text:
### A Pluto.jl notebook ###
# v0.18.4

using Markdown
using InteractiveUtils

# ╔═╡ f56ade1a-c156-11ec-0b95-453ac6cc4736
using NeuronBuilder

# ╔═╡ 939c6ad2-549e-4964-9610-8b3f0a784ba6
using Plots

# ╔═╡ 43ee5aa1-b801-4fa0-a610-aca9a0743712
using ModelingToolkit, Latexify

# ╔═╡ 028bfc47-da2a-4f0a-a27d-9ffa14c1a896
using OrdinaryDiffEq

# ╔═╡ d89bd40e-a5eb-474d-bbdd-61f9bb40a58a
using QuadGK

# ╔═╡ c9c5e405-13d8-44ef-aaf8-e855845fb96e
using ForwardDiff

# ╔═╡ db92afff-5dea-4acf-a385-ec89f7dca875
md"""
# Hodgkin Huxley Tutorial

- With original HH model, as taken from the secondary source [here](https://www.math.mcgill.ca/gantumur/docs/reps/RyanSicilianoHH.pdf)
"""

# ╔═╡ dc0b550c-9706-44b8-9d82-b277a4b9c35d
md"""
## 1. Gate equations
#### Featuring....tracing of numerical code to get symbolic code
- Useful feature of ModelingToolkit.jl (which uses Symbolics.jl)
"""

# ╔═╡ dc5dcd80-fc1e-425a-ac44-7d72d37c713c
αₙ(x) = 0.01(x + 50.0) / (1 - exp(
    -(50.0 + x) / 10.0
)
)

# ╔═╡ ae8b54d2-2ab5-43bd-bf44-2ecbb4f117ab
md"""
αₙ is a numerical function...
"""

# ╔═╡ 24474241-930d-400b-b051-0e6b0c5e5239
βₙ(x) = 0.125exp(-(x + 60) / 80.0)

# ╔═╡ 841a0580-7908-4de6-b287-642e5953c071
αₘ(x) = 0.1(35.0 + x) / (
    -exp(
        -(35.0 + x) / 10.0
    ) + 1.0
);

# ╔═╡ 4ddb805a-5b8d-4845-9425-33bbe26a33b2
βₘ(x) = 4exp(
    -(x + 60) / 18.0
);

# ╔═╡ a823c314-a14b-4af7-950b-cc7d94c38ee6
αₕ(x) = 0.07exp(-(x + 60.0) / 20.0);

# ╔═╡ 6d981c88-ab25-4d6a-b4b8-18f4e65961c4
βₕ(x) = 1.0 / (
    1 + exp(
        (-30.0 - x) / 10.0
    )
);

# ╔═╡ 8f43b2f3-2a01-40e5-94ed-3a38548906a3
τₙ(x) = 1.0 / (αₙ(x) + βₙ(x));

# ╔═╡ 1ed201a4-773b-4c5d-ad19-e5a6ebab27e2
md"""

## Building a channel

### An example sodium channel (hodgkin huxley)
"""

# ╔═╡ 0f0f572c-593b-414a-85b9-471f179ff549
begin
	"""
		Sensors: Reversal{Sodium}
		Actuators: Sodium
		Fields: Default values you may want.
	"""
    struct Na{F,D} <: FlowChannel(Sodium)
        g::D
        m::F
        h::F
    end

	"""
		default constructor
	"""
    Na(x) = Na(x, 0.0, 0.0)

"""
	- Method to build ODESystem from channel.
	- Requires neural parameters only to check whether sensors are parameters or states.
	- E.g. is `ENa` a function of time or static?

"""
    function (ch::Na)(n::Neuron; name=get_name(ch))

		### 1. instantiate any variables/parameters that need to be called programatically
		I, E, V, g = instantiate_hooks(n, ch, currents, reversals, voltage, conductances)

		### 2. Instantiate internal variables as you like
        @variables m(t) h(t)

		### 3. Apportion them as states/parameters
        states, params = vardivide(V, m, h, I, g, E)
        eqs = [
            D(m) ~ αₘ(V) * (1 - m) - βₘ(V) * m,
            D(h) ~ αₕ(V) * (1 - h) - βₕ(V) * h,
            I ~ g * m^3 * h * (E - V)
        ]

        defaultmap = [m => ch.m, h => ch.h, g => ch.g]
        return ODESystem(eqs, t, states, params; defaults=defaultmap, name=name)
    end

end

# ╔═╡ c6e25a10-3f95-4b01-b523-060e1c626587
na = Na(3.)

# ╔═╡ 81f66874-5e5b-46ab-be81-1125d7d39094
αₙ(V)

# ╔═╡ b18e04bd-8ac5-4ad9-9367-a3bf0b882266
βₙ(V)

# ╔═╡ c252fef7-dfb7-4b0b-a8a4-5950bd50a534
αₘ(V)

# ╔═╡ eb93f734-900f-4114-843e-f87d6c21328c
βₘ(V)

# ╔═╡ a9ea693a-5062-4501-9b1e-b295d1ebda9d
αₕ(V)

# ╔═╡ 567f1edd-8474-4418-b985-56c9871b8347
βₕ(V)

# ╔═╡ faea1138-1ab9-4017-87dd-95650eba4ac6
τₙ(V)

# ╔═╡ d8358ea0-8452-4658-a25a-72da16ca6560
na(EmptyNeuron())

# ╔═╡ 5c67938e-1e2b-4783-a938-3e9e70e1903d
na |> typeof |> supertype

# ╔═╡ 4d2b95fb-bc59-4205-be1b-7498e14807a8
md"""
- voltage is not included as it's assumed
"""

# ╔═╡ 11d33cea-5345-4e00-be11-c49595c417bf
sensed(na)

# ╔═╡ 6559cd4d-15b1-4b8f-980d-326a3111ce49
actuated(na)

# ╔═╡ a26230d8-6a33-440b-bb56-2a475bb660e4
md"""
#### Points

- rigid way to define and instantiate sensors and actuators
- freedom to define any internal equations / states
- complicated channel can have multiple sensors / actuators
"""

