def give_me_n_best_periods(i,model,n):
# It's finding the rr_lyraes by index + fit that you specify
rrlyrae = fetch_rrlyrae()
lcid = rrlyrae.ids[i]
t, mag, dmag, filts = rrlyrae.get_lightcurve(lcid)
metadata = rrlyrae.get_metadata(lcid)
# these are the ids of the rr lyrae
idx_of_plot = i
lcid = rrlyrae.ids[i]
metadata = rrlyrae.get_metadata(lcid)
t, mag, dmag, filts = rrlyrae.get_lightcurve(lcid)
# Number of observations - original size
nobs = t.size
# Number of Observations + down-sampled n best periods to be returned
returned_list=[]
# Spelling out the number of periods that you gave
no_of_periods_specified = n
# Let's fit it
model.optimizer.period_range = (0.1, 1.)
# This is all fitted with 4.2 Truncated Fourier Models with VanderPlas paper
model.fit(t,mag,dmag)
model.best_period
# Finding the periods, and then sorting it by argument of the strongest power(score)
periods = np.linspace(0.1, 1., 10000)
scores = model.score(periods)
idx = np.argsort(scores)
##tze_periods = periods[idx[-5:]]
##tze_scores = scores[idx[-5:]]
tze_periods = np.array([])
threshold = 5E-3
for period in periods[idx][::-1]:
if (np.abs(tze_periods - period) > threshold).all() or (len(tze_periods) < 1):
tze_periods = np.append(tze_periods, [period])
if len(tze_periods) >= no_of_periods_specified:
break
tze_periods = tze_periods[::-1]
##print(tze_periods)
##print(tze_scores)
# Getting the infomation to be returned
##No. of observations, 'n' number of periods, and the system's best ave
combo = nobs,tze_periods,metadata['P']
returned_list.append(combo)
##print(no_of_periods_specified,'no_of_periods_specified')
return returned_list
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 test_calc_sig_levels_cases():
r"""Pytest cases for code/utils.py:
calc_sig_levels
"""
# Define function for testing cases.
def test_calc_sig_levels(model,
sig_periods,
ref_sig_powers,
sigs=(95.0, 99.0),
num_shuffles=100):
r"""Pytest for code/utils.py:
calc_sig_levels
"""
test_sig_powers = code.utils.calc_sig_levels(model=model,
sig_periods=sig_periods,
sigs=sigs,
num_shuffles=num_shuffles)
for key in ref_sig_powers:
assert np.all(np.isclose(ref_sig_powers[key],
test_sig_powers[key]))
return None
# Test adapted from
# https://github.com/astroML/gatspy/blob/master/examples/MultiBand.ipynb
rrlyrae = gatspy_data.fetch_rrlyrae()
lcid = rrlyrae.ids[0]
(times, mags, mags_err, filts) = rrlyrae.get_lightcurve(lcid)
model = gatspy_per.LombScargleMultiband(Nterms_base=6, Nterms_band=1)
model.fit(t=times, y=mags, dy=mags_err, filts=filts)
(min_period, max_period, _) = code.utils.calc_period_limits(times=times)
model.optimizer.period_range = (min_period, max_period)
sigs = (95.0, 99.0)
num_shuffles = 100
sig_periods = \
[1.66051856e+03, 2.99875187e-02, 1.49938947e-02, 9.99595991e-03,
7.49698121e-03, 5.99759039e-03, 4.99799500e-03, 4.28399755e-03,
3.74849907e-03, 3.33200001e-03]
ref_sig_powers = \
{95.0: \
[0.25057282, 0.26775067, 0.25570964, 0.27631931, 0.26301363,
0.26418357, 0.2457524 , 0.24058861, 0.24839262, 0.24550175],
99.0: \
[0.32243259, 0.31730944, 0.29074282, 0.33228392, 0.29340864,
0.29399481, 0.26595011, 0.26355087, 0.29701109, 0.2730018]}
test_calc_sig_levels(model=model,
sig_periods=sig_periods,
ref_sig_powers=ref_sig_powers,
sigs=sigs,
num_shuffles=num_shuffles)
