def main(argv):
path = os.path.expanduser('~/Documents/data_old/OPD40cm/20130809/')
airmasstables = ['G9348/timeairmass.dat', 'SA111773/timeairmass.dat', 'SA114750/timeairmass.dat']
#airmasstables = ['SA114750/timeairmass.dat']
targetRa = [21.8736111111111,19.6211111111111,22.6958333333333]
targetDec = [2.38888888888889,0.183055555555556,1.21055555555556]
sitelat =-25.3416666667
sitelong = 3.21148148148
#airmasstables = np.loadtxt(os.path.join(path,airmasstableslis),dtype='S',ndmin=1)
T0 = 2456514.
nightstart = 2456514.4
nightend = 2456514.85
timeStamp = np.linspace(nightstart,nightend,1e3)
lstStamp = [_skysub.lst(time,sitelong) for time in timeStamp]
ax = py.subplot(111)
ylim = [0,0.5]
color = ['b','r','g','y','c','m', 'k']
for i in range(len(airmasstables)):
data = np.loadtxt(os.path.join(path,airmasstables[i]),unpack=True,converters={0:datestr2JD})
secz = np.array([_skysub.true_airmass(_skysub.secant_z(_skysub.altit(targetDec[i],lst - targetRa[i],sitelat)[0])) for lst in lstStamp])
data[1] = np.array([_skysub.true_airmass(_skysub.secant_z(_skysub.altit(targetDec[i],_skysub.lst(time,sitelong) - targetRa[i],sitelat)[0])) for time in data[0]])
mm = np.bitwise_and(data[1] > 0, data[1] < 3)
py.plot(data[0][mm]-T0,np.log10(data[1][mm]),color[i]+'o')
py.plot(data[0][mm]-T0,np.log10(data[1][mm]),color[i]+'o')
mm = np.bitwise_and(secz > 0, secz < 3)
py.plot(timeStamp[mm]-T0,np.log10(secz[mm]),color[i]+'-')
py.plot([nightstart-T0,nightstart-T0],ylim,'k--',lw=1.1,alpha=1.0)
py.plot([nightend-T0,nightend-T0],ylim,'k--',lw=1.1,alpha=1.0)
py.ylim(ylim[1],ylim[0])
a=ax.get_yticks().tolist()
print a
newyticks = ['%.2f'%(10**(val)) for val in a]
print newyticks
ax.set_yticklabels(newyticks)
py.ylabel('airmass',size=18)
py.xlabel('JD - %.0f'%T0,size=18)
py.savefig(os.path.expanduser('~/Develop/SMAPs/Figures/plot_airmasses_obs.pdf'))
py.show()
return 0
def computesky(self) : # computes many quantities so they're self-consistent sidtemp = _skysub.lst(self.jd,self.longit) self.sidereal = ra(sidtemp) # it behaves like an RA self.decimalyr = self.julian_epoch() self.CoordsOfDate = self.precess(self.julian_epoch()) self.hanow = ha(self.sidereal.val - self.CoordsOfDate.ra.val) [self.altit,self.az,self.parang] = \ _skysub.altit(self.CoordsOfDate.dec.val,self.hanow.val, \ self.lat) self.secz = _skysub.secant_z(self.altit) self.airmass = _skysub.true_airmass(self.secz)
def computesky(self):
# computes many quantities so they're self-consistent
sidtemp = _skysub.lst(self.jd, self.longit)
self.sidereal = ra(sidtemp) # it behaves like an RA
self.decimalyr = self.julian_epoch()
self.CoordsOfDate = self.precess(self.julian_epoch())
self.hanow = ha(self.sidereal.val - self.CoordsOfDate.ra.val)
[self.altit,self.az,self.parang] = \
_skysub.altit(self.CoordsOfDate.dec.val,self.hanow.val, \
self.lat)
self.secz = _skysub.secant_z(self.altit)
self.airmass = _skysub.true_airmass(self.secz)
def selectStandardTargets(self,nstars=3,nairmass=3):
'''
Based on configuration parameters, select 'nstars' standard stars to run scheduler on a specified Julian Day. Ideally you
will select standard stars before your science targets so not to have a full queue. Usually standard stars are observed
more than once a night at different airmasses. The user can control this parameter with nairmass and the script will try
to take care of the rest.
