Merge pull request #178 from SciML/Projecttomls

Split project tomls

Author: christopher-dG

.github/workflows/CompatHelper.yml

@@ -8,19 +8,15 @@ on:
 
 jobs:
   build:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        julia-version: [1.2.0]
-        julia-arch: [x86]
-        os: [ubuntu-latest]
+    runs-on: ubuntu-latest
     steps:
-      - uses: julia-actions/setup-julia@latest
-        with:
-          version: ${{ matrix.julia-version }}
       - name: Pkg.add("CompatHelper")
         run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
       - name: CompatHelper.main()
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: julia -e 'using CompatHelper; CompatHelper.main()'
+        run: |
+          julia -e '
+            using CompatHelper
+            dirs = filter(isdir, readdir("tutorials"; join=true))
+            CompatHelper.main(; subdirs=["", dirs...])'

Project.toml

@@ -4,95 +4,12 @@ authors = ["Chris Rackauckas <[email protected]>"]
 version = "0.7.0"
 
 [deps]
-AlgebraicMultigrid = "2169fc97-5a83-5252-b627-83903c6c433c"
-ArbNumerics = "7e558dbc-694d-5a72-987c-6f4ebed21442"
-BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-CUDAnative = "be33ccc6-a3ff-5ff2-a52e-74243cff1e17"
-Cairo = "159f3aea-2a34-519c-b102-8c37f9878175"
-CuArrays = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
-DecFP = "55939f99-70c6-5e9b-8bb0-5071ed7d61fd"
-Decimals = "abce61dc-4473-55a0-ba07-351d65e31d42"
-DiffEqBayes = "ebbdde9d-f333-5424-9be2-dbf1e9acfb5e"
-DiffEqBiological = "eb300fae-53e8-50a0-950c-e21f52c2b7e0"
-DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
-DiffEqDevTools = "f3b72e0c-5b89-59e1-b016-84e28bfd966d"
-DiffEqNoiseProcess = "77a26b50-5914-5dd7-bc55-306e6241c503"
-DiffEqOperators = "9fdde737-9c7f-55bf-ade8-46b3f136cc48"
-DiffEqParamEstim = "1130ab10-4a5a-5621-a13d-e4788d82bd4c"
-DiffEqPhysics = "055956cb-9e8b-5191-98cc-73ae4a59e68a"
-DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
-Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-DoubleFloats = "497a8b3b-efae-58df-a0af-a86822472b78"
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
-Latexify = "23fbe1c1-3f47-55db-b15f-69d7ec21a316"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
-Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7"
-ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
-NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
-NeuralNetDiffEq = "8faf48c0-8b73-11e9-0e63-2155955bfa4d"
-Optim = "429524aa-4258-5aef-a3af-852621145aeb"
-OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
-ParameterizedFunctions = "65888b18-ceab-5e60-b2b9-181511a3b968"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
-PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
-RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
-SparseDiffTools = "47a9eef4-7e08-11e9-0b38-333d64bd3804"
-SparsityDetection = "684fba80-ace3-11e9-3d08-3bc7ed6f96df"
-StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
-StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
-StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
-Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"
-Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 Weave = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9"
 
