JuliaFEM.jl/docs/tutorials/2015-11-23-2d-tie-contact.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2d contacts\n",
    "\n",
    "Author: Jukka Aho\n",
    "\n",
    "Abstract: 2d tie contact.\n",
    "\n",
    "## Model 1: three body tie contact\n",
    "\n",
    "\n",
    "![model](http://s4.postimg.org/u9yqeryul/Screenshot_from_2015_11_24_02_00_00.png)\n",
    "\n",
    "Each element is modelled as own \"body\" and they are connected using tie contacts. Segments 5-6 and 9-10 and 6-7 are slave surfaces, so node 6 or 9 is on at least two tie contacts as slave node. Moreover this model has dirichlet boundary $y=0$ at bottom of body 1 and $x=0$ on left. To get the accurate solution one needs to minimize \n",
    "\\begin{equation}\n",
    "\\frac{15}{2}u_1^4 + 60 u_1^3 + \\frac{15}{4}u_1^2 u_2^2 + 15 u_1^2 u_2 + 120 u_1^2 + 15 u_1 u_2^2 + 60 u_1 u_2 + \\frac{15}{2}u_2^4 + 60 u_2^3 + 120 u_2^2 + 50 u_2,\n",
    "\\end{equation}\n",
    "which gives approximate $u_1 = 0.0634862$ and $u_2 = -0.277183$ for the displacement of upper right corner.\n",
    "[Wolfram](http://www.wolframalpha.com/input/?i=local+minimum+15*x^4%2F2+%2B+60*x^3+%2B+15*x^2*y^2%2F4+%2B+15*x^2*y+%2B+120*x^2+%2B+15*x*y^2+%2B+60*x*y+%2B+15*y^4%2F2+%2B+60*y^3+%2B+120*y^2+%2B+50*y)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "using JuliaFEM.Core: Element, Seg2, Quad4, PlaneStressElasticityProblem,\n",
    "                     DirichletProblem, MortarProblem, DirectSolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nodes = Dict{Int64, Vector{Float64}}(\n",
    "    1 => [0.0, 0.0], 2 => [2.0, 0.0],\n",
    "    3 => [2.0, 1.0], 4 => [0.0, 1.0],\n",
    "    5 => [0.0, 1.0], 6 => [1.0, 1.0],\n",
    "    7 => [1.0, 2.0], 8 => [0.0, 2.0],\n",
    "    9 => [1.0, 1.0], 10 => [2.0, 1.0],\n",
    "    11 => [2.0, 2.0], 12 => [1.0, 2.0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "connectivity = Dict{Int64, Vector{Int64}}(\n",
    "    1 => [1, 2, 3, 4],\n",
    "    2 => [5, 6, 7, 8],\n",
    "    3 => [9, 10, 11, 12]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: number of elements: 3\n"
     ]
    }
   ],
   "source": [
    "elements = Element[]\n",
    "for c in values(connectivity)\n",
    "    element = Quad4(c)\n",
    "    element[\"geometry\"] = Vector{Float64}[nodes[i] for i in c]\n",
    "    element[\"youngs modulus\"] = 900.0\n",
    "    element[\"poissons ratio\"] = 0.25\n",
    "    push!(elements, element)\n",
    "end\n",
    "nelements = length(elements)\n",
    "info(\"number of elements: $nelements\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create three bodies, each containing one element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "body1 = PlaneStressElasticityProblem(\"body 1\")\n",
    "body2 = PlaneStressElasticityProblem(\"body 2\")\n",
    "body3 = PlaneStressElasticityProblem(\"body 3\")\n",
    "push!(body1, elements[1])\n",
    "push!(body2, elements[2])\n",
    "push!(body3, elements[3]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surface traction to the top of bodies 2 and 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t2 = Seg2([8, 7])\n",
    "t2[\"geometry\"] = Vector{Float64}[nodes[8], nodes[7]]\n",
    "t2[\"displacement traction force\"] = Vector{Float64}[[0.0, -100.0], [0.0, -100.0]]\n",
    "t3 = Seg2([12, 11])\n",
    "t3[\"geometry\"] = Vector{Float64}[nodes[12], nodes[11]]\n",
    "t3[\"displacement traction force\"] = Vector{Float64}[[0.0, -100.0], [0.0, -100.0]]\n",
    "push!(body2, t2)\n",
    "push!(body3, t3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boundary conditions: $x=0$ for left boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dx1 = Seg2([1, 4])\n",
    "dx1[\"geometry\"] = Vector[nodes[1], nodes[4]]\n",
    "dx1[\"displacement 1\"] = 0.0\n",
    "dx2 = Seg2([5, 8])\n",
    "dx2[\"geometry\"] = Vector[nodes[5], nodes[8]]\n",
    "dx2[\"displacement 1\"] = 0.0\n",
    "bc1 = DirichletProblem(\"displacement\", 2)\n",
    "push!(bc1, dx1)\n",
    "push!(bc1, dx2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$y=0$ for bottom of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dy1 = Seg2([1, 2])\n",
    "dy1[\"geometry\"] = Vector[nodes[1], nodes[2]]\n",
    "dy1[\"displacement 2\"] = 0.0\n",
    "bc2 = DirichletProblem(\"displacement\", 2)\n",
    "push!(bc2, dy1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mortar boundary conditions: tie contact between body 1 and body 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#rotation_matrix(phi) = [cos(phi) -sin(phi); sin(phi) cos(phi)]\n",
    "using JuliaFEM.Core: calculate_normal_tangential_coordinates!\n",
    "\n",
    "master1 = Seg2([4, 3])\n",
    "master1[\"geometry\"] = Vector[nodes[4], nodes[3]]\n",
    "slave1 = Seg2([5, 6])\n",
    "slave1[\"geometry\"] = Vector[nodes[5], nodes[6]]\n",
    "slave1[\"master elements\"] = Element[master1]\n",
    "calculate_normal_tangential_coordinates!(slave1, 0.0)\n",
    "\n",
    "#slave1[\"nodal ntsys\"] = Matrix[rotation_matrix(-pi/2), rotation_matrix(-pi/2)]\n",
    "contact1 = MortarProblem(\"displacement\", 2)\n",
    "push!(contact1, slave1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tie contact between body 1 and body 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "slave2 = Seg2([9, 10])\n",
    "slave2[\"geometry\"] = Vector[nodes[9], nodes[10]]\n",
    "#slave2[\"nodal ntsys\"] = Matrix[rotation_matrix(-pi/2), rotation_matrix(-pi/2)]\n",
    "slave2[\"master elements\"] = Element[master1]\n",
    "calculate_normal_tangential_coordinates!(slave2, 0.0)\n",
    "contact2 = MortarProblem(\"displacement\", 2)\n",
    "push!(contact2, slave2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tie contact between body 2 and body 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master2 = Seg2([6, 7])\n",
    "master2[\"geometry\"] = Vector[nodes[6], nodes[7]]\n",
    "slave3 = Seg2([9, 12])\n",
    "slave3[\"geometry\"] = Vector[nodes[9], nodes[12]]\n",
    "#slave3[\"nodal ntsys\"] = Matrix[rotation_matrix(0.0), rotation_matrix(0.0)]\n",
    "calculate_normal_tangential_coordinates!(slave3, 0.0)\n",
    "slave3[\"master elements\"] = Element[master2]\n",
    "contact3 = MortarProblem(\"displacement\", 2)\n",
    "push!(contact3, slave3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All defined. Solve it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "solver = DirectSolver()\n",
    "push!(solver, body1)\n",
    "push!(solver, body2)\n",
    "push!(solver, body3)\n",
    "push!(solver, bc1)\n",
    "push!(solver, bc2)\n",
    "push!(solver, contact1)\n",
    "push!(solver, contact2)\n",
    "push!(solver, contact3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Starting solver DirectSolver\n",
      "INFO: # of field problems: 3\n",
      "INFO: # of boundary problems: 5\n",
      "INFO: Starting iteration 1\n",
      "INFO: Assembling field problems...\n",
      "INFO: Assembling body 1: body 1\n",
      "INFO: Assembling body 2: body 2\n",
      "INFO: Assembling body 3: body 3\n",
      "INFO: dim = 24\n",
      "INFO: Assembling boundary problems...\n",
      "INFO: Assembling boundary 1: dirichlet boundary\n",
      "INFO: Assembling boundary 2: dirichlet boundary\n",
      "INFO: Assembling boundary 3: mortar problem\n",
      "INFO: Assembling boundary 4: mortar problem\n",
      "INFO: Assembling boundary 5: mortar problem\n",
      "INFO: Solving system\n",
