Added wiener_filter demo.

demos/wiener_filter.py

+
+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'
+
+    s_space = RGSpace([512, 512], dtype=np.complex128)
+    fft = FFTOperator(s_space)
+    h_space = fft.target[0]
+    p_space = PowerSpace(h_space, distribution_strategy=distribution_strategy)
+
+    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
+    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')
+