diff --git a/notebooks/2015-09-15-2d-segmentation.ipynb b/notebooks/2015-09-15-2d-segmentation.ipynb index 3366f6d..541fe12 100644 --- a/notebooks/2015-09-15-2d-segmentation.ipynb +++ b/notebooks/2015-09-15-2d-segmentation.ipynb @@ -30,7 +30,8 @@ "using JuliaFEM: PSeg, get_field, set_field, interpolate, new_field!, push_field!\n", "using JuliaFEM: calculate_normals!, average_normals!, fit_derivative_field!\n", "using JuliaFEM: set_degree, get_number_of_basis_functions, dinterpolate\n", - "using PyPlot" + "using PyPlot\n", + "using ForwardDiff" ] }, { @@ -46,17 +47,120 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "rlinspace (generic function with 1 method)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "function rlinspace(X1, X2, R, n, p0)\n", + " fx(ϕ, xc) = R*cos(ϕ) + xc\n", + " fy(ϕ, yc) = R*sin(ϕ) + yc\n", + " function F(p)\n", + " ϕ1, ϕ2, xc, yc = p\n", + " return [\n", + " fx(ϕ1, xc) - X1[1]\n", + " fy(ϕ1, yc) - X1[2]\n", + " fx(ϕ2, xc) - X2[1]\n", + " fy(ϕ2, yc) - X2[2]\n", + " ]\n", + " end\n", + " J = ForwardDiff.jacobian(F)\n", + " p = copy(p0)\n", + " for i=1:10\n", + " dp = -J(p) \\ F(p)\n", + " p += dp\n", + " if norm(dp) < 1.0e-9\n", + " println(\"Converged. p = $p\")\n", + " break\n", + " end\n", + " end\n", + " ϕ1, ϕ2, xc, yc = p\n", + " ϕ = linspace(ϕ2, ϕ1, n)\n", + " return fx(ϕ, xc), fy(ϕ, yc)\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converged. p = [4.2905787641613236,5.624156522861784,0.04706336239279979,6.561746550428803]" + ] + }, + { + "data": { + "image/png": [ + 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7cIAQVFxcLLfbrbKyMvXt27fbn9fP6dQtAwfqloHmtqWvGhr0fnm5tpaVafPp01rzxRdySJoYF2fev5OYqGnx8YqNju6lPwkQuQ58eUBZ+VnacGiDnFFO3TfhPmVMydBVl1xl9WgAYGuc4AAhaP78+aqsrNT777/fo8/7WV2dtjad7mwpL9eXDQ1yORy6vn9/zWi6hye1f382tAEXqDHQqE1HNilzd6b+9NmfdFn/y/TYdY/poUkPKTEm0erxACAiEDhACDpx4oROnTqlsWPH9tp/hmEY+nNtbUvsbCsvV7nfr9ioqJYNbTMSEvQtNrQBXaqsr9QL+1/QyvyVKi4v1tTLpiojNUO3Jt8qZxQXSwBAMBE4ACRJjYahj6qqtLVpYUFeRYXOsKEN6NSx08e0Mn+lXjjwgur8dbrz6juVkZqhycMnWz0aAEQsAgdAu5o3tDUHT/5ZG9qaFxbMSEzUcDa0IcIYhqH3//q+svKztOnTTRoQM0BLrl2iRyc/qmH9hlk9HgBEPAIHQLd0tKHtypgY8/6dxETdxIY22