diff --git a/harmpi_materials/BHL.ipynb b/harmpi_materials/BHL.ipynb
new file mode 100755
index 0000000..669e7f2
--- /dev/null
+++ b/harmpi_materials/BHL.ipynb
@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bondi-Hoyle-Lyttleton"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import harm_script as harm\n",
+    "# -i option: run the script in Jupyter's namespace\n",
+    "%run -i harm_script.py   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# change the directory below to your own, which contains \"dumps\" subfolder\n",
+    "os.chdir(\"/Users/astrodoo/Work/WorkShop/2019.07.15_ATA_SummerSchool@UvA/harmpi_output/BHL\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 700.039124 (R_g/c)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\" Read the data \"\"\"\n",
+    "dumpn = 70\n",
+    "\n",
+    "rg(\"gdump\")\n",
+    "rd(\"dump\"+\"%3.3d\"%dumpn)\n",
+    "print(\"time: %f (R_g/c)\"%t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\" compute the coordinates in Cartesian \"\"\"\n",
+    "ph[...] = 0.\n",
+    "cart_coords()\n",
+    "velocity_cart()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig,ax = plt.subplots(figsize=(6,7))\n",
+    "xmax=100  \n",
+    "ymax=100\n",
+    "\n",
+    "plc(np.log10(rho),xy=1,xmax=xmax,ymax=ymax,levels=np.linspace(-0.5,2.,100,endpoint=False), \\\n",
+    "    xla=r'$R\\, [r_g]$',yla=r'$z\\, [r_g]$',cbyla=r'$\\rho$',\n",
+    "    cb=True,isfilled=True,ax=ax,cmap='jet',extend='both',symmx=False)\n",
+    "\n",
+    "skip1 = 10 # reduce the number of arrows\n",
+    "skip2 = 10 # reduce the number of arrows\n",
+    "ax.quiver(X[1,::skip1,::skip2,0],X[3,::skip1,::skip2,0], \\\n",
+    "          uu_cart[1,::skip1,::skip2,0]/np.sqrt(uu_cart[1,::skip1,::skip2,0]**2+uu_cart[3,::skip1,::skip2,0]**2), \\\n",
+    "          uu_cart[3,::skip1,::skip2,0]/np.sqrt(uu_cart[1,::skip1,::skip2,0]**2+uu_cart[3,::skip1,::skip2,0]**2), \\\n",
+    "          scale=15,width=0.005)\n",
+    "\n",
+    "ax.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Draw the contour with stream line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\" Since \"matplot.streamplot\" is requried to input the data in uniform grid, \n",
+    "    the sample of (x,y,ux,uy) is needed to be interpolated from the original data-set \"\"\"\n",
+    "\n",
+    "from scipy.ndimage import map_coordinates as mapcd\n",
+    "\n",
+    "# set the parameters for data sample\n",
+    "rmax = 110   # maximum radius for the stream line\n",
+    "nx = 10      # x sample size\n",
+    "ny = 20      # y sample size\n",
+    "unix = np.linspace(0,rmax,nx)\n",
+    "uniy = np.linspace(-rmax,rmax,ny)\n",
+    "\n",
+    "# generate the grid in 2D\n",
+    "x2d,y2d = np.meshgrid(unix,uniy,indexing='ij')\n",
+    "\n",
+    "# convert to spherical coordinates\n",
+    "r2d = np.sqrt(x2d*x2d + y2d*y2d)\n",
+    "h2d = np.arccos(y2d/r2d)\n",
+    "\n",
+    "# find the indices of the grid in the data-sample\n",
+    "iind_int = np.zeros((nx,ny),dtype=np.float32)\n",
+    "jind_int = np.zeros((nx,ny),dtype=np.float32)\n",
+    "for i in range(nx):\n",
+    "    for j in range(ny):\n",
+    "        if (np.max(r[:,0,0]) > r2d[i,j]):\n",
+    "            iind = np.where(r[:,0,0]>r2d[i,j])[0][0]\n",
+    "            if (iind != 0):\n",
+    "                iind_int[i,j] = iind + (r2d[i,j] - r[iind,0,0])/(r[iind,0,0]-r[iind-1,0,0])\n",
+    "            else:\n",
+    "                iind_int[i,j] = -1\n",
+    "        else: \n",
+    "            iind_int[i,j] = len(r[:,0,0])-1\n",
+    "            \n",
+    "        if (np.max(h[0,:,0]) > h2d[i,j]):\n",
+    "            jind = np.where(h[0,:,0]>h2d[i,j])[0][0]\n",
+    "            if (jind != 0):\n",
+    "                jind_int[i,j] = jind + (h2d[i,j] - h[0,jind,0])/(h[0,jind,0]-h[0,jind-1,0])\n",
+    "            else:\n",
+    "                jind_int[i,j] = -1\n",
+    "        else:\n",
+    "            jind_int[i,j] = len(h[0,:,0])-1\n",
+    "        \n",
+    "# interpolate the velocities\n",
+    "ux2d = (mapcd(uu_cart[1,:,:,0],[[iind_int],[jind_int]],order=1,mode='nearest'))[0]\n",
+    "uy2d = (mapcd(uu_cart[3,:,:,0],[[iind_int],[jind_int]],order=1,mode='nearest'))[0]      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig,ax = plt.subplots(figsize=(6,7))\n",
+    "xmax=100  \n",
+    "ymax=100\n",
+    "\n",
+    "plc(np.log10(rho),xy=1,xmax=xmax,ymax=ymax,levels=np.linspace(-0.5,2.,100,endpoint=False), \\\n",
+    "    xla=r'$R\\, [r_g]$',yla=r'$z\\, [r_g]$',cbyla=r'$\\rho$',\n",
+    "    cb=True,isfilled=True,ax=ax,cmap='jet',extend='both',symmx=False)\n",
+    "\n",
+    "ax.streamplot(x2d[:,0],y2d[0,:],ux2d.T,uy2d.T,color='white')\n",
+    "ax.set_aspect('equal')\n",
+    "\n",
+    "#fig.tight_layout()\n",
+    "#fig.savefig('BHL_stream.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## make a movie"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The quantity you are trying to plot is a constant = 0.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "if (not os.path.isdir('images')): os.mkdir('images')\n",
+    "\n",
+    "movie(0,100,xmax1=20,skip1=16,skip2=16)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/harmpi_materials/BHL_DISK.ipynb b/harmpi_materials/BHL_DISK.ipynb
new file mode 100644
index 0000000..af60dcd
--- /dev/null
+++ b/harmpi_materials/BHL_DISK.ipynb
@@ -0,0 +1,668 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bondi-Hoyle-Lyttleton Disk Problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import harm_script as harm\n",
+    "# -i option: run the script in Jupyter's namespace\n",
+    "%run -i harm_script.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# change the directory below to your own, which contains \"dumps\" subfolder\n",
+    "os.chdir(\"/Users/astrodoo/Work/WorkShop/2019.07.15_ATA_SummerSchool@UvA/harmpi_output/BHL_DISK\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time: 1500.019287 (R_g/c)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\" Read the data \"\"\"\n",
+    "dumpn = 150\n",
+    "\n",
+    "rg(\"gdump\")\n",
+    "rd(\"dump\"+\"%3.3d\"%dumpn)\n",
+    "\n",
+    "# compute the coordinates in Cartesian\n",
+    "ph[...] = 0.\n",
+    "cart_coords()\n",
+    "velocity_cart()\n",
+    "\n",
+    "print(\"time: %f (R_g/c)\"%t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "distance between the black hole and the disk: 200.003857 (Rg)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\" calculate the distance between the black hole and the disk \"\"\"\n",
+    "\n",
+    "dist_disk_init = 100 \n",
+    "v_inf = 0.2\n",
+    "bh_loc = v_inf*t - dist_disk_init\n",
+    "\n",
+    "print(\"distance between the black hole and the disk: %f (Rg)\"%bh_loc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Draw the density contour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig,ax = plt.subplots(figsize=(7,8))\n",
+    "xmax=300  \n",
+    "ymax=400\n",
+    "\n",
+    "# change the coordinate to the disk frame\n",
+    "X_diskfr = X.copy()\n",
+    "X_diskfr[3] = X_diskfr[3] + bh_loc\n",
+    "\n",
+    "plc(np.log10(rho),xy=1,xcoord=X_diskfr[1],ycoord=X_diskfr[3],xmax=xmax,ymax=ymax, \\\n",
+    "    levels=np.linspace(-1,3.,100,endpoint=False), \\\n",
+    "    xla=r'$R\\, [r_g]$',yla=r'$z\\, [r_g]$',cbyla=r'log($\\rho$)',\n",
+    "    cb=True,isfilled=True,ax=ax,cmap='jet',extend='both',symmx=False)\n",
+    "\n",
+    "ax.set_title(r'$t-t_0$ = %.2f (Rg/c)'%(bh_loc/v_inf),fontsize=10)\n",
+    "\n",
+    "\n",
+    "skip1 = 10 # reduce the number of arrows\n",
+    "skip2 = 10 # reduce the number of arrows\n",
+    "ar_width = 0.005 # thickness of the arrows\n",
+    "ar_scale = 2 # scale of the arrows\n",
+    "\n",
+    "#gamma = 1./np.sqrt(1.-v_inf*v_inf)\n",
+    "vu_rel = vu_cart[:,::skip1,::skip2,0].copy()\n",
+    "vu_rel[3] = vu_rel[3] + v_inf\n",
+    "ax.quiver(X_diskfr[1,::skip1,::skip2,0],X_diskfr[3,::skip1,::skip2,0], \\\n",
+    "          vu_rel[1], vu_rel[3], \\\n",
+    "          scale=ar_scale, width=ar_width)\n",
+    "\n",
+    "ax.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig,ax = plt.subplots(figsize=(8,7))\n",
+    "xmax=300  \n",
+    "ymax=400\n",
+    "\n",
+    "# change the coordinate to the disk frame\n",
+    "X_diskfr = X.copy()\n",
+    "X_diskfr[3] = X_diskfr[3] + bh_loc\n",
+    "\n",
+    "plc(np.log10(rho),xy=1,xcoord=X_diskfr[1],ycoord=X_diskfr[3],xmax=xmax,ymax=ymax, \\\n",
+    "    levels=np.linspace(-1,3.,100,endpoint=False), \\\n",
+    "    xla=r'$R\\, [r_g]$',yla=r'$z\\, [r_g]$',cbyla=r'log($\\rho$)',\n",
+    "    cb=True,isfilled=True,ax=ax,cmap='jet',extend='both',symmx=True)\n",
+    "\n",
+    "ax.set_title(r'$t-t_0$ = %.2f (Rg/c)'%(bh_loc/v_inf),fontsize=10)\n",
+    "ax.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig,ax = plt.subplots(figsize=(8,7))\n",
+    "xmax=300  \n",
+    "ymax=400\n",
+    "\n",
+    "# change the coordinate to the disk frame\n",
+    "X_diskfr = X.copy()\n",
+    "X_diskfr[3] = X_diskfr[3] + bh_loc\n",
+    "\n",
+    "plc(np.log10(rho),xy=1,xcoord=X_diskfr[1],ycoord=X_diskfr[3],xmax=xmax,ymax=ymax, \\\n",
+    "    levels=np.linspace(-1,3.,100,endpoint=False), \\\n",
+    "    xla=r'$R\\, [r_g]$',yla=r'$z\\, [r_g]$',cbyla=r'log($\\rho$)',\n",
+    "    cb=True,isfilled=True,ax=ax,cmap='jet',extend='both',symmx=True)\n",
+    "\n",
+    "ax.set_title(r'$t-t_0$ = %.2f (Rg/c)'%(bh_loc/v_inf),fontsize=10)\n",
+    "\n",
+    "\n",
+    "skip1 = 10 # reduce the number of arrows\n",
+    "skip2 = 10 # reduce the number of arrows\n",
+    "ar_width = 0.003 # thickness of the arrows\n",
+    "ar_scale = 4 # scale of the arrows\n",
+    "\n",
+    "#gamma = 1./np.sqrt(1.-v_inf*v_inf)\n",
+    "vu_rel = vu_cart[:,::skip1,::skip2,0].copy()\n",
+    "vu_rel[3] = vu_rel[3] + v_inf\n",
+    "ax.quiver(X_diskfr[1,::skip1,::skip2,0],X_diskfr[3,::skip1,::skip2,0], \\\n",
+    "          vu_rel[1], vu_rel[3], \\\n",
+    "          scale=ar_scale, width=ar_width)\n",
+    "ax.quiver(-X_diskfr[1,::skip1,::skip2,0],X_diskfr[3,::skip1,::skip2,0], \\\n",
+    "          -vu_rel[1], vu_rel[3], \\\n",
+    "          scale=ar_scale, width=ar_width)\n",
+    "\n",
+    "ax.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Density contour with sampled velocity vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sample_velo(bh_loc=0.,v_inf=0.2,xmax=300,ymax=400,smp_nx=10,smp_ny=20):\n",
+    "    \"\"\"\n",
+    "    generate the sample of (x,y,ux,uy) for drawing the velocity vectors\n",
+    "    \n",
+    "    kwargs -\n",
+    "        bh_loc:        black hole location\n",
+    "        v_inf:         velocity at infinity\n",
+    "        smp_nx/smp_ny: sample size of the velocity vectors\n",
+    "        xmax/ymax:     size of the plot\n",
+    "    \"\"\"\n",
+    "    from scipy.ndimage import map_coordinates as mapcd\n",
+    "\n",
+    "    # set the parameters for data sample\n",
+    "    unix = np.linspace(0,xmax,smp_nx)\n",
+    "    uniy = np.linspace(-ymax,ymax,smp_ny) - bh_loc\n",
+    "\n",
+    "    # generate the grid in 2D\n",
+    "    xsmp,ysmp = np.meshgrid(unix,uniy,indexing='ij')\n",
+    "\n",
+    "    # convert to spherical coordinates\n",
+    "    rsmp = np.sqrt(xsmp*xsmp + ysmp*ysmp)\n",
+    "    hsmp = np.arccos(ysmp/rsmp)\n",
+    "\n",
+    "    # find the indices of the grid in the data-sample\n",