      "INFO: UMFPACK: solved in 0.3197059631347656 seconds. norm = 0.5357583756107197\n",
      "INFO: timing info for iteration:\n",
      "INFO: boundary assembly       : 0.3747282028198242\n",
      "INFO: field assembly          : 2.646785020828247\n",
      "INFO: dump matrices to disk   : 9.5367431640625e-7\n",
      "INFO: solve problem           : 0.4615659713745117\n",
      "INFO: update element data     : 0.02040410041809082\n",
      "INFO: non-linear iteration    : 3.5035040378570557\n",
      "INFO: Starting iteration 2\n",
      "INFO: Assembling field problems...\n",
      "INFO: Assembling body 1: body 1\n",
      "INFO: Assembling body 2: body 2\n",
      "INFO: Assembling body 3: body 3\n",
      "INFO: dim = 24\n",
      "INFO: Assembling boundary problems...\n",
      "INFO: Assembling boundary 1: dirichlet boundary\n",
      "INFO: Assembling boundary 2: dirichlet boundary\n",
      "INFO: Assembling boundary 3: mortar problem\n",
      "INFO: Assembling boundary 4: mortar problem\n",
      "INFO: Assembling boundary 5: mortar problem\n",
      "INFO: Solving system\n",
      "INFO: UMFPACK: solved in 0.0003139972686767578 seconds. norm = 0.12311066326855769\n",
      "INFO: timing info for iteration:\n",
      "INFO: boundary assembly       : 0.036119937896728516\n",
      "INFO: field assembly          : 0.0374150276184082\n",
      "INFO: dump matrices to disk   : 9.5367431640625e-7\n",
      "INFO: solve problem           : 0.06756091117858887\n",
      "INFO: update element data     : 0.00013899803161621094\n",
      "INFO: non-linear iteration    : 0.1412510871887207\n",
      "INFO: Starting iteration 3\n",
      "INFO: Assembling field problems...\n",
      "INFO: Assembling body 1: body 1\n",
      "INFO: Assembling body 2: body 2\n",
      "INFO: Assembling body 3: body 3\n",
      "INFO: dim = 24\n",
      "INFO: Assembling boundary problems...\n",
      "INFO: Assembling boundary 1: dirichlet boundary\n",
      "INFO: Assembling boundary 2: dirichlet boundary\n",
      "INFO: Assembling boundary 3: mortar problem\n",
      "INFO: Assembling boundary 4: mortar problem\n",
      "INFO: Assembling boundary 5: mortar problem\n",
      "INFO: Solving system\n",
      "INFO: UMFPACK: solved in 0.0003230571746826172 seconds. norm = 0.006976385449837204\n",
      "INFO: timing info for iteration:\n",
      "INFO: boundary assembly       : 0.03788185119628906\n",
      "INFO: field assembly          : 0.036910057067871094\n",
      "INFO: dump matrices to disk   : 0.0\n",
      "INFO: solve problem           : 0.07133316993713379\n",
      "INFO: update element data     : 0.0001468658447265625\n",
      "INFO: non-linear iteration    : 0.1462879180908203\n",
      "INFO: Starting iteration 4\n",
      "INFO: Assembling field problems...\n",
      "INFO: Assembling body 1: body 1\n",
      "INFO: Assembling body 2: body 2\n",
      "INFO: Assembling body 3: body 3\n",
      "INFO: dim = 24\n",
      "INFO: Assembling boundary problems...\n",
      "INFO: Assembling boundary 1: dirichlet boundary\n",
      "INFO: Assembling boundary 2: dirichlet boundary\n",
      "INFO: Assembling boundary 3: mortar problem\n",
      "INFO: Assembling boundary 4: mortar problem\n",
      "INFO: Assembling boundary 5: mortar problem\n",
      "INFO: Solving system\n",
      "INFO: UMFPACK: solved in 0.00030493736267089844 seconds. norm = 2.1945519744339283e-5\n",
      "INFO: timing info for iteration:\n",
      "INFO: boundary assembly       : 0.037918806076049805\n",
      "INFO: field assembly          : 0.036936044692993164\n",
      "INFO: dump matrices to disk   : 9.5367431640625e-7\n",
      "INFO: solve problem           : 0.07027888298034668\n",
      "INFO: update element data     : 0.00013899803161621094\n",
      "INFO: non-linear iteration    : 0.14528894424438477\n",
      "INFO: Starting iteration 5\n",
      "INFO: Assembling field problems...\n",
      "INFO: Assembling body 1: body 1\n",
      "INFO: Assembling body 2: body 2\n",
      "INFO: Assembling body 3: body 3\n",
      "INFO: dim = 24\n",
      "INFO: Assembling boundary problems...\n",
      "INFO: Assembling boundary 1: dirichlet boundary\n",
      "INFO: Assembling boundary 2: dirichlet boundary\n",
      "INFO: Assembling boundary 3: mortar problem\n",
      "INFO: Assembling boundary 4: mortar problem\n",
      "INFO: Assembling boundary 5: mortar problem\n",
      "INFO: Solving system\n",
      "INFO: UMFPACK: solved in 0.0003120899200439453 seconds. norm = 2.172141514107444e-10\n",
      "INFO: timing info for iteration:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(5,true)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: boundary assembly       : 0.035440921783447266\n",
      "INFO: field assembly          : 0.0363919734954834\n",
      "INFO: dump matrices to disk   : 0.0\n",
      "INFO: solve problem           : 0.06740903854370117\n",
      "INFO: update element data     : 0.0001380443572998047\n",
      "INFO: non-linear iteration    : 0.13939404487609863\n",
      "INFO: solver finished in 4.193101167678833 seconds.\n"
     ]
    }
   ],
   "source": [
    "iterations, converged = call(solver, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: displacement at [2.0,2.0] = [0.06348623177789363,-0.2771830378556528]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Test Passed\n",
       "  Expression: isapprox(u,[0.0634862,-0.277183],atol=1.0e-5)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using JuliaFEM.Test\n",
    "\n",
    "@test converged\n",
    "\n",
    "X = elements[2](\"geometry\", [1.0, 1.0], 0.0)\n",
    "u = elements[2](\"displacement\", [1.0, 1.0], 0.0)\n",
    "info(\"displacement at $X = $u\")\n",
    "@test isapprox(u, [0.0634862, -0.277183], atol=1.0e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2: splitted beam, tie contact\n",
    "\n",
    "<img src=\"http://results.juliafem.org/splitted-2d-beam/2015-12-22-splitted-beam-mesh.png\">\n",
    "\n",
    "Put some load on the top, dx=dy=0 on left boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "using JuliaFEM.Preprocess: parse_aster_med_file\n",
    "using JuliaFEM.Core: PlaneStressLinearElasticityProblem, DirichletProblem,\n",
    "                     get_connectivity, Quad4, Tri3, Seg2, LinearSolver,\n",
    "                     update!, get_elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "create_problems (generic function with 1 method)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using JuliaFEM.Core: Element, MortarProblem, calculate_normal_tangential_coordinates!\n",
    "\n",
    "phi = 0\n",
    "rmat = [cos(phi) -sin(phi); sin(phi) cos(phi)]\n",
    "\n",
    "function create_problems()\n",
    "\n",
    "    mesh = parse_aster_med_file(Pkg.dir(\"JuliaFEM\")*\"/geometry/2d_beam/BEAM.med\")\n",
    "    \n",
    "    for (k, v) in mesh[\"nodes\"]\n",
    "        mesh[\"nodes\"][k] = rmat*v\n",
    "    end\n",
    "    \n",
    "    field_problem = PlaneStressLinearElasticityProblem()\n",
    "\n",
    "    # field problems\n",
    "    joo = Dict(:QU4 => Quad4, :TR3 => Tri3)\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype in keys(joo) || continue\n",
    "        element = joo[eltype](elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        element[\"youngs modulus\"] = 900.0\n",
    "        element[\"poissons ratio\"] = 0.25\n",
    "        push!(field_problem, element)\n",
