Commit c1a84f56 by Philipp Frank

### resolution independent amplitude model priors

parent 6ddae70b
 ... @@ -32,7 +32,7 @@ if __name__ == '__main__': ... @@ -32,7 +32,7 @@ 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 = ift.AmplitudeModel(position_space, 16, 1, 10, -4., 1, 0., 1.) A = ift.AmplitudeModel(position_space, 64, 3, 0.4, -4., 1, 1., 1.) dummy = ift.from_random('normal', A.domain) dummy = ift.from_random('normal', A.domain) # Building the model for a correlated signal # Building the model for a correlated signal ... @@ -46,9 +46,11 @@ if __name__ == '__main__': ... @@ -46,9 +46,11 @@ if __name__ == '__main__': 'xi': harmonic_space 'xi': harmonic_space }))) }))) correlated_field = ht(power_distributor(A)*ift.FieldAdapter(domain, "xi")) vol = harmonic_space.scalar_dvol vol = ift.ScalingOperator(vol ** (-0.5),harmonic_space) correlated_field = ht(vol(power_distributor(A))*ift.FieldAdapter(domain, "xi")) # alternatively to the block above one can do: # alternatively to the block above one can do: # correlated_field = ift.CorrelatedField(position_space, A) #correlated_field = ift.CorrelatedField(position_space, A) # apply some nonlinearity # apply some nonlinearity signal = ift.positive_tanh(correlated_field) signal = ift.positive_tanh(correlated_field) ... ...
 ... @@ -29,7 +29,7 @@ from ..sugar import makeOp, sqrt ... @@ -29,7 +29,7 @@ from ..sugar import makeOp, sqrt def _ceps_kernel(dof_space, k, a, k0): def _ceps_kernel(dof_space, k, a, k0): return a**2/(1+(k/(k0*dof_space.bindistances[0]))**2)**2 return a**2/(1+(k/k0)**2)**2 def create_cepstrum_amplitude_field(domain, cepstrum): def create_cepstrum_amplitude_field(domain, cepstrum): ... ...
 ... @@ -44,9 +44,8 @@ def CorrelatedField(s_space, amplitude_model): ... @@ -44,9 +44,8 @@ def CorrelatedField(s_space, amplitude_model): power_distributor = PowerDistributor(h_space, p_space) power_distributor = PowerDistributor(h_space, p_space) A = power_distributor(amplitude_model) A = power_distributor(amplitude_model) vol = h_space.scalar_dvol vol = h_space.scalar_dvol #vol = 1. vol = ScalingOperator(vol ** (-0.5),h_space) vol = ScalingOperator(vol ** (-0.5),h_space) return ht(vol(A*FieldAdapter(MultiDomain.make({"xi": h_space}), "xi"))) return ht(vol(A)*FieldAdapter(MultiDomain.make({"xi": h_space}), "xi")) def MfCorrelatedField(s_space_spatial, s_space_energy, amplitude_model_spatial, def MfCorrelatedField(s_space_spatial, s_space_energy, amplitude_model_spatial, ... ...
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