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stringlengths 6
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sequencelengths 4
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| ratio_char_token
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| token_count
int64 4
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|---|---|---|---|
<filename>test/runtests.jl
# assert file to test polynomial implementation
using Test
using LinearAlgebra
using Polynomials
using SpecialFunctions
using RecipesBase: apply_recipe
import SparseArrays: sparse, nnz
using OffsetArrays
@testset "Standard basis" begin include("StandardBasis.jl") end
@testset "ChebyshevT" begin include("ChebyshevT.jl") end
@testset "Poly, Pade (compatability)" begin include("Poly.jl") end
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"""
free_energy!(model, config)
Compute the free energy of the SCFT polymer model. The chemical potential is also returned.
Only applicable to two-chain two-component system, e.g. AB/A, AB3/A etc.
"""
function free_energy!(model, config)
cellopt = uppercase(config["Algorithm_Cell_Optimization"]["algorithm"])
if cellopt == "SINGLE"
F = run!(model, config)
elseif cellopt == "BRENT" || cellopt == "AUTO"
res = optimize_cell!(model, config)
config["Algorithm_Cell_Optimization"]["cell_guess"][1] = Optim.minimizer(res)
# F = minimum(res)
end
return Polyorder.H(model)+Polyorder.Hi(model), Polyorder.mu(model)
end
"""
grand_potential(ϕ₁, ϕ₂, ϕ₀, model1, config1; model2=nothing, config2=nothing)
Compute the grand potential and its first order derivatives. Note the same polymer model is assumed is for model1 and model2 but for different phases and chain volume fractions. If model2 and config2 are also specified. Theire consistency with model1 and config1 is not checked! Please make sure they describe the same polymer model!
Only applicable to two-chain two-component system, e.g. AB/A, AB3/A etc.
This method is deprecated and replaced by PhaseDiagram.jl: gibbs_free_energy.
"""
function grand_potential(ϕ₁, ϕ₂, ϕ₀, model1, config1; model2=nothing, config2=nothing)
if model2 === nothing
model2 = model1
end
if config2 === nothing
config2 = config1
end
v₁ = (ϕ₀ - ϕ₂) / (ϕ₁ - ϕ₂)
v₂ = 1 - v₁
config1["Model"]["phi"][1] = 1 - ϕ₁
config1["Model"]["phi"][2] = ϕ₁
F₁, μ₁ = free_energy!(model1, config1)
config2["Model"]["phi"][1] = 1 - ϕ₂
config2["Model"]["phi"][2] = ϕ₂
F₂, μ₂ = free_energy!(model2, config2)
G = v₁ * F₁ + v₂ * F₂
fs = config1["Model"]["f"]
α = fs[1] * fs[end]
dG1 = v₁ * (F₂ - F₁) / (ϕ₁ - ϕ₂) + v₁ * μ₁ / α
dG2 = v₂ * (F₂ - F₁) / (ϕ₁ - ϕ₂) + v₂ * μ₂ / α
return G, dG1, dG2
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] | 2.205882 | 884 |
abstract AbstractRemoteSegments
remotesegments(p::AbstractPolicy,m::AbstractModel,s::AbstractSampler,nproposals::Int) = _remotesegments(p,m,s,nproposals)
numsegments(s::AbstractRemoteSegments) = s.numsegments
numproposalspersegment(s::AbstractRemoteSegments) = s.numproposalspersegment
numtotalproposals(s::AbstractRemoteSegments) = s.numsegments*s.numproposalspersegment
@inline _numjobsegments(::Type{Val{:procs}}) = nprocs()
@inline _numjobsegments(::Type{Val{:workers}}) = nworkers()
@inline _numjobsegments(::Type{Val{:test}}) = 3
@inline _processnumbers(::Type{Val{:procs}}) = procs()
@inline _processnumbers(::Type{Val{:workers}}) = workers()
@inline _processnumbers(::Type{Val{:test}}) = workers()
@inline _numproposalspersegment(nproposals::Int,njobsegments::Int) = ceil(Int,nproposals/njobsegments)
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# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct IoK8sApiCoreV1HTTPGetAction <: SwaggerModel
host::Any # spec type: Union{ Nothing, String } # spec name: host
httpHeaders::Any # spec type: Union{ Nothing, Vector{IoK8sApiCoreV1HTTPHeader} } # spec name: httpHeaders
path::Any # spec type: Union{ Nothing, String } # spec name: path
port::Any # spec type: Union{ Nothing, IoK8sApimachineryPkgUtilIntstrIntOrString } # spec name: port
scheme::Any # spec type: Union{ Nothing, String } # spec name: scheme
function IoK8sApiCoreV1HTTPGetAction(;host=nothing, httpHeaders=nothing, path=nothing, port=nothing, scheme=nothing)
o = new()
validate_property(IoK8sApiCoreV1HTTPGetAction, Symbol("host"), host)
setfield!(o, Symbol("host"), host)
validate_property(IoK8sApiCoreV1HTTPGetAction, Symbol("httpHeaders"), httpHeaders)
setfield!(o, Symbol("httpHeaders"), httpHeaders)
validate_property(IoK8sApiCoreV1HTTPGetAction, Symbol("path"), path)
setfield!(o, Symbol("path"), path)
validate_property(IoK8sApiCoreV1HTTPGetAction, Symbol("port"), port)
setfield!(o, Symbol("port"), port)
validate_property(IoK8sApiCoreV1HTTPGetAction, Symbol("scheme"), scheme)
setfield!(o, Symbol("scheme"), scheme)
o
end
end # type IoK8sApiCoreV1HTTPGetAction
const _property_map_IoK8sApiCoreV1HTTPGetAction = Dict{Symbol,Symbol}(Symbol("host")=>Symbol("host"), Symbol("httpHeaders")=>Symbol("httpHeaders"), Symbol("path")=>Symbol("path"), Symbol("port")=>Symbol("port"), Symbol("scheme")=>Symbol("scheme"))
const _property_types_IoK8sApiCoreV1HTTPGetAction = Dict{Symbol,String}(Symbol("host")=>"String", Symbol("httpHeaders")=>"Vector{IoK8sApiCoreV1HTTPHeader}", Symbol("path")=>"String", Symbol("port")=>"IoK8sApimachineryPkgUtilIntstrIntOrString", Symbol("scheme")=>"String")
Base.propertynames(::Type{ IoK8sApiCoreV1HTTPGetAction }) = collect(keys(_property_map_IoK8sApiCoreV1HTTPGetAction))
Swagger.property_type(::Type{ IoK8sApiCoreV1HTTPGetAction }, name::Symbol) = Union{Nothing,eval(Meta.parse(_property_types_IoK8sApiCoreV1HTTPGetAction[name]))}
Swagger.field_name(::Type{ IoK8sApiCoreV1HTTPGetAction }, property_name::Symbol) = _property_map_IoK8sApiCoreV1HTTPGetAction[property_name]
function check_required(o::IoK8sApiCoreV1HTTPGetAction)
(getproperty(o, Symbol("port")) === nothing) && (return false)
true
end
function validate_property(::Type{ IoK8sApiCoreV1HTTPGetAction }, name::Symbol, val)
end
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] | 2.653885 | 991 |
import DataStructures
import AVLTrees
using BenchmarkTools
include("../src/soalight.jl")
include("../src/allocator.jl")
include("../src/tree_iter.jl")
function testbase1(n)
tree = Set{Int}()
for j in 1:n[]
push!(tree, j)
push!(tree, -j)
end
@assert !(0 in tree)
for j in 1:n[]
@assert j in tree
@assert -j in tree
end
@assert !(n[] + 1 in tree)
@assert !(-n[] - 1 in tree)
for j in 1:n[]
@assert j in tree
@assert -j in tree
pop!(tree, j)
pop!(tree, -j)
@assert !(j in tree)
@assert !(-j in tree)
end
end
function testbase2(n)
tree = DataStructures.SortedSet{Int}()
for j in 1:n[]
push!(tree, j)
push!(tree, -j)
end
@assert !haskey(tree, 0)
for j in 1:n[]
@assert haskey(tree, j)
@assert haskey(tree, -j)
end
@assert !haskey(tree, n[] + 1)
@assert !haskey(tree, -n[] - 1)
for j in 1:n[]
@assert haskey(tree, j)
@assert haskey(tree, -j)
pop!(tree, j)
pop!(tree, -j)
@assert !haskey(tree, j)
@assert !haskey(tree, -j)
end
end
function testbase3(n)
tree = DataStructures.AVLTree{Int}()
for j in 1:n[]
insert!(tree, j)
insert!(tree, -j)
end
@assert !haskey(tree, 0)
for j in 1:n[]
@assert haskey(tree, j)
@assert haskey(tree, -j)
end
@assert !haskey(tree, n[] + 1)
@assert !haskey(tree, -n[] - 1)
for j in 1:n[]
@assert haskey(tree, j)
@assert haskey(tree, -j)
delete!(tree, j)
delete!(tree, -j)
@assert !haskey(tree, j)
@assert !haskey(tree, -j)
end
end
function testbase4(n)
tree = DataStructures.RBTree{Int}()
for j in 1:n[]
insert!(tree, j)
insert!(tree, -j)
end
@assert !haskey(tree, 0)
for j in 1:n[]
@assert haskey(tree, j)
@assert haskey(tree, -j)
end
@assert !haskey(tree, n[] + 1)
@assert !haskey(tree, -n[] - 1)
for j in 1:n[]
@assert haskey(tree, j)
@assert haskey(tree, -j)
delete!(tree, j)
delete!(tree, -j)
@assert !haskey(tree, j)
@assert !haskey(tree, -j)
end
end
function testbase5(n)
tree = DataStructures.SplayTree{Int}()
for j in 1:n[]
push!(tree, j)
push!(tree, -j)
end
@assert !haskey(tree, 0)
for j in 1:n[]
@assert haskey(tree, j)
@assert haskey(tree, -j)
end
@assert !haskey(tree, n[] + 1)
@assert !haskey(tree, -n[] - 1)
for j in 1:n[]
@assert haskey(tree, j)
@assert haskey(tree, -j)
delete!(tree, j)
delete!(tree, -j)
@assert !haskey(tree, j)
@assert !haskey(tree, -j)
end
end
function testbase6(n)
tree = AVLTrees.AVLSet{Int}()
for j in 1:n[]
push!(tree, j)
push!(tree, -j)
end
@assert !(0 in tree)
for j in 1:n[]
@assert j in tree
@assert -j in tree
end
@assert !(n[] + 1 in tree)
@assert !(-n[] - 1 in tree)
for j in 1:n[]
@assert j in tree
@assert -j in tree
delete!(tree, j)
delete!(tree, -j)
@assert !(j in tree)
@assert !(-j in tree)
end
end
function testbase7(n)
tree = nil()
for j in 1:n[]
tree = insert(tree, j, nothing)
tree = insert(tree, -j, nothing)
end
@assert !haskey(tree, 0)
for j in 1:n[]
@assert haskey(tree, j)
@assert haskey(tree, -j)
end
@assert !haskey(tree, n[] + 1)
@assert !haskey(tree, -n[] - 1)
for j in 1:n[]
@assert haskey(tree, j)
@assert haskey(tree, -j)
tree = delete(tree, j)
tree = delete(tree, -j)
@assert !haskey(tree, j)
@assert !haskey(tree, -j)
end
end
function testalloc(n, alloc)
tree = nil(alloc)
for j in 1:n[]
tree = insert(tree, j, nothing, alloc)
tree = insert(tree, -j, nothing, alloc)
end
@assert !haskey(tree, 0, alloc)
for j in 1:n[]
@assert haskey(tree, j, alloc)
@assert haskey(tree, -j, alloc)
end
@assert !haskey(tree, n[] + 1, alloc)
@assert !haskey(tree, -n[] - 1, alloc)
for j in 1:n[]
@assert haskey(tree, j, alloc)
@assert haskey(tree, -j, alloc)
tree = delete(tree, j, alloc)
tree = delete(tree, -j, alloc)
@assert !haskey(tree, j, alloc)
@assert !haskey(tree, -j, alloc)
end
emptyend!(alloc)
end
function testfree(n, alloc)
tree = nil(alloc)
for j in 1:n[]
tree = insert(tree, j, nothing, alloc)
tree = insert(tree, -j, nothing, alloc)
end
@assert !haskey(tree, 0, alloc)
for j in 1:n[]
@assert haskey(tree, j, alloc)
@assert haskey(tree, -j, alloc)
end
@assert !haskey(tree, n[] + 1, alloc)
@assert !haskey(tree, -n[] - 1, alloc)
for j in 1:n[]
@assert haskey(tree, j, alloc)
@assert haskey(tree, -j, alloc)
tree = delete(tree, j, alloc)
tree = delete(tree, -j, alloc)
@assert !haskey(tree, j, alloc)
@assert !haskey(tree, -j, alloc)
end
@assert isempty(alloc)
end
ans = nothing
for n in [100000, 1000, 10]
println(n, " elements")
print(" Set ")
@btime ($ans = testbase1(Ref($n)))
GC.gc()
print(" DataStructures.SortedSet ")
@btime ($ans = testbase2(Ref($n)))
GC.gc()
print(" DataStructures.AVLTree ")
@btime ($ans = testbase3(Ref($n)))
GC.gc()
print(" DataStructures.RBTree ")
@btime ($ans = testbase4(Ref($n)))
GC.gc()
print(" DataStructures.SplayTree ")
@btime ($ans = testbase5(Ref($n)))
GC.gc()
print(" AVLTrees.AVLSet ")
@btime ($ans = testbase6(Ref($n)))
GC.gc()
print(" Tree without allocator ")
@btime ($ans = testbase7(Ref($n)))
GC.gc()
print(" with fixed allocator ")
alloc = Allocator{TreeNode{Int, Int, Nothing}, Int}(2 * n)
@btime ($ans = testalloc(Ref($n), $alloc))
GC.gc()
print(" with resizable allocator ")
alloc = Allocator{TreeNode{Int, Int, Nothing}, Int}(nothing)
@btime ($ans = testalloc(Ref($n), $alloc))
GC.gc()
print(" with fixed free list allocator ")
alloc = FreeListAllocator{TreeNode{Int, Int, Nothing}, Int}(2 * n)
@btime ($ans = testfree(Ref($n), $alloc))
GC.gc()
print(" with resizable free list allocator ")
alloc = FreeListAllocator{TreeNode{Int, Int, Nothing}, Int}(nothing)
@btime ($ans = testfree(Ref($n), $alloc))
GC.gc()
print(" with fixed SOA allocator ")
store = TupleVector{TreeNode{Int, Int, Nothing}}(2 * n)
alloc = SOAllocator{TreeNode{Int, Int, Nothing}, Int, typeof(store)}(store, 2 * n)
@btime ($ans = testalloc(Ref($n), $alloc))
GC.gc()
print(" with resizable SOA allocator ")
store = TupleVector{TreeNode{Int, Int, Nothing}}(N)
alloc = SOAllocator{TreeNode{Int, Int, Nothing}, Int, typeof(store)}(store, nothing)
@btime ($ans = testalloc(Ref($n), $alloc))
GC.gc()
print(" with fixed free list SOA allocator ")
store = TupleVector{TreeNode{Int, Int, Nothing}}(2 * n)
alloc = FreeListSOAllocator{TreeNode{Int, Int, Nothing}, Int, typeof(store)}(store, 2 * n)
@btime ($ans = testfree(Ref($n), $alloc))
GC.gc()
print(" with resizable free list SOA allocator ")
store = TupleVector{TreeNode{Int, Int, Nothing}}(N)
alloc = FreeListSOAllocator{TreeNode{Int, Int, Nothing}, Int, typeof(store)}(store, nothing)
@btime ($ans = testfree(Ref($n), $alloc))
end
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] | 2.014341 | 3,905 |
<gh_stars>0
using Random
using Printf
include("Algencan/AlgencanWrapper.jl")
include("sh.jl")
import Voronoi
import Covering
drawing = (haskey(ENV, "DRAW") && ENV["DRAW"] != "" && ENV["DRAW"] != "0")
drawingAll = (haskey(ENV, "DRAWALL") && ENV["DRAWALL"] != "" && ENV["DRAWALL"] != "0")
TRIES = 100
WIDTH = 1.0
HEIGHT = 1.0
INSTANCE = 1
As = [
Array{Covering.Section, 1}([
Covering.Section(Array{Tuple{Real, Real}, 1}([
(0.1, 0.1),
(0.9, 0.1),
(0.9, 0.9),
(0.1, 0.9),
])),
]),
Array{Covering.Section, 1}([
Covering.Section(Array{Tuple{Real, Real}, 1}([
(0.5, 0.5),
(0.4, 0.4),
(0.5, 0.1),
(0.6, 0.4),
])),
Covering.Section(Array{Tuple{Real, Real}, 1}([
(0.5, 0.5),
(0.6, 0.4),
(0.9, 0.5),
(0.6, 0.6),
])),
Covering.Section(Array{Tuple{Real, Real}, 1}([
(0.5, 0.5),
(0.6, 0.6),
(0.5, 0.9),
(0.4, 0.6),
])),
Covering.Section(Array{Tuple{Real, Real}, 1}([
(0.5, 0.5),
(0.4, 0.6),
(0.1, 0.5),
(0.4, 0.4),
])),
]),
Array{Covering.Section, 1}([
Covering.Section(Array{Tuple{Real, Real}, 1}([
(0, 0.125),
(0.75, 0.5),
(1 ,0.875),
(0.25, 0.5),
])),
Covering.Section(Array{Tuple{Real, Real}, 1}([
(0, 1),
(0, 0.5),
(0.5, 1),
]))
]),
Array{Covering.Section, 1}([
Covering.Section(Array{Tuple{Real, Real}, 1}([
(0.1, 0.1),
(0.2, 0.2),
(0.2, 0.8),
(0.1, 0.9),
])),
Covering.Section(Array{Tuple{Real, Real}, 1}([
(0.1, 0.1),
(0.9, 0.1),
(0.8, 0.2),
(0.2, 0.2),
])),
Covering.Section(Array{Tuple{Real, Real}, 1}([
(0.9, 0.1),
(0.9, 0.9),
(0.8, 0.8),
(0.8, 0.2),
])),
Covering.Section(Array{Tuple{Real, Real}, 1}([
(0.9, 0.9),
(0.1, 0.9),
(0.2, 0.8),
(0.8, 0.8),
])),
]),
]
if haskey(ENV, "INSTANCE")
INSTANCE = parse(Int, ENV["INSTANCE"])
end
A = As[INSTANCE]
function calculateArea()
total_area = 0
for Aⱼ in A
t, _, _ = Covering.areaAndGradient(Aⱼ, 0)
total_area += t
end
return total_area
end
total_area = calculateArea()
function draw(WIDTH, HEIGHT, r, points, n)
if !drawing
return
end
if length(points) == 0
return
end
V = Voronoi.Fortune.compute(points, WIDTH, HEIGHT)
Voronoi.Intersect.intersect(V, Voronoi.Intersect.Rectangle(WIDTH, HEIGHT))
Draw.init(WIDTH, HEIGHT)
for Aⱼ in A
W = SH.intersect(V, Aⱼ, r)
#Draw.coveringPartition(W, "xkcd:pink")
Draw.coveringPartition(W, nothing)
Draw.filledPolygon(Aⱼ, "blue")
end
Draw.circles(r, points)
Draw.commit()
Draw.savefig("bateria/" * string(INSTANCE) * "/" * string(n))
println("...")
end
function coverWithCircles(n, WIDTH, HEIGHT, r, points)
function pack(r, points)
x = zeros(2n + 1)
x[1] = r
for (i, p) in enumerate(points)
x[2i] = p[1]
x[2i + 1] = p[2]
end
return x
end
function unpack(x)
r = x[1]
points = Array{Tuple{Real, Real}}([])
for i in 1:n
append!(points, [(x[2i], x[2i + 1])])
end
return r, points
end
function draw(r, points) if !drawing
return
end
V = Voronoi.Fortune.compute(points, WIDTH, HEIGHT)
Voronoi.Intersect.intersect(V, Voronoi.Intersect.Rectangle(WIDTH, HEIGHT))
Draw.init(WIDTH, HEIGHT)
for Aⱼ in A
W = SH.intersect(V, Aⱼ, r)
Draw.coveringPartition(W, "xkcd:pink")
Draw.filledPolygon(Aⱼ, "xkcd:black")
end
Draw.circles(r, points)
Draw.commit()
print("")
end
function f(x)
r, points = unpack(x)
if drawing && drawingAll
draw(r, points)
end
#println("R: ", r)
return r
end
function ∇f(x)
grad = zeros(2n + 1)
grad[1] = 1
#println("GRAD: ", grad)
return grad
end
function ∇∇f(x)
return [], [], []
end
function c(ind, x)
r, points = unpack(x)
#points, repeats = avoidRepeats(points)
V = Voronoi.Fortune.compute(points, WIDTH, HEIGHT)
Voronoi.Intersect.intersect(V, Voronoi.Intersect.Rectangle(WIDTH, HEIGHT))
uncovered_area = total_area
for Aⱼ in A
W = SH.intersect(V, Aⱼ, r)
area, _, _ = Covering.areaAndGradient(W)
uncovered_area -= area
end
#println("C: ", uncovered_area)
return uncovered_area
end
function ∇c(ind, x)
r, points = unpack(x)
#points, repeats = avoidRepeats(points)
V = Voronoi.Fortune.compute(points, WIDTH, HEIGHT)
Voronoi.Intersect.intersect(V, Voronoi.Intersect.Rectangle(WIDTH, HEIGHT))
gᵣ = 0
gₛ = fill((0.0, 0.0), n)
for Aⱼ in A
W = SH.intersect(V, Aⱼ, r)
_, gᵣⱼ, gₛⱼ = Covering.areaAndGradient(W)
gᵣ += gᵣⱼ
for i in 1:n
gₛ[i] = (gₛ[i][1] + gₛⱼ[i][1], gₛ[i][2] + gₛⱼ[i][2])
end
end
#for i in repeats
# push!(gₛ, (0, 0))
#end
#println("GRAD C.: ", pack(gᵣ, gₛ))
#println("Aperte Enter pra continuar")
#readline(stdin)
return collect(1:2n+1), pack(gᵣ, gₛ)
end
l = zeros(2n + 1)
l[1] = 1e-14
u = zeros(2n + 1)
u[1] = 10e20
for i in 2:2n+1
u[i] = 1.0
end
#draw(r, points)
#println("Aperte Enter pra continuar")
#readline(stdin)
try
x, fx = AlgencanWrapper.optimize(n = 2n + 1, m = 1,
f = f, g = ∇f,# h = ∇∇f,
equatn = [1],
c = c, jac = ∇c,
x = pack(r, points),
l = l,
u = u,
nvparam = 2,
vparam = [
"ITERATIONS-OUTPUT-DETAIL 10",
"PENALTY-PARAMETER-INITIAL-VALUE 1000"
],
#checkder = 1,
epsopt = 1.0e-6,
epsfeas = 1.0e-6
)
r, points = unpack(x)
#draw(r, points)
println("\n\n=================\nRESULTADO\n=================\n")
println("r = ", r)
println("s = ", 1/r)
println("pontos: ")
for p in points
println("(", p[1], ", ", p[2], ")")
end
#if drawing
# println("Digite ENTER para sair.")
# readline(stdin)
#end
return r, points
catch exc
println("Exception: ", exc)
return 1e10, []
end
end
function randf(start, finish, n)
v = rand(n)
return map(x -> start + x*(finish-start), v)
end
if drawing
import Draw
end
function main()
n = tryparse(Int64, ARGS[1])
WIDTH = 1.0
HEIGHT = 1.0
rmin = 1e10
pointsmin = []
#total_area, _, _ = Covering.areaAndGradient(A, 0)
ϵ = 10e-4
Random.seed!(1)
for k in 1:TRIES
#println("Tentativa ", k)
points = convert(Array{Tuple{Real, Real}}, collect(zip(randf(1, WIDTH-1, n), randf(1, HEIGHT-1, n))))
function insideHalfSpace(point, p, q)
pq = Voronoi.Geometry.subVector(q, p)
v = Voronoi.Geometry.rotateVectorCCW(pq)
function dot(a, b)
return a[1]*b[1] + a[2]*b[2]
end
u = Voronoi.Geometry.subVector(point, p)
return (dot(u, v) >= 0)
end
function pointIsInsideConvexPolygon(point, polygon)
p = polygon.borderHead
first = true
while p != polygon.borderHead || first
first = false
q = p.next
if !insideHalfSpace(point, p.origin, q.origin)
return false
end
p = p.next
end
return true
end
function pointIsInsideSomeConvexPolygon(point, polygons)
for polygon in polygons
if pointIsInsideConvexPolygon(point, polygon)
return true
end
end
return false
end
for (i, p) in enumerate(points)
while !pointIsInsideSomeConvexPolygon(points[i], A)
#println("Ponto $i ainda está fora. Re-sorteando.")
points[i] = (randf(1, WIDTH-1, 1)[1], randf(1, HEIGHT-1, 1)[1])
end
end
r = (0.5 + (rand(1)[1]))/n
try
r, points = coverWithCircles(n, WIDTH, HEIGHT, r, points)
#@info "points: $(points)"
catch exc
println("Exception: ", exc)
return 1e10, []
end
#draw(WIDTH, HEIGHT, r, points, n)
@info "n = $(n); k = $(k); rmin = $(rmin); r = $(r)"
if r < rmin
V = Voronoi.Fortune.compute(points, WIDTH, HEIGHT)
Voronoi.Intersect.intersect(V, Voronoi.Intersect.Rectangle(WIDTH, HEIGHT))
uncovered_area = total_area
for Aⱼ in A
W = SH.intersect(V, Aⱼ, r)
area, _, _ = Covering.areaAndGradient(W)
uncovered_area -= area
end
if uncovered_area < ϵ
rmin = r
pointsmin = points
else
@warn "UNFEASIBLE"
end
end
end
println("\n\n=================\nRESULTADO (n = " * string(n) * ")\n=================\n")
println("r = ", rmin)
println("s = ", 1/rmin)
println("pontos: ")
for p in pointsmin
println("(", p[1], ", ", p[2], ")")
end
println("")
if drawing
draw(WIDTH, HEIGHT, rmin, pointsmin, n)
#readline(stdin)
end
end
main()
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] | 1.412656 | 10,367 |
<gh_stars>1-10
include("../src/hypergraph-affinity-functions.jl")
using MAT
using Plots
M = matread("Total_Ns.mat")
Ng = M["Ng"]
Nf = M["Nf"]
Nw = M["Nw"]
r = 10
## Run all
for j = 1:3
for k = 2:4
if j == 1
N = Nf
elseif j == 2
N = Nw
else
N = Ng
end
alpha = 1/2
Hw, Hm = affinities_from_N(N)
alpha = 1/2
Bw = baselines(r,alpha)
Bm = baselines(r,1-alpha)
R1 = Hw./Bw
R2 = Hm./Bm
## Counting balance between men and women
N = Nw
T = 2*sum(N[2,:]) + 3*sum(N[3,:]) + 4*sum(N[4,:])
women = N[2,2] + 2*N[2,3] + N[3,2] + 2*N[3,3] + 3*N[3,4] + N[4,2] + 2*N[4,3] + 3*N[4,4] + 4*N[4]
@show women/T
## Plot
s1 = 375
s2 = 300
gfs = 18
tfs = 18
titlesize = 18
color1 = :green
color2 = :blue
title = ""
leg = false
if j == 3
xlab = "Affinity type t"
else
xlab = ""
end
if k == 2
yl = [.2,1.7]
elseif k ==3
yl = [.2,2]
elseif k == 4
yl = [.1,2.5]
end
if k == 2
ylab = "Affinity / Baseline"
else
ylab = ""
end
p = Plots.plot([1; k],[1; 1],yscale = :identity,fontfamily = "helvetica",linewidth = 2,legend = false,grid = false, size = (s1,s2),color = :gray)
title!(p,title,titlefontsize = titlesize)
xaxis!(p,xlab, xticks = 1:k,tickfontsize=tfs,guidefontsize = gfs)
yaxis!(p,ylab,tickfontsize=tfs,guidefontsize = gfs,ylim = yl)
plot!(p,1:k,R1[k,1:k],color = color1, linewidth = lw, markersize = ms,
markershape = :circle,markerstrokecolor = color1, label = class1)
plot!(p,1:k,R2[k,1:k],color = color2, linewidth = lw, markersize = ms,
markershape = :circle, markerstrokecolor = color2, label = class2)
savefig("Figures/Ratios_$(k)_$(j).pdf")
end
end
## Compute pairs in the clique projection
N = Nw
In1 = 0
In2 = 0
Cross = 0
r = 10
for k = 2:r
global In1, In2, Cross
for j = 1:k+1
i = j-1 # number of women in this type hyperedge
Hik = N[k,j] # number of hyperedges of this type
In1 += binomial(i,2)*Hik # number of extra edges of this type
In2 += binomial(k-i,2)*Hik
Cross += Hik*i*(k-i)
# println("$k, $i")
end
end
hw = round(2*In1/(2*In1 + Cross),digits = 3)
hm = round(2*In2/(2*In2 + Cross),digits = 3)
println("Clique expansion homophily scores: women = $hw, men = $hm")
println("$hw & $hm")
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<gh_stars>0
module AlignedSpans
using Dates, Intervals, Onda
using TimeSpans: TimeSpans, start, stop, format_duration
using StructTypes, ArrowTypes
export SpanRoundingMode, RoundInward, RoundSpanDown, ConstantSamplesRoundingMode
export AlignedSpan, consecutive_subspans, n_samples
# Make our own method so we can add methods for Intervals without piracy
duration(span) = TimeSpans.duration(span)
#####
##### Types and rounding modes
#####
"""
SpanRoundingMode(start::RoundingMode, stop::RoundingMode)
Creates a rounding object for [`AlignedSpan`](@ref) to indicate how the `AlignedSpan`'s
endpoints should be determined from a given `span`s endpoints'.
"""
struct SpanRoundingMode
start::RoundingMode
stop::RoundingMode
end
"""
ConstantSamplesRoundingMode(start::RoundingMode)
Creates a rounding object for [`AlignedSpan`](@ref) to indicate the `AlignedSpan`
should be constructed by the `start` and `duration` of the `span`, without regard to its `stop`.
If two `span`s have the same duration, then the resulting `AlignedSpan`'s will have the same
number of samples when constructed with this rounding mode.
See also [`AlignedSpan(sample_rate, span, mode::ConstantSamplesRoundingMode)`](@ref).
"""
struct ConstantSamplesRoundingMode
start::RoundingMode
end
"""
RoundInward = SpanRoundingMode(RoundUp, RoundDown)
This is a rounding mode where both ends of the continuous time interval are rounded "inwards"
to construct the largest span of indices such that all samples are entirely contained within it.
## Example
Consider a signal with sample rate 1 Hz.
```
Index 1 2 3 4 5
Time (s) 0 1 2 3 4
```
Now, consider the time span 1.5s (inclusive) to 2.5s (exclusive).
Using brackets to highlight this span:
```
Index 1 2 3 4 5
Time (s) 0 1 [ 2 ) 3 4
```
In code, this span is described by
```jldoctest RoundInward
julia> using AlignedSpans, Dates, TimeSpans
julia> ts = TimeSpan(Millisecond(1500), Millisecond(2500))
TimeSpan(00:00:01.500000000, 00:00:02.500000000)
```
The only sample within the span is at index 3. And indeed,
```jldoctest RoundInward
julia> aligned = AlignedSpan(1, ts, RoundInward)
AlignedSpan(1.0, 3, 3)
julia> AlignedSpans.indices(aligned)
3:3
```
gives an `AlignedSpan` with indices `3:3`.
"""
const RoundInward = SpanRoundingMode(RoundUp, RoundDown)
"""
RoundSpanDown = SpanRoundingMode(RoundDown, RoundDown)
This is a rounding mode where *both* ends of the continuous time interval are rounded
downwards.
## Example
Consider a signal with sample rate 1 Hz.
```
Index 1 2 3 4 5
Time (s) 0 1 2 3 4
```
Now, consider the time span 1.5s (inclusive) to 2.5s (exclusive).
Using brackets to highlight this span:
```
Index 1 2 3 4 5
Time (s) 0 1 [ 2 ) 3 4
```
In code, this span is described by
```jldoctest RoundSpanDown
julia> using AlignedSpans, Dates, TimeSpans
julia> ts = TimeSpan(Millisecond(1500), Millisecond(2500))
TimeSpan(00:00:01.500000000, 00:00:02.500000000)
```
If we round both ends of the interval down to the nearest sample,
the start of the interval becomes 1s, and the stop of the interval
becomes 2s. Thus, the associated samples are at indices `2:3`. And indeed,
```jldoctest RoundSpanDown
julia> aligned = AlignedSpan(1, ts, RoundSpanDown)
AlignedSpan(1.0, 2, 3)
julia> AlignedSpans.indices(aligned)
2:3
```
gives an `AlignedSpan` with indices `2:3`.
"""
const RoundSpanDown = SpanRoundingMode(RoundDown, RoundDown)
"""
AlignedSpan(sample_rate::Number, first_index::Int, last_index::Int)
Construct an `AlignedSpan` directly from a `sample_rate` and indices.
"""
struct AlignedSpan
sample_rate::Float64
first_index::Int64
last_index::Int64
function AlignedSpan(sample_rate::Number, first_index::Int, last_index::Int)
if last_index < first_index
throw(ArgumentError("Cannot create `AlignedSpan` with right-endpoint (`last_index=$(last_index)`) strictly smaller than left endpoint (`first_index=$(first_index)`)"))
end
return new(convert(Float64, sample_rate), first_index, last_index)
end
end
#####
##### Continuous -> discrete interface
#####
# Methods for these API functions are provided in `interop.jl`.
"""
AlignedSpans.start_index_from_time(sample_rate, span, rounding_mode)
Returns the index of a sample object obtained by rounding the start of `span` according to `rounding_mode`.
See also [`AlignedSpan(sample_rate, span, mode::SpanRoundingMode)`](@ref) and
[`AlignedSpan(sample_rate, span, mode::ConstantSamplesRoundingMode)`](@ref).
"""
function start_index_from_time end
"""
AlignedSpans.stop_index_from_time(sample_rate, span, rounding_mode)
Returns the index of a sample object obtained by rounding the stop of `span` according to `rounding_mode`.
See also [`AlignedSpan(sample_rate, span, mode::SpanRoundingMode)`](@ref).
"""
function stop_index_from_time end
#####
##### Continuous -> discrete conversions
#####
"""
AlignedSpan(sample_rate, span, mode::SpanRoundingMode)
Creates an `AlignedSpan` by rounding the left endpoint according to `mode.start`,
and the right endpoint by `mode.stop`.
If `mode.start==RoundUp`, then the left index of the resulting span is guaranteed
to be inside `span`. This is accomplished by checking if the left endpoint of the span
is exclusive, and if so, incrementing the index after rounding when necessary.
Likewise, if `mode.start==RoundDown`, then the right index of the resulting span is guaranteed
to be inside `span`. This is accomplished by checking if the right endpoint of the span
is exclusive, and if so, decrementing the index after rounding when necessary.
Note: `span` may be of any type which which provides methods for `AlignedSpans.start_index_from_time` and `AlignedSpans.stop_index_from_time`.
"""
function AlignedSpan(sample_rate, span, mode::SpanRoundingMode)
first_index = start_index_from_time(sample_rate, span, mode.start)
last_index = stop_index_from_time(sample_rate, span, mode.stop)
if last_index < first_index
throw(ArgumentError("No samples lie within `span`"))
end
return AlignedSpan(sample_rate, first_index, last_index)
end
"""
AlignedSpan(sample_rate, span, mode::ConstantSamplesRoundingMode)
Creates an `AlignedSpan` whose left endpoint is rounded according to `mode.start`,
and whose right endpoint is determined so by the left endpoint and the number of samples,
given by `AlignedSpans.n_samples(sample_rate, duration(span))`.
Interface: `span` may be of any type which which provides a method for [`AlignedSpans.start_index_from_time`](@ref) and `TimeSpans.duration`.
## More detailed information
This is designed so that if `AlignedSpan(sample_rate, span, mode::ConstantSamplesRoundingMode)` is applied to multiple `span`s, with the same `sample_rate`, and the same durations, then the resulting `AlignedSpan`'s will have the same number of samples.
For this reason, we ask for `TimeSpans.duration(span)` to be defined, rather than a `n_samples(span)` function: the idea is that we want to only using the duration and the starting time, rather than the *actual* number of samples in this particular `span`.
In contrast, `AlignedSpan(sample_rate, span, RoundInward)` provides an `AlignedSpan` which includes only (and exactly) the samples contained within `span`.
If one wants to create a collection of consecutive, non-overlapping, `AlignedSpans` each with the same number of samples, then use [`consecutive_subspans`](@ref) instead.
"""
function AlignedSpan(sample_rate, span, mode::ConstantSamplesRoundingMode)
first_index = start_index_from_time(sample_rate, span, mode.start)
n = n_samples(sample_rate, duration(span))
last_index = first_index + n - 1
return AlignedSpan(sample_rate, first_index, last_index)
end
include("time_index_conversions.jl")
include("interop.jl")
include("utilities.jl")
end
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] | 3.004917 | 2,644 |
type Layer{T}
data::T
options::Dict{Symbol, Any}
end
function Layer{T}(
data::T;
cmap::String = "nothing",
color::Union{String,Symbol} = "blue",
alpha::Union{Real,Symbol} = 0.5,
markersize::Union{Real,Symbol} = 3.0,
borderwidth::Real = 2.0,
geom::Symbol = :geometry0 # default for geojson(::DataFrame)
)
options = Dict(
:cmap => cmap, :color => color, :alpha => alpha,
:markersize => markersize, :borderwidth => borderwidth, :geom => geom
)
Layer(data, options)
end
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module CalliopeJuMP
export build_julia_model, model_dict
# Import external modules
using NCDatasets; using JuMP; using AxisArrays; using YAML;
include("Util.jl")
include("Variables.jl")
include("Objective.jl")
module load_constraints
export
load_capacity_constraints, load_costs_constraints,
load_dispatch_constraints, load_energy_balance_constraints,
load_milp_constraints, load_network_constraints,
load_conversion_constraints, load_conversion_plus_constraints,
load_export_constraints
include(joinpath("constraints", "capacity.jl"))
include(joinpath("constraints", "costs.jl"))
include(joinpath("constraints", "dispatch.jl"))
include(joinpath("constraints", "energy_balance.jl"))
include(joinpath("constraints", "milp.jl"))
include(joinpath("constraints", "network.jl"))
include(joinpath("constraints", "conversion.jl"))
include(joinpath("constraints", "conversion_plus.jl"))
include(joinpath("constraints", "export.jl"))
end
function build_julia_model(path_to_dataset)
# Bring in Dataset
dataset = NCDatasets.Dataset(path_to_dataset);
#
## Sets
#
sets = Dict()
parameters = Dict()
dimensions = [NCDatasets.nc_inq_dimname(dataset.ncid, i)
for i in NCDatasets.nc_inq_dimids(dataset.ncid, false)]
for var in keys(dataset)
show(var)
if var in dimensions
if var == "timesteps"
show(dataset[var][1])
end
sets[var] = dataset[var][:]
else
parameters[var] = get_variable(dataset, var)
end
end
# Create JuMP model, including assigning a solver
run_solver = dataset.attrib["run.solver"]
if run_solver == "cplex"
using CPLEX
solver = CplexSolver
elseif run_solver == "gurobi"
using Gurobi
solver = GurobiSolver
elseif run_solver == "glpk"
using GLPKMathProgInterface
if haskey(sets, "loc_techs_milp") || haskey(sets, "loc_techs_purchase")
solver = GLPKSolverMIP
else
solver = GLPKSolverLP
end
else
throw("unknown solver", run_solver)
end
backend_model = JuMP.Model(solver = solver());
model_dict = Dict("backend_model"=>backend_model,
"parameters"=>parameters,
"defaults"=>YAML.load(dataset.attrib["defaults"]),
"sets"=>sets,
"dataset"=>dataset)
#
## Variables
#
model_dict["variables"] = initialize_decision_variables(model_dict);
#
## Expressions
#
model_dict["expressions"] = Dict()
#
## Constraints
#
model_dict["constraints"] = merge(
load_constraints.load_capacity_constraints(model_dict),
load_constraints.load_cost_constraints(model_dict),
load_constraints.load_dispatch_constraints(model_dict),
load_constraints.load_energy_balance_constraints(model_dict),
load_constraints.load_network_constraints(model_dict)
)
if haskey(sets, "loc_techs_conversion")
model_dict["constraints"] = merge(
model_dict["constraints"],
load_constraints.load_conversion_constraints(model_dict)
)
end
if haskey(sets, "loc_techs_conversion_plus")
model_dict["constraints"] = merge(
model_dict["constraints"],
load_constraints.load_conversion_plus_constraints(model_dict)
)
end
if haskey(sets, "loc_techs_milp") || haskey(sets, "loc_techs_purchase")
model_dict["constraints"] = merge(
model_dict["constraints"],
load_constraints.load_milp_constraints(model_dict)
)
end
if haskey(sets, "loc_techs_export")
model_dict["constraints"] = merge(
model_dict["constraints"],
load_constraints.load_export_constraints(model_dict)
)
end
#
## Objective
#
cost_minimization(model_dict)
solve(model_dict["backend_model"])
return model_dict
end
end | [
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<reponame>yiminllin/ESDG-PosLimit
using Revise # reduce recompilation time
using Plots
# using Documenter
using LinearAlgebra
using SparseArrays
using BenchmarkTools
using UnPack
push!(LOAD_PATH, "./src")
using CommonUtils
using Basis1D
using SetupDG
push!(LOAD_PATH, "./examples/EntropyStableEuler.jl/src")
using EntropyStableEuler
using EntropyStableEuler.Fluxes1D
function wavespeed_1D(rho,rhou,E)
p = pfun_nd(rho,(rhou,),E)
cvel = @. sqrt(γ*p/rho)
return @. abs(rhou/rho) + cvel
end
unorm(U) = sum(map((x->x.^2),U))
function pfun_nd(rho, rhoU, E)
rhoUnorm2 = unorm(rhoU)./rho
return @. (γ-1)*(E - .5*rhoUnorm2)
end
function primitive_to_conservative_hardcode(rho,U,p)
rhoU = rho.*U
Unorm = unorm(U)
E = @. p/(γ-1) + .5*rho*Unorm
return (rho,rhoU,E)
end
const TOL = 1e-16
"Approximation parameters"
N = 3 # The order of approximation
K = 50
T = 1.0
T = 6.0
#T = 0.0039
# # Sod shocktube
# const γ = 1.4
# const Bl = -0.5
# const Br = 0.5
# const rhoL = 1.0
# const rhoR = 0.125
# const pL = 1.0
# const pR = 0.1
# const xC = 0.0
# const GIFINTERVAL = 20
# T = 0.2
# Leblanc shocktube
const γ = 5/3
const Bl = 0.0
const Br = 9.0
const rhoL = 1.0
const rhoR = 0.001
const pL = 0.1
const pR = 1e-7
const xC = 3.0
const GIFINTERVAL = 600
T = 6.0
"Mesh related variables"
VX = LinRange(Bl,Br,K+1)
EToV = transpose(reshape(sort([1:K; 2:K+1]),2,K))
"Initialize reference element"
r,_ = gauss_lobatto_quad(0,0,N) # Reference nodes
VDM = vandermonde_1D(N,r) # modal to nodal
Dr = grad_vandermonde_1D(N,r)/VDM # nodal differentiation
V1 = vandermonde_1D(1,r)/vandermonde_1D(1,[-1;1]) # nodal linear interpolation
Nq = N
rq,wq = gauss_lobatto_quad(0,0,Nq)
Vq = vandermonde_1D(N,rq)/VDM
M = Vq'*diagm(wq)*Vq
Mlump = zeros(size(M))
Mlump_inv = zeros(size(M))
for i = 1:Nq+1
Mlump[i,i] = sum(M[i,:])
Mlump_inv[i,i] = 1.0/Mlump[i,i]
end
# operators
Qr = M*Dr
B = zeros(N+1,N+1)
B[1,1] = -1
B[end,end] = 1
L = Array(spdiagm(0=>-2*ones(N+1), 1=>ones(N), -1=>ones(N)))
L[1,1] = -1
L[end,end] = -1
psi = pinv(L)*-1/2*B*ones(N+1)
S0 = zeros(N+1,N+1)
for i = 1:N+1
for j = 1:N+1
if L[i,j] != 0
S0[i,j] = psi[j] - psi[i]
end
end
end
Qr0 = S0+1/2*B
# Drop zeros
Qr = Matrix(droptol!(sparse(Qr),TOL))
Qr0 = Matrix(droptol!(sparse(Qr0),TOL))
B = Matrix(droptol!(sparse(B),TOL))
L = Matrix(droptol!(sparse(L),TOL))
S0 = Matrix(droptol!(sparse(S0),TOL))
S = Matrix(droptol!(sparse((Qr-Qr')/2),TOL))
rf = [-1.0;1.0]
nrJ = [-1.0;1.0]
Vf = vandermonde_1D(N,rf)/VDM
"""High order mesh"""
x = V1*VX[transpose(EToV)]
xf = Vf*x
mapM = reshape(1:2*K,2,K)
mapP = copy(mapM)
mapP[1,2:end] .= mapM[2,1:end-1]
mapP[2,1:end-1] .= mapM[1,2:end]
# """Periodic"""
# mapP[1] = mapM[end]
# mapP[end] = mapP[1]
"""Geometric factors"""
J = repeat(transpose(diff(VX)/2),N+1,1)
nxJ = repeat([-1;1],1,K)
rxJ = 1.0
# """Low order mesh"""
# VX0 = [x[1:end-1,:][:]; x[end]]
# #EToV = transpose(reshape(sort([1:K; 2:K+1]),2,K)
# EToV0 = transpose(reshape(sort([1:N*K; 2:N*K+1]),2,N*K))
# x0 = VX0[transpose(EToV0)]
# xf0 = x0
# mapM0 = reshape(1:2*N*K,2,N*K)
# mapP0 = copy(mapM0)
# mapP0[1,2:end] .= mapM0[2,1:end-1]
# mapP0[2,1:end-1] .= mapM0[1,2:end]
"""Geometric factors"""
J = repeat(transpose(diff(VX)/2),N+1,1)
nxJ = repeat([-1;1],1,K)
rxJ = 1.0
"""Initial condition"""
rho_x(x) = (x <= xC) ? rhoL : rhoR
u_x(x) = 0.0
p_x(x) = (x <= xC) ? pL : pR
rho = @. rho_x(x)
u = @. u_x(x)
p = @. p_x(x)
U = primitive_to_conservative_hardcode(rho,u,p)
function limiting_param(U_low, P_ij)
l = 1.0
# Limit density
if U_low[1] + P_ij[1] < -TOL
l = min(abs(U_low[1])/(abs(P_ij[1])+1e-14), 1.0)
end
# limiting internal energy (via quadratic function)
a = P_ij[1]*P_ij[3]-1.0/2.0*P_ij[2]^2
b = U_low[3]*P_ij[1]+U_low[1]*P_ij[3]-U_low[2]*P_ij[2]
c = U_low[3]*U_low[1]-1.0/2.0*U_low[2]^2
l_eps_ij = 1.0
if b^2-4*a*c >= 0
r1 = (-b+sqrt(b^2-4*a*c))/(2*a)
r2 = (-b-sqrt(b^2-4*a*c))/(2*a)
if r1 > TOL && r2 > TOL
l_eps_ij = min(r1,r2)
elseif r1 > TOL && r2 < -TOL
l_eps_ij = r1
elseif r2 > TOL && r1 < -TOL
l_eps_ij = r2
end
end
l = min(l,l_eps_ij)
return l
end
function flux_lowIDP(U_i,U_j,f_i,f_j,c_ij,wavespd)
return c_ij*(f_i+f_j)-abs(c_ij)*wavespd*(U_j-U_i)
end
function flux_high(f_i,f_j,c_ij)
return c_ij*(f_i+f_j)
end
function rhs_IDP(U,K,N,wq,S,S0,dt,Mlump_inv)
p = pfun_nd.(U[1],U[2],U[3])
flux = zero.(U)
@. flux[1] = U[2]
@. flux[2] = U[2]^2/U[1]+p
@. flux[3] = U[3]*U[2]/U[1]+p*U[2]/U[1]
J = (Br-Bl)/K/2 # assume uniform interval
# Low order and high order algebraic fluxes
F_low = [zeros(N+1,N+1),zeros(N+1,N+1),zeros(N+1,N+1)]
F_high = [zeros(N+1,N+1),zeros(N+1,N+1),zeros(N+1,N+1)]
F_low_P = [zeros(2),zeros(2),zeros(2)] # 1: left boundary, 2: right boundary
F_high_P = [zeros(2),zeros(2),zeros(2)]
L = zeros(N+1,N+1) # Array of limiting params
L_P = zeros(K-1) # limiting params at left/right boundary
rhsU = [zeros(N+1,K),zeros(N+1,K),zeros(N+1,K)]
# Determine dt
wavespd_arr = zeros(N+1,K)
for k = 1:K
for i = 1:N+1
wavespd_arr[i,k] = wavespeed_1D(U[1][i,k],U[2][i,k],U[3][i,k])
end
end
for k = 1:K
# Assemble matrix of low and high order algebraic fluxes
# interior of the element
for i = 1:N+1
for j = 1:N+1
if i != j # skip diagonal
wavespd = max(wavespd_arr[i,k],wavespd_arr[j,k])
for c = 1:3
F_low[c][i,j] = flux_lowIDP(U[c][i,k],U[c][j,k],flux[c][i,k],flux[c][j,k],S0[i,j],wavespd)
F_high[c][i,j] = flux_high(flux[c][i,k],flux[c][j,k],S[i,j])
end
end
end
end
# Assemble matrix of low and high order algebraic fluxes
# interface of the element
U_left = (k == 1) ? [rhoL; 0.0; pL/(γ-1)] : [U[1][end,k-1]; U[2][end,k-1]; U[3][end,k-1]]
f_left = (k == 1) ? [0.0; pL; 0.0] : [flux[1][end,k-1]; flux[2][end,k-1]; flux[3][end,k-1]]
U_right = (k == K) ? [rhoR; 0.0; pR/(γ-1)] : [U[1][1,k+1]; U[2][1,k+1]; U[3][1,k+1]]
f_right = (k == K) ? [0.0; pR; 0.0] : [flux[1][1,k+1]; flux[2][1,k+1]; flux[3][1,k+1]]
wavespd_l = max(wavespd_arr[1,k],wavespeed_1D(U_left[1],U_left[2],U_left[3]))
wavespd_r = max(wavespd_arr[end,k],wavespeed_1D(U_right[1],U_right[2],U_right[3]))
for c = 1:3
F_low_P[c][1] = flux_lowIDP(U[c][1,k],U_left[c],flux[c][1,k],f_left[c],-0.5,wavespd_l)
F_low_P[c][2] = flux_lowIDP(U[c][end,k],U_right[c],flux[c][end,k],f_right[c],0.5,wavespd_r)
F_high_P[c][1] = flux_high(flux[c][1,k],f_left[c],-0.5)
F_high_P[c][2] = flux_high(flux[c][end,k],f_right[c],0.5)
end
# Calculate limiting parameters over interior of the element
P_ij = zeros(3,1)
# TODO: redundant
U_low = [zeros(N+1,1),zeros(N+1,1),zeros(N+1,1)]
for c = 1:3
U_low[c] .= sum(-F_low[c],dims=2)
U_low[c][1] -= F_low_P[c][1]
U_low[c][end] -= F_low_P[c][2]
U_low[c] .= U[c][:,k]+dt/J*Mlump_inv*U_low[c]
end
for i = 1:N+1
lambda_j = (i >= 2 && i <= N) ? 1/N : 1/(N+1)
m_i = J*wq[i]
for j = 1:N+1
if i != j
for c = 1:3
P_ij[c] = dt/(m_i*lambda_j)*(F_low[c][i,j]-F_high[c][i,j])
end
L[i,j] = limiting_param([U_low[1][i]; U_low[2][i]; U_low[3][i]],P_ij)
end
end
end
# Symmetrize limiting parameters
for i = 1:N+1
for j = 1:N+1
if i != j
l_ij = min(L[i,j],L[j,i])
L[i,j] = l_ij
L[j,i] = l_ij
end
end
end
# Limiting parameters at the interface
if k <= K-1
lambda_j = 1/(N+1)
rhs_low = zeros(3,1)
U_lowP = [U_low[1][end]; U_low[2][end]; U_low[3][end]]
# current element
m_i = wq[end]*J
for c = 1:3
P_ij[c] = dt/(m_i*lambda_j)*(F_low_P[c][2]-F_high_P[c][2])
end
L_P[k] = limiting_param(U_lowP, P_ij)
# next element
U_lowP = zeros(3,1)
P_ij = zeros(3,1)
m_i = wq[1]*J
lambda_j = 1/(N+1)
# TODO: Very awkward... need to repeat the calculation for the next element
F_low_tmp = [zeros(1,N+1),zeros(1,N+1),zeros(1,N+1)]
F_high_tmp = [zeros(1,N+1),zeros(1,N+1),zeros(1,N+1)]
F_lowP_tmp = zeros(3,1)
F_highP_tmp = zeros(3,1)
i = 1
for j = 1:N+1
if i != j # skip diagonal
wavespd = max(wavespd_arr[i,k+1],wavespd_arr[j,k+1])
for c = 1:3
F_low_tmp[c][i,j] = flux_lowIDP(U[c][i,k+1],U[c][j,k+1],flux[c][i,k+1],flux[c][j,k+1],S0[i,j],wavespd)
F_high_tmp[c][i,j] = flux_high(flux[c][i,k+1],flux[c][j,k+1],S[i,j])
end
end
end
U_left = [U[1][end,k]; U[2][end,k]; U[3][end,k]]
f_left = [flux[1][end,k]; flux[2][end,k]; flux[3][end,k]]
wavespd_l = max(wavespd_arr[1,k+1],wavespeed_1D(U_left[1],U_left[2],U_left[3]))
for c = 1:3
F_lowP_tmp[c] = flux_lowIDP(U[c][1,k+1],U_left[c],flux[c][1,k+1],f_left[c],-0.5,wavespd_l)
F_highP_tmp[c] = flux_high(flux[c][1,k+1],f_left[c],-0.5)
end
U_low = zeros(3,1)
for c = 1:3
U_low[c] = sum(-F_low_tmp[c])-F_lowP_tmp[c][1]
U_low[c] = U[c][1,k+1]+dt*1/J*Mlump_inv[1]*U_low[c]
end
for c = 1:3
P_ij[c] = dt/(m_i*lambda_j)*(F_lowP_tmp[c]-F_highP_tmp[c])
end
Ltmp = limiting_param([U_low[1]; U_low[2]; U_low[3]],P_ij)
L_P[k] = min(Ltmp,L_P[k])
end
# construct rhs
for c = 1:3
# With limiting
rhsU[c][:,k] = sum((L.-1).*F_low[c] - L.*F_high[c],dims=2)
if k > 1
rhsU[c][1,k] += (L_P[k-1]-1)*F_low_P[c][1] - L_P[k-1]*F_high_P[c][1]
else
rhsU[c][1,k] += -F_low_P[c][1]
end
if k < K
rhsU[c][N+1,k] += (L_P[k]-1)*F_low_P[c][2] - L_P[k]*F_high_P[c][2]
else
rhsU[c][N+1,k] += -F_low_P[c][2]
end
rhsU[c][:,k] .= 1/J*Mlump_inv*rhsU[c][:,k]
end
end
return rhsU
end
function rhs_IDPlow(U,K,N,Mlump_inv,p,flux,J)
dfdx = (zeros(N+1,K),zeros(N+1,K),zeros(N+1,K))
for i = 2:K*(N+1)-1
for c = 1:3
dfdx[c][i] = 1/2*(flux[c][mod1(i+1,K*(N+1))] - flux[c][mod1(i-1,K*(N+1))])
end
end
dfdx[1][1] = 1/2*(flux[1][2] - 0.0)
dfdx[2][1] = 1/2*(flux[2][2] - pL)
dfdx[3][1] = 1/2*(flux[3][2] - 0.0)
dfdx[1][end] = 1/2*(0.0 - flux[1][end-1])
dfdx[2][end] = 1/2*(pR - flux[2][end-1])
dfdx[3][end] = 1/2*(0.0 - flux[3][end-1])
visc = (zeros(N+1,K),zeros(N+1,K),zeros(N+1,K))
for i = 2:K*(N+1)-1
wavespd_curr = wavespeed_1D(U[1][i],U[2][i],U[3][i])
wavespd_R = wavespeed_1D(U[1][mod1(i+1,K*(N+1))],U[2][mod1(i+1,K*(N+1))],U[3][mod1(i+1,K*(N+1))])
wavespd_L = wavespeed_1D(U[1][mod1(i-1,K*(N+1))],U[2][mod1(i-1,K*(N+1))],U[3][mod1(i-1,K*(N+1))])
dL = 1/2*max(wavespd_curr,wavespd_L)
dR = 1/2*max(wavespd_curr,wavespd_R)
for c = 1:3
visc[c][i] = dL*(U[c][mod1(i-1,K*(N+1))]-U[c][i]) + dR*(U[c][mod1(i+1,K*(N+1))]-U[c][i])
end
end
# i = 1
wavespd_curr = wavespeed_1D(U[1][1],U[2][1],U[3][1])
wavespd_R = wavespeed_1D(U[1][2],U[2][2],U[3][2])
wavespd_L = wavespeed_1D(rhoL,0.0,pL/(γ-1))
dL = 1/2*max(wavespd_curr,wavespd_L)
dR = 1/2*max(wavespd_curr,wavespd_R)
visc[1][1] = dL*(rhoL-U[1][1]) + dR*(U[1][2]-U[1][1])
visc[2][1] = dL*(0.0-U[2][1]) + dR*(U[2][2]-U[2][1])
visc[3][1] = dL*(pL/(γ-1)-U[3][1]) + dR*(U[3][2]-U[3][1])
# i = end
wavespd_curr = wavespeed_1D(U[1][end],U[2][end],U[3][end])
wavespd_R = wavespeed_1D(rhoR,0.0,pR/(γ-1))
wavespd_L = wavespeed_1D(U[1][end-1],U[2][end-1],U[3][end-1])
dL = 1/2*max(wavespd_curr,wavespd_L)
dR = 1/2*max(wavespd_curr,wavespd_R)
visc[1][end] = dL*(U[1][end-1]-U[1][end]) + dR*(rhoR-U[1][end])
visc[2][end] = dL*(U[2][end-1]-U[2][end]) + dR*(0.0-U[2][end])
visc[3][end] = dL*(U[3][end-1]-U[3][end]) + dR*(pR/(γ-1)-U[3][end])
rhsU = (x->1/J*Mlump_inv*x).(.-dfdx.+visc)
return rhsU
end
function rhs_high(U,K,N,Mlump_inv,S,p,flux,J)
dfdx = (zeros(N+1,K),zeros(N+1,K),zeros(N+1,K))
for k = 1:K
for i = 1:N+1
for j = 1:N+1
if i != j
for c = 1:3
dfdx[c][i,k] += S[i,j]*(flux[c][i,k]+flux[c][j,k])
end
end
end
end
fLB = [0.0;pL;0.0]
fRB = [0.0;pR;0.0]
# At interfaces
for c = 1:3
fL = (k > 1) ? flux[c][end,k-1] : fLB[c]
fR = (k < K) ? flux[c][1,k+1] : fRB[c]
dfdx[c][1,k] += -1.0/2.0*(flux[c][1,k]+fL)
dfdx[c][end,k] += 1.0/2.0*(flux[c][end,k]+fR)
end
end
rhsU = (x->-1/J*Mlump_inv*x).(dfdx)
return rhsU
end
# Time stepping
"Time integration"
t = 0.0
U = collect(U)
resU = [zeros(size(x)),zeros(size(x)),zeros(size(x))]
# # Forward Euler
# while t < T
# dt = 0.0001
# rhsU = rhs_IDP(U,K,N,wq,S,S0,dt,Mlump_inv)
# @. U = U + dt*rhsU
# global t = t + dt
# println("Current time $t with time step size $dt, and final time $T")
# end
# Vp = vandermonde_1D(N,LinRange(-1,1,10))/VDM
# gr(size=(300,300),ylims=(0,1.2),legend=false,markerstrokewidth=1,markersize=2)
# plt = plot(Vp*x,Vp*U[1])
Vp = vandermonde_1D(N,LinRange(-1,1,10))/VDM
gr(size=(300,300),ylims=(0,1.2),legend=false,markerstrokewidth=1,markersize=2)
plot()
dt = 0.0001
Nsteps = Int(T/dt)
@gif for i = 1:Nsteps
rhsU = rhs_IDP(U,K,N,wq,S,S0,dt,Mlump_inv)
@. U = U + dt*rhsU
global t = t + dt
println("Current time $t with time step size $dt, and final time $T")
if i % GIFINTERVAL == 0
plot(Vp*x,Vp*U[1])
end
end every GIFINTERVAL
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] | 1.58513 | 9,321 |
# apply is operations over substitution
#include("primitives.jl")
"""
apply substitution to a term, substitution
"""
function apply(vars::Vlist, sym::Number, subst::Tlist)
return sym
end
function apply(vars::Vlist, sym::Symbol, subst::Tlist)
for i in 1:length(vars)
if sym == vars[i]; return subst[i] end
end
return sym
end
"""
apply: ΣxΣ → Σ
"""
function apply(vars::Vlist, subst1::Tlist, subst2::Tlist)
nterm = []
for arg in subst1
narg = apply(vars, arg, subst2)
if isempty(nterm)
nterm = [narg]
elseif ndims(subst1) == 1
nterm = vcat(nterm, [narg])
else
nterm = hcat(nterm, [narg])
end
end
nterm
end
function apply(vars::Vlist, term::Expr, subst::Tlist)
nterm = deepcopy(term)
for i in 1:length(term.args)
arg = term.args[i]
narg = apply(vars, arg, subst)
nterm.args[i] = narg
end
nterm
end
"""
apply sigma2 to sigma1
simply make v<-t in σ1 to v<-t*σ2
"""
function apply2(vars::Vlist, σ1::Tlist, σ2::Tlist)
map(s->apply(vars, s, σ2), σ1)
end
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437,
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] | 2.364929 | 422 |
# Laplace distribution
export Laplace
@parameterized Laplace() ≪ (1/2) * Lebesgue(ℝ)
for N in AFFINEPARS
@eval begin
proxy(d::Laplace{$N}) = affine(params(d), Laplace())
logdensity(d::Laplace{$N}, x) = logdensity(proxy(d), x)
basemeasure(d::Laplace{$N}) = basemeasure(proxy(d))
end
end
# @affinepars Laplace
function logdensity(d::Laplace{()} , x)
return -abs(x)
end
Base.rand(rng::AbstractRNG, μ::Laplace{()}) = rand(rng, Dists.Laplace())
≪(::Laplace, ::Lebesgue{X}) where X <: Real = true
TV.as(::Laplace) = asℝ
distproxy(::Laplace{()}) = Dists.Laplace()
distproxy(d::Laplace{(:μ,)}) = Dists.Laplace(d.μ, 1.0)
distproxy(d::Laplace{(:σ,)}) = Dists.Laplace(0.0, d.σ)
distproxy(d::Laplace{(:μ,:σ)}) = Dists.Laplace(d.μ, d.σ)
distproxy(d::Laplace{(:ω,)}) = Dists.Laplace(0.0, inv(d.ω))
distproxy(d::Laplace{(:μ,:ω)}) = Dists.Laplace(d.μ, inv(d.ω))
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] | 2.071926 | 431 |
#----------------------------------------
#-- Import or declare all the tools and types
#-- to represent a taxi problem and its solutions
#----------------------------------------
typealias Network DiGraph
typealias Road Edge
# Implementations of Networks must have a "graph" element,
#and "ShortestPath" object, "generateCustomers" and "generateTaxis" methods.
