diff --git a/src/services/KernelEval.jl b/src/services/KernelEval.jl
index c65145b..eaecc3e 100644
--- a/src/services/KernelEval.jl
+++ b/src/services/KernelEval.jl
@@ -4,11 +4,12 @@ function projectSymPosDef(c::AbstractMatrix)
   s = size(c)
   # pretty fast to make or remake isbitstype form matrix
   _c = SMatrix{s...}(c)
+  #TODO likely not intended project here: see AMP#283
   issymmetric(_c) ? _c : project(SymmetricPositiveDefinite(s[1]),_c,_c)
 end
 
 function MvNormalKernel(
-  μ::AbstractVector,
+  μ::AbstractArray,
   Σ::AbstractArray,
   weight::Real=1.0
 )
@@ -58,7 +59,7 @@ function Base.show(io::IO, mvk::MvNormalKernel)
   μ = mean(mvk)
   Σ2 = cov(mvk)
   # Σ=sqrt(Σ2)
-  d = length(μ)
+  d = size(Σ2,1)
   print(io, "MvNormalKernel(d=",d)
   print(io,",μ=",round.(μ;digits=3))
   print(io,",Σ^2=[",round(Σ2[1];digits=3))
@@ -97,10 +98,11 @@ function distanceMalahanobisSq(
   basis=DefaultOrthogonalBasis()
 )
   δc = distanceMalahanobisCoordinates(M,K,q,basis)
-  p = mean(K)
-  ϵ = identity_element(M, q)
-  X = get_vector(M, ϵ, δc, basis)
-  return inner(M, p, X, X)
+  # p = mean(K)
+  # ϵ = identity_element(M, q)
+  # X = get_vector(M, ϵ, δc, basis)
+  # return inner(M, p, X, X)
+  return δc'*δc
 end
 
 
diff --git a/test/manellic/testManellicTree.jl b/test/manellic/testManellicTree.jl
old mode 100644
new mode 100755
index 8a50e88..bcc2328
--- a/test/manellic/testManellicTree.jl
+++ b/test/manellic/testManellicTree.jl
@@ -3,6 +3,7 @@
 using Test
 using ApproxManifoldProducts
 using Random
+using LinearAlgebra
 using StaticArrays
 using TensorCast
 using Manifolds
@@ -255,7 +256,6 @@ permref = sortperm(pts, by=s->getindex(s,1))
 @test norm((pts[mtree.permute] .- mean.(mtree.leaf_kernels)) .|> s->s[1]) < 1e-6
 
 
-
 # for (i,v) in enumerate(dict[:evaltest_1_at])
 #   # @show AMP.evaluate(mtree, [v;]), dict[:evaltest_1_dens][i]
 #   @test isapprox(dict[:evaltest_1_dens][i], AMP.evaluate(mtree, [v;]))
@@ -266,6 +266,316 @@ permref = sortperm(pts, by=s->getindex(s,1))
 ##
 end
 
