Total Cleanup and Restructuring (#248)

* Began the great cleanup of 2020 * Removed pretty much everything else * Forgot a couple * Began restructuring * Moved documentation to it's own project environment * Added back some old expression based model functionality * Began working on cleaning up examples * Continued cleaning up examples * Added ignoring of virtual environment folders * More work cleaning up examples * Stil working on cleaning up examples * removed last manifest * Removed unnecessary travis lines * Updating travis * travis automatically tests * Fixed travis documentation typo * Updated jupyter notebook examples * Updated dockerfile * Removed last of the unnecessary files * Updated README * Removed all documentation errors * Added github actions * Fixed missing light graphs dependency
1 year ago · f85f5febf7
231 changed files with 2918 additions and 42929 deletions
--- a/.github/workflows/doc.yml
+++ b/.github/workflows/doc.yml
@ -0,0 +1,23 @@
+name: Documentation
+
+on: [push, pull_request]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: "Set up Julia"
+        uses: julia-actions/setup-julia@latest
+        with:
+          version: '1.4'
+      - name: "Install system dependencies"
+        run: |
+          sudo apt-get update
+          sudo apt-get install graphviz ttf-dejavu
+      - name: "Install Julia dependencies"
+        run: julia --project=doc -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate();'
+      - name: "Build and deploy docs"
+        env:
+          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
+        run: julia --project=doc doc/make.jl
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@ -0,0 +1,21 @@
+name: Tests
+
+on: [push, pull_request]
+
+jobs:
+  test:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        julia-version: ['1.0', '1.3', '1.4']
+        os: [ubuntu-latest]
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: "Set up Julia"
+        uses: julia-actions/setup-julia@latest
+        with:
+          version: ${{ matrix.julia-version }}
+      - name: "Run tests"
+        uses: julia-actions/julia-runtest@master
--- a/.gitignore
+++ b/.gitignore
@ -1,4 +1,4 @@
-/Manifest.toml
+Manifest.toml
 *.jl.cov
 *.jl.*.cov
 *.jl.mem
@ -278,3 +278,12 @@ examples/*.dot.svg
 doc/src/img/petri/*.png
 /doc/workflow.slides.html
 /examples/petri/covid/img/
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
--- a/.travis.yml
+++ b/.travis.yml
@ -11,24 +11,19 @@ addons:

 julia:
  - 1.0
-
-matrix:
-  allow_failures:
-    - julia: nightly
+  - 1.4
+  - nightly

 notifications:
  email: false

-after_success:
-# - julia -e 'using Pkg; cd(Pkg.dir("SemanticModels")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())';
-#  - julia -e 'using Pkg; cd(Pkg.dir("SemanticModels")); Pkg.add("Coverage"); using Coverage; Codecov.submit(Codecov.process_folder())';
-
 jobs:
+  allow_failures:
+    - julia: nightly
  include:
-    - stage: "Testing"
-      script:
-        - travis_wait 30 julia --project -e 'using Pkg; Pkg.build(); Pkg.test("SemanticModels")'
    - stage: "Documentation"
+      julia: 1.0
      script:
-        - julia --project -e 'using Pkg; Pkg.instantiate();'
-        - julia --project doc/make.jl
+        - julia --project=doc/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd()));
+                                              Pkg.instantiate()'
+        - julia --project=doc/ doc/make.jl
--- a/FluModel.ipynb
+++ b/FluModel.ipynb
@ -1,623 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "using Pkg\n",
-    "Pkg.activate(\".\")\n",
-    "using SemanticModels\n",
-    "using SemanticModels.Unitful: DomainError, s, d, C, uconvert, NoUnits\n",
-    "using DifferentialEquations\n",
-    "using DataFrames\n",
-    "using Unitful\n",
-    "using Test\n",
-    "\n",
-    "using Distributions: Uniform\n",
-    "using GLM\n",
-    "using DataFrames\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "using Plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "stripunits (generic function with 1 method)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stripunits(x) = uconvert(NoUnits, x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CombinedModel{Array{CombinedModel{Array{SpringModel{Array{Quantity{Float64,𝐓^-2,Unitful.FreeUnits{(d^-2,),𝐓^-2,nothing}},1},Tuple{Quantity{Int64,𝐓,Unitful.FreeUnits{(d,),𝐓,nothing}},Quantity{Float64,𝐓,Unitful.FreeUnits{(d,),𝐓,nothing}}},Array{Quantity{Float64,D,U} where U where D,1}},1},getfield(Main, Symbol(\"#create_sir#3\"))},1},getfield(Main, Symbol(\"#create_flu#4\"))}(CombinedModel{Array{SpringModel{Array{Quantity{Float64,𝐓^-2,Unitful.FreeUnits{(d^-2,),𝐓^-2,nothing}},1},Tuple{Quantity{Int64,𝐓,Unitful.FreeUnits{(d,),𝐓,nothing}},Quantity{Float64,𝐓,Unitful.FreeUnits{(d,),𝐓,nothing}}},Array{Quantity{Float64,D,U} where U where D,1}},1},getfield(Main, Symbol(\"#create_sir#3\"))}[CombinedModel{Array{SpringModel{Array{Quantity{Float64,𝐓^-2,FreeUnits{(d^-2,),𝐓^-2,nothing}},1},Tuple{Quantity{Int64,𝐓,FreeUnits{(d,),𝐓,nothing}},Quantity{Float64,𝐓,FreeUnits{(d,),𝐓,nothing}}},Array{Quantity{Float64,D,U} where U where D,1}},1},#create_sir#3}(SpringModel{Array{Quantity{Float64,𝐓^-2,Unitful.FreeUnits{(d^-2,),𝐓^-2,nothing}},1},Tuple{Quantity{Int64,𝐓,Unitful.FreeUnits{(d,),𝐓,nothing}},Quantity{Float64,𝐓,Unitful.FreeUnits{(d,),𝐓,nothing}}},Array{Quantity{Float64,D,U} where U where D,1}}[SpringModel{Array{Quantity{Float64,𝐓^-2,FreeUnits{(d^-2,),𝐓^-2,nothing}},1},Tuple{Quantity{Int64,𝐓,FreeUnits{(d,),𝐓,nothing}},Quantity{Float64,𝐓,FreeUnits{(d,),𝐓,nothing}}},Array{Quantity{Float64,D,U} where U where D,1}}(Quantity{Float64,𝐓^-2,Unitful.FreeUnits{(d^-2,),𝐓^-2,nothing}}[0.000342466 d^-2], (0 d, 753.982 d), Quantity{Float64,D,U} where U where D[25.0 C, 0.0 C d^-1])], #create_sir#3())], getfield(Main, Symbol(\"#create_flu#4\"))())"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "function flusim(tfinal)\n",