Fidv04bDm1Q5u5MHfrqkMYNHqflqct11/i7FOOKsXo8AEATAgfABTl51oa2LeXlOnrmDBvaYEulVaV65sNntPbDtfq69mvdMuYWLU9drhmjZnC5JgCEIAIHQI8oad7Q1nRJW2nThrYp/fubwcOGNoSZfV/sU2Z+pl77+DW5o926P+V+LUtdJu9Ar9WjAQA6QeAA6HFdbWhrvoeHDW0INf6AX9l/zlZmfqbySvI0MmGkHpv8mH406UdK6JNg9XgAgG4gcAD0ukbD0P6qqpbTnXM3tDUHj5cNbbBIeV251n20Tqv2rFJJRYmm/810ZaRmaP6V8xUdxWWWABBOCBwAQVcfCGh3ZWXLwoLmDW0jPB7NTEhoWVrAhjZcrNzcXC1cuFB+v7/lfU6nUxs3btTcuXN15NQRrcxfqZcOvCRfwKdF4xYpIzVDk4ZOsnBqAMDFIHAAWK55Q9vWpuA5yIY29IDs7GwtWLCgw8cn/d0kfdTvIw2OHaxHrn1ES65doiFxQ4I4IQCgNxA4AELOyYYGbWu6nO3cDW0zmhYW3JCQwIY2dMrlcrU6uWkjSnpp30taNG6RPE5OCwHALggcACGvOxvaruvfX242tEUmw5CqqqRTp6Svv255i77vPgW6/FT+CQQAuyFwAISV5g1tW5ti5/2zNrTdkJCgmWxoC39nzrQKlU7fTp40f/X52jyNW1Lb97bGP4EAYD8EDoCw1ryhrTl4tjdtaBvgdOqmpthhQ5uFfD7p9OnWMdLVW9M9WK243dIll0iDBpm/Dhxo/v7cX5s/ZuBAOWJiuhyPfwIBwH4IHAC2cu6Gtj1VVfIbhoa73ZqZmNhyD8+IPn2sHjX8BAJSeXn7JygdvZWXt32eqKj2w6SzaImLk84zULu6B8fpdMrXzskPACC8ETgAbK3K79f2igptKSvT1vJyHaiuliSNiYlpOd2JyA1thiFVV3d96dfZb6dOmZFzrvj4NqcnnUZLYqIZOb0sNzdX6enpHT6+adMmzZ07t9fnAAAEF4EDIKJ83byhremStqKmDW0pcXEtCwu62tDW1WurWKKurvv3qzS/NTS0fZ7Y2A4v+WoVLc2/DhwohXActvffVXR0tN566y3iBgBsisABENHO3tC2taxMX5y1oW1G0z08qWdtaOvqtVV65FTA72+zEazLaOnovpXO7lM594TlkkskG1+69+yzz2rJkiV6+eWXdd9991k9DgCglxA4ANDEMAx9Wlvbcrpz7oa2GQkJ+umVV6qxrq7D52hzX8fZ9610db9Kc9SUlbV94qio7t1cf/bHxMae930rdlZdXa1+/fpp8eLFWrt2rdXjAAB6CYEDAB1oNAwdqK5uWViwvaJCZ2bONE9YOmHccEPX960kJnYcK+1FS3x8UO5bsbvKykr179/f6jEAAL2IwAGAbqoPBNSnk3tzmhn33NN5tAwYENL3rQAAEM4IHAA4D915LR2+rAIAYB2udwCA8+B0Oi/qcQAA0LsIHAA4Dxs3bryoxwEAQO8icADgPMydO1ebNm1S1Dk3/DudTl44EgCAEMA9OABwAZYtW6ZVq1bp888/14gRI6weBwAANOEEBwAuwPHjxyVJw4YNs3gSAABwNu6GBYALMH36dJ06darNpWoAAMBaXKIGAAAAwDb40SMAAAAA2yBwAAAAANgGgQMAAADANggcAAAAALZB4AAAAACwDQIHAAAAgG0QOAAAAABsg8ABAAAAYBsEDgAAAADbIHAAAAAA2AaBAwAAAMA2CBwAAAAAtkHgAAAAALANAgcAgBCUm5srl8slh8PR8uZyuZSbm2v1aAAQ0ggcAEDI4Jt6U3Z2ttLT0+X3+1u93+/3Kz09PeL+PgDgfDgMwzCsHgIAgOzsbC1YsKDDxzdt2qS5c+cGcSLruFyuNnFzNqfTKZ/PF8SJACB8EDgAgJDQU9/UG4Yhw/DLMHwyDJ8CAV/T7xsUCDS0837fOe9v//fffE5DO+9v73m785/X0O77Z80KqJO/CkmSr9wnZ7zzfP+aAcD2+MoIAAgJncVN8+N796a0GxbnBkfPilZUlFsOh0sOh0tRUa6m37s7eb/5a3R0v3be7zrnc1o/j8PhUmPjI11OlZeQpz6X91HcxDj1m9hPcSlxikuJk3uYWw6Ho4f/DgAgfBA4AABLnDlzRnv37tWOHTu0Y8eObn1OfHxaUxicGxbuLt5/dlR42okSV6vP/+Z5nHI4gn+7qmF0HThXvXyVqvdXq/pAtT7/9efyl5uB6LrEZcbOxLiWX/uO6StHNNEDIDJwiRoAIChOnjzZEjM7duzQhx9+KJ/Pp379+un666/XO++80+VzRMo/Wed7uZ5hGKovqVfV/ipVHzCjp3p/tepL6iVJUTFRip0Q23LK029iP8WOj1V03+he/7MAQLAROACAHmcYhoqKipSXl6e8vDzt2LFDR44ckSSNGDFC06ZNU1pamtLS0jRhwgRFR0dzY/1ZcnNzlZ6e3uHj3V244DvtaxU81QeqVVNYIzVKipL6julrnvQ0n/akxMk9yN2DfxIACD4CBwBw0RoaGrRv376WmNm5c6dOnjwph8Oh8ePHa9q0aS1Rk5SU1O5z9NQ39XaRm5urhQsXtoo+p9OpjRs3XtTfQ2Ndo2oP15qnPfurVX3QDJ9ATUCS5B7ubnXSEzcxTn1G9eG+HgBhg8ABAJy3srIy7dy5syVo9u7dq7q6OvXt21epqaktMTNlyhTFx8d3+3l765t6dM4IGDpz9Eyr056q/VXynTBPzKL7RyvuW3Gt7u2JHRurKA8vpwcg9BA4AIBOGYah4uLilpjJy8vTJ598IkkaMmSI0tLSWoImJSVFLpfL4onRU+q/rFfNwZpvTnsOVOtM0RnJkBxOh/pe3bfVBre4lDhWVwOwHIEDAGjF5/Pp4MGDLffP7Ny5U6WlpZKksWPHtsTMtGnTNGrUKC5dijD+ar9qCmpagqf6QLWqD1XLqDe/negzqs83wdN0f49nuIf/nQAIGgIHACJcZWWldu3a1XI6k5+fr9raWnk8Hk2ePLklaKZOnaoBAwZYPS5CUMAfUO2fa79ZZtAUP/4y81JD50DnN/f0NMVPzJUxinJ2fYlbYWGh5s2bp5KSEhmGIYfDoaSkJOXk5Cg5Obm3/2gAwhCBAwARpqSkpGVVc15eng4dOqRAIKBLLrlEU6dObVkIMGnSJHk8HqvHRZgyDEP1x+tbBU/1gWrV/bVOkhTVp/Xq6riUOMVNiFN07Derqw8fPqyUlJR2t+s5nU4VFBQQOQDaIHAAwMYaGxt16NChlpjJy8vT8ePHJUljxoxpdf/MmDFjuIwIvc5Xdtbq6oNm/NR+UivDb0gOKWZMTMtpz7ezvq3i0uIOn2v06NEqKioK4vQAwgGBAwA2UlNTo/z8/JaFALt27VJVVZVcLpeuueaalteeSUtL0+DBg60eF5AkBeoDqjlc02qDW01BjWZUzZBfHb82ktvtVn19fRAnBRAOCBwACAEXuh65tLS0JWZ27Nih/fv3q7GxUQkJCa1iZvLkyYqJiQnGHwXoEUbAkMfjkc/f8Yu7ulwuNTQ0BHEqAOGAwAEAi2VnZ2vBggUdPt78ApeBQECFhYWt1jUXF5uX74waNarlUrO0tDSNHTtWUVG8RgnCm8fj6TRgOMEB0B4CBwAs5nK52r2JupnD4dCcOXO0a9culZWVKTo6WikpKa2CZtiwYUGcGAgOr9ero0ePdvg49+AAaA+BAwAW686N/bNmzWoJmtTUVMXFxQVhMsBahYWFmjBhAlvUAJwXAgcALNadwOFLNSIVr4MD4HwROABgEcOQ3n5bmjvXLanjG6nNj+VLNQAA3cEdqAAQZD6f9Mor0re+JZkL0i7t9OOdTmdQ5gIAwA4IHAAIkpoaadUqyeuV7r1XGjFC2rZNysl5ptPP27hxY3AGBADABvixIAD0slOnpNWrzbgpL5cWLZJycqQJE5o/Yq42bdp0Qa+DAwAAWuMeHADoJZ99Jv3mN9K6deb9Ng8+KP34x9LIkVZPBgCAfRE4ANDDDh2SfvlL6be/leLjpcceM98GDbJ6MgAA7I9L1ACgBxiGtH279OST5ma0yy6TnnrKPLWJjbV6OgAAIgdLBgDgIgQCUna2NHWqdOONUkmJtH69dOyYlJFB3AAAEGwEDgBcgIYG6cUXpauvlhYskFwuKTdXKigwN6S5XFZPCABAZOISNQA4D1VV0nPPSf/5n9L//Z80b570X/9lnuAAAADrETgA0A0nTkgrV0pr1pivZ3PPPdITT0