content
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<reponame>m3g/XLStats.jl
using XLStats
data_dir="./data"
xml_data="$data_dir/hitsDetail.xls"
#xml_data="$data_dir/salbiii_hitsDetail.dat"
links = read_all(xml_file=xml_data,
topolink_log="$data_dir/salbiii_topolink.log",
topolink_input="$data_dir/topolink.inp",
xic_file_name="$data_dir/salbiii_xic.dat",
domain=2:134)
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"""
Exo2SplitBinion{T <: Real} = Parabolic{Exo1SplitBinion{T}}
A 2-exo-split-binion is a parabolic Cayley-Dickson construct with 1-exo-split-binions.
"""
const Exo2SplitBinion{T <: Real} = Parabolic{Exo1SplitBinion{T}}
function Exo2SplitBinion(z::Exo1SplitBinion{T}) where T <: Real
Exo2SplitBinion{T}(z, zero(Exo1SplitBinion{T}))
end
function Exo2SplitBinion(z::SplitBinion{T}) where T <: Real
Exo2SplitBinion{T}(
Exo1SplitBinion{T}(z, zero(SplitBinion{T})),
zero(Exo1SplitBinion{T})
)
end
function Exo2SplitBinion(a::T, b::T, c::T, d::T, f::T, g::T, h::T, j::T) where T <: Real
Exo2SplitBinion{T}(
Exo1SplitBinion{T}(
SplitBinion{T}(a, b),
SplitBinion{T}(c, d)
),
Exo1SplitBinion{T}(
SplitBinion{T}(f, g),
SplitBinion{T}(h, j)
)
)
end
function Exo2SplitBinion(a::T, b::T, c::T, d::T, f::T, g::T, h::T) where T <: Real
Exo2SplitBinion{T}(
Exo1SplitBinion{T}(
SplitBinion{T}(a, b),
SplitBinion{T}(c, d)
),
Exo1SplitBinion{T}(
SplitBinion{T}(f, g),
SplitBinion{T}(h, zero(T))
)
)
end
function Exo2SplitBinion(a::T, b::T, c::T, d::T, f::T, g::T) where T <: Real
Exo2SplitBinion{T}(
Exo1SplitBinion{T}(
SplitBinion{T}(a, b),
SplitBinion{T}(c, d)
),
Exo1SplitBinion{T}(
SplitBinion{T}(f, g),
zero(SplitBinion{T})
)
)
end
function Exo2SplitBinion(a::T, b::T, c::T, d::T, f::T) where T <: Real
Exo2SplitBinion{T}(
Exo1SplitBinion{T}(
SplitBinion{T}(a, b),
SplitBinion{T}(c, d)
),
Exo1SplitBinion{T}(
SplitBinion{T}(f, zero(T)),
zero(SplitBinion{T})
)
)
end
function Exo2SplitBinion(a::T, b::T, c::T, d::T) where T <: Real
Exo2SplitBinion{T}(
Exo1SplitBinion{T}(
SplitBinion{T}(a, b),
SplitBinion{T}(c, d)
),
zero(Exo1SplitBinion{T})
)
end
function Exo2SplitBinion(a::T, b::T, c::T) where T <: Real
Exo2SplitBinion{T}(
Exo1SplitBinion{T}(
SplitBinion{T}(a, b),
SplitBinion{T}(c, zero(T))
),
zero(Exo1SplitBinion{T})
)
end
function Exo2SplitBinion(a::T, b::T) where T <: Real
Exo2SplitBinion{T}(
Exo1SplitBinion{T}(
SplitBinion{T}(a, b),
zero(SplitBinion{T})
),
zero(Exo1SplitBinion{T})
)
end
function Exo2SplitBinion(a::T) where T <: Real
Exo2SplitBinion{T}(
Exo1SplitBinion{T}(
SplitBinion{T}(a, zero(T)),
zero(SplitBinion{T})
),
zero(Exo1SplitBinion{T})
)
end
function Exo2SplitBinion(a::Real, b::Real)
Exo2SplitBinion(promote(a, b)...)
end
function Exo2SplitBinion(a::Real, b::Real, c::Real)
Exo2SplitBinion(promote(a, b, c)...)
end
function Exo2SplitBinion(a::Real, b::Real, c::Real, d::Real)
Exo2SplitBinion(promote(a, b, c, d)...)
end
function Exo2SplitBinion(a::Real, b::Real, c::Real, d::Real, f::Real)
Exo2SplitBinion(promote(a, b, c, d, f)...)
end
function Exo2SplitBinion(a::Real, b::Real, c::Real, d::Real, f::Real, g::Real)
Exo2SplitBinion(promote(a, b, c, d, f, g)...)
end
function Exo2SplitBinion(a::Real, b::Real, c::Real, d::Real, f::Real, g::Real, h::Real)
Exo2SplitBinion(promote(a, b, c, d, f, g, h)...)
end
function Exo2SplitBinion(a::Real, b::Real, c::Real, d::Real, f::Real, g::Real, h::Real, j::Real)
Exo2SplitBinion(promote(a, b, c, d, f, g, h, j)...)
end
function Exo2SplitBinion(z::Exo2Real{T}) where T <: Real
Exo2SplitBinion(z.l.l, zero(T), z.l.r, zero(T), z.r.l, zero(T), z.r.r)
end
"""
unreal(z::Exo2SplitBinion)
The unreal part of a 2-exo-split-binion is a 7-vector.
"""
function unreal(z::Exo2SplitBinion)
vcat(unreal(z.l), array(z.r))
end
function show(io::IO, z::Exo2SplitBinion)
print(io, "[1: ")
print(io, z.l.l.l)
print(io, ", s: ")
print(io, z.l.l.r)
print(io, ", a: ")
print(io, z.l.r.l)
print(io, ", sa: ")
print(io, z.l.r.r)
print(io, ", b: ")
print(io, z.r.l.l)
print(io, ", sb: ")
print(io, z.r.l.r)
print(io, ", ab: ")
print(io, z.r.r.l)
print(io, ", (sa)b: ")
print(io, z.r.r.r)
print(io, "]")
end
function random(::Type{Exo2SplitBinion{T}}) where T <: Real
Exo2SplitBinion{T}(random(Exo1SplitBinion{T}), random(Exo1SplitBinion{T}))
end
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] | 1.8076 | 2,500 |
function labeldims(n)
if n == 1
("\$t\$", "\$x\$")
elseif n == 2
("\$x\$", "\$y\$")
else
("\$x\$", "\$y\$", "\$z\$")
end
end
function stationarycharacteristics(P::Process; n = length(getX0(P)), labels=labeldims(n), xlabel=labels[1], ylabel=labels[2], zlabel=(length(labels) == 3 ? labels[3] : nothing), size=(400, 350))
p1 = marginaltrajectory(P; xlabel, ylabel, zlabel, size, mainalpha=0.35)
display(p1)
AMI = selfmutualinfo(P)
p2 = plot(AMI.dims[1].val, Array((AMI isa AbstractVector ? AMI : AMI[:, end:-1:1])); legend=true, foreground_color_legend = nothing,
background_color_legend = nothing, legendtitle=nothing, size,
#xticks=[0, optimize_ticks(extrema(dims(AMI, 1).val)...; k_min = 3, k_max = 5)[1]...],
yticks=optimize_ticks(extrema(AMI)...; k_min = 3, k_max = 6)[1],
xlim=(0, Inf), xlabel="\$\\tau\$", ylabel="AMI")
display(p2)
return nothing
end
export stationarycharacteristics
function plotlyapunovresponse(P::Process, p, prange; size=(400, 350), N=1000, k=length(getX0(P)), kwargs...)
Ξ»s = lyapunovresponse(P, p, prange, N, k)
plot(prange, Ξ»s; size, xlabel="\$\\theta\$", ylabel="\$\\lambda\$", kwargs...)
end
export plotlyapunovresponse
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<filename>src/run.jl<gh_stars>0
module Run
using FromFile: @from
@from "./ArtefactModelling.jl" import ArtefactModelling
# ArtefactModelling.geo_test()
# ArtefactModelling.vis_test()
# ArtefactModelling.csg_test()
ArtefactModelling.prism_test()
end # module
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<reponame>Sacha0/FixedPointDecimals.jl
using FixedPointDecimals
using FixedPointDecimals: FD, value
using Test
using Printf
using Base.Checked: checked_mul
include("utils.jl")
const SFD2 = FixedDecimal{Int16, 2}
const SFD4 = FixedDecimal{Int16, 4}
const FD1 = FixedDecimal{Int, 1}
const FD2 = FixedDecimal{Int, 2}
const FD3 = FixedDecimal{Int, 3}
const FD4 = FixedDecimal{Int, 4}
const WFD2 = FixedDecimal{Int128, 2}
const WFD4 = FixedDecimal{Int128, 4}
const CONTAINER_TYPES = Base.BitInteger_types # Integer concrete subtypes which are bits
# these arrays should be kept sorted manually
const keyvalues = Dict(
FD2 => [typemin(FD2), # near minimum range
FD2(-289.64), # randomly generated
FD2(-1),
FD2(-0.01), # near zero
FD2(0),
FD2(0.01),
FD2(0.14), # fraction-like
FD2(0.33),
FD2(0.5),
FD2(1), # one
FD2(592.57), # randomly generated
typemax(FD2)], # near maximum range
WFD4 => [typemin(WFD4),
reinterpret(WFD4, -157030247204331916472131926508185768261),
reinterpret(WFD4, -64628160301714851880492570261792833470),
reinterpret(WFD4, -11679287782747983139362515984380939763),
WFD4(-1),
WFD4(-0.0001),
WFD4(0),
WFD4(0.0001),
WFD4(0.01),
WFD4(1),
reinterpret(WFD4, 164435910993133062409572187012743929911),
typemax(WFD4)])
# Floating point values written as integer strings. Useful for testing behaviours of
# trunc, floor, and ceil.
const INTS = Dict(
v => replace(@sprintf("%.200f", v), "." => "")
for v in [
1.22,
1.23,
1.51,
2.2,
2.3,
]
)
const smaller_than_decimal = [1.22, 1.23, 2.3]
const bigger_than_decimal = [1.51, 2.2]
# numbers that may cause overflow
islarge(x) = x == typemin(x) || abs(x) > 1000
# numbers that can never cause overflow
issmall(x) = -1 < x β€ 1
function parse_int(::Type{FD{T, f}}, val::AbstractString; ceil::Bool=false) where {T, f}
reinterpret(FD{T, f}, parse(T, val[1:(f + 1)]) + T(ceil))
end
@testset "FixedPointDecimals" begin
# Basic tests for the methods created above
@testset "alt" begin
@test trunc_alt(FD2, 0.0) == FD2(0)
@test floor_alt(FD2, 0.0) == FD2(0)
@test ceil_alt(FD2, 0.0) == FD2(0)
@test trunc_alt(FD2, 2.149) == FD2(2.14)
@test floor_alt(FD2, 2.149) == FD2(2.14)
@test ceil_alt(FD2, 2.149) == FD2(2.15)
@test trunc_alt(FD2, -2.149) == FD2(-2.14)
@test floor_alt(FD2, -2.149) == FD2(-2.15)
@test ceil_alt(FD2, -2.149) == FD2(-2.14)
@test trunc_alt(FD2, nextfloat(0.0)) == FD2(0)
@test floor_alt(FD2, nextfloat(0.0)) == FD2(0)
@test ceil_alt(FD2, nextfloat(0.0)) == FD2(0.01)
@test trunc_alt(FD2, prevfloat(0.0)) == FD2(0)
@test floor_alt(FD2, prevfloat(0.0)) == FD2(-0.01)
@test ceil_alt(FD2, prevfloat(0.0)) == FD2(0)
end
@testset "max_exp10" begin
@test FixedPointDecimals.max_exp10(Int8) == 2
@test FixedPointDecimals.max_exp10(Int64) == 18
@test FixedPointDecimals.max_exp10(Int128) == 38
@test FixedPointDecimals.max_exp10(UInt8) == 2
@test FixedPointDecimals.max_exp10(UInt64) == 19
@test FixedPointDecimals.max_exp10(UInt128) == 38
@test FixedPointDecimals.max_exp10(BigInt) == -1
for T in CONTAINER_TYPES
x = FixedPointDecimals.max_exp10(T)
@test T(10)^x == widen(T(10))^x
end
@testset "custom integer types" begin
@eval begin
primitive type Int24 <: Integer 24 end
Base.typemax(::Type{Int24}) = 2^24
Base.widen(::Type{Int24}) = Int32
end
@test FixedPointDecimals.max_exp10(Int24) == 7
# Note: we're just pretending that this is unbounded
@eval primitive type IntUnbounded <: Integer 256 end
@test_throws MethodError FixedPointDecimals.max_exp10(IntUnbounded)
end
end
# ensure that the coefficient multiplied by the highest and lowest representable values of
# the container type do not result in overflow.
@testset "coefficient" begin
@testset "overflow $T" for T in CONTAINER_TYPES
f = FixedPointDecimals.max_exp10(T)
powt = FixedPointDecimals.coefficient(FD{T, f})
@test powt % 10 == 0
@test checked_mul(widen(powt), typemax(T)) == widemul(powt, typemax(T))
@test checked_mul(widen(powt), typemin(T)) == widemul(powt, typemin(T))
end
end
@testset "constructor" begin
@testset "invalid $T" for T in CONTAINER_TYPES
f = FixedPointDecimals.max_exp10(T) + 1
@test_throws ArgumentError reinterpret(FD{T,f}, 0)
@test_throws ArgumentError reinterpret(FD{T,-1}, 0)
end
end
@testset "conversion" begin
@testset for x in keyvalues[FD2]
@testset for T in [Rational{Int128}, WFD2, WFD4]
@test convert(FD2, convert(T, x)) == x
@test T(x) == convert(T, x)
end
if 0 β€ abs(x) < 2
@testset for T in [SFD2, SFD4, FD4]
@test convert(FD2, convert(T, x)) == x
end
end
end
@testset "to float" begin
# Convert the rational 5//7 into a FixedDecimal with as much precision as we can
# without using BigInt.
T = Int128
f = FixedPointDecimals.max_exp10(T)
powt = FixedPointDecimals.coefficient(FD{T,f})
val = T(trunc(BigInt, widemul(5//7, powt)))
fd = reinterpret(FD{T,f}, val)
@test convert(Float64, fd) != convert(BigFloat, fd)
@test convert(Float64, fd) == T(val) / T(powt)
@test convert(BigFloat, fd) == BigInt(val) / BigInt(powt)
end
@testset "to rational" begin
fd = reinterpret(FD2, 25)
@test convert(Rational, fd) == 1//4
end
@testset "invalid" begin
@test_throws InexactError convert(FD2, FD4(0.0001))
@test_throws InexactError convert(FD4, typemax(FD2))
@test_throws InexactError convert(SFD2, typemax(FD2))
@test_throws InexactError convert(FD2, 1//3)
@test_throws InexactError convert(FD{Int8,1}, 1//4)
end
@testset "limits of $T" for T in CONTAINER_TYPES
max_exp = FixedPointDecimals.max_exp10(T)
f = max_exp
powt = widen(FixedPointDecimals.coefficient(FD{T,f}))
# Smallest positive integer which is out-of-bounds for the FD
x = max_exp - f + 1
oob = T(10)^(x > 0 ? x : 0)
# ideally we would just use `typemax(T)` but due to precision issues with
# floating-point its possible the closest float will exceed `typemax(T)`.
# Note: we should be doing `trunc(T, ...)` but truncating a BigFloat can be
# problematic (https://github.com/JuliaLang/julia/issues/21914)
max_int = trunc(BigInt, prevfloat(typemax(T) / powt) * powt)
min_int = trunc(BigInt, nextfloat(typemin(T) / powt) * powt)
@test max_int <= typemax(T)
@test value(convert(FD{T,f}, max_int / powt)) == max_int
@test min_int >= typemin(T)
@test value(convert(FD{T,f}, min_int / powt)) == min_int
@test convert(FD{T,f}, typemax(T) // powt) == reinterpret(FD{T,f}, typemax(T))
@test convert(FD{T,f}, typemin(T) // powt) == reinterpret(FD{T,f}, typemin(T))
@test_throws InexactError convert(FD{T,f}, oob)
# Converting to a floating-point
fd = reinterpret(FD{T,f}, typemax(T))
@test convert(Float32, fd) == Float32(typemax(T) / powt)
@test convert(Float64, fd) == Float64(typemax(T) / powt)
@test convert(BigFloat, fd) == BigInt(typemax(T)) / powt
fd = reinterpret(FD{T,f}, typemin(T))
@test convert(Float32, fd) == Float32(typemin(T) / powt)
@test convert(Float64, fd) == Float64(typemin(T) / powt)
@test convert(BigFloat, fd) == BigInt(typemin(T)) / powt
# Converting to a rational
fd = reinterpret(FD{T,f}, typemax(T))
@test convert(Rational, fd) == typemax(T) // powt
fd = reinterpret(FD{T,f}, typemin(T))
@test convert(Rational, fd) == typemin(T) // powt
# The following tests require that the number of decimal places allow for
# `-10 < x < 10` where x is a FD{T,f}. Needed to test `convert(::FD, ::Integer)`.
max_int = typemax(T) Γ· powt * powt
min_int = typemin(T) Γ· powt * powt
@test convert(FD{T,f}, max_int Γ· powt) == reinterpret(FD{T,f}, max_int)
@test convert(FD{T,f}, min_int Γ· powt) == reinterpret(FD{T,f}, min_int)
@test_throws InexactError convert(FD{T,f}, max_int Γ· powt + oob)
@test_throws InexactError convert(FD{T,f}, min_int Γ· powt - oob) # Overflows with Unsigned
end
@testset "limits from $U to $T" for T in CONTAINER_TYPES, U in CONTAINER_TYPES
f = FixedPointDecimals.max_exp10(T)
g = FixedPointDecimals.max_exp10(U)
powt = div(
FixedPointDecimals.coefficient(FD{T, f}),
FixedPointDecimals.coefficient(FD{U, g}),
)
val = typemax(U)
expected = widemul(typemax(U), powt)
# Mixed usage of signed and unsigned types makes testing with typemin hard.
if f >= g && expected <= typemax(T)
@test convert(FD{T,f}, reinterpret(FD{U,g}, val)) == reinterpret(FD{T,f}, expected)
else
@test_throws InexactError convert(FD{T,f}, reinterpret(FD{U,g}, val))
end
end
end
@testset "promotion" begin
@test 1//10 + FD2(0.1) === 1//5
@test 0.1 + FD2(0.1) === 0.2
@test 1 + FD2(0.1) === FD2(1.1)
@test FD2(0.1) + FD4(0.0001) === FD4(0.1001)
@test WFD2(0.1) + FD4(0.0001) === WFD4(0.1001)
end
@testset "float" begin
@test float(-one(SFD2)) === -1.0f0
@test float(zero(SFD2)) === 0.0f0
@test float(one(SFD2)) === 1.0f0
@test float(-one(FD2)) === -1.0
@test float(zero(FD2)) === 0.0
@test float(one(FD2)) === 1.0
end
@testset "comparison" begin
@testset for T in [FD2, WFD4]
@testset for (i, x) in enumerate(keyvalues[T])
@test x == x
@testset for y in keyvalues[T][i+1:end]
@test x β y
@test x < y
@test x β€ y
@test y β x
@test y > x
@test y β₯ x
end
end
end
end
@testset "traits" begin
@testset "zero, one" begin
@test FD2(0) == zero(FD2)
@test FD2(42.42) + FD2(0) == FD2(42.42)
@test FD2(1) == one(FD2)
@test FD2(42.42) * FD2(1) == FD2(42.42)
end
@testset "eps, floatmin, floatmax" begin
@test floatmin(FD2) == eps(FD2) == FD2(0.01)
@test eps(FD2(1.11)) == FD2(0.01)
for x in keyvalues[FD2]
if x β typemax(FD2)
@test x + eps(x) > x
end
if x β typemin(FD2)
@test x - eps(x) < x
if x β 0
@test floatmin(FD2) β€ abs(x) β€ floatmax(FD2)
end
end
end
end
end
@testset "addition" begin
@test FD2(0) + FD2(0) == FD2(0)
@test FD2(1.11) + FD2(2.22) == FD2(3.33)
@test FD2(0.01) + FD2(0.01) == FD2(0.02)
@test FD2(0.01) + FD2(-0.01) == FD2(0)
# overflow
@test typemax(FD2) + eps(FD2) == typemin(FD2)
end
@testset "subtraction" begin
for x in keyvalues[FD2]
@test x - x == 0
for y in keyvalues[FD2]
@test x + y - y == x
@test y + x - y == x
end
end
end
@testset "multiply" begin
@testset "with integer, $T" for T in [FD2, WFD4]
for x in keyvalues[T]
@test 1 * x == x * 1 == x
@test one(x) * x == x * one(x) == x
@test (-1) * x == x * (-1) == -x
@test 2 * x == x + x == (one(x) + one(x)) * x
end
end
@testset "binary" begin
@test FD2(0.33) * FD2(1.00) == FD2(0.33)
@test FD2(0.33) * FD2(3.00) == FD2(0.99)
@test FD2(0.33) * FD2(0.50) == FD2(0.16)
@test FD2(0.33) * FD2(0.33) == FD2(0.11)
@test FD2(0.67) * FD2(0.67) == FD2(0.45)
end
@testset "key values $T" for T in [FD2, WFD4]
totest = [(x, y) for x in keyvalues[T] for y in keyvalues[T] if
issmall(x) || issmall(y) || (!islarge(x) && !islarge(y))]
@testset for (x, y) in totest
# test the multiplication result is correctly rounded
@test x * y == round(typeof(x), Base.widemul(x, y))
end
@test prod(keyvalues[T]) == 0
end
@testset "without promotion" begin
@test_throws InexactError FD{Int8,1}(20)
@test 20 * FD{Int8,1}(0.1) == FD{Int8,1}(2.0)
@test FD{Int8,1}(0.1) * 20 == FD{Int8,1}(2.0)
end
@testset "limits of $T" for T in CONTAINER_TYPES
f = FixedPointDecimals.max_exp10(T)
scalar = convert(FD{T,f}, 1 // 10) # 0.1
# Since multiply will round the result we'll make sure our value does not
# always rounds down.
max_int = typemax(T) - (typemax(T) % 10)
min_int = typemin(T) - (typemin(T) % 10)
@test reinterpret(FD{T,f}, max_int) * scalar ==
reinterpret(FD{T,f}, div(max_int, 10))
@test reinterpret(FD{T,f}, min_int) * scalar ==
reinterpret(FD{T,f}, div(min_int, 10))
end
end
@testset "division" begin
@testset "division by 1" begin
@testset for x in keyvalues[FD2]
@test x / one(x) == x
# signed integers using two's complement have one additional negative value
if x < 0 && x == typemin(x)
@test_throws InexactError x / -one(x)
else
@test x / -one(x) == -x
end
end
end
@testset "division by 2" begin
# even targets
for x in FD2[-0.02, 0, 0.02, 1.00]
for y in [2x-eps(x), 2x, 2x+eps(x)]
@test y / 2 == y / 2one(y) == x == y * FD2(0.5)
end
end
# odd targets
for x in FD2[-0.01, 0.01, 1.01]
y = 2x
@test y / 2 == y / 2one(x) == x == y * FD2(0.5)
end
# big numbers
for T in [SFD2, SFD4, FD2, FD4, WFD2, WFD4]
@test typemin(T) / 2 * 2 == typemin(T)
@test (typemax(T) / 2 - eps(T)) * 2 == typemax(T) - eps(T)
end
end
@testset "division by 3" begin
@test FD2(10) / 3 == FD2(3.33)
@test FD2(20) / 3 == FD2(6.67)
@test FD2(-1.50) / 3 == FD2(-0.50)
@test FD2(-20) / 3 == FD2(-6.67)
# should work with big numbers
@test typemin(FD2) / 3 < 0
@test typemax(FD2) / 3 > 0
end
@testset "reciprocal $x" for x in filter(!iszero, keyvalues[FD2])
# convert each keyvalue to rational to take an exact reciprocal
# and check that it rounds to the in-type reciprocal.
r = 1/Rational{BigInt}(x)
@test round(FD2, r) == 1/x
end
@testset "divide $x by 0" for x in keyvalues[FD2]
@test_throws DivideError x/FD2(0)
end
@testset "rounding" begin
# RoundNearest: 1.27 / 2 == 0.635 rounds up to 0.64
@test FD2(1.27) / FD2(2) == FD2(0.64)
@test FD2(-1.27) / FD2(2) == FD2(-0.64)
@test FD2(1.27) / 2 == FD2(0.64)
@test FD2(-1.27) / 2 == FD2(-0.64)
@test 127 / FD2(200) == FD2(0.64)
@test -127 / FD2(200) == FD2(-0.64)
# RoundNearest: 1.29 / 2 == 0.645 rounds down to 0.64
@test FD2(1.29) / FD2(2) == FD2(0.64)
@test FD2(-1.29) / FD2(2) == FD2(-0.64)
@test FD2(1.29) / 2 == FD2(0.64)
@test FD2(-1.29) / 2 == FD2(-0.64)
@test 129 / FD2(200) == FD2(0.64)
@test -129 / FD2(200) == FD2(-0.64)
# Use of Float or BigFloat internally should not change the calculated result
@test round(Int, 109 / 200 * 100) == 55
@test round(Int, BigInt(109) / 200 * 100) == 54 # Correct
x = FD2(1.09)
y = FD2(200)
for T in [FD2, Int8, Int128, BigInt]
@test x / T(2) == FD2(0.54)
@test T(109) / y == FD2(0.54)
end
end
@testset "without promotion" begin
@test_throws InexactError FD{Int8,1}(20)
@test Int8(20) / FD{Int8,1}(2) == FD{Int8,1}(10.0)
@test FD{Int8,1}(2) / Int8(20) == FD{Int8,1}(0.1)
end
@testset "limits" begin
@test_throws InexactError Int8(1) / FD{Int8,2}(0.4)
@test_throws InexactError FD{Int8,2}(1) / FD{Int8,2}(0.4)
end
@testset "limits of $T" for T in CONTAINER_TYPES
max_exp = FixedPointDecimals.max_exp10(T)
f = max_exp
scalar = convert(FD{T,f}, 1 // 10) # 0.1
# Should be outside of the bounds of a FD{T,f}
x = T(10)
@test_throws InexactError FD{T,f}(x)
# Since multiply will round the result we'll make sure our value always
# rounds down.
max_int = typemax(T) - (typemax(T) % 10)
min_int = typemin(T) - (typemin(T) % 10)
max_fd = reinterpret(FD{T,f}, max_int)
min_fd = reinterpret(FD{T,f}, min_int)
@test (max_fd * scalar) / scalar == max_fd
@test (min_fd * scalar) / scalar == min_fd
@test max_fd / x == reinterpret(FD{T,f}, div(max_int, x))
@test min_fd / x == reinterpret(FD{T,f}, div(min_int, x))
end
end
@testset "truncating div" begin
@testset "div by 1" begin
@testset for x in keyvalues[FD2]
@test x Γ· one(x) === trunc(x)
# signed integers using two's complement have one additional negative value
if x < 0 && trunc(x) === typemin(x)
@test_throws InexactError x Γ· -one(x)
else
@test x Γ· -one(x) === -trunc(x)
end
end
end
@testset "div by 2" begin
@testset for x in keyvalues[FD2]
@test x Γ· 2one(x) === x Γ· 2 === FD2(x.i Γ· FD2(2).i)
end
end
@testset "return types" begin
@test div(2one(FD2), 3) isa FD2
@test one(FD2) Γ· one(FD2) isa FD2
# Promotion to bigger type
@test one(FD4) Γ· one(FD2) isa FD4
@test one(FD2) Γ· one(FD4) isa FD4
@test one(FD{Int32, 2}) Γ· one(FD{Int64, 6}) isa FD{Int64, 6}
end
@testset "div with rounding modes" begin
if VERSION >= v"1.4.0-"
@testset for x in keyvalues[FD2]
# TODO: Test RoundFromZero -- https://github.com/JuliaLang/julia/issues/34519
for R in (RoundToZero, RoundUp, RoundDown, RoundNearest, RoundNearestTiesAway)
@test div(x, 2one(x), R) === div(x, 2, R) === FD2(div(x.i, FD2(2).i, R))
end
end
end
@testset for x in keyvalues[FD2], f in (fld, cld, fld1, div)
@test f(x, 2one(x)) === f(x, 2) === FD2(f(x.i, FD2(2).i))
end
end
end
@testset "abs, sign" begin
@testset for T in [FD2, WFD4]
for x in keyvalues[T]
@test sign(x)^2 β [0, 1]
@test abs(abs(x)) == abs(x)
@test abs(x) * sign(x) == x
@test abs(x) == abs(-x)
if x β typemin(x)
@test abs(x) β₯ 0
@test sign(x) == -sign(-x)
end
@test (abs(x) == 0) === (x == 0)
end
end
end
@testset "isinteger" begin
# Note: Test cannot be used unless we can construct `FD{Int8,6}`
# @testset "overflow" begin
# # Note: After overflow `Int8(10)^6 == 64`
# @test !isinteger(reinterpret(FD{Int8,6}, 64)) # 0.000064
# end
@testset "limits of $T" for T in CONTAINER_TYPES
f = FixedPointDecimals.max_exp10(T)
max_fd = typemax(FD{T,f})
min_fd = typemin(FD{T,f})
@test !isinteger(max_fd)
@test isinteger(trunc(max_fd))
@test isinteger(min_fd) == (min_fd == zero(min_fd))
@test isinteger(trunc(min_fd))
end
end
@testset "round" begin
@testset "to Int" begin
@test round(Int, FD2(-0.51)) === -1
@test round(Int, FD2(-0.50)) === 0
@test round(Int, FD2(-0.49)) === 0
@test round(Int, FD2(0.50)) === 0
@test round(Int, FD2(0.51)) === 1
@test round(Int, FD2(1.50)) === 2
end
@testset "rounding invariant $x" for x in filter(!islarge, keyvalues[FD2])
@test isinteger(round(x))
@test x - FD2(1//2) β€ round(x) β€ x + FD2(1//2)
if x - FD2(1//2) == round(x) || x + FD2(1//2) == round(x)
@test iseven(convert(Int, 100round(x)))
end
@testset "to Int" for T in [Int64, Int32]
@test round(T, x) == round(x)
end
# to FD1
@test x - FD2(1//20) β€ round(FD1, x) β€ x + FD2(1//20)
# to FD2
@test x == round(FD2, x)
end
@testset "limits of $T" for T in CONTAINER_TYPES
f = FixedPointDecimals.max_exp10(T)
powt = FixedPointDecimals.coefficient(FD{T,f})
# Ideally we would just use `typemax(T)` but due to precision issues with
# floating-point its possible the closest float will exceed `typemax(T)`.
# Additionally, when the division results in a `BigFloat` we need to first truncate
# to a `BigInt` before we can truncate the type we want.
max_int = T(trunc(BigInt, prevfloat(typemax(T) / powt) * powt))
min_int = T(trunc(BigInt, nextfloat(typemin(T) / powt) * powt))
@test round(FD{T,f}, max_int / powt) == reinterpret(FD{T,f}, max_int)
@test round(FD{T,f}, min_int / powt) == reinterpret(FD{T,f}, min_int)
@test round(FD{T,f}, typemax(T) // powt) == reinterpret(FD{T,f}, typemax(T))
@test round(FD{T,f}, typemin(T) // powt) == reinterpret(FD{T,f}, typemin(T))
# Note: rounding away from zero will result in an exception.
max_int = typemax(T)
min_int = typemin(T)
max_dec = max_int / powt
min_dec = min_int / powt
if round(T, max_dec) == trunc(T, max_dec)
@test round(reinterpret(FD{T,f}, max_int)) == FD{T,f}(round(T, max_dec))
else
@test_throws InexactError round(reinterpret(FD{T,f}, max_int))
end
if round(T, min_dec) == trunc(T, min_dec)
@test round(reinterpret(FD{T,f}, min_int)) == FD{T,f}(round(T, min_dec))
else
@test_throws InexactError round(reinterpret(FD{T,f}, min_int))
end
end
end
@testset "trunc" begin
@test trunc(Int, FD2(0.99)) === 0
@test trunc(Int, FD2(-0.99)) === 0
@test trunc(Int, FD2(1)) === 1
@test trunc(Int, FD2(-1)) === -1
@test trunc(typemax(FD2)) β€ typemax(FD2)
@test trunc(Int, typemax(FD2)) β€ typemax(FD2)
@test trunc(typemin(FD2)) β₯ typemin(FD2)
@test trunc(Int, typemin(FD2)) β₯ typemin(FD2)
@test trunc(eps(FD2)) == 0
@test trunc(-eps(FD2)) == 0
@testset "truncate invariant" for x in keyvalues[FD2]
@test isinteger(trunc(x))
if x β typemin(FD2)
@test abs(x) - 1 < abs(trunc(x)) β€ abs(x)
else
@test abs(trunc(x)) > 0
end
# to FD1
@test isinteger(Base.widemul(10, FD2(trunc(FD1, x))))
@test abs(FD2(trunc(FD1, x))) β₯ 0
end
@testset "truncate precision" begin
for x in smaller_than_decimal
@test trunc(FD2, x) β trunc(FD3, x)
@test trunc(FD2, x) == FD2(x - 0.01)
@test trunc(FD3, x) == FD3(x - 0.001)
for f in 0:18
@test trunc(FD{Int64, f}, x) == parse_int(FD{Int64, f}, INTS[x])
end
for f in 0:200
@test trunc(FD{BigInt, f}, x) == parse_int(FD{BigInt, f}, INTS[x])
end
end
for x in bigger_than_decimal
exactval = FD3(x)
for f in 3:14
@test trunc(FD{Int64, f}, x) == exactval
end
for f in 0:18
@test trunc(FD{Int64, f}, x) == parse_int(FD{Int64, f}, INTS[x])
end
end
end
@testset "limits of $T" for T in CONTAINER_TYPES
f = FixedPointDecimals.max_exp10(T)
powt = FixedPointDecimals.coefficient(FD{T,f})
# When converting from typemax to a floating-point it is possible that due to
# precision issues that the closest possible float will exceed the typemax.
max_float = prevfloat(convert(AbstractFloat, typemax(FD{T,f})))
min_float = nextfloat(convert(AbstractFloat, typemin(FD{T,f})))
@test trunc(FD{T,f}, max_float) == trunc_alt(FD{T,f}, max_float)
@test trunc(FD{T,f}, min_float) == trunc_alt(FD{T,f}, min_float)
@test trunc(reinterpret(FD{T,f}, typemax(T))) == FD{T,f}(div(typemax(T), powt))
@test trunc(reinterpret(FD{T,f}, typemin(T))) == FD{T,f}(div(typemin(T), powt))
end
end
# eps that works for integers too
epsi(::Type{T}) where T <: Integer = one(T)::T
epsi(::Type{T}) where T = eps(T)
@testset "floor, ceil" begin
@testset for x in filter(!islarge, keyvalues[FD2])
@test floor(x) β€ x < floor(x) + 1
@test ceil(x) - 1 < x β€ ceil(x)
@test isinteger(floor(x))
@test isinteger(ceil(x))
@testset for T in [Int32, Int64, FD1, FD2, FD4, WFD2, WFD4]
@test floor(T, x) β€ x < floor(T, x) + epsi(T)
@test ceil(T, x) - epsi(T) < x β€ ceil(T, x)
end
end
@testset "floor, ceil precision" begin
for x in smaller_than_decimal
@test floor(FD2, x) != floor(FD3, x)
@test floor(FD2, x) == FD2(x - 0.01)
@test floor(FD3, x) == FD3(x - 0.001)
for f in 0:18
@test floor(FD{Int64, f}, x) == parse_int(FD{Int64, f}, INTS[x])
end
@test ceil(FD3, x) == ceil(FD4, x) == FD4(x)
end
for x in bigger_than_decimal
@test ceil(FD2, x) β ceil(FD3, x)
@test ceil(FD2, x) == FD2(x + 0.01)
@test ceil(FD3, x) == FD3(x + 0.001)
for f in 0:18
@test ceil(FD{Int64, f}, x) == parse_int(FD{Int64, f}, INTS[x], ceil=true)
end
@test floor(FD3, x) == floor(FD4, x) == FD4(x)
end
end
@testset "limits of $T" for T in CONTAINER_TYPES
f = FixedPointDecimals.max_exp10(T)
powt = FixedPointDecimals.coefficient(FD{T,f})
# When converting from typemax to a floating-point it is possible that due to
# precision issues that the closest possible float will exceed the typemax.
max_float = prevfloat(convert(AbstractFloat, typemax(FD{T,f})))
min_float = nextfloat(convert(AbstractFloat, typemin(FD{T,f})))
@test floor(FD{T,f}, max_float) == floor_alt(FD{T,f}, max_float)
@test floor(FD{T,f}, min_float) == floor_alt(FD{T,f}, min_float)
@test ceil(FD{T,f}, max_float) == ceil_alt(FD{T,f}, max_float)
@test ceil(FD{T,f}, min_float) == ceil_alt(FD{T,f}, min_float)
# Note: rounding away from zero will result in an exception.
max_int = typemax(T)
min_int = typemin(T)
max_dec = max_int / powt
min_dec = min_int / powt
@test floor(reinterpret(FD{T,f}, max_int)) == FD{T,f}(floor(T, max_dec))
if floor(T, min_dec) == trunc(T, min_dec)
@test floor(reinterpret(FD{T,f}, min_int)) == FD{T,f}(floor(T, min_dec))
else
@test_throws InexactError floor(reinterpret(FD{T,f}, min_int))
end
if ceil(T, max_dec) == trunc(T, max_dec)
@test ceil(reinterpret(FD{T,f}, max_int)) == FD{T,f}(ceil(T, max_dec))
else
@test_throws InexactError ceil(reinterpret(FD{T,f}, max_int))
end
@test ceil(reinterpret(FD{T,f}, min_int)) == FD{T,f}(ceil(T, min_dec))
end
end
@testset "type stability" begin
# Test that basic operations are type stable for all the basic integer types.
fs = [0, 1, 2, 7, 16, 38] # To save time, don't test all possible combinations.
@testset for T in (CONTAINER_TYPES..., BigInt,)
maxF = FixedPointDecimals.max_exp10(T)
frange = filter(f->f<=maxF, fs)
# Unary operations
@testset for f in frange
@test @inferred(zero(FD{T,f}(1))) === FD{T,f}(0)
@test @inferred(one(FD{T,f}(1))) === FD{T,f}(1)
@test @inferred(ceil(FD{T,f}(1))) === FD{T,f}(1)
@test @inferred(round(FD{T,f}(1))) === FD{T,f}(1)
@test @inferred(abs(FD{T,f}(1))) === FD{T,f}(1)
@test @inferred(FD{T,f}(1)^2) === FD{T,f}(1)
@test @inferred(typemax(FD{T,f})) isa FD{T,f}
end
# Binary operations
@testset for (f1,f2) in Iterators.product(frange, frange)
fmax = max(f1,f2)
@test @inferred(FD{T,f1}(1) + FD{T,f2}(0)) === FD{T,fmax}(1)
@test @inferred(FD{T,f1}(1) - FD{T,f2}(0)) === FD{T,fmax}(1)
@test @inferred(FD{T,f1}(1) * FD{T,f2}(1)) === FD{T,fmax}(1)
@test @inferred(FD{T,f1}(1) / FD{T,f2}(1)) === FD{T,fmax}(1)
@test @inferred(FD{T,f1}(1) Γ· FD{T,f2}(1)) === FD{T,fmax}(1)
@test @inferred(max(FD{T,f1}(1), FD{T,f2}(0))) === FD{T,fmax}(1)
@test @inferred(min(FD{T,f1}(1), FD{T,f2}(0))) === FD{T,fmax}(0)
end
end
end
@testset "print" begin
@test string(FD2(1.00)) == "1.00"
@test string(FD2(1.23)) == "1.23"
@test string(FD2(42.40)) == "42.40"
@test string(FD2(-42.40)) == "-42.40"
@test string(FD2(-0.01)) == "-0.01"
@test string(FD2(0)) == "0.00"
@test string(FixedDecimal{Int,0}(123.4)) == "123"
# Displaying a decimal could be incorrect when using a decimal place precision which is
# close to or at the limit for our storage type.
@testset "limits of $T" for T in CONTAINER_TYPES
f = FixedPointDecimals.max_exp10(T)
function fmt(val, f)
str = string(val)
neg = ""
if str[1] == '-'
neg = "-"
str = str[2:end]
end
return string(neg, str[1], ".", rpad(str[2:end], f, '0'))
end
@test string(reinterpret(FD{T,f}, typemax(T))) == fmt(typemax(T), f)
@test string(reinterpret(FD{T,f}, typemin(T))) == fmt(typemin(T), f)
end
end
@testset "show" begin
@testset "compact" begin
@test sprint(show, FD2(1.00), context=:compact=>true) == "1.0"
@test sprint(show, FD2(1.23), context=:compact=>true) == "1.23"
@test sprint(show, FD2(42.40), context=:compact=>true) == "42.4"
@test sprint(show, FD2(-42.40), context=:compact=>true) == "-42.4"
@test sprint(show, FD2(-0.01), context=:compact=>true) == "-0.01"
@test sprint(show, FD2(0), context=:compact=>true) == "0.0"
@test repr(typemin(FixedDecimal{Int64, 2})) ==
"FixedDecimal{Int64,2}(-92233720368547758.08)"
@test repr(typemax(FixedDecimal{Int64, 2})) ==
"FixedDecimal{Int64,2}(92233720368547758.07)"
@test repr(typemin(FixedDecimal{Int32, 2})) ==
"FixedDecimal{Int32,2}(-21474836.48)"
@test repr(typemax(FixedDecimal{Int32, 2})) ==
"FixedDecimal{Int32,2}(21474836.47)"
end
end
@testset "string" begin
for x in keyvalues[FD2]
if 0 β€ abs(x) < 1000
@test Core.eval(@__MODULE__, Meta.parse(string(x))) == x
end
end
end
@testset "parse_round" begin
@test FixedPointDecimals.parse_round(Int, "44", RoundNearest) == 0
@test FixedPointDecimals.parse_round(Int, "45", RoundNearest) == 0
@test FixedPointDecimals.parse_round(Int, "46", RoundNearest) == 1
@test FixedPointDecimals.parse_round(Int, "54", RoundNearest) == 0
@test FixedPointDecimals.parse_round(Int, "55", RoundNearest) == 1
@test FixedPointDecimals.parse_round(Int, "56", RoundNearest) == 1
# Handle a number of digits that exceeds the storage capacity of Int128
@test FixedPointDecimals.parse_round(Int8, "9"^40, RoundNearest) == 1
end
@testset "parse" begin
# Note: the underscore used in the reinterpreted integer is used to indicate the decimal
# place.
@testset "decimal position" begin
@test parse(FD2, "123") == reinterpret(FD2, 123_00)
@test parse(FD2, "0.123") == reinterpret(FD2, 0_12)
@test parse(FD2, ".123") == reinterpret(FD2, 0_12)
@test parse(FD2, "1.23") == reinterpret(FD2, 1_23)
@test parse(FD2, "12.3") == reinterpret(FD2, 12_30)
@test parse(FD2, "123.") == reinterpret(FD2, 123_00)
@test parse(FD2, "123.0") == reinterpret(FD2, 123_00)
@test parse(FD2, "-123") == reinterpret(FD2, -123_00)
@test parse(FD2, "-0.123") == reinterpret(FD2, -0_12)
@test parse(FD2, "-.123") == reinterpret(FD2, -0_12)
@test parse(FD2, "-1.23") == reinterpret(FD2, -1_23)
@test parse(FD2, "-12.3") == reinterpret(FD2, -12_30)
@test parse(FD2, "-123.") == reinterpret(FD2, -123_00)
@test parse(FD2, "-123.0") == reinterpret(FD2, -123_00)
end
@testset "scientific notation" begin
@test parse(FD4, "12e0") == reinterpret(FD4, 00012_0000)
@test parse(FD4, "12e3") == reinterpret(FD4, 12000_0000)
@test parse(FD4, "12e-3") == reinterpret(FD4, 00000_0120)
@test parse(FD4, "1.2e0") == reinterpret(FD4, 00001_2000)
@test parse(FD4, "1.2e3") == reinterpret(FD4, 01200_0000)
@test parse(FD4, "1.2e-3") == reinterpret(FD4, 00000_0012)
@test parse(FD4, "1.2e-4") == reinterpret(FD4, 00000_0001)
@test parse(FD4, "-12e0") == reinterpret(FD4, -00012_0000)
@test parse(FD4, "-12e3") == reinterpret(FD4, -12000_0000)
@test parse(FD4, "-12e-3") == reinterpret(FD4, -00000_0120)
@test parse(FD4, "-1.2e0") == reinterpret(FD4, -00001_2000)
@test parse(FD4, "-1.2e3") == reinterpret(FD4, -01200_0000)
@test parse(FD4, "-1.2e-3") == reinterpret(FD4, -00000_0012)
@test parse(FD2, "999e-1") == reinterpret(FD2, 99_90)
@test parse(FD2, "999e-2") == reinterpret(FD2, 09_99)
@test parse(FD2, "999e-3") == reinterpret(FD2, 01_00)
@test parse(FD2, "999e-4") == reinterpret(FD2, 00_10)
@test parse(FD2, "999e-5") == reinterpret(FD2, 00_01)
@test parse(FD2, "999e-6") == reinterpret(FD2, 00_00)
@test parse(FD2, "-999e-1") == reinterpret(FD2, -99_90)
@test parse(FD2, "-999e-2") == reinterpret(FD2, -09_99)
@test parse(FD2, "-999e-3") == reinterpret(FD2, -01_00)
@test parse(FD2, "-999e-4") == reinterpret(FD2, -00_10)
@test parse(FD2, "-999e-5") == reinterpret(FD2, -00_01)
@test parse(FD2, "-999e-6") == reinterpret(FD2, -00_00)
@test parse(FD4, "9"^96 * "e-100") == reinterpret(FD4, 0_001)
end
@testset "round to nearest" begin
@test parse(FD2, "0.444") == reinterpret(FD2, 0_44)
@test parse(FD2, "0.445") == reinterpret(FD2, 0_44)
@test parse(FD2, "0.446") == reinterpret(FD2, 0_45)
@test parse(FD2, "0.454") == reinterpret(FD2, 0_45)
@test parse(FD2, "0.455") == reinterpret(FD2, 0_46)
@test parse(FD2, "0.456") == reinterpret(FD2, 0_46)
@test parse(FD2, "-0.444") == reinterpret(FD2, -0_44)
@test parse(FD2, "-0.445") == reinterpret(FD2, -0_44)
@test parse(FD2, "-0.446") == reinterpret(FD2, -0_45)
@test parse(FD2, "-0.454") == reinterpret(FD2, -0_45)
@test parse(FD2, "-0.455") == reinterpret(FD2, -0_46)
@test parse(FD2, "-0.456") == reinterpret(FD2, -0_46)
@test parse(FD2, "0.009") == reinterpret(FD2, 0_01)
@test parse(FD2, "-0.009") == reinterpret(FD2, -0_01)
@test parse(FD4, "1.5e-4") == reinterpret(FD4, 0_0002)
end
@testset "round to zero" begin
@test parse(FD2, "0.444", RoundToZero) == reinterpret(FD2, 0_44)
@test parse(FD2, "0.445", RoundToZero) == reinterpret(FD2, 0_44)
@test parse(FD2, "0.446", RoundToZero) == reinterpret(FD2, 0_44)
@test parse(FD2, "0.454", RoundToZero) == reinterpret(FD2, 0_45)
@test parse(FD2, "0.455", RoundToZero) == reinterpret(FD2, 0_45)
@test parse(FD2, "0.456", RoundToZero) == reinterpret(FD2, 0_45)
@test parse(FD2, "-0.444", RoundToZero) == reinterpret(FD2, -0_44)
@test parse(FD2, "-0.445", RoundToZero) == reinterpret(FD2, -0_44)
@test parse(FD2, "-0.446", RoundToZero) == reinterpret(FD2, -0_44)
@test parse(FD2, "-0.454", RoundToZero) == reinterpret(FD2, -0_45)
@test parse(FD2, "-0.455", RoundToZero) == reinterpret(FD2, -0_45)
@test parse(FD2, "-0.456", RoundToZero) == reinterpret(FD2, -0_45)
@test parse(FD2, "0.009", RoundToZero) == reinterpret(FD2, 0_00)
@test parse(FD2, "-0.009", RoundToZero) == reinterpret(FD2, 0_00)
@test parse(FD4, "1.5e-4", RoundToZero) == reinterpret(FD4, 0_0001)
end
@testset "round throws" begin
@test parse(FD2, "0.44", RoundThrows) == reinterpret(FD2, 0_44)
@test parse(FD2, "0.440", RoundThrows) == reinterpret(FD2, 0_44)
@test_throws InexactError parse(FD2, "0.444", RoundThrows)
@test_throws InexactError parse(FD2, "0.445", RoundThrows)
@test_throws InexactError parse(FD2, "0.446", RoundThrows)
@test_throws InexactError parse(FD2, "0.454", RoundThrows)
@test_throws InexactError parse(FD2, "0.455", RoundThrows)
@test_throws InexactError parse(FD2, "0.456", RoundThrows)
@test_throws InexactError parse(FD2, "-0.444", RoundThrows)
@test_throws InexactError parse(FD2, "-0.445", RoundThrows)
@test_throws InexactError parse(FD2, "-0.446", RoundThrows)
@test_throws InexactError parse(FD2, "-0.454", RoundThrows)
@test_throws InexactError parse(FD2, "-0.455", RoundThrows)
@test_throws InexactError parse(FD2, "-0.456", RoundThrows)
@test_throws InexactError parse(FD2, "0.009", RoundThrows)
@test_throws InexactError parse(FD2, "-0.009", RoundThrows)
@test_throws InexactError parse(FD4, "1.5e-4", RoundThrows)
end
@testset "invalid" begin
@test_throws OverflowError parse(FD4, "1.2e100")
@test_throws ArgumentError parse(FD4, "foo")
@test_throws ArgumentError parse(FD4, "1.2.3")
@test_throws ArgumentError parse(FD4, "1.2", RoundUp)
end
@testset "hashing" begin
fd1 = FixedDecimal{Int, 4}(2.5)
fd2 = FixedDecimal{Int, 5}(2.5)
fd3 = FixedDecimal{Int, 4}(3.5)
@test hash(fd1) == hash(fd2)
@test hash(fd1) != hash(fd3)
@test hash(fd1) != hash(fd1.i)
@test hash(FD2(1//10)) == hash(1//10)
@test hash(FD2(1//10)) β hash(0.1)
end
end
end # global testset
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308,
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7,
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8,
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17,
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16,
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16,
8,
198,
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17,
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13,
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8,
198,
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220,
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17,
7,
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16,
8,
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19,
7,
15,
13,
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8,
24844,
370,
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19,
7,
15,
13,
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8,
198,
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198,
198,
31,
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2617,
366,
22468,
1,
2221,
198,
220,
220,
220,
2488,
9288,
12178,
32590,
505,
7,
20802,
35,
17,
4008,
24844,
532,
16,
13,
15,
69,
15,
198,
220,
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220,
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7,
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7,
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35,
17,
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13,
15,
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15,
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220,
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7,
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35,
17,
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13,
15,
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15,
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13,
15,
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7,
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17,
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13,
15,
198,
220,
220,
220,
2488,
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12178,
7,
505,
7,
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17,
4008,
24844,
352,
13,
15,
198,
437,
198,
198,
31,
9288,
2617,
366,
785,
1845,
1653,
1,
2221,
198,
220,
220,
220,
2488,
9288,
2617,
329,
309,
287,
685,
26009,
17,
11,
370,
26009,
19,
60,
198,
220,
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220,
220,
220,
2488,
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2617,
329,
357,
72,
11,
2124,
8,
287,
27056,
378,
7,
2539,
27160,
58,
51,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
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6624,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2617,
329,
331,
287,
1994,
27160,
58,
51,
7131,
72,
10,
16,
25,
437,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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254,
331,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2124,
1279,
331,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2124,
41305,
331,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
331,
15139,
254,
2124,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
331,
1875,
2124,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
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331,
26870,
2124,
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,
437,
198,
198,
31,
9288,
2617,
366,
9535,
896,
1,
2221,
198,
220,
220,
220,
2488,
9288,
2617,
366,
22570,
11,
530,
1,
2221,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
17,
7,
15,
8,
6624,
6632,
7,
26009,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
17,
7,
3682,
13,
3682,
8,
1343,
30002,
17,
7,
15,
8,
6624,
30002,
17,
7,
3682,
13,
3682,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
17,
7,
16,
8,
6624,
530,
7,
26009,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
17,
7,
3682,
13,
3682,
8,
1635,
30002,
17,
7,
16,
8,
6624,
30002,
17,
7,
3682,
13,
3682,
8,
198,
220,
220,
220,
886,
628,
220,
220,
220,
2488,
9288,
2617,
366,
25386,
11,
12178,
1084,
11,
12178,
9806,
1,
2221,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
12178,
1084,
7,
26009,
17,
8,
6624,
304,
862,
7,
26009,
17,
8,
6624,
30002,
17,
7,
15,
13,
486,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
304,
862,
7,
26009,
17,
7,
16,
13,
1157,
4008,
6624,
30002,
17,
7,
15,
13,
486,
8,
198,
220,
220,
220,
220,
220,
220,
220,
329,
2124,
287,
1994,
27160,
58,
26009,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2124,
15139,
254,
2170,
368,
897,
7,
26009,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2124,
1343,
304,
862,
7,
87,
8,
1875,
2124,
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,
611,
2124,
15139,
254,
2170,
14857,
7,
26009,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2124,
532,
304,
862,
7,
87,
8,
1279,
2124,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2124,
15139,
254,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
12178,
1084,
7,
26009,
17,
8,
41305,
2352,
7,
87,
8,
41305,
12178,
9806,
7,
26009,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
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,
437,
198,
198,
31,
9288,
2617,
366,
2860,
653,
1,
2221,
198,
220,
220,
220,
2488,
9288,
30002,
17,
7,
15,
8,
1343,
30002,
17,
7,
15,
8,
6624,
30002,
17,
7,
15,
8,
198,
220,
220,
220,
2488,
9288,
30002,
17,
7,
16,
13,
1157,
8,
1343,
30002,
17,
7,
17,
13,
1828,
8,
6624,
30002,
17,
7,
18,
13,
2091,
8,
198,
220,
220,
220,
2488,
9288,
30002,
17,
7,
15,
13,
486,
8,
1343,
30002,
17,
7,
15,
13,
486,
8,
6624,
30002,
17,
7,
15,
13,
2999,
8,
198,
220,
220,
220,
2488,
9288,
30002,
17,
7,
15,
13,
486,
8,
1343,
30002,
17,
32590,
15,
13,
486,
8,
6624,
30002,
17,
7,
15,
8,
628,
220,
220,
220,
1303,
30343,
198,
220,
220,
220,
2488,
9288,
2170,
368,
897,
7,
26009,
17,
8,
1343,
304,
862,
7,
26009,
17,
8,
6624,
2170,
14857,
7,
26009,
17,
8,
198,
437,
198,
198,
31,
9288,
2617,
366,
7266,
83,
7861,
1,
2221,
198,
220,
220,
220,
329,
2124,
287,
1994,
27160,
58,
26009,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2124,
532,
2124,
6624,
657,
198,
220,
220,
220,
220,
220,
220,
220,
329,
331,
287,
1994,
27160,
58,
26009,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2124,
1343,
331,
532,
331,
6624,
2124,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
331,
1343,
2124,
532,
331,
6624,
2124,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
437,
198,
198,
31,
9288,
2617,
366,
16680,
541,
306,
1,
2221,
198,
220,
220,
220,
2488,
9288,
2617,
366,
4480,
18253,
11,
720,
51,
1,
329,
309,
287,
685,
26009,
17,
11,
370,
26009,
19,
60,
198,
220,
220,
220,
220,
220,
220,
220,
329,
2124,
287,
1994,
27160,
58,
51,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
352,
1635,
2124,
6624,
2124,
1635,
352,
6624,
2124,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
530,
7,
87,
8,
1635,
2124,
6624,
2124,
1635,
530,
7,
87,
8,
6624,
2124,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
13841,
16,
8,
1635,
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6624,
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8,
6624,
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87,
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220,
220,
220,
220,
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220,
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1,
2221,
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7,
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8,
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7,
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8,
6624,
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13,
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8,
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7,
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13,
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7,
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13,
2091,
8,
6624,
30002,
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7,
15,
13,
1157,
8,
198,
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220,
220,
220,
220,
220,
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17,
7,
15,
13,
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8,
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17,
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13,
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8,
6624,
30002,
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7,
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13,
2231,
8,
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2617,
366,
2539,
3815,
720,
51,
1,
329,
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287,
685,
26009,
17,
11,
370,
26009,
19,
60,
198,
220,
220,
220,
220,
220,
220,
220,
2006,
395,
796,
47527,
87,
11,
331,
8,
329,
2124,
287,
1994,
27160,
58,
51,
60,
329,
331,
287,
1994,
27160,
58,
51,
60,
611,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1189,
76,
439,
7,
87,
8,
8614,
1189,
76,
439,
7,
88,
8,
8614,
22759,
3044,
1376,
7,
87,
8,
11405,
5145,
3044,
1376,
7,
88,
4008,
60,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2617,
329,
357,
87,
11,
331,
8,
287,
2006,
395,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1332,
262,
48473,
1255,
318,
9380,
19273,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2124,
1635,
331,
6624,
2835,
7,
4906,
1659,
7,
87,
828,
7308,
13,
28029,
368,
377,
7,
87,
11,
331,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
40426,
7,
2539,
27160,
58,
51,
12962,
6624,
657,
198,
220,
220,
220,
886,
628,
220,
220,
220,
2488,
9288,
2617,
366,
19419,
12148,
1,
2221,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
62,
400,
8516,
554,
1069,
529,
12331,
30002,
90,
5317,
23,
11,
16,
92,
7,
1238,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
1160,
1635,
30002,
90,
5317,
23,
11,
16,
92,
7,
15,
13,
16,
8,
6624,
30002,
90,
5317,
23,
11,
16,
92,
7,
17,
13,
15,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
90,
5317,
23,
11,
16,
92,
7,
15,
13,
16,
8,
1635,
1160,
6624,
30002,
90,
5317,
23,
11,
16,
92,
7,
17,
13,
15,
8,
198,
220,
220,
220,
886,
628,
220,
220,
220,
2488,
9288,
2617,
366,
49196,
286,
720,
51,
1,
329,
309,
287,
7102,
30339,
1137,
62,
9936,
47,
1546,
198,
220,
220,
220,
220,
220,
220,
220,
277,
796,
10832,
12727,
10707,
320,
874,
13,
9806,
62,
11201,
940,
7,
51,
8,
198,
220,
220,
220,
220,
220,
220,
220,
16578,
283,
796,
10385,
7,
26009,
90,
51,
11,
69,
5512,
352,
3373,
838,
8,
220,
1303,
657,
13,
16,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
4619,
29162,
481,
2835,
262,
1255,
356,
1183,
787,
1654,
674,
1988,
857,
407,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1464,
9196,
866,
13,
198,
220,
220,
220,
220,
220,
220,
220,
3509,
62,
600,
796,
2170,
368,
897,
7,
51,
8,
532,
357,
28004,
368,
897,
7,
51,
8,
4064,
838,
8,
198,
220,
220,
220,
220,
220,
220,
220,
949,
62,
600,
796,
2170,
14857,
7,
51,
8,
532,
357,
28004,
14857,
7,
51,
8,
4064,
838,
8,
628,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
302,
27381,
7,
26009,
90,
51,
11,
69,
5512,
3509,
62,
600,
8,
1635,
16578,
283,
6624,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
302,
27381,
7,
26009,
90,
51,
11,
69,
5512,
2659,
7,
9806,
62,
600,
11,
838,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
302,
27381,
7,
26009,
90,
51,
11,
69,
5512,
949,
62,
600,
8,
1635,
16578,
283,
6624,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
302,
27381,
7,
26009,
90,
51,
11,
69,
5512,
2659,
7,
1084,
62,
600,
11,
838,
4008,
198,
220,
220,
220,
886,
198,
437,
198,
198,
31,
9288,
2617,
366,
21426,
1,
2221,
198,
220,
220,
220,
2488,
9288,
2617,
366,
21426,
416,
352,
1,
2221,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2617,
329,
2124,
287,
1994,
27160,
58,
26009,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2124,
1220,
530,
7,
87,
8,
6624,
2124,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4488,
37014,
1262,
734,
338,
16829,
423,
530,
3224,
4633,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2124,
1279,
657,
11405,
2124,
6624,
2170,
14857,
7,
87,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
62,
400,
8516,
554,
1069,
529,
12331,
2124,
1220,
532,
505,
7,
87,
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,
2488,
9288,
2124,
1220,
532,
505,
7,
87,
8,
6624,
532,
87,
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,
628,
220,
220,
220,
2488,
9288,
2617,
366,
21426,
416,
362,
1,
2221,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
772,
6670,
198,
220,
220,
220,
220,
220,
220,
220,
329,
2124,
287,
30002,
17,
58,
12,
15,
13,
2999,
11,
657,
11,
657,
13,
2999,
11,
352,
13,
405,
60,
198,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
329,
331,
287,
685,
17,
87,
12,
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7,
87,
828,
362,
87,
11,
362,
87,
10,
25386,
7,
87,
15437,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
331,
1220,
362,
6624,
331,
1220,
362,
505,
7,
88,
8,
6624,
2124,
6624,
331,
1635,
30002,
17,
7,
15,
13,
20,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
5629,
6670,
198,
220,
220,
220,
220,
220,
220,
220,
329,
2124,
287,
30002,
17,
58,
12,
15,
13,
486,
11,
657,
13,
486,
11,
352,
13,
486,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
796,
362,
87,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
331,
1220,
362,
6624,
331,
1220,
362,
505,
7,
87,
8,
6624,
2124,
6624,
331,
1635,
30002,
17,
7,
15,
13,
20,
8,
198,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
1263,
3146,
198,
220,
220,
220,
220,
220,
220,
220,
329,
309,
287,
685,
20802,
35,
17,
11,
14362,
35,
19,
11,
30002,
17,
11,
30002,
19,
11,
370,
26009,
17,
11,
370,
26009,
19,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2170,
14857,
7,
51,
8,
1220,
362,
1635,
362,
6624,
2170,
14857,
7,
51,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
357,
28004,
368,
897,
7,
51,
8,
1220,
362,
532,
304,
862,
7,
51,
4008,
1635,
362,
6624,
2170,
368,
897,
7,
51,
8,
532,
304,
862,
7,
51,
8,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
628,
220,
220,
220,
2488,
9288,
2617,
366,
21426,
416,
513,
1,
2221,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
17,
7,
940,
8,
1220,
513,
6624,
30002,
17,
7,
18,
13,
2091,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
17,
7,
1238,
8,
1220,
513,
6624,
30002,
17,
7,
21,
13,
3134,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
17,
32590,
16,
13,
1120,
8,
1220,
513,
6624,
30002,
17,
32590,
15,
13,
1120,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
17,
32590,
1238,
8,
1220,
513,
6624,
30002,
17,
32590,
21,
13,
3134,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
815,
670,
351,
1263,
3146,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2170,
14857,
7,
26009,
17,
8,
1220,
513,
1279,
657,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2170,
368,
897,
7,
26009,
17,
8,
1220,
513,
1875,
657,
198,
220,
220,
220,
886,
628,
220,
220,
220,
2488,
9288,
2617,
366,
8344,
541,
43270,
720,
87,
1,
329,
2124,
287,
8106,
7,
0,
271,
22570,
11,
1994,
27160,
58,
26009,
17,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
10385,
1123,
1994,
8367,
284,
9377,
284,
1011,
281,
2748,
48135,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
290,
2198,
326,
340,
9196,
284,
262,
287,
12,
4906,
48135,
13,
198,
220,
220,
220,
220,
220,
220,
220,
374,
796,
352,
14,
49,
864,
90,
12804,
5317,
92,
7,
87,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2835,
7,
26009,
17,
11,
374,
8,
6624,
352,
14,
87,
198,
220,
220,
220,
886,
628,
220,
220,
220,
2488,
9288,
2617,
366,
7146,
485,
720,
87,
416,
657,
1,
329,
2124,
287,
1994,
27160,
58,
26009,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
62,
400,
8516,
46894,
12331,
2124,
14,
26009,
17,
7,
15,
8,
198,
220,
220,
220,
886,
628,
220,
220,
220,
2488,
9288,
2617,
366,
744,
278,
1,
2221,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
10485,
8199,
12423,
25,
352,
13,
1983,
1220,
362,
6624,
657,
13,
48250,
9196,
510,
284,
657,
13,
2414,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
17,
7,
16,
13,
1983,
8,
1220,
30002,
17,
7,
17,
8,
6624,
30002,
17,
7,
15,
13,
2414,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
17,
32590,
16,
13,
1983,
8,
1220,
30002,
17,
7,
17,
8,
6624,
30002,
17,
32590,
15,
13,
2414,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
30002,
17,
7,
16,
13,
1983,
8,
1220,
362,
6624,
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2488,
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27160,
58,
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17,
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357,
69,
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11,
269,
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] | 1.955182 | 19,858 |
<reponame>hashimoto-koh/Wild.jl
import DataStructures: OrderedDict
import StaticArrays: MVector
import Serialization
import SHA
################
# NSitem
################
abstract type AbstNSitem end
struct NScst_item{T} <: AbstNSitem
obj::T
end
mutable struct NSnoncst_item{T} <: AbstNSitem
obj::T
end
Base.copy(x::Wild.AbstNSitem) = typeof(x)(x.obj)
################
# NSX{X}
################
struct NSX{X} <: AbstNS
__dict::OrderedDict{Symbol, AbstNSitem}
__fix_lck::MVector{2, Bool}
# NSX{X}() where X = new{X}(#= __dict =# OrderedDict{Symbol, AbstNSitem}(),
# #= __fix_lck =# MVector{2, Bool}(false, false))
NSX{X}(; ka...) where X =
begin
g = new(#= __dict =# OrderedDict{Symbol, AbstNSitem}(),
#= __fix_lck =# MVector{2, Bool}(false, false))
setprp(g, k, v) =
begin
s = string(k)
if length(s) > 3 && s[4] == '_'
length(s) == 4 &&
Base.error("$(k) can't be used for property.")
pre = Symbol(s[1:3])
if pre in [:cst, :dfn, :prp, :req, :mth, :fnc]
setprp(Base.getproperty(g, pre), Symbol(s[5:end]), v)
else
Base.setproperty!(g, k, v)
end
else
Base.setproperty!(g, k, v)
end
end
for (k,v) in pairs(ka)
setprp(g, k, v)
end
g
end;
end
const NS = NSX{nothing}
abstract type __NSFlgCodeMode end
################
# NS
################
Base.setproperty!(ns::AbstNS, atr::Symbol, x) =
begin
hasfield(typeof(ns), atr) && (return Base.setfield!(ns, atr, x))
atr == :exe && (return x(ns))
haskey(_NSdict0, atr) &&
Base.error("""'$(atr)' can't be used for property""")
d = ns.__dict
if haskey(d, atr)
ns._fixed && Base.error("this NS is fixed!")
o = d[atr].obj
isa(o, NSPrp) && (o.fnc(ns, x); return)
isa(d[atr], NScst_item) && Base.error("""'$(atr)' is const.""")
else
ns._lcked && Base.error("this NS is locked!")
end
y = isa(x, AbstNSitem) ? x : NSnoncst_item(x)
d[atr] = (isa(y.obj, NSDfn)
? (isa(y, NScst_item) ? NScst_item : NSnoncst_item)(y.obj.fnc(ns))
: copy(y))
end
Base.haskey(o::AbstNS, key::Symbol) = key β o._keys
Base.propertynames(ns::AbstNS, private::Bool=false) =
(private
? tuple(Base.keys(ns.__dict)...,
Base.keys(_NSdict0)...,
Base.fieldnames(typeof(ns))...)
