David's final changes: more profiler features, fixes. master
authorDavid Gow <david@ingeniumdigital.com>
Mon, 27 Oct 2014 00:14:02 +0000 (08:14 +0800)
committerDavid Gow <david@ingeniumdigital.com>
Mon, 27 Oct 2014 00:14:02 +0000 (08:14 +0800)
12 files changed:
src/bezier.cpp
src/bezier.h
src/debugscript.cpp
src/feeling_catty.script [new file with mode: 0644]
src/grids_are_go.script [new file with mode: 0644]
src/main.cpp
src/main.h
src/objectrenderer.cpp
src/profiler.cpp
src/profiler.h
src/view.cpp
tools/DavidResults.ipynb [new file with mode: 0644]

index 88d9c1c..83db943 100644 (file)
@@ -96,16 +96,13 @@ vector<BReal> SolveCubic(const BReal & a, const BReal & b, const BReal & c, cons
        //Debug("%u turning points", turns.size());
        for (unsigned i = 1; i < turns.size(); ++i)
        {
-               if (tl > max) break;
-               tu = std::min(turns[i],tu);
+               //if (tl > max) break;
+               tu = turns[i];
                CubicSolveSegment(roots, a, b, c, d, tl, tu,delta);
                tl = turns[i];
        }
-       if (tu < max)
-       {
-               tu = max;
-               CubicSolveSegment(roots, a, b, c, d, tl, tu,delta);
-       }
+       tu = max;
+       CubicSolveSegment(roots, a, b, c, d, tl, tu,delta);
        return roots;
 }
 
index 298bcf3..77429cf 100644 (file)
@@ -20,7 +20,7 @@ namespace IPDF
        
        extern std::vector<BReal> SolveQuadratic(const BReal & a, const BReal & b, const BReal & c, const BReal & min = 0, const BReal & max = 1);
 
-       extern std::vector<BReal> SolveCubic(const BReal & a, const BReal & b, const BReal & c, const BReal & d, const BReal & min = 0, const BReal & max = 1, const BReal & delta = 1e-5);
+       extern std::vector<BReal> SolveCubic(const BReal & a, const BReal & b, const BReal & c, const BReal & d, const BReal & min = 0, const BReal & max = 1, const BReal & delta = 1e-9);
 
        /** A _cubic_ bezier. **/
        struct Bezier
@@ -258,8 +258,8 @@ namespace IPDF
                        // Find points of intersection with the rectangle.
                        Debug("Clipping Bezier to BRect %s", r.Str().c_str());
 
-                       bool isVerticalLine = (x0 == x1 && x1 == x2 && x2 == x3);
-                       bool isHorizontalLine = (y0 == y1 && y1 == y2 && y2 == y3);
+                       bool isVerticalLine = false;//(x0 == x1 && x1 == x2 && x2 == x3);
+                       bool isHorizontalLine = false;//(y0 == y1 && y1 == y2 && y2 == y3);
 
                        // Find its roots.
                        
@@ -269,11 +269,13 @@ namespace IPDF
                        {
                                std::vector<BReal> x_intersection = SolveXParam(r.x);
                                intersection.insert(intersection.end(), x_intersection.begin(), x_intersection.end());
+                               Debug("Number of top intersections: %d", x_intersection.size());
 
                                // And for the other side.
 
                                std::vector<BReal> x_intersection_pt2 = SolveXParam(r.x + r.w);
                                intersection.insert(intersection.end(), x_intersection_pt2.begin(), x_intersection_pt2.end());
+                               Debug("Number of bottom intersections: %d", x_intersection_pt2.size());
                        }
 
                        // Find its roots.
@@ -281,15 +283,18 @@ namespace IPDF
                        {
                                std::vector<BReal> y_intersection = SolveYParam(r.y);
                                intersection.insert(intersection.end(), y_intersection.begin(), y_intersection.end());
+                               Debug("Number of left intersections: %d", y_intersection.size());
 
                                std::vector<BReal> y_intersection_pt2 = SolveYParam(r.y+r.h);
                                intersection.insert(intersection.end(), y_intersection_pt2.begin(), y_intersection_pt2.end());
+                               Debug("Number of right intersections: %d", y_intersection_pt2.size());
                        }
 
                        // Merge and sort.
                        intersection.push_back(BReal(0));
                        intersection.push_back(BReal(1));
                        std::sort(intersection.begin(), intersection.end());
+                       Debug("Number of intersections: %d", intersection.size());
                        
                        std::vector<Bezier> all_beziers;
                        if (intersection.size() <= 2)
index 6d4ba7f..434743d 100644 (file)
@@ -449,14 +449,15 @@ void DebugScript::PrintPerformance(View * view, Screen * scr)
        now.view_bounds = view->GetBounds();
 
        // object_count  clock  delta_clock  x  Log10(x)  y  Log10(y)  w  Log10(w)  Size(w)
-       #ifdef QUADTREE_DISABLED
+       //#ifdef QUADTREE_DISABLED
        printf("%d\t%llu\t%llu\t%s\t%s\t%s\t%s\t%s\t%s\t%u\n",
                now.object_count, (long long unsigned)now.clock,
                (long long unsigned)(now.clock - m_perf_last.clock),
-               Str(now.view_bounds.x).c_str(), Str(Log10(Abs(now.view_bounds.x))).c_str(),
+               "", "", "", "", "", "", 0);
+               /*Str(now.view_bounds.x).c_str(), Str(Log10(Abs(now.view_bounds.x))).c_str(),
                Str(now.view_bounds.y).c_str(), Str(Log10(Abs(now.view_bounds.y))).c_str(),
                Str(now.view_bounds.w).c_str(), Str(Log10(now.view_bounds.w)).c_str(),
-               (unsigned)Size(now.view_bounds.w));
-       #endif
+               (unsigned)Size(now.view_bounds.w));*/
+       //#endif
        m_perf_last = now;
 }
diff --git a/src/feeling_catty.script b/src/feeling_catty.script
new file mode 100644 (file)
index 0000000..385754b
--- /dev/null
@@ -0,0 +1,13 @@
+gpu
+lazy
+debugfont on
+clearperf
+printperf
+clear
+loadsvg svg-tests/cat2.svg
+label start
+debug meow! 
+loop 1000 pxzoom 376 187 1
+debug Repeat
+goto start
+wait
diff --git a/src/grids_are_go.script b/src/grids_are_go.script
new file mode 100644 (file)
index 0000000..3983130
--- /dev/null
@@ -0,0 +1,8 @@
+gpu
+nolazy
+profileon
+debugfont on
+clear
+loadsvg svg-tests/grid.svg
+loop 800 pxzoom 376 187 1
+quit
index c9d2362..23aba12 100644 (file)
@@ -26,7 +26,7 @@ int main(int argc, char ** argv)
        program_name = argv[0];
        
        //Debug("Main!");
-       signal(SIGFPE, sigfpe_handler);
+       signal(SIGFPE, SIG_IGN);// sigfpe_handler);
        #if REALTYPE == REAL_IRRAM
          iRRAM_initialize(argc,argv);
        #endif
@@ -189,12 +189,6 @@ int main(int argc, char ** argv)
 
        Rect bounds(b[0],b[1],b[2],b[3]);
        Screen scr(window_visible);
-       View view(doc,scr, bounds);
-       
-       view.SetLazyRendering(lazy_rendering);
-       view.SetGPURendering(gpu_rendering);
-       view.SetGPUTransform(gpu_transform);
-
        if (input_filename != NULL)
        {
                #ifdef TRANSFORM_OBJECTS_NOT_VIEW
@@ -207,6 +201,12 @@ int main(int argc, char ** argv)
        {
                doc.AddText(input_text, bounds.h/Real(2), bounds.x, bounds.y+bounds.h/Real(2));
        }
+       View view(doc,scr, bounds);
+       
+       view.SetLazyRendering(lazy_rendering);
+       view.SetGPURendering(gpu_rendering);
+       view.SetGPUTransform(gpu_transform);
+
 
