diff --git a/notebooks/2015-09-10-surface-normals.ipynb b/notebooks/2015-09-10-surface-normals.ipynb index ba6029e..a9221a6 100644 --- a/notebooks/2015-09-10-surface-normals.ipynb +++ b/notebooks/2015-09-10-surface-normals.ipynb @@ -17,17 +17,6 @@ "metadata": { "collapsed": false }, - "outputs": [], - "source": [ - "using JuliaFEM" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, "outputs": [ { "name": "stderr", @@ -38,12 +27,18 @@ } ], "source": [ + "using JuliaFEM\n", + "using JuliaFEM: Element, get_connectivity, get_field, set_field\n", + "using JuliaFEM: get_number_of_basis_functions, get_detJ, get_basis\n", + "using JuliaFEM: interpolate, dinterpolate, get_dbasisdxi, new_field!, push_field!, PSeg\n", + "using JuliaFEM: set_degree, get_number_of_basis_functions, dinterpolate\n", + "using ForwardDiff\n", "using PyPlot" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -52,12 +47,12 @@ "data": { "text/plain": [ "3-element Array{Any,1}:\n", - " JuliaFEM.PSeg([1,2],Dict{Any,Any}(:geometry=>Array{T,1}[[-10.0,1.2246467991473533e-15],[-4.999999999999998,8.660254037844387]]),1) \n", - " JuliaFEM.PSeg([2,3],Dict{Any,Any}(:geometry=>Array{T,1}[[-4.999999999999998,8.660254037844387],[5.000000000000001,8.660254037844386]]),1)\n", - " JuliaFEM.PSeg([3,4],Dict{Any,Any}(:geometry=>Array{T,1}[[5.000000000000001,8.660254037844386],[10.0,0.0]]),1) " + " JuliaFEM.PSeg([1,2],Dict{Any,Any}(:Geometry=>Array{T,1}[[-10.0,1.2246467991473533e-15],[-4.999999999999998,8.660254037844387]]),1) \n", + " JuliaFEM.PSeg([2,3],Dict{Any,Any}(:Geometry=>Array{T,1}[[-4.999999999999998,8.660254037844387],[5.000000000000001,8.660254037844386]]),1)\n", + " JuliaFEM.PSeg([3,4],Dict{Any,Any}(:Geometry=>Array{T,1}[[5.000000000000001,8.660254037844386],[10.0,0.0]]),1) " ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -70,71 +65,59 @@ "R = 10.0\n", "for i=1:nelements\n", " con = [i, mod(i, nnodes)+1]\n", - " seg = JuliaFEM.PSeg(con)\n", + " seg = PSeg(con)\n", " # create vector field :geometry for elements\n", " pnts = Vector[]\n", " for deg in phi[con]\n", " X = R*[cos(deg), sin(deg)]\n", " push!(pnts, X)\n", " end\n", - " JuliaFEM.set_field(seg, :geometry, pnts)\n", + " set_field(seg, :Geometry, pnts)\n", " push!(elements, seg)\n", "end\n", "elements" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# calculate \"local\" normals in elements, in a way that\n", + "# n = Nᵢnᵢ gives some reasonable results for ξ ∈ [-1, 1]\n", + "\n", + "function calculate_normals!(el::Element, field_name=:Normals)\n", + " new_field!(el, field_name, Vector)\n", + " for xi in Vector[[-1.0], [1.0]]\n", + " t = dinterpolate(el, :Geometry, xi)\n", + " n = [0 -1; 1 0]*t\n", + " n /= norm(n)\n", + " push_field!(el, field_name, n)\n", + " end\n", + "end\n", + "\n", + "for el in elements\n", + " calculate_normals!(el)\n", + "end" + ] + }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "3-element Array{Any,1}:\n", - " JuliaFEM.PSeg([1,2],Dict{Any,Any}(:geometry=>Array{T,1}[[-10.0,1.2246467991473533e-15],[-4.999999999999998,8.660254037844387]],:normals=>Array{T,1}[[-0.8660254037844386,0.5000000000000002],[-0.8660254037844386,0.5000000000000002]]),1)\n", - " JuliaFEM.PSeg([2,3],Dict{Any,Any}(:geometry=>Array{T,1}[[-4.999999999999998,8.660254037844387],[5.000000000000001,8.660254037844386]],:normals=>Array{T,1}[[1.7763568394002506e-16,1.0],[1.7763568394002506e-16,1.0]]),1) \n", - " JuliaFEM.PSeg([3,4],Dict{Any,Any}(:geometry=>Array{T,1}[[5.000000000000001,8.660254037844386],[10.0,0.0]],:normals=>Array{T,1}[[0.8660254037844387,0.5],[0.8660254037844387,0.5]]),1) " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# calculate \"local\" normals in elements, in a way that\n", - "# n = Nᵢnᵢ gives some reasonable results for ξ ∈ [-1, 1]\n", - "\n", - "for el in elements\n", - " nfld = Vector[]\n", - " for xi in Vector[[-1.0], [1.0]]\n", - " t = JuliaFEM.dinterpolate(el, :geometry, xi)\n", - " n = [0 -1; 1 0]*t\n", - " n /= norm(n)\n", - " push!(nfld, n)\n", - " end\n", - " JuliaFEM.set_field(el, :normals, nfld)\n", - "end\n", - "elements" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, "outputs": [ { "data": { "image/png": [ - 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" 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" ], "text/plain": [ - "PyPlot.Figure(PyObject )" + "PyPlot.Figure(PyObject )" ] }, "metadata": {}, @@ -143,11 +126,10 @@ { "data": { "text/plain": [ - "1-element Array{Any,1}:\n", - " PyObject " + "(-15.0,15.0,-2.0,14.0)" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -157,21 +139,18 @@ " phi2 = linspace(0, pi, 100)\n", " phi3 = linspace(0, pi, 10)\n", " phi4 = linspace(0, pi, 4)\n", - " figure(figsize=(4, 2))\n", + " figure(figsize=(8, 4))\n", " plot(R*cos(phi2), R*sin(phi2))\n", " for fi in phi3\n", " p0 = R*[cos(fi), sin(fi)]\n", " p1 = (R+3)*[cos(fi), sin(fi)]\n", " plot([p0[1], p1[1]], [p0[2], p1[2]], \"-b\")\n", " end\n", - " axis(\"equal\")\n", - " xlim(-1.5*R, 1.5*R)\n", - " ylim(-0.5*R, 1.5*R)\n", "\n", " # create a array of vectors\n", " xis = Vector[[xi] for xi in linspace(-1, 1)]\n", " for el in elements\n", - " coords = JuliaFEM.interpolate(el, :geometry, xis)\n", + " coords = interpolate(el, :Geometry, xis)\n", " # extract 1 and 2 components from array of coordinates\n", " xs = [X[1] for X in coords]\n", " ys = [X[2] for X in coords]\n", @@ -180,8 +159,8 @@ "\n", " xis = Vector[[xi] for xi in linspace(-1, 1, 4)]\n", " for el in elements\n", - " coords = JuliaFEM.interpolate(el, :geometry, xis)\n", - " normals = JuliaFEM.interpolate(el, :normals, xis)\n", + " coords = interpolate(el, :Geometry, xis)\n", + " normals = interpolate(el, :Normals, xis)\n", " for i=1:length(normals)\n", " p0 = coords[i]\n", " p1 = coords[i] + normals[i]/norm(normals[i])*3\n", @@ -189,6 +168,12 @@ " end\n", " end\n", " plot(R*cos(phi4), R*sin(phi4), \"ko\")\n", + "\n", + " xlim(-1.5*R, 1.5*R)\n", + " ylim(-0.5*R, 1.5*R)\n", + "\n", + " axis(\"equal\")\n", + " axis(\"off\")\n", "end\n", "\n", "plot_stuff(elements)" @@ -198,30 +183,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Blue lines \"accurate\" normal direction, other lines are approximations." + "Blue lines \"accurate\" normal direction, other lines are approximations. Notice the discontinuity in the nodes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Strategy 1**: average normals of adjacent elements in common nodes, maybe by weighting it with areas / lenghts ..." + "## Strategy 1\n", + "\n", + "average normals of adjacent elements in common nodes, maybe by weighting it with areas / lenghts ..." ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "using JuliaFEM: get_connectivity, get_field, set_field" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -230,7 +206,7 @@ "\"\"\"\n", "Alter normal field such that normals of adjacent elements are averaged.\n", "\"\"\"\n", - "function average_normals(elements, normal_field=:normals)\n", + "function average_normals!(elements, normal_field=:Normals)\n", " d = Dict()\n", " for el in elements\n", " c = get_connectivity(el)\n", @@ -248,12 +224,12 @@ " set_field(el, normal_field, new_normals)\n", " end\n", "end\n", - "average_normals(elements)" + "average_normals!(elements)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -261,10 +237,10 @@ { "data": { "image/png": [ - 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2bNld4ZgY7cOSNiOTnp4Iy5b9CiAfH34oe0S42Fhg8mS+n/SmTcBnn/E9Jg4f5m+wqbKfcosWLQDw/V61QU5ODu7fv6/yJyHr1weWLuVHD/ztN/7mbYcOQKdOwJkzsh/jP3r0Gp49C4aDQ1sMH95FpTEx2oElbaZMs2f7wdKyISIjj8LJyQVubm5o2rQpRo8ejYiIFEyYwF8JHjwIzJ/PJ5nVq/mkIwRLS0u4uLhozYBjd+/ehVgsFuzxdRMT/kswMpJvOsnJAXr04B/cOX8eSE5OwejRo9G0aVO4ublh0KCOAIAjRzYLEg+jeYI9XMNUDsnJybCwyEVmJhAb+65x9f79+9ix4xKqVbuG5cvtMGkS/5i4Onh6empN0g4PD4eenh7c3d0FrUck4ptI+vUDzp7lvyC7dUuGsXF75OaWbvQePnwgrl27Bjs7O0HjYtSPXWkzZZo9ezYSEp7LWBuNnj1nYeZM9SVsgG/Xvn37NiQSzc+2Ex4ejsaNG5c5jo4qcRzg6wuEhABdusyWmrABIDo6GrNmzVJLTIx6CX6lffPmTSxZsgQ3btxARkYG6tWrh2HDhmHGjBlq+6AzygsNDS1z/e1rwYCa25c9zM2RlZWF6OPH0bBoqD8NuXXpEjwcHdX+N+AAJMUEl1mmvPeO0U2CJu3IyEh06NABDg4O+Oqrr2BjY4OrV69iwYIFCAsLw7Fjx4SsnlGBnJzCMtcXxsQAKhoIR15tARwBUHPgQLXWK80PAEzv3eO7fKhZ2e8MUFhYXglGFwmatPfv34/8/Hz89ddfaNy4MQBg7NixkEgk2LVrFzIyMkoNv1rZZGXxgw3pGrEY+OknIDa27I+IvpMT301EjawA/E+tNcrWQYN16w8ezPcNlCEpSR8vX/JD1OoaXT1v1EHQpF3U/FHjPx10a9WqBT09PRgaanboT6FlZwOtW/M3kFasUP3YEkKJigJGj+aHQ23UyAtRUfdllvXq1Ikf3YlRO69OnXC/jKSdl+eFxo2BH38Ehg/X/BCx8rpwgX8Aa/9+oGtXTUejhVTwhKZMCQkJVKtWLerfvz/dvn2b4uLiaP/+/WRlZUXTp0+Xuk1leoxdIiFau5ZIJCLq169ijyWrg1hM9OOPRMbGRK6uRJcvE919epc4G44AlHo5OzuXOY4MI6zk5GRydnaW+t7Y1a9PDx4k0yef8I/G/+9/RLrwVv36K5G+PtFHHxG9fq3paFRHp8YeiY6OJjc3t+LBpTiOo/nz58ssX5mSdpGTJ4ksLIhatCB6byytMqWnp1O2sgN0KCEhgahrV/4EnzLl3dggY/8cS9bzrcl/uD81adKEXF1dqUmTJjRq1CiWsLVAcnIyjRo1qsR702DAAKp+4gSl/zsWyuHDRLa2RDVqEJ1Szfhfcnvx4oVc5QoLib76iv/8TZ5MpMQwLlpNZ5J2UlISubq6kqurK23ZsoWOHj1Kn332GYlEItq4caPUbSpj0iYiiowkql+fqGZNfoS3sojFYmrdujUNHjyYxGKx4LGdOkVkZ8ePNPfPP++Wh70II24hRxtCNggeA6M6z3NyyDQ4mGY8eVK8LDGRqGdPPinOmEGUlyd8HEeOHCFjY2MKDw8vs1xGBlGvXvyAYjLSgs7TmaQ9efJkMjMzo4SEhBLLR48eTWZmZvTq1atS2xQdXMeOHalv374lXnv37hUyXMG9fEnUvj2RkRHRvn1llz169CgBKPNXSUUVFBDNns2fyD17lvz5LJFIqMO2DtT0p6ZUIK5klz1VwJKYGDIICqJH7/1aE4uJfviByMCAyNubKC5OuPrDwsLI1NSU/Pz8SCKRyCwXE0PUrBmRlRXRmTPCxaNOe/fuLZW7OnbsqBtJu0WLFtShQ4dSy//44w/iOI7Onz9fal1lvdIukpNDNHw4nyi/+45v95ZlxYoVBID27Nmj8jiSk/nmEJGIKCCAP6Hftz9yP2Eh6OyTsyqvmxHe28JCqn/1KvW9c6fUupAQonr1+F9XUk7BCnvx4gXVrl2b2rRpQ2/fvpVZ7vJlPgZnZ6L791UfhzbRmaFZCwoKIJYyp1LRsJpVsR+psTGwaxewbBmweDHw8cf8eBLSzJ49GyNGjMCYMWMQEhKishhu3+Z7tdy5A5w7x09/9f70XW8L3mLmPzPRz60fujt3V1m9jPqY6OlhlbMzTrx6hTNpaSXWeXnxw+W2aMFP87Z2rewBqBSVk5OD/v37AwCOHTsm8wG633/n581s3Jh/uvPfHsGMPFTwJSLT8OHDycjIiB49elRi+YABA0hfX58SExNLbVPZr7Tfd+QIkakpUZs2RLLu1+Tm5lL79u2pZs2aFKeC37PHjvGzxrRqJfvn8cLAhWSw2IAepT6SXoDRCRKJhDreukWNQ0IoX8q9kcJCopkz+V9948ZVvJ1bIpGQn58fmZiYyDx/xWKiuXP5OseMUU/bujbQmTbtiIgIMjExoZo1a9KSJUvop59+op49exLHcfT5559L3aYqJW0iorAwotq1ierUIbp1S3qZly9fUr169ahFixaUpWS/QYmEaNUqIo4jGjxY9swxcelxZLLUhGadnaVUPYx2Cc/MJC4wkNY/fy6zzPbtfDu3j0/FutktWrSIANDhw4elrn/zhmjgQP4zuGpV2U2DlY3OJG0iopCQEOrRowdZWlqSoaEhNWrUiFasWCGzV0RVS9pEfHe71q35q+6jR6WXiYiIIHNzcxowYIDCPUrEYr4bH0A0b17p9uv3+R/2p5qralJGboZCdTDa6/OoKLK+dIlSyrisvXiRqFo1/qZgGfldpgMHDhAAWrp0qdT1z58TeXjwv/L+/FPx/es6nUraiqqKSZuIv/IdMoS/Clm5UvpVyPHjx4njOJo7d67c+83N5fcrEhH9/HPZZS89u0RYCNp2a5uC0TPaLDkvj6wuXqRJDx+WWe7+ff4GZZ06RPfuyb//0NBQMjY2pmHDhkntKRIayncnrVuX6PZtRaOvHFjSrqTEYqL58/kr4k8/5RPuf61atYoA0K5du8rdX3Y2ka8v38Xwjz/KqVsipla/tqJWv7YisUT4vuGMev0QF0eiwECKKKd5LT6eyN2dfxhHnlMwPj6e7O3tydvbm3JySs/6fvAg/4Rt27Z8X/GqiiXtSm7PHj7RduhQ+tFjiURCo0ePJkNDQ7py5YrMfWRmEnXsyP8cPXeu/Dq33dpGWAi6/OxyBaNntFGeWEyu16+TT3h4mf2miYheveJvjltZEV29KrtcdnY2eXp6Ut26dUt1KpBIiBYv5i9A/P35rq5VGUvaVcDVq/xjx46ORHfvllyXm5tLH374IdnZ2VFsbGypbTMz+Yd4LC2JysjrxTJyM6jmqprkf9hfRdEz2uhkaiohMJD+kGP4gYwM/kvf3Fz6Z0gsFtPgwYPJzMyMbv+nzSMnh2jYMD5hL15ctW44yqIz/bQZ5bVrB4SG8sNTtmsHnD79bp2RkRGOHDkCc3Nz9O3bF1lZWcXr3rwBevUC7t4F/vmHn0uwPMsuLkNmXia+7/a9AEfCaIteNjboYWODGdHRyJXy/MT7LC2BU6f4ARx79OD7Ur9v4cKFOHLkCHbv3l082TIAJCUBnTsDR4++mzdUV0YX1Bkq+BJRKXalXVJmJlGfPvyNxHXrSl613L17lywsLKhPnz5UWFhIOTl8ty1z87J/1r7vUeojMlhsQIuCFglzAIxWuf/mDekHBdFyKb/QpMnK4pvprKzedUnds2cPAaCVK1eWKBsRwd9stLcnunFD1ZHrNtY8UsUUFvKD/ABE48cT5ee/W3fq1CkSiUT09dczacAA/qbPxYvy77vfvn5Ub209ys5X34iCjGZ99fgxmQUHU4K0O91SZGbybdw1ahAdOnSdjIyMaOTIkSXaxv/8k79/4uGhXJfByo4l7SpqyxZ+rOGuXYnS0t4tX7NmLQEgkWgbnTwp//7OPjlLWAg6cPeA6oNltFZafj5Vv3yZRiow4EdKCpGz8zMSiWpSmzYfUO6/CV8i4ceu4Th+zO43b4SKWrfpXJv2rVu30K9fP9ja2sLMzAzu7u7YsGGDOqquVD77jB8rJDwcaNsWePyYX56ePhXAOHDceFhaXpJrX4WSQnx15it8WO9DDGkyRLigGa1TzcAAS52csOvlS4RmZsq1jbHxGxgZ9QPHGePt2z+Qn2+E/Hz+MzlrFjB3LnDoEGBmJnDwjPCzsZ89exZ9+/ZFq1at8N1338Hc3BxPnjxBQkKC0FVXSp068Tco+/QBvL2BceOAgAAOixdvxIULjzFw4ECEhoaiQYMGZe7nl5u/4EHKA4R9HgaO3Smqcsba22NTQgKmPnmCqx4eZX4GJBIJhg8fjri4aBw4cBWjR9fAgAFAQQF/g3LXLmDECDUGX9Wp4MpfpoyMDKpZsyYNGjRI7m1Y84h8Xr/m2xkB/kalREKUmppKzs7O1KRJE8rIkP0Yemp2KlVbWY3G/jlWjREz2ibo9WtCYCDtTkoqs9ycOXOI4zg6ceIEERH99Rf/uTMx4YdXZcqnM80je/fuRXJyMpYtWwYAyM7OhkQiEbLKKiMjg5+Iu2FD4MABvluVra0tTp48iYSEBHz88cdSh8UFgIVBCyEmMZZ2WarmqBlt0snaGoOqV8es6Gi8kTFM8q5du7By5UqsWrUKffr0AcB3KR03jh9S+L7sOZ8ZgQiatM+dOwdLS0s8f/4cbm5usLCwgJWVFSZNmoS8vDwhq67U3r4FBgwAzM2Bq1cBU9N36xo1aoSDBw/i7NmzmDlzZqlt7ybfxc83f8b8jvNR07ymGqNmtNEqZ2e8KijAyri4UuuuXLmCcePGYcyYMZg+fXqJdZs3AxMmAJMnA1euqCtaBoCwzSPNmzcnMzMzMjMzo6lTp9LRo0dpypQpxHEc+ftLf/qONY+Ub9QofkTAiAjZZTZs2EAAaPPmzcXLJBIJdd3ZlRqub0h5hVVkIGOmXN9ER5NRUBDFvDfLTExMDNnZ2VHHjh0pT8bogHl5RB9+SOTgwE+lx8imM13+GjRoQBzH0aRJk0osnzBhAnEcR48fPy61DUvaZdu6lW9PLG+8KIlEQhMnTiR9fX0KDAwkIqJjD44RFoJOPDwhfKCMzsgqKCCHK1doUGQkERFlZmZSs2bNyMnJiVJSUsrc9sULvv92t2788wSMdKrMa4L2Himaasjf37/Ecn9/f/z666+4fv06XFxcpG47bdo0WFlZldruv/uqSu7e5X+OjhtX/t16juPw448/4tGjRxg0aBAuXbmEr89+jY+cP0Lvhr3VEzCjE8z19fF9gwYYERWFC69eYe2oUYiLi8O1a9dQvXr1Mre1twf27eOnLVu2DPjuOzUFrcX27duHffv2lViWkZGhugpU8CUiU/fu3YnjuFLTjT148IA4jqP169eX2oZdaUuXk8MPUN+sGVEZc6WWkpaWRq6urmRX345Ec0R0L1mBgZKZKkMskZD3zZtkN3w4iUQiOn36tELbL1zID7UgzwBlVZHO9B5p3bo1ACA+Pr7E8hcvXgAA7OzshKy+Upkzh3+YZu9eQMZcqVJVq1YN2w9sR0pyCjyeeqCJXRPhgmR0lojjMKWgACl79mDIwoXo0aOHQtt/8w3/wNcnnwByPq/DKEnQpD106FAAwNatW0ss37JlCwwMDNC5c2chq680zp8HfvwR+P57wN1d8e23PNsCqwlWOPXbKdUHx1Qaw9q2RZ/Dh3HOxwfpBQUKbauvD+zeDbx6BUydKlCADACBk3bLli0xZswY7N27Fx9//DE2bdqEoUOHYv/+/Zg5cyZq1aolZPWVQlYW/6iwjw/w5ZeKb38j4Qa2396OFf4rUMOyhuoDZCqVX3v3Rq5EgsXPnim8rZMTsHYtsGNHyaGEGdUS/DH2X375BfXq1cP27dtx9OhRODo6Yt26dZgyZYrQVVcKs2cDqalAYCAgUvArlojw1Zmv4F7DHeNajRMmQKZScTAywrz69bEgNhbjHRzg9v5DAHIYM4Yfg2TcOP7GubW1QIFWYYIPGKWvr4/vvvsOMTE+343eAAAgAElEQVQxyMvLw8OHD1nCltPly8DPPwMrV/JXMYrad3cfrj6/inU91kFfJPj3M1NJTK9TB3WNjDDtyROFt+U44Lff+HbtefMECI4RPmkzysnP55848/YGJk1SfPvs/GzM+mcWBjYaiC5OXVQfIFNpGevpYbWzM06npeHUq1cKb1+3LrB0KfDLL6VnvGEqjiVtLbV2LRAVBfz6q+LNIgAQcCUAKW9TsPqj1aoPjqn0BlavDh9ra0x/8gQFSowXNHky4OEBjB8PyBjWhFESS9pa6MULYMkSYMoU4L3p9+T2LP0ZAq4GYHrb6WhQrewhWhlGGo7jsM7FBY9zcvCTEsMo6+nxV9oREXxzCaM6LGlroblz+UGglH26bNa5WahmXA3zPmSNiozympubY7yDAxbGxiIlP1/h7du0AUaP5if3TUsTIMAqiiVtFfj777/x+vVrlewrNJQfVH7ZMuXuvF98dhEH7x3Eiq4rYGFkoZKYmKprsaMjOI7DtzExSm2/fDmQlwcsWqSaeIgIhw4dQmEVbnNhSbuC3r59izFjxqBt27Z49OhRhfZFxHfxa9aM7zqlKLFEjK/+/gpetb0wogWbSoSpuOqGhljo6IgtiYmIePNG4e1r1eJ/Of78M/D0acViyc/Px9ixYzF06FCcrsIdwVnSriBTU1NcvHgRIpEI3t7eOH/+vNL7OnMGCAoCVqzg2wQVtf32doQnhePHHj9CxLG3llGNSQ4OcDU1xdTHj0FECm8/dSpQvXrFBpNKTU1F9+7dsXv3buzcuRN9+/ZVfmc6Tq1n9rJlyyASieCuzLPYWszFxQXXrl2Dl5cXfH198euvvyq8DyK+X2uHDkBvJQbhy8jNwLzz8zC8+XC0rdNW8R0wjAwGIhHWubggOCMDf6SmKry9mRmwYAE/bs6dO4rXf//+fXh7e+PBgwe4cOECRo4cqfhOKhG1Je34+HgsX74cZmZmWjWR7KtXwPTp/AznSlxEFLO2tsZff/2FSZMmYcKECZg6dapC7W7Hj/MxLF3KP6CgqCUXlyC7IBsru65UfGOGKYevjQ1629hgRnQ0cmRMY1eWMWMAR0e+V5QiTp8+jXbt2sHU1BShoaH44IMPFK77fSkpwIYNwLFjFdqNZlV4nEA5+fn5Ubdu3ahz587UrFkzmeXUPTTrpUv8IO4AUdOmRCtXEj1/XrF9btq0ifT09MjX15fS09PLLS+REHl4EHXurFx9D1MfksFiA1oSvES5HTCMHB5mZ5NBUBAtjY1VavvffuPPs3/nWiiTRCKhdevWkUgkoj59+lBmZqZSdRLxwxofPMhPgK2vz7/mzlV6d0rRmZlrigQHB5O+vj7dvXuXOnXqRO7u7jLLamI87fx8fobpjz8mMjYm4jiirl2JduwgUvaz8s8//5C1tTU1btyYnjx5UmbZkyf5D/O/E8worM/ePuS4zpHe5isw0DbDKOHrx4/JNDiY4nNzFd42L4+ofn3+PCtLfn4+ff755wSAZsyYQYVKTIkjFhMFBxONHUtkZcWfX97eRBs3EpUzGY8gdCppFxYWUvPmzWnixIlERFqZtN+XkUG0bRt/1QsQmZgQDRtGdPo0UUGBYvt6+PAhNWzYkGxsbCgoKEhmuY4didq1Uy7e049PExaCDt07pNwOGEYB6QUFZHf5Mg2/f1+p7TduJNLTI4qJkb7+1atX5OPjQwYGBrR161aF9x8VRfTNN/yXA0Dk6Ej07bdEDx8qFa7K6FTS3rhxI1lbW1NqaioRaX/Sft+zZ0TLlxM1bsx/AGrVIpo+nSg8nG/SkEdaWhp17dqV9PX1acuWLaXWh4Tw+/7jD8Xjyy/Mp0YbG1Gn7Z1IIm9ADFNBvyUkEAID6ZocTX//lZ1NZGtLNGVK6XVRUVHk4uJCtra2FBwcLPc+U1KINmwg8vLizyUrK6Jx44guXuSvuLWBziTt1NRUsrGxoTVr1hQv06WkXUQiIbpxg/+g2dnxH4xmzYi+/54oPr787fPz82nChAkEgKZPn17i597QoUQuLspNirru2joSLRJReGK44hszjJIKJRJqeeMGed28SWIlLhbmzycyNSVKS3u37OzZs2RlZUVNmjSh6OjocvdR1E7dt++7duq+ffllOTkKhyQ4nUnaEyZMIFdXVyp4r11B3qTdsWNH6tu3b4nX3r17hQxXLvn5fBu0n9+79u9u3Yh27iTKypK9nUQioQ0bNpBIJKJevXpRRkYGJSTwHzYpU2WWKyU7haxXWtP4E+OVPxiGUVLw69eEwEDakZio8LaJiUQGBkRF13IbN24kPT096tmzZ5k37sVi/up53Lh37dReXvxVdnKyskeienv37i2Vuzp27Kj9SfvRo0ekp6dHGzZsoJiYmOKXt7c3ubm5UWxsLKW9/1X7L2280pYlPZ1oyxaiTp34D5CpKdEnnxD9/bfsK+e///6bLC0tqWnTpjRlylMyM+P3o6gJJyaQ1QorSn6jRZ9WpkoZevcu2V+5QpmK3uwhIn9/ImfnApo4cRIBoK+++krmDceHD/l2aUdH/jyrX59vt46KquABqJFOXGkHBgYSx3FlvqZNm1ZqO11K2u+LjSVatoyoUaN37d9ff010+3bpsvfv3ydnZ2cSiapT