'''
session = Session()
# First of all, standard stars can be obsered multiple times in sucessive nights. I will mark all
# stars an unscheduled.
targets = session.query(Targets).filter(Targets.scheduled == True).filter(Targets.type == self.stdFlag)
for target in targets:
target.scheduled = False
session.commit()
# [To be done] Reject objects that are close to the moon
# Selecting standard stars is not only searching for the higher in that time but select stars than can be observed at 3
# or more (nairmass) different airmasses. It is also important to select stars with different colors (but this will be
# taken care in the future).
if nairmass*nstars > len(self.obsTimeBins):
self.log.warning('Requesting more stars/observations than it will be possible to schedule. Decreasing number of requests to fit in the night.')
nstars = len(self.obsTimeBins)/nairmass
obsStandars = np.zeros(len(self.obsTimeBins))-1 # first selection of observable standards
for tbin,time in enumerate(self.obsTimeBins):
if self.obsTimeMask[tbin] < 1.0:
# 1 - Select objects from database that where not scheduled yet (standard stars may be repited)
# that fits our observing night
targets = session.query(Targets).filter(Targets.scheduled == 0).filter(Targets.type == self.stdFlag)
lst = _skysub.lst(time,self.sitelong) #*360./24.
alt = np.array([_skysub.altit(target.targetDec,lst - target.targetRa,self.sitelat)[0] for target in targets])
stg = alt.argmax()
self.log.info('Selecting %s'%(targets[stg]))
# Marking target as schedule
tst = session.query(Targets).filter(Targets.id == targets[stg].id)
for t in tst:
t.scheduled = True
session.commit()
obsStandars[tbin] = t.id
else:
self.log.info('Bin already filled up with observations. Skipping...')
if len(obsStandars[obsStandars >= 0]) < nstars:
self.log.warning('Could not find %i suitable standard stars in catalog. Only %i where found.'%(nstars,len(obsStandars[obsStandars >= 0])))
#
# Unmarking potential targets as scheduled
#
for id in obsStandars[obsStandars >= 0]:
target = session.query(Targets).filter(Targets.id == id)
for t in target:
t.scheduled = False
session.commit()
tbin+=1
#
# Preparing a grid of altitudes for each target for each observing window
#
amGrid = np.zeros(len(obsStandars)*len(obsStandars)).reshape(len(obsStandars),len(obsStandars))
for i in np.arange(len(obsStandars))[obsStandars >= 0]:
target = session.query(Targets).filter(Targets.id == obsStandars[i])[0]
for j in range(len(obsStandars)):
lst = _skysub.lst(self.obsTimeBins[j],self.sitelong)
amGrid[i][j] = _skysub.true_airmass(_skysub.secant_z(_skysub.altit(target.targetDec,lst - target.targetRa,self.sitelat)[0]))
if amGrid[i][j] < 0:
amGrid [i][j] = 99.
#
# Build a grid mask that specifies the position in time each target should be observed. This means that, when
# selecting a single target we ocuppy more than one, non consecutive, position in the night. This grid shows where are these
# positions.
#
obsMask = np.zeros(len(obsStandars)*len(obsStandars),dtype=np.bool).reshape(len(obsStandars),len(obsStandars))
for i in np.arange(len(obsStandars))[obsStandars >= 0]:
amObs = np.linspace(amGrid[i].min(),self.stdMaxAirmass,nairmass) # requested aimasses
dam = np.mean(np.abs(amGrid[i][amGrid[i]<self.stdMaxAirmass][1:] - amGrid[i][amGrid[i]<self.stdMaxAirmass][:-1])) # how much airmass changes in average
for j,am in enumerate(amObs):
# Mark positions where target is at specified airmass
if j == 0:
obsMask[i] = np.bitwise_or(obsMask[i],amGrid[i] == am)
else:
obsMask[i] = np.bitwise_or(obsMask[i],np.bitwise_and(amGrid[i]>am-dam,amGrid[i]<am+dam))
#print amGrid[i][np.where(obsMask[i])]