 
 
index a96e933..2ead6f6 100644 (file)
@@ -125,6 +125,7 @@ void MainLoop(Document & doc, Screen & scr, View & view, int max_frames = -1)
                real_clock_prev = real_clock_now;
                #endif
                ++frames;
+               g_profiler.BeginZone("MainLoop");
                g_profiler.BeginZone("scr.Clear()");
                scr.Clear();
                g_profiler.EndZone();
@@ -227,6 +228,8 @@ void MainLoop(Document & doc, Screen & scr, View & view, int max_frames = -1)
                g_profiler.BeginZone("scr.Present()");
                scr.Present();
                g_profiler.EndZone();
+               g_profiler.EndZone();
+               g_profiler.AddCounter("Total Objects", doc.ObjectCount());
                g_profiler.EndFrame();
                
                
index 9b84ec2..c0c44b6 100644 (file)
@@ -385,7 +385,7 @@ void BezierRenderer::RenderUsingGPU(unsigned first_obj_id, unsigned last_obj_id)
        while (m_indexes.size() > first_index && m_indexes[first_index] < first_obj_id)
        {
                unsigned new_index = (first_index + first_obj_id) / 2;
-               if (new_index < m_indexes.size() && m_indexes[new_index] < first_obj_id)
+               if (new_index != first_index && new_index < m_indexes.size() && m_indexes[new_index] < first_obj_id)
                        first_index = new_index;
                else
                        first_index ++;
index f718637..13595af 100644 (file)
@@ -15,6 +15,14 @@ void Profiler::BeginZone(std::string name)
        m_zone_stack.push(name);
 }
 
+void Profiler::AddCounter(std::string name, uint64_t amt)
+{
+       if (!m_counters.count(name))
+               m_counters[name] = amt;
+       else
+               m_counters[name] += amt;
+}
+
 void Profiler::EndZone()
 {
        std::string name = m_zone_stack.top();
@@ -38,4 +46,13 @@ void Profiler::EndFrame()
                it.second.tics_frame = 0;
                it.second.calls_frame = 0;
        }
+
+       for (auto& it : m_counters)
+       {
+               if (m_enabled)
+               {
+                       printf("perf_counter\t\"%s\"\t%lu\n", it.first.c_str(), it.second);
+               }
+               it.second = 0;
+       }
 }
index e406536..ffffae9 100644 (file)
@@ -21,6 +21,8 @@ namespace IPDF
                void EndFrame();
 
                void Enable(bool enabled) { m_enabled = enabled; }
+
+               void AddCounter(std::string name, uint64_t amt);
        private:
                struct ProfileZone
                {
@@ -33,6 +35,7 @@ namespace IPDF
                };
 
                std::map<std::string, ProfileZone> m_zones;
+               std::map<std::string, uint64_t> m_counters;
                std::stack<std::string> m_zone_stack;
                bool m_enabled;
        };
index c660a7e..3d984ee 100644 (file)
@@ -476,6 +476,9 @@ void View::RenderRange(int width, int height, unsigned first_obj, unsigned last_
        if (first_obj == last_obj) return;
        PROFILE_SCOPE("View::RenderRange");
        glPushDebugGroup(GL_DEBUG_SOURCE_APPLICATION, 43, -1, "View::RenderRange()");
+
+       g_profiler.AddCounter("Objects Rendered", last_obj-first_obj);
+
        if (m_render_dirty) // document has changed
                PrepareRender();
 
