About to break everything with a merge

diff --git a/src/Makefile b/src/Makefile
index b7489c8..42e793e 100644 (file)
--- a/src/Makefile
+++ b/src/Makefile
@@ -16,10 +16,10 @@ QT_INCLUDE := -I/usr/share/qt4/mkspecs/linux-g++-64 -I. -I/usr/include/qt4/QtCor
 QT_DEF := -DQT_NO_DEBUG -DQT_GUI_LIB -DQT_CORE_LIB
 QT_LIB :=  -L/usr/lib/x86_64-linux-gnu -lQtGui -lQtCore -lpthread 
 
-LIB_x86_64 = ../contrib/lib/libSDL2-2.0.so.0 -lGL -lgmp
-LIB_i386 = ../contrib/lib32/libSDL2-2.0.so.0 -lGL -lgmp
+LIB_x86_64 = ../contrib/lib/libSDL2-2.0.so.0 -lGL -lgmp -lmpfr
+LIB_i386 = ../contrib/lib32/libSDL2-2.0.so.0 -lGL -lgmp -lmpfr
 LIB_i686 = $(LIB_i386)
-LIB_win32 = -mwindows -lmingw32 -L../contrib/win32/lib/ -lSDL2main -lSDL2 -lgmp -static-libgcc -lopengl32 -static-libstdc++
+LIB_win32 = -mwindows -lmingw32 -L../contrib/win32/lib/ -lSDL2main -lSDL2 -lgmp -static-libgcc -lopengl32 -static-libstdc++ -lmpfr
 
 MAINRPATH_x86_64 = -Wl,-rpath,'$$ORIGIN/../contrib/lib'
 MAINRPATH_i386 = -Wl,-rpath,'$$ORIGIN/../contrib/lib32'
@@ -48,6 +48,9 @@ BIN = ../bin/ipdf
 REALTYPE=1
 CONTROLPANEL=enabled
 QUADTREE=disabled
+TRANSFORMATIONS=direct
+MPFR_PRECISION=23
+PATHREAL=0
 DEF = -DREALTYPE=$(REALTYPE)
 
 
@@ -80,6 +83,18 @@ endif
 
 ifeq ($(REALTYPE),6)
        LIB := $(LIB) -lgmp -lmpfr
+       DEF := $(DEF) -DMPFR_PRECISION=$(MPFR_PRECISION)
+endif
+
+ifeq ($(TRANSFORMATIONS),cumulative)
+       DEF := $(DEF) -DTRANSFORM_OBJECTS_NOT_VIEW
+endif
+
+ifeq ($(TRANSFORMATIONS),path)
+       DEF := $(DEF) -DTRANSFORM_OBJECTS_NOT_VIEW -DTRANSFORM_BEZIERS_TO_PATH -DPATHREAL=$(PATHREAL)
+       ifeq ($(PATHREAL), mpfr)
+               LIB:= $(LIB) -lmpfr
+       endif
 endif
 
 ifeq ($(REALTYPE),7)

diff --git a/src/bezier.cpp b/src/bezier.cpp
index 19d89af..3ace267 100644 (file)
--- a/src/bezier.cpp
+++ b/src/bezier.cpp
@@ -14,17 +14,17 @@ namespace IPDF
 vector<BReal> SolveQuadratic(const BReal & a, const BReal & b, const BReal & c, const BReal & min, const BReal & max)
 {
        vector<BReal> roots; roots.reserve(2);
-       if (a == 0 && b != 0)
+       if (a == BReal(0) && b != BReal(0))
        {
                roots.push_back(-c/b);
                return roots;
        }
        BReal disc(b*b - BReal(4)*a*c);
-       if (disc < 0)
+       if (disc < BReal(0))
        {
                return roots;
        }
-       else if (disc == 0)
+       else if (disc == BReal(0))
        {
                BReal x(-b/BReal(2)*a);
                if (x >= min && x <= max)
@@ -55,7 +55,7 @@ static void CubicSolveSegment(vector<BReal> & roots, const BReal & a, const BRea
 {
        BReal l = a*tl*tl*tl + b*tl*tl + c*tl + d;
        BReal u = a*tu*tu*tu + b*tu*tu + c*tu + d;
-       if ((l < 0 && u < 0) || (l > 0 && u > 0))
+       if ((l < BReal(0) && u < BReal(0)) || (l > BReal(0) && u > BReal(0)))
        {
                //Debug("Discarding segment (no roots) l = %f (%f), u = %f (%f)", Double(tl), Double(l), Double(tu), Double(u));
                //return;
@@ -68,7 +68,7 @@ static void CubicSolveSegment(vector<BReal> & roots, const BReal & a, const BRea
                BReal t(tu+tl);
                t /= 2;
                BReal m = a*t*t*t + b*t*t + c*t + d;
-               if (m > 0)
+               if (m > BReal(0))
                {
                        if (negative)
                                tl = t;
@@ -92,7 +92,7 @@ vector<BReal> SolveCubic(const BReal & a, const BReal & b, const BReal & c, cons
        vector<BReal> roots; roots.reserve(3);
        BReal tu(max);
        BReal tl(min);
-       vector<BReal> turns(SolveQuadratic(a*3, b*2, c));
+       vector<BReal> turns(SolveQuadratic(a*BReal(3), b*BReal(2), c));
        //Debug("%u turning points", turns.size());
        for (unsigned i = 1; i < turns.size(); ++i)
        {
@@ -155,20 +155,20 @@ pair<BReal, BReal> BezierTurningPoints(const BReal & p0, const BReal & p1, const
        {
                return pair<BReal,BReal>(0, 1);
        }
-       BReal a = ((p1-p2)*3 + p3 - p0);
-       BReal b = (p2 - p1*2 + p0)*2;
+       BReal a = ((p1-p2)*BReal(3) + p3 - p0);
+       BReal b = (p2 - p1*BReal(2) + p0)*BReal(2);
        BReal c = (p1-p0);
-       if (a == 0)
+       if (a == BReal(0))
        {
-               if (b == 0)
+               if (b == BReal(0))
                        return pair<BReal, BReal>(0,1);
                BReal t = -c/b;
-               if (t > 1) t = 1;
-               if (t < 0) t = 0;
+               if (t > BReal(1)) t = 1;
+               if (t < BReal(0)) t = 0;
                return pair<BReal, BReal>(t, t);
        }
        //Debug("a, b, c are %f, %f, %f", Float(a), Float(b), Float(c));
-       if (b*b - a*c*4 < 0)
+       if (b*b - a*c*BReal(4) < BReal(0))
        {
                //Debug("No real roots");
                return pair<BReal, BReal>(0,1);

diff --git a/src/bezier.h b/src/bezier.h
index d3c0e2d..7b46d14 100644 (file)
--- a/src/bezier.h
+++ b/src/bezier.h
@@ -151,7 +151,7 @@ namespace IPDF
                        // (So can't just use the Copy constructor on the inverse of bounds)
                        // BRect inverse = {-bounds.x/bounds.w, -bounds.y/bounds.h, BReal(1)/bounds.w, BReal(1)/bounds.h};
                        Bezier result;
-                       if (bounds.w == 0)
+                       if (bounds.w == BReal(0))
                        {
                                result.x0 = 0;
                                result.x1 = 0;
@@ -166,7 +166,7 @@ namespace IPDF
                                result.x3 = (x3 - bounds.x)/bounds.w;
                        }
 