# ╔═╡ 76e4277c-ff8f-4def-bbfb-dd918f524921
md"""
### Potassium channel
"""

# ╔═╡ 91f8349e-a5df-44ec-8de0-812302c8f86c
begin
    struct K{F,D} <: FlowChannel(Potassium)
        g::D
        n::F
    end

    K(x) = K(x, 0.0)

    function (ch::K)(neur::Neuron; name=get_name(ch))

        I, E, V = instantiate_hooks(neur, ch, currents, reversals, voltage)
        g, = instantiate_parameters(ch, conductances)
        @variables n(t)

        states, params = vardivide(V, n, I, g, E)
        eqs = [
            D(n) ~ αₙ(V) * (1 - n) - βₙ(V) * n,
            I ~ g * n^4 * (E - V)
        ]

        defaultmap = [n => ch.n, g => ch.g]
        return ODESystem(eqs, t, states, params; defaults=defaultmap, name=name)
    end

end

# ╔═╡ e9fd95b0-0baf-4a51-bd12-a2921b903b93
K(1)(EmptyNeuron())

# ╔═╡ 8f559189-b2d9-44c8-b2d0-2100c5e49888
md"""
### Input channel

- needs custom sensors and actuators
- easy extension to voltage clamp
"""

# ╔═╡ 65dbba1f-4c5f-4f1f-8244-38a83cead934
begin
    struct Inp{F} <: FlowChannel{Tuple{Nothing},Tuple{Leak}}
        f::F
    end

    function (ch::Inp)(neur::Neuron; name=get_name(ch))
        I, V = instantiate_hooks(neur, ch, currents, voltage)

        eqs = [I ~ ch.f(t)]
        states, params = vardivide(V, I)
        return ODESystem(eqs, t, states, params; defaults=[I => ch.f(0)], name)
    end
end

# ╔═╡ e412d90b-4a99-4684-899f-e64adf0a9775
ii = Inp(t -> 10(1 + sin(t)))

# ╔═╡ 61442dee-dabc-4b78-907f-676353a853fa
ii(EmptyNeuron())

# ╔═╡ 02dd5310-11df-443b-af93-bc02f01d3b43
begin
    struct leak{D} <: FlowChannel(Leak)
        g::D
    end

    function (ch::leak)(n::Neuron; name=get_name(ch))
        I, E, V = instantiate_hooks(n, ch, currents, reversals, voltage)
        g, = instantiate_parameters(ch, conductances)

        eqs = [I ~ g * (E - V)]

        states, params = vardivide(V, E, I, g)
        defaultmap = [g => ch.g]

        return ODESystem(eqs, t, states, params; defaults=defaultmap, name=name)
    end
end

# ╔═╡ 672e0024-53f0-46a5-9ae8-9f602cd9c071
md"""### Defining a neuron
"""

# ╔═╡ 93e4e0de-fa09-4857-9da0-ec5742e55445
Capacitance = 0.01

# ╔═╡ cd350bbc-8660-42e7-a9fe-5012b626ca67
geom = NoGeometry(Capacitance)

# ╔═╡ 6225e690-68c5-4e44-a6e6-946a14b1ae37
somatic_parameters = Dict(
    Voltage => -10.0,
    Reversal{Sodium} => 55.17,
    Reversal{Potassium} => -72.14,
    Reversal{Leak} => -49.42
)

# ╔═╡ 6773ab39-2cf2-485a-80f5-632b7570c6cd
dynamics = Dict{DataType,SpeciesDynamics}(
    Voltage => BasicVoltageDynamics(),
)

# ╔═╡ 2db68d74-b25b-4e6b-81cc-3ed1b75c538f
channels = [K(0.36), Na(1.2), leak(0.0003), ii]

# ╔═╡ 4eadd12d-fa37-418a-856a-462479b59d2f
HH = BasicNeuron(geom, dynamics, somatic_parameters, channels, :HH)

# ╔═╡ ef78f53b-78b2-4c46-9eeb-99dfa9b1a429
ODESys = HH()

# ╔═╡ f5d8b94f-03dd-4ecb-8fd8-a545d70acce5
prob = ODEProblem(ODESys, [], (0.0, 100.0), []);

# ╔═╡ 25b4cc97-1947-458a-bf8f-8bf2a7b8a814
sol = solve(prob,Tsit5());

# ╔═╡ c7ba103a-343d-4a8a-8f0d-5277516a62cc
plot(sol; linewidth=4, vars= [ODESys.V])

# ╔═╡ 66da45d9-eeb6-46e4-a477-e3d9adc02624
begin
	p1 = plot(sol, vars=[ODESys.:K₊IK, ODESys.:Na₊INa]; linewidth=4)
	vline!(p1, [20.4]; linewidth=4, linestyle = :dot, label = "visual alignment")
end

# ╔═╡ a2a7e310-9e23-452b-9131-ad63c5edaf26
HH()

# ╔═╡ aad1bf93-6b46-42f4-93c9-5eb5aa04d2e5
md"""### Adding sodium dynamics"""

# ╔═╡ 78a90a0d-cdc0-483e-b21e-1870a31f9648
begin
	struct MySodiumDynamics <: SpeciesDynamics{Sodium} end

	function (b::MySodiumDynamics)(n::Neuron, vars, varnames, flux)
	    Na = vars[findfirst(x -> x == Sodium, varnames)]
	    return D(Na) ~  flux - Na
	end
end

# ╔═╡ 43148316-1de0-466b-b5d2-e57b74de12a8
extended_dynamics = merge(dynamics, Dict(
	Sodium => MySodiumDynamics()
)
)

# ╔═╡ 277b54db-cf00-432e-8ad1-9c63c573117c
extended_parameters = merge(somatic_parameters, Dict(Sodium => 0.))

# ╔═╡ a16841ee-b9b1-4390-b568-c85adb2ce2c3
typeof(extended_dynamics)

# ╔═╡ f4112e87-9a41-4b7e-ae81-fe2f8547da23
SodiumHH = BasicNeuron(geom, extended_dynamics, extended_parameters, channels, :HH)

