def test_ln_prior():
# First, test that using an out of bounds value breaks it
mjd = np.linspace(0., 100., 50)
sigma = np.random.uniform(0.05, 0.15, size=len(mjd))
light_curve = SimulatedLightCurve(mjd=mjd, error=sigma, mag=15)
light_curve.add_microlensing_event(t0=50., u0=0.3, tE=20)
print "Good values:", ln_prior([15, 0.3, 50., 20.], light_curve.mag,
light_curve.mjd)
print "Test values:", ln_prior([15, 0.3, 50., 900.], light_curve.mag,
light_curve.mjd)
print "Bad m0:", ln_prior([25, 0.3, 50., 20.], light_curve.mag,
light_curve.mjd)
print "Bad u0:", ln_prior([15, -0.3, 50., 20.], light_curve.mag,
light_curve.mjd)
print "Bad u0:", ln_prior([15, 1.9, 50., 20.], light_curve.mag,
light_curve.mjd)
print "Bad t0:", ln_prior([15, 0.3, 500., 20.], light_curve.mag,
light_curve.mjd)
print "Bad t0:", ln_prior([15, 0.3, -50., 20.], light_curve.mag,
light_curve.mjd)
print "Bad tE:", ln_prior([15, 0.3, 50., 0.2], light_curve.mag,
light_curve.mjd)
print "Bad tE:", ln_prior([15, 0.3, 50., 2000.], light_curve.mag,
light_curve.mjd)
def test_slice_peak2():
''' Test the slicing around peak '''
light_curve = pdb.get_light_curve(100024, 11, 2693, clean=True) # bad data
ml_chisq = 1E6
ml_params = None
for ii in range(10):
params = pa.fit_microlensing_event(light_curve)
new_chisq = params["result"].chisqr
if new_chisq < ml_chisq:
ml_chisq = new_chisq
ml_params = params
import matplotlib.pyplot as plt
ax = plt.subplot(211)
sliced_lc = light_curve.slice_mjd(ml_params["t0"].value-ml_params["tE"].value, ml_params["t0"].value+ml_params["tE"].value)
print len(sliced_lc)
sliced_lc.plot(ax)
ax2 = plt.subplot(212)
sliced_ml_params = pa.fit_microlensing_event(sliced_lc)
new_sliced_light_curve = pa.fit_subtract_microlensing(sliced_lc, fit_data=sliced_ml_params)
new_sliced_light_curve.plot(ax2)
ax2.set_title(r"Med. Err: {0}, $\sigma$: {1}".format(np.median(sliced_lc.error), np.std(new_sliced_light_curve.mag)))
plt.savefig("plots/test_slice_peak2_bad_data.png")
# Now do with a simulated event
from ptf.lightcurve import SimulatedLightCurve
light_curve = SimulatedLightCurve(mjd=light_curve.mjd, mag=15, error=0.1)
light_curve.add_microlensing_event(u0=0.1, t0=55600, tE=40)
ml_chisq = 1E6
ml_params = None
for ii in range(10):
params = pa.fit_microlensing_event(light_curve)
new_chisq = params["result"].chisqr
if new_chisq < ml_chisq:
ml_chisq = new_chisq
ml_params = params
plt.clf()
ax = plt.subplot(211)
sliced_lc = light_curve.slice_mjd(ml_params["t0"].value-ml_params["tE"].value, ml_params["t0"].value+ml_params["tE"].value)
print len(sliced_lc)
sliced_lc.plot(ax)
ax2 = plt.subplot(212)
sliced_ml_params = pa.fit_microlensing_event(sliced_lc)
new_sliced_light_curve = pa.fit_subtract_microlensing(sliced_lc, fit_data=sliced_ml_params)
new_sliced_light_curve.plot(ax2)
ax2.set_title(r"Med. Err: {0}, $\sigma$: {1}".format(np.median(sliced_lc.error), np.std(new_sliced_light_curve.mag)))
plt.savefig("plots/test_slice_peak2_sim.png")
def test_ln_likelihood():
mjd = np.linspace(0., 100., 50)
sigma = np.random.uniform(0.05, 0.15, size=len(mjd))
light_curve = SimulatedLightCurve(mjd=mjd, error=sigma, mag=15)
light_curve.add_microlensing_event(t0=50., u0=0.3, tE=20)
likelihood1 = ln_likelihood(p=[15, 0.3, 50., 20.], mag=light_curve.mag, mjd=mjd, sigma=sigma)
likelihood2 = ln_likelihood(p=[15, 0.3, 1., 20.], mag=light_curve.mag, mjd=mjd, sigma=sigma)
likelihood3 = ln_likelihood(p=[15, 0.3, 50., 50.], mag=light_curve.mag, mjd=mjd, sigma=sigma)
print "Likelihood with model at correct peak: {}".format(likelihood1)
print "Likelihood with model way off peak: {}".format(likelihood2)
print "Likelihood with model at peak, wrong tE: {}".format(likelihood3)
def test_magnitude_model():
mjd = np.arange(0., 100., 0.2)
sigma = np.zeros_like(mjd) + 0.01
u0 = 0.1
t0 = 50.
