-Add an averaged MatsubaraGreensfunction.

-spacing

SDL_QMC_Master/qmc_master.cpp

@@ -118,6 +118,7 @@ static const ObservableProperties propertycache[] =
     {"SmoothImaginaryGreensfunction", false, VECTORFUNCTION, false, false},
     {"SmoothImaginaryGreensfunction_averaged", false, VECTORFUNCTION, false, false},
     {"MatsubaraFrequencyGreensfunction", true, VECTORFUNCTION, false, false},
+    {"MatsubaraFrequencyGreensfunction_averaged", true, VECTORFUNCTION, false, false},
     {"MatsubaraFrequencyGreensfunctionDerivative", true, VECTORFUNCTION, false, false},
     {"SpinSpinCorrelatedPart_Y", false, VECTORFUNCTION, false, false},
     {"SpinSpinCorrelatedPart_X", false, VECTORFUNCTION, false, false},

common/errordata.h

@@ -121,7 +121,6 @@ public:
     CovType cov;
 };
 
-
 template <typename R> class errordata_signed
 {
 private:

common/jackknife.h

@@ -124,6 +124,7 @@ Cont   the Container that is used for the Storage of the data
 @param data an array of Containers with the stored samples
 @param numcont the number of arrays pointed to by data
 */
+
 template <typename Func, typename Cont>
 errordata<typename Func::res_t > vecjackknife(Func func, const Cont& data, unsigned int numcont, bool covariance)
 {
@@ -141,7 +142,6 @@ errordata<typename Func::res_t > vecjackknife(Func func, const Cont& data, unsig
         x_J = norm *(ElementType(data.size()) * mean - data[k]);
         jackbin[k] = func(x_J);
     }
-
     //calculate the jackknife average of the jackknife samples
     RetType jackmean = expectationvalue(jackbin, data.size());
     RetType fun_mean = func(mean);