void checkcentrality(TString datafilename, TString mcfilename)
{
TFile *fdt = new TFile(datafilename);
TFile *fmc = new TFile(mcfilename);
auto tdt = (TTree *)fdt->Get("nt");
auto tmc = (TTree *)fmc->Get("nt");
auto hdt = new TH1F("centrdt","centrdt",50,0,200); hdt->SetMarkerColor(kBlack);
auto hmc = new TH1F("centrmc","centrmc",50,0,200); hmc->SetMarkerColor(kBlue);
SetMC({hmc}); SetInc({hmc});
SetData({hdt});
tdt->Project("centrdt","bin","weight*(jtpt1>120 && jtpt2>30 && dphi21>2. && hiHF<5500)");
tmc->Project("centrmc","bin","weight*(jtpt1>120 && jtpt2>30 && dphi21>2. && pthatsample>30)");
hmc->Scale(hdt->Integral()/hmc->Integral());
fout->cd();
hdt->Write();
hmc->Write();
fdt->Close();
fmc->Close();
}
LinearProblem prob(const Mesh& mesh) const
{
CellFilter left = domain().left();
CellFilter right = domain().right();
Expr u = new UnknownFunction(new Lagrange(3), "u");
Expr v = new UnknownFunction(new Lagrange(1), "v");
Expr du = new TestFunction(new Lagrange(3), "du");
Expr dv = new TestFunction(new Lagrange(1), "dv");
Expr dx = gradient(1);
Expr x = coord(0);
QuadratureFamily quad = new GaussianQuadrature(10);
Expr eqn = Integral(interior(),
(dx*du)*(dx*u) + du*v + (dx*dv)*(dx*v) + x*dv,
quad);
Expr bc = EssentialBC(left, du*u + dv*v, quad)
+ EssentialBC(right, du*u + dv*v, quad);
return LinearProblem(mesh, eqn, bc,
List(dv,du), List(v,u), vecType());
}
int surfDetDes(IplImage *img, /* image to find Ipoints in */
Ipoint *ipts, /* reference to vector of Ipoints */
int upright, /* run in rotation invariant mode? */
int octaves, /* number of octaves to calculate */
int intervals, /* number of intervals per octave */
int init_sample, /* initial sampling step */
float thres, /* blob response threshold */
int size /*Size of Array */)
{
int ipts_size;
FastHessian fh;
// Create integral-image representation of the image
IplImage *int_img = Integral(img);
// Create Fast Hessian Object
FastH(&fh,int_img);
// Extract interest points and store in vector ipts
ipts_size = getIpoints(&fh);
// Create Surf Descriptor Object
Surf_Constractor(int_img, &fh, upright, ipts, size);
// Deallocate the integral image
cvReleaseImage(&int_img);
return ipts_size;
}
double Envelope::Average( double t0, double t1 )
{
if( t0 == t1 )
return GetValue( t0 );
else
return Integral( t0, t1 ) / (t1 - t0);
}
Expr NitscheStokesNoSlipBC(const CellFilter& cells,
const QuadratureFamily& quad,
const Expr& nu,
const Expr& v,
const Expr& q,
const Expr& u,
const Expr& p,
const Expr& uBC,
const double& gamma1,
const double& gamma2
)
{
TEUCHOS_TEST_FOR_EXCEPT(nu.size() != 1);
TEUCHOS_TEST_FOR_EXCEPT(v.size() != u.size());
TEUCHOS_TEST_FOR_EXCEPT(uBC.size() != u.size());
int dim = uBC.size();
Expr grad = gradient(dim);
Expr n = CellNormalExpr(dim,"n");
Expr h = new CellDiameterExpr();
return Integral(cells,
-nu*(v*((n*grad)*u /*- (n*grad)*uBC*/) + (u-uBC)*((n*grad)*v))
+ p*(v*n) + q*((u-uBC)*n)
+ gamma1/h * v*(u-uBC) + gamma2/h * (v*n) * ((u-uBC)*n),
quad);
}
Double_t RegionIntegratorMultiDim::RegionIntegral(const ROOT::Math::IMultiGenFunction &f, const Regions ®)
{
Double_t result = 0;
Double_t xlow[2], xhigh[2];
for (UInt_t i=0; i<reg.size(); i++)
{
if (fUseCenters == true)
{
xlow[0] = reg[i].cntr_xlow;
xlow[1] = reg[i].cntr_ylow;
xhigh[0] = reg[i].cntr_xhigh;
xhigh[1] = reg[i].cntr_yhigh;
}
else
{
xlow[0] = reg[i].grid_xlow;
xlow[1] = reg[i].grid_ylow;
xhigh[0] = reg[i].grid_xhigh;
xhigh[1] = reg[i].grid_yhigh;
}
result += Integral(f, xlow, xhigh);
}
return result;
}
// We should be able to write a very efficient memoizer for this
// but make sure it gets reset when the envelope is changed.
double Envelope::Integral( double t0, double t1 )
{
if(t0 == t1)
return 0.0;
if(t0 > t1)
{
return -Integral(t1, t0); // this makes more sense than returning the default value
}
unsigned int count = mEnv.Count();
if(count == 0) // 'empty' envelope
return (t1 - t0) * mDefaultValue;
double total = 0.0, lastT, lastVal;
unsigned int i; // this is the next point to check
if(t0 < mEnv[0]->GetT()) // t0 preceding the first point
{
if(t1 <= mEnv[0]->GetT())
return (t1 - t0) * mEnv[0]->GetVal();
i = 1;
lastT = mEnv[0]->GetT();
lastVal = mEnv[0]->GetVal();
total += (lastT - t0) * lastVal;
}
else if(t0 >= mEnv[count - 1]->GetT()) // t0 following the last point
{
return (t1 - t0) * mEnv[count - 1]->GetVal();
}
else // t0 enclosed by points
{
// Skip any points that come before t0 using binary search
int lo, hi;
BinarySearchForTime(lo, hi, t0);
lastVal = InterpolatePoints(mEnv[lo]->GetVal(), mEnv[hi]->GetVal(), (t0 - mEnv[lo]->GetT()) / (mEnv[hi]->GetT() - mEnv[lo]->GetT()), mDB);
lastT = t0;
i = hi; // the point immediately after t0.
}
// loop through the rest of the envelope points until we get to t1
while (1)
{
if(i >= count) // the requested range extends beyond the last point
{
return total + (t1 - lastT) * lastVal;
}
else if(mEnv[i]->GetT() >= t1) // this point follows the end of the range
{
double thisVal = InterpolatePoints(mEnv[i - 1]->GetVal(), mEnv[i]->GetVal(), (t1 - mEnv[i - 1]->GetT()) / (mEnv[i]->GetT() - mEnv[i - 1]->GetT()), mDB);
return total + IntegrateInterpolated(lastVal, thisVal, t1 - lastT, mDB);
}
else // this point preceeds the end of the range
{
total += IntegrateInterpolated(lastVal, mEnv[i]->GetVal(), mEnv[i]->GetT() - lastT, mDB);
lastT = mEnv[i]->GetT();
lastVal = mEnv[i]->GetVal();
i++;
}
}
}
double L2Norm(const Mesh& mesh, const CellFilter& domain,
const Expr& f, const QuadratureFamily& quad,
const WatchFlag& watch)
{
Expr I2 = Integral(domain, f*f, quad, watch);
return sqrt(evaluateIntegral(mesh, I2));
}
int main()
{
double a=0.0, b, eps=0.005, l, t;
while (scanf("%lf %lf %lf", &l, &b, &t) != EOF)
printf("%.2f\n", Integral(a, b, f0, eps, l, t));
return 0;
}
/**
* Return a LP for the specified mesh. This function implements the
* prob() pure virtual function of class LPTestBase.