immutable Customer
id::Int
orig::Int
dest::Int
tcall::Int
tmin::Int
tmaxt::Int
tmax::Int
price::Float64
end
immutable Taxi
id::Int
initPos::Int
end
#------------------------
#-- TaxiProblem: All data needed for simulation
#-- has to include:
#---- network::Network (graph of city)
#---- roadTime::SparseMatrixCSC{Int,Int} Time to cross a road
#---- roadCost::SparseMatrixCSC{Float64,Int} Cost to cross a road
#---- custs::Array{Customer,1} (customers)
#---- taxis::Array{Taxi,1} (taxis)
#---- nTime::Int number of timesteps
#---- waitingCost::Float64 cost of waiting
#---- sp::ShortPaths Shortest paths (time, cost and structure)
abstract TaxiProblem
#Represent the assignement of a customer
#taxi == 0 <=> unassigned
immutable CustomerAssignment
id::Int
timeIn::Int
timeOut::Int
end
#Represent the actions of a taxi during a simulation
#at each time, the road on which the taxi is, and the __ordered__ list of its
#taken customers
immutable TaxiActions
path::Vector{Edge}
custs::Vector{ CustomerAssignment} #customer in order: (id, pickup, dropoff)
end
#Represent the solution of a simulation (paths of taxis, customers, and cost)
immutable TaxiSolution
taxis::Array{TaxiActions, 1}
notTaken::BitVector
cost::Float64
end
immutable ShortPaths
traveltime::Array{Float64,2}
travelcost::Array{Float64,2}
previous::Array{Int,2}
end
ShortPaths() = ShortPaths( Array(Float64, (0,0)), Array(Float64, (0,0)), Array(Int, (0,0)))
immutable RealPaths
previous::Array{Int,2}
traveltime::Array{Float64,2}
real_destinations::Array{Int, 2}
end
# define heap entry data type
immutable DijkstraEntry{Float64}
vertex::Int
dist::Float64
cost::Float64
end
# define appropriate comparators for heap entries
<(e1::DijkstraEntry, e2::DijkstraEntry) = e1.dist < e2.dist
Base.isless(e1::DijkstraEntry, e2::DijkstraEntry) = e1.dist < e2.dist
#Represent an assigned customer (not fixed time-windows)
type AssignedCustomer
id::Int
tInf::Int
tSup::Int
end
#represent a time-window solution
type IntervalSolution
custs::Vector{Vector{AssignedCustomer}}
notTaken::BitVector
cost::Float64
end
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<filename>julia-programming/6-control-flow/short-circuit-evaluation.jl
conTRUE() = (println("Condition is evaluated and is TRUE"); true)
conFALSE() = (println("Condition is evaluated and is FALSE"); false)
# Short Circuit &&
conTRUE() && conTRUE()
conTRUE() && conFALSE()
conFALSE() && conTRUE()
conFALSE() && conFALSE()
# Short Circuit ||
conTRUE() || conTRUE()
conTRUE() || conFALSE()
conFALSE() || conTRUE()
conFALSE() || conFALSE()
# Ternary
x = 5
x >= 0 || println("x less than 0")
x = -1
x >= 0 || println("x less than 0") | [
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<filename>src/ZirconAge/ZirconAges.jl
module ZirconAges
# Julia translation of a R-script provided by <NAME> & <NAME> to compute zircon ages from Tt paths
# Used in the Nat. Comm. publication "Estimating the current size and state of subvolcanic magma reservoirs"
# 15/04/2022, <NAME> & <NAME>
import Base.Threads
using Parameters
using Loess, Statistics, StatsBase, KernelDensity, Loess
export ZirconAgeData,
compute_zircon_age_PDF, compute_zircons_Ttpath, # calculation routines
zircon_age_PDF,
compute_zircons_convert_vecs2mat
"""
Declare function to calculate zircon fraction as function of a temperature profile
"""
function zircon_fraction(T::AbstractArray{_T}, max_x_zr::_T) where _T
A = (1.62.-1.8*(10^4)*exp.((-10^4)./(T .+ 273.15))).*max_x_zr
A[A .<= 0.0] .= 0.0
return A
end
"""
ZirconAgeData
Struct that holds default parameters for the calculations
"""
@with_kw_noshow struct ZirconAgeData
Tsat::Float64 = 825.0 # Maximum zircon saturation temperature [C]
Tmin::Float64 = 690.0 # [C] Minimum zircon saturation Temperature [C]
Tsol::Float64 = 690.0 # [C] Solidus temperature [C]
Tcal_max::Float64 = 800.0 # max temperature to calculate zircon fraction
Tcal_step::Float64 = 1.0 # temperature step to caclulate zircon fraction (resolution of Zircon saturation curve discretization)
max_x_zr::Float64 = 0.001 # max fraction zircons at solidus
zircon_number::Int64 = 100.0 # number of required zircons
time_zr_growth::Float64 = 0.7e6 # Minimum time within T saturation range (This is what the method used in the R script, boils down too)
# -> remain in the Zr saturation zone more than 1/3 of the time the Tt path with the longest time in the saturation zone
end
"""
Loess fit of zircon number over saturation range
"""
function loess_fit_zircon_sat(ZirconData::ZirconAgeData)
@unpack Tsol, Tcal_step, Tcal_max, Tsat, max_x_zr, zircon_number, Tmin = ZirconData
# Get cumulative Zircon fraction (after Tierney et al., 2016; Geology)
T = range(Tsol, stop = Tcal_max, step = Tcal_step )
Tfit = range(Tsol, stop = Tsat, length = length(T))
x_zircon = zircon_fraction(T,max_x_zr) # compute zircon fraction
n_zircon = zero(x_zircon) # number of zircons
n_zircon[2:end] = -diff(x_zircon) # differentiate zircon fraction
n_zircon[1] = n_zircon[2] # set first value
n_zircon = ceil.(( (n_zircon*zircon_number)/maximum(n_zircon)).-minimum(floor.((n_zircon*zircon_number)/maximum(n_zircon))))
# fit n_zircon_N with loess regression
n_zircon_N_fit = loess(Tfit, n_zircon, span=1.0)
return n_zircon_N_fit
end
"""
This employs a loess fit function to compute the number of zircons for each of the Tt paths
"""
function compute_number_zircons!(n_zr::AbstractArray{_T,N}, Tt_paths_Temp::AbstractArray{_T,N}, ZirconData::ZirconAgeData) where {_T,N}
@unpack Tmin, Tsat, Tsol = ZirconData
n_zircon_N_fit = loess_fit_zircon_sat(ZirconData) # loess fit through zircon saturation
Threads.@threads for i in 1:size(Tt_paths_Temp,2)
for j in 1:size(Tt_paths_Temp,1)
T = Tt_paths_Temp[j,i]
if (T > Tsol) & (T < Tsat)
# The following line takes a lot of time, because Loess is not type-stable.
# There is an open PR in the package that may fix it
dat::_T = Loess.predict(n_zircon_N_fit, T)
n_zr[j,i] = floor(dat)
else
n_zr[j,i] = 0.0
end
end
end
nothing
end
"""
prob, ages_eruptible, number_zircons, T_av_time, T_sd_time compute_zircons_Ttpath(time_years::AbstractArray{Float64,1}, Tt_paths_Temp::AbstractArray{Float64,2}; ZirconData::ZirconAgeData)
This computes the number of zircons produced from a series of temperature-time path's.
The Tt-paths are stored in a 2D matrix `Tt_paths_Temp` with rows being the temperature at time `time_years`.
Input:
====
- `time_years` : vector of length `nt` with the time in years (since the beginning of the simulation) of the points provided
- `Tt_paths_Temp` : array of size `(nt,npaths)`` with the temperature of every path.
Output:
- `prob` : a vector that gives the relative probability that a zircon with a given age exists
- `ages_eruptible` : age of eruptble magma
- `number_zircons` : 2D array of size `(nt,)`
- `T_av_time`: vector of size `nt` that contains the average T of the paths
- `T_sd_time`: vector of size `nt` that contains the standard deviation of the T of the paths
This routine was developed based on an R-routine provided as electronic supplement in the paper:
- <NAME>., <NAME>., <NAME>., <NAME>., 2020. Determining the current size and state of subvolcanic magma reservoirs. Nat Commun 11, 5477. https://doi.org/10.1038/s41467-020-19084-2
"""
function compute_zircons_Ttpath(time_years::AbstractArray{_T,1}, Tt_paths_Temp::AbstractArray{_T,2}; ZirconData::ZirconAgeData = ZirconAgeData()) where _T
@unpack Tmin, Tsat, Tsol, time_zr_growth = ZirconData
Tt_paths_Temp1 = copy(Tt_paths_Temp)
Δt = diff(time_years)[1] # timestep [yrs]
time_er_min = maximum(time_years) # backward count
# find all the Tt paths that go through the zircon saturation range & are at the end of the path still below Tsat (otherwise Zr are not yet crystallized)
ID_col_er = findall( (maximum(Tt_paths_Temp1,dims=1).>Tmin) .& (Tt_paths_Temp1[end,:]' .< Tsat))
n_zr = zero(Tt_paths_Temp1)
compute_number_zircons!(n_zr, Tt_paths_Temp1, ZirconData) # computes the number of zircons for every path
# find the number of timesteps for every path, during which the temperature is > Tmin and < Tsat
length_trace = Vector{Float64}(undef,length(ID_col_er))
for i in 1:length(ID_col_er)
length_trace[i] = length( findall( (Tt_paths_Temp1[:,ID_col_er[i][2]] .> Tmin) .& (Tt_paths_Temp1[:,ID_col_er[i][2]] .< Tsat)) )
end
# the next several lines can likely be achieved in a more elegant way...
id = findall( length_trace .== maximum(length_trace))
ID_col_lgst_tr = ID_col_er[ id[1] ][2]
id = findall( Tt_paths_Temp1[:,ID_col_lgst_tr] .< Tsat)
VALID_min_time = findmin( Tt_paths_Temp1[id,ID_col_lgst_tr])
ID_min_time = VALID_min_time[2]
max_age_spread = maximum(length_trace)*Δt # This is defined among all selected paths
T_av_time_1 = replace!(Tt_paths_Temp1, 0.0 => NaN)
T_av_time = zeros(size(T_av_time_1,1))
T_sd_time = zeros(size(T_av_time_1,1))
# get the average temperature of the Tt paths and the standard deviation
for i in 1:size(Tt_paths_Temp1,1)
T_av_time[i] = mean(filter(!isnan, T_av_time_1[i,:]))
T_sd_time[i] = std(filter( !isnan, T_av_time_1[i,:]))
end
# I clarified the R function because the minimum step length to grow a zircon is simply a ratio of the maximum trace between Tmin and Tsol
# This makes sense as we only deal with fractions here. Because no mass is provided the real zircon size cannot possibly be determined
min_step_n = floor( (time_zr_growth/max_age_spread)*(max_age_spread/Δt) )
# find the Tt paths that have a number of timesteps in the saturation range greater than the defined min_step_n ()
# this is to mimic that it takes some time to grow zircons
id = findall( length_trace .> min_step_n)
if isempty(id)
max_Ptpath = maximum(length_trace)*Δt
error("I don't have a single Pt-path that is sufficiently long within time_zr_growth (=$(time_zr_growth) yrs).
The Longest Pt-path I have is $(max_Ptpath) years.
Decrease this value within the ZirconDataAge struct with ZirconData=ZirconAgeData(time_zr_growth=0.1e6) & rerun.")
end
ID_col_er_1 = getindex.(ID_col_er[id], [2])
int_zr_sat = collect(Float64, ID_min_time:1.0:(time_er_min/Δt)-min_step_n)
int_zr_sat = floor.(Int64,int_zr_sat)
T_av_time_slct = Vector{Float64}(undef,length(int_zr_sat)-1) .= 0.0
for i in 1:(size(int_zr_sat,1)-1)
id2 = ID_col_er_1[ findall( (Tt_paths_Temp1[int_zr_sat[i],ID_col_er_1[:]] .> Tmin) .& (Tt_paths_Temp1[int_zr_sat[i],ID_col_er_1[:]] .< Tsat)) ]
if isempty(id2) == true
T_av_time_slct[i] = NaN
else
T_av_time_slct[i] = median(filter(!isnan, Tt_paths_Temp1[int_zr_sat[i],id2]))
end
end
replace!(Tt_paths_Temp1, NaN => 0.0)
ID_col_er = getindex.(ID_col_er, [2])
for i in 1:length(ID_col_er)
ind = findall( (Tt_paths_Temp1[:,ID_col_er[i]]) .== 0.0 );
if ~isempty(ind)
k = maximum(ind)
Tt_paths_Temp1[1:k,ID_col_er[i]] .= 0.0
end
end
zr_select = zero(Tt_paths_Temp1)
zr_select[Tt_paths_Temp1 .> 0.0] .= 1.0
n_zrc2_0 = zr_select.*n_zr # filters out those Tt path that are still >Tsat @ the end
number_zircons = n_zrc2_0[:,ID_col_er_1];
n_measurable_ages = sum(number_zircons, dims=2)
sz = size(number_zircons,1)
ages_eruptible = collect(Float64, 1.0:Δt:sz*Δt)
# probability that a certain zircon is sampled, dependens on how many of a given age ara available:
prob = n_measurable_ages/sum(n_measurable_ages)
prob = prob[:,1]
return prob, ages_eruptible, number_zircons, T_av_time, T_sd_time
end
"""
time_years, prob, ages_eruptible, number_zircons, T_av_time, T_sd_time = compute_zircons_Ttpath(time_years::Vector{Vector{Float64}}, Tt_paths_Temp::Vector{Vector{Float64}}; ZirconData::ZirconAgeData = ZirconAgeData())
This accepts Vector{Vector} as input for time and temperature of each Tt-path. Here, the length of the vector can be variable between different points.
Internally, we interpolate this into a 2D matrix and a longer vector that includes all paths and a single vector with times
"""
function compute_zircons_Ttpath(time_years_vecs::Vector{Vector{_T}}, Tt_paths_Temp_vecs::Vector{Vector{_T}}; ZirconData::ZirconAgeData = ZirconAgeData()) where _T
# convert to a vector with time & matrix with T values at every timestep
time_years, Tt_paths_Temp = compute_zircons_convert_vecs2mat(time_years_vecs, Tt_paths_Temp_vecs)
# call main routine
prob, ages_eruptible, number_zircons, T_av_time, T_sd_time = compute_zircons_Ttpath(time_years, Tt_paths_Temp, ZirconData=ZirconData)
# return, including time_years
return time_years, prob, ages_eruptible, number_zircons, T_av_time, T_sd_time
end
"""
time_years, Ttpaths_mat = compute_zircons_convert_vecs2mat(time_years_vecs::Vector{Vector{Float64}}, Tt_paths_Temp_vecs::Vector{Vector{Float64}})
This converts a vector with Vectors contain time and temperature path's to a single time vector and a matrix that combines all vectors
"""
function compute_zircons_convert_vecs2mat(time_years_vecs::Vector{Vector{_T}}, Tt_paths_Temp_vecs::Vector{Vector{_T}}) where _T
# Create a single vector with the time values:
time_years = unique(reduce(vcat,unique(time_years_vecs)));
# Add the vectors to an array with T values
Tt_paths_mat = zeros(_T,length(time_years), length(Tt_paths_Temp_vecs))
for i in eachindex(Tt_paths_Temp_vecs)
istart = findall(time_years .== time_years_vecs[i][1])
iend = findall(time_years .== time_years_vecs[i][end])
Tt_paths_mat[istart[1]:iend[1], i] .= Tt_paths_Temp_vecs[i]
end
return time_years, Tt_paths_mat
end
"""
zircon_age_PDF(ages_eruptible::AbstractArray{Float64,1}, number_zircons::AbstractArray{Float64,2}, bandwidth=1e5, n_analyses=300)
Compute probability density functions for zircon age path's describes in `number_zircons` with age `ages_eruptible` (both computed ).
`bandwidth` is the smoothening window of the resulting curves (in years), whereas `n_analyses` are the number of analyses done.
"""
function zircon_age_PDF(ages_eruptible::AbstractArray{_T,1}, number_zircons::AbstractArray{_T,2}; bandwidth=1e5, n_analyses=300) where _T
# compute PDF for each of the zircon Tt-paths:
PDF_zircons = []
time_Ma = [];
for i in 1:size(number_zircons,2)
n_meas = number_zircons[:,i]
px = n_meas/sum(n_meas) # probability to have a certain age
# random numbers selected according to the probability
smp = sample( (maximum(ages_eruptible) .- ages_eruptible)/1e6, Weights(px), n_analyses, replace=true)
y = kde(smp, bandwidth=bandwidth/1e6)
# store data
push!(PDF_zircons, y.density)
push!(time_Ma, y.x)
end
n_measurable_ages = sum(number_zircons, dims=2)
pxAv = n_measurable_ages[:,1]./sum(n_measurable_ages[:,1])
smpAv = sample( (maximum(ages_eruptible) .- ages_eruptible)/1e6, Weights(pxAv), n_analyses, replace=true)
yAv = kde(smpAv, bandwidth=bandwidth/1e6)
time_Ma_average = Vector(yAv.x);
PDF_zircon_average = Vector(yAv.density);
return time_Ma, PDF_zircons, time_Ma_average, PDF_zircon_average
end
"""
time_Ma, PDF_zircons, time_Ma_average, PDF_zircon_average, time_years, prob, ages_eruptible, number_zircons, T_av_time, T_sd_time = compute_zircon_age_PDF(time_years_vecs::Vector{Vector}, Tt_paths_Temp_vecs::Vector{Vector}; ZirconData::ZirconAgeData = ZirconAgeData(), bandwidth=bandwidth, n_analyses=300)
This computes the PDF (probability density function) with zircon age data from Vectors with Tt-paths
"""
function compute_zircon_age_PDF(time_years_vecs::Vector{Vector{_T}}, Tt_paths_Temp_vecs::Vector{Vector{_T}}; ZirconData::ZirconAgeData = ZirconAgeData(), bandwidth=1e5, n_analyses=300) where _T
# Compute the probability that a zircon of certain age is sampled:
time_years, prob, ages_eruptible, number_zircons, T_av_time, T_sd_time = compute_zircons_Ttpath(time_years_vecs, Tt_paths_Temp_vecs, ZirconData=ZirconData);
# Use this to compute PDF curves:
time_Ma, PDF_zircons, time_Ma_average, PDF_zircon_average = zircon_age_PDF(ages_eruptible, number_zircons, bandwidth=bandwidth, n_analyses=n_analyses)
return time_Ma, PDF_zircons, time_Ma_average, PDF_zircon_average, time_years, prob, ages_eruptible, number_zircons, T_av_time, T_sd_time
end
end | [
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657,
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15,
60,
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28,
657,
13,
15,
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1441,
317,
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37811,
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220,
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6601,
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44909,
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11,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4613,
3520,
287,
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81,
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6516,
517,
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352,
14,
18,
286,
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309,
83,
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287,
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198,
437,
198,
198,
37811,
198,
220,
220,
220,
6706,
408,
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286,
1976,
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1271,
625,
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2837,
198,
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8818,
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408,
62,
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62,
89,
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62,
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7,
57,
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6601,
3712,
57,
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6601,
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220,
2488,
403,
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349,
11,
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11,
309,
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62,
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11,
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265,
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3509,
62,
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62,
89,
81,
11,
1976,
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62,
17618,
11,
309,
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796,
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6601,
197,
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197,
197,
197,
197,
197,
197,
628,
220,
220,
220,
1303,
3497,
23818,
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357,
8499,
15917,
1681,
2123,
435,
1539,
1584,
26,
2269,
1435,
8,
198,
220,
220,
220,
309,
220,
197,
197,
197,
197,
28,
2837,
7,
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349,
11,
2245,
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309,
9948,
62,
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11,
2239,
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309,
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62,
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220,
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220,
309,
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220,
197,
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197,
28,
2837,
7,
33758,
349,
11,
2245,
796,
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265,
11,
220,
197,
220,
220,
4129,
796,
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7,
51,
4008,
628,
220,
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220,
2124,
62,
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220,
197,
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28,
1976,
343,
1102,
62,
69,
7861,
7,
51,
11,
9806,
62,
87,
62,
89,
81,
8,
197,
197,
197,
2,
24061,
1976,
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198,
220,
220,
220,
299,
62,
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343,
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220,
197,
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28,
6632,
7,
87,
62,
89,
343,
1102,
8,
197,
197,
197,
197,
197,
197,
2,
1271,
286,
1976,
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684,
628,
220,
220,
220,
299,
62,
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1102,
58,
17,
25,
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60,
796,
532,
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7,
87,
62,
89,
343,
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8,
197,
197,
197,
197,
197,
197,
2,
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1976,
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198,
220,
220,
220,
299,
62,
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1102,
58,
16,
60,
220,
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28,
299,
62,
89,
343,
1102,
58,
17,
60,
197,
197,
197,
197,
197,
197,
197,
2,
900,
717,
1988,
628,
220,
220,
220,
299,
62,
89,
343,
1102,
197,
197,
28,
2906,
346,
12195,
7,
357,
77,
62,
89,
343,
1102,
9,
89,
343,
1102,
62,
17618,
20679,
47033,
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77,
62,
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29720,
12,
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7,
28300,
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7,
77,
62,
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9,
89,
343,
1102,
62,
17618,
20679,
47033,
7,
77,
62,
89,
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35514,
628,
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220,
1303,
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299,
62,
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299,
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198,
198,
1212,
24803,
257,
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408,
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2163,
284,
24061,
262,
1271,
286,
1976,
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684,
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286,
262,
309,
83,
13532,
198,
37811,
198,
8818,
220,
24061,
62,
17618,
62,
89,
1980,
684,
0,
7,
77,
62,
89,
81,
3712,
23839,
19182,
90,
62,
51,
11,
45,
5512,
309,
83,
62,
6978,
82,
62,
30782,
3712,
23839,
19182,
90,
62,
51,
11,
45,
5512,
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6601,
3712,
57,
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1102,
23396,
6601,
8,
810,
1391,
62,
51,
11,
45,
92,
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220,
220,
220,
2488,
403,
8002,
309,
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11,
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265,
11,
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220,
220,
299,
62,
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62,
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62,
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7,
57,
343,
1102,
6601,
8,
197,
197,
197,
2,
2376,
408,
4197,
832,
1976,
343,
1102,
36275,
198,
220,
220,
220,
14122,
82,
13,
31,
16663,
82,
329,
1312,
287,
352,
25,
7857,
7,
51,
83,
62,
6978,
82,
62,
30782,
11,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
329,
474,
287,
352,
25,
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7,
51,
83,
62,
6978,
82,
62,
30782,
11,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
309,
796,
309,
83,
62,
6978,
82,
62,
30782,
58,
73,
11,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
357,
51,
1875,
13146,
349,
8,
1222,
357,
51,
1279,
13146,
265,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
383,
1708,
1627,
2753,
257,
1256,
286,
640,
11,
780,
6706,
408,
318,
407,
2099,
12,
31284,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1318,
318,
281,
1280,
4810,
287,
262,
5301,
326,
743,
4259,
340,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4818,
3712,
62,
51,
220,
220,
220,
220,
220,
796,
6706,
408,
13,
79,
17407,
7,
77,
62,
89,
343,
1102,
62,
45,
62,
11147,
11,
309,
8,
197,
197,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
299,
62,
89,
81,
58,
73,
11,
72,
60,
220,
220,
220,
796,
4314,
7,
19608,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
299,
62,
89,
81,
58,
73,
11,
72,
60,
220,
220,
220,
796,
657,
13,
15,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
220,
220,
198,
220,
220,
220,
2147,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
1861,
11,
9337,
62,
263,
37623,
856,
11,
1271,
62,
89,
1980,
684,
11,
309,
62,
615,
62,
2435,
11,
309,
62,
21282,
62,
2435,
220,
24061,
62,
89,
1980,
684,
62,
51,
83,
6978,
7,
2435,
62,
19002,
3712,
23839,
19182,
90,
43879,
2414,
11,
16,
5512,
309,
83,
62,
6978,
82,
62,
30782,
3712,
23839,
19182,
90,
43879,
2414,
11,
17,
19629,
1168,
343,
1102,
6601,
3712,
57,
343,
1102,
23396,
6601,
8,
198,
198,
1212,
552,
1769,
262,
1271,
286,
1976,
1980,
684,
4635,
422,
257,
2168,
286,
5951,
12,
2435,
3108,
338,
13,
220,
198,
464,
309,
83,
12,
6978,
82,
389,
8574,
287,
257,
362,
35,
17593,
4600,
51,
83,
62,
6978,
82,
62,
30782,
63,
351,
15274,
852,
262,
5951,
379,
640,
4600,
2435,
62,
19002,
44646,
198,
198,
20560,
25,
198,
1421,
198,
12,
4600,
2435,
62,
19002,
63,
1058,
15879,
286,
4129,
4600,
429,
63,
351,
262,
640,
287,
812,
357,
20777,
262,
3726,
286,
262,
18640,
8,
286,
262,
2173,
2810,
198,
12,
4600,
51,
83,
62,
6978,
82,
62,
30782,
63,
1058,
7177,
286,
2546,
4600,
7,
429,
11,
77,
6978,
82,
8,
15506,
351,
262,
5951,
286,
790,
3108,
13,
198,
198,
26410,
25,
198,
12,
4600,
1676,
65,
63,
1058,
257,
15879,
326,
3607,
262,
3585,
12867,
326,
257,
1976,
343,
1102,
351,
257,
1813,
2479,
7160,
198,
12,
4600,
1095,
62,
263,
37623,
856,
63,
1058,
2479,
286,
17866,
903,
2153,
2611,
198,
12,
4600,
17618,
62,
89,
1980,
684,
63,
1058,
362,
35,
7177,
286,
2546,
4600,
7,
429,
35751,
63,
198,
12,
4600,
51,
62,
615,
62,
2435,
63,
25,
15879,
286,
2546,
4600,
429,
63,
326,
4909,
262,
2811,
309,
286,
262,
13532,
198,
12,
4600,
51,
62,
21282,
62,
2435,
63,
25,
15879,
286,
2546,
4600,
429,
63,
326,
4909,
262,
3210,
28833,
286,
262,
309,
286,
262,
13532,
198,
198,
1212,
8027,
373,
4166,
1912,
319,
281,
371,
12,
81,
28399,
2810,
355,
7914,
10327,
287,
262,
3348,
25,
198,
12,
1279,
20608,
29,
1539,
1279,
20608,
29,
1539,
1279,
20608,
29,
1539,
1279,
20608,
29,
1539,
12131,
13,
360,
13221,
278,
262,
1459,
2546,
290,
1181,
286,
850,
10396,
5171,
291,
2153,
2611,
46637,
13,
14393,
4440,
1367,
11,
642,
32883,
13,
3740,
1378,
34023,
13,
2398,
14,
940,
13,
940,
2548,
14,
82,
37309,
3134,
12,
33618,
12,
1129,
2919,
19,
12,
17,
198,
198,
37811,
198,
8818,
24061,
62,
89,
1980,
684,
62,
51,
83,
6978,
7,
2435,
62,
19002,
3712,
23839,
19182,
90,
62,
51,
11,
16,
5512,
309,
83,
62,
6978,
82,
62,
30782,
3712,
23839,
19182,
90,
62,
51,
11,
17,
19629,
1168,
343,
1102,
6601,
3712,
57,
343,
1102,
23396,
6601,
796,
1168,
343,
1102,
23396,
6601,
28955,
810,
4808,
51,
628,
220,
220,
220,
2488,
403,
8002,
309,
1084,
11,
13146,
265,
11,
13146,
349,
11,
640,
62,
89,
81,
62,
27922,
796,
1168,
343,
1102,
6601,
198,
220,
220,
220,
220,
198,
220,
220,
220,
309,
83,
62,
6978,
82,
62,
30782,
16,
796,
4866,
7,
51,
83,
62,
6978,
82,
62,
30782,
8,
198,
220,
220,
220,
220,
198,
220,
220,
220,
37455,
83,
220,
197,
197,
197,
197,
28,
197,
26069,
7,
2435,
62,
19002,
38381,
16,
60,
197,
197,
197,
197,
197,
197,
197,
2,
4628,
395,
538,
685,
48489,
60,
198,
220,
220,
220,
640,
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220,
220,
220,
1303,
314,
28464,
262,
371,
2163,
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262,
5288,
2239,
4129,
284,
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257,
1976,
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318,
2391,
257,
8064,
286,
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5415,
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309,
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290,
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349,
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1303,
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355,
356,
691,
1730,
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13,
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645,
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318,
2810,
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1103,
1976,
343,
1102,
2546,
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5457,
307,
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198,
220,
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220,
949,
62,
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62,
77,
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28,
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7,
357,
2435,
62,
89,
81,
62,
27922,
14,
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62,
496,
62,
43639,
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7,
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62,
496,
62,
43639,
14,
138,
242,
83,
8,
1267,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
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309,
83,
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326,
423,
257,
1271,
286,
4628,
395,
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287,
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36275,
2837,
3744,
621,
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62,
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62,
77,
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1303,
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318,
284,
26332,
326,
340,
2753,
617,
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284,
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4686,
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28,
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29,
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62,
77,
8,
220,
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220,
220,
220,
611,
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7,
312,
8,
198,
220,
220,
220,
220,
220,
220,
220,
3509,
62,
47,
83,
6978,
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5415,
7,
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62,
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138,
242,
83,
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220,
220,
220,
220,
220,
220,
220,
4049,
7203,
40,
836,
470,
423,
257,
2060,
34352,
12,
6978,
326,
318,
17338,
890,
1626,
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62,
89,
81,
62,
27922,
357,
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7,
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62,
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62,
27922,
8,
331,
3808,
737,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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383,
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395,
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12,
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314,
423,
318,
29568,
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62,
47,
83,
6978,
8,
812,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36400,
589,
428,
1988,
1626,
262,
1168,
343,
1102,
6601,
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351,
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343,
1102,
6601,
28,
57,
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1102,
23396,
6601,
7,
2435,
62,
89,
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62,
27922,
28,
15,
13,
16,
68,
21,
8,
1222,
302,
5143,
19570,
198,
220,
220,
220,
886,
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220,
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4522,
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62,
263,
62,
16,
197,
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28,
651,
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2389,
62,
4033,
62,
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58,
312,
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17,
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198,
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493,
62,
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28,
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11,
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62,
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62,
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16,
13,
15,
37498,
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62,
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62,
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14,
138,
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83,
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62,
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62,
77,
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198,
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493,
62,
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62,
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28,
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5317,
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11,
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62,
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62,
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8,
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220,
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309,
62,
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62,
2435,
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28,
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7,
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28,
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13,
15,
198,
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220,
220,
198,
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329,
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287,
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37498,
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62,
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62,
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11,
16,
13219,
16,
8,
198,
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220,
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220,
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17,
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197,
197,
28,
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62,
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16,
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16,
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25,
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29,
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8,
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5,
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51,
83,
62,
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62,
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16,
58,
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62,
89,
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62,
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58,
72,
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62,
4033,
62,
263,
62,
16,
58,
25,
11907,
764,
27,
13146,
265,
4008,
2361,
198,
220,
220,
220,
220,
220,
220,
220,
611,
318,
28920,
7,
312,
17,
8,
6624,
2081,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
309,
62,
615,
62,
2435,
62,
6649,
310,
58,
72,
60,
796,
11013,
45,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
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220,
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309,
62,
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58,
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60,
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7,
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7,
0,
271,
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11,
309,
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62,
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82,
62,
30782,
16,
58,
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62,
89,
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62,
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58,
72,
4357,
312,
17,
60,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
220,
220,
220,
220,
198,
220,
220,
220,
6330,
0,
7,
51,
83,
62,
6978,
82,
62,
30782,
16,
11,
11013,
45,
5218,
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13,
15,
8,
198,
220,
220,
220,
4522,
62,
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62,
263,
197,
197,
197,
28,
651,
9630,
12195,
2389,
62,
4033,
62,
263,
11,
685,
17,
12962,
198,
220,
220,
220,
329,
1312,
287,
352,
25,
13664,
7,
2389,
62,
4033,
62,
263,
8,
198,
220,
220,
220,
220,
220,
220,
220,
773,
796,
1064,
439,
7,
357,
51,
83,
62,
6978,
82,
62,
30782,
16,
58,
45299,
2389,
62,
4033,
62,
263,
58,
72,
11907,
8,
764,
855,
657,
13,
15,
5619,
198,
220,
220,
220,
220,
220,
220,
220,
611,
5299,
271,
28920,
7,
521,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
220,
197,
197,
197,
197,
28,
5415,
7,
521,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
309,
83,
62,
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62,
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16,
58,
16,
25,
74,
11,
2389,
62,
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62,
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58,
72,
11907,
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28,
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13,
15,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
628,
220,
220,
220,
220,
198,
220,
220,
220,
1976,
81,
62,
19738,
197,
197,
197,
28,
6632,
7,
51,
83,
62,
6978,
82,
62,
30782,
16,
8,
198,
220,
220,
220,
1976,
81,
62,
19738,
58,
51,
83,
62,
6978,
82,
62,
30782,
16,
764,
29,
657,
13,
15,
60,
764,
28,
352,
13,
15,
197,
198,
220,
220,
220,
299,
62,
89,
6015,
17,
62,
15,
197,
197,
197,
28,
1976,
81,
62,
19738,
15885,
77,
62,
89,
81,
197,
197,
197,
197,
197,
197,
2,
16628,
503,
883,
309,
83,
3108,
326,
389,
991,
1875,
33758,
265,
2488,
262,
886,
220,
198,
220,
220,
220,
1271,
62,
89,
1980,
684,
220,
220,
220,
220,
220,
796,
299,
62,
89,
6015,
17,
62,
15,
58,
45299,
2389,
62,
4033,
62,
263,
62,
16,
11208,
198,
220,
220,
220,
299,
62,
1326,
292,
11970,
62,
1095,
220,
197,
28,
2160,
7,
17618,
62,
89,
1980,
684,
11,
5391,
82,
28,
17,
8,
197,
198,
220,
220,
220,
264,
89,
220,
197,
197,
197,
197,
197,
28,
2546,
7,
17618,
62,
89,
1980,
684,
11,
16,
8,
198,
220,
220,
220,
9337,
62,
263,
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3994,
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338,
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62,
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51,
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51,
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62,
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7,
85,
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11,
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1303,
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30104,
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51,
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51,
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5512,
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17,
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28,
16,
68,
20,
11,
299,
62,
272,
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28,
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8,
198,
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7293,
1133,
12867,
12109,
5499,
329,
1976,
343,
1102,
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3108,
338,
8477,
287,
4600,
17618,
62,
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1980,
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63,
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4600,
1095,
62,
263,
37623,
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63,
357,
16885,
29231,
6739,
198,
63,
3903,
10394,
63,
318,
262,
32746,
258,
768,
4324,
286,
262,
7186,
23759,
357,
259,
812,
828,
9472,
4600,
77,
62,
272,
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63,
389,
262,
1271,
286,
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13,
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8818,
1976,
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62,
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62,
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62,
263,
37623,
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3712,
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62,
51,
11,
16,
5512,
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1980,
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90,
62,
51,
11,
17,
19629,
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28,
16,
68,
20,
11,
299,
62,
272,
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28,
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8,
810,
4808,
51,
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220,
220,
220,
1303,
24061,
12960,
329,
1123,
286,
262,
1976,
343,
1102,
309,
83,
12,
6978,
82,
25,
198,
220,
220,
220,
12960,
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1980,
684,
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17635,
198,
220,
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640,
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21467,
796,
25787,
198,
220,
220,
220,
329,
1312,
287,
352,
25,
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7,
17618,
62,
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1980,
684,
11,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
299,
62,
1326,
292,
220,
197,
197,
197,
796,
1271,
62,
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1980,
684,
58,
45299,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
279,
87,
197,
220,
220,
197,
197,
197,
796,
299,
62,
1326,
292,
14,
16345,
7,
77,
62,
1326,
292,
8,
197,
197,
2,
12867,
284,
423,
257,
1728,
2479,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
4738,
3146,
6163,
1864,
284,
262,
12867,
198,
220,
220,
220,
220,
220,
220,
220,
895,
79,
197,
197,
197,
197,
796,
6291,
7,
357,
47033,
7,
1095,
62,
263,
37623,
856,
8,
764,
12,
9337,
62,
263,
37623,
856,
20679,
16,
68,
21,
11,
775,
2337,
7,
8416,
828,
299,
62,
272,
43710,
11,
6330,
28,
7942,
8,
198,
220,
220,
220,
220,
220,
220,
220,
331,
220,
197,
197,
197,
197,
796,
479,
2934,
7,
82,
3149,
11,
19484,
28,
3903,
10394,
14,
16,
68,
21,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
3650,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
20456,
62,
89,
1980,
684,
11,
220,
197,
88,
13,
43337,
8,
198,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
2435,
62,
21467,
11,
220,
197,
197,
88,
13,
87,
8,
198,
220,
220,
220,
886,
628,
220,
220,
220,
299,
62,
1326,
292,
11970,
62,
1095,
220,
220,
796,
2160,
7,
17618,
62,
89,
1980,
684,
11,
5391,
82,
28,
17,
8,
198,
220,
220,
220,
279,
87,
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197,
220,
220,
197,
197,
197,
28,
299,
62,
1326,
292,
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62,
1095,
58,
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16,
4083,
14,
16345,
7,
77,
62,
1326,
292,
11970,
62,
1095,
58,
45299,
16,
12962,
198,
220,
220,
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895,
79,
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197,
197,
197,
197,
28,
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7,
357,
47033,
7,
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62,
263,
37623,
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8,
764,
12,
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62,
263,
37623,
856,
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16,
68,
21,
11,
775,
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7,
8416,
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828,
299,
62,
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11,
6330,
28,
7942,
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198,
220,
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220,
331,
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197,
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197,
197,
28,
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82,
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28,
3903,
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14,
16,
68,
21,
8,
198,
220,
220,
220,
640,
62,
21467,
62,
23913,
220,
220,
220,
220,
796,
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88,
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62,
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1102,
62,
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88,
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220,
220,
220,
198,
220,
220,
220,
1441,
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62,
21467,
11,
12960,
62,
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1980,
684,
11,
640,
62,
21467,
62,
23913,
11,
12960,
62,
89,
343,
1102,
62,
23913,
198,
437,
628,
198,
37811,
198,
220,
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220,
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62,
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12960,
62,
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1980,
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62,
21467,
62,
23913,
11,
12960,
62,
89,
343,
1102,
62,
23913,
11,
640,
62,
19002,
11,
1861,
11,
9337,
62,
263,
37623,
856,
11,
1271,
62,
89,
1980,
684,
11,
309,
62,
615,
62,
2435,
11,
309,
62,
21282,
62,
2435,
796,
24061,
62,
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1102,
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5512,
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62,
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19629,
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6601,
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57,
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6601,
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28,
3903,
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299,
62,
272,
43710,
28,
6200,
8,
198,
198,
1212,
552,
1769,
262,
12960,
357,
1676,
65,
1799,
12109,
2163,
8,
351,
1976,
343,
1102,
2479,
1366,
422,
569,
478,
669,
351,
309,
83,
12,
6978,
82,
197,
198,
198,
37811,
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8818,
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6601,
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6601,
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6601,
22784,
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28,
16,
68,
20,
11,
299,
62,
272,
43710,
28,
6200,
8,
810,
4808,
51,
198,
220,
220,
220,
220,
198,
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28,
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11,
299,
62,
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28,
77,
62,
272,
43710,
8,
628,
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220,
220,
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62,
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11,
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62,
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11,
309,
62,
21282,
62,
2435,
198,
198,
437,
198,
198,
437
] | 2.299809 | 6,284 |
using Oceananigans.Operators
tracer_diffusivities(tracers, κ::Union{Number, Function}) = with_tracers(tracers, NamedTuple(), (tracers, init) -> κ)
tracer_diffusivities(tracers, ::Nothing) = nothing
function tracer_diffusivities(tracers, κ::NamedTuple)
all(name ∈ propertynames(κ) for name in tracers) ||
throw(ArgumentError("Tracer diffusivities or diffusivity parameters must either be a constants
or a `NamedTuple` with a value for every tracer!"))
return κ
end
convert_diffusivity(FT, κ, discrete) = κ # fallback
convert_diffusivity(FT, κ::Number, discrete) = convert(FT, κ)
convert_diffusivity(FT, κ::F, ::Val{true}) where F<:Function = DiscreteDiffusionFunction(κ)
convert_diffusivity(FT, κ::F, ::Val{false}) where F<:Function = κ
function convert_diffusivity(FT, κ::NamedTuple, discrete)
κ_names = propertynames(κ)
return NamedTuple{κ_names}(Tuple(convert_diffusivity(FT, κi, discrete) for κi in κ))
end
@inline geo_mean_Δᶠ(i, j, k, grid::AbstractGrid) =
cbrt(Δxᶜᶜᶜ(i, j, k, grid) * Δyᶜᶜᶜ(i, j, k, grid) * Δzᶜᶜᶜ(i, j, k, grid))
@kernel function calculate_nonlinear_viscosity!(νₑ, grid, closure, buoyancy, U, C)
i, j, k = @index(Global, NTuple)
@inbounds νₑ[i, j, k] = νᶜᶜᶜ(i, j, k, grid, closure, buoyancy, U, C)
end
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] | 2.240069 | 579 |
<reponame>jw3126/Interpolations.jl
"""
The `extrap_prep` function is used by `getindex_impl` to generate the body of
the `getindex` function for extrapolation objects.
The methods of `extrap_prep` work in "layers", iteratively working out the exact
expression needed.
The first layer takes a specification of the extrapolation scheme(s) to be used
and a `Val` object that specifies the dimensionality of the extrapolation object:
`extrap_prep{T,N}(::Type{T}, Val{N})`. These methods only dispatch to the second
layer, and need not be extended for new schemes.
The second layer also takes a `Val` object that specifies a single dimension on
which to work: `extrap_prep{T,N,d}(::Type{T}, ::Val{N}, ::Val{d}). The methods
with this signature in src/extrapolation/extrap_prep.jl simply expand into a
block with sub-expressions for handling too-low and too-high values separately
(the third layer), but specific interpolation schemes can provide more specific
methods for this layer that handle both ends simultaneously. For example, the
`Flat` scheme has a layer-2 method that uses `clamp` to restrict the coordinate
when used in both directions, but uses `min` and `max` when handling each end
separately.
The third layer, to which the second dispatches if no scheme-specific method is
found, adds a final `Val` object with a symbol `:lo` or `:hi`:
`extrap_prep{T,N,d,l}(::Type{T}, ::Val{N}, ::Val{d}, ::Val{l})`. These methods
must be specified for each extrapolation scheme. However, the general framework
takes care of expanding all possible tuple combinations, so individual schemes
need only care about e.g. `T==Flat`.
In addition to these methods, there is a similar three-layer method hierarchy
for gradient evaluation, in which a `Val{:gradient}` is prepended to the other
arguments:
`extrap_prep{T,N,d,l}(::Val{:gradient}`, ::Type{T}, ::Val{N}, ::Val{d}, ::Val{l})`
If nothing else is specified for the individual schemes, these methods forward
to the same methods without the `:gradient` argument, i.e. the same behavior as
for value extrapolation. This works well with all schemes that are simple
coordinate transformations, but for anything else methods for the low- and high-
value cases need to be implemented for each scheme.
