Aqua + typos CI

.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]
+numer = "numer"

Project.toml

@@ -5,7 +5,6 @@ version = "0.5.1"
 
 [deps]
 AbstractAlgebra = "c3fe647b-3220-5bb0-a1ea-a7954cac585d"
-BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
@@ -27,8 +26,9 @@ TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
 
 [compat]
 AbstractAlgebra = "0.13, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.30, 0.31, 0.32, 0.33, 0.34"
-BenchmarkTools = "1"
+Aqua = "0.8"
 Combinatorics = "1"
+CPUSummary = "0.2"
 DataStructures = "0.18"
 Dates = "1.6, 1.7"
 Groebner = "0.4, 0.5"
@@ -45,14 +45,16 @@ Random = "1.6, 1.7"
 SpecialFunctions = "1, 2"
 SymbolicUtils = "1"
 Symbolics = "5"
+Test = "1"
 TestSetExtensions = "2"
 TimerOutputs = "0.5"
 julia = "1.6, 1.7"
 
 [extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 CPUSummary = "2a0fbf3d-bb9c-48f3-b0a9-814d99fd7ab9"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestSetExtensions = "98d24dd4-01ad-11ea-1b02-c9a08f80db04"
 
 [targets]
-test = ["CPUSummary", "Test", "TestSetExtensions"]
+test = ["Aqua", "CPUSummary", "Test", "TestSetExtensions"]

README.md

@@ -56,7 +56,7 @@ In this example:
   - `y(t)` is the **output variable** which is assumed to be observed in the experiments and, thus, known;
   - `a01, a21, a12, b` are unknown scalar **parameters**.
 
-Note that there may be mulitple inputs and outputs.
+Note that there may be multiple inputs and outputs.
 
 ### Assessing identifiability
 

benchmarking/IdentifiableFunctions/experiments.jl

@@ -1116,7 +1116,7 @@ end
 #! format: off
 
 new_rff = StructuralIdentifiability.RationalFunctionField(funcs1)
-cfs = StructuralIdentifiability.beautifuly_generators(new_rff)
+cfs = StructuralIdentifiability.beautifully_generators(new_rff)
 gb_rff = StructuralIdentifiability.RationalFunctionField(cfs)
 
 K = GF(2^31 - 1)

benchmarking/IdentifiableFunctions/homogenization.jl

@@ -15,7 +15,7 @@ Bilirubin2_io = @ODEmodel(
 funcs = find_identifiable_functions(Bilirubin2_io, with_states = true, strategy = (:gb,))
 
 rff = StructuralIdentifiability.RationalFunctionField(funcs)
-cfs = StructuralIdentifiability.beautifuly_generators(rff)
+cfs = StructuralIdentifiability.beautifully_generators(rff)
 rff = StructuralIdentifiability.RationalFunctionField(cfs)
 
 K = GF(2^31 - 1)

docs/src/tutorials/creating_ode.md

@@ -12,7 +12,7 @@ which involves
 
   - a vector $\mathbf{x}(t)$ of the state variables of the system,
 
-  - a vector $\mathbf{u}(t)$ of extermal inputs,
+  - a vector $\mathbf{u}(t)$ of external inputs,
   - a vector $\mathbf{p}$ of scalar parameters,
   - a vector $\mathbf{y}(t)$ of outputs (i.e., observations),
   - and vectors of rational functions $\mathbf{f}$ and $\mathbf{g}$ (for discussion of the non-rational case, see this [issue](https://github.com/SciML/StructuralIdentifiability.jl/issues/144)).

src/ODE.jl

@@ -359,7 +359,7 @@ Here,
 - `x1`, `x2` are state variables
 - `y` is an output variable
 - `u` is an input variable
-- `a`, `b`, `c` are time-indepdendent parameters
+- `a`, `b`, `c` are time-independent parameters
 
 """
 macro ODEmodel(ex::Expr...)

src/RationalFunctionFields/RationalFunctionField.jl

@@ -200,7 +200,7 @@
 # ------------------------------------------------------------------------------
 
 """
-    beautifuly_generators(rff::RationalFunctionField)
+    beautifully_generators(rff::RationalFunctionField)
 