//6XFK4rIimCRItEtObqmvILM4xAYnNyyDg4mObI0ZzxX6dOpRHQjfT09OnXX38ttT4lhb9p6e3Nn0+WlnxPkOBg7WmnVoROJO3U1FQ6duwY/fnnn8WvY8eOUbNmzcjR0ZH+/PNPunv3bqntdDVpF5FIiEJDib78kqh6df4D5+5OFBBAlJDwrtyePakEdCIDA0PasWOHAvuXkM8OH3Lb4EZ5hXkCHAHDyO+7p0/JMCiIot/K39300aNH5ObmRnp61ahz5wvFy3NyiA4dIurX7107dZ8+RAcOECmwe62kE0lblk6dOmnVwzVCys8nOnGCv9loZEQkEhF17060axd/06RFizwaO3YsAaBZs2bJ1R/1yP0jhIWgU49OqeEIGKZsbwoLqfaVKzRQnidmiOj8+fNUrVo1cnNzo9mzH5GBAf+MxOefE1lb8xc5bdrw93m0qZ26onQ6aXfu3Fnneo+owuvX/BNhHTvyH0yAaPJk/sp5zZo1JBKJqF+/fpRVxt3MnIIcclrnRD1391Rj5AxTtt1JSYTAQDon5R7V+3755RfS19enjz76iF6/fk3Pn787F+rVI5o3j+jBAzUFrWY6nbTLU1mT9vuWLOF7nbx/I+Wvv/4iCwsLat68OT179kzqdssvLif9xfr0IKWSfrIZnSSRSKh9WBi5h4ZSgZQG54KCApo6dSoBoC+++KJEb7ImTfjhI3SxnVoRqsxrbPxODTh7FvD1Bdzc3i3r1asXrl27hqysLLRp0wbXrl0rsc2LrBdYdmkZvvT6Eo2qN1JzxAwjG8dx+NHFBZHZ2dicmFhiXUZGBvr27YuNGzdi06ZN2LBhA/T19YvXz50L3LsHPH+u7qh1F0vaahYfD1y6BPj7l17XtGlThISEwNXVFT4+PtizZ0/xurnn58LEwATfdarAoMQMI5DWlpYYVasWvouJQVpBAQAgOjoa7dq1w/Xr1/H3339j4sSJpbbr3x8wNgYOHFB3xLqLJW01+/NPQF8f6NdP+no7OzucO3cO/v7+GD58OL755htci7uGXRG7sKzLMlgbKzEHGcOowXInJ+QRYWFsLC5evAhvb28UFhbi+vXr6Natm9RtLCyAnj2Bo0fVHKwOY0lbzY4dA3x8yp7/0cjICNu2bUNAQABWrFiB3gN7o5l1M3zm8Zn6AmUYBdkbGeHb+vWxcfNmdO3WDS1atMD169fh9n47oBQDBgDXrwP/aVlhZGBJW43S0/npxAYMKL8sx3GYOXMmvlr3FV7fe43R1UdDT6TEHGQMo0af29hAb98+1BowAKdPn4aNjU252/TuzU+vd/y4GgKsBFjSVqNz54DCQqBPH/nKv8l/gwOFB9B3U19M/2S6sMExjApUMzfHtgsXED9xIs5mZsq1ja0t0L49UIXn6lWIoEn7xo0b+OKLL9C0aVOYm5ujfv368PPzw+PHj4WsVmudOQM0agTUqydf+ZWXV+LV21dY/7/1wgbGMCo0vGFDdK1WDdOjo5Evkci1ja8vcOEC8O89TKYMgibt77//HkePHkX37t2xfv16fP7557h48SI8PT1x7949IavWOkTvuvrJIzY9FquvrsbM9jPhaO0oaGwMo0ocx2Gdiwuic3KwPj5erm18fYGsLOA/PV0ZKfTLL6K8r7/+Gm3atCnRL9PPzw/u7u5YuXIlfv/9dyGr1yrR0UBcHNC9u3zlZ/4zE9VNq2NOhznCBsYwAmhmbo6JDg5Y8uwZRtaqhRqGhmWW9/QEbGz4q+2OHdUUpI4S9Eq7Xbt2JRI2ALi4uKBJkyaIiooSsmqtc/EiP8t6hw7llw2KDcLh+4exsttKmBmaCR8cwwhgkZMT9DgO38TElFtWJAI+/JA/T5iyqf1GJBHh5cuXqF69urqr1qjgYKBlS8DKquxyYokYU/+eirZ12mKY+zD1BMcwArA1MMBiR0dsTUxEeFZWueU7duSbR/Lz1RCcDlN70t6zZw9evHgBPz8/dVetUVeu8FcS5dlyawvuvLyD9T3WQ8Sxzj2Mbpvg4IAmpqaY+uQJiKjMsh06ALm5wK1bagpOR6k1K0RFRWHy5Mlo3749Pv30U3VWrVGvXvFt2l5eZZd7nfMa3wZ+i5EtRqJN7TbqCY5hBKQvEmGdiwsuZWTgUEpKmWVbtAAMDYEbN9QUnI4S9Ebk+5KSktC7d29Uq1YNhw8fBsdxZZafNm0arP7TluDv7w9/aYN2aLmbN/l/y0vai4MXI7cwFyu7rhQ+KIZRk242Nuhva4uZ0dHoa2sLEz3pD4kZGfFNiLqetPft24d9+/aVWJaRkaGy/aslaWdkZKBnz57IzMzEpUuXUKtWrXK3Wbt2LTw9PdUQnfBu3uTbsl1cZJd5kPIAG29sxOLOi2FvYa++4BhGDVY7O6PJjRtY/fw55js6yizXpg1w/rz64hKCtIvLW7duoVWrVirZv+DNI7m5uejbty+ePHmCkydPolGjqjes6J07/E+/sn5cTD87HXUt62Jau2nqC4xh1MTF1BTT6tTBirg4PM/NlVmuRQvg0SMgJ0eNwekYQZO2WCyGn58fQkJCcOjQIXh7ewtZndaKjATc3WWvP/X4FP5+8jd++OgHGOsbqy8whlGjb+rXh6WeHuY8fSqzjLs7IJEADx6oMTAdI2jS/vrrr3HixAn06NEDqamp2L17d4mXpjwt40Ojanl5/JWDrKSdL87HtDPT0MWpCwY0kmMkKYbRUZb6+ljeoAH2Jifjiow23mbN+H8jI9UX1+vXr5GWlqa+CitI0KQdEREBjuNw4sQJjBw5ssRLU71Hrl27BhcXF3z22WdISkoSvL7oaEAs5scckWZj6EY8SXuCdb7ryr05yzC6blStWmhlbo6pjx9DIqULoLk5UKcOf6EjtMLCQvzyyy9o2LAhvvtOdyYXETRpBwYGQiwWQyKRlHqJxWIhq5apTZs22LhxI44dOwZXV1cEBAQgLy9PsPqKxsZq2LD0uuTsZCwKXoTxrcbDvWYZ7ScMU0mIOA4/NmyIsDdvsEPGRVPDhu/OG6EEBgbC09MTEydORN++ffHNN98IW6EKVbmnN/T19TFp0iQ8fvwYo0aNwrx589C0aVMcP3683M7/ynj8GDAzA+yldAj59sK30OP0sMRnicrrZRht9YGVFfxr1MC8p0+RWVhYar2QSfvp06cYNGgQunTpAnNzc4SGhmL79u2wl3aCaqkql7SL2NjYYP369YiIiECDBg3Qv39/+Pr6qnz0wdhYwMmpdM+R20m3seXWFizsvBC2prYqrZNhtN33DRogUyzG8mfPSq1zcuLPG1V68+YNvvnmGzRp0gQhISHYs2cPrly5gjZtdO8htiqbtIs0bdoUZ86cwfHjxxETE4MWLVrgyy+/VNmNifh4oG7dksuICFP/nopG1RthYuvSk50yTGVX19gYs+vVw9r4eDx5+7bkurr8LE9v3lS8HolEgl27dsHV1RVr1qzBrFmz8PDhQwwbNkxn7yFV+aQN8OP/9u3bF3fv3sWKFSuwc+dONGzYED/99BMKpfx8U0R8PH9j5X1HHhzBxWcXsdZ3LQz0DCq0f4bRVTPr1kUtQ0PMiI4usbzoIkfOobhlun79Otq1a4dPP/0UH374IaKiorB48WKYmen2yJksab/HyMgIM2fOxOPHjzFw4EB8+eWXaNmyJc6dO6f0Pl+8ABwc3v1/TkEOZpydgT6ufeDrIueMCAxTCZnq6SHA2Rl/vnqFf977ZVu7Nv9vQoJy+01ISMCIESPQrl075OfnIzg4GAcOHED9+vVVELXmsaQtRc2aNbFlyxbcuHED1tbW6N69OwYMGIAnT54otB8iIDUVqFHj3bIfrv2AF1kvsOajNSqOmmF0z1A7O3SwssJXT56g8N+pyezs+HWpqYrtKycnB8uWLYOrqyvOnDmDzZs34+bNm+hYyWZVYEm7DK1atcKlS5ewf/9+3Lp1C02bNsWcOXOQJcfYwACQmcnPeVc0dHhCZgJWXF6Bqd5T0dBWSh9AhqliOI7Djy4uePD2LX5+8QIAYGEBGBjIn7SJCEeOHEGTJk2wcOFCTJw4EY8fP8a4ceOgJ2NwKl0meNLOy8vD7Nmz4eDgAFNTU7Rt27ZCzQ3qxnEc/Pz8EBUVhXnz5mH9+vVo2LAhtm/fDkk5k5YWfehs/+0cMuf8HJgbmmN+p/kCR80wusPTwgKf2dtjQWwsXhUUgOP4c0aepB0REQEfHx8MHjwYTZs2xd27d7F69epSI4RWJoIn7VGjRmHt2rUYMWIE1q9fDz09PfTq1QtXrlwRumqVMjU1xYIFCxAVFQUfHx+MGTMGXl5euHr1qsxtii7ILS2Ba8+vYfed3VjeZTksjSzVFDXD6IalTk4QE2HBv1OTWVq+O3+kSUlJwYQJE+Dp6YmXL1/i9OnTOHnyJNzc3NQUsQaRgEJCQojjOPrhhx+Kl+Xm5pKLiwu1b99e6jZhYWEEgMLCwoQMrcIuXbpEnp6eBID8/f0pLi5OShkigOjefTG12dyGPH/1pEJxoQaiZRjttzoujvQCAykyK4tatSIaP750mby8PFqzZg1ZWVmRtbU1rVu3jvLz89UfrIJUmdcETdozZ84kAwMDysrKKrF8xYoVxHEcxcfHl9pGV5I2EZFYLKatW7dSjRo1yMTEhBYtWkTZ2dmUnJxMo0aNonr1mhDgSjXrOxBagI6HHdd0yAyjtfLEYnI6fZrs+/YlU9MmZGHhSk2aNKFRo0ZRcnIynTp1itzc3EgkEtHEiRMpJSVF0yHLTWeSdrdu3ahp06allp87d444jqOTJ0+WWqdLSbtIRkZG8RdU7dq1qWbNmgSg1MvZ2ZmSk5M1HS7DaKWXL19SLUdHqeeOqakpASAfHx+KiIjQdKgKU2VeE7RNOzExUeoz/UXLXvx7t1jXWVpaIiAgAPfu3YNIJMLLly+llouOjsasWbPUHB3D6IbZs2cjScbz62/fvoWPjw/Onz+P5s2bqzcwLSPodGM5OTkwMjIqtdzY2Lh4vdDevgWiogSv5l8Noa9vUWaJ0NBQNcXCMLqlvHMjNvYlwsPV9+h5o0aAqanaqpOboEnbxMRE6rCnuf9ON2RiYiJzW1VN7BsVBahoajY5lf3Ye0Ufi2eYyqq8cyMmplCt53JYGKDMNLU6PbGvvb291CaQxMREAIDD+893/4eqJvZt1Ij/46vL4MH6+LfXklT6+mqZS5lhdE5554aTkz4OH1ZTMJA9cUl5hJ7YV9AM4uHhgaCgIGRlZcHC4l2zQUhICACgZcuWQlYPgP95o85J3Tt18kJMzH2Z6728vNQXDMPoEC8vL9y/L/vc6dTJS63nsrYS9Ebk4MGDIRaLsXnz5uJleXl52L59O9q2bYvaRSPDVCIBAQFwdnaWus7Z2RkBAQFqjohhdAM7d+Qj6JW2l5cXhgwZgrlz5yI5ORnOzs7YuXMn4uLisH37diGr1hg7Oztcu3YNs2bNQmhoKAoLC6Gvrw8vLy8EBATArmg0HIZhSmDnjnwEb2DdtWsX5s+fj99//x2vX79GixYtcPLkSXTo0EHoqjXGzs6u0n4pMYyQ2LlTPsGTtpGREQICAthPG4ZhGBVgQ7MyDMPoEJa0GYZhdAhL2gzDMDqEJW2GYRgdwpI2wzCMDmFJm2EYRoewpM0wDKNDBE3a58+fx5gxY+Dq6gozMzM4Oztj3LhxSEpKErJahmGYSkvQh2tmz56N9PR0DBkyBA0bNkR0dDQ2btyIkydP4vbt26hZs6aQ1TMMw1Q6gibtdevWlXpcvUePHujUqRM2btyIJUuWCFk9wzBMpSNo84i08UU+/PBD2NjYIEp908kwDMNUGmq/EfnmzRtkZWWhevXq6q6aYRhG56k9aa9btw4FBQXw8/NTd9UMwzA6T+42bSKSOt+jNEUT9/7XxYsXsWjRIvj5+aFz587yVs0wDMP8S+6kHRwcjC5dushVNioqCq6urqWWDRw4EM2bN8eWLVvK3YeqJvZlGIZRJ6En9uWIiOQp+PLlS5w5c0aunQ4YMACWlpbF///8+XN88MEHMDQ0xJUrV8rs6lc0AWZYWJhKJvbVtH379lWqLxp2PNqrMh0LULmOR6V5jQSWmppKjRo1olq1atGTJ0/KLR8WFkYAKCwsTOjQ1KJv376aDkGl2PFor8p0LESV63hUmdcE7aednZ2NXr16ITExEYGBgTIn7WQYhmHkI2jS/uSTT3Djxg2MGTMG9+7dw71794rXWVhYoH///kJWzzAMU+kImrQjIiLAcRy2bduGbdu2lVjn6OjIkjbDMIyCBE3aMTExCm+Tk5MDAHjw4IGqw9GIjIwM3Lp1S9NhqAw7Hu1VmY4FqFzHU5TPivJbhaigjV2ldu/eTQDYi73Yi70q3Wv37t0VzpFyd/lTl9TUVJw5cwaOjo4wMTHRdDgMwzAVlpOTg9jYWPj6+lZ4CA+tS9oMwzCMbGzmGoZhGB3CkjbDMIwOYUmbYRhGh7CkzTAMo0NY0mYYhtEhWpO0k5KSMGfOHPj4+MDCwgIikQjBwcFSy3bu3BkikajUq2fPnmqOWjpFjgUArl69ig4dOsDMzAz29vaYOnUqsrOz1Rix4nbs2CH1PRCJREhOTtZ0eGXKy8vD7Nmz4eDgAFNTU7Rt2xbnzp3TdFgKCwoKkvkehIaGajq8MmVnZ2PBggXo0aMHbGxsIBKJsHPnTqllHzx4gB49esDCwgK2trYYOXIkUlNT1Rxx2eQ9nlGjRkl9vxo3bix3XYI+EamIqKgoBAQEwNXVFc2bN8e1a9fAcZzM8nXr1sWKFStKLHNwcBA6TLkociy3b99G165d0bRpU6xduxbPnz/H6tWr8fjxY5w6dUrNkStuyZIlcHJyKrHsv+Oga5tRo0bhyJEjmDZtGho2bIjt27ejV69eCAwMxAcffKDp8BQ2depUtGnTpsQybR+cLSUlBUuWLEH9+vXRsmVLBAUFST1H4uPj0bFjR1SrVg0rVqxAVlYWVq9ejcjISISGhsLAwEAD0Zcm7/EAgJGREbZu3VpimULnTIUfz1GRrKwsev36NRERHTp0iDiOo+DgYKllO3XqRO7u7uoMTyGKHEvPnj2pdu3alJWVVbxsy5YtxHEcnT17Vi3xKmP79u3EcZzODaEbEhJCHMfRDz/8ULwsNzeXXFxcqH379hqMTHGBgYHEcRwdOXJE06EoLC8vj16+fElERDdv3iSO42jnzp2lyk2cOJHMzMzo+fPnxcvOnTtHHMfR5s2b1RZveeQ9nk8//ZQsLCwqVJfWNI+Ym5vD2tpa7vJEBLFYjDdv3ggYlXLkPZbMzEycO3cOw4cPh7m5efHykSNHwtzcHAcPHhQyTJUgImRlZUEsFms6FLkcPnwY+vr6+Pzzz4uXGRkZ4bPPPsO1a9eQkJCgweiUU/QeFBYWajoUuRkaGqJGjRoA+PhlOXLkCPr06YM6deoUL+vatStcXV216vyQ93iK1kskEmRmZipVl9YkbUU9evQIZmZmsLS0hL29Pb777jud+tACQGRkJAoLC9G6desSyw0MDNCyZUuEh4drKDL5+fj4wMrKCmZmZujfvz+ePHmi6ZDKFB4eDldX1xJfkgCKmxdu376tibAqZPTo0bCysoKJiQm6dOmCsLAwTYekEgkJCUhJSSl1fgD8+6UL54c0b9++haWlJaytrWFra4svvvhCoXtYWtOmrQgXFxd07doV7u7uyM7OxqFDh7B06VI8evQI+/fv13R4cktMTAQA2Nvbl1pXq1YtXL58Wd0hyc3MzAyjR4+Gj48PLC0tcfPmTaxZswbt27fHrVu3SlwZaZPExESpf++iZS9evFB3SEozMjLC4MGD0atXL1SvXh337t3D6tWr8eGHH+Lq1ato2bKlpkOskLLOD3t7e6SlpaGgoEBr2rXl4eDggNmzZ8PT0xMSiQSnT5/Gpk2bEBERgaCgIOjp6ZW7D0GSNqlg5vay/Hdi4E8++QTjx4/Hb7/9hmnTpsHb21vhfcoi5LEUDdNoZGQkdV8qGcZRDsoc45AhQzBkyJDi5f369YOvry86duyIZcuW4eeffxYk1orKycmR+fcuWq8r2rVrh3bt2hX/f58+fTB48GA0b94cc+fOxenTpzUYXcWVd34UldGlpL18+fIS/z906FC4urrim2++weHDh+Hn51fuPgRpHgkODoapqalcr0ePHqmkzq+//hoAcP78eZXsr4iQx1I0iqG0hJmbmwtTU1OVHEN5VHWMH3zwAby9vbW6+5yJiYnMv3fRel3m7OyM/v37IzAwsNy2VW1X3vnxfhldNm3aNIhEIrlzlyBX2o0bN8aOHfQ2FRsAAAPeSURBVDvkKlurVi2V1Fn0czwtLU0l+ysi5LEU/ewr+hn4vsTERLV1YVTlMdapU0dlX8RCsLe3l9oEUvQeaEu30YqoU6cO8vPzkZ2dXartXpeUd37Y2trq1FW2LMbGxrCxsZE7dwmStGvWrImRI0cKsWuZnj59CgCws7NT6X6FPJZmzZpBX18fN27cwODBg4uX5+fn4/bt2/j4448Fqfe/VHmMT58+Vfl7oEoeHh4ICgpCVlYWLCwsipeHhIQAgM63AwP8e2BiYqLTCRsAateuDTs7O9y4caPUutDQ0ErxXgFAVlYWUlNT5T5vdK73SFZWVqmfS0SEpUuXguM4+Pr6aigyxVlZWaFbt27YvXt3ia6Lv//+O7Kzs0u0GWublJSUUstOnTqFW7duoUePHhqISD6DBw+GWCzG5s2bi5fl5eVh+/btaNu2LWrXrq3B6BQj7T2IiIjA8ePH8dFHH2kgItUbNGgQTp48ifj4+OJl58+fx+PHj7X6/JAmLy8PWVlZpZYvWbIEAOQ+b7Sq98jSpUsBoHjW9l27duHixYsAgG+//RYAEBYWBn9/fwwbNgzOzs7IycnB0aNHcfXqVYwfP15rvn3lORYAWLZsGdq3b49OnTph3LhxiI+Px5o1a+Dr66vVJ1779u3h6emJVq1awcrKCrdu3cK2bdtQr149zJs3T9PhyeTl5YUhQ4Zg7ty5SE5OhrOzM3bu3Im4uDhs375d0+EpxM/PD6ampmjXrh1q1KiB+/fvY/PmzTA3N8fKlSs1HV65Nm7ciPT09OLmquPHjyMuLg4AMGXKFFhaWmLevHk4dOgQfHx8MHXqVGRlZWHVqlVo3rw5Ro8ercnwSynveNLS0uDh4YFhw4bBzc0NAHDmzBmcPn0aPXv2lH+i8wo9mqNiHMeRSCQq8W/RfxeJiYmhoUOHkpOTE5mYmJCZmRm1adNGq56OIpLvWIpcvnyZPvjgAzIxMaGaNWvSl19+SW/evNFA1PL79ttvycPDg6ytrcnQ0JAcHR1p8uTJlJycrOnQypWbm0szZ84ke3t7MjY2Jm9vb61++lSW9evXk7e3N9na2pKBgQHVrl2bRo4cSdHR0ZoOTS6Ojo4lzov3z5dnz54Vl7t37x75+vqSmZkZ2djY0IgRI7Tyc1be8aSnp9OIESOoYcOGZGZmRsbGxuTu7k4rV66kwsJCueth040xDMPoEJ1r02YYhqnKWNJmGIbRISxpMwzD6BCWtBmGYXQIS9oMwzA6hCVthmEYHcKSNsMwjA5hSZthGEaHsKTNMAyjQ1jSZhiG0SEsaTMMw+gQlrQZhmF0yP8B+hsyCdQdquIAAAAASUVORK5CYII=" 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" ], "text/plain": [ - "PyPlot.Figure(PyObject )" + "PyPlot.Figure(PyObject )" ] }, "metadata": {}, @@ -273,11 +249,10 @@ { "data": { "text/plain": [ - "1-element Array{Any,1}:\n", - " PyObject " + "(-15.0,15.0,-2.0,14.0)" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -290,12 +265,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Strategy 2**: Fit field. Here we define normal function and fit field for that." + "A little better, now the interpolant of normal field is continuous." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Strategy 2\n", + "\n", + "Fit field. Here we define normal function and fit field for that. This of course require us to know what is the normal field, in this case it's easily defined $f(x) = X_1^2 + X_2^2 - 10^2$." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -306,14 +290,12 @@ "fit_field! (generic function with 2 methods)" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "using JuliaFEM: Element, get_number_of_basis_functions, get_detJ, get_basis\n", - "\n", "\"\"\"\n", "Fit field s.t. || ∫ (Nᵢ(ξ)αᵢ - f(el, ξ)) dS || -> min!