def plot_periods(ssm_file, mbls_file, rrlyrae):
ids = list(rrlyrae.ids)
sesar_periods = np.array([rrlyrae.get_metadata(lcid)['P'] for lcid in ids])
ssm_periods = gather_results(ssm_file, ids)
mbls_periods = gather_results(mbls_file, ids)
fig, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True)
fig.subplots_adjust(left=0.07,
right=0.95,
wspace=0.1,
bottom=0.15,
top=0.9)
colors = seaborn.color_palette()
plot_period_comparison(ax[0], ssm_periods, sesar_periods, color=colors[0])
plot_period_comparison(ax[1], mbls_periods, sesar_periods, color=colors[1])
ax[0].set_xlabel('supersmoother period (days)')
ax[0].set_ylabel('Sesar 2010 period (days)')
ax[1].set_xlabel('multiband period (days)')
ax[0].set_title("SuperSmoother (g-band)", y=1.04)
ax[1].set_title("Shared-phase Multiband", y=1.04)
return fig, ax
def plot_periods(ssm_file, mbls_file, rrlyrae):
ids = list(rrlyrae.ids)
sesar_periods = np.array([rrlyrae.get_metadata(lcid)['P']
for lcid in ids])
ssm_periods = gather_results(ssm_file, ids)
mbls_periods = gather_results(mbls_file, ids)
fig, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True)
fig.subplots_adjust(left=0.07, right=0.95, wspace=0.1,
bottom=0.15, top=0.9)
colors = seaborn.color_palette()
plot_period_comparison(ax[0], ssm_periods, sesar_periods,
color=colors[0])
plot_period_comparison(ax[1], mbls_periods, sesar_periods,
color=colors[1])
ax[0].set_xlabel('supersmoother period (days)')
ax[0].set_ylabel('Sesar 2010 period (days)')
ax[1].set_xlabel('multiband period (days)')
ax[0].set_title("SuperSmoother (g-band)", y=1.04)
ax[1].set_title("Shared-phase Multiband", y=1.04)
return fig, ax
def plot_LSST_sims(outfile, pointing_indices, ndays, gmags, template_indices):
results_multi = 'resultsLSST.npy'
results_ssm = 'resultsLSST_ssm_{0}.npy'
# Get measured periods
Pobs_multi = gather_results(results_multi,
pointing_indices=pointing_indices,
ndays=ndays,
gmags=gmags,
template_indices=template_indices)
Pobs_multi[np.isnan(Pobs_multi)] = 0
Pobs_ssm = np.array([gather_results(results_ssm.format(band),
pointing_indices=pointing_indices,
ndays=ndays,
gmags=gmags,
template_indices=template_indices)
for band in 'ugriz'])
Pobs_ssm = Pobs_ssm[:, :, :, :, :, 0].transpose(1, 2, 3, 4, 0)
Pobs_ssm[np.isnan(Pobs_ssm)] = 0
# Get true periods
rrlyrae = fetch_rrlyrae()
Ptrue = np.reshape([rrlyrae.get_metadata(rrlyrae.ids[i])['P']
for i in template_indices.ravel()],
template_indices.shape)
# Check for matches
dphimax = 0.37
matches_multi = olusei_period_criterion(Pobs_multi,
Ptrue.reshape(Ptrue.shape + (1,)),
ndays.reshape(ndays.shape + (1,)),
dphimax=dphimax)
results_multi = np.any(matches_multi, -1).mean(-1).mean(-1)
matches_ssm = olusei_period_criterion(Pobs_ssm,
Ptrue.reshape(Ptrue.shape + (1,)),
ndays.reshape(ndays.shape + (1,)),
dphimax=dphimax)
results_ssm = np.any(matches_ssm, -1).mean(-1).mean(-1)
fig, ax = plt.subplots()
for t, frac_multi, frac_ssm in reversed(list(zip(ndays.ravel(),
results_multi.T,
results_ssm.T))):
line = ax.plot(gmags.ravel(), frac_multi,
label='{0:.1f} years'.format(t / 365))
ax.fill_between(gmags.ravel(), frac_ssm, frac_multi,
edgecolor='none',
facecolor=line[0].get_color(), alpha=0.3)
ax.legend(loc='lower left')
ax.set(xlabel='g-band magnitude',
ylabel='Fraction of Periods among Top-5',
title='Multiband Improvement over SuperSmoother for LSST',
xlim=(20, 24.5), ylim=(0, 1))
def period_recovery(band):
outfile = 'resultsLSST_ssm_{0}.npy'.format(band)
results = gather_results(outfile,
pointing_indices=pointing_indices,
ndays=ndays,
gmags=gmags,
template_indices=template_indices)
best_results = results[:, :, :, :, 0]