""" extrap_prep
extrap_prep{T}(::Type{T}, n::Val{1}) = extrap_prep(T, n, Val{1}())
extrap_prep{T}(::Type{Tuple{T}}, n::Val{1}) = extrap_prep(T, n)
extrap_prep{T}(::Type{Tuple{T,T}}, n::Val{1}) = extrap_prep(T, n)
extrap_prep{T}(::Type{Tuple{Tuple{T,T}}}, n::Val{1}) = extrap_prep(T, n)
function extrap_prep{S,T}(::Type{Tuple{S,T}}, n::Val{1})
quote
$(extrap_prep(S, n, Val{1}(), Val{:lo}()))
$(extrap_prep(T, n, Val{1}(), Val{:hi}()))
end
end
extrap_prep{S,T}(::Type{Tuple{Tuple{S,T}}}, n::Val{1}) = extrap_prep(Tuple{S,T}, n)
# needed for ambiguity resolution
extrap_prep{T<:Tuple}(::Type{T}, ::Val{1}) = :(throw(ArgumentError("The 1-dimensional extrap configuration $T is not supported")))
function extrap_prep{T,N}(::Type{T}, n::Val{N})
exprs = Expr[]
for d in 1:N
push!(exprs, extrap_prep(T, n, Val{d}()))
end
return Expr(:block, exprs...)
end
function extrap_prep{N,T<:Tuple}(::Type{T}, n::Val{N})
length(T.parameters) == N || return :(throw(ArgumentError("The $N-dimensional extrap configuration $T is not supported - must be a tuple of length $N (was length $(lenght(T.parameters)))")))
exprs = Expr[]
for d in 1:N
Tdim = T.parameters[d]
if Tdim <: Tuple
length(Tdim.parameters) == 2 || return :(throw(ArgumentError("The extrap configuration $Tdim for dimension $d is not supported - must be a tuple of length 2 or a simple configuration type")))
if Tdim.parameters[1] != Tdim.parameters[2]
push!(exprs, extrap_prep(Tdim, n, Val{d}()))
else
push!(exprs, extrap_prep(Tdim.parameters[1], n, Val{d}()))
end
else
push!(exprs, extrap_prep(Tdim, n, Val{d}()))
end
end
return Expr(:block, exprs...)
end
extrap_prep{T,N,d}(::Type{T}, n::Val{N}, dim::Val{d}) = extrap_prep(Tuple{T,T}, n, dim)
function extrap_prep{S,T,N,d}(::Type{Tuple{S,T}}, n::Val{N}, dim::Val{d})
quote
$(extrap_prep(S, n, dim, Val{:lo}()))
$(extrap_prep(T, n, dim, Val{:hi}()))
end
end
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] | 2.691397 | 1,604 |
<filename>test/runtests.jl
using BranchTests
using Test
Code = quote
@testbranch "Vector" begin
println("in 'Vector'")
v = Vector{Int}()
@test isempty(v)
@testbranch "adds one element" begin
println("in 'adds one element'")
push!(v, 1)
@test length(v) == 1
@testbranch "adds another element" begin
println("in 'adds another element'")
push!(v, 2)
@test length(v) == 2
end
@testbranch DefaultTestSet "removes one" begin # test set type
println("in 'removes one'")
pop!(v)
@test isempty(v)
end
end
@testbranch begin # no name
println("in 'equality'")
@test v == v
end
end
end
eval(Code)
#@testset "vector" begin
# v = Vector{Int}()
#
# @testset "adds one element" begin
# push!(v, 1)
# @test length(v) == 1
#
# @testset "adds another element" begin
# push!(v, 1)
# @test length(v) == 2
# end
#
# @testset "removes - empty" begin
# pop!(v)
# @test isempty(v)
# end
# end
#end
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] | 1.811953 | 686 |
<filename>test/exceptions.jl
using TimeZones: ParseNextError
@testset "Exceptions" begin
# Test exception messages
tz = VariableTimeZone(
"Imaginary/Zone",
[Transition(DateTime(1800,1,1), FixedTimeZone("IST",0,0))],
DateTime(1980,1,1),
)
@test sprint(showerror, AmbiguousTimeError(DateTime(2015,1,1), tz)) ==
"AmbiguousTimeError: Local DateTime 2015-01-01T00:00:00 is ambiguous within Imaginary/Zone"
@test sprint(showerror, NonExistentTimeError(DateTime(2015,1,1), tz)) ==
"NonExistentTimeError: Local DateTime 2015-01-01T00:00:00 does not exist within Imaginary/Zone"
@test sprint(showerror, UnhandledTimeError(tz)) ==
"UnhandledTimeError: TimeZone Imaginary/Zone does not handle dates on or after 1980-01-01T00:00:00 UTC"
end
@testset "ParseNextError" begin
sshowerror(x) = sprint(showerror, x; context=:color => true)
@test ParseNextError("", "", 1) == ParseNextError("", "", 1, 0)
@test ParseNextError("", "A", 1) == ParseNextError("", "A", 1, 1)
@test sshowerror(ParseNextError("", "A", 1)) == "$ParseNextError: \"\e[4mA\e[24m\""
@test sshowerror(ParseNextError("Fail", "A", 1)) == "$ParseNextError: Fail: \"\e[4mA\e[24m\""
@test sshowerror(ParseNextError("", "", 1)) == "$ParseNextError: \"\e[4m\"\e[24m"
@test sshowerror(ParseNextError("", "0:A:0", 3, 3)) == "$ParseNextError: \"0:\e[4mA\e[24m:0\""
@test sshowerror(ParseNextError("", "<>", 2, 0)) == "$ParseNextError: \"<\e[4m>\e[24m\""
@test sshowerror(ParseNextError("", "<>", 2, 4)) == "$ParseNextError: \"<\e[4m>\e[24m\""
end
| [
27,
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1,
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220,
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68,
58,
19,
76,
29,
59,
68,
58,
1731,
76,
7879,
1,
198,
437,
198
] | 2.357353 | 680 |
struct FreqRange{T}
range::T
end
function FreqRange(; min, max)
return FreqRange([min, max])
end
Base.first(r::FreqRange) = first(r.range)
Base.last(r::FreqRange) = last(r.range)
@with_kw struct DispersionModel{S<:AbstractSusceptibility}
inf = 1.0
inf_diag = inf * [1, 1, 1]
inf_offdiag = [0, 0, 0]
susceptibilities::Vector{S}
valid_freq_range::FreqRange
end
function eval_dispersion_model(m::DispersionModel, f)
@unpack inf, inf_diag, inf_offdiag, susceptibilities, valid_freq_range = m
f_min = first(valid_freq_range)
f_max = last(valid_freq_range)
if !(f_min ≤ f ≤ f_max)
throw("frequency outside the model valid range $(f_min):$(f_max)")
end
val =
[
inf_diag[1] inf_offdiag[1] inf_offdiag[2]
inf_offdiag[1] inf_diag[2] inf_offdiag[3]
inf_offdiag[2] inf_offdiag[3] inf_diag[3]
]
for sus in susceptibilities
val += eval_susceptibility(sus, f)
end
return val
end
epsilon(model::DispersionModel, freq) = eval_dispersion_model(model, freq)
mu(model::DispersionModel, freq) = eval_dispersion_model(model, freq)
@recipe function plot(model::DispersionModel;
comp = 1,
transform_epsilon = real,
transform_freq = inv,
nfreq = 100,
xlabel = "λ[μm]",
ylabel = "Re[ϵᵣ]",
label = "ϵ_11"
)
xlabel --> xlabel
ylabel --> ylabel
label --> label
freq_range = model.valid_freq_range.range
fmin = first(freq_range)
fmax = last(freq_range)
freq_points = LinRange(fmin, fmax, nfreq)
ϵ_freqs = map(f -> eval_dispersion_model(model, f)[:], freq_points)
m = reduce(hcat, ϵ_freqs)'
transform_freq.(freq_points), transform_epsilon.(m[:, Int(comp)])
end
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17257,
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69,
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277,
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11,
299,
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80,
8,
628,
220,
220,
220,
18074,
113,
62,
19503,
48382,
796,
3975,
7,
69,
4613,
5418,
62,
6381,
79,
6900,
62,
19849,
7,
19849,
11,
277,
38381,
25,
4357,
2030,
80,
62,
13033,
8,
198,
220,
220,
220,
285,
796,
4646,
7,
71,
9246,
11,
18074,
113,
62,
19503,
48382,
33047,
198,
220,
220,
220,
6121,
62,
19503,
80,
12195,
19503,
80,
62,
13033,
828,
6121,
62,
538,
18217,
261,
12195,
76,
58,
45299,
2558,
7,
5589,
8,
12962,
198,
437,
628
] | 2.154321 | 810 |
using Chemostat_EColi
using Test
@testset "Chemostat_EColi.jl" begin
# Write your tests here.
end
| [
3500,
2580,
1712,
265,
62,
2943,
11106,
198,
3500,
6208,
198,
198,
31,
9288,
2617,
366,
7376,
1712,
265,
62,
2943,
11106,
13,
20362,
1,
2221,
198,
220,
220,
220,
1303,
19430,
534,
5254,
994,
13,
198,
437,
198
] | 2.641026 | 39 |
#
# Constraint interface and implementations
#
import Base: clamp,
show
using LinearAlgebra:
dot,
norm,
normalize,
logabsdet,
logdet
using StatsFuns:
logit,
logistic
"""
VariableConstraint{NC,NF}
Abstract type for transformations on constrained variables represented with
`NC`-dimensional vectors that produce `NF`-dimensional free (unconstrained)
vectors.
The supported interface is the 4 functions `constrain`, `free`,
`constrain_with_pushlogpdf` and `constrain_with_pushlogpdf_grad`.
To implement a new constraint, simply create a new type of `VariableConstraint`
and implement `constrain` and `free`. Various internal functions are used to
provide efficient and accurate defaults; these may be overriden for increased
efficiency when analytical gradients/jacobian determinants are known. The most
common override is `constrain_with_logpdf_correction`.
"""
abstract type VariableConstraint{NC,NF} end
"""
OneToOneConstraint{N}
Alias for dispatch on one-to-one constraints `f: ℝⁿ → ℝⁿ`.
"""
const OneToOneConstraint{N} = VariableConstraint{N,N}
"""
UnivariateConstraint
Alias for dispatch on univariate constraints `f: ℝ → ℝ`. Univariate
constraints can take scalar inputs and produce scalar outputs.
"""
const UnivariateConstraint = OneToOneConstraint{1}
###
### Basic interface for constraints
###
"""
free_dimension(c::VariableConstraint)
Get the number of dimensions (length) of the freed vector.
"""
free_dimension(::VariableConstraint{NC,NF}) where {NC,NF} = NF
"""
constrain_dimension(c::VariableConstraint)
Get the number of dimensions (length) of the constrained vector.
"""
constrain_dimension(::VariableConstraint{NC}) where {NC} = NC
"""
clamp(c::VariableConstraint, x)
Return `x`, ensuring that its value satisfies the constraint. This is useful
to avoid numerical instability when a value nears the boundary condition.
The clamp is invisible during differentiation.
"""
function clamp end
# Passthrough adjoints for clamp
Zygote.@adjoint clamp(c::VariableConstraint, x) = clamp(c, x), Δ -> (nothing, Δ)
"""
free(c::VariableConstraint, x)
From constrained variable `x`, construct free variable.
"""
function free end
Base.@propagate_inbounds function free(c::UnivariateConstraint,
x::AbstractVector)
return [free(c, x[1])]
end
"""
constrain(c::VariableConstraint, y)
From free variable `y`, construct constrained variable.
"""
function constrain end
Base.@propagate_inbounds function constrain(c::UnivariateConstraint,
y::AbstractVector)
return [constrain(c, y[1])]
end
"""
constrain_with_logpdf_correction(c::VariableConstraint, y)
From free variable `y = f(x)` get the constrained variable `x` and the
addtive correction to the log density `log π(x)` to get `log π(x)`. See
[`free_logpdf_correction`](@free_logpdf_correction).
"""
function constrain_with_logpdf_correction(c, y)
x = constrain(c, y)
logdetJ = free_logpdf_correction(c, y)
return x, logdetJ
end
Base.@propagate_inbounds function constrain_with_logpdf_correction(
c::UnivariateConstraint,
y::AbstractVector
)
x, logdetJ = constrain_with_logpdf_correction(c, y[1])
return [x], logdetJ
end
"""
constrain_with_pushlogpdf(c::VariableConstraint, y)
From free variable `y = f(x)` get the constrained variable `x` and a new
function that pushes forward the log density `log π(x)` with respect to `x` and
to the corresponding log density `log π(y)` with respect to `y`.
```julia
x, pushlogpdf = constrain_with_pushlogpdf(c, y)
logπx = ... # Compute density in constrained space
logπy = pushlogpdf(logπx)
```
"""
function constrain_with_pushlogpdf(c, y)
x, logdetJ = constrain_with_logpdf_correction(c, y)
return x, logπx -> logπx + logdetJ
end
Base.@propagate_inbounds function constrain_with_pushlogpdf(
c::UnivariateConstraint,
y::AbstractVector
)
x, pushlogpdf = constrain_with_pushlogpdf(c, y[1])
return [x], pushlogpdf
end
"""
constrain_with_pushlogpdf_grad(c::VariableConstraint, y)
From free variable `y = f(x)` get the constrained variable `x` and a new
function that pushes forward the log density `log π(x)` with respect to `x` and
its gradient to the corresponding log density `log π(y)` with respect to `y` and
its gradient.
```julia
x, pushlogpdf_grad = constrain_with_pushlogpdf_grad(c, y)
logπx, ∇x_logπx = ... # Compute density and its gradient in constrained space
logπy, ∇y_logπy = pushlogpdf_grad(logπx, ∇x_logπx)
```
"""
function constrain_with_pushlogpdf_grad(c, y)
(x, logdetJ), back = Zygote.pullback(constrain_with_logpdf_correction, c, y)
nf = free_dimension(c)
return x, function (logπx, ∇x_logπx)
s = Zygote.sensitivity(logdetJ)
logπy = logπx + logdetJ
T = eltype(logπy)
∇y_logπy = similar(∇x_logπx, T, nf)
copyto!(∇y_logπy, back((∇x_logπx, s))[2])
return logπy, ∇y_logπy
end
end
function constrain_with_pushlogpdf_grad(c::UnivariateConstraint, y::Real)
(x, logdetJ), back = Zygote.pullback(constrain_with_logpdf_correction, c, y)
return x, function (logπx, ∇x_logπx::Real)
s = Zygote.sensitivity(logdetJ)
logπy = logπx + logdetJ
∇y_logπy = back((∇x_logπx, s))[2]
return logπy, ∇y_logπy
end
end
Base.@propagate_inbounds function constrain_with_pushlogpdf_grad(
c::UnivariateConstraint,
y::AbstractVector
)
x, pushlogpdf_grad = constrain_with_pushlogpdf_grad(c, y[1])
return [x], function (logπx, ∇x_logπx::AbstractVector)
logπy, ∇y_logπy = pushlogpdf_grad(logπx, ∇x_logπx[1])
return logπy, [∇y_logπy]
end
end
"""
constrain_jacobian(c::VariableConstraint, y)
From free vector `y = f(x)`, compute the Jacobian matrix of the inverse
transformation `x = f⁻¹(y)` with entries `Jᵢⱼ = ∂xᵢ/∂yⱼ`.
"""
function constrain_jacobian(c, y)
nf = free_dimension(c)
# NOTE: work-around to make forward_jacobian type-inferrable
# see https://github.com/FluxML/Zygote.jl/issues/299
v = Val(min(nf, ForwardDiff.DEFAULT_CHUNK_THRESHOLD))
# NOTE: Zygote's (reverse-mode) Jacobians are adjoints
J′ = last(Zygote.forward_jacobian(y -> constrain(c, y), y, v))
return adjoint(J′)
end
# NOTE: Workaround until Zygote supports nesting Jacobians
# see https://github.com/FluxML/Zygote.jl/issues/305
Zygote.@adjoint function constrain_jacobian(c,
y::AbstractVector)
nf = free_dimension(c)
nc = constrain_dimension(c)
jac(y) = ForwardDiff.jacobian(y->constrain(c, y), y)
J = similar(y, (nc, nf))
diffres = DiffResults.JacobianResult(J, y)
diffres = ForwardDiff.jacobian!(diffres, jac, y)
J = DiffResults.value(diffres)
∇y_J = reshape(DiffResults.jacobian(diffres), (nc, nf, nf))
return J, function (J̄)
@einsum ȳ[k] := J̄[i,j] * ∇y_J[i,j,k]
return (nothing, ȳ)
end
end
"""
constrain_jacobian(c::UnivariateConstraint, y)
From free scalar `y = f(x)`, compute the derivative of the inverse
transformation `x = f⁻¹(y)`, `dx/dy`.
"""
function constrain_jacobian(c::UnivariateConstraint, y)
dx_dy = Zygote.gradient(constrain, c, y)[2]
return dx_dy
end
Base.@propagate_inbounds function constrain_jacobian(
c::UnivariateConstraint,
y::AbstractArray
)
return [constrain_jacobian(c, y[1])]
end
"""
halflogdetmul(x::AbstractMatrix)
For a matrix `x`, compute `½log(det (x' x))`.
"""
halflogdetmul(x) = logdet(x' * x) / 2
# custom adjoint for slight speed-up
Zygote.@adjoint function halflogdetmul(x::AbstractArray)
s = x' * x
return logdet(s) / 2, Δ -> (Δ * (x * inv(s)),) # `Δ * x⁺ᵀ`
end
"""
free_logpdf_correction(c::VariableConstraint, y)
From free vector `y`, compute correction to log pdf for transformation. Given
a transformation `f: x ↦ y`, its inverse `f⁻¹: y ↦ x`, and pdf `π(x)`, the log
pdf of the transformed density is
`log π(y) = log π(f⁻¹(y)) + ½log(det G)`,
where `det G` is the determinant of the matrix `G = Jᵀ J`, and `J` is the
Jacobian matrix of the inverse transformation with entries `Jᵢⱼ = ∂xᵢ/∂yⱼ`.
This result is known as the area formula.
This function returns `½log(det G)` for the general case of `f: ℝᵐ → ℝⁿ`.
"""
function free_logpdf_correction(c, y)
@assert free_dimension(c) <= constrain_dimension(c)
J = constrain_jacobian(c, y)
return halflogdetmul(J)
end
_logabsdet(x) = first(logabsdet(x))
_logabsdet(x::Real) = log(abs(x))
"""
free_logpdf_correction(c::OneToOneConstraint, y)
From free variable `y`, compute correction to log pdf for transformation. For
a one-to-one transformation with a square Jacobian, the correction simplifies
to `log |det J|`.
"""
function free_logpdf_correction(c::OneToOneConstraint, y::AbstractVector)
J = constrain_jacobian(c, y)
return first(logabsdet(J))
end
###
### Constraint implementations
###
"""
IdentityConstraint{N} <: OneToOneConstraint{N}
Do-nothing constraint on `ℝⁿ`, corresponding to the identity function on
`n`-dimensional variables. Included for convenient bundling of constrained
with unconstrained variables.
# Constructor
IdentityConstraint(n::Int)
"""
struct IdentityConstraint{N} <: OneToOneConstraint{N} end
IdentityConstraint(n) = IdentityConstraint{n}()
function Base.show(io::IO, mime::MIME"text/plain",
c::IdentityConstraint{N}) where {N}
print(io, "IdentityConstraint($N)")
end
clamp(::IdentityConstraint, x) = x
clamp(::IdentityConstraint, x::ForwardDiff.Dual) = x
free(::IdentityConstraint{1}, x::Real) = x
free(::IdentityConstraint, x::AbstractVector) = x
constrain(::IdentityConstraint{1}, y::Real) = y
constrain(::IdentityConstraint, y::AbstractVector) = y
free_logpdf_correction(::IdentityConstraint{1}, y::Real) = zero(eltype(y))
free_logpdf_correction(::IdentityConstraint, y::AbstractVector) = zero(eltype(y))
constrain_with_pushlogpdf(::IdentityConstraint{1}, y::Real) = y, identity
constrain_with_pushlogpdf(::IdentityConstraint, y::AbstractVector) = y, identity
function constrain_with_pushlogpdf_grad(::IdentityConstraint{1}, y::Real)
return y, (logπx, ∇x_logπx) -> (logπx, ∇x_logπx)
end
function constrain_with_pushlogpdf_grad(::IdentityConstraint, y::AbstractVector)
return y, (logπx, ∇x_logπx) -> (logπx, ∇x_logπx)
end
"""
LowerBoundedConstraint{T} <: UnivariateConstraint
Constraint on a scalar that is strictly greater than a lower bound.
# Constructor
LowerBoundedConstraint(lb)
"""
struct LowerBoundedConstraint{T} <: UnivariateConstraint
lb::T
end
function Base.show(io::IO, mime::MIME"text/plain", c::LowerBoundedConstraint)
print(io, "LowerBoundedConstraint($(c.lb))")
end
clamp(c::LowerBoundedConstraint, x::Real) = max(x, c.lb + eps(x))
clamp(::LowerBoundedConstraint, x::ForwardDiff.Dual) = x
free(c::LowerBoundedConstraint, x::Real) = log(clamp(c, x) - c.lb)
constrain(c::LowerBoundedConstraint, y::Real) = clamp(c, exp(y) + c.lb)
free_logpdf_correction(c::LowerBoundedConstraint, y::Real) = y
"""
UpperBoundedConstraint{T} <: UnivariateConstraint
Constraint on a scalar that is strictly less than an upper bound.
# Constructor
UpperBoundedConstraint(ub)
"""
struct UpperBoundedConstraint{T} <: UnivariateConstraint
ub::T
end
function Base.show(io::IO, mime::MIME"text/plain", c::UpperBoundedConstraint)
print(io, "UpperBoundedConstraint($(c.ub))")
end
clamp(c::UpperBoundedConstraint, x::Real) = min(x, c.ub - eps(x))
clamp(::UpperBoundedConstraint, x::ForwardDiff.Dual) = x
free(c::UpperBoundedConstraint, x::Real) = log(c.ub - clamp(c, x))
constrain(c::UpperBoundedConstraint, y::Real) = clamp(c, c.ub - exp(y))
free_logpdf_correction(c::UpperBoundedConstraint, y::Real) = y
"""
BoundedConstraint{TL,TU,TD} <: UnivariateConstraint
Constraint on a scalar that is has both an upper and lower bound.
# Constructor
BoundedConstraint(lb, ub)
"""
struct BoundedConstraint{TL,TU,TD} <: UnivariateConstraint
lb::TL
ub::TU
delta::TD
end
function Base.show(io::IO, mime::MIME"text/plain", c::BoundedConstraint)
print(io, "BoundedConstraint($(c.lb), $(c.ub))")
end
function BoundedConstraint(lb::Real, ub::Real)
@assert lb < ub
return BoundedConstraint(lb, ub, ub - lb)
end
clamp(c::BoundedConstraint, x::Real) = clamp(x, c.lb + eps(x), c.ub - eps(x))
clamp(::BoundedConstraint, x::ForwardDiff.Dual) = x
free(c::BoundedConstraint, x::Real) = logit((clamp(c, x) - c.lb) / c.delta)
constrain(c::BoundedConstraint, y::Real) = clamp(c, c.delta * logistic(y) + c.lb)
function constrain_with_logpdf_correction(c::BoundedConstraint, y::Real)
z = logistic(y)
delz = c.delta * z
x = delz + c.lb
dx_dy = delz * (1 - z)
return clamp(c, x), log(dx_dy)
end
"""
TransformConstraint(lb::Real, ub::Real)
Convenient constructor for lower-, upper-, lower- and upper-, and un-bounded
univariate constraints. The correct type is chosen based on the arguments.
"""
function TransformConstraint(lb=-Inf, ub=Inf)
@assert lb < ub
has_lb, has_ub = isfinite(lb), isfinite(ub)
if has_lb && has_ub
return BoundedConstraint(lb, ub)
elseif has_lb
return LowerBoundedConstraint(lb)
elseif has_ub
return UpperBoundedConstraint(ub)
else
return IdentityConstraint(1)
end
end
"""
UnitVectorConstraint{N} <: OneToOneConstraint{N}
Transformation from an `n`-dimensional unit-vector to an unconstrained
`n`-dimensional vector. Note that in this case the inverse transformation
(ℓ²-normalization) is not unique, and therefore the pushforward density cannot
be obtained using the usual Jacobian technique.
However, using the fact that a standard multivariate normally distributed
vector when normalized is uniformly distributed on a sphere, we can push
forward the uniform measure on the sphere by applying a standard multivariate
normal prior to `y`. The corresponding log density correction is `-½ yᵀ y = -½
|y|²`.
The Jacobian of the inverse transformation is a normalized projection matrix
onto the tangent space to the sphere at `x`: `J = Πₓ / |y|`, where
`Πₓ = I - xᵀ x`.
# Constructor
UnitVectorConstraint(n::Int)
"""
struct UnitVectorConstraint{N} <: OneToOneConstraint{N} end
UnitVectorConstraint(n) = UnitVectorConstraint{n}()
function Base.show(io::IO, mime::MIME"text/plain",
c::UnitVectorConstraint{N}) where {N}
print(io, "UnitVectorConstraint($N)")
end
clamp(::UnitVectorConstraint, x) = normalize(x)
clamp(::UnitVectorConstraint, x::AbstractArray{<:ForwardDiff.Dual}) = x
free(c::UnitVectorConstraint, x) = clamp(c, x)
function constrain(::UnitVectorConstraint, y)
x = y ./ norm(y)
return x
end
function normalize_with_norm(y)
ny = norm(y)
return y ./ ny, ny
end
Zygote.@adjoint function normalize_with_norm(y)
x, ny = normalize_with_norm(y)
return (x, ny), function (Δ)
x̄, n̄ȳ = Δ
return (x̄ ./ ny .- x .* (dot(x, x̄) / ny - n̄ȳ),)
end
end
function constrain_with_logpdf_correction(::UnitVectorConstraint, y)
x, ny = normalize_with_norm(y)
logdetJ = -ny^2 / 2
return x, logdetJ
end
"""
UnitVectorScaledConstraint{N} <: OneToOneConstraint{N}
Transformation from an `n`-dimensional unit-vector to an unconstrained
`n`-dimensional vector, scaled by a factor of ``r``.
``r`` is interpreted as a radius. This is intended to bring directional/quaternion
vectors to the same approximate initial scale as other variables.
# Constructor
UnitVectorScaledConstraint(n::Int, r = 1)
"""
struct UnitVectorScaledConstraint{N,T<:Real} <: OneToOneConstraint{N}
r::T
function UnitVectorScaledConstraint{N,T}(r::T) where {N,T}
isinteger(N) && N > 0 || throw(DomainError("N must be a positive integer."))
r > 0 || throw(DomainError("r must be strictly positive."))
return new{N,T}(r)
end
end
UnitVectorScaledConstraint(n, r = 1.0) = UnitVectorScaledConstraint{n,typeof(r)}(r)
function Base.show(io::IO, mime::MIME"text/plain",
c::UnitVectorScaledConstraint{N}) where {N}
print(io, "UnitVectorScaledConstraint($N, c.r)")
end
clamp(::UnitVectorScaledConstraint, x) = normalize(x)
clamp(::UnitVectorScaledConstraint, x::AbstractArray{<:ForwardDiff.Dual}) = x
free(c::UnitVectorScaledConstraint, x) = normalize(x) .* c.r
function constrain(::UnitVectorScaledConstraint, y)
x = y ./ norm(y)
return x
end
function constrain_with_logpdf_correction(c::UnitVectorScaledConstraint, y)
x, ny = normalize_with_norm(y)
logdetJ = -(ny / c.r)^2 / 2
return x, logdetJ
end
"""
UnitSimplexConstraint{N,M} <: VariableConstraint{N,M}
Transformation from an `n`-dimensional vector of positive reals with a unit
ℓ¹-norm to an unconstrained `m = n - 1`-dimensional vector.
This constraint uses the stick-breaking process to define the transformation.
See https://en.wikipedia.org/wiki/Dirichlet_process#The_stick-breaking_process
for more details.
# Constructor
UnitSimplexConstraint(n::Int)
"""
struct UnitSimplexConstraint{N,M} <: VariableConstraint{N,M} end
UnitSimplexConstraint(n::Int) = UnitSimplexConstraint{n,n-1}()
function Base.show(io::IO, mime::MIME"text/plain",
c::UnitSimplexConstraint{N}) where {N}
print(io, "UnitSimplexConstraint($N)")
end
"""
stick_ratio(x, Σx)
Break a piece of length `x` off of a unit-length stick off of which pieces of
total length `Σx` have already been broken. Return the ratio of the length `x`
to the remaining length of the stick.
"""
stick_ratio(x, Σx) = x / (1 - Σx)
"""
stick_length(r, Σx)
Inverse of `stick_ratio`.
"""
stick_length(r, Σx) = r * (1 - Σx)
"""
constrain_stick_ratio(k, y, K)
Constrain the free variable `y` to the `k`th of `K` stick ratios.
"""
constrain_stick_ratio(k, y, K) = logistic(y - log(K - k))
"""
free_stick_ratio(k, r, K)
Free the `k`th stick ratio `r` of `K` stick ratios to an unconstrained variable.
"""
free_stick_ratio(k, r, K) = logit(r) + log(K - k)
function clamp(::UnitSimplexConstraint, x)
ϵ = eps(eltype(x))
return normalize(clamp.(x, ϵ, 1 - ϵ), 1)
end
clamp(::UnitSimplexConstraint, x::AbstractArray{<:ForwardDiff.Dual}) = x
function free(c::UnitSimplexConstraint, x)
K = constrain_dimension(c)
@assert length(x) == K
x = clamp(c, x)
T = eltype(x)
y = similar(x, K - 1)
Σx = zero(T)
@inbounds begin
y[1] = free_stick_ratio(1, x[1], K)
@simd for k = 2:(K - 1)
Σx += x[k - 1]
zₖ = stick_ratio(x[k], Σx)
y[k] = free_stick_ratio(k, zₖ, K)
end
end
return y
end
function constrain(c::UnitSimplexConstraint, y)
K = constrain_dimension(c)
@assert length(y) == free_dimension(c)
x = Zygote.Buffer(y, K)
Σx = zero(eltype(y))
@inbounds begin
for k = 1:(K - 1)
zₖ = constrain_stick_ratio(k, y[k], K)
xₖ = stick_length(zₖ, Σx)
x[k] = xₖ
Σx += xₖ
end
x[K] = 1 - Σx
end
return clamp(c, copy(x))
end
function constrain_with_logpdf_correction(c::UnitSimplexConstraint, y)
K = constrain_dimension(c)
@assert length(y) == free_dimension(c)
T = eltype(y)
x = Zygote.Buffer(y, K)
Σx = zero(eltype(y))
logdetJ = log(K) / 2
@inbounds begin
for k = 1:(K - 1)
zₖ = constrain_stick_ratio(k, y[k], K)
xₖ = stick_length(zₖ, Σx)
x[k] = xₖ
logdetJ += log(xₖ * (1 - zₖ))
Σx += xₖ
end
x[K] = 1 - Σx
end
return clamp(c, copy(x)), logdetJ
end
function constrain_with_pushlogpdf_grad(
c::UnitSimplexConstraint,
y::SubArray
)
return constrain_with_pushlogpdf_grad(c, collect(y))
end
"""
JointConstraint{TC,RC,RF,NC,NF} <: VariableConstraint{NC,NF}
Joint transformation on a series of constraints. This constraint type
conveniently binds together a series of transformations on non-overlapping
subarrays of a longer parameter array.
# Constructor
JointConstraint(constraints::VariableConstraint...)
"""
struct JointConstraint{TC,CR,CF,NC,NF} <: VariableConstraint{NC,NF}
constraints::TC
cranges::CR
franges::CF
function JointConstraint(constraints...)
cs = merge_constraints(constraints...)
ncs = map(constrain_dimension, cs)
nc = sum(ncs)
cranges = _ranges_from_lengths(ncs)
nfs = map(free_dimension, cs)
nf = sum(nfs)
franges = _ranges_from_lengths(nfs)
return new{typeof(cs),typeof(cranges),typeof(franges),nc,nf}(cs, cranges, franges)
end
end
JointConstraint(c) = c
"""
merge_constraints(cs::VariableConstraint...)
Merge adjacent constraints that can be merged. For example,
`IdentityConstraint(3)` and `IdentityConstraint(2)` can be merged into a single
`IdentityConstraint(5)`.
"""
merge_constraints(c1, c2, cs...) = reduce(merge_constraints, (c1, c2, cs...))
@inline merge_constraints(c1) = (c1,)
@inline merge_constraints(c1, c2) = (c1, c2)
@inline function merge_constraints(c1, c2::Tuple)
return (merge_constraints(c1, c2[1])..., Base.tail(c2)...)
end
@inline function merge_constraints(c1::Tuple, c2)
return (Base.front(c1)..., merge_constraints(c1[end], c2)...)
end
@inline function merge_constraints(c1::Tuple, c2::Tuple)
return (Base.front(c1)...,
merge_constraints(c1[end], c2[1])...,
Base.tail(c2)...)
end
@inline function merge_constraints(::IdentityConstraint{M},
::IdentityConstraint{N}) where {M,N}
return (IdentityConstraint(M + N),)
end
function _ranges_from_lengths(lengths)
ranges = UnitRange{Int}[]
k = 1
for len in lengths
push!(ranges, k:(k + len - 1))
k += len
end
return tuple(ranges...)
end
function Base.show(io::IO, mime::MIME"text/plain", jc::JointConstraint)
print(io, "JointConstraint(")
nconstraints = length(jc.constraints)
if nconstraints > 0
print(io, "$(repr(mime, jc.constraints[1]))")
if nconstraints > 10
print(io, ", $(repr(mime, jc.constraints[2]))")
print(io, ", ...")
print(io, ", $(repr(mime, jc.constraints[end-1]))")
print(io, ", $(repr(mime, jc.constraints[end]))")
else
for i in 2:nconstraints
print(io, ", $(repr(mime, jc.constraints[i]))")
end
end
end
print(io, ")")
end
function _parallel_free(cs, cranges, franges, nf, x)
y = similar(x, nf)
@simd for i in 1:length(cs)
@inbounds begin
xᵢ = view(x, cranges[i])
setindex!(y, free(cs[i], xᵢ), franges[i])
end
end
return y
end
function free(jc::JointConstraint, x)
@assert length(x) == constrain_dimension(jc)
return _parallel_free(
jc.constraints,
jc.cranges,
jc.franges,
free_dimension(jc),
x
)
end
function _parallel_constrain(cs, cranges, franges, nc, y)
x = similar(y, nc)
@simd for i in 1:length(cs)
@inbounds begin
yᵢ = view(y, franges[i])
setindex!(x, constrain(cs[i], yᵢ), cranges[i])
end
end
return x
end
function constrain(jc::JointConstraint, y)
@assert length(y) == free_dimension(jc)
return _parallel_constrain(
jc.constraints,
jc.cranges,
jc.franges,
constrain_dimension(jc),
y
)
end
function _parallel_constrain_with_logpdf_correction(cs, cranges, franges, nc, nf, y)
x = similar(y, nc)
TL = eltype(y)
cinds = 1:length(cs)
logdetJ = zero(TL)
@simd for i in cinds
@inbounds begin
yᵢ = view(y, franges[i])
xᵢ, logdetJᵢ = constrain_with_logpdf_correction(cs[i], yᵢ)
setindex!(x, xᵢ, cranges[i])
end
logdetJ += logdetJᵢ
end
return x, logdetJ
end
Zygote.@adjoint function _parallel_constrain_with_logpdf_correction(cs, cranges, franges, nc, nf, y)
x = similar(y, nc)
TL = eltype(y)
cinds = 1:length(cs)
backs = []
logdetJ = zero(TL)
@simd for i in cinds
@inbounds begin
yᵢ = view(y, franges[i])
(xᵢ, logdetJᵢ), backᵢ = (
Zygote.pullback(constrain_with_logpdf_correction, cs[i], yᵢ)
)
setindex!(x, xᵢ, cranges[i])
end
push!(backs, backᵢ)
logdetJ += logdetJᵢ
end
return (x, logdetJ), function (Δ)
x̄, logdetJ̄ = Δ
TA = Base.promote_eltypeof(x̄, logdetJ̄)
ȳ = similar(x̄, TA, nf)
@simd for i in cinds
@inbounds begin
x̄ᵢ = view(x̄, cranges[i])
backᵢ = backs[i]
ȳᵢ = backᵢ((x̄ᵢ, logdetJ̄))[2]
setindex!(ȳ, ȳᵢ, franges[i])
end
end
return (nothing, nothing, nothing, nothing, nothing, ȳ)
end
end
function constrain_with_logpdf_correction(jc::JointConstraint, y)
nf = free_dimension(jc)
@assert length(y) == nf
return _parallel_constrain_with_logpdf_correction(
jc.constraints,
jc.cranges,
jc.franges,
constrain_dimension(jc),
nf,
y
)
end
function _parallel_constrain_with_pushlogpdf(cs, cranges, franges, nc, nf, y)
x = similar(y, nc)
pushes = []
cinds = 1:length(cs)
logdetJs = []
@simd for i in cinds
@inbounds begin
yᵢ = view(y, franges[i])
xᵢ, pushᵢ = constrain_with_pushlogpdf(cs[i], yᵢ)::Tuple
setindex!(x, xᵢ, cranges[i])
end
push!(pushes, pushᵢ)
end
y1 = @inbounds y[1]
function pushlogpdf(logπx)
TL = Base.promote_eltypeof(y1, logπx)
logdetJ = zero(TL)
@simd for i in cinds
@inbounds pushᵢ = pushes[i]
logdetJᵢ = pushᵢ(zero(TL))
logdetJ += logdetJᵢ
end
logπy::TL = logπx + logdetJ
return logπy
end
return x, pushlogpdf
end
function constrain_with_pushlogpdf(jc::JointConstraint, y)
@assert length(y) == free_dimension(jc)
return _parallel_constrain_with_pushlogpdf(
jc.constraints,
jc.cranges,
jc.franges,
constrain_dimension(jc),
free_dimension(jc),
y
)
end
function _parallel_constrain_with_pushlogpdf_grad(cs, cranges, franges, nc, nf, y)
x = similar(y, nc)
pushes = []
cinds = 1:length(cs)
@simd for i in cinds
@inbounds begin
yᵢ = view(y, franges[i])
xᵢ, pushᵢ = constrain_with_pushlogpdf_grad(cs[i], yᵢ)::Tuple
setindex!(x, xᵢ, cranges[i])
end
push!(pushes, pushᵢ)
end
y1 = @inbounds y[1]
function pushlogpdf_grad(logπx, ∇x_logπx)
TL = Base.promote_eltypeof(y1, logπx, ∇x_logπx)
∇y_logπy = similar(∇x_logπx, nf)
logdetJ = zero(TL)
@simd for i in cinds
@inbounds begin
∇x_logπxᵢ = view(∇x_logπx, cranges[i])
pushᵢ = pushes[i]
logdetJᵢ, ∇y_logπyᵢ = pushᵢ(zero(logπx), ∇x_logπxᵢ)::Tuple
setindex!(∇y_logπy, ∇y_logπyᵢ, franges[i])
end
logdetJ += logdetJᵢ
end
logπy::TL = logπx + logdetJ
return logπy, ∇y_logπy
end
return x, pushlogpdf_grad
end
function constrain_with_pushlogpdf_grad(jc::JointConstraint, y)
@assert length(y) == free_dimension(jc)
return _parallel_constrain_with_pushlogpdf_grad(
jc.constraints,
jc.cranges,
jc.franges,
constrain_dimension(jc),
free_dimension(jc),
y
)
end
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] | 2.239881 | 12,402 |
<filename>train.jl
ENV["COLUMNS"]=72
import Base.GC.gc
using DSP.Periodograms: stft, nextfastfft
using DSP.Windows: hanning
using HDF5
using Base.Iterators: flatten, cycle, take, partition
using Statistics: mean, std
using Random: shuffle!
using Knet
using Knet: Data, KnetArray
using LinearAlgebra: diag
function epsnormalise(x::AbstractArray{T}; dims=1, ϵ=eps(T)) where T
μ = mean(x, dims = dims)
σ = std(x, dims = dims, mean = μ, corrected=false)
σ = map((x) -> abs(x) < ϵ ? ϵ : x, σ)
return (x .- μ) ./ σ
end
function make_batch(data, labels, idxs, minibatch_size, cat_mapping; divider = 1, n = 512, noverlap = div(n, 2))
nfft = nextfastfft(n)
sz = div(div(size(data, 1), noverlap), divider) - 1
X_batch = Array{Float32}(undef, div(nfft, 2) + 1, sz, 1, length(idxs))
Y_batch = Array{UInt8}(undef, length(idxs))
win = hanning(nfft)
data_ind = 1:div(size(data, 1), divider)
for i in 1:length(idxs)
st = abs.(stft(Float32.(data[data_ind,idxs[i]][:]./typemax(Int16)), n, noverlap, window=win, nfft=nfft))
X_batch[:, :, 1, i] = epsnormalise(20*log10.(max.(st, eps(Float32))))
Y_batch[i] = cat_mapping[labels[idxs[i]][1]]
end
return minibatch(convert(KnetArray, X_batch), Y_batch, minibatch_size)
end
function confusion(model, data, no_categories, no_labels=no_categories)
conf = zeros(no_categories, no_labels)
sz = 0
for mbatch in data
pred = convert(Array, model(mbatch[1]))
for ind = 1:length(mbatch[2])
conf[findmax(pred[:, ind])[2], mbatch[2][ind]] += 1
end
end
return conf
end
function squish_confusion(conf_ex, cat_mapping)
vals = sort(collect(values(cat_mapping)))
no_categories = length(unique(vals))
conf = zeros(no_categories, no_categories)
for i = 1:length(vals)
conf[:, vals[i]] += conf_ex[:, i]
end
conf
end
function get_idxs(size, batch_size, cat_mapping, start_idx=1)
idxs = Vector{Vector{Int}}()
no_categories = length(unique(values(cat_mapping)))
no_labels = length(cat_mapping)
cat_lengths = zeros(no_categories)
for val in values(cat_mapping)
cat_lengths[val] += 1
end
cat_idxs = zeros(no_categories)
batch_i = 1
lab_i = start_idx
push!(idxs, Vector{UInt}())
while batch_i <= size/batch_size
for i = 1:length(cat_lengths)
push!(idxs[batch_i], lab_i + sum(cat_lengths[1:(i-1)]) + cat_idxs[i])
cat_idxs[i] = mod(cat_idxs[i] + 1, cat_lengths[i])
if length(idxs[batch_i]) == batch_size
batch_i += 1
if batch_i > size/batch_size
break
else
push!(idxs, Vector{UInt}())
end
end
end
lab_i += no_labels
end
idxs
end
cat_mapping =
Dict{Int8, UInt8}(0 => 1, # WAV
11 => 2, # MP3 320
12 => 3, # MP3 192
13 => 4, # MP3 128
21 => 5, # AAC 320
22 => 6, # AAC 192
23 => 7, # AAC 128
31 => 8, # OGG 320
32 => 9, # OGG 192
33 => 10, # OGG 128
41 => 11, # WMA 320
42 => 12, # WMA 192
43 => 13, # WMA 128
51 => 14, # AC3 320
52 => 15, # AC3 192
53 => 16, # AC3 128
)
minibatch_size = 2
train_batch_size = 16
test_batch_size = 512
train_size = 5120
test_size = 2560
all_mapping =
Dict{Int8, UInt8}([(key, i) for (i, key) in enumerate(sort(collect(keys(cat_mapping))))])
no_categories = length(unique(values(cat_mapping)))
no_labels = length(cat_mapping)
train_batch_idxs = get_idxs(train_size, train_batch_size, cat_mapping)
test_batch_idxs = get_idxs(test_size, test_batch_size, cat_mapping, ceil(Int, train_batch_idxs[end][end]/no_labels)*no_labels + 1)
struct Conv; w; b; f; p; end
(c::Conv)(x) = c.f.(pool(conv4(c.w, dropout(x,c.p)) .+ c.b))
Conv(w1::Int,w2::Int,cx::Int,cy::Int,f=relu;pdrop=0) = Conv(param(w1,w2,cx,cy), param0(1,1,cy,1), f, pdrop)
struct Dense; w; b; f; p; end
(d::Dense)(x) = d.f.(d.w * mat(dropout(x,d.p)) .+ d.b) # mat reshapes 4-D tensor to 2-D matrix so we can use matmul
Dense(i::Int,o::Int,f=relu;pdrop=0) = Dense(param(o,i), param0(o), f, pdrop)
struct Chain; layers; Chain(layers...) = new(layers); end
(c::Chain)(x) = (for l in c.layers; x = l(x); end; x)
(c::Chain)(x,y) = nll(c(x),y)
(c::Chain)(d::Data) = mean(c(x,y) for (x,y) in d)
function train!(epochs, load=true, filename="model.jld2", train=true, test=true)
gc()
Knet.gc()
model, test_confs = if load
lf = Knet.load(filename)
lf["model"], lf["test_confs"]
else
Chain(Conv(3, 3, 1, 16),
Conv(3, 3, 16, 16),
Conv(3, 3, 16, 16),
Dense(102240, 300, pdrop=0.3),
Dense(300, 300, pdrop=0.2),
Dense(300, no_categories, identity, pdrop=0.2)),
Array{Array{Float32, 2}, 1}()
end
best_acc = length(test_confs) > 0 ? sum(diag(test_confs[end]))/test_size : 0
h5open("./dataset.h5") do file
data = file["data"]
labels = file["labels"]
for epoch in 1:epochs
@info "Epoch: $epoch"
shuffle!(train_batch_idxs)
if train
for idx in progress(train_batch_idxs)
let dtrn = make_batch(data, labels, idx, minibatch_size, cat_mapping)
for a in adam(model, dtrn); end
end
end
end
if test
test_conf = zeros(no_categories, no_labels)
for idx in progress(test_batch_idxs)
let dtst = make_batch(data, labels, idx, minibatch_size, all_mapping)
test_conf .+= confusion(model, dtst, no_categories, no_labels)
end
end
push!(test_confs, test_conf)
acc = sum(diag(squish_confusion(test_conf, cat_mapping)))/test_size
println("Confusion Matrix:")
display(test_conf)
println("Accuracy: $acc")
else
acc = best_acc + 1
end
if train
if acc > best_acc
best_acc = acc
Knet.save(filename, "model", model, "test_confs", test_confs)
end
end
end
end
return model, test_confs
end
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<gh_stars>0
struct OperationModelResults
variables::Dict{Symbol, DataFrames.DataFrame}
total_cost::Dict{Symbol, Any}
optimizer_log::Dict{Symbol, Any}
time_stamp::DataFrames.DataFrame
end
function get_variable(res_model::OperationModelResults, key::Symbol)
try
!isnothing(res_model.variables)
catch
error("No variable with key $(key) has been found.")
end
return get(res_model.variables, key, nothing)
end
function get_optimizer_log(res_model::OperationModelResults)
return res_model.optimizer_log
end
function get_time_stamp(res_model::OperationModelResults, key::Symbol)
return res_model.time_stamp
end
"""
results = load_operation_results(path, folder_name)
This function can be used to load results from a folder
of results from a single-step problem, or for a single foulder
within a simulation.
# Arguments
-`path::AbstractString = folder path`
-`directory::AbstractString = "2019-10-03T09-18-00"`: the foulder name that contains
feather files of the results.
# Example
```julia
results = load_operation_results("/Users/test/", "2019-10-03T09-18-00")
```
"""
function load_operation_results(path::AbstractString, directory::AbstractString)
if isfile(path)
path = dirname(path)
end
folder_path = joinpath(path, directory)
files_in_folder = collect(readdir(folder_path))
variables = setdiff(files_in_folder, ["time_stamp.feather", "optimizer_log.feather"])
variable_dict = Dict{Symbol, DataFrames.DataFrame}()
for i in 1:length(variables)
variable = variables[i]
variable_name = split("$variable", ".feather")[1]
file_path = joinpath(folder_path,"$variable_name.feather")
variable_dict[Symbol(variable_name)] = Feather.read("$file_path") #change key to variable
end
file_path = joinpath(folder_path,"optimizer_log.feather")
optimizer = Dict{Symbol, Any}(eachcol(Feather.read("$file_path"),true))
file_path = joinpath(folder_path,"time_stamp.feather")
temp_time_stamp = Feather.read("$file_path")
time_stamp = temp_time_stamp[1:(size(temp_time_stamp,1)-1),:]
obj_value = Dict{Symbol, Any}(:OBJECTIVE_FUNCTION => optimizer[:obj_value])
results = OperationModelResults(variable_dict, obj_value, optimizer, time_stamp)
return results
end
# This function returns the length of time_stamp for each step
# that is unique to that step and not overlapping with the next.
function _count_time_overlap(stage::String,
variable::Array,
references::Dict{Any,Any})
date_df = references[stage][variable[1]]
ref = DataFrames.DataFrame()
for (ix,time) in enumerate(date_df.Date)
try
file_path = date_df[ix, :File_Path]
time_file_path = joinpath(dirname(file_path), "time_stamp.feather")
temp_time_stamp = DataFrames.DataFrame(Feather.read("$time_file_path"))
t = size(temp_time_stamp, 1)
append!(ref,temp_time_stamp[(1:t-1),:])
catch
@warn "The given date_range is outside the results time stamp."
end
end
if size(unique(ref),2) == size(ref,2)
return 0.0
else
extra_time_length = size(unique(ref),1)./(length(step)+1)
return extra_time_length
end
end
"""
results = load_simulation_results(stage, step, date_range,variable,references)
This function goes through the reference table of file paths and
aggregates the results over time into a struct of type OperationModelResults
**Note:** the array of steps should match the date range provided.
# Arguments
-`stage::String = "stage-1"``: The stage of the results getting parsed, stage-1 or stage-2
-`step::Array{String} = ["step-1", "step-2", "step-3"]`: the steps of the results getting parsed
-`date_range::StepRange = 2020/01/01T:00:00:00 : 2020/01/03:00:00:00`: the date range to be parsed
-`variable::Array{Symbol} = [:P_ThermalStandard, :P_RenewableDispatch]`: the variables to be parsed
# Example
```julia
date_range = (Dates.DateTime(2020, April, 4):Dates.Hour(24):Dates.DateTime(2020, April, 6))
stage = "stage-1"
step = ["step-1","step-2", "step-3"] # has to match the date range
variable = [:P_ThermalStandard, :P_RenewableDispatch]
results = load_simulation_results(stage,step, date_range, variable, references)
```
"""
function load_simulation_results(stage::String,
references::Dict{Any,Any};
kwargs...)
variable_dict = Dict()
variable = collect(keys(references[stage]))
time_stamp = DataFrames.DataFrame(Range = Dates.DateTime[])
extra_time_length = _count_time_overlap(stage,
variable,
references)
for l in 1:length(variable)
date_df = references[stage][variable[l]]
variable_dict[(variable[l])] = DataFrames.DataFrame()
for (ix,time) in enumerate(date_df.Date)
file_path = date_df[ix, :File_Path]
var = Feather.read("$file_path")
correct_var_length = size(1:(size(var,1) - extra_time_length),1)
variable_dict[(variable[l])] = vcat(variable_dict[(variable[l])],var[1:correct_var_length,:])
if l == 1
time_file_path = joinpath(dirname(file_path), "time_stamp.feather")
temp_time_stamp = DataFrames.DataFrame(Feather.read("$time_file_path"))
non_overlap = size((1:size(temp_time_stamp, 1) - extra_time_length - 1),1)
time_stamp = vcat(time_stamp,temp_time_stamp[(1:non_overlap),:])
end
end
end
# first_file = references[stage][variable[1]]
# file_path = first_file[first_file.Date .== date_range[1], :File_Path][1]
# opt_file_path = joinpath(dirname(file_path),"optimizer_log.feather")
optimizer = Dict{Symbol, Any}()
obj_value = Dict{Symbol, Any}(:OBJECTIVE_FUNCTION => 0.0)
results = OperationModelResults(variable_dict, obj_value, optimizer, time_stamp)
file_path = references[stage][variable[1]][1,:File_Path]
if (:write in keys(kwargs)) == true
write_model_results(results, dirname(dirname(dirname(dirname(file_path)))),"results")
end
return results
end
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437,
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] | 2.34031 | 2,771 |
#!/usr/bin/env julia
using ArgParse
import SpikeSorter
#set up arguments
s = ArgParseSettings()
@add_arg_table s begin
"--session"
help = "The name of the session to be checked"
"--channel"
help = "The channel to be processed"
arg_type = Int
"--chunk"
help = "The chunk for process"
arg_type = Int
"--dir"
help = "Directory to run from"
"files"
action = :store_arg
nargs = '*'
arg_type = String
help = "Files to be processed"
end
D = parse_args(ARGS,s)
if isempty(D["files"])
ss = D["session"]
if !isempty(D["channel"])
ch = @sprintf "%04d" D["channel"]
else
ch = "*"
end
if !isempty(D["chunk"])
chunk = @sprintf "%04d" D["chunk"]
else
chunk = "*"
end
cwd = get(D, "dir", ".")
_files = readchomp(`find $cwd -name "$(ss)_templatesg$(ch).$(chunk).hdf5" -depth 1`)
if isempty(_files)
exit(1)
end
files = split(_files, "\n")
else
#files = convert(Array{ASCIIString,1},D["files"])
files = D["files"]
end
gfiles = filter(SpikeSorter.is_valid_template_file,files)
if isempty(gfiles)
exit(1)
end
println(join(gfiles,"\n"))
exit(0)
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] | 2.304069 | 467 |
Vcheckbounds(A::Operator,kr::Colon) = nothing
checkbounds(A::Operator,kr) =
(maximum(kr) > length(A) || minimum(kr) < 1) && throw(BoundsError(A,kr))
checkbounds(A::Operator,kr::Union{Colon,InfRanges},jr::Union{Colon,InfRanges}) = nothing
checkbounds(A::Operator,kr::Union{Colon,InfRanges},jr) =
(maximum(jr) > size(A,2) || minimum(jr) < 1) && throw(BoundsError(A,(kr,jr)))
checkbounds(A::Operator,kr,jr::Union{Colon,InfRanges}) =
(maximum(kr) > size(A,1) || minimum(kr) < 1 ) && throw(BoundsError(A,(kr,jr)))
checkbounds(A::Operator,kr,jr) =
(!isempty(kr) && (maximum(kr) > size(A,1) || minimum(kr) < 1)) ||
(!isempty(jr) && (maximum(jr) > size(A,2) || minimum(jr) < 1)) &&
throw(BoundsError(A,(kr,jr)))
checkbounds(A::Operator,K::Block,J::Block) =
1 ≤ first(K.n[1]) ≤ length(blocklengths(rangespace(A))) &&
1 ≤ first(J.n[1]) ≤ length(blocklengths(domainspace(A)))
checkbounds(A::Operator,K::BlockRange{1},J::BlockRange{1}) =
isempty(K) || isempty(J) ||
checkbounds(A, Block(maximum(K.indices[1])), Block(maximum(J.indices[1])))
## SubOperator
struct SubOperator{T,B,I,DI,BI} <: Operator{T}
parent::B
indexes::I
dims::DI
bandwidths::BI
end
function SubOperator(A,inds,dims,lu)
checkbounds(A,inds...)
SubOperator{eltype(A),typeof(A),typeof(inds),
typeof(dims),typeof(lu)}(A,inds,dims,lu)
end
# work around strange bug with bool size
SubOperator(A,inds,dims::Tuple{Bool,Bool},lu) = SubOperator(A,inds,Int.(dims),lu)
function SubOperator(A,inds::Tuple{Block,Block},lu)
checkbounds(A,inds...)