-                       if (bounds.h == 0)
+                       if (bounds.h == BReal(0))
                        {
                                result.y0 = 0;
                                result.y1 = 0;

diff --git a/src/debugscript.cpp b/src/debugscript.cpp
index ed10cc3..2784406 100644 (file)
--- a/src/debugscript.cpp
+++ b/src/debugscript.cpp
@@ -172,14 +172,14 @@ void DebugScript::ParseAction(View * view, Screen * scr)
                currentAction.type = AT_ScreenShot;
                inp >> currentAction.textargs;  
        }
-       else if (actionType == "printfps")
+       else if (actionType == "printspf")
        {
-               currentAction.type = AT_PrintFPS;
+               currentAction.type = AT_PrintSPF;
                currentAction.iz = currentAction.loops;
-               m_fps_cpu_mean = 0;
-               m_fps_gpu_mean = 0;
-               m_fps_cpu_stddev = 0;
-               m_fps_gpu_stddev = 0;
+               m_spf_cpu_mean = 0;
+               m_spf_gpu_mean = 0;
+               m_spf_cpu_stddev = 0;
+               m_spf_gpu_stddev = 0;
        }
        else if (actionType == "printbounds")
        {
@@ -215,16 +215,16 @@ bool DebugScript::Execute(View *view, Screen *scr)
        case AT_WaitFrame:
                break;
        case AT_Translate:
-               view->Translate(currentAction.x, currentAction.y);
+               view->Translate(Double(currentAction.x), Double(currentAction.y));
                break;
        case AT_TranslatePx:
                view->Translate(Real(currentAction.ix)/Real(scr->ViewportWidth()), Real(currentAction.iy)/Real(scr->ViewportHeight()));
                break;
        case AT_Zoom:
-               view->ScaleAroundPoint(currentAction.x, currentAction.y, currentAction.z);
+               view->ScaleAroundPoint(Double(currentAction.x), Double(currentAction.y), Double(currentAction.z));
                break;
        case AT_ZoomPx:
-               view->ScaleAroundPoint(Real(currentAction.ix)/Real(scr->ViewportWidth()),Real(currentAction.iy)/Real(scr->ViewportHeight()), Real(expf(-currentAction.iz/20.f)));
+               view->ScaleAroundPoint(Real(currentAction.ix)/Real(scr->ViewportWidth()),Real(currentAction.iy)/Real(scr->ViewportHeight()), exp(Real(-currentAction.iz)/Real(20)));
                break;
        case AT_SetGPURendering:
                view->SetGPURendering(true);
@@ -300,13 +300,13 @@ bool DebugScript::Execute(View *view, Screen *scr)
                
 
                VReal s = target.w/(view->GetBounds().w);
-               if (Real(s) != 1)
+               if (s != VReal(1))
                {
                        VReal x0;
                        VReal y0;
                        x0 = (view->GetBounds().x - target.x)/((s - VReal(1))*view->GetBounds().w);
                        y0 = (view->GetBounds().y - target.y)/((s - VReal(1))*view->GetBounds().h);
-                       view->ScaleAroundPoint(x0, y0, s);
+                       view->ScaleAroundPoint(Double(x0), Double(y0), Double(s));
                        currentAction.loops++;
                }
                else
@@ -324,13 +324,13 @@ bool DebugScript::Execute(View *view, Screen *scr)
                target.w += view->GetBounds().w;
                target.h += view->GetBounds().h;
                VReal s = target.w/(view->GetBounds().w);
-               if (Real(s) != 1)
+               if (s != VReal(1))
                {
                        VReal x0;
                        VReal y0;
                        x0 = (view->GetBounds().x - target.x)/((s - VReal(1))*view->GetBounds().w);
                        y0 = (view->GetBounds().y - target.y)/((s - VReal(1))*view->GetBounds().h);
-                       view->ScaleAroundPoint(x0, y0, s);
+                       view->ScaleAroundPoint(Double(x0), Double(y0), Double(s));
                        currentAction.loops++;
                }
                else
@@ -359,7 +359,7 @@ bool DebugScript::Execute(View *view, Screen *scr)
                currentAction.loops = 1;
                break;
        }
-       case AT_PrintFPS:
+       case AT_PrintSPF:
        {
                // Using a (apparently) Soviet trick to calculate the stddev in one pass
                // This was my favourite algorithm in my Physics honours project
@@ -370,35 +370,35 @@ bool DebugScript::Execute(View *view, Screen *scr)
                if (currentAction.loops <= 1)
                {
                        double n = double(currentAction.iz);
-                       m_fps_cpu_mean /= n;
-                       m_fps_gpu_mean /= n;
+                       m_spf_cpu_mean /= n;
+                       m_spf_gpu_mean /= n;
                        
-                       m_fps_cpu_stddev = sqrt(m_fps_cpu_stddev / n - m_fps_cpu_mean*m_fps_cpu_mean);
-                       m_fps_gpu_stddev = sqrt(m_fps_gpu_stddev / n - m_fps_gpu_mean*m_fps_gpu_mean);
+                       m_spf_cpu_stddev = sqrt(m_spf_cpu_stddev / n - m_spf_cpu_mean*m_spf_cpu_mean);
+                       m_spf_gpu_stddev = sqrt(m_spf_gpu_stddev / n - m_spf_gpu_mean*m_spf_gpu_mean);
                        
                        
                        
                        printf("%d\t%f\t%f\t%f\t%f\n", currentAction.iz,
-                               m_fps_gpu_mean, m_fps_gpu_stddev,
-                               m_fps_cpu_mean, m_fps_cpu_stddev);
+                               m_spf_gpu_mean, m_spf_gpu_stddev,
+                               m_spf_cpu_mean, m_spf_cpu_stddev);
                }
                else
                {
                        
-                       double fps_cpu = 1.0/scr->GetLastFrameTimeCPU();
-                       double fps_gpu = 1.0/scr->GetLastFrameTimeGPU();
+                       double spf_cpu = scr->GetLastFrameTimeCPU();
+                       double spf_gpu = scr->GetLastFrameTimeGPU();
                        
-                       m_fps_cpu_mean += fps_cpu;
-                       m_fps_gpu_mean += fps_gpu;
+                       m_spf_cpu_mean += spf_cpu;
+                       m_spf_gpu_mean += spf_gpu;
                        
-                       m_fps_cpu_stddev += fps_cpu*fps_cpu;
-                       m_fps_gpu_stddev += fps_gpu*fps_gpu;
+                       m_spf_cpu_stddev += spf_cpu*spf_cpu;
+                       m_spf_gpu_stddev += spf_gpu*spf_gpu;
                }
                break;
        }
        case AT_PrintBounds:
        {
-               printf("%s\t%s\t%s\t%s\n", Str(view->GetBounds().x).c_str(), Str(view->GetBounds().y).c_str(), Str(view->GetBounds().w).c_str(), Str(view->GetBounds().h).c_str());
+               printf("%s\t%s\t%s\t%s\t%s\t%s\n", Str(view->GetBounds().x).c_str(), Str(view->GetBounds().y).c_str(), Str(view->GetBounds().w).c_str(), Str(view->GetBounds().h).c_str(), Str(Log10(view->GetBounds().w)).c_str(), Str(Log10(view->GetBounds().h)).c_str());
                break;
        }
        default:

diff --git a/src/debugscript.h b/src/debugscript.h
index 0625535..916425c 100644 (file)
--- a/src/debugscript.h
+++ b/src/debugscript.h
@@ -43,7 +43,7 @@ private:
                AT_SetBounds,
                AT_QueryGPUBounds, // query bounds of Beziers when transformed to GPU
                AT_ScreenShot, // take screenshot
-               AT_PrintFPS, // Print FPS statistics about the frames
+               AT_PrintSPF, // Print FPS statistics about the frames
                AT_PrintBounds, // Print bounds
                AT_Quit
        };
@@ -51,12 +51,12 @@ private:
        struct Action
        {
                ActionType type;
-               Real x, y;
+               VReal x, y;
                int ix, iy;
-               Real z;
+               VReal z;
                int iz;
                int loops;
-               Real w, h;
+               VReal w, h;
                std::string textargs;
                Action() : type(AT_WaitFrame), x(0), y(0), ix(0), iy(0), z(0), loops(0), textargs("") {}
        };
@@ -68,10 +68,10 @@ private:
        std::map<std::string, int> m_labels;
        unsigned m_index;
        
-       double m_fps_cpu_mean;
-       double m_fps_gpu_mean;
-       double m_fps_cpu_stddev;
-       double m_fps_gpu_stddev;
+       double m_spf_cpu_mean;
+       double m_spf_gpu_mean;
+       double m_spf_cpu_stddev;
+       double m_spf_gpu_stddev;
        
        struct PerformanceData
        {

diff --git a/src/eye_of_the_rabbit.script b/src/eye_of_the_rabbit.script
index db76eeb..994d4e9 100644 (file)
--- a/src/eye_of_the_rabbit.script
+++ b/src/eye_of_the_rabbit.script
@@ -1,5 +1,5 @@
 # Test how well document scales back to original...
-cpu
+gpu
 lazy
 debugfont on
 clearperf

diff --git a/src/gmprat.h b/src/gmprat.h
index b92d5af..e0c9849 100644 (file)
--- a/src/gmprat.h
+++ b/src/gmprat.h
@@ -103,6 +103,7 @@ class Gmprat
                