+    "    iind_int = np.zeros((smp_nx,smp_ny),dtype=np.float32)\n",
+    "    jind_int = np.zeros((smp_nx,smp_ny),dtype=np.float32)\n",
+    "    for i in range(smp_nx):\n",
+    "        for j in range(smp_ny):\n",
+    "            if (np.max(r[:,0,0]) > rsmp[i,j]):\n",
+    "                iind = np.where(r[:,0,0]>rsmp[i,j])[0][0]\n",
+    "                if (iind != 0):\n",
+    "                    iind_int[i,j] = iind + (rsmp[i,j] - r[iind,0,0])/(r[iind,0,0]-r[iind-1,0,0])\n",
+    "                else:\n",
+    "                    iind_int[i,j] = -1\n",
+    "            else: \n",
+    "                iind_int[i,j] = len(r[:,0,0])-1\n",
+    "            \n",
+    "            if (np.max(h[0,:,0]) > hsmp[i,j]):\n",
+    "                jind = np.where(h[0,:,0]>hsmp[i,j])[0][0]\n",
+    "                if (jind != 0):\n",
+    "                    jind_int[i,j] = jind + (hsmp[i,j] - h[0,jind,0])/(h[0,jind,0]-h[0,jind-1,0])\n",
+    "                else:\n",
+    "                    jind_int[i,j] = -1\n",
+    "            else:\n",
+    "                jind_int[i,j] = len(h[0,:,0])-1\n",
+    "        \n",
+    "    # interpolate the velocities\n",
+    "    vxsmp  = (mapcd(vu_cart[1,:,:,0],[[iind_int],[jind_int]],order=1,mode='nearest'))[0]\n",
+    "    vysmp  = (mapcd(vu_cart[3,:,:,0],[[iind_int],[jind_int]],order=1,mode='nearest'))[0] + v_inf\n",
+    "    \n",
+    "    # recover y coordinate \n",
+    "    ysmp = ysmp + bh_loc\n",
+    "    \n",
+    "    return (xsmp,ysmp,vxsmp,vysmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xsmp,ysmp,vxsmp,vysmp = sample_velo(bh_loc=bh_loc,v_inf=v_inf,xmax=300,ymax=400,smp_nx=15,smp_ny=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig,ax = plt.subplots(figsize=(8,7))\n",
+    "xmax=300  \n",
+    "ymax=400\n",
+    "\n",
+    "# change the coordinate to the disk frame\n",
+    "X_diskfr = X.copy()\n",
+    "X_diskfr[3] = X_diskfr[3] + bh_loc\n",
+    "\n",
+    "plc(np.log10(rho),xy=1,xcoord=X_diskfr[1],ycoord=X_diskfr[3],xmax=xmax,ymax=ymax, \\\n",
+    "    levels=np.linspace(-1,3.,100,endpoint=False), \\\n",
+    "    xla=r'$R\\, [r_g]$',yla=r'$z\\, [r_g]$',cbyla=r'log($\\rho$)',\n",
+    "    cb=True,isfilled=True,ax=ax,cmap='jet',extend='both',symmx=True)\n",
+    "\n",
+    "ax.set_title(r'$t-t_0$ = %.2f (Rg/c)'%(bh_loc/v_inf),fontsize=10)\n",
+    "\n",
+    "ar_width = 0.003 # thickness of the arrows\n",
+    "ar_scale = 4 # scale of the arrows\n",
+    "\n",
+    "ax.quiver( xsmp,ysmp, vxsmp,vysmp,scale=ar_scale, width=ar_width)\n",
+    "ax.quiver(-xsmp,ysmp,-vxsmp,vysmp,scale=ar_scale, width=ar_width)\n",
+    "\n",
+    "ax.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## make a movie"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def movie_BHLdisk(startn=0,endn=-1,ar_width=0.003,ar_scale=4, smp_nx=15, smp_ny=20, \\\n",
+    "                  xmax=300,ymax=400,dist_disk_init=100.,v_inf=0.2):\n",
+    "    \"\"\"\n",
+    "        kwargs -\n",
+    "            startn:         start dump number\n",
+    "            endn:           end dump number\n",
+    "            ar_width:       arrow width\n",
+    "            ar_scale:       arrow scale\n",
+    "            smp_nx/smp_ny:  sample size of the velocity vectors\n",
+    "            xmax/ymax:      size of the plot\n",
+    "            dist_disk_init: initial distance between the disk and the black hole (Rg)\n",
+    "            v_inf:          velocity at the infinity\n",
+    "    \"\"\"\n",
+    "    import os\n",
+    "    if (not os.path.isdir('images')): os.mkdir('images')\n",
+    "        \n",
+    "    for idump in range(startn,endn+1):\n",
+    "        # set the figure configuration\n",
+    "        fig,ax = plt.subplots(figsize=(8,7))\n",
+    "    \n",
+    "        # read the data\n",
+    "        #rg(\"gdump\")\n",
+    "        rd(\"dump\"+\"%3.3d\"%idump)\n",
+    "\n",
+    "        # compute the coordinates in Cartesian\n",
+    "        ph[...] = 0.\n",
+    "        cart_coords()\n",
+    "        velocity_cart()\n",
+    "        \n",
+    "        # calculate the distance between the black hole and the disk\n",
+    "        bh_loc = v_inf*t - dist_disk_init\n",
+    "        \n",
+    "        # change the coordinate to the disk frame\n",
+    "        X_diskfr = X.copy()\n",
+    "        X_diskfr[3] = X_diskfr[3] + bh_loc\n",
+    "\n",
+    "        plc(np.log10(rho),xy=1,xcoord=X_diskfr[1],ycoord=X_diskfr[3], \\\n",
+    "            xmax=xmax,ymax=ymax,levels=np.linspace(-1,3,100,endpoint=False), \\\n",
+    "            xla=r'$R\\, [r_g]$',yla=r'$z\\, [r_g]$',cbyla=r'log($\\rho$)',\n",
+    "            cb=True,isfilled=True,ax=ax,cmap='jet',extend='max',symmx=True)\n",
+    "\n",
+    "        ax.set_title(r'$t-t_0$ = %.2f (Rg/c)'%(bh_loc/v_inf),fontsize=10)\n",
+    "        \n",
+    "        # draw velocity vectors with sampled data\n",
+    "        xsmp,ysmp,vxsmp,vysmp = sample_velo(bh_loc=bh_loc,v_inf=v_inf,xmax=xmax,ymax=ymax,\\\n",
+    "                                            smp_nx=smp_nx,smp_ny=smp_ny)\n",
+    "        \n",
+    "        ax.quiver( xsmp, ysmp, vxsmp, vysmp, scale=ar_scale, width=ar_width)\n",
+    "        ax.quiver(-xsmp, ysmp,-vxsmp, vysmp, scale=ar_scale, width=ar_width)\n",
+    "        \n",
+    "        ax.set_xlim(-xmax,xmax)\n",
+    "        ax.set_ylim(-ymax,ymax)\n",
+    "        ax.set_aspect('equal')\n",
+    "        \n",
+    "        fig.tight_layout()\n",
+    "        fig.savefig('images/BHLdisk_%3.3d.png'%idump)\n",
+    "        plt.clf()\n",
+    "        plt.close()\n",
+    "        print('%d of %d'%(idump,endn+1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "126 of 201\n",
+      "127 of 201\n",
+      "128 of 201\n",
+      "129 of 201\n",
+      "130 of 201\n",
+      "131 of 201\n",
+      "132 of 201\n",
+      "133 of 201\n",
+      "134 of 201\n",
+      "135 of 201\n",
+      "136 of 201\n",
+      "137 of 201\n",
+      "138 of 201\n",
+      "139 of 201\n",
+      "140 of 201\n",
+      "141 of 201\n",
+      "142 of 201\n",
+      "143 of 201\n",
+      "144 of 201\n",
+      "145 of 201\n",
+      "146 of 201\n",
+      "147 of 201\n",
+      "148 of 201\n",
+      "149 of 201\n",
+      "150 of 201\n",
+      "151 of 201\n",
+      "152 of 201\n",
+      "153 of 201\n",
+      "154 of 201\n",
+      "155 of 201\n",
+      "156 of 201\n",
+      "157 of 201\n",
+      "158 of 201\n",
+      "159 of 201\n",
+      "160 of 201\n",
+      "161 of 201\n",
+      "162 of 201\n",
+      "163 of 201\n",
+      "164 of 201\n",
+      "165 of 201\n",
+      "166 of 201\n",
+      "167 of 201\n",
+      "168 of 201\n",
+      "169 of 201\n",
+      "170 of 201\n",
+      "171 of 201\n",
+      "172 of 201\n",
+      "173 of 201\n",
+      "174 of 201\n",
+      "175 of 201\n",
+      "176 of 201\n",
+      "177 of 201\n",
+      "178 of 201\n",
+      "179 of 201\n",
+      "180 of 201\n",
+      "181 of 201\n",
+      "182 of 201\n",
+      "183 of 201\n",
+      "184 of 201\n",
+      "185 of 201\n",
+      "186 of 201\n",
+      "187 of 201\n",
+      "188 of 201\n",
+      "189 of 201\n",
+      "190 of 201\n",
+      "191 of 201\n",
+      "192 of 201\n",
+      "193 of 201\n",
+      "194 of 201\n",
+      "195 of 201\n",
+      "196 of 201\n",
+      "197 of 201\n",
+      "198 of 201\n",
+      "199 of 201\n",
+      "200 of 201\n"
+     ]
+    }
+   ],
+   "source": [
+    "movie_BHLdisk(startn=0,endn=200)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/harmpi_materials/BHL_stream.png b/harmpi_materials/BHL_stream.png
new file mode 100644
index 0000000..2c55365
Binary files /dev/null and b/harmpi_materials/BHL_stream.png differ
diff --git a/harmpi_materials/BHLdisk.mp4 b/harmpi_materials/BHLdisk.mp4
new file mode 100644
index 0000000..f79f66f
Binary files /dev/null and b/harmpi_materials/BHLdisk.mp4 differ
diff --git a/harmpi_materials/harm_script.py b/harmpi_materials/harm_script.py
new file mode 100755
index 0000000..58ca31b
--- /dev/null
+++ b/harmpi_materials/harm_script.py
@@ -0,0 +1,1254 @@
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+
+import sys
+from matplotlib import rc
+from matplotlib.patches import Ellipse
+from scipy.interpolate import interp1d
+from matplotlib.gridspec import GridSpec
+from matplotlib import cm,ticker
+from numpy import sin, cos, tan, pi
+#matplotlib.use('Agg') #so that it does ok with graphics in batch mode
+
+#choose Computer Modern Roman fonts by default
+#mpl.rcParams['font.serif'] = 'cmr10'
+#mpl.rcParams['font.sans-serif'] = 'cmr10'
+
+#font = { 'size'   : 20}
+#rc('font', **font)
+#rc('xtick', labelsize=20) 
+#rc('ytick', labelsize=20) 
+#rc('xlabel', **font) 
+#rc('ylabel', **font) 
+
+# legend = {'fontsize': 20}
+# rc('legend',**legend)
+#axes = {'labelsize': 20}
+#rc('axes', **axes)
+#rc('mathtext',fontset='cm')
+#use this, but at the expense of slowdown of rendering
+#rc('text', usetex=True)
+# #add amsmath to the preamble
+#matplotlib.rcParams['text.latex.preamble']=[r"\usepackage{amssymb,amsmath}"] 
+import pdb
+
+import numpy as np
+import glob
+import os
+from scipy.interpolate import griddata
+from scipy.interpolate import interp1d
+from numpy import ma
+import matplotlib.colors as colors
+#use_math_text = True
+
+
+def mathify_axes_ticks(ax,fontsize=20,xticks=None,yticks=None):
+    if xticks is None:
+        xticks = ax.get_xticks()
+    if yticks is None:
+        yticks = ax.get_yticks()
+    if ax.get_xscale() != 'log': ax.set_xticklabels([(r'$%g$' % lab) for lab in xticks])
+    if ax.get_yscale() != 'log': ax.set_yticklabels([(r'$%g$' % lab) for lab in yticks])
+    if fontsize is not None:
+        if ax.get_xscale() != 'log':
+            for label in ax.get_xticklabels():
+                label.set_fontsize(fontsize)
+        if ax.get_yscale() != 'log':
+            for label in ax.get_yticklabels():
+                label.set_fontsize(fontsize)
+
+
+def convert_to_single_file(startn=0,endn=-1,ln=10,whichi=0,whichn=1,**kwargs):
+    which = kwargs.pop("which","convert_file")
+    rg("gdump")
+    flist1 = np.sort(glob.glob( os.path.join("dumps/", "dump[0-9][0-9][0-9]_0000") ) )
+    flist2 = np.sort(glob.glob( os.path.join("dumps/", "dump[0-9][0-9][0-9][0-9]_0000") ) )
+    flist1.sort()
+    flist2.sort()
+    flist = np.concatenate((flist1,flist2))
+    firsttime = 1
+    for fldname in flist:
+        #find the index of the file
+        fldindex = np.int(fldname.split("_")[0].split("p")[-1])
+        if fldindex < startn:
+            continue
+        if endn>=0 and fldindex >= endn:
+            break
+        if fldindex % whichn != whichi:
+            #do every whichn'th snapshot starting with whichi'th snapshot
+            continue
+        #print( "Reading " + fldname + " ..." )
+        fname = "dump%03d" % fldindex
+        if os.path.isfile( fname ):
+            print("File %s exists, skipping..." % fname)
+            continue
+        if not os.path.isfile( fname ):
+            rd(fname)
+        
+
+def ellk(a,r):
+    ekval = ek(a,r)
+    lkval = lk(a,r)
+    return(lkval/ekval)
+
+def ek(a,r):
+    #-u_t, I suspect
+    ek = (r**2-2*r+a*r**0.5)/(r*(r**2-3*r+2*a*r**0.5)**0.5)
+    return(ek)
+
+def lk(a,r):
+    udphi = r**0.5*(r**2-2*a*r**0.5+a**2)/(r*(r**2-3*r+2*a*r**0.5)**0.5)
+    return( udphi )
+
+def Risco(ain):
+    eps = np.finfo(np.float64).eps
+    a = np.minimum(ain,1.)
