From 38d89e2d0132033625a4e327a0797d7944d6e100 Mon Sep 17 00:00:00 2001
From: Laura Messio <laura.messio@cea.fr>
Date: Fri, 19 Jul 2013 13:28:30 +0200
Subject: [PATCH] work on gf: scalar_valued, ...

- introducing scalar_valued gf
- Change Fourier routines to run on scalar_valued,
and then use those routines to run on matrix_valued.
- Tools for slices of 2 variables functions
---
 test/triqs/gf/fourier1.cpp                 |   4 +-
 test/triqs/gf/gf_2times_b.cpp              |   2 +-
 test/triqs/gf/pb_affichage.cpp             |  39 +++
 test/triqs/gf/test_fourier_matsubara.cpp   |  64 +++++
 test/triqs/gf/test_fourier_real_time.cpp   | 100 +++++++
 test/triqs/gf/test_fourier_real_time.ipynb | 166 ++++++++++++
 test/triqs/gf/test_gf_triqs.cpp            |   3 +-
 triqs/gf/gf.hpp                            |  25 +-
 triqs/gf/imfreq.hpp                        |  62 +++--
 triqs/gf/imtime.hpp                        | 111 +++++---
 triqs/gf/local/fourier_base.cpp            |  18 +-
 triqs/gf/local/fourier_base.hpp            |   3 +-
 triqs/gf/local/fourier_matsubara.cpp       | 286 +++++++++++----------
 triqs/gf/local/fourier_matsubara.hpp       |  63 ++++-
 triqs/gf/local/fourier_real.cpp            |  66 ++---
 triqs/gf/local/fourier_real.hpp            |  27 +-
 triqs/gf/meshes/product.hpp                |  44 +++-
 triqs/gf/refreq.hpp                        |  50 +++-
 triqs/gf/retime.hpp                        |  65 +++--
 triqs/gf/two_real_times.hpp                |  31 ++-
 20 files changed, 907 insertions(+), 322 deletions(-)
 create mode 100644 test/triqs/gf/pb_affichage.cpp
 create mode 100644 test/triqs/gf/test_fourier_matsubara.cpp
 create mode 100644 test/triqs/gf/test_fourier_real_time.cpp
 create mode 100644 test/triqs/gf/test_fourier_real_time.ipynb

diff --git a/test/triqs/gf/fourier1.cpp b/test/triqs/gf/fourier1.cpp
index 199f93a1..9dbdc359 100644
--- a/test/triqs/gf/fourier1.cpp
+++ b/test/triqs/gf/fourier1.cpp
@@ -25,8 +25,8 @@ int main() {
  auto G3 = make_gf<imfreq> (beta, Fermion, make_shape(2,2));
  auto Gt = make_gf<imtime> (beta, Fermion, make_shape(2,2));
 
- //auto gt = inverse_fourier(G);
- //auto gw = fourier(gt);
+ auto gt = inverse_fourier(G);
+ auto gw = fourier(gt);
 
  //gw() = lazy_fourier(gt);
  G() = lazy_fourier(Gt);
diff --git a/test/triqs/gf/gf_2times_b.cpp b/test/triqs/gf/gf_2times_b.cpp
index c5612df0..7ca41787 100644
--- a/test/triqs/gf/gf_2times_b.cpp
+++ b/test/triqs/gf/gf_2times_b.cpp
@@ -27,7 +27,7 @@ int main(){
 
  std::cout << rg (1,1)<< std::endl ;
  std::cout << R.on_mesh(1,1)<< std::endl ;
- std::cout << R(1,1)<< std::endl ;
+ std::cout << R(0.001,0.001)<< std::endl ;
 
  auto R2 = R;
 