                Gmprat Abs() const {Gmprat a(*this); mpq_abs(a.m_op, a.m_op); return a;}
                
+               
                size_t Size() const
                {
                        return sizeof(uint64_t) * (mpq_numref(m_op)->_mp_alloc + mpq_denref(m_op)->_mp_alloc);
@@ -119,11 +120,14 @@ inline std::ostream & operator<<(std::ostream & os, const Gmprat & fith)
        return os;
 }
 
+
+#if REALTYPE != 9
 inline std::string Str(const Gmprat & g) {return g.Str();}
 inline double Log10(const Gmprat & g) {return g.Log10();}
 inline size_t Size(const Gmprat & g) {return g.Size();}
 inline Gmprat Abs(const Gmprat & g) {return g.Abs();}
 
+#endif
 
 #endif //_GMPRAT_H
 

diff --git a/src/main.cpp b/src/main.cpp
index cdb2d0a..46ea73b 100644 (file)
--- a/src/main.cpp
+++ b/src/main.cpp
@@ -11,6 +11,8 @@
 
 bool ignore_sigfpe = false;
 const char *script_filename;
+bool make_movie = false;
+const char * program_name;
 
 void sigfpe_handler(int sig)
 {
@@ -21,7 +23,7 @@ void sigfpe_handler(int sig)
 
 int main(int argc, char ** argv)
 {      
-       
+       program_name = argv[0];
        
        //Debug("Main!");
        signal(SIGFPE, sigfpe_handler);
@@ -36,10 +38,16 @@ int main(int argc, char ** argv)
        // We want to crash if we ever get a NaN.
        // AH, so *this* is where that got enabled, I was looking for compiler flags
        #ifndef __MINGW32__
-       feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
+       feenableexcept(FE_DIVBYZERO | FE_INVALID); // | FE_OVERFLOW);
        #endif
        #if REALTYPE == REAL_MPFRCPP
-       mpfr_set_default_prec(6);
+       
+               #ifdef MPFR_PRECISION
+               mpfr_set_default_prec(MPFR_PRECISION);
+               #else
+               mpfr_set_default_prec(23);
+               #endif
+               
        #endif
        DebugRealInfo();
 
@@ -63,7 +71,12 @@ int main(int argc, char ** argv)
        bool window_visible = true;
        bool gpu_transform = USE_GPU_TRANSFORM;
        bool gpu_rendering = USE_GPU_RENDERING;
-       
+       #ifdef TRANSFORM_OBJECTS_NOT_VIEW
+               gpu_transform = true;
+       #endif
+       #ifdef TRANSFORM_BEZIERS_TO_PATH
+               gpu_transform = true;
+       #endif
 
        
        int i = 0;
@@ -168,6 +181,9 @@ int main(int argc, char ** argv)
                                        Fatal("Expected filename after -s switch");
                                script_filename = argv[i];
                                break;
+                       case 'm':
+                               make_movie = true;
+                               break;
                }       
        }
 

diff --git a/src/main.h b/src/main.h
index 654aa09..d5ad7a9 100644 (file)
--- a/src/main.h
+++ b/src/main.h
@@ -12,6 +12,8 @@ using namespace IPDF;
 
 
 extern const char *script_filename;
+extern bool make_movie; // whyyy
+extern const char * program_name;
 
 inline void OverlayBMP(Document & doc, const char * input, const char * output, const Rect & bounds = Rect(0,0,1,1), const Colour & c = Colour(0.f,0.f,0.f,1.f))
 {
@@ -69,7 +71,7 @@ void RatCatcher(int x, int y, int buttons, int wheel, Screen * scr, View * view)
                
        if (wheel)
        {
-               view->ScaleAroundPoint(Real(x)/Real(scr->ViewportWidth()),Real(y)/Real(scr->ViewportHeight()), Real(expf(-wheel/20.f)));
+               view->ScaleAroundPoint(Real(x)/Real(scr->ViewportWidth()),Real(y)/Real(scr->ViewportHeight()), exp(Real(-wheel)/Real(20)));
        }
 }
 
@@ -80,7 +82,7 @@ void MainLoop(Document & doc, Screen & scr, View & view, int max_frames = -1)
        
 
        //scr.DebugFontInit("fonts/DejaVuSansMono.ttf", 12);
-       scr.DebugFontInit("fonts/DejaVuSansMono.ttf", 18);
+       scr.DebugFontInit("fonts/DejaVuSansMono.ttf", 36);
        scr.SetMouseHandler(RatCatcher);
 
        ifstream tmp;
@@ -155,11 +157,13 @@ void MainLoop(Document & doc, Screen & scr, View & view, int max_frames = -1)
                
 
                
-
+               scr.DebugFontPrintF("%s\n", program_name);
                scr.DebugFontPrintF("Top Left: (%s,%s)\n", Str(view.GetBounds().x).c_str(),Str(view.GetBounds().y).c_str());
                scr.DebugFontPrintF("Width: %s\n", Str(view.GetBounds().w).c_str());
-               scr.DebugFontPrintF("Zoom: %s %%\n", Str(VReal(100)/VReal(view.GetBounds().w)).c_str());
-               //scr.DebugFontPrintF("Similar size: %s\n", HumanScale(view.GetBounds().w * VReal(22e-3)));
+               Real zoom(100);
+               zoom = zoom/Real(view.GetBounds().w);
+               scr.DebugFontPrintF("Zoom: %s %%\n", Str(zoom).c_str());
+               scr.DebugFontPrintF("Similar size: %s\n", HumanScale(ClampFloat(Double(view.GetBounds().w))));
                
                #if 0
                scr.DebugFontPrintF("Rendered frame %lu\n", (uint64_t)frames);
@@ -205,7 +209,15 @@ void MainLoop(Document & doc, Screen & scr, View & view, int max_frames = -1)
                        scr.DebugFontPrint("Doing rendering using CPU.\n");
                }
                #endif // 0
-               
+
                scr.Present();
+
+               if (make_movie)
+               {
+                       std::stringstream s;
+                       s << "frame" << frames << ".bmp";
+                       scr.ScreenShot(s.str().c_str());
+               }               
+
        }
 }

diff --git a/src/path.h b/src/path.h
index b303fe7..c412428 100644 (file)
--- a/src/path.h
+++ b/src/path.h
@@ -15,11 +15,6 @@
 
 namespace IPDF
 {
-       #ifdef TRANSFORM_BEZIERS_TO_PATH
-               typedef Real PReal;
-       #else
-               typedef Real PReal;
-       #endif
        typedef TRect<PReal> PRect;
        
        struct Colour

diff --git a/src/real.h b/src/real.h
index 13395fb..20ec681 100644 (file)
--- a/src/real.h
+++ b/src/real.h
@@ -102,6 +102,7 @@ namespace IPDF
        inline std::string Str(const mpfr::mpreal & a) {std::stringstream s; s << a; return s.str();}
        inline size_t Size(mpfr::mpreal & a) {return a.get_prec();}
        inline mpfr::mpreal Log10(const mpfr::mpreal & a) {return mpfr::log10(a);}      
+       inline mpfr::mpreal Exp(const mpfr::mpreal & a) {return mpfr::pow(2.817, a);}
        
 #elif REALTYPE == REAL_IRRAM
        typedef iRRAM::REAL Real;
@@ -126,7 +127,7 @@ namespace IPDF
        inline Real Sqrt(const Real & r) {return Real(sqrt(r.ToDouble()));}
        inline Real RealFromStr(const char * str) {return Real(strtod(str, NULL));}
        inline Real Abs(const Real & a) {return (a > Real(0)) ? a : Real(0)-a;}
-       
+       inline std::string Str(const Real & r) {return r.Str();}
        
 #else
        #error "Type of Real unspecified."
@@ -148,8 +149,9 @@ namespace IPDF
        
        
        // Don't cause an exception
-       inline float ClampFloat(double d)
+       inline float ClampFloat(const Real & a)
        {
+               double d = Double(a);
                float f = (fabs(d) < FLT_MAX) ? ((fabs(d) > FLT_MIN) ? (float)d : FLT_MIN) : FLT_MAX;
                return copysign(f, d);
        }
@@ -212,8 +214,9 @@ namespace IPDF
 
