From ad6c6800fabd81fd0842d068aa362aeb84afcf12 Mon Sep 17 00:00:00 2001
From: "J.A. de Jong @ vulgaris" <anne@amdj.nl>
Date: Sun, 22 Feb 2015 09:54:36 +0100
Subject: [PATCH] Still not working

---
 src/solutioninstance.h |   2 +-
 src/tube.cpp           |  20 +++---
 src/tube.h             |   2 +-
 timedomain.i           |  32 ++++++---
 timedomain.ipynb       | 149 +++++++++++++++++++++--------------------
 5 files changed, 113 insertions(+), 92 deletions(-)

diff --git a/src/solutioninstance.h b/src/solutioninstance.h
index 055186d..85fa8e5 100644
--- a/src/solutioninstance.h
+++ b/src/solutioninstance.h
@@ -21,7 +21,7 @@ namespace td{
     vd rho_,m_,rhoE_;
     d time=0;
   public:
-    SolutionInstance(int gp,d rho=1.2);
+    SolutionInstance(int gp,d rho=0);
     ~SolutionInstance(){}
     d getTime() const {return time;}
     void setTime(d t) {time=t;}
diff --git a/src/tube.cpp b/src/tube.cpp
index 8d02bed..079fa52 100644
--- a/src/tube.cpp
+++ b/src/tube.cpp
@@ -9,7 +9,7 @@ d Tube::pleft(d t) {
   d pleft = std::sin(gc.getomg() * t);
   // cout << "t:" << t << endl;
   // cout << "omg:" << gc.getomg() << endl;
-  cout << "pleft:"<< pleft<<endl;
+  // cout << "pleft:"<< pleft<<endl;
   return pleft;
 }
   Tube::Tube(d L, int gp) throw(int) : L(L), gp(gp), sol(SolutionInstance(gp)) {
@@ -49,31 +49,31 @@ void TubeLF::Integrate(d dt) {
   vd Mflux=sol.Mflux();
   vd Eflux=sol.Eflux();
 
-  // std::cout << "poL" << sol.p()(0) << std::endl;
   {  // Left boundary
     d la = dt/dx;
     rho(0) = oldrho(0);
     rho(0)+=-la*(Cflux(1)-Cflux(0));
     d oldu0=oldm(0)/oldrho(0);
     // std::cout << "oldu:"<< oldu0 << std::endl;
-    d momfluxl = oldrho(0)*pow(oldu0, 2) + pleft(t);
+    d momfluxl = pow(oldm(0), 2)/oldrho(0) + pleft(t);
     m(0) = oldm(0);
     m(0)+=-la*(Mflux(1)-momfluxl);
     rhoE(0) = oldrhoE(0);
     rhoE(0)+=-la*(Eflux(1)-Eflux(0));
-
   } //  End left boundary
+
   {				// Inner nodes
     d lambda = dt/(2*dx);
-    rho.subvec(1,gp-2)=0.5*(oldrho.subvec(0,gp-3) + oldrho.subvec(2,gp-1));
-    rho.subvec(1,gp-2)+=-lambda*(Cflux.subvec(0,gp-3) -Cflux.subvec(2,gp-1));
+    rho.subvec(1,gp-2)=0.5*(oldrho.head(gp-2) + oldrho.tail(gp-2));
+    rho.subvec(1,gp-2)+=-lambda*(Cflux.head(gp-2) -Cflux.tail(gp-2));
   
-    m.subvec(1,gp-2)=0.5*(oldm.subvec(0,gp-3) + oldm.subvec(2,gp-1));
-    m.subvec(1,gp-2)+=-lambda*(Mflux.subvec(0,gp-3) -Mflux.subvec(2,gp-1));
+    m.subvec(1,gp-2)=0.5*(oldm.head(gp-2) + oldm.tail(gp-2));
+    m.subvec(1,gp-2)+=-lambda*(Mflux.head(gp-2) -Mflux.tail(gp-2));
 
-    rhoE.subvec(1,gp-2)=0.5*(oldrhoE.subvec(0,gp-3) + oldrhoE.subvec(2,gp-1));
-    rhoE.subvec(1,gp-2)+=-lambda*(Eflux.subvec(0,gp-3) -Eflux.subvec(2,gp-1));
+    rhoE.subvec(1,gp-2)=0.5*(oldrhoE.head(gp-2) + oldrhoE.tail(gp-2));
+    rhoE.subvec(1,gp-2)+=-lambda*(Eflux.head(gp-2) -Eflux.tail(gp-2));
   } // End inner nodes
+
   {				// Right boundary
     int i = gp - 1;
     d la = dt/dx;
diff --git a/src/tube.h b/src/tube.h
index 21f2baa..023c97a 100644
--- a/src/tube.h
+++ b/src/tube.h
@@ -17,7 +17,7 @@ namespace td {
   typedef double d;
 
   class Tube {
-  protected:
+  public:
     d dx, L;              // Grid spacing, total length
     int gp;               // Number of gridpoints
     SolutionInstance sol; // Solutions at time instances
diff --git a/timedomain.i b/timedomain.i
index d5d732d..2759237 100644
--- a/timedomain.i
+++ b/timedomain.i
@@ -43,6 +43,10 @@ class vd{
 %typemap(out) vd {
   $result=npy_from_vd($1);
 }
+%typemap(out) vd& {
+  const vd& res=*$1;
+  $result=npy_from_vd(res);
+}
 typedef double d;
 typedef unsigned us;
 