SubOperator(A,inds,(blocklengths(rangespace(A))[inds[1].n[1]],blocklengths(domainspace(A))[inds[2].n[1]]),lu)
end
SubOperator(A, inds::Tuple{Block,Block}) = SubOperator(A,inds,subblockbandwidths(A))
function SubOperator(A, inds::Tuple{BlockRange{1,R},BlockRange{1,R}}) where R
checkbounds(A,inds...)
dims = (sum(blocklengths(rangespace(A))[inds[1].indices[1]]),
sum(blocklengths(domainspace(A))[inds[2].indices[1]]))
SubOperator(A,inds,dims,(dims[1]-1,dims[2]-1))
end
# cannot infer ranges
SubOperator(A,inds,dims) = SubOperator(A,inds,dims,(dims[1]-1,dims[2]-1))
SubOperator(A,inds) = SubOperator(A,inds,map(length,inds))
convert(::Type{Operator{T}},SO::SubOperator) where {T} =
SubOperator(Operator{T}(SO.parent),SO.indexes,SO.dims,SO.bandwidths)::Operator{T}
function view(A::Operator,kr::InfRanges,jr::InfRanges)
@assert isinf(size(A,1)) && isinf(size(A,2))
st=step(kr)
if isbanded(A) && st==step(jr) # Otherwise, its not a banded operator
kr1=first(kr)
jr1=first(jr)
l,u=(bandwidth(A,1)+jr1-kr1)÷st,(bandwidth(A,2)+kr1-jr1)÷st
else
l,u=∞,∞
end
SubOperator(A,(kr,jr),size(A),(l,u))
end
view(V::SubOperator, kr::AbstractRange, jr::AbstractRange) =
view(V.parent,reindex(V,parentindices(V),(kr,jr))...)
function view(A::Operator, kr::AbstractRange, jr::AbstractRange)
st=step(kr)
if isbanded(A) && st == step(jr)
kr1=first(kr)
jr1=first(jr)
l,u=(bandwidth(A,1)+jr1-kr1)÷st,(bandwidth(A,2)+kr1-jr1)÷st
SubOperator(A,(kr,jr),(length(kr),length(jr)),(l,u))
else
SubOperator(A,(kr,jr))
end
end
function view(A::Operator,kr::UnitRange,jr::UnitRange)
if isbanded(A)
shft=first(kr)-first(jr)
l,u=bandwidth(A,1)-shft,bandwidth(A,2)+shft
SubOperator(A,(kr,jr),(length(kr),length(jr)),(l,u))
else
SubOperator(A,(kr,jr))
end
end
view(A::Operator,::Colon,::Colon) = view(A,1:size(A,1),1:size(A,2))
view(A::Operator,::Colon,jr) = view(A,1:size(A,1),jr)
view(A::Operator,kr,::Colon) = view(A,kr,1:size(A,2))
view(A::Operator,K::Block,J::Block) = SubOperator(A,(K,J))
view(A::Operator,K::Block,j::Colon) = view(A,blockrows(A,K),j)
view(A::Operator,k::Colon,J::Block) = view(A,k,blockcols(A,J))
view(A::Operator, K::Block, j) = view(A,blockrows(A,Int(K)),j)
view(A::Operator, k, J::Block) = view(A,k,blockcols(A,Int(J))) #TODO: fix view
view(A::Operator,KR::BlockRange,JR::BlockRange) = SubOperator(A,(KR,JR))
view(A::Operator,k,j) = SubOperator(A,(k,j))
defaultgetindex(B::Operator,k::InfRanges, j::InfRanges) = view(B, k, j)
defaultgetindex(B::Operator,k::AbstractRange, j::InfRanges) = view(B, k, j)
defaultgetindex(B::Operator,k::InfRanges, j::AbstractRange) = view(B, k, j)
reindex(A::Operator, B::Tuple{Block,Any}, kj::Tuple{Any,Any}) =
(reindex(rangespace(A),(B[1],), (kj[1],))[1], reindex(domainspace(A),tail(B), tail(kj))[1])
# always reindex left-to-right, so if we have only a single tuple, then
# we must be the domainspace
reindex(A::Operator, B::Tuple{Block{1}}, kj::Tuple{Any}) = reindex(domainspace(A),B,kj)
reindex(A::Operator, B::Tuple{BlockRange1,Any}, kj::Tuple{Any,Any}) =
(reindex(rangespace(A),(B[1],), (kj[1],))[1], reindex(domainspace(A),tail(B), tail(kj))[1])
# always reindex left-to-right, so if we have only a single tuple, then
# we must be the domainspace
reindex(A::Operator, B::Tuple{BlockRange1}, kj::Tuple{Any}) =
reindex(domainspace(A),B,kj)
# Blocks are preserved under ranges
for TYP in (:Block,:BlockRange1,:(AbstractVector{Block{1}}))
@eval begin
reindex(A::Operator, B::Tuple{AbstractVector{Int},Any}, kj::Tuple{$TYP,Any}) =
(reindex(rangespace(A), (B[1],), (kj[1],))[1], reindex(domainspace(A),tail(B), tail(kj))[1])
reindex(A::Operator, B::Tuple{AbstractVector{Int}}, kj::Tuple{$TYP}) =
reindex(domainspace(A),B,kj)
end
end
view(V::SubOperator,kr::UnitRange,jr::UnitRange) = view(V.parent,reindex(V,parentindices(V),(kr,jr))...)
view(V::SubOperator,K::Block,J::Block) = view(V.parent,reindex(V,parentindices(V),(K,J))...)
view(V::SubOperator,KR::BlockRange,JR::BlockRange) = view(V.parent, reindex(V,parentindices(V),(KR,JR))...)
function view(V::SubOperator,::Type{FiniteRange},jr::AbstractVector{Int})
cs = (isbanded(V) || isblockbandedbelow(V)) ? colstop(V,maximum(jr)) : mapreduce(j->colstop(V,j),max,jr)
view(V,1:cs,jr)
end
view(V::SubOperator, kr, jr) = view(V.parent,reindex(V,parentindices(V),(kr,jr))...)
view(V::SubOperator,kr::InfRanges,jr::InfRanges) = view(V.parent,reindex(V,parentindices(V),(kr,jr))...)
bandwidths(S::SubOperator) = S.bandwidths
function colstop(S::SubOperator{T,OP,Tuple{UnitRange{Int},UnitRange{Int}}},j::Integer) where {T,OP}
cs = colstop(parent(S),parentindices(S)[2][j])
kr = parentindices(S)[1]
n = size(S,1)
if cs < first(kr)
0
elseif cs ≥ last(kr)
n
else
min(n,findfirst(isequal(cs),kr))
end
end
colstart(S::SubOperator{T,OP,Tuple{UnitRange{Int},UnitRange{Int}}},j::Integer) where {T,OP} =
max(findfirst(parentindices(S)[1],colstart(parent(S),parentindices(S)[2][j])),1)
rowstart(S::SubOperator{T,OP,Tuple{UnitRange{Int},UnitRange{Int}}},j::Integer) where {T,OP} =
max(1,findfirst(parentindices(S)[2],rowstart(parent(S),parentindices(S)[1][j])))
rowstop(S::SubOperator{T,OP,Tuple{UnitRange{Int},UnitRange{Int}}},j::Integer) where {T,OP} =
findfirst(parentindices(S)[2],rowstop(parent(S),parentindices(S)[1][j]))
# blocks don't change
blockcolstop(S::SubOperator{T,OP,Tuple{II,JJ}},J::Integer) where {T,OP,II<:AbstractRange{Int},JJ<:AbstractRange{Int}} =
blockcolstop(parent(S),J)
israggedbelow(S::SubOperator) = israggedbelow(parent(S))
# since blocks don't change with indexex, neither do blockbandwidths
blockbandwidths(S::SubOperator{T,OP,Tuple{II,JJ}}) where {T,OP,II<:AbstractRange{Int},JJ<:AbstractRange{Int}} =
blockbandwidths(parent(S))
function blockbandwidths(S::SubOperator{T,B,Tuple{BlockRange1,BlockRange1}}) where {T,B}
KR,JR = parentindices(S)
l,u = blockbandwidths(parent(S))
sh = first(KR).n[1]-first(JR).n[1]
l-sh,u+sh
end
isblockbanded(S::SubOperator{T,B,Tuple{Block,Block}}) where {T,B} = false
isbanded(S::SubOperator{T,B,Tuple{Block,Block}}) where {T,B} = isbandedblockbanded(parent(S))
bandwidths(S::SubOperator{T,B,Tuple{Block,Block}}) where {T,B} = subblockbandwidths(parent(S))
blockbandwidths(S::SubOperator{T,B,Tuple{Block,Block}}) where {T,B} = 0,0
function BandedBlockBandedMatrix(::Type{Zeros}, S::SubOperator)
kr,jr=parentindices(S)
KO=parent(S)
l,u=blockbandwidths(KO)
λ,μ=subblockbandwidths(KO)
rt=rangespace(KO)
dt=domainspace(KO)
k1,j1=isempty(kr) || isempty(jr) ? (first(kr),first(jr)) :
reindex(S,parentindices(S),(1,1))
# each row/column that we differ from the the block start shifts
# the sub block inds
J = block(dt,j1)
K = block(rt,k1)
jsh=j1-blockstart(dt,J)
ksh=k1-blockstart(rt,K)
rows,cols = blocklengths(rangespace(S)), blocklengths(domainspace(S))
BandedBlockBandedMatrix(Zeros{eltype(KO)}(sum(rows),sum(cols)),
(rows,cols), (l,u), (λ-jsh,μ+ksh))
end
function BandedBlockBandedMatrix(::Type{Zeros}, S::SubOperator{T,B,Tuple{BlockRange1,BlockRange1}}) where {T,B}
KR,JR = parentindices(S)
KO = parent(S)
l,u = blockbandwidths(KO)::Tuple{Int,Int}
λ,μ = subblockbandwidths(KO)::Tuple{Int,Int}
rt = rangespace(KO)
dt = domainspace(KO)
J = first(JR)
K = first(KR)
bl_sh = Int(J) - Int(K)
KBR = blocklengthrange(rt,KR)
KJR = blocklengthrange(dt,JR)
BandedBlockBandedMatrix(Zeros{eltype(KO)}(sum(KBR),sum(KJR)),
(AbstractVector{Int}(KBR),AbstractVector{Int}(KJR)), (l+bl_sh,u-bl_sh), (λ,μ))
end
function domainspace(S::SubOperator)
P =parent(S)
sp=domainspace(P)
kr=parentindices(S)[2]
SubSpace{typeof(sp),typeof(kr),domaintype(sp),rangetype(sp)}(sp,kr)
end
function rangespace(S::SubOperator)
P =parent(S)
sp=rangespace(P)
kr=parentindices(S)[1]
SubSpace{typeof(sp),typeof(kr),domaintype(sp),rangetype(sp)}(sp,kr)
end
size(V::SubOperator) = V.dims
size(V::SubOperator,k::Int) = V.dims[k]
unsafe_getindex(V::SubOperator,k::Integer,j::Integer) = V.parent[reindex(V,parentindices(V),(k,j))...]
getindex(V::SubOperator,k::Integer,j::Integer) = V.parent[reindex(V,parentindices(V),(k,j))...]
getindex(V::SubOperator,k::Integer,j::AbstractRange) = V.parent[reindex(V,parentindices(V),(k,j))...]
getindex(V::SubOperator,k::AbstractRange,j::Integer) = V.parent[reindex(V,parentindices(V),(k,j))...]
getindex(V::SubOperator,k::AbstractRange,j::AbstractRange) = V.parent[reindex(V,parentindices(V),(k,j))...]
Base.parent(S::SubOperator) = S.parent
Base.parentindices(S::SubOperator) = S.indexes
for OP in (:isblockbanded,:isblockbandedabove,:isblockbandedbelow,
:isbandedblockbanded,:isbandedblockbandedabove,
:isbandedblockbandedbelow)
@eval $OP(S::SubOperator) = $OP(parent(S))
end
# TODO: These should be removed as the general purpose case will work,
# once the notion of bandedness of finite dimensional operators is made sense of
_colstops(V) = Int[max(0,colstop(V,j)) for j=1:size(V,2)]
for TYP in (:RaggedMatrix, :Matrix)
def_TYP = Meta.parse("default_" * string(TYP))
@eval begin
function $TYP(V::SubOperator)
if isinf(size(V,1)) || isinf(size(V,2))
error("Cannot convert $V to a $TYP")
end
A = parent(V)
if isbanded(A)
$TYP(BandedMatrix(V))
else
$def_TYP(V)
end
end
function $TYP(V::SubOperator{T,BB,NTuple{2,UnitRange{Int}}}) where {T,BB}
if isinf(size(V,1)) || isinf(size(V,2))
error("Cannot convert $V to a $TYP")
end
A = parent(V)
if isbanded(A)
$TYP(BandedMatrix(V))
elseif isbandedblockbanded(A)
N = block(rangespace(A), last(parentindices(V)[1]))
M = block(domainspace(A), last(parentindices(V)[2]))
B = A[Block(1):N, Block(1):M]
RaggedMatrix(view(B, parentindices(V)...), _colstops(V))
else
$def_TYP(V)
end
end
end
end
# fast converts to banded matrices would be based on indices, not blocks
function BandedMatrix(S::SubOperator{T,B,Tuple{BlockRange1,BlockRange1}}) where {T,B}
A = parent(S)
ds = domainspace(A)
rs = rangespace(A)
KR,JR = parentindices(S)
BandedMatrix(view(A,
blockstart(rs,first(KR)):blockstop(rs,last(KR)),
blockstart(ds,first(JR)):blockstop(ds,last(JR))))
end
function mul_coefficients(A::SubOperator{T,B,Tuple{UnitRange{Int},UnitRange{Int}}},b) where {T,B}
if size(A,2) == length(b)
AbstractMatrix(A)*b
else
view(A,:,1:length(b))*b
end
end
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] | 2.086614 | 6,096 |
<filename>common/md/MolecularDynamicsTrial.jl
################################################################################
#
# Struct for holding all parameters associated with an md simulation
#
################################################################################
abstract MDTrial
################################################################################
#
# AT SOME POINT, IT WOULD BE GOOD TO HAVE A MACRO FOR AUTOMATICALLY
# POPULATING MolecularDynamicsTrial TYPE FIELDS
#
################################################################################
################################################################################
#
# Struct for holding all parameters associated with an md verlet simulation
#
################################################################################
type MolecularDynamicsTrial <: MDTrial
# Parameters
numBodies::Int64 # Number of particles
ρ::Float64 # Density of particles
Td::Float64 # Desired temperature
dx::Float64 # Time step; defaults to Verlet's value
V::Float64 # Volume of box
L::Float64 # Side length of box
steps::Int64 # How long to run the simulation
σ::Float64 # Length scale, from Verlet, in m
ϵ::Float64 # Energy scale, in Kelvin
m::Float64 # Argon atom mass, in kg
τ::Float64 # Time scale, in seconds
# Dynamic Quantities
y::Array{Float64, 3} # Positions
v::Array{Float64, 3} # Velocities
f::Array{Float64, 3} # Accelerations
pe::Array{Float64, 2} # Potential Energies
ke::Array{Float64, 2} # Kinetic Energies
pet::Array{Float64, 1} # Total Potential Energy
ket::Array{Float64, 1} # Total Kinetic Energy
T::Array{Float64, 1} # Temperature at each step
e::Array{Float64, 1} # Total Energy
vir::Array{Float64, 1} # Virial, ∑∑r(∂V/∂r)
currentStep::Int64 # Where this simulation left off
end
################################################################################
#
# Struct for holding all parameters associated with an md metropolis
# simulation
#
################################################################################
type MDMetropolisTrial <: MDTrial
# Parameters
numBodies::Int64 # Number of particles
ρ::Float64 # Density of particles
Td::Float64 # Desired temperature
# dx::Float64 # Time step; defaults to Verlet's value
V::Float64 # Volume of box
L::Float64 # Side length of box
steps::Int64 # How long to run the simulation
σ::Float64 # Length scale, from Verlet, in m
ϵ::Float64 # Energy scale, in Kelvin
m::Float64 # Argon atom mass, in kg
τ::Float64 # Time scale, in seconds
# Dynamic Quantities
y::Array{Float64, 3} # Positions
# v::Array{Float64, 3} # Velocities
# f::Array{Float64, 3} # Accelerations
pe::Array{Float64, 2} # Potential Energies
ke::Array{Float64, 2} # Kinetic Energies
pet::Array{Float64, 1} # Total Potential Energy
ket::Array{Float64, 1} # Total Kinetic Energy
# T::Array{Float64, 1} # Temperature at each step
e::Array{Float64, 1} # Total Energy
vir::Array{Float64, 1} # Virial, ∑∑r(∂V/∂r)
currentStep::Int64 # Where this simulation left off
end
# Outer constructor just for input parameters
MolecularDynamicsTrial( numBodies, ρ, Td, dx, steps, σ, ϵ, m ) =
MolecularDynamicsTrial(
numBodies,
ρ,
Td,
dx,
( numBodies / ρ ), # Volume
( numBodies / ρ )^( 1 / 3 ), # Side length
steps,
σ, # Length scale, in m
ϵ, # Energy scale, in Kelvin
m, # Argon atom mass, in kg
sqrt( m * σ^2 / ( 48ϵ * 1.3806e-23 ) ), # Time scale, in seconds
zeros( 3, numBodies, steps ), # Positions
zeros( 3, numBodies, steps ), # Velocities
zeros( 3, numBodies, steps ), # Accelerations
zeros( numBodies, steps ), # Potential Energies
zeros( numBodies, steps ), # Kinetic Energies
zeros( steps ), # Total Potential Energy
zeros( steps ), # Total Kinetic Energy
zeros( steps ), # Temperature at each step
zeros( steps ), # Total Energy
zeros( steps ), # Virial
0 ); # Completed steps (start at 0)
# Outer constructor for empty object
MolecularDynamicsTrial() = MolecularDynamicsTrial(1,1,0,0,0,0,1,0);
# Comparator for MolecularDynamicsTrial type
function ==( r::MolecularDynamicsTrial, p::MolecularDynamicsTrial )
for field in names( MolecularDynamicsTrial )
if r.( field ) != p.( field )
return false
end
end
return true
end
# Check if trial is finished
function isfinished( r::MolecularDynamicsTrial )
return r.currentStep == r.steps
end
# Duplicate a trial
function duplicate( r::MolecularDynamicsTrial )
p = MolecularDynamicsTrial()
for field in names( MolecularDynamicsTrial )
p.(field) = r.(field)
end
assert(p == r)
return p
end
# Extract a sub-trial TODO : reimplement using slicedim(A,d,i)
function subtrial( r::MolecularDynamicsTrial, n::Int, m::Int )
if r.steps < m
error( "Out of bounds: final step must be less than r.steps" )
elseif m < n
error( "first step must be less than final step" )
elseif n < 1
error( "Out of bounds: first step must be greater than or equal to 1")
elseif n > r.currentStep
error( "first step must be less than current step" )
else
steps = m - n + 1
numBodies = r.numBodies
p = MolecularDynamicsTrial(numBodies,1,0,0,steps,0,1,0);
for field in names( MolecularDynamicsTrial )
if :steps == field
continue # clobber not total steps
elseif isa( r.(field), Array )
fsize = size( p.(field) ) # dimensions of the field
perstep = prod(fsize[1:(end-1)]) # elements per step
first = (n-1)*perstep + 1 # first element to copy
last = m*perstep # last element to copy
assert((last-first+1)==perstep*steps) # correct num elements
i = 1
for j in first:last # linear indexing to set p
p.(field)[i] = r.(field)[j]
i+=1
end
else
p.(field) = r.(field)
end
end
p.currentStep = r.currentStep + 1 - n
return p
end
end
# Lop off unfinished steps
function mdtruncate( r::MolecularDynamicsTrial )
if isfinished( r )
return r
else
return subtrial( r, 1, r.currentStep ) # go till last finished step
end
end
# Add steps, if you need to run for longer
function addsteps( r::MolecularDynamicsTrial, n::Int )
if n<0
error( "Must specify non-negative number of steps to add" )
else
steps = r.steps + n
numBodies = r.numBodies
p = MolecularDynamicsTrial(numBodies,1,0,0,steps,0,1,0);
for field in names( MolecularDynamicsTrial )
if :steps == field
continue # clobber not total steps
elseif isa( r.(field), Array )
i = 1
for val in r.(field)[1:end] # linear indexing to set p
p.(field)[i] = val
i+=1
end
else
p.(field) = r.(field) # copy scalar fields
end
end
return p
end
end
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220,
220,
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220,
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220,
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279,
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3245,
8,
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374,
12195,
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8,
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220,
220,
1441,
279,
198,
220,
220,
220,
886,
198,
437,
198
] | 2.097818 | 4,079 |
using Distributions
interaction_dict = Dict()
interaction_dict["binary"] = [-1.0,1.0]
interaction_dict["normal"] = Normal()
interaction_dict["uniform"] = Uniform(-1.0,1.0)
| [
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<filename>todo/higher-critism.jl
## HC
function hc_score(pValues)
validPvalues(pValues)
n = length(pValues)
f = competerank(pValues) / n ## ties = max
var = f .* (1-f) / n
var[var == 0] = min( var[var > 0] ) # just to make sure we have no zero variance
hc = abs(f-pValues) / sqrt(var)
hc
end
| [
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] | 2.258741 | 143 |
#=
array_initialization:
- Julia version: 1.5.0
- Author: shisa
- Date: 2020-08-10
=#
module ArrayInit
# methods
function main()
println("Not init array = ", Array{Float32}(undef, 3, 2))
println("Init 0 array = ", zeros(Float32, 3, 2))
println("Init 1 array = ", ones(Float32, 3, 2))
println("Init uniform distribution = ", rand(Float32, 3, 2))
println("Init normal distribution = ", randn(Float32, 3, 2))
println("Init fill 8 = ", fill(8, 3, 2))
println("Init zeros fill 10 = ", fill!(zeros(Int32, 4, 4), 10))
println("Similar not init = ", similar(fill(8, 4, 4)))
println("Similar init float32 = ", similar(fill(8, 4, 4), Float32))
println("Similar init float32 3x2 = ", similar(fill(8, 4, 4), Float32, 3, 2))
println("Reshape 3x2 -> 2x3: ", reshape(zeros(Float32, 3, 2), 2, 3))
println("List Comprehension [x for x in 1:10] = ", [x for x in 1:10])
println("List Comprehension [10a + b for a in 0:3, b in 0:4] = ", [10a + b for a in 0:3, b in 0:4])
end
end
if abspath(PROGRAM_FILE) == @__FILE__
using .ArrayInit
ArrayInit.main()
end | [
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<reponame>nw11/MethylUtils.jl
module FeatureTest
using FactCheck
include(Pkg.dir("MethylUtils", "src","FeatureServices.jl"))
path = Pkg.dir("MethylUtils", "testdata","moabs-format-1000lines.txt")
sf = load_moabs_format_as_sequence_features(path)
println(sf)
rf = read_moabs_format_as_sequence_features(path)
ensgene_path = Pkg.dir("MethylUtils","testdata","ensGene1000lines.txt.gz")
ensgene_path = joinpath(homedir(), ".GenomeAnnotations.jl/annotation/mm10/ensGene.txt.gz")
FT = make_ensgene_gene_feature_table( ensgene_path, "mm10")
FR = FeatureRegions(FT)
println( collect(keys(FR.regions)) )
#local_store_path = Pkg.dir("MethylUtils", "testdata","store", "annotation")
#cgs = load_moabs(path)
#SET_LOCAL_STORE_PATH(local_store_path)
#facts("load_moabs") do
# @fact length(cgs) => 1000
#end
#transcripts = ensgene_dataframe("mm10","ensGene1000lines.txt.gz" )
#println(transcripts)
#facts("ensgene_dataframe") do
# @fact size(transcripts,1) => 1000
#end
#genes = ensgene_regions("mm10","ensGene1000lines.txt.gz" )
# println( genes )
# println("length: ", size(genes.dataframe, 1))
# We want to test for ensgene_regions that it is making the kind of ensgene_regions
# we want, i.e. the region covering all transcripts under a gene id
# set config path back to nothing
#SET_LOCAL_STORE_PATH("nothing")
end
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] | 2.477663 | 582 |
<reponame>James-P-D/JuliaDump<filename>src/arrays.jl
using Printf
# ARRAYS ARE INDEXED FROM ONE NOT ZERO IN JULIA!!!!
## I wrote this for displaying arrays, but we don't actually need it. println() works just fine!
#
#function display_array(some_array)
# for x in some_array
# print(x, " ")
# end
# println()
#end
function array_examples()
println("--------------------------------------")
println("Array examples")
println()
println("We can create one-dimensional arrays of zeros")
one_dimensional_array = zeros(Int32, 5)
println(one_dimensional_array)
println("We can create two-dimensional arrays of zeros")
two_dimensional_array = zeros(Int32, 5, 2)
println(two_dimensional_array)
println("We can create arrays where we specify the type, the size, and the initial value (in this case undefined)")
undefined_array = Array{Int32}(undef, 5)
println(undefined_array)
println("We can create a int array and set initial values")
int_array = Int32[1,2,3,4]
println(int_array)
println("We can get individual values. REMEMBER JULIA INDEXED FROM ONE NOT ZERO!")
println(int_array[2]) # The second element really is the second element (not the third as would be the case with zero-indexing)
println("We can create a float array and set initial values")
float_array = Float64[1,2,3,4]
println(float_array)
println("We can create an array where each element is off a different type")
array_of_different_types = [99, 3.1415, "foo", true, 'x']
println(array_of_different_types)
println("We can create an array from a range (2 to 10, stepping at intervals of 2")
even_number_array = collect(2:2:10)
println(even_number_array)
println("We can create an array using comprehension")
square_numbers = [n^2 for n in 1:5]
println(square_numbers)
println("We can also create multi-dimensional arrays using comprehension")
another_multi_dim_array = [n * m for n in 1:5, m in 1:5]
println(another_multi_dim_array)
println("We can get a specific (one-indexed) element from the array")
println(array_of_different_types[1])
println("We can get the last element from the array")
println(array_of_different_types[end])
println("We can get a range, in this case items 2-4")
println(array_of_different_types[2:4])
println("We can check if an item, e.g. 3.1415 exists in the array")
println(3.1415 in array_of_different_types)
println("We can find a specific item in the array, e.g. 'x'")
println(findfirst(isequal('x'), array_of_different_types))
int_array = [1,2,3,4,5,6,7,8,10]
println("We can find all the even numbers in an array using a generic function and findall")
f(a) = mod(a, 2) == 0 ? true : false
println(findall(f, int_array))
println("We can get the number of items that pass the test")
println(count(f, int_array))
println("We can get the row and column size of our 5x2 array we created earlier")
println(size(two_dimensional_array))
println("We can get the length of an array")
println(length(int_array))
println("We can get the sum of all the values in the array")
println(sum(int_array))
println("We can insert items at index 2 (second number after colon seems to specify range in target array to overwrite. increase this number to see old items in array go missing!)")
splice!(int_array, 2:1, [100,200,300,400,500])
println(int_array)
println("We can remove items at index 2 to 6")
splice!(int_array, 2:6)
println(int_array)
println("We get the maximum value in the array")
println(maximum(int_array))
println("We get the minimum value in the array")
println(minimum(int_array))
println("We can perform calculations on an array without looping")
println(int_array * 2)
println("We can store functions in an array")
function_array = [sin, cos, tan]
println(function_array)
for n in function_array
println(n(45))
end
two_dimensional_int_array = [1 2 3 4; 5 6 7 8; 9 10 11 12]
for row = 1:3, col = 1:4
@printf("row %d, col %d = %d\n", row, col, two_dimensional_int_array[row,col])
end
println()
println("We can print just the items from column 2")
println(two_dimensional_int_array[:, 2])
println("We can push items to the end of an array")
push!(int_array, 999)
println(int_array)
end
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] | 2.742771 | 1,660 |
using Documenter, MicrobiomePlots
makedocs(
format = :html,
sitename = "MicrobiomePlots.jl",
pages = [
"Home" => "index.md",
"Recipes" => "recipes.md",
"Contributing" => "contributing.md"
],
authors = "<NAME>, PhD"
)
deploydocs(
repo = "github.com/BioJulia/MicrobiomePlots.jl.git",
julia = "1.0",
osname = "linux",
target = "build",
deps = nothing,
make = nothing
)
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] | 2.147783 | 203 |
<gh_stars>0
function linear_scheme(offset_min::Int64, offset_max::Int64)
float_coeff = float_flux_coefficients(
[i for i in offset_min - 1: offset_max])
return Linear(offset_min,
offset_max,
float_coeff)
end
function flux_positive_linear(scheme::Linear,
vector_in::Array{<:AbstractFloat, 1})
return dot(scheme.flux_coefficients, vector_in)
end # flux_positive_linear
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] | 2.070796 | 226 |
using Mill, Test, Flux
using Mill: partialeval, identity_model
@testset "partialeval" begin
metadata = fill("metadata", 4)
an1 = ArrayNode(rand(3,4))
b = BagNode(an1, [1:4, 0:-1], metadata)
an2 = ArrayNode(randn(5, 4))
wb = WeightedBagNode(an2, [1:2,3:4], rand(1:4, 4), metadata)
pn = ProductNode((b=b,wb=wb))
an3 = ArrayNode(rand(10, 2))
ds = ProductNode((pn, an3))
# printtree(ds)
m = reflectinmodel(ds, d -> Chain(Dense(d, 4, relu), Dense(4,3)), d-> SegmentedMeanMax(d))
@test partialeval(m.ms[2], ds.data[2], an3)[1] === m.ms[2]
@test partialeval(m.ms[2], ds.data[2], an3)[2] === ds.data[2]
@test partialeval(m.ms[2], ds.data[2], an1)[1] === identity_model()
@test partialeval(m.ms[2], ds.data[2], an1)[2].data ≈ m.ms[2](ds.data[2]).data
tm, td = partialeval(m.ms[2], ds.data[2], an3)
@test tm(td).data ≈ m.ms[2](ds.data[2]).data
@test partialeval(m.ms[2], ds.data[2], an1)[2].data ≈ m.ms[2](ds.data[2]).data
@test partialeval(m.ms[1][:b], ds.data[1][:b], an1)[1] === m.ms[1][:b]
@test partialeval(m.ms[1][:b], ds.data[1][:b], an1)[2] === ds.data[1][:b]
@test partialeval(m.ms[1][:b], ds.data[1][:b], b)[1] === m.ms[1][:b]
@test partialeval(m.ms[1][:b], ds.data[1][:b], b)[2] === ds.data[1][:b]
tm, td = partialeval(m.ms[1][:b], ds.data[1][:b], an3)
@test tm(td).data ≈ m.ms[1][:b](ds.data[1][:b]).data
@test partialeval(m.ms[1][:b], ds.data[1][:b], an3)[1] === identity_model()
@test partialeval(m.ms[1][:b], ds.data[1][:b], an3)[2].data ≈ m.ms[1][:b](ds.data[1][:b]).data
@test partialeval(m.ms[1][:wb], ds.data[1][:wb], an2)[1] === m.ms[1][:wb]
@test partialeval(m.ms[1][:wb], ds.data[1][:wb], an2)[2] === ds.data[1][:wb]
@test partialeval(m.ms[1][:wb], ds.data[1][:wb], wb)[1] === m.ms[1][:wb]
@test partialeval(m.ms[1][:wb], ds.data[1][:wb], wb)[2] === ds.data[1][:wb]
tm, td = partialeval(m.ms[1][:wb], ds.data[1][:wb], an3)
@test tm(td).data ≈ m.ms[1][:wb](ds.data[1][:wb]).data
@test partialeval(m.ms[1][:wb], ds.data[1][:wb], an3)[1] === identity_model()
@test partialeval(m.ms[1][:wb], ds.data[1][:wb], an3)[2].data ≈ m.ms[1][:wb](ds.data[1][:wb]).data
@test partialeval(m.ms[1], ds.data[1], an1)[1].ms[1] === m.ms[1].ms[1]
@test partialeval(m.ms[1], ds.data[1], an1)[2].data[1] === ds.data[1].data[1]
@test partialeval(m.ms[1], ds.data[1], an1)[1].ms[2] === identity_model()
@test partialeval(m.ms[1], ds.data[1], an1)[2].data[2].data ≈ m.ms[1].ms[2](ds.data[1].data[2]).data
@test partialeval(m.ms[1], ds.data[1], an3)[1] === identity_model()
@test partialeval(m.ms[1], ds.data[1], an3)[2].data ≈ m.ms[1](ds.data[1]).data
tm, td = partialeval(m.ms[1], ds.data[1], an1)
@test tm(td).data ≈ m.ms[1](ds.data[1]).data
@test partialeval(m.ms[2], ds.data[2], an1)[2].data ≈ m.ms[2](ds.data[2]).data
@test partialeval(m, ds, 1)[1] === identity_model()
@test partialeval(m, ds, 1)[2].data ≈ m(ds).data
tm, td = partialeval(m, ds, an1)
@test tm(td).data ≈ m(ds).data
end
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] | 1.936398 | 1,588 |
export ProductDomain
immutable ProductDomain{D,T,dim} <: Domain{T,dim}
domains::D
end
ProductDomain(d::Tuple) =
ProductDomain{typeof(d),mapreduce(eltype,promote_type,d),mapreduce(dimension,+,d)}(d)
fromcanonical(d::BivariateDomain,x::Tuple)=fromcanonical(d,x...)
tocanonical(d::BivariateDomain,x::Tuple)=tocanonical(d,x...)
# product domains are their own canonical domain
for OP in (:fromcanonical,:tocanonical)
@eval $OP(::ProductDomain,x,y)=(x,y)
end
ProductDomain(A,B)=ProductDomain((A,B))
*(A::ProductDomain,B::ProductDomain)=ProductDomain(tuple(A.domains...,B.domains...))
*(A::ProductDomain,B::Domain)=ProductDomain(tuple(A.domains...,B))
*(A::Domain,B::ProductDomain)=ProductDomain(tuple(A,B.domains...))
*(A::Domain,B::Domain)=ProductDomain(A,B)
Base.length(d::ProductDomain)=length(d.domains)
Base.transpose(d::ProductDomain)=ProductDomain(d[2],d[1])
Base.getindex(d::ProductDomain,k::Integer)=d.domains[k]
==(d1::ProductDomain,d2::ProductDomain)=d1.domains==d2.domains
Base.first(d::ProductDomain)=(first(d[1]),first(d[2]))
function pushappendpts!(ret,xx,pts)
if isempty(pts)
push!(ret,xx)
else
for x in pts[1]
pushappendpts!(ret,(xx...,x),pts[2:end])
end
end
ret
end
function checkpoints(d::ProductDomain)
pts=map(checkpoints,d.domains)
ret=Array(Tuple{map(eltype,d.domains)...},0)
pushappendpts!(ret,(),pts)
ret
end
function points(d::ProductDomain,n::Tuple)
@assert length(d.domains) == length(n)
pts=map(points,d.domains,n)
ret=Array(Tuple{map(eltype,d.domains)...},0)
pushappendpts!(ret,(),pts)
ret
end
Base.reverse(d::ProductDomain)=ProductDomain(map(reverse,d.domains))
domainscompatible(a::ProductDomain,b::ProductDomain) =
length(a.domains)==length(b.domains) &&
all(map(domainscompatible,a.domains,b.domains))
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] | 2.316626 | 818 |
<filename>test/TestAssembleHat.jl
using InvariantMeasures
using ValidatedNumerics
using LinearAlgebra
@testset "Hat assembler" begin
D = Mod1Dynamic(x->2*x)
B = Hat(8)
P = assemble(B, D)
Ptrue = [
0.5 0.25 0 0 0 0 0 0.25;
0 0.25 0.5 0.25 0 0 0 0 ;
0 0 0 0.25 0.5 0.25 0 0 ;
0 0 0 0 0 0.25 0.5 0.25;
0.5 0.25 0 0 0 0 0 0.25;
0 0.25 0.5 0.25 0 0 0 0 ;
0 0 0 0.25 0.5 0.25 0 0 ;
0 0 0 0 0 0.25 0.5 0.25;
]
Ptrue = Ptrue'
@test all(contains_zero.(P-Ptrue))
@test opnormbound(L1, DiscretizedOperator(B, D)) == 1
@test opnormbound(Linf, DiscretizedOperator(B, D)) == 1
Q = DiscretizedOperator(B, D)
@test size(Q) == (8,8)
n = size(Q)[1]
e = randn(n)
x = similar(e)
x[:] .= 2.
mQ = mid(Q)
@test mul!(x, mQ, e, 1, 0) == mQ*e
fQ = mQ.L + mQ.e * mQ.w
@test fQ * e ≈ mQ * e
end
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68,
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230,
285,
48,
1635,
304,
198,
198,
437,
198
] | 1.674296 | 568 |
module TestJaynes
using Test
include("../src/Jaynes.jl")
using .Jaynes
println("\n________________________\n")
@testset "Core" begin
println("Compiler.")
@time @testset "Compiler." begin
include("compiler/update.jl")
end
println("Execution contexts.")
@time @testset "Execution contexts." begin
include("core/contexts.jl")
end
println("Gradients.")
@time @testset "Gradients." begin
include("core/gradients.jl")
end
println("Black-box extensions.")
@time @testset "Black-box extensions." begin
include("core/blackbox.jl")
end
println("Selection query language.")
@time @testset "Selection query language." begin
include("core/selection_query_language.jl")
end
println("Gradient learning.")
@time @testset "Gradient learning." begin
end
println()
end
println("\n________________________\n")
using Gen
@testset "Gen compatibility" begin
println("Generative function interface.")
@time @testset "Generative function interface." begin
include("gen_compat/gen_fn_interface.jl")
end
end
end #module
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] | 2.808354 | 407 |
@testset "Exceptions" begin
function oob_kernel(X)
X[0] = 1
nothing
end
RA = ROCArray(ones(Float32, 4))
try
wait(@roc oob_kernel(RA))
catch err
@test err isa AMDGPU.KernelException
if err isa AMDGPU.KernelException
@test err.exstr !== nothing
@test occursin("BoundsError", err.exstr)
end
end
end
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] | 2.275168 | 149 |
<gh_stars>100-1000
@testset "scc graph wikipedia" begin
n = 8
scc_init = CS.SCCInit(
zeros(Int, n + 1),
zeros(Int, n),
zeros(Int, n),
zeros(Bool, n),
zeros(Int, n),
)
di_ei = [1, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 7, 8, 8]
di_ej = [2, 2, 3, 5, 7, 4, 3, 8, 6, 1, 7, 6, 4, 7]
scc_map = CS.scc(di_ei, di_ej, scc_init)
@test scc_map[1] == scc_map[2] == scc_map[5]
@test scc_map[3] == scc_map[4] == scc_map[8]
@test scc_map[6] == scc_map[7]
@test scc_map[1] != scc_map[3]
@test scc_map[1] != scc_map[6]
@test scc_map[3] != scc_map[6]
end
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] | 1.651475 | 373 |
<filename>examples/hinf_example_tank.jl<gh_stars>10-100
using ControlSystems, RobustAndOptimalControl
using Plots
using LinearAlgebra
"""
This is a simple SISO example with integrator dynamics corresponding to the
quad tank process in the lab.
The example can be set to visualize and save plots using the variables
makeplots - true/false (true if plots are to be generated, false for testing)
SavePlots - true/false (true if plots are to be saved, false for testing)
"""
makeplots = true
# Define the proces parameters
k1, k2, kc, g = 3.33, 3.35, 0.5, 981
A1, A3, A2, A4 = 28, 28, 32, 32
a1, a3, a2, a4= 0.071, 0.071, 0.057, 0.057
h01, h02, h03, h04 = 12.4, 12.7, 1.8, 1.4
T1, T2 = (A1/a1)*sqrt(2*h01/g), (A2/a2)*sqrt(2*h02/g)
T3, T4 = (A3/a3)*sqrt(2*h03/g), (A4/a4)*sqrt(2*h04/g)
c1, c2 = (T1*k1*kc/A1), (T2*k2*kc/A2)
γ1, γ2 = 0.7, 0.6
# Define the process dynamics
A = [-1/T1 0 A3/(A1*T3) 0;
0 -1/T2 0 A4/(A2*T4);
0 0 -1/T3 0;
0 0 0 -1/T4];
B = [γ1*k1/A1 0;
0 γ2*k2/A2;
0 (1-γ2)*k2/A3;
(1-γ1)*k1/A4 0 ];
C = [kc 0 0 0;
0 kc 0 0];
D = zeros(2,2)
G = ss(A,B,C,D);
# Sensitivity weight function
WS = makeweight(100, (0.1, 1), 1/10) * I(2)
# Output sensitivity weight function
# WUelement = 5*tf([1,1],[0.1,1])
WU = tf(0.01) .* I(2)
# Complementary sensitivity weight function
WT = makeweight(1/10, (0.1, 1), 10) * I(2)
# Form augmented P dynamics in state-space
P = hinfpartition(G, WS, WU, WT)
# Check that the assumptions are satisfied
flag = hinfassumptions(P)
# Synthesize the H-infinity optimal controller
flag, C, γ = hinfsynthesize(P)
Pcl, S, CS, T = hinfsignals(P, G, C)
## Plot the specifications
# TODO figure out why I get segmentation errors when using ss instead of tf for
# the weighting functions, makes no sense at all
if makeplots
specificationplot([S, CS, T], [WS[1,1], 0.01, WT[1,1]], γ)
## Plot the closed loop gain from w to z
# TODO figure out why the legends don't seem to work in this case
specificationplot(Pcl, γ; s_labels=["\$\\sigma(P_{cl}(j\\omega))\$"], w_labels=["\$\\gamma\$"])
times = [i for i in range(0, stop=300, length=10000)]
plot(step(T, times))
end
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] | 2.102041 | 1,078 |
<gh_stars>100-1000
"""
flux_first_order!(
bl::BalanceLaw,
flux::Grad,
state::Vars,
aux::Vars,
t::Real
)
Computes (and assembles) flux terms `F¹(Y)` in:
```
∂Y
-- + ∇ • F¹(Y) + ∇ • F²(Y,G) = S(Y, G), G = ∇Y
∂t
```
Computes and assembles non-diffusive
fluxes in the model equations.
For this fallback to work, several methods must be defined:
- [`prognostic_vars`](@ref)
- [`eq_tends`](@ref)
- [`get_prog_state`](@ref)
optionally,
- [`precompute`](@ref)
and individual [`flux`](@ref) kernels that
are defined for each type that `eq_tends` returns.
"""
@inline function flux_first_order!(
bl::BalanceLaw,
flux,
state,
aux,
t,
direction,
)
tend = Flux{FirstOrder}()
_args = (; state, aux, t, direction)
args = merge(_args, (precomputed = precompute(bl, _args, tend),))
map(prognostic_vars(bl)) do prog
var, name = get_prog_state(flux, prog)
val = Σfluxes(prog, eq_tends(prog, bl, tend), bl, args)
setproperty!(var, name, val)
end
nothing
end
"""
flux_second_order!(
bl::BalanceLaw,
flux::Grad,
state::Vars,
diffusive::Vars,
hyperdiffusive::Vars,
aux::Vars,
t::Real
)
Computes (and assembles) flux terms `F²(Y, G)` in:
```
∂Y
-- + ∇ • F¹(Y) + ∇ • F²(Y,G) = S(Y, G), G = ∇Y
∂t
```
Diffusive fluxes in BalanceLaw. Viscosity, diffusivity are calculated
in the turbulence subcomponent and accessed within the diffusive flux
function. Contributions from subcomponents are then assembled (pointwise).
For this fallback to work, several methods must be defined:
- [`prognostic_vars`](@ref)
- [`eq_tends`](@ref)
- [`get_prog_state`](@ref)
optionally,
- [`precompute`](@ref)
and individual [`flux`](@ref) kernels that
are defined for each type that `eq_tends` returns.
"""
@inline function flux_second_order!(
bl::BalanceLaw,
flux,
state,
diffusive,
hyperdiffusive,
aux,
t,
)
tend = Flux{SecondOrder}()
_args = (; state, aux, t, diffusive, hyperdiffusive)
args = merge(_args, (precomputed = precompute(bl, _args, tend),))
map(prognostic_vars(bl)) do prog
var, name = get_prog_state(flux, prog)
val = Σfluxes(prog, eq_tends(prog, bl, tend), bl, args)
setproperty!(var, name, val)
end
nothing
end
"""
source!(
bl::BalanceLaw,
source::Vars,
state::Vars,
diffusive::Vars,
aux::Vars,
t::Real,
direction::Direction,
)
Computes (and assembles) source terms `S(Y)` in:
```
∂Y
-- + ∇ • F¹(Y) + ∇ • F²(Y,G) = S(Y, G), G = ∇Y
∂t
```
For this fallback to work, several methods must be defined:
- [`prognostic_vars`](@ref)
- [`eq_tends`](@ref)
- [`get_prog_state`](@ref)
optionally,
- [`precompute`](@ref)
and individual [`source`](@ref) kernels that
are defined for each type that `eq_tends` returns.
"""
function source!(bl::BalanceLaw, source, state, diffusive, aux, t, direction)
tend = Source()
_args = (; state, aux, t, direction, diffusive)
args = merge(_args, (precomputed = precompute(bl, _args, tend),))
map(prognostic_vars(bl)) do prog
var, name = get_prog_state(source, prog)
val = Σsources(prog, eq_tends(prog, bl, tend), bl, args)
setproperty!(var, name, val)
end
nothing
end
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220,
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886,
198,
220,
220,
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2147,
198,
437,
198
] | 2.197137 | 1,537 |
<gh_stars>0
passwordMatcher = r"(\d*)-(\d*) (.): (\w*)"
validPasswords = 0
for line in eachline()
result = match(passwordMatcher, line)
min = parse(Int32, result[1])
max = parse(Int32, result[2])
count = 0
for match in eachmatch(Regex(result[3]), result[4])
count += 1
end
if count >= min && count <= max
global validPasswords += 1
end
end
println(validPasswords) | [
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35235,
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12102,
14478,
10879,
8
] | 2.441176 | 170 |
## Functions for centered moments of moments feature maps
#const m1s2 = [1,2,3,4,5];
const m2lim2 = 10;
function apply_mofm_full(X::Array{Float64}, rowcounts::Int64, mom::StatisticalMoments,options)
lenresponse = length(mom.moments[1]);
momnums = options.aux;
s=size(momnums);
count =1; i =1;
if(length(s)==1)
for i in momnums
mm = mom.moments[i];
for j = 1:m2lim2
X[rowcounts,count] = get_moment_vec(mm,j);
count = count +1;
end
end
else
for mm in mom.moments
for j = 1:m2lim2
if(options.aux[i,j]==1)
X[rowcounts,count] = get_moment_vec(mm,j);
count = count +1;
end
end
i = i +1;
end
end
end
function get_num_mofm__full_features(mom::StatisticalMoments, options)
s=size(options.aux);
if(length(s)==1)
lenresponse = length(options.aux)*m2lim2;
else
lenresponse = sum(options.aux);
end
return lenresponse;
end
# Defines the feature map
fmofm_full = FeatureMap(apply_mofm_full,get_num_mofm__full_features, "mofm_full_map");
#"returns the mom_num-th moment of resps, where resps is a vector whose elements can be either numbers or vectors."