    "    end\n",
    "\n",
    "    # neumann boundary condition -1 on y direction\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == :LOAD || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        # FIXME\n",
    "        # element[\"displacement traction force\"] = rmat*[0.0, -0.01]\n",
    "        f = rmat*[0.0, -0.01]\n",
    "        element[\"displacement traction force\"] = Vector{Float64}[f, f]\n",
    "        push!(field_problem, element)\n",
    "    end\n",
    "\n",
    "    # boundary conditions\n",
    "    boundary_problem = DirichletProblem(\"displacement\", 2)\n",
    "\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == :LEFT || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        # FIXME\n",
    "        element[\"displacement\"] = (0.0 => Vector{Float64}[[0.0, 0.0], [0.0, 0.0]])\n",
    "        push!(boundary_problem, element)\n",
    "    end\n",
    "\n",
    "    info(\"created $(length(get_elements(field_problem))) field elements.\")\n",
    "    info(\"created $(length(get_elements(boundary_problem))) boundary elements.\")\n",
    "\n",
    "    # Contact definition: contact pair is `LOWER_TO_UPPER <--> UPPER_TO_LOWER`:\n",
    "\n",
    "    slave_surface = :LOWER_TO_UPPER\n",
    "    master_surface = :UPPER_TO_LOWER\n",
    "\n",
    "    contact_problem = MortarProblem(\"displacement\", 2)\n",
    "    master_elements = JuliaFEM.Core.Element[]\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == master_surface || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        element[\"displacement\"] = (0.0 => Vector{Float64}[[0.0, 0.0], [0.0, 0.0]])\n",
    "        push!(master_elements, element)\n",
    "        push!(contact_problem, element)\n",
    "    end\n",
    "\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == slave_surface || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        element[\"master elements\"] = master_elements\n",
    "        calculate_normal_tangential_coordinates!(element, 0.0)\n",
    "        element[\"displacement\"] = (0.0 => Vector{Float64}[[0.0, 0.0], [0.0, 0.0]])\n",
    "        push!(contact_problem, element)\n",
    "    end\n",
    "\n",
    "    info(\"# of master elements: $(length(master_elements))\")\n",
    "    info(\"# of slave elements: $(length(contact_problem.elements))\")\n",
    "\n",
    "    return field_problem, boundary_problem, contact_problem\n",
    "\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Found 5 element sets: LOWER_TO_UPPER, RIGHT, UPPER_TO_LOWER, LEFT, LOAD\n",
      "INFO: created 104 field elements.\n",
      "INFO: created 4 boundary elements.\n",
      "INFO: # of master elements: 14\n",
      "INFO: # of slave elements: 34\n",
      "INFO: Starting solver divided_beam\n",
      "INFO: # of field problems: 1\n",
      "INFO: # of boundary problems: 2\n",
      "INFO: Starting iteration 1\n",
      "INFO: Assembling field problems...\n",
      "INFO: Assembling body 1: plane stress linear elasticity\n",
      "INFO: Assembly: 10.0 % done. \n",
      "INFO: Assembly: 20.0 % done. \n",
      "INFO: Assembly: 30.0 % done. \n",
      "INFO: Assembly: 40.0 % done. \n",
      "INFO: Assembly: 50.0 % done. \n",
      "INFO: Assembly: 60.0 % done. \n",
      "INFO: Assembly: 70.0 % done. \n",
      "INFO: Assembly: 80.0 % done. \n",
      "INFO: Assembly: 90.0 % done. \n",
      "INFO: Assembly: 100.0 % done. \n",
      "INFO: dim = 210\n",
      "INFO: Assembling boundary problems...\n",
      "INFO: Assembling boundary 1: dirichlet boundary\n",
      "INFO: Assembling boundary 2: mortar problem\n",
      "INFO: Solving system\n",
      "INFO: UMFPACK: solved in 0.3899998664855957 seconds. norm = 1.049388621344361\n",
      "INFO: timing info for iteration:\n",
      "INFO: boundary assembly       : 2.3869998455047607\n",
      "INFO: field assembly          : 1.4670000076293945\n",
      "INFO: dump matrices to disk   : 0.0\n",
      "INFO: solve problem           : 0.49900007247924805\n",
      "INFO: update element data     : 0.014999866485595703\n",
      "INFO: non-linear iteration    : 4.367999792098999\n",
      "INFO: Starting iteration 2\n",
      "INFO: Assembling field problems...\n",
      "INFO: Assembling body 1: plane stress linear elasticity\n",
      "INFO: Assembly: 10.0 % done. \n",
      "INFO: Assembly: 20.0 % done. \n",
      "INFO: Assembly: 30.0 % done. \n",
      "INFO: Assembly: 40.0 % done. \n",
      "INFO: Assembly: 50.0 % done. \n",
      "INFO: Assembly: 60.0 % done. \n",
      "INFO: Assembly: 70.0 % done. \n",
      "INFO: Assembly: 80.0 % done. \n",
      "INFO: Assembly: 90.0 % done. \n",
      "INFO: Assembly: 100.0 % done. \n",
      "INFO: dim = 210\n",
      "INFO: Assembling boundary problems...\n",
      "INFO: Assembling boundary 1: dirichlet boundary\n",
      "INFO: Assembling boundary 2: mortar problem\n",
      "INFO: Solving system\n",
      "INFO: UMFPACK: solved in 0.0 seconds. norm = 1.8501897782230326e-13\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2,true)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using JuliaFEM.Core: DirectSolver\n",
    "\n",
    "field_problem, boundary_problem, contact_problem = create_problems()\n",
    "\n",
    "solver = DirectSolver()\n",
    "solver.name = \"divided_beam\"\n",
    "solver.method = :UMFPACK\n",
    "solver.max_iterations = 2\n",
    "solver.dump_matrices = false\n",
    "push!(solver, field_problem)\n",
    "push!(solver, boundary_problem)\n",
    "push!(solver, contact_problem)\n",
    "call(solver, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import PyPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: timing info for iteration:\n",
      "INFO: boundary assembly       : 0.10899996757507324\n",
      "INFO: field assembly          : 0.04700016975402832\n",
      "INFO: dump matrices to disk   : 0.0\n",
      "INFO: solve problem           : 0.06200003623962402\n",
      "INFO: update element data     : 0.015999794006347656\n",
      "INFO: non-linear iteration    : 0.23399996757507324\n",
      "INFO: solver finished in 4.741999864578247 seconds.\n",
      "INFO: displacement at tip: [-0.025032650050967196,-0.1947753325658252]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000002D7D0A58>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function plot(field_problem, scaling_factor=30, time=0.0)\n",
    "\n",
    "    fig = PyPlot.figure(figsize=(15, 4))\n",
    "\n",
    "    g_tip = rmat*[100.0, 0.0]\n",
    "    u_tip = nothing\n",
    "    \n",
    "    for element in field_problem.elements\n",
    "        conn = get_connectivity(element)\n",
    "        X = element(\"geometry\", time)\n",
    "        u = element(\"displacement\", time)\n",
    "        x = X + scaling_factor*u\n",
    "        if isapprox(element(\"geometry\", [1.0, -1.0], time), g_tip)\n",
    "            u_tip = element(\"displacement\", [1.0, -1.0], time)\n",
    "            info(\"displacement at tip: $u_tip\")\n",
    "        end\n",
    "\n",
    "        # undeformed\n",
    "        for i=1:length(X)\n",
    "            px1 = X[i][1]\n",
    "            py1 = X[i][2]\n",
    "            px2 = X[mod(i,length(X))+1][1]\n",
    "            py2 = X[mod(i,length(X))+1][2]\n",
    "            PyPlot.plot([px1, px2], [py1, py2], \"k-\", alpha=0.5)\n",
    "        end\n",
    "\n",
    "        # deformed\n",
    "        for i=1:length(x)\n",
    "            px1 = x[i][1]\n",
