbx.get_psana_event(param_file)
else:
timestamps = run.times()
elif argv.ftype == "h5":
import h5py
dataset_name = "%s_run_%d.h5" % (argv.experiment, argv.run)
dataset_name = os.path.join(argv.xtc_dir, dataset_name)
run = int(argv.run)
f = h5py.File(dataset_name, "r")
timestamps = f.keys()
if rank == 0:
print "Processing run ", argv.run
if argv.first is None:
first = 0
else:
first = argv.first
if argv.last is None:
last = len(timestamps)
else:
last = min(argv.last, len(timestamps))
times = timestamps[first:last]
nevents = len(times)
if rank == 0:
print "Processing events " + str(first) + " to " + str(last)
if size == 1:
plot = argv.plot
else:
plot = 0
FXS = fxs.fluctuation_scattering(
dataset_name=dataset_name,
detector_address=argv.address,
data_type=argv.ftype,
mask_path=argv.mask_path,
mask_angles=None, # np.array([90, 270]) # static masking at 90 and 270
mask_widths=None, # np.array([10, 10]) # +/- degrees
backimg_path=argv.bg_img_path,
backmsk_path=argv.bg_msk_path,
param_path=argv.param_path,
det_dist=argv.det_distance,
det_pix=argv.det_pixel,
beam_l=argv.lambda_b,
mask_thr=argv.thr,
nQ=argv.nQ,
nPhi=argv.nPhi,
dQ=argv.dQ,
dPhi=argv.dP,
cent0=[argv.x, argv.y],
r_max=argv.r_max,
dr=argv.dr,
dx=argv.dx,
dy=argv.dy,
r_0=argv.r0,
q_bound=argv.q_bound,
)
# Initialize iterator
FXS.cnt = np.array([0.0])
# Initialize Index variables
FXS.get_index(nevents)
# 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
if size > 1:
if rank > 0:
hd = pnccd_hit.hit()
# MPI process. Here we set rank 0 to work as a listening server only.
mytimes, myevents = zip(*[(times[i], i) for i in xrange(nevents) if (i + rank) % (size - 1) == 0])
for j in xrange(len(mytimes)):
if j % 10 == 0:
print "Rank", rank, "processing event", rank * len(mytimes) + j, ", ", j, "of", len(mytimes)
FXS.get_image(run, mytimes[j])
# Process hits
if float(FXS.img.sum()) >= hit:
FXS.get_beam(plot=plot) # Beam center refinement
FXS.get_polar(plot=plot) # Polar transform
FXS.get_streak_mask(plot=plot) # Mask out streaks
FXS.get_pixel_mask(plot=plot) # Mask out pixels
FXS.get_norm(plot=plot) # Normalize image, get SAXS
if FXS.r_0 is not None:
FXS.get_size()
FXS.store_index(mytimes[j], myevents[j]) # Store index
FXS.sum_bg() # Sum Background
# Send partial results to master (rank 0)
if (int(FXS.cnt) > 0) and (int(FXS.cnt) % 100 == 0): # Send every 100 events
tmp_n = int(FXS.cnt)
# Average image
tmp_im = FXS.ave / tmp_n
# Total intensity, Size and Score
tmp_ind = np.column_stack((FXS.tot_int, FXS.tot_size, FXS.tot_score))
hd.send(tmp_n, image=tmp_im, ind=tmp_ind)
FXS.cnt += 1
hd.endrun()
else:
FXS.run_nr = int(run.run())
hd = pnccd_hit.hit()
adim = FXS.ave.shape
idim = (nevents, 3)
hd.total_ave = [np.zeros(adim)] * (size - 1)
hd.total_ind = [np.zeros(idim)] * (size - 1)
hd.total_ev_a = [0.0] * (size - 1)
hd.total_ev_i = [0.0] * (size - 1)
nClients = size - 1
while nClients > 0:
# Remove client if the run ended
if hd.recv():
nClients -= 1
else:
na = sum(hd.total_ev_a)
ni = sum(hd.total_ev_i)
if (na == ni) and (na % 100 == 0): # Publish every 100 events
AVE = np.zeros(adim)
IND = np.zeros(idim)
for i in range(size - 1):
AVE = AVE + (hd.total_ave[i] * (hd.total_ev_a[i] / na))
IND = IND + hd.total_ind[i]
FXS.publish(image=AVE, ind=IND, n_a=na, n_i=ni)
else:
# Single CPU
mytimes, myevents = zip(*[(times[i], i) for i in xrange(nevents) if (i + rank) % size == 0])
for j in xrange(len(mytimes)):
if j % 10 == 0:
print "Rank", rank, "processing event", rank * len(mytimes) + j, ", ", j, "of", len(mytimes)
