/usr/include/madness/mra/mraimpl.h is in libmadness-dev 0.10.1~gite4aa500e-10.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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This file is part of MADNESS.
Copyright (C) 2007,2010 Oak Ridge National Laboratory
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
For more information please contact:
Robert J. Harrison
Oak Ridge National Laboratory
One Bethel Valley Road
P.O. Box 2008, MS-6367
email: harrisonrj@ornl.gov
tel: 865-241-3937
fax: 865-572-0680
*/
#ifndef MADNESS_MRA_MRAIMPL_H__INCLUDED
#define MADNESS_MRA_MRAIMPL_H__INCLUDED
#ifndef MPRAIMPLX
#error "mraimpl.h should ONLY be included in one of the mraX.cc files (x=1..6)"
#endif
//#define WORLD_INSTANTIATE_STATIC_TEMPLATES
#include <memory>
#include <math.h>
#include <cmath>
#include <madness/world/world_object.h>
#include <madness/world/worlddc.h>
#include <madness/world/worldhashmap.h>
#include <madness/mra/function_common_data.h>
#include <madness/mra/funcimpl.h>
#include <madness/mra/displacements.h>
namespace std {
template <typename T>
bool isnan(const std::complex<T>& v) {
return ::std::isnan(v.real()) || ::std::isnan(v.imag());
}
}
/// \file mra/mraimpl.h
/// \brief Declaration and initialization of static data, some implementation, some instantiation
namespace madness {
// Definition and initialization of FunctionDefaults static members
// It cannot be an instance of FunctionFactory since we want to
// set the defaults independent of the data type.
template <typename T, std::size_t NDIM>
void FunctionCommonData<T,NDIM>::_init_twoscale() {
if (! two_scale_hg(k, &hg)) throw "failed to get twoscale coefficients";
hgT = copy(transpose(hg));
Slice sk(0,k-1), sk2(k,-1);
hgsonly = copy(hg(Slice(0,k-1),_));
h0 = copy(hg(sk,sk));
h1 = copy(hg(sk,sk2));
g0 = copy(hg(sk2,sk));
g1 = copy(hg(sk2,sk2));
h0T = copy(transpose(hg(sk,sk)));
h1T = copy(transpose(hg(sk,sk2)));
g0T = copy(transpose(hg(sk2,sk)));
g1T = copy(transpose(hg(sk2,sk2)));
}
template <typename T, std::size_t NDIM>
void FunctionCommonData<T,NDIM>::_init_quadrature
(int k, int npt, Tensor<double>& quad_x, Tensor<double>& quad_w,
Tensor<double>& quad_phi, Tensor<double>& quad_phiw, Tensor<double>& quad_phit) {
quad_x = Tensor<double>(npt); // point
quad_w = Tensor<double>(npt); // wheight
quad_phi = Tensor<double>(npt,k);
quad_phiw = Tensor<double>(npt,k);
gauss_legendre(npt,0.0,1.0,quad_x.ptr(),quad_w.ptr());
for (int mu=0; mu<npt; ++mu) {
double phi[200];
legendre_scaling_functions(quad_x(mu),k,phi);
for (int j=0; j<k; ++j) {
quad_phi(mu,j) = phi[j];
quad_phiw(mu,j) = quad_w(mu)*phi[j];
}
}
quad_phit = transpose(quad_phi);
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::verify_tree() const {
PROFILE_MEMBER_FUNC(FunctionImpl);
world.gop.fence(); // Make sure nothing is going on
// Verify consistency of compression status, existence and size of coefficients,
// and has_children() flag.
for (typename dcT::const_iterator it=coeffs.begin(); it!=coeffs.end(); ++it) {
const keyT& key = it->first;
const nodeT& node = it->second;
bool bad;
if (is_compressed()) {
if (node.has_children()) {
bad = (node.coeff().has_data()) and (node.coeff().dim(0) != 2*cdata.k);
}
else {
// bad = node.coeff().size() != 0;
bad = node.coeff().has_data();
}
}
else {
if (node.has_children()) {
// bad = node.coeff().size() != 0;
bad = node.coeff().has_data();
}
else {
bad = (node.coeff().has_data()) and ( node.coeff().dim(0) != cdata.k);
}
}
if (bad) {
print(world.rank(), "FunctionImpl: verify: INCONSISTENT TREE NODE, key =", key, ", node =", node,
", dim[0] =",node.coeff().dim(0),", compressed =",is_compressed());
std::cout.flush();
MADNESS_EXCEPTION("FunctionImpl: verify: INCONSISTENT TREE NODE", 0);
}
}
// Ensure that parents and children exist appropriately
for (typename dcT::const_iterator it=coeffs.begin(); it!=coeffs.end(); ++it) {
const keyT& key = it->first;
const nodeT& node = it->second;
if (key.level() > 0) {
const keyT parent = key.parent();
typename dcT::const_iterator pit = coeffs.find(parent).get();
if (pit == coeffs.end()) {
print(world.rank(), "FunctionImpl: verify: MISSING PARENT",key,parent);
std::cout.flush();
MADNESS_EXCEPTION("FunctionImpl: verify: MISSING PARENT", 0);
}
const nodeT& pnode = pit->second;
if (!pnode.has_children()) {
print(world.rank(), "FunctionImpl: verify: PARENT THINKS IT HAS NO CHILDREN",key,parent);
std::cout.flush();
MADNESS_EXCEPTION("FunctionImpl: verify: PARENT THINKS IT HAS NO CHILDREN", 0);
}
}
for (KeyChildIterator<NDIM> kit(key); kit; ++kit) {
typename dcT::const_iterator cit = coeffs.find(kit.key()).get();
if (cit == coeffs.end()) {
if (node.has_children()) {
print(world.rank(), "FunctionImpl: verify: MISSING CHILD",key,kit.key());
std::cout.flush();
MADNESS_EXCEPTION("FunctionImpl: verify: MISSING CHILD", 0);
}
}
else {
if (! node.has_children()) {
print(world.rank(), "FunctionImpl: verify: UNEXPECTED CHILD",key,kit.key());
std::cout.flush();
MADNESS_EXCEPTION("FunctionImpl: verify: UNEXPECTED CHILD", 0);
}
}
}
}
world.gop.fence();
}
template <typename T, std::size_t NDIM>
const std::shared_ptr< WorldDCPmapInterface< Key<NDIM> > >& FunctionImpl<T,NDIM>::get_pmap() const {
return coeffs.get_pmap();
}
/// perform: this= alpha*f + beta*g, invoked by result
/// f and g are reconstructed, so we can save on the compress operation,
/// walk down the joint tree, and add leaf coefficients; effectively refines
/// to common finest level.
/// @param[in] alpha prefactor for f
/// @param[in] f first addend
/// @param[in] beta prefactor for g
/// @param[in] g second addend
/// @return nothing, but leaves this's tree reconstructed and as sum of f and g
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::gaxpy_oop_reconstructed(const double alpha, const implT& f,
const double beta, const implT& g, const bool fence) {
MADNESS_ASSERT(not f.is_compressed());
MADNESS_ASSERT(not g.is_compressed());
ProcessID owner = coeffs.owner(cdata.key0);
if (world.rank() == owner) {
CoeffTracker<T,NDIM> ff(&f);
CoeffTracker<T,NDIM> gg(&g);
typedef add_op coeff_opT;
coeff_opT coeff_op(ff,gg,alpha,beta);
typedef insert_op<T,NDIM> apply_opT;
apply_opT apply_op(this);
woT::task(world.rank(), &implT:: template forward_traverse<coeff_opT,apply_opT>,
coeff_op, apply_op, cdata.key0);
}
this->compressed=false;
if (fence) world.gop.fence();
}
/// Returns true if the function is compressed.
template <typename T, std::size_t NDIM>
bool FunctionImpl<T,NDIM>::is_compressed() const {
return compressed;
}
/// Returns true if the function is redundant.
template <typename T, std::size_t NDIM>
bool FunctionImpl<T,NDIM>::is_redundant() const {
return redundant;
}
template <typename T, std::size_t NDIM>
bool FunctionImpl<T,NDIM>::is_nonstandard() const {return nonstandard;}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::set_functor(const std::shared_ptr<FunctionFunctorInterface<T,NDIM> > functor1) {
this->on_demand=true;
functor=functor1;
}
template <typename T, std::size_t NDIM>
std::shared_ptr<FunctionFunctorInterface<T,NDIM> > FunctionImpl<T,NDIM>::get_functor() {
MADNESS_ASSERT(this->functor);
return functor;
}
template <typename T, std::size_t NDIM>
std::shared_ptr<FunctionFunctorInterface<T,NDIM> > FunctionImpl<T,NDIM>::get_functor() const {
MADNESS_ASSERT(this->functor);
return functor;
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::unset_functor() {
this->on_demand=false;
functor.reset();
}
template <typename T, std::size_t NDIM>
bool& FunctionImpl<T,NDIM>::is_on_demand() {return on_demand;};
template <typename T, std::size_t NDIM>
const bool& FunctionImpl<T,NDIM>::is_on_demand() const {return on_demand;};
template <typename T, std::size_t NDIM>
TensorType FunctionImpl<T,NDIM>::get_tensor_type() const {return targs.tt;}
template <typename T, std::size_t NDIM>
TensorArgs FunctionImpl<T,NDIM>::get_tensor_args() const {return targs;}
template <typename T, std::size_t NDIM>
double FunctionImpl<T,NDIM>::get_thresh() const {return thresh;}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::set_thresh(double value) {thresh = value;}
template <typename T, std::size_t NDIM>
bool FunctionImpl<T,NDIM>::get_autorefine() const {return autorefine;}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::set_autorefine(bool value) {autorefine = value;}
template <typename T, std::size_t NDIM>
int FunctionImpl<T,NDIM>::get_k() const {return k;}
template <typename T, std::size_t NDIM>
const typename FunctionImpl<T,NDIM>::dcT& FunctionImpl<T,NDIM>::get_coeffs() const {return coeffs;}
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::dcT& FunctionImpl<T,NDIM>::get_coeffs() {return coeffs;}
template <typename T, std::size_t NDIM>
const FunctionCommonData<T,NDIM>& FunctionImpl<T,NDIM>::get_cdata() const {return cdata;}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::accumulate_timer(const double time) const {
timer_accumulate.accumulate(time);
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::print_timer() const {
if (world.rank()==0) {
timer_accumulate.print("accumulate");
timer_target_driven.print("target_driven");
timer_lr_result.print("result2low_rank");
}
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::reset_timer() {
if (world.rank()==0) {
timer_accumulate.reset();
timer_target_driven.reset();
timer_lr_result.reset();
}
}
/// Truncate according to the threshold with optional global fence
/// If thresh<=0 the default value of this->thresh is used
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::truncate(double tol, bool fence) {
// Cannot put tol into object since it would make a race condition
if (tol <= 0.0)
tol = thresh;
if (world.rank() == coeffs.owner(cdata.key0)) {
if (is_compressed()) {
truncate_spawn(cdata.key0,tol);
} else {
truncate_reconstructed_spawn(cdata.key0,tol);
}
}
if (fence)
world.gop.fence();
}
template <typename T, std::size_t NDIM>
const typename FunctionImpl<T,NDIM>::keyT& FunctionImpl<T,NDIM>::key0() const {
return cdata.key0;
}
/// Print a plane ("xy", "xz", or "yz") containing the point x to file
/// works for all dimensions; we walk through the tree, and if a leaf node
/// inside the sub-cell touches the plane we print it in pstricks format
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::print_plane(const std::string filename, const int xaxis, const int yaxis, const coordT& el2) {
// get the local information
Tensor<double> localinfo=print_plane_local(xaxis,yaxis,el2);
// lump all the local information together, and gather on node0
std::vector<Tensor<double> > localinfo_vec(1,localinfo);
std::vector<Tensor<double> > printinfo=world.gop.concat0(localinfo_vec);
world.gop.fence();
// do the actual print
if (world.rank()==0) do_print_plane(filename,printinfo,xaxis,yaxis,el2);
}
/// collect the data for a plot of the MRA structure locally on each node
/// @param[in] xaxis the x-axis in the plot (can be any axis of the MRA box)
/// @param[in] yaxis the y-axis in the plot (can be any axis of the MRA box)
/// @param[in] el2
template <typename T, std::size_t NDIM>
Tensor<double> FunctionImpl<T,NDIM>::print_plane_local(const int xaxis, const int yaxis, const coordT& el2) {
coordT x_sim;
user_to_sim<NDIM>(el2,x_sim);
x_sim[2]+=1.e-10;
// dimensions are: (# boxes)(hue, x lo left, y lo left, x hi right, y hi right)
Tensor<double> plotinfo(coeffs.size(),5);
long counter=0;
// loop over local boxes, if the fit, add the info to the output tensor
typename dcT::const_iterator end = coeffs.end();
for (typename dcT::const_iterator it=coeffs.begin(); it!=end; ++it) {
const keyT& key = it->first;
const nodeT& node = it->second;
// thisKeyContains ignores dim0 and dim1
if (key.thisKeyContains(x_sim,xaxis,yaxis) and node.is_leaf() and (node.has_coeff())) {
Level n=key.level();
Vector<Translation,NDIM> l=key.translation();
// get the diametral edges of the node in the plotting plane
double scale=std::pow(0.5,double(n));
double xloleft = scale*l[xaxis];
double yloleft = scale*l[yaxis];
double xhiright = scale*(l[xaxis]+1);
double yhiright = scale*(l[yaxis]+1);
// convert back to user coordinates
Vector<double,4> user;
user[0]=xloleft*FunctionDefaults<NDIM>::get_cell_width()[xaxis] + FunctionDefaults<NDIM>::get_cell()(xaxis,0);
user[2]=xhiright*FunctionDefaults<NDIM>::get_cell_width()[xaxis] + FunctionDefaults<NDIM>::get_cell()(xaxis,0);
user[1]=yloleft*FunctionDefaults<NDIM>::get_cell_width()[yaxis] + FunctionDefaults<NDIM>::get_cell()(yaxis,0);
user[3]=yhiright*FunctionDefaults<NDIM>::get_cell_width()[yaxis] + FunctionDefaults<NDIM>::get_cell()(yaxis,0);
// if ((xloleft<-5.0) or (yloleft<-5.0) or (xhiright>5.0) or (yhiright>5.0)) continue;
if ((user[0]<-5.0) or (user[1]<-5.0) or (user[2]>5.0) or (user[3]>5.0)) continue;
// do rank or do error
double color=0.0;
if (1) {
const double maxrank=40;
do_convert_to_color hue(maxrank,false);
color=hue(node.coeff().rank());
} else {
// Make quadrature rule of higher order
const int npt = cdata.npt + 1;
Tensor<double> qx, qw, quad_phi, quad_phiw, quad_phit;
FunctionCommonData<T,NDIM>::_init_quadrature(k+1, npt, qx, qw, quad_phi, quad_phiw, quad_phit);
