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nb_2024-02-15_wildlab-frp-duration-overlap.jl
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### A Pluto.jl notebook ###
# v0.19.45
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
el
end
end
# ╔═╡ 03162a1a-9a5b-4dbe-b069-f57d8982cb5a
using PlutoLinks
# ╔═╡ 066e4500-cb80-11ee-0de8-971b5b009656
begin
#using UnfoldBIDS
using UnfoldMakie
using CairoMakie
using PlutoUI
using PyMNE
using BSplineKit
using DataFrames
using Statistics
using ProfileCanvas
using Chain
using CodecZlib
using DataFramesMeta
end
# ╔═╡ 46a7fe46-066e-4559-ae7b-b2e44f09a874
using Unfold
# ╔═╡ d3dc0d2b-8cd9-4831-94c5-17109705cbb6
using StatsModels
# ╔═╡ a964742f-7c26-4bae-9294-62124c136f0d
using HypothesisTests,MultipleTesting
# ╔═╡ 1963a80b-beba-4b60-b993-35981fae309c
md"""
# Setup and load_data etc
"""
# ╔═╡ 7bfade35-524a-41e7-b799-869bfa78a2cc
filefolder = "/store/projects/unfold_duration/fixation_duration_WLFO_dataset/data_Unfold_v7_3/"
# ╔═╡ 699b839a-3044-464d-9e46-d90a7bec1c3b
md"""
# Effects all (200s!)
"""
# ╔═╡ 5b81bcb8-05b3-479c-bf4a-023badab640a
md"""
# Stats
"""
# ╔═╡ aa43d556-fb74-4948-8c3b-da398c5919a3
readdir("../unfoldjl_dev/dev/UnfoldStats/src/")
# ╔═╡ e4bbe515-84dc-445d-b962-6cb401ed1a6c
md"""
### UnfoldStats Code hidden below this cell
"""
# ╔═╡ 267e4358-36b3-459c-bc24-193d7266e9a7
begin
# 1:1 copy of unfoldstats - it's not registered yet, so I had to copy the code in here for simplicities sake
# Helper functions
const BSplineTerm = Base.get_extension(Unfold, :UnfoldBSplineKitExt).BSplineTerm
"""
extract_symbol(t::AbstractTerm)
Return the symbol(s) underlying a term from a model formula, repeated by their actual coefficient number (after StatsModels.apply_schema).
# Examples
```julia
julia> f = @formula 0 ~ 1 + spl(continuous, 4) + continuous + condition + pet + condition & pet
julia> ... # apply schema using an event dataframe, according to StatsModels
julia> extract_symbol(f)
8-element Vector{Any}:
"(Intercept)"
:continuous
:continuous
:continuous
:continuous
:condition
:pet
(:condition, :pet)
```
We get the actual symbols of each predictor - this is different to a function that would return the symbol for each term, which would be `["(Intercept)", :continuous,:continuous,:condition,:pet,(:condition,:pet) ]`
The difference between those two cases would get even more stark, if a basisfunction is in play as it timeexpand terms into many more predictors.
"""
extract_symbol(t::AbstractTerm) = t.sym
extract_symbol(t::InterceptTerm) = "(Intercept)"
extract_symbol(t::BSplineTerm) = repeat([t.term.sym], t.df - 1)
extract_symbol(t::InteractionTerm) = extract_symbol.(t.terms)
extract_symbol(t::FunctionTerm) = extract_symbol.(t.args)
extract_symbol(t::MatrixTerm) = extract_symbol(t.terms)
extract_symbol(t::Unfold.TimeExpandedTerm) =
repeat(extract_symbol(t.term), inner = length(Unfold.colnames(t.basisfunction)))
extract_symbol(f::FormulaTerm) = extract_symbol(f.rhs)
extract_symbol(t::Vector) = vcat(extract_symbol.(t)...)
extract_symbol(t::Tuple) = vcat(extract_symbol.(t)...)
"""
contained_or_equal(p, e)
Test if `p` equals `e` or whether `e` contains `p` if `e` is a tuple.
"""
contained_or_equal(p, e) = (p == e)
contained_or_equal(p, e::Tuple) = (p in e)
"""
get_predictor_string(p)
Return string representation based on the type of `p`.
This function is used for a useful display of variables e.g. in an error message.