@@ -98,24 +102,34 @@ namespace td{
   public:
     SolutionInstance(int gp,d rho=1.2);
     ~SolutionInstance(){}
-    vd rho() const;
     vd p() const;
     vd u() const;
+    const vd& rho() const;
+    const vd& m() const;
+    const vd& rhoE() const;
+
     void setTime(d t);
     void setrho(d rho);
   };
 
   tasystem::Globalconf gc;
 
-  class Tube{
-    virtual void Integrate(d dt)=0;
+  class Tube {
   public:
-    Tube(double L,int gp) throw(int);
-    virtual ~Tube();
-    SolutionInstance& getSol();
-    void setSol(const SolutionInstance& sol);
-    void DoIntegration(d dt,int n=1);
-    d getTime();
+    d dx, L;              // Grid spacing, total length
+    int gp;               // Number of gridpoints
+    SolutionInstance sol; // Solutions at time instances
+    d t = 0;              // Current time
+    d pleft(d t);         // Compute pressure bc
+    virtual void Integrate(d dt) = 0;
+
+  public:
+    Tube(double L, int gp) throw(int);
+    virtual ~Tube() {}
+    SolutionInstance &getSol();
+    void setSol(const SolutionInstance &sol);
+    void DoIntegration(d dt, int n = 1);
+    d getTime() { return t; }
   };
   class TubeLF:public Tube{
     virtual void Integrate(d dt);
diff --git a/timedomain.ipynb b/timedomain.ipynb
index 2b3144e..095904d 100644
--- a/timedomain.ipynb
+++ b/timedomain.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:f48effc45d7e0ce53ecece3d2b83f5c45c2f29c363f0233fd7f2579385deda58"
+  "signature": "sha256:2aa6562ce56a22699d07327912ff516ed48493f67bbddc1babb0722b0ee1a0ff"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -28,11 +28,11 @@
       "f=85.785\n",
       "T=1/f\n",
       "loglevel=20\n",
-      "L=1.0\n",
-      "gp=400\n",
+      "L=1e-2\n",
+      "gp=10\n",
       "gc=cvar.gc #Reference!\n",
       "dx=L/(gp-1);                    # One left and right gp, so\n",
-      "CFL=0.01;\n",
+      "CFL=0.1;\n",
       "gc.setfreq(f)\n",
       "tube=TubeLF(L,gp)\n",
       "dt=min(CFL*dx/gc.c0(),T/50)"
@@ -42,46 +42,6 @@
      "outputs": [],
      "prompt_number": 2
     },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "7.303933869608436e-08"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "gc.c0()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "343.1391506142924"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -92,18 +52,18 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "intsteps=100"
+      "intsteps=1000"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -118,28 +78,76 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "DoIntegration relative time step of one period: 6.27e-06\n"
+        "DoIntegration relative time step of one period: 2.78e-05\n"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 5
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "tube.DoIntegration(dt,intsteps)\n",
+      "tube.gp"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "10"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "tube.DoIntegration(dt,1)\n",
       "sol=tube.getSol()\n",
       "u=sol.u()\n",
       "p=sol.p()\n",
-      "rho=sol.rho()\n",
+      "#rho=sol.rho()\n",
       "figure(figsize=(9,6))\n",
       "subplot(221)\n",
       "plot(p)\n",
       "subplot(222)\n",
       "plot(u)\n",
       "subplot(223)\n",
-      "plot(rho)"
+      "#plot(rho)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a6779fac8>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAF2CAYAAABkjTxiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8/8BoPUqgBBhb9gAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f8a721c74e0>"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "rho"
      ],
      "language": "python",
      "metadata": {},
@@ -149,15 +157,7 @@
        "output_type": "pyout",
        "prompt_number": 10,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x7f338420d828>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAFwCAYAAABeobppAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f3384430a90>"
+        "<timedomaineuler.vd; proxy of <Swig Object of type 'vd *' at 0x7f8a724e5e10> >"
        ]
       }
      ],
@@ -166,18 +166,25 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": [],
+     "input": [
+      "p"
+     ],
      "language": "python",
      "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "array([ -4.29085417e-16,   0.00000000e+00,   0.00000000e+00,\n",
+        "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
+        "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
+        "         0.00000000e+00])"
+       ]
+      }
+     ],
+     "prompt_number": 9
     },
     {
      "cell_type": "code",