do_err_box< FunctionFunctorInterface<T,NDIM> > op(this, this->get_functor().get(), npt, qx, quad_phit, quad_phiw);
do_convert_to_color hue(1000.0,true);
double error=op(it);
error=sqrt(error);//*pow(2,key.level()*6);
color=hue(error);
}
plotinfo(counter,0)=color;
plotinfo(counter,1)=user[0];
plotinfo(counter,2)=user[1];
plotinfo(counter,3)=user[2];
plotinfo(counter,4)=user[3];
++counter;
}
}
// shrink the info
if (counter==0) plotinfo=Tensor<double>();
else plotinfo=plotinfo(Slice(0,counter-1),Slice(_));
return plotinfo;
}
/// print the MRA structure
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::do_print_plane(const std::string filename, std::vector<Tensor<double> > plotinfo,
const int xaxis, const int yaxis, const coordT el2) {
// invoke only on master node
MADNESS_ASSERT(world.rank()==0);
// prepare file
FILE * pFile;
pFile = fopen(filename.c_str(), "w");
Tensor<double> cell=FunctionDefaults<NDIM>::get_cell();
fprintf(pFile,"\\psset{unit=1cm}\n");
fprintf(pFile,"\\begin{pspicture}(%4.2f,%4.2f)(%4.2f,%4.2f)\n",
// cell(xaxis,0),cell(xaxis,1),cell(yaxis,0),cell(yaxis,1));
-5.0,-5.0,5.0,5.0);
fprintf(pFile,"\\pslinewidth=0.1pt\n");
for (std::vector<Tensor<double> >::const_iterator it=plotinfo.begin(); it!=plotinfo.end(); ++it) {
Tensor<double> localinfo=*it;
if (localinfo.has_data()) {
for (long i=0; i<localinfo.dim(0); ++i) {
fprintf(pFile,"\\newhsbcolor{mycolor}{%8.4f 1.0 0.7}\n",localinfo(i,0));
fprintf(pFile,"\\psframe["//linewidth=0.5pt,"
"fillstyle=solid,"
"fillcolor=mycolor]"
"(%12.8f,%12.8f)(%12.8f,%12.8f)\n",
localinfo(i,1),localinfo(i,2),localinfo(i,3),localinfo(i,4));
}
}
}
fprintf(pFile,"\\end{pspicture}\n");
fclose(pFile);
}
/// print the grid (the roots of the quadrature of each leaf box)
/// of this function in user xyz coordinates
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::print_grid(const std::string filename) const {
// get the local information
std::vector<keyT> local_keys=local_leaf_keys();
// lump all the local information together, and gather on node0
std::vector<keyT> all_keys=world.gop.concat0(local_keys);
world.gop.fence();
// do the actual print
if (world.rank()==0) do_print_grid(filename,all_keys);
}
/// return the keys of the local leaf boxes
template <typename T, std::size_t NDIM>
std::vector<typename FunctionImpl<T,NDIM>::keyT> FunctionImpl<T,NDIM>::local_leaf_keys() const {
// coeffs.size is maximum number of keys (includes internal keys)
std::vector<keyT> keys(coeffs.size());
// loop over local boxes, if they are leaf boxes add their quadrature roots
// to the output tensor
int i=0;
typename dcT::const_iterator end = coeffs.end();
for (typename dcT::const_iterator it=coeffs.begin(); it!=end; ++it) {
const keyT& key = it->first;
const nodeT& node = it->second;
if (node.is_leaf()) keys[i++]=key;
}
// shrink the vector to number of leaf keys
keys.resize(i);
return keys;
}
/// print the grid in xyz format
/// the quadrature points and the key information will be written to file,
/// @param[in] filename where the quadrature points will be written to
/// @param[in] keys all leaf keys
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::do_print_grid(const std::string filename, const std::vector<keyT>& keys) const {
// invoke only on master node
MADNESS_ASSERT(world.rank()==0);
// the quadrature points in simulation coordinates of the root node
const Tensor<double> qx=cdata.quad_x;
const size_t npt = qx.dim(0);
// the number of coordinates (grid point tuples) per box ({x1},{x2},{x3},..,{xNDIM})
long npoints=power<NDIM>(npt);
// the number of boxes
long nboxes=keys.size();
// prepare file
FILE * pFile;
pFile = fopen(filename.c_str(), "w");
fprintf(pFile,"%ld\n",npoints*nboxes);
fprintf(pFile,"%ld points per box and %ld boxes \n",npoints,nboxes);
// loop over all leaf boxes
typename std::vector<keyT>::const_iterator key_it=keys.begin();
for (key_it=keys.begin(); key_it!=keys.end(); ++key_it) {
const keyT& key=*key_it;
fprintf(pFile,"# key: %8d",key.level());
for (size_t d=0; d<NDIM; d++) fprintf(pFile,"%8d",int(key.translation()[d]));
fprintf(pFile,"\n");
// this is borrowed from fcube
const Vector<Translation,NDIM>& l = key.translation();
const Level n = key.level();
const double h = std::pow(0.5,double(n));
coordT c; // will hold the point in user coordinates
const Tensor<double>& cell_width = FunctionDefaults<NDIM>::get_cell_width();
const Tensor<double>& cell = FunctionDefaults<NDIM>::get_cell();
if (NDIM == 3) {
for (size_t i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
for (size_t j=0; j<npt; ++j) {
c[1] = cell(1,0) + h*cell_width[1]*(l[1] + qx(j)); // y
for (size_t k=0; k<npt; ++k) {
c[2] = cell(2,0) + h*cell_width[2]*(l[2] + qx(k)); // z
// grid weights
// double scale = pow(0.5,0.5*NDIM*key.level())*
// sqrt(FunctionDefaults<NDIM>::get_cell_volume());
// double w=cdata.quad_phiw[i]*cdata.quad_phiw[j]*cdata.quad_phiw[k];
fprintf(pFile,"%18.12f %18.12f %18.12f\n",c[0],c[1],c[2]);
// fprintf(pFile,"%18.12e %18.12e %18.12e %18.12e\n",c[0],c[1],c[2],w*scale);
}
}
}
} else {
MADNESS_EXCEPTION("only NDIM=3 in print_grid",0);
}
}
fclose(pFile);
}
/// Returns the truncation threshold according to truncate_method
template <typename T, std::size_t NDIM>
double FunctionImpl<T,NDIM>::truncate_tol(double tol, const keyT& key) const {
// RJH ... introduced max level here to avoid runaway
// refinement due to truncation threshold going down to
// intrinsic numerical error
const int MAXLEVEL1 = 20; // 0.5**20 ~= 1e-6
const int MAXLEVEL2 = 10; // 0.25**10 ~= 1e-6
if (truncate_mode == 0) {
return tol;
}
else if (truncate_mode == 1) {
double L = FunctionDefaults<NDIM>::get_cell_min_width();
return tol*std::min(1.0,pow(0.5,double(std::min(key.level(),MAXLEVEL1)))*L);
}
else if (truncate_mode == 2) {
double L = FunctionDefaults<NDIM>::get_cell_min_width();
return tol*std::min(1.0,pow(0.25,double(std::min(key.level(),MAXLEVEL2)))*L*L);
}
else if (truncate_mode == 3) {
// similar to truncate mode 1, but with an additional factor to
// account for an increased number of boxes in higher dimensions
// here is our handwaving argument: this threshold will give each
// FunctionNode an error of less than tol. The total error can
// then be as high as sqrt(#nodes) * tol. Therefore in order to
// account for higher dimensions: divide tol by about the root of
// number of siblings (2^NDIM) that have a large error when we
// refine along a deep branch of the tree. FAB
//
// Nope ... it can easily be as high as #nodes * tol. The real
// fix for this is an end-to-end error analysis of the larger
// application and if desired to include this factor into the
// threshold selected by the application. RJH
const static double fac=1.0/std::pow(2,NDIM*0.5);
tol*=fac;
double L = FunctionDefaults<NDIM>::get_cell_min_width();
return tol*std::min(1.0,pow(0.5,double(std::min(key.level(),MAXLEVEL1)))*L);
} else {
MADNESS_EXCEPTION("truncate_mode invalid",truncate_mode);
}
}
/// Returns patch referring to coeffs of child in parent box
template <typename T, std::size_t NDIM>
std::vector<Slice> FunctionImpl<T,NDIM>::child_patch(const keyT& child) const {
std::vector<Slice> s(NDIM);
const Vector<Translation,NDIM>& l = child.translation();
for (std::size_t i=0; i<NDIM; ++i)
s[i] = cdata.s[l[i]&1]; // Lowest bit of translation
return s;
}
/// Directly project parent NS coeffs to child NS coeffs
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::coeffT FunctionImpl<T,NDIM>::parent_to_child_NS(
const keyT& child, const keyT& parent, const coeffT& coeff) const {
const implT* f=this;
// MADNESS_ASSERT(coeff.tensor_type()==TT_FULL);
coeffT result;
// if the node for child is existent in f, and it is an internal node, we
// automatically have the NS form; if it is a leaf node, we only have the
// sum coeffs, so we take zero difference coeffs
if (child==parent) {
if (coeff.dim(0)==2*f->get_k()) result=coeff; // internal node
else if (coeff.dim(0)==f->get_k()) { // leaf node
tensorT t(f->cdata.v2k);
t(f->cdata.s0)=coeff.full_tensor_copy();
result=coeffT(t,f->get_tensor_args());
} else {
MADNESS_EXCEPTION("confused k in parent_to_child_NS",1);
}
} else if (child.level()>parent.level()) {
// parent and coeff should refer to a leaf node with sum coeffs only
// b/c tree should be compressed with leaves kept.
MADNESS_ASSERT(coeff.dim(0)==f->get_k());
const coeffT coeff1=f->parent_to_child(coeff,parent,child);
tensorT t(f->cdata.v2k);
t(f->cdata.s0)=coeff1.full_tensor_copy();
result=coeffT(t,f->get_tensor_args());
} else {
MADNESS_EXCEPTION("confused keys in parent_to_child_NS",1);
}
return result;
}
/// Returns the box at level n that contains the given point in simulation coordinates
template <typename T, std::size_t NDIM>
Key<NDIM> FunctionImpl<T,NDIM>::simpt2key(const coordT& pt, Level n) const {
Vector<Translation,NDIM> l;
double twon = std::pow(2.0, double(n));
for (std::size_t i=0; i<NDIM; ++i) {
l[i] = Translation(twon*pt[i]);
}
return Key<NDIM>(n,l);
}
/// Get the scaling function coeffs at level n starting from NS form
// N=2^n, M=N/q, q must be power of 2
// q=0 return coeffs [N,k] for direct sum
// q>0 return coeffs [k,q,M] for fft sum
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::tensorT FunctionImpl<T,NDIM>::coeffs_for_jun(Level n, long q) {
MADNESS_ASSERT(compressed && nonstandard && NDIM<=3);
tensorT r,r0;
long N=1<<n;
long M = (q ? N/q: N);
if (q==0) {
q = 1;
long dim[2*NDIM];
for (std::size_t d=0; d<NDIM; ++d) {
dim[d ] = N;
dim[d+NDIM] = cdata.k;
}
tensorT rr(2*NDIM,dim);
r0=r=rr;
//NNkk->MqMqkk, since fuse is not allowed. Now needs to move back to 2*NDIM, since tensor max dim is 6
//for (int d=NDIM-1; d>=0; --d) r.splitdim_inplace_base(d,M,q);
} else {
long dim[2*NDIM];
for (std::size_t d=0; d<NDIM; ++d) {
//dim[d+NDIM*2] = M;
dim[d+NDIM ] = N;
dim[d ] = cdata.k;
}
tensorT rr(2*NDIM,dim);
r0=rr;
/*vector<long> map(3*NDIM);
for (int d=0; d<NDIM; ++d) {
map[d]=d+2*NDIM;
map[NDIM+d]=2*d+1;
map[2*NDIM+d]=2*d;
}
r.mapdim_inplace_base(map);
//print(rr);
//for (int d=1; d<NDIM; ++d) rr.swapdim_inplace_base(2*NDIM+d,NDIM+d); //kkqqMM->kkqMqM
//print(rr);
//for (int d=0; d<NDIM; ++d) rr.swapdim_inplace_base(NDIM+2*d,NDIM+2*d-1); //kkqMqM->kkMqMq
//print(rr);
//for (int d=0; d<NDIM; ++d) rr.fusedim_inplace_base(NDIM+d); //->kkNN
//seems that this fuse is not allowed :(
//print(rr);
*/
r=rr.cycledim(NDIM,0,-1); //->NNkk or MqMqkk
}
print("faking done M q r(fake) r0(real)",M,q,"\n", std::vector<long> (r.dims(),r.dims()+6),std::vector<long> (r0.dims(),r0.dims()+6));
ProcessID me = world.rank();
Vector<long,NDIM> t(N);
Vector<long,NDIM> powq, powN, powM;
long NDIM1 = NDIM-1;
powM[NDIM1]=powq[NDIM1]=powN[NDIM1]=1;
for (int d=NDIM1-1; d>=0; --d) {
powM[d] = powM[d+1]*M;
powq[d] = powq[d+1]*q;
powN[d] = powN[d+1]*N;
}
long powMNDIM = powM[0]*M;
for (IndexIterator it(t); it; ++it) {
keyT key(n, Vector<Translation,NDIM>(*it));
if (coeffs.owner(key) == me) {
typename dcT::iterator it = coeffs.find(key).get();
coeffT qq;
if (it == coeffs.end()) {
// must get from above
typedef std::pair< keyT,coeffT > pairT;
Future<pairT> result;
sock_it_to_me(key, result.remote_ref(world));
const keyT& parent = result.get().first;
// const tensorT& t = result.get().second.full_tensor_copy();
const coeffT& t = result.get().second;
qq = (parent_to_child(t, parent, key));
} else {
qq = copy(it->second.coeff());
}
std::vector<Slice> s(NDIM*2);
long ll = 0;
for (std::size_t d=0; d<NDIM; ++d) {
Translation l = key.translation()[d];
long dum = long(float(l)/q);
ll += (l - dum*q)*powMNDIM*powq[d] + dum*powM[d];
//ll += (l % q)*powM[NDIM]*pow((double)q,NDIM-d-1) + (l/q)*pow((double)M,NDIM-d-1);
//print("translation",l);
//s[d ] = Slice(l,l,0);
//s[d+NDIM ] = Slice(l%q,l%q,0);
//s[d+NDIM] = Slice(0,k-1,1);
}
//long dum = ll;
for (std::size_t d=0; d<NDIM; ++d) {
Translation l = Translation(float(ll) / powN[d]);
//Translation l = ll / pow((double)N,NDIM-d-1);
s[d ] = Slice(l,l,0);
s[d+NDIM] = Slice(0,k-1,1);
ll = ll - l*powN[d];
//ll = ll % long(pow((double)N,NDIM-d-1));
}
//print(s, dum, key.translation());
coeffT qqq=qq(cdata.s0);
r(s) = qqq.full_tensor_copy();
}
}
world.gop.fence();
world.gop.sum(r0);
//print(r,r0);
return r0;
}
/// truncate tree at a certain level
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::erase(const Level& max_level) {
this->make_redundant(true);
typename dcT::iterator end = coeffs.end();
for (typename dcT::iterator it= coeffs.begin(); it!=end; ++it) {
keyT key=it->first;
nodeT& node=it->second;
if (key.level()>max_level) coeffs.erase(key);
if (key.level()==max_level) node.set_has_children(false);
}
this->undo_redundant(true);
}
/// Returns some asymmetry measure ... no comms
template <typename T, std::size_t NDIM>
double FunctionImpl<T,NDIM>::check_symmetry_local() const {
PROFILE_MEMBER_FUNC(FunctionImpl);
typedef Range<typename dcT::const_iterator> rangeT;
return world.taskq.reduce<double,rangeT,do_check_symmetry_local>(rangeT(coeffs.begin(),coeffs.end()),
do_check_symmetry_local(*this));
}
/// Refine multiple functions down to the same finest level
/// @param[v] is the vector of functions we are refining.