#
# Now it is time to actually select the targets. It will start with the first target and then try the others
# until it find enough standard stars, as specified by the user.
#
# Para cada bin em tempo, varro o bin em massa de ar por coisas observaveis. Se acho um, vejo se posso agendar
# os outros bins. Se sim, marco o alvo para observacao, se nao, passo para o proximo. Repito ate completar a
# lista de alvos
#
obsMaskTimeGrid = np.zeros(len(obsStandars),dtype=np.bool)
nrequests = 0
reqId = np.zeros(nstars,dtype=np.int)-1
for tbin,time in enumerate(self.obsTimeBins[:-1]):
# Evaluates if time slots are all available. If yes, mark orbservation and ocuppy slots.
if ( (not obsMaskTimeGrid[obsMask[tbin]].any()) and (len(amGrid[tbin][obsMask[tbin]])>=nairmass) ):
obsMaskTimeGrid = np.bitwise_or(obsMaskTimeGrid,obsMask[tbin])
reqId[nrequests] = tbin
nrequests += 1
if nrequests >= nstars:
break
# Finally, requesting observations
for id in reqId[reqId >= 0]:
target = session.query(Targets).filter(Targets.id == obsStandars[id])[0]
secz = amGrid[id][obsMask[id]]
seczreq = np.zeros(nairmass,dtype=np.bool)
amObs = np.linspace(amGrid[id].min(),self.stdMaxAirmass,nairmass) # requested aimasses
for i,obstime in enumerate(self.obsTimeBins[obsMask[id]]):
sindex = np.abs(amObs-secz[i]).argmin()
if not seczreq[sindex]:
self.log.info('Requesting observations of %s @airmass=%4.2f @mjd=%.3f...'%(target.name,secz[i],obstime-2400000.5))
seczreq[sindex] = True
target.scheduled = True
session.commit()
self.addObservation(target,obstime)
self.obsTimeMask[obsMask[id]] = 1.0
#print self.obsTimeBins[obsMask[id]]
#print
#print i
return 0 #targets
def selectStandardTargets(self, nstars=3, nairmass=3):
'''
Based on configuration parameters, select 'nstars' standard stars to run scheduler on a specified Julian Day. Ideally you
will select standard stars before your science targets so not to have a full queue. Usually standard stars are observed
more than once a night at different airmasses. The user can control this parameter with nairmass and the script will try
to take care of the rest.
'''
session = Session()
# First of all, standard stars can be obsered multiple times in sucessive nights. I will mark all
# stars an unscheduled.
targets = session.query(Targets).filter(
Targets.scheduled == True).filter(Targets.type == self.stdFlag)
for target in targets:
target.scheduled = False
session.commit()
# [To be done] Reject objects that are close to the moon
# Selecting standard stars is not only searching for the higher in that time but select stars than can be observed at 3
# or more (nairmass) different airmasses. It is also important to select stars with different colors (but this will be
# taken care in the future).
if nairmass * nstars > len(self.obsTimeBins):
log.warning(
'Requesting more stars/observations than it will be possible to schedule. Decreasing number of requests to fit in the night.'
)
nstars = len(self.obsTimeBins) / nairmass
obsStandars = np.zeros(len(
self.obsTimeBins)) - 1 # first selection of observable standards
for tbin, time in enumerate(self.obsTimeBins):
if self.obsTimeMask[tbin] < 1.0:
# 1 - Select objects from database that where not scheduled yet (standard stars may be repited)
# that fits our observing night
targets = session.query(Targets).filter(
Targets.scheduled == 0).filter(
Targets.type == self.stdFlag)
lst = _skysub.lst(time, self.sitelong) #*360./24.
alt = np.array([
_skysub.altit(target.targetDec, lst - target.targetRa,
self.sitelat)[0] for target in targets
])
stg = alt.argmax()
log.info('Selecting %s' % (targets[stg]))
# Marking target as schedule
tst = session.query(Targets).filter(
Targets.id == targets[stg].id)
for t in tst:
t.scheduled = True
session.commit()
obsStandars[tbin] = t.id
else:
log.info(
'Bin already filled up with observations. Skipping...')
if len(obsStandars[obsStandars >= 0]) < nstars:
log.warning(
'Could not find %i suitable standard stars in catalog. Only %i where found.'