# ╔═╡ b8fa49cf-cb20-48ea-86a4-37e3eceecf93
SodiumHH()

# ╔═╡ 7c4f5a51-8555-42ce-9c50-9e58e2f4e293
na_sys = SodiumHH()

# ╔═╡ 4dc4dae0-a6b1-4fcf-948e-f8d094329023
states(na_sys)

# ╔═╡ b82bda04-0325-43e6-a551-5541c764979b
na_prob = ODEProblem(na_sys, [], (0.0, 100.0), []);

# ╔═╡ 8283eb9b-1bbc-47c4-9d9f-ab64b75438b9
na_sol = solve(na_prob, Tsit5())

# ╔═╡ 508d29f8-a497-4bf4-b97e-e1dde48c7b71
na_sol(2.; idxs = na_sys.:Na₊INa)

# ╔═╡ 387906ee-e4d1-4a2e-a73d-46f9a6598333
overlapping_current(t::Number, sol) = -prod(
	sol(t, idxs = [na_sys.:Na₊INa, na_sys.:K₊IK])
)

# ╔═╡ 9fe38541-6022-4032-9065-0fdbf9050ab8
function overlapping_current(sol::ODESolution)
	return quadgk(t -> overlapping_current(t, sol), sol.t[1], sol.t[end])
end

# ╔═╡ 7ddf3a4d-21a8-4a0c-84e0-ae7fb51bf2fa
function cost(θ)
	new_prob = remake(prob, p = θ)
	sol = solve(new_prob, Tsit5())
	return overlapping_current(sol)[1]
end

# ╔═╡ 9f476f36-cd15-45ba-ac5f-1805601f0fc9
ForwardDiff.gradient(cost, prob.p)

# ╔═╡ 8225a5ae-0744-4e6b-8f24-851eb7ae4cfd
function gradient_descent(;steps=5, lr=0.01)
	p = deepcopy(prob.p)
	g = deepcopy(p)
	for i = 1:steps
		p -= lr*ForwardDiff.gradient(cost, p)
	end
	return p
end

# ╔═╡ 9893ec68-97a6-45d7-a7e7-1af6003e5cc3
newp = gradient_descent()

# ╔═╡ a120ff89-9f15-4ff4-b655-5e3edc416049
newprob = remake(prob, p=gradient_descent())

# ╔═╡ de0891c3-9ff4-4e07-999c-bef3da82ce31
newsol = solve(newprob, Tsit5());

# ╔═╡ 9f23da53-c232-4030-b936-b069504dd64c
newprob.p

# ╔═╡ 80cd3934-f7ff-4dc3-b881-02ca74758212
plot(newsol; linewidth=4)

# ╔═╡ 12bb5640-54ad-479c-930f-ce914a6e0759
plot(newsol; linewidth=4, vars = [na_sys.:K₊IK, na_sys.:Na₊INa])

# ╔═╡ 90a8c73f-5601-427b-9670-a58b0e71a077
parameters(na_sys)

# ╔═╡ 00a5cf4e-0d98-4c15-b59c-812ff43ebe14
overlapping_current(newsol)[1] - overlapping_current(sol)[1]

# ╔═╡ 4af9ed7a-b79c-4025-b245-bf1127ff2ff8
indexof(sym,syms) = findfirst(isequal(sym),syms)

# ╔═╡ 51c0ff87-74dc-44b2-81a3-9d67540e6f01
getsym(s::Symbol) = ModelingToolkit.getvar(na_sys, s)

# ╔═╡ 2e784191-41e2-4251-b8a7-e1cf92d6f547
a = indexof(getsym(:Cₘ),parameters(na_sys))

# ╔═╡ 48b0b926-5db0-4391-bbbd-05bd533f85d7
sol[na_sys.:K₊IK, 2]

# ╔═╡ 01ace3bc-566b-4507-8dee-3337251f8f57
prod(sol(4.2, idxs=[na_sys.:K₊IK, na_sys.:K₊IK]))

# ╔═╡ ae642025-071d-460f-9c3d-a639cac90dae
ModelingToolkit.getvar(na_sys, :Cₘ)

# ╔═╡ 71c88978-cf46-4098-b773-a41765f372ff
@variables V

# ╔═╡ 76d40232-693c-4de5-b276-04530e7549a3
I, E, V, g = instantiate_hooks(EmptyNeuron(), na, currents, reversals, voltage, conductances)

# ╔═╡ 00000000-0000-0000-0000-000000000001
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uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89"
version = "1.19.0+0"

[[deps.Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4528479aa01ee1b3b4cd0e6faef0e04cf16466da"
uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91"
version = "1.25.0+0"

[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "1acf5bdf07aa0907e0a37d3718bb88d4b687b74a"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.12+0"

[[deps.XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"

[[deps.Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"

[[deps.Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"

[[deps.Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724"
version = "1.2.0+4"

[[deps.Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"

[[deps.Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"

[[deps.Xorg_libXfixes_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
uuid = "d091e8ba-531a-589c-9de9-94069b037ed8"
version = "5.0.3+4"

[[deps.Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
uuid = "a51aa0fd-4e3c-5386-b890-e753decda492"
version = "1.7.10+4"

[[deps.Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"

[[deps.Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"

[[deps.Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"

[[deps.Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"

[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"

[[deps.Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.0+4"

[[deps.Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"

[[deps.Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"

[[deps.Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.2+4"

[[deps.Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.27.0+4"

[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"

[[deps.Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.2+0"

[[deps.ZygoteRules]]
deps = ["MacroTools"]
git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.2"

[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"

[[deps.libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.2+0"

[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"

[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+1"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"

[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"

[[deps.x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"

[[deps.xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
"""

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text:
import Images, Images.ColorTypes
using Base.Test, Color, FixedPointNumbers
import Images.Gray, Images.GrayAlpha
using Compat

macro test_colortype_approx_eq(a, b)
    :(test_colortype_approx_eq($(esc(a)), $(esc(b)), $(string(a)), $(string(b))))
end

function test_colortype_approx_eq(a::ColorValue, b::ColorValue, astr, bstr)
    typeof(a) == typeof(b) || error("types of $astr and $bstr do not match")
    n = length(fieldnames(typeof(a)))
    for i = 1:n
        Base.Test.test_approx_eq(getfield(a, i), getfield(b,i), astr, bstr)
    end
end

function test_colortype_approx_eq(a::AbstractAlphaColorValue, b::AbstractAlphaColorValue, astr, bstr)
    typeof(a) == typeof(b) || error("types of $astr and $bstr do not match")
    n = length(fieldnames(typeof(a.c)))
    for i = 1:n
        Base.Test.test_approx_eq(getfield(a.c, i), getfield(b.c,i), astr, bstr)
    end
    Base.Test.test_approx_eq(a.alpha, b.alpha, astr, bstr)
end

