content
stringlengths 5
1.03M
| input_ids
sequencelengths 4
823k
| ratio_char_token
float64 0.4
12.5
| token_count
int64 4
823k
|
|---|---|---|---|
using Pkg
Pkg.add(PackageSpec(; url="https://github.com/StructuralEquationModels/StructuralEquationModels.jl", rev = "devel")) | [
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] | 2.886364 | 44 |
"""
What element(s) is this material suitable as a standard for?
"""
struct DBStandardFor
database::SQLite.DB
element::Element
material::Material
end
function Base.write(db::SQLite.DB, ::Type{DBStandardFor}, elm::Element, mat::Union{String, Material, Integer})
if mat isa String
matkey = find(db, Material, mat)
mat = read(db, Material, matkey)
elseif mat isa Material
matkey = write(db, mat)
else
matkey = mat
mat = read(db, Material, matkey)
end
@assert haskey(mat, elm) "The material $mat does not contain the element $elm."
stmt1 = SQLite.Stmt(db, "SELECT * FROM STANDARDFOR WHERE ELEMENT=? AND MATKEY=?;")
q1 = DBInterface.execute(stmt1, ( z(elm), matkey, ) )
if SQLite.done(q1)
stmt1 = SQLite.Stmt(db, "INSERT INTO STANDARDFOR ( ELEMENT, MATKEY ) VALUES ( ?, ? );")
DBInterface.execute(stmt1, ( z(elm), matkey, ) )
end
return lastrowid(q1)
end
function Base.findall(db::SQLite.DB, ::Type{DBStandardFor}, elm::Element)::Array{DBMaterial}
stmt1 = SQLite.Stmt(db, "SELECT * FROM STANDARDFOR WHERE ELEMENT=?;")
q1 = DBInterface.execute(stmt1, ( z(elm), ) )
return [ read(db, r[:MATKEY]) for r in q1 ]
end
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] | 2.424063 | 507 |
print("Enter an upper bound: ")
lower = 0
input = readline()
upper = parse(Int, input)
if upper < 1
throw(DomainError)
end
attempts = 1
print("Think of a number, ", lower, "--", upper, ", then press ENTER.")
readline()
const maxattempts = round(Int, ceil(-log(1 / (upper - lower)) / log(2)))
println("I will need at most ", maxattempts, " attempts ",
"(⌈-log(1 / (", upper, " - ", lower, ")) / log(2)⌉ = ",
maxattempts, ").\n")
previous = -1
guess = -1
while true
previous = guess
guess = lower + round(Int, (upper - lower) / 2, RoundNearestTiesUp)
if guess == previous || attempts > maxattempts
println("\nThis is impossible; did you forget your number?")
exit()
end
print("I guess ", guess, ".\n[l]ower, [h]igher, or [c]orrect? ")
input = chomp(readline())
while input ∉ ["c", "l", "h"]
print("Please enter one of \"c\", \"l\", or \"h\". ")
input = chomp(readline())
end
if input == "l"
upper = guess
elseif input == "h"
lower = guess
else
break
end
attempts += 1
end
println("\nI win after ", attempts, attempts == 1 ? " attempt." : " attempts.")
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function heatbath_SU2!(U,NC,temps,β,Dim=4)
temp1 = temps[1]
temp2 = temps[2]
V = temps[3]
ITERATION_MAX = 10^5
temps2 = Array{Matrix{ComplexF64},1}(undef,5)
for i=1:5
temps2[i] = zeros(ComplexF64,2,2)
end
mapfunc!(A,B) = SU2update_KP!(A,B,β,NC,temps2,ITERATION_MAX)
for μ=1:Dim
loops = loops_staple[(Dim,μ)]
iseven = true
evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven)
map_U!(U[μ],mapfunc!,V,iseven)
iseven = false
evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven)
map_U!(U[μ],mapfunc!,V,iseven)
end
end
function heatbath_SU3!(U,NC,temps,β,Dim=4)
temp1 = temps[1]
temp2 = temps[2]
V = temps[3]
ITERATION_MAX = 10^5
temps2 = Array{Matrix{ComplexF64},1}(undef,5)
temps3 = Array{Matrix{ComplexF64},1}(undef,5)
for i=1:5
temps2[i] = zeros(ComplexF64,2,2)
temps3[i] = zeros(ComplexF64,3,3)
end
mapfunc!(A,B) = SU3update_matrix!(A,B,β,NC,temps2,temps3,ITERATION_MAX)
for μ=1:Dim
loops = loops_staple[(Dim,μ)]
iseven = true
evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven)
map_U!(U[μ],mapfunc!,V,iseven)
iseven = false
evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven)
map_U!(U[μ],mapfunc!,V,iseven)
end
end
function heatbath_SUN!(U,NC,temps,β,Dim = 4)
#Dim = 4
temp1 = temps[1]
temp2 = temps[2]
V = temps[3]
ITERATION_MAX = 10^5
temps2 = Array{Matrix{ComplexF64},1}(undef,5)
temps3 = Array{Matrix{ComplexF64},1}(undef,5)
for i=1:5
temps2[i] = zeros(ComplexF64,2,2)
temps3[i] = zeros(ComplexF64,NC,NC)
end
mapfunc!(A,B) = SUNupdate_matrix!(A,B,β,NC,temps2,temps3,ITERATION_MAX)
for μ=1:Dim
loops = loops_staple[(Dim,μ)]
iseven = true
evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven)
map_U!(U[μ],mapfunc!,V,iseven)
iseven = false
evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven)
map_U!(U[μ],mapfunc!,V,iseven)
end
end
function heatbathtest_4D(NX,NY,NZ,NT,β,NC)
Dim = 4
Nwing = 1
#=
u1 = IdentityGauges(NC,Nwing,NX,NY,NZ,NT)
U = Array{typeof(u1),1}(undef,Dim)
U[1] = u1
for μ=2:Dim
U[μ] = IdentityGauges(NC,Nwing,NX,NY,NZ,NT)
end
=#
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
h = Heatbath(U,β)
#=
gauge_action = GaugeAction(U)
plaqloop = make_loops_fromname("plaquette",Dim=Dim)
append!(plaqloop,plaqloop')
βinp = β/2
push!(gauge_action,βinp,plaqloop)
=#
temp1 = similar(U[1])
temp2 = similar(U[1])
temp3 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("polyakov loop = $(real(poly)) $(imag(poly))")
#hnew = Heatbath_update(U,gauge_action)
plaq_ave = 0.0
numhb = 200
for itrj = 1:numhb
#heatbath!(U,hnew)
heatbath!(U,h)
#=
if NC == 2
heatbath_SU2!(U,NC,[temp1,temp2,temp3],β)
elseif NC == 3
heatbath_SU3!(U,NC,[temp1,temp2,temp3],β)
else
heatbath_SUN!(U,NC,[temp1,temp2,temp3],β)
end
=#
plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
plaq_ave += plaq_t
if itrj % 40 == 0
#@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t average: $(plaq_ave/itrj)")
#println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end
#=
if itrj % 40 == 0
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end
=#
end
return plaq_ave/numhb
end
function heatbathtest_2D(NX,NT,β,NC)
Dim = 2
Nwing = 1
#=
u1 = RandomGauges(NC,Nwing,NX,NT)
U = Array{typeof(u1),1}(undef,Dim)
U[1] = u1
for μ=2:Dim
U[μ] = RandomGauges(NC,Nwing,NX,NT)
end
=#
U = Initialize_Gaugefields(NC,Nwing,NX,NT,condition = "hot",randomnumber="Reproducible")
temp1 = similar(U[1])
temp2 = similar(U[1])
temp3 = similar(U[1])
#comb = 6
if Dim == 4
comb = 6 #4*3/2
elseif Dim == 3
comb = 3
elseif Dim == 2
comb = 1
else
error("dimension $Dim is not supported")
end
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("polyakov loop = $(real(poly)) $(imag(poly))")
#=
gauge_action = GaugeAction(U)
plaqloop = make_loops_fromname("plaquette",Dim=Dim)
append!(plaqloop,plaqloop')
βinp = β/2
push!(gauge_action,βinp,plaqloop)
hnew = Heatbath_update(U,gauge_action)
=#
numhb = 200
plaq_ave = 0.0
for itrj = 1:numhb
#heatbath!(U,hnew)
heatbath!(U,[temp1,temp2,temp3],β)
#=
if NC == 2
heatbath_SU2!(U,NC,[temp1,temp2,temp3],β,Dim)
elseif NC == 3
heatbath_SU3!(U,NC,[temp1,temp2,temp3],β,Dim)
else
heatbath_SUN!(U,NC,[temp1,temp2,temp3],β,Dim)
end
=#
plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
plaq_ave += plaq_t
if itrj % 40 == 0
#@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t average: $(plaq_ave/itrj)")
#println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end
#=
if itrj % 40 == 0
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end
=#
end
return plaq_ave/numhb
end
#eps = 1e-1
println("4D system")
@testset "4D" begin
NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 1
@testset "NC=2" begin
β = 2.3
NC = 2
println("NC = $NC")
#val =0.6414596466929057
val = 0.6042874193905048
#@time plaq_t = heatbathtest_2D(NX,NT,β,NC)
@time plaq_ave = heatbathtest_4D(NX,NY,NZ,NT,β,NC)
@test abs(plaq_ave-val)/abs(val) < eps
end
@testset "NC=3" begin
β = 5.7
NC = 3
println("NC = $NC")
#val = 0.5779454661484242
val = 0.5616169101071591
#@time plaq_t = heatbathtest_2D(NX,NT,β,NC)
@time plaq_ave = heatbathtest_4D(NX,NY,NZ,NT,β,NC)
@test abs(plaq_ave-val)/abs(val) < eps
end
@testset "NC=4" begin
β = 5.7
NC = 4
println("NC = $NC")
#val =0.19127260002797497
val = 0.1967198548214144
#@time plaq_t = heatbathtest_2D(NX,NT,β,NC)
@time plaq_ave = heatbathtest_4D(NX,NY,NZ,NT,β,NC)
@test abs(plaq_ave-val)/abs(val) < eps
end
end
println("2D system")
@testset "2D" begin
NX = 4
#NY = 4
#NZ = 4
NT = 4
Nwing = 1
@testset "NC=2" begin
β = 2.3
NC = 2
println("NC = $NC")
val = 0.47007878197368624
#@time plaq_t = heatbathtest_2D(NX,NT,β,NC)
@time plaq_ave = heatbathtest_2D(NX,NT,β,NC)
@test abs(plaq_ave-val)/abs(val) < eps
end
@testset "NC=3" begin
β = 5.7
NC = 3
println("NC = $NC")
val = 0.40073421896125794
#@time plaq_t = heatbathtest_2D(NX,NT,β,NC)
@time plaq_ave = heatbathtest_2D(NX,NT,β,NC)
@test abs(plaq_ave-val)/abs(val) < eps
end
@testset "NC=4" begin
β = 5.7
NC = 4
println("NC = $NC")
val = 0.17476796328668975
#@time plaq_t = heatbathtest_2D(NX,NT,β,NC)
@time plaq_ave = heatbathtest_2D(NX,NT,β,NC)
@test abs(plaq_ave-val)/abs(val) < eps
#@test abs(plaq_t-val)/abs(val) < eps
end
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struct Angle
i :: Int64
j :: Int64
k :: Int64
ktheta :: Float64
theta0 :: Float64
kub :: Float64
s0 :: Float64
end
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using Configurations
import TOML
import Pluto
export SliderServerSettings, ExportSettings, PlutoDeploySettings, get_configuration
using TerminalLoggers: TerminalLogger
using Logging: global_logger
using FromFile
@from "./ConfigurationDocs.jl" import @extract_docs, get_kwdocs, list_options_md
@extract_docs @option struct SliderServerSettings
enabled::Bool = true
"List of notebook files to skip. Provide paths relative to `start_dir`. *If `Export.enabled` is `true` (default), then only paths in `SliderServer_exclude ∩ Export_exclude` will be skipped, paths in `setdiff(SliderServer_exclude, Export_exclude)` will be shut down after exporting.*"
exclude::Vector = String[]
"Port to run the HTTP server on."
port::Integer = 2345
"""Often set to `"0.0.0.0"` on a server."""
host::Any = "127.0.0.1"
"Watch the input directory for file changes, and update the slider server sessions automatically. Only takes effect when running the slider server. More info in the README."
watch_dir::Bool = true
"Besides handling slider server request, should we also run a static file server of the export output folder? Set to `false` if you are serving the HTML files in another way, e.g. using GitHub Pages, and, for some reason, you do not want to *also* serve the HTML files using this serve."
serve_static_export_folder::Bool = true
simulated_lag::Real = 0
end
@extract_docs @option struct ExportSettings
"Generate static HTML files? This setting can only be `false` if you are also running a slider server."
enabled::Bool = true
"Folder to write generated HTML files to (will create directories to preserve the input folder structure). The behaviour of the default value depends on whether you are running the slider server, or just exporting. If running the slider server, we use a temporary directory; otherwise, we use `start_dir` (i.e. we generate each HTML file in the same folder as the notebook file)."
output_dir::Union{Nothing,String} = nothing
"List of notebook files to skip. Provide paths relative to `start_dir`."
exclude::Vector{String} = String[]
"List of notebook files that should always re-run, skipping the `cache_dir` system. Provide paths relative to `start_dir`."
ignore_cache::Vector = String[]
"base64-encode the state object and write it inside the .html file. If `false`, a separate `.plutostate` file is generated. A separate statefile allows us to show a loading bar in pluto while the statefile is loading, but it can complicate setup in some environments."
baked_state::Bool = true
baked_notebookfile::Bool = true
"Hide all buttons and toolbars in Pluto to make it look like an article."
disable_ui::Bool = true
"""Show a "Run on Binder" button on the notebooks."""
offer_binder::Bool = true
"""If 1) you are using this setup to export HTML files for notebooks, AND 2) you are running the slider server **on another setup/computer**, then this setting should be the URL pointing to the slider server, e.g. `"https://sliderserver.mycoolproject.org/"`. For example, you need this if you use GitHub Actions and GitHub Pages to generate HTML files, with a slider server on DigitalOcean. === If you only have *one* server for both the static exports and the slider server, and people will read notebooks directly on your server, then the default value `nothing` will work: it will automatically make the HTML files use their current domain for the slider server."""
slider_server_url::Union{Nothing,String} = nothing
"If provided, use this directory to read and write cached notebook states. Caches will be indexed by the hash of the notebook file, but you need to take care to invalidate the cache when Pluto or this export script updates. Useful in combination with https://github.com/actions/cache, see https://github.com/JuliaPluto/static-export-template for an example."
cache_dir::Union{Nothing,String} = nothing
"Automatically generate an `index.html` file, listing all the exported notebooks (only if no `index.jl` or `index.html` file exists already)."
create_index::Bool = true
"""ADVANCED: URL of the binder repository to load when you click the "Run on binder" button in the top right, this will be set automatically if you leave it at the default value. This setting is quite advanced, and only makes sense if you have a fork of `https://github.com/fonsp/pluto-on-binder/` (because you want to control the binder launch, or because you are using your own fork of Pluto). If so, the setting should be of the form `"https://mybinder.org/v2/gh/fonsp/pluto-on-binder/v0.17.2"`, where `fonsp/pluto-on-binder` is the name of your repository, and `v0.17.2` is a tag or commit hash."""
binder_url::Union{Nothing,String} = nothing
pluto_cdn_root::Union{Nothing,String} = nothing
end
@option struct PlutoDeploySettings
SliderServer::SliderServerSettings = SliderServerSettings()
Export::ExportSettings = ExportSettings()
Pluto::Pluto.Configuration.Options = Pluto.Configuration.Options()
end
function get_configuration(
toml_path::Union{Nothing,String}=nothing;
kwargs...,
)::PlutoDeploySettings
# we set `Pluto_server_notebook_path_suggestion=joinpath(homedir(),"")` to because the default value for this setting changes when the pwd changes. This causes our run_git_directory to exit... Not necessary for Pluto 0.17.3 and up.
if !isnothing(toml_path) && isfile(toml_path)
Configurations.from_toml(
PlutoDeploySettings,
toml_path;
Pluto_server_notebook_path_suggestion=joinpath(homedir(), ""),
kwargs...,
)
else
Configurations.from_kwargs(
PlutoDeploySettings;
Pluto_server_notebook_path_suggestion=joinpath(homedir(), ""),
kwargs...,
)
end
end
merge_recursive(a::AbstractDict, b::AbstractDict) = mergewith(merge_recursive, a, b)
merge_recursive(a, b) = b | [
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] | 3.315031 | 1,803 |
# Residual equation for linear systems
linear_residual!(R, A, x, b) = R .= A * x - b
## Fully-simultaneous Newton method
#==========================================================================================#
# Coupled aero-structural-load-factor residuals
function coupled_residuals!(R, all_horsies, Γs, U, Ω, speed, stiffness_matrix, δs, fem_loads, weight, load_factor, L)
# Get residual vector views
R_A = R.aerodynamics
R_S = R.structures
R_W = @view R[end]
# Aerodynamic residuals
@timeit "Aerodynamic Residuals" solve_nonlinear!(R_A, all_horsies, Γs / speed, U / speed, Ω / speed)
# Structural residuals
@timeit "Structural Residuals" linear_residual!(R_S, stiffness_matrix, δs, fem_loads)
# Weight residual
linear_residual!(R_W, weight, load_factor, L)
nothing
end
# Residual setup for multiple aerostructural surfaces and multiple aerodynamic surfaces
function solve_coupled_residual!(R, x, speed, β, ρ, Ω, syms :: Vector{Symbol}, chord_meshes, camber_meshes, fem_meshes, other_horsies, stiffness_matrix, weight, load_factor)
# Unpack aerodynamic and structural variables
Γs = x.aerodynamics
δs = x.structures
α = x.load_factor
# Compute velocity with new angle of attack
U = freestream_to_cartesian(-speed, α, β)
# Compute displacements
Δs = map((key, fem_mesh) -> reshape(δs[key][7:end], 6, length(fem_mesh)), valkeys(δs), fem_meshes)
# @timeit "Get Translations" dxs =
# @timeit "Get Rotations" Ts =
# New VLM variables
@timeit "New Horseshoes" new_horsies = @. new_horseshoes(mesh_translation(Δs), mesh_rotation(Δs), chord_meshes, camber_meshes, fem_meshes)
# Compute component forces for structural residual
@timeit "Get Circulations" new_Γs = getindex.(Ref(Γs), syms)
@timeit "Get Aerodynamic Centers" new_acs = map(horsies -> bound_leg_center.(horsies), new_horsies)
@timeit "All Horseshoes" all_horsies = [ mapreduce(vec, vcat, new_horsies); vec(other_horsies) ]
@timeit "New Forces" new_forces = map((Γ_comp, hs_comp) -> surface_forces(hs_comp, Γ_comp, all_horsies, Γs, U, Ω, ρ), new_Γs, new_horsies)
# Compute other forces for load factor residual
@timeit "Get Symbols" other_syms = filter(sym -> !(sym ∈ syms), keys(Γs))
@timeit "Get Other Circulations" other_Γs = mapreduce(sym -> vec(getindex(Γs, sym)), vcat, other_syms)
@timeit "Other Forces" other_forces = surface_forces(vec(other_horsies), other_Γs, all_horsies, Γs, U, Ω, ρ)
# Compute lift
@timeit "All Forces" vlm_forces = [ mapreduce(vec, vcat, new_forces); vec(other_forces) ]
@timeit "Transform Summed Forces" D, Y, L = geometry_to_wind_axes(sum(vlm_forces), α, β)
# Build force vector with constraint for structures
@timeit "FEM Loads" fem_loads = mapreduce(vec ∘ fem_load_vector, vcat, new_acs, new_forces, fem_meshes)
# Compute residuals
@timeit "Compute Residuals" coupled_residuals!(R, all_horsies, Γs, U, Ω, speed, stiffness_matrix, δs, fem_loads, weight, load_factor, L * cos(α))
return R
end
# Residual setup for single aerostructural surface
function solve_coupled_residual!(R, x, speed, β, ρ, Ω, chord_mesh, camber_mesh, fem_mesh, stiffness_matrix, weight, load_factor)
# Unpack aerodynamic and structural variables
Γ = x.aerodynamics
δ = x.structures
α = x.load_factor
# Compute velocity with new angle of attack
U = freestream_to_cartesian(-speed, α, β)
# Compute displacements
Δs = δ.displacement
dxs = mesh_translation(Δs)
Ts = mesh_rotation(Δs)
# New VLM variables
@timeit "New Horseshoes" new_horsies = new_horseshoes(dxs, Ts, chord_mesh, camber_mesh, fem_mesh)
# Compute aerodynamic forces
@timeit "Surface Forces" vlm_forces = surface_forces(new_horsies, Γ, U, Ω, ρ)
# Compute structural residual and loads
@timeit "FEM Loads" fem_loads = fem_load_vector(bound_leg_center.(new_horsies), vlm_forces, fem_mesh)
# Compute lift for load factor residual
D, Y, L = geometry_to_wind_axes(sum(vlm_forces), α, β)
# Compute residuals
coupled_residuals!(R, new_horsies, Γ, U, Ω, speed, stiffness_matrix, δ, fem_loads, weight, load_factor, L * cos(α))
return R
end
# # Residual setup for single aerostructural surface and multiple aerodynamic surfaces
# function solve_coupled_residual!(R, x, speed, β, ρ, Ω, chord_mesh, camber_mesh, other_horsies, fem_mesh, stiffness_matrix, weight, load_factor)
# # Unpack aerodynamic and structural variables
# Γ = x.aerodynamics
# δ = x.structures
# α = x.load_factor
# # Get residual vector views
# R_A = R.aerodynamics
# R_S = R.structures
# R_W = @view R[end]
# # Compute velocity with new angle of attack
# U = freestream_to_cartesian(-speed, α, β)
# # Compute displacements
# δs = @views reshape(δ[7:end], 6, length(fem_mesh))
# dxs = mesh_translation(Δs)
# Ts = mesh_rotation(Δs)
# # New VLM variables
# new_horsies = new_horseshoes(dxs, Ts, chord_mesh, camber_mesh, fem_mesh)
# @timeit "Combine Horseshoes" all_horsies = [ vec(new_horsies); other_horsies ]
# # Compute aerodynamic residual and loads
# @timeit "Surface Forces" vlm_forces = surface_forces(Γ, all_horsies, U, Ω, ρ)
# @timeit "FEM Forces" new_forces = @views reshape(vlm_forces[1:length(new_horsies)], size(new_horsies))
# # Compute structural residual and loads
# @timeit "FEM Loads" fem_loads = fem_load_vector(bound_leg_center.(new_horsies), new_forces, fem_mesh)
# # Compute lift for load factor residual
# D, Y, L = geometry_to_wind_axes(sum(vlm_forces), α, β)
# # Compute residuals
# coupled_residuals!(R, all_horsies, Γ, U, Ω, speed, stiffness_matrix, δ, fem_loads, weight, load_factor, L * cos(α))
# return R
# end
# Residual setup for multiple aerostructural surfaces (NEED TO REDUCE REDUNDANCIES)
# function solve_coupled_residual!(R, x, speed, β, ρ, Ω, syms :: Vector{Symbol}, chord_meshes, camber_meshes, fem_meshes, stiffness_matrix, weight, load_factor)
# # Unpack aerodynamic and structural variables
# Γs = x.aerodynamics
# δs = x.structures
# α = x.load_factor
# # Compute velocity with new angle of attack
# U = freestream_to_cartesian(-speed, α, β)
# # Compute displacements
# Δs = map((key, n) -> reshape(δs[key][7:end], 6, n), valkeys(δs), length.(fem_meshes))
# dxs = mesh_translation.(Δs)
# Ts = mesh_rotation.(Δs)
# # New VLM variables
# new_horsies = new_horseshoes.(dxs, Ts, chord_meshes, camber_meshes, fem_meshes)
# all_horsies = mapreduce(vec, vcat, new_horsies)
# # Compute component forces for structural residual
# new_Γs = getindex.(Ref(Γs), syms)
# new_acs = map(horsies -> bound_leg_center.(horsies), new_horsies)
# @timeit "New Forces" new_forces = surface_forces.(new_Γs, new_horsies, Ref(Γs), Ref(all_horsies), Ref(U), Ref(Ω), Ref(ρ))
# # Compute lift
# D, Y, L = geometry_to_wind_axes(sum(mapreduce(vec, vcat, new_forces)), α, β)
# # Build force vector with constraint for structures
# @timeit "FEM Loads" fem_loads = mapreduce(fem_load_vector, vcat, new_acs, new_forces, fem_meshes)
# # Compute residuals
# coupled_residuals!(R, all_horsies, Γs, U, Ω, speed, stiffness_matrix, δs, fem_loads, weight, load_factor, L * cos(α))
# return R
# end
## Nonlinear block Gauss-Seidel
#==========================================================================================#
function aerostruct_gauss_seidel(x0, speed, β, ρ, Ω, chord_mesh, camber_mesh, fem_mesh, stiffness_matrix, weight, load_factor; max_iters = 50, tol = 1e-9)
x = deepcopy(x0)
ε = 1e5
for i = 1:max_iters
xp = deepcopy(x)
@show xp
Γ = @views x.aerodynamics
δ = @views x.structures
α = @views x.load_factor
# Compute velocity with new angle of attack
U = freestream_to_cartesian(-speed, α, β)
# Compute displacements
δs = δ.displacement
dxs = mesh_translation(δs)
Ts = mesh_rotation(δs)
# New geometric variables
new_horsies = new_horseshoes(dxs, Ts, chord_mesh, camber_mesh, fem_mesh)
# Solve circulations
Γ = reshape(influence_matrix(vec(new_horsies), -U / speed) \ boundary_condition(quasi_steady_freestream(vec(new_horsies), U, Ω), horseshoe_normal.(vec(new_horsies))), size(new_horsies))
x.aerodynamics = Γ
@show x
# Compute VLM forces
vlm_forces = surface_forces(Γ, new_horsies, U, Ω, ρ)
# Compute FEM loads
fem_loads = fem_load_vector(bound_leg_center.(new_horsies), vlm_forces, fem_mesh)
# Solve displacements
δ = reshape(stiffness_matrix \ vec(fem_loads), size(fem_loads))
x.structures = δ
@show x
# Compute lift
D, Y, L = geometry_to_wind_axes(sum(vlm_forces), α, β)
# Weight residual
α = acos(abs(weight * load_factor - L) / (weight * load_factor))
x.load_factor = α
@show L
ε = LinearAlgebra.norm(x - xp)
@show (i, ε)
if ε <= tol return x end # Needs NAN checks and everything like NLsolve
end
return x
end | [
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] | 2.330167 | 4,001 |
#using PolyhedralOmega, LinearAlgebra, Test
using LinearAlgebra, Test, PolyhedralOmega
include("../src/SymbolicCone.jl")
@testset "Symbolic Cone Test Set" begin
@test true
end
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using Plots, VortexDistributions
@load "./examples/one_frame.jld2"
heatmap(x,y,abs2.(ψ1'))
psi = Torus(ψ1,x,y)
vortices = findvortices(psi)
# zoom window
x0,y0 = 70,200
Lx,Ly = 100,100
# simple cartesian mask
xm = x[@. abs(x-x0)<Lx/2]
ym = y[@. abs(y-y0)<Ly/2]
psim = @. ψ1[abs(x-x0)<Lx/2,abs(y-y0)<Ly/2]
heatmap(xm,ym,angle.(psim),transpose=true)
psi2 = Torus(psim,xm,ym)
vort = findvortices(psi2)
scatter!([vort[1].xv],[vort[1].yv],alpha=0.4)
scatter!([vort[2].xv],[vort[2].yv],alpha=0.4)
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] | 1.765957 | 282 |
"""
Embedding operator that randomly samples
between parent's value and the nearest parameter boundary
to get the new valid value if target's parameter is out-of-bounds.