# Examples
```jldoctest
julia> UnfoldStats.get_predictor_string(:condition)
":condition"
```
"""
get_predictor_string(p::Symbol) = ":$p"
get_predictor_string(p::String) = "\"$p\""
get_predictor_string(p::Tuple) = "$p"
"""
extract_coefs(model::UnfoldModel, predictor, basisname)
Return the coefficients of an Unfold model for a certain `predictor` and `basisname`.
For extracting the terms of a predictor variable `predictor` must be a symbol e.g. :continuous.
For extracting the intercept `predictor` should be a String, i.e. "(Intercept)".
`basisname` must match one of the basis names which can be found in `coeftable(model)`.
Note: If a predictor variable has more than one term in the formula (e.g. a spline set, a categorical variable with several levels or an interaction),
the coefficients for all terms are returned.
The dimensions of the returned coefficients are channel x times x coefficients.
"""
function extract_coefs(model::UnfoldModel, predictor, basisname)
# Get vector with underlying predictor variable (symbol) for all coefficients
symbols = extract_symbol(Unfold.formulas(model))
# Check whether `predictor` is a predictor in the model
if predictor ∉ symbols
# TODO: Interactions will be listed separately at the moment, maybe don't list them
allowed_predictors = join(get_predictor_string.(unique(symbols)), ", ")
throw(
ArgumentError(
"The given predictor $(get_predictor_string(predictor)) was not found in the model. Possible predictors are: $allowed_predictors.",
),
)
end
basisname_list = Unfold.get_basis_names(model)
# Check whether given `basisname` is in the basisname list of the model
if basisname ∉ vcat(basisname_list...)
allowed_basisnames = join(["\"$b\"" for b in unique(basisname_list)], ", ")
throw(
ArgumentError(
"The given basisname \"$basisname\" was not found in the model. Possible basisnames are: $allowed_basisnames.",
),
)
end
# Create a boolean mask which is true for the model coefficients that belong to the given predictor
mask_predictor = contained_or_equal.(predictor, symbols)
# Create a boolean mask which is true for the model coefficients that belong to the given basis name
mask_basisfunction = vcat(basisname_list...) .== basisname
mask_combined = mask_predictor .* mask_basisfunction
# Check whether the given combination between predictor variable and basisname exist in the model
if sum(mask_combined) == 0
# TODO: Is `ArgumentError` the right exception to use?
throw(
ArgumentError(
"The given predictor $(get_predictor_string(predictor)) does not exist for the given basisname \"$basisname\".",
),
)
end
# Extract the requested coefficients from the coefficient array
if isa(model, UnfoldLinearModel)
coef_subset = coef(model)[:, :, mask_combined]
elseif isa(model, UnfoldLinearModelContinuousTime)
n_coefs = length(
unique(Unfold.extract_coef_info(Unfold.get_coefnames(model), 2)[mask_combined]),
)
coef_subset_temp = @view(coef(model)[:, mask_combined])
# Reshape the coefficient array such that time points and coefficient types get separate dimensions
coef_subset = reshape(coef_subset_temp, size(coef_subset_temp, 1), :, n_coefs)
else
throw("Not implemented.")
end
return coef_subset#::Array{<:Union{<:Missing,<:Float64},3}
end
"""
extract_coefs(models::Vector{<:UnfoldModel}, predictor, basisname)
When applied to a vector of Unfold models, extracts the coefficients (matching the predictor and basisname) for all models (usually subjects) and concatenates them.
The dimensions of the returned coefficients are channel x times x coefficients x subjects.
