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## Lotka-Volterra
function lotka(du,u,p,t)
x = u[1]
y = u[2]
du[1] = p[1]*x - p[2]*x*y
du[2] = -p[3]*y + p[4]*x*y
end
"""
Lotka-Voltera Equations (Non-stiff)
```math
\\frac{dx}{dt} = ax - bxy
```
```math
\\frac{dy}{dt} = -cy + dxy
```
with initial condition ``x=y=1``
"""
prob_ode_lotkavoltera = ODEProblem(lotka,[1.0,1.0],(0.0,1.0),[1.5,1.0,3.0,1.0])
## Fitzhugh-Nagumo
function fitz(du,u,p,t)
v = u[1]
w = u[2]
a = p[1]
b = p[2]
τinv = p[3]
l = p[4]
du[1] = v - v^3/3 -w + l
du[2] = τinv*(v + a - b*w)
end
"""
Fitzhugh-Nagumo (Non-stiff)
```math
\\frac{dv}{dt} = v - \\frac{v^3}{3} - w + I_{est}
```
```math
τ \\frac{dw}{dt} = v + a -bw
```
with initial condition ``v=w=1``
"""
prob_ode_fitzhughnagumo = ODEProblem(fitz,[1.0;1.0],(0.0,1.0),(0.7,0.8,1/12.5,0.5))
#Van der Pol Equations
@parameters t μ
@variables x(t) y(t)
@derivatives D'~t
eqs = [D(x) ~ μ*((1-x^2)*y - x),
D(y) ~ y]
de = ODESystem(eqs)
van = ODEFunction(de, [x,y], [μ], jac=true, Wfact=true)
"""
Van der Pol Equations
```math
\\frac{dx}{dt} = y
```
```math
\\frac{dy}{dt} = μ((1-x^2)y -x)
```
with ``μ=1.0`` and ``u_0=[0,\\sqrt{3}]``
Non-stiff parameters.
"""
prob_ode_vanderpol = ODEProblem(van,[0;sqrt(3)],(0.0,1.0),1.0)
"""
Van der Pol Equations
```math
\\frac{dx}{dt} = y
```
```math
\\frac{dy}{dt} = μ(1-x^2)y -x
```
with ``μ=10^6`` and ``u_0=[0,\\sqrt{3}]``
Stiff parameters.
"""
prob_ode_vanstiff = ODEProblem(van,[0;sqrt(3)],(0.0,1.0),1e6)
# ROBER
@parameters t k₁ k₂ k₃
@variables y₁(t) y₂(t) y₃(t)
@derivatives D'~t
eqs = [D(y₁) ~ -k₁*y₁+k₃*y₂*y₃,
D(y₂) ~ k₁*y₁-k₂*y₂^2-k₃*y₂*y₃,
D(y₃) ~ k₂*y₂^2]
de = ODESystem(eqs)
rober = ODEFunction(de, [y₁,y₂,y₃], [k₁,k₂,k₃], jac=true, Wfact=true)
"""
The Robertson biochemical reactions: (Stiff)
```math
\\frac{dy₁}{dt} = -k₁y₁+k₃y₂y₃
```
```math
\\frac{dy₂}{dt} = k₁y₁-k₂y₂^2-k₃y₂y₃
```
```math
\\frac{dy₃}{dt} = k₂y₂^2
```
where ``k₁=0.04``, ``k₂=3\\times10^7``, ``k₃=10^4``. For details, see:
<NAME> Solving Ordinary Differential Equations I - Nonstiff Problems Page 129
Usually solved on ``[0,1e11]``
"""
prob_ode_rober = ODEProblem(rober,[1.0;0.0;0.0],(0.0,1e11),(0.04,3e7,1e4))
# Three Body
const threebody_μ = big(0.012277471); const threebody_μ′ = 1 - threebody_μ
threebody = (du,u,p,t) -> begin
# 1 = y₁
# 2 = y₂
# 3 = y₁'
# 4 = y₂'
D₁ = ((u[1]+threebody_μ)^2 + u[2]^2)^(3/2)
D₂ = ((u[1]-threebody_μ′)^2 + u[2]^2)^(3/2)
du[1] = u[3]
du[2] = u[4]
du[3] = u[1] + 2u[4] - threebody_μ′*(u[1]+threebody_μ)/D₁ - threebody_μ*(u[1]-threebody_μ′)/D₂
du[4] = u[2] - 2u[3] - threebody_μ′*u[2]/D₁ - threebody_μ*u[2]/D₂
end
@doc doc"""
The ThreeBody problem as written by Hairer: (Non-stiff)
```math
\frac{dy₁}{dt} = y₁ + 2\frac{dy₂}{dt} - \bar{μ}\frac{y₁+μ}{D₁} - μ\frac{y₁-\bar{μ}}{D₂}
```
```math
\frac{dy₂}{dt} = y₂ - 2\frac{dy₁}{dt} - \bar{μ}\frac{y₂}{D₁} - μ\frac{y₂}{D₂}
```
```math
D₁ = ((y₁+μ)^2 + y₂^2)^{3/2}
```
```math
D₂ = ((y₁-\bar{μ})^2+y₂^2)^{3/2}
```
```math
μ = 0.012277471
```
```math
\bar{μ} =1-μ
```
From <NAME> Solving Ordinary Differential Equations I - Nonstiff Problems Page 129
Usually solved on ``t₀ = 0.0`` and ``T = 17.0652165601579625588917206249``
Periodic with that setup.
"""
prob_ode_threebody = ODEProblem(threebody,[0.994, 0.0, 0.0, big(-2.00158510637908252240537862224)],(big(0.0),big(17.0652165601579625588917206249)))
# Rigid Body Equations
@parameters t I₁ I₂ I₃
@variables y₁(t) y₂(t) y₃(t)
@derivatives D'~t
eqs = [D(y₁) ~ I₁*y₂*y₃,
D(y₂) ~ I₂*y₁*y₃,
D(y₃) ~ I₃*y₁*y₂]
de = ODESystem(eqs)
rigid = ODEFunction(de, [y₁,y₂,y₃], [I₁,I₂,I₃], jac=true, Wfact=true)
"""
Rigid Body Equations (Non-stiff)
```math
\\frac{dy₁}{dt} = I₁y₂y₃
```
```math
\\frac{dy₂}{dt} = I₂y₁y₃
```
```math
\\frac{dy₃}{dt} = I₃y₁y₂
```
with ``I₁=-2``, ``I₂=1.25``, and ``I₃=-1/2``.
The initial condition is ``y=[1.0;0.0;0.9]``.
From Solving Differential Equations in R by <NAME>
or <NAME> Solving Ordinary Differential Equations I - Nonstiff Problems Page 244
Usually solved from 0 to 20.
"""
prob_ode_rigidbody = ODEProblem(rigid,[1.0,0.0,0.9],(0.0,20.0),(-2.0,1.25,-0.5))
# Pleiades Problem
pleiades = (du,u,p,t) -> begin
x = view(u,1:7) # x
y = view(u,8:14) # y
v = view(u,15:21) # x′
w = view(u,22:28) # y′
du[1:7] .= v
du[8:14].= w
for i in 14:28
du[i] = zero(eltype(u))
end
for i=1:7,j=1:7
if i != j
r = ((x[i]-x[j])^2 + (y[i] - y[j])^2)^(3/2)
du[14+i] += j*(x[j] - x[i])/r
du[21+i] += j*(y[j] - y[i])/r
end
end
end
@doc doc"""
Pleiades Problem (Non-stiff)
```math
\frac{d^2xᵢ}{dt^2} = \sum_{j≠i} mⱼ(xⱼ-xᵢ)/rᵢⱼ
```
```math
\frac{d^2yᵢ}{dt^2} = \sum_{j≠i} mⱼ(yⱼ-yᵢ)/rᵢⱼ
```
where
```math
rᵢⱼ = ((xᵢ-xⱼ)^2 + (yᵢ-yⱼ)^2)^{3/2}
```
and initial conditions are
```math
x₁(0) = 3
```
```math
x₂(0) = 3
```
```math
x₃(0) = -1
```
```math
x₄(0) = -3
```
```math
x₅(0) = 2
```
```math
x₆(0) = -2
```
```math
x₇(0) = 2
```
```math
y₁(0) = 3
```
```math
y₂(0) = -3
```
```math
y₃(0) = 2
```
```math
y₄(0) = 0
```
```math
y₅(0) = 0
```
```math
y₆(0) = -4
```
```math
y₇(0) = 4
```
and with ``\frac{dxᵢ(0)}{dt}=\frac{dyᵢ(0)}{dt}=0`` except for
```math
\frac{dx₆(0)}{dt} = 1.75
```
```math
\frac{dx₇(0)}{dt} = -1.5
```
```math
\frac{dy₄(0)}{dt} = -1.25
```
```math
\frac{dy₅(0)}{dt} = 1
```
From <NAME> Solving Ordinary Differential Equations I - Nonstiff Problems Page 244
Usually solved from 0 to 3.
"""
prob_ode_pleiades = ODEProblem(pleiades,[3.0,3.0,-1.0,-3.0,2.0,-2.0,2.0,3.0,-3.0,2.0,0,0,-4.0,4.0,0,0,0,0,0,1.75,-1.5,0,0,0,-1.25,1,0,0],(0.0,3.0))
Random.seed!(100)
const mm_A = rand(4,4)
mm_linear = function (du,u,p,t)
mul!(du,mm_A,u)
end
const MM_linear =Matrix(Diagonal(0.5ones(4)))
mm_f = ODEFunction(mm_linear;analytic = (u0,p,t) -> exp(inv(MM_linear)*mm_A*t)*u0, mass_matrix=MM_linear)
prob_ode_mm_linear = ODEProblem(mm_f,rand(4),(0.0,1.0))
@parameters t p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12
@variables y1(t) y2(t) y3(t) y4(t) y5(t) y6(t) y7(t) y8(t)
@derivatives D'~t
eqs = [D(y1) ~ -p1*y1 + p2*y2 + p3*y3 + p4,
D(y2) ~ p1*y1 - p5*y2,
D(y3) ~ -p6*y3 + p2*y4 + p7*y5,
D(y4) ~ p3*y2 + p1*y3 - p8*y4,
D(y5) ~ -p9*y5 + p2*y6 + p2*y7,
D(y6) ~ -p10*y6*y8 + p11*y4 + p1*y5 -
p2*y6 + p11*y7,
D(y7) ~ p10*y6*y8 - p12*y7,
D(y8) ~ -p10*y6*y8 + p12*y7]
de = ODESystem(eqs)
hires = ODEFunction(de, [y1,y2,y3,y4,y5,y6,y7,y8],
[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12],
jac=true, Wfact=true)
u0 = zeros(8)
u0[1] = 1
u0[8] = 0.0057
"""
Hires Problem (Stiff)
It is in the form of
```math
\\frac{dy}{dt} = f(y)
```
with
```math
y(0)=y_0, \\quad y \\in ℝ^8, \\quad 0 ≤ t ≤ 321.8122
```
where ``f`` is defined by
``f(y) = \\begin{pmatrix} −1.71y_1 & +0.43y_2 & +8.32y_3 & +0.0007y_4 & \\\\ 1.71y_1 & −8.75y_2 & & & \\\\ −10.03y_3 & +0.43y_4 & +0.035y_5 & & \\\\ 8.32y_2 & +1.71y_3 & −1.12y_4 & & \\\\ −1.745y_5 & +0.43y_6 & +0.43y_7 & & \\\\ −280y_6y_8 & +0.69y_4 & +1.71y_5 & −0.43y_6 & +0.69y_7 \\\\ 280y_6y_8 & −1.81y_7 & & & \\\\ −280y_6y_8 & +1.81y_7 & & & \\end{pmatrix}``
Reference: [demohires.pdf](http://www.radford.edu/~thompson/vodef90web/problems/demosnodislin/Demos_Pitagora/DemoHires/demohires.pdf)
Notebook: [Hires.ipynb](http://nbviewer.jupyter.org/github/JuliaDiffEq/DiffEqBenchmarks.jl/blob/master/StiffODE/Hires.ipynb)
"""
prob_ode_hires = ODEProblem(hires,u0,(0.0,321.8122), (1.71, 0.43, 8.32, 0.0007, 8.75,
10.03, 0.035, 1.12, 1.745, 280.0,
0.69, 1.81))
@parameters t p1 p2 p3
@variables y1(t) y2(t) y3(t)
@derivatives D'~t
eqs = [D(y1) ~ p1*(y2+y1*(1-p2*y1-y2)),
D(y2) ~ (y3-(1+y1)*y2)/p1,
D(y3) ~ p3*(y1-y3)]
de = ODESystem(eqs)
jac = calculate_jacobian(de)
ModelingToolkit.calculate_factorized_W(de)
orego = ODEFunction(de, [y1,y2,y3], [p1,p2,p3], jac=true, Wfact=true)
"""
Orego Problem (Stiff)
It is in the form of ``\\frac{dy}{dt}=f(y), \\quad y(0)=y0,`` with
```math
y \\in ℝ^3, \\quad 0 ≤ t ≤ 360
```
where ``f`` is defined by
``f(y) = \\begin{pmatrix} s(y_2 - y_1(1-qy_1-y_2)) \\\\ (y_3 - y_2(1+y_1))/s \\\\ w(y_1-y_3) \\end{pmatrix}``
where ``s=77.27``, ``w=0.161`` and ``q=8.375⋅10^{-6}``.
Reference: [demoorego.pdf](http://www.radford.edu/~thompson/vodef90web/problems/demosnodislin/Demos_Pitagora/DemoOrego/demoorego.pdf)
Notebook: [Orego.ipynb](http://nbviewer.jupyter.org/github/JuliaDiffEq/DiffEqBenchmarks.jl/blob/master/StiffODE/Orego.ipynb)
"""
prob_ode_orego = ODEProblem(orego,[1.0,2.0,3.0],(0.0,30.0),[77.27,8.375e-6,0.161])
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<gh_stars>10-100
# Load the shape files
mexicopath = joinpath(testdatapath, "shape_eg_data", "mexico")
states = open_shapefile(joinpath(mexicopath, "states.shp"))
cities = open_shapefile(joinpath(mexicopath, "cities.shp"))
rivers = open_shapefile(joinpath(mexicopath, "rivers.shp"))
# Create plots and test whether they output a Compose.Context (i.e. check if they run)
# Test polygon
canvas1 = plotshape(states)
test1 = (typeof(canvas1) == Compose.Context)
# Test point
canvas2 = plotshape(cities, canvas1, line_width=0.25mm, line_color="red", fill_color=RGB(1,1,0), radius=[0.05cm])
test2 = (typeof(canvas2) == Compose.Context)
# Test polyline
canvas3 = plotshape(rivers, canvas2, line_color=RGB(0.2,0.6,0.8), line_width=0.25mm)
test3 = (typeof(canvas3) == Compose.Context)
# Return
test1 && test2 && test3
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<reponame>UnofficialJuliaMirror/PredictMD.jl-3e7d7328-36f8-4388-bd01-4613c92c7370
import PredictMD
PredictMD.probability_calibration_scores_and_fractions(
[0, 0, 0], [0., 0., 0.];
window = -1,
)
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62,
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69,
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7,
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220,
220,
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685,
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4324,
796,
532,
16,
11,
198,
220,
220,
220,
1267,
198
] | 2.039216 | 102 |
<gh_stars>1-10
module Mancala
export Game, Board
include("game.jl")
module Training
using AlphaZero
include("params.jl")
end
end
| [
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@testset "Natural numbers (example)" begin
# Test the natural numbers and addition
clauses = @julog [
nat(0) <<= true,
nat(s(N)) <<= nat(N),
add(0, Y, Y) <<= true,
add(s(X), Y, s(Z)) <<= add(X, Y, Z)
]
# Is 1 a natural number?
sat, subst = resolve(@julog(nat(s(0))), clauses)
@test sat == true
# Is 5 a natural number?
sat, subst = resolve(@julog(nat(s(s(s(s(s(0))))))), clauses)
@test sat == true
# Is 1 + 1 = 2?
sat, subst = resolve(@julog(add(s(0), s(0), s(s(0)))), clauses)
@test sat == true
# What are all the ways to add up to 3?
sat, subst = resolve(@julog(add(A, B, s(s(s(0))))), clauses)
subst = Set(subst)
@test @varsub({A => 0, B => s(s(s(0)))}) in subst
@test @varsub({A => s(0), B => s(s(0))}) in subst
@test @varsub({A => s(s(0)), B => s(0)}) in subst
@test @varsub({A => s(s(s(0))), B => 0}) in subst
end
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] | 2.187013 | 385 |
using Genie.Renderer, Genie.Requests
greeting = "Welcome"
name = "Genie"
function htmlviewfile_withvars()
raw"
<h1>$(@vars(:greeting))</h1>
<div>
<p>This is a $(@vars(:name)) test</p>
</div>
<hr />
"
end
function htmltemplatefile_withvars()
raw"
<!DOCTYPE HTML>
<html>
<head>
<title>$(@vars(:name)) test</title>
</head>
<body>
<div class=\"template\">
<% @yield %>
</div>
<footer>Just a footer</footer>
</body>
</html>
"
end
@testset "String HTML rendering with vars" begin
r = Requests.HTTP.Response()
@testset "String no layout with vars" begin
r = html(htmlviewfile_withvars(), greeting = greeting, name = name)
@test String(r.body) == "<html><head></head><body><h1>$greeting</h1><div><p>This is a $name test</p></div><hr></body></html>"
end;
@testset "String with layout with vars" begin
r = html(htmlviewfile_withvars(), layout = htmltemplatefile_withvars(), greeting = "Welcome", name = "Genie")
@test String(r.body) == "<html><head><title>$name test</title></head><body><div class=\"template\"><h1>$greeting</h1><div><p>This is a $name test</p></div><hr>\n</div><footer>Just a footer</footer></body></html>"
end;
@test r.status == 200
@test r.headers[1]["Content-Type"] == "text/html; charset=utf-8"
end; | [
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] | 2.437383 | 535 |
import StatsFuns: log2π
@node MvNormalMeanPrecision Stochastic [ out, (μ, aliases = [ mean ]), (Λ, aliases = [ invcov, precision ]) ]
conjugate_type(::Type{ <: MvNormalMeanPrecision }, ::Type{ Val{ :out } }) = MvNormalMeanPrecision
conjugate_type(::Type{ <: MvNormalMeanPrecision }, ::Type{ Val{ :μ } }) = MvNormalMeanPrecision
conjugate_type(::Type{ <: MvNormalMeanPrecision }, ::Type{ Val{ :Λ } }) = Wishart
@average_energy MvNormalMeanPrecision (q_out::Any, q_μ::Any, q_Λ::Any) = begin
(m_mean, v_mean) = mean(q_μ), cov(q_μ)
(m_out, v_out) = mean(q_out), cov(q_out)
0.5 * (ndims(q_out) * log2π + logdet(cholinv(mean(q_Λ))) + tr(mean(q_Λ)*(v_out + v_mean + (m_out - m_mean)*(m_out - m_mean)')))
end
@average_energy MvNormalMeanPrecision (q_out_μ::Any, q_Λ::Any) = begin
(m, V) = mean(q_out_μ), cov(q_out_μ)
d = Int64(ndims(q_out_μ)/2)
@views 0.5*(d*log2π + logdet(cholinv(mean(q_Λ))) + tr(mean(q_Λ)*( V[1:d,1:d] - V[1:d,d+1:end] - V[d+1:end,1:d] + V[d+1:end,d+1:end] + (m[1:d] - m[d+1:end])*(m[1:d] - m[d+1:end])' ) ))
end | [
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10,
16,
25,
437,
12962,
6,
1267,
15306,
198,
437
] | 1.979478 | 536 |
"""
Main module for SimHPC.jl – a discrete event process oriented simulation framework for Julia.
"""
module SimHPC
using DataStructures
using Dates
using ResumableFunctions
import Base.run, Base.isless, Base.show, Base.yield, Base.get
import Base.(&), Base.(|)
import Dates.now
export AbstractEvent, Environment, value, state, environment
export Event, succeed, fail, @callback, remove_callback
export timeout
export Operator, (&), (|)
export @resumable, @yield
export AbstractProcess, Simulation, run, now, active_process, StopSimulation
export Process, @process, interrupt
export Container, Resource, Store, put, get, request, release, cancel
export nowDatetime
include("base.jl")
include("events.jl")
include("operators.jl")
include("simulations.jl")
include("processes.jl")
include("resources/base.jl")
include("resources/containers.jl")
include("resources/stores.jl")
include("utils/time.jl")
end
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] | 3.422939 | 279 |
using Pythia
pythia = newPythia()
icxx"""$pythia->readString("Beams:idA = 2212");"""
icxx"""$pythia->readString("Beams:idB = 2212");"""
icxx"""$pythia->readString("Beams:eCM = 8000.");"""
icxx"""$pythia->readString("HardQCD:gg2qqbar = on");"""
icxx"""$pythia->init();"""
ρ⁺ = Array(Float64,0)
ρ⁻ = Array(Float64,0)
ρ⁰ = Array(Float64,0)
for e in take(events(pythia),5)
for p in particles(e)
id = icxx"$p.id();"
m = icxx"$p.m();"
if id == 213
push!(ρ⁺,m)
elseif id == -213
push!(ρ⁻,m)
elseif id == 113
push!(ρ⁰,m)
end
end
end
particles = [ρ⁺; ρ⁻; ρ⁰]
colors = [
[:ρ⁺ for _ in 1:length(ρ⁺)];
[:ρ⁻ for _ in 1:length(ρ⁻)];
[:ρ⁰ for _ in 1:length(ρ⁰)]]
plot(y = particles, color = colors, Geom.point)
function track_decay(it,id)
p = it[id]
daughter1 = icxx"$p.daughter1();"
daughter2 = icxx"$p.daughter2();"
if daughter1 == daughter2 == 0
println("$id ($(name(it[id]))) does not decay")
return
end
for d in daughter1:daughter2
println("$id ($(name(it[id]))) decays to $d ($(name(it[d])))")
track_decay(it,d)
end
end
for e in take(events(pythia),5)
ps = particles(e)
for (i,p) in enumerate(ps)
# Look for Delta0 or 3112
id = icxx"$p.id();"
(2114 == id || 3112 == id) && track_decay(ps,i-1)
end
println()
end
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] | 1.91069 | 739 |
<gh_stars>1-10
using LinearAlgebra
using SpecialFunctions: erfc
using StaticArrays
using Test
using Zygote
using FiniteDiff
const Mat3{T} = SMatrix{3, 3, T, 9} where T
const Vec3{T} = SVector{3, T} where T
const MVec3{T} = MVector{3, T} where T
"""
A modified ewald summation suitable for AD with Zygote.
Original docstring below.
Compute the electrostatic interaction energy per unit cell between point
charges in a uniform background of compensating charge to yield net
neutrality. the `lattice` and `recip_lattice` should contain the
lattice and reciprocal lattice vectors as columns. `charges` and
`positions` are the point charges and their positions (as an array of
arrays) in fractional coordinates.
"""
function energy_ewald_zygote_faster(lattice, charges, positions; η=nothing)
T = eltype(lattice)
for i=1:3
if norm(lattice[:,i]) == 0
## TODO should something more clever be done here? For now
## we assume that we are not interested in the Ewald
## energy of non-3D systems
return T(0)
end
end
energy_ewald_zygote_faster(lattice, T(2π) * inv(lattice'), charges, positions; η=η)
end
function energy_ewald_zygote_faster(lattice, recip_lattice, charges, positions; η=nothing)
T = eltype(lattice)
@assert T == eltype(recip_lattice)
@assert length(charges) == length(positions)
if η === nothing
# Balance between reciprocal summation and real-space summation
# with a slight bias towards reciprocal summation
η = sqrt(sqrt(T(1.69) * norm(recip_lattice ./ 2T(π)) / norm(lattice))) / 2
end
#
# Numerical cutoffs
#
# The largest argument to the exp(-x) function to obtain a numerically
# meaningful contribution. The +5 is for safety.
max_exponent = -log(eps(T)) + 5
# The largest argument to the erfc function for various precisions.
# To get an idea:
# erfc(5) ≈ 1e-12, erfc(8) ≈ 1e-29, erfc(10) ≈ 2e-45, erfc(14) ≈ 3e-87
max_erfc_arg = get(
Dict(Float32 => 5, Float64 => 8, BigFloat => 14),
T,
something(findfirst(arg -> 100 * erfc(arg) < eps(T), 1:100), 100) # fallback for not yet implemented cutoffs
)
#
# Reciprocal space sum
#
# Initialize reciprocal sum with correction term for charge neutrality
sum_recip = - (sum(charges)^2 / 4η^2)
# Function to return the indices corresponding
# to a particular shell
# TODO switch to an O(N) implementation
function shell_indices(ish)
(Vec3(i,j,k) for i in -ish:ish for j in -ish:ish for k in -ish:ish
if maximum(abs.((i,j,k))) == ish)
end
# Loop over reciprocal-space shells
gsh = 1 # Exclude G == 0
any_term_contributes = true
while any_term_contributes
any_term_contributes = false
# Compute G vectors and moduli squared for this shell patch
for G in shell_indices(gsh)
Gsq = sum(abs2, recip_lattice * Zygote.dropgrad(G))
# Check if the Gaussian exponent is small enough
# for this term to contribute to the reciprocal sum
exponent = Gsq / 4η^2
if exponent > max_exponent
continue
end
cos_strucfac = sum(Z * cos(2T(π) * dot(r, Zygote.dropgrad(G))) for (r, Z) in zip(positions, charges))
sin_strucfac = sum(Z * sin(2T(π) * dot(r, Zygote.dropgrad(G))) for (r, Z) in zip(positions, charges))
sum_strucfac = cos_strucfac^2 + sin_strucfac^2
any_term_contributes = true
sum_recip += sum_strucfac * exp(-exponent) / Gsq
end
gsh += 1
end
# Amend sum_recip by proper scaling factors:
sum_recip *= 4T(π) / abs(det(lattice))
#
# Real-space sum
#
# Initialize real-space sum with correction term for uniform background
sum_real = Zygote.dropgrad(-2η / sqrt(T(π)) * sum(abs2, charges))
# Loop over real-space shells
rsh = 0 # Include R = 0
any_term_contributes = true
while any_term_contributes || rsh <= 1
any_term_contributes = false
# Loop over R vectors for this shell patch
for R in shell_indices(rsh)
for i = 1:length(positions), j = 1:length(positions)
ti = positions[i]
Zi = charges[i]
tj = positions[j]
Zj = charges[j]
# Avoid self-interaction
if rsh == 0 && ti == tj
continue
end
dist = norm(lattice * (ti - tj - Zygote.dropgrad(R)))
# erfc decays very quickly, so cut off at some point
if η * dist > max_erfc_arg
continue
end
any_term_contributes = true
sum_real += Zi * Zj * erfc(η * dist) / dist
end # i,j
end # R
rsh += 1
end
energy = (sum_recip + sum_real) / 2 # Divide by 2 (because of double counting)
energy
end
#=======#
function compute_forces_zygote(positions, lattice, charges)
_positions = reduce(hcat, positions)
forces = first(
Zygote.gradient(_positions -> begin
positions = collect(eachcol(_positions))
-energy_ewald_zygote_faster(lattice, charges, positions)
end,
_positions
)
)
return forces
end
function compute_forces_finitediff(positions, lattice, charges)
_positions = reduce(hcat, positions)
forces = FiniteDiff.finite_difference_gradient(_positions -> begin
positions = collect(eachcol(_positions))
-energy_ewald_zygote_faster(lattice, charges, positions)
end,
_positions
)
return forces
end
#===#
function benchmark_ewald_zygote(num_atoms=20)
lattice = Mat3(
[0.0 5.131570667152971 5.131570667152971;
5.131570667152971 0.0 5.131570667152971;
5.131570667152971 5.131570667152971 0.0]
)
# perturb positions away from equilibrium to get nonzero force
positions = [ones(3)/8+rand(3)/20 for _ in 1:num_atoms]
charges = [14 for _ in 1:num_atoms]
forces_finitediff = compute_forces_finitediff(positions, lattice, charges)
forces_zygote = compute_forces_zygote(positions, lattice, charges)
# check consistency of forces
# @test forces_zygote ≈ forces_finitediff atol=1e-6
@show sum(abs.(forces_finitediff - forces_zygote)) / (3num_atoms)
println("Timings:")
println("Energy without forces")
@time energy_ewald_zygote(lattice, charges, positions)
println("FiniteDiff forces")
@time compute_forces_finitediff(positions, lattice, charges)
println("Zygote forces")
@time compute_forces_zygote(positions, lattice, charges)
return nothing
end
# benchmark_ewald_zygote(2)
# Timings:
# Energy without forces
# 0.004692 seconds (113.77 k allocations: 4.967 MiB)
# FiniteDiff forces
# 0.075556 seconds (1.37 M allocations: 59.604 MiB)
# Zygote forces
# 0.318643 seconds (1.03 M allocations: 48.197 MiB, 11.16% gc time)
# benchmark_ewald_zygote(10)
# Timings:
# Energy without forces
# 0.285002 seconds (2.46 M allocations: 105.066 MiB, 57.46% gc time)
# FiniteDiff forces
# 8.808988 seconds (147.72 M allocations: 6.156 GiB, 18.05% gc time)
# Zygote forces
# 4.530250 seconds (15.03 M allocations: 717.221 MiB, 13.84% gc time)
# Zygote-rewrite required us to
# 1. do not use try-catch
# 2. use Zygote.@ignore on shell_indices
# 3. use Zygote.dropgrad on shell int vectors
const lattice = Mat3(
[0.0 5.131570667152971 5.131570667152971;
5.131570667152971 0.0 5.131570667152971;
5.131570667152971 5.131570667152971 0.0]
)
# perturb positions away from equilibrium to get nonzero force
const positions = [Vec3(ones(3)/8+rand(3)/20) for _ in 1:2]
const charges = [14 for _ in 1:2]
@time energy_ewald_zygote_faster(lattice, charges, positions) # 0.319368 seconds (470.33 k allocations: 24.824 MiB, 6.28% gc time, 99.67% compilation time)
using BenchmarkTools
# @btime energy_ewald_zygote_faster($lattice, $charges, $positions) # 555.540 μs (3378 allocations: 53.33 KiB)
# @time energy_ewald(lattice, charges, positions)
# @time energy_ewald(lattice, charges, positions)
#========#
# A larger physical system with pymatgen
using DFTK
function benchmark_ewald_zygote_pymatgen(num_atoms=20)
# lattice = Mat3(
# [0.0 5.131570667152971 5.131570667152971;
# 5.131570667152971 0.0 5.131570667152971;
# 5.131570667152971 5.131570667152971 0.0]
# )
# # perturb positions away from equilibrium to get nonzero force
# positions = [ones(3)/8+rand(3)/20 for _ in 1:num_atoms]
# charges = [14 for _ in 1:num_atoms]
a = 10.263141334305942 # Lattice constant in Bohr
lattice = a / 2 .* [[0 1 1.]; [1 0 1.]; [1 1 0.]]
Si = ElementPsp(:Si, psp=load_psp("hgh/lda/Si-q4"))
atoms = [Si => [ones(3)/8, -ones(3)/8]];
pystruct = pymatgen_structure(lattice, atoms)
pystruct.make_supercell([1, 1, 1])
lattice = load_lattice(pystruct)
positions = [s.frac_coords for s in pystruct.sites];
charges = [14 for _ in 1:length(positions)]
# forces_finitediff = compute_forces_finitediff(positions, lattice, charges)
forces_zygote = compute_forces_zygote(positions, lattice, charges)
# check consistency of forces
# @test forces_zygote ≈ forces_finitediff atol=1e-6
# @show sum(abs.(forces_finitediff - forces_zygote)) / (3num_atoms)
println("Timings:")
println("Energy without forces")
@time energy_ewald_zygote(lattice, charges, positions)
println("FiniteDiff forces")
# @time compute_forces_finitediff(positions, lattice, charges)
println("Zygote forces")
@time compute_forces_zygote(positions, lattice, charges)
return nothing
end
benchmark_ewald_zygote_pymatgen()
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] | 2.321721 | 4,277 |
#-------------canny.jl---------------------------------------------------------#
#
# Purpose: To implement Canny Edge Detection in Julia
#
#------------------------------------------------------------------------------#
# First, we need to use the appropriate packages
using Images # All filtering and image stuff
using ImageView # imshow for showing images, must be run in REPL
using TestImages # Standart example images in julia
# simple implementation of canny edge detection using in-built Julia functions
function simple_edge_detection()
img = testimage("fabio_color_256")
img_edge = canny(img)
save(string("fabio_edges.png"), img_edge)
end
simple_edge_detection()
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] | 4.13253 | 166 |
<gh_stars>0
struct HighsModel
colcost
collower
colupper
rowlower
rowupper
astart
aindex
avalue
end
struct HighsSolution
colvalue
coldual
rowvalue
rowdual
end
struct HighsBasis
colbasisstatus
rowbasisstatus
end
function Highs_call(model)
n_col = convert(Cint, size(model.colcost, 1))
n_row = convert(Cint, size(model.rowlower, 1))
n_nz = convert(Cint, size(model.aindex, 1))
colcost = convert(Array{Cdouble}, model.colcost)
collower = convert(Array{Cdouble}, model.collower)
colupper = convert(Array{Cdouble}, model.colupper)
rowlower = convert(Array{Cdouble}, model.rowlower)
rowupper = convert(Array{Cdouble}, model.rowupper)
matstart = convert(Array{Cint}, model.astart)
matindex = convert(Array{Cint}, model.aindex)
matvalue = convert(Array{Cdouble}, model.avalue)
solution = HighsSolution(Array{Cdouble, 1}(undef, n_col), Array{Cdouble, 1}(undef, n_col), Array{Cdouble, 1}(undef, n_row), Array{Cdouble, 1}(undef, n_row))
basis = HighsBasis(Array{Cint, 1}(undef, n_col), Array{Cint, 1}(undef, n_row))
modelstatus = convert(Cint, 0)
status = ccall((:Highs_call, "libhighs.so"), Cint, (Cint, Cint, Cint, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cint}, Ptr{Cint}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cint}, Ptr{Cint}, Ref{Cint}),
n_col, n_row, n_nz, colcost, collower, colupper, rowlower, rowupper, matstart, matindex, matvalue, solution.colvalue, solution.coldual, solution.rowvalue, solution.rowdual, basis.colbasisstatus, basis.rowbasisstatus, modelstatus)
return status, solution, basis, modelstatus
end
function Highs_create()
return ccall((:Highs_create, "libhighs.so"), Ptr{Cvoid}, () )
end
function Highs_destroy(highs)
ccall((:Highs_destroy, "libhighs.so"), Cvoid, (Ptr{Cvoid},), highs)
end
function Highs_run(highs)
return ccall((:Highs_run, "libhighs.so"), Cint, (Ptr{Cvoid},), highs)
end
function Highs_readModel(highs, filename)
name = convert(Cstring, pointer(filename))
return ccall((:Highs_readModel, "libhighs.so"), Cint, (Ptr{Cvoid}, Cstring), highs, name)
end
function Highs_writeModel(highs, filename)
name = convert(Cstring, pointer(filename))
return ccall((:Highs_writeModel, "libhighs.so"), Cint, (Ptr{Cvoid}, Cstring), highs, name)
end
function Highs_passLp(highs, model)
n_col = convert(Cint, size(model.colcost, 1))
n_row = convert(Cint, size(model.rowlower, 1))
n_nz = convert(Cint, size(model.aindex, 1))
colcost = convert(Array{Cdouble}, model.colcost)
collower = convert(Array{Cdouble}, model.collower)
colupper = convert(Array{Cdouble}, model.colupper)
rowlower = convert(Array{Cdouble}, model.rowlower)
rowupper = convert(Array{Cdouble}, model.rowupper)
matstart = convert(Array{Cint}, model.astart)
matindex = convert(Array{Cint}, model.aindex)
matvalue = convert(Array{Cdouble}, model.avalue)
return ccall((:Highs_passLp, "libhighs.so"), Cint, (Ptr{Cvoid},Cint, Cint, Cint, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cint},Ptr{Cint},Ptr{Cdouble}),
highs, n_col, n_row, n_nz, colcost, collower, colupper, rowlower, rowupper, matstart, matindex, matvalue)
end
function Highs_setOptionValue(highs, option, value)
opt = convert(Cstring, pointer(option))
val = convert(Cstring, pointer(val))
return ccall((:Highs_setOptionValue, "libhighs.so"), Cint, (Ptr{Cvoid}, Cstring, Cstring), highs, opt, val)
end
function Highs_getNumCols(highs)
return ccall((:Highs_getNumCols, "libhighs.so"), Cint, ())
end
function Highs_getNumRows(highs)
return ccall((:Highs_getNumRows, "libhighs.so"), Cint, ())
end
function Highs_getNumNz(highs)
return ccall((:Highs_getNumNz, "libhighs.so"), Cint, ())
end
function Highs_getSolution(highs)
numcol = Highs_getNumCols(highs)
numrow = Highs_getNumRows(highs)
solution = HighsSolution(Array{Cdouble, 1}(undef, numcol), Array{Cdouble, 1}(undef, numcol), Array{Cdouble, 1}(undef, numrow), Array{Cdouble, 1}(undef, numrow))
ccall((:Highs_getSolution, "libhighs.so"), Cvoid, (Ptr{Cvoid}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}), highs, solution.colvalue, solution.coldual, solution.rowvalue, solution.rowdual)
return solution
end
function Highs_getBasis(highs)
numcol = Highs_getNumCols(highs)
numrow = Highs_getNumRows(highs)
basis = HighsBasis(Array{Cint, 1}(undef, numcol), Array{Cint, 1}(undef, numrow))
ccall((:Highs_getBasis, "libhighs.so"), Cvoid, (Ptr{Cvoid}, Ptr{Cint}, Ptr{Cint}), highs, basis.colbasisstatus, basis.rowbasisstatus)
return basis
end
function Highs_getObjectiveValue(highs)
return ccall((:Highs_getObjectiveValue, "libhighs.so"), Cdouble, (Ptr{Cvoid},), highs)
end
function Highs_getIterationCount(highs)
return ccall((:Highs_getIterationCount, "libhighs.so"), Cint, (Ptr{Cvoid},), highs)
end
function Highs_addRow(highs, lower, upper, indices, values)
n_new_nz = convert(Cint, size(indices, 1))
lo = convert(Cdouble, lower)
hi = convert(Cdouble, upper)
idx = convert(Array{Cint}, indices)
val = convert(Array{Cdouble}, values)
return ccall((:Highs_addRow, "libhighs.so"), Cint, (Ptr{Cvoid}, Cdouble, Cdouble, Cint, Ptr{Cint}, Ptr{Cdouble}), highs, lo, hi, n_new_nz, idx, val)
end
function Highs_addRows(highs, lower, upper, starts, indices, values)
n_new_rows = convert(Cint, size(lower, 1))
n_new_nz = convert(Cint, size(indices, 1))
lo = convert(Array{Cdouble}, lower)
hi = convert(Array{Cdouble}, upper)
st = convert(Array{Cint}, starts)
idx = convert(Array{Cint}, indices)
val = convert(Array{Cdouble}, values)
return ccall((:Highs_addRows, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Ptr{Cdouble}, Ptr{Cdouble}, Cint, Ptr{Cint}, Ptr{Cint}, Ptr{Cdouble}),
highs, n_new_rows, lo, hi, n_new_nz, st, idx, val)
end
function Highs_addCol(highs, cost, lower, upper, indices, values)
n_new_nz = convert(Cint, size(indices, 1))
cc = convert(Cdouble, cost)
lo = convert(Cdouble, lower)
hi = convert(Cdouble, upper)
idx = convert(Array{Cint}, indices)
val = convert(Array{Cdouble}, values)
return ccall((:Highs_addCol, "libhighs.so"), Cint, (Ptr{Cvoid}, Cdouble, Cdouble, Cdouble, Cint, Ptr{Cint}, Ptr{Cdouble}),
highs, cc, lo, hi, n_new_nz, idx, val)
end
function Highs_addCols(highs, costs, lower, upper, starts, indices, values)
n_new_cols = convert(Cint, size(lower, 1))
n_new_nz = convert(Cint, size(indices, 1))
co = convert(Array{Cdouble}, costs)
lo = convert(Array{Cdouble}, lower)
hi = convert(Array{Cdouble}, upper)
st = convert(Array{Cint}, starts)
idx = convert(Array{Cint}, indices)
val = convert(Array{Cdouble}, values)
return ccall((:Highs_addCols, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble}, Cint, Ptr{Cint}, Ptr{Cint}, Ptr{Cdouble}),
highs, n_new_cols, co, lo, hi, n_new_nz, st, idx, val)
end
function Highs_changeObjectiveSense(highs, sense)
sns = convert(Cint, sense)
return ccall((:Highs_changeObjectiveSense, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint), highs, sns)
end
function Highs_changeColCost(highs, colidx, cost)
col = convert(Cint, colidx)
cst = convert(Cdouble, cost)
return ccall((:Highs_changeColCost, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Cdouble), highs, colidx, cost)
end
function Highs_changeColsCostBySet(highs, set, cost)
num_set_entries = convert(Cint, size(set, 1))
st = convert(Array{Cint}, set)
cst = convert(Array{Cdouble}, cost)
return ccall((:Highs_changeColsCostBySet, "libhighs.so"), Cint, (Ptr{Cvoid}, Ptr{Cint}, Ptr{Cdouble}), highs, st, cst)
end
function Highs_changeColsCostByMask(highs, mask, cost)
msk = convert(Array{Cint}, mask)
cst = convert(Array{Cdouble}, cost)
return ccall((:Highs_changeColsCostByMask, "libhighs.so"), Cint, (Ptr{Cvoid}, Ptr{Cint}, Ptr{Cdouble}), highs, msk, cst)
end
function Highs_changeColBounds(highs, col, lower, upper)
colidx = convert(Cint, col)
lo = convert(Cdouble, lower)
hi = convert(Cdouble, upper)
return ccall((:Highs_changeColBounds, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Cdouble, Cdouble), highs, colidx, lo, hi)
end
function Highs_changeColsBoundsByRange(highs, from, to, lower, upper)
f = convert(Cint, from)
t = convert(Int33, to)
lo = convert(Array{Cdouble}, lower)
hi = convert(Array{Cdouble}, upper)
return ccall((:Highs_changeColsBoundsByRange, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Cint, Ptr{Cdouble}, Ptr{Cdouble}),
highs, f, t, lo, hi)
end
function Highs_changeColsBoundsBySet(highs, set, lower, upper)
nset = convert(Cint, size(set, 1))
st = convert(Array{Cint}, set)
lo = convert(Array{Cdouble}, lower)
hi = convert(Array{Cdouble}, upper)
return ccall((:Highs_changeColsBoundsBySet, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Ptr{Cint}, Ptr{Cdouble}, Ptr{Cdouble}),
highs, nset, st, lo, hi)
end
function Highs_changeColsBoundsByMask(highs, mask, lower, upper)
msk = convert(Array{Cint}, mask)
lo = convert(Array{Cdouble}, lower)
hi = convert(Array{Cdouble}, upper)
return ccall((:Highs_changeColsBoundsByMask, "libhighs.so"), Cint, (Ptr{Cvoid}, Ptr{Cint}, Ptr{Cdouble}, Ptr{Cdouble}),
highs, msk, lo, hi)
end
function Highs_changeRowBounds(highs, row, lower, upper)
idx = convert(Cint, row)
lo = convert(Cdouble, lower)
hi = convert(Cdouble, upper)
return ccall((:Highs_changeRowBounds, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Cdouble, Cdouble),
highs, idx, lo, hi)
end
function Highs_changeRowsBoundsBySet(highs, set, lower, upper)
nst = convert(Cint, size(set, 1))
st = convert(Array{Cint}, set)
lo = convert(Array{Cdouble}, lower)
hi = convert(Array(Cdouble), upper)
return ccall((:Highs_changeRowsBoundsBySet, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Ptr{Cint}, Ptr{Cdouble}, Ptr{Cdouble}),
highs, nst, st, lo, hi)
end
function Highs_changeRowsBoundsByMask(highs, mask, lower, upper)
msk = convert(Array{Cint}, mask)
lo = convert(Array{Cdouble}, lower)
hi = convert(Array{Cdouble}, upper)
return ccall((:Highs_changeRowsBoundsByMask, "libhighs.so"), Cint, (Ptr{Cvoid}, Ptr{Cint}, Ptr{Cdouble}, Ptr{Cdouble}),
highs, msk, lo, hi)
end
#=
int Highs_getColsByRange(
void *highs, //!< HiGHS object reference
const int from_col, //!< The index of the first column to
//!< get from the model
const int to_col, //!< One more than the last column to get
//!< from the model
int* num_col, //!< Number of columns got from the model
double *costs, //!< Array of size num_col with costs
double *lower, //!< Array of size num_col with lower bounds
double *upper, //!< Array of size num_col with upper bounds
int* num_nz, //!< Number of nonzeros got from the model
int *matrix_start, //!< Array of size num_col with start
//!< indices of the columns
int *matrix_index, //!< Array of size num_nz with row
//!< indices for the columns
double *matrix_value //!< Array of size num_nz with row
//!< values for the columns
);
int Highs_getColsBySet(
void *highs, //!< HiGHS object reference
const int num_set_entries, //!< The number of indides in the set
const int *set, //!< Array of size num_set_entries with indices
//!< of columns to get
int* num_col, //!< Number of columns got from the model
double *costs, //!< Array of size num_col with costs
double *lower, //!< Array of size num_col with lower bounds
double *upper, //!< Array of size num_col with upper bounds
int* num_nz, //!< Number of nonzeros got from the model
int *matrix_start, //!< Array of size num_col with start indices
//!< of the columns
int *matrix_index, //!< Array of size num_nz with row indices
//!< for the columns
double *matrix_value //!< Array of size num_nz with row values
//!< for the columns
);
int Highs_getColsByMask(
void *highs, //!< HiGHS object reference
const int *mask, //!< Full length array with 1 => get; 0 => not
int* num_col, //!< Number of columns got from the model
double *costs, //!< Array of size num_col with costs
double *lower, //!< Array of size num_col with lower bounds
double *upper, //!< Array of size num_col with upper bounds
int* num_nz, //!< Number of nonzeros got from the model
int *matrix_start, //!< Array of size num_col with start
//!< indices of the columns
int *matrix_index, //!< Array of size num_nz with row indices
//!< for the columns
double *matrix_value //!< Array of size num_nz with row values
//!< for the columns
);
int Highs_getRowsByRange(
void *highs, //!< HiGHS object reference
const int from_row, //!< The index of the first row to get from the model
const int to_row, //!< One more than the last row get from the model
int* num_row, //!< Number of rows got from the model
double *lower, //!< Array of size num_row with lower bounds
double *upper, //!< Array of size num_row with upper bounds
int* num_nz, //!< Number of nonzeros got from the model
int *matrix_start, //!< Array of size num_row with start indices of the
//!< rows
int *matrix_index, //!< Array of size num_nz with column indices for the
//!< rows
double *matrix_value //!< Array of size num_nz with column values for the
//!< rows
);
int Highs_getRowsBySet(
void *highs, //!< HiGHS object reference
const int num_set_entries, //!< The number of indides in the set
const int *set, //!< Array of size num_set_entries with indices
//!< of rows to get
int* num_row, //!< Number of rows got from the model
double *lower, //!< Array of size num_row with lower bounds
double *upper, //!< Array of size num_row with upper bounds
int* num_nz, //!< Number of nonzeros got from the model
int *matrix_start, //!< Array of size num_row with start indices
//!< of the rows
int *matrix_index, //!< Array of size num_nz with column indices
//!< for the rows
double *matrix_value //!< Array of size num_nz with column
//!< values for the rows
);
int Highs_getRowsByMask(
void *highs, //!< HiGHS object reference
const int *mask, //!< Full length array with 1 => get; 0 => not
int* num_row, //!< Number of rows got from the model
double *lower, //!< Array of size num_row with lower bounds
double *upper, //!< Array of size num_row with upper bounds
int* num_nz, //!< Number of nonzeros got from the model
int *matrix_start, //!< Array of size num_row with start indices
//!< of the rows
int *matrix_index, //!< Array of size num_nz with column indices
//!< for the rows
double *matrix_value //!< Array of size num_nz with column
//!< values for the rows
);
=#
function Highs_deleteColsByRange(highs, from, to)
f = convert(Cint, from)
t = convert(Cint, to)
return ccall((:Highs_deleteColsByRange, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Cint), highs, f, t)
end
function Highs_deleteColsBySet(highs, set)
nset = convert(Cint, size(set, 1))
st = convert(Array{Cint}, set)
return ccall((:Highs_deleteColsBySet, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Ptr{Cint}), highs, nset, st)
end
function Highs_deleteColsByMask(highs, mask)
msk = convert(Array{Cint}, mask)
return ccall((:Highs_deleteColsByMask, "libighs.so"), Cint, (Ptr{Cvoid}, Ptr{Cint}), highs, msk)
end
function Highs_deleteRowsByRange(highs, from, to)
f = convert(Cint, from)
t = convert(Cint, to)
return ccall((:Highs_deleteRowsByRange, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Cint), highs, f, t)
end
function Highs_deleteRowsBySet(highs, set)
nset = convert(Cint, size(set, 1))
st = convert(Array{Cint}, set)
return ccall((:Highs_deleteRowsBySet, "libhighs.so"), Cint, (Ptr{Cvoid}, Cint, Ptr{Cint}), highs, nset, st)
end
function Highs_deleteRowsByMask(highs, mask)
msk = convert(Array{Cint}, mask)
return ccall((:Highs_deleteRowsByMask, "libhighs.so"), Cint, (Ptr{Cvoid}, Ptr{Cint}), highs, msk)
end
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600,
92,
828,
28227,
11,
285,
8135,
8,
198,
437,
198
] | 2.348738 | 7,292 |
<gh_stars>0
using MittagLefflerFunctions
using PyPlot
f(x) = exp(x)
nmax = 10
M = nmax + 2
a = chebyshev_coefs(Float64, f, nmax, M)
x = range(-1, 1, length=201)
y = Float64[ chebyshev_sum(a, xval) for xval in x ]
figure(1)
plot(x, y-f.(x))
grid(true)
| [
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] | 2.02381 | 126 |
<reponame>JuliaBinaryWrappers/LibModbus_jll.jl
# Autogenerated wrapper script for LibModbus_jll for aarch64-linux-gnu
export libmodbus
JLLWrappers.@generate_wrapper_header("LibModbus")
JLLWrappers.@declare_library_product(libmodbus, "libmodbus.so.5")
function __init__()
JLLWrappers.@generate_init_header()
JLLWrappers.@init_library_product(
libmodbus,
"lib/libmodbus.so",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@generate_init_footer()
end # __init__()
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] | 2.294931 | 217 |
using OrdinaryDiffEq, DiffEqDevTools, Test, LinearAlgebra
using DiffEqProblemLibrary.ODEProblemLibrary: importodeproblems; importodeproblems()
import DiffEqProblemLibrary.ODEProblemLibrary: prob_ode_bigfloatlinear, prob_ode_bigfloat2Dlinear, prob_ode_linear, prob_ode_2Dlinear
probArr = [prob_ode_bigfloatlinear, prob_ode_bigfloat2Dlinear]
testTol = 0.25
dts = 1 .//(2 .^(10:-1:5))
@testset "Nordsieck Convergence Tests" begin
for i in eachindex(probArr)
sim = test_convergence(dts,probArr[i],AN5())
@test sim.𝒪est[:final] ≈ 5 atol=testTol
@test sim.𝒪est[:l2] ≈ 5 atol=testTol
@test sim.𝒪est[:l∞] ≈ 5 atol=testTol
end
end
probArr = [prob_ode_linear,
prob_ode_2Dlinear]
@testset "Nordsieck Adaptivity Tests: AN5" begin
for i in eachindex(probArr)
prob = probArr[i]
sol = solve(prob, AN5(), reltol=1e-6)
@test length(sol.t) < 11
exact = prob.f(Val{:analytic}, prob.u0, prob.p, prob.tspan[end])
@test exact ≈ sol[end] atol=1e-5
end
end
@testset "Nordsieck Adaptivity Tests: JVODE" begin
for i in eachindex(probArr), sol = [JVODE_Adams(), JVODE_BDF()]
prob = probArr[i]
sol = solve(prob, sol, reltol=1e-4, abstol=1e-7)
@test length(sol.t) < 22
exact = prob.f(Val{:analytic}, prob.u0, prob.p, prob.tspan[end])
@test norm(exact - sol[end], Inf) < 3e-3
end
end
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437,
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4806,
8,
1279,
513,
68,
12,
18,
198,
220,
886,
198,
437,
198
] | 2.199013 | 608 |
<gh_stars>10-100
using Turing, MCMCChains
using Distributions
using ParetoSmooth
samples = randn(100, 1, 1)
data = randn(50)
chain = Chains(samples)
compute_loglike(μ, data) = logpdf(Normal(μ, 1), data)
compute_loglike(μ, σ, data) = logpdf(Normal(μ, σ), data)
pll1 = pointwise_log_likelihoods(compute_loglike, chain, data)
size(pll1) |> display
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] | 2.416667 | 144 |
<gh_stars>1-10
function read_file(file, p, cache, particle_cache)
h, blocks = open(file_summary, file)
SDFFile(file, h, blocks, p, cache, particle_cache)
end
function Base.read(sdf::SDFFile, entry::Symbol)
open(sdf.name) do f
read(f, getindex(sdf.blocks, entry))
end
end
function Base.read(sdf::SDFFile, entries...)
open(sdf.name) do f
asyncmap(i->read(f, getindex(sdf.blocks,i)), entries)
end
end
function read_expensive(sdf, ids)
open(sdf.name) do f
asyncmap(i->make_grid(
Variable(),
sdf.blocks[i],
nothing,
f;
cache=sdf.particle_cache[]
), ids)
end
end
function Base.getindex(sdf::SDFFile, idx::Symbol)
open(sdf.name) do f
read_entry(f, sdf, idx)
end
end
function expensive_grids(ids, idx)
# Values corresponding to "grid/[species]") are expensive to read
cid = [ids...]
for (i, id) in enumerate(ids)
if isnothing(id) && occursin("grid/", string(idx[i]))
cid[i] = idx[i]
end
end
s_id = string.(cid)
expensive = map(i->occursin("grid/", i), s_id)
!reduce(|, expensive) && return nothing, expensive
@debug "Found expensive to read mesh entries"
map(Symbol, unique(s_id[expensive])), expensive
end
# The data for the particles is the most expensive to read since there are
# a lot of particles. Since the ScalarVariables have a grid, that grid
# might be the same for more variables and should only be read once
function Base.getindex(sdf::SDFFile, idx::Vararg{Symbol, N}) where N
mesh_ids = get_mesh_id.((sdf,), idx)
@debug "Reading entries for $idx with mesh ids $mesh_ids"
expensive_ids, is_expensive = expensive_grids(mesh_ids, idx)
if isnothing(expensive_ids)
simple_read(sdf, idx)
else
@debug "Expensive idxs: $is_expensive"
@debug "Reading data without expensive grids"
partial_data = selective_read(sdf, idx, expensive_ids)
@debug "Reading expensive grids: $expensive_ids"
grids = read_expensive(sdf, expensive_ids)
grid_map = (; zip(expensive_ids, grids)...)
complete_data = ()
for (i, d) in enumerate(partial_data)
@debug "Is $(idx[i]) expensive? $(is_expensive[i])"
if is_expensive[i]
id = mesh_ids[i]
@debug "Expensive grid $id at $i"
if isnothing(id)
grid_id = idx[i]
@debug "Storing grid entry $grid_id"
grid = getproperty(grid_map, grid_id)
complete_data = push!!(complete_data, grid)
else
@debug "Storing $id"
grid = getproperty(grid_map, id)
data = d.data
data_block = d.block
f = store_entry(data_block, data, grid)
complete_data = push!!(complete_data, f)
end
else
complete_data = push!!(complete_data, d)
end
end
complete_data
end
end
function selective_read(sdf, idx, expensive_ids)
open(sdf.name) do f
asyncmap(i->read_selected(f, sdf.blocks, i, expensive_ids), idx)
end
end
function simple_read(sdf, idx)
open(sdf.name) do f
asyncmap(i->read_entry(f, sdf, i), idx)
end
end
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] | 2.082108 | 1,632 |
# License for this file: MIT (expat)
# Copyright 2017-2018, DLR Institute of System Dynamics and Control
#
# This file is part of module
# Modia3D.Composition (Modia3D/Composition/_module.jl)
#
# Utility function that should not be directly called (only to be called from attach(..)
function attachAndReverseParents(newParent::Object3D, obj::Object3D)::Nothing
@assert(!(newParent ≡ obj))
# Save elements of obj
r_rel = obj.r_rel
R_rel = obj.R_rel
parent = obj.parent
# Reverse obj, so that newParent is the new parent
obj.parent = newParent
push!(newParent.children, obj)
# Reverse all parents of obj
obj1::Object3D = obj
obj2::Object3D = parent
while !(parent === obj1)
parent_r_rel = parent.r_rel
parent_R_rel = parent.R_rel
if r_rel ≡ Modia3D.ZeroVector3D
parent.r_rel = Modia3D.ZeroVector3D
parent.r_abs = obj1.r_abs
else
parent.r_rel = -R_rel*r_rel
end
if R_rel ≡ Modia3D.NullRotation
parent.R_rel = Modia3D.NullRotation
parent.R_abs = obj1.R_abs
else
parent.R_rel = R_rel'
end
obj2 = parent
parent = obj2.parent
r_rel = parent_r_rel
R_rel = parent_R_rel
obj2.parent = obj1
removeChild!(obj2, obj1)
push!(obj1.children, obj2)
obj1 = obj2
end
# If !isnothing(scene), visit all objs and set scene
#=
if !isnothing(scene)
stack = scene.stack
empty!(stack)
append!(stack, obj.children)
while length(stack) > 0
obj1 = pop!(stack)
obj1.scene = scene
append!(stack, obj1.children)
end
end
=#
return nothing
end
"""
(obj1, obj2, cutJoint) = attach(frame_a, frame_b)
If frame_a and frame_b do not have the same root, they are attached to each other
and cutJoint = false is returned.
If they have the same root, the tree is not modified and cutJoint=true is returned.
"""
function attach(obj1::Object3D, obj2::Object3D)
root1 = rootObject3D(obj1)
root2 = rootObject3D(obj2)
#println("attach: obj1 = ", Modia3D.fullName(obj1), ", root = ", Modia3D.fullName(root1))
#println("attach: obj2 = ", Modia3D.fullName(obj2), ", root = ", Modia3D.fullName(root2))
if root1 ≡ root2
# Compute absolute positions
# updatePosition!(root1)
return (obj1,obj2,true)
end
if isWorld(root2)
attachAndReverseParents(obj2, obj1)
return (obj2,obj1,false)
else
attachAndReverseParents(obj1, obj2)
return (obj1,obj2,false)
end
end
#=
function connect(obj1::Object3D, obj2::Object3D;
r::AbstractVector = Modia3D.ZeroVector3D,
R::Union{Frames.RotationMatrix,Nothing} = nothing,
q::Union{Frames.Quaternion,Nothing} = nothing,
fixed::Bool = true)::Nothing
if !isnothing(R) && !isnothing(q)
error("Modia3D.connect(...): either R or q must be nothing but both have a value.")
end
if !isnothing(R)
Modia3D.assertRotationMatrix(R)
elseif !isnothing(q)
Modia3D.assertQuaternion(q)
end
(parentObject3D, obj, cutJoint) = attach(obj1, obj2)
# println("... connect, fixed = ", fixed, ", obj1=",Modia3D.fullName(obj1), ", obj2 = ", Modia3D.fullName(obj2), ", obj = ", Modia3D.fullName(obj))
if cutJoint
error("Error from Modia3D.Composition.connect(", obj1.name, ",", obj2.name, "):\n",
"Not yet supported to rigidly connect two objs that have the same root.")
end
r_rel = SVector{3,Float64}(r)
R_rel = !isnothing(R) ? R :
!isnothing(q) ? Modia3D.from_q(q) : Modia3D.NullRotation
obj.r_rel = obj===obj2 ? r_rel : -R_rel*r_rel
obj.R_rel = obj===obj2 ? R_rel : R_rel'
#r_abs = parent.r_abs + r_rel
#R_abs = R_rel*parent.R_abs
if fixed
obj.joint = fixedJoint
else
obj1.hasChildJoint = true
q_start = !isnothing(R) ? Modia3D.from_R(R) :
!isnothing(q) ? q : Modia3D.NullQuaternion
q_start = obj===obj2 ? q_start : Modia3D.inverseRotation(q_start)
obj.joint = FreeMotion(obj, r_start = obj.r_rel, q_start = q_start)
end
return nothing
end
=#
function updatePosition!(obj::Object3D)::Nothing
stack = Object3D[]
# Push initial children on stack
append!(stack, obj.children)
while length(stack) > 0
obj = pop!(stack)
# computeKinematics!(obj.joint, obj, Modia3D.KinematicAnalysis, 0.0)
parent = obj.parent
if obj.r_rel ≡ Modia3D.ZeroVector3D
obj.r_abs = parent.r_abs
else
obj.r_abs = parent.r_abs + parent.R_abs'*obj.r_rel
end
if obj.R_rel ≡ Modia3D.NullRotation
obj.R_abs = parent.R_abs
else
obj.R_abs = obj.R_rel*parent.R_abs
end
if length(obj.visualizationFrame) == 1
obj.visualizationFrame[1].r_abs = obj.r_abs
obj.visualizationFrame[1].R_abs = obj.R_abs
end
append!(stack, obj.children)
end
return nothing
end
"""
R = Rfromrot123(rot123::AbstractVector)
Return rotation matrix `R` from Cardan angles (rotation sequence x-y-z).
`rot123` are the Cardan angles (rotation sequence x-y-z) of rotation from frame `1` to frame `2`.
"""
function Rfromrot123(rot123::AbstractVector)
(sal, cal) = sincos(rot123[1])
(sbe, cbe) = sincos(rot123[2])
(sga, cga) = sincos(rot123[3])
return @SMatrix[ cbe*cga sal*sbe*cga+cal*sga -cal*sbe*cga+sal*sga ;
-cbe*sga -sal*sbe*sga+cal*cga cal*sbe*sga+sal*cga ;
sbe -sal*cbe cal*cbe ]
end
"""
w = wfromrot123(rot123::AbstractVector, derrot123::AbstractVector)
Return relative rotational velocity Vector3D `w` from frame `1` to frame `2` resolved in frame `2`.
`rot123` are the Cardan angles (rotation sequence x-y-z) of rotation from frame `1` to frame `2`.
`derrot123` are the time derivatives of `rot123`.
"""
function wfromrot123(rot123::AbstractVector, derrot123::AbstractVector)
# 2_w_12 = (0, 0, gad) + Rga21*[(0, bed, 0) + Rbe21*(ald, 0, 0)]
# = (cbe*cga*ald + sga*bed, -cbe*sga*ald + cga*bed, sbe*ald + gad)
(sbe, cbe) = sincos(rot123[2])
(sga, cga) = sincos(rot123[3])
return @SVector[ cbe*cga*derrot123[1] + sga*derrot123[2] ,
-cbe*sga*derrot123[1] + cga*derrot123[2] ,
sbe*derrot123[1] + derrot123[3] ]
end
# Next function only for backwards compatibility (do not use for new model)
computeKinematics!(scene::Scene, joint::Modia3D.AbstractJoint, obj::Object3D, analysis::Modia3D.AnalysisType, time::Float64)::Nothing =
computeKinematics!(scene, [obj], time)
"""
computeKinematics!(scene::Scene, tree::Vector{Object3D}, time)
Compute position, velocity, acceleration variables of the Object3Ds that are connected
in form of a tree. Variable `tree` contains the Object3Ds in a traversal order (e.g. pre-order traversal).
`tree[1]` is the root object. It is assumed that the kinematic
variables of tree[1].parent have a meaningful value.
"""
function computeKinematics!(scene::Scene, tree::Vector{Object3D}, time)::Nothing
for obj in tree
parent = obj.parent
jointKind = obj.jointKind
if jointKind == FixTranslationKind
obj.r_abs = parent.r_abs + parent.R_abs'*obj.r_rel
obj.R_abs = parent.R_abs
obj.v0 = parent.v0 + parent.R_abs'*cross(parent.w, obj.r_rel)
obj.a0 = parent.a0 + parent.R_abs'*(cross(parent.z, obj.r_rel) + cross(parent.w, cross(parent.w, obj.r_rel)))
obj.w = parent.w
obj.z = parent.z
elseif jointKind == FixKind
obj.r_abs = parent.r_abs + parent.R_abs'*obj.r_rel
obj.R_abs = obj.R_rel*parent.R_abs
obj.v0 = parent.v0 + parent.R_abs'*cross(parent.w, obj.r_rel)
obj.a0 = parent.a0 + parent.R_abs'*(cross(parent.z, obj.r_rel) + cross(parent.w, cross(parent.w, obj.r_rel)))
obj.w = obj.R_rel*parent.w
obj.z = obj.R_rel*parent.z
elseif jointKind == RevoluteKind
revolute = scene.revolute[obj.jointIndex]
obj.r_abs = parent.r_abs
obj.R_rel = Frames.rotAxis(revolute.posAxis, revolute.posMovement, revolute.phi)
obj.R_abs = obj.R_rel*parent.R_abs
obj.v0 = parent.v0
obj.a0 = parent.a0
w_rel = Frames.axisValue(revolute.posAxis, revolute.posMovement, revolute.w)
z_rel = Frames.axisValue(revolute.posAxis, revolute.posMovement, revolute.a)
obj.w = obj.R_rel*(parent.w + w_rel)
obj.z = obj.R_rel*(parent.z + z_rel + cross(parent.w, w_rel))
elseif jointKind == PrismaticKind
prismatic = scene.prismatic[obj.jointIndex]
obj.r_rel = prismatic.eAxis*prismatic.s
obj.r_abs = parent.r_abs + parent.R_abs'*obj.r_rel
obj.R_abs = parent.R_abs
v_rel = prismatic.eAxis*prismatic.v
a_rel = prismatic.eAxis*prismatic.a
obj.v0 = parent.v0 + parent.R_abs'*(v_rel + cross(parent.w, obj.r_rel))
obj.a0 = parent.a0 + parent.R_abs'*(a_rel + cross(parent.z, obj.r_rel) + cross(parent.w, v_rel + cross(parent.w, obj.r_rel)) )
obj.w = parent.w
obj.z = parent.z
elseif jointKind == AbsoluteFreeMotionKind
freeMotion = scene.freeMotion[obj.jointIndex]
obj.r_rel = freeMotion.r
obj.R_rel = Rfromrot123(freeMotion.rot)
obj.r_abs = obj.r_rel
obj.R_abs = obj.R_rel
obj.v0 = freeMotion.v
obj.a0 = freeMotion.a
obj.w = freeMotion.w
obj.z = freeMotion.z
elseif jointKind == FreeMotionKind
freeMotion = scene.freeMotion[obj.jointIndex]
obj.r_rel = freeMotion.r
obj.R_rel = Rfromrot123(freeMotion.rot)
obj.r_abs = parent.r_abs + parent.R_abs'*obj.r_rel
obj.R_abs = obj.R_rel*parent.R_abs
obj.v0 = parent.v0 + parent.R_abs'*(freeMotion.v + cross(parent.w, obj.r_rel))
obj.a0 = parent.a0 + parent.R_abs'*(freeMotion.a + cross(parent.z, obj.r_rel) +
cross(parent.w, cross(parent.w, obj.r_rel)))
obj.w = parent.R_abs*parent.w + freeMotion.w
obj.z = parent.R_abs*parent.z + freeMotion.z
else
error("Bug in Modia3D/src/Composition/specifics/specifics.jl (computeKinematics!): jointKind = $jointKind is not known")
end
end
return nothing
end
"""
computeKinematics_for_leq_mode_pos!(scene::Scene, tree::Vector{Object3D}, time)
Compute accelerations that are only a function of qdd, but not of q and qd.
of the Object3Ds that are connected in form of a tree.
"""
function computeKinematics_for_leq_mode_pos!(scene::Scene, tree::Vector{Object3D}, time)::Nothing
for obj in tree
parent = obj.parent
jointKind = obj.jointKind
if jointKind == FixTranslationKind
obj.a0 = parent.a0 + parent.R_abs'*(cross(parent.z, obj.r_rel))
obj.z = parent.z
elseif jointKind == FixKind
obj.a0 = parent.a0 + parent.R_abs'*(cross(parent.z, obj.r_rel))
obj.z = obj.R_rel*parent.z
elseif jointKind == RevoluteKind
revolute = scene.revolute[obj.jointIndex]
obj.a0 = parent.a0
z_rel = Frames.axisValue(revolute.posAxis, revolute.posMovement, revolute.a)
obj.z = obj.R_rel*(parent.z + z_rel)
elseif jointKind == PrismaticKind
prismatic = scene.prismatic[obj.jointIndex]
eAxis = prismatic.posMovement ? prismatic.posAxis : -prismatic.posAxis
a_rel = prismatic.eAxis*prismatic.a
obj.a0 = parent.a0 + parent.R_abs'*(a_rel + cross(parent.z, obj.r_rel))
obj.z = parent.z
elseif jointKind == AbsoluteFreeMotionKind
freeMotion = scene.freeMotion[obj.jointIndex]
obj.a0 = freeMotion.a
obj.z = freeMotion.z
elseif jointKind == FreeMotionKind
freeMotion = scene.freeMotion[obj.jointIndex]
obj.a0 = parent.a0 + parent.R_abs'*(freeMotion.a + cross(parent.z, obj.r_rel))
obj.z = parent.R_abs*parent.z + freeMotion.z
else
error("Bug in Modia3D/src/Composition/specifics/specifics.jl (computeKinematics_for_leq_mode_pos!): jointKind = $jointKind is not known")
end
end
return nothing
end
"""
computeForcesTorquesAndResiduals!(scene::Scene, tree::Vector{Object3D}, time)
Compute forces/torques and residuals in a backward recursion from tree[end] to tree[1].
Variable `tree` contains the Object3Ds in a traversal order (e.g. pre-order traversal).
It is assumed that all force/torque variables are initialized (e.g. to zero), including
tree[1].parent.
"""
function computeForcesTorquesAndResiduals!(scene::Scene, tree::Vector{Object3D}, time)::Nothing
for i = length(tree):-1:1
obj = tree[i]
parent = obj.parent
jointKind = obj.jointKind
if jointKind == FixTranslationKind
parent.f += obj.f
parent.t += obj.t + cross(obj.r_rel, obj.f)
elseif jointKind == FixKind
parent.f += obj.R_rel'*obj.f
parent.t += obj.R_rel'*obj.t + cross(obj.r_rel, obj.f)
elseif jointKind == RevoluteKind
revolute = scene.revolute[obj.jointIndex]
parent.f += obj.R_rel'*obj.f
parent.t += obj.R_rel'*obj.t
obj.f = -obj.f
obj.t = -obj.t
revolute.residue = -revolute.tau + (revolute.posMovement ? obj.t[revolute.posAxis] : -obj.t[revolute.posAxis])
elseif jointKind == PrismaticKind
prismatic = scene.prismatic[obj.jointIndex]
parent.f += obj.f
parent.t += obj.t + cross(obj.r_rel, obj.f)
obj.f = -obj.f
obj.t = -obj.t
prismatic.residue = -prismatic.f + dot(prismatic.eAxis,obj.f)
elseif jointKind == AbsoluteFreeMotionKind || jointKind == FreeMotionKind
freeMotion = scene.freeMotion[obj.jointIndex]
freeMotion.residue_f = obj.f
freeMotion.residue_t = obj.t
else
error("Bug in Modia3D/src/Composition/specifics/specifics.jl (computeForcesTorquesAndResiduals!): jointKind = $jointKind is not known")
end
end
return nothing
end
"""
setJointVariables!(scene::Scene, objects::Vector{Object3D}, args, qdd, startIndex, ndof)
Copy specific variables into the corresponding Object3Ds.
