diff --git a/julia/Manifest.toml b/julia/Manifest.toml
index 6c504537..9e1101b8 100644
--- a/julia/Manifest.toml
+++ b/julia/Manifest.toml
@@ -1,6 +1,6 @@
 # This file is machine-generated - editing it directly is not advised
 
-julia_version = "1.7.2"
+julia_version = "1.7.0"
 manifest_format = "2.0"
 
 [[deps.AbstractPlutoDingetjes]]
@@ -36,6 +36,12 @@ git-tree-sha1 = "4b859a208b2397a7a623a03449e4636bdb17bcf2"
 uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a"
 version = "1.16.1+1"
 
+[[deps.Calculus]]
+deps = ["LinearAlgebra"]
+git-tree-sha1 = "f641eb0a4f00c343bbc32346e1217b86f3ce9dad"
+uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
+version = "0.5.1"
+
 [[deps.ChainRulesCore]]
 deps = ["Compat", "LinearAlgebra", "SparseArrays"]
 git-tree-sha1 = "9950387274246d08af38f6eef8cb5480862a435f"
@@ -476,6 +482,12 @@ version = "1.0.3"
 deps = ["Artifacts", "Libdl"]
 uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1"
 
+[[deps.Measurements]]
+deps = ["Calculus", "LinearAlgebra", "Printf", "RecipesBase", "Requires"]
+git-tree-sha1 = "88cd033eb781c698e75ae0b680e5cef1553f0856"
+uuid = "eff96d63-e80a-5855-80a2-b1b0885c5ab7"
+version = "2.7.1"
+
 [[deps.Measures]]
 git-tree-sha1 = "e498ddeee6f9fdb4551ce855a46f54dbd900245f"
 uuid = "442fdcdd-2543-5da2-b0f3-8c86c306513e"
@@ -744,7 +756,7 @@ uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [[deps.ThreeBodyDecay]]
 deps = ["Cuba", "Parameters", "PartialWaveFunctions", "QuadGK", "RecipesBase", "StaticArrays"]
-git-tree-sha1 = "d0266d82eac3b189ab492d3bf749239bc332d19b"
+git-tree-sha1 = "dc02cf5ce464aa175435cbd05f0f64df8c8401f5"
 repo-rev = "master"
 repo-url = "https://github.com/mmikhasenko/ThreeBodyDecay.jl"
 uuid = "e6563dab-9ca1-5843-bde3-2ccf38d63843"
diff --git a/julia/Project.toml b/julia/Project.toml
index 1e802287..9f8c65b5 100644
--- a/julia/Project.toml
+++ b/julia/Project.toml
@@ -7,6 +7,7 @@ version = "0.1.0"
 Corpuscles = "e78a0372-c628-4773-9c8d-eb17d149bf93"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7"
 Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781"
diff --git a/julia/notebooks/model0.jl b/julia/notebooks/model0.jl
index 313c0c84..4661a0b7 100644
--- a/julia/notebooks/model0.jl
+++ b/julia/notebooks/model0.jl
@@ -1,5 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.19.3
+# v0.18.0
 
 using Markdown
 using InteractiveUtils
@@ -17,6 +17,8 @@ begin
 	# 
 	using LinearAlgebra
 	using Parameters
+	using Measurements
+	using DataFrames
 	# 
 	using ThreeBodyDecay
 	using ThreeBodyDecay.PartialWaveFunctions
@@ -58,16 +60,27 @@ md"""
 """
 
 # ╔═╡ 8c091c8c-f550-4ea6-b4ad-11aa4027468c
-breakup(m²,m1²,m2²) = sqrt(Kallen(m²,m1²,m2²))
+breakup(m²,m1²,m2²) = sqrt(KallenFact(m²,m1²,m2²))
 