 
        // things stolen from wikipedia and googling
-       inline const char * HumanScale(double f)
+       inline const char * HumanScale(const Real & r)
        {
+               double f = Double(r);
                if (f < 1e-36)
                        return "RATHER SMALL";
                if (f < 1e-35)
@@ -258,15 +261,15 @@ namespace IPDF
                        return "Ant";
                if (f < 1e-2)
                        return "Coin";
-               if (f < 1e-1)
+               if (f < 0.5)
                        return "iPhone";
-               if (f < 1e0)
+               if (f < 5)
                        return "Person";
-               if (f < 1e1)
-                       return "Building";
                if (f < 1e2)
-                       return "Football Field";
+                       return "Building";
                if (f < 1e3)
+                       return "Football Field";
+               if (f < 2e3)
                        return "Mountain";
                if (f < 1e4)
                        return "Clouds";
@@ -304,6 +307,13 @@ namespace IPDF
                                Debug("Size limit of %d is being enforced", PARANOID_SIZE_LIMIT);
                        #endif
                #endif
+               #if REALTYPE == REAL_MPFRCPP
+                       Debug("Precision of MPFR is %d", mpfr_get_default_prec());
+               #endif
+               
+               #ifdef TRANSFORM_BEZIERS_TO_PATH
+               Debug("PathReal = %d => \"%s\"", PATHREAL, g_real_name[PATHREAL]);
+               #endif
        }
 }
 

diff --git a/src/screen.cpp b/src/screen.cpp
index 16a3ab8..628a11a 100644 (file)
--- a/src/screen.cpp
+++ b/src/screen.cpp
@@ -45,7 +45,7 @@ Screen::Screen(bool visible)
        }
 
        SDL_GL_SetAttribute(SDL_GL_CONTEXT_MAJOR_VERSION, 3);
-       SDL_GL_SetAttribute(SDL_GL_CONTEXT_MINOR_VERSION, 1);
+       SDL_GL_SetAttribute(SDL_GL_CONTEXT_MINOR_VERSION, 3);
        SDL_GL_SetAttribute(SDL_GL_CONTEXT_FLAGS, SDL_GL_CONTEXT_DEBUG_FLAG);
        SDL_GL_SetAttribute(SDL_GL_CONTEXT_PROFILE_MASK, SDL_GL_CONTEXT_PROFILE_CORE);
 
@@ -56,17 +56,17 @@ Screen::Screen(bool visible)
        // Why is this so horribly broken?
        if (ogl_IsVersionGEQ(3,2))
        {
-               Fatal("We require OpenGL 3.3, but you have version %d.%d!",ogl_GetMajorVersion(), ogl_GetMinorVersion());
+               Error("We require OpenGL 3.3, but you have version %d.%d!",ogl_GetMajorVersion(), ogl_GetMinorVersion());
        }
 
        if (!SDL_GL_ExtensionSupported("GL_ARB_shading_language_420pack"))
        {
-               Fatal("Your system does not support the ARB_shading_language_420pack extension, which is required.");
+               Error("Your system does not support the ARB_shading_language_420pack extension, which is required.");
        }
 
        if (!SDL_GL_ExtensionSupported("GL_ARB_explicit_attrib_location"))
        {
-               Fatal("Your system does not support the ARB_explicit_attrib_location extension, which is required.");
+               Error("Your system does not support the ARB_explicit_attrib_location extension, which is required.");
        }
 
        m_frame_begin_time = SDL_GetPerformanceCounter();
@@ -227,8 +227,10 @@ void Screen::Present()
 {
        if (!Valid())
                return;
+
        if (m_debug_font_atlas)
                DebugFontFlush();
+
        m_last_frame_time = SDL_GetPerformanceCounter() - m_frame_begin_time;
        glEndQuery(GL_TIME_ELAPSED);
        SDL_GL_SwapWindow(m_window);
@@ -238,6 +240,8 @@ void Screen::Present()
        m_last_frame_gpu_timer = m_frame_gpu_timer;
        glGenQueries(1, &m_frame_gpu_timer);
        glBeginQuery(GL_TIME_ELAPSED, m_frame_gpu_timer);
+
+
 }
 
 double Screen::GetLastFrameTimeGPU() const

diff --git a/src/transformationtype.h b/src/transformationtype.h
index 4f27d5f..b01e2d4 100644 (file)
--- a/src/transformationtype.h
+++ b/src/transformationtype.h
@@ -2,8 +2,58 @@
 #define _TRANSFORMATIONTYPE_H
 
 #ifdef QUADTREE_DISABLED
-#define TRANSFORM_OBJECTS_NOT_VIEW
-#define TRANSFORM_BEZIERS_TO_PATH
+//#define TRANSFORM_OBJECTS_NOT_VIEW
+//#define TRANSFORM_BEZIERS_TO_PATH
 #endif
 
+#include "gmprat.h"
+#include <mpreal.h>
+#include "real.h"
+
+namespace IPDF
+{
+       
+#ifdef TRANSFORM_BEZIERS_TO_PATH
+#if PATHREAL == REAL_SINGLE
+       typedef float PReal;
+#elif PATHREAL == REAL_DOUBLE
+       typedef double PReal;
+#elif PATHREAL == REAL_LONG_DOUBLE
+       typedef long double PReal;
+#elif PATHREAL == REAL_MPFRCPP
+       typedef mpfr::mpreal PReal;
+#elif PATHREAL == REAL_GMPRAT
+       typedef Gmprat PReal;
+#endif
+#else
+       typedef Real PReal;
 #endif
+
+typedef PReal VReal;
+
+
+#ifdef TRANSFORM_BEZIERS_TO_PATH
+
+#if PATHREAL == REAL_MPFRCPP
+
+#if REALTYPE != REAL_MPFRCPP
+       #include <mpreal.h>
+
+       inline double Double(const mpfr::mpreal & r) {return r.toDouble();}
+       inline float Float(const mpfr::mpreal & r) {return r.toDouble();}
+       inline int64_t Int64(const mpfr::mpreal & r) {return r.toLong();}
+       inline mpfr::mpreal Sqrt(const mpfr::mpreal & r) {return mpfr::sqrt(r, mpfr::mpreal::get_default_rnd());}
+       inline mpfr::mpreal Abs(const mpfr::mpreal & r) {return mpfr::abs(r, mpfr::mpreal::get_default_rnd());}
+       //inline mpfr::mpreal RealFromStr(const char * str) {return mpfr::mpreal(strtod(str, NULL));}
+       inline std::string Str(const mpfr::mpreal & a) {std::stringstream s; s << a; return s.str();}
+       inline size_t Size(const mpfr::mpreal & a) {return a.get_prec();}
+       inline mpfr::mpreal Log10(const mpfr::mpreal & a) {return mpfr::log10(a);}              
+       inline mpfr::mpreal Exp(const mpfr::mpreal & a) {return mpfr::pow(2.817, a);}
+
+#endif
+#endif
+#endif
+
+}
+#endif
+

diff --git a/src/turtles_all_the_way_down.script b/src/turtles_all_the_way_down.script
index fcddfe7..ad2647d 100644 (file)
--- a/src/turtles_all_the_way_down.script
+++ b/src/turtles_all_the_way_down.script
@@ -1,9 +1,12 @@
-# BECAUSE I CAN
+# Script for the turtles video
 gpu
 lazy
 #debugfont off
 