FXS.get_image(run, mytimes[j])
# Process hits
if float(FXS.img.sum()) >= hit:
FXS.get_beam(plot=plot) # Beam center refinement
FXS.get_polar(plot=plot) # Polar transform
FXS.get_streak_mask(plot=plot) # Mask out streaks
FXS.get_pixel_mask(plot=plot) # Mask out pixels
FXS.get_norm(plot=plot) # Normalize image, get SAXS
if FXS.r_0 is not None:
FXS.get_size()
FXS.store_index(mytimes[j], myevents[j]) # Store index
FXS.sum_bg() # Sum Background
FXS.cnt += 1
# sum the images across mpi cores
if size > 1:
print "Synchronizing rank", rank
Tot = np.zeros(FXS.cnt.shape)
comm.Reduce(FXS.cnt, Tot)
if rank == 0 and Tot[0] == 0:
raise Sorry("No events found in the run")
# Collect Background variables
if not hasattr(FXS, "Ave"):
FXS.Ave = np.zeros(FXS.msk.shape)
if not hasattr(FXS, "Isaxs"):
FXS.Isaxs = np.zeros(FXS.q.shape)
if not hasattr(FXS, "Vsaxs"):
FXS.Vsaxs = np.zeros(FXS.q.shape)
if not hasattr(FXS, "Back_img"):
FXS.Back_img = np.zeros((len(FXS.q), len(FXS.phi)))
if not hasattr(FXS, "Back_msk_0"):
FXS.Back_msk = np.zeros((len(FXS.q), len(FXS.phi)))
AVE_all = np.zeros(FXS.Ave.shape)
comm.Reduce(FXS.Ave, AVE_all)
BG_img_all = np.zeros(FXS.Back_img.shape)
comm.Reduce(FXS.Back_img, BG_img_all)
BG_msk_all = np.zeros(FXS.Back_msk.shape)
comm.Reduce(FXS.Back_msk, BG_msk_all)
SAXS_all = np.zeros(FXS.Isaxs.shape)
comm.Reduce(FXS.Isaxs, SAXS_all)
VAR_all = np.zeros(FXS.Vsaxs.shape)
comm.Reduce(FXS.Vsaxs, VAR_all)
# Collect Indexing variables
Tot_t = np.zeros(FXS.tot_t.shape)
comm.Reduce(FXS.tot_t, Tot_t)
Tot_s = np.zeros(FXS.tot_s.shape)
comm.Reduce(FXS.tot_s, Tot_s)
Tot_ns = np.zeros(FXS.tot_ns.shape)
comm.Reduce(FXS.tot_ns, Tot_ns)
Tot_fd = np.zeros(FXS.tot_fd.shape)
comm.Reduce(FXS.tot_fd, Tot_fd)
Tot_int = np.zeros(FXS.tot_int.shape)
comm.Reduce(FXS.tot_int, Tot_int)
Tot_cx = np.zeros(FXS.tot_cx.shape)
comm.Reduce(FXS.tot_cx, Tot_cx)
Tot_cy = np.zeros(FXS.tot_cy.shape)
comm.Reduce(FXS.tot_cy, Tot_cy)
Tot_size = np.zeros(FXS.tot_size.shape)
comm.Reduce(FXS.tot_size, Tot_size)
Tot_score = np.zeros(FXS.tot_score.shape)
comm.Reduce(FXS.tot_score, Tot_score)
# Reduce results
if rank == 0:
if size > 1:
print "Synchronized"
# Write out data
if argv.outputdir is None:
opath = os.getcwd()
else:
opath = argv.outputdir
Tot = int(Tot)
Isaxs_ave = SAXS_all / Tot
Isaxs_std = np.sqrt(VAR_all / Tot)
Ave = AVE_all / Tot
tmp = BG_msk_all
ind = tmp == 0
tmp[ind] = 1.0
Bg_img = BG_img_all / tmp
Bg_msk = np.ones(tmp.shape)
Bg_msk[ind] = 0.0
f_index = os.path.join(opath, "Index_run" + str(argv.run) + ".dat")
stamps = [
"Time",
"Seconds",
"Nanoseconds",
"Fiducial",
"Total Intensity",
"Beam X",
"Beam Y",
"Radius [Ang]",
"Score",
]
head = " ".join(stamps)
f_ave = os.path.join(opath, "Average_run" + str(argv.run) + "_" + str(Tot) + ".dat")
f_saxs = os.path.join(opath, "Saxs_run" + str(argv.run) + "_" + str(Tot) + ".dat")
f_bg_im = os.path.join(opath, "Bg_img_" + str(argv.run) + "_" + str(Tot) + ".dat")
f_bg_ms = os.path.join(opath, "Bg_msk_" + str(argv.run) + "_" + str(Tot) + ".dat")
stamps_s = ["q", "Mean", "Std"]
head_s = " ".join(stamps_s)
f = open(f_index, "w")
np.savetxt(
f,
np.c_[Tot_t, Tot_s, Tot_ns, Tot_fd, Tot_int, Tot_cx, Tot_cy, Tot_size, Tot_score],
header=head,
comments="",
)
f.close()
f = open(f_ave, "w")
np.savetxt(f, Ave)
f.close()
f = open(f_saxs, "w")
np.savetxt(f, np.c_[FXS.q, Isaxs_ave, Isaxs_std], header=head_s, comments="")
f.close()
f = open(f_bg_im, "w")
np.savetxt(f, Bg_img)
f.close()
f = open(f_bg_ms, "w")
np.savetxt(f, Bg_msk)
f.close()