-    "    # annual cycle of temperature control flu infectiousness\n",
-    "    springmodel = SpringModel([u\"(1.0/(365*8))d^-2\"], # parameters (frequency)\n",
-    "                              (u\"0d\",tfinal), # time domain\n",
-    "                              [u\"25.0C\", u\"0C/d\"]) # initial_conditions T, T'\n",
-    "    function create_sir(m, solns)\n",
-    "        sol = solns[1]\n",
-    "        initialS = u\"10000person\"\n",
-    "        initialI = u\"1person\" \n",
-    "        initialpop = [initialS, initialI, u\"0.0person\"]\n",
-    "        β = u\"1.0/18\"/u\"d*C\" * sol(sol.t[end-2])[1] #infectiousness\n",
-    "        @show β\n",
-    "        sirprob = SIRSimulation(initialpop, #initial_conditions S,I,R\n",
-    "                                (u\"0.0d\", u\"20d\"), #time domain\n",
-    "                                SIRParams(β, u\"40.0person/d\")) # parameters β, γ\n",
-    "        return sirprob\n",
-    "    end\n",
-    "\n",
-    "    function create_flu(cm, solns)\n",
-    "        sol = solns[1]\n",
-    "        finalI = stripunits(sol(u\"8.0d\")[2]) # X\n",
-    "        population = stripunits(sol(sol.t[end])[2])\n",
-    "        # population = stripunits(sum(sol.u[end]))\n",
-    "        df = SemanticModels.generate_synthetic_data(population, 0,100)\n",
-    "        f = @formula(vaccines_produced ~ flu_patients)\n",
-    "        model =  lm(f,\n",
-    "            df[2:length(df.year),\n",
-    "            [:year, :flu_patients, :vaccines_produced]])\n",
-    "        println(\"GLM Model:\")\n",
-    "        println(model)\n",
-    "\n",
-    "        year_to_predict = 1\n",
-    "        num_flu_patients_from_sim = finalI\n",
-    "        vaccines_produced = missing\n",
-    "        targetDF = DataFrame(year=year_to_predict,\n",
-    "            flu_patients=num_flu_patients_from_sim, \n",
-    "            vaccines_produced=missing)\n",
-    "        @show targetDF\n",
-    "\n",
-    "\n",
-    "        return RegressionProblem(f, model, targetDF, missing)\n",
-    "    end\n",
-    "    cm = CombinedModel([springmodel], create_sir)\n",
-    "    flumodel = CombinedModel([cm], create_flu)\n",
-    "    return flumodel\n",
-    "end\n",
-    "\n",
-    "tfinal = 240π*u\"d\" #(~2 yrs)\n",
-    "flumodel = flusim(tfinal)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
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-     "execution_count": 6,
-     "metadata": {},
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-    }
-   ],
-   "source": [
-    "springmodel = flumodel.deps[1].deps[1]\n",
-    "sirmodel = flumodel.deps[1]\n",
-    "sol = solve(springmodel)\n",
-    "plot(sol.t./d, map(x->x[1], sol.u) ./ C)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "β = 1.0854014119706108 d^-1\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "retcode: Success\n",
-       "Interpolation: specialized 9th order lazy interpolation\n",
-       "t: 16-element Array{Quantity{Float64,𝐓,Unitful.FreeUnits{(d,),𝐓,nothing}},1}:\n",
-       "                0.0 d\n",
-       " 0.2503747748877873 d\n",
-       " 0.9420079061012359 d\n",
-       " 1.9002912698756584 d\n",
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-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot(sirsol.t./d,map(x->stripunits.(x)[2], sirsol.u))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "β = 1.0854014119706108 d^-1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Warning: In the future eachcol will have names argument set to false by default\n",
-      "│   caller = evalcontrasts(::DataFrame, ::Dict{Any,Any}) at modelframe.jl:124\n",
-      "└ @ StatsModels /Users/jamesfairbanks/.julia/packages/StatsModels/AYB2E/src/modelframe.jl:124\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GLM Model:\n",
-      "StatsModels.DataFrameRegressionModel{LinearModel{LmResp{Array{Float64,1}},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}\n",
-      "\n",
-      "Formula: vaccines_produced ~ 1 + flu_patients\n",
-      "\n",
-      "Coefficients:\n",
-      "                 Estimate Std.Error    t value Pr(>|t|)\n",
-      "(Intercept)       4892.06   342.292    14.2921   <1e-24\n",
-      "flu_patients  -0.00529781 0.0655031 -0.0808788   0.9357\n",
-      "\n",
-      "targetDF = 1×3 DataFrame\n",
-      "│ Row │ year  │ flu_patients │ vaccines_produced │\n",
-      "│     │ Int64 │ Float64      │ Missing           │\n",
-      "├─────┼───────┼──────────────┼───────────────────┤\n",
-      "│ 1   │ 1     │ 3627.46      │ missing           │\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Warning: In the future eachcol will have names argument set to false by default\n",