tE = 20
mag = magnitude_model([15, u0, t0, tE], mjd)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.errorbar(mjd, mag, sigma)
light_curve = SimulatedLightCurve(mjd=mjd, error=sigma, mag=15)
light_curve.add_microlensing_event(t0=t0, u0=u0, tE=tE)
light_curve.plot(ax)
plt.show()
def test_magnitude_model():
mjd = np.arange(0., 100., 0.2)
sigma = np.zeros_like(mjd) + 0.01
u0 = 0.1
t0 = 50.
tE = 20
mag = magnitude_model([15, u0, t0, tE], mjd)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.errorbar(mjd, mag, sigma)
light_curve = SimulatedLightCurve(mjd=mjd, error=sigma, mag=15)
light_curve.add_microlensing_event(t0=t0, u0=u0, tE=tE)
light_curve.plot(ax)
plt.show()
def test_slice_peak():
''' Test the slicing around peak '''
light_curve = pdb.get_light_curve(100024, 11, 2693, clean=True) # bad data
ml_chisq = 1E6
ml_params = None
for ii in range(10):
params = pa.fit_microlensing_event(light_curve)
new_chisq = params["result"].chisqr
if new_chisq < ml_chisq:
ml_chisq = new_chisq
ml_params = params
import matplotlib.pyplot as plt
for ii in [1,2,3]:
ax = plt.subplot(3,1,ii)
sliced_lc = light_curve.slice_mjd(ml_params["t0"].value-ii*ml_params["tE"].value, ml_params["t0"].value+ii*ml_params["tE"].value)
sliced_lc.plot(ax)
plt.savefig("plots/test_slice_peak_bad_data.png")
# Now do with a simulated event
from ptf.lightcurve import SimulatedLightCurve
light_curve = SimulatedLightCurve(mjd=light_curve.mjd, mag=15, error=0.1)
light_curve.add_microlensing_event(u0=0.1)
ml_chisq = 1E6
ml_params = None
for ii in range(10):
params = pa.fit_microlensing_event(light_curve)
new_chisq = params["result"].chisqr
if new_chisq < ml_chisq:
ml_chisq = new_chisq
ml_params = params
plt.clf()
for ii in [1,2,3]:
ax = plt.subplot(3,1,ii)
sliced_lc = light_curve.slice_mjd(ml_params["t0"].value-ii*ml_params["tE"].value, ml_params["t0"].value+ii*ml_params["tE"].value)
sliced_lc.plot(ax)
plt.savefig("plots/test_slice_peak_sim.png")
def test_ln_prior():
# First, test that using an out of bounds value breaks it
mjd = np.linspace(0., 100., 50)
sigma = np.random.uniform(0.05, 0.15, size=len(mjd))
light_curve = SimulatedLightCurve(mjd=mjd, error=sigma, mag=15)
light_curve.add_microlensing_event(t0=50., u0=0.3, tE=20)
print "Good values:", ln_prior([15, 0.3, 50., 20.], light_curve.mag, light_curve.mjd)
print "Test values:", ln_prior([15, 0.3, 50., 900.], light_curve.mag, light_curve.mjd)
print "Bad m0:", ln_prior([25, 0.3, 50., 20.], light_curve.mag, light_curve.mjd)
print "Bad u0:", ln_prior([15, -0.3, 50., 20.], light_curve.mag, light_curve.mjd)
print "Bad u0:", ln_prior([15, 1.9, 50., 20.], light_curve.mag, light_curve.mjd)
print "Bad t0:", ln_prior([15, 0.3, 500., 20.], light_curve.mag, light_curve.mjd)
print "Bad t0:", ln_prior([15, 0.3, -50., 20.], light_curve.mag, light_curve.mjd)
print "Bad tE:", ln_prior([15, 0.3, 50., 0.2], light_curve.mag, light_curve.mjd)
print "Bad tE:", ln_prior([15, 0.3, 50., 2000.], light_curve.mag, light_curve.mjd)
def test_ln_likelihood():
mjd = np.linspace(0., 100., 50)
sigma = np.random.uniform(0.05, 0.15, size=len(mjd))
light_curve = SimulatedLightCurve(mjd=mjd, error=sigma, mag=15)
light_curve.add_microlensing_event(t0=50., u0=0.3, tE=20)
