Commit 3213a41c authored by Jakob Knollmueller's avatar 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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