+@testset "Test evaluate MvNormalKernel" begin
+
+M = TranslationGroup(1)
+ker = AMP.MvNormalKernel([0.0], [0.5;;])
+@test isapprox(
+  AMP.evaluate(M, ker, [0.1]),
+  pdf(MvNormal(mean(ker), cov(ker)), [0.1])
+)
+
+
+# Test wrapped cicular distribution 
+function pdf_wrapped_normal(μ, σ, θ; nwrap=1000) 
+  s = 0.0
+  for k = -nwrap:nwrap
+    s += exp(-(θ - μ + 2pi*k)^2 / (2*σ^2))
+  end
+  return 1/(σ*sqrt(2pi)) * s
+end 
+
+M = RealCircleGroup()
+ker = AMP.MvNormalKernel([0.0], [0.1;;])
+@test isapprox(
+  AMP.evaluate(M, ker, [0.1]),
+  pdf_wrapped_normal(mean(ker)[], sqrt(cov(ker))[], 0.1)
+)
+
+ker = AMP.MvNormalKernel([0], [2.0;;])
+@test isapprox(
+  AMP.evaluate(M, ker, [0.]),
+  AMP.evaluate(M, ker, [2pi])
+)
+#TODO wrapped normal distributions broken
+@test_broken isapprox(
+  pdf_wrapped_normal(mean(ker)[], sqrt(cov(ker))[], pi),
+  AMP.evaluate(M, ker, [pi])
+)
+@test_broken isapprox(
+  pdf_wrapped_normal(mean(ker)[], sqrt(cov(ker))[], 0),
+  AMP.evaluate(M, ker, [0.])
+)
+
+##
+M = SpecialEuclidean(2)
+ϵ = identity_element(M)
+Xc = [10, 20, 0.1]
+p = exp(M, ϵ, hat(M, ϵ, Xc))
+kercov = diagm([0.5, 2.0, 0.1].^2)
+ker = AMP.MvNormalKernel(p, kercov)
+@test isapprox(
+  AMP.evaluate(M, ker, p),
+  pdf(MvNormal(Xc, cov(ker)), Xc)
+)
+
+Xc = [10, 22, -0.1]
+q = exp(M, ϵ, hat(M, ϵ, Xc))
+
+@test isapprox(
+  pdf(MvNormal(cov(ker)), [0,0,0]),
+  AMP.evaluate(M, ker, p)
+)
+
+X = log(M, ϵ, Manifolds.compose(M, inv(M, p), q))
+Xc_e = vee(M, ϵ, X)
+pdf_local_coords = pdf(MvNormal(cov(ker)), Xc_e)
+
+@test isapprox(
+  pdf_local_coords,
+  AMP.evaluate(M, ker, q),
+)
+
+delta_c = AMP.distanceMalahanobisCoordinates(M, ker, q)
+X = log(M, ϵ, Manifolds.compose(M, inv(M, p), q))
+Xc_e = vee(M, ϵ, X)
+malad_t = Xc_e'*inv(kercov)*Xc_e
+# delta_t = [10, 20, 0.1] - [10, 22, -0.1] 
+@test isapprox(
+  malad_t,
+  delta_c'*delta_c;
+  atol=1e-10
+)
+
+malad2 = AMP.distanceMalahanobisSq(M,ker,q)
+@test isapprox(
+  malad_t,
+  malad2;
+  atol=1e-10
+)
+
+rbfd = AMP.ker(M, ker, q, 0.5, AMP.distanceMalahanobisSq)
+@test isapprox(
+  exp(-0.5*malad_t),
+  rbfd;
+  atol=1e-10
+)
+
+
+# NOTE 'global' distribution would have been 
+X = log(M, mean(ker), q) 
+Xc_e = vee(M, ϵ, X)
+pdf_global_coords = pdf(MvNormal(cov(ker)), Xc_e)
+
+
+end
+
+@testset "Basic ManellicTree manifolds construction and evaluations" begin
+## 
+
+
+M = TranslationGroup(1)
+ϵ = identity_element(M)
+dis = MvNormal([3.0], diagm([1.0].^2)) 
+Cpts = [rand(dis) for _ in 1:128]
+pts = map(c->exp(M, ϵ, hat(M, ϵ, c)), Cpts)
+mtree = ApproxManifoldProducts.buildTree_Manellic!(M, pts; kernel_bw = [0.2;;],  kernel=AMP.MvNormalKernel)
+
+##
+p = exp(M, ϵ, hat(M, ϵ, [3.0]))
+y_amp = AMP.evaluate(mtree, p)
