Commit df3bdf4c by Martin Reinecke

### use more generic names where applicable

parent 52593191
 import nifty5 as ift import nifty5 as ift 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 import numpy as np from scipy.io import loadmat def get_random_LOS(n_los): def get_random_LOS(n_los): ... @@ -19,7 +15,7 @@ if __name__ == '__main__': ... @@ -19,7 +15,7 @@ if __name__ == '__main__': position_space = ift.RGSpace([128, 128]) position_space = ift.RGSpace([128, 128]) # Setting up an amplitude model # Setting up an amplitude model A, amplitude_internals = make_amplitude_model( A, amplitude_internals = ift.library.make_amplitude_model( position_space, 16, 1, 10, -4., 1, 0., 1.) position_space, 16, 1, 10, -4., 1, 0., 1.) # Building the model for a correlated signal # Building the model for a correlated signal ... @@ -35,14 +31,15 @@ if __name__ == '__main__': ... @@ -35,14 +31,15 @@ if __name__ == '__main__': Amp = power_distributor(A) Amp = power_distributor(A) correlated_field_h = Amp * xi correlated_field_h = Amp * xi correlated_field = ht(correlated_field_h) correlated_field = ht(correlated_field_h) # # alternatively to the block above one can do: # alternatively to the block above one can do: # correlated_field, _ = make_correlated_field(position_space, A) # correlated_field,_ = ift.library.make_correlated_field(position_space, A) # apply some nonlinearity # apply some nonlinearity signal = ift.PointwisePositiveTanh(correlated_field) signal = ift.PointwisePositiveTanh(correlated_field) # Building the Line of Sight response # Building the Line of Sight response LOS_starts, LOS_ends = get_random_LOS(100) LOS_starts, LOS_ends = get_random_LOS(100) R = LOSResponse(position_space, starts=LOS_starts, ends=LOS_ends) R = ift.library.LOSResponse(position_space, starts=LOS_starts, ends=LOS_ends) # build signal response model and model likelihood # build signal response model and model likelihood signal_response = R(signal) signal_response = R(signal) # specify noise # specify noise ... ...
 ... @@ -6,4 +6,4 @@ from .point_sources import PointSources ... @@ -6,4 +6,4 @@ from .point_sources import PointSources from .poissonian_energy import PoissonianEnergy from .poissonian_energy import PoissonianEnergy from .wiener_filter_curvature import WienerFilterCurvature from .wiener_filter_curvature import WienerFilterCurvature from .wiener_filter_energy import WienerFilterEnergy from .wiener_filter_energy import WienerFilterEnergy from .smooth_sky import make_correlated_field, make_mf_correlated_field from .correlated_fields import make_correlated_field, make_mf_correlated_field
 ... @@ -62,6 +62,6 @@ def make_mf_correlated_field(s_space_spatial, s_space_energy, ... @@ -62,6 +62,6 @@ def make_mf_correlated_field(s_space_spatial, s_space_energy, position = MultiField({'xi': Field.from_random('normal', h_space)}) position = MultiField({'xi': Field.from_random('normal', h_space)}) xi = Variable(position)['xi'] xi = Variable(position)['xi'] logsky_h = A*xi correlated_field_h = A*xi logsky = ht(logsky_h) correlated_field = ht(correlated_field_h) return PointwiseExponential(logsky) return PointwiseExponential(correlated_field)
 ... @@ -26,7 +26,7 @@ from ..sugar import log, makeOp ... @@ -26,7 +26,7 @@ from ..sugar import log, makeOp class PoissonianEnergy(Energy): class PoissonianEnergy(Energy): def __init__(self, lamb, d): def __init__(self, lamb, d): """ """ lamb: Sky model object lamb: Model object value = 0.5 * s.vdot(s), i.e. a log-Gauss distribution with unit value = 0.5 * s.vdot(s), i.e. a log-Gauss distribution with unit covariance covariance ... ...
 ... @@ -19,7 +19,7 @@ from .model import Model ... @@ -19,7 +19,7 @@ from .model import Model class Constant(Model): class Constant(Model): """A sky model with a constant (multi-)field as value. """A model with a constant (multi-)field as value. Parameters Parameters ---------- ---------- ... ...
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