    "            py1 = x[i][2]\n",
    "            px2 = x[mod(i,length(x))+1][1]\n",
    "            py2 = x[mod(i,length(x))+1][2]\n",
    "            PyPlot.plot([px1, px2], [py1, py2], \"r--\", alpha=0.5)\n",
    "        end\n",
    "    end\n",
    "\n",
    "    PyPlot.axis(\"equal\")\n",
    "    #PyPlot.axis(\"off\")\n",
    "\n",
    "    return u_tip\n",
    "end\n",
    "\n",
    "u_tip = plot(field_problem, 30.0)\n",
    "#PyPlot.xlim(95, 105)\n",
    "#PyPlot.ylim(-10, 0)\n",
    "PyPlot.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isapprox(u_tip, [-0.025032650050963334,-0.19477533256579582]) || warn(\"different result\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isapprox(norm(u_tip), 0.19637735038616433)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 3: splitted beam, frictionless contact, small sliding\n",
    "\n",
    "Modification to the above: allow beams to slide in tangential direction without a friction. Global assembly operator can be modified by writing own functions `preprocess_assembly!` and `postprocess_assembly!`. Here we simply remove all kinematic constraints in tangential direction to achieve frictionless sliding between bodies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Found 5 element sets: LOWER_TO_UPPER, RIGHT, UPPER_TO_LOWER, LEFT, LOAD\n",
      "INFO: created 104 field elements.\n",
      "INFO: created 4 boundary elements.\n",
      "INFO: # of master elements: 14\n",
      "INFO: # of slave elements: 34\n",
      "INFO: Starting solver divided_beam_small_sliding_contact\n",
      "INFO: # of field problems: 1\n",
      "INFO: # of boundary problems: 2\n",
      "INFO: Starting iteration 1\n",
      "INFO: Assembling field problems...\n",
      "INFO: Assembling body 1: plane stress linear elasticity\n",
      "INFO: Assembly: 10.0 % done. \n",
      "INFO: Assembly: 20.0 % done. \n",
      "INFO: Assembly: 30.0 % done. \n",
      "INFO: Assembly: 40.0 % done. \n",
      "INFO: Assembly: 50.0 % done. \n",
      "INFO: Assembly: 60.0 % done. \n",
      "INFO: Assembly: 70.0 % done. \n",
      "INFO: Assembly: 80.0 % done. \n",
      "INFO: Assembly: 90.0 % done. \n",
      "INFO: Assembly: 100.0 % done. \n",
      "INFO: dim = 210\n",
      "INFO: Assembling boundary problems...\n",
      "INFO: Assembling boundary 1: dirichlet boundary\n",
      "INFO: Assembling boundary 2: mortar problem\n",
      "INFO: assemble: doing postprocess for problem JuliaFEM.Core.BoundaryProblem{JuliaFEM.Core.MortarProblem} assembly\n",
      "INFO: postprocess mortar assembly: remove contraints in tangent direction on boundary.\n",
      "INFO: postprocess mortar assembly: done.\n",
      "INFO: Solving system\n",
      "INFO: UMFPACK: solved in 0.0 seconds. norm = 3.146559185661418\n",
      "INFO: timing info for iteration:\n",
      "INFO: boundary assembly       : 0.7179999351501465\n",
      "INFO: field assembly          : 0.07800006866455078\n",
      "INFO: dump matrices to disk   : 0.0\n",
      "INFO: solve problem           : 0.09299993515014648\n",
      "INFO: update element data     : 0.0\n",
      "INFO: non-linear iteration    : 0.8889999389648438\n",
      "INFO: Starting iteration 2\n",
      "INFO: Assembling field problems...\n",
      "INFO: Assembling body 1: plane stress linear elasticity\n",
      "INFO: Assembly: 10.0 % done. \n",
      "INFO: Assembly: 20.0 % done. \n",
      "INFO: Assembly: 30.0 % done. \n",
      "INFO: Assembly: 40.0 % done. \n",
      "INFO: Assembly: 50.0 % done. \n",
      "INFO: Assembly: 60.0 % done. \n",
      "INFO: Assembly: 70.0 % done. \n",
      "INFO: Assembly: 80.0 % done. \n",
      "INFO: Assembly: 90.0 % done. \n",
      "INFO: Assembly: 100.0 % done. \n",
      "INFO: dim = 210\n",
      "INFO: Assembling boundary problems...\n",
      "INFO: Assembling boundary 1: dirichlet boundary\n",
      "INFO: Assembling boundary 2: mortar problem\n",
      "INFO: assemble: doing postprocess for problem JuliaFEM.Core.BoundaryProblem{JuliaFEM.Core.MortarProblem} assembly\n",
      "INFO: postprocess mortar assembly: remove contraints in tangent direction on boundary.\n",
      "INFO: postprocess mortar assembly: done.\n",
      "INFO: Solving system\n",
      "INFO: UMFPACK: solved in 0.0 seconds. norm = 3.5785351812097138e-12\n",
      "INFO: timing info for iteration:\n",
      "INFO: boundary assembly       : 0.10900020599365234\n",
      "INFO: field assembly          : 0.06299996376037598\n",
      "INFO: dump matrices to disk   : 0.0\n",
      "INFO: solve problem           : 0.09299993515014648\n",
      "INFO: update element data     : 0.0\n",
      "INFO: non-linear iteration    : 0.2650001049041748\n",
      "INFO: solver finished in 1.1540000438690186 seconds.\n",
      "INFO: displacement at tip: [-0.03914822922685343,-0.592340359574268]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x00000000303F3F98>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using JuliaFEM.Core: SparseMatrixCOO, Element, get_integration_points, get_jacobian, get_connectivity, add!\n",
    "using JuliaFEM.Core: BoundaryAssembly, BoundaryProblem, get_elements\n",
    "\n",
    "function calculate_normal_tangential_coordinates(elements::Vector{Element}, time::Real)\n",
    "    P = SparseMatrixCOO()\n",
    "    field_dim = 2\n",
    "    for element in elements\n",
    "        haskey(element, \"normal-tangential coordinates\") || continue\n",
    "        for ip in get_integration_points(element, Val{2})\n",
    "            J = get_jacobian(element, ip, time)\n",
    "            w = ip.weight*norm(J)\n",
    "            nt = transpose(element(\"normal-tangential coordinates\", ip, time))\n",
    "            normal = nt[1,:]\n",
    "            tangent = nt[2,:]\n",
    "            for nid in get_connectivity(element)\n",
    "                ndofs = [2*(nid-1)+1, 2*(nid-1)+2]\n",
    "                add!(P, [2*(nid-1)+1], ndofs, normal)\n",
    "                add!(P, [2*(nid-1)+2], ndofs, tangent)\n",
    "            end\n",
    "        end\n",
    "    end\n",
    "    P = sparse(P)\n",
    "    for i=1:size(P,1)\n",
    "        n = norm(P[i,:])\n",
    "        if n > 0.0\n",
    "            P[i,:] = P[i,:] / n\n",
    "        end\n",
    "    end\n",
    "    return P\n",
    "end\n",
    "\n",
    "import JuliaFEM.Core: postprocess_assembly!\n",
    "\n",
    "function JuliaFEM.Core.postprocess_assembly!(\n",
    "    assembly::BoundaryAssembly, problem::BoundaryProblem{MortarProblem},\n",
    "    time::Real)\n",
    "    info(\"postprocess mortar assembly: remove contraints in tangent direction on boundary.\")\n",
    "    C1 = sparse(assembly.C1)\n",
    "    C2 = sparse(assembly.C2)\n",
    "    dim = size(C1, 1)\n",
    "    P = calculate_normal_tangential_coordinates(get_elements(problem), time)\n",
    "    C1 = P*C1\n",
    "    C2 = P*C2\n",
    "    for i=2:2:dim\n",
    "        C1[i,:] = 0\n",
    "        C2[i,:] = 0\n",
    "    end\n",
    "    assembly.C1 = C1\n",
    "    assembly.C2 = C2\n",
    "    info(\"postprocess mortar assembly: done.\")\n",
    "end\n",
    "\n",
    "\n",
    "field_problem, boundary_problem, contact_problem = create_problems()\n",
    "using JuliaFEM.Core: DirectSolver\n",
    "solver = DirectSolver()\n",
    "solver.name = \"divided_beam_small_sliding_contact\"\n",
    "solver.method = :UMFPACK\n",
    "solver.max_iterations = 10\n",
