diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9743c45..c4a30a3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -14,8 +14,7 @@ SET(CMAKE_SWIG_FLAGS "-Wall")
 add_definitions(-DNDEBUG)	#This is for BOOST functions, and to disable assertions!
 # add_definitions(-DTRACER=0)
 add_definitions(-DEIGEN_NO_DEBUG -DARMA_NO_DEBUG)
-
-add_definitions(-DTRACERNAME=timedomaineulertracer -DARMA_USE_BLAS -DARMA_USE_LAPACK)
+add_definitions(-DTRACERNAME=timedomaineulertracer)
 
 ############################## Not so often changed
 
@@ -53,8 +52,6 @@ link_directories(/home/anne/bin/lib)
 add_library(sources ${src})
 set_source_files_properties(timedomain.i PROPERTIES CPLUSPLUS ON)
 set_source_files_properties(timedomain.i PROPERTIES SWIG_FLAGS "-py3")
-set_source_files_properties(timedomain.i PROPERTIES SWIG_FLAGS "-Wextra")
-set_source_files_properties(timedomain.i PROPERTIES SWIG_FLAGS "-includeall")
 swig_add_module(timedomaineuler python timedomain.i)
 swig_link_libraries(timedomaineuler sources nonlin armadillo ${PYTHON_LIBRARIES})
 
diff --git a/Plot data.ipynb b/Plot data.ipynb
deleted file mode 100644
index 697d32f..0000000
--- a/Plot data.ipynb	
+++ /dev/null
@@ -1,75 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:cf9769ed030dba90588e4e94007d226dd2897d23c8792fd8974c73bb04381b2c"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from tmtubes import gas\n",
-      "air=gas('air')\n",
-      "L=1.\n",
-      "c0=air.cm(293.15)\n",
-      "fres=c0/(4*L)\n",
-      "print(\"fres:\",fres)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "fres: 85.7847876535731\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plot(x,u)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x7f6c683d7278>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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GkKhMflSmbgrHfo277wZ2R49Xmtm7QEdgZfmy48eP/+pxYWEhhYWFCT+IiJSqUwcefBDG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\nQkREklKyEBGRpJQsREQkqf8HcogznVYp2ycAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f6c68d6a080>"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/src/tube.cpp b/src/tube.cpp
index 079fa52..7841160 100644
--- a/src/tube.cpp
+++ b/src/tube.cpp
@@ -65,13 +65,13 @@ void TubeLF::Integrate(d dt) {
   {				// Inner nodes
     d lambda = dt/(2*dx);
     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));
+    rho.subvec(1,gp-2)+=-lambda*(Cflux.tail(gp-2) -Cflux.head(gp-2));
   
     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));
+    m.subvec(1,gp-2)+=-lambda*(Mflux.tail(gp-2) -Mflux.head(gp-2));
 
     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));
+    rhoE.subvec(1,gp-2)+=-lambda*(Eflux.tail(gp-2) -Eflux.head(gp-2));
   } // End inner nodes
 
   {				// Right boundary
diff --git a/timedomain.ipynb b/timedomain.ipynb
index 095904d..869d4e9 100644
--- a/timedomain.ipynb
+++ b/timedomain.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:2aa6562ce56a22699d07327912ff516ed48493f67bbddc1babb0722b0ee1a0ff"
+  "signature": "sha256:4b695d53d8ce9715401825830b83ae0060692d114cf9b5b8e49402b7bbd96c0a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -17,8 +17,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -28,176 +27,45 @@
       "f=85.785\n",
       "T=1/f\n",
       "loglevel=20\n",
-      "L=1e-2\n",
-      "gp=10\n",
+      "L=1.\n",
+      "gp=300\n",
       "gc=cvar.gc #Reference!\n",
       "dx=L/(gp-1);                    # One left and right gp, so\n",
-      "CFL=0.1;\n",
+      "CFL=0.5;\n",
       "gc.setfreq(f)\n",
       "tube=TubeLF(L,gp)\n",
       "dt=min(CFL*dx/gc.c0(),T/50)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "nr_p_period=1\n",
-      "intsteps=int(floor(1./(gc.getfreq()*dt)/nr_p_period))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "intsteps=1000"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(\"DoIntegration relative time step of one period: %0.2e\" %(dt*gc.getfreq()))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "DoIntegration relative time step of one period: 2.78e-05\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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",
-      "figure(figsize=(9,6))\n",
-      "subplot(221)\n",
-      "plot(p)\n",
-      "subplot(222)\n",
-      "plot(u)\n",
-      "subplot(223)\n",
-      "#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": 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19J73ZOuSvPlm+XVt3Qrnnw+XXAJve1v59ZlZ/+OeI7PqcHJUQ0OGwCGHwPLl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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": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "<timedomaineuler.vd; proxy of <Swig Object of type 'vd *' at 0x7f8a724e5e10> >"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "p"
-     ],
-     "language": "python",
-     "metadata": {},
-     "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",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": [],
+     "input": [
+      "nr_p_period=20\n",
+      "intsteps=int(floor(1./(gc.getfreq()*dt)/nr_p_period))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "tube.DoIntegration(dt,intsteps)\n",
+      "sol=tube.getSol()\n",
+      "u=sol.u()\n",
+      "p=sol.p()\n",
+      "#rho=sol.rho()\n",
+      "figure(figsize=(9,3))\n",
+      "subplot(121)\n",
+      "plot(p)\n",
+      "subplot(122)\n",
+      "plot(u)"
+     ],
      "language": "python",
      "metadata": {},
      "outputs": []