remove spurious file

demos/wiener_filter_harmonic - Kopie.py

-from nifty import *
-from mpi4py import MPI
-import plotly.offline as py
-import plotly.graph_objs as go
-
-
-comm = MPI.COMM_WORLD
-rank = comm.rank
-
-
-def plot_maps(x, name):
-
-    trace = [None]*len(x)
-
-    keys = x.keys()
-    field = x[keys[0]]
-    domain = field.domain[0]
-    shape = len(domain.shape)
-    max_n = domain.shape[0]*domain.distances[0]
-    step = domain.distances[0]
-    x_axis = np.arange(0, max_n, step)
-
-    if shape == 1:
-        for ii in xrange(len(x)):
-            trace[ii] = go.Scatter(x= x_axis, y=x[keys[ii]].val.get_full_data(), name=keys[ii])
-        fig = go.Figure(data=trace)
-
-        py.plot(fig, filename=name)
-
-    elif shape == 2:
-        for ii in xrange(len(x)):
-            py.plot([go.Heatmap(z=x[keys[ii]].val.get_full_data().real)], filename=keys[ii])
-    else:
-        raise TypeError("Only 1D and 2D field plots are supported")
-
-def plot_power(x, name):
-
-    layout = go.Layout(
-        xaxis=dict(
-            type='log',
-            autorange=True
-        ),
-        yaxis=dict(
-            type='log',
-            autorange=True
-        )
-    )
-
-    trace = [None]*len(x)
-
-    keys = x.keys()
-    field = x[keys[0]]
-    domain = field.domain[0]
-    x_axis = domain.kindex
-
-    for ii in xrange(len(x)):
-        trace[ii] = go.Scatter(x= x_axis, y=x[keys[ii]].val.get_full_data(), name=keys[ii])
-
-    fig = go.Figure(data=trace, layout=layout)
-    py.plot(fig, filename=name)
-
-np.random.seed(42)
-
-
-
-if __name__ == "__main__":
-
-    distribution_strategy = 'not'
-
-    # setting spaces
-    npix = np.array([500])  # number of pixels
-    total_volume = 1.  # total length
-
-    # setting signal parameters
-    lambda_s = .05  # signal correlation length
-    sigma_s = 10.  # signal variance
-
-
-    #setting response operator parameters
-    length_convolution = .025
-    exposure = 1.
-
-    # calculating parameters
-    k_0 = 4. / (2 * np.pi * lambda_s)
-    a_s = sigma_s ** 2. * lambda_s * total_volume
-
-    # creation of spaces
-#    x1 = RGSpace([npix,npix], distances=total_volume / npix,
-#                 zerocenter=False)
-#    k1 = RGRGTransformation.get_codomain(x1)
-    x1 = HPSpace(32)
-    k1 = HPLMTransformation.get_codomain(x1)
-
-    p1 = PowerSpace(harmonic_partner=k1, logarithmic=False)
-
-
-    # creating Power Operator with given spectrum
-    spec = (lambda k: a_s / (1 + (k / k_0) ** 2) ** 2)
-    p_field = Field(p1, val=spec)
-    S_op = create_power_operator(k1, spec)
-
-    # creating FFT-Operator and Response-Operator with Gaussian convolution
-    Fft_op = FFTOperator(domain=x1, target=k1,
-                        domain_dtype=np.float64,
-                        target_dtype=np.complex128)
-    R_op = ResponseOperator(x1, sigma=[length_convolution],
-                            exposure=[exposure])
-
-    # drawing a random field
-    sk = p_field.power_synthesize(real_signal=True, mean=0.)
-    s = Fft_op.adjoint_times(sk)
-
-    signal_to_noise = 1
-    N_op = DiagonalOperator(R_op.target, diagonal=s.var()/signal_to_noise, bare=True)
-    n = Field.from_random(domain=R_op.target,
-                          random_type='normal',
-                          std=s.std()/np.sqrt(signal_to_noise),
-                          mean=0.)
-
-    d = R_op(s) + n
-
-    # Wiener filter
-    j = Fft_op.times(R_op.adjoint_times(N_op.inverse_times(d)))
-    D = HarmonicPropagatorOperator(S=S_op, N=N_op, R=R_op)
-
-    mk = D(j)
-
-    m = Fft_op.adjoint_times(mk)
-
-#    z={}
-#    z["signal"] = s
-#    z["reconstructed_map"] = m
-#    z["data"] = d
-#    z["lambda"] = R_op(s)
-#    z["j"] = j
-#
-#    plot_maps(z, "Wiener_filter.html")
-
-