ref_SDI = pca.pca(np.mean(simple_hypercube_2, axis=0) / ap.exposure_time,
angle_list=np.zeros((len(diff_cube[0]))),
scale_list=scale_list,
mask_center_px=None)
# quicklook_im(tar_SDI, logAmp=True)
# quicklook_im(ref_SDI, logAmp=True)
# quicklook_im(tar_SDI-ref_SDI, logAmp=True)
cube.append(Lmap)
cube.append(BBmap)
cube.append(SDI)
# cube.append(tar_SDI-ref_SDI)
indep_images(cube,
logAmp=True,
vmins=[0.01, 1, 10],
vmaxs=[6, 6, 1e3],
annos=['BB DSI', 'BB Sim DSI', 'SDI'],
titles=['$I_\mathrm{L}$', '$I_\mathrm{L}$', '$I$'])
iop.hyperFile = iop.datadir + '/noWnoRollHyperWcomp1000cont_Aug_1st.pkl' #5
simple_hypercube = read.get_integ_hypercube(plot=False) #/ap.numframes
wsamples = np.linspace(tp.band[0], tp.band[1], tp.nwsamp)
# scale_list = tp.band[0] / wsamples
scale_list = wsamples / tp.band[0]
angle_list = np.zeros((tp.nwsamp))
print np.mean(simple_hypercube, axis=0).shape
static_psf = pca.pca(np.mean(simple_hypercube, axis=0),
angle_list=angle_list,
scale_list=scale_list,
mask_center_px=None,
# iop.hyperFile = iop.datadir + 'noWnoRollHyperWcomp1000cont_Aug_2ndMKIDs2.pkl'#5
simple_hypercube_2 = read.get_integ_hypercube(plot=False) #/ap.numframes
loop_frames(simple_hypercube_1[::10, 0], logAmp=True)
loop_frames(simple_hypercube_2[:, 0], logAmp=True)
diff_cube = simple_hypercube_1[2:] - simple_hypercube_2[2:]
loop_frames(diff_cube[:, 0], logAmp=False)
# quicklook_im(np.mean(diff_cube[:,0],axis=0), logAmp=False)
quicklook_im(np.mean(diff_cube[:, 0], axis=0), logAmp=True)
quicklook_im(np.median(diff_cube[:, 0], axis=0), logAmp=True)
#
LCcube = np.transpose(diff_cube, (2, 3, 0, 1))
algo_dict = {'thresh': 0}
Dmap = Analysis.stats.get_Dmap(LCcube, algo_dict['thresh'])
# DSI
indep_images([Dmap], vmins=[0.01], vmaxs=[0.5], logAmp=True)
#SDI +DSI
iop.hyperFile = iop.datadir + '/noWnoRollHyperWcomp1000cont_Aug_1st.pkl' #5
simple_hypercube = read.get_integ_hypercube(plot=False) #/ap.numframes
wsamples = np.linspace(tp.band[0], tp.band[1], tp.nwsamp)
# scale_list = tp.band[0] / wsamples
scale_list = wsamples / tp.band[0]
angle_list = np.zeros((tp.nwsamp))
print np.mean(simple_hypercube, axis=0).shape
static_psf = pca.pca(np.mean(simple_hypercube, axis=0),
angle_list=angle_list,
scale_list=scale_list,
mask_center_px=None,
full_output=True)
# print binned
# hist, bins = np.histogram(binned, bins=range(0, int(np.max(binned)) + 1))
# plt.plot(bins[:-1], hist)
# plt.show()
LCmap = np.transpose(bunch_hypercube[:, 0])
SSD_maps = Analysis.stats.get_Iratio(LCmap, xlocs, ylocs, None, None, None)
SSD_maps = np.array(SSD_maps)[:-1]
# # SSD_maps[:2] /= star_phot
# # SSD_maps[2] /= SSD_starphot
# SSD_maps #/= star_phot
#vmins = [2e-11, 2e-8, 1e-12], vmaxs = [5e-7, 1.5e-7, 1e-6]
indep_images(
SSD_maps,
logAmp=True,
titles=[r' $I_C / I^{*}$', r' $I_S / I^{*}$', r' $I_r / I^{*}$'],
annos=['Deterministic', 'Random', 'Beam Ratio'])
print 'here'
# quicklook_im(bunch_hypercube[0,0])
ap.exposure_time = 1
stacked = read.take_exposure(bunch_hypercube)
quicklook_im(stacked[0, 0])
from statsmodels.tsa.stattools import acovf, acf, ccf
mask = Analysis.phot.aperture(64, 64, 1)
aper_idx = np.where(mask)
print aper_idx
# quicklook_im(mask)
axes[0].plot(rad_samp,thruput)
for noise in plotdata[:,1]:
axes[1].plot(rad_samp,noise)
for cont in plotdata[:,2]:
axes[2].plot(rad_samp,cont)
for ax in axes:
ax.set_yscale('log')
ax.set_xlabel('Radial Separation')
ax.tick_params(direction='in',which='both', right=True, top=True)
axes[0].set_ylabel('Throughput')
axes[1].set_ylabel('Noise')
axes[2].set_ylabel('5$\sigma$ Contrast')
axes[2].legend(['Fast','Med','Slow'])
# plt.show()
indep_images(maps, logAmp=True)
plt.show()
# Plot just contrast curves
# rad_samp = np.linspace(0, tp.platescale / 1000. * (plotdata.shape[2]-6), plotdata.shape[2]-6)
rad_samp = np.linspace(0, tp.platescale / 1000. * 100, plotdata.shape[2])
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))
for cont in plotdata[:, 2]:
# dprint((len(cont[:-6]), plotdata.shape[2]-6))
axes.plot(rad_samp, cont, '-')
# axes.plot(rad_samp[:-3], cont[:-9], '-o')
# axes.plot(rad_samp[-4:-2], cont[-10:-8], '--')
axes.plot([0.4, 0.65, 0.9], [1e-4,1e-4,1e-4], 'o')
axes.set_yscale('log')
axes.set_xlabel('Radial Separation')
axes.tick_params(direction='in',which='both', right=True, top=True)