recovery = period_criterion(best_results, Ptrue, ndays)
print(band, recovery.sum() / recovery.size)
return recovery
def plot_LSST_sims(outfile, pointing_indices, ndays, gmags, template_indices):
results_multi = 'resultsLSST.npy'
results_ssm = 'resultsLSST_ssm_{0}.npy'
# Get measured periods
Pobs_multi = gather_results(results_multi,
pointing_indices=pointing_indices,
ndays=ndays,
gmags=gmags,
template_indices=template_indices)
Pobs_multi[np.isnan(Pobs_multi)] = 0
Pobs_ssm = np.array([
gather_results(results_ssm.format(band),
pointing_indices=pointing_indices,
ndays=ndays,
gmags=gmags,
template_indices=template_indices) for band in 'ugriz'
])
Pobs_ssm = Pobs_ssm[:, :, :, :, :, 0].transpose(1, 2, 3, 4, 0)
Pobs_ssm[np.isnan(Pobs_ssm)] = 0
# Get true periods
rrlyrae = fetch_rrlyrae()
Ptrue = np.reshape([
rrlyrae.get_metadata(rrlyrae.ids[i])['P']
for i in template_indices.ravel()
], template_indices.shape)
# Check for matches
dphimax = 0.37
matches_multi = olusei_period_criterion(Pobs_multi,
Ptrue.reshape(Ptrue.shape + (1, )),
ndays.reshape(ndays.shape + (1, )),
dphimax=dphimax)
results_multi = np.any(matches_multi, -1).mean(-1).mean(-1)
matches_ssm = olusei_period_criterion(Pobs_ssm,
Ptrue.reshape(Ptrue.shape + (1, )),
ndays.reshape(ndays.shape + (1, )),
dphimax=dphimax)
results_ssm = np.any(matches_ssm, -1).mean(-1).mean(-1)
fig, ax = plt.subplots()
for t, frac_multi, frac_ssm in reversed(
list(zip(ndays.ravel(), results_multi.T, results_ssm.T))):
line = ax.plot(gmags.ravel(),
frac_multi,
label='{0:.1f} years'.format(t / 365))
ax.fill_between(gmags.ravel(),
frac_ssm,
frac_multi,
edgecolor='none',
facecolor=line[0].get_color(),
alpha=0.3)
ax.legend(loc='lower left')
ax.set(xlabel='g-band magnitude',
ylabel='Fraction of Periods among Top-5',
title='Multiband Improvement over SuperSmoother for LSST',
xlim=(20, 24.5),
ylim=(0, 1))
factor = dphimax / Tdays
return abs(Pobs - Ptrue) <= (Ptrue**2) * factor
template_indices = np.arange(2 * 23).reshape(2, 23).T
pointing_indices = np.arange(1, 24)[:, None]
ndays = np.array([180, 365, 2 * 365, 5 * 365])[:, None, None]
gmags = np.array([20, 21, 22, 23, 24.5])[:, None, None, None]
results_multi = 'LSSTsims/resultsLSST.npy'
results_ssm = 'LSSTsims/resultsLSST_ssm_{0}.npy'
# Get measured periods
Pobs_multi = gather_results(results_multi,
pointing_indices=pointing_indices,
ndays=ndays,
gmags=gmags,
template_indices=template_indices)
Pobs_multi[np.isnan(Pobs_multi)] = 0
Pobs_ssm = np.array([
gather_results(results_ssm.format(band),
pointing_indices=pointing_indices,
ndays=ndays,
gmags=gmags,
template_indices=template_indices) for band in 'ugriz'
])
Pobs_ssm = Pobs_ssm[:, :, :, :, :, 0].transpose(1, 2, 3, 4, 0)
Pobs_ssm[np.isnan(Pobs_ssm)] = 0
# Get true periods
def olusei_period_criterion(Pobs, Ptrue, Tdays, dphimax=0.037):
factor = dphimax / Tdays
return abs(Pobs - Ptrue) <= (Ptrue ** 2) * factor
template_indices = np.arange(2 * 23).reshape(2, 23).T
pointing_indices = np.arange(1, 24)[:, None]
ndays = np.array([180, 365, 2 * 365, 5 * 365])[:, None, None]
gmags = np.array([20, 21, 22, 23, 24.5])[:, None, None, None]
results_multi = "LSSTsims/resultsLSST.npy"
results_ssm = "LSSTsims/resultsLSST_ssm_{0}.npy"
# Get measured periods
Pobs_multi = gather_results(
results_multi, pointing_indices=pointing_indices, ndays=ndays, gmags=gmags, template_indices=template_indices
)
Pobs_multi[np.isnan(Pobs_multi)] = 0
Pobs_ssm = np.array(
[
gather_results(
results_ssm.format(band),
pointing_indices=pointing_indices,
ndays=ndays,
gmags=gmags,
template_indices=template_indices,
)
for band in "ugriz"
]
)