    "solver.dump_matrices = false\n",
    "push!(solver, field_problem)\n",
    "push!(solver, boundary_problem)\n",
    "push!(solver, contact_problem)\n",
    "call(solver, 0.0)\n",
    "\n",
    "u_tip = plot(field_problem, 30)\n",
    "isapprox(u_tip, [-0.03914822922685343,-0.592340359574268]) || warn(\"result changed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frictionless 2d contact with active and inactive nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Found 5 element sets: LOWER_TO_UPPER, RIGHT, UPPER_TO_LOWER, LEFT, LOAD\n",
      "INFO: created 104 field elements.\n",
      "INFO: created 4 boundary elements.\n",
      "INFO: # of master elements: 14\n",
      "INFO: # of slave elements: 34\n",
      "INFO: Starting solver divided_beam_inequality_constraints\n",
      "INFO: # of field problems: 1\n",
      "INFO: # of boundary problems: 2\n",
      "INFO: Starting iteration 1\n",
      "INFO: Assembling field problems...\n",
      "INFO: Assembling body 1: plane stress linear elasticity\n",
      "INFO: Assembly: 10.0 % done. \n",
      "INFO: Assembly: 20.0 % done. \n",
      "INFO: Assembly: 30.0 % done. \n",
      "INFO: Assembly: 40.0 % done. \n",
      "INFO: Assembly: 50.0 % done. \n",
      "INFO: Assembly: 60.0 % done. \n",
      "INFO: Assembly: 70.0 % done. \n",
      "INFO: Assembly: 80.0 % done. \n",
      "INFO: Assembly: 90.0 % done. \n",
      "INFO: Assembly: 100.0 % done. \n",
      "INFO: dim = 210\n",
      "INFO: Assembling boundary problems...\n",
      "INFO: Assembling boundary 1: dirichlet boundary\n",
      "INFO: Assembling boundary 2: mortar problem\n",
      "INFO: assemble: doing postprocess for problem JuliaFEM.Core.BoundaryProblem{JuliaFEM.Core.MortarProblem} assembly\n",
      "INFO: postprocess mortar assembly: peforming PDASS.\n",
      "INFO: weighted gap = [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0,2.5,0.0,0.0,-0.0,2.5,0.0,0.0,0.0,5.0,0.0,0.0,-0.0,5.0,0.0,0.0,-0.0,5.0,0.0,0.0,-0.0,5.0,0.0,0.0,-0.0,5.0,0.0,0.0,-0.0,5.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,5.0,0.0,5.0,0.0,5.0,-0.0,5.0,-0.0,5.0,-0.0,5.0,0.0,5.0,0.0,5.0,0.0,5.0,0.0,5.0,0.0,5.0,-0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]\n",
      "INFO: lambda = [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]\n",
      "INFO: dof 45: normal gap = 2.5, X=[100.0,10.0], C=0.0, la=0.0\n",
      "INFO: contact dof 45 active (C = 0.0)\n",
      "INFO: dof 49: normal gap = 2.5, X=[0.0,10.0], C=-1.0, la=0.0\n",
      "INFO: dof 53: normal gap = 5.0, X=[5.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 57: normal gap = 5.0, X=[10.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 61: normal gap = 5.0, X=[15.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 65: normal gap = 5.0, X=[20.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 69: normal gap = 5.0, X=[25.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 73: normal gap = 5.0, X=[30.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 77: normal gap = 5.0, X=[35.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 81: normal gap = 5.0, X=[40.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 83: normal gap = 5.0, X=[95.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 85: normal gap = 5.0, X=[90.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 87: normal gap = 5.0, X=[85.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 89: normal gap = 5.0, X=[80.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 91: normal gap = 5.0, X=[75.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 93: normal gap = 5.0, X=[70.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 95: normal gap = 5.0, X=[65.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 97: normal gap = 5.0, X=[60.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 99: normal gap = 5.0, X=[55.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 101: normal gap = 5.0, X=[50.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 103: normal gap = 5.0, X=[45.0,10.0], C=0.0, la=0.0\n",
      "INFO: postprocess mortar assembly: done.\n",
      "INFO: Solving system\n",
      "INFO: UMFPACK: solved in 0.0 seconds. norm = 5.426685089269588\n",
      "INFO: timing info for iteration:\n",
      "INFO: boundary assembly       : 0.15599989891052246\n",
      "INFO: field assembly          : 0.06300020217895508\n",
      "INFO: dump matrices to disk   : 0.0\n",
      "INFO: solve problem           : 0.07800006866455078\n",
      "INFO: update element data     : 0.0\n",
      "INFO: non-linear iteration    : 0.2970001697540283\n",
      "INFO: Starting iteration 2\n",
      "INFO: Assembling field problems...\n",
      "INFO: Assembling body 1: plane stress linear elasticity\n",
      "INFO: Assembly: 10.0 % done. \n",
      "INFO: Assembly: 20.0 % done. \n",
      "INFO: Assembly: 30.0 % done. \n",
      "INFO: Assembly: 40.0 % done. \n",
      "INFO: Assembly: 50.0 % done. \n",
      "INFO: Assembly: 60.0 % done. \n",
      "INFO: Assembly: 70.0 % done. \n",
      "INFO: Assembly: 80.0 % done. \n",
      "INFO: Assembly: 90.0 % done. \n",
      "INFO: Assembly: 100.0 % done. \n",
      "INFO: dim = 210\n",
      "INFO: Assembling boundary problems...\n",
      "INFO: Assembling boundary 1: dirichlet boundary\n",
      "INFO: Assembling boundary 2: mortar problem\n",
      "INFO: assemble: doing postprocess for problem JuliaFEM.Core.BoundaryProblem{JuliaFEM.Core.MortarProblem} assembly\n",
      "INFO: postprocess mortar assembly: peforming PDASS.\n",
      "INFO: weighted gap = [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.33,0.0,0.0,0.0,-0.01,2.49,0.0,0.0,-0.07,4.96,0.0,0.0,-0.15,4.89,0.0,0.0,-0.23,4.77,0.0,0.0,-0.3,4.61,0.0,0.0,-0.37,4.41,0.0,0.0,-0.42,4.18,0.0,0.0,-0.47,3.92,0.0,0.0,-0.51,3.65,-0.67,0.2,-0.67,0.51,-0.67,0.82,-0.66,1.14,-0.66,1.45,-0.65,1.77,-0.64,2.09,-0.62,2.42,-0.6,2.73,-0.58,3.05,-0.55,3.35,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]\n",
      "INFO: lambda = [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05,-0.0,0.0,0.0,0.74,-0.19,0.0,0.0,-0.0,0.0,0.0,0.0,0.0,-0.0,0.0,0.0,-0.0,0.0,0.0,0.0,0.0,-0.0,0.0,0.0,-0.0,0.0,0.0,0.0,0.0,-0.0,0.0,0.0,-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,-0.0,0.0,0.0,-4.87,-4.26]\n",
      "INFO: dof 45: normal gap = 0.0, X=[100.0,10.0], C=0.0, la=-0.0\n",
      "INFO: contact dof 45 active (C = 0.0)\n",
      "INFO: dof 49: normal gap = 2.493, X=[0.0,10.0], C=-1.0, la=-0.18516\n",
      "INFO: dof 53: normal gap = 4.959, X=[5.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 57: normal gap = 4.885, X=[10.0,10.0], C=0.0, la=-0.0\n",
      "INFO: dof 61: normal gap = 4.768, X=[15.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 65: normal gap = 4.606, X=[20.0,10.0], C=0.0, la=-0.0\n",
      "INFO: dof 69: normal gap = 4.407, X=[25.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 73: normal gap = 4.179, X=[30.0,10.0], C=0.0, la=-0.0\n",
      "INFO: dof 77: normal gap = 3.924, X=[35.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 81: normal gap = 3.646, X=[40.0,10.0], C=0.0, la=-0.0\n",
      "INFO: dof 83: normal gap = 0.205, X=[95.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 85: normal gap = 0.514, X=[90.0,10.0], C=0.0, la=-0.0\n",
      "INFO: dof 87: normal gap = 0.825, X=[85.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 89: normal gap = 1.138, X=[80.0,10.0], C=0.0, la=-0.0\n",