function get_moment_vec(resps, mom_num)
mu = mean(resps);
if(mom_num==1) return mu end;
num_sim = length(resps);
if(num_sim<2)
println("Not enough simulations to calculate momments, return zeros")
return 0.0*mu
end
moment = sum((mu - r).^mom_num for r in resps)./(num_sim-1.0)
return sign(moment).*abs(moment).^(1.0/mom_num) # I think the sign does make a difference, as it changes with different labels
end
| [
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220,
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7,
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737,
9,
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7,
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61,
7,
16,
13,
15,
14,
32542,
62,
22510,
8,
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314,
892,
262,
1051,
857,
787,
257,
3580,
11,
355,
340,
2458,
351,
1180,
14722,
198,
437,
628
] | 2.094899 | 843 |
mutable struct WindowState
width::Int
height::Int
end
mutable struct MouseState
pos::Tuple{Float64,Float64}
pressed::Bool
scroll::Float64
stale::Bool
end
MouseState() = MouseState((0.0, 0.0), false, 0.0, true)
struct CameraState
offset::Vector{Float32}
scale::Vector{Float32}
end
struct GLBox{N}
center::NTuple{N,Float32}
radius::NTuple{N,Float32}
color::NTuple{4,Float32}
end
struct GLBar1D
center::Float32
radius::Float32
height::Float32
end
function glGenOne(glGenFn)
id = pointer(GLuint[0])
glGenFn(1, id)
return unsafe_load(id)
end
glGenBuffer() = glGenOne(glGenBuffers)
glGenVertexArray() = glGenOne(glGenVertexArrays)
function setup_mouse_callbacks!(mouse_state, window)
mouse_button_callback = let mouse_state = mouse_state
(window, button, action, mods) -> begin
if (button == GLFW.MOUSE_BUTTON_LEFT && action == GLFW.PRESS)
mouse_state.pressed = true
end
if (button == GLFW.MOUSE_BUTTON_LEFT && action == GLFW.RELEASE)
mouse_state.pressed = false
end
end
end
cursor_position_callback = let mouse_state = mouse_state
(window, xpos, ypos) -> begin
mouse_state.pos = (xpos, ypos)
mouse_state.stale = false
end
end
scroll_callback = let mouse_state = mouse_state
(window, xoffset, yoffset) -> begin
mouse_state.scroll += yoffset
end
end
GLFW.SetScrollCallback(window, scroll_callback)
GLFW.SetCursorPosCallback(window, cursor_position_callback)
GLFW.SetMouseButtonCallback(window, mouse_button_callback)
return nothing
end
function setup_window()
GLFW.WindowHint(GLFW.CONTEXT_VERSION_MAJOR, 3)
GLFW.WindowHint(GLFW.CONTEXT_VERSION_MINOR, 3)
GLFW.WindowHint(GLFW.OPENGL_PROFILE, GLFW.OPENGL_CORE_PROFILE)
GLFW.WindowHint(GLFW.OPENGL_FORWARD_COMPAT, true)
GLFW.WindowHint(GLFW.RESIZABLE, true)
window = GLFW.CreateWindow(640, 640, "plot")
GLFW.MakeContextCurrent(window)
window_state = WindowState(640, 640)
framebuffer_size_callback = let window_state = window_state
(window, width, height) -> begin
window_state.width = width
window_state.height = height
glViewport(0, 0, width, height)
end
end
GLFW.SetFramebufferSizeCallback(window, framebuffer_size_callback)
glEnable(GL_BLEND)
glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)
glBlendEquation(GL_FUNC_ADD)
return window, window_state
end
function setup_shader_program(vert, frag)
vertex_shader = compile_shader(read(joinpath(@__DIR__, vert), String), GL_VERTEX_SHADER)
fragment_shader = compile_shader(read(joinpath(@__DIR__, frag), String), GL_FRAGMENT_SHADER)
shader_program = link_program(vertex_shader, fragment_shader)
glDeleteShader(vertex_shader)
glDeleteShader(fragment_shader)
glUseProgram(shader_program)
return shader_program
end
function update_camera2d!(camera_state, window_state, mouse_state, last_pos, last_scroll)
m_to_c = xy -> (2.0*xy[1] / window_state.width - 1.0, 1.0 - 2.0*xy[2] / window_state.height)
if mouse_state.pressed
move_diff = collect(mouse_state.pos .- last_pos)
move_diff[2] *= -1
move_diff[1] *= 2.0 / window_state.width
move_diff[2] *= 2.0 / window_state.height
camera_state.offset .+= move_diff
end
if mouse_state.scroll != last_scroll
yoffset = mouse_state.scroll - last_scroll
if yoffset > 0.5
camera_state.scale .*= 1.2
camera_state.offset .-= m_to_c(mouse_state.pos)
camera_state.offset .*= 1.2
camera_state.offset .+= m_to_c(mouse_state.pos)
elseif yoffset < -0.5
camera_state.scale .*= inv(1.2)
camera_state.offset .-= m_to_c(mouse_state.pos)
camera_state.offset .*= inv(1.2)
camera_state.offset .+= m_to_c(mouse_state.pos)
end
end
camera_state.scale[2] = camera_state.scale[1] * window_state.width / window_state.height
return nothing
end
function plot(boxes::Vector{GLBar1D})
GLFW.WindowHint(GLFW.SAMPLES, 8)
window, window_state = setup_window()
glEnable(GL_MULTISAMPLE)
shader_program = setup_shader_program("bar1d.vert", "bar1d.frag")
camera_offset_loc = glGetUniformLocation(shader_program, "camera_offset")
camera_scale_loc = glGetUniformLocation(shader_program, "camera_scale")
mouse_state = MouseState()
setup_mouse_callbacks!(mouse_state, window)
vbo = glGenBuffer()
glBindBuffer(GL_ARRAY_BUFFER, vbo)
glBufferData(GL_ARRAY_BUFFER, sizeof(boxes), boxes, GL_STATIC_DRAW)
vao = glGenVertexArray()
glBindVertexArray(vao)
glVertexAttribPointer(0, 1, GL_FLOAT, GL_FALSE, 12, Ptr{Cvoid}(0))
glVertexAttribPointer(1, 1, GL_FLOAT, GL_FALSE, 12, Ptr{Cvoid}(4))
glVertexAttribPointer(2, 1, GL_FLOAT, GL_FALSE, 12, Ptr{Cvoid}(8))
glVertexAttribDivisor(0, 1)
glVertexAttribDivisor(1, 1)
glVertexAttribDivisor(2, 1)
glEnableVertexAttribArray(0)
glEnableVertexAttribArray(1)
glEnableVertexAttribArray(2)
last_pos = mouse_state.pos
last_scroll = mouse_state.scroll
camera_state = CameraState(Float32[0.0, 0.0], Float32[1.0, 1.0])
while !GLFW.WindowShouldClose(window)
update_camera2d!(camera_state, window_state, mouse_state, last_pos, last_scroll)
last_pos = mouse_state.pos
last_scroll = mouse_state.scroll
glUniform1f(camera_offset_loc, camera_state.offset[1])
glUniform1f(camera_scale_loc, camera_state.scale[1])
glClearColor(0.0, 0.0, 0.0, 1.0)
glClear(GL_COLOR_BUFFER_BIT)
glDrawArraysInstanced(GL_TRIANGLE_FAN, 0, 4, length(boxes))
GLFW.SwapBuffers(window)
# change to wait events?
GLFW.PollEvents()
end
GLFW.DestroyWindow(window)
return nothing
end
function plot(boxes::Vector{GLBox{1}})
window, window_state = setup_window()
shader_program = setup_shader_program("box1d.vert", "boxnd.frag")
camera_offset_loc = glGetUniformLocation(shader_program, "camera_offset")
camera_scale_loc = glGetUniformLocation(shader_program, "camera_scale")
mouse_state = MouseState()
setup_mouse_callbacks!(mouse_state, window)
vbo = glGenBuffer()
glBindBuffer(GL_ARRAY_BUFFER, vbo)
glBufferData(GL_ARRAY_BUFFER, sizeof(boxes), boxes, GL_STATIC_DRAW)
vao = glGenVertexArray()
glBindVertexArray(vao)
glVertexAttribPointer(0, 1, GL_FLOAT, GL_FALSE, 24, Ptr{Cvoid}(0))
glVertexAttribPointer(1, 1, GL_FLOAT, GL_FALSE, 24, Ptr{Cvoid}(4))
glVertexAttribPointer(2, 4, GL_FLOAT, GL_FALSE, 24, Ptr{Cvoid}(8))
glVertexAttribDivisor(0, 1)
glVertexAttribDivisor(1, 1)
glVertexAttribDivisor(2, 1)
glEnableVertexAttribArray(0)
glEnableVertexAttribArray(1)
glEnableVertexAttribArray(2)
last_pos = mouse_state.pos
last_scroll = mouse_state.scroll
camera_state = CameraState(Float32[0.0, 0.0], Float32[1.0, 1.0])
while !GLFW.WindowShouldClose(window)
update_camera2d!(camera_state, window_state, mouse_state, last_pos, last_scroll)
last_pos = mouse_state.pos
last_scroll = mouse_state.scroll
glUniform1f(camera_offset_loc, camera_state.offset[1])
glUniform1f(camera_scale_loc, camera_state.scale[1])
glClearColor(0.0, 0.0, 0.0, 1.0)
glClear(GL_COLOR_BUFFER_BIT)
glDrawArraysInstanced(GL_TRIANGLE_FAN, 0, 4, length(boxes))
GLFW.SwapBuffers(window)
# change to wait events?
GLFW.PollEvents()
end
GLFW.DestroyWindow(window)
return nothing
end
function plot(boxes::Vector{GLBox{2}})
GLFW.WindowHint(GLFW.SAMPLES, 8)
window, window_state = setup_window()
glEnable(GL_MULTISAMPLE)
shader_program = setup_shader_program("box2d.vert", "boxnd.frag")
camera_offset_loc = glGetUniformLocation(shader_program, "camera_offset")
camera_scale_loc = glGetUniformLocation(shader_program, "camera_scale")
mouse_state = MouseState()
setup_mouse_callbacks!(mouse_state, window)
vbo = glGenBuffer()
glBindBuffer(GL_ARRAY_BUFFER, vbo)
glBufferData(GL_ARRAY_BUFFER, sizeof(boxes), boxes, GL_STATIC_DRAW)
vao = glGenVertexArray()
glBindVertexArray(vao)
glVertexAttribPointer(0, 2, GL_FLOAT, GL_FALSE, 32, Ptr{Cvoid}(0))
glVertexAttribPointer(1, 2, GL_FLOAT, GL_FALSE, 32, Ptr{Cvoid}(8))
glVertexAttribPointer(2, 4, GL_FLOAT, GL_FALSE, 32, Ptr{Cvoid}(16))
glVertexAttribDivisor(0, 1)
glVertexAttribDivisor(1, 1)
glVertexAttribDivisor(2, 1)
glEnableVertexAttribArray(0)
glEnableVertexAttribArray(1)
glEnableVertexAttribArray(2)
last_pos = mouse_state.pos
last_scroll = mouse_state.scroll
camera_state = CameraState(Float32[0.0, 0.0], Float32[1.0, 1.0])
while !GLFW.WindowShouldClose(window)
update_camera2d!(camera_state, window_state, mouse_state, last_pos, last_scroll)
last_pos = mouse_state.pos
last_scroll = mouse_state.scroll
glUniform2f(camera_offset_loc, camera_state.offset[1], camera_state.offset[2])
glUniform2f(camera_scale_loc, camera_state.scale[1], camera_state.scale[2])
glClearColor(0.0, 0.0, 0.0, 1.0)
glClear(GL_COLOR_BUFFER_BIT)
glDrawArraysInstanced(GL_TRIANGLE_FAN, 0, 4, length(boxes))
GLFW.SwapBuffers(window)
# change to wait events?
GLFW.PollEvents()
end
GLFW.DestroyWindow(window)
return nothing
end
function projection_matrix(fovy, aspect, zNear, zFar)
f = 1.0 / tan(fovy / 2.0)
return [
f / aspect 0 0 0;
0 f 0 0;
0 0 (zFar + zNear) / (zNear - zFar) -1;
0 0 (2 * zFar * zNear) / (zNear - zFar) 0;
]
end
function plot(boxes::Vector{GLBox{3}})
window, window_state = setup_window()
shader_program = setup_shader_program("box3d.vert", "boxnd.frag")
camera_loc = glGetUniformLocation(shader_program, "camera")
mouse_state = MouseState()
setup_mouse_callbacks!(mouse_state, window)
key_state = Dict{Int,Bool}()
key_callback = let key_state = key_state
(window, key, scancode, action, mods) -> begin
if action == GLFW.PRESS
key_state[Int(key)] = true
end
if action == GLFW.RELEASE
key_state[Int(key)] = false
end
end
end
GLFW.SetInputMode(window, GLFW.CURSOR, GLFW.CURSOR_DISABLED)
GLFW.SetKeyCallback(window, key_callback)
# Enable depth test
glEnable(GL_DEPTH_TEST)
# Accept fragment if it closer to the camera than the former one
glDepthFunc(GL_LESS)
vbo = glGenBuffer()
glBindBuffer(GL_ARRAY_BUFFER, vbo)
glBufferData(GL_ARRAY_BUFFER, sizeof(boxes), boxes, GL_STATIC_DRAW)
vao = glGenVertexArray()
glBindVertexArray(vao)
glVertexAttribPointer(0, 3, GL_FLOAT, GL_FALSE, 40, Ptr{Cvoid}(0))
glVertexAttribPointer(1, 3, GL_FLOAT, GL_FALSE, 40, Ptr{Cvoid}(12))
glVertexAttribPointer(2, 4, GL_FLOAT, GL_FALSE, 40, Ptr{Cvoid}(24))
glVertexAttribDivisor(0, 1)
glVertexAttribDivisor(1, 1)
glVertexAttribDivisor(2, 1)
glEnableVertexAttribArray(0)
glEnableVertexAttribArray(1)
glEnableVertexAttribArray(2)
camerafps = CameraFps()
last_pos = nothing
t = time()
while !GLFW.WindowShouldClose(window)
next_t = time()
dt = 60*(next_t - t)
t = next_t
if get(key_state, Int(GLFW.KEY_W), false)
walk!(camerafps, -0.3*dt)
end
if get(key_state, Int(GLFW.KEY_A), false)
strafe!(camerafps, -0.3*dt)
end
if get(key_state, Int(GLFW.KEY_S), false)
walk!(camerafps, 0.3*dt)
end
if get(key_state, Int(GLFW.KEY_D), false)
strafe!(camerafps, 0.3*dt)
end
if !mouse_state.stale
if last_pos !== nothing
yaw!(camerafps, (mouse_state.pos[1] - last_pos[1])*dt / 200.0)
end
last_pos = mouse_state.pos
end
camera_mat = projection_matrix(0.87266, window_state.width / window_state.height, 1.0, 1000.0)
camera_mat *= view_matrix(camerafps)
glUniformMatrix4fv(camera_loc, 1, GL_FALSE, Float32.(camera_mat))
glClearColor(0.0, 0.0, 0.0, 1.0)
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
glDrawArraysInstanced(GL_TRIANGLE_STRIP, 0, 14, length(boxes))
GLFW.SwapBuffers(window)
# change to wait events?
GLFW.PollEvents()
end
GLFW.DestroyWindow(window)
return nothing
end
function plot(boxfun::BoxFun)
barlist = GLBar1D[]
for (key, value) in boxfun.dict
box = key_to_box(boxfun.partition, key)
push!(barlist, GLBar1D(box.center[1], box.radius[1], value))
end
plot(barlist)
end
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7,
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1278,
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8763,
62,
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62,
19499,
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62,
26094,
8,
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3163,
20477,
6310,
2903,
7,
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62,
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40,
15567,
2538,
62,
37,
1565,
11,
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11,
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7,
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628,
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13,
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7,
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628,
220,
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220,
220,
220,
220,
220,
1303,
1487,
284,
4043,
2995,
30,
198,
220,
220,
220,
220,
220,
220,
220,
10188,
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13,
39176,
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3419,
198,
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220,
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220,
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220,
10188,
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13,
49174,
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7,
17497,
8,
198,
220,
220,
220,
1441,
2147,
198,
437,
198,
198,
8818,
7110,
7,
29305,
3712,
38469,
90,
8763,
14253,
90,
17,
11709,
8,
198,
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220,
10188,
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13,
27703,
39,
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7,
8763,
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13,
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11,
807,
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1278,
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7,
8763,
62,
44,
16724,
1797,
2390,
16437,
8,
628,
220,
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220,
33030,
62,
23065,
796,
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62,
1477,
5067,
62,
23065,
7203,
3524,
17,
67,
13,
1851,
1600,
366,
3524,
358,
13,
8310,
363,
4943,
628,
220,
220,
220,
4676,
62,
28968,
62,
17946,
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1278,
3855,
3118,
6933,
14749,
7,
1477,
5067,
62,
23065,
11,
366,
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62,
28968,
4943,
198,
220,
220,
220,
4676,
62,
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62,
17946,
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1278,
3855,
3118,
6933,
14749,
7,
1477,
5067,
62,
23065,
11,
366,
25695,
62,
9888,
4943,
628,
220,
220,
220,
10211,
62,
5219,
796,
21839,
9012,
3419,
198,
220,
220,
220,
9058,
62,
35888,
62,
13345,
10146,
0,
7,
35888,
62,
5219,
11,
4324,
8,
628,
220,
220,
220,
410,
2127,
796,
1278,
13746,
28632,
3419,
198,
220,
220,
220,
1278,
36180,
28632,
7,
8763,
62,
1503,
30631,
62,
19499,
45746,
11,
410,
2127,
8,
198,
220,
220,
220,
1278,
28632,
6601,
7,
8763,
62,
1503,
30631,
62,
19499,
45746,
11,
39364,
7,
29305,
828,
10559,
11,
10188,
62,
35744,
2149,
62,
35,
20530,
8,
628,
220,
220,
220,
410,
5488,
796,
1278,
13746,
13414,
16886,
19182,
3419,
198,
220,
220,
220,
1278,
36180,
13414,
16886,
19182,
7,
85,
5488,
8,
198,
220,
220,
220,
1278,
13414,
16886,
8086,
822,
18833,
3849,
7,
15,
11,
362,
11,
10188,
62,
3697,
46,
1404,
11,
10188,
62,
37,
23719,
11,
3933,
11,
350,
2213,
90,
34,
19382,
92,
7,
15,
4008,
198,
220,
220,
220,
1278,
13414,
16886,
8086,
822,
18833,
3849,
7,
16,
11,
362,
11,
10188,
62,
3697,
46,
1404,
11,
10188,
62,
37,
23719,
11,
3933,
11,
350,
2213,
90,
34,
19382,
92,
7,
23,
4008,
198,
220,
220,
220,
1278,
13414,
16886,
8086,
822,
18833,
3849,
7,
17,
11,
604,
11,
10188,
62,
3697,
46,
1404,
11,
10188,
62,
37,
23719,
11,
3933,
11,
350,
2213,
90,
34,
19382,
92,
7,
1433,
4008,
628,
220,
220,
220,
1278,
13414,
16886,
8086,
822,
24095,
271,
273,
7,
15,
11,
352,
8,
198,
220,
220,
220,
1278,
13414,
16886,
8086,
822,
24095,
271,
273,
7,
16,
11,
352,
8,
198,
220,
220,
220,
1278,
13414,
16886,
8086,
822,
24095,
271,
273,
7,
17,
11,
352,
8,
628,
220,
220,
220,
1278,
36695,
13414,
16886,
8086,
822,
19182,
7,
15,
8,
198,
220,
220,
220,
1278,
36695,
13414,
16886,
8086,
822,
19182,
7,
16,
8,
198,
220,
220,
220,
1278,
36695,
13414,
16886,
8086,
822,
19182,
7,
17,
8,
628,
220,
220,
220,
938,
62,
1930,
796,
10211,
62,
5219,
13,
1930,
198,
220,
220,
220,
938,
62,
48728,
796,
10211,
62,
5219,
13,
48728,
628,
220,
220,
220,
4676,
62,
5219,
796,
20432,
9012,
7,
43879,
2624,
58,
15,
13,
15,
11,
657,
13,
15,
4357,
48436,
2624,
58,
16,
13,
15,
11,
352,
13,
15,
12962,
628,
220,
220,
220,
981,
5145,
8763,
24160,
13,
27703,
19926,
26125,
7,
17497,
8,
198,
220,
220,
220,
220,
220,
220,
220,
4296,
62,
25695,
17,
67,
0,
7,
25695,
62,
5219,
11,
4324,
62,
5219,
11,
10211,
62,
5219,
11,
938,
62,
1930,
11,
938,
62,
48728,
8,
628,
220,
220,
220,
220,
220,
220,
220,
938,
62,
1930,
796,
10211,
62,
5219,
13,
1930,
198,
220,
220,
220,
220,
220,
220,
220,
938,
62,
48728,
796,
10211,
62,
5219,
13,
48728,
628,
220,
220,
220,
220,
220,
220,
220,
1278,
3118,
6933,
17,
69,
7,
25695,
62,
28968,
62,
17946,
11,
4676,
62,
5219,
13,
28968,
58,
16,
4357,
4676,
62,
5219,
13,
28968,
58,
17,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
1278,
3118,
6933,
17,
69,
7,
25695,
62,
9888,
62,
17946,
11,
4676,
62,
5219,
13,
9888,
58,
16,
4357,
4676,
62,
5219,
13,
9888,
58,
17,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
1278,
19856,
10258,
7,
15,
13,
15,
11,
657,
13,
15,
11,
657,
13,
15,
11,
352,
13,
15,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1278,
19856,
7,
8763,
62,
46786,
62,
19499,
45746,
62,
26094,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1278,
25302,
3163,
20477,
6310,
2903,
7,
8763,
62,
5446,
40,
15567,
2538,
62,
37,
1565,
11,
657,
11,
604,
11,
4129,
7,
29305,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
10188,
24160,
13,
10462,
499,
36474,
364,
7,
17497,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
1487,
284,
4043,
2995,
30,
198,
220,
220,
220,
220,
220,
220,
220,
10188,
24160,
13,
39176,
37103,
3419,
198,
220,
220,
220,
886,
628,
220,
220,
220,
10188,
24160,
13,
49174,
27703,
7,
17497,
8,
198,
220,
220,
220,
1441,
2147,
198,
437,
198,
198,
8818,
20128,
62,
6759,
8609,
7,
69,
27796,
11,
4843,
11,
1976,
40640,
11,
1976,
21428,
8,
198,
220,
220,
220,
277,
796,
352,
13,
15,
1220,
25706,
7,
69,
27796,
1220,
362,
13,
15,
8,
628,
220,
220,
220,
1441,
685,
198,
220,
220,
220,
220,
220,
220,
220,
277,
1220,
4843,
657,
657,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
657,
26,
198,
220,
220,
220,
220,
220,
220,
220,
657,
220,
220,
220,
220,
220,
220,
220,
220,
220,
277,
657,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
657,
26,
198,
220,
220,
220,
220,
220,
220,
220,
657,
220,
220,
220,
220,
220,
220,
220,
220,
220,
657,
357,
89,
21428,
1343,
1976,
40640,
8,
1220,
357,
89,
40640,
532,
1976,
21428,
8,
220,
220,
220,
220,
532,
16,
26,
198,
220,
220,
220,
220,
220,
220,
220,
657,
220,
220,
220,
220,
220,
220,
220,
220,
220,
657,
357,
17,
1635,
1976,
21428,
1635,
1976,
40640,
8,
1220,
357,
89,
40640,
532,
1976,
21428,
8,
220,
657,
26,
198,
220,
220,
220,
2361,
198,
437,
198,
198,
8818,
7110,
7,
29305,
3712,
38469,
90,
8763,
14253,
90,
18,
11709,
8,
198,
220,
220,
220,
4324,
11,
4324,
62,
5219,
796,
9058,
62,
17497,
3419,
628,
220,
220,
220,
33030,
62,
23065,
796,
9058,
62,
1477,
5067,
62,
23065,
7203,
3524,
18,
67,
13,
1851,
1600,
366,
3524,
358,
13,
8310,
363,
4943,
628,
220,
220,
220,
4676,
62,
17946,
796,
1278,
3855,
3118,
6933,
14749,
7,
1477,
5067,
62,
23065,
11,
366,
25695,
4943,
628,
220,
220,
220,
10211,
62,
5219,
796,
21839,
9012,
3419,
198,
220,
220,
220,
9058,
62,
35888,
62,
13345,
10146,
0,
7,
35888,
62,
5219,
11,
4324,
8,
628,
220,
220,
220,
1994,
62,
5219,
796,
360,
713,
90,
5317,
11,
33,
970,
92,
3419,
628,
220,
220,
220,
1994,
62,
47423,
796,
1309,
1994,
62,
5219,
796,
1994,
62,
5219,
198,
220,
220,
220,
220,
220,
220,
220,
357,
17497,
11,
1994,
11,
629,
1192,
1098,
11,
2223,
11,
13743,
8,
4613,
2221,
198,
220,
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] | 2.171792 | 6,048 |
module CodeInfoTools
using Core: CodeInfo
import Base: iterate, push!, pushfirst!, insert!, delete!, getindex, lastindex, setindex!, display, +, length, identity
import Base: show
#####
##### Exports
#####
export var, Variable, Canvas, renumber, code_info, finish, get_slot
#####
##### Utilities
#####
const Variable = Core.SSAValue
var(id::Int) = Variable(id)
Base.:(+)(v::Variable, id::Int) = Variable(v.id + id)
Base.:(+)(id::Int, v::Variable) = Variable(v.id + id)
function code_info(f, tt; generated=true, debuginfo=:default)
ir = code_lowered(f, tt; generated=generated, debuginfo=:default)
isempty(ir) && return nothing
return ir[1]
end
function get_slot(ci::CodeInfo, s::Symbol)
ind = findfirst(el -> el == s, ci.slotnames)
ind == nothing && return
return Core.Compiler.SlotNumber(ind)
end
@doc(
"""
code_info(f, tt; generate = true, debuginfo = :default)
Return lowered code for function `f` with tuple type `tt`. Equivalent to `InteractiveUtils.@code_lowered` -- but a function call and requires a tuple type `tt` as input.
""", code_info)
walk(fn, x) = fn(x)
walk(fn, x::Variable) = fn(x)
walk(fn, x::Core.ReturnNode) = Core.ReturnNode(walk(fn, x.val))
walk(fn, x::Core.GotoNode) = Core.GotoNode(walk(fn, x.label))
walk(fn, x::Core.GotoIfNot) = Core.GotoIfNot(walk(fn, x.cond), walk(fn, x.dest))
walk(fn, x::Expr) = Expr(x.head, map(a -> walk(fn, a), x.args)...)
function walk(fn, x::Vector)
map(x) do el
walk(fn, el)
end
end
resolve(x) = x
resolve(gr::GlobalRef) = getproperty(gr.mod, gr.name)
#####
##### Canvas
#####
struct Canvas
defs::Vector{Tuple{Int, Int}}
code::Vector{Any}
codelocs::Vector{Int32}
end
Canvas() = Canvas(Tuple{Int, Int}[], Any[], Int32[])
@doc(
"""
```julia
struct Canvas
defs::Vector{Int}
code::Vector{Any}
codelocs::Vector{Int32}
end
Canvas() = Canvas(Int[], Any[], Int32[])
```
A `Vector`-like abstraction for `Core` code nodes.
Properties to keep in mind:
1. Insertion anywhere is slow.
2. Pushing to beginning is slow.
2. Pushing to end is fast.
3. Deletion is fast.
4. Accessing elements is fast.
5. Setting elements is fast.
6. Calling `renumber` must walk the entire `Canvas` instance to update SSA values -- slow.
Thus, if you build up a `Canvas` instance incrementally, everything should be fast.
""", Canvas)
length(c::Canvas) = length(filter(x -> x[2] > 0, c.defs))
function getindex(c::Canvas, idx::Int)
r, ind = c.defs[idx]
@assert ind > 0
getindex(c.code, r)
end
getindex(c::Canvas, v::Variable) = getindex(c, v.id)
function push!(c::Canvas, stmt)
push!(c.code, stmt)
push!(c.codelocs, Int32(1))
l = length(c.defs) + 1
push!(c.defs, (l, l))
return Variable(length(c.defs))
end
function insert!(c::Canvas, idx::Int, x)
r, ind = c.defs[idx]
@assert(ind > 0)
push!(c.code, x)
push!(c.codelocs, Int32(1))
for i in 1 : length(c.defs)
r, k = c.defs[i]
if k > 0 && k >= ind
c.defs[i] = (r, k + 1)
end
end
push!(c.defs, (length(c.defs) + 1, ind))
return Variable(length(c.defs))
end
insert!(c::Canvas, v::Variable, x) = insert!(c, v.id, x)
pushfirst!(c::Canvas, x) = insert!(c, 1, x)
setindex!(c::Canvas, x, v::Int) = setindex!(c.code, x, v)
setindex!(c::Canvas, x, v::Variable) = setindex!(c, x, v.id)
function delete!(c::Canvas, idx::Int)
c.code[idx] = nothing
c.defs[idx] = (idx, -1)
end
delete!(c::Canvas, v::Variable) = delete!(c, v.id)
_get(d::Dict, c, k) = c
_get(d::Dict, c::Variable, k) = haskey(d, c.id) ? Variable(getindex(d, c.id)) : nothing
function renumber(c::Canvas)
s = sort(filter(v -> v[2] > 0, c.defs); by = x -> x[2])
d = Dict((s[i][1], i) for i in 1 : length(s))
ind = first.(s)
swap = walk(k -> _get(d, k, k), c.code)
return Canvas(Tuple{Int, Int}[(i, i) for i in 1 : length(s)],
getindex(swap, ind), getindex(c.codelocs, ind))
end
#####
##### Pretty printing
#####
print_stmt(io::IO, ex) = print(io, ex)
print_stmt(io::IO, ex::Expr) = print_stmt(io::IO, Val(ex.head), ex)
const tab = " "
function show(io::IO, c::Canvas)
indent = get(io, :indent, 0)
bs = get(io, :bindings, Dict())
for (r, ind) in sort(c.defs; by = x -> x[2])
ind > 0 || continue
println(io)
print(io, tab^indent, " ")
print(io, string("%", r), " = ")
ex = get(c.code, r, nothing)
ex == nothing ? print(io, "nothing") : print_stmt(io, ex)
end
end
print_stmt(io::IO, ::Val, ex) = print(io, ex)
function print_stmt(io::IO, ::Val{:enter}, ex)
print(io, "try (outer %$(ex.args[1]))")
end
function print_stmt(io::IO, ::Val{:leave}, ex)
print(io, "end try (start %$(ex.args[1]))")
end
function print_stmt(io::IO, ::Val{:pop_exception}, ex)
print(io, "pop exception $(ex.args[1])")
end
#####
##### Pipe
#####
struct NewVariable
id::Int
end
mutable struct Pipe
from::CodeInfo
to::Canvas
map::Dict{Any, Any}
var::Int
end
function Pipe(ci::CodeInfo)
canv = Canvas()
p = Pipe(ci, canv, Dict(), 0)
return p
end
@doc(
"""
Pipe(ir)
A wrapper around a `Canvas` object. Call [`finish`](@ref) when done to produce a new `CodeInfo` instance.
""", Pipe)
get_slot(p::Pipe, s::Symbol) = get_slot(p.from, s)
# This is used to handle NewVariable instances.
substitute!(p::Pipe, x, y) = (p.map[x] = y; x)
substitute(p::Pipe, x) = get(p.map, x, x)
substitute(p::Pipe, x::Expr) = Expr(x.head, substitute.((p, ), x.args)...)
substitute(p::Pipe, x::Core.GotoNode) = Core.GotoNode(substitute(p, x.label))
substitute(p::Pipe, x::Core.GotoIfNot) = Core.GotoIfNot(substitute(p, x.cond), substitute(p, x.dest))
substitute(p::Pipe, x::Core.ReturnNode) = Core.ReturnNode(substitute(p, x.val))
length(p::Pipe) = length(p.to)
getindex(p::Pipe, v) = getindex(p.to, v)
function getindex(p::Pipe, v::Union{Variable, NewVariable})
tg = substitute(p, v)
return getindex(p.to, tg)
end
lastindex(p::Pipe) = length(p.to)
function pipestate(ci::CodeInfo)
ks = sort([Variable(i) => v for (i, v) in enumerate(ci.code)], by = x -> x[1].id)
return first.(ks)
end
function iterate(p::Pipe, (ks, i) = (pipestate(p.from), 1))
i > length(ks) && return
v = ks[i]
st = walk(resolve, p.from.code[v.id])
substitute!(p, v, push!(p.to, substitute(p, st)))
return ((v, st), (ks, i + 1))
end
var!(p::Pipe) = NewVariable(p.var += 1)
function Base.push!(p::Pipe, x)
tmp = var!(p)
v = push!(p.to, substitute(p, x))
substitute!(p, tmp, v)
return tmp
end
function Base.pushfirst!(p::Pipe, x)
tmp = var!(p)
v = pushfirst!(p.to, substitute(p, x))
substitute!(p, tmp, v)
return tmp
end
function setindex!(p::Pipe, x, v::Union{Variable, NewVariable})
k = substitute(p, v)
setindex!(p.to, substitute(p, x), k)
end
function insert!(p::Pipe, v::Union{Variable, NewVariable}, x; after = false)
v′ = substitute(p, v).id
x = substitute(p, x)
tmp = var!(p)
substitute!(p, tmp, insert!(p.to, v′ + after, x))
return tmp
end
function Base.delete!(p::Pipe, v::Union{Variable, NewVariable})
v′ = substitute(p, v)
delete!(p.to, v′)
end
function finish(p::Pipe)
new_ci = copy(p.from)
c = renumber(p.to)
new_ci.code = c.code
new_ci.codelocs = c.codelocs
new_ci.slotnames = p.from.slotnames
new_ci.slotflags = [0x00 for _ in new_ci.slotnames]
new_ci.inferred = false
new_ci.inlineable = p.from.inlineable
new_ci.ssavaluetypes = length(p.to)
return new_ci
end
@doc(
"""
finish(p::Pipe)
Create a new `CodeInfo` instance from a [`Pipe`](@ref). Renumbers the wrapped `Canvas` in-place -- then copies information from the original `CodeInfo` instance and inserts modifications from the wrapped `Canvas`.
""", finish)
Base.display(p::Pipe) = display(p.to)
function Base.identity(p::Pipe)
for (v, st) in p
end
return p
end
end # module
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] | 2.266457 | 3,494 |
@testset "CookieCutter" begin
problem = SimulationProblem(CartesianGrid(100,100), (:facies => Int, :property => Float64), 3)
solver = CookieCutter(DummySimSolver(:facies=>NamedTuple()), Dict(0=>DummySimSolver(), 1=>DummySimSolver()))
@test sprint(show, solver) == "CookieCutter"
@test sprint(show, MIME"text/plain"(), solver) == "CookieCutter\n └─facies ⇨ DummySimSolver\n └─0 ⇨ DummySimSolver\n └─1 ⇨ DummySimSolver"
Random.seed!(1234)
solution = solve(problem, solver)
if visualtests
@test_reference "data/COOKIE.png" plot(solution)
end
end
| [
31,
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] | 2.495652 | 230 |
importall Bukdu
type TextController <: ApplicationController
end
layout(::Layout, body) = """empty layout"""
index(::TextController) = render(Text)
index_with_layout(::TextController) = render(Text/Layout)
Router() do
get("/index", TextController, index)
get("/index_with_layout", TextController, index_with_layout)
end
using Base.Test
conn = (Router)(get, "/index")
@test 200 == conn.status
@test "text/plain" == conn.resp_header["Content-Type"]
@test "" == conn.resp_body
logs = []
before(render, Text) do
push!(logs, "b")
end
after(render, Text) do
push!(logs, "a")
end
conn = (Router)(get, "/index")
@test "" == conn.resp_body
@test ["b", "a"] == logs
empty!(logs)
conn = (Router)(get, "/index_with_layout")
@test "text/plain" == conn.resp_header["Content-Type"]
@test "empty layout" == conn.resp_body
@test ["b", "a"] == logs
empty!(logs)
layout(::Layout, body::String) = """layout - $body"""
index1(::TextController) = render(Text, "hello")
index_with_layout1(::TextController) = render(Text/Layout, "hello")
Router() do
get("/index1", TextController, index1)
get("/index_with_layout1", TextController, index_with_layout1)
end
conn = (Router)(get, "/index1")
@test "text/plain" == conn.resp_header["Content-Type"]
@test "hello" == conn.resp_body
@test [] == logs
before(render, Text) do body
push!(logs, "b $body")
end
after(render, Text) do body
push!(logs, "a $body")
end
conn = (Router)(get, "/index1")
@test "text/plain" == conn.resp_header["Content-Type"]
@test "hello" == conn.resp_body
@test ["b hello", "a hello"] == logs
empty!(logs)
conn = (Router)(get, "/index_with_layout1")
@test "text/plain" == conn.resp_header["Content-Type"]
@test "layout - hello" == conn.resp_body
@test ["b hello", "a hello"] == logs
empty!(logs)
before(render, Text/Layout) do body
push!(logs, "bl $body")
end
after(render, Text/Layout) do body
push!(logs, "al $body")
end
conn = (Router)(get, "/index1")
@test "text/plain" == conn.resp_header["Content-Type"]
@test "hello" == conn.resp_body
@test ["b hello", "a hello"] == logs
empty!(logs)
conn = (Router)(get, "/index_with_layout1")
@test "text/plain" == conn.resp_header["Content-Type"]
@test "layout - hello" == conn.resp_body
@test ["bl hello","b hello","a hello","al hello"] == logs
empty!(logs)
layout(::Layout, body::String, c::TextController) = """layout2 - $body, $c"""
index2(c::TextController) = render(Text, "foo", c)
index_with_layout2(c::TextController) = render(Text/Layout, "foo", c)
Router() do
get("/index2", TextController, index2)
get("/index_with_layout2", TextController, index_with_layout2)
end
conn = (Router)(get, "/index2")
@test "text/plain" == conn.resp_header["Content-Type"]
@test "foo" == conn.resp_body
@test [] == logs
empty!(logs)
before(render, Text) do body, c
push!(logs, "b $body, $c")
end
after(render, Text) do body, c
push!(logs, "a $body, $c")
end
before(render, Text/Layout) do body, c
push!(logs, "bl")
end
after(render, Text/Layout) do body, c
push!(logs, "al")
end
conn = (Router)(get, "/index_with_layout2")
@test "text/plain" == conn.resp_header["Content-Type"]
@test "layout2 - foo, TextController()" == conn.resp_body
@test ["bl","b foo, TextController()","a foo, TextController()","al"] == logs
empty!(logs)
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] | 2.643144 | 1,247 |
# Active patterns
module Active
using MLStyle
using MLStyle.Qualification
using MLStyle.AbstractPatterns
export @active, active_def
@nospecialize
function active_def(P, body, mod::Module, line::LineNumberNode)
@switch P begin
@case Expr(:call,
Expr(:curly, t, type_args...) || t && let type_args = [] end,
arg
) && if t isa Symbol end
@case _
error("malformed active pattern definition: $P")
end
definition = if isdefined(mod, t)
line
else
:(struct $t end)
end
parametric = isempty(type_args) ? [] : type_args
prepr = "$P"
token = gensym(prepr)
v_ty = Val{(view, token)}
v_val = Val((view, token))
quote
$definition
(::$v_ty)($(parametric...), ) = $arg -> $body
$line
function $MLStyle.pattern_uncall(t::($t isa Function ? typeof($t) : Type{$t}), self::Function, type_params, type_args, args)
$line
isempty(type_params) || error("A ($t) pattern requires no type params.")
parametric = isempty(type_args) ? [] : type_args
n_args = length(args)
function trans(expr)
Expr(:call, Expr(:call, $v_val, parametric...), expr)
end
function guard2(expr)
if n_args === 0
:($expr isa Bool && $expr)
elseif n_args === 1
expr_s = "$t(x)"
msg = "invalid use of active patterns: " *
"1-ary view pattern($expr_s) should accept Union{Some{T}, Nothing} " *
"instead of Union{T, Nothing}! " *
"A simple solution is:\n" *
" (@active $expr_s ex) =>\n (@active $expr_s let r=ex; r === nothing? r : Some(r)) end"
:($expr !== nothing && ($expr isa $Some || begin
$error($msg)
end))
else
:($expr isa $Tuple && length($expr) === $n_args)
end
end
extract = if n_args <= 1
function (expr::Any, i::Int, ::Any, ::Any)
expr
end
else
function (expr::Any, i::Int, ::Any, ::Any)
:($expr[$i])
end
end
type_infer(_...) = Any
comp = $PComp(
$prepr, type_infer;
view=$SimpleCachablePre(trans),
guard2=$NoncachablePre(guard2)
)
ps = if n_args === 0
[]
elseif n_args === 1
[self(Expr(:call, Some, args[1]))]
else
[self(e) for e in args]
end
$decons(comp, extract, ps)
end
end
end
"""
Simple active pattern implementation.
You can give a qualifier in the first argument of `@active` to customize its visibility in other modules.
```julia
@active F(x) begin
if x > 0
nothing
else
:ok
end
end
@match -1 begin
F(:ok) => false
_ => true
end # true
@active public IsEven(x) begin
x % 2 === 0
end
@match 4 begin
IsEven() => :ok
_ => :err
end # :ok
```
"""
macro active(qualifier, case, active_body)
deprecate_qualifier_macro(qualifier, __source__)
active_def(case, active_body, __module__, __source__) |> esc
end
macro active(case, active_body)
active_def(case, active_body, __module__, __source__) |> esc
end
@specialize
end | [
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31,
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] | 1.843996 | 2,032 |
<gh_stars>100-1000
using ..Atmos
using ..ConfigTypes
using ..DiagnosticsMachine
@diagnostics_group(
"AtmosLESDefault", # name
AtmosLESConfigType, # configuration type
Nothing, # params type
(_...) -> nothing, # initialization function
NoInterpolation, # if/when to interpolate
# various horizontal averages
u,
v,
w,
rho,
temp,
pres,
thd,
et,
ei,
ht,
hi,
w_ht_sgs,
# moisture related
qt,
ql,
qi,
qv,
thv,
thl,
w_qt_sgs,
# for variances and co-variances
uu,
vv,
ww,
www,
eiei,
wu,
wv,
wrho,
wthd,
wei,
qtqt,
thlthl,
wqt,
wql,
wqi,
wqv,
wthv,
wthl,
qtthl,
qtei,
#cld_top, TODO
#cld_base, TODO
#cld_cover, TODO
#lwp, TODO
#rwp, TODO
)
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] | 1.74502 | 502 |
<reponame>AShedko/fMRI_ISC_classify
push!(LOAD_PATH, @__DIR__)
using ISCLib
using HDF5, Images, NIfTI
using DataFrames,ExcelReaders, Logging, MultivariateStats, Stats
Logging.configure(level=DEBUG)
function collect_groups(labels)
groups = [Int[] for i = 1:maximum(labels)]
for (i,l) in enumerate(labels)
if l != 0
push!(groups[l], i)
end
end
groups
end
δ(ind::CartesianIndex) = CartesianRange(ind-1, ind+1)
function rndsample(dfr, α::Real=0.2)
dfr[sample(collect(1:size(dfr, 1)), Int(ceil( α * size(dfr, 1)))), :]
end
function iscmask(a)
# load Segmentation
mask_S = niread("$(@__DIR__)/out/S|segmentation_result.nii").raw;
mask_V = niread("$(@__DIR__)/out/V|segmentation_result.nii").raw;
mask_SV = niread("$(@__DIR__)/out/S_V|segmentation_result.nii").raw;
mask_W = max(mask_S,mask_V,mask_SV);
# mask_W = zeros(mask_S)
# idx = (mask_S.> a) $ (mask_V.> a)
# mask_W[idx] = mask_SV[idx]
# mask_W
end
function glmmask(a)
# load Segmentation
# mask_W = niread("../../matlab/glm_res/ResMS.nii")
mask_1 = niread("$(@__DIR__)/../../matlab/glm_res/beta_0001.nii").raw
mask_1 = mask_1 ./ maximum(mask_1[mask_1.>0])
mask_2 = niread("$(@__DIR__)/../../matlab/glm_res/beta_0002.nii").raw
mask_2 = mask_2 ./ maximum(mask_2[mask_2.>0])
mask_W = max(mask_1,mask_2)
mask_W
end
function get_segment(a = .1, b = 0.03, pref="isc")
print("in")
if pref =="isc"
mask_W = iscmask(a)
elseif pref == "ttest" || pref == "tstat_all"
mask_W = niread("$(@__DIR__)/out/Tstat|segmentation_result.nii").raw;
elseif pref == "glm"
mask_W = glmmask(a)
end
SVd = h5read("$(@__DIR__)/out/S_V|disps.h5", "data");
# Average disps
SVd = sum(SVd,2);
med = reduce(+,[get_mean(i) for i in 1:NSUBJ])./NSUBJ
coeff_of_var = reshape(sqrt.(SVd),SHAPE)./med
coeff_of_var[isnan.(coeff_of_var)] = 0
atl = niread("$(@__DIR__)/../../Harvard-Oxford\ cortical\ and\ subcortical\ structural\ atlases/HarvardOxford-cort-maxprob-thr0-2mm.nii.gz").raw
# Compute mask
isc_r = copy(mask_W);
isc_r = dilate(isc_r)
isc_r[!((atl.>0) & (mask_W .> a) & (coeff_of_var .>b ) & (med .> 100)) ]=0;
ni = get_nii(1,1)
res = NIfTI.NIVolume(ni.header, ni.extensions, isc_r)
niwrite("$(@__DIR__)/out/$(pref)_r.nii",res)
# get rid of noise (scanning artifacts have very high CV)
# normed_cv = coeff_of_var./maximum(coeff_of_var)
# get connected components
labels = label_components(isc_r.>0)
groups = filter(x->length(x)>40,collect_groups(labels))
inds = get_result_inds(isc_r, groups)
inds
end
function get_result_inds(isc_r, groups)
mask = falses(isc_r)
for g in groups
mask[g] = true
end
inds = CartesianIndex[]
for g in groups
for el in g
!mask[el] && continue
ind = CartesianIndex(ind2sub(SHAPE,el))
push!(inds,ind)
for i in δ(ind)
mask[i] = false
end
end
end
inds
end
function extract(ni, inds)
raw = ni.raw
out = zeros(length(inds))
for (k,ind) in enumerate(inds)
s = 0
for j in δ(ind)
s += raw[j]
end
out[k] = s/27
end
out
end
function get_params(niis, inds, num)
"""
Gives you one point in feature space
"""
len = length(niis)
stp = Int(ceil(len/num))
frames = hcat(map(x -> extract(x, inds),niis)...)
ms = [zeros(size(frames,1)) for i in 1:num]
ds = [zeros(size(frames,1)) for i in 1:num]
for i = 1:num
ms[i] = squeeze(mean(frames[:,1+stp*(i-1): min(stp*i,len)], 2),2)
ds[i] = squeeze(var(frames[:,1+stp*(i-1): min(stp*i,len)], 2, mean = ms[i]),2)
end
return vcat(ms...,ds...)
end
function get_data(inds, perc::Real = 0.2; transf = s->s[1]=='V',num = 3)
"""
inds is a point cloud, describing a ROI, perc is the percentage of the dataset
Gives you a sequence of points in feature space and labels for them
(X , y)
"""
coll = []
for subj in shuffle(1:NSUBJ)
data = readxl(DataFrame, joinpath(ISCLib.PATH,"logs","$subj.xlsx"), "Лист1!A1:G$(NEXP+1)");
data[:Stimul_1] = map(transf,data[:Stimul_1])
data[:Stimul] = map(Int,(ceil.(2*data[:Stimul])))
data[:Response] = map(Int,(ceil.(2*data[:Response])))
ind_end = data[[:Stimul,:Response,:Stimul_1]][2:end-2,:]
push!(coll,(subj,ind_end))
end
count = 0
X = Vector{Float32}[]
y = []
for col in coll
subj = col[1]
dfr = col[2]
debug(subj)
idxs = Array(rndsample(dfr,perc))
for i in 1:size(idxs,1)
row = view(idxs, i, :)
if row[2]- row[1] < num*2
continue
else
params = get_params(get_niis(subj,row[1]:row[2]),inds, num)
push!(X,params)
push!(y,row[3])
end
end
end
X,y
end
function extract_data(;a = .4, b = 0.02, rnd = false, pref = "", num = 3,)
if rnd
inds = [CartesianIndex((rand(10:80),rand(10:80),rand(10:80))) for i in 1:500]
else
inds = get_segment(a, b, pref);
end
X,y = get_data(inds, 0.99, transf = x->x, num = num);
X = hcat(X...)
y = Vector{typeof(y[1])}(y)
h5open("out/$pref|res_x.h5", "w") do file
write(file, "data", X)
end
h5open("out/$pref|res_y.h5", "w") do file
write(file,"data", y)
end
X,y
end
# extract_data(a = 0.16, b = 0.03, pref = "isc", num=3)
extract_data(a = 40, b = 0.03, pref = "tstat_all", num=3)
# extract_data(a = 0.16, b = 0.03, pref = "glm", num=3)
# extract_data(rnd = true, pref = "rnd")
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] | 1.942684 | 2,966 |
<reponame>dennisprangle/ABCDistances.jl
using ABCDistances
using Base.Test
include("abc.jl")
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] | 2.764706 | 34 |
<filename>test/Solvers/cdcs_tests.jl
include("solver_preamble.jl")
import CDCS
# Iterations:
# dsos_concave_then_convex_cubic : > 2000, < 3000
# chebyshev : > 12000, < 12500
factory = optimizer_with_attributes(CDCS.Optimizer, "verbose" => 0, "maxIter" => 12500)
# chebyshev : > 2e-3, < 3e-3
config = MOI.Test.TestConfig(atol=3e-3, rtol=3e-3, query=false)
@testset "Linear" begin
Tests.linear_test(factory, config)
end
@testset "SOC" begin
Tests.soc_test(factory, config, [
"sdsos_options_pricing"
])
end
@testset "SDP" begin
Tests.sd_test(factory, config, [
"sos_options_pricing"
])
end
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] | 2.193662 | 284 |
<reponame>UnofficialJuliaMirror/ApproxFunBase.jl-fbd15aa5-315a-5a7d-a8a4-24992e37be05
## Linear Solve
for TYP in (:Fun,:StridedVector,:AbstractVector,:Any)
@eval function \(A::Operator,b::$TYP;kwds...)
if isambiguous(domainspace(A))
A=choosespaces(A,b)
if isambiguous(domainspace(A))
error("Cannot infer spaces")
end
\(A,b;kwds...)
else
Fun(domainspace(A),
ldiv_coefficients(A,coefficients(b,rangespace(A));kwds...))
end
end
end
"""
\\(A::Operator,b;tolerance=tol,maxlength=n)
solves a linear equation, usually differential equation, where `A` is an operator
or array of operators and `b` is a `Fun` or array of funs. The result `u`
will approximately satisfy `A*u = b`.
"""
\(::Operator,_)
# Solve each column separately
function \(A::Operator, B::AbstractMatrix; kwds...)
ds=domainspace(A)
if isambiguous(ds)
return choosespaces(A,B[:,1])\B
end
ret=Matrix{VFun{typeof(ds),
promote_type(eltype(A),mapreduce(cfstype,promote_type,B))}}(undef,1,size(B,2))
QR = factorize(A) # reuse computation
for j=1:size(B,2)
ret[1,j] = \(QR,B[:,j];kwds...)
end
Fun(ret)
end
\(A::Operator,B::MatrixFun;kwds...) = \(A,Array(B);kwds...)
ldiv_coefficients(A::Operator,b;kwds...) = ldiv_coefficients(qr(A),b;kwds...)
\(A::Operator,B::Operator) = TimesOperator(inv(A),B)
#TODO: Remove these when interlace is automatic
for TYP in (:Vector,:Matrix)
@eval begin
\(A::$TYP{OO},b::StridedVecOrMat;kwds...) where {OO<:Operator} =
\(interlace(A),b;kwds...)
\(A::$TYP{OO},b::AbstractVecOrMat;kwds...) where {OO<:Operator} =
\(interlace(A),b;kwds...)
\(A::$TYP{OO},b::Fun;kwds...) where {OO<:Operator} =
\(interlace(A),b;kwds...)
end
end
ldiv_coefficients(A::AbstractArray{OO},b;kwds...) where {OO<:Operator} =
ldiv_coefficients(interlace(A),b;kwds...)
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] | 2.084551 | 958 |
<filename>docs/make.jl
using Photodynamics
using Documenter
DocMeta.setdocmeta!(Photodynamics, :DocTestSetup, :(using Photodynamics); recursive=true)
makedocs(;
modules=[Photodynamics],
authors="<NAME> <<EMAIL>> and contributors",
repo="https://github.com/langfzac/Photodynamics.jl/blob/{commit}{path}#{line}",
sitename="Photodynamics.jl",
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
canonical="https://langfzac.github.io/Photodynamics.jl",
assets=String[],
),
pages=[
"Home" => "index.md",
],
)
deploydocs(;
repo="github.com/langfzac/Photodynamics.jl",
devbranch="main"
)
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] | 2.472527 | 273 |
<gh_stars>1-10
instructions = readlines("day12/input.txt")
mutable struct Ship
x::Int
y::Int
angle::Int
end
ship = Ship(0, 0, 0)
function step_instruction(instruction)
global ship
command, amount = instruction[1], parse(Int, instruction[2:end])
if command == 'N'
ship.y += amount
elseif command == 'S'
ship.y -= amount
elseif command == 'E'
ship.x += amount
elseif command == 'W'
ship.x -= amount
elseif command == 'L'
ship.angle = mod((ship.angle + amount), 360)
elseif command == 'R'
ship.angle = mod((ship.angle - amount), 360)
elseif command == 'F'
directions = Dict(90 => 'N', 180 => 'W', 270 => 'S', 0 => 'E')
step_instruction(directions[ship.angle] * string(amount))
end
end
for ins in instructions
step_instruction(ins)
end
println(abs(ship.x)+abs(ship.y)) | [
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] | 2.370667 | 375 |
using Suppressor
using StableRNGs
using Term
using Test
import Term: console_width
const RNG = StableRNG(1337)
include("__test_utils.jl")
using TimerOutputs: TimerOutputs, @timeit
const TIMEROUTPUT = TimerOutputs.TimerOutput()
dotest = console_width() ≥ 88
# ? 1 - text utils
tprint("{bold green}Running: '01_test_text_utils.jl' {/bold green}")
@time @timeit_include("01_test_text_utils.jl")
# ? 2 ansi
tprint("\n\n{bold green}Running: '02_test_ansi.jl' {/bold green}") # ansi & col' {/bold green}")
@time @timeit_include("02_test_ansi.jl")
# ? 3 measure
tprint("\n\n{bold green}Running: '03_test_measure.jl' {/bold green}")
@time @timeit_include("03_test_measure.jl")
# ? 4 style
tprint("\n\n{bold green}Running: '04_test_style.jl' {/bold green}")
@time @timeit_include("04_test_style.jl")
# ? 5 macros
tprint("\n\n{bold green}Running: '05_test_macros.jl' {/bold green}")
@time @timeit_include("05_test_macros.jl")
# ? 6a box
tprint("\n\n{bold green}Running: '6a_test_box.jl' {/bold green}")
@time @timeit_include("6a_test_box.jl")
# ? 6 renderables
tprint("\n\n{bold green}Running: '06_test_renderables.jl' {/bold green}")
@time @timeit_include("06_test_renderables.jl")
# ? 7 panel
tprint("\n\n{bold green}Running: '07_test_panel.jl' {/bold green}")
@time @timeit_include("07_test_panel.jl")
# ? 8 layout
tprint("\n\n{bold green}Running: '08_test_layout.jl' {/bold green}")
@time @timeit_include("08_test_layout.jl")
# ? 9 inspect
tprint("\n\n{bold green}Running: '09_test_inspect.jl' {/bold green}")
@time @timeit_include("09_test_inspect.jl")
# ? 11 console
tprint("\n\n{bold green}Running: '11_test_console.jl' {/bold green}")
@time @timeit_include("11_test_console.jl")
# ? 12 logging
tprint("\n\n{bold green}Running: '12_test_logging.jl' {/bold green}")
@time @timeit_include("12_test_logging.jl")
# ? 14 highlight
tprint("\n\n{bold green}Running: '14_test_highlight.jl' {/bold green}")
@time @timeit_include("14_test_highlight.jl")
# ? 15 progress
tprint("\n\n{bold green}Running: '15_test_progress.jl' {/bold green}")
@time @timeit_include("15_test_progress.jl")
# ? 16 Tree
tprint("\n\n{bold green}Running: '16_test_tree.jl' {/bold green}")
@time @timeit_include("16_test_tree.jl")
# ? 17 Dendogram
tprint("\n\n{bold green}Running: '17_test_dendogram.jl' {/bold green}")
@time @timeit_include("17_test_dendogram.jl")
# ? 18 Table
tprint("\n\n{bold green}Running: '18_test_table.jl' {/bold green}")
@time @timeit_include("18_test_table.jl")
# ? 19 Repr
tprint("\n\n{bold green}Running: '19_test_repr.jl' {/bold green}")
@time @timeit_include("19_test_repr.jl")
# ? 20 Compositor
tprint("\n\n{bold green}Running: '20_test_compositor.jl' {/bold green}")
@time @timeit_include("20_test_compositor.jl")
# ? 21 Markdown
tprint("\n\n{bold green}Running: '21_test_markdown.jl' {/bold green}")
@time @timeit_include("21_test_markdown.jl")
# ? 22 Theme
tprint("\n\n{bold green}Running: '22_test_theme.jl' {/bold green}")
@time @timeit_include("22_test_theme.jl")
# ? 999 EXAMPLES
tprint("\n\n{bold green}Running: '999_test_examples.jl' {/bold green}")
@time @timeit_include("999_test_examples.jl")
# ? 99 ERRORS
tprint("\n\n{bold green}Running: '99_test_errors.jl' {/bold green}")
@time @timeit_include("99_test_errors.jl")
show(TIMEROUTPUT; compact = true, sortby = :firstexec)
println("\n")
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] | 2.416424 | 1,376 |
module HCModelKit
using OrderedCollections: OrderedDict
using StaticArrays: @SVector, @SMatrix
import LinearAlgebra: det, dot
import Latexify
export Expression, Constant, Variable, Operation
export @var,
@unique_var,
evaluate,
subs,
variables,
differentiate,
monomials,
Compiled,
CompiledSystem,
CompiledHomotopy,
compile,
interpret,
interpreted,
System,
Homotopy,
evaluate,
evaluate!,
evaluate_gradient,
evaluate_jacobian,
evaluate_jacobian!,
jacobian,
jacobian!,
dt,
dt!,
dt_jacobian,
dt_jacobian!
include("expression.jl")
include("codegen.jl")
end # module
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198
] | 2.117143 | 350 |
<reponame>nkhedekar/Caesar.jl<gh_stars>100-1000
function sendCmd(cl::TCPSocket, cmd::ASCIIString)
println(cl, cmd)
cmd == "QUIT" ? close(cl) : readline(cl)
end
function getParticles(cl::TCPSocket, lbl::ASCIIString)
res = sendCmd(cl, "GETPARTICLES $(lbl)")
rows = split(res[1:(end-1)],';')
V = readdlm(IOBuffer(rows[1]),',')
for i in 2:length(rows)
v = readdlm(IOBuffer(rows[i]),',')
V = [V;v]
end
return V
end
cl = connect(60001)
println("connected")
sendCmd(cl, "INIT")
sendCmd(cl, "ODOMETRY 1 2 10.0 0.0 0.0 0.1 0 0 0.05 0 0.05")
sendCmd(cl, "ODOMETRY 2 3 10.0 0.0 0.0 0.1 0 0 0.05 0 0.05")
Vb = getParticles(cl, "x3");
sendCmd(cl, "SOLVETREE")
Va = getParticles(cl, "x3");
@show size(Va)
sendCmd(cl, "LANDMBR 3 4 $(pi/2) 10.0 0.01 0 0.5")
sendCmd(cl, "SOLVETREE")
l1 = getParticles(cl, "l1");
@show size(l1)
sendCmd(cl, "QUIT")
close(cl)
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] | 2.103614 | 415 |
module GML
using ...ParserCombinator
using Compat
export parse_raw, parse_dict, GMLError
# symbol required in 0.3
# both work in 0.4
# symbol gives deprecation warning in 0.5
Symbol_ = VERSION >= v"0.5-" ? Symbol : symbol
function mk_parser(string_input)
# this is such a simple grammar that we don't need backtracking, so we can
# use Seq! et al, and Error for useful diagnostics.
# the only tricky things are getting the spaces right so that matching
# spaces doesn't commit us to anything unexpected, and placing errors only
# when we're sure we're wrong (you can't have one in the definition of
# key, for example, because that can fail...).
@with_names begin
expect(x) = Error("Expected $x")
pint(x) = parse(Int64, x)
pflt(x) = parse(Float64, x)
comment = P"(#.*)?"
