maxID = np.max(maxID)
# Crop off some of the early junky observations
minMJD = 56900
minID,mjd = sb.allSkyDB(0,'select min(ID) from dates where mjd > %i;' % minMJD, dtypes='int')
altLimit = 10. # Degrees
sunAltLimit = np.radians(-20.)
maxID = 3000
step = 5
for dateID in np.arange(minID.max(),minID.max()+maxID+1, step):
sqlQ = 'select stars.ra, stars.dec, stars.ID, obs.starMag_inst, obs.starMag_err,obs.sky, obs.filter from obs, stars, dates where obs.starID = stars.ID and obs.dateID = dates.ID and obs.filter = "%s" and obs.dateID = %i and obs.starMag_err != 0 and dates.sunAlt < %f and obs.starMag_inst > %f;' % (filt,dateID,sunAltLimit, starBrightLimit)
# Note that RA,Dec are in degrees
data,mjd = sb.allSkyDB(dateID, sqlQ=sqlQ, dtypes=dtypes)
if data.size > nStarLimit:
alt,az,pa = _altAzPaFromRaDec(np.radians(data['ra']), np.radians(data['dec']),
telescope.lon, telescope.lat, mjd)
for i,nside in enumerate(nsides):
tempMap = healbin(az,alt, az*0+1, nside=nside, reduceFunc=np.size)[hpIndices[i]]
ratio = np.size(np.where(tempMap > 0)[0])/float(np.size(tempMap))
mapFractions[i].append(ratio)
print 'nside, resolution (deg), fraction of healpixes populated'
for mf,nside in zip(mapFractions,nsides):
resol = hp.nside2resol(nside, arcmin=True)/60.
print ' %i, %f, %f' % (nside, resol,np.median(mf))
altLimit = 10. # Degrees
sunAltLimit = np.radians(-20.)
maxID = 3000
step = 5
for dateID in np.arange(minID.max(), minID.max() + maxID + 1, step):
sqlQ = 'select stars.ra, stars.dec, stars.ID, obs.starMag_inst, obs.starMag_err,obs.sky, obs.filter from obs, stars, dates where obs.starID = stars.ID and obs.dateID = dates.ID and obs.filter = "%s" and obs.dateID = %i and obs.starMag_err != 0 and dates.sunAlt < %f and obs.starMag_inst > %f;' % (
filt, dateID, sunAltLimit, starBrightLimit)
# Note that RA,Dec are in degrees
data, mjd = sb.allSkyDB(dateID, sqlQ=sqlQ, dtypes=dtypes)
if data.size > nStarLimit:
alt, az, pa = _altAzPaFromRaDec(np.radians(data['ra']),
np.radians(data['dec']), telescope.lon,
telescope.lat, mjd)
for i, nside in enumerate(nsides):
tempMap = healbin(az,
alt,
az * 0 + 1,
nside=nside,
reduceFunc=np.size)[hpIndices[i]]
ratio = np.size(np.where(tempMap > 0)[0]) / float(np.size(tempMap))
mapFractions[i].append(ratio)
print 'nside, resolution (deg), fraction of healpixes populated'
for mf, nside in zip(mapFractions, nsides):
resol = hp.nside2resol(nside, arcmin=True) / 60.
print ' %i, %f, %f' % (nside, resol, np.median(mf))
starsFit = np.load('solvedStar.npz')['result']
trueStars = fastRead('starInfo.dat', dtype=list(zip(['starID','TrueMag', 'ra','dec'],
[int,float,float,float])),
delimiter=',')
trueStars.sort(order='starID')
# Remove any stars that were observed but not fit
mask = np.in1d(trueStars['starID'], starsFit['starID'], assume_unique=True)
trueStars = trueStars[mask]
resid = starsFit['fitMag'] - trueStars['TrueMag']
resid = resid-np.median(resid)
print('median fitMag - TrueMag = %f'%np.median(resid))
print('std (fitMag - TrueMag) = %f'%np.std(resid))
print('robust RMS = %f'%robustRMS(resid))
rrms = robustRMS(resid)
plt.hist(resid, bins=100, range=(-4*rrms,4*rrms))
plt.xlabel('Fit-True (mags)')
plt.ylabel('#')
plt.savefig('resid_hist.png')
healmap = healbin(np.radians(trueStars['ra']), np.radians(trueStars['dec']), resid)
hp.mollview(healmap, min =-4*rrms, max=4*rrms, unit='Residuals (mag)')
plt.savefig('resid_map.png')
zp = 12.11
#zps = []
counter = 0
maxI = np.size(dateIDs)
for i, dateID in enumerate(dateIDs):
skydata, mjd = sb.allSkyDB(dateID, filt=band) #2744
airmass = 1. / np.cos(np.radians(90. - skydata['alt']))
skydata = skydata[np.where((airmass < 2.1) & (airmass >= 1.))]