"""
function extract_coefs(models::Vector{<:UnfoldModel}, predictor, basisname)
# Extract the coefficients for all subjects
coefs_vector = extract_coefs.(models, predictor, basisname)
# Check that all coefficient arrays have the same size
@assert length(unique(size.(coefs_vector))) == 1
# Concatenate the coefficients to one array
# Dimensions: (channels x times x coefficients x subjects)
coefs_all_subjects = cat(coefs_vector..., dims = ndims(coefs_vector[1]) + 1)#::Array{<:Union{<:Missing,<:Float64},4}
return coefs_all_subjects
end
end;
# ╔═╡ 960d2ff6-2673-42b6-9b3b-3f76f2d2d57b
md"""
# Other Stuff again
"""
# ╔═╡ 16c9a52b-bca0-42ca-a5c8-e3f0e2134fd5
function convert_designmat_nomissing(X::AbstractDesignMatrix) typeof(X).name.wrapper(X.formula,disallowmissing(X.modelmatrix),X.events)
end
# ╔═╡ 5eaa2fb0-b956-4319-a754-3a919e9816ce
function uf_disallowmissing(d::UnfoldModel)
m_nomissing = Unfold.LinearModelFit(disallowmissing(modelfit(d).estimate))
@info typeof(d.designmatrix)
d_nomissing = convert_designmat_nomissing.(d.designmatrix)
return typeof(d).name.wrapper{Float64}(d.design,d_nomissing,m_nomissing)
end
# ╔═╡ a94b1936-5cc9-4ad3-9443-aa121197898f
begin
function load_data()
df_list = []
for (root,dirs,files) in walkdir(filefolder)
for file in files
#println(splitext(file))
if splitext(file)[end] == ".jld2"
fpath = joinpath(root,file)
#println(fpath)
sub = parse(Int,split(file,"-")[2])
duration = parse(Bool,split(split(file,"_")[3],"=")[2])
overlap = parse(Bool,split(split(split(file,"_")[5],"=")[2],".")[1])
#println(sub,duration,overlap)
m = load(fpath,UnfoldModel)
df_single = DataFrame(:model=>uf_disallowmissing(m),:subject=>sub,:duration_modelled=>duration,:overlap_modelled=>overlap)
push!(df_list,df_single)
#break
end
end
end
return vcat(df_list...)
end
data = load_data()
end
# ╔═╡ ba5bd8bc-8c99-4fd0-86c2-70bda1a452f0
effectsAll= DataFramesMeta.combine(groupby(
subset(data,:duration_modelled=>x-> x.==true),
[:subject,:duration_modelled,:overlap_modelled]),
:model=>ByRow(x->(
effects(Dict(:duration=>range(0.2,1,length=5)),x)|>DataFrames.Tables.columntable
)
)=>AsTable, threads=false
)|> x-> flatten(x,[n for n in names(x) if n ∉ ["subject","duration_modelled","overlap_modelled"]])
# ╔═╡ f63b6505-d1a9-497b-aa68-c1c25950dddf
begin
ix = data.duration_modelled.==true
data_statssubset = data.model[ix]
coefs = extract_coefs(data_statssubset, :duration, repeat(["fixation"],length(data.model)÷2))
function calc_pvalues(coefs)
mapslices(c -> pvalue(OneSampleHotellingT2Test(c',
#[mean(c), mean(c), mean(c)]
[0, 0, 0]
)), coefs, dims = (3, 4)) |>
x -> dropdims(x, dims = (3, 4));
end
pvalues_overlap = calc_pvalues(coefs[:,:,:,data.overlap_modelled[ix] .== true])
pvalues_nooverlap = calc_pvalues(coefs[:,:,:,data.overlap_modelled[ix] .== false])
function fdr(p_values)
p_values_corrected = (reshape(adjust(p_values[:], BenjaminiYekutieli()),size(p_values)))
end
pvalues_overlap_fdr = fdr(pvalues_overlap)
pvalues_nooverlap_fdr = fdr(pvalues_nooverlap)
end;