 Given a field of rational functions `rff` returns a set of "simpler" and
 standardized generators for `rff`.
@@ -209,7 +209,7 @@
 1. Filter constants,
 2. Remove redundant generators.
 """
-@timeit _to function beautifuly_generators(
+@timeit _to function beautifully_generators(
     rff::RationalFunctionField;
     discard_redundant = true,
     reversed_order = false,
@@ -390,7 +390,7 @@
     # The first basis in some ordering
     ord = InputOrdering()
     new_rff = groebner_basis_coeffs(rff, seed = seed, ordering = ord)
-    cfs = beautifuly_generators(new_rff)
+    cfs = beautifully_generators(new_rff)
     ordering_to_generators[ord] = cfs
     if isempty(cfs)
         return ordering_to_generators
@@ -415,7 +415,7 @@
                 ordering = ord,
                 up_to_degree = up_to_degree,
             )
-            cfs = beautifuly_generators(new_rff, discard_redundant = false)
+            cfs = beautifully_generators(new_rff, discard_redundant = false)
             ordering_to_generators[ord] = cfs
         end
     end
@@ -432,7 +432,7 @@
                 ordering = ord,
                 up_to_degree = up_to_degree,
             )
-            cfs = beautifuly_generators(new_rff, discard_redundant = false)
+            cfs = beautifully_generators(new_rff, discard_redundant = false)
             ordering_to_generators[ord] = cfs
         end
     end
@@ -450,7 +450,7 @@
                 ordering = ord,
                 up_to_degree = up_to_degree,
             )
-            cfs = beautifuly_generators(new_rff, discard_redundant = false)
+            cfs = beautifully_generators(new_rff, discard_redundant = false)
             ordering_to_generators[ord] = cfs
         end
     end
@@ -467,7 +467,7 @@
 ) where {T}
     @assert strategy in (:monte_carlo,)
     relations = linear_relations_between_normal_forms(
-        beautifuly_generators(rff),
+        beautifully_generators(rff),
         up_to_degree,
         seed = seed,
     )
@@ -532,7 +532,7 @@
         seed = seed,
         rational_interpolator = rational_interpolator,
     )
-    new_fracs = beautifuly_generators(new_rff)
+    new_fracs = beautifully_generators(new_rff)
     if isempty(new_fracs)
         return new_fracs
     end
@@ -556,7 +556,7 @@
 Final cleaning and simplification of generators. 
 Out of $(length(new_fracs)) fractions $(length(new_fracs_unique)) are syntactically unique."""
     runtime =
-        @elapsed new_fracs = beautifuly_generators(RationalFunctionField(new_fracs_unique))
+        @elapsed new_fracs = beautifully_generators(RationalFunctionField(new_fracs_unique))
     @debug "Checking inclusion with probability $p"
     runtime = @elapsed result = issubfield(rff, RationalFunctionField(new_fracs), p)
     _runtime_logger[:id_inclusion_check] = runtime
@@ -565,7 +565,7 @@
         throw("The new subfield generators are not correct.")
     end
     @info "Inclusion checked with probability $p in $(_runtime_logger[:id_inclusion_check]) seconds"
-    @debug "Out of $(length(rff.mqs.nums_qq)) initial generators there are $(length(new_fracs)) indepdendent"
+    @debug "Out of $(length(rff.mqs.nums_qq)) initial generators there are $(length(new_fracs)) independent"
     ranking = generating_set_rank(new_fracs)
     _runtime_logger[:id_ranking] = ranking
     @debug "The ranking of the new set of generators is $ranking"

src/RationalFunctionFields/normalforms.jl

@@ -1,5 +1,5 @@
 
-# Maintans a row echelon form of a set of vectors over the integrals.
+# Maintains a row echelon form of a set of vectors over the integrals.
 # Works well when the ambient dimension is small.
 mutable struct TinyRowEchelonForm{T}
     rows::Vector{Vector{T}}

src/discrete.jl

@@ -6,11 +6,11 @@ Input:
 - `param_values` - parameter values, must be a dictionary mapping parameter to a value
 - `initial_conditions` - initial conditions of `ode`, must be a dictionary mapping state variable to a value
 - `input_values` - input sequences in the form input => list of terms; length of the lists must be at least
-                   teh required number of terms in the result
+                   the required number of terms in the result
 - `num_terms` - number of terms to compute
 
 Output:
-- computes a sequence solution with teh required number of terms prec presented as a dictionary state_variable => corresponding sequence
+- computes a sequence solution with the required number of terms prec presented as a dictionary state_variable => corresponding sequence
 """
 function sequence_solution(
     dds::ODE{P},

src/global_identifiability.jl

@@ -3,7 +3,7 @@
 
 Takes as input input-output equations, the corresponding ode, a list of functions assumed to be known
 and a flag `with_states`.
-Extracts generators of the field of identifiable functions (with or without states) withous
+Extracts generators of the field of identifiable functions (with or without states) without
 any simplifications.
 