likelihood1 = ln_likelihood(p=[15, 0.3, 50., 20.],
mag=light_curve.mag,
mjd=mjd,
sigma=sigma)
likelihood2 = ln_likelihood(p=[15, 0.3, 1., 20.],
mag=light_curve.mag,
mjd=mjd,
sigma=sigma)
likelihood3 = ln_likelihood(p=[15, 0.3, 50., 50.],
mag=light_curve.mag,
mjd=mjd,
sigma=sigma)
print "Likelihood with model at correct peak: {}".format(likelihood1)
print "Likelihood with model way off peak: {}".format(likelihood2)
print "Likelihood with model at peak, wrong tE: {}".format(likelihood3)
all_data = np.zeros((len(sourceIDs), Nfeatures))
for ii, f in enumerate(features):
if f == "sigma_mu":
pdb_f1, pdb_f2 = pdb_index_name[f]
all_data[:, ii] = goodSources[pdb_f1] / goodSources[pdb_f2]
else:
pdb_f = pdb_index_name[f]
all_data[:, ii] = goodSources[pdb_f]
np.save("/home/aprice-whelan/tmp/all_data.npy", all_data)
random_sourceIDs = sourceIDs[np.random.randint(len(sourceIDs), size=Nsources)]
training_data = np.zeros((Nsources, Ntrials, Nfeatures))
for ii, sourceID in enumerate(random_sourceIDs):
d = sourceData.readWhere("matchedSourceID == {0}".format(sourceID))
mjd = d["mjd"]
mag = d["mag"]
err = d["magErr"]
for trial in range(Ntrials):
lc = SimulatedLightCurve(mjd=mjd, mag=mag, error=err)
lc.add_microlensing_event()
stats = compute_variability_indices(lc, indices=features)
training_data[ii, trial, :] = np.array([stats[x] for x in features])
np.save("/home/aprice-whelan/tmp/training_data.npy", training_data)
chip.close()
def make_light_curve_figure(light_curve, sampler, filename=None, title="", x_axis_labels=True, y_axis_labels=True, chains=True):
chain = sampler.flatchain
fig = plt.figure(figsize=(10,12))
fig.suptitle(title, fontsize=23)
gs = gridspec.GridSpec(2, 1, height_ratios=[1,2])
ax = plt.subplot(gs[1])
ax2 = plt.subplot(gs[0])
# "best" parameters
max_idx = np.ravel(sampler.lnprobability).argmax()
best_m0, best_u0, best_t0, best_tE = chain[max_idx]
mjd = np.arange(light_curve.mjd.min()-1000., light_curve.mjd.max()+1000., 0.2)
if chains:
first_t0 = None
for ii in range(150):
walker_idx = np.random.randint(sampler.k)
chain = sampler.chain[walker_idx][-100:]
probs = sampler.lnprobability[walker_idx][-100:]
link_idx = np.random.randint(len(chain))
prob = probs[link_idx]
link = chain[link_idx]
m0, u0, t0, tE = link
if prob/probs.max() > 2:
continue
# More than 100% error
if np.any(np.fabs(link - np.mean(chain,axis=0)) / np.mean(chain,axis=0) > 0.25) or tE < 8 or tE > 250.:
continue
if np.fabs(t0 - best_t0) > 20.:
continue
if first_t0 == None:
first_t0 = t0
s_light_curve = SimulatedLightCurve(mjd=mjd, error=np.zeros_like(mjd), mag=m0)
s_light_curve.add_microlensing_event(t0=t0, u0=u0, tE=tE)
ax.plot(s_light_curve.mjd-first_t0, s_light_curve.mag, linestyle="-", color="#666666", alpha=0.1)
#ax_inset.plot(s_light_curve.mjd-first_t0, s_light_curve.mag, "r-", alpha=0.05)
mean_m0, mean_u0, mean_t0, mean_tE = np.median(chain,axis=0)
std_m0, std_u0, std_t0, std_tE = np.std(chain,axis=0)
print("m0 = ", mean_m0, std_m0)
print("u0 = ", mean_u0, std_u0)
print("tE = ", mean_tE, std_tE)
print("t0 = ", mean_t0, std_t0)
light_curve.mjd -= mean_t0
zoomed_light_curve = light_curve.slice_mjd(-3.*mean_tE, 3.*mean_tE)
if len(zoomed_light_curve) == 0:
mean_t0 = light_curve.mjd.min()
mean_tE = 10.