diff --git a/test/triqs/gf/pb_affichage.cpp b/test/triqs/gf/pb_affichage.cpp
new file mode 100644
index 00000000..af55eb07
--- /dev/null
+++ b/test/triqs/gf/pb_affichage.cpp
@@ -0,0 +1,39 @@
+#define TRIQS_ARRAYS_ENFORCE_BOUNDCHECK
+
+#include <triqs/utility/first_include.hpp>
+#include <iostream>
+#include <stdlib.h>
+#include <stdio.h>
+#include <string.h>
+#include <fstream>
+#include <triqs/gf/gf.hpp>
+#include <triqs/gf/two_real_times.hpp>
+#include <complex>
+using namespace triqs::gf;
+using namespace std;
+using triqs::arrays::make_shape;
+
+int main(){
+
+ double dt=0.001;
+ double tmax=0.005;
+ int nt=tmax/dt;
+ auto  R= make_gf<two_real_times> (tmax,nt,make_shape(1,1));//results
+
+ for(auto point:R.mesh()) R(point)=0;
+
+ const auto rg = on_mesh(R);
+ R.on_mesh(1,1) = 10;
+
+ std::cout << rg (1,1)<< std::endl ;
+ std::cout << R.on_mesh(1,1)<< std::endl ;
+ std::cout << R(0.001,0.001)<< std::endl ;
+
+ auto R2 = R;
+
+ //on_mesh(R2)(1,1) =  on_mesh(R)(1,1) * on_mesh(R)(1,1);
+ on_mesh(R2)(1,1)() =  on_mesh(R)(1,1) * on_mesh(R)(1,1);
+
+ std::cout  << on_mesh(R2)(1,1)<< std::endl; 
+ return 0;
+};
diff --git a/test/triqs/gf/test_fourier_matsubara.cpp b/test/triqs/gf/test_fourier_matsubara.cpp
new file mode 100644
index 00000000..8b81d4e5
--- /dev/null
+++ b/test/triqs/gf/test_fourier_matsubara.cpp
@@ -0,0 +1,64 @@
+//#define TRIQS_ARRAYS_ENFORCE_BOUNDCHECK
+
+#include <triqs/gf/imfreq.hpp> 
+#include <triqs/gf/imtime.hpp> 
+#include <triqs/gf/local/fourier_matsubara.hpp> 
+
+namespace tql= triqs::clef;
+// namespace tqa= triqs::arrays;
+// using tqa::range;
+using triqs::arrays::make_shape;
+using triqs::gf::Fermion;
+using triqs::gf::imfreq;
+using triqs::gf::imtime;
+using triqs::gf::make_gf;
+using triqs::arrays::range;
+#define TEST(X) std::cout << BOOST_PP_STRINGIZE((X)) << " ---> "<< (X) <<std::endl<<std::endl;
+
+int main() {
+ 
+ double precision=10e-9;
+ H5::H5File file("test_fourier_matsubara.h5",H5F_ACC_TRUNC);
+ triqs::clef::placeholder<0> om_;
+ double beta =1;
+ int N=10000;
+ double E=1;
+ 
+ auto Gw1 = make_gf<imfreq> (beta, Fermion, make_shape(1,1), N);
+ Gw1(om_) << 1/(om_-E);
+//  for(auto const& w:Gw1.mesh()){
+//   std::cout<<"w="<<std::complex<double>(w)<<", Gw1=" << Gw1(w)(0,0)<<std::endl;
+//  }
+ h5_write(file, "Gw1", Gw1);   // the original lorentzian
+ 
+ auto Gt1 = make_gf<imtime> (beta, Fermion, make_shape(1,1), N);
+ inverse_fourier_impl( Gt1, Gw1, triqs::gf::matrix_valued() );
+//  for(auto const& t:Gt1.mesh()){
+//   std::cout<<"t="<<t<<",  expected="<<exp(-E*t) * ( (t>0?-1:0)+1/(1+exp(E*beta)) )<<std::endl;
+//  }
+ h5_write(file, "Gt1", Gt1);   // the lorentzian TF : lorentzian_inverse
+ 
+ ///verification that TF(TF^-1)=Id
+ auto Gw1b = make_gf<imfreq> (beta, Fermion, make_shape(1,1), N);
+ fourier_impl(Gw1b, Gt1, triqs::gf::matrix_valued());
+ for(auto const& w:Gw1.mesh()){
+//   std::cout<<"w="<<std::complex<double>(w)<<",Gw1b=" << Gw1b(w)(0,0)<<std::endl;
+//   std::cout<<"w="<<std::complex<double>(w)<<",Delta Gw1b=" << Gw1b(w)(0,0)-Gw1(w)(0,0)<<std::endl;
+  if ( std::abs(Gw1b(w)(0,0)-Gw1(w)(0,0)) > precision) TRIQS_RUNTIME_ERROR<<" fourier_matsubara error : w="<<std::complex<double>(w)<<" ,Gw1b="<<std::abs(Gw1b(w)(0,0))<<"\n";
+ }
+ h5_write(file,"Gw1b",Gw1b); // must be 0
+ 
+ ///verification that the TF is OK
+ for(auto const & t:Gt1.mesh()){
+  Gt1(t)-= exp(-E*t) * ( (t>0?-1:0)+1/(1+exp(E*beta)) );
+  if ( std::abs(Gt1(t)(0,0)) > precision) TRIQS_RUNTIME_ERROR<<" fourier_matsubara error : t="<<t<<" ,G1="<<std::abs(Gt1(t)(0,0))<<"\n";
+ }
+ h5_write(file,"Gt1b",Gt1); // must be 0
+ 
+ ///to verify that lazy_fourier computes
+ auto Gw2 = make_gf<imfreq> (beta, Fermion, make_shape(1,1));
+ Gw2() = lazy_fourier(Gt1);
+ 
+}
+
+
diff --git a/test/triqs/gf/test_fourier_real_time.cpp b/test/triqs/gf/test_fourier_real_time.cpp
new file mode 100644
index 00000000..23c051dd
--- /dev/null
+++ b/test/triqs/gf/test_fourier_real_time.cpp
@@ -0,0 +1,100 @@
+#define TRIQS_ARRAYS_ENFORCE_BOUNDCHECK
+
+#include <triqs/gf/refreq.hpp> 
+#include <triqs/gf/retime.hpp> 
+#include <triqs/gf/local/fourier_real.hpp> 
+#include <triqs/arrays.hpp>
+
+using triqs::arrays::make_shape;
+using triqs::gf::refreq;
+using triqs::gf::retime;
+using triqs::gf::make_gf;
+
+double lorentzian(double w, double a){
+  return 2*a / (w*w + a*a) ; 
+};
+std::complex<double> lorentzian_inverse(double t, double a){
+  return std::exp(-a*std::abs(t)) ; 
+};
+double theta(double x){
+  return x>0 ? 1.0 : ( x<0 ? 0.0 : 0.5 ) ; 
+};
+
+int main() {
+ 
+
+  double precision=10e-10;
+  H5::H5File file("fourier_real_time.h5",H5F_ACC_TRUNC);
+
+  std::complex<double> I(0,1);  
+  
+  //Test on the tail: GF in frequency that is a lorentzian, with its singularity, TF and TF^-1. 
+  
+  double wmax=10;
+  int Nw=1001;
+  
+  auto Gw1 = make_gf<refreq> (-wmax, wmax, Nw, make_shape(1,1),triqs::gf::full_bins);
+  double a = Gw1.mesh().delta() * sqrt( Gw1.mesh().size() );
+  for(auto const & w:Gw1.mesh()) Gw1(w)=lorentzian(w,a);
+  Gw1.singularity()(2)=triqs::arrays::matrix<double>{{2.0*a}};
+  h5_write(file,"Gw1",Gw1);   // the original lorentzian
+  
+  auto Gt1 = inverse_fourier(Gw1);  
+  h5_write(file,"Gt1",Gt1);   // the lorentzian TF : lorentzian_inverse
+  
+  // verification that TF(TF^-1)=Id
+  auto Gw1b = fourier(Gt1);
+  for(auto const & w:Gw1b.mesh()){
+    Gw1b(w)-=Gw1(w);
+    if ( std::abs(Gw1b(w)(0,0)) > precision) TRIQS_RUNTIME_ERROR<<" fourier_real_time error : w="<<w<<" ,G1="<<std::abs(Gw1b(w)(0,0))<<"\n";
+  }
+  h5_write(file,"Gw1b",Gw1b); // must be 0
+  
+  // verification that TF is the lorentzian_inverse function
+  for(auto const & t:Gt1.mesh()){
+    Gt1(t)-=lorentzian_inverse(t,a);
+    if ( std::abs(Gt1(t)(0,0)) > precision) TRIQS_RUNTIME_ERROR<<" fourier_real_time error : t="<<t<<" ,G1="<<std::abs(Gt1(t)(0,0))<<"\n";
+  }
+  h5_write(file,"Gt1b",Gt1); // must be 0
+  
+  
+  
+  //Test on the tail: GF in time that is a decreasing exponential 
+  
+  double tmax=10.;
+  int Nt=501;
+  
+  auto Gt2 = make_gf<retime> (-tmax, tmax, Nt, make_shape(1,1));
+  a = 2*acos(-1.) / ( Gt2.mesh().delta() *sqrt( Gt2.mesh().size() ) );
+  for(auto const & t:Gt2.mesh()) Gt2(t) = 0.5 *I * ( lorentzian_inverse(-t,a)*theta(-t)-lorentzian_inverse(t,a)*theta(t) );
+  //for(auto const & t:Gt2.mesh()) Gt2(t) = 0.5_j * ( lorentzian_inverse(-t,a)*theta(-t)-lorentzian_inverse(t,a)*theta(t) );
+  Gt2.singularity()(1)=triqs::arrays::matrix<double>{{1.0}};
+  h5_write(file,"Gt2",Gt2);
+  
+  auto Gw2 = fourier(Gt2);
+  h5_write(file,"Gw2",Gw2);
+  
+  for(auto const & w:Gw2.mesh()){
+    Gw2(w)-= 0.5/(w+a*I)+0.5/(w-a*I);
+    //Gw2(w)-= 0.5/(w+a*1_j)+0.5/(w-a*1_j);
+    if ( std::abs(Gw2(w)(0,0)) > precision) TRIQS_RUNTIME_ERROR<<" fourier_real_time error : w="<<w<<" ,G2="<<std::abs(Gw2(w)(0,0))<<"\n";
+  }
+  h5_write(file,"Gw2b",Gw2);
+
+  
+  
+  //Test : GF in time is a simple trigonometric function, the result is a sum of Dirac functions 
+  
+  tmax=4*acos(-1.);
+  
+  auto Gt3 = make_gf<retime> (-tmax, tmax, Nt, make_shape(1,1));
+  for(auto const & t:Gt3.mesh()) Gt3(t) = 1.0 * std::cos(10*t) + 0.25*std::sin(4*t) + 0.5 * I*std::sin(8*t+0.3*acos(-1.)) ;
+  //for(auto const & t:Gt3.mesh()) Gt3(t) = 1.0 * std::cos(10*t) + 0.25*std::sin(4*t) + 0.5_j*std::sin(8*t+0.3*acos(-1.)) ;
+  h5_write(file,"Gt3",Gt3);
+  
+  auto Gw3 = fourier(Gt3);
+  h5_write(file,"Gw3",Gw3);
+  
+}
+
+
diff --git a/test/triqs/gf/test_fourier_real_time.ipynb b/test/triqs/gf/test_fourier_real_time.ipynb
new file mode 100644
index 00000000..0b9e7b5e
--- /dev/null
+++ b/test/triqs/gf/test_fourier_real_time.ipynb
@@ -0,0 +1,166 @@
+{
+ "metadata": {
+  "name": "fourier_real_time"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from pytriqs.archive import HDFArchive\n",
+      "from pytriqs.gf.local import GfReFreq, GfReTime\n",
+      "from pytriqs.plot.mpl_interface import oplot\n",
+      "\n",
+      "# Opens the file G.h5, in read mode\n",
+      "R = HDFArchive('../../../../build/test/triqs/gf/fourier_real_time.h5', 'r')\n",
+      "Gw1 = R['Gw1']\n",
+      "Gt1 = R['Gt1']\n",
+      "Gw1b = R['Gw1b']\n",
+      "Gt1b = R['Gt1b']\n",
+      "Gt2 = R['Gt2']\n",
+      "Gw2 = R['Gw2']\n",
+      "Gw2b = R['Gw2b']\n",
+      "Gt3 = R['Gt3']\n",
+      "Gw3 = R['Gw3']"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a0=0.632456\n",
+      "def lorentz(x,a=a0):\n",
+      "    return 2.0*a / (x*x + a*a)\n",
+      "def lorentz_inv(x,a=a0):\n",
+      "    return exp(-a*abs(x))\n",
+      "oplot(Gw1,'o',lorentz,'-')\n",
+      "show()\n",
+      "#verification that TF(TF^{-1})=Id\n",
+      "oplot(Gw1b,'-o')\n",
+      "show()\n",
+      "oplot(Gt1,'-o',lorentz_inv,'-',x_window=(-100,100))\n",
+      "show()\n",
+      "#verification that TF(lorentz)=lorentz_inv\n",
+      "oplot(Gt1b,'o')\n",
+      "show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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DAfjyyy/ZunUreXl5ZGdnM3/+fHJycujevbu95m+++Yb333+f/Px8nnvuOby8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\nRK5CYII=\n"
+      },
+      {
+       "output_type": "display_data",
+       "png": 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Lde+999a4wPrAGngAAAA4g9MuYk1JSdFdd90lNzc3jRs3TtHR0QoICNDx48eV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\nBiHAAwAAAAYhwAMAAAAGIcA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+      },
+      {
+       "output_type": "display_data",
+       "png": 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S7du31+TJk7Vt2zY5jqNnn31WzZs3V6NGjbR8+XJ98MEH2r17txzH0SuvvKIv\nv/xSvXr1Cr3e5ORkpaWlacOGDXriiSfCXs9tt92m77//Xu+9954SEhLCnuvQoYPmzZsXquObb77R\nvHnzQhfRvvjii2rRooXmzZunsWPHav369frzn/+sU089teZv2JF2WMMv9dwx/vIA4IgLWC7zyAOO\necS61zhjHqzrcmLmaP19Ydu2WbhwYejxgTPkmzZtMr169TKpqanmggsuMGPGjDEXXnhh6HmXyxU2\nc33gxY1/+9vfzKmnnmpSUlJMu3btQhftTZ8+3WRnZ5v09HTz1FNPmR9++MG4XK7QHPP69evNxRdf\nbBo1amRyc3PNxIkTQ8ccM2aMGTRoUOjxgfseaP8aS0tLzfXXX2+aNm1qsrKyzIMPPmiCwaAxxphJ\nkyaFvTZjjCkrKzP33nuvadmypUlLSzNt2rQx48ePN8ZUzHhnZWWFrd9/Vn7FihWmY8eOJj093Vx2\n2WVmzJgxxuPxmJSUlNBH3759K//GVPL1rOl8xhizdu1a43K5zMUXX1ztcd966y1z0kknmdTUVHPx\nxRebgoKCsK/pqFGjTNOmTU16enpoZv1ATz/9tGnWrJlJS0szN910U2hufNq0aSYzM9OUlpYaY4zZ\nsWOHOfnkk83UqVNNMBg0vXv3No0aNTKpqanm9NNPD/v3Zowx8+bNM2effbZJS0szWVlZZsqUKcaY\nigswr7rqKtOkSROTnp5uLrzwQvPFF18YY4z57rvvzNlnn21SU1NNo0aNTNeuXc0nn3wSOubSpUvN\nmWeeaVJSUkynTp3MU089Ffpa7r2wNSkpKex7M3Xq1ND+jz/+uGnZsqVJSUkxLVu2NE8//XTouWXL\nloVea6Sq+nlxuD9HrD0HOSYdDfdRBYB6IxBQ0OPVE38OyB75gP50d1Deh8fWvN8xgN8XACJV1c+L\nw/05wvgKAKCC4yjo9srjtRRweWXKGF8BgFghlAMAKjiOAi6v3G4p4PIqaHOhJ1CTtm3bhr2p0N6P\nV199ta5Lw1HGU9cFAADqCceR3+WT2y35XT4ZQjlQo1jfNg/HLjrlAIAKtq2AyyuPRwq4vTLcfQUA\nYoZQDgCo4DgKWBXjK0GXl045AMQQ4ysAgAq2HTa+ojjqlGdkZNT6nTABxKfq3lH3cBDKAQAVHEf+\nPeMrQY9XJo7e0bOqdxUEgFhhfAUAUMFx5N9vfEWMrwBAzBDKAQAVbFt+q2J8JeD2SXb8jK8AQF0j\nlAMAKhxNIN3lAAAgAElEQVQwvqI4Gl8BgLpGKAcAVHAcOXs65UG3L65mygGgrhHKAQAVbFt+7Zkp\nd3tlMb4CADFDKAcAVHAcOVbF+IrxeCU/nXIAiBVCOQCgwn7jKwGPj5lyAIghQjkAoIJty9kzvmLc\nXlkO4ysAECuEcgBABceRX/vGVyw65QAQM7yjJwBARUVztfThV5W5+TvddtnVOslpoF+CP+nfRXOV\nn9+lrssDgGMeoRwA4lxR0VwVFs5U15LWaqLV2rbtdW3VGhnNVmHhTEkimANAlDG+AgBxbty4D1VS\n8rC8+j85OkeS5MgrrxyVlDys8eM/quMKAeDYRygHgDhXXl7xR1OvjGz5JEm2fPKqYqa8rMxdZ7UB\nQLwglANAnEtI8EuSfHLkyCupolPuU8XdVxITA3VWGwDEC0I5AMS5O+7opdzc++RVrhx9Jmnf+Epu\n7r0qKOhZxxUCwLGPCz0BIM7tvYhz04hR2rCqXOlpA+UKNpN3W7n++tfeXOQJADFgGWNMXRcRLZZl\n6Rh+eQBwZN1/v575H6/6fTFKo0cZvTLVJQUCkos/qgJATQ43d/KTFgBQwXFkByvePMjjtRRweyXe\nQAgAYqLehvIZM2aodevWatWqlR577LGDnt+0aZN69+6tjh076vTTT9ekSZNiXyQAHEscR2XGVxHK\nPVLQ4yOUA0CM1MtQHggENHz4cM2YMUPLli3Tq6++qu+++y5szYQJE9SpUyctWrRIxcXF+v3vfy+/\n319HFQPAMcC293XKPVLQ7ZVsu66rAoC4UC9D+cKFC3XyyScrJydHXq9XAwcO1PTp08PWnHDCCdq2\nbZskadu2bWrcuLE8Hq5bBYBD5jgq3y+UB1yMrwBArNTLFLthwwZlZWWFHmdmZmrBggVha2655Rb1\n6NFDJ554orZv36433nij0mONGTMm9Hm3bt3UrVu3aJQMAEc/21ZZcN/4SsDto1MOAFUoLi5WcXHx\nETtevQzllmXVuOaRRx5Rx44dVVxcrJKSEvXs2VOLFy9Wampq2Lr9QzkAoBqOo7L9x1folANAlQ5s\n9o4dO/awjlcvx1eaN2+udevWhR6vW7dOmZmZYWvmz5+vq666SpKUm5urFi1aaPny5TGtEwCOKY6j\n8sB+4yvcfQUAYqZehvLOnTtrxYoVWr16tWzb1uuvv65+/fqFrWndurVmzZolSfrpp5+0fPlytWzZ\nsi7KBYBjwwHjK34X4ysAECv1cnzF4/FowoQJysvLUyAQ0NChQ9WmTRtNnDhRkjRs2DDde++9GjJk\niDp06KBgMKjHH39cjRo1quPKAeDoZRxH5cYrl4sLPQEg1uplKJekPn36qE+fPmHbhg0bFvq8SZMm\n+te//hXrsgDgmGVsRwGXT5a1X6ecUA4AMVEvx1cAALFnyu2Ke5NrTyi3uE85AMQKoRwAIKmiUx70\n7AvlAYvxFQCIFUI5AKCC7ch4fJIkr1dyGF8BgJghlAMAJEnGZnwFAOoKoRwAUMFxZDwHhHI65QAQ\nE4RyAEAFx1Fwz/iKxyM5FuMrABArhHIAQAXbDu+Ui/EVAIgVQjkAoMIB4ysO4ysAEDOEcgCAJMly\nHBnvfuMrYnwFAGKFUA4AqOCEj684jK8AQMwQygEAkiTL71TcoFwVodwW4ysAECuEcgCApIpQvv/4\nis34CgDEDKEcACBJshw7vFNuGF8BgFghlAMApEBAkuTyuiXtmSk3jK8AQKwQygEAkm0r6PHtbZTL\n65XK5aNTDgAxQigHAITuUe7xVDwMja/QKQeAmCCUAwAkx1HQTSgHgLpCKAcAhMZX9g/l5UHGVwAg\nVgjlAAA65QBQxwjlAIA9oTy8U14W5D7lABArhHIAgGTbChzQKS/nPuUAEDOEcgBApeMr5UHGVwAg\nVgjlAADJcRRgfAUA6gyhHABQMb7iOqBTHmB8BQBihVAOAKjolB8YyhlfAYCYIZQDACTHkd8VPr6y\nK8D4CgDECqEcAMD4CgDUMUI5AKCiU2555fVWPPR6pbIA4ysAECuEcgBApeMruxlfAYCYIZQDACrG\nV6x94ytut7Q74JVhfAUAYoJQDgAIja/sDeUulxSwGF8BgFghlAMAJMeRs9/4iiQFPYyvAECsEMoB\nAJJtyy/vAaHcK1PO+AoAxAKhHABQ0Sm3wkO5cTO+AgCxQigHAEi2Lcc6YHzF65PFhZ4AEBOEcgBA\nRaf8gPEVebySn045AMQCoRwAUGkoNx7GVwAgVgjlAADJtmUrfHzF8rglY6RAoO7qAoA4QSgHAFR0\nyk14p9zjtSpGWOiWA0DUEcoBAJLjyJZPXu++TV5vxcWehHIAiD5COQBgz/jKAZ1yz565cu7AAgBR\nRygHAFR0yg8cX/FwsScAxAqhHACwJ5T7KgnljK8AQCwQygEAFeMrlXTKg4yvAEBMEMoBAJLjqLyy\nUO5mfAUAYoFQDgDYE8oPHl8JMr4CADFBKAcAVIyvBKvolDO+AgBRRygHAFR0yqsK5XTKASDqPDUv\nAQAcy4qK5qr5l8u1cterGjHiE+XlnajPPtuoL7/0aFX5f1Q290udd845dV0mABzTCOUAEMeKiuaq\nsHCmJm3O1DbdrM8/N/ryy6ny+/8mSdqiuXrpL5+ptG175ed3qeNqAeDYxfgKAMSxceM+VEnJw/LK\nkSOvpA9DgVySHHn1838Gafz4j+quSACIA4RyAIhj5eUVfzD1ypEtnw78A6otn7xyVFbmroPqACB+\nEMoBII4lJPglST7Zezrl/rDnHXnlk63ExEAdVAcA8YNQDgBx7I47eik39779xld6yeO5NfS8I68y\nj3tZBQU9665IAIgDXOgJAHFs78WbDa/6HxnnOXW7IE1du7bX55/fryVL3Era9Z2GXHeJOnKRJwBE\nFaEcAOJcfn4XqVGKvDv+oLfeylLjxhXbhw+XTv5yrU5r27JuCwSAOMD4CgBAchztdrzyevdt8nol\nv8WbBwFALBDKAQCSbWuX33dQKHcsn2TbdVcXAMQJQjkAQHIc7aqkU+7QKQeAmCCUAwBkHEflxiv3\nfrcj93olvwjlABALhHIAiHfGyLJtyeOVZe3b7PVKNuMrABAThHIAiHeBgIzLJbcv/F07vV7JMXTK\nASAWCOUAEO8cR/KFX+Qp7emUy0coB4AYIJQDQLyzbRmPt4pQ7mV8BQBigFAOAPHOcaoM5YyvAEBs\nEMoBIN45joy38vGVcsZXACAmCOUAEO/2jK94POGbPR7JDjK+AgCxQCgHgHhXzfiKzfgKAMQEoRwA\n4p3jKOipfHylzDC+AgCxQCgHgHhn2wq6q+iUM74CADFBKAeAeOc4VYbycsZXACAmCOUAEO+qG18J\nMr4CALFAKAeAeGfbCrqq6JQHGF8BgFgglANAvHMcBaoYXykLMr4CALFAKAeAeGfbCrirGF8J+OiU\nA0AMEMoBIN45jgJVjK+UBeiUA0AsEMoBIN45jgIWoRwA6hKhHADinW3LX8X4ym7GVwAgJjx1XQAA\noI45jvxVdMp3+72Si045AEQboRwA4l1NodwilANAtNXb8ZUZM2aodevWatWqlR577LFK1xQXF6tT\np046/fTT1a1bt9gWCADHCtuW31X5+MouP+MrABAL9bJTHggENHz4cM2aNUvNmzfXr371K/Xr109t\n2rQJrdmyZYt++9vfaubMmcrMzNSmTZvqsGIAOIo5jvyqplMepFMOANFWLzvlCxcu1Mknn6ycnBx5\nvV4NHDhQ06dPD1szdepUXXnllcrMzJQkNWnSpC5KBYCjn+PIsarplHP3FQCIunrZKd+wYYOysrJC\njzMzM7VgwYKwNStWrJDjOOrevbu2b9+uwsJCDRo06KBjjRkzJvR5t27dGHMBgAPZtpwqZsp3OV5J\njK8AwIGKi4tVXFx8xI5XL0O5ZVk1rnEcR19//bVmz56tXbt26dxzz9U555yjVq1aha3bP5QDACrh\nOHKqGF+pCOV0ygHgQAc2e8eOHXtYx6uXobx58+Zat25d6PG6detCYyp7ZWVlqUmTJkpKSlJSUpK6\ndOmixYsXHxTKAQA1cBw5SjwolLvdkt+4ZVyWrECgYgMAICrq5Ux5586dtWLFCq1evVq2bev1119X\nv379wtZceuml+uSTTxQIBLRr1y4tWLBAp512Wh1VDABHMduutFNuWRXdcnm93IEFAKKsXnbKPR6P\nJkyYoLy8PAUCAQ0dOlRt2rTRxIkTJUnDhg1T69at1bt3b7Vv314ul0u33HILoRwADoXjyK4klEt7\nQrnLW3GxZ1JSzEsDgHhhGWNMXRcRLZZl6Rh+eQBwZAwfrje/ba2Nlw9XYWH4U+np0mZXY7lW/D+p\nceO6qQ8AjgKHmzvrZaccABBDti3bVN0pNxbjKwAQbYRyAIh3jlN9KJeXe5UDQJQRygEg3jmOyo1P\niVWGct5ACACirV7efQUAEEO2LTtYTafcw/gKAEQboRwA4p3jqLya8ZWgm/EVAIg2xlcAIN7ZtsqD\nvqpDueWjUw4AUUYoB4B45zgqr2Z8JWjolANAtDG+AgDxznFUVl0oZ3wFAKKOUA4A8c62VVbN+ErA\nzfgKAEQboRwA4p3jqDxQTafcRaccAKKNUA4A8c5xVFZNKA8wvgIAUceFngAQ72xbu6sbX3ExvgIA\n0UYoB4B45zgqU9Wdcj/jKwAQdYyvAEAcKyqaqx/X/1cr1z6r228fqaKiuaHteXkj9emnY7Ro6TIt\n+uLbOq4UAI5tdMoBIE4VFc1VYeFMfbw7Wdt1l77/NFOFhffpiy++1SuvbFBJycOSpP9qnea/skgb\nLpqr/PwudVw1AByb6JQDQJwaN+5DlZQ8LK8cOaqYXSkpeVgTJnwcCuSS5MirLT9frPHjP6qrUgHg\nmEcoB4A4VV5e8cdSrxzZ8oW2+/1JYets+eSVo7Iyd0zrA4B4Uuvxla+++koPP/ywRo0apRkzZmjx\n4sX64YcftHXrVhljlJ6erpYtW+rMM89Uz5491b59+2jUDQA4TAkJfkmST3aoUy5JHs/usHWOvPLJ\nVmJiIKb1AUA8sYwxJtLF69evV8eOHbVlyxalpaXpggsu0CmnnKKMjAw1btxYwWBQmzdv1ubNm