 [compat]
-AlgebraicMultigrid = "0.2, 0.3"
-ArbNumerics = "1.0"
-BenchmarkTools = "0.4, 0.5"
-CUDA = "1.0"
-CUDAnative = "2.5, 3.0"
-Cairo = "0.8, 1.0"
-CuArrays = "1.4, 2.0"
-DecFP = "0.4, 1.0"
-Decimals = "0.4"
-DiffEqBayes = "2.8"
-DiffEqBiological = "4.0"
-DiffEqCallbacks = "2.9"
-DiffEqDevTools = "2.15"
-DiffEqNoiseProcess = "4.2"
-DiffEqOperators = "4.3"
-DiffEqParamEstim = "1.8"
-DiffEqPhysics = "3.2"
-DifferentialEquations = "6.8"
-Distributions = "0.21, 0.22, 0.23"
-DoubleFloats = "0.9, 1.0"
-Flux = "0.10"
-ForwardDiff = "0.10"
 IJulia = "1.20"
-Latexify = "0.12, 0.13"
-MCMCChains = "3.0, 4.0"
-Measurements = "2.1"
-ModelingToolkit = "0.9, 0.10, 1.0, 2.0, 3.0"
-NLsolve = "4.2"
-NeuralNetDiffEq = "1.5"
-Optim = "0.19, 0.20, 0.21"
-OrdinaryDiffEq = "5.23"
-ParameterizedFunctions = "4.2, 5.0"
-Plots = "0.27, 0.28, 0.29, 1.0"
-PyPlot = "2.8"
-RecursiveArrayTools = "1,2"
-SparseDiffTools = "0.10, 1.0"
-SparsityDetection = "0.1, 0.2, 0.3"
-StaticArrays = "0.10, 0.11, 0.12"
-StatsPlots = "0.12, 0.13, 0.14"
-StochasticDiffEq = "6.23"
-Sundials = "3.8, 4.0"
-Unitful = "0.17, 0.18, 1.0"
 Weave = "0.9, 0.10"
-julia = "1"
+julia = "1.4"

README.md

@@ -44,13 +44,15 @@ DiffEqTutorials.open_notebooks()
   - [DiffEqBiological Tutorial II: Network Properties API](http://tutorials.juliadiffeq.org/html/models/04-diffeqbio_II_networkproperties.html)
   - [DiffEqBiological Tutorial III: Steady-States and Bifurcations](http://tutorials.juliadiffeq.org/html/models/04b-diffeqbio_III_steadystates.html)
   - [Kepler Problem Orbit](http://tutorials.juliadiffeq.org/html/models/05-kepler_problem.html)
-  - [Bayesian Inference of Pendulum Parameters](http://tutorials.juliadiffeq.org/html/models/06-pendulum_bayesian_inference.html)
   - [Kolmogorov Backward Equations](http://tutorials.juliadiffeq.org/html/models/08-kolmogorov_equations.html)
 - Advanced ODE Features
   - [ModelingToolkit.jl, An IR and Compiler for Scientific Models](http://tutorials.juliadiffeq.org/html/ode_extras/01-ModelingToolkit.html)
   - [Feagin's Order 10, 12, and 14 Methods](http://tutorials.juliadiffeq.org/html/ode_extras/02-feagin.html)
   - [Finding Maxima and Minima of DiffEq Solutions](http://tutorials.juliadiffeq.org/html/ode_extras/03-ode_minmax.html)
-  - [Monte Carlo Parameter Estimation from Data](http://tutorials.juliadiffeq.org/html/ode_extras/04-monte_carlo_parameter_estim.html)
+
+- Model Inference
+  - [Bayesian Inference of Pendulum Parameters](http://tutorials.juliadiffeq.org/html/model_inference/01-pendulum_bayesian_inference.html)
+  - [Monte Carlo Parameter Estimation from Data](http://tutorials.juliadiffeq.org/html/model_inference/02-monte_carlo_parameter_estim.html)
 - Type Handling
   - [Solving Equations with Julia-Defined Types](http://tutorials.juliadiffeq.org/html/type_handling/01-number_types.html)
   - [Numbers with Uncertainties](http://tutorials.juliadiffeq.org/html/type_handling/02-uncertainties.html)

src/DiffEqTutorials.jl

@@ -8,6 +8,8 @@ latexfile = joinpath(@__DIR__, "..", "templates", "julia_tex.tpl")
 
 function weave_file(folder,file,build_list=(:script,:html,:pdf,:github,:notebook); kwargs...)
   tmp = joinpath(repo_directory,"tutorials",folder,file)
+  Pkg.activate(dirname(tmp))
+  Pkg.instantiate()
   args = Dict{Symbol,String}(:folder=>folder,:file=>file)
   if :script ∈ build_list
     println("Building Script")

