extensive cleanups

demos/critical_filtering.py

@@ -106,7 +106,9 @@ if __name__ == "__main__":
 
     def ps0(k):
         return (1./(1.+k)**2)
-    t0 = ift.Field(p_space, val=np.log(1./(1+p_space.k_lengths)**2))
+
+    t0 = ift.Field(p_space,
+            val=ift.dobj.from_global_data(np.log(1./(1+p_space.k_lengths)**2)))
 
     for i in range(500):
         S0 = ift.create_power_operator(h_space, power_spectrum=ps0)
@@ -138,6 +140,6 @@ if __name__ == "__main__":
         t0 = power_energy.position.real
 
         # Plot current estimate
-        print(i)
+        ift.dobj.mprint(i)
         if i % 5 == 0:
             plot_parameters(m0, t0, ift.log(sp), data_power)

demos/log_normal_wiener_filter.py

@@ -3,35 +3,38 @@ import numpy as np
 
 if __name__ == "__main__":
     np.random.seed(42)
-    # Setting up parameters    |\label{code:wf_parameters}|
+    # Setting up parameters
     correlation_length = 1.     # Typical distance over which the field is correlated
     field_variance = 2.         # Variance of field in position space
     response_sigma = 0.02       # Smoothing length of response (in same unit as L)
     signal_to_noise = 100       # The signal to noise ratio
     np.random.seed(43)          # Fixing the random seed
+
     def power_spectrum(k):      # Defining the power spectrum
         a = 4 * correlation_length * field_variance**2
         return a / (1 + k * correlation_length) ** 4
 
-    # 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 = ift.RGSpace([N_pixels, N_pixels], distances=L/N_pixels)
+    # Setting up the geometry
+    L = 2.  # Total side-length of the domain
+    N_pixels = 128  # Grid resolution (pixels per axis)
+    # 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)
     power_space = ift.PowerSpace(harmonic_space)
 
-    # Creating the mock signal |\label{code:wf_mock_signal}|
-    S = ift.create_power_operator(harmonic_space, power_spectrum=power_spectrum)
+    # Creating the mock signal
+    S = ift.create_power_operator(harmonic_space,
+                                  power_spectrum=power_spectrum)
     mock_power = ift.PS_field(power_space, power_spectrum)
     mock_signal = fft(ift.power_synthesize(mock_power, real_signal=True))
 
     # Setting up an exemplary response
     mask = ift.Field.ones(signal_space)
     N10 = int(N_pixels/10)
-    #mask.val[N10*5:N10*9, N10*5:N10*9] = 0.
-    R = ift.ResponseOperator(signal_space, sigma=(response_sigma,), exposure=(mask,)) #|\label{code:wf_response}|
+    # mask.val[N10*5:N10*9, N10*5:N10*9] = 0.
+    R = ift.ResponseOperator(signal_space, sigma=(response_sigma,),
+                             exposure=(mask,))
     data_domain = R.target[0]
     R_harmonic = ift.ComposedOperator([fft, R])
 
@@ -39,40 +42,52 @@ if __name__ == "__main__":
     ndiag = ift.Field.full(data_domain, mock_signal.var()/signal_to_noise)
     N = ift.DiagonalOperator(ndiag.weight(1))
     noise = ift.Field.from_random(domain=data_domain, random_type='normal',
-                              std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)
-    data = R(ift.exp(mock_signal)) + noise #|\label{code:wf_mock_data}|
+        std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)
+    data = R(ift.exp(mock_signal)) + noise
 
