def main(source, tofbg=None):
'''
Iterate over the datastream served by source
Input:
source: ip address as string
Output:
none, updates plots
'''
if tofbg is not None:
print('tofbg "{}" loaded...'.format(tofbg))
tofbg = np.load(tofbg)['arr_0'][262000:290000]
print('tofbg average is {}'.format(np.mean(tofbg)))
# for i, tof in enumerate(xfel.getTof(xfel.servedata(source),idx_range=[530000,560000])):
# for i, tof in enumerate(xfel.getTof(xfel.servedata(source),idx_range=[142500,1200000])):
for i, tof in enumerate(xfel.getTof(xfel.servedata(source),idx_range=[100000,500000])):
if tofbg is not None:
tof['tof'] = tof['tof'] - tofbg
plottofavg(tof['tof'])
plotintegral(tof['tof'])
if i%100 == 0:
print('running gc...')
print(tof['tof'].shape)
import gc
gc.collect()
def main(source):
'''
Iterate over the datastream served by source
Input:
source: ip address as string
Output:
none, updates plots
'''
for i, ds in enumerate((online.servedata(source))):
plotdaqalive(isAlive(ds))
def buildDataStreamPipeline():
ds = online.servedata(source)
if _inStream_TOF:
ds = pipeline_TOF(ds, tof_prop)
if _inStream_pnCCD:
ds = pipeline_pnCCD(ds, pnCCD_prop)
if _inStream_XGM:
ds = pipeline_XGM(ds)
return ds
def makeDatastreamPipeline(source):
ds = online.servedata(source) #get the datastream
ds = online.getTof(ds) #get the tofs
ds = processTofs(ds) #treat the tofs
ds = online.getSomeDetector(ds, name='tid', spec0='SQS_DIGITIZER_UTC1/ADC/1:network', spec1='digitizers.trainId') #get current trainids from digitizer property
#ds = online.getSomeDetector(ds, name='tid', spec0='SA3_XTD10_XGM/XGM/DOOCS:output', spec1='timestamp.tid', readFromMeta=True) #get current trainids from gmd property
if pnCCD_in_stream:
ds = online.getSomePnCCD(ds, name='pnCCD', spec0='SQS_NQS_PNCCD1MP/CAL/PNCCD_FMT-0:output', spec1='data.image') #get pnCCD
#ds = online.getSomeDetector(ds, name='tid', spec0='SQS_NQS_PNCCD1MP/CAL/PNCCD_FMT-0:output', spec1='timestamp.tid', readFromMeta=True) #get current trainids from gmd property
ds = online.getSomeDetector(ds, name='gmd', spec0='SA3_XTD10_XGM/XGM/DOOCS:output', spec1='data.intensitySa3TD') #get GMD
return ds
def main(source):
'''
Iterate over the datastream served by source
Input:
source: ip address as string
Output:
none, updates plots
'''
ds = xfel.servedata(source)
#ds = xfel.getImage(ds)
ds = xfel.getImageandTof(ds)
for data in ds:
#plotimage(image)
plotbrightest(data['image'], tid=data['tid'], tof=data['tof'])
def main(source):
'''
Iterate over the datastream served by source
Input:
source: ip address as string
Output:
none, updates plots
'''
ds = online.servedata(source) #get the datastream
ds = online.getTof(ds, idx_range=tof_range) #get the tofs / give the index range!
