def stimcount(band,t0,t1,margin=[90.01,90.01],aspum=68.754932/1000.,
eclipse=None):
"""Return stim counts."""
if not eclipse:
eclipse = 55000 if isPostCSP(t0) else 30000
if isPostCSP(t0):
margin[1]=180.02
avgstim = CalUtils.avg_stimpos(band,eclipse)
return ('{baseURL}select count(*) from {baseDB}.{band}PhotonsNULLV '+
'where time >= {t0} and time < {t1} and ('+
'((x >= {x10} and x < {x11}) and (y >= {y10} and y < {y11})) or '+
'((x >= {x20} and x < {x21}) and (y >= {y20} and y < {y21})) or '+
'((x >= {x30} and x < {x31}) and (y >= {y30} and y < {y31})) or '+
'((x >= {x40} and x < {x41}) and (y >= {y40} and y < {y41}))'+
'){formatURL}').format(baseURL=baseURL, baseDB=baseDB, band=band,
t0=str(long(t0*tscale)), t1=str(long(t1*tscale)),
x10=(avgstim['x1']-margin[0])/aspum,
x11=(avgstim['x1']+margin[0])/aspum,
y10=(avgstim['y1']-margin[1])/aspum,
y11=(avgstim['y1']+margin[1])/aspum,
x20=(avgstim['x2']-margin[0])/aspum,
x21=(avgstim['x2']+margin[0])/aspum,
y20=(avgstim['y2']-margin[1])/aspum,
y21=(avgstim['y2']+margin[1])/aspum,
x30=(avgstim['x3']-margin[0])/aspum,
x31=(avgstim['x3']+margin[0])/aspum,
y30=(avgstim['y3']-margin[1])/aspum,
y31=(avgstim['y3']+margin[1])/aspum,
x40=(avgstim['x4']-margin[0])/aspum,
x41=(avgstim['x4']+margin[0])/aspum,
y40=(avgstim['y4']-margin[1])/aspum,
y41=(avgstim['y4']+margin[1])/aspum,formatURL=formatURL)
def stimcount(data,band,t0,t1,margin=90.001):
"""Given a dict() that contains a list of 'x' and 'y' detector positions
as well as 't' event times, returns the total number of stim events.
"""
pltscl = 68.754932 # arcsec/mm
aspum = pltscl/1000.0 # arcsec/um
eclipse = 100. if isPostCSP(t0) else 40000. # HACK: for backwards comp.
avgstim = avg_stimpos(band,eclipse)
time = ((np.array(data['t'])>=t0) & (np.array(data['t'])<=t1))
stim1 = ((np.array(data['x'])>(avgstim['x1']-margin)) &
(np.array(data['x'])<(avgstim['x1']+margin)) &
(np.array(data['y'])>(avgstim['y1']-margin)) &
(np.array(data['y'])<(avgstim['y1']+margin)))
stim2 = ((np.array(data['x'])>(avgstim['x2']-margin)) &
(np.array(data['x'])<(avgstim['x2']+margin)) &
(np.array(data['y'])>(avgstim['y2']-margin)) &
(np.array(data['y'])<(avgstim['y2']+margin)))
stim3 = ((np.array(data['x'])>(avgstim['x3']-margin)) &
(np.array(data['x'])<(avgstim['x3']+margin)) &
(np.array(data['y'])>(avgstim['y3']-margin)) &
(np.array(data['y'])<(avgstim['y3']+margin)))
stim4 = ((np.array(data['x'])>(avgstim['x4']-margin)) &
(np.array(data['x'])<(avgstim['x4']+margin)) &
(np.array(data['y'])>(avgstim['y4']-margin)) &
(np.array(data['y'])<(avgstim['y4']+margin)))
ix = np.where(time & (stim1 | stim2 | stim3 | stim4))
return len(ix[0])
def deadtime_method2(data,t0,t1,band,refrate=79.,tstep=1., feeclkratio=0.966,refrange=[.4,2.]): """Given a list of global event times, computes the deadtime through direct comparison of the stim rate to the reference rate in bins of depth `tstep` seconds, and trimmed of outliers. This is close to the deadtime method used by the mission pipeline. """ eclipse = 100. if isPostCSP(t0) else 40000. # HACK: for backwards comp. exptime = t1-t0 bins = np.linspace(0.,exptime-exptime%tstep,exptime//tstep+1)+t0 h = np.zeros(len(bins)) for i,t in enumerate(bins): h[i] = stimcount(data,band,t,t+tstep-0.0001,eclipse)/exptime (minrate, maxrate) = (refrate*refrange[0],refrate+refrange[1]) ix = ((h<=maxrate) & (h>=minrate)).nonzero()[0] return (1.-((h[ix]/tstep)/feeclkratio)/refrate).mean()