+
+y_pdf = pdf(dis, [3.0])
+
+@test isapprox(y_amp, y_pdf; atol=0.1)
+
+# ps = [[p] for p = -0:0.01:6]
+# ys_amp = map(p->AMP.evaluate(mtree, exp(M, ϵ, hat(M, ϵ, p))), ps)
+# ys_pdf = pdf(dis, ps)
+
+# lines(first.(ps), ys_pdf)
+# lines!(first.(ps), ys_amp)
+
+# lines!(first.(ps), ys_pdf)
+# lines(first.(ps), ys_amp)
+##
+
+M = SpecialOrthogonal(2)
+ϵ = identity_element(M)
+dis = MvNormal([0.0], diagm([0.1].^2)) 
+Cpts = [rand(dis) for _ in 1:128]
+pts = map(c->exp(M, ϵ, hat(M, ϵ, c)), Cpts)
+mtree = ApproxManifoldProducts.buildTree_Manellic!(M, pts; kernel_bw = [0.005;;], kernel=AMP.MvNormalKernel)
+
+##
+p = exp(M, ϵ, hat(M, ϵ, [0.1]))
+y_amp = AMP.evaluate(mtree, p)
+
+y_pdf = pdf(dis, [0.1])
+
+@test isapprox(y_amp, y_pdf; atol=0.5)
+
+ps = [[p] for p = -0.3:0.01:0.3]
+ys_amp = map(p->AMP.evaluate(mtree, exp(M, ϵ, hat(M, ϵ, p))), ps)
+ys_pdf = pdf(dis, ps)
+
+# lines(first.(ps), ys_pdf)
+# lines!(first.(ps), ys_amp)
+
+
+M = SpecialEuclidean(2)
+ϵ = identity_element(M)
+dis = MvNormal([10,20,0.1], diagm([0.5,2.0,0.1].^2)) 
+Cpts = [rand(dis) for _ in 1:128]
+pts = map(c->exp(M, ϵ, hat(M, ϵ, c)), Cpts)
+mtree = ApproxManifoldProducts.buildTree_Manellic!(M, pts; kernel_bw = diagm([0.05,0.2,0.01]), kernel=AMP.MvNormalKernel)
+
+##
+p = exp(M, ϵ, hat(M, ϵ, [10, 20, 0.1]))
+y_amp = AMP.evaluate(mtree, p)
+y_pdf = pdf(dis, [10,20,0.1])
+#FIXME
+@test_broken isapprox(y_amp, y_pdf; atol=0.1)
+
+end
+
+
+## ========================================================================================
+@testset "Test Product the brute force way" begin
+
+M = SpecialEuclidean(2)
+ϵ = identity_element(M)
+
+Xc_p = [10, 20, 0.1]
+p = exp(M, ϵ, hat(M, ϵ, Xc_p))
+kerp = AMP.MvNormalKernel(p, diagm([0.5, 2.0, 0.1].^2))
+
+Xc_q = [10, 22, -0.1]
+q = exp(M, ϵ, hat(M, ϵ, Xc_q))
+kerq = AMP.MvNormalKernel(q, diagm([1.0, 1.0, 0.1].^2))
+
+kerpq = calcProductGaussians(M, [kerp, kerq])
+
+# brute force way
+xs = 7:0.1:13
+ys = 15:0.1:27
+θs = -0.3:0.01:0.3
+
+grid_points = map(Iterators.product(xs, ys, θs)) do (x,y,θ)
+    exp(M, ϵ, hat(M, ϵ, SVector(x,y,θ)))
+end
+
+# use_global_coords = true
+use_global_coords = false
+
+pdf_ps = map(grid_points) do gp
+  if use_global_coords
+    X = log(M, p, gp) 
+    Xc_e = vee(M, ϵ, X)
+    pdf(MvNormal(cov(kerp)), Xc_e)
+  else
+    X = log(M, ϵ, Manifolds.compose(M, inv(M, p), gp))
+    Xc_e = vee(M, ϵ, X)
+    pdf(MvNormal(cov(kerp)), Xc_e)
+  end
+end
+
+pdf_qs = map(grid_points) do gp
+  if use_global_coords
+    X = log(M, q, gp) 
+    Xc_e = vee(M, ϵ, X)
+    pdf(MvNormal(cov(kerq)), Xc_e)
+  else
+    X = log(M, ϵ, Manifolds.compose(M, inv(M, q), gp))
+    Xc_e = vee(M, ϵ, X)
+    pdf(MvNormal(cov(kerq)), Xc_e)
