Commit 8bf61ef1 authored by Martin Reinecke's avatar Martin Reinecke
Browse files

Merge branch 'poisson_energy' into 'NIFTy_4'

Poisson energy

See merge request ift/NIFTy!242
parents 3f8ac11c c2e26750
Pipeline #27322 passed with stages
in 44 minutes and 50 seconds
# Program to generate figures of article "Information theory for fields"
# by Torsten Ensslin, Annalen der Physik, submitted to special edition
# "Physics of Information" in April 2018
import numpy as np
import nifty4 as ift
import matplotlib.pyplot as plt
class Exp3(object):
def __call__(self, x):
return ift.exp(3*x)
def derivative(self, x):
return 3*ift.exp(3*x)
if __name__ == "__main__":
# Set up physical constants
nu = 1. # excitation field level
kappa = 10. # diffusion constant
eps = 1e-8 # small number to tame zero mode
sigma_n = 0.2 # noise level
sigma_n2 = sigma_n**2
L = 1. # Total length of interval or volume the field lives on
nprobes = 1000 # Number of probes for uncertainty quantification
# Define resolution (pixels per dimension)
N_pixels = 1024
# Define data gaps
N1a = int(0.6*N_pixels)
N1b = int(0.64*N_pixels)
N2a = int(0.67*N_pixels)
N2b = int(0.8*N_pixels)
# Set up derived constants
amp = nu/(2*kappa) # spectral normalization
pow_spec = lambda k: amp / (eps + k**2)
lambda2 = 2*kappa*sigma_n2/nu # resulting correlation length squared
lambda1 = np.sqrt(lambda2)
pixel_width = L/N_pixels
x = np.arange(0, 1, pixel_width)
# Set up the geometry
s_domain = ift.RGSpace([N_pixels], distances=pixel_width)
h_domain = s_domain.get_default_codomain()
HT = ift.HarmonicTransformOperator(h_domain, s_domain)
aHT = HT.adjoint
# Create mock signal
Phi_h = ift.create_power_operator(h_domain, power_spectrum=pow_spec)
phi_h = Phi_h.draw_sample()
# remove zero mode
glob = phi_h.to_global_data()
glob[0] = 0.
phi_h = ift.Field.from_global_data(phi_h.domain, glob)
phi = HT(phi_h)
# Setting up an exemplary response
GeoRem = ift.GeometryRemover(s_domain)
d_domain =[0]
mask = np.ones(d_domain.shape)
mask[N1a:N1b] = 0.
mask[N2a:N2b] = 0.
fmask = ift.Field.from_global_data(d_domain, mask)
Mask = ift.DiagonalOperator(fmask)
R0 = Mask*GeoRem
R = R0*HT
# Linear measurement scenario
N = ift.ScalingOperator(sigma_n2, d_domain) # Noise covariance
n = Mask(N.draw_sample()) # seting the noise to zero in masked region
d = R(phi_h) + n
# Wiener filter
j = R.adjoint_times(N.inverse_times(d))
IC = ift.GradientNormController(name="inverter", iteration_limit=500,
inverter = ift.ConjugateGradient(controller=IC)
D = (ift.SandwichOperator(R, N.inverse) + Phi_h.inverse).inverse
D = ift.InversionEnabler(D, inverter, approximation=Phi_h)
m = HT(D(j))
# Uncertainty
D = ift.SandwichOperator(aHT, D) # real space propagator
Dhat = ift.probe_with_posterior_samples(D.inverse, None,
sig = ift.sqrt(Dhat)
# Plotting
x_mod = np.where(mask > 0, x, None)
plt.rcParams["text.usetex"] = True
c1 = (m-sig).to_global_data()
c2 = (m+sig).to_global_data()
plt.fill_between(x, c1, c2, color='pink', alpha=None)
plt.plot(x, phi.to_global_data(), label=r"$\varphi$", color='black')
plt.scatter(x_mod, d.to_global_data(), label=r'$d$', s=1, color='blue',
plt.plot(x, m.to_global_data(), label=r'$m$', color='red')
plt.xlim([0, L])
plt.ylim([-1, 1])
plt.title('Wiener filter reconstruction')
nonlin = Exp3()
lam = R0(nonlin(HT(phi_h)))
data = ift.Field.from_local_data(
d_domain, np.random.poisson(lam.local_data).astype(np.float64))
# initial guess
psi0 = ift.Field.full(h_domain, 1e-7)
energy = ift.library.PoissonEnergy(psi0, data, R0, nonlin, HT, Phi_h,
IC1 = ift.GradientNormController(name="IC1", iteration_limit=200,
minimizer = ift.RelaxedNewton(IC1)
energy = minimizer(energy)[0]
var = ift.probe_with_posterior_samples(energy.curvature, HT, nprobes)[1]