"""
struct RandomBound{S<:RectSearchSpace} <: EmbeddingOperator
search_space::S
RandomBound(search_space::S) where {S<:RectSearchSpace} = new{S}(search_space)
end
search_space(rb::RandomBound) = rb.search_space
function apply!(eo::RandomBound, target::AbstractIndividual, ref::AbstractIndividual)
length(target) == length(ref) == numdims(eo.search_space) ||
throw(ArgumentError("Dimensions of problem/individuals do not match"))
ss = search_space(eo)
ssmins = dimmin(ss)
ssmaxs = dimmax(ss)
@inbounds for i in eachindex(target)
l, u = ssmins[i], ssmaxs[i]
if target[i] < l
target[i] = l + rand() * (ref[i]-l)
elseif target[i] > u
target[i] = u + rand() * (ref[i]-u)
else # continuous range doesn't need further checks
(ss isa MixedPrecisionRectSearchSpace) || continue
end
if (ss isa MixedPrecisionRectSearchSpace) && (dimdigits(ss, i) >= 0)
target[i] = round(target[i], digits=dimdigits(ss, i))
end
@assert l <= target[i] <= u "target[$i]=$(target[i]) is out of [$l, $u]"
end
return target
end
apply!(eo::RandomBound, target::AbstractIndividual, pop, refIndex::Int) =
apply!(eo, target, viewer(pop, refIndex))
function apply!(eo::RandomBound, target::AbstractIndividual,
pop, parentIndices::AbstractVector{Int})
@assert length(parentIndices) == 1
apply!(eo, target, pop, parentIndices[1])
end
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] | 2.484985 | 666 |
export Image
mutable struct Image
mHandle::vk.VkImage
mType::vk.VkImageViewType
mCreateInfo::vk.VkImageCreateInfo
mUnowned::Bool
mDevice::Device
mMemory::MemoryChunk
function Image(device::Device, createInfo::vk.VkImageCreateInfo, type::vk.VkImageViewType)
this = new()
this.mDevice = device
this.mType = type
this.mCreateInfo = createInfo
this.mUnowned = false
newImage = Ref{vk.VkImage}()
infoRef = Ref(createInfo)
GC.@preserve infoRef begin
if (vk.vkCreateImage(getLogicalDevice(device), infoRef, C_NULL, newImage) != vk.VK_SUCCESS)
error("Failed to create image!")
end
end
this.mHandle = newImage[]
return this
end
# Assumes that the Image is managed somewhere else (e.g. in a swapchain)
# The ImageCreateInfo will not be used to actually create an image, but to
# derive properties of the image (format, extend, ...)
function Image(device::Device, createInfo::vk.VkImageCreateInfo, unowned::vk.VkImage, type::vk.VkImageViewType)
this = new()
this.mDevice = device
this.mType = type
this.mCreateInfo = createInfo
this.mHandle = unowned
this.mUnowned = true
return this
end
end
@class Image [:handle, :destroy]
function destroy(this::Image)
if (this.mUnowned)
return
end
vk.vkDestroyImage(getLogicalDevice(this.mDevice), this.mHandle, C_NULL)
destroy(this.mMemory)
end
function usageToFeatures(usage::vk.VkImageUsageFlags)::vk.VkFormatFeatureFlags
result::vk.VkFormatFeatureFlags = 0
if (usage & vk.VK_IMAGE_USAGE_COLOR_ATTACHMENT_BIT != 0)
result |= vk.VK_FORMAT_FEATURE_COLOR_ATTACHMENT_BIT |
vk.VK_FORMAT_FEATURE_COLOR_ATTACHMENT_BLEND_BIT
end
if (usage & vk.VK_IMAGE_USAGE_DEPTH_STENCIL_ATTACHMENT_BIT != 0)
result |= vk.VK_FORMAT_FEATURE_DEPTH_STENCIL_ATTACHMENT_BIT
end
if (usage & vk.VK_IMAGE_USAGE_INPUT_ATTACHMENT_BIT != 0)
result |= vk.VK_FORMAT_FEATURE_COLOR_ATTACHMENT_BIT
end
if (usage & vk.VK_IMAGE_USAGE_SAMPLED_BIT != 0)
result |= vk.VK_FORMAT_FEATURE_SAMPLED_IMAGE_BIT
end
if (usage & vk.VK_IMAGE_USAGE_STORAGE_BIT != 0)
result |= vk.VK_FORMAT_FEATURE_STORAGE_IMAGE_BIT
end
if (usage & vk.VK_IMAGE_USAGE_TRANSFER_DST_BIT != 0)
# not sure if I should use this, because it's hidden behind an
# extension
# result |= vk.VK_FORMAT_FEATURE_TRANSFER_DST_BIT_KHR
end
if (usage & vk.VK_IMAGE_USAGE_TRANSFER_SRC_BIT != 0)
# not sure if I should use this, because it's hidden behind an
# extension
# result |= vk.VK_FORMAT_FEATURE_TRANSFER_SRC_BIT_KHR
end
return result;
end
function createImage(createInfo::ImageCreateInfo, device::Device)::Image
return Image(device, handleRef(createInfo)[], createInfo.mViewType)
end
function createView(this::Image, range::vk.VkImageSubresourceRange = vk.VkImageSubresourceRange(0, 0, 0, 0, 0))
return createView(this, this.mType, range)
end
function handle(this::Image)::vk.VkImage
return this.mHandle
end
function getLogicalDeviceOf(this::Image)::vk.VkDevice
return getLogicalDevice(this.mDevice)
end
function realizeAttachment(this::Image)
req = VkExt.getImageMemoryRequirements(getLogicalDevice(this.mDevice), this.mHandle)
this.mMemory = allocateDedicated(this.mDevice.mSuballocator, req, VRAM)
bindToImage(this.mMemory, this.mHandle)
end
function getWidth(this::Image)::UInt32
return this.mCreateInfo.extent.width
end
function getHeight(this::Image)::UInt32
return this.mCreateInfo.extent.height
end
# TODO
# setDataVRAM
# setDataRAM
# getData
# realizeVRAM
# realizeRAM
# copyFrom
# copyTo
# blitTo
# changeLayout
# changeOwner
# generateMipmaps
# createView
# changeOwnerImpl
# levelPixels
# levelBytes
# prepStagingBuffer
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] | 2.346177 | 1,687 |
include("layout.jl") # layout
include(joinpath("api", "endpoint.jl")) # Endpoint
include("wasm.jl") # WASM
include("front.jl") # Front
if PROGRAM_FILE == basename(@__FILE__)
using Bukdu
import Bukdu.Actions: index, show, new, edit, create, delete, update
using .Front # WelcomeController
using .Endpoint # CustomerController
import .WASM: WasmController, hello_js, hello_wast
using Sockets
pipeline(:api) do conn::Conn
end
routes(:api) do
resources("/customers", CustomerController)
end
routes(:wasm) do
get("/wasm", WasmController, index)
get("/hello.js", WasmController, hello_js)
get("/hello.wast", WasmController, hello_wast)
plug(Plug.Static, at="/", from=normpath(@__DIR__, "public"))
end
routes(:front) do
get("/", WelcomeController, index)
end
if haskey(ENV, "ON_HEROKU")
Bukdu.start(parse(Int, ENV["PORT"]); host=Sockets.IPAddr(0,0,0,0))
else
Bukdu.start(8080)
end
Router.call(get, "/") #
# CLI.routes()
Base.JLOptions().isinteractive==0 && wait()
# Bukdu.stop()
end # if
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] | 2.345572 | 463 |
# %% Interactive bifurcations for 1D energy balance
using DrWatson
@quickactivate "NonlinearDynamicsTextbook"
include(srcdir("colorscheme.jl"))
using DynamicalSystems, OrdinaryDiffEq, Random
using InteractiveDynamics: record_interaction
import GLMakie
D = 25
omega_vec = randn(D)
ds = Systems.kuramoto(D)
diffeq = (alg = Tsit5(), adaptive = false, dt = 0.05)
integ = integrator(ds; diffeq...)
fig = GLMakie.Figure()
display(fig)
cmap = :curl
axku = GLMakie.Axis(fig[1,1])
# Other static elements
GLMakie.hidedecorations!(axku)
axku.aspect = GLMakie.DataAspect()
GLMakie.lines!(axku, cos.(0:0.001:2π+0.002), sin.(0:0.001:2π+0.002);color = :black)
# Plot balls
phases = GLMakie.Observable(copy(integ.u))
balls = GLMakie.lift(u -> [GLMakie.Point2f0(cos(φ), sin(φ)) for φ in u], phases)
GLMakie.scatter!(axku, balls;
markersize = 12, color = 1:D, colormap = cmap, strokewidth=2, strokecolor = :black
)
# Plot arrows
arrows_end = GLMakie.lift(b -> 0.8b, balls)
arrows_start = [GLMakie.Point2f0(0,0) for i in 1:D]
GLMakie.arrows!(axku, arrows_start, arrows_end;
color = 1:D, colormap = (cmap, 0.5), arrowsize = 10, linewidth = 4,
)
# Plot mean field Vector
# add sliders and buttons
fig[2, 1] = controllayout = GLMakie.GridLayout(tellwidth = false)
run = controllayout[1, 1] = GLMakie.Button(fig; label = "run")
Ks = 0.0:0.1:10.0
kslider = GLMakie.labelslider!(fig, "K =", Ks; sliderkw = Dict(:startvalue => ds.p.K))
controllayout[1, 2] = kslider.layout
# run button functionality
isrunning = GLMakie.Observable(false)
GLMakie.on(run.clicks) do clicks; isrunning[] = !isrunning[]; end
GLMakie.on(run.clicks) do clicks
@async while isrunning[]
GLMakie.isopen(fig.scene) || break # ensures computations stop if closed window
step!(integ)
phases[] = integ.u
yield()
sleep(0.001) # or `yield()` instead
end
end
GLMakie.on(kslider.slider.value) do val
integ.p.K = val
u_modified!(integ, true)
end
# %%
record_interaction(fig, string(@__FILE__)[1:end-2]*"mp4"; total_time = 15, sleep_time = 2)
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] | 2.322435 | 887 |
# Level-2 LinearAlgebra.BLAS interface.
#
# TODO: support gbmv!, sbmv! and hbmv!.
# hpmv! and spmv! is not possible within current frame.
# Level2 BLAS size checker.
bli_check_lv2(trans::BliTrans,
m ::Integer, n ::Integer,
n_::Integer, m_::Integer) = begin
((trans.enum & BLIS_TRANS_BIT) != 0) && ((m, n) = (n, m))
(m == m_) || throw(DimensionMismatch("Target buffer size mismatch."))
(n == n_) || throw(DimensionMismatch("Contracted size mismatch."))
nothing
end
macro blis_interface_linalg_lv2_gemv(Tc1, T1, T2, Tc2, T3, targetfunc, bliname)
# Get method for the object API backend.
blifuncname = Symbol("bli_", bliname)
blifunc = getproperty(ObjectBackend, blifuncname)
return quote
$(esc(targetfunc))(tA::AbstractChar,
α::$Tc1,
A::StridedMatrix{<:$T1},
x::StridedVector{<:$T2},
β::$Tc2,
y::StridedVector{<:$T3}) = begin
bli_tA = char_to_trans[tA]
bli_check_lv2(bli_tA,
size(A)...,
length(x),
length(y))
oα = BliObj(α)
oA = BliObj(A)
ox = BliObj(x)
oβ = BliObj(β)
oy = BliObj(y)
ObjectBackend.bli_obj_set_onlytrans!(bli_tA, oA.obj)
$blifunc(oα, oA, ox, oβ, oy)
y
end
end
end
@doc """
gemv!(tA, α, A, x, β, y)
BLIS-based GEMV with strides support & mixed-precision.
""" gemv!
@blis_interface_linalg_lv2_gemv(BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
gemv!, gemv!)
@blis_interface_linalg_lv2_gemv Float32 Float32 Float32 Float32 Float32 gemv! gemv!
@blis_interface_linalg_lv2_gemv Float64 Float64 Float64 Float64 Float64 gemv! gemv!
@blis_interface_linalg_lv2_gemv ComplexF32 ComplexF32 ComplexF32 ComplexF32 ComplexF32 gemv! gemv!
@blis_interface_linalg_lv2_gemv ComplexF64 ComplexF64 ComplexF64 ComplexF64 ComplexF64 gemv! gemv!
macro blis_interface_linalg_lv2_hemv(Tc1, T1, T2, Tc2, T3, targetfunc, bliname, bli_struc)
# Get method for the object API backend.
blifuncname = Symbol("bli_", bliname)
blifunc = getproperty(ObjectBackend, blifuncname)
return quote
$(esc(targetfunc))(ul::AbstractChar,
α::$Tc1,
A::StridedMatrix{<:$T1},
x::StridedVector{<:$T2},
β::$Tc2,
y::StridedVector{<:$T3}) = begin
bli_ul = char_to_uplo[ul]
bli_check_lv2(BLIS_NO_TRANSPOSE,
size(A)...,
length(x),
length(y))
oα = BliObj(α)
oA = BliObj(A)
ox = BliObj(x)
oβ = BliObj(β)
oy = BliObj(y)
ObjectBackend.bli_obj_set_uplo!(bli_ul, oA)
ObjectBackend.bli_obj_set_struc!($bli_struc, oA.obj)
$blifunc(oα, oA, ox, oβ, oy)
y
end
end
end
@doc """
hemv!(ul, α, A, x, β, y)
BLIS-based HEMV with strides support & mixed-precision.
""" hemv!
@blis_interface_linalg_lv2_hemv(BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
hemv!, hemv!,
BLIS_HERMITIAN)
@blis_interface_linalg_lv2_hemv Float32 Float32 Float32 Float32 Float32 hemv! hemv! BLIS_HERMITIAN
@blis_interface_linalg_lv2_hemv Float64 Float64 Float64 Float64 Float64 hemv! hemv! BLIS_HERMITIAN
@blis_interface_linalg_lv2_hemv ComplexF32 ComplexF32 ComplexF32 ComplexF32 ComplexF32 hemv! hemv! BLIS_HERMITIAN
@blis_interface_linalg_lv2_hemv ComplexF64 ComplexF64 ComplexF64 ComplexF64 ComplexF64 hemv! hemv! BLIS_HERMITIAN
@doc """
symv!(ul, α, A, x, β, y)
BLIS-based SYMV with strides support & mixed-precision.
""" symv!
@blis_interface_linalg_lv2_hemv(BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
symv!, symv!,
BLIS_SYMMETRIC)
@blis_interface_linalg_lv2_hemv Float32 Float32 Float32 Float32 Float32 symv! symv! BLIS_SYMMETRIC
@blis_interface_linalg_lv2_hemv Float64 Float64 Float64 Float64 Float64 symv! symv! BLIS_SYMMETRIC
@blis_interface_linalg_lv2_hemv ComplexF32 ComplexF32 ComplexF32 ComplexF32 ComplexF32 symv! symv! BLIS_SYMMETRIC
@blis_interface_linalg_lv2_hemv ComplexF64 ComplexF64 ComplexF64 ComplexF64 ComplexF64 symv! symv! BLIS_SYMMETRIC
macro blis_interface_linalg_lv2_trmv(T1, T2, targetfunc, bliname)
# Get method for the object API backend.
blifuncname = Symbol("bli_", bliname)
blifunc = getproperty(ObjectBackend, blifuncname)
return quote
$(esc(targetfunc))(ul::AbstractChar,
tA::AbstractChar,
dA::AbstractChar,
A::StridedMatrix{<:$T1},
b::StridedVector{<:$T2}) = begin
bli_ul = char_to_uplo[ul]
bli_dA = char_to_diag[dA]
bli_tA = char_to_trans[tA]
bli_check_lv2(bli_tA,
size(A)...,
length(b),
length(b))
oα = BliObj($T1(1.0))
oA = BliObj(A)
ob = BliObj(b)
ObjectBackend.bli_obj_set_uplo!(bli_ul, oA)
ObjectBackend.bli_obj_set_diag!(bli_dA, oA)
ObjectBackend.bli_obj_set_onlytrans!(bli_tA, oA)
$blifunc(oα, oA, ob)
b
end
end
end
@doc """
trmv!(ul, tA, dA, A, b)
BLIS-based TRMV with strides support & mixed-precision.
"""
@blis_interface_linalg_lv2_trmv(BliCompatibleType,
BliCompatibleType,
trmv!, trmv!)
@blis_interface_linalg_lv2_trmv Float32 Float32 trmv! trmv!
@blis_interface_linalg_lv2_trmv Float64 Float64 trmv! trmv!
@blis_interface_linalg_lv2_trmv ComplexF32 ComplexF32 trmv! trmv!
@blis_interface_linalg_lv2_trmv ComplexF64 ComplexF64 trmv! trmv!
@doc """
trsv!(ul, tA, dA, A, b)
BLIS-based TRSV with strides support & mixed-precision.
"""
@blis_interface_linalg_lv2_trmv(BliCompatibleType,
BliCompatibleType,
trsv!, trsv!)
@blis_interface_linalg_lv2_trmv Float32 Float32 trsv! trsv!
@blis_interface_linalg_lv2_trmv Float64 Float64 trsv! trsv!
@blis_interface_linalg_lv2_trmv ComplexF32 ComplexF32 trsv! trsv!
@blis_interface_linalg_lv2_trmv ComplexF64 ComplexF64 trsv! trsv!
macro blis_interface_linalg_lv2_ger(Tc1, T1, T2, T3, targetfunc, bliname)
# Get method for the object API backend.
blifuncname = Symbol("bli_", bliname)
blifunc = getproperty(ObjectBackend, blifuncname)
return quote
$(esc(targetfunc))(α::$Tc1,
x::StridedVector{<:$T1},
y::StridedVector{<:$T2},
A::StridedMatrix{<:$T3}) = begin
bli_check_lv2(BLIS_NO_TRANSPOSE,
size(A)...,
length(y),
length(x))
oα = BliObj(α)
ox = BliObj(x)
oy = BliObj(y)
oA = BliObj(A)
$blifunc(oα, ox, oy, oA)
A
end
end
end
@doc """
ger!(α, x, y, A)
BLIS-based GER with strides support & mixed-precision.
""" ger!
@blis_interface_linalg_lv2_ger(BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
ger!, ger!)
@blis_interface_linalg_lv2_ger Float32 Float32 Float32 Float32 ger! ger!
@blis_interface_linalg_lv2_ger Float64 Float64 Float64 Float64 ger! ger!
@blis_interface_linalg_lv2_ger ComplexF32 ComplexF32 ComplexF32 ComplexF32 ger! ger!
@blis_interface_linalg_lv2_ger ComplexF64 ComplexF64 ComplexF64 ComplexF64 ger! ger!
macro blis_interface_linalg_lv2_her(Tc1, T1, T2, targetfunc, bliname, bli_struc)
# Get method for the object API backend.
blifuncname = Symbol("bli_", bliname)
blifunc = getproperty(ObjectBackend, blifuncname)
return quote
$(esc(targetfunc))(ul::AbstractChar,
α::$Tc1,
x::StridedVector{<:$T1},
A::StridedMatrix{<:$T2}) = begin
bli_ul = char_to_uplo[ul]
bli_check_lv2(BLIS_NO_TRANSPOSE,
size(A)...,
length(x),
length(x))
oα = BliObj(α)
ox = BliObj(x)
oA = BliObj(A)
ObjectBackend.bli_obj_set_uplo!(bli_ul, oA)
ObjectBackend.bli_obj_set_struc!($bli_struc, oA.obj)
$blifunc(oα, ox, oA)
A
end
end
end
@doc """
her!(uplo, α, x, A)
BLIS-based HER with strides support & mixed-precision.
""" her!
@blis_interface_linalg_lv2_her(BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
her!, her!,
BLIS_HERMITIAN)
@blis_interface_linalg_lv2_her Float32 Float32 Float32 her! her! BLIS_HERMITIAN
@blis_interface_linalg_lv2_her Float64 Float64 Float64 her! her! BLIS_HERMITIAN
@blis_interface_linalg_lv2_her Float32 ComplexF32 ComplexF32 her! her! BLIS_HERMITIAN
@blis_interface_linalg_lv2_her Float64 ComplexF64 ComplexF64 her! her! BLIS_HERMITIAN
@doc """
syr!(uplo, α, x, A)
BLIS-based SYR with strides support & mixed-precision.
""" syr!
@blis_interface_linalg_lv2_her(BliCompatibleType,
BliCompatibleType,
BliCompatibleType,
syr!, syr!,
BLIS_SYMMETRIC)
@blis_interface_linalg_lv2_her Float32 Float32 Float32 syr! syr! BLIS_SYMMETRIC
@blis_interface_linalg_lv2_her Float64 Float64 Float64 syr! syr! BLIS_SYMMETRIC
@blis_interface_linalg_lv2_her ComplexF32 ComplexF32 ComplexF32 syr! syr! BLIS_SYMMETRIC
@blis_interface_linalg_lv2_her ComplexF64 ComplexF64 ComplexF64 syr! syr! BLIS_SYMMETRIC
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] | 1.725754 | 6,465 |
@testset "shapes" begin
inputsize = 20
poollength = 10
datalength = 100
x = rand(Float32, inputsize, poollength, 1, datalength)
m = LSTnet(inputsize, 2, 3, poollength, 20)
@test size(m(x)) == (1, datalength)
if Flux.CUDA.functional()
@test size(gpu(m)(gpu(x))) == (1, datalength)
end
FluxArchitectures.initialize_bias!(m)
@test all(m.RecurLayer.chain.cell.b .== 1)
@test all(m.RecurSkipLayer.cell.b .== 1)
end
@testset "ReluGRU" begin
inputsize = 20
datalength = 100
x = rand(Float32, inputsize, datalength)
m = FluxArchitectures.ReluGRU(inputsize, 10)
@test size(m(x)) == (10, datalength)
if Flux.CUDA.functional()
@test size(gpu(m)(gpu(x))) == (10, datalength)
end
end
@testset "misc" begin
@test repr(LSTnet(10, 2, 3, 10, 20)) == "LSTnet(10, 2, 3, 10, 20)"
@test repr(FluxArchitectures.ReluGRU(20, 10)) == "Recur(ReluGRUCell(20, 10))"
end | [
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] | 2.147126 | 435 |
using Test
using .Stubs
using DandelionWebSockets
@testset "InProcessIO" begin
@testset "Write 1-5 on endpoint 1; Can read 1-5 on endpoint 2" begin
iopair = InProcessIOPair()
readvalues = UInt8[]
@sync begin
@async begin
for i = 1:5
write(iopair.endpoint1, UInt8(i))
end
end
@async begin
for i = 1:5
x = read(iopair.endpoint2, UInt8)
push!(readvalues, x)
end
end
end
@test readvalues == UInt8[1, 2, 3, 4, 5]
end
@testset "Write 1-5 on endpoint 1, and 100-110 on endpoint 2; Values can be read independently" begin
iopair = InProcessIOPair()
readon1 = UInt8[]
readon2 = UInt8[]
@sync begin
@async begin
for i = 1:5
write(iopair.endpoint1, UInt8(i))
end
close(iopair.endpoint1)
end
@async begin
for i = 100:110
write(iopair.endpoint2, UInt8(i))
end
close(iopair.endpoint2)
end
@async begin
try
while true
x = read(iopair.endpoint1, UInt8)
push!(readon1, x)
end
catch ex
if typeof(ex) != EOFError
rethrow()
end
end
end
@async begin
try
while true
x = read(iopair.endpoint2, UInt8)
push!(readon2, x)
end
catch ex
if typeof(ex) != EOFError
rethrow()
end
end
end
end
@test readon1 == UInt8[100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110]
@test readon2 == UInt8[1, 2, 3, 4, 5]
end
@testset "Send 5 values, and then close endpoint 1; EOF is reported on endpoint 2 after 5 values" begin
iopair = InProcessIOPair()
readvalues = []
for i = 1:5
write(iopair.endpoint1, UInt8(i))
end
close(iopair.endpoint1)
for i = 1:5
x = read(iopair.endpoint2, UInt8)
push!(readvalues, x)
end
@test readvalues == UInt8[1, 2, 3, 4, 5]
@test eof(iopair.endpoint2)
end
@testset "Send 5 values, and then close endpoint 1; EOF is not reported on endpoint 2 until after 5 values have been read" begin
iopair = InProcessIOPair()
for i = 1:5
write(iopair.endpoint1, UInt8(1))
end
close(iopair.endpoint1)
for i = 1:5
@test !eof(iopair.endpoint2)
read(iopair.endpoint2, UInt8)
end
end
@testset "Send a frame on endpoint 1; Received a frame on endpoint 2" begin
iopair = InProcessIOPair()
sentframe = Stubs.createserverframe(DandelionWebSockets.OPCODE_TEXT, b"Hello")
write(iopair.endpoint1, sentframe)
receivedframe = read(iopair.endpoint2, DandelionWebSockets.Frame)
@test sentframe.payload == receivedframe.payload
end
@testset "Read from empty stream on endpoint 1 and close endpoint 2; Read throws exception" begin
eofthrown = false
iopair = InProcessIOPair()
@sync begin
@async begin
try
read(iopair.endpoint1, UInt64)
catch ex
if typeof(ex) == EOFError
eofthrown = true
else
rethrow()
end
end
end
@async begin
sleep(0.5)
close(iopair.endpoint2)
end
end
@test eofthrown
end
end | [
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] | 1.711205 | 2,365 |
#MDDatasets DataRS (Recursive Sweep) defninitions
#-------------------------------------------------------------------------------
#==Main types
===============================================================================#
#Linked-list representation of multi-dimensional datasets:
#-------------------------------------------------------------------------------
mutable struct DataRS{T} <: DataMD
sweep::PSweep
elem::Vector{T}
function DataRS{T}(sweep::PSweep, elem::Vector{T}) where {T}
if !elemallowed(DataRS, eltype(elem))
msg = "Can only create DataRS{T} for T ∈ {DataRS, DataF1, DataFloat, DataInt, DataComplex}"
throw(ArgumentError(msg))
elseif length(sweep) != length(elem)
throw(ArgumentError("sweep length does not match number of elem"))
end
return new(sweep, elem)
end
end
#Shorthand (because default (non-parameterized) constructor was overwritten):
DataRS(sweep::PSweep, elem::Vector{T}) where T = DataRS{T}(sweep, elem)
elemallowed(::Type{DataRS}, t::Type{T}) where T = elemallowed(DataMD, t) #Allow basic types
elemallowed(::Type{DataRS}, ::Type{DataRS}) = true #Also allow recursive structures
#Generate empty DataRS structure:
(::Type{DataRS{T}})(sweep::PSweep) where T = DataRS{T}(sweep, Array{T}(undef, length(sweep)))
#==Type promotions
===============================================================================#
Base.promote_rule(::Type{T1}, ::Type{T2}) where {T1<:DataRS, T2<:Number} = DataRS
#==Accessor functions
===============================================================================#
Base.eltype(d::DataRS{T}) where T = T
Base.length(d::DataRS) = length(d.elem)
#==Help with construction
===============================================================================#
#Implement "fill(DataRS, ...) do sweepval" syntax:
function Base.fill!(fn::Function, d::DataRS)
for i in 1:length(d.sweep)
d.elem[i] = fn(d.sweep.v[i])
end
return d
end
Base.fill(fn::Function, ::Type{DataRS{T}}, sweep::PSweep) where T =
fill!(fn, DataRS(sweep, Array{T}(undef, length(sweep))))
Base.fill(fn::Function, ::Type{DataRS}, sweep::PSweep) = fill(fn, DataRS{DataRS}, sweep)
#==Data generation
===============================================================================#
function _ensuresweepunique(d::DataRS, sweepid::String)
if sweepid == d.sweep.id
msg = "Sweep occurs multiple times in DataRS: $sweepid"
throw(ArgumentError(msg))
end
end
#Define "parameter".
#(Generates a DataRS object containing the value of a given swept parameter)
#-------------------------------------------------------------------------------
#Deal with non-leaf elements, once the sweep value is found:
function _parameter(d::DataRS{DataRS}, sweepid::String, sweepval::T) where T
_ensuresweepunique(d, sweepid)
elem = DataRS[_parameter(d.elem[i], sweepid, sweepval) for i in 1:length(d.sweep)]
return DataRS(d.sweep, elem)
end
#Deal with leaf elements, once the sweep value is found:
function _parameter(d::DataRS, sweepid::String, sweepval::T) where T
_ensuresweepunique(d, sweepid)
elem = T[sweepval for i in 1:length(d.sweep)]
return DataRS(d.sweep, elem)
end
#Main "parameter" algorithm (non-leaf elements):
function parameter(d::DataRS{DataRS}, sweepid::String)
if sweepid == d.sweep.id #Sweep found
elem = DataRS[_parameter(d.elem[i], sweepid, d.sweep.v[i]) for i in 1:length(d.sweep)]
else
elem = DataRS[parameter(d.elem[i], sweepid) for i in 1:length(d.sweep)]
end
return DataRS(d.sweep, elem)
end
#Main "parameter" algorithm (leaf elements):
function parameter(d::DataRS, sweepid::String)
T = eltype(d.sweep.v)
if sweepid == d.sweep.id #Sweep found
return DataRS(d.sweep, d.sweep.v)
else
msg = "Sweep not found in DataRS: $sweepid"
throw(ArgumentError(msg))
end
end
#Generate DataRS from DataHR.
#-------------------------------------------------------------------------------
function _buildDataRS(d::DataHR, firstinds::Vector{Int})
curidx = length(firstinds) + 1
sweep = d.sweeps[curidx]
if curidx < length(d.sweeps)
result = DataRS{DataRS}(sweep)
for i in 1:length(sweep.v)
result.elem[i] = _buildDataRS(d, vcat(firstinds, i))
end
else #Last index. Copy data over:
result = DataRS{eltype(d.elem)}(sweep)
for i in 1:length(sweep.v)
result.elem[i] = d.elem[firstinds..., i]
end
end
return result
end
function DataRS(d::DataHR)
return _buildDataRS(d, Int[])
end
#==User-friendly show functions
===============================================================================#
#Print leaf element:
function printDataRSelem(io::IO, ds::DataRS, idx::Int, indent::String)
if isassigned(ds.elem, idx)
println(io, ds.elem[idx])
else
println(io, indent, "UNDEFINED")
end
end
#Print next level of recursive DataRS:
function printDataRSelem(io::IO, ds::DataRS{T}, idx::Int, indent::String) where T<:DataRS
println(io)
if isassigned(ds.elem, idx)
printDataRS(io, ds.elem[idx], indent)
else
println(io, indent, "UNDEFINED")
end
end
#Print DataRS structure:
function printDataRS(io::IO, ds::DataRS, indent::String)
for i in 1:length(ds.elem)
print(io, "$indent", ds.sweep.id, "=", ds.sweep.v[i], ": ")
printDataRSelem(io, ds, i, "$indent ")
end
end
function Base.show(io::IO, ds::DataRS)
print(io, "DataRS[\n")
printDataRS(io, ds, " ")
print(io, "]\n")
end
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] | 2.843095 | 1,861 |
"""
reduce_axes(a, dims)
Returns the appropriate axes for a measure that reduces dimensions along the
dimensions `dims`.
## Example
```jldoctest
julia> reduce_axes((Index{:a}(1:4), Index{:b}(1:4)), 2)
(Index{a}(1:4 => Base.OneTo(4)), Index{b}(1:1 => Base.OneTo(1)))
julia> reduce_axes((Index{:a}(1:4), Index{:b}(1:4)), :a)
(Index{a}(1:1 => Base.OneTo(1)), Index{b}(1:4 => Base.OneTo(4)))
```
"""
reduce_axes(x::AbstractArray, dims) = reduce_axes(axes(x), dims)
reduce_axes(x::Tuple, dims) = _reduce_axes(x, to_dims(x, dims))
reduce_axes(x::Tuple, dims::Colon) = ()
_reduce_axes(x::Tuple{Vararg{Any,D}}, dims::Int) where {D} = _reduce_axes(x, (dims,))
function _reduce_axes(x::Tuple{Vararg{Any,D}}, dims::Tuple{Vararg{Int}}) where {D}
Tuple(map(i -> ifelse(in(i, dims), reduce_axis(x[i]), x[i]), 1:D))
end
"""
reduce_axis(a)
Reduces axis `a` to single value. Allows custom index types to have custom
behavior throughout reduction methods (e.g., sum, prod, etc.)
See also: [`reduce_axes`](@ref)
## Example
```jldoctest
julia> reduce_axis(Index{:a}(1:4))
Index{a}(1:1 => Base.OneTo(1))
julia> reduce_axis(1:4)
1:1
```
"""
function reduce_axis(x::AbstractIndex)
if isempty(x)
error("Cannot reduce empty index.")
else
return unsafe_reindex(x, 1:1)
end
end
reduce_axis(x::OneTo{T}) where {T} = OneTo(one(T))
reduce_axis(x::OneToSRange{T}) where {T} = OneToSRange(one(T))
reduce_axis(x::OneToMRange{T}) where {T} = OneToMRange(one(T))
reduce_axis(x::UnitRange{T}) where {T} = UnitRange{T}(one(T), one(T))
reduce_axis(x::UnitSRange{T}) where {T} = UnitSRange{T}(one(T), one(T))
reduce_axis(x::UnitMRange{T}) where {T} = UnitMRange{T}(one(T), one(T))
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87,
3712,
26453,
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90,
51,
30072,
810,
1391,
51,
92,
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11801,
17257,
90,
51,
92,
7,
505,
7,
51,
828,
530,
7,
51,
4008,
198,
445,
7234,
62,
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7,
87,
3712,
26453,
12562,
858,
90,
51,
30072,
810,
1391,
51,
92,
796,
11801,
12562,
858,
90,
51,
92,
7,
505,
7,
51,
828,
530,
7,
51,
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198,
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62,
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7,
87,
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90,
51,
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51,
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13599,
858,
90,
51,
92,
7,
505,
7,
51,
828,
530,
7,
51,
4008,
198
] | 2.177835 | 776 |
@testset "Postprocessing" begin
@test LatexSVG._adjust_web_svg_dim("16.180339pt") == "1.618034rem"
@test begin
svg_test = """
<?xml version="1.0" encoding="utf-8"?>
<svg width="21.923673pt" height="8.966376pt" viewBox="67.695921 70.081196 21.923673 8.966376">
<defs/>
</svg>
"""
svg_adjusted = LatexSVG._adjust_web_svg_display(svg_test)
svg_adjusted == """
<?xml version="1.0" encoding="UTF-8"?>
<svg height="0.896638rem" viewBox="67.695921 70.081196 21.923673 8.966376" class="latexsvg-display" style="display: block; margin: auto; max-width: 80%">
<defs/>
</svg>
"""
end
@test begin
svg_test = """
<?xml version="1.0" encoding="utf-8"?>
<svg width="21.923673pt" height="8.966376pt" viewBox="67.695921 70.081196 21.923673 8.966376">
<defs/>
</svg>
"""
svg_adjusted = LatexSVG._adjust_web_svg_inline(svg_test)
svg_adjusted == """
<?xml version="1.0" encoding="UTF-8"?>
<svg height="0.896638rem" viewBox="67.695921 70.081196 21.923673 8.966376" class="latexsvg-inline" style="vertical-align: middle">
<defs/>
</svg>
"""
end
end
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] | 1.916159 | 656 |
## https://developer.spotify.com/documentation/web-api/reference/#/operations/get-information-about-the-users-current-playback
"""
player_get_state(;additional_types::String="track", market::String="US")
**Summary**: Get information about the user’s current playback state, including track or episode,
progress, and active device.
# Optional keywords
- `additional_types::String` : "track" (default) or "episode"
- `market::String` : An ISO 3166-1 alpha-2 country code. If a country code is specified,
only episodes that are available in that market will be returned.
Default is set to "US".
# Example
```julia-repl
julia> Spotify.player_get_state()[1]
[ Info: We try the request without checking if current grant includes scope user-read-playback-state.
JSON3.Object{Base.CodeUnits{UInt8, String}, Vector{UInt64}} with 10 entries:
:device => {…
:shuffle_state => false
:repeat_state => "off"
:timestamp => 1636493367689
:context => {…
:progress_ms => 66454
:item => {…
:currently_playing_type => "track"
:actions => {…
:is_playing => true
```
"""
function player_get_state(;additional_types::String="track", market::String="US")
url = "me/player?additional_types=$additional_types&market=$market"
return Spotify.spotify_request(url; scope = "user-read-playback-state")
end
## https://developer.spotify.com/documentation/web-api/reference/#/operations/get-a-users-available-devices
"""
player_get_devices()
**Summary**: Get information about a user’s available devices.
# Example
```julia-repl
julia> Spotify.player_get_devices()[1]["devices"]
2-element JSON3.Array{JSON3.Object, Base.CodeUnits{UInt8, String}, SubArray{UInt64, 1, Vector{UInt64}, Tuple{UnitRange{Int64}}, true}}:
{
"id": "your_device_id",
"is_active": false,
"is_private_session": false,
"is_restricted": false,
"name": "Web Player (Chrome)",
"type": "Computer",
"volume_percent": 100
}
```
"""
function player_get_devices()
return Spotify.spotify_request("me/player/devices"; scope = "user-read-playback-state")
end
## https://developer.spotify.com/documentation/web-api/reference/#/operations/get-the-users-currently-playing-track
"""
player_get_current_track(;additional_types::String="track", market::String="US")
**Summary**: Get the object currently being played on the user's Spotify account.
# Optional keywords
- `additional_types::String` : "track" (default) or "episode"
- `market::String` : An ISO 3166-1 alpha-2 country code. If a country code is specified,
only episodes that are available in that market will be returned.
