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 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_opsim_log(filename='Observations_DD_290_LSSTPG.txt',
i_season=0,
X1=0.,
Color=0.,
label=None,
tofile=None,
zlim=None):
d = NTuple.fromtxt(filename)
cut = (d['band'] != 'LSSTPG::u')
log = snsim.OpSimObsLog(d[cut])
# update_log_with_seeing_and_sky(log,
# seeing = {'LSSTPG::u': 0.92, 'LSSTPG::g': 0.87, 'LSSTPG::r': 0.83, 'LSSTPG::i': 0.80, 'LSSTPG::z': 0.78, 'LSSTPG::y': 0.76},
# mag_sky= {'LSSTPG::u': 22.95, 'LSSTPG::g': 22.24, 'LSSTPG::r': 21.20 , 'LSSTPG::i': 20.47, 'LSSTPG::z': 19.60, 'LSSTPG::y': 18.63})
# update_log_with_seeing_and_sky(log,
# mag_sky= {'LSSTPG::y': 18.63})
seasons = log.split()
log = seasons[i_season]
lcmodel = init_lcmodel(log)
instr = [get_instrument(nm) for nm in np.unique(log.instrument)]
s = snsim.SnSurveyMC(obslog=log,
filename='lsst_survey.conf',
instruments=instr)
sne = s.generate_sample()
sne['X1'] = X1 # -3.
sne['Color'] = Color # 0.3
lc = s.generate_lightcurves(sne, lcmodel, fit=True)
res = reso(lc)
fig = pl.figure()
# pl.plot(sne['z'], res['eColor'], marker='.', color='gray', ls='')
p_first = (s.obslog.mjd.min() - sne['DayMax']) / (1. + sne['z'])
p_last = (s.obslog.mjd.max() - sne['DayMax']) / (1. + sne['z'])
idx = (p_first < -15.) & (p_last > 20.)
print len(idx), idx.sum()
pl.plot(sne['z'][idx], res['eColor'][idx], marker='.', color='b', ls='')
pl.xlabel('$z$', fontsize=20)
pl.ylabel('$\sigma_{\cal{C}}$', fontsize=20)
pl.ylim((0., 0.15))
pl.axhline(0.03, color='r', ls='--')
if label:
pl.annotate(label,
xy=(0.1, 0.8),
xycoords='axes fraction',
fontsize=18)
pl.grid(1)
if zlim:
pl.axvline(zlim, ls='-.', color='gray', lw=2)
if tofile:
fig.savefig(tofile, bbox_inches='tight')
pl.title('season=%d' % i_season)
return s, sne, res, lc
def create_survey(survey_type,
mjd_min=59884.,
color=True,
n=1,
CARD=None,
zrange=(0.05, 1),
graph=False,
record=False):
log = [
snsim.OpSimObsLog(create_log(survey_type, mjd_min)) for i in range(n)
]
lcmodel = init_lcmodel(log[0].band)
#r = log.split()
lc = []
if CARD is None:
CARD = CARDS[survey_type]
for i in range(n):
print('Creating %s survey (# %d over %d seasons) --- ' +
time.ctime()) % (survey_type, i + 1, n)
s = snsim.SnSurveyMC(obslog=log[i], filename='lsst_survey.conf')
s.survey_area = CARD['field']
s.zrange = zrange
sne = s.generate_sample(account_for_edges=True)
sne.sort(order='z')
# sne['X1'] = 0
if color is False:
sne['Color'] = 0
lc += s.generate_lightcurves(sne, lcmodel, fit=1)
if graph:
plot_of_interest(lc, survey_type, record)
return lc
def main(color=None):
log = snsim.OpSimObsLog(NTuple.fromtxt('Observations_DD_290_LSSTPG.txt'))
lcmodel = init_lcmodel(log.band)
r = log.split()
s = snsim.SnSurveyMC(obslog=r[2], filename='lsst_survey.conf')
sne = s.generate_sample()
sne.sort(order='z')
sne['X1'] = 0
if color is not None:
sne['Color'] = color
lc = s.generate_lightcurves(sne, lcmodel, fit=1)
return lc, log, lcmodel
def gen_simple_obslog(
instrument,
opsim_log=None,
cadence=1.,
tmin=-20,
tmax=60.,
excl_bands=['LSSTPG::u'],
texp={
'LSSTPG::g': 300.,
'LSSTPG::r': 600.,
'LSSTPG::i': 600.,
'LSSTPG::z': 780.,
'LSSTPG::y': 600.