"""
function setJointVariables!(scene::Scene, objects::Vector{Object3D}, args, qdd, startIndex, ndof)::Nothing
for (i,obj) in enumerate(objects)
jointKind = obj.jointKind
args_i = args[i]
beg = startIndex[i]
if jointKind == RevoluteKind
@assert(ndof[i] == 1)
revolute = scene.revolute[obj.jointIndex]
revolute.phi = args_i[1]
revolute.w = args_i[2]
revolute.a = qdd[beg]
revolute.tau = args_i[3]
elseif jointKind == PrismaticKind
@assert(ndof[i] == 1)
prismatic = scene.prismatic[obj.jointIndex]
prismatic.s = args_i[1]
prismatic.v = args_i[2]
prismatic.a = qdd[beg]
prismatic.f = args_i[3]
elseif jointKind == AbsoluteFreeMotionKind || jointKind == FreeMotionKind
@assert(ndof[i] == 6)
freeMotion = scene.freeMotion[obj.jointIndex]
freeMotion.r = SVector{3,Float64}(args_i[1])
freeMotion.rot = SVector{3,Float64}(args_i[2])
freeMotion.v = SVector{3,Float64}(args_i[3])
freeMotion.w = SVector{3,Float64}(args_i[4])
freeMotion.a = qdd[beg+0:beg+2]
freeMotion.z = qdd[beg+3:beg+5]
else
error("Bug in Modia3D/src/Composition/specifics/specifics.jl (setJointVariables!): jointKind = $jointKind is not known")
end
end
return nothing
end
"""
getJointResiduals_for_leq_mode_0!(scene::Scene, objects::Vector{Object3D}, residuals, startIndex::Int, ndof::Int, cache_h)
Copy specific variables into their objects for leq_mode = 0.
"""
function getJointResiduals_for_leq_mode_0!(scene::Scene, objects::Vector{Object3D}, residuals, startIndex::Vector{Int}, ndof::Vector{Int}, cache_h)::Nothing
for (i,obj) in enumerate(objects)
jointKind = obj.jointKind
beg = startIndex[i]
if jointKind == RevoluteKind
@assert(ndof[i] == 1)
revolute = scene.revolute[obj.jointIndex]
cache_h[ beg] = revolute.residue + revolute.tau
residuals[beg] = revolute.residue
elseif jointKind == PrismaticKind
@assert(ndof[i] == 1)
prismatic = scene.prismatic[obj.jointIndex]
cache_h[ beg] = prismatic.residue + prismatic.f
residuals[beg] = prismatic.residue
elseif jointKind == AbsoluteFreeMotionKind || jointKind == FreeMotionKind
@assert(ndof[i] == 6)
freeMotion = scene.freeMotion[obj.jointIndex]
cache_h[ beg+0:beg+2] = freeMotion.residue_f
cache_h[ beg+3:beg+5] = freeMotion.residue_t
residuals[beg+0:beg+2] = freeMotion.residue_f
residuals[beg+3:beg+5] = freeMotion.residue_t
else
error("Bug in Modia3D/src/Composition/specifics/specifics.jl (getJointResiduals_for_leq_mode_0!): jointKind = $jointKind is not known")
end
end
return nothing
end
"""
getJointResiduals_for_leq_mode_pos!(scene::Scene, objects::Vector{Object3D}, residuals, startIndex::Int, ndof::Int, cache_h)
Copy specific variables into their objects for leq_mode > 0.
"""
function getJointResiduals_for_leq_mode_pos!(scene::Scene, objects::Vector{Object3D}, residuals, startIndex::Vector{Int}, ndof::Vector{Int}, cache_h)::Nothing
for (i,obj) in enumerate(objects)
jointKind = obj.jointKind
beg = startIndex[i]
if jointKind == RevoluteKind
@assert(ndof[i] == 1)
residuals[beg] = scene.revolute[obj.jointIndex].residue + cache_h[beg]
elseif jointKind == PrismaticKind
@assert(ndof[i] == 1)
residuals[beg] = scene.prismatic[obj.jointIndex].residue + cache_h[beg]
elseif jointKind == AbsoluteFreeMotionKind || jointKind == FreeMotionKind
@assert(ndof[i] == 6)
freeMotion = scene.freeMotion[obj.jointIndex]
residuals[beg+0:beg+2] = freeMotion.residue_f + cache_h[beg+0:beg+2]
residuals[beg+3:beg+5] = freeMotion.residue_t + cache_h[beg+3:beg+5]
else
error("Bug in Modia3D/src/Composition/specifics/specifics.jl (getJointResiduals_for_leq_mode_0!): jointKind = $jointKind is not known")
end
end
return nothing
end
# For backwards compatibility (do not use for new models)
function setAngle!(revolute::Revolute, phi::Float64)
obj = revolute.obj2
revolute.phi = phi
obj.R_rel = Frames.rotAxis(revolute.posAxis, revolute.posMovement, phi)
obj.R_abs = obj.R_rel*obj.parent.R_abs
return revolute
end
# For backwards compatibility (do not use for new models)
function setDistance!(prismatic::Prismatic, s::Float64)
obj = prismatic.obj2
prismatic.s = s
obj.r_rel = prismatic.eAxis*s
obj.r_abs = parent.r_abs + parent.R_abs'*obj.r_rel
return prismatic
end
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26181,
17,
796,
33172,
3401,
544,
18,
35,
13,
12853,
5376,
7,
26801,
17,
828,
33172,
6808,
796,
33172,
3401,
544,
18,
35,
13,
12853,
5376,
7,
15763,
17,
4008,
628,
220,
220,
611,
6808,
16,
38243,
6808,
17,
198,
220,
220,
220,
220,
220,
1303,
3082,
1133,
4112,
6116,
198,
220,
220,
220,
220,
220,
1303,
4296,
26545,
0,
7,
15763,
16,
8,
198,
220,
220,
220,
220,
220,
1441,
357,
26801,
16,
11,
26801,
17,
11,
7942,
8,
198,
220,
220,
886,
628,
220,
220,
611,
318,
10603,
7,
15763,
17,
8,
198,
220,
220,
220,
220,
220,
10199,
1870,
49,
964,
325,
42969,
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26801,
17,
11,
26181,
16,
8,
198,
220,
220,
220,
220,
220,
1441,
357,
26801,
17,
11,
26801,
16,
11,
9562,
8,
198,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
10199,
1870,
49,
964,
325,
42969,
7,
26801,
16,
11,
26181,
17,
8,
198,
220,
220,
220,
220,
220,
1441,
357,
26801,
16,
11,
26801,
17,
11,
9562,
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198,
220,
220,
886,
198,
437,
198,
198,
2,
28,
198,
8818,
2018,
7,
26801,
16,
3712,
10267,
18,
35,
11,
26181,
17,
3712,
10267,
18,
35,
26,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
3712,
23839,
38469,
796,
3401,
544,
18,
35,
13,
28667,
38469,
18,
35,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
371,
3712,
38176,
90,
35439,
13,
49,
14221,
46912,
11,
18465,
92,
796,
2147,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10662,
3712,
38176,
90,
35439,
13,
4507,
9205,
295,
11,
18465,
92,
796,
2147,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5969,
3712,
33,
970,
796,
2081,
2599,
25,
18465,
198,
220,
220,
611,
5145,
271,
22366,
7,
49,
8,
11405,
5145,
271,
22366,
7,
80,
8,
198,
220,
220,
220,
220,
220,
4049,
7203,
5841,
544,
18,
35,
13,
8443,
7,
986,
2599,
2035,
371,
393,
10662,
1276,
307,
2147,
475,
1111,
423,
257,
1988,
19570,
198,
220,
220,
886,
198,
220,
220,
611,
5145,
271,
22366,
7,
49,
8,
198,
220,
220,
220,
220,
220,
3401,
544,
18,
35,
13,
30493,
49,
14221,
46912,
7,
49,
8,
198,
220,
220,
2073,
361,
5145,
271,
22366,
7,
80,
8,
198,
220,
220,
220,
220,
220,
3401,
544,
18,
35,
13,
30493,
4507,
9205,
295,
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80,
8,
198,
220,
220,
886,
628,
198,
220,
220,
357,
8000,
10267,
18,
35,
11,
26181,
11,
2005,
41,
1563,
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796,
10199,
7,
26801,
16,
11,
26181,
17,
8,
198,
220,
1303,
44872,
7203,
986,
2018,
11,
5969,
796,
33172,
5969,
11,
33172,
26181,
16,
28,
1600,
5841,
544,
18,
35,
13,
12853,
5376,
7,
26801,
16,
828,
33172,
26181,
17,
796,
33172,
3401,
544,
18,
35,
13,
12853,
5376,
7,
26801,
17,
828,
33172,
26181,
796,
33172,
3401,
544,
18,
35,
13,
12853,
5376,
7,
26801,
4008,
628,
198,
220,
220,
611,
2005,
41,
1563,
198,
220,
220,
220,
220,
220,
4049,
7203,
12331,
422,
3401,
544,
18,
35,
13,
5377,
9150,
13,
8443,
7,
1600,
26181,
16,
13,
3672,
11,
366,
553,
11,
26181,
17,
13,
3672,
11,
366,
2599,
59,
77,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
3673,
1865,
4855,
284,
20831,
306,
2018,
734,
909,
8457,
326,
423,
262,
976,
6808,
19570,
198,
220,
220,
886,
628,
220,
220,
374,
62,
2411,
796,
20546,
9250,
90,
18,
11,
43879,
2414,
92,
7,
81,
8,
198,
220,
220,
371,
62,
2411,
796,
5145,
271,
22366,
7,
49,
8,
5633,
371,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1058,
198,
220,
220,
5145,
271,
22366,
7,
80,
8,
5633,
3401,
544,
18,
35,
13,
6738,
62,
80,
7,
80,
8,
1058,
3401,
544,
18,
35,
13,
35067,
49,
14221,
628,
220,
220,
26181,
13,
81,
62,
2411,
796,
26181,
18604,
26801,
17,
5633,
374,
62,
2411,
1058,
532,
49,
62,
2411,
9,
81,
62,
2411,
198,
220,
220,
26181,
13,
49,
62,
2411,
796,
26181,
18604,
26801,
17,
5633,
371,
62,
2411,
1058,
220,
371,
62,
2411,
6,
628,
220,
220,
1303,
81,
62,
8937,
796,
2560,
13,
81,
62,
8937,
1343,
374,
62,
2411,
198,
220,
220,
1303,
49,
62,
8937,
796,
371,
62,
2411,
9,
8000,
13,
49,
62,
8937,
628,
198,
220,
220,
611,
5969,
198,
220,
220,
220,
220,
220,
26181,
13,
73,
1563,
796,
5969,
41,
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198,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
26181,
16,
13,
10134,
16424,
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1563,
796,
2081,
198,
220,
220,
220,
220,
220,
10662,
62,
9688,
796,
5145,
271,
22366,
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49,
8,
5633,
3401,
544,
18,
35,
13,
6738,
62,
49,
7,
49,
8,
1058,
198,
220,
220,
220,
220,
220,
5145,
271,
22366,
7,
80,
8,
5633,
10662,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1058,
3401,
544,
18,
35,
13,
35067,
4507,
9205,
295,
198,
220,
220,
220,
220,
220,
10662,
62,
9688,
796,
26181,
18604,
26801,
17,
5633,
10662,
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9688,
1058,
3401,
544,
18,
35,
13,
259,
4399,
49,
14221,
7,
80,
62,
9688,
8,
628,
220,
220,
220,
220,
220,
26181,
13,
73,
1563,
796,
3232,
45740,
7,
26801,
11,
374,
62,
9688,
796,
26181,
13,
81,
62,
2411,
11,
10662,
62,
9688,
796,
10662,
62,
9688,
8,
198,
220,
220,
886,
198,
220,
220,
1441,
2147,
198,
437,
198,
46249,
628,
198,
198,
8818,
4296,
26545,
0,
7,
26801,
3712,
10267,
18,
35,
2599,
25,
18465,
198,
220,
220,
8931,
796,
9515,
18,
35,
21737,
198,
220,
220,
1303,
23691,
4238,
1751,
319,
8931,
198,
220,
220,
24443,
0,
7,
25558,
11,
26181,
13,
17197,
8,
198,
220,
220,
981,
4129,
7,
25558,
8,
1875,
657,
198,
220,
220,
220,
220,
220,
26181,
796,
1461,
0,
7,
25558,
8,
628,
220,
220,
220,
220,
220,
1303,
24061,
42,
7749,
23372,
0,
7,
26801,
13,
73,
1563,
11,
26181,
11,
3401,
544,
18,
35,
13,
42,
7749,
1512,
32750,
11,
657,
13,
15,
8,
628,
220,
220,
220,
220,
220,
2560,
796,
26181,
13,
8000,
628,
220,
220,
220,
220,
220,
611,
26181,
13,
81,
62,
2411,
38243,
3401,
544,
18,
35,
13,
28667,
38469,
18,
35,
198,
220,
220,
220,
220,
220,
220,
220,
220,
26181,
13,
81,
62,
8937,
796,
2560,
13,
81,
62,
8937,
198,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
26181,
13,
81,
62,
8937,
796,
2560,
13,
81,
62,
8937,
1343,
2560,
13,
49,
62,
8937,
6,
9,
26801,
13,
81,
62,
2411,
198,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
611,
26181,
13,
49,
62,
2411,
38243,
3401,
544,
18,
35,
13,
35067,
49,
14221,
198,
220,
220,
220,
220,
220,
220,
220,
220,
26181,
13,
49,
62,
8937,
796,
2560,
13,
49,
62,
8937,
198,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
26181,
13,
49,
62,
8937,
796,
26181,
13,
49,
62,
2411,
9,
8000,
13,
49,
62,
8937,
198,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
611,
4129,
7,
26801,
13,
41464,
1634,
19778,
8,
6624,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
26181,
13,
41464,
1634,
19778,
58,
16,
4083,
81,
62,
8937,
796,
26181,
13,
81,
62,
8937,
198,
220,
220,
220,
220,
220,
220,
220,
220,
26181,
13,
41464,
1634,
19778,
58,
16,
4083,
49,
62,
8937,
796,
26181,
13,
49,
62,
8937,
198,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
24443,
0,
7,
25558,
11,
26181,
13,
17197,
8,
198,
220,
220,
886,
198,
220,
220,
1441,
2147,
198,
437,
628,
628,
198,
37811,
198,
220,
220,
220,
371,
796,
371,
6738,
10599,
10163,
7,
10599,
10163,
3712,
23839,
38469,
8,
198,
198,
13615,
13179,
17593,
4600,
49,
63,
422,
5172,
272,
18333,
357,
10599,
341,
8379,
2124,
12,
88,
12,
89,
737,
198,
63,
10599,
10163,
63,
389,
262,
5172,
272,
18333,
357,
10599,
341,
8379,
2124,
12,
88,
12,
89,
8,
286,
13179,
422,
5739,
4600,
16,
63,
284,
5739,
4600,
17,
44646,
198,
37811,
198,
8818,
371,
6738,
10599,
10163,
7,
10599,
10163,
3712,
23839,
38469,
8,
628,
220,
220,
220,
357,
21680,
11,
2386,
8,
796,
264,
1939,
418,
7,
10599,
10163,
58,
16,
12962,
198,
220,
220,
220,
357,
82,
1350,
11,
269,
1350,
8,
796,
264,
1939,
418,
7,
10599,
10163,
58,
17,
12962,
198,
220,
220,
220,
357,
82,
4908,
11,
269,
4908,
8,
796,
264,
1939,
418,
7,
10599,
10163,
58,
18,
12962,
198,
220,
220,
220,
1441,
2488,
12310,
265,
8609,
58,
269,
1350,
9,
66,
4908,
220,
220,
3664,
9,
82,
1350,
9,
66,
4908,
10,
9948,
9,
82,
4908,
220,
532,
9948,
9,
82,
1350,
9,
66,
4908,
10,
21680,
9,
82,
4908,
2162,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
66,
1350,
9,
82,
4908,
220,
532,
21680,
9,
82,
1350,
9,
82,
4908,
10,
9948,
9,
66,
4908,
220,
220,
2386,
9,
82,
1350,
9,
82,
4908,
10,
21680,
9,
66,
4908,
2162,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
264,
1350,
220,
220,
220,
220,
220,
532,
21680,
9,
66,
1350,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2386,
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66,
1350,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2361,
198,
198,
437,
628,
198,
37811,
198,
220,
220,
220,
266,
796,
266,
6738,
10599,
10163,
7,
10599,
10163,
3712,
23839,
38469,
11,
4587,
10599,
10163,
3712,
23839,
38469,
8,
198,
198,
13615,
3585,
5724,
864,
15432,
20650,
18,
35,
4600,
86,
63,
422,
5739,
4600,
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63,
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5739,
4600,
17,
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287,
5739,
4600,
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63,
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12,
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] | 2.108131 | 9,396 |
using Test, Random
import Flux: activations
@testset "basic" begin
@testset "helpers" begin
@testset "activations" begin
dummy_model = Chain(x->x.^2, x->x .- 3, x -> tan.(x))
x = randn(10)
@test activations(dummy_model, x)[1] == x.^2
@test activations(dummy_model, x)[2] == (x.^2 .- 3)
@test activations(dummy_model, x)[3] == tan.(x.^2 .- 3)
@test activations(Chain(), x) == ()
@test activations(Chain(identity, x->:foo), x)[2] == :foo # results include `Any` type
end
end
@testset "Chain" begin
@test_nowarn Chain(Dense(10, 5, σ), Dense(5, 2))(randn(10))
@test_throws DimensionMismatch Chain(Dense(10, 5, σ),Dense(2, 1))(randn(10))
# numeric test should be put into testset of corresponding layer
end
@testset "Activations" begin
c = Chain(Dense(3,5,relu), Dense(5,1,relu))
X = Float32.([1.0; 1.0; 1.0])
@test_nowarn gradient(()->Flux.activations(c, X)[2][1], params(c))
end
@testset "Dense" begin
@testset "constructors" begin
@test size(Dense(10, 100).W) == (100, 10)
@test Dense(rand(100,10), rand(10)).σ == identity
@test_throws MethodError Dense(10, 10.5)
@test_throws MethodError Dense(10, 10.5, tanh)
end
@testset "dimensions" begin
@test length(Dense(10, 5)(randn(10))) == 5
@test_throws DimensionMismatch Dense(10, 5)(randn(1))
@test_throws MethodError Dense(10, 5)(1) # avoid broadcasting
@test_throws MethodError Dense(10, 5).(randn(10)) # avoid broadcasting
@test size(Dense(10, 5)(randn(10))) == (5,)
@test size(Dense(10, 5)(randn(10,2))) == (5,2)
@test size(Dense(10, 5)(randn(10,2,3))) == (5,2,3)
@test size(Dense(10, 5)(randn(10,2,3,4))) == (5,2,3,4)
end
@testset "zeros" begin
@test Dense(10, 1, identity, initW = ones, initb = zeros)(ones(10,1)) == 10*ones(1, 1)
@test Dense(10, 1, identity, initW = ones, initb = zeros)(ones(10,2)) == 10*ones(1, 2)
@test Dense(10, 2, identity, initW = ones, initb = zeros)(ones(10,1)) == 10*ones(2, 1)
@test Dense(10, 2, identity, initW = ones, initb = zeros)([ones(10,1) 2*ones(10,1)]) == [10 20; 10 20]
@test Dense(10, 2, identity, initW = ones, bias = false)([ones(10,1) 2*ones(10,1)]) == [10 20; 10 20]
end
end
@testset "Diagonal" begin
@test length(Flux.Diagonal(10)(randn(10))) == 10
@test length(Flux.Diagonal(10)(1)) == 10
@test length(Flux.Diagonal(10)(randn(1))) == 10
@test_throws DimensionMismatch Flux.Diagonal(10)(randn(2))
@test Flux.Diagonal(2)([1 2]) == [1 2; 1 2]
@test Flux.Diagonal(2)([1,2]) == [1,2]
@test Flux.Diagonal(2)([1 2; 3 4]) == [1 2; 3 4]
@test Flux.Diagonal(2)(rand(2,3,4)) |> size == (2, 3, 4)
@test Flux.Diagonal(2,3)(rand(2,3,4)) |> size == (2, 3, 4)
@test Flux.Diagonal(2,3,4)(rand(2,3,4)) |> size == (2, 3, 4)
@test Flux.Diagonal(2,3)(rand(2,1,4)) |> size == (2, 3, 4)
end
@testset "Maxout" begin
# Note that the normal common usage of Maxout is as per the docstring
# These are abnormal constructors used for testing purposes
@testset "Constructor" begin
mo = Maxout(() -> identity, 4)
input = rand(40)
@test mo(input) == input
end
@testset "simple alternatives" begin
mo = Maxout((x -> x, x -> 2x, x -> 0.5x))
input = rand(40)
@test mo(input) == 2*input
end
@testset "complex alternatives" begin
mo = Maxout((x -> [0.5; 0.1]*x, x -> [0.2; 0.7]*x))
input = [3.0 2.0]
target = [0.5, 0.7].*input
@test mo(input) == target
end
@testset "params" begin
mo = Maxout(()->Dense(32, 64), 4)
ps = params(mo)
@test length(ps) == 8 #4 alts, each with weight and bias
end
end
@testset "SkipConnection" begin
@testset "zero sum" begin
input = randn(10, 10, 10, 10)
@test SkipConnection(x -> zeros(size(x)), (a,b) -> a + b)(input) == input
end
@testset "concat size" begin
input = randn(10, 2)
@test size(SkipConnection(Dense(10,10), (a,b) -> cat(a, b, dims = 2))(input)) == (10,4)
end
end
@testset "Parallel" begin
@testset "zero sum" begin
input = randn(10, 10, 10, 10)
@test Parallel(+, x -> zeros(size(x)), identity)(input) == input
end
@testset "concat size" begin
input = randn(10, 2)
@test size(Parallel((a, b) -> cat(a, b; dims=2), Dense(10, 10), identity)(input)) == (10, 4)
end
@testset "vararg input" begin
inputs = randn(10), randn(5), randn(4)
@test size(Parallel(+, Dense(10, 2), Dense(5, 2), Dense(4, 2))(inputs)) == (2,)
end
end
end
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] | 2.169705 | 2,139 |
"""
```
augment_states(m::AnSchorfheide, TTT::Matrix{T}, RRR::Matrix{T}, CCC::Matrix{T}) where {T<:AbstractFloat}
```
### Arguments
-`m`: the m object
-`TTT`, `RRR`, and `CCC`: matrices of the state transition equation
### Return values
- `TTT_aug`, `RRR_aug`, and `CCC_aug`: extend the corresponding input matrices to include
jobservables which are growth rates.
### Description
Some observables in the model are growth rates, which are calculated as a linear combination
of a present and lagged state (which is not yet accounted for in the `TTT`, `RRR`,and `CCC`
matrices). To improve the performance of `gensys`, these additional states are added after
the model is solved. `augment_states` assigns an index to each lagged state, and extends the
input `TTT`, `RRR`, and `CCC` matrices to accommodate the additional states and capture the
lagged state value in the current state vector. `RRR` and `CCC` are mostly augmented with
zeros.
The diagram below shows how `TTT` is extended to `TTT_aug`.
TTT_aug
(m.endogenous_states_additional
x
m.endogenous_states_additional)
_________________________________
| | |
| TTT | endog_ |
| (endogenous_states | states_ |
| x | augmented |
| endogenous_states) | |
|_____________________| |
| |
| endogenous_states_augmented |
|_________________________________|
"""
function augment_states(m::AnSchorfheide, TTT::Matrix{T}, RRR::Matrix{T},
CCC::Vector{T}) where {T<:AbstractFloat}
return TTT, RRR, CCC
end
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] | 2.504425 | 678 |
######################################################
# Jacobian of `f(::AbstractArray...)::AbstractArray` #
######################################################
"""
ReverseDiff.jacobian(f, input, cfg::JacobianConfig = JacobianConfig(input))
If `input` is an `AbstractArray`, assume `f` has the form
`f(::AbstractArray{<:Real})::AbstractArray{<:Real}` and return `J(f)(input)`.
If `input` is a tuple of `AbstractArray`s, assume `f` has the form
`f(::AbstractArray{<:Real}...)::AbstractArray{<:Real}` (such that it can be called as
`f(input...)`) and return a `Tuple` where the `i`th element is the Jacobian of `f` w.r.t.
`input[i].`
Note that `cfg` can be preallocated and reused for subsequent calls.
If possible, it is highly recommended to use `ReverseDiff.JacobianTape` to prerecord `f`.
Otherwise, this method will have to re-record `f`'s execution trace for every subsequent
call.
"""
function jacobian(f, input, cfg::JacobianConfig = JacobianConfig(input))
tape = JacobianTape(f, input, cfg)
isa(input, TrackedArray) && empty!(input.tape)
result = jacobian!(tape, input)
empty!(tape.tape)
return result
end
"""
ReverseDiff.jacobian!(result, f, input, cfg::JacobianConfig = JacobianConfig(input))
Returns `result`. This method is exactly like `ReverseDiff.jacobian(f, input, cfg)`, except
it stores the resulting Jacobian(s) in `result` rather than allocating new memory.
`result` can be an `AbstractArray` or a `Tuple` of `AbstractArray`s. The `result` (or any
of its elements, if `isa(result, Tuple)`), can also be a `DiffResults.DiffResult`, in which
case the primal value `f(input)` (or `f(input...)`, if `isa(input, Tuple)`) will be stored
in it as well.
"""
function jacobian!(result, f, input, cfg::JacobianConfig = JacobianConfig(input))
tape = JacobianTape(f, input, cfg)
isa(input, TrackedArray) && empty!(input.tape)
jacobian!(result, tape, input)
empty!(tape.tape)
return result
end
#########################################################
# Jacobian of `f!(::AbstractArray, ::AbstractArray...)` #
#########################################################
"""
ReverseDiff.jacobian(f!, output, input, cfg::JacobianConfig = JacobianConfig(output, input))
Exactly like `ReverseDiff.jacobian(f, input, cfg)`, except the target function has the
form `f!(output::AbstractArray{<:Real}, input::AbstractArray{<:Real}...)`.
"""
function jacobian(f!, output, input, cfg::JacobianConfig = JacobianConfig(output, input))
tape = JacobianTape(f!, output, input, cfg)
isa(input, TrackedArray) && empty!(input.tape)
result = jacobian!(tape, input)
extract_result_value!(output, output_hook(tape))
empty!(tape.tape)
return result
end
"""
ReverseDiff.jacobian!(result, f!, output, input, cfg::JacobianConfig = JacobianConfig(output, input))
Exactly like `ReverseDiff.jacobian!(result, f, input, cfg)`, except the target function has the
form `f!(output::AbstractArray{<:Real}, input::AbstractArray{<:Real}...)`.
"""
function jacobian!(result, f!, output, input, cfg::JacobianConfig = JacobianConfig(output, input))
tape = JacobianTape(f!, output, input, cfg)
isa(input, TrackedArray) && empty!(input.tape)
jacobian!(result, tape, input)
extract_result_value!(output, output_hook(tape))
empty!(tape.tape)
return result
end
###########################
# Executing JacobianTapes #
###########################
"""
ReverseDiff.jacobian!(tape::Union{JacobianTape,CompiledJacobian}, input)
If `input` is an `AbstractArray`, assume `tape` represents a function of the form
`f(::AbstractArray{<:Real})::AbstractArray{<:Real}` or `f!(::AbstractArray{<:Real},
::AbstractArray{<:Real})` and return `tape`'s Jacobian w.r.t. `input`.
If `input` is a tuple of `AbstractArray`s, assume `tape` represents a function of the form
`f(::AbstractArray{<:Real}...)::AbstractArray{<:Real}` or `f!(::AbstractArray{<:Real},
::AbstractArray{<:Real}...)` and return a `Tuple` where the `i`th element is `tape`'s Jacobian
w.r.t. `input[i].`
Note that if `tape` represents a function of the form `f!(output, input...)`, you can only
execute `tape` with new `input` values. There is no way to re-run `tape`'s tape with new
`output` values; since `f!` can mutate `output`, there exists no stable "hook" for loading
new `output` values into the tape.
"""
function jacobian!(tape::Union{JacobianTape,CompiledJacobian}, input)
result = construct_result(output_hook(tape), input_hook(tape))
jacobian!(result, tape, input)
return result
end
"""
ReverseDiff.jacobian!(result, tape::Union{JacobianTape,CompiledJacobian}, input)
Returns `result`. This method is exactly like `ReverseDiff.jacobian!(tape, input)`, except it
stores the resulting Jacobian(s) in `result` rather than allocating new memory.
`result` can be an `AbstractArray` or a `Tuple` of `AbstractArray`s. The `result` (or any
of its elements, if `isa(result, Tuple)`), can also be a `DiffResults.DiffResult`, in which
case the primal value of the target function will be stored in it as well.
"""
function jacobian!(result, tape::Union{JacobianTape,CompiledJacobian}, input)
seeded_forward_pass!(tape, input)
seeded_reverse_pass!(result, tape)
return result
end
##################################################
# unused (but faster) versions of the above code #
##################################################
#=
These commented-out versions of `jacobian` are faster than the ones we're
actually using above, because they avoid a redundant forward pass. This extra
forward pass should be unneccesary - since no input values are changing,
the record pass should be sufficient on its own. However, for some unknown
reason, getting rid of the superfluous forward pass breaks nested
differentation.
=#
# function jacobian(f, input, cfg::JacobianConfig = JacobianConfig(input))
# tape = JacobianTape(f, input, cfg)
# result = construct_result(output_hook(tape), input_hook(tape))
# seeded_reverse_pass!(result, output_hook(tape), input_hook(tape), tape.tape)
# empty!(tape.tape)
# return result
# end
#
# function jacobian!(result, f, input, cfg::JacobianConfig = JacobianConfig(input))
# tape = JacobianTape(f, input, cfg)
# seeded_reverse_pass!(result, output_hook(tape), input_hook(tape), tape.tape)
# empty!(tape.tape)
# return result
# end
#
# function jacobian(f!, output, input, cfg::JacobianConfig = JacobianConfig(output, input))
# tape = JacobianTape(f!, output, input, cfg)
# result = construct_result(output_hook(tape), input_hook(tape))
# seeded_reverse_pass!(result, output_hook(tape), input_hook(tape), tape.tape)
# extract_result_value!(output, output_hook(tape))
# empty!(tape.tape)
# return result
# end
#
# function jacobian!(result, f!, output, input, cfg::JacobianConfig = JacobianConfig(output, input))
# tape = JacobianTape(f!, output, input, cfg)
# seeded_reverse_pass!(result, output_hook(tape), input_hook(tape), tape.tape)
# extract_result_value!(output, output_hook(tape))
# empty!(tape.tape)
# return result
# end
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] | 2.908831 | 2,446 |
### A Pluto.jl notebook ###
# v0.12.6
using Markdown
using InteractiveUtils
# ╔═╡ de7a5e8a-1ee4-11eb-2bbf-0f0eb3e489ec
using Pkg, DrWatson
# ╔═╡ e4eeb13a-1ee4-11eb-0113-91f9a9c3b659
begin
@quickactivate "StatisticsWithJuliaPlutoNotebooks"
using Random, StatsBase, Combinatorics, DataFrames
Random.seed!(1)
end
# ╔═╡ ac3ceabe-1ee4-11eb-3935-313e96aafd62
md"## Listing 2.8"
# ╔═╡ c4bef1a4-1ee9-11eb-0b58-8be033f6fbad
function bruteSetsProbabilityAllMiss(n)
omega = collect(permutations(1:n))
matchEvents = []
for i in 1:n
event = []
for p in omega
if p[i] == i
push!(event,p)
end
end
push!(matchEvents,event)
end
noMatch = setdiff(omega,union(matchEvents...))
return length(noMatch)/length(omega)
end
# ╔═╡ e9f9616e-1eeb-11eb-177d-2fa9d0c18b13
formulaCalcAllMiss(n) = sum([(-1)^k/factorial(k) for k in 0:n])
# ╔═╡ e9f99800-1eeb-11eb-3333-59c96cd555d4
function mcAllMiss(n,N)
function envelopeStuffer()
envelopes = Random.shuffle!(collect(1:n))
return sum([envelopes[i] == i for i in 1:n]) == 0
end
data = [envelopeStuffer() for _ in 1:N]
return sum(data)/N
end
# ╔═╡ e9fa266e-1eeb-11eb-2630-59930c474ed3
begin
N = 10^6
df = DataFrame()
for n in 1:6
bruteForce = bruteSetsProbabilityAllMiss(n)
fromFormula = formulaCalcAllMiss(n)
fromMC = mcAllMiss(n,N)
append!(df, DataFrame(
:bruteForce => round(bruteForce,digits=4),
:fromFormula => round(fromFormula,digits=4),
:fromMC => round(fromMC,digits=4),
:asymptotic => round(1/MathConstants.e,digits=4))
)
end
df
end
# ╔═╡ ea086874-1eeb-11eb-2cb0-4186697d4294
md"## End of listing 2.8"
# ╔═╡ Cell order:
# ╟─ac3ceabe-1ee4-11eb-3935-313e96aafd62
# ╠═de7a5e8a-1ee4-11eb-2bbf-0f0eb3e489ec
# ╠═e4eeb13a-1ee4-11eb-0113-91f9a9c3b659
# ╠═c4bef1a4-1ee9-11eb-0b58-8be033f6fbad
# ╠═e9f9616e-1eeb-11eb-177d-2fa9d0c18b13
# ╠═e9f99800-1eeb-11eb-3333-59c96cd555d4
# ╠═e9fa266e-1eeb-11eb-2630-59930c474ed3
# ╟─ea086874-1eeb-11eb-2cb0-4186697d4294
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] | 1.821747 | 1,122 |
# Autogenerated wrapper script for CUDA_Toolkit_jll for x86_64-w64-mingw32-cuda+10.0
export libcublas, libcudadevrt, libcufft, libcupti, libcurand, libcusolver, libcusparse, libdevice, libnvtoolsext, libnvvm, nvdisasm, nvlink, ptxas
JLLWrappers.@generate_wrapper_header("CUDA_Toolkit")
JLLWrappers.@declare_library_product(libcublas, "cublas64_100.dll")
JLLWrappers.@declare_file_product(libcudadevrt)
JLLWrappers.@declare_library_product(libcufft, "cufft64_100.dll")
JLLWrappers.@declare_library_product(libcupti, "cupti64_100.dll")
JLLWrappers.@declare_library_product(libcurand, "curand64_100.dll")
JLLWrappers.@declare_library_product(libcusolver, "cusolver64_100.dll")
JLLWrappers.@declare_library_product(libcusparse, "cusparse64_100.dll")
JLLWrappers.@declare_file_product(libdevice)
JLLWrappers.@declare_library_product(libnvtoolsext, "nvToolsExt64_1.dll")
JLLWrappers.@declare_library_product(libnvvm, "nvvm64_33_0.dll")
JLLWrappers.@declare_executable_product(nvdisasm)
JLLWrappers.@declare_executable_product(nvlink)
JLLWrappers.@declare_executable_product(ptxas)
function __init__()
JLLWrappers.@generate_init_header()
JLLWrappers.@init_library_product(
libcublas,
"bin\\cublas64_100.dll",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_file_product(
libcudadevrt,
"lib\\cudadevrt.lib",
)
JLLWrappers.@init_library_product(
libcufft,
"bin\\cufft64_100.dll",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_library_product(
libcupti,
"bin\\cupti64_100.dll",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_library_product(
libcurand,
"bin\\curand64_100.dll",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_library_product(
libcusolver,
"bin\\cusolver64_100.dll",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_library_product(
libcusparse,
"bin\\cusparse64_100.dll",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_file_product(
libdevice,
"share\\libdevice\\libdevice.10.bc",
)
JLLWrappers.@init_library_product(
libnvtoolsext,
"bin\\nvToolsExt64_1.dll",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_library_product(
libnvvm,
"bin\\nvvm64_33_0.dll",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_executable_product(
nvdisasm,
"bin\\nvdisasm.exe",
)
JLLWrappers.@init_executable_product(
nvlink,
"bin\\nvlink.exe",
)
JLLWrappers.@init_executable_product(
ptxas,
"bin\\ptxas.exe",
)
JLLWrappers.@generate_init_footer()
end # __init__()
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module TensorProductQuadraturesTests
using Gridap.Integration
using Test
degrees = (2,2)
quad = TensorProductQuadrature(degrees)
test_quadrature(quad)
@test sum(get_weights(quad)) ≈ 1
D = 2
degree = 3
quad = TensorProductQuadrature{D}(degree)
test_quadrature(quad)
@test sum(get_weights(quad)) ≈ 1
end # module
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<filename>src/distances/edit.jl<gh_stars>100-1000
"""
Hamming()
Creates the Hamming distance
The Hamming distance is defined as the number of characters that do not match
"""
struct Hamming <: StringMetric end
function (dist::Hamming)(s1, s2; max_dist::Union{Integer, Nothing} = nothing)
(s1 === missing) | (s2 === missing) && return missing
out = abs(length(s2) - length(s1))
for (ch1, ch2) in zip(s1, s2)
out += ch1 != ch2
if max_dist !== nothing
out > max_dist && return Int(max_dist + 1)
end
end
return out
end
"""
Jaro()
Creates the Jaro distance
The Jaro distance is defined as
``1 - (m / |s1| + m / |s2| + (m - t) / m) / 3``
where ``m`` is the number of matching characters and
``t`` is half the number of transpositions.
"""
struct Jaro <: StringSemiMetric end
## http://alias-i.com/lingpipe/docs/api/com/aliasi/spell/JaroWinklerDistance.html
function (dist::Jaro)(s1, s2)
(s1 === missing) | (s2 === missing) && return missing
len1, len2 = length(s1), length(s2)
if len1 > len2
s1, s2 = s2, s1
len1, len2 = len2, len1
end
# If both iterators empty, formula in Wikipedia gives 1, but it makes more sense to set it to s1 == s2
len2 > 0 || return Float64(s1 == s2)
d = max(0, div(len2, 2) - 1)
flag = fill(false, len2)
ch1_match = Vector{eltype(s1)}()
for (i1, ch1) in enumerate(s1)
for (i2, ch2) in enumerate(s2)
# for each character in s1, greedy search of matching character in s2 within a distance d
i2 >= i1 - d || continue
i2 <= i1 + d || break
if ch1 == ch2 && !flag[i2]
flag[i2] = true
push!(ch1_match, ch1)
break
end
end
end
if isempty(ch1_match)
return 1.0
else
# m counts number matching characters
m = length(ch1_match)
# t/2 counts number transpositions
t = 0
i1 = 0
for (i2, ch2) in enumerate(s2)
if flag[i2]
i1 += 1
@inbounds t += ch2 != ch1_match[i1]
end
end
return 1 - (m / len1 + m / len2 + (m - 0.5 * t) / m) / 3
end
end
"""
JaroWinkler(;p = 0.1, threshold = 0.3, maxlength = 4)
Creates the JaroWinkler distance
The JaroWinkler distance is defined as the Jaro distance, which is multiplied by
``(1-min(l, maxlength) * p)`` as long as it is lower than `threshold`, and where `l` denotes the length of the common prefix.
"""
struct JaroWinkler <: StringSemiMetric
p::Float64 # scaling factor. Default to 0.1
threshold::Float64 # boost limit. Default to 0.3
maxlength::Integer # max length of common prefix. Default to 4
end
JaroWinkler(; p = 0.1, threshold = 0.3, maxlength = 4) = JaroWinkler(p, threshold, maxlength)
## http://alias-i.com/lingpipe/docs/api/com/aliasi/spell/JaroWinklerDistance.html
function (dist::JaroWinkler)(s1, s2)
(s1 === missing) | (s2 === missing) && return missing
out = Jaro()(s1, s2)
if out <= dist.threshold
l = common_prefix(s1, s2)[1]
out = (1 - min(l, dist.maxlength) * dist.p) * out
end
return out
end
"""
Levenshtein()
Creates the Levenshtein distance
The Levenshtein distance is the minimum number of operations (consisting of insertions, deletions,
substitutions of a single character) required to change one string into the other.
"""
struct Levenshtein <: StringMetric end
## Source: http://blog.softwx.net/2014/12/optimizing-levenshtein-algorithm-in-c.html
# Return max_dist + 1 if distance higher than max_dist
# to differentiate distance equal to max_dist or not, which is important for find fctions.
function (dist::Levenshtein)(s1, s2; max_dist::Union{Integer, Nothing} = nothing)
(s1 === missing) | (s2 === missing) && return missing
len1, len2 = length(s1), length(s2)
if len1 > len2
s1, s2 = s2, s1
len1, len2 = len2, len1
end
if max_dist !== nothing
len2 - len1 > max_dist && return Int(max_dist + 1)
end
# prefix common to both strings can be ignored
k = common_prefix(s1, s2)
k == len1 && return len2 - k
# first row of matrix set to distance between "" and s2[1:i]
v = collect(1:(len2-k))
current = 0
for (i1, ch1) in enumerate(s1)
i1 > k || continue
left = current = i1 - k - 1
if max_dist !== nothing
value_lb = left - 1
end
for (i2, ch2) in enumerate(s2)
i2 > k || continue
above = current
# cost on diagonal (substitution)
current = left
@inbounds left = v[i2 - k]
if ch1 != ch2
# minimum between substitution, deletion and insertion
current = min(current + 1, above + 1, left + 1)
end
if max_dist !== nothing
value_lb = min(value_lb, left)
end
@inbounds v[i2 - k] = current
end
if max_dist !== nothing
value_lb > max_dist && return Int(max_dist + 1)
end
end
if max_dist !== nothing
current > max_dist && return Int(max_dist + 1 )
end
return current
end
"""
OptimalStringAlignement()
Creates the OptimalStringAlignement distance (also known ad the unrestricted DamerauLevenshtein distance).
It is the minimum number of operations (consisting of insertions,
deletions or substitutions of a single character, or transposition of two adjacent characters)
required to change one string into the other.
The distance differs slightly from the Damerau-Levenshtein algorithm by imposing
the restriction that no substring is edited more than once. So for example, "CA" to "ABC" has an edit
distance of 2 by a complete application of Damerau-Levenshtein, but a distance of 3 by this method that
uses the optimal string alignment algorithm. In particular, the restricted distance does not satisfy
the triangle inequality.
"""
struct OptimalStringAlignement <: StringSemiMetric end
## http://blog.softwx.net/2015/01/optimizing-damerau-levenshtein_15.html
# Return max_dist + 1 if distance higher than max_dist
function (dist::OptimalStringAlignement)(s1, s2; max_dist::Union{Integer, Nothing} = nothing)
(s1 === missing) | (s2 === missing) && return missing
len1, len2 = length(s1), length(s2)
if len1 > len2
s1, s2 = s2, s1
len1, len2 = len2, len1
end
if max_dist !== nothing
len2 - len1 > max_dist && return Int(max_dist + 1)
end
k = common_prefix(s1, s2)
k == len1 && return len2 - k
v = collect(1:(len2 - k))
w = similar(v)
prevch1, prevch2 = first(s1), first(s2)
if max_dist !== nothing
i2_start = 0
i2_end = max_dist
end
current = 0
for (i1, ch1) in enumerate(s1)
i1 > k || (prevch1 = ch1 ; continue)
left = i1 - k - 1
current = i1 - k
nextTransCost = 0
if max_dist !== nothing
i2_start += i1 - k - 1 + len2 - len1 > max_dist
i2_end += i2_end < len2
end
for (i2, ch2) in enumerate(s2)
i2 > k || (prevch2 = ch2 ; continue)
# no need to look beyond window of lower right diagonal - max distance cells
# lower right diag is i1 - (len2 - len1)) and the upper left diagonal + max_dist cells (upper left is i1)
if max_dist !== nothing
(k + i2_start < i2 < 1 + k + i2_end) || (prevch2 = ch2 ; continue)
end
above = current
thisTransCost = nextTransCost
@inbounds nextTransCost = w[i2 - k]
@inbounds w[i2 - k] = current = left
@inbounds left = v[i2 - k]
if ch1 != ch2
# minimum between substitution, deletion and insertion
current = min(current + 1, above + 1, left + 1)
if i1 > k + 1 && i2 > k + 1 && ch1 == prevch2 && prevch1 == ch2
thisTransCost += 1
current = min(current, thisTransCost)
end
end
@inbounds v[i2 - k] = current
prevch2 = ch2
end
if max_dist !== nothing
v[i1 - k + len2 - len1] > max_dist && return Int(max_dist + 1)
end
prevch1 = ch1
end
if max_dist !== nothing
current > max_dist && return Int(max_dist + 1)
end
return Int(current)
end
"""
DamerauLevenshtein()
Creates the DamerauLevenshtein distance
The DamerauLevenshtein distance is the minimum number of operations (consisting of insertions,
deletions or substitutions of a single character, or transposition of two adjacent characters)
required to change one string into the other.
"""
struct DamerauLevenshtein <: StringMetric end
# https://en.wikipedia.org/wiki/Damerau–Levenshtein_distance
# https://www.lemoda.net/text-fuzzy/damerau-levenshtein/
# Compared to Levenshtein & Restricted distance, cannot get by with only two vectors since transposition can be global
function (dist::DamerauLevenshtein)(s1, s2)
(s1 === missing) | (s2 === missing) && return missing
len1, len2 = length(s1), length(s2)
if len1 > len2
s1, s2 = s2, s1
len1, len2 = len2, len1
end
k = common_prefix(s1, s2)
k == len1 && return len2 - k
# da[ch1] will store last spotted position of ch1 in s1
da = Dict{eltype(s1), UInt32}()
sizehint!(da, len1 - k)
# distm[i1+1, i2+1] will store the distance between first i1 elements of s1 and first i2 elements of s2
distm = zeros(UInt32, len1 + 1 - k, len2 + 1 - k)
distm[:, 1] = 0:(len1-k)
distm[1, :] = 0:(len2-k)
for (i1, ch1) in enumerate(s1)
i1 > k || continue
# j2 is last spotted position of ch1 in s2
# j1 will be last spotted position of ch2 in s1
j2 = 0
for (i2, ch2) in enumerate(s2)
i2 > k || continue
if ch1 == ch2
@inbounds distm[i1 + 1 - k, i2 + 1 - k] = distm[i1 - k, i2 - k]
j2 = i2
else
# minimum between substitution, deletion and insertion
@inbounds pre = min(distm[i1 - k, i2 - k] + one(UInt32),
distm[i1 + 1 - k, i2 - k] + one(UInt32),
distm[i1 - k, i2 + 1 - k] + one(UInt32))
# minimum wrt transposition --- avoid lookup if we know transposition won't be chosen
# either because we're treating first character of s1 or because ch1 has not been spotted in s2 yet
j1 = (i1 == k + 1 || j2 == 0) ? 0 : get(da, ch2, 0)
if j1 > 0
@inbounds pre = min(pre, distm[j1 - k, j2 - k] + (i1 - j1 - 1) + 1 + (i2 - j2 - 1))
end
@inbounds distm[i1 + 1 - k, i2 + 1 - k] = pre
end
end
da[ch1] = i1
end
return Int(distm[end, end])
end
"""
RatcliffObershelp()
Creates the RatcliffObershelp distance
The distance between two strings is defined as one minus the number of matching characters
divided by the total number of characters in the two strings. Matching characters are those
in the longest common subsequence plus, recursively, matching characters in the unmatched
region on either side of the longest common subsequence.
"""
struct RatcliffObershelp <: StringSemiMetric end
function (dist::RatcliffObershelp)(s1, s2)
(s1 === missing) | (s2 === missing) && return missing
len1, len2 = length(s1), length(s2)
n_matched = length_matching_blocks(s1, s2, 1, 1, len1, len2)
len1 + len2 == 0 ? 0.0 : 1 - 2 * n_matched / (len1 + len2)
end
function length_matching_blocks(s1, s2, start1::Integer, start2::Integer, end1::Integer, end2::Integer)
# p is just a storage vector which will be reused
p = zeros(Int, max(end1 - start1, end2 - start2) + 1)
length_matching_blocks!(p, s1, s2, start1, start2, end1, end2)
end
function length_matching_blocks!(p::Vector{Int}, s1, s2, start1::Integer, start2::Integer, end1::Integer, end2::Integer)
end1 >= start1 || return 0
end2 >= start2 || return 0
j1, j2, len = longest_common_pattern!(p, s1, s2, start1, start2, end1, end2)
# exit if there is no common substring
len == 0 && return 0
return len +
length_matching_blocks!(p, s1, s2, start1, start2, j1 - 1, j2 - 1) +
length_matching_blocks!(p, s1, s2, j1 + len, j2 + len, end1, end2)
end
function longest_common_pattern!(p, s1, s2, start1, start2, end1, end2)
if end1 - start1 > end2 - start2
j2, j1, len = longest_common_pattern!(p, s2, s1, start2, start1, end2, end1)
else
j1, j2, len = 0, 0, 0
fill!(p, 0)
# p[i2-start2+1] stores the startingindex of the longest
# common pattern up to i2 with prevch1 as last matching character
for (i1, ch1) in enumerate(s1)
i1 >= start1 || continue
i1 <= end1 || break
oldj2 = 0
for (i2, ch2) in enumerate(s2)
i2 >= start2 || continue
i2 <= end2 || break
if ch1 != ch2
newj2 = 0
else
newj2 = oldj2 > 0 ? oldj2 : i2
newlen = i2 - newj2 + 1
if newlen > len
j1, j2, len = i1 - newlen + 1, newj2, newlen
end
end
p[i2 - start2 + 1], oldj2 = newj2, p[i2 - start2 + 1]
end
end
end
return j1, j2, len
end | [
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<gh_stars>1-10
module Testing
using ..NodalDG
using Test
VX = [0.0, 1.0, 2.0, 3.0]
EToV = [1 2; 2 3; 3 4]
g = NodalDG.Grid1D(NodalDG.Mesh1D(VX, EToV), NodalDG.gaussLobatto1D(2))
println(g.vmapM, g.vmapP)
# Test based on p58/9 in book. Note errata on website
EToV = [1 2; 2 3; 3 5; 5 4]
end
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] | 1.834356 | 163 |
import RowEchelon
import LinearAlgebra
#=
Homework 7
Section 3.2
Problems 26
=#
# Create a matrix with the vectors as columns
A = [3 2 -2 0 ;
5 -6 -1 0 ;
-6 0 3 0 ;
4 7 0 -3]
# Find the detA
detA = LinearAlgebra.det(A)
println("Original Matrix:")
println(A)
println("Determinate:")
println(detA)
# See if the Determine was 0
# Dont forget floating point error
if (detA > 0.000001 || detA < -0.000001)
println("Hence the original matrix is invertable, and its columns form a linearly independent set.")
else
println("Hence the original matrix is NOT invertable, and its columns form a linearly dependent set.")
end
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] | 2.840708 | 226 |
<filename>test/test_vlen_lowlevel.jl
import NCDatasets
filename = tempname()
dimlen = 10
T = Int32
data = Vector{Vector{T}}(undef,dimlen)
for i = 1:length(data)
data[i] = T.(collect(1:i) .+ 100 * i)
end
ncdata = Vector{NCDatasets.nc_vlen_t{T}}(undef,dimlen)
for i = 1:length(data)
ncdata[i] = NCDatasets.nc_vlen_t{T}(length(data[i]), pointer(data[i]))
end
varname = "varname"
vlentypename = "name-vlen"
ncid = NCDatasets.nc_create(filename,NCDatasets.NC_CLOBBER | NCDatasets.NC_NETCDF4)
dimid = NCDatasets.nc_def_dim(ncid, "casts", dimlen)
typeid = NCDatasets.nc_def_vlen(ncid, vlentypename, NCDatasets.ncType[T])
varid = NCDatasets.nc_def_var(ncid, varname, typeid, [dimid])
for i = 1:dimlen
NCDatasets.nc_put_var1(ncid, varid, [i-1], data[i])
end
typeids = NCDatasets.nc_inq_typeids(ncid)
typeid = typeids[1]
name2,datum_size2,base_nc_type2 = NCDatasets.nc_inq_vlen(ncid,typeid)
@test name2 == vlentypename
@test NCDatasets.jlType[base_nc_type2] == T
NCDatasets.nc_close(ncid)
# Reopen
ncid = NCDatasets.nc_open(filename,NCDatasets.NC_NOWRITE)
varid = NCDatasets.nc_inq_varid(ncid,varname)
typeids = NCDatasets.nc_inq_typeids(ncid)
#@show typeids
xtype = NCDatasets.nc_inq_vartype(ncid,varid)
@test xtype == typeids[1]
if xtype >= NCDatasets.NC_FIRSTUSERTYPEID
#@show xtype,NCDatasets.NC_VLEN
typename,shape,base_nc_type,numfields,class = NCDatasets.nc_inq_user_type(ncid,xtype)
#@show typename,shape,base_nc_type,numfields,class
@test base_nc_type == NCDatasets.NC_INT
T2 = NCDatasets.jlType[base_nc_type]
@test T == T2
if class == NCDatasets.NC_VLEN
data2 = Vector{Vector{T}}(undef,dimlen)
NCDatasets.nc_get_var!(ncid,varid,data2)
@test data == data2
for i = 1:dimlen
tmp2 = NCDatasets.nc_get_var1(Vector{T},ncid,varid,[i-1])
@test data[i] == tmp2
end
end
end
NCDatasets.nc_close(ncid)
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] | 1.998965 | 966 |
<filename>test/faraday.jl
using LaserTypes, Test
using Unitful
using StaticArrays
using UnitfulAtomic
using LinearAlgebra
import PhysicalConstants.CODATA2018: c_0
@testset "$laser" for laser in [GaussLaser, LaguerreGaussLaser]
units = [:SI_unitful, :atomic_unitful, :SI, :atomic]
x_si = SVector{4}(uconvert(u"μm", c_0*0u"s"), 0u"μm",0u"μm",1u"μm")
x = (x_si, auconvert.(x_si), ustrip.(x_si), ustrip.(auconvert.(x_si)))
@testset "$unit" for (unit, xᵢ) in zip(units, x)
s = setup_laser(laser, unit, profile=ConstantProfile())
F = Fμν(xᵢ,s)
c = s.constants.c
if unit ∈ [:SI_unitful, :atomic_unitful]
@test all(dimension.(F) .== Ref(dimension(u"T")))
end
@test size(F) == (4, 4)
@test det(F) ≈ 1/c^2 * (E(xᵢ[2:4], xᵢ[1]/c, s) ⋅ B(xᵢ[2:4], xᵢ[1]/c, s))^2
end
end
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27,
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29,
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] | 1.938356 | 438 |
<gh_stars>0
### Instructions for adding a new version of the Go toolchain
#
# * find the latest stable releases at https://go.dev/dl/, update the `version`
# variable and the SHA256 hash of the release tarball in the sources, the
# expected checksum is provided in the download page.
# * To deploy the shard and automatically update your BinaryBuilderBase's
# `Artifacts.toml`, use the `--deploy` flag to the `build_tarballs.jl` script.
# You can build & deploy by running:
#
# julia build_tarballs.jl --debug --verbose --deploy
#
using BinaryBuilder
include("../common.jl")
name = "Go"
version = v"1.17.7"
sources = [
ArchiveSource(
"https://go.dev/dl/go$(version).linux-amd64.tar.gz",
"02b111284bedbfa35a7e5b74a06082d18632eff824fd144312f6063943d49259",
)
]
# Bash recipe for building across all platforms
script = raw"""
mv ${WORKSPACE}/srcdir/go ${prefix}/
"""
# We only build for host platform: x86_64-linux-musl
platforms = [
host_platform,
]
# Dependencies that must be installed before this package can be built
dependencies = []
# The products that we will ensure are always built
products = [
ExecutableProduct("go", :go, "go/bin"),
ExecutableProduct("gofmt", :gofmt, "go/bin"),
]
# Build the tarballs, and possibly a `build.jl` as well.
ndARGS, deploy_target = find_deploy_arg(ARGS)
build_info = build_tarballs(ndARGS, name, version, sources, script, platforms, products, dependencies; skip_audit=true)
if deploy_target !== nothing
upload_and_insert_shards(deploy_target, name, version, build_info)
end
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] | 2.875912 | 548 |
using Random
using PyCall
using ArviZ: attributes
try
ArviZ.initialize_bokeh()
catch
@info "bokeh backend not available. bokeh tests will be skipped."
end
py"""
class PyNullObject(object):
def __init__(self):
pass
"""
function create_model(seed=10)
rng = MersenneTwister(seed)
J = 8
nchains = 4
ndraws = 500
data = Dict(
"J" => J,
"y" => [28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0],
"sigma" => [15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0],
)
posterior = Dict(
"mu" => randn(rng, nchains, ndraws),
"tau" => abs.(randn(rng, nchains, ndraws)),
"eta" => randn(rng, nchains, ndraws, J),
"theta" => randn(rng, nchains, ndraws, J),
)
posterior_predictive = Dict("y" => randn(rng, nchains, ndraws, J))
sample_stats = Dict(
"energy" => randn(rng, nchains, ndraws),
"diverging" => (randn(rng, nchains, ndraws) .> 0.90),
"max_depth" => (randn(rng, nchains, ndraws) .> 0.90),
"log_likelihood" => randn(rng, nchains, ndraws, J),
)
prior = Dict(
"mu" => randn(rng, nchains, ndraws) / 2,
"tau" => abs.(randn(rng, nchains, ndraws)) / 2,
"eta" => randn(rng, nchains, ndraws, data["J"]) / 2,
"theta" => randn(rng, nchains, ndraws, data["J"]) / 2,
)
prior_predictive = Dict("y" => randn(rng, nchains, ndraws, J) / 2)
sample_stats_prior = Dict(
"energy" => randn(rng, nchains, ndraws),
"diverging" => Int.(randn(rng, nchains, ndraws) .> 0.95),
)
model = from_dict(;
posterior=posterior,
posterior_predictive=posterior_predictive,
sample_stats=sample_stats,
prior=prior,
prior_predictive=prior_predictive,
sample_stats_prior=sample_stats_prior,
observed_data=Dict("y" => data["y"]),
dims=Dict("y" => ["obs_dim"], "log_likelihood" => ["obs_dim"]),
coords=Dict("obs_dim" => 1:J),
)
return model
end
models() = (model_1=create_model(10), model_2=create_model(11))
function noncentered_schools_data()
return Dict(
"J" => 8,
"y" => [28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0],
"sigma" => [15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0],
)
end
function check_multiple_attrs(test_dict, parent)
failed_attrs = []
for (dataset_name, attributes) in test_dict
if Symbol(dataset_name) ∈ propertynames(parent)
dataset = getproperty(parent, Symbol(dataset_name))
for attribute in attributes
if Symbol(attribute) ∉ propertynames(dataset)
push!(failed_attrs, (dataset_name, attribute))
end
end
else
push!(failed_attrs, dataset_name)
end
end
return failed_attrs
end
dimsizes(ds) = ds._dims
convertindex(x::AbstractArray) = x
convertindex(o::PyObject) = o.array.values
vardict(ds) = Dict(k => convertindex(v._data) for (k, v) in ds._variables)
dimdict(ds) = Dict(k => v._dims for (k, v) in ds._variables)
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, get(dims, Symbol(name), []))
s = (x -> length(get(coords, x, get(coords, Symbol(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
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] | 2.035003 | 1,857 |
<filename>test/runtests.jl
using RAFF
using Test
using DelimitedFiles
using Distributed
using Random
using SharedArrays
using Logging
include("test_raff.jl")
include("test_solvers.jl")
include("test_utils.jl")
include("test_generator.jl")
include("test_parallel.jl")
include("test_multivariate.jl")
include("test_integration.jl")
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] | 2.888889 | 117 |
<reponame>thalesfr/MagnusCorrection<filename>SNAP/fig6bc.jl
using LinearAlgebra
using StaticArrays
using Plots; pyplot()
using FFTW
using FastClosures
using NPZ
using LaTeXStrings
include("/Users/tfiguei/Work/Magnus/MagnusExpansion.jl")
include("AuxFunctions.jl")
include("HarmonicCorrectionSnap.jl")
include("snap.jl")
σ = pauli_matrices()
χ_GHz = (-8281.0, 0.0, 0.0)#(-8281.0, 48.8, 0.5)
χ = χ_GHz./abs(χ_GHz[1])
N = 10
levels = SA[0, 4]#collect(0:1:9)
α = (pi/2, pi/3)#pi*(rand(10) .- 0.5)
tf = 50.0
Ncorr = 10
system_params = Dict("chi"=>χ, "N"=>N, "levels"=>levels, "alpha"=>α,
"tf"=>tf, "Ncorr"=>Ncorr)
UI = U(system_params, σ; ideal_dynamics=false, frame="interaction", matrix=true)
Identity = Matrix{Complex{Float64}}(I, 2*N, 2*N)
println(1 - fidelity(UI, Identity, 2*N))
Uideal = U(system_params, σ; ideal_dynamics=true, matrix=true)
Ur = U(system_params, σ; ideal_dynamics=false, matrix=true)
println(1 - fidelity(Ur, Uideal, 2*N))
# "First" order correction
magnus_order = 1
max_order = 2
coeffs = get_coeffs(magnus_order, max_order, Ncorr, system_params)
println("---")
Ucorr = U(system_params, σ, coeffs, max_order; frame="qubit", matrix=true)
println(1 - fidelity(Ucorr, Uideal, 2*N))
# "Second" order correction
magnus_order = 2
coeffs2 = get_coeffs(coeffs, magnus_order, max_order, Ncorr, system_params)
println("---")
coeffs2 = coeffs + coeffs2
Ucorr = U(system_params, σ, coeffs2, max_order; frame="qubit", matrix=true)
println(1 - fidelity(Ucorr, Uideal, 2*N))
# In the lab frame
println("Results in the lab frame")
Uideal = U(system_params, σ; frame="lab", ideal_dynamics=true, matrix=true)
Unideal = U(system_params, σ; frame="lab", ideal_dynamics=false, matrix=true)
Ucorr = U(system_params, σ, coeffs2, max_order; frame="lab", matrix=true)
println(1 - fidelity(Unideal, Uideal, 2*N))
println(1 - fidelity(Ucorr, Uideal, 2*N))
#FFT results
Npoints = 1000
data = zeros(Npoints, 5)
for i = 1: Npoints
t = (i-1)*tf/(Npoints-1)
data[i,1] = t
data[i, 2: 3] .= pulse_envelope(t, tf, χ, levels, α)
data[i, 4: 5] .= data[i, 2: 3]
data[i, 4: 5] .+= correction_pulse_envelope(t, tf, χ, coeffs2, max_order, Ncorr)
end
x = data[:, 1]
y = data[:, [2, 4]]
p1 = plot(x, y,
linewidth = 2,
labels = ["Initial\npulse" "Corrected\npulse"],
layout = (2, 1),
title = ["x quadrature" " "])
plot!(xlim=(0, tf),
tickfont = font(12, "Serif"),
xlabel = "time "*L"t/t_\mathrm{f}",
ylabel = "pulse "*L"\times |\chi|^{-1}",
guidefont = font(14, "Serif"),
legendfont = font(12, "Serif"),
margin = 5Plots.mm)
x = data[:, 1]
y = data[:, [3, 5]]
p2 = plot(x, y,
linewidth = 2,
labels = ["Initial\npulse" "Corrected\npulse"],
layout = (2, 1),
title = ["y quadrature" " "])
plot!(xlim=(0, tf),
tickfont = font(12, "Serif"),
xlabel = "time "*L"t/t_\mathrm{f}",
ylabel = "pulse "*L"\times |\chi|^{-1}",
guidefont = font(14, "Serif"),
legendfont = font(12, "Serif"),
background_color_legend = false,
margin = 5Plots.mm)
plot(p1, p2, layout=(1,2), size = (800, 800))
savefig("pulse.pdf")
# Add padding to the pulse
data = vcat(zeros(Npoints, 5), data, zeros(Npoints, 5))
fft_data = zeros(size(data))
fft_data[:, 1] = fftfreq(size(data)[1], tf/(Npoints-1))
for i = 2: 5
fft_pulse = fft(data[:, i])
fft_data[:, i] = abs.(fft_pulse)
end
a = div(size(data)[1], 2)
x = fft_data[1: a, 1]
y = fft_data[1: a, [2, 4]]
p1 = plot(x, y,
linewidth = 2,
labels = ["Initial\npulse" "Corrected\npulse"],
linestyle = [:solid :dash])
plot!(xlim=(0, 10),
tickfont = font(12, "Serif"),
xlabel = "Frequency "*L"\omega/|\chi|",
ylabel = L"|S_x(x)|",
guidefont = font(14, "Serif"),
legendfont = font(12, "Serif"),
background_color_legend = false,
margin = 5Plots.mm)
x = fft_data[1: a, 1]
y = fft_data[1: a, [3, 5]]
p2 = plot(x, y,
linewidth = 2,
labels = ["Initial\npulse" "Corrected\npulse"],
linestyle = [:solid :dash])
plot!(xlim=(0, 10),
tickfont = font(12, "Serif"),
xlabel = "Frequency "*L"\omega/|\chi|",
ylabel = L"|S_y(x)|",
guidefont = font(14, "Serif"),
legendfont = font(12, "Serif"),
background_color_legend = false,
margin = 5Plots.mm)
plot(p1, p2, layout=(1,2), size = (800, 400))
npzwrite("fft.npy", fft_data[1: div(Npoints,2), :])
savefig("fft.pdf")
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<reponame>mehalter/Catlab.jl<gh_stars>1-10
module TestWiringDiagramCore
using Test
using Catlab.WiringDiagrams.WiringDiagramCore
import Catlab.WiringDiagrams.WiringDiagramCore: validate_ports
# For testing purposes, check equality of port symbols.
function validate_ports(source_port::Symbol, target_port::Symbol)
if source_port != target_port
error("Source port $source_port does not equal target port $target_port")
end
end
A, B, C, D = [:A], [:B], [:C], [:D]
f = Box(:f, A, B)
g = Box(:g, B, C)
h = Box(:h, C, D)
# Imperative interface
######################
# Operations on boxes
d = WiringDiagram(A, C)
@test nboxes(d) == 0
@test_throws KeyError box(d,input_id(d))
@test_throws KeyError box(d,output_id(d))
fv = add_box!(d, f)
@test nboxes(d) == 1
@test box(d, fv).value == :f
@test box(d, fv) == f
rem_box!(d, fv)
@test nboxes(d) == 0
fv = add_box!(d, f)
gv = add_box!(d, g)
hv = add_box!(d, h)
@test nboxes(d) == 3
@test [b.value for b in boxes(d)] == [:f,:g,:h]
@test boxes(d) == [f,g,h]
rem_boxes!(d, [fv,hv])
@test nboxes(d) == 1
@test boxes(d) == [g]
# Operations on ports
d = WiringDiagram(A, C)
fv = add_box!(d, f)
gv = add_box!(d, g)
@test input_ports(d, fv) == A
@test output_ports(d, fv) == B
@test input_ports(d, output_id(d)) == C
@test output_ports(d, input_id(d)) == A
@test_throws ErrorException input_ports(d, input_id(d))
@test_throws ErrorException output_ports(d, output_id(d))
@test port_value(d, Port(input_id(d),OutputPort,1)) == :A
@test port_value(d, Port(output_id(d),InputPort,1)) == :C
@test port_value(d, Port(fv,InputPort,1)) == :A
@test port_value(d, Port(fv,OutputPort,1)) == :B
# Operations on wires
@test nwires(d) == 0
@test !has_wire(d, fv, gv)
@test !has_wire(d, (fv,1) => (gv,1))
add_wire!(d, (input_id(d),1) => (fv,1))
add_wire!(d, (fv,1) => (gv,1))
add_wire!(d, (gv,1) => (output_id(d),1))
@test nwires(d) == 3
@test has_wire(d, fv, gv)
@test has_wire(d, (fv,1) => (gv,1))
@test_throws ErrorException add_wire!(d, (gv,1) => (fv,1))
@test wires(d) == map(Wire, [
(input_id(d),1) => (fv,1),
(fv,1) => (gv,1),
(gv,1) => (output_id(d),1),
])
# Shallow copies.
d_copy = copy(d)
rem_boxes!(d_copy, [fv,gv])
@test nboxes(d) == 2
@test nwires(d) == 3
# Graph properties.
@test Set(all_neighbors(d, fv)) == Set([input_id(d),gv])
@test Set(all_neighbors(d, gv)) == Set([fv,output_id(d)])
@test neighbors(d, fv) == [gv]
@test outneighbors(d, fv) == [gv]
@test inneighbors(d, gv) == [fv]
@test wires(d, input_id(d)) == [ Wire((input_id(d),1) => (fv,1)) ]
@test wires(d, fv) == map(Wire, [
((input_id(d),1) => (fv,1)),
((fv,1) => (gv,1))
])
@test out_wires(d, fv) == [ Wire((fv,1) => (gv,1)) ]
@test out_wires(d, Port(fv,OutputPort,1)) == [ Wire((fv,1) => (gv,1)) ]
@test in_wires(d, gv) == [ Wire((fv,1) => (gv,1)) ]
@test in_wires(d, Port(gv,InputPort,1)) == [ Wire((fv,1) => (gv,1)) ]
rem_wires!(d, fv, gv)
@test nwires(d) == 2
@test !has_wire(d, fv, gv)
rem_wire!(d, (input_id(d),1) => (fv,1))
@test wires(d) == [ Wire((gv,1) => (output_id(d),1)) ]
# Induced subgraph.
d = WiringDiagram(A,D)
fv, gv, hv = add_box!(d, f), add_box!(d, g), add_box!(d, h)
add_wires!(d, Pair[
(input_id(d),1) => (fv,1),
(fv,1) => (gv,1),
(gv,1) => (hv,1),
(hv,1) => (output_id(d),1),
])
sub = WiringDiagram(A, D)
fv, gv = add_box!(sub, f), add_box!(sub, g)
add_wires!(sub, Pair[
(input_id(sub),1) => (fv,1),
(fv,1) => (gv,1),
])
@test induced_subdiagram(d, [fv, gv]) == sub
# Substitution
##############
sub = WiringDiagram(B,D)
gv = add_box!(sub, g)
hv = add_box!(sub, h)
add_wires!(sub, Pair[
(input_id(sub),1) => (gv,1),
(gv,1) => (hv,1),
(hv,1) => (output_id(sub),1),
])
d0 = WiringDiagram(A,D)
fv = add_box!(d0, f)
subv = add_box!(d0, sub)
add_wires!(d0, Pair[
(input_id(d0),1) => (fv,1),
(fv,1) => (subv,1),
(subv,1) => (output_id(d0),1),
])
@test boxes(d0) == [ f, sub ]
@test boxes(sub) == [ g, h ]
d = substitute(d0, subv)
@test nboxes(d) == 3
@test boxes(d) == [f, g, h]
box_map = Dict(box(d,v).value => v for v in box_ids(d))
@test nwires(d) == 4
@test Set(wires(d)) == Set(map(Wire, [
(input_id(d),1) => (box_map[:f],1),
(box_map[:f],1) => (box_map[:g],1),
(box_map[:g],1) => (box_map[:h],1),
(box_map[:h],1) => (output_id(d),1),
]))
# Encapsulation
###############
d0 = WiringDiagram(A,D)
fv = add_box!(d0, f)
gv = add_box!(d0, g)
hv = add_box!(d0, h)
add_wires!(d0, Pair[
(input_id(d),1) => (fv,1),
(fv,1) => (gv,1),
(gv,1) => (hv,1),
(hv,1) => (output_id(d),1)
])
d = encapsulate(d0, [fv,gv])
@test nboxes(d) == 2
@test sum(isa(b, WiringDiagram) for b in boxes(d)) == 1
@test nwires(d) == 3
sub = first(b for b in boxes(d) if isa(b, WiringDiagram))
@test nboxes(sub) == 2
@test boxes(sub) == [f, g]
box_map = Dict(box(sub,v).value => v for v in box_ids(sub))
@test Set(wires(sub)) == Set(map(Wire, [
(input_id(sub),1) => (box_map[:f],1),
(box_map[:f],1) => (box_map[:g],1),
(box_map[:g],1) => (output_id(sub),1),
]))
d = encapsulate(d0, [fv,gv], discard_boxes=true, value=:e)
@test boxes(d) == [ Box(:e, A, C), h ]
box_map = Dict(box(d,v).value => v for v in box_ids(d))
@test Set(wires(d)) == Set(map(Wire, [
(input_id(d),1) => (box_map[:e],1),
(box_map[:e],1) => (box_map[:h],1),
(box_map[:h],1) => (output_id(d),1),
]))
d0 = WiringDiagram(A,B)
v1 = add_box!(d0, f)
v2 = add_box!(d0, f)
add_wires!(d0, Pair[
(input_id(d),1) => (v1,1),
(input_id(d),1) => (v2,1),
(v1,1) => (output_id(d),1),
(v2,1) => (output_id(d),1),
])
d = encapsulate(d0, [v1,v2])
@test nboxes(d) == 1
@test nwires(d) == 2
sub = first(boxes(d))
@test sub == d0
end
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] | 1.956079 | 2,846 |
<gh_stars>1-10
module Sigmoid
using LoopVectorization
function func!(output_matrix::Array{Float32}, value_matrix::Array{Float32})
@avxt for i in eachindex(value_matrix)
value_matrix[i] = ifelse(value_matrix[i]>3.0f38, 3.0f38, value_matrix[i])
value_matrix[i] = ifelse(value_matrix[i]<-3.0f38, -3.0f38, value_matrix[i])
output_matrix[i] = ifelse(value_matrix[i]>=0, 1/(1+exp(-value_matrix[i])), exp(value_matrix[i])/(1+exp(value_matrix[i])))
end
end
function get_∇biases!(∇biases::Array{Float32}, value_matrix::Array{Float32}, δ::Array{Float32})
@avxt for i in eachindex(value_matrix)
∇biases[i] = exp(-value_matrix[i])/(1+exp(-value_matrix[i]))^2*δ[i]
end
end
function get_name()
return "Sigmoid"
end
end
# Source: https://timvieira.github.io/blog/post/2014/02/11/exp-normalize-trick/
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<filename>test/test_beta.jl
using MicrobiomeAnalysis
using Microbiome # , SummarizedExperiments
using Test
@testset "MicrobiomeAnalysis.jl" begin
se = SummarizedExperiments.exampleobject(40, 20)
samps = MicrobiomeSample.([samples for samples in se.coldata.name])
taxa = [Taxon("$taxa", :species) for taxa in se.rowdata.name]
mat = assay(se, "bar")
comm = CommunityProfile(mat, taxa, samps)
microjack = Microbiome.jaccard(comm)
miajack = MicrobiomeAnalysis.jaccard(se, "bar")
@test microjack == miajack
microbray = Microbiome.braycurtis(comm)
miabray = MicrobiomeAnalysis.braycurtis(se, "bar")
@test microbray == miabray
microhell = Microbiome.hellinger(comm)
miahell = MicrobiomeAnalysis.hellinger(se, "bar")
@test microhell == miahell
end
# MicrobiomeAnalysis.jl: Error During Test at /Users/giuliobene3000/Desktop/Projects/JuliaProjects/MicrobiomeAnalysis.jl/test/test_beta.jl:5
# Got exception outside of a @test
# MethodError: no method matching pairwise(::Distances.BrayCurtis, ::Array{Float32, 3}, ::Array{Float32, 3}; dims=2)
# Closest candidates are:
# pairwise(::Distances.PreMetric, ::Any, ::Any) at ~/.julia/packages/Distances/6E33b/src/generic.jl:340 got unsupported keyword argument "dims"
# pairwise(::Any, ::Any, ::Any; symmetric, skipmissing) at ~/.julia/packages/StatsBase/pJqvO/src/pairwise.jl:282 got unsupported keyword argument "dims"
# pairwise(::Distances.PreMetric, ::Any) at ~/.julia/packages/Distances/6E33b/src/generic.jl:347 got unsupported keyword argument "dims"
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] | 2.70669 | 583 |
using BlackBoxOptim
# bbo is now extended to support not only AbstractVector{Float64} represetations of solutions, but all AbstractVector{<:Real} solution types.
