Merge pull request #881 from ArnoStrouwen/qa

Aqua and typos CI

Author: ChrisRackauckas

.github/workflows/SpellCheck.yml

@@ -0,0 +1,13 @@
+name: Spell Check
+
+on: [pull_request]
+
+jobs:
+  typos-check:
+    name: Spell Check with Typos
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Actions Repository
+        uses: actions/checkout@v3
+      - name: Check spelling
+        uses: crate-ci/[email protected] 

.typos.toml

@@ -0,0 +1,2 @@
+[default.extend-words]
+Teh = "Teh"

docs/src/examples/GPUs.md

@@ -79,7 +79,7 @@ using Lux, Optimization, OptimizationOptimisers, Zygote, OrdinaryDiffEq,
     Plots, LuxCUDA, SciMLSensitivity, Random, ComponentArrays
 import DiffEqFlux: NeuralODE
 
-CUDA.allowscalar(false) # Makes sure no slow operations are occuring
+CUDA.allowscalar(false) # Makes sure no slow operations are occurring
 
 #rng for Lux.setup
 rng = Random.default_rng()

docs/src/examples/neural_ode_weather_forecast.md

@@ -5,7 +5,7 @@ This example is adapted from [Forecasting the weather with neural ODEs - Sebatia
 
 ## The data
 
-The data is a four-dimensional dataset of daily temperature, humidity, wind speed and pressure meassured over four years in the city Delhi. Let us download and plot it.
+The data is a four-dimensional dataset of daily temperature, humidity, wind speed and pressure measured over four years in the city Delhi. Let us download and plot it.
 
 ```julia
 using Random, Dates, Optimization, ComponentArrays, Lux, OptimizationOptimisers, DiffEqFlux,
@@ -29,7 +29,7 @@ df = download_data()
 
 ```julia
 FEATURES = [:meantemp, :humidity, :wind_speed, :meanpressure]
-UNITS = ["Celcius", "g/m³ of water", "km/h", "hPa"]
+UNITS = ["Celsius", "g/m³ of water", "km/h", "hPa"]
 FEATURE_NAMES = ["Mean temperature", "Humidity", "Wind speed", "Mean pressure"]
 
 function plot_data(df)

docs/src/index.md

@@ -29,7 +29,7 @@ The following layer functions exist:
   - [Collocation-Based Neural ODEs (Neural ODEs without a solver, by far the fastest way!)](https://www.degruyter.com/document/doi/10.1515/sagmb-2020-0025/html)
   - [Multiple Shooting Neural Ordinary Differential Equations](https://arxiv.org/abs/2109.06786)
   - [Neural Stochastic Differential Equations (Neural SDEs)](https://arxiv.org/abs/1907.07587)
-  - [Neural Differential-Algebriac Equations (Neural DAEs)](https://arxiv.org/abs/2001.04385)
+  - [Neural Differential-Algebraic Equations (Neural DAEs)](https://arxiv.org/abs/2001.04385)
   - [Neural Delay Differential Equations (Neural DDEs)](https://arxiv.org/abs/2001.04385)
   - [Augmented Neural ODEs](https://arxiv.org/abs/1904.01681)
   - [Hamiltonian Neural Networks (with specialized second order and symplectic integrators)](https://arxiv.org/abs/1906.01563)
@@ -63,7 +63,7 @@ the precision of the arguments are correct, and anything that requires alternati
 
 Lux.jl has none of these issues, is simpler to work with due to the parameters in its function calls being explicit rather than implicit global
 references, and achieves higher performance. It is built on the same foundations as Flux.jl, such as Zygote and NNLib, and thus it supports the
-same layers underneith and calls the same kernels. The better performance comes from not having the overhead of `restructure` required.
+same layers underneath and calls the same kernels. The better performance comes from not having the overhead of `restructure` required.
 Thus we highly recommend people use Lux instead and only use the Flux fallbacks for legacy code.
 
 ## Citation

src/hnn.jl

@@ -75,7 +75,7 @@ end
 """
     NeuralHamiltonianDE(model, tspan, args...; kwargs...)
 
-Contructs a Neural Hamiltonian DE Layer for solving Hamiltonian Problems parameterized by a
+Constructs a Neural Hamiltonian DE Layer for solving Hamiltonian Problems parameterized by a
 Neural Network [`HamiltonianNN`](@ref).
 
 Arguments:

src/neural_de.jl

@@ -19,7 +19,7 @@ Arguments:
 - `tspan`: The timespan to be solved on.
 - `alg`: The algorithm used to solve the ODE. Defaults to `nothing`, i.e. the
   default algorithm from DifferentialEquations.jl.
-- `sensealg`: The choice of differentiation algorthm used in the backpropogation.
+- `sensealg`: The choice of differentiation algorithm used in the backpropogation.
   Defaults to an adjoint method. See
   the [Local Sensitivity Analysis](https://docs.sciml.ai/DiffEqDocs/stable/analysis/sensitivity/)
   documentation for more details.
@@ -69,7 +69,7 @@ Arguments:
 - `tspan`: The timespan to be solved on.
 - `alg`: The algorithm used to solve the ODE. Defaults to `nothing`, i.e. the
   default algorithm from DifferentialEquations.jl.
-- `sensealg`: The choice of differentiation algorthm used in the backpropogation.
+- `sensealg`: The choice of differentiation algorithm used in the backpropogation.
 - `kwargs`: Additional arguments splatted to the ODE solver. See the
   [Common Solver Arguments](https://docs.sciml.ai/DiffEqDocs/stable/basics/common_solver_opts/)
   documentation for more details.
@@ -118,7 +118,7 @@ Arguments:
 - `nbrown`: The number of Brownian processes
 - `alg`: The algorithm used to solve the ODE. Defaults to `nothing`, i.e. the
   default algorithm from DifferentialEquations.jl.
-- `sensealg`: The choice of differentiation algorthm used in the backpropogation.
+- `sensealg`: The choice of differentiation algorithm used in the backpropogation.
 - `kwargs`: Additional arguments splatted to the ODE solver. See the
   [Common Solver Arguments](https://docs.sciml.ai/DiffEqDocs/stable/basics/common_solver_opts/)
   documentation for more details.
@@ -172,7 +172,7 @@ Arguments:
 - `lags`: Defines the lagged values that should be utilized in the neural network.
 - `alg`: The algorithm used to solve the ODE. Defaults to `nothing`, i.e. the
   default algorithm from DifferentialEquations.jl.
-- `sensealg`: The choice of differentiation algorthm used in the backpropogation.
+- `sensealg`: The choice of differentiation algorithm used in the backpropogation.
   Defaults to using reverse-mode automatic differentiation via Tracker.jl
 - `kwargs`: Additional arguments splatted to the ODE solver. See the
   [Common Solver Arguments](https://docs.sciml.ai/DiffEqDocs/stable/basics/common_solver_opts/)
@@ -219,11 +219,11 @@ Arguments:
   derivative function. Should take an input of size `x` and produce the residual of
   `f(dx,x,t)` for only the differential variables.
 - `constraints_model`: A function `constraints_model(u,p,t)` for the fixed
-  constaints to impose on the algebraic equations.
+  constraints to impose on the algebraic equations.
 - `tspan`: The timespan to be solved on.
 - `alg`: The algorithm used to solve the ODE. Defaults to `nothing`, i.e. the
   default algorithm from DifferentialEquations.jl.
-- `sensealg`: The choice of differentiation algorthm used in the backpropogation.
+- `sensealg`: The choice of differentiation algorithm used in the backpropogation.
   Defaults to using reverse-mode automatic differentiation via Tracker.jl
 - `kwargs`: Additional arguments splatted to the ODE solver. See the
   [Common Solver Arguments](https://docs.sciml.ai/DiffEqDocs/stable/basics/common_solver_opts/)
@@ -286,7 +286,7 @@ constraint equations.
 Arguments:
 
 - `model`: A Flux.Chain or Lux.AbstractExplicitLayer neural network that defines the ̇`f(u,p,t)`
-- `constraints_model`: A function `constraints_model(u,p,t)` for the fixed constaints to
+- `constraints_model`: A function `constraints_model(u,p,t)` for the fixed constraints to
   impose on the algebraic equations.
 - `tspan`: The timespan to be solved on.
 - `mass_matrix`: The mass matrix associated with the DAE
@@ -295,7 +295,7 @@ Arguments:
   compatible with singular mass matrices. Consult the
   [DAE solvers](https://docs.sciml.ai/DiffEqDocs/stable/solvers/dae_solve/) documentation
   for more details.
-- `sensealg`: The choice of differentiation algorthm used in the backpropogation.
+- `sensealg`: The choice of differentiation algorithm used in the backpropogation.
   Defaults to an adjoint method. See
   the [Local Sensitivity Analysis](https://docs.sciml.ai/DiffEqDocs/stable/analysis/sensitivity/)
   documentation for more details.

test/runtests.jl

@@ -72,16 +72,16 @@ const is_CI = haskey(ENV, "CI")
     if GROUP == "All" || GROUP == "Aqua"
         @safetestset "Aqua Q/A" begin
             using Aqua, DiffEqFlux, LinearAlgebra
-
-            # TODO: Enable persistent tasks once the downstream PRs are merged
-            Aqua.test_all(DiffEqFlux; ambiguities = false, piracies = false,
-                persistent_tasks = false)
-
-            Aqua.test_ambiguities(DiffEqFlux; recursive = false)
-
+            Aqua.find_persistent_tasks_deps(DiffEqFlux)
+            Aqua.test_ambiguities(DiffEqFlux, recursive = false)
+            Aqua.test_deps_compat(DiffEqFlux)
+            Aqua.test_piracies(DiffEqFlux; treat_as_own = [LinearAlgebra.Tridiagonal])
+            Aqua.test_project_extras(DiffEqFlux)
+            Aqua.test_stale_deps(DiffEqFlux)
+            Aqua.test_unbound_args(DiffEqFlux)
+            Aqua.test_undefined_exports(DiffEqFlux)
             # FIXME: Remove Tridiagonal piracy after
             # https://github.com/JuliaDiff/ChainRules.jl/issues/713 is merged!
-            Aqua.test_piracies(DiffEqFlux; treat_as_own = [LinearAlgebra.Tridiagonal])
         end
     end
 end