/// @param[key] is the current node.
/// @param[c] is the vector of coefficients passed from above.
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::refine_to_common_level(const std::vector<FunctionImpl<T,NDIM>*>& v,
const std::vector<tensorT>& c,
const keyT key) {
if (key == cdata.key0 && coeffs.owner(key)!=world.rank()) return;
// First insert coefficients from above ... also get write accessors here
std::unique_ptr<typename dcT::accessor[]> acc(new typename dcT::accessor[v.size()]);
for (unsigned int i=0; i<c.size(); i++) {
MADNESS_ASSERT(v[i]->coeffs.get_pmap() == coeffs.get_pmap());
MADNESS_ASSERT(v[i]->coeffs.owner(key) == world.rank());
bool exists = ! v[i]->coeffs.insert(acc[i],key);
if (c[i].size()) {
MADNESS_ASSERT(!exists);
acc[i]->second = nodeT(coeffT(c[i],targs),false);
}
else {
MADNESS_ASSERT(exists);
}
}
// If everyone has coefficients we are done
bool done = true;
for (unsigned int i=0; i<v.size(); i++) {
done &= acc[i]->second.has_coeff();
}
if (!done) {
// Those functions with coefficients need to be refined down
std::vector<tensorT> d(v.size());
for (unsigned int i=0; i<v.size(); i++) {
if (acc[i]->second.has_coeff()) {
tensorT s(cdata.v2k);
// s(cdata.s0) = acc[i]->second.coeff()(___);
s(cdata.s0) = acc[i]->second.coeff().full_tensor_copy();
acc[i]->second.clear_coeff();
d[i] = unfilter(s);
acc[i]->second.set_has_children(true);
}
}
// Loop thru children and pass down
for (KeyChildIterator<NDIM> kit(key); kit; ++kit) {
const keyT& child = kit.key();
std::vector<Slice> cp = child_patch(child);
std::vector<tensorT> childc(v.size());
for (unsigned int i=0; i<v.size(); i++) {
if (d[i].size()) childc[i] = copy(d[i](cp));
}
woT::task(coeffs.owner(child), &implT::refine_to_common_level, v, childc, child);
}
}
}
// horrifically non-scalable
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::put_in_box(ProcessID from, long nl, long ni) const {
if (world.size()> 1000)
throw "NO!";
box_leaf[from] = nl;
box_interior[from] = ni;
}
/// Prints summary of data distribution
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::print_info() const {
if (world.size() >= 1000)
return;
for (int i=0; i<world.size(); ++i)
box_leaf[i] = box_interior[i] == 0;
world.gop.fence();
long nleaf=0, ninterior=0;
typename dcT::const_iterator end = coeffs.end();
for (typename dcT::const_iterator it=coeffs.begin(); it!=end; ++it) {
const nodeT& node = it->second;
if (node.is_leaf())
++nleaf;
else
++ninterior;
}
this->send(0, &implT::put_in_box, world.rank(), nleaf, ninterior);
world.gop.fence();
if (world.rank() == 0) {
for (int i=0; i<world.size(); ++i) {
printf("load: %5d %8ld %8ld\n", i, box_leaf[i], box_interior[i]);
}
}
world.gop.fence();
}
template <typename T, std::size_t NDIM>
bool FunctionImpl<T,NDIM>::noautorefine(const keyT& key, const tensorT& t) const {
return false;
}
/// Returns true if this block of coeffs needs autorefining
template <typename T, std::size_t NDIM>
bool FunctionImpl<T,NDIM>::autorefine_square_test(const keyT& key, const nodeT& t) const {
double lo, hi;
tnorm(t.coeff().full_tensor_copy(), &lo, &hi);
double test = 2*lo*hi + hi*hi;
//print("autoreftest",key,thresh,truncate_tol(thresh, key),lo,hi,test);
return test> truncate_tol(thresh, key);
}
/// is this the same as trickle_down() ?
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::sum_down_spawn(const keyT& key, const coeffT& s) {
typename dcT::accessor acc;
coeffs.insert(acc,key);
nodeT& node = acc->second;
coeffT& c = node.coeff();
//print(key,"received",s.normf(),c.normf(),node.has_children());
if (s.size() > 0) {
if (c.size() > 0)
c.gaxpy(1.0,s,1.0);
else
c = s;
}
if (node.has_children()) {
coeffT d;
if (c.has_data()) {
d = coeffT(cdata.v2k,targs);
d(cdata.s0) += c;
d = unfilter(d);
node.clear_coeff();
}
for (KeyChildIterator<NDIM> kit(key); kit; ++kit) {
coeffT ss;
const keyT& child = kit.key();
if (d.size() > 0) ss = copy(d(child_patch(child)));
//print(key,"sending",ss.normf(),"to",child);
woT::task(coeffs.owner(child), &implT::sum_down_spawn, child, ss);
}
}
else {
// Missing coeffs assumed to be zero
if (c.size() <= 0) c = coeffT(cdata.vk,targs);
}
}
/// After 1d push operator must sum coeffs down the tree to restore correct scaling function coefficients
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::sum_down(bool fence) {
if (world.rank() == coeffs.owner(cdata.key0)) sum_down_spawn(cdata.key0, coeffT());
if (fence) world.gop.fence();
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::forward_do_diff1(const DerivativeBase<T,NDIM>* D,
const implT* f,
const keyT& key,
const std::pair<keyT,coeffT>& left,
const std::pair<keyT,coeffT>& center,
const std::pair<keyT,coeffT>& right) {
D->forward_do_diff1(f,this,key,left,center,right);
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::do_diff1(const DerivativeBase<T,NDIM>* D,
const implT* f,
const keyT& key,
const std::pair<keyT,coeffT>& left,
const std::pair<keyT,coeffT>& center,
const std::pair<keyT,coeffT>& right) {
D->do_diff1(f,this,key,left,center,right);
}
// Called by result function to differentiate f
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::diff(const DerivativeBase<T,NDIM>* D, const implT* f, bool fence) {
typedef std::pair<keyT,coeffT> argT;
typename dcT::const_iterator end = f->coeffs.end();
for (typename dcT::const_iterator it=f->coeffs.begin(); it!=end; ++it) {
const keyT& key = it->first;
const nodeT& node = it->second;
if (node.has_coeff()) {
Future<argT> left = D->find_neighbor(f, key,-1);
argT center(key,node.coeff());
Future<argT> right = D->find_neighbor(f, key, 1);
world.taskq.add(*this, &implT::do_diff1, D, f, key, left, center, right, TaskAttributes::hipri());
}
else {
coeffs.replace(key,nodeT(coeffT(),true)); // Empty internal node
}
}
if (fence) world.gop.fence();
}
/// return the a std::pair<key, node>, which MUST exist
template <typename T, std::size_t NDIM>
std::pair<Key<NDIM>,ShallowNode<T,NDIM> > FunctionImpl<T,NDIM>::find_datum(keyT key) const {
MADNESS_ASSERT(coeffs.probe(key));
ShallowNode<T,NDIM> snode(coeffs.find(key).get()->second);
return std::pair<Key<NDIM>,ShallowNode<T,NDIM> >(key,snode);
}
/// multiply the ket with a one-electron potential rr(1,2)= f(1,2)*g(1)
/// @param[in] val_ket function values of f(1,2)
/// @param[in] val_pot function values of g(1)
/// @param[in] particle if 0 then g(1), if 1 then g(2)
/// @return the resulting function values
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::coeffT FunctionImpl<T,NDIM>::multiply(const coeffT& val_ket,
const coeffT& val_pot, int particle) const {
MADNESS_ASSERT(particle==0 or particle==1);
MADNESS_ASSERT(val_pot.tensor_type()==TT_FULL);
MADNESS_ASSERT(val_ket.tensor_type()==TT_2D);
std::vector<long> vkhalf=std::vector<long>(NDIM/2,cdata.vk[0]);
tensorT ones=tensorT(vkhalf);
ones=1.0;
TensorArgs targs(-1.0,val_ket.tensor_type());
coeffT pot12;
if (particle==0) pot12=outer(val_pot.full_tensor(),ones,targs);
else if (particle==1) pot12=outer(ones,val_pot.full_tensor(),targs);
coeffT result=copy(val_ket);
result.emul(pot12);
return result;
}
/// given several coefficient tensors, assemble a result tensor
/// the result looks like: (v(1,2) + v(1) + v(2)) |ket(1,2)>
/// or (v(1,2) + v(1) + v(2)) |p(1) p(2)>
/// i.e. coefficients for the ket and coefficients for the two particles are
/// mutually exclusive. All potential terms are optional, just pass in empty coeffs.
/// @param[in] key the key of the FunctionNode to which these coeffs belong
/// @param[in] cket coefficients of the ket
/// @param[in] vpotential1 function values of the potential for particle 1
/// @param[in] vpotential2 function values of the potential for particle 2
/// @param[in] veri function values for the 2-particle potential
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::coeffT FunctionImpl<T,NDIM>::assemble_coefficients(
const keyT& key, const coeffT& coeff_ket, const coeffT& vpotential1,
const coeffT& vpotential2, const tensorT& veri) const {
// take a shortcut if we are already done
bool ket_only=(not (vpotential1.has_data() or vpotential2.has_data() or veri.has_data()));
if (ket_only) return coeff_ket;
// switch to values instead of coefficients
coeffT val_ket=coeffs2values(key,coeff_ket);
// the result tensor
coeffT val_result;
coeffT coeff_result;
// potential for particles 1 and 2, must be done in TT_2D
if (vpotential1.has_data() or vpotential2.has_data()) {
val_ket=val_ket.convert(TensorArgs(-1.0,TT_2D));
}
if (vpotential1.has_data()) val_result+=multiply(val_ket,vpotential1,0);
if (vpotential2.has_data()) val_result+=multiply(val_ket,vpotential2,1);
// values for eri: this must be done in full rank...
if (veri.has_data()) {
tensorT val_ket2=val_ket.full_tensor_copy().emul(veri);
if (val_result.has_data()) val_ket2+=val_result.full_tensor_copy();
// values2coeffs expensive (30%), coeffT() (relatively) cheap (8%)
coeff_result=coeffT(values2coeffs(key,val_ket2),this->get_tensor_args());
} else {
// convert back to original tensor type
val_ket=val_ket.convert(get_tensor_args());
MADNESS_ASSERT(val_result.has_data());
coeff_result=values2coeffs(key,val_result);
coeff_result.reduce_rank(this->get_tensor_args().thresh);
}
return coeff_result;
}
/// Permute the dimensions of f according to map, result on this
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::mapdim(const implT& f, const std::vector<long>& map, bool fence) {
PROFILE_MEMBER_FUNC(FunctionImpl);
const_cast<implT*>(&f)->flo_unary_op_node_inplace(do_mapdim(map,*this),fence);
}
/// take the average of two functions, similar to: this=0.5*(this+rhs)
/// works in either basis and also in nonstandard form
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::average(const implT& rhs) {
rhs.flo_unary_op_node_inplace(do_average(*this),true);
this->scale_inplace(0.5,true);
flo_unary_op_node_inplace(do_reduce_rank(targs),true);
}
/// change the tensor type of the coefficients in the FunctionNode
/// @param[in] targs target tensor arguments (threshold and full/low rank)
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::change_tensor_type1(const TensorArgs& targs, bool fence) {
flo_unary_op_node_inplace(do_change_tensor_type(targs),fence);
}
/// reduce the rank of the coefficients tensors
/// @param[in] targs target tensor arguments (threshold and full/low rank)
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::reduce_rank(const TensorArgs& targs, bool fence) {
flo_unary_op_node_inplace(do_reduce_rank(targs),fence);
}
/// Transform sum coefficients at level n to sums+differences at level n-1
/// Given scaling function coefficients s[n][l][i] and s[n][l+1][i]
/// return the scaling function and wavelet coefficients at the
/// coarser level. I.e., decompose Vn using Vn = Vn-1 + Wn-1.
/// \code
/// s_i = sum(j) h0_ij*s0_j + h1_ij*s1_j
/// d_i = sum(j) g0_ij*s0_j + g1_ij*s1_j
// \endcode
/// Returns a new tensor and has no side effects. Works for any
/// number of dimensions.
///
/// No communication involved.
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::tensorT FunctionImpl<T,NDIM>::filter(const tensorT& s) const {
tensorT r(cdata.v2k,false);
tensorT w(cdata.v2k,false);
return fast_transform(s,cdata.hgT,r,w);
//return transform(s,cdata.hgT);
}
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::coeffT FunctionImpl<T,NDIM>::filter(const coeffT& s) const {
coeffT result=transform(s,cdata.hgT);
return result;
}
/// Transform sums+differences at level n to sum coefficients at level n+1
/// Given scaling function and wavelet coefficients (s and d)
/// returns the scaling function coefficients at the next finer
/// level. I.e., reconstruct Vn using Vn = Vn-1 + Wn-1.
/// \code
/// s0 = sum(j) h0_ji*s_j + g0_ji*d_j
/// s1 = sum(j) h1_ji*s_j + g1_ji*d_j
/// \endcode
/// Returns a new tensor and has no side effects
///
/// If (sonly) ... then ss is only the scaling function coeff (and
/// assume the d are zero). Works for any number of dimensions.
///
/// No communication involved.
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::tensorT FunctionImpl<T,NDIM>::unfilter(const tensorT& s) const {
tensorT r(cdata.v2k,false);
tensorT w(cdata.v2k,false);
return fast_transform(s,cdata.hg,r,w);
//return transform(s, cdata.hg);
}
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::coeffT FunctionImpl<T,NDIM>::unfilter(const coeffT& s) const {
return transform(s,cdata.hg);
}
/// downsample the sum coefficients of level n+1 to sum coeffs on level n
/// specialization of the filter method, will yield only the sum coefficients
/// @param[in] key key of level n
/// @param[in] v vector of sum coefficients of level n+1
/// @param[in] args TensorArguments for possible low rank approximations
/// @return sum coefficients on level n in full tensor format
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::tensorT FunctionImpl<T,NDIM>::downsample(const keyT& key, const std::vector< Future<coeffT > >& v) const {
tensorT result(cdata.vk);
// the twoscale coefficients: for downsampling use h0/h1; see Alpert Eq (3.34a)
const tensorT h[2] = {cdata.h0T, cdata.h1T};
tensorT matrices[NDIM];
// loop over all child nodes, transform and accumulate
long i=0;
for (KeyChildIterator<NDIM> kit(key); kit; ++kit,++i) {
// get the appropriate twoscale coefficients for each dimension
for (size_t ii=0; ii<NDIM; ++ii) matrices[ii]=h[kit.key().translation()[ii]%2];
// transform and accumulate on the result
result+=general_transform(v[i].get(),matrices).full_tensor_copy();
}
return result;
}
/// upsample the sum coefficients of level 1 to sum coeffs on level n+1
/// specialization of the unfilter method, will transform only the sum coefficients
/// @param[in] key key of level n+1
/// @param[in] coeff sum coefficients of level n (does NOT belong to key!!)
