down_time = binning(time, i, b_bin_steps)
down_flux = binning(flux, i, b_bin_steps)
eep = EEP(down_time, down_flux)
eep.estimate()
print "binned N: ", len(eep.flux), eep.get_params()
results = eep.get_params()
width = max(results[0] * 3.0, 0.4)
# 3 times of the width. The data point at zero time
# must be centered.
half_width = int((width / 2.0) * base_sampling)
center_index = np.searchsorted(time, results[3])
flux_cut = flux[max(center_index - half_width, 0) : min(center_index + half_width + 1, len(flux))]
pl.subplot(122)
pl.step(eep.time, eep.flux, color=colors[i % len(colors)], marker="x", where="mid")
v = pl.axis()
x = np.linspace(v[0], v[1], 1000)
y = eep.gaussian_step(eep.p1, x)
pl.plot(x, y, color=colors[i % len(colors)], ls="--", lw=2)
v = pl.axis()
# pl.axis([-0.5, 0.5, v[2], v[3]])
pl.grid()
pl.title("b: %.2f, a: %.1f AU, d:%.1f km" % (b, a, d))
pl.xlabel("Time/seconds")
pl.ylabel("Scaled flux")
pl.show()
