Merge branch 'master' into mg/hdf-0.17

Team-RADDISH · Mar 12, 2024 · 06676c1 · 06676c1
2 parents bcd1b71 + 9284bba
commit 06676c1
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Showing 4 changed files with 11 additions and 18 deletions.
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "ParticleDA"
 uuid = "61cd1fb4-f4c4-4bc8-80c6-ea5639a6ca2e"
 authors = ["Mosè Giordano and Tuomas Koskela and Dan Giles and Matt Graham"]
-version = "1.0.1"
+version = "1.1.0"
 
 [deps]
 ChunkSplitters = "ae650224-84b6-46f8-82ea-d812ca08434e"

diff --git a/test/Project.toml b/test/Project.toml
@@ -2,28 +2,24 @@
 ChunkSplitters = "ae650224-84b6-46f8-82ea-d812ca08434e"
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 GaussianRandomFields = "e4b2fa32-6e09-5554-b718-106ed5adafe9"
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
 
 [compat]
 Distributions = "0.22, 0.23, 0.24, 0.25"
-FillArrays = "0.13"
 GaussianRandomFields = "2.2.1"
 HDF5 = "0.14, 0.15, 0.16, 0.17"
 MPI = "0.20.8"
 OrdinaryDiffEq = "6.40"
-SciMLBase = "1.81"
 PDMats = "0.11"
 StableRNGs = "1"
 YAML = "0.4"

diff --git a/test/models/lineargaussian.jl b/test/models/lineargaussian.jl
@@ -1,7 +1,6 @@
 module LinearGaussian
 
 using Distributions
-using FillArrays
 using HDF5
 using Random
 using PDMats
@@ -49,7 +48,7 @@ function diagonal_linear_gaussian_model_parameters(
         :observation_matrix => ScalMat(
             state_dimension, observation_coefficient
         ),
-        :initial_state_mean => Zeros(state_dimension),
+        :initial_state_mean => zeros(state_dimension),
         :initial_state_covar => ScalMat(
             state_dimension, initial_state_std^2
         ),
@@ -81,7 +80,7 @@ function stochastically_driven_dsho_model_parameters(
             ]'
         ],
         :observation_matrix => ScalMat(2, 1.),
-        :initial_state_mean => Zeros(2),
+        :initial_state_mean => zeros(2),
         :initial_state_covar => ScalMat(2, 1.),
         :state_noise_covar => PDMat(
             Q * exp(-ω * δ / Q) * [
@@ -120,8 +119,8 @@ function init(parameters_dict::Dict, n_tasks::Int=1)
             MvNormal(m, c)
             for (m, c) in (
                 (parameters.initial_state_mean, parameters.initial_state_covar), 
-                (Zeros(state_dimension), parameters.state_noise_covar), 
-                (Zeros(observation_dimension), parameters.observation_noise_covar), 
+                (zeros(state_dimension), parameters.state_noise_covar), 
+                (zeros(observation_dimension), parameters.observation_noise_covar), 
             )
         )...
     )

diff --git a/test/models/lorenz63.jl b/test/models/lorenz63.jl
@@ -2,12 +2,10 @@ module Lorenz63
 
 using Base.Threads
 using Distributions
-using FillArrays
 using HDF5
 using Random
 using PDMats
 using OrdinaryDiffEq
-using SciMLBase
 using ParticleDA
 
 Base.@kwdef struct Lorenz63ModelParameters{S <: Real, T <: Real}
@@ -27,9 +25,9 @@ function get_params(
     return P(; (; (Symbol(k) => v for (k, v) in model_params_dict)...)...)
 end
 
-struct Lorenz63Model{S <: Real, T <: Real}
+struct Lorenz63Model{S <: Real, T <: Real, I}
     parameters::Lorenz63ModelParameters{S, T}
-    integrators::Vector{<:SciMLBase.AbstractODEIntegrator}
+    integrators::Vector{I}
     initial_state_distribution::MvNormal{S}
     state_noise_distribution::MvNormal{S}
     observation_noise_distribution::MvNormal{T}
@@ -57,7 +55,7 @@ function init(
             Tsit5();
             save_everystep=false
         ) 
-        for u in eachcol(Matrix{S}(undef, 3, n_tasks))
+        for u in eachcol(zeros(S, 3, n_tasks))
     ]
     state_dimension = 3
     observation_dimension = length(parameters.observed_indices)
@@ -67,9 +65,9 @@ function init(
         (
             MvNormal(m, isa(s, Vector) ? PDiagMat(s.^2) : ScalMat(length(m), s.^2))
             for (m, s) in (
-                (Ones{S}(state_dimension), parameters.initial_state_std), 
-                (Zeros{S}(state_dimension), parameters.state_noise_std), 
-                (Zeros{T}(observation_dimension), parameters.observation_noise_std), 
+                (ones(S, state_dimension), parameters.initial_state_std), 
+                (zeros(S, state_dimension), parameters.state_noise_std), 
+                (zeros(T, observation_dimension), parameters.observation_noise_std), 
             )
         )...
     )