Update tests

Author: roflmaostc

src/analysis_tools.jl

@@ -9,17 +9,18 @@ Assumes that both arrays are 2 dimensional
 * [Rainer Heintzmann, \"Estimating missing information by maximum likelihood deconvolution\"](https://www.sciencedirect.com/science/article/abs/pii/S0968432806001272)
 """
 function relative_energy_regain(ground_truth, rec)
+    T = eltype(ground_truth)
     # go to fourier space
     ground_truth_fft = fft(ground_truth)
     rec_fft = fft(rec)
 
     # a dict to store the values for certain frequencies
     # we store a list since some (rounded) frequencies occur more than once
-    ΔE_R_dict = Dict{Float64, Vector{Float64}}()
-    E_R_dict = Dict{Float64, Vector{Float64}}()
+    ΔE_R_dict = Dict{T, Vector{T}}()
+    E_R_dict = Dict{T, Vector{T}}()
 
     # round the frequencies to 4 digits, alternative would be to bin
-    round4(x) = round(x, digits=3)
+    round4(x) = T(round(x, digits=3))
 
 
     # iterate over the frequencies and calculate the relative energy regain
@@ -37,13 +38,12 @@ function relative_energy_regain(ground_truth, rec)
 
     # finally transform everything into a list of frequencies and 
     # a list of relative energy regains
-    freqs = Float64[]
-    G_R_list = Float64[]
-    for f in sort(Float64.(keys(ΔE_R_dict)))
+    freqs = T[]
+    G_R_list = T[]
+    for f in sort(T.(keys(ΔE_R_dict)))
         push!(freqs, f)
         mean_ΔE_r = mean(ΔE_R_dict[f])
         mean_E_r = mean(E_R_dict[f])
-
         push!(G_R_list, (mean_E_r - mean_ΔE_r) / mean_E_r)
     end
 

src/hessian_schatten_norm.jl

@@ -69,7 +69,7 @@ end
 
 function schatten_norm_tullio(H11, H12, H22, p)
     λ₁, λ₂ = eigvals_symmetric_tullio(H11, H12, H22) 
-    return @tullio res = (1f-8 + λ₁[i, j]^p + λ₂[i, j]^p)^(1/p)
+    return @tullio res = abs((1f-8 + λ₁[i, j]^p + λ₂[i, j]^p))^(1/p)
 end
 
 """

test/hessian_schatten_norm.jl

@@ -0,0 +1,25 @@
+
+@testset "Test eigvals" begin
+    
+    function f(a1::T,b1,c1) where T
+        a = Array{T, 2}(undef, 1, 1)
+        a[1,1] = a1
+        b = Array{T, 2}(undef, 1, 1)
+        b[1,1] = b1
+        c = Array{T, 2}(undef, 1, 1)
+        c[1,1] = c1
+        @test all(.≈(DeconvOptim.eigvals_symmetric_tullio(a,b,c), DeconvOptim.eigvals_symmetric(a,b,c)))
+    end
+
+    f(10.0, 20.0, -10.0)
+    f(0f0, -12f0, 13f0)
+end
+
+
+@testset "Schatten norm consistent" begin
+
+    x = [1 2 3; 1 1 1; 0 0 -1f0]
+    @test DeconvOptim.HSp(x, p = 1) ≈ 0.9999999900000001
+    @test DeconvOptim.HSp(x, p = 2) ≈ 1.732050831647567
+    @test abs.(DeconvOptim.HSp(x, p = 1)) ≈ DeconvOptim.HS1(x) 
+end

test/runtests.jl

@@ -18,6 +18,7 @@ end
 
 
 include("analysis_tools.jl")
+include("hessian_schatten_norm.jl")
 include("conv.jl")
 include("mappings.jl")