@test length(RGB) == 3
@test length(ColorTypes.BGR) == 3
@test length(AlphaColorValue{ColorTypes.BGR{Float32},Float32}) == 4  # TypeConstructor mess, would be length(BGRA) otherwise
@test length(ColorTypes.AlphaColor{RGB, Ufixed8}) == 4  # length(ARGB)
@test length(ColorTypes.RGB1) == 4
@test length(ColorTypes.RGB4) == 4
@test length(RGB24) == 4
@test length(ColorTypes.Gray) == 1
@test length(AlphaColorValue{ColorTypes.Gray,Ufixed8}) == 2
@test length(ColorTypes.Gray24) == 4
# @test length(ColorTypes.AGray32) == 4
@test length(ColorTypes.ARGB{Float32}) == 4
@test length(ColorTypes.YIQ) == 3
c = ColorTypes.AlphaColor(RGB{Ufixed8}(0.8,0.2,0.4), Ufixed8(0.5))
@test length(c) == 4
@test length(zero(GrayAlpha{Float32})) == 2
@test length(one(RGB{Float32})) == 3

# Arithmetic with Gray
cf = Gray{Float32}(0.1)
ccmp = Gray{Float32}(0.2)
@test 2*cf == ccmp
@test cf*2 == ccmp
@test ccmp/2 == cf
@test 2.0f0*cf == ccmp
@test eltype(2.0*cf) == Float64
cu = Gray{Ufixed8}(0.1)
@test 2*cu == Gray(2*cu.val)
@test 2.0f0*cu == Gray(2.0f0*cu.val)
f = Ufixed8(0.5)
@test_approx_eq (f*cu).val f*cu.val
@test 2.*cf == ccmp
@test cf.*2 == ccmp
@test cf/2.0f0 == Gray{Float32}(0.05)
@test cu/2 == Gray(cu.val/2)
@test cu/0.5f0 == Gray(cu.val/0.5f0)
@test cf+cf == ccmp
@test isfinite(cf)
@test !isinf(cf)
@test !isnan(cf)
@test isfinite(Gray(NaN)) == false
@test isinf(Gray(NaN)) == false
@test isnan(Gray(NaN)) == true
@test isfinite(Gray(Inf)) == false
@test isinf(Gray(Inf)) == true
@test isnan(Gray(Inf)) == false
@test_approx_eq abs(Gray(0.1)) 0.1
@test eps(Gray{Ufixed8}) == Gray(eps(Ufixed8))  # #282

acu = Gray{Ufixed8}[cu]
acf = Gray{Float32}[cf]
@test typeof(acu+acf) == Vector{Gray{Float32}}
@test typeof(2*acf) == Vector{Gray{Float32}}
@test typeof(0x02*acu) == Vector{Gray{Float32}}
@test typeof(acu/2) == Vector{Gray{typeof(Ufixed8(0.5)/2)}}

# Arithmetic with GrayAlpha
p1 = GrayAlpha{Float32}(Gray(0.8), 0.2)
p2 = GrayAlpha{Float32}(Gray(0.6), 0.3)
@test_colortype_approx_eq p1+p2 GrayAlpha{Float32}(Gray(1.4),0.5)
@test_colortype_approx_eq (p1+p2)/2 GrayAlpha{Float32}(Gray(0.7),0.25)
@test_colortype_approx_eq 0.4f0*p1+0.6f0*p2 GrayAlpha{Float32}(Gray(0.68),0.26)
@test_colortype_approx_eq ([p1]+[p2])[1] GrayAlpha{Float32}(Gray(1.4),0.5)
@test_colortype_approx_eq ([p1].+[p2])[1] GrayAlpha{Float32}(Gray(1.4),0.5)
@test_colortype_approx_eq ([p1]/2)[1] GrayAlpha{Float32}(Gray(0.4),0.1)
@test_colortype_approx_eq (0.4f0*[p1]+0.6f0*[p2])[1] GrayAlpha{Float32}(Gray(0.68),0.26)

# Arithemtic with RGB
cf = RGB{Float32}(0.1,0.2,0.3)
ccmp = RGB{Float32}(0.2,0.4,0.6)
@test 2*cf == ccmp
@test cf*2 == ccmp
@test ccmp/2 == cf
@test 2.0f0*cf == ccmp
@test eltype(2.0*cf) == Float64
cu = RGB{Ufixed8}(0.1,0.2,0.3)
@test 2*cu == RGB(2*cu.r, 2*cu.g, 2*cu.b)
@test 2.0f0*cu == RGB(2.0f0*cu.r, 2.0f0*cu.g, 2.0f0*cu.b)
f = Ufixed8(0.5)
@test_approx_eq (f*cu).r f*cu.r
@test 2.*cf == ccmp
@test cf.*2 == ccmp
@test cf/2.0f0 == RGB{Float32}(0.05,0.1,0.15)
@test cu/2 == RGB(cu.r/2,cu.g/2,cu.b/2)
@test cu/0.5f0 == RGB(cu.r/0.5f0, cu.g/0.5f0, cu.b/0.5f0)
@test cf+cf == ccmp
@test_colortype_approx_eq (cf*[0.8f0])[1] RGB{Float32}(0.8*0.1,0.8*0.2,0.8*0.3)
@test_colortype_approx_eq ([0.8f0]*cf)[1] RGB{Float32}(0.8*0.1,0.8*0.2,0.8*0.3)
@test_colortype_approx_eq (cf.*[0.8f0])[1] RGB{Float32}(0.8*0.1,0.8*0.2,0.8*0.3)
@test_colortype_approx_eq ([0.8f0].*cf)[1] RGB{Float32}(0.8*0.1,0.8*0.2,0.8*0.3)
@test isfinite(cf)
@test !isinf(cf)
@test !isnan(cf)
@test isfinite(RGB(NaN, 1, 0.5)) == false
@test isinf(RGB(NaN, 1, 0.5)) == false
@test isnan(RGB(NaN, 1, 0.5)) == true
@test isfinite(RGB(1, Inf, 0.5)) == false
@test isinf(RGB(1, Inf, 0.5)) == true
@test isnan(RGB(1, Inf, 0.5)) == false
@test_approx_eq abs(RGB(0.1,0.2,0.3)) 0.6