     # Wiener filter
     m0 = ift.Field.zeros(harmonic_space)
-    ctrl = ift.GradientNormController(verbose=False,tol_abs_gradnorm=1)
-    ctrl2 = ift.GradientNormController(verbose=True,tol_abs_gradnorm=0.1, name="outer")
+    ctrl = ift.GradientNormController(verbose=False, tol_abs_gradnorm=1)
+    ctrl2 = ift.GradientNormController(verbose=True, tol_abs_gradnorm=0.1,
+                                       name="outer")
     inverter = ift.ConjugateGradient(controller=ctrl)
-    energy = ift.library.LogNormalWienerFilterEnergy(m0, data, R_harmonic, N, S, inverter=inverter)
-    minimizer1 = ift.VL_BFGS(controller=ctrl2,max_history_length=20)
+    energy = ift.library.LogNormalWienerFilterEnergy(m0, data, R_harmonic,
+                                                     N, S, inverter=inverter)
+    # minimizer1 = ift.VL_BFGS(controller=ctrl2, max_history_length=20)
     minimizer2 = ift.RelaxedNewton(controller=ctrl2)
-    minimizer3 = ift.SteepestDescent(controller=ctrl2)
+    # minimizer3 = ift.SteepestDescent(controller=ctrl2)
 
-    #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)
 
-    #Plotting #|\label{code:wf_plotting}|
-    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")
+    # Plotting
+    ift.plotting.plot(mock_signal.real, name='mock_signal.pdf',
+                      colormap="plasma", xlabel="Pixel Index",
+                      ylabel="Pixel Index")
+    logdata = np.log(ift.dobj.to_global_data(data.val.real)).reshape(signal_space.shape)
+    ift.plotting.plot(ift.Field(signal_space,
+                                val=ift.dobj.from_global_data(logdata)),
+                      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")
 
     # Probing the variance
-    class Proby(ift.DiagonalProberMixin, ift.Prober): pass
+    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))))
 
     sm = ift.FFTSmoothingOperator(signal_space, sigma=0.02)
     variance = sm(proby.diagonal.weight(-1))
-    ift.plotting.plot(variance, name = 'variance.pdf')
+    ift.plotting.plot(variance, name='variance.pdf')

demos/paper_demos/cartesian_wiener_filter.py

@@ -2,82 +2,82 @@ import numpy as np
 import nifty2go as ift
 
 if __name__ == "__main__":
-    signal_to_noise = 1.5 # The signal to noise ratio
+    signal_to_noise = 1.5  # The signal to noise ratio
 
-    # Setting up parameters    |\label{code:wf_parameters}|
-    correlation_length_1 = 1. # Typical distance over which the field is correlated
-    field_variance_1 = 2. # Variance of field in position space
+    # Setting up parameters
+    correlation_length_1 = 1.  # Typical distance over which the field is correlated
+    field_variance_1 = 2.  # Variance of field in position space
 
-    response_sigma_1 = 0.05 # Smoothing length of response (in same unit as L)
+    response_sigma_1 = 0.05  # Smoothing length of response (in same unit as L)
 
-    def power_spectrum_1(k): # note: field_variance**2 = a*k_0/4.
+    def power_spectrum_1(k):  # note: field_variance**2 = a*k_0/4.
         a = 4 * correlation_length_1 * field_variance_1**2
         return a / (1 + k * correlation_length_1) ** 4.
 
-    # Setting up the geometry |\label{code:wf_geometry}|
-    L_1 = 2. # Total side-length of the domain
-    N_pixels_1 = 512 # Grid resolution (pixels per axis)
+    # Setting up the geometry
+    L_1 = 2.  # Total side-length of the domain
+    N_pixels_1 = 512  # Grid resolution (pixels per axis)
 
     signal_space_1 = ift.RGSpace([N_pixels_1], distances=L_1/N_pixels_1)
     harmonic_space_1 = signal_space_1.get_default_codomain()
-    # Setting up the geometry |\label{code:wf_geometry}|
-    L_2 = 2. # Total side-length of the domain
-    N_pixels_2 = 512 # Grid resolution (pixels per axis)
+    # Setting up the geometry
+    L_2 = 2.  # Total side-length of the domain
+    N_pixels_2 = 512  # Grid resolution (pixels per axis)
     signal_space_2 = ift.RGSpace([N_pixels_2], distances=L_2/N_pixels_2)
     harmonic_space_2 = signal_space_2.get_default_codomain()
 