# ╔═╡ 76bc096c-43e1-43e0-8b3e-635f49afaca7
# all effects took 221s in Unfold 0.6, and a single one 0.3 - 4.3s (with a lot of GC, median maybe 2s?)
# ╔═╡ 6f280f2b-5d41-4586-833e-1f59b0cc8bda
effectsAvg = groupby(effectsAll,[:time,:eventname,:duration,:channel,:duration_modelled,:overlap_modelled])|>x-> DataFramesMeta.combine(x,:yhat=>(x->median(skipmissing(x)))=>:yhat)
# ╔═╡ 1c56775d-1137-4c66-a5ff-097a9a692354
begin
test = @chain effectsAvg begin
#@subset :overlap_modelled .==true
@subset :duration_modelled .== true
@subset :eventname .== "fixation"
#@combine :effect = :yhat[:duration .== 0.2] .- :yhat[:duration .== 1]
#plot_topoplotseries()
end
end
# ╔═╡ b385676d-01be-49ba-95f4-5f50f9418b59
#dur_diff = combine(groupby(test,[:time,:channel]),AsTable([:yhat,:duration])=>(x->x.yhat[x.duration .≈0.1] .- x.yhat[x.duration.≈1])=>:diff)
begin
dur_diff = DataFramesMeta.combine(groupby(test,[:overlap_modelled,:time,:channel]),:yhat => (x-> -(-(extrema(x)...)))=>:diff)
dur_diff_diff =DataFramesMeta.combine(groupby(dur_diff,[:channel,:time]),[:diff,:overlap_modelled]=>((y,o)->y[o].-y[.!o])=>:diff)
dur_diff_diff.overlap_modelled .= "difference"
dur_diff = vcat(dur_diff,dur_diff_diff)
end
# ╔═╡ 8f149b30-ffeb-4d08-b3ef-f0761c8fab5b
# ╔═╡ 9e11982b-3584-4193-a23d-66577a49ef0e
begin
z = UnfoldMakie.df_timebin(UnfoldMakie.eeg_matrix_to_dataframe(pvalues_overlap_fdr,string.(1:size(pvalues_overlap_fdr,1))),0.2,fun = minimum,col_y=:estimate,grouping=[:label])
#z = reshape(z.estimate,size(pvalues_overlap_fdr,1),:) .<=α
end
# ╔═╡ 9ca377f9-1bdb-4f79-a8e3-615033cd4355
function topoplot_scatter_modifier!(ftopo,mask,Δ,α)
z = UnfoldMakie.df_timebin(UnfoldMakie.eeg_matrix_to_dataframe(mask,string.(1:size(mask,1))),Δ,fun = minimum,col_y=:estimate,grouping=[:label])
z = reshape(z.estimate,size(mask,1),:) .<=α
z = z.*5
@show "start"
#@assert length(ftopo) == size(mask,2)
for (ix,single_topo) = enumerate(ftopo)
@show z[1,ix]
single_topo.scene.plots[1].label_scatter.markersize.val = z[:,ix]
end
end
# ╔═╡ 14a34ca7-4704-4b80-bf41-8b7cf3221cb3
# ╠═╡ disabled = true
#=╠═╡
plot_erp(subset(effectsAll,:overlap_modelled=>x->x.==true,:duration_modelled=>x->x.==true,:basisname=>x->x.=="fixation",:channel=>x->x.==ch_ix),mapping=(;color=:duration,group=:duration,layout=:subject=>nonnumeric),categorical_color=false)
╠═╡ =#
# ╔═╡ 6800102a-952f-41d0-98eb-0a255a75796c
# ╠═╡ disabled = true
#=╠═╡
let
d = subset(effectsAvg,:overlap_modelled=>x->x.==true,:duration_modelled=>x->x.==true,:basisname=>x->x.=="fixation",:channel=>x->x.==ch_ix)
@show d.yhat[1]
d = transform!(groupby(d,:duration),["yhat","time"] => ((yhat,time)->yhat.-mean(yhat[time.<0]))=>"yhat")
@show first(d)
plot_erp(d,mapping=(;color=:duration,group=:duration),categorical_color=false)
end
╠═╡ =#
# ╔═╡ cd270188-b32e-4d62-9a12-4b70a093f7ba
# ╔═╡ b096ce93-de2a-475a-9fe7-f8a87150fd1d
raw = PyMNE.io.read_raw_eeglab(joinpath(filefolder,"sourcedata","sub-08","eeg","sub-08_task-WLFO_eeg.set"), preload=false)
# ╔═╡ ceddf0bc-077d-430b-85f3-8308b7abb221
pos = UnfoldMakie.to_positions(raw)
# ╔═╡ 42cbd598-0d87-494d-90dc-eb84dec276a9
plot_butterfly(effects(Dict(:duration=>[0.5]),data.model[1])|>x->@subset(x,:eventname.=="fixation"),mapping=(;col=:eventname),positions=pos)
# ╔═╡ 9edeae83-432d-4168-a511-20ab8f7eb08c
begin
function topoplot_indicator!(f,ix)
x = zeros(128)
x[ix] =1
clist = [:gray,:darkred][Int.(x.+1)]
ax = f[1,1] = Axis(f, width = Relative(0.4),
