def testBinDeg(self):
"""
Test that healbin returns correct values and valid healpy maps.
"""
ra = np.zeros(3)
dec = np.zeros(3)
values = ra * 0. + 1.
nside = 128
hpid = utils.raDec2Hpid(nside, ra[0], dec[0])
map1 = utils.healbin(ra, dec, values, nside=nside)
self.assertEqual(map1[hpid], 1.)
self.assertEqual(hp.maptype(map1), 0)
map2 = utils.healbin(ra, dec, values, nside=nside, reduceFunc=np.sum)
self.assertEqual(map2[hpid], 3.)
self.assertEqual(hp.maptype(map2), 0)
map3 = utils.healbin(ra, dec, values, nside=nside, reduceFunc=np.std)
self.assertEqual(map3[hpid], 0.)
self.assertEqual(hp.maptype(map3), 0)
import healpy as hp
from lsst.sims.utils import healbin
import matplotlib.pylab as plt
# Just look at the number of stars we're getting on the sky
nside = 8
data = np.load('phot.npz')
phot = data['phot'].copy()
data.close()
umjd = np.unique(phot['mjd'])
phot.sort(order='mjd')
left = np.searchsorted(phot['mjd'], umjd)
right = np.searchsorted(phot['mjd'], umjd, side='right')
nstars = right - left
goodMJD = umjd[np.where(nstars == nstars.max())]
good = np.where(phot['mjd'] == goodMJD)
hmap = healbin(phot['RA'][good],
phot['dec'][good],
phot['dec'][good],
nside=nside,
reduceFunc=np.size)
fig = plt.figure(1)
hp.mollview(hmap, fig=1, title='%i stars' % np.size(good[0]), unit='N stars')
fig.savefig('stellar_density.png')
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())
nstars.extend(starmap[good].tolist())
print 'ack'
names = ['mjd', 'hpid', 'nstars']
types = [float, int, int]
result = np.array(zip(mjds, hpids, nstars), dtype=zip(names, types))
np.savez('starDensity_nside%i.npz' % nside, starmap=result, nside=nside)
import numpy as np
import healpy as hp
from lsst.sims.utils import healbin
import matplotlib.pylab as plt
# Just look at the number of stars we're getting on the sky
nside = 8
data = np.load('phot.npz')
phot = data['phot'].copy()
data.close()
umjd = np.unique(phot['mjd'])
phot.sort(order='mjd')
left = np.searchsorted(phot['mjd'], umjd)
right = np.searchsorted(phot['mjd'], umjd, side='right')
nstars = right-left
goodMJD = umjd[np.where(nstars == nstars.max())]
good = np.where(phot['mjd'] == goodMJD)
hmap = healbin(phot['RA'][good], phot['dec'][good], phot['dec'][good], nside=nside, reduceFunc=np.size)
fig = plt.figure(1)
hp.mollview(hmap, fig=1, title='%i stars' % np.size(good[0]), unit='N stars')
fig.savefig('stellar_density.png')
darkTimestdResid = data['darkTimestdResid'].copy()
darkTimesCount = data['darkTimesCount'].copy()
else:
darkTimeMaps = []
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 = 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)
# switch moon to ra dec
moonRA, moonDec = _raDecFromAltAz(sm.moonAlt, sm.moonAz,
telescope.lon, telescope.lat,
mjd)
# compute distances
moonDistances = haversine(hpra[good], hpdec[good], moonRA,