alt, az, pa = altAzPaFromRaDec(np.radians(skydata['ra']),
np.radians(skydata['dec']), telescope.lon,
telescope.lat, mjd)
skyhp = healplots.healbin(az, alt, skydata['sky'], nside=nside)
sm.setRaDecMjd(np.radians(skydata['ra']),
np.radians(skydata['dec']),
mjd,
degrees=False)
if sm.sunAlt < np.radians(-12.):
sm.computeSpec()
mags = sm.computeMags(canonDict[band])
good = np.where(mags > 0)
if np.size(good[0]) > 10:
modelhp = healplots.healbin(az[good],
alt[good],
mags[good],
nside=nside)
zp = 12.11
#zps = []
counter = 0
maxI = np.size(dateIDs)
for i,dateID in enumerate(dateIDs):
skydata, mjd = sb.allSkyDB(dateID , filt=band)#2744
airmass = 1./np.cos(np.radians(90.-skydata['alt']))
skydata = skydata[np.where((airmass < 2.1) & (airmass >= 1.))]
alt,az,pa = altAzPaFromRaDec(np.radians(skydata['ra']), np.radians(skydata['dec']),
telescope.lon, telescope.lat, mjd)
skyhp = healplots.healbin(az, alt, skydata['sky'], nside=nside)
sm.setRaDecMjd(np.radians(skydata['ra']), np.radians(skydata['dec']), mjd, degrees=False)
if sm.sunAlt < np.radians(-12.):
sm.computeSpec()
mags = sm.computeMags(canonDict[band])
good = np.where(mags > 0)
if np.size(good[0]) > 10:
modelhp = healplots.healbin(az[good], alt[good], mags[good], nside=nside)
zp = np.median(mags[good] - skydata['sky'][good])
#zps.append(zp)
fig = plt.figure(num=1)
hp.mollview(skyhp+zp, rot=(0,90), sub=(2,1,1), fig=1, title='Cannon '+band+' mjd=%0.2f'%sm.mjd,
min=cmin, max=cmax)
hp.mollview(modelhp, rot=(0,90), sub=(2,1,2), fig=1,
for j, (le, ri, mjd) in enumerate(zip(left, right, umjd)):
Observatory.date = mjd2djd(mjd)
sun.compute(Observatory)
alt, az, pa = altAzPaFromRaDec(data['ra'][le:ri],
data['dec'][le:ri], telescope.lon,
telescope.lat, mjd)
az = wrapRA(az - sun.az)
altaz['alt'][le:ri] += alt
altaz['az'][le:ri] += az
moon.compute(Observatory)
moonAlt[le:ri] += moon.alt
print 'making maps'
good = np.where(moonAlt < 0)
magMap[:, i] = healbin(altaz['az'][good],
altaz['alt'][good],
data['sky'][good],
nside=nside,
reduceFunc=np.median)
rmsMap[:, i] = healbin(altaz['az'][good],
altaz['alt'][good],
data['sky'][good],
nside=nside,
reduceFunc=robustRMS)
print 'saving maps'
np.savez('TwilightMaps/twiMaps_%s.npz' % filterName,
magMap=magMap,
rmsMap=rmsMap,
sunAlts=sunAlts)
if read:
data = np.load('Plots/valAr_%s.npz' % filterName)
validationArr = data['validationArr'].copy()
else:
for i, indx in enumerate(indices):
skydata, mjd = sb.allSkyDB(dateData[indx]['dateID'],
filt=filterName)
if skydata.size > starLimit:
airmass = 1. / np.cos(np.radians(90. - skydata['alt']))
skydata = skydata[np.where((airmass < amMax)
& (airmass >= 1.))]