: tuple(Base.keys(ns.__dict)...,
Base.keys(_NSdict0)...))
Base.hasproperty(ns::AbstNS, atr::Symbol) =
Base.hasfield(typeof(ns), atr) ||
haskey(_NSdict0, atr) ||
haskey(ns.__dict, atr)
Base.getproperty(ns::AbstNS, atr::Symbol) =
begin
Base.hasfield(typeof(ns), atr) && (return Base.getfield(ns, atr))
haskey(_NSdict0, atr) && (return _NSdict0[atr](ns))
d = ns.__dict
haskey(d, atr) ||
error("""this NS does not have a property named '$(atr)'.""")
x = d[atr].obj;
isa(x, Union{NSPrp, NSMth}) && (return x(ns))
isa(x, NSFnc) && (return x.fnc)
isa(x, NSReq) &&
(y = x(ns);
d[atr] = (isa(d[atr], NScst_item) ? NScst_item : NSnoncst_item)(y);
return y)
return x
end
################
# __NSX_CodeMode
################
__NSX_CodeMode_CodeType = Vector{NamedTuple{(:atr, :obj),Tuple{Symbol,Any}}}
struct __NSX_CodeMode <: AbstNS
__code::__NSX_CodeMode_CodeType
__instances
__parallel::NSnoncst_item{Bool}
__NSX_CodeMode() =
new(#= __code =# __NSX_CodeMode_CodeType(),
#= __instances =# [],
#= __parallel =# NSnoncst_item{Bool}(false))
end
__divNn(N::Integer, n::Integer) =
begin
k1 = (let v = fill(N Γ· n, n); v[1:N%n] .+= 1; accumulate(+, v); end)
k0 = (let v = similar(k1); v[1] = 1; @. v[2:end] = k1[1:end-1] + 1; v; end)
[i0:i1 for (i0,i1) in zip(k0,k1)]
end
Base.setproperty!(ns::__NSX_CodeMode, atr::Symbol, x) =
begin
hasfield(typeof(ns), atr) && (return Base.setfield!(ns, atr, x))
haskey(_NSdict0, atr) &&
Base.error("""'$(atr)' can't be used for property""")
y = atr == :exe ? x : isa(x, AbstNSitem) ? x : NSnoncst_item(x)
push!(ns.__code, NamedTuple{(:atr, :obj), Tuple{Symbol, Any}}((atr, y)))
inst = ns.__instances[1]
if ns.__parallel.obj
@sync for r in __divNn(length(inst), Threads.nthreads())
Threads.@spawn foreach(i -> Base.setproperty!(i.o, atr, x), inst[r])
end
else
foreach(i -> Base.setproperty!(i.o, atr, x), inst)
end
end
Base.haskey(ns::__NSX_CodeMode, key::Symbol) = key β propertynames(ns)
Base.propertynames(ns::__NSX_CodeMode, private::Bool=false) =
tuple(Base.keys(_NSdict0)..., Base.fieldnames(typeof(ns))...)
Base.hasproperty(ns::__NSX_CodeMode, atr::Symbol) =
Base.hasfield(typeof(ns), atr) || haskey(_NSdict0, atr)
Base.getproperty(ns::__NSX_CodeMode, atr::Symbol) =
begin
Base.hasfield(typeof(ns), atr) && (return Base.getfield(ns, atr))
haskey(_NSdict0, atr) && (return _NSdict0[atr](ns))
error("""This NS does not have a property named '$(atr)'.""")
end;
################
# __NSClsInstance{X}
################
struct __NSClsInstance{X} <: AbstNS
__dict::OrderedDict{Symbol, AbstNSitem}
__fix_lck::MVector{2, Bool}
cls::NS
__NSClsInstance{X}(cls) where X =
new{X}(#= __dict =# OrderedDict{Symbol, AbstNSitem}(),
#= __fix_lck =# MVector{2, Bool}(false, false),
#= cls =# cls)
end
Base.getproperty(nsi::__NSClsInstance, atr::Symbol) =
begin
Base.hasfield(typeof(nsi), atr) && (return Base.getfield(nsi, atr))
haskey(_NSdict0, atr) && (return _NSdict0[atr](nsi))
d = nsi.__dict
if haskey(d, atr)
x = d[atr].obj;
isa(x, Union{NSTagFunc{:prp}, NSTagFunc{:mth}}) && (return x(nsi))
isa(x, NSTagFunc{:fnc}) && (return x.fnc)
isa(x, NSTagFunc{:req}) &&
(y = x(nsi);
d[atr] = (isa(d[atr], NScst_item) ? NScst_item : NSnoncst_item)(y);
return y)
return x
else
haskey(nsi.cls, atr) && (return Base.getproperty(nsi.cls, atr))
error("""This NS does not have a property named '$(atr)'.""")
end
end
################
# >>, >>>
################
Base.:>>( g::AbstNS, h::AbstNS) = h.import(g)
Base.:>>>(g::AbstNS, h::AbstNS) = h.deepimport(g)
################
# genNSX, ns
################
# genNSX() = NSX{Symbol("NS_", string(bytes2hex(SHA.sha256(string(time_ns())))))}
genNSX() = NSX{gensym()}
genNSX(X) = NSX{X}
nsx() = genNSX()()
nsx(X) = genNSX(X)()
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] | 1.861032 | 3,857 |
<reponame>aerappa/Gridap.jl
module AlgebraTests
using Test
@testset "AlgebraInterfaces" begin include("AlgebraInterfacesTests.jl") end
@testset "SparseMatrixCSC" begin include("SparseMatrixCSCTests.jl") end
@testset "SparseMatrixCSR" begin include("SparseMatrixCSRTests.jl") end
@testset "SymSparseMatrixCSR" begin include("SymSparseMatrixCSRTests.jl") end
@testset "NonlinearOperators" begin include("NonlinearOperatorsTests.jl") end
@testset "NonlinearSolvers" begin include("NonlinearSolversTests.jl") end
@testset "NLSolvers" begin include("NLSolversTests.jl") end
@testset "LinearSolvers" begin include("LinearSolversTests.jl") end
end # module
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] | 2.933333 | 225 |
<reponame>frederikgeth/PowerModelsPrivacyPreserving.jl
"""
Creates Ohms constraints (yt post fix indicates that Y and T values are in rectangular form)
```
p[f_idx] == (g+g_fr)/tm*v[f_bus]^2 + (-g*tr+b*ti)/tm^2*(v[f_bus]*v[t_bus]*cos(t[f_bus]-t[t_bus])) + (-b*tr-g*ti)/tm^2*(v[f_bus]*v[t_bus]*sin(t[f_bus]-t[t_bus]))
q[f_idx] == -(b+b_fr)/tm*v[f_bus]^2 - (-b*tr-g*ti)/tm^2*(v[f_bus]*v[t_bus]*cos(t[f_bus]-t[t_bus])) + (-g*tr+b*ti)/tm^2*(v[f_bus]*v[t_bus]*sin(t[f_bus]-t[t_bus]))
```
"""
function constraint_ohms_from_variable_impedance(pm::_PM.AbstractACPModel, n::Int, f_bus, t_bus, f_idx, t_idx, g, b, g_fr, b_fr, tr, ti, tm)
(l,i,j) = f_idx
p_fr = _PM.var(pm, n, :p, f_idx)
q_fr = _PM.var(pm, n, :q, f_idx)
vm_fr = _PM.var(pm, n, :vm, f_bus)
vm_to = _PM.var(pm, n, :vm, t_bus)
va_fr = _PM.var(pm, n, :va, f_bus)
va_to = _PM.var(pm, n, :va, t_bus)
b = _PM.var(pm, n, :b, l)
g = _PM.var(pm, n, :g, l) # g = y*r
b_fr = _PM.var(pm, n, :b_shunt, l)
g_fr = _PM.var(pm, n, :g_shunt, l)
JuMP.@NLconstraint(pm.model, p_fr == (g+g_fr)/tm^2*vm_fr^2
+ (-g*tr+b*ti)/tm^2*(vm_fr*vm_to*cos(va_fr-va_to))
+ (-b*tr-g*ti)/tm^2*(vm_fr*vm_to*sin(va_fr-va_to)) )
JuMP.@NLconstraint(pm.model, q_fr == -(b+b_fr)/tm^2*vm_fr^2
- (-b*tr-g*ti)/tm^2*(vm_fr*vm_to*cos(va_fr-va_to))
+ (-g*tr+b*ti)/tm^2*(vm_fr*vm_to*sin(va_fr-va_to)) )
end
"""
Creates Ohms constraints (yt post fix indicates that Y and T values are in rectangular form)
```
p[t_idx] == (g+g_to)*v[t_bus]^2 + (-g*tr-b*ti)/tm^2*(v[t_bus]*v[f_bus]*cos(t[t_bus]-t[f_bus])) + (-b*tr+g*ti)/tm^2*(v[t_bus]*v[f_bus]*sin(t[t_bus]-t[f_bus]))
q[t_idx] == -(b+b_to)*v[t_bus]^2 - (-b*tr+g*ti)/tm^2*(v[t_bus]*v[f_bus]*cos(t[f_bus]-t[t_bus])) + (-g*tr-b*ti)/tm^2*(v[t_bus]*v[f_bus]*sin(t[t_bus]-t[f_bus]))
```
"""
function constraint_ohms_to_variable_impedance(pm::_PM.AbstractACPModel, n::Int, f_bus, t_bus, f_idx, t_idx, g, b, g_to, b_to, tr, ti, tm)
(l,i,j) = f_idx
p_to = _PM.var(pm, n, :p, t_idx)
q_to = _PM.var(pm, n, :q, t_idx)
vm_fr = _PM.var(pm, n, :vm, f_bus)
vm_to = _PM.var(pm, n, :vm, t_bus)
va_fr = _PM.var(pm, n, :va, f_bus)
va_to = _PM.var(pm, n, :va, t_bus)
# r = g/b
b = _PM.var(pm, n, :b, l)
g = _PM.var(pm, n, :g, l)
b_to = _PM.var(pm, n, :b_shunt, l)
g_to = _PM.var(pm, n, :g_shunt, l)
# g = y*r
JuMP.@NLconstraint(pm.model, p_to == (g+g_to)*vm_to^2
+ (-g*tr-b*ti)/tm^2*(vm_to*vm_fr*cos(va_to-va_fr))
+ (-b*tr+g*ti)/tm^2*(vm_to*vm_fr*sin(va_to-va_fr)) )
JuMP.@NLconstraint(pm.model, q_to == -(b+b_to)*vm_to^2
- (-b*tr+g*ti)/tm^2*(vm_to*vm_fr*cos(va_to-va_fr))
+ (-g*tr-b*ti)/tm^2*(vm_to*vm_fr*sin(va_to-va_fr)) )
end
"""
Defines the faithfullness in terms of grid losses
|actual_loss - reference_loss|/(reference_loss) <= beta
we split this in two constraints to deal with the absolute value
|x| <= y iff x <=y and -x<=y
"""
function constraint_loss_faithfulness(pm::_PM.AbstractPowerModel, n::Int, ref_loss, beta)
arcs_from = _PM.ref(pm, :arcs_from)
p = _PM.var(pm, :p)
loss = sum(p[(l,i,j)] + p[(l,j,i)] for (l,i,j) in arcs_from)
JuMP.@constraint(pm.model, (loss - ref_loss)/(ref_loss) <= beta)
JuMP.@constraint(pm.model, (ref_loss - loss)/(ref_loss) <= beta)
end
function constraint_cost_faithfulness(pm::_PM.AbstractPowerModel, n::Int, ref_cost, beta)
cost = _PM.var(pm, :cost)
JuMP.@constraint(pm.model, (cost - ref_cost)/(ref_cost) <= beta)
JuMP.@constraint(pm.model, (ref_cost - cost)/(ref_cost) <= beta)
end
"Supporting only quadratic cost functions for generators breaks compatibility with Matpower to some extent"
function constraint_fuel_cost_quadratic(pm::_PM.AbstractPowerModel)
gen_cost = Dict()
for (n, nw_ref) in _PM.nws(pm)
for (i,gen) in nw_ref[:gen]
pg = sum( _PM.var(pm, n, :pg, i)[c] for c in _PM.conductor_ids(pm, n) )
if length(gen["cost"]) == 1
gen_cost[(n,i)] = gen["cost"][1]
elseif length(gen["cost"]) == 2
gen_cost[(n,i)] = gen["cost"][1]*pg + gen["cost"][2]
elseif length(gen["cost"]) == 3
gen_cost[(n,i)] = gen["cost"][1]*pg^2 + gen["cost"][2]*pg + gen["cost"][3]
else
gen_cost[(n,i)] = 0.0
end
end
end
cost = _PM.var(pm, :cost)
JuMP.@constraint(pm.model, cost ==
sum(
sum( gen_cost[(n,i)] for (i,gen) in nw_ref[:gen] )
for (n, nw_ref) in _PM.nws(pm))
)
end
function constraint_gen_bounds_cc(pm::_PM.AbstractPowerModel, n::Int, i, pmin, pmax, qmin, qmax, eta)
pg = _PM.var(pm, n, :pg, i)
qg = _PM.var(pm, n, :qg, i)
d = Distributions.Normal()
z = Distributions.quantile(d,eta)
# JuMP.@constraint(pm.model, (cost - ref_cost)/(ref_cost) <= beta)
end
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] | 1.811918 | 2,685 |
#=
ellipse_sino.jl
2019-07-12, <NAME>
2019-07-13, <NAME>, refactor to use sg.grid
=#
export ellipse_sino
#using MIRT: sino_geom, SinoGeom, downsample2
"""
`sino = ellipse_sino(sg, ells ; oversample=1, xscale=1, yscale=1)`
Create sinogram projection of one or more ellipses.
Works for any sinogram geometry.
in
- `sg::SinoGeom` sinogram geometry object from `sino_geom()`
- `ells::Matrix` `[ne 6]` ellipse parameters
* `[centx centy radx rady angle_degrees amplitude]`
options
- `oversample::Int` oversampling factor for emulating "strips"
* default 1: just 1 ray per detector element
- `xscale::Int` use -1 to flip in x (not recommended); default 1
- `yscale::Int` use -1 to flip in y (not recommended); default 1
out
- `sino` `[nb na]` sinogram
To get the sample locations, use `(pos,ang) = sg.grid`
"""
function ellipse_sino(
sg::SinoGeom,
ells::AbstractMatrix{<:Real} ;
oversample::Int = 1,
kwargs...,
)
sg = sg.over(oversample)
(rg, Οg) = sg.grid
sino = ellipse_sino(rg, Οg, ells ; kwargs...)
if oversample > 1
sino = downsample2(sino, (oversample, 1))
end
return sino
end
"""
`sino = ellipse_sino(rg::AbstractArray{<:Real}, Οg::AbstractArray{<:Real},
ells ; oversample=1, xscale=1, yscale=1)`
Create sinogram projection of one or more ellipses,
for arbitrary radial/angular sampling grid locations `(rg, Οg)`
in
- `rg::AbstractArray{<:Real}` radial sampling locations
- `Οg::AbstractArray{<:Real}` angular sampling locations (radians)
- `ells::Matrix` `[ne 6]` ellipse parameters
* `[centx centy radx rady angle_degrees amplitude]`
options
- `xscale::Int` use -1 to flip in x (not recommended); default 1
- `yscale::Int` use -1 to flip in y (not recommended); default 1
out
- `sino::AbstractArray{Float32}` same size as `rg` and `Οg`
"""
function ellipse_sino(
rg::AbstractArray{<:Real},
Οg::AbstractArray{<:Real},
ells::AbstractMatrix{<:Real} ;
xscale::Int = 1,
yscale::Int = 1,
)
size(ells,2) != 6 && throw("6 parameters per ellipse")
size(rg) != size(Οg) && throw("rg and Οg size mismatch")
sino = zeros(Float32, size(rg))
cangs = cos.(Οg)
sangs = sin.(Οg)
#loop over ellipses
#ticker reset
ne = size(ells, 1)
for ie in 1:ne
#ticker(mfilename, ie, ne)
ell = ells[ie, :]
cx = ell[1] * xscale
cy = ell[2] * yscale
rx = ell[3]
ry = ell[4]
(rx <= 0) || (ry <= 0) && throw("need positive radii")
eang = deg2rad(ell[5])
val = ell[6]
if yscale == -1
eang = -eang
end
if xscale == -1
eang = pi - eang
end
scale = 2 * val * rx * ry
# square of projected radius:
rp2 = @. (rx * (cangs * cos(eang) + sangs * sin(eang)))^2 +
(ry * (sangs * cos(eang) - cangs * sin(eang)))^2
sp = cx * cangs + cy * sangs # radial shift
dis2 = (rg - sp).^2 # square of distances from center
@. sino += scale / rp2 * sqrt(max(rp2 - dis2, 0))
end
return sino
end
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] | 2.370675 | 1,214 |
module Car
export nb_months
function nb_months(startPriceOld, startPriceNew, savingperMonth, percentLossByMonth)
priceDelta = startPriceNew-startPriceOld
lossFactor = 1 - percentLossByMonth/100
saving = 0
month = 0
while saving < priceDelta
month += 1
saving += savingperMonth
priceDelta *= lossFactor
if isodd(month)
lossFactor -= 0.5/100
end
end
[month, round(Int,saving-priceDelta)]
end
end | [
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] | 2.122047 | 254 |
<reponame>jlchan/EntropyStableEuler.jl<filename>test/runtests.jl<gh_stars>1-10
using EntropyStableEuler
using Test
using StaticArrays
@testset "Logmean tests" begin
uL,uR = 1,2
@test logmean(uL,uR) == logmean(uL,uR,log(uL),log(uR))
@test logmean(uL,uR) == logmean(uR,uL)
@test logmean(uL,uL) β uL
end
function init_prim(d)
if d==1
rho,u,p = 1,.1,2
Q = (rho,u,p)
elseif d==2
rho,u,v,p = 1,.1,.2,2
Q = (rho,u,v,p)
elseif d==3
rho,u,v,w,p = 1,.1,.2,.3,2
Q = (rho,u,v,w,p)
end
return Q
end
@testset "Tests for d = $d" for d in (1:3)
@testset "Entropy variable tests" begin
U = prim_to_cons(Euler{d}(),init_prim(d))
V = v_ufun(Euler{d}(),U)
h = 1e-7
central_diff(f,x) = (f(x+h) - f(x-h))/(2*h)
swapentry(x,y,i) = (x[begin:i-1]...,y,x[i+1:end]...)
for j = 1:d+2
@test abs(V[j] - central_diff(x->Sfun(Euler{d}(),swapentry(U,x,j)),U[j])) < h
end
UV = u_vfun(Euler{d}(),V)
@test all(UV .β U)
end
@testset "Symmetry" begin
# test symmetry
UL = prim_to_cons(Euler{d}(),init_prim(d))
UR = prim_to_cons(Euler{d}(),init_prim(d).*1.1)
FLR = fS(Euler{d}(),UL,UR)
FRL = fS(Euler{d}(),UR,UL)
@test all(FLR .β FRL)
end
@testset "Consistency" begin
Q = init_prim(d)
U = prim_to_cons(Euler{d}(),Q)
F = fS(Euler{d}(),U,U)
p = pfun(Euler{d}(),U)
if d==1
rho,rhou,E = U
u = rhou./rho
Fexact = SVector{3}(rho*u, rho*u^2 + p, u*(E+p))
elseif d==2
rho,rhou,rhov,E = U
u,v = rhou./rho, rhov./rho
Fx = SVector{4}(rho*u, rho*u^2 + p, rho*u*v, u*(E+p))
Fy = SVector{4}(rho*v, rho*u*v, rho*v^2 + p, v*(E+p))
Fexact = (Fx,Fy)
elseif d==3
rho,rhou,rhov,rhow,E = U
u,v,w = rhou./rho, rhov./rho, rhow./rho
Fx = SVector{5}(rho*u, rho*u^2 + p, rho*u*v, rho*u*w, u*(E+p))
Fy = SVector{5}(rho*v, rho*u*v, rho*v^2 + p, rho*v*w, v*(E+p))
Fz = SVector{5}(rho*w, rho*u*w, rho*v*w, rho*w^2 + p, w*(E+p))
Fexact = (Fx,Fy,Fz)
end
@test all(F .β Fexact)
end
@testset "Entropy conservation property" begin
Ξ³ = Euler{d}().Ξ³
UL = prim_to_cons(Euler{d}(),init_prim(d))
UR = prim_to_cons(Euler{d}(),init_prim(d).*1.1)
VL = v_ufun(Euler{d}(),UL)
VR = v_ufun(Euler{d}(),UR)
Ο(U) = (Ξ³-1).*U[2:d+1]
F = fS(Euler{d}(),UL,UR)
vTF(VL,VR,F) = sum((VL .- VR).*F)
for j = 1:d
if d==1
@test vTF(VL,VR,F) β Ο(UL)[j]-Ο(UR)[j]
else
@test vTF(VL,VR,F[j]) β Ο(UL)[j]-Ο(UR)[j]
end
end
end
@testset "Type stability tests" begin
Q = init_prim(d)
U = prim_to_cons(Euler{d}(),Q)
V = v_ufun(Euler{d}(),U)
@inferred prim_to_cons(Euler{d}(),Q)
@inferred cons_to_prim_beta(Euler{d}(),U)
@inferred v_ufun(Euler{d}(),U)
@inferred u_vfun(Euler{d}(),V)
@inferred fS(Euler{d}(),U,U)
end
end
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] | 1.560976 | 2,091 |
# The format of simplegraph files is as follows:
# a one line header: <num_vertices>, <num_edges>, {"d" | "u"}, <name>
# - num_vertices is an integer
# - num_edges is an integer
# - "d" for directed graph, "u" for undirected. Note that this
# option does not perform any additional edge construction; it's
# merely used to return the correct type of graph.
# - name is a string
# header followed by a list of (comma-delimited) edges - src,dst.
# Multiple graphs may be present in one file.
function _lg_read_one_graph(f::IO, n_v::Integer, n_e::Integer, directed::Bool)
if directed
g = DiGraph(n_v)
else
g = Graph(n_v)
end
for i = 1:n_e
line = chomp(readline(f))
if length(line) > 0
src_s, dst_s = split(line,r"\s*,\s*")
src = parse(Int, src_s)
dst = parse(Int, dst_s)
add_edge!(g, src, dst)
end
end
return g
end
function _lg_skip_one_graph(f::IO, n_e::Integer)
for _ in 1:n_e
readline(f)
end
end
"""Returns a dictionary of (name=>graph) loaded from file `fn`."""
function loadlg_mult(io::IO)
graphs = Dict{AbstractString, SimpleGraph}()
while !eof(io)
line = strip(chomp(readline(io)))
if startswith(line,"#") || line == ""
next
else
nvstr, nestr, dirundir, graphname = split(line, r"s*,s*", limit=4)
n_v = parse(Int, nvstr)
n_e = parse(Int, nestr)
dirundir = strip(dirundir)
graphname = strip(graphname)
directed = !(dirundir == "u")
g = _lg_read_one_graph(io, n_v, n_e, directed)
graphs[graphname] = g
end
end
return graphs
end
function loadlg(io::IO, gname::AbstractString)
while !eof(io)
line = strip(chomp(readline(io)))
(startswith(line,"#") || line == "") && continue
nvstr, nestr, dirundir, graphname = split(line, r"s*,s*", limit=4)
n_v = parse(Int, nvstr)
n_e = parse(Int, nestr)
graphname = strip(graphname)
if gname == graphname
dirundir = strip(dirundir)
directed = !(dirundir == "u")
return _lg_read_one_graph(io, n_v, n_e, directed)
else
_lg_skip_one_graph(io, n_e)
end
end
error("Graph $gname not found")
end
"""Writes a graph `g` with name `graphname` in a proprietary format
to the IO stream designated by `io`.
Returns 1 (number of graphs written).
"""
function savelg(io::IO, g::SimpleGraph, gname::AbstractString)
# write header line
dir = is_directed(g)? "d" : "u"
line = join([nv(g), ne(g), dir, gname], ",")
write(io, "$line\n")
# write edges
for e in edges(g)
write(io, "$(src(e)),$(dst(e))\n")
end
return 1
end
"""Writes a dictionary of (name=>graph) to a file `fn`,
with default `GZip` compression.
Returns number of graphs written.
"""
function savelg_mult(io::IO, graphs::Dict)
ng = 0
for (gname, g) in graphs
ng += savelg(io, g, gname)
end
return ng
end
# savelg(io::IO, g::SimpleGraph, n::AbstractString) =
# savelg_mult(io, Dict(n=>g))
# write(g::Graph, fn::AbstractString; compress::Bool=true) = write(g, "graph", fn; compress=compress)
# write(g::DiGraph, fn::AbstractString; compress::Bool=true) = write(g, "digraph", fn; compress=compress)
filemap[:lg] = (loadlg, loadlg_mult, savelg, savelg_mult)
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] | 2.168342 | 1,592 |
<gh_stars>1-10
module Truss
using LinearAlgebra
using Plots
using GraphRecipes
export define, solve, show
"""
truss_object
This object holds all the definitions (material properties, section properties, geometry) and the solution (displacements, reactions, internal forces) for a system of truss elements.
num_dof_per_node::Int64
members::Array{NTuple{4,Int64},1}
node_geometry::Array{Float64,2}
section_properties::Array{Float64,1}
material_properties::Array{Float64,1}
supports::Array{Int64,1}
external_forces::Array{Float64,1}
L::Array{Float64,1}
E::Array{Float64,1}
A::Array{Float64,1}
ΞΈ::Array{Float64,1}
K::Array{Float64,2}
Kff::Array{Float64,2}
Ksf::Array{Float64,2}
Kfs::Array{Float64,2}
Kss::Array{Float64,2}
F::Array{Float64,1}
Fs::Array{Float64,1}
Ff::Array{Float64,1}
s::Array{Int64,1}
f::Array{Int64,1}
T::Array{Array{Float64,2},1}
k_element_local::Array{Array{Float64,2},1}
k_element_global::Array{Array{Float64,2},1}
u::Array{Float64,1}
uf::Array{Float64,1}
f_element::Array{Array{Float64,1},2}
"""
mutable struct truss_object
num_dof_per_node::Int64
members::Array{NTuple{4,Int64},1}
node_geometry::Array{Float64,2}
section_properties::Array{Float64,1}
material_properties::Array{Float64,1}
supports::Array{Int64,1}
external_forces::Array{Float64,1}
L::Array{Float64,1}
E::Array{Float64,1}
A::Array{Float64,1}
ΞΈ::Array{Float64,1}
K::Array{Float64,2}
Kff::Array{Float64,2}
Ksf::Array{Float64,2}
Kfs::Array{Float64,2}
Kss::Array{Float64,2}
F::Array{Float64,1}
Fs::Array{Float64,1}
Ff::Array{Float64,1}
s::Array{Int64,1}
f::Array{Int64,1}
T::Array{Array{Float64,2},1}
k_element_local::Array{Array{Float64,2},1}
k_element_global::Array{Array{Float64,2},1}
u::Array{Float64,1}
uf::Array{Float64,1}
f_element::Array{Array{Float64,1},2}
#This allows for the construction of a new object.
truss_object() = new()
end
"""
calculate_truss_element_lengths(members, node_geometry)
Calculate the truss member lengths in a structural system.
`members` is a NTuple{4, Int64} containing node i, node j, A, E assignments
`node_geometry` is array with the global x-y coordinates
"""
function calculate_truss_element_lengths(members, node_geometry)
#Initialize array to hold truss lengths.
truss_lengths=zeros(Float64, length(members))
#Loop over each truss element.
for i=1:length(members)
#Define truss node numbers.
node_i=members[i][1]
node_j=members[i][2]
#Define truss node coordinates.
node_i_xy=node_geometry[node_i,:]
node_j_xy=node_geometry[node_j,:]
#Calculate the truss element length.
truss_lengths[i] =norm(node_i_xy - node_j_xy)
end
return truss_lengths
end
"""
define_local_truss_element_stiffness_matrix(E, A, L)
Define a truss element stiffness matrix in its local coordinate system.
`E` is elastic modulus.
`A` is the cross-sectional area.
`L` is truss length.
There are 4 degrees of freedom for the truss element.
2 4
1>^--------------->3^
"""
function define_local_truss_element_stiffness_matrix(E, A, L)
k_truss_element_local = E * A/ L * [1 0 -1 0
0 0 0 0
-1 0 1 0
0 0 0 0]
return k_truss_element_local
end
"""
define_truss_element_orientations(members, node_geometry)
Define the truss element orientation angle.
`members` is a NTuple{4, Int64} containing node i, node j, A, E assignments
`node_geometry` is array with the global x-y coordinates
A positive angle is defined counterclockwise from the horizon, rotating about node i.
"""
function define_truss_element_orientations(members, node_geometry)
# Initialize the orientation array for all truss elements in a structural system.
ΞΈ = zeros(Float64, length(members))
# Loop over the truss elements.
for i=1:length(members)
point_i = node_geometry[members[i][1], :]
point_j = node_geometry[members[i][2], :]
element_vector = point_j - point_i
#Calculate the orientation angle.
#https://en.wikipedia.org/wiki/Atan2
ΞΈ[i] = atan(element_vector[2], element_vector[1])
end
return ΞΈ
end
"""
vector_rotation_operator(ΞΈ)
Define an operator matrix that converts truss element displacements to a new, rotated coordinate system.
`ΞΈ` is the orientation angle of a truss element.
A positive angle is defined counterclockwise from the horizon, rotating about node i.
"""
function vector_rotation_operator(ΞΈ)
T = [cos(ΞΈ) sin(ΞΈ) 0 0
-sin(ΞΈ) cos(ΞΈ) 0 0
0 0 cos(ΞΈ) sin(ΞΈ)
0 0 -sin(ΞΈ) cos(ΞΈ)]
return T
end
"""
assign_member_properties(members, member_property, property_order, property_type)
Define an array containing the member property values for each element in a structural system. For example, define all the cross-sectional areas for all the members in a truss.
`members` is a NTuple{4, Int64} containing node i, node j, A, E assignments.
`member_property` is an array containing all the available properties that a member can take on.
`property order` is the index in the `members` NTuple where the specific property is assigned.
"""
function assign_member_properties(members, member_property, property_order, property_type)
#Initialize the property array
property = zeros(Float64, length(members))
#Loop over all the members.
for i=1:length(members)
#Assign a specific property (area, elastic modulus) to each element in a structural system.
property[i] = member_property[members[i][property_order]][property_type]
end
return property
end
"""
calculate_global_element_stiffness_matrix(k_element_local, ΞΈ)
Transform a local element stiffness matrix to global coordinates.
`k_element_local` is the element stiffness matrix in its local coordinate system.
`ΞΈ` is the orientation angle of the element.
"""
function calculate_global_element_stiffness_matrix(k_element_local, T)
#Transform the local element stiffness matrix to global coordinates.
k_element_global = T' * k_element_local * T
return k_element_global
end
"""
assemble_global_stiffness_matrix(node_geometry, members, k_element_global, num_dof_per_node)
Assemble all the element stiffness matrices into the global system stiffness matrix.
`node_geometry` is an array with the global node x-y coordinates.
`members` is a NTuple{4, Int64} containing node i, node j, and property assignments.
`k_element_global` are all the element stiffness matrices in a 3D array.
`num_dof_per_node` defines the number of degrees of freedom per element node. For a truss, this is 2, for a beam it is 3, ...
"""
function assemble_global_stiffness_matrix(node_geometry, members, k_element_global, num_dof_per_node)
num_nodes = size(node_geometry)[1]
k_system_global = zeros(Float64, num_nodes * num_dof_per_node, num_nodes * num_dof_per_node)
for i=1:length(members)
node_i = members[i][1]
node_j = members[i][2]
node_i_dof = [1;2] .+ (node_i - 1) * num_dof_per_node
node_j_dof = [1;2] .+ (node_j - 1) * num_dof_per_node
global_dof = [node_i_dof; node_j_dof]
k_system_global[global_dof, global_dof] = k_system_global[global_dof, global_dof] + k_element_global[i]
end
return k_system_global
end
"""
define(members, section_properties, material_properties, node_geometry, supports, external_forces)
Define and partition the global system stiffness matrix and external force vector.
`members` is a NTuple{4, Int64} containing node i, node j, and property assignments.
'section_properties' defines a library of section properties.
'material_properties' defines a library of material properties.
`node_geometry` is an array with the global node x-y coordinates.
`supports` defines the degrees of freedom that are free (0) and fixed(1).
`external_forces` is an array of the external forces applied to the nodes, in global coordinates.
Partition the stiffness matrix into Kff (free free), Kfs (free supported), Ksf (supported free), and Kss (supported supported). Partition the external forces at the free dof into Ff. Also return `f` array of free global degrees of freedom, `s` fixed global degrees of freedom, `T` all the rotation matrices for each truss element, and the `k_element_local` and `k_element_global` matrices.
"""
function define(members, section_properties, material_properties, node_geometry, supports, external_forces)
truss = truss_object()
truss.members = members
truss.section_properties = section_properties
truss.material_properties = material_properties
truss.node_geometry = node_geometry
truss.supports = supports
num_nodes = size(node_geometry)[1]
truss.external_forces = zeros(Float64, num_nodes * 2)
truss.external_forces .= external_forces
#Define the number of degrees of freedom per node.
truss.num_dof_per_node = 2
#Calculate truss member lengths.
truss.L = calculate_truss_element_lengths(members, node_geometry)
#Calculate truss member orientations.
truss.ΞΈ = define_truss_element_orientations(members, node_geometry)
#Define truss properties for stiffness matrix calculations.
truss.A = assign_member_properties(members, section_properties, 3, 1)
truss.E = assign_member_properties(members, material_properties, 4, 1)
#Calculate the local stiffness matrix for each element.
truss.k_element_local = define_local_truss_element_stiffness_matrix.(truss.A, truss.E, truss.L)
#Define rotation matrix for each element.
truss.T = vector_rotation_operator.(truss.ΞΈ)
#Rotate local stiffness matrix into global coordinates.
truss.k_element_global = calculate_global_element_stiffness_matrix.(truss.k_element_local, truss.T)
#Assemble the global stiffness matrix.
truss.K = assemble_global_stiffness_matrix(node_geometry, members, truss.k_element_global, truss.num_dof_per_node)
#Define free degrees of freedom.
truss.f = findall(x-> x==0, supports)
#Define number of free dof.
num_free_dof = length(truss.f)
#Define fixed degrees of freedom.
truss.s = findall(x-> x==1, supports)
#Define number of fixed dof.
num_fixed_dof = length(truss.s)
#Initialize partitioned stiffness matrices.
truss.Kff = zeros(Float64, (num_free_dof, num_free_dof))
truss.Ksf = zeros(Float64, (num_fixed_dof, num_free_dof))
truss.Kfs = zeros(Float64, (num_free_dof, num_fixed_dof))
truss.Kss = zeros(Float64, (num_fixed_dof, num_fixed_dof))
#Partition the global stiffness matrix.
truss.Kff .= truss.K[truss.f, truss.f]
truss.Ksf .= truss.K[truss.s, truss.f]
truss.Kfs .= truss.K[truss.f, truss.s]
truss.Kss .= truss.K[truss.s, truss.s]
#Partition the external force vectors.
truss.F = external_forces
truss.Ff = zeros(Float64, length(truss.f))
truss.Ff .= truss.F[truss.f]
return truss
end
"""
solve(K, Kff, Ksf, F, Ff, s, f, T, k_element_local)
Solve for system displacements, reactions, and internal forces.
`K` is the global stiffness matrix.
'Kff' is the partitioned portion of the global stiffness matrix, free by free.
'Ksf' is the partitioned portion of the global stiffness matrix, support by free.
`F` is an array of the external forces.
`s` is an array of the fixed degrees of freedom.
`f` is an array of the support degrees of freedom.
`T` are all the rotation matrices for each element.
`k_element_local` are all the element local stiffness matrices.
Calculate the global displacement vector `uf` for the free degrees of freedom, provide the full global displacement vector `u`, and calculate all the element internal forces as an array of arrays `f_element`.
"""
function solve(truss)
#Define the total number of degrees of freedom in the system.
num_dof = size(truss.K)[1]
#Initialize the global displacement vector.
truss.u = zeros(Float64, num_dof)
#Calculate the global displacements at the free degrees of freedom.
truss.uf = truss.Kff^-1 * truss.Ff
#Insert uf into u.
truss.u[truss.f] .= truss.uf
#Calculate the global system reactions.
truss.Fs = truss.Ksf * truss.uf
#Insert Fs into F.
truss.F[truss.s] .= truss.Fs
#Define the number of elements.
num_elem = length(truss.A)
#Initialize an array of element force vectors.
truss.f_element = fill(Float64[], num_elem, 1)
#Calculate the internal forces in each element.
for i = 1:num_elem
#Define node i and j for an element.
node_i = truss.members[i][1]
node_j = truss.members[i][2]
#Calculate the global dof numbers at each element nodes i and j.
node_i_dof = [1;2] .+ (node_i - 1) * truss.num_dof_per_node
node_j_dof = [1;2] .+ (node_j - 1) * truss.num_dof_per_node
#Assemble all the global dof numbers for the element.
global_dof = [node_i_dof; node_j_dof]
#Find the element displacements in global coordinates.
u_element_global = zeros(Float64, length(global_dof))
u_element_global .= truss.u[global_dof]
#Transform the element displacements into local coordinates.
u_element_local = truss.T[i] * u_element_global
#Calculate the element local forces.
truss.f_element[i] = truss.k_element_local[i] * u_element_local
end
return truss
end
function show(truss_object, scale_x, scale_y)
Ξ΄x = truss_object.u[1:2:end]
Ξ΄y = truss_object.u[2:2:end]
num_nodes = floor(Int, length(truss_object.u)/2)
g = zeros(Int64, (num_nodes, num_nodes))
start_nodes = [x[1] for x in truss_object.members]
end_nodes = [x[2] for x in truss_object.members]
for i = 1:num_nodes
index = findall(x->x==i, start_nodes)
g[i, end_nodes[index]] .= 1
end
graphplot(g,
x=truss_object.node_geometry[:,1], y=truss_object.node_geometry[:,2],
nodeshape=:circle, nodesize=2,
axis_buffer=0.01,
curves=false,
color=:red,
linewidth=0.5,
arrowlengthfrac=0)
graphplot!(g,
x=truss_object.node_geometry[:,1] .+ Ξ΄x .* scale_x, y=truss_object.node_geometry[:,2] .+ Ξ΄y .* scale_y,
nodeshape=:circle, nodesize=2,
axis_buffer=0.01,
curves=false,
color=:black,
linewidth=2,
arrowlengthfrac=0)
quiver!(truss_object.node_geometry[:,1],truss_object.node_geometry[:,2],quiver=(truss_object.external_forces[1:2:end]./10, truss_object.external_forces[2:2:end]./10))
return current()
end
end #module
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628,
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] | 2.559896 | 5,760 |
<gh_stars>0
using Test
using NSGAII
include("./cornercases.jl")
@testset "Domination" begin
L=[[2,1,1],[1,2,2],[1,0,0]]
fitness(x::FloatIndividual)=0
cfg=Cambrian.get_config("./test.yaml";n_population= 3,n_offsprings=5,d_fitness=3)
e=NSGA2Evolution{FloatIndividual}(cfg,fitness)
for i in 1:3
copyto!(e.population[i].fitness,L[i])
end
@test dominates(e,e.population[1],e.population[2])==false
@test dominates(e,e.population[2],e.population[1])==false
@test dominates(e,e.population[1],e.population[3])==true
@test dominates(e,e.population[3],e.population[1])==false
end
@testset "fastNonDominatedSort" begin
fitness1,ranks1=pop1D()
fitness2,ranks2=pop3D()
fitness(x::FloatIndividual)=0
cfg=Cambrian.get_config("./test.yaml";n_population= 20,n_offsprings=5,d_fitness=1)
e=NSGA2Evolution{FloatIndividual}(cfg,fitness)
for i in 1:20
copyto!(e.population[i].fitness,fitness1[i])
end
oldPop1D=copy(e.population)
fastNonDominatedSort!(e)
@test length(e.population)==length(oldPop1D)
for x in oldPop1D
@test x in e.population
end
for i in 1:e.config.n_population
@test ranks1[i]==e.rank[objectid(e.population[i])]
end
cfg=Cambrian.get_config("./test.yaml";n_population= 16,n_offsprings=5,d_fitness=3)
e=NSGA2Evolution{FloatIndividual}(cfg,fitness)
for i in 1:16
copyto!(e.population[i].fitness,fitness2[i])
end
oldPop3D=copy(e.population)
fastNonDominatedSort!(e)
@test length(e.population)==length(oldPop3D)
for x in oldPop3D
@test x in e.population
end
for i in 1:e.config.n_population
@test ranks2[i]==e.rank[objectid(e.population[i])]
end
end
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] | 2.160248 | 805 |
<filename>src/gp_de.jl<gh_stars>1-10
export SparseGP, GPmodel, GPODE
export samplegp, gpmeanvar
# https://github.com/SciML/DiffEqFlux.jl/blob/c59971fd4d3ee84aff39f88b7073d7e8cf51c34c/src/neural_de.jl#L38
abstract type TrajType end
struct SampleTraj <: TrajType end
struct MeanTraj <: TrajType end
abstract type SparseGPMethod end
struct FITC <: SparseGPMethod end
struct VLB <: SparseGPMethod end
struct Vanilla <: SparseGPMethod end
###
# Struct that contains everything needed for prediction
###
struct SparseGP{K, T<:Real, N, A<: NTuple{N, Array{<:Array{<:Real,1},1}}, M<:SparseGPMethod}
kernel::K
Ο_n::T
inP::A # inducing points
mean::Function
trafo::Function
method::M
# type? FITC, SOR, PITC
end
#ToDo: Update Ο_n in this object
function zeromean(n)
return x -> fill(0, n)
end
identitytrafo(x) = x
function SparseGP(kernel, Z, U; Ο_n = 1e-6, mean = zeromean(length(U[1])), trafo = identitytrafo, method = Vanilla())
indP = (trafo.(Z), U)
N = length(indP)
SparseGP{typeof(kernel), typeof(Ο_n), N, typeof(indP), typeof(method)}(kernel, Ο_n, indP, mean, trafo, method)
end
function SparseGP(kernel, Z, X, Y; Ο_n = 1e-6, mean = zeromean(length(Y[1])), trafo = identitytrafo, method = FITC())
indP = (trafo.(Z), trafo.(X), Y)
N = length(indP)
SparseGP{typeof(kernel), typeof(Ο_n), N, typeof(indP), typeof(method)}(kernel, Ο_n, indP, mean, trafo, method)
end
function (sgp::SparseGP)(x::T) where T <: Real
Z = sgp.inP[1]
ker = sgp.kernel
Kx = kernelmatrix(ker, [[x]], Z)
end
function (sgp::SparseGP)(x::Array{T,1}) where T <: Real
Z = sgp.inP[1]
ker = sgp.kernel
Kx = kernelmatrix(ker, [x], Z)
end
###
# GP model, that contains the sparse GP object with all necessary data, and
struct GPmodel{SGP <: SparseGP, T<:Real}
sgp::SGP
KinvU::Array{T,2}
Ξ£::Array{T,2}
end
# maybe with data
function GPmodel(sgp::SparseGP)
KiU, Ξ£ = computeKinvU(sgp)
GPmodel(sgp, KiU, Ξ£)
end
function (gpm::GPmodel)(x, traj::MeanTraj = MeanTraj())
Kx = gpm.sgp(gpm.sgp.trafo(x))
ΞΌ = gpm.sgp.mean
return (ΞΌ(x) .+ (Kx * gpm.KinvU)[:])
end
function (gpm::GPmodel)(x, traj::SampleTraj)
Kx = gpm.sgp(gpm.sgp.trafo(x))
ΞΌ = gpm.sgp.mean
m = (ΞΌ(x) .+ (Kx * gpm.KinvU))[:]
Ξ£ = gpm.Ξ£
std = sqrt.(diag(Kx * (Ξ£ \ Kx')))
# var = diag(Kx*Kuu*Kx')
s = randn(length(x)) .* std
return (m .+ s)
end
# this can almost certainly be removed. might be interesting for comparison purposes
function (gpm::GPmodel)(xv::MS) where MS <: Array{<:Measurement{<:Real}, 1}
x = getfield.(xv, :val)
Sigma = diagm(getfield.(xv, err))
Kx = gpm.sgp(gpm.sgp.trafo(x))
ΞΌ = gpm.sgp.mean
m = (ΞΌ(x) .+ (Kx * gpm.KinvU))[:]
Ξ£ = gpm.Ξ£
var = sqrt.(diag(Kx * (Ξ£ \ Kx')))
# var = diag(Kx*Kuu*Kx')
return (m .Β± var)
end
####
# functions to facilitate efficient computation, as per Q-C&R
function computeKinvU(sgp::SparseGP)
return _computeKinvU(sgp, sgp.inP, sgp.method)
end
function _computeKinvU(sgp::SparseGP, indP::NTuple{2, Array{<:Array{<:Real,1},1}}, method::M) where M<:SparseGPMethod
Z = indP[1]
U = indP[2]
Ο_n = sgp.Ο_n
ΞΌ = sgp.mean
U = U .- ΞΌ.(Z)
# vU = reduce(vcat, U)
# ToDo: might have to make output(?) dimensions more explicit
vU = reshape(reduce(vcat, U), :, length(Z)*length(Z[1]))
vU = permutedims(vU)
ker = sgp.kernel
K = kernelmatrix(ker, Z) + Ο_n * I
KinvU = K \ vU
return (KinvU, K)
end
function _computeKinvU(sgp::SparseGP, indP::NTuple{3, Array{<:Array{<:Real,1},1}}, method::M) where M <: SparseGPMethod
Z = indP[1]
X = indP[2]
Y = indP[3]
ΞΌ = sgp.mean
Y = Y .- ΞΌ.(X)
ker = sgp.kernel
Kff = kernelmatrix(ker, X)
Kfu = kernelmatrix(ker, X, Z)
Kuu = kernelmatrix(ker, Z)
Qff = Kfu * ( Kuu \ Kfu' )
noise = sgp.Ο_n
Ξ = _computelambda(Kff, Qff, noise, method)
Ξ£ = _computesigma(Kuu, Kfu, Ξ)
vY = reshape(reduce(vcat, Y), :, length(X)*length(X[1]))
vY = permutedims(vY)
KinvU = Ξ£ \ (Kfu' * (Ξ \ vY))
return (KinvU, Ξ£)
end
function _computesigma(Kuu, Kfu, Ξ)
return Kuu + Kfu' * (Ξ \ Kfu)
end
function _computelambda(Kff, Qff, noise, method::VLB)
return noise * I
end
function _computelambda(Kff, Qff, noise, method::FITC)
return Diagonal(diag( Kff - Qff) .+ noise)
end
#####
# complete GPODE construct
#####
basic_tgrad(u,p,t) = zero(u)
struct GPODE{M<:GPmodel,T,A,K,TT<: TrajType} #<: NeuralDELayer
model::M
# p::P, parameters, maybe one day
tspan::T
args::A
kwargs::K
trajtype::TT
function GPODE(model::GPM,tspan,args...; sample = false, kwargs...) where GPM <: GPmodel
tt = sample ? SampleTraj() : MeanTraj()
new{typeof(model),typeof(tspan),typeof(args),typeof(kwargs), typeof(tt)}(
model,tspan,args,kwargs,tt)
end
end
function GPODE(sgp::SGP,tspan,args...;sample::Bool=false, kwargs...) where SGP <: SparseGP
gpm = GPmodel(sgp)
return GPODE(gpm,tspan,args..., sample = sample, kwargs...)
end
function (gp::GPODE)(x0)
dudt_(u,p,t) = gp.model(u, gp.trajtype)
ff = ODEFunction{false}(dudt_,tgrad=basic_tgrad)
prob = ODEProblem{false}(ff,x0,getfield(gp,:tspan))
solve(prob,gp.args...;gp.kwargs...)
end
function samplegp(x, gp, n, args = gp.args; kwargs...)
