From 5e03b40a079948192ccd97594f8e045f7623fa63 Mon Sep 17 00:00:00 2001 From: Alex Alegre <6503910+Saityi@users.noreply.github.com> Date: Wed, 30 Aug 2023 15:53:14 -0600 Subject: [PATCH] Add a validate-notebooks CI step; tag first cells of the notebooks used as "parameters", as required by the notebook runner action --- .github/workflows/tests.yml | 1 - .github/workflows/validate-notebooks.yml | 50 + .../convert_from_SEIR_stockFlowDiagram.ipynb | 755 +--- .../composition/diabetes_model.ipynb | 3093 +---------------- .../stratification/diabetes_diagnose.ipynb | 2207 +----------- 5 files changed, 90 insertions(+), 6016 deletions(-) create mode 100644 .github/workflows/validate-notebooks.yml diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index be32c0a0..1b874699 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -9,7 +9,6 @@ jobs: matrix: julia-version: ['1.9'] os: [ubuntu-latest, macOS-latest] - steps: - uses: actions/checkout@v2 - uses: julia-actions/setup-julia@latest diff --git a/.github/workflows/validate-notebooks.yml b/.github/workflows/validate-notebooks.yml new file mode 100644 index 00000000..06cf8297 --- /dev/null +++ b/.github/workflows/validate-notebooks.yml @@ -0,0 +1,50 @@ +name: Check notebook examples for regressions + +on: [push, pull_request] + +env: + JULIA_NUM_THREADS: 'auto' +jobs: + notebooks-examples-regression-test: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v3 + - name: Setup Julia + uses: julia-actions/setup-julia@v1 + with: + version: 1.9 + - name: Cache Julia dependencies + uses: julia-actions/cache@v1 + - name: Add IJulia Jupyter kernel + run: julia --color=yes --project=. -e 'ENV["JULIA_PKG_PRECOMPILE_AUTO"]=0; using Pkg; pkg"add IJulia"' + - name: Install Julia dependencies + run: julia --color=yes --project=. -e 'import Pkg; Pkg.instantiate(); Pkg.resolve(); Pkg.precompile()' + - uses: yaananth/run-notebook@v2 + env: + RUNNER: ${{ toJson(runner) }} + SECRETS: ${{ toJson(secrets) }} + GITHUB: ${{ toJson(github) }} + with: + notebook: "examples/full_fledged_schema_examples_new/stratification/diabetes_diagnose.ipynb" + - uses: yaananth/run-notebook@v2 + env: + RUNNER: ${{ toJson(runner) }} + SECRETS: ${{ toJson(secrets) }} + GITHUB: ${{ toJson(github) }} + with: + notebook: "examples/full_fledged_schema_examples_new/composition/diabetes_model.ipynb" + - uses: yaananth/run-notebook@v2 + env: + RUNNER: ${{ toJson(runner) }} + SECRETS: ${{ toJson(secrets) }} + GITHUB: ${{ toJson(github) }} + with: + notebook: "examples/full_fledged_schema_examples_new/CausalLoopDiagrams/convert_from_SEIR_stockFlowDiagram.ipynb" + - uses: actions/upload-artifact@v3 + if: always() + with: + name: output + path: ${{ RUNNER.temp }}/nb-runner + env: + RUNNER: ${{ toJson(runner) }} 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 e0fa8e37..6b3e6362 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 @@ -3,7 +3,11 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "tags": [ + "parameters" + ] + }, "outputs": [], "source": [ "using GraphViz\n", @@ -659,428 +663,7 @@ "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "G\n", - "\n", - "\n", - "\n", - "s1\n", - "\n", - "S\n", - "\n", - "\n", - "\n", - "v3\n", - "S * (I / N)\n", - "\n", - "\n", - "\n", - "s1->v3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "v4\n", - "β * (S * (I / N))\n", - "\n", - "\n", - "\n", - "s1->v4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "v7\n", - "S * δ\n", - "\n", - "\n", - 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"\n\n\n\n\n\nG\n\n\n\nn1\nS\n\n\n\nn7\nv_deathS\n\n\n\nn1->n7\n\n\n\n\n\nn13\nN\n\n\n\nn1->n13\n\n\n\n\n\nn15\nv_incid₂\n\n\n\nn1->n15\n\n\n\n\n\nn2\nE\n\n\n\nn8\nf_inf\n\n\n\nn2->n8\n\n\n\n\n\nn9\nf_deathE\n\n\n\nn2->n9\n\n\n\n\n\nn2->n13\n\n\n\n\n\nn3\nI\n\n\n\nn10\nf_rec\n\n\n\nn3->n10\n\n\n\n\n\nn11\nf_deathI\n\n\n\nn3->n11\n\n\n\n\n\nn3->n13\n\n\n\n\n\nn14\nv_incid₁\n\n\n\nn3->n14\n\n\n\n\n\nn4\nR\n\n\n\nn12\nf_deathR\n\n\n\nn4->n12\n\n\n\n\n\nn4->n13\n\n\n\n\n\nn5\nf_birth\n\n\n\nn5->n1\n\n\n\n\n\nn6\nf_incid\n\n\n\nn6->n1\n\n\n\n\n\nn6->n2\n\n\n\n\n\nn7->n1\n\n\n\n\n\nn8->n2\n\n\n\n\n\nn8->n3\n\n\n\n\n\nn9->n2\n\n\n\n\n\nn10->n3\n\n\n\n\n\nn10->n4\n\n\n\n\n\nn11->n3\n\n\n\n\n\nn12->n4\n\n\n\n\n\nn13->n5\n\n\n\n\n\nn13->n14\n\n\n\n\n\nn14->n15\n\n\n\n\n\nn15->n6\n\n\n\n\n\nn16\nμ\n\n\n\nn16->n5\n\n\n\n\n\nn17\nβ\n\n\n\nn17->n6\n\n\n\n\n\nn18\ntlatent\n\n\n\nn18->n8\n\n\n\n\n\nn19\ntrecovery\n\n\n\nn19->n10\n\n\n\n\n\nn20\nδ\n\n\n\nn20->n7\n\n\n\n\n\nn20->n9\n\n\n\n\n\nn20->n11\n\n\n\n\n\nn20->n12\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 => \"E\", :shape => \"plaintext\")), Catlab.Graphics.Graphviz.Node(\"n3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"I\", :shape => \"plaintext\")), Catlab.Graphics.Graphviz.Node(\"n4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"R\", :shape => \"plaintext\")), Catlab.Graphics.Graphviz.Node(\"n5\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"f_birth\", :shape => \"plaintext\")), Catlab.Graphics.Graphviz.Node(\"n6\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"f_incid\", :shape => \"plaintext\")), Catlab.Graphics.Graphviz.Node(\"n7\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"v_deathS\", :shape => \"plaintext\")), Catlab.Graphics.Graphviz.Node(\"n8\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"f_inf\", :shape => \"plaintext\")), Catlab.Graphics.Graphviz.Node(\"n9\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"f_deathE\", :shape => \"plaintext\")), Catlab.Graphics.Graphviz.Node(\"n10\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"f_rec\", :shape => \"plaintext\")) … Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n16\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n5\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n17\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n6\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n18\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n8\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n19\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n10\