height = Relative(0.4),
halign = 1.2,
valign = 1,aspect=1)
UnfoldMakie.TopoPlots.eeg_topoplot!(ax, x; positions=pos, enlarge=0.9,label_scatter=(;color=clist,markersize=((x.+0.25).*40)./5,strokewidth=0),interpolation=UnfoldMakie.TopoPlots.NullInterpolator())
hidespines!(ax)
hidedecorations!(ax)
end
end
# ╔═╡ b4921506-384f-439b-a375-20c6115ed309
ch_names = pyconvert(Array,raw.ch_names)
# ╔═╡ acb96e40-2afb-4fc3-86db-4fc83ce1817d
@bind ch_ix PlutoUI.Select(1:128 .=> ch_names)
# ╔═╡ f11334da-68b3-469a-b5f3-dbb52bf9dec2
begin
f = Figure(size=(800,600))
ch_l = ["AFz","Cz","Oz"]
ch_l_ix = findall((ch_names).∈Ref(ch_l) )
@info ch_l_ix
#plt_effects = transform(groupby(effectsAvg[1:9:end,:],:duration),["yhat","time"] => ((yhat,time)->yhat.-mean(yhat[time.<0]))=>"yhat")
plt_effects = effectsAvg
d_subset = subset(plt_effects,
:time => x->x .>-0.1,
:duration_modelled =>x->x.== true,
:eventname=>x->x.=="fixation",
:channel=>x->x.∈ Ref(ch_l_ix))
d_subset.channel = ch_names[d_subset.channel]
plot_erp!(f[1,1],d_subset,
mapping=(;color=:duration,
group=:duration,
row=:overlap_modelled,
col=:channel),
categorical_color=false)
vs = (;colorrange=[-2,2],enlarge=0.9,contours=false,label_scatter=(markersize=0, strokewidth=1,strokecolor=:black))
plot_topoplotseries!(f[2,1],subset(dur_diff,:time => x->x .>=-0.1,),0.2;positions=pos,
axis=(;xlim_topo =(-0.05,1.05),ylim_topo=(-0.05,1.05)),
mapping=(;y=:diff,row=:overlap_modelled),
visual=vs)
rowsize!(f.layout,1,Relative(0.55))
#rowsize!(f.layout,2,Relative(0.25))
f
end
# ╔═╡ 0b0e1c26-7c95-4d4c-aa2e-9e5b23e568aa
ch_names[ch_l_ix]
# ╔═╡ 36a76e53-2b5c-4e31-a45b-ecf2b8e686e8
ch_l_ix
# ╔═╡ eff22111-fa13-4a78-8bc7-65b72863cc27
begin
topoplot_indicator!(f.layout[1,1][1,1][1,1],ch_l_ix[3])
topoplot_indicator!(f.layout[1,1][1,1][1,2],ch_l_ix[1])
topoplot_indicator!(f.layout[1,1][1,1][1,3],ch_l_ix[2])
f
end
# ╔═╡ 845bbc07-9251-45d6-a96e-55877f8d4976
begin
topos= f.content[15:15+18-1]
topoplot_scatter_modifier!(topos[1:6],pvalues_nooverlap_fdr,0.2*512,0.005)
topoplot_scatter_modifier!(topos[7:12],pvalues_overlap_fdr,0.2*512,0.005)
f
end
# ╔═╡ e7828099-75ff-4788-ab81-bf2897e4b983
plot_erp(@subset(d_subset,:channel.=="16",:eventname .=="fixation",:overlap_modelled .==true),mapping=(;color=:duration,group=:duration),categorical_color=false,categorical_group=true)
# ╔═╡ 36ea1c90-af08-4ade-914f-22e0b1d2acc4
let
d = zeros(128)
d[ch_ix] = 10
#UnfoldMakie.eeg_matrix_to_dataframe(d,1:128)
UnfoldMakie.eeg_topoplot(d;positions=pos,labels=ch_names,interpolation=UnfoldMakie.TopoPlots.NullInterpolator(),enlarge=0.9,axis=(aspect=DataAspect(),), label_text = true,label_scatter=(;markersize=10)
)
end
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
BSplineKit = "093aae92-e908-43d7-9660-e50ee39d5a0a"
CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
Chain = "8be319e6-bccf-4806-a6f7-6fae938471bc"
CodecZlib = "944b1d66-785c-5afd-91f1-9de20f533193"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
MultipleTesting = "f8716d33-7c4a-5097-896f-ce0ecbd3ef6b"
PlutoLinks = "0ff47ea0-7a50-410d-8455-4348d5de0420"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
ProfileCanvas = "efd6af41-a80b-495e-886c-e51b0c7d77a3"
PyMNE = "6c5003b2-cbe8-491c-a0d1-70088e6a0fd6"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
Unfold = "181c99d8-e21b-4ff3-b70b-c233eddec679"
UnfoldMakie = "69a5ce3b-64fb-4f22-ae69-36dd4416af2a"
[compat]
BSplineKit = "~0.17.2"