7Vs\n2TLNnz9f2dnZ+v3vf6/BgwfLsqxovpaDWJalWrw8AIgre2fKl5U8oVRtl60E5ebeq+uvzwybKf+z\n/iR3o8912pQHmCkHgCocbu6MOJR/9913ateunfr27asxY8aoY8eOcrmqn37x+/1auHChnnnmGa1d\nu1ZTp05Vbm7uIRdbW4RyAKhe0f9+rPxLusmtUbqoZ1CFhT2Vn99FRUVzNX78R1q2zK27d87Rr/u0\n0qmvvFjX5QJAvRWTUD5//nw99NBD+v777zV8+HDdeeedtT7R8uXLVVBQoEceeUSdO3c+pGJri1AO\nADVwHJmkJLmDfjmO5D5gbPz++6WL5j+obueWSw89VDc1AsBR4HBzZ40Xevr9fs2aNUvTp09XeXm5\nTj311EM60amnnqr33ntP77333iHtDwCIAseRvF65XAcHckny+STb8OZBABBtNV7o6fF4NGrUKEnS\njz/+qJNOOumQT5aYmKgHHnjgkPcHABxhjiP5fPJVccmPz1dx9xVCOQBEV42d8kAgoMmTJ0uSjDFK\nS0s75JMZYzRu3LhD3h8AcITZtozHK5+v8qd9PqnceCXbjm1dABBnagzlbrdbqampGjFihCTJOcRu\nSWlpqa666iq1adPmkPYHAESB49QcyoOMrwBAtEX05kFXXHGFLr/8cnk8Hr3zzjsqLS2N+AQbN27U\n3Xffra5du+ruu+9Wz549D7lYAMARZtsyXl8NnXIfnXIAiLKI3zyoa9euSk5O1gsvvKBHH31UOTk5\nOv/889WuXTulp6crPT09dJ/yX375RcuWLdPcuXP1448/avjw4fr888/VoEGDaL4WAEBtOY6M2ytf\nFW/W6fNJu+mUA0DU1eodPXv06KFTTz1V9913n4qKivTRRx/p+eef1+rVq7V161ZZlqX09HS1aNFC\nF1xwgf7yl7/owgsvVEJCQrTqBwAcDsdR0ONVQhWd8oQEaQuhHACirlahvH///nrkkUf0yCOPaMCA\nARowYEC06gIAxIJtK+ipfnylLMj4CgBEW0Qz5XtdffXVatKkiR544AHelAcAjgWOo6C7hgs9A3TK\nASDaahXKXS6XZsyYoZNOOkk7d+6MVk0AgFhxHAVc1YfyMkI5AERdrcZXJCkhIUE33nhjNGoBAMSa\nbSvorn58ZXeA8RUAiLZadcpr46677lLLli2jdXgAwJFApxwA6oWohfKff/5Zq1evjtbhAQBHQgSh\nfLefUA4A0Ra1UA4AOArYtvyMrwBAnYt4pnzQoEGyLCviA8+fP79W6wEAdcBx5Leq75TvolMOAFEX\ncSj/5z//WeuDE8oBoJ5zHPld1XfKdzk+QjkARFnEoTwlJUWZmZl67rnnIrpH+aOPPqqPPvrosIoD\nAESZbdfYKd/t9zK+AgBRFnEo79Chg5YsWaKuXbtGtH7SpEmHWhMAIFYcR37VML7ieCUPnXIAiKaI\nL/Ts2LGjtm/frpKSkogPzrt+AkA95ziyrerHV3bYjK8AQLRF3Cnv2rWr5s2bp3Xr1ik3N7fG9Zdd\ndplatGhxWMUBAKLMtuVE0ilnfAUAosoyx3A727IsuvUAUJ0nntD8aT/pgx5P6sEHD37a75ca+7Zr\na4MTpB07Yl8fABwlDjd3cp9yAIhnjiNHPiUkVP60xyPZ8skwvgIAUXVIoXzy5MlasmRJtWv+7//+\nT1OmTDmkogAAMWLbsqsZX5Eky+eVZdsSf3kEgKg5pFA+ZMgQTZs2rdo106dP15AhQw6pKABAjDiO\nbFN9KPcmuGRcLikQiF1dABBnoja+EuCHNwDUf7Yt21R99xWp4mJP+Xxc7AkAURS1UL5ixQplZGRE\n6/AAgCPBcVReQ6fc55OMx8ttEQEgiiK+JeKQIUPCriqdNm2aVq9efdC6QCCgNWvWaN68ecrPzz9i\nhQIAosBxZAcJ5QBQ1yIO5ZMnTw57vGjRIi1atKjK9eecc46eeeaZQ68MABB9tq0y41NaTaHcy/gK\nAERTxKF81apVoU55y5YtVVhYqBEjRhx0P0a3262MjAylpKQc8WIBAEeY46g8gk550E2nHACiKeJQ\nnpOTE/p81KhR6t69u0466aRo1AQAiJUIQ7khlANAVEUcyvc3ZsyYI1wGAKBO2LbKAjXffSXgYXwF\nAKKpVndfefbZZ/Xoo4+G3e7wr3/9q1q0aKGWLVuGfQwePPhI1woAONIcR2WMrwBAnYs4lH/99dca\nPny4tm/fLrfbHdpeWlqqNWvWaPXq1WEfL7/8crUXggIA6gHHUZk/glDuIpQDQDRFHMpfffVV+Xw+\njRgxotLnHceRbduybVv//e9/5fV69fLLLx+xQgEAUWDb2h3J+Iqb8RUAiKaIZ8rnzZunc889V02b\nNq30+f27502aNNGvf/1rffLJJ4dfIQAgehxHuyPolAfolANAVEXcKV+xYoU6dOgQ8YFzcnJUUlJy\nSEUBAGLEcbQ74FNCQtVLEhIkv9tHKAeAKIo4lG/fvl2pqakHbR88eLDmzJlz0Pb09HRt27bt8KoD\nAERNUdFcLf/2B63/aZJuuWWkiormHvR8Xt5IFReP0YrVq7Xwk6/rqFIAOPZFPL6SkpKizZs3H7Q9\nJycn7B7me23evFnJycmHVRwAIDqKiuaqsHCm3tp2nHbodi2a10kbN94nScrP7xJ6vqTkYUlSqb5W\n0cQv9POv5io/v0tdlg4Ax6SIO+U5OTlauHBhxAf+4osvKg3rAIC6N27chyopeVheObJVMVBeUvKw\nxo//KOz5vWz59MuPV4eeBwAcWRGH8m7duumrr77SZ599VuPazz77TF999ZW6d+9+WMUBAKKjvLzi\nD6U+2XLkDW0vK3OHPb+XI698skPPAwCOrIhD+a233irLsnTNNdfou+++q3Ld999/r2uvvVYul0u3\n3nrrESkSAHBkJST4JUleOWGhPDExEPb8Xo688soJPQ8AOLIinik/5ZRTNGrUKI0dO1ZnnHGG+vfv\nrx49eqh58+aSpA0bNmj27Nl66623ZNu2Ro8erVNOOSVqhQMADt0dd/RSScl98pbsG1/Jzb1XBQW9\nw57fO8Jiy6cTm76uAQV/qrOaAeBYZhljTG12GDt2rB566CEFApV3Szwej0aOHKlRo0YdkQIPh2VZ\nquXLA4C4UVQ0V+df3kenOrerU16iCgp6hl3EWVQ0V+PHf6T/9//cemDTv9Tx+q46/dmn67BiAKi/\nDjd31jqUS9KqVav00ksv6dNPP9WPP/4oSTr++ON1wQUXaPDgwWrZsuUhF3QkEcoBoHomPV3Z/h+0\nbkdGlWuefFI6+5936MLBuVJhYQyrA4Cjx+HmzojHV/bXsmVLPfjgg4d8UgBAPWHbcjWo5u08JSUm\nSuXySbYdo6IAIP5EfKEnAOAY5DhyJXirXZKQINlBL+/oCQBRRCgHgHhljCy/X+7E6kN5YqJUbgjl\nABBNhHIAiFeOI+PxKCHRqnZZQoJUFmR8BQCiiVAOAPHKcWTcXiUkVL8sIUEqZ3wFAKKKUA4A8cpx\nFPR4lZhY/bLERKksQCgHgGgilANAvLJtBT2+iDrlZQHGVwAgmgjlABCvHEdBN51yAKgPCOUAEK/2\nhPJIOuW7CeUAEFWEcgCIV7atgDuy8ZXdfsZXACCaCOUAEK8cRwFXZOMru/x0ygEgmgjlABCvHEd+\nV2Sd8l1+H6EcAKKIUA4A8cq2I+6U73a8jK8AQBQRygEgXjmO/FZkF3rudBhfAYBoIpQDQLxyHDlW\nZOMrOx3GVwAgmgjlABCvbFt+1Ty+4vFIjvHKlDO+AgDRUm9D+YwZM9S6dWu1atVKjz32WJXrvvji\nC3k8Hr3zzjsxrA4AjgGOIyeC8RXLkuTzKmjTKQeAaKmXoTwQCGj48OGaMWOGli1bpldffVXfffdd\npevuvvtu9e7dW8aYOqgUAI5ijiNHvho75ZJk+XwyhHIAiBpPXRdQmYULF+rkk09WTk6OJGngwIGa\nPn262rRpE7Zu/Pjx6t+/v7744osqjzVmzJjQ5926dVO3bt2iUDEAHIVsW7Zq7pRLkuVjfAUA9ldc\nXKzi4uIjdrx6Gco3bNigrKys0OPMzEwtWLDgoDXTp0/XnDlz9MUXX8iyrEqPtX8oBwDsx3HkmMhC\nuSvBK9EpB4CQA5u9Y8eOPazj1cvxlaoC9v5GjBihRx99VJZlyRjD+AoA1JZtq9xEOL6S4JPhPuUA\nEDX1slPevHlzrVu3LvR43bp1yszMDFvz1VdfaeDAgZKkTZs26YMPPpDX61W/fv1iWisAHLUcR3Zt\nOuU76JQDQLTUy1DeuXNnrVixQqtXr9aJJ56o119/Xa+++mrYmlWrVoU+HzJkiC655BICOQDUhuOo\n3NR8S0RJ8iR5ZfkJ5QAQLfUylHs8Hk2YMEF5eXkKBAIaOnSo2rRpo4kTJ0qShg0bVscVAsAxwLZV\nHvQpJZILPRN8shzGVwAgWuplKJekPn36qE+fPmHbqgrjL730UixKAoBji+OoPBjZ+Io70cs7egJA\nFNXLCz0BADHgOCoLRD6+4goQygEgWgjlABCvbFtlQV9EnXJPklcuvyNxpysAiApCOQDEq1p0yhOS\nXAq63JLfH/26ACAOEcoBIM4UFc1VXt5Ivfbyx9q0bY7mzZtb49rZs8fINpY+eO/fMawUAOJHvb3Q\nEwBw5BUVzVVh4UyVlDysvirUbrXQ2LEz1bSplJ/fpcq1klSuZ3TvH2YqmJh40FoAwOGhUw4AcWTc\nuA9DIdsrR468Wr36YY0f/1G1ayXJkVfrV/+p0rUAgMNDKAeAOFJevu8PpHtDuSSVlbmrXStVhHKv\nnErXAgAOD6EcAOJIQsK+CzV9smXLJ0lKTAxUu1aSbPnkk13pWgDA4SGUA0AcueOOXsrNvU/Svk55\nbu69KijoWe1aqaJTnpv1aKVrAQCHhws9ASCO7L1Ac/z4+3X8l4sVKC3XX/9aWOmFm/uvLSlxK7h6\nq0bdc566cpEnABxxljHH7jtBWJalY/jlAcBh2dXrUhV8PUQvbrqsxrV//7uU98cOypozWerYMQbV\nAcDR5XBzJ+MrABCnguWOXAneiNYmJUmO5ZUcJ8pVAUB8IpQDQJwy5Y5cCb6I1iYlVVzoSSgHgOgg\nlANAnAqW23InRt4pt41Xsu0oVwUA8YlQDgDxynZqH8rplANAVBDKASBOGduWKzHy8ZVy46NTDgBR\nQigHgHjlOPIkRd4pLw/SKQeAaCGUA0C8IpQDQL1BKAeAOGU5tjwNIh9fKQsyvgIA0UIoB4A4Zfkd\neRtE3ikvC9ApB4BoIZQDQJwilANA/UEoB4A45fLb8iZHNr6SmCjtDvhkyhlfAYBoIJQDQJxyBRz5\nkiPrlLtcUtDtlbObTjkARAOhHADilDsYeSiXJOPxyr+LUA4A0UAoB4A45Q7Y8qVENr4iScbjk7OL\n8RUAiAZCOQDEI2PkNgElpngi38frVYDxFQCICkI5AMQjx5Hf8iipgRX5Pj6v/IRyAIgKQjkAxCPb\nlt/lU1JSLfbx+RTYzfgKAEQDoRwA4pHjyJG3VqHc8nkVKKNTDgDRQCgHgHjkOHKs2nXKLZ9PgXJC\nOQBEA6EcAOKRbde6U+5K8CpYxvgKAEQDoRwA4tEhjK+4ErwK0ikHgKgglANAPHIc2aZ24yuuRJ+M\nTSgHgGgglANAHCkqmqu8vJEact1ftDtQqnnz5ka8z1f/N00rl32toqKa9wEA1E4t3jUCAHA0Kyqa\nq8LCmSopeVgd9Y0czdf9989Uw4ZSfn6XGve5TO+qTJP1h8KZkqreBwBQe3TKASBOjBv3oUpKHpYk\n+WTLlk+rVj2s8eM/imgfWz75ZKukpPp9AAC1RygHgDhRXr7vj6NeVVzoKUllZe6I9nHklVdOjfsA\nAAbOIckAAB2NSURBVGqPUA4AcSIhwR/6fP9QnpgYiGif/UN5dfsAAGqPUA4AceKOO3opN/c+SfvG\nV3Jz71VBQc+I9tk7vlLTPgCA2rOMMaaui4gWy7J0DL88AKi1oqK5Gj/+I7VZVaK8ki8UeO/FGi/Y\n3LtPw+U/6p71Rf+/vXuPj7q+9zz+mtxIuARkVKBApQIe0AMW10t9VKkagWosp/ZsvbTdWqstpStQ\na6tWxRO1eDtbz5aorWuxtdrTWh+7qGcHA0jFsVZgi6hVPArxxi1cfiEQIPfM/pFOTLhokplkkpnX\n8/GYhyT5/T6/78TfY+ad73wvbHnqD07ylKSDJJo7DeWSlIHK/8diPvzpo5xb9VSHz1n5b+v4dMmV\nHL/n1W5smST1TYnmToevSFIGqquuJ5ab16lz+g3KI6uxvptaJEmZzVAuSRmofn8D5OZ26pz8Qblk\nNbmjpyR1B0O5JGWghgMNhDobygtzyW42lEtSdzCUS1IGajhQD/06N3yloDCPnGaHr0hSd8j55EMk\nSemm8UADobzO9ZT3H5xLlj3lktQtDOWSlIEaD9ST069zobxgcB7NMXvKJak7OHxFkjJQVvUemgcO\n7tQ5/Y8dyAD209zgbp6SlGyGcknKQDl7AxqHhDt1Tna/HKoZRM22qm5qlSRlLkO5JGWgfvsCYkd1\nLpQD7M4KU7sl6IYWSVJmM5RLUgbK3x9AuPOhfE9OmLqthnJJSjZDuSRloP41AaGjOx/Kq3PDNFQY\nyiUp2QzlkpSBBtYGZB/b+VC+r1+Yxu2GcklKNkO5JGWggQ2V5A7vfCg/UBCmeVdlN7RIkjKboVyS\nMk19PXlNNeQfW9jpU2v6h4kF9pRLUrIZyiUp01RWsid7KAMGhjp9at3AMKFKQ7kkJZuhXJIyTRBQ\nGQozYEDnT20oDJNdZSiXpGQzlEtSpgkCgljXQnnj4DC5ewzlkpRshnJJyhCRSJQZM27hlu8/wI6m\n7USj0U6f++zqJ6n+4E0ikY6fK0n6ZDmpboAkqftFIlHmzVtKefkCRvMrAgZw54+XkpcHxcVTO3zu\np/mAQSxh3rylwCefK0nqGHvKJSkDLFy4jPLyBQCECQgIU16+gNLS5Z06NyBMmKDD50qSOsZQLkkZ\noK7uow9G46EcoLY2u1Pn7mcA2TSRT02HzpUkdYyhXJIyQL9+ja3/bhvK8/ObOnUuhFp7yztyriSp\nYwzlkpQB5s6dztixNwMfhfKxY29izpxpnToXWoawfHbUv3ToXElSxzjRU5IyQHxCZmnpfD7953U0\nZBXy859f3aGJmm3PrajIZs+b1dwyexKfc5KnJCVNKBaLxVLdiO4SCoVI46cnSV2yd/SJ3Dzuj5Q+\n/4+dPveVV6DyvH/m/Icvg69+tRtaJ0l9U6K50+ErkpRhcvcGxIaGu3TuoEGwszkMgRsISVIyGcol\nKZPEYuTtqyR0dNdCeWEh7Gg0lEtSshnKJSmT7N1LY04+/Yfkden0QYNgW4OhXJKSzVAuSZkkCDhQ\nEKawsGunFxTAzqYwzTsN5ZKUTIZyScokQUB1XphBg7p2eigEB/qHadxhKJekZDKUS1ImCQL25HQ9\nlAPUDbCnXJKSzVAuSZkkCKjK7vrwFYCGwjChSkO5JCWToVySMknQsptnIj3ljYPDZO02lEtSMhnK\nJSmTVFayqzmxnnKOOorsfXuguTlpzZKkTGcol6RMEgRsb0qsp3zA4BwaCwZBVVXy2iVJGc5QLkmZ\nJAioqE+sp7ywEGr7u1a5JCWToVySMkkQsKUusZ7yQYNgf4GhXJKSyVAuSRkgEokyY8YtvP2Xv7Hp\nwB+JRqNdrvHUUyW8vStgTdkL3dBSScpMOalugCSpe0UiUebNW0p5+QLy+B0Bt/DDHy4iOxuKi6d2\nugbAJsp57RcvsfO0aIdrSJKOrNf2lJeVlTFhwgTGjx/PPffcc8jPf/e733HyySczefJkPv/5z/P6\n66+noJWS1PstXLisNUyHaVkSsbx8AaWly7tUAyAgTNOOcztVQ5J0ZL2yp7ypqYlrrrmG5557jpEj\nR3Laaacxc+ZMJk6c2HrM8ccfTzQaZfDgwZSVlfHd736XVatWpbDVktQ71dW1vNTnUk8BNeylZZZn\nbW12p2vEBYQJE1Bb2yvfRiSpz+mVPeVr1qxh3LhxjBkzhtzcXC677DKefvrpdseceeaZDB48GIAz\nzjiDzZs3p6KpktTr9evXCMBQKqlkKBACID+/qdM14uKhvDM1JElH1iu7OLZs2cLo0aNbvx41ahSr\nV68+4vGLFi3iwgsvPOzPSkpKWv99zjnncM455ySrmZLUJ8ydO53y8pvpV/61v4dyGDv2JubM+WKn\na8SHsASEOW7ASubMeahb2ixJvd3KlStZuXJl0ur1ylAeCoU6fOzzzz/PI488wksvvXTYn7cN5ZKU\nieITMZ+/7R4OvLGfYYXz+fnPv9ipCZrxY0tL51NZmc2Bv23g9HH9OdpJnpIy1MGdvbfddltC9Xpl\nKB85ciSbNm1q/XrTpk2MGjXqkONef/11vvOd71BWVsZRRx3Vk02UpD6luHgqxfUB75Xs4Z8+dwfF\nxV2sUTyVrVvhW5Nf4Wi+nfyGSlKG6pVjyk899VQ2bNjA+++/T319PU888QQzZ85sd8yHH37IV77y\nFR5//HHGjRuXopZKUh8SBOzNDTNkSGJlhgyB96vdPEiSkqlX9pTn5ORw//33M2PGDJqamrjqqquY\nOHEiDz3UMnZx1qxZ3H777ezevZvZs2cDkJuby5o1a1LZbEnq3YKAqqwwiX6wWFAAu2JhYkFAxwcb\nSpI+TigWi8VS3YjuEgqFSOOnJ0mdc/31/J8XhrLjyhv53vcSKzXs2BgVe/IJVVW1pHRJynCJ5s5e\nOXxFktQNgoCdTYkPXwEYclSIxsEOYZGkZDGUS1KmCAIqGhIfvgJw1FFQP8hQLknJYiiXpEwRBGyp\nS1JP+RCoHWAol6RkMZRLUqYIAjYfSF4o359vKJekZOmVq69IkrpBEPBBY/KGr1TnGcolKVnsKZek\nNBeJRJkx/WYad+5iQ+VC/vKXaGK1ZtzCkiUlvLj+Td7+y9oktlSSMpc95ZKUxiKRKPPmLWVn+fXU\nsJAG7uJHP7qZ3NyWHTq7Uqu8fAEAGxlI3jNPsjES7XQtSVJ79pRLUhpbuHAZ5eULGEolAWEAyssX\nUFq6vMu14gLCZO2Z2KVakqT2DOWSlMbq6lo+EA0TtIZygNra7C7XigsIEyboUi1JUnuGcklKY/36\nNQKHhvL8/KYu14qLh/Ku1JIktWcol6Q0NnfudMaOvbldKB879ibmzJnW5VpxAWGG577dpVqSpPZC\nsVgslupGdJdQKEQaPz1J6pBIJMoHP7qTwopKbhwwg4cemtbliZmRSJTS0uXs2ZPNh2v38l7BI+Tt\nqUpyiyWp70k0dxrKJSkTlJQQjcZ49ozbuOuuxMvV1MDQwkYOxPIJ1ddDlh+8SspsieZOX0UlKRME\nAUEszNFHJ6dcQQFk98uBQYOgyp5ySUqUoVySMkEQUNGQvFAOcPTR0DjYXT0lKRkM5ZKUCYKArXXJ\nD+V1Aw3lkpQMhnJJygRBwIcHkh/KDxQYyiUpGQzlkpQJgoD39yY/lO/LM5RLUjIYyiUpEwQB5VXJ\nD+VVOYZySUoGQ7kkpbFIJErxtJ/QuG8/W/bdy4svRpNSc8aMW3j66RJWvr6ODavXJaGlkpTZclLd\nAElS94hEosybt5R95fOoZBGwgB/84GZCIRLaPGjevKWUly8AoJxjWPPsI7wTiXa5piTJnnJJSlsL\nFy6jvHwBYQICwgCUly+gtHR5wjXjAsLk7h2bUE1JkqFcktJWXV3Lh6FtQzlAbW12wjXjAsKECRKq\nKUkylEtS2urXrxE4NJTn5zclXDMuHsoTqSlJMpRLUtqaO3c6Y8fe3C6Ujx17E3PmTEu4ZlxAmGHZ\n7yZUU5IEoVgsFkt1I7pLKBQijZ+eJH2iSCRKxbW30VBRy7+NOI/77puW8ITMSCRKaelyamuzWftC\nDbvzfk5OXW2SWixJfVOiudNQLknp7vrr+cPyodRfeyPf/GZyS39mTIx3t+UTqqqCgoLkFpekPiTR\n3OnwFUlKd0HA1towI0Ykv/SIT4VoGOQGQpKUKEO5JKW7yko+2B9m+PDklx4+HGoGhKGyMvnFJSmD\nGMolKd0FAe9WdVNP+QjYl2dPuSQlylAuSWkqEokyY8YtvLf2P3mv+ne8/HI06bWXLCnhb1srWLvs\nxaTVlqRMlPPJh0iS+ppIJMq8eUspL19AAb8i4DauvfZ+srJIyuor8doAH7KVP//qZSrOiiZcW5Iy\nlT3lkpSGFi5c9vfQHGMolQSEKS9fQGnp8iTWbhEQpnnX2UmpLUmZylAuSWmorq7lg9BBVFNHPxrI\nA6C2NjtptePiu3omo7YkZSpDuSSloX79GgHa7eYJkJ/flLTacfFQnozakpSpDOWSlIbmzp3O2LE3\ntwvlY8fexJw505JWOy4gzOiCl5JSW5IylRM9JSkNxSdc/uVf7qP69SomT5zPnXd+MSkTMeM1Skvn\nU1ubzb6X3+ekEf0Y7iRPSeqyUCyN96FPdLtTSerz/v3fWT7vPyhY/HvOOqt7LvGtz/0nD2z5JwZs\nert7LiBJfUCiudPhK5KUzoKALTVhRo/uvksMGhMmu8rNgyQpEYZySUpD8c19fv2z/80H+1/n1VeT\nt3HQwddY8nIpOfsqifzHyqRfQ5IyhWPKJSnNtN3c5yLmEDCe665bSk5O4hsHHe4aANWUcuvcZyAr\nyw2EJKkL7CmXpDTTdnOf+Oorydo46HDXgJYVWPa+P9sNhCSpiwzlkpRm2m7u03ZJxGRu7uMGQpKU\nXA5fkaQ003Zzn7ahPJmb+xxpA6EmNxCSpC6xp1yS0kzbzX3ioTxZGwcd7hrQEspPGPpLNxCSpC6y\np1yS0kzbzX3CS7cy5pRfcN3tX0rqBMyDNxDav+odLjjjNKY7yVOSusSecklKM5FIlIULl9F4AApo\n5Mp5F3XLiijFxVOZM2ca/fo1Up3bn3fXvEokkvylFyUpE9hTLklppO1ShcOoYDcPcdvtyxgaDiU9\nmLe91jge4B/3vcG8eUuB5C29KEmZwp5ySUojbZcqHMdGPuC4pC+HeLhrfcBxjGNjt11LktKdoVyS\n0kjbpQrP4088z7lAcpdDPNy1XuRsPscq+lHrsoiS1AWGcklKI22XKjyf51hBEZDc5RAPd629DOZN\nTuJMXu6Wa0lSujOUS1IaiS9V2J/9nMIr/Jmzkr4c4sHXiltBERcX3uKyiJLUBU70lKQ0U1i4nc8O\nLGbtvoEcN/F6/vVfL+221VcAbr31O7z3XjUv7K3k7rpytiX9SpKU/gzlkpQmPloN5Vdczo9ZwXnU\n1dV1+3X37DmW3bsfJkot45uO4dtzngFcgUWSOsPhK5KUJtquhlLEClZQxLvvdu9qKG2vWUc+qzmD\n0e99wRVYJKmTDOWSlCbiq6GE2cVYylnD6UD3rLxy8DXjnuN8iljhCiyS1EmGcklKE/HVUM7leV7k\nbBrJBbpn5ZWDrxm3giLO5zlXYJGkTjKUS1KaOPPMT1FQ8L3WoStAt628Etd+BZYor7CYT7GBpi2b\niUSi3XZdSUo3TvSUpDQQiUR5/PEt1NR8jSK+zINcSkHBpXzjG1/o1gmXbVdgWb8+h9raX/ACb3HM\nG9OYN29pu2MkSUdmT7kkpYH4hMvRjGEwObzBA9TUPMGqVd2/QGFx8VSOPnoYtbW/AFqGsBSxgvLy\n7p1kKknpxFAuSWkgPuGyiBX8ifOI/f3lvacmXLad8BkP5RBzwqckdZDDVyQpDezduxOg3Xhy6N5J\nnm19NOEzyn+ylDwCjucaqqvre+T6ktTX2VMuSX1cJBJl27Za4CaKWMFznA/A8OHX9tiW93PnTmf4\n8KuApcACnuNiijiZrVsHOeFTkjrAUC5JfdzChcuoqFjEifwDNdTyPo8C8/nUp/b12CTL4uKpjBiR\nD7RsJBRfGrGi4j7HlUtSBxjKJamP+2g8+R5W8F+BEuAOBg0a2aPtKCw8pvXfKyjiXJ4nRLPjyiWp\nAxxTLkl9XNvx5L/n8tbv9/QGPm3HlW9hGZXEmMx3qa4O9Wg7JKkvsqdckvqw+HjybH7CF3iBP3Ee\n0LPjyePajyv/KSu4hCImOq5ckjrAUC5Jfdj8+U9QUbGI/8JxfEg/dvIgPT2ePK79uPIoK9hBEb+k\noqI/t976WI+2RZL6GkO5JPVRkUiUt97aB0ARlazga6RqPHlcy7jyKLCU5/lfnMV2crmV9etz7C2X\npI9hKJekPmrhwmXU1o4G4Hyea10KEXp+PHlcy7jyZcACdjOUdziBM1hNbe0vXIVFkj6GoVyS+qht\n2/YD08nnek7j/xGlZbhKfv73enw8edzcudPJz9/096+irCCPIuYDt7B5846UtEmS+gJDuST1QSUl\nD/Lmm+8DU/k8w3mdIezjZ8B8TjyxqcfHk8cVF09l4sSBxIewrOCfKeJdIIe33tpBScmDKWmXJPV2\nhnJJ6mMikSj33vsCzc3XAjdTxE5WcCVQQkHBDm6//b+ltH133HEpBQUPADP4M9v5LLsZwI9obl7M\nvfe+7thySTqMUCwWi6W6Ed0lFAqRxk9PUoY65ZT/zrp1xwAl5LOctXyN2RQTZTQnnbSDN954KNVN\nZPLka/nb3wYAP+VPnMKvGcVjnAI0csop21m79uFUN1GSkirR3GlPuST1ISUlD/Laa7uBRnJo4AlK\neZVpvMgjwB2MGnVsqpsIwIgRA2jZny7K9ZzMz4gylXIgh3XrAoexSNJBDOWS1EeUlDzIHXcsp7n5\neEKczyOcQjZNXMGjxMiioGBWyiZ4Hmzu3OkUFLwFLOOvXMllFPMk/5cpbCIWm8wddyw3mEtSG4Zy\nSeoDPgrkJwPT+J/8mDGE+CqTaGQBodBXuP76k1M2wfNgxcVTuf76L5CV9S6wjD8xi1kUEeEpTmA7\nzc0nG8wlqQ3HlEtSLxWJRJk//7e888429u/PBT4LNHIruXyZxzmXL7OHAUATp5xS0SvHaX80/r0R\nmM63uJcS/spZnMNmABo5/vj+LFx4da/5g0KSuiLR3Gkol6ReJB7E169/l7q6IcBEIETL+OxG5lDN\nNTzO2axnB8MAKCiYxZNPfr1XhtpIJMpXv/oANTUTiQfza/kx36Wcs3mEXTwDbANC5OcPZOLEMHfc\ncWmvfC6S9HEM5R/DUK7eauXKlZxzzjmpboZSpG0PeE1NHc3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+      },
+      {
+       "output_type": "display_data",
+       "png": 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LRVfLNUSb9H119d0QbdI3fZvcJn3Td2P23UJSO0mDLh46dEYpb82WJAK9mzVY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\nhiLMAwAAAIb6f85o483yTXmJAAAAAElFTkSuQmCC\n"
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a0=7.03184\n",
+      "def expo(x,a=a0):\n",
+      "    if(x>0):return -0.5*1j*exp(-a*x)\n",
+      "    else:return 0.5*1j*exp(a*x)\n",
+      "def expo_inv(x,a=a0):\n",
+      "    return 0.5/(x+a*1j)+0.5/(x-a*1j)\n",
+      "\n",
+      "oplot(Gt2,'o',expo,'-')\n",
+      "show()\n",
+      "oplot(Gw2,'o',expo_inv,'-',x_window=(-50,50))\n",
+      "show()\n",
+      "oplot(Gw2b,'o',x_window=(-50,50))\n",
+      "show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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uXbvik08+wZAhQ/Dyyy/r3MvWrVtx8eJF5OfnY9GiRRg9ejQkScLo0aMRFRWF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\nqFGjtHbtWs2dO1eSVFFRoezs7Dbfh7noAAAAiDUxF9T79eunN954Q2V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+      },
+      {
+       "output_type": "display_data",
+       "png": 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pW7cuBw8eBB78eqampgIYxFA6nc5gVd1ly5axa9cu2rdvT8+ePenatav+uYCA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\nRK5CYII=\n"
+      },
+      {
+       "output_type": "display_data",
+       "png": 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RqAMAAAAWRKAOAAAAWBCBOgAAAGBBBOoAAACABRGoAwAAABZEoA4AAABYEIE6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\nAAAASUVORK5CYII=\n"
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "oplot(Gt3,'o')\n",
+      "show()\n",
+      "oplot(Gw3,'o',x_window=(-15,15))\n",
+      "show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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7Yg8k0gkhMCugROlAjCywZSGeRbl/QYyICzeLiiDxMFtgBCccUBbtzWy/aeb7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\n"
+      },
+      {
+       "output_type": "display_data",
+       "png": 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A5+XlaejQoR5tgwcPliRdf/31noMGBSkhIUEHDhxohBIB/xNmq/7mUuHB4YYr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\n"
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/test/triqs/gf/test_gf_triqs.cpp b/test/triqs/gf/test_gf_triqs.cpp
index 562da39f..58a20c3f 100644
--- a/test/triqs/gf/test_gf_triqs.cpp
+++ b/test/triqs/gf/test_gf_triqs.cpp
@@ -34,8 +34,7 @@ void print_to_file(std::string const s, gf<imtime> const & gt){
 
