with open('save.pkl', 'wb') as handle:
pickle.dump(plotdata, handle, protocol=pickle.HIGHEST_PROTOCOL)
# for cont in plotdata[:,2]:
# plt.plot(rad_samp,cont)
# print plotdata
# print plotdata[:,2]
# Plotting
plotdata = np.array(plotdata)
rad_samp = np.linspace(0, tp.platescale / 1000. * plotdata.shape[2],
plotdata.shape[2])
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 3.4))
for thruput in plotdata[:, 0]:
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(['RDI', 'ADI', 'SDI', 'DSI', 'SSD'])
compare_images(maps, logAmp=True)
plt.show()
width=1,
titles=r'$I$',
annos=['Before Coron.', 'After Coron.'],
logAmp=True)
plt.show(block=True)
tp.detector = 'MKIDs'
tp.w_bins = 12
if __name__ == '__main__':
mkid = gpd.run()[0, :]
compare_images(mkid[::2],
vmax=200,
logAmp=True,
vmin=1,
title=r'$I (cts)$',
annos=[
'MKIDs 800 nm', '940 nm', '1080 nm', '1220 nm',
'1360 nm', '1500 nm'
])
quicklook_im(np.mean(mkid[5:-1], axis=0),
anno='MEDIS J Band',
vmax=400,
axis=None,
title=r'$I (cts)$',
logAmp=True,
label='e')
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(9, 3.8))
labels = ['Ideal', 'MKID']
# images = [ideal,mkid]
titles = ['A.U.', 'cts']
tp.rot_rate = 4.5 # deg/s
iop.hyperFile = iop.datadir + '/ADIHyper.pkl'
ADI_hypercube = read.get_integ_hypercube(plot=False) #/ap.numframes
# star_phots = np.ones((len(ADI_hypercube))) * star_phot
algo_dict = {}
angle_list = -1 * np.arange(0, num_exp * tp.rot_rate * cp.frame_time,
tp.rot_rate * cp.frame_time)
print angle_list.shape
method_out = eval_method(ADI_hypercube[:, 0], pca.pca, angle_list,
algo_dict)
plotdata.append(method_out[0])
maps.append(method_out[1])
quicklook_im(method_out[1])
#
annos = ['ADI', 'RDI']
compare_images([maps[1], maps[0]], logAmp=True, scale=1e3, annos=annos)
# #
# # SDI
# tp.rot_rate = 0 # deg/s
# tp.nwsamp = 8
# iop.hyperFile = iop.datadir + '/SDIHyper.pkl'
# wsamples = np.linspace(tp.band[0], tp.band[1], tp.nwsamp)
# scale_list = tp.band[0] / wsamples
# SDI_hypercube = read.get_integ_hypercube(plot=False)
# tp.nwsamp = 1
# algo_dict = {'scale_list': scale_list}
# ap.exposure_time = 1
# # datacube = read.med_collapse(SDI_hypercube)
# datacube = (read.take_exposure(SDI_hypercube)/ap.numframes)[0]
# # star_phots = star_phot
# angle_list = np.zeros((len(SDI_hypercube[0])))
tp.NCPA_type = None#'Static'
tp.CPA_type = 'Amp'
# tp.CPA_type = 'test'
# tp.CPA_type = None#'Static'#'test'
ap.lods = [[2, 0]] # initial location (no rotation)
tp.active_null = True
tp.speckle_kill = True
iop.hyperFile = iop.datadir + '/amp_abs2.pkl'
# tp.active_modulate = True
perfect_hypercube = read.get_integ_hypercube(plot=False) # /ap.numframes
quicklook_im(np.sum(perfect_hypercube[:, 0], axis=0), axis=None, title=r' $I / I^{*}$', anno='Integration')
loop_frames(perfect_hypercube[::20, 0])