Default is set to "US".
# Example
```julia-repl
julia> Spotify.player_get_current_track()[1]
[ Info: We try the request without checking if current grant includes scope user-read-playback-state.
JSON3.Object{Base.CodeUnits{UInt8, String}, Vector{UInt64}} with 7 entries:
:timestamp => 1636491068506
:context => {…
:progress_ms => 5265
:item => {…
:currently_playing_type => "track"
:actions => {…
:is_playing => true
```
"""
function player_get_current_track(;additional_types::String="track", market::String="US")
url = "me/player/currently-playing?additional_types=$additional_types&market=$market"
return Spotify.spotify_request(url; scope = "user-read-playback-state")
end
## https://developer.spotify.com/documentation/web-api/reference/#/operations/get-recently-played
"""
player_get_recent_tracks(;duration::Int64=1, limit::Int64=20)
**Summary**: Get current user's recently played tracks.
# Optional keywords
- `duration::Int64` : Number of days to look in the past, default is set to 1
- `limit::Int64` : Maximum number of items to return, default is set to 20
# Example
```julia-repl
julia> Spotify.player_get_recent_tracks()[1]
[ Info: We try the request without checking if current grant includes scope user-read-recently-played.
JSON3.Object{Base.CodeUnits{UInt8, String}, Vector{UInt64}} with 5 entries:
:items => JSON3.Object[{…
:next => "https://api.spotify.com/v1/me/player/recently-played?after=1636123644988&limit=20"
:cursors => {…
:limit => 20
:href => "https://api.spotify.com/v1/me/player/recently-played?after=1636410050&limit=20"
```
"""
function player_get_recent_tracks(;duration::Int64=1, limit::Int64=20)
# Subtract duration from current date and convert to Int64
starting_date = Dates.datetime2unix(Dates.now() - Dates.Day(duration))
after = round(Int64, starting_date)
url = "me/player/recently-played?after=$after&limit=$limit"
return Spotify.spotify_request(url; scope = "user-read-recently-played")
end
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] | 2.601358 | 1,914 |
using Test
using Covers
cc=Cover(4,8)
cc(1,1,true)
cc(2,2,true)
@test all(cc .^ Bool[1 1 1 1 1 1 1 1;
0 1 0 0 0 0 0 0;
0 1 0 0 0 0 0 0;
0 1 0 0 0 0 0 0])
@test all(ncovered(cc) .== [1 2 1 1 1 1 1 1; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0])
@test all(multicovered(cc) .== Bool[0 1 0 0 0 0 0 0; 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0])
@test cc[9] && (!cc[10])
cc[9] =false
@test all(cc .^ Bool[0 1 0 0 0 0 0 0;
0 1 0 0 0 0 0 0;
0 1 0 0 0 0 0 0;
0 1 0 0 0 0 0 0])
cc[10] =true
@test all(cc .^ Bool[0 1 1 0 0 0 0 0;
0 1 1 0 0 0 0 0;
0 1 1 0 0 0 0 0;
0 1 1 0 0 0 0 0])
cc[9] =true
@test all(cc .^ Bool[0 1 1 0 0 0 0 0;
0 1 1 0 0 0 0 0;
0 1 1 0 0 0 0 0;
0 1 1 0 0 0 0 0])
cc[10] =false
@test all(cc .^ Bool[0 1 0 0 0 0 0 0;
0 1 0 0 0 0 0 0;
0 1 0 0 0 0 0 0;
0 1 0 0 0 0 0 0])
cc=Cover(4,8)
cc(1,1,true)
cc(2,2,true)
@test cc[1,3] && (!cc[2,3])
cc[1,3] =false
@test all(cc .^ Bool[0 1 0 0 0 0 0 0;
0 1 0 0 0 0 0 0;
0 1 0 0 0 0 0 0;
0 1 0 0 0 0 0 0])
cc[2,3] =true
@test all(cc .^ Bool[0 1 1 0 0 0 0 0;
0 1 1 0 0 0 0 0;
0 1 1 0 0 0 0 0;
0 1 1 0 0 0 0 0])
cc.=false
@test all(cc .^ Bool[0 0 0 0 0 0 0 0;
0 0 0 0 0 0 0 0;
0 0 0 0 0 0 0 0;
0 0 0 0 0 0 0 0])
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] | 1.496855 | 1,113 |
abstract type AbstractProblem end
struct CellProblemAdvecTemp{MeshType,TbfType,TypeK,TypeS,VType,BcondType,LaplacianStruct,AdvectionStruct} <: AbstractProblem
Tc::Vector{Float64}
k::TypeK
s::TypeS
ρC::Float64
u::VType
bcond::BcondType
mesh::MeshType
laplacian!::LaplacianStruct
advection!::AdvectionStruct
∇Tc::Vector{Vec2D{Float64}}
Tbf::TbfType
end
function CellProblemAdvecTemp(Tc,mesh,d)
bcond = boundary_conditions(d)
k = ConstVec{d[:conductivity]}()
s = ConstVec{d[:source]}()
ρC = d[Symbol("rho*C")]
uvec = ConstVec{d[:velocity]}()
∇Tc = zeros(Vec2D{Float64},length(Tc))
Tbf = FieldAtBoundary(Tc,mesh,bcond)
advection = get_advection_method(d)
laplacian = get_laplacian_method(Tc,bcond,k,mesh,d)
types = typeof.((mesh,Tbf,k,s,uvec,bcond,laplacian,advection))
return CellProblemAdvecTemp{types...}(Tc,k,s,ρC,uvec,bcond,mesh,laplacian,advection,∇Tc,Tbf)
end
@inline needs_gradient_calculation(p) = (typeof(p.advection!) === UpWind2ndOrder) && (typeof(p.laplacian!) === MethodB) && (typeof(p.u) <: ConstVec && p.u[1] == zero(eltype(p.u)))
function calculate_rhs!(rhs,p::CellProblemAdvecTemp)
fill!(rhs,zero(eltype(rhs)))
is_implicit(p.laplacian!) || p.laplacian!(rhs,p)
needs_gradient_calculation(p) && gradient!(p.∇Tc,FaceSimpleInterpolation(p.Tc,p.mesh),p.Tbf,p.mesh.f2cloops)
if !(typeof(p.u) <: ConstVec && p.u[1] == zero(eltype(p.u)))
p.advection!(rhs,p)
end
if !(typeof(p.s) <: ConstVec && p.s[1] == zero(eltype(p.s)))
add_source!(rhs,p)
end
is_implicit(p.laplacian!) && p.laplacian!(rhs,p)
return nothing
end
function add_source!(rhs,p)
s = p.s
@inbounds @simd for i=1:length(rhs)
rhs[i] += s[i]
end
end
struct StokesProblem{UfType,UbfType,MeshType,uBcondType,LaplacianStruct,Ma,PMa,PMap} <: AbstractProblem
u::Vec2DArray{Float64}
u_old::Vec2DArray{Float64}
uf::UfType
ubf::UbfType
δu::Vec2DArray{Float64}
ru::Vec2DArray{Float64}
p::Vector{Float64}
δp::Vector{Float64}
ν::ConstVec{Float64}
ρ::ConstVec{Float64}
bcond::uBcondType
mesh::MeshType
laplacian!::LaplacianStruct
∇u::Vector{FiniteVolumeMesh.Ten2D{Float64}}
A::Ma
pcgA::PMa
s::Vector{Float64}
Ap::PoissonP{MeshType}
pcgAp::PMap
dt::Float64
end
function StokesProblem(u,mesh,d)
u_old = similar(u)
uf = FaceSimpleInterpolation(u,mesh)
bcond = uboundary_conditions(d)
ubf = FieldAtBoundary(u,mesh,bcond)
δu = similar(u)
ru = similar(u)
ν = ConstVec(d[:viscosity])
ρ = ConstVec(d[:density])
∇u = zeros(Ten2D{Float64},length(u))
p = zeros(length(u))
δp = zeros(length(u))
laplacian = get_laplacian_method(u,bcond,ν,mesh,d)
A = aIpDbG(d,mesh)
pcgA = PCG(δu)
pcgAp = PCG(δp)
types = typeof.((uf,ubf,mesh,bcond,laplacian,A,pcgA,pcgAp))
s = zeros(length(mesh.cells))
Ap = PoissonP(d,mesh)
dt = d[:dt]
return StokesProblem{types...}(u,u_old,uf,ubf,δu,ru,p,δp,ν,ρ,bcond,mesh,laplacian,∇u,A,pcgA,s,Ap,pcgAp,dt)
end
struct NSProblem{UfType,UbfType,MeshType,uBcondType,LaplacianStruct,AdvecStruct,Ma,PMa,PMap,TBcondType,TLaplacianStruct,TAdvectionStruct,TbfType,MaT,PMaT} <: AbstractProblem
u::Vec2DArray{Float64}
uf::UfType
ubf::UbfType
δu::Vec2DArray{Float64}
ru::Vec2DArray{Float64}
p::Vector{Float64}
δp::Vector{Float64}
ν::ConstVec{Float64}
ρ::ConstVec{Float64}
bcond::uBcondType
mesh::MeshType
laplacian!::LaplacianStruct
uadvection!::AdvecStruct
∇u::Vector{FiniteVolumeMesh.Ten2D{Float64}}
A::Ma
pcgA::PMa
sp::Vector{Float64}
Ap::PoissonP{MeshType}
pcgAp::PMap
dt::Float64
Tc::Vector{Float64}
∇Tc::Vec2DArray{Float64}
rT::Vector{Float64}
δT::Vector{Float64}
k::ConstVec{Float64}
s::ConstVec{Float64}
ρC::Float64
Tbcond::TBcondType
Tlaplacian!::TLaplacianStruct
Tadvection!::TAdvectionStruct
Tbf::TbfType
AT::MaT
pcgAT::PMaT
end
function NSProblem(u,mesh,d)
uf = FaceSimpleInterpolation(u,mesh)
bcond = uboundary_conditions(d)
ubf = FieldAtBoundary(u,mesh,bcond)
δu = Vec2DArray{Float64}(length(u))
ru = Vec2DArray{Float64}(length(u))
ν = ConstVec(d[:viscosity])
ρ = ConstVec(d[:density])
∇u = zeros(Ten2D{Float64},length(u))
p = zeros(length(u))
δp = zeros(length(u))
laplacian = get_laplacian_method(u,bcond,ConstVec{Float64}(0.5*ν[1]),mesh,d)
A = aIpDbG(ConstVec(d[:density]/d[:dt]),-0.5*ν[1],d,mesh)
pcgA = PCG(δu,KrylovCtrl("vPCG.set"))
pcgAp = PCG(δp,KrylovCtrl("pPCG.set"))
uadvection = UpWindAdvection(u,ubf,u,ubf,mesh)
sp = zeros(length(mesh.cells))
Ap = PoissonP(d,mesh)
dt = d[:dt]
Tc = zeros(Float64,length(mesh.cells))
∇Tc = Vec2DArray{Float64}(length(u))
rT = zeros(Float64,length(mesh.cells))
dT = zeros(Float64,length(mesh.cells))
Tbcond = boundary_conditions(d)
k = ConstVec{Float64}(d[:conductivity])
s = ConstVec{Float64}(d[:source])
ρC = d[Symbol("rho*C")]
Tbf = FieldAtBoundary(Tc,mesh,Tbcond)
Tadvection = UpWindAdvection(Tc,Tbf,u,ubf,mesh)
Tlaplacian = get_laplacian_method(Tc,Tbcond,ConstVec{Float64}(0.5*k[1]),mesh,d)
AT = aIpDbG(ConstVec{Float64}(ρC/dt),-0.5*k[1],d,mesh)
pcgAT = PCG(dT,KrylovCtrl("tPCG.set"))
types = typeof.((uf,ubf,mesh,bcond,laplacian,uadvection,A,pcgA,pcgAp,Tbcond,Tlaplacian,Tadvection,Tbf,AT,pcgAT))
return NSProblem{types...}(u,uf,ubf,δu,ru,p,δp,ν,ρ,bcond,mesh,laplacian,uadvection,∇u,A,pcgA,sp,Ap,pcgAp,dt,Tc,∇Tc,rT,dT,k,s,ρC,Tbcond,Tlaplacian,Tadvection,Tbf,AT,pcgAT)
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] | 1.912357 | 2,978 |
__precompile__()
module UnitFloat
import Base:
+,
-,
*,
bits,
convert,
exponent,
isfinite,
isinf,
isless,
one,
show,
signbit,
significand,
typemax,
typemin,
zero
export UFloat
"""
UFloat
The 64-bit type storing the *fraction* and the *exponent* of the unit-interval
floating point number.
```
|-----------------------------------------|----------------------|
| Exponent (41 bits) | Fraction (23 bits) |
|-----------------------------------------|----------------------|
bit no |63 23|22 0|
```
"""
primitive type UFloat 64 end # TODO can we extend AbstractFloat, like BigFloat?
"""
_UFloat
Construct a `UFloat` given the exponent and the fraction. The exponent must be
strictly negative and >= `-2^41`. Only the last 23 bits of the given fraction
are used.
"""
@inline function _UFloat(exponent::Int64, fraction::Float32)
bits = UInt64(reinterpret(UInt32, fraction)) & 0x00000000007fffff # ~UInt(0) >> 41
bits |= ~reinterpret(UInt64, -exponent) << 23
reinterpret(UFloat, bits)
end
@inline _is_zero(x) = x == zero(typeof(x))
@inline _is_one(x) = x == one(typeof(x))
@inline zero(::Type{UFloat}) = reinterpret(UFloat, 0x0000000000000000)
@inline one(::Type{UFloat}) = reinterpret(UFloat, 0xffffffffff800000)
zero(::UFloat) = zero(UFloat)
one(::UFloat) = one(UFloat)
typemin(::Type{UFloat}) = zero(UFloat)
typemax(::Type{UFloat}) = one(UFloat)
typemin(::UFloat) = zero(UFloat)
typemax(::UFloat) = one(UFloat)
isinf(::UFloat) = false
isfinite(::UFloat) = true
bits(u::UFloat) = bits(reinterpret(UInt64, u))
signbit(u::UFloat) = false
@inline function _exponent_unsafe(u::UFloat)
# gives incorrect results for zero(UFloat) and one(UFloat)
-reinterpret(Int64, ~reinterpret(UInt64, u) >> 23)
end
@inline function _exponent_unsafe(f::Float32)
Int64((reinterpret(UInt32, f) & 0x7f800000) >> 23) - 127
end
@inline function _exponent_unsafe(f::Float64)
Int64((reinterpret(UInt64, f) & 0x7ff0000000000000) >> 52) - 1023
end
function exponent(u::UFloat)
_is_zero(u) && throw(DomainError())
_exponent_unsafe(u)
end
@inline function _significand_unsafe(u::UFloat)
# incorrect for zero(UFloat) and one(UFloat)
bits = UInt32((reinterpret(UInt64, u) & 0x00000000007fffff)) # zero first 41 bits
bits |= 0x3f800000 # add zero exponent to Float32
reinterpret(Float32, bits)
end
@inline function _significand_unsafe(f::Float32)
reinterpret(Float32, (reinterpret(UInt32, f) & 0x007fffff) | 0x3f800000)
end
@inline function _significand_unsafe(f::Float64)
_significand_unsafe(Float32(f))
end
function significand(u::UFloat)
_is_zero(u) && return 0.0f0
_is_one(u) && return 1.0f0
_significand_unsafe(u)
end
function convert(::Type{UFloat}, f::F) where {F<:AbstractFloat}
f < zero(F) && throw(InexactError())
f == zero(F) && return zero(UFloat)
f == one(F) && return one(UFloat) # one special case because exponent==0
exp, frac = exponent(f), significand(f)
exp >= 0 && throw(InexactError())
_UFloat(exp, Float32(frac))
end
function convert(::Type{F}, u::UFloat) where {F<:AbstractFloat}
_is_zero(u) && return zero(F)
_is_one(u) && return one(F)
frac = _significand_unsafe(u)
exp = _exponent_unsafe(u)
ldexp(F(frac), exp)
end
convert(::Type{UFloat}, b::Bool) = b ? one(UFloat) : zero(UFloat)
convert(::Type{Bool}, u::UFloat) = u == one(UFloat)
###############################################################################
function _multiply(a, b)::UFloat
sa = _significand_unsafe(a)
sb = _significand_unsafe(b)
ea = _exponent_unsafe(a)
eb = _exponent_unsafe(b)
sn = sa * sb
en = ea + eb + _exponent_unsafe(sn) # cannot be <= 1<<41
en > -2199023255552 ? _UFloat(en, sn) : zero(UFloat)
end
@inline function _add(a, b)::UFloat
_is_zero(a) && return UFloat(b)
_is_zero(b) && return UFloat(a)
sa = _significand_unsafe(a)
sb = _significand_unsafe(b)
ea = _exponent_unsafe(a)
eb = _exponent_unsafe(b)
ed = ea - eb
sn = sb + ldexp(sa, ed)
en = eb + _exponent_unsafe(sn)
en >= 0 ? one(UFloat) : _UFloat(en, sn)
end
+(a::UFloat, b::UFloat) = _add(a, b)
*(a::UFloat, b::UFloat) = _multiply(a, b)
for T in [Float32, Float64], (op, fn) in [(:+, :_add), (:*, :_multiply)]
@eval begin
($op)(a::UFloat, b::$T) = ($fn)(a, b)
($op)(a::$T, b::UFloat) = ($fn)(a, b)
end
end
###############################################################################
@inline function _isless(a, b)::Bool
a == one(typeof(a)) && return false
b == one(typeof(b)) && return true # assumed a ≠ 1.0
b == zero(typeof(b)) && return false
a == zero(typeof(a)) && return true # assumed b ≠ 0.0
ea = _exponent_unsafe(a)
eb = _exponent_unsafe(b)
ea < eb && return true
ea > eb && return false
# ea == eb
sa = _significand_unsafe(a)
sb = _significand_unsafe(b)
sa < sb
end
isless(a::UFloat, b::UFloat) = _isless(a, b)
begin
local T = [UFloat, Float32, Float64]
for T1 in T, T2 in T
if T1 != T2
@eval begin
isless(a::$T1, b::$T2) = _isless(a, b)
end
end
end
end
###############################################################################
function show(io::IO, u::UFloat)
if _is_zero(u)
print(io, "0.0uf")
elseif _is_one(u)
print(io, "1.0uf")
else
# represent the number as a×10^b, with b int
e2 = exponent(u)
e10 = e2 * log10(2.0)
e10i = round(e10)
e10r = e10 - e10i
s = Float32(significand(u) * exp10(e10r))
while round(s, 5) >= 10.0f0
s /= 10.0f0; e10i += 1
end
while round(s, 5) < 1.0f0
s *= 10.0f0; e10i -= 1
end
@printf(io, "%.5fuf%d", s, e10i)
end
end
end # module
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] | 2.241405 | 2,676 |
using Test, PlotReferenceImages
@testset "PlotReferenceImages" begin
@test !isdir(reference_path(:notabackend, v"1"))
@test isfile(reference_file(:gr, 1, v"1"))
end
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] | 2.636364 | 66 |
struct DisjointPartitioner{Mtd, Mtd′}
mtd::Mtd
mtd′::Mtd′
end
function partition_plaid(A::SparseMatrixCSC, K, method::DisjointPartitioner; kwargs...)
Φ = partition_stripe(A, K, method.mtd; kwargs...)
Π = partition_stripe(PermutedDimsArray(A, (2, 1)), K, method.mtd′, Φ; kwargs...)
return (Π, Φ)
end
struct AlternatingPartitioner{Mtds}
mtds::Mtds
end
AlternatingPartitioner(mtds...) = AlternatingPartitioner{typeof(mtds)}(mtds)
function partition_plaid(A::SparseMatrixCSC, K, method::AlternatingPartitioner; adj_A = nothing, kwargs...)
if adj_A === nothing
adj_A = adjointpattern(A)
end
Φ = partition_stripe(A, K, method.mtds[1]; adj_A=adj_A, kwargs...)
Π = partition_stripe(adj_A, K, method.mtds[2], Φ; adj_A=A, kwargs...)
for (i, mtd) in enumerate(method.mtds[3:end])
if isodd(i)
Φ = partition_stripe(A, K, mtd, Π; adj_A=adj_A, kwargs...)
else
Π = partition_stripe(adj_A, K, mtd, Φ; adj_A=A, kwargs...)
end
end
return (Π, Φ)
end
struct AlternatingNetPartitioner{Hint, Mtds}
hint::Hint
mtds::Mtds
end
AlternatingNetPartitioner(mtds...) = AlternatingNetPartitioner{NoHint, typeof(mtds)}(NoHint(), mtds)
AlternatingNetPartitioner(hint::Hint, mtds...) where {Hint<:AbstractHint} = AlternatingNetPartitioner{Hint, typeof(mtds)}(hint, mtds)
function partition_plaid(A::SparseMatrixCSC, K, method::AlternatingNetPartitioner; adj_A = nothing, net = nothing, kwargs...)
if adj_A === nothing
adj_A = adjointpattern(A)
end
if net === nothing
net = netcount(method.hint, A; kwargs...)
end
Φ = partition_stripe(A, K, method.mtds[1]; net=net, adj_A=adj_A, kwargs...)
Π = partition_stripe(adj_A, K, method.mtds[2], Φ; adj_A=A, kwargs...)
for (i, mtd) in enumerate(method.mtds[3:end])
if isodd(i)
Φ = partition_stripe(A, K, mtd, Π; net=net, adj_A=adj_A, kwargs...)
else
Π = partition_stripe(adj_A, K, mtd, Φ; adj_A=A, adj_net = net, kwargs...)
end
end
return (Π, Φ)
end
struct SymmetricPartitioner{Mtds}
mtds::Mtds
function SymmetricPartitioner{Mtds}(mtds::Mtds) where {Mtds}
return new{Mtds}(mtds)
end
end
SymmetricPartitioner(mtd) = SymmetricPartitioner{Tuple{typeof(mtd)}}((mtd,))
SymmetricPartitioner(mtds...) = SymmetricPartitioner{typeof(mtds)}(mtds)
function partition_plaid(A::SparseMatrixCSC, K, method::SymmetricPartitioner; adj_A = nothing, kwargs...)
if length(method.mtds) > 1
if adj_A === nothing
adj_A = adjointpattern(A)
end
Π = partition_stripe(A, K, method.mtds[1]; adj_A=adj_A, kwargs...)
for (i, mtd) in enumerate(method.mtds[2:end])
if isodd(i)
Π = partition_stripe(A, K, mtd, Π; adj_A=adj_A, kwargs...)
else
Π = partition_stripe(adj_A, K, mtd, Π; adj_A=A, kwargs...)
end
end
else
Π = partition_stripe(A, K, method.mtds[1]; kwargs...)
end
return (Π, Π)
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] | 2.009217 | 1,519 |
using CSV, DataFrames, Plots, StatsPlots
export plot_trade_union_membership, plot_GDP_per_capita, plot_maddison_GDP_data
export plot_GDP
export north_vs_south_countries, anglo_germanic_countries
export plot_relative_GDP
ENV["GKS_ENCODING"]="utf8" # Fix some annoying warning when plotting
# Find names of colors here: http://juliagraphics.github.io/Colors.jl/stable/namedcolors/
function plot_trade_union_membership()
path = joinpath(datadir, "trade-union-membership.csv")
unions = CSV.File(path) |> DataFrame
unions = select(unions, :Country, :Time, :Value)
country_unions = groupby(unions, :Country)
country_names = ["Norway", "Sweden", "Denmark", "Finland",
"United Kingdom", "United States", "Canada"]
colors = [:purple, :blue, :cyan, :green, :orange, :red, :brown]
plot()
for (i, cname) in enumerate(country_names)
country = country_unions[(Country=cname,)]
@df country plot!(:Time, :Value, label=cname, lw=2, seriescolor=colors[i])
end
plot!(minorgrid=true, # turn on minor grid lines
gridalpha=0.3, # Make major grid lines a bit more visible
legend=:outertopleft # Place box with legends outside of plot on top left
)
end
function plot_GDP_per_capita()
gdp = load_GDP_per_capita()
p = @df gdp plot(
:year,
[:norway, :sweden, :denmark, :finland, :uk, :usa],
lw=2,
minorgrid=true,
gridalpha=0.3,
legend=:bottomright,
label=["norway" "sweden" "denmark" "finland" "uk" "usa"],
seriescolor=[:magenta4 :mediumblue :deeppink2 :purple4 :lime :green]
# seriescolor=[:aqua :teal :dodgerblue :blue :gold :orange :orangered3]
)
# p = plot(gdp.year, # x-axis
# [gdp.norway, gdp.sweden, gdp.finland, gdp.denmark, gdp.uk, gdp.usa, gdp.canada],
# lw=2, # line width
# legend=:bottomright, # where to put legend
# label=["norway" "sweden" "finland" "denmark" "uk" "usa" "canada"] # labels in legend
# )
# savefig(p, "nordic-GDP-growth-from-60s.svg")
end
function plot_maddison_GDP_data()
gdp = load_maddison_GDP_data()
country_gdp = groupby(gdp, :Country)
plot_GDP(country_gdp)
end
# This is a smarter way of doing the plotting
function simpler_maddison_GDP_plot()
gdp = load_maddison_GDP_data()
countries = DataFrame(Country=["Norway", "Sweden", "Denmark", "Finland", "United Kingdom", "United States"])
# Like a set intersection. Match on :Country column
sample = innerjoin(gdp, countries, on=:Country)
# Filter out irrelevant years
sample = filter(row -> 1950 < row.Year < 2020, sample)
# Creates a separate series for every unique value in value provided to
# group property. Every row with that group value gets assigned to the same series
plot(sample.Year, sample.GDP, group=sample.Country)
# You can also write this as:
@df sample plot(:Year, :GDP, group=:Country)
end
const north_vs_south_countries = [
"Norway" => :purple,
"Sweden" => :cyan,
"Finland" => :blue,
"Portugal" => :goldenrod2,
"Argentina" => :orange,
"Spain" => :brown,
"Italy" => :red,
"Venezuela" => :orangered3,
]
const anglo_germanic_countries = [
"Denmark" => :purple,
"Germany" => :cyan,
"Austria" => :blue,
"United Kingdom" => :goldenrod2,
"United States" => :orange,
"Canada" => :brown,
"Italy" => :red,
"France" => :orangered3,
]
"""
plot_GDP(country_gdp::GroupedDataFrame, range)
Plots GDP data for the year `range` given.
"""
function plot_GDP(country_gdp::GroupedDataFrame, range = 1800:2020, countries = north_vs_south_countries)
plot()
for (cname, color) in countries
country = country_gdp[(Country=cname,)]
country = filter(row -> row.Year in range, country)
@df country plot!(:Year, :GDP, label=cname, lw=2, seriescolor=color)
# @df country plot!(:Year, :GDP, label=cname, lw=1)
end
plot!(minorgrid=true, # turn on minor grid lines
gridalpha=0.3, # Make major grid lines a bit more visible
# legend=:outertopleft # Place box with legends outside of plot on top left
legend=:topleft
)
end
function plot_relative_GDP(base_country::DataFrame, country_gdp::GroupedDataFrame, range = 1900:2020, countries = anglo_germanic_countries)
bcountry = base_country = filter(row -> row.Year in range, base_country)
plot(ylims = (40, 130))
for (cname, color) in countries
country = country_gdp[(Country=cname,)]
country = filter(row -> row.Year in range, country)
country.rGDP = 100*(country.GDP ./ bcountry.GDP)
@df country plot!(:Year, :rGDP, label=cname, lw=2, seriescolor=color)
# @df country plot!(:Year, :GDP, label=cname, lw=1)
end
plot!(minorgrid=true, # turn on minor grid lines
gridalpha=0.3, # Make major grid lines a bit more visible
# legend=:outertopleft # Place box with legends outside of plot on top left
legend=:topleft
)
end
function plot_cultural_diff()
path = joinpath(datadir, "geerthofstede-2015-08-16.csv")
cultural = CSV.File(path) |> DataFrame
cultures = select(cultural, :country,
:pdi => :hierarchy,
:idv => :individualism,
:mas => :masculinity,
:uai => :uncertainty,
:ltowvs => :long_term,
:ivr => :indulgence)
# Countries we want to filter on by performing an innerjoin
# countries = DataFrame(country=["Norway", "Sweden", "Denmark", "Finland", "U.S.A."])
countries = DataFrame(country=["Norway", "Sweden", "Denmark", "Japan", "China"])
# Pick cultures where the :country column matches in both DataFrame objects
sample = innerjoin(cultures, countries, on = :country)
# Stack turns column names into row values. E.g. if you got the row:
# country hierarchy individualism
# China 80 20
#
# then this becomes:
# country variable value
# China hierarchy 80
# China individualism 20
#
# So every column entry gets a separate row with the name of that column
data = stack(sample, 2:7)
# Why is this useful? Because the `group` attribute allows you to bundle values
# having identical column value such as "hierarchy" into a seprate series.
# Here each series is one country. The x-axis values for each country is the
# cultural dimensions under the `variable` column
groupedbar(data.variable, data.value, group=data.country)
# Turn into CSV viewable in Numbers
# clipboard(repr(MIME("text/csv"), sample))
end
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] | 2.328428 | 2,990 |
mutable struct Rake
stop_words::Vector{String}
min_char_length::Int
max_words_length::Int
min_keyword_frequency::Int
min_words_length_adj::Int
max_words_length_adj::Int
min_phrase_freq_adj::Int
end
struct Score
rank::Float64
frequency::Int
end
function Rake(stop_words; min_char_length=3, max_words_length=3, min_keyword_frequency=10,
min_words_length_adj=1, max_words_length_adj=1, min_phrase_freq_adj=2)
Rake(stop_words, min_char_length, max_words_length, min_keyword_frequency,
min_words_length_adj, max_words_length_adj, min_phrase_freq_adj)
end
import Base.run
function run(self::Rake, text)
# Convert all multiple whitespace to single whitespace
cleaned_text = remove_redundant_whitespace(text)
# Convert to lower case
cleaned_text = lowercase(cleaned_text)
# Split on punctuation
split_on = ['.', '?', '!', '\n', ';', ',', ':', '\u2019', '\u2013']
sentences = split(cleaned_text, split_on)
# Split on stopwords
phrases = split_at_stopwords(sentences, Rake.stop_words)
# Find all possible keywords
all_possible_keyphrases = find_all_possible_keyphrases(phrases)
# Score all possible keywords
keyphrase_with_score = calculate_keyphrase_scores(all_possible_keyphrases)
# Function to compare our (keyword, score) tuples
#tuple_compare(x,y) = x[2].rank > y[2].rank
#sorted_keywords = sort(keyword_candidates, lt=tuple_compare)
return keyphrase_with_score
end
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8,
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220,
220,
1441,
1994,
34675,
62,
4480,
62,
26675,
198,
437,
198
] | 2.519263 | 597 |
using CairoMakie, Makie.GeometryBasics
n = 20
function ngonShape(h, k, r, n)
Polygon([Point2f(h .+ r*sin.(m*2π/n),k .+ r*cos.(m*2π/n)) for m in 1:n])
end
polysCentric = [ngonShape(0, 0, 3/i^1.5, i) for i in 3:n]
polysCircular = [ngonShape(√2/2*sin(θ), √2/2*cos(θ), 0.15/√idx, idx + 2)
for (idx, θ) in enumerate(LinRange(0,2π*(1 -1/(n-2)), n-2))]
cmap = to_colormap(:linear_protanopic_deuteranopic_kbw_5_98_c40_n256)[3:end]
with_theme(theme_black()) do
fig, ax, = poly(polysCentric; color = 1:n-2, colormap = cmap,
axis = (;aspect = DataAspect()), figure = (;resolution = (600,400)))
poly!(polysCircular; color = 1:n-2, colormap = cmap)
hidedecorations!(ax; grid = false)
hidespines!(ax)
save("polygons.svg", fig)
display(fig)
end
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3500,
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220,
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273,
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0,
7,
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26,
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220,
220,
220,
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0,
7,
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220,
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3613,
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70,
1600,
2336,
8,
198,
220,
220,
220,
3359,
7,
5647,
8,
198,
437,
198
] | 1.979487 | 390 |
import Dagger: DArray, ArrayDomain, Thunk, chunks, domainchunks
# Domain munging conveniences
struct ChunkDomain
domain::AbstractVector{Int}
chunks::Vector{<:AbstractVector{Int}}
end
ArrayDomain(d::ChunkDomain...) = ArrayDomain(map(d -> d.domain, d)...)
mapcart(f, xs) = f.(xs)
mapcart(f, xs, ys) = f.(xs, reshape(xs, 1, :))
domprod(ds...) = mapcart(tuple, ds...)
chunkinds(i::ChunkDomain...) = domprod(map(i -> i.chunks, i)...)
chunkinds(xs::DArray, dim::Integer) =
map(d -> d.indexes[dim],
domainchunks(xs)[ntuple(i -> i == dim ? (:) : 1 , ndims(xs))...])
domain(xs::DArray, i::Integer) = ChunkDomain(indices(xs, i), chunkinds(xs, i))
# Constructors
DArray{T,N}(dom::NTuple{N,ChunkDomain}, chunks::AbstractArray{<:Any,N}) where {T,N} =
DArray(T, ArrayDomain(dom...), ArrayDomain.(chunkinds(dom...)), chunks)
DArray{T,N}(f, dom::NTuple{N,ChunkDomain}) where {T,N} =
DArray{T,N}(dom, f.(chunkinds(dom...)))