-      "│   caller = evalcontrasts(::DataFrame, ::Dict{Symbol,StatsModels.ContrastsMatrix}) at modelframe.jl:124\n",
-      "└ @ StatsModels /Users/jamesfairbanks/.julia/packages/StatsModels/AYB2E/src/modelframe.jl:124\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1-element Array{Union{Missing, Float64},1}:\n",
-       " 4872.838053691122"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sol = solve(flumodel)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ErrorException",
-     "evalue": "type Array has no field u",
-     "output_type": "error",
-     "traceback": [
-      "type Array has no field u",
-      "",
-      "Stacktrace:",
-      " [1] getproperty(::Any, ::Symbol) at ./sysimg.jl:18",
-      " [2] top-level scope at In[10]:1"
-     ]
-    }
-   ],
-   "source": [
-    "print(typeof(sol.u))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,jl:light",
-   "text_representation": {
-    "extension": ".jl",
-    "format_name": "light",
-    "format_version": "1.3",
-    "jupytext_version": "0.8.6"
-   }
-  },
-  "kernel_info": {
-   "name": "julia-1.0"
-  },
-  "kernelspec": {
-   "display_name": "Julia 1.0.0",
-   "language": "julia",
-   "name": "julia-1.0"
-  },
-  "language_info": {
-   "file_extension": ".jl",
-   "mimetype": "application/julia",
-   "name": "julia",
-   "version": "1.0.2"
-  },
-  "nteract": {
-   "version": "0.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
--- a/FluModel.jl
+++ b/FluModel.jl
@ -1,105 +0,0 @@
-# -*- coding: utf-8 -*-
-# ---
-# jupyter:
-#   jupytext:
-#     formats: ipynb,jl:light
-#     text_representation:
-#       extension: .jl
-#       format_name: light
-#       format_version: '1.3'
-#       jupytext_version: 0.8.6
-#   kernel_info:
-#     name: julia-1.0
-#   kernelspec:
-#     display_name: Julia 1.0.0
-#     language: julia
-#     name: julia-1.0
-# ---
-
-# +
-using Pkg
-Pkg.activate(".")
-using SemanticModels
-using SemanticModels.Unitful: DomainError, s, d, C, uconvert, NoUnits
-using DifferentialEquations
-using DataFrames
-using Unitful
-using Test
-
-using Distributions: Uniform
-using GLM
-using DataFrames
-
-# -
-
-using Plots
-
-stripunits(x) = uconvert(NoUnits, x)
-
-# +
-function flusim(tfinal)
-    # annual cycle of temperature control flu infectiousness
-    springmodel = SpringModel([u"(1.0/(365*8))d^-2"], # parameters (frequency)
-                              (u"0d",tfinal), # time domain
-                              [u"25.0C", u"0C/d"]) # initial_conditions T, T'
-    function create_sir(m, solns)
-        sol = solns[1]
-        initialS = u"10000person"
-        initialI = u"1person" 
-        initialpop = [initialS, initialI, u"0.0person"]
-        β = u"1.0/18"/u"d*C" * sol(sol.t[end-2])[1] #infectiousness
-        @show β
-        sirprob = SIRSimulation(initialpop, #initial_conditions S,I,R
-                                (u"0.0d", u"20d"), #time domain
-                                SIRParams(β, u"40.0person/d")) # parameters β, γ
-        return sirprob
-    end
-
-    function create_flu(cm, solns)
-        sol = solns[1]
-        finalI = stripunits(sol(u"8.0d")[2]) # X
-        population = stripunits(sol(sol.t[end])[2])
-        # population = stripunits(sum(sol.u[end]))
-        df = SemanticModels.generate_synthetic_data(population, 0,100)
-        f = @formula(vaccines_produced ~ flu_patients)
-        model =  lm(f,
-            df[2:length(df.year),
-            [:year, :flu_patients, :vaccines_produced]])
-        println("GLM Model:")
-        println(model)
-
-        year_to_predict = 1
-        num_flu_patients_from_sim = finalI
-        vaccines_produced = missing
-        targetDF = DataFrame(year=year_to_predict,
-            flu_patients=num_flu_patients_from_sim, 
-            vaccines_produced=missing)
-        @show targetDF
-
-
-        return RegressionProblem(f, model, targetDF, missing)
-    end
-    cm = CombinedModel([springmodel], create_sir)
-    flumodel = CombinedModel([cm], create_flu)
-    return flumodel
-end
-
-tfinal = 240π*u"d" #(~2 yrs)
-flumodel = flusim(tfinal)
-
-# -
-
-springmodel = flumodel.deps[1].deps[1]
-sirmodel = flumodel.deps[1]
-sol = solve(springmodel)
-plot(sol.t./d, map(x->x[1], sol.u) ./ C)
-
-sirsol = solve(sirmodel)
-
-plot(sirsol.t./d,map(x->stripunits.(x)[2], sirsol.u))
-
-sol = solve(flumodel)
-
-print(typeof(sol.u))
-
-
--- a/Project.toml
+++ b/Project.toml
@ -4,71 +4,17 @@ authors = ["James Fairbanks <james.fairbanks@gtri.gatech.edu>", "Micah Halter <m
 version = "0.3.0"

 [deps]
-BoundaryValueDiffEq = "764a87c0-6b3e-53db-9096-fe964310641d"
-CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
-Cassette = "7057c7e9-c182-5462-911a-8362d720325c"
 Catlab = "134e5e36-593f-5add-ad60-77f754baafbe"
-Compose = "a81c6b42-2e10-5240-aca2-a61377ecd94b"
-DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
-DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
-Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
-GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
-GeneralizedGenerated = "6b9d7cbe-bcb9-11e9-073f-15a7a543e2eb"
-GraphDataFrameBridge = "3c71623a-a715-5176-9801-629b201a4880"
-JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
-LabelledArrays = "2ee39098-c373-598a-b85f-a56591580800"
-Latexify = "23fbe1c1-3f47-55db-b15f-69d7ec21a316"