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n20\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n7\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n20\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n9\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n20\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n11\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n20\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n12\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n14\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n15\", \"\", \"\")], OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:color => \"blue\")), Catlab.Graphics.Graphviz.Edge(Catlab.Graphics.Graphviz.NodeID[Catlab.Graphics.Graphviz.NodeID(\"n15\", \"\", \"\"), Catlab.Graphics.Graphviz.NodeID(\"n6\", \"\", \"\")], 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}}())" ] diff --git a/examples/full_fledged_schema_examples_new/composition/diabetes_model.ipynb b/examples/full_fledged_schema_examples_new/composition/diabetes_model.ipynb index 5feb4602..d67e851d 100644 --- a/examples/full_fledged_schema_examples_new/composition/diabetes_model.ipynb +++ b/examples/full_fledged_schema_examples_new/composition/diabetes_model.ipynb @@ -3,7 +3,11 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "tags": [ + "parameters" + ] + }, "outputs": [], "source": [ "using GraphViz\n", @@ -529,311 +533,7 @@ "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "G\n", - "\n", - "\n", - "\n", - "s1\n", - "\n", - "NormalWeight\n", - "\n", - "\n", - "\n", - "v1\n", - "NormalWeight * rMortalityWeight\n", - "\n", - "\n", - "\n", - "s1->v1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "s1->v1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "v2\n", - "NormalWeight * rOverWeight\n", - "\n", - "\n", - "\n", - 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- "\n", - "rBirth\n", - "\n", - "\n", - "\n", - "v6\n", - "N * rBirth\n", - "\n", - "\n", - "\n", - "p1->v6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p2\n", - "\n", - "rMortalityWeight\n", - "\n", - "\n", - "\n", - "p2->v1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p2->v3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p3\n", - "\n", - "rObese\n", - "\n", - "\n", - "\n", - "p3->v4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p4\n", - "\n", - "rOverWeight\n", - "\n", - "\n", - "\n", - "p4->v2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p5\n", - "\n", - "rMortalityobese\n", - "\n", - "\n", - "\n", - "p5->v5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "fs_6u\n", - "\n", - "\n", - "\n", - "\n", - "fs_6u->v6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "fs_1d\n", - "\n", - "\n", - "\n", - "\n", - "fs_3d\n", - "\n", - "\n", - "\n", - "\n", - "fs_5d\n", - "\n", - "\n", - "\n", - "\n", - "v1->fs_1d\n", - "\n", - "\n", - "\n", - "\n", - "f_DeathNormalWeight\n", - "\n", - "\n", - "\n", - "v2->s2\n", - "\n", - "\n", - "\n", - "\n", - "f_BecomingOverWeight\n", - "\n", - "\n", - "\n", - "v3->fs_3d\n", - "\n", - "\n", - "\n", - "\n", - "f_DeathOverWeight\n", - "\n", - "\n", - "\n", - "v4->s3\n", - "\n", - "\n", - "\n", - "\n", - "f_BecomingObese\n", - "\n", - "\n", - "\n", - "v5->fs_5d\n", - "\n", - "\n", - "\n", - "\n", - "f_DeathObese\n", - "\n", - "\n", - "\n", - "v6->s1\n", - "\n", - "\n", - "\n", - "\n", - "f_NewBorn\n", - "\n", - "\n", - "\n", - "sv1->v6\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], + "image/svg+xml": "\n\n\n\n\n\nG\n\n\n\ns1\n\nNormalWeight\n\n\n\nv1\nNormalWeight * rMortalityWeight\n\n\n\ns1->v1\n\n\n\n\n\n\ns1->v1\n\n\n\n\n\nv2\nNormalWeight * rOverWeight\n\n\n\ns1->v2\n\n\n\n\n\n\ns1->v2\n\n\n\n\n\nsv1\n\nN\n\n\n\ns1->sv1\n\n\n\n\n\ns2\n\nOverWeight\n\n\n\nv3\nOverWeight * rMortalityWeight\n\n\n\ns2->v3\n\n\n\n\n\n\ns2->v3\n\n\n\n\n\nv4\nOverWeight * rObese\n\n\n\ns2->v4\n\n\n\n\n\n\ns2->v4\n\n\n\n\n\ns2->sv1\n\n\n\n\n\ns3\n\nObese\n\n\n\nv5\nObese * rMortalityobese\n\n\n\ns3->v5\n\n\n\n\n\n\ns3->v5\n\n\n\n\n\ns3->sv1\n\n\n\n\n\np1\n\nrBirth\n\n\n\nv6\nN * rBirth\n\n\n\np1->v6\n\n\n\n\n\np2\n\nrMortalityWeight\n\n\n\np2->v1\n\n\n\n\n\np2->v3\n\n\n\n\n\np3\n\nrObese\n\n\n\np3->v4\n\n\n\n\n\np4\n\nrOverWeight\n\n\n\np4->v2\n\n\n\n\n\np5\n\nrMortalityobese\n\n\n\np5->v5\n\n\n\n\n\nfs_6u\n\n\n\n\nfs_6u->v6\n\n\n\n\n\n\nfs_1d\n\n\n\n\nfs_3d\n\n\n\n\nfs_5d\n\n\n\n\nv1->fs_1d\n\n\n\n\nf_DeathNormalWeight\n\n\n\nv2->s2\n\n\n\n\nf_BecomingOverWeight\n\n\n\nv3->fs_3d\n\n\n\n\nf_DeathOverWeight\n\n\n\nv4->s3\n\n\n\n\nf_BecomingObese\n\n\n\nv5->fs_5d\n\n\n\n\nf_DeathObese\n\n\n\nv6->s1\n\n\n\n\nf_NewBorn\n\n\n\nsv1->v6\n\n\n\n\n\n", "text/plain": [ "Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"NormalWeight\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"OverWeight\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"Obese\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"p1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"rBirth\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"rMortalityWeight\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"rObese\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"rOverWeight\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"p5\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"rMortalityobese\", :shape => \"circle\", :color => \"black\")), Catlab.Graphics.Graphviz.Node(\"fs_6u\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"\", :shape => \"point\", :color => \"white\")), Catlab.Graphics.Graphviz.Node(\"fs_1d\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"\", :shape => \"point\", :color => \"white\")) … 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(\"v6\", \"\", \"\")], 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(\"v6\", \"\", \"\")], 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(\"v5\", \"\", \"\")], 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\", \"\", \"\")], 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rDevelopingLate\n\n\n\ns3->v5\n\n\n\n\n\n\ns3->v5\n\n\n\n\n\nv6\nDiabeticEarly_U * rMortalityDiabeticEarly_U\n\n\n\ns3->v6\n\n\n\n\n\n\ns3->v6\n\n\n\n\n\nv17\nDiabeticEarly_U * rDiabeticEarly\n\n\n\ns3->v17\n\n\n\n\n\n\ns3->v17\n\n\n\n\n\ns3->sv1\n\n\n\n\n\ns4\n\nDiabeticLate_U\n\n\n\nv7\nDiabeticLate_U * rMortalityDiabeticLate\n\n\n\ns4->v7\n\n\n\n\n\n\ns4->v7\n\n\n\n\n\nv18\nDiabeticLate_U * rDiabeticLate\n\n\n\ns4->v18\n\n\n\n\n\n\ns4->v18\n\n\n\n\n\ns4->sv1\n\n\n\n\n\ns5\n\nPrediabetic_D\n\n\n\nv8\nPrediabetic_D * rDevelopingDiabetic_D\n\n\n\ns5->v8\n\n\n\n\n\n\ns5->v8\n\n\n\n\n\nv9\nPrediabetic_D * rMortalityPrediabetic\n\n\n\ns5->v9\n\n\n\n\n\n\ns5->v9\n\n\n\n\n\ns5->sv1\n\n\n\n\n\ns6\n\nDiabeticWtComp_D\n\n\n\nv10\nDiabeticWtComp_D * rDevelopingEarly_D\n\n\n\ns6->v10\n\n\n\n\n\n\ns6->v10\n\n\n\n\n\nv11\nDiabeticWtComp_D * rMortalityDiabeticWtComp_D\n\n\n\ns6->v11\n\n\n\n\n\n\ns6->v11\n\n\n\n\n\ns6->sv1\n\n\n\n\n\ns7\n\nDiabeticEarly_D\n\n\n\nv12\nDiabeticEarly_D * rDevelopingLate\n\n\n\ns7->v12\n\n\n\n\n\n\ns7->v12\n\n\n\n\n\nv13\nDiabeticEarly_D * rMortalityDiabeticEarly_D\n\n\n\ns7->v13\n\n\n\n\n\n\ns7->v13\n\n\n\n\n\ns7->sv1\n\n\n\n\n\ns8\n\nDiabeticLate_D\n\n\n\nv14\nDiabeticLate_D * 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"\n", - "\n", - "v19->fs_19d\n", - "\n", - "\n", - "\n", - "\n", - "DeathDiabeticEarly_D\n", - "\n", - "\n", - "\n", - "v20->fs_20d\n", - "\n", - "\n", - "\n", - "\n", - "DeathDiabeticLate_D\n", - "\n", - "\n", - "\n", - "v21->s8\n", - "\n", - "\n", - "\n", - "\n", - "DiagnosisPrediabetic\n", - "\n", - "\n", - "\n", - "v22->s9\n", - "\n", - "\n", - "\n", - "\n", - "DiagnosisDiabeticWtComp\n", - "\n", - "\n", - "\n", - "v23->s10\n", - "\n", - "\n", - "\n", - "\n", - "DiagnosisDiabeticEarly\n", - "\n", - "\n", - "\n", - "v24->s11\n", - "\n", - "\n", - "\n", - "\n", - "DiagnosisDiabeticLate\n", - "\n", - "\n", - "\n", - "v25->s2\n", - "\n", - "\n", - "\n", - "\n", - "fRecoveryToOWFromDx\n", - "\n", - "\n", - "\n", - "v26->s1\n", - "\n", - "\n", - "\n", - "\n", - "fRecoveryToNWFromDx\n", - "\n", - "\n", - "\n", - "v27->s3\n", - "\n", - "\n", - "\n", - "\n", - "fRecoveryToOBFromDx\n", - "\n", - "\n", - "\n", - "v28->s4\n", - "\n", - "\n", - "\n", - "\n", - "fDevelopingPrediabeticNW\n", - "\n", - "\n", - "\n", - "v29->s2\n", - "\n", - "\n", - "\n", - "\n", - "fRecoveryToOWFromUx\n", - "\n", - "\n", - "\n", - "v30->s3\n", - "\n", - "\n", - "\n", - "\n", - "fRecoveryToOBFromUx\n", - "\n", - "\n", - "\n", - "v31->s1\n", - "\n", - "\n", - "\n", - "\n", - "fRecoveryToNWFromUx\n", - "\n", - "\n", - "\n", - "v32->s4\n", - "\n", - "\n", - "\n", - "\n", - "fDevelopingPrediabeticOW\n", - "\n", - "\n", - "\n", - "v33->s4\n", - "\n", - "\n", - "\n", - "\n", - "fDevelopingPrediabeticOB\n", - "\n", - "\n", - "\n", - "sv1->v6\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], + "image/svg+xml": "\n\n\n\n\n\nG\n\n\n\ns1\n\nNormalWeight\n\n\n\nv1\nNormalWeight * rMortalityWeight\n\n\n\ns1->v1\n\n\n\n\n\n\ns1->v1\n\n\n\n\n\nv2\nNormalWeight * rOverWeight\n\n\n\ns1->v2\n\n\n\n\n\n\ns1->v2\n\n\n\n\n\nv28\nNormalWeight * rIncidenceNW\n\n\n\ns1->v28\n\n\n\n\n\n\ns1->v28\n\n\n\n\n\nsv1\n\nN\n\n\n\ns1->sv1\n\n\n\n\n\ns2\n\nOverWeight\n\n\n\nv3\nOverWeight * rMortalityWeight\n\n\n\ns2->v3\n\n\n\n\n\n\ns2->v3\n\n\n\n\n\nv4\nOverWeight * rObese\n\n\n\ns2->v4\n\n\n\n\n\n\ns2->v4\n\n\n\n\n\nv32\nOverWeight * rIncidenceOW\n\n\n\ns2->v32\n\n\n\n\n\n\ns2->v32\n\n\n\n\n\ns2->sv1\n\n\n\n\n\ns3\n\nObese\n\n\n\nv5\nObese * rMortalityobese\n\n\n\ns3->v5\n\n\n\n\n\n\ns3->v5\n\n\n\n\n\nv33\nObese * rIncidenceOB\n\n\n\ns3->v33\n\n\n\n\n\n\ns3->v33\n\n\n\n\n\ns3->sv1\n\n\n\n\n\ns4\n\nPrediabetic_U\n\n\n\nv7\nPrediabetic_U * rDevelopingDiabetic_U\n\n\n\ns4->v7\n\n\n\n\n\n\ns4->v7\n\n\n\n\n\nv8\nPrediabetic_U * rMortalityPrediabetic\n\n\n\ns4->v8\n\n\n\n\n\n\ns4->v8\n\n\n\n\n\nv21\nPrediabetic_U * rPrediabetic\n\n\n\ns4->v21\n\n\n\n\n\n\ns4->v21\n\n\n\n\n\nv29\nPrediabetic_U * rRecovery\n\n\n\ns4->v29\n\n\n\n\n\n\ns4->v29\n\n\n\n\n\nv30\nPrediabetic_U * rRecovery\n\n\n\ns4->v30\n\n\n\n\n\n\ns4->v30\n\n\n\n\n\nv31\nPrediabetic_U * rRecovery\n\n\n\ns4->v31\n\n\n\n\n\n\ns4->v31\n\n\n\n\n\ns4->sv1\n\n\n\n\n\ns5\n\nDiabeticWtComp_U\n\n\n\nv9\nDiabeticWtComp_U * rDevelopingEarly_U\n\n\n\ns5->v9\n\n\n\n\n\n\ns5->v9\n\n\n\n\n\nv10\nDiabeticWtComp_U * rMortalityDiabeticWtComp_U\n\n\n\ns5->v10\n\n\n\n\n\n\ns5->v10\n\n\n\n\n\nv22\nDiabeticWtComp_U * rDiabeticWtComp\n\n\n\ns5->v22\n\n\n\n\n\n\ns5->v22\n\n\n\n\n\ns5->sv1\n\n\n\n\n\ns6\n\nDiabeticEarly_U\n\n\n\nv11\nDiabeticEarly_U * rDevelopingLate\n\n\n\ns6->v11\n\n\n\n\n\n\ns6->v11\n\n\n\n\n\nv12\nDiabeticEarly_U * rMortalityDiabeticEarly_U\n\n\n\ns6->v12\n\n\n\n\n\n\ns6->v12\n\n\n\n\n\nv23\nDiabeticEarly_U * rDiabeticEarly\n\n\n\ns6->v23\n\n\n\n\n\n\ns6->v23\n\n\n\n\n\ns6->sv1\n\n\n\n\n\ns7\n\nDiabeticLate_U\n\n\n\nv13\nDiabeticLate_U * rMortalityDiabeticLate\n\n\n\ns7->v13\n\n\n\n\n\n\ns7->v13\n\n\n\n\n\nv24\nDiabeticLate_U * rDiabeticLate\n\n\n\ns7->v24\n\n\n\n\n\n\ns7->v24\n\n\n\n\n\ns7->sv1\n\n\n\n\n\ns8\n\nPrediabetic_D\n\n\n\nv14\nPrediabetic_D * rDevelopingDiabetic_D\n\n\n\ns8->v14\n\n\n\n\n\n\ns8->v14\n\n\n\n\n\nv15\nPrediabetic_D * rMortalityPrediabetic\n\n\n\ns8->v15\n\n\n\n\n\n\ns8->v15\n\n\n\n\n\nv25\nPrediabetic_D * rRecovery\n\n\n\ns8->v25\n\n\n\n\n\n\ns8->v25\n\n\n\n\n\nv26\nPrediabetic_D * rRecovery\n\n\n\ns8->v26\n\n\n\n\n\n\ns8->v26\n\n\n\n\n\nv27\nPrediabetic_D * rRecovery\n\n\n\ns8->v27\n\n\n\n\n\n\ns8->v27\n\n\n\n\n\ns8->sv1\n\n\n\n\n\ns9\n\nDiabeticWtComp_D\n\n\n\nv16\nDiabeticWtComp_D * rDevelopingEarly_D\n\n\n\ns9->v16\n\n\n\n\n\n\ns9->v16\n\n\n\n\n\nv17\nDiabeticWtComp_D * rMortalityDiabeticWtComp_D\n\n\n\ns9->v17\n\n\n\n\n\n\ns9->v17\n\n\n\n\n\ns9->sv1\n\n\n\n\n\ns10\n\nDiabeticEarly_D\n\n\n\nv18\nDiabeticEarly_D * rDevelopingLate\n\n\n\ns10->v18\n\n\n\n\n\n\ns10->v18\n\n\n\n\n\nv19\nDiabeticEarly_D * rMortalityDiabeticEarly_D\n\n\n\ns10->v19\n\n\n\n\n\n\ns10->v19\n\n\n\n\n\ns10->sv1\n\n\n\n\n\ns11\n\nDiabeticLate_D\n\n\n\nv20\nDiabeticLate_D * rMortalityDiabeticLate\n\n\n\ns11->v20\n\n\n\n\n\n\ns11->v20\n\n\n\n\n\ns11->sv1\n\n\n\n\n\np1\n\nrBirth\n\n\n\nv6\nN * rBirth\n\n\n\np1->v6\n\n\n\n\n\np2\n\nrMortalityWeight\n\n\n\np2->v1\n\n\n\n\n\np2->v3\n\n\n\n\n\np3\n\nrObese\n\n\n\np3->v4\n\n\n\n\n\np4\n\nrOverWeight\n\n\n\np4->v2\n\n\n\n\n\np5\n\nrMortalityobese\n\n\n\np5->v5\n\n\n\n\n\np6\n\nrDevelopingDiabetic_U\n\n\n\np6->v7\n\n\n\n\n\np7\n\nrMortalityPrediabetic\n\n\n\np7->v8\n\n\n\n\n\np8\n\nrDevelopingEarly_U\n\n\n\np8->v9\n\n\n\n\n\np9\n\nrMortalityDiabeticEarly_U\n\n\n\np9->v12\n\n\n\n\n\np10\n\nrMortalityDiabeticLate\n\n\n\np10->v13\n\n\n\n\n\np11\n\nrMortalityDiabeticWtComp_U\n\n\n\np11->v10\n\n\n\n\n\np12\n\nrDevelopingLate\n\n\n\np12->v11\n\n\n\n\n\np13\n\nrDevelopingDiabetic_D\n\n\n\np13->v14\n\n\n\n\n\np14\n\nrMortalityPrediabetic\n\n\n\np14->v15\n\n\n\n\n\np15\n\nrDevelopingEarly_D\n\n\n\np15->v16\n\n\n\n\n\np16\n\nrMortalityDiabeticEarly_D\n\n\n\np16->v19\n\n\n\n\n\np17\n\nrMortalityDiabeticLate\n\n\n\np17->v20\n\n\n\n\n\np18\n\nrMortalityDiabeticWtComp_D\n\n\n\np18->v17\n\n\n\n\n\np19\n\nrDevelopingLate\n\n\n\np19->v18\n\n\n\n\n\np20\n\nrPrediabetic\n\n\n\np20->v21\n\n\n\n\n\np21\n\nrDiabeticWtComp\n\n\n\np21->v22\n\n\n\n\n\np22\n\nrDiabeticEarly\n\n\n\np22->v23\n\n\n\n\n\np23\n\nrDiabeticLate\n\n\n\np23->v24\n\n\n\n\n\np24\n\nrRecovery\n\n\n\np24->v25\n\n\n\n\n\np24->v26\n\n\n\n\n\np24->v27\n\n\n\n\n\np24->v29\n\n\n\n\n\np24->v30\n\n\n\n\n\np24->v31\n\n\n\n\n\np25\n\nrIncidenceNW\n\n\n\np25->v28\n\n\n\n\n\np26\n\nrIncidenceOW\n\n\n\np26->v32\n\n\n\n\n\np27\n\nrIncidenceOB\n\n\n\np27->v33\n\n\n\n\n\nfs_6u\n\n\n\n\nfs_6u->v6\n\n\n\n\n\n\nfs_1d\n\n\n\n\nfs_3d\n\n\n\n\nfs_5d\n\n\n\n\nfs_8d\n\n\n\n\nfs_10d\n\n\n\n\nfs_12d\n\n\n\n\nfs_13d\n\n\n\n\nfs_15d\n\n\n\n\nfs_17d\n\n\n\n\nfs_19d\n\n\n\n\nfs_20d\n\n\n\n\nv1->fs_1d\n\n\n\n\nf_DeathNormalWeight\n\n\n\nv2->s2\n\n\n\n\nf_BecomingOverWeight\n\n\n\nv3->fs_3d\n\n\n\n\nf_DeathOverWeight\n\n\n\nv4->s3\n\n\n\n\nf_BecomingObese\n\n\n\nv5->fs_5d\n\n\n\n\nf_DeathObese\n\n\n\nv6->s1\n\n\n\n\nf_NewBorn\n\n\n\nv7->s5\n\n\n\n\nDevelopingDiabetic_U\n\n\n\nv8->fs_8d\n\n\n\n\nDeathPrediabetic_U\n\n\n\nv9->s6\n\n\n\n\nDevelopingEarly_U\n\n\n\nv10->fs_10d\n\n\n\n\nDeathDiabeticWtComp_U\n\n\n\nv11->s7\n\n\n\n\nDevelopingLate_U\n\n\n\nv12->fs_12d\n\n\n\n\nDeathDiabeticEarly_U\n\n\n\nv13->fs_13d\n\n\n\n\nDeathDiabeticLate_U\n\n\n\nv14->s9\n\n\n\n\nDevelopingDiabetic_D\n\n\n\nv15->fs_15d\n\n\n\n\nDeathPrediabetic_D\n\n\n\nv16->s10\n\n\n\n\nDevelopingEarly_D\n\n\n\nv17->fs_17d\n\n\n\n\nDeathDiabeticWtComp_D\n\n\n\nv18->s11\n\n\n\n\nDevelopingLate_D\n\n\n\nv19->fs_19d\n\n\n\n\nDeathDiabeticEarly_D\n\n\n\nv20->fs_20d\n\n\n\n\nDeathDiabeticLate_D\n\n\n\nv21->s8\n\n\n\n\nDiagnosisPrediabetic\n\n\n\nv22->s9\n\n\n\n\nDiagnosisDiabeticWtComp\n\n\n\nv23->s10\n\n\n\n\nDiagnosisDiabeticEarly\n\n\n\nv24->s11\n\n\n\n\nDiagnosisDiabeticLate\n\n\n\nv25->s2\n\n\n\n\nfRecoveryToOWFromDx\n\n\n\nv26->s1\n\n\n\n\nfRecoveryToNWFromDx\n\n\n\nv27->s3\n\n\n\n\nfRecoveryToOBFromDx\n\n\n\nv28->s4\n\n\n\n\nfDevelopingPrediabeticNW\n\n\n\nv29->s2\n\n\n\n\nfRecoveryToOWFromUx\n\n\n\nv30->s3\n\n\n\n\nfRecoveryToOBFromUx\n\n\n\nv31->s1\n\n\n\n\nfRecoveryToNWFromUx\n\n\n\nv32->s4\n\n\n\n\nfDevelopingPrediabeticOW\n\n\n\nv33->s4\n\n\n\n\nfDevelopingPrediabeticOB\n\n\n\nsv1->v6\n\n\n\n\n\n", "text/plain": [ "Graph(\"G\", true, \"dot\", Catlab.Graphics.Graphviz.Statement[Catlab.Graphics.Graphviz.Node(\"s1\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"NormalWeight\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s2\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"OverWeight\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s3\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"Obese\", :shape => \"square\", :color => \"black\", :style => \"filled\", :fillcolor => \"#9ACEEB\")), Catlab.Graphics.Graphviz.Node(\"s4\", OrderedCollections.OrderedDict{Symbol, Union{String, Catlab.Graphics.Graphviz.Html}}(:label => \"Prediabetic_U\", :shape => \"square\", :color => \"black\", 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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" - ], + "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", "text/html": [ "\n", "\n", diff --git a/examples/full_fledged_schema_examples_new/stratification/diabetes_diagnose.ipynb b/examples/full_fledged_schema_examples_new/stratification/diabetes_diagnose.ipynb index 17800a5e..e4b49dd5 100644 --- a/examples/full_fledged_schema_examples_new/stratification/diabetes_diagnose.ipynb +++ b/examples/full_fledged_schema_examples_new/stratification/diabetes_diagnose.ipynb @@ -1,9 +1,22 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "using GraphViz\n", @@ -474,161 +487,7 @@ "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "G\n", - "\n", - "\n", - "\n", - "s1\n", - "\n", - "Pop\n", - "\n", - "\n", - "\n", - "v1\n", - "rDeath * Pop\n", - "\n", - "\n", - "\n", - "s1->v1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "s1->v1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "v2\n", - "rProgress * Pop\n", - "\n", - "\n", - "\n", - "s1->v2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "s1->v2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "v3\n", - "rStrata * Pop\n", - "\n", - "\n", - "\n", - "s1->v3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "s1->v3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sv1\n", - "\n", - "N\n", - "\n", - "\n", - "\n", - "s1->sv1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p1\n", - "\n", - "rDeath\n", - "\n", - "\n", - "\n", - "p1->v1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p2\n", - "\n", - "rProgress\n", - "\n", - "\n", - "\n", - "p2->v2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "p3\n", - "\n", - "rStrata\n", - "\n", - "\n", - "\n", - "p3->v3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "fs_1d\n", - "\n", - "\n", - "\n", - "\n", - "v1->fs_1d\n", - "\n", - "\n", - "\n", - "\n", - "f_death\n", - "\n", - "\n", - "\n", - "v2->s1\n", - "\n", - "\n", - "\n", - "\n", - "f_progress\n", - "\n", - "\n", - "\n", - "v3->s1\n", - "\n", - "\n", - "\n", - "\n", - "f_strata\n", - "\n", - "\n", - "\n" - ], + "image/svg+xml": 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