/// @param[in] args TensorArguments for possible low rank approximations
/// @return sum coefficients on level n+1
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::coeffT FunctionImpl<T,NDIM>::upsample(const keyT& key, const coeffT& coeff) const {
// the twoscale coefficients: for upsampling use h0/h1; see Alpert Eq (3.35a/b)
// note there are no difference coefficients; if you want that use unfilter
const tensorT h[2] = {cdata.h0, cdata.h1};
tensorT matrices[NDIM];
// get the appropriate twoscale coefficients for each dimension
for (size_t ii=0; ii<NDIM; ++ii) matrices[ii]=h[key.translation()[ii]%2];
// transform and accumulate on the result
const coeffT result=general_transform(coeff,matrices);
return result;
}
/// Projects old function into new basis (only in reconstructed form)
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::project(const implT& old, bool fence) {
long kmin = std::min(cdata.k,old.cdata.k);
std::vector<Slice> s(NDIM,Slice(0,kmin-1));
typename dcT::const_iterator end = old.coeffs.end();
for (typename dcT::const_iterator it=old.coeffs.begin(); it!=end; ++it) {
const keyT& key = it->first;
const nodeT& node = it->second;
if (node.has_coeff()) {
coeffT c(cdata.vk,targs);
c(s) += node.coeff()(s);
coeffs.replace(key,nodeT(c,false));
}
else {
coeffs.replace(key,nodeT(coeffT(),true));
}
}
if (fence)
world.gop.fence();
}
template <typename T, std::size_t NDIM>
bool FunctionImpl<T,NDIM>::exists_and_has_children(const keyT& key) const {
return coeffs.probe(key) && coeffs.find(key).get()->second.has_children();
}
template <typename T, std::size_t NDIM>
bool FunctionImpl<T,NDIM>::exists_and_is_leaf(const keyT& key) const {
return coeffs.probe(key) && (not coeffs.find(key).get()->second.has_children());
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::broaden_op(const keyT& key, const std::vector< Future <bool> >& v) {
for (unsigned int i=0; i<v.size(); ++i) {
if (v[i]) {
refine_op(true_refine_test(), key);
break;
}
}
}
// For each local node sets value of norm tree to 0.0
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::zero_norm_tree() {
typename dcT::iterator end = coeffs.end();
for (typename dcT::iterator it=coeffs.begin(); it!=end; ++it) {
it->second.set_norm_tree(0.0);
}
}
// Broaden tree
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::broaden(std::vector<bool> is_periodic, bool fence) {
typename dcT::iterator end = coeffs.end();
for (typename dcT::iterator it=coeffs.begin(); it!=end; ++it) {
const keyT& key = it->first;
typename dcT::accessor acc;
MADNESS_ASSERT(coeffs.find(acc,key));
nodeT& node = acc->second;
if (node.has_coeff() &&
node.get_norm_tree() != -1.0 &&
node.coeff().normf() >= truncate_tol(thresh,key)) {
node.set_norm_tree(-1.0); // Indicates already broadened or result of broadening/refining
//int ndir = std::pow(3,NDIM);
int ndir = static_cast<int>(std::pow(static_cast<double>(3), static_cast<int>(NDIM)));
std::vector< Future <bool> > v = future_vector_factory<bool>(ndir);
keyT neigh;
int i=0;
for (HighDimIndexIterator it(NDIM,3); it; ++it) {
Vector<Translation,NDIM> l(*it);
for (std::size_t d=0; d<NDIM; ++d) {
const int odd = key.translation()[d] & 0x1L; // 1 if odd, 0 if even
l[d] -= 1; // (0,1,2) --> (-1,0,1)
if (l[d] == -1)
l[d] = -1-odd;
else if (l[d] == 1)
l[d] = 2 - odd;
}
keyT neigh = neighbor(key, keyT(key.level(),l), is_periodic);
if (neigh.is_valid()) {
v[i++] = this->send(coeffs.owner(neigh), &implT::exists_and_has_children, neigh);
}
else {
v[i++].set(false);
}
}
woT::task(world.rank(), &implT::broaden_op, key, v);
}
}
// Reset value of norm tree so that can repeat broadening
if (fence) {
world.gop.fence();
zero_norm_tree();
world.gop.fence();
}
}
/// sum all the contributions from all scales after applying an operator in mod-NS form
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::trickle_down(bool fence) {
// MADNESS_ASSERT(is_redundant());
nonstandard = compressed = redundant = false;
// this->print_size("in trickle_down");
if (world.rank() == coeffs.owner(cdata.key0))
woT::task(world.rank(), &implT::trickle_down_op, cdata.key0,coeffT());
if (fence)
world.gop.fence();
}
/// sum all the contributions from all scales after applying an operator in mod-NS form
/// cf reconstruct_op
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::trickle_down_op(const keyT& key, const coeffT& s) {
// Note that after application of an integral operator not all
// siblings may be present so it is necessary to check existence
// and if absent insert an empty leaf node.
//
// If summing the result of an integral operator (i.e., from
// non-standard form) there will be significant scaling function
// coefficients at all levels and possibly difference coefficients
// in leaves, hence the tree may refine as a result.
typename dcT::iterator it = coeffs.find(key).get();
if (it == coeffs.end()) {
coeffs.replace(key,nodeT(coeffT(),false));
it = coeffs.find(key).get();
}
nodeT& node = it->second;
// The integral operator will correctly connect interior nodes
// to children but may leave interior nodes without coefficients
// ... but they still need to sum down so just give them zeros
if (node.coeff().has_no_data()) node.coeff()=coeffT(cdata.vk,targs);
// if (node.has_children() || node.has_coeff()) { // Must allow for inconsistent state from transform, etc.
if (node.has_children()) { // Must allow for inconsistent state from transform, etc.
coeffT d = node.coeff();
if (key.level() > 0) d += s; // -- note accumulate for NS summation
node.clear_coeff();
for (KeyChildIterator<NDIM> kit(key); kit; ++kit) {
const keyT& child = kit.key();
coeffT ss= upsample(child,d);
ss.reduce_rank(thresh);
PROFILE_BLOCK(recon_send);
woT::task(coeffs.owner(child), &implT::trickle_down_op, child, ss);
}
}
else {
node.coeff()+=s;
node.coeff().reduce_rank(thresh);
}
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::reconstruct(bool fence) {
// Must set true here so that successive calls without fence do the right thing
MADNESS_ASSERT(not is_redundant());
nonstandard = compressed = redundant = false;
if (world.rank() == coeffs.owner(cdata.key0))
woT::task(world.rank(), &implT::reconstruct_op, cdata.key0,coeffT());
if (fence)
world.gop.fence();
}
/// compress the wave function
/// after application there will be sum coefficients at the root level,
/// and difference coefficients at all other levels; furthermore:
/// @param[in] nonstandard keep sum coeffs at all other levels, except leaves
/// @param[in] keepleaves keep sum coeffs (but no diff coeffs) at leaves
/// @param[in] redundant keep only sum coeffs at all levels, discard difference coeffs
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::compress(bool nonstandard, bool keepleaves, bool redundant, bool fence) {
MADNESS_ASSERT(not is_redundant());
// Must set true here so that successive calls without fence do the right thing
this->compressed = true;
this->nonstandard = nonstandard;
this->redundant = redundant;
// these two are exclusive
MADNESS_ASSERT(not (redundant and nonstandard));
// otherwise we loose information
if (redundant) {MADNESS_ASSERT(keepleaves);}
// this->print_tree();
if (world.rank() == coeffs.owner(cdata.key0)) {
compress_spawn(cdata.key0, nonstandard, keepleaves, redundant);
}
if (fence)
world.gop.fence();
}
/// convert this to redundant, i.e. have sum coefficients on all levels
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::make_redundant(const bool fence) {
// fast return if possible
if (is_redundant()) return;
// NS form might have leaf sum coeffs, but we don't know
// change to standard compressed form
if (is_nonstandard()) this->standard(true);
// we need the leaf sum coeffs, so reconstruct
if (is_compressed()) reconstruct(true);
compress(false,true,true,fence);
compressed=false;
}
/// convert this from redundant to standard reconstructed form
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::undo_redundant(const bool fence) {
if (!is_redundant()) return;
redundant = compressed = nonstandard = false;
flo_unary_op_node_inplace(remove_internal_coeffs(),fence);
}
/// compute for each FunctionNode the norm of the function inside that node
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::norm_tree(bool fence) {
if (world.rank() == coeffs.owner(cdata.key0))
norm_tree_spawn(cdata.key0);
if (fence)
world.gop.fence();
}
template <typename T, std::size_t NDIM>
double FunctionImpl<T,NDIM>::norm_tree_op(const keyT& key, const std::vector< Future<double> >& v) {
//PROFILE_MEMBER_FUNC(FunctionImpl);
double sum = 0.0;
int i=0;
for (KeyChildIterator<NDIM> kit(key); kit; ++kit,++i) {
double value = v[i].get();
sum += value*value;
}
sum = sqrt(sum);
coeffs.task(key, &nodeT::set_norm_tree, sum); // why a task? because send is deprecated to keep comm thread free
//if (key.level() == 0) std::cout << "NORM_TREE_TOP " << sum << "\n";
return sum;
}
template <typename T, std::size_t NDIM>
Future<double> FunctionImpl<T,NDIM>::norm_tree_spawn(const keyT& key) {
nodeT& node = coeffs.find(key).get()->second;
if (node.has_children()) {
std::vector< Future<double> > v = future_vector_factory<double>(1<<NDIM);
int i=0;
for (KeyChildIterator<NDIM> kit(key); kit; ++kit,++i) {
v[i] = woT::task(coeffs.owner(kit.key()), &implT::norm_tree_spawn, kit.key());
}
return woT::task(world.rank(),&implT::norm_tree_op, key, v);
}
else {
// return Future<double>(node.coeff().normf());
const double norm=node.coeff().normf();
// invoked locally anyways
node.set_norm_tree(norm);
return Future<double>(norm);
}
}
/// truncate using a tree in reconstructed form
/// must be invoked where key is local
template <typename T, std::size_t NDIM>
Future<typename FunctionImpl<T,NDIM>::coeffT> FunctionImpl<T,NDIM>::truncate_reconstructed_spawn(const keyT& key, const double tol) {
MADNESS_ASSERT(coeffs.probe(key));
nodeT& node = coeffs.find(key).get()->second;
// if this is a leaf node just return the sum coefficients
if (not node.has_children()) return Future<coeffT>(node.coeff());
// if this is an internal node, wait for all the children's sum coefficients
// and use them to determine if the children can be removed
std::vector<Future<coeffT> > v = future_vector_factory<coeffT>(1<<NDIM);
int i=0;
for (KeyChildIterator<NDIM> kit(key); kit; ++kit,++i) {
v[i] = woT::task(coeffs.owner(kit.key()), &implT::truncate_reconstructed_spawn, kit.key(),tol,TaskAttributes::hipri());
}
// will return (possibly empty) sum coefficients
return woT::task(world.rank(),&implT::truncate_reconstructed_op,key,v,tol,TaskAttributes::hipri());
}
/// given the sum coefficients of all children, truncate or not
/// @return new sum coefficients (empty if internal, not empty, if new leaf); might delete its children
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::coeffT FunctionImpl<T,NDIM>::truncate_reconstructed_op(const keyT& key, const std::vector< Future<coeffT > >& v, const double tol) {
MADNESS_ASSERT(coeffs.probe(key));
// the sum coefficients might be empty, which means they come from an internal node
// and we must not truncate; so just return empty coeffs again
for (size_t i=0; i<v.size(); ++i) if (v[i].get().has_no_data()) return coeffT();
// do not truncate below level 1
if (key.level()<2) return coeffT();
// compute the wavelet coefficients from the child nodes
typename dcT::accessor acc;
MADNESS_ASSERT(coeffs.find(acc, key));
int i=0;
tensorT d(cdata.v2k);
for (KeyChildIterator<NDIM> kit(key); kit; ++kit,++i) {
// d(child_patch(kit.key())) += v[i].get();
d(child_patch(kit.key())) += v[i].get().full_tensor_copy();
}
d = filter(d);
tensorT s=copy(d(cdata.s0));
d(cdata.s0) = 0.0;
const double error=d.normf();
nodeT& node = coeffs.find(key).get()->second;
if (error < truncate_tol(tol,key)) {
node.set_has_children(false);
for (KeyChildIterator<NDIM> kit(key); kit; ++kit) {
coeffs.erase(kit.key());
}
// "replace" children with new sum coefficients
coeffT ss=coeffT(s,targs);
acc->second.set_coeff(ss);
return ss;
} else {
return coeffT();
}
}
/// calculate the wavelet coefficients using the sum coefficients of all child nodes
/// @param[in] key this's key
/// @param[in] v sum coefficients of the child nodes
/// @param[in] nonstandard keep the sum coefficients with the wavelet coefficients
/// @param[in] redundant keep only the sum coefficients, discard the wavelet coefficients
/// @return the sum coefficients
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::coeffT FunctionImpl<T,NDIM>::compress_op(const keyT& key, const std::vector< Future<coeffT > >& v, bool nonstandard, bool redundant) {
//PROFILE_MEMBER_FUNC(FunctionImpl);
MADNESS_ASSERT(not redundant);
double cpu0=cpu_time();
// Copy child scaling coeffs into contiguous block
tensorT d(cdata.v2k);
// coeffT d(cdata.v2k,targs);
int i=0;
for (KeyChildIterator<NDIM> kit(key); kit; ++kit,++i) {
// d(child_patch(kit.key())) += v[i].get();
d(child_patch(kit.key())) += v[i].get().full_tensor_copy();
}
d = filter(d);
double cpu1=cpu_time();
timer_filter.accumulate(cpu1-cpu0);
cpu0=cpu1;
typename dcT::accessor acc;
MADNESS_ASSERT(coeffs.find(acc, key));
if (acc->second.has_coeff()) {
print(" stuff in compress_op");
// const coeffT& c = acc->second.coeff();
const tensorT c = acc->second.coeff().full_tensor_copy();
if (c.dim(0) == k) {
d(cdata.s0) += c;
}
else {
d += c;
}
}
// tighter thresh for internal nodes
TensorArgs targs2=targs;
targs2.thresh*=0.1;
// need the deep copy for contiguity
coeffT ss=coeffT(copy(d(cdata.s0)),targs2);
if (key.level()> 0 && !nonstandard)
d(cdata.s0) = 0.0;
// insert either sum or difference coefficients
if (redundant) {
acc->second.set_coeff(ss);
} else {
coeffT dd=coeffT(d,targs2);
acc->second.set_coeff(dd);
}
cpu1=cpu_time();
timer_compress_svd.accumulate(cpu1-cpu0);
// return sum coefficients
return ss;
}
/// similar to compress_op, but insert only the sum coefficients in the tree
/// @param[in] key this's key
/// @param[in] v sum coefficients of the child nodes
/// @return the sum coefficients
template <typename T, std::size_t NDIM>
typename FunctionImpl<T,NDIM>::coeffT FunctionImpl<T,NDIM>::make_redundant_op(const keyT& key, const std::vector< Future<coeffT > >& v) {
// get the sum coefficients of this level given the sum coefficients of level n+1
TensorArgs targs2=targs;
targs2.thresh*=0.1;
coeffT s(this->downsample(key,v),targs2);
// insert sum coefficients into tree
typename dcT::accessor acc;
MADNESS_ASSERT(coeffs.find(acc, key));
MADNESS_ASSERT(not (acc->second.has_coeff()));
acc->second.set_coeff(s);
return s;
}
/// Changes non-standard compressed form to standard compressed form
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::standard(bool fence) {
flo_unary_op_node_inplace(do_standard(this),fence);
nonstandard = false;
}
/// after apply we need to do some cleanup;
template <typename T, std::size_t NDIM>
double FunctionImpl<T,NDIM>::finalize_apply(const bool fence) {
TensorArgs tight_args(targs);
tight_args.thresh*=0.01;
double begin=wall_time();
flo_unary_op_node_inplace(do_consolidate_buffer(tight_args),true);
// reduce the rank of the final nodes, leave full tensors unchanged
// flo_unary_op_node_inplace(do_reduce_rank(tight_args.thresh),true);
flo_unary_op_node_inplace(do_reduce_rank(targs),true);
// change TT_FULL to low rank
flo_unary_op_node_inplace(do_change_tensor_type(targs),true);
// truncate leaf nodes to avoid excessive tree refinement
flo_unary_op_node_inplace(do_truncate_NS_leafs(this),true);
double end=wall_time();
double elapsed=end-begin;
this->compressed=true;
this->nonstandard=true;
this->redundant=false;
if (fence) world.gop.fence();
return elapsed;
}
/// Returns the square of the local norm ... no comms
template <typename T, std::size_t NDIM>
double FunctionImpl<T,NDIM>::norm2sq_local() const {
PROFILE_MEMBER_FUNC(FunctionImpl);
typedef Range<typename dcT::const_iterator> rangeT;
return world.taskq.reduce<double,rangeT,do_norm2sq_local>(rangeT(coeffs.begin(),coeffs.end()),
do_norm2sq_local());
}
/// Returns the maximum local depth of the tree ... no communications.