# dudt_(u,p,t) = gp.model(u, GaussianProcessODEs.SampleTraj())
# ff = ODEFunction{false}(dudt_,tgrad=GaussianProcessODEs.basic_tgrad)
# prob = ODEProblem{false}(ff,x,getfield(gp,:tspan))
# ensemble = EnsembleProblem(prob)
# solve(ensemble, gp.args..., EnsembleThreads(); trajectories = n, gp.kwargs...)
sample_gpODE = GPODE(gp.model, gp.tspan,
args...; sample = true, kwargs...);
t = @elapsed sols = ThreadsX.collect(sample_gpODE(x) for i in 1:n)
EnsembleSolution(sols, t, true)
end
using DifferentialEquations.EnsembleAnalysis
function gpmeanvar(samplesols, ts)
meanvar = timepoint_meanvar(samplesols,ts)
mtrajs = meanvar[1]
stdtrajs = sqrt.(meanvar[2])
return mtrajs, stdtrajs
end | [
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] | 2.086178 | 2,959 |
module mesh_examples
using FinEtools
using FinEtoolsFlexStructures.FESetCorotBeamModule: FESetL2CorotBeam
using FinEtoolsFlexStructures.CrossSectionModule: CrossSectionCircle, CrossSectionRectangle
using FinEtoolsFlexStructures.MeshFrameMemberModule: frame_member, merge_members
using VisualStructures: plot_nodes, plot_midline, render, plot_space_box, plot_solid, space_aspectratio
using PlotlyJS
using JSON
function curve_mesh()
L = 42
xyz = [0 0 0;
0 L/4 L*1/4;
L/4 L/4 L*2/4;
L/4 0 L*3/4;
0 0 L]
nL = 20
cs = CrossSectionCircle(s -> 5.9910, s -> [0.0, 0.0, 1.0])
fens, fes = frame_member(xyz, nL, cs)
plots = cat(plot_nodes(fens),
plot_midline(fens, fes; color = "rgb(155, 155, 255)", lwidth = 4),
dims = 1)
# push!(plots, plot_nodes(fens))
render(plots; aspectratio = [1.0 1.0 4.0])
true
end # curve_mesh
function line_mesh_solid()
L = 40.2
xyz = [0 0 0;
0 0 L]
nL = 1
cs = CrossSectionCircle(s -> 2.5, s -> [0.0, 1.0, 0.0])
fens, fes = frame_member(xyz, nL, cs)
plots = cat(plot_nodes(fens),
plot_solid(fens, fes);
dims = 1)
# push!(plots, plot_nodes(fens))
render(plots; aspectratio = [1.0 1.0 1.0])
true
end # curve_mesh
function curve_mesh_solid()
L = 42
xyz = [0 0 0;
0 L/4 L*1/4;
L/4 L/4 L*2/4;
L/4 0 L*3/4;
0 0 L]
nL = 20
cs = CrossSectionCircle(s -> 0.5, s -> [0.0, 0.0, 1.0])
fens, fes = frame_member(xyz, nL, cs)
plots = cat(plot_nodes(fens),
plot_solid(fens, fes);
dims = 1)
# push!(plots, plot_nodes(fens))
render(plots; aspectratio = space_aspectratio(fens.xyz))
true
end # curve_mesh
function argyr_l_frame()
# Parameters:
E=71240.0;#MPa
nu=0.31;# Poisson ratio
rho=5e-9;
b=3.0; h=30.0; L=240.0; # cross-sectional dimensions and length of each leg in millimeters
# Cross-sectional properties
cs = CrossSectionRectangle(s -> b, s -> h, s -> [0.0, 1.0, 0.0])
##
# Choose the mass formulation:
mass_type=2;
##
# Reference frequencies
reffs = [11.2732, 30.5269]
neigvs = 2;
# Select the number of elements per half the length.
xyz =
n=8;
members = []
push!(members, frame_member([0 0 L; L 0 L], n, cs))
push!(members, frame_member([L 0 L; L 0 0], n, cs))
fens, fes = merge_members(members; tolerance = L / 10000);
plots = cat(plot_space_box([[0 -L/2 0]; [L L/2 L]]),
plot_nodes(fens),
plot_midline(fens, fes; color = "rgb(155, 155, 255)", lwidth = 4), dims = 1)
render(plots; aspectratio = [1.0 1.0 1.0])
true
end # argyr_l_frame
function argyr_l_frame_movable()
# Parameters:
E=71240.0;#MPa
nu=0.31;# Poisson ratio
rho=5e-9;
b=3.0; h=30.0; L=240.0; # cross-sectional dimensions and length of each leg in millimeters
# Cross-sectional properties
cs = CrossSectionRectangle(s -> b, s -> h, s -> [0.0, 1.0, 0.0])
##
# Choose the mass formulation:
mass_type=2;
##
# Reference frequencies
reffs = [11.2732, 30.5269]
neigvs = 2;
# Select the number of elements per half the length.
xyz =
n=1;
members = []
push!(members, frame_member([0 0 L; L 0 L], n, cs))
push!(members, frame_member([L 0 L; L 0 0], n, cs))
fens, fes = merge_members(members; tolerance = L / 10000);
plots = cat(plot_space_box([[0 -L/2 0]; [L L/2 L]]),
plot_nodes(fens),
plot_midline(fens, fes; color = "rgb(155, 155, 255)", lwidth = 4), dims = 1)
p = render(plots; aspectratio = [1.0 1.0 1.0])
@show p.plot.data
true
end # argyr_l_frame
function argyr_l_frame_solid()
# Parameters:
E=71240.0;#MPa
nu=0.31;# Poisson ratio
rho=5e-9;
b=3.0; h=30.0; L=240.0; # cross-sectional dimensions and length of each leg in millimeters
# Cross-sectional properties
cs = CrossSectionRectangle(s -> b, s -> h, s -> [0.0, 1.0, 0.0])
##
# Choose the mass formulation:
mass_type=2;
##
# Reference frequencies
reffs = [11.2732, 30.5269]
neigvs = 2;
# Select the number of elements per half the length.
xyz =
n=8;
members = []
push!(members, frame_member([0 0 L; L 0 L], n, cs))
push!(members, frame_member([L 0 L; L 0 0], n, cs))
fens, fes = merge_members(members; tolerance = L / 10000);
plots = cat(plot_space_box([[0 -L/2 0]; [L L/2 L]]),
plot_nodes(fens),
plot_solid(fens, fes; facecolor = "rgb(155, 0, 0)"), dims = 1)
render(plots; aspectratio = [1.0 1.0 1.0])
true
end # argyr_l_frame
function curve_mesh_change_view()
L = 42
xyz = [0 0 0;
0 L/4 L*1/4;
L/4 L/4 L*2/4;
L/4 0 L*3/4;
0 0 L]
nL = 20
cs = CrossSectionCircle(s -> 5.9910, s -> [0.0, 0.0, 1.0])
fens, fes = frame_member(xyz, nL, cs)
plots = cat(plot_nodes(fens),
plot_midline(fens, fes; color = "rgb(155, 155, 255)", lwidth = 4),
dims = 1)
# push!(plots, plot_nodes(fens))
pl = render(plots; aspectratio = [1.0 1.0 4.0])
savejson(pl, "plot.json")
true
end # curve_mesh
function allrun()
println("#####################################################")
println("# curve_mesh ")
curve_mesh()
println("#####################################################")
println("# curve_mesh_solid ")
curve_mesh_solid()
println("#####################################################")
println("# argyr_l_frame ")
argyr_l_frame()
return true
end # function allrun
@info "All examples may be executed with "
println("using .$(@__MODULE__); $(@__MODULE__).allrun()")
end # module
nothing
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] | 2.167813 | 2,616 |
using FastRationals
using Polynomials, LinearAlgebra, BenchmarkTools, MacroTools
BenchmarkTools.DEFAULT_PARAMETERS.evals = 1;
BenchmarkTools.DEFAULT_PARAMETERS.samples = 200;
BenchmarkTools.DEFAULT_PARAMETERS.time_tolerance = 1.0e-8;
BenchmarkTools.DEFAULT_PARAMETERS.overhead = BenchmarkTools.estimate_overhead();
walk(x, inner, outer) = outer(x)
walk(x::Expr, inner, outer) = outer(Expr(x.head, map(inner, x.args)...))
postwalk(f, x) = walk(x, x -> postwalk(f, x), f)
function referred(expr::Expr)
if expr.head == :$
:($(Expr(:$, :(Ref($(expr.args...)))))[])
else
expr
end
end
referred(x) = x
"""
@noelide _bmacro_ expression
where _bmacro_ is one of @btime, @belapsed, @benchmark
Wraps all interpolated code in _expression_ in a __Ref()__ to
stop the compiler from cheating at simple benchmarks. Works
with any macro that accepts interpolation
#Example
julia> @btime \$a + \$b
0.024 ns (0 allocations: 0 bytes)
3
julia> @noelide @btime \$a + \$b
1.277 ns (0 allocations: 0 bytes)
3
"""
macro noelide(expr)
out = postwalk(referred, expr) |> esc
end
function testadd(x,y,z)
a = x + y
b = a + z
c = b + a
d = c + x
return d
end
function testmul(x,y,z)
a = x * y
b = a * z
c = z * x
d = a * b
return d
end
function testarith(x,y,z)
a = x + y
b = x * y
c = z - b
d = a / c
return d
end
w32,x32,y32,z32 = Rational{Int32}.([1//12, -2//77, 3//54, -4//17]); q32 = Int32(1)//Int32(7);
u32,v32 = w32+z32, w32-z32
w64,x64,y64,z64 = Rational{Int64}.([1//12, -2//77, 3//54, -4//17]); q64 = Int64(1)//Int64(7);
u64,v64 = w64+z64, w64-z64
ply32 = Poly([w32, x32, y32, z32]);
ply64 = Poly([w64, x64, y64, z64]);
ply64w = Poly([u64, v64, w64, x64, y64, z64]);
a32,b32,c32,d32,e32,f32 = FastQ32.((w32,x32,y32,z32,u32,v32)); fastq32 = FastQ32(q32);
fastply32=Poly([a32,b32,c32,d32]);
a64,b64,c64,d64,e64,f64 = FastQ64.((w64,x64,y64,z64,u64,v64)); fastq64 = FastQ64(q64);
fastply64=Poly([a64,b64,c64,d64]);
fastply64w=Poly([a64,b64,c64,d64,e64,f64]);
m = [1//1 1//5 1//9 1//13; 1//2 1//6 1//10 1//14; 1//3 1//7 1//11 1//15; 1//4 1//8 1//12 1//16];
m32 = Rational{Int32}.(m);
m64 = Rational{Int64}.(m);
mfast32 = FastQ32.(m);
mfast64 = FastQ64.(m);
relspeed_arith32 =
round( (@noelide @belapsed testarith($x32,$y32,$z32)) /
(@noelide @belapsed testarith($a32,$b32,$c32)), digits=1);
relspeed_arith64 =
round( (@noelide @belapsed testarith($x64,$y64,$z64)) /
(@noelide @belapsed testarith($a64,$b64,$c64)), digits=1);
relspeed_add32 =
round( (@noelide @belapsed testadd($x32,$y32,$z32)) /
(@noelide @belapsed testadd($a32,$b32,$c32)), digits=1);
relspeed_add64 =
round( (@noelide @belapsed testadd($x64,$y64,$z64)) /
(@noelide @belapsed testadd($a64,$b64,$c64)), digits=1);
relspeed_mul32 =
round( (@noelide @belapsed testmul($x32,$y32,$z32)) /
(@noelide @belapsed testmul($a32,$b32,$c32)), digits=1);
relspeed_mul64 =
round( (@noelide @belapsed testmul($x64,$y64,$z64)) /
(@noelide @belapsed testmul($a64,$b64,$c64)), digits=1);
relspeed_ply32 =
round( (@noelide @belapsed polyval($ply32, $q32)) /
(@noelide @belapsed polyval($fastply32, $fastq32)), digits=1);
relspeed_ply64 =
round( (@noelide @belapsed polyval($ply64, $q64)) /
(@noelide @belapsed polyval($fastply64, $fastq64)), digits=1);
relspeed_ply64w =
round( (@noelide @belapsed polyval($ply64w, $q64)) /
(@noelide @belapsed polyval($fastply64w, $fastq64)), digits=1);
relspeeds = string(
"\n\n\trelative speeds",
"\n\t (32)\t (64)\n\n",
"mul: \t $relspeed_mul32 \t $relspeed_mul64 \n",
"muladd:\t $relspeed_arith32 \t $relspeed_arith64 \n",
"add: \t $relspeed_add32 \t $relspeed_add64 \n",
"poly: \t $relspeed_ply32 \t $relspeed_ply64w \n");
print(relspeeds);
;
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] | 2.087805 | 1,845 |
import Test: @test, @testset, @test_throws
import Luna: Capillary, Tools, PhysData
@testset "Params" begin
m = Capillary.MarcatiliMode(125e-6, :He, 0.4, model=:reduced)
p = Tools.params(300e-6, 10e-15, 800e-9, m, :He, P=0.4)
# compare to Pufe
@test isapprox(p.N, 2.239, rtol=1e-3)
@test isapprox(p.Lfiss, 1.768, rtol=1e-3)
@test isapprox(p.zdw, 378.8e-9, rtol=1e-2)
@test isapprox(p.P0/p.Pcr, 0.0398, rtol=2e-2)
# test backup zdw method
p = Tools.capillary_params(6e-9, 20e-15, 800e-9, 14e-6, :Kr, P=15.0)
@test isapprox(p.zdw, 7.693023014958748e-7, rtol=1e-7)
end
@testset "RDW phasematching" begin
a = 125e-6
gas = :HeJ
pressure = 1
Ξ»0 = 800e-9
Ξ»RDW = Tools.Ξ»RDW(a, gas, pressure, Ξ»0)
@test Ξ»RDW - 188e-9 < 1e-9
@test Tools.pressureRDW(a, gas, Ξ»RDW, Ξ»0) β pressure
end
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] | 1.906404 | 406 |
<filename>stdlib/Random/src/deprecated.jl
# This file is a part of Julia. License is MIT: https://julialang.org/license
# PR #25567
Base.@deprecate_binding dSFMT DSFMT
# PR #21359
@deprecate srand(r::MersenneTwister, filename::AbstractString, n::Integer=4) srand(r, read!(filename, Vector{UInt32}(uninitialized, Int(n))))
@deprecate srand(filename::AbstractString, n::Integer=4) srand(read!(filename, Vector{UInt32}(uninitialized, Int(n))))
@deprecate MersenneTwister(filename::AbstractString) srand(MersenneTwister(0), read!(filename, Vector{UInt32}(uninitialized, Int(4))))
function randjump(mt::MersenneTwister, jumps::Integer, jumppoly::AbstractString)
depwarn("`randjump(rng, jumps, jumppoly::AbstractString)` is deprecated; use `randjump(rng, steps, jumps)` instead", :randjump)
Base.Random._randjump(mt, DSFMT.GF2X(jumppoly), jumps)
end
@deprecate randjump(mt::MersenneTwister, jumps::Integer) randjump(mt, big(10)^20, jumps)
@deprecate convert(::Type{UInt128}, u::UUID) UInt128(u)
@deprecate convert(::Type{UUID}, s::AbstractString) UUID(s)
# PR #25429
@deprecate rand(r::AbstractRNG, dims::Dims) rand(r, Float64, dims)
@deprecate rand( dims::Dims) rand(Float64, dims)
# PR #25668
@deprecate RandomDevice(unlimited::Bool) RandomDevice(; unlimited=unlimited)
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5391,
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31,
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8344,
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3712,
33,
970,
8,
14534,
24728,
7,
26,
15822,
28,
403,
10698,
8,
198
] | 2.579882 | 507 |
# ---------------
# Global Commands
# ---------------
handle("julia.set-global-client") do req, data
global global_client = req[1]
Base.notify(connect_notify)
global_client
end
handle("cwd") do _, path
path == nothing && return
path = isfile(path) ? dirname(path) : path
cd(path)
end
function command(cmd, data = Dict())
data[:cmd] = cmd
raise(global_client, "editor.eval.julia.command", data)
end
function popup(header, body="", buttons = [@d(:label => "Ok")])
command("popup",
@d(:header => header,
:body => body,
:buttons => buttons))
end
function ltprint(message; error = false)
command("print",
@d(:value => message,
:error => error))
end
function notify_done(msg = nothing)
command("done", @d(:msg => msg))
end
function notify(message; class = "")
command("notify",
@d(:msg => message,
:class => class))
end
function notify_error(message)
notify(message, class = "error")
end
function console(value::AbstractString; html = false)
command("console",
@d("value" => value,
"html" => html))
end
| [
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11,
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220,
220,
366,
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1,
220,
5218,
27711,
4008,
198,
437,
198
] | 2.511013 | 454 |
# FIXME temporary copy/paste from:
# https://github.com/FluxML/MacroTools.jl/blob/master/src/utils.jl#L55-L87
isexpr(x::Expr) = true
isexpr(x) = false
isexpr(x::Expr, ts...) = x.head in ts
isexpr(x, ts...) = any(T->isa(T, Type) && isa(x, T), ts)
isline(ex) = isexpr(ex, :line) || isa(ex, LineNumberNode)
rmlines(x) = x
function rmlines(x::Expr)
# Do not strip the first argument to a macrocall, which is
# required.
if x.head == :macrocall && length(x.args) >= 2
Expr(x.head, x.args[1], nothing, filter(x->!isline(x), x.args[3:end])...)
else
Expr(x.head, filter(x->!isline(x), x.args)...)
end
end
striplines(ex) = prewalk(rmlines, ex)
walk(x, inner, outer) = outer(x)
walk(x::Expr, inner, outer) = outer(Expr(x.head, map(inner, x.args)...))
prewalk(f, x) = walk(f(x), x -> prewalk(f, x), identity)
| [
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7,
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13,
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7,
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11,
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13,
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7,
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11,
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87,
828,
2124,
4613,
662,
11152,
7,
69,
11,
2124,
828,
5369,
8,
198
] | 2.239782 | 367 |
using StatsModels
using RegressionFormulae
using RegressionFormulae: fulldummy
using Test
include("dummymod.jl")
dat = (; y=zeros(10), a=["u","i","o"], b=["q","w","e"], c=["s","d","f"], x=1:10)
@testset "error checking" begin
@test_throws ArgumentError fulldummy(term(:a))
sch = schema(dat)
@test_throws ArgumentError apply_schema(@formula(y ~ fulldummy(x)), sch, RegressionModel)
end
| [
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3310,
2234,
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198,
437,
198
] | 2.537975 | 158 |
using Plots
V(x,y) = (1/2)*(x^2 + y^2 + 2*x^2*y - (2/3)*y^3)
x = y = range(-1,stop = 1, length = 1000)
V(x,y) = (1/2)*(x^2 + y^2 + 2*x^2*y - (2/3)*y^3)
x = y = range(-1,stop = 1, length = 1000)
contourf(x,y,V)
contour!(x,y,V,levels=0:1/6:1,title = "Henon-Heiles potential: Intensity map",linecolor = :white)
# savefig("potential_intensity.png")
plot(x,y,V,title = "Henon-Heiles potential: Surface map")
# savefig("potential_surface.png") | [
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7203,
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1843,
62,
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13,
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4943
] | 1.99115 | 226 |
<reponame>kiranshila/SIGPROC.jl
using RecipesBase, Statistics, DimensionalData
@recipe f(::Type{Filterbank}, fb::Filterbank) = fb.data
@userplot Waterfall
@recipe function f(h::Waterfall)
if h.args[1] isa Filterbank
data = h.args[1].data
elseif h.args[1] isa DimArray
data = h.args[1]
else
@error "Argument must be a filterbank or DimArray"
end
# set up the subplots
fc := :viridis
legend := false
link := :both
framestyle := [:none :axes :none]
grid := false
layout := @layout [topav _
heatmap{0.9w,0.9h} _]
# Main Waterfall
@series begin
seriestype := :heatmap
subplot := 2
data
end
# these are common to both average plots
linecolor := :black
seriestype := :line
# upper
@series begin
title := ""
subplot := 1
dropdims(mean(data,dims=Freq),dims=Freq)
end
end | [
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<filename>src/incidence_to_triangles.jl
function find_zero_indices(a)
eachindex(a)'[a .== 0]
end
function find_nonzero_indices(a)
eachindex(a)'[a .!= 0]
end
"""
`INCIDENCE_TO_INCIDENT_LIST_ARRAY` - takes in M edges on N nodes in NxM sparse format and returns an 3xN array where the N-th column includes the indices of the nodes connected (one-direction) to node N
* Currently assumes that there's at most 3 points, and furthermore that the nonzeros alternate
* 0 in the incidence_array means that there's less than 3 points
"""
function incidence_to_incident_list_array(E)
incident_list_array = zeros(Int64, 3, size(E, 1))
incident_list_counts = ones(Int64, size(E, 1))
rows = rowvals(E)
vals = nonzeros(E)
ind = 1
# for each edge
for j in 1:size(E, 2)
node_first = rows[ind]
node_second = rows[ind+1]
incident_list_count = incident_list_counts[node_first]
incident_list_array[incident_list_count, node_first] = node_second
incident_list_counts[node_first] = incident_list_count+1
ind = ind+2
end
return incident_list_array
end
# checks if p -> p(pa,pb) is the same as p -> pc -> pca(b,c) and writes the triangle
function check_and_write_triangles!(triangles, cur_triangle, p, pa, pb, pc, pca, pcb, pcc)
if p == 0 return cur_triangle end
if pc == 0 return cur_triangle end
if pa != 0
if pa == pca || pa == pcb || pa == pcc
triangles[1, cur_triangle] = p
triangles[2, cur_triangle] = pc
triangles[3, cur_triangle] = pa
cur_triangle += 1
end
end
if pb != 0
if pb == pca || pb == pcb || pb == pcc
triangles[1, cur_triangle] = p
triangles[2, cur_triangle] = pc
triangles[3, cur_triangle] = pb
cur_triangle += 1
end
end
return cur_triangle
end
"""
`INCIDENT_LIST_ARRAY_TO_TRIANGLES` - Convert incidence array to 3xN list triangle node index vectors
"""
function incident_list_array_to_triangles(incident_list_array)
triangles = zeros(Int64, 3, size(incident_list_array, 2) * 10)
max_edges = size(incident_list_array, 1)
cur_triangle = 1
# absolutely awful, but really fast
# for each node at j
for p in 1:size(incident_list_array, 2)
# the up to 3 points that have edges from j
pa = incident_list_array[1, p]
pb = incident_list_array[2, p]
pc = incident_list_array[3, p]
if pa != 0
# the up to 9 points that have edges from pa, pb, pc
paa = incident_list_array[1, pa]; pab = incident_list_array[2, pa]; pac = incident_list_array[3, pa]
else
paa = 0; pab = 0; pac = 0
end
if pb != 0
pba = incident_list_array[1, pb]; pbb = incident_list_array[2, pb]; pbc = incident_list_array[3, pb]
else
pba = 0; pbb = 0; pbc = 0
end
if pc != 0
pca = incident_list_array[1, pc]; pcb = incident_list_array[2, pc]; pcc = incident_list_array[3, pc]
else
pca = 0; pcb = 0; pcc = 0
end
cur_triangle = check_and_write_triangles!(triangles, cur_triangle, p, pa, pb, pc, pca, pcb, pcc)
cur_triangle = check_and_write_triangles!(triangles, cur_triangle, p, pa, pc, pb, pba, pbb, pbc)
cur_triangle = check_and_write_triangles!(triangles, cur_triangle, p, pb, pc, pa, paa, pab, pac)
end
return triangles[:, 1:(cur_triangle - 1)]
end
"""
`INCIDENCE_TO_TRIANGLES` - Convert node-edge incidence matrix into triangle
array (triples of node indices defining all triangles in the mesh). The triangle
array will be used when applying piecewise affine transforms through the
meshwarp function.
See Mesh type documentation for definition of an incidence matrix.
```
triangles = incidence_to_triangles(E)
```
* E: node-edge incidence matrix
* triangles: 3xN array of ints, each column represents the indices of three nodes
in one triangle.
"""
function incidence_to_triangles(E)
incident_list_array = incidence_to_incident_list_array(E)
return incident_list_array_to_triangles(incident_list_array)
end
#= THESE FUNCTIONS ARE SLOW
"""
`INCIDENCE_TO_DICT` - Create dictionary of node sets connected to indexing node
"""
function incidence_to_dict(D)
D = abs.(D)
node_dict = Dict()
for i = 1:size(D,1)
j = 1
while D[i,j] == 0
j += 1
end
if !(j in keys(node_dict))
node_dict[j] = Set{Int64}()
end
k = j+1
while D[i,k] == 0
k += 1
end
push!(node_dict[j], k)
end
return node_dict
end
"""
`DICT_TO_TRIANGLES` - Convert node dict to Nx3 list triangle node index vectors
"""
function dict_to_triangles(node_dict)
triangles = Array{Int64}(0, 3)
for a in sort(collect(keys(node_dict)))
setA = node_dict[a]
for b in sort(collect(setA))
if !(b in keys(node_dict))
continue
end
setB = node_dict[b]
setC = intersect(setA, setB)
for c in sort(collect(setC))
triangles = vcat(triangles, [a b c])
end
end
end
return triangles
end
"""
`INCIDENCE_TO_TRIANGLES` - Convert edge-node incidence matrix into triangle
array (triples of node indices defining all triangles in the mesh). The triangle
array will be used when applying piecewise affine transforms through the
meshwarp function.
See Mesh type documentation for definition of an incidence matrix.
```
triangles = incidence_to_triangles(D)
```
* D: edge-node incidence matrix
* triangles: Nx3 array of ints, each row represents the indices of three nodes
in one triangle.
"""
function incidence_to_triangles(D)
node_dict = incidence_to_dict(D)
return dict_to_triangles(node_dict)
end
=#
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] | 2.595124 | 2,092 |
<filename>test/runtests.jl
using AtomBase
using Test
@testset "Atom.jl" begin
atom1 = Atom()
atom2 = Atom("H", [1.0, 0.0, 0.0])
io = IOBuffer()
show(io, atom2)
@test atom2() == ("H", [1.0, 0.0, 0.0])
@test !(atom1 == atom2)
@test String(take!(io)) == "H\t[1.0, 0.0, 0.0]"
end
@testset "Atoms.jl Properties" begin
property1 = Properties()
property2 = Properties{Float64}("Force", 1.0)
property3 = Properties("energy")
energy1 = Energy()
energy2 = Energy(1.0)
energy3 = Energy(2)
@test eltype(property1) == eltype(property2) == eltype(energy1) == eltype(energy2) == eltype(energy3) == Float64
@test property2() == 1.0
@test energy3() == 2.0
energy1(4.0)
@test energy1() == 4.0
io = IOBuffer()
show(io, property2)
@test String(take!(io)) == "Force=1.0"
show(io, energy3)
@test String(take!(io)) == "energy=2.0"
end
@testset "Atoms.jl Atoms" begin
atom = Atom("H", [1.0, 0.0, 0.0])
property = Energy(1.0)
atoms0 = Atoms()
atoms1 = Atoms([atom, atom], 2, [property, property])
atoms2 = Atoms([atom, atom, atom], [property,])
atoms3 = Atoms([atom,])
atoms4 = Atoms(atom, [property, ])
atoms5 = Atoms(atom)
atoms6 = Atoms([atom, atom], 2, property)
atoms7 = Atoms([atom, atom], property)
atoms8 = Atoms(atom, property)
@test atoms2.number == 3
addAtom!(atoms2, atom)
@test atoms2.number == 4
addProperty!(atoms3, property)
@test atoms3.properties == [property,]
@test iterate(atoms2) == (atoms2.atom[1], 2)
@test iterate(atoms2, 2) == (atoms2.atom[2], 3)
@test length(atoms2) == 4
@test getindex(atoms2, 1) == atom
@test firstindex(atoms2) == 1
@test lastindex(atoms2) == 4
@test getindex(atoms2, 1.0) == atom
@test getindex(atoms2, [1, 2]) == [atom, atom]
io = IOBuffer()
show(io, atoms8)
@test String(take!(io)) == "1\nenergy=1.0 \nH\t[1.0, 0.0, 0.0]\n"
end
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13,
15,
11,
657,
13,
15,
60,
59,
77,
1,
198,
437,
198
] | 2.193946 | 892 |
using Dash
app = dash()
app.layout = html_div() do
dcc_input(id="input", value = "initial value"),
html_div(id = "output")
end
callback!(app,
[Output("output","children")],
Input("input","value")
) do value
return (value,)
end
run_server(app)
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] | 2.509259 | 108 |
using NMRPulses
using Test
id = IdlePulse(1, 1e-3)
Ξ© = 2*pi
pi_2 = PiHalfPulse(Ξ©, 0, 1e-3)
pi_pulse = PiPulse(Ξ©, 0, 1e-3)
s = Sequence([pi_2, id, pi_pulse, id, pi_2])
a, b, c = prepare_sequence(s)
@testset "NMRPulses.jl" begin
# Write your tests here.
@test s(0.0)[1] == a[1]
@test pi_2(0.0)[1] == a[1]
end | [
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] | 1.827778 | 180 |
# *********************************************************************************
# REopt, Copyright (c) 2019-2020, Alliance for Sustainable Energy, LLC.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# Redistributions of source code must retain the above copyright notice, this list
# of conditions and the following disclaimer.
#
# Redistributions in binary form must reproduce the above copyright notice, this
# list of conditions and the following disclaimer in the documentation and/or other
# materials provided with the distribution.
#
# Neither the name of the copyright holder nor the names of its contributors may be
# used to endorse or promote products derived from this software without specific
# prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
# IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
# INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
# OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
# OF THE POSSIBILITY OF SUCH DAMAGE.
# *********************************************************************************
struct MPCInputs <: AbstractInputs
s::MPCScenario
techs::Techs
existing_sizes::DenseAxisArray{Float64, 1} # (techs.all)
time_steps::UnitRange
time_steps_with_grid::Array{Int, 1}
time_steps_without_grid::Array{Int, 1}
hours_per_time_step::Float64
months::UnitRange
production_factor::DenseAxisArray{Float64, 2} # (techs.all, time_steps)
levelization_factor::Dict{String, Float64} # (techs.all)
value_of_lost_load_per_kwh::Array{R, 1} where R<:Real #default set to 1 US dollar per kwh
pwf_e::Float64
pwf_om::Float64
pwf_fuel::Dict{String, Float64}
third_party_factor::Float64
ratchets::UnitRange
techs_by_exportbin::DenseAxisArray{Array{String,1}} # indexed on [:NEM, :WHL]
export_bins_by_tech::Dict{String, Array{Symbol, 1}}
cop::Dict{String, Float64} # (techs.cooling)
thermal_cop::Dict{String, Float64} # (techs.absorption_chiller)
end
function MPCInputs(fp::String)
s = MPCScenario(JSON.parsefile(fp))
MPCInputs(s)
end
function MPCInputs(s::MPCScenario)
time_steps = 1:length(s.electric_load.loads_kw)
hours_per_time_step = 1 / s.settings.time_steps_per_hour
techs, production_factor, existing_sizes = setup_tech_inputs(s)
months = 1:length(s.electric_tariff.monthly_demand_rates)
techs_by_exportbin = DenseAxisArray([ techs.all, techs.all, techs.all], s.electric_tariff.export_bins)
# TODO account for which techs have access to export bins (when we add more techs than PV)
levelization_factor = Dict(t => 1.0 for t in techs.all)
pwf_e = 1.0
pwf_om = 1.0
pwf_fuel = Dict{String, Float64}()
pwf_fuel["Generator"] = 1.0
third_party_factor = 1.0
time_steps_with_grid, time_steps_without_grid, = setup_electric_utility_inputs(s)
export_bins_by_tech = Dict{String, Array{Symbol, 1}}()
for t in techs.elec
export_bins_by_tech[t] = s.electric_tariff.export_bins
end
# TODO implement export bins by tech (rather than assuming that all techs share the export_bins)
#Placeholder COP because the REopt model expects it
cop = Dict("ExistingChiller" => s.cooling_load.cop)
thermal_cop = Dict{String, Float64}()
MPCInputs(
s,
techs,
existing_sizes,
time_steps,
time_steps_with_grid,
time_steps_without_grid,
hours_per_time_step,
months,
production_factor,
levelization_factor, # TODO need this?
typeof(s.financial.value_of_lost_load_per_kwh) <: Array{<:Real, 1} ? s.financial.value_of_lost_load_per_kwh : fill(s.financial.value_of_lost_load_per_kwh, length(time_steps)),
pwf_e,
pwf_om,
pwf_fuel,
third_party_factor,
# maxsize_pv_locations,
1:length(s.electric_tariff.tou_demand_ratchet_time_steps), # ratchets
techs_by_exportbin,
export_bins_by_tech,
cop,
thermal_cop
# s.site.min_resil_time_steps,
# s.site.mg_tech_sizes_equal_grid_sizes,
# s.site.node
)
end
function setup_tech_inputs(s::MPCScenario)
techs = Techs(s)
time_steps = 1:length(s.electric_load.loads_kw)
# REoptInputs indexed on techs:
existing_sizes = DenseAxisArray{Float64}(undef, techs.all)
production_factor = DenseAxisArray{Float64}(undef, techs.all, time_steps)
if !isempty(techs.pv)
setup_pv_inputs(s, existing_sizes, production_factor)
end
if "Generator" in techs.all
setup_gen_inputs(s, existing_sizes, production_factor)
end
return techs, production_factor, existing_sizes
end
function setup_pv_inputs(s::MPCScenario, existing_sizes, production_factor)
for pv in s.pvs
production_factor[pv.name, :] = pv.prod_factor_series
existing_sizes[pv.name] = pv.size_kw
end
return nothing
end
function setup_gen_inputs(s::MPCScenario, existing_sizes, production_factor)
existing_sizes["Generator"] = s.generator.size_kw
production_factor["Generator", :] = ones(length(s.electric_load.loads_kw))
return nothing
end
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] | 2.599462 | 2,232 |
<filename>docs/make.jl
using Documenter
deploydocs(repo = "github.com/uva-bi-sdad/CareerOneStop.jl.git",
push_preview = true,
devbranch = "main")
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] | 2.1125 | 80 |
<gh_stars>1-10
module MultiInterface
export Interface,
Policy,
@interface,
@impl,
enable_multiinterface_validation,
get_policy,
set_policy,
with_policy,
@unpack,
list_impls,
get_imp,
get_method
using MacroTools
import Parameters: with_kw, @unpack
SE = Union{Symbol,Expr}
__policy = nothing
__num_imps = Dict()
__modules = Dict{Symbol,Module}()
# __interface = Dict{Symbol, Signature}()
enable_display() = global display = true
disable_display() = global display = false
display = false
gen_argname() = Symbol(replace(string(gensym("arg")), "#"=>""))
mi_types(e::Expr) = length(e.args) == 1 ? e.args[1] : e.args[2]
mi_types(::Symbol) = :Any
mi_argname(arg::Expr) = length(arg.args) == 1 ? gen_argname() : arg.args[1]
mi_argname(arg::Symbol) = arg
mi_getname(f::Symbol) = f
mi_getname(f::Expr) = f.args[2].value
function register_module(str, mod)
global __modules
push!(__modules, str=>mod)
end
function get_module(mod, str)
global __modules
get(__modules, str, mod)
end
# TODO do we want to allow the same interface name in different modules?
function register_interface(source, str, mod, args, retval, wparams)
global __interface
haskey(__interface, str) && @warn "reregistering interface for $str at $source" was___=get_interface(str) willbe=Signature(mod, args, retval, wparams)
push!(__interface, str=>Signature(mod, args, retval, wparams))
end
function get_interface(str)
get(__interface, str, nothing)
end
function getvars(t::Type)
v = []
getvars(t) = nothing
getvars(t::DataType) = append!(v, collect(t.parameters))
getvars(t::UnionAll) = begin
push!(v, t.var)
getvars(t.body)
end
unique(v)
end
function get_and_inc_imp_number(imp_type)
global __num_imps
if ~(imp_type in keys(__num_imps))
__num_imps[imp_type] = 0
end
__num_imps[imp_type] += 1
return __num_imps[imp_type]
end
function get_policy()
global __policy
return __policy
end
function set_policy(new_policy)
global __policy
old_policy = __policy
__policy = new_policy
return old_policy
end
abstract type Interface end
abstract type Policy end
get_imp(policy::Policy, args...) = nothing
OptionalPolicy = Union{Policy, Nothing}
function list_impls(imp_type, args_type=nothing)
if isnothing(args_type)
args_type = Tuple{<:Interface, Vararg{Any}}
else
args_type = Tuple{<:Interface, args_type.parameters...}
end
return [get_impl(m.sig) for m in methods(imp_type, args_type)]
end
function get_impl(t::UnionAll)
return get_impl(t.body)
end
function get_impl(t::DataType)
return t.parameters[2]
end
function strip_module(s)
l = findlast('.', s)
l === nothing || return s[(l+1):end]
s
end
function firstcaps_to_lowerunder(s)
s = strip_module(s)
return lowercase(strip(replace(s,r"[A-Z]"=>s"_\0"),['_']))
end
function lowerunder_to_firstcaps(s)
s = strip_module(s)
return replace(titlecase(s),"_"=>"")
end
nothing2any(::Nothing) = :Any
nothing2any(x::Union{Symbol,Expr}) = x
"""
macro interface(interface_exp)
Called
```
@interface a(x::I)::O where {I,O}
```
Generates the following code:
```
abstract type A <: Interface end
function (a(x::I; )::O) where {I,O}
policy = get_policy()
return a(policy, x::I)
end
function (a(policy::Policy, x::I; )::O) where {I,O}
imp = get_imp(policy, A, x::I)
return a(imp, x::I)
end
```
"""
macro interface(interface_exp)
return esc(interface_macro(__module__, __source__, interface_exp))
end
function interface_macro(mod, source, interface_exp)
f, args, R, W = interface_capture(interface_exp)
args = add_missing_argnames(args)
if display
println("name: ", f, ": ", typeof(f))
println("args: ", args, ": ", typeof(args))
println("returntype: ", R, ": ", typeof(R))
println("where: ", W, ": ", typeof(W))
end
W = isnothing(W) ? () : W
interface_type_name = Symbol(lowerunder_to_firstcaps(String(f)))
body = quote
policy = get_policy()
return $f(policy, $(get_arg_vars(args)...))
end
use_policy_body = quote
imp = get_imp(policy, $interface_type_name, $(get_arg_vars(args)...))
return $f(imp, $(get_arg_vars(args)...))
end
bare_func_sig_dict = Dict(:name => f,
:args => args,
:kwargs => Any[],
:body => body,
:rtype => R,
:whereparams => W,
)
default_func_sig_dict = Dict(:name => f,
:args => [:(policy::Policy); args],
:kwargs => Any[],
:body => use_policy_body,
:rtype => R,
:whereparams => W,
)
register_module(interface_type_name, mod)
# WIP
# register_interface(source, interface_type_name, mod, get_arg_types(args), nothing2any(R), W)
result = quote
abstract type $interface_type_name <: Interface end
$(combinedef(bare_func_sig_dict))
$(combinedef(default_func_sig_dict))
end
if display
println(prettify(result))
end
return result
end
function get_arg_vars(args)
tovar(s::Symbol) = s
tovar(expr::Expr) = tovar(Val(expr.head), expr)
tovar(::Val{:(::)}, expr) = expr.args[1]
map(tovar, args)
end
function add_missing_argnames(args)
map(args) do arg
m = match(r"^::\s*(.*)", string(arg))
if m !== nothing
arg = copy(arg)
insert!(arg.args, 1, gen_argname())
end
return arg
end
end
function get_arg_type_sigs(args)
map(args) do arg
m = match(r".*?::\s*(.*)", string(arg))
s = m === nothing ? :Type : Meta.parse(string("Type{<:", m.captures[1],"}"))
return Expr(:(::), s)
end
end
function get_arg_types(args)::Vector{Union{Symbol, Expr}}
map(args) do arg
m = match(r".*?::\s*(.*)", string(arg))
return m === nothing ? :Any : Meta.parse(m.captures[1])
end
end
function mi_join_args(func_sig_dicts)
zippedargs = zip([[mi_types(a) for a in fsd[:args]] for fsd in func_sig_dicts]...)
argnames = [mi_argname(a) for a in first(func_sig_dicts)[:args]]
function mi_merge_into!(s::Expr, t)
t in s.args && return s
push!(s.args, t)
return s
end
do_union(s,t) = :(Union{$s,$t})
mi_merge(::Val, s::Expr, t::Symbol) = do_union(s,t)
mi_merge(::Val, s::Expr, ::Val, t::Expr) = do_union(s,t)
function mi_merge(::Val{:Union}, s::Expr, ::Val{:Union}, t::Expr)
v = deepcopy(s)
append!(v.args, t.args)
unique!(v.args)
return v
end
mi_merge(::Val{:Union}, s::Expr, t::Symbol) = mi_merge_into!(deepcopy(s), t)
mi_merge(::Val{:Union}, s::Expr, ::Val, t::Expr) = mi_merge_into!(deepcopy(s), t)
mi_merge(::Val, s::Expr, ::Val{:Union}, t::Expr) = mi_merge(t, s)
mi_merge(s::Symbol, t::Symbol) = s === t ? s : do_union(s,t)
mi_merge(s::Expr, t::Symbol) = mi_merge(Val(s.args[1]), s, t)
mi_merge(s::Symbol, t::Expr) = mi_merge(t,s)
mi_merge(s::Expr, t::Expr) = mi_merge(Val(s.args[1]), s, Val(t.args[1]), t)
args = [reduce(mi_merge, arg) for arg in zippedargs]
exprs = map(argnames, args) do n,arg
:($(n)::$(arg))
end
collect(exprs)
end
parameter_names(e::Symbol) = e
parameter_names(e::Expr) = length(e.args) == 1 ? gen_argname() : e.args[1]
"""
macro impl(expr)
Called
```
@impl begin
struct MyA
precision::Int64
end
function a(x::Int)
c = 1
return x + c + precision
end
end
```
The `MyA` can be considered an identifier for this particular method, as opposed to different implementations of
this function.
This macro generates the following code:
```
begin
Base.@__doc__ struct MyA <: A
precision::Int64
MyA(; precision = error("Field '" * "precision" * "' has no default, supply it with keyword.")) = MyA(precision)
MyA(precision) = new(precision)
end
()
()
MyA(pp::MyA; kws...) = (Parameters).reconstruct(pp, kws)
MyA(pp::MyA, di::(Parameters).AbstractDict) = (Parameters).reconstruct(pp, di)
MyA(pp::MyA, di::Vararg{Tuple{Symbol, Any}}) = (Parameters).reconstruct(pp, di)
nothing
macro unpack_MyA(ex)
esc((Parameters)._unpack(ex, Any[:precision]))
end
macro pack_MyA()
esc((Parameters)._pack_new(MyA, Any[:precision]))
end
MyA
function a(impl::MyA, x::Int; )
@unpack (precision,) = impl
c = 1
return x + c + precision
end
function a(policy::Nothing, x::Int; )
return a(MyA(), x::Int)
end
@generated function get_method(imp::Type{MyA})
m = ([m for m = methods(a, Tuple{MyA, Vararg})])[1]
return m
end
(s::MyA)(args...) = a(s, args...)
end
```
"""
macro impl(expr)
return esc(implement_macro(__module__, __source__, expr))
end
function implement_macro(mod, source, implement_expr)
stripped = MacroTools.prewalk(rmlines, implement_expr)
T, fields, fname, func_sig_dicts = struct_capture(stripped)
interface_name = Symbol(lowerunder_to_firstcaps(string(fname)))
impl_type_name = isnothing(T) ?
Symbol(string(interface_name, get_and_inc_imp_number(interface_name))) :
T
first_func_sig_dict = first(func_sig_dicts)
specific_imp_func_decs = Dict[]
for func_sig_dict in func_sig_dicts
specific_imp_func_dec = deepcopy(func_sig_dict)
specific_imp_func_dec[:args] = [:(impl::$impl_type_name); specific_imp_func_dec[:args]]
if length(fields) > 0
fieldnames = [namify(f) for f in fields]
# Have to do some wonky thing here to get this to parse right
unpack_expr = :(@unpack $(fieldnames[1:(end-1)]...), $(fieldnames[end]) = impl)
else
unpack_expr = :(begin end)
end
specific_imp_func_dec[:body] = quote
try
$unpack_expr
$(specific_imp_func_dec[:body])
catch
src = $(string(source))
@error "error at $(src)"
rethrow()
end
end
push!(specific_imp_func_decs, specific_imp_func_dec)
end
joined_args = mi_join_args(func_sig_dicts)
joined_params = [parameter_names(e) for e in joined_args]
default_imp_func_dec = deepcopy(first_func_sig_dict)
default_imp_func_dec[:args] = [:(impl::Nothing); joined_args]
default_imp_func_dec[:whereparams] = unique(reduce((x,fsd)->append!(x,get(fsd,:whereparams,[])), func_sig_dicts;init=[]))
default_imp_func_dec[:body] = quote
try
return $(default_imp_func_dec[:name])($impl_type_name(), $(joined_params...))
catch
src = $(string(source))
@error "error at $(src)"
rethrow()
end
end
# TODO should do a typejoin on all retvals
default_imp_func_dec[:rtype] = :Any
if ~(:rtype in keys(first_func_sig_dict))
first_func_sig_dict[:rtype] = :Any
end
interface_module = get_module(mod, interface_name)
if length(fields) >= 1
struct_exp = with_kw(:(struct $impl_type_name <: $(interface_module).$(interface_name)
$(fields...)
end),
@__MODULE__,
false)
else
struct_exp = quote
struct $impl_type_name <: $(interface_module).$(interface_name)
$(fields...)
end
end
end
wparams = reduce(append!, [get(fsd, :whereparams, []) for fsd in func_sig_dicts]; init=[])
wparams = unique(wparams)
argtypes = [[get(fsd,:rtype,:Any);get_arg_types(fsd[:args])] for fsd in func_sig_dicts]
source_str = string(source)
result = quote
$struct_exp
$([combinedef(sifd) for sifd in specific_imp_func_decs]...)
$(combinedef(default_imp_func_dec))
@generated function get_method(imp::Type{$impl_type_name})
m = [m for m in methods($(first_func_sig_dict[:name]), Tuple{$impl_type_name, Vararg{Any}})][1]
return m
end
(s::$impl_type_name)(args...) = $(first_func_sig_dict[:name])(s, args...)
end
if display
println(prettify(result))
end
return result
end
function interface_capture(interface_exp)
@capture(interface_exp, (f_(args__)::R_ where W__) |
(f_(args__) where W__) |
(f_(args__)::R_) |
(f_(args__)))
return f, args, R, W
end
function struct_capture(expr)
if expr.args[1].head === :struct
@capture(expr.args[1], (struct T_ fields__ end) | (mutable struct T_ fields__ end))
fs = [splitdef(a) for a in expr.args[2:end]]
else
T = nothing
fields = []
fs = [splitdef(a) for a in expr.args]
end
fname = first(fs)[:name]
T,fields,fname,fs
end
###
### USING POLICIES
###
function with_policy(f, new_policy)
last_policy = set_policy(new_policy)
result = nothing
try
result = f()
catch err
rethrow()
finally
_ = set_policy(last_policy)
end
return result
end
end # module
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2011,
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51,
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309,
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198,
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incx = 1
incy = 1
| [
1939,
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] | 1.8 | 10 |
<filename>src/GeometricTools_gridabstract.jl
#=##############################################################################
# DESCRIPTION
Abstract grid type definition.
# AUTHORSHIP
* Author : <NAME>
* Email : <EMAIL>
* Created : May 2018
* License : MIT License
=###############################################################################
################################################################################
# ABSTRACT GRID TYPE
################################################################################
"""
Implementations of AbstractGrid are expected to have the following fields:
* `dims::Int64` : Number of dimensions.
* `nnodes::Int64` : Number of nodes in the grid.
* `ncells::Int64` : Number of cells in the grid.
<!-- * `bbox::Array{Int64, 1}` : Bounding box of the grid (cells in each dim). -->
and the following functions
```julia
function get_node(self::MyGrid, i::Int64)
# Returns the position of the i-th node (1-indexed) in the grid
end
function get_node(self::MyGrid, coor::Array{Int64,1})
# Returns the position of the node of subscript coordinates `coor`
# (1-indexed)
end
function get_cell(self::MyGrid, i::Int64)
# Returns the nodes indices of i-th cell in the grid (1-indexed)
end
function get_cell(self::MyGrid, coor::Array{Int64,1})
# Returns the node indices of the cell with subscript coordinates `coor`
# (1-indexed). The format corresponds to VTK_HEXAHEDRON (=12) in 3D,
# VTK_QUAD (=9) in 2D, or VTK_LINE (=3) in 1D---except that points are
# 1-indexed instead of 0-indexed.
end
function get_fieldval(self::MyGrid, field_name::String, coor::Array{Int64,1})
# Returns the value of node of coordinates `coor` (1-indexed) in the field
# 'field_name'.
end
function get_fieldval(self::MyGrid, field_name::String, i::Int64)
# Returns the value of i-th node (1-indexed) in the field 'field_name'.
end
function add_field(self::MyGrid, field_name::String, field_type::String,
field_data)
# Adds a field of data associated to each node.
#
# NOTE: each data entry must be a single value if `field_type==scalar`, or a
# 3-element array if `field_type==vector`.
end
function calculate_field(self::MyGrid, f, field_name::String, field_type::String)
# Evaluates the function `f` at each nodes and stores the values as a new
# field.
#
# NOTE: f must return a single value if `field_type==scalar`, or a 3-element
# array if `field_type==vector`.
end
function lintransform!(self::MyGrid, M::Array{Float64,2}, T::Array{Float64,1};
reset_fields::Bool=true)
# Rotates and translates the grid by the rotation matrix `M` and translation
# vector `T` (linear transformation).
end
function transform!(self::MyGrid, f; reset_fields::Bool=true)
# Applies the space transformation given by function `f` to the grid.
end
function save(self::MyGrid, filename::String; args...)
# Outputs a vtk file of this grid
end
function plot(self::MyGrid; fig_name="gridplot", fontsize=15,
xlims=nothing, ylims=nothing, zlims=nothing,
labelcells=true, labelnodes=false, labelndivs=true,
title_str=nothing)
# Plots the grid on PyPlot
end
```
"""
abstract type AbstractGrid end
# Implementations
for header_name in ["grid", "gridmulti", "gridspecials"]
include("GeometricTools_"*header_name*".jl")
end
# Implementations of AbstractGrid
GridTypes = Union{Grid, MultiGrid, GridTriangleSurface}
#= Extension of the Grid type
(extensions are any type that have properties `_ndivsnodes` and `_ndivsnodes`)
=#
GridExtentions = Union{Grid, GridTriangleSurface}
# Extensions
for header_name in ["gridextensions"]
include("GeometricTools_"*header_name*".jl")
end
##### GENERIC FUNCTIONS #######################################################
"Returns the centroid of the cell"
function get_cellcenter(self::GridTypes, args...)
nodes = get_cell(self, args...)
C = sum([get_node(self, node) for node in nodes])/size(nodes, 1)
return C
end
##### END OF ABSTRACT GRID #####################################################
| [
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] | 2.836589 | 1,536 |
function write_xsf( filnam, LL_in, atpos; molecule=false )
#
f = open(filnam, "w")
Natoms = size(atpos)[2]
#
if molecule
@printf(f, "MOLECULE\n")
else
@printf(f, "CRYSTAL\n")
end
LL = LL_in'
@printf(f, "PRIMVEC\n")
@printf(f, "%18.10f %18.10f %18.10f\n", LL[1,1], LL[1,2], LL[1,3])
@printf(f, "%18.10f %18.10f %18.10f\n", LL[2,1], LL[2,2], LL[2,3])
@printf(f, "%18.10f %18.10f %18.10f\n", LL[3,1], LL[3,2], LL[3,3])
@printf(f, "PRIMCOORD\n")
@printf(f, "%8d %8d\n", Natoms, 1)
for ia = 1:Natoms
@printf(f, "H %18.10f %18.10f %18.10f\n", atpos[1,ia], atpos[2,ia], atpos[3,ia])
end
close(f)
end
function write_xsf_3d_crystal( filnam, Ns, LL_in, data3d; center=zeros(3) )
#
f = open(filnam, "a") # FIXME: What if filnam is not exist?
#
LL = LL_in'
@printf(f, "BEGIN_BLOCK_DATAGRID_3D\n")
@printf(f, "made_by_ffr\n")
@printf(f, "DATAGRID_3D_UNKNOWN\n")
@printf(f, "%8d %8d %8d\n", Ns[1]+1, Ns[2]+1, Ns[3]+1 )
@printf(f, "%18.10f %18.10f %18.10f\n", center[1], center[2], center[3])
@printf(f, "%18.10f %18.10f %18.10f\n", LL[1,1], LL[1,2], LL[1,3])
@printf(f, "%18.10f %18.10f %18.10f\n", LL[2,1], LL[2,2], LL[2,3])
@printf(f, "%18.10f %18.10f %18.10f\n", LL[3,1], LL[3,2], LL[3,3])
#
rDat3d = reshape( data3d, (Ns[1],Ns[2],Ns[3]) )
for k = 1:Ns[3]+1
for j = 1:Ns[2]+1
for i = 1:Ns[1]+1
ii = ( i == Ns[1]+1 ? 1 : i )
jj = ( j == Ns[2]+1 ? 1 : j )
kk = ( k == Ns[3]+1 ? 1 : k )
#
@printf(f, "%18.10f\n", rDat3d[ii,jj,kk])
end
end
end
@printf(f, "END_DATAGRID_3D\n")
@printf(f, "END_BLOCK_DATAGRID_3D\n")
close(f)
end
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] | 1.64 | 1,100 |
<reponame>climate-machine/JRRTMGP
function setup_rfmip_as(
ds_lw_in,
idx_gases,
exp_no,
lookup,
::Type{FT},
::Type{DA},
::Type{VMR},
max_threads,
) where {FT <: AbstractFloat, DA, VMR}
FTA1D = DA{FT, 1}
FTA2D = DA{FT, 2}
deg2rad = FT(Ο) / FT(180)
nlay = Int(ds_lw_in.dim["layer"])
ncol = Int(ds_lw_in.dim["site"])
nlev = nlay + 1
ngas = lookup.n_gases
nbnd = lookup.n_bnd
lon = DA{FT, 1}(ds_lw_in["lon"][:])
lat = DA{FT, 1}(ds_lw_in["lat"][:])
lon = nothing # This example skips latitude dependent gravity computation
lat = nothing # to be consistent with the FORTRAN RRTMGP test case.
sfc_emis = DA{FT, 2}(repeat(reshape(Array{FT}(ds_lw_in["surface_emissivity"][:]), 1, :), nbnd, 1)) # all bands use same emissivity
sfc_alb = DA{FT, 2}(repeat(reshape(Array{FT}(ds_lw_in["surface_albedo"][:]), 1, :), nbnd, 1)) # all bands use same albedo
#--------------------------------------------------------------
zenith = Array{FT, 1}(deg2rad .* ds_lw_in["solar_zenith_angle"][:])
irrad = Array{FT, 1}(ds_lw_in["total_solar_irradiance"][:])
# block out coluumns with zenith > Ο/2
usecol = BitArray(undef, ncol)
usecol .= 1
for i in 1:ncol
if zenith[i] > FT(Ο) / 2 - 2 * eps(FT)
irrad[i] = FT(0)
usecol[i] = 0
end
end
zenith = DA{FT, 1}(zenith)
irrad = DA{FT, 1}(irrad)
#--------------------------------------------------------------
p_lev = ds_lw_in["pres_level"][:]
lev_ind = p_lev[1, 1] > p_lev[end, 1] ? (1:nlev) : (nlev:-1:1)
lay_ind = p_lev[1, 1] > p_lev[end, 1] ? (1:nlay) : (nlay:-1:1)
p_lev[lev_ind[end], :] .= lookup.p_ref_min
p_lev = DA{FT, 2}(p_lev[lev_ind, :])
p_lay = DA{FT, 2}(ds_lw_in["pres_layer"][:][lay_ind, :])
t_lev = DA{FT, 2}(ds_lw_in["temp_level"][:][lev_ind, :, exp_no])
t_lay = DA{FT, 2}(ds_lw_in["temp_layer"][:][lay_ind, :, exp_no])
t_sfc = DA{FT, 1}(ds_lw_in["surface_temperature"][:, exp_no])
col_dry = DA{FT, 2}(undef, nlay, ncol)
# Reading volume mixing ratios
vmr_h2o = FTA2D(ds_lw_in["water_vapor"][:][lay_ind, :, exp_no]) # vmr of H2O and O3
vmr_o3 = FTA2D(ds_lw_in["ozone"][:][lay_ind, :, exp_no]) # vary with height
vmrat = zeros(FT, ngas)
vmrat[idx_gases["co2"]] =
FT(ds_lw_in["carbon_dioxide_GM"][exp_no]) * parse(FT, ds_lw_in["carbon_dioxide_GM"].attrib["units"])
vmrat[idx_gases["n2o"]] =
FT(ds_lw_in["nitrous_oxide_GM"][exp_no]) * parse(FT, ds_lw_in["nitrous_oxide_GM"].attrib["units"])
vmrat[idx_gases["co"]] =
FT(ds_lw_in["carbon_monoxide_GM"][exp_no]) * parse(FT, ds_lw_in["carbon_monoxide_GM"].attrib["units"])
vmrat[idx_gases["ch4"]] = FT(ds_lw_in["methane_GM"][exp_no]) * parse(FT, ds_lw_in["methane_GM"].attrib["units"])
vmrat[idx_gases["o2"]] = FT(ds_lw_in["oxygen_GM"][exp_no]) * parse(FT, ds_lw_in["oxygen_GM"].attrib["units"])
vmrat[idx_gases["n2"]] = FT(ds_lw_in["nitrogen_GM"][exp_no]) * parse(FT, ds_lw_in["nitrogen_GM"].attrib["units"])
vmrat[idx_gases["ccl4"]] =
FT(ds_lw_in["carbon_tetrachloride_GM"][exp_no]) * parse(FT, ds_lw_in["carbon_tetrachloride_GM"].attrib["units"])
vmrat[idx_gases["cfc11"]] = FT(ds_lw_in["cfc11_GM"][exp_no]) * parse(FT, ds_lw_in["cfc11_GM"].attrib["units"])
vmrat[idx_gases["cfc12"]] = FT(ds_lw_in["cfc12_GM"][exp_no]) * parse(FT, ds_lw_in["cfc12_GM"].attrib["units"])
vmrat[idx_gases["cfc22"]] = FT(ds_lw_in["hcfc22_GM"][exp_no]) * parse(FT, ds_lw_in["hcfc22_GM"].attrib["units"])
vmrat[idx_gases["hfc143a"]] = FT(ds_lw_in["hfc143a_GM"][exp_no]) * parse(FT, ds_lw_in["hfc143a_GM"].attrib["units"])
vmrat[idx_gases["hfc125"]] = FT(ds_lw_in["hfc125_GM"][exp_no]) * parse(FT, ds_lw_in["hfc125_GM"].attrib["units"])
vmrat[idx_gases["hfc23"]] = FT(ds_lw_in["hfc23_GM"][exp_no]) * parse(FT, ds_lw_in["hfc23_GM"].attrib["units"])
vmrat[idx_gases["hfc32"]] = FT(ds_lw_in["hfc32_GM"][exp_no]) * parse(FT, ds_lw_in["hfc32_GM"].attrib["units"])
vmrat[idx_gases["hfc134a"]] = FT(ds_lw_in["hfc134a_GM"][exp_no]) * parse(FT, ds_lw_in["hfc134a_GM"].attrib["units"])
vmrat[idx_gases["cf4"]] = FT(ds_lw_in["cf4_GM"][exp_no]) * parse(FT, ds_lw_in["hfc23_GM"].attrib["units"])
# vmr[idx_gases["no2"]] = FT(ds_lw_in["no2_GM"][exp_no]) * # missing from input file
# parse(FT, ds_lw_in["hfc32_GM"].attrib["units"])
# This example skips latitude dependent gravity compution to be consistent with the
# FORTRAN RRTMGP test case.
compute_col_dry!(p_lev, col_dry, param_set, vmr_h2o, lat) # the example skips lat based gravity calculation
vmr = VMR(vmr_h2o, vmr_o3, FTA1D(vmrat))
cld_r_eff_liq = nothing
cld_r_eff_ice = nothing
cld_path_liq = nothing
cld_path_ice = nothing
cld_mask = nothing
ice_rgh = 1
#------------------
return (
AtmosphericState{
FT,
typeof(t_sfc),
typeof(lat),
typeof(p_lev),
typeof(cld_r_eff_liq),
typeof(cld_mask),
typeof(vmr),
Int,
}(
lon,
lat,
p_lay,
p_lev,
t_lay,
t_lev,
t_sfc,
col_dry,
vmr,
cld_r_eff_liq,
cld_r_eff_ice,
cld_path_liq,
cld_path_ice,
cld_mask,
ice_rgh,
nlay,
ncol,
ngas,
),
sfc_emis,
sfc_alb,
zenith,
irrad,
usecol,
)
#=
return (
ClearAtmosphericState{
FT,
DA{FT,1},
typeof(lat),
DA{FT,2},
typeof(vmr),
Int,
}(
lon,
lat,
p_lay,
p_lev,
t_lay,
t_lev,
t_sfc,
col_dry,
vmr,
nlay,
ncol,
ngas,
),
sfc_emis,
sfc_alb,
zenith,
irrad,
)
=#
end
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1267,
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796,
2,
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] | 1.765647 | 3,499 |
<reponame>xijiang/AGH.jl<gh_stars>0
"""
function read_g012(file)
---
This function read a `plink 012` genotype `file`.
It return a named tuple, with an `ID` string vector,
and `genotype` Int8 matrix.
Note, this function doesn't do error check.
The file integrity should be checked before this function.
The genotype matrix is of `nLoci` by `nID`, i.e., ID column majored.
Genotypes of the same ID are in the same column.
"""
function read_g012(file)
# Determine the dimensions of the genotypes
nid = countlines(file)
nlc = length(split(readline(file))) - 1
id = String[]
genotype = zeros(Int8, nlc, nid)
i = 1
for line in eachline(file)
# ToDo: speed up this reading using buffer.
t = split(line)
push!(id, t[1])
genotype[:, i] = parse.(Int8, t[2:end])
i += 1
end
return (ID = id, genotype = genotype)
end
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] | 2.537143 | 350 |
<filename>misc/judge.jl
using PkgBenchmark: judge, target_result, baseline_result,
writeresults, export_markdown
mkpath("tmp")
JUDGE_BASELINE = get(ENV, "JUDGE_BASELINE", "^HEAD")
results = judge("LyapunovExponents", JUDGE_BASELINE)
writeresults("tmp/benchmarks_target.json", target_result(results))
writeresults("tmp/benchmarks_baseline.json", baseline_result(results))
export_markdown("tmp/benchmarks_judge.md", results)
showall(results)
println()
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] | 2.826087 | 161 |
"""
Re-factoring and re-implementation of MGT
"""
module GapLas
import Printf: @printf
import GraphIdx
import GraphIdx: Graph, EdgeGraph, Edge, Weights, WeightedGraph
import GraphIdx: PrimMstMem, prim_mst_edges
import GraphIdx.Tree: RootedTree
import ..Dual: dual!, gap_vec!
import ..TreeDP: TreeDPMem, tree_dp!
import ..TreeFlow: extract_non_tree!, update_tree!
struct Sol{N}
x::Array{Float64, N}
Ξ±::Vector{Float64}
end
struct GapMem{N, WL<:Weights{Float64}}
sol::Sol{N}
y_tree::Array{Float64,N}
Ξ±_tree::Vector{Float64}
Ξ»_tree::WL
gamma::Vector{Float64}
wgraph::WeightedGraph
egraph::EdgeGraph
dp_mem::TreeDPMem
mst::PrimMstMem
tree::RootedTree
end
function GapMem(y::Array{Float64,N}, graph::Graph, lambda::Weights{Float64}) where {N}
@assert length(y) == GraphIdx.num_nodes(graph)
root_node = 1
m = GraphIdx.num_edges(graph)
n = GraphIdx.num_nodes(graph)
Ξ»_tree = similar(lambda, n)
Ξ±_tree = Vector{Float64}(undef, n)
egraph = collect(graph)
mst = PrimMstMem(egraph)
lam = Float64[lambda[i] for i=1:m]
tree = RootedTree(root_node, mst.parent)
return GapMem(
Sol(copy(y), zeros(Float64, m)), # sol
similar(y), # y_tree
Ξ±_tree,
Ξ»_tree,
Vector{Float64}(undef, m), # gamma
WeightedGraph(mst.neighbors, lam),
egraph,
TreeDPMem(n),
mst,
tree,
)
end
"""
General framework for iterated tree optimizer:
1. Allocate memory (of type `Mem`)
2. Call `gaplas!(mem, y, graph, Ξ», ΞΌ)` in every iteration (`max_iter` many times).
!!! note
`mem` must contain a `gamma` and an `x` field
"""
function gaplas(
y::Array{Float64,N},
graph::GraphT,
lambda::Weights{Float64},
mu::W2,
::Type{Mem} = GapMem;
max_iter::Int = 5,
verbose::Bool = true,
)::Array{Float64,N} where {N, GraphT<:Graph, W2<:Weights{Float64}, Mem}
mem = Mem(y, graph, lambda)
for it in 1:max_iter
gaplas!(mem, y, graph, lambda, mu)
if verbose
let gap = -sum(mem.gamma)
@printf("%4d %12.4f\n", it, gap)
end
end
end
return mem.sol.x
end
"""
Optional: node weights `ΞΌ` are set to `GraphIdx.Ones{Float64}`
"""
function gaplas(
y::Array{Float64,N},
graph::Graph,
lambda::Weights{Float64},
::Type{Mem} = GapMem;
max_iter::Int = 5,
verbose::Bool = true,
)::Array{Float64,N} where {N, Mem}
gaplas(
y, graph, lambda, GraphIdx.Ones{Float64}(), Mem; max_iter=max_iter, verbose=verbose
)
end
"""
Called in every iteration.
Implements the actual optimization, i.e.
"""
function gaplas!(
mem::GapMem{N, W1},
y::Array{Float64,N},
graph::Graph,
lambda::Weights{Float64},
mu::Weights{Float64},
) where {N, W1<:Weights{Float64}}
find_gap_tree!(mem, y, graph, lambda)
tree_dp!(mem.sol.x, mem.y_tree, mem.tree, mem.Ξ»_tree, mu, mem.dp_mem)
mem.Ξ±_tree .= vec(mem.sol.x) .- vec(mem.y_tree)
dual!(mem.Ξ±_tree, mem.dp_mem.proc_order, mem.mst.parent)
update_tree!(
mem.sol.Ξ±,
mem.Ξ±_tree,
mem.mst.selected,
mem.egraph,
mem.mst.parent,
)
end
"""
Determine a tree by choosing edges with high gap value.
