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
9 months ago · f85f5febf7
--- 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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     "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",
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       "               20.0 d\n",
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       " [10000.0 person, 1.0 person, 0.0 person]           \n",
       " [9999.69 person, 1.31091 person, 0.00115006 person]\n",
       " [9998.22 person, 2.76907 person, 0.0065442 person] \n",
       " [9993.17 person, 7.80133 person, 0.0251661 person] \n",
       " [9976.09 person, 24.8203 person, 0.0882149 person] \n",
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   "source": [
    "sirsol = solve(sirmodel)"
   ]
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  {
   "cell_type": "code",
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   ],
   "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": []
  }
 ],
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--- 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"

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 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/+ 1
+++ b/+ 1
@ -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: [`findassign`](@ref).
 """
 function findassigns(expr::Expr)
    # g(y) = filter(x->x!=nothing, y)
    matches = Dict{Any,Set{Expr}}()
    g(y::Any) = :()
    f(x::Any) = :()
    f(x::Expr) = begin
        if x.head == :(=)
            s = get(matches, x.args[1], Set{Expr}())
            matches[x.args[1]] = push!(s, x)
            return x
        end
        walk(x, f, g)
    end
    walk(expr, f, g)
    return matches
 end

 """    findvars(file)

 finds all the variable names from a julia script.

 Example:
    file = "epicookbook/src/DiscreteTimeSIR.jl"
    matches = findvars(file)
 """
 function findvars(file)
    ex = parsefile(file)
    matches = findassigns(ex)
    matches = matches
    return matches
 end

 if length(ARGS) > 0
    for file in ARGS
        try
            matches = findvars(file)
            for k in keys(matches)
                println("$file,\"$k\"")
            end
        catch
            @warn "Processing failed" file=file
        end
    end
 end
--- a/bin/transformations.jl
+++ b/bin/transformations.jl
@ -1,57 +0,0 @@
 # -*- coding: utf-8 -*-
 # + {}
 module Transformations