Merge pull request #201 from JuliaGNI/compathelper/new_version/2025-06-28-01-04-20-013-01885103614

CompatHelper: bump compat for GeometricEquations to 0.19, (keep existing compat)

Author: michakraus

Project.toml

@@ -1,7 +1,7 @@
 name = "GeometricMachineLearning"
 uuid = "194d25b2-d3f5-49f0-af24-c124f4aa80cc"
-authors = ["Michael Kraus <[email protected]>"]
 version = "0.4.4"
+authors = ["Michael Kraus <[email protected]>"]
 
 [deps]
 AbstractNeuralNetworks = "60874f82-5ada-4c70-bd1c-fa6be7711c8a"
@@ -13,6 +13,7 @@ Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 GeometricBase = "9a0b12b7-583b-4f04-aa1f-d8551b6addc9"
 GeometricEquations = "c85262ba-a08a-430a-b926-d29770767bf2"
+GeometricIntegrators = "dcce2d33-59f6-5b8d-9047-0defad88ae06"
 GeometricSolutions = "7843afe4-64f4-4df4-9231-049495c56661"
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
@@ -41,10 +42,9 @@ ChainRulesCore = "1"
 ChainRulesTestUtils = "1"
 Distances = "0.10"
 ForwardDiff = "0.10, 1"
-GeometricBase = "0.10"
-GeometricEquations = "0.18"
-GeometricIntegrators = "0.14.2"
-GeometricSolutions = "0.3.24, 0.4"
+GeometricBase = "0.11"
+GeometricEquations = "0.19"
+GeometricSolutions = "0.5"
 HDF5 = "0.16, 0.17"
 KernelAbstractions = "0.9"
 LazyArrays = "=2.3.2"
@@ -74,13 +74,4 @@ SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = [
-    "Documenter",
-    "ChainRulesTestUtils",
-    "Random",
-    "SafeTestsets",
-    "Test",
-    "GeometricProblems",
-    "FiniteDifferences",
-    "GeometricIntegrators",
-]
+test = ["Documenter", "ChainRulesTestUtils", "Random", "SafeTestsets", "Test", "GeometricProblems", "FiniteDifferences", "GeometricIntegrators"]

README.md

@@ -28,7 +28,7 @@ include("scripts/pendulum.jl")
 
 type = Float32 # Float16 etc.
 # get data 
-qp_data = GeometricMachineLearning.apply_toNT(a -> CuArray(type.(a)), pendulum_data((q=[0.], p=[1.]); tspan=(0.,100.)))
+qp_data = GeometricMachineLearning.apply_toNT(a -> CuArray(type.(a)), pendulum_data((q=[0.], p=[1.]); timespan=(0.,100.)))
 # call the DataLoader
 dl = DataLoader(qp_data)
 

docs/src/optimizers/manifold_related/parallel_transport.md

@@ -134,10 +134,10 @@ Y_increments = []
 Δ₂_transported = []
 
 const n_steps = 6
-const tstep = 2
+const timestep = 2
 
 for _ in 1:n_steps
-    update_section!(λY, tstep * B, geodesic)
+    update_section!(λY, timestep * B, geodesic)
     push!(Y_increments, copy(λY.Y))
     push!(Δ_transported, Matrix(λY) * B * E)
     push!(Δ₂_transported, Matrix(λY) * B₂ * E)

docs/src/tutorials/adjusting_the_loss_function.md

@@ -20,7 +20,7 @@ using GeometricProblems.HarmonicOscillator: hodeproblem
 import Random # hide
 Random.seed!(123) # hide
 
-sol = integrate(hodeproblem(; tspan = 100), ImplicitMidpoint()) 
+sol = integrate(hodeproblem(; timespan = 100), ImplicitMidpoint()) 
 data = DataLoader(sol; suppress_info = true)
 
 nn = NeuralNetwork(GSympNet(2))

docs/src/tutorials/linear_symplectic_transformer.md

@@ -31,11 +31,11 @@ CairoMakie.activate!() # hide
 import Random # hide
 Random.seed!(123) # hide
 