hjx1o9GQAAOBuBAwCdOHbMXBbwwguS0yk9/LC56nnECKsnAwAA7WFNNAC0Y/9+cyPa669LAwdKy5ZJjz4qDRhg9WQAAKAznOAAQBPDkLZuNcPm3XelUaOkVauk+++XYmKsng4AAHQHW9QARLzGRvOkZvJk6TvfkU6elDZskI4cMU9tiBsAAMIHJzgAIlZdnfmaNb/+tVRUJM2YIW3eLM2aJTkcVk8HAAAuBIEDIOJUVEhr10qZmeZ2tFtvlV591TzBAQAA4Y3AARAxvvhCysoy46auTvrBD8xVz2PGWD0ZAADoKQQOANs7ckT61a/My9H69JGWLJGWL5eGDrV6MgAA0NNYEw3AtvbsMTeibdwoXXqptGKFtHixFB9v9WQAAKC3cIIDwFYMw1wU8OST0rZtktcrPfecdO+9ksdj9XQAAKC3sSYagC34/eZq54kTpTlzpJoa6Y03pMJC6cEHiRsAACIFgQMgrNXWSk8/bZ7U3H23NGSI+WKd+fnSbbdJ0dFWTwgAAIKJS9QAhKXTp82wWbnS/P2dd5r32qSkWD0ZAACwEic4AEJSYWGhvF6vPB6P3G63PB6PvF6vtm4t1IoVUlKS9ItfSHfcYb5I54YNxA0AAGCLGoAQdPjwYaWkpMjv97fzqFNxcQXKyEjWsmXS4MFBHw8AAIQwAgdAyPF6vTp69GiHj19++WgdO1YUxIkAAEC4IHAAhByPx6OGhoYOH3e73aqvrw/iRAAAIFxwDw6AkNPVz134uQwAAOgIgQMg5Dgcjot6HAAARC4CB0DISUpKuqjHAQBA5CJwAIScnJwcOZ3tv0yX0+lUTk5OkCcCAADhgsABEHKSk5NVUFCg0aNHy+12y+Vyye12a/To0SooKFBycrLVIwIAgBDFFjUAAAAAtsEJDgAAAADbIHAAAAAA2AaBAwAAAMA2CBwAAAAAtkHgAAAAALANAgcAAACAbRA4AAAAAGyDwAEAAABgGwQOAAAAANsgcAAAAADYBoEDAAAAwDYIHAAAAAC2QeAAAAAAsA0CBwAAAIBtEDgAAAAAbIPAAQAAAGAbBA4AAAAA2yBwAAAAANgGgQMAAADANggcAAAAALZB4AAAAACwDQIHAAAAgG0QOAAAAABsg8ABAAAAYBsEDgAAAADbIHAAAAAA2AaBAwAAAMA2CBwAAAAAtkHgAAAAALANAgcAAACAbRDetTbUAAABD0lEQVQ4AAAAAGyDwAEAAABgGwQOAAAAANsgcAAAAADYBoEDAAAAwDYIHAAAAAC2QeAAAAAAsA0CBwAAAIBtEDgAAAAAbIPAAQAAAGAbBA4AAAAA2yBwAAAAANgGgQMAAADANggcAAAAALZB4AAAAACwDQIHAAAAgG0QOAAAAABsg8ABAAAAYBsEDgAAAADbIHAAAAAA2AaBAwAAAMA2CBwAAAAAtkHgAAAAALANAgcAAACAbRA4AAAAAGyDwAEAAABgGwQOAAAAANsgcAAAAADYBoEDAAAAwDYIHAAAAAC2QeAAAAAAsA0CBwAAAIBtEDgAAAAAbIPAAQAAAGAbBA4AAAAA2yBwAAAAANjG/wMm9hwIfRLtAgAAAABJRU5ErkJggg==" + ], + "text/plain": [ + "PyPlot.Figure(PyObject )" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(-3.0,5.0,-0.1,3.6)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Converged. p = [2.110067133637596,1.2802355090457196,1.5675520985912952,-3.7904167887303712]\n" + ] + } + ], "source": [ "srand(42)\n", "\n", "nsl = 5\n", "nm = 5\n", "\n", - "x1 = linspace(0, 3, nsl) + rand(nsl)*0.1\n", - "y1 = 0.5*rand(nsl)\n", - "x2 = linspace(0, 3, nm) + rand(nm)*0.1 + 0.4\n", - "y2 = 0.5*rand(nm) + 0.5\n", + "ϕ11 = 5.4\n", + "ϕ12 = 