+    Z1 = 1 + (1. - a**2)**(1./3.) * ((1. + a)**(1./3.) + (1. - a)**(1./3.))
+    Z2 = (3*a**2 + Z1**2)**(1./2.)
+    risco = 3 + Z2 - np.sign(a)* ( (3 - Z1)*(3 + Z1 + 2*Z2) )**(1./2.)
+    return(risco)
+
+def Ebind(r,a):
+    #1+u_t, I suspect
+    Eb = 1 - (r**2-2*r+a*r**0.5)/(r*(r**2-3*r+2*a*r**0.5)**0.5)
+    return( Eb )
+
+def etaNT(a):
+    return( Ebindisco(a) )
+
+def Ebindisco(a):
+    eps = np.finfo(np.float64).eps
+    a0 = 0.99999 #1.-1e8*eps
+    if a > a0: 
+        a = a0
+        Eb = Ebind( Risco(a), a )
+        return((a-a0)/(1.-a0)*(1.-3.**(-0.5)) + (1.-a)/(1.-a0)*Eb)
+    Eb = Ebind( Risco(a), a)
+    #Eb = (1.-3.**(-0.5))*a**2
+    return( Eb )
+
+def mkmov_simple(starti=0,endi=400):
+    for i in range(starti,endi+1):
+        rd("dump%03d" % i);
+        aphi=psicalc()
+        if i == starti: amax = aphi.max()
+        cs, cb = plco(np.log10(rho),levels=np.linspace(-8,0,100),isfilled=1,k=0,xy=1,xmax=10,ymax=5,dobh=1,cb=1,extend="both",pretty=1)
+        ax = plt.gca()
+        ax.set_xlabel(r"$R\ [r_g]$",fontsize=20,labelpad=-5)
+        ax.set_ylabel(r"$z\ [r_g]$",fontsize=20,labelpad=-5)
+        cb.ax.set_xlabel(r"$\log\rho$",fontsize=20,ha="left")
+        plc(aphi,levels=np.linspace(-amax,amax,10)[1:-1],colors="white",linewidths=2,xy=-1)
+        print(i);
+        plt.title("t=%.4g"%np.round(t)); 
+        plt.draw();
+        plt.savefig("frame%03d.png"%i)
+
+def convert_wrapper(**kwargs):
+    if len(sys.argv[2:])==2 and sys.argv[2].isdigit() and sys.argv[3].isdigit():
+        whichi = int(sys.argv[2])
+        whichn = int(sys.argv[3])
+    else:
+        print( "Usage: %s %s <whichi> <whichn>" % (sys.argv[0], sys.argv[1]) )
+        return
+    convert_to_single_file(whichi = whichi, whichn = whichn, **kwargs)
+
+def mkmov_wrapper(**kwargs):
+    if len(sys.argv[2:])==2 and sys.argv[2].isdigit() and sys.argv[3].isdigit():
+        whichi = int(sys.argv[2])
+        whichn = int(sys.argv[3])
+    else:
+        print( "Usage: %s %s <whichi> <whichn>" % (sys.argv[0], sys.argv[1]) )
+        return
+    mkmov(whichi = whichi, whichn = whichn, **kwargs)
+
+def mkmov(startn=0,endn=-1,ln=10,whichi=0,whichn=1,**kwargs):
+    which = kwargs.pop("which","mkfrm8panel")
+    dosavefig = kwargs.pop("dosavefig",1)
+    print("Doing %s movie" % which)
+    rg("gdump")
+    #compute the total magnetic flux at t = 0
+    rd("dump000")
+    aphi=psicalc()
+    aphimax = aphi.max()
+    #construct file list
+    flist1 = np.sort(glob.glob( os.path.join("dumps/", "dump[0-9][0-9][0-9]") ) )
+    flist2 = np.sort(glob.glob( os.path.join("dumps/", "dump[0-9][0-9][0-9][0-9]") ) )
+    flist1.sort()
+    flist2.sort()
+    flist = np.concatenate((flist1,flist2))
+    if len(flist) == 0:
+        flist1 = np.sort(glob.glob( os.path.join("dumps/", "dump[0-9][0-9][0-9]_0000") ) )
+        flist2 = np.sort(glob.glob( os.path.join("dumps/", "dump[0-9][0-9][0-9][0-9]_0000") ) )
+        flist1.sort()
+        flist2.sort()
+        flist = np.concatenate((flist1,flist2))
+    firsttime = 1
+    dpi = 135
+    for fldname in flist:
+        #find the index of the file
+        fldindex = np.int(fldname.split("_")[0].split("p")[-1])
+        if fldindex < startn:
+            continue
+        if endn>=0 and fldindex >= endn:
+            break
+        if fldindex % whichn != whichi:
+            #do every whichn'th snapshot starting with whichi'th snapshot
+            continue
+        if dosavefig:
+            fname = "%s%04d.png" % (which,fldindex)
+            if os.path.isfile( fname ):
+                print("File %s exists, skipping..." % fname)
+                continue
+        #print( "Reading " + fldname + " ..." )
+        rd("dump%03d" % fldindex);
+        if which == "mkfrmsimple":
+            if firsttime:
+                firsttime = 0
+                fig = plt.figure(figsize=(12,8))
+                plt.clf()
+            mkfrmsimple(fig=fig,aphimax = aphimax)
+        else:
+            print("Unknown movie type: %s" % which)
+            return
+        print(fldindex)
+        plt.draw()
+        if dosavefig:
+            plt.savefig(fname,dpi = dpi)
+
+            
+#############
+def mkfrmsimple(fig=None,aphimax=None,lnx=100,lny=100,vmin=-10,vmax=1,fntsize=20,asp=1.):
+    if fig is None: fig = plt.gcf();
+    aphi = psicalc() #vpot[3].mean(-1)
+    if aphimax is None: aphimax = aphi.max()
+    #ax.set_aspect(asp)
+    res,cb=plco(lrho,xy=1,xmax=lnx,ymax=lny,symmx=1,
+                isfilled=1,cb=1,pretty=1,
+                levels=np.linspace(vmin,vmax,100),
+                extend="both",cbxla=r"$\ \ \ \ \ \ \ \ \log_{10}\rho$")
+    plt.xlabel(r"$x\ [r_g]$",fontsize=fntsize)
+    plt.ylabel(r"$z\ [r_g]$",fontsize=fntsize,labelpad=-30)
+    ax = plt.gca()
+    #cmap = cm.jet
+    #label = r"$\log_{10}\rho$"
+    #cx1,cb1 = mkvertcolorbar(ax,fig,gap=0.02,width=0.05,vmin=vmin,vmax=vmax,loc="right",
+    #            label=label,ticks=tcks,fntsize=fntsize,cmap=cmap,extend="both")
+    plc(aphi/aphimax,symmx=1,xy=-1,levels=np.linspace(0.,1.,20)[1:],colors="black",linewidths=1.)