 LightGraphs = "093fc24a-ae57-5d10-9952-331d41423f4d"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
-LsqFit = "2fda8390-95c7-5789-9bda-21331edee243"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
-MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
-OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Petri = "4259d249-1051-49fa-8328-3f8ab9391c33"
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
-TikzPictures = "37f6aa50-8035-52d0-81c2-5a1d08754b2d"
-Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"

 [compat]
-CSV = "<1.0"
-Cassette = "<1.0"
-Catlab = "<1.0"
-DataFrames = "<1.0"
-DataFramesMeta = "<1.0"
-DelimitedFiles = "<2.0"
-Distributions = "0.23.0"
-Documenter = "<1.0"
-GLM = "<2.0"
-GeneralizedGenerated = "0.1.4"
-GraphDataFrameBridge = "<1.0"
-JSON = "<1.0"
-Latexify = "<1.0"
-LightGraphs = "<2.0"
-LinearAlgebra = "<2.0"
-Logging = "<2.0"
-LsqFit = "<1.0"
-MacroTools = "<1.0"
-MetaGraphs = "<1.0"
-Petri = "0.1.3"
-Plots = "<1.0"
-Random = "<2.0"
-StaticArrays = "0.11"
-Unitful = "<1.0"
 julia = "1.0"

 [extras]
-DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
-OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
-Polynomials = "f27b6e38-b328-58d1-80ce-0feddd5e7a45"
-Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
-Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

 [targets]
-test = ["OrdinaryDiffEq", "Polynomials", "Printf", "Statistics", "Test"]
+test = ["Test"]
--- a/README.md
+++ b/README.md
@ -44,10 +44,6 @@ Pkg.test("SemanticModels")

 ```

-Note that running the tests for the first time can take a while because `DifferentialEquations` is a large library that
-requires a long precompilation step. Various functions in the `SemanticModels.Dubstep` module can also have long
-precompile times, due to heavy use of generated functions.
-
 Then you can load it at the julia REPL with `using SemanticModels`

 You should start exploring the notebooks in the examples folder. These notebooks are represented in jupytext format,
@ -56,7 +52,7 @@ and are stored as julia programs you can run at the repl or in the notebook inte
 1. Model augmentation: an example script `examples/decorations/graphs.jl` shows how to augment an agent based simulation to add new
   modeling components using an API for changing models at the semantic level.

-2. Model Representations: SemanticModels supports extracting diagram representations of scripts and creating scripts from wiring diagram representations. See the `examples/petri/malaria.ipynb` notebook for a demonstration, as well as expanding on model augmentation by combining and composing models to build a more complex simulation.
+2. Model Representations: SemanticModels supports extracting diagram representations of scripts and creating scripts from wiring diagram representations. See the `examples/malaria/malaria.ipynb` notebook for a demonstration, as well as expanding on model augmentation by combining and composing models to build a more complex simulation.


 There are scripts in the folder `SemanticModels/bin` which provide command line access to some functionality of the
@ -92,27 +88,20 @@ In addition to the examples in the documentation, there are fully worked out exa
 https://github.com/jpfairbanks/SemanticModels.jl/tree/master/examples/. Each subdirectory represents one self contained
 example, starting with `epicookbook`.

-## Concepts
-
-Here is a preview of the concepts used in SemanticModels, please see the full documentation for a more thorough description.
-
-### Model Augmentation
+## Model Augmentation

 The primary usecase for SemanticModels.jl is to assist scientists in what we call *model augmentation*. This is the
 process of taking a known model developed by another researcher (potentially a past version of yourself) and
 transforming the model to create a novel model. This process can help fit an existing theory to new data, explore
 alternative hypotheses about the mechanisms of a natural phenomena, or conduct counterfactual thought experiments.

-SemanticModels.ModelTool is the current home for this capability.
-You can call `m = ModelTool.model(ExpAgentProblem, expr)` to lift an agent based model up to the semantic level, then apply
-transformations on that `m` and then call `eval(m.expr)` to generate code for that new model. This allows you to compare
+SemanticModels is the current home for this capability.
+You can call `m = model(PetriModel, model)` to lift a `Petri.jl` based model up to the semantic level, then apply
+transformations on that `m` and then call `solve(m)` to generate code for that new model. This allows you to compare
 different variations on a theme to conduct your research.

-If you are working with `ODEProblem` or agent based odels, there are prebuilt types for representing those models, but
-if you want to add a new class of models you will just have to write:
-
 1. a struct `T` that holds a structured representation of instances of the model class
-2. extend `ModelTool.model(::DataType{T}, expr::Expr)` to extract that information from a code representation of your model
+2. extend `model(::DataType{T}, model::Model{R})` to extract that information from a model generated by a specific DSL, and lift it to the semantic level
 3. a set of *valid transforms* that can be done to your model.