# To illustrate this you here find a variant with binary representations through UInt8, instead of the default (Individual = Vector{Float64}).
# The toy problem implemented below is maxone, which maximizes the amount of ones in a binary array (as a proof of concept).
# The example uses a new optimizer, a Genetic Algorithm (GA), which by default uses the newly implemented operators BinaryFlipMutation and MultiPointCrossover.
# fitness function
maxones(genotype::AbstractVector{<:Integer})::Float64 = sum(genotype)
# search space with unsigned integers
ss = RectSearchSpace(1000, (0x00, 0x01), UInt8)
bboptimize(maxones; SearchSpace = ss,
MaxTime = 10,
FitnessScheme = MaximizingFitnessScheme,
Method = :ga)
# Per default, the MultiPointCrossover uses 2-point crossover and a mutation rate of 1%, which has the algorithm converge slowly. Below we set the number of points to 50 and mutation rate to .1 % to speed-up convergence.
using BlackBoxOptim: MultiPointCrossover, BinaryFlipMutation
bboptimize(maxones; CrossoverOperator = MultiPointCrossover(50),
MutationOperator = BinaryFlipMutation(.001),
SearchSpace = ss,
MaxTime = 10,
FitnessScheme = MaximizingFitnessScheme,
Method = :ga)
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] | 2.90613 | 522 |
export stationdensity_selection
"""
stationdensity_selection(InputDict::Dict)
return stationlist which is density-averaged over space.
"""
function stationdensity_selection(InputDict::Dict)
starttime = InputDict["starttime"]
endtime = InputDict["endtime"]
kmlfile = InputDict["kmlfile"]
studyarea = InputDict["studyarea"]
includenet = InputDict["includenet"]
priority = InputDict["priority"]
TotalNumofStation = InputDict["TotalNumofStation"]
nx = InputDict["nx"]
ny = InputDict["ny"]
IsPlotFigure = InputDict["IsPlotFigure"]
# parse kml station file
println("---Parse KML file---")
xdoc = parse_file(kmlfile);
xroot = LightXML.root(xdoc);
ces = collect(child_elements(xroot));
itmp = 1
stid = 0
while true
itmp
if !isempty(ces[itmp]["name"])
stid = itmp
break;
end
itmp += 1
if itmp > 100
error("Station network is more than 100. abort.")
end
end
s1 = ces[stid:end]
stationnum = length(s1)
stationlist = []
# 1. read station info
for i = 1:stationnum
stationname = content(s1[i]["name"][1])
stationloc = content(s1[i]["Point"][1]["coordinates"][1])
coord_lon = parse.(Float64, split(stationloc, ","))[1]
coord_lat = parse.(Float64, split(stationloc, ","))[2]
# find operating range
txt = string(s1[i]["Style"][1]["BalloonStyle"][1]["text"])
tl =split(string(txt), ";")
operatingtime =tl[findfirst(occursin.("Operating Range", tl))]
startoptime = DateTime(split(operatingtime)[3][1:10])
endoptime = DateTime(split(operatingtime)[5][1:10])
datacentertxt =tl[findfirst(occursin.("Data Center", tl))]
if (startoptime <= starttime && endtime <= endoptime
&& any(occursin.(stationname[1:2], includenet)))
# take only stations which is operated over study time.
st = Dict("stationname" => stationname,
"coord_lon" => coord_lon,
"coord_lat" => coord_lat)
push!(stationlist, st)
end
end
# 2. plot location
if IsPlotFigure
pyplot()
fodir = "./fig"
if !ispath(fodir) mkpath(fodir); end
px = [stationlist[x]["coord_lon"] for x in 1:length(stationlist)]
py = [stationlist[x]["coord_lat"] for x in 1:length(stationlist)]
text = [stationlist[x]["stationname"] for x in 1:length(stationlist)]
Plots.plot(bg=:white)
p1 = Plots.scatter!(px, py,
markershape = :dtriangle,
markersize = 12,
label="",
size=(1200,1200),
xlabel = "lon",
xlims = (studyarea[4], studyarea[2]),
ylabel = "lat",
ylims = (studyarea[3], studyarea[1]),
aspect_ratio=:equal)
Plots.savefig(p1, fodir*"/"*InputDict["figname"])
end
# 3. compute station density
println("---Compute station density---")
dx = (studyarea[2] - studyarea[4])/nx
dy = (studyarea[1] - studyarea[3])/ny
NG = nx * ny
grid = []
for i=1:nx
for j=1:ny
xl = (i-1) * dx + studyarea[4]
xr = (i) * dx + studyarea[4]
yb = (j-1) * dy + studyarea[3]
yt = (j) * dy + studyarea[3]
xm = (xl+xr)/2
ym = (yb+yt)/2
gridtmp = Dict("xm"=>xm, "ym"=>ym, "xl"=>xl, "xr"=>xr, "yb"=>yb, "yt"=>yt)
push!(grid, gridtmp)
end
end
stationidlist = []
for gid = 1:NG
xl = grid[gid]["xl"]
xr = grid[gid]["xr"]
yb = grid[gid]["yb"]
yt = grid[gid]["yt"]
numstationpergrid=0
stationid=Int[]
for i = 1:length(stationlist)
x1 = stationlist[i]["coord_lon"]
y1 = stationlist[i]["coord_lat"]
if insquare(x1, y1, xl, xr, yb, yt, edgeinclude=true)
numstationpergrid += 1
push!(stationid, i)
end
end
grid[gid]["numstationpergrid"] = numstationpergrid
push!(stationidlist, stationid)
end
# 4. reduce the station density
# - sort from higher num of station
# - remove one of pairs with closest distance and priority
println("---Averaging station density---")
r_grid= deepcopy(grid)
r_stationlist = deepcopy(stationlist)
r_stationidlist = deepcopy(stationidlist)
removestations!(TotalNumofStation, r_grid, r_stationlist, r_stationidlist, includenet, priority)
r_stationlist = [stationlist[x] for x in collect(Iterators.flatten(r_stationidlist))]
if IsPlotFigure
px = [r_stationlist[x]["coord_lon"] for x in 1:length(r_stationlist)]
py = [r_stationlist[x]["coord_lat"] for x in 1:length(r_stationlist)]
text = [r_stationlist[x]["stationname"] for x in 1:length(r_stationlist)]
Plots.plot(bg=:white)
p2 = Plots.scatter!(px, py,
markershape = :dtriangle,
markersize = 12,
label="",
size=(1200,1200),
xlabel = "lon",
xlims = (studyarea[4], studyarea[2]),
ylabel = "lat",
ylims = (studyarea[3], studyarea[1]),
aspect_ratio=:equal)
Plots.savefig(p2, fodir*"/"*"densityaveraged_"*InputDict["figname"])
end
#===
save for SeisDownload input file
===#
fofiledir = "./files"
if !ispath(fofiledir) mkpath(fofiledir); end
stationlistfoname = fofiledir*"/input_stationlist.jld2"
jldopen(stationlistfoname, "w") do file
file["stationlist"] = stationlist;
end
#===
# save stationlist for gmt
===#
# gather network name
networks = Dict()
println("---Station networks---")
for i = 1:length(stationlist)
networkname = split(stationlist[i]["stationname"], ".")[1]
if !haskey(networks, networkname)
println(networkname)
#add this network to dict
networks["$(networkname)"] = Array[]
end
end
for i = 1:length(stationlist)
stationname = stationlist[i]["stationname"]
coord_lon = stationlist[i]["coord_lon"]
coord_lat = stationlist[i]["coord_lat"]
# Here, elevation is not included because no elevetion in IRIS kml file.
# However, you can find elevation when analysing dv/v as CorrData has the elevation of stations.
for key = keys(networks)
if occursin(stationname[1:3], "$(key).", )
push!(networks["$(key)"], [coord_lon, coord_lat])
end
end
end
#save as jld2
jldopen(fofiledir*"/gmt_stationcoords.jld2", "w") do file
for key = keys(networks)
file["$(key)"] = networks["$(key)"]
end
end
return nothing
end
"""
insquare(x1, y1, xl, xr, yb, yt; edgeinclude::Bool=true)
return if x1, y1 is in square
"""
function insquare(x1, y1, xl, xr, yb, yt; edgeinclude::Bool=true)
if edgeinclude
if x1 >= xl && x1 <= xr && y1 >= yb && y1 <= yt
return true
else
return false
end
else
if x1 > xl && x1 < xr && y1 > yb && y1 < yt
return true
else
return false
end
end
end
"""
removestations!(TotalNumofStation::Int, r_grid::Array{Any,1}, r_stationlist::Array{Any,1},
r_stationidlist::Array{Any,1}, includenet::Array{String,1}, priority::Array{String,1})
remove stations with averaging density in grid
"""
function removestations!(TotalNumofStation::Int, r_grid::Array{Any,1}, r_stationlist::Array{Any,1},
r_stationidlist::Array{Any,1}, includenet::Array{String,1}, priority::Array{String,1})
NG = length(r_grid)
r_numstlistsort = [r_grid[x]["numstationpergrid"] for x in 1:NG]
r_sortid = sortperm(r_numstlistsort, rev=true)
icount = 1
while true
if sum(r_numstlistsort) <= TotalNumofStation
break
elseif sum(r_numstlistsort) == 0
error("All station is removed from list. abort.")
elseif icount > 10000
error("iteration number exceeds 10000. too many station removal. abort.")
end
r_gid = first(r_sortid) # remove from this grid
st = r_stationidlist[r_gid]
# compute distance with eachother
rdist = 1e12
r_ii = -1
r_jj = -1
r_netii = ""
r_netjj = ""
for i = 1:length(st)-1
for j = i+1:length(st)
ii = st[i]
jj = st[j]
id1 = r_stationlist[ii]["stationname"]
id2 = r_stationlist[jj]["stationname"]
net1 = string(split(id1, ".")[1])
net2 = string(split(id2, ".")[1])
x1 = r_stationlist[ii]["coord_lon"]
y1 = r_stationlist[ii]["coord_lat"]
x2 = r_stationlist[jj]["coord_lon"]
y2 = r_stationlist[jj]["coord_lat"]
trial_dist = norm([(x2-x1), (y2-y1)])
# decide if we remove this
if @isdefined includenet
if any(occursin.(includenet, net1)) || any(occursin.(includenet, net2))
continue;
end
end
if trial_dist < rdist
#update potential removal station
r_ii = ii
r_jj = jj
r_netii = net1
r_netjj = net2
rdist = trial_dist
end
end
end
if r_ii == -1 || r_jj == -1
error("no station is chosen. abort.")
end
#evaluate removing either r_ii or r_jj
if !@isdefined priority
priority = []
end
ri1 = findfirst(x -> x==r_netii, priority)
ri2 = findfirst(x -> x==r_netjj, priority)
if isnothing(ri1) || isnothing(ri2)
removedid = r_ii #no priority
elseif ri1 >= ri2
removedid = r_ii
else
removedid = r_jj
end
# pop out removedid from list
r_grid[r_gid]["numstationpergrid"] = r_numstlistsort[r_gid] - 1
filter!(e -> e != removedid, r_stationidlist[r_gid])
r_numstlistsort = [r_grid[x]["numstationpergrid"] for x in 1:NG]
r_sortid = sortperm(r_numstlistsort, rev=true)
icount += 1
end
end
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1303,
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3419,
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44872,
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25,
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8,
198,
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3127,
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6626,
7,
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58,
72,
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1,
17529,
3672,
33116,
366,
19570,
58,
16,
60,
198,
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220,
220,
220,
220,
220,
220,
611,
5145,
10134,
2539,
7,
3262,
5225,
11,
3127,
3672,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44872,
7,
27349,
3672,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2860,
428,
3127,
284,
8633,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7686,
14692,
3,
7,
27349,
3672,
8,
8973,
796,
15690,
21737,
198,
220,
220,
220,
220,
220,
220,
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220,
220,
886,
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329,
1312,
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25,
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7,
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8,
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220,
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220,
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4429,
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58,
72,
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1,
17529,
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8973,
198,
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220,
220,
220,
220,
220,
220,
6349,
62,
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4429,
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58,
72,
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1,
37652,
62,
14995,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
6349,
62,
15460,
796,
4429,
4868,
58,
72,
7131,
1,
37652,
62,
15460,
8973,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
3423,
11,
22910,
318,
407,
3017,
780,
645,
9766,
303,
5378,
287,
14826,
1797,
479,
4029,
2393,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2102,
11,
345,
460,
1064,
22910,
618,
11090,
278,
288,
85,
14,
85,
355,
2744,
81,
6601,
468,
262,
22910,
286,
8985,
13,
628,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
796,
8251,
7,
3262,
5225,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
8833,
259,
7,
17529,
3672,
58,
16,
25,
18,
4357,
17971,
7,
2539,
21387,
11,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
3262,
5225,
14692,
3,
7,
2539,
16725,
4357,
685,
37652,
62,
14995,
11,
6349,
62,
15460,
12962,
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,
1303,
21928,
355,
474,
335,
17,
198,
220,
220,
220,
474,
335,
9654,
7,
69,
1659,
3902,
343,
9,
1,
14,
70,
16762,
62,
17529,
1073,
3669,
13,
73,
335,
17,
1600,
366,
86,
4943,
466,
2393,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
796,
8251,
7,
3262,
5225,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2393,
14692,
3,
7,
2539,
8,
8973,
796,
7686,
14692,
3,
7,
2539,
8,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1441,
2147,
198,
198,
437,
198,
198,
37811,
198,
1040,
421,
533,
7,
87,
16,
11,
331,
16,
11,
2124,
75,
11,
2124,
81,
11,
331,
65,
11,
331,
83,
26,
5743,
17256,
3712,
33,
970,
28,
7942,
8,
198,
7783,
611,
2124,
16,
11,
331,
16,
318,
287,
6616,
198,
37811,
198,
8818,
1035,
421,
533,
7,
87,
16,
11,
331,
16,
11,
2124,
75,
11,
2124,
81,
11,
331,
65,
11,
331,
83,
26,
5743,
17256,
3712,
33,
970,
28,
7942,
8,
198,
220,
220,
220,
611,
5743,
17256,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2124,
16,
18189,
2124,
75,
11405,
2124,
16,
19841,
2124,
81,
11405,
331,
16,
18189,
331,
65,
11405,
331,
16,
19841,
331,
83,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2081,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
3991,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2124,
16,
1875,
2124,
75,
11405,
2124,
16,
1279,
2124,
81,
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331,
16,
1875,
331,
65,
11405,
331,
16,
1279,
331,
83,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2081,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
3991,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
437,
628,
198,
37811,
198,
2787,
709,
395,
602,
0,
7,
14957,
33111,
1659,
12367,
3712,
5317,
11,
374,
62,
25928,
3712,
19182,
90,
7149,
11,
16,
5512,
374,
62,
17529,
4868,
3712,
19182,
90,
7149,
11,
16,
5512,
198,
220,
220,
220,
220,
374,
62,
17529,
312,
4868,
3712,
19182,
90,
7149,
11,
16,
5512,
846,
268,
316,
3712,
19182,
90,
10100,
11,
16,
5512,
8475,
3712,
19182,
90,
10100,
11,
16,
30072,
198,
28956,
8985,
351,
20430,
12109,
287,
10706,
198,
37811,
198,
8818,
816,
709,
395,
602,
0,
7,
14957,
33111,
1659,
12367,
3712,
5317,
11,
374,
62,
25928,
3712,
19182,
90,
7149,
11,
16,
5512,
374,
62,
17529,
4868,
3712,
19182,
90,
7149,
11,
16,
5512,
198,
220,
220,
220,
220,
374,
62,
17529,
312,
4868,
3712,
19182,
90,
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11,
16,
5512,
846,
268,
316,
3712,
19182,
90,
10100,
11,
16,
5512,
8475,
3712,
19182,
90,
10100,
11,
16,
30072,
628,
220,
220,
220,
220,
39058,
796,
4129,
7,
81,
62,
25928,
8,
628,
220,
220,
220,
220,
374,
62,
22510,
301,
20713,
419,
796,
685,
81,
62,
25928,
58,
87,
7131,
1,
22510,
17529,
525,
25928,
8973,
329,
2124,
287,
352,
25,
10503,
60,
198,
220,
220,
220,
220,
374,
62,
30619,
312,
796,
3297,
16321,
7,
81,
62,
22510,
301,
20713,
419,
11,
2710,
28,
7942,
8,
628,
220,
220,
220,
220,
14158,
608,
796,
352,
198,
220,
220,
220,
220,
981,
2081,
628,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2160,
7,
81,
62,
22510,
301,
20713,
419,
8,
19841,
7472,
33111,
1659,
12367,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
2160,
7,
81,
62,
22510,
301,
20713,
419,
8,
6624,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4049,
7203,
3237,
4429,
318,
4615,
422,
1351,
13,
15614,
19570,
198,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
14158,
608,
1875,
33028,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4049,
7203,
2676,
341,
1271,
21695,
33028,
13,
1165,
867,
4429,
9934,
13,
15614,
19570,
198,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
220,
374,
62,
70,
312,
796,
717,
7,
81,
62,
30619,
312,
8,
1303,
4781,
422,
428,
10706,
198,
220,
220,
220,
220,
220,
220,
220,
220,
336,
796,
374,
62,
17529,
312,
4868,
58,
81,
62,
70,
312,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
24061,
5253,
351,
1123,
847,
198,
220,
220,
220,
220,
220,
220,
220,
220,
374,
17080,
796,
352,
68,
1065,
198,
220,
220,
220,
220,
220,
220,
220,
220,
374,
62,
4178,
796,
532,
16,
198,
220,
220,
220,
220,
220,
220,
220,
220,
374,
62,
41098,
796,
532,
16,
198,
220,
220,
220,
220,
220,
220,
220,
220,
374,
62,
3262,
4178,
796,
13538,
198,
220,
220,
220,
220,
220,
220,
220,
220,
374,
62,
3262,
41098,
796,
13538,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
796,
352,
25,
13664,
7,
301,
13219,
16,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
474,
796,
1312,
10,
16,
25,
13664,
7,
301,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21065,
796,
336,
58,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
474,
73,
796,
336,
58,
73,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
16,
796,
374,
62,
17529,
4868,
58,
4178,
7131,
1,
17529,
3672,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
17,
796,
374,
62,
17529,
4868,
58,
41098,
7131,
1,
17529,
3672,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2010,
16,
796,
220,
4731,
7,
35312,
7,
312,
16,
11,
366,
19570,
58,
16,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2010,
17,
796,
220,
4731,
7,
35312,
7,
312,
17,
11,
366,
19570,
58,
16,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
16,
796,
374,
62,
17529,
4868,
58,
4178,
7131,
1,
37652,
62,
14995,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
16,
796,
374,
62,
17529,
4868,
58,
4178,
7131,
1,
37652,
62,
15460,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
17,
796,
374,
62,
17529,
4868,
58,
41098,
7131,
1,
37652,
62,
14995,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
17,
796,
374,
62,
17529,
4868,
58,
41098,
7131,
1,
37652,
62,
15460,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4473,
62,
17080,
796,
2593,
26933,
7,
87,
17,
12,
87,
16,
828,
357,
88,
17,
12,
88,
16,
8,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
5409,
611,
356,
4781,
428,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
220,
2488,
271,
23211,
846,
268,
316,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
220,
597,
7,
13966,
1834,
259,
12195,
259,
758,
268,
316,
11,
2010,
16,
4008,
8614,
597,
7,
13966,
1834,
259,
12195,
259,
758,
268,
316,
11,
2010,
17,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
26,
198,
220,
220,
220,
220,
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] | 1.949692 | 5,526 |
<filename>src/utils/base.jl
export nframes,
frame_type,
select_last_dim,
select_last_frame,
consecutive_view,
find_all_max,
huber_loss,
huber_loss_unreduced,
discount_rewards,
discount_rewards!,
discount_rewards_reduced,
generalized_advantage_estimation,
generalized_advantage_estimation!,
logitcrossentropy_unreduced,
flatten_batch,
unflatten_batch
using StatsBase
nframes(a::AbstractArray{T,N}) where {T,N} = size(a, N)
frame_type(::Array{T,N}) where {T,N} = Array{T,N - 1}
frame_type(::Vector{T}) where {T} = T
select_last_dim(xs::AbstractArray{T,N}, inds) where {T,N} =
@views xs[ntuple(_ -> (:), N - 1)..., inds]
select_last_frame(xs::AbstractArray{T,N}) where {T,N} = select_last_dim(xs, size(xs, N))
"""
flatten_batch(x::AbstractArray)
Merge the last two dimension.
# Example
```julia-repl
julia> x = reshape(1:12, 2, 2, 3)
2×2×3 reshape(::UnitRange{Int64}, 2, 2, 3) with eltype Int64:
[:, :, 1] =
1 3
2 4
[:, :, 2] =
5 7
6 8
[:, :, 3] =
9 11
10 12
julia> flatten_batch(x)
2×6 reshape(::UnitRange{Int64}, 2, 6) with eltype Int64:
1 3 5 7 9 11
2 4 6 8 10 12
```
"""
flatten_batch(x::AbstractArray) =
reshape(x, (size(x) |> reverse |> Base.tail |> Base.tail |> reverse)..., :) # much faster than `reshape(x, size(x)[1:end-2]..., :)`
unflatten_batch(x::AbstractArray, i::Int...) =
reshape(x, (size(x) |> reverse |> Base.tail |> reverse)..., i...)
consecutive_view(
cb::AbstractArray,
inds::Vector{Int};
n_stack = nothing,
n_horizon = nothing,
) = consecutive_view(cb, inds, n_stack, n_horizon)
consecutive_view(cb::AbstractArray, inds::Vector{Int}, ::Nothing, ::Nothing) =
select_last_dim(cb, inds)
consecutive_view(cb::AbstractArray, inds::Vector{Int}, n_stack::Int, ::Nothing) =
select_last_dim(
cb,
reshape([i for x in inds for i in x-n_stack+1:x], n_stack, length(inds)),
)
consecutive_view(cb::AbstractArray, inds::Vector{Int}, ::Nothing, n_horizon::Int) =
select_last_dim(
cb,
reshape([i for x in inds for i in x:x+n_horizon-1], n_horizon, length(inds)),
)
consecutive_view(cb::AbstractArray, inds::Vector{Int}, n_stack::Int, n_horizon::Int) =
select_last_dim(
cb,
reshape(
[j for x in inds for i in x:x+n_horizon-1 for j in i-n_stack+1:i],
n_stack,
n_horizon,
length(inds),
),
)
function find_all_max(x)
v = maximum(x)
v, findall(==(v), x)
end
function find_all_max(x, mask::AbstractVector{Bool})
v = maximum(view(x, mask))
v, [k for (m, k) in zip(mask, keys(x)) if m && x[k] == v]
end
# !!! watch https://github.com/JuliaLang/julia/pull/35316#issuecomment-622629895
Base.findmax(f, domain) = mapfoldl(x -> (f(x), x), _rf_findmax, domain)
_rf_findmax((fm, m), (fx, x)) = isless(fm, fx) ? (fx, x) : (fm, m)
# !!! type piracy
Base.findmax(A::AbstractVector, mask::AbstractVector{Bool}) =
findmax(i -> A[i], view(keys(A), mask))
function logitcrossentropy_unreduced(logŷ::AbstractVecOrMat, y::AbstractVecOrMat)
return vec(-sum(y .* logsoftmax(logŷ), dims = 1))
end
"""
huber_loss_unreduced(labels, predictions; δ = 1.0f0)
Similar to [`huber_loss`](@ref), but it doesn't do the `mean` operation in the last step.
"""
function huber_loss_unreduced(labels, predictions; δ = 1.0f0)
abs_error = abs.(predictions .- labels)
quadratic = min.(abs_error, δ)
linear = abs_error .- quadratic
0.5f0 .* quadratic .* quadratic .+ δ .* linear
end
"""
huber_loss(labels, predictions; δ = 1.0f0)
See [Huber loss](https://en.wikipedia.org/wiki/Huber_loss)
"""
huber_loss(labels, predictions; δ = 1.0f0) =
huber_loss_unreduced(labels, predictions; δ = δ) |> mean
const VectorOrMatrix = Union{AbstractMatrix,AbstractVector}
"""
discount_rewards(rewards::VectorOrMatrix, γ::Number;kwargs...)
Calculate the gain started from the current step with discount rate of `γ`.
`rewards` can be a matrix.
# Keyword argments
- `dims=:`, if `rewards` is a `Matrix`, then `dims` can only be `1` or `2`.
- `terminal=nothing`, specify if each reward follows by a terminal. `nothing` means the game is not terminated yet. If `terminal` is provided, then the size must be the same with `rewards`.
- `init=nothing`, `init` can be used to provide the the reward estimation of the last state.
# Example
"""
function discount_rewards(rewards::VectorOrMatrix, γ::T; kwargs...) where {T<:Number}
res = similar(rewards, promote_type(eltype(rewards), T))
discount_rewards!(res, rewards, γ; kwargs...)
res
end
discount_rewards!(new_rewards, rewards, γ; terminal = nothing, init = nothing, dims = :) =
_discount_rewards!(new_rewards, rewards, γ, terminal, init, dims)
function _discount_rewards!(
new_rewards::AbstractMatrix,
rewards::AbstractMatrix,
γ,
terminal::Nothing,
init::Nothing,
dims::Int,
)
dims = ndims(rewards) - dims + 1
for (r′, r) in zip(eachslice(new_rewards, dims = dims), eachslice(rewards, dims = dims))
_discount_rewards!(r′, r, γ, nothing, nothing)
end
end
function _discount_rewards!(
new_rewards::AbstractMatrix,
rewards::AbstractMatrix,
γ,
terminal::Nothing,
init,
dims::Int,
)
dims = ndims(rewards) - dims + 1
for (i, (r′, r)) in
enumerate(zip(eachslice(new_rewards, dims = dims), eachslice(rewards, dims = dims)))
_discount_rewards!(r′, r, γ, nothing, init[i])
end
end
function _discount_rewards!(
new_rewards::AbstractMatrix,
rewards::AbstractMatrix,
γ,
terminal,
init::Nothing,
dims::Int,
)
dims = ndims(rewards) - dims + 1
for (r′, r, t) in zip(
eachslice(new_rewards, dims = dims),
eachslice(rewards, dims = dims),
eachslice(terminal, dims = dims),
)
_discount_rewards!(r′, r, γ, t, nothing)
end
end
function _discount_rewards!(
new_rewards::AbstractMatrix,
rewards::AbstractMatrix,
γ,
terminal,
init,
dims::Int,
)
dims = ndims(rewards) - dims + 1
for (i, (r′, r, t)) in enumerate(zip(
eachslice(new_rewards, dims = dims),
eachslice(rewards, dims = dims),
eachslice(terminal, dims = dims),
))
_discount_rewards!(r′, r, γ, t, init[i])
end
end
_discount_rewards!(
new_rewards::AbstractVector,
rewards::AbstractVector,
γ,
terminal,
init,
dims::Colon,
) = _discount_rewards!(new_rewards, rewards, γ, terminal, init)
"assuming rewards and new_rewards are Vector"
_discount_rewards!(new_rewards, rewards, γ, terminal, init::Nothing) =
_discount_rewards!(new_rewards, rewards, γ, terminal, zero(eltype(new_rewards)))
function _discount_rewards!(new_rewards, rewards, γ, terminal, init)
gain = init
for i in length(rewards):-1:1
is_continue = isnothing(terminal) ? true : (!terminal[i])
gain = rewards[i] + γ * gain * is_continue
new_rewards[i] = gain
end
new_rewards
end
discount_rewards_reduced(rewards::AbstractVector, γ; terminal = nothing, init = nothing) =
_discount_rewards_reduced(rewards, γ, terminal, init)
function discount_rewards_reduced(
rewards::AbstractMatrix,
γ::T;
terminal = nothing,
init = nothing,
dims,
) where {T<:Number}
dims = ndims(rewards) - dims + 1
res = Array{promote_type(eltype(rewards), T)}(undef, size(rewards, dims))
_discount_rewards_reduced!(res, rewards, γ, terminal, init, dims)
res
end
_discount_rewards_reduced(rewards, γ, terminal, init::Nothing) =
_discount_rewards_reduced(rewards, γ, terminal, zero(eltype(rewards)))
function _discount_rewards_reduced(rewards, γ, terminal, init)
gain = init
for i in length(rewards):-1:1
is_continue = isnothing(terminal) ? true : (!terminal[i])
gain = rewards[i] + γ * gain * is_continue
end
gain
end
discount_rewards_reduced!(
reduced_rewards::AbstractVector,
rewards::AbstractMatrix,
γ;
terminal = nothing,
init = nothing,
dims,
) = _discount_rewards_reduced!(reduced_rewards, rewards, γ, terminal, init, dims)
function _discount_rewards_reduced!(
reduced_rewards,
rewards,
γ,
terminal::Nothing,
init::Nothing,
dims::Int,
)
for (i, r) in enumerate(eachslice(rewards, dims = dims))
reduced_rewards[i] = _discount_rewards_reduced(r, γ, nothing, nothing)
end
end
function _discount_rewards_reduced!(
reduced_rewards,
rewards,
γ,
terminal::Nothing,
init,
dims::Int,
)
for (i, r) in enumerate(eachslice(rewards, dims = dims))
reduced_rewards[i] = _discount_rewards_reduced(r, γ, nothing, init[i])
end
end
function _discount_rewards_reduced!(
reduced_rewards,
rewards,
γ,
terminal,
init::Nothing,
dims::Int,
)
for (i, (r, t)) in
enumerate(zip(eachslice(rewards, dims = dims), eachslice(terminal, dims = dims)))
reduced_rewards[i] = _discount_rewards_reduced(r, γ, t, nothing)
end
end
function _discount_rewards_reduced!(reduced_rewards, rewards, γ, terminal, init, dims::Int)
for (i, (r, t)) in
enumerate(zip(eachslice(rewards, dims = dims), eachslice(terminal, dims = dims)))
reduced_rewards[i] = _discount_rewards_reduced(r, γ, t, init[i])
end
end
"""
generalized_advantage_estimation(rewards::VectorOrMatrix, values::VectorOrMatrix, γ::Number, λ::Number;kwargs...)
Calculate the generalized advantage estimate started from the current step with discount rate of `γ` and a lambda for GAE-Lambda of 'λ'.
`rewards` and 'values' can be a matrix.
# Keyword argments
- `dims=:`, if `rewards` is a `Matrix`, then `dims` can only be `1` or `2`.
- `terminal=nothing`, specify if each reward follows by a terminal. `nothing` means the game is not terminated yet. If `terminal` is provided, then the size must be the same with `rewards`.
# Example
"""
function generalized_advantage_estimation(
rewards::VectorOrMatrix,
values::VectorOrMatrix,
γ::T,
λ::T;
kwargs...,
) where {T<:Number}
res = similar(rewards, promote_type(eltype(rewards), T))
generalized_advantage_estimation!(res, rewards, values, γ, λ; kwargs...)
res
end
generalized_advantage_estimation!(
advantages,
rewards,
values,
γ,
λ;
terminal = nothing,
dims = :,
) = _generalized_advantage_estimation!(advantages, rewards, values, γ, λ, terminal, dims)
function _generalized_advantage_estimation!(
advantages::AbstractMatrix,
rewards::AbstractMatrix,
values::AbstractMatrix,
γ,
λ,
terminal::Nothing,
dims::Int,
)
dims = ndims(rewards) - dims + 1
for (r′, r, v) in zip(
eachslice(advantages, dims = dims),
eachslice(rewards, dims = dims),
eachslice(values, dims = dims),
)
_generalized_advantage_estimation!(r′, r, v, γ, λ, nothing)
end
end
function _generalized_advantage_estimation!(
advantages::AbstractMatrix,
rewards::AbstractMatrix,
values::AbstractMatrix,
γ,
λ,
terminal,
dims::Int,
)
dims = ndims(rewards) - dims + 1
for (r′, r, v, t) in zip(
eachslice(advantages, dims = dims),
eachslice(rewards, dims = dims),
eachslice(values, dims = dims),
eachslice(terminal, dims = dims),
)
_generalized_advantage_estimation!(r′, r, v, γ, λ, t)
end
end
_generalized_advantage_estimation!(
advantages::AbstractVector,
rewards::AbstractVector,
values::AbstractVector,
γ,
λ,
terminal,
dims::Colon,
) = _generalized_advantage_estimation!(advantages, rewards, values, γ, λ, terminal)
"assuming rewards and advantages are Vector"
function _generalized_advantage_estimation!(advantages, rewards, values, γ, λ, terminal)
gae = 0
for i in length(rewards):-1:1
is_continue = isnothing(terminal) ? true : (!terminal[i])
delta = rewards[i] + γ * values[i+1] * is_continue - values[i]
gae = delta + γ * λ * is_continue * gae
advantages[i] = gae
end
advantages
end
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2017,
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2017,
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2017,
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2017,
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2017,
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2017,
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2017,
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2017,
3712,
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] | 2.328651 | 5,197 |
###############################################################################
#
# Calculate SIF fluxes
#
###############################################################################
"""
SIF_fluxes!(leaves::Array{LeafBios{FT},1},
can_opt::CanopyOpticals{FT},
can_rad::CanopyRads{FT},
can::Canopy4RT{FT},
soil::SoilOpticals{FT},
wls::WaveLengths{FT},
rt_con::RTCache{FT},
rt_dim::RTDimensions;
photon::Bool = true
) where {FT<:AbstractFloat}
Computes 2-stream diffusive radiation transport for SIF radiation (calls
[`diffusive_S!`] internally). Layer reflectance and transmission is
computed from SW optical properties, layer sources from absorbed light and
SIF efficiencies. Boundary conditions are zero SIF incoming from atmosphere
or soil.
- `leaves` Array of [`LeafBios`](@ref) type struct
- `can_opt` [`CanopyOpticals`](@ref) type struct
- `can_rad` [`CanopyRads`](@ref) type struct
- `can` [`Canopy4RT`](@ref) type struct
- `soil` [`SoilOpticals`](@ref) type struct
- `wls` [`WaveLengths`](@ref) type struct
- `rt_con` [`RTCache`](@ref) type cache
- `rt_dim` [`RTDimensions`](@ref) type struct
- `photon` If true, use photon unit in the matrix conversion
"""
function SIF_fluxes!(
leaves::Array{LeafBios{FT},1},
can_opt::CanopyOpticals{FT},
can_rad::CanopyRads{FT},
can::Canopy4RT{FT},
soil::SoilOpticals{FT},
wls::WaveLengths{FT},
rt_con::RTCache{FT},
rt_dim::RTDimensions;
photon::Bool = true
) where {FT<:AbstractFloat}
# unpack variables from structures
@unpack LAI, lidf, nLayer, Ω = can;
@unpack a, absfo, absfs, absfsfo, cosΘ_l, cos2Θ_l, fo, fs, fsfo, Po, Ps, Pso, sigb, vb, vf = can_opt;
@unpack E_down, E_up, ϕ_shade, ϕ_sun = can_rad;
@unpack ρ_SW_SIF = soil;
@unpack dWL_iWLE, iWLE, iWLF, WLE, WLF = wls;
sf_con = rt_con.sf_con;
# 1. define some useful parameters
iLAI = LAI * Ω / nLayer;
# 2. calculate some useful parameters
sf_con.τ_dd .= 1 .- view(a, iWLF, :) .* iLAI;
sf_con.ρ_dd .= view(sigb, iWLF, :) .* iLAI;
# 3. Compute mid layer Ps,Po,Pso
# Qso = (Pso[1:nLayer] + Pso[2:nLayer+1]) / 2;
# Qs = ( Ps[1:nLayer] + Ps[2:nLayer+1]) / 2;
# Qo = ( Po[1:nLayer] + Po[2:nLayer+1]) / 2;
Qso = view(Pso, 1:nLayer);
Qs = view(Ps , 1:nLayer);
Qo = view(Po , 1:nLayer);
# 4. reflectance, transmittance factors in a thin layer the following are
# vectors with length [nl,nWL]
sf_con.sun_dwl_iWlE .= view(can_opt.Es_, iWLE, 1) .* dWL_iWLE;
if photon sf_con.sun_dwl_iWlE .*= WLE .* _FAC(FT) end;
@inbounds for i=1:nLayer
if length(leaves)>1
Mb = leaves[i].Mb;
Mf = leaves[i].Mf;
else
Mb = leaves[1].Mb;
Mf = leaves[1].Mf;
end
sf_con.M⁺ .= (Mb .+ Mf) ./ 2;
sf_con.M⁻ .= (Mb .- Mf) ./ 2;
# Need to normalize incoming radiation bin
# change from mSCOPE to enable different WL grids!
mul!(sf_con.M⁻_sun, sf_con.M⁻, sf_con.sun_dwl_iWlE);
mul!(sf_con.M⁺_sun, sf_con.M⁺, sf_con.sun_dwl_iWlE);
sf_con.tmp_dwl_iWlE .= view(E_down, iWLE, i) .* dWL_iWLE;
if photon sf_con.tmp_dwl_iWlE .*= WLE .* _FAC(FT) end;
mul!(sf_con.M⁺⁻, sf_con.M⁺, sf_con.tmp_dwl_iWlE);
sf_con.tmp_dwl_iWlE .= view(E_up, iWLE, i+1) .* dWL_iWLE;
if photon sf_con.tmp_dwl_iWlE .*= WLE .* _FAC(FT) end;
mul!(sf_con.M⁺⁺, sf_con.M⁺, sf_con.tmp_dwl_iWlE);
sf_con.tmp_dwl_iWlE .= view(E_up, iWLE, i+1) .* dWL_iWLE;
if photon sf_con.tmp_dwl_iWlE .*= WLE .* _FAC(FT) end;
mul!(sf_con.M⁻⁺, sf_con.M⁻, sf_con.tmp_dwl_iWlE);
sf_con.tmp_dwl_iWlE .= view(E_down, iWLE, i) .* dWL_iWLE;
if photon sf_con.tmp_dwl_iWlE .*= WLE .* _FAC(FT) end;
mul!(sf_con.M⁻⁻, sf_con.M⁻, sf_con.tmp_dwl_iWlE);
# Here comes the tedious part:
# TODO move them to a seprate function
sf_con.ϕ_cosΘ .= view(ϕ_sun, :, :, i) .* cosΘ_l;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
sunCos = mean(sf_con.ϕ_cosΘ_lidf);
sf_con.ϕ_cosΘ .= ϕ_shade[i] .* cosΘ_l;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
shadeCos = mean(sf_con.ϕ_cosΘ_lidf);
sf_con.ϕ_cosΘ .= view(ϕ_sun, :, :, i) .* cos2Θ_l;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
sunCos2 = mean(sf_con.ϕ_cosΘ_lidf);
sf_con.ϕ_cosΘ .= ϕ_shade[i] .* cos2Θ_l;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
shadeCos2 = mean(sf_con.ϕ_cosΘ_lidf);
mul!(sf_con.ϕ_cosΘ_lidf, view(ϕ_sun, :, :, i)', lidf);
sunLidf = mean(sf_con.ϕ_cosΘ_lidf);
shadeLidf = mean(lidf) * ϕ_shade[i];
sf_con.ϕ_cosΘ .= view(ϕ_sun, :, :, i) .* absfsfo;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
_mean_absfsfo = mean(sf_con.ϕ_cosΘ_lidf);
sf_con.ϕ_cosΘ .= view(ϕ_sun, :, :, i) .* fsfo;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
_mean_fsfo = mean(sf_con.ϕ_cosΘ_lidf);
sf_con.wfEs .= _mean_absfsfo .* sf_con.M⁺_sun .+
_mean_fsfo .* sf_con.M⁻_sun;
sf_con.ϕ_cosΘ .= view(ϕ_sun, :, :, i) .* absfs;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
_mean_absfs = mean(sf_con.ϕ_cosΘ_lidf);
sf_con.ϕ_cosΘ .= view(ϕ_sun, :, :, i) .* fs .* cosΘ_l;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
_mean_fs = mean(sf_con.ϕ_cosΘ_lidf);
sf_con.sfEs .= _mean_absfs .* sf_con.M⁺_sun .- _mean_fs .* sf_con.M⁻_sun;
sf_con.sbEs .= _mean_absfs .* sf_con.M⁺_sun .+ _mean_fs .* sf_con.M⁻_sun;
sf_con.ϕ_cosΘ .= ϕ_shade[i] .* absfo;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
_mean_absfo = mean(sf_con.ϕ_cosΘ_lidf);
sf_con.ϕ_cosΘ .= ϕ_shade[i] .* fo .* cosΘ_l;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
_mean_fo = mean(sf_con.ϕ_cosΘ_lidf);
sf_con.vfEplu_shade .= _mean_absfo .* sf_con.M⁺⁺ .- _mean_fo .* sf_con.M⁻⁺;
sf_con.vbEmin_shade .= _mean_absfo .* sf_con.M⁺⁻ .+ _mean_fo .* sf_con.M⁻⁻;
sf_con.ϕ_cosΘ .= view(ϕ_sun, :, :, i) .* absfo;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
_mean_absfo = mean(sf_con.ϕ_cosΘ_lidf);
sf_con.ϕ_cosΘ .= view(ϕ_sun, :, :, i) .* fo .* cosΘ_l;
mul!(sf_con.ϕ_cosΘ_lidf, adjoint(sf_con.ϕ_cosΘ), lidf);
_mean_fo = mean(sf_con.ϕ_cosΘ_lidf);
sf_con.vfEplu_sun .= _mean_absfo .* sf_con.M⁺⁺ .- _mean_fo .* sf_con.M⁻⁺;
sf_con.vbEmin_sun .= _mean_absfo .* sf_con.M⁺⁻ .+ _mean_fo .* sf_con.M⁻⁻;
sf_con.sigfEmin_shade .= shadeLidf .* sf_con.M⁺⁻ .- shadeCos2 .* sf_con.M⁻⁻;
sf_con.sigbEmin_shade .= shadeLidf .* sf_con.M⁺⁻ .+ shadeCos2 .* sf_con.M⁻⁻;
sf_con.sigfEmin_sun .= sunLidf .* sf_con.M⁺⁻ .- sunCos2 .* sf_con.M⁻⁻;
sf_con.sigbEmin_sun .= sunLidf .* sf_con.M⁺⁻ .+ sunCos2 .* sf_con.M⁻⁻;
sf_con.sigfEplu_shade .= shadeLidf .* sf_con.M⁺⁺ .- shadeCos2 .* sf_con.M⁻⁺;
sf_con.sigbEplu_shade .= shadeLidf .* sf_con.M⁺⁺ .+ shadeCos2 .* sf_con.M⁻⁺;
sf_con.sigfEplu_sun .= sunLidf .* sf_con.M⁺⁺ .- sunCos2 .* sf_con.M⁻⁺;
sf_con.sigbEplu_sun .= sunLidf .* sf_con.M⁺⁺ .+ sunCos2 .* sf_con.M⁻⁺;
# Fluxes:
# sunlit for each layer
# shade leaf for each layer
# Eq. 29a for sunlit leaf
# Eq. 29b for sunlit leaf
# Eq. 29a for shade leaf
# Eq. 29b for shade leaf
sf_con.piLs[:,i] .= sf_con.wfEs .+ sf_con.vfEplu_sun .+ sf_con.vbEmin_sun;
sf_con.piLd[:,i] .= sf_con.vbEmin_shade .+ sf_con.vfEplu_shade;
sf_con.Fsmin[:,i] .= sf_con.sfEs .+ sf_con.sigfEmin_sun .+ sf_con.sigbEplu_sun;
sf_con.Fsplu[:,i] .= sf_con.sbEs .+ sf_con.sigbEmin_sun .+ sf_con.sigfEplu_sun;
sf_con.Fdmin[:,i] .= sf_con.sigfEmin_shade .+ sf_con.sigbEplu_shade;
sf_con.Fdplu[:,i] .= sf_con.sigbEmin_shade .+ sf_con.sigfEplu_shade;
# Total weighted fluxes
_qs_iLAI = Qs[i] * iLAI;
_1_qs_iLAI = (1 - Qs[i]) * iLAI;
sf_con.S⁻[:,i] .= _qs_iLAI .* view(sf_con.Fsmin, :, i) .+ _1_qs_iLAI .* view(sf_con.Fdmin, :, i);
sf_con.S⁺[:,i] .= _qs_iLAI .* view(sf_con.Fsplu, :, i) .+ _1_qs_iLAI .* view(sf_con.Fdplu, :, i);
sf_con.Femo[:,i] .= _qs_iLAI .* view(sf_con.piLs , :, i) .+ _1_qs_iLAI .* view(sf_con.piLd , :, i);
end
# 5. Compute diffusive fluxes within canopy
# Use Zero SIF fluxes as top and bottom boundary:
# TODO pre-allocate these!
diffusive_S!(sf_con, soil, rt_dim);
# 6. Save in output structures!
# direct Sunlit leaves
# direct shaded leaves
_iLAI_pi = iLAI / FT(pi);
# why this step is so slow?
sf_con.tmp_1d_nLayer .= Qso .* _iLAI_pi;
mul!(can_rad.SIF_obs_sunlit, sf_con.piLs, sf_con.tmp_1d_nLayer);
sf_con.tmp_1d_nLayer .= (Qo .- Qso) .* _iLAI_pi;
mul!(can_rad.SIF_obs_shaded, sf_con.piLd, sf_con.tmp_1d_nLayer);
# 7. SIF from scattered internally and soil contribution
sf_con.tmp_2d_nWlF_nLayer .= view(vb, iWLF, :) .* view(sf_con.F⁻, :, 1:nLayer) .+ view(vf, iWLF, :) .* view(sf_con.F⁺, :, 1:nLayer);
mul!(can_rad.SIF_obs_scattered, sf_con.tmp_2d_nWlF_nLayer, Qo);
can_rad.SIF_obs_scattered .= can_rad.SIF_obs_scattered .* _iLAI_pi;
can_rad.SIF_obs_soil .= ( ρ_SW_SIF .* view(sf_con.F⁻, :, nLayer+1) ) .* Po[end] ./ FT(pi);
can_rad.SIF_hemi .= view(sf_con.F⁺, :, 1);
can_rad.SIF_obs .= can_rad.SIF_obs_sunlit .+ can_rad.SIF_obs_shaded .+ can_rad.SIF_obs_scattered .+ can_rad.SIF_obs_soil;
# can_rad.SIF_sum[:] = sum(sf_con.S⁻ + sf_con.S⁺, dims=2);
@inbounds for j in eachindex(can_rad.SIF_sum)
can_rad.SIF_sum[j] = sum( view(sf_con.S⁻, j, :) ) + sum( view(sf_con.S⁺, j, :) );
end
if photon
can_rad.SIF_obs ./= WLF .* _FAC(FT);
end;
return nothing
end
"""
SIF_fluxes!(leaf::LeafBios{FT},
in_rad::IncomingRadiation{FT},
wls::WaveLengths{FT},
rt_con::RTCache{FT},
fqe::FT = FT(0.01);
photon::Bool = true
) where {FT<:AbstractFloat}
Leaf level SIF, given
- `leaf` [`LeafBios`](@ref) type struct
- `in_rad` [`IncomingRadiation`](@ref) type struct
- `wls` [`WaveLengths`](@ref) type struct
- `rt_con` [`RTCache`](@ref) type cache
- `fqe` Fluorescence quantum yield (default at 1%)
- `photon` If true, use photon unit in the matrix conversion
Note that `in_rad` assumes direct light with zenith angle of 0, and a zenith
angle correction needs to be made before passing it to this function. The
up- and down-ward SIF are stored in `sf_con` as `M⁻_sun` and `M⁺_sun`.
"""
function SIF_fluxes!(
leaf::LeafBios{FT},
in_rad::IncomingRadiation{FT},
wls::WaveLengths{FT},
rt_con::RTCache{FT},
fqe::FT = FT(0.01);
photon::Bool = true
) where {FT<:AbstractFloat}
# unpack the values
@unpack Mb, Mf = leaf;
@unpack dWL_iWLE, iWLE, WLE, WLF = wls;
sf_con = rt_con.sf_con;
sf_con.tmp_dwl_iWlE .= (view(in_rad.E_direct , iWLE, 1) .+ view(in_rad.E_diffuse, iWLE, 1)) .* dWL_iWLE;
if photon
sf_con.tmp_dwl_iWlE .*= WLE .* _FAC(FT);
end;
# calculate the SIF spectra for direct light
# sf_con.M⁺ .= (Mb .+ Mf) ./ 2;
# sf_con.M⁻ .= (Mb .- Mf) ./ 2;
# mul!(sf_con.M⁺_sun, sf_con.M⁺, sf_con.tmp_dwl_iWlE);
# mul!(sf_con.M⁻_sun, sf_con.M⁻, sf_con.tmp_dwl_iWlE);
mul!(sf_con.M⁺_sun, Mb, sf_con.tmp_dwl_iWlE);
mul!(sf_con.M⁻_sun, Mf, sf_con.tmp_dwl_iWlE);
if photon
sf_con.M⁺_sun ./= WLF .* _FAC(FT);
sf_con.M⁻_sun ./= WLF .* _FAC(FT);
end;
# divide by pi to account for scattering
sf_con.M⁻_sun .*= fqe / pi;
sf_con.M⁺_sun .*= fqe / pi;
return nothing
end
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] | 1.706088 | 7,145 |
<gh_stars>1-10
#
# interp/sparse.jl --
#
# Implement sparse linear interpolator.
#
#------------------------------------------------------------------------------
#
# This file is part of the LinearInterpolators package licensed under the MIT
# "Expat" License.
#
# Copyright (C) 2015-2016, <NAME>, <NAME> & <NAME>.
# Copyright (C) 2016-2021, <NAME>.
#
# All code is in a module to "hide" private methods.
module SparseInterpolators
export
SparseInterpolator,
SparseUnidimensionalInterpolator
using InterpolationKernels
using LazyAlgebra
using LazyAlgebra.Foundations
import LazyAlgebra: apply, apply!, vcreate, output_size, input_size
import Base: axes, eltype, size
import SparseArrays: sparse
using ..LinearInterpolators
using ..LinearInterpolators: limits, getcoefs
import ..LinearInterpolators.Meta
import ..LinearInterpolators: coefficients, columns, rows,
fit, regularize, regularize!
abstract type AbstractSparseInterpolator{T<:AbstractFloat} <: LinearMapping end
eltype(A::AbstractSparseInterpolator) = eltype(typeof(A))
eltype(::Type{<:AbstractSparseInterpolator{T}}) where {T} = T
struct SparseInterpolator{T<:AbstractFloat,S,N} <: AbstractSparseInterpolator{T}
C::Vector{T}
J::Vector{Int}
nrows::Int
ncols::Int
dims::Dims{N} # dimensions of result
function SparseInterpolator{T,S,N}(C::Vector{T},
J::Vector{Int},
dims::Dims{N},
ncols::Int) where {T,S,N}
@assert S ≥ 1
@assert minimum(dims) ≥ 1
nrows = prod(dims)
nvals = S*nrows # number of non-zero coefficients
@assert length(C) == nvals
@assert length(J) == nvals
new{T,S,N}(C, J, nrows, ncols, dims)
end
end
# Interpolator can be used as a function.
(A::SparseInterpolator)(x::AbstractVector) = apply(A, x)
output_size(A::SparseInterpolator) = A.dims
input_size(A::SparseInterpolator) = (A.ncols,)
width(A::SparseInterpolator{T,S,N}) where {T,S,N} = S
coefficients(A::SparseInterpolator) = A.C
columns(A::SparseInterpolator) = A.J
function rows(A::SparseInterpolator{T,S,N}) where {T,S,N}
nrows = A.nrows
nvals = S*nrows # number of non-zero coefficients
@assert length(A.C) == nvals
@assert length(A.J) == nvals
I = Array{Int}(undef, nvals)
k0 = 0
for i in 1:nrows
for s in 1:S
k = k0 + s
@inbounds I[k] = i
end
k0 += S
end
return I
end
# Convert to a sparse matrix.
sparse(A::SparseInterpolator) =
sparse(rows(A), columns(A), coefficients(A), A.nrows, A.ncols)
"""
A = SparseInterpolator{T=eltype(ker)}(ker, pos, grd)
yields a sparse linear interpolator suitable for interpolating with kernel
`ker` at positions `pos` a function sampled on the grid `grd`. Optional
parameter `T` is the floating-point type of the coefficients of the operator
`A`. Call `eltype(A)` to query the type of the coefficients of the sparse
interpolator `A`.
Then `y = apply(A, x)` or `y = A(x)` or `y = A*x` yield the result of
interpolation array `x`. The shape of `y` is the same as that of `pos`.
Formally, this amounts to computing:
y[i] = sum_j ker((pos[i] - grd[j])/step(grd))*x[j]
with `step(grd)` the (constant) step size between the nodes of the grid `grd`
and `grd[j]` the `j`-th position of the grid.
"""
SparseInterpolator(ker::Kernel{T}, args...) where {T<:AbstractFloat} =
SparseInterpolator{T}(ker, args...)
@deprecate SparseInterpolator(T::Type{<:AbstractFloat}, ker::Kernel, args...) SparseInterpolator{T}(ker, args...)
SparseInterpolator{T}(ker::Kernel, args...) where {T<:AbstractFloat} =
SparseInterpolator{T}(T(ker), args...)
function SparseInterpolator{T}(ker::Kernel{T},
pos::AbstractArray{<:Real},
grd::AbstractRange) where {T<:AbstractFloat}
SparseInterpolator{T}(ker, fractional_index(T, pos, grd),
CartesianIndices(axes(pos)), length(grd))
end
function SparseInterpolator{T}(ker::Kernel{T},
pos::AbstractArray{<:Real},
len::Integer) where {T<:AbstractFloat}
SparseInterpolator{T}(ker, fractional_index(T, pos),
CartesianIndices(axes(pos)), len)
end
function SparseInterpolator{T}(ker::Kernel{T,S},
f::Function,
R::CartesianIndices{N},
ncols::Integer) where {T<:AbstractFloat,S,N}
C, J = _sparsecoefs(R, Int(ncols), ker, f)
return SparseInterpolator{T,S,N}(C, J, size(R), ncols)
end
@generated function _sparsecoefs(R::CartesianIndices{N},
ncols::Int,
ker::Kernel{T,S},
f::Function) where {T,S,N}
J_ = [Symbol(:j_,s) for s in 1:S]
C_ = [Symbol(:c_,s) for s in 1:S]
code = (Meta.generate_getcoefs(J_, C_, :ker, :lim, :x),
[:( J[k+$s] = $(J_[s]) ) for s in 1:S]...,
[:( C[k+$s] = $(C_[s]) ) for s in 1:S]...)
quote
lim = limits(ker, ncols)
nvals = S*length(R)
J = Array{Int}(undef, nvals)
C = Array{T}(undef, nvals)
k = 0
@inbounds for i in R
x = convert(T, f(i))
$(code...)
k += S
end
return C, J
end
end
function _check(A::SparseInterpolator{T,S,N},
out::AbstractArray{T,N},
inp::AbstractVector{T}) where {T,S,N}
nvals = S*A.nrows # number of non-zero coefficients
J, ncols = A.J, A.ncols
length(A.C) == nvals ||
error("corrupted sparse interpolator (bad number of coefficients)")
length(J) == nvals ||
error("corrupted sparse interpolator (bad number of indices)")
length(inp) == ncols ||
error("bad vector length (expecting $(A.ncols), got $(length(inp)))")
size(out) == A.dims ||
error("bad output array size (expecting $(A.dims), got $(size(out)))")
length(out) == A.nrows ||
error("corrupted sparse interpolator (bad number of \"rows\")")
@inbounds for k in 1:nvals
1 ≤ J[k] ≤ ncols ||
error("corrupted sparse interpolator (out of bound indices)")
end
end
function vcreate(::Type{Direct},
A::SparseInterpolator{T,S,N},
x::AbstractVector{T},
scratch::Bool=false) where {T,S,N}
return Array{T}(undef, output_size(A))
end
function vcreate(::Type{Adjoint},
A::SparseInterpolator{T,S,N},
x::AbstractArray{T,N},
scratch::Bool=false) where {T,S,N}
return Array{T}(undef, input_size(A))
end
function apply!(α::Real,
::Type{Direct},
A::SparseInterpolator{Ta,S,N},
x::AbstractVector{Tx},
scratch::Bool,
β::Real,
y::AbstractArray{Ty,N}) where {Ta,Tx<:Real,
Ty<:AbstractFloat,S,N}
_check(A, y, x)
if α == 0
vscale!(y, β)
else
T = float(promote_type(Ta, Tx))
alpha = convert(T, α)
nrows, ncols = A.nrows, A.ncols
C, J = coefficients(A), columns(A)
k0 = 0
if β == 0
@inbounds for i in 1:nrows
sum = zero(T)
@simd for s in 1:S
k = k0 + s
j = J[k]
sum += C[k]*x[j]
end
y[i] = alpha*sum
k0 += S
end
else
beta = convert(Ty, β)
@inbounds for i in 1:nrows
sum = zero(T)
@simd for s in 1:S
k = k0 + s
j = J[k]
sum += C[k]*x[j]
end
y[i] = alpha*sum + beta*y[i]
k0 += S
end
end
end
return y
end
function apply!(α::Real,
::Type{Adjoint},
A::SparseInterpolator{Ta,S,N},
x::AbstractArray{Tx,N},
scratch::Bool,
β::Real,
y::AbstractVector{Ty}) where {Ta,Tx<:Real,
Ty<:AbstractFloat,S,N}
_check(A, x, y)
vscale!(y, β)
if α != 0
T = float(promote_type(Ta, Tx))
alpha = convert(T, α)
nrows, ncols = A.nrows, A.ncols
C, J = coefficients(A), columns(A)
k0 = 0
@inbounds for i in 1:nrows
c = alpha*x[i]
if c != 0
@simd for s in 1:S
k = k0 + s
j = J[k]
y[j] += C[k]*c
end
end
k0 += S
end
end
return y
end
"""
`AtWA(A,w)` yields the matrix `A'*W*A` from a sparse linear operator `A` and
weights `W = diag(w)`.
"""
function AtWA(A::SparseInterpolator{T,S,N},
w::AbstractArray{T,N}) where {T,S,N}
ncols = A.ncols
AtWA!(Array{T}(undef, ncols, ncols), A, w)
end
"""
`AtA(A)` yields the matrix `A'*A` from a sparse linear operator `A`.
"""
function AtA(A::SparseInterpolator{T,S,N}) where {T,S,N}
ncols = A.ncols
AtA!(Array{T}(undef, ncols, ncols), A)
end
# Build the `A'*A` matrix from a sparse linear operator `A`.
function AtA!(dst::AbstractArray{T,2},
A::SparseInterpolator{T,S,N}) where {T,S,N}
nrows, ncols = A.nrows, A.ncols
@assert size(dst) == (ncols, ncols)
fill!(dst, zero(T))
C, J = coefficients(A), columns(A)
k0 = 0
@assert length(J) == length(C)
@inbounds for i in 1:nrows
for s in 1:S
k = k0 + s
1 ≤ J[k] ≤ ncols || error("corrupted interpolator table")
end
for s1 in 1:S
k1 = k0 + s1
j1, c1 = J[k1], C[k1]
@simd for s2 in 1:S
k2 = k0 + s2
j2, c2 = J[k2], C[k2]
dst[j1,j2] += c1*c2
end
end
k0 += S
end
return dst
end
# Build the `A'*W*A` matrix from a sparse linear operator `A` and weights `W`.
function AtWA!(dst::AbstractArray{T,2}, A::SparseInterpolator{T,S,N},
wgt::AbstractArray{T,N}) where {T,S,N}
nrows, ncols = A.nrows, A.ncols
@assert size(dst) == (ncols, ncols)
@assert size(wgt) == output_size(A)
fill!(dst, zero(T))
C, J = coefficients(A), columns(A)
k0 = 0
@assert length(J) == length(C)
@inbounds for i in 1:nrows
for s in 1:S
k = k0 + s
1 ≤ J[k] ≤ ncols || error("corrupted interpolator table")
end
w = wgt[i]
for s1 in 1:S
k1 = k0 + s1
j1 = J[k1]
wc1 = w*C[k1]
@simd for s2 in 1:S
k2 = k0 + s2
j2 = J[k2]
dst[j1,j2] += C[k2]*wc1
end
end
k0 += S
end
return dst
end
# Default regularization levels.
const RGL_EPS = 1e-9
const RGL_MU = 0.0
"""
fit(A, y [, w]; epsilon=1e-9, mu=0.0) -> x
performs a linear fit of `y` by the model `A*x` with `A` a linear interpolator.
The returned value `x` minimizes:
sum(w.*(A*x - y).^2)
where `w` are given weights. If `w` is not specified, all weights are assumed
to be equal to one; otherwise `w` must be an array of nonnegative values and of
same size as `y`.
Keywords `epsilon` and `mu` may be specified to regularize the solution and
minimize:
sum(w.*(A*x - y).^2) + rho*(epsilon*norm(x)^2 + mu*norm(D*x)^2)
where `D` is a finite difference operator, `rho` is the maximum diagonal
element of `A'*diag(w)*A` and `norm` is the Euclidean norm.
"""
function fit(A::SparseInterpolator{T,S,N},
y::AbstractArray{T,N},
w::AbstractArray{T,N};
epsilon::Real = RGL_EPS,
mu::Real = RGL_MU) where {T,S,N}
@assert size(y) == output_size(A)
@assert size(w) == size(y)
# Compute RHS vector A'*W*y with W = diag(w).
rhs = A'*(w.*y)
# Compute LHS matrix A'*W*A with W = diag(w).
lhs = AtWA(A, w)
# Regularize a bit.
regularize!(lhs, epsilon, mu)
# Solve the linear equations.
cholfact!(lhs,:U,Val{true})\rhs
end
function fit(A::SparseInterpolator{T,S,N},
y::AbstractArray{T,N};
epsilon::Real = RGL_EPS,
mu::Real = RGL_MU) where {T,S,N}
@assert size(y) == output_size(A)
@assert size(w) == size(y)
# Compute RHS vector A'*y.
rhs = A'*y
# Compute LHS matrix A'*W*A with W = diag(w).
lhs = AtA(A)
# Regularize a bit.
regularize!(lhs, epsilon, mu)
# Solve the linear equations.
cholfact!(lhs,:U,Val{true})\rhs
end
"""
regularize(A, ϵ, μ) -> R
regularizes the symmetric matrix `A` to produce the matrix:
R = A + ρ*(ϵ*I + μ*D'*D)
where `I` is the identity, `D` is a finite difference operator and `ρ` is the
maximum diagonal element of `A`.
"""
regularize(A::AbstractArray{T,2}, args...) where {T<:AbstractFloat} =
regularize!(copyto!(Array{T}(undef, size(A)), A), args...)
"""
regularize!(A, ϵ, μ) -> A
stores the regularized matrix in `A` (and returns it). This is the in-place
version of [`LinearInterpolators.SparseInterpolators.regularize`].
"""
function regularize!(A::AbstractArray{T,2},
eps::Real = RGL_EPS,
mu::Real = RGL_MU) where {T<:AbstractFloat}
regularize!(A, T(eps), T(mu))
end
function regularize!(A::AbstractArray{T,2},
eps::T, mu::T) where {T<:AbstractFloat}
local rho::T
@assert eps ≥ zero(T)
@assert mu ≥ zero(T)
@assert size(A,1) == size(A,2)
n = size(A,1)
if eps > zero(T) || mu > zero(T)
rho = A[1,1]
for j in 2:n
d = A[j,j]
rho = max(rho, d)
end
rho > zero(T) || error("we have a problem!")
end
if eps > zero(T)
q = eps*rho
for j in 1:n
A[j,j] += q
end
end
if mu > zero(T)
q = rho*mu
if n ≥ 2
r = q + q
A[1,1] += q
A[2,1] -= q
for i in 2:n-1
A[i-1,i] -= q
A[i, i] += r
A[i+1,i] -= q
end
A[n-1,n] -= q
A[n, n] += q
elseif n == 1
A[1,1] += q
end
end
return A
end
# Yields a function that takes an index and returns the corresponding
# interpolation position as fractional index into the source array.
function fractional_index(T::Type{<:AbstractFloat},
pos::AbstractArray{<:Real},
grd::AbstractRange)
# Use the central position of the grid to minimize rounding errors.
c = (convert(T, first(grd)) + convert(T, last(grd)))/2
q = 1/convert(T, step(grd))
r = convert(T, 1 + length(grd))/2
return i -> q*(convert(T, pos[i]) - c) + r
end
function fractional_index(T::Type{<:AbstractFloat},
pos::AbstractArray{<:Real})
return i -> T(pos[i])
end
#------------------------------------------------------------------------------
"""
SparseUnidimensionalInterpolator{T<:AbstractFloat,S,D} <: AbstractSparseInterpolator{T}
* `T` is the floating-point type of the coefficients,
* `S` is the size of the kernel
(number of nodes to combine for a single interpolator)
* `D` is the dimension of interpolation.
"""
struct SparseUnidimensionalInterpolator{T<:AbstractFloat,S,D} <: AbstractSparseInterpolator{T}
nrows::Int # number of rows
ncols::Int # number of columns
C::Vector{T} # coefficients along the dimension of interpolation
J::Vector{Int} # columns indices along the dimension of interpolation
end
(A::SparseUnidimensionalInterpolator)(x) = apply(A, x)
interp_dim(::SparseUnidimensionalInterpolator{T,S,D}) where {T,S,D} = D
coefficients(A::SparseUnidimensionalInterpolator) = A.C
columns(A::SparseUnidimensionalInterpolator) = A.J
size(A::SparseUnidimensionalInterpolator) = (A.nrows, A.ncols)
size(A::SparseUnidimensionalInterpolator, i::Integer) =
(i == 1 ? A.nrows :
i == 2 ? A.ncols : error("out of bounds dimension"))
"""
SparseUnidimensionalInterpolator{T=eltype(ker)}(ker, d, pos, grd)
yields a linear mapping which interpolates the `d`-th dimension of an array
with kernel `ker` at positions `pos` along the dimension of interpolation `d`
and assuming the input array has grid coordinates `grd` along the the `d`-th
dimension of interpolation. Argument `pos` is a vector of positions, argument
`grd` may be a range or the length of the dimension of interpolation. Optional
parameter `T` is the floating-point type of the coefficients of the operator.
This kind of interpolator is suitable for separable multi-dimensional
interpolation with precomputed interpolation coefficients. Having precomputed
coefficients is mostly interesting when the operator is to be applied multiple
times (for instance in iterative methods). Otherwise, separable operators
which compute the coefficients *on the fly* may be preferable.
A combination of instances of `SparseUnidimensionalInterpolator` can be built
to achieve sperable multi-dimensional interpolation. For example:
using LinearInterpolators
ker = CatmullRomSpline()
n1, n2 = 70, 50
x1 = linspace(1, 70, 201)
x2 = linspace(1, 50, 201)
A1 = SparseUnidimensionalInterpolator(ker, 1, x1, 1:n1)
A2 = SparseUnidimensionalInterpolator(ker, 2, x2, 1:n2)
A = A1*A2
"""
SparseUnidimensionalInterpolator(ker::Kernel{T}, args...) where {T<:AbstractFloat} =
SparseUnidimensionalInterpolator{T}(ker, args...)