4.6\n", + "R1 = 5\n", + "xc1 = -0.8\n", + "yc1 = 9.9\n", + "X1 = [-2.0, 2.0]\n", + "X2 = [4.0, 3.5]\n", + "x1, y1 = rlinspace(X1, X2, R1, nsl, [ϕ11, ϕ12, xc1, yc1])\n", + "\n", + "ϕ11 = 2.2\n", + "ϕ12 = 1.1\n", + "R1 = 5\n", + "xc1 = 1.9\n", + "yc1 = -4.1\n", + "X1 = [-1.0, 0.5]\n", + "X2 = [3.0, 1.0]\n", + "x2, y2 = rlinspace(X1, X2, R1, nsl, [ϕ11, ϕ12, xc1, yc1])\n", + "\n", + "#x1 = linspace(0, 3, nsl) + rand(nsl)*0.1\n", + "#y1 = 0.5*rand(nsl)\n", + "#x2 = linspace(0, 3, nm) + rand(nm)*0.1 + 0.4\n", + "#y2 = 0.5*rand(nm) + 0.5\n", "\n", "function create_elements(X, sid=0)\n", " Γ = []\n", @@ -78,12 +182,44 @@ " calculate_normals!(el)\n", "end\n", "average_normals!(Γ₁)\n", - "average_normals!(Γ₂)" + "average_normals!(Γ₂)\n", + "\n", + "function plot_element(el; plot_with_normal=false)\n", + " # create a array of vectors\n", + " xis = Vector[[xi] for xi in linspace(-1, 1)]\n", + " coords = interpolate(el, :Geometry, xis)\n", + " normals = interpolate(el, :Normals, Vector[[-1.0], [1.0]])\n", + " ncoords = interpolate(el, :Geometry, Vector[[-1.0], [1.0]])\n", + " xs = [X[1] for X in coords]\n", + " ys = [X[2] for X in coords]\n", + " plot(xs, ys, \"-\")\n", + " plot([xs[1], xs[end]], [ys[1], ys[end]], \"ko\")\n", + " plot([xs[1], xs[end]], [ys[1], ys[end]], \"ko\")\n", + " if plot_with_normal\n", + " for i=1:2\n", + " p0 = ncoords[i]\n", + " p1 = ncoords[i]+0.1*normals[i]\n", + " plot([p0[1], p1[1]], [p0[2], p1[2]], \"-k\")\n", + " end\n", + " end\n", + "end\n", + "\n", + "\n", + "figure(figsize=(10, 3))\n", + "for el in Γ₁\n", + " plot_element(el; plot_with_normal=true)\n", + "end\n", + "for el in Γ₂\n", + " plot_element(el; plot_with_normal=false)\n", + "end\n", + "axis(\"equal\")\n", + "ylim(-0.1, 3.6)\n", + "axis(\"off\")" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -94,7 +230,7 @@ "calc_projection (generic function with 1 method)" ] }, - "execution_count": 3, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -171,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -179,10 +315,10 @@ { "data": { "image/png": [ - 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" 