sig = ift.sqrt(var)
m = HT(energy.position)
phi = HT(phi_h)
plt.rcParams["text.usetex"] = True
c1 = nonlin(m-sig).to_global_data()
c2 = nonlin(m+sig).to_global_data()
plt.fill_between(x, c1, c2, color='pink', alpha=None)
plt.plot(x, nonlin(phi).to_global_data(), label=r"$e^{3\varphi}$",
plt.scatter(x_mod, data.to_global_data(), label=r'$d$', s=1, color='blue',
plt.plot(x, nonlin(m).to_global_data(),
label=r'$e^{3\varphi_\mathrm{cl}}$', color='red')
plt.xlim([0, L])
plt.ylim([-0.1, 7.5])
plt.title('Poisson log-normal reconstruction')
......@@ -3,4 +3,5 @@ from .wiener_filter_curvature import WienerFilterCurvature
from .noise_energy import NoiseEnergy
from .nonlinear_power_energy import NonlinearPowerEnergy
from .nonlinear_wiener_filter_energy import NonlinearWienerFilterEnergy
from .poisson_energy import PoissonEnergy
from .nonlinearities import Exponential, Linear, Tanh, PositiveTanh
......@@ -24,11 +24,11 @@ from ..utilities import memo
def _LinearizedPowerResponse(Instrument, nonlinearity, ht, Distributor, tau,
power = exp(0.5*tau)
position = ht(Distributor(power)*xi)
linearization = nonlinearity.derivative(position)
return 0.5*Instrument*linearization*ht*xi*Distributor*power
power = exp(0.5*tau)
position = ht(Distributor(power)*xi)
linearization = nonlinearity.derivative(position)
return 0.5*Instrument*linearization*ht*xi*Distributor*power
class NonlinearPowerEnergy(Energy):
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <>.
# Copyright(C) 2013-2018 Max-Planck-Society
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
from import Energy
from ..operators.diagonal_operator import DiagonalOperator
from ..operators.sandwich_operator import SandwichOperator
from ..operators.inversion_enabler import InversionEnabler
from ..field import log
class PoissonEnergy(Energy):
def __init__(self, position, d, Instrument, nonlinearity, ht, Phi_h,
super(PoissonEnergy, self).__init__(position=position)
self._inverter = inverter
self._d = d
self._Instrument = Instrument
self._nonlinearity = nonlinearity
self._ht = ht
self._Phi_h = Phi_h
htpos = ht(position)
lam = Instrument(nonlinearity(htpos))
Rho = DiagonalOperator(nonlinearity.derivative(htpos))
eps = 1e-100 # to catch harmless 0/0 where data is zero
W = DiagonalOperator((d+eps)/(lam**2+eps))
phipos = Phi_h.inverse_adjoint_times(position)
prior_energy = 0.5*position.vdot(phipos)
likel_energy = lam.sum()-d.vdot(log(lam+eps))
self._value = prior_energy + likel_energy
R1 = Instrument*Rho*ht
self._grad = (phipos + R1.adjoint_times((lam-d)/(lam+eps))).lock()
self._curv = Phi_h.inverse + SandwichOperator(R1, W)
def at(self, position):
return self.__class__(position, self._d, self._Instrument,
self._nonlinearity, self._ht, self._Phi_h,
def value(self):
return self._value
def gradient(self):
return self._grad
def curvature(self):
return InversionEnabler(self._curv, self._inverter,
......@@ -20,7 +20,7 @@ from ..minimization.quadratic_energy import QuadraticEnergy
from .wiener_filter_curvature import WienerFilterCurvature
def WienerFilterEnergy(position, d, R, N, S, inverter):
def WienerFilterEnergy(position, d, R, N, S, inverter=None):
"""The Energy for the Wiener filter.
It covers the case of linear measurement with
......@@ -39,8 +39,9 @@ def WienerFilterEnergy(position, d, R, N, S, inverter):
The noise covariance in data space.
S : EndomorphicOperator
The prior signal covariance in harmonic space.
inverter : Minimizer
inverter : Minimizer, optional
the minimization strategy to use for operator inversion
If None, the energy object will not support curvature computation.
op = WienerFilterCurvature(R, N, S, inverter)
vec = R.adjoint_times(N.inverse_times(d))
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