+# Wait to start video
+loop 1000 wait
 
+#Load first turtle...
 loadsvg svg-tests/turtle.svg
 loop 50 pxzoom 430 170 1
 loadsvg svg-tests/turtle.svg
@@ -17,6 +20,15 @@ loop 50 pxzoom 430 170 1
 loadsvg svg-tests/turtle.svg
 loop 50 pxzoom 430 170 1
 loadsvg svg-tests/turtle.svg
+
+# Wait near last visible turtle at float
+#loop 1000 wait
+# Zoom back out
+#loop 350 pxzoom 430 170 -1
+#loop 350 pxzoom 430 170 1
+
+# Continue
+loadsvg svg-tests/turtle.svg
 loop 50 pxzoom 430 170 1
 loadsvg svg-tests/turtle.svg
 loop 50 pxzoom 430 170 1
@@ -92,7 +104,7 @@ loop 50 pxzoom 430 170 -1
 clearperf
 label start
 printperf
-loop 3000 pxzoom 430 170 1
-loop 3000 pxzoom 430 170 -1
+loop 2000 pxzoom 430 170 1
+loop 2000 pxzoom 430 170 -1
 goto start
 wait

diff --git a/src/turtles_sans_turtles.script b/src/turtles_sans_turtles.script
new file mode 100644 (file)
index 0000000..5a60bc9
--- /dev/null
+++ b/src/turtles_sans_turtles.script
@@ -0,0 +1,5 @@
+loop 1000 pxzoom 430 170 1
+label start
+loop 2000 pxzoom 430 170 -1
+loop 2000 pxzoom 430 170 1
+goto start

diff --git a/src/view.cpp b/src/view.cpp
index 3de7fb4..af5f53e 100644 (file)
--- a/src/view.cpp
+++ b/src/view.cpp
@@ -165,6 +165,10 @@ void View::ScaleAroundPoint(Real x, Real y, Real scale_amount)
        m_bounds.y = vy - top;
        m_bounds.w *= scale_amount;
        m_bounds.h *= scale_amount;
+       if (m_bounds.w == VReal(0))
+       {
+               Debug("Scaled to zero!!!");
+       }
        //Debug("Scale at {%s, %s} by %s View Bounds => %s", x.Str().c_str(), y.Str().c_str(), scale_amount.Str().c_str(), m_bounds.Str().c_str());
        
        
@@ -523,7 +527,7 @@ void View::UpdateObjBoundsVBO(unsigned first_obj, unsigned last_obj)
 {
        if (m_query_gpu_bounds_on_next_frame != NULL)
        {
-               fprintf(m_query_gpu_bounds_on_next_frame,"# View: %s\t%s\t%s\t%s", Str(m_bounds.x).c_str(), Str(m_bounds.y).c_str(), Str(m_bounds.w).c_str(), Str(m_bounds.h).c_str());
+               fprintf(m_query_gpu_bounds_on_next_frame,"# View: %s\t%s\t%s\t%s\n", Str(m_bounds.x).c_str(), Str(m_bounds.y).c_str(), Str(m_bounds.w).c_str(), Str(m_bounds.h).c_str());
        }       
        
        //m_objbounds_vbo.Invalidate();
@@ -585,7 +589,6 @@ void View::UpdateObjBoundsVBO(unsigned first_obj, unsigned last_obj)
                                continue;
                                
                        Rect obj_bounds = m_document.m_objects.bounds[id];
-
                        obj_bounds.x *= pbounds.w;
                        obj_bounds.x += pbounds.x;
                        obj_bounds.y *= pbounds.h;

diff --git a/src/view.h b/src/view.h
index 124ae07..05be33e 100644 (file)
--- a/src/view.h
+++ b/src/view.h
@@ -8,7 +8,7 @@
 #include "path.h"
 #include "transformationtype.h"
 
-#define USE_GPU_TRANSFORM true 
+#define USE_GPU_TRANSFORM false 
 #define USE_GPU_RENDERING true
 #define USE_SHADING !(USE_GPU_RENDERING) && true
 
@@ -18,11 +18,6 @@
 
 namespace IPDF
 {
-       #ifdef TRANSFORM_BEZIERS_TO_PATH
-               typedef Real VReal;
-       #else
-               typedef Real VReal;
-       #endif
        typedef TRect<VReal> VRect;
        
        class Screen;

