dateIDs = []
hpIDs = []
starMags = []
starMags_err = []
mjds = []
airmasses = []
altLimit = 25. # Degrees
sunAltLimit = np.radians(-20.)
starBrightLimit = -9.
# Demand this many stars before using the frame in ubercal
nStarLimit = 200
# get the max dateID
maxID, mjd = sb.allSkyDB(0, 'select max(ID) from dates;', dtypes='int')
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')
# See if there are any nights where there were observations, but no
# stars made it into the DB (i.e., it was totally cloudy all night).
allmjds, blah = sb.allSkyDB(0,
'select mjd from dates where mjd > %i;' % minMJD,
dtypes='float')
db_mjds = np.unique(np.floor(allmjds))
alt, az = hp.pix2ang(nside,indices)
alt = np.pi/2.-alt
airmass = 1./np.cos(np.pi/2.-alt)
good = np.where((airmass >=1) & (airmass <= airmassLimit) )
hpIndices.append(indices[good])
# Demand this many stars before using the frame in ubercal
nStarLimit = 200
names = ['ra','dec','starID','starMag', 'starMag_err', 'sky', 'filter']
types = [float,float,float, float,float,float,'|S1']
dtypes = zip(names,types)
# get the max dateID
maxID,mjd = sb.allSkyDB(0,'select max(ID) from dates;', dtypes='int')
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)
# 8191. 8649
#dateIDs = np.arange(8191, 8694, 2) #np.arange(1665, 2506, 5) #[2133,2134]
dateIDs = np.arange(28793, 29651, 2)
outDir = 'Movie'
cmin = 18.
cmax = 21.5
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:
# 8191. 8649
#dateIDs = np.arange(8191, 8694, 2) #np.arange(1665, 2506, 5) #[2133,2134]
dateIDs = np.arange(28793, 29651, 2)
outDir = 'Movie'
cmin = 18.
cmax = 21.5
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()
'B': 'blue_canon.csv'
}
path = os.path.join(os.environ.get('SIMS_SKYBRIGHTNESS_DATA_DIR'), 'Canon')
for key in canonFiles.keys():
data = np.loadtxt(os.path.join(path, canonFiles[key]),
delimiter=',',
dtype=zip(['wave', 'throughput'], [float, float]))
band = Bandpass()
band.setBandpass(data['wave'], data['throughput'])
canonDict[key] = band
sqlQ = 'select id,mjd,sunAlt,moonAlt from dates'
dateData, mjd = sb.allSkyDB(2744,
sqlQ=sqlQ,
filt=None,
dtypes=zip(['dateID', 'mjd', 'sunAlt', 'moonAlt'],
[int, float, float, float]))
skipsize = 10
indices = np.arange(0, dateData.size, skipsize)
# Maybe bin things on 15-min timescales to cut down the number of calls I need to make to the model
#binsize = 15./60./24.
#edges = np.arange(skydata['mjd'].min(),skydata['mjd'].max()+binsize*2, binsize)
read = True
filters = ['R', 'G', 'B']
sm = sb.SkyModel(mags=False)
telescope = TelescopeInfo('LSST')
canonDict = {}
canonFiles = {'R':'red_canon.csv','G':'green_canon.csv','B':'blue_canon.csv'}
path = os.path.join(os.environ.get('SIMS_SKYBRIGHTNESS_DATA_DIR'), 'Canon')
for key in canonFiles.keys():
data = np.loadtxt(os.path.join(path,canonFiles[key]), delimiter=',',
dtype=zip(['wave','throughput'],[float,float]))
band = Bandpass()
band.setBandpass(data['wave'], data['throughput'])
canonDict[key]=band
sqlQ = 'select id,mjd,sunAlt,moonAlt from dates'
dateData,mjd = sb.allSkyDB(2744 , sqlQ=sqlQ, filt=None,dtypes=zip(['dateID', 'mjd','sunAlt', 'moonAlt'],
[int,float,float,float]))
skipsize = 10
indices = np.arange(0,dateData.size, skipsize)
# Maybe bin things on 15-min timescales to cut down the number of calls I need to make to the model
#binsize = 15./60./24.
#edges = np.arange(skydata['mjd'].min(),skydata['mjd'].max()+binsize*2, binsize)
read = True
filters = ['R','G','B']
sm = sb.SkyModel(mags=False)
telescope = TelescopeInfo('LSST')
nside = 16
import numpy as np import lsst.sims.skybrightness as sb import healpy as hp from lsst.sims.utils import healbin nside = 4 filt = 'R' mjds = [] hpids = [] nstars = [] # Crop off junky early things minMJD = 56900 minID,mjd = sb.allSkyDB(0,'select min(ID) from dates where mjd > %i;' % minMJD, dtypes='int') maxID,mjd = sb.allSkyDB(0,'select max(ID) from dates where mjd > %i;' % minMJD, dtypes='int') # Temp pad for testing # maxID = minID+100 full_range = float(maxID - minID) for i,dateID in enumerate(np.arange(minID.max(),maxID.max())): print i, mjd data,mjd = sb.allSkyDB(dateID)# , sqlQ=sqlQ, dtypes=dtypes) starmap = healbin(data['ra'], data['dec'], data['dec'], nside=nside, reduceFunc=np.size) good = np.where(starmap != hp.UNSEEN)[0] mjds.extend([mjd]*good.size) hpids.extend(good.tolist())