     signal_domain = ift.DomainTuple.make((signal_space_1, signal_space_2))
     mid_domain = ift.DomainTuple.make((signal_space_1, harmonic_space_2))
-    harmonic_domain = ift.DomainTuple.make((harmonic_space_1, harmonic_space_2))
+    harmonic_domain = ift.DomainTuple.make((harmonic_space_1,
+                                            harmonic_space_2))
 
     fft_1 = ift.FFTOperator(harmonic_domain, space=0)
     power_space_1 = ift.PowerSpace(harmonic_space_1)
 
-    mock_power_1 = ift.Field(power_space_1, val=power_spectrum_1(power_space_1.k_lengths))
+    mock_power_1 = ift.PS_field(power_space_1, power_spectrum_1)
 
+    # Setting up parameters
+    correlation_length_2 = 1.  # Typical distance over which the field is correlated
+    field_variance_2 = 2.  # Variance of field in position space
 
+    response_sigma_2 = 0.01  # Smoothing length of response (in same unit as L)
 
-    # Setting up parameters    |\label{code:wf_parameters}|
-    correlation_length_2 = 1. # Typical distance over which the field is correlated
-    field_variance_2 = 2. # Variance of field in position space
-
-    response_sigma_2 = 0.01 # Smoothing length of response (in same unit as L)
-
-    def power_spectrum_2(k): # note: field_variance**2 = a*k_0/4.
+    def power_spectrum_2(k):  # note: field_variance**2 = a*k_0/4.
         a = 4 * correlation_length_2 * field_variance_2**2
         return a / (1 + k * correlation_length_2) ** 2.5
 
     fft_2 = ift.FFTOperator(mid_domain, space=1)
     power_space_2 = ift.PowerSpace(harmonic_space_2)
 
-    mock_power_2 = ift.Field(power_space_2, val=power_spectrum_2(power_space_2.k_lengths))
+    mock_power_2 = ift.PS_field(power_space_2, power_spectrum_2)
 
     fft = ift.ComposedOperator((fft_1, fft_2))
 
     mock_power = ift.Field(domain=(power_space_1, power_space_2),
-                           val=np.outer(mock_power_1.val, mock_power_2.val))
+                           val=ift.dobj.from_global_data(np.outer(ift.dobj.to_global_data(mock_power_1.val), ift.dobj.to_global_data(mock_power_2.val))))
 
     diagonal = ift.power_synthesize_special(mock_power, spaces=(0, 1))**2
     diagonal = diagonal.real
 
     S = ift.DiagonalOperator(diagonal)
 
-
     np.random.seed(10)
     mock_signal = fft(ift.power_synthesize(mock_power, real_signal=True))
 
     # Setting up a exemplary response
     N1_10 = int(N_pixels_1/10)
-    mask_1 = ift.Field.ones(signal_space_1)
-    mask_1.val[N1_10*7:N1_10*9] = 0.
+    mask_1 = np.ones(signal_space_1.shape)
+    mask_1[N1_10*7:N1_10*9] = 0.
+    mask_1 = ift.Field(signal_space_1, ift.dobj.from_global_data(mask_1))
 
     N2_10 = int(N_pixels_2/10)
-    mask_2 = ift.Field.ones(signal_space_2)
-    mask_2.val[N2_10*7:N2_10*9] = 0.
+    mask_2 = np.ones(signal_space_2.shape)
+    mask_2[N2_10*7:N2_10*9] = 0.
+    mask_2 = ift.Field(signal_space_2, ift.dobj.from_global_data(mask_2))
 
