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221
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\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Nnname
1S
2i
3I
\n", + "
\n" + ], + "text/plain": [ + "CausalLoop with elements E = 1:4, N = 1:3\n", + "┌───┬───┬───┐\n", + "│\u001b[1m E \u001b[0m│\u001b[1m s \u001b[0m│\u001b[1m t \u001b[0m│\n", + "├───┼───┼───┤\n", + "│ 1 │ 1 │ 2 │\n", + "│ 2 │ 2 │ 1 │\n", + "│ 3 │ 2 │ 3 │\n", + "│ 4 │ 3 │ 2 │\n", + "└───┴───┴───┘\n", + "┌───┬───────┐\n", + "│\u001b[1m N \u001b[0m│\u001b[1m nname \u001b[0m│\n", + "├───┼───────┤\n", + "│ 1 │ S │\n", + "│ 2 │ i │\n", + "│ 3 │ I │\n", + "└───┴───────┘\n" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# test the causal loop diagram\n", + "si_causalLoop=CausalLoop(\n", + " [:S,:i,:I],\n", + " [:S=>:i,:i=>:S,:i=>:I,:I=>:i]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "G\n", + "\n", + "\n", + "\n", + "n1\n", + "S\n", + "\n", + "\n", + "\n", + "n2\n", + "i\n", + "\n", + "\n", + "\n", + "n1->n2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "n2->n1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "n3\n", + "I\n", + "\n", + "\n", + "\n", + "n2->n3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "n3->n2\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"n1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"S\", :shape => \"plaintext\")), Catlab.Graphics.Graphviz.Node(\"n2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"i\", :shape => \"plaintext\")), Catlab.Graphics.Graphviz.Node(\"n3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I\", :shape => \"plaintext\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\"))], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GraphCL(si_causalLoop)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.10.4", + "language": "julia", + "name": "julia-1.10" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/full_fledged_schema_examples/SIR_example_function_structure_test_compostion_and_stratification.ipynb b/examples/full_fledged_schema_examples/SIR_example_function_structure_test_compostion_and_stratification.ipynb index e66db17f..712e6d4e 100644 --- a/examples/full_fledged_schema_examples/SIR_example_function_structure_test_compostion_and_stratification.ipynb +++ b/examples/full_fledged_schema_examples/SIR_example_function_structure_test_compostion_and_stratification.ipynb @@ -407,9 +407,8 @@ "└─────┴──────┴──────┴──────────────┘\n" ] }, - "execution_count": 2, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -432,190 +431,190 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "S\n", + "\n", + "S\n", "\n", "\n", "\n", "v4\n", - "S * (beta * (c * (I / N)))\n", + "S * (beta * (c * (I / N)))\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "v1\n", - "I / N\n", + "I / N\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "I / tRec\n", + "I / tRec\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "R\n", + "\n", + "R\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "c * (I / N)\n", + "c * (I / N)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c * (I / N))\n", + "beta * (c * (I / N))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "tRec\n", + "\n", + "tRec\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "inf\n", + "\n", + "\n", + "\n", + "\n", + "inf\n", "\n", "\n", "\n", "v5->s3\n", - "\n", - "\n", - "\n", - "\n", - "rec\n", + "\n", + "\n", + "\n", + "\n", + "rec\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -624,9 +623,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"S\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"R\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"tRec\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I / N\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c * (I / N)\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta * (c * (I / N))\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"S * (beta * (c * (I / N)))\", :shape => \"plaintext\", :fontcolor => \"black\")) … Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:splines => \"splines\"))" ] }, - "execution_count": 3, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -672,133 +670,133 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "S\n", + "\n", + "S\n", "\n", "\n", "\n", "v2\n", - "S * vRate\n", + "S * vRate\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "V\n", + "\n", + "V\n", "\n", "\n", "\n", "v1\n", - "V * lambda\n", + "V * lambda\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "vRate\n", + "\n", + "vRate\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "lambda\n", + "\n", + "lambda\n", "\n", "\n", "\n", "p2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->s3\n", - "\n", - "\n", - "\n", - "\n", - "infV\n", + "\n", + "\n", + "\n", + "\n", + "infV\n", "\n", "\n", "\n", "v2->s2\n", - "\n", - "\n", - "\n", - "\n", - "vac\n", + "\n", + "\n", + "\n", + "\n", + "vac\n", "\n", "\n", "\n" @@ -807,9 +805,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"S\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"V\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"vRate\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"lambda\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"V * lambda\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"S * vRate\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"sv1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"N\", :shape => \"circle\", :color => \"black\", :fillcolor => \"cornflowerblue\", :style => \"filled\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"\", :labelfontsize => \"6\", :color => \"black:invis:black\", :arrowhead => \"none\", :splines => \"ortho\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"vac\", :labelfontsize => \"6\", :color => \"black:invis:black\", :splines => \"ortho\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"\", :labelfontsize => \"6\", :color => \"black:invis:black\", :arrowhead => \"none\", :splines => \"ortho\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"s3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"infV\", :labelfontsize => \"6\", :color => \"black:invis:black\", :splines => \"ortho\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:splines => \"splines\"))" ] }, - "execution_count": 5, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -835,71 +832,71 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "n1\n", - "\n", - "seir\n", + "\n", + "seir\n", "\n", "\n", "\n", "\n", "n5\n", - "\n", - "S\n", + "\n", + "S\n", "\n", "\n", "\n", "n1--n5\n", - "\n", + "\n", "\n", "\n", "\n", "\n", "n6\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "n1--n6\n", - "\n", + "\n", "\n", "\n", "\n", "n2\n", - "\n", - "svi\n", + "\n", + "svi\n", "\n", "\n", "\n", "n2--n5\n", - "\n", + "\n", "\n", "\n", "\n", "n2--n6\n", - "\n", + "\n", "\n", "\n", "\n", "\n", "n3--n5\n", - "\n", + "\n", "\n", "\n", "\n", "\n", "n4--n6\n", - "\n", + "\n", "\n", "\n", "\n" @@ -908,9 +905,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", false, \"neato\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"n1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:id => \"box1\", :label => \"seir\")), Catlab.Graphics.Graphviz.Node(\"n2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:id => \"box2\", :label => \"svi\")), Catlab.Graphics.Graphviz.Node(\"n3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:height => \"0\", :id => \"outer1\", :label => \"\", :margin => \"0\", :shape => \"none\", :style => \"invis\", :width => \"0\")), Catlab.Graphics.Graphviz.Node(\"n4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:height => \"0\", :id => \"outer2\", :label => \"\", :margin => \"0\", :shape => \"none\", :style => \"invis\", :width => \"0\")), Catlab.Graphics.Graphviz.Node(\"n5\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:comment => \"junction\", :fillcolor => \"black\", :height => \"0.075\", :id => \"junction1\", :label => \"\", :shape => \"circle\", :style => \"filled\", :width => \"0.075\", :xlabel => \"S\")), Catlab.Graphics.Graphviz.Node(\"n6\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:comment => \"junction\", :fillcolor => \"black\", :height => \"0.075\", :id => \"junction2\", :label => \"\", :shape => \"circle\", :style => \"filled\", :width => \"0.075\", :xlabel => \"I\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n4\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:fontname => \"Serif\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:fontname => \"Serif\", :shape => \"ellipse\", :margin => \"0.05,0.025\", :width => \"0.5\", :height => \"0.5\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:fontname => \"Serif\", :len => \"1\"))" ] }, - "execution_count": 6, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -928,47 +924,23 @@ "metadata": {}, "outputs": [ { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "G\n", - "\n", - "\n", - "\n", - "s1\n", - "\n", - "S\n", - "\n", - "\n", - "\n", - "sv1\n", - "\n", - "N\n", - "\n", - "\n", - "\n", - "s1->sv1\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"S\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"sv1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"N\", :shape => \"circle\", :color => \"black\", :fillcolor => \"cornflowerblue\", :style => \"filled\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:splines => \"splines\"))" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: both Catlab and StockFlow export \"Graph\"; uses of it in module Main must be qualified\n" + ] + }, + { + "ename": "UndefVarError", + "evalue": "UndefVarError: `Graph` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `Graph` not defined\n", + "\n", + "Stacktrace:\n", + " [1] top-level scope\n", + " @ ~/Documents/Git/StockFlow.jl/examples/full_fledged_schema_examples/SIR_example_function_structure_test_compostion_and_stratification.ipynb:3" + ] } ], "source": [ @@ -983,47 +955,16 @@ "metadata": {}, "outputs": [ { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "G\n", - "\n", - "\n", - "\n", - "s1\n", - "\n", - "I\n", - "\n", - "\n", - "\n", - "sv1\n", - "\n", - "N\n", - "\n", - "\n", - "\n", - "s1->sv1\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"sv1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"N\", :shape => \"circle\", :color => \"black\", :fillcolor => \"cornflowerblue\", :style => \"filled\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:splines => \"splines\"))" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" + "ename": "UndefVarError", + "evalue": "UndefVarError: `Graph` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `Graph` not defined\n", + "\n", + "Stacktrace:\n", + " [1] top-level scope\n", + " @ ~/Documents/Git/StockFlow.jl/examples/full_fledged_schema_examples/SIR_example_function_structure_test_compostion_and_stratification.ipynb:3" + ] } ], "source": [ @@ -1043,280 +984,280 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "S\n", + "\n", + "S\n", "\n", "\n", "\n", "v4\n", - "S * (beta * (c * (I / N)))\n", + "S * (beta * (c * (I / N)))\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "S * vRate\n", + "S * vRate\n", "\n", "\n", "\n", "s1->v7\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "v1\n", - "I / N\n", + "I / N\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "I / tRec\n", + "I / tRec\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "R\n", + "\n", + "R\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4\n", - "\n", - "V\n", + "\n", + "V\n", "\n", "\n", "\n", "v6\n", - "V * lambda\n", + "V * lambda\n", "\n", "\n", "\n", "s4->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "c * (I / N)\n", + "c * (I / N)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c * (I / N))\n", + "beta * (c * (I / N))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "tRec\n", + "\n", + "tRec\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "vRate\n", + "\n", + "vRate\n", "\n", "\n", "\n", "p4->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p5\n", - "\n", - "lambda\n", + "\n", + "lambda\n", "\n", "\n", "\n", "p5->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "inf\n", + "\n", + "\n", + "\n", + "\n", + "inf\n", "\n", "\n", "\n", "v5->s3\n", - "\n", - "\n", - "\n", - "\n", - "rec\n", + "\n", + "\n", + "\n", + "\n", + "rec\n", "\n", "\n", "\n", "v6->s2\n", - "\n", - "\n", - "\n", - "\n", - "infV\n", + "\n", + "\n", + "\n", + "\n", + "infV\n", "\n", "\n", "\n", "v7->s4\n", - "\n", - "\n", - "\n", - "\n", - "vac\n", + "\n", + "\n", + "\n", + "\n", + "vac\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -1325,9 +1266,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"S\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"R\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"V\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"tRec\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"vRate\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p5\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"lambda\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I / N\", :shape => \"plaintext\", :fontcolor => \"black\")) … Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s4\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p4\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v7\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p5\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:splines => \"splines\"))" ] }, - "execution_count": 9, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -1366,145 +1306,145 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "V\n", + "\n", + "V\n", "\n", "\n", "\n", "v4\n", - "V * lambda\n", + "V * lambda\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "v1\n", - "I / N\n", + "I / N\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "c * (I / N)\n", + "c * (I / N)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c * (I / N))\n", + "beta * (c * (I / N))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "lambda\n", + "\n", + "lambda\n", "\n", "\n", "\n", "p3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "infV\n", + "\n", + "\n", + "\n", + "\n", + "infV\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -1513,9 +1453,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"V\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"lambda\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I / N\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c * (I / N)\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta * (c * (I / N))\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"V * lambda\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"sv1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"N\", :shape => \"circle\", :color => \"black\", :fillcolor => \"cornflowerblue\", :style => \"filled\")) … Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:splines => \"splines\"))" ] }, - "execution_count": 10, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -1540,133 +1479,133 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "V\n", + "\n", + "V\n", "\n", "\n", "\n", "v4\n", - "(*)(V)\n", + "(*)(V)\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "v1\n", - "I / N\n", + "I / N\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "c * (I / N)\n", + "c * (I / N)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c * (I / N))\n", + "beta * (c * (I / N))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "infV\n", + "\n", + "\n", + "\n", + "\n", + "infV\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -1675,9 +1614,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"V\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I / N\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c * (I / N)\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta * (c * (I / N))\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"(*)(V)\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"sv1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"N\", :shape => \"circle\", :color => \"black\", :fillcolor => \"cornflowerblue\", :style => \"filled\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"\", :labelfontsize => \"6\", :color => \"black:invis:black\", :arrowhead => \"none\", :splines => \"ortho\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"infV\", :labelfontsize => \"6\", :color => \"black:invis:black\", :splines => \"ortho\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:splines => \"splines\"))" ] }, - "execution_count": 11, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -1701,139 +1639,139 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "V\n", + "\n", + "V\n", "\n", "\n", "\n", "v4\n", - "V * (beta * (c * (I / N)))\n", + "V * (beta * (c * (I / N)))\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "v1\n", - "I / N\n", + "I / N\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "c * (I / N)\n", + "c * (I / N)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c * (I / N))\n", + "beta * (c * (I / N))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "infV\n", + "\n", + "\n", + "\n", + "\n", + "infV\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -1842,9 +1780,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"V\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I / N\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c * (I / N)\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta * (c * (I / N))\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"V * (beta * (c * (I / N)))\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"sv1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"N\", :shape => \"circle\", :color => \"black\", :fillcolor => \"cornflowerblue\", :style => \"filled\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"\", :labelfontsize => \"6\", :color => \"black:invis:black\", :arrowhead => \"none\", :splines => \"ortho\")) … Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:splines => \"splines\"))" ] }, - "execution_count": 12, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -1869,274 +1806,274 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "V\n", + "\n", + "V\n", "\n", "\n", "\n", "v4\n", - "V * (beta * (c * (I / N)))\n", + "V * (beta * (c * (I / N)))\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "v1\n", - "I / N\n", + "I / N\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v6\n", - "I / tRec\n", + "I / tRec\n", "\n", "\n", "\n", "s2->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "S\n", + "\n", + "S\n", "\n", "\n", "\n", "v5\n", - "S * (beta * (c * (I / N)))\n", + "S * (beta * (c * (I / N)))\n", "\n", "\n", "\n", "s3->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "S * vRate\n", + "S * vRate\n", "\n", "\n", "\n", "s3->v7\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4\n", - "\n", - "R\n", + "\n", + "R\n", "\n", "\n", "\n", "s4->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "c * (I / N)\n", + "c * (I / N)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c * (I / N))\n", + "beta * (c * (I / N))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "tRec\n", + "\n", + "tRec\n", "\n", "\n", "\n", "p3->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "vRate\n", + "\n", + "vRate\n", "\n", "\n", "\n", "p4->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "infV\n", + "\n", + "\n", + "\n", + "\n", + "infV\n", "\n", "\n", "\n", "v5->s2\n", - "\n", - "\n", - "\n", - "\n", - "inf\n", + "\n", + "\n", + "\n", + "\n", + "inf\n", "\n", "\n", "\n", "v6->s4\n", - "\n", - "\n", - "\n", - "\n", - "rec\n", + "\n", + "\n", + "\n", + "\n", + "rec\n", "\n", "\n", "\n", "v7->s1\n", - "\n", - "\n", - "\n", - "\n", - "vac\n", + "\n", + "\n", + "\n", + "\n", + "vac\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -2145,9 +2082,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"V\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"S\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"R\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"tRec\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"vRate\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I / N\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c * (I / N)\", :shape => \"plaintext\", :fontcolor => \"black\")) … Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s4\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p4\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v7\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:splines => \"splines\"))" ] }, - "execution_count": 13, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -2182,123 +2118,122 @@ "outputs": [ { "data": { - "image/png": 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", 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bt2/v7u6eOXPm7t27kx0Rw6QxlggZhmGYUY11jTIMwzCjGkuEDMMwzKjGEiHDMAwzqrFEyDAMw4xqLBEyDMMwoxpLhAzDMMyoxhIhwzAMM6r9f/c1TM8wbQCiAAAAAElFTkSuQmCC", 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"\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, - "execution_count": 15, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -2318,129 +2253,129 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "V\n", + "\n", + "V\n", "\n", "\n", "\n", "v4\n", - "V * lambda\n", + "V * lambda\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "v1\n", - "I / N\n", + "I / N\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "c * (I / N)\n", + "c * (I / N)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c * (I / N))\n", + "beta * (c * (I / N))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "lambda\n", + "\n", + "lambda\n", "\n", "\n", "\n", "p3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -2449,9 +2384,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"V\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"lambda\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I / N\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"c * (I / N)\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"beta * (c * (I / N))\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"v4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"V * lambda\", :shape => \"plaintext\", :fontcolor => \"black\")), Catlab.Graphics.Graphviz.Node(\"sv1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"N\", :shape => \"circle\", :color => \"black\", :fillcolor => \"cornflowerblue\", :style => \"filled\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"s2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"sv1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"v1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p3\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p2\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}()), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"p1\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:splines => \"splines\"))" ] }, - "execution_count": 16, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -2477,7 +2411,15 @@ "cell_type": "code", "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: using Graphviz.Graph in module Main conflicts with an existing identifier.\n" + ] + } + ], "source": [ "using Catlab.Graphics.Graphviz: Html\n", "using Catlab.Graphics.Graphviz" @@ -2494,9 +2436,8 @@ "GraphF_typed (generic function with 5 methods)" ] }, - "execution_count": 18, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -2608,205 +2549,205 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "pop\n", + "\n", + "pop\n", "\n", "\n", "\n", "v1\n", - "pop / N\n", + "pop / N\n", "\n", "\n", "\n", "s1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4\n", - "pop * (beta * (c * (pop / N)))\n", + "pop * (beta * (c * (pop / N)))\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "pop * rFstOrder\n", + "pop * rFstOrder\n", "\n", "\n", "\n", "s1->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v6\n", - "pop * rAge\n", + "pop * rAge\n", "\n", "\n", "\n", "s1->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "c * (pop / N)\n", + "c * (pop / N)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c * (pop / N))\n", + "beta * (c * (pop / N))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "rFstOrder\n", + "\n", + "rFstOrder\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "rAge\n", + "\n", + "rAge\n", "\n", "\n", "\n", "p4->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s1\n", - "\n", - "\n", - "\n", - "\n", - "inf\n", + "\n", + "\n", + "\n", + "\n", + "inf\n", "\n", "\n", "\n", "v5->s1\n", - "\n", - "\n", - "\n", - "\n", - "fstOrder\n", + "\n", + "\n", + "\n", + "\n", + "fstOrder\n", "\n", "\n", "\n", "v6->s1\n", - "\n", - "\n", - "\n", - "\n", - "aging\n", + "\n", + "\n", + "\n", + "\n", + "aging\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -2815,9 +2756,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"pop\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c\", :shape => \"circle\", :color => \"black\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"beta\", :shape => \"circle\", :color => \"black\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rFstOrder\", :shape => \"circle\", :color => \"black\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rAge\", :shape => \"circle\", :color => \"black\")), Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"pop / N\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c * (pop / N)\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"beta * (c * (pop / N))\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"pop * (beta * (c * (pop / N)))\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"pop * rFstOrder\", :shape => \"plaintext\", :fontcolor => \"black\")) … Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v3\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 19, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -2842,205 +2782,205 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "pop\n", + "\n", + "pop\n", "\n", "\n", "\n", "v1\n", - "pop / N\n", + "pop / N\n", "\n", "\n", "\n", "s1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4\n", - "pop * (beta * (c * (pop / N)))\n", + "pop * (beta * (c * (pop / N)))\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "pop * rFstOrder\n", + "pop * rFstOrder\n", "\n", "\n", "\n", "s1->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v6\n", - "pop * rAge\n", + "pop * rAge\n", "\n", "\n", "\n", "s1->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "c * (pop / N)\n", + "c * (pop / N)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c * (pop / N))\n", + "beta * (c * (pop / N))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "rFstOrder\n", + "\n", + "rFstOrder\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "rAge\n", + "\n", + "rAge\n", "\n", "\n", "\n", "p4->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s1\n", - "\n", - "\n", - "\n", - "\n", - "inf\n", + "\n", + "\n", + "\n", + "\n", + "inf\n", "\n", "\n", "\n", "v5->s1\n", - "\n", - "\n", - "\n", - "\n", - "fstOrder\n", + "\n", + "\n", + "\n", + "\n", + "fstOrder\n", "\n", "\n", "\n", "v6->s1\n", - "\n", - "\n", - "\n", - "\n", - "aging\n", + "\n", + "\n", + "\n", + "\n", + "aging\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -3049,9 +2989,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"pop\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c\", :shape => \"circle\", :color => \"gold\", :fontcolor => \"gold\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"beta\", :shape => \"circle\", :color => \"gold4\", :fontcolor => \"gold4\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rFstOrder\", :shape => \"circle\", :color => \"darkorange1\", :fontcolor => \"darkorange1\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rAge\", :shape => \"circle\", :color => \"lightgoldenrod\", :fontcolor => \"lightgoldenrod\")), Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"pop / N\", :shape => \"plaintext\", :fontcolor => \"antiquewhite4\")), Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c * (pop / N)\", :shape => \"plaintext\", :fontcolor => \"antiquewhite\")), Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"beta * (c * (pop / N))\", :shape => \"plaintext\", :fontcolor => \"gold\")), Node(\"v4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"pop * (beta * (c * (pop / N)))\", :shape => \"plaintext\", :fontcolor => \"saddlebrown\")), Node(\"v5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"pop * rFstOrder\", :shape => \"plaintext\", :fontcolor => \"slateblue\")) … Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v3\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 20, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -3087,9 +3026,8 @@ "1:4" ] }, - "execution_count": 22, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -3619,9 +3557,8 @@ "└─────┴──────┴──────┴──────────────┘\n" ] }, - "execution_count": 23, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -3669,295 +3606,295 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "S\n", + "\n", + "S\n", "\n", "\n", "\n", "v4\n", - "S * (beta * (c * (I / N)))\n", + "S * (beta * (c * (I / N)))\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v6\n", - "S * rAge\n", + "S * rAge\n", "\n", "\n", "\n", "s1->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "v1\n", - "I / N\n", + "I / N\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "I * rRec\n", + "I * rRec\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "I * rAge\n", + "I * rAge\n", "\n", "\n", "\n", "s2->v7\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "R\n", + "\n", + "R\n", "\n", "\n", "\n", "v8\n", - "R * rAge\n", + "R * rAge\n", "\n", "\n", "\n", "s3->v8\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "c * (I / N)\n", + "c * (I / N)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c * (I / N))\n", + "beta * (c * (I / N))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "rRec\n", + "\n", + "rRec\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "rAge\n", + "\n", + "rAge\n", "\n", "\n", "\n", "p4->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "inf\n", + "\n", + "\n", + "\n", + "\n", + "inf\n", "\n", "\n", "\n", "v5->s3\n", - "\n", - "\n", - "\n", - "\n", - "rec\n", + "\n", + "\n", + "\n", + "\n", + "rec\n", "\n", "\n", "\n", "v6->s1\n", - "\n", - "\n", - "\n", - "\n", - "id_S\n", + "\n", + "\n", + "\n", + "\n", + "id_S\n", "\n", "\n", "\n", "v7->s2\n", - "\n", - "\n", - "\n", - "\n", - "id_I\n", + "\n", + "\n", + "\n", + "\n", + "id_I\n", "\n", "\n", "\n", "v8->s3\n", - "\n", - "\n", - "\n", - "\n", - "id_R\n", + "\n", + "\n", + "\n", + "\n", + "id_R\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -3966,9 +3903,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"S\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"R\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c\", :shape => \"circle\", :color => \"gold\", :fontcolor => \"gold\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"beta\", :shape => \"circle\", :color => \"gold4\", :fontcolor => \"gold4\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rRec\", :shape => \"circle\", :color => \"darkorange1\", :fontcolor => \"darkorange1\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rAge\", :shape => \"circle\", :color => \"lightgoldenrod\", :fontcolor => \"lightgoldenrod\")), Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"I / N\", :shape => \"plaintext\", :fontcolor => \"antiquewhite4\")), Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c * (I / N)\", :shape => \"plaintext\", :fontcolor => \"antiquewhite\")), Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"beta * (c * (I / N))\", :shape => \"plaintext\", :fontcolor => \"gold\")) … Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v3\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v8\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v7\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 25, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -4546,9 +4482,8 @@ "└─────┴──────┴──────┴──────────────┘\n" ] }, - "execution_count": 26, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -4597,346 +4532,346 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "Child\n", + "\n", + "Child\n", "\n", "\n", "\n", "v1\n", - "Child / NC\n", + "Child / NC\n", "\n", "\n", "\n", "s1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4\n", - "Child * (beta * (c_C * (Child / NC)))\n", + "Child * (beta * (c_C * (Child / NC)))\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "Child * r\n", + "Child * r\n", "\n", "\n", "\n", "s1->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v6\n", - "Child * rAge\n", + "Child * rAge\n", "\n", "\n", "\n", "s1->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "NC\n", + "\n", + "NC\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "Adult\n", + "\n", + "Adult\n", "\n", "\n", "\n", "v7\n", - "Adult / NA\n", + "Adult / NA\n", "\n", "\n", "\n", "s2->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v10\n", - "Adult * (beta * (c_A * (Adult / NA)))\n", + "Adult * (beta * (c_A * (Adult / NA)))\n", "\n", "\n", "\n", "s2->v10\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v11\n", - "Adult * r\n", + "Adult * r\n", "\n", "\n", "\n", "s2->v11\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv2\n", - "\n", - "NA\n", + "\n", + "NA\n", "\n", "\n", "\n", "s2->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c_C\n", + "\n", + "c_C\n", "\n", "\n", "\n", "v2\n", - "c_C * (Child / NC)\n", + "c_C * (Child / NC)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c_C * (Child / NC))\n", + "beta * (c_C * (Child / NC))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v9\n", - "beta * (c_A * (Adult / NA))\n", + "beta * (c_A * (Adult / NA))\n", "\n", "\n", "\n", "p2->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "r\n", + "\n", + "r\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3->v11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "rAge\n", + "\n", + "rAge\n", "\n", "\n", "\n", "p4->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p5\n", - "\n", - "c_A\n", + "\n", + "c_A\n", "\n", "\n", "\n", "v8\n", - "c_A * (Adult / NA)\n", + "c_A * (Adult / NA)\n", "\n", "\n", "\n", "p5->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s1\n", - "\n", - "\n", - "\n", - "\n", - "infC\n", + "\n", + "\n", + "\n", + "\n", + "infC\n", "\n", "\n", "\n", "v5->s1\n", - "\n", - "\n", - "\n", - "\n", - "frsC\n", + "\n", + "\n", + "\n", + "\n", + "frsC\n", "\n", "\n", "\n", "v6->s2\n", - "\n", - "\n", - "\n", - "\n", - "agingC\n", + "\n", + "\n", + "\n", + "\n", + "agingC\n", "\n", "\n", "\n", "v7->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v8->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v9->v10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v10->s2\n", - "\n", - "\n", - "\n", - "\n", - "infA\n", + "\n", + "\n", + "\n", + "\n", + "infA\n", "\n", "\n", "\n", "v11->s2\n", - "\n", - "\n", - "\n", - "\n", - "frsA\n", + "\n", + "\n", + "\n", + "\n", + "frsA\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv2->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -4945,9 +4880,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"Child\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"Adult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c_C\", :shape => \"circle\", :color => \"gold\", :fontcolor => \"gold\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"beta\", :shape => \"circle\", :color => \"gold4\", :fontcolor => \"gold4\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"r\", :shape => \"circle\", :color => \"darkorange1\", :fontcolor => \"darkorange1\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rAge\", :shape => \"circle\", :color => \"lightgoldenrod\", :fontcolor => \"lightgoldenrod\")), Node(\"p5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c_A\", :shape => \"circle\", :color => \"gold\", :fontcolor => \"gold\")), Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"Child / NC\", :shape => \"plaintext\", :fontcolor => \"antiquewhite4\")), Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c_C * (Child / NC)\", :shape => \"plaintext\", :fontcolor => \"antiquewhite\")), Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"beta * (c_C * (Child / NC))\", :shape => \"plaintext\", :fontcolor => \"gold\")) … Edge(NodeID[NodeID(\"v3\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v11\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v9\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p5\", \"\", \"\"), NodeID(\"v8\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 28, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -4983,460 +4917,460 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "SChild\n", + "\n", + "SChild\n", "\n", "\n", "\n", "v4\n", - "SChild * (betabeta * (cc_C * (IChild / NNC)))\n", + "SChild * (betabeta * (cc_C * (IChild / NNC)))\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v6\n", - "SChild * rAgerAge\n", + "SChild * rAgerAge\n", "\n", "\n", "\n", "s1->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "NNC\n", + "\n", + "NNC\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "IChild\n", + "\n", + "IChild\n", "\n", "\n", "\n", "v1\n", - "IChild / NNC\n", + "IChild / NNC\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "IChild * rRecr\n", + "IChild * rRecr\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "IChild * rAgerAge\n", + "IChild * rAgerAge\n", "\n", "\n", "\n", "s2->v7\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "RChild\n", + "\n", + "RChild\n", "\n", "\n", "\n", "v8\n", - "RChild * rAgerAge\n", + "RChild * rAgerAge\n", "\n", "\n", "\n", "s3->v8\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4\n", - "\n", - "SAdult\n", + "\n", + "SAdult\n", "\n", "\n", "\n", "v12\n", - "SAdult * (betabeta * (cc_A * (IAdult / NNA)))\n", + "SAdult * (betabeta * (cc_A * (IAdult / NNA)))\n", "\n", "\n", "\n", "s4->v12\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->v12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv2\n", - "\n", - "NNA\n", + "\n", + "NNA\n", "\n", "\n", "\n", "s4->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s5\n", - "\n", - "IAdult\n", + "\n", + "IAdult\n", "\n", "\n", "\n", "v9\n", - "IAdult / NNA\n", + "IAdult / NNA\n", "\n", "\n", "\n", "s5->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v13\n", - "IAdult * rRecr\n", + "IAdult * rRecr\n", "\n", "\n", "\n", "s5->v13\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s5->v13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s5->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s6\n", - "\n", - "RAdult\n", + "\n", + "RAdult\n", "\n", "\n", "\n", "s6->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "cc_C\n", + "\n", + "cc_C\n", "\n", "\n", "\n", "v2\n", - "cc_C * (IChild / NNC)\n", + "cc_C * (IChild / NNC)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "betabeta\n", + "\n", + "betabeta\n", "\n", "\n", "\n", "v3\n", - "betabeta * (cc_C * (IChild / NNC))\n", + "betabeta * (cc_C * (IChild / NNC))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v11\n", - "betabeta * (cc_A * (IAdult / NNA))\n", + "betabeta * (cc_A * (IAdult / NNA))\n", "\n", "\n", "\n", "p2->v11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "rRecr\n", + "\n", + "rRecr\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3->v13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "rAgerAge\n", + "\n", + "rAgerAge\n", "\n", "\n", "\n", "p4->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p5\n", - "\n", - "cc_A\n", + "\n", + "cc_A\n", "\n", "\n", "\n", "v10\n", - "cc_A * (IAdult / NNA)\n", + "cc_A * (IAdult / NNA)\n", "\n", "\n", "\n", "p5->v10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "infinfC\n", + "\n", + "\n", + "\n", + "\n", + "infinfC\n", "\n", "\n", "\n", "v5->s3\n", - "\n", - "\n", - "\n", - "\n", - "recfrsC\n", + "\n", + "\n", + "\n", + "\n", + "recfrsC\n", "\n", "\n", "\n", "v6->s4\n", - "\n", - "\n", - "\n", - "\n", - "id_SagingC\n", + "\n", + "\n", + "\n", + "\n", + "id_SagingC\n", "\n", "\n", "\n", "v7->s5\n", - "\n", - "\n", - "\n", - "\n", - "id_IagingC\n", + "\n", + "\n", + "\n", + "\n", + "id_IagingC\n", "\n", "\n", "\n", "v8->s6\n", - "\n", - "\n", - "\n", - "\n", - "id_RagingC\n", + "\n", + "\n", + "\n", + "\n", + "id_RagingC\n", "\n", "\n", "\n", "v9->v10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v10->v11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v11->v12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v12->s5\n", - "\n", - "\n", - "\n", - "\n", - "infinfA\n", + "\n", + "\n", + "\n", + "\n", + "infinfA\n", "\n", "\n", "\n", "v13->s6\n", - "\n", - "\n", - "\n", - "\n", - "recfrsA\n", + "\n", + "\n", + "\n", + "\n", + "recfrsA\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv2->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -5445,9 +5379,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"SChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"RChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"SAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s6\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"RAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"cc_C\", :shape => \"circle\", :color => \"black\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"betabeta\", :shape => \"circle\", :color => \"black\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rRecr\", :shape => \"circle\", :color => \"black\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rAgerAge\", :shape => \"circle\", :color => \"black\")) … Edge(NodeID[NodeID(\"v1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v13\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v11\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p5\", \"\", \"\"), NodeID(\"v10\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v8\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v7\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 30, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -5472,153 +5405,153 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "SChild\n", + "\n", + "SChild\n", "\n", "\n", "\n", "sv1\n", - "\n", - "NNC\n", + "\n", + "NNC\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "IChild\n", + "\n", + "IChild\n", "\n", "\n", "\n", "v1\n", - "IChild / NNC\n", + "IChild / NNC\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "SAdult\n", + "\n", + "SAdult\n", "\n", "\n", "\n", "sv2\n", - "\n", - "NNA\n", + "\n", + "NNA\n", "\n", "\n", "\n", "s3->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4\n", - "\n", - "IAdult\n", + "\n", + "IAdult\n", "\n", "\n", "\n", "v2\n", - "IAdult / NNA\n", + "IAdult / NNA\n", "\n", "\n", "\n", "s4->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "cc_C\n", + "\n", + "cc_C\n", "\n", "\n", "\n", "v3\n", - "cc_C * (IChild / NNC)\n", + "cc_C * (IChild / NNC)\n", "\n", "\n", "\n", "p1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "cc_A\n", + "\n", + "cc_A\n", "\n", "\n", "\n", "v4\n", - "cc_A * (IAdult / NNA)\n", + "cc_A * (IAdult / NNA)\n", "\n", "\n", "\n", "p2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv2->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -5627,9 +5560,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"SChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"SAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"cc_C\", :shape => \"circle\", :color => \"black\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"cc_A\", :shape => \"circle\", :color => \"black\")), Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IChild / NNC\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IAdult / NNA\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"cc_C * (IChild / NNC)\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"cc_A * (IAdult / NNA)\", :shape => \"plaintext\", :fontcolor => \"black\")) … Edge(NodeID[NodeID(\"s3\", \"\", \"\"), NodeID(\"sv2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s4\", \"\", \"\"), NodeID(\"sv2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s2\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"sv2\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v2\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v1\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 31, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -5654,141 +5586,141 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "SChild\n", + "\n", + "SChild\n", "\n", "\n", "\n", "sv1\n", - "\n", - "NNC\n", + "\n", + "NNC\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "IChild\n", + "\n", + "IChild\n", "\n", "\n", "\n", "v1\n", - "IChild / NNC\n", + "IChild / NNC\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "SAdult\n", + "\n", + "SAdult\n", "\n", "\n", "\n", "sv2\n", - "\n", - "NNA\n", + "\n", + "NNA\n", "\n", "\n", "\n", "s3->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4\n", - "\n", - "IAdult\n", + "\n", + "IAdult\n", "\n", "\n", "\n", "v2\n", - "IAdult / NNA\n", + "IAdult / NNA\n", "\n", "\n", "\n", "s4->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "cc_C\n", + "\n", + "cc_C\n", "\n", "\n", "\n", "v3\n", - "(*)(cc_C)\n", + "(*)(cc_C)\n", "\n", "\n", "\n", "p1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "cc_A\n", + "\n", + "cc_A\n", "\n", "\n", "\n", "v4\n", - "(*)(cc_A)\n", + "(*)(cc_A)\n", "\n", "\n", "\n", "p2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv2->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -5797,9 +5729,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"SChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"SAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"cc_C\", :shape => \"circle\", :color => \"black\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"cc_A\", :shape => \"circle\", :color => \"black\")), Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IChild / NNC\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IAdult / NNA\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"(*)(cc_C)\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"(*)(cc_A)\", :shape => \"plaintext\", :fontcolor => \"black\")) … Edge(NodeID[NodeID(\"s4\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s2\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s3\", \"\", \"\"), NodeID(\"sv2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s4\", \"\", \"\"), NodeID(\"sv2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s2\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"sv2\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 32, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -5824,279 +5755,279 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "SChild\n", + "\n", + "SChild\n", "\n", "\n", "\n", "sv1\n", - "\n", - "NNC\n", + "\n", + "NNC\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "IChild\n", + "\n", + "IChild\n", "\n", "\n", "\n", "v1\n", - "IChild / NNC\n", + "IChild / NNC\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "SAdult\n", + "\n", + "SAdult\n", "\n", "\n", "\n", "sv2\n", - "\n", - "NNA\n", + "\n", + "NNA\n", "\n", "\n", "\n", "s3->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4\n", - "\n", - "IAdult\n", + "\n", + "IAdult\n", "\n", "\n", "\n", "v2\n", - "IAdult / NNA\n", + "IAdult / NNA\n", "\n", "\n", "\n", "s4->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "fcc\n", + "\n", + "fcc\n", "\n", "\n", "\n", "v3\n", - "fcc * (IChild / NNC)\n", + "fcc * (IChild / NNC)\n", "\n", "\n", "\n", "p1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "fca\n", + "\n", + "fca\n", "\n", "\n", "\n", "v4\n", - "fca * (IAdult / NNA)\n", + "fca * (IAdult / NNA)\n", "\n", "\n", "\n", "p2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "fac\n", + "\n", + "fac\n", "\n", "\n", "\n", "v5\n", - "fac * (IChild / NNC)\n", + "fac * (IChild / NNC)\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "faa\n", + "\n", + "faa\n", "\n", "\n", "\n", "v6\n", - "faa * (IAdult / NNA)\n", + "faa * (IAdult / NNA)\n", "\n", "\n", "\n", "p4->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p5\n", - "\n", - "cc_C\n", + "\n", + "cc_C\n", "\n", "\n", "\n", "v9\n", - "cc_C * (fcc * (IChild / NNC) + fca * (IAdult / NNA))\n", + "cc_C * (fcc * (IChild / NNC) + fca * (IAdult / NNA))\n", "\n", "\n", "\n", "p5->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p6\n", - "\n", - "cc_A\n", + "\n", + "cc_A\n", "\n", "\n", "\n", "v10\n", - "cc_A * (fac * (IChild / NNC) + faa * (IAdult / NNA))\n", + "cc_A * (fac * (IChild / NNC) + faa * (IAdult / NNA))\n", "\n", "\n", "\n", "p6->v10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "fcc * (IChild / NNC) + fca * (IAdult / NNA)\n", + "fcc * (IChild / NNC) + fca * (IAdult / NNA)\n", "\n", "\n", "\n", "v3->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v8\n", - "fac * (IChild / NNC) + faa * (IAdult / NNA)\n", + "fac * (IChild / NNC) + faa * (IAdult / NNA)\n", "\n", "\n", "\n", "v5->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v6->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v8->v10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv2->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -6105,9 +6036,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"SChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"SAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"fcc\", :shape => \"circle\", :color => \"black\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"fca\", :shape => \"circle\", :color => \"black\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"fac\", :shape => \"circle\", :color => \"black\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"faa\", :shape => \"circle\", :color => \"black\")), Node(\"p5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"cc_C\", :shape => \"circle\", :color => \"black\")), Node(\"p6\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"cc_A\", :shape => \"circle\", :color => \"black\")) … Edge(NodeID[NodeID(\"v2\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v1\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v2\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v1\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p6\", \"\", \"\"), NodeID(\"v10\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p5\", \"\", \"\"), NodeID(\"v9\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 33, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -6133,586 +6063,586 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "SChild\n", + "\n", + "SChild\n", "\n", "\n", "\n", "v12\n", - "SChild * (betabeta * (cc_C * (fcc * (IChild / NNC) + fca * (IAdult / NNA))))\n", + "SChild * (betabeta * (cc_C * (fcc * (IChild / NNC) + fca * (IAdult / NNA))))\n", "\n", "\n", "\n", "s1->v12\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v14\n", - "SChild * rAgerAge\n", + "SChild * rAgerAge\n", "\n", "\n", "\n", "s1->v14\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "NNC\n", + "\n", + "NNC\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "IChild\n", + "\n", + "IChild\n", "\n", "\n", "\n", "v1\n", - "IChild / NNC\n", + "IChild / NNC\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v13\n", - "IChild * rRecr\n", + "IChild * rRecr\n", "\n", "\n", "\n", "s2->v13\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v15\n", - "IChild * rAgerAge\n", + "IChild * rAgerAge\n", "\n", "\n", "\n", "s2->v15\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v15\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "SAdult\n", + "\n", + "SAdult\n", "\n", "\n", "\n", "v18\n", - "SAdult * (betabeta * (cc_A * (fac * (IChild / NNC) + faa * (IAdult / NNA))))\n", + "SAdult * (betabeta * (cc_A * (fac * (IChild / NNC) + faa * (IAdult / NNA))))\n", "\n", "\n", "\n", "s3->v18\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v18\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv2\n", - "\n", - "NNA\n", + "\n", + "NNA\n", "\n", "\n", "\n", "s3->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4\n", - "\n", - "IAdult\n", + "\n", + "IAdult\n", "\n", "\n", "\n", "v2\n", - "IAdult / NNA\n", + "IAdult / NNA\n", "\n", "\n", "\n", "s4->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v19\n", - "IAdult * rRecr\n", + "IAdult * rRecr\n", "\n", "\n", "\n", "s4->v19\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->v19\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s5\n", - "\n", - "RChild\n", + "\n", + "RChild\n", "\n", "\n", "\n", "v16\n", - "RChild * rAgerAge\n", + "RChild * rAgerAge\n", "\n", "\n", "\n", "s5->v16\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s5->v16\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s5->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s6\n", - "\n", - "RAdult\n", + "\n", + "RAdult\n", "\n", "\n", "\n", "s6->sv2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "fcc\n", + "\n", + "fcc\n", "\n", "\n", "\n", "v3\n", - "fcc * (IChild / NNC)\n", + "fcc * (IChild / NNC)\n", "\n", "\n", "\n", "p1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "fca\n", + "\n", + "fca\n", "\n", "\n", "\n", "v4\n", - "fca * (IAdult / NNA)\n", + "fca * (IAdult / NNA)\n", "\n", "\n", "\n", "p2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "fac\n", + "\n", + "fac\n", "\n", "\n", "\n", "v5\n", - "fac * (IChild / NNC)\n", + "fac * (IChild / NNC)\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "faa\n", + "\n", + "faa\n", "\n", "\n", "\n", "v6\n", - "faa * (IAdult / NNA)\n", + "faa * (IAdult / NNA)\n", "\n", "\n", "\n", "p4->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p5\n", - "\n", - "cc_C\n", + "\n", + "cc_C\n", "\n", "\n", "\n", "v9\n", - "cc_C * (fcc * (IChild / NNC) + fca * (IAdult / NNA))\n", + "cc_C * (fcc * (IChild / NNC) + fca * (IAdult / NNA))\n", "\n", "\n", "\n", "p5->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p6\n", - "\n", - "cc_A\n", + "\n", + "cc_A\n", "\n", "\n", "\n", "v10\n", - "cc_A * (fac * (IChild / NNC) + faa * (IAdult / NNA))\n", + "cc_A * (fac * (IChild / NNC) + faa * (IAdult / NNA))\n", "\n", "\n", "\n", "p6->v10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p7\n", - "\n", - "betabeta\n", + "\n", + "betabeta\n", "\n", "\n", "\n", "v11\n", - "betabeta * (cc_C * (fcc * (IChild / NNC) + fca * (IAdult / NNA)))\n", + "betabeta * (cc_C * (fcc * (IChild / NNC) + fca * (IAdult / NNA)))\n", "\n", "\n", "\n", "p7->v11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v17\n", - "betabeta * (cc_A * (fac * (IChild / NNC) + faa * (IAdult / NNA)))\n", + "betabeta * (cc_A * (fac * (IChild / NNC) + faa * (IAdult / NNA)))\n", "\n", "\n", "\n", "p7->v17\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p8\n", - "\n", - "rRecr\n", + "\n", + "rRecr\n", "\n", "\n", "\n", "p8->v13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p8->v19\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p9\n", - "\n", - "rAgerAge\n", + "\n", + "rAgerAge\n", "\n", "\n", "\n", "p9->v14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p9->v15\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p9->v16\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "fcc * (IChild / NNC) + fca * (IAdult / NNA)\n", + "fcc * (IChild / NNC) + fca * (IAdult / NNA)\n", "\n", "\n", "\n", "v3->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v8\n", - "fac * (IChild / NNC) + faa * (IAdult / NNA)\n", + "fac * (IChild / NNC) + faa * (IAdult / NNA)\n", "\n", "\n", "\n", "v5->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v6->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v8->v10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v9->v11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v10->v17\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v11->v12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v12->s2\n", - "\n", - "\n", - "\n", - "\n", - "infinfC\n", + "\n", + "\n", + "\n", + "\n", + "infinfC\n", "\n", "\n", "\n", "v13->s5\n", - "\n", - "\n", - "\n", - "\n", - "recfrsC\n", + "\n", + "\n", + "\n", + "\n", + "recfrsC\n", "\n", "\n", "\n", "v14->s3\n", - "\n", - "\n", - "\n", - "\n", - "id_SagingC\n", + "\n", + "\n", + "\n", + "\n", + "id_SagingC\n", "\n", "\n", "\n", "v15->s4\n", - "\n", - "\n", - "\n", - "\n", - "id_IagingC\n", + "\n", + "\n", + "\n", + "\n", + "id_IagingC\n", "\n", "\n", "\n", "v16->s6\n", - "\n", - "\n", - "\n", - "\n", - "id_RagingC\n", + "\n", + "\n", + "\n", + "\n", + "id_RagingC\n", "\n", "\n", "\n", "v17->v18\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v18->s4\n", - "\n", - "\n", - "\n", - "\n", - "infinfA\n", + "\n", + "\n", + "\n", + "\n", + "infinfA\n", "\n", "\n", "\n", "v19->s6\n", - "\n", - "\n", - "\n", - "\n", - "recfrsA\n", + "\n", + "\n", + "\n", + "\n", + "recfrsA\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv2->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -6721,9 +6651,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"SChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"SAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"IAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"RChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s6\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"RAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"fcc\", :shape => \"circle\", :color => \"black\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"fca\", :shape => \"circle\", :color => \"black\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"fac\", :shape => \"circle\", :color => \"black\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"faa\", :shape => \"circle\", :color => \"black\")) … Edge(NodeID[NodeID(\"p9\", \"\", \"\"), NodeID(\"v15\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p9\", \"\", \"\"), NodeID(\"v14\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p8\", \"\", \"\"), NodeID(\"v13\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p7\", \"\", \"\"), NodeID(\"v11\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p6\", \"\", \"\"), NodeID(\"v10\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p5\", \"\", \"\"), NodeID(\"v9\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 34, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -6764,135 +6693,134 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "image/svg+xml": [ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ], "text/html": [ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, - "execution_count": 36, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -6919,197 +6847,197 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "pop\n", + "\n", + "pop\n", "\n", "\n", "\n", "v1\n", - "pop * rage\n", + "pop * rage\n", "\n", "\n", "\n", "s1->v1\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2\n", - "pop * rFstOrder\n", + "pop * rFstOrder\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4\n", - "pop * δ\n", + "pop * δ\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "μ\n", + "\n", + "μ\n", "\n", "\n", "\n", "v3\n", - "N * μ\n", + "N * μ\n", "\n", "\n", "\n", "p1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "δ\n", + "\n", + "δ\n", "\n", "\n", "\n", "p2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "rFstOrder\n", + "\n", + "rFstOrder\n", "\n", "\n", "\n", "p3->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "rage\n", + "\n", + "rage\n", "\n", "\n", "\n", "p4->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "fs_3u\n", - "\n", + "\n", "\n", "\n", "\n", "fs_3u->v3\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "fs_4d\n", - "\n", + "\n", "\n", "\n", "\n", "v1->s1\n", - "\n", - "\n", - "\n", - "\n", - "aging\n", + "\n", + "\n", + "\n", + "\n", + "aging\n", "\n", "\n", "\n", "v2->s1\n", - "\n", - "\n", - "\n", - "\n", - "fstOrder\n", + "\n", + "\n", + "\n", + "\n", + "fstOrder\n", "\n", "\n", "\n", "v3->s1\n", - "\n", - "\n", - "\n", - "\n", - "birth\n", + "\n", + "\n", + "\n", + "\n", + "birth\n", "\n", "\n", "\n", "v4->fs_4d\n", - "\n", - "\n", - "\n", - "\n", - "death\n", + "\n", + "\n", + "\n", + "\n", + "death\n", "\n", "\n", "\n", "sv1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -7118,9 +7046,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"pop\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"μ\", :shape => \"circle\", :color => \"gold\", :fontcolor => \"gold\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"δ\", :shape => \"circle\", :color => \"gold4\", :fontcolor => \"gold4\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rFstOrder\", :shape => \"circle\", :color => \"darkorange1\", :fontcolor => \"darkorange1\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rage\", :shape => \"circle\", :color => \"lightgoldenrod\", :fontcolor => \"lightgoldenrod\")), Node(\"fs_3u\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"\", :shape => \"point\", :color => \"white\")), Node(\"fs_4d\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"\", :shape => \"point\", :color => \"white\")), Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"pop * rage\", :shape => \"plaintext\", :fontcolor => \"antiquewhite4\")), Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"pop * rFstOrder\", :shape => \"plaintext\", :fontcolor => \"antiquewhite\")), Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"N * μ\", :shape => \"plaintext\", :fontcolor => \"gold\")) … Edge(NodeID[NodeID(\"v4\", \"\", \"\"), NodeID(\"fs_4d\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => Html(\"death\"), :labelfontsize => \"6\", :color => \"saddlebrown:invis:saddlebrown\", :splines => \"ortho\")), Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 37, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -7155,9 +7082,8 @@ "1:4" ] }, - "execution_count": 39, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -7185,408 +7111,408 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "NormalWeight\n", + "\n", + "NormalWeight\n", "\n", "\n", "\n", "v2\n", - "NormalWeight * δw\n", + "NormalWeight * δw\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3\n", - "NormalWeight * rw\n", + "NormalWeight * rw\n", "\n", "\n", "\n", "s1->v3\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "NormalWeight * rage\n", + "NormalWeight * rage\n", "\n", "\n", "\n", "s1->v7\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "OverWeight\n", + "\n", + "OverWeight\n", "\n", "\n", "\n", "v4\n", - "OverWeight * δw\n", + "OverWeight * δw\n", "\n", "\n", "\n", "s2->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "OverWeight * ro\n", + "OverWeight * ro\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v8\n", - "OverWeight * rage\n", + "OverWeight * rage\n", "\n", "\n", "\n", "s2->v8\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "Obese\n", + "\n", + "Obese\n", "\n", "\n", "\n", "v6\n", - "Obese * δo\n", + "Obese * δo\n", "\n", "\n", "\n", "s3->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v9\n", - "Obese * rage\n", + "Obese * rage\n", "\n", "\n", "\n", "s3->v9\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "μ\n", + "\n", + "μ\n", "\n", "\n", "\n", "v1\n", - "N * μ\n", + "N * μ\n", "\n", "\n", "\n", "p1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "δw\n", + "\n", + "δw\n", "\n", "\n", "\n", "p2->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "rw\n", + "\n", + "rw\n", "\n", "\n", "\n", "p3->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "ro\n", + "\n", + "ro\n", "\n", "\n", "\n", "p4->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p5\n", - "\n", - "δo\n", + "\n", + "δo\n", "\n", "\n", "\n", "p5->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p6\n", - "\n", - "rage\n", + "\n", + "rage\n", "\n", "\n", "\n", "p6->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p6->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p6->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "fs_1u\n", - "\n", + "\n", "\n", "\n", "\n", "fs_1u->v1\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "fs_2d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_4d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_6d\n", - "\n", + "\n", "\n", "\n", "\n", "v1->s1\n", - "\n", - "\n", - "\n", - "\n", - "NewBorn\n", + "\n", + "\n", + "\n", + "\n", + "NewBorn\n", "\n", "\n", "\n", "v2->fs_2d\n", - "\n", - "\n", - "\n", - "\n", - "DeathNormalWeight\n", + "\n", + "\n", + "\n", + "\n", + "DeathNormalWeight\n", "\n", "\n", "\n", "v3->s2\n", - "\n", - "\n", - "\n", - "\n", - "BecomingOverWeight\n", + "\n", + "\n", + "\n", + "\n", + "BecomingOverWeight\n", "\n", "\n", "\n", "v4->fs_4d\n", - "\n", - "\n", - "\n", - "\n", - "DeathOverWeight\n", + "\n", + "\n", + "\n", + "\n", + "DeathOverWeight\n", "\n", "\n", "\n", "v5->s3\n", - "\n", - "\n", - "\n", - "\n", - "BecomingObese\n", + "\n", + "\n", + "\n", + "\n", + "BecomingObese\n", "\n", "\n", "\n", "v6->fs_6d\n", - "\n", - "\n", - "\n", - "\n", - "DeathObese\n", + "\n", + "\n", + "\n", + "\n", + "DeathObese\n", "\n", "\n", "\n", "v7->s1\n", - "\n", - "\n", - "\n", - "\n", - "idNW\n", + "\n", + "\n", + "\n", + "\n", + "idNW\n", "\n", "\n", "\n", "v8->s2\n", - "\n", - "\n", - "\n", - "\n", - "idOW\n", + "\n", + "\n", + "\n", + "\n", + "idOW\n", "\n", "\n", "\n", "v9->s3\n", - "\n", - "\n", - "\n", - "\n", - "idOb\n", + "\n", + "\n", + "\n", + "\n", + "idOb\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -7595,9 +7521,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"NormalWeight\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"OverWeight\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"Obese\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"μ\", :shape => \"circle\", :color => \"black\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"δw\", :shape => \"circle\", :color => \"black\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rw\", :shape => \"circle\", :color => \"black\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"ro\", :shape => \"circle\", :color => \"black\")), Node(\"p5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"δo\", :shape => \"circle\", :color => \"black\")), Node(\"p6\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rage\", :shape => \"circle\", :color => \"black\")), Node(\"fs_1u\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"\", :shape => \"point\", :color => \"white\")) … Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p6\", \"\", \"\"), NodeID(\"v9\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p6\", \"\", \"\"), NodeID(\"v8\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p6\", \"\", \"\"), NodeID(\"v7\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p5\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"TB\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 40, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -7637,408 +7562,408 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "NormalWeight\n", + "\n", + "NormalWeight\n", "\n", "\n", "\n", "v2\n", - "NormalWeight * δw\n", + "NormalWeight * δw\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3\n", - "NormalWeight * rw\n", + "NormalWeight * rw\n", "\n", "\n", "\n", "s1->v3\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "NormalWeight * rage\n", + "NormalWeight * rage\n", "\n", "\n", "\n", "s1->v7\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "OverWeight\n", + "\n", + "OverWeight\n", "\n", "\n", "\n", "v4\n", - "OverWeight * δw\n", + "OverWeight * δw\n", "\n", "\n", "\n", "s2->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "OverWeight * ro\n", + "OverWeight * ro\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v8\n", - "OverWeight * rage\n", + "OverWeight * rage\n", "\n", "\n", "\n", "s2->v8\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "Obese\n", + "\n", + "Obese\n", "\n", "\n", "\n", "v6\n", - "Obese * δo\n", + "Obese * δo\n", "\n", "\n", "\n", "s3->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v9\n", - "Obese * rage\n", + "Obese * rage\n", "\n", "\n", "\n", "s3->v9\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "μ\n", + "\n", + "μ\n", "\n", "\n", "\n", "v1\n", - "N * μ\n", + "N * μ\n", "\n", "\n", "\n", "p1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "δw\n", + "\n", + "δw\n", "\n", "\n", "\n", "p2->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "rw\n", + "\n", + "rw\n", "\n", "\n", "\n", "p3->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "ro\n", + "\n", + "ro\n", "\n", "\n", "\n", "p4->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p5\n", - "\n", - "δo\n", + "\n", + "δo\n", "\n", "\n", "\n", "p5->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p6\n", - "\n", - "rage\n", + "\n", + "rage\n", "\n", "\n", "\n", "p6->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p6->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p6->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "fs_1u\n", - "\n", + "\n", "\n", "\n", "\n", "fs_1u->v1\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "fs_2d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_4d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_6d\n", - "\n", + "\n", "\n", "\n", "\n", "v1->s1\n", - "\n", - "\n", - "\n", - "\n", - "NewBorn\n", + "\n", + "\n", + "\n", + "\n", + "NewBorn\n", "\n", "\n", "\n", "v2->fs_2d\n", - "\n", - "\n", - "\n", - "\n", - "DeathNormalWeight\n", + "\n", + "\n", + "\n", + "\n", + "DeathNormalWeight\n", "\n", "\n", "\n", "v3->s2\n", - "\n", - "\n", - "\n", - "\n", - "BecomingOverWeight\n", + "\n", + "\n", + "\n", + "\n", + "BecomingOverWeight\n", "\n", "\n", "\n", "v4->fs_4d\n", - "\n", - "\n", - "\n", - "\n", - "DeathOverWeight\n", + "\n", + "\n", + "\n", + "\n", + "DeathOverWeight\n", "\n", "\n", "\n", "v5->s3\n", - "\n", - "\n", - "\n", - "\n", - "BecomingObese\n", + "\n", + "\n", + "\n", + "\n", + "BecomingObese\n", "\n", "\n", "\n", "v6->fs_6d\n", - "\n", - "\n", - "\n", - "\n", - "DeathObese\n", + "\n", + "\n", + "\n", + "\n", + "DeathObese\n", "\n", "\n", "\n", "v7->s1\n", - "\n", - "\n", - "\n", - "\n", - "idNW\n", + "\n", + "\n", + "\n", + "\n", + "idNW\n", "\n", "\n", "\n", "v8->s2\n", - "\n", - "\n", - "\n", - "\n", - "idOW\n", + "\n", + "\n", + "\n", + "\n", + "idOW\n", "\n", "\n", "\n", "v9->s3\n", - "\n", - "\n", - "\n", - "\n", - "idOb\n", + "\n", + "\n", + "\n", + "\n", + "idOb\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -8047,9 +7972,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"NormalWeight\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"OverWeight\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"Obese\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"μ\", :shape => \"circle\", :color => \"gold\", :fontcolor => \"gold\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"δw\", :shape => \"circle\", :color => \"gold4\", :fontcolor => \"gold4\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rw\", :shape => \"circle\", :color => \"darkorange1\", :fontcolor => \"darkorange1\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"ro\", :shape => \"circle\", :color => \"darkorange1\", :fontcolor => \"darkorange1\")), Node(\"p5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"δo\", :shape => \"circle\", :color => \"gold4\", :fontcolor => \"gold4\")), Node(\"p6\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rage\", :shape => \"circle\", :color => \"lightgoldenrod\", :fontcolor => \"lightgoldenrod\")), Node(\"fs_1u\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"\", :shape => \"point\", :color => \"white\")) … Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p6\", \"\", \"\"), NodeID(\"v9\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p6\", \"\", \"\"), NodeID(\"v8\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p6\", \"\", \"\"), NodeID(\"v7\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p5\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"TB\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 41, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -8082,414 +8006,414 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "Child\n", + "\n", + "Child\n", "\n", "\n", "\n", "v2\n", - "Child * δC\n", + "Child * δC\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3\n", - "Child * r\n", + "Child * r\n", "\n", "\n", "\n", "s1->v3\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4\n", - "Child * rageCA\n", + "Child * rageCA\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "Adult\n", + "\n", + "Adult\n", "\n", "\n", "\n", "v5\n", - "Adult * δA\n", + "Adult * δA\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v6\n", - "Adult * r\n", + "Adult * r\n", "\n", "\n", "\n", "s2->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "Adult * rageAS\n", + "Adult * rageAS\n", "\n", "\n", "\n", "s2->v7\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "Senior\n", + "\n", + "Senior\n", "\n", "\n", "\n", "v8\n", - "Senior * δS\n", + "Senior * δS\n", "\n", "\n", "\n", "s3->v8\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v9\n", - "Senior * r\n", + "Senior * r\n", "\n", "\n", "\n", "s3->v9\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "μ\n", + "\n", + "μ\n", "\n", "\n", "\n", "v1\n", - "N * μ\n", + "N * μ\n", "\n", "\n", "\n", "p1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "δC\n", + "\n", + "δC\n", "\n", "\n", "\n", "p2->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "δA\n", + "\n", + "δA\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "δS\n", + "\n", + "δS\n", "\n", "\n", "\n", "p4->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p5\n", - "\n", - "rageCA\n", + "\n", + "rageCA\n", "\n", "\n", "\n", "p5->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p6\n", - "\n", - "rageAS\n", + "\n", + "rageAS\n", "\n", "\n", "\n", "p6->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p7\n", - "\n", - "r\n", + "\n", + "r\n", "\n", "\n", "\n", "p7->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p7->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p7->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "fs_1u\n", - "\n", + "\n", "\n", "\n", "\n", "fs_1u->v1\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "fs_3d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_6d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_9d\n", - "\n", + "\n", "\n", "\n", "\n", "v1->s1\n", - "\n", - "\n", - "\n", - "\n", - "NB\n", + "\n", + "\n", + "\n", + "\n", + "NB\n", "\n", "\n", "\n", "v2->fs_3d\n", - "\n", - "\n", - "\n", - "\n", - "DeathC\n", + "\n", + "\n", + "\n", + "\n", + "DeathC\n", "\n", "\n", "\n", "v3->s1\n", - "\n", - "\n", - "\n", - "\n", - "idC\n", + "\n", + "\n", + "\n", + "\n", + "idC\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "agingCA\n", + "\n", + "\n", + "\n", + "\n", + "agingCA\n", "\n", "\n", "\n", "v5->fs_6d\n", - "\n", - "\n", - "\n", - "\n", - "DeathA\n", + "\n", + "\n", + "\n", + "\n", + "DeathA\n", "\n", "\n", "\n", "v6->s2\n", - "\n", - "\n", - "\n", - "\n", - "idA\n", + "\n", + "\n", + "\n", + "\n", + "idA\n", "\n", "\n", "\n", "v7->s3\n", - "\n", - "\n", - "\n", - "\n", - "agingAS\n", + "\n", + "\n", + "\n", + "\n", + "agingAS\n", "\n", "\n", "\n", "v8->fs_9d\n", - "\n", - "\n", - "\n", - "\n", - "DeathS\n", + "\n", + "\n", + "\n", + "\n", + "DeathS\n", "\n", "\n", "\n", "v9->s3\n", - "\n", - "\n", - "\n", - "\n", - "idS\n", + "\n", + "\n", + "\n", + "\n", + "idS\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -8498,9 +8422,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"Child\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"Adult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"Senior\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"μ\", :shape => \"circle\", :color => \"black\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"δC\", :shape => \"circle\", :color => \"black\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"δA\", :shape => \"circle\", :color => \"black\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"δS\", :shape => \"circle\", :color => \"black\")), Node(\"p5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rageCA\", :shape => \"circle\", :color => \"black\")), Node(\"p6\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rageAS\", :shape => \"circle\", :color => \"black\")), Node(\"p7\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"r\", :shape => \"circle\", :color => \"black\")) … Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p7\", \"\", \"\"), NodeID(\"v9\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v8\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p6\", \"\", \"\"), NodeID(\"v7\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p7\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p5\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p7\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 42, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -8541,414 +8464,414 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "Child\n", + "\n", + "Child\n", "\n", "\n", "\n", "v2\n", - "Child * δC\n", + "Child * δC\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3\n", - "Child * r\n", + "Child * r\n", "\n", "\n", "\n", "s1->v3\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4\n", - "Child * rageCA\n", + "Child * rageCA\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "Adult\n", + "\n", + "Adult\n", "\n", "\n", "\n", "v5\n", - "Adult * δA\n", + "Adult * δA\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v6\n", - "Adult * r\n", + "Adult * r\n", "\n", "\n", "\n", "s2->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "Adult * rageAS\n", + "Adult * rageAS\n", "\n", "\n", "\n", "s2->v7\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "Senior\n", + "\n", + "Senior\n", "\n", "\n", "\n", "v8\n", - "Senior * δS\n", + "Senior * δS\n", "\n", "\n", "\n", "s3->v8\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v9\n", - "Senior * r\n", + "Senior * r\n", "\n", "\n", "\n", "s3->v9\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "μ\n", + "\n", + "μ\n", "\n", "\n", "\n", "v1\n", - "N * μ\n", + "N * μ\n", "\n", "\n", "\n", "p1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "δC\n", + "\n", + "δC\n", "\n", "\n", "\n", "p2->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "δA\n", + "\n", + "δA\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p4\n", - "\n", - "δS\n", + "\n", + "δS\n", "\n", "\n", "\n", "p4->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p5\n", - "\n", - "rageCA\n", + "\n", + "rageCA\n", "\n", "\n", "\n", "p5->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p6\n", - "\n", - "rageAS\n", + "\n", + "rageAS\n", "\n", "\n", "\n", "p6->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p7\n", - "\n", - "r\n", + "\n", + "r\n", "\n", "\n", "\n", "p7->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p7->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p7->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "fs_1u\n", - "\n", + "\n", "\n", "\n", "\n", "fs_1u->v1\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "fs_3d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_6d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_9d\n", - "\n", + "\n", "\n", "\n", "\n", "v1->s1\n", - "\n", - "\n", - "\n", - "\n", - "NB\n", + "\n", + "\n", + "\n", + "\n", + "NB\n", "\n", "\n", "\n", "v2->fs_3d\n", - "\n", - "\n", - "\n", - "\n", - "DeathC\n", + "\n", + "\n", + "\n", + "\n", + "DeathC\n", "\n", "\n", "\n", "v3->s1\n", - "\n", - "\n", - "\n", - "\n", - "idC\n", + "\n", + "\n", + "\n", + "\n", + "idC\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "agingCA\n", + "\n", + "\n", + "\n", + "\n", + "agingCA\n", "\n", "\n", "\n", "v5->fs_6d\n", - "\n", - "\n", - "\n", - "\n", - "DeathA\n", + "\n", + "\n", + "\n", + "\n", + "DeathA\n", "\n", "\n", "\n", "v6->s2\n", - "\n", - "\n", - "\n", - "\n", - "idA\n", + "\n", + "\n", + "\n", + "\n", + "idA\n", "\n", "\n", "\n", "v7->s3\n", - "\n", - "\n", - "\n", - "\n", - "agingAS\n", + "\n", + "\n", + "\n", + "\n", + "agingAS\n", "\n", "\n", "\n", "v8->fs_9d\n", - "\n", - "\n", - "\n", - "\n", - "DeathS\n", + "\n", + "\n", + "\n", + "\n", + "DeathS\n", "\n", "\n", "\n", "v9->s3\n", - "\n", - "\n", - "\n", - "\n", - "idS\n", + "\n", + "\n", + "\n", + "\n", + "idS\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -8957,9 +8880,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"Child\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"Adult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"Senior\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"deeppink\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"μ\", :shape => \"circle\", :color => \"gold\", :fontcolor => \"gold\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"δC\", :shape => \"circle\", :color => \"gold4\", :fontcolor => \"gold4\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"δA\", :shape => \"circle\", :color => \"gold4\", :fontcolor => \"gold4\")), Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"δS\", :shape => \"circle\", :color => \"gold4\", :fontcolor => \"gold4\")), Node(\"p5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rageCA\", :shape => \"circle\", :color => \"lightgoldenrod\", :fontcolor => \"lightgoldenrod\")), Node(\"p6\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rageAS\", :shape => \"circle\", :color => \"lightgoldenrod\", :fontcolor => \"lightgoldenrod\")), Node(\"p7\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"r\", :shape => \"circle\", :color => \"darkorange1\", :fontcolor => \"darkorange1\")) … Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p7\", \"\", \"\"), NodeID(\"v9\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v8\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p6\", \"\", \"\"), NodeID(\"v7\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p7\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p5\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p7\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 43, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -8992,969 +8914,969 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "NormalWeightChild\n", + "\n", + "NormalWeightChild\n", "\n", "\n", "\n", "v2\n", - "NormalWeightChild * δwδC\n", + "NormalWeightChild * δwδC\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "NormalWeightChild * rwr\n", + "NormalWeightChild * rwr\n", "\n", "\n", "\n", "s1->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "NormalWeightChild * ragerageCA\n", + "NormalWeightChild * ragerageCA\n", "\n", "\n", "\n", "s1->v7\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "NN\n", + "\n", + "NN\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "OverWeightChild\n", + "\n", + "OverWeightChild\n", "\n", "\n", "\n", "v3\n", - "OverWeightChild * δwδC\n", + "OverWeightChild * δwδC\n", "\n", "\n", "\n", "s2->v3\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v6\n", - "OverWeightChild * ror\n", + "OverWeightChild * ror\n", "\n", "\n", "\n", "s2->v6\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v8\n", - "OverWeightChild * ragerageCA\n", + "OverWeightChild * ragerageCA\n", "\n", "\n", "\n", "s2->v8\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "ObeseChild\n", + "\n", + "ObeseChild\n", "\n", "\n", "\n", "v4\n", - "ObeseChild * δoδC\n", + "ObeseChild * δoδC\n", "\n", "\n", "\n", "s3->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v9\n", - "ObeseChild * ragerageCA\n", + "ObeseChild * ragerageCA\n", "\n", "\n", "\n", "s3->v9\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->v9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4\n", - "\n", - "NormalWeightAdult\n", + "\n", + "NormalWeightAdult\n", "\n", "\n", "\n", "v10\n", - "NormalWeightAdult * δwδA\n", + "NormalWeightAdult * δwδA\n", "\n", "\n", "\n", "s4->v10\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->v10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v13\n", - "NormalWeightAdult * rwr\n", + "NormalWeightAdult * rwr\n", "\n", "\n", "\n", "s4->v13\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->v13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v15\n", - "NormalWeightAdult * ragerageAS\n", + "NormalWeightAdult * ragerageAS\n", "\n", "\n", "\n", "s4->v15\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->v15\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s4->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s5\n", - "\n", - "OverWeightAdult\n", + "\n", + "OverWeightAdult\n", "\n", "\n", "\n", "v11\n", - "OverWeightAdult * δwδA\n", + "OverWeightAdult * δwδA\n", "\n", "\n", "\n", "s5->v11\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", 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"fs_1u\n", - "\n", + "\n", "\n", "\n", "\n", "fs_1u->v1\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "fs_4d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_5d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_6d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_12d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_13d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_14d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_20d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_21d\n", - "\n", + "\n", "\n", "\n", "\n", "fs_22d\n", - "\n", + "\n", "\n", "\n", "\n", "v1->s1\n", - "\n", - "\n", - "\n", - "\n", - "NewBornNB\n", + "\n", + "\n", + "\n", + "\n", + "NewBornNB\n", "\n", "\n", "\n", "v2->fs_4d\n", - "\n", - "\n", - "\n", - "\n", - "DeathNormalWeightDeathC\n", + "\n", + "\n", + "\n", + "\n", + "DeathNormalWeightDeathC\n", "\n", "\n", "\n", "v3->fs_5d\n", - "\n", - "\n", - "\n", - "\n", - "DeathOverWeightDeathC\n", + "\n", + "\n", + "\n", + "\n", + "DeathOverWeightDeathC\n", "\n", "\n", "\n", "v4->fs_6d\n", - "\n", - "\n", - "\n", - "\n", - "DeathObeseDeathC\n", + "\n", + "\n", + "\n", + "\n", + "DeathObeseDeathC\n", "\n", "\n", "\n", "v5->s2\n", - "\n", - "\n", - "\n", - "\n", - "BecomingOverWeightidC\n", + "\n", + "\n", + "\n", + "\n", + "BecomingOverWeightidC\n", "\n", "\n", "\n", "v6->s3\n", - "\n", - "\n", - "\n", - "\n", - "BecomingObeseidC\n", + "\n", + "\n", + "\n", + "\n", + "BecomingObeseidC\n", "\n", "\n", "\n", "v7->s4\n", - "\n", - "\n", - "\n", - "\n", - "idNWagingCA\n", + "\n", + "\n", + "\n", + "\n", + "idNWagingCA\n", "\n", "\n", "\n", "v8->s5\n", - "\n", - "\n", - "\n", - "\n", - "idOWagingCA\n", + "\n", + "\n", + "\n", + "\n", + "idOWagingCA\n", "\n", "\n", "\n", "v9->s6\n", - "\n", - "\n", - "\n", - "\n", - "idObagingCA\n", + "\n", + "\n", + "\n", + "\n", + "idObagingCA\n", "\n", "\n", "\n", "v10->fs_12d\n", - "\n", - "\n", - "\n", - "\n", - "DeathNormalWeightDeathA\n", + "\n", + "\n", + "\n", + "\n", + "DeathNormalWeightDeathA\n", "\n", "\n", "\n", "v11->fs_13d\n", - "\n", - "\n", - "\n", - "\n", - "DeathOverWeightDeathA\n", + "\n", + "\n", + "\n", + "\n", + "DeathOverWeightDeathA\n", "\n", "\n", "\n", "v12->fs_14d\n", - "\n", - "\n", - "\n", - "\n", - "DeathObeseDeathA\n", + "\n", + "\n", + "\n", + "\n", + "DeathObeseDeathA\n", "\n", "\n", "\n", "v13->s5\n", - "\n", - "\n", - "\n", - "\n", - "BecomingOverWeightidA\n", + "\n", + "\n", + "\n", + "\n", + "BecomingOverWeightidA\n", "\n", "\n", "\n", "v14->s6\n", - "\n", - "\n", - "\n", - "\n", - "BecomingObeseidA\n", + "\n", + "\n", + "\n", + "\n", + "BecomingObeseidA\n", "\n", "\n", "\n", "v15->s7\n", - "\n", - "\n", - "\n", - "\n", - "idNWagingAS\n", + "\n", + "\n", + "\n", + "\n", + "idNWagingAS\n", "\n", "\n", "\n", "v16->s8\n", - "\n", - "\n", - "\n", - "\n", - "idOWagingAS\n", + "\n", + "\n", + "\n", + "\n", + "idOWagingAS\n", "\n", "\n", "\n", "v17->s9\n", - "\n", - "\n", - "\n", - "\n", - "idObagingAS\n", + "\n", + "\n", + "\n", + "\n", + "idObagingAS\n", "\n", "\n", "\n", "v18->fs_20d\n", - "\n", - "\n", - "\n", - "\n", - "DeathNormalWeightDeathS\n", + "\n", + "\n", + "\n", + "\n", + "DeathNormalWeightDeathS\n", "\n", "\n", "\n", "v19->fs_21d\n", - "\n", - "\n", - "\n", - "\n", - "DeathOverWeightDeathS\n", + "\n", + "\n", + "\n", + "\n", + "DeathOverWeightDeathS\n", "\n", "\n", "\n", "v20->fs_22d\n", - "\n", - "\n", - "\n", - "\n", - "DeathObeseDeathS\n", + "\n", + "\n", + "\n", + "\n", + "DeathObeseDeathS\n", "\n", "\n", "\n", "v21->s8\n", - "\n", - "\n", - "\n", - "\n", - "BecomingOverWeightidS\n", + "\n", + "\n", + "\n", + "\n", + "BecomingOverWeightidS\n", "\n", "\n", "\n", "v22->s9\n", - "\n", - "\n", - "\n", - "\n", - "BecomingObeseidS\n", + "\n", + "\n", + "\n", + "\n", + "BecomingObeseidS\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -9963,9 +9885,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"NormalWeightChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"OverWeightChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"ObeseChild\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"NormalWeightAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"OverWeightAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s6\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"ObeseAdult\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s7\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"NormalWeightSenior\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s8\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"OverWeightSenior\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s9\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"ObeseSenior\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"μμ\", :shape => \"circle\", :color => \"black\")) … Edge(NodeID[NodeID(\"p4\", \"\", \"\"), NodeID(\"v10\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p8\", \"\", \"\"), NodeID(\"v9\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p8\", \"\", \"\"), NodeID(\"v8\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p8\", \"\", \"\"), NodeID(\"v7\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p11\", \"\", \"\"), NodeID(\"v6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p10\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 44, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -9998,143 +9919,142 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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((:I, :N) => :/), :v_meanInfectiousContactsPerS => ((:c, :v_prevalence) => :*), :v_perSIncidenceRate => ((:beta, :v_meanInfectiousContactsPerS) => :*), :v_newInfetions => ((:S, :v_perSIncidenceRate) => :*), :v_newRecovery => ((:I, :tRec) => :/))" ] }, - "execution_count": 48, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -10196,190 +10115,190 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "S\n", + "\n", + "S\n", "\n", "\n", "\n", "v4\n", - "S * (beta * (c * (I / N)))\n", + "S * (beta * (c * (I / N)))\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "v1\n", - "I / N\n", + "I / N\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "I / tRec\n", + "I / tRec\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "R\n", + "\n", + "R\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "c * (I / N)\n", + "c * (I / N)\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "beta * (c * (I / N))\n", + "beta * (c * (I / N))\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "tRec\n", + "\n", + "tRec\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "inf\n", + "\n", + "\n", + "\n", + "\n", + "inf\n", "\n", "\n", "\n", "v5->s3\n", - "\n", - "\n", - "\n", - "\n", - "rec\n", + "\n", + "\n", + "\n", + "\n", + "rec\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -10388,9 +10307,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"S\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"R\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c\", :shape => \"circle\", :color => \"black\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"beta\", :shape => \"circle\", :color => \"black\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"tRec\", :shape => \"circle\", :color => \"black\")), Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"I / N\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c * (I / N)\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"beta * (c * (I / N))\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"S * (beta * (c * (I / N)))\", :shape => \"plaintext\", :fontcolor => \"black\")) … Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s2\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s3\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v3\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 49, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -10409,190 +10327,190 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", - "\n", - "S\n", + "\n", + "S\n", "\n", "\n", "\n", "v4\n", - "v_newInfetions\n", + "v_newInfetions\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", - "I\n", + "\n", + "I\n", "\n", "\n", "\n", "v1\n", - "v_prevalence\n", + "v_prevalence\n", "\n", "\n", "\n", "s2->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v5\n", - "v_newRecovery\n", + "v_newRecovery\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s3\n", - "\n", - "R\n", + "\n", + "R\n", "\n", "\n", "\n", "s3->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p1\n", - "\n", - "c\n", + "\n", + "c\n", "\n", "\n", "\n", "v2\n", - "v_meanInfectiousContactsPerS\n", + "v_meanInfectiousContactsPerS\n", "\n", "\n", "\n", "p1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p2\n", - "\n", - "beta\n", + "\n", + "beta\n", "\n", "\n", "\n", "v3\n", - "v_perSIncidenceRate\n", + "v_perSIncidenceRate\n", "\n", "\n", "\n", "p2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "p3\n", - "\n", - "tRec\n", + "\n", + "tRec\n", "\n", "\n", "\n", "p3->v5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v1->v2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v2->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v3->v4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4->s2\n", - "\n", - "\n", - "\n", - "\n", - "inf\n", + "\n", + "\n", + "\n", + "\n", + "inf\n", "\n", "\n", "\n", "v5->s3\n", - "\n", - "\n", - "\n", - "\n", - "rec\n", + "\n", + "\n", + "\n", + "\n", + "rec\n", "\n", "\n", "\n", "sv1->v1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -10601,9 +10519,8 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"S\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"I\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"R\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c\", :shape => \"circle\", :color => \"black\")), Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"beta\", :shape => \"circle\", :color => \"black\")), Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"tRec\", :shape => \"circle\", :color => \"black\")), Node(\"v1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"v_prevalence\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"v_meanInfectiousContactsPerS\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"v_perSIncidenceRate\", :shape => \"plaintext\", :fontcolor => \"black\")), Node(\"v4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"v_newInfetions\", :shape => \"plaintext\", :fontcolor => \"black\")) … Edge(NodeID[NodeID(\"s1\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s2\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"s3\", \"\", \"\"), NodeID(\"sv1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"sv1\", \"\", \"\"), NodeID(\"v1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v3\", \"\", \"\"), NodeID(\"v4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"v1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p3\", \"\", \"\"), NodeID(\"v5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p2\", \"\", \"\"), NodeID(\"v3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}()), Edge(NodeID[NodeID(\"p1\", \"\", \"\"), NodeID(\"v2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:splines => \"splines\"))" ] }, - "execution_count": 50, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -10626,12 +10543,11 @@ { "data": { "text/plain": [ - "Graph (generic function with 3 methods)" + "Catlab.Graphics.Graphviz.Graph" ] }, - "execution_count": 51, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -10649,175 +10565,175 @@ "\n", "\n", - "\n", "\n", - "\n", + "\n", "\n", "G\n", - "\n", + "\n", "\n", "\n", "n1\n", - "S\n", + "S\n", "\n", "\n", "\n", "n4\n", - "inf\n", + "inf\n", "\n", "\n", "\n", "n1->n4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n6\n", - "N\n", + "N\n", "\n", "\n", "\n", "n1->n6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n2\n", - "I\n", + "I\n", "\n", "\n", "\n", "n5\n", - "rec\n", + "rec\n", "\n", "\n", "\n", "n2->n5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n2->n6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n7\n", - "v_prevalence\n", + "v_prevalence\n", "\n", "\n", "\n", "n2->n7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n3\n", - "R\n", + "R\n", "\n", "\n", "\n", "n3->n6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n4->n1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n4->n2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n5->n2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n5->n3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n6->n7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n8\n", - "v_meanInfectiousContactsPerS\n", + "v_meanInfectiousContactsPerS\n", "\n", "\n", "\n", "n7->n8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n9\n", - "v_perSIncidenceRate\n", + "v_perSIncidenceRate\n", "\n", "\n", "\n", "n8->n9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n9->n4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n10\n", - "c\n", + "c\n", "\n", "\n", "\n", "n10->n8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n11\n", - "beta\n", + "beta\n", "\n", "\n", "\n", "n11->n9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "n12\n", - "tRec\n", + "tRec\n", "\n", "\n", "\n", "n12->n5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" @@ -10826,22 +10742,14 @@ "Catlab.Graphics.Graphviz.Graph(\"G\", true, \"dot\", Statement[Node(\"n1\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"S\", :shape => \"plaintext\")), Node(\"n2\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"I\", :shape => \"plaintext\")), Node(\"n3\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"R\", :shape => \"plaintext\")), Node(\"n4\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"inf\", :shape => \"plaintext\")), Node(\"n5\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"rec\", :shape => \"plaintext\")), Node(\"n6\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"N\", :shape => \"plaintext\")), Node(\"n7\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"v_prevalence\", :shape => \"plaintext\")), Node(\"n8\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"v_meanInfectiousContactsPerS\", :shape => \"plaintext\")), Node(\"n9\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"v_perSIncidenceRate\", :shape => \"plaintext\")), Node(\"n10\", OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:label => \"c\", :shape => \"plaintext\")) … Edge(NodeID[NodeID(\"n4\", \"\", \"\"), NodeID(\"n2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:color => \"blue\")), Edge(NodeID[NodeID(\"n5\", \"\", \"\"), NodeID(\"n3\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:color => \"blue\")), Edge(NodeID[NodeID(\"n4\", \"\", \"\"), NodeID(\"n1\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:color => \"blue\")), Edge(NodeID[NodeID(\"n5\", \"\", \"\"), NodeID(\"n2\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:color => \"blue\")), Edge(NodeID[NodeID(\"n10\", \"\", \"\"), NodeID(\"n8\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:color => \"blue\")), Edge(NodeID[NodeID(\"n11\", \"\", \"\"), NodeID(\"n9\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:color => \"blue\")), Edge(NodeID[NodeID(\"n12\", \"\", \"\"), NodeID(\"n5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:color => \"blue\")), Edge(NodeID[NodeID(\"n7\", \"\", \"\"), NodeID(\"n8\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:color => \"blue\")), Edge(NodeID[NodeID(\"n8\", \"\", \"\"), NodeID(\"n9\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:color => \"blue\")), Edge(NodeID[NodeID(\"n9\", \"\", \"\"), NodeID(\"n4\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:color => \"blue\"))], OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(:rankdir => \"LR\"), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}(), OrderedCollections.OrderedDict{Symbol, Union{String, Html}}())" ] }, - "execution_count": 52, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ "causalloop=convertToCausalLoop(SIRstructure)\n", "GraphCL(causalloop)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/full_fledged_schema_examples_new/CausalLoopDiagrams/convert_from_SEIR_stockFlowDiagram.ipynb b/examples/full_fledged_schema_examples_new/CausalLoopDiagrams/convert_from_SEIR_stockFlowDiagram.ipynb index b38137d0..6c0661b7 100644 --- a/examples/full_fledged_schema_examples_new/CausalLoopDiagrams/convert_from_SEIR_stockFlowDiagram.ipynb +++ b/examples/full_fledged_schema_examples_new/CausalLoopDiagrams/convert_from_SEIR_stockFlowDiagram.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "parameters" @@ -23,9 +23,593 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "StockAndFlowF {S:4, SV:1, LS:4, F:8, I:4, O:7, V:10, LV:8, LSV:2, P:5, LVV:2, LPV:8, Name:0, Op:0, Position:0}\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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LSVlsvsvlsvvlsvsvposition
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Ppname
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LVVlvsrclvtgtlvsrcposition
1232
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LPVlpvplpvvlpvpposition
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"├───┼────┼──────────┤\n", + "│\u001b[1m 1 \u001b[0m│ 1 │ f_birth │\n", + "│\u001b[1m 2 \u001b[0m│ 4 │ f_incid │\n", + "│\u001b[1m 3 \u001b[0m│ 7 │ v_deathS │\n", + "│\u001b[1m 4 \u001b[0m│ 5 │ f_inf │\n", + "│\u001b[1m 5 \u001b[0m│ 8 │ f_deathE │\n", + "│\u001b[1m 6 \u001b[0m│ 6 │ f_rec │\n", + "│\u001b[1m 7 \u001b[0m│ 9 │ f_deathI │\n", + "│\u001b[1m 8 \u001b[0m│ 10 │ f_deathR │\n", + "└───┴────┴──────────┘\n", + "┌───┬─────┬────┐\n", + "│\u001b[1m I \u001b[0m│\u001b[1m ifn \u001b[0m│\u001b[1m is \u001b[0m│\n", + "├───┼─────┼────┤\n", + "│\u001b[1m 1 \u001b[0m│ 1 │ 1 │\n", + "│\u001b[1m 2 \u001b[0m│ 2 │ 2 │\n", + "│\u001b[1m 3 \u001b[0m│ 4 │ 3 │\n", + "│\u001b[1m 4 \u001b[0m│ 6 │ 4 │\n", + "└───┴─────┴────┘\n", + "┌───┬─────┬────┐\n", + "│\u001b[1m O \u001b[0m│\u001b[1m ofn \u001b[0m│\u001b[1m os \u001b[0m│\n", + "├───┼─────┼────┤\n", + "│\u001b[1m 1 \u001b[0m│ 2 │ 1 │\n", + "│\u001b[1m 2 \u001b[0m│ 3 │ 1 │\n", + "│\u001b[1m 3 \u001b[0m│ 4 │ 2 │\n", + "│\u001b[1m 4 \u001b[0m│ 5 │ 2 │\n", + "│\u001b[1m 5 \u001b[0m│ 6 │ 3 │\n", + "│\u001b[1m 6 \u001b[0m│ 7 │ 3 │\n", + "│\u001b[1m 7 \u001b[0m│ 8 │ 4 │\n", + "└───┴─────┴────┘\n", + "┌────┬──────────┬─────┐\n", + "│\u001b[1m V \u001b[0m│\u001b[1m vname \u001b[0m│\u001b[1m vop \u001b[0m│\n", + "├────┼──────────┼─────┤\n", + "│\u001b[1m 1 \u001b[0m│ v_birth │ * │\n", + "│\u001b[1m 2 \u001b[0m│ v_incid₁ │ / │\n", + "│\u001b[1m 3 \u001b[0m│ v_incid₂ │ * │\n", + "│\u001b[1m 4 \u001b[0m│ v_incid₃ │ * │\n", + "│\u001b[1m 5 \u001b[0m│ v_inf │ / │\n", + "│\u001b[1m 6 \u001b[0m│ v_rec │ / │\n", + "│\u001b[1m 7 \u001b[0m│ v_deathS │ * │\n", + "│\u001b[1m 8 \u001b[0m│ v_deathE │ * │\n", + "│\u001b[1m 9 \u001b[0m│ v_deathI │ * │\n", + "│\u001b[1m 10 \u001b[0m│ v_deathR │ * │\n", + "└────┴──────────┴─────┘\n", + "┌────┬─────┬─────┬─────────────┐\n", + "│\u001b[1m LV \u001b[0m│\u001b[1m lvs \u001b[0m│\u001b[1m lvv \u001b[0m│\u001b[1m lvsposition \u001b[0m│\n", + "├────┼─────┼─────┼─────────────┤\n", + "│\u001b[1m 1 \u001b[0m│ 3 │ 2 │ 1 │\n", + "│\u001b[1m 2 \u001b[0m│ 1 │ 3 │ 1 │\n", + "│\u001b[1m 3 \u001b[0m│ 2 │ 5 │ 1 │\n", + "│\u001b[1m 4 \u001b[0m│ 3 │ 6 │ 1 │\n", + "│\u001b[1m 5 \u001b[0m│ 1 │ 7 │ 1 │\n", + "│\u001b[1m 6 \u001b[0m│ 2 │ 8 │ 1 │\n", + "│\u001b[1m 7 \u001b[0m│ 3 │ 9 │ 1 │\n", + "│\u001b[1m 8 \u001b[0m│ 4 │ 10 │ 1 │\n", + "└────┴─────┴─────┴─────────────┘\n", + "┌─────┬───────┬──────┬───────────────┐\n", + "│\u001b[1m LSV \u001b[0m│\u001b[1m lsvsv \u001b[0m│\u001b[1m lsvv \u001b[0m│\u001b[1m lsvsvposition \u001b[0m│\n", + "├─────┼───────┼──────┼───────────────┤\n", + "│\u001b[1m 1 \u001b[0m│ 1 │ 1 │ 2 │\n", + "│\u001b[1m 2 \u001b[0m│ 1 │ 2 │ 2 │\n", + "└─────┴───────┴──────┴───────────────┘\n", + "┌───┬───────────┐\n", + "│\u001b[1m P \u001b[0m│\u001b[1m pname \u001b[0m│\n", + "├───┼───────────┤\n", + "│\u001b[1m 1 \u001b[0m│ μ │\n", + "│\u001b[1m 2 \u001b[0m│ β │\n", + "│\u001b[1m 3 \u001b[0m│ tlatent │\n", + "│\u001b[1m 4 \u001b[0m│ trecovery │\n", + "│\u001b[1m 5 \u001b[0m│ δ │\n", + "└───┴───────────┘\n", + "┌─────┬───────┬───────┬───────────────┐\n", + "│\u001b[1m LVV \u001b[0m│\u001b[1m lvsrc \u001b[0m│\u001b[1m lvtgt \u001b[0m│\u001b[1m lvsrcposition \u001b[0m│\n", + "├─────┼───────┼───────┼───────────────┤\n", + "│\u001b[1m 1 \u001b[0m│ 2 │ 3 │ 2 │\n", + "│\u001b[1m 2 \u001b[0m│ 3 │ 4 │ 2 │\n", + "└─────┴───────┴───────┴───────────────┘\n", + "┌─────┬──────┬──────┬──────────────┐\n", + "│\u001b[1m LPV \u001b[0m│\u001b[1m lpvp \u001b[0m│\u001b[1m lpvv \u001b[0m│\u001b[1m lpvpposition \u001b[0m│\n", + "├─────┼──────┼──────┼──────────────┤\n", + "│\u001b[1m 1 \u001b[0m│ 1 │ 1 │ 1 │\n", + "│\u001b[1m 2 \u001b[0m│ 2 │ 4 │ 1 │\n", + "│\u001b[1m 3 \u001b[0m│ 3 │ 5 │ 2 │\n", + "│\u001b[1m 4 \u001b[0m│ 4 │ 6 │ 2 │\n", + "│\u001b[1m 5 \u001b[0m│ 5 │ 7 │ 2 │\n", + "│\u001b[1m 6 \u001b[0m│ 5 │ 8 │ 2 │\n", + "│\u001b[1m 7 \u001b[0m│ 5 │ 9 │ 2 │\n", + "│\u001b[1m 8 \u001b[0m│ 5 │ 10 │ 2 │\n", + "└─────┴──────┴──────┴──────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "seir = @stock_and_flow begin\n", " :stocks\n", @@ -71,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -80,420 +664,420 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "\n", "s1\n", "\n", - "S\n", + "S\n", "\n", "\n", "\n", "v3\n", - "S * (I / N)\n", + "S * (I / N)\n", "\n", "\n", "\n", "s1->v3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "v4\n", - "β * (S * (I / N))\n", + "β * (S * (I / N))\n", "\n", "\n", "\n", "s1->v4\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "v7\n", - "S * δ\n", + "S * δ\n", "\n", "\n", "\n", "s1->v7\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "s1->v7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sv1\n", - "\n", - "N\n", + "\n", + "N\n", "\n", "\n", "\n", "s1->sv1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "s2\n", - "\n", 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Nnname
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17β
18tlatent
19trecovery
20δ
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\u001b[0m│\n", + "├────┼───────────┤\n", + "│\u001b[1m 1 \u001b[0m│ S │\n", + "│\u001b[1m 2 \u001b[0m│ E │\n", + "│\u001b[1m 3 \u001b[0m│ I │\n", + "│\u001b[1m 4 \u001b[0m│ R │\n", + "│\u001b[1m 5 \u001b[0m│ f_birth │\n", + "│\u001b[1m 6 \u001b[0m│ f_incid │\n", + "│\u001b[1m 7 \u001b[0m│ v_deathS │\n", + "│\u001b[1m 8 \u001b[0m│ f_inf │\n", + "│\u001b[1m 9 \u001b[0m│ f_deathE │\n", + "│\u001b[1m 10 \u001b[0m│ f_rec │\n", + "│\u001b[1m 11 \u001b[0m│ f_deathI │\n", + "│\u001b[1m 12 \u001b[0m│ f_deathR │\n", + "│\u001b[1m 13 \u001b[0m│ N │\n", + "│\u001b[1m 14 \u001b[0m│ v_incid₁ │\n", + "│\u001b[1m 15 \u001b[0m│ v_incid₂ │\n", + "│\u001b[1m 16 \u001b[0m│ μ │\n", + "│ ⋮ │ ⋮ │\n", + "└────┴───────────┘\n", + "\u001b[36m 4 rows omitted\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "seir_causalLoop = convertToCausalLoop(seir)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + 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"display_data" + } + ], "source": [ "GraphCL(seir_causalLoop)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [] diff --git a/examples/full_fledged_schema_examples_new/stratification/sir_linear_stratification.ipynb b/examples/full_fledged_schema_examples_new/stratification/sir_linear_stratification.ipynb index 53398c1f..4e740fbc 100644 --- a/examples/full_fledged_schema_examples_new/stratification/sir_linear_stratification.ipynb +++ b/examples/full_fledged_schema_examples_new/stratification/sir_linear_stratification.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 18, "id": "38c8b82a", "metadata": {}, "outputs": [], @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, "id": "25d17bab", "metadata": {}, "outputs": [ @@ -38,7 +38,7 @@ "GraphF_typed (generic function with 5 methods)" ] }, - "execution_count": 2, + 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"LoadError", + "evalue": "UndefVarError: `typed_WeightModel` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `typed_WeightModel` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[30]:1" + ] } ], "source": [ @@ -4799,7 +1462,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "id": "b0fa738f", "metadata": {}, "outputs": [], @@ -4818,149 +1481,21 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 32, "id": "ad6be5bd", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" + "ename": "LoadError", + "evalue": "UndefVarError: `aged_weight` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `aged_weight` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[32]:1" + ] } ], "source": [ @@ -4971,7 +1506,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 33, "id": "22218a89", "metadata": {}, "outputs": [ @@ -4989,7 +1524,7 @@ "HTML{String}(\"\\n\")" ] }, - "execution_count": 16, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } diff --git a/validate-notebook-examples.sh b/validate-notebook-examples.sh new file mode 100755 index 00000000..92507461 --- /dev/null +++ b/validate-notebook-examples.sh @@ -0,0 +1,8 @@ +#!/bin/env bash + +mkdir -p jlexamples +./ipynb-to-jl.sh "full_fledged_schema_examples" +./ipynb-to-jl.sh "full_fledged_schema_examples_new" +rm jlexamples/full_fledged_schema_examples*checkpoint* +julia -p auto --project="." -e 'include("./run_notebooks.jl")' +rm -rf jlexamples/