def set_maps(self, map1, map2, mjd1, mjd2):
"""
set the two cloud maps to use to generate cloud motion predictions
"""
# Put things in correct order if not already
if mjd2 < mjd1:
self.mjd1 = mjd2
self.mjd2 = mjd1
self.map2 = map1
self.map1 = map2
else:
self.mjd1 = mjd1
self.mjd2 = mjd2
self.map2 = map2
self.map1 = map1
self.nside = hp.npix2nside(map1.size)
if hp.npix2nside(map2.size) != self.nside:
raise ValueError('nside of map1 and map2 do not match.')
# What is the time between images
self.dt = self.mjd2 - self.mjd1
# Diff the maps, project to a screen.
# hmm, should probably change this to be a diff on screen rather than on sphere?
diff = diff_hp(self.map1, self.map2)
cloudyArea, self.cloudmask = cloudyness(diff)
self._find_dx_dy()
def set_maps(self, map1, map2, mjd1, mjd2):
"""
set the two cloud maps to use to generate cloud motion predictions
"""
# Put things in correct order if not already
if mjd2 < mjd1:
self.mjd1 = mjd2
self.mjd2 = mjd1
self.map2 = map1
self.map1 = map2
else:
self.mjd1 = mjd1
self.mjd2 = mjd2
self.map2 = map2
self.map1 = map1
self.nside = hp.npix2nside(map1.size)
if hp.npix2nside(map2.size) != self.nside:
raise ValueError('nside of map1 and map2 do not match.')
# What is the time between images
self.dt = self.mjd2-self.mjd1
# Diff the maps, project to a screen.
# hmm, should probably change this to be a diff on screen rather than on sphere?
diff = diff_hp(self.map1, self.map2)
cloudyArea, self.cloudmask = cloudyness(diff)
self._find_dx_dy()
dec = np.pi / 2. - dec
lmst, last = calcLmstLast(mjd, site.longitude_rad)
lmst = lmst / 12. * 180.
alt, az = stupidFast_RaDec2AltAz(ra, dec, site.latitude_rad,
site.longitude_rad, mjd)
frame = single_frame(mjd, filter_name='R')
seen = np.where((frame != hp.UNSEEN) & (previous != hp.UNSEEN))
unseen = np.where((frame == hp.UNSEEN) | (previous == hp.UNSEEN))
frame = fixBias(frame)
previous = fixBias(previous)
diff = frame - previous
diff[unseen] = hp.UNSEEN
diff_frac = diff / previous
diff_frac[unseen] = hp.UNSEEN
diff_frac[np.where(previous == 0)] = hp.UNSEEN
gdiff = np.where((diff != hp.UNSEEN) & (~np.isnan(diff)))[0]
if np.size(gdiff) > 0:
cf = cloudyness(diff_frac) / (gdiff.size * hp.nside2pixarea(nside) *
(180. / np.pi)**2)
print 'cloudy fraction, 5 deg scale = %.2f' % cf
cf = cloudyness(diff_frac,
fwhm=30.) / (gdiff.size * hp.nside2pixarea(nside) *
(180. / np.pi)**2)
print 'cloudy fraction, 30 deg scale = %.2f' % cf
dec, ra = hp.pix2ang(nside, np.arange(previous.size))
dec = np.pi/2. - dec
lmst, last = calcLmstLast(mjd, site.longitude_rad)
lmst = lmst/12.*180.