*/
LinearProblem prob(const Mesh& mesh) const
{
const double pi = 4.0*atan(1.0);
CellFilter left = domain().left();
Expr u = new UnknownFunction(new Lagrange(2), "u");
Expr v = new TestFunction(new Lagrange(2), "v");
Expr dx = gradient(1);
Expr x = coord(0);
QuadratureFamily quad = new GaussianQuadrature(4);
Expr eqn = Integral(interior(), (dx*v)*(dx*u), quad)
+ Integral(interior(), -0.25*pi*pi*sin(pi*x/2.0)*v, quad);
Expr bc = EssentialBC(left, v*u, quad);
return LinearProblem(mesh, eqn, bc, v, u, vecType());
}
void checkvertex(TString datafilename, TString mcfilename)
{
TFile *fmc = new TFile(mcfilename);
auto ntmc = (TTree *)fmc->Get("nt");
TFile *fdt = new TFile(datafilename);
auto ntdt = (TTree *)fdt->Get("nt");
auto vdt0 = new TH1F("vdt0","Data no weighting",30,-15,15); vdt0->Sumw2();
auto vmc0 = new TH1F("vmc0","MC no weighting",30,-15,15); vmc0->Sumw2();
ntdt->Project("vdt0","vz","weight*(jtpt2>120 && jtpt2>30)");
ntmc->Project("vmc0","vz","pthatweight*(jtpt2>120 && jtpt2>30 && pthatsample>30)");
vdt0->Scale(1/vdt0->Integral());
vmc0->Scale(1/vmc0->Integral());
plotylog = false;
Draw({vdt0,vmc0});
auto vdt = new TH1F("vdt","Data weighted",30,-15,15); vdt->Sumw2();
auto vmc = new TH1F("vmc","MC weighted",30,-15,15); vmc->Sumw2();
ntdt->Project("vdt","vz","weight*(jtpt2>120 && jtpt2>30)");
ntmc->Project("vmc","vz","weight*(jtpt2>120 && jtpt2>30 && pthatsample>30)");
vdt->Scale(1/vdt->Integral());
vmc->Scale(1/vmc->Integral());
SetMC({vmc, vmc0});
SetData({vdt,vdt0});
SetInc({vmc, vmc0});
fout->cd();
vdt->Write();
vmc->Write();
vdt0->Write();
vmc0->Write();
fmc->Close();
fdt->Close();
}
double H1Norm(
const Mesh& mesh,
const CellFilter& filter,
const Expr& f,
const QuadratureFamily& quad,
const WatchFlag& watch)
{
Expr grad = gradient(mesh.spatialDim());
Expr g = grad*f;
Expr I2 = Integral(filter, f*f + g*g, quad, watch);
return sqrt(evaluateIntegral(mesh, I2));
}
LinearProblem prob(const Mesh& mesh) const
{
Expr u = new UnknownFunction(basis_, "u");
Expr v = new TestFunction(basis_, "v");
Expr x = coord(0);
int p = basis_.order();
QuadratureFamily quad = new GaussianQuadrature(2*p);
Expr ex = exactSoln();
Expr eqn = Integral(interior(), v*(u-ex), quad);
Expr bc;
return LinearProblem(mesh, eqn, bc, v, u, vecType());
}
Expr NitschePoissonDirichletBC(int dim,
const CellFilter& cells,
const QuadratureFamily& quad,
const Expr& kappa,
const Expr& v,
const Expr& u,
const Expr& uBC,
const double& gamma)
{
Expr grad = gradient(dim);
Expr n = CellNormalExpr(dim,"n");
Expr h = new CellDiameterExpr();
return Integral(cells, -kappa*(v*((n*grad)*u) + (u-uBC)*((n*grad)*v))
+ gamma/h * v*(u-uBC), quad);
}
LinearProblem prob(const Mesh& mesh) const
{
CellFilter left = domain().left();
CellFilter right = domain().right();
Expr u = new UnknownFunction(new Lagrange(2), "u");
Expr v = new TestFunction(new Lagrange(2), "v");
Expr dx = gradient(1);
Expr x = coord(0);
QuadratureFamily quad = new GaussianQuadrature(4);
Expr eqn = Integral(interior(), (dx*v)*(dx*u) - v*u, quad);
Expr bc = EssentialBC(left, v*(u-cos(x)), quad);
return LinearProblem(mesh, eqn, bc, v, u, vecType());
}
//计算LC
static void CalcStd(u8 *pu8Gray, f32 *pf32Contrast, l32 * pl32IImg, l32 *pl32IImg2, l32 l32Width, l32 l32Height, l32 l32BlockW, l32 l32BlockH)
{
l32 l32Row, l32Col, l32BlockSize;
l32 l32Left, l32Right, l32Top, l32Bot;
f32 f32Std, f32Var, f32EX, f32EX2;
memset(pl32IImg, 0, (l32Width + 1) * (l32Height + 1) * sizeof(l32));
memset(pl32IImg2, 0, (l32Width + 1) * (l32Height + 1) * sizeof(l32));
//计算积分图像
Integral(pu8Gray, pl32IImg, l32Width, l32Height);
Integral2(pu8Gray, pl32IImg2, l32Width, l32Height);
for(l32Row = 0; l32Row < l32Height; l32Row++)
{
for(l32Col = 0; l32Col < l32Width; l32Col++)
{
//compute local contrast
l32Left = MAX(l32Col - l32BlockW/2, 0);
l32Right = MIN(l32Col + l32BlockW/2 + 1, l32Width);
l32Top = MAX(l32Row - l32BlockH/2, 0);
l32Bot = MIN(l32Row + l32BlockH/2 + 1, l32Height);
l32BlockSize = (l32Right - l32Left) * (l32Bot - l32Top);
f32EX = 1.0f * (pl32IImg[l32Bot * (l32Width + 1) + l32Right] - pl32IImg[l32Bot * (l32Width + 1) + l32Left]
- pl32IImg[l32Top * (l32Width + 1) + l32Right] + pl32IImg[l32Top * (l32Width + 1) + l32Left]) / l32BlockSize;
f32EX2 = 1.0f * (pl32IImg2[l32Bot * (l32Width + 1) + l32Right] - pl32IImg2[l32Bot * (l32Width + 1) + l32Left]
- pl32IImg2[l32Top * (l32Width + 1) + l32Right] + pl32IImg2[l32Top * (l32Width + 1) + l32Left]) / l32BlockSize;
//计算方差
f32Var = f32EX2 - f32EX * f32EX;
f32Std = sqrt(f32Var);
*pf32Contrast = f32Std;
pf32Contrast++;
}
}
}
int scaling(SDL_Surface *image, feature feat)
{
int a,b,c,d,e,f;
int n,w,i,j,h;
int **arr = Integral(image);
if(feat.t == 1)
{
int x = 1;
n = 2*feat.w*feat.h;
i = (int)(round((feat.i*image->h)/24));
j = (int)(round((feat.j*image->h)/24));
h = (int)(round((feat.h*image->h)/24));
while(x < (((int)(round(1+(2*feat.w*image->h)/24)))/2)&& 2*x<((image->h-j+1)))
{
x++;
}
int S1,S2;
w = x;
a = i+h-1;
b = j+w-1;
c = j-1;
d = i-1;
e = j+2*w-1;