 # ╔═╡ 01217062-003d-4496-b0fe-f54d32fbb2bf
 md"""
 ### `BreitWignerMinL`
 """
 
-# ╔═╡ 7a5a7e49-5454-4021-a698-766b1242f757
+# ╔═╡ 9f936b78-9235-40ef-8e34-e6ce1d88734e
+function F²(l,p,p0,d)
+	pR = p*d
+	p0R = p0*d
+	l==0 && return 1.0
+	l==1 && return (1+p0R^2)/(1+pR^2)
+	l!=2 && error("l>2 cannot be")
+	return (9+3p0R^2+3p0R^4)/(9+3pR^2+3pR^4)
+end
+
+# ╔═╡ fe3e1d61-1ad0-4509-92c7-dbac6a81343f
 begin
-	@with_kw struct BreitWignerMinL{T}
+	abstract type Lineshape end
+	@with_kw struct BreitWignerMinL{T} <: Lineshape
 		pars::T
 		l::Int
 		minL::Int
@@ -80,25 +93,20 @@ begin
 		m0::Float64
 	end
 	BreitWignerMinL(pars::T; kw...) where T = BreitWignerMinL(; pars, kw...)
-	function (BW::BreitWignerMinL)(s,σ)
+	function (BW::BreitWignerMinL)(σ)
+		dR, dΛc = 1.5, 5.0 # /GeV
 		m,Γ₀ = BW.pars
 		@unpack l, minL = BW
 		@unpack m1,m2,mk,m0 = BW
 		p,p0 = breakup(σ,m1^2,m2^2), breakup(m^2,m1^2,m2^2)
-		q,q0 = breakup(s,σ,mk^2), breakup(s,m^2,mk^2)
-		Γ = Γ₀*(p/p0)^(2l+1)*m/sqrt(σ)  # should also have F
-		1/(m^2-σ-1im*m*Γ) * (p/p0)^l * (q/q0)^minL # should also have F
+		q,q0 = breakup(m0^2,σ,mk^2), breakup(m0^2,m^2,mk^2)
+		Γ = Γ₀*(p/p0)^(2l+1)*m/sqrt(σ)*F²(l,p,p0,dR)
+		1/(m^2-σ-1im*m*Γ) * (p/p0)^l * (q/q0)^minL *
+			sqrt(F²(l,p,p0,dR) * F²(minL,q,q0,dΛc))
 	end
-end
-
-# ╔═╡ 60a8cb20-6041-494d-9523-397ce329c7b6
-md"""
-### `BuggBreitWignerMinL`
-"""
-
-# ╔═╡ 94c6394f-d00c-462c-ae29-ea107b66e191
-begin
-	@with_kw struct BuggBreitWignerMinL{T}
+	
+	# BuggBreitWignerMinL
+	@with_kw struct BuggBreitWignerMinL{T} <: Lineshape
 		pars::T
 		l::Int
 		minL::Int
@@ -114,22 +122,15 @@ begin
 		T <: NamedTuple{X, Tuple{Float64, Float64}} where X =
 			BuggBreitWignerMinL(; pars=merge(pars, (γ=1.1,)), kw...)
 	# 
-	function (BW::BuggBreitWignerMinL)(s,σ)
+	function (BW::BuggBreitWignerMinL)(σ)
 		σA = mK^2-mπ^2/2
 		m, Γ₀, γ = BW.pars
 		mΓ = (σ-σA) / (m^2-σA) * Γ₀^2*exp(-γ*σ)
 		1/(m^2-σ-1im*mΓ)
 	end
-end
 