% (nstars, len(obsStandars[obsStandars >= 0])))
#
# Unmarking potential targets as scheduled
#
for id in obsStandars[obsStandars >= 0]:
target = session.query(Targets).filter(Targets.id == id)
for t in target:
t.scheduled = False
session.commit()
tbin += 1
#
# Preparing a grid of altitudes for each target for each observing window
#
amGrid = np.zeros(len(obsStandars) * len(obsStandars)).reshape(
len(obsStandars), len(obsStandars))
for i in np.arange(len(obsStandars))[obsStandars >= 0]:
target = session.query(Targets).filter(
Targets.id == obsStandars[i])[0]
for j in range(len(obsStandars)):
lst = _skysub.lst(self.obsTimeBins[j], self.sitelong)
amGrid[i][j] = _skysub.true_airmass(
_skysub.secant_z(
_skysub.altit(target.targetDec, lst - target.targetRa,
self.sitelat)[0]))
if amGrid[i][j] < 0:
amGrid[i][j] = 99.
#
# Build a grid mask that specifies the position in time each target should be observed. This means that, when
# selecting a single target we ocuppy more than one, non consecutive, position in the night. This grid shows where are these
# positions.
#
obsMask = np.zeros(len(obsStandars) * len(obsStandars),
dtype=np.bool).reshape(len(obsStandars),
len(obsStandars))
for i in np.arange(len(obsStandars))[obsStandars >= 0]:
amObs = np.linspace(amGrid[i].min(), self.stdMaxAirmass,
nairmass) # requested aimasses
dam = np.mean(
np.abs(amGrid[i][amGrid[i] < self.stdMaxAirmass][1:] -
amGrid[i][amGrid[i] < self.stdMaxAirmass][:-1])
) # how much airmass changes in average
for j, am in enumerate(amObs):
# Mark positions where target is at specified airmass
if j == 0:
obsMask[i] = np.bitwise_or(obsMask[i], amGrid[i] == am)
else:
obsMask[i] = np.bitwise_or(
obsMask[i],
np.bitwise_and(amGrid[i] > am - dam,
amGrid[i] < am + dam))
#print amGrid[i][np.where(obsMask[i])]
#
# Now it is time to actually select the targets. It will start with the first target and then try the others
# until it find enough standard stars, as specified by the user.
#
# Para cada bin em tempo, varro o bin em massa de ar por coisas observaveis. Se acho um, vejo se posso agendar
# os outros bins. Se sim, marco o alvo para observacao, se nao, passo para o proximo. Repito ate completar a
# lista de alvos
#
obsMaskTimeGrid = np.zeros(len(obsStandars), dtype=np.bool)
nrequests = 0
reqId = np.zeros(nstars, dtype=np.int) - 1
for tbin, time in enumerate(self.obsTimeBins[:-1]):
# Evaluates if time slots are all available. If yes, mark orbservation and ocuppy slots.
if ((not obsMaskTimeGrid[obsMask[tbin]].any())
and (len(amGrid[tbin][obsMask[tbin]]) >= nairmass)):
obsMaskTimeGrid = np.bitwise_or(obsMaskTimeGrid, obsMask[tbin])
reqId[nrequests] = tbin
nrequests += 1
if nrequests >= nstars:
break
# Finally, requesting observations
for id in reqId[reqId >= 0]:
target = session.query(Targets).filter(
Targets.id == obsStandars[id])[0]
secz = amGrid[id][obsMask[id]]
seczreq = np.zeros(nairmass, dtype=np.bool)
amObs = np.linspace(amGrid[id].min(), self.stdMaxAirmass,
nairmass) # requested aimasses
for i, obstime in enumerate(self.obsTimeBins[obsMask[id]]):
sindex = np.abs(amObs - secz[i]).argmin()
if not seczreq[sindex]:
log.info(
'Requesting observations of %s @airmass=%4.2f @mjd=%.3f...'
% (target.name, secz[i], obstime - 2400000.5))
seczreq[sindex] = True
target.scheduled = True
session.commit()
self.addObservation(target, obstime)
self.obsTimeMask[obsMask[id]] = 1.0
#print self.obsTimeBins[obsMask[id]]
#print
#print i
return 0 #targets
def selectStandardTargets(self,flag,nstars=3,nairmass=3):
'''
Based on configuration parameters, select 'nstars' standard stars to run scheduler on a specified Julian Day. Ideally you
will select standard stars before your science targets so not to have a full queue. Usually standard stars are observed
more than once a night at different airmasses. The user can control this parameter with nairmass and the script will try
to take care of the rest.