-    R = ift.ResponseOperator(signal_domain,spaces=(0,1),
+    R = ift.ResponseOperator(signal_domain, spaces=(0, 1),
                              sigma=(response_sigma_1, response_sigma_2),
-                             exposure=(mask_1, mask_2)) #|\label{code:wf_response}|
+                             exposure=(mask_1, mask_2))
     data_domain = R.target
     R_harmonic = ift.ComposedOperator([fft, R])
 
@@ -87,21 +87,23 @@ if __name__ == "__main__":
     noise = ift.Field.from_random(domain=data_domain, random_type='normal',
                                   std=mock_signal.std()/np.sqrt(signal_to_noise),
                                   mean=0)
-    data = R(mock_signal) + noise #|\label{code:wf_mock_data}|
+    data = R(mock_signal) + noise
 
     # Wiener filter
     j = R_harmonic.adjoint_times(N.inverse_times(data))
     ctrl = ift.GradientNormController(verbose=True, tol_abs_gradnorm=0.1)
     inverter = ift.ConjugateGradient(controller=ctrl)
-    wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic)
+    wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N,
+                                                         R=R_harmonic)
     wiener_curvature = ift.InversionEnabler(wiener_curvature, inverter)
 
-    m_k = wiener_curvature.inverse_times(j) #|\label{code:wf_wiener_filter}|
+    m_k = wiener_curvature.inverse_times(j)
     m = fft(m_k)
 
     # Probing the variance
-    class Proby(ift.DiagonalProberMixin, ift.Prober): pass
-    proby = Proby((signal_space_1, signal_space_2), probe_count=1,ncpu=1)
+    class Proby(ift.DiagonalProberMixin, ift.Prober):
+        pass
+    proby = Proby((signal_space_1, signal_space_2), probe_count=1, ncpu=1)
     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))

demos/paper_demos/wiener_filter.py

@@ -3,34 +3,38 @@ import numpy as np
 
 
 if __name__ == "__main__":
-    # Setting up parameters    |\label{code:wf_parameters}|
+    # Setting up parameters
     correlation_length_scale = 1.  # Typical distance over which the field is correlated
     fluctuation_scale = 2.         # Variance of field in position space
     response_sigma = 0.05          # Smoothing length of response (in same unit as L)
     signal_to_noise = 1.5          # The signal to noise ratio
     np.random.seed(43)             # Fixing the random seed
+
     def power_spectrum(k):         # Defining the power spectrum
         a = 4 * correlation_length_scale * fluctuation_scale**2
         return a / (1 + (k * correlation_length_scale)**2) ** 2
 
-    # Setting up the geometry |\label{code:wf_geometry}|
+    # Setting up the geometry
     L = 2.  # Total side-length of the domain
     N_pixels = 512  # Grid resolution (pixels per axis)
     signal_space = ift.RGSpace([N_pixels, N_pixels], distances=L/N_pixels)
     harmonic_space = signal_space.get_default_codomain()
     fft = ift.FFTOperator(harmonic_space, target=signal_space)
-    power_space = ift.PowerSpace(harmonic_space,binbounds=ift.PowerSpace.useful_binbounds(harmonic_space,logarithmic=True))
+    power_space = ift.PowerSpace(harmonic_space, binbounds=ift.PowerSpace.useful_binbounds(harmonic_space,logarithmic=True))
 
-    # Creating the mock signal |\label{code:wf_mock_signal}|
-    S = ift.create_power_operator(harmonic_space, power_spectrum=power_spectrum)
+    # Creating the mock signal
+    S = ift.create_power_operator(harmonic_space,
+                                  power_spectrum=power_spectrum)
     mock_power = ift.PS_field(power_space, power_spectrum)
     mock_signal = fft(ift.power_synthesize(mock_power, real_signal=True))
 