-# ╔═╡ 8baf8a30-fda8-4094-bcfd-4f48c71dc1d1
-md"""
-### `Flatte1405`
-"""
-
-# ╔═╡ 6b9a1178-7863-4493-a4fa-78676cc2695c
-begin
-	@with_kw struct Flatte1405{T}
+	# Flatte1405
+	@with_kw struct Flatte1405{T} <: Lineshape
 		pars::T
 		l::Int
 		minL::Int
@@ -143,11 +144,11 @@ begin
 	end
 	#
 	Flatte1405(pars::T; kw...) where T = Flatte1405(; pars, kw...)
-	function (BW::Flatte1405)(s,σ)
+	function (BW::Flatte1405)(σ)
 		m,Γ₀ = BW.pars
-		@unpack m1, m2 = BW
-		p,p0 = breakup(σ,m1^2,m2^2), breakup(m^2,m1^2,m2^2)
-		p′,p0′ = breakup(σ,mπ^2,mΣ^2), breakup(m^2,mp^2,mΣ^2)
+		@unpack m1, m2, m0, mk = BW
+		p,p0 = breakup(σ,m1^2,m2^2), breakup(m^2,mπ^2,mΣ^2)
+		p′,p0′ = breakup(σ,mπ^2,mΣ^2), breakup(m^2,mπ^2,mΣ^2)
 		Γ1 = Γ₀*(p/p0)*m/sqrt(σ)  # should also have F
 		Γ2 = Γ₀*(p′/p0′)*m/sqrt(σ)  # should also have F
 		Γ = Γ1+Γ2
@@ -171,57 +172,66 @@ function buildchain(key, dict)
 	@unpack mass, width, lineshape = dict
 	#
 	k = Dict('K'=>1,'D'=>3,'L'=>2)[first(key)]
-	# 
-	parity = dict["jp"][end]
-	two_j = Int(2*eval(Meta.parse(dict["jp"][1:end-2])))
 	#
-	massval, widthval = ifhyphenaverage.((mass,width)) ./ 1e3
+	jp_R = str2jp(dict["jp"])
+	parity = jp_R.p
+	two_j = jp_R.j |> x2
 	#
-	LS = vcat(
-		[possible_ls(
-			jp(two_j // 2, parity),
-			jp(tbs.two_js[k] // 2, parities[k]);
-			jp=jp(tbs.two_js[0] // 2, P0)) for P0 in ['+', '-']]...)
+	massval, widthval = ifhyphenaverage.((mass,width)) ./ 1e3
 	#
 	i,j = ij_from_k(k)
-	ls = possible_ls(
-		jp(tbs.two_js[i] // 2, parities[i]),
-		jp(tbs.two_js[j] // 2, parities[j]);
-		jp=jp(two_j // 2, parity))
-	length(ls) != 1 && error("wierd")
-	onlyls = first(ls)
+	# 
+	@unpack two_js = tbs
+	# 
+	reaction_ij = jp_R=>(jp(two_js[i]//2,parities[i]), jp(two_js[j]//2,parities[j]))
+	reaction_Rk(P0) = jp(two_js[0]//2,P0) => (jp_R, jp(two_js[k]//2,parities[k]))
+	# 
+	LS = vcat(possible_ls(reaction_Rk('+')), possible_ls(reaction_Rk('-')))
 	minLS = first(sort(vcat(LS...); by=x->x[1]))
 	# 
-	code = quote
-		decay_chain(
-			k=$k,
-			Xlineshape = $(Symbol(lineshape))(
+	ls = possible_ls(reaction_ij)
+	length(ls) != 1 && error("expected the only on ls: $(ls)")
+	onlyls = first(ls)
+	#
+	Hij = ParityRecoupling(two_js[i],two_js[j],reaction_ij)
+	# HRk = RecouplingLS(minLS |> x2, reaction_Rk('+'))
+	Xlineshape = eval(
+		quote 
+		$(Symbol(lineshape))(
 				(; m=$massval, Γ=$widthval);
 				name = $key,
 				l = $(onlyls[1]),
 				minL = $(minLS[1]),
-				m1=$(ms[i]), m2=$(ms[j]), mk=$(ms[k]), m0=$(ms[0])),
-			two_LS = $(minLS .|> x2), 
-			two_ls = $(onlyls .|> x2),
-			two_s=$two_j, tbs=$tbs,
-			)
-	end
-	eval(code)
-	# code
+				m1=$(ms[i]), m2=$(ms[j]), mk=$(ms[k]), m0=$(ms[0]))
+		end)
+	return (; k, Xlineshape, Hij, two_s=two_j, parity)
 end
 
-# ╔═╡ c034441c-95b0-409f-a3cf-2714789b2d0f
+# ╔═╡ 7cc4c5f9-4392-4b57-88af-59d1cf308162
 isobarsinput = readjson(joinpath("..","data","isobars.json"))["isobars"];
 
+# ╔═╡ f9449a5b-7d92-43d7-95a6-c9e4632172f5
+typeof(
+	[buildchain("K(892)", isobarsinput["K(892)"]).Xlineshape,
+	 buildchain("L(1405)", isobarsinput["L(1405)"]).Xlineshape])
+
 # ╔═╡ b0f5c181-dcb2-48f8-a510-57eac44ca4d9
-ds = [buildchain(keys, dict) for (keys, dict) in isobarsinput]
+begin
+	isobars = Dict()
+	#
+	for (key, dict) in isobarsinput
+		isobars[key] = buildchain(key, dict)
+	end
+	isobars
+end
 