"""
function find_gap_tree!(
mem::GapMem{N, W1}, y::Array{Float64,N}, graph::Graph, lambda::Weights{Float64}
) where {N, W1<:Weights{Float64}}
gap_vec!(mem.gamma, mem.sol.x, mem.sol.Ξ±, mem.wgraph, -1.0)
prim_mst_edges(mem.gamma, mem.tree.root, mem.mst)
mem.y_tree .= y
extract_non_tree!(
graph,
mem.mst.parent,
mem.y_tree,
mem.sol.Ξ±,
mem.Ξ»_tree,
lambda,
)
end
end
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220,
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1267,
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628,
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198
] | 2.085297 | 1,782 |
using DrWatson
@quickactivate "dummy_project"
using DrWatsonSim
using BSON
using Dates
function long_running_computation(p,output_path)
sleep(p[:duration])
result = p[:a]^p[:b]
BSON.bson(output_path, Dict(:result=>result))
return nothing
end
duration = [0.1, 1]
a = [1,3]
b = 1
parameter = @dict duration a b
if in_simulation_mode()
# Using simid() here is actually better, because then the
# no lookup it the index file is need which means, that the
# database must not be loked.
m = Metadata(simdir())
println("$(simid()): Loaded metadata file")
m["type"] = "Simple Computation"
m["started at"] = Dates.now()
println("$(simid()): Creating new files")
m_new = Metadata(simdir("newfile"))
m_new["extra"] = "This should be blocked"
println("$(simid()): Done creating new file")
end
@runsync x->long_running_computation(x, simdir("output.bson")) dict_list(parameter) datadir("sims")
if in_simulation_mode()
@info "Simulation done" simid()
isfile(simdir("output_first_run.bson")) || mv(simdir("output.bson"),simdir("output_first_run.bson"))
end
if !in_simulation_mode()
@info "All simulations done"
for folder in readdir(datadir("sims"))
path = datadir("sims", folder)
@rerunsync x->long_running_computation(x, simdir("output.bson")) path
end
end
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] | 2.567619 | 525 |
<reponame>igutierrezm/ANOVADDPTest.jl<filename>src/poisson.jl
struct PoissonData
X::Matrix{Int}
x::Vector{Int}
y::Vector{Int}
Xunique::Vector{Vector{Int}}
function PoissonData(X::Matrix{Int}, y::Vector{Int})
x = denserank([X[i, :] for i in 1:size(X, 1)])
Xunique = sort(unique([X[i, :] for i in 1:size(X, 1)]))
new(X, x, y, Xunique)
end
end
PoissonData(x::Vector{Int}, y::Vector{Int}) = PoissonData(x[:, :], y)
struct PoissonDDP <: AbstractDPM
parent::DPM
a0::Float64
b0::Float64
a1::Vector{Vector{Int}}
b1::Vector{Vector{Int}}
sumlogu::Vector{Vector{Float64}}
gammaprior::Womack
gamma::Vector{Bool}
G::Int
function PoissonDDP(
rng::AbstractRNG,
N::Int,
G::Int;
K0::Int = 1,
Ξ±a0::Float64 = 2.0,
Ξ±b0::Float64 = 4.0,
a0::Float64 = 2.0,
b0::Float64 = 4.0,
ΞΆ0::Float64 = 1.0,
)
parent = DPM(rng, N; K0, a0 = Ξ±a0, b0 = Ξ±b0)
a1 = [a0 * ones(Int, G)]
b1 = [b0 * ones(Int, G)]
sumlogu = [zeros(G)]
gammaprior = Womack(G - 1, ΞΆ0)
gamma = ones(Bool, G)
new(parent, a0, b0, a1, b1, sumlogu, gammaprior, gamma, G)
end
end
function parent_dpm(m::PoissonDDP)
m.parent
end
function add_cluster!(m::PoissonDDP)
@extract m : G a0 b0 a1 b1 sumlogu
push!(a1, a0 * ones(G))
push!(b1, b0 * ones(G))
push!(sumlogu, zeros(G))
end
function update_suffstats!(m::PoissonDDP, data)
@extract data : y x
@extract m : a0 b0 a1 b1 sumlogu gamma
d = cluster_labels(m)
while length(a1) < cluster_capacity(m)
add_cluster!(m)
end
for k in active_clusters(m)
a1[k] .= a0
b1[k] .= b0
sumlogu[k] .= 0.0
end
for i = 1:length(y)
di = d[i]
zi = iszero(gamma[x[i]]) ? 1 : x[i]
sumlogu[di][zi] += logfactorial(y[i])
a1[di][zi] += y[i]
b1[di][zi] += 1
end
end
function update_suffstats!(m::PoissonDDP, data, i::Int, k1::Int, k2::Int)
@extract data : y x
@extract m : a0 b0 a1 b1 sumlogu gamma
while length(a1) < cluster_capacity(m)
add_cluster!(m)
end
zi = iszero(gamma[x[i]]) ? 1 : x[i]
# Modify cluster/group k2/zi
a1[k2][zi] += y[i]
b1[k2][zi] += 1
sumlogu[k2][zi] += logfactorial(y[i])
# Modify cluster/group k1/zi
a1[k1][zi] -= y[i]
b1[k1][zi] -= 1
sumlogu[k1][zi] -= logfactorial(y[i])
end
function logpredlik(m::PoissonDDP, data, i::Int, k::Int)
d = cluster_labels(m)
@extract m : a1 b1 gamma
@extract data : y x
j = iszero(gamma[x[i]]) ? 1 : x[i]
a1kj = a1[k][j] - (d[i] == k) * y[i]
b1kj = b1[k][j] - (d[i] == k)
return logpdf(NegativeBinomial(a1kj, b1kj / (b1kj + 1)), y[i])
end
function logpredlik(m::PoissonDDP, train, predict, i::Int, k::Int)
@extract m : a1 b1 gamma
@extract predict : y x
j = iszero(gamma[x[i]]) ? 1 : x[i]
a1kj = a1[k][j]
b1kj = b1[k][j]
return logpdf(NegativeBinomial(a1kj, b1kj / (b1kj + 1)), y[i])
end
function logmglik(m::PoissonDDP, j::Int, k::Int)
@extract m : a0 b0 a1 b1 sumlogu
return (
a0 * log(b0) - a1[k][j] * log(b1[k][j]) +
loggamma(a1[k][j]) - loggamma(a0) -
sumlogu[k][j]
)
end
function update_gamma!(rng::AbstractRNG, m::PoissonDDP, data)
@extract m : gammaprior gamma
A = active_clusters(m)
# Resample gamma[g], given the other gamma's
for g = 2:length(gamma)
# log-odds (numerator)
gamma[g] = 1
update_suffstats!(m, data)
log_num = logpdf(gammaprior, gamma[2:end])
for k β A, j β (1, g)
log_num += logmglik(m, j, k)
end
# log-odds (denominator)
gamma[g] = 0
update_suffstats!(m, data)
log_den = logpdf(gammaprior, gamma[2:end])
for k β A, j β (1)
log_den += logmglik(m, j, k)
end
# log-odds and new gamma[g]
log_odds = log_num - log_den
gamma[g] = rand(rng) <= 1 / (1 + exp(-log_odds))
end
end
function update_hyperpars!(rng::AbstractRNG, m::PoissonDDP, data)
update_gamma!(rng, m, data)
end
# <NAME> (<EMAIL>) University of Tartu, Tartu, Estonia
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] | 1.858834 | 2,281 |
<gh_stars>0
using FuzzySets, Test
step_size = 0.005
levels = collect(0:step_size:1)
Aβ = FuzzyNumber(levels, number=1)
Aβ = FuzzyNumber(levels, number=2)
Bβ = FuzzyNumber(levels, number=0)
Bβ = FuzzyNumber(levels, number=0)
Aβ = FuzzyVector([Aβ, Aβ])
@test length(Aβ) == 2
@test Aβ[1] == Aβ
@test Aβ[2] == Aβ
@test iterate(Aβ, 1) == Aβ
@test iterate(Aβ, 2) == Aβ
@test isnothing(iterate(Aβ, 3))
Bβ = FuzzyVector([Bβ])
@test length(Bβ) == 1
@test Bβ[1] == Bβ
Bβ = Bβ βͺ [Bβ]
@test length(Bβ) == 2
@test Bβ[1] == Bβ
@test Bβ[2] == Bβ
@test Aβ == Aβ
@test Aβ == FuzzyVector([Aβ, Aβ])
@test Bβ != FuzzyVector([Aβ, Aβ])
@test cut(Aβ, 0) == [Interval(0.5, 1.5), Interval(1.5, 2.5)]
@test cut(Aβ, 1) == [Interval(1), Interval(2)]
Cβ = FuzzyNumber(levels, number=-1)
Cβ = FuzzyNumber(levels, number=3)
Cβ = FuzzyVector([Cβ, Cβ])
patterns = [Aβ, Cβ]
@test cut(patterns, 1, 1) == [Interval(1), Interval(-1)]
@test cut(patterns, 2, 1) == [Interval(2), Interval(3)] | [
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module PeriodizeSC
using Cubature: hcubature
using Cuba
using JSON
using NPZ
_Nc = 4
_NcN = 8 #Sites for nambu = _Nc*2
# Global Variables
II = eye(Complex{Float64}, _Nc)
ZEROS = zeros(Complex{Float64}, _Nc, _Nc)
"""
Type ModelVector holds all the relevant quantities needed for computations. The built-in member function ModelVector() creates an
instance of the type ModelVector.
#Arguments of ModelVector():
- t_::Float64: Nearest-neighbor hopping amplitude (Float64)
- tp_::Float64: Second nearest-neighbor hopping amplitude (Float64)
- tpp_::Float64: Third nearest-neighbor hopping amplitude (Float64)
- mu_::Float64: Chemical potential (Float64)
- wvec_::Float64: Matsubara frequency grid (Array{Complex{Float64}, 1})
- sEvec_c::Array{Complex{Float64}, 3}: Vector of self-energies (Array{Complex{Float64}, 3})
- cumulants_::Array{Complex{Float64}, 3}: Vector of cumulants of the Green's function (Array{Complex{Float64}, 3})
#Returns:
- ModelVector instance.
"""
type ModelVector
t_::Float64 ; tp_::Float64 ; tpp_::Float64; mu_::Float64
wvec_::Array{Complex{Float64}, 1} ; sEvec_c_::Array{Complex{Float64}, 3}
cumulants_::Array{Complex{Float64}, 3}
function ModelVector(t::Float64, tp::Float64, tpp::Float64, mu::Float64,
wvec::Array{Complex{Float64}, 1}, sEvec_c::Array{Complex{Float64}, 3})
cumulants = build_cumulants(wvec, mu, sEvec_c)
return new(t, tp, tpp, mu, wvec, sEvec_c, cumulants)
end
end
"""
Function member of the type ModelVector returning the cumulant of the Green's function.
Useful to compute the superfluid stiffness by periodizing the cumulant.
#Arguments:
- wvec_::Array{Complex{Float64}, 1}: Matsubara frequency grid (Array{Complex{Float64}, 1})
- mu_::Float64: Chemical potential (Float64)
- sEvec_c_::Array{Complex{Float64}, 3}: Vector of self-energies (Array{Complex{Float64}, 3})
#Returns:
- cumulants: Cumulants of the Green's function (Complex{Float64} matrix of size 2*Nc X 2*Nc)
"""
function build_cumulants(wvec::Array{Complex{Float64}, 1}, mu::Float64, sEvec_c::Array{Complex{Float64}, 3})
cumulants = zeros(Complex{Float64}, size(sEvec_c))
for (ii, ww) in enumerate(wvec)
tmp = zeros(Complex{Float64}, (_NcN, _NcN))
tmp[1, 1] = tmp[2, 2] = tmp[3, 3] = tmp[4, 4] = (ww + mu)
tmp[5, 5] = tmp[6, 6] = tmp[7, 7] = tmp[8, 8] = -conj((ww + mu))
tmp -= sEvec_c[ii, :, :]
cumulants[ii, :, :] = inv(tmp)
end
return cumulants
end
"""
Type Model holds all the relevant quantities needed for computations for a given Matsubara frequency. The built-in member function Model() instantiates
an object that a priori has a type ModelVector.
#Arguments of Model():
- modelvec: Object of type ModelVector
- ii::Integer: Matsubara frequency index number (Int64)
#Returns:
- Model instance.
"""
type Model
t_::Float64 ; tp_::Float64 ; tpp_::Float64 ; mu_::Float64
w_::Complex{Float64} ; sE_::Array{Complex{Float64}, 2}
cumulant_::Array{Complex{Float64}, 2}
function Model(modelvec::ModelVector, ii::Integer)
(t, tp, tpp, mu, w, sE_c, cumulant) = (modelvec.t_, modelvec.tp_, modelvec.tpp_, modelvec.mu_, modelvec.wvec_[ii], modelvec.sEvec_c_[ii, :, :], modelvec.cumulants_[ii, :, :])
return new(t, tp, tpp, mu, w, sE_c, cumulant)
end
end
"""
Function to construct the nearest-neighbor dispersion relation on CuO2 square lattice.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- epsilonk: Value of the dispersion relation on the 2d reciprocal lattice
"""
function epsilonk(model::Model, kx::Float64, ky::Float64)
epsilonk = -2.0*model.t_*(cos(kx) + cos(ky))
return epsilonk
end
"""
Function to construct the nearest-neighbor dispersion relation derivative with respect to kx on CuO2 square lattice.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- dxepsilonk: Value of the dispersion relation derivative along kx on the 2d reciprocal lattice
"""
function Dxepsilonk(model::Model, kx::Float64, ky::Float64)
dxepsilonk = 2.0*model.t_*sin(kx)
return dxepsilonk
end
"""
Function to construct the nearest-neighbor dispersion relation derivative with respect to ky on CuO2 square lattice.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- dyepsilonk: Value of the dispersion relation derivative along ky on the 2d reciprocal lattice
"""
function Dyepsilonk(model::Model, kx::Float64, ky::Float64)
dyepsilonk = 2.0*model.t_*sin(ky)
return dyepsilonk
end
"""
Function to construct the second nearest-neighbor dispersion relation on CuO2 square lattice.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- zetak: Value of the dispersion relation on the 2d reciprocal lattice
"""
function zetak(model::Model, kx::Float64, ky::Float64)
zetak = -2.0*model.tp_*(cos(kx+ky) + cos(kx-ky))
return zetak
end
"""
Function to construct the second nearest-neighbor dispersion relation derivative along kx on CuO2 square lattice.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- dxzetak: Value of the dispersion relation derivative along kx on the 2d reciprocal lattice
"""
function Dxzetak(model::Model, kx::Float64, ky::Float64)
dxzetak = 2.0*model.tp_*(sin(kx+ky)+sin(kx-ky))
return dxzetak
end
"""
Function to construct the second nearest-neighbor dispersion relation derivative along ky on CuO2 square lattice.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- dyzetak: Value of the dispersion relation derivative along ky on the 2d reciprocal lattice
"""
function Dyzetak(model::Model, kx::Float64, ky::Float64)
dyzetak = 2.0*model.tp_*(sin(kx+ky)-sin(kx-ky))
return dyzetak
end
"""
Function to construct the third nearest-neighbor dispersion relation on CuO2 square lattice.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- omegak: Value of the dispersion relation on the 2d reciprocal lattice
"""
function omegak(model::Model, kx::Float64, ky::Float64)
omegak = -2.0*model.tpp_*(cos(2.0*kx)+cos(2.0*ky))
return omegak
end
"""
Function to construct the third nearest-neighbor dispersion relation derivative along kx on CuO2 square lattice
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- dxomegak: Value of the dispersion relation derivative along kx on the 2d reciprocal lattice
"""
function Dxomegak(model::Model, kx::Float64, ky::Float64)
dxomegak = 4.0*model.tpp_*sin(2.0*kx)
return dxomegak
end
"""
Function to construct the third-nearest-neighbor dispersion relation derivative along ky on CuO2 square lattice
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- dyomegak: Value of the dispersion relation derivative along ky on the 2d reciprocal lattice
"""
function Dyomegak(model::Model, kx::Float64, ky::Float64)
dyomegak = 4.0*model.tpp_*sin(2.0*ky)
return dyomegak
end
"""
Function to construct the full dispersion relation on CuO2 square lattice
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- eps_0: Value of the full dispersion relation on the 2d reciprocal lattice
"""
function eps_0(model::Model, kx::Float64, ky::Float64)
return(epsilonk(model,kx,ky)+zetak(model,kx,ky)+omegak(model,kx,ky))
end
############################################################################## Important Note ###########################################################################
## The following functions construct the bare current vertices used when computing the in-plane superfluid stiffness. The situation in which each and every one of the ##
## functions is used is specified. ##
#########################################################################################################################################################################
######## Cumulant and Green's function periodization schemes SC only
function DxDxEpsilonbark(model::Model, kx::Float64, ky::Float64)
return (Dxepsilonk(model,kx,ky)+Dxzetak(model,kx,ky)+Dxomegak(model,kx,ky))*(Dxepsilonk(model,kx,ky)+Dxzetak(model,kx,ky)+Dxomegak(model,kx,ky))
end
function DyDyEpsilonbark(model::Model, kx::Float64, ky::Float64)
return (Dyepsilonk(model,kx,ky)+Dyzetak(model,kx,ky)+Dyomegak(model,kx,ky))*(Dyepsilonk(model,kx,ky)+Dyzetak(model,kx,ky)+Dyomegak(model,kx,ky))
end
####### Cumulant and Green's function periodization schemes SC+AFM mixed state
function DxDxZetakbar(model::Model, kx::Float64, ky::Float64)
return (Dxzetak(model,kx,ky)+Dxomegak(model,kx,ky))*(Dxzetak(model,kx,ky)+Dxomegak(model,kx,ky))
end
function DyDyZetakbar(model::Model, kx::Float64, ky::Float64)
return (Dyzetak(model,kx,ky)+Dyomegak(model,kx,ky))*(Dyzetak(model,kx,ky)+Dyomegak(model,kx,ky))
end
function DxDxZetakbarepsilonk(model::Model, kx::Float64, ky::Float64)
return (Dxzetak(model,kx,ky)+Dxomegak(model,kx,ky))*Dxepsilonk(model,kx,ky)
end
function DyDyZetakbarepsilonk(model::Model, kx::Float64, ky::Float64)
return (Dyzetak(model,kx,ky)+Dyomegak(model,kx,ky))*Dyepsilonk(model,kx,ky)
end
######## Trace scheme for SC+AFM mixed state
function DxDxzetak(model::Model, kx::Float64, ky::Float64)
return Dxzetak(model,kx,ky)*Dxzetak(model,kx,ky)
end
function DyDyzetak(model::Model, kx::Float64, ky::Float64)
return Dyzetak(model,kx,ky)*Dyzetak(model,kx,ky)
end
function DxDxomegak(model::Model, kx::Float64, ky::Float64)
return Dxomegak(model,kx,ky)*Dxomegak(model,kx,ky)
end
function DyDyomegak(model::Model, kx::Float64, ky::Float64)
return Dyomegak(model,kx,ky)*Dyomegak(model,kx,ky)
end
function DxDxepsilonk(model::Model, kx::Float64, ky::Float64)
return Dxepsilonk(model,kx,ky)*Dxepsilonk(model,kx,ky)
end
function DyDyepsilonk(model::Model, kx::Float64, ky::Float64)
return Dyepsilonk(model,kx,ky)*Dyepsilonk(model,kx,ky)
end
function DxDxzetakomegak(model::Model, kx::Float64, ky::Float64)
return Dxzetak(model,kx,ky)*Dxomegak(model,kx,ky)
end
function DyDyzetakomegak(model::Model, kx::Float64, ky::Float64)
return Dyzetak(model,kx,ky)*Dyomegak(model,kx,ky)
end
function DxDxzetakepsilonk(model::Model, kx::Float64, ky::Float64)
return Dxzetak(model,kx,ky)*Dxepsilonk(model,kx,ky)
end
function DyDyzetakepsilonk(model::Model, kx::Float64, ky::Float64)
return Dyzetak(model,kx,ky)*Dyepsilonk(model,kx,ky)
end
function DxDxomegakepsilonk(model::Model, kx::Float64, ky::Float64)
return Dxomegak(model,kx,ky)*Dxepsilonk(model,kx,ky)
end
function DyDyomegakepsilonk(model::Model, kx::Float64, ky::Float64)
return Dyomegak(model,kx,ky)*Dyepsilonk(model,kx,ky)
end
###################################################################### End of Important Note ################################################################################
#############################################################################################################################################################################
"""
Function defining the hopping between two layers (CuO2 planes) within a unit cell.
#Arguments:
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- tperp_squared: Value of the bilayer hopping in terms of the in-plane wavevectors
(Represents the current vertices in the current-current correlation function)
"""
function tperp(kx::Float64,ky::Float64)
coskx = cos(kx)
cosky = cos(ky)
tperp = -(coskx-cosky)*(coskx-cosky)
tperp_squared = tperp*tperp
return 1/2*tperp_squared ### In reality, one must add a factor of 1/2 to account of the integral along z-axis.
end
"""
Function adding the proper phases proportional to the reduced supercluster BZ to the intercluster hoppings.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- 2N_c x 2N_c complex-valued matrix t(k^{tilde})
"""
function tktilde(model::Model, kx::Float64, ky::Float64)
t = model.t_ ; tp = model.tp_; tpp = model.tpp_
k = [kx, ky]
r_sites = [[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
K_sites = pi*deepcopy(r_sites)
t_array = zeros(Complex{Float64}, (_Nc, _Nc))
for i in 1:_Nc
for j in 1:_Nc
for K in K_sites
t_array[i, j] += 1.0/_Nc * exp(1.0im*dot(K + k, r_sites[i] - r_sites[j])) * eps_0(model, (K + k)...)
end
end
end
return (vcat(hcat(t_array, ZEROS), hcat(ZEROS, -t_array)))
end
"""
Function building the full cluster Green's function.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements, self-energy and chemical potential
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- gf_ktilde: 2N_c x 2N_c complex-valued matrix G_c(i omega_n,k^{tilde})
"""
function build_gf_ktilde(model::Model, kx::Float64, ky::Float64)
zz = (model.w_ + model.mu_)*II
zz_c = -conj(-conj(model.w_) + model.mu_)*II
tmp_zz = (vcat(hcat(zz, ZEROS), hcat(ZEROS, -conj(zz)))) #### -conj(zz)
gf_ktilde = inv(tmp_zz - tktilde(model, kx, ky) - model.sE_)
return gf_ktilde
end
"""
Function pre-computing the full cluster Green's function on the BZ. Useful to speed up in-plane computations.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements, self-energy and chemical potential
- tk_el::Array{Complex{Float64},2}: pre-computed k-tilde-dependent hopping term (t(k^{tilde}))
#Returns:
- gf_ktilde: 2N_c x 2N_c complex-valued matrix G_c(i omega_n,k^{tilde})
"""
function build_gf_ktilde_prebuild(model::Model, tk_el::Array{Complex{Float64},2})
zz = (model.w_ + model.mu_)*II
tmp_zz = (vcat(hcat(zz, ZEROS), hcat(ZEROS, -conj(zz)))) #Same thing as zz_c = -conj(-conj(model.w_) + model.mu_)*II
gf_ktilde = inv(tmp_zz - tk_el - model.sE_)
return gf_ktilde
end
"""
Function used to periodize the Green's function with SC only.
#Arguments:
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
- args::Array{Complex{Float64},2}: Lattice Green's function in terms of k_tilde before being periodized (Array{Complex{Float64}, 2})
#Returns:
- nambu_periodized: Periodized lattice Green's function (Complex{Float64}-valued matrix 0.5*Nc X 0.5*Nc)
"""
function periodize_nocoex(kx::Float64, ky::Float64, arg::Array{Complex{Float64},2})
ex = exp(1.0im*kx)
ey = exp(1.0im*ky)
emx = conj(ex)
emy = conj(ey)
vk1 = [1.0,-1.0*ex,-1.0*ey,ex*ey] #equivalent transformation matrix for Green per. would be:
vk2 = [-1.0*emx,1.0,emx*ey,-1.0*ey] #[1.0,-1.0*ex,-1.0*ey,ex*ey]
vk3 = [-1.0*emy,emy*ex,1.0,-1.0*ex] #[-1.0*emx,1.0,emx*ey,-1.0*ey]
vk4 = [emx*emy,-1.0*emy,-1.0*emx,1.0] #[-1.0*emy,emy*ex,1.0,-1.0*ex]
nambu_periodized = zeros(Complex{Float64},(2,2)) #[emx*emy,-1.0*emy,-1.0*emx,1.0]
gup = arg[1:4,1:4]
ff = arg[1:4,5:end]
ffdag = arg[5:end,1:4]
gdown = arg[5:end,5:end]
llperiodized = [gup,ff,ffdag,gdown]
vk = [vk1,vk2,vk3,vk4]
list_total = []
for elem in llperiodized
list_i = []
summ=0.0
for i in 1:size(elem)[1]
for j in 1:size(elem)[2]
push!(list_i, elem[i,j]*vk[i][j])
end
end
summ=sum(list_i)
#println("sum = ", summ)
push!(list_total,summ)
end
nambu_periodized[1,1] = list_total[1]
nambu_periodized[1,2] = list_total[2]
nambu_periodized[2,1] = list_total[3]
nambu_periodized[2,2] = list_total[4]
return 0.25*nambu_periodized
end
"""
Function used to periodize the cumulant with SC only.
#Arguments:
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
- K::Array{Float64,1}: Array of superlattice reciprocal wavevectors (noted K)
- args::Array{Complex{Float64},2}: Lattice Green's function in terms of k_tilde before being periodized (Array{Complex{Float64}, 2})
#Returns:
- nambu_cum: Periodized lattice Green's function (Complex{Float64}-valued matrix 0.5*Nc X 0.5*Nc)
"""
function periodize_nocoex_cum(kx::Float64, ky::Float64, K::Array{Float64,1}, arg::Array{Complex{Float64},2})
nambu_cum = zeros(Complex{Float64},(2,2))
R = [[0,0],[1,0],[0,1],[1,1]]
k = [kx,ky]
gup = arg[1:4,1:4]
ff = arg[1:4,5:end]
ffdag = arg[5:end,1:4]
gdown = arg[5:end,5:end]
llperiodized = [gup,ff,ffdag,gdown]
nambu_sum = Array{Complex{Float64},1}()
for elem in llperiodized
tot = 0.0
for (i,R1) in enumerate(R)
for (j,R2) in enumerate(R)
tot += exp(-1.0im*dot(k + K + [pi,pi],R1 - R2))*elem[i,j]
end
end
push!(nambu_sum,tot)
end
nambu_cum[1,1] = nambu_sum[1]; nambu_cum[1,2] = nambu_sum[2]
nambu_cum[2,1] = nambu_sum[3]; nambu_cum[2,2] = nambu_sum[4]
return nambu_cum
end
"""
Function associated to the function periodizing the cumulant with SC only. Adds the dispersion relations after having built the cumulant.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements, self-energy and chemical potential
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
- K::Array{Float64,1}: Array of superlattice reciprocal wavevectors (noted K)
#Returns:
- Cumulant of the Green's function (Complex{Float64}-valued matrix Nc X Nc)
"""
function periodize_nocoex_cum_suite(model::Model, kx::Float64, ky::Float64, K::Array{Float64,1})
nambu_cum_inv = zeros(Complex{Float64},(2,2))
nambu_cum_inv = inv(periodize_nocoex_cum(kx,ky,K,model.cumulant_))
k = [kx,ky]
nambu_cum_inv[1,1] -= eps_0(model,(k + K + [pi,pi])...); nambu_cum_inv[2,2] += eps_0(model,(k + K + [pi,pi])...)
return inv(nambu_cum_inv)
end
"""
Function associated to the function periodizing the cumulant with SC only. Last inversion step corresping to the
full Green's function.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements, self-energy and chemical potential
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- nambu_periodized: Periodized lattice Green's function (Complex{Float64}-valued matrix Nc X Nc)
"""
function periodize_nocoex_cum_finale(model::Model, kx::Float64, ky::Float64)
nambu_periodized = zeros(Complex{Float64},(2,2))
Ks = [[0.,0.],[pi,0.],[0.,pi],[pi,pi]]
for K in Ks
#println(K)
nambu_periodized += periodize_nocoex_cum_suite(model,kx,ky,K)
end
return 0.25*nambu_periodized
end
"""
Function to periodize the Green's function with coexistence between AF and SC.
#Arguments:
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
- args::Array{Complex{Float64},2}: Lattice Green's function in terms of k_tilde before periodized (Array{Complex{Float64}, 2})
#Returns:
- nambu_periodized: Periodized lattice Green's function (Complex{Float64}-valued matrix Nc X Nc)
"""
function periodize_AFM_orb(arg::Array{Complex{Float64},2}, kx::Float64, ky::Float64) ###Appropriate periodization method for AF-SC systems
R_A = [[0.0,0.0],[1.0,1.0]]
R_B = [[1.0,0.0],[0.0,1.0]]
K_x = [[0.0,0.0],[pi,0.0]]; K_y = [[0.0,0.0],[0.0,pi]]
k = [kx,ky]
nambu_periodized = zeros(Complex{Float64}, _Nc, _Nc)
gup = arg[1:4, 1:4]
ff = arg[1:4, 5:end]
ffdag = arg[5:end, 1:4]
gdown = arg[5:end, 5:end]
llgreen = [gup, ff, ffdag, gdown]
blocks = zeros(Complex{Float64},2,2,4)
for (ii,elem) in enumerate(llgreen)
gAA = [[elem[1,1],elem[1,4]],[elem[4,1],elem[4,4]]]
gAB = [[elem[1,2],elem[1,3]],[elem[4,2],elem[4,3]]]
gBA = [[elem[2,1],elem[2,4]],[elem[3,1],elem[3,4]]]
gBB = [[elem[2,2],elem[2,3]],[elem[3,2],elem[3,3]]]
summAA = summAB = summBA = summBB = 0.0
for K in K_y ####K_x and K_y should be equivalent
for RA in 1:size(R_A)[1]
for RAprime in 1:size(R_A)[1]
summAA += exp(1.0im*dot(K + k, R_A[RAprime] - R_A[RA]))*gAA[RA][RAprime]
end
end
end
blocks[1,1,ii] = summAA
for K in K_y
for RA in 1:size(R_A)[1]
for RBprime in 1:size(R_B)[1]
summAB += exp(1.0im*dot(K + k, R_B[RBprime] - R_A[RA]))*gAB[RA][RBprime]
end
end
end
blocks[1,2,ii] = summAB
for K in K_y
for RB in 1:size(R_B)[1]
for RAprime in 1:size(R_A)[1]
summBA += exp(1.0im*dot(K + k, R_A[RAprime] - R_B[RB]))*gBA[RB][RAprime]
end
end
end
blocks[2,1,ii] = summBA
for K in K_y
for RB in 1:size(R_B)[1]
for RBprime in 1:size(R_B)[1]
summBB += exp(1.0im*dot(K + k, R_B[RBprime] - R_B[RB]))*gBB[RB][RBprime]
end
end
end
blocks[2,2,ii] = summBB
end
nambu_periodized[1:2,1:2] = blocks[:,:,1]
nambu_periodized[1:2,3:end] = blocks[:,:,2]
nambu_periodized[3:end,1:2] = blocks[:,:,3]
nambu_periodized[3:end,3:end] = blocks[:,:,4]
return(0.5*nambu_periodized)
end
"""
Function to periodize the cumulant in the case of coexistence between AF and SC.
#Arguments:
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
- args::Array{Complex{Float64},2}: Lattice Green's function in terms of k_tilde before periodized (Array{Complex{Float64}, 2})
- K::Array{Float64,1}: Array of superlattice reciprocal wavevectors (noted K)
#Returns:
- nambu_cum: Periodized lattice Green's function (Complex{Float64}-valued matrix of size Nc x Nc)
"""
function periodize_AFM_orb_cum(arg::Array{Complex{Float64},2}, kx::Float64, ky::Float64, K::Array{Float64,1}) ###Appropriate periodization method for AFM-SC systems
nambu_cum = zeros(Complex{Float64},4,4)
R_A = [[0.0,0.0],[1.0,1.0]]
R_B = [[1.0,0.0],[0.0,1.0]]
k = [kx,ky]
gup = arg[1:4, 1:4]
ff = arg[1:4, 5:end]
ffdag = arg[5:end, 1:4]
gdown = arg[5:end, 5:end]
llgreen = [gup, ff, ffdag, gdown]
sum_blocks = zeros(Complex{Float64},2,2,4)
for (ii,elem) in enumerate(llgreen)
gAA = [[elem[1,1],elem[1,4]],[elem[4,1],elem[4,4]]]
gAB = [[elem[1,2],elem[1,3]],[elem[4,2],elem[4,3]]]
gBA = [[elem[2,1],elem[2,4]],[elem[3,1],elem[3,4]]]
gBB = [[elem[2,2],elem[2,3]],[elem[3,2],elem[3,3]]]
summAA = summAB = summBA = summBB = 0.0
for RA in 1:size(R_A)[1]
for RAprime in 1:size(R_A)[1]
summAA += exp(1.0im*dot(K + k, R_A[RAprime] - R_A[RA]))*gAA[RA][RAprime]
end
end
sum_blocks[1,1,ii] = summAA
for RA in 1:size(R_A)[1]
for RBprime in 1:size(R_B)[1]
summAB += exp(1.0im*dot(K + k, R_B[RBprime] - R_A[RA]))*gAB[RA][RBprime]
end
end
sum_blocks[1,2,ii] = summAB
for RB in 1:size(R_B)[1]
for RAprime in 1:size(R_A)[1]
summBA += exp(1.0im*dot(K + k, R_A[RAprime] - R_B[RB]))*gBA[RB][RAprime]
end
end
sum_blocks[2,1,ii] = summBA
for RB in 1:size(R_B)[1]
for RBprime in 1:size(R_B)[1]
summBB += exp(1.0im*dot(K + k, R_B[RBprime] - R_B[RB]))*gBB[RB][RBprime]
end
end
sum_blocks[2,2,ii] = summBB
end
nambu_cum[1:2,1:2] = sum_blocks[:,:,1]
nambu_cum[1:2,3:end] = sum_blocks[:,:,2]
nambu_cum[3:end,1:2] = sum_blocks[:,:,3]
nambu_cum[3:end,3:end] = sum_blocks[:,:,4]
return nambu_cum
end
"""
Function associated to the function periodizing the cumulant in the case of coexistence between AFM and SC. Adds the
dispersion relations after having built the cumulant.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements, self-energy and chemical potential
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
- K::Array{Float64,1}: Array of superlattice reciprocal wavevectors (noted K)
#Returns:
- Cumulant of the lattice Green's function (Complex{Float64}-valued matrix of size Nc x Nc)
"""
function periodize_cum_coex_suite(model::Model, kx::Float64, ky::Float64, K::Array{Float64,1})
nambu_cum_inv = inv(periodize_AFM_orb_cum(model.cumulant_,kx,ky,K))
k = [kx,ky]
nambu_cum_inv[1,1]-=zetak(model,(k + K)...)+omegak(model,(k + K)...); nambu_cum_inv[2,2]-=zetak(model,(k + K)...)+omegak(model,(k + K)...); nambu_cum_inv[1,2]-=epsilonk(model,(k + K)...); nambu_cum_inv[2,1]-=epsilonk(model,(k + K)...)
nambu_cum_inv[3,3]+=zetak(model,(k + K)...)+omegak(model,(k + K)...); nambu_cum_inv[4,4]+=zetak(model,(k + K)...)+omegak(model,(k + K)...); nambu_cum_inv[4,3]+=epsilonk(model,(k + K)...); nambu_cum_inv[3,4]+=epsilonk(model,(k + K)...)
nambu_periodized_final = inv(nambu_cum_inv)
return nambu_periodized_final
end
"""
Function associated to the function periodizing the cumulant in the case of coexistence between AF and SC. Last inversion step corresping to the
full Green's function.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements, self-energy and chemical potential
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- nambu_periodized: Periodized lattice Green's function (Complex{Float64}-valued matrix Nc X Nc)
"""
function periodize_coex_cum_finale(model::Model, kx::Float64, ky::Float64)
nambu_periodized = zeros(Complex{Float64},(4,4))
Kx = [[0,0],[pi,0]]; Ky = [[0,0],[0,pi]]
for K in Kx ## or Ky
nambu_periodized += periodize_cum_coex_suite(model,kx,ky,K)
end
return 0.5*nambu_periodized
end
"""
Function to compute the superfluid stiffness in the case dSC and AF coexist (with cumulant periodization) perpendicular to the CuO2 cuprates plane
neglecting the current vertex corrections.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- Superfluid stiffness: Value of the superfluid stiffness perpendicular to the plane for dSC+AF data with cumulant periodization
"""
function stiffness_cumulant_AFM_SC(model::Model, kx::Float64, ky::Float64)
nambu_periodized_final = periodize_coex_cum_finale(model,kx,ky)
return 1.0*real(tperp(kx,ky)*4.0*(nambu_periodized_final[2,4]*nambu_periodized_final[3,1]+nambu_periodized_final[1,4]*nambu_periodized_final[3,2]+
nambu_periodized_final[2,3]*nambu_periodized_final[4,1]+nambu_periodized_final[1,3]*nambu_periodized_final[4,2]))
end
"""
Function to compute the superfluid stiffness in the case there is only dSC (with cumulant periodization) perpendicular to the CuO2 cuprates plane
neglecting the current vertex corrections.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- Superfluid stiffness: Value of the superfluid stiffness perpendicular to the plane for dSC-only data with cumulant periodization
"""
function stiffness_cum_SC(model::Model, kx::Float64, ky::Float64)
nambu_periodized_final = periodize_nocoex_cum_finale(model,kx,ky)
return 1.0*real(tperp(kx,ky)*4.0*(nambu_periodized_final[1,2]*nambu_periodized_final[2,1]))
end
"""
Function to compute the superfluid stiffness in the case there is only dSC (with Green periodization) perpendicular to the CuO2 cuprates plane and
neglecting the current vertex corrections
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- Superfluid stiffness: Value of the superfluid stiffness perpendicular to the ab plane for dSC-only data with Green periodization
"""
function stiffness_SC(model::Model, kx::Float64, ky::Float64)
nambu_periodized = periodize_nocoex(kx, ky, build_gf_ktilde(model, kx, ky)) ###### NEW or not NEW <-------------
return 1.0*real(4.0*tperp(kx,ky)*(nambu_periodized[1,2]*nambu_periodized[2,1])) ##Removed minus sign
end
"""
Function to compute the superfluid stiffness in the case there is only dSC (with Green periodization) in the CuO2 cuprates plane
neglecting the current vertex corrections
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
- inplane_axis::String: String-valued argument to specify in-plane axis along which computing the superfluid stiffness (\'xx\' or \'yy\')
- AFM_SC_1::Int64: Integer value set to 1 to compute pure SC superfluid stiffness using the formula in coexisting AF+dSC regime. Otherwize, let it be 0
#Returns:
- Superfluid stiffness: Value of the superfluid stiffness in the ab plane for dSC-only data with Green periodization
"""
function stiffness_nocoex_per_ab(model::Model, kx::Float64, ky::Float64, inplane_axis::String, AFM_SC_1::Int64)
if AFM_SC_1 == 1
G = periodize_AFM_orb(build_gf_ktilde(model, kx, ky), kx, ky)
if inplane_axis == "xx"
kxky = [DxDxZetakbar,DxDxZetakbarepsilonk,DxDxepsilonk]
elseif inplane_axis == "yy"
kxky = [DyDyZetakbar,DyDyZetakbarepsilonk,DyDyepsilonk]
else
println("OUPS!") && throw("inplane_axis parameter takes only two possible values: \"xx\" or \"yy\".")
end
return 1.0*real(kxky[1](model,kx,ky)*4.0*(G[1,3]*G[3,1]+G[2,3]*G[3,2]+G[1,4]*G[4,1]+G[2,4]*G[4,2])+
kxky[2](model,kx,ky)*2.0*((G[1,3]+G[2,4])*(G[3,2]+G[4,1])+(G[1,4]+G[2,3])*(G[3,1]+G[4,2]))+
kxky[2](model,kx,ky)*2.0*((G[1,3]+G[2,4])*(G[3,2]+G[4,1])+(G[1,4]+G[2,3])*(G[3,1]+G[4,2]))+
kxky[3](model,kx,ky)*4.0*(G[2,4]*G[3,1]+G[1,4]*G[3,2]+G[2,3]*G[4,1]+G[1,3]*G[4,2]))
elseif AFM_SC_1 == 0
G = periodize_nocoex(kx, ky, build_gf_ktilde(model, kx, ky))
if inplane_axis == "xx"
kxky = DxDxEpsilonbark
elseif inplane_axis == "yy"
kxky = DyDyEpsilonbark
else
println("OUPS!") && throw("inplane_axis parameter takes only two possible values: \"xx\" or \"yy\".")
end
return 1.0*real(kxky(model,kx,ky)*4.0*(G[1,2]*G[2,1]))
end
end
"""
Function to compute the superfluid stiffness in the case dSC+AF coexist (with Green periodization) perpendicular to the CuO2 cuprates plane and
neglecting the current vertex corrections
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- Superfluid stiffness: Value of the superfluid stiffness perpendicular to the ab plane for dSC+AF data with Green periodization
"""
function stiffness_test(model::Model, kx::Float64, ky::Float64)
nambu_periodized = periodize_AFM_orb(build_gf_ktilde(model, kx, ky), kx, ky)
return 1.0*real(4.0*tperp(kx,ky)*(nambu_periodized[2,4]*nambu_periodized[3,1]+nambu_periodized[1,4]*nambu_periodized[3,2]+
nambu_periodized[2,3]*nambu_periodized[4,1]+nambu_periodized[1,3]*nambu_periodized[4,2]))
end
"""
Function to compute the superfluid stiffness in the case dSC+AF coexist (with Green periodization) in the CuO2 cuprates plane
neglecting the current vertex corrections
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
- inplane_axis::String: String-valued argument to specify in-plane axis along which computing the superfluid stiffness (\'xx\' or \'yy\')
- AFM_SC_1::Int64: Integer value set to 1 to compute pure SC superfluid stiffness using the formula in coexisting AF+dSC regime. Otherwise let it be 0
#Returns:
- Superfluid stiffness: Value of the superfluid stiffness in the ab plane for dSC+AF data with Green periodization
"""
function stiffness_coex_per_ab(model::Model, kx::Float64, ky::Float64, inplane_axis::String)
G = periodize_AFM_orb(build_gf_ktilde(model, kx, ky), kx, ky)
if inplane_axis == "xx"
kxky = [DxDxEpsilonbark,DxDxZetakbar,DxDxZetakbarepsilonk,DxDxepsilonk]
elseif inplane_axis == "yy"
kxky = [DyDyEpsilonbark,DyDyZetakbar,DyDyZetakbarepsilonk,DyDyepsilonk]
else
println("OUPS!") && throw("inplane_axis parameter takes only two possible values: \"xx\" or \"yy\".")
end
return 1.0*real(kxky[1](model,kx,ky)*4.0*(G[1,3]*G[3,1]+G[2,3]*G[3,2]+G[1,4]*G[4,1]+G[2,4]*G[4,2])+
kxky[2](model,kx,ky)*2.0*((G[1,3]+G[2,4])*(G[3,2]+G[4,1])+(G[1,4]+G[2,3])*(G[3,1]+G[4,2]))+
kxky[2](model,kx,ky)*2.0*((G[1,3]+G[2,4])*(G[3,2]+G[4,1])+(G[1,4]+G[2,3])*(G[3,1]+G[4,2]))+
kxky[3](model,kx,ky)*4.0*(G[2,4]*G[3,1]+G[1,4]*G[3,2]+G[2,3]*G[4,1]+G[1,3]*G[4,2]))
end
"""
Function to compute the superfluid stiffness in the case there is dSC and AF (without periodization) perpendicular to the CuO2 cuprates plane
neglecting the current vertex corrections
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- Superfluid stiffness: Value of the superfluid stiffness perpendicular to the ab plane for dSC+AF data without periodization (tracing)
"""
function stiffness_cluster_G(model::Model, kx::Float64, ky::Float64)
G = build_gf_ktilde(model,kx,ky)
return 1.0*real(tperp(kx,ky)*4.0*(sum(G[1:4,5:end]))*(sum(G[5:end,1:4])))
end
"""
Function used to compute the superfluid stiffness in the case there is dSC and AF (without periodization) perpendicular to the CuO2 cuprates plane
neglecting the current vertex corrections
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- Superfluid stiffness: Value of the superfluid stiffness perpendicular to the ab plane for dSC+AF data without periodization
"""
function stiffness_cluster_G_first_neighbor(model::Model, kx::Float64, ky::Float64)
G = build_gf_ktilde(model,kx,ky)
return 1.0*real(tperp(kx,ky)*4.0*((G[3,7]+G[3,8]+G[4,7]+G[4,8])*(G[5,1]+G[5,2]+G[6,1]+G[6,2])+(G[1,7]+G[1,8]+
G[2,7]+G[2,8])*(G[5,3]+G[5,4]+G[6,3]+G[6,4])+(G[3,5]+G[3,6]+G[4,5]+G[4,6])*(G[7,1]+G[7,2]+G[8,1]+G[8,2])+(G[1,5]+G[1,6]+
G[2,5]+G[2,6])*(G[7,3]+G[7,4]+G[8,3]+G[8,4])))
end
"""
Function used to compute the superfluid stiffness in the case there is dSC and AF (without periodization) in the CuO2 cuprates plane
neglecting the current vertex corrections
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
- inplane_axis::String: In-plane axis along which the superfluid stiffness is to be computed.
#Returns:
- Superfluid stiffness: Value of the superfluid stiffness in the ab plane for dSC+AF data without periodization
"""
function stiffness_cluster_G_ab(model::Model, kx::Float64, ky::Float64, inplane_axis::String)
G = build_gf_ktilde(model,kx,ky)
if inplane_axis == "xx"
kxky = [DxDxomegak, DxDxzetakomegak, DxDxomegakepsilonk, DxDxzetakepsilonk, DxDxzetak, DxDxzetakepsilonk, DxDxomegakepsilonk, DxDxzetakepsilonk, DxDxepsilonk]
elseif inplane_axis == "yy"
kxky = [DyDyomegak, DyDyzetakomegak, DyDyomegakepsilonk, DyDyzetakepsilonk, DyDyzetak, DyDyzetakepsilonk, DyDyomegakepsilonk, DyDyzetakepsilonk, DyDyepsilonk]
else
println("OUPS!") && throw("inplane_axis parameter takes only two possible values: \"xx\" or \"yy\".")
end
return 1.0*real((kxky[1](model,kx,ky)*4.0*(G[1,5]*G[5,1]+G[2,5]*G[5,2]+G[3,5]*G[5,3]+G[4,5]*G[5,4]+G[1,6]*G[6,1]+G[2,6]*G[6,2]+G[3,6]*G[6,3]+G[4,6]*G[6,4]+G[1,7]*G[7,1]+G[2,7]*G[7,2]+G[3,7]*G[7,3]+G[4,7]*G[7,4]+G[1,8]*G[8,1]+G[2,8]*G[8,2]+G[3,8]*G[8,3]+G[4,8]*G[8,4])+
kxky[2](model,kx,ky)*2.0*((G[1,5]+G[2,6])*(G[5,2]+G[6,1])+(G[1,6]+G[2,5])*(G[5,1]+G[6,2])+(G[3,5]+G[4,6])*(G[5,4]+G[6,3])+(G[3,6]+G[4,5])*(G[5,3]+G[6,4])+(G[1,7]+G[2,8])*(G[7,2]+G[8,1])+(G[1,8]+G[2,7])*(G[7,1]+G[8,2])+(G[3,7]+G[4,8])*(G[7,4]+G[8,3])+(G[3,8]+G[4,7])*(G[7,3]+G[8,4]))+
kxky[3](model,kx,ky)*2.0*(G[3,5]*G[5,1]+G[4,5]*G[5,1]+G[3,5]*G[5,2]+G[4,5]*G[5,2]+G[3,7]*G[5,3]+G[3,8]*G[5,3]+(G[2,7]+G[2,8])*(G[5,2]+G[6,2])+(G[3,6]+G[4,6])*(G[6,1]+G[6,2])+G[3,7]*G[6,3]+G[3,8]*G[6,3]+(G[4,7]+G[4,8])*(G[5,4]+G[6,4])+G[2,6]*(G[6,3]+G[6,4])+G[3,7]*G[7,1]+G[4,7]*G[7,1]+G[3,7]*G[7,2]+G[4,7]*G[7,2]+G[3,5]*G[7,3]+G[3,6]*G[7,3]+G[4,5]*G[7,4]+G[4,6]*G[7,4]+G[2,7]*(G[7,3]+G[7,4])+G[1,7]*(G[5,1]+G[6,1]+G[7,3]+G[7,4])+G[3,8]*G[8,1]+G[4,8]*G[8,1]+G[1,5]*(G[5,3]+G[5,4]+G[7,1]+G[8,1])+G[1,6]*(G[6,3]+G[6,4]+G[7,1]+G[8,1])+(G[3,8]+G[4,8])*G[8,2]+G[2,6]*(G[7,2]+G[8,2])+G[2,5]*(G[5,3]+G[5,4]+G[7,2]+G[8,2])+G[3,5]*G[8,3]+G[3,6]*G[8,3]+(G[4,5]+G[4,6])*G[8,4]+G[2,8]*(G[8,3]+G[8,4])+G[1,8]*(G[5,1]+G[6,1]+G[8,3]+G[8,4]))+
kxky[4](model,kx,ky)*2.0*((G[1,5]+G[2,6])*(G[5,2]+G[6,1])+(G[1,6]+G[2,5])*(G[5,1]+G[6,2])+(G[3,5]+G[4,6])*(G[5,4]+G[6,3])+(G[3,6]+G[4,5])*(G[5,3]+G[6,4])+(G[1,7]+G[2,8])*(G[7,2]+G[8,1])+(G[1,8]+G[2,7])*(G[7,1]+G[8,2])+(G[3,7]+G[4,8])*(G[7,4]+G[8,3])+(G[3,8]+G[4,7])*(G[7,3]+G[8,4]))+
kxky[5](model,kx,ky)*4.0*(G[2,6]*G[5,1]+G[1,6]*G[5,2]+G[4,6]*G[5,3]+G[3,6]*G[5,4]+G[2,5]*G[6,1]+G[1,5]*G[6,2]+G[4,5]*G[6,3]+G[3,5]*G[6,4]+G[2,8]*G[7,1]+G[1,8]*G[7,2]+G[4,8]*G[7,3]+G[3,8]*G[7,4]+G[2,7]*G[8,1]+G[1,7]*G[8,2]+G[4,7]*G[8,3]+G[3,7]*G[8,4])+
kxky[6](model,kx,ky)*2.0*(G[3,6]*G[5,1]+G[4,6]*G[5,1]+G[3,6]*G[5,2]+G[4,6]*G[5,2]+G[4,7]*G[5,3]+G[4,8]*G[5,3]+G[3,7]*G[5,4]+G[3,8]*G[5,4]+G[3,5]*G[6,1]+G[4,5]*G[6,1]+G[3,5]*G[6,2]+G[4,5]*G[6,2]+G[4,7]*G[6,3]+G[4,8]*G[6,3]+G[3,7]*G[6,4]+G[3,8]*G[6,4]+G[3,8]*G[7,1]+G[4,8]*G[7,1]+G[3,8]*G[7,2]+G[4,8]*G[7,2]+G[4,5]*G[7,3]+G[4,6]*G[7,3]+G[3,5]*G[7,4]+G[3,6]*G[7,4]+G[2,8]*(G[5,1]+G[6,1]+G[7,3]+G[7,4])+G[1,8]*(G[5,2]+G[6,2]+G[7,3]+G[7,4])+G[3,7]*G[8,1]+G[4,7]*G[8,1]+G[2,6]*(G[5,3]+G[5,4]+G[7,1]+G[8,1])+G[2,5]*(G[6,3]+G[6,4]+G[7,1]+G[8,1])+(G[3,7]+G[4,7])*G[8,2]+G[1,6]*(G[5,3]+G[5,4]+G[7,2]+G[8,2])+G[1,5]*(G[6,3]+G[6,4]+G[7,2]+G[8,2])+G[4,5]*G[8,3]+G[4,6]*G[8,3]+(G[3,5]+G[3,6])*G[8,4]+G[2,7]*(G[5,1]+G[6,1]+G[8,3]+G[8,4])+G[1,7]*(G[5,2]+G[6,2]+G[8,3]+G[8,4]))+
kxky[7](model,kx,ky)*2.0*(G[3,5]*G[5,1]+G[4,5]*G[5,1]+G[3,5]*G[5,2]+G[4,5]*G[5,2]+G[3,7]*G[5,3]+G[3,8]*G[5,3]+(G[2,7]+G[2,8])*(G[5,2]+G[6,2])+(G[3,6]+G[4,6])*(G[6,1]+G[6,2])+G[3,7]*G[6,3]+G[3,8]*G[6,3]+(G[4,7]+G[4,8])*(G[5,4]+G[6,4])+G[2,6]*(G[6,3]+G[6,4])+G[3,7]*G[7,1]+G[4,7]*G[7,1]+G[3,7]*G[7,2]+G[4,7]*G[7,2]+G[3,5]*G[7,3]+G[3,6]*G[7,3]+G[4,5]*G[7,4]+G[4,6]*G[7,4]+G[2,7]*(G[7,3]+G[7,4])+G[1,7]*(G[5,1]+G[6,1]+G[7,3]+G[7,4])+G[3,8]*G[8,1]+G[4,8]*G[8,1]+G[1,5]*(G[5,3]+G[5,4]+G[7,1]+G[8,1])+G[1,6]*(G[6,3]+G[6,4]+G[7,1]+G[8,1])+(G[3,8]+G[4,8])*G[8,2]+G[2,6]*(G[7,2]+G[8,2])+G[2,5]*(G[5,3]+G[5,4]+G[7,2]+G[8,2])+G[3,5]*G[8,3]+G[3,6]*G[8,3]+(G[4,5]+G[4,6])*G[8,4]+G[2,8]*(G[8,3]+G[8,4])+G[1,8]*(G[5,1]+G[6,1]+G[8,3]+G[8,4]))+
kxky[8](model,kx,ky)*2.0*(G[3,6]*G[5,1]+G[4,6]*G[5,1]+G[3,6]*G[5,2]+G[4,6]*G[5,2]+G[4,7]*G[5,3]+G[4,8]*G[5,3]+G[3,7]*G[5,4]+G[3,8]*G[5,4]+G[3,5]*G[6,1]+G[4,5]*G[6,1]+G[3,5]*G[6,2]+G[4,5]*G[6,2]+G[4,7]*G[6,3]+G[4,8]*G[6,3]+G[3,7]*G[6,4]+G[3,8]*G[6,4]+G[3,8]*G[7,1]+G[4,8]*G[7,1]+G[3,8]*G[7,2]+G[4,8]*G[7,2]+G[4,5]*G[7,3]+G[4,6]*G[7,3]+G[3,5]*G[7,4]+G[3,6]*G[7,4]+G[2,8]*(G[5,1]+G[6,1]+G[7,3]+G[7,4])+G[1,8]*(G[5,2]+G[6,2]+G[7,3]+G[7,4])+G[3,7]*G[8,1]+G[4,7]*G[8,1]+G[2,6]*(G[5,3]+G[5,4]+G[7,1]+G[8,1])+G[2,5]*(G[6,3]+G[6,4]+G[7,1]+G[8,1])+(G[3,7]+G[4,7])*G[8,2]+G[1,6]*(G[5,3]+G[5,4]+G[7,2]+G[8,2])+G[1,5]*(G[6,3]+G[6,4]+G[7,2]+G[8,2])+G[4,5]*G[8,3]+G[4,6]*G[8,3]+(G[3,5]+G[3,6])*G[8,4]+G[2,7]*(G[5,1]+G[6,1]+G[8,3]+G[8,4])+G[1,7]*(G[5,2]+G[6,2]+G[8,3]+G[8,4]))+
kxky[9](model,kx,ky)*4.0*((G[3,7]+G[3,8]+G[4,7]+G[4,8])*(G[5,1]+G[5,2]+G[6,1]+G[6,2])+(G[1,7]+G[1,8]+G[2,7]+G[2,8])*(G[5,3]+G[5,4]+G[6,3]+G[6,4])+(G[3,5]+G[3,6]+G[4,5]+G[4,6])*(G[7,1]+G[7,2]+G[8,1]+G[8,2])+(G[1,5]+G[1,6]+G[2,5]+G[2,6])*(G[7,3]+G[7,4]+G[8,3]+G[8,4]))))
end
"""
Function to compute the superfluid stiffness in the case there is dSC only (without periodization) perpendicular to the CuO2 cuprates plane
neglecting the current vertex corrections
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- Superfluid stiffness: Value of the superfluid stiffness perpendicular to the ab plane for dSC-only data without periodization
"""
function stiffness_trace_G(model::Model, kx::Float64, ky::Float64)
G = build_gf_ktilde(model,kx,ky)
return 1.0*real(tperp(kx,ky)*4.0*trace(G[1:4,5:end]*G[5:end,1:4]))
end
"""
Function to compute the superfluid stiffness in the case there is dSC only (without periodization) in the CuO2 cuprates plane neglecting the current vertex corrections
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- kx::Float64: Wavevector in x direction
- ky::Float64: Wavevector in y direction
#Returns:
- Superfluid stiffness: Value of the superfluid stiffness in the ab plane for dSC-only data without periodization
"""
function stiffness_trace_G_ab(model::Model, kx::Float64, ky::Float64, inplane_axis::String)
G = build_gf_ktilde(model,kx,ky)
if inplane_axis == "xx"
kxky = DxDxEpsilonbark
elseif inplane_axis == "yy"
kxky = DyDyEpsilonbark
else
println("OUPS!") && throw("inplane_axis parameter takes only two possible values: \"xx\" or \"yy\".")
end
return 1.0*real(kxky(model,kx,ky)*kxky(model,kx,ky)*4.0*trace(G[1:4,5:end]*G[5:end,1:4]))
end
"""
Template function useful to perform integration over the BZ when computing in-plane superfluid stiffness without periodization.
Called in module Stiffness.jl.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- param::Tuple{String,String}: Tuple of two strings specifying the direction along which to compute the superfluid stiffness and the state (pure or mixed).
#Returns:
- Superfluid stiffness integrand. It is a function object used to compute integrals.
"""
function make_stiffness_cluster_G_kintegrand_ab(model::PeriodizeSC.Model, param::T) where T <: Tuple{AbstractString,AbstractString}
function stiffness_kintegrand(kk::Array{Float64,1})
if isa(param,Tuple{String,String})
if param[1] == "xx" && param[2] == "COEX"
return(PeriodizeSC.stiffness_cluster_G_ab(model,kk[1],kk[2],"xx"))
elseif param[1] == "yy" && param[2] == "COEX"
return(PeriodizeSC.stiffness_cluster_G_ab(model,kk[1],kk[2],"yy"))
elseif param[1] == "xx" && param[2] == "NOCOEX"
return(PeriodizeSC.stiffness_trace_G_ab(model,kk[1],kk[2],"xx"))
elseif param[1] == "yy" && param[2] == "NOCOEX"
return(PeriodizeSC.stiffness_trace_G_ab(model,kk[1],kk[2],"yy"))
end
else
throw(ErrorException("Error occurred in function PeriodizeSC.make_stiffness_cluster_G_kintegrand_ab. Type not permitted in template."))
end
end
return stiffness_kintegrand
end
"""
Template function useful to perform integration over the BZ when computing in-plane superfluid stiffness with periodization.
Called in module Stiffness.jl.
#Arguments:
- model: model instance to have access to the attributes such as hopping elements
- param::Tuple{String,String,Int64}: Tuple of three elements specifying the direction along which to compute the superfluid stiffness, the state (pure or mixed) and an internal debug parameter.
#Returns:
- Superfluid stiffness integrand. It is a function object used to compute integrals.
"""
function make_stiffness_kintegrand_per_ab(model::PeriodizeSC.Model, param::T) where T <: Tuple{AbstractString,AbstractString,Number}
function stiffness_kintegrand(kk::Array{Float64,1})
if isa(param,Tuple{String,String,Int64})
if param[1] == "xx" && param[2] == "COEX"
return(PeriodizeSC.stiffness_coex_per_ab(model,kk[1],kk[2],"xx"))
elseif param[1] == "yy" && param[2] == "COEX"
return(PeriodizeSC.stiffness_coex_per_ab(model,kk[1],kk[2],"yy"))
elseif param[1] == "xx" && param[2] == "NOCOEX" && param[3] == 0
return(PeriodizeSC.stiffness_nocoex_per_ab(model,kk[1],kk[2],"xx",0))
elseif param[1] == "xx" && param[2] == "NOCOEX" && param[3] == 1
return(PeriodizeSC.stiffness_nocoex_per_ab(model,kk[1],kk[2],"xx",1))
elseif param[1] == "yy" && param[2] == "NOCOEX" && param[3] == 0
return(PeriodizeSC.stiffness_nocoex_per_ab(model,kk[1],kk[2],"yy",0))
elseif param[1] == "yy" && param[2] == "NOCOEX" && param[3] == 1
return(PeriodizeSC.stiffness_nocoex_per_ab(model,kk[1],kk[2],"yy",1))
end
else
throw(ErrorException("Error occurred in function PeriodizeSC.make_stiffness_cluster_G_kintegrand_ab. Type not permitted in template."))
end
end
return stiffness_kintegrand
end
############################################################################## Important Note ###########################################################################
## The following functions act as decorators to the superfluid stiffness functions. These functions are necessary to compute the integrals over k-space. The situation ##
## in which each and every one of the functions is used is specified by its name. ##
#########################################################################################################################################################################
function make_stiffness_trace_G_kintegrand(model::PeriodizeSC.Model)
function stiffness_kintegrand(kk::Array{Float64,1})
return(PeriodizeSC.stiffness_trace_G(model,kk[1],kk[2]))
end
return stiffness_kintegrand
end
function make_stiffness_cluster_G_kintegrand(model::PeriodizeSC.Model)
function stiffness_kintegrand(kk::Array{Float64,1})
return(PeriodizeSC.stiffness_cluster_G(model,kk[1],kk[2]))
end
return stiffness_kintegrand
end
function make_stiffness_cluster_G_kintegrand_fourth(model::PeriodizeSC.Model)
function stiffness_kintegrand(kk::Array{Float64,1})
return(PeriodizeSC.stiffness_cluster_G_first_neighbor(model,kk[1],kk[2]))
end
return stiffness_kintegrand
end
function make_stiffness_kintegrand_cum_AFM_SC(model::PeriodizeSC.Model)
function stiffness_kintegrand(kk::Array{Float64, 1})
return(PeriodizeSC.stiffness_cumulant_AFM_SC(model, kk[1], kk[2]))
end
return stiffness_kintegrand
end
function make_stiffness_kintegrand_cum_SC(model::PeriodizeSC.Model)
function stiffness_kintegrand(kk::Array{Float64, 1})
return(PeriodizeSC.stiffness_cum_SC(model, kk[1], kk[2]))
end
return stiffness_kintegrand
end
function make_stiffness_kintegrand_SC(model::PeriodizeSC.Model)
function stiffness_kintegrand(kk::Array{Float64,1})
return(PeriodizeSC.stiffness_SC(model, kk[1], kk[2]))
end
return stiffness_kintegrand
end
function make_stiffness_kintegrand_test(model::PeriodizeSC.Model)
function stiffness_kintegrand(kk::Array{Float64,1})
return(PeriodizeSC.stiffness_test(model,kk[1],kk[2]))
end
return stiffness_kintegrand
end
###################################################################### End of Important Note ################################################################################
#############################################################################################################################################################################
"""
Function used to sum over a grid of the reduced BZ.