     # Setting up an exemplary response
-    mask = ift.Field.ones(signal_space)
+    mask = np.ones(signal_space.shape)
     N10 = int(N_pixels/10)
-    mask.val[N10*5:N10*9, N10*5:N10*9] = 0.
-    R = ift.ResponseOperator(signal_space, sigma=(response_sigma,), exposure=(mask,))  #|\label{code:wf_response}|
+    mask[N10*5:N10*9, N10*5:N10*9] = 0.
+    mask = ift.Field(signal_space, ift.dobj.from_global_data(mask))
+    R = ift.ResponseOperator(signal_space, sigma=(response_sigma,),
+                             exposure=(mask,))
     data_domain = R.target[0]
     R_harmonic = ift.ComposedOperator([fft, R])
 
@@ -39,28 +43,29 @@ if __name__ == "__main__":
     N = ift.DiagonalOperator(ndiag.weight(1))
     noise = ift.Field.from_random(domain=data_domain, random_type='normal',
                                   std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)
-    data = R(mock_signal) + noise  #|\label{code:wf_mock_data}|
+    data = R(mock_signal) + noise
 
     # Wiener filter
     j = R_harmonic.adjoint_times(N.inverse_times(data))
-    ctrl = ift.GradientNormController(verbose=True,tol_abs_gradnorm=0.1)
+    ctrl = ift.GradientNormController(verbose=True, tol_abs_gradnorm=0.1)
     inverter = ift.ConjugateGradient(controller=ctrl)
-    wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic)
-    wiener_curvature =ift.InversionEnabler(wiener_curvature, inverter)
-    m_k = wiener_curvature.inverse_times(j)  #|\label{code:wf_wiener_filter}|
+    wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N,
+                                                         R=R_harmonic)
+    wiener_curvature = ift.InversionEnabler(wiener_curvature, inverter)
+    m_k = wiener_curvature.inverse_times(j)
     m = fft(m_k)
 
-    # Probing the uncertainty |\label{code:wf_uncertainty_probing}|
-    class Proby(ift.DiagonalProberMixin, ift.Prober): pass
-    proby = Proby(signal_space, probe_count=1,ncpu=1)
-    proby(lambda z: fft(wiener_curvature.inverse_times(fft.inverse_times(z))))  #|\label{code:wf_variance_fft_wrap}|
+    # Probing the uncertainty
+    class Proby(ift.DiagonalProberMixin, ift.Prober):
+        pass
+    proby = Proby(signal_space, probe_count=1, ncpu=1)
+    proby(lambda z: fft(wiener_curvature.inverse_times(fft.inverse_times(z))))
 
     sm = ift.FFTSmoothingOperator(signal_space, sigma=0.03)
-    variance = ift.sqrt(sm(proby.diagonal.weight(-1)))  #|\label{code:wf_variance_weighting}|
+    variance = ift.sqrt(sm(proby.diagonal.weight(-1)))
 
-    # Plotting #|\label{code:wf_plotting}|
+    # Plotting
     ift.plotting.plot(variance,name="uncertainty.pdf",xlabel='Pixel index', ylabel='Pixel index')
     ift.plotting.plot(mock_signal,name="mock_signal.pdf",xlabel='Pixel index', ylabel='Pixel index')
     ift.plotting.plot(ift.Field(signal_space, val=data.val),name="data.pdf",xlabel='Pixel index', ylabel='Pixel index')
     ift.plotting.plot(m,name="map.pdf",xlabel='Pixel index', ylabel='Pixel index')
-

demos/probing.py

-from __future__ import print_function
 import nifty2go as ift
 import numpy as np
 
@@ -20,9 +19,9 @@ diagOp = ift.DiagonalOperator(f)
 
 diagProber = DiagonalProber(domain=x)
 diagProber(diagOp)
-print((f - diagProber.diagonal).norm())
+ift.dobj.mprint((f - diagProber.diagonal).norm())
 
 multiProber = MultiProber(domain=x)
 multiProber(diagOp)
-print((f - multiProber.diagonal).norm())
-print(f.sum() - multiProber.trace)
+ift.dobj.mprint((f - multiProber.diagonal).norm())
+ift.dobj.mprint(f.sum() - multiProber.trace)

demos/wiener_filter_via_curvature.py

@@ -3,7 +3,8 @@ import nifty2go as ift
 import numericalunits as nu
 
 if __name__ == "__main__":
-    nu.reset_units("SI")
+    # In MPI mode, the random seed for numericalunits must be set by hand
+    nu.reset_units(43)
     dimensionality = 2
     np.random.seed(43)
 