S1=(((a<0)|(b<0))?0:arr[a][b])-(((a<0)|(c<0))?0:arr[a][c])-(((d<0)|(b<0))?0:arr[d][b])+(((d<0)|(c<0))?0:arr[d][c]);
S2=(((a<0)|(e<0))?0:arr[a][e])-(((a<0)|(b<0))?0:arr[a][b])-(((d<0)|(e<0))?0:arr[d][e])+(((d<0)|(b<0))?0:arr[d][b]);
return ((S1-S2)*n)/(2*w*h);
}
if(feat.t==2)
{
int x=1;
i = (int)(round((feat.i*image->h)/24));
j = (int)(round((feat.j*image->h)/24));
h = (int)(round((feat.h*image->h)/24));
n = 3*feat.w*feat.h;
while(x < (((int)(round(1+(3*feat.w*image->h)/24)))/3)&& 3*x<((image->h-j+1)))
{
x++;
}
w=x;
int S1,S2,S3;
a = i+h-1;
b = j+w-1;
c = j-1;
d = i-1;
e = j+2*w-1;
f = j+3*w-1;
S1 = (((a<0)|(b<0))?0:arr[a][b])-(((a<0)|(c<0))?0:arr[a][c])-(((d<0)|(b<0))?0:arr[d][b])+(((d<0)|(c<0))?0:arr[d][c]);
S2=(((a<0)|(e<0))?0:arr[a][e])-(((a<0)|(b<0))?0:arr[a][b])-(((d<0)|(e<0))?0:arr[d][e])+(((d<0)|(b<0))?0:arr[d][b]);
S3 = (((a<0)|(f<0))?0:arr[a][f])-(((a<0)|(e<0))?0:arr[a][e])-(((d<0)|(f<0))?0:arr[d][f])+(((d<0)|(e<0))?0:arr[d][e]);
return ((S1-S2+S3)*n)/(3*w*h);
}
if(feat.t==3)
{
int x=1;
i = (int)(round((feat.i*image->h)/24));
j = (int)(round((feat.j*image->h)/24));
w = (int)(round((feat.w*image->h)/24));
n = 2*feat.w*feat.h;
while(x < (((int)(round(1+(2*feat.h*image->h)/24)))/2)&& 2*x<((image->h-j+1)))
{
x++;
}
h=x;
int S1,S2;
a = i+h-1;
b = j+w-1;
c = j-1;
d = i-1;
e = i+2*h-1;
S1=(((a<0)|(b<0))?0:arr[a][b])-(((a<0)|(c<0))?0:arr[a][c])-(((d<0)|(b<0))?0:arr[d][b])+(((d<0)|(c<0))?0:arr[d][c]);
S2=(((e<0)|(b<0))?0:arr[e][b])-(((e<0)|(c<0))?0:arr[e][c])-(((a<0)|(b<0))?0:arr[a][b])+(((a<0)|(c<0))?0:arr[a][c]);
return ((S1-S2)*n)/(2*w*h);
}
if(feat.t==4)
{
int x=1;
i = (int)(round((feat.i*image->h)/24));
j = (int)(round((feat.j*image->h)/24));
w = (int)(round((feat.w*image->h)/24));
n = 3*feat.w*feat.h;
while(x < (((int)(round(1+(3*feat.h*image->h)/24)))/3)&& 3*x<((image->h-j+1)))
{
x++;
}
h=x;
int S1,S2,S3;
a = i+h-1;
b = j+w-1;
c = j-1;
d = i-1;
e = i+2*h-1;
f = i+3*h-1;
S1 = (((a<0)|(b<0))?0:arr[a][b])-(((a<0)|(c<0))?0:arr[a][c])-(((d<0)|(b<0))?0:arr[d][b])+(((d<0)|(c<0))?0:arr[d][c]);
S2=(((e<0)|(b<0))?0:arr[e][b])-(((e<0)|(c<0))?0:arr[e][c])-(((a<0)|(b<0))?0:arr[a][b])+(((a<0)|(c<0))?0:arr[a][c]);
S3=(((f<0)|(b<0))?0:arr[f][b])-(((f<0)|(c<0))?0:arr[f][c])-(((e<0)|(b<0))?0:arr[e][b])+(((e<0)|(c<0))?0:arr[e][c]);
return ((S1-S2+S3)*n)/(3*w*h);
}
if(feat.t==5)
{
int x=1;
i = (int)(round((feat.i*image->h)/24));
j = (int)(round((feat.j*image->h)/24));
n = 4*feat.w*feat.h;
while(x < (((int)(round(1+(2*feat.h*image->h)/24)))/2)&& 2*x<((image->h-j+1)))
{
x++;
}
h=x;
x=1;
while(x < (((int)(round(1+(2*feat.w*image->h)/24)))/2)&& 2*x<((image->h-j+1)))
{
x++;
}
w=x;
int S1,S2,S3,S4;
a = i+h-1;
b = j+w-1;
c = j-1;
d = i-1;
e = i+2*h-1;
f = j+2*w-1;
S1=(((a<0)|(b<0))?0:arr[a][b])-(((a<0)|(c<0))?0:arr[a][c])-(((d<0)|(b<0))?0:arr[d][b])+(((d<0)|(c<0))?0:arr[d][c]);
S2=(((e<0)|(b<0))?0:arr[e][b])-(((e<0)|(c<0))?0:arr[e][c])-(((a<0)|(b<0))?0:arr[a][b])+(((a<0)|(c<0))?0:arr[a][c]);
S3=(((a<0)|(f<0))?0:arr[a][f])-(((a<0)|(b<0))?0:arr[a][b])-(((d<0)|(f<0))?0:arr[d][f])+(((d<0)|(b<0))?0:arr[d][b]);
S4=(((e<0)|(f<0))?0:arr[e][f])-(((e<0)|(b<0))?0:arr[e][b])-(((a<0)|(f<0))?0:arr[a][f])+(((a<0)|(b<0))?0:arr[a][b]);
return ((S1-S2-S3+S4)*n)/(4*w*h);
}
return 0;
}
void Envelope::testMe()
{
double t0=0, t1=0;
SetInterpolateDB(false);
Mirror(false);
SetDefaultValue(0.5);
Flatten(0.5);
checkResult( 1, Integral(0.0,100.0), 50);
checkResult( 2, Integral(-10.0,10.0), 10);
SetDefaultValue(1.0);
Flatten(0.5);
checkResult( 3, Integral(0.0,100.0), 50);
checkResult( 4, Integral(-10.0,10.0), 10);
checkResult( 5, Integral(-20.0,-10.0), 5);
SetDefaultValue(0.5);
Flatten(0.5);
Insert( 5.0, 0.5 );
checkResult( 6, Integral(0.0,100.0), 50);
checkResult( 7, Integral(-10.0,10.0), 10);
SetDefaultValue(0.5);
Flatten(0.0);
Insert( 0.0, 0.0 );
Insert( 5.0, 1.0 );
Insert( 10.0, 0.0 );
t0 = 10.0 - .1;
t1 = 10.0 + .1;
double result = Integral(0.0,t1);
double resulta = Integral(0.0,t0);
double resultb = Integral(t0,t1);
// Integrals should be additive
checkResult( 8, result - resulta - resultb, 0);
SetDefaultValue(0.5);
Flatten(0.0);
Insert( 0.0, 0.0 );
Insert( 5.0, 1.0 );
Insert( 10.0, 0.0 );
t0 = 10.0 - .1;
t1 = 10.0 + .1;
checkResult( 9, Integral(0.0,t1), 5);
checkResult( 10, Integral(0.0,t0), 4.999);
checkResult( 11, Integral(t0,t1), .001);
WX_CLEAR_ARRAY(mEnv);
Insert( 0.0, 0.0 );
Insert( 5.0, 1.0 );
Insert( 10.0, 0.0 );
checkResult( 12, NumberOfPointsAfter( -1 ), 3 );
checkResult( 13, NumberOfPointsAfter( 0 ), 2 );
checkResult( 14, NumberOfPointsAfter( 1 ), 2 );
checkResult( 15, NumberOfPointsAfter( 5 ), 1 );
checkResult( 16, NumberOfPointsAfter( 7 ), 1 );
checkResult( 17, NumberOfPointsAfter( 10 ), 0 );
checkResult( 18, NextPointAfter( 0 ), 5 );
checkResult( 19, NextPointAfter( 5 ), 10 );
}
// This one scales the y-axis before integrating.
// To re-scale [0,1] to [minY,maxY] we use the mapping y -> minY + (maxY - minY)y
// So we want to find the integral of (minY + (maxY - minY)f(t)), where f is our envelope.
