Add Downgrade CI and update lower package compats

.github/workflows/Downgrade.yml

@@ -0,0 +1,33 @@
+name: Downgrade
+on:
+  pull_request:
+    branches:
+      - main
+      - dev
+    paths-ignore:
+      - 'docs/**'
+  push:
+    branches:
+      - main
+      - dev
+    paths-ignore:
+      - 'docs/**'
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        version: ['1.10', '1', 'pre']
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v2
+        with:
+          version: ${{ matrix.version }}
+      - name: Add LegendJuliaRegistry
+        run: julia -e 'using Pkg; Pkg.Registry.add("General"); Pkg.Registry.add(Pkg.RegistrySpec(url = "https://github.com/legend-exp/LegendJuliaRegistry"))'
+        shell: bash
+      - uses: julia-actions/julia-downgrade-compat@v1
+        with:
+          skip: Pkg,TOML
+      - uses: julia-actions/julia-buildpkg@v1
+      - uses: julia-actions/julia-runtest@v1

Project.toml

@@ -53,44 +53,44 @@ LegendSpecFitsRecipesBaseExt = ["RecipesBase", "Plots"]
 
 [compat]
 ArgCheck = "1, 2"
-ArraysOfArrays = "0.6"
-BAT = "3"
-ChangesOfVariables = "0.1.1"
+ArraysOfArrays = "0.5, 0.6"
+BAT = "3.1"
+ChangesOfVariables = "0.1.3"
 DensityInterface = "0.4"
-Distributions = "0.24, 0.25"
-FillArrays = "0.7, 0.8, 0.9, 0.10, 0.11, 0.12, 0.13, 1"
+Distributions = "0.25.87"
+FillArrays = "1.4.1"
 Format = "1.2, 1.3"
-ForwardDiff = "0.10"
+ForwardDiff = "0.10.26"
 GaussianMixtures = "0.3.11"
 IntervalSets = "0.7"
-InverseFunctions = "0.1"
-IrrationalConstants = "0.1, 0.2"
-KernelDensity = "0.5, 0.6"
+InverseFunctions = "0.1.8"
+IrrationalConstants = "0.1.1, 0.2"
+KernelDensity = "0.6.4"
 LaTeXStrings = "1.3"
-LogExpFunctions = "0.3"
-LinearAlgebra = "1"
+LogExpFunctions = "0.3.16"
+LinearAlgebra = "<0.0.1, 1"
 LinearRegression = "0.2"
 LsqFit = "0.14, 0.15"
-Measurements = "2"
+Measurements = "2.5"
 Measures = "0.3"
-Optim = "1"
-Optimization = "3, 4"
-OptimizationBBO = "0.3"
-OptimizationNLopt = "0.2"
-OptimizationOptimJL = "0.3"
+Optim = "1.7.5"
+Optimization = "3.25, 4"
+OptimizationBBO = "0.3, 0.4"
+OptimizationNLopt = "0.2, 0.3"
+OptimizationOptimJL = "0.3, 0.4"
 Plots = "1"
 PropDicts = "0.2"
 RadiationSpectra = "0.5.12"
-Random = "1"
-RecipesBase = "1"
+Random = "<0.0.1, 1"
+RecipesBase = "1.3.4"
 Roots = "2"
 SnoopPrecompile = "1"
-SpecialFunctions = "0.10, 1, 2"
-Statistics = "1"
-StatsBase = "0.32, 0.33, 0.34"
-StructArrays = "0.4, 0.5, 0.6, 0.7"
-Tables = "0.2, 1.0"
-TypedTables = "1"
-Unitful = "1"
-ValueShapes = "0.10.1, 0.11"
+SpecialFunctions = "2.1.4"
+Statistics = "<0.0.1, 1"
+StatsBase = "0.33.7, 0.34"
+StructArrays = "0.6.17, 0.7"
+Tables = "1.11"
+TypedTables = "1.2"
+Unitful = "1.6"
+ValueShapes = "0.10.13, 0.11"
 julia = "1.10"

test/Project.toml

@@ -10,10 +10,10 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 
 [compat]
-Distributions = "0.24, 0.25"
+Distributions = "0.25"
 Documenter = "1"
-Interpolations = "0.15"
+Interpolations = "0.13, 0.14, 0.15"
 LegendDataTypes = "0.1"
 Measurements = "2"
-StatsBase = "0.32, 0.33, 0.34"
+StatsBase = "0.33, 0.34"
 Unitful = "1"

test/test_utils.jl

@@ -1,5 +1,9 @@
 # This file is a part of LegendSpecFits.jl, licensed under the MIT License (MIT).
 
+using Interpolations: Throw, extrapolate, interpolate, Gridded, Linear
+_linear_interpolation(range::AbstractVector, vs::AbstractVector; extrapolation_bc = Throw()) =
+    extrapolate(interpolate((range, ), vs, Gridded(Linear())), extrapolation_bc)
+
 """
     generate_mc_spectrum(n_tot::Int=200000,; f_fit::Base.Callable=th228_fit_functions.f_fit)
 Sample Legend200 calibration data based on "Inverse Transform Sampling" method
@@ -53,7 +57,7 @@ function generate_mc_spectrum(n_tot::Int=200000,; f_fit::Base.Callable=LegendSpe
     for i=1:length(th228_lines)
         bandwidth = maximum(model_cdf_all[i])-minimum(model_cdf_all[i])
         rand_i = minimum(model_cdf_all[i]).+bandwidth.*rand(n_i[i]); # make sure sample is within model range 
-        interp_cdf_inv = linear_interpolation(model_cdf_all[i], bin_centers_all[i]) # inverse cdf
+        interp_cdf_inv = _linear_interpolation(model_cdf_all[i], bin_centers_all[i]) # inverse cdf
         energy_mc_all[i] = interp_cdf_inv.(rand_i) 
     end