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https://github.com/triqs/dft_tools
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gf curry: clean call for a single index
- if the index is not a tuple, make it a tuple... - better API.
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@ -37,7 +37,7 @@ try {
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//std::cout << G_t_tau_N (0.1,0.2) << std::endl;
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auto G_w_wn_view = G_w_wn();
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auto G_w_wn_sl0_a = partial_eval<0>(G_w_wn(), std::make_tuple(size_t(8)));
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auto G_w_wn_sl0_a = partial_eval<0>(G_w_wn(), 8);
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static_assert(std::is_same<typename std::remove_reference<decltype(G_w_wn_sl0_a.mesh())>::type, const gf_mesh<imfreq>>::value, "oops");
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//auto G_w_wn_curry0_a = curry0(G_w_wn);
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//auto G_w_wn_sl0_a = slice_mesh0(G_w_wn(), 8);
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@ -72,17 +72,15 @@ try {
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// test curry
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std::cout << "curry no"<< G_w_wn.on_mesh(8,3) << std::endl ;
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/*
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auto G_w_wn_curry0 = curry<0>(G_w_wn);
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static_assert(std::is_same<typename std::remove_reference<decltype(G_w_wn_curry0[0].mesh())>::type, const gf_mesh<imfreq>>::value, "oops");
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static_assert(std::is_same<typename std::remove_reference<decltype(G_w_wn_curry0.mesh())>::type, const gf_mesh<refreq>>::value, "oops");
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auto G_w_wn_curry1 = curry<1>(G_w_wn);
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auto G_w_wn_view2 = G_w_wn();
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std::cout << " curry "<<G_w_wn_curry0[8] << G_w_wn_curry0[8][3] << G_w_wn_view2.on_mesh(8,3) << G_w_wn_view2.on_mesh(8,3) <<std::endl ;
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std::cout << " curry "<<G_w_wn_curry1[3][8] << std::endl;
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std::cout << "G_w_wn_sl0_a [3]"<<G_w_wn_sl0_a[3] << std::endl ;
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*/
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// test hdf5
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H5::H5File file("gf_re_im_freq_time.h5", H5F_ACC_TRUNC );
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h5_write(file, "g_t_tau", G_t_tau);
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@ -49,15 +49,21 @@ namespace triqs { namespace gfs {
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template<typename M0, typename M1, typename ...M> auto rm_tuple_of_size_one(std::tuple<M0,M1,M...> const & t) DECL_AND_RETURN(t);
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template<typename M> auto rm_tuple_of_size_one(std::tuple<M> const & t) DECL_AND_RETURN(std::get<0>(t));
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// as_tuple leaves a tuple intact and wrap everything else in a tuple...
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template<typename T> std::tuple<T> as_tuple(T && x) { return std::tuple<T> {std::forward<T>(x)};}
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template<typename ... T> std::tuple<T...> as_tuple(std::tuple<T...> && x) { return std::forward<T...>(x);}
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template<typename ... T> std::tuple<T...> const & as_tuple(std::tuple<T...> const & x) { return x;}
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template<typename ... T> std::tuple<T...> & as_tuple(std::tuple<T...> & x) { return x;}
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template<int ... pos, typename Opt, typename Target, bool B, bool IsConst, typename IT, typename ... Ms>
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gf_view< typename cart_prod_impl< triqs::tuple::filter_out_t<std::tuple<Ms...>, pos...>>::type ,Target, Opt,IsConst>
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partial_eval(gf_impl< cartesian_product<Ms...>, Target,Opt,B,IsConst> const & g, IT index) {
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// meshes of the returned gf_view : just drop the mesh of the evaluated variables
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auto meshes_tuple_partial = triqs::tuple::filter_out<pos...>(g.mesh().components());
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// a view of the array of g, with the dimension sizeof...(Ms)
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auto arr = reinterpret_linear_array(g.mesh(),g.data());
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auto arr = reinterpret_linear_array(g.mesh(),g.data()); // NO the second () forces a view
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// now rebuild a tuple of the size sizeof...(Ms), containing the indices and range at the position of evaluated variables.
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auto arr_args = triqs::tuple::inverse_filter<sizeof...(Ms),pos...>(index, arrays::range());
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auto arr_args = triqs::tuple::inverse_filter<sizeof...(Ms),pos...>(as_tuple(index), arrays::range());
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// from it, we make a slice of the array of g, corresponding to the data of the returned gf_view
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auto arr2 = triqs::tuple::apply(arr, arr_args);
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// finally, we build the view on this data.
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