diff --git a/notebooks/2015-09-15-2d-segmentation.ipynb b/notebooks/2015-09-15-2d-segmentation.ipynb
index 8c5f147..1531e0e 100644
--- a/notebooks/2015-09-15-2d-segmentation.ipynb
+++ b/notebooks/2015-09-15-2d-segmentation.ipynb
@@ -4,23 +4,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Contact segmentation in 2d\n",
+    "# Calculating mortar projection matrices\n",
     "\n",
     "Author(s): Jukka Aho\n",
     "\n",
-    "**Abstract**: Calculate mortar projection for 2d"
+    "**Abstract**: Evaluate mortar projection matrices in 2d and 3d"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 54,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "using JuliaFEM: new_fieldset!, add_field!, dinterpolate\n",
-    "using JuliaFEM: Field, FieldSet, Seg2\n",
+    "using JuliaFEM\n",
+    "using JuliaFEM: Seg2, Basis, Field, FieldSet, dinterpolate, interpolate, get_connectivity\n",
     "using PyPlot\n",
     "using ForwardDiff"
    ]
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 55,
    "metadata": {
     "collapsed": false
    },
@@ -45,7 +45,7 @@
        "rlinspace (generic function with 1 method)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false
    },
@@ -89,23 +89,24 @@
     {
      "data": {
       "text/plain": [
-       "1-element Array{Array{T,1},1}:\n",
-       " [0,1]"
+       "interpolate (generic function with 11 methods)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "f = Field(0.0, Vector[])\n",
-    "push!(f.values, [0, 1])"
+    "function FEM.interpolate(basis::Basis, field::Field, xis::Array{Vector,1})\n",
+    "    [FEM.interpolate(basis, field, xi) for xi in xis]\n",
+    "end\n",
+    "#FEM.interpolate(Γ₁[1], \"geometry\", Vector[[-1.0], [1.0]], 0.0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 78,
    "metadata": {
     "collapsed": false
    },
@@ -118,19 +119,24 @@
      ]
     },
     {
-     "ename": "LoadError",
-     "evalue": "LoadError: \"Empty set of fields.\"\nwhile loading In[9], in expression starting on line 60",
-     "output_type": "error",
-     "traceback": [
-      "LoadError: \"Empty set of fields.\"\nwhile loading In[9], in expression starting on line 60",
-      "",
-      " in interpolate at C:\\Users\\jahx06\\.julia\\v0.4\\JuliaFEM\\src\\interpolate.jl:28",
-      " in dinterpolate at C:\\Users\\jahx06\\.julia\\v0.4\\JuliaFEM\\src\\elements.jl:208",
-      " in calculate_normals! at In[9]:36",
-      " in calculate_normals! at In[9]:33",
-      " [inlined code] from In[9]:61",
-      " in anonymous at no file:0"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000002F321F98>)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-0.1,4.0)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
      "name": "stdout",
@@ -173,16 +179,17 @@
     "\"\"\"\n",
     "Calculate element local normal field\n",
     "\"\"\"\n",
-    "function calculate_normals!(el::Seg2, time, field_name=\"normal\")\n",
-    "    new_fieldset!(el, field_name)\n",
+    "function calculate_normals!(element::Seg2, time, field_name=\"normal\")\n",
+    "    normal_fieldset = FieldSet(field_name)\n",
     "    normal_field = Field(time, Vector[])\n",
+    "    tangent(xi) = dinterpolate(element, \"geometry\", xi, time)\n",
     "    for xi in Vector[[-1.0], [1.0]]\n",
-    "        tangent = dinterpolate(el, \"geometry\", xi, time)\n",
-    "        normal = [0 -1; 1 0]*tangent\n",
-    "        normal /= norm(normal)\n",