# TODO: insert additional test cases here.
return None
def test_ls_model_fluxes_rel_cases():
r"""Pytest cases for code/utils.py:
ls_model_fluxes_rel
"""
# Define function for testing cases.
def test_ls_model_fluxes_rel(params, model, ref_modeled_fluxes_rel):
r"""Pytest for code/utils.py:
ls_model_fluxes_rel
"""
test_modeled_fluxes_rel = code.utils.ls_model_fluxes_rel(params=params,
model=model)
assert np.all(
np.isclose(ref_modeled_fluxes_rel, test_modeled_fluxes_rel))
return None
# Test adapted from
# https://github.com/astroML/gatspy/blob/master/examples/MultiBand.ipynb
rrlyrae = gatspy_data.fetch_rrlyrae()
lcid = rrlyrae.ids[0]
(times, mags, mags_err, filts) = rrlyrae.get_lightcurve(lcid)
fluxes_rel = np.empty_like(mags)
fluxes_rel_err = np.empty_like(mags_err)
for filt in np.unique(filts):
tfmask = (filt == filts)
fluxes_rel[tfmask] = \
map(lambda mag_1: \
bss.utils.calc_flux_intg_ratio_from_mags(
mag_1=mag_1,
mag_2=np.median(mags[tfmask])),
mags[tfmask])
fluxes_rel_err[tfmask] = \
map(lambda mag_1, mag_2: \
abs(1.0 - bss.utils.calc_flux_intg_ratio_from_mags(
mag_1=mag_1,
mag_2=mag_2)),
np.add(mags[tfmask], mags_err[tfmask]),
mags[tfmask])
model = gatspy_per.LombScargleMultiband(Nterms_base=6, Nterms_band=1)
best_period = rrlyrae.get_metadata(lcid)['P']
params = (best_period)
model.fit(t=times, y=fluxes_rel, dy=fluxes_rel_err, filts=filts)
test_ls_model_fluxes_rel(params=params,
model=model,
ref_modeled_fluxes_rel=model.predict(
t=model.t,
filts=model.filts,
period=best_period))
# TODO: insert additional test cases here.
return None
def test_ls_model_fluxes_rel_cases():
r"""Pytest cases for code/utils.py:
ls_model_fluxes_rel
"""
# Define function for testing cases.
def test_ls_model_fluxes_rel(params, model, ref_modeled_fluxes_rel):
r"""Pytest for code/utils.py:
ls_model_fluxes_rel
"""
test_modeled_fluxes_rel = code.utils.ls_model_fluxes_rel(
params=params, model=model)
assert np.all(np.isclose(
ref_modeled_fluxes_rel, test_modeled_fluxes_rel))
return None
# Test adapted from
# https://github.com/astroML/gatspy/blob/master/examples/MultiBand.ipynb
rrlyrae = gatspy_data.fetch_rrlyrae()
lcid = rrlyrae.ids[0]
(times, mags, mags_err, filts) = rrlyrae.get_lightcurve(lcid)
fluxes_rel = np.empty_like(mags)
fluxes_rel_err = np.empty_like(mags_err)
for filt in np.unique(filts):
tfmask = (filt == filts)
fluxes_rel[tfmask] = \
map(lambda mag_1: \
bss.utils.calc_flux_intg_ratio_from_mags(
mag_1=mag_1,
mag_2=np.median(mags[tfmask])),
mags[tfmask])
fluxes_rel_err[tfmask] = \
map(lambda mag_1, mag_2: \
abs(1.0 - bss.utils.calc_flux_intg_ratio_from_mags(
mag_1=mag_1,
mag_2=mag_2)),
np.add(mags[tfmask], mags_err[tfmask]),
mags[tfmask])
model = gatspy_per.LombScargleMultiband(Nterms_base=6, Nterms_band=1)
best_period = rrlyrae.get_metadata(lcid)['P']
params = (best_period)
model.fit(t=times, y=fluxes_rel, dy=fluxes_rel_err, filts=filts)
test_ls_model_fluxes_rel(
params=params, model=model,
ref_modeled_fluxes_rel=model.predict(
t=model.t, filts=model.filts, period=best_period))
