def compute_mean_rms(file_path, plot=False):
"""Step through a charge-injection binary file and calculate the
mean and standard deviation of the charge distribution in each pixel.
Warning
-------
Note that, since the signal in ADC counts can be a relatively large
number, we use Welford's algorithm for the running variance:
https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
And yes, this is not a nuisance---the first attempt at using the
dumb running variance ended up in flames (in case you're tempted to
change this).
"""
assert(file_path.endswith('.mdat'))
n = numpy.zeros((300, 352), 'd')
mean = numpy.zeros((300, 352), 'd')
M2 = numpy.zeros((300, 352), 'd')
for event in xpeBinaryFileWindowed(file_path):
indices = (slice(event.xmin, event.xmax + 1),
slice(event.ymin, event.ymax + 1))
n[indices] += 1
delta = event.adc_values - mean[indices]
mean[indices] += delta/n[indices]
M2[indices] += delta*(event.adc_values - mean[indices])
rms = numpy.sqrt(M2/(n - 1))
if plot:
fig, axes = plt.subplots(nrows=1, ncols=2)
img0 = axes[0].matshow(mean)
img1 = axes[1].matshow(rms)
plt.show()
return mean, rms
def compute_mean_rms(file_path, plot=False):
"""Step through a charge-injection binary file and calculate the
mean and standard deviation of the charge distribution in each pixel.
Warning
-------
Note that, since the signal in ADC counts can be a relatively large
number, we use Welford's algorithm for the running variance:
https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
And yes, this is not a nuisance---the first attempt at using the
dumb running variance ended up in flames (in case you're tempted to
change this).
"""
assert (file_path.endswith('.mdat'))
n = numpy.zeros((300, 352), 'd')
mean = numpy.zeros((300, 352), 'd')
M2 = numpy.zeros((300, 352), 'd')
for event in xpeBinaryFileWindowed(file_path):
indices = (slice(event.xmin,
event.xmax + 1), slice(event.ymin, event.ymax + 1))
n[indices] += 1
delta = event.adc_values - mean[indices]
mean[indices] += delta / n[indices]
M2[indices] += delta * (event.adc_values - mean[indices])
rms = numpy.sqrt(M2 / (n - 1))
if plot:
fig, axes = plt.subplots(nrows=1, ncols=2)
img0 = axes[0].matshow(mean)
img1 = axes[1].matshow(rms)
plt.show()
return mean, rms
def run_xpe_recon(file_path,
num_events=1000000000,
zero_suppression=9,
coordinate_system='pixy',
output_path=None,
min_cluster_size=6,
max_cluster_size=400,
small_radius=1.5,
large_radius=3.5,
weight_scale=0.05):
"""Run the event reconstruction on a binary file.
"""
assert (file_path.endswith('.mdat'))
if output_path is None:
output_path = file_path.replace('.mdat', '_xpe.root')
logger.info('Opening output file %s...' % output_path)
output_file = ROOT.TFile(output_path, 'RECREATE')
output_tree = xpeReconTree()
event_id = 0
for event in xpeBinaryFileWindowed(file_path):
cluster_list = hierarchical_clustering(event, zero_suppression,
coordinate_system)
cluster = cluster_list[0]
recon = xpePixyRecon(cluster)
if not recon.error_summary:
output_tree.set_value('fRunId', -1)
output_tree.set_value('fEventId', event_id)
output_tree.set_value('fNClusters', len(cluster_list))
output_tree.set_value('fTrigWindow', event.num_pixels())
output_tree.set_value('fTimeTick', -1)
output_tree.set_value('fTimeStamp', -1)
output_tree.set_value('fBufferId', event.buffer_id)
output_tree.set_value('fCluSize', cluster.num_pixels())
output_tree.set_value('fPHeight', cluster.pulse_height)
output_tree.set_value('fStoN', -1)
output_tree.set_value('fTotNoise', -1)
output_tree.set_value('fBaricenterX', cluster.baricenter.x())
output_tree.set_value('fBaricenterY', cluster.baricenter.y())
output_tree.set_value('fTheta0', recon.phi0)
output_tree.set_value('fTheta1', recon.phi1)
output_tree.set_value('fMomX', recon.ma0.mom2_long)
output_tree.set_value('fMomY', recon.ma0.mom2_trans)
output_tree.set_value('fMomThirdX', recon.mom3_long)
output_tree.set_value('fImpactX', recon.conversion_baricenter.x())
output_tree.set_value('fImpactY', recon.conversion_baricenter.y())
output_tree.Fill()
event_id += 1
if event_id == num_events:
break
num_proc_events = output_tree.GetEntries()
output_tree.Write()
output_file.Close()
logger.info('Done, %d event(s) written to the output file.' %\
num_proc_events)
return output_path
def test(filePath, num_events, zero_suppression=9, coordinate_system='xpedaq'):
"""
"""
from pyxpe.recon.binio import xpeBinaryFileWindowed
input_file = xpeBinaryFileWindowed(filePath)
for i in xrange(num_events):
event = input_file.next()
print event
cluster = hierarchical_clustering(event, zero_suppression,
coordinate_system)[0]
print cluster
cluster.draw(coordinate_system)
def run_xpe_recon(file_path, num_events=1000000000, zero_suppression=9,
coordinate_system='pixy', output_path=None,
min_cluster_size=6, max_cluster_size=400,
small_radius=1.5, large_radius=3.5, weight_scale=0.05):
"""Run the event reconstruction on a binary file.