'''
session = Session()
# query project information
projQuery = session.query(Projects).filter(Projects.flag == flag)
totobstime = 0.
# Calculate total observation time
for block in projQuery:
totobstime += block.exptime
totobstime /= 86400.0
# First of all, standard stars can be observed multiple times in sucessive nights. I will mark all
# stars as unscheduled.
targets = session.query(Targets).filter(Targets.scheduled == True).filter(Targets.type == flag)
for target in targets:
target.scheduled = False
session.commit()
# [To be done] Reject objects that are close to the moon
# [To be done] Apply all sorts of rejections
# Selecting standard stars is not only searching for the higher in that time but select stars than can be observed at 3
# or more (nairmass) different airmasses. It is also important to select stars with different colors (but this will be
# taken care in the future).
if nairmass*nstars > len(self.obsTimeBins):
self.log.warning('Requesting more stars/observations than it will be possible to schedule. Decreasing number of requests to fit in the night.')
nstars = len(self.obsTimeBins)/nairmass
# Build a grid of desired times for higher airmass observation of each standard star.
stdObsTimeBin = np.arange(10,len(self.obsTimeBins)-10,(len(self.obsTimeBins)-10)/nstars)
obsStandars = np.zeros(nstars)
print stdObsTimeBin
# selecting the closest bin without observation
stdObsTimeBin,status = self.findSuitableTimeBin(stdObsTimeBin)
if status != 0:
raise Exception('Could not find suitable time to start observations! Try cleaning queue.')
print stdObsTimeBin
site = Site()
calclst = lambda time: np.sum(np.array([float(tt) / 60.**i for i,tt in enumerate(str(site._getEphem(datetimeFromJD(time)).sidereal_time()).split(':'))]))
nightlst = np.array([calclst(obstime) for obstime in self.obsTimeBins])
for i,tbin in enumerate(stdObsTimeBin):
# selecting the closest bin without observation
closestcleanbin = tbin
while self.obsTimeMask[closestcleanbin] > 0.0:
closestcleanbin += 1
if i+1 < len(stdObsTimeBin):
if closestcleanbin > stdObsTimeBin[i+1]:
raise Exception('Could not find suitable place to start observations of standard star. Try cleaning queue.')
time = self.obsTimeBins[closestcleanbin]
# 1 - Select objects from database that where not scheduled yet (standard stars may be repited)
# that fits our observing night
#targetSched = False
# Will try until a good match is obtained
#while( not targetSched ):
targets = session.query(Targets).filter(Targets.scheduled == 0).filter(Targets.type == flag)
if len(targets[:]) > 0:
#ephem = site._getEphem(datetimeFromJD(time))
lst = calclst(time) #np.sum(np.array([float(tt) / 60.**i for i,tt in enumerate(str(ephem.sidereal_time()).split(':'))]))
sitelat = np.sum(np.array([float(tt) / 60.**i for i,tt in enumerate(str(site['latitude']).split(':'))]))
alt = np.array([_skysub.altit(target.targetDec,lst - target.targetRa,sitelat)[0] for target in targets])
stg = alt.argmax()
print('Selecting %s'%(targets[stg]))
# Marking target as schedule
tst = session.query(Targets).filter(Targets.id == targets[stg].id)
# Build airmass table for object
objsecz = np.array([_skysub.true_airmass(_skysub.secant_z(_skysub.altit(targets[stg].targetDec,nlst - targets[stg].targetRa,sitelat)[0])) for nlst in nightlst])
# Build desired airmass table
#obsairmass = np.linspace(_skysub.true_airmass(_skysub.secant_z(alt[stg])),projQuery[0].maxairmass,nairmass)
obsairmass = np.logspace(np.log10(np.min(objsecz[objsecz > 0])),np.log10(projQuery[0].maxairmass),nairmass)
np.savetxt('airmass_%04i.dat'%(stg),X=zip(self.obsTimeBins,objsecz))
# Build mask with scheduled airmasses
#mask = np.zeros(len(objsecz),dtype=bool) == 1
pltobstime,pltobsairmass = np.array([]),np.array([])
# Try scheduling observations on all airmasses
for airmass in obsairmass:
# Get times where the object is close to the desired airmass and there are no observations scheduled
timeobsmask = np.bitwise_and(self.obsTimeMask < 1.0,np.abs(objsecz - airmass) < self.tolairmass)
# Check that there are times available
if not timeobsmask.any():
#raise Exception('No time available for scheduling observations of standard star %s at airmass %.3f'%(targets[stg],airmass))
self.log.warning('No time available for scheduling observations of standard star %s at airmass %.3f'%(targets[stg],airmass))
# Start trying to schedule observations
indexes = np.arange(len(self.obsTimeMask))[timeobsmask] #np.bitwise_and(self.obsTimeMask, timeobsmask)
obsSched = False
for index in indexes:
print('[%.3f] - Time bin available for observation of standard star at airmass %.3f'%(self.obsTimeBins[index], airmass))
print '- Require %i extra time bins'%(totobstime/self.tbin)
if (self.obsTimeMask[index:index+totobstime/self.tbin] < 1.0).all():
print 'Observation fit in this block.'