#Arguments:
- modelvector: modelvector instance
- fct: Function object to be used to sum over the reduced BZ.
- gridK::Int64: k-space grid NxN, where N is set to 100 by default.
- len_sEvec_c::Int64: Length of the Matsubara frequency grid. Set by default to 500.
#Returns:
- Returns an array of two columns contaning the k-integrated Green's function and the associated Matsubara frequency.
"""
function sum_RBZ(modelvector::ModelVector, fct; gridK::Int64 = 100, len_sEvec_c::Int64=500)
len_sEvec_c > size(modelvector.sEvec_c_)[1] && throw(ErrorException("You have just exceeded the number of Matsubara frequencies available for calculations!"))
kx = linspace(-pi,pi,gridK)
ky = linspace(-pi,pi,gridK)
println("Length of len_sEvec_c and length of modelvector.sEvec_c_, respectively :", len_sEvec_c, size(modelvector.sEvec_c_)[1])
result_k_ind = Array{Float64}(len_sEvec_c)
for n in 1:len_sEvec_c
stiffness = 0.0
model = Model(modelvector,n)
for i in 1:size(ky)[1]
for j in 1:size(kx)[1]
stiffness += fct(model,kx[j],ky[i])
end
end
stiffness = 1./(2.0*gridK^2)*stiffness
result_k_ind[n] = stiffness
println("stiffness = ", stiffness)
end
result_out = hcat(modelvector.wvec_[1:len_sEvec_c], result_k_ind)
return(result_out)
end
function sum_BZ(modelvector::ModelVector, fct; gridK::Int64 = 100, len_sEvec_c::Int64=500)
println("in sum_BZ: 2.0*sum_RBZ")
result_out = 2.0*sum_RBZ(modelvector, fct, gridK=gridK, len_sEvec_c=len_sEvec_c)
return result_out
end
"""
Function used to integrate over the BZ when computing out-of-plane superfluid stiffness.
#Arguments:Integer value set to 1 to compute pure SC superfluid stiffness using the formula in coexisting AF+dSC regime. Otherwize, let it be 0
- modelvectInteger value set to 1 to compute pure SC superfluid stiffness using the formula in coexisting AF+dSC regime. Otherwize, let it be 0
- fct: FuncInteger value set to 1 to compute pure SC superfluid stiffness using the formula in coexisting AF+dSC regime. Otherwize, let it be 0
- gridK::InInteger value set to 1 to compute pure SC superfluid stiffness using the formula in coexisting AF+dSC regime. Otherwize, let it be 0
- len_sEvecInteger value set to 1 to compute pure SC superfluid stiffness using the formula in coexisting AF+dSC regime. Otherwize, let it be 0
#Returns:
- Returns an array of two columns contaning the k-integrated Green's function and the associated Matsubara frequency.
"""
function calcintegral_RBZ(modelvector::ModelVector, fct; maxevals::Int64=100_000, len_sEvec_c::Int64=500)
len_sEvec_c > size(modelvector.sEvec_c_)[1] && throw(ErrorException("You have just exceeded the number of Matsubara frequencies available for calculations!"))
println("Length of len_sEvec_c and length of modelvector.sEvec_c_, respectively : ", len_sEvec_c, " ", size(modelvector.sEvec_c_)[1])
result = Array{Float64,1}(len_sEvec_c)
println("in calcintegral_RBZ, kwargs")
for n in 1:len_sEvec_c
model = Model(modelvector, n)
#println("w_: ", model.w_)
result[n] = (1.0/2.0)*(2.0*pi)^(-2.0)*hcubature(fct(model), (-pi,-pi), (pi,pi), reltol=1.49e-8, abstol=1.49e-8, maxevals=maxevals)[1]
#println("stiff: ", result[n])
end
result_out = hcat(modelvector.wvec_[1:len_sEvec_c], result)
return result_out
end
function calcintegral_BZ(modelvector::ModelVector, fct; maxevals::Int64=100_000, len_sEvec_c::Int64=500)
println("in calcintegral_BZ: 2.0*calcintegral_RBZ")
result_out = 2.0*calcintegral_RBZ(modelvector,fct,len_sEvec_c=len_sEvec_c)
return result_out
end
"""
Function used to integrate over the BZ when computing in-plane superfluid stiffness.
#Arguments:
- modelvector: modelvector instance
- fct: Function object to be used to integrate over the BZ.
- param::Tuple{String,String,Int64}: Tuple of three elements necessary to provide because of the form of the parameter fct used.
- gridK::Int64: k-space grid NxN, where N is set to 100 by default.
- len_sEvec_c::Int64: Length of the Matsubara frequency grid. Set by default to 500.
#Returns:
- Returns an array of two columns contaning the k-integrated Green's function and the associated Matsubara frequency.
"""
function calcintegral_RBZ_ab(modelvector::ModelVector, fct, param::T; maxevals::Int64=100_000, len_sEvec_c::Int64=500) where T <: Tuple
len_sEvec_c > size(modelvector.sEvec_c_)[1] && throw(ErrorException("You have just exceeded the number of Matsubara frequencies available for calculations!"))
println("Length of len_sEvec_c and length of modelvector.sEvec_c_, respectively : ", len_sEvec_c, " ",size(modelvector.sEvec_c_)[1])
result = Array{Float64,1}(len_sEvec_c)
println("in calcintegral_RBZ_ab, kwargs")
for n in 1:len_sEvec_c
model = Model(modelvector, n)
result[n] = (1.0/2.0)*(2.0*pi)^(-2.0)*hcubature(fct(model,param), (-pi,-pi), (pi,pi), reltol=1.49e-8, abstol=1.49e-8, maxevals=maxevals)[1]
println("stiff: ", result[n])
end
result_out = hcat(modelvector.wvec_[1:len_sEvec_c], result)
return result_out
end
function calcintegral_BZ_ab(modelvector::ModelVector, fct, param::T; maxevals::Int64=100_000, len_sEvec_c::Int64=500) where T <: Tuple
println("in calcintegral_BZ_ab: 2.0*calcintegral_RBZ_ab")
result_out = 2.0*calcintegral_RBZ_ab(modelvector,fct,param,maxevals=maxevals,len_sEvec_c=len_sEvec_c)
return result_out
end
"""
Function used to map any functions on desired k-space grid. Used when pre-computing current vertices.
#Arguments:
- model: model instance
- grid::Int64: k-space grid dimension.
- fct: Function object to be mapped onto the BZ grid.
#Returns:
- Returns an array object of type Array{Float64}(Grid,Grid). This object contains the fct values on the BZ grid.
"""
function k_grid(model::Model,Grid::Int64,fct) #This function can take tktilde or dispersion relation
try
BZ_grid = Array{Float64}(Grid,Grid)
for (i,ky) in enumerate(linspace(-pi,pi,Grid))
for (j,kx) in enumerate(linspace(-pi,pi,Grid))
BZ_grid[i,j] = fct(model,kx,ky)
end
end
return BZ_grid
catch ex
if isa(ex,MethodError)
BZ_grid = Array{Array{Complex{Float64},2}}(Grid,Grid)
for (i,ky) in enumerate(linspace(-pi,pi,Grid))
for (j,kx) in enumerate(linspace(-pi,pi,Grid))
BZ_grid[i,j] = fct(model,kx,ky)
end
end
return BZ_grid
else
throw(ErrorException("Problem in definition of the type of BZ_grid. See function PeriodizeSC.k_grid."))
end
end
end
"""
Function used to in-plane AF+dSC superfluid stiffness without periodization.
#Arguments:
- modelvector: modelvector instance
- kxky::Array{Array{Float64,2},1}: Pre-computed current vertices.
- tk::Array{Array{Complex{Float64},2},2}: Pre-computed hopping matrix represented in the reduced supercluster BZ.
- Grid::Int64: Value of the k-space grid dimension.
- len_sEvec_c::Int64: Length of the Matsubara frequency grid. Set by default to 500.
#Returns:
- Returns an array of two columns contaning the k-integrated Green's function and the associated Matsubara frequency.
"""
function stiffness_cluster_G_ab_k_grid(modelvector::ModelVector, kxky::Array{Array{Float64,2},1}, tk::Array{Array{Complex{Float64},2},2}, Grid::Int64; len_sEvec_c::Int64=500)
len_sEvec_c > size(modelvector.sEvec_c_)[1] && throw(ErrorException("You have exceeded the number of Matsubara frequencies available for calculations."))
result_n = Array{Float64,1}(len_sEvec_c)
for n in 1:len_sEvec_c
model = Model(modelvector,n) ########################### Should devide by two when tracing ???
Sum = 0.0
for (i,ky) in enumerate(linspace(-pi,pi,Grid))
for (j,kx) in enumerate(linspace(-pi,pi,Grid))
G = build_gf_ktilde_prebuild(model,tk[i,j])
Sum += 1.0*(kxky[1][i,j]*4.0*(G[1,5]*G[5,1]+G[2,5]*G[5,2]+G[3,5]*G[5,3]+G[4,5]*G[5,4]+G[1,6]*G[6,1]+G[2,6]*G[6,2]+G[3,6]*G[6,3]+G[4,6]*G[6,4]+G[1,7]*G[7,1]+G[2,7]*G[7,2]+G[3,7]*G[7,3]+G[4,7]*G[7,4]+G[1,8]*G[8,1]+G[2,8]*G[8,2]+G[3,8]*G[8,3]+G[4,8]*G[8,4])+
kxky[2][i,j]*2.0*((G[1,5]+G[2,6])*(G[5,2]+G[6,1])+(G[1,6]+G[2,5])*(G[5,1]+G[6,2])+(G[3,5]+G[4,6])*(G[5,4]+G[6,3])+(G[3,6]+G[4,5])*(G[5,3]+G[6,4])+(G[1,7]+G[2,8])*(G[7,2]+G[8,1])+(G[1,8]+G[2,7])*(G[7,1]+G[8,2])+(G[3,7]+G[4,8])*(G[7,4]+G[8,3])+(G[3,8]+G[4,7])*(G[7,3]+G[8,4]))+
kxky[3][i,j]*2.0*(G[3,5]*G[5,1]+G[4,5]*G[5,1]+G[3,5]*G[5,2]+G[4,5]*G[5,2]+G[3,7]*G[5,3]+G[3,8]*G[5,3]+(G[2,7]+G[2,8])*(G[5,2]+G[6,2])+(G[3,6]+G[4,6])*(G[6,1]+G[6,2])+G[3,7]*G[6,3]+G[3,8]*G[6,3]+(G[4,7]+G[4,8])*(G[5,4]+G[6,4])+G[2,6]*(G[6,3]+G[6,4])+G[3,7]*G[7,1]+G[4,7]*G[7,1]+G[3,7]*G[7,2]+G[4,7]*G[7,2]+G[3,5]*G[7,3]+G[3,6]*G[7,3]+G[4,5]*G[7,4]+G[4,6]*G[7,4]+G[2,7]*(G[7,3]+G[7,4])+G[1,7]*(G[5,1]+G[6,1]+G[7,3]+G[7,4])+G[3,8]*G[8,1]+G[4,8]*G[8,1]+G[1,5]*(G[5,3]+G[5,4]+G[7,1]+G[8,1])+G[1,6]*(G[6,3]+G[6,4]+G[7,1]+G[8,1])+(G[3,8]+G[4,8])*G[8,2]+G[2,6]*(G[7,2]+G[8,2])+G[2,5]*(G[5,3]+G[5,4]+G[7,2]+G[8,2])+G[3,5]*G[8,3]+G[3,6]*G[8,3]+(G[4,5]+G[4,6])*G[8,4]+G[2,8]*(G[8,3]+G[8,4])+G[1,8]*(G[5,1]+G[6,1]+G[8,3]+G[8,4]))+
kxky[4][i,j]*2.0*((G[1,5]+G[2,6])*(G[5,2]+G[6,1])+(G[1,6]+G[2,5])*(G[5,1]+G[6,2])+(G[3,5]+G[4,6])*(G[5,4]+G[6,3])+(G[3,6]+G[4,5])*(G[5,3]+G[6,4])+(G[1,7]+G[2,8])*(G[7,2]+G[8,1])+(G[1,8]+G[2,7])*(G[7,1]+G[8,2])+(G[3,7]+G[4,8])*(G[7,4]+G[8,3])+(G[3,8]+G[4,7])*(G[7,3]+G[8,4]))+
kxky[5][i,j]*4.0*(G[2,6]*G[5,1]+G[1,6]*G[5,2]+G[4,6]*G[5,3]+G[3,6]*G[5,4]+G[2,5]*G[6,1]+G[1,5]*G[6,2]+G[4,5]*G[6,3]+G[3,5]*G[6,4]+G[2,8]*G[7,1]+G[1,8]*G[7,2]+G[4,8]*G[7,3]+G[3,8]*G[7,4]+G[2,7]*G[8,1]+G[1,7]*G[8,2]+G[4,7]*G[8,3]+G[3,7]*G[8,4])+
kxky[6][i,j]*2.0*(G[3,6]*G[5,1]+G[4,6]*G[5,1]+G[3,6]*G[5,2]+G[4,6]*G[5,2]+G[4,7]*G[5,3]+G[4,8]*G[5,3]+G[3,7]*G[5,4]+G[3,8]*G[5,4]+G[3,5]*G[6,1]+G[4,5]*G[6,1]+G[3,5]*G[6,2]+G[4,5]*G[6,2]+G[4,7]*G[6,3]+G[4,8]*G[6,3]+G[3,7]*G[6,4]+G[3,8]*G[6,4]+G[3,8]*G[7,1]+G[4,8]*G[7,1]+G[3,8]*G[7,2]+G[4,8]*G[7,2]+G[4,5]*G[7,3]+G[4,6]*G[7,3]+G[3,5]*G[7,4]+G[3,6]*G[7,4]+G[2,8]*(G[5,1]+G[6,1]+G[7,3]+G[7,4])+G[1,8]*(G[5,2]+G[6,2]+G[7,3]+G[7,4])+G[3,7]*G[8,1]+G[4,7]*G[8,1]+G[2,6]*(G[5,3]+G[5,4]+G[7,1]+G[8,1])+G[2,5]*(G[6,3]+G[6,4]+G[7,1]+G[8,1])+(G[3,7]+G[4,7])*G[8,2]+G[1,6]*(G[5,3]+G[5,4]+G[7,2]+G[8,2])+G[1,5]*(G[6,3]+G[6,4]+G[7,2]+G[8,2])+G[4,5]*G[8,3]+G[4,6]*G[8,3]+(G[3,5]+G[3,6])*G[8,4]+G[2,7]*(G[5,1]+G[6,1]+G[8,3]+G[8,4])+G[1,7]*(G[5,2]+G[6,2]+G[8,3]+G[8,4]))+
kxky[7][i,j]*2.0*(G[3,5]*G[5,1]+G[4,5]*G[5,1]+G[3,5]*G[5,2]+G[4,5]*G[5,2]+G[3,7]*G[5,3]+G[3,8]*G[5,3]+(G[2,7]+G[2,8])*(G[5,2]+G[6,2])+(G[3,6]+G[4,6])*(G[6,1]+G[6,2])+G[3,7]*G[6,3]+G[3,8]*G[6,3]+(G[4,7]+G[4,8])*(G[5,4]+G[6,4])+G[2,6]*(G[6,3]+G[6,4])+G[3,7]*G[7,1]+G[4,7]*G[7,1]+G[3,7]*G[7,2]+G[4,7]*G[7,2]+G[3,5]*G[7,3]+G[3,6]*G[7,3]+G[4,5]*G[7,4]+G[4,6]*G[7,4]+G[2,7]*(G[7,3]+G[7,4])+G[1,7]*(G[5,1]+G[6,1]+G[7,3]+G[7,4])+G[3,8]*G[8,1]+G[4,8]*G[8,1]+G[1,5]*(G[5,3]+G[5,4]+G[7,1]+G[8,1])+G[1,6]*(G[6,3]+G[6,4]+G[7,1]+G[8,1])+(G[3,8]+G[4,8])*G[8,2]+G[2,6]*(G[7,2]+G[8,2])+G[2,5]*(G[5,3]+G[5,4]+G[7,2]+G[8,2])+G[3,5]*G[8,3]+G[3,6]*G[8,3]+(G[4,5]+G[4,6])*G[8,4]+G[2,8]*(G[8,3]+G[8,4])+G[1,8]*(G[5,1]+G[6,1]+G[8,3]+G[8,4]))+
kxky[8][i,j]*2.0*(G[3,6]*G[5,1]+G[4,6]*G[5,1]+G[3,6]*G[5,2]+G[4,6]*G[5,2]+G[4,7]*G[5,3]+G[4,8]*G[5,3]+G[3,7]*G[5,4]+G[3,8]*G[5,4]+G[3,5]*G[6,1]+G[4,5]*G[6,1]+G[3,5]*G[6,2]+G[4,5]*G[6,2]+G[4,7]*G[6,3]+G[4,8]*G[6,3]+G[3,7]*G[6,4]+G[3,8]*G[6,4]+G[3,8]*G[7,1]+G[4,8]*G[7,1]+G[3,8]*G[7,2]+G[4,8]*G[7,2]+G[4,5]*G[7,3]+G[4,6]*G[7,3]+G[3,5]*G[7,4]+G[3,6]*G[7,4]+G[2,8]*(G[5,1]+G[6,1]+G[7,3]+G[7,4])+G[1,8]*(G[5,2]+G[6,2]+G[7,3]+G[7,4])+G[3,7]*G[8,1]+G[4,7]*G[8,1]+G[2,6]*(G[5,3]+G[5,4]+G[7,1]+G[8,1])+G[2,5]*(G[6,3]+G[6,4]+G[7,1]+G[8,1])+(G[3,7]+G[4,7])*G[8,2]+G[1,6]*(G[5,3]+G[5,4]+G[7,2]+G[8,2])+G[1,5]*(G[6,3]+G[6,4]+G[7,2]+G[8,2])+G[4,5]*G[8,3]+G[4,6]*G[8,3]+(G[3,5]+G[3,6])*G[8,4]+G[2,7]*(G[5,1]+G[6,1]+G[8,3]+G[8,4])+G[1,7]*(G[5,2]+G[6,2]+G[8,3]+G[8,4]))+
kxky[9][i,j]*4.0*((G[3,7]+G[3,8]+G[4,7]+G[4,8])*(G[5,1]+G[5,2]+G[6,1]+G[6,2])+(G[1,7]+G[1,8]+G[2,7]+G[2,8])*(G[5,3]+G[5,4]+G[6,3]+G[6,4])+(G[3,5]+G[3,6]+G[4,5]+G[4,6])*(G[7,1]+G[7,2]+G[8,1]+G[8,2])+(G[1,5]+G[1,6]+G[2,5]+G[2,6])*(G[7,3]+G[7,4]+G[8,3]+G[8,4])))
end
end
result_n[n] = 2./(Grid^2)*real(Sum) # Factor 2 accounts for the imaginary axis
end
result_out = hcat(modelvector.wvec_[1:len_sEvec_c],result_n)
println(result_out)
return result_out
end
"""
Function used to in-plane AF+dSC superfluid stiffness with periodization.
#Arguments:
- modelvector: modelvector instance
- kxky::Array{Array{Float64,2},1}: Pre-computed current vertices.
- tk::Array{Array{Complex{Float64},2},2}: Pre-computed hopping matrix represented in the reduced supercluster BZ.
- Grid::Int64: Value of the k-space grid dimension.
- Cum::Int64: 1 if the periodization is to be done on the cumulant. 0 if the periodization is to be done on Green's function.
- super_data_M_el::Float64: Value of the AF order parameter amplitude.
- M_tol::Float64: Relevant for debugging purposes only.
- len_sEvec_c::Int64: Length of the Matsubara frequency grid. Set by default to 500.
#Returns:
- Returns an array of two columns contaning the k-integrated Green's function and the associated Matsubara frequency.
"""
function stiffness_COEX_Per_Cum_ab_k_grid(modelvector::ModelVector, kxky::Array{Array{Float64,2},1}, tk::Array{Array{Complex{Float64},2},2}, Grid::Int64, Cum::Int64, super_data_M_el::Float64, M_tol::Float64; len_sEvec_c::Int64=500)
len_sEvec_c > size(modelvector.sEvec_c_)[1] && throw(ErrorException("You have exceeded the number of Matsubara frequencies available for calculations."))
result_n = Array{Float64,1}(len_sEvec_c)
for n in 1:len_sEvec_c
model = Model(modelvector,n)
Sum = 0.0
cond = abs(super_data_M_el) > M_tol
if cond
for (i,ky) in enumerate(linspace(-pi,pi,Grid))
for (j,kx) in enumerate(linspace(-pi,pi,Grid))
if Cum == 0
gf_ktilde = build_gf_ktilde_prebuild(model,tk[i,j])
G = periodize_AFM_orb(gf_ktilde, kx, ky)
elseif Cum == 1
G = periodize_coex_cum_finale(model, kx, ky)
end
Sum += 1.0*(kxky[2][i,j]*4.0*(G[1,3]*G[3,1]+G[2,3]*G[3,2]+G[1,4]*G[4,1]+G[2,4]*G[4,2])+
kxky[3][i,j]*2.0*((G[1,3]+G[2,4])*(G[3,2]+G[4,1])+(G[1,4]+G[2,3])*(G[3,1]+G[4,2]))+
kxky[3][i,j]*2.0*((G[1,3]+G[2,4])*(G[3,2]+G[4,1])+(G[1,4]+G[2,3])*(G[3,1]+G[4,2]))+
kxky[4][i,j]*4.0*(G[2,4]*G[3,1]+G[1,4]*G[3,2]+G[2,3]*G[4,1]+G[1,3]*G[4,2]))
end
end
else
for (i,ky) in enumerate(linspace(-pi,pi,Grid))
for (j,kx) in enumerate(linspace(-pi,pi,Grid))
if Cum == 0
gf_ktilde = build_gf_ktilde_prebuild(model,tk[i,j])
G = periodize_nocoex(kx, ky, gf_ktilde)
elseif Cum == 1
G = stiffness_cum_SC(model, kx, ky)
end
Sum += 2.0*(kxky[1][i,j]*4.0*(G[1,2]*G[2,1])) ## Factor 2.0 for the spin degrees of freedom
end
end
end
result_n[n] = (1./(2*Grid^2))*real(Sum) # Factor 1/2 to avoid counting the spin degree of freedom twice
end
result_out = hcat(modelvector.wvec_[1:len_sEvec_c],result_n)
println(result_out)
return result_out
end
"""
Function used to in-plane dSC superfluid stiffness with periodization.
#Arguments:
- modelvector: modelvector instance
- kxky::Array{Array{Float64,2},1}: Pre-computed current vertices.
- tk::Array{Array{Complex{Float64},2},2}: Pre-computed hopping matrix represented in the reduced supercluster BZ.
- Grid::Int64: Value of the k-space grid dimension.
- Cum::Int64: 1 if the periodization is to be done on the cumulant. 0 if the periodization is to be done on Green's function.
- AFMSC::Int64: Integer value set to 1 to compute pure SC superfluid stiffness using the formula in coexisting AF+dSC regime. Otherwize, let it be 0.
- len_sEvec_c::Int64: Length of the Matsubara frequency grid. Set by default to 500.
#Returns:
- Returns an array of two columns contaning the k-integrated Green's function and the associated Matsubara frequency.
"""
function stiffness_NOCOEX_Per_Cum_ab_k_grid(modelvector::ModelVector, kxky::Array{Array{Float64,2},1}, tk::Array{Array{Complex{Float64},2},2}, Grid::Int64, Cum::Int64, AFMSC::Int64; len_sEvec_c::Int64=500)
len_sEvec_c > size(modelvector.sEvec_c_)[1] && throw(ErrorException("You have exceeded the number of Matsubara frequencies available for calculations."))
result_n = Array{Float64,1}(len_sEvec_c)
for n in 1:len_sEvec_c
model = Model(modelvector,n)
Sum = 0.0
for (i,ky) in enumerate(linspace(-pi,pi,Grid))
for (j,kx) in enumerate(linspace(-pi,pi,Grid))
if Cum == 0 && AFMSC == 0
gf_ktilde = build_gf_ktilde_prebuild(model,tk[i,j])
G = periodize_nocoex(kx, ky, gf_ktilde)
Sum += 1.0*(kxky[1][i,j]*4.0*(G[1,2]*G[2,1]))
elseif Cum == 1 && AFMSC == 0
G = periodize_cumulant_SC(model, kx, ky)
Sum += 1.0*(kxky[1][i,j]*4.0*(G[1,2]*G[2,1]))
elseif Cum == 0 && AFMSC == 1
gf_ktilde = build_gf_ktilde_prebuild(model,tk[i,j])
G = periodize_AFM_orb(gf_ktilde, kx, ky)
Sum += 1.0*(kxky[1][i,j]*4.0*(G[1,3]*G[3,1]+G[2,3]*G[3,2]+G[1,4]*G[4,1]+G[2,4]*G[4,2])+
kxky[2][i,j]*2.0*((G[1,3]+G[2,4])*(G[3,2]+G[4,1])+(G[1,4]+G[2,3])*(G[3,1]+G[4,2]))+
kxky[2][i,j]*2.0*((G[1,3]+G[2,4])*(G[3,2]+G[4,1])+(G[1,4]+G[2,3])*(G[3,1]+G[4,2]))+
kxky[3][i,j]*4.0*(G[2,4]*G[3,1]+G[1,4]*G[3,2]+G[2,3]*G[4,1]+G[1,3]*G[4,2]))
elseif Cum == 1 && AFMSC == 1
G = periodize_coex_cum_finale(model, kx, ky)
Sum += 1.0*(kxky[1][i,j]*4.0*(G[1,3]*G[3,1]+G[2,3]*G[3,2]+G[1,4]*G[4,1]+G[2,4]*G[4,2])+
kxky[2][i,j]*2.0*((G[1,3]+G[2,4])*(G[3,2]+G[4,1])+(G[1,4]+G[2,3])*(G[3,1]+G[4,2]))+
kxky[2][i,j]*2.0*((G[1,3]+G[2,4])*(G[3,2]+G[4,1])+(G[1,4]+G[2,3])*(G[3,1]+G[4,2]))+
kxky[3][i,j]*4.0*(G[2,4]*G[3,1]+G[1,4]*G[3,2]+G[2,3]*G[4,1]+G[1,3]*G[4,2]))
end
end
end
result_n[n] = 1./(Grid^2)*real(Sum) # No factor 2 to count the spin degree of freedom
end
result_out = hcat(modelvector.wvec_[1:len_sEvec_c],result_n)
println(result_out)
return result_out
end
end ## End of module
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618,
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312,
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13,
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2,
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2886,
25,
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900,
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352,
284,
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5899,
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312,
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1262,
262,
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287,
763,
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10,
67,
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13,
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86,
1096,
11,
1309,
340,
307,
657,
198,
198,
12,
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303,
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1988,
900,
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284,
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1988,
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312,
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340,
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2,
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25,
198,
198,
12,
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281,
7177,
286,
734,
15180,
542,
7574,
262,
479,
12,
18908,
4111,
3469,
338,
2163,
290,
262,
3917,
30107,
549,
3301,
8373,
13,
198,
198,
37811,
198,
8818,
42302,
18908,
1373,
62,
27912,
57,
7,
4666,
9954,
2715,
3712,
17633,
38469,
11,
277,
310,
26,
3509,
1990,
874,
3712,
5317,
2414,
28,
3064,
62,
830,
11,
18896,
62,
82,
36,
35138,
62,
66,
3712,
5317,
2414,
28,
4059,
8,
198,
220,
220,
220,
18896,
62,
82,
36,
35138,
62,
66,
1875,
2546,
7,
4666,
9954,
2715,
13,
82,
36,
35138,
62,
66,
62,
38381,
16,
60,
11405,
3714,
7,
12331,
16922,
7203,
1639,
423,
655,
20672,
262,
1271,
286,
30107,
549,
3301,
19998,
1695,
329,
16765,
2474,
4008,
198,
220,
220,
220,
44872,
7203,
24539,
286,
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62,
82,
36,
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62,
66,
290,
4129,
286,
2746,
31364,
13,
82,
36,
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62,
66,
62,
11,
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1058,
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62,
82,
36,
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62,
66,
11,
366,
220,
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2546,
7,
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9954,
2715,
13,
82,
36,
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62,
66,
62,
38381,
16,
12962,
198,
220,
220,
220,
1255,
796,
15690,
90,
43879,
2414,
11,
16,
92,
7,
11925,
62,
82,
36,
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62,
66,
8,
198,
220,
220,
220,
44872,
7203,
259,
42302,
18908,
1373,
62,
27912,
57,
11,
479,
86,
22046,
4943,
198,
220,
220,
220,
329,
299,
287,
352,
25,
11925,
62,
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36,
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62,
66,
198,
220,
220,
220,
220,
220,
220,
220,
2746,
796,
9104,
7,
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9954,
2715,
11,
299,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
35235,
7203,
86,
62,
25,
33172,
2746,
13,
86,
62,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1255,
58,
77,
60,
796,
357,
16,
13,
15,
14,
17,
13,
15,
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7,
17,
13,
15,
9,
14415,
8,
61,
32590,
17,
13,
15,
27493,
71,
66,
549,
1300,
7,
69,
310,
7,
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12095,
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828,
357,
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11,
14415,
828,
823,
83,
349,
28,
16,
13,
2920,
68,
12,
23,
11,
16552,
349,
28,
16,
13,
2920,
68,
12,
23,
11,
3509,
1990,
874,
28,
9806,
1990,
874,
38381,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
35235,
7203,
301,
733,
25,
33172,
1255,
58,
77,
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198,
220,
220,
220,
886,
198,
220,
220,
220,
1255,
62,
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289,
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7,
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13,
86,
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62,
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16,
25,
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62,
82,
36,
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62,
66,
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1255,
8,
198,
220,
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220,
1441,
1255,
62,
448,
198,
437,
198,
198,
8818,
42302,
18908,
1373,
62,
33,
57,
7,
4666,
9954,
2715,
3712,
17633,
38469,
11,
277,
310,
26,
3509,
1990,
874,
3712,
5317,
2414,
28,
3064,
62,
830,
11,
18896,
62,
82,
36,
35138,
62,
66,
3712,
5317,
2414,
28,
4059,
8,
198,
220,
220,
220,
44872,
7203,
259,
42302,
18908,
1373,
62,
33,
57,
25,
362,
13,
15,
9,
9948,
66,
18908,
1373,
62,
27912,
57,
4943,
198,
220,
220,
220,
1255,
62,
448,
796,
362,
13,
15,
9,
9948,
66,
18908,
1373,
62,
27912,
57,
7,
4666,
9954,
2715,
11,
69,
310,
11,
11925,
62,
82,
36,
35138,
62,
66,
28,
11925,
62,
82,
36,
35138,
62,
66,
8,
198,
220,
220,
220,
1441,
1255,
62,
448,
198,
437,
198,
198,
37811,
198,
22203,
973,
284,
19386,
625,
262,
347,
57,
618,
14492,
287,
12,
14382,
48713,
312,
49586,
13,
220,
198,
198,
2,
28100,
2886,
25,
198,
198,
12,
2746,
31364,
25,
2746,
31364,
4554,
198,
12,
277,
310,
25,
15553,
2134,
284,
307,
973,
284,
19386,
625,
262,
347,
57,
13,
198,
12,
5772,
3712,
51,
29291,
90,
10100,
11,
10100,
11,
5317,
2414,
38362,
309,
29291,
286,
1115,
4847,
3306,
284,
2148,
780,
286,
262,
1296,
286,
262,
11507,
277,
310,
973,
13,
198,
12,
10706,
42,
3712,
5317,
2414,
25,
479,
12,
13200,
10706,
399,
87,
45,
11,
810,
399,
318,
900,
284,
1802,
416,
4277,
13,
220,
198,
12,
18896,
62,
82,
36,
35138,
62,
66,
3712,
5317,
2414,
25,
22313,
286,
262,
30107,
549,
3301,
8373,
10706,
13,
5345,
416,
4277,
284,
5323,
13,
198,
198,
2,
35561,
25,
198,
198,
12,
16409,
281,
7177,
286,
734,
15180,
542,
7574,
262,
479,
12,
18908,
4111,
3469,
338,
2163,
290,
262,
3917,
30107,
549,
3301,
8373,
13,
198,
198,
37811,
198,
8818,
42302,
18908,
1373,
62,
27912,
57,
62,
397,
7,
4666,
9954,
2715,
3712,
17633,
38469,
11,
277,
310,
11,
5772,
3712,
51,
26,
3509,
1990,
874,
3712,
5317,
2414,
28,
3064,
62,
830,
11,
18896,
62,
82,
36,
35138,
62,
66,
3712,
5317,
2414,
28,
4059,
8,
810,
309,
1279,
25,
309,
29291,
198,
220,
220,
220,
18896,
62,
82,
36,
35138,
62,
66,
1875,
2546,
7,
4666,
9954,
2715,
13,
82,
36,
35138,
62,
66,
62,
38381,
16,
60,
11405,
3714,
7,
12331,
16922,
7203,
1639,
423,
655,
20672,
262,
1271,
286,
30107,
549,
3301,
19998,
1695,
329,
16765,
2474,
4008,
198,
220,
220,
220,
44872,
7203,
24539,
286,
18896,
62,
82,
36,
35138,
62,
66,
290,
4129,
286,
2746,
31364,
13,
82,
36,
35138,
62,
66,
62,
11,
8148,
1058,
33172,
18896,
62,
82,
36,
35138,
62,
66,
11,
366,
220,
33172,
7857,
7,
4666,
9954,
2715,
13,
82,
36,
35138,
62,
66,
62,
38381,
16,
12962,
198,
220,
220,
220,
1255,
796,
15690,
90,
43879,
2414,
11,
16,
92,
7,
11925,
62,
82,
36,
35138,
62,
66,
8,
198,
220,
220,
220,
44872,
7203,
259,
42302,
18908,
1373,
62,
27912,
57,
62,
397,
11,
479,
86,
22046,
4943,
198,
220,
220,
220,
329,
299,
287,
352,
25,
11925,
62,
82,
36,
35138,
62,
66,
198,
220,
220,
220,
220,
220,
220,
220,
2746,
796,
9104,
7,
4666,
9954,
2715,
11,
299,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1255,
58,
77,
60,
796,
357,
16,
13,
15,
14,
17,
13,
15,
27493,
7,
17,
13,
15,
9,
14415,
8,
61,
32590,
17,
13,
15,
27493,
71,
66,
549,
1300,
7,
69,
310,
7,
19849,
11,
17143,
828,
13841,
14415,
12095,
14415,
828,
357,
14415,
11,
14415,
828,
823,
83,
349,
28,
16,
13,
2920,
68,
12,
23,
11,
16552,
349,
28,
16,
13,
2920,
68,
12,
23,
11,
3509,
1990,
874,
28,
9806,
1990,
874,
38381,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
44872,
7203,
301,
733,
25,
33172,
1255,
58,
77,
12962,
198,
220,
220,
220,
886,
198,
220,
220,
220,
1255,
62,
448,
796,
289,
9246,
7,
4666,
9954,
2715,
13,
86,
35138,
62,
58,
16,
25,
11925,
62,
82,
36,
35138,
62,
66,
4357,
1255,
8,
198,
220,
220,
220,
1441,
1255,
62,
448,
198,
437,
198,
198,
8818,
42302,
18908,
1373,
62,
33,
57,
62,
397,
7,
4666,
9954,
2715,
3712,
17633,
38469,
11,
277,
310,
11,
5772,
3712,
51,
26,
3509,
1990,
874,
3712,
5317,
2414,
28,
3064,
62,
830,
11,
18896,
62,
82,
36,
35138,
62,
66,
3712,
5317,
2414,
28,
4059,
8,
810,
309,
1279,
25,
309,
29291,
198,
220,
220,
220,
44872,
7203,
259,
42302,
18908,
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<filename>test/runtests.jl
# Workaround for libz loading confusion.
@static if Sys.islinux()
using ImageMagick
end
using GtkReactive, Gtk.ShortNames, IntervalSets, Graphics, Colors,
TestImages, FileIO, FixedPointNumbers, RoundingIntegers, Dates, Cairo,
IdentityRanges
using Test
rtask = Reactive.runner_task # starting with Reactive 0.7.0, this became a Ref
if isa(rtask, Base.RefValue)
rtask = rtask[]
end
if !istaskdone(rtask)
Reactive.stop()
fetch(rtask)
end
include("tools.jl")
@testset "Widgets" begin
## label
l = label("Hello")
@test signal(l) == l.signal
@test signal(signal(l)) == l.signal
@test get_gtk_property(l, :label, String) == "Hello"
push!(signal(l), "world")
rr()
@test get_gtk_property(l, :label, String) == "world"
@test string(l) == string("Gtk.GtkLabelLeaf with ", string(signal(l)))
# map with keywords
lsig0 = map(l) do lbl # "regular" map runs the function
lbl
end
rr()
@test value(lsig0) == "world"
lsig = map(l; init="foo") do lbl # with "init", you avoid running
lbl
end
rr()
@test value(lsig) == "foo"
push!(l, "bar")
rr()
@test value(lsig) == "bar"
## checkbox
w = Window("Checkbox")
check = checkbox(label="click me")
push!(w, check)
Gtk.showall(w)
@test value(check) == false
@test Gtk.G_.active(check.widget) == false
push!(check, true)
rr()
@test value(check)
@test Gtk.G_.active(check.widget)
destroy(w)
## togglebutton
w = Window("Togglebutton")
tgl = togglebutton(label="click me")
push!(w, tgl)
Gtk.showall(w)
@test value(tgl) == false
@test Gtk.G_.active(tgl.widget) == false
push!(tgl, true)
rr()
@test value(tgl)
@test Gtk.G_.active(tgl.widget)
destroy(w)
## textbox (aka Entry)
txt = textbox("Type something")
num = textbox(5, range=1:10)
win = Window("Textboxes") |> (bx = Box(:h))
push!(bx, txt)
push!(bx, num)
Gtk.showall(win)
@test get_gtk_property(txt, :text, String) == "Type something"
push!(txt, "ok")
rr()
@test get_gtk_property(txt, :text, String) == "ok"
set_gtk_property!(txt, :text, "other direction")
signal_emit(widget(txt), :activate, Nothing)
rr()
@test value(txt) == "other direction"
@test get_gtk_property(num, :text, String) == "5"
push!(signal(num), 11, (sig, val, osig, capex) -> throw(capex.ex))
@test_throws ArgumentError rr()
push!(num, 8)
rr()
@test get_gtk_property(num, :text, String) == "8"
meld = map(txt, num) do t, n
join((t, n), 'X')
end
rr()
@test value(meld) == "other directionX8"
push!(num, 4)
rr()
@test value(meld) == "other directionX4"
push!(txt, "4")
rr()
@test value(meld) == "4X4"
destroy(win)
## textarea (aka TextView)
v = textarea("Type something longer")
win = Window(v)
Gtk.showall(win)
@test value(v) == "Type something longer"
push!(v, "ok")
rr()
@test get_gtk_property(Gtk.G_.buffer(v.widget), :text, String) == "ok"
destroy(win)
## slider
s = slider(1:15)
sleep(0.01) # For the Gtk eventloop
@test value(s) == 8
push!(s, 3)
rr()
@test value(s) == 3
s3 = slider(IdentityRange(-3:3))
sleep(0.01)
@test value(s3) == 0
push!(s3, -3)
rr()
@test value(s3) == -3
# Use a single signal for two widgets
s2 = slider(1:15, signal=signal(s), orientation='v')
@test value(s2) == 3
push!(s2, 11)
rr()
@test value(s) == 11
destroy(s2)
destroy(s)
# Updating the limits of the slider
s = slider(1:15)
sleep(0.01) # For the Gtk eventloop
@test value(s) == 8
push!(s, 1:7, 5)
sleep(0.01)
rr()
@test value(s) == 5
## dropdown
dd = dropdown(("Strawberry", "Vanilla", "Chocolate"))
@test value(dd) == "Strawberry"
push!(dd, "Chocolate")
rr()
@test get_gtk_property(dd, :active, Int) == 2
destroy(dd.widget)
r = Ref(0)
dd = dropdown(["Five"=>x->x[]=5,
"Seven"=>x->x[]=7])
ddsig = map(f->f(r), dd.mappedsignal)
rr()
@test value(dd) == "Five"
@test r[] == 5
push!(dd, "Seven")
run_till_empty()
@test value(dd) == "Seven"
@test r[] == 7
push!(dd, "Five")
rr()
@test r[] == 5
destroy(dd.widget)
## spinbutton
s = spinbutton(1:15)
sleep(0.01) # For the Gtk eventloop
@test value(s) == 1
push!(s, 3)
rr()
@test value(s) == 3
destroy(s)
s = spinbutton(0:59, orientation="vertical")
@test G_.orientation(Orientable(widget(s))) == Gtk.GConstants.GtkOrientation.VERTICAL
destroy(s)
# Updating the limits of the spinbutton
s = spinbutton(1:15)
sleep(0.01) # For the Gtk eventloop
@test value(s) == 1
push!(s, 1:7, 5)
rr()
@test value(s) == 5
## cyclicspinbutton
a = spinbutton(1:10, value = 5)
carry_up = Signal(false)
foreach(carry_up; init=nothing) do up
push!(a, value(a) - (-1)^up)
end
b = cyclicspinbutton(1:3, carry_up)
run_till_empty()
@test value(a) == 5
@test value(b) == 1
push!(b, 2)
run_till_empty()
@test value(a) == 5
@test value(b) == 2
push!(b, 0)
run_till_empty()
@test value(a) == 4
@test value(b) == 3
push!(b, 4)
run_till_empty()
@test value(a) == 5
@test value(b) == 1
destroy(a)
s = cyclicspinbutton(0:59, carry_up, orientation="vertical")
@test G_.orientation(Orientable(widget(s))) == Gtk.GConstants.GtkOrientation.VERTICAL
destroy(s)
# timewidget
t = Dates.Time(1,1,1)
s = Signal(t)
tw = timewidget(t, signal=s)
run_till_empty()
@test value(tw) == value(s) == t
t = Dates.Time(2,2,2)
push!(tw, t)
run_till_empty()
@test value(tw) == value(s) == t
t = Dates.Time(3,3,3)
push!(s, t)
run_till_empty()
@test value(tw) == value(s) == t
# datetimewidget
t = DateTime(1,1,1,1,1,1)
s = Signal(t)
tw = datetimewidget(t, signal=s)
run_till_empty()
@test value(tw) == value(s) == t
t = DateTime(2,2,2,2,2,2)
push!(tw, t)
run_till_empty()
@test value(tw) == value(s) == t
t = DateTime(3,3,3,3,3,3)
push!(s, t)
run_till_empty()
@test value(tw) == value(s) == t
# progressbar
pb = progressbar(1..10)
@test value(pb) == 1
push!(pb, 5)
run_till_empty()
@test value(pb) == 5
pb = progressbar(2:8)
@test value(pb) == 2
end
## button
# For reasons I don't understand, this often fails if it's inside a @testset
counter = 0
w = Window("Widgets")
b = button("Click me")
push!(w, b)
action = map(b) do val
global counter
counter::Int += 1
end
Gtk.showall(w)
rr()
cc = counter # map seems to fire it once, so record the "new" initial value
click(b::GtkReactive.Button) = ccall((:gtk_button_clicked,Gtk.libgtk),Cvoid,(Ptr{Gtk.GObject},),b.widget)
GC.gc(true)
click(b)
rr()
@test counter == cc+1
destroy(w)
# Make sure we can also put a ToolButton in a Button
button(; widget=ToolButton("Save as..."))
if Gtk.libgtk_version >= v"3.10"
# To support GtkBuilder, we need this as the minimum libgtk version
@testset "Compound widgets" begin
## player widget
s = Signal(1)
p = player(s, 1:8)
win = Window() |> (g = Grid())
g[1,1] = p
Gtk.showall(win)
rr()
btn_fwd = p.widget.step_forward
@test value(s) == 1
push!(btn_fwd, nothing)
run_till_empty()
@test value(s) == 2
push!(p.widget.play_forward, nothing)
for i = 1:7
run_till_empty()
sleep(0.1)
end
@test value(s) == 8
destroy(win)
p = player(1:1000)
win = Window(frame(p))
Gtk.showall(win)
push!(widget(p).direction, 1)
destroy(win) # this should not generate a lot of output
end
end
@testset "CairoUnits" begin
x = UserUnit(0.2)
@test UserUnit(x) === x
@test convert(UserUnit, x) === x
@test x+x === UserUnit(0.2+0.2)
@test x-x === UserUnit(0.0)
@test Float64(x) === 0.2
@test convert(Float64, x) === 0.2
y = UserUnit(-0.3)
@test x > y
@test y < x
@test abs(x) === x
@test abs(y) === UserUnit(0.3)
@test min(x, y) === y
@test max(x, y) === x
z = DeviceUnit(2.0)
@test_throws ErrorException x+z
@test Bool(DeviceUnit(1.0)) === true
@test Integer(DeviceUnit(3.0)) === 3
end
@testset "Canvas" begin
@test XY(5, 5) === XY{Int}(5, 5)
@test XY(5, 5.0) === XY{Float64}(5.0, 5.0)
@test XY{UserUnit}(5, 5.0) === XY{UserUnit}(5.0, 5.0) === XY{UserUnit}(UserUnit(5), UserUnit(5))
@test XY(5.0, 5)+XY(4, 4.1) === XY(9, 9.1)
@test XY(5, 5)-XY(4, 4) === XY(1, 1)
@test isa(MouseButton{UserUnit}(), MouseButton{UserUnit})
@test isa(MouseButton{DeviceUnit}(), MouseButton{DeviceUnit})
@test isa(MouseScroll{UserUnit}(), MouseScroll{UserUnit})
@test isa(MouseScroll{DeviceUnit}(), MouseScroll{DeviceUnit})
@test BoundingBox(XY(2..4, -15..15)) === BoundingBox(2, 4, -15, 15)
c = canvas(208, 207)
win = Window(c)
Gtk.showall(win)
sleep(0.1)
@test Graphics.width(c) == 208
@test Graphics.height(c) == 207
@test isa(c, GtkReactive.Canvas{DeviceUnit})
destroy(win)
c = canvas(UserUnit, 208, 207)
win = Window(c)
Gtk.showall(win)
reveal(c, true)
sleep(0.3)
@test isa(c, GtkReactive.Canvas{UserUnit})
corner_dev = (DeviceUnit(208), DeviceUnit(207))
for (coords, corner_usr) in ((BoundingBox(0, 1, 0, 1), (UserUnit(1), UserUnit(1))),
(ZoomRegion((5:10, 3:5)), (UserUnit(5), UserUnit(10))),
((-1:1, 101:110), (UserUnit(110), UserUnit(1))))
set_coordinates(c, coords)
@test GtkReactive.convertunits(UserUnit, c, corner_dev...) == corner_usr
@test GtkReactive.convertunits(DeviceUnit, c, corner_dev...) == corner_dev
@test GtkReactive.convertunits(UserUnit, c, corner_usr...) == corner_usr
@test GtkReactive.convertunits(DeviceUnit, c, corner_usr...) == corner_dev
end
destroy(win)
c = canvas()
f = Frame(c)
@test isa(f, Gtk.GtkFrameLeaf)
destroy(f)
c = canvas()
f = AspectFrame(c, "Some title", 0.5, 0.5, 3.0)
@test isa(f, Gtk.GtkAspectFrameLeaf)
@test get_gtk_property(f, :ratio, Float64) == 3.0
destroy(f)
end
# @testset "Canvas events" begin
win = Window() |> (c = canvas(UserUnit))
Gtk.showall(win)
sleep(0.2)
lastevent = Ref("nothing")
press = map(btn->lastevent[] = "press", c.mouse.buttonpress)
release = map(btn->lastevent[] = "release", c.mouse.buttonrelease)
motion = map(btn->lastevent[] = string("motion to ", btn.position.x, ", ", btn.position.y),
c.mouse.motion)
scroll = map(btn->lastevent[] = "scroll", c.mouse.scroll)
rr()
lastevent[] = "nothing"
@test lastevent[] == "nothing"
signal_emit(widget(c), "button-press-event", Bool, eventbutton(c, BUTTON_PRESS, 1))
sleep(0.1)
rr()
@test lastevent[] == "press"
signal_emit(widget(c), "button-release-event", Bool, eventbutton(c, GtkReactive.BUTTON_RELEASE, 1))
sleep(0.1)
rr()
sleep(0.1)
rr()
@test lastevent[] == "release"
signal_emit(widget(c), "scroll-event", Bool, eventscroll(c, UP))
sleep(0.1)
rr()
sleep(0.1)
rr()
@test lastevent[] == "scroll"
signal_emit(widget(c), "motion-notify-event", Bool, eventmotion(c, 0, UserUnit(20), UserUnit(15)))
sleep(0.1)
rr()
sleep(0.1)
rr()
@test lastevent[] == "motion to UserUnit(20.0), UserUnit(15.0)"
destroy(win)
# end
@testset "Popup" begin
popupmenu = Menu()
popupitem = MenuItem("Popup menu...")
push!(popupmenu, popupitem)
Gtk.showall(popupmenu)
win = Window() |> (c = canvas())
popuptriggered = Ref(false)
push!(c.preserved, map(c.mouse.buttonpress) do btn
if btn.button == 3 && btn.clicktype == BUTTON_PRESS
popup(popupmenu, btn.gtkevent) # use the raw Gtk event
popuptriggered[] = true
nothing
end
end)
yield()
@test !popuptriggered[]
evt = eventbutton(c, BUTTON_PRESS, 1)
signal_emit(widget(c), "button-press-event", Bool, evt)
yield()
@test !popuptriggered[]
evt = eventbutton(c, BUTTON_PRESS, 3)
signal_emit(widget(c), "button-press-event", Bool, evt)
run_till_empty()
@test popuptriggered[]
destroy(win)
destroy(popupmenu)
end
@testset "Drawing" begin
img = testimage("lighthouse")
c = canvas(UserUnit, size(img, 2), size(img, 1))
win = Window(c)
xsig, ysig = Signal(20), Signal(20)
draw(c, xsig, ysig) do cnvs, x, y
copy!(c, img)
ctx = getgc(cnvs)
set_source(ctx, colorant"red")
set_line_width(ctx, 2)
circle(ctx, x, y, 5)
stroke(ctx)
end
Gtk.showall(win)
rr()
push!(xsig, 100)
rr()
sleep(1)
# Check that we get the right answer
fn = tempname()
Cairo.write_to_png(getgc(c).surface, fn)
imgout = load(fn)
rm(fn)
@test imgout[25,100] == imgout[16,100] == imgout[20,105] == colorant"red"
@test imgout[20,100] == img[20,100]
destroy(win)
end
# For testing ZoomRegion support for non-AbstractArray objects
struct Foo end
Base.axes(::Foo) = (Base.OneTo(7), Base.OneTo(9))
@testset "Zoom/pan" begin
@test string(UserUnit(3)) == "UserUnit(3.0)"
@test string(DeviceUnit(3)) == "DeviceUnit(3.0)"
xy = @inferred(XY(1, 3))
@test isa(xy, XY{Int})
@test xy.x == 1
@test xy.y == 3
@test string(xy) == "XY(1, 3)"
xy = @inferred(XY{Float64}(1, 3))
@test isa(xy, XY{Float64})
@test xy.x == 1
@test xy.y == 3
@test string(xy) == "XY(1.0, 3.0)"
@test isa(convert(XY{Int}, xy), XY{Int})
xy = XY{Float64}(3.2, 4.8)
xyr = convert(XY{RInt}, xy)
@test isa(xyr, XY{RInt}) && xyr.x == 3 && xyr.y == 5
xy = XY(UserUnit(3), UserUnit(5))
@test string(xy) == "XY{UserUnit}(3.0, 5.0)"
@test @inferred(XY{UserUnit}(3, 5)) == xy
zr = ZoomRegion((1:80, 1:100)) # y, x order
zrz = GtkReactive.zoom(zr, 0.5)
@test zrz.currentview.x == 26..75
@test zrz.currentview.y == 21..60
zrp = GtkReactive.pan_x(zrz, 0.2)
@test zrp.currentview.x == 36..85
@test zrp.currentview.y == 21..60
zrp = GtkReactive.pan_x(zrz, -0.2)
@test zrp.currentview.x == 16..65
@test zrp.currentview.y == 21..60
zrp = GtkReactive.pan_y(zrz, -0.2)
@test zrp.currentview.x == 26..75
@test zrp.currentview.y == 13..52
zrp = GtkReactive.pan_y(zrz, 0.2)
@test zrp.currentview.x == 26..75
@test zrp.currentview.y == 29..68
zrp = GtkReactive.pan_x(zrz, 1.0)
@test zrp.currentview.x == 51..100
@test zrp.currentview.y == 21..60
zrp = GtkReactive.pan_y(zrz, -1.0)
@test zrp.currentview.x == 26..75
@test zrp.currentview.y == 1..40
zrz2 = GtkReactive.zoom(zrz, 2.0001)
@test zrz2 == zr
zrz2 = GtkReactive.zoom(zrz, 3)
@test zrz2 == zr
zrz2 = GtkReactive.zoom(zrz, 1.9)
@test zrz2.currentview.x == 4..97
@test zrz2.currentview.y == 3..78
zrz = GtkReactive.zoom(zr, 0.5, GtkReactive.XY{DeviceUnit}(50.5, 40.5))
@test zrz.currentview.x == 26..75
@test zrz.currentview.y == 21..60
zrz = GtkReactive.zoom(zr, 0.5, GtkReactive.XY{DeviceUnit}(60.5, 30.5))
@test zrz.currentview.x == 31..80
@test zrz.currentview.y == 16..55
zrr = GtkReactive.reset(zrz)
@test zrr == zr
zrbb = ZoomRegion(zr.fullview, BoundingBox(5, 15, 35, 75))
@test zrbb.fullview === zr.fullview
@test zrbb.currentview.x == 5..15
@test zrbb.currentview.y == 35..75
@test typeof(zrbb.currentview) == typeof(zr.currentview)
zrsig = Signal(zr)
push!(zrsig, (3:5, 4:7))
rr()
zr = value(zrsig)
@test zr.fullview.y == 1..80
@test zr.fullview.x == 1..100
@test zr.currentview.y == 3..5
@test zr.currentview.x == 4..7
push!(zrsig, XY(1..2, 3..4))
rr()
zr = value(zrsig)
@test zr.fullview.y == 1..80
@test zr.fullview.x == 1..100
@test zr.currentview.y == 3..4
@test zr.currentview.x == 1..2
zr = ZoomRegion(Foo())
@test zr.fullview.y == 1..7
@test zr.fullview.x == 1..9
zr = ZoomRegion((1:100, 1:80), (11:20, 8:12))
@test zr.fullview.x == 1..80
@test zr.fullview.y == 1..100
@test zr.currentview.x == 8..12
@test zr.currentview.y == 11..20
@test axes(zr) == (11:20, 8:12)
end
### Simulate the mouse clicks, etc. to trigger zoom/pan
# Again, this doesn't seem to work inside a @testset
win = Window() |> (c = canvas(UserUnit))
zr = Signal(ZoomRegion((1:11, 1:20)))
zoomrb = init_zoom_rubberband(c, zr)
zooms = init_zoom_scroll(c, zr)
pans = init_pan_scroll(c, zr)
pand = init_pan_drag(c, zr)
draw(c) do cnvs
set_coordinates(c, value(zr))
fill!(c, colorant"blue")
end
Gtk.showall(win)
sleep(0.1)
# Zoom by rubber band
signal_emit(widget(c), "button-press-event", Bool,
eventbutton(c, BUTTON_PRESS, 1, UserUnit(5), UserUnit(3), CONTROL))
sleep(0.1)
rr()
signal_emit(widget(c), "motion-notify-event", Bool,
eventmotion(c, mask(1), UserUnit(10), UserUnit(4)))
sleep(0.1)
rr()
signal_emit(widget(c), "button-release-event", Bool,
eventbutton(c, GtkReactive.BUTTON_RELEASE, 1, UserUnit(10), UserUnit(4)))
sleep(0.1)
rr()
sleep(0.1)
rr()
@test value(zr).currentview.x == 5..10
@test value(zr).currentview.y == 3..4
# Ensure that the rubber band damage has been repaired
fn = tempname()
Cairo.write_to_png(getgc(c).surface, fn)
imgout = load(fn)
rm(fn)
@test all(x->x==colorant"blue", imgout)
# Pan-drag
signal_emit(widget(c), "button-press-event", Bool,
eventbutton(c, BUTTON_PRESS, 1, UserUnit(6), UserUnit(3), 0))
sleep(0.1)
rr()
signal_emit(widget(c), "motion-notify-event", Bool,
eventmotion(c, mask(1), UserUnit(7), UserUnit(2)))
sleep(0.1)
rr()
sleep(0.1)
rr()
@test value(zr).currentview.x == 4..9
@test value(zr).currentview.y == 4..5
# Reset
signal_emit(widget(c), "button-press-event", Bool,
eventbutton(c, DOUBLE_BUTTON_PRESS, 1, UserUnit(5), UserUnit(4.5), CONTROL))
sleep(0.1)
rr()
sleep(0.1)
rr()
@test value(zr).currentview.x == 1..20
@test value(zr).currentview.y == 1..11
# Zoom-scroll
signal_emit(widget(c), "scroll-event", Bool,
eventscroll(c, UP, UserUnit(8), UserUnit(4), CONTROL))
sleep(0.1)
rr()
sleep(0.1)
rr()
@test value(zr).currentview.x == 4..14
@test value(zr).currentview.y == 2..8
# Pan-scroll
signal_emit(widget(c), "scroll-event", Bool,
eventscroll(c, RIGHT, UserUnit(8), UserUnit(4), 0))
sleep(0.1)
rr()
sleep(0.1)
rr()
@test value(zr).currentview.x == 5..15
@test value(zr).currentview.y == 2..8
signal_emit(widget(c), "scroll-event", Bool,
eventscroll(c, DOWN, UserUnit(8), UserUnit(4), 0))
sleep(0.1)
rr()
sleep(0.1)
rr()
@test value(zr).currentview.x == 5..15
@test value(zr).currentview.y == 3..9
destroy(win)
@testset "Surfaces" begin
for (val, cmp) in ((0.2, Gray24(0.2)),
(Gray(N0f8(0.5)), Gray24(0.5)),
(RGB(0, 1, 0), RGB24(0, 1, 0)),
(RGBA(1, 0, 0.5, 0.8), ARGB32(1, 0, 0.5, 0.8)))
surf = GtkReactive.image_surface(fill(val, 3, 5))
@test surf.height == 3 && surf.width == 5
@test all(x->x == reinterpret(UInt32, cmp), surf.data)
destroy(surf)
end
end
@testset "Layout" begin
g = Grid()
g[1,1] = textbox("hello")
end
# Ensure that the examples run (but the Reactive queue is stopped, so
# they won't work unless one calls `@async Reactive.run()` manually)
examplepath = joinpath(dirname(dirname(@__FILE__)), "examples")
include(joinpath(examplepath, "imageviewer.jl"))
include(joinpath(examplepath, "widgets.jl"))
include(joinpath(examplepath, "drawing.jl"))
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] | 2.084305 | 9,608 |
<filename>src/api.jl
## Token and registration related API
"""
register(code::String) -> String
register() -> String
Create a new authentification token from a code obtained on https://my.remarkable.com/connect/desktop
If a code is not provided a tutorial is given.
"""
function register(code::String; path_to_token::String = "", kwargs...)
data = Dict(
"code" => code,
"deviceDesc" => "desktop-windows",
"deviceID" => string(uuid4())
)
@info "Registering device"
response = HTTP.request_json(
"POST",
AUTH_API,
data;
kwargs...
)
token = String(response.body)
@info "Token : $token"
if isempty(path_to_token)
path_to_token = pwd()
end
write(joinpath(path_to_token, ".token"), token)
@info "Token saved at $(joinpath(path_to_token))"
return token
end
function register()
@warn """
Hi, to use this API you first need to register this device.
To do so go to https://my.remarkable.com/connect/desktop and ask for a new code!
Once you have it run `register(code)` (where `code` is a `String`).
It will return your authentification token and also save your token in a local file.
"""
end
## Items accessors
"""
list_items(client::RemarkableClient) -> Collection
Return a `Collection` of all `Document`s and `Collection` present on the server
You can visualize them nicely via `print_tree`
"""
function list_items(client::RemarkableClient; kwargs...)
@info "Listing all items"
body = HTTP.request(client, "GET", STORAGE_API[] * ITEM_LIST; kwargs...)
items = JSON.parse(String(body))
docs = RemarkableObject[]
for item in items
item = Dict(Symbol(key)=>value for (key, value) in item)
if item[:Type] == "DocumentType"
doc = Document(;item...)
push!(docs, doc)
elseif item[:Type] == "CollectionType"
collec = Collection(;item...)
push!(docs, collec)
end
end
return create_tree(docs)
end
"""
get_item(client, id::String, download = false)
get_item(client, id::RemarkableObject, download = false)
Return a `RemarkableObject` given an ID or an existing `RemarkableObject`,
using `download=true` will give the `BlobURLGet` to download the files
"""
function get_item(client::RemarkableClient, id::String, download::Bool = false; kwargs...)
get_item(client, Document(ID = id), download; kwargs...)
end
function get_item(client::RemarkableClient, doc::RemarkableObject, download::Bool = false; kwargs...)
query = Dict("doc" => doc.ID)
if download
query["withBlob"] = "true"
end
@info "Listing item"
body = HTTP.request(
client,
"GET",
STORAGE_API[] * ITEM_LIST;
query = query,
kwargs...