light_curve.mjd -= mean_t0
zoomed_light_curve = light_curve.slice_mjd(-3.*mean_tE, 3.*mean_tE)
zoomed_light_curve.plot(ax)
light_curve.plot(ax2)
ax.set_xlim(-3.*mean_tE, 3.*mean_tE)
#ax.text(-2.5*mean_tE, np.min(s_light_curve.mag), r"$u_0=${u0:.3f}$\pm${std:.3f}".format(u0=u0, std=std_u0) + "\n" + r"$t_E=${tE:.1f}$\pm${std:.1f} days".format(tE=mean_tE, std=std_tE), fontsize=19)
if chains:
fig.text(0.15, 0.48,
r"$u_0=${u0:.3f}$\pm${std:.3f}".format(u0=mean_u0, std=std_u0) +
"\n" + r"$t_E=${tE:.1f}$\pm${std:.1f} days".format(tE=mean_tE, std=std_tE), fontsize=19)
if y_axis_labels:
ax.set_ylabel(r"$R$ (mag)", fontsize=24)
if x_axis_labels:
ax.set_xlabel(r"time-$t_0$ [days]", fontsize=24)
# Now plot the "best fit" line in red
if chains:
s_light_curve = SimulatedLightCurve(mjd=mjd, error=np.zeros_like(mjd), mag=best_m0)
s_light_curve.add_microlensing_event(t0=best_t0, u0=best_u0, tE=best_tE)
ax.plot(s_light_curve.mjd-best_t0, s_light_curve.mag, "r-", alpha=0.6, linewidth=2)
if y_axis_labels:
ax2.set_ylabel(r"$R$ (mag)", fontsize=24)
#light_curve.plot(ax_inset)
#ax_inset.set_ylim(s_light_curve.mag.min()+abs(s_light_curve.mag.min()-m0)/5., s_light_curve.mag.min()-0.05)
#ax_inset.set_xlim(-0.3*tE, 0.3*tE)
#ax_inset.set_xticklabels([])
#ax_inset.set_yticklabels([])
fig.subplots_adjust(hspace=0.1, left=0.12)
if filename == None:
return fig, axes
else:
fig.savefig(os.path.join("plots/fit_events", filename))
def make_light_curve_figure(light_curve,
sampler,
filename=None,
title="",
x_axis_labels=True,
y_axis_labels=True,
chains=True):
chain = sampler.flatchain
fig = plt.figure(figsize=(10, 12))
fig.suptitle(title, fontsize=23)
gs = gridspec.GridSpec(2, 1, height_ratios=[1, 2])
ax = plt.subplot(gs[1])
ax2 = plt.subplot(gs[0])
# "best" parameters
max_idx = np.ravel(sampler.lnprobability).argmax()
best_m0, best_u0, best_t0, best_tE = chain[max_idx]
mjd = np.arange(light_curve.mjd.min() - 1000.,
light_curve.mjd.max() + 1000., 0.2)
if chains:
first_t0 = None
for ii in range(150):
walker_idx = np.random.randint(sampler.k)
chain = sampler.chain[walker_idx][-100:]
probs = sampler.lnprobability[walker_idx][-100:]
link_idx = np.random.randint(len(chain))
prob = probs[link_idx]
link = chain[link_idx]
m0, u0, t0, tE = link
if prob / probs.max() > 2:
continue
# More than 100% error
if np.any(
np.fabs(link - np.mean(chain, axis=0)) /
np.mean(chain, axis=0) > 0.25) or tE < 8 or tE > 250.:
continue
if np.fabs(t0 - best_t0) > 20.:
continue
if first_t0 == None:
first_t0 = t0
s_light_curve = SimulatedLightCurve(mjd=mjd,
error=np.zeros_like(mjd),
mag=m0)
s_light_curve.add_microlensing_event(t0=t0, u0=u0, tE=tE)
ax.plot(s_light_curve.mjd - first_t0,
s_light_curve.mag,
linestyle="-",
color="#666666",
alpha=0.1)
#ax_inset.plot(s_light_curve.mjd-first_t0, s_light_curve.mag, "r-", alpha=0.05)
mean_m0, mean_u0, mean_t0, mean_tE = np.median(chain, axis=0)
std_m0, std_u0, std_t0, std_tE = np.std(chain, axis=0)
print("m0 = ", mean_m0, std_m0)
print("u0 = ", mean_u0, std_u0)
print("tE = ", mean_tE, std_tE)
print("t0 = ", mean_t0, std_t0)
light_curve.mjd -= mean_t0
zoomed_light_curve = light_curve.slice_mjd(-3. * mean_tE, 3. * mean_tE)
if len(zoomed_light_curve) == 0:
mean_t0 = light_curve.mjd.min()
mean_tE = 10.
