def check_m5sigma_depth():
"""
Compare Philippe Gris' m5 with mine.
"""
log = snsim.OpSimObsLog(NTuple.fromtxt('Observations_DD_290_LSSTPG.txt'))
lsstpg = psf.find('LSSTPG')
sky_flux = lsstpg.mag_to_flux(log.mag_sky, log.band)
m5 = lsstpg.mag_lim(30., sky_flux, log.seeing, log.band)
pl.figure()
pl.plot(log.mjd, log.m5sigmadepth, marker='.', color='gray', ls='')
pl.plot(log.mjd, m5, marker='.', color='r', ls='')
pl.xlabel('MJD')
for b in np.unique(log.band):
idx = log.band == b
pl.figure()
pl.plot(log.mjd[idx],
m5[idx] - log.m5sigmadepth[idx],
marker='.',
color='gray',
ls='')
pl.title(b)
pl.xlabel('MJD')
pl.xlabel('$\Delta m_{5\sigma} [%s]$' % b)
def main_depth_wide(
instr_name='LSSTPG',
# bands=['g', 'r', 'i', 'z'],
SNR=dict(zip(['LSSTPG::' + b for b in "griz"], [30., 40., 30., 20.])),
target={
'LSSTPG::g': (24.83, 3.), # 23.86
'LSSTPG::r': (24.35, 3.), # 23.82
'LSSTPG::i': (23.88, 3.), # 23.51
'LSSTPG::z': (23.30, 3.), #
}):
instr = psf.find(instr_name)
bands = SNR.keys()
# Wide survey
logs = [
snsim.OpSimObsLog(NTuple.fromtxt(fn))
for fn in glob.glob('OpSimLogs/*_WFD_*.txt')
]
m = np.hstack([log.median_values() for log in logs])
lims = f5_cadence_lims(zs=[0.1, 0.2, 0.3, 0.4, 0.5], SNR=SNR)
# bands=[instr_name + '::' + b for b in bands])
for bn in bands:
f5_cadence_plot(
instr,
bn, # instr_name + '::' + bn,
lims,
mag_range=(21., 24.8),
dt_range=(0.5, 30.),
median_log_summary=m,
target=target)
pl.gcf().savefig('m5_cadence_limits_wide_%s.png' % bn.split(':')[-1],
bbox_inches='tight')
pl.gcf().savefig('m5_cadence_limits_wide_%s.pdf' % bn.split(':')[-1],
bbox_inches='tight')
def __init__(self, pxlobs, z=0.5, rf_phase_range=(-20., 45.), nside=64, etc=None, lc_template=None, model_filename=None):
"""
Constructor. Takes a file
"""
self.pixel_obs = pxlobs
self.mjd = np.unique(np.floor(pxlobs['mjd'])).astype(int)
self.mjd = self.mjd[self.mjd>0]
self.nside, self.npix = nside, hp.nside2npix(nside)
self.rf_phase_range = np.array(rf_phase_range)
self.min_rf_phase_range = np.array(rf_phase_range) + np.array([10., -10.])
self.accept = []
self.fig_odometer = 0
self.cache_mjd_min = 0.
self.cache_mjd_max = 0.
self.cache = None
# add an etc
logging.info('loading ETC')
self.etc = psf.find(instrument_name) if etc is None else etc
suffix = self.etc.instrument.name + '::'
self.pixel_obs['bandname'][:] = np.core.defchararray.add(suffix, self.pixel_obs['band'])
# add a LC model
self.sn = np.rec.fromarrays(([0.] * 7),
names=['z', 'dL', 'DayMax', 'X1', 'Color', 'ra', 'dec'])
self.sn['X1'], self.sn['Color'] = -2., 0.2
logging.info('instantiating Cosmo model')
self.cosmo = cosmo.CosmoLambda()
logging.info('loading SALT2 from: %s' % model_filename)
# self.lcmodel = snsim.init_lc_model(np.unique(self.pixel_obs['bandname']),
# model_filename)
logging.info('done.')
# add something to manage the LC templates
# print lc_template
if lc_template is not None:
self.lc_template_cache = self._build_lc_template_cache(lc_template)
else:
self.lc_template_cache = None
def main_depth_ddf(
instr_name='LSSTPG',
# bands=['r', 'i', 'z', 'y'],
SNR=dict(zip(['LSSTPG::' + b for b in "grizy"],
[25., 25., 60., 35., 20.])),
target={ # 'LSSTPG::g': (26.91, 3.), # was 25.37
'LSSTPG::r': (26.43, 3.), # was 25.37
'LSSTPG::i': (26.16, 3.), # was 25.37 # could be 25.3 (400-s)
'LSSTPG::z': (25.56, 3.), # was 24.68 # could be 25.1 (1000-s)
'LSSTPG::y': (24.68, 3.)