-const tstep = .3
+const timestep = .3
 const n_init_con = 5
 
 # ensemble problem
-ep = hodeensemble([rand(2) for _ in 1:n_init_con], [rand(2) for _ in 1:n_init_con]; tstep = tstep)
+ep = hodeensemble([rand(2) for _ in 1:n_init_con], [rand(2) for _ in 1:n_init_con]; timestep = timestep)
 dl = DataLoader(integrate(ep, ImplicitMidpoint()); suppress_info = true)
 # dl = DataLoader(vcat(dl_nt.input.q, dl_nt.input.p))  # hide
 

docs/src/tutorials/matrix_softmax.md

@@ -47,11 +47,11 @@ mpurple = RGBf(148 / 256, 103 / 256, 189 / 256) # hide
 mblue = RGBf(31 / 256, 119 / 256, 180 / 256) # hide
 mgreen = RGBf(44 / 256, 160 / 256, 44 / 256) # hide
 
-const tstep = .3
+const timestep = .3
 const n_init_con = 5
 
 # ensemble problem
-ep = hodeensemble([rand(2) for _ in 1:n_init_con], [rand(2) for _ in 1:n_init_con]; tstep = tstep)
+ep = hodeensemble([rand(2) for _ in 1:n_init_con], [rand(2) for _ in 1:n_init_con]; timestep = timestep)
 dl = DataLoader(integrate(ep, ImplicitMidpoint()); suppress_info = true)
 # dl = DataLoader(vcat(dl_nt.input.q, dl_nt.input.p))  # hide
 

docs/src/tutorials/softmax_comparison.md

@@ -40,11 +40,11 @@ CairoMakie.activate!() # hide
 import Random # hide
 Random.seed!(123) # hide
 
-const tstep = .3
+const timestep = .3
 const n_init_con = 5
 
 # ensemble problem
-ep = hodeensemble([rand(2) for _ in 1:n_init_con], [rand(2) for _ in 1:n_init_con]; tstep = tstep)
+ep = hodeensemble([rand(2) for _ in 1:n_init_con], [rand(2) for _ in 1:n_init_con]; timestep = timestep)
 dl = DataLoader(integrate(ep, ImplicitMidpoint()); suppress_info = true)
 
 nothing # hide

docs/src/tutorials/symplectic_autoencoder.md

@@ -79,7 +79,7 @@ using GeometricIntegrators: integrate, ImplicitMidpoint
 using GeometricMachineLearning 
 import Random # hide
 
-pr = tl.hodeproblem(; tspan = (0.0, 800.))
+pr = tl.hodeproblem(; timespan = (0.0, 800.))
 sol = integrate(pr, ImplicitMidpoint())
 nothing # hide
 ```

docs/src/tutorials/symplectic_transformer.md

@@ -31,11 +31,11 @@ CairoMakie.activate!() # hide
 import Random # hide
 Random.seed!(123) # hide
 
-const tstep = .3
+const timestep = .3
 const n_init_con = 5
 
 # ensemble problem
-ep = hodeensemble([rand(2) for _ in 1:n_init_con], [rand(2) for _ in 1:n_init_con]; tstep = tstep)
+ep = hodeensemble([rand(2) for _ in 1:n_init_con], [rand(2) for _ in 1:n_init_con]; timestep = timestep)
 dl = DataLoader(integrate(ep, ImplicitMidpoint()); suppress_info = true)
 # dl = DataLoader(vcat(dl_nt.input.q, dl_nt.input.p))  # hide
 

docs/src/tutorials/sympnet_tutorial.md

@@ -27,7 +27,7 @@ import Random # hide
 Random.seed!(1234) # hide
 
 # the problem is the ODE of the harmonic oscillator
-ho_problem = ho.hodeproblem(; tspan = 500)
+ho_problem = ho.hodeproblem(; timespan = 500)
 
 # integrate the system
 solution = integrate(ho_problem, ImplicitMidpoint())