adjustments for new plotter

demos/critical_filtering.py

 import numpy as np
 import nifty2go as ift
 
-import plotly.graph_objs as go
-import plotly.offline as pl
-
 np.random.seed(42)
 #np.seterr(all="raise",under="ignore")
 
 def plot_parameters(m, t, p, p_d):
     x = ift.log(t.domain[0].kindex)
     m = fft.adjoint_times(m)
-    m = m.val.real
     t = t.val.real
     p = p.val.real
     p_d = p_d.val.real
-    pl.plot([go.Heatmap(z=m)], filename='map.html', auto_open=False)
-    pl.plot([go.Scatter(x=x, y=t), go.Scatter(x=x, y=p),
-             go.Scatter(x=x, y=p_d)], filename="t.html", auto_open=False)
+    ift.plotting.plot(m.real, name='map.pdf')
+    #pl.plot([go.Scatter(x=x, y=t), go.Scatter(x=x, y=p),
+    #         go.Scatter(x=x, y=p_d)], filename="t.html", auto_open=False)
 
 
 class AdjointFFTResponse(ift.LinearOperator):
@@ -90,7 +86,7 @@ if __name__ == "__main__":
     realized_power = ift.log(sh.power_analyze(binbounds=p_space.binbounds))
     data_power = ift.log(fft(d).power_analyze(binbounds=p_space.binbounds))
     d_data = d.val.real
-    pl.plot([go.Heatmap(z=d_data)], filename='data.html', auto_open=False)
+    ift.plotting.plot(d.real, name="data.pdf")
 
     IC1 = ift.DefaultIterationController(verbose=True,iteration_limit=100)
     minimizer1 = ift.RelaxedNewton(IC1)

demos/log_normal_wiener_filter.py

@@ -18,7 +18,7 @@ if __name__ == "__main__":
     # Setting up the geometry |\label{code:wf_geometry}|
     L = 2. # Total side-length of the domain
     N_pixels = 128 # Grid resolution (pixels per axis)
-    #signal_space = RGSpace([N_pixels, N_pixels], distances=L/N_pixels)
+    #signal_space = ift.RGSpace([N_pixels, N_pixels], distances=L/N_pixels)
     signal_space = ift.HPSpace(16)
     harmonic_space = signal_space.get_default_codomain()
     fft = ift.FFTOperator(harmonic_space, target=signal_space)
@@ -53,39 +53,27 @@ if __name__ == "__main__":
     minimizer2 = ift.RelaxedNewton(controller=ctrl2)
     minimizer3 = ift.SteepestDescent(controller=ctrl2)
 
-    print type(energy.value)
-    me1 = minimizer1(energy)
+    #me1 = minimizer1(energy)
     me2 = minimizer2(energy)
-    me3 = minimizer3(energy)
+    #me3 = minimizer3(energy)
 
-    m1 = fft(me1[0].position)
+    #m1 = fft(me1[0].position)
     m2 = fft(me2[0].position)
-    m3 = fft(me3[0].position)
+    #m3 = fft(me3[0].position)
 
 
-    # Probing the variance
-    #class Proby(ift.DiagonalProberMixin, ift.Prober): pass
-    #proby = Proby(signal_space, probe_count=100)
-    #proby(lambda z: fft(wiener_curvature.inverse_times(fft.inverse_times(z))))
-
-    #sm = SmoothingOperator(signal_space, sigma=0.02)
-    #variance = sm(proby.diagonal.weight(-1))
-
     #Plotting #|\label{code:wf_plotting}|
-    #plotter = plotting.RG2DPlotter(color_map=plotting.colormaps.PlankCmap())
-    plotter = ift.plotting.HealpixPlotter(color_map=ift.plotting.colormaps.PlankCmap())
+    ift.plotting.plot(mock_signal.real,name='mock_signal.pdf', colormap="plasma",xlabel="Pixel Index",ylabel="Pixel Index")
+    ift.plotting.plot(ift.Field(signal_space, val=np.log(data.val.real).reshape(signal_space.shape)),name="log_of_data.pdf", colormap="plasma",xlabel="Pixel Index",ylabel="Pixel Index")
+    #ift.plotting.plot(m1.real,name='m_LBFGS.pdf', colormap="plasma",xlabel="Pixel Index",ylabel="Pixel Index")
+    ift.plotting.plot(m2.real,name='m_Newton.pdf', colormap="plasma",xlabel="Pixel Index",ylabel="Pixel Index")
+    #ift.plotting.plot(m3.real,name='m_SteepestDescent.pdf', colormap="plasma",xlabel="Pixel Index",ylabel="Pixel Index")
 