// But that's just (t1 - t0)minY + (maxY - minY)Integral( t0, t1 ).
double Envelope::Integral( double t0, double t1, double minY, double maxY )
{
return ((t1 - t0) * minY) + ((maxY - minY) * Integral( t0, t1 ));
}
void
Triangle_Processor::Process( Stack<AtomicRegion>& Offspring)
{
TimesCalled ++;
if (TimesCalled == 1)
{
TheRule->Apply(LocalIntegrand(),Geometry(),Integral(),AbsoluteError());
Offspring.MakeEmpty();
return;
};
if(TimesCalled == 2)
{
real NewVolume
= Geometry().Volume()/2;
Stack<Triangle> Parts;
Vector<unsigned int> DiffOrder(Diffs.Size());
const real difffac = real(1)/real(0.45);
const real difftreshold = 1e-3;
TheRule->ComputeDiffs(LocalIntegrand(),Geometry(),Diffs);
// Sort the differences in descending order.
for (unsigned int ik=0 ; ik<=2 ; ik++) { DiffOrder[ik] = ik; }
for (unsigned int i=0 ; i<=1 ; i++)
{
for (unsigned int k=i+1 ; k<=2 ; k++)
if (Diffs[DiffOrder[k]]>Diffs[DiffOrder[i]])
{
unsigned int h = DiffOrder[i];
DiffOrder[i] = DiffOrder[k];
DiffOrder[k] = h;
}
}
if (Diffs[DiffOrder[0]] < difftreshold)
{
TheDivisor4->Apply(Geometry(),Parts,DiffOrder);
NewVolume /=2;
}
else
{
if (Diffs[DiffOrder[0]]>difffac*Diffs[DiffOrder[2]])
{
TheDivisor2->Apply (Geometry(),Parts,DiffOrder);
}
else
{
TheDivisor4->Apply(Geometry(),Parts,DiffOrder);
NewVolume /=2;
}
};
unsigned int N = Parts.Size();
for (unsigned int ii =0;ii<N;ii++)
{
Triangle* g = Parts.Pop();
g->Volume(NewVolume);
Processor<Triangle>* p = Descendant();
Atomic<Triangle>* a = new Atomic<Triangle>(g,p);
a->LocalIntegrand(&LocalIntegrand());
Offspring.Push(a);
};
return;
};
Error(TimesCalled > 2,
"Triangle_Processor : more than two calls of Process()");
}
int main(int argc, char** argv)
{
try
{
int nx = 32;
double convTol = 1.0e-8;
double lambda = 0.5;
Sundance::setOption("nx", nx, "Number of elements");
Sundance::setOption("tol", convTol, "Convergence tolerance");
Sundance::setOption("lambda", lambda, "Lambda (parameter in Bratu's equation)");
Sundance::init(&argc, &argv);
Out::root() << "Bratu problem (lambda=" << lambda << ")" << endl;
Out::root() << "Newton's method, linearized by hand" << endl << endl;
VectorType<double> vecType = new EpetraVectorType();
MeshType meshType = new BasicSimplicialMeshType();
MeshSource mesher = new PartitionedLineMesher(0.0, 1.0, nx, meshType);
Mesh mesh = mesher.getMesh();
CellFilter interior = new MaximalCellFilter();
CellFilter sides = new DimensionalCellFilter(mesh.spatialDim()-1);
CellFilter left = sides.subset(new CoordinateValueCellPredicate(0, 0.0));
CellFilter right = sides.subset(new CoordinateValueCellPredicate(0, 1.0));
BasisFamily basis = new Lagrange(1);
Expr w = new UnknownFunction(basis, "w");
Expr v = new TestFunction(basis, "v");
Expr grad = gradient(1);
Expr x = new CoordExpr(0);
const double pi = 4.0*atan(1.0);
Expr uExact = sin(pi*x);
Expr R = pi*pi*uExact - lambda*exp(uExact);
QuadratureFamily quad4 = new GaussianQuadrature(4);
QuadratureFamily quad2 = new GaussianQuadrature(2);
DiscreteSpace discSpace(mesh, basis, vecType);
Expr uPrev = new DiscreteFunction(discSpace, 0.5);
Expr stepVal = copyDiscreteFunction(uPrev);
Expr eqn
= Integral(interior, (grad*v)*(grad*w) + (grad*v)*(grad*uPrev)
- v*lambda*exp(uPrev)*(1.0+w) - v*R, quad4);
Expr h = new CellDiameterExpr();
Expr bc = EssentialBC(left+right, v*(uPrev+w)/h, quad2);
LinearProblem prob(mesh, eqn, bc, v, w, vecType);
LinearSolver<double> linSolver
= LinearSolverBuilder::createSolver("amesos.xml");
Out::root() << "Newton iteration" << endl;
int maxIters = 20;
Expr soln ;
bool converged = false;
for (int i=0; i<maxIters; i++)
{
/* solve for the next u */
prob.solve(linSolver, stepVal);
Vector<double> stepVec = getDiscreteFunctionVector(stepVal);
double deltaU = stepVec.norm2();
Out::root() << "Iter=" << setw(3) << i << " ||Delta u||=" << setw(20)
<< deltaU << endl;
addVecToDiscreteFunction(uPrev, stepVec);
if (deltaU < convTol)
{
soln = uPrev;
converged = true;
break;
}
}
TEUCHOS_TEST_FOR_EXCEPTION(!converged, std::runtime_error,
"Newton iteration did not converge after "
<< maxIters << " iterations");
FieldWriter writer = new DSVWriter("HandCodedBratu.dat");
writer.addMesh(mesh);
writer.addField("soln", new ExprFieldWrapper(soln[0]));
writer.write();
Out::root() << "Converged!" << endl << endl;
double L2Err = L2Norm(mesh, interior, soln-uExact, quad4);
Out::root() << "L2 Norm of error: " << L2Err << endl;
Sundance::passFailTest(L2Err, 1.5/((double) nx*nx));
}
catch(std::exception& e)
{
Sundance::handleException(e);
}
Sundance::finalize();
}
bool CNBugTest()
{
/* We will do our linear algebra using Epetra */
VectorType<double> vecType = new EpetraVectorType();
MeshType meshType = new BasicSimplicialMeshType();
int nx = 1;
MeshSource mesher = new PartitionedLineMesher(0.0, 1.0, nx, meshType);
Mesh mesh = mesher.getMesh();
/* Create a cell filter that will identify the maximal cells
* in the interior of the domain */
CellFilter interior = new MaximalCellFilter();
/* Create unknown function */
BasisFamily L1=new Lagrange(1);
Expr u = new UnknownFunction(L1, "u");
Expr w = new TestFunction(L1, "w");
Expr dx = new Derivative(0);
Expr x = new CoordExpr(0);
/* We need a quadrature rule for doing the integrations */
QuadratureFamily quad = new GaussianQuadrature(4);
DiscreteSpace discSpaceL1(mesh, L1, vecType);
Expr ud = x; //proj.project();
WatchFlag watch("watch");
watch.setParam("evaluation", 5);
watch.setParam("evaluator setup", 5);
watch.setParam("discrete function evaluation", 1);
watch.setParam("integration setup", 0);
watch.setParam("symbolic preprocessing", 3);
watch.setParam("integration", 0);
Expr eqn1 = Integral(interior, w*(dx*(u+ud)), quad, watch);
Expr eqn2 = Integral(interior, w*(dx*u)+w*(dx*ud),
quad, watch);
Expr bc;
Out::root() << " @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n"
<< " @@@@@@@@@@@@@@@ creating BAD operator @@@@@@@@@@@@@\n"
<< " @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n"
<< endl;
LinearProblem prob1(mesh, eqn1, bc, w, u, vecType);
LinearOperator<double> A1 = prob1.getOperator();
Out::root() << "BAD operator = " << endl << A1 << endl << endl << endl;
Out::root() << " @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n"
<< " @@@@@@@@@@@@@@@ creating GOOD operator @@@@@@@@@@@@\n"
<< " @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n"
<< endl;
LinearProblem prob2(mesh, eqn2, bc, w, u, vecType);
LinearOperator<double> A2 = prob2.getOperator();
Out::root() << "GOOD operator = " << endl << A2 << endl;
Vector<double> b = A2.domain().createMember();
b.randomize();
Vector<double> r = A2*b - A1*b;
Out::root() << "difference in operator application = "
<< r.norm2() << endl;
double tol = 1000.0;
return SundanceGlobal::checkTest(r.norm2(), tol);
}
void FaceMasker::Run(int min_depth, int min_pixels, int open_size,
int head_width, int head_height, int head_depth,
int face_size, int extended_size,
int window_size, int width, int height,
float focal_length, const Depth *depth,
Color *color) {
width_ = width;
height_ = height;
window_size_ = window_size;
if (size_ < (width_ * height_)) {
size_ = width_ * height_;
if (valid_mask_ != NULL)
delete [] valid_mask_;
if (head_mask_ != NULL)
delete [] head_mask_;
if (depth_integral_ != NULL)
delete [] depth_integral_;
if (valid_integral_ != NULL)
delete [] valid_integral_;
if (head_integral_ != NULL)
delete [] head_integral_;
if (min_sizes_ != NULL)
delete [] min_sizes_;
if (max_sizes_ != NULL)
delete [] max_sizes_;
valid_mask_ = new bool[size_];
head_mask_ = new bool[size_];
depth_integral_ = new int[size_];
valid_integral_ = new int[size_];
head_integral_ = new int[size_];
min_sizes_ = new float[size_];
max_sizes_ = new float[size_];
}
int max_depth = (head_width * focal_length) / min_pixels;
#pragma omp parallel for
for (int i = 0; i < (width * height); i++) {
valid_mask_[i] = ((depth[i] > min_depth) && (depth[i] < max_depth)) ?