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" ], "text/plain": [ - "PyPlot.Figure(PyObject )" + "PyPlot.Figure(PyObject )" ] }, "metadata": {}, @@ -191,35 +327,15 @@ { "data": { "text/plain": [ - "(-0.5,4.0)" + "(-3.0,5.0,-0.1,3.6)" ] }, - "execution_count": 4, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "function plot_element(el; plot_with_normal=false)\n", - " # create a array of vectors\n", - " xis = Vector[[xi] for xi in linspace(-1, 1)]\n", - " coords = interpolate(el, :Geometry, xis)\n", - " normals = interpolate(el, :Normals, Vector[[-1.0], [1.0]])\n", - " ncoords = interpolate(el, :Geometry, Vector[[-1.0], [1.0]])\n", - " xs = [X[1] for X in coords]\n", - " ys = [X[2] for X in coords]\n", - " plot(xs, ys, \"-\")\n", - " plot([xs[1], xs[end]], [ys[1], ys[end]], \"ko\")\n", - " plot([xs[1], xs[end]], [ys[1], ys[end]], \"ko\")\n", - " if plot_with_normal\n", - " for i=1:2\n", - " p0 = ncoords[i]\n", - " p1 = ncoords[i]+0.1*normals[i]\n", - " plot([p0[1], p1[1]], [p0[2], p1[2]], \"-k\")\n", - " end\n", - " end\n", - "end\n", - "\n", "function plot_all()\n", " figure(figsize=(10, 3))\n", " for el in Γ₁\n", @@ -242,9 +358,8 @@ " end\n", " end\n", " axis(\"equal\")\n", + " ylim(-0.1, 3.6)\n", " axis(\"off\")\n", - " ylim(-0.0, 0.5)\n", - " xlim(-0.5, 4.0)\n", "end\n", "\n", "plot_all()" @@ -259,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -267,10 +382,10 @@ { "data": { "image/png": [ - 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" 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" ], "text/plain": [ - "PyPlot.Figure(PyObject )" + "PyPlot.Figure(PyObject )" ] }, "metadata": {}, @@ -279,10 +394,10 @@ { "data": { "text/plain": [ - "(-0.5,4.0)" + "(-3.0,5.0,-0.1,3.6)" ] }, - "execution_count": 5, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -305,17 +420,28 @@ "end\n", "plot_all()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Julia 0.5.0-dev", + "display_name": "Julia 0.4.0-dev", "language": "julia", - "name": "julia-0.5" + "name": "julia-0.4" }, "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", "name": "julia", - "version": "0.5.0" + "version": "0.4.0" } }, "nbformat": 4,