ptw -> direct calls

src/operators/energy_operators.py

@@ -200,9 +200,9 @@ class VariableCovarianceGaussianEnergy(LikelihoodOperator):
         self._check_input(x)
         r, i = x[self._kr], x[self._ki]
         if self._cplx:
-            res = 0.5*r.vdot(r*i.real).real - i.ptw("log").sum()
+            res = 0.5*r.vdot(r*i.real).real - i.log().sum()
         else:
-            res = 0.5*(r.vdot(r*i) - i.ptw("log").sum())
+            res = 0.5*(r.vdot(r*i) - i.log().sum())
         if not x.want_metric:
             return res
         if self._use_full_fisher:
@@ -260,16 +260,16 @@ class _SpecialGammaEnergy(LikelihoodOperator):
         self._check_input(x)
         r = self._resi
         if self._cplx:
-            res = 0.5*(r*x.real).vdot(r).real - x.ptw("log").sum()
+            res = 0.5*(r*x.real).vdot(r).real - x.log().sum()
         else:
-            res = 0.5*((r*x).vdot(r) - x.ptw("log").sum())
+            res = 0.5*((r*x).vdot(r) - x.log().sum())
         if not x.want_metric:
             return res
         return res.add_metric(self.get_metric_at(x.val))
 
     def get_transformation(self):
         sc = 1. if self._cplx else np.sqrt(0.5)
-        return self._dt, sc*ScalingOperator(self._domain, 1.).ptw('log')
+        return self._dt, sc*ScalingOperator(self._domain, 1.).log()
 
 
 class GaussianEnergy(LikelihoodOperator):
@@ -399,7 +399,7 @@ class PoissonianEnergy(LikelihoodOperator):
 
     def apply(self, x):
         self._check_input(x)
-        res = x.sum() - x.ptw("log").vdot(self._d)
+        res = x.sum() - x.log().vdot(self._d)
         if not x.want_metric:
             return res
         return res.add_metric(self.get_metric_at(x.val))
@@ -446,14 +446,14 @@ class InverseGammaLikelihood(LikelihoodOperator):
 
     def apply(self, x):
         self._check_input(x)
-        res = x.ptw("log").vdot(self._alphap1) + x.ptw("reciprocal").vdot(self._beta)
+        res = x.log().vdot(self._alphap1) + x.reciprocal().vdot(self._beta)
         if not x.want_metric:
             return res
         return res.add_metric(self.get_metric_at(x.val))
 
     def get_transformation(self):
-        fact = self._alphap1.ptw('sqrt')
-        res = makeOp(fact)@ScalingOperator(self._domain,1.).ptw('log')
+        fact = self._alphap1.sqrt()
+        res = makeOp(fact) @ ScalingOperator(self._domain, 1.).log()
         return self._sampling_dtype, res
 
 
@@ -481,7 +481,7 @@ class StudentTEnergy(LikelihoodOperator):
 
     def apply(self, x):
         self._check_input(x)
-        res = (((self._theta+1)/2)*(x**2/self._theta).ptw("log1p")).sum()
+        res = (((self._theta+1)/2)*(x**2/self._theta).log1p()).sum()
         if not x.want_metric:
             return res
         return res.add_metric(self.get_metric_at(x.val))
@@ -492,7 +492,7 @@ class StudentTEnergy(LikelihoodOperator):
         else:
             from ..extra import full
             th = full(self._domain, self._theta)
-        return np.float64, makeOp(((th+1)/(th+3)).ptw('sqrt'))
+        return np.float64, makeOp(((th+1)/(th+3)).sqrt())
 
 
 class BernoulliEnergy(LikelihoodOperator):
@@ -522,16 +522,16 @@ class BernoulliEnergy(LikelihoodOperator):
 
     def apply(self, x):
         self._check_input(x)
-        res = -x.ptw("log").vdot(self._d) + (1.-x).ptw("log").vdot(self._d-1.)
+        res = -x.log().vdot(self._d) + (1.-x).log().vdot(self._d-1.)
         if not x.want_metric:
             return res
         return res.add_metric(self.get_metric_at(x.val))
 
     def get_transformation(self):
         from ..extra import full
-        res = Adder(full(self._domain,1.))@ScalingOperator(self._domain,-1)
-        res = res * ScalingOperator(self._domain,1).ptw('reciprocal')
-        return np.float64, -2.*res.ptw('sqrt').ptw('arctan')
+        res = Adder(full(self._domain, 1.)) @ ScalingOperator(self._domain, -1)
+        res = res * ScalingOperator(self._domain, 1).reciprocal()
+        return np.float64, -2.*res.sqrt().arctan()
 
 
 class StandardHamiltonian(EnergyOperator):