-    "        push!(normal_field.values, normal)\n",
+    "        t = tangent(xi)\n",
+    "        n = [-t[2], t[1]]\n",
+    "        push!(normal_field, n/norm(n))\n",
     "    end\n",
-    "    #add_field!(el, field_name, normal_field)\n",
+    "    push!(normal_fieldset, normal_field)\n",
+    "    push!(element, normal_fieldset)\n",
     "end\n",
     "\n",
     "function create_elements(X, sid=0)\n",
@@ -192,24 +199,49 @@
     "    for i=1:nelements\n",
     "        con = sid+[i, i+1]\n",
     "        el = Seg2(con)\n",
-    "        new_fieldset!(el, \"geometry\")\n",
-    "        #add_field!(el, \"geometry\", Field(0.0, Vector[X[:, i], X[:, i+1]]))\n",
+    "        push!(el, FieldSet(\"geometry\", [Field(0.0, Vector[X[:, i], X[:, i+1]])]))\n",
     "        push!(Γ, el)\n",
     "    end\n",
     "    return Γ\n",
     "end\n",
+    "\n",
+    "function plot_element(el; plot_with_normal=false)\n",
+    "    # create a array of vectors\n",
+    "    xis = linspace([-1.0], [1.0], 3)\n",
+    "    time = 0.0\n",
+    "    coords = interpolate(el, \"geometry\", xis, time)\n",
+    "    ncoords = interpolate(el, \"geometry\", xis, time)\n",
+    "    normals = interpolate(el, \"normal\", xis, time)\n",
+    "    xs = [X[1] for X in coords]\n",
+    "    ys = [X[2] for X in coords]\n",
+    "    plot(xs, ys, \"-\")\n",
+    "    plot([xs[1], xs[end]], [ys[1], ys[end]], \"ko\")\n",
+    "    plot([xs[1], xs[end]], [ys[1], ys[end]], \"ko\")\n",
+    "    if plot_with_normal\n",
+    "        for i=1:3\n",
+    "            p0 = ncoords[i]\n",
+    "            p1 = ncoords[i]+0.3*normals[i]\n",
+    "            plot([p0[1], p1[1]], [p0[2], p1[2]], \"-k\")\n",
+    "        end\n",
+    "    end\n",
+    "end\n",
+    "\n",
     "Γ₁ = create_elements([x1 y1]', 0)\n",
     "Γ₂ = create_elements([x2 y2]', nsl);\n",
-    "\n",
     "for el in [Γ₁; Γ₂]\n",
     "    calculate_normals!(el, 0.0)\n",
     "end\n",
     "\n",
     "figure(figsize=(10, 3))\n",
-    "plot(x1, y1, \"-ko\")\n",
-    "plot(x2, y2, \"-ko\")\n",
+    "for el in Γ₁\n",
+    "    plot_element(el; plot_with_normal=true)\n",
+    "end\n",
+    "for el in Γ₂\n",
+    "    plot_element(el; plot_with_normal=false)\n",
+    "end\n",
     "axis(\"equal\")\n",
-    "ylim(-0.1, 4.0)"
+    "ylim(-0.1, 4.0)\n",
+    "#axis(\"off\")"
    ]
   },
   {
@@ -218,28 +250,48 @@
    "source": [
     "### Calculating surface normals\n",
     "\n",
-    "- must have unique normal"
+    "To define so called \"continuous normal field\", normals must be unambiguous in nodes. This can be done by averaging normals of adjacent elements:\n",
+    "\\begin{equation}\n",
+    "\\mathbf{n}_{k}=\\frac{\\sum_{e=1}^{n_{k}^{\\mathrm{adj}}} \\mathbf{n}_{k}^{\\left(e\\right)}}{\\left\\Vert \\sum_{e=1}^{n_{k}^{\\mathrm{adj}}} \\mathbf{n}_{k}^{\\left(e\\right)}\\right\\Vert }.\n",
+    "\\end{equation}\n",
+    "\n",
+    "In practice, we take average of all normal vectors connecting to some arbitrary node."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 79,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converged. p = [4.2905787641613236,5.624156522861784,0.04706336239279979,6.561746550428803]\n"
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "JuliaFEM.Field{Array{Array{T,1},1}}(0.0,1,Array{T,1}[[0.7021480119880804,-0.7120310170639945],[0.7021480119880804,-0.7120310170639945]])"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "FEM.interpolate(Γ₁[1][\"normal\"], 0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7faeec09ee10>)"