template <typename T, std::size_t NDIM>
std::size_t FunctionImpl<T,NDIM>::max_local_depth() const {
std::size_t maxdepth = 0;
typename dcT::const_iterator end = coeffs.end();
for (typename dcT::const_iterator it=coeffs.begin(); it!=end; ++it) {
std::size_t N = (std::size_t) it->first.level();
if (N> maxdepth)
maxdepth = N;
}
return maxdepth;
}
/// Returns the maximum depth of the tree ... collective ... global sum/broadcast
template <typename T, std::size_t NDIM>
std::size_t FunctionImpl<T,NDIM>::max_depth() const {
std::size_t maxdepth = max_local_depth();
world.gop.max(maxdepth);
return maxdepth;
}
/// Returns the max number of nodes on a processor
template <typename T, std::size_t NDIM>
std::size_t FunctionImpl<T,NDIM>::max_nodes() const {
std::size_t maxsize = 0;
maxsize = coeffs.size();
world.gop.max(maxsize);
return maxsize;
}
/// Returns the min number of nodes on a processor
template <typename T, std::size_t NDIM>
std::size_t FunctionImpl<T,NDIM>::min_nodes() const {
std::size_t minsize = 0;
minsize = coeffs.size();
world.gop.min(minsize);
return minsize;
}
/// Returns the size of the tree structure of the function ... collective global sum
template <typename T, std::size_t NDIM>
std::size_t FunctionImpl<T,NDIM>::tree_size() const {
std::size_t sum = 0;
sum = coeffs.size();
world.gop.sum(sum);
return sum;
}
/// Returns the number of coefficients in the function ... collective global sum
template <typename T, std::size_t NDIM>
std::size_t FunctionImpl<T,NDIM>::size() const {
std::size_t sum = 0;
#if 1
typename dcT::const_iterator end = coeffs.end();
for (typename dcT::const_iterator it=coeffs.begin(); it!=end; ++it) {
const nodeT& node = it->second;
if (node.has_coeff())
sum+=node.size();
}
// print("proc",world.rank(),sum);
#else
typename dcT::const_iterator end = coeffs.end();
for (typename dcT::const_iterator it=coeffs.begin(); it!=end; ++it) {
const nodeT& node = it->second;
if (node.has_coeff())
++sum;
}
if (is_compressed())
for (std::size_t i=0; i<NDIM; ++i)
sum *= 2*cdata.k;
else
for (std::size_t i=0; i<NDIM; ++i)
sum *= cdata.k;
#endif
world.gop.sum(sum);
return sum;
}
/// Returns the number of coefficients in the function ... collective global sum
template <typename T, std::size_t NDIM>
std::size_t FunctionImpl<T,NDIM>::real_size() const {
std::size_t sum = coeffs.size() * (sizeof(keyT) + sizeof(nodeT));
typename dcT::const_iterator end = coeffs.end();
for (typename dcT::const_iterator it=coeffs.begin(); it!=end; ++it) {
const nodeT& node = it->second;
if (node.has_coeff()) sum+=node.coeff().real_size();
}
world.gop.sum(sum);
return sum;
}
/// print tree size and size
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::print_size(const std::string name) const {
const size_t tsize=this->tree_size();
const size_t size=this->size();
const size_t rsize=this->real_size();
const double wall=wall_time();
const double d=sizeof(T);
const double fac=1024*1024*1024;
double norm=0.0;
{
double local = norm2sq_local();
this->world.gop.sum(local);
this->world.gop.fence();
norm=sqrt(local);
}
if (this->world.rank()==0) {
printf("%40s at time %.1fs: norm/tree/real/size: %7.5f %zu, %6.3f, %6.3f GByte\n",
(name.c_str()), wall, norm, tsize,double(rsize)/fac,double(size)/fac*d);
}
}
/// print the number of configurations per node
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::print_stats() const {
if (this->targs.tt==TT_FULL) return;
int dim=NDIM/2;
int k0=k;
if (is_compressed()) k0=2*k;
Tensor<long> n(int(std::pow(double(k0),double(dim))+1));
long n_full=0;
long n_large=0;
if (world.rank()==0) print("n.size(),k0,dim",n.size(),k0,dim);
typename dcT::const_iterator end = coeffs.end();
for (typename dcT::const_iterator it=coeffs.begin(); it!=end; ++it) {
const nodeT& node = it->second;
if (node.has_coeff()) {
if (node.coeff().rank()>long(n.size())) {
++n_large;
} else if (node.coeff().rank()==-1) {
++n_full;
} else if (node.coeff().rank()<0) {
print("small rank",node.coeff().rank());
} else {
n[node.coeff().rank()]++;
}
}
}
world.gop.sum(n.ptr(), n.size());
if (world.rank()==0) {
print("configurations number of nodes");
if (world.rank()==0) print(" full rank ",n_full);
for (unsigned int i=0; i<n.size(); i++) {
long m=n[i];
if (world.rank()==0) print(" ",i," ",m);
}
if (world.rank()==0) print(" large rank ",n_large);
}
}
template <typename T, std::size_t NDIM>
T FunctionImpl<T,NDIM>::eval_cube(Level n, coordT& x, const tensorT& c) const {
PROFILE_MEMBER_FUNC(FunctionImpl);
const int k = cdata.k;
double px[NDIM][k];
T sum = T(0.0);
for (std::size_t i=0; i<NDIM; ++i) legendre_scaling_functions(x[i],k,px[i]);
if (NDIM == 1) {
for (int p=0; p<k; ++p)
sum += c(p)*px[0][p];
}
else if (NDIM == 2) {
for (int p=0; p<k; ++p)
for (int q=0; q<k; ++q)
sum += c(p,q)*px[0][p]*px[1][q];
}
else if (NDIM == 3) {
for (int p=0; p<k; ++p)
for (int q=0; q<k; ++q)
for (int r=0; r<k; ++r)
sum += c(p,q,r)*px[0][p]*px[1][q]*px[2][r];
}
else if (NDIM == 4) {
for (int p=0; p<k; ++p)
for (int q=0; q<k; ++q)
for (int r=0; r<k; ++r)
for (int s=0; s<k; ++s)
sum += c(p,q,r,s)*px[0][p]*px[1][q]*px[2][r]*px[3][s];
}
else if (NDIM == 5) {
for (int p=0; p<k; ++p)
for (int q=0; q<k; ++q)
for (int r=0; r<k; ++r)
for (int s=0; s<k; ++s)
for (int t=0; t<k; ++t)
sum += c(p,q,r,s,t)*px[0][p]*px[1][q]*px[2][r]*px[3][s]*px[4][t];
}
else if (NDIM == 6) {
for (int p=0; p<k; ++p)
for (int q=0; q<k; ++q)
for (int r=0; r<k; ++r)
for (int s=0; s<k; ++s)
for (int t=0; t<k; ++t)
for (int u=0; u<k; ++u)
sum += c(p,q,r,s,t,u)*px[0][p]*px[1][q]*px[2][r]*px[3][s]*px[4][t]*px[5][u];
}
else {
MADNESS_EXCEPTION("FunctionImpl:eval_cube:NDIM?",NDIM);
}
return sum*pow(2.0,0.5*NDIM*n)/sqrt(FunctionDefaults<NDIM>::get_cell_volume());
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::reconstruct_op(const keyT& key, const coeffT& s) {
//PROFILE_MEMBER_FUNC(FunctionImpl);
// Note that after application of an integral operator not all
// siblings may be present so it is necessary to check existence
// and if absent insert an empty leaf node.
//
// If summing the result of an integral operator (i.e., from
// non-standard form) there will be significant scaling function
// coefficients at all levels and possibly difference coefficients
// in leaves, hence the tree may refine as a result.
typename dcT::iterator it = coeffs.find(key).get();
if (it == coeffs.end()) {
coeffs.replace(key,nodeT(coeffT(),false));
it = coeffs.find(key).get();
}
nodeT& node = it->second;
// The integral operator will correctly connect interior nodes
// to children but may leave interior nodes without coefficients
// ... but they still need to sum down so just give them zeros
if (node.has_children() && !node.has_coeff()) {
node.set_coeff(coeffT(cdata.v2k,targs));
}
if (node.has_children() || node.has_coeff()) { // Must allow for inconsistent state from transform, etc.
coeffT d = node.coeff();
if (!d.has_data()) d = coeffT(cdata.v2k,targs);
if (key.level() > 0) d(cdata.s0) += s; // -- note accumulate for NS summation
if (d.dim(0)==2*get_k()) { // d might be pre-truncated if it's a leaf
d = unfilter(d);
node.clear_coeff();
node.set_has_children(true);
for (KeyChildIterator<NDIM> kit(key); kit; ++kit) {
const keyT& child = kit.key();
coeffT ss = copy(d(child_patch(child)));
ss.reduce_rank(thresh);
//PROFILE_BLOCK(recon_send); // Too fine grain for routine profiling
woT::task(coeffs.owner(child), &implT::reconstruct_op, child, ss);
}
} else {
MADNESS_ASSERT(node.is_leaf());
// node.coeff()+=s;
node.coeff().reduce_rank(targs.thresh);
}
}
else {
coeffT ss=s;
if (s.has_no_data()) ss=coeffT(cdata.vk,targs);
if (key.level()) node.set_coeff(copy(ss));
else node.set_coeff(ss);
}
}
template <typename T, std::size_t NDIM>
Tensor<T> fcube(const Key<NDIM>& key, T (*f)(const Vector<double,NDIM>&), const Tensor<double>& qx) {
// fcube(key,typename FunctionFactory<T,NDIM>::FunctorInterfaceWrapper(f) , qx, fval);
std::vector<long> npt(NDIM,qx.dim(0));
Tensor<T> fval(npt);
fcube(key,ElementaryInterface<T,NDIM>(f) , qx, fval);
return fval;
}
template <typename T, std::size_t NDIM>
Tensor<T> fcube(const Key<NDIM>& key, const FunctionFunctorInterface<T,NDIM>& f, const Tensor<double>& qx) {
// fcube(key,typename FunctionFactory<T,NDIM>::FunctorInterfaceWrapper(f) , qx, fval);
std::vector<long> npt(NDIM,qx.dim(0));
Tensor<T> fval(npt);
fcube(key, f, qx, fval);
return fval;
}
template <typename T, std::size_t NDIM>
// void FunctionImpl<T,NDIM>::fcube(const keyT& key, const FunctionFunctorInterface<T,NDIM>& f, const Tensor<double>& qx, tensorT& fval) const {
void fcube(const Key<NDIM>& key, const FunctionFunctorInterface<T,NDIM>& f, const Tensor<double>& qx, Tensor<T>& fval) {
//~ template <typename T, std::size_t NDIM> template< typename FF>
//~ void FunctionImpl<T,NDIM>::fcube(const keyT& key, const FF& f, const Tensor<double>& qx, tensorT& fval) const {
typedef Vector<double,NDIM> coordT;
//PROFILE_MEMBER_FUNC(FunctionImpl);
const Vector<Translation,NDIM>& l = key.translation();
const Level n = key.level();
const double h = std::pow(0.5,double(n));
coordT c; // will hold the point in user coordinates
const int npt = qx.dim(0);
const Tensor<double>& cell_width = FunctionDefaults<NDIM>::get_cell_width();
const Tensor<double>& cell = FunctionDefaults<NDIM>::get_cell();
// Do pre-screening of the FunctionFunctorInterface, f, before calculating f(r) at quadrature points
coordT c1, c2;
for (std::size_t i = 0; i < NDIM; i++) {
c1[i] = cell(i,0) + h*cell_width[i]*(l[i] + qx((long)0));
c2[i] = cell(i,0) + h*cell_width[i]*(l[i] + qx(npt-1));
}
if (f.screened(c1, c2)) {
fval(___) = 0.0;
return;
}
Tensor<double> vqx;
bool vectorized = f.supports_vectorized();
if (vectorized) {
T* fvptr = fval.ptr();
if (NDIM == 1) {
double* x1 = new double[npt];
int idx = 0;
for (int i=0; i<npt; ++i, ++idx) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
x1[idx] = c[0];
}
Vector<double*,1> xvals {x1};
f(xvals, fvptr, npt);
delete [] x1;
}
else if (NDIM == 2) {
double* x1 = new double[npt*npt];
double* x2 = new double[npt*npt];
int idx = 0;
for (int i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
for (int j=0; j<npt; ++j, ++idx) {
c[1] = cell(1,0) + h*cell_width[1]*(l[1] + qx(j)); // y
x1[idx] = c[0];
x2[idx] = c[1];
}
}
Vector<double*,2> xvals {x1, x2};
f(xvals, fvptr, npt*npt);
delete [] x1;
delete [] x2;
}
else if (NDIM == 3) {
double* x1 = new double[npt*npt*npt];
double* x2 = new double[npt*npt*npt];
double* x3 = new double[npt*npt*npt];
int idx = 0;
for (int i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
for (int j=0; j<npt; ++j) {
c[1] = cell(1,0) + h*cell_width[1]*(l[1] + qx(j)); // y
for (int k=0; k<npt; ++k, ++idx) {
c[2] = cell(2,0) + h*cell_width[2]*(l[2] + qx(k)); // z
x1[idx] = c[0];
x2[idx] = c[1];
x3[idx] = c[2];
}
}
}
Vector<double*,3> xvals {x1, x2, x3};
f(xvals, fvptr, npt*npt*npt);
delete [] x1;
delete [] x2;
delete [] x3;
}
else if (NDIM == 4) {
double* x1 = new double[npt*npt*npt*npt];
double* x2 = new double[npt*npt*npt*npt];
double* x3 = new double[npt*npt*npt*npt];
double* x4 = new double[npt*npt*npt*npt];
int idx = 0;
for (int i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
for (int j=0; j<npt; ++j) {
c[1] = cell(1,0) + h*cell_width[1]*(l[1] + qx(j)); // y
for (int k=0; k<npt; ++k) {
c[2] = cell(2,0) + h*cell_width[2]*(l[2] + qx(k)); // z
for (int m=0; m<npt; ++m, ++idx) {
c[3] = cell(3,0) + h*cell_width[3]*(l[3] + qx(m)); // xx
x1[idx] = c[0];
x2[idx] = c[1];
x3[idx] = c[2];
x4[idx] = c[3];
}
}
}
}
Vector<double*,4> xvals {x1, x2, x3, x4};
f(xvals, fvptr, npt*npt*npt*npt);
delete [] x1;
delete [] x2;
delete [] x3;
delete [] x4;
}
else if (NDIM == 5) {