self.obsTimeMask[index:index+totobstime/self.tbin] = 1.0
self.log.info('Requesting observations of %s @airmass=%4.2f @mjd=%.3f...'%(target.name,airmass,self.obsTimeBins[index]-2400000.5))
pltobstime = np.append(pltobstime,self.obsTimeBins[index:index+totobstime/self.tbin])
pltobsairmass = np.append(pltobsairmass, objsecz[index:index+totobstime/self.tbin])
#for nblock,ii in enumerate(range(index,int(index+totobstime/self.tbin),1)):
self.addObservation(targets[stg],self.obsTimeBins[index],projQuery)
break
np.savetxt('obsairmass_%04i.dat'%stg,X = zip(pltobstime,pltobsairmass))
#self.obsTimeMask[index] = 1.0
#for iobsbins in range(index+1,index+int(totobstime/self.tbin)):
#print '[%i] - require extra time bin'%(iobsbins)
#if self.obsTimeMask[iobsbins] < 1.0:
# self.obsTimeMask[iobsbins] = 1.0
#else:
# raise Exception('Time bin [%i/%i] not available for observation of standard star at airmass %.3f'%(iobsbins,len(self.obsTimeMask),airmass))
#else:
#raise Exception('Time bin not available for observation of standard star at airmass %.3f'%(airmass))
for t in tst:
t.scheduled = True
session.commit()
obsStandars[i] = t.id
else:
self.log.warning('No suitable standard star for jd:%.3f in database...'%(time))
return 0
return 0
if len(obsStandars[obsStandars >= 0]) < nstars:
self.log.warning('Could not find %i suitable standard stars in catalog. Only %i where found.'%(nstars,len(obsStandars[obsStandars >= 0])))
obsStandars = np.zeros(len(self.obsTimeBins))-1 # first selection of observable standards
for tbin,time in enumerate(self.obsTimeBins):
if self.obsTimeMask[tbin] < 1.0:
# 1 - Select objects from database that where not scheduled yet (standard stars may be repited)
# that fits our observing night
targets = session.query(Targets).filter(Targets.scheduled == 0).filter(Targets.type == flag)
if len(targets[:]) > 0:
ephem = site._getEphem(datetimeFromJD(time))
lst = np.sum(np.array([float(tt) / 60.**i for i,tt in enumerate(str(ephem.sidereal_time()).split(':'))]))
sitelat = np.sum(np.array([float(tt) / 60.**i for i,tt in enumerate(str(site['latitude']).split(':'))]))
secz = np.array([_skysub.secant_z(_skysub.altit(target.targetDec,lst - target.targetRa,sitelat)[0]) for target in targets])
stg = secz.argmax()
self.log.info('Selecting %s'%(targets[stg]))
# Marking target as schedule
tst = session.query(Targets).filter(Targets.id == targets[stg].id)
for t in tst:
t.scheduled = True
session.commit()
obsStandars[tbin] = t.id
else:
print('No suitable target for jd:%.3f in database...'%(time))
break
else:
self.log.info('Bin already filled up with observations. Skipping...')