 SemanticModels.jl provides library functions to help with steps 2 and 3 and functions for executing and comparing then
@ -121,29 +110,6 @@ outputs of different variations of the model.
 We think of SemanticModels as a _post hoc_ modeling framework the enters the scene after scientific code has been
 written. As opposed to a standard modeling framework that you develop before you write the scientific code.

-<!---
-### Overdubbing
-
-`SemanticModels.Dubstep` provides functionality for manipulating models at execution time. Where `ModelTool` allows you
-to manipulate models at the syntactic level, `Dubstep` allows you to manipulate their execution. This falls along
-similar lines of static vs dynamic analysis.
-
-You can modify a program's execution using `Cassette.overdub` and replace function calls with your own functions. For an example, see `test/transform/ode.jl`. Or you can use a new compiler pass if you need more control over the values that you want to manipulate.
-->
-
-### Knowledge Graphs
-
-MetaGraphs.jl is used to model the relationships between models and concepts in a knowledge graph.
-
-There are a few different forms of knowledge graphs that can be extracted from codes.
-
-1. The type graph: Vertices are types, edges are functions between types see `examples/agenttypes2.jl`.
-
-2. Vertices are functions and variables, edges represent dataflow, function references variable or function calls function.
-
-3. Conceptual knowledge graph from text, vertices are concepts edges are relations between concepts.
-
-
 ## Acknowledgements

 This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Agreement No. HR00111990008.
--- a/16
+++ b/16
@ -1,19 +1,5 @@
-julia 1.0.0
-CSV
-Cassette
-Distributions
-Documenter
+Catlab
 LightGraphs
-MetaGraphs
-DataFrames
-DataFramesMeta
-GraphDataFrameBridge
-GLM
-JSON
-Latexify
-Unitful
-Plots
 MacroTools
-Catlab
 ModelingToolkit
 Petri
--- a/SemanticModelsIn10Mins.ipynb
+++ b/SemanticModelsIn10Mins.ipynb
--- a/bin/extract.jl
+++ b/bin/extract.jl
@ -1,449 +0,0 @@
-module Edges
-#using Pkg
-#Pkg.update()
-using DataFrames
-using GraphDataFrameBridge
-using MetaGraphs
-using CSV
-using LightGraphs
-using Random
-using DataFramesMeta
-using Colors
-using Logging
-
-using SemanticModels.Graphs
-using SemanticModels.Extraction
-
-export edgetype,
-    edges,
-    create_kg_from_code_edges,
-    create_kg_from_markdown_edges,
-    format_edges_dataframe,
-    assign_vertex_colors_by_key,
-    write_graph_to_dot_file
-
-function edgetype(var, val::Expr)
-    if val.head == :call
-        return :output
-    elseif length(val.args) >=2 && typeof(val.args[2]) <: Expr && val.args[2].head == :tuple
-        @show val.args
-        return :structure
-    else
-        return :takes
-    end
-end
-
-edgetype(var, val::Symbol) = :destructure
-edgetype(var, val) = :takes
-edgetype(var::Symbol, val::Symbol) = :takes
-edgetype(args...) = @show args
-
-
-function edges(mc, subdef, scope)
-    @info("Making edges",scope=scope)
-    edg = Any[]
-    for ( var,val ) in mc.vc
-        @show var, val
-        typ = edgetype(var, val)
-        val = typ==:structure ? val.args[2] : val
-        e = (scope, typ, :var, var, :val, val)
-        push!(edg, e)
-        if typ == :output
-            e = (scope, typ, :var, var, :exp, val)
-            push!(edg, e)
-        end
-        if typ == :structure
-            e = (scope, typ, :var, var, :tuple, val.args[2])
-            push!(edg, e)
-        end
-
-        if typeof(val) <: Expr && val.head == :vect
-            push!(edg, (scope, :has, :value, var, :property, :collection))
-        end
-        if typeof(val) <: Expr && val.head == :call
-            push!(edg, (scope, :input, :func, val.args[1], :args, Symbol.(val.args[2:end])))
-        end
-        if typ == :destructure
-            @debug var.args
-            for lhs in var.args
-                push!(edg, (scope, :comp, :var, val, :var, lhs))
-            end
-        end
-        if typ == :structure
-            @debug var, val
-            for rhs in val.args
-                push!(edg, (scope, :comp, :var, var, :val, rhs))
-            end
-        end
-
-    end
-    for (funcname, smc) in subdef
-        @debug "Recursing"
-        # @show funcname, smc
-        subedges = edges(smc, [], "$scope.$funcname")
-        for e in subedges
-            push!(edg, e)
-        end
-    end
-    return edg
-end
-
-function preprocess_vertex_name(orig_str_rep)
-    #TODO this should work and be faster:
-    # replace(orig_str_rep, "^" => "exp", "*" => "star", "-" => "neg", "\"" => " ") # \" #this comment works around a bug in emacs julia formater
-    clean_str = replace(
-                  replace(
-                      replace(
-                          replace(orig_str_rep, "^" => "exp"),
-                          "*" => "star"),
-                      "-" => "neg"),
-                  "\"" => " ") # \" #this comment works around a bug in emacs julia formater
-    return clean_str
-end
-
-function format_edges_dataframe(code_edges, output_path::String)
-
-    edges_df = []
-
-    for e in code_edges
-
-        # Going from left to right, there are two edges we need to create (?)