alt, az = stupidFast_RaDec2AltAz(ra, dec, site.latitude_rad, site.longitude_rad, mjd)
frame = single_frame(mjd, filter_name='R')
seen = np.where((frame != hp.UNSEEN) & (previous != hp.UNSEEN))
unseen = np.where((frame == hp.UNSEEN) | (previous == hp.UNSEEN))
frame = fixBias(frame)
previous = fixBias(previous)
diff = frame - previous
diff[unseen] = hp.UNSEEN
diff_frac = diff/previous
diff_frac[unseen] = hp.UNSEEN
diff_frac[np.where(previous == 0)] = hp.UNSEEN
gdiff = np.where((diff != hp.UNSEEN) & (~np.isnan(diff)))[0]
if np.size(gdiff) > 0:
cf = cloudyness(diff_frac) / (gdiff.size*hp.nside2pixarea(nside)*(180./np.pi)**2)
print 'cloudy fraction, 5 deg scale = %.2f' % cf
cf = cloudyness(diff_frac, fwhm=30.) / (gdiff.size*hp.nside2pixarea(nside)*(180./np.pi)**2)
print 'cloudy fraction, 30 deg scale = %.2f' % cf
def gen_cloud_maps():
sm = sb.SkyModel(mags=True)
hpindx = np.arange(hp.nside2npix(32))
ra, dec = utils._hpid2RaDec(32, hpindx)
do_query = False
dbAddress = 'sqlite:///meddata.sqlite'
engine = sqla.create_engine(dbAddress)
connection = engine.raw_connection()
cursor = connection.cursor()
# Check all the mjd values in the db
if do_query:
query = 'select distinct(mjd) from medskybrightness;'
cursor.execute(query)
data = cursor.fetchall()
mjd_clean = [ack[0] for ack in data]
mjd_clean = np.array(mjd_clean)
# Let's go 57200 as a start
mjd_start = 57200
mjd_length = 40.
query = 'select distinct(mjd) from medskybrightness where mjd > %f and mjd < %f;' % (
mjd_start, mjd_start + mjd_length)
cursor.execute(query)
data = cursor.fetchall()
mjd_clean = np.array([ack[0] for ack in data])
cloud_maps = []
mjd_final = []
moon_limit = np.radians(30.)
am_limit = 2.5
maxI = mjd_clean[1:].size
for i, mjd in enumerate(mjd_clean[1:]):
if (mjd_clean[i] - mjd_clean[i - 1]) < (2. / 60. / 24.):
sf = single_frame(mjd_clean[i - 1])
sf2 = single_frame(mjd_clean[i])
if (sf.max() != hp.UNSEEN) & (sf2.max() != hp.UNSEEN):
diff = (sf - sf2) / sf
mask = np.where((sf == hp.UNSEEN) | (sf2 == hp.UNSEEN))
diff[mask] = hp.UNSEEN
if diff.max() != hp.UNSEEN:
cf, cloud_mask = cloudyness(diff, skyRMS_max=0.05)
sm.setRaDecMjd(ra, dec, mjd)
skymap = sm.returnMags()['r']
mask = np.where((skymap == hp.UNSEEN)
| np.isnan(skymap))[0]
cloud_mask[mask] = 2
moon_dist = utils.haversine(ra, dec, sm.moonRA, sm.moonDec)
mask = np.where((moon_dist < moon_limit)
| (sm.airmass < 1.)
| (sm.airmass > am_limit))
cloud_mask[mask] = 2
cloud_maps.append(cloud_mask)
# put in some masking near the moon
mjd_final.append(mjd)
progress = i / float(maxI) * 100
text = "\r progress = %.1f%%" % progress
sys.stdout.write(text)
sys.stdout.flush()
cloud_maps = np.array(cloud_maps)
map_key = {
'good': 0,
'masked': 2,
'bright_in_current': -1,
'bright_in_last': 1
}
np.savez('month_o_clouds.npz',
cloud_maps=cloud_maps,
mjds=mjd_final,
map_key=map_key)
diff[out] = hp.UNSEEN
frame[out] = hp.UNSEEN
diff_frac[out] = hp.UNSEEN
diff_frac[np.where(previous == 0)] = hp.UNSEEN
# maybe rotate based on LMST and latitude?
gdiff = np.where((diff != hp.UNSEEN) & (~np.isnan(diff)))[0]
if np.size(gdiff) > 0:
rms = robustRMS(diff_frac[gdiff])
median_value = np.median(diff_frac[gdiff])
nout = np.size(
np.where((
np.abs(diff[gdiff] - np.median(diff[gdiff])) > outlier_mag)
& (alt[gdiff] > alt_limit))[0])
nabove = float(np.size(np.where(alt[gdiff] > alt_limit)[0])) * 100
cf, cloud_mask = cloudyness(diff_frac, skyRMS_max=0.05)
cf = cf / (gdiff.size * hp.nside2pixarea(nside) *
(180. / np.pi)**2)
if nabove != 0:
nout = nout / nabove
else:
nout = -666
else:
rms = hp.UNSEEN
median_value = hp.UNSEEN
nout = -666
cf = -666
cloud_mask = diff_frac * 0
rms_diff_frame.append(rms)
med_diff_frame.append(median_value)
def gen_cloud_maps():
sm = sb.SkyModel(mags=True)
hpindx = np.arange(hp.nside2npix(32))
ra, dec = utils._hpid2RaDec(32, hpindx)
do_query = False
dbAddress = 'sqlite:///meddata.sqlite'
engine = sqla.create_engine(dbAddress)
connection = engine.raw_connection()
cursor = connection.cursor()
# Check all the mjd values in the db
if do_query:
query = 'select distinct(mjd) from medskybrightness;'
cursor.execute(query)
data = cursor.fetchall()
mjd_clean = [ack[0] for ack in data]
mjd_clean = np.array(mjd_clean)
# Let's go 57200 as a start
mjd_start = 57200
mjd_length = 40.