diff --git a/tools/DavidResults.ipynb b/tools/DavidResults.ipynb
new file mode 100644 (file)
index 0000000..9b271ab
--- /dev/null
@@ -0,0 +1,238 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      }
+     ],
+     "prompt_number": 67
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "qt_mainlooptimes = loadtxt(\"../data/qt-mainlooptimes-novb\")\n",
+      "qt_genquadchildtimes = loadtxt(\"../data/qt-genquadchild-novb\")\n",
+      "gmp_mainlooptimes = loadtxt(\"../data/gmp-mainlooptimes\")\n",
+      "qt_objsrendered = loadtxt(\"../data/qt-objsrendered-novb\")\n",
+      "gmp_objsrendered = loadtxt(\"../data/gmp-objsrendered\")\n",
+      "qt_totalobjs = loadtxt(\"../data/qt-totalobjs-novb\")\n",
+      "gmp_totalobjs = loadtxt(\"../data/gmp-totalobjs\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 68
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot(qt_mainlooptimes[:800], label=\"Quadtree\")\n",
+      "plot(gmp_mainlooptimes[:800], label=\"GMP Rationals\")\n",
+      "xlabel(\"Frame\")\n",
+      "ylabel(\"Time (ms)\")\n",
+      "semilogy()\n",
+      "legend()\n",
+      "#show()\n",
+      "savefig(\"frametime.pdf\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
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f/o4An0DcanwEgrLChHiH4L6wfUtLAc+Eulc7jnM3T8X9992PwDAOMMIZaaCUR1GVckzEARBeIJa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\nOiwHIiLSYTkQEZEOy4GIiHT+AfmuMwh9276KAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe572628c90>"
+       ]
+      }
+     ],
+     "prompt_number": 84
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot(qt_totalobjs[:800], label=\"In Document\")\n",
+      "plot(qt_objsrendered[:800], label=\"Rendered\")\n",
+      "plot(gmp_totalobjs[:800], label=\"In Original Document\")\n",
+      "xlabel(\"Frame\")\n",
+      "ylabel(\"Objects\")\n",
+      "legend()\n",
+      "#show()\n",
+      "savefig(\"objects.pdf\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
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Yetz0czFi0B4OPDbb40cFBGRDWvIxYY1/h8+a9YsrFmzBiEhIVVec3JywpgxY/Dyyy+b9aa2gkN8\niYgapsaWyLVr19CuXTtcuHChUutDCAGdToeJEyfihx9+sFqgdVWfjHrmDPDkk8afRET2qlGmPWnX\nrh0A44n1uXPnYvjw4Vi4cCHkcjm6du2Kbdu2mRetDeE5ESKihql1qNXUqVORkJCAffv24amnnsLk\nyZMBAA899FCjB9fYmESIiBqm1iQiSRJiY2PRqlUrjBo1CjqdzhpxWQWTCBFRw9RYhebm5gIAWrVq\nhdTUVERERCA7OxsPP/yw1YJrbEwiREQNU2MVmpKSAkmS0LJlSyQnJ2P//v2QJKnZXWzIaeCJiMxX\nY0ZISUlBQUEBTp8+jcGDB+PkyZO4du0a3n//fWvG16g4xJeIqGFqTCKvv/46nnrqKRw8eBBbtmxB\nTk4Oxo4di1dffdWa8TUqdmcRETVMjUnk+PHjVWbsTUhIwPHjxxs9KGthEiEiapgak4hcLq+23LEZ\n1bpMIkREDVNjEmndujVycnIqleXk5KBNmzaNHpS1MIkQETVMjVXoP/7xD4waNQqRkZHo3LkzLly4\ngG+//RZff/21NeNrVEwiREQNc89ZfEtKSrBz5078+uuv6NChA0aNGgU3NzdrxldvNc0Bo9EAR48C\nFV/6/ntj2ZdfWjFAIiIb05C5s+o8Fbyl/P777xgwYAC+++47ODg4YNKkSXBwcIC/vz/WrFkDSZKQ\nmpqKTz75BI6Ojli8eDGGDx+OkpISPP/88ygoKIC7uzs2btwIb2/vqgdUw4exZQswezbQtWvl8gkT\ngLlzG+toiYhsX6NMwNgYdDodpk2bBjc3NwghMHfuXCQnJyMrKwtCCOzYsQP5+flISUnBgQMHsHv3\nbixYsABarRZr165FQEAAsrKyEBcXh6SkpHq9t1YLDB8OHDxYeWECISIyn1WTyPz58zFjxgy0b98e\nAHDkyBEoFAoAQGxsLDIyMpCTk4PQ0FDI5XJ4eHjAz88PJ06cwP79+xETEwMAiImJQUZGRr3eW68H\nmtHF9kRENsFq1eqGDRvg4+ODoUOHAjDel6Ri88nd3R0qlQpFRUXw9PSsttzDw6NSWX0YDJzihIjI\n0qw2NiktLQ2SJCEjIwPHjh1DfHw8CgoKTK8XFRXBy8sLHh4eUKvVpnK1Wl2lvLysPgwGtkSIiCzN\nakkkMzPT9DgqKgofffQR5s+fj8zMTERERCA9PR3R0dEIDg7GokWLoNFoUFpailOnTsHf3x+hoaHY\ntWsXgoJfodL0AAATa0lEQVSCkJ6ebuoGq05iYqLpcWRkJCIjI9mdRUT0J6VSCaVSaZF9WX10FmBM\nIh9//DEkScLUqVOh1WrRq1cvpKamQpIkrFu3Dp988gkMBgMWLVqEMWPGoKSkBPHx8bh69SqcnZ2x\nefNmtG3btuoB1TDKYM0a4KefjD+JiOiO+2qIb2Or6cNISTHeSz0lpQmCIiKyYffNEN+mxHMiRESW\nZzfVql7P0VlERJZmN0mELREiIsuzm2qVSYSIyPLsplrlEF8iIsuzm2qVV6wTEVmeXSURtkSIiCzL\nbqpVJhEiIsuzm2qVQ3yJiCzPbpIIWyJERJZnN9UqkwgRkeXZTbXK7iwiIsuzmyTClggRkeXZTbXK\nJEJEZHl2U62yO4uIyPLsJomwJUJEZHl2U60yiRARWZ7dVKucO4uIyPLsJolwFl8iIsuzm2qV3VlE\nRJZnN9UqkwgRkeXZTbXKIb5ERJbn2NQBWIMQAmUGPYQElBmMZTJJBkmSmjYwIqL7nF20RObtmYdN\nnZ2QcMEFLkkucHrDCUuVS5s6LCKi+55dJJHrJdcxKD8N/+xWhrIlZfgg9gNcL77e1GEREd337CKJ\nGIQBwuBgOrEuk2QoM5Q1bVBERM2AXSQRvUEPVEgijg6O0At90wZFRNQM2EUSMQgDhHAwjc6SObAl\nQkRkCfaTRPQytkSIiCzMfpLIXedE9AYmESKihrKLJKIX+krdWY4OjuzOIiKyALtIIgZhAAx3urNk\nDjJ2ZxERWYDdJBGDvvLoLLZEiIgazi6SSPkQX9PoLJ4TISKyCLtIInefWGdLhIjIMuwmiRj0PCdC\nRGRpdpNE2BIhIrI8qyURnU6HF154AQqFAoMGDcLXX3+Ns2fPIiwsDAqFAjNnzoQQAgCQmpqKoKAg\nhISEYOfOnQCAkpISjBs3DgqFAsOHD0dhYWGd31sv9BA8J0JEZHFWSyKbNm2Cj48PsrKy8M0332DW\nrFmYN28ekpOTkZWVBSEEduzYgfz8fKSkpODAgQPYvXs3FixYAK1Wi7Vr1yIgIABZWVmIi4tDUlJS\nnd/b2BKp2p11KO8QNh7biN9UvzXSURMRNW9WSyITJkzA8uXLAQAGgwFyuRxHjhyBQqEAAMTGxiIj\nIwM5OTkIDQ2FXC6Hh4cH/Pz8cOLECezfvx8xMTEAgJiYGGRkZNT5vQ3CAH2ZAxz/vAVXeXfWvD3z\nsPD7hfj0yKeWPVgiIjthtSTi5uaGli1bQq1WY8KECUhKSoLBYDC97u7uDpVKhaKiInh6elZb7uHh\nUamsrvQGPXRaBzg7G5+Xd2dp9VoEdwiGVq+1zEESEdkZq94e99KlSxg7dixmzZqFZ599Fq+++qrp\ntaKiInh5ecHDwwNqtdpUrlarq5SXl9UkMTHR9DgyMhIGYaiURCqeWHeTuzGJEJFdUSqVUCqVFtmX\n1ZLItWvXMHToUHz44YeIiooCAAQGBiIzMxMRERFIT09HdHQ0goODsWjRImg0GpSWluLUqVPw9/dH\naGgodu3ahaCgIKSnp5u6wapTMYkAgOEXA3Qa2Z2WyJ/nRPQGPVo6tWQSISK7EhkZicjISNPzZcuW\nmb0vqyWR5ORkqFQqLF++3HRuZNWqVZgzZw60Wi169eqF8ePHQ5IkzJkzB+Hh4TAYDEhOToazszNm\nzJiB+Ph4hIeHw9nZGZs3b67ze9fUEikzlMFN7obbutuNcchERM2eJMrH1TYTkiTh7kMK/DgQ5977\nFBcO9kfr1sDpwtMYsWUENHoN4gPikVeUh/Wj1jdRxERETau6erOu7OZiQ51GBicn4/Pym1Jp9Vqe\nEyEiagC7SSLaiqOz/rw9rk6vg5uTG3QGXdMGSER0n7KLJKI36GHQV75OpHyIL0+sExGZz06SiAFO\njjJIkvG5TDK2RNidRUTUMPaTROR3DrX8nIjOoENLp5bQ6dmdRURkDrtMIjIHGTRlGgCAi6MLWyJE\nRGayiyRSZtDD2alCEpFkKCkrgdxBDieZE5MIEZGZ7CKJGAwGOMllpuflFxs6yZzgJHPi6CwiIjNZ\nde4sqykurvTUSVMGdweNqVxWpoGrFvCUyeGkKYNDSWmVbYiIqHbN8or129WkRgeDC1ycjcOzBIAS\nXQkAwEXuAk2ZBq6OLqZ1tQYdhBBwkBwgd2ieeZaIqJxUUmL2FevNMokkf3AFsU8Yn2/+eR3ePbwE\n52ZcRee2D5jWG//FeGw9tRW/zP4FMf+Mwdk5Z02vtVnRBjMHzsQ3575BztQcax8CEZFVcdqTu3Rp\n1x79uhiXzu18AAAt3Sofag/vHgAAuYO8yon1Yl0xYrvGcugvEVEtmmVfjWOFo3KWGec6cZAqJ5EH\n3R8EADjJnHCz9CbeO/geAECChNKyUrg7ufOEOxFRLZplEpHdGYgFJ5lx1sW7k8gLfV+Au5M7fNx8\nMGfQHOQV5QEANv+42bQdh/4SEd1bs08izo7GlohMklVax93ZHS8EvAAASHo0yVR+LP8Yrt2+Zhz6\ny+4sIqJ7apbnROrSnVXjtn+OxpLL5OzOIiKqRbNMItW1ROqbRNidRURUu2aZRKpricgcZDWsXZlc\nJjf+dJCzO4uIqBbNMok0pCVSfu6E3VlERLVrlknEEudE2J1FRFS7ZplE6jLEtyatXVsDMHZnlRnK\nzL6Kk4jIHjTLJFKxJVLfJPLu4+/ip5k/QZIk02y/RERUvWaZRCq2RMpPlNeVu7M7evr0NG5bzZQo\nRER0R/NPIg71SyIV8eQ6EdG9NcskUrE7y7EBU7nzqnUiontrlkmkYkuktWtrBHcINms/FbuzDMKA\ntKNplgiPiKjZaJZJpGJLRC6T41DCIbP2U/HWuQW3CzD535N5op2IqIJmmURkdbs4vVZy2Z2r1lUa\nFQDgevF1y+yciKgZaJZJxNFCcxNX7M76o+QPAMDvt3+3zM6JiJqBZj8VfEO4yl0Rvz0eLZ1aIvNi\nJgAmESKiiprlPdYLCgS8vRu+r1+u/4LL6ssAgKiNUQCARzs9ik5enUzreLfwxtuPvd3wNyMiaiIN\nucd6s0wiN24ItGpl4f0ukwAAqSNSK5XPTp+N3//2O1o6taw2FiIiW8ckUoEkSSgqEnB3t+x+1x1Z\nhyJNEeaGzK38fsuqTxTBHYLNHhVGRGRNDUkiPCdSRwn9E2p8rZ1bO+T/Ld/0vLC4EN1Xd7d8EERE\nNqZZjs5qjCRyLx08OlR63tq1NYo0RbzanYiavWbZErHUEN+6ODrtKLxbVD6L7yA5oLVra1wvuY4H\nWj5gvWCIiKzsvkoiBoMBM2fOxIkTJ+Ds7Ix169ahS5cuVdZzsGL7qt8D/aotb+vWFhduXqhywt3V\n0bXOt+olIrJ191V31vbt26HVanHgwAG8/fbbmDdvXrXr2cKgqL7t+uKxzx7DA/94wLS0fbctRmwZ\nAaVS2dTh1QnjtCzGaVmM0zbcV0lk//79iImJAQAMGjQIhw8fbuKIarZp7CbcWnir0nLz9Zv43+//\nQ0xSDFq908q0dHivA04Xnm7qkKu4X778jNOyGKdl3S9xmuu+6s4qKiqCh4eH6blMJoPBYICDNfuv\nGsBJ5oQzs89gaeFSvD7ndVN5yg8pCPgowHQDrdnBsxEXEAcnmRN+LvwZeoMekiRB0VEBD2ePmnZP\nRGR191US8fDwgFqtNj2/nxJIORdHF7jKXdHK9c7VkH9X/B2vDH4FgHFalbjtcdh6aitKdCXo3Koz\n3J3doSpV4bmtz6GFvAUEhGlMd8XHrV1bw93ZHS6OLjXejEug+rHg1Y0Rv3j0Ir5P+75e25QrLStF\nQXFBja83RPuW7eHm5AYHyQEOkgPOnTiHnM05yL+VjyJNUaO8pyVcP3Qdae+n4bb2dp3W93HzQQt5\nC7g6utZ4Xxxzfje1bfPb0d+QsT6j0mvFumJcL2mcyUc7uHeAq9zV9PuUIMFBckCxrtg0Y0R1rh+6\njk0pmxolprqQIKGkrAQluhLj8z/70SXc6U+XJAm3DtzCR//4CABQZijDQx4PQZIkSJDq9LP8M6lt\nXZmDDDJJZv2LnMV9ZOvWrWLSpElCCCEOHjwonnjiiSrrdOnSRQDgwoULFy51XLp06WJ2vXxfXbEu\nhDCNzgKAtLQ0dOvWrYmjIiKyX/dVEiEiIttyf51QICIim9IskojBYMD06dMxZMgQREVF4dy5c00d\nEgDg0KFDiIoyTiF/9uxZhIWFQaFQYObMmaaTmKmpqQgKCkJISAh27txp1fh0Oh1eeOEFKBQKDBo0\nCF9//bVNxqnX6zF58mSEhYUhPDwcJ0+etMk4y/3+++94+OGHcebMGZuNs3///oiKikJUVBSmTJli\ns3G+9dZbGDJkCIKCgrBx40abjHPjxo2mz3Lw4MFwdXVFbm6uTcVpMBhMf0MKhQKnT5+23Gdp9tkU\nG7J161bxl7/8RQghRHZ2thg1alQTRyTEO++8I/r06SNCQkKEEEKMGDFCZGZmCiGEmD59uvjqq6/E\n1atXRZ8+fYRWqxUqlUr06dNHaDQaq8WYlpYmXnnlFSGEEDdu3BAPP/ywGDlypM3FuX37djFlyhQh\nhBBKpVKMHDnSJuMUQgitVitGjx4tunfvLn7++Web/L2XlJSIwMDASmW2GOfevXvFiBEjhBBC3Lp1\nSyxZssRmf+/lZs2aJVJTU20uzvT0dPHUU08JIYT49ttvxdixYy0WY7NoidjiRYh+fn7Ytm2bKbsf\nOXIECoUCABAbG4uMjAzk5OQgNDQUcrkcHh4e8PPzMw0asIYJEyZg+fLlAIz/qcjlcpuMc9SoUfj4\n448BABcuXECrVq2Qm5trc3ECwPz58zFjxgy0b98egG3+3o8fP47i4mIMGzYM0dHRyM7Otsk49+zZ\ngz59+mD06NEYMWIERo4cabO/dwA4fPgwfvrpJyQkJNhcnK6urlCpVBBCQKVSwcnJyWIxNoskUtNF\niE1p7NixcKwwE6SoMH7B3d0dKpUKRUVF8PT0rFJuLW5ubmjZsiXUajUmTJiApKSkSp+brcQJGH+n\nkyZNwksvvYSJEyfa5Oe5YcMG+Pj4YOjQoQCMv3NbjNPNzQ3z58/H7t278dFHH2HixImVXreVOAsK\nCpCbm4svv/wSH330EZ577jmb/DzLJScnY+nSpQBs7+89NDQUpaWl6NGjB6ZNm4Y5c+ZYLMZmkUTu\nh4sQK8ZTVFQELy+vKnGr1Wq0svQtGWtx6dIlPProo4iLi8Ozzz5rs3ECxkr69OnTSEhIQGlpqc3F\nmZaWhm+//RZRUVE4duwY4uPjUVBw54JLW4mzW7dupsTRtWtXtGnTBteuXbO5OL29vTF06FA4Ojqi\nW7ducHFxqVSh2UqcAHDz5k2cOXMGERERAGzv733FihUIDQ3F6dOncezYMcTFxUGnu3OriobEaFs1\nrZlCQ0Oxa9cuAEB2djb69u3bxBFVFRgYiMzMTABAeno6FAoFgoODsW/fPmg0GqhUKpw6dQr+/v5W\ni+natWsYOnQoVqxYgUmTJtlsnJ9//jneeustAMZmuUwmw8CBA20uzszMTCiVSuzduxf9+vXDZ599\nhpiYGJuLMy0tzTR56ZUrV6BWqzF06FCbizMsLAzffPONKc7i4mJER0fbXJwAkJWVhejoaNNzW/s7\nun37tqm3plWrVigrK7NcjI11IseaDAaDmD59uhgyZIgYMmSIOH36dFOHJIQQ4tdffzWdWD9z5oyI\niIgQISEhYsqUKcJgMAghhEhNTRVBQUFiwIABYtu2bVaNb86cOaJ9+/YiMjLStBw/ftzm4iwuLhZP\nPfWUUCgUIiQkRPz73/+2yc+zosjISHH69GmbjFOn04nnn39ehIeHi/DwcHHw4