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_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")
