def empirical_deadtime(band,trange,verbose=0,retries=20,feeclkratio=0.966):
"""Calculate empirical deadtime (per global count rate) using revised
formulas.
"""
model = {'FUV':[-0.000386611005025,76.5419507472],
'NUV':[-0.000417794996843,77.1516557638]}
rawexpt = trange[1]-trange[0]
gcr = gQuery.getValue(gQuery.globalcounts(band,trange[0],trange[1]))/rawexpt
refrate = model[band][1]/feeclkratio
scr = model[band][0]*gcr+model[band][1]
return (1-scr/feeclkratio/refrate)
def globalcount_shuttered(band,trange,verbose=0,timestamplist=False):
try:
t = (timestamplist if np.array(timestamplist).any() else
np.array(gQuery.getArray(
gQuery.uniquetimes(band,trange[0],trange[1],flag=True),
verbose=verbose),dtype='float64')[:,0]/gQuery.tscale)
except IndexError: # Shutter this whole time range.
if verbose:
print 'No data in {t0},{t1}'.format(t0=trange[0],t1=trange[1])
return 0
times = np.sort(np.unique(np.append(t,trange)))
tranges = distinct_tranges(times,maxgap=0.05)
nonnullevents,nullevents = 0,0
for trange in tranges:
nullevents += gQuery.getValue(
gQuery.deadtime2(band,trange[0],trange[1]),verbose=verbose)
nonnullevents += gQuery.getValue(gQuery.deadtime1(band,trange[0],
trange[1]),verbose=verbose)
return nullevents+nonnullevents
def stimcount_shuttered(band,trange,verbose=0,retries=20.,timestamplist=False):
try:
t = (timestamplist if np.array(timestamplist).any() else
np.array(gQuery.getArray(
gQuery.uniquetimes(band,trange[0],trange[1]),
verbose=verbose),dtype='float64')[:,0]/gQuery.tscale)
except IndexError: # Shutter this whole time range.
if verbose:
print 'No data in {t0},{t1}'.format(t0=trange[0],t1=trange[1])
return 0
times = np.sort(np.unique(np.append(t,trange)))
tranges = distinct_tranges(times,maxgap=0.05)
stimcount = 0
for trange in tranges:
stimcount += gQuery.getValue(gQuery.stimcount(band,trange[0],trange[1]),
verbose=verbose)+gQuery.getValue(
gQuery.stimcount(band,trange[0],trange[1],
null=False),verbose=verbose)
return stimcount
def netdead(band,t0,t1,tstep=1.,refrate=79.,verbose=0): print 'Time range: ['+str(t0)+', '+str(t1)+']' refrate = 79. # counts per second, nominal stim rate feeclkratio = 0.966 # not sure what detector property this adjusts for tec2fdead = 5.52e-6 # TEC to deadtime correction (Method 2) stimcount = gQuery.getValue(gQuery.stimcount(band,t0,t1)) totcount = (gQuery.getValue(gQuery.deadtime1(band,t0,t1)) + gQuery.getValue(gQuery.deadtime2(band,t0,t1))) exptime = t1-t0 # Method 0 # Empirical formula dead0 = tec2fdead*(totcount/exptime)/feeclkratio minrate,maxrate = refrate*.4,refrate+2. # Method 2 # Direct measurement of stims bins = np.linspace(0.,exptime-exptime%tstep,exptime//tstep+1)+t0 h = np.zeros(len(bins)) for i,t in enumerate(bins): print_inline(t1-t) h[i] = gQuery.getValue(gQuery.stimcount(band,t,t+tstep-0.0001)) #h,xh = np.histogram(stimt-trange[0],bins=bins) ix = ((h<=maxrate) & (h>=minrate)).nonzero()[0] dead2 = (1.-((h[ix]/tstep)/feeclkratio)/refrate).mean() # Method 1 # Empirical formula except with counts binned into 1s h = np.zeros(len(bins)) for i,t in enumerate(bins): print_inline(t1-t) h[i] = gQuery.getValue(gQuery.deadtime1(band,t,t+tstep-0.0001)) + gQuery.getValue(gQuery.deadtime2(band,t,t+tstep-0.0001)) dead1 = (tec2fdead*(h/tstep)/feeclkratio).mean() expt = compute_exptime(band,[t0,t1]) print dead0,dead1,dead2 return [t0, t1, dead0, dead1, dead2, expt, stimcount, totcount]
def exposure(band,trange,verbose=0,retries=20):
"""Compute the effective exposure time for a time range."""
rawexpt = trange[1]-trange[0]
if rawexpt<=0:
return 0.
shutdead = gQuery.getArray(gQuery.shutdead(band,trange[0],trange[1]),
verbose=verbose,retries=retries)
# NOTE: The deadtime correction in shutdead does not work properly in FUV
# so we're doing it separately for now.
deadtime = gQuery.getValue(gQuery.deadtime(band,trange[0],trange[1]),
verbose=verbose,retries=retries)
#return (rawexpt-shutdead[0][0])*(1.-shutdead[1][0])
return (rawexpt-shutdead[0][0])*(1.-deadtime)
def netdead(band, t0, t1, tstep=1., refrate=79., verbose=0):
print 'Time range: [' + str(t0) + ', ' + str(t1) + ']'
refrate = 79. # counts per second, nominal stim rate
feeclkratio = 0.966 # not sure what detector property this adjusts for
tec2fdead = 5.52e-6 # TEC to deadtime correction (Method 2)
stimcount = gQuery.getValue(gQuery.stimcount(band, t0, t1))
totcount = (gQuery.getValue(gQuery.deadtime1(band, t0, t1)) +
gQuery.getValue(gQuery.deadtime2(band, t0, t1)))
exptime = t1 - t0
# Method 0
# Empirical formula
dead0 = tec2fdead * (totcount / exptime) / feeclkratio
minrate, maxrate = refrate * .4, refrate + 2.
# Method 2
# Direct measurement of stims
bins = np.linspace(0., exptime - exptime % tstep,
exptime // tstep + 1) + t0
h = np.zeros(len(bins))
for i, t in enumerate(bins):
print_inline(t1 - t)
h[i] = gQuery.getValue(gQuery.stimcount(band, t, t + tstep - 0.0001))
#h,xh = np.histogram(stimt-trange[0],bins=bins)
ix = ((h <= maxrate) & (h >= minrate)).nonzero()[0]
dead2 = (1. - ((h[ix] / tstep) / feeclkratio) / refrate).mean()
# Method 1
# Empirical formula except with counts binned into 1s
h = np.zeros(len(bins))
for i, t in enumerate(bins):
print_inline(t1 - t)
h[i] = gQuery.getValue(gQuery.deadtime1(
band, t, t + tstep - 0.0001)) + gQuery.getValue(
gQuery.deadtime2(band, t, t + tstep - 0.0001))
dead1 = (tec2fdead * (h / tstep) / feeclkratio).mean()
expt = compute_exptime(band, [t0, t1])
print dead0, dead1, dead2
return [t0, t1, dead0, dead1, dead2, expt, stimcount, totcount]