double* x1 = new double[npt*npt*npt*npt*npt];
double* x2 = new double[npt*npt*npt*npt*npt];
double* x3 = new double[npt*npt*npt*npt*npt];
double* x4 = new double[npt*npt*npt*npt*npt];
double* x5 = new double[npt*npt*npt*npt*npt];
int idx = 0;
for (int i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
for (int j=0; j<npt; ++j) {
c[1] = cell(1,0) + h*cell_width[1]*(l[1] + qx(j)); // y
for (int k=0; k<npt; ++k) {
c[2] = cell(2,0) + h*cell_width[2]*(l[2] + qx(k)); // z
for (int m=0; m<npt; ++m) {
c[3] = cell(3,0) + h*cell_width[3]*(l[3] + qx(m)); // xx
for (int n=0; n<npt; ++n, ++idx) {
c[4] = cell(4,0) + h*cell_width[4]*(l[4] + qx(n)); // yy
x1[idx] = c[0];
x2[idx] = c[1];
x3[idx] = c[2];
x4[idx] = c[3];
x5[idx] = c[4];
}
}
}
}
}
Vector<double*,5> xvals {x1, x2, x3, x4, x5};
f(xvals, fvptr, npt*npt*npt*npt*npt);
delete [] x1;
delete [] x2;
delete [] x3;
delete [] x4;
delete [] x5;
}
else if (NDIM == 6) {
double* x1 = new double[npt*npt*npt*npt*npt*npt];
double* x2 = new double[npt*npt*npt*npt*npt*npt];
double* x3 = new double[npt*npt*npt*npt*npt*npt];
double* x4 = new double[npt*npt*npt*npt*npt*npt];
double* x5 = new double[npt*npt*npt*npt*npt*npt];
double* x6 = new double[npt*npt*npt*npt*npt*npt];
int idx = 0;
for (int i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
for (int j=0; j<npt; ++j) {
c[1] = cell(1,0) + h*cell_width[1]*(l[1] + qx(j)); // y
for (int k=0; k<npt; ++k) {
c[2] = cell(2,0) + h*cell_width[2]*(l[2] + qx(k)); // z
for (int m=0; m<npt; ++m) {
c[3] = cell(3,0) + h*cell_width[3]*(l[3] + qx(m)); // xx
for (int n=0; n<npt; ++n) {
c[4] = cell(4,0) + h*cell_width[4]*(l[4] + qx(n)); // yy
for (int p=0; p<npt; ++p, ++idx) {
c[5] = cell(5,0) + h*cell_width[5]*(l[5] + qx(p)); // zz
x1[idx] = c[0];
x2[idx] = c[1];
x3[idx] = c[2];
x4[idx] = c[3];
x5[idx] = c[4];
x6[idx] = c[5];
}
}
}
}
}
}
Vector<double*,6> xvals {x1, x2, x3, x4, x5, x6};
f(xvals, fvptr, npt*npt*npt*npt*npt*npt);
delete [] x1;
delete [] x2;
delete [] x3;
delete [] x4;
delete [] x5;
delete [] x6;
}
else {
MADNESS_EXCEPTION("FunctionImpl: fcube: confused about NDIM?",NDIM);
}
}
else {
if (NDIM == 1) {
for (int i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
fval(i) = f(c);
MADNESS_ASSERT(!std::isnan(fval(i)));
}
}
else if (NDIM == 2) {
for (int i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
for (int j=0; j<npt; ++j) {
c[1] = cell(1,0) + h*cell_width[1]*(l[1] + qx(j)); // y
fval(i,j) = f(c);
MADNESS_ASSERT(!std::isnan(fval(i,j)));
}
}
}
else if (NDIM == 3) {
for (int i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
for (int j=0; j<npt; ++j) {
c[1] = cell(1,0) + h*cell_width[1]*(l[1] + qx(j)); // y
for (int k=0; k<npt; ++k) {
c[2] = cell(2,0) + h*cell_width[2]*(l[2] + qx(k)); // z
fval(i,j,k) = f(c);
MADNESS_ASSERT(!std::isnan(fval(i,j,k)));
}
}
}
}
else if (NDIM == 4) {
for (int i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
for (int j=0; j<npt; ++j) {
c[1] = cell(1,0) + h*cell_width[1]*(l[1] + qx(j)); // y
for (int k=0; k<npt; ++k) {
c[2] = cell(2,0) + h*cell_width[2]*(l[2] + qx(k)); // z
for (int m=0; m<npt; ++m) {
c[3] = cell(3,0) + h*cell_width[3]*(l[3] + qx(m)); // xx
fval(i,j,k,m) = f(c);
MADNESS_ASSERT(!std::isnan(fval(i,j,k,m)));
}
}
}
}
}
else if (NDIM == 5) {
for (int i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
for (int j=0; j<npt; ++j) {
c[1] = cell(1,0) + h*cell_width[1]*(l[1] + qx(j)); // y
for (int k=0; k<npt; ++k) {
c[2] = cell(2,0) + h*cell_width[2]*(l[2] + qx(k)); // z
for (int m=0; m<npt; ++m) {
c[3] = cell(3,0) + h*cell_width[3]*(l[3] + qx(m)); // xx
for (int n=0; n<npt; ++n) {
c[4] = cell(4,0) + h*cell_width[4]*(l[4] + qx(n)); // yy
fval(i,j,k,m,n) = f(c);
MADNESS_ASSERT(!std::isnan(fval(i,j,k,m,n)));
}
}
}
}
}
}
else if (NDIM == 6) {
for (int i=0; i<npt; ++i) {
c[0] = cell(0,0) + h*cell_width[0]*(l[0] + qx(i)); // x
for (int j=0; j<npt; ++j) {
c[1] = cell(1,0) + h*cell_width[1]*(l[1] + qx(j)); // y
for (int k=0; k<npt; ++k) {
c[2] = cell(2,0) + h*cell_width[2]*(l[2] + qx(k)); // z
for (int m=0; m<npt; ++m) {
c[3] = cell(3,0) + h*cell_width[3]*(l[3] + qx(m)); // xx
for (int n=0; n<npt; ++n) {
c[4] = cell(4,0) + h*cell_width[4]*(l[4] + qx(n)); // yy
for (int p=0; p<npt; ++p) {
c[5] = cell(5,0) + h*cell_width[5]*(l[5] + qx(p)); // zz
fval(i,j,k,m,n,p) = f(c);
MADNESS_ASSERT(!std::isnan(fval(i,j,k,m,n,p)));
}
}
}
}
}
}
}
else {
MADNESS_EXCEPTION("FunctionImpl: fcube: confused about NDIM?",NDIM);
}
}
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::fcube(const keyT& key, T (*f)(const coordT&), const Tensor<double>& qx, tensorT& fval) const {
// fcube(key,typename FunctionFactory<T,NDIM>::FunctorInterfaceWrapper(f) , qx, fval);
madness::fcube(key,ElementaryInterface<T,NDIM>(f) , qx, fval);
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::fcube(const keyT& key, const FunctionFunctorInterface<T,NDIM>& f, const Tensor<double>& qx, tensorT& fval) const {
madness::fcube(key,f,qx,fval);
}
/// project the functor into this functionimpl, and "return" a tree in reconstructed,
/// rank-reduced form.
/// @param[in] key current FunctionNode
/// @param[in] do_refine
/// @param[in] specialpts in case these are very spiky functions -- don't undersample
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::project_refine_op(const keyT& key,
bool do_refine,
const std::vector<Vector<double,NDIM> >& specialpts) {
//PROFILE_MEMBER_FUNC(FunctionImpl);
if (do_refine && key.level() < max_refine_level) {
// Restrict special points to this box
std::vector<Vector<double,NDIM> > newspecialpts;
if (key.level() < functor->special_level() && specialpts.size() > 0) {
BoundaryConditions<NDIM> bc = FunctionDefaults<NDIM>::get_bc();
std::vector<bool> bperiodic = bc.is_periodic();
for (unsigned int i = 0; i < specialpts.size(); ++i) {
coordT simpt;
user_to_sim(specialpts[i], simpt);
Key<NDIM> specialkey = simpt2key(simpt, key.level());
if (specialkey.is_neighbor_of(key,bperiodic)) {
newspecialpts.push_back(specialpts[i]);
}
}
}
// If refining compute scaling function coefficients and
// norm of difference coefficients
tensorT r, s0;
double dnorm = 0.0;
//////////////////////////if (newspecialpts.size() == 0)
{
// Make in r child scaling function coeffs at level n+1
r = tensorT(cdata.v2k);
for (KeyChildIterator<NDIM> it(key); it; ++it) {
const keyT& child = it.key();
r(child_patch(child)) = project(child);
}
// Filter then test difference coeffs at level n
tensorT d = filter(r);
if (truncate_on_project) s0 = copy(d(cdata.s0));
d(cdata.s0) = T(0);
dnorm = d.normf();
}
// If have special points always refine. If don't have special points
// refine if difference norm is big
if (newspecialpts.size() > 0 || dnorm >=truncate_tol(thresh,key.level())) {
coeffs.replace(key,nodeT(coeffT(),true)); // Insert empty node for parent
for (KeyChildIterator<NDIM> it(key); it; ++it) {
const keyT& child = it.key();
ProcessID p;
if (FunctionDefaults<NDIM>::get_project_randomize()) {
p = world.random_proc();
}
else {
p = coeffs.owner(child);
}
//PROFILE_BLOCK(proj_refine_send); // Too fine grain for routine profiling
woT::task(p, &implT::project_refine_op, child, do_refine, newspecialpts);
}
}
else {
if (truncate_on_project) {
coeffT s(s0,thresh,FunctionDefaults<NDIM>::get_tensor_type());
coeffs.replace(key,nodeT(s,false));
}
else {
coeffs.replace(key,nodeT(coeffT(),true)); // Insert empty node for parent
for (KeyChildIterator<NDIM> it(key); it; ++it) {
const keyT& child = it.key();
coeffT s(r(child_patch(child)),thresh,FunctionDefaults<NDIM>::get_tensor_type());
coeffs.replace(child,nodeT(s,false));
}
}
}
}
else {
coeffs.replace(key,nodeT(coeffT(project(key),targs),false));
}
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::add_scalar_inplace(T t, bool fence) {
std::vector<long> v0(NDIM,0L);
std::vector<long> v1(NDIM,1L);
std::vector<Slice> s(NDIM,Slice(0,0));
const TensorArgs full_args(-1.0,TT_FULL);
if (is_compressed()) {
if (world.rank() == coeffs.owner(cdata.key0)) {
typename dcT::iterator it = coeffs.find(cdata.key0).get();
MADNESS_ASSERT(it != coeffs.end());
nodeT& node = it->second;
MADNESS_ASSERT(node.has_coeff());
// node.node_to_full_rank();
// node.full_tensor_reference()(v0) += t*sqrt(FunctionDefaults<NDIM>::get_cell_volume());
// node.node_to_low_rank();
change_tensor_type(node.coeff(),full_args);
node.coeff().full_tensor()(v0) += t*sqrt(FunctionDefaults<NDIM>::get_cell_volume());
change_tensor_type(node.coeff(),targs);
}
}
else {
for (typename dcT::iterator it=coeffs.begin(); it!=coeffs.end(); ++it) {
Level n = it->first.level();
nodeT& node = it->second;
if (node.has_coeff()) {
// this looks funny, but is necessary for GenTensor, since you can't access a
// single matrix element. Therefore make a (1^NDIM) tensor, convert to GenTensor, then
// add to the original one by adding a slice.
tensorT ttt(v1);
ttt=t*sqrt(FunctionDefaults<NDIM>::get_cell_volume()*pow(0.5,double(NDIM*n)));
coeffT tt(ttt,get_tensor_args());
node.coeff()(s) += tt;
// this was the original line:
// node.coeff().full_tensor()(v0) += t*sqrt(FunctionDefaults<NDIM>::get_cell_volume()*pow(0.5,double(NDIM*n)));
}
}
}
if (fence) world.gop.fence();
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::insert_zero_down_to_initial_level(const keyT& key) {
PROFILE_MEMBER_FUNC(FunctionImpl);
if (compressed) initial_level = std::max(initial_level,1); // Otherwise zero function is confused
if (coeffs.is_local(key)) {
if (compressed) {
if (key.level() == initial_level) {
coeffs.replace(key, nodeT(coeffT(), false));
}
else {
coeffs.replace(key, nodeT(coeffT(cdata.v2k,targs), true));
}
}
else {
if (key.level()<initial_level) {
coeffs.replace(key, nodeT(coeffT(), true));
}
else {
coeffs.replace(key, nodeT(coeffT(cdata.vk,targs), false));
}
}
}
if (key.level() < initial_level) {
for (KeyChildIterator<NDIM> kit(key); kit; ++kit) {
insert_zero_down_to_initial_level(kit.key());
}
}
}
template <typename T, std::size_t NDIM>
Future<bool> FunctionImpl<T,NDIM>::truncate_spawn(const keyT& key, double tol) {
//PROFILE_MEMBER_FUNC(FunctionImpl);
typename dcT::iterator it = coeffs.find(key).get();
if (it == coeffs.end()) {
// In a standard tree all children would exist but some ops (transform)
// can leave the tree in a messy state. Just make the missing node as an
// empty leaf.
coeffs.replace(key,nodeT());
it = coeffs.find(key).get();
}
nodeT& node = it->second;
if (node.has_children()) {
std::vector< Future<bool> > v = future_vector_factory<bool>(1<<NDIM);
int i=0;
for (KeyChildIterator<NDIM> kit(key); kit; ++kit,++i) {
v[i] = woT::task(coeffs.owner(kit.key()), &implT::truncate_spawn, kit.key(), tol, TaskAttributes::generator());
}
return woT::task(world.rank(),&implT::truncate_op, key, tol, v);
}
else {
// In compressed form leaves should not have coeffs ... however the
// transform op could leave the tree with leaves that do have coeffs
// in which case we want something sensible to happen
//MADNESS_ASSERT(!node.has_coeff());
if (node.has_coeff() && key.level()>1) {
double dnorm = node.coeff().normf();
if (dnorm < truncate_tol(tol,key)) {
node.clear_coeff();
}
}
return Future<bool>(node.has_coeff());
}
}
template <typename T, std::size_t NDIM>
bool FunctionImpl<T,NDIM>::truncate_op(const keyT& key, double tol, const std::vector< Future<bool> >& v) {
//PROFILE_MEMBER_FUNC(FunctionImpl); // Too fine grain for routine profiling
// If any child has coefficients, a parent cannot truncate
for (int i=0; i<(1<<NDIM); ++i) if (v[i].get()) return true;
nodeT& node = coeffs.find(key).get()->second;
// Interior nodes should always have coeffs but transform might
// leave empty interior nodes ... hence just force no coeffs to
// be zero coeff unless it is a leaf.