+    plt.title(r"$t=%g$" % int(t+0.5), fontsize=fntsize)
+    plt.xlim(-lnx,lnx)
+    plt.ylim(-lny,lny)
+    mathify_axes_ticks(ax)
+    
+def mkvertcolorbar(ax,fig,vmin=0,vmax=1,label=None,ylabel=None,ticks=None,fntsize=20,cmap=mpl.cm.jet,gap=0.03,width=0.02,extend="neither",loc="right"):
+    box = ax.get_position()
+    #pdb.set_trace()
+    # cpos = [box.x0,box.y0+box.height+0.05,box.width,0.03]
+    locs = loc.split()
+    loc0 = locs[0]
+    if len(locs)>1:
+        loc1 = locs[1]
+    else:
+        loc1 = None
+    if loc0 == "left":
+        cpos = box.x0-gap-width,box.y0,width,box.height
+    elif loc0 == "right":
+        cpos = box.x0+box.width+gap,box.y0,width,box.height
+    elif loc0 == "top":
+        if loc1 == "right":
+            cpos = box.x0+box.width*0.55,box.y0+box.height+gap,box.width*0.45,width
+        elif loc1 == "left":
+            cpos = box.x0+box.width*0.0,box.y0+box.height+gap,box.width*0.45,width
+        else:
+            cpos = box.x0,box.y0+box.height+gap,box.width,width
+    ax1 = fig.add_axes(cpos)
+    #cmap = mpl.cm.jet
+    norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
+    if loc0 == "left" or loc0 == "right":
+        ori = "vertical"
+    else:
+        ori = "horizontal"
+    if ticks is not None:
+        cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cmap,
+                                        norm=norm,
+                                        orientation=ori,
+                                        ticks=ticks,
+                                        extend=extend)
+    else:
+        cb1 = mpl.colorbar.ColorbarBase(ax1,  cmap=cmap,
+                                        norm=norm,
+                                        orientation=ori,
+                                        extend=extend)
+    if loc0 == "top" or loc0 == "bottom":
+        cb1.ax.xaxis.set_ticks_position(loc0)
+        mathify_axes_ticks(cb1.ax,fontsize=fntsize,xticks=ticks)
+    elif loc0 == "left" or loc0 == "right":
+        cb1.ax.yaxis.set_ticks_position(loc0)
+        mathify_axes_ticks(cb1.ax,fontsize=fntsize,yticks=ticks)
+    if label is not None:
+        ax1.set_xlabel(label,fontsize=fntsize)
+    if ylabel is not None:
+        ax1.set_ylabel(ylabel,fontsize=fntsize)
+    for label in ax1.get_xticklabels() + ax1.get_yticklabels():
+        label.set_fontsize(fntsize)
+    return ax1,cb1
+
+def Qmri(dir=2):
+    """
+    APPROXIMATELY Computes number of theta cells resolving one MRI wavelength
+    """
+    global bu,rho,uu,_dx2,_dx3
+    #cvel()
+    #corrected this expression to include both 2pi and dxdxp[3][3]
+    #also corrected defition of va^2 to contain bsq+gam*ug term
+    #need to figure out how to properly measure this in fluid frame
+    vaudir = np.abs(bu[dir])/np.sqrt(rho+bsq+gam*ug)
+    omega = dxdxp[3][3]*uu[3]/uu[0]+1e-15
+    lambdamriudir = 2*np.pi * vaudir / omega
+    if dir == 2:
+        res=lambdamriudir/_dx2
+    elif dir == 3:
+        res=lambdamriudir/_dx3
+    return(res)
+
+def goodlabs(fntsize=20):
+    ax = plt.gca()
+    for label in ax.get_xticklabels() + ax.get_yticklabels():
+        label.set_fontsize(fntsize)
+
+
+
+def iofr(rval):
+    rval = np.array(rval)
+    if np.max(rval) < r[0,0,0]:
+        return 0
+    res = interp1d(r[:,0,0], ti[:,0,0], kind='linear', bounds_error = False, fill_value = 0)(rval)
+    if len(res.shape)>0 and len(res)>0:
+        res[rval<r[0,0,0]]*=0
+        res[rval>r[nx-1,0,0]]=res[rval>r[nx-1,0,0]]*0+nx-1
+    else:
+        res = np.float64(res)
+    return(np.floor(res+0.5).astype(int))
+
+
+
+#read in a dump file
+def rd(dump):
+    read_file(dump,type="dump")
+
+
+#read in a grid file
+def rg(dump):
+    read_file(dump,type="gdump")
+
+#read in a grid file
+def rg2(dump):
+    read_file(dump,type="gdump2",noround=True)
+
+#high-level function that reads either MPI or serial gdump's
+def read_file(dump,type=None,savedump=True,saverdump=False,noround=False):
+    if type is None:
+        if dump.startswith("dump"):
+            type = "dump"
+            print("Reading a dump file %s ..." % dump)
+        elif dump.startswith("gdump2"):
+            type = "gdump2"
+            print("Reading a gdump2 file %s ..." % dump)
+        elif dump.startswith("gdump"):
+            type = "gdump"
+            print("Reading a gdump file %s ..." % dump)
+        elif dump.startswith("rdump"):
+            type = "rdump"
+            print("Reading a rdump file %s ..." % dump)
+        elif dump.startswith("fdump"):
+            type = "fdump"
+            print("Reading a fdump file %s ..." % dump)
+        else:
+            print("Couldn't guess dump type; assuming it is a data dump")
+            type = "dump"
+    #normal dump
+    if os.path.isfile( "dumps/" + dump ):
+        headerline = read_header("dumps/" + dump, returnheaderline = True)
+        gd = read_body("dumps/" + dump,nx=N1+2*N1G,ny=N2+2*N2G,nz=N3+2*N3G,noround=1)
+        if noround:
+            res = data_assign(         gd,type=type,nx=N1+2*N1G,ny=N2+2*N2G,nz=N3+2*N3G)
+        else:
+            res = data_assign(myfloat(gd),type=type,nx=N1+2*N1G,ny=N2+2*N2G,nz=N3+2*N3G)
+        return res
+    #MPI-type dump that is spread over many files
+    else:
+        flist = np.sort(glob.glob( "dumps/" + dump + "_[0-9][0-9][0-9][0-9]" ))
+        if len(flist) == 0:
+            print( "Could not find %s or its MPI counterpart" % dump )
+            return
+        sys.stdout.write( "Reading %s (%d files)" % (dump, len(flist)) )
+        sys.stdout.flush()
+        ndots = 10
+        dndot = len(flist)/ndots
+        if dndot == 0: dndot = 1
+        for i,fname in enumerate(flist):
+            #print( "Reading file %d out of %d..." % (i,len(flist)) )
+            #header for each file might be different, so read each
+            header = read_header(fname,issilent=1)
+            if header is None:
+                print( "Error reading header of %s, aborting..." % fname )
+                return
+            lgd = read_body(fname,nx=N1+2*N1G,ny=N2+2*N2G,nz=N3+2*N3G)
+            #this gives an array of dimensions (-1,N1,N2,N3)+potentially ghost cells
+            if 0 == i:
+                #create full array: of dimensions, (-1,nx,ny,nz)
+                fgd = np.zeros( (lgd.shape[0], nx+2*N1G, ny+2*N2G, nz+2*N3G), dtype=np.float32)
+            if not type == "rdump":
+                #construct full indices: ti, tj, tk
+                #fti,ftj,ftk = mgrid[0:nx,0:ny,0:nz]
+                lti,ltj,ltk = lgd[0:3,:,:].view();
+                lti = np.int64(lti)
+                ltj = np.int64(ltj)
+                ltk = np.int64(ltk)
+                fgd[:,lti+N1G,ltj+N2G,ltk+N3G] = lgd[:,:,:,:]
+            else:
+                print(starti,startj,startk)
+                fgd[:,starti:starti+N1+2*N1G,startj:startj+N2+2*N2G,startk:startk+N3+2*N3G] = lgd[:,:,:,:]
+            del lgd
+            if i%dndot == 0:
+                sys.stdout.write(".")
+                sys.stdout.flush()
+        res = data_assign(fgd,type=type,nx=nx+2*N1G,ny=ny+2*N2G,nz=nz+2*N3G)
+        if savedump:
+            #if the full dump file does not exist, create it
+            dumpfullname = "dumps/" + dump
+            if (type == "dump" or type == "gdump") and not os.path.isfile(dumpfullname):
+                sys.stdout.write("Saving full dump to %s..." % dumpfullname)
+                sys.stdout.flush()
+                header[1] = header[4] #N1 = nx
+                header[2] = header[5] #N2 = ny
+                header[3] = header[6] #N3 = nz
+                fout = open( dumpfullname, "wb" )
+                #join header items with " " (space) as a glue
+                #see http://stackoverflow.com/questions/12377473/python-write-versus-writelines-and-concatenated-strings
+                #write it out with a new line char at the end
+                fout.write(" ".join(header) + "\n")
+                fout.flush()
+                os.fsync(fout.fileno())
+                #reshape the dump content
+                gd1 = fgd.transpose(1,2,3,0)
+                gd1.tofile(fout)
+                fout.close()
+                print( " done!" )
+                if res is not None:
+                    return res
+        return res
+
+#read in a header
+def read_header(dump,issilent=True,returnheaderline=False):
+    global t,nx,ny,nz,N1,N2,N3,N1G,N2G,N3G,starti,startj,startk,_dx1,_dx2,_dx3,a,gam,Rin,Rout,hslope,R0,ti,tj,tk,x1,x2,x3,r,h,ph,gcov,gcon,gdet,drdx,gn3,gv3,guu,gdd,dxdxp, games, startx1, startx2, startx3, x10, x20, tf, NPR, DOKTOT, BL
+    global fractheta
+    global fracphi
+    global rbr
+    global npow2
+    global cpow2
+    #read image
+    fin = open( dump, "rb" )
+    headerline = fin.readline()
+    header = headerline.split()
+    nheadertot = len(header)
+    fin.close()
+    if not dump.startswith("dumps/rdump"):
+        if not issilent: print( "dump header: len(header) = %d" % len(header) )
+        nheader = 57
+        n = 0
+        t = myfloat(np.float64(header[n])); n+=1
+        #per tile resolution
+        N1 = int(header[n]); n+=1
+        N2 = int(header[n]); n+=1
+        N3 = int(header[n]); n+=1
+        #total resolution
+        nx = int(header[n]); n+=1
+        ny = int(header[n]); n+=1
+        nz = int(header[n]); n+=1
+        #numbers of ghost cells
+        N1G = int(header[n]); n+=1
+        N2G = int(header[n]); n+=1
+        N3G = int(header[n]); n+=1
+        startx1 = myfloat(float(header[n])); n+=1
+        startx2 = myfloat(float(header[n])); n+=1
+        startx3 = myfloat(float(header[n])); n+=1
+        _dx1=myfloat(float(header[n])); n+=1
+        _dx2=myfloat(float(header[n])); n+=1
+        _dx3=myfloat(float(header[n])); n+=1
+        tf=myfloat(float(header[n])); n+=1
+        nstep=myfloat(float(header[n])); n+=1
+        a=myfloat(float(header[n])); n+=1
+        gam=myfloat(float(header[n])); n+=1
+        cour=myfloat(float(header[n])); n+=1
+        DTd=myfloat(float(header[n])); n+=1
+        DTl=myfloat(float(header[n])); n+=1
+        DTi=myfloat(float(header[n])); n+=1
+        DTr=myfloat(float(header[n])); n+=1
+        DTr01=myfloat(float(header[n])); n+=1
+        dump_cnt=myfloat(float(header[n])); n+=1
+        image_cnt=myfloat(float(header[n])); n+=1
+        rdump_cnt=myfloat(float(header[n])); n+=1
+        rdump01_cnt=myfloat(float(header[n])); n+=1
+        dt=myfloat(float(header[n])); n+=1
+        lim=myfloat(float(header[n])); n+=1
+        failed=myfloat(float(header[n])); n+=1
+        Rin=myfloat(float(header[n])); n+=1
+        Rout=myfloat(float(header[n])); n+=1
+        hslope=myfloat(float(header[n])); n+=1
+        R0=myfloat(float(header[n])); n+=1
+        NPR=int(header[n]); n+=1
+        DOKTOT=int(header[n]); n+=1
+        DOCYLINDRIFYCOORDS=int(header[n]); n+=1
+        fractheta = myfloat(header[n]); n+=1
+        fracphi   = myfloat(header[n]); n+=1
+        rbr       = myfloat(header[n]); n+=1
+        npow2     = myfloat(header[n]); n+=1
+        cpow2     = myfloat(header[n]); n+=1
+        x10 = myfloat(header[n]); n+=1
+        x20 = myfloat(header[n]); n+=1
+        fracdisk = myfloat(header[n]); n+=1
+        fracjet = myfloat(header[n]); n+=1
+        r0disk = myfloat(header[n]); n+=1
+        rdiskend = myfloat(header[n]); n+=1
+        r0jet = myfloat(header[n]); n+=1
+        rjetend = myfloat(header[n]); n+=1
+        jetnu = myfloat(header[n]); n+=1
+        rsjet = myfloat(header[n]); n+=1
+        r0grid = myfloat(header[n]); n+=1
+        BL = myfloat(header[n]); n+=1
+    else:
+        print("rdump header")
+        nheader = 48
+        n = 0
+        #per tile resolution
+        N1 = int(header[n]); n+=1
+        N2 = int(header[n]); n+=1
+        N3 = int(header[n]); n+=1
+        #total resolution
+        nx = int(header[n]); n+=1
+        ny = int(header[n]); n+=1
+        nz = int(header[n]); n+=1
+        #numbers of ghost cells
+        N1G = int(header[n]); n+=1
+        N2G = int(header[n]); n+=1
+        N3G = int(header[n]); n+=1
+        #starting indices
+        starti = int(header[n]); n+=1
+        startj = int(header[n]); n+=1
+        startk = int(header[n]); n+=1
+        t = myfloat(header[n]); n+=1
+        tf = myfloat(header[n]); n+=1
+        nstep = int(header[n]); n+=1
+        a = myfloat(header[n]); n+=1
+        gam = myfloat(header[n]); n+=1
+        game = myfloat(header[n]); n+=1
+        game4 = myfloat(header[n]); n+=1
+        game5 = myfloat(header[n]); n+=1
+        cour = myfloat(header[n]); n+=1
+        DTd = myfloat(header[n]); n+=1
+        DTl = myfloat(header[n]); n+=1
+        DTi = myfloat(header[n]); n+=1
+        DTr = myfloat(header[n]); n+=1
+        DTr01 = myfloat(header[n]); n+=1
+        dump_cnt = myfloat(header[n]); n+=1
+        image_cnt = myfloat(header[n]); n+=1
+        rdump_cnt = myfloat(header[n]); n+=1
+        rdump01_cnt=myfloat(float(header[n])); n+=1
+        dt = myfloat(header[n]); n+=1
+        lim = myfloat(header[n]); n+=1
+        failed = myfloat(header[n]); n+=1
+        Rin = myfloat(header[n]); n+=1
+        Rout = myfloat(header[n]); n+=1
+        hslope = myfloat(header[n]); n+=1
+        R0 = myfloat(header[n]); n+=1
+        fractheta = myfloat(header[n]); n+=1
+        fracphi = myfloat(header[n]); n+=1
+        rbr = myfloat(header[n]); n+=1
+        npow2 = myfloat(header[n]); n+=1
+        cpow2 = myfloat(header[n]); n+=1
+        x10 = myfloat(header[n]); n+=1
+        x20 = myfloat(header[n]); n+=1
+        mrat = myfloat(header[n]); n+=1
+        fel0 = myfloat(header[n]); n+=1
+        felfloor = myfloat(header[n]); n+=1
+        tdump = myfloat(header[n]); n+=1
+        trdump = myfloat(header[n]); n+=1
+        timage = myfloat(header[n]); n+=1
+        tlog  = myfloat(header[n]); n+=1
+    if n < len(header):
+        nheader = 60
+        global_fracdisk   = myfloat(header[n]); n+=1
+        global_fracjet    = myfloat(header[n]); n+=1
+        global_r0disk     = myfloat(header[n]); n+=1
+        global_rdiskend   = myfloat(header[n]); n+=1
+        global_r0jet      = myfloat(header[n]); n+=1
+        global_rjetend    = myfloat(header[n]); n+=1
+        global_jetnu      = myfloat(header[n]); n+=1
+        global_rsjet      = myfloat(header[n]); n+=1
+        global_r0grid     = myfloat(header[n]); n+=1
+    if n != nheader or n != nheadertot:
+        print("Wrong number of elements in header: nread = %d, nexpected = %d, nototal = %d: incorrect format?"