true : false;
}
Integral(width, height, true, valid_mask_, valid_integral_);
Integral(width, height, valid_mask_, depth, depth_integral_);
#pragma omp parallel for
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
int i = x + y * width;
head_mask_[i] = false;
if (valid_mask_[i]) {
int head_cols = (int)((head_width * focal_length) / depth[i]);
int head_rows = (int)((head_height * focal_length) / depth[i]);
int center_average = Mean(width, height,
x - head_cols / 2, x + head_cols / 2,
y - head_rows / 2, y + head_rows / 2,
depth_integral_, valid_integral_);
int left_average = Mean(width, height,
x - (5 * head_cols / 4), x - (3 * head_cols / 4),
y - head_rows / 2, y + head_rows / 2,
depth_integral_, valid_integral_);
int right_average = Mean(width, height,
x + (3 * head_cols / 4), x + (5 * head_cols / 4),
y - head_rows / 2, y + head_rows / 2,
depth_integral_, valid_integral_);
int top_average = Mean(width, height,
x - head_cols / 2, x + head_cols / 2,
y - (5 * head_rows / 4), y - (3 * head_rows / 4),
depth_integral_, valid_integral_);
int top_left_average = Mean(width, height,
x - (5 * head_cols / 4), x - (3 * head_cols / 4),
y - (5 * head_rows / 4), y - (3 * head_rows / 4),
depth_integral_, valid_integral_);
int top_right_average = Mean(width, height,
x + (3 * head_cols / 4), x + (5 * head_cols / 4),
y - (5 * head_rows / 4), y - (3 * head_rows / 4),
depth_integral_, valid_integral_);
int center_difference = ABS(depth[i] - center_average);
int left_difference = ABS(depth[i] - left_average);
int right_difference = ABS(depth[i] - right_average);
int top_difference = ABS(depth[i] - top_average);
int top_left_difference = ABS(depth[i] - top_left_average);
int top_right_difference = ABS(depth[i] - top_right_average);
int alpha = head_depth;
int beta = 2 * head_depth;
head_mask_[i] = ((center_difference < alpha) &&
(left_difference > beta) &&
(right_difference > beta) &&
(top_difference > beta) &&
(top_left_difference > beta) &&
(top_right_difference > beta)) ? true : false;
}
}
}
Integral(width, height, false, head_mask_, head_integral_);
Erode(width, height, open_size, head_integral_, head_mask_);
Integral(width, height, true, head_mask_, head_integral_);
Dilate(width, height, open_size, head_integral_, head_mask_);
Integral(width, height, true, head_mask_, head_integral_);
#pragma omp parallel for
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
int i = x + y * width;
min_sizes_[i] = max_sizes_[i] = 0.0f;
if (valid_mask_[i]) {
int face_pixels = (int)((face_size * focal_length) / depth[i]);
if (Sum(width, height, x - face_pixels / 2, x + face_pixels / 2,
y - face_pixels / 2, y + face_pixels / 2, head_integral_) > 0) {
int extended_pixels =(int)((extended_size * focal_length) / depth[i]);
min_sizes_[i] = face_pixels - extended_pixels;
max_sizes_[i] = face_pixels + extended_pixels;
}
}
}
}
frame_++;
scale_ = 0;
//#define HEAD_DEBUG
#ifdef HEAD_DEBUG
Mat image(height_, width_, CV_8UC3, color);
Mat head_region = Mat::zeros(height_, width_, CV_8UC3);
#pragma omp parallel for
for (int y = 0; y < height_; y++) {
for (int x = 0; x < width_; x++) {
int i = x + y * width_;
if (head_mask_[i])
head_region.at<Vec3b>(y, x) = Vec3b(255, 255, 255);
}
}
char filename[256];
sprintf(filename, "head-%d.png", frame_);
Mat output;
addWeighted(image, 0.5, head_region, 0.5, 0.0, output);
imwrite(filename, output);
scale_++;
#endif
}
int main(int argc, char** argv)
{
try
{
Sundance::init(&argc, &argv);
int np = MPIComm::world().getNProc();
int nx = 64;
const double pi = 4.0*atan(1.0);
MeshType meshType = new BasicSimplicialMeshType();
MeshSource mesher = new PartitionedLineMesher(0.0, pi, nx, meshType);
Mesh mesh = mesher.getMesh();
CellFilter interior = new MaximalCellFilter();
CellFilter bdry = new BoundaryCellFilter();
/* Create a vector space factory, used to
* specify the low-level linear algebra representation */
VectorType<double> vecType = new EpetraVectorType();
/* create symbolic coordinate functions */
Expr x = new CoordExpr(0);
/* create target function */
double R = 0.01;
Expr sx = sin(x);
Expr cx = cos(x);
Expr ssx = sin(sx);
Expr sx2 = sx*sx;
Expr cx2 = cx*cx;
Expr f = sx2 - sx - ssx;
Expr uStar = 2.0*R*(sx2-cx2) + R*sx2*ssx + sx;
/* Form exact solution */
Expr uEx = sx;
Expr lambdaEx = R*sx2;
Expr alphaEx = -lambdaEx/R;
/* create a discrete space on the mesh */
BasisFamily bas = new Lagrange(1);
DiscreteSpace discreteSpace(mesh, bas, vecType);
/* initialize the design, state, and multiplier vectors to constants */
Expr alpha0 = new DiscreteFunction(discreteSpace, 0.25, "alpha0");
Expr u0 = new DiscreteFunction(discreteSpace, 0.5, "u0");
Expr lambda0 = new DiscreteFunction(discreteSpace, 0.25, "lambda0");
/* create symbolic objects for test and unknown functions */
Expr u = new UnknownFunction(bas, "u");
Expr lambda = new UnknownFunction(bas, "lambda");
Expr alpha = new UnknownFunction(bas, "alpha");
/* create symbolic differential operators */
Expr dx = new Derivative(0);
Expr grad = dx;
/* create quadrature rules of different orders */
QuadratureFamily q1 = new GaussianQuadrature(1);
QuadratureFamily q2 = new GaussianQuadrature(2);
QuadratureFamily q4 = new GaussianQuadrature(4);
/* Form objective function */
Expr reg = Integral(interior, 0.5 * R * alpha*alpha, q2);
Expr fit = Integral(interior, 0.5 * pow(u-uStar, 2.0), q4);
Expr constraintEqn = Integral(interior,
(grad*lambda)*(grad*u) + lambda*(alpha + sin(u) + f), q4);
Expr L = reg + fit + constraintEqn;
Expr constraintBC = EssentialBC(bdry, lambda*u, q2);
Functional Lagrangian(mesh, L, constraintBC, vecType);
LinearSolver<double> adjSolver
= LinearSolverBuilder::createSolver("amesos.xml");
ParameterXMLFileReader reader("nox-amesos.xml");
ParameterList noxParams = reader.getParameters();
NOXSolver nonlinSolver(noxParams);
RCP<PDEConstrainedObjBase> obj = rcp(new NonlinearPDEConstrainedObj(
Lagrangian, u, u0, lambda, lambda0, alpha, alpha0,
nonlinSolver, adjSolver));
Vector<double> xInit = obj->getInit();
bool doFDCheck = true;
if (doFDCheck)
{
Out::root() << "Doing FD check of gradient..." << endl;
bool fdOK = obj->fdCheck(xInit, 1.0e-6, 0);