acu = RGB{Ufixed8}[cu]
acf = RGB{Float32}[cf]
@test typeof(acu+acf) == Vector{RGB{Float32}}
@test typeof(2*acf) == Vector{RGB{Float32}}
@test typeof(convert(UInt8, 2)*acu) == Vector{RGB{Float32}}
@test typeof(acu/2) == Vector{RGB{typeof(Ufixed8(0.5)/2)}}

c = ColorTypes.Gray{Ufixed16}(0.8)
@test convert(RGB, c) == RGB{Ufixed16}(0.8,0.8,0.8)
@test convert(RGB{Float32}, c) == RGB{Float32}(0.8,0.8,0.8)
r4 = ColorTypes.RGB4(1,0,0)
@test convert(RGB, r4) == RGB(1,0,0)
@test convert(RGB{Ufixed8}, r4) == RGB{Ufixed8}(1,0,0)
@test convert(ColorTypes.RGB4{Ufixed8}, r4) == ColorTypes.RGB4{Ufixed8}(1,0,0)
@test convert(ColorTypes.RGB4{Float32}, r4) == ColorTypes.RGB4{Float32}(1,0,0)
@test convert(ColorTypes.BGR{Float32}, r4) == ColorTypes.BGR{Float32}(1,0,0)

iob = IOBuffer()
c = ColorTypes.BGR{Ufixed8}(0.1,0.2,0.301)
show(iob, c)
@test takebuf_string(iob) == "BGR{Ufixed8}(0.102,0.2,0.302)"
c = ColorTypes.Gray{Ufixed16}(0.8)
show(iob, c)
@test takebuf_string(iob) == "Gray{Ufixed16}(0.8)"
ca = AlphaColorValue(c, Ufixed16(0.2))
show(iob, ca)
@test takebuf_string(iob) == "GrayAlpha{Ufixed16}(0.8,0.2)"

# YIQ
@test convert(ColorTypes.YIQ, RGB(1,0,0)) == ColorTypes.YIQ(0.299, 0.595716, 0.211456)
@test convert(ColorTypes.YIQ, RGB(0,1,0)) == ColorTypes.YIQ(0.587, -0.274453, -0.522591)
@test convert(ColorTypes.YIQ, RGB(0,0,1)) == ColorTypes.YIQ(0.114, -0.321263, 0.311135)
@test convert(RGB, ColorTypes.YIQ(1.0,0.0,0.0)) == RGB(1,1,1)
v = 0.5957
@test convert(RGB, ColorTypes.YIQ(0.0,1.0,0.0)) == RGB(0.9563*v,-0.2721*v,-1.1070*v)  # will be clamped to v
v = -0.5226
@test convert(RGB, ColorTypes.YIQ(0.0,0.0,-1.0)) == RGB(0.6210*v,-0.6474*v,1.7046*v)  # will be clamped to v

ext: jl


text:
function euclidean_distance(vec1, vec2)
    sqrt(sum([(vec1[i]-vec2[i])^2 for i in 1:length(vec1)]))
end

function k_nearest_neighbour(eigenface_table, new_eigenface, k)
    n=length(eigenface_table)
    distances = [euclidean_distance(eigenface_table[i][2], new_eigenface) for i in 1:n]
    nearest_neighbours = [eigenface_table[i] for i in sortperm(distances)[1:k]]
    classes = unique([neighbour[1] for neighbour in nearest_neighbours])
    nearest = nearest_neighbours[1][1]
    inverse_distance(neighbour) = 1/(euclidean_distance(neighbour, new_eigenface))
    min = 0
    for class in classes
        freq = sum([inverse_distance(neighbour[2]) for neighbour in filter(x->x[1]==class, nearest_neighbours)])
        if freq > min
            min = freq
            nearest = class
        end
    end
    nearest
end

function test_model(proj, images, d, n, k)
    labels = unique(map(x->x[2], images))
    training_set = reduce(vcat,[map(x->x[1], filter(x->x[2] == labels[i], images))[1:n+1] for i in 1:size(labels, 1)])
    image_matrix = images_to_image_matrix(training_set)
    transformed = transpose(transpose(proj[:,1:d])*image_matrix)
    eigenface_table = reduce(vcat, [[[labels[i+1] [transformed[j,:]]] for j in (i*(n+1)+1):(i*(n+1)+n+1)] for i in 0:size(labels, 1)-1])
    correct = 0
    for i in 1:length(training_set)
        test_eigenface = eigenface_table[i]
        data = reduce(vcat, [eigenface_table[1:i-1], eigenface_table[i+1:end]])
        if k_nearest_neighbour(data, test_eigenface[2], k) == test_eigenface[1]
            correct = correct + 1
        end
    end
    return correct/length(training_set)
end


text:
module Clustering

using Clustering, ParallelKMeans

export kmeans,
       kmeanspar

function kmeans(X, k)
    Clustering.kmeans(X, k);
end

function kmeanspar(X, k)
    # https://pydatablog.github.io/ParallelKMeans.jl/stable/#How-To-Use
    ParallelKMeans.kmeans(X, k);
end

end


text:
# abstract types -
abstract type PSAbstractVisualizationTheme end
abstract type PSAbstractLatticeModel end

struct PSEquityAsset <: PSAbstractAsset

    # data -
    assetSymbol::String
    purchasePricePerShare::Float64
    numberOfShares::Int64
    purchaseDate::Date

    function PSEquityAsset(assetSymbol::String, purchasePricePerShare::Float64, numberOfShares::Int64, purchaseDate::Date)
        this = new(assetSymbol, purchasePricePerShare, numberOfShares, purchaseDate)
    end
end

struct PSCallOptionContract <: PSAbstractAsset

    # data -
    assetSymbol::String
    strikePrice::Float64
    expirationDate::Date
    premimumValue::Float64
    numberOfContracts::Int64
    sense::Symbol
    contractMultiplier::Float64