# TODO: insert additional test cases here.
return None
def test_calc_sig_levels_cases():
r"""Pytest cases for code/utils.py:
calc_sig_levels
"""
# Define function for testing cases.
def test_calc_sig_levels(
model, sig_periods, ref_sig_powers, sigs=(95.0, 99.0),
num_shuffles=100):
r"""Pytest for code/utils.py:
calc_sig_levels
"""
test_sig_powers = code.utils.calc_sig_levels(
model=model, sig_periods=sig_periods, sigs=sigs,
num_shuffles=num_shuffles)
for key in ref_sig_powers:
assert np.all(np.isclose(ref_sig_powers[key], test_sig_powers[key]))
return None
# Test adapted from
# https://github.com/astroML/gatspy/blob/master/examples/MultiBand.ipynb
rrlyrae = gatspy_data.fetch_rrlyrae()
lcid = rrlyrae.ids[0]
(times, mags, mags_err, filts) = rrlyrae.get_lightcurve(lcid)
model = gatspy_per.LombScargleMultiband(Nterms_base=6, Nterms_band=1)
model.fit(t=times, y=mags, dy=mags_err, filts=filts)
(min_period, max_period, _) = code.utils.calc_period_limits(times=times)
model.optimizer.period_range = (min_period, max_period)
sigs = (95.0, 99.0)
num_shuffles = 100
sig_periods = \
[1.66051856e+03, 2.99875187e-02, 1.49938947e-02, 9.99595991e-03,
7.49698121e-03, 5.99759039e-03, 4.99799500e-03, 4.28399755e-03,
3.74849907e-03, 3.33200001e-03]
ref_sig_powers = \
{95.0: \
[0.25057282, 0.26775067, 0.25570964, 0.27631931, 0.26301363,
0.26418357, 0.2457524 , 0.24058861, 0.24839262, 0.24550175],
99.0: \
[0.32243259, 0.31730944, 0.29074282, 0.33228392, 0.29340864,
0.29399481, 0.26595011, 0.26355087, 0.29701109, 0.2730018]}
test_calc_sig_levels(
model=model, sig_periods=sig_periods, ref_sig_powers=ref_sig_powers,
sigs=sigs, num_shuffles=num_shuffles)
# TODO: insert additional test cases here.
return None
def give_me_nobs(i,model,n):
# It's finding the rr_lyraes by index + fit that you specify
rrlyrae = fetch_rrlyrae()
lcid = rrlyrae.ids[i]
t, mag, dmag, filts = rrlyrae.get_lightcurve(lcid)
metadata = rrlyrae.get_metadata(lcid)
# these are the ids of the rr lyrae
idx_of_plot = i
lcid = rrlyrae.ids[i]
metadata = rrlyrae.get_metadata(lcid)
t, mag, dmag, filts = rrlyrae.get_lightcurve(lcid)
# Number of observations - original size
nobs = t.size
return nobs
def rr_info(i): ''' This function takes all the rr_lyrae info in one convenient package Parameters ---------- i : <type 'int'> The index number of light curve you want to examine Returns ------- t: <type 'numpy.ndarray'> The time/period of each of the light curve of each RR_Lyrae mag: <type 'numpy.ndarray'> The magnitude of each of the light curve of each RR_Lyrae dmag: <type 'numpy.ndarray'> The error in magnitude of each of the light curve of each RR_Lyrae nobs: <type 'int'> The number of observations metadata: <type 'numpy.void'> All the information of each RR_Lyrae. Most important is metadata['P'] = Period ''' # It's finding the rr_lyraes by index + fit that you specify rrlyrae = fetch_rrlyrae() lcid = rrlyrae.ids[i] t, mag, dmag, filts = rrlyrae.get_lightcurve(lcid) # Number of observations - original size nobs = t.size # The data with all the magnitude in different filter bands + Period metadata = rrlyrae.get_metadata(lcid) return t,mag,dmag,metadata,nobs
from gatspy import datasets, periodic rrlyrae = datasets.fetch_rrlyrae() lcid = rrlyrae.ids[0] t, mag, dmag, filts = rrlyrae.get_lightcurve(lcid) mask = (filts == 'r') t_r, mag_r, dmag_r = t[mask], mag[mask], dmag[mask] model = periodic.LombScargleFast(fit_period=True) model.optimizer.period_range = (0.2, 1.2) model.fit(t_r, mag_r, dmag_r) print(model.best_period) metadata = rrlyrae.get_metadata(lcid) true_period = metadata['P'] print(true_period) import numpy as np tfit = np.linspace(0, model.best_period, 4) print(model.predict(tfit)) model = periodic.LombScargleFast(fit_period=True) model.optimizer.period_range = (0.2, 1.2) model.fit(t_r, mag_r, dmag_r) print(model.best_period) model = periodic.LombScargleFast(fit_period=True) model.optimizer.set(period_range=(0.5, 0.7), first_pass_coverage=10) model.fit(t_r, mag_r, dmag_r) print(model.best_period)
