### everything seems to work and looks nice

parent df578915
 ... ... @@ -22,13 +22,13 @@ if __name__ == '__main__': # Choose problem geometry and masking # # One dimensional regular grid # position_space = ift.RGSpace() # mask = np.ones(position_space.shape) # One dimensional regular grid position_space = ift.RGSpace() mask = np.ones(position_space.shape) # Two dimensional regular grid with chess mask position_space = ift.RGSpace([128,128]) mask = make_chess_mask() # # Two dimensional regular grid with chess mask # position_space = ift.RGSpace([128,128]) # mask = make_chess_mask() # # Sphere with half of its locations randomly masked # position_space = ift.HPSpace(128) ... ...
 ... ... @@ -19,10 +19,10 @@ def get_2D_exposure(): if __name__ == '__main__': # ABOUT THIS CODE np.random.seed(42) np.random.seed(41) # Set up the position space of the signal # # # One dimensional regular grid with uniform exposure # position_space = ift.RGSpace(1024) # exposure = np.ones(position_space.shape) ... ... @@ -84,5 +84,6 @@ if __name__ == '__main__': # Plot results result_sky = sky.at(H.position).value ##PLOTTING
 ... ... @@ -3,6 +3,7 @@ from nifty5.library.los_response import LOSResponse from nifty5.library.amplitude_model import make_amplitude_model from nifty5.library.smooth_sky import make_correlated_field import numpy as np from scipy.io import loadmat def get_random_LOS(n_los): ... ... @@ -11,9 +12,9 @@ def get_random_LOS(n_los): return starts, ends if __name__ == '__main__': np.random.seed(41) ### ABOUT THIS TUTORIAL np.random.seed(42) position_space = ift.RGSpace([128,128]) # Setting up an amplitude model ... ... @@ -33,14 +34,13 @@ if __name__ == '__main__': correlated_field_h = Amp * xi correlated_field = ht(correlated_field_h) # # alternatively to the block above one can do: # correlated_field, _ = make_correlated_field(position_space,A) # correlated_field, _ = make_correlated_field(position_space, A) # apply some nonlinearity signal = ift.PointwisePositiveTanh(correlated_field) # Building the Line of Sight response LOS_starts, LOS_ends = get_random_LOS(1000) LOS_starts, LOS_ends = get_random_LOS(100) R = LOSResponse(position_space, starts=LOS_starts, ends=LOS_ends) # build signal response model and model likelihood signal_response = R(signal) # specify noise ... ... @@ -73,7 +73,7 @@ if __name__ == '__main__': ift.plot([ A.at(MOCK_POSITION).value], name='power.pdf') # number of samples used to estimate the KL N_samples = 10 N_samples = 20 for i in range(5): H = H.at(position) samples = [H.curvature.draw_sample(from_inverse=True) for _ in range(N_samples)] ... ...
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!