@deprecate SparseUnidimensionalInterpolator(T::Type{<:AbstractFloat}, ker::Kernel, args...) SparseUnidimensionalInterpolator{T}(ker, args...)
SparseUnidimensionalInterpolator{T}(ker::Kernel, args...) where {T<:AbstractFloat} =
SparseUnidimensionalInterpolator{T}(T(ker), args...)
function SparseUnidimensionalInterpolator{T}(ker::Kernel{T},
d::Integer,
pos::AbstractVector{<:Real},
len::Integer) where {T<:AbstractFloat}
len ≥ 1 || throw(ArgumentError("invalid dimension length"))
return SparseUnidimensionalInterpolator{T}(ker, d, pos, 1:Int(len))
end
# FIXME: not type-stable
function SparseUnidimensionalInterpolator{T}(ker::Kernel{T,S},
d::Integer,
pos::AbstractVector{<:Real},
grd::AbstractRange
) where {T<:AbstractFloat,S}
SparseUnidimensionalInterpolator{T,S,Int(d)}(ker, pos, grd)
end
function SparseUnidimensionalInterpolator{T,S,D}(ker::Kernel{T,S},
pos::AbstractVector{<:Real},
grd::AbstractRange
) where {D,T<:AbstractFloat,S}
isa(D, Int) || throw(ArgumentError("invalid type for dimension of interpolation"))
D ≥ 1 || throw(ArgumentError("invalid dimension of interpolation"))
nrows = length(pos)
ncols = length(grd)
C, J = _sparsecoefs(CartesianIndices((nrows,)), ncols, ker,
fractional_index(T, pos, grd))
return SparseUnidimensionalInterpolator{T,S,D}(nrows, ncols, C, J)
end
function vcreate(::Type{Direct},
A::SparseUnidimensionalInterpolator,
x::AbstractArray,
scratch::Bool=false)
nrows, ncols = size(A)
return _vcreate(nrows, ncols, A, x)
end
function vcreate(::Type{Adjoint},
A::SparseUnidimensionalInterpolator,
x::AbstractArray,
scratch::Bool=false)
nrows, ncols = size(A)
return _vcreate(ncols, nrows, A, x)
end
function _vcreate(ny::Int, nx::Int,
A::SparseUnidimensionalInterpolator{Ta,S,D},
x::AbstractArray{Tx,N}) where {Ta,Tx<:Real,S,D,N}
xdims = size(x)
1 ≤ D ≤ N ||
throw(DimensionMismatch("out of range dimension of interpolation"))
xdims[D] == nx ||
throw(DimensionMismatch("dimension $D of `x` must be $nx"))
Ty = float(promote_type(Ta, Tx))
ydims = [(d == D ? ny : xdims[d]) for d in 1:N]
return Array{Ty,N}(undef, ydims...)
end
function apply!(α::Real, ::Type{Direct},
A::SparseUnidimensionalInterpolator{Ta,S,D},
x::AbstractArray{Tx,N},
scratch::Bool,
β::Real,
y::AbstractArray{Ty,N}) where {Ta<:AbstractFloat,
Tx<:Real,
Ty<:AbstractFloat,S,D,N}
# Check arguments.
_check(A, N)
xdims = size(x)
ydims = size(y)
nrows, ncols = size(A)
xdims[D] == ncols ||
throw(DimensionMismatch("dimension $D of `x` must be $ncols"))
ydims[D] == nrows ||
throw(DimensionMismatch("dimension $D of `y` must be $nrows"))
for k in 1:N
k == D || xdims[k] == ydims[k] ||
throw(DimensionMismatch("`x` and `y` have incompatible dimensions"))
end
# Apply operator.
if α == 0
vscale!(y, β)
else
C = coefficients(A)
J = columns(A)
I_pre = CartesianIndices(xdims[1:D-1])
I_post = CartesianIndices(xdims[D+1:N])
T = promote_type(Ta,Tx)
alpha = convert(T, α)
if β == 0
_apply_direct!(T, Val{S}, C, J, alpha, x, y,
I_pre, nrows, I_post)
else
beta = convert(Ty, β)
_apply_direct!(T, Val{S}, C, J, alpha, x, beta, y,
I_pre, nrows, I_post)
end
end
return y
end
function apply!(α::Real, ::Type{Adjoint},
A::SparseUnidimensionalInterpolator{Ta,S,D},
x::AbstractArray{Tx,N},
scratch::Bool,
β::Real,
y::AbstractArray{Ty,N}) where {Ta<:AbstractFloat,
Tx<:Real,
Ty<:AbstractFloat,S,D,N}
# Check arguments.
_check(A, N)
xdims = size(x)
ydims = size(y)
nrows, ncols = size(A)
xdims[D] == nrows ||
throw(DimensionMismatch("dimension $D of `x` must be $nrows"))
ydims[D] == ncols ||
throw(DimensionMismatch("dimension $D of `y` must be $ncols"))
for k in 1:N
k == D || xdims[k] == ydims[k] ||
throw(DimensionMismatch("`x` and `y` have incompatible dimensions"))
end
# Apply adjoint operator.
vscale!(y, β)
if α != 0
T = promote_type(Ta,Tx)
_apply_adjoint!(Val{S}, coefficients(A), columns(A),
convert(T, α), x, y,
CartesianIndices(xdims[1:D-1]), nrows,
CartesianIndices(xdims[D+1:N]))
end
return y
end
# The 3 following private methods are needed to achieve type invariance and win
# a factor ~1000 in speed! Also note the way the innermost loop is written
# with a constant range and an offset k0 which is updated; this is critical for
# saving a factor 2-3 in speed.
#
# The current version takes ~ 4ms (7 iterations of linear conjugate gradients)
# to fit a 77×77 array of weights interpolated by Catmull-Rom splines to
# approximate a 256×256 image.
function _apply_direct!(::Type{T},
::Type{Val{S}},
C::Vector{<:AbstractFloat},
J::Vector{Int},
α::AbstractFloat,
x::AbstractArray{<:Real,N},
y::AbstractArray{<:AbstractFloat,N},
I_pre::CartesianIndices{N_pre},
len::Int,
I_post::CartesianIndices{N_post}
) where {T<:AbstractFloat,S,N,N_post,N_pre}
@assert N == N_post + N_pre + 1
@inbounds for i_post in I_post
for i_pre in I_pre
k0 = 0
for i in 1:len
sum = zero(T)
@simd for s in 1:S
k = k0 + s
sum += C[k]*x[i_pre,J[k],i_post]
end
y[i_pre,i,i_post] = α*sum
k0 += S
end
end
end
end
function _apply_direct!(::Type{T},
::Type{Val{S}},
C::Vector{<:AbstractFloat},
J::Vector{Int},
α::AbstractFloat,
x::AbstractArray{<:Real,N},
β::AbstractFloat,
y::AbstractArray{<:AbstractFloat,N},
I_pre::CartesianIndices{N_pre},
len::Int,
I_post::CartesianIndices{N_post}
) where {T<:AbstractFloat,S,N,N_post,N_pre}
@assert N == N_post + N_pre + 1
@inbounds for i_post in I_post
for i_pre in I_pre
k0 = 0
for i in 1:len
sum = zero(T)
@simd for s in 1:S
k = k0 + s
sum += C[k]*x[i_pre,J[k],i_post]
end
y[i_pre,i,i_post] = α*sum + β*y[i_pre,i,i_post]
k0 += S
end
end
end
end
function _apply_adjoint!(::Type{Val{S}},
C::Vector{<:AbstractFloat},
J::Vector{Int},
α::AbstractFloat,
x::AbstractArray{<:Real,N},
y::AbstractArray{<:AbstractFloat,N},
I_pre::CartesianIndices{N_pre},
len::Int,
I_post::CartesianIndices{N_post}
) where {S,N,N_post,N_pre}
@assert N == N_post + N_pre + 1
@inbounds for i_post in I_post
for i_pre in I_pre
k0 = 0
for i in 1:len
c = α*x[i_pre,i,i_post]
@simd for s in 1:S
k = k0 + s
y[i_pre,J[k],i_post] += C[k]*c
end
k0 += S
end
end
end
end
function _check(A::SparseUnidimensionalInterpolator{T,S,D},
N::Int) where {T<:AbstractFloat,S,D}
1 ≤ D ≤ N ||
throw(DimensionMismatch("out of range dimension of interpolation"))
nrows, ncols = size(A)
nvals = S*nrows
C = coefficients(A)
J = columns(A)
length(C) == nvals ||
throw(DimensionMismatch("array of coefficients must have $nvals elements (has $(length(C)))"))
length(J) == nvals ||
throw(DimensionMismatch("array of indices must have $nvals elements (has $(length(C)))"))
for k in eachindex(J)
1 ≤ J[k] ≤ ncols || throw(ErrorException("out of bounds indice(s)"))
end
end
end # module
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] | 1.924742 | 14,138 |
push!(LOAD_PATH,"../src/")
using SlepcWrap
using Documenter
using Literate
# Generate examples
example_src = joinpath(@__DIR__,"..","example")
example_dir = joinpath(@__DIR__,"src","example")
Sys.rm(example_dir; recursive=true, force=true)
Literate.markdown(joinpath(example_src, "helmholtz.jl"), example_dir; documenter = false, execute = false) # documenter = false to avoid Documenter to execute cells
Literate.markdown(joinpath(example_src, "helmholtz_fancy.jl"), example_dir; documenter = false, execute = false) # documenter = false to avoid Documenter to execute cells
Literate.markdown(joinpath(example_src, "complex.jl"), example_dir; documenter = false, execute = false) # documenter = false to avoid Documenter to execute cells
makedocs(;
modules=[SlepcWrap],
authors="bmxam",
sitename="SlepcWrap.jl",
clean=true,doctest=false,
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
canonical="https://github.com/bmxam/SlepcWrap.jl",
assets=String[],
),
pages=[
"Home" => "index.md",
"Examples" => Any[
"example/helmholtz.md",
],
"Fancy examples" => Any[
"example/helmholtz_fancy.md",
"example/complex.md",
],
"API Reference" => Any[
"api/init.md",
"api/eps.md",
],
"API fancy" => "api/fancy/fancy.md",
],
)
deploydocs(;
repo="github.com/bmxam/SlepcWrap.jl.git",
push_preview = true
)
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] | 2.357367 | 638 |
# ---
# title: 75. Sort Colors
# id: problem75
# author: <NAME>
# date: 2020-10-31
# difficulty: Medium
# categories: Array, Two Pointers, Sort
# link: <https://leetcode.com/problems/sort-colors/description/>
# hidden: true
# ---
#
# Given an array `nums` with `n` objects colored red, white, or blue, sort them
# **[in-place](https://en.wikipedia.org/wiki/In-place_algorithm) **so that
# objects of the same color are adjacent, with the colors in the order red,
# white, and blue.
#
# Here, we will use the integers `0`, `1`, and `2` to represent the color red,
# white, and blue respectively.
#
# **Follow up:**
#
# * Could you solve this problem without using the library's sort function?
# * Could you come up with a one-pass algorithm using only `O(1)` constant space?
#
#
#
# **Example 1:**
#
#
#
# Input: nums = [2,0,2,1,1,0]
# Output: [0,0,1,1,2,2]
#
#
# **Example 2:**
#
#
#
# Input: nums = [2,0,1]
# Output: [0,1,2]
#
#
# **Example 3:**
#
#
#
# Input: nums = [0]
# Output: [0]
#
#
# **Example 4:**
#
#
#
# Input: nums = [1]
# Output: [1]
#
#
#
#
# **Constraints:**
#
# * `n == nums.length`
# * `1 <= n <= 300`
# * `nums[i]` is `0`, `1`, or `2`.
#
#
## @lc code=start
using LeetCode
## add your code here:
## @lc code=end
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] | 2.169872 | 624 |
__precompile__()
module AnimalBreedingTools
using Printf
using Statistics
using LinearAlgebra
using Base.Threads
using StatsBase
using Jacobi
struct AnimalBreedingDataSetRaw
pedlist::Matrix{Int}
genid::Vector{Int}
G::Matrix{Float64}
y::Vector{Float64}
X::Matrix{Float64}
end
export get_design_matrix, get_interaction_matrix, directsum, solve_pcg, approximate_trace_of_inverse
export v2r, c2r, r2v, r2c, vech, bending, bending2
export AnimalBreedingDataSetRaw, load_data_set
export chol_symtrid, ldlt_symtrid, takahashi_ldlt_symtrid!
export normalized_legendre, normalized_legendre_matrix, hrv_class
export disp
include("designmatrix.jl")
include("solver.jl")
include("tools.jl")
include("data.jl")
include("polynomials.jl")
include("io.jl")
end
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] | 2.74552 | 279 |
<reponame>giordano/RegistryCI.jl
function meets_compat_for_all_deps(working_directory::AbstractString, pkg, version)
deps = Pkg.TOML.parsefile(joinpath(working_directory, uppercase(pkg[1:1]), pkg, "Deps.toml"))
compat = Pkg.TOML.parsefile(joinpath(working_directory, uppercase(pkg[1:1]), pkg, "Compat.toml"))
# First, we construct a Dict in which the keys are the package's
# dependencies, and the value is always false.
dep_has_compat_with_upper_bound = Dict{String, Bool}()
@debug("We always have julia as a dependency")
dep_has_compat_with_upper_bound["julia"] = false
for version_range in keys(deps)
if version in Pkg.Types.VersionRange(version_range)
for name in keys(deps[version_range])
if (!is_jll_name(name)) & (!is_julia_stdlib(name))
@debug("Found a new (non-stdlib non-JLL) dependency: $(name)")
dep_has_compat_with_upper_bound[name] = false
end
end
end
end
# Now, we go through all the compat entries. If a dependency has a compat
# entry with an upper bound, we change the corresponding value in the Dict
# to true.
for version_range in keys(compat)
if version in Pkg.Types.VersionRange(version_range)
for compat_entry in compat[version_range]
name = compat_entry[1]
value = compat_entry[2]
if value isa Vector
if !isempty(value)
value_ranges = Pkg.Types.VersionRange.(value)
each_range_has_upper_bound = _has_upper_bound.(value_ranges)
if all(each_range_has_upper_bound)
@debug("Dependency \"$(name)\" has compat entries that all have upper bounds")
dep_has_compat_with_upper_bound[name] = true
end
end
else
value_range = Pkg.Types.VersionRange(value)
if _has_upper_bound(value_range)
@debug("Dependency \"$(name)\" has a compat entry with an upper bound")
dep_has_compat_with_upper_bound[name] = true
end
end
end
end
end
meets_this_guideline = all(values(dep_has_compat_with_upper_bound))
if meets_this_guideline
return true, ""
else
bad_dependencies = Vector{String}()
for name in keys(dep_has_compat_with_upper_bound)
if !(dep_has_compat_with_upper_bound[name])
@error("Dependency \"$(name)\" does not have a compat entry that has an upper bound")
push!(bad_dependencies, name)
end
end
sort!(bad_dependencies)
message = string("The following dependencies ",
"do not have a ",
"compat entry that has ",
"an upper bound: ",
join(bad_dependencies,
", "),
". You may find ",
"[CompatHelper]",
"(https://github.com/bcbi/CompatHelper.jl) ",
"helpful for keeping ",
"your compat entries ",
"up-to-date.",
"Note: If your package works for the current version `x.y.z` of a dependency `foo`, ",
"then a compat entry `foo = x.y.z` implies a compatibility upper bound ",
"for packages following semver. You can additionally include earlier versions ",
"your package is compatible with. ",
"See https://julialang.github.io/Pkg.jl/v1/compatibility/ for details.")
return false, message
end
end
function meets_patch_release_does_not_narrow_julia_compat(pkg::String,
new_version::VersionNumber;
registry_head::String,
registry_master::String)
old_version = latest_version(pkg, registry_master)
julia_compats_for_old_version = julia_compat(pkg, old_version, registry_master)
julia_compats_for_new_version = julia_compat(pkg, new_version, registry_head)
if Set(julia_compats_for_old_version) == Set(julia_compats_for_new_version)
return true, ""
end
meets_this_guideline = range_did_not_narrow(julia_compats_for_old_version, julia_compats_for_new_version)
if meets_this_guideline
return true, ""
else
if (old_version >= v"1") || (new_version >= v"1")
msg = string("A patch release is not allowed to narrow the ",
"supported ranges of Julia versions. ",
"The ranges have changed from ",
"$(julia_compats_for_old_version) ",
"(in $(old_version)) ",
"to $(julia_compats_for_new_version) ",
"(in $(new_version)).")
return false, msg
else
@info("Narrows Julia compat, but it's OK since package is pre-1.0")
return true, ""
end
end
end
function meets_name_length(pkg)
meets_this_guideline = length(pkg) >= 5
if meets_this_guideline
return true, ""
else
return false, "Name is not at least five characters long"
end
end
function meets_normal_capitalization(pkg)
meets_this_guideline = occursin(r"^[A-Z]\w*[a-z]\w*[0-9]?$", pkg)
if meets_this_guideline
return true, ""
else
return false, "Name does not meet all of the following: starts with an uppercase letter, ASCII alphanumerics only, not all letters are uppercase.**"
end
end
function meets_repo_url_requirement(pkg::String; registry_head::String)
url = Pkg.TOML.parsefile(joinpath(registry_head, uppercase(pkg[1:1]), pkg, "Package.toml"))["repo"]
meets_this_guideline = url_has_correct_ending(url, pkg)
if meets_this_guideline
return true, ""
else
return false, "Repo URL does not end with /name.jl.git, where name is the package name"
end
end
function _invalid_sequential_version(reason::AbstractString)
return false, "Does not meet sequential version number guideline: $reason", :invalid
end
function _valid_change(old_version::VersionNumber, new_version::VersionNumber)
diff = difference(old_version, new_version)
@debug("Difference between versions: ", old_version, new_version, diff)
if diff == v"0.0.1"
return true, "", :patch
elseif diff == v"0.1.0"
return true, "", :minor
elseif diff == v"1.0.0"
return true, "", :major
else
return _invalid_sequential_version("increment is not one of: 0.0.1, 0.1.0, 1.0.0")
end
end
function meets_sequential_version_number(existing::Vector{VersionNumber}, ver::VersionNumber)
always_assert(!isempty(existing))
if ver in existing
return _invalid_sequential_version("version $ver already exists")
end
issorted(existing) || (existing = sort(existing))
idx = searchsortedlast(existing, ver)
idx > 0 || return _invalid_sequential_version("version $ver less than least existing version $(existing[1])")
prv = existing[idx]
always_assert(ver != prv)
nxt = thismajor(ver) != thismajor(prv) ? nextmajor(prv) :
thisminor(ver) != thisminor(prv) ? nextminor(prv) : nextpatch(prv)
ver <= nxt || return _invalid_sequential_version("version $ver skips over $nxt")
return _valid_change(prv, ver)
end
function _has_no_prerelease_data(version)
result = version.prerelease == ()
return result
end
function _has_no_build_data(version)
result = version.build == ()
return result
end
_has_prerelease_data(version) = !( _has_no_prerelease_data(version) )
_has_build_data(version) = !( _has_no_build_data(version) )
_has_prerelease_andor_build_data(version) = _has_prerelease_data(version) || _has_build_data(version)
function meets_sequential_version_number(pkg::String,
new_version::VersionNumber;
registry_head::String,
registry_master::String)
if _has_prerelease_andor_build_data(new_version)
return false, "Version number is not allowed to contain prerelease or build data", :invalid
end
_all_versions = all_versions(pkg, registry_master)
return meets_sequential_version_number(_all_versions, new_version)
end
function meets_standard_initial_version_number(version)
if _has_prerelease_andor_build_data(version)
return false, "Version number is not allowed to contain prerelease or build data"
end
meets_this_guideline = version == v"0.0.1" || version == v"0.1.0" || version == v"1.0.0" || _is_x_0_0(version)
if meets_this_guideline
return true, ""
else
return false, "Version number is not 0.0.1, 0.1.0, 1.0.0, or X.0.0"
end
end
function _is_x_0_0(version::VersionNumber)
if _has_prerelease_andor_build_data(version)
return false
end
result = (version.major >= 1) && (version.minor == 0) && (version.patch == 0)
return result
end
function _generate_pkg_add_command(pkg::String,
version::VersionNumber)::String
return "Pkg.add(Pkg.PackageSpec(name=\"$(pkg)\", version=v\"$(string(version))\"));"
end
function meets_version_can_be_pkg_added(working_directory::String,
pkg::String,
version::VersionNumber;
registry_deps::Vector{<:AbstractString} = String[])
pkg_add_command = _generate_pkg_add_command(pkg,
version)
_registry_deps = convert(Vector{String}, registry_deps)
code = """
import Pkg;
Pkg.Registry.add(Pkg.RegistrySpec(path=\"$(working_directory)\"));
_registry_deps = $(_registry_deps);
for regdep in _registry_deps
Pkg.Registry.add(Pkg.RegistrySpec(url = regdep))
end
@info("Attempting to `Pkg.add` package...");
$(pkg_add_command)
@info("Successfully `Pkg.add`ed package");
"""
before_message = "Attempting to `Pkg.add` the package"
success_message = "Successfully `Pkg.add`ed the package"
success_return_1 = true
success_return_2 = ""
failure_message = "Was not able to successfully `Pkg.add` the package"
failure_return_1 = false
failure_return_2 = string("I was not able to install the package ",
"(i.e. `Pkg.add(\"$(pkg)\")` failed). ",
"See the CI logs for details.")
return _run_pkg_commands(working_directory,
pkg,
version;
code = code,
before_message = before_message,
success_message = success_message,
success_return_1 = success_return_1,
success_return_2 = success_return_2,
failure_message = failure_message,
failure_return_1 = failure_return_1,
failure_return_2 = failure_return_2)
end
function meets_version_can_be_imported(working_directory::String,
pkg::String,
version::VersionNumber;
registry_deps::Vector{<:AbstractString} = String[])
pkg_add_command = _generate_pkg_add_command(pkg,
version)
_registry_deps = convert(Vector{String}, registry_deps)
code = """
import Pkg;
Pkg.Registry.add(Pkg.RegistrySpec(path=\"$(working_directory)\"));
_registry_deps = $(_registry_deps);
for regdep in _registry_deps
Pkg.Registry.add(Pkg.RegistrySpec(url = regdep))
end
@info("Attempting to `Pkg.add` package...");
$(pkg_add_command)
@info("Successfully `Pkg.add`ed package");
@info("Attempting to `import` package");
import $(pkg);
@info("Successfully `import`ed package");
"""
before_message = "Attempting to `import` the package"
success_message = "Successfully `import`ed the package"
success_return_1 = true
success_return_2 = ""
failure_message = "Was not able to successfully `import` the package"
failure_return_1 = false
failure_return_2 = string("I was not able to load the package ",
"(i.e. `import $(pkg)` failed). ",
"See the CI logs for details.")
return _run_pkg_commands(working_directory,
pkg,
version;
code = code,
before_message = before_message,
success_message = success_message,
success_return_1 = success_return_1,
success_return_2 = success_return_2,
failure_message = failure_message,
failure_return_1 = failure_return_1,
failure_return_2 = failure_return_2)
end
function _run_pkg_commands(working_directory::String,
pkg::String,
version::VersionNumber;
code,
before_message,
success_message,
success_return_1,
success_return_2,
failure_message,
failure_return_1,
failure_return_2)
original_directory = pwd()
tmp_dir_1 = mktempdir()
tmp_dir_2 = mktempdir()
atexit(() -> rm(tmp_dir_1; force = true, recursive = true))
atexit(() -> rm(tmp_dir_2; force = true, recursive = true))
cd(tmp_dir_1)
# We need to be careful with what environment variables we pass to the child
# process. For example, we don't want to pass an environment variable containing
# our GitHub token to the child process. Because if the Julia package that we are
# testing has malicious code in its __init__() function, it could try to steal
# our token. So we only pass four environment variables:
# 1. PATH. If we don't pass PATH, things break. And PATH should not contain any
# sensitive information.
# 2. PYTHON. We set PYTHON to the empty string. This forces any packages that use
# PyCall to install their own version of Python instead of using the system
# Python.
# 3. JULIA_DEPOT_PATH. We set JULIA_DEPOT_PATH to the temporary directory that
# we created. This is because we don't want the child process using our
# real Julia depot. So we set up a fake depot for the child process to use.
# 4. R_HOME. We set R_HOME to "*".
cmd = Cmd(`$(Base.julia_cmd()) -e $(code)`;
env = Dict("PATH" => ENV["PATH"],
"PYTHON" => "",
"JULIA_DEPOT_PATH" => tmp_dir_2,
"R_HOME" => "*"))
# GUI toolkits may need a display just to load the package
xvfb = Sys.which("xvfb-run")
@info("xvfb: ", xvfb)
if xvfb !== nothing
pushfirst!(cmd.exec, "-a")
pushfirst!(cmd.exec, xvfb)
end
@info(before_message)
cmd_ran_successfully = success(pipeline(cmd, stdout=stdout, stderr=stderr))
cd(original_directory)
rm(tmp_dir_1; force = true, recursive = true)
rm(tmp_dir_2; force = true, recursive = true)
if cmd_ran_successfully
@info(success_message)
return success_return_1, success_return_2
else
@error(failure_message)
return failure_return_1, failure_return_2
end
end
url_has_correct_ending(url, pkg) = endswith(url, "/$(pkg).jl.git")
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] | 2.079349 | 7,864 |
<gh_stars>0
# https://developer.mozilla.org/en-US/docs/Web/HTTP/Status#successful_responses
const status_codes = Dict(
100 => "Continue",
101 => "Switching Protocols",
102 => "Processing",
103 => "Early Hints",
200 => "OK",
201 => "Created",
202 => "Accepted",
203 => "Non-Authoritative Information",
204 => "No Content",
205 => "Reset Content",
206 => "Partial Content",
207 => "Multi-Status",
208 => "Already Reported",
226 => "IM Used",
300 => "Multiple Choices",
301 => "Moved Permanently",
302 => "Found",
303 => "See Other",
304 => "Not Modified",
305 => "Use Proxy",
307 => "Temporary Redirect",
308 => "Permanent Redirect",
400 => "Bad Request",
401 => "Unauthorized",
402 => "Payment Required",
403 => "Forbidden",
404 => "Not Found",
405 => "Method Not Allowed",
406 => "Not Acceptable",
407 => "Proxy Authentication Required",
408 => "Request Timeout",
409 => "Conflict",
410 => "Gone",
411 => "Length Required",
412 => "Precondition Failed",
413 => "Payload Too Large",
414 => "URI Too Long",
415 => "Unsupported Media Type",
416 => "Range Not Satisfiable",
417 => "Expectation Failed",
418 => "I'm a teapot",
421 => "Misdirected Request",
422 => "Unprocessable Entity",
423 => "Locked",
424 => "Failed Dependency",
425 => "Too Early",
426 => "Upgrade Required",
428 => "Precondition Required",
429 => "Too Many Requests",
431 => "Request Header Fields Too Large",
451 => "Unavailable For Legal Reasons",
500 => "Internal Server Error",
501 => "Not Implemented",
502 => "Bad Gateway",
503 => "Service Unavailable",
504 => "Gateway Time-out",
505 => "HTTP Version Not Supported",
506 => "Variant Also Negotiates",
507 => "Insufficient Storage",
508 => "Loop Detected",
509 => "Bandwidth Limit Exceeded",
510 => "Not Extended",
511 => "Network Authentication Required"
)
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] | 2.701195 | 753 |
using QuasinormalModes
using RootsAndPoles
# ------------------------------------------------------------------
# 1. Setting up Schwarzschild black hole data structure for
# computing the QNMs numerically.
# ------------------------------------------------------------------
struct NSchwarzschildData{N,T} <: NumericAIMProblem{N,T}
nIter::N
x0::T
l::N
s::N
end
function NSchwarzschildData(nIter::N, x0::T, l::N, s::N) where {N,T}
return NSchwarzschildData{N,T}(nIter, x0, l, s)
end
QuasinormalModes.λ0(::NSchwarzschildData{N,T}) where {N,T} = (x,ω) -> (-1 + (2*im)*ω + x*(4 - 3*x + (4*im)*(-2 + x)*ω))/((-1 + x)^2*x)
QuasinormalModes.S0(d::NSchwarzschildData{N,T}) where {N,T} = (x,ω) -> (d.l + d.l^2 + (-1 + d.s^2)*(-1 + x) + (4*im)*(-1 + x)*ω + 4*(-2 + x)*ω^2)/((-1 + x)^2*x)
QuasinormalModes.get_niter(d::NSchwarzschildData{N,T}) where {N,T} = d.nIter
QuasinormalModes.get_x0(d::NSchwarzschildData{N,T}) where {N,T} = d.x0
# ------------------------------------------------------------------
# 2. Benchmark
# ------------------------------------------------------------------
"""
bench_iter(iter_start, iter_end, x0, l, s, xt, ft, nls_iter, reference_ωr, reference_ωi, repeat)
Measures the time required to compute a pair of quasinormal frequencies as a function of the
number of AIM iterations performed. The result is also compared with a know result in order
to provide a error measurement.
# Input
- `iter_start`: First number of iterations to perform.
- `iter_end`: Last number of iterations.
- `x0`: The point around which AIM functions will be Taylor expanded.
- `l`: The l value of the sought mode.
- `s`: The s value of the sought mode.
- `xt`: The `xtol` value passed to NLSolve.
- `ft`: The `ftol` value passed to NLSolve.
- `nls_iter`: The number of iterations that NLSolve will perform.
- `reference_ωr`: The reference value for the real QNM frequency.
- `reference_ωi`: The reference value for the imaginary QNM frequency.
- `repeat`: The amount of times to repeat the operation.
# Output
nothing
"""
function bench_iter(iter_start, iter_end, x0, l, s, xt, ft, nls_iter, reference_ωr, reference_ωi, repeat)
for i in 1:repeat
println("Benchmark iteration ", i, ":")
file = open("run_$(i).dat", "w")
println(file, "# 1:time(ns) 2:iter 3:x0 4:l 5:s 6:xt 7:ft 8:w_r 9:w_i 10:error in w_r 11:error in w_i")
for iter in iter_start:iter_end
println(" AIM iteration ", iter)
p_num = NSchwarzschildData(iter, x0, l, s);
c_num = AIMCache(p_num)
t0 = time_ns();
ev = computeEigenvalues(Threaded(), p_num, c_num, typeof(x0)(reference_ωr, reference_ωi), nls_xtol = xt, nls_ftol = ft, nls_iterations = nls_iter)
elapsed_time = time_ns() - t0
if(ev.x_converged || ev.f_converged)
println(file,
elapsed_time, " ",
iter, " ",
convert(Float64, x0), " ",
l, " ",
s, " ",
convert(Float64, xt), " ",
convert(Float64, ft), " ",
ev.zero[1], " ",
ev.zero[2], " ",
ev.zero[1] - reference_ωr, " ",
ev.zero[2] - reference_ωi
)
flush(file)
end
end
close(file)
end
end
# We call the function once and discard the results in order so that the compilation time does not get included in the benchmark.
bench_iter(0x00001, 0x00064, Complex(big"0.39", big"0.0"), 0x00000, 0x00000, big"1.0e-55", big"1.0e-55", 5000000, big"0.2209098781608393", big"-0.2097914341737619", 1)
bench_iter(0x00001, 0x00064, Complex(big"0.39", big"0.0"), 0x00000, 0x00000, big"1.0e-55", big"1.0e-55", 5000000, big"0.2209098781608393", big"-0.2097914341737619", 20)
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] | 2.12474 | 1,924 |
module FluxMNIST
using Flux, Flux.Data.MNIST, Statistics
using Flux: onehotbatch, onecold, crossentropy, throttle, @epochs
import Flux: glorot_uniform
using Base.Iterators: repeated, partition
using BSON: @save
using Dates
# using CuArrays
include("util.jl")
#=
Initialization is from
He et al., 2015,
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
https://arxiv.org/abs/1502.01852
=#
kaiming(::Type{T}, h, w, i, o) where {T<:AbstractFloat} = T(sqrt(2 / (w * h * o))) .* randn(T, h, w, i, o)
glorot_uniform(::Type{T}, dims...) where {T<:AbstractFloat} = (rand(T, dims...) .- T(0.5)) .* sqrt(T(24.0)/(sum(dims)))
# Classify MNIST digits with a convolutional network
function loadMNIST(::Type{T}, batch_size::Int = 1000) where {T<:AbstractFloat}
imgs = MNIST.images()
labels = onehotbatch(MNIST.labels(), 0:9)
_train = BatchProducer(T.(cat((cat(float.(imgs[idxs])..., dims = 4)
for idxs in partition(1:60_000, batch_size))..., dims=4)),
labels, batch_size, true)
train = (gpu.(minibatch) for minibatch in _train)
# Prepare test set (first 1,000 images)
tX = T.(cat(float.(MNIST.images(:test)[1:batch_size])..., dims = 4)) |> gpu
tY = onehotbatch(MNIST.labels(:test)[1:batch_size], 0:9) |> gpu
return (train, tX, tY)
end
# for Float32 and kaiming initialization
(::Type{Conv})(::Type{T}, k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
init = kaiming, stride = 1, pad = 0, dilation = 1) where {T<:AbstractFloat, N} =
Conv(param(init(T, k..., ch...)), param(zeros(T, ch[2])), σ, stride = stride, pad = pad, dilation = dilation)
function (::Type{Dense})(::Type{T}, in::Integer, out::Integer, σ = identity;
initW = glorot_uniform, initb = zeros) where {T<:AbstractFloat}
return Dense(param(initW(T, out, in)), param(initb(T, out)), σ)
end
# Model
mutable struct Model{T,M}
m::M
(::Type{Model{T}})() where {T<:AbstractFloat} = new{T,Chain}(Chain(
Conv(T, (5, 5), 1=>32, relu),
x -> maxpool(x, (2, 2)),
Conv(T, (5, 5), 32=>64, relu),
x -> maxpool(x, (2, 2)),
x -> reshape(x, :, size(x, 4)),
Dense(T, 1024, 1024, relu),
Dropout(0.5),
Dense(T, 1024, 10),
softmax) |> gpu
)
end
# loss(x, y) = crossentropy(m(x), y)
mutable struct Loss{M} <: Function
m::M
end
(loss::Loss)(x, y) = crossentropy(loss.m(x), y)
# accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))
mutable struct Accuracy{M} <:Function
m::M
end
(accuracy::Accuracy)(x, y) = mean(onecold(accuracy.m(x)) .== onecold(y))
# train!
function train!(m::Model, traindata; epochs = 10, cb = identity)
loss = Loss(m.m)
opt = ADAM(params(m.m)) # TODO: patameterize
@epochs epochs Flux.train!(loss, traindata, opt; cb=cb)
end
# save Model (weights only)
function savemodel(m::Model{Float32})
ts = Dates.format(now(), dateformat"yyyymmddHHMMSS")
savemodel(m, "model-flux-f32_$(ts).bson")
end
function savemodel(m::Model)
ts = Dates.format(now(), dateformat"yyyymmddHHMMSS")
savemodel(m, "model-flux-f64_$(ts).bson")
end
function savemodel(m::Model, filename::AbstractString)
weights = Tracker.data.(params(m.m))
@save filename weights
end
end # module
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198,
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198
] | 2.280412 | 1,455 |
using Flux
using Zygote
import Zygote:gradient
gradient(f, ::Val{:Zygote}, args...) = gradient(f, args...)
Zygote.@adjoint argmax(xs; dims = :) = argmax(xs;dims=dims), _ -> nothing
# ??? can safely removed now
Zygote.@adjoint function Base.broadcasted(::typeof(relu), x::Array{T}) where T<:Real
y = relu.(x)
return y, Δ -> begin
res = similar(Δ)
for i in 1:length(res)
if y[i] > 0
res[i] = Δ[i]
else
res[i] = zero(T)
end
end
(nothing, res)
end
end | [
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] | 1.941379 | 290 |
<gh_stars>100-1000
### Extensible system for multimedia display of Python objects
const PYSHOW_RULES = Function[]
function pyshow_add_rule(rule::Function)
push!(PYSHOW_RULES, rule)
return
end
function pyshow(io::IO, mime::MIME, x)
x_ = Py(x)
for rule in PYSHOW_RULES
rule(io, string(mime), x_) && return
end
throw(MethodError(show, (io, mime, x_)))
end
function pyshowable(mime::MIME, x)
x_ = Py(x)
for rule in PYSHOW_RULES
rule(devnull, string(mime), x_) && return true
end
return false
end
### Particular rules
# x._repr_mimebundle_()
function pyshow_rule_mimebundle(io::IO, mime::String, x::Py)
try
ans = x._repr_mimebundle_(include=pylist((mime,)))
if pyisinstance(ans, pybuiltins.tuple)
data = ans[0][mime]
else
data = ans[mime]
end
data = pyconvert(Union{String,Vector{UInt8}}, data)
if mime == "text/html"
data = String(data)
if occursin("altair-viz-", data) && occursin("document.currentScript.previousElementSibling", data)
# unsatisfactory hack to get altair plots to display properly in pluto
# TODO: fix this upstream (in altair or pluto??)
data = replace(data, "document.currentScript.previousElementSibling" => "((document.currentScript || {}).previousElementSibling || {})")
end
end
write(io, data)
return true
catch exc
if exc isa PyException
return false
else
rethrow()
end
end
end
const MIME_TO_REPR_METHOD = Dict(
"text/plain" => "__repr__",
"text/html" => "_repr_html_",
"text/markdown" => "_repr_markdown_",
"text/json" => "_repr_json_",
"text/latex" => "_repr_latex_",
"application/javascript" => "_repr_javascript_",
"application/pdf" => "_repr_pdf_",
"image/jpeg" => "_repr_jpeg_",
"image/png" => "_repr_png_",
"image/svg+xml" => "_repr_svg_",
)
# x._repr_FORMAT_()
function pyshow_rule_repr(io::IO, mime::String, x::Py)
method = get(MIME_TO_REPR_METHOD, mime, "")
isempty(method) && return false
try
ans = pygetattr(x, method)()
if pyisinstance(ans, pybuiltins.tuple)
data = ans[0]
else
data = ans
end
write(io, pyconvert(Union{String,Vector{UInt8}}, data))
return true
catch exc
if exc isa PyException
return false
else
rethrow()
end
end
end
const MIME_TO_MATPLOTLIB_FORMAT = Dict(
"image/png" => "png",
"image/jpeg" => "jpeg",
"image/tiff" => "tiff",
"image/svg+xml" => "svg",
"application/pdf" => "pdf",
)
# x.savefig()
# Requires x to be a matplotlib.pyplot.Figure, or x.figure to be one.
# Closes the underlying figure.
function pyshow_rule_savefig(io::IO, mime::String, x::Py)
format = get(MIME_TO_MATPLOTLIB_FORMAT, mime, "")
isempty(format) && return false
pyhasattr(x, "savefig") || return false
try
plt = pysysmodule.modules["matplotlib.pyplot"]
Figure = plt.Figure
fig = x
while !pyisinstance(fig, Figure)
fig = fig.figure
end
buf = pyimport("io").BytesIO()
x.savefig(buf, format=format)
data = pyconvert(Vector{UInt8}, buf.getvalue())
write(io, data)
plt.close(fig)
return true
catch exc
if exc isa PyException
return false
else
rethrow()
end
end
end
function init_pyshow()
pyshow_add_rule(pyshow_rule_mimebundle)
pyshow_add_rule(pyshow_rule_repr)
pyshow_add_rule(pyshow_rule_savefig)
end
| [
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286,
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] | 2.077697 | 1,789 |
# # Sequential Simulations with [PowerSimulations.jl](https://github.com/NREL-SIIP/PowerSimulations.jl)
# **Originally Contributed by**: <NAME>
# ## Introduction
# PowerSimulations.jl supports simulations that consist of sequential optimization problems
# where results from previous problems inform subsequent problems in a variety of ways. This
# example demonstrates some of these capabilities to represent electricity market clearing.
# ## Dependencies
# Since the `OperatiotnsProblem` is the fundamental building block of a sequential
# simulation in PowerSimulations, we will build on the [OperationsProblem example](../../notebook/3_PowerSimulations_examples/1_operations_problems.ipynb)
# by sourcing it as a dependency.
using SIIPExamples
pkgpath = dirname(dirname(pathof(SIIPExamples)))
include(
joinpath(pkgpath, "test", "3_PowerSimulations_examples", "01_operations_problems.jl"),
)
# ### 5-Minute system
# We had already created a `sys::System` from hourly RTS data in the OperationsProblem example.
# The RTS data also includes 5-minute resolution time series data. So, we can create another
# `System`:
sys_RT = System(rawsys; time_series_resolution = Dates.Minute(5))
transform_single_time_series!(sys_RT, 12, Hour(1))
# ## `OperationsProblemTemplate`s define `Stage`s
# Sequential simulations in PowerSimulations are created by defining `OperationsProblems`
# that represent `Stages`, and how information flows between executions of a `Stage` and
# between different `Stage`s.
#
# Let's start by defining a two stage simulation that might look like a typical day-Ahead
# and real-time electricity market clearing process.
# ### Define the reference model for the day-ahead unit commitment
devices = Dict(
:Generators => DeviceModel(ThermalStandard, ThermalStandardUnitCommitment),
:Ren => DeviceModel(RenewableDispatch, RenewableFullDispatch),
:Loads => DeviceModel(PowerLoad, StaticPowerLoad),
:HydroROR => DeviceModel(HydroDispatch, FixedOutput),
:Hydro => DeviceModel(HydroEnergyReservoir, HydroDispatchRunOfRiver),
:RenFx => DeviceModel(RenewableFix, FixedOutput),
)
template_uc = template_unit_commitment(devices = devices)
# ### Define the reference model for the real-time economic dispatch
devices = Dict(
:Generators => DeviceModel(ThermalStandard, ThermalDispatch),
:Ren => DeviceModel(RenewableDispatch, RenewableFullDispatch),
:Loads => DeviceModel(PowerLoad, StaticPowerLoad),
:HydroROR => DeviceModel(HydroDispatch, FixedOutput),
:Hydro => DeviceModel(HydroEnergyReservoir, HydroDispatchRunOfRiver),
:RenFx => DeviceModel(RenewableFix, FixedOutput),
)
template_ed = template_economic_dispatch(devices = devices)
# ### Define the `Stage`s
# Stages define models. The actual problem will change as the stage gets updated to represent
# different time periods, but the formulations applied to the components is constant within
# a stage. In this case, we want to define two stages with the `OperationsProblemTemplate`s
# and the `System`s that we've already created.
stages_definition = Dict(
"UC" => Stage(GenericOpProblem, template_uc, sys, solver),
"ED" => Stage(
GenericOpProblem,
template_ed,
sys_RT,
solver,
balance_slack_variables = true,
),
)
# Note that the "ED" stage has a `balance_slack_variables = true` argument. This adds slack
# variables with a default penalty of 1e6 to the nodal energy balance constraint and helps
# ensure feasibility with some performance impacts.
# ### `SimulationSequence`
# Similar to an `OperationsProblemTemplate`, the `SimulationSequence` provides a template of
# how to execute a sequential set of operations problems.
#nb # print_struct(SimulationSequence)
# Let's review some of the `SimulationSequence` arguments.
# ### Chronologies
# In PowerSimulations, chronologies define where information is flowing. There are two types
# of chronologies.
# - inter-stage chronologies: Define how information flows between stages. e.g. day-ahead
# solutions are used to inform economic dispatch problems
# - intra-stage chronologies: Define how information flows between multiple executions of a
# single stage. e.g. the dispatch setpoints of the first period of an economic dispatch problem
# are constrained by the ramping limits from setpoints in the final period of the previous problem.
#
# Let's define an inter-stage chronology that synchronizes information from 24 periods of
# the first stage with a set of executions of the second stage:
feedforward_chronologies = Dict(("UC" => "ED") => Synchronize(periods = 24))
# ### `FeedForward` and `Cache`
# The definition of exactly what information is passed using the defined chronologies is
# accomplished with `FeedForward` and `Cache` objects. Specifically, `FeedForward` is used
# to define what to do with information being passed with an inter-stage chronology. Let's
# define a `FeedForward` that affects the semi-continuous range constraints of thermal generators
# in the economic dispatch problems based on the value of the unit-commitment variables.
feedforward = Dict(
("ED", :devices, :Generators) => SemiContinuousFF(
binary_source_stage = PSI.ON,
affected_variables = [PSI.ACTIVE_POWER],
),
)
# ### Sequencing
# The stage problem length, look-ahead, and other details surrounding the temporal Sequencing
# of stages are controlled using the `order`, `horizons`, and `intervals` arguments.
# - order::Dict(Int, String) : the hierarchical order of stages in the simulation
# - horizons::Dict(String, Int) : defines the number of time periods in each stage (problem length)
# - intervals::Dict(String, Dates.Period) : defines the interval with which stage problems
# advance after each execution. e.g. day-ahead problems have an interval of 24-hours
#
# So, to define a typical day-ahead - real-time sequence, we can define the following:
# - Day ahead problems should represent 48 hours, advancing 24 hours after each execution (24-hour look-ahead)
# - Real time problems should represent 1 hour (12 5-minute periods), advancing 1 hour after each execution (no look-ahead)
order = Dict(1 => "UC", 2 => "ED")
horizons = Dict("UC" => 24, "ED" => 12)
intervals = Dict("UC" => (Hour(24), Consecutive()), "ED" => (Hour(1), Consecutive()))
# Finally, we can put it all together:
DA_RT_sequence = SimulationSequence(
step_resolution = Hour(24),
order = order,
horizons = horizons,
intervals = intervals,
ini_cond_chronology = InterStageChronology(),
feedforward_chronologies = feedforward_chronologies,
feedforward = feedforward,
)
# ## `Simulation`
# Now, we can build and execute a simulation using the `SimulationSequence` and `Stage`s
# that we've defined.
sim = Simulation(
name = "rts-test",
steps = 1,
stages = stages_definition,
stages_sequence = DA_RT_sequence,
simulation_folder = rts_dir,
)
# ### Build simulation
build!(sim)
# ### Execute simulation
# the following command returns the status of the simulation (0: is proper execution) and
# stores the results in a set of HDF5 files on disk.
execute!(sim)
# ## Results
# To access the results, we need to load the simulation result metadata and then make
# requests to the specific data of interest. This allows you to efficiently access the
# results of interest without overloading resources.
results = SimulationResults(sim);
uc_results = get_stage_results(results, "UC"); # UC stage result metadata
ed_results = get_stage_results(results, "ED"); # ED stage result metadata
# Now we can read the specific results of interest for a specific stage, time window (optional),
# and set of variables, duals, or parameters (optional)
read_variables(uc_results, names = [:P__ThermalStandard, :P__RenewableDispatch])
# Or if we want the result of just one variable, parameter, or dual (must be defined in the
# stage definition), we can use:
read_parameter(
ed_results,
:P__max_active_power__RenewableFix,
initial_time = DateTime("2020-01-01T06:00:00"),
count = 5,
)
# * note that this returns the results of each execution step in a separate dataframe *
# If you want the realized results (without lookahead periods), you can call `read_realized_*`:
read_realized_variables(uc_results, names = [:P__ThermalStandard, :P__RenewableDispatch])
# ## Plotting
# Take a look at the examples in [the plotting folder.](../../notebook/3_PowerSimulations_examples/Plotting)
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] | 3.501239 | 2,422 |
<filename>src/aggregate.jl
using Feather
using DataFrames
using LightGraphs
using GraphIO
using ParserCombinator
data = Feather.read(
joinpath("experiments", "test_run_grid", "agents", "adata.feather")
)
graph = LightGraphs.loadgraph(
joinpath("experiments", "test_run_grid", "graphs", "rep_01.gml"),
GraphIO.GML.GMLFormat()
)
connected_comps = LightGraphs.connected_components(graph)
connected_comps_by_size = [length(i) for i in connected_comps]
# SizeBiggestComponent
maximum(connected_comps_by_size)
# SeperateComponents
length(connected_comps)
data
# StubbornCount in data
# NetworkType in data
# UniqueCultures -> aggregate from Culture
# SeperateComponents
# ExcessZeros -> aggregate from Culture
function aggregate()
end
function read_raw_data()
end
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] | 3.007722 | 259 |
<reponame>longhua8800w/GeometricFlux.jl<filename>src/layers/pool.jl
samesize_float = Dict(Int8=>Float16, UInt8=>Float16, Int16=>Float16, UInt16=>Float16,
Int32=>Float32, UInt32=>Float32, Int64=>Float64, UInt64=>Float64)
# GlobalPool(x, aggr, batch, size=nothing) # aggr=sum, mean, max
#
# TopKPool()
# struct MaxPool
#
# end
#
# function MaxPool(adj::AbstractMatrix, ch::Pair{<:Integer,<:Integer}, σ = identity)
# MaxPool()
# end
#
# (m::MaxPool)(X::AbstractMatrix) = maxpool(m.cluster, X)
#
#
#
# struct MeanPool
#
# end
#
# (m::MeanPool)(X::AbstractMatrix) = meanpool(m.cluster, X)
function sumpool(cluster::Array{Int}, X::Array{T}) where {T<:Real}
dims = _pooling_dim_check(cluster, X)
c = length(Set(cluster))
Y = zeros(T, dims..., c)
scatter_add!(Y, X, cluster)
Y
end
function subpool(cluster::Array{Int}, X::Array{T}) where {T<:Real}
dims = _pooling_dim_check(cluster, X)
c = length(Set(cluster))
Y = zeros(T, dims..., c)
scatter_sub!(Y, X, cluster)
Y
end
function prodpool(cluster::Array{Int}, X::Array{T}) where {T<:Real}
dims = _pooling_dim_check(cluster, X)
c = length(Set(cluster))
Y = ones(T, dims..., c)
scatter_mul!(Y, X, cluster)
Y
end
function divpool(cluster::Array{Int}, X::Array{T}) where {T<:Real}
dims = _pooling_dim_check(cluster, X)
c = length(Set(cluster))
FT = (T <: Integer) ? samesize_float[T] : T
Y = ones(FT, dims..., c)
scatter_div!(Y, FT.(X), cluster)
Y
end
function maxpool(cluster::Array{Int}, X::Array{T}) where {T<:Real}
dims = _pooling_dim_check(cluster, X)
c = length(Set(cluster))
Y = fill(typemin(T), dims..., c)
scatter_max!(Y, X, cluster)
Y
end
function minpool(cluster::Array{Int}, X::Array{T}) where {T<:Real}
dims = _pooling_dim_check(cluster, X)
c = length(Set(cluster))
Y = fill(typemax(T), dims..., c)
scatter_min!(Y, X, cluster)
Y
end
function meanpool(cluster::Array{Int}, X::Array{T}) where {T<:Real}
dims = _pooling_dim_check(cluster, X)
c = length(Set(cluster))
FT = (T <: Integer) ? samesize_float[T] : T
Y = zeros(FT, dims..., c)
scatter_mean!(Y, FT.(X), cluster)
Y
end
function _pooling_dim_check(cluster::Array{Int}, X::Array{T}) where {T<:Real}
dim_c = size(cluster)
d = length(dim_c)
dim_X = size(X)
n = length(dim_X) - d
@assert n > 0 "X must have more dimensions than cluster."
@assert dim_c == dim_X[n+1:end] "X must have the same latter dimension with cluster."
dim_X[1:n]
end
pool(op::Symbol, cluster::Array, X::Array) = pool(Val(op), cluster, X)
pool(::Val{:add}, cluster::Array, X::Array) = sumpool(cluster, X)
pool(::Val{:sub}, cluster::Array, X::Array) = subpool(cluster, X)
pool(::Val{:mul}, cluster::Array, X::Array) = prodpool(cluster, X)
pool(::Val{:div}, cluster::Array, X::Array) = divpool(cluster, X)
pool(::Val{:max}, cluster::Array, X::Array) = maxpool(cluster, X)
pool(::Val{:min}, cluster::Array, X::Array) = minpool(cluster, X)
pool(::Val{:mean}, cluster::Array, X::Array) = meanpool(cluster, X)
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] | 2.255474 | 1,370 |
<reponame>pulsipher/InfiniteOpt.jl
################################################################################
# DATATYPES
################################################################################
# Extend addchild to take the root of another graph as input
function _LCRST.addchild(parent::_LCRST.Node{T}, newc::_LCRST.Node{T}) where T
# copy the new node if it is not a root
# otherwise, we are just merging 2 graphs together
if !_LCRST.isroot(newc)
newc = copy(newc)
end
# add it on to the tree
newc.parent = parent
prevc = parent.child
if prevc == parent
parent.child = newc
else
prevc = _LCRST.lastsibling(prevc)
prevc.sibling = newc
end
return newc
end
# Extend addchild with convenient nothing dispatch for empty previous child
function _LCRST.addchild(
parent::_LCRST.Node{T},
oldc::Nothing,
newc::_LCRST.Node{T}
) where T
return _LCRST.addchild(parent, newc)
end
# Extend addchild to efficiently add multiple children if the previous is known
function _LCRST.addchild(
parent::_LCRST.Node{T},
prevc::_LCRST.Node{T},
data::T
) where T
# add it on to the tree
newc = _LCRST.Node(data, parent)
prevc.sibling = newc
return newc
end
# Extend addchild to efficiently add multiple children if the previous is known
function _LCRST.addchild(
parent::_LCRST.Node{T},
prevc::_LCRST.Node{T},
newc::_LCRST.Node{T}
) where T
# check if the prev is actually a child of the parent
@assert prevc.parent === parent "Previous child doesn't belong to parent."
# copy the new node if it is not a root
# otherwise, we are just merging 2 graphs together
if !_LCRST.isroot(newc)
newc = copy(newc)
end
# add it on to the tree
newc.parent = parent
prevc.sibling = newc
return newc
end
# Map a LCRST tree based by operating each node with a function
function _map_tree(map_func::Function, node::_LCRST.Node)
new_node = map_func(node)
prev = nothing
for child in node
prev = _LCRST.addchild(new_node, prev, _map_tree(map_func, child))
end
return new_node
end
# Extend copying for graph nodes
function Base.copy(node::_LCRST.Node)
return _map_tree(n -> _LCRST.Node(n.data), node)
end
# Replace a node with its only child if it only has 1 child
function _merge_parent_and_child(node::_LCRST.Node)
if _LCRST.islastsibling(node.child)
child = node.child
node.data = child.data
for n in child
n.parent = node
end
node.child = child.child
child.child = child
child.parent = child
end
return node
end
# This is ambiguous but faster than the concrete alternatives tested so far
# Even better than using Node{Any}...
"""
NodeData
A `DataType` for storing values in an expression tree that is used in a
[`NLPExpr`](@ref). Acceptable value types include:
- `Real`: Constants
- `GeneralVariableRef`: Optimization variables
- `JuMP.GenericAffExpr{Float64, GeneralVariableRef}`: Affine expressions
- `JuMP.GenericQuadExpr{Float64, GeneralVariableRef}`: Quadratic expressions
- `Symbol`: Registered NLP function name.
**Fields**
- `value`: The stored value.
"""
struct NodeData
value
end
# Getter function for the node value (so it is easy to change later on if needed)
function _node_value(data::NodeData)
return data.value
end
# Recursively determine if node is effectively zero
function _is_zero(node::_LCRST.Node{NodeData})
raw = _node_value(node.data)
if isequal(raw, 0)
return true
elseif _LCRST.isleaf(node)
return false
elseif raw in (:+, :-) && all(_is_zero(n) for n in node)
return true
elseif raw == :* && any(_is_zero(n) for n in node)
return true
elseif raw in (:/, :^) && _is_zero(node.child)
return true
elseif all(_is_zero(n) for n in node) && iszero(get(_NativeNLPFunctions, (raw, length(collect(node))), (i...) -> true)((0.0 for n in node)...))
return true
else
return false
end
end
# Prone any nodes that are effectively zero
function _drop_zeros!(node::_LCRST.Node{NodeData})
if _LCRST.isleaf(node)
return node
elseif _is_zero(node)
node.data = NodeData(0.0)
_LCRST.makeleaf!(node)
return node
end
raw = _node_value(node.data)
if raw == :+
for n in node
if _is_zero(n)
_LCRST.prunebranch!(n)
end
end
_merge_parent_and_child(node)
elseif raw == :-
if _is_zero(node.child)
_LCRST.prunebranch!(node.child)
elseif _is_zero(node.child.sibling)
_LCRST.prunebranch!(node.child.sibling)
_merge_parent_and_child(node)
end
end
for n in node
_drop_zeros!(n)
end
return node
end
# Extend Base.isequal for our node types
function Base.isequal(n1::_LCRST.Node{NodeData}, n2::_LCRST.Node{NodeData})
isequal(_node_value(n1.data), _node_value(n2.data)) || return false
count(i -> true, n1) != count(i -> true, n2) && return false
for (c1, c2) in zip(n1, n2)
if !isequal(c1, c2)
return false
end
end
return true
end
"""
NLPExpr <: JuMP.AbstractJuMPScalar
A `DataType` for storing scalar nonlinear expressions. It stores the expression
algebraically via an expression tree where each node contains [`NodeData`](@ref)
that can store one of the following:
- a registered function name (stored as a `Symbol`)
- a constant
- a variable
- an affine expression
- a quadratic expression.
Specifically, it employs a left-child right-sibling tree
(from `LeftChildRightSiblingTrees.jl`) to represent the expression tree.
**Fields**
- `tree_root::LeftChildRightSiblingTrees.Node{NodeData}`: The root node of the
expression tree.
"""
struct NLPExpr <: JuMP.AbstractJuMPScalar
tree_root::_LCRST.Node{NodeData}
# Constructor
function NLPExpr(tree_root::_LCRST.Node{NodeData})
return new(tree_root)
end
end
# Extend basic functions
Base.broadcastable(nlp::NLPExpr) = Ref(nlp)
Base.copy(nlp::NLPExpr) = NLPExpr(copy(nlp.tree_root))
Base.zero(::Type{NLPExpr}) = NLPExpr(_LCRST.Node(NodeData(0.0)))
Base.one(::Type{NLPExpr}) = NLPExpr(_LCRST.Node(NodeData(1.0)))
function Base.isequal(nlp1::NLPExpr, nlp2::NLPExpr)
return isequal(nlp1.tree_root, nlp2.tree_root)
end
"""
JuMP.drop_zeros!(nlp::NLPExpr)::NLPExpr
Removes the zeros (possibly introduced by deletion) from an nonlinear expression.
Note this only uses a few simple heuristics and will not remove more complex
relationships like `cos(π/2)`.
**Example**
```julia-repl
julia> expr = x^2.3 * max(0, zero(NLPExpr)) - exp(1/x + 0)
x^2.3 * max(0, 0) - exp(1 / x + 0)
julia> drop_zeros!(expr)
-exp(1 / x)
```
"""
function JuMP.drop_zeros!(nlp::NLPExpr)
_drop_zeros!(nlp.tree_root) # uses a basic simplification scheme
return nlp
end
# Extend JuMP.isequal_canonical (uses some heuristics but is not perfect)
function JuMP.isequal_canonical(nlp1::NLPExpr, nlp2::NLPExpr)
n1 = _drop_zeros!(copy(nlp1.tree_root))
n2 = _drop_zeros!(copy(nlp2.tree_root))
return isequal(n1, n2)
end
# Print the tree structure of the expression tree
function print_expression_tree(io::IO, nlp::NLPExpr)
return AbstractTrees.print_tree(io, nlp.tree_root)
end
print_expression_tree(io::IO, expr) = println(io, expr)
"""
print_expression_tree(nlp::NLPExpr)
Print a tree representation of the nonlinear expression `nlp`.
**Example**
```julia-repl
julia> expr = (x * sin(x)^3) / 2
(x * sin(x)^3) / 2
julia> print_expression_tree(expr)
/
├─ *
│ ├─ x
│ └─ ^
│ ├─ sin
│ │ └─ x
│ └─ 3
└─ 2
```
"""
print_expression_tree(nlp::NLPExpr) = print_expression_tree(stdout::IO, nlp)
# Convenient expression alias
const AbstractInfOptExpr = Union{
NLPExpr,
JuMP.GenericQuadExpr{Float64, GeneralVariableRef},
JuMP.GenericAffExpr{Float64, GeneralVariableRef},
GeneralVariableRef
}
## Dispatch function for ast mapping
# Constant
function _ast_process_node(map_func::Function, c)
return c
end
# Variable
function _ast_process_node(map_func::Function, v::GeneralVariableRef)
return map_func(v)
end
# AffExpr
function _ast_process_node(map_func::Function, aff::JuMP.GenericAffExpr)
ex = Expr(:call, :+)
for (v, c) in aff.terms
if isone(c)
push!(ex.args, map_func(v))
else
push!(ex.args, Expr(:call, :*, c, map_func(v)))
end
end
if !iszero(aff.constant)
push!(ex.args, aff.constant)
end
return ex
end
# QuadExpr
function _ast_process_node(map_func::Function, quad::JuMP.GenericQuadExpr)
ex = Expr(:call, :+)
for (xy, c) in quad.terms
if isone(c)
push!(ex.args, Expr(:call, :*, map_func(xy.a), map_func(xy.b)))
else
push!(ex.args, Expr(:call, :*, c, map_func(xy.a), map_func(xy.b)))
end
end
append!(ex.args, _ast_process_node(map_func, quad.aff).args[2:end])
return ex
end
# Map an expression tree to a Julia AST tree that is compatible with JuMP
function _tree_map_to_ast(map_func::Function, node::_LCRST.Node)
if _LCRST.isleaf(node)
return _ast_process_node(map_func, _node_value(node.data))
else
ex = Expr(:call, _node_value(node.data)) # will be function symbol name
append!(ex.args, (_tree_map_to_ast(map_func, n) for n in node))
return ex
end
end
"""
map_nlp_to_ast(map_func::Function, nlp::NLPExpr)::Expr
Map the nonlinear expression `nlp` to a Julia AST expression where each variable
is mapped via `map_func` and is directly interpolated into the AST expression.
This is intended as an internal method that can be helpful for developers that
wish to map a `NLPExpr` to a Julia AST expression that is compatible with
`JuMP.add_NL_expression`.
"""
function map_nlp_to_ast(map_func::Function, nlp::NLPExpr)
return _tree_map_to_ast(map_func, nlp.tree_root)
end
################################################################################
# EXPRESSION CREATION HELPERS
################################################################################
## Make convenient dispatch methods for raw child input
# NLPExpr
function _process_child_input(nlp::NLPExpr)
return nlp.tree_root
end
# An InfiniteOpt expression (not general nonlinear)
function _process_child_input(v::AbstractInfOptExpr)
return NodeData(v)
end
# Function symbol
function _process_child_input(f::Symbol)
return NodeData(f)
end
# A constant
function _process_child_input(c::Union{Real, Bool})
return NodeData(c)
end
# Fallback
function _process_child_input(v)
error("Unrecognized algebraic expression input `$v`.")
end
# Generic graph builder
function _call_graph(func::Symbol, arg1, args...)
root = _LCRST.Node(NodeData(func))
prevc = _LCRST.addchild(root, _process_child_input(arg1))
for a in args
prevc = _LCRST.addchild(root, prevc, _process_child_input(a))
end
return root
end
################################################################################
# SUMS AND PRODUCTS
################################################################################
## Define helper functions for sum reductions
# Container of NLPExprs
function _reduce_by_first(::typeof(sum), first_itr::NLPExpr, itr)
root = _LCRST.Node(NodeData(:+))
prevc = _LCRST.addchild(root, first_itr.tree_root)
for ex in itr
prevc = _LCRST.addchild(root, prevc, _process_child_input(ex))
end
return NLPExpr(root)
end
# Container of InfiniteOpt exprs
function _reduce_by_first(
::typeof(sum),
first_itr::JuMP.AbstractJuMPScalar,
itr
)
result = first_itr
for i in itr
result = _MA.operate!(_MA.add_mul, result, i)
end
return result
end
# Fallback
function _reduce_by_first(::typeof(sum), first_itr, itr; kw...)
return isempty(itr) ? first_itr : first_itr + sum(identity, itr; kw...)
end
# Hyjack Base.sum for better efficiency on iterators --> this is type piracy...
function Base.sum(itr::Base.Generator; kw...)
isempty(itr) && return sum(identity, itr; kw...)
itr1, new_itr = Iterators.peel(itr)
return _reduce_by_first(sum, itr1, new_itr; kw...)
end
# Extend Base.sum for container of NLPExprs
function Base.sum(arr::AbstractArray{<:NLPExpr}; init = zero(NLPExpr))
isempty(arr) && return init
itr1, new_itr = Iterators.peel(arr)
return _reduce_by_first(sum, itr1, new_itr)
end
# Extend Base.sum for container of InfiniteOpt exprs
function Base.sum(
arr::AbstractArray{<:AbstractInfOptExpr};
init = zero(JuMP.GenericAffExpr{Float64, GeneralVariableRef})
)
isempty(arr) && return init
result = _MA.Zero()
for i in arr
result = _MA.operate!(_MA.add_mul, result, i)
end
return result
end
## Define helper functions for reducing products
# Container of InfiniteOpt exprs
function _reduce_by_first(::typeof(prod), first_itr::AbstractInfOptExpr, itr)
root = _LCRST.Node(NodeData(:*))
prevc = _LCRST.addchild(root, _process_child_input(first_itr))
for ex in itr
prevc = _LCRST.addchild(root, prevc, _process_child_input(ex))
end
return NLPExpr(root)
end
# Fallback
function _reduce_by_first(::typeof(prod), first_itr, itr; kw...)
return first_itr * prod(identity, itr; kw...)
end
# Hyjack Base.prod for better efficiency on iterators --> this is type piracy...
function Base.prod(itr::Base.Generator; kw...)
isempty(itr) && return prod(identity, itr; kw...)
itr1, new_itr = Iterators.peel(itr)
return _reduce_by_first(prod, itr1, new_itr; kw...)
end
# Extend Base.prod for container of InfiniteOpt exprs
function Base.prod(arr::AbstractArray{<:AbstractInfOptExpr}; init = one(NLPExpr))
isempty(arr) && return init
itr1, new_itr = Iterators.peel(arr)
return _reduce_by_first(prod, itr1, new_itr)
end
################################################################################
# MULTIPLICATION OPERATORS
################################################################################
# TODO more intelligently operate with constants
# QuadExpr * expr
function Base.:*(
quad::JuMP.GenericQuadExpr{Float64, GeneralVariableRef},
expr::AbstractInfOptExpr
)
return NLPExpr(_call_graph(:*, quad, expr))
end
# expr * QuadExpr
function Base.:*(
expr::AbstractInfOptExpr,
quad::JuMP.GenericQuadExpr{Float64, GeneralVariableRef}
)
return NLPExpr(_call_graph(:*, expr, quad))
end
# QuadExpr * QuadExpr
function Base.:*(
quad1::JuMP.GenericQuadExpr{Float64, GeneralVariableRef},
quad2::JuMP.GenericQuadExpr{Float64, GeneralVariableRef}
)
return NLPExpr(_call_graph(:*, quad1, quad2))
end
# NLPExpr * QuadExpr
function Base.:*(
nlp::NLPExpr,
quad::JuMP.GenericQuadExpr{Float64, GeneralVariableRef}
)
return NLPExpr(_call_graph(:*, nlp, quad))
end
# QuadExpr * NLPExpr
function Base.:*(
quad::JuMP.GenericQuadExpr{Float64, GeneralVariableRef},
nlp::NLPExpr
)
return NLPExpr(_call_graph(:*, quad, nlp))
end
# NLPExpr * expr/constant
function Base.:*(nlp::NLPExpr, expr::Union{AbstractInfOptExpr, Real})
return NLPExpr(_call_graph(:*, nlp, expr))
end
# expr/constant * NLPExpr
function Base.:*(expr::Union{AbstractInfOptExpr, Real}, nlp::NLPExpr)
return NLPExpr(_call_graph(:*, expr, nlp))
end
# NLPExpr * NLPExpr
function Base.:*(nlp1::NLPExpr, nlp2::NLPExpr)
return NLPExpr(_call_graph(:*, nlp1, nlp2))
end
# expr * expr * expr ...
function Base.:*(
expr1::AbstractInfOptExpr,
expr2::AbstractInfOptExpr,
expr3::AbstractInfOptExpr,
exprs::Vararg{AbstractInfOptExpr}
)
return NLPExpr(_call_graph(:*, expr1, expr2, expr3, exprs...))
end
# *NLPExpr
function Base.:*(nlp::NLPExpr)
return nlp
end
################################################################################
# DIVISION OPERATORS
################################################################################
# expr/constant / expr
function Base.:/(
expr1::Union{AbstractInfOptExpr, Real},
expr2::AbstractInfOptExpr
)
return NLPExpr(_call_graph(:/, expr1, expr2))
end
# NLPExpr / constant
function Base.:/(nlp::NLPExpr, c::Real)
if iszero(c)
error("Cannot divide by zero.")
elseif isone(c)
return nlp
else
return NLPExpr(_call_graph(:/, nlp, c))
end
end
################################################################################
# POWER OPERATORS
################################################################################
# expr ^ Integer
function Base.:^(expr::AbstractInfOptExpr, c::Integer)
if iszero(c)
return one(JuMP.GenericAffExpr{Float64, GeneralVariableRef})
elseif isone(c)
return expr
elseif c == 2
return expr * expr
else
return NLPExpr(_call_graph(:^, expr, c))
end
end
# expr ^ Real
function Base.:^(expr::AbstractInfOptExpr, c::Real)
if iszero(c)
return one(JuMP.GenericAffExpr{Float64, GeneralVariableRef})
elseif isone(c)
return expr
elseif c == 2
return expr * expr
else
return NLPExpr(_call_graph(:^, expr, c))
end
end
# NLPExpr ^ Integer
function Base.:^(expr::NLPExpr, c::Integer)
if iszero(c)
return one(JuMP.GenericAffExpr{Float64, GeneralVariableRef})
elseif isone(c)
return expr
else
return NLPExpr(_call_graph(:^, expr, c))
end
end
# NLPExpr ^ Real
function Base.:^(expr::NLPExpr, c::Real)
if iszero(c)
return one(JuMP.GenericAffExpr{Float64, GeneralVariableRef})
elseif isone(c)
return expr
else
return NLPExpr(_call_graph(:^, expr, c))
end
end
# expr/constant ^ expr
function Base.:^(
expr1::Union{AbstractInfOptExpr, Real},
expr2::AbstractInfOptExpr
)
return NLPExpr(_call_graph(:^, expr1, expr2))
end
################################################################################
# SUBTRACTION OPERATORS
################################################################################
# TODO more intelligently operate with constants
# NLPExpr - expr/constant
function Base.:-(nlp::NLPExpr, expr::Union{AbstractInfOptExpr, Real})
return NLPExpr(_call_graph(:-, nlp, expr))
end
# expr/constant - NLPExpr
function Base.:-(expr::Union{AbstractInfOptExpr, Real}, nlp::NLPExpr)
return NLPExpr(_call_graph(:-, expr, nlp))
end
# NLPExpr - NLPExpr
function Base.:-(nlp1::NLPExpr, nlp2::NLPExpr)
return NLPExpr(_call_graph(:-, nlp1, nlp2))
end
# -NLPExpr
function Base.:-(nlp::NLPExpr)
return NLPExpr(_call_graph(:-, nlp))
end
# Var - Var (to avoid using v == v)
function Base.:-(lhs::V, rhs::V) where {V<:GeneralVariableRef}
if isequal(lhs, rhs)
return zero(JuMP.GenericAffExpr{Float64,V})
else
return JuMP.GenericAffExpr(0.0,
DataStructures.OrderedDict(lhs => 1.0, rhs => -1.0))
end
end
################################################################################
# ADDITION OPERATORS
################################################################################
# TODO more intelligently operate with constants
# NLPExpr + expr/constant
function Base.:+(nlp::NLPExpr, expr::Union{AbstractInfOptExpr, Real})
return NLPExpr(_call_graph(:+, nlp, expr))
end
# expr/constant + NLPExpr
function Base.:+(expr::Union{AbstractInfOptExpr, Real}, nlp::NLPExpr)
return NLPExpr(_call_graph(:+, expr, nlp))
end
# NLPExpr + NLPExpr
function Base.:+(nlp1::NLPExpr, nlp2::NLPExpr)
return NLPExpr(_call_graph(:+, nlp1, nlp2))
end
# +NLPExpr
function Base.:+(nlp::NLPExpr)
return nlp
end
################################################################################
# MUTABLE ARITHMETICS
################################################################################
# Define NLPExpr as a mutable type for MA
_MA.mutability(::Type{NLPExpr}) = _MA.IsMutable()
# Extend MA.promote_operation for bettered efficiency
for type in (:Real, :GeneralVariableRef,
:(JuMP.GenericAffExpr{Float64, GeneralVariableRef}),
:(JuMP.GenericQuadExpr{Float64, GeneralVariableRef}))
@eval begin
function _MA.promote_operation(
::Union{typeof(+),typeof(-),typeof(*),typeof(/),typeof(^)},
::Type{<:$type},
::Type{NLPExpr}
)
return NLPExpr
end
function _MA.promote_operation(
::Union{typeof(+),typeof(-),typeof(*),typeof(/),typeof(^)},
::Type{NLPExpr},
::Type{<:$type}
)
return NLPExpr
end
end
end
function _MA.promote_operation(
::Union{typeof(+),typeof(-),typeof(*),typeof(/),typeof(^)},
::Type{NLPExpr},
::Type{NLPExpr}
)
return NLPExpr
end
for type in (:GeneralVariableRef,
:(JuMP.GenericAffExpr{Float64, GeneralVariableRef}))
@eval begin
function _MA.promote_operation(
::Union{typeof(*),typeof(/),typeof(^)},
::Type{<:$type},
::Type{JuMP.GenericQuadExpr{Float64, GeneralVariableRef}}
)
return NLPExpr
end
function _MA.promote_operation(
::Union{typeof(*),typeof(/),typeof(^)},
::Type{JuMP.GenericQuadExpr{Float64, GeneralVariableRef}},
::Type{<:$type}
)
return NLPExpr
end
end
end
function _MA.promote_operation(
::Union{typeof(*),typeof(/),typeof(^)},
::Type{<:JuMP.GenericQuadExpr{Float64, GeneralVariableRef}},
::Type{<:JuMP.GenericQuadExpr{Float64, GeneralVariableRef}}
)
return NLPExpr
end
for type in (:GeneralVariableRef,
:(JuMP.GenericAffExpr{Float64, GeneralVariableRef}),
:(JuMP.GenericQuadExpr{Float64, GeneralVariableRef}))
@eval begin
function _MA.promote_operation(
::Union{typeof(/),typeof(^)},
::Type{<:Real},
::Type{<:$type}
)
return NLPExpr
end
end
end
for type in (:GeneralVariableRef,
:(JuMP.GenericAffExpr{Float64, GeneralVariableRef}))
@eval begin
function _MA.promote_operation(
::Union{typeof(/),typeof(^)},
::Type{GeneralVariableRef},
::Type{<:$type}
)
return NLPExpr
end
end
end
for type in (:GeneralVariableRef,
:(JuMP.GenericAffExpr{Float64, GeneralVariableRef}))
@eval begin
function _MA.promote_operation(
::Union{typeof(/),typeof(^)},
::Type{JuMP.GenericAffExpr{Float64, GeneralVariableRef}},
::Type{<:$type}
)
return NLPExpr
end
end
end
# Extend MA.scaling in case an NLPExpr needs to be converted to a number
function _MA.scaling(nlp::NLPExpr)
c = _node_value(nlp.tree_root.data)
if !(c isa Real)
error("Cannot convert `$nlp` to `$Float64`.")
end
return _MA.scaling(c)
end
# Extend MA.mutable_Copy to avoid unnecessary copying
function _MA.mutable_copy(nlp::NLPExpr)
return nlp # we don't need to copy since we build from the leaves up
end
# Extend MA.mutable_operate! as required
function _MA.mutable_operate!(
op::Union{typeof(zero), typeof(one)},
::NLPExpr
)
return op(NLPExpr) # not actually mutable for safety and efficiency
end
function _MA.mutable_operate!(
op::Union{typeof(+), typeof(-), typeof(*), typeof(/), typeof(^)},
nlp::NLPExpr,
v
)
return op(nlp, v)
end
function _MA.mutable_operate!(
op::Union{typeof(+), typeof(-), typeof(*), typeof(/), typeof(^)},
v,
nlp::NLPExpr
)
return op(v, nlp)
end
function _MA.mutable_operate!(
op::typeof(+),
v::Union{JuMP.GenericAffExpr{Float64, GeneralVariableRef},
JuMP.GenericQuadExpr{Float64, GeneralVariableRef}},
nlp::NLPExpr
)
return op(v, nlp)
end
function _MA.mutable_operate!(
op::typeof(-),
v::Union{JuMP.GenericAffExpr{Float64, GeneralVariableRef},
JuMP.GenericQuadExpr{Float64, GeneralVariableRef}},
nlp::NLPExpr
)
return op(v, nlp)
end
function _MA.mutable_operate!(
op::Union{typeof(+), typeof(-), typeof(*), typeof(/), typeof(^)},
nlp1::NLPExpr,
nlp2::NLPExpr
)
return op(nlp1, nlp2)
end
function _MA.mutable_operate!(op::_MA.AddSubMul, nlp::NLPExpr, args...)
return _MA.add_sub_op(op)(nlp, *(args...))
end
# TODO maybe extend _MA.add_mul/_MA_.sub_mul as well
################################################################################
# NATIVE NLP FUNCTIONS
################################################################################
# Store all of the native registered functions
const _NativeNLPFunctions = Dict{Tuple{Symbol, Int}, Function}(
(:-, 2) => -,
(:/, 2) => /,
(:^, 2) => ^
)
# List of 1 argument base functions to register
const _Base1ArgFuncList = (
:sqrt => sqrt,
:cbrt => cbrt,
:abs => abs,
:abs2 => abs2,
:inv => inv,
:log => log,
:log10 => log10,
:log2 => log2,
:log1p => log1p,
:exp => exp,
:exp2 => exp2,
:expm1 => expm1,
:sin => sin,
:cos => cos,
:tan => tan,
:sec => sec,
:csc => csc,
:cot => cot,
:sind => sind,
:cosd => cosd,
:tand => tand,
:secd => secd,
:cscd => cscd,
:cotd => cotd,
:asin => asin,
:acos => acos,
:atan => atan,
:asec => asec,
:acsc => acsc,
:acot => acot,
:asind => asind,
:acosd => acosd,
:atand => atand,
:asecd => asecd,
:acscd => acscd,
:acotd => acotd,
:sinh => sinh,
:cosh => cosh,
:tanh => tanh,
:sech => sech,
:csch => csch,
:coth => coth,
:asinh => asinh,
:acosh => acosh,
:atanh => atanh,
:asech => asech,
:acsch => acsch,
:acoth => acoth,
:deg2rad => deg2rad,
:rad2deg => rad2deg
)
# Setup the base 1 argument functions
for (name, func) in _Base1ArgFuncList
# add it to the main storage dict
_NativeNLPFunctions[(name, 1)] = func
# make an expression constructor
@eval begin
function Base.$name(v::AbstractInfOptExpr)
return NLPExpr(_call_graph($(quot(name)), v))
end
end
end
# Setup the Base functions with 2 arguments
for (name, func) in (:min => min, :max => max)
# add it to the main storage dict
_NativeNLPFunctions[(name, 2)] = func
# make an expression constructor
@eval begin
function Base.$name(
v1::Union{AbstractInfOptExpr, Real},
v2::Union{AbstractInfOptExpr, Real}
)
return NLPExpr(_call_graph($(quot(name)), v1, v2))
end
end
end
# Setup the ifelse function
_NativeNLPFunctions[(:ifelse, 3)] = Core.ifelse
"""
InfiniteOpt.ifelse(cond::NLPExpr, v1::Union{AbstractInfOptExpr, Real},
v2::Union{AbstractInfOptExpr, Real})::NLPExpr
A symbolic version of `Core.ifelse` that can be used to establish symbolic
expressions with logic conditions. Note that is must be written
`InfiniteOpt.ifelse` since it conflicts with `Core.ifelse`.