# compare_images([perfect_hypercube[0,0],perfect_hypercube[10,0],perfect_hypercube[100,0],perfect_hypercube[1000,0]], logAmp=True)
annos = ['Frame: 0', 'Frame: 20', 'Frame: 500', 'Frame: 2000']
compare_images(
[perfect_hypercube[0, 0], perfect_hypercube[20, 0], perfect_hypercube[500, 0], perfect_hypercube[-1, 0]],
logAmp=True, title='A.U', annos= annos)
# Perfect
tp.detector = 'ideal'#'MKIDs' #
tp.use_ao = False
tp.use_atmos = False
tp.NCPA_type = None#'Static'
tp.CPA_type = None#'Static'
tp.active_null = False
iop.hyperFile = iop.datadir + '/perfect.pkl'
# tp.active_modulate = True
perfect_hypercube = read.get_integ_hypercube(plot=False) # /ap.numframes
# quicklook_im(np.sum(perfect_hypercube[:, 0], axis=0), axis=None, title=r' $I_r / I^{*}$', anno='Integration')
# loop_frames(perfect_hypercube[:, 0])
# Plotting
dprint(np.shape(plotdata))
plotdata = np.array(plotdata)
# rad_samp = np.linspace(0,tp.platescale/1000.*mp.array_size[0]/2,plotdata.shape[2])
rad_samp = plotdata[:, 3][0]
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 3.4))
for thruput in plotdata[:, 0]:
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(['Exp.', 'RDI', 'RDI DSI', 'RDI DSI SDI'])
compare_images(
maps,
logAmp=True,
vmin=0.01,
vmax=100, # vmins = [0.1,0.1,0.1,0.1], vmaxs = [100,100,0.5,0.5],
annos=['Exp.', 'RDI', 'RDI DSI', 'RDI DSI SDI']) # ,
# titles=[r' $I / I^{*}$', r' $I / I^{*}$', r' $I_L / I^{*}$', r' $I_L / I^{*}$'])
# quicklook_im(maps[0], logAmp=True, vmin = 0.01, vmax = 1)
plt.show()
starphot=star_phot,
algo=pca.pca,
debug=True)
# from vip_hci import pca
# loop_frames(hypercube[:,0])
ADI = pca.pca(
hypercube[:, 0],
angle_list=-1 * np.arange(0, num_exp * tp.rot_rate * cp.frame_time,
tp.rot_rate * cp.frame_time)
) #, scale_list=np.ones((len(hypercube[:,0]))), mask_center_px=None)
quicklook_im(ADI)
ADI = ADI.reshape(1, 128, 128)
wsamples = np.linspace(tp.band[0], tp.band[1], tp.nwsamp)
scale_list = tp.band[0] / wsamples
print scale_list, scale_list[-1], 'SL', 1. / scale_list[-1]
wframe = clipped_zoom(hypercube[0, 0], 1. / scale_list[-1])
quicklook_im(wframe)
wframe = wframe.reshape(1, 128, 128)
cube = np.vstack((hypercube[0, [0, -1]], wframe, SDI))
# cube = np.dstack((hypercube[0,0],wframe,hypercube[0,-1],SDI)).transpose()
annos = [
'$\lambda_0$=860 nm', '$\lambda_8=$1250 nm', '$\lambda_0$ Scaled',
'SDI Residual'
]
print 'change titles to bottom left text in figure white'
# view_datacube(cube, logAmp=True,)
compare_images(cube, logAmp=True, annos=annos)
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(R_sigs)
maps = np.array(maps)
compare_images(maps, logAmp=True, scale=1e1) #[[0,1,-1]]
plt.show()
plt.plot(R_sigs, plotdata[:, 2, 16])
plt.show()
# # ***** pixel yield *****
#
# import copy
# orig_psf_template = copy.deepcopy(psf_template)
# # yields = np.logspace(0, 1.7, 3)
# # Rs = [50]
# yields = [0.8,0.9,0.95,0.99,0.999]#[0.05,0.2,0.4,0.8,0.995]#[0.99995,0.4]#0.4,0.8,
# mp.R_mean = 50
#
# import Detector