-
-        # Edge one
-        src_1_vname = "$(e[1])"
-        src_1_vtype = "missing"
-        #dst_1_vname = preprocess_vertex_name(("\""*"\""*"$(e[4])"*"\""*"\""))
-        dst_1_vname = "$(e[4])"
-        #dst_1_vtype = e[3] # this is the data type; we'll use it once we have reconciliation rules in place
-        dst_1_vtype = "missing"
-
-        src_1_vhash = Graphs.gen_vertex_hash("$src_1_vname", "src_1_vtype")
-        dst_1_vhash = Graphs.gen_vertex_hash("$dst_1_vname", "dst_1_vtype")
-
-        edge_1_relation = e[2]
-        edge_1_description = e[2]
-        edge_1_value = e[6] # check this; unclear if we want to (only) store as metadata or actually create edge 2
-
-        edge_1_attrs = (src_vhash = "$src_1_vhash",
-            src_name = src_1_vname,
-            src_vtype = src_1_vtype,
-            dst_vhash = "$dst_1_vhash",
-            dst_name = dst_1_vname,
-            dst_vtype = dst_1_vtype,
-            edge_relation = edge_1_relation,
-            edge_description = edge_1_description,
-            value = "$edge_1_value"
-         )
-
-        push!(edges_df, edge_1_attrs)
-
-        # Edge two
-        src_2_vname = dst_1_vname
-        src_2_vtype = dst_1_vtype
-        #dst_2_vname = preprocess_vertex_name(("\""*"\""*"$(e[6])"*"\""*"\""))
-        dst_2_vname = "$(e[6])"
-        dst_2_vtype = "missing"
-
-        src_2_vhash = dst_1_vhash
-        dst_2_vhash = Graphs.gen_vertex_hash("$dst_2_vname", "dst_2_vtype")
-
-        edge_2_relation = string(e[5])
-        edge_2_description = string(e[5])
-        edge_2_value = string(e[6])
-
-        edge_2_attrs = (src_vhash = "$src_2_vhash",
-            src_name = src_2_vname,
-            src_vtype = src_2_vtype,
-            dst_vhash = "$dst_2_vhash",
-            dst_name = dst_2_vname,
-            dst_vtype = dst_2_vtype,
-            edge_relation = edge_2_relation,
-            edge_description = edge_2_description,
-            value = "$edge_2_value"
-         )
-
-        push!(edges_df, edge_2_attrs)
-    end
-
-    open(output_path, "w") do io
-        print(io, repr(Graphs.load_graph_data(edges_df)))
-    end
-
-    return edges_df
-
-end
-
-
-function create_kg_from_code_edges(code_edges)
-
-    G = MetaDiGraph()
-    G_prime = Graphs.insert_edges_from_jl(code_edges, G)
-    return G_prime
-end
-
-function create_kg_from_code_edges(code_edges, G::MetaDiGraph)
-
-    G_prime = Graphs.insert_edges_from_jl(code_edges, G)
-    return G_prime
-end
-
-function create_kg_from_markdown_edges(path)
-
-    G = Extraction.definitiongraph(path, Extraction.sequentialnamer())
-
-    # this is a hack for now..
-    # TODO: modify the markdown definitions.jl script to ensure vertex/edge props match the schema
-    for v in vertices(G)
-
-        v_hash = Graphs.gen_vertex_hash(get_prop(G, v, :name), "missing")
-        set_indexing_prop!(G, v, :v_hash, "$v_hash")
-
-        set_props!(G, v,
-            Dict(:v_name=>get_prop(G, v, :name),
-                 :v_type=>"missing"))
-    end
-
-    for e in LightGraphs.edges(G)
-
-        src_vhash = Graphs.gen_vertex_hash(get_prop(G, e.src, :name), "missing")
-        dst_vhash = Graphs.gen_vertex_hash(get_prop(G, e.dst, :name), "missing")
-
-        set_props!(G, Edge(e.src, e.dst), Dict(:e_rel=>"verb",
-                                                :e_desc=>"Verb",
-                                                :e_value=>"is defined as",
-                                                :weight=>1))
-    end
-
-    return G
-end
-
-
-function create_kg_from_markdown_edges(path, extraction_rule="definition")
-
-    G = Extraction.definitiongraph(path, Extraction.sequentialnamer())
-
-#     vertex_df = DataFrame(vertex=String[],
-#                           v_hash=String[],
-#                           v_name=String[],
-#                           v_type=String[],
-#                           v_text=String[])
-
-#     edges_df = DataFrame(src_vhash = String[],
-#                         src_name = String[],
-#                         src_vtype = String[],
-#                         dst_vhash = String[],
-#                         dst_name = String[],
-#                         dst_vtype = String[],
-#                         edge_relation = String[],
-#                         edge_description = String[],
-#                         value = String[])
-
-    # this is a hack for now..