query = 'select distinct(mjd) from medskybrightness where mjd > %f and mjd < %f;' % (mjd_start,
mjd_start+mjd_length)
cursor.execute(query)
data = cursor.fetchall()
mjd_clean = np.array([ack[0] for ack in data])
cloud_maps = []
mjd_final = []
moon_limit = np.radians(30.)
am_limit = 2.5
maxI = mjd_clean[1:].size
for i, mjd in enumerate(mjd_clean[1:]):
if (mjd_clean[i]-mjd_clean[i-1]) < (2./60./24.):
sf = single_frame(mjd_clean[i-1])
sf2 = single_frame(mjd_clean[i])
if (sf.max() != hp.UNSEEN) & (sf2.max() != hp.UNSEEN):
diff = (sf-sf2)/sf
mask = np.where((sf == hp.UNSEEN) | (sf2 == hp.UNSEEN))
diff[mask] = hp.UNSEEN
if diff.max() != hp.UNSEEN:
cf, cloud_mask = cloudyness(diff, skyRMS_max=0.05)
sm.setRaDecMjd(ra, dec, mjd)
skymap = sm.returnMags()['r']
mask = np.where((skymap == hp.UNSEEN) | np.isnan(skymap))[0]
cloud_mask[mask] = 2
moon_dist = utils.haversine(ra, dec, sm.moonRA, sm.moonDec)
mask = np.where((moon_dist < moon_limit) | (sm.airmass < 1.) | (sm.airmass > am_limit))
cloud_mask[mask] = 2
cloud_maps.append(cloud_mask)
# put in some masking near the moon
mjd_final.append(mjd)
progress = i/float(maxI)*100
text = "\r progress = %.1f%%" % progress
sys.stdout.write(text)
sys.stdout.flush()
cloud_maps = np.array(cloud_maps)
map_key = {'good': 0, 'masked': 2, 'bright_in_current': -1, 'bright_in_last': 1}
np.savez('month_o_clouds.npz', cloud_maps=cloud_maps, mjds=mjd_final, map_key=map_key)
(previous == hp.UNSEEN) | (alt < np.radians(10.)))
previous = frame.copy()
diff[out] = hp.UNSEEN
frame[out] = hp.UNSEEN
diff_frac[out] = hp.UNSEEN
diff_frac[np.where(previous == 0)] = hp.UNSEEN
# maybe rotate based on LMST and latitude?
gdiff = np.where((diff != hp.UNSEEN) & (~np.isnan(diff)))[0]
if np.size(gdiff) > 0:
rms = robustRMS(diff_frac[gdiff])
median_value = np.median(diff_frac[gdiff])
nout = np.size(np.where((np.abs(diff[gdiff] - np.median(diff[gdiff])) > outlier_mag) &
(alt[gdiff] > alt_limit))[0])
nabove = float(np.size(np.where(alt[gdiff] > alt_limit)[0]))*100
cf, cloud_mask = cloudyness(diff_frac, skyRMS_max=0.05)
cf = cf / (gdiff.size*hp.nside2pixarea(nside)*(180./np.pi)**2)
if nabove != 0:
nout = nout/nabove
else:
nout = -666
else:
rms = hp.UNSEEN
median_value = hp.UNSEEN
nout = -666
cf = -666
cloud_mask = diff_frac*0
rms_diff_frame.append(rms)
med_diff_frame.append(median_value)
above_limit = np.where(alt > alt_limit)[0]