light_curve.mjd -= mean_t0
zoomed_light_curve = light_curve.slice_mjd(-3. * mean_tE, 3. * mean_tE)
zoomed_light_curve.plot(ax)
light_curve.plot(ax2)
ax.set_xlim(-3. * mean_tE, 3. * mean_tE)
#ax.text(-2.5*mean_tE, np.min(s_light_curve.mag), r"$u_0=${u0:.3f}$\pm${std:.3f}".format(u0=u0, std=std_u0) + "\n" + r"$t_E=${tE:.1f}$\pm${std:.1f} days".format(tE=mean_tE, std=std_tE), fontsize=19)
if chains:
fig.text(
0.15,
0.48,
r"$u_0=${u0:.3f}$\pm${std:.3f}".format(u0=mean_u0, std=std_u0) +
"\n" + r"$t_E=${tE:.1f}$\pm${std:.1f} days".format(tE=mean_tE,
std=std_tE),
fontsize=19)
if y_axis_labels:
ax.set_ylabel(r"$R$ (mag)", fontsize=24)
if x_axis_labels:
ax.set_xlabel(r"time-$t_0$ [days]", fontsize=24)
# Now plot the "best fit" line in red
if chains:
s_light_curve = SimulatedLightCurve(mjd=mjd,
error=np.zeros_like(mjd),
mag=best_m0)
s_light_curve.add_microlensing_event(t0=best_t0,
u0=best_u0,
tE=best_tE)
ax.plot(s_light_curve.mjd - best_t0,
s_light_curve.mag,
"r-",
alpha=0.6,
linewidth=2)
if y_axis_labels:
ax2.set_ylabel(r"$R$ (mag)", fontsize=24)
#light_curve.plot(ax_inset)
#ax_inset.set_ylim(s_light_curve.mag.min()+abs(s_light_curve.mag.min()-m0)/5., s_light_curve.mag.min()-0.05)
#ax_inset.set_xlim(-0.3*tE, 0.3*tE)
#ax_inset.set_xticklabels([])
#ax_inset.set_yticklabels([])
fig.subplots_adjust(hspace=0.1, left=0.12)
if filename == None:
return fig, axes
else:
fig.savefig(os.path.join("plots/fit_events", filename))
all_data = np.zeros((len(sourceIDs), Nfeatures))
for ii, f in enumerate(features):
if f == "sigma_mu":
pdb_f1, pdb_f2 = pdb_index_name[f]
all_data[:, ii] = goodSources[pdb_f1] / goodSources[pdb_f2]
else:
pdb_f = pdb_index_name[f]
all_data[:, ii] = goodSources[pdb_f]
np.save("/home/aprice-whelan/tmp/all_data.npy", all_data)
random_sourceIDs = sourceIDs[np.random.randint(len(sourceIDs), size=Nsources)]
training_data = np.zeros((Nsources, Ntrials, Nfeatures))
for ii, sourceID in enumerate(random_sourceIDs):
d = sourceData.readWhere("matchedSourceID == {0}".format(sourceID))
mjd = d['mjd']
mag = d['mag']
err = d['magErr']
for trial in range(Ntrials):
lc = SimulatedLightCurve(mjd=mjd, mag=mag, error=err)
lc.add_microlensing_event()
stats = compute_variability_indices(lc, indices=features)
training_data[ii, trial, :] = np.array([stats[x] for x in features])
np.save("/home/aprice-whelan/tmp/training_data.npy", training_data)
chip.close()
def test_iscandidate(plot=False):
''' Use test light curves to test selection:
- Periodic
- Bad data
- Various simulated events
- Flat light curve
- Transients (SN, Nova, etc.)
