wiener_filter_hamiltonian.py 3.9 KB
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from nifty import *
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import plotly.offline as pl
import plotly.graph_objs as go

from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.rank


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class WienerFilterEnergy(Energy):
    def __init__(self, position, D, j):
        # in principle not necessary, but useful in order to make the signature
        # explicit
        super(WienerFilterEnergy, self).__init__(position)
        self.D = D
        self.j = j

    def at(self, position):
        return self.__class__(position, D=self.D, j=self.j)

    @property
    def value(self):
        D_inv_x = self.D_inverse_x()
        H = 0.5 * D_inv_x.dot(self.position) - self.j.dot(self.position)
        return H.real

    @property
    def gradient(self):
        D_inv_x = self.D_inverse_x()
        g = D_inv_x - self.j
        return_g = g.copy_empty(dtype=np.float)
        return_g.val = g.val.real
        return return_g

    def D_inverse_x(self):
        return D.inverse_times(self.position)


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if __name__ == "__main__":

    distribution_strategy = 'fftw'

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    # Set up spaces and fft transformation
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    s_space = RGSpace([512, 512], dtype=np.float)
    fft = FFTOperator(s_space)
    h_space = fft.target[0]
    p_space = PowerSpace(h_space, distribution_strategy=distribution_strategy)

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    # create the field instances and power operator
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    pow_spec = (lambda k: (42 / (k + 1) ** 3))
    S = create_power_operator(h_space, power_spectrum=pow_spec,
                              distribution_strategy=distribution_strategy)

    sp = Field(p_space, val=lambda z: pow_spec(z)**(1./2),
               distribution_strategy=distribution_strategy)
    sh = sp.power_synthesize(real_signal=True)
    ss = fft.inverse_times(sh)

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    # model the measurement process
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    R = SmoothingOperator(s_space, sigma=0.01)
#    R = DiagonalOperator(s_space, diagonal=1.)
#    R._diagonal.val[200:400, 200:400] = 0

    signal_to_noise = 1
    N = DiagonalOperator(s_space, diagonal=ss.var()/signal_to_noise, bare=True)
    n = Field.from_random(domain=s_space,
                          random_type='normal',
                          std=ss.std()/np.sqrt(signal_to_noise),
                          mean=0)

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    # create mock data
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    d = R(ss) + n
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    # set up reconstruction objects
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    j = R.adjoint_times(N.inverse_times(d))
    D = PropagatorOperator(S=S, N=N, R=R)

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    def distance_measure(energy, iteration):
        pass
        #print (iteration, ((x-ss).norm()/ss.norm()).real)
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    minimizer = SteepestDescent(convergence_tolerance=0,
                                iteration_limit=50,
                                callback=distance_measure)

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#    minimizer = VL_BFGS(convergence_tolerance=0,
#                        iteration_limit=50,
#                        callback=distance_measure,
#                        max_history_length=5)
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    m0 = Field(s_space, val=1)

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    energy = WienerFilterEnergy(position=m0, D=D, j=j)

    (energy, convergence) = minimizer(energy)



#
#
#
#    grad = gradient(m)
#
#    d_data = d.val.get_full_data().real
#    if rank == 0:
#        pl.plot([go.Heatmap(z=d_data)], filename='data.html')
#
#
#    ss_data = ss.val.get_full_data().real
#    if rank == 0:
#        pl.plot([go.Heatmap(z=ss_data)], filename='ss.html')
#
#    sh_data = sh.val.get_full_data().real
#    if rank == 0:
#        pl.plot([go.Heatmap(z=sh_data)], filename='sh.html')
#
#    j_data = j.val.get_full_data().real
#    if rank == 0:
#        pl.plot([go.Heatmap(z=j_data)], filename='j.html')
#
#    jabs_data = np.abs(j.val.get_full_data())
#    jphase_data = np.angle(j.val.get_full_data())
#    if rank == 0:
#        pl.plot([go.Heatmap(z=jabs_data)], filename='j_abs.html')
#        pl.plot([go.Heatmap(z=jphase_data)], filename='j_phase.html')
#
#    m_data = m.val.get_full_data().real
#    if rank == 0:
#        pl.plot([go.Heatmap(z=m_data)], filename='map.html')
#
#    grad_data = grad.val.get_full_data().real
#    if rank == 0:
#        pl.plot([go.Heatmap(z=grad_data)], filename='grad.html')