# Views
using Dagger: lookup_parts
function catchunks(chs)
for i = 1:ndims(chs)
chs = mapslices(xs -> [cat(i, xs...)], chs, i)
end
return chs[1]
end
function chslice(xs::DArray, d::ArrayDomain)
subchunks, subdomains = lookup_parts(chunks(xs), domainchunks(xs), d)
chsize = size(subdomains)
subchunks
Thunk(subchunks...) do subchunks...
catchunks(reshape(collect(subchunks), chsize))
end
end
chslice(xs::DArray, i...) = chslice(xs, ArrayDomain(i))
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7,
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828,
288,
8,
198,
220,
442,
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2546,
7,
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3438,
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8,
198,
220,
850,
354,
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220,
536,
2954,
7,
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466,
850,
354,
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986,
198,
220,
220,
220,
4929,
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7,
3447,
1758,
7,
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7,
7266,
354,
14125,
828,
442,
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4008,
198,
220,
886,
198,
437,
198,
198,
354,
48369,
7,
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3712,
35,
19182,
11,
1312,
23029,
796,
442,
48369,
7,
34223,
11,
15690,
43961,
7,
72,
4008,
198
] | 2.408547 | 585 |
export Catalog
"""
Catalog( data, ssm)
Data type to store analogs and succesors observations
from the Space State model
- [Example Catalog](@ref)
"""
struct Catalog
nt::Int64
nv::Int64
data::Array{Float64,2}
analogs::Array{Float64,2}
successors::Array{Float64,2}
sources::StateSpaceModel
function Catalog(data::Array{Float64,2}, ssm::StateSpaceModel)
nv, nt = size(data)
analogs = data[:, 1:end-ssm.dt_states]
successors = data[:, ssm.dt_states+1:end]
new(nt, nv, data, analogs, successors, ssm)
end
end
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25,
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12,
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76,
13,
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62,
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60,
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220,
220,
220,
220,
220,
220,
220,
41491,
796,
1366,
58,
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264,
5796,
13,
28664,
62,
27219,
10,
16,
25,
437,
60,
628,
220,
220,
220,
220,
220,
220,
220,
649,
7,
429,
11,
299,
85,
11,
1366,
11,
15075,
82,
11,
41491,
11,
264,
5796,
8,
628,
220,
220,
220,
886,
198,
198,
437,
198
] | 2.410788 | 241 |
input = read("2020/data/day_8.txt", String)
test_input = """
nop +0
acc +1
jmp +4
acc +3
jmp -3
acc -99
acc +1
jmp -4
acc +6
"""
mutable struct State
total::Int
index::Int
used_codes::Array{Int,1}
end
function apply_rule!(state::State, rule::AbstractString)
push!(state.used_codes, state.index)
key, str_val = split(rule)
val = parse(Int, str_val)
if key == "nop"
state.index += 1
elseif key == "jmp"
state.index += val
elseif key == "acc"
state.total += val
state.index += 1
end
# @info state
end
function run_program(rules)
state = State(0, 1, [])
for unused in 1:length(rules) # theoretical max iterations
if state.index ∈ state.used_codes
return (state, "ERROR: Infinite Loop")
elseif state.index == length(rules) + 1
return state, "SUCCESS"
else
rule = rules[state.index]
apply_rule!(state, rule)
end
end
end
function part_1(input)
rules = split(strip(input), "\n")
state, exit_code = run_program(rules)
@assert exit_code == "ERROR: Infinite Loop"
state
end
@assert part_1(test_input).total == 5
@info part_1(input)
function part_2(input)
state = State(0, 1, [])
rules = split(strip(input), "\n")
for i in 1:length(rules)
if occursin("nop", rules[i]) | occursin("jmp", rules[i])
new_rules = copy(rules)
if occursin("nop", rules[i])
new_rules[i] = replace(rules[i], "nop" => "jmp")
else
new_rules[i] = replace(rules[i], "jmp" => "nop")
end
state, exit_code = run_program(new_rules)
if exit_code == "SUCCESS"
return state, i
end
end
end
end
@info part_2(input)
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] | 2.092997 | 871 |
using PerfChecker
using BenchmarkTools
using PatternFolds
target = PatternFolds
function bench() # 0.2.x
# Intervals
itv = Interval{Open,Closed}(0.0, 1.0)
i = IntervalsFold(itv, 2.0, 1000)
unfold(i)
collect(i)
reverse(collect(i))
# rand(i, 1000)
# Vectors
vf = make_vector_fold([0, 1], 2, 1000)
# @info "Checking VectorFold" vf pattern(vf) gap(vf) folds(vf) length(vf)
unfold(vf)
collect(vf)
reverse(collect(vf))
rand(vf, 1000)
return nothing
end
# function bench() # 0.1.1-0.1.5
# # Intervals
# i = IntervalsFold(Interval((0.0, true), (1.0, false)), 2.0, 1000)
# unfold(i)
# collect(i)
# reverse(collect(i))
# # rand(i, 1000)
# # Vectors
# vf = VectorFold([0, 1], 2, 1000)
# # @info "Checking VectorFold" vf pattern(vf) gap(vf) folds(vf) length(vf)
# unfold(vf)
# collect(vf)
# reverse(collect(vf))
# map(_ -> rand(vf), 1:1000)
# return nothing
# end
t = @benchmark bench() evals = 1 samples = 1000 seconds = 3600
# Actual call to PerfChecker
store_benchmark(t, target; path=@__DIR__)
| [
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] | 2.117202 | 529 |
using Base.Test
import ArchGDAL; const AG = ArchGDAL
@testset "Test methods for dataset" begin
AG.registerdrivers() do
AG.read("data/utmsmall.tif") do dataset
AG.createcopy(dataset, "tmp/utmcopy.tif","GTiff") do copydataset
@test AG.ngcp(copydataset) == 0
@test AG.noverview(AG.getband(copydataset,1)) == 0
AG.buildoverviews!(copydataset, Cint[2,4,8])
@test AG.noverview(AG.getband(copydataset,1)) == 3
AG.copywholeraster(dataset, copydataset,
progressfunc=GDAL.C.GDALTermProgress)
end
AG.copyfiles("GTiff", "tmp/utmcopy2.tif", "tmp/utmcopy.tif")
AG.update("tmp/utmcopy2.tif") do copydataset
AG.copywholeraster(dataset, copydataset, ["COMPRESS=LZW"])
end
end
end
rm("tmp/utmcopy.tif")
rm("tmp/utmcopy2.tif")
end
# untested: AG.deletelayer!(copydataset, 0) | [
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220,
886,
198,
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220,
220,
42721,
7203,
22065,
14,
315,
23209,
11081,
13,
49929,
4943,
198,
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220,
220,
42721,
7203,
22065,
14,
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23209,
11081,
17,
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49929,
4943,
198,
437,
198,
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2,
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2934,
1616,
417,
2794,
0,
7,
22163,
5173,
265,
292,
316,
11,
657,
8
] | 1.852552 | 529 |
module PkgTemplates
using Dates
using InteractiveUtils
using LibGit2
using Mustache
using Pkg
using REPL.TerminalMenus
using URIParser
export
# Template/package generation.
Template,
generate,
interactive_template,
generate_interactive,
# Licenses.
show_license,
available_licenses,
# Plugins.
GitHubPages,
GitLabPages,
AppVeyor,
TravisCI,
GitLabCI,
CirrusCI,
Codecov,
Coveralls,
Citation
"""
A plugin to be added to a [`Template`](@ref), which adds some functionality or integration.
New plugins should almost always extend [`GenericPlugin`](@ref) or [`CustomPlugin`](@ref).
"""
abstract type Plugin end
include("licenses.jl")
include("template.jl")
include("generate.jl")
include("plugin.jl")
include(joinpath("plugins", "documenter.jl"))
include(joinpath("plugins", "coveralls.jl"))
include(joinpath("plugins", "appveyor.jl"))
include(joinpath("plugins", "codecov.jl"))
include(joinpath("plugins", "travisci.jl"))
include(joinpath("plugins", "gitlabci.jl"))
include(joinpath("plugins", "cirrusci.jl"))
include(joinpath("plugins", "githubpages.jl"))
include(joinpath("plugins", "gitlabpages.jl"))
include(joinpath("plugins", "citation.jl"))
const DEFAULTS_DIR = normpath(joinpath(@__DIR__, "..", "defaults"))
const BADGE_ORDER = [GitHubPages, GitLabPages, TravisCI, AppVeyor, GitLabCI, Codecov, Coveralls]
end
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25690,
11,
39298,
709,
11,
17546,
5691,
60,
198,
198,
437,
198
] | 2.854209 | 487 |
include("boundconstraint_enum.jl")
include("misc.jl")
include("abstract_bc_quadsolver_conf.jl")
include("abstract_bc_quadsolver_result.jl")
include("bc_quadsolver_interface.jl")
include("Kunisch-Rendl.jl")
| [
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] | 2.64557 | 79 |
using OCToolbox
using LinearAlgebra
using QuantumInformation
i2 = Matrix{Complex{Float64}}(I, 2, 2)
# we define our initial and final states
Ψ = [1+0.0im, 0.0]
ρ = [0+0.0im, 1.0]
N_ensemble = 10
ΔRange = 10 * 2π
H_drift = collect(range(-ΔRange, ΔRange, length = N_ensemble)) .* [sz]
H_ctrl = [π * sx, π * sy]
# we set up a functional for a robust pulse
function fn(controls)
controls = complex.(real.(controls))
err = 0
for i = 1:N_ensemble
U = pw_full_evolution(H_drift[i], H_ctrl, controls, Δt, i2)
err += C2(ρ, U * Ψ)
end
err
end
K = 2
N = 20
T = 10
Δt = T/N
control_guess = rand(K, N).*0.001
fn(control_guess)
using Zygote
Zygote.gradient(fn, control_guess)
o = GRAPE(fn, control_guess, K, N)
fn(o.minimizer)
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] | 2.140845 | 355 |
module CcallMacros
export @ccall, @cdef, @disable_sigint, @nonzero_systemerror
"""
`calltoccall` is an implementation detail of @ccall and @cdef
takes and expression like :(printf("%d"::Cstring, value::Cuint)::Cvoid)
returns: a tuple of (function_name, return_type, arg_types, args
The above input outputs this:
(:printf, :Cvoid, :((Cstring, Cuint)), ["%d", :value])
Note that the args are in an array, not a quote block and have to be
appended to the ccall in a separate step.
"""
function calltoccall(expr)
expr.head != :(::) &&
error("@ccall needs a function signature with a return type")
rettype = expr.args[2]
call = expr.args[1]
call.head != :call &&
error("@ccall has to be a function call")
if (f = call.args[1]) isa Expr
lib = f.args[1]
fname = f.args[2]
func = :(($fname, $lib))
else
func = QuoteNode(f)
end
argtypes = :(())
args = []
for arg in call.args[2:end]
varargs = false
if arg.head == :...
varargs = true
arg = arg.args[1]
end
arg.head != :(::) &&
error("args in @ccall must be annotated")
value = arg.args[1]
type_ = arg.args[2]
# This currently doesn't work.
if varargs
value = :($value...)
type_ = :($type_...)
end
push!(args, value)
push!(argtypes.args, type_)
end
func, rettype, argtypes, args
end
"""
convert a julia-style function definition to a ccall:
`@ccall printf("%d"::Cstring, 10::Cint)::Cvoid`
same as:
`ccall(:printf, Cvoid, (Cstring, Cint), "%d", 10)`
"""
macro ccall(expr)
func, rettype, argtypes, args = calltoccall(expr)
output = :(ccall($func, $rettype, $argtypes))
append!(output.args, args)
esc(output)
end
"""
define a _very_ thin wrapper function on a ccall. Mostly for wrapping
libraries quickly as a foundation for a higher-level interface.
@cdef mkfifo(path::Cstring, mode::Cuint)::Cint
becomes:
mkfifo(path, mode) = ccall(:mkfifo, Cint, (Cstring, Cuint), path, mode)
"""
macro cdef(expr)
func, rettype, argtypes, args = calltoccall(expr)
call = :(ccall($func, $rettype, $argtypes))
append!(call.args, args)
name = func isa QuoteNode ? func.value : func.args[1].value
definition = :($name())
append!(definition.args, args)
esc(:($definition = $call))
end
"""
disable SIGINT while expr is being executed. Mostly useful for calling
C functions that call back into Julia in a concurrent context because
memory corruption can occur and crash the whole program.
"""
macro disable_sigint(expr)
out = quote
disable_sigint() do
$expr
end
end
esc(out)
end
const comment = r"#=.*?=# "
"""
throw a system error if the expression returns a non-zero exit status.
"""
macro nonzero_systemerror(expr)
str = replace(string(expr), comment => "")
out = quote
err = $expr
systemerror($str, err != 0)
err
end
esc(out)
end
end # module
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] | 2.420635 | 1,260 |
__precompile__(false)
module Buildkite
import HTTP
import JSON
export BuildkiteAPI
abstract type AbstractBuildkiteAPI end
Base.@kwdef struct BuildkiteAPI <: AbstractBuildkiteAPI
base_url::HTTP.URI = HTTP.URI("https://api.buildkite.com/v2/")
access_token::String = ""
end
function buildkite_api_uri(api::BuildkiteAPI, path)
HTTP.URIs.merge(api.base_url, path = api.base_url.path * path)
end
function buildkite_request(api::BuildkiteAPI, request_method, endpoint;
handle_error = true,
headers = Dict(),
params = Dict(),
allowredirects::Bool = true,
idle_timeout = 20,
status_exception = false)
api_endpoint = buildkite_api_uri(api, endpoint)
request_headers = convert(Dict{String, String}, headers)
if !haskey(request_headers, "Authorization")
request_headers["Authorization"] = "Bearer $(api.access_token)"
end
if !haskey(request_headers, "User-Agent")
request_headers["User-Agent"] = "Buildkite-jl"
end
if request_method == HTTP.get
api_uri = HTTP.URIs.merge(api_endpoint, query = params)
println("DEBUG: ", api_uri)
r = request_method(api_uri, request_headers,
redirect = allowredirects, status_exception = false, idle_timeout=idle_timeout)
else
api_uri = string(api_uri)
r = request_method(api_uri, request_headers, JSON.json(params),
redirect=allowredirects,
status_exception=status_exception,
idle_timeout=idle_timeout)
end
if handle_error
#handle_response_error(r)
end
return r
end
# REST primitives
function buildkite_get(api::BuildkiteAPI, endpoint = ""; options...)
buildkite_request(api, HTTP.get, endpoint; options...)
end
function buildkite_post(api::BuildkiteAPI, endpoint = ""; options...)
buildkite_request(api, HTTP.post, endpoint; options...)
end
function buildkite_put(api::BuildkiteAPI, endpoint = ""; options...)
buildkite_request(api, HTTP.put, endpoint; options...)
end
function buildkite_delete(api::BuildkiteAPI, endpoint = ""; options...)
buildkite_request(api, HTTP.delete, endpoint; options...)
end
function buildkite_patch(api::BuildkiteAPI, endpoint = ""; options...)
buildkite_request(api, HTTP.patch, endpoint; options...)
end
function buildkite_get_json(api::BuildkiteAPI, endpoint = ""; options...)
JSON.parse(HTTP.payload(buildkite_get(api, endpoint; options...), String))
end
function buildkite_post_json(api::BuildkiteAPI, endpoint = ""; options...)
JSON.parse(HTTP.payload(buildkite_post(api, endpoint; options...), String))
end
function buildkite_put_json(api::BuildkiteAPI, endpoint = ""; options...)
JSON.parse(HTTP.payload(buildkite_put(api, endpoint; options...), String))
end
function buildkite_delete_json(api::BuildkiteAPI, endpoint = ""; options...)
JSON.parse(HTTP.payload(buildkite_delete(api, endpoint; options...), String))
end
function buildkite_patch_json(api::BuildkiteAPI, endpoint = ""; options...)
JSON.parse(HTTP.payload(buildkite_patch(api, endpoint; options...), String))
end
function hello_world()
base_url = HTTP.URI("https://api.buildkite.com")
r = buildkite_get_json(BuildkiteAPI(base_url=base_url), "")
return r["response"]
end
# organization api
struct Organization
api::BuildkiteAPI
data::Dict
end
function organization(api::BuildkiteAPI, name)
return Organization(api, buildkite_get_json(api, "organizations/$(lowercase(name))"))
end
# pipelines api
struct Pipeline
api::BuildkiteAPI
data::Dict
end
function pipelines(api::BuildkiteAPI, organization; page=0, pagination=false)
query_params = Dict("page" => page)
endpoint = "organizations/$(lowercase(organization))/pipelines"
return [Pipeline(api, p) for p in buildkite_get_json(api, endpoint; params = query_params)]
end
function pipelines(org::Buildkite.Organization)
return pipelines(org.api, org.data["name"])
end
# build api
struct Build
api::BuildkiteAPI
data::Dict
end
function builds(api::BuildkiteAPI; state=nothing)
query_params = Dict("state" => state)
endpoint = "builds"
return [Build(api, b) for b in buildkite_get_json(api, endpoint; params = query_params)]
end
build_state(b::Build) = b.data["state"]
end
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] | 2.518269 | 1,779 |
function count_nucleotides(strand::AbstractString)
a = 0
c = 0
g = 0
t = 0
for i in strand
if i == 'A'
a += 1
elseif i == 'C'
c += 1
elseif i == 'G'
g += 1
elseif i == 'T'
t += 1
else
return throw(DomainError())
end
end
return Dict( 'A' => a, 'C' => c, 'G' => g, 'T' => t)
end
count_nucleotides("GAGAGACGTT")
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] | 1.716475 | 261 |
using AutonomousMerging
using Random
using AutomotiveDrivingModels
using POMDPs
using POMDPPolicies
using POMDPSimulators
using Test
function test_state(mdp::GenerativeMergingMDP, s,v=0.0, acc=0.0)
ego = Vehicle(vehicle_state(35.0, merge_lane(mdp.env), 5.0, mdp.env.roadway), VehicleDef(), EGO_ID)
veh1 = Vehicle(vehicle_state(s, main_lane(mdp.env), 4.9, mdp.env.roadway), VehicleDef(), EGO_ID + 1)
scene = Scene()
push!(scene, ego)
push!(scene, veh1)
return AugScene(scene, (acc=acc,))
end
@testset "Environment" begin
env = MergingEnvironment(main_lane_angle = 0.0, merge_lane_angle = pi/12)
main = main_lane(env)
merge = merge_lane(env)
@test main == env.roadway[LaneTag(MAIN_LANE_ID, 1)]
@test main.curve[end].s == env.main_lane_length + env.after_merge_length
@test merge.curve[end].s == env.merge_lane_length
end
@testset "GenerativeMDP" begin
rng = MersenneTwister(1)
mdp = GenerativeMergingMDP(max_cars=12,
min_cars=10,
driver_type = :random,
observe_cooperation = true,
initial_ego_velocity=0.0)
s0 = initialstate(mdp, rng)
policy = RandomPolicy(mdp, rng=rng)
hr = HistoryRecorder(rng = rng, max_steps=100)
hist = POMDPSimulators.simulate(hr, mdp, policy, s0)
s = s0
svec = convert_s(Vector{Float64}, s, mdp)
srec = convert_s(AugScene, svec, mdp)
@test get_by_id(srec.scene, EGO_ID).state.posG ≈ get_by_id(s.scene, EGO_ID).state.posG
s = state_hist(hist)[end]
svec = convert_s(Vector{Float64}, s, mdp)
srec = convert_s(AugScene, svec, mdp)
@test get_by_id(srec.scene, EGO_ID).state.posG ≈ get_by_id(s.scene, EGO_ID).state.posG
end
@testset "CooperativeIDM" begin
rng = MersenneTwister(1)
mdp = GenerativeMergingMDP(random_n_cars=true, dt=0.5)
mdp.driver_models[2] = CooperativeIDM(c=1.0)
set_desired_speed!(mdp.driver_models[2], 5.0)
s0 = test_state(mdp, 85.0)
policy = FunctionPolicy(s->7)
hr = HistoryRecorder(rng = rng, max_steps=40)
hist = POMDPSimulators.simulate(hr, mdp, policy, s0)
end
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] | 2.079545 | 1,056 |
# this should exist?
"""
filename(path)
Sring -> String
Extract core name of the file from path
E.g. filename("/some/path/to/file_name.ext") -> "file_name"
"""
filename(path) = splitext(basename(path))[1]
# this should be in PropDicts?
"""
propdict(json_file)
AbstractString -> PropDict
Construct a PropDict based on given <json_file>.
1) Why can't I name it function PropDict()?
2) Why doesn't this already exist in PropDicts?
I find the PropDicts.read(PropDict, String) format kinda cumbersome
"""
function propdict(json_file::AbstractString)
PropDicts.read(PropDict, json_file)
end
"""
remove_negative(value)
Real -> Real
Replace negative values by zeros.
Used to remove a glitch in SSD pulses as a quick fix
while the glitch is being debugged.
"""
function remove_negative(value::Real)
value < 0 ? 0 : value
end
"""
preamp_gain(preamp, noise_model)
GenericPreAmp, NoiseFromData -> Real
Calculate gain of <preamp> based on its max energy and
the offset observed in data baselines contained in <noise_model>
In principle we should not do this in this simulation, and user has to provide
the precise parameters of the electronics chain used in producing data
the baselines from which are contained in <noise_model>.
"""
function preamp_gain(preamp::PreAmp, noise_model::NoiseFromData)
# average offset in baselines
offset = mean(mean.(noise_model.baseline_catalog.waveform.value))
# calculate gain based on offset in data baselines
(typemax(UInt16) - offset) * germanium_ionization_energy / uconvert(u"eV", preamp.max_e)
end
"""
preamp_gain(preamp, ::NoiseFromSim)
GenericPreAmp -> Float64
Do nothing and return original value of gain parameter in <preamp>,
since when NoiseFromSim model is used, gain is provided by the user
(or calculated based on user given max energy)
"""
function preamp_gain(preamp::GenericPreAmp, ::NoiseFromSim)
preamp.gain
end
# """
# trigger_threshold(trigger, noise_model, preamp)
# Trigger, NoiseFromSim, GenericPreAmp -> Real
# Calculate trigger threshold based on whether
# the user provided it in the simulation settings
# """
# function trigger_threshold(trigger::Trigger, noise_model::NoiseFromSim, preamp::PreAmp)
# # if trigger threshold not given, take noise level as reference
# threshold = trigger.threshold_keV == 0u"keV" ? noise_model.noise_σ * 3 : trigger.threshold_keV
# uconvert(u"eV", threshold) / germanium_ionization_energy * preamp.gain
# end
"""
trigger_threshold(trigger, preamp, ::NoiseFromSim)
Trigger, GenericPreAmp -> Real
In NoiseFromSim setting, non-zero trigger threshold in keV MUST be given by the user.
Calculate final threshold based the value contained in <trigger>, and <preamp> gain.
"""
function trigger_threshold(trigger::Trigger, preamp::GenericPreAmp, ::NoiseFromSim)
uconvert(u"eV", trigger.threshold_keV) / germanium_ionization_energy * preamp.gain
# threshold = trigger.threshold_keV == 0u"keV" ? noise_model.noise_σ * 3 : trigger.threshold_keV
# uconvert(u"eV", threshold) / germanium_ionization_energy * preamp.gain
end
"""
trigger_threshold(trigger, preamp, noise_model)
Trigger, GenericPreAmp, NoiseFromData -> Real
In NoiseFromData setting, if trigger threshold in keV is not provided,
it is calculated based on noise levels in the baselines contained in
<noise_model>.
Otherwise the final threshold is calculated based on <preamp> gain.
"""
function trigger_threshold(trigger::Trigger, preamp::GenericPreAmp, noise_model::NoiseFromData)
trigger.threshold_keV == 0u"keV" ? std(noise_model.baseline_catalog.waveform[1].value) * 3 : uconvert(u"eV", trigger.threshold_keV) / germanium_ionization_energy * preamp.gain
end | [
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"""
abstract type AbstractHyperRectangle{N,T} <: AbstractElement{ReferenceHyperCube{N},SVector{N,T}}
Axis-aligned hyperrectangle in `N` dimensions with coordinates of type
`SVector{N,T}`.
"""
abstract type AbstractHyperRectangle{N,T} <: AbstractElement{ReferenceHyperCube{N},SVector{N,T}} end
Base.eltype(::AbstractHyperRectangle{N,T}) where {N,T} = T
ambient_dimension(::AbstractHyperRectangle{N}) where {N} = N
geometric_dimension(::AbstractHyperRectangle{N}) where {N} = N
low_corner(r::AbstractHyperRectangle) = abstractmethod(r)
high_corner(r::AbstractHyperRectangle) = abstractmethod(r)
diameter(r::AbstractHyperRectangle) = abstractmethod(r)
center(r::AbstractHyperRectangle) = abstractmethod(r)
radius(r::AbstractHyperRectangle) = abstractmethod(r)
vertices(r::AbstractHyperRectangle) = abstractmethod(r)
Base.in(point,h::AbstractHyperRectangle) = all(low_corner(h) .<= point .<= high_corner(h))
function vertices(rec::AbstractHyperRectangle{2})
lc = low_corner(rec)
hc = high_corner(rec)
return SVector(
SVector(lc[1],lc[2]),
SVector(hc[1],lc[2]),
SVector(hc[1],hc[2]),
SVector(lc[1],hc[2])
)
end
function vertices(rec::AbstractHyperRectangle{3})
lc = low_corner(rec)
hc = high_corner(rec)
return SVector(
# lower face
SVector(lc[1],lc[2],lc[3]),
SVector(hc[1],lc[2],lc[3]),
SVector(hc[1],hc[2],lc[3]),
SVector(lc[1],hc[2],lc[3]),
# upper face
SVector(lc[1],lc[2],hc[3]),
SVector(hc[1],lc[2],hc[3]),
SVector(hc[1],hc[2],hc[3]),
SVector(lc[1],hc[2],hc[3])
)
end
## implement the AbstractElement interface for convenience
function (el::AbstractHyperRectangle)(u)
@assert u in domain(el)
lc = low_corner(el)
hc = high_corner(el)
# map from reference domain to
v = @. lc + (hc - lc)*u
return v
end
function jacobian(el::AbstractHyperRectangle,u)
@assert u in domain(el)
lc = low_corner(el)
hc = high_corner(el)
return SDiagonal(hc - lc)
end
"""
distance(r1::AbstractHyperRectangle,r2::AbstractHyperRectangle)
The (minimal) Euclidean distance between a point `x ∈ r1` and `y ∈ r2`.
"""
function distance(rec1::AbstractHyperRectangle{N},rec2::AbstractHyperRectangle{N}) where {N}
d2 = 0
rec1_low_corner = low_corner(rec1)
rec1_high_corner = high_corner(rec1)
rec2_low_corner = low_corner(rec2)
rec2_high_corner = high_corner(rec2)
for i=1:N
d2 += max(0,rec1_low_corner[i] - rec2_high_corner[i])^2 +
max(0,rec2_low_corner[i] - rec1_high_corner[i])^2
end
return sqrt(d2)
end
"""
distance(x::SVector,r::HyperRectangle)
The (minimal) Euclidean distance between the point `x` and any point `y ∈ r`.
"""
function distance(pt::SVector{N},rec::AbstractHyperRectangle{N}) where {N}
d2 = 0
rec_low_corner = low_corner(rec)
rec_high_corner = high_corner(rec)
for i=1:N
d2 += max(0,pt[i] - rec_high_corner[i])^2 +
max(0,rec_low_corner[i] - pt[i])^2
end
return sqrt(d2)
end
distance(rec::AbstractHyperRectangle{N},pt::SVector{N}) where {N} = distance(pt,rec)
######
# HyperRectangle
######
"""
struct HyperRectangle{N,T}
Axis-aligned hyperrectangle in `N` dimensions given by `low_corner::SVector{N,T}` and
`high_corner::SVector{N,T}`.
"""
struct HyperRectangle{N,T} <: AbstractHyperRectangle{N,T}
low_corner::SVector{N,T}
high_corner::SVector{N,T}
end
HyperRectangle(l::Tuple,h::Tuple) = HyperRectangle(SVector(l),SVector(h))
HyperRectangle(l::SVector,h::SVector) = HyperRectangle(promote(l,h)...)
# 1d case
HyperRectangle(a::Number,b::Number) = HyperRectangle(SVector(a),SVector(b))
low_corner(r::HyperRectangle) = r.low_corner
high_corner(r::HyperRectangle) = r.high_corner
center(r::HyperRectangle) = (low_corner(r) + high_corner(r)) / 2
diameter(r::HyperRectangle) = norm(high_corner(r) .- low_corner(r),2)
radius(r::HyperRectangle) = diameter(r) / 2
Base.isapprox(h1::HyperRectangle,h2::HyperRectangle;kwargs...) = isapprox(h1.low_corner,h2.low_corner;kwargs...) && isapprox(h1.high_corner,h2.high_corner;kwargs...)
######
# HyperCube
######
"""
struct HyperCube{N,T}
Axis-aligned hypercube in `N` dimensions given by `low_corner::SVector{N,T}` and
`side::T`.
"""
struct HyperCube{N,T} <: AbstractHyperRectangle{N,T}
low_corner::SVector{N,T}
side::T
end
# 1d case
HyperCube(low_corner::Number,side::Number) = HyperCube(SVector(low_corner),side)
side(r::HyperCube) = r.side
low_corner(r::HyperCube) = r.low_corner
high_corner(r::HyperCube) = low_corner(r) .+ side(r)
center(r::HyperCube) = low_corner(r) .+ side(r)/2
diameter(r::HyperCube{N}) where N = side(r)*sqrt(N)
radius(r::HyperCube) = diameter(r) / 2
Base.isapprox(h1::HyperCube,h2::HyperCube;kwargs...) = isapprox(h1.low_corner,h2.low_corner;kwargs...) && isapprox(h1.side,h2.side;kwargs...)
######
# Utils
######
function HyperRectangle(els,cube=false)
isempty(els) && (error("data cannot be empty") )
lb = first(els) |> coords
ub = first(els) |> coords
for el in els
pt = coords(el)
lb = min.(lb,pt)
ub = max.(ub,pt)
end
if cube # fit a square/cube instead
w = maximum(ub-lb)
xc = (ub + lb) / 2
lb = xc .- w/2
ub = xc .+ w/2
# TODO: return HyperCube instead
end
return HyperRectangle(lb,ub)
end
"""
split(rec::AbstractHyperRectangle,[axis]::Int,[place])
Split a hyperrectangle in two along the `axis` direction at the position
`place`. Returns a tuple with the two resulting hyperrectangles.
When no `place` is given, defaults to splitting in the middle of the axis.
When no axis and no place is given, defaults to splitting along the largest axis.
