Commit 3213a41c by Jakob Knollmueller

### cleanup

parent d2fb3aee
 ... ... @@ -6,33 +6,6 @@ from nifty5.library.amplitude_model import make_amplitude_model from nifty5.library.smooth_sky import make_correlated_field import numpy as np # def get_radial_LOS(lines=100, rotations=100, scale=1/400.): # def make_los(n=10, angle=0, d=1 / 40.): # starts_list = [] # ends_list = [] # for i in xrange(n): # starts_list += [[-(110.) * d + 0.5, d * 0 + 0.5]] # # ends_list += [[(190.) * d + 0.5, (-57.4 + (114.8 * i) / n) * d + 0.5]] # starts_list = np.array(starts_list) # ends_list = np.array(ends_list) # # rot_matrix = np.array([[np.cos(angle), -np.sin(angle)], # [np.sin(angle), np.cos(angle)]]) # starts_list = rot_matrix.dot(starts_list.T - 0.5).T + 0.5 # ends_list = rot_matrix.dot(ends_list.T - 0.5).T + 0.5 # # return (starts_list, ends_list) # # rotation_angle = np.pi*2 # temp_coords = (np.empty((0, 2)), np.empty((0, 2))) # for alpha in [-rotation_angle/rotations*j for j in xrange(rotations)]: # temp = make_los(n=lines, angle=alpha, d = scale) # temp_coords = np.concatenate([temp_coords, temp], axis=1) # # starts = list(temp_coords[0].T) # ends = list(temp_coords[1].T) # return starts, ends def get_random_LOS(n_los): starts = list(np.random.uniform(0,1,(n_los,2)).T) ... ... @@ -48,10 +21,8 @@ if __name__ == '__main__': A, __ = make_amplitude_model(position_space,16, 1, 10, -4., 1, 0., 1.) log_signal, _ = make_correlated_field(position_space,A) signal = ift.PointwisePositiveTanh(log_signal) # LOS_starts, LOS_ends = get_radial_LOS() LOS_starts, LOS_ends = get_random_LOS(100) R = LOSResponse(position_space, starts=LOS_starts, ends=LOS_ends) # R = ift.GeometryRemover(position_space) data_space = R.target signal_response = R(signal) noise = .001 ... ... @@ -74,8 +45,6 @@ if __name__ == '__main__': ift.plot(R.adjoint_times(data),name='data.pdf') ift.plot([ A.at(MOCK_POSITION).value], name='power.pdf') # H, convergence = minimizer(H) # position = H.position for i in range(5): H = H.at(position) samples = [H.curvature.draw_sample(from_inverse=True) for _ in range(N_samples)] ... ...
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