-    # TODO: modify the markdown definitions.jl script to ensure vertex/edge props match the schema
-    for v in vertices(G)
-
-        v_hash = Graphs.gen_vertex_hash(get_prop(G, v, :name), "concept")
-        set_indexing_prop!(G, v, :v_hash, "$v_hash")
-
-        set_props!(G, v,
-            Dict(:v_name=>get_prop(G, v, :name),
-                 :v_type=>"concept",
-                 :v_text=>length(get_prop(G, v, :text))==0 ? "no_text" : get_prop(G, v, :text)))
-
-#         push!(vertex_df, (vertex="$v", v_hash="$v_hash", v_name=get_prop(G, v, :name), v_type="concept",  v_text=length(get_prop(G, v, :text))==0 ? "no_text" : get_prop(G, v, :text)))
-
-    end
-
-    for e in LightGraphs.edges(G)
-
-        src_vhash = Graphs.gen_vertex_hash(get_prop(G, e.src, :name), "concept")
-        dst_vhash = Graphs.gen_vertex_hash(get_prop(G, e.dst, :name), "concept")
-
-
-        set_props!(G, Edge(e.src, e.dst), Dict(:e_rel=>"verb",
-                                                :e_desc=>"Verb",
-                                                :e_value=>"is defined as",
-                                                :weight=>1))
-
-#         push!(edges_df, (src_vhash="$src_vhash", src_name = get_prop(G, e.src, :name), src_vtype="concept",
-#                 dst_vhash="$dst_vhash", dst_name = get_prop(G, e.dst, :name), dst_vtype = "concept",
-#                 edge_relation= "verb", edge_description="Verb", value="is defined as"))
-    end
-
-    # for debugging
-    #CSV.write("../examples/epicookbook/data/markdown_vertices.csv",vertex_df)
-    #CSV.write("../examples/epicookbook/data/markdown_edges.csv",edges_df)
-    return G
-end
-
-
-"""    assign_vertex_colors_by_key(G::MetaDiGraph, color_by::Symbol)
-
-Takes as input an existing MetaDiGraph, and a group_by, which is assumed to correspond to an existing vertex prop.
-Groups the vertices in G by the group_by field, computes the number of unique colors needed, and generates a hash table, with key equal to the vertex hash, and value equal to the assigned color.
-
-see also: [`assign_edge_styles_by_key`](@ref), [1write_graph_to_dot_file1](@ref)
-"""
-function assign_vertex_colors_by_key(G::MetaDiGraph, group_by::Symbol)
-
-    # element 1 = white; element 2 = black; for sake of making graph easier to read, add +2 buffer and start indexing colors at 3
-    cols = distinguishable_colors(length(unique(get_prop(G, v, group_by) for v in vertices(G)))+2, [RGB(1,1,1)])[3:end]
-
-    color_type_lookup = Dict()
-
-    for (i, v_type) in enumerate(unique([get_prop(G, v, group_by) for v in vertices(G)]))
-        color_type_lookup[v_type] = "#$(hex(cols[i]))"
-    end
-
-    vertex_color_lookup = Dict()
-
-    for v in vertices(G)
-        vertex_color_lookup[get_prop(G, v, :v_hash)] = color_type_lookup[get_prop(G, v,group_by)]
-    end
-
-    return vertex_color_lookup
-
-end
-
-
-"""    assign_edge_styles_by_key(G::MetaDiGraph, group_by::Symbol)
-
-Takes as input an existing MetaDiGraph, and a group_by field, which is assumed to correspond to an existing edge prop.
-Groups the directed edges in G by the group_by field, computes the number of unique edge styles needed, and generates a hash table, with key equal to the edge id, and value equal to the assigned (line) style.
-
-see also: [`assign_edge_styles_by_key`](@ref), [1write_graph_to_dot_file1](@ref)
-"""
-function assign_edge_style_by_key(G::MetaDiGraph, groupy_by::Symbol)
-    # TODO; need to figure out how to generate arbitrary styles, or put a cap on number based on schema
-end
-
-
-# TODO: fix syntax and puncutuation errors so the graph can be loaded from a dot file
-function write_graph_to_dot_file(G::MetaDiGraph, output_path::String, graph_name::String, v_color_lookup)
-
-    head = "digraph " * graph_name * " {"
-
-     open(output_path, "w") do io
-
-        println(io, head)
-
-        for v in vertices(G)
-            v_color = v_color_lookup[get_prop(G, v, :v_hash)]
-            vname = preprocess_vertex_name(get_prop(G, v, :v_name))
-            println(io, string("$v" *  " [color=" * "\"" * "$v_color" *  "\"" * " ," * " label=" * "\"" * "$vname" * "\"" * "];"))
-
-        end
-
-        for e in LightGraphs.edges(G)
-
-            src_vname = preprocess_vertex_name(get_prop(G, e.src, :v_name))
-            dst_vname = preprocess_vertex_name(get_prop(G, e.dst, :v_name))
-            e_rel = get_prop(G, e, :e_rel)
-            #e_value = get_prop(G, e, :e_value)
-
-            println(io, string("$(e.src)" * " -> " * "$(e.dst)" * " [label=" * "\"" * "$(e_rel)" * "\""  * "];"))
-
-        end
-
-        println(io, "}")
-
-    end
-
-    # Example dot file
-    #         digraph graphname {
-    #          // The label attribute can be used to change the label of a node
-    #          a [label="Foo"];
-    #          // Here, the node shape is changed.