'''
np.random.seed(10)
logger.setLevel(logging.DEBUG)
from ptf.lightcurve import SimulatedLightCurve
import ptf.db.mongodb as mongo
db = mongo.PTFConnection()
logger.info("---------------------------------------------------")
logger.info(greenText("Periodic light curves"))
logger.info("---------------------------------------------------")
# Periodic light curves
periodics = [(4588, 7, 13227), (4588, 2, 15432), (4588, 9, 17195), (2562, 10, 28317), (4721, 8, 11979), (4162, 2, 14360)]
for field_id, ccd_id, source_id in periodics:
periodic_light_curve = pdb.get_light_curve(field_id, ccd_id, source_id, clean=True)
periodic_light_curve.indices = pa.compute_variability_indices(periodic_light_curve, indices=["eta", "delta_chi_squared", "j", "k", "sigma_mu"])
assert pa.iscandidate(periodic_light_curve, lower_eta_cut=10**db.fields.find_one({"_id" : field_id}, {"selection_criteria" : 1})["selection_criteria"]["eta"]) in ["subcandidate" , False]
if plot: plot_lc(periodic_light_curve)
logger.info("---------------------------------------------------")
logger.info(greenText("Bad light curves"))
logger.info("---------------------------------------------------")
# Bad data
bads = [(3756, 0, 14281), (1983, 10, 1580)]
for field_id, ccd_id, source_id in bads:
bad_light_curve = pdb.get_light_curve(field_id, ccd_id, source_id, clean=True)
bad_light_curve.indices = pa.compute_variability_indices(bad_light_curve, indices=["eta", "delta_chi_squared", "j", "k", "sigma_mu"])
assert not pa.iscandidate(bad_light_curve, lower_eta_cut=10**db.fields.find_one({"_id" : field_id}, {"selection_criteria" : 1})["selection_criteria"]["eta"])
if plot: plot_lc(bad_light_curve)
logger.info("---------------------------------------------------")
logger.info(greenText("Simulated light curves"))
logger.info("---------------------------------------------------")
# Simulated light curves
for field_id,mjd in [(4721,periodic_light_curve.mjd)]:
for err in [0.01, 0.05, 0.1]:
logger.debug("field: {0}, err: {1}".format(field_id,err))
light_curve = SimulatedLightCurve(mjd=mjd, mag=15, error=[err])
light_curve.indices = pa.compute_variability_indices(light_curve, indices=["eta", "delta_chi_squared", "j", "k", "sigma_mu"])
assert not pa.iscandidate(light_curve, lower_eta_cut=10**db.fields.find_one({"_id" : field_id}, {"selection_criteria" : 1})["selection_criteria"]["eta"])
light_curve.add_microlensing_event(u0=np.random.uniform(0.2, 0.8), t0=light_curve.mjd[int(len(light_curve)/2)], tE=light_curve.baseline/8.)
light_curve.indices = pa.compute_variability_indices(light_curve, indices=["eta", "delta_chi_squared", "j", "k", "sigma_mu"])
if plot:
plt.clf()
light_curve.plot()
plt.savefig("plots/tests/{0}_{1}.png".format(field_id,err))
assert pa.iscandidate(light_curve, lower_eta_cut=10**db.fields.find_one({"_id" : field_id}, {"selection_criteria" : 1})["selection_criteria"]["eta"])
logger.info("---------------------------------------------------")
logger.info(greenText("Transient light curves"))
logger.info("---------------------------------------------------")
# Transients (SN, Novae)
transients = [(4564, 0, 4703), (4914, 6, 9673), (100041, 1, 4855), (100082, 5, 7447), (4721, 8, 3208), (4445, 7, 11458),\
(100003, 6, 10741), (100001, 10, 5466), (4789, 6, 11457), (2263, 0, 3214), (4077, 8, 15293), (4330, 10, 6648), \
(4913, 7, 13436), (100090, 7, 2070), (4338, 2, 10330), (5171, 0, 885)]
for field_id, ccd_id, source_id in transients:
transient_light_curve = pdb.get_light_curve(field_id, ccd_id, source_id, clean=True)
logger.debug(transient_light_curve)
transient_light_curve.indices = pa.compute_variability_indices(transient_light_curve, indices=["eta", "delta_chi_squared", "j", "k", "sigma_mu"])