+              % (n, nheader, nheadertot) )
+        return headerline
+    if returnheaderline:
+        return headerline
+    else:
+        return header
+            
+def read_body(dump,nx=None,ny=None,nz=None,noround=False):
+        fin = open( dump, "rb" )
+        header = fin.readline()
+        if dump.startswith("dumps/rdump"):
+            dtype = np.float64
+            body = np.fromfile(fin,dtype=dtype,count=-1)
+            gd = body.view().reshape((nx,ny,nz,-1), order='C')
+            if noround:
+                gd=gd.transpose(3,0,1,2)
+            else:
+                gd=myfloat(gd.transpose(3,0,1,2))
+        elif dump.startswith("dumps/gdump2"):
+            dtype = np.float64
+            body = np.fromfile(fin,dtype=dtype,count=-1)
+            gd = body.view().reshape((nx,ny,nz,-1), order='C')
+            if noround:
+                gd=gd.transpose(3,0,1,2)
+            else:
+                gd=myfloat(gd.transpose(3,0,1,2))
+        elif dump.startswith("dumps/fdump"):
+            dtype = np.int64
+            body = np.fromfile(fin,dtype=dtype,count=-1)
+            gd = body.view().reshape((-1,nz,ny,nx), order='F')
+            gd=myfloat(gd.transpose(0,3,2,1))
+        else:
+            dtype = np.float32
+            body = np.fromfile(fin,dtype=dtype,count=-1)
+            gd = body.view().reshape((-1,nz,ny,nx), order='F')
+            gd=myfloat(gd.transpose(0,3,2,1))
+        return gd
+
+def data_assign(gd,type=None,**kwargs):
+    if type is None:
+        print("Please specify data type")
+        return
+    if type == "gdump":
+        gdump_assign(gd,**kwargs)
+        return None
+    elif type == "gdump2":
+        gdump2_assign(gd,**kwargs)
+        return None
+    elif type == "dump":
+        dump_assign(gd,**kwargs)
+        return None
+    elif type == "rdump":
+        gd = rdump_assign(gd,**kwargs)
+        return gd
+    elif type == "fdump":
+        gd = fdump_assign(gd,**kwargs)
+        return gd
+    else:
+        print("Unknown data type: %s" % type)
+        return gd
+    
+def gdump_assign(gd,**kwargs):
+    global t,nx,ny,nz,N1,N2,N3,_dx1,_dx2,_dx3,a,gam,Rin,Rout,hslope,R0,ti,tj,tk,x1,x2,x3,r,h,ph,gcov,gcon,gdet,drdx,gn3,gv3,guu,gdd,dxdxp, games
+    nx = kwargs.pop("nx",nx)
+    ny = kwargs.pop("ny",ny)
+    nz = kwargs.pop("nz",nz)
+    ti,tj,tk,x1,x2,x3,r,h,ph = gd[0:9,:,:].view();  n = 9
+    gv3 = gd[n:n+16].view().reshape((4,4,nx,ny,nz),order='F').transpose(1,0,2,3,4); n+=16
+    gn3 = gd[n:n+16].view().reshape((4,4,nx,ny,nz),order='F').transpose(1,0,2,3,4); n+=16
+    gcov = gv3
+    gcon = gn3
+    guu = gn3
+    gdd = gv3
+    gdet = gd[n]; n+=1
+    drdx = gd[n:n+16].view().reshape((4,4,nx,ny,nz),order='F').transpose(1,0,2,3,4); n+=16
+    dxdxp = drdx
+    if n != gd.shape[0]:
+        print("rd: WARNING: nread = %d < ntot = %d: incorrect format?" % (n, gd.shape[0]) )
+        return 1
+    return 0
+
+def gdump2_assign(gd,**kwargs):
+    global t,nx,ny,nz,N1,N2,N3,_dx1,_dx2,_dx3,a,gam,Rin,Rout,hslope,R0,ti,tj,tk,x1,x2,x3,gdet,games,rf1,hf1,phf1,rf2,hf2,phf2,rf3,hf3,phf3,rcorn,hcord,phcorn,re1,he1,phe1,re2,he2,phe2,re3,he3,phe3
+    nx = kwargs.pop("nx",nx)
+    ny = kwargs.pop("ny",ny)
+    nz = kwargs.pop("nz",nz)
+    ti,tj,tk,x1,x2,x3 = gd[0:6,:,:].view();  n = 6
+    rf1,hf1,phf1,rf2,hf2,phf2,rf3,hf3,phf3 = gd[0:9,:,:].view();  n += 9
+    rcorn,hcord,phcorn,rcent,hcent,phcen = gd[0:6,:,:].view();  n += 6
+    re1,he1,phe1,re2,he2,phe2,re3,he3,phe3 = gd[0:9,:,:].view();  n += 9
+    gdet = gd[n]; n+=1
+    if n != gd.shape[0]:
+        print("rd: WARNING: nread = %d < ntot = %d: incorrect format?" % (n, gd.shape[0]) )
+        return 1
+    return 0
+
+#read in a dump file
+def dump_assign(gd,**kwargs):
+    global t,nx,ny,nz,_dx1,_dx2,_dx3,gam,hslope,a,R0,Rin,Rout,ti,tj,tk,x1,x2,x3,r,h,ph,rho,ug,vu,B,pg,cs2,Sden,U,gdetB,divb,uu,ud,bu,bd,v1m,v1p,v2m,v2p,gdet,bsq,gdet,alpha,rhor, ktot, pg
+    nx = kwargs.pop("nx",nx)
+    ny = kwargs.pop("ny",ny)
+    nz = kwargs.pop("nz",nz)
+    ti,tj,tk,x1,x2,x3,r,h,ph,rho,ug = gd[0:11,:,:].view(); n = 11
+    pg = (gam-1)*ug
+    lrho=np.log10(rho)
+    vu=np.zeros_like(gd[0:4])
+    B=np.zeros_like(gd[0:4])
+    vu[1:4] = gd[n:n+3]; n+=3
+    B[1:4] = gd[n:n+3]; n+=3
+    #if total entropy equation is evolved (on by default)
+    if DOKTOT == 1:
+      ktot = gd[n]; n+=1
+    divb = gd[n]; n+=1
+    uu = gd[n:n+4]; n+=4
+    ud = gd[n:n+4]; n+=4
+    bu = gd[n:n+4]; n+=4
+    bd = gd[n:n+4]; n+=4
+    bsq = mdot(bu,bd)
+    v1m,v1p,v2m,v2p,v3m,v3p=gd[n:n+6]; n+=6
+    gdet=gd[n]; n+=1
+    rhor = 1+(1-a**2)**0.5
+    if "guu" in globals():
+        #lapse
+        alpha = (-guu[0,0])**(-0.5)
+    if n != gd.shape[0]:
+        print("rd: WARNING: nread = %d < ntot = %d: incorrect format?" % (n, gd.shape[0]) )
+        return 1
+    return 0
+
+def rdump_assign(gd,**kwargs):
+    global t,nx,ny,nz,_dx1,_dx2,_dx3,gam,hslope,a,R0,Rin,Rout,ti,tj,tk,x1,x2,x3,r,h,ph,rho,ug,vu,B,pg,cs2,Sden,U,gdetB,divb,uu,ud,bu,bd,v1m,v1p,v2m,v2p,gdet,bsq,gdet,alpha,rhor, ktot, Ttot, game, qisosq, pflag, qisodotb, kel, uelvar, Tel4, Tel5,Teldis, Tels, kel4, kel5,ugel,ugeldis, ugcon, sel, ugscon, ugel4, ugel5,stot, uelvar, Telvar, Tsel, sel, ugels, games, phi, keldis, phihat,csphib,lrho
+    nx = kwargs.pop("nx",nx)
+    ny = kwargs.pop("ny",ny)
+    nz = kwargs.pop("nz",nz)
+    n = 0
+    rho = gd[n]; n+=1
+    ug = gd[n]; n+=1
+    vu=np.zeros_like(gd[0:4])
+    B=np.zeros_like(gd[0:4])
+    vu[1:4] = gd[n:n+3]; n+=3
+    B[1:4] = gd[n:n+3]; n+=3
+    # if n != gd.shape[0]:
+    #     print("rd: WARNING: nread = %d < ntot = %d: incorrect format?" % (n, gd.shape[0]) )
+    #     return 1
+    return gd
+
+def fdump_assign(gd,**kwargs):
+    global t,nx,ny,nz,_dx1,_dx2,_dx3,gam,hslope,a,R0,Rin,Rout,ti,tj,tk,x1,x2,x3,r,h,ph,rho,ug,vu,B,pg,cs2,Sden,U,gdetB,divb,uu,ud,bu,bd,v1m,v1p,v2m,v2p,gdet,bsq,gdet,alpha,rhor, ktot, Ttot, game, qisosq, pflag, qisodotb, kel, uelvar, Tel4, Tel5,Teldis, Tels, kel4, kel5,ugel,ugeldis, ugcon, sel, ugscon, ugel4, ugel5,stot, uelvar, Telvar, Tsel, sel, ugels, games, phi, keldis, phihat,csphib,lrho,fail
+    nx = kwargs.pop("nx",nx)
+    ny = kwargs.pop("ny",ny)
+    nz = kwargs.pop("nz",nz)
+    fail = gd
+    return gd
+
+
+
+def mdot(a,b):
+    """
+    Computes a contraction of two tensors/vectors.  Assumes
+    the following structure: tensor[m,n,i,j,k] OR vector[m,i,j,k], 
+    where i,j,k are spatial indices and m,n are variable indices. 
