minor changes; documentation updated.

nifty_explicit.py

@@ -1150,7 +1150,6 @@ class explicit_operator(operator):
             X = X.astype(max(min(X.dtype,self.domain.datatype),min(X.dtype,self.target.datatype)))
         else:
             raise ValueError(about._errors.cstring("ERROR: dimension mismatch ( "+str(np.size(X))+" <> "+str(self.ncol())+" x "+str(self.ncol())+" )."))
-
         return explicit_operator(self.domain,self._calc_mul(X,1),bare=True,sym=sym,uni=uni,target=newtarget)
 
     def __imul__(self,X): ## __imul__ : self *= X
@@ -1386,7 +1385,7 @@ class explicit_operator(operator):
     ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
     def __repr__(self):
-        return "<nifty.explicit_operator>"
+        return "<nifty_explicit.explicit_operator>"
 
 ##-----------------------------------------------------------------------------
 
@@ -1846,7 +1845,7 @@ class explicit_probing(probing):
     ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
     def __repr__(self):
-        return "<nifty.explicit_probing>"
+        return "<nifty_explicit.explicit_probing>"
 
 ##-----------------------------------------------------------------------------
 

nifty_power.py

@@ -339,7 +339,7 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
         var : {scalar, list, array}, *optional*
             Variance of the assumed spectral smoothness prior (default: 10).
         force : bool, *optional*, *experimental*
-            Indicates whether smoothness is to be enforces or not
+            Indicates whether smoothness is to be enforced or not
             (default: False).
         bare : bool, *optional*
             Indicates whether the power spectrum entries returned are "bare"

nifty_tools.py

@@ -260,7 +260,7 @@ class invertible_operator(operator):
     ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
     def __repr__(self):
-        return "<nifty.invertible_operator>"
+        return "<nifty_tools.invertible_operator>"
 
 ##-----------------------------------------------------------------------------
 
@@ -417,10 +417,10 @@ class propagator_operator(operator):
 
     ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-    def _standard_M_times_1(self,x): ## applies > R_adjoint N_inverse R assuming N is diagonal
+    def _standard_M_times_1(self,x,**kwargs): ## applies > R_adjoint N_inverse R assuming N is diagonal
         return self.RN[0].adjoint_times(self.RN[1]._inverse_multiply(self.RN[0].times(x))) ## N.imp = True
 
-    def _standard_M_times_2(self,x): ## applies > R_adjoint N_inverse R
+    def _standard_M_times_2(self,x,**kwargs): ## applies > R_adjoint N_inverse R
         return self.RN[0].adjoint_times(self.RN[1].inverse_times(self.RN[0].times(x)))
 
     ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
@@ -552,7 +552,7 @@ class propagator_operator(operator):
     ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
     def __repr__(self):
-        return "<nifty.propagator_operator>"
+        return "<nifty_tools.propagator_operator>"
 
 ##-----------------------------------------------------------------------------
 
@@ -866,7 +866,7 @@ class conjugate_gradient(object):
     ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
     def __repr__(self):
-        return "<nifty.conjugate_gradient>"
+        return "<nifty_tools.conjugate_gradient>"
 
 ##=============================================================================
 
@@ -1130,7 +1130,7 @@ class steepest_descent(object):
     ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
     def __repr__(self):
-        return "<nifty.steepest_descent>"
+        return "<nifty_tools.steepest_descent>"
 
 ##=============================================================================