assert pa.iscandidate(transient_light_curve, lower_eta_cut=10**db.fields.find_one({"_id" : field_id}, {"selection_criteria" : 1})["selection_criteria"]["eta"])
if plot: plot_lc(transient_light_curve)
def fit_candidates(Nburn, Nsamples, Nwalkers, seed=None):
final_candidates = ["PTFS1206i", "PTFS1216bt", "PTFS1217cv"]
fig1 = plt.figure(figsize=(22,8))
fontsize = 16
gs = gridspec.GridSpec(2, 3, height_ratios=[1,2])
u0s,t0s,m0s,tEs = [],[],[],[]
du0s,dt0s,dm0s,dtEs = [],[],[],[]
# Make 3-panel figure with candidates
for ii,PTFID in enumerate(final_candidates):
if ii == 2:
Nsamples = 1000
chains = True
filename = "data/candidates/lc_{0}.dat".format(PTFID)
figtext = ptfid_to_longname[PTFID] #candidate["long_ptf_name"]
light_curve = lightcurve_from_filename(filename)
if ii == 0:
y_axis_labels = True
else:
y_axis_labels = False
if ii == 1:
x_axis_labels = True
else:
x_axis_labels = False
logger.info("Fitting {0}".format(PTFID))
sampler = fit.fit_model_to_light_curve(light_curve, nwalkers=Nwalkers, nsamples=Nsamples, nburn_in=Nburn, seed=seed)
chain = sampler.flatchain
print "Mean acceptance fraction: {0:.3f}".format(np.mean(sampler.acceptance_fraction))
ax = plt.subplot(gs[1,ii])
ax2 = plt.subplot(gs[0,ii])
# "best" parameters
flatchain = np.vstack(sampler.chain)
best_m0, best_u0, best_t0, best_tE = np.median(flatchain, axis=0)
mjd = np.arange(light_curve.mjd.min()-1000., light_curve.mjd.max()+1000., 0.2)
first_t0 = None
for jj in range(100):
walker_idx = np.random.randint(sampler.k)
chain = sampler.chain[walker_idx][-100:]
probs = sampler.lnprobability[walker_idx][-100:]
link_idx = np.random.randint(len(chain))
prob = probs[link_idx]
link = chain[link_idx]
m0, u0, t0, tE = link
if prob/probs.max() > 2:
continue
# More than 100% error
if np.any(np.fabs(link - np.mean(chain,axis=0)) / np.mean(chain,axis=0) > 0.25) or tE < 8 or tE > 250.:
continue
if np.fabs(t0 - best_t0) > 20.:
continue
if first_t0 == None:
first_t0 = t0
s_light_curve = SimulatedLightCurve(mjd=mjd, error=np.zeros_like(mjd), mag=m0)
s_light_curve.add_microlensing_event(t0=t0, u0=u0, tE=tE)
ax.plot(s_light_curve.mjd-first_t0, s_light_curve.mag, linestyle="-", color="#ababab", alpha=0.05, marker=None)
#ax_inset.plot(s_light_curve.mjd-first_t0, s_light_curve.mag, "r-", alpha=0.05)
mean_m0, mean_u0, mean_t0, mean_tE = np.median(chain,axis=0)
std_m0, std_u0, std_t0, std_tE = np.std(chain,axis=0)
m0s.append(mean_m0)
dm0s.append(std_m0)
t0s.append(mean_t0+54832.) # HJD to MJD
dt0s.append(std_t0)
tEs.append(mean_tE)
dtEs.append(std_tE)
u0s.append(mean_u0)
du0s.append(std_u0)
print("m0 = ", mean_m0, std_m0)
print("u0 = ", mean_u0, std_u0)
print("tE = ", mean_tE, std_tE)
print("t0 = ", mean_t0, std_t0)
light_curve.mjd -= mean_t0
zoomed_light_curve = light_curve.slice_mjd(-3.*mean_tE, 3.*mean_tE)
if len(zoomed_light_curve) == 0:
mean_t0 = light_curve.mjd.min()
mean_tE = 10.
light_curve.mjd -= mean_t0
zoomed_light_curve = light_curve.slice_mjd(-3.*mean_tE, 3.*mean_tE)
zoomed_light_curve.plot(ax)
light_curve.plot(ax2)
ax.set_xlim(-3.*mean_tE, 3.*mean_tE)
#ax.text(-2.5*mean_tE, np.min(s_light_curve.mag), r"$u_0=${u0:.3f}$\pm${std:.3f}".format(u0=u0, std=std_u0) + "\n" + r"$t_E=${tE:.1f}$\pm${std:.1f} days".format(tE=mean_tE, std=std_tE), fontsize=19)
ylim, xlim = ax.get_ylim(), ax.get_xlim()
dx, dy = (xlim[1]-xlim[0]), (ylim[0]-ylim[1])
ax.text(xlim[0]+dx/20., ylim[1]+dy/8.,
r"$u_0=${u0:.3f}$\pm${std:.3f}".format(u0=mean_u0, std=std_u0) +
"\n" + r"$t_E=${tE:.1f}$\pm${std:.1f} days".format(tE=mean_tE, std=std_tE), fontsize=fontsize)
ylim, xlim = ax2.get_ylim(), ax2.get_xlim()