+    """
+    if (a.ndim == 3 and b.ndim == 3) or (a.ndim == 4 and b.ndim == 4):
+          c = (a*b).sum(0)
+    elif a.ndim == 5 and b.ndim == 4:
+          c = np.empty(np.maximum(a[:,0,:,:,:].shape,b.shape),dtype=b.dtype)
+          for i in range(a.shape[0]):
+                c[i,:,:,:] = (a[i,:,:,:,:]*b).sum(0)
+    elif a.ndim == 4 and b.ndim == 5:
+          c = np.empty(np.maximum(b[0,:,:,:,:].shape,a.shape),dtype=a.dtype)
+          for i in range(b.shape[1]):
+                c[i,:,:,:] = (a*b[:,i,:,:,:]).sum(0)
+    elif a.ndim == 5 and b.ndim == 5:
+          c = np.empty((a.shape[0],b.shape[1],a.shape[2],a.shape[3],max(a.shape[4],b.shape[4])),dtype=a.dtype)
+          for i in range(c.shape[0]):
+                for j in range(c.shape[1]):
+                      c[i,j,:,:,:] = (a[i,:,:,:,:]*b[:,j,:,:,:]).sum(0)
+    elif a.ndim == 5 and b.ndim == 6:
+          c = np.empty((a.shape[0],b.shape[1],b.shape[2],max(a.shape[2],b.shape[3]),max(a.shape[3],b.shape[4]),max(a.shape[4],b.shape[5])),dtype=a.dtype)
+          for mu in range(c.shape[0]):
+              for k in range(c.shape[1]):
+                  for l in range(c.shape[2]):
+                      c[mu,k,l,:,:,:] = (a[mu,:,:,:,:]*b[:,k,l,:,:,:]).sum(0)
+    else:
+           raise Exception('mdot', 'wrong dimensions')
+    return c
+
+
+def psicalc(B1=None):
+    """
+    Computes the field vector potential
+    """
+    global B
+    if B1 is None: B1 = B[1]
+    daphi = -(gdet*B1).mean(-1)*_dx2
+    aphi=daphi[:,::-1].cumsum(axis=1)[:,::-1]
+    aphi-=0.5*daphi #correction for half-cell shift between face and center in theta
+    return(aphi)
+
+
+def myfloat(f,acc=1):
+    """ acc=1 means np.float32, acc=2 means np.float64 """
+    if acc==1:
+        return( np.float32(f) )
+    else:
+        return( np.float64(f) )
+
+def get_fracphi():
+    fracphi = dxdxp[3,3,0,0,0]*_dx3*nz/(2*np.pi)
+    return( fracphi )
+
+def plco(myvar,**kwargs):
+    global r,h,ph
+    plt.clf()
+    return plc(myvar,**kwargs)
+
+def plc(myvar,**kwargs): #plc
+    global r,h,ph
+    #xcoord = kwargs.pop('x1', None)
+    #ycoord = kwargs.pop('x2', None)
+    if(np.min(myvar)==np.max(myvar)):
+        print("The quantity you are trying to plot is a constant = %g." % np.min(myvar))
+        return
+    cb = kwargs.pop('cb', False)
+    nc = kwargs.pop('nc', 15)
+    k = kwargs.pop('k',0)
+    mirrorx = kwargs.pop('mirrorx',0)
+    mirrory = kwargs.pop('mirrory',0)
+    symmx = kwargs.pop('symmx',0)
+    #cmap = kwargs.pop('cmap',cm.jet)
+    isfilled = kwargs.pop('isfilled',False)
+    xy = kwargs.pop('xy',0)
+    xcoord = kwargs.pop("xcoord",None)
+    ycoord = kwargs.pop("ycoord",None)
+    lin = kwargs.pop('lin',0)
+    xmax = kwargs.pop('xmax',10)
+    ymax = kwargs.pop('ymax',5)
+    cbxlabel = kwargs.pop('cbxla',None)
+    cbylabel = kwargs.pop('cbyla',None)
+    fntsize = kwargs.pop("fntsize",20)
+    cbgoodticks = kwargs.pop("cbgoodticks",1)
+    xlabel = kwargs.pop("xla",None)
+    ylabel = kwargs.pop("yla",None)
+    dobh = kwargs.pop("dobh",1)
+    pretty = kwargs.pop("pretty",0)
+    ax = kwargs.pop("ax",None)
+    cbticks = kwargs.pop("cbticks",None)
+    domathify = kwargs.pop("domathify",0)
+    if np.abs(xy)==1:
+        if xcoord is None: xcoord = r * np.sin(h)
+        if ycoord is None: ycoord = r * np.cos(h)
+        if mirrory: ycoord *= -1
+        if mirrorx: xcoord *= -1
+    if xcoord is not None and ycoord is not None:
+        xcoord = xcoord[:,:,None] if xcoord.ndim == 2 else xcoord[:,:,k:k+1]
+        ycoord = ycoord[:,:,None] if ycoord.ndim == 2 else ycoord[:,:,k:k+1]
+    if np.abs(xy)==1 and symmx:
+        if myvar.ndim == 2:
+            myvar = myvar[:,:,None] if myvar.ndim == 2 else myvar[:,:,k:k+1]
+            myvar=np.concatenate((myvar[:,::-1],myvar),axis=1)
+            xcoord=np.concatenate((-xcoord[:,::-1],xcoord),axis=1)
+            ycoord=np.concatenate((ycoord[:,::-1],ycoord),axis=1)
+        else:
+            if myvar.shape[-1] > 1: 
+                symmk = (k+nz//2)%nz 
+            else: 
+                symmk = k
+            myvar=np.concatenate((myvar[:,ny-1:ny,k:k+1],myvar[:,::-1,symmk:symmk+1],myvar[:,:,k:k+1]),axis=1)
+            xcoord=np.concatenate((xcoord[:,ny-1:ny,k:k+1],-xcoord[:,::-1],xcoord),axis=1)
+            ycoord=np.concatenate((ycoord[:,ny-1:ny,k:k+1],ycoord[:,::-1],ycoord),axis=1)
+    elif np.abs(xy) == 2 and symmx:
+        #if fracphi == 0.5 done in a robust way
+        if get_fracphi() < 0.75:
+            r1 = np.concatenate((r,r,r[...,0:1]),axis=2)
+            ph1 = np.concatenate((ph,ph+np.pi,ph[...,0:1]+2*np.pi),axis=2)
+            myvar = np.concatenate((myvar,myvar,myvar[...,0:1]),axis=2)
+        else:
+            r1 = np.concatenate((r,r[...,0:1]),axis=2)
+            ph1 = np.concatenate((ph,ph[...,0:1]+2*np.pi),axis=2)
+            myvar = np.concatenate((myvar,myvar[...,0:1]),axis=2)
+        xcoord=(r1*cos(ph1))[:,ny//2,:,None]
+        ycoord=(r1*sin(ph1))[:,ny//2,:,None]
+        myvar = myvar[:,ny//2,:,None]
+    else:
+        myvar = myvar[:,:,None] if myvar.ndim == 2 else myvar[:,:,k:k+1]
+    if lin:
+        xcoord = r
+        ycoord = h
+    if ax is None:
+        ax = plt.gca()
+    if  xcoord is None or ycoord is None:
+        if isfilled:
+            res = ax.contourf(myvar[:,:,0].transpose(),nc,**kwargs)
+        else:
+            res = ax.contour(myvar[:,:,0].transpose(),nc,**kwargs)
+    else:
+        if isfilled:
+            res = ax.contourf(xcoord[:,:,0],ycoord[:,:,0],myvar[:,:,0],nc,**kwargs)
+        else:
+            res = ax.contour(xcoord[:,:,0],ycoord[:,:,0],myvar[:,:,0],nc,**kwargs)
+    if xy>0 and not symmx:
+        ax.set_xlim(0,xmax)
+        ax.set_ylim(-ymax,ymax)
+    if xy> 0 and symmx:
+        ax.set_xlim(-xmax,xmax)
+        ax.set_ylim(-ymax,ymax)
+    if xlabel is not None:
+        ax.set_xlabel(xlabel,fontsize=fntsize)
+    if ylabel is not None:
+        ax.set_ylabel(ylabel,fontsize=fntsize)
+    if pretty:
+        for label in ax.get_xticklabels() + ax.get_yticklabels():
+            label.set_fontsize(fntsize)
+            if domathify: mathify_axes_ticks(ax,fontsize=fntsize)
+    if cb: #use color bar
+        cb = plt.colorbar(res,ax=ax)
+        if pretty and cbgoodticks and cbticks is None:
+            vmin = cb.vmin
+            vmax = cb.vmax
+            #this returns incorrect ticks! so ignore it
+            #ticks = cb.ax.get_yticks()
+            #nticks = len(ticks)
+            #if not too many ticks, then pretty them up
+            rvmin = np.round(vmin)
+            rvmax = np.round(vmax)
+            if rvmin == vmin and rvmax == vmax and vmax-vmin <= 10:
+                ticks = np.arange(rvmin,rvmax+1)
+                cb.set_ticks(ticks)
+                mathify_axes_ticks(cb.ax,fontsize=fntsize,yticks=ticks)
+            elif rvmin == vmin and rvmax == vmax and vmax-vmin <= 20:
+                ticks = np.arange(rvmin,rvmax+1)[::2]
+                cb.set_ticks(ticks)
+                mathify_axes_ticks(cb.ax,fontsize=fntsize,yticks=ticks)
+        if cbticks is not None:
+            cb.set_ticks(cbticks)
+            mathify_axes_ticks(cb.ax,fontsize=fntsize,yticks=cbticks)
+        if cbxlabel is not None:
+            cb.ax.set_xlabel(cbxlabel,fontsize=fntsize)
+        if cbxlabel is not None:
+            cb.ax.set_xlabel(cbxlabel,fontsize=fntsize)
+        if cbylabel is not None:
+            cb.ax.set_ylabel(cbylabel,fontsize=fntsize)
+        if pretty:
+            for label in cb.ax.get_yticklabels():
+                label.set_fontsize(fntsize)
+    if xy and dobh and "rhor" in globals(): 
+        el = Ellipse((0,0), 2*rhor, 2*rhor, facecolor='k', alpha=1)
+        art=ax.add_artist(el)
+        art.set_zorder(20)
+    if cb:
+        return res, cb
+    else:
+        return res
+
+def faraday():
+    global omegaf1, omegaf2
+    if 'omegaf1' in globals():
+        del omegaf1
+    if 'omemaf2' in globals():
+        del omegaf2
+    omegaf1=fFdd(0,1)/fFdd(1,3)
+    omegaf2=fFdd(0,2)/fFdd(2,3)
+
+def Tcalcud():
+    global Tud, TudEM, TudMA
+    global mu, sigma
+    global enth
+    global unb, isunbound
+    pg = (gam-1)*ug
+    w=rho+ug+pg
+    eta=w+bsq
+    if 'Tud' in globals():
+        del Tud
+    if 'TudMA' in globals():
+        del TudMA
+    if 'TudEM' in globals():
+        del TudEM
+    if 'mu' in globals():
+        del mu
+    if 'sigma' in globals():
+        del sigma
+    if 'unb' in globals():
+        del unb
+    if 'isunbound' in globals():
+        del isunbound
+    Tud = np.zeros((4,4,nx,ny,nz),dtype=np.float32,order='F')
+    TudMA = np.zeros((4,4,nx,ny,nz),dtype=np.float32,order='F')
+    TudEM = np.zeros((4,4,nx,ny,nz),dtype=np.float32,order='F')
+    for kapa in np.arange(4):
+        for nu in np.arange(4):
+            if(kapa==nu): delta = 1
+            else: delta = 0
+            TudEM[kapa,nu] = bsq*uu[kapa]*ud[nu] + 0.5*bsq*delta - bu[kapa]*bd[nu]
+            TudMA[kapa,nu] = w*uu[kapa]*ud[nu]+pg*delta
+            #Tud[kapa,nu] = eta*uu[kapa]*ud[nu]+(pg+0.5*bsq)*delta-bu[kapa]*bd[nu]
+            Tud[kapa,nu] = TudEM[kapa,nu] + TudMA[kapa,nu]
+    mu = -Tud[1,0]/(rho*uu[1])
+    sigma = TudEM[1,0]/TudMA[1,0]
+    enth=1+ug*gam/rho
+    unb=enth*ud[0]
+    isunbound=(-unb>1.0)
+
+
+def aux():
+    faraday()
+    Tcalcud()
+
+if __name__ == "__main__":
+    if False:
+        #1D plot example
+        plt.clf()
+        rg("gdump")
+        rd("dump000")
+        plt.plot(r[:,ny//2,0],rho[:,ny//2,0])
+        plt.xscale("log")
+        plt.yscale("log")
+        plt.xlabel("r")
+        plt.ylabel("rho")
+    if False:
+        #2D plot example
+        plt.clf()
+        rg("gdump")
+        rd("dump000")
+        #R-z plot of the logarithm of density distribution
+        plc(r,np.log10(rho),cb=True,xy=1,xmax=100,ymax=50)
+
+
+
+
+def bhole():
+
+    ax = plt.gca()
+    el = Ellipse((0,0), 2*rhor, 2*rhor, facecolor='k', alpha=1)
+    art=ax.add_artist(el)
+    art.set_zorder(20)
+    plt.draw()
+
+
+
+def testfail(fldname = "dump000"):
+    try: 
+        rd(fldname)
+    except IOError as e:
+        print("I/O error({0}): {1}".format(e.errno, e.strerror))
+
+
+def get_sorted_file_list(prefix="dump"):
+    flist0 = np.sort(glob.glob( os.path.join("dumps/", "%s[0-9][0-9][0-9]"%prefix) ) )
+    flist1 = np.sort(glob.glob( os.path.join("dumps/", "%s[0-9][0-9][0-9][0-9]"%prefix) ) )