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 Give_Me_Light_Curves_Down_Sampled(i,nobs,model,n,d_array):
# Number of Observations + down-sampled n best periods to be returned
returned_list=[]
# Spelling out the number of periods that you gave
no_of_periods_specified = n
# It's finding the rr_lyraes by index + fit that you specify
rrlyrae = fetch_rrlyrae()
lcid = rrlyrae.ids[i]
t, mag, dmag, filts = rrlyrae.get_lightcurve(lcid)
metadata = rrlyrae.get_metadata(lcid)
# these are the ids of the rr lyrae
idx_of_plot = i
lcid = rrlyrae.ids[i]
metadata = rrlyrae.get_metadata(lcid)
t, mag, dmag, filts = rrlyrae.get_lightcurve(lcid)
for items in d_array:
# Down-sampling the data !!
nobs = items
legend ='# of Observation:',nobs
dd = random.sample(zip(t,mag,dmag), nobs)
# Shuffling the random sample back to their respective houses
ee = []
ff= []
gg = []
for i in range(len(dd)):
ee.append(dd[i][0])
ff.append(dd[i][1])
gg.append(dd[i][2])
t = np.asarray(ee)
mag = np.asarray(ff)
dmag = np.asarray(gg)
# Let's fit it
model.optimizer.period_range = (0.1, 1.)
# This is all fitted with 4.2 Truncated Fourier Models with VanderPlas paper
model.fit(t,mag,dmag)
model.best_period
# Finding the periods, and then sorting it by argument of the strongest power(score)
periods = np.linspace(0.1, 1., 10000)
scores = model.score(periods)
idx = np.argsort(scores)
##tze_periods = periods[idx[-5:]]
##tze_scores = scores[idx[-5:]]
tze_periods = np.array([])
threshold = 5E-3
for period in periods[idx][::-1]:
if (np.abs(tze_periods - period) > threshold).all() or (len(tze_periods) < 1):
tze_periods = np.append(tze_periods, [period])
if len(tze_periods) >= no_of_periods_specified:
break
tze_periods = tze_periods[::-1]
##print(tze_periods)
##print(tze_scores)
# Getting the infomation to be returned
##No. of observations, 'n' number of periods, and the system's best ave
combo = nobs,tze_periods,metadata['P']
returned_list.append(combo)
##print(no_of_periods_specified,'no_of_periods_specified')
return returned_list
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
if __name__ == '__main__':
rrlyrae = fetch_rrlyrae()
lcid = list(rrlyrae.ids)[482]
fig, ax = plot_example_lightcurve(rrlyrae, lcid)
fig.savefig('fig07a.pdf')
fig, ax = plot_periods(ssm_file='S82sims/res_supersmoother_g.npy',
mbls_file='S82sims/res_multiband_1_0.npy',
rrlyrae=rrlyrae)
fig.savefig('fig07b.pdf')
plt.show()
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from gatspy.datasets import fetch_rrlyrae
rrlyrae = fetch_rrlyrae()
rrlyrae_partial = fetch_rrlyrae(partial=True)
data = [rrlyrae.get_lightcurve(lcid)
for lcid in rrlyrae.ids]
data_partial = [rrlyrae_partial.get_lightcurve(lcid)
for lcid in rrlyrae.ids]
tminmax = np.array([[t.min(), t.max()] for t,_,_,_ in data])
print(tminmax.min(0), tminmax.max(0))
counts = np.array([[np.sum(filts == f) for f in 'ugriz']
for t, y, dy, filts in data])
counts_partial = np.array([[np.sum(filts == f) for f in 'ugriz']
for t, y, dy, filts in data_partial])
print(np.mean(counts, 0), np.median(counts, 0))
print(np.mean(counts_partial, 0), np.median(counts_partial, 0))
fig, ax = plt.subplots(2)
sns.violinplot(counts, ax=ax[0], names='ugriz')
sns.violinplot(counts_partial, ax=ax[1], names='ugriz')
def test_calc_min_flux_time_cases():
r"""pytest cases for code/utils.py:
calc_min_flux_time
"""
