def locAcquire(
Naves=1,
Nds=1,
):
numAttempts = 1
while 1:
numAttempts += 1
try:
if 0:
while acq.glbSOCKET_PUBSUB.poll(50):
while acq.glbSOCKET_PUBSUB.poll(50):
print("Clearing old data...")
acq.checkForPublished()
sleep(0.2)
_, out, dt = acq.acquireStreaming(Nmin=Naves,
bNoStale=True) #Raw()
#out=MT.smoothalong(out,Nds, axis=-1)[...,::Nds]
out = MT.downsample_npts(out, Nds, axis=-1)
#outL.append(out)
#out=np.mean(out, axis=0)
except DAQError:
print("trigger stopped, try again")
else:
break
t = np.arange(out.shape[-1]) * dt * Nds
return t, out
def __call__(self, handle, noOfSamples, startIndex, overflow, triggerAt, triggered, autoStop, parameter):
self.streamReady=True
noOfSamples=int(noOfSamples/self.Nds)*self.Nds #Only take a multiple of the down-sample factor
#print("start index: {}, nSamps: {}, qvsize: {}".format(startIndex, noOfSamples, self._incData.view().shape))
if noOfSamples:
#print("doing things")
self._incData.advanceEnd(noOfSamples)
#print("qvsize: {}".format(self._incData.view().shape))
newDat=self._incData.view()
trigInds=self.findTrigs(newDat[1])
if len(trigInds)>1:
#print("trig0: {}".format(trigInds[0]))
if self.NptsPerSegment is None:
self.NptsPerSegment=int(np.mean(np.diff(trigInds)))-5
diffs=np.diff(np.hstack([[0], trigInds]) )-self.NptsPerSegment
if any(abs(diffs)<0):
print("oh no! diffs: {}".format(diffs))
#print("Npts per seg: {}".format(self.NptsPerSegment))
#print("len(TrigInds) {} in {} samples".format(len(trigInds), noOfSamples))
Nprocessed=trigInds[-1]
self._incData.dumpStart(Nprocessed) #Dump points up until the last trigger
tA=trigInds*self.sampleInterval+self.tLast
self.tLast=tA[-1]
for t,trgI in zip(tA, [0]+list(trigInds[:-1])):
#print(trgI)
self.q.put_nowait((t, newDat[0,trgI:trgI+self.NptsPerSegment]) )
self.totalSegments+=len(trigInds)
else:
Nprocessed=0
Nsave=int(Nprocessed/self.Nds)
if self.curSaveInd+Nsave< self.data.size and Nsave!=0:
self.data[self.curSaveInd:self.curSaveInd+Nsave]=MT.downsample_npts(newDat[0, :Nprocessed], self.Nds)
self.curSaveInd+=Nsave
#else:
# self.data[self.curInd:self.curInd+noOfSamples]=ps.data[1,0,startIndex:startIndex+noOfSamples]
#self._incData.dumpStart(noOfSamples) #Dump points up until the last trigger
else:
print("no samples")