@@ -44,8 +45,7 @@ if __name__ == "__main__":
                                   power_spectrum=power_spectrum)
     np.random.seed(43)
 
-    mock_power = ift.Field(power_space,
-                           val=ift.dobj.from_global_data(power_spectrum(power_space.k_lengths)))
+    mock_power = ift.PS_field(power_space, power_spectrum)
     mock_harmonic = ift.power_synthesize(mock_power, real_signal=True)
     mock_harmonic = mock_harmonic.real
     mock_signal = fft(mock_harmonic)
@@ -81,7 +81,7 @@ if __name__ == "__main__":
 
     ift.plotting.plot(ift.Field(sspace2, mock_signal.real.val)/nu.K,
                       name="mock_signal.pdf")
-    ift.plotting.plot(ift.Field(
-        sspace2, val=ift.dobj.from_global_data(ift.dobj.to_global_data(data.val.real).reshape(signal_space.shape)))/nu.K,
-        name="data.pdf")
+    data = ift.dobj.to_global_data(data.val.real).reshape(sspace2.shape)/nu.K
+    data = ift.Field(sspace2, val=ift.dobj.from_global_data(data))/nu.K
+    ift.plotting.plot(ift.Field(sspace2, val=data), name="data.pdf")
     ift.plotting.plot(ift.Field(sspace2, m_s.real.val)/nu.K, name="map.pdf")

demos/wiener_filter_via_hamiltonian.py

@@ -50,7 +50,7 @@ if __name__ == "__main__":
     S = ift.create_power_operator(h_space, power_spectrum=p_spec)
 
     # Drawing a sample sh from the prior distribution in harmonic space
-    sp = ift.Field(p_space, ift.dobj.from_global_data(p_spec(p_space.k_lengths)))
+    sp = ift.PS_field(p_space, p_spec)
     sh = ift.power_synthesize(sp, real_signal=True)
     ss = fft.adjoint_times(sh)
 
@@ -58,7 +58,7 @@ if __name__ == "__main__":
     # Instrument = ift.FFTSmoothingOperator(s_space, sigma=0.05)
     diag = np.ones(s_space.shape)
     diag[20:80, 20:80] = 0
-    diag = ift.Field(s_space,ift.dobj.from_global_data(diag))
+    diag = ift.Field(s_space, ift.dobj.from_global_data(diag))
     Instrument = ift.DiagonalOperator(diag)
 
     # Adding a harmonic transformation to the instrument

nifty/basic_arithmetics.py

@@ -17,7 +17,6 @@
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
 from __future__ import division
-import numpy as np
 from .field import Field
 from . import dobj
 
@@ -29,7 +28,7 @@ def _math_helper(x, function, out):
     if not isinstance(x, Field):
         raise TypeError("This function only accepts Field objects.")
     if out is not None:
-        if not isinstance(out, Field) or x.domain!=out.domain:
+        if not isinstance(out, Field) or x.domain != out.domain:
             raise ValueError("Bad 'out' argument")
         function(x.val, out=out.val)
         return out

nifty/data_objects/distributed_do.py

+from __future__ import print_function
 import numpy as np
 from .random import Random
 from mpi4py import MPI
 
-__all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
-           "empty", "zeros", "ones", "empty_like", "vdot", "abs", "exp",
-           "log", "sqrt", "from_object", "from_random",
-           "local_data", "ibegin", "np_allreduce_sum", "distaxis",
-           "from_local_data", "from_global_data", "to_global_data",
-           "redistribute", "default_distaxis"]
-
 _comm = MPI.COMM_WORLD
 ntask = _comm.Get_size()
 rank = _comm.Get_rank()
 master = (rank == 0)
 