# Define function for testing cases.
def test_calc_min_flux_time(model,
filt,
ref_min_flux_time,
best_period=None,
lwr_time_bound=None,
upr_time_bound=None,
tol=0.1,
maxiter=10):
r"""Pytest for code/utils.py:
calc_min_flux_time
"""
test_min_flux_time = \
code.utils.calc_min_flux_time(
model=model, filt=filt, best_period=best_period,
lwr_time_bound=lwr_time_bound, upr_time_bound=upr_time_bound,
tol=tol, maxiter=maxiter)
assert np.isclose(ref_min_flux_time, test_min_flux_time)
return None
# Test adapted from
# https://github.com/astroML/gatspy/blob/master/examples/MultiBand.ipynb
rrlyrae = gatspy_data.fetch_rrlyrae()
lcid = rrlyrae.ids[0]
(times, mags, mags_err, filts) = rrlyrae.get_lightcurve(lcid)
fluxes_rel = np.empty_like(mags)
fluxes_rel_err = np.empty_like(mags_err)
for filt in np.unique(filts):
tfmask = (filt == filts)
fluxes_rel[tfmask] = \
map(lambda mag_1: \
bss.utils.calc_flux_intg_ratio_from_mags(
mag_1=mag_1,
mag_2=np.median(mags[tfmask])),
mags[tfmask])
fluxes_rel_err[tfmask] = \
map(lambda mag_1, mag_2: \
abs(1.0 - bss.utils.calc_flux_intg_ratio_from_mags(
mag_1=mag_1,
mag_2=mag_2)),
np.add(mags[tfmask], mags_err[tfmask]),
mags[tfmask])
model = gatspy_per.LombScargleMultiband(Nterms_base=6, Nterms_band=1)
best_period = rrlyrae.get_metadata(lcid)['P']
model.fit(t=times, y=fluxes_rel, dy=fluxes_rel_err, filts=filts)
min_flux_time_init = \
code.utils.calc_min_flux_time(
model=model, filt='z', best_period=best_period, tol=0.1, maxiter=10)
for (filt, ref_min_flux_time) in \
zip(['u', 'g', 'r', 'i', 'z'],
[0.370657590606, 0.366563989108, 0.375194445097, 0.377970590837,
0.378704402065]):
time_window_halfwidth = 0.1 * best_period
test_calc_min_flux_time(
model=model,
filt=filt,
ref_min_flux_time=ref_min_flux_time,
best_period=best_period,
lwr_time_bound=min_flux_time_init - time_window_halfwidth,
upr_time_bound=min_flux_time_init + time_window_halfwidth,
tol=1e-5,
maxiter=10)
# TODO: insert additional test cases here.
return None
find_periods,
lcids,
save_every=save_every,
parallel=parallel,
client=client)
def gather_results(outfile, ids):
if not os.path.exists(outfile):
raise ValueError("Cannot gather results from {0}".format(outfile))
results = NumpyCache(outfile)
return np.array([results.get_row(lcid) for lcid in ids])
if __name__ == '__main__':
rrlyrae = fetch_rrlyrae()
rrlyrae_partial = fetch_rrlyrae(partial=True)
parallel = True
ssm_bands = ['g']
if parallel:
# Need some imports on the engine
from IPython.parallel import Client
client = Client()
dview = client.direct_view()
with dview.sync_imports():
from gatspy.periodic import (LombScargleMultiband, SuperSmoother,
SuperSmootherMultiband)