 # ╔═╡ cee9dc28-8048-49e7-8caf-8e07bcd884c4
 let
 	plot(layout=grid(1,3), size=(1000,240))
-	for d in ds
-		plot!(sp=d.k, x->abs2(d.Xlineshape(ms[0]^2,x)),lims(d.k,ms)...,
-			lab=d.Xlineshape.name)
+	for (k,v) in isobars
+		xv = range(lims(v.k,ms)..., length=300)
+		calv = abs2.(v.Xlineshape.(xv))
+		plot!(sp=v.k, xv, calv ./ sum(calv), lab=k)
 	end
 	plot!()
 end
@@ -232,6 +242,110 @@ end
 # 	summed_over_polarization((x,y)->abs2(amplitude(x,y,d)), tbs.two_js)
 # 	; iσx=1, iσy=2, title=d.Xlineshape.name) for d in ds]...)
 
+# ╔═╡ 7ecc65a4-9fe7-4209-ad54-f1c8abe52ee5
+ modelparameters =
+	 readjson(joinpath("..","data","modelparameters.json"))["modelstudies"]
+
+# ╔═╡ 24d051e5-4d9e-48c8-aace-225f3a0218cb
+begin
+	defaultparameters = first(modelparameters)["parameters"]
+	defaultparameters["ArK(892)1"] = "1.0 ± 0.0"
+	defaultparameters["AiK(892)1"] = "0.0 ± 0.0"
+end
+
+# ╔═╡ 468884de-530f-4903-acfa-72946e330866
+couplingindexmap = Dict(
+	r"L.*" => Dict(
+			'1' => (1, 0),
+			'2' => (-1, 0)),
+	r"D.*" => Dict(
+			'1' => (1, 0),
+			'2' => (-1, 0)),
+	r"K\(892\)" => Dict(
+			'1' => (0,  1),
+			'2' => (-2, 1),
+			'3' => (2, -1),
+			'4' => (0, -1)),
+	r"K\(700|1430\)" => Dict(
+			'1' => (0, -1),
+			'2' => (0, 1)))
+
+# ╔═╡ f3e20fe4-1e26-47e0-9a2d-a794ece9e0d9
+begin
+	modelterms = DataFrame()
+	# 
+	couplingkeys = filter(x->x[1:2]=="Ar", keys(defaultparameters))
+	for c in couplingkeys
+		isobarname = c[3:end-1]
+		couplingindex = c[end]
+		c_re = c
+		c_im = "Ai" * c[3:end]
+		value_re = eval(Meta.parse(defaultparameters[c_re])).val
+		value_im = eval(Meta.parse(defaultparameters[c_im])).val
+		value_re + 1im*value_im
+		# 
+		r = filter(keys(couplingindexmap)) do k
+			match(k, isobarname) !== nothing
+		end
+		(two_λR,two_λk) = couplingindexmap[first(r)][couplingindex]
+		# 
+		push!(modelterms, (; isobarname, two_λR,two_λk, c=value_re + 1im*value_im, ))
+	end
+	modelterms
+end
+
+# ╔═╡ 5f8973a8-faa7-4663-b896-e7f4ff9600a2
+function convertLHCb2DPD(two_λR, two_λk, c; k, parity, two_j)
+	if k == 2
+		@assert two_λk == 0
+		c′ = - (2*(parity == '+')-1) * c
+		return (-two_λR, two_λk, c′)
+	elseif k == 3
+		c′ = - (2*(parity == '+')-1) * minusone()^(two_j//2-1//2) * c
+		return (-two_λR, two_λk, c′)
+	end
+	k!=1 && error("cannot be!")
+	return (two_λR, -two_λk, c)
+end
+
+# ╔═╡ fe72915f-51c7-48bd-8745-9bc97d05162c
+const terms = [
+	begin
+		@unpack k, Hij, two_s, Xlineshape, parity = isobars[t.isobarname]
