anticipate a few nice changes from symbolicDifferentiation

demos/nonlinear_wiener_filter.py

@@ -10,8 +10,8 @@ if __name__ == "__main__":
     p_spec = lambda k: (1. / (k*correlation_length + 1) ** 4)
 
     nonlinearity = Tanh()
-    #nonlinearity = Linear()
-    #nonlinearity = Exponential()
+    # nonlinearity = Linear()
+    # nonlinearity = Exponential()
 
     # Set up position space
     s_space = ift.RGSpace(1024)

nifty4/__init__.py

@@ -28,5 +28,5 @@ from .multi import *
 
 __all__ = ["__version__", "dobj", "DomainTuple"] + \
           domains.__all__ + operators.__all__ + minimization.__all__ + \
-          ["DomainTuple", "Field", "sqrt", "exp", "log"] + \
+          ["DomainTuple", "Field", "sqrt", "exp", "log", "tanh"] + \
           multi.__all__

nifty4/minimization/line_energy.py

@@ -97,5 +97,5 @@ class LineEnergy(object):
         if abs(res.imag) / max(abs(res.real), 1.) > 1e-12:
             from ..logger import logger
             logger.warning("directional derivative has non-negligible "
-                           "imaginary part:", res)
+                           "imaginary part: {}".format(res))
         return res.real

nifty4/minimization/scipy_minimizer.py

@@ -98,7 +98,7 @@ class ScipyMinimizer(Minimizer):
         r = opt.minimize(hlp.fun, x, method=self._method, jac=hlp.jac,
                          hessp=hessp, options=self._options, bounds=bounds)
         if not r.success:
-            logger.error("Problem in Scipy minimization:", r.message)
+            logger.error("Problem in Scipy minimization: {}".format(r.message))
             return hlp._energy, IterationController.ERROR
         return hlp._energy, IterationController.CONVERGED
 

nifty4/operators/linear_operator.py

@@ -130,16 +130,22 @@ class LinearOperator(NiftyMetaBase()):
 
     def __mul__(self, other):
         from .chain_operator import ChainOperator
+        if np.isscalar(other) and other == 1.:
+            return self
         other = self._toOperator(other, self.domain)
         return ChainOperator.make([self, other])
 
     def __rmul__(self, other):
         from .chain_operator import ChainOperator
+        if np.isscalar(other) and other == 1.:
+            return self
         other = self._toOperator(other, self.target)
         return ChainOperator.make([other, self])
 
     def __add__(self, other):
         from .sum_operator import SumOperator
+        if np.isscalar(other) and other == 0.:
+            return self
         other = self._toOperator(other, self.domain)
         return SumOperator.make([self, other], [False, False])
 
@@ -148,6 +154,8 @@ class LinearOperator(NiftyMetaBase()):
 
     def __sub__(self, other):
         from .sum_operator import SumOperator
+        if np.isscalar(other) and other == 0.:
+            return self
         other = self._toOperator(other, self.domain)
         return SumOperator.make([self, other], [False, True])