EGbjFMIIV599VXT\n++/Zs8dm43z33XfFqlWrTM9tLc4//vhDjB49WoSFhYlBgwaJLVu2WCxGXmxIRERmaxbdWURE1DSY\nRIiIyGxMIkREZDYmESIiMhuTCBERmY1JhIiIzHZf3R6XyBZcuHABffv2xYABA0xl0dHRWLx4cRNG\nRdQ0mESIzNC7d2/s3bu3qcMganLsziKyAKVSiUGDBkGhUOCf//wnvvzySzz66KMIDw+HQqHA9evX\noVQqMWzYMIwaNQr9+/fHJ598gmeeeQY9e/bERx99BMA4dUp4eDgiIyMxZcoUlJWVNfGREd0bWyJE\nZvjpp59MNxwDgKlTp0Kj0eDQoUMAjDdT2rlzJ1xdXTF9+nTs3r0bHTp0wOXLl3H8+HEcPnwYEyZM\nwPnz55GXl4cxY8Zg+vTpmDp1Kg4cOABvb28sWbIEGzZsQEJCQlMdJlGtmESIzNCrV69K3VmZmZno\n3r276bmPjw/i4+PRsmVL/PzzzwgJCQEA+Pv7QyaTwdPTE126dIGjoyO8vLxQWlqKgoIC5OfnY8KE\nCQCAkpIS07TyRLaKSYTIAoQQpum/VSoVEhMTcenSJRgMBgwdOtR07wZJkmrch7e3Nx566CH8+9//\nhru7O7Zv347WrVtbJX4iczGJEJnh7mQgSZKpzNPTE6GhoQgJCUHbtm3RvXt3XL16FZ06daq03d2P\nJUnCqlWr8MQTT8BgMMDT0xOfffaZdQ6IyEycxZeIiMzG0VlERGQ2JhEiIjIbkwgREZmNSYSIiMzG\nJEJERGZjEiEiIrMxiRARkdmYRIiIyGz/HzOzeULQ/rWvAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe572f28cd0>"
+       ]
+      }
+     ],
+     "prompt_number": 70
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot(qt_totalobjs[:800], qt_mainlooptimes[:800], label=\"Quadtree\")\n",
+      "plot(gmp_totalobjs[:800], gmp_mainlooptimes[:800], label=\"GMP Rationals\")\n",
+      "xlabel(\"Total Objects\")\n",
+      "ylabel(\"Time (ms)\")\n",
+      "legend()\n",
+      "#show()\n",
+      "savefig(\"totalobjectstime.pdf\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
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1PwOtxWyWCy0xvJnfTqLdp7tmBUxq7lLEZE2qLGXnTogUURETFGQiIiIKQoSERExRUEiIiKmKEhE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\ngkREREz5/5G5CmPVzT9MAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe572f8be10>"
+       ]
+      }
+     ],
+     "prompt_number": 71
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot(qt_objsrendered[:800], qt_mainlooptimes[:800], label=\"Quadtree\")\n",
+      "plot(gmp_objsrendered[:800], gmp_mainlooptimes[:800], label=\"GMP Rationals\")\n",
+      "xlabel(\"Objects Rendered\")\n",
+      "ylabel(\"Time (ms)\")\n",
+      "legend()\n",
+      "#show()\n",
+      "savefig(\"totalobjectstime.pdf\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAENCAYAAAD34uk0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtcFOX+B/DPcglNWTVT85i/LPFGKJEuFxeWJUqWyLuW\nl0Lyco5iR48oGdFJ6BBdTckKj2he08o0zQA9bbGQeAmBpAtqqHkp9Ugqu8plkX1+f3CYQEAcZWHB\nz/v12hfLs8PM92FgPjszO88ohBACREREMtg1dwFERNTyMDyIiEg2hgcREcnG8CAiItkYHkREJBvD\ng4iIZLNaeKxduxYBAQEICAiAt7c32rZti+zsbPj6+kKj0SA8PBxVnxJOSkqCSqWCj48PkpOTAQAl\nJSUYO3YsNBoNQkJCUFhYaK1SiYhIJkVTXOfx3HPP4aGHHsKOHTswf/58aDQazJo1C0FBQfD29saw\nYcOQnZ2NkpIS+Pr64sCBA3jvvfdw+fJlvPzyy/jkk0+wd+9eLF261NqlEhHRDbD6YasDBw7g559/\nxvTp05GdnQ2NRgMACA4Ohl6vR1ZWFtRqNRwdHaFUKuHi4oK8vDxkZmZCp9MBAHQ6HfR6vbVLJSKi\nG2T18IiPj8eiRYsAANV3cpydnVFUVASj0YgOHTrU2a5UKmu0ERGRbbBqeFy6dAlHjhyBv79/5cLs\n/lyc0WhEx44doVQqYTKZpHaTyVSrvaqNiIhsg4M1Z56RkYHAwEDpew8PD6Snp8Pf3x+pqakIDAyE\np6cnoqOjUVZWhtLSUuTn58PNzQ1qtRopKSlQqVRITU2VDndV5+LigqNHj1qzC0RErU7v3r1RUFBw\nazMRVvTWW2+JhIQE6fsjR44If39/4ePjI6ZNmyYsFosQQoikpCShUqnE4MGDxdatW4UQQhQXF4vx\n48cLX19fERgYKM6dO1dr/lYuv9ktWrSouUuwqtbcv9bcNyHYv5auMbadVt3zWLBgQY3v+/TpA4PB\nUGu66dOnY/r06TXa2rZti08//dSa5RER0U3iRYJERCQbw8OGabXa5i7Bqlpz/1pz3wD2j5roIkFr\nUSgUaMHlExE1i8bYdlr1nAcR2Z677roLFy9ebO4yqAl06tQJFy5csMq8uedBdJvh/83to7513Rh/\nAzznQUREsjE8iIhINoYHERHJxvAgIiLZGB5EZDOuXr2KN954A+7u7hg0aBDc3Nzw3HPPNeqnw557\n7jnExsYCAFauXInExMRGm/fthOFBRDbj6aefRm5uLjIyMpCXl4fvv/8e9913H3x8fHD58uVGWYZC\noYBCoQAA7N69G8XFxY0y39sNw4OIbEJWVhYyMjKwZs0a6R4/Dg4OiIyMRP/+/ZGYmIj7778f2dnZ\n0s/06tULOTk5ACrvHeTl5QV3d3e4uLhg27ZtACpv//Dkk0+if//+0Gq1OHToEIQQ2LZtG3bs2IEl\nS5bggw8+QExMDIKCguDu7o7Q0FAAwKuvvorBgwfDw8MDo0ePxpkzZwAARUVFCAsLw5AhQ+Du7o6I\niAhUVFQ05a+r2TE8iKgGhaJxHnLt3r0bKpUKbdq0qfXaY489hj179vyvvj9nXvX8xIkT+Prrr5GR\nkYGDBw8iLi4OL7/8MgBg0aJFaNeuHQ4dOoQtW7bgl19+gUKhwKhRozBixAhEREQgPDwcAHDq1Cnk\n5uZi3bp1WLduHX788Ud89913yM3NRXBwsDSA67x58zBkyBAcOHAAOTk5OH/+PN555x35nW7BeIU5\nEdXQXNcPXu/CNSEEysvL6/3Z++67D2vXrsX69etx9OhR7Nu3D1euXAEAfP3110hISAAAdO7cGWPH\njq017yre3t7STeu+/PJLZGVlYciQIQCAiooKlJSU1Hht1apVAIDS0tIaN7u7HdxevSUim6VWq5GV\nlSVtoMvLy6XbT3/zzTcYMmRIrYAxm80AgJycHOm8SFBQEBYuXAiLxQKgMpSqngOAvb19jeVW7b0o\nFAq0a9dOardYLHjhhReQm5uL3NxcHDhwABkZGdJrn332mfTa3r178e677zb2r8SmMTyIyCaoVCoE\nBAQgLCwMly5dwrFjxzB06FCMGzcO+/fvx+zZs9GlSxdkZWUBAPbt24czZ85ACIFvv/0WKpUK//jH\nP+Dn54fPP/9cOgeh0+mwatUqCCFw6dIl6VwIUHlOpSqArt3rCQoKQlJSknQ77JiYGEyZMkV67Z13\n3oEQAmazGaNHj8YHH3xg9d+RLWF4EJHNWL9+PQYPHgx/f3+MGzcO5eXlcHBwwD333IOdO3fijTfe\nQEJCAjw8PLBy5Uppb2TixIkoLCyEm5sbHnnkETz00EO4dOkSrly5gpiYGDg6OqJ///4YPnw43Nzc\npOUFBwfj3Xffxeuvv17jU1hA5U3qnnjiCXh7e8PNzQ0HDx7E2rVrAQDvvvsurly5gkGDBkkfKX7+\n+eeb/PfVnDgwItFtpiX+3xQVFeHAgQMIDAxs7lJaFGsOjMjwIJJBEauAWNSy/+b4f3P74Ki6RERk\nUxgeREQkG8