**Example**
```julia
julia> InfiniteOpt.ifelse(x >= y, 0, y^3)
ifelse(x >= y, 0, y^3)
```
"""
function ifelse(
cond::NLPExpr,
v1::Union{AbstractInfOptExpr, Real},
v2::Union{AbstractInfOptExpr, Real}
)
return NLPExpr(_call_graph(:ifelse, cond, v1, v2))
end
function ifelse(
cond::Bool,
v1::Union{AbstractInfOptExpr, Real},
v2::Union{AbstractInfOptExpr, Real}
)
return cond ? v1 : v2
end
# Setup the Base comparison functions
for (name, func) in (:< => Base.:(<), :(==) => Base.:(==), :> => Base.:(>),
:<= => Base.:(<=), :>= => Base.:(>=))
# add it to the main storage dict
_NativeNLPFunctions[(name, 2)] = func
# make an expression constructor
@eval begin
function Base.$name(v::AbstractInfOptExpr, c::Real)
return NLPExpr(_call_graph($(quot(name)), v, c))
end
function Base.$name(c::Real, v::AbstractInfOptExpr)
return NLPExpr(_call_graph($(quot(name)), c, v))
end
function Base.$name(v1::AbstractInfOptExpr, v2::AbstractInfOptExpr)
return NLPExpr(_call_graph($(quot(name)), v1, v2))
end
if $(quot(name)) in (:<, :>)
function Base.$name(v1::GeneralVariableRef, v2::GeneralVariableRef)
if isequal(v1, v2)
return false
else
return NLPExpr(_call_graph($(quot(name)), v1, v2))
end
end
else
function Base.$name(v1::GeneralVariableRef, v2::GeneralVariableRef)
if isequal(v1, v2)
return true
else
return NLPExpr(_call_graph($(quot(name)), v1, v2))
end
end
end
end
end
# Setup the Base logical functions (we cannot extend && and || directly)
_NativeNLPFunctions[(:&&, 2)] = Base.:&
_NativeNLPFunctions[(:||, 2)] = Base.:|
# Logical And
function Base.:&(v::Union{GeneralVariableRef, NLPExpr}, c::Bool)
return c ? v : false
end
function Base.:&(c::Bool, v::Union{GeneralVariableRef, NLPExpr})
return c ? v : false
end
function Base.:&(
v1::Union{GeneralVariableRef, NLPExpr},
v2::Union{GeneralVariableRef, NLPExpr}
)
return NLPExpr(_call_graph(:&&, v1, v2))
end
# Logical Or
function Base.:|(v::Union{GeneralVariableRef, NLPExpr}, c::Bool)
return c ? true : v
end
function Base.:|(c::Bool, v::Union{GeneralVariableRef, NLPExpr})
return c ? true : v
end
function Base.:|(
v1::Union{GeneralVariableRef, NLPExpr},
v2::Union{GeneralVariableRef, NLPExpr})
return NLPExpr(_call_graph(:||, v1, v2)
)
end
const _Special1ArgFuncList = (
:erf => SpecialFunctions.erf,
:erfinv => SpecialFunctions.erfinv,
:erfc => SpecialFunctions.erfc,
:erfcinv => SpecialFunctions.erfcinv,
:erfi => SpecialFunctions.erfi,
:gamma => SpecialFunctions.gamma,
:lgamma => SpecialFunctions.lgamma,
:digamma => SpecialFunctions.digamma,
:invdigamma => SpecialFunctions.invdigamma,
:trigamma => SpecialFunctions.trigamma,
:airyai => SpecialFunctions.airyai,
:airybi => SpecialFunctions.airybi,
:airyaiprime => SpecialFunctions.airyaiprime,
:airybiprime => SpecialFunctions.airybiprime,
:besselj0 => SpecialFunctions.besselj0,
:besselj1 => SpecialFunctions.besselj1,
:bessely0 => SpecialFunctions.bessely0,
:bessely1 => SpecialFunctions.bessely1,
:erfcx => SpecialFunctions.erfcx,
:dawson => SpecialFunctions.dawson
)
# Setup the SpecialFunctions 1 argument functions
for (name, func) in _Special1ArgFuncList
# add it to the main storage dict
_NativeNLPFunctions[(name, 1)] = func
# make an expression constructor
@eval begin
function SpecialFunctions.$name(v::AbstractInfOptExpr)
return NLPExpr(_call_graph($(quot(name)), v))
end
end
end
################################################################################
# USER FUNCTIONS
################################################################################
"""
RegisteredFunction{F <: Function, G <: Union{Function, Nothing},
H <: Union{Function, Nothing}}
A type for storing used defined registered functions and their information that
is needed by JuMP for build an `NLPEvaluator`. The constructor is of the form:
```julia
RegisteredFunction(name::Symbol, num_args::Int, func::Function,
[gradient::Function, hessian::Function])
```
**Fields**
- `name::Symbol`: The name of the function that is used in `NLPExpr`s.
- `num_args::Int`: The number of function arguments.
- `func::F`: The function itself.
- `gradient::G`: The gradient function if one is given.
- `hessian::H`: The hessian function if one is given.
"""
struct RegisteredFunction{F <: Function, G, H}
name::Symbol
num_args::Int
func::F
gradient::G
hessian::H
# Constructors
function RegisteredFunction(
name::Symbol,
num_args::Int,
func::F
) where {F <: Function}
return new{F, Nothing, Nothing}(name, num_args, func, nothing, nothing)
end
function RegisteredFunction(
name::Symbol,
num_args::Int,
func::F,
gradient::G
) where {F <: Function, G <: Function}
if isone(num_args) && !hasmethod(gradient, Tuple{Real})
error("Invalid gradient function form, see the docs for details.")
elseif !isone(num_args) && !hasmethod(gradient, Tuple{AbstractVector{Real}, ntuple(_->Real, num_args)...})
error("Invalid multi-variate gradient function form, see the docs for details.")
end
return new{F, G, Nothing}(name, num_args, func, gradient, nothing)
end
function RegisteredFunction(
name::Symbol,
num_args::Int,
func::F,
gradient::G,
hessian::H
) where {F <: Function, G <: Function, H <: Function}
if isone(num_args) && !hasmethod(gradient, Tuple{Real})
error("Invalid gradient function form, see the docs for details.")
elseif isone(num_args) && !hasmethod(hessian, Tuple{Real})
error("Invalid hessian function form, see the docs for details.")
end
return new{F, G, H}(name, num_args, func, gradient, hessian)
end
end
# Helper function for @register
function _register(
_error::Function,
call_mod::Module,
model::InfiniteModel,
name::Symbol,
num_args::Int,
funcs...
)
if !all(f -> f isa Function, funcs)
_error("Gradient and/or hessian must be functions.")
elseif haskey(_NativeNLPFunctions, (name, num_args)) ||
haskey(model.func_lookup, (name, num_args))
_error("A function with name `$name` and $num_args arguments is already " *
"registered. Please use a function with a different name.")
elseif !hasmethod(funcs[1], NTuple{num_args, Real})
_error("The function `$name` is not defined for arguments of type `Real`.")
elseif length(unique!([m.module for m in methods(funcs[1])])) > 1 ||
first(methods(funcs[1])).module !== call_mod
_error("Cannot register function names that are used by packages. Try " *
"wrapping `$(funcs[1])` in a user-defined function.")
end
push!(model.registrations, RegisteredFunction(name, num_args, funcs...))
model.func_lookup[name, num_args] = funcs[1]
return
end
# Helper function to check the inputs of created functions
function _check_function_args(model::InfiniteModel, f_name, args...)
for a in args
m = _model_from_expr(a)
if m !== nothing && m !== model
error("`$f_name` is a registered function in a different model than " *
"`$a` belongs to. Try registering `$f_name` to the current " *
"model.")
end
end
return
end
"""
@register(model::InfiniteModel, func_expr, [gradient::Function], [hessian::Function])
Register a user-defined function in accordance with `func_expr` such that it can
be used in `NLPExpr`s that are used with `model` without being traced.
**Argument Information**
Here `func_expr` is of the form: `myfunc(a, b)` where `myfunc` is the function
name and the number of arguments are given symbolically. Note that the choice
of argument symbols is arbitrary. Each function argument must support anything
of type `Real` to specified.
Here we can also specify a gradient function `gradient` which for 1 argument
functions must taken in the 1 argument and return its derivative. For
multi-argument functions the gradient function must be of the form:
```julia
function gradient(g::AbstractVector{T}, args::T...) where {T <: Real}
# fill g vector with the gradient of the function
end
```
For 1 argument functions we can also specify a hessian function with takes that
argument and return the 2nd derivative. Hessians can ge specified for
multi-argument functions, but `JuMP` backends do not currently support this.
If no gradient and/or hessian is given, the automatic differentation capabilities
of the backend (e.g., `JuMP`) will be used to determine them. Note that the
`JuMP` backend does not use Hessian's for user-defined multi-argument functions.
**Notes**
- When possible, tracing is preferred over registering a function (see
[Function Tracing](@ref) for more info).
- Only user-defined functions can be specified. If the function is used by a
package then it can not be used directly. However, we can readily wrap it in a
new function `newfunc(a) = pkgfunc(a)`.
- We can only register functions in the same scope that they are defined in.
- Registered functions can only be used in or below the scope in which they are
registered. For instance, if we register some function inside of another
function then we can only use it inside that function (not outside of it).
- A function with a given name and number of arguments can only be registered
once in a particular model.
**Examples**
```julia-repl
julia> @variable(model, x)
x
julia> f(a) = a^3;
julia> f(x) # user-function gets traced
x^3
julia> @register(model, f(a)) # register function
f (generic function with 2 methods)
julia> f(x) # function is no longer traced and autodifferentiation will be used
f(x)
julia> f2(a) = a^2; g2(a) = 2 * a; h2(a) = 2;
julia> @register(model, f2(a), g2, h2) # register with explicit gradient and hessian
f2 (generic function with 2 methods)
julia> f2(x)
f2(x)
julia> f3(a, b) = a * b^2;
julia> function g3(v, a, b)
v[1] = b^2
v[2] = 2 * a * b
return
end;
julia> @register(model, f3(a, b), g3) # register multi-argument function
f3 (generic function with 4 methods)
julia> f3(42, x)
f3(42, x)
```
"""
macro register(model, f, args...)
# define error message function
_error(str...) = _macro_error(:register, (f, args...), __source__, str...)
# parse the arguments and check
pos_args, extra_kwargs, _, _ = _extract_kwargs(args)
if !isempty(extra_kwargs)
_error("Keyword arguments were given, but none are accepted.")
elseif length(pos_args) > 2
_error("Too many position arguments given, should be of form " *
"`@register(myfunc(a), [gradient], [hessian])` where " *
"`gradient` and `hessian` are optional arguments.")
end
# process the function input
if isexpr(f, :call) && all(a -> a isa Symbol, f.args)
f_name = f.args[1]
f_args = f.args[2:end]
num_args = length(f_args)
else
_error("Unexpected function format, should be of form `myfunc(a, b)`.")
end
# start creating the register code and register
code = Expr(:block)
push!(code.args, quote
$model isa InfiniteModel || $_error("Expected an `InfiniteModel`.")
end)
calling_mod = __module__ # get the module the macro is being called from
push!(code.args, quote
InfiniteOpt._register($_error, $calling_mod, $model, $(quot(f_name)),
$num_args, $(f_name), $(args...))
end)
# define the function overloads needed to create expressions
Ts = [Real, AbstractInfOptExpr]
type_combos = vec(collect(Iterators.product(ntuple(_->Ts, num_args)...)))
filter!(ts -> !all(T -> T == Real, ts), type_combos) # remove combo with only Reals
annotype(name, T) = :($name :: $T)
set_args(xs, vs) = (xs = map(annotype, xs, vs); xs)
for ts in type_combos
push!(code.args, quote
function $(f_name)($(set_args(f_args, ts)...))
InfiniteOpt._check_function_args($model, $(quot(f_name)), $(f_args...))
return NLPExpr(InfiniteOpt._call_graph($(quot(f_name)), $(f_args...)))
end
end)
end
# return the code
return esc(code)
end
"""
name_to_function(model::InfiniteModel, name::Symbol, num_args::Int)::Union{Function, Nothing}
Return the registered function that corresponds to `name` with `num_args`.
Returns `nothing` if no such registered function exists. This helps retrieve the
functions of function names stored in `NLPExpr`s.
"""
function name_to_function(model::InfiniteModel, name::Symbol, num_args::Int)
if name == :+
return +
elseif name == :*
return *
else
return get(_NativeNLPFunctions, (name, num_args),
get(model.func_lookup, (name, num_args), nothing))
end
end
"""
all_registered_functions(model::InfiniteModel)::Vector{Function}
Retrieve all the functions that are currently registered to `model`.
"""
function all_registered_functions(model::InfiniteModel)
funcs = append!(collect(values(_NativeNLPFunctions)), (+, *))
return append!(funcs, values(model.func_lookup))
end
"""
user_registered_functions(model::InfiniteModel)::Vector{RegisteredFunction}
Return all the functions (and their associated information) that the user has
registered to `model`. Each is stored as a [`RegisteredFunction`](@ref).
"""
function user_registered_functions(model::InfiniteModel)
return model.registrations
end
## Define helper function to add registered functions to JuMP
# No gradient or hessian
function _add_func_data_to_jump(
model::JuMP.Model,
data::RegisteredFunction{F, Nothing, Nothing}
) where {F <: Function}
JuMP.register(model, data.name, data.num_args, data.func, autodiff = true)
return
end
# Only gradient information
function _add_func_data_to_jump(
model::JuMP.Model,
data::RegisteredFunction{F, G, Nothing}
) where {F <: Function, G <: Function}
JuMP.register(model, data.name, data.num_args, data.func, data.gradient,
autodiff = isone(data.num_args))
return
end
# Gradient and hessian information
function _add_func_data_to_jump(model::JuMP.Model, data::RegisteredFunction)
if data.num_args > 1
error("JuMP does not support hessians for multi-argument registered " *
"functions.")
end
JuMP.register(model, data.name, data.num_args, data.func, data.gradient,
data.hessian)
return
end
"""
add_registered_to_jump(opt_model::JuMP.Model, inf_model::InfiniteModel)::Nothing
Add the user registered functions in `inf_model` to a `JuMP` model `opt_model`.
This is intended as an internal method, but it is provided for developers that
extend `InfiniteOpt` to use other optimizer models.
"""
function add_registered_to_jump(opt_model::JuMP.Model, inf_model::InfiniteModel)
for data in user_registered_functions(inf_model)
_add_func_data_to_jump(opt_model, data)
end
return
end
################################################################################
# LINEAR ALGEBRA
################################################################################
# Extend LinearAlgebra.dot for increased efficiency
LinearAlgebra.dot(lhs::AbstractInfOptExpr, rhs::AbstractInfOptExpr) = lhs * rhs
LinearAlgebra.dot(lhs::AbstractInfOptExpr, rhs::Real) = lhs * rhs
LinearAlgebra.dot(lhs::Real, rhs::AbstractInfOptExpr) = lhs * rhs
# Implement promote_rule to help build better containers
function Base.promote_rule(::Type{NLPExpr}, ::Type{<:Real})
return NLPExpr
end
function Base.promote_rule(::Type{NLPExpr}, ::Type{GeneralVariableRef})
return NLPExpr
end
function Base.promote_rule(::Type{NLPExpr}, ::Type{<:JuMP.GenericAffExpr})
return NLPExpr
end
function Base.promote_rule(::Type{NLPExpr}, ::Type{<:JuMP.GenericQuadExpr})
return NLPExpr
end
# TODO make proper MA extensions to enable efficient definition
################################################################################
# PRINTING
################################################################################
# Define better printing for NodeData
function Base.show(io::IO, data::NodeData)
return print(io, string(_node_value(data)))
end
# Map operators to their respective precedence (largest is highest priority)
const _Precedence = (; :^ => 6, Symbol("+u") => 5, Symbol("-u") => 5, :* => 4,
:/ => 4, :+ => 3, :- => 3, :(==) => 2, :<= => 2, :>= => 2,
:> => 2, :< => 2, :&& => 1, :|| => 1)
## Make functions to determine the precedence of a leaf
# AffExpr
function _leaf_precedence(aff::JuMP.GenericAffExpr)
has_const = !iszero(JuMP.constant(aff))
itr = JuMP.linear_terms(aff)
num_terms = length(itr)
if iszero(num_terms)
# we have only a constant
return 10 # will always have precedence
elseif has_const || num_terms > 1
# we have an expr with multiple terms
return 3
elseif isone(first(itr)[1])
# we have a single variable
return 10 # will always have precedence
elseif first(itr)[1] == -1
# we have a single unary negative variable
return 5
else
# we have a single variable multiplied by some coefficient
return 4
end
end
# QuadExpr
function _leaf_precedence(quad::JuMP.GenericQuadExpr)
has_aff = !iszero(quad.aff)
itr = JuMP.quad_terms(quad)
num_terms = length(itr)
if iszero(num_terms)
# we have an affine expression
return _leaf_precedence(quad.aff)
elseif has_aff || num_terms > 1
# we have a general quadratic expression
return 3
else
# we only have a single quadratic term
return 4
end
end
# Other
function _leaf_precedence(v)
return 10
end
# Recursively build an expression string, starting with a root node
function _expr_string(
node::_LCRST.Node{NodeData},
str::String = "";
prev_prec = 0,
prev_comm = false
)
# prepocess the raw value
raw_value = _node_value(node.data)
is_op = raw_value isa Symbol && haskey(_Precedence, raw_value)
data_str = _string_round(raw_value)
# make a string according to the node structure
if _LCRST.isleaf(node) && _leaf_precedence(raw_value) > prev_prec
# we have a leaf that doesn't require parentheses
return str * data_str
elseif _LCRST.isleaf(node)
# we have a leaf that requires parentheses
return str * string("(", data_str, ")")
elseif is_op && !_LCRST.islastsibling(node.child)
# we have a binary operator
curr_prec = _Precedence[raw_value]
has_prec = curr_prec > prev_prec || (prev_comm && curr_prec == prev_prec)
if !has_prec
str *= "("
end
op_str = data_str == "^" ? data_str : string(" ", data_str, " ")
is_comm = raw_value == :* || raw_value == :+
for child in node
str = string(_expr_string(child, str, prev_prec = curr_prec,
prev_comm = is_comm), op_str)
end
str = str[1:prevind(str, end, length(op_str))]
return has_prec ? str : str * ")"
elseif is_op
# we have a unary operator
curr_prec = _Precedence[Symbol(raw_value, :u)]
has_prec = curr_prec > prev_prec
if !has_prec
str *= "("
end
str *= string(data_str, _expr_string(node.child, str,
prev_prec = curr_prec))
return has_prec ? str : str * ")"
else
# we have a function
str *= string(data_str, "(")
for child in node
str = _expr_string(child, str)
str *= ", "
end
return str[1:prevind(str, end, 2)] * ")"
end
end
# Extend JuMP.function_string for nonlinear expressions
function JuMP.function_string(mode, nlp::NLPExpr)
return _expr_string(nlp.tree_root)
end
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] | 2.423276 | 19,935 |
ipopt_optimizer =
JuMP.optimizer_with_attributes(Ipopt.Optimizer, "tol" => 1e-6, "print_level" => 0)
fast_ipopt_optimizer = JuMP.optimizer_with_attributes(
Ipopt.Optimizer,
"print_level" => 0,
"max_cpu_time" => 5.0,
)
# use default print_level = 5 # set to 0 to disable
GLPK_optimizer = JuMP.optimizer_with_attributes(GLPK.Optimizer)
Cbc_optimizer = JuMP.optimizer_with_attributes(Cbc.Optimizer)
OSQP_optimizer =
JuMP.optimizer_with_attributes(OSQP.Optimizer, "verbose" => false, "max_iter" => 50000)
fast_lp_optimizer = JuMP.optimizer_with_attributes(Cbc.Optimizer, "seconds" => 3.0)
scs_solver = JuMP.optimizer_with_attributes(
SCS.Optimizer,
"max_iters" => 100000,
"eps" => 1e-4,
"verbose" => 0,
)
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] | 2.267692 | 325 |
<gh_stars>10-100
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
@doc raw"""Represents storage that is managed by an external CSI volume driver (Beta feature)
IoK8sApiCoreV1CSIPersistentVolumeSource(;
controllerExpandSecretRef=nothing,
controllerPublishSecretRef=nothing,
driver=nothing,
fsType=nothing,
nodePublishSecretRef=nothing,
nodeStageSecretRef=nothing,
readOnly=nothing,
volumeAttributes=nothing,
volumeHandle=nothing,
)
- controllerExpandSecretRef::IoK8sApiCoreV1SecretReference : ControllerExpandSecretRef is a reference to the secret object containing sensitive information to pass to the CSI driver to complete the CSI ControllerExpandVolume call. This is an alpha field and requires enabling ExpandCSIVolumes feature gate. This field is optional, and may be empty if no secret is required. If the secret object contains more than one secret, all secrets are passed.
- controllerPublishSecretRef::IoK8sApiCoreV1SecretReference : ControllerPublishSecretRef is a reference to the secret object containing sensitive information to pass to the CSI driver to complete the CSI ControllerPublishVolume and ControllerUnpublishVolume calls. This field is optional, and may be empty if no secret is required. If the secret object contains more than one secret, all secrets are passed.
- driver::String : Driver is the name of the driver to use for this volume. Required.
- fsType::String : Filesystem type to mount. Must be a filesystem type supported by the host operating system. Ex. \"ext4\", \"xfs\", \"ntfs\".
- nodePublishSecretRef::IoK8sApiCoreV1SecretReference : NodePublishSecretRef is a reference to the secret object containing sensitive information to pass to the CSI driver to complete the CSI NodePublishVolume and NodeUnpublishVolume calls. This field is optional, and may be empty if no secret is required. If the secret object contains more than one secret, all secrets are passed.
- nodeStageSecretRef::IoK8sApiCoreV1SecretReference : NodeStageSecretRef is a reference to the secret object containing sensitive information to pass to the CSI driver to complete the CSI NodeStageVolume and NodeStageVolume and NodeUnstageVolume calls. This field is optional, and may be empty if no secret is required. If the secret object contains more than one secret, all secrets are passed.
- readOnly::Bool : Optional: The value to pass to ControllerPublishVolumeRequest. Defaults to false (read/write).
- volumeAttributes::Dict{String, String} : Attributes of the volume to publish.
- volumeHandle::String : VolumeHandle is the unique volume name returned by the CSI volume plugin’s CreateVolume to refer to the volume on all subsequent calls. Required.
"""
mutable struct IoK8sApiCoreV1CSIPersistentVolumeSource <: SwaggerModel
controllerExpandSecretRef::Any # spec type: Union{ Nothing, IoK8sApiCoreV1SecretReference } # spec name: controllerExpandSecretRef
controllerPublishSecretRef::Any # spec type: Union{ Nothing, IoK8sApiCoreV1SecretReference } # spec name: controllerPublishSecretRef
driver::Any # spec type: Union{ Nothing, String } # spec name: driver
fsType::Any # spec type: Union{ Nothing, String } # spec name: fsType
nodePublishSecretRef::Any # spec type: Union{ Nothing, IoK8sApiCoreV1SecretReference } # spec name: nodePublishSecretRef
nodeStageSecretRef::Any # spec type: Union{ Nothing, IoK8sApiCoreV1SecretReference } # spec name: nodeStageSecretRef
readOnly::Any # spec type: Union{ Nothing, Bool } # spec name: readOnly
volumeAttributes::Any # spec type: Union{ Nothing, Dict{String, String} } # spec name: volumeAttributes
volumeHandle::Any # spec type: Union{ Nothing, String } # spec name: volumeHandle
function IoK8sApiCoreV1CSIPersistentVolumeSource(;controllerExpandSecretRef=nothing, controllerPublishSecretRef=nothing, driver=nothing, fsType=nothing, nodePublishSecretRef=nothing, nodeStageSecretRef=nothing, readOnly=nothing, volumeAttributes=nothing, volumeHandle=nothing)
o = new()
validate_property(IoK8sApiCoreV1CSIPersistentVolumeSource, Symbol("controllerExpandSecretRef"), controllerExpandSecretRef)
setfield!(o, Symbol("controllerExpandSecretRef"), controllerExpandSecretRef)
validate_property(IoK8sApiCoreV1CSIPersistentVolumeSource, Symbol("controllerPublishSecretRef"), controllerPublishSecretRef)
setfield!(o, Symbol("controllerPublishSecretRef"), controllerPublishSecretRef)
validate_property(IoK8sApiCoreV1CSIPersistentVolumeSource, Symbol("driver"), driver)
setfield!(o, Symbol("driver"), driver)
validate_property(IoK8sApiCoreV1CSIPersistentVolumeSource, Symbol("fsType"), fsType)
setfield!(o, Symbol("fsType"), fsType)
validate_property(IoK8sApiCoreV1CSIPersistentVolumeSource, Symbol("nodePublishSecretRef"), nodePublishSecretRef)
setfield!(o, Symbol("nodePublishSecretRef"), nodePublishSecretRef)
validate_property(IoK8sApiCoreV1CSIPersistentVolumeSource, Symbol("nodeStageSecretRef"), nodeStageSecretRef)
setfield!(o, Symbol("nodeStageSecretRef"), nodeStageSecretRef)
validate_property(IoK8sApiCoreV1CSIPersistentVolumeSource, Symbol("readOnly"), readOnly)
setfield!(o, Symbol("readOnly"), readOnly)
validate_property(IoK8sApiCoreV1CSIPersistentVolumeSource, Symbol("volumeAttributes"), volumeAttributes)
setfield!(o, Symbol("volumeAttributes"), volumeAttributes)
validate_property(IoK8sApiCoreV1CSIPersistentVolumeSource, Symbol("volumeHandle"), volumeHandle)
setfield!(o, Symbol("volumeHandle"), volumeHandle)
o
end
end # type IoK8sApiCoreV1CSIPersistentVolumeSource
const _property_map_IoK8sApiCoreV1CSIPersistentVolumeSource = Dict{Symbol,Symbol}(Symbol("controllerExpandSecretRef")=>Symbol("controllerExpandSecretRef"), Symbol("controllerPublishSecretRef")=>Symbol("controllerPublishSecretRef"), Symbol("driver")=>Symbol("driver"), Symbol("fsType")=>Symbol("fsType"), Symbol("nodePublishSecretRef")=>Symbol("nodePublishSecretRef"), Symbol("nodeStageSecretRef")=>Symbol("nodeStageSecretRef"), Symbol("readOnly")=>Symbol("readOnly"), Symbol("volumeAttributes")=>Symbol("volumeAttributes"), Symbol("volumeHandle")=>Symbol("volumeHandle"))
const _property_types_IoK8sApiCoreV1CSIPersistentVolumeSource = Dict{Symbol,String}(Symbol("controllerExpandSecretRef")=>"IoK8sApiCoreV1SecretReference", Symbol("controllerPublishSecretRef")=>"IoK8sApiCoreV1SecretReference", Symbol("driver")=>"String", Symbol("fsType")=>"String", Symbol("nodePublishSecretRef")=>"IoK8sApiCoreV1SecretReference", Symbol("nodeStageSecretRef")=>"IoK8sApiCoreV1SecretReference", Symbol("readOnly")=>"Bool", Symbol("volumeAttributes")=>"Dict{String, String}", Symbol("volumeHandle")=>"String")
Base.propertynames(::Type{ IoK8sApiCoreV1CSIPersistentVolumeSource }) = collect(keys(_property_map_IoK8sApiCoreV1CSIPersistentVolumeSource))
Swagger.property_type(::Type{ IoK8sApiCoreV1CSIPersistentVolumeSource }, name::Symbol) = Union{Nothing,eval(Base.Meta.parse(_property_types_IoK8sApiCoreV1CSIPersistentVolumeSource[name]))}
Swagger.field_name(::Type{ IoK8sApiCoreV1CSIPersistentVolumeSource }, property_name::Symbol) = _property_map_IoK8sApiCoreV1CSIPersistentVolumeSource[property_name]
function check_required(o::IoK8sApiCoreV1CSIPersistentVolumeSource)
(getproperty(o, Symbol("driver")) === nothing) && (return false)
(getproperty(o, Symbol("volumeHandle")) === nothing) && (return false)
true
end
function validate_property(::Type{ IoK8sApiCoreV1CSIPersistentVolumeSource }, name::Symbol, val)
end
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] | 3.228157 | 2,415 |
mutable struct Eggbox_Ensemble <: GMC_NS_Ensemble
path::String
model_initλ::Function
models::Vector{Eggbox_Record}
contour::Float64
log_Li::Vector{Float64}
log_Xi::Vector{Float64}
log_wi::Vector{Float64}
log_Liwi::Vector{Float64}
log_Zi::Vector{Float64}
Hi::Vector{Float64}
obs::Nothing
priors::Vector{<:Distribution}
constants::Vector{<:Real}
box::Matrix{Float64}
sample_posterior::Bool
posterior_samples::Vector{Eggbox_Record}
GMC_Nmin::Int64
GMC_τ_death::Float64
GMC_init_τ::Float64
GMC_tune_μ::Int64
GMC_tune_α::Float64
GMC_tune_PID::NTuple{3,Float64}
GMC_timestep_η::Float64
GMC_reflect_η::Float64
GMC_exhaust_σ::Float64
GMC_chain_κ::Int64
t_counter::Int64
end
Eggbox_Ensemble(path::String, no_models::Integer, prior, box, GS_settings...; sample_posterior::Bool=true) =
Eggbox_Ensemble(
path,
construct_eggbox_model,
assemble_EMs(path, no_models, prior, box, Vector{Real}())...,
[-Inf], #L0 = 0
[0.], #X0 = 1
[-Inf], #w0 = 0
[-Inf], #Liwi0 = 0
[-Inf], #Z0 = 0
[0.], #H0 = 0,
nothing,
length(prior)==1 ? ([GMC_NS.marginals(prior)...]) : (prior),
Vector{Real}(),
length(prior)==1 ? (to_unit_ball.(box,[GMC_NS.marginals(prior)...])) : (to_unit_ball.(box,prior)),
sample_posterior,
Vector{Eggbox_Record}(),
GS_settings...,
no_models+1)
function Base.show(io::IO, e::Eggbox_Ensemble; progress=true)
x1o=[0.0, 0.4, 0.8, 0.2, 0.6, 1.0, 0.0, 0.4, 0.8, 0.2, 0.6, 1.0, 0.0,0.4,0.8,0.2,0.6,1.0]
x2o=[0.0, 0.0, 0.0, 0.2, 0.2, 0.2, 0.4, 0.4, 0.4, 0.6, 0.6, 0.6, 0.8, 0.8, 0.8,1.0, 1.0, 1.0]
x1m=[m.pos[1] for m in e.models]
x2m=[m.pos[2] for m in e.models]
x1m.+=1.; x2m.+=1.
x1m./=2.; x2m./=2
plt=scatterplot(x1o,x2o,title="Eggbox Ensemble: Contour $(round(e.contour,digits=2))",color=:green,name="Optima")
scatterplot!(plt, x1m, x2m, color=:magenta, name="Model Particles")
show(io, plt)
println()
(progress && return 19)
end
function assemble_EMs(path::String, no_trajectories::Integer, prior, box, constants)
ensemble_records = Vector{Eggbox_Record}()
!isdir(path) && mkpath(path)
length(prior)==1 ? (marginals=[GMC_NS.marginals(prior)...]) : (marginals=prior)
box=to_unit_ball.(box,marginals)
@showprogress 1 "Assembling Normal Model ensemble..." for trajectory_no in 1:no_trajectories
model_path = string(path,'/',trajectory_no,'.',1)
if !isfile(model_path)
proposal=[rand.(prior)...]
pos=to_unit_ball.(proposal,marginals)
box_bound!(pos,box)
θvec=to_prior.(pos,marginals)
model = construct_eggbox_model(trajectory_no, 1, θvec, pos, [0.], nothing, constants...; v_init=true)
serialize(model_path, model) #save the model to the ensemble directory
push!(ensemble_records, Eggbox_Record(trajectory_no,1,pos,model_path,model.log_Li))
else #interrupted assembly pick up from where we left off
model = deserialize(model_path)
push!(ensemble_records, Eggbox_Record(trajectory_no,1,model.pos,model_path,model.log_Li))
end
end
return ensemble_records, minimum([record.log_Li for record in ensemble_records])
end | [
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] | 2.119843 | 1,527 |
<filename>src/PolyhedralOmega.jl
module PolyhedralOmega
using LinearAlgebra, IterTools, MultivariatePolynomials, TypedPolynomials, AbstractAlgebra, TaylorSeries
#using LazySet
include("SymbolicCone.jl")
include("./SmithNormalForm/src/SmithNormalForm.jl")
function macmahon(A::Matrix{Int64}, b::Vector{Int64})
sizeA = size(A)
Id = Matrix(1I, sizeA[2], sizeA[2])
V = vcat(Id, A)
q = append!(zeros(Float64, sizeA[2]), -b)
#o = zeros(Bool, size(q,1))
o = zeros(Bool, sizeA[2])
return SymbolicCone(V, q, o)
end
function flip(C::SymbolicCone)
V = C.V
s = 1
for i = 1:size(V, 2)
for j = 1:size(V, 1)
if (V[j, i] != 0)
forward = (sign(V[j, i]) == 1)
if (!forward)
s = s * (-1)
V[:, i] = (-1) * V[:, i]
C.o[i] = xor(C.o[i], true)
end
break
end
end
end
C.sign = s
return C
end
function elimLastCoordinate(C::SymbolicCone)
V = C.V
n = size(V, 1)
k = size(V, 2)
q = C.q
o = C.o
res = []
II = []
Iplus = filter(x -> V[n, x] > 0, Vector(1:k)) #one of them should have zero eq or not?
Iminus = filter(x -> V[n, x] < 0, Vector(1:k))
#if (q[n] > 0 || (q[n] == 0 && size(Iplus, 1) <= size(Iminus, 1) && size(Iplus, 1) >= 1) )
#if (q[n] == 0)
# println("Iequal")
# return [SymbolicCone(prim(V[1:n-1, :]), q[1:n-1], o[1:k])]
#end
if (q[n] < 0)
#res = [SymbolicCone(prim(V[1:n-1, :]), q[1:n-1], o[1:k])]
println("Iplus")
II = Iplus
else
println("Iminus")
II = Iminus
res = [SymbolicCone(prim(V[1:n-1, :]), q[1:n-1], o[1:k])]
end
#Cprime if q[n] is zero
println("II: ", II)
sgnq = q[n]
for j in II
L = []
for i in 1:k
if i == j
push!(L, (sgnq >= 0 ? -1 : 1)*V[:,j])
else
push!(L, (sgnq >= 0 ? 1 : -1)*( (V[n,i] * V[:,j]) + (-V[n,j]*V[:,i]) ))
end
end
println("L: ", L)
new_o = o[j] != false ? vcat(o[1:j-1], false, o[j+1:end]) : deepcopy(o)
new_V = prim(hcat(L...))
new_V = new_V[1:(size(new_V, 1)-1),:]
new_q = map(+, q, (-q[n] / (V[n, j])) * V[:, j])
new_q = new_q[1:n-1]
new_cone = SymbolicCone(new_V, new_q, new_o)
print("\nnew cone:")
println(new_cone)
flip_res = flip(new_cone)
push!(res, flip_res)
print("\nflip:")
println(flip_res)
end
return res
end
function prim_v(v::Array{Int64})
d = abs(gcd(v))
if (d == 1)
return v
else
return reduce(vcat, map(vi -> floor(Int, vi / d), v))
end
end
function prim(V::Matrix{Int64})
return Matrix{Int64}(transpose(reduce(hcat, map(i -> prim_v(V[i, :]), Vector(1:size(V, 1))))))
end
function eliminateCoordinates(C::SymbolicCone, k::Int)
res = elimLastCoordinate(C)
innerres = []
println("first elc: ", res)
for i = 1:(k-1)#becuse we call eliminate last coordinate before
for j = 1:length(res)
eres = elimLastCoordinate(res[j])
append!(innerres, eres)
println("j: ", j, " eres: ", eres)
end
res = innerres
innerres = []
end
return res
end
function enumerateFundamentalParallelePiped(C::SymbolicCone)
SMFRes = SmithNormalForm.smith(C.V)
S = SmithNormalForm.diagm(SMFRes)
Uinv = inv(SMFRes.S)
Winv = inv(SMFRes.T)
dimension = size(C.V, 2) # num of rows
ambientDimension = size(C.V, 1) # num of cols
#println("C.V: ", C.V)
#println("\nUinv: ", Uinv, "\nWinv: ", Winv)
#println("size 1: ", size(S,1), "\nsize 2: ", size(S,2))
diagonals = Int64[]
for i in 1:dimension
if(i <= size(S,1) && i <= size(S,2))
#println("i: ", i)
push!(diagonals, S[i,i])
end
end
#println("Diagonals: ", diagonals)
lastDiagonal = diagonals[end]
# sprime = [Integer(sk / si) for si in s]
primeDiagonals = Int64[]
for d in diagonals
push!(primeDiagonals, Int64(lastDiagonal/d))
end
#println("Prime diagonals: ", primeDiagonals)
# qhat = Uinv * q
apex = C.q
#println("q: ", apex, "\nV: ", C.V)
qhat = Uinv*apex
#println("qhat: ", qhat)
# Wprime
Wprime = [Winv[j,i]*primeDiagonals[i] for i = 1:dimension, j = 1:dimension] #Winv * primeDiagonals
#println("wprime: ", Wprime)
tmpWprime = deepcopy(Wprime)
#println("ftmpwprime: ", tmpWprime)
for i in 1:dimension
tmpWprime[dimension-i+1,:] = Wprime[i,:]
end
#println("tmpwprime: ", tmpWprime)
#Wprime = tmpWprime
# qtrans
qtrans = [sum([-Wprime[j,i] * qhat[i] for i = 1:dimension]) for j = 1:dimension]
#println("qtrans: ", qtrans)
#qfrac
qfrac = [qtrans[i] - floor.(Int, qtrans[i]) for i = 1:dimension]
#println("qfrac: ", qfrac)
#qint
qint = [ floor.(Int, qi) for qi in qtrans ]
#println("qint: ", qint)
#qsummand
qsummand = [Int64(qi) for qi in (lastDiagonal*apex + C.V*qfrac) ]
#println("qsummand", qsummand)
#println("dim: ", dimension, " adim: ", ambientDimension)
#println("o: ", C.o, " qfrac: ", qfrac)
#openness
openness = [ (qfrac[j] == 0 ? C.o[j] : 0) for j in 1:ambientDimension]
#println("openness: ", openness)
#bigP
#res1 = [[1:1:diagonals[i];] for i= 1:dimension]
#println("res1: ", res1)
# CartesianProduct( *[xrange(s[i]) for i in 1:k] )
L = []
P = []
for v in IterTools.product([1:diagonals[i] for i in 1:dimension]...)
push!(P, v)
innerRes = []
j = 1
for qj in qint
inner = 0
i = 1
for vi in v
inner += Wprime[i,j] * vi
i += 1
end
inner += qj
inner = inner % lastDiagonal
if inner == 0 && C.o[j]
inner = lastDiagonal
end
append!(innerRes, inner)
j += 1
end
#println("inneerres: ", innerRes)
outerRes = []
for l in 1:ambientDimension
outer = 0
j = 1
for innerResi in innerRes
outer += C.V[l,j] * innerResi
j += 1
end
append!(outerRes, outer) # outerRes is an integral vector
end
#println("outerRes: ", outerRes)
#push!(L, tuple(collect( ((ai + bi) / lastDiagonal) for (ai,bi) in collect(zip(outerRes, qsummand)) )))
push!(L, collect( Int64((ai + bi) / lastDiagonal) for (ai,bi) in collect(zip(outerRes, qsummand)) ))
end
C.fp = L
end
function computeRationalFunction(C::SymbolicCone)
numOfVars = size(C.q,1)
VV = [string("x_",i) for i in 1:numOfVars]
S, X = PolynomialRing(QQ, VV, ordering=:deglex)
num = 0
#println("VV: ", VV, "\nQQ: ", QQ, "\nS: ", S, "\nX: ", X, "\n")
for p in C.fp
tmp = 1
for i in 1:length(p)
tmp *=X[i]^p[i]
end
num += tmp
end
#println("Numerator:", num)
den = 1
for j in 1:size(C.V,2)
tmp = 1
for i in 1:size(C.V,1)
tmp *=X[i]^C.V[:,j][i]
end
den *= 1 - tmp
end
#println("Denominator:", den)
ratfun = num//den
#println("ratfun:", ratfun)
C.ratfun = ratfun
end
function solve(A::Matrix{Int64}, b::Vector{Int64})
@polyvar RationalFunctions
ratfun = 0
C = macmahon(A, b)
print("MacMahon cone: ")
println(C)
ListOfSymbolicCones = eliminateCoordinates(C, size(C.V, 1) - size(C.V, 2))
#println("eliminate coordinates: ", ListOfSymbolicCones)
println("Result Cones: ")
for cone in ListOfSymbolicCones
#enumerateFundamentalParallelePiped(cone)
#computeRationalFunction(cone)
#ratfun += cone.sign*cone.ratfun
println(cone)
#println("fp: ", cone.fp)
#println("ratfun: ", cone.ratfun)
#println()
end
return ratfun
end
end
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] | 1.913248 | 4,242 |
<filename>src/ELF/ELFHandle.jl
# Export Datatypes
export ELFHandle
# Import Base methods
import Base: start, getindex
"""
ELFHandle
An `ObjectHandle` subclass for ELF files, this is the primary object by which
client applications will interact with ELF files.
"""
struct ELFHandle{T<:IO} <: ObjectHandle
# Backing IOS and start point within the IOStream of this ELF object
io::T
start::Int
# Elf Internal data such as endianness, version, OS ABI, etc...
ei::ELFInternal
# The parsed-out header of the ELF object
header::ELFHeader
# The path of the file this was created with
path::String
end
## Define creation methods
function readmeta(io::IO, ::Type{H}) where {H <: ELFHandle}
# This is the magic that we know we must find
elven_magic = UInt8['\177', 'E', 'L', 'F']
# Save the starting position of `io`
start = position(io)
# Check for magic bytes
magic = [read(io, UInt8) for idx in 1:4]
if any(magic .!= elven_magic)
msg = """
Magic Number 0x$(join(hex.(magic),"")) does not match expected ELF
magic number 0x$(join("", hex.(elven_magic)))
"""
throw(MagicMismatch(replace(strip(msg), "\n", " ")))
end
# Read the ELF Internal data, then skip its padding
ei = unpack(io, ELFInternal)
skip(io, 7)
# Build different Header objects for 32 or 64-bit ELF
header_type = elf_internal_is64bit(ei) ? ELFHeader64{H} : ELFHeader32{H}
# Unpack the header, but pretend we didn't by rewinding `io`
header = unpack(io, header_type, elf_internal_endianness(ei))
seek(io, start)
# Construct our ELFHandle, pilfering the filename from the IOStream
return ELFHandle(io, start, ei, header, path(io))
end
## IOStream-like operations:
start(oh::ELFHandle) = oh.start
iostream(oh::ELFHandle) = oh.io
## Format-specific properties:
header(oh::ELFHandle) = oh.header
endianness(oh::ELFHandle) = elf_internal_endianness(oh.ei)
is64bit(oh::ELFHandle) = elf_internal_is64bit(oh.ei)
isrelocatable(oh::ELFHandle) = header(oh).e_type == ET_REL
isexecutable(oh::ELFHandle) = header(oh).e_type == ET_EXEC
islibrary(oh::ELFHandle) = header(oh).e_type == ET_DYN
mangle_section_name(oh::ELFHandle, name::AbstractString) = string(".", name)
mangle_symbol_name(oh::ELFHandle, name::AbstractString) = name
format_string(::Type{H}) where {H <: ELFHandle} = "ELF"
# Section information
section_header_offset(oh::ELFHandle) = header(oh).e_shoff
section_header_size(oh::ELFHandle) = header(oh).e_shentsize
function section_header_type(oh::H) where {H <: ELFHandle}
if is64bit(oh)
return ELFSection64{H}
else
return ELFSection32{H}
end
end
# Segment information (Note this is NOT a part of the generic ObjectFile API,
# this is an ELF-only extension)
"""
segment_header_offset(oh::ELFHandle)
Return the offset of the segment header table within the given ELF object.
"""
segment_header_offset(oh::ELFHandle) = header(oh).e_phoff
"""
segment_header_offset(oh::ELFHandle)
Return the size of a segment header within the given ELF object.
"""
segment_header_size(oh::ELFHandle) = header(oh).e_phentsize
"""
segment_header_type(oh::ELFHandle)
Return the type of a segment header within the given ELF object. E.g. within a
64-bit ELF object, this will return `ELFSegment64`.
"""
function segment_header_type(oh::H) where {H <: ELFHandle}
if is64bit(oh)
return ELFSegment64{H}
else
return ELFSegment32{H}
end
end
# Symbol information
symtab_entry_offset(oh::ELFHandle) = section_offset(Symbols(oh).section_ref)
symtab_entry_size(oh::ELFHandle) = sizeof(symtab_entry_type(oh))
function symtab_entry_type(oh::H) where {H <: ELFHandle}
if is64bit(oh)
return ELFSymtabEntry64{H}
else
return ELFSymtabEntry32{H}
end
end
# Dynamic Linkage information
function dyn_entry_type(oh::H) where {H <: ELFHandle}
if is64bit(oh)
return ELFDynEntry64{H}
else
return ELFDynEntry32{H}
end
end
## Misc. operations
path(oh::ELFHandle) = oh.path
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] | 2.65627 | 1,539 |
#=
The following code is modified from
https://github.com/billmclean/GaussQuadrature.jl with the original license:
> The MIT License (MIT)
> Copyright (c) 2013 billmclean
> Permission is hereby granted, free of charge, to any person obtaining a copy
> of this software and associated documentation files (the "Software"), to deal
> in the Software without restriction, including without limitation the rights
> to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
> copies of the Software, and to permit persons to whom the Software is
> furnished to do so, subject to the following conditions:
> The above copyright notice and this permission notice shall be included in
> all copies or substantial portions of the Software.
> THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
> IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
> FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
> AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
> LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
> OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
> SOFTWARE.
=#
# October 2013 by <NAME>, School of Maths and Stats,
# The University of New South Wales.
#
# Based on earlier Fortran codes
#
# gaussq.f original version 20 Jan 1975 from Stanford
# gaussq.f modified 21 Dec by <NAME>
# gaussquad.f95 Nov 2005 by <NAME>
#
# This module provides functions to compute the abscissae x[j] and
# weights w[j] for the classical Gauss quadrature rules, including
# the Radau and Lobatto variants. Thus, the sum
#
# n
# ∑ w[j] f(x[j])
# j=1
#
# approximates
#
# hi
# ∫ f(x) w(x) dx
# lo
#
# where the weight function w(x) and interval lo < x < hi are as shown
# in the table below.
#
# Name Interval Weight Function
#
# Legendre -1 < x < 1 1
# Chebyshev (first kind) -1 < x < 1 1 / sqrt(1-x²)
# Chebyshev (second kind) -1 < x < 1 sqrt(1-x²)
# Jacobi -1 < x < 1 (1-x)ᵅ (1+x)ᵝ
# Laguerre 0 < x < ∞ xᵅ exp(-x)
# Hermite -∞ < x < ∞ exp(-x²)
#
# In addition to these classical rules, the module generates Gauss rules
# for logarithmic weights of the form
#
# w(x) = x^ρ log(1/x) for 0 < x < 1.
#
# For the Jacobi and Laguerre rules we require α > -1 and
# β > -1, so that the weight function is integrable. Likewise, for
# log weight we require ρ > -1.
#
# Use the endpoint argument to include one or both of the end points
# of the interval of integration as an abscissa in the quadrature
# rule, as follows.
#
# endpoint = NeitherEndPoint() Default lo < x[j] < hi, j = 1:n.
# endpoint = LeftEndPoint() Left Radau lo = x[1] < x[j] < hi, j = 2:n.
# endpoint = RightEndPoint() Right Radau lo < x[j] < x[n] = hi, j = 1:n-1.
# endpoint = BothEndPoint() Lobatto lo = x[1] < x[j] < x[n] = hi, j = 2:n-1.
#
#
# The code uses the Golub and Welsch algorithm, in which the abscissae
# x[j] are the eigenvalues of a symmetric tridiagonal matrix whose
# entries depend on the coefficients in the 3-term recurrence relation
# for the othonormal polynomials generated by the weighted inner product.
#
# References:
#
# 1. <NAME>., and <NAME>., Calculation of Gaussian
# quadrature rules, Mathematics of Computation 23 (April,
# 1969), pp. 221-230.
# 2. <NAME>., Some modified matrix eigenvalue problems,
# Siam Review 15 (april, 1973), pp. 318-334 (section 7).
# 3. <NAME>, Gaussian Quadrature Formulas, Prentice-
# Hall, <NAME>., 1966.
# Enumeration type used to specify which endpoints of the integration
# interval should be included amongst the quadrature points: neither,
# left, right, or both.
abstract type EndPoint end
struct NeitherEndPoint <: EndPoint end
struct LeftEndPoint <: EndPoint end
struct RightEndPoint <: EndPoint end
struct BothEndPoint <: EndPoint end
"""
x, w = legendregauss(T, n, endpoint::EndPoint=Bennu.NeitherEndPoint())
Returns points `x` and weights `w` for the `n`-point Gauss-Legendre rule
for the interval `-1 < x < 1` with weight function `w(x) = 1`.
Use `endpoint=LeftEndPoint()`, `RightEndPoint() ` or `BothEndPoints()` for the
left Radau, right Radau, or Lobatto rules, respectively.
"""
function legendregauss(::Type{T}, n, endpoint=NeitherEndPoint()) where {T}
@assert n ≥ 1
a, b = legendrecoefficients(T, n)
return gaussrule(-one(T), one(T), a, b, endpoint)
end
"""
x, w = legendregauss(n, endpoint::EndPoint=Bennu.NeitherEndPoint())
Convenience function with type `T = Float64`:
"""
legendregauss(n, endpoint::EndPoint=NeitherEndPoint()) = legendregauss(Float64, n, endpoint)
"""
x, w = legendregausslobatto(T, n)
Returns points `x` and weights `w` for the `n`-point Legendre-Gauss-Lobatto rule
for the interval `-1 ≤ x ≤ 1` with weight function `w(x) = 1`.
"""
function legendregausslobatto(::Type{T}, n) where {T}
return legendregauss(T, n, BothEndPoint())
end
"""
x, w = legendregausslobatto(n)
Convenience function with type `T = Float64`:
"""
legendregausslobatto(n) = legendregausslobatto(Float64, n)
function legendrecoefficients(::Type{T}, n) where {T}
a = zeros(T, n)
b = zeros(T, n + 1)
b[1] = sqrt(convert(T, 2))
for k in 2:(n + 1)
b[k] = (k - 1) / sqrt(convert(T, (2k - 1) * (2k - 3)))
end
return a, b
end
"""
x, w = gaussrule(lo, hi, a, b, endpoint, maxiterations=100)
Generates the points `x` and weights `w` for a Gauss rule with weight
function `w(x)` on the interval `lo < x < hi`.
The arrays `a` and `b` hold the coefficients (as given, for instance, by
`legendrecoefficients`) in the three-term recurrence relation for the monic
orthogonal polynomials `p(0,x)`, `p(1,x)`, `p(2,x)`, ... , that is,
p(k, x) = (x-a[k]) p(k-1, x) - b[k]² p(k-2, x), k ≥ 1,
where `p(0, x) = 1` and, by convention, `p(-1, x) = 0` with
hi
b[1]^2 = ∫ w(x) dx.
lo
Thus, `p(k, x) = xᵏ + lower degree terms` and
hi
∫ p(j, x) p(k, x) w(x) dx = 0 if j ≠ k.
lo
"""
function gaussrule(lo, hi, a, b, endpoint::EndPoint, maxiterations=100)
T = promote_type(typeof(lo), typeof(hi), eltype(a), eltype(b))
n = length(a)
@assert length(b) == n + 1
if endpoint isa LeftEndPoint
if n == 1
a[1] = lo
else
a[n] = tridiagonalshiftsolve(n, lo, a, b) * b[n]^2 + lo
end
elseif endpoint isa RightEndPoint
if n == 1
a[1] = hi
else
a[n] = tridiagonalshiftsolve(n, hi, a, b) * b[n]^2 + hi
end
elseif endpoint isa BothEndPoint
if n == 1
error("Must have at least two points for both ends.")
end
g = tridiagonalshiftsolve(n, lo, a, b)
t1 = (hi - lo) / (g - tridiagonalshiftsolve(n, hi, a, b))
b[n] = sqrt(t1)
a[n] = lo + g * t1
end
w = zero(a)
tridiagonaleigenproblem!(a, b, w, maxiterations)
for i in 1:n
w[i] = (b[1] * w[i])^2
end
idx = sortperm(a)
# Ensure end point values are exact.
if endpoint isa LeftEndPoint || endpoint isa BothEndPoint
a[idx[1]] = lo
elseif endpoint isa RightEndPoint || endpoint isa BothEndPoint
a[idx[n]] = hi
end
return a[idx], w[idx]
end
function tridiagonalshiftsolve(n, shift, a, b) where {T}
#
# Perform elimination to find the nth component s = delta[n]
# of the solution to the nxn linear system
#
# ( J_n - shift I_n ) delta = e_n,
#
# where J_n is the symmetric tridiagonal matrix with diagonal
# entries a[i] and off-diagonal entries b[i], and e_n is the nth
# standard basis vector.
#
t = a[1] - shift
for i in 2:(n - 1)
t = a[i] - shift - b[i]^2 / t
end
return one(t) / t
end
function tridiagonaleigenproblem!(d, e, z, maxiterations)
#
# Finds the eigenvalues and first components of the normalised
# eigenvectors of a symmetric tridiagonal matrix by the implicit
# QL method.
#
# d[i] On entry, holds the ith diagonal entry of the matrix.
# On exit, holds the ith eigenvalue.
#
# e[i] On entry, holds the [i,i-1] entry of the matrix for
# i = 2, 3, ..., n. (The value of e[1] is not used.)
# On exit, e is overwritten.
#
# z[i] On exit, holds the first component of the ith normalised
# eigenvector associated with d[i].
#
# maxiterations The maximum number of QL iterations.
#
# Martin and Wilkinson, Numer. Math. 12: 377-383 (1968).
# Dubrulle, Numer. Math. 15: 450 (1970).
# Handbook for Automatic Computation, Vol ii, Linear Algebra,
# pp. 241-248, 1971.
#
# This is a modified version of the Eispack routine imtql2.
#
T = promote_type(eltype(d), eltype(e), eltype(z))
n = length(z)
z[1] = one(T)
z[2:n] .= zero(T)
e[n + 1] = zero(T)
if n == 1 # Nothing to do for a 1x1 matrix.
return
end
for l in 1:n
for j in 1:maxiterations
# Look for small off-diagonal elements.
m = n
for i in l:(n - 1)
if abs(e[i + 1]) <= eps(T) * (abs(d[i]) + abs(d[i + 1]))
m = i
break
end
end
p = d[l]
if m == l
continue
end
if j == maxiterations
msg = string("No convergence after ", j, " iterations",
" (try increasing maxiterations)")
error(msg)
end
# Form shift
g = (d[l + 1] - p) / (2 * e[l + 1])
r = hypot(g, one(T))
g = d[m] - p + e[l + 1] / (g + copysign(r, g))
s = one(T)
c = one(T)
p = zero(T)
for i in (m - 1):-1:l
f = s * e[i + 1]
b = c * e[i + 1]
if abs(f) < abs(g)
s = f / g
r = hypot(s, one(T))
e[i + 2] = g * r
c = one(T) / r
s *= c
else
c = g / f
r = hypot(c, one(T))
e[i + 2] = f * r
s = one(T) / r
c *= s
end
g = d[i + 1] - p
r = (d[i] - g) * s + 2 * c * b
p = s * r
d[i + 1] = g + p
g = c * r - b
# Form first component of vector.
f = z[i + 1]
z[i + 1] = s * z[i] + c * f
z[i] = c * z[i] - s * f
end
d[l] -= p
e[l + 1] = g
e[m + 1] = zero(T)
end
end
return
end
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] | 2.162896 | 5,083 |
# SLAMinDB service functions
using Dates
function getcredentials(;nparticles=true, drawdepth=true, multisession=false, drawedges=true)
cloudGraph, addrdict = standardcloudgraphsetup(nparticles=nparticles, drawdepth=drawdepth, drawedges=drawedges, multisession=multisession)
return addrdict
end
function slamindbsavejld(fgl::G, session::AbstractString, itercount::Int) where G <: AbstractDFG
dt = Base.Dates.now()
filenamejld = "$(session)_$(Dates.format(dt, "dduyy-HH:MM:SS"))_slamindb_$(itercount).jld"
println("------Save fg to file: $(filenamejld)------")
savejld(fgl,file=filenamejld)
nothing
end
"""
$(SIGNATURES)
Runs SlamInDb for given number of iterations against a specific session.
"""
function runSlamInDbOnSession(
caesarConfig::CaesarConfig,
cloudGraph::CloudGraph,
userId::String,
robotId::String,
sessionId::String,
iterations::Int64,
isRecursiveSolver::Bool,
solverStatus::SolverStatus,
iterationCompleteCallback)::Nothing
#
N = caesarConfig.numParticles
# TODO: Constants to refactor
drawbayestree = false
# Update the status parameters
solverStatus.isAttached = true
solverStatus.attachedSessionTimestamp = string(unix2datetime(time()))
solverStatus.userId = userId
solverStatus.robotId = robotId
solverStatus.sessionId = sessionId
solverStatus.result = "IN PROGRESS"
itercount = 0
while ((iterations > 0 || iterations == -1) && solverStatus.isAttached)
iterationStats = IterationStatistics()
try
iterations = iterations == -1 ? iterations : iterations-1 # stop at 0 or continue indefinitely if -1
startns = time_ns()
solverStatus.iteration = itercount
println("===================CONVERT===================")
solverStatus.currentStep = "Prep_Convert"
fg = initfg(sessionname=sessionId, robotname=robotId, username=userId, cloudgraph=cloudGraph)
updatenewverts!(fg, N=N)
println()
println("=============ENSURE INITIALIZED==============")
solverStatus.currentStep = "Prep_EnsureInitialized"
ensureAllInitialized!(fg)
println()
println("================MULTI-SESSION================")
solverStatus.currentStep = "Prep_MultiSession"
rmInstMultisessionPriors!(cloudGraph, session=sessionId, robot=robotId, user=userId, multisessions=caesarConfig.multiSession)
println()
println("====================SOLVE====================")
solverStatus.currentStep = "Init_Solve"
fg = initfg(sessionname=sessionId, robotname=robotId, username=userId, cloudgraph=cloudGraph)
setBackendWorkingSet!(cloudGraph.neo4j.connection, sessionId, robotId, userId)
println("Get local copy of graph")
solverStatus.currentStep = "Init_LocalGraphCopy"
if fullLocalGraphCopy!(fg)
iterationStats.numNodes = length(fg.IDs) + length(fg.fIDs) #Variables and factors
# (savejlds && itercount == 0) ? slamindbsavejld(fg, sessionId, itercount) : nothing
itercount += 1
println("-------------Ensure Initialization-----------")
solverStatus.currentStep = "Solve_EnsureInitialized"
ensureAllInitialized!(fg)
println("------------Bayes (Junction) Tree------------")
solverStatus.currentStep = "Solve_BuildBayesTree"
tree = wipeBuildNewTree!(fg,drawpdf=drawbayestree)
solverStatus.currentStep = "Solve_InferOverTree"
if !isRecursiveSolver
inferOverTree!(fg, tree, N=N)
else
inferOverTreeR!(fg, tree, N=N)
end
else
sleep(0.2)
end
# Notify iteration update.
solverStatus.lastIterationDurationSeconds = (time_ns() - startns) / 1e9
solverStatus.currentStep = "Idle"
iterationStats.result = "GOOD"
solverStatus.result = "GOOD"
catch ex
io = IOBuffer()
showerror(io, ex, catch_backtrace())
err = String(take!(io))
msg = "ERROR\r\n$err"
iterationStats.result = msg
solverStatus.result = msg
finally
iterationStats.endTimestamp = Dates.now()
iterationCompleteCallback(iterationStats)
end
end
solverStatus.isAttached = false
solverStatus.detachedSessionTimestamp = string(unix2datetime(time()))
solverStatus.userId = ""
solverStatus.robotId = ""
solverStatus.sessionId = ""
return nothing
end
"""
$(SIGNATURES)
Low-level call to iterate the SlamInDb solver for given number of iterations against a specific session and keyword parameters.
"""
function runDbSolver(cloudGraph::CloudGraphs.CloudGraph,
robotId::A,
sessionId::A,
userId::A;
N::Int=100,
loopctrl::Vector{Bool}=Bool[true],
iterations::Int=-1,
multisession::Vector{A}=Sting[""],
savejlds::Bool=false,
recursivesolver::Bool=false,
drawbayestree::Bool=false ) where {A <: AbstractString}
itercount = 0
while loopctrl[1] && (iterations > 0 || iterations == -1) # loopctrl for future use
iterations = iterations == -1 ? iterations : iterations-1 # stop at 0 or continue indefinitely if -1
println("===================CONVERT===================")
fgl = initfg(sessionname=sessionId, robotname=robotId, username=userId, cloudgraph=cloudGraph)
updatenewverts!(fgl, N=N)
println()
println("=============ENSURE INITIALIZED==============")
ensureAllInitialized!(fgl)
println()
println("================MULTI-SESSION================")
rmInstMultisessionPriors!(cloudGraph, session=sessionId, robot=robotId, user=userId, multisessions=multisession)
println()
println("====================SOLVE====================")
fgl = initfg(sessionname=sessionId, robotname=robotId, username=userId, cloudgraph=cloudGraph)
setBackendWorkingSet!(cloudGraph.neo4j.connection, sessionId, robotId, userId)
println("get local copy of graph")
if fullLocalGraphCopy!(fgl)
(savejlds && itercount == 0) ? slamindbsavejld(fgl, sessionName, itercount) : nothing
itercount += 1
println("-------------Ensure Initialization-----------")
ensureAllInitialized!(fgl)
println("------------Bayes (Junction) Tree------------")
tree = wipeBuildNewTree!(fgl,drawpdf=drawbayestree)
if !recursivesolver
inferOverTree!(fgl, tree, N=N)
else
inferOverTreeR!(fgl, tree, N=N)
end
savejlds ? slamindbsavejld(fgl, sessionName, itercount) : nothing
else
sleep(0.2)
end
end
end
"""
Manually call the SLAMinDB solver to perform inference on a specified session given the keyword parameter settings.