if len(obsStandars[obsStandars >= 0]) < nstars:
self.log.warning('Could not find %i suitable standard stars in catalog. Only %i where found.'%(nstars,len(obsStandars[obsStandars >= 0])))
#
# Unmarking potential targets as scheduled
#
for id in obsStandars[obsStandars >= 0]:
target = session.query(Targets).filter(Targets.id == id)
for t in target:
t.scheduled = False
session.commit()
tbin+=1
#
# Preparing a grid of altitudes for each target for each observing window
#
amGrid = np.zeros(len(obsStandars)*len(obsStandars)).reshape(len(obsStandars),len(obsStandars))
for i in np.arange(len(obsStandars))[obsStandars >= 0]:
target = session.query(Targets).filter(Targets.id == obsStandars[i])[0]
for j in range(len(obsStandars)):
lst = _skysub.lst(self.obsTimeBins[j],self.sitelong)
amGrid[i][j] = _skysub.true_airmass(_skysub.secant_z(_skysub.altit(target.targetDec,lst - target.targetRa,self.sitelat)[0]))
if amGrid[i][j] < 0:
amGrid [i][j] = 99.
#
# Build a grid mask that specifies the position in time each target should be observed. This means that, when
# selecting a single target we ocuppy more than one, non consecutive, position in the night. This grid shows where are these
# positions.
#
obsMask = np.zeros(len(obsStandars)*len(obsStandars),dtype=np.bool).reshape(len(obsStandars),len(obsStandars))
for i in np.arange(len(obsStandars))[obsStandars >= 0]:
amObs = np.linspace(amGrid[i].min(),self.stdMaxAirmass,nairmass) # requested aimasses
dam = np.mean(np.abs(amGrid[i][amGrid[i]<self.stdMaxAirmass][1:] - amGrid[i][amGrid[i]<self.stdMaxAirmass][:-1])) # how much airmass changes in average
for j,am in enumerate(amObs):
# Mark positions where target is at specified airmass
if j == 0:
obsMask[i] = np.bitwise_or(obsMask[i],amGrid[i] == am)
else:
obsMask[i] = np.bitwise_or(obsMask[i],np.bitwise_and(amGrid[i]>am-dam,amGrid[i]<am+dam))
#print amGrid[i][np.where(obsMask[i])]
#
# Now it is time to actually select the targets. It will start with the first target and then try the others
# until it find enough standard stars, as specified by the user.
#
# Para cada bin em tempo, varro o bin em massa de ar por coisas observaveis. Se acho um, vejo se posso agendar
# os outros bins. Se sim, marco o alvo para observacao, se nao, passo para o proximo. Repito ate completar a
# lista de alvos
#
obsMaskTimeGrid = np.zeros(len(obsStandars),dtype=np.bool)
nrequests = 0
reqId = np.zeros(nstars,dtype=np.int)-1
for tbin,time in enumerate(self.obsTimeBins[:-1]):
# Evaluates if time slots are all available. If yes, mark orbservation and ocuppy slots.
if ( (not obsMaskTimeGrid[obsMask[tbin]].any()) and (len(amGrid[tbin][obsMask[tbin]])>=nairmass) ):
obsMaskTimeGrid = np.bitwise_or(obsMaskTimeGrid,obsMask[tbin])
reqId[nrequests] = tbin
nrequests += 1
if nrequests >= nstars:
break
# Finally, requesting observations
for id in reqId[reqId >= 0]:
target = session.query(Targets).filter(Targets.id == obsStandars[id])[0]
secz = amGrid[id][obsMask[id]]
seczreq = np.zeros(nairmass,dtype=np.bool)
amObs = np.linspace(amGrid[id].min(),self.stdMaxAirmass,nairmass) # requested aimasses
for i,obstime in enumerate(self.obsTimeBins[obsMask[id]]):
sindex = np.abs(amObs-secz[i]).argmin()
if not seczreq[sindex]:
self.log.info('Requesting observations of %s @airmass=%4.2f @mjd=%.3f...'%(target.name,secz[i],obstime-2400000.5))
seczreq[sindex] = True
target.scheduled = True
session.commit()
self.addObservation(target,obstime)
self.obsTimeMask[obsMask[id]] = 1.0
#print self.obsTimeBins[obsMask[id]]
#print
#print i
return 0 #targets