cosmetics

demos/wiener_filter_via_hamiltonian.py

@@ -70,7 +70,7 @@ if __name__ == "__main__":
     # Generate plots
     zmax = max(ht(sh).max(), ht(m).max())
     zmin = min(ht(sh).min(), ht(m).min())
-    plotdict = {"zmax": zmax, "zmin": zmin,"colormap": "Planck-like"}
+    plotdict = {"zmax": zmax, "zmin": zmin, "colormap": "Planck-like"}
     plotdict2 = {"colormap": "Planck-like"}
     ift.plot(ht(sh), name="mock_signal.png", **plotdict)
     ift.plot(ht(m), name="reconstruction.png", **plotdict)

nifty4/library/nonlinear_power_curvature.py

@@ -20,12 +20,12 @@ from ..operators.inversion_enabler import InversionEnabler
 from .response_operators import LinearizedPowerResponse
 
 
-def NonlinearPowerCurvature(tau, ht, Instrument, nonlinearity, Projection, N,
+def NonlinearPowerCurvature(tau, ht, Instrument, nonlinearity, Distribution, N,
                             T, xi_sample_list, inverter):
     result = None
     for xi_sample in xi_sample_list:
         LinearizedResponse = LinearizedPowerResponse(
-            Instrument, nonlinearity, ht, Projection, tau, xi_sample)
+            Instrument, nonlinearity, ht, Distribution, tau, xi_sample)
         op = LinearizedResponse.adjoint*N.inverse*LinearizedResponse
         result = op if result is None else result + op
     result = result*(1./len(xi_sample_list)) + T

nifty4/library/response_operators.py

@@ -24,7 +24,7 @@ def LinearizedSignalResponse(Instrument, nonlinearity, ht, power, m):
 
 
 def LinearizedPowerResponse(Instrument, nonlinearity, ht, Distribution, tau,
-        xi):
+                            xi):
     power = exp(0.5*tau)
     position = ht(Distribution(power)*xi)
     linearization = nonlinearity.derivative(position)