# read and estimate observables
#print folder + '/' + lc_name_template
lc_name = glob.glob(folder + '/' + lc_name_template)[0]
# get flux
time, flux = np.loadtxt(lc_name, dtype=(np.float, np.float),
usecols=(1, 2), unpack=True)
# Scale the light curve so that the mean level is one.
flux /= np.median(flux)
#pl.subplot(211)
#pl.title(lc_name_template)
#pl.step(time, flux, where='mid')
# estimate event properties
eep = EEP(time, flux)
eep.estimate()
results = eep.get_params()
# Cut flux with three times of the event width.
# At least 1 seconds.
width = max(results[0] * 3., 1.)
# 3 times of the width. The data point at zero time
# must be centered.
half_width = int((width / 2.) / 0.05)
center_index = np.searchsorted(time, results[3])
#print half_width, center_index
# This make always the number of data points "odd".
flux_cut = flux[max(center_index - half_width, 0):
min(center_index + half_width + 1, len(flux))]
b = rows[0][4]
t = 0.0
a = rows[0][5]
d = rows[0][6]
print rows[0][7], rows[0][8], rows[0][9]
# gen_lc = generate_event_lc(rows[0][tel], np.inf)
time = np.arange(len(rows[0][tel])) / 120.0
time -= np.median(time)
pl.figure(figsize=(16, 6))
pl.subplot(121)
pl.grid()
pl.step(time, rows[0][tel], "bx", where="mid")
eep = EEP(time, rows[0][tel])
# eep.estimate()
v = pl.axis()
x = np.linspace(v[0], v[1], 1000)
# y = eep.gaussian_step(eep.p1, x)
# pl.plot(x, y, 'r-')
pl.xlabel("Time/seconds")
pl.ylabel("Scaled flux")
# print eep.p1, len(eep.flux), eep.get_params()
# From file.
folder = "/Users/kim/Server/taos2/taos_lc/a%06.1fau_d%04.1fkm" % (a, d)
lc_name_template = "TEL%d_bzero%06.3fomega_tzero%06.3fms_*_a%06.1fau_d%04.1fkm_*.lcv" % (tel, b, t, a, d)
# Read and estimate observables
for i in range(10):
logger.info('\t%dth search..' % (i + 1))
# Select one random event (three light curves).
results, c_id = select_random(conn)
true_id.append(c_id)
# Add noise to the selected event.
#Also, make the light curves having 200 data points.
# 20 Hz time sampling. Centered at zero.
#print len(results['flux_a']), len(results['flux_b']), len(results['flux_c'])
results['flux_a'] = add_noise(results['flux_a'], SNRs[m])
results['flux_b'] = add_noise(results['flux_b'], SNRs[m])
results['flux_c'] = add_noise(results['flux_c'], SNRs[m])
# Estimate observables for each light curve.
eep = EEP(results['time_a'], results['flux_a'])
eep.estimate()
a_eep = eep.get_params()
width = max(a_eep[0] * 3., 1.)
half_width = int((width / 2.) / 0.05)
center_index = np.searchsorted(results['time_a'], a_eep[3])
start_index = center_index - half_width
end_index = center_index + half_width + 1
#start_index = max(center_index - half_width, 0)
#end_index = min(center_index + half_width + 1, len(results['flux_a']))
results['flux_a'] = results['flux_a'][start_index:end_index]
results['time_a'] = results['time_a'][start_index:end_index]
eep = EEP(results['time_b'], results['flux_b'])
eep.estimate()
b_eep = eep.get_params()
# Binning and add noise to the selected event.
for tel in tels:
# Binning, with offset 0.
results['flux_%d' % tel] = \
binning(results['flux_%d' % tel], 0, b_bin_steps)
results['time_%d' % tel] = \
binning(results['time_%d' % tel], 0, b_bin_steps)
# Add noise.
results['flux_%d' % tel] = \
add_noise(results['flux_%d' % tel], SNRs[m])
# Estimate observables for each light curve.
for tel in tels:
eep = EEP(results['time_%d' % tel], results['flux_%d' % tel])
eep.estimate()
results['eep_%d' % tel] = eep.get_params()
width = max(results['eep_%d' % tel][0] * 3., 0.4)
half_width = int((width / 2.) * base_sampling)
center_index = np.searchsorted(results['time_%d' % tel],
results['eep_%d' % tel][3])
results['flux_%d' % tel] = \
results['flux_%d' % tel] \
[max(center_index - half_width, 0):
min(center_index + half_width + 1,
len(results['flux_%d' % tel]))]
results['time_%d' % tel] = \
results['time_%d' % tel] \
[max(center_index - half_width, 0):
