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snow visualization added
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147
rogueviz/snow.cpp
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147
rogueviz/snow.cpp
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#include "../hyper.h"
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/** \brief Snowball visualization
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*
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* This visualization puts small objects ('snowballs') randomly throughout the space.
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* It provides a way to visualize the geometry without any tessellation.
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*
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* Should work for tessellations where every tile is congruent.
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*
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* The snow_lambda parameter gives the expected number of snowballs per cell.
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* (The number in every region has Poisson distribution with mean proportional to its area.)
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*
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* Not implemented for: product
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*
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**/
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namespace hr {
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ld snow_lambda = 1;
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bool snow_test = false;
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/* a funny glitch */
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bool snow_glitch = false;
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int snow_shape = 0;
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map<cell*, vector<transmatrix> > matrices_at;
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hpcshape& shapeid(int i) {
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switch(i) {
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case 0:
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return cgi.shSnowball;
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case 1:
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return cgi.shHeptaMarker;
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case 2:
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return cgi.shDisk;
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default:
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return cgi.shDisk;
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}
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}
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bool draw_snow(cell *c, const transmatrix& V) {
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if(!matrices_at.count(c)) {
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auto& v = matrices_at[c];
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int cnt = 0;
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ld prob = randd();
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ld poisson = exp(-snow_lambda);
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while(cnt < 2*snow_lambda+100) {
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if(prob < poisson) break;
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prob -= poisson;
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cnt++;
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poisson *= snow_lambda / cnt;
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}
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if(snow_test) {
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if(c != cwt.at)
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cnt = 0;
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else {
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c->wall = waFloorA;
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cnt = snow_lambda;
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}
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}
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if(snow_glitch) {
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// in the standard tiling, this is incorrect but fun
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for(int t=0; t<cnt; t++) {
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hyperpoint h = C0;
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h[0] = randd() - .5;
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h[1] = randd() - .5;
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h[2] = randd() - .5;
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h[2] = -h[2];
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v.push_back(rgpushxto0(h));
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}
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}
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else if(nonisotropic || bt::in()) {
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int co = bt::expansion_coordinate();
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ld aer = bt::area_expansion_rate();
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for(int t=0; t<cnt; t++) {
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hyperpoint h;
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// randd() - .5;
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for(int a=0; a<3; a++) {
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if(a != co || aer == 1)
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h[a] = randd() * 2 - 1;
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else {
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ld r = randd();
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h[co] = log(lerp(1, aer, r)) / log(aer) * 2 - 1;
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}
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}
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h = tC0(bt::normalized_at(h));
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v.push_back(rgpushxto0(h));
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}
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}
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else {
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while(isize(v) < cnt) {
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ld maxr = WDIM == 2 ? cgi.rhexf : cgi.corner_bonus;
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ld vol = randd() * wvolarea_auto(maxr);
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ld r = binsearch(0, maxr, [vol] (ld r) { return wvolarea_auto(r) > vol; });
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transmatrix T = random_spin();
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hyperpoint h = T * xpush0(r);
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cell* c1 = c;
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virtualRebase(c1, h);
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if(c1 == c)
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v.push_back(T * xpush(r));
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}
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}
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}
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poly_outline = 0xFF;
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for(auto& T: matrices_at[c])
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queuepoly(V * T, shapeid(snow_shape), 0xFFFFFFFF).tinf = nullptr;
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return false;
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}
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bool cylanim = false;
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auto hchook = addHook(hooks_drawcell, 100, draw_snow)
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+ addHook(clearmemory, 40, [] () {
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matrices_at.clear();
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})
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+ addHook(hooks_args, 100, [] {
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using namespace arg;
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if(0) ;
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else if(argis("-snow-lambda")) {
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shift_arg_formula(snow_lambda);
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}
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else if(argis("-snow-shape")) {
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shift(); snow_shape = argi();
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}
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else if(argis("-snow-test")) {
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snow_test = true;
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}
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else if(argis("-snow-glitch")) {
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snow_test = true;
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}
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else return 1;
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return 0;
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});
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}
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