 Returns a tuple consisting of

src/states.jl

@@ -6,7 +6,7 @@ Input:
 - `vars` - list of variables
 
 The function considers `f` as `A / B`, where `A` and `B` are polynomials in `vars` with
-coefficients in rational fucntion field in the remaining variables such that at least one of the
+coefficients in rational function field in the remaining variables such that at least one of the
 coefficients is equal to one.
 
 Output:

src/submodels.jl

@@ -90,7 +90,7 @@ end
 
 # ------------------------------------------------------------------------------
 
-# filters the models containin all states or no states
+# filters the models containing all states or no states
 function filter_max_empty(
     ode::ODE{P},
     submodels::Array{Set{fmpq_mpoly}, 1},

src/util.jl

@@ -373,7 +373,7 @@ Input:
 - `poly` - multivariate polynomial
 - `variables` - a list of variables from the generators of the ring of p
 Output:
-- dictionary with keys being tuples of length `lenght(variables)` and values being polynomials in the variables other than those which are the coefficients at the corresponding monomials (in a smaller polynomial ring)
+- dictionary with keys being tuples of length `length(variables)` and values being polynomials in the variables other than those which are the coefficients at the corresponding monomials (in a smaller polynomial ring)
 """
 function extract_coefficients(poly::P, variables::Array{P, 1}) where {P <: MPolyElem}
     xs = gens(parent(poly))

test/extract_coefficients.jl

@@ -1,4 +1,4 @@
-@testset "Coefficient extraction for rational fucntions" begin
+@testset "Coefficient extraction for rational functions" begin
     R, (x, y, z) = PolynomialRing(QQ, ["x", "y", "z"])
     C = extract_coefficients_ratfunc(
         (x^2 + y * z - y^2 * z^3 + 3 * x * z^3) // (x + y + z + z^2 * (x^2 + 1)),

test/ode_ps_solution.jl

@@ -76,7 +76,7 @@ end
         end
     end
 
-    # Testing ps_ode_solution in conjuntion with the ODE class
+    # Testing ps_ode_solution in conjunction with the ODE class
     for i in 1:30
         # Setting up the ring
         NUMX = 3
@@ -91,7 +91,7 @@ end
         PType = gfp_mpoly
         TDict = Dict{PType, Union{PType, Generic.Frac{PType}}}
 
-        # Generating the intial conditions etc
+        # Generating the initial conditions etc
         ic = Dict(vars[i] => F(rand(-5:5)) for i in 1:NUMX)
         param_vals = Dict(vars[i + NUMX] => F(rand(-5:5)) for i in 1:NUMP)
         inputs =

test/qa.jl

@@ -0,0 +1,12 @@
+using StructuralIdentifiability, Aqua
+@testset "Aqua" begin
+    Aqua.find_persistent_tasks_deps(StructuralIdentifiability)
+    Aqua.test_ambiguities(StructuralIdentifiability, recursive = false)
+    Aqua.test_deps_compat(StructuralIdentifiability)
+    Aqua.test_piracies(StructuralIdentifiability,
+        treat_as_own = [])
+    Aqua.test_project_extras(StructuralIdentifiability)
+    Aqua.test_stale_deps(StructuralIdentifiability)
+    Aqua.test_unbound_args(StructuralIdentifiability)
+    Aqua.test_undefined_exports(StructuralIdentifiability)
+end

test/runtests.jl

@@ -79,8 +79,7 @@ end
 
 @info "Testing started"
 
-@test isempty(Test.detect_ambiguities(StructuralIdentifiability))
-@test isempty(Test.detect_unbound_args(StructuralIdentifiability))
+@time @testset "Quality Assurance" include("qa.jl")
 
 @time @testset "All the tests" verbose = true begin
     @includetests ARGS