if (fdOK)
{
Out::root() << "FD check OK" << endl;
}
else
{
Out::root() << "FD check FAILED" << endl;
TEUCHOS_TEST_FOR_EXCEPT(!fdOK);
}
}
RCP<UnconstrainedOptimizerBase> opt
= OptBuilder::createOptimizer("basicLMBFGS.xml");
opt->setVerb(3);
OptState state = opt->run(obj, xInit);
if (state.status() != Opt_Converged)
{
Out::root()<< "optimization failed: " << state.status() << endl;
TEUCHOS_TEST_FOR_EXCEPT(state.status() != Opt_Converged);
}
else
{
Out::root() << "opt converged: " << state.iter() << " iterations"
<< endl;
}
FieldWriter w = new MatlabWriter("NonlinControl1D");
w.addMesh(mesh);
w.addField("u", new ExprFieldWrapper(u0));
w.addField("alpha", new ExprFieldWrapper(alpha0));
w.addField("lambda", new ExprFieldWrapper(lambda0));
w.write();
double uErr = L2Norm(mesh, interior, u0-uEx, q4);
double lamErr = L2Norm(mesh, interior, lambda0-lambdaEx, q4);
double aErr = L2Norm(mesh, interior, alpha0-alphaEx, q4);
Out::root() << "error in u = " << uErr << endl;
Out::root() << "error in lambda = " << lamErr << endl;
Out::root() << "error in alpha = " << aErr << endl;
double tol = 0.05;
Sundance::passFailTest(uErr + lamErr + aErr, tol);
}
catch(exception& e)
{
cerr << "main() caught exception: " << e.what() << endl;
}
Sundance::finalize();
return Sundance::testStatus();
}
int main(int argc, char** argv)
{
try
{
const double pi = 4.0*atan(1.0);
double lambda = 1.25*pi*pi;
int nx = 32;
int nt = 10;
double tFinal = 1.0/lambda;
Sundance::setOption("nx", nx, "Number of elements");
Sundance::setOption("nt", nt, "Number of timesteps");
Sundance::setOption("tFinal", tFinal, "Final time");
Sundance::init(&argc, &argv);
/* Creation of vector type */
VectorType<double> vecType = new EpetraVectorType();
/* Set up mesh */
MeshType meshType = new BasicSimplicialMeshType();
MeshSource meshSrc = new PartitionedRectangleMesher(
0.0, 1.0, nx,
0.0, 1.0, nx,
meshType);
Mesh mesh = meshSrc.getMesh();
/*
* Specification of cell filters
*/
CellFilter interior = new MaximalCellFilter();
CellFilter edges = new DimensionalCellFilter(1);
CellFilter west = edges.coordSubset(0, 0.0);
CellFilter east = edges.coordSubset(0, 1.0);
CellFilter south = edges.coordSubset(1, 0.0);
CellFilter north = edges.coordSubset(1, 1.0);
/* set up test and unknown functions */
BasisFamily basis = new Lagrange(1);
Expr u = new UnknownFunction(basis, "u");
Expr v = new TestFunction(basis, "v");
/* set up differential operators */
Expr grad = gradient(2);
Expr x = new CoordExpr(0);
Expr y = new CoordExpr(1);
Expr t = new Sundance::Parameter(0.0);
Expr tPrev = new Sundance::Parameter(0.0);
DiscreteSpace discSpace(mesh, basis, vecType);
Expr uExact = cos(0.5*pi*y)*sin(pi*x)*exp(-lambda*t);
L2Projector proj(discSpace, uExact);
Expr uPrev = proj.project();
/*
* We need a quadrature rule for doing the integrations
*/
QuadratureFamily quad = new GaussianQuadrature(2);
double deltaT = tFinal/nt;
Expr gWest = -pi*exp(-lambda*t)*cos(0.5*pi*y);
Expr gWestPrev = -pi*exp(-lambda*tPrev)*cos(0.5*pi*y);
/* Create the weak form */
Expr eqn = Integral(interior, v*(u-uPrev)/deltaT
+ 0.5*(grad*v)*(grad*u + grad*uPrev), quad)
+ Integral(west, -0.5*v*(gWest+gWestPrev), quad);
Expr bc = EssentialBC(east + north, v*u, quad);
LinearProblem prob(mesh, eqn, bc, v, u, vecType);
LinearSolver<double> solver
= LinearSolverBuilder::createSolver("amesos.xml");
FieldWriter w0 = new VTKWriter("TransientHeat2D-0");
w0.addMesh(mesh);
w0.addField("T", new ExprFieldWrapper(uPrev[0]));
w0.write();
for (int i=0; i<nt; i++)
{
t.setParameterValue((i+1)*deltaT);
tPrev.setParameterValue(i*deltaT);
Out::root() << "t=" << (i+1)*deltaT << endl;
Expr uNext = prob.solve(solver);
ostringstream oss;
oss << "TransientHeat2D-" << i+1;
FieldWriter w = new VTKWriter(oss.str());
w.addMesh(mesh);
w.addField("T", new ExprFieldWrapper(uNext[0]));
w.write();
updateDiscreteFunction(uNext, uPrev);
}
double err = L2Norm(mesh, interior, uExact-uPrev, quad);
Out::root() << "error norm=" << err << endl;
double h = 1.0/(nx-1.0);
double tol = 0.1*(pow(h,2.0) + pow(lambda*deltaT, 2.0));
Out::root() << "tol=" << tol << endl;
Sundance::passFailTest(err, tol);
}
catch(std::exception& e)
{
Sundance::handleException(e);
}
Sundance::finalize();
return Sundance::testStatus();
}
int main(int argc, char** argv)
{
try
{
Sundance::init(&argc, &argv);
int np = MPIComm::world().getNProc();
/* We will do our linear algebra using Epetra */
VectorType<double> vecType = new EpetraVectorType();
/* Create a mesh. It will be of type BasisSimplicialMesh, and will
* be built using a PartitionedLineMesher. */
MeshType meshType = new BasicSimplicialMeshType();
MeshSource mesher = new PartitionedLineMesher(0.0, 1.0, 1*np, meshType);
Mesh mesh = mesher.getMesh();
/* Create a cell filter that will identify the maximal cells
* in the interior of the domain */
CellFilter interior = new MaximalCellFilter();
/* Create the Spectral Basis */
int ndim = 1;
int order = 2;
SpectralBasis sbasis = new HermiteSpectralBasis(ndim, order);
/* Create unknown and test functions, discretized using first-order
* Lagrange interpolants */
Expr u = new UnknownFunction(new Lagrange(1), sbasis, "u");
Expr v = new TestFunction(new Lagrange(1), sbasis, "v");
/* Create the stochastic input function. */
Expr a0 = new Sundance::Parameter(1.0);
Expr a1 = new Sundance::Parameter(0.1);
Expr a2 = new Sundance::Parameter(0.01);
Expr alpha = new SpectralExpr(sbasis, tuple(a0, a1, a2));
/* Create a discrete space, and discretize the function 1.0 on it */
cout << "forming discrete space" << std::endl;
DiscreteSpace discSpace(mesh, new Lagrange(1), sbasis, vecType);
cout << "forming discrete func" << std::endl;
Expr u0 = new DiscreteFunction(discSpace, 0.5, "u0");
/* We need a quadrature rule for doing the integrations */
QuadratureFamily quad = new GaussianQuadrature(2);
/* Now we set up the weak form of our equation. */
Expr eqn = Integral(interior, v*(u*u-alpha), quad);
cout << "equation = " << eqn << std::endl;
/* There are no boundary conditions for this problem, so the
* BC expression is empty */
Expr bc;
/* We can now set up the nonlinear problem! */
