mod = np.array([[0, 1.8, 0, 0.95, 0, 1 / 6], [1.9, 0, 0, 0.95, 0, 1 / 6],
[0, 1.8, 0, 0.95, 2 * np.pi / 3, 1 / 6],
[1.9, 0, 0, 0.95, 2 * np.pi / 3, 1 / 6],
[0, 1.8, 0, 0.95, 4 * np.pi / 3, 1 / 6],
[1.9, 0, 0, 0.95, 4 * np.pi / 3, 1 / 6]])
def plot_traces(traces, theta, axis=0):
plt.figure()
for k in range(len(traces)):
plt.plot(np.arange(len(traces[k])), traces[k][:, axis])
plt.plot([len(traces[k])], [theta[k, axis]], 'o')
with Context(debugMode=False) as ctx:
g = gaussian.Gaussian(ctx)
roisize = 10
sf_theta = [[roisize // 2, roisize // 2, 0, 10000, 5]]
sf_theta = np.repeat(sf_theta, 20, 0)
psf = g.CreatePSF_XYZIBg(roisize,
gaussian.Gauss3D_Calibration(),
cuda=True)
spot_mod = mod.reshape(len(mod) * 6)[None].repeat(len(sf_theta), 0)
s = SIMFLUX(ctx)
sum_estim = s.CreateEstimator(psf, len(mod), False)
sf_estim = s.CreateEstimator(psf, len(mod), True)
s0_x = 1.3
gamma_x = -20
d_x = 20
A_x = 0 #2e-04
s0_y = 1.3
gamma_y = 20
d_y = 20
A_y = 0 #1e-05
calib = gaussian.Gauss3D_Calibration([s0_x, gamma_x, d_x, A_x],
[s0_y, gamma_y, d_y, A_y])
with Context() as ctx:
sigma = 1.5
roisize = 12
theta = [[roisize // 2, roisize // 2, 3, 1000, 5]]
g_api = gaussian.Gaussian(ctx)
psf = g_api.CreatePSF_XYZIBg(roisize, calib, True)
imgs = psf.ExpectedValue(theta)
plt.figure()
plt.imshow(imgs[0])
sample = np.random.poisson(imgs)
# Run localization on the sample
estimated, diag, traces = psf.Estimate(sample)
print(f"Estimated position: {estimated[0]}")