"""
function Base.split(rec::AbstractHyperRectangle,axis,place)
rec_low_corner = low_corner(rec)
rec_high_corner = high_corner(rec)
N = ambient_dimension(rec)
high_corner1 = svector(n-> n==axis ? place : rec_high_corner[n], N)
low_corner2 = svector(n-> n==axis ? place : rec_low_corner[n], N)
rec1 = HyperRectangle(rec_low_corner, high_corner1)
rec2 = HyperRectangle(low_corner2,rec_high_corner)
return (rec1, rec2)
end
function Base.split(rec::AbstractHyperRectangle,axis)
place = (high_corner(rec)[axis] + low_corner(rec)[axis])/2
return split(rec,axis,place)
end
function Base.split(rec::AbstractHyperRectangle)
axis = argmax(high_corner(rec) .- low_corner(rec))
return split(rec,axis)
end
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] | 2.290998 | 2,866 |
function _add_forces!(
f::Tuple,
p,
idx::Integer,
jdx::Integer,
fp::AbstractFloat,
rp::AbstractFloat,
)
@inbounds for (k, c) in zip(p, f)
c[idx] += (fp * k) / rp
c[jdx] -= (fp * k) / rp
end
end
function _compute_distance(
x::SubArray,
y::SubArray,
z::SubArray,
i::Integer,
j::Integer,
boxl::Real,
)
xij = x[i] - x[j]
yij = y[i] - y[j]
zij = z[i] - z[j]
xij -= boxl * round(xij / boxl)
yij -= boxl * round(yij / boxl)
zij -= boxl * round(zij / boxl)
rij2 = xij * xij + yij * yij + zij * zij
st_positions = @SVector [xij, yij, zij]
return st_positions, rij2
end
function _compressibilityz(
pos::StaticVector,
fp::AbstractFloat,
rij::AbstractFloat,
zfactor::ZFactor,
)
tmp_sum = @. (pos^2.0 * -fp) / rij
zfactor.zval += sum(tmp_sum)
end
function _compute_energy!(
positions::AbstractArray,
forces::AbstractArray,
params::NamedTuple,
pot::PairwisePotential;
rdfobj = nothing,
zfactor = nothing,
)
# Initialize necessary variables
energy = zero(params.rc2)
x = view(positions, :, 1)
y = view(positions, :, 2)
z = view(positions, :, 3)
fx = view(forces, :, 1)
fy = view(forces, :, 2)
fz = view(forces, :, 3)
for i = 1:params.N-1
@inbounds @fastmath for j = (i+1):params.N
(static_positions, rij2) = _compute_distance(x, y, z, i, j, params.boxl)
if rij2 < params.rc2
rij2 = √rij2
u_pair, f_pair = apply!(pot, rij2)
_add_forces!((fx, fy, fz), static_positions, i, j, f_pair, rij2)
energy += u_pair
if !isnothing(rdfobj)
simple_rdf!(rdfobj, rij2)
end
if !isnothing(zfactor)
_compressibilityz(static_positions, f_pair, rij2, zfactor)
end
end
end
end
return energy / params.N
end
function energy_force!(
positions::AbstractArray,
forces::AbstractArray,
params::NamedTuple,
pot::PairwisePotential;
gofr = nothing,
zfactor = nothing,
)
# Retrieve data from the system
fill!(forces, zero(params.boxl))
energy =
_compute_energy!(positions, forces, params, pot; rdfobj = gofr, zfactor = zfactor)
end
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] | 1.954432 | 1,207 |
module TestDocumentation
using Base.Test
using DataFrames
using TimeData
println("\n Running documentation tests\n")
using WorldBankDataTd
## single indicator, single country
gnp = WorldBankDataTd.wdi("NY.GNP.PCAP.CD", "BR")
## single indicator, multiple countries
gnp = WorldBankDataTd.wdi("NY.GNP.PCAP.CD", ["BR", "US", "DE"])
## multiple indicators, single country
data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"],
"BR")
## multiple indicators, multiple countries
data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"],
["BR", "US", "DE"])
## same, but as DataFrame
data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"],
["BR", "US", "DE"],
format = DataFrame)
## multiple indicators, multiple countries, additional information
data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"],
["BR", "US", "DE"], extra = true)
data[1:5, :]
countryData = getWBMeta("countries")
countryData[1:5, :]
names(countryData)
size(countryData)
indicatorData = getWBMeta("indicators")
indicatorData[1:5, :]
names(indicatorData)
size(indicatorData)
WorldBankDataTd.country_cache[1:5, :]
WorldBankDataTd.indicator_cache[1:5, :]
res = search_wdi("countries", :name, r"united"i)
res
res = search_wdi("indicators", :description, r"gross national expenditure"i)
res[:name]
search_wdi("countries", :iso2c, r"TZ"i)
search_wdi("countries", :income, r"upper middle"i)
search_wdi("countries", :region, r"Latin America"i)
search_wdi("countries", :capital, r"^Ka"i)
search_wdi("countries", :lending, r"IBRD"i)
search_wdi("indicators", :name, r"gross national expenditure"i)
search_wdi("indicators", :description, r"gross national expenditure"i)
search_wdi("indicators", :source_database, r"Sustainable"i)
search_wdi("indicators", :source_organization,
r"Global Partnership"i)[1:5, :]
data = wdi("NY.GNP.PCAP.CD", ["US","BR"], 1980, 2012, extra = true)
usData = chkDates(x-> x[:iso2c] .== "US", eachdate(data)) |>
x -> asArr(x, Bool, false) |>
x -> data[x[:], :]
usData
dfAS = wdi("EN.ATM.CO2E.KT", "AS")
end
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17,
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1600,
366,
1921,
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198,
437,
198
] | 2.294792 | 960 |
module TexAssistant
import ArgParse
import TOML
include("utils.jl")
include("regexs.jl")
include("split_comment.jl")
include("expand_input.jl")
include("extract_sections_to_files.jl")
include("settings.jl")
include("extractors.jl")
include("tex_match.jl")
function __init__()
_fill_regexs!()
end
end | [
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] | 2.409396 | 149 |
# Julia program to Left Rotate a number by a specific bits.
# Left Rotate 'cnt' number of bits of the given number 'n'
function left_rotate_bits(n, cnt)
cnt = cnt % 31
while(cnt > 0)
# Store the current MSB in a temporary variable
msb = (n >> 31) & 1;
# Left rotate the given number by one bit
n = (n<<1);
# Set the dropped MSB as the new LSB
n = n | msb;
# Decrement cnt
cnt = cnt - 1
end
return n
end
print("Enter the number? ")
num = readline()
num = parse(Int, num)
print("How many bits do you want to rotate? ")
cnt = readline()
cnt = parse(Int, cnt)
left = left_rotate_bits(num, cnt)
print("The Left-rotated number is: $left")
"""
Time Complexity: O(n)
Space Complexity: O(1)
SAMPLE INPUT AND OUTPUT
Enter the number? 39
How many bits do you want to rotate? 17
The Left-rotated number is: 5111808
"""
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318,
25,
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220,
198,
37811,
198
] | 2.452055 | 365 |
include("differencial.jl")
include("RungeKutta.jl")
"""
`dynamics(q::SatelliteToolbox.Quaternion,ω::Vector,T::Vector,I::Matrix,dt)`
トルク`T`を与えられた剛体の回転を刻み時間`dt`秒ぶん計算する関数
## Arguments
- `q::SatelliteToolbox.Quaternion` :`t`秒時点の姿勢を表す単位クォータニオン 衛星軌道面座標系の基底をボディ座標系の基底に変換する
- `ω::Vector` :`t`秒時点の角速度ベクトル ボディ座標系基準
- `T::Vector` :剛体に与えられるトルク ボディ座標系基準
- `I::Matrix` :慣性テンソル
- `dt::Number` :刻み時間
## Returns
- `(qk/norm(qk), ωk)::Tuple{SatelliteToolbox.Quaternion,Vector}` :`(t+dt)`秒時点の姿勢を表す単位クォータニオンと角速度ベクトルの組
## Example
`qk,ωk=dynamics(q,ω,T,I,dt)`
"""
function dynamics(q::SatelliteToolbox.Quaternion,ω::Vector,T::Vector,I::Matrix,dt::Number)
qk,ωk=RK4((q,ω),(T,I),dt)
return qk/norm(qk),ωk
end
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] | 1.304577 | 568 |
Dict{Float32,String}(
19=>"North Europe",
8=>"Central Europe",
15=>"South Europe",
33=>"West South America",
2=>"Amazon",
18=>"Northeast Brazil",
26=>"Southeastern South America",
5=>"Central America/Mexico",
1=>"Alaska/N.W. Canada",
13=>"East North America",
32=>"West North America",
10=>"Central North America",
9=>"Canada/Greenland/Iceland" ,
11=>"East Africa",
30=>"West Asia" ,
29=>"West Africa" ,
22=>"Sahara",
21=>"Southern Africa",
7=>"Central Asia",
12=>"East Asia",
28=>"Tibetan Plateau",
16=>"North Asia",
25=>"Southeast Asia",
23=>"South Asia",
17=>"Northern Australia",
24=>"Southern Australia")
| [
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] | 2.772727 | 220 |
using Metal
@show devices()
dev = MtlDevice(1)
#lib = MTL.LibraryWithFile(d, "default.metallib")
src = read(dirname(pathof(Metal))*"/Metal/kernels/add.metal", String)
bufferSize = 128
bufferA = MtlArray{Float32,1}(undef, tuple(bufferSize), storage=Shared)
bufferB = MtlArray{Float32,1}(undef, tuple(bufferSize), storage=Shared)
bufferC = MtlArray{Float32,1}(undef, tuple(bufferSize), storage=Shared)
vecA = unsafe_wrap(Vector{Float32}, bufferA.buffer, tuple(bufferSize))
vecB = unsafe_wrap(Vector{Float32}, bufferB.buffer, tuple(bufferSize))
vecC = unsafe_wrap(Vector{Float32}, bufferC.buffer, tuple(bufferSize))
using Random
rand!.([vecA, vecB])
## Setup
opts = MtlCompileOptions()
lib = MtlLibrary(dev, src, opts)
fun = MtlFunction(lib, "add_arrays")
pip_addfun = MtlComputePipelineState(dev, fun)
queue = global_queue(dev) #MtlCommandQueue(dev)
##
vecA .= 0.0; vecB .= 0.0; vecC .= 0.0;
cmd = MTL.commit!(queue) do cmdbuf
MtlComputeCommandEncoder(cmdbuf) do enc
MTL.set_function!(enc, pip_addfun)
MTL.set_buffer!(enc, bufferA.buffer, 0, 1)
MTL.set_buffer!(enc, bufferB.buffer, 0, 2)
MTL.set_buffer!(enc, bufferC.buffer, 0, 3)
#MTL.set_buffers!(enc,
# [bufferA.buffer, bufferB.buffer, bufferC.buffer],
# [0,0,0], 1:3)
gridSize = MtSize(length(vecA), 1, 1)
threadGroupSize = min(length(vecA), pip_addfun.maxTotalThreadsPerThreadgroup)
threadGroupSize = MTL.MtSize(threadGroupSize, 1, 1)
@info threadGroupSize
MTL.append_current_function!(enc, gridSize, threadGroupSize)
end
end
# Execute
wait(cmd)
@show vecC
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] | 2.264228 | 738 |
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Description
# ==============================================================================
#
# Structures related to the orbit propagators.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
export OrbitPropagator
"""
OrbitPropagator{T}
Abstract type of the orbit propagator. Every propagator structure must be a
subtype of this type and must implement the following API functions:
propagate!(orbp, t::Number)
propagate!(orbp, t::AbstractVector)
propagate_to_epoch!(orbp, JD::Number)
propagate_to_epoch!(orbp, JD::AbstractVector)
step!(orbp, Δt::Number)
"""
abstract type OrbitPropagator{T} end
# Two Body Orbit Propagator
# ==============================================================================
export TwoBody_Structure, OrbitPropagatorTwoBody
"""
TwoBody_Structure{T}
Low level Two Body orbit propagator structure.
"""
@with_kw mutable struct TwoBody_Structure{T}
# Initial mean orbital elements
# ==========================================================================
epoch::T # ......................... Epoch of the initial mean elements [JD]
a_0::T # ..................... Initial mean normalized semi-major axis [m]
n_0::T # ..................................... Initial mean motion [rad/s]
e_0::T # .................................. Initial mean eccentricity [ ]
i_0::T # .................................. Initial mean inclination [rad]
Ω_0::T # ......................................... Initial mean RAAN [rad]
ω_0::T # .......................... Initial mean argument of perigee [rad]
f_0::T # ................................. Initial mean true anomaly [rad]
M_0::T # ................................. Initial mean mean anomaly [rad]
μ::T # .. Standard gravitational parameter of the central body [m^3/s^2]
# Current mean orbital elements
# ==========================================================================
Δt::T # .............. Timespan from the epoch of the initial elements [s]
f_k::T # .................................. Current mean true anomaly [rad]
M_k::T # .................................. Current mean mean anomaly [rad]
end
"""
OrbitPropagatorTwoBody{T} <: OrbitPropagator{T}
Structure that holds the information related to the Two Body orbit propagator.
# Fields
* `orb`: Mean orbital elements (see `Orbit`).
* `tbd`: Structure that stores the Two Body orbit propagator data (see
`TwoBody_Structure`).
"""
struct OrbitPropagatorTwoBody{T} <: OrbitPropagator{T}
tbd::TwoBody_Structure{T}
end
# J2 Orbit Propagator
# ==============================================================================
export J2_GravCte, J2_Structure, OrbitPropagatorJ2
"""
J2_GravCte{T}
Gravitational constants for J2 orbit propagator.
# Fields
* `R0`: Earth equatorial radius [m].
* `μm`: √GM [er/s]^(3/2).
* `J2`: The second gravitational zonal harmonic of the Earth.
"""
@with_kw struct J2_GravCte{T}
R0::T
μm::T
J2::T
end
"""
J2_Structure{T}
Low level J2 orbit propagator structure.
"""
@with_kw mutable struct J2_Structure{T}
# Initial mean orbital elements
# ==========================================================================
epoch::T # ............ Epoch of the initial mean elements [JD]
al_0::T # ....... Initial mean normalized semi-major axis [er]
n_0::T # ........................ Initial mean motion [rad/s]
e_0::T # ..................... Initial mean eccentricity [ ]
i_0::T # ..................... Initial mean inclination [rad]
Ω_0::T # ............................ Initial mean RAAN [rad]
ω_0::T # ............. Initial mean argument of perigee [rad]
f_0::T # .................... Initial mean true anomaly [rad]
M_0::T # .................... Initial mean mean anomaly [rad]
dn_o2::T # ...... First time derivative of mean motion [rad/s²]
ddn_o6::T # ..... Second time derivative of mean motion [rad/s³]
j2_gc::J2_GravCte{T} # ........ J2 orbit propagator gravitational constants
# Current mean orbital elements
# ==========================================================================
Δt::T # .............. Timespan from the epoch of the initial elements [s]
al_k::T # ..................... Current mean normalized semi-major axis [er]
e_k::T # ................................... Current mean eccentricity [ ]
i_k::T # ................................... Current mean inclination [rad]
Ω_k::T # .......................................... Current mean RAAN [rad]
ω_k::T # ........................... Current mean argument of perigee [rad]
f_k::T # .................................. Current mean true anomaly [rad]
M_k::T # .................................. Current mean mean anomaly [rad]
# Auxiliary variables
# ==========================================================================
δa::T # ........................... Semi-major axis time derivative [er/s]
δe::T # ............................... Eccentricity time derivative [1/s]
δΩ::T # ..................................... RAAN time derivative [rad/s]
δω::T # ...................... Argument of perigee time derivative [rad/s]
δM_0::T # ............................. Mean anomaly time derivative [rad/s]
end
"""
OrbitPropagatorJ2{T} <: OrbitPropagator{T}
Structure that holds the information related to the J2 orbit propagator.
# Fields
* `j2d`: Structure that stores the J2 orbit propagator data (see
`J2_Structure`).
"""
struct OrbitPropagatorJ2{T} <: OrbitPropagator{T}
j2d::J2_Structure{T}
end
# J2 osculating orbit propagator
# ==============================================================================
export J2osc_Strutcture, OrbitPropagatorJ2osc
"""
J2osc_Structure{T}
Low level J2 osculating orbit propagator structure.
"""
@with_kw mutable struct J2osc_Structure{T}
# J2 orbit propagator to propagate the mean elements.
j2d::J2_Structure{T}
# Propagation time from epoch.
Δt::T
# Current osculating Keplerian elements
# ==========================================================================
a_k::T # ................................... Osculating semi-major axis [er]
e_k::T # ....................................... Osculating eccentricity [ ]
i_k::T # ...................................... Osculating inclination [rad]
Ω_k::T # ............................................. Osculating RAAN [rad]
ω_k::T # .............................. Osculating argument of perigee [rad]
f_k::T # ..................................... Osculating true anomaly [rad]
M_k::T # ..................................... Osculating mean anomaly [rad]
end
"""
OrbitPropagatorJ2osc{T} <: OrbitPropagator{T}
Structure that holds the information related to the J2 osculating orbit
propagator.
# Fields
* `j2oscd`: Structure that stores the J2 osculating orbit propagator data (see
`J2osc_Structure`).
"""
struct OrbitPropagatorJ2osc{T} <: OrbitPropagator{T}
j2oscd::J2osc_Structure{T}
end
# J4 orbit propagator
# ==============================================================================
export J4_GravCte, J4_Structure, OrbitPropagatorJ4
"""
J4_GravCte{T}
Gravitational constants for J4 orbit propagator.
# Fields
* `R0`: Earth equatorial radius [m].
* `μm`: √GM [er/s]^(3/2).
* `J2`: The second gravitational zonal harmonic of the Earth.
* `J4`: The fourth gravitational zonal harmonic of the Earth.
"""
@with_kw struct J4_GravCte{T}
R0::T
μm::T
J2::T
J4::T
end
"""
J4_Structure{T}
Low level J4 orbit propagator structure.
"""
@with_kw mutable struct J4_Structure{T}
# Initial mean orbital elements
# ==========================================================================
epoch::T # ............ Epoch of the initial mean elements [JD]
al_0::T # ....... Initial mean normalized semi-major axis [er]
n_0::T # ........................ Initial mean motion [rad/s]
e_0::T # ..................... Initial mean eccentricity [ ]
i_0::T # ..................... Initial mean inclination [rad]
Ω_0::T # ............................ Initial mean RAAN [rad]
ω_0::T # ............. Initial mean argument of perigee [rad]
f_0::T # .................... Initial mean true anomaly [rad]
M_0::T # .................... Initial mean mean anomaly [rad]
dn_o2::T # ...... First time derivative of mean motion [rad/s²]
ddn_o6::T # ..... Second time derivative of mean motion [rad/s³]
j4_gc::J4_GravCte{T} # ........ J4 orbit propagator gravitational constants
# Current mean orbital elements
# ==========================================================================
Δt::T # .............. Timespan from the epoch of the initial elements [s]
al_k::T # ..................... Current mean normalized semi-major axis [er]
e_k::T # ................................... Current mean eccentricity [ ]
i_k::T # ................................... Current mean inclination [rad]
Ω_k::T # .......................................... Current mean RAAN [rad]
ω_k::T # ........................... Current mean argument of perigee [rad]
f_k::T # .................................. Current mean true anomaly [rad]
M_k::T # .................................. Current mean mean anomaly [rad]
# Auxiliary variables
# ==========================================================================
δa::T # ........................... Semi-major axis time derivative [er/s]
δe::T # ............................... Eccentricity time derivative [1/s]
δΩ::T # ..................................... RAAN time derivative [rad/s]
δω::T # ...................... Argument of perigee time derivative [rad/s]
δM_0::T # ............................. Mean anomaly time derivative [rad/s]
end
"""
OrbitPropagatorJ4{T} <: OrbitPropagator{T}
Structure that holds the information related to the J4 orbit propagator.
# Fields
* `j4d`: Structure that stores the J4 orbit propagator data (see
`J4_Structure`).
"""
struct OrbitPropagatorJ4{T} <: OrbitPropagator{T}
j4d::J4_Structure{T}
end
# SGP4
# ==============================================================================
export OrbitPropagatorSGP4
"""
OrbitPropagatorSGP4{T} <: OrbitPropagator{T}
Structure that holds the information related to the SGP4 propagator.
# Fields
* `sgp4d`: Structure that stores the SGP4 data (see `SGP4_Structure`).
"""
struct OrbitPropagatorSGP4{T} <: OrbitPropagator{T}
sgp4d::SGP4_Structure{T}
end
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16960,
19,
62,
1273,
5620,
90,
51,
92,
198,
437,
198
] | 2.980428 | 3,781 |
function clean(phone_number)
end
| [
8818,
3424,
7,
4862,
62,
17618,
8,
198,
220,
220,
220,
220,
198,
437,
198
] | 2.533333 | 15 |
@testset "Ensembles" begin
d = PointSet(rand(2,100))
r = (z=[1:100 for i in 1:10],)
s = Ensemble(d, r)
@test domain(s) == d
@test s[:z] == r[:z]
for i in 1:10
@test s[i] == georef((z=1:100,), d)
end
s1 = Ensemble(d, r)
s2 = Ensemble(d, r)
@test s1 == s2
@test sprint(show, s) == "2D Ensemble"
@test sprint(show, MIME"text/plain"(), s) == "2D Ensemble\n domain: 100 PointSet{2,Float64}\n variables: z\n N° reals: 10"
d = CartesianGrid(10,10)
r = (z=[1:100 for i in 1:10],)
s = Ensemble(d, r)
@test domain(s) == d
@test s[:z] == r[:z]
for i in 1:10
@test s[i] == georef((z=1:100,), d)
end
@test sprint(show, s) == "2D Ensemble"
@test sprint(show, MIME"text/plain"(), s) == "2D Ensemble\n domain: 10×10 CartesianGrid{2,Float64}\n variables: z\n N° reals: 10"
if visualtests
@test_reference "data/ensemble.png" plot(s,size=(800,300))
end
grid = CartesianGrid(3,3)
reals = (z=[i*ones(nelements(grid)) for i in 1:3],)
ensemble = Ensemble(grid, reals)
# mean
mean2D = mean(ensemble)
@test mean2D.z == 2.0*ones(nelements(mean2D))
@test domain(mean2D) == ensemble.domain
# variance
var2D = var(ensemble)
@test var2D.z == 1.0*ones(nelements(var2D))
@test domain(var2D) == ensemble.domain
# quantile (scalar)
p = 0.5
quant2D = quantile(ensemble, p)
@test quant2D.z == 2.0*ones(nelements(quant2D))
@test domain(quant2D) == ensemble.domain
# quantile (vector)
ps = [0.0, 0.5, 1.0]
quants2D = quantile(ensemble, ps)
@test quants2D[2].z == quant2D.z
end
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198,
437,
198
] | 2.20255 | 706 |
""
function get_solution_tf(pm::GenericPowerModel, sol::Dict{String,Any})
PowerModels.add_bus_voltage_setpoint(sol, pm)
PowerModels.add_generator_power_setpoint(sol, pm)
PowerModels.add_branch_flow_setpoint(sol, pm)
PowerModels.add_dcline_flow_setpoint(sol, pm)
add_risk(sol, pm)
add_branch_shift_setpoint(sol, pm)
add_branch_tap_setpoint(sol, pm)
add_load_power_setpoint(sol, pm)
add_nodal_power_setpoint(sol, pm)
add_pdelta(sol, pm)
add_qdelta(sol, pm)
return sol
end
""
function add_branch_shift_setpoint(sol, pm::GenericPowerModel)
PowerModels.add_setpoint(sol, pm, "branch", "shiftf", :va_shift; extract_var = (var,idx,item) -> var[(idx, item["f_bus"], item["t_bus"])], default_value = (item) -> 0)
PowerModels.add_setpoint(sol, pm, "branch", "shiftt", :va_shift; extract_var = (var,idx,item) -> var[(idx, item["t_bus"], item["f_bus"])], default_value = (item) -> 0)
end
""
function add_branch_tap_setpoint(sol, pm::GenericPowerModel)
dict_name = "branch"
index_name = "index"
if pm.data["multinetwork"] == false
sol_dict = get(sol, dict_name, Dict{String,Any}())
if length(pm.data[dict_name]) > 0
sol[dict_name] = sol_dict
end
for (i,item) in pm.data[dict_name]
idx = Int(item[index_name])
fbus = Int(item["f_bus"])
tbus = Int(item["t_bus"])
sol_item = sol_dict[i] = get(sol_dict, i, Dict{String,Any}())
sol_item["tapf"] = 1
sol_item["tapt"] = 1
try
extract_vtap_fr = (var,idx,item) -> var[(idx, item["f_bus"], item["t_bus"])]
extract_vtap_to = (var,idx,item) -> var[(idx, item["t_bus"], item["f_bus"])]
vtap_fr = getvalue(extract_vtap_fr(PowerModels.var(pm, :vm_tap), idx, item))
vtap_to = getvalue(extract_vtap_to(PowerModels.var(pm, :vm_tap), idx, item))
vf = getvalue(PowerModels.var(pm,:vm))[fbus]
vt = getvalue(PowerModels.var(pm,:vm))[tbus]
sol_item["tapf"] = vf / vtap_fr
sol_item["tapt"] = vt / vtap_to
catch
end
end
else
for (n,nw_data) in pm.data["nw"]
sol_dict = get(sol, dict_name, Dict{String,Any}())
if length(pm.data["nw"][n][dict_name]) > 0
sol[dict_name] = sol_dict
end
for (i,item) in pm.data["nw"][n][dict_name]
idx = Int(item[index_name])
fbus = Int(item["f_bus"])
tbus = Int(item["t_bus"])
sol_item = sol_dict[i] = get(sol_dict, i, Dict{String,Any}())
sol_item["tapf"] = 1
sol_item["tapt"] = 1
try
extract_vtap_fr = (var,idx,item) -> var[(idx, item["f_bus"], item["t_bus"])]
extract_vtap_to = (var,idx,item) -> var[(idx, item["t_bus"], item["f_bus"])]
vtap_fr = getvalue(extract_vtap_fr(PowerModels.var(pm, :vm_tap), idx, item))
vtap_to = getvalue(extract_vtap_to(PowerModels.var(pm, :vm_tap), idx, item))
vf = getvalue(PowerModels.var(pm,:vm))[fbus]
vt = getvalue(PowerModels.var(pm,:vm))[tbus]
sol_item["tapf"] = vf / vtap_fr
sol_item["tapt"] = vt / vtap_to
catch
end
end
end
end
end
""
function add_load_power_setpoint(sol, pm::GenericPowerModel)
mva_base = pm.data["baseMVA"]
PowerModels.add_setpoint(sol, pm, "load", "pl", :pl)
PowerModels.add_setpoint(sol, pm, "load", "ql", :ql)
end
""
function add_nodal_power_setpoint(sol, pm::GenericPowerModel)
mva_base = pm.data["baseMVA"]
PowerModels.add_setpoint(sol, pm, "bus", "pnode", :pnode)
PowerModels.add_setpoint(sol, pm, "bus", "qnode", :qnode)
end
""
function add_pdelta(sol, pm::GenericPowerModel)
mva_base = pm.data["baseMVA"]
PowerModels.add_setpoint(sol, pm, "load", "pdelta", :pl_delta)
PowerModels.add_setpoint(sol, pm, "gen", "pdelta", :pg_delta)
end
""
function add_qdelta(sol, pm::GenericPowerModel)
mva_base = pm.data["baseMVA"]
PowerModels.add_setpoint(sol, pm, "load", "qdelta", :ql_delta)
PowerModels.add_setpoint(sol, pm, "gen", "qdelta", :qg_delta)
end
""
function add_risk(sol, pm::GenericPowerModel)
add_case_level_setpoint(sol, pm, "first_stage_cost", :first_stage_cost)
add_case_level_setpoint(sol, pm, "second_stage_risk", :second_stage_risk)
PowerModels.add_setpoint(sol, pm, "contingencies", "dispatch_cost", :dispatch_cost)
PowerModels.add_setpoint(sol, pm, "contingencies", "redispatch_cost", :redispatch_cost)
PowerModels.add_setpoint(sol, pm, "contingencies", "loadshedding_cost", :loadshedding_cost)
# add_cont_level_setpoint(sol, pm, "contingencies", "dispatch_cost", :dispatch_cost)
# add_cont_level_setpoint(sol, pm, "contingencies", "redispatch_cost", :redispatch_cost)
# add_cont_level_setpoint(sol, pm, "contingencies", "loadshedding_cost", :loadshedding_cost)
end
""
function add_case_level_setpoint(sol, pm::GenericPowerModel, param_name, variable_symbol; scale = (x) -> x, extract_var = var -> var)
if !haskey(sol, param_name)
sol[param_name] = Dict()
end
# print(pm.var[:nw][1][:cnd][pm.ccnd][variable_symbol])
# print(sol)
variable = extract_var(pm.var[:nw][1][:cnd][pm.ccnd][variable_symbol])
sol[param_name] = scale(getvalue(variable))
# catch
# end
end
""
function add_cont_level_setpoint(sol, pm::GenericPowerModel, dict_name, param_name, variable_symbol; scale = (x) -> x, extract_var = var -> var)
if !haskey(sol, dict_name)
sol[dict_name] = Dict()
end
sol[dict_name][param_name] = Dict()
sol[dict_name][param_name] = getvalue(extract_var(pm.var[:nw][1][:cnd][pm.ccnd][variable_symbol]))
end
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] | 2.024872 | 2,935 |
using Random
using LightGraphs
mutable struct Agent
id::Int32
values::AbstractArray
vote::Int8
energy::Float32
end
function initNetwork(N)
#Initialize agents
global nodes = []
for i in 1:N
push!(nodes,Agent(i,rand(0:10,(5)),rand(1:10),0))
end
#=
#Generate graph with N
A = bitrand((N,N)) #Adjacency matrix #TODO:Make sparse
for i in 1:N
A[i,i] = 0 #Make sure nodes are not be connected to themselves
end
global Network = DiGraph(A) #Generate graph
A = nothing #Clear A
#We are now implementing a Barabasi-Albert graph, instead of a random one
=#
#Generate Barabasi graph with N nodes, 3 conntections each, 10 initial nodes
global Network = barabasi_albert(N, 10, 3, seed=1, is_directed=true)
#global Network = erdos_renyi(N, 4, is_directed=true)
#Initialize energy
for i in 1:N
dE(i)
end
computeEnergy()
#Count initial preferences
trackPreference()
end
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] | 2.437653 | 409 |
using Plots
using Random
Random.seed!(123)
# Solver options
tf = 5.0
N = 101
integration = :rk4
opts = SolverOptions()
opts.verbose = true
opts.cost_tolerance = 1e-8
opts.cost_tolerance_intermediate = 1e-8
opts.constraint_tolerance = 1e-8
opts.constraint_tolerance_intermediate = 1e-8
opts.gradient_norm_tolerance = 1e-12
opts.gradient_norm_tolerance_intermediate = 1e-12
opts.use_gradient_aula = true
opts.active_constraint_tolerance = 0.0
opts.penalty_scaling = 10.0
opts.penalty_initial = 1e-3
opts.penalty_update_frequency = 1
opts.constraint_decrease_ratio = .25
opts.iterations = 1000
opts.iterations_outerloop = 50
opts.iterations_innerloop = 300
opts.outer_loop_update_type = :individual
# Set up model, objective, solver
model, = TrajectoryOptimization.Dynamics.quadrotor
n = model.n
m = model.m
# -initial state
x0 = zeros(n)
x0[1:3] = [0.; 0.; 0.]
q0 = [1.;0.;0.;0.]