diff --git a/tools/analysis.ipynb b/tools/analysis.ipynb
index 902b8da..f7e4e02 100644 (file)
--- a/tools/analysis.ipynb
+++ b/tools/analysis.ipynb
@@ -24,7 +24,7 @@
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 1
     },
     {
      "cell_type": "code",
@@ -35,7 +35,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 62
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -43,12 +43,13 @@
      "input": [
       "from common import *\n",
       "import build\n",
-      "import scaling"
+      "import scaling\n",
+      "import saveload"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 137
+     "prompt_number": 23
     },
     {
      "cell_type": "code",
@@ -56,12 +57,13 @@
      "input": [
       "# If things are changed run this instead of restarting kernel\n",
       "scaling = reload(scaling)\n",
-      "build = reload(build)"
+      "build = reload(build)\n",
+      "saveload = reload(saveload)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 190
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -76,12 +78,19 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "options[\"tobuild\"] += [\"mpfrc++\"]"
+      "try:\n",
+      "    options[\"built\"] = saveload.load_obj(\"built\")\n",
+      "except:\n",
+      "    options[\"built\"] = []\n",
+      "options[\"tobuild\"] = [\"float\", \"double\", \"mpfr-16\",\"mpfr-32\", \"mpfr-64\", \"mpfr-256\", \"mpfr-512\", \"mpfr-1024\", \"Gmprat\", \"cumul-float\", \"path-float\", \"path-Gmprat\", \"path-mpfr-1024\"]\n",
+      "options[\"tobuild\"] = [b for b in options[\"tobuild\"] if b not in options[\"built\"]]\n",
+      "options[\"tobuild\"] = [\"path-Gmprat\",\"path-mpfr-1024\"]\n",
+      "    "
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 46
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
@@ -96,7 +105,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Building: ['single', 'double', 'GMPrat', 'mpfrc++']\n",
+        "Building: ['path-Gmprat', 'path-mpfr-1024']\n",
         "\r",
         "[                  0%                  ]"
        ]
@@ -106,30 +115,7 @@
        "stream": "stdout",
        "text": [
         " \r",
-        "[**********       25%                  ]  1 of 4 complete"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " \r",
-        "[*****************50%                  ]  2 of 4 complete"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "'Failed to build GMPrat'"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " \r",
-        "[*****************75%*********         ]  3 of 4 complete"
+        "[*****************50%                  ]  1 of 2 complete"
        ]
       },
       {
@@ -137,7 +123,7 @@
        "stream": "stdout",
        "text": [
         " \r",
-        "[****************100%******************]  4 of 4 complete"
+        "[****************100%******************]  2 of 2 complete"
        ]
       },
       {
@@ -148,18 +134,42 @@
        ]
       }
      ],
-     "prompt_number": 47
+     "prompt_number": 15
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "options[\"built\"] = [\"single\", \"double\", \"cumul-single\", \"cumul-double\", \"path-single\", \"path-double\", \"path-rat\"] # Hack for now, these were manually compiled"
+      "saveload.save_obj(options[\"built\"], \"built\")\n",
+      "options[\"built\"]\n"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 91
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 16,
+       "text": [
+        "['float',\n",
+        " 'double',\n",
+        " 'mpfr-16',\n",
+        " 'mpfr-32',\n",
+        " 'mpfr-64',\n",
+        " 'mpfr-256',\n",
+        " 'mpfr-512',\n",
+        " 'mpfr-1024',\n",
+        " 'Gmprat',\n",
+        " 'cumul-float',\n",
+        " 'path-float',\n",
+        " 'path-Gmprat',\n",
+        " 'path-mpfr-1024',\n",
+        " 'path-Gmprat',\n",
+        " 'path-mpfr-1024']"
+       ]
+      }
+     ],
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -172,15 +182,104 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "scaling_data = {}\n",
-      "for b in options[\"built\"]:\n",
-      "    scaling_data[b] = scaling.FixedScales(\"./\"+b, fps=100, xz=0.5, yz=0.5)\n",
-      "    "
+      "scaling_data = saveload.load_dict(\"scaling_data\")\n",
+      "scaling_data.keys()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 20,
+       "text": [
+        "['mpfr-32',\n",
+        " 'mpfr-256',\n",
+        " 'double',\n",
+        " 'float',\n",
+        " 'mpfr-512',\n",
+        " 'path-mpfr-1024',\n",
+        " 'mpfr-1024',\n",
+        " 'cumul-float',\n",
+        " 'mpfr-64',\n",
+        " 'path-float',\n",
+        " 'path-Gmprat',\n",
+        " 'mpfr-16',\n",
+        " 'Gmprat']"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "del scaling_data[\"path-Gmprat\"]\n",
+      "del scaling_data[\"path-mpfr-1024\"]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 92
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#scaling_data = {}\n",
+      "p = ProgressBar(len(options[\"built\"]))\n",
+      "p.animate(0)\n",
+      "for i,b in enumerate(options[\"built\"]):\n",
+      "    if b in scaling_data.keys():\n",
+      "        #print \"Skip %s\" % b\n",
+      "        continue\n",
+      "    print b\n",
+      "    p.animate(i)\n",
+      "    scaling_data[b] = scaling.FixedScales(\"./\"+b, steps=2000, fps=10, xz=0.5, yz=0.5)\n",
+      "saveload.save_obj(scaling_data, \"scaling_data\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\r",
+        "[                  0%                  ]"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " path-Gmprat\n",
+        "\r",
+        "[***               7%                  ]  1 of 15 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " ./path-Gmprat - Quit early after 149 steps - Exception [Errno 32] Broken pipe\n",
+        "./path-Gmprat - Couldn't exit - [Errno 32] Broken pipe\n",
+        "path-mpfr-1024\n",
+        "\r",
+        "[*****************80%**********        ]  12 of 15 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " ./path-mpfr-1024 - Quit early after 149 steps - Exception [Errno 32] Broken pipe\n",
+        "./path-mpfr-1024 - Couldn't exit - [Errno 32] Broken pipe\n"
+       ]
+      }
+     ],
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -197,12 +296,14 @@
      "collapsed": false,
      "input": [
       "fig = figure(figsize=(6,4))\n",
-      "for b in scaling_data:\n",
-      "    plot(1.0/scaling_data[b][\"accuracy\"][:,2], scaling_data[b][\"accuracy\"][:,5])\n",
-      "xscale(\"log\")\n",
-      "legend(scaling_data.keys(), loc=\"best\")\n",
+      "l = []\n",
+      "for b in [\"mpfr-16\", \"mpfr-32\", \"mpfr-64\", \"mpfr-256\", \"mpfr-512\", \"mpfr-1024\",\"path-Gmprat\"]: \n",
+      "    plot(-1.0*scaling_data[b][\"accuracy\"][:,4], scaling_data[b][\"accuracy\"][:,-1])\n",
+      "    l += [b]\n",
+      "#xscale(\"log\")\n",
+      "legend(l, loc=\"best\")\n",
       "title(\"Loss of Precision for a 1x1 pixel grid\\nView Top Left: (0.5,0.5)\")\n",
-      "xlabel(\"Magnification (1/width)\")\n",
+      "xlabel(\"Log10(Magnification)\")\n",
       "ylabel(\"Representable Lines\")\n",
       "\n",
       "fig.savefig('../../sam/figures/loss_of_precision_grid_0.5.pdf', format='PDF')"
@@ -213,23 +314,26 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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NJYgRI0bA2NhY4djo0aMhFNZW+Y9//AOZmbU7ep08eRI+Pj7Q0dGBXC6Hg4MD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\ne6OgoAAzZszApEmTUK1k4MmOHTsQGRmJS5cuISc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Hr68vAPmCWIwxhIaGwtHREe3bt8fUqVP5pS78/PywadMmAJVXootEIoSFhQH43zLhtWGV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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f305427d810>"
+        "<matplotlib.figure.Figure at 0x7f1de96875d0>"
        ]
       }
      ],
-     "prompt_number": 97
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "fig = figure(figsize=(6,4))\n",
-      "for b in scaling_data:\n",
-      "    plot(1.0/scaling_data[b][\"accuracy\"][:,2], scaling_data[b][\"performance\"][:,3])\n",
-      "xscale(\"log\")\n",
-      "legend(scaling_data.keys())\n",
+      "l = []\n",
+      "for b in [\"mpfr-1024\", \"Gmprat\"]:\n",
+      "    plot(-1.0*scaling_data[b][\"accuracy\"][:,4], scaling_data[b][\"performance\"][:,3])\n",
+      "    l += [b]\n",
+      "#xscale(\"log\")\n",
+      "\n",
+      "legend(l, loc=\"best\")\n",
       "xlabel(\"Magnification\")\n",
       "ylabel(\"Average FPS (100 frames)\")"
      ],
@@ -239,21 +343,21 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 98,
+       "prompt_number": 42,
        "text": [
-        "<matplotlib.text.Text at 0x7f3053e62150>"
+        "<matplotlib.text.Text at 0x7f37e035ca10>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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slliytLQkm1WbyOVrP7p0UZkC++4mUYWIduzYQb16W9DpDe/RvA/TSWFrOO09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IiGD58uUAJCQk4OPjQ9u2bc3mzc6uqCzWrl1L165dHXULEifDmGLRCI0Dr+ew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\nrkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f3053fc1b90>"
+        "<matplotlib.figure.Figure at 0x7f37e04233d0>"
        ]
       }
      ],
-     "prompt_number": 98
+     "prompt_number": 42
     },
     {
      "cell_type": "markdown",
@@ -266,35 +370,103 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "invariance_data = {}\n",
-      "for b in options[\"built\"]:\n",
-      "    invariance_data[b] = scaling.TestInvariance(\"./\"+b, fps=100, x0 = 0.5, y0 = 0.5, w0 = 1e-6, h0 = 1e-6, xz=400, yz=300)\n",
-      "    "
+      "invariance_data = saveload.load_dict(\"invariance_data\")\n",
+      "invariance_data.keys()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 191
+     "outputs": []
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "invariance_data.keys()"
+      "invariance_data = {}\n",
+      "p = ProgressBar(len(options[\"built\"]))\n",
+      "p.animate(0)\n",
+      "for i,b in enumerate(options[\"built\"]):\n",
+      "    if b in invariance_data.keys():\n",
+      "        continue\n",
+      "    invariance_data[b] = scaling.TestInvariance(\"./\"+b, fps=10, x0 = 0.5, y0 = 0.5, w0 = 1e-6, h0 = 1e-6, xz=400, yz=300)\n",
+      "    p.animate(i)\n",
+      "   \n",
+      "#saveload.save_obj(invariance_data, \"invariance_data\")"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 187,
+       "output_type": "stream",
+       "stream": "stdout",
        "text": [
-        "['path-single', 'double', 'single', 'cumul-double', 'cumul-single']"
+        "\r",
+        "[                  0%                  ]"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " \r",
+        "[***               7%                  ]  1 of 15 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " \r",
+        "[***               7%                  ]  1 of 15 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " ./mpfr-16 - Quit early after 1 steps - No precision left\n",
+        "\r",
+        "[*****            13%                  ]  2 of 15 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " ./mpfr-32 - Quit early after 801 steps - Took too long to render frames\n",
+        "\r",
+        "[********         20%                  ]  3 of 15 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " \r",
+        "[**********       27%                  ]  4 of 15 complete"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " \r",
+        "[*************    33%                  ]  5 of 15 complete"
+       ]
+      },
+      {
+       "ename": "KeyboardInterrupt",
+       "evalue": "",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+        "\u001b[1;32m<ipython-input-28-471152fea0f5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minvariance_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0minvariance_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscaling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTestInvariance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"./\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e-6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e-6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m400\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0manimate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32m/home/sam/Documents/University/2014/ipdf/code/tools/scaling.py\u001b[0m in \u001b[0;36mTestInvariance\u001b[1;34m(binname, x0, y0, w0, h0, s, steps, xz, yz, testsvg, renderer, fps)\u001b[0m\n\u001b[0;32m    102\u001b[0m                         \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"loop %d pxzoom %s %s %s\\n\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m                         \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"querygpubounds step%d.dat\\n\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m                         \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"step%d.dat\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    105\u001b[0m                                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m                         \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"loop %d printspf\\n\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mfps\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Print an FPS count to signal it is safe to read the file\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32m/usr/lib/python2.7/genericpath.pyc\u001b[0m in \u001b[0;36misfile\u001b[1;34m(path)\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[1;34m\"\"\"Test whether a path is a regular file\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m         \u001b[0mst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
        ]
       }
      ],
-     "prompt_number": 187
+     "prompt_number": 28
     },
     {
      "cell_type": "code",
@@ -302,13 +474,13 @@
      "input": [
       "fig = figure(figsize=(6,4))\n",
       "for b in invariance_data.keys():\n",
-      "    plot(invariance_data[b][\"accuracy\"][:,2], invariance_data[b][\"accuracy\"][:,4])\n",
-      "xscale(\"log\")\n",
+      "    plot(invariance_data[b][\"accuracy\"][:,4], invariance_data[b][\"accuracy\"][:,-2])\n",
+      "#xscale(\"log\")\n",
       "yscale(\"log\")\n",
       "legend(scaling_data.keys(), loc=\"best\")\n",
       "xlabel(\"Total scaling factor\")\n",
       "ylabel(\"Accumulated Error\")\n",
-      "fig.savefig(\"../../sam/figures/cumulative_error_grid.pdf\", format=\"PDF\")"
+      "#fig.savefig(\"../../sam/figures/cumulative_error_grid.pdf\", format=\"PDF\")"
      ],
      "language": "python",
      "metadata": {},
@@ -341,21 +513,21 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 118,
+       "prompt_number": 196,
        "text": [
-        "<matplotlib.text.Text at 0x7f30539630d0>"
+        "<matplotlib.text.Text at 0x7f3051dd37d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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t59SAGE7f0uBtJo5K5N4tVzn4mwiGe52lXtvUy9Tr9RwQF8e9jTyhjJoadtkV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\nJ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LWL0auHFDt6QQE4NmuxofB2bOBJ56imDG1grwf86BjwcNWwb7YIqDQ6OdyBq5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\n4EBRFEVJ+H8hbiqovByBAQAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f30537655d0>"
+        "<matplotlib.figure.Figure at 0x7f30521fc8d0>"
        ]
       }
      ],
-     "prompt_number": 118
+     "prompt_number": 196
     },
     {
      "cell_type": "markdown",