+  end
+end
+
+pdf_pqs = pdf_ps .* pdf_qs
+# pdf_pqs ./= sum(pdf_pqs) * 0.01
+# pdf_pqs .*= 15.9672
+
+amp_ps = map(grid_points) do gp
+  AMP.evaluate(M, kerp, gp)
+end
+amp_qs = map(grid_points) do gp
+  AMP.evaluate(M, kerq, gp)
+end
+
+amp_pqs = map(grid_points) do gp
+  AMP.evaluate(M, kerpq, gp)
+end
+
+amp_bf_pqs = amp_ps .* amp_qs
+
+#FIXME 
+normalized_compare_test = isapprox.(normalize(amp_pqs), normalize(amp_bf_pqs); atol=0.001)
+@test_broken all(normalized_compare_test)
+@warn "Brute force product test overlap $(round(count(normalized_compare_test) / length(amp_pqs) * 100, digits=2))%"
+
+#TODO should this be local or global coords?
+@test_broken findmax(pdf_pqs[:,60,30])[2] == findmax(amp_pqs[:,60,30])[2]
+
+# these are all correct
+# lines(xs, pdf_ps[:,60,30])
+# lines!(xs, amp_ps[:,60,30])
+
+# lines(ys, pdf_ps[30,:,30])
+# lines!(ys, amp_ps[30,:,30])
+
+# lines(θs, pdf_ps[30,60,:])
+# lines!(θs, amp_ps[30,60,:])
+
+#these are different for "local" vs "global"
+# lines(xs, normalize(pdf_pqs[:,60,30]))
+# lines!(xs, normalize(amp_pqs[:,60,30]))
+# lines!(xs, normalize(amp_bf_pqs[:,60,30]))
+
+# lines(ys, normalize(pdf_pqs[30,:,30]))
+# lines!(ys, normalize(amp_pqs[30,:,30]))
+
+# lines(θs, normalize(pdf_pqs[30,60,:]))
+# lines!(θs, normalize(amp_pqs[30,60,:]))
+
+# contour(xs, ys, pdf_pqs[:,:,30]; color = :blue)
+# contour!(xs, ys, amp_pqs[:,:,30]; color = :red)
+# contour!(xs, ys, amp_bf_pqs[:,:,30]; color = :green)
+
+#just some exploration
+# pdf_p = pdf(Normal(10, 0.5), xs)
+# pdf_q = pdf(Normal(10, 1.0), xs)
+# pdf_pq = (pdf_p .* pdf_q)
+# pdf_pq ./= sum(pdf_pq) * 0.01 
+
+# lines(xs, pdf_p)
+# lines!(xs, pdf_q)
+# lines!(xs, pdf_pq)
+
+# pdf_p = pdf(Normal(20, 2.0), ys)
+# pdf_q = pdf(Normal(22, 1.0), ys)
+# pdf_pq = (pdf_p .* pdf_q)
+# pdf_pq ./= sum(pdf_pq) * 0.01 
+
+# lines(ys, pdf_p)
+# lines!(ys, pdf_q)
+# lines!(ys, pdf_pq)
+
+# pdf_p = pdf(Normal(0.1, 0.1), θs)
+# pdf_q = pdf(Normal(-0.1, 0.1), θs)
+# pdf_pq = (pdf_p .* pdf_q)
+# pdf_pq ./= sum(pdf_pq) * 0.01 
+
+# lines(θs, pdf_p)
+# lines!(θs, pdf_q)
+# lines!(θs, pdf_pq)
+
+
+end
+
+## ========================================================================================
+
+
 @testset "Manellic basic evaluation test 1D" begin
 ##
 
@@ -274,7 +584,7 @@ pts = [zeros(1) for _ in 1:100]
 bw = ones(1,1)
 mtree = ApproxManifoldProducts.buildTree_Manellic!(M, pts; kernel_bw=bw, kernel=AMP.MvNormalKernel)
 
-@test isapprox( 0.4, AMP.evaluate(mtree, SA[0.0;]); atol=0.1)
+@test isapprox( pdf(Normal(0,1), 0), AMP.evaluate(mtree, SA[0.0;]))
 
 @error "expectedLogL for different number of test points not working yet."
 # AMP.expectedLogL(mtree, [randn(1) for _ in 1:5])