+    flist2 = np.sort(glob.glob( os.path.join("dumps/", "%s[0-9][0-9][0-9][0-9][0-9]"%prefix) ) )
+    flist0.sort()
+    flist1.sort()
+    flist2.sort()
+    flist = np.concatenate((flist0,flist1,flist2))
+    return flist
+    
+
+
+def fFdd(i,j):
+    if i==0 and j==1:
+        fdd =  gdet*(uu[2]*bu[3]-uu[3]*bu[2]) # f_tr
+    elif i==1 and j==0:
+        fdd = -gdet*(uu[2]*bu[3]-uu[3]*bu[2]) # -f_tr
+    elif i==0 and j==2:
+        fdd =  gdet*(uu[3]*bu[1]-uu[1]*bu[3]) # f_th
+    elif i==2 and j==0:
+        fdd = -gdet*(uu[3]*bu[1]-uu[1]*bu[3]) # -f_th
+    elif i==0 and j==3:
+        fdd =  gdet*(uu[1]*bu[2]-uu[2]*bu[1]) # f_tp
+    elif i==3 and j==0:
+        fdd = -gdet*(uu[1]*bu[2]-uu[2]*bu[1]) # -f_tp
+    elif i==1 and j==3:
+        fdd =  gdet*(uu[2]*bu[0]-uu[0]*bu[2]) # f_rp = gdet*B2
+    elif i==3 and j==1:
+        fdd = -gdet*(uu[2]*bu[0]-uu[0]*bu[2]) # -f_rp = gdet*B2
+    elif i==2 and j==3:
+        fdd =  gdet*(uu[0]*bu[1]-uu[1]*bu[0]) # f_hp = gdet*B1
+    elif i==3 and j==2:
+        fdd = -gdet*(uu[0]*bu[1]-uu[1]*bu[0]) # -f_hp = gdet*B1
+    elif i==1 and j==2:
+        fdd =  gdet*(uu[0]*bu[3]-uu[3]*bu[0]) # f_rh = gdet*B3
+    elif i==2 and j==1:
+        fdd = -gdet*(uu[0]*bu[3]-uu[3]*bu[0]) # -f_rh = gdet*B3
+    else:
+        fdd = np.zeros_like(uu[0])
+    return fdd
+
+delta = lambda kapa,nu: (kapa==nu)
+fTudEM = lambda kapa,nu: bsq*uu[kapa]*ud[nu] + 0.5*bsq*delta(kapa,nu) - bu[kapa]*bd[nu]
+fTudMA = lambda kapa,nu: (rho+gam*ug)*uu[kapa]*ud[nu]+(gam-1)*ug*delta(kapa,nu)
+fTud = lambda kapa,nu: fTudEM(kapa,nu) + fTudMA(kapa,nu)
+fRud = lambda kapa,nu: 4./3.*Erf*uradu[kapa]*uradd[nu]+1./3.*Erf*delta(kapa,nu)
+
+def odot(a,b):
+    """ Outer product of two vectors a^mu b_nu"""
+    #the shape of the product is (4,4,nx,ny,max(a.nz,b.nz))
+    outer_product = np.zeros(np.concatenate((np.array((4,4)),amax(a[0].shape,b[0].shape))),dtype=np.float32,order='F')
+    for mu in np.arange(4):
+        for nu in np.arange(4):
+            outer_product[mu,nu] = a[mu]*b[nu]
+    return(outer_product)
+
+
+def amax(arg1,arg2):
+    return(np.maximum(arg1,arg2))
+
+def amin(arg1,arg2):
+    return(np.minimum(arg1,arg2))
+
+def sph_to_cart(var,bs1,bs2,bs3):
+    var_cart=np.zeros_like(var)
+    J_cart=np.zeros((4,4,bs1,bs2,bs3))
+    J_cart[0,0,:,:,:]=1.0
+    J_cart[1,1,:,:,:]=(np.sin(h)*np.cos(ph))[:,:,:]
+    J_cart[1,2,:,:,:]=(r*np.cos(h)*np.cos(ph))[:,:,:]
+    J_cart[1,3,:,:,:]=0.;#-(r*np.sin(h)*np.sin(ph))[:,:,:]
+    J_cart[2,1,:,:,:]=0.;#(np.sin(h)*np.sin(ph))[:,:,:]
+    J_cart[2,2,:,:,:]=0.;#(r*np.cos(h)*np.sin(ph))[:,:,:]
+    J_cart[2,3,:,:,:]=(r*np.sin(h)*np.cos(ph))[:,:,:]
+    J_cart[3,1,:,:,:]=np.cos(h[:,:,:])
+    J_cart[3,2,:,:,:]=-(r*np.sin(h))[:,:,:]
+    var_cart[:]=mdot(J_cart[:,:],var[:])
+    return var_cart
+
+def velocity_cart():
+	global vu_cart, vkerr, uu_cart, uukerr
+	beta=np.zeros((4,bs1,bs2,1))
+	vkerr=np.zeros_like(uu)
+	uukerr=np.zeros_like(uu)
+	uu_cart=np.zeros_like(uu)
+	uu_KS=np.zeros_like(uu)
+	vu_cart=np.zeros_like(uu)
+	uu_cart=np.zeros_like(uu)
+			
+	alpha = 1./(-gcon[0,0])**0.5
+	beta[1:4] = gcon[0,1:4]*alpha*alpha
+	vkerr[1:4] = (beta[1:4] + uu[1:4]/uu[0])/alpha
+	vkerr[:]=mdot(dxdxp[:,:], vkerr[:])
+	uukerr[:]=mdot(dxdxp[:,:],uu[:])
+	vu_cart=sph_to_cart(vkerr,bs1,bs2,bs3)
+	uu_cart=sph_to_cart(uukerr,bs1,bs2,bs3)
+
+
+def cart_coords():
+	global coord_cart,coord_KS,bs1,bs2,bs3,X
+	bs1=len(r[:,0,0])
+	bs2=len(r[0,:,0])
+	bs3=len(r[0,0,:])
+	coord_KS=np.zeros_like(uu)
+	X=np.zeros_like(uu)
+	coord_cart=np.zeros_like(uu)
+	coord_KS[1]=r
+	coord_KS[2]=h
+	coord_KS[3]=ph
+	coord_cart=sph_to_cart(coord_KS,bs1,bs2,bs3)
+	X[1]=r*np.sin(h)
+	X[3]=r*np.cos(h)
+
+def sph_to_cart2(X, h, ph):
+    X[1] = np.sin(h) * np.cos(ph)
+    X[2] = np.sin(h) * np.sin(ph)
+    X[3] = np.cos(h)
+
+
+#plot in cartesian coordinates
+def movie(dumpstart,dumpend, xmax1=20, skip1=16, skip2=16):
+	str=os.getcwd()
+	for itot in range(dumpstart,dumpend+1):
+		rg("gdump")
+		rd("dump%03d" %itot)
+		ph[:,:,:]=0.0;
+		cart_coords()
+
+		velocity_cart()
+		plco(np.log10(rho),xy=1,xmax=xmax1,ymax=xmax1/2,cb=True,isfilled=1,levels=np.arange(-0.5,2,0.01),cmap=cm.jet)
+		plt.suptitle(r"$log_{10}(\rho)$ at $t=%d r_g/c$"%t)
+		ax=plt.gca()
+		#ax.quiver(X[1,::skip1,::skip2,0],X[3,::skip1,::skip2,0],vu_cart[1,::skip1,::skip2,0]/np.sqrt(vu_cart[1,::skip1,::skip2,0]**2+vu_cart[3,::skip1,::skip2,0]**2),vu_cart[3,::skip1,::skip2,0]/np.sqrt(vu_cart[1,::skip1,::skip2,0]**2+vu_cart[3,::skip1,::skip2,0]**2))
+		ax.quiver(X[1,::skip1,::skip2,0],X[3,::skip1,::skip2,0],uu_cart[1,::skip1,::skip2,0]/np.sqrt(uu_cart[1,::skip1,::skip2,0]**2+uu_cart[3,::skip1,::skip2,0]**2),uu_cart[3,::skip1,::skip2,0]/np.sqrt(uu_cart[1,::skip1,::skip2,0]**2+uu_cart[3,::skip1,::skip2,0]**2))
+		plt.savefig(str+"/images/BHL_%d.png"%itot,dpi=100)
+		plt.close()
+		plt.cla()
+		plt.clf()
+
+
+#plot in the code coordinates, dubbed as internal coordinates
+def int_movie(dumpstart,dumpend, xmax1=20, skip1=16, skip2=16):
+	str=os.getcwd()
+	for itot in range(dumpstart,dumpend+1):
+		rg("gdump")
+		rd("dump%03d" %itot)
+		ph[:,:,:]=0.0;
+		cart_coords()
+
+		velocity_cart()
+		plt.figure(figsize=(8,8))
+		plc(np.log10(rho),xy=0,xcoord=np.log10(r),ycoord=x2,cb=True,isfilled=1,levels=np.arange(-0.5,2,0.01),cmap=cm.jet)
+		plt.suptitle(r"$log_{10}(\rho)$ at $t=%d r_g/c$"%t)
+		ax=plt.gca()
+		#ax.quiver(X[1,::skip1,::skip2,0],X[3,::skip1,::skip2,0],vu_cart[1,::skip1,::skip2,0]/np.sqrt(vu_cart[1,::skip1,::skip2,0]**2+vu_cart[3,::skip1,::skip2,0]**2),vu_cart[3,::skip1,::skip2,0]/np.sqrt(vu_cart[1,::skip1,::skip2,0]**2+vu_cart[3,::skip1,::skip2,0]**2))
+		ax.quiver(np.log10(r[::skip1,::skip2,0]),x2[::skip1,::skip2,0],uu[1,::skip1,::skip2,0]/np.sqrt(gcov[1,1,::skip1,::skip2,0]*uu[1,::skip1,::skip2,0]**2+gcov[2,2,::skip1,::skip2,0]*uu[2,::skip1,::skip2,0]**2),uu[2,::skip1,::skip2,0]/np.sqrt(gcov[1,1,::skip1,::skip2,0]*uu[1,::skip1,::skip2,0]**2+gcov[2,2,::skip1,::skip2,0]*uu[2,::skip1,::skip2,0]**2))
+		plt.savefig(str+"/images/BHL_one_%d.png"%itot,dpi=100)
+		
+		plt.close()
+		plt.cla()
+		plt.clf()
+
+
+#plot accretion rate vs time
+def accretion_rate(dumpstart,dumpend, v_inf):
+	global time, Mdot
+	time=np.zeros(dumpend-dumpstart)
+	Mdot=np.zeros(dumpend-dumpstart)
+	
+	Mdot_HL=4.*np.pi/(v_inf**3)
+	time_HL=2./(v_inf**3)
+	for itot in range(dumpstart,dumpend):
+		rg("gdump")
+		rd("dump%03d" %itot)
+		cell=0
+		while(r[cell,0,0]<rhor):
+			cell+=1
+		ph[:,:,:]=0.0;
+		Mdot[itot-dumpstart]=((-gdet*rho*uu[1]*_dx2*_dx3).sum(-1).sum(-1))[cell]/Mdot_HL
+		time[itot-dumpstart]=t/time_HL
+	plt.plot(time,Mdot)
+	plt.ylabel(r"$\dot{M}/\dot{M}_{\rm HL}$")
+	plt.xlabel(r"$t/t_{\rm HL}$")
+	plt.show()
+		
+
+#############################
+#
+# End of movie making
+#
+#############################
+        
+    
+if __name__ == "__main__":
+    if len(sys.argv)>1:
+        if sys.argv[1].startswith("mkfrm"):
+            mkmov_wrapper(which=sys.argv[1])
+        elif sys.argv[1].startswith("convertdump"):
+            convert_wrapper(which=sys.argv[1])
+        else:
+            print( "Unknown command %s" % sys.argv[1] )
+    else:
+        if False:
+            #1D plot example
+            plt.clf()
+            rg("gdump")
+            rd("dump000")
+            plt.loglog(r[:,ny//2,0],rho[:,ny//2,0])
+            plt.xlabel("r")
+            plt.ylabel("rho")
+        if False:
+            #2D plot example
+            plt.clf()
+            rg("gdump")
+            rd("dump000")
+            #R-z plot of the logarithm of density distribution
+            plc(np.log10(rho),cb=True,xy=1,xmax=100,ymax=50)
+
diff --git a/harmpi_BHL.mp4 b/harmpi_materials/harmpi_BHL.mp4
similarity index 100%
rename from harmpi_BHL.mp4
rename to harmpi_materials/harmpi_BHL.mp4
diff --git a/harmpi_BHLdisk2D.mp4 b/harmpi_materials/harmpi_BHLdisk2D.mp4
similarity index 100%
rename from harmpi_BHLdisk2D.mp4
rename to harmpi_materials/harmpi_BHLdisk2D.mp4
diff --git a/harmpi_materials/harmpi_analysis_tutorial.ipynb b/harmpi_materials/harmpi_analysis_tutorial.ipynb
new file mode 100755
index 0000000..210b16d
--- /dev/null
+++ b/harmpi_materials/harmpi_analysis_tutorial.ipynb
@@ -0,0 +1,537 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## harmpi analysis tutorial\n",
+    "\n",
+    "the purpose of this tutorial is to get a first feeling for analyzing output of a numerical simulation\n",
+    "\n",
+    "here we will use a harmpi magnetized torus run. please make sure you have the files dump000, dump100, gdump in the dumps/ folder and place this notebook in the main simulation directory (next to harm_script.py)\n",
+    "\n",
+    "as we go, think about items marked \"Q:\", discuss with your neighbors, and ask for help at any step where you get stuck"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "first import some things -- **if it fails**, make sure harm_script.py is in the same directory and there are data files in a dumps/ directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "%matplotlib inline\n",
+    "import matplotlib; matplotlib.rcParams['axes.unicode_minus']=False; matplotlib.rcParams['image.cmap']='inferno'\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "# make plots out to radius r=40 r_g\n",
+    "xr=40.\n",
+    "\n",
+    "%run -i harm_script.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### read in the simulation grid file\n",
+    "\n",
+    "the data are stored as arrays with 3 dimensions, corresponding to simulation coordinates x1,x2,x3<br>\n",
+    "variables are stored as arrays with dimensions (nx1,nx2,nx3)\n",
+    "\n",
+    "### Q: why is our 3rd dimension of size 1?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rg(\"gdump\")\n",
+    "print(np.shape(x1))\n",
+    "\n",
+    "print(x1[:5,0,0])\n",
+    "print(x1[0,:5,0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### read the dump file of the initial conditions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rd(\"dump000\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### harm_script.py has built in functions for making 2D contour plots\n",
+    "### Q: what variables did we make plots of? (refer to harmpi_tutorial.md for definitions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cs, cb = plco(rho,isfilled=1,k=0,xy=1,xmax=xr,ymax=25,dobh=1,cb=1,pretty=1)\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')\n",