diff --git a/tools/build.py b/tools/build.py
index 82e9aa1..699facb 100755 (executable)
--- a/tools/build.py
+++ b/tools/build.py
@@ -4,26 +4,42 @@ from common import *
 
 import subprocess
 
-def Build(real_type, quadtree=False, controlpanel=False):
-    global options
-    real_name = ""
-    if (type(real_type) == str):
-        quadtree = "enabled" if (real_type.split("-")[-1] == "qtree") else quadtree
-        real_type = real_type.split("-")[0]
-        real_name = real_type
-        real_type = options["real_names"].index(real_type)
-    else:
-        real_name = options["real_names"][real_type]
-        
-    quadtree = "enabled" if quadtree else "disabled"
-    controlpanel = "enabled" if controlpanel else "disabled"
-    if (os.system("make -C %s clean" % options["ipdf_src"]) != 0):
-        raise Exception("Make clean failed.")
-    if (os.system("make -C %s REALTYPE=%d QUADTREE=%s CONTROLPANEL=%s" % (options["ipdf_src"], real_type, quadtree, controlpanel)) != 0):
-        raise Exception("Make failed.")
-        
-    q = "-qtree" if quadtree == "enabled" else ""
-    os.rename(options["ipdf_bin"], options["local_bin"]+real_name+q)
+def Build(binname):
+       global options
+       
+       tokens = binname.split("-")
+       transformations = "direct"
+       mpfr_prec = 23
+       if tokens[0] == "path":
+               transformations = "path"
+               tokens = tokens[1:]
+       elif tokens[0] == "cumul":
+               transformations = "cumulative"
+               tokens = tokens[1:]
+
+       realname = tokens[0]
+       realtype = options["real_names"].index(realname)
+       
+       mainreal = 0
+       pathreal = 0
+       if transformations == "direct":
+               mainreal = realtype
+       else:
+               pathreal = realtype # hackky.
+               realtype = 1
+       
+       if realname == "mpfr":
+               mpfr_prec = int(tokens[1])
+       
+       quadtree = "disabled"
+       if "qtree" in tokens:
+               quadtree = "enabled"
+       
+       
+       if (os.system("make -C %s clean; make -C %s REALTYPE=%d MPFR_PRECISION=%d QUADTREE=%s CONTROLPANEL=disabled TRANSFORMATIONS=%s PATHREAL=%d" % (options["ipdf_src"], options["ipdf_src"], mainreal, mpfr_prec, quadtree, transformations, pathreal)) != 0):
+               raise Exception("Make failed.")
+
+       os.rename(options["ipdf_bin"], options["local_bin"]+binname)
 
 
 def BuildAll():
@@ -33,11 +49,11 @@ def BuildAll():
        for (i,b) in enumerate(options["tobuild"]): #options["real_names"]:
                if b in options["ignore"]:
                        continue
-               try:
-                       Build(b, False, False)
-                       options["built"] += [b]
-               except:
-                       display("Failed to build %s" % b)
+               #try:
+               Build(b)
+               options["built"] += [b]
+               #except:
+               #       display("Failed to build %s" % b)
                p.animate(i+1)
 
 if __name__ == "__main__":

diff --git a/tools/common.py b/tools/common.py
index 4ebfb1b..8b296ab 100644 (file)
--- a/tools/common.py
+++ b/tools/common.py
@@ -9,13 +9,13 @@ from IPython.display import display, clear_output
 import threading
 
 options = {
-    "real_names" : ["single", "double", "long", "virtual", "Rational_GMPint", "Rational_Arbint", "mpfrc++", "iRRAM", "ParanoidNumber", "GMPrat"],
+    "real_names" : ["float", "double", "long", "virtual", "Rational_Gmpint", "Rational_Arbint", "mpfr", "iRRAM", "ParanoidNumber", "Gmprat"],
     "ipdf_src" : "../src/",
     "ipdf_bin" : "../bin/ipdf",
     "local_bin" : "./",
     "tests" : "../src/tests/",
     "ignore" : ["virtual", "Rational_Arbint", "iRRAM", "ParanoidNumber"],
-    "tobuild" : ["single", "double", "mpfrc++","GMPrat"],
+    "tobuild" : ["float", "double", "mpfr-1024","Gmprat", "path-Gmprat", "path-mpfr-1025"],
     "numerical_tests" : ["identitytests"],
     "built" : []
 }

diff --git a/tools/saveload.py b/tools/saveload.py
new file mode 100644 (file)
index 0000000..7f43a43
--- /dev/null
+++ b/tools/saveload.py
@@ -0,0 +1,16 @@
+import pickle
+
+def save_obj(obj, name ):
+    with open('obj/'+ name + '.pkl', 'wb') as f:
+        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
+
+def load_obj(name ):
+    with open('obj/' + name + '.pkl', 'r') as f:
+        return pickle.load(f)
+
+
+def load_dict(name):
+       try:
+               return load_obj(name)
+       except:
+               return {}

diff --git a/tools/scaling.py b/tools/scaling.py
index 6240718..a354e72 100755 (executable)
--- a/tools/scaling.py
+++ b/tools/scaling.py
@@ -1,5 +1,7 @@
 #!/usr/bin/python -u
 
+#much copy paste such python
+
 import sys
 import os
 from matplotlib.pyplot import *
@@ -14,83 +16,122 @@ def FixedScales(binname, x0=0, y0=0, w0=1, h0=1, s=0.5, steps=100, xz = 0.5, yz
        performance = []
        n = open("/dev/null", "w")
        #n = sys.stderr
-       p = subprocess.Popen(binname + " -s stdin", bufsize=0, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=n, shell=True)
-       p.stdin.write("%s\n" % renderer)
-       p.stdin.write("setbounds %s %s %s %s\n" % (str(x0),str(y0),str(w0),str(h0)))
-       p.stdin.write("loadsvg %s\n" % testsvg)
-       p.stdin.write("querygpubounds original.dat\n")
-       p.stdin.write("screenshot original.bmp\n")
-       for i in xrange(steps):
-               p.stdin.write("clear\n")
-               p.stdin.write("loop 1 zoom %s %s %s\n" % (str(xz), str(yz), str(s)))
+       try:
+               p = subprocess.Popen(binname + " -s stdin", bufsize=0, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=n, shell=True)
+               p.stdin.write("%s\n" % renderer)
+               p.stdin.write("setbounds %s %s %s %s\n" % (str(x0),str(y0),str(w0),str(h0)))
                p.stdin.write("loadsvg %s\n" % testsvg)
-               p.stdin.write("querygpubounds step%d.dat\n" % i)
-               while not os.path.isfile("step%d.dat" % i):
-                       pass
-               p.stdin.write("loop %d printfps\n" % fps) # Print an FPS count to signal it is safe to read the file
-               fpsout = p.stdout.readline().strip(" \r\n").split("\t")
+               p.stdin.write("querygpubounds original.dat\n")
+               p.stdin.write("screenshot original.bmp\n")
+       except Exception, e:
+               print "%s - Couldn't start - %s" % (binname, str(e))
+               return {"accuracy" : asarray(accuracy), \
+                       "performance" : asarray(performance)}
+       
+       for i in xrange(steps):
+               try:
+                       start_time = time.time()
+                       p.stdin.write("clear\n")
+                       p.stdin.write("loop 1 zoom %s %s %s\n" % (str(xz), str(yz), str(s)))
+                       p.stdin.write("loadsvg %s\n" % testsvg)
+                       p.stdin.write("querygpubounds step%d.dat\n" % i)
+               #while not os.path.isfile("step%d.dat" % i):
+               #       pass
+                       p.stdin.write("loop %d printspf\n" % fps) # Print an FPS count to signal it is safe to read the file
+                       fpsout = p.stdout.readline().strip(" \r\n").split("\t")
                #print(str(fpsout))
-               p.stdin.write("printbounds\n")
-               bounds = map(float, p.stdout.readline().strip(" \r\n").split("\t"))
+                       p.stdin.write("printbounds\n")
+                       bounds = p.stdout.readline().strip(" \r\n").split("\t")
+                       try:
+                               bounds = map(float, bounds)
+                       except:
+                               pass
                