"""
assert(file_path.endswith('.mdat'))
if output_path is None:
output_path = file_path.replace('.mdat', '_xpe.root')
logger.info('Opening output file %s...' % output_path)
output_file = ROOT.TFile(output_path, 'RECREATE')
output_tree = xpeReconTree()
event_id = 0
for event in xpeBinaryFileWindowed(file_path):
cluster_list = hierarchical_clustering(event, zero_suppression,
coordinate_system)
cluster = cluster_list[0]
recon = xpePixyRecon(cluster)
if not recon.error_summary:
output_tree.set_value('fRunId', -1)
output_tree.set_value('fEventId', event_id)
output_tree.set_value('fNClusters', len(cluster_list))
output_tree.set_value('fTrigWindow', event.num_pixels())
output_tree.set_value('fTimeTick', -1)
output_tree.set_value('fTimeStamp', -1)
output_tree.set_value('fBufferId', event.buffer_id)
output_tree.set_value('fCluSize', cluster.num_pixels())
output_tree.set_value('fPHeight', cluster.pulse_height)
output_tree.set_value('fStoN', -1)
output_tree.set_value('fTotNoise', -1)
output_tree.set_value('fBaricenterX', cluster.baricenter.x())
output_tree.set_value('fBaricenterY', cluster.baricenter.y())
output_tree.set_value('fTheta0', recon.phi0)
output_tree.set_value('fTheta1', recon.phi1)
output_tree.set_value('fMomX', recon.ma0.mom2_long)
output_tree.set_value('fMomY', recon.ma0.mom2_trans)
output_tree.set_value('fMomThirdX', recon.mom3_long)
output_tree.set_value('fImpactX', recon.conversion_baricenter.x())
output_tree.set_value('fImpactY', recon.conversion_baricenter.y())
output_tree.Fill()
event_id += 1
if event_id == num_events:
break
num_proc_events = output_tree.GetEntries()
output_tree.Write()
output_file.Close()
logger.info('Done, %d event(s) written to the output file.' %\
num_proc_events)
return output_path
def get_ci_run_noise_stat(run_id, verbose=False):
""" Noisy channel analysis of charge injection runs
"""
print("Processing run Id %d" % run_id)
run_info = load_run_info('/data1/xpe/xpedata/033_%07d' % run_id)
thr_dac = run_info['config']['threshold_dacs'][0]
if verbose:
print(run_info)
nMinWin = 0
nLargerWin = 0
RoiDict = {}
PixelDict = {}
file_path = run_info['data_file_path']
if not os.path.isfile(file_path):
# No event found, upper limits not implemented yet
return (thr_dac, 0, 0, -1, {}, [])
for event in xpeBinaryFileWindowed(file_path):
# check event has minimum window of 396
if event.num_pixels() in SINGLE_PXL_WINDOW:
nMinWin += 1
# Eval ROI and check if already found, if not add it
roi = event.roi()
if RoiDict.has_key(roi):
RoiDict[roi] += 1
else:
RoiDict[roi] = 1
else:
nLargerWin += 1
if verbose:
print("******************* ROI different than Minimum:")
print(event.roi(), event.num_pixels())
