# CORRELATION_RADIUS
source_kernel = np.ones((5, 5))
background_kernel = np.ones((100, 10))
kbe = KernelBackgroundEstimator(images=images,
source_kernel=source_kernel,
background_kernel=background_kernel,
significance_threshold=SIGNIFICANCE_THRESHOLD,
mask_dilation_radius=MASK_DILATION_RADIUS)
kbe.run(n_iterations=3)
# In[ ]:
kbe.run_iteration()
# In[ ]:
kbe._data[1].print_info()
# ## Plot results
#
# TODO
# In[ ]:
n_iterations = 5
#plt.clf()
plt.figure(figsize=(10, 10))
kbe = KernelBackgroundEstimator(
images=images,
source_kernel=source_kernel,
background_kernel=background_kernel,
significance_threshold=SIGNIFICANCE_THRESHOLD,
mask_dilation_radius=MASK_DILATION_RADIUS,
)
n_iterations = 4
# *** RUN & PLOT ***
plt.figure(figsize=(8, 4))
for iteration in range(n_iterations):
kbe.run_iteration()
mask_hdu = kbe.mask_image_hdu
mask = mask_hdu.data[:, 1400:2000]
plt.subplot(n_iterations, 2, 2 * iteration + 1)
background_hdu = kbe.background_image_hdu
data = background_hdu.data[:, 1400:2000]
plt.imshow(data, vmin=0, vmax=1)
plt.contour(mask, levels=[0], linewidths=2, colors='white')
plt.axis('off')
plt.title('Background Estimation, Iteration {0}'.format(iteration),
fontsize='small')
plt.subplot(n_iterations, 2, 2 * iteration + 2)
significance_hdu = kbe.significance_image_hdu
data = significance_hdu.data[:, 1400:2000]