 
 void test_0(){
-
- triqs::gf::freq_infty inf;
+ 
  int Ntau = 10001;
  double beta =1;
 
diff --git a/triqs/gf/gf.hpp b/triqs/gf/gf.hpp
index c87c8e77..bdd7a09e 100644
--- a/triqs/gf/gf.hpp
+++ b/triqs/gf/gf.hpp
@@ -385,20 +385,33 @@ namespace triqs { namespace gf {
   }
 
  // tool for lazy transformation
- template<typename Tag, typename D> struct gf_keeper{ gf_view<D> g; gf_keeper (gf_view<D> const & g_) : g(g_) {} };
+ template<typename Tag, typename D, typename Target = matrix_valued> struct gf_keeper{ gf_view<D,Target> g; gf_keeper (gf_view<D,Target> const & g_) : g(g_) {} };
 
  // ---------------------------------- slicing ------------------------------------
-
+ 
+ //slice
  template<typename Variable, typename Target, typename Opt, bool V, typename... Args>
-  gf_view<Variable,Target,Opt> slice_target (gf_impl<Variable,Target,Opt,V> const & g, Args... args) {
-   return gf_view<Variable,Target,Opt>(g.mesh(), g.data()(tqa::range(), args... ), slice_target (g.singularity(),args...), g.symmetry());
+  gf_view<Variable,matrix_valued,Opt> slice_target (gf_impl<Variable,Target,Opt,V> const & g, Args... args) {
+   static_assert(std::is_same<Target,matrix_valued>::value, "slice_target only for matrix_valued GF's");
+   using arrays::range;
+   //auto sg=slice_target (g.singularity(),range(args,args+1)...);
+   return gf_view<Variable,matrix_valued,Opt>(g.mesh(), g.data()(tqa::range(), args... ),  slice_target (g.singularity(),args...) , g.symmetry());
   }
 
  template<typename Variable, typename Target, typename Opt, bool V, typename... Args>
-  gf_view<Variable,Target,Opt> slice_mesh (gf_impl<Variable,Target,Opt,V> const & g, Args... args) {
-   return gf_view<Variable,Target,Opt>(g.mesh().slice(args...), g.data()(g.mesh().slice_get_range(args...),arrays::ellipsis()), g.singularity(), g.symmetry());
+  gf_view<Variable,scalar_valued,Opt> slice_target_to_scalar (gf_impl<Variable,Target,Opt,V> const & g, Args... args) {
+   static_assert(std::is_same<Target,matrix_valued>::value, "slice_target only for matrix_valued GF's");
+   using arrays::range;
+   auto sg=slice_target (g.singularity(),range(args,args+1)...);
+   return gf_view<Variable,scalar_valued,Opt>(g.mesh(), g.data()(tqa::range(), args... ), sg , g.symmetry());
   }
 
+/*
+  template<typename Variable1,typename Variable2, typename Target, typename Opt, bool V, typename... Args>
+  gf_view<Variable2,Target,Opt> slice_mesh (gf_impl<Variable1,Target,Opt,V> const & g, Args... args) {
+   return gf_view<Variable2,Target,Opt>(g.mesh().slice(args...), g.data()(g.mesh().slice_get_range(args...),arrays::ellipsis()), g.singularity(), g.symmetry());
+  }*/
+
 }}
 
 #include "./gf_expr.hpp"
diff --git a/triqs/gf/imfreq.hpp b/triqs/gf/imfreq.hpp
index 79775e35..01ef422f 100644
--- a/triqs/gf/imfreq.hpp
+++ b/triqs/gf/imfreq.hpp
@@ -44,45 +44,71 @@ namespace triqs { namespace gf {
 
   //singularity
   template<typename Opt> struct singularity<imfreq,matrix_valued,Opt>  { typedef local::tail type;};
+  template<typename Opt> struct singularity<imfreq,scalar_valued,Opt>  { typedef local::tail type;};
   
   //h5 name
   template<typename Opt> struct h5_name<imfreq,matrix_valued,Opt>      { static std::string invoke(){ return "GfImFreq";}};
+  template<typename Opt> struct h5_name<imfreq,scalar_valued,Opt>      { static std::string invoke(){ return "GfImFreq_s";}};
 
   /// ---------------------------  evaluator ---------------------------------
-
-  template<typename Opt>
-   struct evaluator<imfreq,matrix_valued,Opt> {
+  template<typename Opt, typename Target>
+   struct evaluator<imfreq,Target,Opt> {
     static constexpr int arity = 1;
+    typedef typename std::conditional < std::is_same<Target, matrix_valued>::value, arrays::matrix_view<std::complex<double>>, std::complex<double>>::type rtype; 
     template<typename G>
-     arrays::matrix_view<std::complex<double> >  operator() (G const * g, long n)  const {return g->data()(n, arrays::range(), arrays::range()); }
+     rtype operator() (G const * g, long n)  const {return g->data()(n, arrays::ellipsis()); }
     template<typename G>
      local::tail_view operator()(G const * g, freq_infty const &) const {return g->singularity();}
    };
 