# get the highest pixel per event:
# I'm sure there is a better way to do it...
(highest_x, highest_y) = event.highest_pixel()
h_pxl = (highest_x + event.xmin, highest_y + event.ymin)
if PixelDict.has_key(h_pxl):
PixelDict[h_pxl] += 1
else:
PixelDict[h_pxl] = 1
PixelList = []
for ((x, y), v) in PixelDict.items():
PixelList.append((v, x, y))
PixelList.sort()
PixelList.reverse()
timeLastEvt = event.microseconds
# dac, nGoodRoi, nBadRoi, timeLastEvt, RoiDict
return (thr_dac, nMinWin, nLargerWin, timeLastEvt, RoiDict, PixelList)
def plot_pixel_distribution(file_path, x, y):
assert (file_path.endswith('.mdat'))
heightList = []
for event in xpeBinaryFileWindowed(file_path):
try:
heightList.append(event.adc_value(x - event.xmin, y - event.ymin))
except IndexError:
pass
#print heightList
heights = numpy.array(heightList)
print len(heights), numpy.mean(heights), numpy.std(heights)
xmin = -1
xmax = numpy.amax(heights)
nbins = xmax - xmin
histRange = (xmin, xmax)
plt.hist(heights, nbins, histRange, facecolor='g')
plt.show()
def plot_pixel_distribution(file_path, x, y):
assert(file_path.endswith('.mdat'))
heightList = []
for event in xpeBinaryFileWindowed(file_path):
try:
heightList.append(event.adc_value(x - event.xmin, y- event.ymin))
except IndexError:
pass
#print heightList
heights = numpy.array(heightList)
print len(heights), numpy.mean(heights), numpy.std(heights)
xmin = -1
xmax = numpy.amax(heights)
nbins = xmax - xmin
histRange = (xmin, xmax)
plt.hist(heights, nbins, histRange, facecolor='g')
plt.show()
file_path = '/data/xpedata/001/001_0000669/001_0000669_data.mdat'
#runId = 670 # ci(100,100)
#runId = 679 # ci(9,101)
#runId = 680 # ci(8,101)
#runId = 681 # ci(7,101)
#runId = 682 # ci(2,101)
#runId = 683 # ci(3,101)
#runId = 684 # ci(0,101)
#runId = 685 # ci(298,10)
#runId = 686 # ci(298,3)
#runId = 687 # ci(298,350)
#runId = 688 # ci(0,0)
#file_path = '/data/xpedata/001/001_%07d/001_%07d_data.mdat'% (runId,runId)
input_file = xpeBinaryFileWindowed(file_path)
# matrix for pixel occupancy
# DON"T FORGET: np.zeros([row,col])
pxl_occupancy = np.zeros([XPOL_NUM_ROWS, XPOL_NUM_COLUMNS])
for i in range(N_MAX):
event = input_file.next()
#event.draw_ascii(ZERO_SUPP)
#print (event)
#print guess_trg_pxl(event)
#col, row = pixels_overthr(event, ZERO_SUPP)
#for (c,r) in zip(col, row):
# pxl_occupancy[r][c] +=event.adc_value(c-event.xmin, r-event.ymin)
# pxl_occupancy[r][c] += 1
col, row = guess_trg_pxl(event)
'--full-frame',
action='store_true',
dest='full_frame',
default=False,
help='read events in full frame mode')
parser.add_argument('-o',
'--offset',
type=int,
default=0,
help='offset (in bytes) of the desired event header')
parser.add_argument('-n',
'--num-events',
type=int,
default=10,
help='number of events to be read')
parser.add_argument('-z',
'--zero-suppression',
type=int,
default=10,
help='zero-suppression threshold')
args = parser.parse_args()
if args.full_frame:
input_file = xpeBinaryFileFullFrame(args.binfile)
else:
input_file = xpeBinaryFileWindowed(args.binfile)
input_file.seek(args.offset)
for i in xrange(args.num_events):
event = input_file.next()
print('%d - %s' % (i, event))
if __name__ == '__main__':
import argparse
formatter = argparse.ArgumentDefaultsHelpFormatter
parser = argparse.ArgumentParser(formatter_class=formatter)
parser.add_argument('binfile', type=str,
help='the input binary file')
parser.add_argument('-f', '--full-frame', action='store_true',
dest = 'full_frame', default=False,
help='read events in full frame mode')
parser.add_argument('-o', '--offset', type=int, default=0,
help='offset (in bytes) of the desired event header')
parser.add_argument('-n', '--num-events', type=int, default=10,
help='number of events to be read')