dx, dy = (xlim[1]-xlim[0]), (ylim[0]-ylim[1])
if ii == 2:
ax2.text((xlim[0]+xlim[1])/2.-30,ylim[1]+dy/8.,figtext,fontsize=fontsize)
else:
ax2.text(xlim[0]+dx/20., ylim[1]+dy/8., figtext,fontsize=fontsize)
if y_axis_labels:
ax.set_ylabel(r"$R$ (mag)", fontsize=fontsize+2)
if x_axis_labels:
ax.set_xlabel(r"time-$t_0$ [days]", fontsize=fontsize+2)
# Now plot the "best fit" line in red
s_light_curve = SimulatedLightCurve(mjd=mjd, error=np.zeros_like(mjd), mag=best_m0)
s_light_curve.add_microlensing_event(t0=best_t0, u0=best_u0, tE=best_tE)
ax.plot(s_light_curve.mjd-best_t0, s_light_curve.mag, "k-", alpha=0.6, linewidth=3)
if y_axis_labels:
ax2.set_ylabel(r"$R$ (mag)", fontsize=fontsize+2)
import astropy.table as at
from astropy.io import ascii
tbl = at.Table([at.Column(data=u0s, name="u0"),
at.Column(data=du0s, name="e_u0"),
at.Column(data=tEs, name="tE"),
at.Column(data=dtEs, name="e_tE"),
at.Column(data=t0s, name="t0"),
at.Column(data=dt0s, name="e_t0"),
at.Column(data=m0s, name="m0"),
at.Column(data=dm0s, name="e_m0")])
ascii.write(tbl, output=os.path.join("plots/fit_events/metadata.tex"), Writer=ascii.Latex)
fig1.subplots_adjust(hspace=0.15, wspace=0.15, left=0.06, right=0.95, top=0.98, bottom=0.09)
fig1.savefig(os.path.join("plots/fit_events", "fig10.eps"))
def microlensing_event_sim():
""" Create the multi-panel figure with simulated microlensing events for a single
'typical' PTF light curve.
"""
#field = pdb.Field(100062, "R")
#ccd = field.ccds[1]
#chip = ccd.read()
#sources = chip.sources.readWhere("(ngoodobs > 300) & (vonNeumannRatio > 1.235)")
#light_curve = ccd.light_curve(sources["matchedSourceID"][np.random.randint(0, len(sources))], clean=True)
#print sources["matchedSourceID"]
light_curve = pdb.get_light_curve(100062, 1, 13268, clean=True)
num = 4
fig, axes = plt.subplots(num,1, sharex=True, figsize=(11,15))
sim_light_curve = SimulatedLightCurve(mjd=light_curve.mjd, mag=light_curve.mag, error=light_curve.error)
t0 = sim_light_curve.mjd[int(len(sim_light_curve.mjd)/2)]
kwarg_list = [None, {"u0" : 1.0, "t0" : t0, "tE" : 20},
{"u0" : 0.5, "t0" : t0, "tE" : 20},
{"u0" : 0.01, "t0" : t0, "tE" : 20}]
args_list = [(16.66, "a)"), (16.4, "b)"), (16.0, "c)"), (12, "d)")]
args_list2 = [16.68, 16.5, 16.2, 13]
for ii in range(num):
axes[ii].xaxis.set_visible(False)
if ii != 0:
#sim_light_curve.reset()
sim_light_curve = SimulatedLightCurve(mjd=light_curve.mjd, mag=light_curve.mag, error=light_curve.error)
sim_light_curve.add_microlensing_event(**kwarg_list[ii])
sim_light_curve.plot(axes[ii], marker="o", ms=3, alpha=0.75)
axes[ii].axhline(14.3, color='r', linestyle="--")
if kwarg_list[ii] == None:
u0_str = ""
else:
u0 = kwarg_list[ii]["u0"]
u0_str = r"$u_0={:.2f}$".format(u0)
#axes[ii].set_ylabel(u0_str, rotation="horizontal")
#for tick in axes[ii].yaxis.get_major_ticks():
# tick.label.set_fontsize(tick_font_size)
if ii == 0:
[tick.set_visible(False) for jj,tick in enumerate(axes[ii].get_yticklabels()) if jj % 2 != 0]
if ii % 2 != 0:
axes[ii].yaxis.tick_right()
else:
axes[ii].yaxis.set_label_position("right")
if ii == 0:
axes[ii].set_ylabel(r"$R$", rotation="horizontal", fontsize=26)
axes[ii].yaxis.set_label_position("left")
axes[ii].text(56100, *args_list[ii], fontsize=24)
axes[ii].text(56100, args_list2[ii], u0_str, fontsize=24)
#fig.suptitle("PTF light curve with simulated microlensing events", fontsize=24)
for ax in fig.axes:
for ticklabel in ax.get_yticklabels():
ticklabel.set_fontsize(18)
fig.subplots_adjust(hspace=0.0, left=0.1, right=0.9)
fig.savefig(os.path.join(pg.plots_path, "paper_figures", "simulated_events.pdf"), bbox_inches="tight", facecolor="white")