}): # was 24.72
bands = SNR.keys()
instr = psf.find(instr_name)
# DDF survey
# logs = [snsim.OpSimObsLog(NTuple.fromtxt(fn)) for fn in glob.glob('OpSimLogs_LSST/*_DD_*.txt')]
logs = [
snsim.OpSimObsLog(NTuple.fromtxt(fn))
for fn in glob.glob('OpSimLogs/*_DD_*.txt')
]
m = np.hstack([log.median_values() for log in logs])
lims = f5_cadence_lims(zs=[0.6, 0.7, 0.8, 0.9, 1.0, 1.1], SNR=SNR)
# bands=[instr_name + '::' + b for b in bands])
for bn in bands:
f5_cadence_plot(
instr,
bn, # instr_name + '::' + bn,
lims,
target=target,
mag_range=(23., 26.5),
median_log_summary=m)
pl.gcf().savefig('m5_cadence_limits_%s.png' % bn.split(':')[-1],
bbox_inches='tight')
pl.gcf().savefig('m5_cadence_limits_%s.pdf' % bn.split(':')[-1],
bbox_inches='tight')
# open the obslog
with np.load(args.obslog) as f:
obslog = f['l']
# global stats
# r = cadence_stats(cad, bands='ugrizy', title=args.cadence_filename)
# numbers of visits
plot_number_of_visits(obslog,
nside=args.nside,
max_nv=400,
output_dir=args.output_dir)
# average seeing
plot_average_seeing(obslog, nside=args.nside, output_dir=args.output_dir)
# survey depth
etc = psf.find('LSSTPG')
plot_survey_depth(obslog,
etc,
nside=args.nside,
maxv=28.,
output_dir=args.output_dir)
# ... aaaaand finally ! (long !)
if True:
plot_seasons(obslog, nside=args.nside, output_dir=args.output_dir)
plot_average_cadence(obslog,
nside=args.nside,
output_dir=args.output_dir)
def plot_figure_3():
log = snsim.OpSimObsLog(NTuple.fromtxt('Observations_DD_290_LSSTPG.txt'))
r = log.split()
log = r[2]
# Wide
lsstpg = psf.find('LSSTPG')
r = plot_sigc_vs_z(lsstpg,
delta=4.,
exptimes={
'LSSTPG::g': 30,
'LSSTPG::r': 30,
'LSSTPG::i': 30.,
'LSSTPG::z': 30.
},
zmax=0.5,
sigc_filename='sigc_lsstpg_wide_30',
snr_filename='snr_lsstpg_wide_30')
r = plot_sigc_vs_z(lsstpg,
delta=3.,
exptimes={
'LSSTPG::g': 30,
'LSSTPG::r': 30,
'LSSTPG::i': 30.,
'LSSTPG::z': 30.
},
zmax=0.5,
sigc_filename='sigc_lsstpg_wide_30_cad3',
snr_filename='snr_lsstpg_wide_30_cad3')
r = plot_sigc_vs_z(lsstpg,
delta=4.,
exptimes={
'LSSTPG::g': 30,
'LSSTPG::r': 30,
'LSSTPG::i': 30.,
'LSSTPG::z': 30.
},
zmax=0.5,
sigc_filename='sigc_lsstpg_wide_30_minion',
snr_filename='snr_lsstpg_wide_30_minion',
reference_log=log)
# DDF
r = plot_sigc_vs_z(lsstpg,
delta=4.,
exptimes={
'LSSTPG::r': 600,
'LSSTPG::i': 600.,
'LSSTPG::z': 780.,
'LSSTPG::y': 600
},
zmax=1.1,
sigc_filename='sigc_lsstpg_ddf_600',
snr_filename='snr_lsstpg_ddf_600')
r = plot_sigc_vs_z(lsstpg,
delta=3.,
exptimes={
'LSSTPG::r': 1200,
'LSSTPG::i': 1800.,
'LSSTPG::z': 1800.,
'LSSTPG::y': 1800
},
zmax=1.1,
sigc_filename='sigc_lsstpg_ddf_1800_cad3',
snr_filename='snr_lsstpg_ddf_1800_cad3')
# r = plot_sigc_vs_z(lsstpg, delta=3., exptimes={'LSSTPG::r': 600, 'LSSTPG::i': 600., 'LSSTPG::z': 780., 'LSSTPG::y': 600},
# zmax=1.1, sigc_filename='sigc_lsstpg_ddf_1200_minion', snr_filename='snr_lsstpg_ddf_1200_minion', reference_log=log)
# r = plot_sigc_vs_z(lsstpg, delta=3., exptimes={'LSSTPG::r': 1200, 'LSSTPG::i': 1200., 'LSSTPG::z': 1200., 'LSSTPG::y': 1200},
# zmax=1.1, sigc_filename='sigc_lsstpg_ddf_1200_cad2', snr_filename='snr_lsstpg_ddf_1200_cad2')
lsst = psf.find('LSST')
r = plot_sigc_vs_z(lsst,
delta=4.,
exptimes={
'LSST::r': 600.,
'LSST::i': 600.,
'LSST::z': 780.,
'LSST::y4': 600
},
zmax=1.1,
sigc_filename='sigc_lsst_ddf_600',
snr_filename='snr_lsst_ddf_600')
r = plot_sigc_vs_z(lsst,
delta=3.,
exptimes={
'LSST::r': 1200,
'LSST::i': 1800.,
'LSST::z': 1800.,
'LSST::y4': 1800
},
zmax=1.1,
sigc_filename='sigc_lsst_ddf_1800_cad3',
snr_filename='snr_lsst_ddf_1800_cad3')
def plot_lc_snr(filename='Observations_DD_290_LSSTPG.txt',
z=1.1,
X1=0.,
Color=0.,
DayMax=0.,
bands=['LSSTPG::' + b for b in "rizy"],
rest_frame_margins=(-15., 30.),
snr_min=20.,
fig=None):
"""Return a metric from a log and a fiducial supernova.