\n", "\"\"\"\n", @@ -378,71 +360,63 @@ "end" ] }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "f (generic function with 1 method)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Return accurate normal for element based on a known geometry.\n", + "\"\"\"\n", + "function f(el, xi)\n", + " geom_info(X) = X[1]^2 + X[2]^2 - 10^2\n", + " X = interpolate(el, :Geometry, xi)\n", + " n = ForwardDiff.gradient(geom_info, X)\n", + " n / norm(n)\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for el in elements\n", + " fit_field!(el, :Normals, f)\n", + "end" + ] + }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "2-element Array{Float64,1}:\n", - " -0.866025\n", - " 0.5 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "using JuliaFEM: interpolate\n", - "using ForwardDiff\n", - "\n", - "\"\"\"\n", - "Return accurate normal for element based on a geometry.\n", - "\"\"\"\n", - "function f(el, xi)\n", - " geom_info(X) = X[1]^2 + X[2]^2 - 10^2\n", - " X = interpolate(el, :geometry, xi)\n", - " n = ForwardDiff.gradient(geom_info, X)\n", - " n / norm(n)\n", - "end\n", - "\n", - "#fit_field(elements[1], :normals, f)\n", - "f(elements[1], [0.0])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for el in elements\n", - " fit_field!(el, :normals, f)\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, "outputs": [ { "data": { "image/png": [ - 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" 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" ], "text/plain": [ - "PyPlot.Figure(PyObject )" + "PyPlot.Figure(PyObject )" ] }, "metadata": {}, @@ -451,11 +425,10 @@ { "data": { "text/plain": [ - "1-element Array{Any,1}:\n", - " PyObject " + "(-15.0,15.0,-2.0,14.0)" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -468,21 +441,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Normal direction fitted in least squares sense. Why not also fit geometry based on normal direction information?" + "Normal direction fitted in least squares sense. Getting better all the time. Now the field is accurate in nodal points and quite close in all around the faceted surface. Why not also fit geometry based on normal direction information? Let's increase the degree of approximation by introducing new basis function to hierarchical basis and use that to get more accurate solution:" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "using JuliaFEM: set_degree, get_number_of_basis_functions, dinterpolate\n", "for el in elements\n", " set_degree(el, 2)\n", - " for field in (:geometry, :normals)\n", + " for field in (:Geometry, :Normals)\n", " push!(el.fields[field], [0.0, 0.0])\n", " el.fields[field] = el.fields[field][1:get_number_of_basis_functions(el)]\n", " end\n", @@ -491,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -502,14 +474,12 @@ "fit_derivative_field! (generic function with 2 methods)" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "using JuliaFEM: get_dbasisdxi\n", - "\n", "\"\"\"\n", "Fit field s.t. || ∫ ∂/∂ξ(∑Nᵢ(ξ)αᵢ)f(el, ξ) dS || -> min!\n", "\"\"\"\n", @@ -577,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -587,21 +557,21 @@ "Return tangent vector in point ξ for element el.\n", "\"\"\"\n", "function tangent(el, xi)\n", - " normal = JuliaFEM.interpolate(el, :normals, xi)\n", + " normal = interpolate(el, :Normals, xi)\n", " [0 -1; 1 0]'*normal\n", "end\n", "\n", "for i=1:5\n", " for el in elements\n", - " fit_field!(el, :normals, f)\n", - " fit_derivative_field!(el, :geometry, tangent, Int[1, 2])\n", + " fit_field!(el, :Normals, f)\n", + " fit_derivative_field!(el, :Geometry, tangent, Int[1, 2])\n", " end\n", "end" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -609,10 +579,10 @@ { "data": { "image/png": [ - 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" 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" ], "text/plain": [ - "PyPlot.Figure(PyObject )" + "PyPlot.Figure(PyObject )" ] }, "metadata": {}, @@ -621,11 +591,10 @@ { "data": { "text/plain": [ - "1-element Array{Any,1}:\n", - " PyObject " + "(-15.0,15.0,-2.0,14.0)" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } diff --git a/src/elements.jl b/src/elements.jl index 5c63aa8..0da9e8f 100644 --- a/src/elements.jl +++ b/src/elements.jl @@ -27,7 +27,7 @@ Several functions are inherited from Element abstract type: - get_number_of_basis_functions* - get_element_dimension * - get_basis * -- get_dbasisdxi* +- get_dbasisdxi * - get_dbasisdX - get_field - set_field @@ -36,46 +36,15 @@ Several functions are inherited from Element abstract type: Which should work if element is defined following some rules. Functions marked with asterisk * are the ones which must necessarily to implement by your own. -=# - -# These must be implemented for your own element -get_number_of_basis_functions(el::Type{Element}) = nothing -get_number_of_basis_functions(el::Element) = nothing -get_element_dimension(el::Element) = nothing -get_basis(el::Element, xi) = nothing -get_dbasisdxi(el::Element, xi) = nothing -get_connectivity(el::Element) = el.connectivity - -""" -Create new element with element_name to family element_family - -Examples --------- ->>> @create_element(Seg2, CG, "2 node linear segment") -""" -macro