CairoMakie = "~0.12.3"
Chain = "~0.6.0"
CodecZlib = "~0.7.4"
DataFrames = "~1.6.1"
DataFramesMeta = "~0.15.2"
HypothesisTests = "~0.11.0"
MultipleTesting = "~0.6.0"
PlutoLinks = "~0.1.6"
PlutoUI = "~0.7.59"
ProfileCanvas = "~0.1.6"
PyMNE = "~0.2.2"
StatsModels = "~0.7.3"
Unfold = "~0.7"
UnfoldMakie = "~0.5.4"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
julia_version = "1.10.0"
manifest_format = "2.0"
project_hash = "85ede050d21db835500468c1e03a33286f915a94"
[[deps.AbstractFFTs]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
version = "1.5.0"
weakdeps = ["ChainRulesCore", "Test"]
[deps.AbstractFFTs.extensions]
AbstractFFTsChainRulesCoreExt = "ChainRulesCore"
AbstractFFTsTestExt = "Test"
[[deps.AbstractPlutoDingetjes]]
deps = ["Pkg"]
git-tree-sha1 = "6e1d2a35f2f90a4bc7c2ed98079b2ba09c35b83a"
uuid = "6e696c72-6542-2067-7265-42206c756150"
version = "1.3.2"
[[deps.AbstractTrees]]
git-tree-sha1 = "2d9c9a55f9c93e8887ad391fbae72f8ef55e1177"
uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
version = "0.4.5"
[[deps.Accessors]]
deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "LinearAlgebra", "MacroTools", "Markdown", "Test"]
git-tree-sha1 = "c0d491ef0b135fd7d63cbc6404286bc633329425"
uuid = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
version = "0.1.36"
[deps.Accessors.extensions]
AccessorsAxisKeysExt = "AxisKeys"
AccessorsIntervalSetsExt = "IntervalSets"
AccessorsStaticArraysExt = "StaticArrays"
AccessorsStructArraysExt = "StructArrays"
AccessorsUnitfulExt = "Unitful"
[deps.Accessors.weakdeps]
AxisKeys = "94b1ba4f-4ee9-5380-92f1-94cde586c3c5"
IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
Requires = "ae029012-a4dd-5104-9daa-d747884805df"
StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
StructArrays = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
[[deps.Adapt]]
deps = ["LinearAlgebra", "Requires"]
git-tree-sha1 = "6a55b747d1812e699320963ffde36f1ebdda4099"
uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
version = "4.0.4"
weakdeps = ["StaticArrays"]
[deps.Adapt.extensions]
AdaptStaticArraysExt = "StaticArrays"
[[deps.AlgebraOfGraphics]]
deps = ["Colors", "Dates", "Dictionaries", "FileIO", "GLM", "GeoInterface", "GeometryBasics", "GridLayoutBase", "KernelDensity", "Loess", "Makie", "PlotUtils", "PooledArrays", "PrecompileTools", "RelocatableFolders", "StatsBase", "StructArrays", "Tables"]
git-tree-sha1 = "215a2dc9a286831bdad694475357619a9d99698d"
uuid = "cbdf2221-f076-402e-a563-3d30da359d67"
version = "0.6.20"
[[deps.AliasTables]]
deps = ["PtrArrays", "Random"]
git-tree-sha1 = "9876e1e164b144ca45e9e3198d0b689cadfed9ff"
uuid = "66dad0bd-aa9a-41b7-9441-69ab47430ed8"
version = "1.1.3"
[[deps.Animations]]
deps = ["Colors"]
git-tree-sha1 = "e81c509d2c8e49592413bfb0bb3b08150056c79d"
uuid = "27a7e980-b3e6-11e9-2bcd-0b925532e340"
version = "0.4.1"
[[deps.ArgTools]]
uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"
version = "1.1.1"
[[deps.ArrayInterface]]
deps = ["Adapt", "LinearAlgebra", "SparseArrays", "SuiteSparse"]
git-tree-sha1 = "ed2ec3c9b483842ae59cd273834e5b46206d6dda"
uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
version = "7.11.0"
[deps.ArrayInterface.extensions]
ArrayInterfaceBandedMatricesExt = "BandedMatrices"
ArrayInterfaceBlockBandedMatricesExt = "BlockBandedMatrices"
ArrayInterfaceCUDAExt = "CUDA"
ArrayInterfaceCUDSSExt = "CUDSS"
ArrayInterfaceChainRulesExt = "ChainRules"