Args:
z, X1, Color, DayMax: fiducial supernova
"""
lsstpg = psf.find('LSSTPG')
colors = band_colors(lsstpg)
mjd, shapes = get_pulse_shapes(bands, z=z, X1=X1, Color=Color)
mjd_margins = rest_frame_margins[0] * (1. +
z), rest_frame_margins[1] * (1. + z)
log = snsim.OpSimObsLog(NTuple.fromtxt(filename))
r = log.split(delta=100.)
# f5 = lsstpg.mag_to_flux(log.m5sigmadepth, log.band)
flux_sky = lsstpg.mag_to_flux(log.mag_sky, log.band)
f5 = lsstpg.flux_lim(log.exptime, flux_sky, log.seeing, log.band)
# print lsstpg.mag_lim(log.exptime, flux_sky, log.seeing, log.band)
# print lsstpg.mag_to_flux(lsstpg.mag_lim(log.exptime, flux_sky, log.seeing, log.band), log.band)
if fig is None:
fig = pl.figure(figsize=(12, 12))
ax = None
mjd = np.arange(log.mjd.min(), log.mjd.max(), 1.)
for i, bn in enumerate(bands):
if ax is None:
ax = pl.subplot(len(bands), 1, i + 1)
pl.title('$SN[X1=%5.1f,C=%5.1f]\ at\ z=%4.2f$ [%s]' %
(X1, Color, z, filename))
else:
ax = pl.subplot(len(bands), 1, i + 1)
# runs
for rr in r:
pl.axvspan(rr.mjd.min(), rr.mjd.max(), color='gray', alpha=0.25)
pl.axvspan(rr.mjd.min() - rest_frame_margins[0],
rr.mjd.max() - rest_frame_margins[1],
color='gray',
alpha=0.35)
# plot the cadence metric
idx = log.band == bn
c = CadenceMetric(log.mjd[idx], f5[idx], log.band[idx], shapes[bn])
y = c(mjd)
pl.plot(mjd, y, color=colors[bn], marker='.', ls=':')
pl.ylabel('$SNR [%s]$' % bn.split(':')[-1],
fontsize=16) # color=colors[bn])
pl.axhline(snr_min, ls='--', color='r')
pl.ylim((0., max((y.max(), snr_min + 1))))
# plot the average number of observations averaged over a
# window of ~ 21 days.
c_sched = SimpleCadenceMetric(log.mjd[idx], 21.)
print c_sched.sumw.min(), c_sched.sumw.max()
y_sched = c_sched(mjd)
ax = pl.gca().twinx()
ax.plot(mjd, y_sched, color='gray', ls='-')
pl.ylim((0., 0.28))
pl.ylabel("Cadence [day$^{-1}$]", color='black')
cad = 1. / (4. * (1. + z))
pl.axhline(cad, ls=':', color='gray')
if i < len(bands) - 1:
ax.get_xaxis().set_ticklabels([])
pl.subplots_adjust(hspace=0.06)
pl.xlabel('$MJD$ [days]', fontsize=16)
return c
bands = 'grizy'
opsim_dir = '/pbs/throng/lsst/users/gris/Read_sqlite_python3/OpSimLogs_' + simu_name + '_' + thetype + '/WFD'
opsim_dir += '/Season_' + str(season)
opsim_files = glob.glob(opsim_dir + '/WFD_*.txt')
MultiProc_Median_log_summary(opsim_files, season, bands, simu_name,
thetype)
if display:
display_metric = True
Plots = False
if display_metric:
atmos = False
instr_snsim = psf.find('LSSTPG')
instr = Telescope(atmos=atmos, aerosol=False, airmass=-1.)
instrb = Telescope(atmos=True, aerosol=False, airmass=1.2)
zs = [0.1, 0.2, 0.3, 0.4, 0.5]
SNR = dict(zip(['LSSTPG::' + b for b in "griz"], [30., 40., 30., 20.]))
SNR_sncosmo = dict(zip([b for b in "griz"], [30., 40., 30., 20.]))
bands = SNR.keys()
target = {
'g': (24.83, 3.), # 23.86
'r': (24.35, 3.), # 23.82
'i': (23.88, 3.), # 23.51
'z': (23.30, 3.)
}