    function PSCallOptionContract(assetSymbol::String, expirationDate::Date, strikePrice::Float64, premimumValue::Float64, numberOfContracts::Int64;
            sense::Symbol=:buy, contractMultiplier::Float64 = 100.0)

            this = new(assetSymbol, strikePrice, expirationDate, premimumValue, numberOfContracts, sense, contractMultiplier)
    end
end

struct PSPutOptionContract <: PSAbstractAsset

    # data -
    assetSymbol::String
    strikePrice::Float64
    expirationDate::Date
    premimumValue::Float64
    numberOfContracts::Int64
    sense::Symbol
    contractMultiplier::Float64

    function PSPutOptionContract(assetSymbol::String, expirationDate::Date, strikePrice::Float64, premimumValue::Float64, numberOfContracts::Int64;
            sense::Symbol=:buy, contractMultiplier::Float64 = 100.0)

        this = new(assetSymbol, strikePrice, expirationDate, premimumValue, numberOfContracts, sense, contractMultiplier)
    end
end

struct LocalExpectationRegressionModel

    a0::Float64
    a1::Float64
    a2::Float64
    a3::Float64

    function LocalExpectationRegressionModel(a0,a1,a2,a3)
        this = new(a0,a1,a2,a3)
    end
end

mutable struct PSBinaryLatticeModel <: PSAbstractLatticeModel

    # data -
    volatility::Float64
    timeToExercise::Float64
    riskFreeRate::Float64
    dividendRate::Float64
    numberOfLevels::Int64

    function PSBinaryLatticeModel(volatility,timeToExercise,riskFreeRate,dividendRate; numberOfLevels = 10)
        this = new(volatility,timeToExercise,riskFreeRate,dividendRate, numberOfLevels)
    end
end

mutable struct PSTernaryLatticeModel <: PSAbstractLatticeModel

    # data -
    volatility::Float64
    timeToExercise::Int64
    riskFreeRate::Float64
    dividendRate::Float64

    function PSTernaryLatticeModel(volatility,timeToExercise,riskFreeRate,dividendRate)
        this = new(volatility,timeToExercise,riskFreeRate,dividendRate)
    end
end


text:
"""
    Cauchy(μ, σ)

The *Cauchy distribution* with location `μ` and scale `σ` has probability density function

```math
f(x; \\mu, \\sigma) = \\frac{1}{\\pi \\sigma \\left(1 + \\left(\\frac{x - \\mu}{\\sigma} \\right)^2 \\right)}
```

```julia
Cauchy()         # Standard Cauchy distribution, i.e. Cauchy(0, 1)
Cauchy(u)        # Cauchy distribution with location u and unit scale, i.e. Cauchy(u, 1)
Cauchy(u, b)     # Cauchy distribution with location u and scale b

params(d)        # Get the parameters, i.e. (u, b)
location(d)      # Get the location parameter, i.e. u
scale(d)         # Get the scale parameter, i.e. b
```

External links

* [Cauchy distribution on Wikipedia](http://en.wikipedia.org/wiki/Cauchy_distribution)

"""
struct Cauchy{T<:Real} <: ContinuousUnivariateDistribution
    μ::T
    σ::T

    function Cauchy{T}(μ::T, σ::T) where T
        @check_args(Cauchy, σ > zero(σ))
        new{T}(μ, σ)
    end
end

Cauchy(μ::T, σ::T) where {T<:Real} = Cauchy{T}(μ, σ)
Cauchy(μ::Real, σ::Real) = Cauchy(promote(μ, σ)...)
Cauchy(μ::Integer, σ::Integer) = Cauchy(Float64(μ), Float64(σ))
Cauchy(μ::Real) = Cauchy(μ, 1.0)
Cauchy() = Cauchy(0.0, 1.0)

@distr_support Cauchy -Inf Inf

#### Conversions
function convert(::Type{Cauchy{T}}, μ::Real, σ::Real) where T<:Real
    Cauchy(T(μ), T(σ))
end
function convert(::Type{Cauchy{T}}, d::Cauchy{S}) where {T <: Real, S <: Real}
    Cauchy(T(d.μ), T(d.σ))
end

#### Parameters

location(d::Cauchy) = d.μ
scale(d::Cauchy) = d.σ

params(d::Cauchy) = (d.μ, d.σ)
@inline partype(d::Cauchy{T}) where {T<:Real} = T

#### Statistics

mean(d::Cauchy{T}) where {T<:Real} = T(NaN)
median(d::Cauchy) = d.μ
mode(d::Cauchy) = d.μ

var(d::Cauchy{T}) where {T<:Real} = T(NaN)
skewness(d::Cauchy{T}) where {T<:Real} = T(NaN)
kurtosis(d::Cauchy{T}) where {T<:Real} = T(NaN)

entropy(d::Cauchy) = log4π + log(d.σ)

#### Functions

zval(d::Cauchy, x::Real) = (x - d.μ) / d.σ
xval(d::Cauchy, z::Real) = d.μ + z * d.σ

pdf(d::Cauchy, x::Real) = 1 / (π * scale(d) * (1 + zval(d, x)^2))
logpdf(d::Cauchy, x::Real) = - (log1psq(zval(d, x)) + logπ + log(d.σ))

function cdf(d::Cauchy, x::Real)
    μ, σ = params(d)
    invπ * atan2(x - μ, σ) + 1//2
end

function ccdf(d::Cauchy, x::Real)
    μ, σ = params(d)
    invπ * atan2(μ - x, σ) + 1//2
end

function quantile(d::Cauchy, p::Real)
    μ, σ = params(d)
    μ + σ * tan(π * (p - 1//2))
end

function cquantile(d::Cauchy, p::Real)
    μ, σ = params(d)
    μ + σ * tan(π * (1//2 - p))
end

mgf(d::Cauchy{T}, t::Real) where {T<:Real} = t == zero(t) ? one(T) : T(NaN)
cf(d::Cauchy, t::Real) = exp(im * (t * d.μ) - d.σ * abs(t))