create_element(element_name, element_family, element_description) -# Logging.debug("Creating element ", element_name, ": ", element_description, "\n") - eltype = esc(element_name) - elfam = esc(element_family) - quote - global get_element_description - type $eltype <: $elfam - connectivity :: Array{Int, 1} - fields :: Dict{Any, Any} - end - $eltype(connectivity) = $eltype(connectivity, Dict{Any, Any}()) - get_element_description(el::Type{$eltype}) = $element_description - end -end - -#= - Start of example +---------------- -Example how to create new element. This is commented because I use code +This is example how to create new element. This is commented because I use code generation for simple elements like Lagrage elements. Feel free to use code generation but elements can be of course created manually too! +abstract CG <: Element # create new element family "Continous Galerkin" + type Quad4 <: CG connectivity :: Array{Int, 1} fields :: Dict{Any, Any} @@ -110,96 +79,25 @@ End of example. =# +# These must be implemented for your own element +get_number_of_basis_functions(el::Type{Element}) = nothing +get_number_of_basis_functions(el::Element) = nothing +get_element_dimension(el::Element) = nothing +get_basis(el::Element, xi) = nothing +get_dbasisdxi(el::Element, xi) = nothing +get_connectivity(el::Element) = el.connectivity + ### LAGRANGE ELEMENTS ### +include("lagrange.jl") -abstract CG <: Element # Lagrange (continous Galerkin) element family - -""" -Given polynomial P and coordinates of reference element, calculate -Lagrange basis function and partial derivatives. -""" -function calculate_lagrange_basis(P, X) - dim, nbasis = size(X) - A = zeros(nbasis, nbasis) - for i=1:nbasis - A[i,:] = P(X[:, i]) - end -# Logging.debug("Calculating inverse of A") - invA = inv(A)' - basis(xi) = invA*P(xi) - dbasisdxi = ForwardDiff.jacobian(basis) - basis, dbasisdxi -end - -""" -Assign Lagrange basis for element. -""" -macro create_lagrange_basis(element_name, X, P) - -# Logging.debug("Creating Lagrange basis for element ", element_name, ". ") - eltype = esc(element_name) - - quote - - global get_number_of_basis_functions, get_element_dimension - global get_basis, get_dbasisdxi - - dim = size($X, 1) - nbasis = size($X, 2) -# Logging.debug("Number of basis functions: ", nbasis, ". ") -# Logging.debug("Element dimension: ", dim) - - get_number_of_basis_functions(el::Type{$(esc(element_name))}) = nbasis - get_number_of_basis_functions(el::$(esc(element_name))) = nbasis - get_element_dimension(el::$(esc(element_name))) = dim - - basis, dbasisdxi = calculate_lagrange_basis($P, $X) - get_basis(el::$eltype, xi) = basis(xi) - get_dbasisdxi(el::$eltype, xi) = dbasisdxi(xi) -# Logging.debug("Element ", $element_name, " created.") - end - -end - -# 0d Lagrange element - -@create_element(Point1, CG, "1 node point element") - -# 1d Lagrange elements - -@create_element(Seg2, CG, "2 node linear line element") -@create_lagrange_basis(Seg2, [-1.0 1.0], (xi) -> [1.0, xi[1]]) - -@create_element(Seg3, CG, "3 node quadratic line element") -@create_lagrange_basis(Seg3, [-1.0 1.0 0.0], (xi) -> [1.0, xi[1], xi[1]^2]) - -# 2d Lagrange elements - -@create_element(Quad4, CG, "4 node bilinear quadrangle element") -@create_lagrange_basis(Quad4, - [-1.0 1.0 1.0 -1.0 - -1.0 -1.0 1.0 1.0], - (xi) -> [1.0, xi[1], xi[2], xi[1]*xi[2]]) - -# 3d Lagrange elements - -@create_element(Tet10, CG, "10 node quadratic tetrahedron") -@create_lagrange_basis(Tet10, - [0.0 1.0 0.0 0.0 0.5 0.5 0.0 0.0 0.5 0.0 - 0.0 0.0 1.0 0.0 0.0 0.5 0.5 0.0 0.0 0.5 - 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.5 0.5 0.5], - (xi) -> [ 1.0, xi[1], xi[2], xi[3], xi[1]^2, - xi[2]^2, xi[3]^2, xi[1]*xi[2], xi[2]*xi[3], xi[3]*xi[1]]) - - -### HIERARCHICAL ELEMENTS ### - +### HIERARCHICAL P-ELEMENTS ### include("hierarchical.jl") -# Common element routines +### COMMON ELEMENT ROUTINES ### """ -Test routine for element. +Test routine for element. If this passes, element interface is properly +defined. Parameters ---------- @@ -208,7 +106,7 @@ eltype::Type{Element} Raises ------ -This uses FactCheck and throws exception if element is not passing. +This uses FactCheck and throws exceptions if element is not passing all tests. """ function test_element(eltype) Logging.info("Testing element $eltype") @@ -264,38 +162,46 @@ function test_element(eltype) Logging.info("Element $eltype passed tests.") end + """ Get jacobian of element evaluated at point ξ on element in reference configuration. +Parameters +---------- +el::Element +xi::Vector +geometry_field::Any, optional + +Returns +------- +Vector or Matrix + depending on element type + Notes ----- -This function assumes that element has field :geometry defined. +Big "J" comes from reference (undeformed) configuration. """ -#function get_Jacobian(el::Element, xi) -# dbasisdxi = get_dbasisdxi(el, xi) -# X = get_field(el, :geometry) -# J = X*dbasisdxi -# return J -#end -function get_Jacobian(el::Element, xi, geometry_field=:geometry) +function get_Jacobian(el::Element, xi, geometry_field=:Geometry) dinterpolate(el, geometry_field, xi) end + """ Get jacobian of element evaluated at point ξ on element in current configuration. Notes ----- -This function assumes that element has fields :geometry and :displacement defined. +Small "j" comes from current (deformed) configuration. """ -function