 
+def mprint(*args):
+    if master:
+        print(*args)
+
+
 def _shareSize(nwork, nshares, myshare):
     return (nwork//nshares) + int(myshare < nwork % nshares)
 

nifty/data_objects/numpy_do.py

 # Data object module for NIFTy that uses simple numpy ndarrays.
 
+from __future__ import print_function
 import numpy as np
 from numpy import ndarray as data_object
 from numpy import full, empty, empty_like, sqrt, ones, zeros, vdot, abs, \
                   exp, log
 from .random import Random
 
-__all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
-           "empty", "zeros", "ones", "empty_like", "vdot", "abs", "exp",
-           "log", "sqrt", "from_object", "from_random",
-           "local_data", "ibegin", "np_allreduce_sum", "distaxis",
-           "from_local_data", "from_global_data", "to_global_data",
-           "redistribute", "default_distaxis"]
-
 ntask = 1
 rank = 0
 master = True
 
 
+def mprint(*args):
+    print(*args)
+
+
 def from_object(object, dtype=None, copy=True):
     return np.array(object, dtype=dtype, copy=copy)
 

nifty/dobj.py

-#from __future__ import print_function
-
 try:
     from mpi4py import MPI
-    if MPI.COMM_WORLD.Get_size()==1:
-        #print ("MPI found, but only with one task, using numpy_do...")
+    if MPI.COMM_WORLD.Get_size() == 1:
         from .data_objects.numpy_do import *
+        # mprint("MPI found, but only with one task, using numpy_do...")
     else:
-        #if MPI.COMM_WORLD.Get_rank() == 0:
-        #    print ("MPI with multiple tasks found, using distributed_do...")
         from .data_objects.distributed_do import *
+        # mprint("MPI with multiple tasks found, using distributed_do...")
 except ImportError:
-    #print ("MPI not found, using numpy_do...")
     from .data_objects.numpy_do import *
+    # mprint("MPI not found, using numpy_do...")
+
+__all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
+           "empty", "zeros", "ones", "empty_like", "vdot", "abs", "exp",
+           "log", "sqrt", "from_object", "from_random",
+           "local_data", "ibegin", "np_allreduce_sum", "distaxis",
+           "from_local_data", "from_global_data", "to_global_data",
+           "redistribute", "default_distaxis", "mprint"]

nifty/domain_tuple.py

@@ -19,6 +19,7 @@
 from functools import reduce
 from .domain_object import DomainObject
 
+
 class DomainTuple(object):
     _tupleCache = {}
 
@@ -122,7 +123,6 @@ class DomainTuple(object):
         of pixels in the requested space in res[1], and the remaining pixels in
         res[2].
         """
-        dims = (dom.dim for dom in self._dom)
         return (self._accdims[ispace],
                 self._accdims[ispace+1]//self._accdims[ispace],
                 self._accdims[-1]//self._accdims[ispace+1])

nifty/energies/line_energy.py

@@ -16,8 +16,6 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from __future__ import print_function
-
 
 class LineEnergy(object):
     """ Evaluates an underlying Energy along a certain line direction.
@@ -114,6 +112,7 @@ class LineEnergy(object):
     def directional_derivative(self):
         res = self.energy.gradient.vdot(self.line_direction)
         if abs(res.imag) / max(abs(res.real), 1.) > 1e-12:
-            print("directional derivative has non-negligible "
-                  "imaginary part:", res)
+            from ..dobj import mprint
+            mprint("directional derivative has non-negligible "
+                   "imaginary part:", res)
         return res.real

nifty/field.py

@@ -16,7 +16,7 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from __future__ import division, print_function
+from __future__ import division