cosmetics

demos/critical_filtering.py

@@ -14,8 +14,8 @@ def adjust_zero_mode(m0, t0):
     return (ift.Field.from_global_data(m0.domain, mtmp),
             ift.Field.from_global_data(t0.domain, ttmp))
 
-if __name__ == "__main__":
 
+if __name__ == "__main__":
     noise_level = 1.
     p_spec = (lambda k: (.5 / (k + 1) ** 3))
 

demos/nonlinear_critical_filter.py

@@ -14,8 +14,8 @@ def adjust_zero_mode(m0, t0):
     return (ift.Field.from_global_data(m0.domain, mtmp),
             ift.Field.from_global_data(t0.domain, ttmp))
 
-if __name__ == "__main__":
 
+if __name__ == "__main__":
     noise_level = 1.
     p_spec = (lambda k: (1. / (k + 1) ** 2))
 

demos/poisson_demo.py

 # Program to generate figures of article "Information theory for fields"
 # by Torsten Ensslin, Annalen der Physik, submitted to special edition
 # "Physics of Information" in April 2018, arXiv:1804.03350
-# to get exact figure of paper comment out lines marked by "COMMENT OUT" 
+# to get exact figure of paper comment out lines marked by "COMMENT OUT"
 
 import numpy as np
 import nifty4 as ift
@@ -28,7 +28,7 @@ if __name__ == "__main__":
     L = 1.          # Total length of interval or volume the field lives on
     nprobes = 1000  # Number of probes for uncertainty quantification used in paper
     nprobes = 10    # COMMENT OUT TO REPRODUCE PAPER FIGURE EXACTLY
-    
+
     # Define resolution (pixels per dimension)
     N_pixels = 1024
 

demos/wiener_filter_data_space_noiseless.py

@@ -43,6 +43,7 @@ class CustomResponse(ift.LinearOperator):
     def capability(self):
         return self.TIMES | self.ADJOINT_TIMES
 
+
 if __name__ == "__main__":
     np.random.seed(43)
     # Set up physical constants

demos/wiener_filter_via_curvature.py

@@ -11,6 +11,7 @@ def init_nu():
         data = comm.bcast(data, root=0)
         nu.reset_units(data[0])
 
+
 if __name__ == "__main__":
     # In MPI mode, the random seed for numericalunits must be set by hand
     init_nu()

nifty4/operators/sandwich_operator.py

@@ -80,7 +80,7 @@ class SandwichOperator(EndomorphicOperator):
                 try:
                     s = self._cheese.draw_sample(from_inverse, dtype)
                     return self._bun.inverse_times(s)
-                except:
+                except NotImplementedError:
                     pass
             raise NotImplementedError(
                 "cannot draw from inverse of this operator")