def setUp(self):
print("setting up")
self.tempdir = tempfile.mkdtemp()
now = "2020-05-17T00:00:00.1234567"
t = sm.Time(now)
N = 1024
SN = 15.0
theta = 0
xfwhm = 4
yfwhm = 3
weights = np.random.random(100) * 20000 + 10
zero = 10 # for zero in [5, 10, 25]:
filenames = []
x = np.random.randint(low=30, high=900, size=100)
y = np.random.randint(low=30, high=900, size=100)
xy = [(x[i], y[i]) for i in range(100)]
m = sm.delta_point(N, center=False, xy=xy, weights=weights)
img_dir = os.path.join(self.tempdir, "zp_{}".format(zero))
os.makedirs(img_dir)
for i in range(4):
im = sm.image(
m,
N,
t_exp=2 * i + 1,
X_FWHM=xfwhm,
Y_FWHM=yfwhm,
theta=theta,
SN=SN,
bkg_pdf="gaussian",
)
filenames.append(
sm.capsule_corp(im,
t,
t_exp=i + 1,
i=i,
zero=zero + i,
path=img_dir))
self.filenames = filenames
self.img = im
self.img_masked = np.ma.MaskedArray(im, mask=np.zeros(im.shape))
def random_4psf_simage():
frames = []
for theta in [0, 45, 105, 150]:
image_seed = int(random.rand() * 1000)
N = 512 # side
X_FWHM = 5 + 5.5 * theta / 180
Y_FWHM = 5
t_exp = 5
max_fw = max(X_FWHM, Y_FWHM)
x = random.randint(low=6 * max_fw, high=N - 6 * max_fw, size=80)
y = random.randint(low=6 * max_fw, high=N - 6 * max_fw, size=80)
xy = [(x[i], y[i]) for i in range(80)]
SN = 30.0 # SN para poder medir psf
weights = list(np.linspace(10, 1000.0, len(xy)))
m = simtools.delta_point(N, center=False, xy=xy, weights=weights)
im = simtools.image(
m,
N,
t_exp,
X_FWHM,
Y_FWHM=Y_FWHM,
theta=theta,
SN=SN,
bkg_pdf="gaussian",
seed=image_seed,
)
frames.append(im + 100.0)
frame = np.zeros((1024, 1024))
for j in range(2):
for i in range(2):
frame[i * 512 : (i + 1) * 512, j * 512 : (j + 1) * 512] = frames[
i + 2 * j
]
return s.SingleImage(frame)
filenames = []
x = np.random.randint(low=30, high=900, size=25)
y = np.random.randint(low=30, high=900, size=25)
xy = [(x[i], y[i]) for i in range(25)]
m = sm.delta_point(N, center=False, xy=xy, weights=weights)
img_dir = os.path.join(test_dir, 'zp={}'.format(zero))
if not os.path.exists(img_dir):
os.makedirs(img_dir)
for i in range(40):
im = sm.image(m,
N,
t_exp=i + 1,
X_FWHM=xfwhm,
Y_FWHM=yfwhm,
theta=theta,
SN=SN,
bkg_pdf='poisson')
filenames.append(
sm.capsule_corp(im, t, t_exp=i + 1, i=i, zero=zero + i, path=img_dir))
ensemble = pc.ImageEnsemble(filenames)
S_hat_stack, S_stack, S_hat, S, R_hat = ensemble.calculate_R(n_procs=4,
debug=True)
S_inv = pc._ifftwn(S_hat)
d = S - S_inv
X_FWHM = 2 + 2.5 * theta / 180
Y_FWHM = 2.8
t_exp = 1
max_fw = max(X_FWHM, Y_FWHM)
x = np.linspace(6 * max_fw, N - 6 * max_fw, 6)
y = np.linspace(6 * max_fw, N - 6 * max_fw, 6)
xy = simtools.cartesian_product([x, y])
SN = 30. # SN para poder medir psf
weights = list(np.linspace(1000, 3000, len(xy)))
m = simtools.delta_point(N, center=False, xy=xy, weights=weights)
im = simtools.image(m,
N,
t_exp,
X_FWHM,
Y_FWHM=Y_FWHM,
theta=theta,
SN=SN,
bkg_pdf='gaussian')
frames.append(im)
frame = np.zeros((2 * N, 2 * N))
for j in range(2):
for i in range(2):
frame[i * N:(i + 1) * N, j * N:(j + 1) * N] = frames[i + 2 * j]
now = '2016-05-17T00:00:00.1234567'
t = Time(now)
filenames.append(
simtools.capsule_corp(frame,
t,
t_exp=1,
for theta in [0, 45, 105, 150]:
N = 512 # side
X_FWHM = 5 + 2.5*theta/180
Y_FWHM = 5
t_exp = 1
max_fw = max(X_FWHM, Y_FWHM)
test_dir = os.path.abspath('./test/test_images/psf_basis_kl_gs')
x = np.random.randint(low=6*max_fw, high=N-6*max_fw, size=80)
y = np.random.randint(low=6*max_fw, high=N-6*max_fw, size=80)
xy = [(x[i], y[i]) for i in range(80)]
SN = 5. # SN para poder medir psf
weights = list(np.linspace(10, 1000., len(xy)))
m = simtools.delta_point(N, center=False, xy=xy, weights=weights)
im = simtools.image(m, N, t_exp, X_FWHM, Y_FWHM=Y_FWHM, theta=theta,
SN=SN, bkg_pdf='poisson')
frames.append(im+100.)
frame = np.zeros((1024, 1024))
for j in range(2):
for i in range(2):
frame[i*512:(i+1)*512, j*512:(j+1)*512] = frames[i+2*j]
with si.SingleImage(frame) as sim:
a_fields, psf_basis = sim.get_variable_psf(inf_loss=0.025)
x, y = sim.get_afield_domain()
plt.imshow(np.log(frame), interpolation='none')
plt.colorbar()
FWHM = 10
test_dir = os.path.abspath('./test/test_images/one_star')
x = np.linspace(5 * FWHM, N - 5 * FWHM, 10)
y = np.linspace(5 * FWHM, N - 5 * FWHM, 10)
xy = simtools.cartesian_product([x, y])
now = '2016-05-17T00:00:00.1234567'
t = Time(now)
filenames = []
for i in range(100):
SN = 2. # + i
weights = list(np.linspace(0.00001, 1, len(xy)))
m = simtools.delta_point(N, center=False, xy=xy, weights=weights)
im = simtools.image(m, N, t_exp=1, X_FWHM=FWHM, SN=SN, bkg_pdf='gaussian')
filenames.append(
simtools.capsule_corp(im, t, t_exp=1, i=i, zero=3.1415, path=test_dir))
S = np.zeros(shape=(N, N))
psf = simtools.Psf(N, FWHM)
for afile in filenames:
px = pc.SingleImage(afile)
# Now I must combine this images, normalizing by the var(noise)
var = px.meta['std']
conv = convolve_fft(px.imagedata, psf)
S = S + conv / var**2.
fits.writeto(data=S, filename='./test_images/S.fits', overwrite=True)