-    plotter.figure.xaxis = ift.plotting.Axis(label='Pixel Index')
-    plotter.figure.yaxis = ift.plotting.Axis(label='Pixel Index')
+    # Probing the variance
+    class Proby(ift.DiagonalProberMixin, ift.Prober): pass
+    proby = Proby(signal_space, probe_count=1)
+    proby(lambda z: fft(me2[0].curvature.inverse_times(fft.adjoint_times(z))))
 
-    plotter.plot.zmax = 5; plotter.plot.zmin = -5
-    #plotter(variance, path = 'variance.html')
-#    #plotter.plot.zmin = exp(mock_signal.min());
-#    plotter(mock_signal.real, path='mock_signal.html')
-#    plotter(Field(signal_space, val=np.log(data.val.get_full_data().real).reshape(signal_space.shape)),
-#            path = 'log_of_data.html')
-#
-    plotter(m1.real, path='m_LBFGS.html')
-    plotter(m2.real, path='m_Newton.html')
-    plotter(m3.real, path='m_SteepestDescent.html')
-#
+    sm = ift.FFTSmoothingOperator(signal_space, sigma=0.02)
+    variance = sm(proby.diagonal.weight(-1))
+    ift.plotting.plot(variance, name = 'variance.pdf')

demos/paper_demos/cartesian_wiener_filter.py

@@ -58,7 +58,7 @@ if __name__ == "__main__":
 
     diagonal = mock_power.power_synthesize(spaces=(0, 1), mean=1, std=0,
                                            real_signal=False)**2
-    #diagonal = diagonal.real
+    diagonal = diagonal.real
 
     S = ift.DiagonalOperator(domain=(harmonic_space_1, harmonic_space_2),
                              diagonal=diagonal)
@@ -101,20 +101,19 @@ if __name__ == "__main__":
 
     # Probing the variance
     class Proby(ift.DiagonalProberMixin, ift.Prober): pass
-    proby = Proby((signal_space_1, signal_space_2), probe_count=100)
+    proby = Proby((signal_space_1, signal_space_2), probe_count=2,ncpu=2)
     proby(lambda z: fft(wiener_curvature.inverse_times(fft.inverse_times(z))))
 #    sm = SmoothingOperator(signal_space, sigma=0.02)
 #    variance = sm(proby.diagonal.weight(-1))
     variance = proby.diagonal.weight(-1)
 
     plot_space = ift.RGSpace((N_pixels_1, N_pixels_2))
-    plotter = plotting.RG2DPlotter(color_map=plotting.colormaps.PlankCmap())
-    plotter.figure.xaxis = ift.plotting.Axis(label='Pixel Index')
-    plotter.figure.yaxis = ift.plotting.Axis(label='Pixel Index')
-
-    plotter.plot.zmin = 0.
-    plotter.plot.zmax = 3.
     sm = ift.FFTSmoothingOperator(plot_space, sigma=0.03)
+    ift.plotting.plot(ift.log(ift.sqrt(sm(ift.Field(plot_space, val=variance.val.real)))), name='uncertainty.pdf',zmin=0.,zmax=3.,title="Uncertainty map",xlabel="x")
+    ift.plotting.plot(ift.Field(plot_space, val=mock_signal.val.real), name='mock_signal.pdf')
+    ift.plotting.plot(ift.Field(plot_space, val=data.val.real), name='data.pdf')
+    ift.plotting.plot(ift.Field(plot_space, val=m.val.real), name='map.pdf')
+    exit()
     plotter(ift.log(ift.sqrt(sm(ift.Field(plot_space, val=variance.val.real)))), path='uncertainty.html')
 
     plotter.plot.zmin = np.real(mock_signal.min());

demos/wiener_filter_via_curvature.py

@@ -66,8 +66,7 @@ if __name__ == "__main__":
     m = wiener_curvature.inverse_times(j)
     m_s = fft(m)
 
-    ift.plotting.plot(mock_signal.real,"mock_signal.pdf")
-    ift.plotting.plot(mock_power.real,"power.pdf")
+    ift.plotting.plot(mock_signal.real,name="mock_signal.pdf")
     ift.plotting.plot(ift.Field(signal_space,
-                val=data.val.real.reshape(signal_space.shape)), "data.pdf")
-    ift.plotting.plot(m_s.real, "map.pdf")
+                val=data.val.real.reshape(signal_space.shape)), name="data.pdf")
+    ift.plotting.plot(m_s.real, name="map.pdf")