jdadj, obsobject, lightdata = f.openfile(kplr_id, kplr_file)
time, flux, flux_err = f.fix_data(lightdata)
time -= np.median(time)
inj_period = 300.00
inj_offset = -12.0
inj_depth = 0.000336
inj_width = 0.54
flux = f.raw_injection(inj_period,inj_offset,inj_depth,inj_width,time,flux)
flux, variance = f.rescale(flux, flux_err)
depth_interval = np.linspace(0.00,0.001, 200)
width_interval = np.linspace(0.40,0.70, 200)
ln_like_grid = [[f.ln_like(flux, f.push_box_model(inj_offset, d, w, time), variance) for d in depth_interval] for w in width_interval]
ln_like_flat = f.ln_like(flux, f.flat_model(time), variance)
ln_like_grid -= ln_like_flat
#The following two lines would net negatiev likelihood values to NaN
# negative = ln_like_grid < 0
# ln_like_grid[negative] = np.nan
plt.imshow(ln_like_grid, cmap= 'spectral', aspect = 'auto', extent = [depth_interval[0], depth_interval[-1], width_interval[0], width_interval[-1]], origin = 'lower', interpolation = 'nearest')
plt.colorbar()
plt.xlabel('Depth (unitless)')
plt.ylabel('Width (days)')
plt.title(r'$\Delta \ln L = \ln L_m - \ln L_f$')
print t.clock() - t0
time -= np.median(time)
inj_period = 300.00
inj_offset = -12.0
inj_depth = 0.000336
inj_width = 0.54
flux = f.raw_injection(inj_period, inj_offset, inj_depth, inj_width, time,
flux)
flux, variance = f.rescale(flux, flux_err)
depth_interval = np.linspace(0.00, 0.001, 200)
width_interval = np.linspace(0.40, 0.70, 200)
ln_like_grid = [[
f.ln_like(flux, f.push_box_model(inj_offset, d, w, time), variance)
for d in depth_interval
] for w in width_interval]
ln_like_flat = f.ln_like(flux, f.flat_model(time), variance)
ln_like_grid -= ln_like_flat
#The following two lines would net negatiev likelihood values to NaN
# negative = ln_like_grid < 0
# ln_like_grid[negative] = np.nan
plt.imshow(ln_like_grid,
cmap='spectral',
aspect='auto',
extent=[
depth_interval[0], depth_interval[-1], width_interval[0],
width_interval[-1]
#The following 5 lines of code create a fake transit signal inside the data. inj_period = 100.00 inj_offset = -12.0 inj_depth = 0.000336 inj_width = 0.54 flux = f.raw_injection(inj_period,inj_offset,inj_depth,inj_width,time,flux) flux, variance = f.rescale(flux, flux_err) width = 1.5 depth = 0.000336 # offset_interval = np.linspace(time[0], time[-1], 10000) ln_like_perfect = np.asarray([f.ln_like(flux, f.push_box_model(o, depth, width, time), variance) for o in time]) ln_like_flat = f.ln_like(flux, f.flat_model(time), variance) #subtract the flat model likelihood from the ln_likelihood array ln_like_array = ln_like_perfect - ln_like_flat index_max_like = np.argmax(ln_like_array) found_offset = time[index_max_like] print found_offset fig1 = plt.figure() sub1 = fig1.add_subplot(211) sub1.plot(time, flux, '.k') sub1.plot(time, f.push_box_model(found_offset,depth,width,time)) # sub1.vlines(inj_offset + 0.5*inj_width, flux.min(),flux.max(), 'r')
time -= np.median(time)
# The following 5 lines of code create a fake transit signal inside the data.
inj_period = 225.00
inj_offset = 0.0
inj_depth = 0.000336 * 3
inj_width = 0.54
flux = f.raw_injection(inj_period, inj_offset, inj_depth, inj_width, time,
flux)
flux = f.ma_filter(flux, 10)
width = 0.54
depth = 0.000336 * 3
ln_like_perfect = np.asarray([
f.ln_like(flux, f.push_box_model(o, depth, width, time), variance)
for o in time
])
ln_like_flat = f.ln_like(flux, f.flat_model(time), variance)
#subtract the flat model likelihood from the ln_likelihood array
ln_like_array = ln_like_perfect - ln_like_flat
index_max_like = np.argmax(ln_like_array)
found_offset = time[index_max_like]
print found_offset
fig1 = plt.figure()
sub1 = fig1.add_subplot(211)
sub1.plot(time, flux, ',k')
ylim_range = 0.001