# we need string input as we match multiple lines
if string_input && ParserCombinator.FAST_REGEX
wspace = "([\t ]+|[\r\n]+(#.*)?)"
wstar(x) = string(x, wspace, "*")
wplus(x) = string(x, wspace, "+")
space = ~Pattern(wplus(""))
spc = ~Pattern(wstar(""))
open = ~Pattern(wstar("\\["))
close = ~Pattern(wstar("]"))
key = Pattern(wplus("([a-zA-Z][a-zA-Z0-9]*)"), 1) > Symbol_
int = Pattern(wstar("((\\+|-)?\\d+)"), 1) > pint
real = Pattern(wstar("((\\+|-)?\\d+.\\d+((E|e)(\\+|-)?\\d+)?)"), 1) > pflt
str = Pattern(wstar("\"([^\"]*)\""), 1)
else
wspace = Alt!(P"[\t ]+", Seq!(P"[\r\n]+", comment))
space = wspace[1:end,:!]
spc = wspace[0:end,:!]
open = Seq!(E"[", spc)
close = Seq!(E"]", spc)
key = Seq!(p"[a-zA-Z][a-zA-Z0-9]*", space) > Symbol_
int = Seq!(p"(\+|-)?\d+", spc) > pint
real = Seq!(p"(\+|-)?\d+.\d+((E|e)(\+|-)?\d+)?", spc) > pflt
str = Seq!(Pattern("\"([^\"]*)\"", 1), spc)
end
list = Delayed()
sublist = Seq!(open, list, Alt!(close, expect("]")))
value = Alt!(real, int, str, sublist, expect("value"))
element = Seq!(key, value) > tuple
list.matcher = Nullable{Matcher}(element[0:end,:!] > vcat)
# first line comment must be explicit (no previous linefeed)
Seq!(comment, spc, list, Alt!(Seq!(spc, Eos()), expect("key")))
end
end
# this returns the "natural" representation as nested arrays and tuples
function parse_raw(s; debug=false)
parser = mk_parser(isa(s, AbstractString))
try
if ParserCombinator.FAST_REGEX
(debug ? parse_one_dbg : parse_one)(s, Trace(parser); debug=debug)
else
(debug ? parse_lines_dbg : parse_lines)(s, Trace(parser); debug=debug)
end
catch x
if (debug)
Base.show_backtrace(STDOUT, catch_backtrace())
end
rethrow()
end
end
# (semi) structured model of GML graph files
# the GML specs that i have found are really rather frustrating, because they
# don't seme to acknowledge a fundamental problem with this format, which is
# that you cannot tell, from the file alone, whether a particuar field is a
# list or a single value.
# obviously, a name that occurs multiple times in a single scope is a list.
# but the opposite - that an isolated name is a single value - is not
# necessarily true, because it may be a singleton list.
# one solution is to make a model that very closely follows the "predefined
# keys" part of himsolt's 1996 document, available as part of the tarball from
# http://www.fim.uni-passau.de/fileadmin/files/lehrstuhl/brandenburg/projekte/gml/gml-documentation.tar.gz
# but that seems very specific, perhaps dated, and still doesn't help with
# additional fields.
# another solution is to treat everything as a list, and use dictionaries of
# lists. but that means that the idea of "keys" is messed up with additional
# [1] indexes into singleton lists.
# after some reflection, i've decide to take a list of names of lists, and to
# validate against that. this isn't perfect - it doesn't allow for the same
# name to have different meanings in different contexts, for example - but it
# seems to be a good middle ground for "doing the right thing" in general
# cases.
# users with different requirements are free to take the "raw" parse and build
# their own object models.
type GMLError<:Exception
msg::AbstractString
end
LISTS = [:graph,:node,:edge]
typealias GMLDict Dict{Symbol, Any}
function build_dict(raw; lists=LISTS, unsafe=false)
root = GMLDict()
if length(raw) > 0
build_dict(root, raw[1]; lists=lists, unsafe=unsafe)
end
root
end
function build_dict(dict::GMLDict, raw; lists=LISTS, unsafe=false)
for (name, value) in raw
if isa(value, Vector)
entry = GMLDict()
build_dict(entry, value; lists=lists, unsafe=unsafe)
else
entry = value
end
if name in lists
if !haskey(dict, name)
dict[name] = Any[]
end
push!(dict[name], entry)
else
if !haskey(dict, name)
dict[name] = entry
elseif !unsafe
throw(GMLError("$name is a list"))
end
end
end
end
# lists describes which symbols should be modelled as lists
# if unsafe is false, multiple values for non-list symbols throw an error;
# if true they are silently discarded
parse_dict(s; debug=false, lists=LISTS, unsafe=false) = build_dict(parse_raw(s; debug=debug); lists=lists, unsafe=unsafe)
end
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] | 2.305896 | 2,527 |
using DataFrames, Query
df1 = DataFrame(a=[1,2,3], b=[1.,2.,3.])
df2 = DataFrame(c=[2.,4.,2.], d=["John", "Jim","Sally"])
x = @from i in df1 begin
@join j in df2 on i.a equals convert(Int,j.c)
@select {i.a,i.b,j.c,j.d,e="Name: $(j.d)"}
@collect DataFrame
end
println(x)
| [
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7,
87,
8,
198
] | 2.007042 | 142 |
# This file is part of Kpax3. License is MIT.
mutable struct KWeight
c::Vector{Float64}
w::Matrix{Float64}
z::Matrix{Float64}
end
mutable struct MCMCSupport
m::Int
n::Int
u::Vector{Int}
t::Vector{Float64}
lp::Array{Float64, 3}
lq::Array{Float64, 3}
lr::Array{Float64, 3}
vi::Int
ni::Vector{Float64}
ui::Vector{Int}
wi::KWeight
lpi::Matrix{Float64}
vj::Int
nj::Vector{Float64}
uj::Vector{Int}
wj::KWeight
lpj::Matrix{Float64}
tmp::Vector{Float64}
cl::Vector{Int}
k::Int
lograR::Float64
logmlik::Float64
logmlikcandidate::Float64
end
function MCMCSupport(state::State,
priorC::AminoAcidPriorCol)
n = length(state.R)
(maxclust, m) = size(state.C)
u = Int[a for a in 1:n]
t = zeros(Float64, n)
lp = zeros(Float64, 4, maxclust, m)
lq = zeros(Float64, 4, maxclust, m)
lr = zeros(Float64, 3, maxclust, m)
g = 0
for b in 1:m, l in 1:state.k
g = state.cl[l]
lp[1, g, b] = logmarglik(state.n1s[g, b], state.v[g], priorC.A[1, b],
priorC.B[1, b])
lp[2, g, b] = logmarglik(state.n1s[g, b], state.v[g], priorC.A[2, b],
priorC.B[2, b])
lp[3, g, b] = logmarglik(state.n1s[g, b], state.v[g], priorC.A[3, b],
priorC.B[3, b])
lp[4, g, b] = logmarglik(state.n1s[g, b], state.v[g], priorC.A[4, b],
priorC.B[4, b])
end
vi = 0
ni = zeros(Float64, m)
ui = zeros(Int, n)
wi = KWeight(zeros(Float64, m), zeros(Float64, 4, m), zeros(Float64, 4, m))
lpi = zeros(Float64, 4, m)
vj = 0
nj = zeros(Float64, m)
uj = zeros(Int, n)
wj = KWeight(zeros(Float64, m), zeros(Float64, 4, m), zeros(Float64, 4, m))
lpj = zeros(Float64, 4, m)
tmp = zeros(Float64, 4)
cl = zeros(Int, n)
k = 0
logmlik = logmarglikelihood(state.cl, state.k, lp, priorC)
MCMCSupport(m, n, u, t, lp, lq, lr, vi, ni, ui, wi, lpi, vj, nj, uj, wj, lpj,
tmp, cl, k, 0.0, logmlik, 0.0)
end
function resizesupport!(support::MCMCSupport,
maxclust::Int)
if size(support.lp, 2) < maxclust
# we don't want to allocate new resources too often, so allocate double the
# previous size. this should guarantee a logarithmic number of allocations
newsize = min(support.n, max(maxclust, 2 * size(support.lp, 2)))
lp = zeros(Float64, 4, newsize, support.m)
for b in 1:support.m, g in 1:size(support.lp, 2)
lp[1, g, b] = support.lp[1, g, b]
lp[2, g, b] = support.lp[2, g, b]
lp[3, g, b] = support.lp[3, g, b]
lp[4, g, b] = support.lp[4, g, b]
end
support.lp = lp
support.lq = zeros(Float64, 4, newsize, support.m)
support.lr = zeros(Float64, 3, newsize, support.m)
end
nothing
end
mutable struct KOffspring
R::Vector{Int}
v::Vector{Int}
end
mutable struct GASupport
m::Int
n::Int
oi::KOffspring
oj::KOffspring
end
function GASupport(m::Int,
n::Int)
oi = KOffspring(zeros(Int, n), zeros(Int, n))
oj = KOffspring(zeros(Int, n), zeros(Int, n))
GASupport(m, n, oi, oj)
end
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] | 1.975301 | 1,579 |
using Test
using Bigsimr
using Distributions
import LinearAlgebra: PosDefException
import Bigsimr: ValidCorrelationError
@testset "Random Vector Utilities" begin
@testset "Fast Standard Random Normal" begin
# Must work for each bits floating point type
for T in (Float64, Float32, Float16)
@test_nowarn Bigsimr._randn(T, 4, 4)
@test eltype(Bigsimr._randn(T, 4, 4)) === T
end
@test_nowarn Bigsimr._randn(Float64, 4.0, 4.0)
@test_nowarn Bigsimr._randn(Float64, 4.0, 4)
@test_nowarn Bigsimr._randn(Float64, 4, 4.0)
@test_nowarn Bigsimr._randn(4.0, 4.0)
@test_nowarn Bigsimr._randn(4.0, 4)
@test_nowarn Bigsimr._randn(4 , 4.0)
@test_throws InexactError Bigsimr._randn(4.5, 5.5)
@test_throws InexactError Bigsimr._randn(4.5, 5)
@test_throws InexactError Bigsimr._randn(4 , 5.5)
end
@testset "Random Multivariate Normal Generation" begin
r_negdef = [
1.00 0.82 0.56 0.44
0.82 1.00 0.28 0.85
0.56 0.28 1.00 0.22
0.44 0.85 0.22 1.00
]
# Must fail for negative semidefinite matrices
@test_throws PosDefException Bigsimr._rmvn(10, r_negdef)
# Must work for each bits floating point type
for T in (Float64, Float32, Float16)
r = cor_randPD(T, 4)
@test_nowarn Bigsimr._rmvn(10, r)
@test eltype(Bigsimr._rmvn(10, r)) === T
end
end
@testset "User Random Multivariate Normal Generation" begin
# Must work for each bits floating point type
for T in (Float64, Float32, Float16)
r = cor_randPD(T, 4)
@test_nowarn rmvn(10, r)
@test eltype(rmvn(10, r)) === T
end
r = cor_randPD(4)
@test_nowarn rmvn(10.0, r)
@test_throws InexactError rmvn(10.5, r)
end
@testset "Normal to Marginal" begin
# Must work for scalars, vectors, and matrices of type Float64
D = Binomial(10, 0.5)
x = randn(Float64)
y = randn(Float64, 2)
z = randn(Float64, 2, 2)
ω = randn(Float64, 2, 2, 2)
γ = randn(Float32)
@test_nowarn Bigsimr.normal_to_margin(D, x)
@test_nowarn Bigsimr.normal_to_margin(D, y)
@test_nowarn Bigsimr.normal_to_margin(D, z)
@test_nowarn Bigsimr.normal_to_margin(D, ω)
@test_nowarn Bigsimr.normal_to_margin(D, γ)
# Standard normal to standard normal should be invariant
z = rand(Normal(0, 1), 100000)
@test z ≈ Bigsimr.normal_to_margin(Normal(0, 1), z)
# Estimated parameters must be close to true parameters
d1 = Binomial(20, 0.2)
d2 = Poisson(3)
d3 = Normal(12, π)
x1 = Bigsimr.normal_to_margin(d1, z)
x2 = Bigsimr.normal_to_margin(d2, z)
x3 = Bigsimr.normal_to_margin(d3, z)
f1 = fit_mle(Binomial, 20, x1)
f2 = fit_mle(Poisson, x2)
f3 = fit_mle(Normal, x3)
@test all(isapprox.(params(d1), params(f1), rtol=0.01))
@test all(isapprox.(params(d2), params(f2), rtol=0.01))
@test all(isapprox.(params(d3), params(f3), rtol=0.01))
end
end
@testset "Random Vector Generation" begin
@testset "rvec" begin
# Must throw an error if a margin is not a univariate distribution
r = cor_randPD(2)
m = [Binomial(10, 0.2), MvNormal(zeros(2), r)]
@test_throws MethodError rvec(2, r, m)
# Must throw an error if r is not a valid correlation matrix
m = [Binomial(10, 0.3), Gamma(10, 3)]
r = Float64[1.0 2.33333; 0.333333 1.0] # Not positive definite
c = Float64[2 4; 4 100] # Is covariance, not correlation
@test_throws ValidCorrelationError rvec(3, r, m)
@test_throws ValidCorrelationError rvec(3, c, m)
# Must throw an arror if the dimensions of r do not match the number of margins
m = [Binomial(10, 0.3), Gamma(10, 3)]
r = cor_randPD(3)
@test_throws DimensionMismatch rvec(4, r, m)
# Distributions.jl only returns random Float64 types, but `rvec` should
# still accept correlation matrices of different floating point types
for T in (Float64, Float32, Float16)
r = cor_randPD(T, 2)
@test_nowarn rvec(10, r, m)
@test_nowarn rvec(10.0, r, m)
@test_throws InexactError rvec(10.5, r, m)
end
end
end
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] | 2.012946 | 2,240 |
using JuMP, JuMPeR, Gurobi, Random, Distributions, LinearAlgebra, DataFrames, Plots
n = 10 # Number of facilities
m = 50 # Number of customers
# Generating random data (please don't change the seeds.)
facilities = 0.6.*rand(MersenneTwister(5), n,2) .+ 0.2;
customers = rand(MersenneTwister(2), m, 2);
c = [LinearAlgebra.norm(customers[i, :] .- facilities[j, :])[1] for j=1:n, i=1:m];
f = rand(MersenneTwister(3), n)*1 .+ 5;
s = rand(MersenneTwister(4), n)*2 .+ 15;
d = rand(MersenneTwister(5), m)*0.5 .+ 0.75
# P matrix
R_D = 0.25
P = [0.2*exp(-1/R_D .*LinearAlgebra.norm(customers[i, :] .- customers[j, :])[1]) for j=1:m, i=1:m];
P = (P .>= 0.2*exp(-1/R_D .* R_D)) .* P
""" Cutting plane facility location model.
Note this is just the nominal problem, but with linear policies y(z) = u + Vz when z = 0. """
function CP_facility_model(c::Matrix, f::Vector)
n, m = size(c)
@assert length(f) == n
model = Model(solver = GurobiSolver(OutputFlag = 0))
# VARIABLES
@variable(model, x[1:n], Bin) # Facility locations
@variable(model, V[1:n, 1:m, 1:m]) # Linear decision rule w.r.t. z
@variable(model, u[1:n, 1:m]) # Fixed policy
# CONSTRAINTS
for j=1:m # Demand constraints
dt = zeros(n, m);
[dt[i,j] = 1 for i=1:n];
@constraint(model, sum(dt .* u) >= d[j])
end
for i=1:n # Capacity constraints
dt = zeros(n, m);
[dt[i,j] = 1 for j=1:m];
@constraint(model, sum(dt .* u) <= s[i] * x[i])
end
# OBJECTIVE
@variable(model, F)
@constraint(model, F >= sum(f[j] * x[j] for j = 1:n) + sum(c[i, j] * u[i, j] for i=1:n, j=1:m))
@objective(model, Min, F)
return model, x, u, V
end
function apply_heuristic(model, x, u, V)
@constraint(model, u .>= 0)
@constraint(model, V .>= 0)
return
end
""" Finds and adds worst case cuts for the facility location problem. """
function find_wc_cuts(model, x, u, V, xvals, uvals, Vvals, rho, Gamm)
wc_model = Model(solver = GurobiSolver(OutputFlag = 0)) # suppressing printouts.
@variable(wc_model, -rho <= z[1:m] <= rho)
@variable(wc_model, normdummy[1:m] >= 0)
@constraint(wc_model, [i=1:m], normdummy[i] >= z[i])
@constraint(wc_model, [i=1:m], normdummy[i] >= -z[i])
@constraint(wc_model, sum(normdummy) <= Gamm)
count = length(model.linconstr)
for i = 1:n # nonnegativity constraints
for j=1:m
@objective(wc_model, Min, uvals[i,j] + sum(Vvals[i,j,:] .* z))
solve(wc_model)
if getobjectivevalue(wc_model) < -1e-5 # if constraint is violated
new_z = getvalue(z)
@constraint(model, u[i,j] + sum(V[i,j,:] .* new_z) >= 0)
end
end
end
@info("$(length(model.linconstr) - count) nonnegativity cuts added. ")
count = length(model.linconstr)
for j = 1:m # demand constraints
dt = zeros(n, m);
[dt[i,j] = 1 for i=1:n];
@objective(wc_model, Min, sum(dt .* uvals) + sum(Vvals[:,j,:] * z) - d[j] - (P*z)[j])
solve(wc_model)
if getobjectivevalue(wc_model) < -1e-5 # if constraint is violated
new_z = getvalue(z)
@constraint(model, sum(dt .* u) + sum(V[:,j,:] * new_z) >= d[j] + (P*new_z)[j])
end
end
@info("$(length(model.linconstr) - count) demand constraint cuts added.")
count = length(model.linconstr)
for i = 1:n # capacity constraints
dt = zeros(n, m);
[dt[i,j] = 1 for j=1:m];
@objective(wc_model, Min, - sum(dt .* uvals) - sum(Vvals[i,:,:] * z) + s[i] * xvals[i])
solve(wc_model)
if getobjectivevalue(wc_model) < -1e-5 # if constraint is violated
new_z = getvalue(z)
@constraint(model, sum(dt .* u) + sum(V[i,:,:] * new_z) <= s[i] * x[i])
end
end
@info("$(length(model.linconstr) - count) capacity constraint cuts added.")
# objective
@objective(wc_model, Max, sum(f[j] * xvals[j] for j = 1:n) + sum(c[i, j] * (uvals[i, j] + sum(Vvals[i,j,:] .* z)) for i=1:n, j=1:m))
solve(wc_model)
if getobjectivevalue(wc_model) > getvalue(model.obj) + 1e-5 # if constraint is violated
new_z = getvalue(z)
@constraint(model, model.obj >= sum(f[j] * x[j] for j = 1:n) + sum(c[i, j] * (u[i, j] + sum(V[i,j,:] .* new_z)) for i=1:n, j=1:m))
@info("Objective cut added.")
end
end
""" Plots the solution of the facility location model.
Blue circles are active facilities with different capacities.
Orange plus signs are other potential facility locations.
Rays describe connections between facilities and demand nodes.
"""
function plot_solution(model, x, y, cost = nothing)
plt = scatter(facilities[:, 1], facilities[:, 2], markersize = 0.4 .* s .* getvalue(x))
scatter!(facilities[:, 1], facilities[:, 2], markersize = 0.4 .* s, markershape = :+)
for i=1:n
for j=1:m
if getvalue(y[i,j]) >= 1e-10
plot!([customers[j, 1], facilities[i,1]], [customers[j,2], facilities[i,2]], linewidth = getvalue(y[i,j]), legend=false)
end
end
end
if cost == nothing
scatter!(customers[:, 1], customers[:, 2], markersize = 3*d,
title = "Total cost: $(round(getobjectivevalue(model), sigdigits=5))")
else
scatter!(customers[:, 1], customers[:, 2], markersize = 3*d,
title = "Total cost: $(round(cost,sigdigits=5))")
end
println("Facility cost: $(getvalue(sum(f[j] * x[j] for j = 1:n)))")
println("Transportation cost: $(getvalue(sum(c[i, j] * y[i, j] for i=1:n, j=1:m)))")
return plt
end
rho = 1
Gamm = 5
model, x, u, V = CP_facility_model(c, f)
apply_heuristic(model, x, u, V)
# Do 15 iterations of cuts
for i=1:15
@info("Iteration $(i).")
modelsize = length(model.linconstr)
solve(model)
xvals, uvals, Vvals = getvalue(x), getvalue(u), getvalue(V)
find_wc_cuts(model, x, u, V, xvals, uvals, Vvals , rho, Gamm)
if length(model.linconstr) == modelsize
@info("Optimum reached.")
@info("Optimal cost:$(getobjectivevalue(model)).")
break
end
end
# Problem should converge in ~100 adversarial iterations.
plot_solution(model, x, u) | [
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] | 2.118327 | 2,941 |
export children, has_children, is_left_child, is_right_child
import AbstractTrees: children, has_children
function children(jlt::AbstractJLBoostTree)
jlt.children
end
function has_children(jlt::AbstractJLBoostTree)
length(children(jlt)) > 0
end
function is_left_child(jlt::AbstractJLBoostTree)
jlt == jlt.parent.children[1]
end
is_right_child(jlt::AbstractJLBoostTree) = !is_left_child(jlt) | [
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] | 2.731544 | 149 |
<gh_stars>0
include("Graph.jl")
include("NodeCentrality.jl")
include("EdgeCentrality.jl")
function NormalizedBetweennessCentrality(G)
NC = ((G.n > 10000) || (G.m > 10000)) ? ApproxBetweennessCentrality(G) : BetweennessCentrality(G)
EC = ((G.n > 10000) || (G.m > 10000)) ? ApproxEdgeBetweenness(G) : EdgeBetweenness(G)
NC ./= sum(NC)
EC ./= sum(EC)
return NC, EC
end
function selectNodes(G, selectedNodes, selectedEdges, selectMode)
NC, EC = NormalizedBetweennessCentrality(G)
nl = zeros(Int, G.n)
el = zeros(Int, G.m)
foreach(i -> nl[i] = i, 1 : G.n)
foreach(i -> el[i] = i, 1 : G.m)
sort!(nl, by = x->-NC[x])
sort!(el, by = x->-EC[x])
Selected = zeros(Bool, G.n)
if ('n' in selectMode)
foreach(i -> Selected[nl[i]] = true, 1 : min(selectedNodes, G.n))
end
if ('e' in selectMode)
for i = 1 : min(selectedEdges, G.m)
Selected[G.E[el[i]][2]] = true
Selected[G.E[el[i]][3]] = true
end
end
return NC, EC, Selected
end
function SearchWay(g, status)
n = size(g, 1)
h = copy(status)
Q = zeros(Int, n)
front = 1
rear = 0
pushQ(x) = begin
rear += 1
Q[rear] = x
end
popQ() = begin
tmp = Q[front]
front += 1
return tmp
end
for i = 1 : n
(h[i] == 1) ? pushQ(i) : nothing
end
prior = zeros(Int, n)
while front <= rear
u = popQ()
for v in g[u]
if h[v] == 0
h[v] = 1
prior[v] = u
pushQ(v)
elseif h[v] == 2
nodeList = Array{Int, 1}()
push!(nodeList, v)
push!(nodeList, u)
p = prior[u]
while p != 0
push!(nodeList, p)
p = prior[p]
end
return nodeList
end
end
end
return nothing
end
function KeepOne(G, Selected, NC, EC)
g = getAdjacentList(G)
F = zeros(Int, G.n)
foreach(i -> F[i] = i, 1 : G.n)
find(x) = begin
if F[x] != x
F[x] = find(F[x])
end
return F[x]
end
SetUnion(x, y) = F[find(x)] = find(y)
for (ID, u, v, w) in G.E
(Selected[u] && Selected[v]) ? SetUnion(u, v) : nothing
end
status = zeros(Int, G.n)
while true
clist = Array{Int, 1}()
for i = 1 : G.n
if Selected[i] && (F[i] == i)
push!(clist, i)
end
end
Bst = Array{Int, 1}()
bstlen = G.n
fill!(status, 0)
for u in clist
for i = 1 : G.n
if Selected[i]
status[i] = (find(i) == u) ? 1 : 2
end
end
tmp = SearchWay(g, status)
if (tmp != nothing) && (size(tmp, 1) < bstlen)
bstlen = size(tmp, 1)
Bst = copy(tmp)
end
end
if bstlen == G.n
break
end
for i = 1 : bstlen
Selected[Bst[i]] = true
(i > 1) ? SetUnion(Bst[i], Bst[i - 1]) : nothing
end
end
NodeC = Dict{Int, Float64}()
NCmax = NC[argmax(NC)]
for i = 1 : G.n
if Selected[i]
NodeC[G.V[i]] = NC[i] / NCmax
end
end
return SubGraph(G, Selected, EC), NodeC, NCmax
end
function SearchAll(g, st, selected)
n = size(g, 1)
visited = zeros(Bool, n)
visited[st] = true
Q = zeros(Int, n)
front = 1
rear = 0
pushQ(x) = begin
rear += 1
Q[rear] = x
end
popQ() = begin
tmp = Q[front]
front += 1
return tmp
end
pushQ(st)
prior = zeros(Int, n)
while front <= rear
u = popQ()
for v in g[u]
if !visited[v]
visited[v] = true
prior[v] = u
pushQ(v)
end
end
end
needNodes = zeros(Bool, n)
for v = 1 : n
if selected[v]
x = v
while (x > 0)
needNodes[x] = true
x = prior[x]
end
end
end
return needNodes
end
function KeepAll(G, Selected, NC, EC)
g = getAdjacentList(G)
Needed = copy(Selected)
for i = 1 : G.n
if Selected[i]
needNodes = SearchAll(g, i, Selected)
foreach(j -> Needed[j] = Needed[j] || needNodes[j], 1 : G.n)
end
end
NodeC = Dict{Int, Float64}()
NCmax = NC[argmax(NC)]
for i = 1 : G.n
if Needed[i]
NodeC[G.V[i]] = NC[i] / NCmax
end
end
return SubGraph(G, Needed, EC), NodeC, NCmax
end
function Compress(G, selectedNodes, selectedEdges, selectMode, addMode)
NC, EC, Selected = selectNodes(G, selectedNodes, selectedEdges, selectMode)
return (addMode == "KeepOne") ? KeepOne(G, Selected, NC, EC) : KeepAll(G, Selected, NC, EC)
end
function CentralityInformation(G, NodeC, maxNodeC)
nci = 0.0
eci = 0.0
foreach(i -> nci += (NodeC[G.V[i]]*maxNodeC), 1 : G.n)
foreach(i -> eci += G.E[i][4], 1 : G.m)
return nci, eci, (nci+eci)/2
end
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] | 1.791595 | 2,903 |
<reponame>sp94/PlaneWaveExpansionMethod.jl<gh_stars>10-100
"""
Mode(k0, frequency, data, weighting, basis, label)
Eigenmode of a photonic crystal expressed on a plane-wave `basis` with a weighted inner product.
"""
struct Mode
k0::Vector{Float64}
frequency::ComplexF64
data::Vector{ComplexF64}
weighting::Matrix{ComplexF64}
basis::PlaneWaveBasis
label::String
end
"""
HilbertSpace(k0, data, weighting, basis)
Hilbert space spanned by the eigenvectors in each column of `data`.
The eigenvectors will be orthonormalised using Gram-Schmidt orthonormalisation,
see [`orthonormalise`](@ref).
"""
struct HilbertSpace
k0::Vector{Float64}
data::Matrix{ComplexF64}
weighting::Matrix{ComplexF64}
basis::PlaneWaveBasis
function HilbertSpace(k0::Vector{Float64}, data::Matrix{ComplexF64},
weighting::Matrix{ComplexF64}, basis::PlaneWaveBasis)
# Inner constructor guarantees data will be orthonormalised
data = orthonormalise(data, weighting=weighting)
return new(k0, data, weighting, basis)
end
end
"""
HilbertSpace(modes::Array{Mode,1})
Returns the Hilbert space spanned by the `modes`.
The `data` of the Hilbert space is guaranteed to be orthonormal
under the weighting of the `modes`.
"""
function HilbertSpace(modes::Array{Mode,1})
k0 = modes[1].k0
data = zeros(ComplexF64, length(modes[1].data), length(modes))
weighting = modes[1].weighting
basis = modes[1].basis
for (col,mode) in enumerate(modes)
@assert mode.k0 == k0
@assert mode.weighting == weighting
@assert mode.basis == basis
data[:,col] = mode.data
end
return HilbertSpace(k0, data, weighting, basis)
end
"""
shift_k0(space::HilbertSpace, dp::Int, dq::Int)
Shift the basis of the Hilbert space by `dp*b1 + dq*b2`, where `b1` and `b2`
are reciprocal lattice vectors.
This is required when we need the overlaps of modes that are at the same k-point
but in different Brillouin zones.
"""
function shift_k0(space::HilbertSpace, dp::Int, dq::Int)
data = space.data
ps = space.basis.ps
qs = space.basis.qs
data_new = zeros(ComplexF64, size(data))
for row in 1:size(data,1)
# Identify the corresponding (p_new,q_new) indices
# in the plane wave basis
p_new = ps[row] - dp
q_new = qs[row] - dq
row_new = findfirst(x->x==(p_new,q_new), collect(zip(ps,qs)))
if !isnothing(row_new)
data_new[row_new,:] = data[row,:]
end
end
k0_new = space.k0 + dp*space.basis.b1 + dq*space.basis.b2
return HilbertSpace(k0_new, data_new, space.weighting, space.basis)
end
"""
normalise(data; weighting=I)
Normalisation of vectors with a weighted inner product.
"""
function normalise(data; weighting=I)
out = copy(data)
for n in 1:size(out,2)
out[:,n] /= sqrt(abs(dot(out[:,n],weighting*out[:,n])))
end
return out
end
"""
orthonormalise(data; weighting=I)
Gram-Schmidt orthonormalisation of vectors with a weighted inner product.
"""
function orthonormalise(data; weighting=I)
out = copy(data)
out[:,1] = normalise(out[:,1], weighting=weighting)
# Gram-Schmidt orthogonalisation
projector = zeros(size(out,1),size(out,1))
for n in 2:size(out,2)
projector += out[:,n-1]*out[:,n-1]' * weighting
out[:,n] = out[:,n] - projector*out[:,n]
out[:,n] = normalise(out[:,n], weighting=weighting)
end
return out
end
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] | 2.41931 | 1,450 |
using Test
using IncrementalInference
using Random
##
@testset "test basic multihypo" begin
## A simple multihypo example
Random.seed!(42) # The answer to reproducable noise
fg = LocalDFG(solverParams=SolverParams(graphinit=false, gibbsIters=5, spreadNH=5.0))
pRight = 0.99
pWrong = 0.01
pr_noise = 0.01
od_noise = 0.1
lm_noise = 0.01
# true positions
# x0 at 0
# x1 at 1
# l1 at 1
# l2 at 2
#x0 prior
addVariable!(fg, :x0, ContinuousScalar)
prpo = Normal(0.0, pr_noise)
addFactor!(fg, [:x0], Prior(Normal(rand(prpo), pr_noise)))
#l1 and l2
addVariable!(fg, :l1, ContinuousScalar, tags=[:LANDMARK])
addVariable!(fg, :l2, ContinuousScalar, tags=[:LANDMARK])
#x0 to l1 or l2
p2ln = Normal(1.0, lm_noise)
p2p = LinearRelative(Normal(rand(p2ln), lm_noise))
addFactor!(fg, [:x0; :l1; :l2], p2p, multihypo = [1, pRight, pWrong])
# addFactor!(fg, [:x0; :l1], p2p) #this one used for sanity check
#x0 to x1
addVariable!(fg, :x1, ContinuousScalar)
pp = Normal(1.0, od_noise)
addFactor!(fg, [:x0,:x1], LinearRelative(Normal(rand(pp), od_noise)))
#x1 to l1 or l2
p2ln = Normal(0.0, lm_noise)
p2p = LinearRelative(Normal(rand(p2ln), lm_noise))
addFactor!(fg, [:x1; :l1; :l2], p2p, multihypo = [1, pRight, pWrong])
# addFactor!(fg, [:x1; :l1], p2p) #this one used for sanity check
#x1 to l2 or l1
p2ln = Normal(1.0, lm_noise)
p2p = LinearRelative(Normal(rand(p2ln), lm_noise))
addFactor!(fg, [:x1; :l2; :l1], p2p, multihypo = [1, pRight, pWrong])
# addFactor!(fg, [:x1; :l2], p2p) #this one used for sanity check
##
# prescribe an elimination order to get a single clique
eo = [:l2,:x1,:x0,:l1]
# fg.solverParams.graphinit=true
smtasks = Task[]
tree = solveTree!(fg, eliminationOrder=eo) #, smtasks=smtasks, recordcliqs=ls(fg));
# hists = fetchCliqHistoryAll!(smtasks)
# plotKDE(fg, ls(fg))
##
@test isapprox(DFG.getPPESuggested(fg, :x0)[], 0, atol = 0.2)
@test isapprox(DFG.getPPESuggested(fg, :x1)[], 1, atol = 0.2)
@test isapprox(DFG.getPPESuggested(fg, :l1)[], 1, atol = 0.2)
L2 = getBelief(fg, :l2)
npts = length(getPoints(L2))
pts = [2.0.+0.1*randn(1) for _ in 1:npts]
L2_ = manikde!(ContinuousScalar, pts)
# test that there is at least a mode present
@test mmd(L2_, L2, ContinuousScalar) < 1e-3
# @test isapprox(DFG.getPPESuggested(fg, :l2)[], 2, atol = 0.2)
##
end
@testset "test multihypo chain example (see #462)..." begin
##
l1 = -10.0
l2 = +10.0
lnoise = 1.0
x1 = 0
x2 = 0
Onoise = 0.1
fg = initfg()
addVariable!(fg, :x1, ContinuousScalar)
addVariable!(fg, :x2, ContinuousScalar)
addVariable!(fg, :l1, ContinuousScalar)
addVariable!(fg, :l1_0, ContinuousScalar)
addVariable!(fg, :l2, ContinuousScalar)
addVariable!(fg, :l2_0, ContinuousScalar)
# priors on two landmarks only
addFactor!(fg, [:l1], Prior(Normal(l1, lnoise)))
addFactor!(fg, [:l2], Prior(Normal(l2, lnoise)))
# relative constraints
addFactor!(fg, [:x1;:l1;:l1_0], LinearRelative(Normal(l1-x1, lnoise)), multihypo=[1;1/2;1/2])
addFactor!(fg, [:x2;:l2;:l2_0], LinearRelative(Normal(l2-x2, lnoise)), multihypo=[1;1/2;1/2])
addFactor!(fg, [:x1;:x2], LinearRelative(Normal(0, Onoise)))
tree = solveTree!(fg)
# drawTree(tree, show=true)
# expect x1 x2 to have at least one mode at 0
@test getPPE(fg, :x1).suggested[1] - x1 |> abs < 1.2
@test getPPE(fg, :x2).suggested[1] - x2 |> abs < 1.2
@test getPPE(fg, :l1).suggested[1] - l1 |> abs < 1.2
@test getPPE(fg, :l2).suggested[1] - l2 |> abs < 1.2
# l1_0, l2_0 should be nearby around l1 and l2, but cannot confirm 100%
@test getPPE(fg, :l1_0).suggested[1] - l1 |> abs < 10
@test getPPE(fg, :l2_0).suggested[1] - l2 |> abs < 10
##
end
# using RoMEPlotting
# Gadfly.set_default_plot_size(35cm,25cm)
#
# plotKDE(fg, [:l1;:l2])
# plotKDE(fg, [:l1_0;:l2_0])
# plotKDE(fg, [:x1;:x2])
#
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] | 2.103099 | 1,775 |
<gh_stars>10-100
module LibNuklear
import Libdl
# Load in `deps.jl`, complaining if it does not exist
const depsjl_path = joinpath(@__DIR__, "..", "deps", "deps.jl")
if !isfile(depsjl_path)
error("Nuklear was not build properly. Please run Pkg.build(\"Nuklear\").")
end
include(depsjl_path)
# Module initialization function
function __init__()
check_deps()
end
include("CEnum.jl")
using .CEnum
include("ctypes.jl")
export Ctm, Ctime_t, Cclock_t
include(joinpath(@__DIR__, "..", "gen", "libnuklear_common.jl"))
include(joinpath(@__DIR__, "..", "gen", "libnuklear_api.jl"))
include(joinpath(@__DIR__, "..", "gen", "helper_api.jl"))
const nk_false = 0
const nk_true = 1
export nk_false, nk_true
foreach(names(@__MODULE__, all=true)) do s
if startswith(string(s), "nk_") || startswith(string(s), "NK_")
@eval export $s
end
end
end # module
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] | 2.404432 | 361 |
<filename>test/tsvd.jl
using Elemental, TSVD, LinearAlgebra, Test
using Random
A = Elemental.DistMatrix(Float64)
Elemental.gaussian!(A, 500, 200)
# Warning! If seed is not set, each worker will end up with a different initial vector
# making the Lanczos processes inconsistent
Random.seed!(123)
vals_Elemental = tsvd(A, 5)[2]
localA = Array(A)
vals_LAPACK = svdvals(localA)[1:5]
@test vals_Elemental ≈ vals_LAPACK
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] | 2.843537 | 147 |
<filename>gtk/signals.jl
# https://juliagraphics.github.io/Gtk.jl/latest/manual/signals/
using Gtk
# b = GtkButton("Press me")
# win = GtkWindow(b, "Callbacks")
# showall(win)
# function button_clicked_callback(widget)
# println(widget, " was clicked!")
# end
# id = signal_connect(button_clicked_callback, b, "clicked")
win = GtkWindow("Callbacks")
b = GtkButton("Press me")
push!(win, b)
showall(win)
function clicked_callback(widget)
println(widget, " was clicked!")
end
# https://developer.gnome.org/gtk3/stable/GtkButton.html#gtk-button-clicked
id = signal_connect(clicked_callback, b, "clicked") # emit "clicked" signal
# Use do- syntax
win2 = GtkWindow("Callbacks")
b2 = GtkButton("Press me 2")
push!(win2, b2)
# See https://docs.julialang.org/en/v1/manual/functions/index.html#Do-Block-Syntax-for-Function-Arguments-1
id2 = signal_connect(b2, "clicked") do widget
println(widget, " was clicked 2!")
end
id2_2 = signal_connect(b2, "clicked") do widget
println("\"", get_gtk_property(widget,:label,String), "\" was clicked!")
end
# Disconnect signal
signal_handler_disconnect(b2, id2)
# Block signal
#signal_handler_block(b2, id2_2)
#signal_handler_unblock(b2, id2_2)
showall(win2)
# https://developer.gnome.org/gtk3/stable/GtkWidget.html#GtkWidget-button-press-event
b = GtkButton("Pick a mouse button")
win = GtkWindow(b, "Callbacks")
id = signal_connect(b, "button-press-event") do widget, event # must have event
println("widget: $widget")
println(event)
println("You pressed button ", event.button)
end
showall(win)
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16539,
8,
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437,
198,
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439,
7,
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8,
198
] | 2.597682 | 604 |
include("Map/Map.jl")
include("Map/ResidueRingPoly.jl")
include("Map/NfOrd.jl")
include("Map/NumberField.jl")
include("Map/AbGrp.jl")
include("Map/NfRel.jl")
include("Map/FqPolyRing.jl")
| [
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] | 2.428571 | 77 |
import Zygote
struct ZygoteGradAlg <: GradAlg end
const ZygoteGrad = ZygoteGradAlg()
import ..Omega
Zygote.@nograd Omega.Space.increment
function gradmap(rv, ω::Ω)
l = apl(rv, ω)
params = Params(values(ω)) # We can avoid doing this every time.
g = gradient(params) do
rv(ω)
end
g
end
function lineargradient(rv, ω, ::ZygoteGradAlg)
Zygote.gradient(ωvec -> apl(rv, unlinearize(ωvec, ω)), linearize(ω))
end
function grad(rv, ω, v, ::ZygoteGradAlg)
grads = Zygote.gradient(Zygote.Params(v)) do
rv(ω)
end
end
grad(rv, ω, ::ZygoteGradAlg) = grad(rv, ω, values(ω), ZygoteGrad)
function gradarray(rv, ω, ::ZygoteGradAlg)
vs = values(ω)
grads_ = grad(rv, ω, vs, ZygoteGrad)
map(v -> grads_[v], vs)
end | [
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] | 2.160237 | 337 |
<reponame>JuliaBinaryWrappers/HelFEM_jll.jl
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule HelFEM_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("HelFEM")
JLLWrappers.@generate_main_file("HelFEM", UUID("584ea77b-75d0-51ad-9ad6-7fb559b95557"))
end # module HelFEM_jll
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# AUTO GENERATED FILE - DO NOT EDIT
export uishell
"""
uishell(;kwargs...)
An UIShell component.
UIShell is a default layout with the header and a sidebar
Keyword arguments:
- `id` (String; optional): Element id
- `name` (String; required): Platform Name
- `sidebarItems` (optional): Items of the sidebar. sidebarItems has the following type: Array of lists containing elements 'name', 'url'.
Those elements have the following types:
- `name` (String; optional)
- `url` (String; optional)s
- `headerItems` (optional): Items of the header. headerItems has the following type: Array of lists containing elements 'name', 'url'.
Those elements have the following types:
- `name` (String; optional)
- `url` (String; optional)s
- `loading_state` (optional): Object that holds the loading state object coming from dash-renderer. loading_state has the following type: lists containing elements 'is_loading', 'prop_name', 'component_name'.
Those elements have the following types:
- `is_loading` (Bool; optional): Determines if the component is loading or not
- `prop_name` (String; optional): Holds which property is loading
- `component_name` (String; optional): Holds the name of the component that is loading
"""
function uishell(; kwargs...)
available_props = Symbol[:id, :name, :sidebarItems, :headerItems, :loading_state]
wild_props = Symbol[]
return Component("uishell", "UIShell", "dash_carbon_components", available_props, wild_props; kwargs...)
end
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] | 3.386878 | 442 |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct LoadBalancerBackendAddressPropertiesFormat <: SwaggerModel
virtualNetwork::Any # spec type: Union{ Nothing, SubResource } # spec name: virtualNetwork
ipAddress::Any # spec type: Union{ Nothing, String } # spec name: ipAddress
networkInterfaceIPConfiguration::Any # spec type: Union{ Nothing, SubResource } # spec name: networkInterfaceIPConfiguration
function LoadBalancerBackendAddressPropertiesFormat(;virtualNetwork=nothing, ipAddress=nothing, networkInterfaceIPConfiguration=nothing)
o = new()
validate_property(LoadBalancerBackendAddressPropertiesFormat, Symbol("virtualNetwork"), virtualNetwork)
setfield!(o, Symbol("virtualNetwork"), virtualNetwork)
validate_property(LoadBalancerBackendAddressPropertiesFormat, Symbol("ipAddress"), ipAddress)
setfield!(o, Symbol("ipAddress"), ipAddress)
validate_property(LoadBalancerBackendAddressPropertiesFormat, Symbol("networkInterfaceIPConfiguration"), networkInterfaceIPConfiguration)
setfield!(o, Symbol("networkInterfaceIPConfiguration"), networkInterfaceIPConfiguration)
o
end
end # type LoadBalancerBackendAddressPropertiesFormat
const _property_map_LoadBalancerBackendAddressPropertiesFormat = Dict{Symbol,Symbol}(Symbol("virtualNetwork")=>Symbol("virtualNetwork"), Symbol("ipAddress")=>Symbol("ipAddress"), Symbol("networkInterfaceIPConfiguration")=>Symbol("networkInterfaceIPConfiguration"))
const _property_types_LoadBalancerBackendAddressPropertiesFormat = Dict{Symbol,String}(Symbol("virtualNetwork")=>"SubResource", Symbol("ipAddress")=>"String", Symbol("networkInterfaceIPConfiguration")=>"SubResource")
Base.propertynames(::Type{ LoadBalancerBackendAddressPropertiesFormat }) = collect(keys(_property_map_LoadBalancerBackendAddressPropertiesFormat))
Swagger.property_type(::Type{ LoadBalancerBackendAddressPropertiesFormat }, name::Symbol) = Union{Nothing,eval(Base.Meta.parse(_property_types_LoadBalancerBackendAddressPropertiesFormat[name]))}
Swagger.field_name(::Type{ LoadBalancerBackendAddressPropertiesFormat }, property_name::Symbol) = _property_map_LoadBalancerBackendAddressPropertiesFormat[property_name]
function check_required(o::LoadBalancerBackendAddressPropertiesFormat)
true
end
function validate_property(::Type{ LoadBalancerBackendAddressPropertiesFormat }, name::Symbol, val)
end
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] | 3.644412 | 689 |
#=
Copyright 2021 BlackRock, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
=#
using Convex
using ECOS
using PiecewiseQuadratics
using LinearAlgebra
@testset "Only quadratic" begin
P = ones(1, 1)
q = -2 * ones(1)
A = zeros(0, 1)
b = zeros(0)
g = zeros(PiecewiseQuadratic, 1)
ρ = zeros(0)
σ = ones(1)
params = AdmmParams(P, q, A, b, g)
settings = Settings(; ρ=ρ, σ=σ)
vars, _ = optimize(params, settings)
@test abs(vars.x[1] - 2) < 1e-3
@test abs(vars.xt[1] - 2) < 1e-3
end
@testset "Only separable" begin
P = zeros(1, 1)
q = zeros(1)
A = zeros(0, 1)
b = zeros(0)
g = [PiecewiseQuadratic([BoundedQuadratic(2.0, 3.0, 0.0, -1.0, 10.0),
BoundedQuadratic(3.0, 5.0, 0.0, 1.0, 4.0)])]
ρ = zeros(0)
σ = ones(1)
params = AdmmParams(P, q, A, b, g)
settings = Settings(; ρ=ρ, σ=σ)
vars, _ = optimize(params, settings)
display(vars)
@test abs(vars.x[1] - 3) < 1e-3
@test abs(vars.xt[1] - 3) < 1e-3
end
@testset "Random quadratic program" begin
# Original generating code:
# P = randn(5, 5)
# P = P'*P
# A = randn(2, 5)
P = [1.29585 2.90775 -0.421317 1.52657 0.209435
2.90775 10.5604 0.298516 4.64058 -2.92845
-0.421317 0.298516 1.94608 -1.61207 -0.198397
1.52657 4.64058 -1.61207 4.06981 -2.02592
0.209435 -2.92845 -0.198397 -2.02592 4.66317]
q = -10 * ones(5)
A = [1.33694 -0.745464 -0.0531773 -2.11537 1.22246
-0.0731486 -1.22006 -0.165136 -0.066768 0.567695]
b = A * ones(5)
g = repeat([indicator(0, Inf)], 5)
x = Variable(5)
problem = Convex.minimize(0.5 * quadform(x, P) + q' * x, [x >= 0, A * x == b])
solve!(problem, ECOS.Optimizer)
ρ = ones(2)
σ = ones(5)
params = AdmmParams(P, q, A, b, g)
settings = Settings(; ρ=ρ, σ=σ, term_cond_freq=1000, max_iters=1000)
vars, stats = optimize(params, settings)
@test norm(vars.x - x.value) < 1e-3
@test norm(vars.xt - x.value) < 1e-3
end
@testset "Random Lasso problem" begin
n = 2 # num features
m = 0 # num constraints
X = [1.33694 -0.745464
-0.0531773 -2.11537
1.22246 -0.0731486
-1.22006 -0.165136
-0.066768 0.567695]
y = X * ones(n)
λ = 1.0
β = Variable(n)
problem = Convex.minimize(0.5 * sumsquares(X * β - y) + λ * sum(abs(β)))
solve!(problem, ECOS.Optimizer)
ρ = ones(m)
σ = ones(n)
P = X' * X
q = -X' * y
A = zeros(m, n)
b = zeros(m)
g = [PiecewiseQuadratic([BoundedQuadratic(-Inf, 0.0, 0.0, -λ, 0.0),
BoundedQuadratic(0.0, Inf, 0.0, λ, 0.0)]) for i in 1:n]
params = AdmmParams(P, q, A, b, g)
settings = Settings(; ρ=ρ, σ=σ, term_cond_freq=1000, max_iters=1000)
vars, _ = optimize(params, settings)
@test norm(vars.x - β.value) < 1e-3
@test norm(vars.xt - β.value) < 1e-3
# first vars term cache
settings = Settings(; ρ=ρ, σ=σ, term_cond_freq=1000, max_iters=1000,
term_cond_type=FIRST_VARS_TERM_COND_FLAG, compute_stats=true)
vars, stats = optimize(params, settings)
@test norm(vars.x - β.value) < 1e-3
@test norm(vars.xt - β.value) < 1e-3
end
@testset "small example" begin
# NOTE: example from README.md
# construct problem data (ensuring the problem is feasible)
x0 = [0.0036165677461501566, 0.9751164348173793, 0.4825907494313493,
0.47578842808561417]
A = [0.477388 0.796388 0.509418 0.426954;
0.772124 0.381946 0.412414 0.815745]
b = A * x0
X = [0.444226 0.331317 0.870612 0.576343;
0.236767 0.392398 0.000545117 0.0703828;
0.413522 0.893505 0.366298 0.654319;
0.149137 0.0738117 0.737134 0.403253]
P = X'X # ensure P is positive definite
@assert isposdef(P)
q = [0.007228400155782522, 0.9979590358695292, 0.6284406861482683, 0.08716574428367818]
# x1 has to be in union([-1, 2], [2.5, 3.5]) and has a quadratic penalty if
# it lies in [-1, 2] and a linear penalty if it lies in [2.5, 3.5]
g1 = PiecewiseQuadratic([BoundedQuadratic(-1, 2, 1, 0, 0),
BoundedQuadratic(2.5, 3.5, 0, 1, 0)])
# x2 has to be between -20 and 10
g2 = indicator(-20, 10)
# x3 has to be between -5 and 10
g3 = indicator(-5, 10)
# x4 has to be exactly 1.2318
g4 = indicator(1.2318, 1.2318)
g = [g1, g2, g3, g4]
# solve
params = AdmmParams(P, q, A, b, g)
# display
display(params)
vars, _ = optimize(params)
expected = [-0.39769, 1.73815, -0.96784, 1.2318]
@test norm(vars.x - expected) < 1e-4
end
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] | 2.004277 | 2,572 |
<reponame>joachimbrand/Rimu.jl
"""
MPIData(data; kwargs...)
Wrapper used for signaling that this data is part of a distributed data structure and
communication should happen with MPI. `MPIData` can generally be used where an
[`AbstractDVec`](@ref) would be used otherwise. Unlike [`AbstractDVec`](@ref)s, `MPIData`
does not support indexing, or iteration over `keys`, `values`, and `pairs`.
Keyword arguments:
* `setup = mpi_point_to_point` - controls the communication stratgy
* [`mpi_one_sided`](@ref) uses one-sided communication with remote memory access (RMA), sets [`MPIOneSided`](@ref) strategy.
* [`mpi_point_to_point`](@ref) uses [`MPIPointTOPoint`](@ref) strategy.
* [`mpi_all_to_all`](@ref) uses [`MPIAllToAll`](@ref) strategy.
* [`mpi_no_exchange`](@ref) sets [`MPINoWalkerExchange`](@ref) strategy. Experimental. Use with caution!
* `comm = mpi_comm()`
* `root = mpi_root`
* The rest of the keyword arguments are passed to `setup`.
"""
struct MPIData{D,S}
data::D # local data, e.g. a DVec
comm::MPI.Comm
root::Int32 # rank of root process
isroot::Bool # true if running on root process
s::S # type (struct) with further details needed for communication
function MPIData(data::D, comm, root, s::S) where {D, S<:DistributeStrategy}
return new{D,S}(data, comm, root, s.id == root, s)
end
end
# convenient constructor with setup function
function MPIData(data; setup=mpi_point_to_point, comm=mpi_comm(), root=mpi_root, kwargs...)
return setup(data, comm, root; kwargs...)
end
Base.eltype(md::MPIData) = eltype(md.data)
Base.valtype(md::MPIData) = valtype(md.data)
Base.keytype(md::MPIData) = keytype(md.data)
Rimu.localpart(md::MPIData) = md.data
Rimu.StochasticStyle(d::MPIData) = Rimu.StochasticStyle(d.data)
function Base.summary(io::IO, md::MPIData)
data = nameof(typeof(md.data))
len = length(md)
style = StochasticStyle(md)
strat = nameof(typeof(md.s))
print(io, "MPIData($data) with $len entries, style = $style, strategy = $strat")
end
function Base.show(io::IO, md::MPIData)
summary(io, md)
limit, _ = displaysize()
for (i, p) in enumerate(pairs(localpart(md)))
if length(md) > i > limit - 4
print(io, "\n ⋮ => ⋮")
break
else
print(io, "\n ", p)
end
end
end
###
### Iterators
###
"""
MPIDataIterator{I,M<:MPIData}
Iterator over `keys`, `values`, or `pairs` of a `dv::MPIData`. Unlike its name would
suggest, it does not actually support iteration. To perform computations with it, use
`mapreduce`, or its derivatives (`sum`, `prod`, `reduce`...), which will perform the
reduction accross MPI ranks.
"""
struct MPIDataIterator{I,M<:MPIData}
iter::I
data::M
end
function Base.iterate(it::MPIDataIterator, args...)
error(
"iterating over `::MPIData` is not supported. ",
"Use `localpart` to iterate over the local part of the vector or `mapreduce` to ",
"perform a reduction accross ranks",
)
end
function Base.mapreduce(f, op, it::MPIDataIterator; kwargs...)
res = mapreduce(f, op, it.iter; kwargs...)
return MPI.Allreduce(res, op, it.data.comm)
end
Base.IteratorSize(::MPIDataIterator) = Base.SizeUnknown()
Base.pairs(data::MPIData) = MPIDataIterator(pairs(localpart(data)), data)
Base.keys(data::MPIData) = MPIDataIterator(keys(localpart(data)), data)
Base.values(data::MPIData) = MPIDataIterator(values(localpart(data)), data)
Rimu.localpart(it::MPIDataIterator) = it.iter
"""
length(md::MPIData)
Compute the length of the distributed data on every MPI rank with
`MPI.Allreduce`. MPI syncronizing.
"""
Base.length(md::MPIData) = MPI.Allreduce(length(md.data), +, md.comm)
"""
norm(md::MPIData, p=2)
Compute the norm of the distributed data on every MPI rank with `MPI.Allreduce`.
MPI syncronizing.
"""
function LinearAlgebra.norm(md::MPIData, p::Real=2)
if p === 2
return sqrt(sum(abs2, values(md)))
elseif p === 1
return float(sum(abs, values(md)))
elseif p === Inf
return float(mapreduce(abs, max, values(md); init=real(zero(valtype(md)))))
else
error("$p-norm of MPIData is not implemented.")
end
end
"""
walkernumber(md::MPIData)
Compute the walkernumber of the distributed data on every MPI rank with `MPI.Allreduce`.
MPI syncronizing.
"""
function Rimu.DictVectors.walkernumber(md::MPIData)
return MPI.Allreduce(walkernumber(md.data), +, md.comm)
end
"""
mpi_synchronize!(md::MPIData)
Synchronize `md`, ensuring its contents are distributed among ranks correctly.