if (node.has_children() && !node.has_coeff()) node.set_coeff(coeffT(cdata.v2k,targs));
if (key.level() > 1) { // >1 rather >0 otherwise reconstruct might get confused
double dnorm = node.coeff().normf();
if (dnorm < truncate_tol(tol,key)) {
node.clear_coeff();
if (node.has_children()) {
node.set_has_children(false);
for (KeyChildIterator<NDIM> kit(key); kit; ++kit) {
coeffs.erase(kit.key());
}
}
}
}
return node.has_coeff();
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::print_tree(std::ostream& os, Level maxlevel) const {
if (world.rank() == 0) do_print_tree(cdata.key0, os, maxlevel);
world.gop.fence();
if (world.rank() == 0) os.flush();
world.gop.fence();
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::do_print_tree(const keyT& key, std::ostream& os, Level maxlevel) const {
typename dcT::const_iterator it = coeffs.find(key).get();
if (it == coeffs.end()) {
//MADNESS_EXCEPTION("FunctionImpl: do_print_tree: null node pointer",0);
for (int i=0; i<key.level(); ++i) os << " ";
os << key << " missing --> " << coeffs.owner(key) << "\n";
}
else {
const nodeT& node = it->second;
for (int i=0; i<key.level(); ++i) os << " ";
os << key << " " << node << " --> " << coeffs.owner(key) << "\n";
if (key.level() < maxlevel && node.has_children()) {
for (KeyChildIterator<NDIM> kit(key); kit; ++kit) {
do_print_tree(kit.key(),os,maxlevel);
}
}
}
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::print_tree_graphviz(std::ostream& os, Level maxlevel) const {
if (world.rank() == 0) do_print_tree_graphviz(cdata.key0, os, maxlevel);
world.gop.fence();
if (world.rank() == 0) os.flush();
world.gop.fence();
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::do_print_tree_graphviz(const keyT& key, std::ostream& os, Level maxlevel) const {
struct uniqhash {
static int64_t value(const keyT& key) {
int64_t result = 0;
for (int64_t j = 0; j <= key.level()-1; ++j) {
result += (1 << j*NDIM);
}
result += key.translation()[0];
return result;
}
};
typename dcT::const_iterator it = coeffs.find(key).get();
if (it != coeffs.end()) {
const nodeT& node = it->second;
if (key.level() < maxlevel && node.has_children()) {
for (KeyChildIterator<NDIM> kit(key); kit; ++kit) {
os << uniqhash::value(key) << " -> " << uniqhash::value(kit.key()) << "\n";
do_print_tree_graphviz(kit.key(),os,maxlevel);
}
}
}
}
template <typename T, std::size_t NDIM>
Tensor<T> FunctionImpl<T,NDIM>::project(const keyT& key) const {
//PROFILE_MEMBER_FUNC(FunctionImpl);
if (not functor) MADNESS_EXCEPTION("FunctionImpl: project: confusion about function?",0);
// if functor provides coeffs directly, awesome; otherwise use compute by yourself
if (functor->provides_coeff()) return functor->coeff(key).full_tensor_copy();
MADNESS_ASSERT(cdata.npt == cdata.k); // only necessary due to use of fast transform
tensorT fval(cdata.vq,false); // this will be the returned result
tensorT work(cdata.vk,false); // initially evaluate the function in here
tensorT workq(cdata.vq,false); // initially evaluate the function in here
// compute the values of the functor at the quadrature points and scale appropriately
madness::fcube(key,*functor,cdata.quad_x,work);
work.scale(sqrt(FunctionDefaults<NDIM>::get_cell_volume()*pow(0.5,double(NDIM*key.level()))));
//return transform(work,cdata.quad_phiw);
return fast_transform(work,cdata.quad_phiw,fval,workq);
}
template <typename T, std::size_t NDIM>
Future<double> FunctionImpl<T,NDIM>::get_norm_tree_recursive(const keyT& key) const {
if (coeffs.probe(key)) {
return Future<double>(coeffs.find(key).get()->second.get_norm_tree());
}
MADNESS_ASSERT(key.level());
keyT parent = key.parent();
return woT::task(coeffs.owner(parent), &implT::get_norm_tree_recursive, parent, TaskAttributes::hipri());
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::sock_it_to_me(const keyT& key,
const RemoteReference< FutureImpl< std::pair<keyT,coeffT> > >& ref) const {
//PROFILE_MEMBER_FUNC(FunctionImpl);
if (coeffs.probe(key)) {
const nodeT& node = coeffs.find(key).get()->second;
Future< std::pair<keyT,coeffT> > result(ref);
if (node.has_coeff()) {
//madness::print("sock found it with coeff",key);
result.set(std::pair<keyT,coeffT>(key,node.coeff()));
}
else {
//madness::print("sock found it without coeff",key);
result.set(std::pair<keyT,coeffT>(key,coeffT()));
}
}
else {
keyT parent = key.parent();
//madness::print("sock forwarding to parent",key,parent);
//PROFILE_BLOCK(sitome_send); // Too fine grain for routine profiling
woT::task(coeffs.owner(parent), &FunctionImpl<T,NDIM>::sock_it_to_me, parent, ref, TaskAttributes::hipri());
}
}
// like sock_it_to_me, but it replaces empty node with averaged coeffs from further down the tree
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::sock_it_to_me_too(const keyT& key,
const RemoteReference< FutureImpl< std::pair<keyT,coeffT> > >& ref) const {
PROFILE_MEMBER_FUNC(FunctionImpl);
if (coeffs.probe(key)) {
const nodeT& node = coeffs.find(key).get()->second;
Future< std::pair<keyT,coeffT> > result(ref);
if (node.has_coeff()) {
result.set(std::pair<keyT,coeffT>(key,node.coeff()));
}
else {
result.set(std::pair<keyT,coeffT>(key,nodeT(coeffT(project(key),targs),false).coeff()));
}
}
else {
keyT parent = key.parent();
//PROFILE_BLOCK(sitome2_send); // Too fine grain for routine profiling
woT::task(coeffs.owner(parent), &FunctionImpl<T,NDIM>::sock_it_to_me_too, parent, ref, TaskAttributes::hipri());
}
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::eval(const Vector<double,NDIM>& xin,
const keyT& keyin,
const typename Future<T>::remote_refT& ref) {
PROFILE_MEMBER_FUNC(FunctionImpl);
// This is ugly. We must figure out a clean way to use
// owner computes rule from the container.
Vector<double,NDIM> x = xin;
keyT key = keyin;
Vector<Translation,NDIM> l = key.translation();
ProcessID me = world.rank();
while (1) {
ProcessID owner = coeffs.owner(key);
if (owner != me) {
//PROFILE_BLOCK(eval_send); // Too fine grain for routine profiling
woT::task(owner, &implT::eval, x, key, ref, TaskAttributes::hipri());
return;
}
else {
typename dcT::futureT fut = coeffs.find(key);
typename dcT::iterator it = fut.get();
nodeT& node = it->second;
if (node.has_coeff()) {
Future<T>(ref).set(eval_cube(key.level(), x, node.coeff().full_tensor_copy()));
return;
}
else {
for (std::size_t i=0; i<NDIM; ++i) {
double xi = x[i]*2.0;
int li = int(xi);
if (li == 2) li = 1;
x[i] = xi - li;
l[i] = 2*l[i] + li;
}
key = keyT(key.level()+1,l);
}
}
}
//MADNESS_EXCEPTION("should not be here",0);
}
template <typename T, std::size_t NDIM>
std::pair<bool,T>
FunctionImpl<T,NDIM>::eval_local_only(const Vector<double,NDIM>& xin, Level maxlevel) {
Vector<double,NDIM> x = xin;
keyT key(0);
Vector<Translation,NDIM> l = key.translation();
const ProcessID me = world.rank();
while (key.level() <= maxlevel) {
if (coeffs.owner(key) == me) {
typename dcT::futureT fut = coeffs.find(key);
typename dcT::iterator it = fut.get();
if (it != coeffs.end()) {
nodeT& node = it->second;
if (node.has_coeff()) {
return std::pair<bool,T>(true,eval_cube(key.level(), x, node.coeff().full_tensor_copy()));
}
}
}
for (std::size_t i=0; i<NDIM; ++i) {
double xi = x[i]*2.0;
int li = int(xi);
if (li == 2) li = 1;
x[i] = xi - li;
l[i] = 2*l[i] + li;
}
key = keyT(key.level()+1,l);
}
return std::pair<bool,T>(false,0.0);
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::evaldepthpt(const Vector<double,NDIM>& xin,
const keyT& keyin,
const typename Future<Level>::remote_refT& ref) {
PROFILE_MEMBER_FUNC(FunctionImpl);
// This is ugly. We must figure out a clean way to use
// owner computes rule from the container.
Vector<double,NDIM> x = xin;
keyT key = keyin;
Vector<Translation,NDIM> l = key.translation();
ProcessID me = world.rank();
while (1) {
ProcessID owner = coeffs.owner(key);
if (owner != me) {
//PROFILE_BLOCK(eval_send); // Too fine grain for routine profiling
woT::task(owner, &implT::evaldepthpt, x, key, ref, TaskAttributes::hipri());
return;
}
else {
typename dcT::futureT fut = coeffs.find(key);
typename dcT::iterator it = fut.get();
nodeT& node = it->second;
if (node.has_coeff()) {
Future<Level>(ref).set(key.level());
return;
}
else {
for (std::size_t i=0; i<NDIM; ++i) {
double xi = x[i]*2.0;
int li = int(xi);
if (li == 2) li = 1;
x[i] = xi - li;
l[i] = 2*l[i] + li;
}
key = keyT(key.level()+1,l);
}
}
}
//MADNESS_EXCEPTION("should not be here",0);
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::evalR(const Vector<double,NDIM>& xin,
const keyT& keyin,
const typename Future<long>::remote_refT& ref) {
PROFILE_MEMBER_FUNC(FunctionImpl);
// This is ugly. We must figure out a clean way to use
// owner computes rule from the container.
Vector<double,NDIM> x = xin;
keyT key = keyin;
Vector<Translation,NDIM> l = key.translation();
ProcessID me = world.rank();
while (1) {
ProcessID owner = coeffs.owner(key);
if (owner != me) {
//PROFILE_BLOCK(eval_send); // Too fine grain for routine profiling
woT::task(owner, &implT::evalR, x, key, ref, TaskAttributes::hipri());
return;
}
else {
typename dcT::futureT fut = coeffs.find(key);
typename dcT::iterator it = fut.get();
nodeT& node = it->second;
if (node.has_coeff()) {
Future<long>(ref).set(node.coeff().rank());
return;
}
else {
for (std::size_t i=0; i<NDIM; ++i) {
double xi = x[i]*2.0;
int li = int(xi);
if (li == 2) li = 1;
x[i] = xi - li;
l[i] = 2*l[i] + li;
}
key = keyT(key.level()+1,l);
}
}
}
//MADNESS_EXCEPTION("should not be here",0);
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::tnorm(const tensorT& t, double* lo, double* hi) const {
//PROFILE_MEMBER_FUNC(FunctionImpl); // Too fine grain for routine profiling
// Chosen approach looks stupid but it is more accurate
// than the simple approach of summing everything and
// subtracting off the low-order stuff to get the high
// order (assuming the high-order stuff is small relative
// to the low-order)
tensorT work = copy(t);
tensorT tlo = work(cdata.sh);
*lo = tlo.normf();
tlo.fill(0.0);
*hi = work.normf();
}
namespace detail {
template <typename A, typename B>
struct noop {
void operator()(const A& a, const B& b) const {};
template <typename Archive> void serialize(Archive& ar) {}
};
template <typename T, std::size_t NDIM>
struct scaleinplace {
T q;
scaleinplace() {}
// G++ 4.1.2 ICEs on BGP ... scaleinplace(T q) : q(q) {}
scaleinplace(T q) {this->q = q;}
void operator()(const Key<NDIM>& key, Tensor<T>& t) const {
t.scale(q);
}
void operator()(const Key<NDIM>& key, FunctionNode<T,NDIM>& node) const {
node.coeff().scale(q);
}
template <typename Archive> void serialize(Archive& ar) {
ar & q;
}
};
template <typename T, std::size_t NDIM>
struct squareinplace {
void operator()(const Key<NDIM>& key, Tensor<T>& t) const {
t.emul(t);
}
template <typename Archive> void serialize(Archive& ar) {}
};
template <typename T, std::size_t NDIM>
struct absinplace {
void operator()(const Key<NDIM>& key, Tensor<T>& t) const {t=abs(t);}
template <typename Archive> void serialize(Archive& ar) {}
};
template <typename T, std::size_t NDIM>
struct abssquareinplace {
void operator()(const Key<NDIM>& key, Tensor<T>& t) const {abs(t.emul(t));}
template <typename Archive> void serialize(Archive& ar) {}
};
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::scale_inplace(const T q, bool fence) {
// unary_op_coeff_inplace(detail::scaleinplace<T,NDIM>(q), fence);
unary_op_node_inplace(detail::scaleinplace<T,NDIM>(q), fence);
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::square_inplace(bool fence) {
//unary_op_value_inplace(&implT::autorefine_square_test, detail::squareinplace<T,NDIM>(), fence);
unary_op_value_inplace(detail::squareinplace<T,NDIM>(), fence);
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::abs_inplace(bool fence) {
unary_op_value_inplace(detail::absinplace<T,NDIM>(), fence);
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::abs_square_inplace(bool fence) {
unary_op_value_inplace(detail::abssquareinplace<T,NDIM>(), fence);
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::phi_for_mul(Level np, Translation lp, Level nc, Translation lc, Tensor<double>& phi) const {
//PROFILE_MEMBER_FUNC(FunctionImpl); // Too fine grain for routine profiling
double p[200];
double scale = pow(2.0,double(np-nc));
for (int mu=0; mu<cdata.npt; ++mu) {
double xmu = scale*(cdata.quad_x(mu)+lc) - lp;
MADNESS_ASSERT(xmu>-1e-15 && xmu<(1+1e-15));
legendre_scaling_functions(xmu,cdata.k,p);
for (int i=0; i<k; ++i) phi(i,mu) = p[i];
}
phi.scale(pow(2.0,0.5*np));
}
template <typename T, std::size_t NDIM>
const GenTensor<T> FunctionImpl<T,NDIM>::parent_to_child(const coeffT& s, const keyT& parent, const keyT& child) const {
//PROFILE_MEMBER_FUNC(FunctionImpl); // Too fine grain for routine profiling
// An invalid parent/child means that they are out of the box
// and it is the responsibility of the caller to worry about that
// ... most likely the coefficients (s) are zero to reflect
// zero B.C. so returning s makes handling this easy.