+    "plt.figure()\n",
+    "cs, cb = plco(ug,isfilled=1,k=0,xy=1,xmax=xr,ymax=25,dobh=1,cb=1,pretty=1)\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')\n",
+    "plt.figure()\n",
+    "cs, cb = plco(bsq,isfilled=1,k=0,xy=1,xmax=xr,ymax=25,dobh=1,cb=1,pretty=1)\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### visualization is a key part of numerical simulation work! compare to different versions below\n",
+    "\n",
+    "### Q: what is the difference? what do you think are the pros/cons?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cs, cb = plco(np.log10(rho),isfilled=1,k=0,xy=1,xmax=xr,ymax=25,dobh=1,cb=1,pretty=1,levels=-10.+10*np.arange(60)/59.,extend=\"both\")\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')\n",
+    "plt.figure()\n",
+    "cs, cb = plco(np.log10(ug),isfilled=1,k=0,xy=1,xmax=xr,ymax=25,dobh=1,cb=1,pretty=1,levels=-10.+8.5*np.arange(60)/59.,extend=\"both\")\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')\n",
+    "plt.figure()\n",
+    "cs, cb = plco(np.log10(bsq),isfilled=1,k=0,xy=1,xmax=xr,ymax=25,dobh=1,cb=1,pretty=1,levels=-14.+12*np.arange(60)/59.,extend=\"both\")\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q: experiment on your own using the \"plco\" function in harm_script.py. enter one or more new plots in the blank line below.\n",
+    "\n",
+    "### some first changes could be the plot bounds xmax, ymax; the contour levels defined by \"levels\", or try plotting a different variable from harmpi_tutorial.md"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## ADD ONE OR MORE NEW PLOTS HERE!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### quantitatively it's usually easier to look at 1D data, and as a result we want to do slicing or averaging\n",
+    "\n",
+    "### Q: first, define a radius coordinate in 1D which has size nx1. Call it r1d and enter it in the line below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r1d = ## ENTER HERE##\n",
+    "\n",
+    "print(np.shape(r1d))\n",
+    "# did it work correctly? how could you check?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### define a 1D slice through the equatorial plane"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def midplane(x):\n",
+    "    return x[:,ny/2,0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### define the integral over surface area ,\\int dA, as a sum over angles on our grid, with dA in each cell related to the metric determinant gdet\n",
+    "### example: in flat space spherical coordinates, dA = r^2 sin theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sum_angles(x):\n",
+    "    return np.sum(np.sum(x*gdet,-1),-1)*_dx2*_dx3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### finally define a radial weighted average by the fluid mass density rho, <x> = Sum dA x rho / Sum dA rho\n",
+    "### Q: why might density-weighted averages might be useful? why or why not? talk to your neighbors!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def shell(x):\n",
+    "    return sum_angles(x*rho)/sum_angles(rho)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### the accretion rate is the inward mass flux as a function of radius, Mdot(r) = \\int dA \\rho u^r\n",
+    "### where u^r is the radial coordinate velocity as seen at infinity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FM_000 = sum_angles(-rho*uu[1])\n",
+    "plt.plot(r1d,FM_000)\n",
+    "plt.xlim(0,xr)\n",
+    "plt.xlabel('$r$ ($r_g$)'); plt.ylabel('accretion rate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### let's look at some average properties for the initial dump000\n",
+    "**Omega** = u^phi / u^t is the rotation frequency,<br>\n",
+    "**v^r** = u^r / u^t is a radial velocity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Omega_r_000=shell(uu[3]/uu[0])\n",
+    "vr_r_000=shell(uu[1]/uu[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q: how would you summarize the motion of the torus?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(r1d,vr_r_000,label='radial velocity')\n",
+    "plt.plot(r1d,Omega_r_000*r1d,label='rotational velocity')\n",
+    "plt.xlim(6,xr)\n",
+    "plt.xlabel('$r$ ($r_g$)'); plt.ylabel('velocity (c)')\n",
+    "plt.legend(loc='upper right',fontsize=12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### define some more quantities:\n",
+    "**pgas** is the gas pressure,<br>\n",
+    "**b^2** is the squared magnetic field strength,<br>\n",
+    "**beta** is the relative pressure contributed by gas and magnetic fields,<br>\n",
+    "**hr** is the scale height,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hr_000=np.sqrt(shell((r*np.cos(h))**2.))/r1d\n",
+    "pgas_r_000 = shell(ug*(gam-1.))\n",
+    "bsq_r_000 = shell(bsq)\n",
+    "beta_r_000 = pgas_r_000/bsq_r_000*2."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**beta ~ 1** means that the magnetic fields push back on the gas. usually we call this \"strong\" while **beta >> 1** means the field is weak.\n",
+    "### Q: by that definition, are the magnetic fields in our initial torus weak or strong?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.semilogy(r1d,beta_r_000)\n",
+    "plt.xlabel(r'$r$ ($r_g$)'); plt.ylabel(r'plasma beta $\\beta$')\n",
+    "plt.xlim(0,xr); plt.ylim(1.,1e6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the scale height tells us how the material is distributed vertically, **h/r << 1 \"thin\"** or **h/r ~ 1 \"thick\"**\n",
+    "### Q: would you say our initial torus is geometrically thin or thick?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(r1d,hr_000)\n",
+    "plt.xlim(6,xr)\n",
+    "plt.xlabel('$r$ ($r_g$)'); plt.ylabel('scale height $h/r$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### now let's look at a later time step!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "rd(\"dump100\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### what has **changed** by t = 1000 GM/c^3?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cs, cb = plco(np.log10(rho),isfilled=1,k=0,xy=1,xmax=xr,ymax=25,dobh=1,cb=1,pretty=1,levels=-10.+10*np.arange(60)/59.,extend=\"both\")\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')\n",
+    "plt.figure()\n",
+    "cs, cb = plco(np.log10(ug),isfilled=1,k=0,xy=1,xmax=xr,ymax=25,dobh=1,cb=1,pretty=1,levels=-10.+8.5*np.arange(60)/59.,extend=\"both\")\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')\n",
+    "plt.figure()\n",
+    "cs, cb = plco(np.log10(bsq),isfilled=1,k=0,xy=1,xmax=xr,ymax=25,dobh=1,cb=1,pretty=1,levels=-14.+12*np.arange(60)/59.,extend=\"both\")\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### now zoom in by changing the bounds of the plot xmax,ymax\n",
+    "\n",
+    "### Q: what has changed by t=1000 GM/c^3?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cs, cb = plco(np.log10(rho),isfilled=1,k=0,xy=1,xmax=10,ymax=5,dobh=1,cb=1,pretty=1,levels=-10.+10*np.arange(60)/59.,extend=\"both\")\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')\n",
+    "plt.figure()\n",
+    "cs, cb = plco(np.log10(ug),isfilled=1,k=0,xy=1,xmax=10,ymax=5,dobh=1,cb=1,pretty=1,levels=-10.+8.5*np.arange(60)/59.,extend=\"both\")\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')\n",
+    "plt.figure()\n",
+    "cs, cb = plco(np.log10(bsq),isfilled=1,k=0,xy=1,xmax=10,ymax=5,dobh=1,cb=1,pretty=1,levels=-14.+12*np.arange(60)/59.,extend=\"both\")\n",
+    "plt.xlabel('$x$ ($r_g$)'); plt.ylabel('$z$ ($r_g$)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "calculate the **inward mass flux** at this later time\n",
+    "### Q: where is material infalling or outflowing? is that what you expect? \n",
+    "### Q: what about at the event horizon? is there gas falling onto the black hole?\n",
+    "the inward mass flux at the event horizon is called the **accretion rate**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FM_100 = sum_angles(-rho*uu[1])\n",
+    "plt.plot(r1d,FM_000,label='t=0')\n",
+    "plt.plot(r1d,FM_100,label='1000')\n",
+    "plt.legend(fontsize=12)\n",
+    "plt.xlim(0,xr)\n",
+    "plt.xlabel(r'$r$ ($r_g$)'); plt.ylabel('inward radial mass flux')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "re-calculate our averaged 1D quantities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hr_100=np.sqrt(shell((r*np.cos(h))**2.))/r1d\n",
+    "Omega_r_100=shell(uu[3]/uu[0])\n",
+    "vr_r_100=shell(uu[1]/uu[0])\n",
+    "pgas_r_100 = shell(ug*(gam-1.))\n",
+    "bsq_r_100 = shell(bsq)\n",
+    "beta_r_100 = pgas_r_100/bsq_r_100*2."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### plot comparisons of b^2, beta between early and late time\n",
+    "### Q: how does the magnetic field strength compare?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.semilogy(r1d,bsq_r_000,label='t=0')\n",
+    "plt.semilogy(r1d,bsq_r_100,label='1000')\n",
+    "plt.xlim(0,xr); plt.ylim(1e-8,1e-2)\n",
+    "plt.legend(fontsize=12)\n",
+    "plt.xlabel(r'$r$ ($r_g$)'); plt.ylabel(r'$b^2$')\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.semilogy(r1d,beta_r_000,label='t=0')\n",
+    "plt.semilogy(r1d,beta_r_100,label='1000')\n",
+    "plt.xlim(0,xr); plt.ylim(1.,1e6)\n",
+    "plt.legend(fontsize=12)\n",
+    "plt.xlabel(r'$r$ ($r_g$)'); plt.ylabel(r'plasma $\\beta$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### optional further experiments:\n",
+    "make plots of the accretion rate and beta vs. time<br>\n",
+    "i) transfer all dump files to your scratch space<br>\n",
+    "ii) define lists of mdot, beta, b2<br>\n",
+    "iii) loop over dump files, calculate quantities as we have done so far, append them to lists<br>\n",
+    "iv) make plots vs. time at fixed radius (for beta near 10 r_g, for Mdot near event horizon)<br>\n",
+    "**Q: how do the accretion rate and magnetic field strength grow with time?**\n",
+    "\n",
+    "calculate jet power<br>\n",
+    "**Q: does our torus launch a jet?**<br>\n",
+    "check the harmpi_tutorial.md and ask instructors "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/harmpi_ataschool_version.zip b/harmpi_materials/harmpi_ataschool_version.zip
similarity index 100%
rename from harmpi_ataschool_version.zip
rename to harmpi_materials/harmpi_ataschool_version.zip
diff --git a/harmpi_torus2D.mp4 b/harmpi_materials/harmpi_torus2D.mp4
similarity index 100%
rename from harmpi_torus2D.mp4
rename to harmpi_materials/harmpi_torus2D.mp4