)
item = JSON.parse(String(body))
return Document(first(item))
end
## Item modificators
"""
delete_item!(client, id::String)
delete_item!(client, obj::remarkable)
Delete the object from your collection (online)
"""
function delete_item!(client::RemarkableClient, id::String; kwargs...)
delete_item!(client, Document(ID = id); kwargs...)
end
function delete_item!(client::RemarkableClient, obj::RemarkableObject; kwargs...)
@info "Deleting item `$(title(obj))`"
return storage_request(client, "PUT", "delete", obj_to_dict(obj); kwargs...);
end
"""
update_metadata!(client, obj::RemarkableObject) ->
Update the metadata of an object, can be used to modify a file or create a
Collection
"""
function update_metadata!(client::RemarkableClient, obj::RemarkableObject; kwargs...)
@info "Updating item metadata `$(title(obj))`"
storage_request(client, "PUT", UPDATE_STATUS, obj_to_dict(obj); kwargs...)
end
"""
create_folder!(client, name::String, parent::String="") ->
Create a folder (Collection) in `parent` (root by default)
"""
function create_folder!(client::RemarkableClient, name::String, parent::String = ""; kwargs...)
item = Collection(
Parent = parent,
VissibleName = name,
)
@info "Creating folder $name"
res = update_metadata!(client, item; kwargs...)
return Collection(res)
end
## Download files
"""
download_document(client, id::String, [path_target::String]) -> ZipFile Body
download_document(client, doc::Document, [path_target::String]) -> Zipfile Body
Download a document object with given id/doc.
The document is always given as a `ZipFile` and can be saved via `write(filepath, body)`
if `path_target` is given, the zip file is automatically written and named.
"""
function Base.download(client::RemarkableClient, id::String; kwargs...)
download(client, Document(ID= id); kwargs...)
end
function Base.download(client::RemarkableClient, doc::Document; kwargs...)
doc = get_item(client, doc.ID, true)
@info "Downloading data"
return HTTP.request(client, "GET", doc.BlobURLGet; kwargs...)
end
function Base.download(client::RemarkableClient, id::String, path_target::String; kwargs...)
download_document(client, Document(ID=id), path_target; kwargs...)
end
function Base.download(client::RemarkableClient, doc::Document, path_target::String; kwargs...)
file_name = isempty(title(doc)) ?
doc.ID :
(ispdf(doc) ?
title(doc)[1:end-4] :
title(doc))
file_path = joinpath(path_target, file_name * ".zip")
body = download(client, doc; kwargs...)
write(file_path, body)
@info "File downloaded at $(file_path)"
return file_path
end
function download_pdf(client::RemarkableClient, doc::Document, path_target::String; kwargs...)
file_path = download(client, doc, path_target; kwargs...)
file_name = isempty(title(doc)) ?
doc.ID :
(ispdf(doc) ?
title(doc)[1:end-4] :
title(doc))
z = ZipFile.Reader(file_path)
for f in z.files
if endswith(f, ".pdf")
write(joinpath(path_target, file_name * ".pdf"), f)
@info "Extracted $(file_name).pdf"
return joinpath(path_target, file_name * ".pdf")
end
end
end
## Upload files
"""
create_upload_request(client, doc::Document=Document()) -> Document
Create a request to upload a document with a given id.
"""
function create_upload_request(client::RemarkableClient, doc::Document=Document(); kwargs...)
@info "Creating upload request"
data = storage_request(client, "PUT", "upload/request", obj_to_dict(doc); kwargs...)
if isempty(data["BlobURLPut"])
error("Failed to get URL for upload")
end
return Document(doc; BlobURLPut = data["BlobURLPut"])
end
"""
upload_document!(client, obj::RemarkableObject, zip) -> RemarkableObject
Upload `zip` file (actual zip file) with metadata from obj
"""
function upload_document!(client::RemarkableClient, obj::RemarkableObject, zipfile; kwargs...)
obj = create_upload_request(client, obj; kwargs...)
@info "Uploading data"
body = HTTP.request(client, "PUT", obj.BlobURLPut, Dict(), zipfile; kwargs...)
item = update_metadata!(client, obj)
return item
end
"""
upload_pdf!(client, pdf_path::String, pdf_name::String = basename(pdf_path), parent)
Create a document for the given pdf and upload it.
"""
function upload_pdf!(client::RemarkableClient, pdf_path::String, pdf_name::String = basename(pdf_path), parent::String = ""; kwargs...)
upload_pdf!(client, read(pdf_path), pdf_name, parent; kwargs...)
end
function upload_pdf!(client::RemarkableClient, pdf, pdfname::String, parent::String=""; kwargs...)
doc = Document(
Parent = parent,
VissibleName = pdfname
)
## Create the ZIP file here with ZipFile
tmpdir = mktempdir()
tmpfile = joinpath(tmpdir, doc.ID * ".zip")
zip = ZipFile.Writer(tmpfile)
pdffile = ZipFile.addfile(zip, doc.ID * ".pdf")
write(pdffile, pdf)
pagedata = ZipFile.addfile(zip, doc.ID * ".pagedata")
write(pagedata, "")
content = ZipFile.addfile(zip, doc.ID * ".content")
json_content = Dict(
"extraMetaData" => Dict(),
"fileType" => "pdf",
"lastOpenedPage" => 0,
"lineHeight" => -1,
"margins" => 100,
"pageCount" => 0,
"textScale" => 1,
"transform" => Dict()
)
write(content, JSON.json(json_content))
close(zip)
# zip = ZipFile.Reader(tmpfile)
@info "Zip file temporarily saved at $(tmpfile)."
upload_document!(client, doc, read(joinpath(tmpfile)); kwargs...)
end
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] | 2.514815 | 3,510 |
using MonteCarloMeasurements: Particles
function test_namedtuple_data(
idata,
group,
names,
nchains,
ndraws;
library = "MyLib",
coords = Dict(),
dims = Dict(),
)
@test idata isa InferenceData
@test group in ArviZ.groupnames(idata)
ds = getproperty(idata, group)
sizes = dimsizes(ds)
@test length(sizes) == 2 + length(coords)
vars = vardict(ds)
for name in string.(names)
@test name in keys(vars)
dim = get(dims, name, [])
s = (x -> length(get(coords, x, []))).(dim)
@test size(vars[name]) == (nchains, ndraws, s...)
end
@test "inference_library" in keys(attributes(ds))
@test attributes(ds)["inference_library"] == library
return nothing
end
@testset "from_namedtuple" begin
rng = MersenneTwister(42)
nchains, ndraws = 4, 10
sizes = (x = (), y = (2,), z = (3, 4))
dims = Dict("y" => ["yx"], "z" => ["zx", "zy"])
coords = Dict("yx" => ["y1", "y2"], "zx" => 1:3, "zy" => 1:4)
nts = [
"NamedTuple" =>
(; (k => randn(rng, nchains, ndraws, v...) for (k, v) in pairs(sizes))...),
"Vector{NamedTuple}" => [
(; (k => randn(rng, ndraws, v...) for (k, v) in pairs(sizes))...)
for _ in 1:nchains
],
"Matrix{NamedTuple}" => [
(; (k => randn(rng, v...) for (k, v) in pairs(sizes))...)
for _ in 1:nchains, _ in 1:ndraws
],
"Vector{Vector{NamedTuple}}" => [
[(; (k => randn(rng, v...) for (k, v) in pairs(sizes))...) for _ in 1:ndraws] for _ in 1:nchains
],
"Vector{NamedTuple} particles" => [
(; (k => Particles(randn(rng, ndraws, v...)) for (k, v) in pairs(sizes))...) for _ in 1:nchains
],
]
@testset "posterior::$(type)" for (type, nt) in nts
idata1 = from_namedtuple(nt; dims = dims, coords = coords, library = "MyLib")
test_namedtuple_data(
idata1,
:posterior,
keys(sizes),
nchains,
ndraws;
library = "MyLib",
coords = coords,
dims = dims,
)
idata2 =
convert_to_inference_data(nt; dims = dims, coords = coords, library = "MyLib")
test_namedtuple_data(
idata2,
:posterior,
keys(sizes),
nchains,
ndraws;
library = "MyLib",
coords = coords,
dims = dims,
)
end
@testset "$(group)" for group in [
:posterior_predictive,
:sample_stats,
:predictions,
:log_likelihood,
]
@testset "::$(type)" for (type, nt) in nts
idata1 = convert_to_inference_data(
nt;
(group => nt,)...,
dims = dims,
coords = coords,
library = "MyLib",
)
test_namedtuple_data(
idata1,
:posterior,
keys(sizes),
nchains,
ndraws;
library = "MyLib",
coords = coords,
dims = dims,
)
test_namedtuple_data(
idata1,
group,
keys(sizes),
nchains,
ndraws;
library = "MyLib",
coords = coords,
dims = dims,
)
idata2 = convert_to_inference_data(
nt;
(group => keys(sizes),)...,
dims = dims,
coords = coords,
library = "MyLib",
)
test_namedtuple_data(
idata2,
group,
keys(sizes),
nchains,
ndraws;
library = "MyLib",
coords = coords,
dims = dims,
)
end
end
@testset "$(group)" for group in [:prior_predictive, :sample_stats_prior]
@testset "::$(type)" for (type, nt) in nts
idata1 = from_namedtuple(;
prior = nt,
(group => nt,)...,
dims = dims,
coords = coords,
library = "MyLib",
)
test_namedtuple_data(
idata1,
:prior,
keys(sizes),
nchains,
ndraws;
library = "MyLib",
coords = coords,
dims = dims,
)
test_namedtuple_data(
idata1,
group,
keys(sizes),
nchains,
ndraws;
library = "MyLib",
coords = coords,
dims = dims,
)
idata2 = from_namedtuple(;
prior = nt,
(group => keys(sizes),)...,
dims = dims,
coords = coords,
library = "MyLib",
)
test_namedtuple_data(
idata2,
group,
keys(sizes),
nchains,
ndraws;
library = "MyLib",
coords = coords,
dims = dims,
)
end
end
@testset "$(group)" for group in
[:observed_data, :constant_data, :predictions_constant_data]
_, nt = nts[1]
idata = from_namedtuple(
nt;
(group => Dict("w" => [1.0, 2.0]),)...,
dims = Dict("w" => ["wx"]),
coords = Dict("wx" => 1:2),
library = "MyLib",
)
@test idata isa InferenceData
@test group in ArviZ.groupnames(idata)
ds = getproperty(idata, group)
sizes = dimsizes(ds)
@test length(sizes) == 1
@test "w" in keys(vardict(ds))
@test "inference_library" in keys(attributes(ds))
@test attributes(ds)["inference_library"] == "MyLib"
end
end
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<filename>21/21.jl<gh_stars>1-10
using Base.Iterators
using DataStructures
using ArgParse
function readInput(filename::String)
regex = r"Player (\d+) starting position: (\d+)"
player1, value1 = -1, -1
player2, value2 = -1, -1
open(filename) do file
for line in readlines(file)
m = match(regex, line)
if m[1] == "1"
player1, value1 = parse.(Int, (m[1], m[2]))
else
player2, value2 = parse.(Int, (m[1], m[2]))
end
end
end
return (player1, value1), (player2, value2)
end
function playGame(p1, p2; winning_score::Int = 1000)
roll_dice(offset::Int) = sum(((offset:offset+2) .- 1) .% 100 .+ 1)
p1, p2 = p1 - 1, p2 - 1
s1, s2, dice_i = 0, 0, 0
for i in countfrom(1)
if i % 2 == 1
p1 = (p1 + roll_dice(dice_i + 1)) % 10
s1 += p1 + 1
s1 >= winning_score && return i * s2 *3
else
p2 = (p2 + roll_dice(dice_i + 1)) % 10
s2 += p2 + 1
s2 >= winning_score && return i * s1 * 3
end
dice_i = (dice_i + 3) % 100
end
end
function dirac_game(p1, p2; winning_score::Int=21)
s1, p1 = 0, p1 - 1
s2, p2 = 0, p2 - 1
wins = [0, 0]
three_dice = vec(sum.(collect(product(1:3, 1:3, 1:3))))
prev_step = Dict{Tuple{Int, Int, Int, Int}, Int}((p1, s1, p2, s2) => 1)
turn = true
while length(prev_step) != 0
next_step = DefaultDict{Tuple{Int, Int, Int, Int}, Int}(0)
for k in keys(prev_step)
v = prev_step[k]
p1, s1, p2, s2 = k
for dice in three_dice
if turn
s1_ = s1 + (p1 + dice) % 10 + 1
if s1_ >= winning_score
wins[1] += v
else
p1_ = (p1 + dice) % 10
next_step[(p1_, s1_, p2, s2)] += v
end
else
s2_ = s2 + (p2 + dice) % 10 + 1
if s2_ >= winning_score
wins[2] += v
else
p2_ = (p2 + dice) % 10
next_step[(p1, s1, p2_, s2_)] += v
end
end
end
end
turn = !turn
prev_step = next_step
end
return maximum(wins)
end
function parse_cli()
s = ArgParseSettings()
@add_arg_table s begin
"--example"
help = "Calculate example"
action = :store_true
end
return parse_args(s)
end
args = parse_cli();
if args["example"]
(_, p1), (_, p2) = readInput("example.txt")
score = playGame(p1, p2)
@assert score == 739785
w = dirac_game(p1, p2)
@assert w == 444356092776315
end
(_, p1), (_, p2) = readInput("input.txt")
score = playGame(p1, p2)
w = dirac_game(p1, p2)
println(score)
println(w) | [
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] | 1.737801 | 1,701 |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
@doc raw"""CrossVersionObjectReference contains enough information to let you identify the referred resource.
IoK8sApiAutoscalingV2beta1CrossVersionObjectReference(;
apiVersion=nothing,
kind=nothing,
name=nothing,
)
- apiVersion::String : API version of the referent
- kind::String : Kind of the referent; More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds\"
- name::String : Name of the referent; More info: http://kubernetes.io/docs/user-guide/identifiers#names
"""
mutable struct IoK8sApiAutoscalingV2beta1CrossVersionObjectReference <: SwaggerModel
apiVersion::Any # spec type: Union{ Nothing, String } # spec name: apiVersion
kind::Any # spec type: Union{ Nothing, String } # spec name: kind
name::Any # spec type: Union{ Nothing, String } # spec name: name
function IoK8sApiAutoscalingV2beta1CrossVersionObjectReference(;apiVersion=nothing, kind=nothing, name=nothing)
o = new()
validate_property(IoK8sApiAutoscalingV2beta1CrossVersionObjectReference, Symbol("apiVersion"), apiVersion)
setfield!(o, Symbol("apiVersion"), apiVersion)
validate_property(IoK8sApiAutoscalingV2beta1CrossVersionObjectReference, Symbol("kind"), kind)
setfield!(o, Symbol("kind"), kind)
validate_property(IoK8sApiAutoscalingV2beta1CrossVersionObjectReference, Symbol("name"), name)
setfield!(o, Symbol("name"), name)
o
end
end # type IoK8sApiAutoscalingV2beta1CrossVersionObjectReference
const _property_map_IoK8sApiAutoscalingV2beta1CrossVersionObjectReference = Dict{Symbol,Symbol}(Symbol("apiVersion")=>Symbol("apiVersion"), Symbol("kind")=>Symbol("kind"), Symbol("name")=>Symbol("name"))
const _property_types_IoK8sApiAutoscalingV2beta1CrossVersionObjectReference = Dict{Symbol,String}(Symbol("apiVersion")=>"String", Symbol("kind")=>"String", Symbol("name")=>"String")
Base.propertynames(::Type{ IoK8sApiAutoscalingV2beta1CrossVersionObjectReference }) = collect(keys(_property_map_IoK8sApiAutoscalingV2beta1CrossVersionObjectReference))
Swagger.property_type(::Type{ IoK8sApiAutoscalingV2beta1CrossVersionObjectReference }, name::Symbol) = Union{Nothing,eval(Base.Meta.parse(_property_types_IoK8sApiAutoscalingV2beta1CrossVersionObjectReference[name]))}
Swagger.field_name(::Type{ IoK8sApiAutoscalingV2beta1CrossVersionObjectReference }, property_name::Symbol) = _property_map_IoK8sApiAutoscalingV2beta1CrossVersionObjectReference[property_name]
function check_required(o::IoK8sApiAutoscalingV2beta1CrossVersionObjectReference)
(getproperty(o, Symbol("kind")) === nothing) && (return false)
(getproperty(o, Symbol("name")) === nothing) && (return false)
true
end
function validate_property(::Type{ IoK8sApiAutoscalingV2beta1CrossVersionObjectReference }, name::Symbol, val)
end
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] | 2.939571 | 1,026 |
<gh_stars>0
module BandedMatrices
using Base, FillArrays, ArrayLayouts, LinearAlgebra, SparseArrays, Random
using LinearAlgebra.LAPACK
import Base: axes, axes1, getproperty, iterate, tail
import LinearAlgebra: BlasInt, BlasReal, BlasFloat, BlasComplex, axpy!,
copy_oftype, checksquare, adjoint, transpose, AdjOrTrans, HermOrSym,
_chol!, rot180
import LinearAlgebra.BLAS: libblas
import LinearAlgebra.LAPACK: liblapack, chkuplo, chktrans
import LinearAlgebra: cholesky, cholesky!, cholcopy, norm, diag, eigvals!, eigvals, eigen!, eigen,
qr, qr!, axpy!, ldiv!, mul!, lu, lu!, ldlt, ldlt!, AbstractTriangular,
chkstride1, kron, lmul!, rmul!, factorize, StructuredMatrixStyle, logabsdet,
svdvals, svdvals!, QRPackedQ, checknonsingular, ipiv2perm, tril!,
triu!, Givens, diagzero
import SparseArrays: sparse
import Base: getindex, setindex!, *, +, -, ==, <, <=, >, isassigned,
>=, /, ^, \, transpose, showerror, reindex, checkbounds, @propagate_inbounds
import Base: convert, size, view, unsafe_indices,
first, last, size, length, unsafe_length, step,
to_indices, to_index, show, fill!, promote_op,
MultiplicativeInverses, OneTo, ReshapedArray,
similar, copy, convert, promote_rule, rand,
IndexStyle, real, imag, Slice, pointer, unsafe_convert, copyto!,
hcat, vcat, hvcat
import Base.Broadcast: BroadcastStyle, AbstractArrayStyle, DefaultArrayStyle, Broadcasted, broadcasted,
materialize, materialize!
import ArrayLayouts: MemoryLayout, transposelayout, triangulardata,
conjlayout, symmetriclayout, symmetricdata,
triangularlayout, MatLdivVec, hermitianlayout, hermitiandata,
materialize!, BlasMatMulMatAdd, BlasMatMulVecAdd, BlasMatLmulVec, BlasMatLdivVec,
colsupport, rowsupport, symmetricuplo, MatMulMatAdd, MatMulVecAdd,
sublayout, sub_materialize, _fill_lmul!,
reflector!, reflectorApply!, _copyto!,
_qr!, _qr, _lu!, _lu, _factorize, TridiagonalLayout
import FillArrays: AbstractFill, getindex_value
export BandedMatrix,
bandrange,
brand,
bandwidth,
BandError,
band,
Band,
BandRange,
bandwidths,
colrange,
rowrange,
isbanded,
Zeros,
Fill,
Ones,
Eye
if VERSION < v"1.2-"
import Base: has_offset_axes
require_one_based_indexing(A...) = !has_offset_axes(A...) || throw(ArgumentError("offset arrays are not supported but got an array with index other than 1"))
else
import Base: require_one_based_indexing
end
if VERSION < v"1.3-"
const _apply_ipiv_rows! = LinearAlgebra._apply_ipiv!
else
import LinearAlgebra: _apply_ipiv_rows!
end
include("blas.jl")
include("lapack.jl")
include("generic/AbstractBandedMatrix.jl")
include("generic/broadcast.jl")
include("generic/matmul.jl")
include("generic/Band.jl")
include("generic/utils.jl")
include("generic/indexing.jl")
include("banded/BandedMatrix.jl")
include("banded/BandedLU.jl")
include("banded/bandedqr.jl")
include("banded/gbmm.jl")
include("banded/linalg.jl")
include("symbanded/symbanded.jl")
include("symbanded/ldlt.jl")
include("symbanded/BandedCholesky.jl")
include("symbanded/SplitCholesky.jl")
include("symbanded/bandedeigen.jl")
include("tribanded.jl")
include("interfaceimpl.jl")
# function _precompile_()
# precompile(Tuple{typeof(gbmm!), Char, Char, Float64, BandedMatrix{Float64,Array{Float64,2},Base.OneTo{Int64}}, BandedMatrix{Float64,Array{Float64,2},Base.OneTo{Int64}}, Float64, BandedMatrix{Float64,Array{Float64,2},Base.OneTo{Int64}}})
# end
# _precompile_()
end #module
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] | 2.263219 | 1,721 |
<gh_stars>0
using Base.Test
using MLDatasets
include("MNIST/runtests.jl")
# CIFAR10
x, y = CIFAR10.traindata()
x, y = CIFAR10.testdata()
# CIFAR100
x, y = CIFAR100.traindata()
x, y = CIFAR100.testdata()
# PTBLM
x, y = PTBLM.traindata()
x, y = PTBLM.testdata()
# UD_English
x = UD_English.traindata()
x = UD_English.devdata()
x = UD_English.testdata()
nothing
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] | 2.206061 | 165 |
# Example Julia script calling the typical workflow to search PubMed and store
# the results in a database
# Date: September 6, 2016
# Authors: <NAME>
# BCBI - Brown University
# Version: Julia 0.4.5
using BioMedQuery.Processes
using BioMedQuery.Entrez
#************************ LOCALS TO CONFIGURE!!!! **************************
email= ENV["NCBI_EMAIL"] #This is an enviroment variable that you need to setup
search_term="(obesity[MeSH Major Topic]) AND (\"2010\"[Date - Publication] : \"2012\"[Date - Publication])"
max_articles = 20
overwrite=true
verbose = false
results_dir = "./results"
#Database backend
using_sqlite=false
using_mysql=false
using_endnote=true
using_xml=false
#***************************************************************************
if !isdir(results_dir)
mkdir(results_dir)
end
if using_mysql
#************************ LOCALS TO CONFIGURE!!!! **************************
host="localhost" #If want to hide - use enviroment variables instead
mysql_usr="root"
mysql_pswd=""
dbname="pubmed_obesity_2010_2012"
#***************************************************************************
config = Dict(:host=>host,
:dbname=>dbname,
:username=>mysql_usr,
:pswd=>mysql_pswd,
:overwrite=>overwrite)
save_func = save_efetch_mysql
elseif using_sqlite
#************************ LOCALS TO CONFIGURE!!!! **************************
db_path="results_dir/pubmed_obesity_2010_2012.db"
#***************************************************************************
config = Dict(:db_path=>db_path,
:overwrite=>overwrite)
save_func = save_efetch_sqlite
elseif using_endnote
#************************ LOCALS TO CONFIGURE!!!! **************************
citation_type="endnote"
output_file="results_dir/pubmed_obesity_2010_2012.enw"
#***************************************************************************
config = Dict(:type => citation_type, :output_file => output_file, :overwrite=> overwrite)
save_func = save_article_citations
elseif using_xml
#************************ LOCALS TO CONFIGURE!!!! **************************
output_file="results_dir/pubmed_obesity_2010_2012.xml"
#***************************************************************************
config = Dict(:output_file => output_file, :overwrite=> overwrite)
save_func = save_efetch_xml
else
error("Unsupported database backend, options are: sqlite, mysql, endnote")
end
@time begin
db = pubmed_search_and_save(email, search_term, max_articles,
save_func, config, verbose)
end
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] | 3.087558 | 868 |
function check_bipartiteness(A)
return true
end
function check_unipartiteness(A)
size(A,1) == size(A,2) || throw(ArgumentError("The matrix for a unipartite network must be square (yours has size $(size(A)))"))
end
function check_bipartiteness(A, T, B)
check_bipartiteness(A)
check_name_vector(T)
check_name_vector(B)
eltype(T) == eltype(B) || throw(ArgumentError("The species in a bipartite network levels must have the same type (you gave $(eltype(T)) and $(eltype(B)))"))
length(unique(vcat(T,B))) == sum(size(A)) || throw(ArgumentError("Species names cannot be shared between the levels of a bipartite network"))
length(T) == size(A,1) || throw(ArgumentError("The length of top-level species names must match matrix size ($(length(T)) v. $(size(A,1)))"))
length(B) == size(A,2) || throw(ArgumentError("The length of bottom-level species names must match matrix size ($(length(B)) v. $(size(A,2)))"))
end
function check_unipartiteness(A, S)
check_unipartiteness(A)
check_name_vector(S)
length(S) == size(A,1) || throw(ArgumentError("The length of species names must match matrix size ($(length(S)) v. $(size(A,1)))"))
end
function check_name_vector(N)
length(N) == length(unique(N)) || throw(ArgumentError("The names of species must be unique"))
end
function check_probability_values(A)
minimum(A) >= zero(eltype(A)) || throw(ArgumentError("The probabilities must be at least 0.0"))
maximum(A) <= one(eltype(A)) || throw(ArgumentError("The probabilities must be at most 1.0"))
end
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] | 2.885932 | 526 |
abstract type AbstractEvent end
abstract type AbstractContinuousEvent <: AbstractEvent end
abstract type AbstractDiscreteEvent <: AbstractEvent end
# evaluate the functional whose events are sought
(eve::AbstractEvent)(iter, state) = eve.condition(iter, state)
# initialize function, must return the same type as eve(iter, state)
initialize(eve::AbstractEvent, T) = throw("Initialization method not implemented for event ", eve)
# whether the event requires computing eigen-elements
@inline computeEigenElements(::AbstractEvent) = false
length(::AbstractEvent) = throw("length not implemented")
# default label used to record event in ContResult
labels(::AbstractEvent, ind) = "user"
# whether the user provided its own labels
hasCustomLabels(::AbstractEvent) = false
# general condition for detecting a continuous event.
# Basically, we want to detect if some component of `eve(fct(iter, state))` is below Ο΅
# the ind is used to specify which par of the event is tested
function isEventCrossed(::AbstractContinuousEvent, iter, state, ind = :)
test(x, y) = x * y < 0
if state.eventValue[1] isa Real
return test(state.eventValue[1], state.eventValue[2])
else
for u in zip(state.eventValue[1][ind], state.eventValue[2][ind])
if test(u[1], u[2])
return true
end
end
return false
end
end
# general condition for detecting a discrete event
function isEventCrossed(::AbstractDiscreteEvent, iter, state, ind = :)
test(x, y) = x != y
if state.eventValue[1] isa Integer
return test(state.eventValue[1], state.eventValue[2])
else
for u in zip(state.eventValue[1][ind], state.eventValue[2][ind])
if test(u[1], u[2])
return true
end
end
return false
end
end
####################################################################################################
# for AbstractContinuousEvent and AbstractDiscreteEvent
# return type when calling eve.fct(iter, state)
initialize(eve::AbstractContinuousEvent, T) = eve.nb == 1 ? T(1) : ntuple(x -> T(1), eve.nb)
initialize(eve::AbstractDiscreteEvent, T) = eve.nb == 1 ? Int64(1) : ntuple(x -> Int64(1), eve.nb)
####################################################################################################
"""
$(TYPEDEF)
Structure to pass a ContinuousEvent function to the continuation algorithm.
A continuous call back returns a **tuple/scalar** value and we seek its zeros.
$(TYPEDFIELDS)
"""
struct ContinuousEvent{Tcb, Tl} <: AbstractContinuousEvent
"number of events, ie the length of the result returned by the callback function"
nb::Int64
", ` (iter, state) -> NTuple{nb, T}` callback function which, at each continuation state, returns a tuple. For example, to detect crossing 1.0 and -2.0, you can pass `(iter, state) -> (getp(state)+2, getx(state)[1]-1)),`. Note that the type `T` should match the one of the parameter specified by the `::Lens` in `continuation`."
condition::Tcb
"whether the event requires to compute eigen elements"
computeEigenElements::Bool
"Labels used to display information. For example `labels[1]` is used top qualify an event of the type `(0,1.3213,3.434)`. For example, you can use `labels = (\"hopf\",)` or `labels = (\"hopf\", \"fold\")`. You must have `labels::Union{Nothing, NTuple{N, String}}`."
labels::Tl
end
ContinuousEvent(nb::Int, fct, labels::Union{Nothing, NTuple{N, String}} = nothing) where N = (@assert nb > 0 "You need to return at least one callback"; ContinuousEvent(nb, fct, false, labels))
@inline computeEigenElements(eve::ContinuousEvent) = eve.computeEigenElements
@inline length(eve::ContinuousEvent) = eve.nb
@inline hasCustomLabels(eve::ContinuousEvent{Tcb, Tl}) where {Tcb, Tl} = ~(Tl == Nothing)
####################################################################################################
"""
$(TYPEDEF)
Structure to pass a DiscreteEvent function to the continuation algorithm.
A continuous call back returns a discrete value and we seek when it changes.
$(TYPEDFIELDS)
"""
struct DiscreteEvent{Tcb, Tl} <: AbstractDiscreteEvent
"number of events, ie the length of the result returned by the callback function"
nb::Int64
", ` (iter, state) -> NTuple{nb, Int64}` callback function which at each continuation state, returns a tuple. For example, to detect a value change."
condition::Tcb
"whether the event requires to compute eigen elements"
computeEigenElements::Bool
"Labels used to display information. For example `labels[1]` is used top qualify an event occuring in the first component. For example, you can use `labels = (\"hopf\",)` or `labels = (\"hopf\", \"fold\")`. You must have `labels::Union{Nothing, NTuple{N, String}}`."
labels::Tl
end
DiscreteEvent(nb::Int, fct, labels::Union{Nothing, NTuple{N, String}} = nothing) where N = (@assert nb > 0 "You need to return at least one callback"; DiscreteEvent(nb, fct, false, labels))
@inline computeEigenElements(eve::DiscreteEvent) = eve.computeEigenElements
@inline length(eve::DiscreteEvent) = eve.nb
@inline hasCustomLabels(eve::DiscreteEvent{Tcb, Tl}) where {Tcb, Tl} = ~(Tl == Nothing)
function labels(eve::Union{ContinuousEvent{Tcb, Nothing}, DiscreteEvent{Tcb, Nothing}}, ind) where Tcb
return "userC" * mapreduce(x->"-$x", *, ind)
end
function labels(eve::Union{ContinuousEvent{Tcb, Tl}, DiscreteEvent{Tcb, Tl}}, ind) where {Tcb, Tl}
if isempty(ind)
return "user"
end
return mapreduce(x->eve.labels[x], *, ind)
end
####################################################################################################
"""
$(TYPEDEF)
Structure to pass a PairOfEvents function to the continuation algorithm. It is composed of a pair ContinuousEvent / DiscreteEvent.
$(TYPEDFIELDS)
"""
struct PairOfEvents{Tc <: AbstractContinuousEvent, Td <: AbstractDiscreteEvent} <: AbstractEvent
"Continuous event"
eventC::Tc
"Discrete event"
eventD::Td
end
@inline computeEigenElements(eve::PairOfEvents) = computeEigenElements(eve.eventC) || computeEigenElements(eve.eventD)
@inline length(event::PairOfEvents) = length(event.eventC) + length(event.eventD)
function (eve::PairOfEvents)(iter, state)
outc = eve.eventC(iter, state)
outd = eve.eventD(iter, state)
return outc..., outd...
end
initialize(eve::PairOfEvents, T) = initialize(eve.eventC, T)..., initialize(eve.eventD, T)...
function isEventCrossed(eve::PairOfEvents, iter, state, ind = :)
nc = length(eve.eventC)
n = length(eve)
resC = isEventCrossed(eve.eventC, iter, state, 1:nc)
resD = isEventCrossed(eve.eventD, iter, state, nc+1:n)
return resC || resD
end
####################################################################################################
"""
$(TYPEDEF)
Multiple events can be chained together to form a `SetOfEvents`. A `SetOfEvents`
is constructed by passing the constructor `ContinuousEvent`, `DiscreteEvent` or other `SetOfEvents` instances:
SetOfEvents(cb1, cb2, cb3)
# Example
BK.SetOfEvents(BK.FoldDetectCB, BK.BifDetectCB)
You can pass as many events as you like.
$(TYPEDFIELDS)
"""
struct SetOfEvents{Tc <: Tuple, Td <: Tuple} <: AbstractEvent
"Continuous event"
eventC::Tc
"Discrete event"
eventD::Td
end
SetOfEvents(callback::AbstractDiscreteEvent) = SetOfEvents((),(callback,))
SetOfEvents(callback::AbstractContinuousEvent) = SetOfEvents((callback,),())
SetOfEvents() = SetOfEvents((),())
SetOfEvents(cb::Nothing) = SetOfEvents()
# For Varargs, use recursion to make it type-stable
SetOfEvents(events::Union{AbstractEvent, Nothing}...) = SetOfEvents(split_events((), (), events...)...)
"""
split_events(cs, ds, args...)
Split comma seperated callbacks into sets of continous and discrete callbacks. Inspired by DiffEqBase.
"""
@inline split_events(cs, ds) = cs, ds
@inline split_events(cs, ds, c::Nothing, args...) = split_events(cs, ds, args...)
@inline split_events(cs, ds, c::AbstractContinuousEvent, args...) = split_events((cs..., c), ds, args...)
@inline split_events(cs, ds, d::AbstractDiscreteEvent, args...) = split_events(cs, (ds..., d), args...)
@inline function split_events(cs, ds, d::SetOfEvents, args...)
split_events((cs...,d.eventC...), (ds..., d.eventD...), args...)
end
@inline computeEigenElements(eve::SetOfEvents) = mapreduce(computeEigenElements, |, eve.eventC) || mapreduce(computeEigenElements, |, eve.eventD)
function (eve::SetOfEvents)(iter, state)
outc = map(x -> x(iter, state), eve.eventC)
outd = map(x -> x(iter, state), eve.eventD)
return (outc..., outd...)
end
initialize(eve::SetOfEvents, T) = map(x->initialize(x,T),eve.eventC)..., map(x->initialize(x,T),eve.eventD)...
function isEventCrossed(event::SetOfEvents, iter, state)
res = false
nC = length(event.eventC)
nD = length(event.eventD)
nCb = nC+nD
for (i, eve) in enumerate(event.eventC)
res = res | isEventCrossed(eve, iter, state, i)
end
for (i, eve) in enumerate(event.eventD)
res = res | isEventCrossed(eve, iter, state, nC + i)
end
return res
end
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] | 3.087579 | 2,866 |
"""
Convert a FrequencySimulationResult into a TimeSimulationResult by using the inverse fourier transform.
Assumes only positive frequencies and a real time signal
"""
function frequency_to_time(simres::FrequencySimulationResult{T,Dim,FieldDim};
t_vec::AbstractVector{T} = Ο_to_t(simres.Ο),
impulse::ContinuousImpulse{T} = TimeDiracImpulse(zero(T)), #GaussianImpulse(maximum(simres.Ο)),
discrete_impulse::DiscreteImpulse{T} = continuous_to_discrete_impulse(impulse, t_vec, simres.Ο),
method = :dft
) where {Dim,FieldDim,T}
t_vec = discrete_impulse.t
time_field = frequency_to_time(transpose(field(simres)), simres.Ο, t_vec;
discrete_impulse = discrete_impulse, method = method)
return TimeSimulationResult(transpose(time_field), simres.x, t_vec)
end
"""
Convert a TimeSimulationResult into a FrequencySimulationResult by using the fourier transform.
Assumes only positive frequencies and a real time signal
"""
function time_to_frequency(timres::TimeSimulationResult{T,Dim,FieldDim};
Ο_vec = t_to_Ο(timres.t),
impulse::ContinuousImpulse{T} = TimeDiracImpulse(zero(T)), #GaussianImpulse(maximum(Ο_vec)),
discrete_impulse::DiscreteImpulse{T} = continuous_to_discrete_impulse(impulse,timres.t, Ο_vec),
method =:dft
) where {Dim,FieldDim,T}
Ο_vec = discrete_impulse.Ο
freq_field = time_to_frequency(transpose(field(timres)), timres.t, Ο_vec;
discrete_impulse = discrete_impulse, method = method)
return FrequencySimulationResult(transpose(freq_field), deepcopy(timres.x), Ο_vec)
end
"""
returns an array of time from the frequency array Ο_vec.
Uses the same convention for sampling the time as the discrete Fourier transfrom.
Assumes Ο_vec is ordered and non-negative.
"""
function Ο_to_t(Ο_arr::AbstractArray{T}) where T <: AbstractFloat
N = length(Ο_arr)
if Ο_arr[1] == zero(T)
N -= 1
elseif minimum(Ο_arr) < zero(T)
error("expected only non-negative values for the frequencies")
end
dΟ = median(abs.((circshift(Ο_arr,1) - Ο_arr)[2:end]))
t_arr = LinRange(zero(T),2Ο/dΟ,2N+2)[1:(2N+1)]
return t_arr
end
"The inverse of Ο_to_t if Ο_vec[1] == 0"
function t_to_Ο(t_arr::AbstractArray{T}) where T <: AbstractFloat
N = Int(round((length(t_arr)-one(T))/T(2)))
maxt = t_arr[2] - t_arr[1] + t_arr[end] - t_arr[1] # subtract t_arr[1] in case t_arr[1] != zero(T)
maxΟ = N*2Ο/maxt
Ο_vec = LinRange(zero(T),maxΟ,N+1)
return Ο_vec
end
"""
Returns the first element of array which isn't zero (assumes elements are
increasing and distinct)
"""
function firstnonzero(arr::AbstractArray{T}) where T <: AbstractFloat
if arr[1] != 0
return arr[1]
else
return arr[2]
end
end
"""
See also: [`DiscreteImpulse`](@ref), [`ContinuousImpulse`](@ref)
Calculates the time response from the frequency response by approximating an
inverse Fourier transform. The time signal is assumed to be real and the
frequenices Ο_vec are assumed to be positive (can include zero) and sorted. The
result is convoluted in time Οith the user specified impulse.
We use the Fourier transform convention:
F(Ο) = β« f(t)*exp(im*Ο*t) dt
f(t) = (2Ο)^(-1) * β« F(Ο)*exp(-im*Ο*t) dt
To easily sample any time, the default is not FFT, but a discrete version of the transform above.
"""
function frequency_to_time(field_mat::AbstractArray{Complex{T}}, Ο_vec::AbstractVector{T},
t_vec::AbstractArray{T} = Ο_to_t(Ο_vec);
impulse::ContinuousImpulse{T} = TimeDiracImpulse(zero(T)),
discrete_impulse::DiscreteImpulse{T} = continuous_to_discrete_impulse(impulse, t_vec, Ο_vec),
method=:dft) where T <: AbstractFloat
# In case the used specifies discrete_impulse but not t_vec
t_vec = discrete_impulse.t
if size(field_mat,1) != size(Ο_vec,1) error("Vector of frequencies Ο_vec expected to be same size as size(field_mat,1)") end
function f(t::T,j::Int)
fs = discrete_impulse.in_freq.*field_mat[:,j] .* exp.(-(im*t) .* Ο_vec)
if method == :dft && Ο_vec[1] == zero(T)
fs[1] = fs[1]/T(2) # so as to not add Ο=0 tΟice in the integral of Ο over [-inf,inf]
end
fs
end
inverse_fourier_integral = (t,j) -> numerical_integral(Ο_vec, f(t,j), method)
u = [inverse_fourier_integral(t,j) for t in discrete_impulse.t, j in axes(field_mat,2)]
return real.(u)/pi # a constant 1/(2pi) appears due to our Fourier convention, but because we only use positive frequencies, and assume a real time signal, this becomes 1/pi.
end
"""
The inverse of the function frequency_to_time (only an exact inverse when using
:dft integration). We use the Fourier transform convention:
F(Ο) = β« f(t)*exp(im*Ο*t) dt
"""
function time_to_frequency(field_mat::Union{AbstractArray{T},AbstractArray{Complex{T}}}, t_vec::AbstractVector{T},
Ο_vec::AbstractArray{T} = t_to_Ο(t_vec);
impulse::ContinuousImpulse{T} = TimeDiracImpulse(zero(T)),
discrete_impulse::DiscreteImpulse{T} = continuous_to_discrete_impulse(impulse, t_vec, Ο_vec),
method=:dft) where T <: AbstractFloat
# In case the used specifies discrete_impulse but not Ο_vec
Ο_vec = discrete_impulse.Ο
# to use an impulse below in time we would need to do a discrete convolution, which we decided against.
f(Ο::T, j::Int) = field_mat[:,j] .* exp.((im*Ο) .* t_vec)
fourier_integral = (Ο,j) -> numerical_integral(t_vec, f(Ο,j), method)
uhat = [discrete_impulse.in_freq[i]*fourier_integral(discrete_impulse.Ο[i],j) for i in eachindex(discrete_impulse.Ο), j in axes(field_mat,2)]
return uhat
end
function numerical_integral(xs::AbstractArray{T}, fs::Union{AbstractArray{T},AbstractArray{Complex{T}}}, method = :dft) where T <: AbstractFloat
if method == :trapezoidal
sum(1:(length(xs)-1)) do xi
(fs[xi] + fs[xi+1])*(xs[xi+1] - xs[xi])/T(2)
end
elseif method == :dft
fs[1]*(xs[2] - xs[1]) +
sum(2:length(xs)) do xi
fs[xi]*(xs[xi] - xs[xi-1])
end
else
error("The method $method for numerical integration is not known.")
end
end
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] | 2.482675 | 2,482 |
<reponame>wxuejing/AutomotiveDrivingModels.jl<gh_stars>1-10
export
LaneFollowingDriver,
StaticLaneFollowingDriver,
PrincetonDriver,
IntelligentDriverModel,
ProportionalSpeedTracker,
track_longitudinal!
include("lane_following_drivers.jl")
include("static_lane_following_drivers.jl")
include("princeton_drivers.jl")
include("speed_trackers.jl")
include("intelligent_driver_models.jl")
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] | 2.921429 | 140 |
"""
JuMPNorm
Abstract type for norms to a JuMP model.
"""
abstract type JuMPNorm end
"""
L1NormLinear
L1NormLinear()
Add a set of linear constraints to a model to map an expression to a variable which is the L1 norm of that expression.
"""
struct L1NormLinear <: JuMPNorm end
"""
MaxNormLinear
MaxNormLinear()
Add a set of linear constraints to a model to map an expression to a variable which is the max norm of that expression.
"""
struct MaxNormLinear <: JuMPNorm end
"""
ScaleNorm{S<:Real,N} <: JuMPNorm
ScaleNorm(scale, n)
Scales result from `n` with a factor `scale`.
"""
struct ScaleNorm{S<:Real,N<:JuMPNorm} <: JuMPNorm
scale::S
n::N
end
"""
SumNorm{N<:JuMPNorm} <: JuMPNorm
Sum of `ns`.
"""
struct SumNorm{N<:JuMPNorm} <: JuMPNorm
ns::Vector{N}
end
SumNorm(ns::JuMPNorm...) = SumNorm(collect(ns))
SumNorm(sns::Pair{<:Real, <:JuMPNorm}...) = SumNorm(ScaleNorm.(first.(sns), last.(sns))...)
"""
norm!(s::L1NormLinear, model, X)
Add a set of linear constraints to a model to map `X` to an expression `Xβ²` which is the L1 norm of `X`.
Note that it only works for the objective function and only for minimization.
"""
function norm!(s::L1NormLinear, model, X, denom=1)
# Use trick from http://lpsolve.sourceforge.net/5.1/absolute.htm to make min abs(expression) linear
Xβ² = @variable(model, [1:length(X)])
@constraint(model, X .<= Xβ² .* denom)
@constraint(model, -X .<= Xβ² .* denom)
return @expression(model, sum(Xβ²))
end
"""
norm!(s::L1NormLinear, model, X)
Add a set of linear constraints to a model to map `X` to a variable `Xβ²` which is the max norm of `X`.
Note that it only works for the objective function and only for minimization.
"""
function norm!(s::MaxNormLinear, model, X, denom=1)
# Use trick from https://math.stackexchange.com/questions/2589887/how-can-the-infinity-norm-minimization-problem-be-rewritten-as-a-linear-program to make min abs(expression) linear
Xβ² = @variable(model)
@constraint(model, X .<= Xβ² .* denom)
@constraint(model, -X .<= Xβ² .* denom)
return Xβ²
end
function norm!(s::ScaleNorm, model, X, denom=1)
Xβ² = norm!(s.n, model, X, denom)
return @expression(model, s.scale * Xβ²)
end
norm!(s::SumNorm, model, X, denom=1) = mapfoldl(n -> norm!(n, model, X, denom), (Xβ²,Xβ³) -> @expression(model, Xβ²+Xβ³), s.ns, init=@expression(model, 0))
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] | 2.570968 | 930 |
using DelayedKalmanFilter
using DelimitedFiles
using Test
@testset "Kalman Filter tests" begin
loading_path = string(pwd(),"/data/");
protein_at_observations = readdlm(string(loading_path,"kalman_filter_test_trace_observations.csv"),',');
model_parameters = [10000.0,5.0,log(2)/30,log(2)/90,1.0,1.0,29.0];
measurement_variance = 10000;
a,b,c,d,e,f,g = kalman_filter(protein_at_observations,model_parameters,measurement_variance);
# check arrays are correct shape
@test size(a) == (protein_at_observations[end,1] + 1 + model_parameters[end],3)
@test size(b) == ((protein_at_observations[end,1] + 1 + model_parameters[end])*2,(protein_at_observations[end,1] + 1 + model_parameters[end])*2)
@test size(c) == (protein_at_observations[end,1] + 1 + model_parameters[end],length(model_parameters),2)
@test size(d) == (length(model_parameters),(protein_at_observations[end,1] + 1 + model_parameters[end])*2,(protein_at_observations[end,1] + 1 + model_parameters[end])*2)
@test size(e) == (size(protein_at_observations,1),3)
@test size(f) == (size(protein_at_observations,1),length(model_parameters),2)
@test size(g) == (size(protein_at_observations,1),length(model_parameters),2,2)
end
# using ForwardDiff
# log_likelihood_gradient(model_parameters,protein_at_observations,measurement_variance) = ForwardDiff.gradient(model_parameters -> calculate_log_likelihood_at_parameter_point(model_parameters,protein_at_observations,measurement_variance),model_parameters)
# log_likelihood_gradient(model_parameters,protein_at_observations,measurement_variance)
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] | 2.595779 | 616 |
module AsyncFinalizersTests
include("utils.jl")
include("test_bags.jl")
include("test_finalizers.jl")
include("test_chaos.jl")
end # module AsyncFinalizersTests
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using MOCNeutronTransport
@testset "QuadraticSegment_2D" begin
for F in [Float32, Float64, BigFloat]
@testset "Constructors" begin
# Constructor
xββ = Point_2D{F}(0, 0)
xββ = Point_2D{F}(2, 0)
xββ = Point_2D{F}(1, 1)
q = QuadraticSegment_2D(xββ, xββ, xββ)
@test q.points == SVector(xββ, xββ, xββ)
end
@testset "Methods" begin
# interpolation
xββ = Point_2D{F}(0, 0)
xββ = Point_2D{F}(2, 0)
xββ = Point_2D{F}(1, 1)
q = QuadraticSegment_2D(xββ, xββ, xββ)
for r = LinRange{F}(0, 1, 11)
@test q(r) β Point_2D{F}(2r, -(2r)^2 + 4r)
end
# gradient
for r = LinRange{F}(0, 1, 11)
@test β(q, r) β Point_2D{F}(2, -(8r) + 4)
end
# arclength
xββ = Point_2D{F}(0, 0)
xββ = Point_2D{F}(2, 0)
xββ = Point_2D{F}(1, 0)
q = QuadraticSegment_2D(xββ, xββ, xββ)
# straight edge
@test abs(arclength(q) - 2) < 1.0e-6
# curved
xββ = Point_2D{F}(1, 1)
q = QuadraticSegment_2D(xββ, xββ, xββ)
@test abs(arclength(q) - 2.9578857151786138) < 1.0e-6
# boundingbox
xββ = Point_2D{F}(0, 0)
xββ = Point_2D{F}(2, 0)
xββ = Point_2D{F}(1, 1)
q = QuadraticSegment_2D(xββ, xββ, xββ)
bb = boundingbox(q)
@test bb.xmin β 0
@test bb.ymin β 0
@test bb.xmax β 2
@test bb.ymax β 1
xββ = Point_2D{F}(0, 0)
xββ = Point_2D{F}(2, 2)
xββ = Point_2D{F}(1, 1)
q = QuadraticSegment_2D(xββ, xββ, xββ)
bb = boundingbox(q)
@test bb.xmin β 0
@test bb.ymin β 0
@test bb.xmax β 2
@test bb.ymax β 2
xββ = Point_2D{F}(0, 0)
xββ = Point_2D{F}(2, 0)
xββ = Point_2D{F}(2.1, 1)
q = QuadraticSegment_2D(xββ, xββ, xββ)
bb = boundingbox(q)
@test bb.xmin β 0
@test bb.ymin β 0
@test bb.xmax β 2.3272727272727276
@test bb.ymax β 1
# isstraight
xββ = Point_2D{F}(0, 0)
xββ = Point_2D{F}(2, 0)
xββ = Point_2D{F}(1, 0)
q = QuadraticSegment_2D(xββ, xββ, xββ)
@test isstraight(q)
xββ = Point_2D{F}(2, 0.0001)
q = QuadraticSegment_2D(xββ, xββ, xββ)
@test !isstraight(q)
# laplacian
xββ = Point_2D{F}(0, 0)
xββ = Point_2D{F}(2, 0)
xββ = Point_2D{F}(1, 1)
q = QuadraticSegment_2D(xββ, xββ, xββ)
p = βΒ²(q, 0)
@test p.x β 0
@test p.y β -8
p = βΒ²(q, 1)
@test p.x β 0
@test p.y β -8
# # closest_point
# xββ = Point_2D{F}(0, 0)
# xββ = Point_2D{F}(2, 0)
# xββ = Point_2D{F}(1, 1)
# q = QuadraticSegment_2D(xββ, xββ, xββ)
# p = Point_2D{F}(1, 1.1)
# r, p_c = closest_point(p, q)
# @test xββ β p_c
# p = Point_2D{F}(-0.1, 0)
# r, p_c = closest_point(p, q)
# @test xββ β p_c
#
# # intersect
# xββ = Point_2D{F}(0, 0)
# xββ = Point_2D{F}(2, 0)
# xββ = Point_2D{F}(1, 1)
# xββ = Point_2D{F}(1, 0)
# xββ
= Point_2D{F}(1, 2)
#
# # 1 intersection
# q = QuadraticSegment_2D(xββ, xββ, xββ)
# l = LineSegment_2D(xββ, xββ
)
# npoints, (point1, point2) = intersect(l, q)
# @test npoints == 1
# @test point1 β Point_2D{F}(1, 1)
#
# # 2 intersections
# xββ = Point_2D{F}(0, 3//4)
# xββ
= Point_2D{F}(2, 3//4)
# l = LineSegment_2D(xββ, xββ
)
# npoints, (point1, point2) = l β© q
# @test npoints == 2
# @test point1 β Point_2D{F}(1//2, 3//4)
# @test point2 β Point_2D{F}(3//2, 3//4)
#
# # 0 intersections
# xββ = Point_2D{F}(0, 3)
# xββ
= Point_2D{F}(2, 3)
# l = LineSegment_2D(xββ, xββ
)
# npoints, (point1, point2) = intersect(l, q)
# @test npoints == 0
# @test point1 β Point_2D{F}(0)
# @test point2 β Point_2D{F}(0)
#
# # is_left
# xββ = Point_2D{F}(0, 0)
# xββ = Point_2D{F}(2, 0)
# xββ = Point_2D{F}(1, 1)
# q = QuadraticSegment_2D(xββ, xββ, xββ)
# @test !is_left(Point_2D{F}(1, 0), q)
# @test is_left(Point_2D{F}(1, 2), q)
end
end
end
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] | 1.29209 | 3,742 |
<filename>src/distributions/exponential.jl
#
# IID case
#
function min(Xs::IIDRandomSequence{Continuous,Exponential})
N,Ξ» = length(Xs),rate(Xs.d)
return Exponential(1./(N*Ξ»))
end
function spacing(Xs::IIDRandomSequence,space::Integer)
N,Ξ» = length(Xs),rate(Xs.d)
return Exponential(1./((N-space+1)*Ξ»s))
end
#
# INID case
#
function min(Xs::INIDRandomSequence{Continuous,Exponential})
N,Ξ»s = length(Xs),[rate(X) for X in Xs.distributions]
return Exponential(1./sum(Ξ»s))
end
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] | 2.405941 | 202 |
#=
https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl/blob/master/src/ReinforcementLearningZoo/src/algorithms/cfr/deep_cfr.jl
=#
function CounterfactualRegret.train!(sol::DeepCFRSolver, N::Int; show_progress::Bool = false, cb = () -> ())
cb = Flux.Optimise.runall(cb)
initialize!(sol)
h0 = initialhist(sol.game)
prog = Progress(N; enabled=show_progress)
for t in 1:N
cb()
for p in 1:2
for _ in 1:sol.traversals
sol.traverser(sol, h0, p, sol.T)
end
train_value!(sol, p)
end
next!(prog)
sol.T += 1.0f0
end
train_policy!(sol)
end
function initialize!(nn, init=Flux.glorot_normal)
for p in Flux.params(nn)
p .= init(size(p)...)
end
end
"""
reset advantage networks and empty memory buffers
"""
function initialize!(sol::DeepCFRSolver)
initialize!.(sol.V)
initialize!(sol.Ξ )
end
function train_value!(sol::DeepCFRSolver, p::Int)
initialize!(sol.V[p])
opt = deepcopy(sol.advantage_opt)
train_net!(
sol.gpu,
sol.V[p],
sol.Mv[p].I,
sol.Mv[p].r,
sol.Mv[p].t,
sol.batch_size,
sol.value_batches,
opt
)
end
function train_policy!(sol::DeepCFRSolver, batches::Int=sol.strategy_batches, opt=sol.strategy_opt)
train_net!(
sol.gpu,
sol.Ξ ,
sol.MΟ.I,
sol.MΟ.Ο,
sol.MΟ.t,
sol.batch_size,
sol.strategy_batches,
opt
)
end
function train_net!(
::Val{true},
dest_net,
x_data,
y_data,
w,
batch_size,
n_batches,
opt)
isempty(x_data) && return nothing
src_net = dest_net |> gpu
input_size = length(first(x_data))
output_size = length(first(y_data))
p = Flux.params(src_net)
_X = Matrix{Float32}(undef, input_size, batch_size)
_Y = Matrix{Float32}(undef, output_size, batch_size)
sample_idxs = Vector{Int}(undef, batch_size)
idxs = 1:length(w)
X = _X |> gpu
Y = _Y |> gpu
W = Vector{Float32}(undef, batch_size) |> gpu
for i in 1:n_batches
rand!(sample_idxs, idxs)
fillmat!(_X, x_data, sample_idxs)
fillmat!(_Y, y_data, sample_idxs)
copyto!(X, _X)
copyto!(Y, _Y)
copyto!(W, w[sample_idxs])
Loss = NetLoss(src_net, X, Y, W)
gs = gradient(Loss, p)
Flux.update!(opt, p::Flux.Params, gs)
end
Flux.loadmodel!(dest_net, src_net)
nothing
end
function train_net!(
::Val{false},
net,
x_data,
y_data,
w,
batch_size,
n_batches,
opt)
isempty(x_data) && return nothing
input_size = length(first(x_data))
output_size = length(first(y_data))
X = Matrix{Float32}(undef, input_size, batch_size)
Y = Matrix{Float32}(undef, output_size, batch_size)
W = Vector{Float32}(undef, batch_size)
sample_idxs = Vector{Int}(undef, batch_size)
idxs = 1:length(w)
Loss = NetLoss(net, X, Y, W)
p = Flux.params(net)
for i in 1:n_batches
rand!(sample_idxs, idxs)
fillmat!(X::Matrix{Float32}, x_data, sample_idxs)
fillmat!(Y::Matrix{Float32}, y_data, sample_idxs)
copyto!(W::Vector{Float32}, @view w[sample_idxs])
gs = gradient(Loss, p)
Flux.update!(opt, p::Flux.Params, gs)
end
nothing
end
train_net!(args...) = train_net!(Val(false), args...)
function fillmat!(mat::AbstractMatrix, vecvec::AbstractVector, idxs)
@inbounds for i in 1:size(mat, 2)
mat[:,i] .= vecvec[idxs[i]]
end
return mat
end
"""
Weighted mean squared error
"""
wmse(yΜ,y,w) = sum(abs2.(yΜ .- y)*w ./ length(w))
struct NetLoss{NN,M,WGT}
net::NN
X::M
Y::M
W::WGT
end
(n::NetLoss)() = wmse(n.net(n.X), n.Y, n.W)
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] | 1.976563 | 1,920 |
precompile(Ipopt.eval_jac_g_wrapper, (Int32, Ptr{Float64}, Int32, Int32, Int32, Ptr{Int32}, Ptr{Int32}, Ptr{Float64}, Ptr{Void},))
precompile(Ipopt.eval_h_wrapper, (Int32, Ptr{Float64}, Int32, Float64, Int32, Ptr{Float64}, Int32, Int32, Ptr{Int32}, Ptr{Int32}, Ptr{Float64}, Ptr{Void},))
precompile(Ipopt.createProblem, (Int64, Array{Float64, 1}, Array{Float64, 1}, Int64, Array{Float64, 1}, Array{Float64, 1}, Int64, Int64, Function, Function, Function, Function, Function,))
precompile(Ipopt.eval_f_wrapper, (Int32, Ptr{Float64}, Int32, Ptr{Float64}, Ptr{Void},))
precompile(Ipopt.eval_g_wrapper, (Int32, Ptr{Float64}, Int32, Int32, Ptr{Float64}, Ptr{Void},))
precompile(Ipopt.eval_grad_f_wrapper, (Int32, Ptr{Float64}, Int32, Ptr{Float64}, Ptr{Void},))
precompile(Ipopt.call, (Type{Ipopt.IpoptProblem}, Ptr{Void}, Int64, Int64, Function, Function, Function, Function, Function,))
precompile(Ipopt.call, (Array{Any, 1}, Type{Ipopt.IpoptMathProgModel},))
precompile(Ipopt.optimize!, (Ipopt.IpoptMathProgModel,))
precompile(Ipopt.call, (Array{Any, 1}, Type{Ipopt.IpoptSolver},))
precompile(Ipopt.addOption, (Ipopt.IpoptProblem, ASCIIString, ASCIIString,))
precompile(Ipopt.NonlinearModel, (Ipopt.IpoptSolver,))
precompile(Ipopt.status, (Ipopt.IpoptMathProgModel,))
precompile(Ipopt.solveProblem, (Ipopt.IpoptProblem,))
precompile(Ipopt.call, (Type{Ipopt.IpoptMathProgModel},))
precompile(Ipopt.addOption, (Ipopt.IpoptProblem, ASCIIString, Int64,))
precompile(Ipopt.getobjval, (Ipopt.IpoptMathProgModel,))
precompile(Ipopt.setwarmstart!, (Ipopt.IpoptMathProgModel, Array{Float64, 1},))
precompile(Ipopt.getsolution, (Ipopt.IpoptMathProgModel,))
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13,
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37372,
2964,
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] | 2.328612 | 706 |
module ReinforcementLearningZoo
using ReinforcementLearningBase
using ReinforcementLearningCore
const RLZoo = ReinforcementLearningZoo
export RLZoo
include("algorithms/algorithms.jl")
end # module
| [
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<gh_stars>1-10
"""
timederiv(x) Take time derivative of symbolic expression x.
timederiv(x,subdiffout,subin) also substitutes the derivative of subdiffout with
the symbols in subin. IE the symbols in subin are the time derivatives of
the symbols in subdiffout.
timederiv(x,body) uses the properties of body to determine the relationship between
variables (that the velocity states are the time derivative of position states, etc.).
timederiv(x,model) same as timederiv(x,model) but with the info of the whole model
"""
function timederiv(x::Array{SymPy.Sym})
dxdt = diff(x,t)
end
function timederiv(x::SymPy.Sym)
dxdt = diff(x,t)
end
"""
Take time derivative of x, where subdiffout specifies what symbols in x depend on time.
Also replace the time derivatives of subdiffout with the symbols in subin.
For example if you have a variable x that depends on time, this looks for x,
replaces it with x(t), take the derivative which produces expressions like:
Derivative(x(t),t), which then are replaced with the symbol defined in subin,
for example vx.
julia> @syms x vx
julia> pos = x^2
julia> vel = timederiv(pos,x,vx)
2*x*vx
"""
function timederiv(x,subdiffout::Array{SymPy.Sym,1},subin::Array{SymPy.Sym,1})
if length(subdiffout) != length(subin)
error("subdiffout and subin must have same dimensions")
end
# Make subdiffout depend on time
subdiffoutT = AddTimeDependence(subdiffout)
x = subs(x,subdiffout,subdiffoutT)
# Take derivative
dxdt = timederiv(x)
# Substitute deriviative of subdiffout for diffin
diffout = diff(subdiffoutT,t)
for i in eachindex(diffout)
dxdt = subs(dxdt,diffout[i],subin[i])
end
# remove time dependence
return dxdt = subs(dxdt,subdiffoutT,subdiffout)
end
# function timederiv(x::SymPy.Sym,v...)
# return dxdt = timederiv([x],v...)
# end
function timederiv(x::Array{SymPy.Sym},body::Body)
# the derivative of the position and velocity states are the velocity and accelerations
# respectively
return timederiv(x,body.connection.q,body.connection.u)
end
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] | 3 | 679 |
<reponame>AnderGray/IntervalArithmetic.jl
using IntervalArithmetic
using Test
using StaticArrays
using Random
@testset "rand tests" begin
X = 3..4
for i in 1:100
@test rand(X) β X
end
Y = IntervalBox(3..4, 5..6)
for i in 1:100
@test rand(Y) β Y
end
X = 3..4
for i in 1:100
@test rand(X,4)[(i%4)+1] β X
end
# Y = IntervalBox(3..4, 5..6)
# for i in 1:100
# @test rand(Y,2)[(i%2)+1][(i%2)+1] β Y[(i%2)+1]
# end
for T in (Float32, Float64, BigFloat)
X = Interval{T}(3, 4)
@test rand(X) isa T
Y = IntervalBox(X, X)
@test rand(Y) isa SVector{2,T}
end
for T in (Float32, Float64, BigFloat)
X = Interval{T}(3, 7)
Y = rand(X, 7)
@test Y isa Array{T, 1}
for x in Y
@test x isa T
end
end
end
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] | 1.790123 | 486 |
<reponame>Wikunia/hakank
#=
Linear regression.
