def get_unoccult_perf_psf(plot=False, hyperFile='/IntHyperUnOccult.pkl'):
import copy
tp_orig = copy.copy(tp)
ap_orig = copy.copy(ap)
iop_orig = copy.copy(iop)
tp.detector = 'ideal'
ap.companion = False
# tp.NCPA_type = 'Wave'
iop.hyperFile = iop.datadir + '/perfIntHyperUnOccult.pkl'
tp.occulter_type = 'None'
num_exp = 1
ap.exposure_time = 0.001 # 0.001
ap.numframes = int(num_exp * ap.exposure_time / cp.frame_time)
tp.use_atmos = False
tp.nwsamp = 1
tp.CPA_type = None#'Quasi'# None
tp.NCPA_type = None#'Wave'# #None
tp.aber_params = {'CPA': False,
'NCPA': False,
'QuasiStatic': False, # or 'Static'
'Phase': False,
'Amp': False,
'n_surfs': 2}
# Yup this is 'if' is necessary
hypercube = read.get_integ_hypercube()
# PSF = hypercube[0,0]
PSF = (read.take_exposure(hypercube))[0,0]
if plot:
quicklook_im(PSF)
tp.__dict__ = tp_orig.__dict__
ap.__dict__ = ap_orig.__dict__
iop.__dict__ = iop_orig.__dict__
# # print tp.occulter_type
return PSF
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)
star_corr = 0
# for x, y in np.transpose(aper_idx):
# # for y in aper_idx[0]:
# print x, y
# corr, ljb, pvalue = acf(bunch_hypercube[:, 0, x, y], unbiased=False, qstat=True, nlags=len(range(5000)))
# # plt.plot(range(4999), corr[:-1])
readnoises = [0.1, 1, 5, 10, 30]
for rn in [50]: #readnoises:
# ++++++++ H2RG - Full Obs ++++++
iop.hyperFile = iop.datadir + '/nocompnorm_BinH2RG_with_coron_hyper_%i.pkl' % rn
tp.occulter_type = 'Gaussian'
tp.nwsamp = 10
hp.readnoise = rn
if __name__ == '__main__':
hypercube = read.get_integ_hypercube(plot=False)
# view_datacube(hypercube[0], logAmp=True)
wsamples = np.linspace(tp.band[0], tp.band[1], tp.nwsamp)
scale_list = tp.band[0] / wsamples
print scale_list
algo_dict = {'scale_list': scale_list}
# datacube = hypercube[0]
datacube = (read.take_exposure(hypercube) / ap.numframes)[0]
# star_phots = star_phot
angle_list = np.zeros((len(hypercube[0])))
print datacube.shape
method_out = eval_method(datacube, pca.pca, angle_list, algo_dict)
plotdata.append(method_out[0])
maps.append(method_out[1])
# quicklook_im(method_out[1])
# +++++++++++++++++++++++++++++++
# *********************************************************
# if __name__ == "__main__":
# 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]:
wsamples = np.linspace(tp.band[0], tp.band[1], tp.w_bins)
scale_list = wsamples/(tp.band[1]-tp.band[0])
scale_list = scale_list#[::-1]
algo_dict = {'scale_list': scale_list}
ap.exposure_time = 0.1 # 0.001
ap.numframes = int(num_exp * ap.exposure_time / cp.frame_time)
iop.hyperFile = iop.datadir + 'HR8799_phot_tag%i_tar_%i.h5' % (ap.numframes, np.log10(ap.star_photons))
dprint(iop.hyperFile)
orig_hyper = read.get_integ_hypercube(plot=False)[:, :]
# fast_hyper = fast_hyper[:100]
# ap.numframes = int(100 * ap.exposure_time / cp.frame_time)
# dprint(fast_hyper.shape)
fast_hyper = read.take_exposure(orig_hyper)
ap.exposure_time = 1 # 0.001
med_hyper = read.take_exposure(orig_hyper)
ap.exposure_time = 10#1.0 # 0.001
slow_hyper = read.take_exposure(orig_hyper)
ap.exposure_time = 100#1.0 # 0.001
v_slow_hyper = read.take_exposure(orig_hyper)
# this is crucial for the PCA
fast_hyper /= np.sum(fast_hyper) # /ap.numframes
med_hyper /= np.sum(med_hyper) # /ap.numframes
slow_hyper /= np.sum(slow_hyper) # /ap.numframes
# v_slow_hyper /= np.sum(v_slow_hyper) # /ap.numframes
fast_hyper = np.transpose(fast_hyper, (1, 0, 2, 3))
med_hyper = np.transpose(med_hyper, (1, 0, 2, 3))
tp.detector = 'H2RG' #''MKIDs'#
sp.save_obs = False
sp.show_wframe = False #True#'continuous'
if sp.show_wframe == 'continuous':
sp.fig = plt.figure()
sp.show_cube = False
sp.return_cube = True
tp.nwsamp = 4
# tp.use_atmos = True # have to for now because ao wfs reads in map produced but not neccessary
# tp.use_ao = True
# tp.active_null=False
# tp.satelite_speck = True
# tp.speck_locs = [[40,40]]
# ap.frame_time = 0.001
num_exp = 25
ap.exposure_time = 0.01
ap.numframes = int(num_exp * ap.exposure_time / cp.frame_time)
sp.num_processes = 1
if __name__ == '__main__':
hypercube = run()
print np.shape(hypercube)
hypercube = read.take_exposure(hypercube)
print np.shape(hypercube[0, 3]), np.shape(hypercube)
loop_frames(hypercube[:, 0])
plt.close()
loop_frames(hypercube[0])
plt.show()
loop_frames(hypercube[:, 0])
plt.show()
for R in np.round(R_sigs):
# for R in [51]:
mp.R_sig = R
iop.hyperFile = iop.datadir + '/SDIHyper_C1e5_Rsig%i_5w_5.h5' % R
iop.device_params = iop.datadir + '/deviceParams_Rsig%i_5w_5.pkl' % R
wsamples = np.linspace(tp.band[0], tp.band[1], tp.nwsamp)
scale_list = tp.band[0] / wsamples
# print tp.nwsamp
SDI_hypercube = read.get_integ_hypercube(plot=False)
# print SDI_hypercube.shape
noncor_frames = np.zeros((10, 128, 128))
thruputs = np.zeros((10, 41))
for t in range(10):
ap.exposure_time = 0.1 #1.
datacube = read.take_exposure(
SDI_hypercube[t * 100:(t + 1) * 100]) / (ap.numframes / 10)
# view_datacube(datacube[0])
# datacube = read.med_collapse(SDI_hypercube)
# dprint(datacube.shape)
# loop_frames(SDI_hypercube[0])
# loop_frames(SDI_hypercube[:,0])
# loop_frames(datacube[0], logAmp=False)
# view_datacube(datacube[0], logAmp=False)
# ap.exposure_time = 0.005
ap.exposure_time = 0.001
# loop_frames(datacube[0])
algo_dict = {'scale_list': scale_list[::-1]}
# star_phots = star_phot
angle_list = np.zeros((len(SDI_hypercube[0])))
method_out = eval_method(datacube[0], pca.pca, angle_list,