   /// ---------------------------  data access  ---------------------------------
-
   template<typename Opt> struct data_proxy<imfreq,matrix_valued,Opt> : data_proxy_array<std::complex<double>,3> {};
+  template<typename Opt> struct data_proxy<imfreq,scalar_valued,Opt> : data_proxy_array<std::complex<double>,1> {};
 
   // -------------------------------   Factories  --------------------------------------------------
-
+  
+  // matrix_valued
   template<typename Opt> struct factories<imfreq,matrix_valued,Opt> { 
-   typedef gf<imfreq,matrix_valued,Opt> gf_t;
+    typedef gf<imfreq,matrix_valued,Opt> gf_t;
+    typedef gf_view<imfreq,matrix_valued,Opt> gf_view_t;
+    
+    template<typename MeshType>
+    static gf_t make_gf(MeshType && m, tqa::mini_vector<size_t,2> shape, local::tail_view const & t) {
+      typename gf_t::data_non_view_t A(shape.front_append(m.size())); A() =0;
+      return gf_t ( std::forward<MeshType>(m), std::move(A), t, nothing() ) ;
+    }
+    static gf_t make_gf(double beta, statistic_enum S, tqa::mini_vector<size_t,2> shape) {
+      return make_gf(mesh<imfreq,Opt>::make(beta,S), shape, local::tail(shape));
+    }
+    static gf_t make_gf(double beta, statistic_enum S,  tqa::mini_vector<size_t,2> shape, size_t Nmax) {
+      return make_gf(mesh<imfreq,Opt>::make(beta,S,Nmax), shape, local::tail(shape));
+    }
+    static gf_t make_gf(double beta, statistic_enum S, tqa::mini_vector<size_t,2> shape, size_t Nmax, local::tail_view const & t) {
+      return make_gf(mesh<imfreq,Opt>::make(beta,S,Nmax), shape, t);
+    }
+  };
+  
+  // scalar_valued
+  template<typename Opt> struct factories<imfreq,scalar_valued,Opt> { 
+   typedef gf<imfreq,scalar_valued,Opt> gf_t;
+   typedef gf_view<imfreq,scalar_valued,Opt> gf_view_t;
 
    template<typename MeshType>
-    static gf_t make_gf(MeshType && m, tqa::mini_vector<size_t,2> shape, local::tail_view const & t) {
-     typename gf_t::data_non_view_t A(shape.front_append(m.size())); A() =0;
-     return gf_t ( std::forward<MeshType>(m), std::move(A), t, nothing() ) ;
-    }
-   static gf_t make_gf(double beta, statistic_enum S, tqa::mini_vector<size_t,2> shape) {
-    return make_gf(mesh<imfreq,Opt>::make(beta,S), shape, local::tail(shape));
+   static gf_t make_gf(MeshType && m, local::tail_view const & t) {
+    typename gf_t::data_non_view_t A(m.size()); A() =0;
+    return gf_t ( std::forward<MeshType>(m), std::move(A), t, nothing() ) ;
    }
-   static gf_t make_gf(double beta, statistic_enum S,  tqa::mini_vector<size_t,2> shape, size_t Nmax) {
-    return make_gf(mesh<imfreq,Opt>::make(beta,S,Nmax), shape, local::tail(shape));
+   static gf_t make_gf(double beta, statistic_enum S) {
+     return make_gf(mesh<imfreq,Opt>::make(beta,S), local::tail(tqa::mini_vector<size_t,2> (1,1)));
    }
-   static gf_t make_gf(double beta, statistic_enum S, tqa::mini_vector<size_t,2> shape, size_t Nmax, local::tail_view const & t) {
-    return make_gf(mesh<imfreq,Opt>::make(beta,S,Nmax), shape, t);
+   static gf_t make_gf(double beta, statistic_enum S, size_t Nmax) {
+    return make_gf(mesh<imfreq,Opt>::make(beta,S,Nmax), local::tail(tqa::mini_vector<size_t,2> (1,1)));
+   }
+   static gf_t make_gf(double beta, statistic_enum S, size_t Nmax, local::tail_view const & t) {
+    return make_gf(mesh<imfreq,Opt>::make(beta,S,Nmax), t);
    }
   };
- } // gf_implementation
+ } // gf_implementation 
+
 }}
 #endif
diff --git a/triqs/gf/imtime.hpp b/triqs/gf/imtime.hpp
index 3712a4f3..ff9fa422 100644
--- a/triqs/gf/imtime.hpp
+++ b/triqs/gf/imtime.hpp
@@ -43,14 +43,21 @@ namespace triqs { namespace gf {
  
   // singularity 
   template<typename Opt> struct singularity<imtime,matrix_valued,Opt>  { typedef local::tail type;};
+  template<typename Opt> struct singularity<imtime,scalar_valued,Opt>  { typedef local::tail type;};
   
   // h5 name
   template<typename Opt> struct h5_name<imtime,matrix_valued,Opt>      { static std::string invoke(){ return  "GfImTime";}};
+  template<typename Opt> struct h5_name<imtime,scalar_valued,Opt>      { static std::string invoke(){ return  "GfImTime_s";}};
 
-  /// ---------------------------  closest mesh point on the grid ---------------------------------
+  /// ---------------------------  data access  ---------------------------------
 
-  template<typename Opt>
-   struct get_closest_point <imtime,matrix_valued,Opt> {
+  template<typename Opt> struct data_proxy<imtime,matrix_valued,Opt> : data_proxy_array<double,3> {};
+  template<typename Opt> struct data_proxy<imtime,scalar_valued,Opt> : data_proxy_array<double,1> {};
+
+   /// ---------------------------  closest mesh point on the grid ---------------------------------
+
+  template<typename Opt, typename Target>
+   struct get_closest_point <imtime,Target,Opt> {
     // index_t is size_t
     template<typename G, typename T>
      static size_t invoke(G const * g, closest_pt_wrap<T> const & p) {
@@ -63,6 +70,33 @@ namespace triqs { namespace gf {
 
   /// ---------------------------  evaluator ---------------------------------
 
+  // NOT TESTED
+  // TEST THE SPPED when q_view are incorporated...
+  // true evaluator with interpolation ...
+  template<typename G, typename ReturnType>
+   ReturnType evaluator_imtime_impl (G const * g, double tau, ReturnType && _tmp) {
+    // interpolate between n and n+1, with weight
+    double beta = g->mesh().domain().beta;
+    int p = std::floor(tau/beta);
+    tau -= p*beta;
+    double a = tau/g->mesh().delta();
+    long n = std::floor(a);
+    double w = a-n;
+    assert(n < g->mesh().size()-1);
+    auto _ = arrays::ellipsis();
+    if ((g->mesh().domain().statistic == Fermion) && (p%2==1))
+     _tmp = - w*g->data()(n, _) - (1-w)*g->data()(n+1, _);
+    else
+     _tmp =   w*g->data()(n, _) + (1-w)*g->data()(n+1, _);
+    //else { // Speed test to redo when incoparated qview in main branch
+    // _tmp(0,0) =   w*g->data()(n, 0,0) + (1-w)*g->data()(n+1, 0,0);
+    // _tmp(0,1) =   w*g->data()(n, 0,1) + (1-w)*g->data()(n+1, 0,1);
+    // _tmp(1,0) =   w*g->data()(n, 1,0) + (1-w)*g->data()(n+1, 1,0);
+    // _tmp(1,1) =   w*g->data()(n, 1,1) + (1-w)*g->data()(n+1, 1,1);
+    // }
+    return _tmp;
+   }
+
   template<typename Opt>
    struct evaluator<imtime,matrix_valued,Opt> {
     private:
@@ -70,61 +104,37 @@ namespace triqs { namespace gf {
     public :
      static constexpr int arity = 1;
      evaluator() = default;
-     evaluator(size_t n1, size_t n2) : _tmp(n1,n2) {}
-     // WHAT happen in resize ??
+     evaluator(size_t n1, size_t n2) : _tmp(n1,n2) {} // WHAT happen in resize ??
 
-     // NOT TESTED
-     // TEST THE SPPED when q_view are incorporated...
-     // true evaluator with interpolation ...
      template<typename G>
-      arrays::matrix<double> const & operator()(G const * g, double tau) const {
-       // interpolate between n and n+1, with weight
-       double beta = g->mesh().domain().beta;
-       int p = std::floor(tau/beta);
-       tau -= p*beta;
-       double a = tau/g->mesh().delta();
-       long n = std::floor(a);
-       double w = a-n;
-       assert(n < g->mesh().size()-1);
-       if ((g->mesh().domain().statistic == Fermion) && (p%2==1))
-	_tmp = - w*g->data()(n, arrays::range(), arrays::range()) - (1-w)*g->data()(n+1, arrays::range(), arrays::range());
-       else
-	_tmp =   w*g->data()(n, arrays::range(), arrays::range()) + (1-w)*g->data()(n+1, arrays::range(), arrays::range());
-       //else { // Speed test to redo when incoparated qview in main branch
-       // _tmp(0,0) =   w*g->data()(n, 0,0) + (1-w)*g->data()(n+1, 0,0);
-       // _tmp(0,1) =   w*g->data()(n, 0,1) + (1-w)*g->data()(n+1, 0,1);
-       // _tmp(1,0) =   w*g->data()(n, 1,0) + (1-w)*g->data()(n+1, 1,0);
-       // _tmp(1,1) =   w*g->data()(n, 1,1) + (1-w)*g->data()(n+1, 1,1);
-       // }
-       return _tmp;
-      }
+      arrays::matrix<double> const & operator()(G const * g, double tau) const { return evaluator_imtime_impl(g, tau, _tmp);}
 
      template<typename G>
       typename G::singularity_t const & operator()(G const * g,freq_infty const &) const {return g->singularity();}
    };
 
-  /// ---------------------------  data access  ---------------------------------
+  template<typename Opt>
+   struct evaluator<imtime,scalar_valued,Opt> {
+    public :
+     static constexpr int arity = 1;
 
-  template<typename Opt> struct data_proxy<imtime,matrix_valued,Opt> : data_proxy_array<double,3> {};
- 
-  // -------------------------------   Factories  --------------------------------------------------
+     template<typename G> double operator()(G const * g, double tau) const { return evaluator_imtime_impl(g, tau, 0.0);}
 
+     template<typename G>
+      typename G::singularity_t const & operator()(G const * g,freq_infty const &) const {return g->singularity();}
+   };
+
+ // -------------------------------   Factories  --------------------------------------------------
+
+  // matrix_valued
   template<typename Opt> struct factories<imtime,matrix_valued,Opt> { 
    typedef gf<imtime,matrix_valued,Opt> gf_t;
-
    template<typename MeshType>
     static gf_t make_gf(MeshType && m, tqa::mini_vector<size_t,2> shape, local::tail_view const & t) {
      typename gf_t::data_non_view_t A(shape.front_append(m.size())); A() =0;
      //return gf_t ( m, std::move(A), t, nothing() ) ;
      return gf_t (std::forward<MeshType>(m), std::move(A), t, nothing(), evaluator<imtime,matrix_valued,Opt>(shape[0],shape[1]) ) ;
     }
-   /*static gf_t make_gf(double beta, statistic_enum S, tqa::mini_vector<size_t,2> shape) {
-     return make_gf(make_mesh(beta,S,1025,half_bins), shape, local::tail(shape));
-     }
-     static gf_t make_gf(double beta, statistic_enum S, tqa::mini_vector<size_t,2> shape, size_t Nmax) {
-     return make_gf(make_mesh(beta,S,Nmax,half_bins), shape, local::tail(shape));
-     }
-     */
    static gf_t make_gf(double beta, statistic_enum S,  tqa::mini_vector<size_t,2> shape, size_t Nmax=1025, mesh_kind mk= half_bins) {
     return make_gf(mesh<imtime,Opt>::make(beta,S,Nmax,mk), shape, local::tail(shape));
    }
@@ -132,7 +142,24 @@ namespace triqs { namespace gf {
     return make_gf(mesh<imtime,Opt>::make(beta,S,Nmax,mk), shape, t);
    }
   };
- } // gf_implementation
+
+  // scalar_valued 
+  template<typename Opt> struct factories<imtime,scalar_valued,Opt> { 
+   typedef gf<imtime,scalar_valued,Opt> gf_t;
+   template<typename MeshType>
+    static gf_t make_gf(MeshType && m, local::tail_view const & t) {
+     typename gf_t::data_non_view_t A(m.size()); A() =0;
+     return gf_t (std::forward<MeshType>(m), std::move(A), t, nothing());
+    }
+   static gf_t make_gf(double beta, statistic_enum S, size_t Nmax=1025, mesh_kind mk= half_bins) {
+    return make_gf(mesh<imtime,Opt>::make(beta,S,Nmax,mk), local::tail(tqa::mini_vector<size_t,2> (1,1)));
+   }
+   static gf_t make_gf(double beta, statistic_enum S, size_t Nmax, mesh_kind mk, local::tail_view const & t) {
+    return make_gf(mesh<imtime,Opt>::make(beta,S,Nmax,mk), t);
+   }
+  };
+ } // gf_implementation.
+ 
 }}
 #endif
 
diff --git a/triqs/gf/local/fourier_base.cpp b/triqs/gf/local/fourier_base.cpp
index e12a2ec2..525790c5 100644
--- a/triqs/gf/local/fourier_base.cpp
+++ b/triqs/gf/local/fourier_base.cpp
@@ -1,5 +1,5 @@
 /*******************************************************************************
- *
+ * 
  * TRIQS: a Toolbox for Research in Interacting Quantum Systems
  *
  * Copyright (C) 2011 by M. Ferrero, O. Parcollet
@@ -23,32 +23,30 @@
 #include <algorithm>
 
 namespace triqs { namespace gf { namespace details { 
-
+ 
  void fourier_base(const tqa::vector<dcomplex> &in, tqa::vector<dcomplex> &out, size_t L, bool direct) {
-
+  
   // !!!! L must always be the number of time bins !!!!
   //const size_t L( (direct ? in.size() : out.size()) );
   //const int L(max(in.size(),out.size()));  <-- bug
-
+  
   fftw_complex *inFFT, *outFFT;
   fftw_plan p;
   inFFT = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * L);
   outFFT = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * L);
-
+  
   const dcomplex * restrict in_ptr = in.data_start();
   dcomplex * restrict out_ptr = out.data_start();
   const size_t imax = std::min(L,in.size());
   for (size_t i =0; i<imax; ++i) { inFFT[i][0] = real(in_ptr[i]); inFFT[i][1] = imag(in_ptr[i]);}
   for (size_t i =imax; i<L; ++i) { inFFT[i][0] = 0; inFFT[i][1] = 0;}
-
+//   for (size_t i =0; i<L; ++i) { inFFT[i][0] = real(in_ptr[i]); inFFT[i][1] = imag(in_ptr[i]);}
   p = fftw_plan_dft_1d(L, inFFT, outFFT, (direct ? FFTW_BACKWARD : FFTW_FORWARD), FFTW_ESTIMATE);
   fftw_execute(p); 
-
   const size_t jmax = std::min(L,out.size());
-  for (size_t j =0; j<jmax; ++j)  {out_ptr[j] = dcomplex(outFFT[j][0] ,outFFT[j][1]);}
-
+  for (size_t j =0; j<jmax; ++j) out_ptr[j] = dcomplex(outFFT[j][0] ,outFFT[j][1]);
   fftw_destroy_plan(p); 
   fftw_free(inFFT); fftw_free(outFFT);
  }
-
+ 
 }}}
diff --git a/triqs/gf/local/fourier_base.hpp b/triqs/gf/local/fourier_base.hpp
index 77006c1c..a7022ae8 100644
--- a/triqs/gf/local/fourier_base.hpp
+++ b/triqs/gf/local/fourier_base.hpp
@@ -28,8 +28,9 @@ namespace triqs { namespace gf {
 
    namespace tqa = triqs::arrays;
    typedef std::complex<double> dcomplex;
-
+   
    void fourier_base(const tqa::vector<dcomplex> &in, tqa::vector<dcomplex> &out, size_t L, bool direct);
+   void fourier_base(const tqa::vector<dcomplex> &in, tqa::vector<dcomplex> &out, size_t L1, size_t L2, bool direct);
 
