def convtest(test_signal, delta, func):
domain = test_signal.domain
# Create Convolution Operator
conv_op = ift.FuncConvolutionOperator(domain, func)
# Convolve, Adjoint-Convolve
conv_signal = conv_op(test_signal)
cac_signal = conv_op.adjoint_times(conv_signal)
print(test_signal.integrate(), conv_signal.integrate(),
cac_signal.integrate())
# generate kernel image
conv_delta = conv_op(delta)
# Plot results
plot = ift.Plot()
plot.add(signal, title='Signal')
plot.add(conv_signal, title='Signal Convolved')
plot.add(cac_signal, title='Signal, Conv, Adj-Conv')
plot.add(conv_delta, title='Kernel')
plot.output()
MOCK_NOISE = N.draw_sample()
data = R(MOCK_SIGNAL) + MOCK_NOISE
# Build inverse propagator D and information source j
D_inv = R.adjoint @ N.inverse @ R + S.inverse
j = R.adjoint_times(N.inverse_times(data))
# Make D_inv invertible (via Conjugate Gradient)
IC = ift.GradientNormController(iteration_limit=500, tol_abs_gradnorm=1e-3)
D = ift.InversionEnabler(D_inv, IC, approximation=S.inverse).inverse
# Calculate WIENER FILTER solution
m = D(j)
# Plotting
rg = isinstance(position_space, ift.RGSpace)
plot = ift.Plot()
filename = "getting_started_1_mode_{}.png".format(mode)
if rg and len(position_space.shape) == 1:
plot.add([HT(MOCK_SIGNAL), GR.adjoint(data),
HT(m)],
label=['Mock signal', 'Data', 'Reconstruction'],
alpha=[1, .3, 1])
plot.add(mask_to_nan(mask, HT(m - MOCK_SIGNAL)), title='Residuals')
plot.output(nx=2, ny=1, xsize=10, ysize=4, name=filename)
else:
plot.add(HT(MOCK_SIGNAL), title='Mock Signal')
plot.add(mask_to_nan(mask, (GR(Mask)).adjoint(data)), title='Data')
plot.add(HT(m), title='Reconstruction')
plot.add(mask_to_nan(mask, HT(m - MOCK_SIGNAL)), title='Residuals')
plot.output(nx=2, ny=2, xsize=10, ysize=10, name=filename)
print("Saved results as '{}'.".format(filename))