_tofplot = pg.plot(title='ToF Simple Live {}'.format(tools.__version__))
for data in ds:
_tofplot.plot(data['tof'].flatten(), clear=True)
pg.QtGui.QApplication.processEvents()
def makeDatastreamPipeline(source):
'''
build the datastream pipeline
'''
# get the data stream
ds = online.servedata(source)
# get the pnccds from datastream
if pnCCD_in_stream:
ds = online.getSomePnCCD(
ds,
name='pnCCD',
spec0='SQS_NQS_PNCCD1MP/CAL/PNCCD_FMT-0:output',
spec1='data.image') #get pnCCD
ds = online.getSomeDetector(
ds,
name='tid',
spec0='SQS_NQS_PNCCD1MP/CAL/PNCCD_FMT-0:output',
spec1='timestamp.tid',
readFromMeta=True) #get current trainids from gmd property
ds = processPnCCDs(ds) # convert pnccd adu to photon count
# get the tofs from datastream directly cut the tof
if tof_in_stream:
if not set_tof_trace_to_zero:
ds = online.getTof(
ds, idx_range=[start_tof, end_tof], baselineTo=1000
) #get the tofs, uses element specs from experiment defaults
ds = processTofs(ds) #treat the tofs
ds = online.getSomeDetector(
ds,
name='tid',
spec0='SQS_DIGITIZER_UTC1/ADC/1:network',
spec1='digitizers.trainId'
) #get current trainids from digitizer property
# get gmd data from datastream
if gmd_in_stream:
ds = online.getSomeDetector(
ds,
name='gmd',
spec0='SA3_XTD10_XGM/XGM/DOOCS:output',
spec1='data.intensitySa3TD'
) #get GMD pulse energies returns an array with a list of pulse energie for each bunch
#~ ds = online.getSomeDetector(ds, name='gmd_x', spec0='SA3_XTD10_XGM/XGM/DOOCS:output', spec1='data.xSa3TD') #get GMD pointing x
#~ ds = online.getSomeDetector(ds, name='gmd_y', spec0='SA3_XTD10_XGM/XGM/DOOCS:output', spec1='data.ySa3TD') #get GMD pointing y
#~ ds = online.getSomeDetector(ds, name='tid', spec0='SA3_XTD10_XGM/XGM/DOOCS:output', spec1='timestamp.tid', readFromMeta=True)
return ds
def makeDatastreamPipeline(source):
ds = online.servedata(source) #get the datastream
ds = online.getTof(ds, idx_range=[start_tof, end_tof]) #get the tofs
ds = processTofs(ds) #treat the tofs
ds = online.getSomeDetector(
ds,
name='tid',
spec0=defaultConf['tofDevice'],
spec1='digitizers.trainId'
) #get current trainids from digitizer property
#ds = online.getSomeDetector(ds, name='tid', spec0='SA3_XTD10_XGM/XGM/DOOCS:output', spec1='timestamp.tid', readFromMeta=True) #get current trainids from gmd property
#if pnCCD_in_stream:
#ds = online.getSomePnCCD(ds, name='pnCCD', spec0='SQS_NQS_PNCCD1MP/CAL/CORR_CM:output', spec1='data.image') #get pnCCD
#ds = online.getSomeDetector(ds, name='tid', spec0='SQS_NQS_PNCCD1MP/CAL/PNCCD_FMT-0:output', spec1='timestamp.tid', readFromMeta=True) #get current trainids from gmd property
#ds = online.getSomeDetector(ds, name='gmd', spec0='SA3_XTD10_XGM/XGM/DOOCS:output', spec1='data.intensitySa3TD') #get GMD
return ds
def main(source):
'''
Iterate over the datastream served by source
Input:
source: ip address as string
Output:
none, updates plots
'''
#ds is the datastream - everything is done in pipeline form
ds = online.servedata(source) #get the datastream
ds = online.getTof(ds, idx_range=tof_range) #get the tofs
#~ Option to let multi bunch tof trace appear like a single bunch tof trace but actually containing a sum of each bunches trace
#~ ds = foldTofs(ds) #fold tofs from shots in the pulsetrain
#~ Example on how to add some other detector to the datastream
#~ ds = online.getSomeDetector(ds, name='phoFlux', spec0='SA3_XTD10_XGM/XGM/DOOCS', spec1='pulseEnergy.photonFlux.value')
#~ Run the live analysis
for data in ds:
plotHits(data)
def makeDatastreamPipeline(source):
ds = online.servedata(source) #get the datastream
if pnCCD_in_stream:
ds = online.getSomePnCCD(
ds,
name='pnCCD',
spec0='SQS_NQS_PNCCD1MP/CAL/PNCCD_FMT-0:output',
spec1='data.image') #get pnCCD