-               performance += [map(float, fpsout)]
-               accuracy += [bounds + [gpubounds.ComputeError("original.dat", "step%d.dat" % i), \
+                       performance += [map(float, fpsout) + [time.time() - start_time]]
+                       accuracy += [bounds + [gpubounds.ComputeError("original.dat", "step%d.dat" % i), \
                                 gpubounds.UniqueBounds("step%d.dat" % i)]]
                                 
-               os.unlink("step%d.dat" % i) # Don't need it any more
-               if accuracy[-1][-1] <= 0:
-                       print "%s - Quit early after %d steps" % (binname, i)
+                       os.unlink("step%d.dat" % i) # Don't need it any more
+               #print accuracy[-1][-1]
+                       if accuracy[-1][-1] <= 1:
+                               print "%s - Quit early after %d steps - No precision left" % (binname, i)
+                               break
+                       if performance[-1][-1] > 60:
+                               print "%s - Quit early after %d steps - Took too long to render frames" % (binname, i)
+                               break
+               except Exception, e:
+                       print "%s - Quit early after %d steps - Exception %s" % (binname, i, str(e))
                        break
        
-       p.stdin.write("screenshot final.bmp\n")
-       p.stdin.write("quit\n")
-       p.stdin.close()
+       try:
+               p.stdin.write("screenshot final.bmp\n")
+               p.stdin.write("quit\n")
+               p.stdin.close()
+       except Exception, e:
+               print "%s - Couldn't exit - %s" % (binname, str(e))
+               
        return {"accuracy" : asarray(accuracy),
                        "performance" : asarray(performance)}
 
 
-def TestInvariance(binname, x0=0, y0=0, w0=1, h0=1, s=-1, steps=1000, xz = 0.5, yz = 0.5, testsvg="svg-tests/grid.svg", renderer="gpu", fps=1):
+def TestInvariance(binname, x0=0, y0=0, w0=1, h0=1, s=-1, steps=1000, xz = 400, yz = 300, testsvg="svg-tests/grid.svg", renderer="gpu", fps=1):
        accuracy = []
        performance = []
        n = open("/dev/null", "w")
        #n = sys.stderr
-       p = subprocess.Popen(binname + " -s stdin", bufsize=0, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=n, shell=True)
-       p.stdin.write("%s\n" % renderer)
-       p.stdin.write("setbounds %s %s %s %s\n" % (str(x0),str(y0),str(w0),str(h0)))
-       p.stdin.write("loadsvg %s\n" % testsvg)
-       p.stdin.write("querygpubounds original.dat\n")
-       p.stdin.write("screenshot original.bmp\n")
-       p.stdin.write("printbounds\n")
-       bounds_orig = map(float, p.stdout.readline().strip(" \r\n").split("\t"))
-       for i in xrange(1,steps,50):
-               p.stdin.write("loop %d pxzoom %s %s %s\n" % (i, str(xz), str(yz), str(int(s))))
+       try:
+               p = subprocess.Popen(binname + " -s stdin", bufsize=0, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=n, shell=True)
+               p.stdin.write("%s\n" % renderer)
+               p.stdin.write("setbounds %s %s %s %s\n" % (str(x0),str(y0),str(w0),str(h0)))
+               p.stdin.write("loadsvg %s\n" % testsvg)
+               p.stdin.write("querygpubounds original.dat\n")
+               p.stdin.write("screenshot original.bmp\n")
                p.stdin.write("printbounds\n")
-               bounds = map(float, p.stdout.readline().strip(" \r\n").split("\t"))
-               p.stdin.write("loop %d pxzoom %s %s %s\n" % (i, str(xz), str(yz), str(-int(s))))
-               p.stdin.write("querygpubounds step%d.dat\n" % i)
-               while not os.path.isfile("step%d.dat" % i):
-                       pass
-               p.stdin.write("loop %d printfps\n" % fps) # Print an FPS count to signal it is safe to read the file
-               fpsout = p.stdout.readline().strip(" \r\n").split("\t")
-               #print(str(fpsout))
+               bounds_orig = map(float, p.stdout.readline().strip(" \r\n").split("\t"))
+       except Exception, e:
+               print "%s - Couldn't start - %s" % (binname, str(e))
+               return {"accuracy" : asarray(accuracy),
+                       "performance" : asarray(performance)}
+       
+       for i in xrange(1,steps,50):
+               try:
+                       start_time = time.time()
+                       p.stdin.write("loop %d pxzoom %s %s %s\n" % (i, str(xz), str(yz), str(int(s))))
+                       p.stdin.write("printbounds\n")
+                       bounds = map(float, p.stdout.readline().strip(" \r\n").split("\t"))
+                       p.stdin.write("loop %d pxzoom %s %s %s\n" % (i, str(xz), str(yz), str(-int(s))))
+                       p.stdin.write("querygpubounds step%d.dat\n" % i)
+                       while not os.path.isfile("step%d.dat" % i):
+                               pass
+                       p.stdin.write("loop %d printspf\n" % fps) # Print an FPS count to signal it is safe to read the file
+                       fpsout = p.stdout.readline().strip(" \r\n").split("\t")
+                       #print(str(fpsout))
                
                
-               bounds[0] = bounds[0]-bounds_orig[0]
-               bounds[1] = bounds[1]-bounds_orig[1]
-               bounds[2] = bounds[2]/bounds_orig[2]
-               bounds[3] = bounds[3]/bounds_orig[3]
-               performance += [map(float, fpsout)]
-               accuracy += [bounds + [gpubounds.ComputeError("original.dat", "step%d.dat" % i), \
+                       bounds[0] = bounds[0]-bounds_orig[0]
+                       bounds[1] = bounds[1]-bounds_orig[1]
+                       bounds[2] = bounds[2]/bounds_orig[2]
+                       bounds[3] = bounds[3]/bounds_orig[3]
+                       performance += [map(float, fpsout) + [time.time() - start_time]]
+                       accuracy += [bounds + [gpubounds.ComputeError("original.dat", "step%d.dat" % i), \
                                 gpubounds.UniqueBounds("step%d.dat" % i)]]
                                 
-               os.unlink("step%d.dat" % i) # Don't need it any more
-               if accuracy[-1][-1] <= 0:
-                       print "%s - Quit early after %d steps" % (binname, i)
-                       break
-               
-       p.stdin.write("screenshot final.bmp\n")
-       p.stdin.write("quit\n")
-       p.stdin.close()
+                       os.unlink("step%d.dat" % i) # Don't need it any more
+                       if accuracy[-1][-1] <= 10:
+                               print "%s - Quit early after %d steps - No precision left" % (binname, i)
+                               break
+                       if performance[-1][-1] > 60:
+                               print "%s - Quit early after %d steps - Took too long to render frames" % (binname, i)
+                               break
+               except Exception, e:
+                       print "%s - Quit early after %d steps - %s" % (binname, i, str(e))
+                       
+       try:
+               p.stdin.write("screenshot final.bmp\n")
+               p.stdin.write("quit\n")
+               p.stdin.close()
+       except Exception, e:
+               print "%s - Couldn't exit - %s" % (binname, str(e))
        return {"accuracy" : asarray(accuracy),
                        "performance" : asarray(performance)}