def _eventStats(self):
"""
Query a database and return the fractions of normal events, events with warnings, events with errors and total events.
"""
import random
normalEvents = len(
query(
self.analysisDB,
"select AbsEventStart from metadata where ProcessingStatus='normal' order by AbsEventStart ASC"
))
warnEvents = len(
query(
self.analysisDB,
"select AbsEventStart from metadata where ProcessingStatus like 'w%' order by AbsEventStart ASC"
))
errorEvents = len(
query(
self.analysisDB,
"select AbsEventStart from metadata where ProcessingStatus like 'e%' order by AbsEventStart ASC"
))
totalEvents = len(
query(self.analysisDB, "select ProcessingStatus from metadata"))
return round(normalEvents / float(totalEvents),
3), round(warnEvents / float(totalEvents),
3), round(errorEvents / float(totalEvents),
3), totalEvents
def _timePerEvent(self): q=query( self.analysisDB, "select ProcessTime from metadata where ProcessingStatus='normal'" ) return round(np.mean(np.hstack(q)),1), round(np.std(np.hstack(q)), 1)
def _openChanCurrent(self): q=query( self.analysisDB, "select OpenChCurrent from metadata where ProcessingStatus='normal'" ) return round(np.mean(np.hstack(q)),1), round(np.std(np.hstack(q)), 1)
def _openChanCurrent(self):
q = query(
self.analysisDB,
"select OpenChCurrent from metadata where ProcessingStatus='normal'"
)
return round(np.mean(np.hstack(q)), 1), round(np.std(np.hstack(q)), 1)
def _timeseriesSaved(self): try: q=query( self.analysisDB, "select TimeSeries from metadata" ) return len(q[0][0]) > 0 except: return False
def _hist(self): q=query( self.AnalysisDBFile, self.queryString ) x=np.hstack( np.hstack( np.array( q ) ) ) # xmin=np.max([0, np.min(x)]) xmin=np.min(x) xmax=np.max(x) return np.array(np.histogram(x, bins=self.numBins, density=self.density))
def _caprate(self):
q = np.hstack(
query(
self.analysisDB,
"select AbsEventStart from metadata where ProcessingStatus='normal' order by AbsEventStart ASC"
))
# save the start time of the latest event
self.lastEventStartTimeSec = q[-1] / 1000
c = caprate(q)
return round(c[0], 1), round(c[1], 1)
def _caprate(self): q=np.hstack( query( self.analysisDB, "select AbsEventStart from metadata where ProcessingStatus='normal' order by AbsEventStart ASC" ) ) # save the start time of the latest event self.lastEventStartTimeSec=q[-1]/1000; c=caprate(q) return round(c[0], 1), round(c[1], 1)
def _eventStats(self): """ Query a database and return the fractions of normal events, events with warnings, events with errors and total events. """ import random normalEvents=len(query( self.analysisDB, "select AbsEventStart from metadata where ProcessingStatus='normal' order by AbsEventStart ASC" )) warnEvents=len(query( self.analysisDB, "select AbsEventStart from metadata where ProcessingStatus like 'w%' order by AbsEventStart ASC" )) errorEvents=len(query( self.analysisDB, "select AbsEventStart from metadata where ProcessingStatus like 'e%' order by AbsEventStart ASC" )) totalEvents=len(query( self.analysisDB, "select ProcessingStatus from metadata" )) return round(normalEvents/float(totalEvents), 3), round(warnEvents/float(totalEvents), 3), round(errorEvents/float(totalEvents), 3), totalEvents
def timeSeries(self):
try:
q=query(self.analysisDB, self.qstr)[0]
decimate=self._calculateDecimation(len(q[1]))
dt=(1./self.FsHz)*decimate
ydat=np.array(q[1])
polarity=float(np.sign(np.mean(ydat)))
ydat=polarity*ydat[::decimate]
xdat=np.arange(0, dt*len(ydat), dt)
dat={}
if q[0]=='normal':
if self.fitFuncHnd:
# xfit=np.arange(0,float((len(q[1]))/self.FsHz), float(1/(100*self.FsHz)))
xfit=np.arange(0, dt*len(ydat), dt)
yfit=self.fitFuncHnd( *eval(self.fitFuncArgs) )
if self.stepFuncHnd:
# xstep=np.arange(0,float((len(q[1]))/self.FsHz), float(1/(100*self.FsHz)))
xstep=np.arange(0, dt*len(ydat), dt)
ystep=self.stepFuncHnd( *eval(self.stepFuncArgs) )
dat['data'] = [
plotlyWrapper.plotlyTrace(list(xdat), list(ydat), "NormalEvent"),
plotlyWrapper.plotlyTrace(list(xfit), list(yfit), "NormalEventFit"),
plotlyWrapper.plotlyTrace(list(xstep), list(ystep), "NormalEventStep")
]
elif q[0].startswith('w'):
dat['data'] = [ plotlyWrapper.plotlyTrace(list(xdat), list(ydat), "WarnEvent") ]
self.returnMessageJSON['errorText']="WARNING: "+self.errTextObject[q[0]]
else:
dat['data'] = [ plotlyWrapper.plotlyTrace(list(xdat), list(ydat), "ErrorEvent") ]
self.returnMessageJSON['errorText']="ERROR: "+self.errTextObject[q[0]]
dat['layout']=plotlyWrapper.plotlyLayout("EventViewLayout")
dat['options']=plotlyWrapper.plotlyOptions()
self.returnMessageJSON['eventViewPlot']=dat
self.returnMessageJSON['parameterTable']=self._paramTable(*eval(self.paramTableFuncArgs))
return self.returnMessageJSON
except IndexError:
raise EndOfDataError
def _hist2d(self):
try:
q = query(self.AnalysisDBFile, self.queryString)
y, x = np.transpose(np.array(q))
Y = np.hstack(y)
X = np.hstack(x)
xmin = np.max([0, 0.9 * np.min(X)])
xmax = 1.1 * np.max(X)
ymin = np.max([0, 0.9 * np.min(Y)])
ymax = np.min([1, 1.1 * np.max(Y)])
return np.histogram2d(X,
Y,
bins=(self.numBins, self.numBins),
range=[[xmin, xmax], [ymin, ymax]])
except ValueError:
raise QuerySyntaxError("")
def _hist2d(self):
try:
q=query(
self.AnalysisDBFile,
self.queryString
)
y,x=np.transpose(np.array(q))
Y=np.hstack(y)
X=np.hstack(x)
xmin=np.max([0, 0.9*np.min(X)])
xmax=1.1*np.max(X)
ymin=np.max([0, 0.9*np.min(Y)])
ymax=np.min([1, 1.1*np.max(Y)])
return np.histogram2d(X, Y, bins=(self.numBins, self.numBins), range=[[xmin, xmax], [ymin, ymax]])
except ValueError:
raise QuerySyntaxError("")
def _timePerEvent(self):
q = query(
self.analysisDB,
"select ProcessTime from metadata where ProcessingStatus='normal'")
return round(np.mean(np.hstack(q)), 1), round(np.std(np.hstack(q)), 1)
def _timeseriesSaved(self):
try:
q = query(self.analysisDB, "select TimeSeries from metadata")
return len(q[0][0]) > 0
except:
return False