+		two_λR, two_λk, c =
+			convertLHCb2DPD(t.two_λR, t.two_λk, t.c; k, two_j=two_s, parity)
+		HRk = NoRecoupling(two_λR, two_λk)
+		(c, DecayChain(; k, Xlineshape, Hij, HRk, two_s, tbs))
+	end for t in eachrow(modelterms)]
+
+# ╔═╡ abdeb1ac-19dc-45b2-94dd-5e64fb3d8f14
+A(σs,two_λs) = sum(c*amplitude(σs,two_λs, d) for (c,d) in terms)
+
+# ╔═╡ e50afc73-d0a6-4a8d-ae06-c95c9946998d
+begin
+	I = summed_over_polarization(abs2 ∘ A, tbs.two_js)
+end
+
+# ╔═╡ 99be3b6e-4462-4bf9-bf04-5c4e61966e28
+begin
+	λσs = randomPoint(tbs)
+	I(λσs.σs)
+end
+
+# ╔═╡ 88c58ce2-c98b-4b60-901f-ed95099c144b
+pdata = flatDalitzPlotSample(ms; Nev=10000)
+
+# ╔═╡ b833843f-c8b6-44c6-847c-efce0ed989d4
+plot(ms,σs->I(σs), iσx=2, iσy=1)
+
+# ╔═╡ e2346d61-a4c7-40bf-9e0d-87948da4e160
+Iv = I.(pdata)
+
+# ╔═╡ 8e06d5ec-4b98-441d-919b-7b90071e6674
+histogram2d(
+	getproperty.(pdata, :σ2),
+	getproperty.(pdata, :σ1), weights=Iv, bins=100)
+
 # ╔═╡ Cell order:
 # ╠═dea88954-c7b2-11ec-1a3d-717739cfd08b
 # ╠═e04157cc-7697-41e5-8e4e-3556332929ef
@@ -243,15 +357,26 @@ end
 # ╟─65473482-5487-4d10-88a6-aedd62538014
 # ╠═8c091c8c-f550-4ea6-b4ad-11aa4027468c
 # ╟─01217062-003d-4496-b0fe-f54d32fbb2bf
-# ╠═7a5a7e49-5454-4021-a698-766b1242f757
-# ╟─60a8cb20-6041-494d-9523-397ce329c7b6
-# ╠═94c6394f-d00c-462c-ae29-ea107b66e191
-# ╟─8baf8a30-fda8-4094-bcfd-4f48c71dc1d1
-# ╠═6b9a1178-7863-4493-a4fa-78676cc2695c
+# ╠═9f936b78-9235-40ef-8e34-e6ce1d88734e
+# ╠═fe3e1d61-1ad0-4509-92c7-dbac6a81343f
 # ╟─877f6aab-d0ff-44d7-9ce0-e43d895be297
 # ╠═469a9d3e-6c82-4cd6-afb6-5e92c481a7a2
 # ╠═680c04bb-027f-46ad-b53b-039e33dacd86
-# ╠═c034441c-95b0-409f-a3cf-2714789b2d0f
+# ╠═7cc4c5f9-4392-4b57-88af-59d1cf308162
+# ╠═f9449a5b-7d92-43d7-95a6-c9e4632172f5
 # ╠═b0f5c181-dcb2-48f8-a510-57eac44ca4d9
 # ╠═cee9dc28-8048-49e7-8caf-8e07bcd884c4
 # ╠═3971792d-a41f-4792-92b7-8b36e51f70b1
+# ╠═7ecc65a4-9fe7-4209-ad54-f1c8abe52ee5
+# ╠═24d051e5-4d9e-48c8-aace-225f3a0218cb
+# ╠═468884de-530f-4903-acfa-72946e330866
+# ╠═f3e20fe4-1e26-47e0-9a2d-a794ece9e0d9
+# ╠═5f8973a8-faa7-4663-b896-e7f4ff9600a2
+# ╠═fe72915f-51c7-48bd-8745-9bc97d05162c
+# ╠═abdeb1ac-19dc-45b2-94dd-5e64fb3d8f14
+# ╠═e50afc73-d0a6-4a8d-ae06-c95c9946998d
+# ╠═99be3b6e-4462-4bf9-bf04-5c4e61966e28
+# ╠═88c58ce2-c98b-4b60-901f-ed95099c144b
+# ╠═b833843f-c8b6-44c6-847c-efce0ed989d4
+# ╠═e2346d61-a4c7-40bf-9e0d-87948da4e160
+# ╠═8e06d5ec-4b98-441d-919b-7b90071e6674