-    #          b [shape=box];
-    #          // These edges both have different line properties
-    #          a -- b -- c [color=blue];
-    #          b -- d [style=dotted];
-    #      }
-    end
-
-end
-
-
-# command-line usage
-# julia -i --project ../bin/extract.jl ../examples/epicookbook/src/ScalingModel.jl ../examples/epicookbook/src/SEIRmodel.jl
-
-using SemanticModels.Parsers
-using SemanticModels.Graphs
-using SemanticModels.Extraction
-@debug "Done Loading Package"
-using DataFrames
-using MetaGraphs
-using LightGraphs
-
-if length(ARGS) < 1
-    error("You must provide a file path to a .jl file", args=ARGS)
-end
-
-mdown_path = "../examples/epicookbook/epireceipes_Automates_GTRI_ASKE_2rules_output/json"
-
-function markdowngraph(mdown_path)
-    try
-        return Edges.create_kg_from_markdown_edges(mdown_path, "definition")
-    catch
-        @warn "Failed to read markdown" path=mdown_path
-        return MetaDiGraph()
-    end
-end
-
-G_markdown = markdowngraph(mdown_path)
-
-@info("Graph created from markdown has v vertices and e edges.", v=nv(G_markdown), e=ne(G_markdown))
-
-num_files = length(ARGS)
-global G_temp = MetaDiGraph()
-
-for i in 1:num_files
-
-    output_path = "../examples/epicookbook/data/edges_from_code_$i.jl"
-
-    path = ARGS[i]
-    @info "Parsing julia script" file=path
-    expr = parsefile(path)
-    mc = defs(expr.args[3].args)
-    @info "script uses modules" modules=mc.modc
-    @info "script defines functions" funcs=mc.fc.defs
-    @info "script defines glvariables" funcs=mc.vc
-    subdefs = recurse(mc)
-    @info "local scope definitions" subdefs=subdefs
-
-    for func in subdefs
-        funcname = func[1]
-        mc = func[2]
-        @info "$funcname uses modules" modules=mc.modc
-        @info "$funcname defines functions" funcs=mc.fc.defs
-        @info "$funcname defines variables" funcs=mc.vc
-    end
-
-    edg = Edges.edges(mc, subdefs, expr.args[2])
-    @info("Edges found", path=path)
-    #for e in edg
-        #println(e)
-    #end
-
-    code_edges_df = Edges.format_edges_dataframe(edg, output_path)
-
-    if i == 1
-        # We only need to ingest the markdown info once.
-        G_code = Edges.create_kg_from_code_edges(output_path, G_markdown)
-    else
-        G_code = Edges.create_kg_from_code_edges(output_path, G_temp)
-    end
-
-    @info("Code graph $i has v vertices and e edges.", v=nv(G_code), e=ne(G_code))
-
-    global vcolors = Edges.assign_vertex_colors_by_key(G_code, :v_type)
-    global G_temp = copy(G_code)
-
-end
-
-@info("All markdown and code files have been parsed; writing final knowledge graph to dot file")
-dot_file_path = "../examples/epicookbook/data/dot_file_ex1.dot"
-Edges.write_graph_to_dot_file(G_temp, dot_file_path, "G_code_and_markdown", vcolors)
-
-# Generate svg file
-run(`dot -Tsvg -O $dot_file_path`)
--- a/bin/extractvars.jl
+++ b/bin/extractvars.jl
@ -1,109 +0,0 @@
-#!/usr/bin/env julia
-# extractvars.jl is a script to print out the variables contained in a julia program
-# Example Usage:
-#
-#      julia ../bin/extractvars.jl epicookbook/src/SIRModel.jl epicookbook/src/ScalingModel.jl
-#
-# Example Output:
-# loaded
-# epicookbook/src/SIRModel.jl, "sir_sol"
-# epicookbook/src/SIRModel.jl, "tspan"
-# epicookbook/src/SIRModel.jl, "du[2]"
-# epicookbook/src/SIRModel.jl, "(b, g)"
-# epicookbook/src/SIRModel.jl, "S"
-# epicookbook/src/SIRModel.jl, "pram"
-# epicookbook/src/SIRModel.jl, "init"
-# epicookbook/src/SIRModel.jl, "sir_prob2"
-# epicookbook/src/SIRModel.jl, "(S, I, R)"
-# epicookbook/src/SIRModel.jl, "du[3]"
-# epicookbook/src/SIRModel.jl, "parms"
-# epicookbook/src/SIRModel.jl, "β"
-# epicookbook/src/SIRModel.jl, "γ"
-# epicookbook/src/SIRModel.jl, "I"
-# epicookbook/src/SIRModel.jl, "du[1]"
-# epicookbook/src/SIRModel.jl, "sir_prob"
-# epicookbook/src/ScalingModel.jl, "α"
-# epicookbook/src/ScalingModel.jl, "sir_sol"
-# epicookbook/src/ScalingModel.jl, "tspan"
-# epicookbook/src/ScalingModel.jl, "βs[:, i]"
-# epicookbook/src/ScalingModel.jl, "w"
-# epicookbook/src/ScalingModel.jl, "μ"
-# epicookbook/src/ScalingModel.jl, "K"
-# epicookbook/src/ScalingModel.jl, "init"
-# epicookbook/src/ScalingModel.jl, "(β, r, μ, K, α)"
-# epicookbook/src/ScalingModel.jl, "dS"
-# epicookbook/src/ScalingModel.jl, "ws"
-# epicookbook/src/ScalingModel.jl, "(S, I)"
-# epicookbook/src/ScalingModel.jl, "parms"
-# epicookbook/src/ScalingModel.jl, "du"
-# epicookbook/src/ScalingModel.jl, "r"
-# epicookbook/src/ScalingModel.jl, "dI"
-# epicookbook/src/ScalingModel.jl, "β"
-# epicookbook/src/ScalingModel.jl, "m"
-# epicookbook/src/ScalingModel.jl, "βs"
-# epicookbook/src/ScalingModel.jl, "sir_prob"
-# epicookbook/src/ScalingModel.jl, "i"
-try
-    using SemanticModels
-catch
-
-    import Pkg;
-    Pkg.add("SemanticModels")
-end
-
-using SemanticModels.Parsers
-import SemanticModels.Parsers.walk
-
-"""    findassigns(expr::Expr)
-
-findassign walks the AST of `expr` to find the assignments to variables.
-
-This function returns a reference to the original expression so that you can modify it inplace
-and is intended to help users rewrite expressions for generating new models.
-
-See also: [`findassig