"""
function slamindb(;addrdict=nothing,
N::Int=-1,
loopctrl::Vector{Bool}=Bool[true],
iterations::Int=-1,
multisession::Bool=false,
savejlds::Bool=false,
recursivesolver::Bool=false,
drawbayestree::Bool=false )
nparticles = false
if N > 0
addrdict["num particles"] = string(N)
else
nparticles = true
end
cloudGraph, addrdict = standardcloudgraphsetup(addrdict=addrdict,
nparticles=nparticles, multisession=multisession)
N = parse(Int, addrdict["num particles"])
session = addrdict["session"]
robot = addrdict["robot"]
user = addrdict["user"]
if !haskey(addrdict, "multisession")
addrdict["multisession"]=String[]
end
runDbSolver(cloudGraph,
addrdict["robotId"],
session,
N=N,
loopctrl=loopctrl,
iterations=iterations,
multisession=addrdict["multisession"],
savejlds=savejlds,
recursivesolver=recursivesolver,
drawbayestree=drawbayestree)
nothing
end
function convertdb(;addrdict=nothing,
N::Int=-1 )
#
nparticles = false
if N > 0
addrdict["num particles"] = string(N)
else
nparticles = true
end
cloudGraph, addrdict = standardcloudgraphsetup(addrdict=addrdict, nparticles=nparticles)
N = parse(Int, addrdict["num particles"])
fg = initfg(sessionname=addrdict["session"], cloudgraph=cloudGraph)
updatenewverts!(fg, N=N)
end
function resetconvertdb(;addrdict=nothing)
cloudGraph, addrdict = standardcloudgraphsetup(addrdict=addrdict)
println("Clearing slamindb data, leaving front-end data, session: $(addrdict["session"])")
resetentireremotesession(cloudGraph.neo4j.connection, addrdict["session"])
end
#
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] | 2.417853 | 3,652 |
include("BinDeps.jl")
using .BinDeps
@static if Base.VERSION < v"1.3-" || (Sys.iswindows() && Sys.WORD_SIZE == 32)
@BinDeps.setup()
using Libdl
function validate_libz_version(name, handle)
f = Libdl.dlsym_e(handle, "zlibVersion")
f == C_NULL && return false
ver = VersionNumber(unsafe_string(ccall(f, Cstring, ())))
println("Version Libz is: $ver")
# Version 1.2.8 or above
return ver >= v"1.2.8"
end
function validate_openssl_version(name, handle)
f = Libdl.dlsym_e(handle, "OpenSSL_version_num")
f == C_NULL && return false
v = ccall(f, Culong, ())
println("Version OpenSSL is: $(string(v, base=16))")
# Version 1.1.0 or above
return v >= 0x1010000f
end
libz = library_dependency("libz", aliases=["libz", "libzlib", "zlib1"], validate=validate_libz_version)
libcrypto = library_dependency("libcrypto", aliases=["libcrypto", "libcrypto-1_1-x64", "libcrypto-1_1"], validate=validate_openssl_version)
prefix = joinpath(@__DIR__, "usr")
if !Sys.iswindows()
provides(Sources,
URI("https://github.com/madler/zlib/archive/v1.2.11.tar.gz"),
libz, unpacked_dir="zlib-1.2.11")
provides(SimpleBuild,
(@build_steps begin
GetSources(libz)
@build_steps begin
ChangeDirectory(joinpath(BinDeps.depsdir(libz), "src", "zlib-1.2.11"))
`./configure --prefix=$prefix`
`make`
`make install`
end
end), libz, os = :Unix)
osslver = "1_1_0k"
osslfn = "OpenSSL_$(osslver)"
ossldir = "openssl-$(osslfn)"
osslpkg = "$(osslfn).tar.gz"
provides(Sources,
URI("https://github.com/openssl/openssl/archive/$(osslpkg)"),
libcrypto, unpacked_dir="$(ossldir)")
provides(SimpleBuild,
(@build_steps begin
GetSources(libcrypto)
@build_steps begin
ChangeDirectory(joinpath(BinDeps.depsdir(libcrypto), "src", "$(ossldir)"))
`./config --prefix=$prefix`
`make depend`
`make install`
end
end), libcrypto, os = :Unix)
else
zlib_bn = "zlib-1.2.11-win$(Sys.WORD_SIZE)-mingw"
zlib_fn = "$(zlib_bn).zip"
zlib_uri = "https://bintray.com/vszakats/generic/download_file?file_path=$(zlib_fn)"
provides(Binaries, URI(zlib_uri), libz, filename="$(zlib_fn)", unpacked_dir="$(zlib_bn)")
openssl_bn = "openssl-1.1.0i-win$(Sys.WORD_SIZE)-mingw"
openssl_fn = "$(openssl_bn).zip"
openssl_uri = "https://bintray.com/vszakats/generic/download_file?file_path=$(openssl_fn)"
provides(Binaries, URI(openssl_uri), libcrypto, filename="$(openssl_fn)", unpacked_dir="$(openssl_bn)")
end
@BinDeps.install Dict([:libz => :libz, :libcrypto => :libcrypto])
end
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] | 1.932373 | 1,597 |
function feedforward!(net::Network,input::Vector{Float64})::Vector{Float64}
net.layers[1] = input
# a^(l+1) = σ(w^l*a^l + b^1)
for i=1:(length(net.layers)-1)
net.z[i] = net.weights[i]*net.neurons[i] + net.biases[i]
net.neurons[i+1] = net.z[i].net.act()
end
return net.neurons[end]
end
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] | 1.9 | 170 |
#========================================================================================#
"""
QuBits
Module QuBits: My Very Own Quantum Computer
Author: <NAME>, 03/05/22
"""
module QuBits
# Externally available names:
export Tensor, ⊗, kron, ⊚, kpow, isclose, ishermitian, isunitary
export State, qubit, ampl, phase, prob, maxprob, nbits
# Imports:
import Base:kron
using LinearAlgebra
#========================================================================================#
# Tensor definitions:
#-----------------------------------------------------------------------------------------
"""
Tensor
Tensor is a synonym for AbstractArray.
"""
Tensor = AbstractArray
#-----------------------------------------------------------------------------------------
"""
Kronecker product ⊗
Define ⊗ as an infix operator for the Kronecker product of two Tensors
"""
⊗ = kron
#-----------------------------------------------------------------------------------------
# Tensor methods:
#-----------------------------------------------------------------------------------------
"""
kpow( t::Tensor, n::Int)
Kronecker the Tensor t n times with itself.
"""
function kpow( t::Tensor, n::Int)
if n==0
# Base case:
return 1.0
end
# Build Kronecker product:
tn = t
for _ in 1:(n-1)
tn = tn ⊗ t
end
tn
end
# Use circled ring as operator for kpow:
⊚ = kpow
#-----------------------------------------------------------------------------------------
"""
isclose( t::Tensor, u::Tensor, atol::Float64=1e-6)
Check whether all elements of the Tensor t are close to those of the Tensor u.
"""
function isclose( t, u, atol::Float64=1e-6)
all(abs.(t.-u) .< atol)
end
#-----------------------------------------------------------------------------------------
"""
ishermitian( t::Tensor)
Check whether the Tensor t is hermitian.
"""
function ishermitian( t::Tensor)
sz = size(t)
if length(sz) != 2 || sz[1] != sz[2]
return false
end
isclose(t,t')
end
#-----------------------------------------------------------------------------------------
"""
isunitary( t::Tensor)
Check whether the Tensor t is unitary.
"""
function isunitary( t::Tensor)
sz = size(t)
if length(sz) != 2 || sz[1] != sz[2]
return false
end
isclose(t*t',Matrix(I,size(t)))
end
#========================================================================================#
# State definitions:
#-----------------------------------------------------------------------------------------
"""
State
A State is a collection of one or more qubits: a wrapper for Vector{Complex}.
"""
struct State <: AbstractVector{Complex}
amp::Vector{Complex} # The State amplitudes
function State(amp::Vector)
if isclose(amp,0.0)
error("State vectors must be non-zero")
end
new(amp/norm(amp))
end
end
#-----------------------------------------------------------------------------------------
# Delegated methods:
Base.length(s::State) = length(s.amp)
Base.size(s::State) = size(s.amp)
Base.setindex(s::State,i) = setindex(s.amp,i)
Base.getindex(s::State,i) = getindex(s.amp,i)
#-----------------------------------------------------------------------------------------
"""
kron( s1::State, s2::State)
Return Kronecker product of two States.
"""
function kron( s1::State, s2::State)
State(kron(s1.amp,s2.amp))
end
#-----------------------------------------------------------------------------------------
"""
ampl( s::State, bits::BitVector)
Return amplitude for qubit indexed by bits.
"""
function ampl( s::State, bits::BitVector)
s.amp[1+bits2dec(bits)] # Add 1 to index because bits counts from 0
end
ampl( s::State, bits...) = ampl(s,BitVector(bits))
#-----------------------------------------------------------------------------------------
"""
phase( s::State, bits::BitVector)
Return phase of the amplitude of the qubit indexed by bits.
"""
function phase( s::State, bits::BitVector)
angle(ampl(s,bits))
end
phase( s::State, bits...) = phase(s,BitVector(bits))
#-----------------------------------------------------------------------------------------
"""
prob( s::State, bits::BitVector)
Return probability for qubit indexed by bits.
"""
function prob( s::State, bits::BitVector)
amp = ampl(s,bits) # Retrieve own amplitude
real(amp'*amp) # Calculate its squared norm
end
prob( s::State, bits...) = prob(s,BitVector(bits))
#-----------------------------------------------------------------------------------------
"""
normalise( s::State)
Normalise the state s (but throw an error if it's a zero state).
"""
function normalise(s::State)
nrm = norm(s)
if isclose(nrm,0.0)
error("Attempting to normaise zero-probability state.")
end
s /= nrm
end
#-----------------------------------------------------------------------------------------
"""
qubit( alpha::Complex, beta::Complex)
Create a single qubit State from the given amplitudes.
"""
function qubit( alpha::Number, beta=nothing)
if beta===nothing
beta = sqrt(1.0 - alpha'*alpha)
end
#= if !isclose( alpha'*alpha+beta'*beta, 1.0)
error("Qubit probabilities do not add up to 1.")
end=#
State([alpha,beta])
end
function qubit(; alpha=nothing, beta=nothing)
if alpha === beta === nothing
# Neither amplitude is given:
error("alpha, beta or both are required.")
end
if alpha === nothing
# alpha not given:
alpha = sqrt(1.0 - beta'*beta)
end
qubit(alpha,beta)
end
#-----------------------------------------------------------------------------------------
"""
nbits( stt::State)
Return number of qubits in this state.
"""
function nbits( s::State)
Int(log2(length(s)))
end
#-----------------------------------------------------------------------------------------
"""
maxprob( stt::State)
Return tuple (bitindex,prob) of highest probability qubit in this State.
"""
function maxprob( s::State)
mxbindex, mxprob = BitVector([]), 0.0
for bindex in bitprod(nbits(s))
thisprob = prob(s,bindex)
if thisprob > mxprob
mxbindex, mxprob = bindex, thisprob
end
end
(mxbindex, mxprob)
end
#-----------------------------------------------------------------------------------------
"""
Base.show( s::State)
Display the given State.
"""
function Base.show( io::IO, s::State)
print( io, "State{", nbits(s), "}[")
for i in 1:length(s)-1
print( round(s[i],digits=4), ", ")
end
print( io, round(s[end],digits=4), "]")
end
#========================================================================================#
# Helper methods:
#-----------------------------------------------------------------------------------------
"""
bits2dec( bits::BitVector)
Compute decimal representation of a BitVector.
"""
function bits2dec( bits::BitVector)
s = 0; v = 1
for i in view(bits,length(bits):-1:1)
s += v*i
v <<= 1
end
s
end
#-----------------------------------------------------------------------------------------
"""
dec2bits( dec::Int, nbits::Int)
Compute a hi2lo nbit binary representation of a decimal value.
"""
function dec2bits( dec::Int, nbits::Int)
BitVector(reverse( digits(dec, base=2, pad=nbits)))
end
#-----------------------------------------------------------------------------------------
"""
bitprod( nbits::Int)
Construct in numerical order a list of all binary numbers containing nbits.
"""
function bitprod( nbits::Int)
[dec2bits(i,nbits) for i in 0:2^nbits-1]
end
#-----------------------------------------------------------------------------------------
function demo()
p1 = qubit(alpha=rand())
x1 = qubit(beta=rand())
psi = p1 ⊗ x1
println( isclose( psi'*psi, 1.0))
println( isclose( p1'*p1*x1'*x1, 1.0))
println( ampl(psi,1,0))
println( prob(psi,1,0))
end
end # ... of module QuBits | [
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] | 3.339705 | 2,302 |
<filename>src/univariate/continuous/lognormal.jl<gh_stars>0
doc"""
LogNormal(μ,σ)
The *log normal distribution* is the distribution of the exponential of a [`Normal`](:func:`Normal`) variate: if $X \sim \operatorname{Normal}(\mu, \sigma)$ then $\exp(X) \sim \operatorname{LogNormal}(\mu,\sigma)$. The probability density function is
$f(x; \mu, \sigma) = \frac{1}{x \sqrt{2 \pi \sigma^2}}
\exp \left( - \frac{(\log(x) - \mu)^2}{2 \sigma^2} \right),
\quad x > 0$
```julia
LogNormal() # Log-normal distribution with zero log-mean and unit scale
LogNormal(mu) # Log-normal distribution with log-mean mu and unit scale
LogNormal(mu, sig) # Log-normal distribution with log-mean mu and scale sig
params(d) # Get the parameters, i.e. (mu, sig)
meanlogx(d) # Get the mean of log(X), i.e. mu
varlogx(d) # Get the variance of log(X), i.e. sig^2
stdlogx(d) # Get the standard deviation of log(X), i.e. sig
```
External links
* [Log normal distribution on Wikipedia](http://en.wikipedia.org/wiki/Log-normal_distribution)
"""
immutable LogNormal <: ContinuousUnivariateDistribution
μ::Float64
σ::Float64
LogNormal(μ::Real, σ::Real) = (@check_args(LogNormal, σ > zero(σ)); new(μ, σ))
LogNormal(μ::Real) = new(μ, 1.0)
LogNormal() = new(0.0, 1.0)
end
@distr_support LogNormal 0.0 Inf
#### Parameters
params(d::LogNormal) = (d.μ, d.σ)
#### Statistics
meanlogx(d::LogNormal) = d.μ
varlogx(d::LogNormal) = abs2(d.σ)
stdlogx(d::LogNormal) = d.σ
mean(d::LogNormal) = ((μ, σ) = params(d); exp(μ + 0.5 * σ^2))
median(d::LogNormal) = exp(d.μ)
mode(d::LogNormal) = ((μ, σ) = params(d); exp(μ - σ^2))
function var(d::LogNormal)
(μ, σ) = params(d)
σ2 = σ^2
(exp(σ2) - 1.0) * exp(2.0 * μ + σ2)
end
function skewness(d::LogNormal)
σ2 = varlogx(d)
e = exp(σ2)
(e + 2.0) * sqrt(e - 1.0)
end
function kurtosis(d::LogNormal)
σ2 = varlogx(d)
e = exp(σ2)
e2 = e * e
e3 = e2 * e
e4 = e3 * e
e4 + 2.0 * e3 + 3.0 * e2 - 6.0
end
function entropy(d::LogNormal)
(μ, σ) = params(d)
0.5 * (1.0 + log(twoπ * σ^2)) + μ
end
#### Evalution
pdf(d::LogNormal, x::Float64) = normpdf(d.μ, d.σ, log(x)) / x
function logpdf(d::LogNormal, x::Float64)
if !insupport(d, x)
return -Inf
else
lx = log(x)
return normlogpdf(d.μ, d.σ, lx) - lx
end
end
cdf(d::LogNormal, x::Float64) = x > 0.0 ? normcdf(d.μ, d.σ, log(x)) : 0.0
ccdf(d::LogNormal, x::Float64) = x > 0.0 ? normccdf(d.μ, d.σ, log(x)) : 1.0
logcdf(d::LogNormal, x::Float64) = x > 0.0 ? normlogcdf(d.μ, d.σ, log(x)) : -Inf
logccdf(d::LogNormal, x::Float64) = x > 0.0 ? normlogccdf(d.μ, d.σ, log(x)) : 0.0
quantile(d::LogNormal, q::Float64) = exp(norminvcdf(d.μ, d.σ, q))
cquantile(d::LogNormal, q::Float64) = exp(norminvccdf(d.μ, d.σ, q))
invlogcdf(d::LogNormal, lq::Float64) = exp(norminvlogcdf(d.μ, d.σ, lq))
invlogccdf(d::LogNormal, lq::Float64) = exp(norminvlogccdf(d.μ, d.σ, lq))
function gradlogpdf(d::LogNormal, x::Float64)
(μ, σ) = params(d)
x > 0.0 ? - ((log(x) - μ) / (σ^2) + 1.0) / x : 0.0
end
# mgf(d::LogNormal)
# cf(d::LogNormal)
#### Sampling
rand(d::LogNormal) = exp(randn() * d.σ + d.μ)
## Fitting
function fit_mle{T <: Real}(::Type{LogNormal}, x::AbstractArray{T})
lx = log(x)
μ, σ = mean_and_std(lx)
LogNormal(μ, σ)
end
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] | 2.079777 | 1,617 |
<gh_stars>1-10
using JLD
using DiffPrecTest
using LaTeXStrings
include("../qq_plot.jl")
pArr = [100, 200]
elemArr = [(5,5), (8, 7), (50, 25)]
methodArr = ["Sym-N", "Asym-N", "YinXia", "Sym-B", "Asym-B", "O-Sym-N", "O-Asym-N", "O-Sym-B", "O-Asym-B"]
## qq-plots
fig_num = 0
fig = figure(figsize=(7.08661, 4.72441), dpi=1000)
for ip=1:2
for iElem=1:3
global fig_num
res = load("results/sim1_res_$(ip)_$(iElem).jld", "results")
fig_num += 1
qq_my = [res[1, i].p / res[1, i].std for i=1:1000]
qq_oracle = [res[6, i].p / res[6, i].std for i=1:1000]
ax = subplot(2, 3, fig_num)
qqplot(qq_oracle, color="grey")
qqplot(qq_my)
title(
latexstring("p = $(pArr[ip]), \\Delta_{$(elemArr[iElem][1]),$(elemArr[iElem][2])} = 0"),
size="small"
)
ax[:tick_params]("both", labelsize="xx-small", length=2, pad=2)
end
end
tight_layout()
savefig("qq_plot_sim1.pdf")
close(fig)
## hist-plots
fig_num = 0
x = range(-3.290, stop=3.290, length=100)
y = pdf.(Normal(), x)
fig = figure(figsize=(7.08661, 4.72441), dpi=1000)
for ip=1:2
for iElem=1:3
global fig_num
res = load("results/sim1_res_$(ip)_$(iElem).jld", "results")
fig_num += 1
ax = subplot(2, 3, fig_num)
plt[:hist]([res[1, i].p / res[1, i].std for i=1:1000], 100, density=true)
plot(x,y)
xlim(-3.290,3.290)
title(
latexstring("p = $(pArr[ip]), \\Delta_{$(elemArr[iElem][1]),$(elemArr[iElem][2])} = 0"),
size="small"
)
ax[:tick_params]("both", labelsize="xx-small", length=2, pad=2)
end
end
tight_layout()
savefig("hist_plot_sim1.pdf")
close(fig)
## irina plots | [
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] | 1.935522 | 853 |
<reponame>ranocha/PartitionedArrays.jl
using LinearAlgebra
using SparseArrays
using PartitionedArrays
using Test
using IterativeSolvers
function test_fdm(parts)
u(x) = x[1]+x[2]
f(x) = zero(x[1])
lx = 2.0
ls = (lx,lx,lx)
nx = 10
ns = (nx,nx,nx)
n = prod(ns)
h = lx/(nx-1)
points = [(0,0,0),(-1,0,0),(1,0,0),(0,-1,0),(0,1,0),(0,0,-1),(0,0,1)]
coeffs = [-6,1,1,1,1,1,1]/(h^2)
stencil = [ (coeff,CartesianIndex(point)) for (coeff,point) in zip(coeffs,points) ]
#lx = 2.0
#ls = (lx,lx)
#nx = 4
#ns = (nx,nx)
#n = prod(ns)
#h = lx/(nx-1)
#points = [(0,0),(-1,0),(1,0),(0,-1),(0,1)]
#coeffs = [-4,1,1,1,1]/(h^2)
#stencil = [ (coeff,CartesianIndex(point)) for (coeff,point) in zip(coeffs,points) ]
# Use a Cartesian partition if possible
if ndims(parts) == length(ns)
rows = PRange(parts,ns)
else
rows = PRange(parts,n)
end
# We don't need the ghost layer for the rhs
# So, it can be allocated right now.
b = PVector{Float64}(undef,rows)
# We don't need the ghost layer for the exact solution
# So, it can be allocated right now.
x̂ = similar(b)
# Loop over (owned) rows, fill the coo-vectors, rhs, and the exact solution
# In this case, we always touch local rows, but arbitrary cols.
# Thus, row ids can be readily stored in local numbering so that we do not need to convert
# them later.
I,J,V = map_parts(rows.partition,b.values,x̂.values) do rows,b,x̂
cis = CartesianIndices(ns)
lis = LinearIndices(cis)
I = Int[]
J = Int[]
V = Float64[]
for lid in rows.oid_to_lid
i = rows.lid_to_gid[lid]
ci = cis[i]
xi = (Tuple(ci) .- 1) .* h
x̂[lid] = u(xi)
boundary = any(s->(1==s||s==nx),Tuple(ci))
if boundary
push!(I,lid)
push!(J,i)
push!(V,one(eltype(V)))
b[lid] = u(xi)
else
for (v,dcj) in stencil
cj = ci + dcj
j = lis[cj]
push!(I,lid)
push!(J,j)
push!(V,-v)
end
b[lid] = f(xi)
end
end
I,J,V
end
# TODO fill b and x̂ while add_gids is communicating values.
# Build a PRange taking the owned ids in rows plus ghost ids from the touched cols
cols = add_gids(rows,J)
# Now we can convert J to local numbering, I is already in local numbering.
to_lids!(J,cols)
# Build the PSparseMatrix from the coo-vectors (in local numbering)
# and the data distribution described by rows and cols.
A = PSparseMatrix(I,J,V,rows,cols;ids=:local)
# The initial guess needs the ghost layer (that why we take cols)
# in other to perform the product A*x in the cg solver.
# We also need to set the boundary values
x0 = PVector(0.0,cols)
map_parts(x0.values,x0.rows.partition) do x,rows
for lid in rows.oid_to_lid
cis = CartesianIndices(ns)
i = rows.lid_to_gid[lid]
ci = cis[i]
xi = (Tuple(ci) .- 1) .* h
boundary = any(s->(1==s||s==nx),Tuple(ci))
if boundary
x[lid] = u(xi)
end
end
end
# When this call returns, x has the correct answer only in the owned values.
# The values at ghost ids can be recovered with exchange!(x)
x = copy(x0)
IterativeSolvers.cg!(x,A,b,verbose=i_am_main(parts))
# This compares owned values, so we don't need to exchange!
@test norm(x-x̂) < 1.0e-5
end
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] | 2.140746 | 1,556 |
function transit_orb(t,x,dt,nsub)
# Computes a transit lightcurve, normalized to unity, as a
# function of time, t, usually in HJD or BJD.
#
# Input parameters (x) are:
# x[1] = P (units of day)
# x[2] = inc = inclination angle
# x[3] = p = R_p/R_* = radius of planet in units of radius of star
# x[4] = t0 = mid-point of transit
# x[5] = u1 = linear limb-darkening coefficient
# x[6] = u2 = quadratic limb-darkening coefficient
# x[7] = f0 = uneclipsed flux
# x[8] = a/R_* = semi-major axis divided by R_*
# x[9] = e = eccentricity
# x[10] = omega = longitude of pericentre
# Compute time of pericentre passage:
# The true anomaly at the time of transit:
f1=1.5*pi-x[10]*pi/180.0
ecc=x[9]
tp=(x[4]+x[1]*sqrt(1.0-ecc^2)/2.0/pi*(ecc*sin(f1)/(1.0+ecc*cos(f1))
-2.0/sqrt(1.0-ecc^2)*atan2(sqrt(1.0-ecc^2)*tan(0.5*f1),1.0+ecc)))
fluxoft = 0.0
for j=1:nsub
tsub = t-dt/2.0+dt*(j-0.5)/nsub
m=2.0*pi/x[1]*(tsub-tp)
f=kepler(m,ecc)
radius=x[8]*(1.0-ecc^2)/(1.0+ecc*cos(f))
# Now compute sky separation:
z0=radius*sqrt(1.0-(sin(x[2]*pi/180.0)*sin(x[10]*pi/180.0+f))^2)
if (sin(x[10]*pi/180.0+f) < 0.0) && (z0 <= (1.0+x[3]))
if x[3] < 1.0
fluxoft += occultquad(z0,x[5],x[6],x[3])
else
# We'll assume that the smaller object is not limb-darkened:
fluxoft += occultquad(z0, 0., 0.,x[3])
end
else
fluxoft += 1.0
end
end
# Take mean of flux over sub-time steps:
flux = fluxoft*x[7]/nsub
return flux
end
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58,
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14,
5907,
549,
198,
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28462,
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437,
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] | 2.028169 | 710 |
using DashBootstrapComponents, DashHtmlComponents
button_groups = html_div([
dbc_buttongroup(
[dbc_button("Left"), dbc_button("Middle"), dbc_button("Right")],
size = "lg",
className = "me-1",
),
dbc_buttongroup(
[dbc_button("Left"), dbc_button("Middle"), dbc_button("Right")],
size = "md",
className = "me-1",
),
dbc_buttongroup(
[dbc_button("Left"), dbc_button("Middle"), dbc_button("Right")],
size = "sm",
),
]);
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220,
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198,
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] | 2.117647 | 238 |
<filename>src/bond_hamiltonian/concrete_bond_hopping_hamiltonian_dict.jl<gh_stars>0
################################################################################
#
# ABSTRACT TYPE
#
# BondHoppingHamiltonianDict <: AbstractBondHamiltonian{L,1}
# --> L is the label type of bonds
# --> N is the dimension the bond term matrix (NxN matrix)
#
# FILE CONTAINS
# - abstract type definition
# - interface definition
# - generator function
#
################################################################################
################################################################################
#
# ABSTRACT TYPE DEFINITION
#
################################################################################
mutable struct BondHoppingHamiltonianDict{L} <: AbstractBondHamiltonian{L,1}
# coupling
couplings :: Dict{L,Float64}
end
# export the type
export BondHoppingHamiltonianDict
################################################################################
#
# INTERFACING / ACCESSING BOND HAMILTONIANS
# (functions have to be overwritten by concrete types)
#
################################################################################
# get bond term of Hamiltonian
function bondterm(
h :: BondHoppingHamiltonianDict{L},
b :: AbstractBond{L,NB}
) :: Float64 where {N,L,NB,NS}
# return the matching coupling and 0.0 if not found
return get(h.couplings, label(b), 0.0)
end
# export the function
export bondterm
# set a couplint within the dictonary of couplings and add it if not present yet
function setCoupling(
h :: BondHoppingHamiltonianDict{L},
coupling :: L,
coupling_strength :: Real
) where {L}
# add the coupling to the dictonary
h.couplings[coupling] = coupling_strength
end
function setCoupling(
h :: BondHoppingHamiltonianDict{L},
bond :: B,
coupling_strength :: Real
) where {L,NB,B<:AbstractBond{L,NB}}
# add the coupling to the dictonary
h.couplings[label(bond)] = coupling_strength
end
# export the function
export setCoupling
function getHoppingHamiltonianDict(
unitcell :: U
) :: BondHoppingHamiltonianDict{L} where {L,NB,S,B<:AbstractBond{L,NB},U<:AbstractUnitcell{S,B}}
# just create a new hamiltonian
return BondHoppingHamiltonianDict{L}(Dict{L,Float64}())
end
function getHoppingHamiltonianDict(
unitcell :: U,
couplings :: Dict{L,<:Real}
) :: BondHoppingHamiltonianDict{L} where {L,NB,S,B<:AbstractBond{L,NB},U<:AbstractUnitcell{S,B}}
# just create a new hamiltonian
hb = BondHoppingHamiltonianDict{L}(Dict{L,Float64}())
# set all copulings
for c in keys(couplings)
setCoupling(hb, c, couplings[c])
end
# return the bond hamiltonian
return hb
end
export getHoppingHamiltonianDict
function getBondHamiltonianType(
::Val{:BondHoppingHamiltonianDict}
)
# return the type
return BondHoppingHamiltonianDict
end
function saveBondHamiltonian(
hb :: HB,
fn :: AbstractString,
group :: AbstractString = "bond_hamiltonian"
;
append :: Bool = false
) where {L,HB<:BondHoppingHamiltonianDict{L}}
# determine the mode based on if one wants to append stuff
if append
mode = "r+"
else
mode = "w"
end
# open the file in mode
h5open(fn, mode) do file
# create the group in which the bonds are saved
group_hb = g_create(file, group)
# save the type identifier
attrs(group_hb)["type"] = "BondHoppingHamiltonianDict"
# save the parameters
attrs(group_hb)["N"] = 1
attrs(group_hb)["L"] = string(L)
# save the labels
if L <: Number
group_hb["labels"] = [p[1] for p in hb.couplings]
else
group_hb["labels"] = [string(p[1]) for p in hb.couplings]
end
# save the Float64 couplings
group_hb["couplings"] = [p[2] for p in hb.couplings]
end
# return nothing
return nothing
end
function loadBondHamiltonian(
::Type{HB},
fn :: AbstractString,
group :: AbstractString = "bond_hamiltonian"
) where {LI,HB<:Union{BondHoppingHamiltonianDict{LI},BondHoppingHamiltonianDict}}
# read attribute data
attr_data = h5readattr(fn, group)
# determine D based on this
L = Meta.eval(Meta.parse(attr_data["L"]))
N = attr_data["N"]
# load coupling
couplings = h5read(fn, group*"/couplings")
# load label
labels = L.(h5read(fn, group*"/labels"))
# return the new bond hamiltonian
return BondHoppingHamiltonianDict{L}(Dict([(labels[i], couplings[i]) for i in 1:length(labels)]))
end
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] | 2.554791 | 1,889 |
<gh_stars>10-100
benchmarks = [
Dict(
:name => "Modified LV for testing",
:ode => @ODEmodel(
x1'(t) = (a + b) * x1(t) - c * x1(t) * x2(t),
x2'(t) = -a * b * x2(t) + d * x1(t) * x2(t),
y1(t) = x1(t)
)
),
Dict(
:name => "SIWR original",
:ode => @ODEmodel(
S'(t) = mu - bi * S(t) * I(t) - bw * S(t) * W(t) - mu * S(t) + a * R(t),
I'(t) = bw * S(t) * W(t) + bi * S(t) * I(t) - (gam + mu) * I(t),
W'(t) = xi * (I(t) - W(t)),
R'(t) = gam * I(t) - (mu + a) * R(t),
y(t) = k * I(t)
),
:skip => false
),
Dict(
:name => "SIWR with extra output",
:ode => @ODEmodel(
S'(t) = mu - bi * S(t) * I(t) - bw * S(t) * W(t) - mu * S(t) + a * R(t),
I'(t) = bw * S(t) * W(t) + bi * S(t) * I(t) - (gam + mu) * I(t),
W'(t) = xi * (I(t) - W(t)),
R'(t) = gam * I(t) - (mu + a) * R(t),
y(t) = k * I(t),
y2(t) = S(t) + I(t) + R(t)
),
:skip => false
),
Dict(
:name => "Pharm",
:ode => @ODEmodel(
x0'(t) = a1 * (x1(t) - x0(t)) - (ka * n * x0(t)) / (kc * ka + kc * x2(t) + ka * x0(t)),
x1'(t) = a2 * (x0(t) - x1(t)),
x2'(t) = b1 * (x3(t) - x2(t)) - (kc * n * x2(t)) / (kc * ka + kc * x2(t) + ka * x0(t)),
x3'(t) = b2 * (x2(t) - x3(t)),
y1(t) = x0(t)
),
:skip => false
),
Dict(
:name => "SEAIJRC Covid model",
:ode => @ODEmodel(
S'(t) = -b * S(t) * (I(t) + J(t) + q * A(t)) * Ninv(t),
E'(t) = b * S(t) * (I(t) + J(t) + q * A(t)) * Ninv(t) - k * E(t),
A'(t) = k * (1 - r) * E(t) - g1 * A(t),
I'(t) = k * r * E(t) - (alpha + g1) * I(t),
J'(t) = alpha * I(t) - g2 * J(t),
C'(t) = alpha * I(t),
Ninv'(t) = 0,
y(t) = C(t),
y2(t) = Ninv(t)
),
:skip => false
),
Dict(
:name => "MAPK model (5 outputs)",
:ode => @ODEmodel(
KS00'(t) = -a00 * K(t) * S00(t) + b00 * KS00(t) + gamma0100 * FS01(t) + gamma1000 * FS10(t) + gamma1100 * FS11(t),
KS01'(t) = -a01 * K(t) * S01(t) + b01 * KS01(t) + c0001 * KS00(t) - alpha01 * F(t) * S01(t) + beta01 * FS01(t) + gamma1101 * FS11(t),
KS10'(t) = -a10 * K(t) * S10(t) + b10 * KS10(t) + c0010 * KS00(t) - alpha10 * F(t) * S10(t) + beta10 * FS10(t) + gamma1110 * FS11(t),
FS01'(t) = -alpha11 * F(t) * S11(t) + beta11 * FS11(t) + c0111 * KS01(t) + c1011 * KS10(t) + c0011 * KS00(t),
FS10'(t) = a00 * K(t) * S00(t) - (b00 + c0001 + c0010 + c0011) * KS00(t),
FS11'(t) = a01 * K(t) * S01(t) - (b01 + c0111) * KS01(t),
K'(t) = a10 * K(t) * S10(t) - (b10 + c1011) * KS10(t),
F'(t) = alpha01 * F(t) * S01(t) - (beta01 + gamma0100) * FS01(t),
S00'(t) = alpha10 * F(t) * S10(t) - (beta10 + gamma1000) * FS10(t),
S01'(t) = alpha11 * F(t) * S11(t) - (beta11 + gamma1101 + gamma1110 + gamma1100) * FS11(t),
S10'(t) = -a00 * K(t) * S00(t) + (b00 + c0001 + c0010 + c0011) * KS00(t) - a01 * K(t) * S01(t) + (b01 + c0111) * KS01(t) - a10 * K(t) * S10(t) + (b10 + c1011) * KS10(t),
S11'(t) = -alpha01 * F(t) * S01(t) + (beta01 + gamma0100) * FS01(t) - alpha10 * F(t) * S10(t) + (beta10 + gamma1000) * FS10(t) - alpha11 * F(t) * S11(t) + (beta11 + gamma1101 + gamma1110 + gamma1100) * FS11(t),
y1(t) = F(t),
y2(t) = S00(t),
y3(t) = S01(t),
y4(t) = S10(t),
y5(t) = S11(t)
)
),
Dict(
:name => "MAPK model (5 outputs bis)",
:ode => @ODEmodel(
KS00'(t) = -a00 * K(t) * S00(t) + b00 * KS00(t) + gamma0100 * FS01(t) + gamma1000 * FS10(t) + gamma1100 * FS11(t),
KS01'(t) = -a01 * K(t) * S01(t) + b01 * KS01(t) + c0001 * KS00(t) - alpha01 * F(t) * S01(t) + beta01 * FS01(t) + gamma1101 * FS11(t),
KS10'(t) = -a10 * K(t) * S10(t) + b10 * KS10(t) + c0010 * KS00(t) - alpha10 * F(t) * S10(t) + beta10 * FS10(t) + gamma1110 * FS11(t),
FS01'(t) = -alpha11 * F(t) * S11(t) + beta11 * FS11(t) + c0111 * KS01(t) + c1011 * KS10(t) + c0011 * KS00(t),
FS10'(t) = a00 * K(t) * S00(t) - (b00 + c0001 + c0010 + c0011) * KS00(t),
FS11'(t) = a01 * K(t) * S01(t) - (b01 + c0111) * KS01(t),
K'(t) = a10 * K(t) * S10(t) - (b10 + c1011) * KS10(t),
F'(t) = alpha01 * F(t) * S01(t) - (beta01 + gamma0100) * FS01(t),
S00'(t) = alpha10 * F(t) * S10(t) - (beta10 + gamma1000) * FS10(t),
S01'(t) = alpha11 * F(t) * S11(t) - (beta11 + gamma1101 + gamma1110 + gamma1100) * FS11(t),
S10'(t) = -a00 * K(t) * S00(t) + (b00 + c0001 + c0010 + c0011) * KS00(t) - a01 * K(t) * S01(t) + (b01 + c0111) * KS01(t) - a10 * K(t) * S10(t) + (b10 + c1011) * KS10(t),
S11'(t) = -alpha01 * F(t) * S01(t) + (beta01 + gamma0100) * FS01(t) - alpha10 * F(t) * S10(t) + (beta10 + gamma1000) * FS10(t) - alpha11 * F(t) * S11(t) + (beta11 + gamma1101 + gamma1110 + gamma1100) * FS11(t),
y0(t) = K(t),
y1(t) = F(t),
y2(t) = S00(t),
y3(t) = S01(t) + S10(t),
y4(t) = S11(t)
),
:skip => false
),
Dict(
:name => "MAPK model (6 outputs)",
:ode => @ODEmodel(
KS00'(t) = -a00 * K(t) * S00(t) + b00 * KS00(t) + gamma0100 * FS01(t) + gamma1000 * FS10(t) + gamma1100 * FS11(t),
KS01'(t) = -a01 * K(t) * S01(t) + b01 * KS01(t) + c0001 * KS00(t) - alpha01 * F(t) * S01(t) + beta01 * FS01(t) + gamma1101 * FS11(t),
KS10'(t) = -a10 * K(t) * S10(t) + b10 * KS10(t) + c0010 * KS00(t) - alpha10 * F(t) * S10(t) + beta10 * FS10(t) + gamma1110 * FS11(t),
FS01'(t) = -alpha11 * F(t) * S11(t) + beta11 * FS11(t) + c0111 * KS01(t) + c1011 * KS10(t) + c0011 * KS00(t),
FS10'(t) = a00 * K(t) * S00(t) - (b00 + c0001 + c0010 + c0011) * KS00(t),
FS11'(t) = a01 * K(t) * S01(t) - (b01 + c0111) * KS01(t),
K'(t) = a10 * K(t) * S10(t) - (b10 + c1011) * KS10(t),
F'(t) = alpha01 * F(t) * S01(t) - (beta01 + gamma0100) * FS01(t),
S00'(t) = alpha10 * F(t) * S10(t) - (beta10 + gamma1000) * FS10(t),
S01'(t) = alpha11 * F(t) * S11(t) - (beta11 + gamma1101 + gamma1110 + gamma1100) * FS11(t),
S10'(t) = -a00 * K(t) * S00(t) + (b00 + c0001 + c0010 + c0011) * KS00(t) - a01 * K(t) * S01(t) + (b01 + c0111) * KS01(t) - a10 * K(t) * S10(t) + (b10 + c1011) * KS10(t),
S11'(t) = -alpha01 * F(t) * S01(t) + (beta01 + gamma0100) * FS01(t) - alpha10 * F(t) * S10(t) + (beta10 + gamma1000) * FS10(t) - alpha11 * F(t) * S11(t) + (beta11 + gamma1101 + gamma1110 + gamma1100) * FS11(t),
y0(t) = K(t),
y1(t) = F(t),
y2(t) = S00(t),
y3(t) = S01(t),
y4(t) = S10(t),
y5(t) = S11(t)
)
),
Dict(
:name => "<NAME>",
:ode => @ODEmodel(
x1'(t) = -b * x1(t) + 1 / (c + x4(t)),
x2'(t) = alpha * x1(t) - beta * x2(t),
x3'(t) = gama * x2(t) - delta * x3(t),
x4'(t) = sigma * x4(t) * (gama * x2(t) - delta * x3(t)) / x3(t),
y(t) = x1(t)
)
),
Dict(
:name => "HIV",
:ode => @ODEmodel(
x'(t) = lm - d * x(t) - beta * x(t) * v(t),
y'(t) = beta * x(t) * v(t) - a * y(t),
v'(t) = k * y(t) - u * v(t),
w'(t) = c * x(t) * y(t) * w(t) - c * q * y(t) * w(t) - b * w(t),
z'(t) = c * q * y(t) * w(t) - h * z(t),
y1(t) = w(t),
y2(t) = z(t)
)
),
Dict(
:name => "<NAME>",
:ode => @ODEmodel(
s'(t) = mu - mu * s(t) - b0 * (1 + b1 * x1(t)) * i(t) * s(t) + g * r(t),
i'(t) = b0 * (1 + b1 * x1(t)) * i(t) * s(t) - (nu + mu) * i(t),
r'(t) = nu * i(t) - (mu + g) * r(t),
x1'(t) = -M * x2(t),
x2'(t) = M * x1(t),
y1(t) = i(t),
y2(t) = r(t)
)
),
Dict(
:name => "<NAME>",
:ode => @ODEmodel(
EGFR'(t) = EGFR_turnover * pro_EGFR(t) + EGF_EGFR(t) * reaction_1_k2 - EGFR(t) * EGFR_turnover - EGF_EGFR(t) * reaction_1_k1,
pEGFR'(t) = EGF_EGFR(t) * reaction_9_k1 - pEGFR(t) * reaction_4_k1 + pEGFR_Akt(t) * reaction_2_k2 + pEGFR_Akt(t) * reaction_3_k1 - Akt(t) * pEGFR(t) * reaction_2_k1,
pEGFR_Akt'(t) = Akt(t) * pEGFR(t) * reaction_2_k1 - pEGFR_Akt(t) * reaction_3_k1 - pEGFR_Akt(t) * reaction_2_k2,
Akt'(t) = pAkt(t) * reaction_7_k1 + pEGFR_Akt(t) * reaction_2_k2 - Akt(t) * pEGFR(t) * reaction_2_k1,
pAkt'(t) = pAkt_S6(t) * reaction_5_k2 - pAkt(t) * reaction_7_k1 + pAkt_S6(t) * reaction_6_k1 + pEGFR_Akt(t) * reaction_3_k1 - S6(t) * pAkt(t) * reaction_5_k1,
S6'(t) = pAkt_S6(t) * reaction_5_k2 + pS6(t) * reaction_8_k1 - S6(t) * pAkt(t) * reaction_5_k1,
pAkt_S6'(t) = S6(t) * pAkt(t) * reaction_5_k1 - pAkt_S6(t) * reaction_6_k1 - pAkt_S6(t) * reaction_5_k2,
pS6'(t) = pAkt_S6(t) * reaction_6_k1 - pS6(t) * reaction_8_k1,
EGF_EGFR'(t) = EGF_EGFR(t) * reaction_1_k1 - EGF_EGFR(t) * reaction_9_k1 - EGF_EGFR(t) * reaction_1_k2,
y1(t) = a1 * (pEGFR(t) + pEGFR_Akt(t)),
y2(t) = a2 * (pAkt(t) + pAkt_S6(t)),
y3(t) = a3 * pS6(t)
)
),
Dict(
:name => "CD8 T cell differentiation",
:ode => @ODEmodel(
N'(t) = - N(t) * mu_N - N(t) * P(t) * delta_NE,
E'(t) = N(t) * P(t) * delta_NE - E(t)^2 * mu_EE - E(t) * delta_EL + E(t) * P(t) * rho_E,
S'(t) = S(t) * delta_EL - S(t) * delta_LM - S(t)^2 * mu_LL - E(t) * S(t) * mu_LE,
M'(t) = S(t) * delta_LM - mu_M * M(t),
P'(t) = P(t)^2 * rho_P - P(t) * mu_P - E(t) * P(t) * mu_PE - S(t) * P(t) * mu_PL,
y1(t) = N(t),
y2(t) = E(t) + S(t),
y3(t) = M(t)
)
),
Dict(
:name => "Chemical reaction network",
:ode => @ODEmodel(
x1'(t) = -k1 * x1(t) * x2(t) + k2 * x4(t) + k4 * x6(t),
x2'(t) = -k1 * x1(t) * x2(t) + k2 * x4(t) + k3 * x4(t),
x3'(t) = k3 * x4(t) + k5 * x6(t) - k6 * x3(t) * x5(t),
x4'(t) = k1 * x1(t) * x2(t) - k2 * x4(t) - k3 * x4(t),
x5'(t) = k4 * x6(t) + k5 * x6(t) - k6 * x3(t) * x5(t),
x6'(t) = -k4 * x6(t) - k5 * x6(t) + k6 * x3(t) * x5(t),
y1(t) = x3(t),
y2(t) = x2(t)
)
)
]
# the NFkB example
ode = @ODEmodel(
x1'(t) = k_prod - k_deg * x1(t) - k1 * x1(t) * u(t),
x2'(t) = -k3 * x2(t) - k_deg * x2(t) - a2 * x2(t) * x10(t) + t1 * x4(t) - a3 * x2(t) * x13(t) + t2 * x5(t) + (k1 * x1(t) - k2 * x2(t) * x8(t)) * u(t),
x3'(t) = k3 * x2(t) - k_deg * x3(t) + k2 * x2(t) * x8(t) * u(t),
x4'(t) = a2 * x2(t) * x10(t) - t1 * x4(t),
x5'(t) = a3 * x2(t) * x13(t) - t2 * x5(t),
x6'(t) = c6a * x13(t) - a1 * x6(t) * x10(t) + t2 * x5(t) - i1 * x6(t),
x7'(t) = i1 * kv * x6(t) - a1 * x11(t) * x7(t),
x8'(t) = c4 * x9(t) - c5 * x8(t),
x9'(t) = c2 - c1 * x7(t) - c3 * x9(t),
x10'(t) = -a2 * x2(t) * x10(t) - a1 * x10(t) * x6(t) + c4a * x12(t) - c5a * x10(t) - i1a * x10(t) + e1a * x11(t),
x11'(t) = -a1 * x11(t) * x7(t) + i1a * kv * x10(t) - e1a * kv * x11(t),
x12'(t) = c2a + c1a * x7(t) - c3a * x12(t),
x13'(t) = a1 * x10(t) * x6(t) - c6a * x13(t) - a3 * x2(t) * x13(t) + e2a * x14(t),
x14'(t) = a1 * x11(t) * x7(t) - e2a * kv * x14(t),
x15'(t) = c2c + c1c * x7(t) - c3c * x15(t),
y1(t) = x7(t),
y2(t) = x10(t) + x13(t),
y3(t) = x9(t),
y4(t) = x1(t) + x2(t) + x3(t),
y5(t) = x2(t),
y6(t) = x12(t)
)
QQ = StructuralIdentifiability.Nemo.QQ
ode = set_parameter_values(ode, Dict(
a1 => QQ(1, 2),
a2 => QQ(1, 5),
a3 => QQ(1),
c1a => QQ(5, 10^(7)),
c2a => QQ(0),
c5a => QQ(1, 10^(4)),
c6a => QQ(2, 10^(5)),
c1 => QQ(5, 10^(7)),
c2 => QQ(0),
c3 => QQ(4, 10^(4)),
c4 => QQ(1, 2),
kv => QQ(5),
e1a => QQ(5, 10^(4)),
c1c => QQ(5, 10^(7)),
c2c => QQ(0),
c3c => QQ(4, 10^(4))
))
push!(
benchmarks,
Dict(
:name => "NFkB",
:ode => ode,
:skip => true
)
)
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] | 1.543652 | 8,121 |
<reponame>d9w/Spark.jl
using JSON
using Spark
function start(worker::Worker)
server = listen(IPv4(0), worker.port)
println("Starting worker")
while worker.active
sock = accept(server)
@async while worker.active
try
line = readline(sock)
result = handle(worker, line)
println(sock, json({"result" => result}))
catch e
showerror(STDERR, e)
break
end
end
end
end
function handle(worker::Worker, line::ASCIIString)
# General format of a message:
# {:call => "funcname", :args => {anything}}
# this is dispatched to any function call - fine since we're assuming
# a private non-adversarial network.
msg = JSON.parse(strip(line))
r = false
if "call" in keys(msg)
r = eval(Expr(:call, symbol(msg["call"]), worker, msg["args"]))
end
return r
end
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] | 2.211765 | 425 |
<filename>src/figures.jl
"""
Figure(workstation::Tuple{Float64, Float64}, plots::Vector{PlotObject})
Return a new figure, defined by:
* **`workstation`**: a `Tuple{Float64, Float64}` with the width and height of the
overall plot container (workstation), in pixels.
* **`plots`**: a vector of [`PlotObject`](@ref) elements, which contain the information of
the individual plots included in the figure.
"""
struct Figure
workstation::Tuple{Float64, Float64}
plots::Vector{PlotObject}
end
const EMPTYFIGURE = Figure((0.0, 0.0), [PlotObject()])
const CURRENTFIGURE = Ref(EMPTYFIGURE)
"""
gcf()
gcf(fig::Figure)
Get the global current figure, or set it to be `fig`.
"""
gcf() = (CURRENTFIGURE[] == EMPTYFIGURE) ? Figure() : CURRENTFIGURE[]
gcf(fig::Figure) = (CURRENTFIGURE[] = fig)
"""
Figure([figsize::Tuple{Float64, Float64}, units::String])
Create a new figure of a given size.
The figure size is defined by `figsize` (a 2-tuple with the target width and height),
and `units` (a string with the abbreviation of the units: "px" for pixels,
"in" for inches, "cm" for centimeters or "m" for meters).
The default dimensions used by `Figure()` with no arguments is 600×450 pixels
— or a proportional increased size, if the detected display resolution is high.
This constructor also sets the "current figure" to the one that has just been
created..
"""
function Figure(figsize=(600,450), units::String="px")
# Calculate the display resolution (dpi)
mwidth, mheight, width, height = GR.inqdspsize()
dpi = width / mwidth * 0.0254
# Transform figsize to pixels
sizepx = Tuple(float(s) for s in figsize)
if units == "px" # keep it as is
elseif units == "in"
sizepx = figsize .* dpi
elseif units == "cm"
sizepx = figsize .* (dpi / 2.54)
elseif units == "m"
sizepx = figsize .* (dpi / 0.0254)
end
# increase dimensions if dpi is too big (the figure would be tiny)
w, h = (dpi > 200) ? Tuple(s * dpi / 100 for s in sizepx) : sizepx
# Workstation size
wssize = mwidth / width * w
ratio = (w > h) ? (1.0, float(h)/w) : (float(w)/h, 1.0)
workstation = (wssize * ratio[1], wssize * ratio[2])
plots = [PlotObject()]
CURRENTFIGURE[] = Figure(workstation, plots)
end
function Base.Multimedia.display(fig::Figure)
output = draw(fig)
output isa Nothing && return nothing
display(output)
return nothing
end
# Caution! This depends on GR internals
# Base.showable(::MIME"image/svg+xml", ::Figure) = GR.mime_type[] == "svg"
# Base.showable(::MIME"image/png", ::Figure) = GR.mime_type[] == "png"
Base.showable(::MIME"text/html", ::Figure) = GR.mime_type[] ∈ ("mov", "mp4", "webm")
# Base.show(io::IO, mime::M, fig::Figure) where {
# M <: Union{MIME"image/svg+xml", MIME"image/png", MIME"text/html"}
# } = show(io, mime, draw(fig))
Base.show(io::IO, mime::MIME"text/html", fig::Figure) = show(io, mime, draw(fig))
for (mime, fmt) in (
"image/png" => "png",
"image/svg+xml" => "svg"
)
@eval function Base.show(io::IO, mime::MIME{Symbol($mime)}, fig::Figure)
gr_mime = GR.isinline() ? string(GR.mime_type[]) : ""
GR.inline($fmt)
try
show(io, mime, draw(fig))
catch err
throw(err)
finally
resetmime(gr_mime)
end
end
end
"""
currentplot([fig::Figure])
Get the "current plot", i.e. the target of the next plotting operations in the
(optionally) given figure `fig`.
If no figure is given, the "current figure" is used (cf. [`gcf`](@ref)).
"""
currentplot(f::Figure=gcf()) = f.plots[end]
# Normalized width and height of a figure'w workstation
wswindow(f::Figure) = f.workstation ./ maximum(f.workstation)
## Subplots
function subplot!(f::Figure, nr, nc, p, replace=true)
xmin, xmax, ymin, ymax = 1.0, 0.0, 1.0, 0.0
for i in collect(p)
r = nr - div(i-1, nc)
c = (i-1) % nc + 1
xmin = min(xmin, (c-1)/nc)
xmax = max(xmax, c/nc)
ymin = min(ymin, (r-1)/nr)
ymax = max(ymax, r/nr)
end
coords = [xmin, xmax, ymin, ymax]
po = PlotObject(; subplot = coords)
if replace
po = replaceplot!(f.plots, po)
end
push!(f.plots, po)
return po
end
"""
subplot(num_rows, num_columns, indices[, replace])
Set a subplot in the current figure.
By default, the current plot covers the whole window. To display more
than one plot, the window can be split into a number of rows and columns,
with each plot covering one or more cells in the resulting grid.
Subplot indices are one-based and start at the upper left corner, with a
new row starting after every `num_cols` subplots.
The arguments `num_rows` and `num_cols` indicate the number of rows and columns
of the grid of plots into which the figure is meant to be divided, and `indices`
is an integer or a collection of integers that identify a group of cells in that
grid. This function returns a plot with the minimum size that spans over all
those cells, and appends it to the array of plots of the figure,
such that it becomes its current plot.
If the viewport of the new subplot coincides with the viewport of an existing
plot, by default the older one is moved to the first plane, and taken as the
"current plot"; but if there is only a partial overlap between the new subplot
and other plots, the overlapping plots are removed.
To override this behavior and keep all previous plots without changes,
set the optional argument `replace` to false.
# Examples
```julia
$(_example("subplot"))
```
"""
subplot(args...) = subplot!(gcf(), args...)
function replaceplot!(plotcollection, p)
# If subplot is not specified in p, empty the whole collection
if !haskey(p.attributes, :subplot)
empty!(plotcollection)
else
coords = p.attributes[:subplot]
i = 1
while i ≤ length(plotcollection)
# Check intersection
coords_i = get(plotcollection[i].attributes, :subplot, [0.0, 1.0, 0.0, 1.0])
bottomleft = (max(coords[1], coords_i[1]), max(coords[3], coords_i[3]))
topright = (min(coords[2], coords_i[2]), min(coords[4], coords_i[4]))
if all(bottomleft .< topright)
(coords ≈ coords_i) && (p = plotcollection[i])
deleteat!(plotcollection, i)
else
i += 1
end
end
end
return p
end
function draw(f::Figure)
GR.clearws()
w, h = f.workstation
GR.setwsviewport(0.0, w, 0.0, h)
ratio_w, ratio_h = wswindow(f)
GR.setwswindow(0.0, ratio_w, 0.0, ratio_h)
drawn = false
for p in f.plots
drawn = draw(p) || drawn
end
GR.updatews()
if GR.isinline() && drawn
return GR.show()
else
return
end
end
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] | 2.497252 | 2,729 |
<reponame>anupam-mitra/ITensors.jl
using ITensors
using ProfileView
# Examples of optimizing simple chained matrix multiplications,
# useful for getting an idea for the overhead.
function main(d=100)
i = Index(d, "i")
A = randomITensor(i', dag(i))
#
# 2 tensors
#
println("\n2 tensors")
# Simple pair contraction
@btime $A' * $A
#
# 3 tensors
#
println("\n3 tensors")
println("Pick an optimal sequence explicitly")
ITensors.disable_contraction_sequence_optimization()
@show ITensors.using_contraction_sequence_optimization()
@btime $A'' * $A' * $A
# Alternative syntax
#@btime $A'' * ($A' * $A)
#@btime contract([$A'', $A', $A]; sequence = "right_associative")
#@btime contract([$A'', $A', $A]; sequence = "left_associative")
#@btime contract([$A'', $A', $A]; sequence = $([[1, 2], 3]))
#@btime contract([$A'', $A', $A]; sequence = $([[2, 3], 1]))
println("Pick a bad sequence explicitly")
@btime $A'' * $A * $A'
# Alternative syntax
#@btime contract([$A'', $A', $A]; sequence = $([[1, 3], 2]))
println("Let it optimize")
ITensors.enable_contraction_sequence_optimization()
@show ITensors.using_contraction_sequence_optimization()
# Already starting from an optimal sequence, there
# is still overhead to checking if there isn't a better sequence
println("Starting from optimal sequence")
@btime $A'' * $A' * $A
# Find a better sequence
println("Starting from non-optimal sequence")
@btime $A'' * $A * $A'
f(A, N) =
for _ in 1:N
A'' * A' * A
end
@profview f(A, 1e6)
ITensors.disable_contraction_sequence_optimization()
#
# 4 tensors
#
println("\n4 tensors")
println("Pick an optimal sequence explicitly")
ITensors.disable_contraction_sequence_optimization()
@show ITensors.using_contraction_sequence_optimization()
@btime $A''' * $A'' * $A' * $A
# Alternative syntax
#@btime $A''' * ($A'' * ($A' * $A))
#@btime contract([$A''', $A'', $A', $A]; sequence = $([[[1, 2], 3], 4]))
println("Pick a bad sequence explicitly")
@btime $A'' * $A * $A''' * $A'
println("Let it optimize")
ITensors.enable_contraction_sequence_optimization()
@show ITensors.using_contraction_sequence_optimization()
println("Starting from optimal sequence")
@btime $A''' * $A'' * $A' * $A
println("Starting from non-optimal sequence")
@btime $A'' * $A * $A''' * $A'
#
# 5 tensors
#
println("\n5 tensors")
println("Pick an optimal sequence explicitly")
ITensors.disable_contraction_sequence_optimization()
@show ITensors.using_contraction_sequence_optimization()
@btime $A'''' * $A''' * $A'' * $A' * $A
println("Pick a bad sequence explicitly")
@btime $A'' * $A * $A''' * $A' * $A''''
println("Let it optimize")
ITensors.enable_contraction_sequence_optimization()
@show ITensors.using_contraction_sequence_optimization()
println("Starting from optimal sequence")
@btime $A'''' * $A''' * $A'' * $A' * $A
println("Starting from non-optimal sequence")
@btime $A'' * $A'''' * $A * $A''' * $A'
return ITensors.disable_contraction_sequence_optimization()
end
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] | 2.773736 | 1,127 |
# """
# Function applies the transformations for loading a parameter to
# fp = (quote end).args
# sp = (quote end).args
# M == param_length == 0 => scalar
# """
function load_transformations!(
fp, sp, b::Bounds{T}, out, shape::Vector{Int},
partial::Bool, logjac::Bool, sptr::Union{Symbol,Nothing},
m::Module = DistributionParameters,
θ = Symbol("##θparameter##"), ∂θ = Symbol("##∂θparameter##"),
exportparam::Bool = false
) where {T}
maybe_align = x -> exportparam ? x : VectorizationBase.align(x)
N = length(shape)
scalar = iszero(N)
M = prod(shape)
if !scalar
X = similar(shape); X[1] = 1
for n in 2:N
X[n] = X[n-1] * shape[n-1]
end
end
use_sptr = sptr isa Symbol
outinit = if scalar
quote end
elseif use_sptr
if N == 1
quote
$out = $m.PtrVector{$(first(shape)),$T}(pointer($sptr, $T))
$sptr += $(maybe_align(sizeof(T)*M))
end
else
quote
$out = $m.PtrArray{$(Tuple{shape...}),$T,$N,$(Tuple{X...}),$M,true}(pointer($sptr, $T))
$sptr += $(maybe_align(sizeof(T)*M))
end
end
else
quote # Do we want to pad these?
$out = $m.FixedSizeArray{$(Tuple{shape...}),$T,$N,$(Tuple{X...}),$M}(undef)
end
end
outlifestart = if !scalar && !exportparam && use_sptr
push!(outinit.args, :($m.lifetime_start!($out)))
true
else
false
end
adjout = ReverseDiffExpressionsBase.adj(out)
if isunbounded(b)
if scalar
push!(fp, :($out = $m.VectorizationBase.load($θ)))
elseif exportparam
isym = gensym(:i)
loop_quote = quote
LoopVectorization.@vvectorize_unsafe $T $m for $isym ∈ 1:$M
$out[$isym] = $θ[$isym]
end
end
copy_q = quote
$outinit
$(macroexpand(m, loop_quote))
end
push!(fp, copy_q)
else
push!(fp, :($out = $m.PtrArray{$(Tuple{shape...}),$T,$N,$(Tuple{X...}),$M,true}(pointer($θ))))
end
elseif islowerbounded(b)
logout = gensym(Symbol(:log_, out))
if scalar
outdef = ((b.lb == zero(T)) && !exportparam ) ? :(RealFloat{$(Bounds(zero(T),typemax(T))),$T,$T}(Base.exp($logout),$logout)) : :(exp($logout) + $(T(b.lb)))
load_expr = quote
$logout = $m.VectorizationBase.load($θ)
$out = $outdef
end
logjac && push!(load_expr.args, :(target = $m.vadd(target, $logout)))
else
outdef = (b.lb == zero(T)) ? :(exp($logout)) : :(exp($logout) + $(T(b.lb)))
isym = gensym(:i)
loopbody = quote
$logout = $θ[$isym]
$out[$isym] = $outdef
end
logjac && push!(loopbody.args, :(target = $m.vadd(target, $logout)))
loop_quote = quote
LoopVectorization.@vvectorize_unsafe $T $m for $isym ∈ 1:$M
$loopbody
end
end
if b.lb == zero(T) && !exportparam && sptr !== nothing
outinit = quote
$out = $m.RealArray{$(Tuple{shape...}),$(Bounds(zero(T),typemax(T))),$T,$N,$(Tuple{X...}),$M,Ptr{$T}}(pointer($sptr, $T),pointer($θ))
$sptr += $(maybe_align(sizeof(T)*M))
end
end
load_expr = quote
$outinit
$(macroexpand(m, loop_quote))
end
end
push!(fp, load_expr)
# (exportparam || scalar) || push!(fp, load_expr)
if partial
if scalar
# push!(sp, :($m.VectorizationBase.store!($∂θ, muladd($adjout, $out, one($T)))))
push!(sp, :($m.VectorizationBase.store!($adjout, muladd($m.VectorizationBase.load($adjout), $out, one($T)))))
else
isym = gensym(:i)
storeloop = quote
LoopVectorization.@vvectorize_unsafe $T $m for $isym ∈ 1:$M
$adjout[$isym] = $m.SIMDPirates.vmuladd($adjout[$isym], $out[$isym], one($T))
end
end
push!(sp, macroexpand(m, storeloop))
end
end
outlifestart && push!(sp, :($m.lifetime_end!($out)))
elseif isupperbounded(b)
logout = gensym(Symbol(:log_, out))
outdef = ((b.ub == zero(T)) && !exportparam) ? :(- exp($logout)) : :($(T(b.ub)) - exp($logout))
if scalar
load_expr = quote
$logout = $m.VectorizationBase.load($θ)
$out = $outdef
end
logjac && push!(load_expr.args, :(target = $m.vadd(target, $logout)))
else
isym = gensym(:i)
loopbody = quote
$logout = $θ[$isym]
$out[$isym] = $outdef
end
logjac && push!(loopbody.args, :(target = $m.vadd(target, $logout)))
loop_quote = quote
LoopVectorization.@vvectorize_unsafe $T $m for $isym ∈ 1:$M
$loopbody
end
end
load_expr = quote
$outinit
$(macroexpand(m, loop_quote))
end
end
push!(fp, load_expr)
if partial
if scalar
push!(sp, :($m.VectorizationBase.store!($adjout, $m.SIMDPirates.vfnmadd($m.VectorizationBase.load($adjout), $out, one($T)))))
else
isym = gensym(:i)
storeloop = quote
LoopVectorization.@vvectorize_unsafe $T $m for $isym ∈ 1:$M
$adjout[$isym] = $m.SIMDPirates.vfnmadd($adjout[$isym], $out[$isym], one($T))
end
end
push!(sp, macroexpand(m, storeloop))
end
end
outlifestart && push!(sp, :($m.lifetime_end!($out)))
elseif isbounded(b)
scale = b.ub - b.lb
invlogit = gensym(:invlogit)
ninvlogit = gensym(:ninvlogit)
∂invlogit = gensym(:∂invlogit)
if scalar
if b.lb == 0 && b.ub == 1
unconstrained = gensym(:unconstrained)
q = quote
$unconstrained = $m.VectorizationBase.load($θ)
$ninvlogit = one($T) / (one($T) + exp($unconstrained))
$invlogit = one($T) - $ninvlogit
$out = RealFloat{$(Bounds(zero(T),one(T))),$T,$T}($invlogit, $unconstrained)
$∂invlogit = $invlogit * $ninvlogit
end
else
q = quote
$ninvlogit = one($T) / (one($T) + exp($m.VectorizationBase.load($θ)))
$invlogit = one($T) - $ninvlogit
$out = $(b.lb == zero(T) ?
(scale == one(T) ? invlogit : :($scale * $invlogit)) :
(scale == one(T) ? :($(b.lb) + $invlogit) : :(muladd($scale, $invlogit, $(b.lb)))))
$∂invlogit = $invlogit * $ninvlogit
end
end
logjac && push!(q.args, :( target = $m.vadd(target, $∂invlogit)))
push!(fp, q)
if partial
∂q = quote
VectorizationBase.store!(
$adjout, @fastmath one($T) - $(T(2)) * $invlogit +
$m.VectorizationBase.load($adjout) * $(scale == one(T) ? ∂invlogit : :($scale * $∂invlogit))
)
end
push!(sp, macroexpand(m, ∂q)) #Expand the fastmath? Why not Base.FastMath.add_fast / mul_fast directly?
end
else
isym = gensym(:i)
if b.lb == 0 && b.ub == 1
outinit = quote
$out = $m.RealArray{$(Tuple{shape...}),$(Bounds(zero(T),one(T))),$T,$N,$(Tuple{X...}),$M,Ptr{$T}}(pointer($sptr, $T), pointer($θ))
$sptr += $(maybe_align(sizeof(T)*M))
end
outlifestart && push!(outinit.args, :($m.lifetime_start!($out)))
end
if partial
invlogitinits = if use_sptr
quote
$outinit
$invlogit = $m.PtrVector{$M,$T}(pointer($sptr,$T))
$sptr += $(maybe_align(M*sizeof(T)))
$∂invlogit = $m.PtrVector{$M,$T}(pointer($sptr,$T))
$sptr += $(maybe_align(M*sizeof(T)))
end
else
quote
$outinit
$invlogit = FixedSizeVector{$M,$T}(undef)
$∂invlogit = FixedSizeVector{$M,$T}(undef)
end
end
if outlifestart
push!(invlogitinits.args, :($m.lifetime_start!($invlogit)))
push!(invlogitinits.args, :($m.lifetime_start!($∂invlogit)))
end
push!(fp, invlogitinits)
ilt = gensym(:ilt)
∂ilt = gensym(:∂ilt)
loop_body = quote
$ninvlogit = one($T) / (one($T) + $m.SLEEFPirates.exp($θ[$isym]))
$ilt = one($T) - $ninvlogit
$invlogit[$isym] = $ilt
$∂ilt = $ilt * $ninvlogit
$∂invlogit[$isym] = $∂ilt
$out[$isym] = $(b.lb == zero(T) ?
(scale == one(T) ? ilt : :($scale * $ilt)) :
(scale == one(T) ? :($(b.lb) + $ilt) : :($m.SIMDPirates.vmuladd($scale, $ilt, $(b.lb)))))
end
logjac && push!(loop_body.args, :(target = $m.vadd(target, $m.SLEEFPirates.log($∂ilt))))
loop_quote = quote
LoopVectorization.@vvectorize_unsafe $T $((m)) for $isym ∈ 1:$M
$loop_body
end
end
push!(fp, macroexpand(m, loop_quote))
storeloop = quote
LoopVectorization.@vvectorize_unsafe $T $((m)) for $isym ∈ 1:$M
$adjout[$isym] = one($T) - $(T(2)) * $invlogit[$isym] +
($adjout)[$isym] * $(scale == one(T) ? :($∂invlogit[$isym]) : :($∂invlogit[$isym] * $scale))
end
end
push!(sp, macroexpand(m, storeloop))
if outlifestart
push!(sp, :($m.lifetime_end!($invlogit)))
push!(sp, :($m.lifetime_end!($∂invlogit)))
end
else
loop_body = quote
$ninvlogit = one($T) / (one($T) + $m.SLEEFPirates.exp($θ[$isym]))
$invlogit = one($T) - $ninvlogit
$out[$isym] = $(b.lb == zero(T) ?
(scale == one(T) ? invlogit : :($scale * $invlogit)) :
(scale == one(T) ? :($(b.lb) + $invlogit) : :($m.SIMDPirates.vmuladd($scale, $invlogit, $(b.lb)))))
end
logjac && push!(loop_body.args, :(target = $m.vadd(target, $m.SLEEFPirates.log($invlogit * $ninvlogit))))
loop_quote = quote
$outinit
LoopVectorization.@vvectorize_unsafe $T $((m)) for $isym ∈ 1:$M
$loop_body
end
end
push!(fp, macroexpand(m, loop_quote))
end
outlifestart && push!(sp, :($m.lifetime_end!($out)))
end
end
if partial
if scalar
push!(fp, :($adjout = $∂θ))
else
push!(fp, :($adjout = $m.PtrArray{$(Tuple{shape...}),$T,$N,$(Tuple{X...}),$M,true}(pointer($∂θ))))
end
end
if exportparam && scalar
push!(fp, :($m.VectorizationBase.store!(pointer($sptr, $T), convert($T, $out)); $sptr += $(sizeof(T))))
end
push!(fp, :($θ += $M))
partial && push!(fp, :($∂θ += $M))
nothing
end
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] | 1.633468 | 7,691 |
<reponame>AlexandreChern/CIS561_Proj2<filename>src/vec.jl<gh_stars>1-10
export PetscVec, VecSetType, VecSetValues, VecAssemblyBegin, VecAssemblyEnd, VecSetSizes, VecGetSize, VecGetLocalSize, VecNorm, VecGetValues, VecGetOwnershipRange, VecGetArray, VecRestoreArray, VecGetArrayRead, VecRestoreArrayRead, VecSet, VecSqrtAbs, VecLog, VecExp, VecAbs, VecMax, VecMin, VecCopy, VecDuplicate, VecAXPY, VecAXPBY, VecAYPX, VecWAXPY, VecMAXPY, VecAXPBYPCZ, VecScale, VecDot, VecTDot, VecSum, VecSwap, VecReciprocal, VecShift, VecPointwiseMult, VecPointwiseDivide
export getLocalIndices
"""
Petsc Vector type.
Not a subtype of `AbstractArray` because Petsc vectors do not conform to that
API.