"""
function mpi_synchronize!(md::MPIData)
P = eltype(md)
myrank = mpi_rank(md.comm)
buffers = Vector{P}[P[] for _ in 1:mpi_size(md.comm)]
for (add, val) in pairs(localpart(md))
tr = targetrank(add, mpi_size(md.comm))
if tr ≠ myrank
push!(buffers[tr + 1], add => val)
localpart(md)[add] = zero(valtype(md))
end
end
mpi_communicate_buffers!(localpart(md), buffers, md.comm)
return md
end
"""
mpi_communicate_buffers!(target::AbstractDVec{K,V}, buffers::Vector{<:Vector{V}})
Use MPI to communicate the contents of `buffers` and sort them into `target`. The length
of `buffers` should be equal to [`mpi_size`](@ref).
"""
function mpi_communicate_buffers!(target, buffers, comm)
myrank = mpi_rank(comm)
recbuf = buffers[myrank + 1]
datatype = MPI.Datatype(eltype(target))
# Receive from lower ranks.
for id in 0:(myrank - 1)
resize!(recbuf, MPI.Get_count(MPI.Probe(id, 0, comm), datatype))
MPI.Recv!(recbuf, id, 0, comm)
for (add, value) in recbuf
target[add] += value
end
end
# Perform sends.
for id in 0:(mpi_size(comm) - 1)
id == myrank && continue
MPI.Send(buffers[id + 1], id, 0, comm)
end
# Receive from higher ranks.
for id in (myrank + 1):(mpi_size(comm) - 1)
resize!(recbuf, MPI.Get_count(MPI.Probe(id, 0, comm), datatype))
MPI.Recv!(recbuf, id, 0, comm)
for (add, value) in recbuf
target[add] += value
end
end
return target
end
"""
*(lop::AbstractHamiltonian, md::MPIData)
Allocating "Matrix"-"vector" multiplication with MPI-distributed "vector" `md`. The result is similar to
[`localpart(md)`](@ref) with all content having been communicated to the correct [`targetrank`](@ref).
MPI communicating.
See [`MPIData`](@ref).
"""
function Base.:*(lop, md::MPIData)
T = promote_type(eltype(lop),valtype(md))
P = Pair{keytype(md),T}
buffers = Vector{P}[P[] for _ in 1:mpi_size(md.comm)]
myrank = mpi_rank()
result = similar(localpart(md), T)
# Sort values into buffers and communicate.
for (key, val) in pairs(localpart(md))
result[key] += diagonal_element(lop, key)*val
for (add, elem) in offdiagonals(lop, key)
tr = targetrank(add, mpi_size(md.comm))
if tr == myrank
result[add] += elem * val
else
push!(buffers[tr + 1], add => elem * val)
end
end
end
mpi_communicate_buffers!(result, buffers, md.comm)
return result
end
# Note: the following methods assume MPIDatas are distributed correctly.
function LinearAlgebra.dot(x, md::MPIData)
return MPI.Allreduce(localpart(x)⋅localpart(md), +, md.comm)
end
function LinearAlgebra.dot(md::MPIData, x)
return MPI.Allreduce(localpart(md)⋅localpart(x), +, md.comm)
end
function LinearAlgebra.dot(md_left::MPIData, md_right::MPIData)
return MPI.Allreduce(localpart(md_left)⋅localpart(md_right), +, md_left.comm)
end
# Note: the following two methods work with x::DVec and assume `x` is the same on all ranks.
function LinearAlgebra.dot(x, lop, md::MPIData)
return MPI.Allreduce(dot(x, lop, localpart(md)), +, md.comm)
end
function LinearAlgebra.dot(md::MPIData, lop, x)
return MPI.Allreduce(dot(localpart(md), lop, x), +, md.comm)
end
"""
copy_to_local(md::MPIData)
Collect all pairs in `md` from all ranks and store them in a local `AbstractDVec`.
"""
copy_to_local(md::MPIData) = copy_to_local!(similar(localpart(md)), md)
"""
copy_to_local!(target, md::MPIData)
Collect all pairs in `md` from all ranks and store them in `target`.
In-place version of [`copy_to_local`](@ref).
"""
function copy_to_local!(target, md::MPIData)
copy!(target, localpart(md))
comm = md.comm
myrank = mpi_rank(comm)
datatype = MPI.Datatype(eltype(md))
# Store all pairs to a buffer.
sendbuf = collect(pairs(localpart(md)))
recbuf = eltype(md)[]
# Receive from lower ranks.
for id in 0:(myrank - 1)
resize!(recbuf, MPI.Get_count(MPI.Probe(id, 0, comm), datatype))
MPI.Recv!(recbuf, id, 0, comm)
for (add, value) in recbuf
target[add] += value
end
end
# Perform sends.
for id in 0:(mpi_size(comm) - 1)
id == myrank && continue
MPI.Send(sendbuf, id, 0, comm)
end
# Receive from higher ranks.
for id in (myrank + 1):(mpi_size(comm) - 1)
resize!(recbuf, MPI.Get_count(MPI.Probe(id, 0, comm), datatype))
MPI.Recv!(recbuf, id, 0, comm)
for (add, value) in recbuf
target[add] += value
end
end
MPI.Barrier(comm)
return target
end
function LinearAlgebra.dot(md_left::MPIData, lop, md_right::MPIData)
# Idea: lop * md_right can be huge. It might be better to just collect the full left
# vector and do the multiplication locally.
left = copy_to_local(md_left)
return dot(left, lop, md_right)
end
function Rimu.freeze(md::MPIData)
mpi_synchronize!(md)
return freeze(localpart(md))
end
function Rimu.all_overlaps(operators::Tuple, vecs::NTuple{N,MPIData}) where {N}
local_vec_i = similar(localpart(vecs[1]))
T = promote_type((valtype(v) for v in vecs)..., eltype.(operators)...)
names = String[]
values = T[]
for i in 1:N, j in i+1:N
push!(names, "c$(i)_dot_c$(j)")
push!(values, dot(vecs[i], vecs[j]))
copy_to_local!(local_vec_i, vecs[i])
for (k, op) in enumerate(operators)
push!(names, "c$(i)_Op$(k)_c$(j)")
push!(values, dot(local_vec_i, op, vecs[j]))
end
end
num_reports = (N * (N - 1) ÷ 2) * (length(operators) + 1)
return SVector{num_reports,String}(names).data, SVector{num_reports,T}(values).data
end
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] | 2.353791 | 4,432 |
module AverageShiftedHistograms
import StatsBase
import StatsBase: nobs, fit!
import Distributions
import UnicodePlots
using RecipesBase
export
ash, fit!, extendrange, xy, xyz, nout, nobs,
# Kernels
Kernels,
biweight, cosine, epanechnikov, triangular, tricube, triweight, uniform,
gaussian, logistic
# common
abstract AbstractAsh
StatsBase.nobs(o::AbstractAsh) = o.nobs
nout(o::AbstractAsh) = nobs(o) - sum(o.v)
"""
`extendrange(x, s = .5, n = 200)`
Create a `LinSpace` of length `n` starting at `s` standard deviations below
`minimum(x)` and ending at `s` standard deviations above `maximum(x)`
"""
function extendrange(y::AbstractVector, s = 0.5, n = 200)
σ = std(y)
linspace(minimum(y) - s * σ, maximum(y) + s * σ, n)
end
include("kernels.jl")
include("univariate.jl")
include("bivariate.jl")
end
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] | 2.653968 | 315 |
<gh_stars>10-100
### A Pluto.jl notebook ###
# v0.12.7
using Markdown
using InteractiveUtils
# ╔═╡ 6fd616f6-1ea1-11eb-3814-8bfb4a096c49
using Pkg, DrWatson
# ╔═╡ 6ffe2628-1ea1-11eb-24ea-57f985146a72
begin
@quickactivate "StatisticsWithJuliaPlutoNotebooks"
using Random, StatsBase, DataFrames, Plots, Measures
Random.seed!(1)
end;
# ╔═╡ ed174bc4-1ea0-11eb-1e2f-a32874cec549
md"## Listing 3.2"
# ╔═╡ 70179b62-1ea1-11eb-13ce-7fb4d54bad4b
begin
pDiscrete = [0.25, 0.25, 0.5]
xGridD = 0:2
end;
# ╔═╡ 7e20eac0-1f6d-11eb-23c8-f7f93c8ec92b
pContinuous(x) = 3/4*(1 - x^2)
# ╔═╡ 7e212a44-1f6d-11eb-17a5-a56e8476b5cf
xGridC = -1:0.01:1;
# ╔═╡ 7e21ad70-1f6d-11eb-1e7a-5982011bd333
pContinuous2(x) = x < 0 ? x+1 : 1-x
# ╔═╡ 7e2dde18-1f6d-11eb-2165-fd23fbaf928e
begin
p1 = plot(xGridD, line=:stem, pDiscrete, marker=:circle, c=:blue, ms=6, msw=0)
p2 = plot(xGridC, pContinuous.(xGridC), c=:blue)
p3 = plot(xGridC, pContinuous2.(xGridC), c=:blue)
plot(p1, p2, p3, layout=(1,3), legend=false, ylims=(0,1.1), xlabel="x",
ylabel=["Probability" "Density" "Density"], size=(1200, 400), margin=5mm)
end
# ╔═╡ 70182cd8-1ea1-11eb-094d-8d0d49cf15f3
md"## End of listing 3.2"
# ╔═╡ Cell order:
# ╟─ed174bc4-1ea0-11eb-1e2f-a32874cec549
# ╠═6fd616f6-1ea1-11eb-3814-8bfb4a096c49
# ╠═6ffe2628-1ea1-11eb-24ea-57f985146a72
# ╠═70179b62-1ea1-11eb-13ce-7fb4d54bad4b
# ╠═7e20eac0-1f6d-11eb-23c8-f7f93c8ec92b
# ╠═7e212a44-1f6d-11eb-17a5-a56e8476b5cf
# ╠═7e21ad70-1f6d-11eb-1e7a-5982011bd333
# ╠═7e2dde18-1f6d-11eb-2165-fd23fbaf928e
# ╟─70182cd8-1ea1-11eb-094d-8d0d49cf15f3
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] | 1.662407 | 939 |
<reponame>UnofficialJuliaMirror/StochasticPrograms.jl-8b8459f2-c380-502b-8633-9aed2d6c2b35<filename>src/methods/evaluation.jl
# Problem evaluation #
# ========================== #
function _eval_first_stage(stochasticprogram::StochasticProgram, x::AbstractVector)
first_stage = get_stage_one(stochasticprogram)
return eval_objective(first_stage.obj, x)
end
function _eval_second_stage(stochasticprogram::TwoStageStochasticProgram, x::AbstractVector, scenario::AbstractScenario, solver::MPB.AbstractMathProgSolver)
outcome = outcome_model(stochasticprogram, x, scenario, solver)
solve(outcome)
return probability(scenario)*getobjectivevalue(outcome)
end
function _eval_second_stages(stochasticprogram::TwoStageStochasticProgram{S,SP},
x::AbstractVector,
solver::MPB.AbstractMathProgSolver) where {S, SP <: ScenarioProblems}
outcome_generator = scenario -> outcome_model(stochasticprogram, x, scenario; solver = solver)
return outcome_mean(outcome_generator, scenarios(stochasticprogram))
end
function _eval_second_stages(stochasticprogram::TwoStageStochasticProgram{S,SP},
x::AbstractVector,
solver::MPB.AbstractMathProgSolver) where {S, SP <: DScenarioProblems}
Qs = Vector{Float64}(undef, nworkers())
outcome_generator = scenario -> outcome_model(stochasticprogram, x, scenario; solver = solver)
@sync begin
for (i,w) in enumerate(workers())
@async Qs[i] = remotecall_fetch((sp,outcome_generator)->begin
scenarioproblems = fetch(sp)
isempty(scenarioproblems.scenarios) && return 0.0
return outcome_mean(outcome_generator, scenarioproblems.scenarios)
end,
w,
stochasticprogram.scenarioproblems[w-1],
outcome_generator)
end
end
return sum(Qs)
end
function _stat_eval_second_stages(stochasticprogram::TwoStageStochasticProgram{S,SP},
x::AbstractVector,
solver::MPB.AbstractMathProgSolver) where {S, SP <: ScenarioProblems}
N = nscenarios(stochasticprogram)
outcome_generator = scenario -> outcome_model(stochasticprogram, x, scenario; solver = solver)
𝔼Q, σ² = outcome_welford(outcome_generator, scenarios(stochasticprogram))
return 𝔼Q, sqrt(σ²)
end
function _stat_eval_second_stages(stochasticprogram::TwoStageStochasticProgram{S,SP},
x::AbstractVector,
solver::MPB.AbstractMathProgSolver) where {S, SP <: DScenarioProblems}
N = nscenarios(stochasticprogram)
partial_welfords = Vector{Tuple{Float64,Float64,Int}}(undef, nworkers())
@sync begin
for (i,w) in enumerate(workers())
@async partial_welfords[i] = remotecall_fetch((sp,stage_one_generator,stage_two_generator,stage_one_params,stage_two_params,x,solver)->begin
scenarioproblems = fetch(sp)
isempty(scenarioproblems.scenarios) && return zero(eltype(x)), zero(eltype(x))
outcome_generator = scenario -> begin
outcome_model = Model(solver = solver)
_outcome_model!(outcome_model,
stage_one_generator,
stage_two_generator,
stage_one_params,
stage_two_params,
x,
scenario)
return outcome_model
end
return (outcome_welford(outcome_generator, scenarioproblems.scenarios)..., length(scenarioproblems.scenarios))
end,
w,
stochasticprogram.scenarioproblems[w-1],
stochasticprogram.generator[:stage_1_vars],
stochasticprogram.generator[:stage_2],
stage_parameters(stochasticprogram, 1),
stage_parameters(stochasticprogram, 2),
x,
solver)
end
end
𝔼Q, σ², _ = reduce(aggregate_welford, partial_welfords)
return 𝔼Q, sqrt(σ²)
end
function _eval(stochasticprogram::StochasticProgram{2}, x::AbstractVector, solver::MPB.AbstractMathProgSolver)
xlength = decision_length(stochasticprogram)
length(x) == xlength || error("Incorrect length of given decision vector, has ", length(x), " should be ", xlength)
all(.!(isnan.(x))) || error("Given decision vector has NaN elements")
cᵀx = _eval_first_stage(stochasticprogram, x)
𝔼Q = _eval_second_stages(stochasticprogram, x, solver)
return cᵀx+𝔼Q
end
# Mean/variance calculations #
# ========================== #
function outcome_mean(outcome_generator::Function, scenarios::Vector{<:AbstractScenario})
Qs = zeros(length(scenarios))
for (i,scenario) in enumerate(scenarios)
outcome = outcome_generator(scenario)
status = solve(outcome)
if status != :Optimal
error("Outcome model could not be solved, returned status: $status")
end
Qs[i] = probability(scenario)*getobjectivevalue(outcome)
end
return sum(Qs)
end
function outcome_welford(outcome_generator::Function, scenarios::Vector{<:AbstractScenario})
Q̄ₖ = 0
Sₖ = 0
N = length(scenarios)
for k = 1:N
Q̄ₖ₋₁ = Q̄ₖ
outcome = outcome_generator(scenarios[k])
status = solve(outcome)
if status != :Optimal
error("Outcome model could not be solved, returned status: $status")
end
Q = getobjectivevalue(outcome)
Q̄ₖ = Q̄ₖ + (Q-Q̄ₖ)/k
Sₖ = Sₖ + (Q-Q̄ₖ)*(Q-Q̄ₖ₋₁)
end
return Q̄ₖ, Sₖ/(N-1)
end
function aggregate_welford(left::Tuple, right::Tuple)
x̄ₗ, σₗ², nₗ = left
x̄ᵣ, σᵣ², nᵣ = right
δ = x̄ᵣ-x̄ₗ
N = nₗ+nᵣ
x̄ = (nₗ*x̄ₗ+nᵣ*x̄ᵣ)/N
Sₗ = σₗ²*(nₗ-1)
Sᵣ = σᵣ²*(nᵣ-1)
S = Sₗ+Sᵣ+nₗ*nᵣ/N*δ^2
return (x̄, S/(N-1), N)
end
# Evaluation API #
# ========================== #
"""
evaluate_decision(stochasticprogram::TwoStageStochasticProgram,
decision::AbstractVector;
solver = JuMP.UnsetSolver())
Evaluate the first-stage `decision` in `stochasticprogram`.
In other words, evaluate the first-stage objective at `decision` and solve outcome models of `decision` for every available scenario. Optionally, supply a capable `solver` to solve the outcome models. Otherwise, any previously set solver will be used.
"""
function evaluate_decision(stochasticprogram::StochasticProgram{2}, decision::AbstractVector; solver::SPSolverType = JuMP.UnsetSolver())
# Use cached solver if available
supplied_solver = pick_solver(stochasticprogram, solver)
# Abort if no solver was given
if isa(supplied_solver, JuMP.UnsetSolver)
error("Cannot evaluate decision without a solver.")
end
return _eval(stochasticprogram, decision, internal_solver(supplied_solver))
end
"""
evaluate_decision(stochasticprogram::TwoStageStochasticProgram,
decision::AbstractVector,
scenario::AbstractScenario;
solver = JuMP.UnsetSolver())
Evaluate the result of taking the first-stage `decision` if `scenario` is the actual outcome in `stochasticprogram`.
"""
function evaluate_decision(stochasticprogram::StochasticProgram{2},
decision::AbstractVector,
scenario::AbstractScenario;
solver::SPSolverType = JuMP.UnsetSolver())
# Use cached solver if available
supplied_solver = pick_solver(stochasticprogram, solver)
# Abort if no solver was given
if isa(supplied_solver, JuMP.UnsetSolver)
error("Cannot evaluate decision without a solver.")
end
outcome = outcome_model(stochasticprogram, decision, scenario; solver = solver)
status = solve(outcome)
if status == :Optimal
return _eval_first_stage(stochasticprogram, decision) + getobjectivevalue(outcome)
end
error("Outcome model could not be solved, returned status: $status")
end
"""
evaluate_decision(stochasticmodel::StochasticModel{2},
decision::AbstractVector,
sampler::AbstractSampler;
solver = JuMP.UnsetSolver(),
confidence = 0.9,
n = 1000,
N = 100,
M = 10)
Return a statistical estimate of the objective of the two-stage `stochasticmodel` at `decision` in the form of a confidence interval at level `confidence`, when the underlying scenario distribution is inferred by `sampler`.
In other words, evaluate `decision` on an SAA model of size `n`. Generate an upper bound using the sample variance of the evaluation. The lower bound is calculated from the lower bound around the true optimum generated by `confidence_interval`
See also: [`confidence_interval`](@ref)
"""
function evaluate_decision(stochasticmodel::StochasticModel{2},
decision::AbstractVector,
sampler::AbstractSampler{S};
solver::SPSolverType = JuMP.UnsetSolver(),
confidence::AbstractFloat = 0.9,
n::Integer = 1000,
N::Integer = 100,
M::Integer = 10) where S <: AbstractScenario
eval_model = SAA(stochasticmodel, sampler, n)
# Condidence level
α = (1-confidence)/2
# Compute confidence interval around true optimum for lower bound
CI = confidence_interval(stochasticmodel, sampler; solver = solver, confidence = 1-α, N = N, M = M)
L = lower(CI)
# Upper bound
cᵀx = _eval_first_stage(eval_model, decision)
𝔼Q, σ = _stat_eval_second_stages(eval_model, decision, internal_solver(solver))
U = cᵀx + 𝔼Q + quantile(Normal(0,1), 1-α)*σ
return ConfidenceInterval(L, U, confidence)
end
"""
confidence_interval(stochasticmodel::StochasticModel{2},
sampler::AbstractSampler;
solver = JuMP.UnsetSolver(),
confidence = 0.95,
N = 100,
M = 10)
Generate a confidence interval around the true optimum of the two-stage `stochasticmodel` at level `confidence`, when the underlying scenario distribution is inferred by `sampler`.
`N` is the size of the SAA models used to generate the interval and generally governs how tight it is. `M` is the amount of SAA samples used.
"""
function confidence_interval(stochasticmodel::StochasticModel{2}, sampler::AbstractSampler{S}; solver::SPSolverType = JuMP.UnsetSolver(), confidence::AbstractFloat = 0.95, N::Integer = 100, M::Integer = 10) where {S <: AbstractScenario}
# Condidence level
α = 1-confidence
# Lower bound
Qs = Vector{Float64}(undef, M)
for i = 1:M
saa = SAA(stochasticmodel, sampler, N)
Qs[i] = VRP(saa, solver = solver)
end
Q̂ = mean(Qs)
σ = std(Qs)
t = quantile(TDist(M-1), 1-α)
L = Q̂ - t*σ
U = Q̂ + t*σ
return ConfidenceInterval(L, U, 1-α)
end
# ========================== #
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] | 2.182116 | 5,189 |
module LibSodiumClangWrap
using Clang
using Printf
using libsodium_jll
m = @eval module Anon end
const LIBSODIUM_JLL_DIR = dirname(dirname(pathof(libsodium_jll)))
m.include(joinpath(LIBSODIUM_JLL_DIR, "src", "libsodium_jll.jl"))
const LIBSODIUM_DIR = dirname(dirname(m.libsodium_jll.libsodium_path))
const LIBSODIUM_INCLUDE = joinpath(LIBSODIUM_DIR, "include", "sodium")
const HEADER_FNAMES = ["core.h",
"export.h",
"crypto_hash_sha512.h",
"crypto_sign_ed25519.h"]
const HEADER_PATHS = map(hn -> joinpath(LIBSODIUM_INCLUDE, hn) |> normpath,
HEADER_FNAMES)
const IGNORE_DEFINITIONS = ["crypto_sign_ed25519_MESSAGEBYTES_MAX",
"crypto_sign_ed25519_open"]
function cursor_wrapped(cursor_name, cursor)
any(map(def -> startswith(cursor_name, def), IGNORE_DEFINITIONS)) ? false : true
end
const OUTPUT_DIR = abspath(@__DIR__, "out")
wc = init(; headers = HEADER_PATHS,
output_file = joinpath(OUTPUT_DIR, "api.jl"),
common_file = joinpath(OUTPUT_DIR, "common.jl"),
clang_includes = vcat(LIBSODIUM_INCLUDE, CLANG_INCLUDE),
clang_args = ["-I", joinpath(LIBSODIUM_INCLUDE, "..")],
header_wrapped = (root, current)->root == current,
header_library = x->"libsodium",
cursor_wrapped = cursor_wrapped,
clang_diagnostics = true,
)
run(wc)
end
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] | 1.994573 | 737 |
<reponame>80386DX40/Lasso.jl<gh_stars>10-100
using Lasso
using Random, SparseArrays
DATADIR = joinpath(dirname(@__FILE__), "data")
function diffmatslow(order, n)
D = sparse(SparseArrays.spdiagm_internal(0 => fill(-1., n-1), 1=>fill(1., n-1))...)
for i = 1:order
D = sparse(SparseArrays.spdiagm_internal(0 => fill(-1., n-i-1), 1=>fill(1., n-i-1))...)*D
end
D
end
# Test that DifferenceMatrix behaves as expected
@testset "DifferenceMatrix" begin
@testset "order = $(order)" for order in (1, 2, 3)
D1 = diffmatslow(order, 100)
D2 = Lasso.TrendFiltering.DifferenceMatrix{Float64}(order, 100)
x = randn(testrng, 100)
@test D2'D2 == D1'D1
@test D2*x ≈ D1*x
@test D2'*x[1:size(D1, 1)] ≈ D1'*x[1:size(D1, 1)]
end
end
# Test against results from glmgen
lakehuron = readcsvmat(joinpath(DATADIR, "LakeHuron.csv"); header=true)[:, 3]
@testset "TrendFiltering" begin
@testset "order = $(order)" for order in (1, 2, 3)
@testset "lambda = $(lambda)" for lambda in (1., 10., 100.)
@test coef(fit(TrendFilter, lakehuron, order, lambda; tol=1e-9)) ≈
vec(readcsvmat(joinpath(DATADIR, "LakeHuron_order_$(order)_lambda_$(lambda).csv")))
end
end
end
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] | 2.134907 | 593 |
set_theory = [
Axiom("Definition of the universal set", P ⊂ Ω, TRUE),
Axiom("Definition of the universal set", x ∈ Ω, TRUE),
Axiom("Definition of the empty set", ∅ ⊂ A, TRUE),
Axiom("Definition of the empty set", x ∈ ∅, FALSE),
Axiom("Equal sets are subsets", A ⊂ A, TRUE),
Axiom("Definition of Element Of", x ∈ (A ∩ B), (x ∈ A) ∧ (x ∈ B)),
Axiom("Definition of Element Of", (x ∈ A) ∧ (x ∈ B), x ∈ (A ∩ B)),
Axiom("Definition of Element Of", x ∈ (A ∪ B), (x ∈ A) ∨ (x ∈ B)),
Axiom("Definition of Element Of", (x ∈ A) ∨ (x ∈ B), x ∈ (A ∪ B)),
Axiom("Flatten Condition Set", ConditionSet(y, y ∈ A), A), # todo: change to f(x)
Axiom("Expand Condition Set", A, ConditionSet(_y, _y ∈ A)), # todo: change to f(x)
Axiom("Definition of Subset", A ⊂ B, (_y ∈ A) ⟹ (_y ∈ B)),
Axiom("Definition of Subset", (_y ∈ A) ⟹ (_y ∈ B), A ⊂ B),
Axiom("Definition of the universal set", x ∈ (P ∪ Ω), x ∈ Ω),
#Theorem("Intersections are subsets of Unions", x ∈ (A ∩ B), x ∈ (A ∪ B), Proof([])),
#Theorem("Intersections are subsets of Unions", (A ∩ B) ⊂ (A ∪ B), TRUE, Proof([])),
]
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198,
220,
220,
220,
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] | 2.26 | 500 |
<gh_stars>10-100
# ---
# title: 951. Flip Equivalent Binary Trees
# id: problem951
# author: <NAME>
# date: 2020-10-31
# difficulty: Medium
# categories: Tree
# link: <https://leetcode.com/problems/flip-equivalent-binary-trees/description/>
# hidden: true
# ---
#
# For a binary tree **T** , we can define a **flip operation** as follows:
# choose any node, and swap the left and right child subtrees.
#
# A binary tree **X** is _flip equivalent_ to a binary tree **Y** if and only
# if we can make **X** equal to **Y** after some number of flip operations.
#
# Given the roots of two binary trees `root1` and `root2`, return `true` if the
# two trees are flip equivelent or `false` otherwise.
#
#
#
# **Example 1:**
#
# 
#
#
#
# Input: root1 = [1,2,3,4,5,6,null,null,null,7,8], root2 = [1,3,2,null,6,4,5,null,null,null,null,8,7]
# Output: true
# Explanation: We flipped at nodes with values 1, 3, and 5.
#
#
# **Example 2:**
#
#
#
# Input: root1 = [], root2 = []
# Output: true
#
#
# **Example 3:**
#
#
#
# Input: root1 = [], root2 = [1]
# Output: false
#
#
# **Example 4:**
#
#
#
# Input: root1 = [0,null,1], root2 = []
# Output: false
#
#
# **Example 5:**
#
#
#
# Input: root1 = [0,null,1], root2 = [0,1]
# Output: true
#
#
#
#
# **Constraints:**
#
# * The number of nodes in each tree is in the range `[0, 100]`.
# * Each tree will have **unique node values** in the range `[0, 99]`.
#
#
## @lc code=start
using LeetCode
## add your code here:
## @lc code=end
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using IntroML
using Documenter
using Pluto
const DOCS_PATH = @__DIR__
const SRC_PATH = joinpath(DOCS_PATH, "src")
const NB_PATH = joinpath(dirname(DOCS_PATH), "notebooks")
function build_notebook(nbpath, htmlpath)
@info "Building notebook at $nbpath to HTML file at $htmlpath"
s = Pluto.ServerSession()
nb = Pluto.SessionActions.open(s, nbpath; run_async=false)
write(htmlpath, Pluto.generate_html(nb))
return htmlpath
end
# build Pluto notebooks
for fn in readdir(NB_PATH)
nbpath = joinpath(NB_PATH, fn)
htmlpath = joinpath(SRC_PATH, first(splitext(fn)) * ".html")
build_notebook(nbpath, htmlpath)
end
DocMeta.setdocmeta!(IntroML, :DocTestSetup, :(using IntroML); recursive=true)
makedocs(;
modules=[IntroML],
authors="<NAME> <<EMAIL>> and contributors",
repo="https://github.com/mlcolab/IntroML.jl/blob/{commit}{path}#{line}",
sitename="IntroML.jl",
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
canonical="https://mlcolab.github.io/IntroML.jl",
assets=String[],
),
pages=["Home" => "index.md"],
)
deploydocs(; repo="github.com/mlcolab/IntroML.jl", devbranch="main", push_preview=true)
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<gh_stars>1-10
#!/usr/bin/env julia
using Plots, Random, Printf
function initCondGen() #get random initial conditions for mass/radius, position, and velocity
function getMass(nBodies) #generate random masses that better reflect actual stellar populations
mList=zeros(nBodies)
N=(0.5^(-1.3)-150^(-1.3))/1.3 #crude approximation of IMF integral assuming alpha = 2.3, stellar mass range of 0.5:150 solar masses
rescale=1e6
max=floor(Int,N*rescale)
for i=1:nBodies
intTarget=rand(0:max,1)[1]/rescale
m=(0.5^(-1.3)-intTarget*1.3)^(-1/1.3) #just algebra from above
mList[i]=round(m,digits=2)
end
return mList
end
m=rand(1:1500,3)./10 #3 random masses between 0.1 and 150 solar masses, uniform distribution
#m=getMass(3) #get mass from IMF -- this way is kind of boring...so not using it, but left here in case I change my mind?
rad=m.^0.8 #3 radii based on masses in solar units
m=m.*2e30 #convert to SI kg
rad=rad.*7e8 #convert to SI m
pos1=rand(-10:10,3) #random initial coordinates x & y for first body, AU
function genPos2(pos1)
accept2=false
while accept2==false
pos2=rand(-10:10,3) #random initial coordinates for second body, AU
dist21=sqrt((pos1[1]-pos2[1])^2+(pos1[2]-pos2[2])^2+(pos1[3]-pos2[3])^2)
if (dist21*1.5e11)>(rad[1]+rad[2]) #they aren't touching
accept2=true
return pos2
end
end
end
pos2=genPos2(pos1)
function genPos3(pos1,pos2)
accept3=false
while accept3==false
pos3=rand(-10:10,3) #random initial coordinates for third body, AU
dist31=sqrt((pos1[1]-pos3[1])^2+(pos1[2]-pos3[2])^2+(pos1[3]-pos3[3])^2)
dist32=sqrt((pos2[1]-pos3[1])^2+(pos2[2]-pos3[2])^2+(pos2[3]-pos3[3])^2)
if (dist31*1.5e11)>(rad[1]+rad[3]) && (dist32*1.5e11)>(rad[2]+rad[3]) #3rd isn't touching either
accept3=true
return pos3
end
end
end
pos3=genPos3(pos1,pos2)
pos=[pos1[1],pos1[2],pos1[3],pos2[1],pos2[2],pos2[3],pos3[1],pos3[2],pos3[3]].*1.5e11 #convert accepted positions to SI, m
v=rand(-7e3:7e3,9) #random xyz velocities with mag between -10 & 10 km/s, totally arbitrary...
#r=[x1,y1,x2,y2,x3,y3,v1x,v1y,v2x,v2y,v3x,v3y]
r=[pos[1],pos[2],pos[3],pos[4],pos[5],pos[6],pos[7],pos[8],pos[9],v[1],v[2],v[3],v[4],v[5],v[6],v[7],v[8],v[9]]
open("initCond.txt","w") do f #save initial conditions to file in folder where script is run
write(f,"m1=$(@sprintf("%.1f",(m[1]/2e30))) m2=$(@sprintf("%.1f",(m[2]/2e30))) m3=$(@sprintf("%.1f",(m[3]/2e30))) (solar masses)\nv1x=$(v[1]/1e3) v1y=$(v[2]/1e3) v1z=$(v[3]/1e3) v2x=$(v[4]/1e3) v2y=$(v[5]/1e3) v2z=$(v[6]/1e3) v3x=$(v[7]/1e3) v3y=$(v[8]/1e3) v3z=$(v[9]/1e3) (km/s)\nx1=$(pos1[1]) y1=$(pos1[2]) z1=$(pos1[3]) x2=$(pos2[1]) y2=$(pos2[2]) z2=$(pos2[3]) x3=$(pos3[1]) y3=$(pos3[2]) z3=$(pos3[3]) (AU from center)")
end
return r, rad, m
end
function dR(r,m) #function we will use RK4 on to approximate solution
G=6.67408313131313e-11# Nm^2/kg^2
M1,M2,M3=m[1],m[2],m[3] #kg
x1,x2,x3=r[1],r[4],r[7] #m
y1,y2,y3=r[2],r[5],r[8] #m
z1,z2,z3=r[3],r[6],r[9]
c1,c2,c3=G*M1,G*M2,G*M3 #Nm^2/kg
r1_2=sqrt((x1-x2)^2+(y1-y2)^2+(z1-z2)^2) #distance from 1->2, m
r1_3=sqrt((x1-x3)^2+(y1-y3)^2+(z1-z3)^2) #distance from 1->3, m
r2_3=sqrt((x2-x3)^2+(y2-y3)^2+(z2-z3)^2) #distance from 2->3, m
v1X,v2X,v3X=r[10],r[13],r[16] #these are our change in position after dt (dr/dt*dt=dr)
v1Y,v2Y,v3Y=r[11],r[14],r[17] #m after * dt
v1Z,v2Z,v3Z=r[12],r[15],r[18]
#get change in velocity from accelerations (d^2r/dt^2*dt=dv/dt*dt=dv)
dx1=-(c2*(x1-x2)/(r1_2^3))-(c3*(x1-x3)/(r1_3^3)) #d^2x/dt^2 for 1, m/s after * dt
dx2=-(c1*(x2-x1)/(r1_2^3))-(c3*(x2-x3)/(r2_3^3)) #d^2x/dt^2 for 2, m/s
dx3=-(c1*(x3-x1)/(r1_3^3))-(c2*(x3-x2)/(r2_3^3)) #d^2x/dt^2 for 3, m/s
dy1=-(c2*(y1-y2)/(r1_2^3))-(c3*(y1-y3)/(r1_3^3)) #d^2y/dt^2 for 1, m/s
dy2=-(c1*(y2-y1)/(r1_2^3))-(c3*(y2-y3)/(r2_3^3)) #d^2y/dt^2 for 2, m/s
dy3=-(c1*(y3-y1)/(r1_3^3))-(c2*(y3-y2)/(r2_3^3)) #d^2y/dt^2 for 3, m/s
dz1=-(c2*(z1-z2)/(r1_2^3))-(c3*(z1-z3)/(r1_3^3)) #d^2y/dt^2 for 1, m/s
dz2=-(c1*(z2-z1)/(r1_2^3))-(c3*(z2-z3)/(r2_3^3)) #d^2y/dt^2 for 2, m/s
dz3=-(c1*(z3-z1)/(r1_3^3))-(c2*(z3-z2)/(r2_3^3)) #d^2y/dt^2 for 3, m/s
return [v1X,v1Y,v1Z,v2X,v2Y,v2Z,v3X,v3Y,v3Z,dx1,dy1,dz1,dx2,dy2,dz2,dx3,dy3,dz3]
end
function gen3Body(stopCond=[10,100],numSteps=10000) #default stop conditions of 10 yrs and 100 AU sep
tStop=stopCond[1]*365*24*3600 #convert to SI s
sepStop=stopCond[2]*1.5e11 #convert to SI m
stop=false
currentT=0
t=range(0,stop=tStop,length=(numSteps+1)) #+1 because I don't want 0 to count
stepSize=tStop/numSteps
x1=zeros(length(t))
y1=zeros(length(t))
z1=zeros(length(t))
x2=zeros(length(t))
y2=zeros(length(t))
z2=zeros(length(t))
x3=zeros(length(t))
y3=zeros(length(t))
z3=zeros(length(t))
r,rad,m=initCondGen()
min12=rad[1]+rad[2]
min13=rad[1]+rad[3]
min23=rad[2]+rad[3]
i=1
stopT=maximum(t)
#implement RK4 to model solutions to differential equations
while stop==false
if currentT==stopT || currentT>stopT #in case of rounding error or something
stop=true
elseif i>numSteps+1 #inf loop failsafe
stop=true
println("error: shouldn't have gotten here")
else
x1[i]=r[1]
y1[i]=r[2]
z1[i]=r[3]
x2[i]=r[4]
y2[i]=r[5]
z2[i]=r[6]
x3[i]=r[7]
y3[i]=r[8]
z3[i]=r[9]
k1=stepSize*dR(r,m)
k2=stepSize*dR(r.+0.5.*k1,m)
k3=stepSize*dR(r.+0.5.*k2,m)
k4=stepSize*dR(r.+k3,m)
r+=(k1.+2.0*k2.+2.0.*k3.+k4)./6
#check separation after each dt step
sep12=sqrt((x1[i]-x2[i])^2+(y1[i]-y2[i])^2+(z1[i]-z2[i])^2)
sep13=sqrt((x1[i]-x3[i])^2+(y1[i]-y3[i])^2+(z1[i]-z3[i])^2)
sep23=sqrt((x3[i]-x2[i])^2+(y3[i]-y2[i])^2+(z3[i]-z2[i])^2)
if sep12<min12 || sep13<min13 || sep23<min23 || sep12>sepStop || sep13>sepStop || sep23>sepStop
stop=true #stop if collision happens or body is ejected
t=range(0,stop=currentT,length=i) #t should match pos vectors
x1=x1[1:i] #don't want trailing zeros
y1=y1[1:i]
z1=z1[1:i]
x2=x2[1:i]
y2=y2[1:i]
z2=z2[1:i]
x3=x3[1:i]
y3=y3[1:i]
z3=z3[1:i]
end
i+=1
currentT+=stepSize #next step
end
end
return [x1,y1,z1,x2,y2,z2,x3,y3,z3], t, m, rad
end
function getInteresting3Body(minTime=0) #in years, defaults to 0
#sometimes random conditions result in a really short animation where things
#just crash into each other/fly away, so this function throws away those
yearSec=365*24*3600
interesting=false
i=1
while interesting==false
plotData,t,m,rad=gen3Body([60,150],600000)
if (maximum(t)/yearSec)>minTime #only return if simulation runs for longer than minTime
println(maximum(t)/yearSec) #tell me how many years we are simulating
open("cron_log.txt","a") do f #for cron logging, a flag = append
write(f,"$(maximum(t)/yearSec)\n")
end
return plotData,t,m,rad
interesting=true
elseif i>5000 #computationally expensive so don't want to go forever
interesting=true #render it anyways I guess because sometimes it's fun?
println("did not find interesting solution in number of tries allotted, running anyways")
println(maximum(t)/yearSec) #how many years simulation runs for
open("cron_log.txt","a") do f #for cron logging
write(f,"$(maximum(t)/yearSec)\n")
end
return plotData,t,m,rad
end
i+=1
end
end
function getLims(pos,padding) #determines plot limits at each frame, padding in units of pos
x=[pos[1],pos[4],pos[7]]
xMin=minimum(x)
xMax=maximum(x)
dx=xMax-xMin
y=[pos[2],pos[5],pos[8]]
yMin=minimum(y)
yMax=maximum(y)
dy=yMax-yMin
z=[pos[3],pos[6],pos[9]]
zMin=minimum(z)
zMax=maximum(z)
dz=zMax-zMin
dList=[dx,dy,dz]
if maximum(dList)==dx
#use x for square
xlims=[xMin-padding,xMax+padding]
ylims=[yMin-padding,yMin+dx+padding]
zlims=[zMin-padding,zMin+dx+padding]
elseif maximum(dList)==dy
#use y for square
xlims=[xMin-padding,xMin+dy+padding]
ylims=[yMin-padding,yMax+padding]
zlims=[zMin-padding,zMin+dy+padding]
else
#use z for cube
xlims=[xMin-padding,xMin+dz+padding]
ylims=[yMin-padding,yMin+dz+padding]
zlims=[zMin-padding,zMax+padding]
end
return xlims,ylims,zlims
end
function getColors(m,c) #places colors of objects according to mass/size
#c=[:biggest,:medium,:smallest] (order of input colors)
maxM=maximum(m)
minM=minimum(m)
colors=[:blue,:blue,:blue] #testing
if m[1]==maxM
colors[1]=c[1]
if m[2]==minM
colors[2]=c[3]
colors[3]=c[2]
else
colors[3]=c[3]
colors[2]=c[2]
end
elseif m[2]==maxM
colors[2]=c[1]
if m[1]==minM
colors[1]=c[3]
colors[3]=c[2]
else
colors[3]=c[3]
colors[1]=c[2]
end
else
colors[3]=c[1]
if m[1]==minM
colors[1]=c[3]
colors[2]=c[2]
else
colors[2]=c[3]
colors[1]=c[2]
end
end
return colors
end
function makeCircleVals(r,center=[0,0,0])
#(x,y,z)=(rcos(theta)sin(phi),rsin(theta)sin(phi),rcos(phi))
xOffset=center[1]
yOffset=center[2]
zOffset=center[3]
xVals=[]
yVals=[]
zVals=[]
for i=0:pi/128:2*pi
for j=0:pi/64:pi
x=r*cos(i)*sin(j)+xOffset
y=r*sin(i)*sin(j)+yOffset
z=r*cos(j)+zOffset
push!(xVals,x)
push!(yVals,y)
push!(zVals,z)
end
end
return xVals,yVals,zVals
end
plotData,t,m,rad=getInteresting3Body(15)
c=[:DodgerBlue,:Gold,:Tomato] #most massive to least massive, also roughly corresponds to temp
colors=getColors(m,c)
#adding fake stars
numStars=2500
starsX=zeros(numStars)
starsY=zeros(numStars)
starsZ=zeros(numStars)
for i=1:numStars
num=rand(-200:200,3) #box size is 70 AU but we need some extra padding for movement
starsX[i]=num[1]
starsY[i]=num[2]
starsZ[i]=num[3]
end
#this new way runs significantly faster (~2x improvement over @anim)
#Downside is it spams folder with png images of every frame and must manually compile with ffmpeg
#Comment out and use older way (after this below) if performance/specific formatting is not an issue
plotLoadPath="/home/kirk/Documents/3Body/tmpPlots/"
threeBodyAnim=Animation(plotLoadPath,String[])
for i=1:333:length(t) #this makes animation scale ~1 sec/year with other conditions
GR.inline("png") #added to eneable cron/jobber compatibility, also this makes frames generate WAY faster? Prior to adding this when run from cron/jobber frames would stop generating at 408 for some reason.
gr(legendfontcolor = plot_color(:white)) #legendfontcolor=:white plot arg broken right now (at least in this backend)
print("$(@sprintf("%.2f",i/length(t)*100)) % complete\r") #output percent tracker
pos=[plotData[1][i],plotData[2][i],plotData[3][i],plotData[4][i],plotData[5][i],plotData[6][i],plotData[7][i],plotData[8][i],plotData[9][i]] #current pos
limx,limy,limz=getLims(pos./1.5e11,10) #convert to AU, 10 AU padding
p=plot3d(plotData[1][1:33:i]./1.5e11,plotData[2][1:33:i]./1.5e11,plotData[3][1:33:i]./1.5e11,label="",linecolor=colors[1],linealpha=max.((1:33:i) .+ 10000 .- i,2500)/10000) #plot orbits up to i
p=plot3d!(plotData[4][1:33:i]./1.5e11,plotData[5][1:33:i]./1.5e11,plotData[6][1:33:i]./1.5e11,label="",linecolor=colors[2],linealpha=max.((1:33:i) .+ 10000 .- i,2500)/10000) #linealpha argument causes lines to decay
p=plot3d!(plotData[7][1:33:i]./1.5e11,plotData[8][1:33:i]./1.5e11,plotData[9][1:33:i]./1.5e11,label="",linecolor=colors[3],linealpha=max.((1:33:i) .+ 10000 .- i,2500)/10000) #example: alpha=max.((1:i) .+ 100 .- i,0) causes only last 100 to be visible
p=scatter3d!(starsX,starsY,starsZ,markercolor=:white,markersize=:1,label="") #fake background stars
star1=makeCircleVals(rad[1],[plotData[1][i],plotData[2][i],plotData[3][i]]) #generate spheres with appropriate sizes for each star
star2=makeCircleVals(rad[2],[plotData[4][i],plotData[5][i],plotData[6][i]]) #at current positions
star3=makeCircleVals(rad[3],[plotData[7][i],plotData[8][i],plotData[9][i]])
p=plot3d!(star1[1]./1.5e11,star1[2]./1.5e11,star1[3]./1.5e11,label="$(@sprintf("%.1f", m[1]./2e30))",color=colors[1],fill=true) #plot star circles with labels
p=plot3d!(star2[1]./1.5e11,star2[2]./1.5e11,star2[3]./1.5e11,label="$(@sprintf("%.1f", m[2]./2e30))",color=colors[2],fill=true)
p=plot3d!(star3[1]./1.5e11,star3[2]./1.5e11,star3[3]./1.5e11,label="$(@sprintf("%.1f", m[3]./2e30))",color=colors[3],fill=true)
p=plot3d!(background_color=:black,background_color_legend=:transparent,foreground_color_legend=:transparent,
background_color_outside=:white,aspect_ratio=:equal,legendtitlefontcolor=:white) #formatting for plot frame
p=plot3d!(title="Random Three-Body Problem\nt: $(@sprintf("%0.2f",t[i]/365/24/3600)) years after start",
legend=:best,xaxis=("x: AU",(limx[1],limx[2]),font(9,"Courier")),yaxis=("y: AU",(limy[1],limy[2]),font(9,"Courier")),zaxis=("z: AU",(limz[1],limz[2]),font(9,"Courier")),
gridalpha=0.5,gridcolor=:white,titlefont=font(14,"Courier"),size=(720,721),legendfontsize=8,legendtitle="Mass (in solar masses)",legendtitlefontsize=8) #add in axes/title/legend with formatting
frame(threeBodyAnim,p) #generate the frame
closeall() #close plots
end
#threeBodyFile="3Body_fps30.mp4"
#crf is compression value (17 or 18 "visually lossless"), pix_fmt is for twitter specific vid req, -b:v specifies target bitrate, -vcodec specifies codec (h264 in this case) -y says overwrite existing file
#run( `ffmpeg -framerate 30 -i $plotLoadPath"%06d.png" -vcodec libx264 -pix_fmt yuv420p -profile:v high -b:v 2048K -y -vf "scale=720:720,setdar=1/1" $threeBodyFile` ) #-vf scale=720:72 -crf 25
#run( `ffmpeg -framerate 30 -i $plotLoadPath"%06d.png" -c:v libx264 -preset slow -coder 1 -movflags +faststart -g 15 -crf 18 -pix_fmt yuv420p -profile:v high -y -bf 2 -vf "scale=720:720,setdar=1/1" $threeBodyFile` ) #all this bullshit to hopefully satisfy twitter requirements
#NOTE: moved ffmpeg commands to shell script
#old (simpler) way of generating animation
#uncomment and use this way if you just want a simple animation saved and don't
#care about performance/specific formatting of video.