x0[4:7] = q0
# -final state
xf = copy(x0)
xf[1:3] = [0.;40.;0.] # xyz position
xf[4:7] = q0
# -control limits
u_min = 0.0
u_max = 10.0
# Q = (1e-1)*Matrix(I,n,n)
# Q[4,4] = 1.0
# Q[5,5] = 1.0
# Q[6,6] = 1.0
# Q[7,7] = 1.0
# R = (1e-1)*Matrix(I,m,m)
Q = (1e-1)*Matrix(I,n,n)
R = (1e-1)*Matrix(I,m,m)
Qf = (1000.0)*Matrix(I,n,n)
# obstacles constraint
r_quad = 3.0
r_sphere = 3.0
spheres = ((0.,10.,0.,r_sphere),(0.,20.,0.,r_sphere),(0.,30.,0.,r_sphere))
n_spheres = 3
function cI(c,x,u)
for i = 1:n_spheres
c[i] = sphere_constraint(x,spheres[i][1],spheres[i][2],spheres[i][3],spheres[i][4]+r_quad)
end
c
end
# unit quaternion constraint
function cE(c,x,u)
c = sqrt(x[4]^2 + x[5]^2 + x[6]^2 + x[7]^2) - 1.0
end
obj_uncon = LQRObjective(Q, R, Qf, tf, x0, xf)
obj_con = TrajectoryOptimization.ConstrainedObjective(obj_uncon,u_min=u_min,u_max=u_max,cI=cI,cE=cE)
solver_uncon = Solver(model,obj_uncon,integration=integration,N=N,opts=opts)
solver_con = Solver(model,obj_con,integration=integration,N=N,opts=opts)
U0 = zeros(solver_uncon.model.m, solver_uncon.N-1)
@time results_uncon, stats_uncon = solve(solver_uncon,U0)
plot(to_array(results_uncon.X)[1:3,:]')
solver_con = Solver(model,obj_con,integration=integration,N=N,opts=opts)
results_con.μ
@time results_con, stats_con = solve(solver_con,U0)
plot(to_array(results_con.U)[:,1:solver_con.N-1]')
plot(to_array(results_con.X)[1:3,:]')
plot(to_array(results_con.λ[1:N-1])')
max_violation(results_con)
total_time(solver_con,results_con)
plot!(log.(stats_con["max_condition_number"]))
plot(log.(stats_con["c_max"]).+15.5)
plot(log.(stats_con["cost"]))
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4008,
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] | 2.075783 | 1,214 |
<html>
<head>
<style type="text/css">
body {
margin: 2em;
}
h1 {
text-align: center;
}
table {
border-spacing: 0;
box-shadow: 0 0 0.25em #888;
margin: auto;
}
table,
tr,
th,
td {
border-collapse: collapse;
}
th {
color: white;
background-color: rgb(43, 53, 59);
}
th,
td {
padding: 0.5em;
}
table tr:nth-child(even) td {
background-color: rgba(218, 224, 229, 0.850);
}
</style>
</head>
<body>
<h1>csv2html Example</h1>
<table>
<tr>
<th>Character</th>
<th>Speech</th>
</tr>
<tr>
<td>The multitude</td>
<td>The messiah! Show us the messiah!</td>
</tr>
<tr>
<td>Brians mother</td>
<td>Now you listen here! He's not the messiah; he's a very naughty boy! Now go away!</td>
</tr>
<tr>
<td>The multitude</td>
<td>Who are you?</td>
</tr>
<tr>
<td>Brians mother</td>
<td>I'm his mother; that's who!</td>
</tr>
<tr>
<td>The multitude</td>
<td>Behold his mother! Behold his mother!</td>
</tr>
</table>
</body>
</html>
| [
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] | 1.628409 | 880 |
# length(pdp::VkPhysicalDeviceProperties) = 1
# iterate(pdp::VkPhysicalDeviceProperties) = (pdp, nothing) | [
2,
4129,
7,
79,
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3712,
53,
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8
] | 2.837838 | 37 |
# Test Linear System Optimizaiton
using SwitchTimeOpt
using MathOptInterface
using LinearAlgebra
const MOI = MathOptInterface
using Ipopt
# Define test options
objtol = 1e-04
primaltol = 1e-03
# Define Solver options
maxiter = 50
maxtime = 100.0
verbose = 0
tolerance = 1e-06
#Define solver
solver = Ipopt.Optimizer()
MOI.set(solver, MOI.RawOptimizerAttribute("tol"), tolerance)
MOI.set(solver, MOI.RawOptimizerAttribute("print_level"), verbose)
MOI.set(solver, MOI.RawOptimizerAttribute("max_cpu_time"), maxtime)
MOI.set(solver, MOI.RawOptimizerAttribute("max_iter"), maxiter)
# Size of the state space
nx = 2;
# Cost function Matrix
Q = 1.0 * Matrix(I, nx, nx)
# Initial State
x0 = [1.0; 1.0]
# Define initial and final time
t0 = 0.0
tf = 1.0
### Define System Dynamics
A = zeros(nx, nx, 2)
A[:, :, 1] = [-1 0;
1 2]
A[:, :, 2] = [1 1;
1 -2]
m = stoproblem(x0, A, solver=solver)
solve!(m)
@testset "Test optimal switching time" begin
@test isapprox(gettau(m)[1], 0.26486646235103123, atol=primaltol)
end
@testset "Test status and optimal objective value" begin
@test isapprox(getobjval(m), 5.2545429449272145, atol=objtol)
@test string(getstat(m)) == "LOCALLY_SOLVED"
end
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] | 2.362934 | 518 |
## ScalarAffineFunction-in-Set
## ScalarQuadraticFunction-in-Set
function _build_constraint(
model::Optimizer,
f::F,
s::MOI.GreaterThan{T},
) where {
T <: Real,
F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}},
}
return cpo_java_ge(model.inner, _parse(model, f), s.lower)
end
function _build_constraint(
model::Optimizer,
f::F,
s::MOI.LessThan{T},
) where {
T <: Real,
F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}},
}
return cpo_java_le(model.inner, _parse(model, f), s.upper)
end
function _build_constraint(
model::Optimizer,
f::F,
s::MOI.EqualTo{T},
) where {
T <: Real,
F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}},
}
return cpo_java_eq(model.inner, _parse(model, f), s.value)
end
function _build_constraint(
model::Optimizer,
f::F,
s::MOI.EqualTo{T},
) where {
T <: Integer,
F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}},
}
return cpo_java_eq(model.inner, _parse(model, f), Int32(s.value))
end
function _build_constraint(
model::Optimizer,
f::F,
s::MOI.Interval{T},
) where {
T <: Real,
F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}},
}
if s.lower == Inf
return _build_constraint(model, f, MOI.LessThan(s.upper))
elseif s.upper == Inf
return _build_constraint(model, f, MOI.GreaterThan(s.lower))
elseif s.lower == s.upper
return _build_constraint(model, f, MOI.EqualTo(s.lower))
end
return cpo_java_range(model.inner, s.lower, _parse(model, f), s.upper)
end
# No vector of constraints, there is no more efficient way to do it.
function MOI.delete(
model::Optimizer,
c::MOI.ConstraintIndex{F, S},
) where {
T <: Real,
F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}},
S <:
Union{MOI.GreaterThan{T}, MOI.LessThan{T}, MOI.EqualTo{T}, MOI.Interval{T}},
}
cpo_java_remove(model.inner, _info(model, c).constraint)
delete!(model.constraint_info, c)
return
end
# TODO: function MOI.set(model::Optimizer, ::MOI.ConstraintSet, c::MOI.ConstraintIndex{MOI.ScalarAffineFunction{Float64}, S}, s::S) where {S}
## VectorOfVariables-in-SOS{I|II}
# Not available. Bridge it?
## VectorOfVariables-in-SecondOrderCone
function _build_constraint(
model::Optimizer,
f::MOI.VectorOfVariables,
s::MOI.SecondOrderCone,
)
if length(f.variables) != s.dimension
error("Dimension of $(s) does not match number of terms in $(f)")
end
# First, check the lower bound on t.
t_info = _info(model, f.variables[1])
if !_has_lb(model, t_info.index) || _get_lb(model, t_info.index) < 0.0
if _get_lb(model, t_info.index) < 0.0
t_info.old_lb = _get_lb(model, t_info.index)
end
t_info.n_socs += 1
_set_lb(model, t_info.index, 0.0)
end
# Then, add the quadratic constraint.
expr = _parse(model, f, s)
return cpo_java_gt(model, expr, 0)
end
function MOI.delete(
model::Optimizer,
c::MOI.ConstraintIndex{MOI.VectorOfVariables, MOI.SecondOrderCone},
)
# Remove the constraint.
cpo_java_remove(model, _info(model, c).constraint)
# Maybe restore the old bound on t.
t_info = _info(model, f.variables[1])
t_info.n_socs -= 1
if t_info.n_socs == 0
_set_lb(model, t_info.index, t_info.old_lb, typeof(t_info.old_lb))
t_info.old_lb = nothing
end
return
end
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] | 2.172414 | 1,624 |
module InterfaceTesting
using Base.Test
export partial_method_exists,
test_iterator_interface,
test_index_interface,
test_abstractarray_interface
"""
Checks if a method exist.
The first parameter is the function.
The second parameter is a list of types, or wild cards.
THe `:` is used as a wildcard.
Wild cards match any type (Not just `Any`)
"""
function partial_method_exists(f, types)
meths = methods(f)
for mm in meths
@show mm
sig_types = mm.sig.types[2:end]
length(types) != length(sig_types) && continue
for (spec, sig) in zip(types, sig_types)
spec==(:) && continue
spec <: sig || @goto next_meth
end
return true
@label next_meth
end
return false
end
"""
Checks that all the method expected of an Interator are defined.
This is the most formal and common of informal interfaced.
It is practically useless to not implement it properly.
"""
function test_iterator_interface(itertype; testset_type=Test.DefaultTestSet)
@testset testset_type "$(itertype)" begin
#Basic Methods
@test method_exists(start, (itertype,))
@test partial_method_exists(next, (itertype,:))
@test partial_method_exists(done, (itertype,:))
#Iterators size
@test which(Base.iteratorsize, (itertype,)) == which(Base.iteratorsize, (Any,))#Method for Instance of type should always be the fall back -- never set directly
if Base.iteratorsize(itertype)==Base.HasShape()
@test method_exists(size, (itertype,))
@test method_exists(length, (itertype,))
elseif Base.iteratorsize(itertype)==Base.HasLength()
@test method_exists(length, (itertype,))
else
@test Base.iteratorsize(itertype) in (Base.SizeUnknown(), Base.IsInfinite())
end
#Iterator Eltype
if Base.iteratoreltype(itertype) == Base.HasEltype()
@test method_exists(eltype, (itertype,))
end
end
end
"""
Checks Indexing Interface implemented.
This is the least formal of informal interfaces.
There a plenty of useful partial implementatons of it.
Eg a read-only things
"""
function test_index_interface(kind; testset_type=Test.DefaultTestSet)
@testset testset_type "$(kind)" begin
if method_exists(getindex, (kind, Vararg))
@test partial_method_exists(size, (kind, :)) #otherwise `end` won't work in multidimentional index expressions
else #Assume 1D indexing only has been defined
@test partial_method_exists(getindex, (kind,:))
end
@test partial_method_exists(setindex!, (kind,:,:)) #Got to have something. 1D will do. Might have more, kinda complex
@test method_exists(endof, (kind,)) #If not defined then `end` won't work
end
end
"""
Checks AbstractArray Interface implemented.
This is required for all subtypes of AbstractArray.
Filling this will mean automatically furfilling `test_index_interface`
"""
function test_abstractarray_interface(kind; testset_type=Test.DefaultTestSet)
@testset testset_type "$(kind)" begin
@test method_exists(size, (kind,))
@test method_exists(getindex, (kind, Vararg{Int}))
@test partial_method_exists(setindex!, (kind, :, :))
end
end
end # module
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] | 2.61326 | 1,267 |
######################################################################
# Suite of macros to automatically generate getters and setters for
# object properties along with the accompanying `hasproperty` and
# `propertynames` specializations.
# -----
# Licensed under MIT License
module GenerateProperties
const Optional{T} = Union{T, Nothing}
const GetterSetterBody = Tuple{Optional{LineNumberNode}, Expr}
struct StructProp{S, F} end
struct StructField{S, F} end
assign(inst, field::Symbol, value) = assign(StructProp{typeof(inst), field}, inst, value)
assign( ::Type{StructProp{ S, F}}, inst::S, value) where {S, F} = assign(StructField{S, F}, inst, value)
assign(T::Type{StructField{S, F}}, inst::S, value) where {S, F} = assign(getfieldtype(T), inst, F, value)
assign(::Type{T}, inst, field::Symbol, value) where {T} = setfield!(inst, field, convert(T, value))
assign(::Type{T1}, inst, field::Symbol, value::T2) where {T1, T2<:T1} = setfield!(inst, field, value)
assign(::Type{T1}, inst, field::Symbol, value::T2) where {T1<:Number, T2<:Number} = setfield!(inst, field, T1(value))
retrieve(inst, field::Symbol) = retrieve(StructProp{typeof(inst), field}, inst)
retrieve(::Type{StructProp{ S, F}}, inst::S) where {S, F} = retrieve(StructField{S, F}, inst)
retrieve(::Type{StructField{S, F}}, inst::S) where {S, F} = getfield(inst, F)
getparambody(x) = x
getparambody(unionall::UnionAll) = getparambody(unionall.body)
getfieldtype(S::Type, F::Symbol) = getfieldtype(StructField{S, F})
@generated function getfieldtype(::Type{StructField{S, F}}) where {S, F}
@assert !isa(S, UnionAll) "Cannot reliably use getfieldtype with UnionAll"
idx = findfirst(field->field==F, fieldnames(S))
@assert idx !== nothing "Field $F not found in type $S"
T = S.types[idx]
:($T)
end
export @generate_properties
macro generate_properties(T, block)
if !isa(block, Expr) || block.head != :block
throw(ArgumentError("Second argument to @generate_properties must be a block"))
end
result = Expr(:block)
props = Set{Symbol}()
symget = Symbol("@get")
symset = Symbol("@set")
symeq = Symbol("=")
lastlinenumber = nothing
for expr ∈ block.args
if isa(expr, LineNumberNode)
lastlinenumber = expr
else
if expr.head != :macrocall || expr.args[1] ∉ (symget, symset)
throw(ArgumentError("Every line must be a call to either @get or @set"))
end
args = filterlinenumbers(expr.args)
if args[2].head != symeq throw(ArgumentError("Getter/Setter not an assignment")) end
prop, body = filterlinenumbers(args[2].args)
body = replace_self(T, body)
push!(props, prop)
if expr.args[1] == symget
push!(result.args, generate_getter(T, prop, lastlinenumber, body))
elseif expr.args[1] == symset
push!(result.args, generate_setter(T, prop, lastlinenumber, body))
end
lastlinenumber = nothing
end
end
# Generate propertynames
push!(result.args, quote
@generated function Base.propertynames(::$T)
res = tuple(union($props, fieldnames($T))...)
:($res)
end
end)
push!(result.args, :(Base.getproperty(self::$T, prop::Symbol) = GenerateProperties.retrieve(self, prop)))
push!(result.args, :(Base.setproperty!(self::$T, prop::Symbol, value) = GenerateProperties.assign(self, prop, value)))
esc(result)
end
export @get, @set
macro get(args...) end
macro set(args...) end
filterlinenumbers(exprs) = filter(expr->!isa(expr, LineNumberNode), exprs)
replace_self(_, expr) = expr
function replace_self(T::Union{Symbol, Expr}, expr::Expr)
@assert isa(T, Symbol) || T.head == :curly
if expr.head == :(=)
lhs, rhs = expr.args
if isa(lhs, Expr) && lhs.head == :. && lhs.args[1] == :self
prop = lhs.args[2]::QuoteNode
expr = :(GenerateProperties.assign(self, $rhs))
insert!(expr.args, 2, structfieldexpr(prop))
end
elseif expr.head == :.
if expr.args[1] == :self
prop = expr.args[2]::QuoteNode
expr = :(GenerateProperties.retrieve(self))
insert!(expr.args, 2, structfieldexpr(prop))
end
end
expr.args = map(sub->replace_self(T, sub), expr.args)
expr
end
function generate_getter(T, prop::Symbol, linenumber::Optional{LineNumberNode}, body)
block = Expr(:block)
if linenumber !== nothing push!(block.args, linenumber) end
push!(block.args, body)
fnexpr = :(GenerateProperties.retrieve(self::$T) = $block)
insert!(fnexpr.args[1].args, 2, argsubtypeexpr(structpropexpr(T, prop)))
fnexpr
end
function generate_setter(T, prop::Symbol, linenumber::Optional{LineNumberNode}, body)
block = Expr(:block)
if linenumber !== nothing push!(block.args, linenumber) end
push!(block.args, body)
fnexpr = :(GenerateProperties.assign(self::$T, value) = $block)
insert!(fnexpr.args[1].args, 2, argsubtypeexpr(structpropexpr(T, prop)))
fnexpr
end
structpropexpr(T, prop::QuoteNode) = Expr(:curly, :(GenerateProperties.StructProp), :(<:$T), prop)
structpropexpr(T, prop::Symbol) = structpropexpr(T, QuoteNode(prop))
structfieldexpr(prop::QuoteNode) = Expr(:curly, :(GenerateProperties.StructField), :(typeof(self)), prop)
structfieldexpr(prop::Symbol) = structfieldexpr(QuoteNode(prop))
function argtypeexpr(type::Expr)
Expr(:(::), Expr(:curly, :Type, type))
end
function argsubtypeexpr(type::Expr)
Expr(:(::), Expr(:curly, :Type, Expr(:<:, type)))
end
end # module GenerateProperties
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] | 2.367948 | 2,446 |
import Base: +, -, *, /, length, iterate, zero, convert, Vector, Array
"""
This correspond to coordinate of atom.
This contain 3 fields like below.
- x <: Real
- y <: Real
- z <: Real
Some operators and functions were overloaded to make Coordinate objects broadcastable.
"""
mutable struct Coordinate{RealT <: Real}
# This class is for make vector operation broadcastable.
x::RealT
y::RealT
z::RealT
function Coordinate(vector::VectorT
) where VectorT <: AbstractArray{RealT} where RealT <: Real
if length(vector) != 3
ArgumentError("Argument for Coordinate constructor must be array of length 3")
end
new{RealT}(vector[1], vector[2], vector[3])
end
function Coordinate(x::RealT, y::RealT, z::RealT) where RealT <: Real
new{RealT}(x, y, z)
end
end
function +(arg1::Coordinate{T}, arg2::Coordinate{U}) where T <: Real where U <: Real
Coordinate(arg1.x + arg2.x, arg1.y + arg2.y, arg1.z + arg2.z)
end
function -(arg1::Coordinate{T}, arg2::Coordinate{U}) where T <: Real where U <: Real
Coordinate(arg1.x - arg2.x, arg1.y - arg2.y, arg1.z - arg2.z)
end
function *(arg1::Coordinate{T}, arg2::U) where T <: Real where U <: Real
Coordinate(arg1.x * arg2, arg1.y * arg2, arg1.z * arg2)
end
function *(arg1::U, arg2::Coordinate{T}) where T <: Real where U <: Real
Coordinate(arg1 * arg2.x, arg1 * arg2.y, arg1 * arg2.z)
end
function *(arg1::Coordinate{T}, arg2::Coordinate{U}) where T <: Real where U <: Real
arg1.x * arg2.x + arg1.y * arg2.y + arg1.z * arg2.z
end
function /(arg1::Coordinate{T}, arg2::U) where T <: Real where U <: Real
Coordinate(arg1.x / arg2, arg1.y / arg2, arg1.z / arg2)
end
# for operator broadcast
function length(arg::Coordinate{T}) where T <: Real
1
end
function iterate(arg::Coordinate{T}) where T <: Real
(arg, nothing)
end
function iterate(arg::Coordinate{T}, nothing) where T <: Real
nothing
end
function zero(arg::Coordinate{T}) where T <: Real
Coordinate(T(0.0), T(0.0), T(0.0))
end
function convert(::Type{Coordinate{RealT}}, coord::Coordinate{<:Real}) where RealT <: Real
return Coordinate(RealT(coord.x), RealT(coord.y), RealT(coord.z))
end
function Vector(arg::Coordinate{RealT}) where RealT <: Real
[arg.x, arg.y, arg.z]
end
function Array(arg::Coordinate{RealT}) where RealT <: Real
[arg.x, arg.y, arg.z]
end
function norm(arg::Coordinate{T}) where T <: Real
sqrt(abs2(arg.x) + abs2(arg.y) + abs2(arg.z))
end
function distance(first_coord::Coordinate{T}, second_coord::Coordinate{T}) where T <: Real
norm(first_coord - second_coord)
end
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] | 2.492424 | 1,056 |
const Orbits = [(1,0), (2,0), (0,1), (1,1), (0,2)]
"""
flatten(pdm::Dict{Int, <:PersistenceDiagram}) -> PersistenceDiagram
Flatten a multi-dimensional pd into a barcode.
"""
flatten(pdm::Dict{Int, PD}) where {T<:Real, PD<:PersistenceDiagram{T}} =
(collect∘Iterators.flatten∘values)(pdm)
"""
maxsize(pds) -> Int
Return the number of bars in the largest barcode from the collection of barcodes `pds`
"""
maxsize(pds::AbstractVector{PD}) where {T<:Real, PD<:PersistenceDiagram{T}} =
maximum(map(length, pds))
tropicdim(n) = n+(div(n*(n+1),2)) # codomain dimension
function tropicstats(pds::AbstractVector{PD}) where {T<:Real, PD<:PersistenceDiagram{T}}
n = ComputationalHomology.maxsize(pds)
d = ComputationalHomology.tropicdim(n)
maxval = filter!(!isinf, map(i->max(birth(i),death(i)), Iterators.flatten(pds))) |> maximum
m = map(i->birth(i)/-ComputationalHomology.birthx(i), Iterators.flatten(pds)) |> maximum
(d, n, ceil(Int, m), maxval)
end
"""
Caluclate 2-symmetric tropical rational polynomial for the persistence diagram
"""
function tropicp(pd::PersistenceDiagram{T},m,l,p; maxval=T(Inf)) where {T<:Real}
B = Tuple{T,T}[]
for i in pd
d = min(maxval, death(i))
b = birth(i)
push!(B, (min(m*(d-b), b), d-b))
end
# pad zeros
while length(B) < l+p
push!(B, (zero(T), zero(T)))
end
n = length(B)
if l == 0 && p == 0
0
elseif l == 0
sort!(map(sum, B), rev=true)[1:p] |> sum
elseif p == 0
sort!(map(last,B), rev=true)[1:l] |> sum
else
res = 0
L = hcat(combinations(1:n,l)...)'
for i in 1:length(L)
ii = L[i,:]
e0 = sum(last, B[ii])
K = hcat(combinations(setdiff(1:n, ii),p)...)'
for j = 1:length(K)
jj = K[j,:]
e1 = sum(map(first, B[jj]) .+ map(last, B[jj]))
res = max(res, e0 + e1)
end
end
res
end
end
"""
tropic(pd)
Calculate a tropical coordinates for a persistent diagram `pd`
Paper: "Tropical Sufficient Statistics for Persistent Homology" by A.Monod et al.
Ref: https://arxiv.org/abs/1709.02647
"""
function tropic(pds::AbstractVector{PD}) where {T<:Real, PD<:PersistenceDiagram{T}}
d,n,m,mv = ComputationalHomology.tropicstats(pds)
[tropicp(pd,m,i,j,maxval=mv) for (i,j) in ComputationalHomology.Orbits, pd in pds ]
end
tropic(pd::PersistenceDiagram{T}) where {T<:Real} = tropic([pd]) |> vec
tropic(pdd::Dict{Int, PD}) where {T<:Real, PD<:PersistenceDiagram{T}} = tropic(flatten(pdd))
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] | 2.113729 | 1,231 |
using ImageFiltering
using Statistics
using StatsBase
function badpixremoval_clump(frame::AbstractMatrix{T}; max_niter=15, kwargs...) where T
# create bad pixel map by sigma clipping outliers
bad_pixels = badpixel_clip(frame; kwargs...)
bad_pixels_cumul = copy(bad_pixels)
total = sum(bad_pixels)
# iterate replacing bad pixels with sigma clipping
out = copy(frame)
it = 0
while total > 0 && it < max_niter
it += 1
out = sigma_filter!(out, bad_pixels)
bad_pixels = badpixel_clip(out)
bad_pixels_cumul .|= bad_pixels
total = sum(bad_pixels)
end
return out, bad_pixels_cumul
end
function badpixel_clip(frame; sigma=4, box_size=9)
med = mapwindow(median, frame, (box_size, box_size); border="reflect")
std = mapwindow(mad, frame, (box_size, box_size); border="reflect")
return @. abs(frame - med) > sigma * std
end
function sigma_filter!(frame, bad_pixels; box_size=9)
bpm = copy(bad_pixels)
min_neigh = sum(3:2:box_size)
half_box = box_size ÷ 2
while sum(bpm) > 0
pad_bpm = BorderArray(bpm, Pad(:reflect, half_box, half_box))
pad_im = BorderArray(frame, Pad(:reflect, half_box, half_box))
for idx in findall(bpm)
ax1 = idx.I[1] - half_box:idx.I[1] + half_box
ax2 = idx.I[2] - half_box:idx.I[2] + half_box
block_bpm = @view pad_bpm[ax1, ax2]
# ensure enough "good" neighbors for imputation
if sum(block_bpm) < min_neigh
block_im = @view pad_im[ax1, ax2]
frame[idx] = median(block_im)
bpm[idx] = false
end
end
end
return frame
end | [
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] | 2.134253 | 797 |
using Dates
function hfun_datetext(date=locvar("rss_pubdate"))
datetext = "$(monthname(date)) $(day(date)), $(year(date))"
return datetext
end
# Based on https://github.com/JuliaLang/www.julialang.org/blob/master/utils.jl
function hfun_blogposts()
curyear = year(Dates.today())
io = IOBuffer()
for year in curyear:-1:2020
ys = "$year"
for month in 12:-1:1
ms = "0"^(month < 10) * "$month"
base = joinpath("blog", ys, ms)
isdir(base) || continue
posts = filter!(p -> endswith(p, ".md"), readdir(base))
days = zeros(Int, length(posts))
lines = Vector{String}(undef, length(posts))
for (i, post) in enumerate(posts)
ps = splitext(post)[1]
url = "/blog/$ys/$ms/$ps/"
surl = strip(url, '/')
title = pagevar(surl, :title)
rss = pagevar(surl, :rss)
date = pagevar(surl, :rss_pubdate)
datetext = hfun_datetext(date)
days[i] = day(date)
lines[i] =
"""
~~~
<div class="card border-dark mb-4">
<div class="card-body">
<h3 class="card-title">
<a class="text-dark" href="$url">$title</a>
</h3>
<p class="card-text">$rss</p>
<p class="card-text text-muted">$datetext</p>
</div>
</div>
~~~
"""
end
# Sort by day
foreach(line -> write(io, line), lines[sortperm(days, rev=true)])
end
end
# markdown conversion adds `<p>` beginning and end but
# we want to avoid this to avoid an empty separator
r = Franklin.fd2html(String(take!(io)), internal=true)
return r
end
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] | 1.769369 | 1,110 |
"""
`PrincipalAxes` is an abstract type to be used
as the argument of a function.
"""
abstract PrincipalAxes | [
37811,
198,
63,
42904,
8521,
31554,
274,
63,
318,
281,
12531,
2099,
284,
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262,
4578,
286,
257,
2163,
13,
198,
37811,
198,
397,
8709,
32641,
31554,
274
] | 3.483871 | 31 |
using BenchmarkTools
import ParticleSwarmOptimizer
pso = ParticleSwarmOptimizer
pso.get_localbest([0.5, 0.5, 0.5, 0.4, 0.5], [2, 4, 5], <)
@benchmark pso.get_localbest([0.5, 0.5, 0.5, 0.4, 0.5], [2, 4, 5], <)
@benchmark pso.get_localbest([0.5, 0.5, 0.5, 0.4, 0.5], [2], <)
| [
3500,
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4102,
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198,
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2142,
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320,
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1548,
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657,
13,
20,
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685,
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] | 1.923077 | 143 |
"""
loglike_beta(nij, A, cij)
Profile likelihood for the frequency response vectors.
*Reference*: Based on Equation (9) of Stompor et al., MNRAS, 392, 216 (2009)
# Arguments
- `nij::Array{<:AbstractFloat,2}`: symmetric noise covariance matrix with the dimention of `(nν, nν)` where `nν` is the number of frequency bands.
- `A::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the frequency response, for `nc` components in sky.
- E.g., ``A = [a B]`` where `a = ones(nν)` for CMB and `B` is a `nν`-by-`nc-1` matrix for the frequency response of foreground components.
- `cij::Array{<:AbstractFloat,2}`: symmetric covariance matrix with the dimention of `(nν, nν)`.
"""
function loglike_beta(
nij::Array{T,2},
A::Array{T,2},
cij::Array{T,2},
) where {T<:AbstractFloat}
if size(nij)[1] ≠ size(nij)[2] || size(cij)[1] ≠ size(cij)[2]
throw(DimensionMismatch("covariance matrix must be a square matrix"))
elseif size(nij)[1] ≠ size(A)[1] || size(nij)[1] ≠ size(cij)[1]
throw(DimensionMismatch("dimensions of input matrices do not match"))
else
N = Symmetric(nij)
M = Symmetric(cij)
end
Ninv = inv(N)
lnlike = 0.5 * tr(Ninv * A * inv(A' * Ninv * A) * A' * Ninv * M)
end
"""
loglike_beta(nij, A, d)
Profile likelihood for the frequency response vectors.
*Reference*: Equation (9) of Stompor et al., MNRAS, 392, 216 (2009)
# Arguments
- `nij::Array{<:AbstractFloat,2}`: symmetric noise covariance matrix with the dimention of `(nν, nν)` where `nν` is the number of frequency bands.
- `A::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the frequency response, for `nc` components in sky.
- E.g., ``A = [a B]`` where `a = ones(nν)` for CMB and `B` is a `nν`-by-`nc-1` matrix for the frequency response of foreground components.
- `d::Array{<:AbstractFloat,1}`: data vector for a given pixel (or any other appropriate domain). The number of elements is `nν`.
"""
function loglike_beta(
nij::Array{T,2},
A::Array{T,2},
d::Array{T,1},
) where {T<:AbstractFloat}
if size(nij)[1] ≠ size(nij)[2]
throw(DimensionMismatch("covariance matrix must be a square matrix"))
elseif size(nij)[1] ≠ size(A)[1] || size(nij)[1] ≠ length(d)
throw(DimensionMismatch("dimensions of matrices/vector do not match"))
else
M = Symmetric(nij)
end
x = M \ d
s = inv(A' * inv(M) * A) * A'x
lnlike = 0.5 * x'A * s
end
"""
loglike_beta_grad(nij, A, dAdβ, d)
Derivative of the profile likelihood for the frequency response vectors with respect to a foreground parameter
*Reference*: Equation (A1) of Errard et al., PRD, 84, 063005 (2011)
# Arguments
- `nij::Array{<:AbstractFloat,2}`: symmetric noise covariance matrix with the dimention of `(nν, nν)` where `nν` is the number of frequency bands.
- `A::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the frequency response, for `nc` components in sky.
- E.g., ``A = [a B]`` where `a = ones(nν)` for CMB and `B` is a `nν`-by-`nc-1` matrix for the frequency response of foreground components.
- `dAdβ::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the derivative of the frequency response with respect to a foreground parameter.
- E.g., ``dAdβ = [zeros(nν) dsynch/dβs zeros(nν)]`` where `zeros(nν)` for CMB and dust because they do not depend on the synchrotron index `βs`.
- `d::Array{<:AbstractFloat,1}`: data vector for a given pixel (or any other appropriate domain). The number of elements is `nν`.
"""
function loglike_beta_grad(
nij::Array{T,2},
A::Array{T,2},
dAdβ::Array{T,2},
d::Array{T,1},
) where {T<:AbstractFloat}
if size(nij)[1] ≠ size(nij)[2]
throw(DimensionMismatch("covariance matrix must be a square matrix"))
elseif size(nij)[1] ≠ size(A)[1] ||
size(nij)[1] ≠ length(d) ||
size(A)[1] ≠ size(dAdβ)[1] ||
size(A)[2] ≠ size(dAdβ)[2]
throw(DimensionMismatch("dimensions of matrices/vector do not match"))
else
M = Symmetric(nij)
end
x = M \ d
s = inv(A' * inv(M) * A) * A'x
lnlike_grad = s' * dAdβ' * (x - M \ (A * s))
end
"""
loglike_beta_hess(nij, A, dAdβI, dAdβJ, d)
Hessian of the profile likelihood for the frequency response vectors with respect to foreground parameters
*Reference*: Equation (5) of Errard et al., PRD, 84, 063005 (2011)
# Arguments
- `nij::Array{<:AbstractFloat,2}`: symmetric noise covariance matrix with the dimention of `(nν, nν)` where `nν` is the number of frequency bands.
- `A::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the frequency response, for `nc` components in sky.
- E.g., ``A = [a B]`` where `a = ones(nν)` for CMB and `B` is a `nν`-by-`nc-1` matrix for the frequency response of foreground components.
- `dAdβI::Array{<:AbstractFloat,2}` and `dAdβJ::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the derivative of the frequency response with respect to a foreground parameter.
- E.g., ``dAdβI = [zeros(nν) dsynch/dβs zeros(nν)]`` where `zeros(nν)` for CMB and dust because they do not depend on the synchrotron index `βs`.