parser.add_argument('-z', '--zero-suppression', type=int, default=10,
help='zero-suppression threshold')
args = parser.parse_args()
if args.full_frame:
input_file = xpeBinaryFileFullFrame(args.binfile)
else:
input_file = xpeBinaryFileWindowed(args.binfile)
input_file.seek(args.offset)
for i in xrange(args.num_events):
event = input_file.next()
if args.full_frame:
event.draw()
else:
event.draw_ascii(args.zero_suppression)
event.draw(args.zero_suppression)
output_tree.set_value('fBaricenterY', cluster.baricenter.y())
output_tree.set_value('fTheta0', recon.phi0)
output_tree.set_value('fTheta1', recon.phi1)
output_tree.set_value('fMomX', recon.ma0.mom2_long)
output_tree.set_value('fMomY', recon.ma0.mom2_trans)
output_tree.set_value('fMomThirdX', recon.mom3_long)
output_tree.set_value('fImpactX', recon.conversion_baricenter.x())
output_tree.set_value('fImpactY', recon.conversion_baricenter.y())
output_tree.Fill()
event_id += 1
if event_id == num_events:
break
num_proc_events = output_tree.GetEntries()
output_tree.Write()
output_file.Close()
logger.info('Done, %d event(s) written to the output file.' %\
num_proc_events)
return output_path
if __name__ == '__main__':
file_path = '/data/work/xpe/xpedaq/data/test_fe_500evts.mdat'
for event in xpeBinaryFileWindowed(file_path):
cluster_list = hierarchical_clustering(event, 9, 'xpe')
cluster = cluster_list[0]
recon = xpePixyRecon(cluster)
print(cluster.pulse_height, cluster.num_pixels(),\
cluster.baricenter.x(), cluster.baricenter.y(), recon.phi0,\
recon.ma0.mom2_long, recon.ma0.mom2_trans)
raw_input()
output_tree.set_value('fBaricenterY', cluster.baricenter.y())
output_tree.set_value('fTheta0', recon.phi0)
output_tree.set_value('fTheta1', recon.phi1)
output_tree.set_value('fMomX', recon.ma0.mom2_long)
output_tree.set_value('fMomY', recon.ma0.mom2_trans)
output_tree.set_value('fMomThirdX', recon.mom3_long)
output_tree.set_value('fImpactX', recon.conversion_baricenter.x())
output_tree.set_value('fImpactY', recon.conversion_baricenter.y())
output_tree.Fill()
event_id += 1
if event_id == num_events:
break
num_proc_events = output_tree.GetEntries()
output_tree.Write()
output_file.Close()
logger.info('Done, %d event(s) written to the output file.' %\
num_proc_events)
return output_path
if __name__ == '__main__':
file_path = '/data/work/xpe/xpedaq/data/test_fe_500evts.mdat'
for event in xpeBinaryFileWindowed(file_path):
cluster_list = hierarchical_clustering(event, 9, 'xpe')
cluster = cluster_list[0]
recon = xpePixyRecon(cluster)
print(cluster.pulse_height, cluster.num_pixels(),\
cluster.baricenter.x(), cluster.baricenter.y(), recon.phi0,\
recon.ma0.mom2_long, recon.ma0.mom2_trans)
raw_input()
_phi1 = numpy.degrees(ma0.phi) + 90
_phi2 = _phi1 - 180.
w2 = Wedge(cluster.baricenter, r2, _phi1, _phi2, facecolor='none',
edgecolor=_color, lw=_lw, hatch='///')
plt.gca().add_artist(w2)
recon.conversion_point.draw(color='green')
annotate('Absorption point', recon.conversion_point, (0.16, 0.9))
plt.savefig('sample_evt_conv_point.pdf')
dir2_fig = cluster.draw(coordinate_system, hexcol_padding=0.1, show=False)
cluster.baricenter.draw(color='blue')
annotate('Baricenter', cluster.baricenter, (0.13, 0.65))
ma0.axis.draw(color='blue', lw=_lw, ls='dashed')
p = ma0.axis.at(0.4)
annotate('Principal axis', p, (0.45, 0.95))
ma1.draw(color='green', ellipse=False, semiaxes=False)
annotate('Absorption point', ma1.pivot, (0.16, 0.9))
p = ma1.axis.at(0.5)
annotate('Final direction', p, (0.86, 0.35))
plt.savefig('sample_evt_phi2.pdf')
plt.show()
if __name__ == '__main__':
file_path = '/data/work/xpe/xpedaq/data/test_fe_500evts.mdat'
input_file = xpeBinaryFileWindowed(file_path)
for i in range(108):
event = input_file.next()
display_recon(event)