  }
 
diff --git a/triqs/gf/local/fourier_matsubara.cpp b/triqs/gf/local/fourier_matsubara.cpp
index e426d2ea..90447f60 100644
--- a/triqs/gf/local/fourier_matsubara.cpp
+++ b/triqs/gf/local/fourier_matsubara.cpp
@@ -1,5 +1,5 @@
 /*******************************************************************************
- *
+ * 
  * TRIQS: a Toolbox for Research in Interacting Quantum Systems
  *
  * Copyright (C) 2011 by M. Ferrero, O. Parcollet
@@ -23,151 +23,165 @@
 #include <fftw3.h>
 
 namespace triqs { namespace gf { 
-
+ 
  namespace impl_local_matsubara {
-
-  inline dcomplex oneFermion(dcomplex a,double b,double tau,double Beta) {
-   return -a*( b >=0 ? exp(-b*tau)/(1+exp(-Beta*b)) : exp(b*(Beta-tau))/(1+exp(Beta*b)) );
+  
+  inline dcomplex oneFermion(dcomplex a,double b,double tau,double beta) {
+   return -a*( b >=0 ? exp(-b*tau)/(1+exp(-beta*b)) : exp(b*(beta-tau))/(1+exp(beta*b)) );
   }
-
-  inline dcomplex oneBoson(dcomplex a,double b,double tau,double Beta) {
-   return a*( b >=0 ? exp(-b*tau)/(exp(-Beta*b)-1) : exp(b*(Beta-tau))/(1-exp(b*Beta)) );
+  
+  inline dcomplex oneBoson(dcomplex a,double b,double tau,double beta) {
+   return a*( b >=0 ? exp(-b*tau)/(exp(-beta*b)-1) : exp(b*(beta-tau))/(1-exp(b*beta)) );
   }
  }
-
+ 
+ template<typename GfElementType> GfElementType convert_green         ( dcomplex const& x) { return x;}
+ template<>                       double        convert_green<double> ( dcomplex const& x) { return real(x);}
+ 
  //--------------------------------------------------------------------------------------
-
- void fourier_impl  (gf_view<imfreq> &gw , gf_view<imtime> const & gt){
-
-  // set behavior according to mesh kind
-  double shift;
-  size_t L;
-  switch(gt.mesh().kind()) {
-    case half_bins: shift = 0.5; L = gt.mesh().size(); break;
-    case full_bins: shift = 0.0; L = gt.mesh().size()-1; break;
-    case without_last: shift = 0.0; L = gt.mesh().size(); break;
-  }
-
-  auto ta = gt(freq_infty());
-  long numberTimeSlices = gt.mesh().size();
-  double Beta = gt.domain().beta, Pi = std::acos(-1);
-  dcomplex I(0,1);
-  tqa::vector<dcomplex> g_in(gt.mesh().size()), g_out (gw.mesh().size()); 
-
-  using namespace impl_local_matsubara;
-  for (size_t n1=0; n1<gw.data().shape()[1];n1++)
-   for (size_t n2=0; n2<gw.data().shape()[2];n2++) {
-    dcomplex d= ta(1)(n1,n2), A= ta.get_or_zero(2)(n1,n2),B = ta.get_or_zero(3)(n1,n2);
-    //dcomplex d= ta(1)(n1,n2), A= ta(2)(n1,n2),B = ta(3)(n1,n2);
-    double b1, b2, b3;
-    dcomplex a1, a2, a3;
-    if (gw.domain().statistic == Fermion){ b1 = 0; b2 =1; b3 =-1; a1 = d-B; a2 = (A+B)/2; a3 = (B-A)/2; }
-    else { b1 = -0.5; b2 =-1; b3 =1; a1=4*(d-B)/3; a2=B-(d+A)/2; a3=d/6+A/2+B/3; }
-    g_in() = 0;
-
-    if (gw.domain().statistic == Fermion){
-     for (auto & t : gt.mesh())  
-      g_in(t.index()) = exp(I*Pi*t/Beta)*( gt(t)(n1,n2) - ( oneFermion(a1,b1,t,Beta) + oneFermion(a2,b2,t,Beta)+ oneFermion(a3,b3,t,Beta) ) );
-    }
-    else { 
-     for (auto & t : gt.mesh()) 
-      g_in(t.index()) =  gt(t)(n1,n2) -  ( oneBoson(a1,b1,t,Beta) + oneBoson(a2,b2,t,Beta) + oneBoson(a3,b3,t,Beta) );  
-    }
-
-    g_in *= Beta/numberTimeSlices;
-
-    details::fourier_base(g_in, g_out, L, true);
-
-    for (auto & w : gw.mesh()) {
-     gw(w)(n1,n2) = g_out(w.index())*exp(2*I*w.index()*shift*Pi/Beta*gt.mesh().delta()) + a1/(w-b1) + a2/(w-b2) + a3/(w-b3); 
-    }
-
-    // set tail
-    gw.singularity() = gt.singularity();
-
+ 
+ struct impl_worker {
+  
+  tqa::vector<dcomplex> g_in, g_out;
+  
+  void direct ( gf_view<imfreq,scalar_valued> &gw , gf_view<imtime,scalar_valued> const& gt) {
+   using namespace impl_local_matsubara;
+   auto ta = gt(freq_infty());
+   //TO BE MODIFIED AFTER SCALAR IMPLEMENTATION TODO
+   dcomplex d= ta(1)(0,0), A= ta.get_or_zero(2)(0,0), B = ta.get_or_zero(3)(0,0);
+   double b1=0, b2=0, b3=0;
+   dcomplex a1, a2, a3;
+   double beta=gt.mesh().domain().beta;
+   auto L = ( gt.mesh().kind() == full_bins ? gt.mesh().size()-1 : gt.mesh().size());
+   double fact= beta/ gt.mesh().size();
+   dcomplex iomega = dcomplex(0.0,1.0) * std::acos(-1) / beta;
+   dcomplex iomega2 = iomega * 2 * gt.mesh().delta() * ( gt.mesh().kind() == half_bins ? 0.5 : 0.0);
+   g_in.resize(gt.mesh().size());
+   g_out.resize(gw.mesh().size());
+   if (gw.domain().statistic == Fermion){
+    b1 = 0; b2 =1; b3 =-1; 
+    a1 = d-B; a2 = (A+B)/2; a3 = (B-A)/2; 
    }
- }
- //---------------------------------------------------------------------------
-
- template<typename GfElementType> GfElementType convert_green ( dcomplex const & x) { return x;}
- template<> double convert_green<double> ( dcomplex const & x) { return real(x);}
-
- //---------------------------------------------------------------------------
-
- void inverse_fourier_impl (gf_view<imtime> &gt,  gf_view<imfreq> const & gw) { 
-
-  // set behavior according to mesh kind
-  double shift;
-  size_t L;
-  switch(gt.mesh().kind()) {
-    case half_bins: shift = 0.5; L = gt.mesh().size(); break;
-    case full_bins: shift = 0.0; L = gt.mesh().size()-1; break;
-    case without_last: shift = 0.0; L = gt.mesh().size(); break;
+   else { 
+    b1 = -0.5; b2 =-1; b3 =1; 
+    a1 = 4*(d-B)/3; a2 = B-(d+A)/2; a3 = d/6+A/2+B/3; 
+   }
+   if (gw.domain().statistic == Fermion){
+    for (auto & t : gt.mesh())  
+     g_in(t.index()) = fact * exp(iomega*t) * ( gt(t) - ( oneFermion(a1,b1,t,beta) + oneFermion(a2,b2,t,beta)+ oneFermion(a3,b3,t,beta) ) );
+   }
+   else {
+    for (auto & t : gt.mesh()) 
+     g_in(t.index()) =  fact * ( gt(t) -  ( oneBoson(a1,b1,t,beta) + oneBoson(a2,b2,t,beta) + oneBoson(a3,b3,t,beta) ) );  
+   }
+   details::fourier_base(g_in, g_out, L, true);
+   for (auto & w : gw.mesh()) {
+    gw(w) = g_out( w.index() ) * exp(iomega2*w.index() ) + a1/(w-b1) + a2/(w-b2) + a3/(w-b3); 
+   }
+   gw.singularity() = gt.singularity();// set tail
   }
-
-  static bool Green_Function_Are_Complex_in_time = false;
-  auto ta = gw(freq_infty());
-
-  double Beta = gt.domain().beta, Pi = std::acos(-1);
-  dcomplex I(0,1);
-  tqa::vector<dcomplex> g_in(gw.mesh().size()), g_out (gt.mesh().size());
-
-  using namespace impl_local_matsubara;
-  for (size_t n1=0; n1<gt.data().shape()[1];n1++)
-   for (size_t n2=0; n2<gt.data().shape()[2];n2++) {
-    dcomplex d= ta(1)(n1,n2), A= ta.get_or_zero(2)(n1,n2),B = ta.get_or_zero(3)(n1,n2);
-    //dcomplex d= ta(1)(n1,n2), A= ta(2)(n1,n2),B = ta(3)(n1,n2);
-
-    double b1, b2, b3;
-    dcomplex a1, a2, a3;
-    if (gw.domain().statistic == Fermion){ b1 = 0; b2 =1; b3 =-1; a1 = d-B; a2 = (A+B)/2; a3 = (B-A)/2; }
-    else { b1 = -0.5; b2 =-1; b3 =1; a1=4*(d-B)/3; a2=B-(d+A)/2; a3=d/6+A/2+B/3; }
-    g_in() = 0;
-
-    for (auto & w: gw.mesh()) {
-     g_in(w.index()) =  exp(-I*2*w.index()*shift*Pi/Beta*gt.mesh().delta()) * ( gw(w)(n1,n2) - (a1/(w-b1) + a2/(w-b2) + a3/(w-b3)) );
+  
+  
+  
+  void inverse(gf_view<imtime,scalar_valued> gt, gf_view<imfreq,scalar_valued> const gw){
+   using namespace impl_local_matsubara;
+   static bool Green_Function_Are_Complex_in_time = false;
+   // If the Green function are NOT complex, then one use the symmetry property
+   // fold the sum and get a factor 2
+   auto ta = gw(freq_infty());
+   //TO BE MODIFIED AFTER SCALAR IMPLEMENTATION TODO
+   dcomplex d= ta(1)(0,0), A= ta.get_or_zero(2)(0,0), B = ta.get_or_zero(3)(0,0);
+   double b1, b2, b3;
+   dcomplex a1, a2, a3;
+   
+   double beta=gw.domain().beta;
+   size_t L= gt.mesh().size() - ( gt.mesh().kind() == full_bins ? 1 : 0); //L can be different from gt.mesh().size() (depending on the mesh kind) and is given to the FFT algorithm 
+   dcomplex iomega = dcomplex(0.0,1.0) * std::acos(-1) / beta;
+   dcomplex iomega2 = -iomega * 2 * gt.mesh().delta() * (gt.mesh().kind() == half_bins ? 0.5 : 0.0) ; 
+   double fact = (Green_Function_Are_Complex_in_time ? 1 : 2)/beta;
+   g_in.resize( gw.mesh().size());
+   g_out.resize(gt.mesh().size());
+   
+   if (gw.domain().statistic == Fermion){
+    b1 = 0; b2 =1; b3 =-1; 
+    a1 = d-B; a2 = (A+B)/2; a3 = (B-A)/2; 
+   }
+   else { 
+    b1 = -0.5; b2 =-1; b3 =1; 
+    a1=4*(d-B)/3; a2=B-(d+A)/2; a3=d/6+A/2+B/3;
+   }
+   g_in() = 0;
+   for (auto & w: gw.mesh()) {
+    g_in( w.index() ) =  fact * exp(w.index()*iomega2) * ( gw(w) - (a1/(w-b1) + a2/(w-b2) + a3/(w-b3)) );
+   }
+   // for bosons GF(w=0) is divided by 2 to avoid counting it twice
+   if (gw.domain().statistic == Boson && !Green_Function_Are_Complex_in_time ) g_in(0) *= 0.5;
+   
+   details::fourier_base(g_in, g_out, L, false);
+   
+   // CORRECT FOR COMPLEX G(tau) !!!
+   typedef double gt_result_type;
+   //typedef typename gf<imtime>::mesh_type::gf_result_type gt_result_type;
+   if (gw.domain().statistic == Fermion){
+    for (auto & t : gt.mesh()){
+     gt(t) = convert_green<gt_result_type> ( g_out( t.index() == L ? 0 : t.index() ) * exp(-iomega*t)
+     + oneFermion(a1,b1,t,beta) + oneFermion(a2,b2,t,beta)+ oneFermion(a3,b3,t,beta) );
     }
-    // for bosons GF(w=0) is divided by 2 to avoid counting it twice
-    if (gw.domain().statistic == Boson && !Green_Function_Are_Complex_in_time ) g_in(0) *= 0.5; 
-
-    details::fourier_base(g_in, g_out, L, false);
-
-    // If the Green function are NOT complex, then one use the symmetry property
-    // fold the sum and get a factor 2
-    double fact = (Green_Function_Are_Complex_in_time ? 1 : 2);
-    g_out = g_out*(fact/Beta);
-
-    // CORRECT FOR COMPLEX G(tau) !!!
-    typedef double gt_result_type;
-    //typedef boost::mpl::if_<gt_result_type;
-    //typedef typename gf<imtime>::mesh_type::gf_result_type gt_result_type;
-
-    if (gw.domain().statistic == Fermion){
-     for (auto & t : gt.mesh()) 
-       gt(t)(n1,n2) = convert_green<gt_result_type> (g_out(t.index())*exp(-I*Pi*t/Beta)
-                         + oneFermion(a1,b1,t,Beta) + oneFermion(a2,b2,t,Beta)+ oneFermion(a3,b3,t,Beta) );
-    }
-    else {
-     for (auto & t : gt.mesh()) 
-       gt(t)(n1,n2) = convert_green<gt_result_type> (g_out(t.index())
-                         + oneBoson(a1,b1,t,Beta) + oneBoson(a2,b2,t,Beta) + oneBoson(a3,b3,t,Beta) );
-    }
-
-    if (gt.mesh().kind() == full_bins)
-      gt.on_mesh(L)(n1,n2) = -gt.on_mesh(0)(n1,n2)-convert_green<gt_result_type>(ta(1)(n1,n2));
-
-    // set tail
+   }
+   else {
+    for (auto & t : gt.mesh()) 
+     gt(t) = convert_green<gt_result_type> ( g_out( t.index() == L ? 0 : t.index() )
+     + oneBoson(a1,b1,t,beta) + oneBoson(a2,b2,t,beta) + oneBoson(a3,b3,t,beta) );
+   }
+   if (gt.mesh().kind() == full_bins)
+    gt.on_mesh(L) = -gt.on_mesh(0)-convert_green<gt_result_type>(ta(1)(0,0));
+   // set tail
     gt.singularity() = gw.singularity();
-
+  }
+  
+ };
+ 
+ void fourier_impl  (gf_view<imfreq,scalar_valued> gw , gf_view<imtime,scalar_valued> const gt, scalar_valued){
+  impl_worker w;
+  w.direct(gw,gt);
+ }
+ 
+ void fourier_impl  (gf_view<imfreq,matrix_valued> gw , gf_view<imtime,matrix_valued> const gt, matrix_valued){
+  impl_worker w;
+  for (size_t n1=0; n1<gt.data().shape()[1];n1++)
+   for (size_t n2=0; n2<gt.data().shape()[2];n2++){
+    auto gw_sl=slice_target_to_scalar(gw,n1,n2);
+    auto gt_sl=slice_target_to_scalar(gt,n1,n2);
+    w.direct(gw_sl, gt_sl);
    }
  }
-
-
- gf_keeper<tags::fourier,imtime> lazy_fourier         (gf_view<imtime> const & g) { return g;}
- gf_keeper<tags::fourier,imfreq> lazy_inverse_fourier (gf_view<imfreq> const & g) { return g;}
-
- void triqs_gf_view_assign_delegation( gf_view<imfreq> &g, gf_keeper<tags::fourier,imtime> const & L) { fourier_impl (g,L.g);}
- void triqs_gf_view_assign_delegation( gf_view<imtime> &g, gf_keeper<tags::fourier,imfreq> const & L) { inverse_fourier_impl(g,L.g);}
-
+ 
+ 
+ 
+ //---------------------------------------------------------------------------
+ 
+ void inverse_fourier_impl  (gf_view<imtime,scalar_valued> gt , gf_view<imfreq,scalar_valued> const gw, scalar_valued){ 
+  impl_worker w;
+  w.inverse(gt,gw);
+ }
+ 
+ void inverse_fourier_impl  (gf_view<imtime,matrix_valued> gt , gf_view<imfreq,matrix_valued> const gw, matrix_valued){
+  impl_worker w;
+  for (size_t n1=0; n1<gw.data().shape()[1];n1++)
+   for (size_t n2=0; n2<gw.data().shape()[2];n2++){
+    auto gt_sl=slice_target_to_scalar(gt, n1, n2);
+    auto gw_sl=slice_target_to_scalar(gw, n1, n2);
+    w.inverse( gt_sl, gw_sl);
+   }
+ }
+ 
+ 
+ 
+ //---------------------------------------------------------------------------
+ void triqs_gf_view_assign_delegation( gf_view<imfreq,scalar_valued> g, gf_keeper<tags::fourier,imtime,scalar_valued> const & L) { fourier_impl ( g ,L.g, scalar_valued() ); }
+ void triqs_gf_view_assign_delegation( gf_view<imfreq,matrix_valued> g, gf_keeper<tags::fourier,imtime,matrix_valued> const & L) { fourier_impl ( g, L.g, matrix_valued() ); }
+ void triqs_gf_view_assign_delegation( gf_view<imtime,scalar_valued> g, gf_keeper<tags::fourier,imfreq,scalar_valued> const & L) { inverse_fourier_impl( g, L.g, scalar_valued() ); }
+ void triqs_gf_view_assign_delegation( gf_view<imtime,matrix_valued> g, gf_keeper<tags::fourier,imfreq,matrix_valued> const & L) { inverse_fourier_impl( g, L.g, matrix_valued() ); }
+ 
 }}
 
diff --git a/triqs/gf/local/fourier_matsubara.hpp b/triqs/gf/local/fourier_matsubara.hpp
index d6f43ee2..fab132a4 100644
--- a/triqs/gf/local/fourier_matsubara.hpp
+++ b/triqs/gf/local/fourier_matsubara.hpp
@@ -1,5 +1,5 @@
 /*******************************************************************************
- *
+ * 
  * TRIQS: a Toolbox for Research in Interacting Quantum Systems
  *
  * Copyright (C) 2011 by M. Ferrero, O. Parcollet
@@ -25,18 +25,57 @@
 #include <triqs/gf/imfreq.hpp> 
 #include <triqs/gf/imtime.hpp> 
 
