#include "../hyper.h"

/** \brief Snowball visualization
 *
 *  This visualization puts small objects ('snowballs') randomly throughout the space. 
 *  It provides a way to visualize the geometry without any tessellation.
 *
 *  Should work for tessellations where every tile is congruent.
 *
 *  The snow_lambda parameter gives the expected number of snowballs per cell.
 *  (The number in every region has Poisson distribution with mean proportional to its area.)
 *
 *  Freezes for tessellations with ideal vertices
 *
 * 
 *
 **/

namespace hr {

ld snow_lambda = 1;

color_t snow_color = 0xFFFFFFFF;

bool snow_test = false;

/* intense brightness */
bool snow_intense = false;

/* a funny glitch */
bool snow_glitch = false;

/* disable textures */
bool snow_texture = true;

int snow_shape = 0;

map<cell*, vector<transmatrix> > matrices_at;

hpcshape& shapeid(int i) {
  switch(i) {
    case 0: 
      return cgi.shSnowball;
    case 1:
      return cgi.shHeptaMarker;
    case 2:
      return cgi.shDisk;
    default:
      return cgi.shDisk;
    }
  }

transmatrix random_snow_matrix(cell *c) {
  if(snow_glitch) {
    // in the standard tiling, this is incorrect but fun
    hyperpoint h = C0;
    h[0] = randd() - .5;
    h[1] = randd() - .5;
    h[2] = randd() - .5;
    h[2] = -h[2];
    return rgpushxto0(h);
    }
  else if(prod) {
    transmatrix T = PIU(random_snow_matrix(c));
    return mscale(T, (randd() - .5) * cgi.plevel);
    }
  else if(hybri && !prod) {
    return rots::lift_matrix(PIU(random_snow_matrix(c))); // * zpush((randd() - .5) * cgi.plevel);
    }
  else if(nonisotropic || bt::in()) {

    int co = bt::expansion_coordinate();
    ld aer = bt::area_expansion_rate();

    hyperpoint h;
    // randd() - .5;
    
    for(int a=0; a<3; a++) {
      if(a != co || aer == 1)
        h[a] = randd() * 2 - 1;
      else {
        ld r = randd();
        h[co] = log(lerp(1, aer, r)) / log(aer) * 2 - 1;
        }
      }
    return bt::normalized_at(h);
    }
  else {
    while(true) {
      ld maxr = WDIM == 2 ? cgi.rhexf : cgi.corner_bonus;
      ld vol = randd() * wvolarea_auto(maxr);
      ld r = binsearch(0, maxr, [vol] (ld r) { return wvolarea_auto(r) > vol; });
      transmatrix T = random_spin();
      hyperpoint h = T * xpush0(r);
      cell* c1 = c;
      virtualRebase(c1, h);
      if(c1 == c)
        return T * xpush(r);
      }
    }
  }

bool draw_snow(cell *c, const transmatrix& V) {
  
  if(!matrices_at.count(c)) {
    auto& v = matrices_at[c];
    int cnt = 0;
    ld prob = randd();
    ld poisson = exp(-snow_lambda);
    while(cnt < 2*snow_lambda+100) {
      if(prob < poisson) break;
      prob -= poisson;
      cnt++;
      poisson *= snow_lambda / cnt;
      }
    if(snow_test) {
      if(c != cwt.at) 
        cnt = 0;
      else {
        c->wall = waFloorA;
        cnt = snow_lambda;
        }
      }      

    for(int t=0; t<cnt; t++) 
      v.push_back(random_snow_matrix(c));
    }
  
  poly_outline = 0xFF;
  for(auto& T: matrices_at[c]) {
    auto& p = queuepoly(V * T, shapeid(snow_shape), snow_color);
    if(!snow_texture) p.tinf = nullptr;
    if(snow_intense) p.flags |= POLY_INTENSE;
    }

  return false;
  }

bool cylanim = false;

auto hchook = addHook(hooks_drawcell, 100, draw_snow)

+ addHook(clearmemory, 40, [] () {
    matrices_at.clear();
    })

+ addHook(hooks_args, 100, [] {
  using namespace arg;
           
  if(0) ;
  else if(argis("-snow-lambda")) {
    shift_arg_formula(snow_lambda);
    }
  else if(argis("-snow-shape")) {
    shift(); snow_shape = argi();
    }
  else if(argis("-snow-test")) {
    snow_test = true;
    }
  else if(argis("-snow-color")) {
    shift(); snow_color = arghex();
    }
  else if(argis("-snow-intense")) {
    snow_intense = true;
    }
  else if(argis("-snow-no-texture")) {
    snow_texture = false;
    }
  else if(argis("-snow-glitch")) {
    snow_test = true;
    }
  else return 1;
  return 0;
  });

}