ODiIhkY3gQEZFsDA8iIpLNauHx2muvYejQoVCpVFi7di0KCgrg6+sLjUaD8PBw6Ux/\nUlISVCoVfHx8kJycDAAoKSnB2LFjodFoEBISgsLCQmuVSUQ2ZtWqVfD29oarqytcXFwwbNgwfPfd\nd9LrWq0WdnZ2OH78eI2fS09Ph52dnTTGlFarxQMPPAAPDw88/PDDcHNzQ1hYmHQFe3VhYWG49957\n4eHhAQ8PD7i7u6N379546623Gqz3+PHjGDduHADg999/h1qtvpXu1yssLAyLFy+2yrxvhlXCw2Aw\nYO/evdizZw8MBgOOHTuG+fPnIz4+HhkZGRBCYPv27Th79iyWLVuGPXv2YNeuXYiKioLZbEZiYiLc\n3d2RkZGB0NBQxMXFWaNMIrIxL774ItauXYvNmzfj559/RkFBAaKiovDEE0/g9OnT0nT33XcfNmzY\nUONn165di27duknfKxQKvP3228jNzUVOTg5+/PFHFBcXSwMmVqdQKBARESENN3Lw4EEYDAbExsbi\nyJEj1635xIkTOHz4MADgL3/5CzIzM2/lV1Cvay9ibG5WCY///Oc/GDhwIEaNGoXhw4djxIgRyM7O\nhkajAVB5Vader0dWVhbUajUcHR2hVCrh4uKCvLw8ZGZmQqfTAagcWkCv11ujTCKyIefOnUNCQgI2\nb96Mnj17Su0BAQFYsmSJNNChQqHA5MmT8dFHH0nTFBcXIzMzE48++uh1l1E1JHtdrr3u4dSpU1Ao\nFHB2dgZQ95DvFosF06dPx9GjRxEcHIwTJ06gffv2ACrH5vr73/+OBx98EIMGDcKMGTOke5L06tUL\nsbGx0Gg06NWrFxYuXAigcsysuXPnwtvbGw8++CBcXV2l0YSrW7RoEdzd3aFSqaDT6XD27Nnr9tsa\nrBIe58+fR3Z2Nj777DMsX74ckyZNqrFinJ2dUVRUBKPRKI3bf227Uqms0UZErdvevXsxYMCAGnsP\nVSZPnox+/fpJ33t4eOCOO+6QDmdt3boVI0aMgINDzYHCq293Ll68iE8//RSPPPJIrfkLIbBkyRJ4\neHjAxcUFXbp0wVtvvYUvv/wS3bt3x4kTJ/DNN9/UGvLdzs4Oq1atQu/evZGamgohhLR3EBcXh7Nn\nzyIvLw8HDx6ExWJBZGQkgMoAvHLlCjIyMrBnzx4sW7YMJ06cwP79+3H27Fns27cPP/30E0JDQ/H6\n66/XqPXUqVNISEjAgQMHkJWVVeuwXlOxypDsd999NwYMGAAHBwf07dsXbdq0wW+//Sa9bjQa0bFj\nRyiVSmnQMQAwmUy12qva6hMTEyM912q10Gq1jd4fotuJIrZxDo3czJX41Q/LmEwm6WjF5cuX8eST\nT+LVV1+VXg8NDcWGDRvg6emJdevWYcmSJXj77bf/XL4QiIyMRFxcnBQiw4cPx9y5c+tcbkREBCIi\nIlBcXIynnnoKdnZ28PPzA1B5mGzNmjV1Dvle35XaO3fuRHx8vDSK79///neMGjVKen3kyJEAKg91\nde3aFRcuXICPjw86d+6MxMREHDt2DAaDQXojXeXee++Fu7s7PDw8EBwcjODg4DoDsTqDwQCDwXDd\naeSySnj4+voiISEBERER+P3331FcXIzAwECkp6fD398fqampCAwMhKenJ6Kjo1FWVobS0lLk5+fD\nzc0NarUaKSkpUKlUSE1Nlf6A6lI9PIjo1jXX8Cuenp44dOgQLly4gLvuugvOzs7Izc0FAMTGxtb4\n4EzVoavBgwcjIiICRqMRDz74YI35VZ3zGDNmzA0tvyoE7rzzTqxfvx4DBgzAO++8gwULFiAnJwcj\nR47E/PnzERQUBH9/f8yaNeu687NYLDWCpaKiosY9Sdq2bVujViEEkpOT8Y9//AMLFizAqFGj0L9/\n/xrndqr2bNLT05GdnY2vvvoK8+bNQ0BAAJYuXVpvLde+sa66h/utsEp4hISEICMjA56enrBYLPjg\ngw/Qq1cvzJgxA2azGa6urhg3bhwUCgXmzJkDPz8/WCwWxMfHw8nJCbNmzcKUKVPg5+cHJycnbNy4\n0RplEpEN+ctf/oK5c+di/PjxWLNmjXTe4+TJk8jMzKwVDt27d8egQYMwdepU6bax1+4F3Oz4TR07\ndsTixYvxt7/9DZMmTaox5HtFRQXCw8OlId8dHBzqvFFVUFAQli9fLn067P3338ewYcPqXaYQAnq9\nHsOHD8ff/vY3lJaW4rXXXpOWU9WXvLw8TJo0Cfv378fgwYPRrVs3rF+//qb6eSusdifBN954o1Zb\nXbtN06dPl27tWKVt27b49NNPrVUaEdmouLg4bNy4EZMnT8bly5dRXl6ONm3aYMKECZg9e3at6UND\nQzFt2jRs3boVAGp9GknOp5OunXbSpElISkrCggULsHTpUmzZsgVubm7o3LkzJkyYgE2bNuHKlStw\nc3ODvb09vL298fHHH0vzeemll7BgwQI89NBDuHr1Kry8vLBs2bLrLn/mzJmYNGkSPDw80KlTJ4wc\nORKLFy+W9jgUCgUGDRqEJ598EkOGDEH79u1x5513NsuNqDiqLpEMHFWXWhKOqktERDaF4UFERLIx\nPIiISDaGBxERycbwICIi2RgeREQkm9Wu8yAi29SpUyebGp2VrKdTp05WmzfDg+g2c+HCheYugVoB\nHrYiIiLZGB5ERCQbw4OIiGRjeBARkWwMDyIiko3hQUREsjE8iIhINoYHERHJxvAgIiLZGB5ERCQb\nw4OIiGRjeBARkWwMDyIiko3hQUREsjE8iIhINquGx8MPP4yAgAAEBARg2rRpKCgogK+vLzQaDcLD\nwyGEAAAkJSVBpVLBx8cHycnJAICSkhKMHTsWGo0GISEhKCwstGapREQkg9XCo7S0FACQlpaGtLQ0\nrFq1ChEREYiPj0dGRgaEENi+fTvOnj2LZcuWYc+ePdi1axeioqJgNpuRmJgId3d3ZGRkIDQ0FHFx\ncdYqlYiIZLJaeBw8eBDFxcUICgpCYGAg9u3bh5ycHGg0GgBAcHAw9Ho9srKyoFar4ejoCKVSCRcX\nF+Tl5SEzMxM6nQ4AoNPpoNfrrVUqERHJZLXb0LZr1w6RkZGYNm0afvnlFykIqjg7O6OoqAhGoxEd\nOnSos12pVNZoIyIi22C18Ojbty9cXFwAAH369EHnzp2Rm5srvW40GtGxY0colUqYTCap3WQy1Wqv\naqtLTEyM9Fyr1UKr1TZ+Z4iIWjCDwQCDwdCo87RaeKxevRp5eXl4//338fvvv8NkMmHYsGFIT0+H\nv78/UlNTERgYCE9PT0RHR6OsrAylpaXIz8+Hm5sb1Go1UlJSoFKpkJqaKh3uulb18CAiotqufWMd\nGxt7y/NUiKqPPDWyq1ev4tlnn8WJEycAAG+++SY6d+6MGTNmwGw2w9XVFUlJSVAoFFi5ciVWrFgB\ni8WC6OhojB49GiUlJZgyZQrOnDkDJycnbNy4EV27dq1ZvEIBK5VPVCdFrAJiEf/mqGVrjG2n1cKj\nKTA8qKkxPKg1aIxtJy8SJCIi2RgeREQkG8ODiIhkY3gQEZFsDA8iIpKN4UFERLIxPIiISDaGBxER\nycbwICIi2RgeREQkG8ODiIhkY3gQEZFsDA8iIpKN4UFERLIxPIiISDaGBxERycbwICIi2RgeREQk\nG8ODiIhkY3gQEZFsDA8iIpKN4UFERLIxPIiISDaHhib44YcfYDAY8Mcff6Bbt24IDAxE3759m6I2\nIiKyUfXueeTn5yMkJASxsbG4ePEievToAZPJhKioKIwYMQI//fRTgzP/73//i549e+LIkSMoKCiA\nr68vNBoNwsPDIYQAACQlJUGlUsHHxwfJyckAgJKSEowdOxYajQYhISEoLCxspO4SEVGjEPVYtGiR\nuHTpUp2v/fHHH+Kf//xnfT8qhBDCbDaLUaNGiX79+olDhw6J4cOHi/T0dCGEEDNnzhSff/65OHPm\njBg4cKAwm82iqKhIDBw4UJSVlYnFixeL2NhYIYQQH3/8sZg7d26dy7hO+URWgRj+zVHL1xjbznr3\nPGJiYtChQ4cabRaLBQBw11134ZVXXrluKEVGRmLWrFno3r07ACAnJwcajQYAEBwcDL1ej6ysLKjV\najg6OkKpVMLFxQV5eXnIzMyETqcDAOh0Ouj1+pvNRiIisoIGT5hv2LABmzZtwpo1a3DPPffgrbfe\nanCma9asQZcuXTBs2DAAgBBCOkwFAM7OzigqKoLRaKwRUNXblUpljTYiIrIdDZ4wT0hIwM6dO/HU\nU0/h5MmTGDZsGCIjI6/7M6tXr4ZCoYBer8f333+PKVOm4Pz589LrRqMRHTt2hFKphMlkktpNJlOt\n9qq2+sTExEjPtVottFptQ10iIrqtGAwGGAyGRp1ng+HRtm1bAIBSqUSbNm1QUVHR4EzT09Ol5wEB\nAVi+fDkiIyORnp4Of39/pKamIjAwEJ6enoiOjkZZWRlKS0uRn58PNzc3qNVqpKSkQKVSITU1VTrc\nVZfq4UFERLVd+8Y6Njb2lufZYHj07t0bXl5eWLp0KWJjYzFo0CDZC1EoFFi8eDFmzJgBs9kMV1dX\njBs3DgqFAnPmzIGfnx8sFgvi4+Ph5OSEWbNmYcqUKfDz84OTkxM2btx4U50jIiLrUIjqJyPqcfny\nZbRv3x5nz57FPffc0xR13RCFQoEbKJ+o0ShiFRCL+DdHLVtjbDsb3PP44osvsHr1apSWlkoLTUlJ\nuaWFEhFRy9ZgeCxYsAArVqy47klrIiK6vTQYHm5ubvwEExER1dBgeIwcORLe3t4YMGAAgMrDVh9+\n+KHVCyMiItt1Q9d5LFy4ULqYT6FQWL0oIiKybQ2GR/fu3fHUU081RS1ERNRCNBgebdq0gU6nw0MP\nPQSFQgGFQoH4+PimqI2IiGxUg+ExfPhwAH9+LpiHrYiIqN6LBD///HOMHj263h/cunUrxowZY7XC\nbgQvEqSmxosEqTWw6kWCxcXF0Ol0CAoKwqBBg9CtWzdcvHgR+/fvx86dOxEaGnpLCyYiopbrusOT\nXLlyBR999BHS09NRWFiIrl27QqvV4qmnnkL79u2bss46cc+Dmhr3PKg1aIxt5w2NbWWrGB7U1Bge\n1Bo0xrazwZtBERERXYvhQUREsjX4UV0A0Ov1OHbsGHx8fODi4iLdIIqIiG5PDYZHVFQUfvvtN/z8\n889wcHBAfHw8Nm3a1BS1ERGRjWrwsNXu3buxbt06ODs7Y+rUqTh+/HhT1EVERDaswfCoqKiQbgRV\nUVEBe3t7qxdFRES2rcHDVvPmzcPgwYNx/vx5eHp6IiIioinqIiIiG3ZD13lcvHgRBQUFuP/++3H3\n3Xc3RV03hNd5UFPjdR7UGvAe5kRE1Cx4D3MiIpKN9zAnIiLZeA9zIiKSzWr3MK+oqMCMGTNw5MgR\nKBQKLF++HE5OTggLC4OdnR3c3Nzw/vvvQ6FQICkpCStWrICDgwNeeuklhISEoKSkBE8//TTOnz8P\nZ2dnrF271qZO1hMR3c6sdg/zL7/8EnZ2dti9ezfS09Px4osvAgDi4+Oh0Wgwa9YsbN++Hd7e3li2\nbBmys7NRUlICX19fPPbYY0hMTIS7uztefvllfPLJJ4iLi8PSpUvl95CIiBrdDd3DPCgoCB4eHrLu\nYT5y5Eg88cQTAIBff/0VnTp1gl6vh0ajAQAEBwfjP//5D+zt7aFWq+Ho6AhHR0e4uLggLy8PmZmZ\nWLhwIQBAp9PhX//61630k4iIGtEN3cP8Zu9bbm9vj7CwMGzbtg2bN2/GV199Jb3m7OyMoqIiGI1G\n6ZDYte1KpbJGGxER2YZ6wyMrKwsqlQrdu3e/pQWsWbMG586dg6enp3StCAAYjUZ07NgRSqUSJpNJ\najeZTLXaq9rqEhMTIz3XarX8ZBgR0TUMBgMMBkOjzrPe8Pjmm2+gUqmwadOmWnseQUFBDc54/fr1\nOH36NKKiotC2bVvY29tjyJAhSE9Ph7+/P1JTUxEYGAhPT09ER0ejrKwMpaWlyM/Ph5ubG9RqNVJS\nUqBSqZCamiod7rpW9fAgIqLarn1jHRsbe8vzrDc8srOzAVTuOdyMcePGISwsDP7+/igvL0dCQgL6\n9++PGTNmwGw2w9XVFePGjYNCocCcOXPg5+cHi8WC+Ph4ODk5YdasWZgyZQr8/Pzg5OSEjRs33lQd\nRETU+Ood2yogIABpaWlNXY8sHNuKmhrHtqLWwKpjWx07dgwvvvhirQXc6KetiIio9ao3PO688070\n69evKWshIqIWot7wuOeeezBlypSmrIWIiFqIeu8kOHjw4Kasg4iIWpAbuhmUreIJc2pqPGFOrUFj\nbDsbvIc5ERHRtRgeREQkG8ODiIhkY3gQEZFsDA8iIpKN4UFERLIxPIiISDaGBxERycbwICIi2Rge\nREQkG8ODiIhkY3gQEZFsDA8iIpKN4UFERLIxPIiISDaGBxERycbwICIi2RgeREQkG8ODiIhks0p4\nlJeX45lnnoFGo4GXlxd27NiBgoIC+Pr6QqPRIDw8XLp/blJSElQqFXx8fJCcnAwAKCkpwdixY6HR\naBASEoLCwkJrlElERDfJKuHx0UcfoUuXLsjIyMDOnTsxe/ZszJ8/H/Hx8cjIyIAQAtu3b8fZs2ex\nbNky7NmzB7t27UJUVBTMZjMSExPh7u6OjIwMhIaGIi4uzhplEhHRTbJKeIwfPx6vvPIKAMBiscDR\n0RE5OTnQaDQAgODgYOj1emRlZUGtVsPR0RFKpRIuLi7Iy8tDZmYmdDodAECn00Gv11ujTCIiuklW\nCY927dqhffv2MJlMGD9+POLi4mCxWKTXnZ2dUVRUBKPRiA4dOtTZrlQqa7QREZHtcLDWjE+dOoUx\nY8Zg9uzZmDhxIp5//nnpNaPRiI4dO0KpVMJkMkntJpOpVntVW31iYmKk51qtFlqtttH7QkTUkhkM\nBhgMhkadp1XC49y5cxg2bBg++OADBAQEAAA8PDyQnp4Of39/pKamIjAwEJ6enoiOjkZZWRlKS0uR\nn58PNzc3qNVqpKSkQKVSITU1VTrcVZfq4UFERLVd+8Y6Njb2luepEFUfe2pEc+fOxebNm9GvXz+p\nLSEhAXPmzIHZbIarqyuSkpKgUCiwcuVKrFixAhaLBdHR0Rg9ejRKSkowZcoUnDlzBk5OTti4cSO6\ndu1au3iFAlYon6heilgFxCL+zVHL1hjbTquER1NheFBTY3hQa9AY205eJEhERLIxPIiISDaGBxER\nycbwICIi2RgeREQkG8ODiIhkY3gQEZFsDA8iIpKN4UFERLIxPIiISDaGBxERycbwICIi2RgeREQk\nG8ODiIhkY3gQEZFsDA8iIpKN4UFERLIxPIiISDaGBxERycbwICIi2RgeREQkG8ODiIhkY3gQEZFs\nDA8iIpLNquGxf/9+BAQEAAAKCgrg6+sLjUaD8PBwCCEAAElJSVCpVPDx8UFycjIAoKSkBGPHjoVG\no0FISAgKCwutWSYREclktfB48803MWPGDJSVlQEAIiIiEB8fj4yMDAghsH37dpw9exbLli3Dnj17\nsGvXLkRFRcFsNiMxMRHu7u7IyMhAaGgo4uLirFUmERHdBKuFh4uLC7Zu3SrtYeTk5ECj0QAAgoOD\nodfrkZWVBbVaDUdHRyiVSri4uCAvLw+ZmZnQ6XQAAJ1OB71eb60yiYjoJlgtPMaMGQMHBwfp+6oQ\nAQBnZ2cUFRXBaDSiQ4cOdbYrlcoabUREZDscGp6kcdjZ/ZlTRqMRHTt2hFKphMlkktpNJlOt9qq2\n+sTExEjPtVottFpto9dORNSSGQwGGAyGRp1nk4WHh4cH0tPT4e/vj9TUVAQGBsLT0xPR0dEoKytD\naWkp8vPz4ebmBrVajZSUFKhUKqSmpkqHu+pSPTyIiKi2a99Yx8bG3vI8rR4eCoUCALB48WLMmDED\nZrMZrq6uGDduHBQKBebMmQM/Pz9YLBbEx8fDyckJs2bNwpQpU+Dn5wcnJyds3LjR2mUSEZEMClH9\nZEQLo1Ao0ILLpxZIEauAWMS/OWrZGmPbyYsEiYhINoYHERHJxvAgIiLZGB5ERCQbw4OIiGRjeBAR\nkWwMDyIiko3hQUREsjE8iIhINoYHERHJxvAgIiLZGB5ERCQbw4OIiGRjeBARkWwMDyIiko3hQURE\nsjE8iIhINoYHERHJxvAgIiLZGB5ERCQbw4OIiGRjeBDdgICAygdRUzh6tLkraJhDcxdA1BIYDP97\nom3GIui24eICnD4N9OjR3JXUz2b3PCwWC2bOnImhQ4ciICAAR1tCFBOR5OJF4IknmruKlqukpLkr\nuD6bDY9t27bBbDZjz549eP311zF//vzmLqnJGaS3u61Ta+5fa+4bcGP9+/prIDnZ+rVYQ2tff43B\nZg9bZWZmQqfTAQC8vLxw4MCBZq6o6RkMBmi12uYuw2paSv/+9S/5P9PcfRMCqKgAyssBs7nya/XH\njbbV175zpwFpadrrTvvtt83W/VvW3OuvJbDZ8DAajVAqldL39vb2sFgssLNrvJ2ljAxg6FDAwYq/\nhR9/BNwIgdIpAAAKzUlEQVTc6n998mRgwwZAobBeDSSPEDU3hi+/XPP1n35qeAP7ww/AunW3vpG+\nlWnt7ABHR+COOyq/Xvu4lfby8srfRbt29U/r4gK88krTrz9qGjYbHkqlEiaTSfq+vuBQKIBHH/3z\n+z17gOLimm16feXX6m3V2x95pHKDAVR+vfZ51fcnT1Y+GtvGjfW/FhvbOMtwcan8WlDQOPNrLI3V\nv6Z0vTcD1W3dat06GmKxAGVllQ9r2L37xqZrqW+Mmvtv09bPeUDYqC1btoiwsDAhhBB79+4Vjz/+\neK1pevfuLQDwwQcffPAh49G7d+9b3kYrhKh6X21bhBAIDw9HXl4eAGD16tXo27dvM1dFREQAYLPh\nQUREtstmP6pLRES2q0WGR2u6gPDhhx9GQEAAAgICMG3aNBQUFMDX1xcajQbh4eGo2jFMSkqCSqWC\nj48Pkm38w/P79+9HwP/G8pDTn5KSEowdOxYajQYhISEoLCxstj5cT/X+5ebm4t5775XW4ebNmwG0\nzP6Vl5fjmWeegUajgZeXF3bs2NGq1l9d/cvNzUWPHj1axfqrqKjA1KlT4evrCz8/P/z000/