from nifty import * import plotly.offline as pl import plotly.graph_objs as go from mpi4py import MPI comm = MPI.COMM_WORLD rank = comm.rank if __name__ == "__main__": distribution_strategy = 'fftw' # Setting up the geometry s_space = RGSpace([512, 512], dtype=np.float64) fft = FFTOperator(s_space) h_space = fft.target[0] p_space = PowerSpace(h_space, distribution_strategy=distribution_strategy) # Creating the mock data 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=pow_spec, distribution_strategy=distribution_strategy) sh = sp.power_synthesize(real_signal=True) ss = fft.inverse_times(sh) R = SmoothingOperator(s_space, sigma=0.1) 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) d = R(ss) + n # Wiener filter j = R.adjoint_times(N.inverse_times(d)) D = PropagatorOperator(S=S, N=N, R=R) m = D(j) d_data = d.val.get_full_data().real m_data = m.val.get_full_data().real ss_data = ss.val.get_full_data().real if rank == 0: pl.plot([go.Heatmap(z=d_data)], filename='data.html') pl.plot([go.Heatmap(z=m_data)], filename='map.html') pl.plot([go.Heatmap(z=ss_data)], filename='map_orig.html')