      "INFO: dof 91: normal gap = 1.455, X=[75.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 93: normal gap = 1.774, X=[70.0,10.0], C=0.0, la=-0.0\n",
      "INFO: dof 95: normal gap = 2.094, X=[65.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 97: normal gap = 2.415, X=[60.0,10.0], C=0.0, la=-0.0\n",
      "INFO: dof 99: normal gap = 2.734, X=[55.0,10.0], C=0.0, la=0.0\n",
      "INFO: dof 101: normal gap = 3.048, X=[50.0,10.0], C=0.0, la=-0.0\n",
      "INFO: dof 103: normal gap = 3.353, X=[45.0,10.0], C=0.0, la=0.0\n",
      "INFO: postprocess mortar assembly: done.\n",
      "INFO: Solving system\n",
      "INFO: UMFPACK: solved in 0.0 seconds. norm = 1.303211404108607e-12\n",
      "INFO: timing info for iteration:\n",
      "INFO: boundary assembly       : 0.1399998664855957\n",
      "INFO: field assembly          : 0.06200003623962402\n",
      "INFO: dump matrices to disk   : 0.0\n",
      "INFO: solve problem           : 0.07800006866455078\n",
      "INFO: update element data     : 0.0\n",
      "INFO: non-linear iteration    : 0.2799999713897705\n",
      "INFO: solver finished in 0.5770001411437988 seconds.\n",
      "INFO: displacement at tip: [-0.036167518632111914,-0.4890644234713899]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x0000000033496E10>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2-element Array{Float64,1}:\n",
       " -0.0361675\n",
       " -0.489064 "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function create_problems_2()\n",
    "\n",
    "    mesh = parse_aster_med_file(Pkg.dir(\"JuliaFEM\")*\"/geometry/2d_beam/BEAM.med\")\n",
    "    \n",
    "    upper_body_nodes = Set()\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :TR3 || continue\n",
    "        push!(upper_body_nodes, elcon...)\n",
    "    end\n",
    "    for (nid, coords) in mesh[\"nodes\"]\n",
    "        nid in upper_body_nodes || continue\n",
    "        mesh[\"nodes\"][nid] = mesh[\"nodes\"][nid] + [0.0, 1.0]\n",
    "    end\n",
    "    \n",
    "    field_problem = PlaneStressLinearElasticityProblem()\n",
    "\n",
    "    # field problems\n",
    "    joo = Dict(:QU4 => Quad4, :TR3 => Tri3)\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype in keys(joo) || continue\n",
    "        element = joo[eltype](elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        element[\"youngs modulus\"] = 900.0\n",
    "        element[\"poissons ratio\"] = 0.25\n",
    "        push!(field_problem, element)\n",
    "    end\n",
    "\n",
    "    # neumann boundary condition -1 on y direction\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == :LOAD || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        # FIXME\n",
    "        # element[\"displacement traction force\"] = rmat*[0.0, -0.01]\n",
    "        f = rmat*[0.0, -0.0125]*1.5\n",
    "        element[\"displacement traction force\"] = Vector{Float64}[f, f]\n",
    "        push!(field_problem, element)\n",
    "    end\n",
    "\n",
    "    # boundary conditions\n",
    "    boundary_problem = DirichletProblem(\"displacement\", 2)\n",
    "\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == :LEFT || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        element[\"displacement\"] = (0.0 => Vector{Float64}[[0.0, 0.0], [0.0, 0.0]])\n",
    "        push!(boundary_problem, element)\n",
    "    end\n",
    "\n",
    "    info(\"created $(length(get_elements(field_problem))) field elements.\")\n",
    "    info(\"created $(length(get_elements(boundary_problem))) boundary elements.\")\n",
    "\n",
    "    # Contact definition: contact pair is `LOWER_TO_UPPER <--> UPPER_TO_LOWER`:\n",
    "\n",
    "    master_surface = :UPPER_TO_LOWER\n",
    "    slave_surface = :LOWER_TO_UPPER\n",
    "\n",
    "    contact_problem = MortarProblem(\"displacement\", 2)\n",
    "    master_elements = JuliaFEM.Core.Element[]\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == master_surface || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        element[\"displacement\"] = (0.0 => Vector{Float64}[[0.0, 0.0], [0.0, 0.0]])\n",
    "        push!(master_elements, element)\n",
    "        push!(contact_problem, element)\n",
    "    end\n",
    "\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == slave_surface || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        element[\"master elements\"] = master_elements\n",
    "        element[\"displacement\"] = (0.0 => Vector{Float64}[[0.0, 0.0], [0.0, 0.0]])\n",
    "        calculate_normal_tangential_coordinates!(element, 0.0)\n",
    "        push!(contact_problem, element)\n",
    "    end\n",
    "\n",
    "    info(\"# of master elements: $(length(master_elements))\")\n",
    "    info(\"# of slave elements: $(length(contact_problem.elements))\")\n",
    "\n",
    "    return field_problem, boundary_problem, contact_problem\n",
    "\n",
    "end\n",
    "\n",
    "using JuliaFEM.Core: calculate_nodal_vector, Element\n",
    "\n",
    "function JuliaFEM.Core.postprocess_assembly!(\n",
    "    assembly::BoundaryAssembly, problem::BoundaryProblem{MortarProblem},\n",
    "    time::Real)\n",
    "    info(\"postprocess mortar assembly: peforming PDASS.\")\n",
    "    \n",
    "    C1 = sparse(assembly.C1)\n",
    "    C2 = sparse(assembly.C2)\n",
    "    dim = size(C1, 1)\n",
    "    elements = get_elements(problem)\n",
    "    P = -calculate_normal_tangential_coordinates(elements, time)\n",
    "    resize!(C1, 156, 156)\n",
    "    resize!(C2, 156, 156)\n",
    "    resize!(P, 156, 156)\n",
    "    la = calculate_nodal_vector(\"reaction force\", 2, elements, time)\n",
    "    X = calculate_nodal_vector(\"geometry\", 2, elements, time)\n",
    "    u = calculate_nodal_vector(\"displacement\", 2, elements, time)\n",
    "    x = X+u\n",
    "    #info(\"x = \", x)\n",
    "    #info(\"lambda = \", la)\n",
    "    gh = -C1*x  # weighted gap vector\n",
    "    info(\"weighted gap = \", round(gh, 2))\n",
    "    info(\"lambda = \", round(la, 2))\n",
    "\n",
    "    #=\n",
    "    info(\"size P = \", size(P))\n",
    "    info(\"size C1 = \", size(C1))\n",
    "    info(\"size C2 = \", size(C2))\n",
    "    info(\"size la = \", size(la))\n",
    "    info(\"size gh = \", size(gh))\n",
    "    info(\"displacement = \", round(u, 2))\n",
    "    =#\n",
    "\n",
    "    #nz = sort(unique(rowvals(P)))\n",
    "    #dump(round(full(P[nz,nz]), 3))\n",
    "    \n",
    "    C1 = P*C1\n",
    "    C2 = P*C2\n",
    "    gh = P*gh\n",
    "    la = P*la\n",
    "    c = 1.0\n",
    "    # complementarity function\n",
    "    C = la - clamp(la - c*gh, 0, Inf)\n",
    "    C[abs(C) .< 1.0e-12] = 0.0\n",
    "    C[49] = -1 # node in support\n",
    "    #info(\"complementarity function = \", round(C, 2))\n",
    "    X2 = reshape(X, 2, round(Int, length(X)/2))\n",
    "    j = 0\n",
    "    for i=1:2:dim\n",
    "        j += 1\n",
    "        X2[1,j] == 0 && continue\n",
    "        info(\"dof $i: normal gap = $(round(gh[i], 3)), X=$(round(X2[:,j], 2)), C=$(round(C[i], 5)), la=$(round(la[i], 5))\")\n",
    "        #if C[i] > 0\n",
    "        if i == 45\n",
    "            info(\"contact dof $i active (C = $(C[i]))\")\n",
    "            #gh[i] = -gh[i]\n",
    "        else\n",
    "            C1[i, :] = 0\n",
    "            C2[i, :] = 0\n",
    "            gh[i] = 0\n",
    "        end\n",
    "    end\n",
    "    for i=2:2:dim\n",