# threeBodyAnim=@animate for i=1:length(t)
# gr(legendfontcolor = plot_color(:white)) #plot arg broken right now in Julia
# print("$(@sprintf("%.2f",i/length(t)*100)) % complete\r") #output percent tracker
# pos=[plotData[1][i],plotData[2][i],plotData[3][i],plotData[4][i],plotData[5][i],plotData[6][i]] #current pos
# limx,limy=getLims(pos./1.5e11,5) #convert to AU, 5 AU padding
# plot(plotData[1][1:i]./1.5e11,plotData[2][1:i]./1.5e11,label="",linecolor=colors[1])
# plot!(plotData[3][1:i]./1.5e11,plotData[4][1:i]./1.5e11,label="",linecolor=colors[2])
# plot!(plotData[5][1:i]./1.5e11,plotData[6][1:i]./1.5e11,label="",linecolor=colors[3])
# scatter!(starsX,starsY,markercolor=:white,markersize=:1,label="") #fake background stars
# star1=makeCircleVals(rad[1],[plotData[1][i],plotData[2][i]])
# star2=makeCircleVals(rad[2],[plotData[3][i],plotData[4][i]])
# star3=makeCircleVals(rad[3],[plotData[5][i],plotData[6][i]])
# plot!(star1[1]./1.5e11,star1[2]./1.5e11,label="$(@sprintf("%.1f", m[1]./2e30))",color=colors[1],fill=true)
# plot!(star2[1]./1.5e11,star2[2]./1.5e11,label="$(@sprintf("%.1f", m[2]./2e30))",color=colors[2],fill=true)
# plot!(star3[1]./1.5e11,star3[2]./1.5e11,label="$(@sprintf("%.1f", m[3]./2e30))",color=colors[3],fill=true)
# plot!(background_color=:black,background_color_legend=:transparent,background_color_outside=:white,aspect_ratio=:equal,legendtitlefontcolor=:white) #legendfontcolor=:white
# plot!(xlabel="x: AU",ylabel="y: AU",title="Random Three Body Problem\nt: $(@sprintf("%0.2f",t[i]/365/24/3600)) yrs after start",
# legend=:best,xaxis=("x: AU",(limx[1],limx[2]),font(12,"Courier")),yaxis=("y: AU",(limy[1],limy[2]),font(12,"Courier")),
# grid=false,titlefont=font(24,"Courier"),size=(720,720),legendfontsize=12,legendtitle="Mass (in solar masses)",legendtitlefontsize=14)
# end every 25
#mp4(threeBodyAnim,"3Body_fps30.mp4",fps=30)
#OR
#gif(threeBodyAnim,"3Body_fps30.gif",fps=30)
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18,
4008,
30420,
66,
18,
9,
7,
89,
16,
12,
89,
18,
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7,
81,
16,
62,
18,
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18,
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67,
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17,
88,
14,
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61,
17,
329,
352,
11,
285,
14,
82,
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288,
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17,
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7,
66,
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9,
7,
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12,
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16,
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62,
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66,
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7,
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12,
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18,
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7,
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17,
62,
18,
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18,
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67,
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17,
88,
14,
28664,
61,
17,
329,
362,
11,
285,
14,
82,
198,
220,
220,
220,
288,
89,
18,
10779,
7,
66,
16,
9,
7,
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18,
12,
89,
16,
20679,
7,
81,
16,
62,
18,
61,
18,
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30420,
66,
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9,
7,
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12,
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17,
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7,
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17,
62,
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14,
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14,
82,
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220,
1441,
685,
85,
16,
55,
11,
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16,
56,
11,
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16,
57,
11,
85,
17,
55,
11,
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17,
56,
11,
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17,
57,
11,
85,
18,
55,
11,
85,
18,
56,
11,
85,
18,
57,
11,
34350,
16,
11,
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16,
11,
67,
89,
16,
11,
34350,
17,
11,
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17,
11,
67,
89,
17,
11,
34350,
18,
11,
9892,
18,
11,
67,
89,
18,
60,
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198,
8818,
2429,
18,
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7,
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41888,
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11,
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4357,
22510,
8600,
82,
28,
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8,
1303,
12286,
2245,
3403,
286,
838,
331,
3808,
290,
1802,
27548,
41767,
198,
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220,
220,
256,
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28,
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58,
16,
60,
9,
24760,
9,
1731,
9,
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1303,
1102,
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264,
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220,
220,
220,
41767,
19485,
28,
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25559,
58,
17,
60,
9,
16,
13,
20,
68,
1157,
1303,
1102,
1851,
284,
25861,
285,
198,
220,
220,
220,
2245,
28,
9562,
198,
220,
220,
220,
1459,
51,
28,
15,
198,
220,
220,
220,
256,
28,
9521,
7,
15,
11,
11338,
28,
83,
19485,
11,
13664,
16193,
22510,
8600,
82,
10,
16,
4008,
1303,
10,
16,
780,
314,
836,
470,
765,
657,
284,
954,
198,
220,
220,
220,
2239,
10699,
28,
83,
19485,
14,
22510,
8600,
82,
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220,
220,
220,
2124,
16,
28,
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418,
7,
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7,
83,
4008,
198,
220,
220,
220,
331,
16,
28,
9107,
418,
7,
13664,
7,
83,
4008,
198,
220,
220,
220,
1976,
16,
28,
9107,
418,
7,
13664,
7,
83,
4008,
198,
220,
220,
220,
2124,
17,
28,
9107,
418,
7,
13664,
7,
83,
4008,
198,
220,
220,
220,
331,
17,
28,
9107,
418,
7,
13664,
7,
83,
4008,
198,
220,
220,
220,
1976,
17,
28,
9107,
418,
7,
13664,
7,
83,
4008,
198,
220,
220,
220,
2124,
18,
28,
9107,
418,
7,
13664,
7,
83,
4008,
198,
220,
220,
220,
331,
18,
28,
9107,
418,
7,
13664,
7,
83,
4008,
198,
220,
220,
220,
1976,
18,
28,
9107,
418,
7,
13664,
7,
83,
4008,
198,
220,
220,
220,
374,
11,
6335,
11,
76,
28,
15003,
25559,
13746,
3419,
198,
220,
220,
220,
949,
1065,
28,
6335,
58,
16,
48688,
6335,
58,
17,
60,
198,
220,
220,
220,
949,
1485,
28,
6335,
58,
16,
48688,
6335,
58,
18,
60,
198,
220,
220,
220,
949,
1954,
28,
6335,
58,
17,
48688,
6335,
58,
18,
60,
198,
220,
220,
220,
1312,
28,
16,
198,
220,
220,
220,
2245,
51,
28,
47033,
7,
83,
8,
628,
220,
220,
220,
1303,
320,
26908,
371,
42,
19,
284,
2746,
8136,
284,
22577,
27490,
198,
220,
220,
220,
981,
2245,
855,
9562,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1459,
51,
855,
11338,
51,
8614,
1459,
51,
29,
11338,
51,
1303,
259,
1339,
286,
38185,
4049,
393,
1223,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2245,
28,
7942,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
1312,
29,
22510,
8600,
82,
10,
16,
1303,
10745,
9052,
10143,
8635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2245,
28,
7942,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44872,
7203,
18224,
25,
6584,
470,
423,
7891,
994,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
16,
58,
72,
22241,
81,
58,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
16,
58,
72,
22241,
81,
58,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
16,
58,
72,
22241,
81,
58,
18,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
17,
58,
72,
22241,
81,
58,
19,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
17,
58,
72,
22241,
81,
58,
20,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
17,
58,
72,
22241,
81,
58,
21,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
18,
58,
72,
22241,
81,
58,
22,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
18,
58,
72,
22241,
81,
58,
23,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
18,
58,
72,
22241,
81,
58,
24,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
16,
28,
9662,
10699,
9,
67,
49,
7,
81,
11,
76,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
17,
28,
9662,
10699,
9,
67,
49,
7,
81,
13,
10,
15,
13,
20,
15885,
74,
16,
11,
76,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
18,
28,
9662,
10699,
9,
67,
49,
7,
81,
13,
10,
15,
13,
20,
15885,
74,
17,
11,
76,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
19,
28,
9662,
10699,
9,
67,
49,
7,
81,
13,
10,
74,
18,
11,
76,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
10,
16193,
74,
16,
13,
10,
17,
13,
15,
9,
74,
17,
13,
10,
17,
13,
15,
15885,
74,
18,
13,
10,
74,
19,
737,
14,
21,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
9122,
14139,
706,
1123,
288,
83,
2239,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
41767,
1065,
28,
31166,
17034,
19510,
87,
16,
58,
72,
45297,
87,
17,
58,
72,
12962,
61,
17,
33747,
88,
16,
58,
72,
45297,
88,
17,
58,
72,
12962,
61,
17,
33747,
89,
16,
58,
72,
45297,
89,
17,
58,
72,
12962,
61,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
41767,
1485,
28,
31166,
17034,
19510,
87,
16,
58,
72,
45297,
87,
18,
58,
72,
12962,
61,
17,
33747,
88,
16,
58,
72,
45297,
88,
18,
58,
72,
12962,
61,
17,
33747,
89,
16,
58,
72,
45297,
89,
18,
58,
72,
12962,
61,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
41767,
1954,
28,
31166,
17034,
19510,
87,
18,
58,
72,
45297,
87,
17,
58,
72,
12962,
61,
17,
33747,
88,
18,
58,
72,
45297,
88,
17,
58,
72,
12962,
61,
17,
33747,
89,
18,
58,
72,
45297,
89,
17,
58,
72,
12962,
61,
17,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
41767,
1065,
27,
1084,
1065,
8614,
41767,
1485,
27,
1084,
1485,
8614,
41767,
1954,
27,
1084,
1954,
8614,
41767,
1065,
29,
325,
79,
19485,
8614,
41767,
1485,
29,
325,
79,
19485,
8614,
41767,
1954,
29,
325,
79,
19485,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2245,
28,
7942,
1303,
11338,
611,
17661,
4325,
393,
1767,
318,
38632,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
28,
9521,
7,
15,
11,
11338,
28,
14421,
51,
11,
13664,
28,
72,
8,
1303,
83,
815,
2872,
1426,
30104,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
16,
28,
87,
16,
58,
16,
25,
72,
60,
1303,
9099,
470,
765,
25462,
1976,
27498,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
16,
28,
88,
16,
58,
16,
25,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
16,
28,
89,
16,
58,
16,
25,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
17,
28,
87,
17,
58,
16,
25,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
17,
28,
88,
17,
58,
16,
25,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
17,
28,
89,
17,
58,
16,
25,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
18,
28,
87,
18,
58,
16,
25,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
18,
28,
88,
18,
58,
16,
25,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
18,
28,
89,
18,
58,
16,
25,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1312,
47932,
16,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1459,
51,
47932,
9662,
10699,
1303,
19545,
2239,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
220,
220,
220,
1441,
685,
87,
16,
11,
88,
16,
11,
89,
16,
11,
87,
17,
11,
88,
17,
11,
89,
17,
11,
87,
18,
11,
88,
18,
11,
89,
18,
4357,
256,
11,
285,
11,
2511,
198,
437,
198,
198,
8818,
651,
19302,
278,
18,
25842,
7,
1084,
7575,
28,
15,
8,
1303,
259,
812,
11,
26235,
284,
657,
198,
220,
220,
220,
1303,
29810,
4738,
3403,
1255,
287,
257,
1107,
1790,
11034,
810,
1243,
198,
220,
220,
220,
1303,
3137,
7014,
656,
1123,
584,
14,
12254,
1497,
11,
523,
428,
2163,
12542,
1497,
883,
198,
220,
220,
220,
614,
6558,
28,
24760,
9,
1731,
9,
2623,
405,
198,
220,
220,
220,
3499,
28,
9562,
198,
220,
220,
220,
1312,
28,
16,
198,
220,
220,
220,
981,
3499,
855,
9562,
198,
220,
220,
220,
220,
220,
220,
220,
7110,
6601,
11,
83,
11,
76,
11,
6335,
28,
5235,
18,
25842,
26933,
1899,
11,
8628,
4357,
8054,
830,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
357,
47033,
7,
83,
20679,
1941,
6558,
8,
29,
1084,
7575,
1303,
8807,
1441,
611,
18640,
4539,
329,
2392,
621,
949,
7575,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44872,
7,
47033,
7,
83,
20679,
1941,
6558,
8,
1303,
33331,
502,
703,
867,
812,
356,
389,
985,
8306,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1280,
7203,
66,
1313,
62,
6404,
13,
14116,
2430,
64,
4943,
466,
277,
1303,
1640,
1067,
261,
18931,
11,
257,
6056,
796,
24443,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3551,
7,
69,
553,
3,
7,
47033,
7,
83,
20679,
1941,
6558,
19415,
77,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
7110,
6601,
11,
83,
11,
76,
11,
6335,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3499,
28,
7942,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
1312,
29,
27641,
1303,
785,
1996,
15208,
5789,
523,
836,
470,
765,
284,
467,
8097,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3499,
28,
7942,
1303,
13287,
340,
32845,
314,
4724,
780,
3360,
340,
338,
1257,
30,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44872,
7203,
20839,
407,
1064,
3499,
4610,
287,
1271,
286,
8404,
44554,
11,
2491,
32845,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44872,
7,
47033,
7,
83,
20679,
1941,
6558,
8,
1303,
4919,
867,
812,
18640,
4539,
329,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1280,
7203,
66,
1313,
62,
6404,
13,
14116,
2430,
64,
4943,
466,
277,
1303,
1640,
1067,
261,
18931,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3551,
7,
69,
553,
3,
7,
47033,
7,
83,
20679,
1941,
6558,
19415,
77,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
7110,
6601,
11,
83,
11,
76,
11,
6335,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
1312,
47932,
16,
198,
220,
220,
220,
886,
198,
437,
198,
198,
8818,
651,
43,
12078,
7,
1930,
11,
39231,
8,
1303,
67,
13221,
274,
7110,
7095,
379,
1123,
5739,
11,
24511,
287,
4991,
286,
1426,
198,
220,
220,
220,
2124,
41888,
1930,
58,
16,
4357,
1930,
58,
19,
4357,
1930,
58,
22,
11907,
198,
220,
220,
220,
2124,
9452,
28,
39504,
7,
87,
8,
198,
220,
220,
220,
2124,
11518,
28,
47033,
7,
87,
8,
198,
220,
220,
220,
44332,
28,
87,
11518,
12,
87,
9452,
198,
220,
220,
220,
331,
41888,
1930,
58,
17,
4357,
1930,
58,
20,
4357,
1930,
58,
23,
11907,
198,
220,
220,
220,
331,
9452,
28,
39504,
7,
88,
8,
198,
220,
220,
220,
331,
11518,
28,
47033,
7,
88,
8,
198,
220,
220,
220,
20268,
28,
88,
11518,
12,
88,
9452,
198,
220,
220,
220,
1976,
41888,
1930,
58,
18,
4357,
1930,
58,
21,
4357,
1930,
58,
24,
11907,
198,
220,
220,
220,
1976,
9452,
28,
39504,
7,
89,
8,
198,
220,
220,
220,
1976,
11518,
28,
47033,
7,
89,
8,
198,
220,
220,
220,
288,
89,
28,
89,
11518,
12,
89,
9452,
198,
220,
220,
220,
288,
8053,
41888,
34350,
11,
9892,
11,
67,
89,
60,
198,
220,
220,
220,
611,
5415,
7,
67,
8053,
8,
855,
34350,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1904,
2124,
329,
6616,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
2475,
82,
41888,
87,
9452,
12,
39231,
11,
87,
11518,
10,
39231,
60,
198,
220,
220,
220,
220,
220,
220,
220,
331,
2475,
82,
41888,
88,
9452,
12,
39231,
11,
88,
9452,
10,
34350,
10,
39231,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1976,
2475,
82,
41888,
89,
9452,
12,
39231,
11,
89,
9452,
10,
34350,
10,
39231,
60,
198,
220,
220,
220,
2073,
361,
5415,
7,
67,
8053,
8,
855,
9892,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1904,
331,
329,
6616,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
2475,
82,
41888,
87,
9452,
12,
39231,
11,
87,
9452,
10,
9892,
10,
39231,
60,
198,
220,
220,
220,
220,
220,
220,
220,
331,
2475,
82,
41888,
88,
9452,
12,
39231,
11,
88,
11518,
10,
39231,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1976,
2475,
82,
41888,
89,
9452,
12,
39231,
11,
89,
9452,
10,
9892,
10,
39231,
60,
198,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1904,
1976,
329,
23441,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
2475,
82,
41888,
87,
9452,
12,
39231,
11,
87,
9452,
10,
67,
89,
10,
39231,
60,
198,
220,
220,
220,
220,
220,
220,
220,
331,
2475,
82,
41888,
88,
9452,
12,
39231,
11,
88,
9452,
10,
67,
89,
10,
39231,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1976,
2475,
82,
41888,
89,
9452,
12,
39231,
11,
89,
11518,
10,
39231,
60,
198,
220,
220,
220,
886,
198,
220,
220,
220,
1441,
2124,
2475,
82,
11,
88,
2475,
82,
11,
89,
2475,
82,
198,
437,
198,
198,
8818,
651,
5216,
669,
7,
76,
11,
66,
8,
1303,
23625,
7577,
286,
5563,
1864,
284,
2347,
14,
7857,
198,
220,
220,
220,
1303,
66,
41888,
25,
14261,
3495,
11,
25,
24132,
11,
25,
17470,
395,
60,
357,
2875,
286,
5128,
7577,
8,
198,
220,
220,
220,
3509,
44,
28,
47033,
7,
76,
8,
198,
220,
220,
220,
949,
44,
28,
39504,
7,
76,
8,
198,
220,
220,
220,
7577,
41888,
25,
17585,
11,
25,
17585,
11,
25,
17585,
60,
1303,
33407,
198,
220,
220,
220,
611,
285,
58,
16,
60,
855,
9806,
44,
198,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
16,
22241,
66,
58,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
611,
285,
58,
17,
60,
855,
1084,
44,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
17,
22241,
66,
58,
18,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
18,
22241,
66,
58,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
18,
22241,
66,
58,
18,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
17,
22241,
66,
58,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
2073,
361,
285,
58,
17,
60,
855,
9806,
44,
198,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
17,
22241,
66,
58,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
611,
285,
58,
16,
60,
855,
1084,
44,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
16,
22241,
66,
58,
18,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
18,
22241,
66,
58,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
18,
22241,
66,
58,
18,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
16,
22241,
66,
58,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
18,
22241,
66,
58,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
611,
285,
58,
16,
60,
855,
1084,
44,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
16,
22241,
66,
58,
18,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
17,
22241,
66,
58,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
17,
22241,
66,
58,
18,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7577,
58,
16,
22241,
66,
58,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
220,
220,
220,
1441,
7577,
198,
437,
198,
198,
8818,
787,
31560,
293,
53,
874,
7,
81,
11,
16159,
41888,
15,
11,
15,
11,
15,
12962,
198,
220,
220,
220,
1303,
7,
87,
11,
88,
11,
89,
35793,
6015,
418,
7,
1169,
8326,
8,
31369,
7,
34846,
828,
3808,
259,
7,
1169,
8326,
8,
31369,
7,
34846,
828,
6015,
418,
7,
34846,
4008,
198,
220,
220,
220,
2124,
34519,
28,
16159,
58,
16,
60,
198,
220,
220,
220,
331,
34519,
28,
16159,
58,
17,
60,
198,
220,
220,
220,
1976,
34519,
28,
16159,
58,
18,
60,
198,
220,
220,
220,
2124,
53,
874,
28,
21737,
198,
220,
220,
220,
331,
53,
874,
28,
21737,
198,
220,
220,
220,
1976,
53,
874,
28,
21737,
198,
220,
220,
220,
329,
1312,
28,
15,
25,
14415,
14,
12762,
25,
17,
9,
14415,
198,
220,
220,
220,
220,
220,
220,
220,
329,
474,
28,
15,
25,
14415,
14,
2414,
25,
14415,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
28,
81,
9,
6966,
7,
72,
27493,
31369,
7,
73,
47762,
87,
34519,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
28,
81,
9,
31369,
7,
72,
27493,
31369,
7,
73,
47762,
88,
34519,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
28,
81,
9,
6966,
7,
73,
47762,
89,
34519,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
87,
53,
874,
11,
87,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
88,
53,
874,
11,
88,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
89,
53,
874,
11,
89,
8,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
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220,
220,
1441,
2124,
53,
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11,
88,
53,
874,
11,
89,
53,
874,
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198,
198,
29487,
6601,
11,
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11,
76,
11,
6335,
28,
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19302,
278,
18,
25842,
7,
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8,
198,
66,
41888,
25,
35,
375,
1362,
14573,
11,
25,
13306,
11,
25,
13787,
5549,
60,
1303,
1712,
4858,
284,
1551,
4858,
11,
635,
7323,
24866,
284,
20218,
198,
4033,
669,
28,
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5216,
669,
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11,
66,
8,
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2,
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5788,
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28,
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55,
28,
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56,
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1312,
28,
16,
25,
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29366,
198,
220,
220,
220,
997,
28,
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32590,
2167,
25,
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18,
8,
1303,
3524,
2546,
318,
4317,
27548,
475,
356,
761,
617,
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649,
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31034,
17,
87,
9025,
625,
2488,
11227,
8,
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2,
8048,
1589,
318,
340,
599,
4105,
9483,
351,
279,
782,
4263,
286,
790,
5739,
290,
1276,
14500,
17632,
351,
31246,
43913,
198,
2,
21357,
503,
290,
779,
4697,
835,
357,
8499,
428,
2174,
8,
611,
2854,
14,
11423,
33313,
318,
407,
281,
2071,
198,
198,
29487,
8912,
15235,
35922,
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14,
74,
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14,
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14,
18,
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14,
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3646,
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198,
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35320,
28,
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8912,
15235,
11,
10100,
58,
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1312,
28,
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25,
13664,
7,
83,
8,
1303,
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1838,
11034,
5046,
5299,
16,
792,
14,
1941,
351,
584,
3403,
198,
220,
220,
220,
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1600,
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] | 1.930915 | 8,960 |
module UtilTest
using Parametron
using Test
using Parametron: sort_and_combine!
using Base.Sort: QuickSort
combinepair(x, y) = first(x) => last(x) + last(y)
@testset "sort_and_combine!" begin
n = 100
@test sort_and_combine!([2 => 1.0, 2 => 2.0]; combine=combinepair, by=first) == [2 => 3.0]
@test sort_and_combine!([3 => 1.0, 2 => 4.0, 3 => 2.0, 1 => 2.0], combine=combinepair, by=first) == [1 => 2.0, 2 => 4.0, 3 => 3.0]
@test length(sort_and_combine!(Pair.(1 : n, rand(n)), combine=combinepair, by=first)) == n
@test length(sort_and_combine!(Pair.(fill(3, n), rand(n)), combine=combinepair, by=first)) == 1
v = [rand(1 : round(Int, n / 2)) => rand() for i = 1 : n]
global allocs
for i = 1 : 2
vcopy = copy(v)
allocs = @allocated sort_and_combine!(vcopy; combine=combinepair, by=first, alg=QuickSort)
end
@test allocs == 0
end
end # module
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] | 2.268844 | 398 |
function evaluate(p::MPoly{T}, es...) where {T}
r = zero(T)
for (m, c) in p
t = c
for i = 1:length(m)
t *= es[i]^m[i]
end
r += t
end
r
end
function evaluate_basis(p::MPoly{T}, es...) where {T}
r = Array{T}(undef, 0)
for (m, c) in p
r1 = c
for i = 1:length(m)
r1 *= es[i]^m[i]
end
push!(r, r1)
end
r
end
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] | 1.612167 | 263 |
using PosteriorAnalysis
using Base.Test
using MacroTools
import PosteriorAnalysis: _transform_expr
######################################################################
# test transformations (internal)
######################################################################
"Test that expression is passed through."
macro test_passthrough(expr)
(expr2, captured_names) = _transform_expr(expr)
@test isequal(expr, expr2)
@test isempty(captured_names)
end
test_template(f, expr) = f(_transform_expr(expr)...)
macro test_template(expr, template, variables...)
evar = gensym(:expr)
cvar = gensym(:captured_names)
quote
test_template($(Meta.quot(expr))) do $(evar), $(cvar)
@match $(evar) begin
$template => begin
$([:(@test get($cvar, $(Meta.quot(v)), nothing) == $v)
for v in variables]...)
end
_ => throw(error("Result $($evar) does not match template"))
end
end
end
end
@testset "macros" begin
@test_throws ErrorException _transform_expr(:(@v())) # no arguments
@test_throws ErrorException _transform_expr(:(@v a b)) # too many arguments
@test_passthrough(:(a+b))
@test_passthrough(:(a.b))
@test_passthrough(:(quote a+@v(b)+c end))
@test_template @v(a) a_ a
@test_template @v(a)+b a_+b a
@test_template @v(a).b a_.b a
@test_template a+@v(b)+@v(c) a+b_+c_ b c
end
######################################################################
# test operations
######################################################################
@testset "operations" begin
a = 1:3
b = 4:6
pd_z = PosteriorDraws(length(a))
pd_a = PosteriorDraws(:a => 1:3)
pd_ab = PosteriorDraws([:b,:a], [b,a]) # constructor will enforce sorting
@test size(pd_ab) == (2, length(a))
@test keys(pd_ab) == [:a,:b]
@test addvars(pd_a, :b => b) == pd_ab
@test addvars(pd_z, :a => a, :b => b) == pd_ab
@test vcat(pd_z, pd_ab) == pd_ab
@test dropvars(pd_ab, :b) == pd_a
@test all(pd_ab[:a] .== a)
@test all(pd_ab[:a,2:3] .== a[2:3])
pd_abc0 = addvars(pd_ab, :c => a+b)
pd_abc1 = map(pd_ab, :c, +, :a, :b)
pd_abc2 = @pdmap pd_ab c => @v(a) + @v(b)
@test pd_abc0 == pd_abc1
@test pd_abc0 == pd_abc2
io = IOBuffer()
@test (show(io, pd_ab); true)
@test takebuf_string(io) ==
"""PosteriorDraws with 3 observations
a => Int64
b => Int64
"""
end
@testset "elttype test" begin
f(a,b) = Float64(a+b)
g(a,b) = [string(a),string(b)]
pd1 = PosteriorDraws(:a => 1:3, :b => 11:13)
@test pd1[:a] == collect(1:3)
@test pd1[:b] == collect(11:13)
@test eltype(pd1[:a]) ≡ eltype(pd1[:b]) ≡ Int
pd2 = @pdmap pd1 c => f(@v(a), @v(b))
pd3 = @pdmap pd2 d => g(@v(a), @v(b))
@test eltype(pd2[:c]) ≡ eltype(pd3[:c]) ≡ Float64
@test eltype(pd3[:d]) ≡ Array{String, 1}
end
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] | 2.113731 | 1,442 |
<gh_stars>10-100
ENV["PKG_CONFIG_PATH"] = "/Users/dc/anaconda/envs/python35/lib/pkgconfig"
using OpenCV
using Images
using Cxx
import ImageView
function opencv_to_image(img_opencv)
converted_image = zeros(Float16, (3, rows(img_opencv), cols(img_opencv)));
for i = 1:size(converted_image, 2)
for j = 1:size(converted_image, 3)
pixel_value = @cxx at_v3b(img_opencv, i, j)
converted_image[:, i, j] = map(x -> Int(at(pixel_value, x)), [2, 1, 0]) ./ 255
end
end
return converted_image
end
# C++ OpenCV code
cxx"""
#include "opencv2/videoio.hpp"
#include "opencv2/imgproc.hpp"
using namespace std;
using namespace cv;
/// Retrieve video device by index
cv::VideoCapture get_video_device(int device_index) {
cv::VideoCapture capture(device_index);
cv::Mat frame;
capture.read(frame);
return capture;
}
/// Capture frame
cv::Mat capture_frame(cv::VideoCapture capture) {
cv::Mat frame;
bool Success = capture.read(frame);
return frame;
}
/// Capture and save frame
void capture_save_frame(cv::VideoCapture capture, String dest) {
cv::Mat frame;
bool success = capture.read(frame);
if (success) {
cv::imwrite(dest, frame);
}
}
/// Release an active camera
void release_camera(cv::VideoCapture capture) {
capture.release();
}
"""
video_device = @cxx get_video_device(CAP_ANY);
@time current_frame = @cxx capture_frame(video_device);
@time current_frame_image = opencv_to_image(current_frame);
ImageView.imshow(colorview(RGB, current_frame_image))
filename = joinpath(pwd(), "camera-frame.jpg");
@time @cxx capture_save_frame(video_device, pointer(filename));
@time current_frame_image_2 = load(filename);
ImageView.imshow(current_frame_image_2) | [
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] | 2.573913 | 690 |
using UnivariateFunctions: evaluate, years_between, years_from_global_base, PE_Function, Sum_Of_Functions, change_base_of_PE_Function, derivative, indefinite_integral, right_integral, left_integral, evaluate_integral
using Dates
tol = 10*eps()
today = Date(2000,1,1)
pe_func = PE_Function(1.0,2.0,today, 3)
(pe_func.base_ - years_from_global_base(today)) < tol
date_in_2020 = Date(2020,1,1)
pe_func2 = PE_Function(1.0,2.0,date_in_2020, 3)
(pe_func2.base_ - years_from_global_base(date_in_2020)) < tol
abs(evaluate(pe_func, date_in_2020) - evaluate(pe_func, years_from_global_base(date_in_2020)) ) < tol
#Sum of functions
sum_func = Sum_Of_Functions([pe_func, PE_Function(2.0,2.5,today, 3) ])
abs(evaluate(sum_func, date_in_2020) - evaluate(sum_func, years_from_global_base(date_in_2020)) ) < tol
# left and right integrals
l_int = left_integral(pe_func, today)
(evaluate(l_int, date_in_2020) - evaluate_integral(pe_func,today,date_in_2020)) < tol
r_int = right_integral(pe_func, date_in_2020)
(evaluate(r_int, today) - evaluate_integral(pe_func,today,date_in_2020)) < tol
# With DateTimes
today_time = DateTime(2000,1,1, 10, 10, 1)
pe_func = PE_Function(1.0,2.0,today_time, 3)
# left and right integrals
later_today = DateTime(2000,1,1, 10, 14, 1)
l_int = left_integral(pe_func, today)
(evaluate(l_int, later_today) - evaluate_integral(pe_func,today,later_today)) < tol
r_int = right_integral(pe_func, later_today)
(evaluate(r_int, today) - evaluate_integral(pe_func,today,later_today)) < tol
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] | 2.500832 | 601 |
randdual(x, ::Val{N}=Val(3)) where {N} = ForwardDiff.Dual(x, ntuple(_ -> randn(), Val(N))...)
@time @testset "ForwardDiff.jl" begin
m = 53
n = 63
k = 73
A1 = rand(Float64, m, k)
B1 = rand(Float64, k, n)
C1 = rand(Float64, m, n)
A2 = deepcopy(A1)
B2 = deepcopy(B1)
C2 = deepcopy(C1)
α = Float64(2.0)
β = Float64(2.0)
Octavian.matmul!(C1, A1, B1, α, β)
LinearAlgebra.mul!(C2, A2, B2, α, β)
@test C1 ≈ C2
@testset "real array from the left" begin
config = ForwardDiff.JacobianConfig(nothing, C1, B1)
I = LinearAlgebra.I(size(B1, 2))
J1 = ForwardDiff.jacobian((C, B) -> Octavian.matmul!(C, A1, B), C1, B1, config)
@test J1 ≈ kron(I, A1)
J2 = ForwardDiff.jacobian((C, B) -> LinearAlgebra.mul!(C, A2, B), C2, B2, config)
@test J1 ≈ kron(I, A2)
@test J1 ≈ J2
J3 = ForwardDiff.jacobian((C, B) -> Octavian.matmul_serial!(C, A1, B), C1, B1, config)
@test J3 ≈ kron(I, A1)
@test J3 ≈ J2
end
@testset "real array from the right" begin
# dense and column-major arrays
config = ForwardDiff.JacobianConfig(nothing, C1, A1)
J1 = ForwardDiff.jacobian((C, A) -> Octavian.matmul!(C, A, B1), C1, A1, config)
J2 = ForwardDiff.jacobian((C, A) -> LinearAlgebra.mul!(C, A, B2), C2, A2, config)
J3 = ForwardDiff.jacobian((C, A) -> Octavian.matmul_serial!(C, A, B1), C1, A1, config)
@test J1 ≈ J2 ≈ J3
# transposed arrays
A1new = Matrix(A1')'
A2new = Matrix(A2')'
config = ForwardDiff.JacobianConfig(nothing, C1, A1new)
J1 = ForwardDiff.jacobian((C, A) -> Octavian.matmul!(C, A, B1), C1, A1new, config)
J2 = ForwardDiff.jacobian((C, A) -> LinearAlgebra.mul!(C, A, B2), C2, A2new, config)
J3 = ForwardDiff.jacobian((C, A) -> Octavian.matmul_serial!(C, A, B1), C1, A1new, config)
@test J1 ≈ J2 ≈ J3
# direct version using dual numbers
A1dual = zeros(eltype(config), reverse(size(A1))...)
A1dual .= A1'
C1dual = zeros(eltype(config), size(C1)...)
A2dual = deepcopy(A1dual)
C2dual = deepcopy(C1dual)
C3dual = similar(C1dual); C4dual = similar(C2dual)
Octavian.matmul!(C1dual, A1dual', B1)
Octavian.matmul!(C2dual, A2dual', B2)
Octavian.matmul_serial!(C3dual, A1dual', B1)
Octavian.matmul_serial!(C4dual, A2dual', B2)
@test C1dual ≈ C2dual ≈ C3dual ≈ C4dual
end
@testset "two dual arrays" begin
A1d = randdual.(A1)
B1d = randdual.(B1)
@test reinterpret(Float64, Octavian.matmul(A1d, B1d, 1.3)) ≈ reinterpret(Float64, Octavian.matmul_serial(A1d, B1d, 1.3)) ≈ reinterpret(Float64, (A1d * B1d) .* 1.3)
@test reinterpret(Float64, Octavian.matmul(@view(A1d[begin:end-1,:]), B1d)) ≈ reinterpret(Float64, Octavian.matmul_serial(@view(A1d[begin:end-1,:]), B1d)) ≈ reinterpret(Float64, @view(A1d[begin:end-1,:]) * B1d)
end
end
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] | 1.895807 | 1,574 |
<reponame>abraemer/SimLib.jl<filename>src/halfchainentropy_zblock.jl
module HCE_ZBlock_Module
import ..ED
using ..SimLib
using ..SimLib: FArray
using LinearAlgebra: eigvals!, Hermitian, mul!, svdvals!
using SharedArrays: sdata
using SpinSymmetry
using XXZNumerics: entropy
export HalfChainEntropyZBlock, HCEDataDescriptor, HCEData, load_entropy
### Descriptor
struct HCEDataDescriptor <: ED.EDDerivedDataDescriptor
L::Int
symm::Bool
derivedfrom::ED.EDDataDescriptor
end
HCEDataDescriptor(L, args...; kwargs...) = HCEDataDescriptor(L, true, EDDataDescriptor(args...; kwargs...))
HCEDataDescriptor(L, symm::Bool, args...; kwargs...) = HCEDataDescriptor(L, symm, EDDataDescriptor(args...; kwargs...))
### Data obj
struct HCEData{N} <: SimLib.AbstractSimpleData
descriptor::HCEDataDescriptor
data::FArray{N}
end
ED._default_folder(::HCEDataDescriptor) = "entropy"
ED._filename_addition(hcedd::HCEDataDescriptor) = "-l_$(hcedd.L)" * (hcedd.symm ? "_symm" : "")
"""
load_entropy(L[, symm], edd)
load_entropy(L[, symm], model[, diagtype][, location])
"""
load_entropy(args...; kwargs...) = load(HCEDataDescriptor(args...; kwargs...))
## Functions
_zblock_inds(N, k) = SpinSymmetry._indices(zbasis(N, k))
struct SymmZBlockEntanglementEntropy
Nfull::Int
kfull::Int
N1::Int
indexLookup::Vector{Int}## ToDo: is that a sensible data structure?
indsA::Vector{Vector{Int}} # note that indsA contains fewer indices -> should be used as column
indsB::Vector{Vector{Int}}
size::Int
function SymmZBlockEntanglementEntropy(zblockbasis, N1)
N = zblockbasis.N
k = zblockbasis.k
N1 = max(N1, N-N1)
indexLookup = zeros(2^N)
indexLookup[_zblock_inds(N, k)] = 1:binomial(N,k)
krange = max(0,N1+k-N):min(N1, k)
indsA = [2^N1 .* (_zblock_inds(N-N1, k-ki) .- 1) for ki in krange]
indsB = [_zblock_inds(N1, ki) .- 1 for ki in krange]
size = 2*N1 == N ? N1 : N
new(N, k, N1, indexLookup, indsA, indsB, size)
end
end
# scalar for broadcasting
Base.broadcastable(s::SymmZBlockEntanglementEntropy) = Ref(s)
entanglement_entropy(s::SymmZBlockEntanglementEntropy, ψ) = entanglement_entropy!(zeros(Float64, s.size),s,ψ)
function entanglement_entropy!(out, s::SymmZBlockEntanglementEntropy, ψ)
NB = s.Nfull
fill!(out, 0)
for (indA, indB) in zip(s.indsA, s.indsB)
## No point in optimizing this allocation further.
mat = Matrix{eltype(ψ)}(undef, length(indA), length(indB))
for shift in 1:s.size
mat .= getindex.(Ref(ψ), getindex.(Ref(s.indexLookup), SpinSymmetry._roll_bits.(NB, indA .+ indB', shift-1) .+ 1))
out[shift] += entropy(svdvals!(mat) .^ 2) # most allocations come from svdvals!
end
end
out
end
### Task
mutable struct HalfChainEntropyTask{S} <: ED.EDTask
L::Int
symm::Bool
entropy_strategy::S
data
end
## ToDo: This always symmetrizes over the chain right now.
HalfChainEntropyZBlock(basis::SymmetrizedBasis, L=div(basis.basis.N,2)) = HalfChainEntropyTask(L, true, SymmZBlockEntanglementEntropy(basis.basis, L), nothing)
HalfChainEntropyZBlock(; basis, L=div(basis.basis.N,2)) = HalfChainEntropyZBlock(basis, L)
function ED.initialize!(task::HalfChainEntropyTask, arrayconstructor, spectral_size)
task.data = arrayconstructor(Float64, task.entropy_strategy.size, spectral_size)
end
function ED.compute_task!(task::HalfChainEntropyTask, evals, evecs, inds...)
n = min(size(task.data,1), size(evecs,2))
for (i, ψ) in enumerate(eachcol(evecs))
i <= n || break
entanglement_entropy!(view(task.data, :, i, inds...), task.entropy_strategy, ψ)
end
end
function ED.failed_task!(task::HalfChainEntropyTask, inds...)
task.data[:, :, inds...] .= NaN64
end
function ED.assemble(task::HalfChainEntropyTask, edd)
HCEData(HCEDataDescriptor(task.L, task.symm, edd), sdata(task.data))
end
Base.summary(task::HalfChainEntropyTask) = string(typeof(task)) * "(L=$(task.L), symm=$(task.symm))"
end # module
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] | 2.327221 | 1,745 |
<gh_stars>0
# maybe put this script with the other one together?
export sudoku_1, sudoku_2
sudoku_1 = [0 0 4 7 2 0 9 0 0; 0 3 9 0 0 8 0 0 5; 0 0 1 5 0 6 0 0 4; 0 4 0 0 1 0 5 2 0; 0 2 8 0 5 0 1 7 0; 0 1 6 0 3 0 0 9 0; 4 0 0 9 0 1 3 0 0; 1 0 0 3 0 0 8 4 0; 0 0 7 0 8 5 6 0 0];
sudoku_2 = [5 3 0 0 7 0 0 0 0; 6 0 0 1 9 5 0 0 0; 0 9 8 0 0 0 0 6 0; 8 0 0 0 6 0 0 0 3; 4 0 0 8 0 3 0 0 1; 7 0 0 0 2 0 0 0 6;0 6 0 0 0 0 2 8 0; 0 0 0 4 1 9 0 0 5; 0 0 0 0 8 0 0 7 9]; | [
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] | 2.004367 | 229 |
# Script for running DA and ADA using the same traninig data
# go to Ricker model folder
try
cd("Ricker model")
catch
warn("Already in the Ricker model folder")
end
# load algorithms
include("rickermodel.jl")
# packages for storing data
using JLD
using HDF5
################################################################################
### set up problem ###
################################################################################
problem = set_up_gp_problem(ploton = false)
# set adaptive updating
#problem.adaptive_update = AMUpdate(eye(3), 2.4/sqrt(3), 1., 0.7, 50)
#problem.adaptive_update = noAdaptation(2.4/sqrt(3)*eye(3))
# or, use AM gen alg for adaptive updating
#problem.adaptive_update = AMUpdate_gen(eye(3), 2.4/sqrt(3), 0.2, 1., 0.8, 25)
problem.adaptive_update = AMUpdate_gen(eye(3), 2.4/sqrt(3), 0.3, 1., 0.8, 25)
# set algorithm parameters
problem.alg_param.N = 1000 # nbr particels
problem.alg_param.R = 50000 # nbr iterations
problem.alg_param.burn_in = 0 # burn in
problem.alg_param.length_training_data = 2000
problem.alg_param.alg = "MCWM" # we should only! use the MCWM algorithm
problem.alg_param.compare_GP_and_PF = false
problem.alg_param.noisy_est = false
problem.alg_param.pred_method = "sample"
problem.alg_param.print_interval = 10000 # problem.alg_param.R#
problem.alg_param.beta_MH = 0.15 # "local_loglik_approx" # "max_loglik"
problem.alg_param.lasso = false
#problem.data.y = Array(readtable("y.csv"))[:,1]
#problem.data.y = Array(readtable("y_data_set_1.csv"))[:,1]
problem.data.y = Array(readtable("y_data_set_2.csv"))[:,1]
################################################################################
### generate traning data ###
################################################################################
# set up training problem
#accelerated_da = true
problem_training = set_up_problem(ploton = false)
length_training_data = 2000
length_test_data = 2000
burn_in = 2000
problem_training.alg_param.N = 1000 # nbr particels
problem_training.alg_param.R = length_training_data + length_test_data + burn_in # nbr iterations
problem_training.alg_param.burn_in = burn_in # burn_in
problem_training.data.y = Array(readtable("y_data_set_2.csv"))[:,1] #Array(readtable("y.csv"))[:,1]
problem_training.alg_param.print_interval = 1000
# test starting at true parameters
#problem.model_param.theta_0 = problem.model_param.theta_true
# PMCMC
problem_training.alg_param.alg = "MCWM"
# use AM alg for adaptive updating
#problem.adaptive_update = AMUpdate(eye(3), 2.4/sqrt(3), 1., 0.7, 25)
#problem.adaptive_update = noAdaptation(2.4/sqrt(3)*eye(3))
# or, use AM gen alg for adaptive updating
#problem_training.adaptive_update = AMUpdate_gen(eye(3), 2.4/sqrt(3), 0.2, 1., 0.8, 25)
problem_training.adaptive_update = AMUpdate_gen(eye(3), 2.4/sqrt(3), 0.4, 1., 0.8, 25)
load_training_data = true
if !load_training_data
# generate training data
tic()
# collect data
res_training, Theta_star_training, loglik_star_training,Theta_old_training,loglik_old_training, cov_matrix = mcmc(problem_training, true, true)
time_pre_er = toc()
# write outputs
res = res_training[1]
Theta = res.Theta_est
loglik = res.loglik_est
accept_vec = res.accept_vec
prior_vec = res.prior_vec
loglik_avec_priorvec = zeros(3, length(loglik))
loglik_avec_priorvec[1,:] = loglik
loglik_avec_priorvec[2,:] = accept_vec
loglik_avec_priorvec[3,:] = prior_vec
algorithm_parameters = zeros(10, 2)
algorithm_parameters[1,1] = problem_training.alg_param.burn_in
algorithm_parameters[2:4,1] = problem_training.model_param.theta_true
algorithm_parameters[5:7,1] = problem_training.model_param.theta_0
algorithm_parameters[8:end,:] = problem_training.prior_dist.prior_parameters
writetable("Results/Theta_training.csv", convert(DataFrame, Theta))
writetable("Results/loglik_avec_priorvec_training.csv", convert(DataFrame, loglik_avec_priorvec))
writetable("Results/algorithm_parameters_training.csv", convert(DataFrame, algorithm_parameters))
# split tranining and test data
Theta_test_star = Theta_star_training[:,(end-length_test_data+1):end]
loglik_test_star = loglik_star_training[(end-length_test_data+1):end]
Theta_test_old = Theta_old_training[:,(end-length_test_data+1):end]
loglik_test_old = loglik_old_training[(end-length_test_data+1):end]
data_test_star = [Theta_test_star; loglik_test_star']
data_test_old = [Theta_test_old; loglik_test_old']
Theta_training_star = Theta_star_training[:,1:length_training_data]
loglik_training_star = loglik_star_training[1:length_training_data]
Theta_training_old = Theta_old_training[:,1:length_training_data]
loglik_training_old = loglik_old_training[1:length_training_data]
data_training_star = [Theta_training_star; loglik_training_star']
data_training_old = [Theta_training_old; loglik_training_old']
save("gp_training_and_test_data.jld",
"res_training", res_training,
"data_training_star", data_training_star,
"data_training_old", data_training_old,
"data_test_star", data_test_star,
"data_test_old", data_test_old,
"cov_matrix",cov_matrix)
else
@load "gp_training_and_test_data.jld"
end
################################################################################
### fit gp model ###
################################################################################
# fit gp model
tic()
# create gp object
gp = GPModel("est_method",zeros(6), zeros(4),
eye(problem.alg_param.length_training_data-20), zeros(problem.alg_param.length_training_data-20),zeros(2,problem.alg_param.length_training_data-20),
collect(1:10))
data_training = data_training_star
data_test = data_test_star
# fit GP model
if problem.alg_param.est_method == "ml"
# fit GP model using ml
perc_outlier = 0.1 # used when using PMCMC for trainig data 0.05
tail_rm = "left"
problem.alg_param.lasso = false
ml_est(gp, data_training,"SE", problem.alg_param.lasso,perc_outlier,tail_rm)
else
error("The two stage estimation method is not in use")
#two_stage_est(gp, data_training)
end
time_fit_gp = toc()
# save fitted gp model
# @save "gp_fitted_model.jld" gp
################################################################################
### DA-GP-MCMC ###
################################################################################
accelerated_da = false
problem.model_param.theta_0 = mean(res_training[1].Theta_est[:,problem_training.alg_param.burn_in+1:problem_training.alg_param.burn_in+length_training_data],2)
res = @time dagpmcmc(problem_training, problem, gp, cov_matrix)
# calc res
mcmc_results = Result(res[1].Theta_est, res[1].loglik_est, res[1].accept_vec, res[1].prior_vec)
# write output
Theta = mcmc_results.Theta_est
loglik = mcmc_results.loglik_est
accept_vec = mcmc_results.accept_vec
prior_vec = mcmc_results.prior_vec
loglik_avec_priorvec = zeros(3, length(loglik))
loglik_avec_priorvec[1,:] = loglik
loglik_avec_priorvec[2,:] = accept_vec
loglik_avec_priorvec[3,:] = prior_vec
algorithm_parameters = zeros(10, 2)
algorithm_parameters[1,1] = problem.alg_param.burn_in + 1
algorithm_parameters[2:4,1] = problem.model_param.theta_true
algorithm_parameters[5:7,1] = problem.model_param.theta_0
algorithm_parameters[8:end,:] = problem.prior_dist.prior_parameters
if !accelerated_da
writetable("Results/Theta_dagpmcmc_lunarc.csv", convert(DataFrame, Theta))
writetable("Results/loglik_avec_priorvec_dagpmcmc_lunarc.csv", convert(DataFrame, loglik_avec_priorvec))
writetable("Results/algorithm_parameters_dagpmcmc_lunarc.csv", convert(DataFrame, algorithm_parameters))
else
writetable("Results/Theta_adagpmcmc_lunarc.csv", convert(DataFrame, Theta))
writetable("Results/loglik_avec_priorvec_adagpmcmc_lunarc.csv", convert(DataFrame, loglik_avec_priorvec))
writetable("Results/algorithm_parameters_adagpmcmc_lunarc.csv", convert(DataFrame, algorithm_parameters))
end
################################################################################
### A-DA-GP-MCMC ###
################################################################################
accelerated_da = true
problem.model_param.theta_0 = mean(res_training[1].Theta_est[:,problem_training.alg_param.burn_in+1:problem_training.alg_param.burn_in+length_training_data],2)
################################################################################
## Create features for classification models ##
################################################################################
n = size(data_training,2)
n_burn_in = problem_training.alg_param.burn_in
dim = length(problem.model_param.theta_true)
data_gp_loglik_star_old = zeros(4,n)
data_gp_loglik_star_old[3,:] = data_training_star[dim+1,:]
data_gp_loglik_star_old[4,:] = data_training_old[dim+1,:]
# data_gp_loglik_star_old = [gp_star, gp_old, ll_star, ll_old]
std_pred_gp_star = zeros(n)
noisy_pred = problem.alg_param.noisy_est
for i = 1:n
(loglik_est_star, var_pred_ml_star, prediction_sample_ml_star) = predict(data_training_star[1:dim,i],gp,noisy_pred)
(loglik_est_old, var_pred_ml, prediction_sample_ml_old) = predict(data_training_old[1:dim,i],gp,noisy_pred)
data_gp_loglik_star_old[1,i] = prediction_sample_ml_star[1]
data_gp_loglik_star_old[2,i] = prediction_sample_ml_old[1]
std_pred_gp_star[i] = sqrt(var_pred_ml_star[1])
end
nbr_GP_star_geq_GP_old = zero(Int64)
nbr_case_1 = zero(Int64)
nbr_case_2 = zero(Int64)
targets_case_1_and_3 = []
data_case_1_and_3 = []
targets_case_2_and_4 = []
data_case_2_and_4 = []
for i = 1:n
if data_gp_loglik_star_old[1,i] > data_gp_loglik_star_old[2,i]
nbr_GP_star_geq_GP_old += 1
data_case_1_and_3 = vcat(data_case_1_and_3, [data_training[1:dim,i]; data_gp_loglik_star_old[1,i]/data_gp_loglik_star_old[2,i]; std_pred_gp_star[i]])
if data_gp_loglik_star_old[3,i] > data_gp_loglik_star_old[4,i]
append!(targets_case_1_and_3, 1)
nbr_case_1 += 1
else
append!(targets_case_1_and_3, 0)
end
else
data_case_2_and_4 = vcat(data_case_2_and_4, [data_training[1:dim,i]; data_gp_loglik_star_old[1,i]/data_gp_loglik_star_old[2,i]; std_pred_gp_star[i]])
if data_gp_loglik_star_old[3,i] < data_gp_loglik_star_old[4,i]
append!(targets_case_2_and_4, 1)
nbr_case_2 += 1
else
append!(targets_case_2_and_4, 0)
end
end
end
# tansform features and set input data
# convert matricies to floats
data_case_1_and_3 = convert(Array{Float64,2},reshape(data_case_1_and_3, (dim+2, length(targets_case_1_and_3))))
data_case_2_and_4 = convert(Array{Float64,2},reshape(data_case_2_and_4, (dim+2, length(targets_case_2_and_4))))
targets_case_1_and_3 = convert(Array{Float64,1}, targets_case_1_and_3)
targets_case_2_and_4 = convert(Array{Float64,1}, targets_case_2_and_4)
################################################################################
## set case model ###
################################################################################
select_case_model = "dt" # logisticregression or dt
nbr_GP_star_led_GP_old = n-nbr_GP_star_geq_GP_old
prob_case_1 = nbr_case_1/nbr_GP_star_geq_GP_old
prob_case_2 = (nbr_case_2)/nbr_GP_star_led_GP_old
prob_case_3 = 1-prob_case_1
prob_case_4 = (nbr_GP_star_led_GP_old-nbr_case_2)/nbr_GP_star_led_GP_old
prob_cases = [prob_case_1;prob_case_2;prob_case_3;prob_case_4]
println("Est prob:")
println(prob_cases)
#prob_cases = [0.2;0.2;0.8;0.8]
if select_case_model == "biasedcoin"
casemodel = BiasedCoin(prob_cases)
elseif select_case_model == "logisticregression"
mean_posterior = mean(theta_training,2)[:]
input_data_case_1_and_3 = zeros(length(targets_case_1_and_3), dim+3)
input_data_case_1_and_3[:,1] = sqrt((mean_posterior[1] - data_case_1_and_3[1,:]).^2)
input_data_case_1_and_3[:,2] = sqrt((mean_posterior[2] - data_case_1_and_3[2,:]).^2)
input_data_case_1_and_3[:,3] = sqrt((mean_posterior[3] - data_case_1_and_3[3,:]).^2)
input_data_case_1_and_3[:,4] = sqrt(sum((repmat(mean_posterior', size(data_case_1_and_3,2))'-data_case_1_and_3[1:3,:]).^2,1))
input_data_case_1_and_3[:,5] = data_case_1_and_3[4,:]
input_data_case_1_and_3[:,end] = targets_case_1_and_3
input_data_case_1_and_3 = DataFrame(input_data_case_1_and_3)
input_data_case_2_and_4 = zeros(length(targets_case_2_and_4), dim+3)
input_data_case_2_and_4[:,1] = sqrt((mean_posterior[1] - data_case_2_and_4[1,:]).^2)
input_data_case_2_and_4[:,2] = sqrt((mean_posterior[2] - data_case_2_and_4[2,:]).^2)
input_data_case_2_and_4[:,3] = sqrt((mean_posterior[3] - data_case_2_and_4[3,:]).^2)
input_data_case_2_and_4[:,4] = sqrt(sum((repmat(mean_posterior', size(data_case_2_and_4,2))'-data_case_2_and_4[1:3,:]).^2,1))
input_data_case_2_and_4[:,5] = data_case_2_and_4[4,:]
input_data_case_2_and_4[:,end] = targets_case_2_and_4
input_data_case_2_and_4 = DataFrame(input_data_case_2_and_4)
log_reg_model_case_1_and_3 = glm(@formula(x6 ~ x1 + x2 + x3 + x4 + x5), input_data_case_1_and_3, Binomial(), LogitLink())
log_reg_model_case_2_and_4 = glm(@formula(x6 ~ x1 + x2 + x3 + x4 + x5), input_data_case_2_and_4, Binomial(), LogitLink())
β_for_model1or3 = coef(log_reg_model_case_1_and_3)
β_for_model2or4 = coef(log_reg_model_case_2_and_4)
casemodel = LogisticRegression(β_for_model1or3, β_for_model2or4, mean_posterior)
elseif select_case_model == "dt"
#standardization!(data_case_1_and_3)
input_data_case_1_and_3 = zeros(length(targets_case_1_and_3), dim+2)
input_data_case_1_and_3[:,1] = data_case_1_and_3[1,:]
input_data_case_1_and_3[:,2] = data_case_1_and_3[2,:]
input_data_case_1_and_3[:,3] = data_case_1_and_3[3,:]
input_data_case_1_and_3[:,4] = data_case_1_and_3[4,:]
input_data_case_1_and_3[:,end] = targets_case_1_and_3
#standardization!(data_case_2_and_4)
input_data_case_2_and_4 = zeros(length(targets_case_2_and_4), dim+2)
input_data_case_2_and_4[:,1] = data_case_2_and_4[1,:]
input_data_case_2_and_4[:,2] = data_case_2_and_4[2,:]
input_data_case_2_and_4[:,3] = data_case_2_and_4[3,:]
input_data_case_2_and_4[:,4] = data_case_2_and_4[4,:]
input_data_case_2_and_4[:,end] = targets_case_2_and_4
# 4 features model
features_1_and_3 = convert(Array, input_data_case_1_and_3[:, 1:dim+1])
labels_1_and_3 = convert(Array, input_data_case_1_and_3[:, end])
labels_1_and_3 = Array{String}(size(features_1_and_3,1))
for i = 1:length(labels_1_and_3)
if input_data_case_1_and_3[i,end] == 0
labels_1_and_3[i] = "case 3"
else
labels_1_and_3[i] = "case 1"
end
end
decisiontree1or3 = build_tree(labels_1_and_3, features_1_and_3)
decisiontree1or3 = prune_tree(decisiontree1or3, 0.9)
# 4 features model
features_case_2_and_4 = convert(Array, input_data_case_2_and_4[:, 1:dim+1])
labels_case_2_and_4 = Array{String}(size(features_case_2_and_4,1))
for i = 1:length(labels_case_2_and_4)
if input_data_case_2_and_4[i,end] == 0
labels_case_2_and_4[i] = "case 4"
else
labels_case_2_and_4[i] = "case 2"
end
end
# train full-tree classifier
decisiontree2or4 = build_tree(labels_case_2_and_4, features_case_2_and_4)
decisiontree2or4 = prune_tree(decisiontree2or4, 0.9)
casemodel = DT(decisiontree1or3, decisiontree2or4)
end
# run ADA
res = @time adagpmcmc(problem_training, problem, gp, casemodel, cov_matrix)
# write results
mcmc_results = Result(res[1].Theta_est, res[1].loglik_est, res[1].accept_vec, res[1].prior_vec)
# write output
Theta = mcmc_results.Theta_est
loglik = mcmc_results.loglik_est
accept_vec = mcmc_results.accept_vec
prior_vec = mcmc_results.prior_vec
loglik_avec_priorvec = zeros(3, length(loglik))
loglik_avec_priorvec[1,:] = loglik
loglik_avec_priorvec[2,:] = accept_vec
loglik_avec_priorvec[3,:] = prior_vec
algorithm_parameters = zeros(10, 2)
algorithm_parameters[1,1] = problem.alg_param.burn_in + 1
algorithm_parameters[2:4,1] = problem.model_param.theta_true
algorithm_parameters[5:7,1] = problem.model_param.theta_0
algorithm_parameters[8:end,:] = problem.prior_dist.prior_parameters
if !accelerated_da
writetable("Results/Theta_dagpmcmc_lunarc.csv", convert(DataFrame, Theta))
writetable("Results/loglik_avec_priorvec_dagpmcmc_lunarc.csv", convert(DataFrame, loglik_avec_priorvec))
writetable("Results/algorithm_parameters_dagpmcmc_lunarc.csv", convert(DataFrame, algorithm_parameters))
else
writetable("Results/Theta_adagpmcmc_lunarc.csv", convert(DataFrame, Theta))
writetable("Results/loglik_avec_priorvec_adagpmcmc_lunarc.csv", convert(DataFrame, loglik_avec_priorvec))
writetable("Results/algorithm_parameters_adagpmcmc_lunarc.csv", convert(DataFrame, algorithm_parameters))
end
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] | 2.379763 | 7,165 |
# ------------ Branch trace ------------ #
struct BranchTrace{T <: RecordSite, B <: RecordSite} <: Trace
cond::T
branch::B
BranchTrace(cond::T, branch::B) where {T, B} = new{T, B}(cond, branch)
end
# If-else branch site
struct ConditionalBranchCallSite{C, A, J, L, R} <: CallSite
trace::BranchTrace
score::Float64
cond_kernel::C
cond_args::J
cond::Bool
branch::A
branch_args::L
ret::R
end
get_score(cbs::ConditionalBranchCallSite) = cbs.score
| [
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] | 2.432836 | 201 |
# Custom pretty-printing
Base.show(io::IO, ::MIME"text/plain", ::ScalarQuantity) = print(io, "Scalar")
Base.show(io::IO, ::MIME"text/plain", ::VectorQuantity) = print(io, "Vector")
function grid_description(grid)
mins = minimum(grid)
maxs = maximum(grid)
join(join.(zip(mins, maxs), " … "), " × " )
end
function Base.show(io::IO, m::MIME"text/plain", f::AbstractPICDataStructure)
show(io, m, scalarness(typeof(f)))
data = unwrapdata(f)
grid = getdomain(f)
print(io, " with data:\n")
ctx = IOContext(io, :limit=>true, :compact=>true, :displaysize => (10,50))
Base.print_array(ctx, data)
print(io, "\nand $(parameterless_type(typeof(grid))) grid ")
print(io, grid_description(grid))
end
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] | 2.451505 | 299 |
<reponame>PseudoCodeNerd/StackOverflow.jl<filename>test/runtests.jl<gh_stars>1-10
using Test
using StackOverflow
qHolder = getrecentquestionsfortag()
@test_nowarn qHolder[1].answer_count
@test_nowarn getrecentbadgerecipients()
@test_nowarn geterrors()
siteinfoholder = getsiteinfo()
@test_nowarn dump(siteinfoholder)
@test_nowarn searchtag("julia")
@test_nowarn StackOverflow.searcherror("404")
aHolder = getanswers()
@test typeof(aHolder[1].question_id) == Int
@test_nowarn StackOverflow.getanswerfromquestion(qHolder[1])
@test_nowarn StackOverflow.getquestionfromanswer(aHolder[1])
@test_nowarn getusers()
@test_nowarn getcomments()
@test_nowarn getsuggested_edits()
@test_nowarn StackOverflow.getmoderators()
@test_nowarn StackOverflow.getopenquestions()
@test_nowarn StackOverflow.getnoanswerquestions()
@test_nowarn StackOverflow.getfeaturedquestions()
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] | 2.726415 | 318 |