tutorials/advanced/01-beeler_reuter.jmd

@@ -5,7 +5,7 @@ author: Shahriar Iravanian
 
 ## Background
 
-[JuliaDiffEq](https://github.com/JuliaDiffEq) is a suite of optimized Julia libraries to solve ordinary differential equations (ODE). *JuliaDiffEq* provides a large number of explicit and implicit solvers suited for different types of ODE problems. It is possible to reduce a system of partial differential equations into an ODE problem by employing the [method of lines (MOL)](https://en.wikipedia.org/wiki/Method_of_lines). The essence of MOL is to discretize the spatial derivatives (by finite difference, finite volume or finite element methods) into algebraic equations and to keep the time derivatives as is. The resulting differential equations are left with only one independent variable (time) and can be solved with an ODE solver. [Solving Systems of Stochastic PDEs and using GPUs in Julia](http://www.stochasticlifestyle.com/solving-systems-stochastic-pdes-using-gpus-julia/) is a brief introduction to MOL and using GPUs to accelerate PDE solving in *JuliaDiffEq*. Here we expand on this introduction by developing an implicit/explicit (IMEX) solver for a 2D cardiac electrophysiology model and show how to use [CuArray](https://github.com/JuliaGPU/CuArrays.jl) and [CUDAnative](https://github.com/JuliaGPU/CUDAnative.jl) libraries to run the explicit part of the model on a GPU.
+[SciML](https://github.com/SciML) is a suite of optimized Julia libraries to solve ordinary differential equations (ODE). *SciML* provides a large number of explicit and implicit solvers suited for different types of ODE problems. It is possible to reduce a system of partial differential equations into an ODE problem by employing the [method of lines (MOL)](https://en.wikipedia.org/wiki/Method_of_lines). The essence of MOL is to discretize the spatial derivatives (by finite difference, finite volume or finite element methods) into algebraic equations and to keep the time derivatives as is. The resulting differential equations are left with only one independent variable (time) and can be solved with an ODE solver. [Solving Systems of Stochastic PDEs and using GPUs in Julia](http://www.stochasticlifestyle.com/solving-systems-stochastic-pdes-using-gpus-julia/) is a brief introduction to MOL and using GPUs to accelerate PDE solving in *JuliaDiffEq*. Here we expand on this introduction by developing an implicit/explicit (IMEX) solver for a 2D cardiac electrophysiology model and show how to use [CuArray](https://github.com/JuliaGPU/CuArrays.jl) and [CUDAnative](https://github.com/JuliaGPU/CUDAnative.jl) libraries to run the explicit part of the model on a GPU.
 
 Note that this tutorial does not use the [higher order IMEX methods built into DifferentialEquations.jl](https://docs.juliadiffeq.org/latest/solvers/split_ode_solve/#Implicit-Explicit-(IMEX)-ODE-1) but instead shows how to hand-split an equation when the explicit portion has an analytical solution (or approxiate), which is common in many scenarios.
 

tutorials/advanced/Project.toml

@@ -0,0 +1,16 @@
+[deps]
+AlgebraicMultigrid = "2169fc97-5a83-5252-b627-83903c6c433c"
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+CUDAnative = "be33ccc6-a3ff-5ff2-a52e-74243cff1e17"
+CuArrays = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
+DiffEqOperators = "9fdde737-9c7f-55bf-ade8-46b3f136cc48"
+DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
+NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+SparseDiffTools = "47a9eef4-7e08-11e9-0b38-333d64bd3804"
+SparsityDetection = "684fba80-ace3-11e9-3d08-3bc7ed6f96df"
+Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"

tutorials/introduction/Project.toml

@@ -0,0 +1,8 @@
+[deps]
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+ParameterizedFunctions = "65888b18-ceab-5e60-b2b9-181511a3b968"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"

tutorials/models/06-pendulum_bayesian_inference.jmd renamed to tutorials/model_inference/01-pendulum_bayesian_inference.jmd

@@ -0,0 +1,14 @@
+[deps]
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+CmdStan = "593b3428-ca2f-500c-ae53-031589ec8ddd"
+DiffEqBayes = "ebbdde9d-f333-5424-9be2-dbf1e9acfb5e"
+DiffEqParamEstim = "1130ab10-4a5a-5621-a13d-e4788d82bd4c"
+DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb"
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
+StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
+TransformVariables = "84d833dd-6860-57f9-a1a7-6da5db126cff"