#### Fitting

# Note: this is not a Maximum Likelihood estimator
function fit(::Type{Cauchy}, x::AbstractArray{T}) where T<:Real
    l, m, u = quantile(x, [0.25, 0.5, 0.75])
    Cauchy(m, (u - l) / 2)
end


text:
#==============================================================================#
# runtests.jl
#
# Tests for Retry.jl
#
# Copyright Sam O'Connor 2014 - All rights reserved
#==============================================================================#

using Retry
using Test

mutable struct TestException <: Exception
    code
end

# @protected try re-throws the exception by default...

@test_throws TestException  @protected try
                                throw(TestException(7))
                            catch e
                            end

# @protected try re-throws the exception if the @ignore condition is false...

@test_throws TestException  @protected try
                                throw(TestException(7))
                            catch e
                                @ignore if ecode(e) == "Nothing to see here" end
                            end

# Exception ignored by error code...

@test   @protected try
            throw(TestException(7))
        catch e
            @ignore if ecode(e) == 7 end
        end

# Exception ignored by type...

@test   @protected try
            throw(TestException(7))
        catch e
            @ignore if typeof(e) == TestException end
        end

# Try 4 times and re-thow exception...

count = 0
@test_throws TestException  @repeat 4 try
                                global count += 1
                                throw(TestException(7))
                            catch e
                                @retry if ecode(e) == 7 end
                            end
@test count == 4

# Only try 1 time and re-thow exception if @retry condition is not met...

count = 0
@test_throws TestException  @repeat 4 try
                                global count += 1
                                throw(TestException(5))
                            catch e
                                @retry if ecode(e) == 7 end
                            end
@test count == 1

# Only try 1 time and re-thow exception if @retry condition is not met...

count = 0
@test @repeat 4 try
            global count += 1
            throw(TestException(7))
        catch e
            @ignore if ecode(e) == 7 end
        end
@test count == 1

# Check that retry delay gets longer each time...

count = 0
start = time()
last_t = 0
delay = -1
i = -1
@test_throws TestException @repeat 3 try
                                global count, start, last_t
                                t = time() - start
                                @test t > last_t * 9.9
                                start = time()
                                count += 1
                                throw(TestException(7))
                            catch e
                                @delay_retry if ecode(e) == 7 end
                            end
@test count == 3

# Check for leakage of macro local variables...
@test delay == -1
@test i == -1

# Re-throw after 2 attempts...

count = 0
@test_throws TestException @repeat 2 try
                                global count += 1
                                throw(TestException(count))
                            catch e
                                @retry if ecode(e) < 3 end
                                @ignore if ecode(e) == 3 end
                            end
@test count == 2

# @ignore condition met after 3 attempts...

count = 0
@test                       @repeat 3 try
                                global count += 1
                                throw(TestException(count))
                            catch e
                                @retry if ecode(e) < 3 end
                                @ignore if ecode(e) == 3 end
                            end
@test count == 3

# No more attempts after @ignore condition met...

count = 0
@test                       @repeat 10 try
                                global count += 1
                                throw(TestException(count))
                            catch e
                                @retry if ecode(e) < 3 end
                                @ignore if ecode(e) == 3 end
                            end
@test count == 3

#==============================================================================#
# End of file.
#==============================================================================#


text:
using Pkg
Pkg.activate(@__DIR__) # Activating environment
Pkg.instantiate() # Downloads packages if needed

using CSV
using Glob
using Random
using Statistics
using DataFrames
using Distributions
using LinearAlgebra
using AnnotationFusion

path = joinpath(dirname(@__DIR__), "data/DOME/")
file = joinpath(path, "dome_annotations_M1.csv")
df = CSV.read(file, DataFrame)

function aggregate_row(row; method::AnnotationFusion.FusionMethod = Copeland(), annotators = ["A$i" for i in 1:3], persons = 1:4)

    columns = vcat(["Person", annotators]...)
    rankings = hcat([Vector(row[[Symbol(string(a, i)) for i in 1:4]]) for a in annotators]...)
    annotations = DataFrame(hcat(persons, rankings), columns)
    aggregates = fuse(annotations, :Person, method)

    return DataFrame([Symbol("P$(i)_$(name(method))") for i in persons] .=> aggregates)

end

merged_copeland = vcat(map(row -> aggregate_row(row; method=Copeland()), eachrow(df))...)
merged_mean = vcat(map(row -> aggregate_row(row; method=Mean()), eachrow(df))...)

df = hcat(df, merged_mean, merged_copeland)

CSV.write(joinpath(path, "dome_annotations_M1_merged.csv"), df)


text:
###################################################
## geo/stats/datasplit.jl
## split data into training/testing subsets
###################################################

"""
	`DataSplit`: abstract type with information about how the dataset is split into training and testing sets
	must implement attributes:
	- trainingIDs: list of IDs of trips of training set
	- testingIDs: list of IDs of trips of testing set
	- geodata: GeoData object that this DataSplit object is related to
"""
abstract type DataSplit end

function Base.show(io::IO, ds::DataSplit)
	typeName = split(string(typeof(ds)),".")[end]
	println(io, "DataSplit: $(typeName)")
end

trainSet(s::DataSplit) = s.trainingIDs
testSet(s::DataSplit) = s.testingIDs

"""
	`RandomSplit`: split data set randomly, with a given fraction of the data in the training set
	and the remainder in the testing set
"""
mutable struct RandomSplit <: DataSplit
	# compulsory
	trainingIDs::Vector{Int}
	testingIDs::Vector{Int}
	geodata::GeoData

	# particular
	fractionTrain::Float64
	function RandomSplit(geodata::GeoData, fractionTrain::Float64, trips::AbstractArray{Int, 1} = eachindex(geodata))
		obj = new()
		obj.geodata = geodata
		obj.fractionTrain = fractionTrain
		# split set of trips into training and testing
		shuf = shuffle(collect(trips))
		endTraining = round(Int, fractionTrain * length(trips))
		obj.trainingIDs = sort(shuf[1:endTraining])
		obj.testingIDs = sort(shuf[(endTraining + 1):end])
		return obj
	end
end

"""
	`LocSplit`: split data set geographically. All testing rides are included in a 4D sphere, everything else forms training set.
"""
mutable struct LocSplit <: DataSplit
	# compulsory
	trainingIDs::Vector{Int}
	testingIDs::Vector{Int}
	geodata::GeoData