- `d::Array{<:AbstractFloat,1}`: data vector for a given pixel (or any other appropriate domain). The number of elements is `nν`.
"""
function loglike_beta_hess(
nij::Array{T,2},
A::Array{T,2},
dAdβI::Array{T,2},
dAdβJ::Array{T,2},
d::Array{T,1},
) where {T<:AbstractFloat}
if size(nij)[1] ≠ size(nij)[2]
throw(DimensionMismatch("covariance matrix must be a square matrix"))
elseif size(nij)[1] ≠ size(A)[1] ||
size(nij)[1] ≠ length(d) ||
size(A)[1] ≠ size(dAdβI)[1] ||
size(A)[2] ≠ size(dAdβI)[2] ||
size(A)[1] ≠ size(dAdβJ)[1] ||
size(A)[2] ≠ size(dAdβJ)[2]
throw(DimensionMismatch("dimensions of matrices/vector do not match"))
else
M = Symmetric(nij)
end
x = M \ d
AtMinvAinv = inv(A' * inv(M) * A)
s = AtMinvAinv * A'x
u = M \ (dAdβJ * s)
v = M \ (A * AtMinvAinv * A'u)
lnlike_hess = tr(dAdβI' * (v - u) * s')
end
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] | 2.348855 | 2,577 |
{"score": 7.42, "score_count": 48775, "timestamp": 1573578230.0}
{"score": 7.43, "score_count": 45911, "timestamp": 1562142618.0}
{"score": 7.44, "score_count": 35854, "timestamp": 1523189633.0}
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# Main file for solving the J-SAA and J+-SAA problems
using Distributions, StatsFuns, StatsBase, JuMP, Gurobi, GLMNet
const gurobi_env = Gurobi.Env()
# read these as inputs from the corresponding "instance file"
modRepNum = parse(ARGS[1])
degNum = parse(ARGS[2])
dataRepNum = parse(ARGS[3])
sampSizeNum = parse(ARGS[4])
#*========= MODEL INFORMATION ==========
const caseNum = 2
const paramsFile = "params_case" * string(caseNum) * "_jsaa.jl"
#*======================================
#*========= INCLUDE FILES ====================
include("maxParameters.jl")
include("randomSeeds.jl")
include(paramsFile)
# the next two data files are defined in paramsFile
include(modelFile)
include(dataFile)
include("regressionMethods.jl")
include("genJSAAScenarios.jl")
include("solveModel.jl")
include("estimateSolnCost.jl")
include("evaluateSoln.jl")
#*======================================
# set seed for reproducibility
srand(startingSeed)
# directory name for storing results
const baseDirName = "case" * string(caseNum) * "_jsaa/" * "mod_" * string(modRepNum) * "/" * "deg_" * string(degNum) * "/"
const subDirName = baseDirName * "rep_" * string(dataRepNum) * "/"
const subDirName2 = subDirName * regressionMethod * "/" * "samp_" * string(sampSizeNum) * "/"
mkpath(subDirName2)
#*========= PRINT OPTIONS ====================
const storeResults = true
const infoFile = "ddsp.txt"
const modelDataFile = "model_data.txt"
const numSampFile = "num_samples_" * regressionMethod * ".txt"
const jsaaObjFile = "jsaa_obj.txt"
const jsaaDDObjFile = "jsaa_ddobj.txt"
const jpsaaObjFile = "jpsaa_obj.txt"
const jpsaaDDObjFile = "jpsaa_ddobj.txt"
const jsaaTimeFile = "jsaa_time.txt"
const covRealFile = "covariate_obs.txt"
#*======================================
#*========= GENERATE MODEL PARAMETERS ====================
srand(randomSeeds_models[modRepNum])
covariate_mean, covariate_covMat, coeff_true = generateBaseDemandModel(numCovariates)
#*========= STORE RESULTS ====================
if(storeResults)
# write details to text file, including some key details about the test instance
details_file = baseDirName * infoFile
open(details_file, "w") do f
write(f,"case number: $caseNum \n")
write(f,"numResources: $numResources \n")
write(f,"numCustomers: $numCustomers \n")
write(f,"model replicate number: $modRepNum \n")
write(f,"degree: $(degree[degNum]) \n")
write(f,"sample sizes: $numDataSamples \n")
write(f,"demand_errors_scaling: $demand_errors_scaling \n")
write(f,"regressionMethod: $regressionMethod \n\n")
write(f,"randomSeeds_MC: $randomSeeds_MC \n")
write(f,"randomSeeds_models: $randomSeeds_models \n")
write(f,"randomSeeds_data: $randomSeeds_data \n")
end
mod_data_file = baseDirName * modelDataFile
open(mod_data_file, "w") do f
write(f,"covariate_mean = $covariate_mean \n")
write(f,"covariate_covMat = $covariate_covMat \n")
write(f,"coeff_true = $coeff_true \n")
end
samp_size_file = baseDirName * numSampFile
open(samp_size_file, "w") do f
for i = 1:length(numDataSamples)
write(f,"$(numDataSamples[i]) \n")
end
end
end
#*============================================
#*========= GENERATE DATA REPLICATE ====================
srand(randomSeeds_data[dataRepNum])
covariate_obs = generateCovariateReal(numCovariates,covariate_mean,covariate_covMat)
demand_data, covariate_data = generateDemandData(numCovariates,numDataSamples[sampSizeNum],degree[degNum],covariate_mean,covariate_covMat,coeff_true)
#*========= STORE RESULTS ====================
if(storeResults)
cov_real_file = subDirName * covRealFile
open(cov_real_file, "w") do f
write(f,"covariate_obs = $covariate_obs")
end
end
#*============================================
#*========= SOLVE THE J-SAA and J+-SAA MODELS ====================
tic()
demand_scen_jsaa, demand_scen_jpsaa = generateJSAAScenarios(demand_data,covariate_data,covariate_obs,regressionMethod)
z_soln_jsaa, objDDJSAA = solveSAAModel(demand_scen_jsaa)
jsaaObjEstimates = estimateSolnQuality(z_soln_jsaa,covariate_obs,degree[degNum],numMCScenarios,numMCReplicates,coeff_true)
z_soln_jpsaa, objDDJpSAA = solveSAAModel(demand_scen_jpsaa)
jpsaaObjEstimates = estimateSolnQuality(z_soln_jpsaa,covariate_obs,degree[degNum],numMCScenarios,numMCReplicates,coeff_true)
jsaaTime = toq()
#*========= STORE RESULTS ====================
if(storeResults)
# write data to text file
obj_est_file1 = subDirName2 * jsaaObjFile
open(obj_est_file1, "w") do f
for i = 1:length(jsaaObjEstimates)
write(f,"$(jsaaObjEstimates[i]) \n")
end
end
obj_est_file2 = subDirName2 * jpsaaObjFile
open(obj_est_file2, "w") do f
for i = 1:length(jpsaaObjEstimates)
write(f,"$(jpsaaObjEstimates[i]) \n")
end
end
ddobj_est_file1 = subDirName2 * jsaaDDObjFile
open(ddobj_est_file1, "w") do f
write(f,"$objDDJSAA")
end
ddobj_est_file2 = subDirName2 * jpsaaDDObjFile
open(ddobj_est_file2, "w") do f
write(f,"$objDDJpSAA")
end
time_obj_file = subDirName2 * jsaaTimeFile
open(time_obj_file, "w") do f
write(f,"$jsaaTime")
end
end
#*============================================
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2,
9,
10052,
25609,
201,
198
] | 2.446633 | 2,183 |
function py2ju_obj(py_obj)
py_attr = keys(py_obj)
n_attr = length(py_attr)
ju_obj = Dict()
for i = 1:n_attr
# skip base class attributes
if !contains(String(py_attr[i]), "__")
if py_obj[py_attr[i]] != nothing
error("Cannot convert Python object with non-None attribute value $(prop[i]).")
end
attr_name = String(py_attr[i])
ju_obj[attr_name] = nothing
end
end
return ju_obj
end
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1441,
7544,
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198,
437,
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] | 2.004082 | 245 |
"""
"""
struct ConditionalGaussian <: SciMLBase.AbstractLinearAlgorithm end
struct LinearGaussian <: SciMLBase.AbstractLinearAlgorithm end
| [
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] | 3.564103 | 39 |
export MtlArgumentEncoder, set_buffer!, set_buffers!, set_constant!
const MTLArgumentEncoder = Ptr{MtArgumentEncoder}
mutable struct MtlArgumentEncoder
handle::MTLArgumentEncoder
end
function MtlArgumentEncoder(fun::MtlFunction, entry::Integer)
handle = mtNewArgumentEncoderWithBufferIndexFromFunction(fun, entry-1)
obj = MtlArgumentEncoder(handle)
finalizer(unsafe_destroy!, obj)
return obj
end
function unsafe_destroy!(enc::MtlArgumentEncoder)
mtRelease(enc.handle)
end
Base.unsafe_convert(::Type{MTLArgumentEncoder}, enc::MtlArgumentEncoder) = enc.handle
Base.:(==)(a::MtlArgumentEncoder, b::MtlArgumentEncoder) = a.handle == b.handle
Base.hash(fun::MtlArgumentEncoder, h::UInt) = hash(mod.handle, h)
## properties
Base.propertynames(o::MtlArgumentEncoder) = (
# identification
#=:device, :label,=#
# creation
:encodedLength,
# alignment
:alignment,
)
function Base.getproperty(o::MtlArgumentEncoder, f::Symbol)
if f === :encodedLength
mtArgumentEncoderLength(o)
elseif f === :alignment
mtArgumentEncoderAlignment(o)
else
getfield(o, f)
end
end
Base.sizeof(a::MtlArgumentEncoder) = Int(mtArgumentEncoderLength(a))
## operations
# NOTE: indices aren't 1-based here, because they map onto exact IDs in the metadata
function assign_argument_buffer!(enc::MtlArgumentEncoder, buf::MtlBuffer, offset::Integer=0)
mtArgumentEncoderSetArgumentBufferWithOffset(enc, buf, offset)
end
function assign_argument_buffer!(enc::MtlArgumentEncoder, buf::MtlBuffer, offset::Integer, element::Integer)
mtArgumentEncoderSetArgumentBufferWithOffsetForElement(enc, buf, offset, element)
end
set_buffer!(enc::MtlArgumentEncoder, buf::MtlBuffer, offset::Integer, index::Integer) =
mtArgumentEncoderSetBufferOffsetAtIndex(enc, buf, offset, index)
set_buffers!(enc::MtlArgumentEncoder, bufs::Vector{<:MtlBuffer},
offsets::Vector{Int}, indices::UnitRange{Int}) =
mtArgumentEncoderSetBuffersOffsetsWithRange(enc, handle_array(bufs), offsets, indices)
function set_constant!(enc::MtlArgumentEncoder, val, index::Integer)
dst = Base.bitcast(Ptr{typeof(val)}, mtArgumentEncoderConstantDataAtIndex(enc, index))
Base.unsafe_store!(dst, val, 1)
return
end
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] | 2.69395 | 843 |
# Thanks @tlienart
using Franklin, JSON
using Markdown, Dates
include("youtube_videos.jl")
const DATEFMT = dateformat"yyyy-mm-dd HH:MMp"
const TZ = "America/New_York"
function hfun_doc(params)
fname = join(params[1:max(1, length(params)-2)], " ")
head = params[end-1]
type = params[end]
doc = eval(Meta.parse("@doc $fname"))
txt = Markdown.plain(doc)
# possibly further processing here
body = Franklin.fd2html(txt, internal=true)
return """
<div class="docstring">
<h2 class="doc-header" id="$head">
<a href="#$head">$head</a>
<div class="doc-type">$type</div></h2>
<div class="doc-content">$body</div>
</div>
"""
end
function hfun_youtube(params)
id = params[1]
return """
<iframe id="$id" width="100%" height="360"
src="https://www.youtube.com/embed/$(get(videos, id, id))"
frameborder="0"
allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen></iframe>
"""
end
function hfun_showtime(params)
id = params[1]
str = locvar(id)
if isnothing(str)
@warn "Unknown datetime variable $str"
return ""
end
try
DateTime(str, DATEFMT)
catch err
@warn "There was an error parsing date $str, the format is yyyy-mm-dd HH:MMp (see ?DateFormat)"
rethrow(err)
end
end
function parse_duration(str)
str = replace(str, r"^PT"=>"")
hrex, mrex, srex = Regex.(string.("^([0-9]+)", ["H","M","S"]))
t = 0
hmatch = match(hrex, str)
if !isnothing(hmatch)
h = parse(Int, hmatch[1])
t += 60*60*h
str = replace(str, hrex=>"")
end
mmatch = match(mrex, str)
if !isnothing(mmatch)
m = parse(Int, mmatch[1])
t += 60*m
str = replace(str, mrex=>"")
end
smatch = match(srex, str)
if !isnothing(smatch)
s = parse(Int, smatch[1])
t += s
str = replace(str, srex=>"")
end
t
end
function hfun_go_live()
seq = locvar("sequence")
airtime = locvar("airtime")
if isnothing(seq)
@warn "airtime set, but no `sequence` variable not defined." *
"sequence is an array of video IDs to play in order on this page"
end
vid_ids = [get(videos, s, s) for s in seq]
f = tempname()
# Get the duration of each video
download("https://www.googleapis.com/youtube/v3/videos?id=$(join(vid_ids, ","))&part=contentDetails&key=AIzaSyDZhbWHc2PTEFTx173MaTgddnWCGPqdbB8", f)
dict = JSON.parse(String(read(f)))
durations = [parse_duration(video["contentDetails"]["duration"])
for video in dict["items"]]
jrepr(x) = sprint(io->JSON.print(io, x))
"""
<script src="/assets/moment.min.js"></script>
<script src="/assets/moment-timezone.js"></script>
<script src="/assets/live-player.js"></script>
<script>
play_live($(jrepr(string(DateTime(airtime, DATEFMT)))), $(jrepr(TZ)), $(jrepr(seq)), $(jrepr(durations)))
</script>
"""
end
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] | 2.239146 | 1,359 |
struct BitsIntChar
a::Int
b::Char
end
struct BitsCharBitsIntChar
a::Char
b::BitsIntChar
end
@testset "Bits types with custom fields" begin
@test begin
b = JlrsReflect.reflect([BitsCharBitsIntChar])
sb = JlrsReflect.StringWrappers(b)
sb[BitsIntChar] === """#[repr(C)]
#[derive(Clone, Debug, Unbox, ValidLayout, Typecheck, IntoJulia)]
#[jlrs(julia_type = "Main.BitsIntChar")]
pub struct BitsIntChar {
pub a: i64,
pub b: ::jlrs::wrappers::inline::char::Char,
}"""
sb[BitsCharBitsIntChar] === """#[repr(C)]
#[derive(Clone, Debug, Unbox, ValidLayout, Typecheck, IntoJulia)]
#[jlrs(julia_type = "Main.BitsCharBitsIntChar")]
pub struct BitsCharBitsIntChar {
pub a: ::jlrs::wrappers::inline::char::Char,
pub b: BitsIntChar,
}"""
end
end
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] | 2.038462 | 442 |
using MatrixFactorizations, LinearAlgebra, Random, ArrayLayouts, Test
using LinearAlgebra: BlasComplex, BlasFloat, BlasReal, rmul!, lmul!, require_one_based_indexing, checksquare
n = 10
# Split n into 2 parts for tests needing two matrices
n1 = div(n, 2)
n2 = 2*n1
Random.seed!(1234321)
areal = randn(n,n)/2
aimg = randn(n,n)/2
a2real = randn(n,n)/2
a2img = randn(n,n)/2
breal = randn(n,2)/2
bimg = randn(n,2)/2
using MatrixFactorizations: RQPackedQ
using MatrixFactorizations: rq, rq!
const Our=MatrixFactorizations
@testset "RQ" begin
@testset "LAPACK $elty" for elty in (Float32,Float64,ComplexF32,ComplexF64)
@testset "Compare with LAPACK (square $elty)" begin
n = 10
A = randn(n,n)
R, Q = @inferred rq(A)
Ṟ, Q̄ = Our._rqfactUnblocked!(copy(A))
fla,τla = LinearAlgebra.LAPACK.gerqf!(copy(A))
@test Q.factors ≈ fla
@test Q.τ ≈ τla
@test Q.factors ≈ Q̄.factors
@test Q.τ ≈ Q̄.τ
@test R ≈ Ṟ
@test Q ≈ Q̄
end
@testset "Compare with LAPACK (rectangular $elty)" begin
n = 10
A = randn(n,n+2)
R, Q = rq(A)
Ṟ, Q̄ = Our._rqfactUnblocked!(copy(A))
fla,τla = LinearAlgebra.LAPACK.gerqf!(copy(A))
@test Q.factors ≈ fla
@test Q.τ ≈ τla
@test Q.factors ≈ Q̄.factors
@test Q.τ ≈ Q̄.τ
@test R ≈ Ṟ
@test Q ≈ Q̄
A = randn(n+2,n)
R, Q = rq(A)
Ṟ, Q̄ = Our._rqfactUnblocked!(copy(A))
fla,τla = LinearAlgebra.LAPACK.gerqf!(copy(A))
@test Q.factors ≈ fla
@test Q.τ ≈ τla
@test Q.factors ≈ Q̄.factors
@test Q.τ ≈ Q̄.τ
@test R ≈ Ṟ
@test Q ≈ Q̄
end
end
@testset for eltya in (Float32, Float64, ComplexF32, ComplexF64, BigFloat, Complex{BigFloat}, Int)
# @testset for eltya in (Float32,ComplexF32)
raw_a = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(areal, aimg) : areal)
raw_a2 = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(a2real, a2img) : a2real)
asym = raw_a' + raw_a # symmetric indefinite
apd = raw_a' * raw_a # symmetric positive-definite
ε = εa = eps(abs(float(one(eltya))))
@testset for eltyb in (Float32, Float64, ComplexF32, ComplexF64, Int)
# @info "stubbed eltyb loop"
# @testset for eltyb in (eltya, )
raw_b = eltyb == Int ? rand(1:5, n, 2) : convert(Matrix{eltyb}, eltyb <: Complex ? complex.(breal, bimg) : breal)
εb = eps(abs(float(one(eltyb))))
ε = max(εa, εb)
tab = promote_type(eltya, eltyb)
@testset "RQ decomposition of a Number" begin
α = rand(eltyb)
aα = fill(α, 1, 1)
@test rq(α).R * rq(α).Q ≈ rq(aα).R * rq(aα).Q
@test abs(rq(α).Q[1,1]) ≈ one(eltyb)
end
for (a, b) in ((raw_a, raw_b),
(view(raw_a, 1:n-1, 1:n-1), view(raw_b, 1:n-1, 1)))
a_1 = size(a, 1)
@testset "RQ decomposition" begin
rqa = @inferred rq(a)
@inferred rq(a)
q, r = rqa.Q, rqa.R
@test_throws ErrorException rqa.Z
@test q'*q ≈ Matrix(I, a_1, a_1)
@test q*q' ≈ Matrix(I, a_1, a_1)
@test q'*Matrix(1.0I, a_1, a_1)' ≈ q'
@test q'q ≈ Matrix(I, a_1, a_1)
@test Matrix(1.0I, a_1, a_1)'q' ≈ q'
@test r*q ≈ a
@test a*(rqa\b) ≈ b atol=5000ε
@test Array(rqa) ≈ a
sq = size(q.factors, 2)
@test *(Matrix{eltyb}(I, sq, sq), adjoint(q)) * q ≈ Matrix(I, sq, sq) atol=5000ε
if eltya != Int
@test Matrix{eltyb}(I, a_1, a_1)*q ≈ convert(AbstractMatrix{tab}, q)
ac = copy(a)
# would need rectangular ldiv! method
@test_throws DimensionMismatch rq!(a[:, 1:5])\b == rq!(view(ac, :, 1:5))\b
end
rqstring = sprint((t, s) -> show(t, "text/plain", s), rqa)
rstring = sprint((t, s) -> show(t, "text/plain", s), r)
qstring = sprint((t, s) -> show(t, "text/plain", s), q)
@test rqstring == "$(summary(rqa))\nR factor:\n$rstring\nQ factor:\n$qstring"
end
end
if eltya != Int
@testset "Matmul with RQ factorizations" begin
a = raw_a
rqa = rq(a[:,1:n1])
q, r = rqa.Q, rqa.R
@test rmul!(copy(q'), q) ≈ Matrix(I, n1, n1)
@test_throws DimensionMismatch rmul!(Matrix{eltya}(I, n1+1, n1+1),q)
@test rmul!(copy(q), adjoint(q)) ≈ Matrix(I, n1, n1)
@test_throws DimensionMismatch rmul!(Matrix{eltya}(I, n1+1, n1+1),adjoint(q))
@test_throws ErrorException size(q,-1)
@test_throws DimensionMismatch q * Matrix{Int8}(I, n+4, n+4)
end
end
end
@testset "Wide RQ" begin
m = n-2
A = raw_a[1:m,1:n]
R,Q = rq(A)
@test Q'*Q ≈ Matrix(I, n, n)
@test istriu(R)
@test hcat(zeros(m,n-m),R)*Q ≈ A
# test the perverse padded product
@test R*Q ≈ A
Qm = Matrix(Q)
@test Qm' * Qm ≈ Matrix(I, n, n)
@test hcat(zeros(m,n-m),R)*Qm ≈ A
end
@testset "Tall RQ" begin
p = n-2
A = raw_a[1:n,1:p]
R,Q = rq(A)
@test Q'*Q ≈ Matrix(I, p, p)
@test istriu(R,p-n)
@test R*Q ≈ A
Qm = Matrix(Q)
@test Qm' * Qm ≈ Matrix(I, p, p)
@test R*Qm ≈ A
end
end
@testset "transpose errors" begin
@test_throws MethodError transpose(rq(randn(3,3)))
@test_throws MethodError adjoint(rq(randn(3,3)))
@test_throws MethodError transpose(rq(big.(randn(3,3))))
@test_throws MethodError adjoint(rq(big.(randn(3,3))))
end
@testset "Issue 7304" begin
A = [-√.5 -√.5; -√.5 √.5]
Q = rq(A).Q
@test norm(A+Q) < eps()
end
@testset "rq on AbstractVector" begin
vl = [3.0, 4.0]
for Tl in (Float32, Float64)
for T in (Tl, Complex{Tl})
v = convert(Vector{T}, vl)
nv, nm = rq(v)
@test nv*nm ≈ v
end
end
end
@testset "Issue 24589. Promotion of rational matrices" begin
A = rand(1//1:5//5, 4,3)
@test first(rq(A)) == first(rq(float(A)))
end
# omit "Issue Test Factorization fallbacks for rectangular problems"
@testset "lmul!/rmul! $elty" for elty in (:real, :cplx)
s = elty == :real ? 0.0 : 0.25im
A = randn(100,100) .+ s
R,Q = rq(A)
x = randn(100) .+ s
b = randn(100,2) .+ s
@test lmul!(Q, copy(x)) ≈ Matrix(Q)*x
@test lmul!(Q, copy(b)) ≈ Matrix(Q)*b
@test lmul!(Q', copy(x)) ≈ Matrix(Q)'*x
@test lmul!(Q', copy(b)) ≈ Matrix(Q)'*b
c = randn(2,100) .+ s
@test rmul!(copy(c), Q) ≈ c*Matrix(Q)
@test rmul!(copy(c), Q') ≈ c*Matrix(Q')
A = randn(103,100) .+ s
R,Q = rq(A)
x = randn(100) .+ s
b = randn(100,2) .+ s
@test lmul!(Q, copy(x)) ≈ Matrix(Q)*x
@test lmul!(Q, copy(b)) ≈ Matrix(Q)*b
@test lmul!(Q', copy(x)) ≈ Matrix(Q)'*x
@test lmul!(Q', copy(b)) ≈ Matrix(Q)'*b
c = randn(2,100) .+ s
@test rmul!(copy(c), Q) ≈ c*Matrix(Q)
@test rmul!(copy(c), Q') ≈ c*Matrix(Q')
end
end
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] | 1.640429 | 4,942 |
#-------------------------------------------------------------------
#* EMSO Model Library (EML) Copyright (C) 2004 - 2007 ALSOC.
#*
#* This LIBRARY is free software; you can distribute it and/or modify
#* it under the therms of the ALSOC FREE LICENSE as available at
#* http://www.enq.ufrgs.br/alsoc.
#*
#* EMSO Copyright (C) 2004 - 2007 ALSOC, original code
#* from http://www.rps.eng.br Copyright (C) 2002-2004.
#* All rights reserved.
#*
#* EMSO is distributed under the therms of the ALSOC LICENSE as
#* available at http://www.enq.ufrgs.br/alsoc.
#*
#*----------------------------------------------------------------------
#* Authors: Rafael de Pelegrini Soares
#* Andrey Copat, Estefane S. Horn, Marcos L. Alencastro
#* $Id$
#*--------------------------------------------------------------------
#Needs to be reformulated
type HidraulicTurbine
HidraulicTurbine()=begin
NComp=outers.NComp
PP=outers.PP
new(
DanaInteger(Dict{Symbol,Any}(
:Brief=>"Number of chemical components",
:Lower=>1
)),
DanaPlugin(Dict{Symbol,Any}(
:Brief=>"External Physical Properties",
:Type=>"PP"
)),
fill(molweight(Dict{Symbol,Any}(
:Brief=>"Molar Weight"
)),(NComp)),
efficiency(Dict{Symbol,Any}(
:Brief=>"Turbine efficiency"
)),
efficiency(Dict{Symbol,Any}(
:Brief=>"Brake efficiency"
)),
positive(Dict{Symbol,Any}(
:Brief=>"Volumetric expansivity",
:Unit=>"1/K"
)),
head(Dict{Symbol,Any}(
:Brief=>"Head Developed"
)),
power(Dict{Symbol,Any}(
:Brief=>"Fluid Power"
)),
power(Dict{Symbol,Any}(
:Brief=>"Brake Power"
)),
positive(Dict{Symbol,Any}(
:Brief=>"Pressure Ratio"
)),
press_delta(Dict{Symbol,Any}(
:Brief=>"Pressure Drop",
:DisplayUnit=>"kPa",
:Symbol=>"\\Delta P"
)),
molweight(Dict{Symbol,Any}(
:Brief=>"Mixture Molar Weight"
)),
dens_mass(Dict{Symbol,Any}(
:Brief=>"Specific Mass"
)),
cp_mol(Dict{Symbol,Any}(
:Brief=>"Heat Capacity"
)),
stream(Dict{Symbol,Any}(
:Brief=>"Inlet stream",
:PosX=>0.05,
:PosY=>0.0,
:Symbol=>"_{in}"
)),
stream(Dict{Symbol,Any}(
:Brief=>"Outlet stream",
:PosX=>0.65,
:PosY=>1,
:Symbol=>"_{out}"
)),
power(Dict{Symbol,Any}(
:Brief=>"Work Outlet",
:PosX=>1,
:PosY=>0.46
)),
[
:(Mwm = sum(Mw*Inlet.z)),
:(rho = PP.LiquidDensity(Inlet.T,Inlet.P,Inlet.z)),
:(Outlet.v = PP.VapourFraction(Outlet.T, Outlet.P, Outlet.z)),
:(Cp = PP.LiquidCp(Inlet.T,Inlet.P,Inlet.z)),
:(Outlet.P = Inlet.P * Pratio),
:(Outlet.P = Inlet.P - Pdrop),
:(FPower * rho = -Pdrop * Inlet.F * Mwm),
:(BPower = FPower * Eff),
:(BPower = WorkOut),
:((Outlet.T - Inlet.T) * rho * Cp = (Outlet.h - Inlet.h) * rho + Pdrop * Mwm * (1-Beta*Inlet.T)),
:((Outlet.h - Inlet.h) * rho = -Pdrop * Mwm),
:(Outlet.F = Inlet.F),
:(Outlet.z = Inlet.z),
:(Head = Outlet.h - Inlet.h),
],
[
"Calculate Mwm for Inlet Mixture","Calculate rho using a External Physical Properties Routine","Calculate Outlet Vapour Fraction","Calculate Cp Using a External Physical Properties Routine","Pressure Ratio","Pressure Drop","Calculate Fluid Power","Calculate Brake Power","","Calculate Outlet Temperature","Calculate Outlet Enthalpy","Molar Balance","Calculate Outlet Composition","Calculate Head",
],
[:NComp,:PP,:Mw,],
[:Eff,:Meff,:Beta,:Head,:FPower,:BPower,:Pratio,:Pdrop,:Mwm,:rho,:Cp,:Inlet,:Outlet,:WorkOut,]
)
end
NComp::DanaInteger
PP::DanaPlugin
Mw::Array{molweight}
Eff::efficiency
Meff::efficiency
Beta::positive
Head::head
FPower::power
BPower::power
Pratio::positive
Pdrop::press_delta
Mwm::molweight
rho::dens_mass
Cp::cp_mol
Inlet::stream
Outlet::stream
WorkOut::power
equations::Array{Expr,1}
equationNames::Array{String,1}
parameters::Array{Symbol,1}
variables::Array{Symbol,1}
attributes::Dict{Symbol,Any}
end
export HidraulicTurbine
function set(in::HidraulicTurbine)
Mw = PP.MolecularWeight()
end
function setEquationFlow(in::HidraulicTurbine)
#Mixtures Properties
addEquation(1)
addEquation(2)
addEquation(3)
addEquation(4)
addEquation(5)
addEquation(6)
addEquation(7)
addEquation(8)
addEquation(9)
addEquation(10)
addEquation(11)
addEquation(12)
addEquation(13)
addEquation(14)
end
function atributes(in::HidraulicTurbine,_::Dict{Symbol,Any})
fields::Dict{Symbol,Any}=Dict{Symbol,Any}()
fields[:Pallete]=true
fields[:Icon]="icon/HidraulicTurbine"
fields[:Brief]="Testing Model of a Hidraulic Turbine."
drive!(fields,_)
return fields
end
HidraulicTurbine(_::Dict{Symbol,Any})=begin
newModel=HidraulicTurbine()
newModel.attributes=atributes(newModel,_)
newModel
end
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] | 2.221856 | 2,123 |
module TestExpressionsTVL
import Test: @testset, @test
import Volcanito: tvl, @tvl
@testset "TVL || truth table" begin
@test @tvl(true || true) === true
@test @tvl(true || false) === true
@test @tvl(true || missing) === true
@test @tvl(false || true) === true
@test @tvl(false || false) === false
@test @tvl(false || missing) === missing
@test @tvl(missing || true) === true
@test @tvl(missing || false) === missing
@test @tvl(missing || missing) === missing
end
@testset "TVL && truth table" begin
@test @tvl(true && true) === true
@test @tvl(true && false) === false
@test @tvl(true && missing) === missing
@test @tvl(false && true) === false
@test @tvl(false && false) === false
@test @tvl(false && missing) === false
@test @tvl(missing && true) === missing
@test @tvl(missing && false) === false
@test @tvl(missing && missing) === missing
end
# We define a function that prints out a unique ID for each argument
# to a Boolean operator. By wrapping all Boolean values in calls to this
# function, we're able to check that the order of evaluation and
# side-effects of the short-circuiting operators are retained by our
# macro rewrites.
function f(io, i, x)
print(io, i)
x
end
@testset "Order of evaluation for tvl" begin
for x in (true, false)
for y in (true, false)
for z in (true, false)
io = IOBuffer()
a = f(io, 1, x) && f(io, 2, y) || f(io, 3, z)
order_a = String(take!(io))
b = @tvl f(io, 1, x) && f(io, 2, y) || f(io, 3, z)
order_b = String(take!(io))
@test a === b
@test order_a === order_b
a = f(io, 1, x) || f(io, 2, y) && f(io, 3, z)
order_a = String(take!(io))
b = @tvl f(io, 1, x) || f(io, 2, y) && f(io, 3, z)
order_b = String(take!(io))
@test a === b
@test order_a === order_b
end
end
end
end
@testset "@tvl with local variables" begin
let x = 1, y = 2
@test (@tvl x > 1 || y < 2) === false
end
end
end
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] | 2.187 | 1,000 |
module DoublyPeriodicTaylorGreen
using Printf
using Oceananigans, Oceananigans.OutputWriters
# Advected vortex: ψ(x, y, t) = exp(-2t) * cos(x - U*t) * cos(y)
u(x, y, t, U=1) = U + exp(-2t) * cos(x - U*t) * sin(y)
v(x, y, t, U=1) = - exp(-2t) * sin(x - U*t) * cos(y)
#####
##### x, y
#####
function setup_simulation(; Nx, Δt, stop_iteration, U=1, architecture=CPU(), dir="data")
grid = RegularCartesianGrid(size=(Nx, Nx, 1), x=(0, 2π), y=(0, 2π), z=(0, 1),
topology=(Periodic, Periodic, Bounded))
model = IncompressibleModel(architecture = architecture,
grid = grid,
coriolis = nothing,
buoyancy = nothing,
tracers = nothing,
closure = ConstantIsotropicDiffusivity(ν=1))
set!(model, u = (x, y, z) -> u(x, y, 0, U),
v = (x, y, z) -> v(x, y, 0, U))
simulation = Simulation(model, Δt=Δt, stop_iteration=stop_iteration, progress_frequency=stop_iteration)
simulation.output_writers[:fields] = JLD2OutputWriter(model, FieldOutputs(model.velocities);
dir = dir, force = true,
prefix = @sprintf("taylor_green_Nx%d_Δt%.1e", Nx, Δt),
interval = stop_iteration * Δt / 10)
return simulation
end
function setup_and_run(; setup...)
simulation = setup_simulation(; setup...)
println("""
Running decaying Taylor-Green vortex simulation in x, y with
Nx = $(setup[:Nx])
Δt = $(setup[:Δt])
""")
@time run!(simulation)
return nothing
end
end # module
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] | 1.817204 | 1,023 |
#=
Log-likelihood for ODEs, integrating along the tree
using flow algorithm
Ignacio Quintero Mächler
t(-_-t)
Created 23 10 2019
=#
"""
make_Et(Et::Function,
p0::Array{Float64,1},
u0::Array{Float64,1},
ts::Array{Float64,1},
ti::Float64,
Make Extinction through time, `E(t)`.