}):
"""
"""
def select(data, excl):
bands = data['band']
cut = np.in1d(bands, excl)
return data[~cut]
#
if opsim_log is None:
data = select(instrument.data, excl=excl_bands)
else:
data = update_instrument_summary_data_from_obslog(
instrument, opsim_log)
data = select(data, excl=excl_bands)
seq = np.arange(tmin, tmax + cadence, cadence)
nbands = len(data)
n = len(seq)
#
band = np.repeat(data['band'], n)
mjd = np.tile(seq, nbands)
exptime = np.repeat([texp[b] for b in data['band']], n)
seeing = np.repeat(data['iq'], n)
moon_frac = np.zeros(len(band))
mag_sky = np.repeat(data['mag_sky'], n)
flux_sky = instrument.mag_to_flux(mag_sky, band)
kAtm = np.zeros(len(band))
airmass = np.ones(len(band))
m5sigmadepth = instrument.mag_lim(exptime, flux_sky, seeing, band, snr=5.0)
Nexp = np.ones(len(band))
data = np.rec.array((band, mjd, exptime, seeing, moon_frac, mag_sky, kAtm,
airmass, m5sigmadepth, Nexp),
names=[
'band', 'mjd', 'exptime', 'seeing', 'moon_frac',
'sky', 'kAtm', 'airmass', 'm5sigmadepth', 'Nexp'
])
return snsim.OpSimObsLog(data)
def reso_vs_lc_amplitude():
opsim = NTuple.fromtxt('Observations_DD_290_LSSTPG.txt')
idx = opsim['band'] != 'LSSTPG::u'
log = snsim.OpSimObsLog(opsim[idx])
r = log.split()
lcmodel = init_lcmodel(log)
s = snsim.SnSurveyMC(obslog=r[2], filename='lsst_survey.conf')
sne = s.generate_sample()
lc = s.generate_lightcurves(sne, lcmodel, fit=1)
bands = np.unique(log.band)
r = []
for l in lc:
C = l.covmat()
sa = []
for b in bands:
sa.append(l.amplitude_snr(b))
r.append(np.sqrt(np.diag(C)).tolist() + sa)
return sne, np.rec.fromrecords(r,
names=['eX0', 'eX1', 'eColor', 'eDayMax'] +
['sig_' + b for b in bands])
def create_survey(survey_type, mjd_min=59884., color=True, n=1, record=False, zs=None):
log = [snsim.OpSimObsLog(create_log(survey_type, mjd_min)) for i in range(n)]
lcmodel = init_lcmodel(log[0].band)
#r = log.split()
lc = []
for i in range(n):
print ('Creating %s survey (# %d over %d seasons) --- ' + time.ctime()) % (survey_type, i+1, n)
s = snsim.SnSurveyMC(obslog=log[i], filename='lsst_survey.conf')
s.survey_area = CARDS[survey_type]['field']
if survey_type[:4] == 'deep':
s.zrange = 0.1, 0.9
elif survey_type[:4] == 'wide':
s.zrange = 0.05, 0.35
sne = s.generate_sample(account_for_edges=True, z=zs)
sne.sort(order='z')
sne['X1'] = 0
if color is False:
sne['Color'] = 0
lc += s.generate_lightcurves(sne, lcmodel, fit=1)
plot_of_interest(lc, survey_type, record)
return lc, log, lcmodel
def load_instruments():
# load instruments
lsst = psf.ImagingInstrument('LSST')
lsst.precompute(full=1)
lsst_pg = psf.ImagingInstrument('LSSTPG')
lsst_pg.precompute(full=1)
lsst_pg_opsim = psf.ImagingInstrument('LSSTPG')
log = snsim.OpSimObsLog(NTuple.fromtxt('Observations_DD_290_LSSTPG.txt'))
d = update_instrument_summary_data_from_obslog(lsst_pg_opsim, log)
lsst_pg_opsim.data[:] = d
lsst_pg_opsim.precompute(full=1)
# dump summary tables
print "***** LSST [~LSE-40] ******"
lsst.dump(exptime=30.)