WXX+3\nfNakGWzZskU8++yzQggh9u3bJ0aOHNnMFd2ckpIS4eHhUaNt+PDhIj09XQghxMyZM8Xnn38uzpw5\nIwYOHCjMZrMoKioSAwcOFGVlZc1RcoPeeOMNMXDgQOHj4yOEkNefxYsXi9jYWCGEEB9//LGYO3du\ns/WjPtf2LykpSSxevLjGNC21f6tXrxbz5s0TQghx4cIF0bNnTzFixIhWs/7q6t/KlStbzfrbtm2b\nmDZtmhBCCIPBIEaMGGHV9dci9zxaywWEBw8eRHFxMYKCghAYGIh9+/YhJycHGo0GABAcHAy9Xo+s\nrCyo1Wo4OjpCqVTCxcVF+iCBrXFxccHWrVuldzhy+lN9vep0OuirPkttQ67tX3Z2NpKTk+Hv74/p\n06fj8uXL+O6771pk/8aPH49X/ndhhsVigaOjY6taf3X1rzWtv5EjR+Lf//43AODXX39Fp06dkJ2d\nbbX11yLDo74LCFuadu3aITIyErt27cLy5csxefLkGq87OzujqKgIRqMRHTp0qNVui8aMGQOHaldd\nimqfx2ioP9XXq6328dr+eXl54e2330Z6ejoeeOABxMbGwmQytcj+tWvXDu3bt4fJZML48eMRFxdX\n4/+qpa+/a/v36quvwtPTs9WsP6ByWxgWFoa5c+di8uTJVv3/a5HhcaMXENq6vn37SoHRp08fdO7c\nGefOnZNeNxqN6NixY63+mkwmdOrUqcnrvRnV18v1+nNte1WbrRs9ejQ8PDyk57m5uS26f6dOncIj\njzyC0NBQTJw4sdWtv+r9mzBhQqtbfwCwZs0aHD58GNOnT0dpaanU3tjrr+VtcQGo1WqkpKQAAPbt\n24dBgwY1c0U3Z/Xq1dKAj7///jtMJhOGDRuG9PR0AEBqaio0Gg08PT3x7bffoqysDEVFRcjPz4fb\njV7m3Mw8PDxuuD/V12vVtLZOp9MhKysLAKDX6zFkyJAW279z585h2LBhePPNNxEWFgagda2/uvrX\nmtbf+vXr8dprrwEA2rZtC3t7ewwZMsR66896p2+sx2KxiJkzZ4qhQ4eKoUOHisOHDzd3STelvLxc\nPP3008LPz0/4+fmJvXv3iiNHjgh/f3/h4+Mjpk2bJiwWixCi8sSsSqUSgwcPFlu3bm3myq/v+PHj\n0gllOf0pLi4W48ePF76+viIwMFCcO3eu2fpwPdX79/333wu1Wi20Wq2YOHGiMJlMQoiW2b85c+aI\n7t27C61WKz0OHjzYatZfXf3bt29fq1l/xcXF4sknnxQajUb4+PiIL774wqr/f7xIkIiIZGuRh62I\niKh5MTyIiEg2hgcREcnG8CAiItkYHkREJBvDg4iIZGN4UIt1/PhxjB07FgEBAfD19cXs2bNx+fJl\nAEBYWBiys7NrTH/u3DnMnj1b1jLKysqwatWqG5o2JiYG/fr1k0ZoHTRoEOLj42Utry4DBw685XlY\nc350e2J4UItUUlKCkSNH4oUXXkBaWhp2794NLy8vTJw4EQCgqOPG2d26dcP7778vazlnzpzBypUr\nb2hahUKB+fPnIy0tDWlpaThw4AA+/PBDmxu6m6gxODQ8CZHtSU5OhlarhUqlktpCQ0ORmJiIX3/9\nFQDw2muv4eLFixBCICkpCfb29pg4cSL27t2L9PR0vPTSS7C3t0fv3r3x73//G+Xl5Xj22Wdx8uRJ\nmM1mvPfee1i1ahV+/vlnxMXFISAgAPPnz8cdd9yBO++8E5999hnat29fo67q19wWFhaivLwcbdu2\nRVFREaZNm4YLFy4AAN599124ubmhT58+8PX1xeHDh9GtWzds2bIFpaWlePrpp1FYWIjevXujoqIC\nAPDDDz9g7ty5EEKgc+fO+PDDD5GTk4OFCxfCyckJf/3rX9GzZ89a/TKbzXXOj+iWWPNyeSJrefPN\nN8WSJUtqtU+cOFEYDAYRFhYmVqxYIYQQIiUlRYwZM0b8+uuvwtvbWwghRJ8+fcT58+eFEEL885//\nFElJSeKdd94RUVFRQgghfvnlF7F06dIaPxMZGSmWLFkiLBaL2LZtmzh58mSNZS9atEj07dtX+Pv7\niwceeED4+fmJr776SgghxPPPPy8SExOFEJVDtvj6+gohhLC3txenT58WQgihVqvFvn37xOLFi8VL\nL70khBDi0KFDonfv3kIIIby8vER+fr4QQohVq1aJ6OhoYTAYhLu7uxCictievn371upXffMjuhXc\n86AWqUePHvjuu+9qtRcUFOD//u//AAD+/v4AAG9vb0RGRkrTnD9/HmfPnsX48eMBVB4Ce+yxx1BY\nWIjg4GAAlfftmDt3rrQXAwAvvvgiXn31VQQGBqJHjx7w8vKqseyqw1Z//etfkZOTgwkTJqBPnz4A\nKvca0tLS8MknnwAALl68CAC4++670aNHDwBAz549UVpaisOHD+Pxxx8HAPTr1w9dunQBAOTn52PW\nrFkAKu+K17dvX2maqn6dOXOmVr/++9//1jk/olvBcx7UIo0cORJfffWVNCIqAKxcuRJdunTB/fff\nD6ByxGUAyMjIgLu7uzTd3XffjXvvvRdffPEF0tLS8MILL+DRRx/FgAEDpPkdO3YMzzzzTI17xWzY\nsAFhYWH45ptv4OrqihUrVtSqS/zvsNXDDz+MF154ARMmTIAQAgMGDMC8efOQlpYmzQeo+9yMq6sr\nMjMzAQBHjx6Vzpn0798f69evR1paGuLj4zF8+HAAfw57X1+/6psf0a3gnge1SO3atcOOHTswb948\n/PHHH7h69Src3d2xadMmaZqvv/4aa9asgaOjIz788EOUl5cDqNxgJyQk4PHHH4fFYkGHDh2wbt06\neHt7Y+rUqdBqtaioqEBCQgK6du0Ks9mMqKgojB49GtOnT0e7du1gb29fZ3hUD4OpU6fik08+wfLl\nyxEdHY1p06ZhxYoVMBqNiI2NrTV91fczZ86U7kXdq1cv3HXXXQCAxMREPPPMM7h69Srs7OywatUq\n/Pbbb9I87Ozsrtuva+dHdCs4qi7dNn755RdMnz5dur8BEd08Hrai28Lp06cxefJkjB49urlLIWoV\nuOdBRESycc+DiIhkY3gQEZFsDA8iIpKN4UFERLIxPIiISDaGBxERyfb/bwAUZKQppBgAAAAASUVO\nRK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe572dd7090>"
+       ]
+      }
+     ],
+     "prompt_number": 72
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot(qt_mainlooptimes[:800], label=\"Quadtree\")\n",
+      "#plot(qt_genquadchildtimes[:800], label=\"Child Generation\")\n",
+      "xlabel(\"Frame\")\n",
+      "ylabel(\"Time (ms)\")\n",
+      "legend()\n",
+      "#show()\n",
+      "savefig(\"qtframetime.pdf\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
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fpEcTCYTZsyYgaFDhyI1NRVHbPdKFpTq2pAh8n2zZuoKBNSFHRDg6Hesj9yUha3UHAC1OaFzZ7ny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\nRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe572eb0590>"
+       ]
+      }
+     ],
+     "prompt_number": 83
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print sum(qt_genquadchildtimes) / sum(qt_mainlooptimes)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0.237412858249\n"
+       ]
+      }
+     ],
+     "prompt_number": 74
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print sum(gmp_mainlooptimes) / sum(qt_mainlooptimes)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "2127.35360186\n"
+       ]
+      }
+     ],
+     "prompt_number": 75
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 75
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 75
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

UCC git Repository :: git.ucc.asn.au