+       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000002F310630>)"
       ]
      },
      "metadata": {},
@@ -251,43 +303,39 @@
        "(-0.1,3.6)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converged. p = [2.110067133637596,1.2802355090457196,1.5675520985912952,-3.7904167887303712]\n"
-     ]
     }
    ],
    "source": [
-    "# calculate and average normals like we did in last notebook\n",
-    "average_normals!(Γ₁)\n",
-    "average_normals!(Γ₂)\n",
-    "\n",
-    "function plot_element(el; plot_with_normal=false)\n",
-    "    # create a array of vectors\n",
-    "    xis = Vector[[xi] for xi in linspace(-1, 1)]\n",
-    "    coords = interpolate(el, :Geometry, xis)\n",
-    "    normals = interpolate(el, :Normals, Vector[[-1.0], [1.0]])\n",
-    "    ncoords = interpolate(el, :Geometry, Vector[[-1.0], [1.0]])\n",
-    "    xs = [X[1] for X in coords]\n",
-    "    ys = [X[2] for X in coords]\n",
-    "    plot(xs, ys, \"-\")\n",
-    "    plot([xs[1], xs[end]], [ys[1], ys[end]], \"ko\")\n",
-    "    plot([xs[1], xs[end]], [ys[1], ys[end]], \"ko\")\n",
-    "    if plot_with_normal\n",
-    "        for i=1:2\n",
-    "            p0 = ncoords[i]\n",
-    "            p1 = ncoords[i]+0.3*normals[i]\n",
-    "            plot([p0[1], p1[1]], [p0[2], p1[2]], \"-k\")\n",
+    "function average_normals!(elements, time, normal_field=\"normal\")\n",
+    "    d = Dict()\n",
+    "    # calculate sum of normals connecting to node k\n",
+    "    for el in elements\n",
+    "        c = get_connectivity(el)\n",
+    "        n = interpolate(el[normal_field], time).values\n",
+    "        for (ci, ni) in zip(c, n)\n",
+    "            d[ci] = haskey(d, ci) ? d[ci] + ni : ni\n",
     "        end\n",
     "    end\n",
+    "    # norm\n",
+    "    for (ci, ni) in d\n",
+    "        d[ci] /= norm(d[ci])\n",
+    "    end\n",
+    "    # update back to elements\n",
+    "    for el in elements\n",
+    "        c = get_connectivity(el)\n",
+    "        #new_normals = Field(time, [d[ci] for ci in c])\n",
+    "        #set_field(el, normal_field, new_normals)\n",
+    "        #el[normal_field][end].values = new_normals\n",
+    "        #push!(el[normal_field], new_normals)\n",
+    "        el[normal_field][end].values = [d[ci] for ci in c]\n",
+    "    end\n",
     "end\n",
     "\n",
+    "average_normals!(Γ₁, 0.0)\n",
+    "average_normals!(Γ₂, 0.0)\n",
     "\n",
     "figure(figsize=(10, 3))\n",
     "for el in Γ₁\n",
@@ -301,62 +349,6 @@
     "#axis(\"off\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6x2 Array{Float64,2}:\n",
-       "  4.0       3.5    \n",
-       "  3.05326   2.5664 \n",
-       "  1.89394   1.91534\n",
-       "  0.60401   1.59286\n",
-       " -0.725305  1.62176\n",
-       " -2.0       2.0    "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[x1 y1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6x2 Array{Float64,2}:\n",
-       "  3.0       1.0    \n",
-       "  2.18891   1.17082\n",
-       "  1.36075   1.2053 \n",
-       "  0.538273  1.10249\n",
-       " -0.255919  0.86522\n",
-       " -1.0       0.5    "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[x2 y2]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,