See ~/stan/linear_regression.R
"""
# mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
# alpha 4.03 0.00 0.13 3.79 3.95 4.03 4.12 4.28 1937 1
# beta 2.00 0.00 0.00 2.00 2.00 2.00 2.00 2.00 1975 1
# sigma 0.64 0.00 0.05 0.55 0.61 0.63 0.67 0.74 2179 1
# lp__ -4.85 0.03 1.20 -7.96 -5.42 -4.55 -3.96 -3.46 1594 1
"""
See ~/blog/linear_regression.blog
~/webppl/linear_regression.wppl
Note: I have problem with this model.
- MH(), SMC, PG,give bad (or at least not great) rhat's
- NUTS, HMC, etc throw errors
=#
using Turing, StatsPlots, DataFrames
include("jl_utils.jl")
@model function linear_regression(y)
n = length(y)
alpha ~ Normal(2,sqrt(2))
beta ~ Normal(2,sqrt(2))
sigma ~ Gamma(2,1/2)
# mu = Array{Float64}(undef, n)
# mu = Vector{Float64}(undef, n)
mu = tzeros(n)
for i in 1:n
mu[i] = alpha + beta*i
y[i] ~ Normal(mu[i], sigma)
# y[i] ~ Normal(alpha + beta*i, sigma)
end
end
# Generated data (R):
# n = 100
# x <- 1:n
# y <- 4+2*x+rnorm(n,0,0.6)
#
# We are trying to restore these values:
# alpha: 4.0
# beta: 2.0
# sigma: 0.6
#
#=
y = [5.983543,7.363850,9.001728,11.478878,14.753256,16.245218,
18.758869,19.370087,23.417882,23.014029,25.900169,27.840894,
29.350226,33.157245,34.046848,36.086745,38.871855,40.375736,
42.633400,44.724727,45.845138,48.671348,50.828773,51.793457,
54.159697,56.117874,57.545261,60.188977,62.063218,63.913408,
66.528169,68.641637,70.790472,72.934844,73.421080,76.197684,
78.432745,80.136338,81.058943,84.328133,85.410062,89.205925,
89.229415,92.975393,93.531872,95.862181,97.878907,99.553297,
102.068987,103.838061,106.568636,107.375497,110.583279,112.123316,
113.890922,116.537840,117.444612,119.756863,123.567937,124.637554,
125.430167,127.414399,129.799403,131.492455,134.129614,136.107248,
138.823907,140.231367,142.460227,143.936719,146.620783,147.043767,
150.176601,152.500756,152.473417,155.145638,158.793813,161.411058,
163.030423,163.591843,165.696041,167.556486,169.848393,172.043780,
173.372439,175.591097,177.409328,179.357999,181.339012,184.016542,
186.234897,187.865354,190.316685,191.390721,193.391863,196.904094,
197.551148,200.483832,201.944348,203.700001
]
=#
n = 100
x = 1:100
alpha = 4
beta = 2
sigma = 0.6
# y = alpha+beta*x+rand(Normal(0,sigma))
y = alpha.+beta .* x .+rand(Normal(0,sigma),n)
# println("y:$y")
model = linear_regression(y)
num_chains = 4
# chains = sample(model, Prior(), MCMCThreads(), 10_000, num_chains)
# chains = sample(model, MH(), MCMCThreads(), 40_000, num_chains)
# chains = sample(model, MH(
# # :alpha => Normal(2,sqrt(2)),
# # :beta => Normal(2,sqrt(2)),
# # :sigma => Gamma(2,2)
# ), MCMCThreads(), 40_000, num_chains)
# chains = sample(model, PG(15), MCMCThreads(), 1_000, num_chains)
chains = sample(model, SMC(1_000), MCMCThreads(), 1_000, num_chains)
# chains = sample(model, IS(), MCMCThreads(), 10_000, num_chains)
# Both HMC and NUTS give the following error:
# ERROR: LoadError: TaskFailedException:
# TypeError: in typeassert, expected Float64, got a value of type ForwardDiff.Dual{Nothing,Float64,3}
# chains = sample(model, HMC(0.1,5), MCMCThreads(), 10_000, num_chains) # Error
# chains = sample(model, NUTS(0.65), MCMCThreads(), 10_000, num_chains) # Error
display(chains)
# display(plot(chains))
# gen = generated_quantities(model, chains)
# show_var_dist_pct(gen, 40)
| [
27,
7856,
261,
480,
29,
33010,
39934,
14,
43573,
962,
198,
2,
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299,
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220,
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220,
264,
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43595,
7,
17,
11,
16,
14,
17,
8,
628,
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220,
220,
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2414,
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299,
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220,
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220,
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77,
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220,
220,
220,
329,
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25,
77,
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220,
220,
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38547,
42980,
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13,
486,
1821,
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1495,
13,
12865,
22172,
11,
1983,
13,
40675,
4531,
19,
11,
198,
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220,
220,
2808,
13,
14877,
24909,
11,
2091,
13,
18458,
22995,
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2682,
13,
3023,
3104,
2780,
11,
2623,
13,
2919,
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2231,
11,
2548,
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5774,
1507,
2816,
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1821,
13,
22318,
49150,
11,
198,
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220,
220,
5433,
13,
21,
2091,
7029,
11,
2598,
13,
22,
23753,
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11,
2231,
13,
23,
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20107,
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2780,
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1485,
2780,
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1120,
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23,
2078,
46871,
11,
4349,
13,
3720,
27712,
22,
11,
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220,
7175,
13,
19707,
40035,
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13,
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3312,
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23,
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220,
220,
15143,
13,
15,
3104,
44183,
11,
15197,
13,
5999,
1795,
5333,
11,
15801,
13,
20,
33808,
2623,
11,
15982,
13,
22318,
38073,
11,
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13,
46239,
26050,
11,
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13,
1065,
2091,
1433,
11,
198,
220,
220,
220,
17318,
13,
4531,
2931,
1828,
11,
18298,
13,
20,
30695,
1821,
11,
17657,
13,
2598,
3510,
1065,
11,
16315,
13,
2425,
3104,
5066,
11,
10163,
13,
20,
37601,
2718,
11,
17464,
13,
5066,
2425,
4051,
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198,
220,
220,
220,
13151,
13,
3559,
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11,
16799,
13,
19,
21139,
2079,
11,
18741,
13,
45455,
31552,
11,
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13,
2920,
1731,
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13,
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4846,
1415,
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15982,
23045,
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21503,
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13,
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29626,
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13,
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11,
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13,
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2996,
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11,
19782,
13,
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35978,
11,
26492,
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2670,
2998,
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24943,
13,
2670,
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25272,
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3829,
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24,
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] | 1.989125 | 1,839 |
<gh_stars>0
using SimpleHttpIO
using StreamReader
if !isdefined(:TEST_DIR)
include("utils.jl")
end
facts("Test Request.jl") do
context("parse headers") do
context("parse headers from IO") do
io = IOBuffer()
sio = IOSocket(io)
wclf(io)
seekstart(io)
frqe(;message="Empty first line.") do
S.readfirstline(sio)
end
io = IOBuffer()
sio = IOSocket(io)
line = "first line"
ws = wclf(io, line)
seekstart(io)
@fact S.readfirstline(sio) --> (ws, line)
io = IOBuffer()
sio = IOSocket(io)
ws = wclf(io, "H1: H1 value")
ws += wclf(io, "HM: HM value 1")
ws += wclf(io, "HM: HM value 2")
ws += wclf(io)
seekstart(io)
hs = sizeof(io.data)
eh = ["H1"=>"H1 value","HM"=>String["HM value 1","HM value 2"]]
s, lnum, h = S.readheaders(sio)
@fact ws --> hs
@fact s --> hs
@fact lnum --> 4
for (k, v) in h
@fact h[k] --> eh[k] ("Mismatch " * k * " Header")
end
end
context("parse info") do
for line in String["GET /index.html", "GET /index.html HTTP/1.1",
"GET /directory%20name/file.txt HTTP/1.1"]
parts = split(line, " ")
if length(parts) == 2
m, r = parts
pr = default_protocol = astuple(S.FAKE_PROTOCOL)
else
m, r, pr = parts
pr = split(pr, "/")
default_protocol = N
end
method, resource, protocol = S.parseinfo(line, default_protocol)
@fact method --> m
@fact r --> resource
@fact protocol --> pr
end
end
context("read request info") do
for line in String["GET /index.html", "GET /index.html HTTP/1.1",
"GET /directory%20name/file.txt HTTP/1.1"]
context("read request info at " * repr(line)) do
io = IOBuffer()
wclf(io, line)
seekstart(io)
parts = split(line, " ")
if length(parts) == 2
m, rs = parts
pr = default_protocol = astuple(S.FAKE_PROTOCOL)
protocol!(S.FAKE_PROTOCOL)
else
m, rs, pr = parts
pr = split(pr, "/")
default_protocol = N
end
r = S.Request(IOSocket(io))
S.readinfo(r, S.PROTOCOLS, default_protocol)
@fact r.method --> m
@fact r.resource --> rs
if pr != N
@fact r.protocol.name --> pr[1]
@fact r.protocol.version --> pr[2]
else
@fact r.protocol --> pr
end
end
end
end
context("post request") do
types = ["application/x-www-form-urlencoded" => ("postvar=pvalue&postvar2=pvalue2",
["postvar2"=>{"pvalue2"},"postvar"=>{"pvalue"}]),
"application/json" => ("{\"postvar\":\"pvalue\",\"postvar2\":\"pvalue2\"}",
["postvar2"=>"pvalue2","postvar"=>"pvalue"])]
for (tk, (tdata, tdatae)) in types
context("test $tk content-type") do
req_io = IOBuffer()
write(req_io, "POST /page?getvar=gvalue HTTP/1.1\r\n" *
"Host: 0.0.0.0:7000\r\n" *
"Content-Type: $tk\r\n" *
"Content-Length: $(sizeof(tdata))\r\n" *
"\r\n" *
tdata)
seekstart(req_io)
req = Request(IOSocket(req_io))
init(req, S.PROTOCOLS)
data_parsed = parsedata(req)
@fact data_parsed --> true
@fact req.protocol --> HTTP_1_1
@fact req.content_length --> sizeof(tdata)
@fact req.content_type --> tk
@fact req.get --> ["getvar"=>{"gvalue"}]
@fact req.post --> tdatae
end
end
end
end
context("parse multipart/form-data") do
sample_path = joinpath(TEST_DIR, "data", "multipart", "rawdata", "sample.txt")
sio = IOSocket(open(sample_path, "r"))
s, info = S.readinfo(sio, S.PROTOCOLS)
size, linenum, headers = S.readheaders(sio)
content_size = parseint(headers["Content-Length"])
boundary = S.multipartboundary(headers["Content-Type"])
s, d, files = S.parsempfd(sio, boundary)
for f in files
fpath = joinpath(TEST_DIR, "data", "multipart", "files", f.name)
open(fpath, "r") do fio
@fact f.io.data --> readbytes(fio, filesize(fpath)) "File Data mismatch '$(f.name)' -> '$(fpath)'"
end
end
end
end
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] | 1.68005 | 3,188 |
<filename>test/foo.jl
# this file is defined to test @changeprecision T include(...)
foo(x) = x/3
| [
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] | 2.882353 | 34 |
<reponame>zhangliye/SimHPC
using SimHPC
@resumable function charge(env::Environment, duration::Number)
@yield timeout(env, duration) # will continue t later
end
@resumable function driver(env::Environment, car_process::Process)
@yield timeout(env, 3)
@yield interrupt(car_process)
end
@resumable function car(env::Environment, id::Int)
while true
println("Car$(id) Start parking at ", now(env))
parking_duration = convert(Int, round(rand()*10) )
@yield timeout(env, parking_duration)
println("Car$(id) start charging at ", now(env))
charge_duration = convert(Int, round(rand()*10) )
charge_process = @process charge(sim, charge_duration) # Process is a event
#@yield charge_process #schedule the behavior here
try
@yield charge_process
catch
println("Was interrupted. Hopefully, the battery is full enough ...")
end
println("Car$(id) Start driving at ", now(env))
trip_duration = convert(Int, round(rand()*10) )
@yield timeout(env, trip_duration) # drive 2 mins
end
end
rand(1,10)
sim = Simulation()
for i in 1:100
@process car(sim, i) # Process is scheduled here
end
@time run(sim, 1000) # start the processes
println("Finished SimHPC") | [
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<reponame>cgroll/AssetMgmt.jl<filename>src/investments.jl
##############
## contents ##
##############
## types:
## - Investments :: quite similar to Timematr, with fields ~vals~ and ~idx~
#################################
## definition Investments type ##
#################################
type Investments{T}
vals::DataFrame
idx::Array{T, 1}
function Investments(vals::DataFrame, idx::Array{T, 1})
## consists of time index and numeric data (no NAs allowed)
## with equal row numbers and rowsums of data equal to one
## check constraints
TimeData.chkIdx(idx)
TimeData.chkNum(vals) # numeric values only?
chkEqualsOne(vals)
## check equal number of rows
if(size(vals, 1) != length(idx))
if (length(idx) == 0) | (size(vals, 1) == 0)
return new(DataFrame([]), Array{T, 1}[])
end
error(length(idx), " idx entries, but ", size(vals, 1), " rows of data")
end
return new(vals, idx)
end
end
## required for parametric type declaration
function Investments{T}(vals::DataFrame, idx::Array{T, 1})
return Investments{T}(vals, idx)
end
## initialization without dates
function Investments(vals::DataFrame)
nPortfolios = size(vals, 1)
return Investments(vals, [1:nPortfolios])
end
####################
## display method ##
####################
import Base.Multimedia.display
function display(invs::Investments)
## display information about an array
## set display parameters
maxDispCols = 5;
## get type and field information
typ = typeof(invs)
println("\ntype: $typ")
print("dimensions: ")
print(size(invs))
print("\n")
## get first entries
(nrow, ncol) = size(invs)
showCols = minimum([maxDispCols ncol]);
Peekidx = DataFrame(idx = invs.idx);
Peek = [Peekidx invs.vals[:, 1:showCols]];
display(Peek)
end
function str(invs::Investments)
display(invs)
end
######################
## Investments size ##
######################
import Base.size
function size(invs::Investments)
return size(invs.vals)
end
function size(invs::Investments, ind::Int)
return size(invs.vals, ind)
end
import Base.ndims
function ndims(invs::Investments)
return ndims(invs.vals)
end
#####################
## get investments ##
#####################
function weights(invs::Investments)
return array(invs.vals)
end
import TimeData.core
function core(invs::Investments)
return convert(Array, invs.vals)
end
###############
## get index ##
###############
import TimeData.idx
function idx(invs::Investments)
return invs.idx
end
import Base.names
function names(invs::Investments)
return names(invs.vals)
end
#############
## convert ##
#############
import Base.convert
function convert(::Type{Timematr}, invs::Investments)
return Timematr(invs.vals, idx(invs))
end
function convert(::Type{DataFrame}, invs::Investments)
return [DataFrame(idx = invs.idx) invs.vals]
end
## asArr
##------
import TimeData.asArr
function asArr(invs::Investments)
return convert(Array, invs.vals)
end
########
## IO ##
########
function readInvestments(filename::String)
td = readTimedata(filename)
return Investments(td.vals, idx(td))
end
function writeInvestments(filename::String, invs::Investments)
## create large dataframe
idxDf = DataFrame(idx = idx(invs));
df = [idxDf invs.vals];
writetable(filename, df)
end
#############
## isequal ##
#############
import Base.isequal
function isequal(invs::Investments, invs2::Investments)
typeEqu = isequal(typeof(invs), typeof(invs2))
valsEqu = isequal(invs.vals, invs2.vals)
idxEqu = isequal(invs.idx, invs2.idx)
equ = (valsEqu & idxEqu & typeEqu)
return equ
end
##########
## copy ##
##########
function Base.copy(invs::Investments)
copiedVals = copy(invs.vals)
copiedInvs = AssetMgmt.Investments(copiedVals, idx(invs))
return copiedInvs
end
###############################################
## check matching investment and return data ##
###############################################
function chkMatchInvData(invs::Investments, discRet::Timematr)
## test whether investments and return data are matching
##
## Matching: all investement dates are entailed in return data
## dates.
##
## Output: error when inputs don't match
## check for conforming dates and assets
if !issubset(idx(invs), idx(discRet))
error("indices / dates of investments must be contained in
dates of return data")
end
if AssetMgmt.names(invs) != names(discRet)
error("asset names of investments and returns must coincide")
end
end
#######################################
## find trading days for investments ##
#######################################
function isTradingDay(invs::Investments, discRet::Timematr)
## most changes in investment weights are caused by simple price
## changes without actual trading. This function finds all days
## with active rebalancing of weights. If evening weights
## (achieved through price changes) deviate from next day morning
## weights, next day will be called trading day.
##
## return dates may deviate from investment dates as long as all
## investment dates are also contained in return dates.
##
## trading day indicators are returned for all investment dates.
## get automatic daily changes of weights
wgtsPriceChanges = evolWgts(invs, discRet)
## get values without metadata
invsArr = AssetMgmt.core(invs)
wgtsPriceChangesArr = AssetMgmt.core(wgtsPriceChanges)
## preallocate trading indicator
nObs = size(invs, 1)
isTrading = falses(nObs)
## first day is defined as trading day
isTrading[1] = true
for ii=2:nObs
## get weight changes from evening weights last day to morning
## weights of current day
wgtChange =
sum(abs(invsArr[ii, :] - wgtsPriceChangesArr[ii-1, :]))
if wgtChange > 1e-12
isTrading[ii] = true
end
end
return isTrading
end
######################
## getindex methods ##
######################
## invs[SingleColumnIndex] => Timematr
## invs[MultiColumnIndex] => Timematr (if columns sum up to one:
## Investments?)
## invs[SingleRowIndex, SingleColumnIndex] => Scalar
## invs[SingleRowIndex, MultiColumnIndex] => Timematr
## invs[MultiRowIndex, SingleColumnIndex] => Timematr
## invs[MultiRowIndex, MultiColumnIndex] => Timematr (if columns sum
## up to one: Investments)
## TimeData types do ALWAYS preserve types when indexing.
typealias ColumnIndex Union(Real, Symbol)
# invs[SingleColumnIndex] => Timematr
function Base.getindex(invs::Investments, col_ind::ColumnIndex)
tm = convert(Timematr, invs)
return tm[col_ind]
end
# invs[MultiColumnIndex] => Timematr (Investments for simple
# re-ordering)
function Base.getindex{T <: ColumnIndex}(invs::Investments,
col_inds::AbstractVector{T})
output = convert(Timematr, invs)[col_inds]
## are all columns selected?
nAss = size(invs, 2)
if length(col_inds) == nAss
if sort(col_inds) == sort(names(invs))
output = AssetMgmt.Investments(output.vals, idx(invs))
end
end
return output
end
# invs[SingleRowIndex, SingleColumnIndex] => Timematr
function Base.getindex(invs::Investments, row_ind::Real,
col_ind::ColumnIndex)
tm = convert(Timematr, invs)
return tm[row_ind, col_ind]
end
# invs[SingleRowIndex, MultiColumnIndex] => Timematr
function Base.getindex{T <: ColumnIndex}(invs::Investments,
row_ind::Real,
col_inds::AbstractVector{T})
output = convert(Timematr, invs)[row_ind, col_inds]
## are all columns selected?
nAss = size(invs, 2)
if length(col_inds) == nAss
if sort(col_inds) == sort(names(invs))
output = AssetMgmt.Investments(output.vals, idx(invs))
end
end
return output
end
# invs[MultiRowIndex, SingleColumnIndex] => Timematr
function Base.getindex{T <: Real}(invs::Investments,
row_inds::AbstractVector{T},
col_ind::ColumnIndex)
tm = convert(Timematr, invs)
return tm[row_inds, col_ind]
end
# invs[MultiRowIndex, MultiColumnIndex] => Timematr
function Base.getindex{R <: Real, T <: ColumnIndex}(invs::Investments,
row_inds::AbstractVector{R},
col_inds::AbstractVector{T})
output = convert(Timematr, invs)[row_inds, col_inds]
## are all columns selected?
nAss = size(invs, 2)
if length(col_inds) == nAss
if sort(col_inds) == sort(names(invs))
output = AssetMgmt.Investments(output.vals, idx(invs))
end
end
return output
end
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] | 2.535144 | 3,571 |
module LogitTest
using Test
using ForneyLab
using ForneyLab: outboundType, isApplicable
using ForneyLab: VBLogitOut, VBLogitIn1, VBLogitXi
#-------------
# Update rules
#-------------
@testset "VBLogitOut" begin
@test VBLogitOut <: NaiveVariationalRule{Logit}
@test outboundType(VBLogitOut) == Message{Bernoulli}
@test isApplicable(VBLogitOut, [Nothing, ProbabilityDistribution, ProbabilityDistribution])
@test ruleVBLogitOut(nothing, ProbabilityDistribution(Univariate, GaussianMeanVariance, m=2.0, v=1.0), ProbabilityDistribution(Univariate, PointMass, m=3.0)) == Message(Univariate, Bernoulli, p=1/(1+exp(-2.0)))
end
@testset "VBLogitIn1" begin
@test VBLogitIn1 <: NaiveVariationalRule{Logit}
@test outboundType(VBLogitIn1) == Message{GaussianWeightedMeanPrecision}
@test isApplicable(VBLogitIn1, [ProbabilityDistribution, Nothing, ProbabilityDistribution])
@test ruleVBLogitIn1(ProbabilityDistribution(Univariate, Bernoulli, p=0.8), nothing, ProbabilityDistribution(Univariate, PointMass, m=3.0)) == Message(Univariate, GaussianWeightedMeanPrecision, xi=0.3, w=(1/(1+exp(-3.0)) - 0.5)/3.0)
end
@testset "VBLogitXi" begin
@test VBLogitXi <: NaiveVariationalRule{Logit}
@test outboundType(VBLogitXi) == Message{Function}
@test isApplicable(VBLogitXi, [ProbabilityDistribution, ProbabilityDistribution, Nothing])
@test ruleVBLogitXi(ProbabilityDistribution(Univariate, Bernoulli, p=0.8), ProbabilityDistribution(Univariate, GaussianMeanVariance, m=2.0, v=1.0), nothing) == Message(Univariate, Function, mode=sqrt(5.0))
end
@testset "averageEnergy" begin
@test averageEnergy(Logit, ProbabilityDistribution(Univariate, Bernoulli, p=0.8), ProbabilityDistribution(Univariate, GaussianMeanVariance, m=2.0, v=1.0), ProbabilityDistribution(Univariate, PointMass, m=3.0)) == (1/(1+exp(-3.0)) - 0.5)/6.0*(5.0 - 9.0) + 2.5 + log(1+exp(-3.0)) - 1.6
end
end # module | [
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] | 2.614966 | 735 |
# SPD-License-Identifier: MIT
using ArgParse
using JLD2
using MarkovModels
function parse_commandline()
s = ArgParseSettings()
@add_arg_table s begin
"lexicon"
required = true
help = "input lexicon file"
"lexiconfsm"
required = true
help = "output lexicon in JLD2 format"
end
s.description = """
Build the lexicon FSM. The lexicon input file should be formatted
as:
word1 p r o n u n
word1 p R o N u n
word2 w o r d
"""
parse_args(s)
end
function LinearFSM(seq)
SF = LogSemifield{Float32}
fsm = VectorFSM{SF}()
prev = nothing
for (i, label) in enumerate(seq)
initweight = i == 1 ? one(SF) : zero(SF)
finalweight = i == length(seq) ? one(SF) : zero(SF)
s = addstate!(fsm, label; initweight, finalweight)
i > 1 && addarc!(fsm, prev, s)
prev = s
end
fsm
end
function main(args)
pronuns = Dict()
open(args["lexicon"], "r") do f
for line in eachline(f)
tokens = split(line)
word = tokens[1]
pronun = tokens[2:end]
list = get(pronuns, word, [])
push!(list, pronun)
pronuns[word] = list
end
end
jldopen(args["lexiconfsm"], "w") do f
for word in keys(pronuns)
fsms = [LinearFSM(pronun) for pronun in pronuns[word]]
fsm = union(fsms...)
f[word] = fsm |> minimize |> renormalize
end
end
end
args = parse_commandline()
main(args)
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] | 2.059289 | 759 |
<filename>test/runtests.jl
module TestTextAnalysis
using SparseArrays
using Test
using Languages
using TextAnalysis
using WordTokenizers
# @testset "TextAnalysis" begin
println("Running tests:")
include("tokenizer.jl")
include("ngramizer.jl")
include("document.jl")
include("metadata.jl")
include("corpus.jl")
include("preprocessing.jl")
include("dtm.jl")
include("stemmer.jl")
include("tf_idf.jl")
include("lda.jl")
include("summarizer.jl")
include("sentiment.jl")
include("bayes.jl")
include("rouge.jl")
include("averagePerceptronTagger.jl")
include("utils.jl")
# end
end
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] | 2.954082 | 196 |
using OmegaCore
using OmegaTest
using OmegaCore.Traits
using Test
using Distributions
using Random
function testtraits()
Ο = SimpleΞ©(Dict())
Ο2 = OmegaCore.appendscope(Ο, [1,])
# traits(typeof(Ο2.tags))
g(traits(typeof(Ο2)))
end
function testsimple1()
Ο = defΞ©()()
Ο = OmegaCore.tagrng(Ο, Random.GLOBAL_RNG)
Ο2 = defΞ©()()
Ο2 = OmegaCore.tagrng(Ο2, Random.GLOBAL_RNG)
x = 1 ~ Normal(0, 1)
map(x, [Ο, Ο2])
end
function testsimple2()
x = 1 ~ Normal(0, 1)
rng = Random.GLOBAL_RNG
Ξ©T = defΞ©()
y = OmegaCore.condvar(x)
Ο = OmegaCore.OmegaRejectionSample.condomegasample1(rng, Ξ©T, y, OmegaCore.RejectionSample)
x(Ο)
end
function testsimple3a()
x = 1 ~ Normal(0, 1)
rng = Random.GLOBAL_RNG
Ξ©T = defΞ©()
y = OmegaCore.condvar(x)
Ο = OmegaCore.OmegaRejectionSample.condomegasample1(rng, Ξ©T, y, OmegaCore.RejectionSample)
map(x, typeof(Ο)[Ο, Ο])
end
function testsimple3()
x = 1 ~ Normal(0, 1)
rng = Random.GLOBAL_RNG
Ξ©T = defΞ©()
y = OmegaCore.condvar(x)
Ο = OmegaCore.OmegaRejectionSample.condomegasample1(rng, Ξ©T, y, OmegaCore.RejectionSample)
x.([Ο, Ο])
end
function testsimple4()
x = 1 ~ Normal(0, 1)
rng = Random.GLOBAL_RNG
Ξ©T = defΞ©()
y = OmegaCore.condvar(x)
Ο = OmegaCore.OmegaRejectionSample.condomegasample(rng, Ξ©T, y, 5, OmegaCore.RejectionSample)
map(x, Ο)
end
@testset "infer types" begin
@test isinferred(testsimple1)
@test isinferred(testsimple2)
@test isinferred(testsimple3)
@test isinferred(testsimple3a)
@test isinferred(testsimple4)
end | [
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320,
1154,
18,
8,
198,
220,
2488,
9288,
318,
259,
18186,
7,
41989,
320,
1154,
18,
64,
8,
198,
220,
2488,
9288,
318,
259,
18186,
7,
41989,
320,
1154,
19,
8,
198,
437,
220,
220
] | 2.214697 | 694 |
include("NonSimpleNumField/Field.jl")
| [
17256,
7203,
15419,
26437,
33111,
15878,
14,
15878,
13,
20362,
4943,
198
] | 3.166667 | 12 |
<filename>test/runtests.jl
include("../src/nn.jl")
using Test
Random.seed!(1)
"""
charge distribution -> potential (Poisson's eqn), electric field (Gauss's law)
"""
# input Scalar field
inranks = [0]
# output scalar field, vector field
outranks = [0, 1]
sz=(8,8,8)
dx = 0.1
dV=dx^3
rmax = 2dx
lmax = 1
# charge distribution
x = [zeros(sz...)]
ix=[5, 5, 5]
x[1][ix...] = 1.0
X=[x]
# generate data
# Green's fn for Poisson, Gauss
f1 = LinearOperator(:potential,dx;rmax=rmax)
f2 = LinearOperator(:field,dx;rmax=rmax)
y1 = f1(X[1])
y2 = f2(X[1])
# check
v=[1,1,1]
ix = ix.+v
r=norm(v)*dx
@test y1[1][ix...]βdV/r
@test [y2[i][ix...] for i = 1:3]βdV/r^2*ones(3)/sqrt(3)
##
# train
# linear layer: tensor field convolution
L = EquivConv(inranks, outranks, dx; rmax = rmax)
function nn(X)
L(X)
end
function loss()
y1hat, y2hat = nn(X)
l1 = Flux.mae(toArray(y1), toArray(y1hat))
l2 = Flux.mae(toArray(y2), toArray(y2hat))
l = l1 + l2
println(l)
l
end
loss()
##
ps = Flux.params(L)
data = [()]
opt = ADAM(0.1)
println("===\nTraining")
for i = 1:5
# global doplot = i % 50 == 0
Flux.train!(loss, ps, data, opt)
end
##
Random.seed!(1)
n=4
inranks=[0,0]
outranks=[0]
X=[[rand(n,n,n)],[rand(n,n,n)]]
y=[[X[1][1].*X[2][1]]]
# train
# linear layer: tensor field convolution
A = EquivAttn(inranks, outranks)
function nn(X)
A(X)
end
function loss()
yhat = nn(X)
l = Flux.mae(toArray(y[1]), toArray(yhat[1]))
println(l)
l
end
loss()
ps = Flux.params(A)
data = [()]
opt = ADAM(0.1)
println("===\nTraining")
for i = 1:10
# global doplot = i % 50 == 0
Flux.train!(loss, ps, data, opt)
end
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] | 1.997582 | 827 |
<reponame>UnofficialJuliaMirror/StaticRanges.jl-d8176aec-3168-11e9-3c98-e3954798be3a<filename>src/findvalue.jl
# unsafe_findvalue doesn't confirm that the integer is in bounds or r[idx] == val
unsafe_findvalue(val, r::Union{OneToRange,OneTo}) = round(Integer, val)
function unsafe_findvalue(val, r::Union{StaticUnitRange,UnitRange})
return round(Integer, (val - first(r)) + 1)
end
function unsafe_findvalue(val, r::Union{AbstractStepRangeLen,StepRangeLen})
return round(Integer, ((val - r.ref) / step_hp(r)) + r.offset)
end
unsafe_findvalue(val, r::AbstractRange) = round(Integer, (val - r.start) / r.step) + 1
function unsafe_findvalue(val, r::Union{AbstractLinRange,LinRange})
return round(Integer, (((val - r.start) / (r.stop - r.start)) * r.lendiv) + 1)
end
unsafe_findvalue(val, ::LinearIndices{1,Tuple{OneTo{Int64}}}) = Int(val)
Base.in(x::Integer, r::OneToRange{<:Integer}) = (1 <= x) & (x <= last(r))
Base.findall(f::Function, r::UnionRange) = find_all(f, r)
Base.findall(f::Fix2{typeof(in)}, r::UnionRange) = find_all(f, r)
Base.findlast(f::Function, x::UnionRange) = find_last(f, x)
Base.findfirst(f::Function, r::UnionRange) = find_first(f, r)
# TODO this could easily be optimized more
@propagate_inbounds Base.count(f::Function, r::UnionRange) = length(find_all(f, r))
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2,
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46,
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517,
198,
31,
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796,
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7,
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62,
439,
7,
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11,
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198
] | 2.568898 | 508 |
using RMInsurance
using Distributions
using DataFrames
include("SSTLife_Input.jl")
include("SSTLife.jl")
println("Start SSTLife test")
@test round(rtk_start, digits = 2) β 158.58
@test round(tc, digits = 2) β 147.60
@test round(sst_ratio, digits = 4) β 1.0744
println("End SSTLife test")
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] | 2.630631 | 111 |
using HSA
include("helpers.jl")
using FactCheck
@with_agents facts("The ISA") do
rt = Runtime()
context("Can be retrieved from an agent") do
agent = get_testagent()
isa = HSA.agent_info_isa(agent)
name = HSA.isa_info_name(isa)
@fact name --> not("")
end
end
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] | 2.315789 | 133 |
<reponame>jagot/QuantumInt.jl
abstract AbstractObservable
using SimpleFields
import Base: call
call{T<:AbstractFloat}(obs::AbstractObservable,
Ξ¨, i::Integer, Ο::T, field::Field) = (obs.v[i] = value(obs, Ξ¨, i, Ο, field))
type TimeObservable{T<:AbstractFloat} <: AbstractObservable
v::Vector{T}
TimeObservable(N::Int) = new(Vector{T}(N))
end
value{T<:AbstractFloat}(obs::TimeObservable{T},
Ξ¨, i::Integer, Ο::T, field::Field) = (i-1)*Ο
type NormObservable{T<:AbstractFloat} <: AbstractObservable
v::Vector{T}
NormObservable(N::Int) = new(Vector{T}(N))
end
value{T<:AbstractFloat}(obs::NormObservable{T},
Ξ¨, i::Integer, Ο::T, field::Field) = norm(Ξ¨)
type FieldObservable{T<:AbstractFloat} <: AbstractObservable
v::Vector{T}
FieldObservable(N::Int) = new(Vector{T}(N))
end
value{T<:AbstractFloat}(obs::FieldObservable{T},
Ξ¨, i::Integer, Ο::T, field::Field) = field((i-1)*Ο/field.T)
const observable_types = Dict("time" => TimeObservable,
"norm" => NormObservable,
"field" => FieldObservable)
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] | 2.048951 | 572 |
<gh_stars>0
using WordCloud
using Random
words = [randstring(rand(1:8)) for i in 1:300]
weights = randexp(length(words)) .* 1000 .+ rand(1:100, length(words))
wordcloud(words, weights) |> generate! | [
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struct Model{F1<:Function,F2<:Function,F3<:Function}
ΞΌ!::F1
G::F2
n::Int64
simulate::F3
Tx::Type
Ty::Type
end
function simulateModel(model::Model, n::Int64)
ys = Vector{model.Ty}(undef, n)
rng = getRNG()
x::model.Tx = model.Tx()
for p in 1:n
model.ΞΌ!(x, rng)
ys[p] = model.simulate(x, rng)
end
return ys
end
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] | 2.066667 | 165 |
<filename>test/runtests.jl
using PaddedBlocks
using Test
include("blocks.jl") | [
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using JUDI, SegyIO, LinearAlgebra, PyPlot
# Set up model structure
n = (120, 100) # (x,y,z) or (x,z)
d = (10., 10.)
o = (0., 0.)
# Velocity [km/s]
v = ones(Float32,n) .+ 0.4f0
v[:,Int(round(end/2)):end] .= 3f0
v0 = ones(Float32,n) .+ 0.4f0
# Slowness squared [s^2/km^2]
m0 = (1f0 ./ v0).^2
m = (1f0 ./ v).^2
dm = vec(m - m0)
# Setup info and model structure
nsrc = 2 # number of sources
model0 = Model(n, d, o, m0)
model = Model(n, d, o, m)
# Set up receiver geometry
nxrec = 120
xrec = range(50f0, stop=1150f0, length=nxrec)
yrec = 0f0
zrec = range(50f0, stop=50f0, length=nxrec)
# receiver sampling and recording time
time = 1000f0 # receiver recording time [ms]
dt = 4f0 # receiver sampling interval [ms]
# Set up receiver structure
recGeometry = Geometry(xrec, yrec, zrec; dt=dt, t=time, nsrc=nsrc)
# Source wavelet
f0 = 0.01f0 # kHz
wavelet = ricker_wavelet(time, dt, f0)
# Set up info structure for linear operators
ntComp = get_computational_nt(recGeometry, model)
info = Info(prod(n), nsrc, ntComp)
###################################################################################################
# Write shots as segy files to disk
opt = Options(return_array=false, dt_comp=1.0, free_surface=true)
# Setup operators
Pr = judiProjection(info, recGeometry)
F = judiModeling(info, model; options=opt)
# Extended source weights
weights = Array{Array}(undef, nsrc)
for j=1:nsrc
weights[j] = randn(Float32, model.n)
end
w = judiWeights(weights)
# Create operator for injecting the weights, multiplied by the provided wavelet(s)
Pw = judiLRWF(info, wavelet)
# Model observed data w/ extended source
F = Pr*F*adjoint(Pw)
# Simultaneous observed data
d_sim = F*w
dw = adjoint(F)*d_sim
# Jacobian
J = judiJacobian(F, w)
d_lin = J*dm
g = adjoint(J)*d_lin
# Plot results
figure()
subplot(1,2,1)
imshow(d_sim.data[1], vmin=-5e2, vmax=5e2, cmap="gray"); title("Non-linear shot record")
subplot(1,2,2)
imshow(d_lin.data[1], vmin=-5e3, vmax=5e3, cmap="gray"); title("Linearized shot record")
figure()
subplot(1,2,1)
imshow(adjoint(dw.weights[1]), vmin=-5e6, vmax=5e6, cmap="gray"); title("Weights 1")
subplot(1,2,2)
imshow(adjoint(reshape(g, model0.n)), vmin=-1e8, vmax=1e8, cmap="gray"); title("Gradient w.r.t. m") | [
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] | 2.425946 | 925 |
<reponame>UnofficialJuliaMirrorSnapshots/BlockArrays.jl-8e7c35d0-a365-5155-bbbb-fb81a777f24e
"""
BlockSlice(indices)
Represent an AbstractUnitRange of indices that attaches a block.
Upon calling `to_indices()`, Blocks are converted to BlockSlice objects to represent
the indices over which the Block spans.
This mimics the relationship between `Colon` and `Base.Slice`.
"""
struct BlockSlice{BB} <: AbstractUnitRange{Int}
block::BB
indices::UnitRange{Int}
end
Block(bs::BlockSlice{<:Block}) = bs.block
for f in (:axes, :unsafe_indices, :axes1, :first, :last, :size, :length,
:unsafe_length, :start)
@eval $f(S::BlockSlice) = $f(S.indices)
end
getindex(S::BlockSlice, i::Integer) = getindex(S.indices, i)
show(io::IO, r::BlockSlice) = print(io, "BlockSlice(", r.block, ",", r.indices, ")")
next(S::BlockSlice, s) = next(S.indices, s)
done(S::BlockSlice, s) = done(S.indices, s)
function _unblock(cum_sizes, I::Tuple{Block{1, T},Vararg{Any}}) where {T}
B = first(I)
b = first(B.n)
range = cum_sizes[b]:cum_sizes[b + 1] - 1
BlockSlice(B, range)
end
function _unblock(cum_sizes, I::Tuple{BlockRange{1,R}, Vararg{Any}}) where {R}
B = first(I)
b_start = first(first(B.indices))
b_stop = last(first(B.indices))
range = cum_sizes[b_start]:cum_sizes[b_stop + 1] - 1
BlockSlice(B, range)
end
_sub_cumul_sizes(cs, inds) = _sub_cumul_sizes(cs, inds[1], tail(inds))
_sub_cumul_sizes(::Tuple{}, ::Tuple{}) = ()
function _sub_cumul_sizes(cs, inds1::BlockSlice{Block{1,Int}}, inds)
B = Int(inds1.block)
ret = view(cs[1], B:B+1)
(ret .- ret[1] .+ 1, _sub_cumul_sizes(tail(cs), inds)...)
end
function _sub_cumul_sizes(cs, inds1::BlockSlice{BlockRange{1,Tuple{UnitRange{Int}}}}, inds)
ret = view(cs[1], inds1.block.indices[1][1]:(inds1.block.indices[1][end]+1))
(ret .- ret[1] .+ 1, _sub_cumul_sizes(tail(cs), inds)...)
end
blocksizes(V::SubArray) = BlockSizes(_sub_cumul_sizes(cumulsizes(parent(V)), parentindices(V)))
"""
unblock(block_sizes, inds, I)
Returns the indices associated with a block as a `BlockSlice`.
"""
function unblock(A::AbstractArray{T,N}, inds, I) where {T, N}
if length(inds) == 0
# Allow `ones(2)[Block(1)[1:1], Block(1)[1:1]]` which is
# similar to `ones(2)[1:1, 1:1]`.
_unblock(Base.OneTo(2), I)
else
_unblock(cumulsizes(A, N - length(inds) + 1), I)
end
end
to_index(::Block) = throw(ArgumentError("Block must be converted by to_indices(...)"))
to_index(::BlockRange) = throw(ArgumentError("BlockRange must be converted by to_indices(...)"))
@inline to_indices(A, inds, I::Tuple{Block{1}, Vararg{Any}}) =
(unblock(A, inds, I), to_indices(A, _maybetail(inds), tail(I))...)
# splat out higher dimensional blocks
# this mimics view of a CartesianIndex
@inline to_indices(A, inds, I::Tuple{Block, Vararg{Any}}) =
to_indices(A, inds, (Block.(I[1].n)..., tail(I)...))
@inline to_indices(A, inds, I::Tuple{BlockRange{1,R}, Vararg{Any}}) where R =
(unblock(A, inds, I), to_indices(A, _maybetail(inds), tail(I))...)
# splat out higher dimensional blocks
# this mimics view of a CartesianIndex
@inline to_indices(A, inds, I::Tuple{BlockRange, Vararg{Any}}) =
to_indices(A, inds, (BlockRange.(tuple.(I[1].indices))..., tail(I)...))
# In 0.7, we need to override to_indices to avoid calling linearindices
@inline to_indices(A, I::Tuple{Block, Vararg{Any}}) =
to_indices(A, axes(A), I)
@inline to_indices(A, I::Tuple{BlockRange, Vararg{Any}}) =
to_indices(A, axes(A), I)
# The first argument for `reindex` is removed as of
# https://github.com/JuliaLang/julia/pull/30789 in Julia `Base`. So,
# we define 2-arg `reindex` for Julia 1.2 and later.
if VERSION >= v"1.2-"
# BlockSlices map the blocks and the indices
# this is loosely based on Slice reindex in subarray.jl
reindex(idxs::Tuple{BlockSlice{<:BlockRange}, Vararg{Any}},
subidxs::Tuple{BlockSlice{<:BlockRange}, Vararg{Any}}) =
(@_propagate_inbounds_meta; (BlockSlice(BlockRange(idxs[1].block.indices[1][Int.(subidxs[1].block)]),
idxs[1].indices[subidxs[1].indices]),
reindex(tail(idxs), tail(subidxs))...))
reindex(idxs::Tuple{BlockSlice{BlockRange{1,Tuple{UnitRange{Int}}}}, Vararg{Any}},
subidxs::Tuple{BlockSlice{Block{1,Int}}, Vararg{Any}}) =
(@_propagate_inbounds_meta; (BlockSlice(Block(idxs[1].block.indices[1][Int(subidxs[1].block)]),
idxs[1].indices[subidxs[1].indices]),
reindex(tail(idxs), tail(subidxs))...))
function reindex(idxs::Tuple{BlockSlice{Block{1,Int}}, Vararg{Any}},
subidxs::Tuple{BlockSlice{Block{1,Int}}, Vararg{Any}})
(idxs[1], reindex(tail(idxs), tail(subidxs))...)
end
else # if VERSION >= v"1.2-"
reindex(V, idxs::Tuple{BlockSlice{<:BlockRange}, Vararg{Any}},
subidxs::Tuple{BlockSlice{<:BlockRange}, Vararg{Any}}) =
(@_propagate_inbounds_meta; (BlockSlice(BlockRange(idxs[1].block.indices[1][Int.(subidxs[1].block)]),
idxs[1].indices[subidxs[1].indices]),
reindex(V, tail(idxs), tail(subidxs))...))
reindex(V, idxs::Tuple{BlockSlice{BlockRange{1,Tuple{UnitRange{Int}}}}, Vararg{Any}},
subidxs::Tuple{BlockSlice{Block{1,Int}}, Vararg{Any}}) =
(@_propagate_inbounds_meta; (BlockSlice(Block(idxs[1].block.indices[1][Int(subidxs[1].block)]),
idxs[1].indices[subidxs[1].indices]),
reindex(V, tail(idxs), tail(subidxs))...))
function reindex(V, idxs::Tuple{BlockSlice{Block{1,Int}}, Vararg{Any}},
subidxs::Tuple{BlockSlice{Block{1,Int}}, Vararg{Any}})
subidxs[1].block == Block(1) || throw(BoundsError(V, subidxs[1].block))
(idxs[1], reindex(V, tail(idxs), tail(subidxs))...)
end
end # if VERSION >= v"1.2-"
#################
# support for pointers
#################
const BlockOrRangeIndex = Union{RangeIndex, BlockSlice}
function unsafe_convert(::Type{Ptr{T}},
V::SubArray{T, N, BlockArray{T,N,AT,BS}, NTuple{N, BlockSlice{Block{1,Int}}}}) where {AT <: AbstractArray{<:AbstractArray{T,N},N}, BS <: AbstractBlockSizes{N}} where {T,N}
unsafe_convert(Ptr{T}, parent(V).blocks[Int.(Block.(parentindices(V)))...])
end
unsafe_convert(::Type{Ptr{T}}, V::SubArray{T,N,PseudoBlockArray{T,N,AT},<:Tuple{Vararg{BlockOrRangeIndex}}}) where {T,N,AT} =
unsafe_convert(Ptr{T}, V.parent) + (Base.first_index(V)-1)*sizeof(T)
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] | 2.173899 | 3,042 |
<filename>src/kmeans.jl
# All Abstract types defined
"""
AbstractKMeansAlg
Abstract base type inherited by all sub-KMeans algorithms.
"""
abstract type AbstractKMeansAlg end
"""
ClusteringResult
Base type for the output of clustering algorithm.
"""
abstract type ClusteringResult end
# Here we mimic `Clustering` output structure
"""
KmeansResult{C,D<:Real,WC<:Real} <: ClusteringResult
The output of [`kmeans`](@ref) and [`kmeans!`](@ref).
# Type parameters
* `C<:AbstractMatrix{<:AbstractFloat}`: type of the `centers` matrix
* `D<:Real`: type of the assignment cost
* `WC<:Real`: type of the cluster weight
# C is the type of centers, an (abstract) matrix of size (d x k)
# D is the type of pairwise distance computation from points to cluster centers
# WC is the type of cluster weights, either Int (in the case where points are
# unweighted) or eltype(weights) (in the case where points are weighted).
"""
struct KmeansResult{C<:AbstractMatrix{<:AbstractFloat},D<:Real,WC<:Real} <: ClusteringResult
centers::C # cluster centers (d x k)
assignments::Vector{Int} # assignments (n)
costs::Vector{D} # cost of the assignments (n)
counts::Vector{Int} # number of points assigned to each cluster (k)
wcounts::Vector{WC} # cluster weights (k)
totalcost::D # total cost (i.e. objective)
iterations::Int # number of elapsed iterations
converged::Bool # whether the procedure converged
end
"""
@parallelize(n_threads, ncol, f)
Parallelize function and run it over n_threads. Function should require following conditions:
1. It should not return any values.
1. It should accept parameters two parameters at the end of the argument list. First
accepted parameter is `range`, which defines chunk used in calculations. Second
parameter is `idx` which defines id of the container where results can be stored.
`ncol` argument defines range 1:ncol which is sliced in `n_threads` chunks.
"""
macro parallelize(n_threads, ncol, f)
for i in 1:length(f.args)
f.args[i] = :($(esc(f.args[i])))
end
single_thread_chunk = copy(f)
push!(single_thread_chunk.args, :(1:$(esc(ncol))))
push!(single_thread_chunk.args, 1)
multi_thread_chunk = copy(f)
push!(multi_thread_chunk.args, :(ranges[i]))
push!(multi_thread_chunk.args, :(i))
last_multi_thread_chunk = copy(f)
push!(last_multi_thread_chunk.args, :(ranges[end]))
push!(last_multi_thread_chunk.args, :($(esc(n_threads))))
return quote
if $(esc(n_threads)) == 1
$single_thread_chunk
else
local ranges = splitter($(esc(ncol)), $(esc(n_threads)))
local waiting_list = $(esc(Vector)){$(esc(Task))}(undef, $(esc(n_threads)) - 1)
for i in 1:$(esc(n_threads)) - 1
waiting_list[i] = @spawn $multi_thread_chunk
end
$last_multi_thread_chunk
for i in 1:$(esc(n_threads)) - 1
wait(waiting_list[i])
end
end
end
end
"""
distance(X1, X2, i1, i2)
Allocationless calculation of square eucledean distance between vectors X1[:, i1] and X2[:, i2]
"""
function distance(X1, X2, i1, i2)
d = 0.0
# TODO: break of the loop if d is larger than threshold (known minimum disatnce)
@inbounds @simd for i in axes(X1, 1)
d += (X1[i, i1] - X2[i, i2])^2
end
return d
end
"""
sum_of_squares(x, labels, centre, k)
This function computes the total sum of squares based on the assigned (labels)
design matrix(x), centroids (centre), and the number of desired groups (k).
A Float type representing the computed metric is returned.
"""
function sum_of_squares(containers, x, labels, centre, r, idx)
s = 0.0
@inbounds for j in r
for i in axes(x, 1)
s += (x[i, j] - centre[i, labels[j]])^2
end
end
containers.sum_of_squares[idx] = s
end
"""
Kmeans([alg::AbstractKMeansAlg,] design_matrix, k; n_threads = nthreads(), k_init="k-means++", max_iters=300, tol=1e-6, verbose=true)
This main function employs the K-means algorithm to cluster all examples
in the training data (design_matrix) into k groups using either the
`k-means++` or random initialisation technique for selecting the initial
centroids.
At the end of the number of iterations specified (max_iters), convergence is
achieved if difference between the current and last cost objective is
less than the tolerance level (tol). An error is thrown if convergence fails.
Arguments:
- `alg` defines one of the algorithms used to calculate `k-means`. This
argument can be omitted, by default Lloyd algorithm is used.
- `n_threads` defines number of threads used for calculations, by default it is equal
to the `Threads.nthreads()` which is defined by `JULIA_NUM_THREADS` environmental
variable. For small size design matrices it make sense to set this argument to 1 in order
to avoid overhead of threads generation.
- `k_init` is one of the algorithms used for initialization. By default `k-means++` algorithm is used,
alternatively one can use `rand` to choose random points for init.
- `max_iters` is the maximum number of iterations
- `tol` defines tolerance for early stopping.
- `verbose` is verbosity level. Details of operations can be either printed or not by setting verbose accordingly.
A `KmeansResult` structure representing labels, centroids, and sum_squares is returned.
"""
function kmeans(alg, design_matrix, k;
n_threads = Threads.nthreads(),
k_init = "k-means++", max_iters = 300,
tol = 1e-6, verbose = false, init = nothing)
nrow, ncol = size(design_matrix)
containers = create_containers(alg, k, nrow, ncol, n_threads)
return kmeans!(alg, containers, design_matrix, k, n_threads = n_threads,
k_init = k_init, max_iters = max_iters, tol = tol,
verbose = verbose, init = init)
end
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] | 2.636324 | 2,274 |
<gh_stars>1-10
highlighter_exists(config::CthulhuConfig) =
Sys.which(config.highlighter.exec[1]) !== nothing
__init__() = CONFIG.enable_highlighter = highlighter_exists(CONFIG)
function highlight(io, x, lexer, config::CthulhuConfig)
_print = endswith(x, '\n') ? print : println
config.enable_highlighter || return _print(io, x)
if !highlighter_exists(config)
@warn "Highlighter command $(config.highlighter.exec[1]) does not exist."
return _print(io, x)
end
cmd = `$(config.highlighter) $lexer`
open(pipeline(cmd; stdout=io, stderr=stderr), "w") do io
_print(io, x)
end
end
function cthulhu_llvm(io::IO, mi, optimize, debuginfo, params, config::CthulhuConfig,
dump_module = false)
dump = InteractiveUtils._dump_function_linfo_llvm(
mi, params.world,
#=wrapper=# false, #=strip_ir_metadata=# true,
dump_module,
optimize, debuginfo ? :source : :none, Base.CodegenParams())
highlight(io, dump, "llvm", config)
end
function cthulhu_native(io::IO, mi, optimize, debuginfo, params, config::CthulhuConfig)
dump = InteractiveUtils._dump_function_linfo_native(
mi, params.world,
#=wrapper=# false, #=syntax=# config.asm_syntax,
debuginfo ? :source : :none)
highlight(io, dump, "asm", config)
end
function cthulhu_ast(io::IO, mi, optimize, debuginfo, params, config::CthulhuConfig)
meth = mi.def
ast = definition(Expr, meth)
if ast!==nothing
if !config.pretty_ast
dump(io, ast; maxdepth=typemax(Int))
else
show(io, ast)
# Meta.show_sexpr(io, ast)
# Could even highlight the above as some kind-of LISP
end
else
@info "Could not retrieve AST. AST display requires Revise.jl to be loaded." meth
end
end
function cthulhu_source(io::IO, mi, optimize, debuginfo, params, config::CthulhuConfig)
meth = mi.def
src, line = definition(String, meth)
highlight(io, src, "julia", config)
end
cthulhu_warntype(args...) = cthulhu_warntype(stdout, args...)
function cthulhu_warntype(io::IO, src, rettype, debuginfo, stable_code)
debuginfo = Base.IRShow.debuginfo(debuginfo)
lineprinter = Base.IRShow.__debuginfo[debuginfo]
lambda_io::IOContext = io
if hasfield(typeof(src), :slotnames) && src.slotnames !== nothing
slotnames = Base.sourceinfo_slotnames(src)
lambda_io = IOContext(lambda_io, :SOURCE_SLOTNAMES => slotnames)
show_variables(io, src, slotnames)
end
print(io, "Body")
InteractiveUtils.warntype_type_printer(io, rettype, true)
println(io)
if isa(src, IRCode)
show(io, src)
# XXX this doesn't properly show warntype
else
ir_printer = stable_code ? Base.IRShow.show_ir : show_ir
ir_printer(lambda_io, src, lineprinter(src), InteractiveUtils.warntype_type_printer)
end
return nothing
end
function cthulu_typed(io::IO, debuginfo_key, CI, rettype, mi, iswarn, stable_code)
println(io)
println(io, "β β $(string(Callsite(-1, MICallInfo(mi, rettype), :invoke)))")
if iswarn
cthulhu_warntype(io, CI, rettype, debuginfo_key, stable_code)
elseif isa(CI, IRCode)
show(io, CI)
else
show(io, CI, debuginfo = debuginfo_key)
end
println(io)
end
function show_variables(io, src, slotnames)
println(io, "Variables")
slottypes = src.slottypes
for i = 1:length(slotnames)
print(io, " ", slotnames[i])
if isa(slottypes, Vector{Any})
InteractiveUtils.warntype_type_printer(io, slottypes[i], true)
end
println(io)
end
println(io)
end
# These are standard code views that don't need any special handling,
# This namedtuple maps toggle::Symbol to function
const codeviews = (;
llvm=cthulhu_llvm,
native=cthulhu_native,
ast=cthulhu_ast,
source=cthulhu_source,
)
"""
Cthulhu.Bookmark
A `Cthulhu.Bookmark` remembers a method marked by `b` key during a descent.
It can be used with the following functions:
* `descend(::Bookmark)`, `descend_code_typed(::Bookmark)`,
`descend_code_warntype(::Bookmark)`: continue the descent.
* `code_typed(::Bookmark)`, `code_warntype([::IO,] ::Bookmark)`: show typed IR
* `code_llvm([::IO,] ::Bookmark)`: pretty-print LLVM IR
* `code_native([::IO,] ::Bookmark)`: pretty-print native code
"""
struct Bookmark
mi::MethodInstance
params::CompilerParams
end
"""
Cthulhu.BOOKMARKS :: Vector{Bookmark}
During a descent, methods can be "bookmarked" by pressing `b` key. It
pushes a [`Cthulhu.Bookmark`](@ref) into `Cthulhu.BOOKMARKS`. This can be
used to, e.g., continue descending by `descend(Cthulhu.BOOKMARKS[end])`.
See [`Cthulhu.Bookmark`](@ref) for other usages.
"""
const BOOKMARKS = Bookmark[]
# Default `show` is broken for `Core.Compiler.Params`. Trying not invoke it.
Base.show(io::IO, b::Bookmark) =
print(io, "Cthulhu.Bookmark(", b.mi, ", ::", CompilerParams, ")")
# Turn off `optimize` and `debuginfo` for default `show` so that the
# output is smaller.
function Base.show(io::IO, ::MIME"text/plain", b::Bookmark;
optimize = false, debuginfo = :none, iswarn=false)
CI, rt = InteractiveUtils.code_typed(b, optimize = optimize)
if get(io, :typeinfo, Any) === Bookmark # a hack to check if in Vector etc.
print(io, Callsite(-1, MICallInfo(b.mi, rt)), :invoke)
print(io, " (world: ", b.params.world, ")")
return
end
println(io, "Cthulhu.Bookmark (world: ", b.params.world, ")")
cthulu_typed(io, debuginfo, CI, rt, b.mi, iswarn)
end
function InteractiveUtils.code_typed(b::Bookmark; optimize = true)
(CI, rt, slottypes) = do_typeinf_slottypes(b.mi, optimize, b.params)
preprocess_ci!(CI, b.mi, optimize, CONFIG)
return CI => rt
end
InteractiveUtils.code_warntype(b::Bookmark; kw...) =
InteractiveUtils.code_warntype(stdout, b; kw...)
function InteractiveUtils.code_warntype(io::IO, b::Bookmark; debuginfo = :source, kw...)
CI, rt = InteractiveUtils.code_typed(b; kw...)
cthulhu_warntype(io, CI, rt, debuginfo)
end
InteractiveUtils.code_llvm(b::Bookmark) = InteractiveUtils.code_llvm(stdout, b)
InteractiveUtils.code_llvm(io::IO, b::Bookmark; optimize = true, debuginfo = :source,
dump_module = false, config = CONFIG) =
cthulhu_llvm(io, b.mi, optimize, debuginfo == :source, b.params, config, dump_module)
InteractiveUtils.code_native(b::Bookmark; kw...) =
InteractiveUtils.code_native(stdout, b; kw...)