"""
type PetscVec
pobj::Ptr{Void}
"""
Constructor
**Inputs**
* comm: MPI Communicator
"""
function PetscVec(comm::MPI_Comm)
# comm = PETSC_COMM_SELF();
vec = Array{Ptr{Void}}(1)
err = ccall(( :VecCreate, libpetsclocation ), PetscErrorCode,(comm_type,Ptr{Void}),comm,vec);
vec = new(vec[1])
# finalizer(vec,PetscDestroy)
# does not seem to be called immediately when vec is no longer visible, is it called later during garbage collection? - yes
return vec
end
"""
Constructor from existing pointer to a Petsc Vec
**Inputs**
pobj: a Ptr{Void} to an existing Petsc object
"""
function PetscVec(pobj::Ptr{Void}) # default constructor
return new(pobj)
end
end
"""
Union{AbstractVector, PetscVec}
"""
const AllVectors = Union{AbstractVector, PetscVec}
"""
Free a Petsc vec. Safe to call multiple times
"""
function PetscDestroy(vec::PetscVec)
if (vec.pobj != 0)
err = ccall(( :VecDestroy, libpetsclocation), PetscErrorCode, (Ptr{Ptr{Void}},), &vec.pobj);
end
vec.pobj = 0 # unnecessary? vec no longer has any references to it
# println("VecDestroy called")
end
"""
VecSetType
"""
function VecSetType(vec::PetscVec,name)
err = ccall((:VecSetType, libpetsclocation), PetscErrorCode, (Ptr{Void}, Cstring), vec.pobj,name);
end
#TODO: make this better: use VecFromArray
function PetscVec(array::Array{PetscScalar})
vec = PetscVec()
err = ccall(( :VecSetType, libpetsclocation), PetscErrorCode,(Ptr{Void},Cstring), vec.pobj,"seq");
err = ccall( (:VecSetSizes, libpetsclocation), PetscErrorCode, (Ptr{Void}, PetscInt, PetscInt), vec.pobj,length(array),length(array));
# want a PetscInt array so build it ourselves
idx = Array{PetscInt}(length(array));
for i=1:length(array); idx[i] = i-1; end
err = ccall( ( :VecSetValues, libpetsclocation), PetscErrorCode,(Ptr{Void},PetscInt, Ptr{PetscInt},Ptr{PetscScalar},Int32), vec.pobj,length(idx),idx,array,INSERT_VALUES);
err = ccall( ( :VecAssemblyBegin, libpetsclocation), PetscErrorCode,(Ptr{Void},), vec.pobj);
err = ccall( ( :VecAssemblyEnd, libpetsclocation), PetscErrorCode, (Ptr{Void},), vec.pobj);
return vec
end
"""
VecSetValues
"""
function VecSetValues(vec::PetscVec,idx::Array{PetscInt},array::Array{PetscScalar},flag::Integer)
err = ccall( ( :VecSetValues, libpetsclocation), PetscErrorCode,
(Ptr{Void},PetscInt ,Ptr{PetscInt},Ptr{PetscScalar},Int32),
vec.pobj,length(idx), idx,array,flag);
return err
end
"""
VecSetValues method that implicitly uses `INSERT_VALUES`
"""
function VecSetValues(vec::PetscVec,idx::Array{PetscInt},array::Array{PetscScalar})
VecSetValues(vec,idx,array,INSERT_VALUES)
end
#=
function VecSetValues(vec::PetscVec,array::Array{PetscScalar})
idx = Array{PetscInt}(ength(array))
for i=1:length(array); idx[i] = i-1; end
VecSetValues(vec,idx,array,INSERT_VALUES)
end
=#
"""
VecAssemblyBegin
"""
function VecAssemblyBegin(obj::PetscVec)
err = ccall( ( :VecAssemblyBegin, libpetsclocation), PetscErrorCode, (Ptr{Void},), obj.pobj);
end
"""
VecAssemblyEnd
"""
function VecAssemblyEnd(obj::PetscVec)
err = ccall( ( :VecAssemblyEnd, libpetsclocation), PetscErrorCode,(Ptr{Void},), obj.pobj);
end
"""
Convenience function for calling VecAssemblyBegin, and VecAssemblyEnd, in
one go.
"""
function VecAssemble(obj::PetscVec)
VecAssemblyBegin(obj)
VecAssemblyEnd(obj)
end
"""
VecSetSizes
"""
function VecSetSizes(vec::PetscVec,n::Integer, N::Integer)
err = ccall( ( :VecSetSizes, libpetsclocation), PetscErrorCode, (Ptr{Void}, PetscInt, PetscInt), vec.pobj,n,N);
end
"""
PetscView for Petsc vector
"""
function PetscView(obj::PetscVec,viewer)
err = ccall( ( :VecView, libpetsclocation), PetscErrorCode, (Ptr{Void},Int64),obj.pobj,0);
end
"""
VeGetSize
**Inputs**
* vec: a Petsc vector
**Outputs**
* the size
"""
function VecGetSize(obj::PetscVec)
n = Array{PetscInt}(1)
err = ccall( ( :VecGetSize, libpetsclocation), PetscErrorCode, (Ptr{Void},Ptr{PetscInt}), obj.pobj,n);
return n[1]
end
"""
VecGetLocalSize
**Inputs**
* vec: a Petsc vector
**Outputs**
* the size
"""
function VecGetLocalSize(arg1::PetscVec)
arg2 = Ref{PetscInt}()
ccall((:VecGetLocalSize,petsc),PetscErrorCode,(Ptr{Void},Ptr{PetscInt}),arg1.pobj,arg2)
return arg2[]
end
"""
VecNorm
**Inputs**
* obj: Petsc vector
* normtype: the Petsc enum for the norm type
**Output**
* the norm value
"""
function VecNorm(obj::PetscVec,normtype::Integer)
n = Array{PetscReal}()
err = ccall( ( :VecNorm, libpetsclocation), PetscScalar, (Ptr{Void},Int32,Ptr{PetscReal}), obj.pobj,normtype, n);
return n[1]
end
#=
function VecNorm(obj::PetscVec)
return VecNorm(obj,PETSC_NORM_2)
end
=#
"""
VecGetValues
"""
function VecGetValues(vec::PetscVec, ni::Integer, ix::AbstractArray{PetscInt,1}, y::AbstractArray{PetscScalar,1})
# need indices to be PetscInt
# ix_local = Array{PetscInt}(ni)
# for i=1:ni
# ix_local[i] = ix[i]
# end
err = ccall((:VecGetValues,petsc),PetscErrorCode,(Ptr{Void},PetscInt,Ptr{PetscInt},Ptr{PetscScalar}),vec.pobj, ni, ix, y)
return nothing
end
"""
VecGetValues with length of idx inferred from idx
**Inputs**
* vec: the Petsc vector
* idx: array of PetscInt indices
* y: array of PetscScalar values
"""
function VecGetValues(vec::PetscVec,idx::AbstractArray{PetscInt,1}, y::AbstractArray{PetscScalar, 1})
VecGetValues(vec, length(idx), idx, y)
end
"""
VecGetOwnershipRange
**Inputs**
* vec: Petsc vector
**Outputs**
* low: lowest index (zero-based) that is owned
* high: highest index + 1 that is owned
"""
function VecGetOwnershipRange(vec::PetscVec)
low = Array{PetscInt}(1)
high = Array{PetscInt}(1)
ccall((:VecGetOwnershipRange,petsc),PetscErrorCode,(Ptr{Void},Ptr{PetscInt},Ptr{PetscInt}),vec.pobj, low, high)
return low[1], high[1]
end
# new functions
"""
VecGetArray. Users must call `VecRestoreArray` when finished.
**Inputs**
* vec: the Petsc vector
**Outputs**
* arr: a Julia Array{PetscScalar, 1}
"""
function VecGetArray(vec::PetscVec)
# gets a pointer to the data underlying a Petsc vec, turns it into a Julia
# array
# ptr_arr must be passed into PetscVecRestoreArray
ptr_arr = Array{Ptr{PetscScalar}}(1)
ccall((:VecGetArray,petsc),PetscErrorCode,(Ptr{Void}, Ptr{Ptr{PetscScalar}}),vec.pobj, ptr_arr)
first, last = VecGetOwnershipRange(vec)
len = last - first
arr = unsafe_wrap(Array, ptr_arr[1], len)
return arr
end
"""
VecRestoreArray. Users must not access the array after calling this function.
**Inputs**
* vec: the PetscVector passed into `VecGetArray`
* arr: the array returned by `VecGetArray
"""
function VecRestoreArray(vec::PetscVec, arr::Array{PetscScalar, 1})
ptr_arr = Ref{Ptr{PetscScalar}}(pointer(arr))
ccall((:VecRestoreArray,petsc),PetscErrorCode,(Ptr{Void}, Ptr{Ptr{PetscScalar}}),vec.pobj, ptr_arr)
end
"""
Similar to `VecGetArray`, but produces the array must not be written to.
"""
function VecGetArrayRead(vec::PetscVec)
ptr_arr = Array{Ptr{PetscScalar}}(1)
ccall((:VecGetArrayRead,petsc),PetscErrorCode,(Ptr{Void}, Ptr{Ptr{PetscScalar}}),vec.pobj, ptr_arr)
first, last = VecGetOwnershipRange(vec)
len = last - first
arr = unsafe_wrap(Array, ptr_arr[1], len)
return arr
end
"""
Similar to `VecRestoreArray`, but corresponds to `VecGetArrayRead`
"""
function VecRestoreArrayRead(vec::PetscVec, arr::Array{PetscScalar, 1})
ptr_arr = Ref{Ptr{PetscScalar}}(pointer(arr))
ccall((:VecRestoreArrayRead,petsc),PetscErrorCode,(Ptr{Void}, Ptr{Ptr{PetscScalar}}),vec.pobj, ptr_arr)
# ccall((:VecRestoreArrayRead,petsc),PetscErrorCode,(Vec,Ptr{Ptr{PetscScalar}}),arg1,arg2)
end
"""
VecSet
"""
function VecSet(vec::PetscVec, val::PetscScalar)
ccall((:VecSet,petsc),PetscErrorCode,(Ptr{Void},PetscScalar), vec.pobj, val)
end
"""
VecSqrtAbs
"""
function VecSqrtAbs(vec::PetscVec)
ccall((:VecSqrtAbs,petsc),PetscErrorCode,(Ptr{Void},), vec.pobj)
end
"""
VecLog
"""
function VecLog(vec::PetscVec)
ccall((:VecLog,petsc),PetscErrorCode,(Ptr{Void},),vec.pobj)
end
"""
VecExp
"""
function VecExp(vec::PetscVec)
ccall((:VecExp,petsc),PetscErrorCode,(Ptr{Void},),vec.pobj)
end
function VecAbs(vec::PetscVec)
ccall((:VecAbs,petsc),PetscErrorCode,(Ptr{Void},),vec.pobj)
end
"""
VecMax
**Inputs**
* vec: PetscVec
**Output**
* r: the maximum value
* idx: the (zero-based) index of the maximum value
"""
function VecMax(vec::PetscVec)
r = Array{PetscReal}(1) # max value
idx = Array{PetscInt}(1) # index of max value
ccall((:VecMax,petsc),PetscErrorCode,(Ptr{Void}, Ptr{PetscInt},Ptr{PetscReal}), vec.pobj, idx, r)
return r[1], idx[1]
end
"""
VecMin. Same interface as `VecMax`
"""
function VecMin(vec::PetscVec)
r = Array{PetscReal}(1) # min value
idx = Array{PetscInt}(1) # index of min value
ccall((:VecMin,petsc),PetscErrorCode,(Ptr{Void},Ptr{PetscInt},Ptr{PetscReal}), vec.pobj, idx, r)
return r[1], idx[1]
end
"""
VecReciprocal
"""
function VecReciprocal(vec::PetscVec)
ccall((:VecReciprocal,petsc),PetscErrorCode,(Ptr{Void},),vec.pobj)
end
"""
VecShift
"""
function VecShift(vec::PetscVec, a::PetscScalar)
ccall((:VecShift,petsc),PetscErrorCode,(Ptr{Void},PetscScalar), vec.pobj, a)
end
"""
VecPointwiseMult
"""
function VecPointwiseMult(w::PetscVec, x::PetscVec,y::PetscVec)
ccall((:VecPointwiseMult,petsc),PetscErrorCode,(Ptr{Void}, Ptr{Void}, Ptr{Void}), w.pobj, x.pobj, y.pobj)
end
"""
VecPointwiseDivide
"""
function VecPointwiseDivide(w::PetscVec, x::PetscVec, y::PetscVec)
ccall((:VecPointwiseDivide,petsc),PetscErrorCode,(Ptr{Void}, Ptr{Void}, Ptr{Void}), w.pobj, x.pobj, y.pobj)
end
"""
VecCopy
"""
function VecCopy(vec::PetscVec , vec2::PetscVec)
ccall((:VecCopy,petsc),PetscErrorCode,(Ptr{Void}, Ptr{Void}), vec.pobj, vec2.pobj)
end
"""
VecDulicate
"""
function VecDuplicate( vec::PetscVec)
ptr_arr = Array{Ptr{Void}}(1)
ccall((:VecDuplicate,petsc),PetscErrorCode,( Ptr{Void}, Ptr{Ptr{Void}}), vec.pobj, ptr_arr)
return PetscVec(ptr_arr[1])
end
# Some vector linear algebra
"""
VecAXPY
"""
function VecAXPY( vec1::PetscVec, a::PetscScalar, vec2::PetscVec)
ccall((:VecAXPY,petsc),PetscErrorCode,(Ptr{Void},PetscScalar,Ptr{Void}), vec1.pobj, a, vec2.pobj)
end
"""
VecAXPBY
"""
function VecAXPBY(vec1::PetscVec, a::PetscScalar, b::PetscScalar, vec2::PetscVec)
ccall((:VecAXPBY,petsc),PetscErrorCode,(Ptr{Void},PetscScalar,PetscScalar,Ptr{Void}),vec1.pobj, a, b, vec2.pobj)
end
"""
VecMAXPY
"""
function VecMAXPY(vec1::PetscVec, n::Integer, a::AbstractArray{PetscScalar, 1}, x::AbstractArray{Ptr{Void}, 1})
# the vector x must contains the pointers from the PetscVec objects, not the PetscVec objects themselves
ccall((:VecMAXPY,petsc),PetscErrorCode,(Ptr{Void},PetscInt,Ptr{PetscScalar},Ptr{Ptr{Void}}),vec1.pobj, n, a, x)
end
"""
VecAYPX
"""
function VecAYPX(vec1::PetscVec, a::PetscScalar, vec2::PetscVec)
ccall((:VecAYPX,petsc),PetscErrorCode,(Ptr{Void},PetscScalar,Ptr{Void}),vec1.pobj, a, vec2.pobj)
end
"""
VecWAXPY
"""
function VecWAXPY(w::PetscVec, a::PetscScalar, x::PetscVec, y::PetscVec)
ccall((:VecWAXPY,petsc),PetscErrorCode,(Ptr{Void},PetscScalar,Ptr{Void},Ptr{Void}), w.pobj, a, x.pobj, y.pobj)
end
"""
VecAXPBYPCZ
"""
function VecAXPBYPCZ(z::PetscVec, alpha::PetscScalar, beta::PetscScalar, gamma::PetscScalar, x::PetscVec, y::PetscVec)
ccall((:VecAXPBYPCZ,petsc),PetscErrorCode,(Ptr{Void},PetscScalar,PetscScalar,PetscScalar,Ptr{Void},Ptr{Void}), z.pobj, alpha,beta, gamma, x.pobj, y.pobj)
end
"""
VecScale
"""
function VecScale(vec::PetscVec, a::PetscScalar)
ccall((:VecScale,petsc),PetscErrorCode,(Ptr{Void},PetscScalar), vec.pobj, a)
end
"""
VecDot
"""
function VecDot(x::PetscVec, y::PetscVec)
r = Array{PetscScalar}(1)
err = ccall((:VecDot,petsc),PetscErrorCode,( Ptr{Void}, Ptr{Void}, Ptr{PetscScalar}), x.pobj, y.pobj, r)
println("PetscVecDot error code = ", err)
return r[1]
end
"""
VecTDot
"""
function VecTDot(x::PetscVec, y::PetscVec)
r = Array{PetscScalar}(1)
ccall((:VecTDot,petsc),PetscErrorCode,(Ptr{Void}, Ptr{Void},Ptr{PetscScalar}), x.pobj, y.pobj, r)
return r[1]
end
"""
VecSum
"""
function VecSum(vec::PetscVec)
r = Array{PetscScalar}(1)
ccall((:VecSum,petsc),PetscErrorCode,(Ptr{Void},Ptr{PetscScalar}),vec.pobj, r)
return r[1]
end
"""
VecSwap
"""
function VecSwap(x::PetscVec, y::PetscVec)
ccall((:VecSwap,petsc),PetscErrorCode,(Ptr{Void}, Ptr{Void}), x.pobj, y.pobj)
end
include("vec_interface.jl")
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# *********************************************************************************
# 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.
# *********************************************************************************
"""
add_electric_tariff_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="")
Adds the ElectricTariff results to the dictionary passed back from `run_reopt` using the solved model `m` and the `REoptInputs` for node `_n`.
Note: the node number is an empty string if evaluating a single `Site`.
ElectricTariff results:
- `lifecycle_energy_cost_after_tax` lifecycle cost of energy from the grid in present value, after tax
- `year_one_energy_cost_before_tax` cost of energy from the grid over the first year, before considering tax benefits
- `lifecycle_demand_cost_after_tax` lifecycle cost of power from the grid in present value, after tax
- `year_one_demand_cost_before_tax` cost of power from the grid over the first year, before considering tax benefits
- `lifecycle_fixed_cost_after_tax` lifecycle fixed cost in present value, after tax
- `year_one_fixed_cost_before_tax` fixed cost over the first year, before considering tax benefits
- `lifecycle_min_charge_adder_after_tax` lifecycle minimum charge in present value, after tax
- `year_one_min_charge_adder_before_tax` minimum charge over the first year, before considering tax benefits
- `year_one_bill_before_tax` sum of `year_one_energy_cost_before_tax`, `year_one_demand_cost_before_tax`, `year_one_fixed_cost_before_tax`, `year_one_min_charge_adder_before_tax`, and `year_one_coincident_peak_cost_before_tax`
- `lifecycle_export_benefit_after_tax` lifecycle export credits in present value, after tax
- `year_one_export_benefit_before_tax` export credits over the first year, before considering tax benefits
- `lifecycle_coincident_peak_cost_after_tax` lifecycle coincident peak charge in present value, after tax
- `year_one_coincident_peak_cost_before_tax` coincident peak charge over the first year
"""
function add_electric_tariff_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="")
r = Dict{String, Any}()
m[Symbol("Year1UtilityEnergy"*_n)] = p.hours_per_time_step *
sum(m[Symbol("dvGridPurchase"*_n)][ts, tier] for ts in p.time_steps, tier in 1:p.s.electric_tariff.n_energy_tiers)
r["lifecycle_energy_cost_after_tax"] = round(value(m[Symbol("TotalEnergyChargesUtil"*_n)]) * (1 - p.s.financial.offtaker_tax_pct), digits=2)
r["year_one_energy_cost_before_tax"] = round(value(m[Symbol("TotalEnergyChargesUtil"*_n)]) / p.pwf_e, digits=2)
r["lifecycle_demand_cost_after_tax"] = round(value(m[Symbol("TotalDemandCharges"*_n)]) * (1 - p.s.financial.offtaker_tax_pct), digits=2)
r["year_one_demand_cost_before_tax"] = round(value(m[Symbol("TotalDemandCharges"*_n)]) / p.pwf_e, digits=2)
r["lifecycle_fixed_cost_after_tax"] = round(m[Symbol("TotalFixedCharges"*_n)] * (1 - p.s.financial.offtaker_tax_pct), digits=2)
r["year_one_fixed_cost_before_tax"] = round(m[Symbol("TotalFixedCharges"*_n)] / p.pwf_e, digits=0)
r["lifecycle_min_charge_adder_after_tax"] = round(value(m[Symbol("MinChargeAdder"*_n)]) * (1 - p.s.financial.offtaker_tax_pct), digits=2)
r["year_one_min_charge_adder_before_tax"] = round(value(m[Symbol("MinChargeAdder"*_n)]) / p.pwf_e, digits=2)
r["lifecycle_export_benefit_after_tax"] = -1 * round(value(m[Symbol("TotalExportBenefit"*_n)]) * (1 - p.s.financial.offtaker_tax_pct), digits=2)
r["year_one_export_benefit_before_tax"] = -1 * round(value(m[Symbol("TotalExportBenefit"*_n)]) / p.pwf_e, digits=0)
r["lifecycle_coincident_peak_cost_after_tax"] = round(value(m[Symbol("TotalCPCharges"*_n)]) * (1 - p.s.financial.offtaker_tax_pct), digits=2)
r["year_one_coincident_peak_cost_before_tax"] = round(r["lifecycle_coincident_peak_cost_after_tax"] / p.pwf_e, digits=2)
r["year_one_bill_before_tax"] = r["year_one_energy_cost_before_tax"] + r["year_one_demand_cost_before_tax"] +
r["year_one_fixed_cost_before_tax"] + r["year_one_min_charge_adder_before_tax"] + r["year_one_coincident_peak_cost_before_tax"]
d["ElectricTariff"] = r
nothing
end
function add_electric_tariff_results(m::JuMP.AbstractModel, p::MPCInputs, d::Dict; _n="")
r = Dict{String, Any}()
m[Symbol("energy_purchased"*_n)] = p.hours_per_time_step *
sum(m[Symbol("dvGridPurchase"*_n)][ts] for ts in p.time_steps)
r["energy_cost"] = round(value(m[Symbol("TotalEnergyChargesUtil"*_n)]), digits=2)
r["demand_cost"] = round(value(m[Symbol("TotalDemandCharges"*_n)]), digits=2)
r["export_benefit"] = -1 * round(value(m[Symbol("TotalExportBenefit"*_n)]), digits=0)
d["ElectricTariff"] = r
nothing
end | [
2,
41906,
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2147,
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] | 2.834751 | 2,233 |
# Compressor Codec
# ================
struct ZstdCompressor <: TranscodingStreams.Codec
cstream::CStream
level::Int
end
function Base.show(io::IO, codec::ZstdCompressor)
print(io, summary(codec), "(level=$(codec.level))")
end
# Same as the zstd command line tool (v1.2.0).
const DEFAULT_COMPRESSION_LEVEL = 3
"""
ZstdCompressor(;level=$(DEFAULT_COMPRESSION_LEVEL))
Create a new zstd compression codec.
Arguments
---------
- `level`: compression level (1..$(MAX_CLEVEL))
"""
function ZstdCompressor(;level::Integer=DEFAULT_COMPRESSION_LEVEL)
if !(1 ≤ level ≤ MAX_CLEVEL)
throw(ArgumentError("level must be within 1..$(MAX_CLEVEL)"))
end
return ZstdCompressor(CStream(), level)
end
const ZstdCompressorStream{S} = TranscodingStream{ZstdCompressor,S} where S<:IO
"""
ZstdCompressorStream(stream::IO; kwargs...)
Create a new zstd compression stream (see `ZstdCompressor` for `kwargs`).
"""
function ZstdCompressorStream(stream::IO; kwargs...)
x, y = splitkwargs(kwargs, (:level,))
return TranscodingStream(ZstdCompressor(;x...), stream; y...)
end
# Methods
# -------
function TranscodingStreams.initialize(codec::ZstdCompressor)
code = initialize!(codec.cstream, codec.level)
if iserror(code)
zstderror(codec.cstream, code)
end
return
end
function TranscodingStreams.finalize(codec::ZstdCompressor)
if codec.cstream.ptr != C_NULL
code = free!(codec.cstream)
if iserror(code)
zstderror(codec.cstream, code)
end
codec.cstream.ptr = C_NULL
end
return
end
function TranscodingStreams.startproc(codec::ZstdCompressor, mode::Symbol, error::Error)
code = reset!(codec.cstream, 0 #=unknown source size=#)
if iserror(code)
error[] = ErrorException("zstd error")
return :error
end
return :ok
end
function TranscodingStreams.process(codec::ZstdCompressor, input::Memory, output::Memory, error::Error)
cstream = codec.cstream
cstream.ibuffer.src = input.ptr
cstream.ibuffer.size = input.size
cstream.ibuffer.pos = 0
cstream.obuffer.dst = output.ptr
cstream.obuffer.size = output.size
cstream.obuffer.pos = 0
if input.size == 0
code = finish!(cstream)
else
code = compress!(cstream)
end
Δin = Int(cstream.ibuffer.pos)
Δout = Int(cstream.obuffer.pos)
if iserror(code)
error[] = ErrorException("zstd error")
return Δin, Δout, :error
else
return Δin, Δout, input.size == 0 && code == 0 ? :end : :ok
end
end
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] | 2.477756 | 1,034 |
<reponame>shamazmazum/CorrelationFunctions.jl<gh_stars>1-10
extract_edge(array :: AbstractArray, mode :: Symbol) =
extract_edge(array, Val(mode))
extract_edge(array :: AbstractArray, :: Val{:Sobel}) =
let norm(x) = sqrt.(sum(map(x -> x.^2, x)))
norm(imgradients(array, Kernel.sobel))
end
extract_edge(array :: AbstractArray, :: Val{:distance_map}) =
let distances = Bool.(array) |> feature_transform |> distance_transform
Float64.(distances .== 1.0)
end
"""
surfsurf(array, phase[; len][, directions][, plans,] periodic = false, edgemode = :Sobel)
surfsurf(array, χ[; len][, directions][, plans,] periodic = false, edgemode = :Sobel)
Calculate surface-surface correlation function for one-, two- or
three-dimensional multiphase system.
Surface-surface CF equals to probability that corner elements of a
line segment with the length `x` cut from the array belong to the
boundary of a cluster with the phase `phase`. This implementation
calculates surface-surface function for all `x`s in the range from `1`
to `len` which defaults to half of the minimal dimension of the
array.
You can chose how an edge between phases are selected by passing
`edgemode` argument which can be either `:Sobel` or
`:distance_map`. Usually, `:Sobel` gives much better results.
You can specify a custom indicator function `χ(x)` instead of `phase`.
An argument `plans` can be used to support precomputed FFT plans which
can be helpful if you call `surfsurf` often with the array of the same
size. Plans can be computed with `S2FTPlans` constructor.
See also: [`direction1Dp`](@ref), [`direction2Dp`](@ref),
[`direction3Dp`](@ref), [`S2FTPlans`](@ref).
"""
function surfsurf end
surfsurf(array :: AbstractArray,
phase;
len :: Integer = (array |> size |> minimum) ÷ 2,
directions :: Vector{Symbol} = array |> default_directions,
periodic :: Bool = false,
plans :: S2FTPlans = S2FTPlans(array, periodic),
edgemode :: Symbol = :Sobel) =
surfsurf(array, x -> x == phase;
len = len,
directions = directions,
periodic = periodic,
plans = plans,
edgemode = edgemode)
function surfsurf(array :: AbstractArray,
χ :: Function;
len :: Integer = (array |> size |> minimum) ÷ 2,
directions :: Vector{Symbol} = array |> default_directions,
periodic :: Bool = false,
plans :: S2FTPlans = S2FTPlans(array, periodic),
edgemode :: Symbol = :Sobel)
ph = map(χ, array)
edge = extract_edge(ph, edgemode)
return s2(edge, SeparableIndicator(identity);
len = len,
directions = directions,
periodic = periodic,
plans = plans)
end
"""
surfvoid(array, phase[; len][, directions][, plans,] periodic = false, edgemode = :Sobel)
surfvoid(array, χ[; len][, directions][, plans,] periodic = false, edgemode = :Sobel)
Calculate surface-void correlation function for one-, two- or
three-dimensional multiphase system.
Surface-void CF equals to probability that one corner of a line
segment with the length `x` cut from the array belongs to the boundary
of a cluster with the phase `phase` and the other belongs to the void
phase `0`. This implementation calculates surface-void function for
all `x`s in the range from `1` to `len` which defaults to half of the
minimal dimension of the array.
You can chose how an edge between phases are selected by passing
`edgemode` argument which can be either `:Sobel` or
`:distance_map`. Usually, `:Sobel` gives much better results.
You can specify a custom indicator function `χ(x)` instead of `phase`.
An argument `plans` can be used to support precomputed FFT plans which
can be helpful if you call `surfvoid` often with the array of the same
size. Plans can be computed with `S2FTPlans` constructor.
See also: [`direction1Dp`](@ref), [`direction2Dp`](@ref),
[`direction3Dp`](@ref), [`S2FTPlans`](@ref).
"""
function surfvoid end
surfvoid(array :: AbstractArray,
phase;
len :: Integer = (array |> size |> minimum) ÷ 2,
directions :: Vector{Symbol} = array |> default_directions,
periodic :: Bool = false,
plans :: S2FTPlans = S2FTPlans(array, periodic),
edgemode :: Symbol = :Sobel) =
surfvoid(array, x -> x == phase;
len = len,
directions = directions,
periodic = periodic,
plans = plans,
edgemode = edgemode)
function surfvoid(array :: AbstractArray,
χ :: Function;
len :: Integer = (array |> size |> minimum) ÷ 2,
directions :: Vector{Symbol} = array |> default_directions,
periodic :: Bool = false,
plans :: S2FTPlans = S2FTPlans(array, periodic),
edgemode :: Symbol = :Sobel)
ph = map(χ, array)
edge = extract_edge(ph, edgemode)
χ1(x) = array[x] == 0
χ2(x) = edge[x]
return s2(CartesianIndices(array), SeparableIndicator(χ1, χ2);
len = len,
directions = directions,
periodic = periodic,
plans = plans)
end
# Frequency analisys of an input data (helps to check how an input is
# suitable for correlation functions which work with the surface).
"""
normalized_fft(a)
Perform FFT on `a` which preserves the norm of `a`.
"""
function normalized_fft(a :: AbstractArray)
f = fft(a)
n1 = norm(a)
n2 = norm(f)
return f .* (n1/n2)
end
"""
cut_from_center(a, fraction)
Return a slice from the center of `a`. A slice will have dimensions
`fraction*size(a)`.
"""
function cut_from_center(a :: AbstractArray, fraction :: Float64)
start = ((1 - fraction)*x÷2 |> Int for x in size(a))
stop = ((1 + fraction)*x÷2 |> Int for x in size(a))
ranges = (start+1:stop+1 for (start, stop) in zip(start, stop))
return a[ranges...]
end
@doc raw"""
lowfreq_energy_ratio(array, fraction = 0.5)
Calculate a ratio $E_a/E$ where $E$ is a total energy of a signal
`array` and $E_a$ is the energy concentrated in frequencies $[0, af/2]$
where $f$ is the sampling rate and $a$ is set via parameter
`fraction`. `mean(array)` is subtracted from the array before
calculations.
This function can be helpful in estimating if `array` is suitable for
calculating surface-surface or surface-void function. An empirical
criterion is that if this function returns a value greater than `0.95`,
the array is good.
"""
function lowfreq_energy_ratio(array :: AbstractArray,
fraction :: Float64 = 0.5)
f = normalized_fft(array .- mean(array))
total_energy = f |> norm
highfreq_energy = cut_from_center(f, 1 - fraction) |> norm
return (total_energy - highfreq_energy) / total_energy
end
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] | 2.36396 | 3,091 |
module MIMEBundles
export MIMEBundle, mimerepr, stringmime
using JSON
include("./mimerepr.jl")
include("./mimes.jl")
struct MIMEBundle
data::Dict{MIME, Union{String, JSONText}}
metadata::Dict{Any, Any}
end
function MIMEBundle(obj)
data = Dict{MIME, Any}()
metadata = Dict()
for mime in MIMEOrVec[DEFAULT_MIMES..., extramimes(obj)...]
mime, result = mimerepr(mime, obj)
if result !== nothing
if isjsonmime(mime)
result = JSONText(result)
end
data[mime] = result
end
end
return MIMEBundle(data, metadata)
end
end # module
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] | 2.086093 | 302 |
include("Shape.jl")
"""
morph_to(to_func::Function; object=:stroke)
A closure for the [`_morph_to`](@ref) function.
This makes it easier to write the function inside an `Object`.
Currently morphing is quite simple and only works for basic shapes.
It especially does not work with functions which produce more than one polygon
or which produce filled polygons.
Blending between fills of polygons is definitely coming at a later stage.
**Important:** The functions itself should not draw the polygon
i.e. use `circle(Point(100,100), 50)` instead of `circle(Point(100,100), 50, :stroke)`
# Arguments
- `to_func::Function`: Same as `from_func` but it defines the "result" polygon,
which will be displayed at the end of the Object
# Keywords
- `do_action::Symbol` defines whether the object has a fill or just a stroke. Defaults to `:stroke`.
# Example
This creates a star that morphs into a circle and back.
```
astar(args...; do_action=:stroke) = star(O, 50, 5, 0.5, 0, do_action)
acirc(args...; do_action=:stroke) = circle(Point(100,100), 50, do_action)
video = Video(500, 500)
back = Background(1:20, ground)
star_obj = Object(1:10, astar)
act!(star_obj, Action(linear(), morph_to(acirc)))
circle_obj = Object(11:20, acirc)
act!(circle_obj, Action(:same, morph_to(astar)))
```
"""
function morph_to(to_func::Function; do_action = :stroke)
return (video, object, action, frame) ->
_morph_to(video, object, action, frame, to_func; do_action = do_action)
end
"""
_morph_to(video::Video, object::Object, action::Action, frame, to_func::Function; do_action=:stroke)
Internal version of [`morph_to`](@ref) but described there.
"""
function _morph_to(
video::Video,
object::Object,
action::Action,
frame,
to_func::Function;
do_action = :stroke,
)
newpath()
object.func(video, object, frame; do_action = :none)
closepath()
from_polys = pathtopoly()
newpath()
to_func(video, object, frame; do_action = :none)
closepath()
to_polys = pathtopoly()
return morph_between(video, action, frame, from_polys, to_polys; do_action = do_action)
end
"""
morph_between(video::Video, action::Action, frame,
from_polys::Vector{Vector{Point}}, to_polys::Vector{Vector{Point}};
do_action=:stroke)
Internal version of [`morph_to`](@ref) after the from poly is defined.
"""
function morph_between(
video::Video,
action::Action,
frame,
from_polys::Vector{Vector{Point}},
to_polys::Vector{Vector{Point}};
do_action = :stroke,
)
cs = get_current_setting()
cs.show_object = false
# computation of the polygons and the best way to morph in the first frame
if frame == first(get_frames(action))
save_morph_polygons!(action, from_polys, to_polys)
end
# obtain the computed polygons. These polygons have the same number of points.
number_of_shapes = length(action.defs[:from_shape])
bool_move = ones(Bool, number_of_shapes)
bool_appear = zeros(Bool, number_of_shapes)
for si in 1:number_of_shapes
from_shape = action.defs[:from_shape][si]
to_shape = action.defs[:to_shape][si]
inter_shape = action.defs[:inter_shape][si]
if isempty(from_shape) || isempty(to_shape)
if isempty(to_shape)
action.defs[:inter_shape][si] = from_shape
else
action.defs[:inter_shape][si] = to_shape
bool_appear[si] = true
end
bool_move[si] = false
continue
end
# compute the interpolation variable `t` for the current frame
t = get_interpolation(action, frame)
interpolate_shape!(inter_shape, from_shape, to_shape, t)
draw_shape(inter_shape, do_action)
end
# let new paths appear
t = get_interpolation(action, frame)
setopacity(t)
shape_ids = [si for si in 1:number_of_shapes if (!bool_move[si] && bool_appear[si])]
shapes = action.defs[:inter_shape][shape_ids]
draw_shape.(shapes, do_action)
# let old paths disappear
t = get_interpolation(action, frame)
setopacity(1 - t)
shape_ids = [si for si in 1:number_of_shapes if (!bool_move[si] && !bool_appear[si])]
shapes = action.defs[:inter_shape][shape_ids]
draw_shape.(shapes, do_action)
setopacity(1)
end
"""
match_num_points(poly_1::Vector{Point}, poly_2::Vector{Point})
This is a helper function for [`morph_to`](@ref).
Given two polygons `poly_1` and `poly_2` points are added to the polygon with less points
until both polygons have the same number of points.
The polygon with less points gets mutated during this process.
# Arguments
- `poly_1::Vector{Point}`: The points which define the first polygon
- `poly_2::Vector{Point}`: The points which define the second polygon
"""
function match_num_points(poly_1::Vector{Point}, poly_2::Vector{Point})
l1 = length(poly_1)
l2 = length(poly_2)
# if both have the same number of points => we are done
l1 == l2 && return poly_1, poly_2
new_poly_1 = simplify(poly_1)
new_poly_2 = simplify(poly_2)
l1 = length(new_poly_1)
l2 = length(new_poly_2)
# poly_1 should have less points than poly_2 so we flip if this is not the case
flipped = false
if l1 > l2
new_poly_1, new_poly_2 = new_poly_2, new_poly_1
l1, l2 = l2, l1
flipped = true
end
# the difference of the length of points
missing_nodes = l2 - l1
add_points!(new_poly_1, missing_nodes)
if flipped
new_poly_1, new_poly_2 = new_poly_2, new_poly_1
end
@assert length(new_poly_1) == length(new_poly_2)
return new_poly_1, new_poly_2
end
"""
add_points!(poly, missing_nodes)
Add #`missing_nodes` to poly.
"""
function add_points!(poly, missing_nodes)
pdists = polydistances(poly)
every_dist = pdists[end] / missing_nodes
pdiffs = diff(pdists)
ct = 0.0
poly_orig = copy(poly)
npi = 1
for pi in 1:length(poly_orig)
add_nodes = convert(Int, round(pdiffs[pi] / every_dist))
t = pdiffs[pi] / (add_nodes + 1)
ct_local = ct
for i in 1:add_nodes
ct_local += t
new_point = get_polypoint_at(poly_orig, ct_local / pdists[end]; pdist = pdists)
npi += 1
insert!(poly, npi, new_point)
end
npi += 1
missing_nodes -= add_nodes
ct += pdiffs[pi]
every_dist = (pdists[end] - ct) / missing_nodes
end
end
"""
save_morph_polygons!(action::Action, from_func::Vector{Vector{Point}},
to_func::Vector{Vector{Point}})
Calls the functions to polygons and calls [`match_num_points`](@ref)
such that both polygons have the same number of points.
This is done once inside [`_morph_to`](@ref).
Saves the two polygons inside `action.defs[:from_poly]` and `action.defs[:to_poly]`.
**Assumption:** Both functions create the same number of polygons.
"""
function save_morph_polygons!(
action::Action,
from_polys::Vector{Vector{Point}},
to_polys::Vector{Vector{Point}},
)
# delete polygons with less than 2 points
for i in length(from_polys):-1:1
length(from_polys[i]) <= 1 && splice!(from_polys, i)
end
for i in length(to_polys):-1:1
length(to_polys[i]) <= 1 && splice!(to_polys, i)
end
if length(from_polys) != length(to_polys)
try_merge_polygons(from_polys)
try_merge_polygons(to_polys)
end
from_shapes = create_shapes(from_polys)
to_shapes = create_shapes(to_polys)
if length(from_shapes) > 1
from_shapes, to_shapes = reorder_match(from_shapes, to_shapes)
end
action.defs[:from_shape] = Vector{Shape}()
action.defs[:to_shape] = Vector{Shape}()
action.defs[:inter_shape] = Vector{Shape}()
counter = 0
for (from_shape, to_shape) in zip(from_shapes, to_shapes)
counter += 1
if isempty(from_shape) || isempty(to_shape)
if isempty(from_shape)
push!(action.defs[:from_shape], EmptyShape())
push!(action.defs[:to_shape], to_shape)
push!(
action.defs[:inter_shape],
Shape(
length(to_shape.points),
[length(subpath) for subpath in to_shape.subpaths],
),
)
else
push!(action.defs[:from_shape], from_shape)
push!(action.defs[:to_shape], EmptyShape())
push!(
action.defs[:inter_shape],
Shape(
length(from_shape.points),
[length(subpath) for subpath in from_shape.subpaths],
),
)
end
else
from_shape, to_shape = prepare_to_interpolate(from_shape, to_shape)
push!(action.defs[:from_shape], from_shape)
push!(action.defs[:to_shape], to_shape)
push!(
action.defs[:inter_shape],
Shape(
length(from_shape.points),
[length(subpath) for subpath in from_shape.subpaths],
),
)
end
end
end
"""
try_merge_polygons(polys)
Try to merge polygons together to match the number of polygons that get morphed.
The only example I encountered is that the `[` of a 3xY matrix consists of 3 parts which
are merged together.
"""
function try_merge_polygons(polys)
for pi in 1:length(polys)
!ispolyclockwise(polys[pi]) && continue
for pj in 1:length(polys)
pi >= pj && continue
!ispolyclockwise(polys[pj]) && continue
smallest_dist = Inf
found_Ap = O
found_Bp = O
start_with_A = true
for (pA, pointA1, pointA2) in
zip(1:length(polys[pi]), polys[pi], circshift(polys[pi], -1))
for (pB, pointB1, pointB2) in
zip(1:length(polys[pj]), polys[pj], circshift(polys[pj], -1))
dist1 = distance(pointA1, pointB2)
dist2 = distance(pointA2, pointB1)
if dist1 <= 3 && dist2 <= 3
if isinside((pointB1 + pointB2) / 2, polys[pi]; allowonedge = true)
pB1 = pB + 1 <= length(polys[pj]) ? pB + 1 : 1
polys[pj][pB1] = pointA1 - (pointB2 - pointA1)
polys[pj][pB] = pointA2 - (pointB1 - pointA2)
pointB2 = polys[pj][pB1]
pointB1 = polys[pj][pB]
end
if ispolyclockwise([pointA1, pointA2, pointB1, pointB2])
start_with_A = false
smallest_dist = dist1
found_Ap = pA
found_Bp = pB + 1 <= length(polys[pj]) ? pB + 1 : 1
else
start_with_A = true
smallest_dist = dist2
found_Ap = pA + 1 <= length(polys[pi]) ? pA + 1 : 1
found_Bp = pB + 1
end
end
end
end
if smallest_dist <= 3
if start_with_A
tmp_polyA = circshift(polys[pi], length(polys[pi]) - found_Ap + 1)
tmp_polyB = circshift(polys[pj], length(polys[pj]) - found_Bp)
polys[pi] = vcat(tmp_polyA, tmp_polyB[1:(end - 1)])
polys[pj] = Point[]
else
tmp_polyB = circshift(polys[pj], length(polys[pj]) - found_Bp + 1)
tmp_polyA = circshift(polys[pi], length(polys[pi]) - found_Ap)
polys[pi] = vcat(tmp_polyB, tmp_polyA)
polys[pj] = Point[]
end
end
end
end
for pi in length(polys):-1:1
if isempty(polys[pi])
splice!(polys, pi)
end
end
end
"""
compute_shortest_morphing_dist(from_poly::Vector{Point}, to_poly::Vector{Point})
Rotates `from_poly` internally to check which mapping produces the smallest morphing distance.
It returns the start index of the rotation of `from_poly` as well as the smallest distance.
"""
function compute_shortest_morphing_dist(from_poly::Vector{Point}, to_poly::Vector{Point})
# find the smallest morphing distance to match the points in a more natural way
# smallest_i holds the best starting point of from_path
smallest_i = 1
smallest_distance = typemax(Float64)
for i in 1:length(from_poly)
overall_distance = 0.0
for j in 1:length(from_poly)
p1 = from_poly[mod1(j + i - 1, length(from_poly))]
p2 = to_poly[j]
overall_distance += distance(p1, p2)
end
if overall_distance < smallest_distance
smallest_distance = overall_distance
smallest_i = i
end
end
return smallest_i, smallest_distance
end
"""
reorder_match(from_shapes::Vector{Shape}, to_shapes::Vector{Shape})
Computes the similiarty of the shapes and finds the best mapping such that the sum of similiarty
is maximized.
Additionally it creates empty shapes when needed such that
`reordered_from` and `reordered_to` contain the same number of shapes.
# Returns
- `reordered_from::Vector{Shape}`
- `reordered_to::Vector{Shape}`
"""
function reorder_match(from_shapes::Vector{Shape}, to_shapes::Vector{Shape})
num_shapes = max(length(from_shapes), length(to_shapes))
similiarity_matrix = zeros(length(from_shapes), length(to_shapes))
for fi in 1:length(from_shapes)
from_shape = from_shapes[fi]
for ti in 1:length(to_shapes)
to_shape = to_shapes[ti]
similiarity_matrix[fi, ti] = -get_similarity(from_shape, to_shape)
end
end
assignment, cost = hungarian(similiarity_matrix)
new_from_shapes = Vector{Shape}(undef, num_shapes)
for i in 1:num_shapes
new_from_shapes[i] = EmptyShape()
end
new_to_shapes = Vector{Shape}(undef, num_shapes)
for i in 1:num_shapes
if i <= length(to_shapes)
new_to_shapes[i] = to_shapes[i]
else
new_to_shapes[i] = EmptyShape()
end
end
ptr = length(to_shapes) + 1
for i in 1:length(from_shapes)
if assignment[i] == 0
new_from_shapes[ptr] = from_shapes[i]
ptr += 1
else
new_from_shapes[assignment[i]] = from_shapes[i]
end
end
return new_from_shapes, new_to_shapes
end
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] | 2.110683 | 7,002 |
<reponame>isaacsas/ReactionNetworkImporters.jl
module ReactionNetworkImporters
using DataStructures, Symbolics, SymbolicUtils, Catalyst, SparseArrays
# creates a ModelingToolkit function-like Symbol
# can then do stuff like
# @parameters t
# S₁ = funcsym(S,1)
# u = S₁(t)
function funcsym(S::Symbol, t, args...)
S = Symbol(S,args...)
(@variables $(S)(t))[1]
end
abstract type NetworkFileFormat end
# exported data types
#struct RSSANetwork <: NetworkFileFormat end
struct BNGNetwork <: NetworkFileFormat end
struct MatrixNetwork <: NetworkFileFormat end
struct ParsedReactionNetwork
"Catalyst Network"
rn::ReactionSystem
"Initial Conditions"
u₀
"Parameters"
p
"Expressions for the Parameters"
paramexprs
"Dict from `Variable` in species(rn) to full string for species name"
varstonames
"Dict from lumped species name (as string) to group of species ids"
groupstoids
end
ParsedReactionNetwork(rn::ReactionSystem, u₀; p=nothing, paramexprs=nothing, varstonames=nothing, groupstoids=nothing) =
ParsedReactionNetwork(rn, u₀, p, paramexprs, varstonames, groupstoids)
export RSSANetwork, BNGNetwork, MatrixNetwork, ParsedReactionNetwork
# parsers
#include("parsing_routines_rssafiles.jl")
include("parsing_routines_bngnetworkfiles.jl")
include("parsing_routines_matrixnetworks.jl")
export loadrxnetwork
end # module
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] | 2.716763 | 519 |
<reponame>JuliaTagBot/OrthogonalPolynomials.jl
module OrthogonalPolynomials
# 0. Our mission
# Dear codes, should you choose to accept it, is to learn and implement the following
# mathematical in Julia, whilst learning the magic of multiple dispatch as a design
# paradigm. Ready? Steady? Go!
# If you are a beginner and this code just looks like squiggles, worry not!
# There is a video tutorial online at my youtube channel, BrainRPG.
# Happy Hacking!
# - <NAME>
# http://people.math.sfu.ca/~cbm/aands/page_789.jpg
# 1. First design - Laguerre only
# Note the _names with underscore are used as convention for package internals.
_d(n, α=0) = binomial(n+α, n)
_b(n, m) = n-m+1
_c(α, m) = m*(α+m)
_k(n, α=0) = [-_b(n,i)*inv(_c(α, i)) for i in 1:n]
_f(x) = x
_α = 0
_a(x, n, ks = _k(n,α), i=0) = i == n ? :(1) : return :(muladd( $(ks[i+1]*_f(x)), $((_a)(x, n, ks, i+1)) , 1))
# 2. Macro function as a for loop
macro _a(x,n,ks=k(n,α))
ex = 1
for i in n-1 : -1 : 0
ex = :(muladd( $(ks[i+1]*_f(x)), $ex, 1))
end
return :($ex)
end
# 2. Second design, now with dispatching on types.
# 2.1 FIRST, we will define all the infrastructure for dispatching for Laguerre.
# 2.2 THEN, we will extend that design by adding dispatches for the Hermite case.
# 2.3 LAST, we will extend for all the other cases.
# 2.1 FIRST, the Laguerre case.
abstract type OP end
struct Laguerre{α,n} <: OP end
params(a :: T) where T = Tuple(T.parameters) # Credit to @yingboma
Laguerre(α, n :: Int) = Laguerre{α, n}()
Laguerre(n :: Int) = n >= 0 ? Laguerre{0,n}() : throw("The degree n must be non-negative.")
# Generalized binomial, useful too in the Jacobi case.
function Base.binomial(α :: T,k :: Int) where T<:AbstractFloat
res = 1
for i in 0:k-1
res = res * (α - i)
end
return res/factorial(k)
end
d(p :: T, x) where T<:Laguerre = d(p)
function d(p :: T) where T<:Laguerre
α,n = params(p)
return binomial(α + n, n)
end
function b(p :: T, m) where T<:Laguerre
α,n = params(p)
return n-m+1
end
function c(p :: T, m) where T<:Laguerre
α, n = params(p)
return m*(α + m)
end
f(p :: T, x) where T<:Laguerre = x
# This now works for all Orthogonal Polynomials!
function k(p :: T, x) where T<:OP
n = params(p)[end]
return [-b(p, i)*inv(c(p, i)) for i in 1:n]
end
# Notice this works for all types Orthogonal Polynomials!
# Note: It doesn't return a specialized function yet - try to run it with @code_llvm
# We will make this specialized in the Third stage of design, after we add all the dispatches
# for all the polynomials.
function a(p :: T, x, m = params(p)[end]) where T<:OP
res = oneunit(x)
ks = k(p, x)
for i in m-1: -1 : 0
res = muladd(res, ks[i+1]*f(p, x),1)
end
return res * d(p, x)
end
# function a(p :: T, x, i=
# 2.2 SECOND - add a little abstract type machinery to handle dispatches for even/odd cases.
abstract type EvenOP <: OP end
abstract type OddOP <: OP end
struct Hermite{n} <: OP end
struct HermiteEven{n} <: EvenOP end
struct HermiteOdd{n} <: OddOP end
Hermite{n}() where n = iseven(n) ? HermiteEven{n ÷ 2}() : HermiteOdd{(n-1) ÷ 2}()
Hermite(n) = n>= 0 ? Hermite{n}() : throw("The degree n must be non-negative.")
#Heven
function d(p :: T, x) where T<:HermiteEven
n = params(p)[1]
return (-1) ^ n * (factorial(2n)/factorial(n))
end
function b(p :: T, m) where T<:HermiteEven
n = params(p)[1]
return 2*(n - m + 1)
end
# This now covers half the cases!
c(p :: T, m) where T<:EvenOP = m*(2m - 1)
# This catches almost every case!
f(p :: T, x) where T<:OP = x^2
#Hodd
function d(p :: T, x) where T<:HermiteOdd
n = params(p)[1]
return (-1) ^ n * (factorial(2n +1)/factorial(n))*2x
end
function b(p :: T, m) where T<:HermiteOdd
n = params(p)[1]
return 2*(n - m + 1)
end
# This catches almost every case left over! We are only missing the Jacobi case.
c(p :: T, m) where T<:OddOP = m * (2m + 1)
# 2.3 THIRD, we now add dispatches to all the other cases.
struct Legendre{n} <: OP end
struct LegendreEven{n} <: EvenOP end
struct LegendreOdd{n} <: OddOP end
Legendre{n}() where n = iseven(n) ? LegendreEven{n ÷ 2}() : LegendreOdd{(n-1) ÷ 2}()
Legendre(n :: Int) = n>=0 ? Legendre{n}() : throw("The degree n must be non-negative.")
# PEven
# Note! We only have to define two functions per case here, because the other cases already got caught by
# some adequate planning and dispatching.
function d(p :: T, x) where T<:LegendreEven
n = params(p)[1]
return (-1/4)^n * binomial(2n,n)
end
function b(p :: T, m) where T<:LegendreEven
n = params(p)[1]
return (n - m + 1) * (2n + 2m -1)
end
# POdd
function d(p :: T, x) where T<:LegendreOdd
n = params(p)[1]
return (-1/4)^n * binomial(2n+1,n) * (n+1) * x
end
function b(p :: T, m) where T<:LegendreOdd
n = params(p)[1]
return (n - m + 1) * (2n + 2m + 1)
end
# TEven
struct ChebyshevFirstKind{n} <: OP end
struct ChebyshevFirstKindEven{n} <: EvenOP end
struct ChebyshevFirstKindOdd{n} <: OddOP end
ChebyshevFirstKind{n}() where n = iseven(n) ? ChebyshevFirstKindEven{n ÷ 2}() : ChebyshevFirstKindOdd{(n-1) ÷ 2}()
ChebyshevFirstKind(n :: Int) = n>=0 ? ChebyshevFirstKind{n}() : throw("The degree n must be non-negative")
function d(p :: T, x) where T<:ChebyshevFirstKindEven
n = params(p)[1]
return (-1)^n
end
function b(p :: T, m) where T<:ChebyshevFirstKindEven
n = params(p)[1]
return 2(n-m+1)*(n+m-1)
end
# TOdd
function d(p :: T, x) where T<: ChebyshevFirstKindOdd
n = params(p)[1]
return (-1^n)*(2n+1)*x
end
function b(p ::T, m) where T<: ChebyshevFirstKindOdd
n = params(p)[1]
return 2*(n-m+1)*(n+m)
end
# UEven
struct ChebyshevSecondKind{n} <: OP end
struct ChebyshevSecondKindEven{n} <: EvenOP end
struct ChebyshevSecondKindOdd{n} <: OddOP end
ChebyshevSecondKind{n}() where n = iseven(n) ? ChebyshevSecondKindEven{n ÷ 2}() : ChebyshevSecondKindOdd{(n-1) ÷ 2}()
ChebyshevSecondKind(n :: Int) = n>=0 ? ChebyshevSecondKind{n}() : throw("The degree n must be non-negative")
function d(p :: T, x) where T<:ChebyshevSecondKindEven
n = params(p)[1]
return (-1)^n
end
function b(p :: T, m) where T<:ChebyshevSecondKindEven
n = params(p)[1]
return 2*(n-m+1)*(n+m)
end
# UOdd
function d(p :: T, x) where T<:ChebyshevSecondKindOdd
n = params(p)[1]
return (-1)^n * 2(n+1)*x
end
function b(p :: T, m) where T<:ChebyshevSecondKindOdd
n = params(p)[1]
return 2*(n-m+1)*(n+m)
end
# CEven
struct Gegenbauer{α, n} <: OP end
struct GegenbauerEven{α, n} <: EvenOP end
struct GegenbauerOdd{α, n} <: OddOP end
function Gegenbauer(α, n :: Int)
n >= 0 && α >= 0 ? nothing : throw("The degree n must be non-negative")
iseven(n) ? GegenbauerEven{α, n ÷ 2}() : GegenbauerOdd{α, (n-1) ÷ 2}()
end
Gegenbauer(n :: Int) = Gegenbauer(0, n)
# We need to define a pochhammer symbol here!
function poch(a,::Val{N}) where N
res = a; for i in Base.OneTo(N-1)
res = res * (a+i)
end
return res
end
poch(a, ::Val{0}) = one(a)
poch(a, ::Val{1}) = a
function d(p :: T, x = 0) where T<:GegenbauerEven
α, n = params(p)
return (-1)^n * (poch(α,Val(n))/factorial(n))
end
function b(p :: T, m) where T<:GegenbauerEven
α, n = params(p)
return 2*(n-m+1)*(α+n+m-1)
end
# COdd
function d(p :: T, x) where T<:GegenbauerOdd
α, n = params(p)
return (-1)^n * (poch(α,Val(n+1))/factorial(n))*2x
end
function b(p :: T, m) where T<:GegenbauerOdd
α, n = params(p)
return 2*(n-m+1)*(α + n + m)
end
# Jacobi
struct Jacobi{α, β, n} <: OP end
Jacobi(α, β, n :: Int) = α >= 0 && β >= 0 && n >= 0 ? Jacobi{α, β, n}() : throw("The parameters must be non-negative")
function d(p :: T, x) where T<:Jacobi
α, β, n = params(p)
return binomial(n + α, n)
end
function b(p :: T, m) where T<:Jacobi
α, β, n = params(p)
return (n - m + 1) * (α + β + n + m)
end
function c(p :: T, m) where T<:Jacobi
α, β, n = params(p)
return 2m * (α + m)
end
function f(p :: T, x) where T<:Jacobi
return 1 - x
end
# Pass: Laguerre, ChebyFE, ChebySE,
# Check Jacobi-binomial, HermiteEven, HermiteOdd, LegendreE, LegendreO, ChebySO, ChebyFO, GegenbauerE, GegenbauerO
# Strat, look at the a(j,2,0) and k(j,.5) methods
export d,b,c,k,f,a
export Jacobi # J(α,β,n)
export GegenbauerEven, GegenbauerOdd, Gegenbauer # C(α,n)
export ChebyshevSecondKindEven, ChebyshevSecondKindOdd, ChebyshevSecondKind # U(n)
export ChebyshevFirstKindEven, ChebyshevFirstKindOdd, ChebyshevFirstKind # T(n)
export Legendre # L(n)
export HermiteOdd, HermiteEven, Hermite # H(n)
export Laguerre
end # module
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] | 2.187933 | 4,044 |
<filename>test/feature_transform.jl
@testset "feature_transform" begin
function ind2cart(F)
s = CartesianIndices(axes(F))
return map(i -> CartesianIndex(s[i]), F)
end
@testset "Square Images" begin
# (1)
A = [true false; false true]
F = feature_transform(A)
@test F == ind2cart([1 1; 1 4])
D = distance_transform(F)
@test D == [0 1; 1 0]
# (2)
A = [true true; false true]
F = feature_transform(A)
@test F == ind2cart([1 3; 1 4])
D = distance_transform(F)
@test D == [0 0; 1 0]
# (3)
A = [false false; false true]
F = feature_transform(A)
@test F == ind2cart([4 4; 4 4])
D = distance_transform(F)
@test D ≈ [sqrt(2) 1.0; 1.0 0.0]
# (4)
A = [true false true; false true false; true true false]
F = feature_transform(A)
@test F == ind2cart([1 1 7; 1 5 5; 3 6 6])
D = distance_transform(F)
@test D == [0 1 0; 1 0 1; 0 0 1]
# (5)
A = [false false true; true true false; true true true]
F = feature_transform(A)
@test F == ind2cart([2 5 7; 2 5 5; 3 6 9])
D = distance_transform(F)
@test D == [1 1 0; 0 0 1; 0 0 0]
# (6)
A = [
true false true true
false true false false
false true true false
true false false false
]
F = feature_transform(A)
@test F == ind2cart([1 1 9 13; 1 6 6 13; 4 7 11 11; 4 4 11 11])
D = distance_transform(F)
@test D ≈ [0.0 1.0 0.0 0.0; 1.0 0.0 1.0 1.0; 1.0 0.0 0.0 1.0; 0.0 1.0 1.0 sqrt(2)]
end
@testset "Rectangular Images" begin
# (1)
A = [true false true; false true false]
F = feature_transform(A)
@test F == ind2cart([1 1 5; 1 4 4])
D = distance_transform(F)
@test D == [0 1 0; 1 0 1]
# (2)
A = [true false; false false; false true]
F = feature_transform(A)
@test F == ind2cart([1 1; 1 6; 6 6])
D = distance_transform(F)
@test D == [0 1; 1 1; 1 0]
# (3)
A = [
true false false
true false false
false true true
true true true
false true false
]
F = feature_transform(A)
@test F == ind2cart([1 1 1; 2 2 13; 2 8 13; 4 9 14; 4 10 10])
D = distance_transform(F)
@test D == [0.0 1.0 2.0; 0.0 1.0 1.0; 1.0 0.0 0.0; 0.0 0.0 0.0; 1.0 0.0 1.0]
@test feature_transform(Gray.(A)) == F
end
@testset "Corner Case Images" begin
null1 = CartesianIndex((typemin(Int),))
null2 = CartesianIndex((typemin(Int), typemin(Int)))
# (1)
A = [false]
F = feature_transform(A)
@test F == [null1]
D = distance_transform(F)
@test D == [Inf]
# (2)
A = [true]
F = feature_transform(A)
@test F == ind2cart([1])
D = distance_transform(F)
@test D == [0]
# (3)
A = [true false]
F = feature_transform(A)
@test F == ind2cart([1 1])
D = distance_transform(F)
@test D == [0 1]
# (4)
A = [false; false]
F = feature_transform(A)
@test F == [null1; null1]
D = distance_transform(F)
@test D == [Inf; Inf]
# (5)
A = [true; true]
F = feature_transform(A)
@test F == ind2cart([1; 2])
D = distance_transform(F)
@test D == [0; 0]
# (6)
A = [true; true; false]
F = feature_transform(A)
@test F == ind2cart([1; 2; 2])
D = distance_transform(F)
@test D == [0; 0; 1]
# (7)
A = falses(3, 3)
F = feature_transform(A)
@test all(x -> x == null2, F)
D = distance_transform(F)
@test all(x -> x == Inf, D)
# (8)
A = trues(4, 2, 3)
F = feature_transform(A)
@test F == ind2cart(reshape(1:length(A), size(A)))
D = distance_transform(F)
@test all(x -> x == 0, D)
# (9)
A = falses(4, 2, 3)
A[3, 1, 2] = true
@test all(==(CartesianIndex(3, 1, 2)), feature_transform(A))
end
@testset "Anisotropic images" begin
A, w = [false false; false true], (3, 1)
F = feature_transform(A; weights=w)
@test F == ind2cart([4 4; 4 4])
D = distance_transform(F, w)
@test D ≈ [sqrt(10) 3.0; 1.0 0.0]
A, w = [false false; false true], (1, 3)
F = feature_transform(A; weights=w)
@test F == ind2cart([4 4; 4 4])
D = distance_transform(F, w)
@test D ≈ [sqrt(10) 1.0; 3.0 0.0]
A, w = [true false; false true], (3, 1)
F = feature_transform(A; weights=w)
@test F == ind2cart([1 1; 4 4])
D = distance_transform(F, w)
@test D == [0 1; 1 0]
A, w = [true false; false true], (1, 3)
F = feature_transform(A; weights=w)
@test F == ind2cart([1 4; 1 4])
D = distance_transform(F, w)
@test D == [0 1; 1 0]
end
end
nothing
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] | 1.876812 | 2,760 |
<filename>src/EPOCHInput.jl
module EPOCHInput
export parse_input
using Unitful
using PhysicalConstants.CODATA2018: c_0, ε_0, μ_0, m_e, e
using BangBang
include("parse.jl")
include("constant.jl")
include("control.jl")
include("no_globals.jl")
include("no_parsing.jl")
include("output.jl")
end
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] | 2.529915 | 117 |
module TimedTests
export @timedtestset, TimedTestSet
import Test
using Test: AbstractTestSet, DefaultTestSet, Broken, Fail, Error, Pass, TestSetException
import Test: record, finish, print_test_errors, print_test_results, print_counts,
get_testset, get_testset_depth,get_test_counts, get_alignment, filter_errors
macro timedtestset(ex...)