print "***** LSST [~SMTN-002 ] ******"
lsst_pg.dump(exptime=30.)
print "***** LSST [~SMTN-002 + OpSim default values ] ******"
lsst_pg_opsim.dump(exptime=30.)
return lsst, lsst_pg, lsst_pg_opsim
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')
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_sigc_vs_z(instrument,
reference_log=None,
fig=None,
delta=4.,
zmax=1.1,
sigc_filename=None,
snr_filename=None,
exptimes={
'LSSTPG::r': 600.,
'LSSTPG::i': 600.,
'LSSTPG::z': 600.,
'LSSTPG::y': 600.
}):
cad = regular_cadence(instrument,
log=reference_log,
delta=delta,
exptimes=exptimes)
bands = exptimes.keys()
cad = snsim.OpSimObsLog(cad)
lcmodel = init_lcmodel(cad.band)
lcmodel.restframe_wavelength_range = (3600., 8000.)
s = snsim.SnSurveyMC(obslog=cad, filename='lsst_survey.conf')
s.survey_area = 20.
z = np.linspace(0.005, zmax, 100)
sne = s.generate_sample(z=z)
sne.sort(order='z')
sne_mean = sne.copy()
sne_mean['X1'] = 0.
sne_mean['Color'] = 0.
sne_mean['DayMax'] = 0.
lc_mean = s.generate_lightcurves(sne_mean, lcmodel, fit=1)
magres_mean = reso(lc_mean, bands=bands)
sne_red = sne.copy()
sne_red['X1'] = -2.
sne_red['Color'] = 0.2
sne_red['DayMax'] = 0.
lc_red = s.generate_lightcurves(sne_red, lcmodel, fit=1)
magres_red = reso(lc_red, bands=bands)
sne_blue = sne.copy()
sne_blue['X1'] = 2.
sne_blue['Color'] = -0.2
sne_blue['DayMax'] = 0.
lc_blue = s.generate_lightcurves(sne_blue, lcmodel, fit=1)
magres_blue = reso(lc_blue, bands=bands)
# plot with the
if fig is None:
fig = pl.figure()
pl.fill_between(sne['z'],
magres_red['eColor'],
magres_blue['eColor'],
color='blue',
alpha=0.25)
pl.plot(sne['z'], magres_mean['eColor'], color='k', ls='-')
pl.plot(sne['z'], magres_blue['eColor'], color='b', ls='-', lw=1)
pl.plot(sne['z'], magres_red['eColor'], color='r', ls='-', lw=1)
pl.axhline(0.04, color='red', ls='--')
pl.xlabel('$z$', fontsize=18)
pl.ylabel('$\sigma_C$', fontsize=18)
pl.xlim((0., zmax))
pl.ylim((0., 0.15))
pl.grid(1)
if sigc_filename is not None:
pl.gcf().savefig(sigc_filename + '.png', bbox_inches='tight')
pl.gcf().savefig(sigc_filename + '.pdf', bbox_inches='tight')
pl.figure()
colors = psf.band_colors(exptimes.keys())
for bn in exptimes.keys():
bb = bn.split(':')[-1]
idx = magres_red[bb + '_snr'] > 0.
pl.plot(sne_red['z'][idx],
magres_red[bb + '_snr'][idx],
color=colors[bn],
marker='.',
label=bb)
pl.xlabel('$z$', fontsize=18)
pl.ylabel('amplitude SNR', fontsize=18)
pl.ylim((0., 100.))
pl.legend(loc='best')
pl.grid(1)
pl.xlim((0., zmax))
if snr_filename is not None:
pl.gcf().savefig(snr_filename + '.png', bbox_inches='tight')
pl.gcf().savefig(snr_filename + '.pdf', bbox_inches='tight')
return fig, sne, magres_red, magres_mean, magres_blue, lc_red
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