    "        C1[i, :] = 0\n",
    "        C2[i, :] = 0\n",
    "        gh[i] = 0\n",
    "    end\n",
    "    #assembly.g = g\n",
    "    assembly.C1 = C1\n",
    "    assembly.C2 = C2\n",
    "    assembly.g = sparse(gh)\n",
    "    info(\"postprocess mortar assembly: done.\")\n",
    "end\n",
    "\n",
    "field_problem, boundary_problem, contact_problem = create_problems_2()\n",
    "using JuliaFEM.Core: DirectSolver\n",
    "solver = DirectSolver()\n",
    "solver.name = \"divided_beam_inequality_constraints\"\n",
    "solver.method = :UMFPACK\n",
    "solver.max_iterations = 5\n",
    "solver.dump_matrices = false\n",
    "push!(solver, field_problem)\n",
    "push!(solver, boundary_problem)\n",
    "push!(solver, contact_problem)\n",
    "call(solver, 0.0)\n",
    "\n",
    "u_tip = plot(field_problem, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4x4 Array{Float64,2}:\n",
       " 0.340028  0.654971  0.587056   0.0\n",
       " 0.28579   0.626868  0.0744826  0.0\n",
       " 0.215966  0.159712  0.301785   0.0\n",
       " 0.0       0.0       0.0        0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = sparse(rand(3, 3))\n",
    "resize!(A, 4, 4)\n",
    "full(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Array{Float64,1}:\n",
       " 0.0\n",
       " 0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contact_problem.elements[5](\"displacement\", [0.0], 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Int64,1}:\n",
       " 137\n",
       " 138\n",
       " 139\n",
       " 140"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JuliaFEM.Core.get_gdofs(contact_problem.elements[5], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: assemble: doing postprocess for problem JuliaFEM.Core.BoundaryProblem{JuliaFEM.Core.MortarProblem} assembly\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array(Float64,(0,2)) "
     ]
    },
    {
     "data": {
      "text/plain": [
       "4x1 Array{Float64,2}:\n",
       "  0.0\n",
       " -2.0\n",
       "  0.0\n",
       " -2.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: postprocess mortar assembly: remove contraints in tangent direction on boundary.\n",
      "INFO: postprocess mortar assembly: done.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0x2 Array{Float64,2}\n"
     ]
    }
   ],
   "source": [
    "function JuliaFEM.Core.postprocess_assembly!(\n",
    "    assembly::BoundaryAssembly, problem::BoundaryProblem{MortarProblem},\n",
    "    time::Real)\n",
    "    info(\"postprocess mortar assembly: remove contraints in tangent direction on boundary.\")\n",
    "    C1 = sparse(assembly.C1)\n",
    "    C2 = sparse(assembly.C2)\n",
    "    g = sparse(assembly.g)\n",
    "    n = round(Int, length(g)/2)\n",
    "    dump(reshape(full(g), 2, n)[:,60:end]')\n",
    "    dim = size(C1, 1)\n",
    "    P = calculate_normal_tangential_coordinates(get_elements(problem), time)\n",
    "    C1 = P*C1\n",
    "    C2 = P*C2\n",
    "    for i=2:2:dim\n",
    "        C1[i,:] = 0\n",
    "        C2[i,:] = 0\n",
    "    end\n",
    "    assembly.g = g\n",
    "    assembly.C1 = C1\n",
    "    assembly.C2 = C2\n",
    "    info(\"postprocess mortar assembly: done.\")\n",
    "end\n",
    "\n",
    "el1 = Seg2([1, 2])\n",
    "el1[\"geometry\"] = Vector{Float64}[[0.0, 0.0], [4.0, 0.0]]\n",
    "el2 = Seg2([3, 4])\n",
    "el2[\"geometry\"] = Vector{Float64}[[0.0, 1.0], [4.0, 1.0]]\n",
    "el1[\"master elements\"] = Element[el2]\n",
    "JuliaFEM.Core.calculate_normal_tangential_coordinates!(el1, 0.0)\n",
    "p = MortarProblem(\"displacement\", 2)\n",
    "push!(p, el1)\n",
    "a = JuliaFEM.Core.assemble(p, 0.0)\n",
    "full(a.g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple test case, sliding contact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{Int64,Array{Float64,1}} with 8 entries:\n",
       "  7 => [2.0,0.0]\n",
       "  4 => [2.0,4.0]\n",
       "  2 => [2.0,6.0]\n",
       "  3 => [2.0,2.0]\n",
       "  5 => [0.0,0.0]\n",
       "  8 => [4.0,0.0]\n",
       "  6 => [2.0,0.0]\n",
       "  1 => [0.0,2.0]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using JuliaFEM.Core: Seg2, Tri3, Quad4, Node, update!,\n",
    "    calculate_normal_tangential_coordinates!, PlaneStressLinearElasticityProblem, \n",
    "    ContactProblem, SmallSlidingContact, Element\n",
    "using JuliaFEM.Core: PlaneStressLinearElasticityProblem, DirichletProblem,\n",
    "                     get_connectivity, Quad4, Tri3, Seg2, LinearSolver,\n",
    "                     update!, get_elements, BiorthogonalBasis, StandardBasis\n",
    "nodes = Dict{Int64, Node}(\n",
    "    1 => [0.0, 2.0],\n",
    "    2 => [2.0, 6.0],\n",
    "    3 => [2.0, 2.0],\n",
    "    4 => [2.0, 4.0],\n",
    "    5 => [0.0, 0.0],\n",
    "    6 => [2.0, 0.0],\n",
    "    7 => [2.0, 0.0],\n",
    "    8 => [4.0, 0.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "basis = StandardBasis\n",
    "\n",
    "el1 = Quad4([5, 6, 3, 1])\n",
    "el2 = Tri3([1, 3, 4])\n",
    "el3 = Tri3([7, 8, 2])\n",
    "del1 = Seg2([5, 6])\n",
    "del2 = Seg2([7, 8])\n",
    "sel1 = Seg2([4, 3])\n",
    "sel2 = Seg2([3, 6])\n",
    "mel1 = Seg2([2, 7])\n",
    "update!([el1, el2, el3, sel1, sel2, mel1, del1, del2], \"geometry\", nodes)\n",
    "for el in [el1, el2, el3]\n",
    "    el[\"youngs modulus\"] = 90.0\n",
    "    el[\"poissons ratio\"] = 0.25\n",
    "end\n",
    "for el in [del1, del2]\n",
    "    el[\"displacement\"] = 0.0\n",
    "end\n",
    "for el in [sel1, sel2]\n",
    "    el[\"master elements\"] = Element[mel1]\n",
    "    calculate_normal_tangential_coordinates!(el, 0.0)\n",
    "end\n",
    "prob = PlaneStressLinearElasticityProblem()\n",
    "push!(prob, el1, el2, el3)\n",
    "bc = DirichletProblem(\"displacement\", 2; basis=basis)\n",
    "push!(bc, del1, del2)\n",
    "con = ContactProblem(\"cont\", \"displacement\", 2;\n",
    "                     contact_type=SmallSlidingContact)\n",
    "push!(con, sel1, sel2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: slave dofs of element: [7,8,5,6]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normal tangential = \n",
      "[1.0 0.0\n",
      " 0.0 -1.0]\n",
      "normal tangential = "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: slave dofs of element: [5,6,11,12]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "JuliaFEM.Core.BoundaryAssembly(JuliaFEM.Core.SparseMatrixCOO([7,5,7,5,7,5,7,5,8,6  …  5,11,6,12,6,12,6,12,6,12],[7,7,5,5,3,3,13,13,8,8  …  13,13,6,6,12,12,4,4,14,14],[0.21522,0.0105929,0.0105929,0.000521371,-0.147011,-0.00723572,-0.0788018,-0.00387854,0.21522,0.0105929  …  -0.0109405,-0.222282,0.000521371,0.0105929,0.0105929,0.21522,-0.00017379,-0.00353096,-0.0109405,-0.222282]),JuliaFEM.Core.SparseMatrixCOO([7,5,7,5,7,5,7,5,8,6  …  5,11,6,12,6,12,6,12,6,12],[7,7,5,5,3,3,13,13,8,8  …  13,13,6,6,12,12,4,4,14,14],[0.21522,0.0105929,0.0105929,0.000521371,-0.147011,-0.00723572,-0.0788018,-0.00387854,0.21522,0.0105929  …  -0.0109405,-0.222282,0.000521371,0.0105929,0.0105929,0.21522,-0.00017379,-0.00353096,-0.0109405,-0.222282]),JuliaFEM.Core.SparseMatrixCOO(Int64[],Int64[],Float64[]),JuliaFEM.Core.SparseMatrixCOO(Int64[],Int64[],Float64[]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[1.0 0.0\n",
      " 0.0 -1.0]\n",
      "normal tangential = \n",
      "[1.0 0.0\n",
      " 0.0 -1.0]\n",
      "normal tangential = \n",
      "[1.0 0.0\n",