NonlinearProblem prob(mesh, eqn, bc, v, u, u0, vecType);
#ifdef HAVE_CONFIG_H
ParameterXMLFileReader reader(searchForFile("SolverParameters/nox.xml"));
#else
ParameterXMLFileReader reader("nox.xml");
#endif
ParameterList noxParams = reader.getParameters();
std::cerr << "solver params = " << noxParams << std::endl;
NOXSolver solver(noxParams);
prob.solve(solver);
/* Inspect solution values. The solution is constant in space,
* so we can simply take the first NTerms entries in the vector */
Vector<double> vec = DiscreteFunction::discFunc(u0)->getVector();
int k=0;
for (SequentialIterator<double> i=vec.space().begin(); i!=vec.space().end(); i++, k++)
{
cout << "u[" << k << "] = " << vec[i] << std::endl;
}
double tol = 1.0e-12;
double errorSq = 0.0;
Sundance::passFailTest(errorSq, tol);
}
catch(std::exception& e)
{
std::cerr << e.what() << std::endl;
}
Sundance::finalize(); return Sundance::testStatus();
}
int main(int argc, char** argv)
{
try
{
Sundance::init(&argc, &argv);
int np = MPIComm::world().getNProc();
int nx = 48;
int ny = 48;
int npx = -1;
int npy = -1;
PartitionedRectangleMesher::balanceXY(np, &npx, &npy);
TEUCHOS_TEST_FOR_EXCEPT(npx < 1);
TEUCHOS_TEST_FOR_EXCEPT(npy < 1);
TEUCHOS_TEST_FOR_EXCEPT(npx * npy != np);
MeshType meshType = new BasicSimplicialMeshType();
MeshSource mesher = new PartitionedRectangleMesher(0.0, 1.0, nx, npx,
0.0, 1.0, ny, npy, meshType);
Mesh mesh = mesher.getMesh();
CellFilter interior = new MaximalCellFilter();
CellFilter bdry = new BoundaryCellFilter();
/* Create a vector space factory, used to
* specify the low-level linear algebra representation */
VectorType<double> vecType = new EpetraVectorType();
/* create a discrete space on the mesh */
DiscreteSpace discreteSpace(mesh, new Lagrange(1), vecType);
/* initialize the design, state, and multiplier vectors */
Expr alpha0 = new DiscreteFunction(discreteSpace, 1.0, "alpha0");
Expr u0 = new DiscreteFunction(discreteSpace, 1.0, "u0");
Expr lambda0 = new DiscreteFunction(discreteSpace, 1.0, "lambda0");
/* create symbolic objects for test and unknown functions */
Expr u = new UnknownFunction(new Lagrange(1), "u");
Expr lambda = new UnknownFunction(new Lagrange(1), "lambda");
Expr alpha = new UnknownFunction(new Lagrange(1), "alpha");
/* create symbolic differential operators */
Expr dx = new Derivative(0);
Expr dy = new Derivative(1);
Expr grad = List(dx, dy);
/* create symbolic coordinate functions */
Expr x = new CoordExpr(0);
Expr y = new CoordExpr(1);
/* create target function */
const double pi = 4.0*atan(1.0);
Expr uStar = sin(pi*x)*sin(pi*y);
/* create quadrature rules of different orders */
QuadratureFamily q1 = new GaussianQuadrature(1);
QuadratureFamily q2 = new GaussianQuadrature(2);
QuadratureFamily q4 = new GaussianQuadrature(4);
/* Regularization weight */
double R = 0.001;
double U0 = 1.0/(1.0 + 4.0*pow(pi,4.0)*R);
double A0 = -2.0*pi*pi*U0;
/* Form objective function */
Expr reg = Integral(interior, 0.5 * R * alpha*alpha, q2);
Expr fit = Integral(interior, 0.5 * pow(u-uStar, 2.0), q4);
Expr constraintEqn = Integral(interior,
(grad*lambda)*(grad*u) + lambda*alpha, q2);
Expr L = reg + fit + constraintEqn;
Expr constraintBC = EssentialBC(bdry, lambda*u, q2);
Functional Lagrangian(mesh, L, constraintBC, vecType);
LinearSolver<double> solver
= LinearSolverBuilder::createSolver("amesos.xml");
RCP<ObjectiveBase> obj = rcp(new LinearPDEConstrainedObj(
Lagrangian, u, u0, lambda, lambda0, alpha, alpha0,
solver));
Vector<double> xInit = obj->getInit();
bool doFDCheck = false;
if (doFDCheck)
{
Out::root() << "Doing FD check of gradient..." << endl;
bool fdOK = obj->fdCheck(xInit, 1.0e-6, 2);
if (fdOK)
{
Out::root() << "FD check OK" << endl;
}
else
{
Out::root() << "FD check FAILED" << endl;
}
}
RCP<UnconstrainedOptimizerBase> opt
= OptBuilder::createOptimizer("basicLMBFGS.xml");
opt->setVerb(2);
OptState state = opt->run(obj, xInit);
bool ok = true;
if (state.status() != Opt_Converged)
{
Out::root()<< "optimization failed: " << state.status() << endl;
TEUCHOS_TEST_FOR_EXCEPT(state.status() != Opt_Converged);
}
Out::root() << "opt converged: " << state.iter() << " iterations"
<< endl;
Out::root() << "exact solution: U0=" << U0 << " A0=" << A0 << endl;
FieldWriter w = new VTKWriter("PoissonSourceInversion");
w.addMesh(mesh);
w.addField("u", new ExprFieldWrapper(u0));
w.addField("alpha", new ExprFieldWrapper(alpha0));
w.addField("lambda", new ExprFieldWrapper(lambda0));
w.write();
double uErr = L2Norm(mesh, interior, u0-U0*uStar, q4);
double aErr = L2Norm(mesh, interior, alpha0-A0*uStar, q4);
Out::root() << "error in u = " << uErr << endl;
Out::root() << "error in alpha = " << aErr << endl;
double tol = 0.01;
Sundance::passFailTest(uErr + aErr, tol);
}
catch(std::exception& e)
{
cerr << "main() caught exception: " << e.what() << endl;
}
Sundance::finalize();
return Sundance::testStatus();
}
int main(int argc, char** argv)
{
try
{
Sundance::init(&argc, &argv);
int np = MPIComm::world().getNProc();
// DOFMapBase::classVerbosity() = VerbExtreme;
const double density = 1000.0; // kg/m^3
const double porosity = 0.442; // dimensionless %
const double A = 175.5; // dimensionless fit parameter
const double B = 1.83; // dimensionless fit parameter
const double criticalRe = 36.73; // dimensionless fit parameter
const double dynvisc = 1.31; // kg/(m-s)
const double graindia = 1.9996e-4; // m
const double charvel = 1.0; // m/s
double Reynolds = density*graindia*charvel/(dynvisc*porosity);
Expr Re = new Parameter(Reynolds);
/* We will do our linear algebra using Epetra */
VectorType<double> vecType = new EpetraVectorType();
/* Create a mesh. It will be of type BasisSimplicialMesh, and will
* be built using a PartitionedLineMesher. */
MeshType meshType = new BasicSimplicialMeshType();
MeshSource mesher = new PartitionedLineMesher(0.0, 1000.0, 100*np, meshType);
Mesh mesh = mesher.getMesh();
/* Create a cell filter that will identify the maximal cells
* in the interior of the domain */
CellFilter interior = new MaximalCellFilter();
CellFilter points = new DimensionalCellFilter(0);
CellPredicate leftPointFunc = new PositionalCellPredicate(leftPointTest);