-namespace triqs { namespace gf { 
-
+namespace triqs { namespace gf {
+ 
  // First the implementation of the fourier transform
- void fourier_impl         (gf_view<imfreq> &gw , gf_view<imtime> const & gt);
- void inverse_fourier_impl (gf_view<imtime> &gt,  gf_view<imfreq> const & gw);
-
- gf_keeper<tags::fourier,imtime> lazy_fourier         (gf_view<imtime> const & g);
- gf_keeper<tags::fourier,imfreq> lazy_inverse_fourier (gf_view<imfreq> const & g);
-
- void triqs_gf_view_assign_delegation( gf_view<imfreq> &g, gf_keeper<tags::fourier,imtime> const & L);
- void triqs_gf_view_assign_delegation( gf_view<imtime> &g, gf_keeper<tags::fourier,imfreq> const & L);
-
+ void fourier_impl         (gf_view<imfreq,scalar_valued> gw , gf_view<imtime,scalar_valued> const gt, scalar_valued);
+ void fourier_impl         (gf_view<imfreq,matrix_valued> gw , gf_view<imtime,matrix_valued> const gt, matrix_valued);
+ void inverse_fourier_impl (gf_view<imtime,scalar_valued> gt,  gf_view<imfreq,scalar_valued> const gw, scalar_valued);
+ void inverse_fourier_impl (gf_view<imtime,matrix_valued> gt,  gf_view<imfreq,matrix_valued> const gw, matrix_valued);
+ 
+ inline gf_view<imfreq,matrix_valued> fourier (gf_view<imtime,matrix_valued> const & gt) {
+  size_t L = (gt.mesh().kind() == full_bins ? gt.mesh().size()-1 : gt.mesh().size() );
+  auto gw = make_gf<imfreq,matrix_valued>(gt.domain().beta, gt.domain().statistic , gt.data().shape().front_pop(), L);
+  auto V = gw();
+  fourier_impl(V, gt, matrix_valued());
+  return gw;
+ }
+ inline gf_view<imfreq,scalar_valued> fourier (gf_view<imtime,scalar_valued> const & gt) {
+  size_t L = (gt.mesh().kind() == full_bins ? gt.mesh().size()-1 : gt.mesh().size() );
+  auto gw = make_gf<imfreq,scalar_valued>(gt.domain().beta, gt.domain().statistic, L);
+  auto V = gw();
+  fourier_impl(V, gt, scalar_valued());
+  return gw;
+ }
+ 
+ inline gf_view<imtime, matrix_valued> inverse_fourier (gf_view<imfreq, matrix_valued> const & gw, mesh_kind mk = half_bins) {
+  double pi = std::acos(-1);
+  size_t L = (mk == full_bins ? gw.mesh().size()+1 : gw.mesh().size() );
+  auto gt = make_gf<imtime,matrix_valued>(gw.domain().beta, gw.domain().statistic, gw.data().shape().front_pop(), L);
+  auto V = gt();
+  inverse_fourier_impl(V, gw, matrix_valued());
+  return gt;
+ }
+ inline gf_view<imtime,scalar_valued> inverse_fourier (gf_view<imfreq,scalar_valued> const & gw, mesh_kind mk = half_bins) {
+  double pi = std::acos(-1);
+  size_t L = (mk == full_bins ? gw.mesh().size()+1 : gw.mesh().size() );
+  auto gt = make_gf<imtime,scalar_valued>(gw.domain().beta, gw.domain().statistic, L);
+  auto V = gt();
+  inverse_fourier_impl(V, gw,scalar_valued());
+  return gt;
+ }
+ 
+ inline gf_keeper<tags::fourier,imtime,scalar_valued> lazy_fourier         (gf_view<imtime,scalar_valued> const & g) { return g;}
+ inline gf_keeper<tags::fourier,imfreq,scalar_valued> lazy_inverse_fourier (gf_view<imfreq,scalar_valued> const & g) { return g;}
+ inline gf_keeper<tags::fourier,imtime,matrix_valued> lazy_fourier         (gf_view<imtime,matrix_valued> const & g) { return g;}
+ inline gf_keeper<tags::fourier,imfreq,matrix_valued> lazy_inverse_fourier (gf_view<imfreq,matrix_valued> const & g) { return g;}
+ 
+ void triqs_gf_view_assign_delegation( gf_view<imfreq,scalar_valued> g, gf_keeper<tags::fourier,imtime,scalar_valued> const & L);
+ void triqs_gf_view_assign_delegation( gf_view<imfreq,matrix_valued> g, gf_keeper<tags::fourier,imtime,matrix_valued> const & L);
+ void triqs_gf_view_assign_delegation( gf_view<imtime,scalar_valued> g, gf_keeper<tags::fourier,imfreq,scalar_valued> const & L);
+ void triqs_gf_view_assign_delegation( gf_view<imtime,matrix_valued> g, gf_keeper<tags::fourier,imfreq,matrix_valued> const & L);
+ 
+ 
 }}
 #endif
 
diff --git a/triqs/gf/local/fourier_real.cpp b/triqs/gf/local/fourier_real.cpp
index e4c300e1..664f0202 100644
--- a/triqs/gf/local/fourier_real.cpp
+++ b/triqs/gf/local/fourier_real.cpp
@@ -24,24 +24,28 @@
 
 namespace triqs { namespace gf { 
 
-  namespace impl_local_real {
-    inline dcomplex th_expo(double t, double a ) { return (t < 0 ? 0 : -I * exp(-a*t)); }
-    inline dcomplex th_expo_neg(double t, double a ) { return (t > 0 ? 0 : I * exp(a*t)); }
+  namespace {
+    double pi = std::acos(-1);
+    dcomplex I(0,1);
+    inline dcomplex th_expo(double t, double a )     { return (t > 0 ? -I * exp(-a*t) : ( t < 0 ? 0 : -0.5 * I * exp(-a*t) ) ) ; }
+    inline dcomplex th_expo_neg(double t, double a ) { return (t < 0 ?  I * exp( a*t) : ( t > 0 ? 0 :  0.5 * I * exp( a*t) ) ) ; }
+    inline dcomplex th_expo_inv(double w, double a )     { return 1./(w+I*a) ; }
+    inline dcomplex th_expo_neg_inv(double w, double a ) { return 1./(w-I*a) ; }
   }
 
   //--------------------------------------------------------------------------------------
 
-  void fourier_impl(gf_view<refreq> & gw, gf_view<retime> const & gt) {
-
-    using namespace impl_local_real;
+  void fourier_impl(gf_view<refreq> gw, gf_view<retime> const gt) {
 
     size_t L = gt.mesh().size();
-    if (gw.mesh().size() != gt.mesh().size()) TRIQS_RUNTIME_ERROR << "Meshes are different";
+    if (gw.mesh().size() != L) TRIQS_RUNTIME_ERROR << "Meshes are different";
     double test = std::abs(gt.mesh().delta() * gw.mesh().delta() * L / (2*pi) -1);
     if (test > 1.e-10) TRIQS_RUNTIME_ERROR << "Meshes are not compatible";
 
-    const double tmin = gt.mesh().x_min() + (gt.mesh().kind() == half_bins ? 0.5 : 0.0) * gt.mesh().delta();
-    const double wmin = gw.mesh().x_min() + (gw.mesh().kind() == half_bins ? 0.5 : 0.0) * gw.mesh().delta();
+    const double tmin = gt.mesh().x_min();
+    const double wmin = gw.mesh().x_min(); 
+    //a is a number very larger than delta_w and very smaller than wmax-wmin, used in the tail computation
+    const double a = gw.mesh().delta() * sqrt( double(L) );
 
     auto ta = gt(freq_infty());
     tqa::vector<dcomplex> g_in(L), g_out(L);
@@ -50,19 +54,19 @@ namespace triqs { namespace gf {
       for (size_t n2=0; n2<gw.data().shape()[2];n2++) {
 
         dcomplex t1 = ta(1)(n1,n2), t2= ta.get_or_zero(2)(n1,n2);
-        dcomplex a1 = (t1 - I * t2)/2, a2 = (t1 + I * t2)/2;
+        dcomplex a1 = (t1 + I * t2/a )/2., a2 = (t1 - I * t2/a )/2.;
 
         g_in() = 0;
 
-        for (auto & t : gt.mesh()) {
-          g_in(t.index()) = (gt(t)(n1,n2) - (a1*th_expo(t,1) + a2*th_expo_neg(t,1))) * std::exp(I*t*wmin);
+        for (auto const & t : gt.mesh()) {
+          g_in(t.index()) = (gt(t)(n1,n2) - (a1*th_expo(t,a) + a2*th_expo_neg(t,a))) * std::exp(I*t*wmin);
         }
-
+        
         details::fourier_base(g_in, g_out, L, true);
 
-        for (auto & w : gw.mesh()) {
-          gw(w)(n1,n2) = gt.mesh().delta() * std::exp(I*w*tmin) * std::exp(-I*wmin*tmin) * g_out(w.index())
-                         + (a1/(w+I) + a2/(w-I));
+        for (auto const & w : gw.mesh()) {
+          gw(w)(n1,n2) = gt.mesh().delta() * std::exp(I*(w-wmin)*tmin) * g_out(w.index())
+                         + a1*th_expo_inv(w,a) + a2*th_expo_neg_inv(w,a);
         }
       }
     }
@@ -74,17 +78,17 @@ namespace triqs { namespace gf {
 
   //---------------------------------------------------------------------------
 
-  void inverse_fourier_impl (gf_view<retime> & gt, gf_view<refreq> const & gw) { 
-
-    using namespace impl_local_real;
+  void inverse_fourier_impl (gf_view<retime> gt, gf_view<refreq> const gw) { 
 
     size_t L = gw.mesh().size();
-    if (gw.mesh().size() != gt.mesh().size()) TRIQS_RUNTIME_ERROR << "Meshes are different";
+    if ( L != gt.mesh().size()) TRIQS_RUNTIME_ERROR << "Meshes are different";
     double test = std::abs(gt.mesh().delta() * gw.mesh().delta() * L / (2*pi) -1);
     if (test > 1.e-10) TRIQS_RUNTIME_ERROR << "Meshes are not compatible";
 
-    const double tmin = gt.mesh().x_min() + (gt.mesh().kind() == half_bins ? 0.5 : 0.0) * gt.mesh().delta();
-    const double wmin = gw.mesh().x_min() + (gw.mesh().kind() == half_bins ? 0.5 : 0.0) * gw.mesh().delta();
+    const double tmin = gt.mesh().x_min();
+    const double wmin = gw.mesh().x_min();
+    //a is a number very larger than delta_w and very smaller than wmax-wmin, used in the tail computation
+    const double a = gw.mesh().delta() * sqrt( double(L) );
 
     auto ta = gw(freq_infty());
     tqa::vector<dcomplex> g_in(L), g_out(L);
@@ -93,20 +97,18 @@ namespace triqs { namespace gf {
       for (size_t n2=0; n2<gt.data().shape()[2];n2++) {
 
         dcomplex t1 = ta(1)(n1,n2), t2 = ta.get_or_zero(2)(n1,n2);
-        dcomplex a1 = (t1 - I * t2)/2, a2 = (t1 + I * t2)/2;
+        dcomplex a1 = (t1 + I * t2/a )/2., a2 = (t1 - I * t2/a )/2.;
         g_in() = 0;
 
-        for (auto & w: gw.mesh()) {
-          g_in(w.index()) = (gw(w)(n1,n2) - (a1/(w+I) + a2/(w-I))) * std::exp(-I*w*tmin);
-        }
+        for (auto const & w: gw.mesh())
+          g_in(w.index()) = (gw(w)(n1,n2) - a1*th_expo_inv(w,a) - a2*th_expo_neg_inv(w,a)  ) * std::exp(-I*w*tmin);
 
         details::fourier_base(g_in, g_out, L, false);
 
         const double corr = 1.0/(gt.mesh().delta()*L);
-        for (auto & t : gt.mesh()) {
-          gt(t)(n1,n2) = corr * std::exp(I*wmin*tmin) * std::exp(-I*wmin*t) *
-                                g_out(t.index()) + a1*th_expo(t,1) + a2*th_expo_neg(t,1);
-        }
+        for (auto const & t : gt.mesh())
+          gt(t)(n1,n2) = corr * std::exp(I*wmin*(tmin-t)) *
+                                 g_out( t.index() ) + a1 * th_expo(t,a) + a2 * th_expo_neg(t,a) ;
       }
     }
 
@@ -120,7 +122,7 @@ namespace triqs { namespace gf {
   gf_keeper<tags::fourier,retime> lazy_fourier         (gf_view<retime> const & g) { return g;}
   gf_keeper<tags::fourier,refreq> lazy_inverse_fourier (gf_view<refreq> const & g) { return g;}
 
-  void triqs_gf_view_assign_delegation( gf_view<refreq> &g, gf_keeper<tags::fourier,retime> const & L) { fourier_impl (g,L.g);}
-  void triqs_gf_view_assign_delegation( gf_view<retime> &g, gf_keeper<tags::fourier,refreq> const & L) { inverse_fourier_impl(g,L.g);}
+  void triqs_gf_view_assign_delegation( gf_view<refreq> g, gf_keeper<tags::fourier,retime> const & L) { fourier_impl (g,L.g);}
+  void triqs_gf_view_assign_delegation( gf_view<retime> g, gf_keeper<tags::fourier,refreq> const & L) { inverse_fourier_impl(g,L.g);}
 
 }}
diff --git a/triqs/gf/local/fourier_real.hpp b/triqs/gf/local/fourier_real.hpp
index f94f4e9d..81909ebc 100644
--- a/triqs/gf/local/fourier_real.hpp
+++ b/triqs/gf/local/fourier_real.hpp
@@ -27,29 +27,26 @@
 
 namespace triqs { namespace gf { 
 
- namespace impl_local_real {
-   dcomplex I(0,1);
-   double pi = std::acos(-1);
- }
-
  // First the implementation of the fourier transform
- void fourier_impl         (gf_view<refreq> &gw , gf_view<retime> const & gt);
- void inverse_fourier_impl (gf_view<retime> &gt,  gf_view<refreq> const & gw);
+ void fourier_impl         (gf_view<refreq> gw , gf_view<retime> const gt);
+ void inverse_fourier_impl (gf_view<retime> gt,  gf_view<refreq> const gw);
 
- gf_view<refreq> fourier (gf_view<retime> const & gt) { 
+ inline gf_view<refreq> fourier (gf_view<retime> const & gt) { 
+   double pi = std::acos(-1);
    size_t L = gt.mesh().size();
-   double wmin = -impl_local_real::pi * (L-1) / (L*gt.mesh().delta());
-   double wmax =  impl_local_real::pi * (L-1) / (L*gt.mesh().delta());
+   double wmin = -pi * (L-1) / (L*gt.mesh().delta());
+   double wmax =  pi * (L-1) / (L*gt.mesh().delta());
    auto gw = make_gf<refreq>(wmin, wmax, L, gt.data().shape().front_pop());
    auto V = gw();
    fourier_impl(V, gt);
    return gw;
  }
 
- gf_view<retime> inverse_fourier (gf_view<refreq> const & gw) { 
+ inline gf_view<retime> inverse_fourier (gf_view<refreq> const & gw) { 
+   double pi = std::acos(-1);
    size_t L = gw.mesh().size();
-   double tmin = -impl_local_real::pi * (L-1) / (L*gw.mesh().delta());
-   double tmax =  impl_local_real::pi * (L-1) / (L*gw.mesh().delta());
+   double tmin = -pi * (L-1) / (L*gw.mesh().delta());
+   double tmax =  pi * (L-1) / (L*gw.mesh().delta());
    auto gt = make_gf<retime>(tmin, tmax, L, gw.data().shape().front_pop());
    auto V = gt();
    inverse_fourier_impl(V, gw);
@@ -59,8 +56,8 @@ namespace triqs { namespace gf {
  gf_keeper<tags::fourier,retime> lazy_fourier         (gf_view<retime> const & g);
  gf_keeper<tags::fourier,refreq> lazy_inverse_fourier (gf_view<refreq> const & g);
 
- void triqs_gf_view_assign_delegation( gf_view<refreq> &g, gf_keeper<tags::fourier,retime> const & L);
- void triqs_gf_view_assign_delegation( gf_view<retime> &g, gf_keeper<tags::fourier,refreq> const & L);
+ void triqs_gf_view_assign_delegation( gf_view<refreq> g, gf_keeper<tags::fourier,retime> const & L);
+ void triqs_gf_view_assign_delegation( gf_view<retime> g, gf_keeper<tags::fourier,refreq> const & L);
 
 }}
 #endif
diff --git a/triqs/gf/meshes/product.hpp b/triqs/gf/meshes/product.hpp
index fc6adec3..95627f2f 100644
--- a/triqs/gf/meshes/product.hpp
+++ b/triqs/gf/meshes/product.hpp
@@ -23,6 +23,7 @@
 #include "./mesh_tools.hpp"
 #include "../domains/product.hpp"
 #include <triqs/utility/tuple_tools.hpp>
+#include <triqs/utility/mini_vector.hpp>
 namespace triqs { namespace gf {
 
  template<typename... Meshes> struct mesh_product : tag::composite {
@@ -57,6 +58,14 @@ namespace triqs { namespace gf {
    struct _aux3 { template<typename P, typename M> size_t operator()(M const & m, P const & p,size_t R) {return p.linear_index() + R * m.size();}};
    size_t mp_to_linear(m_pt_tuple_t const & mp) const { return triqs::tuple::fold_on_zip(_aux3(), m_tuple, mp, size_t(0)); }
 