InteractiveUtils.code_native(io::IO, b::Bookmark; optimize = true, debuginfo = :source,
config = CONFIG) =
cthulhu_native(io, b.mi, optimize, debuginfo == :source, b.params, config)
@nospecialize
using Base.IRShow: compute_basic_blocks, scan_ssa_use!, should_print_ssa_type, print_stmt, GotoIfNot, GotoNode, PhiNode, block_for_inst
function show_ir(io::IO, code::Core.CodeInfo, line_info_preprinter, line_info_postprinter)
cols = displaysize(io)[2]
used = BitSet()
stmts = code.code
types = code.ssavaluetypes
cfg = compute_basic_blocks(stmts)
max_bb_idx_size = length(string(length(cfg.blocks)))
for stmt in stmts
scan_ssa_use!(push!, used, stmt)
end
bb_idx = 1
if isempty(used)
maxlength_idx = 0
else
maxused = maximum(used)
maxlength_idx = length(string(maxused))
end
for idx in eachindex(stmts)
if !isassigned(stmts, idx)
# This is invalid, but do something useful rather
# than erroring, to make debugging easier
printstyled(io, "#UNDEF\n", color=:red)
continue
end
stmt = stmts[idx]
show_type = types isa Vector{Any} && should_print_ssa_type(stmt)
if types isa Vector{Any} # ignore types for pre-inference code
if isassigned(types, idx) && show_type
typ = types[idx]
if (idx in used) && typ isa Type && (!Base.isdispatchelem(typ) || typ == Core.Box)
else
continue
end
else
continue
end
end
# Compute BB guard rail
if bb_idx > length(cfg.blocks)
# If invariants are violated, print a special leader
linestart = " "^(max_bb_idx_size + 2) # not inside a basic block bracket
inlining_indent = line_info_preprinter(io, linestart, idx)
printstyled(io, "!!! ", "β"^max_bb_idx_size, color=:light_black)
else
bbrange = cfg.blocks[bb_idx].stmts
bbrange = bbrange.start:bbrange.stop
# Print line info update
linestart = idx == first(bbrange) ? " " : sprint(io -> printstyled(io, "β ", color=:light_black), context=io)
linestart *= " "^max_bb_idx_size
inlining_indent = line_info_preprinter(io, linestart, idx)
if idx == first(bbrange)
bb_idx_str = string(bb_idx)
bb_pad = max_bb_idx_size - length(bb_idx_str)
bb_type = length(cfg.blocks[bb_idx].preds) <= 1 ? "β" : "β"
printstyled(io, bb_idx_str, " ", bb_type, "β"^bb_pad, color=:light_black)
elseif idx == last(bbrange) # print separator
printstyled(io, "β", "β"^(1 + max_bb_idx_size), color=:light_black)
else
printstyled(io, "β ", " "^max_bb_idx_size, color=:light_black)
end
if idx == last(bbrange)
bb_idx += 1
end
end
print(io, inlining_indent, " ")
# convert statement index to labels, as expected by print_stmt
if stmt isa Expr
if stmt.head === :gotoifnot && length(stmt.args) == 2 && stmt.args[2] isa Int
stmt = GotoIfNot(stmt.args[1], block_for_inst(cfg, stmt.args[2]::Int))
elseif stmt.head === :enter && length(stmt.args) == 1 && stmt.args[1] isa Int
stmt = Expr(:enter, block_for_inst(cfg, stmt.args[1]::Int))
end
elseif isa(stmt, GotoIfNot)
stmt = GotoIfNot(stmt.cond, block_for_inst(cfg, stmt.dest))
elseif stmt isa GotoNode
stmt = GotoNode(block_for_inst(cfg, stmt.label))
elseif stmt isa PhiNode
e = stmt.edges
stmt = PhiNode(Int32[block_for_inst(cfg, Int(e[i])) for i in 1:length(e)], stmt.values)
end
print_stmt(io, idx, stmt, used, maxlength_idx, true, show_type)
if types isa Vector{Any} # ignore types for pre-inference code
if !isassigned(types, idx)
# This is an error, but can happen if passes don't update their type information
printstyled(io, "::#UNDEF", color=:red)
elseif show_type
typ = types[idx]
if typ isa Union && Base.is_expected_union(typ)
Base.emphasize(io, "::$typ", Base.warn_color()) # more mild user notification
else
Base.emphasize(io, "::$typ")
end
end
end
println(io)
end
let linestart = " "^(max_bb_idx_size + 2)
line_info_preprinter(io, linestart, 0)
end
nothing
end
@specialize
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] | 2.185257 | 5,182 |
using ManifoldsBase
using Test
struct ErrorTestManifold <: Manifold{β} end
function ManifoldsBase.check_manifold_point(::ErrorTestManifold, x)
if any(u -> u < 0, x)
return DomainError(x, "<0")
end
return nothing
end
function ManifoldsBase.check_tangent_vector(M::ErrorTestManifold, x, v)
mpe = check_manifold_point(M, x)
mpe === nothing || return mpe
if any(u -> u < 0, v)
return DomainError(v, "<0")
end
return nothing
end
@testset "Domain errors" begin
M = ErrorTestManifold()
@test isa(check_manifold_point(M, [-1, 1]), DomainError)
@test check_manifold_point(M, [1, 1]) === nothing
@test !is_manifold_point(M, [-1, 1])
@test is_manifold_point(M, [1, 1])
@test_throws DomainError is_manifold_point(M, [-1, 1], true)
@test isa(check_tangent_vector(M, [1, 1], [-1, 1]), DomainError)
@test check_tangent_vector(M, [1, 1], [1, 1]) === nothing
@test !is_tangent_vector(M, [1, 1], [-1, 1])
@test is_tangent_vector(M, [1, 1], [1, 1])
@test_throws DomainError is_tangent_vector(M, [1, 1], [-1, 1], true)
end
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437,
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] | 2.23374 | 492 |
@testset "Testing Lots of things" begin
localcache = joinpath(@__DIR__, "cache", "fuzz.jls")
backend = AutoTM.Backend("CPU")
cache = AutoTM.Profiler.CPUKernelCache(localcache)
# Queue up a collection of models and optimizers.
batchsize = 16
fns = [
() -> AutoTM.Zoo.resnet_training(AutoTM.Zoo.Resnet50(), batchsize),
() -> AutoTM.Zoo.vgg_training(AutoTM.Zoo.Vgg19(), batchsize),
() -> AutoTM.Zoo.inception_v4_training(batchsize),
# TODO: Finish profiling to add this test.
#() -> AutoTM.Zoo.densenet_training(batchsize)
]
# Go through the static and synchronous optimizers.
#
# Just do one where we know we'll get move nodes / a mix of DRAM and PMM
# Keep the total number down to facilitate quicker testing.
optimizers = [
AutoTM.Optimizer.Static(1 // 1),
AutoTM.Optimizer.Synchronous(1 // 1),
]
for (i, f) in enumerate(fns)
for (j, opt) in enumerate(optimizers)
printstyled("Function $i -- Optimizer $j\n"; color = :green)
@test AutoTM.Verifier.verify(backend, f, opt, cache)
end
end
end
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] | 2.387967 | 482 |
<reponame>JuliaPackageMirrors/RingBuffers.jl<gh_stars>0
using RingBuffers
if VERSION >= v"0.5.0-"
using Base.Test
else
using BaseTestNext
end
@testset "RingBuffer Tests" begin
include("lockfree.jl")
@testset "Construction" begin
r = RingBuffer(Float64, 8, 1)
@test eltype(r) == Float64
@test RingBuffers.read_space(r) == 0
@test RingBuffers.write_space(r) == 8
end
@testset "Basic read/write" begin
r = RingBuffer(Int, 8, 2)
data = reshape(1:12, 6, 2)
write(r, data[1:4, :])
@test read(r, 4) == data[1:4, :]
write(r, data)
@test read(r, 6) == data
end
@testset "Overwriting overflow" begin
r = RingBuffer(Int, 8, 2; overflow=OVERWRITE)
data = reshape(1:12, 6, 2)
@test write(r, data[1:4, :]) == 4
@test write(r, data) == 6
@test read(r, 4) == [3 9; 4 10; 1 7; 2 8]
end
@testset "Single-write overwrite overflow" begin
r = RingBuffer(Int, 8, 2; overflow=OVERWRITE)
data = reshape(1:20, 10, 2)
@test write(r, data) == 10
@test read(r, 4) == [3 13; 4 14; 5 15; 6 16]
end
@testset "truncating overflow" begin
r = RingBuffer(Int, 8, 2; overflow=TRUNCATE)
data = reshape(1:12, 6, 2)
@test write(r, data[1:4, :]) == 4
@test write(r, data) == 4
@test write(r, data) == 0
@test read(r, 5) == [1 7; 2 8; 3 9; 4 10; 1 7]
end
@testset "truncating underflow" begin
r = RingBuffer(Int, 8, 2; underflow=TRUNCATE)
data = reshape(1:6, 3, 2)
@test write(r, data) == 3
@test read(r, 5) == [1 4; 2 5; 3 6]
end
@testset "padded underflow" begin
r = RingBuffer(Int, 8, 2; underflow=PAD)
data = reshape(1:6, 3, 2)
@test write(r, data) == 3
@test read(r, 5) == [1 4; 2 5; 3 6; 0 0; 0 0]
end
@testset "queuing blocking writes" begin
r = RingBuffer(Int, 4, 2)
data = reshape(1:12, 6, 2)
res1 = @async write(r, data)
res2 = @async write(r, data)
res3 = @async write(r, data)
yield()
@test read(r, 5) == [1 7; 2 8; 3 9; 4 10; 5 11]
@test read(r, 13) == [6 12; 1 7; 2 8; 3 9; 4 10;
5 11; 6 12; 1 7; 2 8; 3 9;
4 10; 5 11; 6 12]
@test wait(res1) == 6
@test wait(res2) == 6
@test wait(res3) == 6
end
@testset "queueing blocking reads" begin
r = RingBuffer(Int, 8, 2)
data = reshape(1:12, 6, 2)
res1 = @async read(r, 2)
res2 = @async read(r, 2)
res3 = @async read(r, 2)
yield()
write(r, data)
@test wait(res1) == [1 7; 2 8]
@test wait(res2) == [3 9; 4 10]
@test wait(res3) == [5 11; 6 12]
end
end
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] | 1.896598 | 1,499 |
<reponame>VEZY/MultiScaleTreeGraph.jl<gh_stars>1-10
"""
MetaGraph(g::Node)
Convert an MTG into a [MetaGraph](https://juliagraphs.org/MetaGraphsNext.jl/dev/).
# Examples
```julia
# Importing an mtg from the package:
file = joinpath(dirname(dirname(pathof(MultiScaleTreeGraph))),"test","files","simple_plant.mtg")
mtg = read_mtg(file)
MetaGraph(mtg)
```
"""
function MetaGraph(g::Node)
meta_mtg =
MetaGraph(
DiGraph(),
Label = Int,
VertexMeta = typeof(g.attributes),
EdgeMeta = String,
gprops = "MTG"
)
traverse!(g, to_MetaGraph, meta_mtg)
return meta_mtg
end
function to_MetaGraph(node, meta_mtg)
meta_mtg[node.id] = node.attributes
if !isroot(node)
code_node = code_for(meta_mtg, node.id)
code_parent = code_for(meta_mtg, parent(node).id)
add_edge!(meta_mtg, code_parent, code_node, node.MTG.link)
end
end
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] | 2.131222 | 442 |
## Bubble pressure solver
function x0_bubble_pressure(model::EoSModel,T,x)
#TODO
#on sufficiently large temps,
#the joule-thompson inversion occurs
#making the virial coeff positive
#on those cases, use an strategy that supposes pure gas on that side
#Pbi = inf
#xi = 0
#check each T with T_scale, if treshold is over, replace Pi with inf
pure = split_model(model)
crit = crit_pure.(pure)
T_c = [tup[1] for tup in crit]
V_c = [tup[3] for tup in crit]
_0 = zero(T+first(x))
nan = _0/_0
sat_nan = (nan,nan,nan)
replaceP = ifelse.(T_c .< T,true,false)
eachx = eachcol(Diagonal(ones(eltype(x),length(x))))
# Bi = second_virial_coefficient.(model,T,eachx)
#using P_B(2B) as a sat aproximation
#z = 1 + B/v
#P_B = RT/v(1+B/v)
#P_B(2B) = -RT/2B(1-B/2B)
#P_B(2B) = -0.25*RT/B
sat = [if !replaceP[i] sat_pure(pure[i],T) else sat_nan end for i in 1:length(pure)]
P_sat = [tup[1] for tup in sat]
V_l_sat = [tup[2] for tup in sat]
V_v_sat = [tup[3] for tup in sat]
# P_Bi = @. -0.25*RΜ*T/Bi
#=xP0 = yP
#dot(x,P0) = P
P = dot(x,P0)
=#
P = zero(T)
V0_l = zero(T)
V0_v = zero(T)
Pi = zero(x)
for i in 1:length(x)
if !replaceP[i]
Pi[i] = P_sat[i][1]
P+=x[i]*Pi[i]
V0_l += x[i]*V_l_sat[i]
else
Pi[i] = pressure(pure[i],V_c[i],T)
P+=x[i]*Pi[i]
V0_l += x[i]*V_c[i]
end
end
#@show P_Bi
#P = dot(x,P_Bi)
y = @. x*Pi/P
ysum = 1/β(y)
y = y.*ysum
for i in 1:length(x)
if !replaceP[i]
V0_v += y[i]*V_v_sat[i]
else
V0_v += y[i]*V_c[i]*1.2
end
end
prepend!(y,log10.([V0_l,V0_v]))
return y
end
function bubble_pressure(model::EoSModel, T, x; v0 =nothing)
TYPE = promote_type(eltype(T),eltype(x))
# lb_v = lb_volume(model,x)
ts = T_scales(model,x)
pmix = p_scale(model,x)
if v0 === nothing
v0 = x0_bubble_pressure(model,T,x)
end
len = length(v0[1:end-1])
#xcache = zeros(eltype(x0),len)
Fcache = zeros(eltype(v0[1:end-1]),len)
f! = (F,z) -> Obj_bubble_pressure(model, F, T, exp10(z[1]), exp10(z[2]), x,z[3:end],ts,pmix)
r =Solvers.nlsolve(f!,v0[1:end-1],LineSearch(Newton()))
sol = Solvers.x_sol(r)
v_l = exp10(sol[1])
v_v = exp10(sol[2])
y = FractionVector(sol[3:end])
P_sat = pressure(model,v_l,T,x)
return (P_sat, v_l, v_v, y)
end
function Obj_bubble_pressure(model::EoSModel, F, T, v_l, v_v, x, y,ts,ps)
y = FractionVector(y) #julia magic, check misc.jl
ΞΌ_l = VT_chemical_potential(model,v_l,T,x)
ΞΌ_v = VT_chemical_potential(model,v_v,T,y)
p_l = pressure(model,v_l,T,x)
p_v = pressure(model,v_v,T,y)
for i in 1:length(x)
F[i] = (ΞΌ_l[i]-ΞΌ_v[i])/(RΜ*ts[i])
end
F[end] = (p_l-p_v)/ps
return F
end
#j! = (J,z) -> Jac_bubble_pressure(model, J, T, exp10(z[1]), exp10(z[2]), x[i,:], z[3:end])
#=
_y0 = collect(FractionVector(v0[3:end]))
@show _y0
@show _pl0 = pressure(model,exp10(v0[1]),T,x)
@show _pv0 = pressure(model,exp10(v0[2]),T,_y0)
_P = 0.5*(_pl0+_pv0)
for _ in 1:3
_y0,_P = rr_bubble_pressure_refine(model,x,_y0,_P,T)
end
=#
# function bubble_pressure_rr(model, T, x; P = 40000)
# sol0 = x0_bubble_pressure(model,T,x)
# vl0 = exp10(sol0[1])
# vv0 = exp10(sol0[2])
# @show y0 = collect(FractionVector(sol0[3:end]))
# @show vl0 = volume(model,P,T,x,phase=:l)
# @show vv0 = volume(model,P,T,y0,phase=:v)
# @show ΞΌ_l = vt_chemical_potential(model,vl0,T,x)
# @show ΞΌ_v = vt_chemical_potential(model,vv0,T,y0)
# y1 = ΞΌ_l ./ ΞΌ_v .* x
# @show y1 = y1 ./ sum(y1)
# @show pl0 = pressure(model,vl0,T,x)
# @show pv0 = pressure(model,vv0,T,y1)
# @show P = (pl0 - pv0)/(log(pl0) - log(pv0))
# @show vl = volume(model,P,T,x,phase=:l)
# @show vv = volume(model,P,T,y1,phase=:v)
# #=
# ΞΌ_l = vt_chemical_potential(model,vl,T,x)
# ΞΌ_v = vt_chemical_potential(model,vv,T,y)
# K = log.(ΞΌ_v) ./ log.(ΞΌ_l)
# y = K .* x
# y = y./sum(y)
# pl = pressure(model,vl,T,x)
# pv = pressure(model,vv,T,y)
# P = (pl+pv)/2
# @show vl,vv,y
# return y,P
# =#
# end
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220,
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796,
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198,
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886,
198
] | 1.755135 | 2,483 |
<reponame>davibarreira/NotebookToLaTeX.jl
### A Pluto.jl notebook ###
# v0.16.3
using Markdown
using InteractiveUtils
# βββ‘ e56ad662-0c01-48ca-addb-615e9350c6d9
1
# βββ‘ 43e5dfe4-90b2-4f7b-b467-3107b78520f4
x = 1 + 1
# βββ‘ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
"""
# βββ‘ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
julia_version = "1.7.0-rc1"
manifest_format = "2.0"
[deps]
"""
# βββ‘ Cell order:
# β βe56ad662-0c01-48ca-addb-615e9350c6d9
# β β43e5dfe4-90b2-4f7b-b467-3107b78520f4
# ββ00000000-0000-0000-0000-000000000001
# ββ00000000-0000-0000-0000-000000000002
| [
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] | 1.991525 | 354 |
<gh_stars>100-1000
# This file is a part of BAT.jl, licensed under the MIT License (MIT).
struct GradFunc{
OF<:Function,
UF<:Function,
VS<:AbstractValueShape,
GS<:AbstractValueShape,
Alg<:DifferentiationAlgorithm
} <: GradientFunction
_orig_f::OF
_unshaped_f::UF
_input_shape::VS
_grad_shape::GS
_diffalg::Alg
end
function GradFunc(f::Function, diffalg::DifferentiationAlgorithm)
input_shape = varshape(f)
unshaped_f = unshaped(f)
grad_shape = gradient_shape(input_shape)
GradFunc(f, unshaped_f, input_shape, grad_shape, diffalg)
end
function Base.show(io::IO, gf::GradFunc)
print(io, Base.typename(typeof(gf)).name, "(")
show(io, gf._orig_f)
print(io, ", ")
show(io, gf._diffalg)
print(io, ")")
end
function Base.show(io::IO, M::MIME"text/plain", gf::GradFunc)
print(io, Base.typename(typeof(gf)).name, "(")
show(io, M, gf._orig_f)
print(io, ", ")
show(io, M, gf._diffalg)
print(io, ")")
end
_fixup_var(shape::AbstractValueShape, v::Any) = v
_fixup_var(shape::NamedTupleShape, v::NamedTuple) = shape(unshaped(v, shape))
function (gf::GradFunc)(v::Any)
input_shape = gf._input_shape
v_shaped = _fixup_var(input_shape, v)
v_unshaped = unshaped(v_shaped)
grad_f_unshaped = similar(v_unshaped)
value = unshaped_gradient!(grad_f_unshaped, gf._unshaped_f, v_unshaped, gf._diffalg)
(value, gf._grad_shape(grad_f_unshaped))
end
function (gf::GradFunc)(::typeof(!), grad_f::Any, v::Any)
input_shape = gf._input_shape
v_shaped = _fixup_var(input_shape, v)
v_unshaped = unshaped(v_shaped)
if isnothing(grad_f)
R(gf._unshaped_f(v_unshaped))
else
grad_f_unshaped = unshaped(grad_f, gf._grad_shape)
unshaped_gradient!(grad_f_unshaped, gf._unshaped_f, v_unshaped, gf._diffalg)
end
end
function valgradof(f::Function, algorithm::DifferentiationAlgorithm = vjp_algorithm(f))
GradFunc(f, algorithm)
end
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<filename>src/core/DataStream.jl
# Functions
import Polynomials: ImmutablePolynomial
# Macros
import MLStyle: @match
# Modules
import MPI
mutable struct PayloadRange
a::Int32
b::Int32
end
# PayloadRange(r::NTuple{2,<:Integer}) = PayloadRange(r[1], r[2])
function Base.copy(r::PayloadRange)
return PayloadRange(r.a, r.b)
end
function Base.:(==)(r::PayloadRange, t::NTuple{2,<:Integer})
return r.a == t[1] && r.b == t[2]
end
struct PayloadGroup
range::PayloadRange
ratio::Float64
end
PayloadGroup(range::Vector{<:Integer}, ratio::T) where {T} =
PayloadGroup(PayloadRange(range[1], range[2]), get_ratio(ratio))
abstract type EvolutionFunction end
struct DataStreamConfig
name::String
evolve::EvolutionFunction
payload_groups::Vector{PayloadGroup}
end
function get_payload_group_id(
rank::Integer,
nranks::Integer,
cfg::DataStreamConfig,
)
percentile = (rank + 1) / nranks
current = 0.0
for id = 1:length(cfg.payload_groups)
grp = cfg.payload_groups[id]
current += grp.ratio
if percentile <= current
return id
end
end
# TODO: throw an error here ?
end
function get_payload_group_id(cfg::DataStreamConfig)
comm = MPI.COMM_WORLD
return get_payload_group_id(MPI.Comm_rank(comm), MPI.Comm_size(comm), cfg)
end
abstract type DataObject end
mutable struct DataVector <: DataObject
vec::Vector{Float64}
end
DataVector() = DataVector(Float64[])
mutable struct DataStream
initial_range::PayloadRange
range::PayloadRange
evolve::EvolutionFunction
data::DataObject
end
DataStream(range::PayloadRange, evolve::EvolutionFunction) =
DataStream(range, copy(range), evolve, DataVector())
DataStream(cfg::DataStreamConfig) =
DataStream(cfg.payload_groups[get_payload_group_id(cfg)].range, cfg.evolve)
struct GrowthFactorEvFn <: EvolutionFunction
factor::Float64
end
GrowthFactorEvFn(params::Vector{<:Real}) = GrowthFactorEvFn(params[1])
function evolve_payload_range!(
stream::DataStream,
step::Integer,
fn::GrowthFactorEvFn,
)
growth = fn.factor^step
stream.range.a = round(stream.initial_range.a * growth)
stream.range.b = round(stream.initial_range.b * growth)
end
struct PolynomialEvFn <: EvolutionFunction
poly::ImmutablePolynomial
end
PolynomialEvFn(params::Vector{<:Real}) =
PolynomialEvFn(ImmutablePolynomial(vcat(0, params)))
function evolve_payload_range!(
stream::DataStream,
step::Integer,
fn::PolynomialEvFn,
)
growth = fn.poly(step)
stream.range.a = stream.initial_range.a + growth
stream.range.b = stream.initial_range.b + growth
end
function evolve_payload_range!(stream::DataStream, step::Integer)
evolve_payload_range!(stream, step, stream.evolve)
end
function check_length(expected::Integer, params::Vector{<:Real})
if length(params) != expected
@error "Wrong number of parameters"
end
return params
end
function get_evolution_function(evcfg::Config)
params = evcfg[:params]
@match evcfg[:function] begin
"GrowthFactor" => return GrowthFactorEvFn(check_length(1, params))
"Polynomial" => return PolynomialEvFn(params)
"Linear" => return PolynomialEvFn(check_length(1, params))
_ => @error "Unsupported stream size evolution function"
end
end
get_evolution_function(nothing) = GrowthFactorEvFn(1.0)
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] | 2.596947 | 1,310 |
# stitch images and estimate together
using ImageMagick
# using ImageShow
using Images, ImageView, ImageDraw
datadir = joinpath(ENV["HOME"],"data","racecar")
resultsparentdir = joinpath(datadir, "results")
imgfolder = "images"
currdirtime = "2018-11-07T01:36:52.274"
resultsdir = joinpath(resultsparentdir, "$(currdirtime)")
i = 1
for i in 1:10
tag_img = load( joinpath(resultsdir, imgfolder, "x$(i).jpg") )
top_img = load( joinpath(resultsdir, imgfolder, "img_$(i).png") )
rows = size(im1)[1]+size(im1t)[1]
end
#
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] | 2.61194 | 201 |
<gh_stars>0
"""
compute_col_dry_kernel!(
col_dry,
p_lev,
mol_m_dry,
mol_m_h2o,
avogadro
helmert1,
vmr_h2o
lat,
glaycol,
)
This function computes the column amounts of dry or moist air.
"""
function compute_col_dry_kernel!(
col_dry::AbstractArray{FT, 2},
p_lev::AbstractArray{FT, 2},
mol_m_dry::FT,
mol_m_h2o::FT,
avogadro::FT,
helmert1::FT,
vmr_h2o::Union{AbstractArray{FT, 2}, Nothing},
lat::Union{AbstractArray{FT, 1}, Nothing},
glaycol::Tuple{Int, Int},
) where {FT <: AbstractFloat}
glay, gcol = glaycol[1], glaycol[2] # global col & lay ids
helmert2 = FT(0.02586) # second constant of Helmert formula
m2_to_cm2 = FT(100 * 100) # m^2 to cm^2
if lat isa AbstractArray
g0 = helmert1 - helmert2 * cos(FT(2) * FT(Ο) * lat[gcol] / FT(180)) # acceleration due to gravity [m/s^2]
else
g0 = helmert1
end
Ξp = p_lev[glay, gcol] - p_lev[glay + 1, gcol]
if vmr_h2o isa AbstractArray
fact = FT(1) / (FT(1) + vmr_h2o[glay, gcol])
# Get average mass of moist air per mole of moist air
m_air = (mol_m_dry + mol_m_h2o * vmr_h2o[glay, gcol]) * fact
# Hydrostatic equation
col_dry[glay, gcol] = (Ξp * avogadro / (m2_to_cm2 * m_air * g0)) * fact # molecules/cm^2
else
col_dry[glay, gcol] = (Ξp * avogadro / (m2_to_cm2 * mol_m_air * g0)) # molecules/cm^2
end
end
"""
compute_interp_fractions(
lkp::AbstractLookUp{I,FT},
vmr,
p_lay,
t_lay,
tropo,
ibnd,
glaycol,
) where {I<:Int,FT<:AbstractFloat}
compute interpolation fractions for binary species parameter, pressure and temperature.
"""
@inline function compute_interp_fractions(
lkp::AbstractLookUp{I, FT},
vmr,
p_lay,
t_lay,
tropo,
ibnd,
glaycol,
) where {I <: Int, FT <: AbstractFloat}
jftemp = compute_interp_frac_temp(lkp, t_lay, glaycol...)
jfpress = compute_interp_frac_press(lkp, p_lay, tropo, glaycol...)
jfΞ·, col_mix = compute_interp_frac_Ξ·(lkp, vmr, tropo, jftemp[1], ibnd, glaycol...)
return (jftemp, jfpress, jfΞ·, col_mix)
end
"""
compute_interp_frac_temp(
lkp::AbstractLookUp{I,FT},
t_lay,
glay,
gcol,
) where {I<:Int,FT<:AbstractFloat}
compute interpolation fraction for temperature.
"""
@inline function compute_interp_frac_temp(
lkp::AbstractLookUp{I, FT},
t_lay,
glay,
gcol,
) where {I <: Int, FT <: AbstractFloat}
(; Ξ_t_ref, n_t_ref, t_ref) = lkp
@inbounds jtemp = loc_lower(t_lay, Ξ_t_ref, n_t_ref, t_ref)
@inbounds ftemp = (t_lay - t_ref[jtemp]) / Ξ_t_ref
return (jtemp, ftemp)
end
"""
compute_interp_frac_press(
lkp::AbstractLookUp,
p_lay,
tropo,
glay,
gcol,
)
Compute interpolation fraction for pressure.
"""
@inline function compute_interp_frac_press(lkp::AbstractLookUp, p_lay, tropo, glay, gcol)
(; Ξ_ln_p_ref, p_ref, n_p_ref) = lkp
@inbounds jpress = Int(min(max(fld(log(p_ref[1]) - log(p_lay), Ξ_ln_p_ref) + 1, 1), n_p_ref - 1) + 1)
@inbounds fpress = (log(p_ref[jpress - 1]) - log(p_lay)) / Ξ_ln_p_ref
jpress = jpress + tropo - 1
return (jpress, fpress)
end
"""
compute_interp_frac_Ξ·(
lkp::AbstractLookUp{I,FT},
vmr,
tropo,
jtemp,
ibnd,
glay,
gcol,
) where {FT<:AbstractFloat,I<:Int}
Compute interpolation fraction for binary species parameter.
"""
@inline function compute_interp_frac_Ξ·(
lkp::AbstractLookUp{I, FT},
vmr,
tropo,
jtemp,
ibnd,
glay,
gcol,
) where {FT <: AbstractFloat, I <: Int}
(; n_Ξ·, key_species, vmr_ref) = lkp
ig = view(key_species, :, tropo, ibnd)
vmr1 = get_vmr(vmr, ig[1], glay, gcol)
vmr2 = get_vmr(vmr, ig[2], glay, gcol)
itemp = 1
@inbounds Ξ·_half = vmr_ref[tropo, ig[1] + 1, jtemp + itemp - 1] / vmr_ref[tropo, ig[2] + 1, jtemp + itemp - 1]
col_mix1 = vmr1 + Ξ·_half * vmr2
Ξ· = col_mix1 β₯ eps(FT) * 2 ? vmr1 / col_mix1 : FT(0.5)
loc_Ξ· = FT(Ξ· * (n_Ξ· - 1))
jΞ·1 = min(unsafe_trunc(I, loc_Ξ·) + 1, n_Ξ· - 1)
#fΞ·1 = loc_Ξ· % FT(1) # TODO: "%: operator seems unstable on GPU
#fΞ·1 = FT(loc_Ξ· % 1) # to be revisited
fΞ·1 = loc_Ξ· - fld(loc_Ξ·, FT(1))
itemp = 2
@inbounds Ξ·_half = vmr_ref[tropo, ig[1] + 1, jtemp + itemp - 1] / vmr_ref[tropo, ig[2] + 1, jtemp + itemp - 1]
col_mix2 = vmr1 + Ξ·_half * vmr2
Ξ· = col_mix2 β₯ eps(FT) * 2 ? vmr1 / col_mix2 : FT(0.5)
loc_Ξ· = FT(Ξ· * (n_Ξ· - 1))
jΞ·2 = min(unsafe_trunc(I, loc_Ξ·) + 1, n_Ξ· - 1)
#fΞ·2 = loc_Ξ· % FT(1) # TODO: "%" operator seems unstable on GPU
#fΞ·2 = FT(loc_Ξ· % 1) # to be revisited
fΞ·2 = loc_Ξ· - fld(loc_Ξ·, FT(1))
return ((jΞ·1, jΞ·2, fΞ·1, fΞ·2), (col_mix1, col_mix2))#nothing
end
"""
compute_Ο_ssa_lw_src!(
lkp::AbstractLookUp{I,FT},
vmr,
col_dry,
igpt,
ibnd,
p_lay::FT,
t_lay,
glaycol,
src_args...,
) where {FT<:AbstractFloat,I<:Int}
Compute optical thickness, single scattering albedo, asymmetry parameter
and longwave sources whenever applicable.
"""
@inline function compute_Ο_ssa_lw_src!(
lkp::AbstractLookUp{I, FT},
vmr,
col_dry,
igpt,
ibnd,
p_lay::FT,
t_lay,
glaycol,
src_args...,
) where {FT <: AbstractFloat, I <: Int}
# upper/lower troposphere
tropo = p_lay > lkp.p_ref_tropo ? 1 : 2
# volume mixing ration of h2o
vmr_h2o = get_vmr(vmr, lkp.idx_h2o, glaycol...)
jftemp, jfpress, jfΞ·, col_mix = compute_interp_fractions(lkp, vmr, p_lay, t_lay, tropo, ibnd, glaycol)
# computing Ο_major
Ο_major = interp3d(jfΞ·..., jftemp..., jfpress..., lkp.kmajor, igpt, col_mix...) * col_dry
# computing Ο_minor
Ο_minor =
compute_Ο_minor(lkp, tropo, vmr, vmr_h2o, col_dry, p_lay, t_lay, jftemp..., jfΞ·..., igpt, ibnd, glaycol...)
# compute Ο_Rayleigh
Ο_ray = compute_Ο_rayleigh(lkp, tropo, col_dry, vmr_h2o, jftemp..., jfΞ·..., igpt)
Ο = Ο_major + Ο_minor + Ο_ray
ssa = FT(0)
if Ο > 2 * eps(FT) # single scattering albedo
ssa = Ο_ray / Ο
end
# computing Planck sources for longwave problem
compute_lw_planck_src!(lkp, jfΞ·..., jfpress..., jftemp..., t_lay, igpt, ibnd, glaycol..., src_args...)
return (Ο, ssa)
end
"""
compute_Ο_minor(
lkp::AbstractLookUp,
tropo::I,
vmr,
vmr_h2o::FT,
col_dry,
p_lay::FT,
t_lay::FT,
jtemp::I,
ftemp::FT,
jΞ·1::I,
jΞ·2::I,
fΞ·1::FT,
fΞ·2::FT,
igpt,
ibnd,
glay,
gcol,
) where {FT<:AbstractFloat,I<:Int}
Compute optical thickness contributions from minor gases.
"""
@inline function compute_Ο_minor(
lkp::AbstractLookUp,
tropo::I,
vmr,
vmr_h2o::FT,
col_dry,
p_lay::FT,
t_lay::FT,
jtemp::I,
ftemp::FT,
jΞ·1::I,
jΞ·2::I,
fΞ·1::FT,
fΞ·2::FT,
igpt,
ibnd,
glay,
gcol,
) where {FT <: AbstractFloat, I <: Int}
if tropo == 1 # in lower atmosphere
minor_bnd_st = lkp.minor_lower_bnd_st
idx_gases_minor = lkp.idx_gases_minor_lower
minor_scales_with_density = lkp.minor_lower_scales_with_density
idx_scaling_gas = lkp.idx_scaling_gas_lower
scale_by_complement = lkp.lower_scale_by_complement
minor_gpt_sh = lkp.minor_lower_gpt_sh
kminor = lkp.kminor_lower
else # in upper atmosphere
minor_bnd_st = lkp.minor_upper_bnd_st
idx_gases_minor = lkp.idx_gases_minor_upper
minor_scales_with_density = lkp.minor_upper_scales_with_density
idx_scaling_gas = lkp.idx_scaling_gas_upper
scale_by_complement = lkp.upper_scale_by_complement
minor_gpt_sh = lkp.minor_upper_gpt_sh
kminor = lkp.kminor_upper
end
Ο_minor = FT(0)
pa2hpa = FT(0.01) # pascals to hectopascals
dry_fact = FT(1) / (FT(1) + vmr_h2o)
@inbounds loc_in_bnd = igpt - (lkp.bnd_lims_gpt[1, ibnd] - 1)
@inbounds for i in minor_bnd_st[ibnd]:(minor_bnd_st[ibnd + 1] - 1)
vmr_imnr = get_vmr(vmr, idx_gases_minor[i], glay, gcol)
if vmr_imnr > eps(FT) * 2
scaling = vmr_imnr * col_dry
if minor_scales_with_density[i] == 1
scaling *= (pa2hpa * p_lay / t_lay)
sgas = idx_scaling_gas[i]
if sgas > 0
if scale_by_complement[i] == 1
scaling *= (FT(1) - get_vmr(vmr, sgas, glay, gcol) * dry_fact)
else
scaling *= get_vmr(vmr, sgas, glay, gcol) * dry_fact
end
end
end
k_loc = minor_gpt_sh[i] + loc_in_bnd
Ο_minor += interp2d(fΞ·1, fΞ·2, ftemp, kminor, k_loc, jΞ·1, jΞ·2, jtemp) * scaling
end
end
return Ο_minor
end
"""
compute_Ο_rayleigh(
lkp::LookUpSW,
tropo::I,
col_dry::FT,
vmr_h2o::FT,
jtemp::I,
ftemp::FT,
jΞ·1::I,
jΞ·2::I,
fΞ·1::FT,
fΞ·2::FT,
igpt::I,
) where {FT<:AbstractFloat,I<:Int}
Compute Rayleigh scattering optical depths for shortwave problem
"""
@inline function compute_Ο_rayleigh(
lkp::LookUpSW,
tropo::I,
col_dry::FT,
vmr_h2o::FT,
jtemp::I,
ftemp::FT,
jΞ·1::I,
jΞ·2::I,
fΞ·1::FT,
fΞ·2::FT,
igpt::I,
) where {FT <: AbstractFloat, I <: Int}
if tropo == 1
Ο_ray = interp2d(fΞ·1, fΞ·2, ftemp, lkp.rayl_lower, igpt, jΞ·1, jΞ·2, jtemp) * (vmr_h2o + FT(1)) * col_dry
else
Ο_ray = interp2d(fΞ·1, fΞ·2, ftemp, lkp.rayl_upper, igpt, jΞ·1, jΞ·2, jtemp) * (vmr_h2o + FT(1)) * col_dry
end
return Ο_ray
end
@inline function compute_Ο_rayleigh(lkp::LookUpLW{I, FT}, args...) where {FT <: AbstractFloat, I <: Int}
return FT(0)
end
"""
compute_lw_planck_src!(
lkp::LookUpLW,
jΞ·1,
jΞ·2,
fΞ·1,
fΞ·2,
jpresst,
fpress,
jtemp,
ftemp,
t_lay,
igpt,
ibnd,
glay,
gcol,
sf,
t_lev,
t_sfc,
)
Computes Planck sources for the longwave problem.
"""
@inline function compute_lw_planck_src!(
lkp::LookUpLW,
jΞ·1,
jΞ·2,
fΞ·1,
fΞ·2,
jpresst,
fpress,
jtemp,
ftemp,
t_lay,
igpt,
ibnd,
glay,
gcol,
sf,
t_lev,
t_sfc,
)
(; planck_fraction, t_planck, n_t_plnk, totplnk) = lkp
(; lay_source, lev_source_inc, lev_source_dec, sfc_source) = sf
# compute Planck fraction
p_frac = interp3d(jΞ·1, jΞ·2, fΞ·1, fΞ·2, jtemp, ftemp, jpresst, fpress, planck_fraction, igpt)
planck_args = (t_planck, totplnk, ibnd)
# computing lay_source
@inbounds lay_source[glay, gcol] = interp1d(t_lay, planck_args...) * p_frac
# computing lev_source_inc
@inbounds lev_source_inc[glay, gcol] = interp1d(t_lev[glay + 1, gcol], planck_args...) * p_frac
# computing lev_source_dec
@inbounds lev_source_dec[glay, gcol] = interp1d(t_lev[glay, gcol], planck_args...) * p_frac
if glay == 1 # computing sfc_source
@inbounds sfc_source[gcol] = interp1d(t_sfc, planck_args...) * p_frac
end
return nothing
end
@inline function compute_lw_planck_src!(lkp::LookUpSW, args...)
return nothing
end
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###=============================================================================
#
# FILE: Infra.jl
#
# USAGE: include("../Infra.jl")
#
# DESCRIPTION: Julia interface to launch containers through Azure VM
#
# OPTIONS: ---
# DEPENDENCIES: sshpass
# BUGS: ---
# NOTES: ---
# AUTHOR: <NAME> <<EMAIL>>
# ORGANIZATION: GSD-UFAL
# CREATED: 23-09-2015 17:16
#
###=============================================================================
module Infra
using Requests: get, parse
using Docker
###== Top-level variables ======================================================
const nprocs=16 # number of cpu cores available from server
const ssh_key=homedir()*"/.ssh/azkey"
const ssh_pubkey=homedir()*"/.ssh/azkey.pub"
const carray_dir= length(LOAD_PATH) == 3 ? LOAD_PATH[3]*"Infra/src" : Pkg.dir("Infra")*"/src" # Infra package directory
###=============================================================================
type GlobalSettings
host::ASCIIString
url::ASCIIString
passwd::ASCIIString
cpuset::Array{Int64,1} # number of cpu cores available from server
end
type Container # Abstraction for Docker container
cid::AbstractString
pid::Integer
cpuset::Array{Int64,1}
mem_size::Integer
function Container(cid,pid,cpuset=[],mem_size=512)
return new(cid,pid,cpuset,mem_size)
end
end
const map_containers = Dict{Integer, Container}()
GlobalSettings() = GlobalSettings("","","",collect(0:nprocs-1))
const SETTINGS = GlobalSettings()
set_h(h) = SETTINGS.host=h
set_url(u) = SETTINGS.url=u
set_passwd(p) = SETTINGS.passwd=p
set_cpuset(c) = SETTINGS.cpuset=c
let next_key = 1 # cid -> container id
global get_next_key
function get_next_key() retval = next_key
next_key += 1
retval
end
end
function parse_cpuset(n_of_cpus)
if (n_of_cpus == 0 || length(SETTINGS.cpuset) < n_of_cpus)
"",[]
else
s = strip(string(SETTINGS.cpuset[1:n_of_cpus]),['[',']'])
a = SETTINGS.cpuset[1:n_of_cpus]
deleteat!(SETTINGS.cpuset,1:n_of_cpus)
s,a
end
end
@doc """
### set_host(h::AbstractString,p::AbstractString)
Configures passwordless SSH connections at host `h` whose password is `p`.
This function calls the `cloud_setup.sh` script which requires `sshpass`.
```Example
set_host("cloudarray.cloudapp.net","password")
```
""" ->
function set_host(h::AbstractString,p::AbstractString)
reply = success(`$(carray_dir)/cloud_setup.sh $h $p`) # set up ssh. if errors occurs, return false
if (reply)
set_h(h)
set_url(h*":4243")
set_passwd(p)
true
else
error("There is an error during SSH configuration. Please see the log for more details: cloud_setup.log")
false
end
end
function get_port()
response = get("http://$(SETTINGS.host):8000")
parse(Int,join(map(Char,response.data)))
end
@doc """
### create_containers(n_of_containers::Integer, n_of_cpus::Integer, mem_size::Integer; tunnel::bool)
Launches Docker containers and adds them as Julia workers configured with passwordless SSH.
This function requires `sshpass` to be installed:
* Debian-based Linux distros as Ubuntu:
```
sudo apt-get install sshpass
```
* OS X through [macports](http://macports.org):
```
sudo port install sshpass
```
```Example
create_containers(2,3,1024) # 2 containers with 3 CPU Cores and 1gb RAM
create_containers(1,2,512) # 1 container with 2 CPU Cores and 512mb RAM
```
""" ->
function create_containers(n_of_containers::Integer, n_of_cpus=0, mem_size=512;tunnel=false)
reserved_mem=200 # reserved memory for initializing a worker into a container
mem_size=mem_size+reserved_mem
for i in 1:n_of_containers
ssh_config = false
key = get_next_key()
port = 3000+get_port()
n_of_cpus=parse_cpuset(n_of_cpus)
# Creating a docker container at VM
info("Creating container ($key)...")
container = Docker.create_container("$(SETTINGS.url)","cloudarray:latest",memory=mem_size*(10^6),cpuSets=n_of_cpus[1],portBindings=[22,"$port"])
Docker.start_container("$(SETTINGS.url)",container["Id"])
info("Creating container ($key)... OK")
# Configuring ssh without password (transfer public key to container)
info("SSH configuration ($key)... ")
while !ssh_config
ssh_config = success(pipeline(`cat $ssh_pubkey`,`sshpass -p $(SETTINGS.passwd) ssh -o StrictHostKeyChecking=no -p $port root@$(SETTINGS.host) 'umask 077; mkdir -p ~/.ssh; cat >> ~/.ssh/authorized_keys'`)) # if ssh configuration is successful: return true or false
if !ssh_config
info("SSH configuration ($key) failed! Trying again...")
end
end
info("SSH configuration ($key)... OK")
info("Adding worker ($key)...")
pid = addprocs(["root@$(SETTINGS.host)"];tunnel=tunnel,sshflags=`-i $ssh_key -p $port`,dir="/opt/julia/bin",exename="/opt/julia/bin/julia")
info("Adding worker ($key)... OK")
map_containers[key] = Container(chomp(container["Id"]),pid[1],n_of_cpus[2],mem_size) # Adding Container to Dict
end
end
@doc """
### delete_containers(args...)
Removes the specified container(s)/worker(s).
```Example
delete_containers(3) # delete container 3
create_containers(1:5) # delete from 1st to 5th container
create_containers(all) # delete all containers
```
""" ->
function delete_containers(args...) # (splat) variable number of arguments. Ex.: delete_containers(1,2,3) or delete_containers(1:3)
containers_rmlist = Dict()
if vcat(args...)[1] == all
for i in collect(keys(map_containers))
if haskey(map_containers, i) # container exist?
container = map_containers[i]
rmprocs(container.pid)
delete!(map_containers,i)
set_cpuset(collect(0:nprocs-1))
Docker.remove_container("$(SETTINGS.url)","$(container.cid)")
end
end
else
for i in vcat(args...) # vcat -> concatenate to a array 1 dimension
if haskey(map_containers, i) # container exist?
container = map_containers[i]
rmprocs(container.pid)
delete!(map_containers,i)
set_cpuset([container.cpuset;SETTINGS.cpuset])
Docker.remove_container("$(SETTINGS.url)","$(container.cid)")
end
end
end
end
@doc """
### containers()
Returns the list of all containers' processes identifiers (IDs).
```Example
containers()
```
""" ->
function containers()
sort(collect(keys(map_containers)))
end
@doc """
### ncontainers()
Gets the number of available container processes.
```Example
ncontainers()
```
""" ->
function ncontainers()
length(map_containers)
end
@doc """
### list_containers()
List container(s) as a sorted list.
```Example
list_containers()
```
""" ->
function list_containers()
for key in sort(collect(keys(map_containers)))
println("$key => $(map_containers[key])")
end
end
@doc """
### mem_usage(key::Integer)
Returns the container memory usage.
```Example
mem_usage(number_of_container)
```
""" ->
function mem_usage(key::Integer)
Docker.stats_container("$(SETTINGS.url)","$(map_containers[key].cid)")["memory_stats"]["usage"]/10^6
end
@doc """
### cpu_usage(key::Integer)
Returns the container CPU usage (%).
```Example
cpu_usage(number_of_container)
```
""" ->
function cpu_usage(key)
stats = Docker.stats_container("$(SETTINGS.url)","$(map_containers[key].cid)")
percpu_usage = stats["cpu_stats"]["cpu_usage"]["percpu_usage"]
previousSystem = stats["precpu_stats"]["system_cpu_usage"]
previousCPU = stats["precpu_stats"]["cpu_usage"]["total_usage"]
totalUsage = stats["cpu_stats"]["cpu_usage"]["total_usage"]
systemUsage = stats["cpu_stats"]["system_cpu_usage"]
cpuPercent = 0.0
cpuDelta = totalUsage - previousCPU
systemDelta = systemUsage - previousSystem
cpuPercent = (cpuDelta / systemDelta) * length(percpu_usage) * 100.0
cpuPercent
end
@doc """
### io_usage(key::Integer)
Returns the number of kilobytes read and written by the cgroup.
```Example
io_usage(number_of_container)
```
""" ->
function io_usage(key::Integer)
stats = Docker.stats_container("$(SETTINGS.url)","$(map_containers[key].cid)")["blkio_stats"]
w = stats["io_service_bytes_recursive"][1]["value"]/10^3 # write
r = stats["io_service_bytes_recursive"][2]["value"]/10^3 # read
[w,r]
end
@doc """
### net_usage(key::Integer)
Returns networking TX/RX usage.
tx = number of bytes transmitted
rx = number of bytes reiceved
```Example
net_usage(number_of_container)
```
""" ->
function net_usage(key::Integer)
stats = Docker.stats_container("$(SETTINGS.url)","$(map_containers[key].cid)")["networks"]["eth0"]
tx = stats["tx_bytes"]
rx = stats["rx_bytes"]
[tx,rx]
end
end
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<reponame>andrew-saydjari/DHC
push!(LOAD_PATH, "/Users/saydjari/Dropbox/GradSchool_AKS/Doug/Projects/DHC/main")
using DHC_2DUtils
using Profile
using BenchmarkTools
using FFTW
using HDF5
using Test
using SparseArrays
filter_hash = fink_filter_hash(1, 8, nx=256, pc=1, wd=1)
@time filt_3d = fink_filter_bank_3dizer(filter_hash, 1, nz=256)
filter_temp = zeros(256,256,256)
filter_temp[filt_3d["filt_index"][56]] = filt_3d["filt_value"][56]
h5write("../DHC/scratch_AKS/data/filt_3d_rs.h5", "main/data", fftshift(real(fft(filter_temp))))
h5write("../DHC/scratch_AKS/data/filt_3d_fs.h5", "main/data", fftshift(filter_temp))
| [
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<filename>notebooks/3.quantum-computing.jl
### A Pluto.jl notebook ###
# v0.12.21
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing
el
end
end
# βββ‘ 1c3ff5c4-90d5-11eb-1459-7d7d43335a80
using Yao, YaoPlots, YaoExtensions, PlutoUI, Flux, Plots
# βββ‘ e9342104-91cf-11eb-01d7-ffd747975c7c
using LinearAlgebra
# βββ‘ aa09eef8-8eac-11eb-00e6-6794d5c9e0b5
md"""
# Quantum Computing Basics
In case you are not familar with some basic concept of quantum computing or quantum algorithms, we will have a quick and short introduction for quantum computing.
"""
# βββ‘ 22f46d6e-90d5-11eb-3094-e7015012995c
md"""
## Qubits
The computation states are often represented as qubits in quantum computing model. A qubit is a quantum state that has two basis states ``|0β©`` and ``|1β©``
## Quantum Registers
A quantum register is an abstraction of the qubit memory, it usualy contains many qubits.
## Quantum Gates
A quantum gate is a unitary operator that can be applied on the (quantum) register. we usually visualize it as a square block with two wires, e.g the following is a Pauli X gate.
"""
# βββ‘ 920da102-90d5-11eb-0437-495807d8b345
YaoPlots.plot(X)
# βββ‘ a5d5d56a-90d5-11eb-1e4a-8bf893d8534a
md"""
## Parameterized Quantum Gates
There are some quantum gates that can take classical parameters, e.g the rotation gates on X, Y, Z or other directions.
"""
# βββ‘ be1f51c6-90d5-11eb-3db1-99708f112467
YaoPlots.plot(Rx(0.1))
# βββ‘ 4f4a700e-90d6-11eb-3058-b1809082853a
md"""
## Controlled Gate
The gate operation can be controlled by another qubits, we usually represent them as the following diagram
"""
# βββ‘ 6b2079e2-90d6-11eb-330c-0194f2906114
YaoPlots.plot(control(2, 1, 2=>Rx(0.1)))
# βββ‘ 7854398c-90d6-11eb-2aad-b75727ab7b79
md"for controlled Pauli X gate, we also call it the CNOT gate, it can be plotted in a simpler way"
# βββ‘ 9003bb5c-90d6-11eb-102e-f1954fe71ec6
YaoPlots.plot(cnot(2, 1, 2))
# βββ‘ debe78d2-90d5-11eb-1526-87f514b030bf
md"""
## Quantum Circuits & Quantum Algorithms
Now with the concept of register and gates, we can define quantum circuits. The quantum circuits are composition of gates and measurements.
e.g the following is a quantum fourier transformation circuit, it is defined using hadamard gate ``H`` and control phase shift gate.
"""
# βββ‘ 0a379552-90d6-11eb-1a07-130ebf3c1264
begin
A(i, j) = control(i, j=>shift(2Ο/(1<<(i-j+1))))
B(n, k) = chain(n, j==k ? put(k=>H) : A(j, k) for j in k:n)
qft(n) = chain(B(n, k) for k in 1:n)
YaoPlots.plot(qft(3))
end
# βββ‘ e4f1f268-91ca-11eb-231a-712cb8cf4a55
md"""
## Program with Yao
checkout the website: [yaoquantum.org](https://yaoquantum.org/)
"""
# βββ‘ 0478efb2-91cb-11eb-034e-0b0c3be90bb7
html"""
<iframe src="https://yaoquantum.org/" width=100% height=1000></iframe>
"""
# βββ‘ 2a546ef0-91cb-11eb-17fd-4b8c74878e6a
md"""
### the quantum blocks
Yao represents the quantum circuits as blocks
"""
# βββ‘ 76b6bf64-91cb-11eb-3c39-576a66e70cf4
qft(3)
# βββ‘ c75b0244-8eac-11eb-2528-b9f59f61634d
md"""
## Variational Quantum Algorithms
*in some context, this is also called quantum machine learning.*
Traditionally, a quantum algorithm usually requires a well designed circuits that provides an exponential speed up comparing to its classical counterparts, e.g the well known Shor algorithm.
However, designing and finding such algorithms can be extremely hard, and may not be possible implemented on near term quantum devices that is noisy.
"""
# βββ‘ e1345ee8-8eac-11eb-3c22-0db185142833
md"""
## Differentiating Quantum Circuits: the forward mode
In literature, this is usually refered as the **Parameter Shift Rule**, it was first discovered in [Quantum Circuit Learning, arXiv: 1803.00745](https://arxiv.org/pdf/1803.00745.pdf)
we will now derive the rules, assume the unitary of our parameterized quantum circuit is ``U(\mathbf{ΞΈ}) = \prod_{i=1}^n U(ΞΈ_i)``, and for convenience, we will denote ``U_{j:k} = U_jβ―U_k``
then for an observable ``B``, the expectation value is ``β¨B(\mathbf{ΞΈ})β© = Tr(B U_{l:1}ΟU_{l:1}^{β })``
now if we assume our unitary is generated by Pauli strings (which is not always true), we have ``U_j(ΞΈ) = exp(-iΞΈ_j P_j/2)``.
The gradient of ``β¨B(\mathbf{ΞΈ})β©`` can be calculated as following
```math
\frac{\partial β¨Bβ©}{\partial ΞΈ_j} = -\frac{i}{2} (B U_{l:j} [P_j, U_{j-1:1}ΟU_{j-1:1}^{β }] U_{l:j}^{β })
```
and because we have the following relationship for arbitrary ``Ο``
```math
[P_j, Ο] = i[U_j(\frac{Ο}{2})Ο U_j^{β }(\frac{Ο}{2}) - U_j(-\frac{Ο}{2})Ο U_j(-\frac{Ο}{2})]
```
thus the gradient can be evaluated by
```math
\begin{aligned}
\frac{\partial B}{\partial ΞΈ_j} =& \frac{1}{2} (Tr[B U_{l:j+1}U(ΞΈ_j+\frac{Ο}{2})U_{j-1:1}ΟU_{j-1:1}^{β }U_j^{β }(ΞΈ_j+\frac{Ο}{2})U_{l:j+1}^{β }] -\\
&Tr[B U_{l:j+1}U(ΞΈ_j-\frac{Ο}{2})U_{j-1:1}ΟU_{j-1:1}^{β }U_j^{β }(ΞΈ_j-\frac{Ο}{2})U_{l:j+1}^{β }])
\end{aligned}
```
which means
```math
\frac{\partial B}{\partial ΞΈ_j} = \frac{1}{2} (β¨B(\theta_j + \frac{Ο}{2})β© - β¨B(\theta_j - \frac{Ο}{2})β©)
```
"""
# βββ‘ f8270b02-8eac-11eb-2ab3-3323e01f4f62
md"""
## Differentiating Quantum Circuits: the reverse mode
is it possible to differentiate quantum circuits using reverse mode?
"""
# βββ‘ c97fffa8-91d0-11eb-0a0c-092545e368f8
md"""
Only for simulation
"""
# βββ‘ 3c919a50-8ead-11eb-058f-27eceb1deeaa
md"""
## Implementing A Simple Variational Quantum Eigensolver (VQE)
You have learned how variational Monte Carlo works, now let's:
1. use a quantum circuit as our ansatz
2. directly calculate the energy by measure the Hamiltonian
3. use gradient based optimization to optimize the energy
"""
# βββ‘ 68e75bba-91ca-11eb-128f-1d6b0b7cbb78
md"first let's create a variational circuit using the `variational_circuit` function"
# βββ‘ 4422a0fa-91ca-11eb-3bda-f37b2dab68b3
YaoPlots.plot(variational_circuit(5, 3))
# βββ‘ 82f2d962-91ca-11eb-2e66-597a90047e87
md"then create a quantum register at state ``|00β―00β©``"
# βββ‘ 895600fc-91ca-11eb-3d03-e5aedfa44576
r = zero_state(5)
# βββ‘ 940657d8-91ca-11eb-33fd-63a019b4ebfc
md"we can evaluate this circuit on the emulator"
# βββ‘ a0527026-91ca-11eb-0604-21a298350c8d
r |> variational_circuit(5, 3)
# βββ‘ b8a71bf4-91ca-11eb-2f81-eb67cfeeada0
md"""
we can now create a 1-D heisenberg hamiltonian
```math
\sum_i \mathbf{S}_i \mathbf{S}_{i+1}
```
"""
# βββ‘ da9ba108-91ca-11eb-28a4-755cf76ea605
h = heisenberg(5)
# βββ‘ b3cc56a2-91cb-11eb-3736-87df27d04545
md"we can calculate the expectation value of this hamiltonian"
# βββ‘ 848d146c-91cb-11eb-17cc-87a1aa3c0d76
expect(heisenberg(5), r)
# βββ‘ bd3c4bca-91cb-11eb-29cf-657eb95db73d
@doc expect
# βββ‘ 9fe3db9c-91cb-11eb-1888-cf7dd2aa3aa1
expect(heisenberg(5), r=>variational_circuit(5, 3))
# βββ‘ a6c13932-91cb-11eb-3822-7da569e520e0
reg, βΞΈ = expect'(heisenberg(5), zero_state(5)=>variational_circuit(5, 3))
# βββ‘ cbacc1f8-91cb-11eb-316c-37e3da59855c
md"we can use the optimizers from the ML framework `Flux`"
# βββ‘ dddcbf20-91cd-11eb-2d73-2daa1643074d
opt = ADAM()
# βββ‘ 18d83a62-91d0-11eb-2d8d-2bc2e69785e8
md"depth=$(@bind depth Slider(1:10; show_value=true))"
# βββ‘ 12aff314-91ce-11eb-36d3-772d32cb8f3e
circuit = variational_circuit(5, depth)
# βββ‘ e172b6f0-91cd-11eb-35be-2d44f6f26a80
begin
history = []
ΞΈ = rand(nparameters(circuit))
for _ in 1:1000
reg, βΞΈ = expect'(heisenberg(5), zero_state(5)=>dispatch!(circuit, ΞΈ))
Flux.Optimise.update!(opt, ΞΈ, βΞΈ)
current = expect(heisenberg(5), zero_state(5)=>dispatch!(circuit, ΞΈ))
push!(history, real(current))
end
Plots.plot(history)
end
# βββ‘ ec0935e0-91cf-11eb-1837-334e0e92edee
minimum(eigvals(Matrix(heisenberg(5))))
# βββ‘ ad0071da-8ead-11eb-31e8-b94b8bff7635
md"""
## Placement Ads: Google Summer of Code 2022
https://summerofcode.withgoogle.com/
"""
# βββ‘ Cell order:
# β β1c3ff5c4-90d5-11eb-1459-7d7d43335a80
# ββaa09eef8-8eac-11eb-00e6-6794d5c9e0b5
# ββ22f46d6e-90d5-11eb-3094-e7015012995c
# ββ920da102-90d5-11eb-0437-495807d8b345
# ββa5d5d56a-90d5-11eb-1e4a-8bf893d8534a
# ββbe1f51c6-90d5-11eb-3db1-99708f112467
# ββ4f4a700e-90d6-11eb-3058-b1809082853a
# ββ6b2079e2-90d6-11eb-330c-0194f2906114
# ββ7854398c-90d6-11eb-2aad-b75727ab7b79
# ββ9003bb5c-90d6-11eb-102e-f1954fe71ec6
# ββdebe78d2-90d5-11eb-1526-87f514b030bf
# ββ0a379552-90d6-11eb-1a07-130ebf3c1264
# ββe4f1f268-91ca-11eb-231a-712cb8cf4a55
# ββ0478efb2-91cb-11eb-034e-0b0c3be90bb7
# ββ2a546ef0-91cb-11eb-17fd-4b8c74878e6a
# β β76b6bf64-91cb-11eb-3c39-576a66e70cf4
# ββc75b0244-8eac-11eb-2528-b9f59f61634d
# ββe1345ee8-8eac-11eb-3c22-0db185142833
# ββf8270b02-8eac-11eb-2ab3-3323e01f4f62
# ββc97fffa8-91d0-11eb-0a0c-092545e368f8
# ββ3c919a50-8ead-11eb-058f-27eceb1deeaa
# ββ68e75bba-91ca-11eb-128f-1d6b0b7cbb78
# β β4422a0fa-91ca-11eb-3bda-f37b2dab68b3
# ββ82f2d962-91ca-11eb-2e66-597a90047e87
# β β895600fc-91ca-11eb-3d03-e5aedfa44576
# ββ940657d8-91ca-11eb-33fd-63a019b4ebfc
# β βa0527026-91ca-11eb-0604-21a298350c8d
# ββb8a71bf4-91ca-11eb-2f81-eb67cfeeada0
# β βda9ba108-91ca-11eb-28a4-755cf76ea605
# ββb3cc56a2-91cb-11eb-3736-87df27d04545
# β β848d146c-91cb-11eb-17cc-87a1aa3c0d76
# β βbd3c4bca-91cb-11eb-29cf-657eb95db73d
# β β9fe3db9c-91cb-11eb-1888-cf7dd2aa3aa1
# β βa6c13932-91cb-11eb-3822-7da569e520e0
# ββcbacc1f8-91cb-11eb-316c-37e3da59855c
# β βdddcbf20-91cd-11eb-2d73-2daa1643074d
# β β18d83a62-91d0-11eb-2d8d-2bc2e69785e8
# β β12aff314-91ce-11eb-36d3-772d32cb8f3e
# β βe172b6f0-91cd-11eb-35be-2d44f6f26a80
# β βe9342104-91cf-11eb-01d7-ffd747975c7c
# β βec0935e0-91cf-11eb-1837-334e0e92edee
# ββad0071da-8ead-11eb-31e8-b94b8bff7635
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] | 2.026455 | 4,914 |
<reponame>wesleyjholt/FlowFarm.jl
"""
Author: <NAME>
Date: May 29, 2020
Test based on:
[1] An Aero-acoustic Noise Distribution Prediction Methodology for Offshore Wind Farms
by <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>
"""
using FLOWFarm; const ff = FLOWFarm
using DelimitedFiles
using PyPlot
# load data
data = readdlm("inputfiles/velocity_def_row_of_10_turbs.txt", ',', skipstart=4)
# load problem set up
include("./model_sets/model_set_2.jl")
# calculate wind turbine velocities and corresponding aerodynamic operational states
turbine_inflow_velcities, turbine_ct, turbine_ai, turbine_local_ti = ff.turbine_velocities_one_direction(turbine_x, turbine_y, turbine_z, rotor_diameter, hub_height, turbine_yaw,
sorted_turbine_index, ct_model, rotor_sample_points_y, rotor_sample_points_z, windresource,
model_set, wind_farm_state_id=1)
# calculate the power production of each wind turbine
ff.turbine_powers_one_direction(generator_efficiency, cut_in_speed, cut_out_speed, rated_speed, rated_power, rotor_diameter, turbine_inflow_velcities, air_density, power_model)
# set up the point locations to be used in generating the plot
stepsize = 5
xrange = 1:stepsize:10*rotor_diameter[1]*nturbines
yrange = -1*rotor_diameter[1]*nturbines:stepsize:1*rotor_diameter[1]*nturbines
zrange = hub_height[1]:stepsize:hub_height[1]
# calculate the wind speed at each point in the flow field
velh = ff.calculate_flow_field(xrange, yrange, zrange,
model_set, turbine_x, turbine_y, turbine_z, turbine_yaw, turbine_ct, turbine_ai,
rotor_diameter, hub_height, turbine_local_ti, sorted_turbine_index, turbine_inflow_velcities,
windresource; wind_farm_state_id=1)
# visualize the resulting data
flowfieldplot = contourf(xrange, yrange, velh, cmap="Blues_r") | [
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