      " 0.0 -1.0]\n",
      "normal tangential = \n",
      "[1.0 0.0\n",
      " 0.0 -1.0]\n",
      "normal tangential = \n",
      "[1.0 0.0\n",
      " 0.0 -1.0]\n",
      "normal tangential = \n",
      "[1.0 0.0\n",
      " 0.0 -1.0]\n",
      "normal tangential = \n",
      "[1.0 0.0\n",
      " 0.0 -1.0]\n",
      "normal tangential = \n",
      "[1.0 0.0\n",
      " 0.0 -1.0]\n",
      "normal tangential = \n",
      "[1.0 0.0\n",
      " 0.0 -1.0]\n"
     ]
    }
   ],
   "source": [
    "ass1 = assemble(prob, 0.0)\n",
    "dbc = assemble(bc, 0.0)\n",
    "cbc = assemble(con, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8x8 Array{Float64,2}:\n",
       "  92.0  -15.0  0.0   0.0  -74.0   15.0    0.0  -12.0\n",
       " -15.0   62.0  0.0   0.0   15.0  -14.0  -18.0    0.0\n",
       "   0.0    0.0  6.0   0.0    0.0    0.0    0.0    0.0\n",
       "   0.0    0.0  0.0  16.0    0.0    0.0    0.0    0.0\n",
       " -74.0   15.0  0.0   0.0  110.0  -15.0  -18.0   12.0\n",
       "  15.0  -14.0  0.0   0.0  -15.0  110.0   18.0  -48.0\n",
       "   0.0  -18.0  0.0   0.0  -18.0   18.0   18.0    0.0\n",
       " -12.0    0.0  0.0   0.0   12.0  -48.0    0.0   48.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = full(ass1.stiffness_matrix)\n",
    "dims = size(K)\n",
    "K[1:8,1:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16x16 Array{Float64,2}:\n",
       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
       " 0.0  0.0  -12.0    0.0  24.0   0.0   6.0   0.0  0.0  0.0   6.0   0.0  -24.0    0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  -12.0   0.0  24.0   0.0   6.0  0.0  0.0   0.0   6.0    0.0  -24.0  0.0  0.0\n",
       " 0.0  0.0  -10.0    0.0   6.0   0.0  12.0   0.0  0.0  0.0   0.0   0.0   -8.0    0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  -10.0   0.0   6.0   0.0  12.0  0.0  0.0   0.0   0.0    0.0   -8.0  0.0  0.0\n",
       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
       " 0.0  0.0   -2.0    0.0   6.0   0.0   0.0   0.0  0.0  0.0  12.0   0.0  -16.0    0.0  0.0  0.0\n",
       " 0.0  0.0    0.0   -2.0   0.0   6.0   0.0   0.0  0.0  0.0   0.0  12.0    0.0  -16.0  0.0  0.0\n",
       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fixed = [9, 10, 11, 12, 13, 14, 15, 16]\n",
    "ENV[\"COLUMNS\"] = 300\n",
    "C1 = full(cbc.C1, dims...)\n",
    "C1[abs(C1) .< 1.0e-9] = 0\n",
    "#C1[fixed, fixed] = 0\n",
    "C1*18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16x16 Array{Float64,2}:\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D = full(cbc.D, dims...)\n",
    "D[abs(D) .< 1.0e-9] = 0\n",
    "D[fixed, fixed] = 0\n",
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16x16 Array{Float64,2}:\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  -12.0  0.0  24.0  0.0   6.0  0.0  0.0  0.0   6.0  0.0  -24.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  -10.0  0.0   6.0  0.0  12.0  0.0  0.0  0.0   0.0  0.0   -8.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0   -2.0  0.0   6.0  0.0   0.0  0.0  0.0  0.0  12.0  0.0  -16.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C2 = full(cbc.C2, dims...)\n",
    "C2[abs(C2) .< 1.0e-9] = 0\n",
    "#C2[fixed, :] = 0\n",
    "for i=2:2:16\n",
    "    C1[i,:] = 0\n",
    "    C2[i,:] = 0\n",
    "end\n",
    "C2*18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16x16 Array{Float64,2}:\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16x16 Array{Float64,2}:\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  12.0   0.0   6.0   0.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  12.0   0.0   6.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   6.0   0.0  12.0   0.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   6.0   0.0  12.0   0.0   0.0   0.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0  12.0   0.0   6.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0  12.0   0.0   6.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   6.0   0.0  12.0   0.0\n",
       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   6.0   0.0  12.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DC1 = full(dbc.C1, dims...)\n",
    "DC2 = full(dbc.C2, dims...)\n",
    "DD = full(dbc.D, dims...)\n",
    "DC1*18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32-element Array{Float64,1}:\n",
       " 18.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  ⋮  \n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0\n",
       "  0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = zeros(16)\n",
    "f[1] = 18.0\n",
    "g = zeros(16)\n",
    "A = [K (C1+DC1)'; (C2+DC2) (D+DD)]\n",
    "b = [f; g]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: zero line 17\n",
      "INFO: zero line 18\n",
      "INFO: zero line 19\n",
      "INFO: zero line 20\n",
      "INFO: zero line 22\n",
      "INFO: zero line 24\n"
     ]
    }
   ],
   "source": [
    "for i=1:size(A,1)\n",
    "    if sum(abs(A[i,:])) == 0\n",
    "        info(\"zero line $i\")\n",
    "        A[i,i] = 1.0\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2x8 Array{Float64,2}:\n",
       " 0.406155  0.659049   0.219683   0.439366   0.0  0.0  0.0  0.0\n",
       " 0.149452  0.0       -0.102834  -0.0562157  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u = A \\ b\n",
    "u = reshape(u, 2, 16)\n",
    "u[abs(u) .< 1.0e-9] = 0\n",
    "disp = u[:,1:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# tie contact\n",
    "@assert isapprox(norm(vec(disp)), 0.8223229928825583)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# frictionless sliding\n",
    "@assert isapprox(norm(vec(disp)), 0.9363129098080032)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2x8 Array{Float64,2}:\n",
       " 0.0  2.0  2.0  2.0  0.0  2.0  2.0  4.0\n",
       " 2.0  6.0  2.0  4.0  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = [0.0 2; 2 6; 2 2; 2 4; 0 0; 2 0; 2 0; 4 0]'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2x8 Array{Float64,2}:\n",
       " 0.406155  2.65905  2.21968  2.43937  0.0  2.0  2.0  4.0\n",
       " 2.14945   6.0      1.89717  3.94378  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = X + disp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{Int64,1}:\n",
       " 7\n",
       " 8\n",
       " 2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eldofs = Dict()\n",
    "eldofs[1] = [5, 6, 3, 1]\n",
    "eldofs[2] = [1, 3, 4]\n",
    "eldofs[3] = [7, 8, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000002615EAC8>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(-0.1,6.1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PyPlot\n",
    "\n",
    "function get_coords(X)\n",
    "    return [X X[:, 1]]\n",
    "end\n",
    "\n",
    "function plot(X, args...; kwargs...)\n",
    "    X1 = X[:, eldofs[1]]\n",
    "    X2 = X[:, eldofs[2]]\n",
    "    X3 = X[:, eldofs[3]]\n",
    "    PyPlot.plot(get_coords(X1)[1,:]', get_coords(X1)[2,:]', args...; kwargs...)\n",
    "    PyPlot.plot(get_coords(X2)[1,:]', get_coords(X2)[2,:]', args...; kwargs...)\n",
    "    PyPlot.plot(get_coords(X3)[1,:]', get_coords(X3)[2,:]', args...; kwargs...)\n",
    "    axis(\"off\")\n",
    "end\n",
    "figure(figsize=(4, 5))\n",
    "plot(X, \"-k\")\n",
    "plot(x, \"--r\")\n",
    "xlim(-0.1, 4.1)\n",
    "ylim(-0.1, 6.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.4.1",
   "language": "julia",
   "name": "julia-0.4"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}