ArrayInterfaceGPUArraysCoreExt = "GPUArraysCore"
ArrayInterfaceReverseDiffExt = "ReverseDiff"
ArrayInterfaceStaticArraysCoreExt = "StaticArraysCore"
ArrayInterfaceTrackerExt = "Tracker"
[deps.ArrayInterface.weakdeps]
BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
BlockBandedMatrices = "ffab5731-97b5-5995-9138-79e8c1846df0"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
CUDSS = "45b445bb-4962-46a0-9369-b4df9d0f772e"
ChainRules = "082447d4-558c-5d27-93f4-14fc19e9eca2"
GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
[[deps.ArrayLayouts]]
deps = ["FillArrays", "LinearAlgebra"]
git-tree-sha1 = "600078184f7de14b3e60efe13fc0ba5c59f6dca5"
uuid = "4c555306-a7a7-4459-81d9-ec55ddd5c99a"
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weakdeps = ["SparseArrays"]
[deps.ArrayLayouts.extensions]
ArrayLayoutsSparseArraysExt = "SparseArrays"
[[deps.Artifacts]]
uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
[[deps.Automa]]
deps = ["PrecompileTools", "TranscodingStreams"]
git-tree-sha1 = "588e0d680ad1d7201d4c6a804dcb1cd9cba79fbb"
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[[deps.AxisAlgorithms]]
deps = ["LinearAlgebra", "Random", "SparseArrays", "WoodburyMatrices"]
git-tree-sha1 = "01b8ccb13d68535d73d2b0c23e39bd23155fb712"
uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950"
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[[deps.AxisArrays]]
deps = ["Dates", "IntervalSets", "IterTools", "RangeArrays"]
git-tree-sha1 = "16351be62963a67ac4083f748fdb3cca58bfd52f"
uuid = "39de3d68-74b9-583c-8d2d-e117c070f3a9"
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[[deps.BSplineKit]]
deps = ["ArrayLayouts", "BandedMatrices", "FastGaussQuadrature", "LinearAlgebra", "PrecompileTools", "Random", "Reexport", "SparseArrays", "Static", "StaticArrays", "StaticArraysCore"]
git-tree-sha1 = "212437e58494e55c67917436ee17f795cf4e8117"
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[[deps.BandedMatrices]]
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git-tree-sha1 = "71f605effb24081b09cae943ba39ef9ca90c04f4"
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[deps.BandedMatrices.extensions]
BandedMatricesSparseArraysExt = "SparseArrays"
[[deps.Base64]]
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
[[deps.Bzip2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "9e2a6b69137e6969bab0152632dcb3bc108c8bdd"
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[[deps.CRC32c]]
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[[deps.CRlibm_jll]]
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git-tree-sha1 = "e329286945d0cfc04456972ea732551869af1cfc"
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[[deps.CairoMakie]]
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git-tree-sha1 = "3441d68ea63944a2b9b6de76603ec1c8b0fd4e3e"
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git-tree-sha1 = "a2f1c8c668c8e3cb4cca4e57a8efdb09067bb3fd"
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weakdeps = ["JSON", "RecipesBase", "SentinelArrays", "StructTypes"]
[deps.CategoricalArrays.extensions]
CategoricalArraysJSONExt = "JSON"
CategoricalArraysRecipesBaseExt = "RecipesBase"
CategoricalArraysSentinelArraysExt = "SentinelArrays"
CategoricalArraysStructTypesExt = "StructTypes"
[[deps.Chain]]
git-tree-sha1 = "9ae9be75ad8ad9d26395bf625dea9beac6d519f1"
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