CellPredicate rightPointFunc = new PositionalCellPredicate(rightPointTest);
CellFilter leftPoint = points.subset(leftPointFunc);
CellFilter rightPoint = points.subset(rightPointFunc);
/* Create unknown and test functions, discretized using first-order
* Lagrange interpolants */
Expr p = new UnknownFunction(new Lagrange(2), "p");
Expr q = new UnknownFunction(new Lagrange(2), "q");
Expr u = new TestFunction(new Lagrange(2), "u");
Expr v = new TestFunction(new Lagrange(2), "v");
/* Create differential operator and coordinate function */
Expr dx = new Derivative(0);
Expr x = new CoordExpr(0);
/* We need a quadrature rule for doing the integrations */
QuadratureFamily quad = new GaussianQuadrature(4);
/* Define the weak form */
Expr MassEqn = Integral(interior, q*(dx*u), quad)
+ Integral(leftPoint, - q*u,quad)
+ Integral(rightPoint, - q*u,quad);
Expr MomEqn = Integral(interior, (density/porosity)*q*q*(dx*v) + porosity*p*(dx*v) - porosity*q*v*A - (porosity*q*v*B*Re*Re)/((Re+criticalRe)*(1-porosity)), quad)
+ Integral(leftPoint, - density*q*q*v/porosity - porosity*p*v,quad)
+ Integral(rightPoint,- density*q*q*v/porosity - porosity*p*v,quad);
/* Define the Dirichlet BC */
Expr leftbc = EssentialBC(leftPoint, v*(q-charvel), quad);
Expr rightbc = EssentialBC(rightPoint, v*(q-charvel), quad);
/* Create a discrete space, and discretize the function 1.0 on it */
BasisFamily L2 = new Lagrange(2);
Array<BasisFamily> basis = tuple(L2, L2);
DiscreteSpace discSpace(mesh, basis, vecType);
Expr u0 = new DiscreteFunction(discSpace, 1.0, "u0");
Expr p0 = u0[0];
Expr q0 = u0[1];
/* Create a TSF NonlinearOperator object */
std::cerr << "about to make nonlinear object" << std::endl;
std::cerr.flush();
NonlinearOperator<double> F
= new NonlinearProblem(mesh, MassEqn+MomEqn, leftbc+rightbc, Sundance::List(u,v),Sundance::List(p,q) , u0, vecType);
// F.verbosity() = VerbExtreme;
/* Get the initial guess */
Vector<double> x0 = F.getInitialGuess();
/* Create an Aztec solver for solving the linear subproblems */
std::map<int,int> azOptions;
std::map<int,double> azParams;
azOptions[AZ_solver] = AZ_gmres;
azOptions[AZ_precond] = AZ_dom_decomp;
azOptions[AZ_subdomain_solve] = AZ_ilu;
azOptions[AZ_graph_fill] = 1;
azOptions[AZ_max_iter] = 1000;
azParams[AZ_tol] = 1.0e-13;
LinearSolver<double> linSolver = new AztecSolver(azOptions,azParams);
/* Now let's create a NOX solver */
NOX::TSF::Group grp(x0, F, linSolver);
grp.verbosity() = VerbExtreme;
// Set up the status tests
NOX::StatusTest::NormF statusTestA(grp, 1.0e-10);
NOX::StatusTest::MaxIters statusTestB(20);
NOX::StatusTest::Combo statusTestsCombo(NOX::StatusTest::Combo::OR, statusTestA, statusTestB);
// Create the list of solver parameters
NOX::Parameter::List solverParameters;
// Set the solver (this is the default)
solverParameters.setParameter("Nonlinear Solver", "Line Search Based");
// Create the line search parameters sublist
NOX::Parameter::List& lineSearchParameters = solverParameters.sublist("Line Search");
// Set the line search method
lineSearchParameters.setParameter("Method","More'-Thuente");
// Create the solver
NOX::Solver::Manager solver(grp, statusTestsCombo, solverParameters);
// Solve the nonlinear system
NOX::StatusTest::StatusType status = solver.solve();
// Print the answer
cout << "\n" << "-- Parameter List From Solver --" << "\n";
solver.getParameterList().print(cout);
// Get the answer
grp = solver.getSolutionGroup();
// Print the answer
cout << "\n" << "-- Final Solution From Solver --" << "\n";
grp.print();
double tol = 1.0e-12;
Sundance::passFailTest(0, tol);
}
catch(std::exception& e)
{
Sundance::handleException(e);
}
Sundance::finalize(); return Sundance::testStatus();
}
double Parameters::Mean(double time1, double time2) const
{
double total = Integral(time1,time2);
return total/(time2-time1);
}
void detectAndDisplay(IplImage *frame)
{
IplImage *frame_gray;
int ForCount1, ForCount2;
ForCount1 = ForCount2 = 0;
memset(pointXY, 0, sizeof(char)*column*row);
frame_gray = cvCreateImage( cvGetSize(frame),IPL_DEPTH_8U,1);
//frame_gray = cvCreateImage( cvSize(column, row),IPL_DEPTH_8U,1);
dst = cvCreateImage(cvGetSize(frame),IPL_DEPTH_8U,1);
//dst = cvCreateImage(cvSize(column,row),IPL_DEPTH_8U,1);
#ifdef showXY
showSumX = cvCreateImage(cvGetSize(frame),IPL_DEPTH_8U,1);
showSumY = cvCreateImage(cvSize(row,column),IPL_DEPTH_8U,1);
#endif
cvCvtColor(frame, frame_gray, CV_BGR2GRAY);
//cvCanny(frame_gray, dst, 40, 40*3, 3);
cvThreshold(frame_gray, dst, Threshold, 255, CV_THRESH_BINARY);
for(ForCount1 = 0; ForCount1 < column; ForCount1++)
{
for(ForCount2 = 0; ForCount2 < row; ForCount2++)
{
CvScalar s = cvGet2D(dst,ForCount2,ForCount1);
//char s = ((uchar *)(dst->imageData + ForCount1*dst->widthStep))[ForCount2];
#ifdef Detail
printf("%3d %3d %f\n",ForCount1,ForCount2, s.val[0]);
#endif
/*if( s.val[0] <= Threshold)
{
pointXY[ForCount1][ForCount2] = 1;
}*/
if(s.val[0] <= Threshold)
{
pointXY[ForCount1][ForCount2] = 0;
}
else
{
pointXY[ForCount1][ForCount2] = 1;
}
}
}
Integral(row);
Integral(column);
Error1();
Error2();
cvShowImage("Webcam",dst);
cvShowImage("Webcam1",frame_gray);
#ifdef Detail
for(ForCount1 = 0; ForCount1 < column; ForCount1++)
{
printf("x[%3d]:%d\n", ForCount1, SumX[ForCount1]);
}
printf("\n");
for(ForCount1 = 0; ForCount1 < row; ForCount1++)
{
printf("y[%3d]:%d\n", ForCount1, SumY[ForCount1]);
}
printf("\n");
#endif
#ifdef showXY
for(ForCount1 = 0; ForCount1 < column; ForCount1++)
{
for(ForCount2 = 0; ForCount2 < (int)SumX[ForCount1]; ForCount2++)
{
CvScalar s = cvGet2D(showSumX,ForCount2,ForCount1);
s.val[0] = 255;
cvSet2D(showSumX, ForCount2, ForCount1, s);
}
}
cvShowImage("SumX", showSumX);
for(ForCount1 = 0; ForCount1 < row; ForCount1++)
{
for(ForCount2 = 0; ForCount2 < (int)SumY[ForCount1]; ForCount2++)
{
CvScalar s = cvGet2D(showSumY,ForCount2,ForCount1);
s.val[0] = 255;
cvSet2D(showSumY, ForCount2, ForCount1, s);
}
}
#endif
//cvShowImage("SumY", showSumY);
cvReleaseImage( &dst );
cvReleaseImage( &frame_gray );
#ifdef showXY
cvReleaseImage( &showSumX );
cvReleaseImage( &showSumY );
#endif
}