+   //
+   struct _aux4 { template< typename M, typename V> V * operator()(M const & m, V * v) {*v = m.size(); return ++v;}};
+   utility::mini_vector<size_t,dim> all_size_as_mini_vector () const { 
+    utility::mini_vector<size_t,dim> res;
+    triqs::tuple::fold(_aux4(), m_tuple, &res[0] ); 
+    return res;
+   }
+
    // Same but a variadic list of mesh_point_t
    template<typename ... MP> size_t mesh_pt_components_to_linear(MP const &  ... mp) const {
     static_assert(std::is_same< std::tuple<MP...>, m_pt_tuple_t>::value, "Call incorrect ");
@@ -75,6 +84,7 @@ namespace triqs { namespace gf {
     mesh_point_t(mesh_product const & m_)                  : m(&m_), _c (triqs::tuple::apply(F1(), m_.m_tuple)), _atend(false)    {}
     m_pt_tuple_t const & components_tuple() const { return _c;}
     size_t linear_index() const { return m->mp_to_linear(_c);}
+    const mesh_product * mesh() const { return m;}
 
     typedef domain_pt_t cast_t;
     operator cast_t() const { return m->index_to_point(index);}
@@ -141,10 +151,40 @@ namespace triqs { namespace gf {
      triqs::tuple::fold(_aux_ser<Archive>(ar), m_tuple, size_t(0));
     }
 
- private:
+  private:
    m_tuple_t  m_tuple;
    domain_t _dom;
-};
+ };
+
+ //template<int pos, typename ... M> 
+ //typename std::tuple_element<pos,typename mesh_product<M...>::index_t>::type get_index1(typename mesh_product<M...>::mesh_point_t const & p) { return std::get<pos>(p.components_tuple());}
+
+ template<int pos, typename P> 
+  auto get_index(P const & p) DECL_AND_RETURN( std::get<pos>(p.components_tuple()).index());
+
+ template<int pos, typename P> 
+  auto get_point(P const & p) DECL_AND_RETURN( std::get<pos>( p.mesh()->components() ).index_to_point( std::get<pos>(p.components_tuple()).index() ) );
+ 
+ // C++14
+ //auto get_point(P const & p) { return std::get<pos> (p.mesh()->components()).index_to_point( std::get<pos>(p.components_tuple()));}
+
+ // Given a composite mesh m , and a linear array of storage A
+ // reinterpret_linear_array(m,A) returns a d-dimensionnal view of the array
+ // with indices egal to the indices of the components of the mesh.
+ // Very useful for slicing, currying functions.
+ template<typename ... Meshes, typename T, ull_t OptionsFlags >
+  arrays::array_view<T, sizeof...(Meshes),OptionsFlags, arrays::indexmaps::mem_layout::fortran_order(sizeof...(Meshes)) >
+  reinterpret_linear_array(mesh_product<Meshes...> const & m, arrays::array_view<T,1,OptionsFlags> const & A) {
+   static int constexpr rank = sizeof...(Meshes);
+   typedef arrays::array_view<T, sizeof...(Meshes),OptionsFlags, arrays::indexmaps::mem_layout::fortran_order(rank)> return_t;
+   typedef typename return_t::indexmap_type im_t;
+   auto l = m.all_size_as_mini_vector();
+   typename im_t::strides_type sv;
+   std::ptrdiff_t s= 1;
+   for (int u=0; u<rank; ++u) { sv[u] = s; s *= l[u];} // fortran type folding 
+   return return_t (im_t (l,sv,0) , A.storage());
+  }
+
 
 }}
 #endif
diff --git a/triqs/gf/refreq.hpp b/triqs/gf/refreq.hpp
index 489bf04e..b0620a04 100644
--- a/triqs/gf/refreq.hpp
+++ b/triqs/gf/refreq.hpp
@@ -31,45 +31,50 @@ namespace triqs { namespace gf {
  struct refreq {};
 
  namespace gf_implementation { 
-
+  
   template<typename Opt> struct mesh<refreq,Opt>                { 
    typedef linear_mesh<R_domain> type;
    typedef typename type::domain_t domain_t;
-   static type make(double wmin, double wmax, size_t n_freq, mesh_kind mk) {
+   static type make(double wmin, double wmax, size_t n_freq, mesh_kind mk=full_bins) {
     return type(domain_t(), wmin, wmax, n_freq, mk);
    }
   };
   
+  // singularity 
   template<typename Opt> struct singularity<refreq,matrix_valued,Opt>  { typedef local::tail type;};
+  template<typename Opt> struct singularity<refreq,scalar_valued,Opt>  { typedef local::tail type;};
+  
+  // h5 name
   template<typename Opt> struct h5_name<refreq,matrix_valued,Opt>      { static std::string invoke(){ return "GfReFreq";}};
+  template<typename Opt> struct h5_name<refreq,scalar_valued,Opt>      { static std::string invoke(){ return "GfReFreq_s";}};
 
   /// ---------------------------  evaluator ---------------------------------
-
-  template<typename Opt>
-   struct evaluator<refreq,matrix_valued,Opt> {
+  template<typename Opt, typename Target>
+   struct evaluator<refreq,Target,Opt> {
     static constexpr int arity = 1;
+    typedef typename std::conditional < std::is_same<Target, matrix_valued>::value, arrays::matrix_view<std::complex<double>>, std::complex<double>>::type rtype; 
     template<typename G>
-     arrays::matrix_view<std::complex<double> >  operator() (G const * g,double w0)  const {
+     rtype operator() (G const * g,double w0)  const {
       auto & data = g->data();
       auto & mesh = g->mesh();
       size_t index; double w; bool in;
       std::tie(in, index, w) = windowing(mesh,w0);
       if (!in) TRIQS_RUNTIME_ERROR <<" Evaluation out of bounds";
-      arrays::matrix<std::complex<double> > res = w*data(mesh.index_to_linear(index), arrays::ellipsis()) + (1-w)*data(mesh.index_to_linear(index+1), arrays::ellipsis());
-      return res;
+      return w*data(mesh.index_to_linear(index), arrays::ellipsis()) + (1-w)*data(mesh.index_to_linear(index+1), arrays::ellipsis());
      }
     template<typename G>
      local::tail_view operator()(G const * g,freq_infty const &) const {return g->singularity();}
    };
 
   /// ---------------------------  data access  ---------------------------------
-
   template<typename Opt> struct data_proxy<refreq,matrix_valued,Opt> : data_proxy_array<std::complex<double>,3> {};
-
+  template<typename Opt> struct data_proxy<refreq,scalar_valued,Opt> : data_proxy_array<std::complex<double>,1> {};
+   
   // -------------------------------   Factories  --------------------------------------------------
-
+  
+  //matrix_valued
   template<typename Opt> struct factories<refreq, matrix_valued,Opt> {
-   typedef gf<refreq> gf_t;
+   typedef gf<refreq,matrix_valued> gf_t;
 
    template<typename MeshType>
     static gf_t make_gf(MeshType && m, tqa::mini_vector<size_t,2> shape, local::tail_view const & t) {
@@ -86,6 +91,27 @@ namespace triqs { namespace gf {
     typename gf_t::data_non_view_t A(shape.front_append(n_freq)); A() =0;
     return gf_t(mesh<refreq,Opt>::make(wmin, wmax, n_freq, mk), std::move(A), local::tail(shape), nothing());
    }
+  };
+   
+   //scalar_valued
+  template<typename Opt> struct factories<refreq,scalar_valued,Opt> {
+   typedef gf<refreq,scalar_valued> gf_t;
+
+   template<typename MeshType>
+    static gf_t make_gf(MeshType && m, local::tail_view const & t) {
+     typename gf_t::data_non_view_t A(m.size()); A() =0;
+     return gf_t ( std::forward<MeshType>(m), std::move(A), t, nothing() ) ;
+    }
+    
+    static gf_t make_gf(double wmin, double wmax, size_t n_freq) {
+     typename gf_t::data_non_view_t A(n_freq); A() =0;
+     return gf_t(mesh<refreq,Opt>::make(wmin, wmax, n_freq), std::move(A), local::tail(tqa::mini_vector<size_t,2>(1,1)), nothing());
+    }
+
+   static gf_t make_gf(double wmin, double wmax, size_t n_freq, mesh_kind mk) {
+    typename gf_t::data_non_view_t A(n_freq); A() =0;
+    return gf_t(mesh<refreq,Opt>::make(wmin, wmax, n_freq, mk), std::move(A), local::tail(tqa::mini_vector<size_t,2>(1,1)), nothing());
+   }
 
   };
  } // gf_implementation
diff --git a/triqs/gf/retime.hpp b/triqs/gf/retime.hpp
index c480c788..cb48e8bf 100644
--- a/triqs/gf/retime.hpp
+++ b/triqs/gf/retime.hpp
@@ -36,53 +36,80 @@ namespace triqs { namespace gf {
    typedef linear_mesh<R_domain> type;
    typedef typename type::domain_t domain_t;
 
-   static type make(double tmin, double tmax, size_t n_points, mesh_kind mk) {
-    return type(domain_t(), tmin, tmax, n_points, mk);
+   static type make(double tmin, double tmax, size_t n_points, mesh_kind mk=full_bins) {
+     return type(domain_t(), tmin, tmax, n_points, mk);
    }
+   
   };
   
+  // singularity 
   template<typename Opt> struct singularity<retime,matrix_valued,Opt>  { typedef local::tail type;};
+  template<typename Opt> struct singularity<retime,scalar_valued,Opt>  { typedef local::tail type;};
+  
+  // h5 name
   template<typename Opt> struct h5_name<retime,matrix_valued,Opt>      { static std::string invoke(){ return  "GfReTime";}};
+  template<typename Opt> struct h5_name<retime,scalar_valued,Opt>      { static std::string invoke(){ return  "GfReTime_s";}};
 
   /// ---------------------------  evaluator ---------------------------------
-
-  template<typename Opt>
-   struct evaluator<retime,matrix_valued,Opt> {
+  template<typename Opt, typename Target>
+   struct evaluator<retime,Target,Opt> {
     static constexpr int arity = 1;
+    //typedef typename std::conditional < std::is_same<Target, matrix_valued>::value, arrays::matrix_view<std::complex<double>>, std::complex<double>>::type rtype; 
+    typedef typename std::conditional < std::is_same<Target, matrix_valued>::value, arrays::matrix<std::complex<double>>, std::complex<double>>::type rtype; 
     template<typename G>
-     arrays::matrix_view<std::complex<double> >  operator() (G const * g,double t0)  const {
+      rtype operator() (G const * g,double t0)  const {
       auto & data = g->data();
       auto & mesh = g->mesh();
       size_t index; double w; bool in;
       std::tie(in, index, w) = windowing(mesh,t0);
       if (!in) TRIQS_RUNTIME_ERROR <<" Evaluation out of bounds";
-      arrays::matrix<std::complex<double> > res = w*data(mesh.index_to_linear(index), arrays::ellipsis()) + (1-w)*data(mesh.index_to_linear(index+1), arrays::ellipsis());
-      return res;
+      return 
+        (1-w) * data(mesh.index_to_linear(index  ), arrays::ellipsis() ) 
+        + w *   data(mesh.index_to_linear(index+1), arrays::ellipsis() );
      }
     template<typename G>
      local::tail_view operator()(G const * g,freq_infty const &) const {return g->singularity();}
    };
 
   /// ---------------------------  data access  ---------------------------------
-
-  template<typename Opt> struct data_proxy<retime,matrix_valued,Opt> : data_proxy_array<std::complex<double>,3> {};
+   template<typename Opt> struct data_proxy<retime,matrix_valued,Opt> : data_proxy_array<std::complex<double>,3> {};
+   template<typename Opt> struct data_proxy<retime,scalar_valued,Opt> : data_proxy_array<std::complex<double>,1> {};
 
   // -------------------------------   Factories  --------------------------------------------------
 
+  //matrix_valued
   template<typename Opt> struct factories<retime, matrix_valued,Opt> {
-   typedef gf<retime> gf_t;
-
-   static gf_t make_gf(double tmin, double tmax, size_t n_points, tqa::mini_vector<size_t,2> shape) {
-    typename gf_t::data_non_view_t A(shape.front_append(n_points)); A() =0;
-    return gf_t(mesh<retime,Opt>::make(tmin, tmax, n_points, full_bins), std::move(A), local::tail(shape), nothing());
-   }
-
+   typedef gf<retime,matrix_valued> gf_t;
+   
    static gf_t make_gf(double tmin, double tmax, size_t n_points, tqa::mini_vector<size_t,2> shape, mesh_kind mk) {
     typename gf_t::data_non_view_t A(shape.front_append(n_points)); A() =0;
-    return gf_t(mesh<retime,Opt>::make(tmin, tmax, n_points, mk), std::move(A), local::tail(shape), nothing());
+    return gf_t(mesh<retime,Opt>::make(tmin, tmax, n_points,mk), std::move(A), local::tail(shape), nothing());
    }
-
+   
+   static gf_t make_gf(double tmin, double tmax, size_t n_points, tqa::mini_vector<size_t,2> shape) {
+    typename gf_t::data_non_view_t A(shape.front_append(n_points)); A() =0;
+    return gf_t(mesh<retime,Opt>::make(tmin, tmax, n_points), std::move(A), local::tail(shape), nothing());
+   }
+   
   };
+  
+  //scalar_valued
+  template<typename Opt> struct factories<retime, scalar_valued,Opt> {
+   typedef gf<retime,scalar_valued> gf_t;
+   
+   static gf_t make_gf(double tmin, double tmax, size_t n_points, mesh_kind mk) {
+    typename gf_t::data_non_view_t A(n_points); A() =0;
+    return gf_t(mesh<retime,Opt>::make(tmin, tmax, n_points,mk), std::move(A), local::tail(tqa::mini_vector<size_t,2>(1,1)), nothing());
+   }
+   
+   static gf_t make_gf(double tmin, double tmax, size_t n_points) {
+    typename gf_t::data_non_view_t A(n_points); A() =0;
+    return gf_t(mesh<retime,Opt>::make(tmin, tmax, n_points), std::move(A), local::tail(tqa::mini_vector<size_t,2>(1,1)), nothing());
+   }
+   
+  };
+  
+  
  } // gf_implementation
 }}
 #endif
diff --git a/triqs/gf/two_real_times.hpp b/triqs/gf/two_real_times.hpp
index 2e693974..349947e1 100644
--- a/triqs/gf/two_real_times.hpp
+++ b/triqs/gf/two_real_times.hpp
@@ -69,15 +69,21 @@ namespace triqs { namespace gf {
   /// ---------------------------  evaluator ---------------------------------
 
   template<typename Opt>
-   struct evaluator<two_real_times,matrix_valued,Opt> {
+  struct evaluator<two_real_times,matrix_valued,Opt> {
     static constexpr int arity = 2;
     template<typename G>
-     arrays::matrix_view<std::complex<double> > operator() (G const * g, double t0, double t1)  const {
-      auto & m0 = std::get<0>(g->mesh().components()); 
-      double s= m0.x_max()/m0.size();
-      return g->data()(g->mesh().index_to_linear( typename G::mesh_t::index_t(t0*s, t1*s)), arrays::range(), arrays::range());//mesh.index_to_linear(mesh.point_to_index (t1,t2)));
-     } 
-   };
+    arrays::matrix<std::complex<double> > operator() (G const * g, double t0, double t1)  const {
+      auto & data = g->data();
+      auto & m = std::get<0>(g->mesh().components()); 
+      size_t n0,n1; double w0,w1; bool in;
+      std::tie(in, n0, w0) = windowing(m,t0);
+      if (!in) TRIQS_RUNTIME_ERROR <<" Evaluation out of bounds";
+      std::tie(in, n1, w1) = windowing(m,t1);
+      if (!in) TRIQS_RUNTIME_ERROR <<" Evaluation out of bounds";
+      auto gg = [g,data]( size_t n0, size_t n1) {return data(g->mesh().index_to_linear(std::tuple<size_t,size_t>{n0,n1}), arrays::ellipsis());};
+      return  w0 * ( w1*gg(n0,n1) + (1-w1)*gg(n0,n1+1) ) + (1-w0) * ( w1*gg(n0+1,n1) + (1-w1)*gg(n0+1,n1+1)); 
+    } 
+  };
 
   /// ---------------------------  data access  ---------------------------------
 
@@ -128,15 +134,16 @@ namespace triqs { namespace gf {
  } // gf_implementation
 
   // -------------------------------   Additionnal free function for this gf  --------------------------------------------------
-
+  
   // from g(t,t') and t, return g(t-t') for any t'>t 
+  // 
  gf<retime> slice (gf_view<two_real_times> const & g, double t) { 
-  auto const & m = std::get<0> (g.mesh().components());
-  long it = get_closest_mesh_pt_index(m, t);
+  auto const & m = std::get<0> (g.mesh().components());   //one-time mesh
+  long it = get_closest_mesh_pt_index(m, t);              //index of t on this mesh
   long nt = m.size() - it;
-  if (it < nt) nt = it ;
+  if (it+1 < nt) nt = it+1 ;                              //nt=length of the resulting GF's mesh
   double dt = m.delta();
-  auto res = make_gf<retime>(0, nt*dt, nt, g(t,t).shape());
+  auto res = make_gf<retime>(0, 2*(nt-1)*dt, nt, g(t,t).shape());
   res() = 0;
   auto _ = arrays::range();// everyone
   for(long sh=0; sh<nt; sh++){