get_jacobian(el::Element, xi) +function get_jacobian(el::Element, xi, geometry_field=:Geometry, displacement_field=:displacement) dbasisdxi = get_dbasisdxi(el, xi) - X = get_field(el, :geometry) - u = get_field(el, :displacement) + X = get_field(el, geometry_field) + u = get_field(el, displacement_field) j = (X+u)*dbasisdxi return j end + """ Evaluate partial derivatives of basis, dbasis/dX """ @@ -305,6 +211,7 @@ function get_dbasisdX(el::Element, xi) dbasisdxi*inv(J) end + """ Evaluate partial derivatives of basis, dbasis/dx """ @@ -314,32 +221,38 @@ function get_dbasisdx(el::Element, xi) dbasisdxi*inv(j) end + """ Set field variable. """ function set_field(el::Element, field_name, field_value) el.fields[field_name] = field_value end + """ Create new empty field of some type. """ -function new_field(el::Element, field_name, field_type) +function new_field!(el::Element, field_name, field_type) el.fields[field_name] = field_type[] end + """ Push to existing field. """ function push_field!(el::Element, field_name, field_value) push!(el.fields[field_name], field_value) end + """ Get field variable. """ function get_field(el::Element, field_name) el.fields[field_name] end -"""Evaluate some field in point ξ on element using basis functions. + +""" +Evaluate some field in point ξ on element using basis functions. Parameters ---------- el :: Element -field :: Union{ASCIIString, Symbol} +field :: Any xi :: Vector Returns @@ -375,3 +288,177 @@ function dinterpolate(el::Element, field, xi::Vector) end return sum([fld[i]*dbasis[i,:] for i in 1:length(fld)]) end + +""" +calculate "local" normals in elements, in a way that +n = Nᵢnᵢ gives some reasonable results for ξ ∈ [-1, 1] +""" +function calculate_normals!(el::Element, field_name=:Normals) + new_field!(el, field_name, Vector) + for xi in Vector[[-1.0], [1.0]] + t = dinterpolate(el, :Geometry, xi) + n = [0 -1; 1 0]*t + n /= norm(n) + push_field!(el, field_name, n) + end +end + +""" +Alter normal field such that normals of adjacent elements are averaged. +""" +function average_normals!(elements, normal_field=:Normals) + d = Dict() + for el in elements + c = get_connectivity(el) + n = get_field(el, normal_field) + for (ci, ni) in zip(c, n) + d[ci] = haskey(d, ci) ? d[ci] + ni : ni + end + end + for (ci, ni) in d + d[ci] /= norm(d[ci]) + end + for el in elements + c = get_connectivity(el) + new_normals = [d[ci] for ci in c] + set_field(el, normal_field, new_normals) + end +end + + +# FIXME: These two needs integration -- maybe not in elements.jl ..? +""" +Fit field s.t. || ∫ (Nᵢ(ξ)αᵢ - f(el, ξ)) dS || -> min! + +Parameters +---------- +f::Function + Needs to take (el::Element, xi::Vector) as argument +fixed_coeffs::Int[] + These coefficients are not changed during fitting -> constrained optimizatio +""" +function fit_field!(el::Element, field, f, fixed_coeffs=Int[]) + w = [ + 128/225, + (332+13*sqrt(70))/900, + (332+13*sqrt(70))/900, + (332-13*sqrt(70))/900, + (332-13*sqrt(70))/900] + xi = Vector[ + [0.0], + [ 1/3*sqrt(5 - 2*sqrt(10/7))], + [-1/3*sqrt(5 - 2*sqrt(10/7))], + [ 1/3*sqrt(5 + 2*sqrt(10/7))], + [-1/3*sqrt(5 + 2*sqrt(10/7))]] + n = get_number_of_basis_functions(el) + fld = get_field(el, field) + nfld = length(fld[1]) + #Logging.debug("dim of field $field: $nfld") + + M = zeros(n, n) + b = zeros(n, nfld) + for i=1:length(w) + detJ = get_detJ(el, xi[i]) + N = get_basis(el, xi[i]) + M += w[i]*N*N'*detJ + fi = f(el, xi[i]) + for j=1:nfld + b[:, j] += w[i]*N*fi[j]*detJ + end + end + + coeffs = zeros(n) + for j=1:nfld + for k=1:n + coeffs[k] = fld[k][j] + end + if length(fixed_coeffs) != 0 + # constrained problem, some coefficients are fixed + N = Int[] # rest of coeffs + S = Int[] # fixed coeffs + for i = 1:n + if i in fixed_coeffs + push!(S, i) + else + push!(N, i) + end + end + lhs = M[N,N] + rhs = b[N,j] - M[N,S]*coeffs[S] + coeffs[N] = lhs \ rhs + else + coeffs[:] = M \ b[:,j] + end + for k=1:n + fld[k][j] = coeffs[k] + end + end + set_field(el, field, fld) + return +end + + +""" +Fit field s.t. || ∫ ∂/∂ξ(∑Nᵢ(ξ)αᵢ)f(el, ξ) dS || -> min! +""" +function fit_derivative_field!(el::Element, field, f, fixed_coeffs=Int[]) + w = [ + 128/225, + (332+13*sqrt(70))/900, + (332+13*sqrt(70))/900, + (332-13*sqrt(70))/900, + (332-13*sqrt(70))/900] + xi = Vector[ + [0.0], + [ 1/3*sqrt(5 - 2*sqrt(10/7))], + [-1/3*sqrt(5 - 2*sqrt(10/7))], + [ 1/3*sqrt(5 + 2*sqrt(10/7))], + [-1/3*sqrt(5 + 2*sqrt(10/7))]] + n = get_number_of_basis_functions(el) + fld = get_field(el, field) + nfld = length(fld[1]) + #Logging.debug("dim of field $field: $nfld") + + M = zeros(n, n) + b = zeros(n, nfld) + for i=1:length(w) + detJ = get_detJ(el, xi[i]) + dNdxi = get_dbasisdxi(el, xi[i]) + dNdX = dNdxi / detJ + M += w[i]*dNdX*dNdX'*detJ + fi = f(el, xi[i]) + for j=1:nfld + b[:, j] += w[i]*dNdX*fi[j]*detJ + end + end + + coeffs = zeros(n) + for j=1:nfld + for k=1:n + coeffs[k] = fld[k][j] + end + if length(fixed_coeffs) != 0 + #Logging.info("constrained problem, some coefficients are fixed") + N = Int[] # rest of coeffs + S = Int[] # fixed coeffs + for i = 1:n + if i in fixed_coeffs + push!(S, i) + else + push!(N, i) + end + end + lhs = M[N,N] + rhs = b[N,j] - M[N,S]*coeffs[S] + coeffs[N] = lhs \ rhs + else + coeffs[:] = M \ b[:,j] + end + for k=1:n + fld[k][j] = coeffs[k] + end + end + set_field(el, field, fld) + return +end +