if (parent == child || parent.is_invalid() || child.is_invalid()) return s;
coeffT result = fcube_for_mul<T>(child, parent, s);
result.scale(sqrt(FunctionDefaults<NDIM>::get_cell_volume()*pow(0.5,double(NDIM*child.level()))));
result = transform(result,cdata.quad_phiw);
return result;
}
template <typename T, std::size_t NDIM>
T FunctionImpl<T,NDIM>::trace_local() const {
PROFILE_MEMBER_FUNC(FunctionImpl);
std::vector<long> v0(NDIM,0);
T sum = 0.0;
if (compressed) {
if (world.rank() == coeffs.owner(cdata.key0)) {
typename dcT::const_iterator it = coeffs.find(cdata.key0).get();
if (it != coeffs.end()) {
const nodeT& node = it->second;
if (node.has_coeff()) sum = node.coeff().full_tensor_copy()(v0);
}
}
}
else {
for (typename dcT::const_iterator it=coeffs.begin(); it!=coeffs.end(); ++it) {
const keyT& key = it->first;
const nodeT& node = it->second;
if (node.has_coeff()) sum += node.coeff().full_tensor_copy()(v0)*pow(0.5,NDIM*key.level()*0.5);
}
}
return sum*sqrt(FunctionDefaults<NDIM>::get_cell_volume());
}
static inline bool enforce_bc(bool is_periodic, Level n, Translation& l) {
Translation two2n = 1ul << n;
if (l < 0) {
if (is_periodic)
l += two2n; // Periodic BC
else
return false; // Zero BC
}
else if (l >= two2n) {
if (is_periodic)
l -= two2n; // Periodic BC
else
return false; // Zero BC
}
return true;
}
template <typename T, std::size_t NDIM>
Key<NDIM> FunctionImpl<T,NDIM>::neighbor(const keyT& key, const Key<NDIM>& disp, const std::vector<bool>& is_periodic) const {
Vector<Translation,NDIM> l = key.translation();
for (std::size_t axis=0; axis<NDIM; ++axis) {
l[axis] += disp.translation()[axis];
//if (!enforce_bc(bc(axis,0), bc(axis,1), key.level(), l[axis])) {
if (!enforce_bc(is_periodic[axis], key.level(), l[axis])) {
return keyT::invalid();
}
}
return keyT(key.level(),l);
}
template <typename T, std::size_t NDIM>
Future< std::pair< Key<NDIM>, GenTensor<T> > >
FunctionImpl<T,NDIM>::find_me(const Key<NDIM>& key) const {
//PROFILE_MEMBER_FUNC(FunctionImpl); // Too fine grain for routine profiling
typedef std::pair< Key<NDIM>,coeffT > argT;
Future<argT> result;
//PROFILE_BLOCK(find_me_send); // Too fine grain for routine profiling
woT::task(coeffs.owner(key), &implT::sock_it_to_me_too, key, result.remote_ref(world), TaskAttributes::hipri());
return result;
}
template <typename T, std::size_t NDIM>
Future< GenTensor<T> > FunctionImpl<T,NDIM>::compress_spawn(const Key<NDIM>& key,
bool nonstandard, bool keepleaves, bool redundant) {
if (!coeffs.probe(key)) print("missing node",key);
MADNESS_ASSERT(coeffs.probe(key));
// get fetches remote data (here actually local)
nodeT& node = coeffs.find(key).get()->second;
if (node.has_children()) {
std::vector< Future<coeffT > > v = future_vector_factory<coeffT >(1<<NDIM);
int i=0;
for (KeyChildIterator<NDIM> kit(key); kit; ++kit,++i) {
//PROFILE_BLOCK(compress_send); // Too fine grain for routine profiling
// readily available
v[i] = woT::task(coeffs.owner(kit.key()), &implT::compress_spawn, kit.key(),
nonstandard, keepleaves, redundant, TaskAttributes::hipri());
}
if (redundant) return woT::task(world.rank(),&implT::make_redundant_op, key, v);
return woT::task(world.rank(),&implT::compress_op, key, v, nonstandard, redundant);
}
else {
Future<coeffT > result(node.coeff());
if (!keepleaves) node.clear_coeff();
return result;
}
}
template <typename T, std::size_t NDIM>
void FunctionImpl<T,NDIM>::plot_cube_kernel(archive::archive_ptr< Tensor<T> > ptr,
const keyT& key,
const coordT& plotlo, const coordT& plothi, const std::vector<long>& npt,
bool eval_refine) const {
Tensor<T>& r = *ptr;
coordT h; // Increment between points in each dimension
for (std::size_t i=0; i<NDIM; ++i) {
if (npt[i] > 1) {
h[i] = (plothi[i]-plotlo[i])/(npt[i]-1);
}
else {
MADNESS_ASSERT(plotlo[i] == plothi[i]);
h[i] = 0.0;
}
}
const Level n = key.level();
const Vector<Translation,NDIM>& l = key.translation();
const double twon = pow(2.0,double(n));
const tensorT& coeff = coeffs.find(key).get()->second.coeff().full_tensor_copy(); // Ugh!
// const tensorT coeff = coeffs.find(key).get()->second.full_tensor_copy(); // Ugh!
long ind[NDIM];
coordT x;
coordT boxlo, boxhi;
Vector<int,NDIM> boxnpt;
double fac = pow(0.5,double(key.level()));
int npttotal = 1;
for (std::size_t d=0; d<NDIM; ++d) {
// Coords of box
boxlo[d] = fac*key.translation()[d];
boxhi[d] = boxlo[d]+fac;
if (boxlo[d] > plothi[d] || boxhi[d] < plotlo[d]) {
// Discard boxes out of the plot range
npttotal = boxnpt[d] = 0;
//print("OO range?");
break;
}
else if (npt[d] == 1) {
// This dimension is only a single point
boxlo[d] = boxhi[d] = plotlo[d];
boxnpt[d] = 1;
}
else {
// Restrict to plot range
boxlo[d] = std::max(boxlo[d],plotlo[d]);
boxhi[d] = std::min(boxhi[d],plothi[d]);
// Round lo up to next plot point; round hi down
double xlo = long((boxlo[d]-plotlo[d])/h[d])*h[d] + plotlo[d];
if (xlo < boxlo[d]) xlo += h[d];
boxlo[d] = xlo;
double xhi = long((boxhi[d]-plotlo[d])/h[d])*h[d] + plotlo[d];
if (xhi > boxhi[d]) xhi -= h[d];
// MADNESS_ASSERT(xhi >= xlo); // nope
boxhi[d] = xhi;
boxnpt[d] = long(round((boxhi[d] - boxlo[d])/h[d])) + 1;
}
npttotal *= boxnpt[d];
}
//print(" box", boxlo, boxhi, boxnpt, npttotal);
if (npttotal > 0) {
for (IndexIterator it(boxnpt); it; ++it) {
for (std::size_t d=0; d<NDIM; ++d) {
double xd = boxlo[d] + it[d]*h[d]; // Sim. coords of point
x[d] = twon*xd - l[d]; // Offset within box
MADNESS_ASSERT(x[d]>=0.0 && x[d] <=1.0); // sanity
if (npt[d] > 1) {
ind[d] = long(round((xd-plotlo[d])/h[d])); // Index of plot point
}
else {
ind[d] = 0;
}
MADNESS_ASSERT(ind[d]>=0 && ind[d]<npt[d]); // sanity
}
if (eval_refine) {
r(ind) = n;
}
else {
T tmp = eval_cube(n, x, coeff);
r(ind) = tmp;
//print(" eval", ind, tmp, r(ind));
}
}
}
}
/// Set plot_refine=true to get a plot of the refinment levels of
/// the given function (defaulted to false in prototype).
template <typename T, std::size_t NDIM>
Tensor<T> FunctionImpl<T,NDIM>::eval_plot_cube(const coordT& plotlo,
const coordT& plothi,
const std::vector<long>& npt,
const bool eval_refine) const {
PROFILE_MEMBER_FUNC(FunctionImpl);
Tensor<T> r(NDIM, &npt[0]);
//r(___) = 99.0;
MADNESS_ASSERT(!compressed);
for (typename dcT::const_iterator it=coeffs.begin(); it!=coeffs.end(); ++it) {
const keyT& key = it->first;
const nodeT& node = it->second;
if (node.has_coeff()) {
woT::task(world.rank(), &implT::plot_cube_kernel,
archive::archive_ptr< Tensor<T> >(&r), key, plotlo, plothi, npt, eval_refine);
}
}
// ITERATOR(r, if (r(IND) == 99.0) {print("BAD", IND); error("bad",0);});
world.taskq.fence();
world.gop.sum(r.ptr(), r.size());
world.gop.fence();
return r;
}
static inline void dxprintvalue(FILE* f, const double t) {
fprintf(f,"%.6e\n",t);
}
static inline void dxprintvalue(FILE* f, const double_complex& t) {
fprintf(f,"%.6e %.6e\n", t.real(), t.imag());
}
template <typename T, std::size_t NDIM>
void plotdx(const Function<T,NDIM>& function,
const char* filename,
const Tensor<double>& cell,
const std::vector<long>& npt,
bool binary) {
PROFILE_FUNC;
MADNESS_ASSERT(NDIM<=6);
const char* element[6] = {"lines","quads","cubes","cubes4D","cubes5D","cubes6D"};
function.verify();
World& world = const_cast< Function<T,NDIM>& >(function).world();
FILE *f=0;
if (world.rank() == 0) {
f = fopen(filename, "w");
if (!f) MADNESS_EXCEPTION("plotdx: failed to open the plot file", 0);
fprintf(f,"object 1 class gridpositions counts ");
for (std::size_t d=0; d<NDIM; ++d) fprintf(f," %ld",npt[d]);
fprintf(f,"\n");
fprintf(f,"origin ");
for (std::size_t d=0; d<NDIM; ++d) fprintf(f, " %.6e", cell(d,0));
fprintf(f,"\n");
for (std::size_t d=0; d<NDIM; ++d) {
fprintf(f,"delta ");
for (std::size_t c=0; c<d; ++c) fprintf(f, " 0");
double h = 0.0;
if (npt[d]>1) h = (cell(d,1)-cell(d,0))/(npt[d]-1);
fprintf(f," %.6e", h);
for (std::size_t c=d+1; c<NDIM; ++c) fprintf(f, " 0");
fprintf(f,"\n");
}
fprintf(f,"\n");
fprintf(f,"object 2 class gridconnections counts ");
for (std::size_t d=0; d<NDIM; ++d) fprintf(f," %ld",npt[d]);
fprintf(f,"\n");
fprintf(f, "attribute \"element type\" string \"%s\"\n", element[NDIM-1]);
fprintf(f, "attribute \"ref\" string \"positions\"\n");
fprintf(f,"\n");
int npoint = 1;
for (std::size_t d=0; d<NDIM; ++d) npoint *= npt[d];
const char* iscomplex = "";
if (TensorTypeData<T>::iscomplex) iscomplex = "category complex";
const char* isbinary = "";
if (binary) isbinary = "binary";
fprintf(f,"object 3 class array type double %s rank 0 items %d %s data follows\n",
iscomplex, npoint, isbinary);
}
world.gop.fence();
Tensor<T> r = function.eval_cube(cell, npt);
if (world.rank() == 0) {
if (binary) {
// This assumes that the values are double precision
fflush(f);
fwrite((void *) r.ptr(), sizeof(T), r.size(), f);
fflush(f);
}
else {
for (IndexIterator it(npt); it; ++it) {
//fprintf(f,"%.6e\n",r(*it));
dxprintvalue(f,r(*it));
}
}
fprintf(f,"\n");
fprintf(f,"object \"%s\" class field\n",filename);
fprintf(f,"component \"positions\" value 1\n");
fprintf(f,"component \"connections\" value 2\n");
fprintf(f,"component \"data\" value 3\n");
fprintf(f,"\nend\n");
fclose(f);
}
world.gop.fence();
}
template <std::size_t NDIM>
void FunctionDefaults<NDIM>::set_defaults(World& world) {
k = 6;
thresh = 1e-4;
initial_level = 2;
max_refine_level = 30;
truncate_mode = 0;
refine = true;
autorefine = true;
debug = false;
truncate_on_project = true;
apply_randomize = false;
project_randomize = false;
bc = BoundaryConditions<NDIM>(BC_FREE);
tt = TT_FULL;
cell = Tensor<double>(NDIM,2);
cell(_,1) = 1.0;
recompute_cell_info();
//pmap = std::shared_ptr< WorldDCPmapInterface< Key<NDIM> > >(new WorldDCDefaultPmap< Key<NDIM> >(world));
pmap = std::shared_ptr< WorldDCPmapInterface< Key<NDIM> > >(new madness::LevelPmap< Key<NDIM> >(world));
//pmap = std::shared_ptr< WorldDCPmapInterface< Key<NDIM> > >(new SimplePmap< Key<NDIM> >(world));
}
template <std::size_t NDIM>
void FunctionDefaults<NDIM>::print(){
std::cout << "Function Defaults:" << std::endl;
std::cout << " Dimension " << ": " << NDIM << std::endl;
std::cout << " k" << ": " << k << std::endl;
std::cout << " thresh" << ": " << thresh << std::endl;
std::cout << " initial_level" << ": " << initial_level << std::endl;
std::cout << " max_refine_level" << ": " << max_refine_level << std::endl;
std::cout << " truncate_mode" << ": " << truncate_mode << std::endl;
std::cout << " refine" << ": " << refine << std::endl;
std::cout << " autorefine" << ": " << autorefine << std::endl;
std::cout << " debug" << ": " << debug << std::endl;
std::cout << " truncate_on_project" << ": " << truncate_on_project << std::endl;
std::cout << " apply_randomize" << ": " << apply_randomize << std::endl;
std::cout << " project_randomize" << ": " << project_randomize << std::endl;
std::cout << " bc" << ": " << bc << std::endl;
std::cout << " tt" << ": " << tt << std::endl;
std::cout << " cell" << ": " << cell << std::endl;
}
template <typename T, std::size_t NDIM>
const FunctionCommonData<T,NDIM>* FunctionCommonData<T,NDIM>::data[MAXK] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
template <std::size_t NDIM> int FunctionDefaults<NDIM>::k;
template <std::size_t NDIM> double FunctionDefaults<NDIM>::thresh;
template <std::size_t NDIM> int FunctionDefaults<NDIM>::initial_level;
template <std::size_t NDIM> int FunctionDefaults<NDIM>::max_refine_level;
template <std::size_t NDIM> int FunctionDefaults<NDIM>::truncate_mode;
template <std::size_t NDIM> bool FunctionDefaults<NDIM>::refine;
template <std::size_t NDIM> bool FunctionDefaults<NDIM>::autorefine;
template <std::size_t NDIM> bool FunctionDefaults<NDIM>::debug;
template <std::size_t NDIM> bool FunctionDefaults<NDIM>::truncate_on_project;
template <std::size_t NDIM> bool FunctionDefaults<NDIM>::apply_randomize;
template <std::size_t NDIM> bool FunctionDefaults<NDIM>::project_randomize;
template <std::size_t NDIM> BoundaryConditions<NDIM> FunctionDefaults<NDIM>::bc;
template <std::size_t NDIM> TensorType FunctionDefaults<NDIM>::tt;
template <std::size_t NDIM> Tensor<double> FunctionDefaults<NDIM>::cell;
template <std::size_t NDIM> Tensor<double> FunctionDefaults<NDIM>::cell_width;
template <std::size_t NDIM> Tensor<double> FunctionDefaults<NDIM>::rcell_width;
template <std::size_t NDIM> double FunctionDefaults<NDIM>::cell_volume;
template <std::size_t NDIM> double FunctionDefaults<NDIM>::cell_min_width;
template <std::size_t NDIM> std::shared_ptr< WorldDCPmapInterface< Key<NDIM> > > FunctionDefaults<NDIM>::pmap;
template <std::size_t NDIM> std::vector< Key<NDIM> > Displacements<NDIM>::disp;
template <std::size_t NDIM> std::vector< Key<NDIM> > Displacements<NDIM>::disp_periodicsum[64];
}
#endif // MADNESS_MRA_MRAIMPL_H__INCLUDED
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