	"graph with which this is identified"
	network::Network
	"center of 4D sphere containing testing set"
	center::Tuple{Float64, Float64, Float64, Float64}
	"radius of 4D sphere containing testing set"
	radius::Float64
	function LocSplit(geodata::GeoData, network::Network, center::Tuple{Float64, Float64, Float64, Float64}, radius::Float64, trips::AbstractArray{Int,1} = eachindex(geodata))
		obj = new()
		obj.geodata = geodata
		obj.center = center
		obj.radius = radius
		obj.network = network
		mask = BitArray(length(trips))
		for i in trips
			pX, pY = toENU(geodata[i].pLon, geodata[i].pLat, network)
			dX, dY = toENU(geodata[i].dLon, geodata[i].dLat, network)
			mask[i] = ( (center[1] - pX)^2 + (center[2] - pY)^2 + (center[3] - dX)^2 + (center[4] - dY)^2 < radius^2 )
		end
		obj.trainingIDs = trips[!mask]
		obj.testingIDs = trips[mask]
		return obj
	end
end

# extra classes for center/radius given as Int
LocSplit(geodata::GeoData, network::Network, center::Tuple{Int, Int, Int, Int}, radius::Float64, trips::AbstractArray{Int,1} = eachindex(geodata)) = LocSplit(geodata, network, (Float64(center[1]), Float64(center[2]), Float64(center[3]), Float64(center[4])), radius, trips)
LocSplit(geodata::GeoData, network::Network, center::Tuple{Int, Int, Int, Int}, radius::Int, trips::AbstractArray{Int,1} = eachindex(geodata)) = LocSplit(geodata, network, (Float64(center[1]), Float64(center[2]), Float64(center[3]), Float64(center[4])), Float64(radius), trips)
LocSplit(geodata::GeoData, network::Network, center::Tuple{Float64, Float64, Float64, Float64}, radius::Int, trips::AbstractArray{Int,1} = eachindex(geodata)) = LocSplit(geodata, network, center, Float64(radius), trips)


text:
using RootHitFiles, Test, Tables

@testset "RootHitFiles.jl" begin

    @testset "Construction" begin
        @test RootHitFile("test.root.hits") isa RootHitFile

        @test RootHitFile(open("test.root.hits")) isa RootHitFile

        @test all(RootHitFile(open("test.root.hits")) .== RootHitFile("test.root.hits"))

        # Not a .root.hits file
        @test_throws ArgumentError RootHitFile("../Project.toml")
    end

    @testset "Parsing" begin
        f = RootHitFile("test.root.hits")

        e1 = read(f)
        @test e1.eventnum == 624
        @test e1.primcount == 3

        @test all(e1.pos[1]     .≈ (1.60738, -2.07026, -201.594))
        @test e1.E[1]            ≈ 0.1638
        @test e1.time[1]        == 0
        @test e1.particleID[1]  == 22
        @test e1.trkID[1]       == 187
        @test e1.trkparentID[1] == 4
        @test e1.volumeID[1]    == "physiDet"

        @test all(e1.pos[end]     .≈ (1.60714, -2.06979, -201.594))
        @test e1.E[end]            ≈ 1.09771
        @test e1.time[end]        == 0
        @test e1.particleID[end]  == 11
        @test e1.trkID[end]       == 269
        @test e1.trkparentID[end] == 235
        @test e1.volumeID[end]    == "physiDet"

        e2 = read(f)
        @test e2.eventnum == 632
        @test e2.primcount == 3

        @test all(e2.pos[1]     .≈ (-1.16375, 1.16453, -197.114))
        @test e2.E[1]            ≈ 0.01478
        @test e2.time[1]        == 0
        @test e2.particleID[1]  == 22
        @test e2.trkID[1]       == 8
        @test e2.trkparentID[1] == 5
        @test e2.volumeID[1]    == "physiDet"

        @test all(e2.pos[end]     .≈ (-1.16539, 1.16754, -197.116))
        @test e2.E[end]            ≈ 6.3739
        @test e2.time[end]        == 0
        @test e2.particleID[end]  == 11
        @test e2.trkID[end]       == 108
        @test e2.trkparentID[end] == 101
        @test e2.volumeID[end]    == "physiDet"

        ef = collect(f)[end]
        @test ef.eventnum == 1559
        @test ef.primcount == 5

        @test all(ef.pos[1]     .≈ (-0.109196, 2.10771, -196.751))
        @test ef.E[1]            ≈ 0.03819
        @test ef.time[1]        == 0
        @test ef.particleID[1]  == 22
        @test ef.trkID[1]       == 10
        @test ef.trkparentID[1] == 7
        @test ef.volumeID[1]    == "physiDet"

        @test all(ef.pos[end]     .≈ (-1.02351, 2.28199, -198.087))
        @test ef.E[end]            ≈ 9.71922
        @test ef.time[end]        == 0
        @test ef.particleID[end]  == 11
        @test ef.trkID[end]       == 111
        @test ef.trkparentID[end] == 16
        @test ef.volumeID[end]    == "physiDet"
    end

    @testset "Iteration" begin
        f = RootHitFile("test.root.hits")
        for e in RootHitFile("test.root.hits")
            @test e == read(f)
        end

        @test Base.IteratorSize(RootHitFile) == Base.SizeUnknown()

        @test Base.IteratorEltype(RootHitFile) == Base.HasEltype()

        @test eltype(RootHitFile("test.root.hits")) <: NamedTuple
    end

    @testset "File Interface" begin
        f = RootHitFile("test.root.hits")

        @test eof(f) == false

        collect(f)

        @test eof(f) == true
    end

    @testset "Tables Interface" begin
        f = RootHitFile("test.root.hits")

        @test Tables.istable(typeof(f))

        @test Tables.rowaccess(typeof(f))
        @test Tables.rows(f) === f

        ctbl = columntable(f)
        @test ctbl.eventnum == [624, 632, 1150, 1266, 1447, 1488, 1559]

        rtbl = rowtable(RootHitFile("test.root.hits"))
        @test first(rtbl) == first(RootHitFile("test.root.hits"))
    end
end