if length(ex) == 2
name = ex[1]
testset = ex[2]
else
name = "timed test set"
testset = ex[1]
end
timedtestsetvar = gensym()
# for some reason, you cannot do @testset TestExtras.TimedTests.TimedTestSet begin ...
# instead we do: var = TestExtras.TimedTests.TimedTestSet; @testset var begin ...
return esc(Expr(:block,
Expr(:(=), timedtestsetvar, :($TimedTestSet)),
Expr(:macrocall, Symbol("@testset"), __source__, timedtestsetvar, name, testset)))
end
@nospecialize
mutable struct TimedTestSet <: AbstractTestSet
description::AbstractString
results::Vector
n_passed::Int
anynonpass::Bool
ti::Float64
tf::Float64
TimedTestSet(desc) = new(desc, [], 0, false, time())
end
# For a broken result, simply store the result
Test.record(ts::TimedTestSet, t::Broken) = (push!(ts.results, t); t)
# For a passed result, do not store the result since it uses a lot of memory
record(ts::TimedTestSet, t::Pass) = (ts.n_passed += 1; t)
# For the other result types, immediately print the error message
# but do not terminate. Print a backtrace.
function record(ts::TimedTestSet, t::Union{Fail, Error})
if Test.TESTSET_PRINT_ENABLE[]
printstyled(ts.description, ": ", color=:white)
# don't print for interrupted tests
if !(t isa Error) || t.test_type !== :test_interrupted
print(t)
# don't print the backtrace for Errors because it gets printed in the show
# method
if !isa(t, Error)
Base.show_backtrace(stdout, Test.scrub_backtrace(backtrace()))
end
println()
end
end
push!(ts.results, t)
t, isa(t, Error) || backtrace()
end
# When a TimedTestSet finishes, it records itself to its parent
# testset, if there is one. This allows for recursive printing of
# the results at the end of the tests
record(ts::TimedTestSet, t::AbstractTestSet) = push!(ts.results, t)
@specialize
function print_test_errors(ts::TimedTestSet)
for t in ts.results
if isa(t, Error) || isa(t, Fail)
println("Error in testset $(ts.description):")
show(t)
println()
elseif isa(t, TimedTestSet)
print_test_errors(t)
end
end
end
function print_test_results(ts::TimedTestSet, depth_pad=0)
# Calculate the overall number for each type so each of
# the test result types are aligned
passes, fails, errors, broken, c_passes, c_fails, c_errors, c_broken = get_test_counts(ts)
total_pass = passes + c_passes
total_fail = fails + c_fails
total_error = errors + c_errors
total_broken = broken + c_broken
dig_pass = total_pass > 0 ? ndigits(total_pass) : 0
dig_fail = total_fail > 0 ? ndigits(total_fail) : 0
dig_error = total_error > 0 ? ndigits(total_error) : 0
dig_broken = total_broken > 0 ? ndigits(total_broken) : 0
total = total_pass + total_fail + total_error + total_broken
dig_total = total > 0 ? ndigits(total) : 0
# For each category, take max of digits and header width if there are
# tests of that type
pass_width = dig_pass > 0 ? max(length("Pass"), dig_pass) : 0
fail_width = dig_fail > 0 ? max(length("Fail"), dig_fail) : 0
error_width = dig_error > 0 ? max(length("Error"), dig_error) : 0
broken_width = dig_broken > 0 ? max(length("Broken"), dig_broken) : 0
total_width = dig_total > 0 ? max(length("Total"), dig_total) : 0
# Calculate the alignment of the test result counts by
# recursively walking the tree of test sets
align = max(get_alignment(ts, 0), length("Test Summary:"))
# Print the outer test set header once
pad = total == 0 ? "" : " "
printstyled(rpad("Test Summary:", align, " "), " |", pad; bold=true, color=:white)
if pass_width > 0
printstyled(lpad("Pass", pass_width, " "), " "; bold=true, color=:green)
end
if fail_width > 0
printstyled(lpad("Fail", fail_width, " "), " "; bold=true, color=Base.error_color())
end
if error_width > 0
printstyled(lpad("Error", error_width, " "), " "; bold=true, color=Base.error_color())
end
if broken_width > 0
printstyled(lpad("Broken", broken_width, " "), " "; bold=true, color=Base.warn_color())
end
if total_width > 0
printstyled(lpad("Total", total_width, " "); bold=true, color=Base.info_color())
end
printstyled(" Time: "; bold = true, color = Base.info_color())
printstyled(string(round(ts.tf-ts.ti; sigdigits=3)); color = Base.info_color())
println()
# Recursively print a summary at every level
print_counts(ts, depth_pad, align, pass_width, fail_width, error_width, broken_width, total_width)
end
# Called at the end of a @testset, behaviour depends on whether
# this is a child of another testset, or the "root" testset
function finish(ts::TimedTestSet)
# If we are a nested test set, do not print a full summary
# now - let the parent test set do the printing
ts.tf = time()
if get_testset_depth() != 0
# Attach this test set to the parent test set
parent_ts = get_testset()
record(parent_ts, ts)
return ts
end
passes, fails, errors, broken, c_passes, c_fails, c_errors, c_broken = get_test_counts(ts)
total_pass = passes + c_passes
total_fail = fails + c_fails
total_error = errors + c_errors
total_broken = broken + c_broken
total = total_pass + total_fail + total_error + total_broken
if Test.TESTSET_PRINT_ENABLE[]
print_test_results(ts)
end
# Finally throw an error as we are the outermost test set
if total != total_pass + total_broken
# Get all the error/failures and bring them along for the ride
efs = filter_errors(ts)
throw(TestSetException(total_pass, total_fail, total_error, total_broken, efs))
end
# return the testset so it is returned from the @testset macro
ts
end
# Recursive function that finds the column that the result counts
# can begin at by taking into account the width of the descriptions
# and the amount of indentation. If a test set had no failures, and
# no failures in child test sets, there is no need to include those
# in calculating the alignment
function get_alignment(ts::TimedTestSet, depth::Int)
# The minimum width at this depth is
ts_width = 2*depth + length(ts.description)
# If all passing, no need to look at children
!ts.anynonpass && return ts_width
# Return the maximum of this width and the minimum width
# for all children (if they exist)
isempty(ts.results) && return ts_width
child_widths = map(t->get_alignment(t, depth+1), ts.results)
return max(ts_width, maximum(child_widths))
end
# Recursive function that fetches backtraces for any and all errors
# or failures the testset and its children encountered
function filter_errors(ts::TimedTestSet)
efs = []
for t in ts.results
if isa(t, Union{TimedTestSet,DefaultTestSet})
append!(efs, filter_errors(t))
elseif isa(t, Union{Fail, Error})
append!(efs, [t])
end
end
efs
end
# Recursive function that counts the number of test results of each
# type directly in the testset, and totals across the child testsets
function get_test_counts(ts::TimedTestSet)
passes, fails, errors, broken = ts.n_passed, 0, 0, 0
c_passes, c_fails, c_errors, c_broken = 0, 0, 0, 0
for t in ts.results
isa(t, Fail) && (fails += 1)
isa(t, Error) && (errors += 1)
isa(t, Broken) && (broken += 1)
if isa(t, Union{TimedTestSet,DefaultTestSet})
np, nf, ne, nb, ncp, ncf, nce , ncb = get_test_counts(t)
c_passes += np + ncp
c_fails += nf + ncf
c_errors += ne + nce
c_broken += nb + ncb
end
end
ts.anynonpass = (fails + errors + c_fails + c_errors > 0)
return passes, fails, errors, broken, c_passes, c_fails, c_errors, c_broken
end
# Recursive function that prints out the results at each level of
# the tree of test sets
function print_counts(ts::TimedTestSet, depth, align,
pass_width, fail_width, error_width, broken_width, total_width)
# Count results by each type at this level, and recursively
# through any child test sets
passes, fails, errors, broken, c_passes, c_fails, c_errors, c_broken = get_test_counts(ts)
subtotal = passes + fails + errors + broken + c_passes + c_fails + c_errors + c_broken
# Print test set header, with an alignment that ensures all
# the test results appear above each other
print(rpad(string(" "^depth, ts.description), align, " "), " | ")
np = passes + c_passes
if np > 0
printstyled(lpad(string(np), pass_width, " "), " ", color=:green)
elseif pass_width > 0
# No passes at this level, but some at another level
print(lpad(" ", pass_width), " ")
end
nf = fails + c_fails
if nf > 0
printstyled(lpad(string(nf), fail_width, " "), " ", color=Base.error_color())
elseif fail_width > 0
# No fails at this level, but some at another level
print(lpad(" ", fail_width), " ")
end
ne = errors + c_errors
if ne > 0
printstyled(lpad(string(ne), error_width, " "), " ", color=Base.error_color())
elseif error_width > 0
# No errors at this level, but some at another level
print(lpad(" ", error_width), " ")
end
nb = broken + c_broken
if nb > 0
printstyled(lpad(string(nb), broken_width, " "), " ", color=Base.warn_color())
elseif broken_width > 0
# None broken at this level, but some at another level
print(lpad(" ", broken_width), " ")
end
if np == 0 && nf == 0 && ne == 0 && nb == 0
printstyled("No tests", color=Base.info_color())
else
printstyled(lpad(string(subtotal), total_width, " "), color=Base.info_color())
end
println()
# Only print results at lower levels if we had failures
if np + nb != subtotal
for t in ts.results
if isa(t, Union{TimedTestSet,DefaultTestSet})
print_counts(t, depth + 1, align,
pass_width, fail_width, error_width, broken_width, total_width)
end
end
end
end
end
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] | 2.501161 | 4,308 |
<filename>test/test_VolterraIntegral-MonKernel.jl
using ApproxFun, BandedMatrices, BlockBandedMatrices, LinearAlgebra, BlockArrays, MultivariateOrthogonalPolynomials, Test
using SparseVolterraExamples
#############################
## Tests for Volterra integral operator
## with non-Clenshaw monomial expansion of the kernel.
####
## Includes tests for 1 to x and 1 to 1-x limits of integration.
#############################
@testset "Volterra Integral operator testing, no kernel" begin
N=15;
S = Jacobi(0,1,0..1);
g = Fun(x -> 1-sin(x)^2+x*exp(x^3), S);
V = triVolterraFunP01(g,[1.,0.],N,false)(0);
@test V ≈ triNoKernelDirect(g,1.);
end
@testset "Volterra Integral operator testing, with kernel" begin
N=15;
S = Jacobi(0,1,0..1);
g = Fun(x -> 1-sin(x)^2, S);
# K given in monomial basis
K = [1.,0.,2.,1.]; # = K(x,y)=1+2*x+x*y
val=0.4;
Kfval = Fun(y -> 1+2*val+val*y, S);
@test triVolterraFunP01(g,K,N,false)(val) ≈ triNoKernelDirect(Kfval*g,1.0-val);
#########################################################################
N=15;
S = Jacobi(0,1,0..1);
g = Fun(x -> 1-sin(x)^2, S);
# K given in monomial basis
K = [2.3,0.33,2.1,1.2,7.6]; # = K(x,y)=2.3+0.33*y+2.1*x+1.2*x*y+7.6*y^2
val=0.6;
Kfval = Fun(y -> 2.3+0.33*y+2.1*val+1.2*val*y+7.6*y^2, S);
@test triVolterraFunP01(g,K,N,false)(val) ≈ triNoKernelDirect(Kfval*g,1.0-val);
end
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] | 2.173252 | 658 |
<filename>wind_mixing/train_NDE_triples.jl
using Flux
using WindMixing
using JLD2
using FileIO
using OceanParameterizations
using OrdinaryDiffEq
using Random
using GalacticOptim
using LinearAlgebra
BLAS.set_num_threads(1)
# Training data
# train_files = [
# "wind_-1e-3_heating_-4e-8",
# "wind_-5e-4_cooling_4e-8",
# ]
train_files = [
"-1e-3"
]
𝒟train = WindMixing.data(train_files, scale_type=ZeroMeanUnitVarianceScaling, enforce_surface_fluxes=false)
PATH = pwd()
OUTPUT_PATH = joinpath(PATH, "training_output")
OUTPUT_PATH = "D:\\University Matters\\MIT\\CLiMA Project\\OceanParameterizations.jl\\training_output"
EXTRACTED_OUTPUT_PATH = joinpath(PATH, "extracted_training_output")
# FILE_PATH = joinpath(OUTPUT_PATH, "NDE_training_mpp_3sim_-1e-3_-8e-4_-5e-4_diffusivity_1e-1_Ri_1e-1_weights_divide1f5_gradient_smallNN_scale_1e-2_rate_2e-4.jld2")
# @assert !isfile(FILE_PATH)
# FILE_PATH_uw = joinpath(PATH, "extracted_training_output", "uw_NN_training_1sim_-1e-3_extracted.jld2")
# FILE_PATH_vw = joinpath(PATH, "extracted_training_output", "vw_NN_training_1sim_-1e-3_extracted.jld2")
# FILE_PATH_wT = joinpath(PATH, "extracted_training_output", "wT_NN_training_1sim_-1e-3_extracted.jld2")
# uw_file = jldopen(FILE_PATH_uw, "r")
# vw_file = jldopen(FILE_PATH_vw, "r")
# wT_file = jldopen(FILE_PATH_wT, "r")
# uw_NN = uw_file["neural_network"]
# vw_NN = vw_file["neural_network"]
# wT_NN = wT_file["neural_network"]
N_inputs = 96
hidden_units = 400
N_outputs = 31
weights, re = Flux.destructure(Chain(Dense(N_inputs, hidden_units, relu), Dense(hidden_units, N_outputs)))
uw_NN = re(weights ./ 1f5)
vw_NN = re(weights ./ 1f5)
wT_NN = re(weights ./ 1f5)
task_id = parse(Int,ARGS[1]) + 1
num_tasks = parse(Int,ARGS[2])
task_id = 1
FILE_NAME = ["NDE_training_mpp_2sim_windcooling_MS_windheating_SS_diffusivity_1e-1_Ri_1e-1_divide1f5_gradient_smallNN_scale_5e-3_rate_1e-4",
"NDE_training_mpp_2sim_windcooling_MS_windheating_SS_diffusivity_1e-1_Ri_1e-1_divide1f5_gradient_smallNN_scale_1e-2_rate_1e-4",
"NDE_training_mpp_2sim_windcooling_MS_windheating_SS_diffusivity_1e-1_Ri_1e-1_divide1f5_gradient_smallNN_scale_1.5e-2_rate_1e-4",
"NDE_training_mpp_2sim_windcooling_MS_windheating_SS_diffusivity_1e-1_Ri_1e-1_divide1f5_gradient_smallNN_scale_2e-2_rate_1e-4",
][task_id]
FILE_PATH = joinpath(OUTPUT_PATH, "$(FILE_NAME).jld2")
@assert !isfile(FILE_PATH)
EXTRACTED_FILE_NAME = "$(FILE_NAME)_extracted"
EXTRACTED_FILE_PATH = joinpath(EXTRACTED_OUTPUT_PATH, "$EXTRACTED_FILE_NAME.jld2")
# FILE_NAME_NN = ["NDE_training_mpp_5sim_-1e-3_-9e-4_-8e-4_-7e-4_-5e-4_diffusivity_1e-1_Ri_1e-1_weights_divide1f5_gradient_smallNN_scale_5e-3_rate_1e-4_extracted.jld2",
# "NDE_training_mpp_5sim_-1e-3_-9e-4_-8e-4_-7e-4_-5e-4_diffusivity_1e-1_Ri_1e-1_weights_divide1f5_gradient_smallNN_scale_5e-3_rate_2e-4_extracted.jld2",
# "NDE_training_mpp_5sim_-1e-3_-9e-4_-8e-4_-7e-4_-5e-4_diffusivity_1e-1_Ri_1e-1_weights_divide1f5_gradient_smallNN_scale_1e-2_rate_1e-4_extracted.jld2",
# "NDE_training_mpp_5sim_-1e-3_-9e-4_-8e-4_-7e-4_-5e-4_diffusivity_1e-1_Ri_1e-1_weights_divide1f5_gradient_smallNN_scale_1e-2_rate_2e-4_extracted.jld2"
# ][task_id]
# FILE_NAME_NN = "NDE_training_mpp_9sim_windcooling_diffusivity_1e-1_Ri_1e-1_weights_divide1f5_gradient_smallNN_scale_5e-3_rate_1e-4_extracted.jld2"
# FILE_PATH_NN = joinpath(EXTRACTED_OUTPUT_PATH, FILE_NAME_NN)
# @assert isfile(FILE_PATH_NN)
# file = jldopen(FILE_PATH_NN, "r")
# uw_NN = file["neural_network/uw"]
# vw_NN = file["neural_network/vw"]
# wT_NN = file["neural_network/wT"]
# close(file)
gradient_scaling = [5f-3, 1f-2, 1.5f-2, 2f-2][task_id]
train_parameters = Dict("ν₀" => 1f-4, "ν₋" => 0.1f0, "Riᶜ" => 0.25f0, "ΔRi" => 1f-1, "Pr" => 1f0, "κ" => 10f0,
"modified_pacanowski_philander" => true, "convective_adjustment" => false,
"smooth_profile" => false, "smooth_NN" => false, "smooth_Ri" => false, "train_gradient" => true,
"zero_weights" => true, "unscaled" => false, "gradient_scaling" => gradient_scaling)
# train_epochs = [1]
# train_tranges = [1:9:1153]
# train_iterations = [600]
# # train_optimizers = [[[ADAM(1e-4)]], [[ADAM(2e-4)]], [[ADAM(1e-4)]], [[ADAM(2e-4)]], [[ADAM(1e-4)]], [[ADAM(2e-4)]], [[ADAM(1e-4)]], [[ADAM(2e-4)]]][task_id]
# train_optimizers = [[ADAM(1e-4)]]
train_epochs = [1]
train_tranges = [1:35:200]
train_iterations = [3]
train_optimizers = [[ADAM(2e-4)]]
# train_tranges = [1:10:100, 1:10:200, 1:20:500, 1:30:700, 1:30:800, 1:30:900, 1:35:1153]
# train_epochs = [1 for i in 1:length(train_tranges)]
# train_iterations = [50, 50, 100, 30, 20, 50, 150]
# train_optimizers = [[ADAM(0.1), ADAM(0.01)], [ADAM(0.01)], [ADAM(0.01)], [ADAM(0.01)], [ADAM(0.01)], [ADAM(0.01)], [ADAM(0.01), ADAM(0.001), ADAM(5e-4), ADAM(2e-4)]]
# train_tranges = [1:10:100, 1:10:200, 1:20:500, 1:20:800, 1:35:1153]
# train_epochs = [1 for i in 1:length(train_tranges)]
# train_iterations = [30, 30, 50, 30, 200]
# train_optimizers = [[[ADAM(0.01)] for i in 1:6]; [[ADAM(0.01), ADAM(1e-3)]]]
# # train_optimizers = [[ADAM(1e-5)] for i in 1:6]
timestepper = Rosenbrock23()
# train_optimizers = [[ADAM(2e-4), ADAM(1e-4), ADAM(5e-5), RMSProp(1e-4)]]
# train_optimizers=[[ADAM(5e-4)]]
function train(FILE_PATH, train_files, train_epochs, train_tranges, train_parameters, train_optimizers, train_iterations, uw_NN, vw_NN, wT_NN, 𝒟train, timestepper, unscaled)
write_metadata_NDE_training(FILE_PATH, train_files, train_epochs, train_tranges, train_parameters, train_optimizers, uw_NN, vw_NN, wT_NN)
if unscaled
for i in 1:length(train_epochs)
@info "iteration $i/$(length(train_epochs)), time range $(train_tranges[i])"
# uw_NN, vw_NN, wT_NN = train_NDE_convective_adjustment(uw_NN, vw_NN, wT_NN, 𝒟train, train_tranges[i], timestepper, train_optimizers[i], train_epochs[i], FILE_PATH, 1, 1, 10f0, 5)
if train_parameters["modified_pacanowski_philander"]
uw_NN, vw_NN, wT_NN = train_NDE_unscaled(uw_NN, vw_NN, wT_NN, 𝒟train, train_tranges[i], timestepper, train_optimizers[i], train_epochs[i], FILE_PATH, i, n_simulations=length(train_files), maxiters=train_iterations[i],
modified_pacanowski_philander=train_parameters["modified_pacanowski_philander"], convective_adjustment=train_parameters["convective_adjustment"],
ν₀=train_parameters["ν₀"], ν₋=train_parameters["ν₋"], ΔRi=train_parameters["ΔRi"], Riᶜ=train_parameters["Riᶜ"],
κ=train_parameters["κ"],
smooth_profile=train_parameters["smooth_profile"], smooth_NN=train_parameters["smooth_NN"], smooth_Ri=train_parameters["smooth_Ri"], train_gradient=train_parameters["train_gradient"],
gradient_scaling=train_parameters["gradient_scaling"])
else
uw_NN, vw_NN, wT_NN = train_NDE_unscaled(uw_NN, vw_NN, wT_NN, 𝒟train, train_tranges[i], timestepper, train_optimizers[i], train_epochs[i], FILE_PATH, i, n_simulations=length(train_files), maxiters=train_iterations[i],
modified_pacanowski_philander=train_parameters["modified_pacanowski_philander"], convective_adjustment=train_parameters["convective_adjustment"],
κ=train_parameters["κ"],
smooth_profile=train_parameters["smooth_profile"], smooth_NN=train_parameters["smooth_NN"], smooth_Ri=train_parameters["smooth_Ri"], train_gradient=train_parameters["train_gradient"],
gradient_scaling=train_parameters["gradient_scaling"])
end
end
else
for i in 1:length(train_epochs)
@info "iteration $i/$(length(train_epochs)), time range $(train_tranges[i])"
# uw_NN, vw_NN, wT_NN = train_NDE_convective_adjustment(uw_NN, vw_NN, wT_NN, 𝒟train, train_tranges[i], timestepper, train_optimizers[i], train_epochs[i], FILE_PATH, 1, 1, 10f0, 5)
if train_parameters["modified_pacanowski_philander"]
uw_NN, vw_NN, wT_NN = train_NDE(uw_NN, vw_NN, wT_NN, 𝒟train, train_tranges[i], timestepper, train_optimizers[i], train_epochs[i], FILE_PATH, i, n_simulations=length(train_files), maxiters=train_iterations[i],
modified_pacanowski_philander=train_parameters["modified_pacanowski_philander"], convective_adjustment=train_parameters["convective_adjustment"],
ν₀=train_parameters["ν₀"], ν₋=train_parameters["ν₋"], ΔRi=train_parameters["ΔRi"], Riᶜ=train_parameters["Riᶜ"],
κ=train_parameters["κ"],
smooth_profile=train_parameters["smooth_profile"], smooth_NN=train_parameters["smooth_NN"], smooth_Ri=train_parameters["smooth_Ri"], train_gradient=train_parameters["train_gradient"],
zero_weights = train_parameters["zero_weights"],
gradient_scaling=train_parameters["gradient_scaling"])
else
uw_NN, vw_NN, wT_NN = train_NDE(uw_NN, vw_NN, wT_NN, 𝒟train, train_tranges[i], timestepper, train_optimizers[i], train_epochs[i], FILE_PATH, i, n_simulations=length(train_files), maxiters=train_iterations[i],
modified_pacanowski_philander=train_parameters["modified_pacanowski_philander"], convective_adjustment=train_parameters["convective_adjustment"],
κ=train_parameters["κ"],
smooth_profile=train_parameters["smooth_profile"], smooth_NN=train_parameters["smooth_NN"], smooth_Ri=train_parameters["smooth_Ri"], train_gradient=train_parameters["train_gradient"],
zero_weights = train_parameters["zero_weights"],
gradient_scaling=train_parameters["gradient_scaling"])
end
end
end
return uw_NN, vw_NN, wT_NN
end
# uw_NN_res, vw_NN_res, wT_NN_res = train(FILE_PATH, train_files, train_epochs, train_tranges, train_parameters, train_optimizers, train_iterations, uw_NN, vw_NN, wT_NN, 𝒟train, timestepper, train_parameters["unscaled"])
uw_NN_res, vw_NN_res, wT_NN_res = train(FILE_PATH, train_files, train_epochs, train_tranges, train_parameters, train_optimizers, train_iterations, uw_NN, vw_NN, wT_NN, 𝒟train, timestepper, train_parameters["unscaled"])
extract_NN(FILE_PATH, EXTRACTED_FILE_PATH, "NDE")
test_files = train_files
animate_training_results(test_files, FILE_NAME, trange=1:1:1153)
OCEANANIGANS_OUTPUT_DIR = joinpath(pwd(), "NDE_output_oceananigans", FILE_NAME)
animate_training_results_oceananigans(test_files, 60, FILE_NAME, OCEANANIGANS_OUTPUT_DIR) | [
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] | 2.089579 | 5,057 |
"""
Load and plot simulations results on confidence bands' coverages
"""
#region import and models
using Pkg
Pkg.activate(".")
Pkg.instantiate()
using DrWatson
using DataFrames
using PyPlot
pygui(true)
using JLD2
using ScoreDrivenERGM
using ScoreDrivenERGM.DynNets
using Statistics
using Logging
using SharedArrays
using LinearAlgebra
using ProjUtilities
#endregion
@elapsed dfEst = collect_results( datadir("sims", "dgp&FIl_est"))
dfEst["modelTag"] = string.(dfEst["model"])
@elapsed df = dfEst
# @elapsed dfConf = collect_results( datadir("sims", "dgp&FIl_conf"))
# dfConf["modelTag"] = string.(dfConf["model"])
# @elapsed df = antijoin(dfEst, dfConf, on = [:allParDgpT, :modelTag])
df.dgpSettings
begin
# limitSample = 10
limitSample = nothing
# listParUncMethod = ["PB-MVN"] #"WHITE-MLE"
winsorProp=0
listParUncMethod = ["WHITE-MLE"]
#listParUncMethod = ["PB-MVN"] #"WHITE-MLE"
# m = listParUncMethod[1]
# estResRow = collect(eachrow(df))[1]
for estResRow in eachrow(df)
processFlag = false
if (estResRow.N in [100]) & (estResRow.T in [300, 3000]) & (estResRow.model.scoreScalingType == "FISH_D") & (estResRow.dgpSettings.type == "AR")
if estResRow.dgpSettings.opt.B[1] == 1
processFlag = true
if haskey(estResRow.model.options, "integrated")
if estResRow.model.options["integrated"]
end
end
end
end
if processFlag
for m in listParUncMethod
if winsorProp!=0
m = "$m$winsorProp"
end
timeConf = @elapsed avgCover, constInds, errInds, allConfBandsFiltPar, allConfBandsPar, allmvSDUnParEstCovWhite = average_coverages(estResRow, m; limitSample = limitSample, winsorProp=winsorProp)
coverDict = (;m, avgCover, constInds, errInds, allConfBandsFiltPar, allConfBandsPar, allmvSDUnParEstCovWhite) |>DrWatson.ntuple2dict |> DrWatson.tostringdict
coverDict = merge(DrWatson.tostringdict(estResRow), coverDict)
delete!(coverDict, "path")
delete!(coverDict, "modelTag")
coverDict["dgp"] = coverDict["dgpSettings"]
delete!(coverDict, "dgpSettings")
coverDict["S"] = coverDict["nSample"]
delete!(coverDict, "nSample")
# loadPath = estResRow.path
saveName = replace.( savename(coverDict, "jld2"; allowedtypes = (Real, String, Symbol, NamedTuple, Tuple, ScoreDrivenERGM.DynNets.SdErgm) ), r"[\"]" => "") #loadPath[findlast("\\", loadPath)[end]+1:end]
timeSave = save( datadir("sims", "dgp&FIl_conf", saveName), coverDict)
Logging.@info(" errRatio = $(mean(errInds)) , time = $timeConf")
end
end
end
end
# T = 100
# N = 100
# nSample = 100
# modelTag = string(DynNets.SdErgmDirBin0Rec0_pmle("HESS_D"))
# dgpSetting = DynNets.list_example_dgp_settings(DynNets.SdErgmDirBin0Rec0_mle()).dgpSetSDhigh
# res = filter([:modelTag, :T, :N, :dgpSettings, :nSample] => (m,t,n, d, s) -> all((m==modelTag, t==T, n==N, d == dgpSetting, s == nSample)), df)[1,:]
# res.allvEstSdResPar
# vEstSdUnParBootDist = mapslices(x -> DynNets.unrestrict_all_par(res.model, x), res.allvEstSdResPar, dims=1)
# # @show res.allvEstSdResPar
# covParBoot = cov(Utilities.drop_bad_un_estimates(vEstSdUnParBootDist)')
# covParBoot, minEigenVal = Utilities.make_pos_def(covParBoot)
# mvSDUnParEstCov = Symmetric(covParBoot)
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] | 2.17709 | 1,615 |
<filename>src/solvers/structured/progressivehedging/MOI_wrapper.jl
# MIT License
#
# Copyright (c) 2018 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Optimizer(; <keyword arguments>)
Return a progressive-hedging optimizer.
...
# Arguments
- `subproblem_optimizer::AbstractOptimizer`: MathOptInterface solver capable of solving quadratic programs.
- `penalty::AbstractPenalizer = Fixed()`: Specify penalty update procedure (Fixed, Adaptive)
- `execution::AbstractExecuter = Serial`: Specify how algorithm should be executed (Serial, Synchronous, Asynchronous). Distributed variants requires worker cores.
- `penaltyterm::PenaltyTerm = Quadratic`: Specify penaltyterm variant ([`Quadratic`](@ref), [`Linearized`](@ref), [`InfNorm`](@ref), [`ManhattanNorm`][@ref])
- <keyword arguments>: Algorithm specific parameters, consult individual docstrings (see above list) for list of possible arguments and default values.
...
"""
mutable struct Optimizer <: AbstractStructuredOptimizer
subproblem_optimizer
sub_params::Dict{MOI.AbstractOptimizerAttribute, Any}
execution::AbstractExecution
penalizer::AbstractPenalizer
penaltyterm::AbstractPenaltyTerm
parameters::ProgressiveHedgingParameters{Float64}
status::MOI.TerminationStatusCode
primal_status::MOI.ResultStatusCode
dual_status::MOI.ResultStatusCode
raw_status::String
solve_time::Float64
progressivehedging::Union{AbstractProgressiveHedging, Nothing}
function Optimizer(; subproblem_optimizer = nothing,
execution::AbstractExecution = nworkers() == 1 ? Serial() : Synchronous(),
penalty::AbstractPenalizer = Fixed(),
penaltyterm::AbstractPenaltyTerm = Quadratic(),
kw...)
return new(subproblem_optimizer,
Dict{MOI.AbstractOptimizerAttribute, Any}(),
execution,
penalty,
penaltyterm,
ProgressiveHedgingParameters{Float64}(; kw...),
MOI.OPTIMIZE_NOT_CALLED,
MOI.NO_SOLUTION,
MOI.NO_SOLUTION,
"Progressive-hedging optimizer has not been run.",
NaN,
nothing)
end
end
# Interface #
# ========================== #
function supports_structure(optimizer::Optimizer, ::ScenarioDecompositionStructure)
return true
end
function default_structure(::UnspecifiedInstantiation, optimizer::Optimizer)
if optimizer.execution isa Serial && nworkers() == 1
return ScenarioDecomposition()
else
return DistributedScenarioDecomposition()
end
end
function check_loadable(optimizer::Optimizer, ::ScenarioDecompositionStructure)
if optimizer.subproblem_optimizer === nothing
msg = "Subproblem optimizer not set. Consider setting `SubProblemOptimizer` attribute."
throw(UnloadableStructure{Optimizer, ScenarioDecompositionStructure}(msg))
end
return nothing
end
function ensure_compatible_execution!(optimizer::Optimizer, ::ScenarioDecompositionStructure{2, 1, <:Tuple{ScenarioProblems}})
if !(optimizer.execution isa Serial)
@warn "Distributed execution policies are not compatible with a single-core scenario-decomposition structure. Switching to `Serial` execution by default."
MOI.set(optimizer, Execution(), Serial())
end
return nothing
end
function ensure_compatible_execution!(optimizer::Optimizer, ::ScenarioDecompositionStructure{2, 1, <:Tuple{DistributedScenarioProblems}})
if optimizer.execution isa Serial
@warn "Serial execution not compatible with distributed scenario-decomposition structure. Switching to `Synchronous` execution by default."
MOI.set(optimizer, Execution(), Synchronous())
end
return nothing
end
function load_structure!(optimizer::Optimizer, structure::ScenarioDecompositionStructure, x₀::AbstractVector)
# Sanity check
check_loadable(optimizer, structure)
# Restore structure if optimization has been run before
restore_structure!(optimizer)
# Ensure that execution policy is compatible
ensure_compatible_execution!(optimizer, structure)
# Set subproblem optimizers
set_subproblem_optimizer!(structure, optimizer.subproblem_optimizer)
# Set subproblem optimizer attributes
for (attr, value) in optimizer.sub_params
MOI.set(scenarioproblems(structure), attr, value)
end
# Create new progressive-hedging algorithm
optimizer.progressivehedging = ProgressiveHedgingAlgorithm(structure,
x₀,
optimizer.execution,
optimizer.penalizer,
optimizer.penaltyterm;
type2dict(optimizer.parameters)...)
return nothing
end
function restore_structure!(optimizer::Optimizer)
if optimizer.progressivehedging !== nothing
restore_subproblems!(optimizer.progressivehedging)
end
return nothing
end
function MOI.optimize!(optimizer::Optimizer)
if optimizer.progressivehedging === nothing
throw(StochasticProgram.UnloadedStructure{Optimizer}())
end
# Run progressive-hedging procedure
optimizer.status = optimizer.progressivehedging()
# Check if optimal
if optimizer.status == MOI.OPTIMAL
optimizer.primal_status = MOI.FEASIBLE_POINT
optimizer.dual_status = MOI.FEASIBLE_POINT
optimizer.raw_status = "Progressive-hedging procedure converged to optimal solution."
end
# Extract solve time
optimizer.solve_time = optimizer.progressivehedging.progress.tlast - optimizer.progressivehedging.progress.tinit
return nothing
end
function num_iterations(optimizer::Optimizer)
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return num_iterations(optimizer.progressivehedging)
end
function optimizer_name(optimizer::Optimizer)
return "$(str(optimizer.execution))Progressive-hedging with $(str(optimizer.penalizer))"
end
# MOI #
# ========================== #
function MOI.get(optimizer::Optimizer, ::MOI.Silent)
return !MOI.get(optimizer, MOI.RawParameter("log"))
end
function MOI.set(optimizer::Optimizer, attr::MOI.Silent, flag::Bool)
MOI.set(optimizer, MOI.RawParameter("log"), !flag)
optimizer.sub_params[attr] = flag
return nothing
end
function MOI.get(optimizer::Optimizer, param::MOI.RawParameter)
name = Symbol(param.name)
if !(name in fieldnames(ProgressiveHedgingParameters))
error("Unrecognized parameter name: $(name).")
end
return getfield(optimizer.parameters, name)
end
function MOI.set(optimizer::Optimizer, param::MOI.RawParameter, value)
name = Symbol(param.name)
if !(name in fieldnames(ProgressiveHedgingParameters))
error("Unrecognized parameter name: $(name).")
end
setfield!(optimizer.parameters, name, value)
return nothing
end
function MOI.get(optimizer::Optimizer, ::MOI.TimeLimitSec)
limit = MOI.get(optimizer, MOI.RawParameter("time_limit"))
return isinf(limit) ? nothing : limit
end
function MOI.set(optimizer::Optimizer, ::MOI.TimeLimitSec, limit::Union{Real, Nothing})
limit = limit === nothing ? Inf : limit
MOI.set(optimizer, MOI.RawParameter("time_limit"), limit)
return
end
function MOI.get(optimizer::Optimizer, ::PrimalTolerance)
return MOI.get(optimizer, MOI.RawParameter("ϵ₁"))
end
function MOI.set(optimizer::Optimizer, ::PrimalTolerance, limit::Real)
MOI.set(optimizer, MOI.RawParameter("ϵ₁"), limit)
return nothing
end
function MOI.get(optimizer::Optimizer, ::DualTolerance)
return MOI.get(optimizer, MOI.RawParameter("ϵ₂"))
end
function MOI.set(optimizer::Optimizer, ::DualTolerance, limit::Real)
MOI.set(optimizer, MOI.RawParameter("ϵ₂"), limit)
return nothing
end
function MOI.get(optimizer::Optimizer, ::MasterOptimizer)
return MOI.get(optimizer, SubProblemOptimizer())
end
function MOI.get(optimizer::Optimizer, ::SubProblemOptimizer)
if optimizer.subproblem_optimizer === nothing
return MOI.get(optimizer, MasterOptimizer())
end
return MOI.OptimizerWithAttributes(optimizer.subproblem_optimizer, collect(optimizer.sub_params))
end
function MOI.set(optimizer::Optimizer, ::SubProblemOptimizer, optimizer_constructor)
optimizer.subproblem_optimizer = optimizer_constructor
# Clear any old parameters
empty!(optimizer.sub_params)
return nothing
end
function MOI.get(optimizer::Optimizer, ::SubProblemOptimizerAttribute, attr::MOI.AbstractOptimizerAttribute)
if !haskey(optimizer.sub_params, attr)
error("Subproblem optimizer attribute $(attr) has not been set.")
end
return optimizer.sub_params[attr]
end
function MOI.set(optimizer::Optimizer, ::SubProblemOptimizerAttribute, attr::MOI.AbstractOptimizerAttribute, value)
optimizer.sub_params[attr] = value
return nothing
end
function MOI.get(optimizer::Optimizer, param::RawSubProblemOptimizerParameter)
moi_param = MOI.RawParameter(param.name)
if !haskey(optimizer.sub_params, moi_param)
error("Subproblem optimizer attribute $(param.name) has not been set.")
end
return optimizer.sub_params[moi_param]
end
function MOI.set(optimizer::Optimizer, param::RawSubProblemOptimizerParameter, value)
moi_param = MOI.RawParameter(param.name)
optimizer.sub_params[moi_param] = value
return nothing
end
function MOI.get(optimizer::Optimizer, ::Execution)
return optimizer.execution
end
function MOI.set(optimizer::Optimizer, ::Execution, execution::AbstractExecution)
optimizer.execution = execution
return nothing
end
function MOI.get(optimizer::Optimizer, ::Penalizer)
return optimizer.penalizer
end
function MOI.set(optimizer::Optimizer, ::Penalizer, penalizer::AbstractPenalizer)
optimizer.penalizer = penalizer
return nothing
end
function MOI.get(optimizer::Optimizer, ::PenaltyTerm)
return optimizer.penaltyterm
end
function MOI.set(optimizer::Optimizer, ::PenaltyTerm, penaltyterm::AbstractPenaltyTerm)
optimizer.penaltyterm = penaltyterm
return nothing
end
function MOI.get(optimizer::Optimizer, param::ExecutionParameter)
return MOI.get(optimizer.execution, param)
end
function MOI.set(optimizer::Optimizer, param::ExecutionParameter, value)
MOI.set(optimizer.execution, param, value)
return nothing
end
function MOI.get(optimizer::Optimizer, param::PenalizationParameter)
return MOI.get(optimizer.penalizer, param)
end
function MOI.set(optimizer::Optimizer, param::PenalizationParameter, value)
MOI.set(optimizer.penalizer, param, value)
return nothing
end
function MOI.get(optimizer::Optimizer, ::MOI.TerminationStatus)
return optimizer.status
end
function MOI.get(optimizer::Optimizer, ::MOI.PrimalStatus)
return optimizer.primal_status
end
function MOI.get(optimizer::Optimizer, ::MOI.DualStatus)
return optimizer.dual_status
end
function MOI.get(optimizer::Optimizer, ::MOI.RawStatusString)
return optimizer.raw_status
end
function MOI.get(optimizer::Optimizer, ::MOI.VariablePrimal, index::MOI.VariableIndex)
if optimizer.progressivehedging === nothing
throw(StochasticProgram.UnloadedStructure{Optimizer}())
end
return decision(optimizer.progressivehedging, index)
end
function MOI.get(optimizer::Optimizer, ::MOI.ConstraintPrimal, ci::MOI.ConstraintIndex)
if optimizer.progressivehedging === nothing
throw(StochasticProgram.UnloadedStructure{Optimizer}())
end
return scalar_subproblem_reduction(optimizer.progressivehedging) do subproblem
return MOI.get(subproblem.optimizer, MOI.ConstraintPrimal(), ci)
end
end
function MOI.get(optimizer::Optimizer, ::MOI.ConstraintDual, ci::MOI.ConstraintIndex)
if optimizer.progressivehedging === nothing
throw(StochasticProgram.UnloadedStructure{Optimizer}())
end
return scalar_subproblem_reduction(optimizer.progressivehedging) do subproblem
return MOI.get(subproblem.optimizer, MOI.ConstraintDual(), ci)
end
end
function MOI.get(optimizer::Optimizer, ::MOI.ObjectiveValue)
if optimizer.progressivehedging === nothing
throw(StochasticProgram.UnloadedStructure{Optimizer}())
end
return objective_value(optimizer.progressivehedging)
end
function MOI.get(optimizer::Optimizer, ::MOI.DualObjectiveValue)
if optimizer.progressivehedging === nothing
throw(StochasticProgram.UnloadedStructure{Optimizer}())
end
if optimizer.status == MOI.OPTIMAL
return objective_value(optimizer.progressivehedging)
elseif optimizer.status == MOI.INFEASIBLE
sense = MOI.get(optimizer.progressivehedging.structure, MOI.ObjectiveSense())
return sense == MOI.MAX_SENSE ? Inf : -Inf
elseif optimizer.status == MOI.DUAL_INFEASIBLE
sense = MOI.get(optimizer.progressivehedging.structure, MOI.ObjectiveSense())
return sense == MOI.MAX_SENSE ? -Inf : Inf
else
return NaN
end
end
function MOI.get(optimizer::Optimizer, ::MOI.SolveTime)
return optimizer.solve_time
end
function MOI.get(optimizer::Optimizer, attr::Union{MOI.AbstractOptimizerAttribute, MOI.AbstractModelAttribute})
# Fallback to through structure
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return MOI.get(optimizer.progressivehedging.structure, attr)
end
function MOI.get(optimizer::Optimizer, attr::MOI.AbstractConstraintAttribute, ci::MOI.ConstraintIndex)
# Fallback to first-stage optimizer through structure
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return MOI.get(optimizer.progressivehedging.structure, attr, ci)
end
function MOI.set(optimizer::Optimizer, attr::Union{MOI.AbstractOptimizerAttribute, MOI.AbstractModelAttribute}, value)
# Fallback to first-stage optimizer through structure
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return MOI.set(optimizer.progressivehedging.structure, attr, value)
end
function MOI.set(optimizer::Optimizer, attr::MOI.AbstractVariableAttribute, index::MOI.VariableIndex, value)
# Fallback to first-stage optimizer through structure
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return MOI.set(optimizer.progressivehedging.structure, attr, index, value)
end
function MOI.set(optimizer::Optimizer, attr::MOI.AbstractConstraintAttribute, ci::MOI.ConstraintIndex, value)
# Fallback to first-stage optimizer through structure
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return MOI.set(optimizer.progressivehedging.structure, attr, ci, value)
end
function MOI.get(optimizer::Optimizer, attr::ScenarioDependentModelAttribute)
# Fallback to subproblem optimizer through structure
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return MOI.get(optimizer.progressivehedging.structure, attr)
end
function MOI.get(optimizer::Optimizer, attr::ScenarioDependentVariableAttribute, index::MOI.VariableIndex)
# Fallback to subproblem optimizer through structure
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return MOI.get(optimizer.progressivehedging.structure, attr, index)
end
function MOI.get(optimizer::Optimizer, attr::ScenarioDependentConstraintAttribute, ci::MOI.ConstraintIndex)
# Fallback to subproblem optimizer through structure
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return MOI.get(optimizer.progressivehedging.structure, attr, ci)
end
function MOI.set(optimizer::Optimizer, attr::ScenarioDependentModelAttribute, value)
# Fallback to subproblem optimizer through structure
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return MOI.set(optimizer.progressivehedging.structure, attr, value)
end
function MOI.set(optimizer::Optimizer, attr::ScenarioDependentVariableAttribute, index::MOI.VariableIndex, value)
# Fallback to subproblem optimizer through structure
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return MOI.set(optimizer.progressivehedging.structure, attr, index, value)
end
function MOI.set(optimizer::Optimizer, attr::ScenarioDependentConstraintAttribute, ci::MOI.ConstraintIndex, value)
# Fallback to subproblem optimizer through structure
if optimizer.progressivehedging === nothing
throw(UnloadedStructure{Optimizer}())
end
return MOI.set(optimizer.progressivehedging.structure, attr, ci, value)
end
function MOI.is_empty(optimizer::Optimizer)
return optimizer.progressivehedging === nothing
end
MOI.supports(::Optimizer, ::MOI.Silent) = true
MOI.supports(::Optimizer, ::MOI.TimeLimitSec) = true
MOI.supports(::Optimizer, ::MOI.RawParameter) = true
MOI.supports(::Optimizer, ::AbstractStructuredOptimizerAttribute) = true
MOI.supports(::Optimizer, ::RelativeTolerance) = false
MOI.supports(::Optimizer, ::RawInstanceOptimizerParameter) = true
MOI.supports(::Optimizer, ::AbstractProgressiveHedgingAttribute) = true
# High-level attribute setting #
# ========================== #
"""
get_penalization_attribute(stochasticprogram::StochasticProgram, name::String)
Return the value associated with the penalization-specific attribute named `name` in `stochasticprogram`.
See also: [`set_penalization_attribute`](@ref), [`set_penalization_attributes`](@ref).
"""
function get_penalization_attribute(stochasticprogram::StochasticProgram, name::String)
return return MOI.get(optimizer(stochasticprogram), RawPenalizationParameter(name))
end
"""
set_penalization_attribute(stochasticprogram::StochasticProgram, name::Union{Symbol, String}, value)
Sets the penalization-specific attribute identified by `name` to `value`.
"""
function set_penalization_attribute(stochasticprogram::StochasticProgram, name::Union{Symbol, String}, value)
return set_optimizer_attribute(stochasticprogram, RawPenalizationParameter(String(name)), value)
end
"""
set_penalization_attributes(stochasticprogram::StochasticProgram, pairs::Pair...)
Given a list of `attribute => value` pairs or a collection of keyword arguments, calls
`set_penalization_attribute(stochasticprogram, attribute, value)` for each pair.
"""
function set_penalization_attributes(stochasticprogram::StochasticProgram, pairs::Pair...)
for (name, value) in pairs
set_penalization_attribute(stochasticprogram, name, value)
end
end
function set_penalization_attributes(stochasticprogram::StochasticProgram; kw...)
for (name, value) in kw
set_penalization_attribute(stochasticprogram, name, value)
end
end
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] | 2.782992 | 7,267 |
function draw!(camera::CameraModel, scene::AbstractScene)
optical_center = Point3f0(camera.𝐜)
image_width = camera.image_width
image_height = camera.image_height
f = camera.focal_length
𝐞₁ = camera.𝐞₁
𝐞₂ = camera.𝐞₂
𝐞₃ = camera.𝐞₃
bottom_right = optical_center + Point3f0((image_width/2) * 𝐞₁ + (image_height/2) * 𝐞₂ + f*𝐞₃)
top_right = optical_center + Point3f0((image_width/2) * 𝐞₁ + (-image_height/2) * 𝐞₂ + f*𝐞₃)
top_left = optical_center + Point3f0((-image_width/2) * 𝐞₁ + (-image_height/2) * 𝐞₂ + f*𝐞₃)
bottom_left = optical_center + Point3f0((-image_width/2) * 𝐞₁ + (image_height/2) * 𝐞₂ + f*𝐞₃)
centroid2film = [
optical_center => bottom_right;
optical_center => top_right;
optical_center => top_left;
optical_center => bottom_left;
]
film = [
bottom_right => top_right;
top_right => top_left;
top_left => bottom_left;
bottom_left => bottom_right;
]
scale = 20.0f0
coordinate_system = [
optical_center => optical_center + Point3f0(scale*𝐞₁);
optical_center => optical_center + Point3f0(scale*𝐞₂);
optical_center => optical_center + Point3f0(scale*𝐞₃);
]
linesegments!(scene, centroid2film, color = :black, linewidth = 2)
linesegments!(scene, film, color = :black, linewidth = 2)
linesegments!(scene, coordinate_system, color = [:red, :green, :blue ], linewidth = 2)
end
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] | 1.950904 | 774 |
<gh_stars>0
# LSL.jl: Julia interface for Lab Streaming Layer
# Copyright (C) 2019 <NAME>
# test/streaminfo.jl: test stream information functions
@testset "StreamInfo" begin
info = StreamInfo(name="testname",
type="testtype",
channel_count=2,
nominal_srate=10,
channel_format=Float64,
source_id="testid")
@test name(info) == "testname"
@test type(info) == "testtype"
@test channel_count(info) == 2
@test nominal_srate(info) == 10
@test channel_format(info) == Float64
@test source_id(info) == "testid"
@test version(info) == protocol_version()
channels = append_child(desc(info), "channels")
for label in ["C3", "C4", "Cz", "FPz", "POz", "CPz", "O1", "O2"]
ch = append_child(channels, "channel")
append_child_value(ch, "label", label)
append_child_value(ch, "unit", "microvolts")
append_child_value(ch, "type", "EEG")
end
append_child_value(desc(info), "manufacturer", "SCCN")
cap = append_child(desc(info), "cap")
append_child_value(cap, "name", "EasyCap")
append_child_value(cap, "size", "54")
append_child_value(cap, "labelscheme", "10-20")
end
# Force cleanup
GC.gc()
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] | 2.398374 | 492 |
<gh_stars>1-10
function intersectingvertices(S1, S2; tol::Float64 = 1/10^10)
# Dimension
n = size(S1, 1)
# Centroid and radii
c1, c2 = Circumsphere(S1)[2:n+1], Circumsphere(S2)[2:n+1]
r1, r2 = Circumsphere(S1)[1], Circumsphere(S2)[1]
# Orientation of simplices
orientation_S1 = det([ones(1, n + 1); S1])
orientation_S2 = det([ones(1, n + 1); S2])
if abs(orientation_S1) < tol || abs(orientation_S2) < tol
return Array{Float64, 2}(undef, 0, 0)
end
# Set volume to zero initially. Change only if there is intersection
IntVol = 0.0
# -------------------------------------
# Simplices intersect in some way
# -------------------------------------
# If the (distance between centroids)^2-(sum of radii)^2 < 0,
# then the simplices intersect in some way.
dist_difference::Float64 = (transpose(c1 - c2) * (c1 - c2) - (r1 + r2)^2)[1]
if dist_difference < 0
#@show "The simplices intersect in some way (possibly only along a boundary, with zero volume intersection)"
# Find the number of points of each simplex contained within the
# circumsphere of the other simplex
vertices1InCircum2 = SomeVertexInCircumsphere(S1, r2, c2)
vertices2InCircum1 = SomeVertexInCircumsphere(S2, r1, c1)
if vertices1InCircum2 + vertices2InCircum1 >= 1
βs1in2, βs2in1, ordered_vertices1, ordered_vertices2, numof1in2, numof2in1 =
BarycentricCoordinates(S1,S2,orientation_S1,orientation_S2,tol)
# Trivial intersections
TriviallyContained = heaviside0([numof1in2 numof2in1] .- (n+1))
IsSomeContained = sum(TriviallyContained, dims=2)[1]
if IsSomeContained == 2.0 # The simplices coincide
#@show "The simplices coincide"
return copy(transpose(S1))
elseif IsSomeContained == 1.0 # One simplex is contained in the other
if TriviallyContained[1] == 1.0 # Simplex1 is contained in Simplex2
#@show "Simplex 1 is contained in simplex 2"
return copy(transpose(S1))
else # Simplex2 is contained in Simplex1
#@show "Simplex 2 is contained in simplex 1"
return copy(transpose(S2))
end
else # No simplex contains the other
#@show "No simplex contains the other"
Ncomm, ordered_vertices1, ordered_vertices2 = SharedVertices(βs1in2,ordered_vertices1,ordered_vertices2,numof1in2,numof2in1)
# Is there any shared face?
if Ncomm == n
#@show "The simplices share a face"
IntVol = SharedFaceVolume(S2, βs1in2, ordered_vertices1, ordered_vertices2)
return transpose(SharedFaceVertices(S2, βs1in2, ordered_vertices1, ordered_vertices2))
else # The simplices do not share a face.
#@show "The simplices do not share a face"
IntVert, ConvexExpIntVert = IntersectionOfBoundaries_NoStorage(S1,S2,βs1in2,βs2in1, ordered_vertices1, ordered_vertices2, numof1in2, numof2in1, Ncomm, tol)
if !isempty(IntVert)
IntVert,ConvexExpIntVert = PolytopeGeneratingVertices(S1,S2,IntVert,ConvexExpIntVert,βs1in2,βs2in1,ordered_vertices1,ordered_vertices2,numof1in2,numof2in1,Ncomm);
IntVol = VolumeComputation(IntVert, ConvexExpIntVert)
return IntVert
else
return Array{Float64, 2}(undef, 0, 0)
end
end
end
else
IntVert = Array{Float64, 2}(undef, 0, 0)
end
else
IntVert = Array{Float64, 2}(undef, 0, 0)
end
return IntVert
end
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] | 2.058635 | 1,876 |
using eFEMpart, JLD
using LinearAlgebra # for condition number calculation
function main()
function computeNorms(N,order)
start = time()
# load mesh
mesh = squareMesh([-2,2,0,1],N,order)
OperatorType = :AdvDiffAS
Pe = 2.3
windX(x,y) = -x^4*y^2 + 5.9; windY(x,y) = x^3*y^3
wx = [windX.(mesh.xy[i].x,mesh.xy[i].y) for i=1:length(mesh.xy)]
wy = [windY.(mesh.xy[i].x,mesh.xy[i].y) for i=1:length(mesh.xy)]
param = AdvDiffParam(wx,wy,Pe*ones(length(mesh.xy)))
dNodes = Dirichlet(:left,:right,:top,:bottom)
dBCf = Dirichlet((x,y) -> x^4*y^4)
ff = Forcing((x,y) -> 4*5.9*x^3*y^4 - 4/Pe*x^2*y^2*(4*x^2 + 3*y^2))
Nodes = [dNodes]
bcfun = [dBCf,ff]
prob = Problem(mesh,Nodes,bcfun,OperatorType)
sol = solve(prob,mesh,param)
elapsed = time() - start
# compute condition number of operator matrix
#LinOp = GenerateSystem(mesh,prob,param)
#ApplyBC!(LinOp,mesh,prob,param,OperatorType)
#κ = cond(Array(LinOp.Op),2)
κ = 0.5
# compute mesh h
h = hCalc(mesh)
# generate exact solution
Uexact(x,y) = dBCf.f(x,y)
UexactArr = [Uexact.(mesh.xy[i].x,mesh.xy[i].y) for i=1:length(mesh.xy)]
# compute difference
err = sol.u - UexactArr
# compute Li norm
L1 = DomainNorm(mesh.xy,mesh.cm,err;normID="1")
L2 = DomainNorm(mesh.xy,mesh.cm,err;normID="2")
Linf = DomainNorm(mesh.xy,mesh.cm,err;normID="Inf")
return κ,h,L1,L2,Linf,elapsed
end
Narr = [8,8,16,32]#,64]#,128]#,256]
order = [1,2]
N = length(Narr)
κarr = zeros(N,2)
harr = zeros(N,2)
L1arr = zeros(N,2)
L2arr = zeros(N,2)
LInfarr = zeros(N,2)
timearr = zeros(N,2)
for i=1:N
for j=1:2
n = Narr[i]
o = order[j]
κarr[i,j],harr[i,j],L1arr[i,j],L2arr[i,j],LInfarr[i,j],timearr[i,j] = computeNorms(n,o)
println("completed N=$(n), order=$(o)")
end
end
κarr = κarr[2:end,:]
harr = harr[2:end,:]
L1arr = L1arr[2:end,:]
L2arr = L2arr[2:end,:]
LInfarr = LInfarr[2:end,:]
timearr = timearr[2:end,:]
# export output to *.jld file
run(`mkdir -p data`)
save("data/TEMP_advdiffAS_validation.jld", "harr",harr[:,1],
"o1L1arr", L1arr[:,1],
"o1L2arr", L2arr[:,1],
"o1LInfarr",LInfarr[:,1],
"o1timearr",timearr[:,1],
"o2L1arr", L1arr[:,2],
"o2L2arr", L2arr[:,2],
"o2LInfarr",LInfarr[:,2],
"o2timearr",timearr[:,2],
"κarr",κarr[:,2])
nothing
end
main()
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] | 1.670796 | 1,695 |
<gh_stars>0
using StaticArrays
struct Member{Tval, Tid}
val::Tval
id::Tid
end
import Base.isless
function isless(a::Member{Tval, Tid}, b::Member{Tval, Tid}) where{Tval, Tid}
return isless((a.val, a.id), (b.val, b.id))
end
struct Family{Tval, Tid}
n_columns::MVector{1, Tid}
n_nz ::MVector{1, Tid}
n_buffer::MVector{1, Tid}
# This array gives for contains the ordering. The i-th parent in the
#daycare has id P[i]
P::Vector{Tid}
# This array gives for contains the inverse ordering.
revP::Vector{Tid}
# The array that contains the first "child" for every parent
colptr::Vector{Tid}
# The array that contains the global id-s of the children
rowval::Vector{Member{Tval,Tid}}
# parents[id] contains a member struct specifying the distance of id to its parent and the position in the ordering of the parent
parents::Vector{Member{Tval,Tid}}
end
function Family{Tval,Tid}(dofs::AbstractArray{<:AbstractDOF}) where {Tval,Tid}
N = length(dofs)
return Family{Float64, Int64}(MVector(zero(Tid)), MVector(zero(Tid)), MVector(N), zeros(Tid, N), zeros(Tid, N), zeros(Tid, N + one(Tid)), Vector{Member{Tval, Tid}}(undef, N), Vector{Member{Tval, Tid}}(undef, N))
end
function Family(dofs::AbstractArray{Point{d,Tval}}) where {d, Tval}
return Family{Tval, Int}(dofs)
end
#Function that begins a new parent aka column in daycare
function new_column!(f::Family, Id)
f.n_columns[1] += 1
f.P[f.n_columns[1]] = Id
f.revP[Id] = f.n_columns[1]
f.colptr[f.n_columns[1]] = f.n_nz[1] + 1
f.colptr[f.n_columns[1] + 1] = f.n_nz[1] + 1
end
function new_child!(f::Family, new_child)
# If the buffer is flowing over, increase it
if f.n_nz[1] >= f.n_buffer[1]
if f.n_nz[1] <= 1e6
f.n_buffer[1] = 2 * f.n_buffer[1]
else
f.n_buffer[1] = f.n_buffer[1] + 1e6
end
resize!( f.rowval, f.n_buffer[1] )
end
f.n_nz[1] += 1
f.colptr[f.n_columns[1] + 1] += 1
f.rowval[f.n_nz[1]] = new_child
end
function new_children!(f::Family, new_children)
# If the buffer is flowing over, increase it
while f.n_nz[1] + size(new_children,1) >= f.n_buffer[1] - 1
if f.n_nz[1] <= 1e6
f.n_buffer[1] = 2 * f.n_buffer[1]
else
f.n_buffer[1] = f.n_buffer[1] + 1e6
end
resize!(f.rowval, f.n_buffer[1])
end
f.n_nz[1] += size(new_children,1)
f.colptr[f.n_columns[1] + 1] += size(new_children,1)
f.rowval[f.n_nz[1] - size(new_children,1) + 1 : f.n_nz[1]] .= new_children
end
function column_iterator(f, column_index)
return f.colptr[column_index] : (f.colptr[column_index + 1] - 1)
end
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] | 2.274735 | 1,132 |
<filename>src/Bijectors.jl
module Bijectors
#=
NOTE: Codes below are adapted from
https://github.com/brian-j-smith/Mamba.jl/blob/master/src/distributions/transformdistribution.jl
The Mamba.jl package is licensed under the MIT License:
> Copyright (c) 2014: <NAME> and other contributors:
>
> https://github.com/brian-j-smith/Mamba.jl/contributors
>
> Permission is hereby granted, free of charge, to any person obtaining
> a copy of this software and associated documentation files (the
> "Software"), to deal in the Software without restriction, including
> without limitation the rights to use, copy, modify, merge, publish,
> distribute, sublicense, and/or sell copies of the Software, and to
> permit persons to whom the Software is furnished to do so, subject to
> the following conditions:
>
> The above copyright notice and this permission notice shall be
> included in all copies or substantial portions of the Software.
>
> THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
> EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
> MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
> IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
> CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
> TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
> SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
=#
using Reexport, Requires
@reexport using Distributions
using StatsFuns
using LinearAlgebra
using MappedArrays
using Base.Iterators: drop
using LinearAlgebra: AbstractTriangular
import Functors
import NonlinearSolve
import ChainRulesCore
export TransformDistribution,
PositiveDistribution,
UnitDistribution,
SimplexDistribution,
PDMatDistribution,
link,
invlink,
logpdf_with_trans,
isclosedform,
transform,
forward,
logabsdetjac,
logabsdetjacinv,
Bijector,
ADBijector,
Inverse,
Composed,
compose,
Stacked,
stack,
Identity,
bijector,
transformed,
UnivariateTransformed,
MultivariateTransformed,
logpdf_with_jac,
logpdf_forward,
PlanarLayer,
RadialLayer,
CouplingLayer,
InvertibleBatchNorm
if VERSION < v"1.1"
using Compat: eachcol
end
const DEBUG = Bool(parse(Int, get(ENV, "DEBUG_BIJECTORS", "0")))
_debug(str) = @debug str
_eps(::Type{T}) where {T} = T(eps(T))
_eps(::Type{Real}) = eps(Float64)
_eps(::Type{<:Integer}) = eps(Float64)
function _clamp(x, a, b)
T = promote_type(typeof(x), typeof(a), typeof(b))
ϵ = _eps(T)
clamped_x = ifelse(x < a, convert(T, a), ifelse(x > b, convert(T, b), x))
DEBUG && _debug("x = $x, bounds = $((a, b)), clamped_x = $clamped_x")
return clamped_x
end
function mapvcat(f, args...)
out = map(f, args...)
init = vcat(out[1])
return reshape(reduce(vcat, drop(out, 1); init = init), size(out))
end
function maphcat(f, args...)
out = map(f, args...)
init = reshape(out[1], :, 1)
return reduce(hcat, drop(out, 1); init = init)
end
function eachcolmaphcat(f, x1, x2)
out = [f(x1[:,i], x2[i]) for i in 1:size(x1, 2)]
init = reshape(out[1], :, 1)
return reduce(hcat, drop(out, 1); init = init)
end
function eachcolmaphcat(f, x)
out = map(f, eachcol(x))
init = reshape(out[1], :, 1)
return reduce(hcat, drop(out, 1); init = init)
end
function sumeachcol(f, x1, x2)
# Using a view below for x1 breaks Tracker
return sum(f(x1[:,i], x2[i]) for i in 1:size(x1, 2))
end
# Distributions
link(d::Distribution, x) = bijector(d)(x)
invlink(d::Distribution, y) = inv(bijector(d))(y)
function logpdf_with_trans(d::Distribution, x, transform::Bool)
if ispd(d)
return pd_logpdf_with_trans(d, x, transform)
elseif isdirichlet(d)
l = logpdf(d, x .+ eps(eltype(x)))
else
l = logpdf(d, x)
end
if transform
return l - logabsdetjac(bijector(d), x)
else
return l
end
end
## Univariate
const TransformDistribution = Union{
T,
Truncated{T},
} where T <: ContinuousUnivariateDistribution
const PositiveDistribution = Union{
BetaPrime, Chi, Chisq, Erlang, Exponential, FDist, Frechet, Gamma, InverseGamma,
InverseGaussian, Kolmogorov, LogNormal, NoncentralChisq, NoncentralF, Rayleigh, Weibull,
}
const UnitDistribution = Union{Beta, KSOneSided, NoncentralBeta}
function logpdf_with_trans(d::UnivariateDistribution, x, transform::Bool)
if transform
return map(x -> logpdf(d, x), x) - logabsdetjac(bijector(d), x)
else
return map(x -> logpdf(d, x), x)
end
end
## Multivariate
const SimplexDistribution = Union{Dirichlet}
isdirichlet(::SimplexDistribution) = true
isdirichlet(::Distribution) = false
###########
# ∑xᵢ = 1 #
###########
function link(
d::Dirichlet,
x::AbstractVecOrMat{<:Real},
proj::Bool = true,
)
return SimplexBijector{proj}()(x)
end
function link_jacobian(
d::Dirichlet,
x::AbstractVector{T},
proj::Bool = true,
) where {T<:Real}
return jacobian(SimplexBijector{proj}(), x)
end
function invlink(
d::Dirichlet,
y::AbstractVecOrMat{<:Real},
proj::Bool = true
)
return inv(SimplexBijector{proj}())(y)
end
function invlink_jacobian(
d::Dirichlet,
y::AbstractVector{T},
proj::Bool = true
) where {T<:Real}
return jacobian(inv(SimplexBijector{proj}()), y)
end
## Matrix
#####################
# Positive definite #
#####################
const PDMatDistribution = Union{MatrixBeta, InverseWishart, Wishart}
ispd(::Distribution) = false
ispd(::PDMatDistribution) = true
function logpdf_with_trans(
d::MatrixDistribution,
X::AbstractArray{<:AbstractMatrix{<:Real}},
transform::Bool,
)
return map(X) do x
logpdf_with_trans(d, x, transform)
end
end
function pd_logpdf_with_trans(d, X::AbstractMatrix{<:Real}, transform::Bool)
T = eltype(X)
Xcf = cholesky(X, check = false)
if !issuccess(Xcf)
Xcf = cholesky(X + max(eps(T), eps(T) * norm(X)) * I)
end
lp = getlogp(d, Xcf, X)
if transform && isfinite(lp)
U = Xcf.U
d = dim(d)
lp += sum((d .- (1:d) .+ 2) .* log.(diag(U)))
lp += d * log(T(2))
end
return lp
end
function getlogp(d::MatrixBeta, Xcf, X)
n1, n2 = params(d)
p = dim(d)
return ((n1 - p - 1) / 2) * logdet(Xcf) + ((n2 - p - 1) / 2) * logdet(I - X) + d.logc0
end
function getlogp(d::Wishart, Xcf, X)
return 0.5 * ((d.df - (dim(d) + 1)) * logdet(Xcf) - tr(d.S \ X)) + d.logc0
end
function getlogp(d::InverseWishart, Xcf, X)
Ψ = Matrix(d.Ψ)
return -0.5 * ((d.df + dim(d) + 1) * logdet(Xcf) + tr(Xcf \ Ψ)) + d.logc0
end
include("utils.jl")
include("interface.jl")
include("chainrules.jl")
# Broadcasting here breaks Tracker for some reason
maporbroadcast(f, x::AbstractArray{<:Any, N}...) where {N} = map(f, x...)
maporbroadcast(f, x::AbstractArray...) = f.(x...)
# optional dependencies
function __init__()
@require LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02" begin
function maporbroadcast(f, x1::LazyArrays.BroadcastArray, x...)
return copy(f.(x1, x...))
end
function maporbroadcast(f, x1, x2::LazyArrays.BroadcastArray, x...)
return copy(f.(x1, x2, x...))
end
function maporbroadcast(f, x1, x2, x3::LazyArrays.BroadcastArray, x...)
return copy(f.(x1, x2, x3, x...))
end
end
@require ForwardDiff="f6369f11-7733-5829-9624-2563aa707210" include("compat/forwarddiff.jl")
@require Tracker="9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" include("compat/tracker.jl")
@require Zygote="e88e6eb3-aa80-5325-afca-941959d7151f" include("compat/zygote.jl")
@require ReverseDiff="37e2e3b7-166d-5795-8a7a-e32c996b4267" include("compat/reversediff.jl")
@require DistributionsAD="ced4e74d-a319-5a8a-b0ac-84af2272839c" include("compat/distributionsad.jl")
end
end # module
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] | 2.354003 | 3,435 |
<reponame>JobJob/GymWrappers.jl
module GymWrappers
using OpenAIGym
import OpenAIGym: AbstractGymEnv, actionset
import Reinforce: reward, total_reward
abstract type AbstractGymWrapper <: AbstractGymEnv end
export AbstractGymEnv, AbstractGymWrapper, gymenv, getwrapper, maybe_wrap
# other exports in included files
# Defaults
for fn in [:state, :reset!, :finished, :actions, :step!, :reward, :total_reward]
@eval Reinforce.$fn(wrapped_env::AbstractGymWrapper, args...; kwargs...) =
Reinforce.$fn(wrapped_env.env, args...; kwargs...)
end
# for fn in [:render]
# @eval OpenAIGym.$fn(wrapped_env::AbstractGymWrapper; kwargs...) =
# OpenAIGym.$fn(wrapped_env.env; kwargs...)
# end
# With multiple wraps, eventually this will get to the actual GymEnv
getwrapper{WrapType <: AbstractGymEnv}(::Type{WrapType}, wrpenv::AbstractGymWrapper) =
getwrapper(WrapType, wrpenv.env) # recurses down the wrappers
# If the env is of the right type, return it
getwrapper{WrapType <: AbstractGymWrapper}(::Type{WrapType}, env::WrapType) = env
# Special case when looking for a GymEnv, to win the method specialisation race
getwrapper(::Type{GymEnv}, env::GymEnv) = env
# If we're here we looked through all the wrappers and there weren't any `WrapType` wrappers
getwrapper{WrapType <: AbstractGymEnv}(::Type{WrapType}, env::GymEnv) =
error("env is not wrapped with a wrapper of type $WrapType")
# short syntax for getting the gym env
gymenv(env::AbstractGymEnv) = getwrapper(GymEnv, env)
# The Business
"""
`maybe_wrap(env::AbstractGymEnv; wrapper_specs=default_wrapper_specs, wrap_deepmind=true)`
Wrap an environment using the wrapper_specs in `wrapper_specs[gamename]`, where
`gamename` is "Breakout" for `env.name == "BreakoutNoFrameskip-v4"`.
If `!haskey(wrapper_specs, gamename)` then the `env` is returned unwrapped.
Example wrapper spec:
```
default_wrapper_specs = Dict(
"Pong"=>[ActionSetWrapper=>([2, 3],), GreyMeanWrapper=>(false,)]
)
```
Which will call `ActionSetWrapper(env, [2,3])`, `GreyMeanWrapper(env, false)`
"""
function maybe_wrap(env::AbstractGymEnv; wrapper_specs=default_wrapper_specs,
wrap_deepmind=true)
gamename, gamever = env.name, env.ver
replace(gamename, r"(NoFrameskip|Deterministic)","")
wrapped_env = env
all_wrappers = get(wrapper_specs, gamename, [])
wrap_deepmind && append!(all_wrappers, wrapper_specs["deepmind_defaults"])
for wrapper_spec in all_wrappers
wrapper, args = wrapper_spec
wrapped_env = wrapper(wrapped_env, args...)
end
wrapped_env
end
include("misc_utils.jl")
include("reward_wrappers.jl")
include("observation_wrappers.jl")
include("action_wrappers.jl")
include("episode_wrappers.jl")
# --------------------------------
# Default Wrappers
# --------------------------------
default_wrapper_specs = Dict{String, Vector{Pair{Type{T} where T <: AbstractGymWrapper, Tuple}}}(
"deepmind_defaults"=>[
# wrappers applied from inner most (top) -> outer most (bottom)
EpisodicLifeWrapper=>(),
GreyMeanWrapper=>(),
# GreyChanWrapper=>(2,),
DownsizeWrapper=>(84, 84), # (210, 160) -> (84, 84)
MaxAndSkipWrapper=>(4,), # repeat action for 4 frames
MultiFrameWrapper=>(4,), # stack last 4 frames
]
)
# --------------------------------
# Tests
# --------------------------------
#---
# envname = "Pong-v0"
# env = GymEnv(envname) |> maybe_wrap
# #---
# gryimg = OpenAIGym.render(env; mode="rgb_array")
# #---
# colorview(Gray, (env.env.env.state[:,:,3] ./ 255)) == gryimg
# #---
# imshow(gryimg)
# #---
# OpenAIGym.render(env) # should call imshow automatically
# #---
# actions(env)
#---
end
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] | 2.665468 | 1,390 |
"""
CSV.write(file::Union{String, IO}, file; kwargs...) => file
table |> CSV.write(file::Union{String, IO}; kwargs...) => file
Write a [Tables.jl interface input](https://github.com/JuliaData/Tables.jl) to a csv file, given as an `IO` argument or String representing the file name to write to.
Keyword arguments include:
* `delim::Union{Char, String}=','`: a character or string to print out as the file's delimiter
* `quotechar::Char='"'`: character to use for quoting text fields that may contain delimiters or newlines
* `openquotechar::Char`: instead of `quotechar`, use `openquotechar` and `closequotechar` to support different starting and ending quote characters
* `escapechar::Char='\\'`: character used to escape quote characters in a text field
* `missingstring::String=""`: string to print
* `dateformat=Dates.default_format(T)`: the date format string to use for printing out Date & DateTime columns
* `append=false`: whether to append writing to an existing file/IO, if `true`, it will not write column names by default
* `writeheader=!append`: whether to write an initial row of delimited column names, not written by default if appending
* `header`: pass a list of column names (Symbols or Strings) to use instead of the column names of the input table
"""
function write end
_seekstart(p::Base.Process) = return
_seekstart(io::IO) = seekstart(io)
function with(f::Function, io::IO, append)
!append && _seekstart(io)
f(io)
end
function with(f::Function, file::String, append)
open(file, append ? "a" : "w") do io
f(io)
end
end
const VALUE_BUFFERS = [IOBuffer()]
function _reset(io::IOBuffer)
io.ptr = 1
io.size = 0
end
function bufferedwrite(io, val::Number, df)
print(io, val)
return false
end
getvalue(x, df) = x
getvalue(x::T, df) where {T <: Dates.TimeType} = Dates.format(x, df === nothing ? Dates.default_format(T) : df)
function bufferedwrite(io, val, df)
VALUE_BUFFER = VALUE_BUFFERS[Threads.threadid()]
_reset(VALUE_BUFFER)
print(VALUE_BUFFER, getvalue(val, df))
return true
end
function bufferedescape(io, delim, oq, cq, e)
VALUE_BUFFER = VALUE_BUFFERS[Threads.threadid()]
n = position(VALUE_BUFFER)
n == 0 && return
needtoescape, needtoquote = check(n, delim, oq, cq)
seekstart(VALUE_BUFFER)
if needtoquote
if needtoescape
Base.write(io, oq)
buf = VALUE_BUFFER.data
for i = 1:n
@inbounds b = buf[i]
if b === cq || b === oq
Base.write(io, e, b)
else
Base.write(io, b)
end
end
Base.write(io, cq)
else
Base.write(io, oq, VALUE_BUFFER, cq)
end
else
Base.write(io, VALUE_BUFFER)
end
return
end
function check(n, delim::Char, oq, cq)
needtoescape = false
buf = VALUE_BUFFERS[Threads.threadid()].data
@inbounds needtoquote = buf[1] === oq
d = delim % UInt8
@simd for i = 1:n
@inbounds b = buf[i]
needtoquote |= (b === d) | (b === UInt8('\n')) | (b === UInt8('\r'))
needtoescape |= b === cq
end
return needtoescape, needtoquote
end
function check(n, delim::String, oq, cq)
needtoescape = false
buf = VALUE_BUFFERS[Threads.threadid()].data
@inbounds needtoquote = buf[1] === oq
@simd for i = 1:n
@inbounds b = buf[i]
needtoquote |= (b === UInt8('\n')) | (b === UInt8('\r'))
needtoescape |= b === cq
end
needtoquote |= occursin(delim, String(buf[1:n]))
return needtoescape, needtoquote
end
write(file::Union{String, IO}; kwargs...) = x->write(file, x; kwargs...)
function write(file::Union{String, IO}, itr; kwargs...)
rows = Tables.rows(itr)
sch = Tables.schema(rows)
return write(sch, rows, file; kwargs...)
end
function printheader(io, header, delim, oq, cq, e, df)
cols = length(header)
for (col, nm) in enumerate(header)
bufferedwrite(io, string(nm), nothing) && bufferedescape(io, delim, oq, cq, e)
Base.write(io, ifelse(col == cols, UInt8('\n'), delim))
end
return
end
function write(sch::Tables.Schema{schema_names}, rows, file::Union{String, IO};
delim::Union{Char, String}=',',
quotechar::Char='"',
openquotechar::Union{Char, Nothing}=nothing,
closequotechar::Union{Char, Nothing}=nothing,
escapechar::Char='\\',
missingstring::AbstractString="",
dateformat=nothing,
append::Bool=false,
writeheader::Bool=!append,
header::Vector=String[],
kwargs...) where {schema_names}
oq, cq = openquotechar !== nothing ? (openquotechar % UInt8, closequotechar % UInt8) : (quotechar % UInt8, quotechar % UInt8)
e = escapechar % UInt8
names = isempty(header) ? schema_names : header
cols = length(names)
with(file, append) do io
writeheader && printheader(io, names, delim, oq, cq, escapechar, dateformat)
for row in rows
Tables.eachcolumn(sch, row) do val, col, nm
bufferedwrite(io, coalesce(val, missingstring), dateformat) && bufferedescape(io, delim, oq, cq, e)
Base.write(io, ifelse(col == cols, UInt8('\n'), delim))
end
end
end
return file
end
# handle unknown schema case
function write(::Nothing, rows, file::Union{String, IO};
delim::Union{Char, String}=',',
quotechar::Char='"',
openquotechar::Union{Char, Nothing}=nothing,
closequotechar::Union{Char, Nothing}=nothing,
escapechar::Char='\\',
missingstring::AbstractString="",
dateformat=nothing,
append::Bool=false,
writeheader::Bool=!append,
header::Vector=String[],
kwargs...)
oq, cq = openquotechar !== nothing ? (openquotechar % UInt8, closequotechar % UInt8) : (quotechar % UInt8, quotechar % UInt8)
e = escapechar % UInt8
state = iterate(rows)
if state === nothing
if writeheader && !isempty(header)
with(file, append) do io
printheader(io, header, delim, oq, cq, escapechar, dateformat)
end
end
return file
end
row, st = state
names = isempty(header) ? propertynames(row) : header
sch = Tables.Schema(names, nothing)
cols = length(names)
with(file, append) do io
writeheader && printheader(io, names, delim, oq, cq, escapechar, dateformat)
while true
Tables.eachcolumn(sch, row) do val, col, nm
bufferedwrite(io, coalesce(val, missingstring), dateformat) && bufferedescape(io, delim, oq, cq, e)
Base.write(io, ifelse(col == cols, UInt8('\n'), delim))
end
state = iterate(rows, st)
state === nothing && break
row, st = state
end
end
return file
end
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] | 2.380537 | 2,867 |
<gh_stars>10-100
# todo: ^, fma, muladd, div, fld, cld, rem, mod, mod1, fld1
Base.:+(x::T) where {T<:InfExtendedReal} = T(+x.val)
Base.:-(x::T) where {T<:InfExtendedReal} = T(-x.val)
Base.:+(x::T, y::T) where {T<:InfExtendedReal} = isinf(x) ? isinf(y) ? T(x.val+y.val) : x : isinf(y) ? y : T(x.val+y.val)
Base.:-(x::T, y::T) where {T<:InfExtendedReal} = isinf(x) ? isinf(y) ? T(x.val-y.val) : x : isinf(y) ? -y : T(x.val-y.val)
Base.:*(x::T, y::T) where {T<:InfExtendedReal} =
if isinf(x)
if isinf(y)
T(x.val * y.val)
else
iszero(y) ? throw(DivideError()) : T(Infinite(signbit(x) ⊻ signbit(y)))
end
else
if isinf(y)
iszero(x) ? throw(DivideError()) : T(Infinite(signbit(x) ⊻ signbit(y)))
else
T(x.val * y.val)
end
end
@generated Base.:/(x::InfExtendedReal{T}, y::InfExtendedReal{T}) where {T<:Real} = :(convert($(InfExtendedReal(typeof(one(T)/one(T)))),
if isinf(x)
if isinf(y)
throw(DivideError())
else
Infinite(signbit(x) ⊻ signbit(y))
end
else
if isinf(y)
zero(T)
else
x.val / y.val
end
end))
Base.abs(x::InfExtendedReal{T}) where {T<:Real} = InfExtendedReal{T}(abs(x.val))
@generated Base.:(//)(x::InfExtendedReal{T}, y::InfExtendedReal{S}) where {T<:Real,S<:Real} = :(convert($(InfExtendedReal(typeof(one(T)//one(S)))),
if isinf(x)
if isinf(y)
throw(DivideError())
else
Infinite(signbit(x) ⊻ signbit(y))
end
else
if isinf(y)
zero(T)
else
x.val // y.val
end
end))
Base.:(//)(x::InfExtendedReal, y::Real) = //(promote(x,y)...)
Base.:(//)(x::Real, y::InfExtendedReal) = //(promote(x,y)...)
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] | 1.96568 | 845 |