ds = online.getSomeDetector(
ds,
name='tid',
spec0='SQS_NQS_PNCCD1MP/CAL/PNCCD_FMT-0:output',
spec1='timestamp.tid',
readFromMeta=True) #get current trainids from gmd property
ds = processPnCCDs(ds) # convert pnccd adu to photon count
if gmd_in_stream:
ds = online.getSomeDetector(ds,
name='gmd',
spec0='SA3_XTD10_XGM/XGM/DOOCS:output',
spec1='data.intensitySa3TD') #get GMD
#~ ds = online.getSomeDetector(ds, name='gmd_x', spec0='SA3_XTD10_XGM/XGM/DOOCS:output', spec1='data.xSa3TD') #get GMD
#~ ds = online.getSomeDetector(ds, name='gmd_y', spec0='SA3_XTD10_XGM/XGM/DOOCS:output', spec1='data.ySa3TD') #get GMD
#~ ds = online.getSomeDetector(ds, name='tid', spec0='SA3_XTD10_XGM/XGM/DOOCS:output', spec1='timestamp.tid', readFromMeta=True)
return ds
def makeSomeData():
source = 'tcp://10.253.0.142:6666'
source = 'tcp://127.0.0.1:8010'
ds = online.servedata(source) #get the datastream
ds = online.getTof(ds) #get the tofs
ds = foldTofs(ds)
print("get some detector")
ds = online.getSomeDetector(ds,
name='tid',
spec0='SQS_DIGITIZER_UTC1/ADC/1:network',
spec1='digitizers.trainId')
print("done")
# initialize variables for performance monitoring
t_start = time.time()
for_loop_step_dur = 0
n = -1
freq_avg = 0
dt_avg = 0
trainId = 0
trainId_old = -1
skip_count = 0
# start with for loop
print("Start With For Loop")
for data in ds:
# performance monitor - frequency of displaying data + loop duration
n += 1
dt = (time.time() - t_start)
t_start = time.time()
freq = 1 / dt
if n > 0:
dt_avg = (dt_avg * (n - 1) + dt) / n
freq_avg = 1 / dt_avg
loop_classification_percent = for_loop_step_dur / 0.1 * 100
if loop_classification_percent < 100:
loop_classification_msg = "OK"
else:
loop_classification_msg = "TOO LONG!!!"
print("Frequency: " + str(round(freq_avg, 2)) +
" Hz | skipped: " + str(skip_count) + " ( " +
str(round(skip_count / n * 100, 1)) + " %) | n: " +
str(n) + "/" + str(trainId) + " | Loop benchmark: " +
str(round(loop_classification_percent, 1)) +
" % (OK if <100%) - " + loop_classification_msg)
t_1 = time.time()
# extract TOF data
#tof = data['tof']
t_2 = time.time()
#tof = tof[200000:200000+N_datapts]
# print(tof.shape) # just in case debugging because of shape errors
#N_samples = len(tof)
#print(data['tof'].shape)
x = np.arange(N_datapts)
y = np.squeeze(data['tof'])
t_3 = time.time()
# Train ID Data
trainId_old = trainId
trainId = str(data['tid'])
if int(trainId) - int(trainId_old) is not 1:
#print('SKIP')
skip_count += 1
# update from callback
doc.add_next_tick_callback(partial(update, x=x, y=y, tid=trainId))
# monitor for loop step duration
for_loop_step_dur = time.time() - t_start
def makeDatastreamPipeline(source):
ds = online.servedata(source) #get the datastream
ds = online.getTof(ds) #get the tofs
ds = processTofs(ds) #treat the tofs
ds = online.getSomeDetector(ds, name='tid', spec0='SQS_DIGITIZER_UTC1/ADC/1:network', spec1='digitizers.trainId') #get current trainids from digitizer property
return ds
def integral(d, int_range=[400000, 450000]):
int_val = np.sum(d[int_range[0]:int_range[1]])
## initialize plot windows
### tofplot
_tofplot = pg.plot(title='ToF Simple Live {}'.format(tools.__version__))
## initialize some global lists
_int_value_list = list()
_scan_paramter_list = list()
## setup the pipeline
ds = online.servedata(source) #get the datastream
ds = online.getTof(ds, idx_range=[0, 1200000]) #get the tofs
ds = online.getSomeDetector(
ds,
name='parker',
spec0='SQS_NQS_CRSC/TSYS/PARKER_TRIGGER',
spec1='actualDelay.value') #get a random piece of data
## start pulling the data
for data in ds: #this could be made into a pipeline maybe
tof = data['tof']
parker = data['parker']
print(parker)
plotTOF(tof)
#_int_value_list.append(integral(tof, int_range=[400000,450000]))