alt, az, pa = _altAzPaFromRaDec(np.radians(skydata['ra']),
np.radians(skydata['dec']),
telescope.lon, telescope.lat,
mjd)
skyhp = healplots.healbin(az, alt, skydata['sky'], nside=nside)
skyhp[np.isnan(skyhp)] = hp.UNSEEN
good = np.where(skyhp != hp.UNSEEN)
sm.setRaDecMjd(hpra[good],
hpdec[good],
mjd,
degrees=False,
azAlt=True)
sm.computeSpec()
modelhp = np.zeros(npix, dtype=float) + hp.UNSEEN
modelhp[good] = sm.computeMags(canonDict[filterName])
validationArr['frameZP'][i] = np.median(modelhp[good] -
skyhp[good])
notnan = np.where(skyhp != hp.UNSEEN)
hpdec = np.pi/2.-lat
for filterName in filters:
if read:
data = np.load('Plots/valAr_%s.npz' % filterName)
validationArr = data['validationArr'].copy()
else:
for i,indx in enumerate(indices):
skydata,mjd = sb.allSkyDB(dateData[indx]['dateID'], filt=filterName)
if skydata.size > starLimit:
airmass = 1./np.cos(np.radians(90.-skydata['alt']))
skydata = skydata[np.where((airmass < amMax) & (airmass >= 1.))]
alt,az,pa = _altAzPaFromRaDec(np.radians(skydata['ra']), np.radians(skydata['dec']),
telescope.lon, telescope.lat, mjd)
skyhp = healplots.healbin(az,alt, skydata['sky'], nside=nside)
skyhp[np.isnan(skyhp)] = hp.UNSEEN
good = np.where(skyhp != hp.UNSEEN)
sm.setRaDecMjd(hpra[good], hpdec[good], mjd, degrees=False, azAlt=True)
sm.computeSpec()
modelhp = np.zeros(npix,dtype=float)+hp.UNSEEN
modelhp[good] = sm.computeMags(canonDict[filterName])
validationArr['frameZP'][i] = np.median(modelhp[good]-skyhp[good] )
notnan = np.where(skyhp != hp.UNSEEN)
validationArr['obsDarkestHP'][i] = np.where(skyhp == skyhp[notnan].max() )[0].min()
validationArr['obsBrightestHP'][i] = np.where(skyhp == skyhp[notnan].min() )[0].min()
notnan =np.where(modelhp != hp.UNSEEN)
validationArr['modelDarkestHP'][i] = np.where(modelhp == modelhp[notnan].max() )[0]
validationArr['modelBrightestHP'][i] = np.where(modelhp == modelhp[notnan].min() )[0]
umjd = np.unique(data['mjd'])
left = np.searchsorted(data['mjd'], umjd)
right = np.searchsorted(data['mjd'], umjd, side='right')
altaz = np.zeros(data.size, dtype=zip(['alt','az'], [float]*2))
moonAlt = np.zeros(data.size, dtype=float)
print 'computing alts and azs'
for j, (le, ri, mjd) in enumerate(zip(left,right,umjd)):
Observatory.date = mjd2djd(mjd)
sun.compute(Observatory)
alt, az, pa = altAzPaFromRaDec(data['ra'][le:ri], data['dec'][le:ri],
telescope.lon, telescope.lat, mjd)
az = wrapRA(az - sun.az)
altaz['alt'][le:ri] += alt
altaz['az'][le:ri] += az
moon.compute(Observatory)
moonAlt[le:ri] += moon.alt
print 'making maps'
good = np.where(moonAlt < 0)
magMap[:,i] = healbin(altaz['az'][good],altaz['alt'][good], data['sky'][good],
nside=nside, reduceFunc=np.median)
rmsMap[:,i] = healbin(altaz['az'][good],altaz['alt'][good],data['sky'][good],
nside=nside, reduceFunc=robustRMS)
print 'saving maps'
np.savez('TwilightMaps/twiMaps_%s.npz' % filterName, magMap=magMap, rmsMap=rmsMap, sunAlts=sunAlts)
trueStars = fastRead('starInfo.dat',
dtype=list(
zip(['starID', 'TrueMag', 'ra', 'dec'],
[int, float, float, float])),
delimiter=',')
trueStars.sort(order='starID')
# Remove any stars that were observed but not fit
mask = np.in1d(trueStars['starID'], starsFit['starID'], assume_unique=True)
trueStars = trueStars[mask]
resid = starsFit['fitMag'] - trueStars['TrueMag']
resid = resid - np.median(resid)
print('median fitMag - TrueMag = %f' % np.median(resid))
print('std (fitMag - TrueMag) = %f' % np.std(resid))
print('robust RMS = %f' % robustRMS(resid))
rrms = robustRMS(resid)
plt.hist(resid, bins=100, range=(-4 * rrms, 4 * rrms))
plt.xlabel('Fit-True (mags)')
plt.ylabel('#')
plt.savefig('resid_hist.png')
healmap = healbin(np.radians(trueStars['ra']), np.radians(trueStars['dec']),
resid)
hp.mollview(healmap, min=-4 * rrms, max=4 * rrms, unit='Residuals (mag)')
plt.savefig('resid_map.png')