# Make sure necessary data is fetched on all clients.
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
if __name__ == '__main__':
rrlyrae = fetch_rrlyrae()
lcid = list(rrlyrae.ids)[482]
fig, ax = plot_example_lightcurve(rrlyrae, lcid)
fig.savefig('fig07a.pdf')
fig, ax = plot_periods(ssm_file='S82sims/res_supersmoother_g.npy',
mbls_file='S82sims/res_multiband_1_0.npy',
rrlyrae=rrlyrae)
fig.savefig('fig07b.pdf')
plt.show()
def __init__(self, lsst_file=LSST_FILE):
self.pointings = self._load_sims(lsst_file)
self.S82data = fetch_rrlyrae()
def __init__(self, lsst_file=LSST_FILE):
self.pointings = self._load_sims(lsst_file)
self.S82data = fetch_rrlyrae()
def test_calc_min_flux_time_cases():
r"""pytest cases for code/utils.py:
calc_min_flux_time
"""
# Define function for testing cases.
def test_calc_min_flux_time(
model, filt, ref_min_flux_time, best_period=None,
lwr_time_bound=None, upr_time_bound=None, tol=0.1, maxiter=10):
r"""Pytest for code/utils.py:
calc_min_flux_time
"""
test_min_flux_time = \
code.utils.calc_min_flux_time(
model=model, filt=filt, best_period=best_period,
lwr_time_bound=lwr_time_bound, upr_time_bound=upr_time_bound,
tol=tol, maxiter=maxiter)
assert np.isclose(ref_min_flux_time, test_min_flux_time)
return None
# Test adapted from
# https://github.com/astroML/gatspy/blob/master/examples/MultiBand.ipynb
rrlyrae = gatspy_data.fetch_rrlyrae()
lcid = rrlyrae.ids[0]
(times, mags, mags_err, filts) = rrlyrae.get_lightcurve(lcid)
fluxes_rel = np.empty_like(mags)
fluxes_rel_err = np.empty_like(mags_err)
for filt in np.unique(filts):
tfmask = (filt == filts)
fluxes_rel[tfmask] = \
map(lambda mag_1: \
bss.utils.calc_flux_intg_ratio_from_mags(
mag_1=mag_1,
mag_2=np.median(mags[tfmask])),
mags[tfmask])
fluxes_rel_err[tfmask] = \
map(lambda mag_1, mag_2: \
abs(1.0 - bss.utils.calc_flux_intg_ratio_from_mags(
mag_1=mag_1,
mag_2=mag_2)),
np.add(mags[tfmask], mags_err[tfmask]),
mags[tfmask])
model = gatspy_per.LombScargleMultiband(Nterms_base=6, Nterms_band=1)
best_period = rrlyrae.get_metadata(lcid)['P']
model.fit(t=times, y=fluxes_rel, dy=fluxes_rel_err, filts=filts)
min_flux_time_init = \
code.utils.calc_min_flux_time(
model=model, filt='z', best_period=best_period, tol=0.1, maxiter=10)
for (filt, ref_min_flux_time) in \
zip(['u', 'g', 'r', 'i', 'z'],
[0.370657590606, 0.366563989108, 0.375194445097, 0.377970590837,
0.378704402065]):
time_window_halfwidth = 0.1 * best_period
test_calc_min_flux_time(
model=model, filt=filt, ref_min_flux_time=ref_min_flux_time,
best_period=best_period,
lwr_time_bound=min_flux_time_init - time_window_halfwidth,
upr_time_bound=min_flux_time_init + time_window_halfwidth,
tol=1e-5, maxiter=10)
# TODO: insert additional test cases here.
return None