"""
function make_Et(Et::Function,
p0::Array{Float64,1},
u0::Array{Float64,1},
ts::Array{Float64,1},
ti::Float64,
tf::Float64)
prob = ODEProblem(Et, u0, (ti,tf), p0)
int = init(prob,
Tsit5(),
saveat = ts,
save_everystep = false,
calck = false,
force_dtmin = true,
save_start = true,
initialize_save = false,
maxiters = 100_000_000,
verbose = false)
function f(p::Array{Float64,1})
int.p = p
reinit!(int)
solve!(int).u::Array{Array{Float64,1},1}
end
return f
end
"""
make_Gt(At::Function,
p0::Array{Array{Float64,1},1},
u0::Array{Float64,2},
ts::Array{Float64,1},
ti::Float64,
tf::Float64)
Make flow equation for likelihoods through time, `G(t)`.
"""
function make_Gt(At::Function,
p0::Array{Array{Float64,1},1},
u0::Array{Float64,2},
ts::Array{Float64,1},
ti::Float64,
tf::Float64)
prob = ODEProblem(At, u0, (ti,tf), p0)
int = init(prob,
Tsit5(),
saveat = ts,
save_everystep = false,
calck = false,
force_dtmin = true,
save_start = true,
initialize_save = false,
maxiters = 100_000_000,
verbose = false)
function f(p::Array{Array{Float64,1},1})
int.p = p
reinit!(int)
solve!(int).u::Array{Array{Float64,2},1}
end
return f
end
"""
make_loglik(Gt ::Function,
Et ::Function,
X ::Array{Array{Float64,1},1},
triads ::Array{Array{Int64,1},1},
lbts ::Array{Int64,2},
bts ::Array{Float64,1},
ns ::Int64,
ned ::Int64,
nets ::Int64,
ntip ::Int64,
λevent!::Function,
rootll ::Function)
Make log-likelihood function using the flow algorithm.
"""
function make_loglik(Gt ::Function,
Et ::Function,
X ::Array{Array{Float64,1},1},
triads ::Array{Array{Int64,1},1},
lbts ::Array{Int64,2},
bts ::Array{Float64,1},
ns ::Int64,
ned ::Int64,
nets ::Int64,
λevent!::Function,
rootll ::Function)
# preallocate arrays
X0 = Array{Float64,1}(undef,ns)
Xp1 = Array{Float64,1}(undef,ns)
Xp2 = Array{Float64,1}(undef,ns)
Xr = Array{Float64,1}(undef,ns)
wg = Array{Float64,1}(undef,ns)
Er = Array{Float64,1}(undef,ns)
rtime = bts[end]
netsm1 = nets - 1
nbts = length(bts)
# start ll function for parameters
function f(p::Array{Float64,1})
@inbounds begin
Ets = Et(p)
# push parameters as the last vector in Ets
push!(Ets, p)
# estimate `Gts` according to `Ets` and `p`
Gts = Gt(Ets)
ll = 0.0
if rank(Gts[nbts]) == ns
for i in Base.OneTo(nbts)
if rank(Gts[i]) != ns
@show Gts[nbts]
@show Gts[i]
end
end
for trio in triads
pr, d1, d2 = trio
## first daughter
X0 = Gts[lbts[d1,2]]\X[d1]
mul!(Xp1, Gts[lbts[d1,1]], X0)
check_negs(Xp1, ns) && return -Inf
## second daughter
X0 = Gts[lbts[d2,2]]\X[d2]
mul!(Xp2, Gts[lbts[d2,1]], X0)
check_negs(Xp2, ns) && return -Inf
λtime = bts[lbts[d1,1]]
λevent!(λtime, X, Xp1, Xp2, p, pr)
if pr != ned
mdlus = sum(X[pr])
X[pr] /= mdlus
ll += log(mdlus)
end
end
else
for trio in triads
pr, d1, d2 = trio
## first daughter
ldiv!(X0, qr!(Gts[lbts[d1,2]], Val(true)), X[d1])
mul!(Xp1, Gts[lbts[d1,1]], X0)
check_negs(Xp1, ns) && return -Inf
## second daughter
ldiv!(X0, qr!(Gts[lbts[d2,2]], Val(true)), X[d2])
mul!(Xp2, Gts[lbts[d2,1]], X0)
check_negs(Xp2, ns) && return -Inf
λtime = bts[lbts[d1,1]]
λevent!(λtime, X, Xp1, Xp2, p, pr)
if pr != ned
mdlus = sum(X[pr])
X[pr] /= mdlus
ll += log(mdlus)
end
end
end
# assign root likelihoods
@simd for i in Base.OneTo(ns)
Xr[i] = X[ned][i]
end
# assign root extinction
@simd for i in Base.OneTo(ns)
Er[i] = Ets[netsm1][i]
end
# estimate weights
normbysum!(Xr, wg, ns)
ll += log(rootll(rtime, Xr, Er, wg, p)::Float64)
end
return ll
end
return f
end
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] | 1.66575 | 3,273 |
type BitsContainer <: RoaringContainer
card::UInt
data::Vector{UInt64}
end
function BitsContainer()
Vector{UInt64}()
return BitsContainer()
end
bitset_container_t *bitset_container_create(void) {
bitset_container_t *bitset =
(bitset_container_t *)calloc(1, sizeof(bitset_container_t));
if (!bitset) {
return NULL;
}
// sizeof(__m256i) == 32
bitset->array = (uint64_t *) aligned_malloc(32, sizeof(uint64_t) * BITSET_CONTAINER_SIZE_IN_WORDS);
if (! bitset->array) {
free(bitset);
return NULL;
}
bitset_container_clear(bitset);
return bitset;
}
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] | 2.327206 | 272 |
@diffusion_process SIRAux begin
:parameters
(α, β, σ1, σ2, t, T) --> Float64
(u, v) --> ℝ{2}
:additional
linear --> true
constdiff --> true
statespace -->BoundedStateSpace(((1,2), (0.0,0.0)), ((1,2), (1.0,1.0)))
end
function B(t, P::SIRAux)
@SMatrix[
(P.α*(1 - P.v[1] - P.v[2]) - P.β) 0.0;
P.β 0.0
]
end
β(t, P::SIRAux) = ℝ{2}(0.0, 0.0)
function σ(t, P::SIRAux)
@SMatrix Float64[
(-P.σ1*(1 - v[1] - v[2])*v[1]) -P.σ2*v[1];
0.0 P.σ2*v[1]
]
end
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] | 1.610942 | 329 |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: jl:light,ipynb
# text_representation:
# extension: .jl
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.4.2
# kernelspec:
# display_name: Julia 1.4.0
# language: julia
# name: julia-1.4
# ---
# +
using Gen
using Statistics
include("DistributionsBacked.jl")
using AdvancedHMC
const my_normal = DistributionsBacked{Float64}((mu, sigma) ->
Distributions.Normal(mu, sigma), [true, true], true)
const my_unif = DistributionsBacked{Float64}((lo, hi) ->
Distributions.Uniform(lo, hi), [true, true], true)
;
# +
@gen function corbiv_model() #correlated bivariate normal
ρ ~ my_unif(-1., 1.)
x ~ my_normal(0., 1.)
y ~ my_normal(ρ*x, sqrt(1. - ρ^2))
end
@gen function cormiv_model() #correlated bivariate normal
ρ ~ my_unif(0., 1.) #correlation between x and y
ρ2 ~ my_unif(0., ρ) #correlation between z and (x or y)
zy_cond_zx = (1-ρ2^2)*ρ2
x ~ my_normal(0., 1.)
y ~ my_normal(ρ*x, sqrt(1. - ρ^2))
z ~ my_normal(ρ2*x + zy_cond_zx*y, sqrt(1. - ρ2^2 - zy_cond_zx^2))
end
;
# +
function constrainρ(ρ::Float64)
constraints = Gen.choicemap()
constraints[:ρ] = ρ
constraints
end
function constrainρ2(ρ::Float64, ρ2, z)
constraints = Gen.choicemap()
constraints[:ρ] = ρ
constraints[:ρ2] = ρ2
constraints[:z] = z
constraints
end
function mcmc_inference(ρ, num_iters, update, selection)
observation = constrainρ(ρ)
(trace, _) = generate(corbiv_model, (), observation)
samples = Array{Float64}(undef,num_iters,2)
for i=1:num_iters
trace = update(trace, selection)
ch = get_choices(trace)
samples[i,1] = ch[:x]
samples[i,2] = ch[:y]
end
samples
end
function mcmc_m_inference(ρ, ρ2, z, num_iters, update)
observation = constrainρ2(ρ, ρ2, z)
(trace, _) = generate(cormiv_model, (), observation)
samples = Array{Float64}(undef,num_iters,2)
for i=1:num_iters
trace = update(trace)
ch = get_choices(trace)
samples[i,1] = ch[:x]
samples[i,2] = ch[:y]
end
samples
end
function block_mh(tr, selection)
(tr, _) = mh(tr, select(:x, :y))
tr
end
function simple_hmc(tr, selection)
(tr, _) = hmc(tr, select(:x, :y))
tr
end
;
# -
iters = 10_000
show = 5
ρ = -.5
samps = mcmc_inference(ρ, iters, block_mh, select(:x,:y))
samps[(iters-show+1):iters,:]
iters = 100
show = 5
ρ = .8
samps = mcmc_inference(ρ, iters, simple_hmc)
samps[(iters-show+1):iters,:]
println(mean(samps))
println(cor(samps[:,1],samps[:,2]))
# Disable AdvancedHMC's NUTS logging
# +
using Logging
using LoggingExtras
function ignore_sampling_filter(log_args)
!(occursin("sampling steps",log_args.message) || occursin("adapation steps",log_args.message))
end
logger = ActiveFilteredLogger(ignore_sampling_filter, global_logger())
if !(@isdefined old_logger) #do this only once
old_logger = global_logger(logger)
end
# -
function my_nuts(trace, selection,
n_postadapt_steps = 2,
n_adapts = 1,
initial_ϵ_reduce_fac = 10)
n_NUTS_steps = n_postadapt_steps + n_adapts
filtered_choices = get_selected(get_choices(trace), selection)
cur_xy = to_array(filtered_choices, Float64)
dimension = length(cur_xy)
metric = DiagEuclideanMetric(dimension)
retval_grad = nothing #accepts_output_grad(get_gen_fn(trace)) ? zero(get_retval(trace)) : nothing
function update_xy(val)
extra_constraints = from_array(filtered_choices, val)
update(trace, (), (NoChange(),), extra_constraints)
end
function val_to_lp_plus_c(val)
(new_trace, weight, discard, retdiff) = update_xy(val)
weight
end
function val_to_grad(val)
(new_trace, weight, discard, retdiff) = update_xy(val)
(retval_grad_out, values_trie, gradient_trie) = choice_gradients(new_trace, selection, retval_grad)
grad = [gradient_trie[:x], gradient_trie[:y]]
(weight, grad)
end
# Define a Hamiltonian system, using metric defined globally above
hamiltonian = Hamiltonian(metric, val_to_lp_plus_c, val_to_grad)
# Define a leapfrog solver, with initial step size chosen heuristically
initial_ϵ = find_good_stepsize(hamiltonian, cur_xy) ./ initial_ϵ_reduce_fac
integrator = Leapfrog(initial_ϵ)
# Define an HMC sampler, with the following components
# - multinomial sampling scheme,
# - generalised No-U-Turn criteria, and
# - windowed adaption for step-size and diagonal mass matrix
proposal = NUTS{MultinomialTS, GeneralisedNoUTurn}(integrator)
adaptor = StanHMCAdaptor(MassMatrixAdaptor(metric), StepSizeAdaptor(0.8, integrator))
# Run the sampler to draw samples from the specified Gaussian, where
# - `samples` will store the samples
# - `stats` will store diagnostic statistics for each sample
samples, stats = sample(hamiltonian, proposal, cur_xy, n_NUTS_steps, adaptor, n_adapts; progress=false)
#println(samples[3])
(new_trace, weight, discard, retdiff) = update_xy(samples[n_NUTS_steps])
new_trace
end
# +
iters = 200
show = 5
ρ = .99
samps = mcmc_inference(ρ, iters, my_nuts, select(:x,:y))
samps[(iters-show+1):iters,:]
# -
println(cor(samps[1:iters-1,1],samps[2:iters,1])) #serial correlation; lower is better
println(ρ^4) #for comparison, gibbs would be ρ² for each step; ρ⁴ for two steps
positive
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796,
532,
13,
20,
198,
82,
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796,
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66,
62,
259,
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7,
33643,
11,
340,
364,
11,
2512,
62,
76,
71,
11,
2922,
7,
25,
87,
11,
25,
88,
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198,
82,
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58,
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10,
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628,
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66,
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71,
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2,
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399,
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50,
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198,
198,
2,
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198,
3500,
5972,
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11627,
8847,
198,
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8818,
8856,
62,
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11347,
62,
24455,
7,
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62,
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8,
198,
220,
220,
220,
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7,
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1834,
259,
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37687,
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4831,
1600,
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62,
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13,
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8,
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8833,
259,
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324,
499,
341,
4831,
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62,
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13,
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198,
437,
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1362,
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11928,
4400,
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1362,
7,
46430,
62,
37687,
11347,
62,
24455,
11,
3298,
62,
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1362,
28955,
628,
198,
361,
5145,
7,
31,
271,
23211,
1468,
62,
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1362,
8,
1303,
4598,
428,
691,
1752,
198,
220,
220,
220,
1468,
62,
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1362,
796,
3298,
62,
6404,
1362,
7,
6404,
1362,
8,
198,
437,
198,
2,
532,
198,
198,
8818,
616,
62,
31381,
7,
40546,
11,
6356,
11,
220,
198,
220,
220,
220,
220,
220,
220,
220,
299,
62,
7353,
42552,
62,
20214,
796,
362,
11,
198,
220,
220,
220,
220,
220,
220,
220,
299,
62,
42552,
82,
796,
352,
11,
198,
220,
220,
220,
220,
220,
220,
220,
4238,
62,
139,
113,
62,
445,
7234,
62,
38942,
796,
838,
8,
198,
220,
220,
220,
220,
198,
220,
220,
220,
299,
62,
45,
3843,
50,
62,
20214,
796,
299,
62,
7353,
42552,
62,
20214,
1343,
299,
62,
42552,
82,
198,
220,
220,
220,
220,
198,
220,
220,
220,
29083,
62,
6679,
1063,
796,
651,
62,
34213,
7,
1136,
62,
6679,
1063,
7,
40546,
828,
6356,
8,
198,
220,
220,
220,
1090,
62,
5431,
796,
284,
62,
18747,
7,
10379,
4400,
62,
6679,
1063,
11,
48436,
2414,
8,
198,
220,
220,
220,
15793,
796,
4129,
7,
22019,
62,
5431,
8,
198,
220,
220,
220,
18663,
796,
6031,
363,
36,
36616,
485,
272,
9171,
1173,
7,
46156,
8,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1005,
2100,
62,
9744,
796,
2147,
1303,
13635,
82,
62,
22915,
62,
9744,
7,
1136,
62,
5235,
62,
22184,
7,
40546,
4008,
5633,
6632,
7,
1136,
62,
1186,
2100,
7,
40546,
4008,
1058,
2147,
198,
220,
220,
220,
220,
198,
220,
220,
220,
2163,
4296,
62,
5431,
7,
2100,
8,
198,
220,
220,
220,
220,
220,
220,
220,
3131,
62,
1102,
2536,
6003,
796,
422,
62,
18747,
7,
10379,
4400,
62,
6679,
1063,
11,
1188,
8,
198,
220,
220,
220,
220,
220,
220,
220,
4296,
7,
40546,
11,
29994,
357,
2949,
19400,
22784,
828,
3131,
62,
1102,
2536,
6003,
8,
198,
220,
220,
220,
886,
198,
220,
220,
220,
220,
198,
220,
220,
220,
2163,
1188,
62,
1462,
62,
34431,
62,
9541,
62,
66,
7,
2100,
8,
198,
220,
220,
220,
220,
220,
220,
220,
357,
3605,
62,
40546,
11,
3463,
11,
27537,
11,
1005,
26069,
8,
796,
4296,
62,
5431,
7,
2100,
8,
198,
220,
220,
220,
220,
220,
220,
220,
3463,
198,
220,
220,
220,
886,
198,
220,
220,
220,
220,
198,
220,
220,
220,
2163,
1188,
62,
1462,
62,
9744,
7,
2100,
8,
198,
220,
220,
220,
220,
220,
220,
220,
357,
3605,
62,
40546,
11,
3463,
11,
27537,
11,
1005,
26069,
8,
796,
4296,
62,
5431,
7,
2100,
8,
198,
220,
220,
220,
220,
220,
220,
220,
357,
1186,
2100,
62,
9744,
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448,
11,
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62,
83,
5034,
11,
31312,
62,
83,
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8,
796,
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62,
9744,
2334,
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3605,
62,
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11,
6356,
11,
1005,
2100,
62,
9744,
8,
198,
220,
220,
220,
220,
220,
220,
220,
3915,
796,
685,
49607,
62,
83,
5034,
58,
25,
87,
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31312,
62,
83,
5034,
58,
25,
88,
11907,
198,
220,
220,
220,
220,
220,
220,
220,
357,
6551,
11,
3915,
8,
198,
220,
220,
220,
886,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
2896,
500,
257,
11582,
666,
1080,
11,
1262,
18663,
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18309,
2029,
198,
220,
220,
220,
8891,
9044,
666,
796,
11582,
666,
7,
4164,
1173,
11,
1188,
62,
1462,
62,
34431,
62,
9541,
62,
66,
11,
1188,
62,
1462,
62,
9744,
8,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
2896,
500,
257,
16470,
49956,
1540,
332,
11,
351,
4238,
2239,
2546,
7147,
339,
333,
16772,
198,
220,
220,
220,
4238,
62,
139,
113,
796,
1064,
62,
11274,
62,
9662,
7857,
7,
2763,
9044,
666,
11,
1090,
62,
5431,
8,
24457,
4238,
62,
139,
113,
62,
445,
7234,
62,
38942,
198,
220,
220,
220,
4132,
12392,
796,
33927,
49956,
7,
36733,
62,
139,
113,
8,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
2896,
500,
281,
367,
9655,
6072,
20053,
11,
351,
262,
1708,
6805,
198,
220,
220,
220,
1303,
220,
220,
532,
1963,
259,
49070,
19232,
7791,
11,
198,
220,
220,
220,
1303,
220,
220,
532,
2276,
1417,
1400,
12,
52,
12,
17278,
9987,
11,
290,
198,
220,
220,
220,
1303,
220,
220,
532,
4324,
276,
512,
64,
1159,
329,
2239,
12,
7857,
290,
40039,
2347,
17593,
198,
220,
220,
220,
6961,
796,
399,
3843,
50,
90,
15205,
259,
49070,
4694,
11,
3611,
1417,
2949,
3843,
700,
92,
7,
18908,
12392,
8,
198,
220,
220,
220,
6068,
273,
796,
7299,
39,
9655,
48003,
273,
7,
20273,
46912,
48003,
273,
7,
4164,
1173,
828,
5012,
10699,
48003,
273,
7,
15,
13,
23,
11,
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12392,
4008,
628,
220,
220,
220,
1303,
5660,
262,
6072,
20053,
284,
3197,
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422,
262,
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12822,
31562,
11,
810,
198,
220,
220,
220,
1303,
220,
220,
532,
4600,
82,
12629,
63,
481,
3650,
262,
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198,
220,
220,
220,
1303,
220,
220,
532,
4600,
34242,
63,
481,
3650,
23584,
7869,
329,
1123,
6291,
198,
220,
220,
220,
220,
198,
220,
220,
220,
8405,
11,
9756,
796,
6291,
7,
2763,
9044,
666,
11,
6961,
11,
1090,
62,
5431,
11,
299,
62,
45,
3843,
50,
62,
20214,
11,
6068,
273,
11,
299,
62,
42552,
82,
26,
4371,
28,
9562,
8,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
35235,
7,
82,
12629,
58,
18,
12962,
198,
220,
220,
220,
220,
198,
220,
220,
220,
220,
198,
220,
220,
220,
357,
3605,
62,
40546,
11,
3463,
11,
27537,
11,
1005,
26069,
8,
796,
4296,
62,
5431,
7,
82,
12629,
58,
77,
62,
45,
3843,
50,
62,
20214,
12962,
198,
220,
220,
220,
649,
62,
40546,
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437,
628,
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2,
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270,
364,
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939,
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12860,
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642,
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33643,
796,
764,
2079,
628,
198,
82,
9430,
796,
285,
11215,
66,
62,
259,
4288,
7,
33643,
11,
340,
364,
11,
616,
62,
31381,
11,
2922,
7,
25,
87,
11,
25,
88,
4008,
198,
82,
9430,
58,
7,
270,
364,
12,
12860,
10,
16,
2599,
270,
364,
11,
47715,
198,
2,
532,
198,
198,
35235,
7,
10215,
7,
82,
9430,
58,
16,
25,
270,
364,
12,
16,
11,
16,
4357,
82,
9430,
58,
17,
25,
270,
364,
11,
16,
60,
4008,
1303,
46911,
16096,
26,
2793,
318,
1365,
198,
35235,
7,
33643,
61,
19,
8,
1303,
1640,
7208,
11,
46795,
1443,
561,
307,
18074,
223,
31185,
329,
1123,
2239,
26,
18074,
223,
46256,
112,
329,
734,
4831,
198,
198,
24561,
628,
198
] | 2.243287 | 2,495 |
export rule
@rule BIFMHelper(:out, Marginalisation) (q_in::Any, ) = ProdFinal(q_in) | [
39344,
3896,
198,
198,
31,
25135,
347,
5064,
44,
47429,
7,
25,
448,
11,
11899,
1292,
5612,
8,
357,
80,
62,
259,
3712,
7149,
11,
1267,
796,
1041,
67,
19006,
7,
80,
62,
259,
8
] | 2.4 | 35 |
"A macro to make calling KNITRO's KN_* C API a little cleaner"
macro kn_ccall(func, args...)
f = Base.Meta.quot(Symbol("KN_$(func)"))
args = [esc(a) for a in args]
quote
ccall(($f, libknitro), $(args...))
end
end
macro kn_get_values(function_name, type)
name_singular = "KN_" * string(function_name)
name_plural = "KN_" * string(function_name) * "s"
fname = Symbol(name_plural)
fnameshort = Symbol(name_singular)
# Names of C function in knitro.h
c_fname = Symbol(name_singular)
c_fnames = Symbol(name_plural)
c_fnames_all = Symbol(name_plural * "_all")
n = if occursin("var", name_singular)
:(KN_get_number_vars(kc))
elseif occursin("con", name_singular)
:(KN_get_number_cons(kc))
end
quote
function $(esc(fname))(kc::Model)
result = zeros($type, $n)
$c_fnames_all(kc, result)
return result
end
function $(esc(fname))(kc::Model, index::Vector{Cint})
result = zeros($type, length(index))
$c_fnames(kc, length(index), index, result)
return result
end
function $(esc(fname))(kc::Model, index::Integer)
result = zeros($type, 1)
$c_fname(kc, index, result)
return result[1]
end
$(esc(fnameshort))(kc::Model, index::Integer) = $(esc(fname))(kc, index)
end
end
macro kn_get_attribute(function_name, type)
fname = Symbol("KN_" * string(function_name))
quote
function $(esc(fname))(m::Model)
val = zeros($type, 1)
ret = $fname(m, val)
return val[1]
end
end
end
"Format output returned by KNITRO as proper Julia string."
function _format_output(output::AbstractString)
# remove trailing whitespace
res = strip(output)
# remove special characters
return res = strip(res, '\0')
end
"Return the current KNITRO version."
function get_release()
len = 15
out = zeros(Cchar, len)
KN_get_release(len, out)
return String(strip(String(convert(Vector{UInt8}, out)), '\0'))
end
"Wrapper for KNITRO KN_context."
mutable struct Env
ptr_env::Ptr{Cvoid}
function Env()
ptrptr_env = Ref{Ptr{Cvoid}}()
res = KN_new(ptrptr_env)
if res != 0
error("Fail to retrieve a valid KNITRO KN_context. Error $res")
end
return new(ptrptr_env[])
end
Env(ptr::Ptr{Cvoid}) = new(ptr)
end
Base.unsafe_convert(ptr::Type{Ptr{Cvoid}}, env::Env) = env.ptr_env::Ptr{Cvoid}
is_valid(env::Env) = env.ptr_env != C_NULL
"""
Free all memory and release any Knitro license acquired by calling KN_new.
"""
function free_env(env::Env)
if env.ptr_env != C_NULL
ptrptr_env = Ref{Ptr{Cvoid}}(env.ptr_env)
KN_free(ptrptr_env)
env.ptr_env = C_NULL
end
return
end
"""
Structure specifying the callback context.
Each evaluation callbacks (for objective, gradient or hessian)
is attached to a unique callback context.
"""
mutable struct CallbackContext
context::Ptr{Cvoid}
n::Int
m::Int
# Add a dictionnary to store user params.
userparams::Any
# Oracle's callbacks are context dependent, so store
# them inside dedicated CallbackContext.
eval_f::Function
eval_g::Function
eval_h::Function
eval_rsd::Function
eval_jac_rsd::Function
function CallbackContext(ptr_cb::Ptr{Cvoid})
return new(ptr_cb, 0, 0, nothing)
end
end
Base.unsafe_convert(ptr::Type{Ptr{Cvoid}}, cb::CallbackContext) = cb.context::Ptr{Cvoid}
mutable struct Model
# KNITRO context environment.
env::Env
# Keep reference to callbacks for garbage collector.
callbacks::Vector{CallbackContext}
# Some structures for userParams
puts_user::Any
multistart_user::Any
mip_user::Any
newpoint_user::Any
# Solution values.
# Optimization status. Equal to 1 if problem is unsolved.
status::Cint
obj_val::Cdouble
x::Vector{Cdouble}
mult::Vector{Cdouble}
# Special callbacks (undefined by default).
# (this functions do not depend on callback environments)
ms_process::Function
newpt_callback::Function
mip_callback::Function
user_callback::Function
ms_initpt_callback::Function
puts_callback::Function
# Constructor.
function Model()
model = new(
Env(),
CallbackContext[],
nothing,
nothing,
nothing,
nothing,
1,
Inf,
Cdouble[],
Cdouble[],
)
# Add a destructor to properly delete model.
finalizer(KN_free, model)
return model
end
# Instantiate a new Knitro instance in current environment `env`.
function Model(env::Env)
return new(
env,
CallbackContext[],
nothing,
nothing,
nothing,
nothing,
1,
Inf,
Cdouble[],
Cdouble[],
)
end
end
Base.unsafe_convert(ptr::Type{Ptr{Cvoid}}, kn::Model) = kn.env.ptr_env::Ptr{Cvoid}
"Free solver object."
KN_free(m::Model) = free_env(m.env)
"Create solver object."
KN_new() = Model()
is_valid(m::Model) = is_valid(m.env)
has_callbacks(m::Model) = !isempty(m.callbacks)
register_callback(model::Model, cb::CallbackContext) = push!(model.callbacks, cb)
function Base.show(io::IO, m::Model)
if is_valid(m)
println(io, "$(get_release())")
println(io, "-----------------------")
println(io, "Problem Characteristics")
println(io, "-----------------------")
println(io, "Objective goal: Minimize")
println(io, "Objective type: $(KN_get_obj_type(m))")
println(
io,
"Number of variables: $(KN_get_number_vars(m))",
)
println(
io,
"Number of constraints: $(KN_get_number_cons(m))",
)
println(
io,
"Number of nonzeros in Jacobian: $(KN_get_jacobian_nnz(m))",
)
println(
io,
"Number of nonzeros in Hessian: $(KN_get_hessian_nnz(m))",
)
else
println(io, "KNITRO Problem: NULL")
end
end
#=
LM license manager
=#
"""
Type declaration for the Artelys License Manager context object.
Applications must not modify any part of the context.
"""
mutable struct LMcontext
ptr_lmcontext::Ptr{Cvoid}
# Keep a pointer to instantiated models in order to free
# memory properly.
linked_models::Vector{Model}
function LMcontext()
ptrref = Ref{Ptr{Cvoid}}()
res = KN_checkout_license(ptrref)
if res != 0
error("KNITRO: Error checkout license")
end
lm = new(ptrref[], Model[])
finalizer(KN_release_license, lm)
return lm
end
end
Base.unsafe_convert(ptr::Type{Ptr{Cvoid}}, lm::LMcontext) = lm.ptr_lmcontext::Ptr{Cvoid}
function Env(lm::LMcontext)
ptrptr_env = Ref{Ptr{Cvoid}}()
res = KN_new_lm(lm, ptrptr_env)
if res != 0
error("Fail to retrieve a valid KNITRO KN_context. Error $res")
end
return Env(ptrptr_env[])
end
function attach!(lm::LMcontext, model::Model)
push!(lm.linked_models, model)
return
end
# create Model with license manager
function Model(lm::LMcontext)
model = Model(Env(lm))
attach!(lm, model)
return model
end
KN_new_lm(lm::LMcontext) = Model(lm)
function KN_release_license(lm::LMcontext)
# First, ensure that all linked models are properly freed
# before releasing license manager!
KN_free.(lm.linked_models)
if lm.ptr_lmcontext != C_NULL
refptr = Ref{Ptr{Cvoid}}(lm.ptr_lmcontext)
KN_release_license(refptr)
lm.ptr_lmcontext = C_NULL
end
end
#=
VARIABLES
=#
function KN_add_var(m::Model)
index = Cint[0]
KN_add_var(m, index)
return index[1]
end
function KN_add_vars(m::Model, nvars::Int)
indexes = zeros(Cint, nvars)
KN_add_vars(m, nvars, indexes)
return indexes
end
@kn_get_values get_var_lobnd Cdouble
@kn_get_values get_var_upbnd Cdouble
@kn_get_values get_var_eqbnd Cdouble
@kn_get_values get_var_fxbnd Cdouble
@kn_get_values get_var_primal_value Cdouble
@kn_get_values get_var_dual_value Cdouble
@kn_get_values get_var_type Cint
#=
OBJECTIVE
=#
function KN_add_obj_linear_struct(
m::Model,
objIndices::Vector{Cint},
objCoefs::Vector{Cdouble},
)
nnz = length(objIndices)
@assert nnz == length(objCoefs)
return KN_add_obj_linear_struct(m, nnz, objIndices, objCoefs)
end
function KN_add_obj_linear_struct(m::Model, objindex::Int, objCoefs::Cdouble)
return KN_add_obj_linear_struct(m, Cint[objindex], [objCoefs])
end
function KN_add_obj_quadratic_struct(
m::Model,
indexVars1::Vector{Cint},
indexVars2::Vector{Cint},
coefs::Vector{Cdouble},
)
nnz = length(indexVars1)
@assert nnz == length(indexVars2) == length(coefs)
return KN_add_obj_quadratic_struct(m, nnz, indexVars1, indexVars2, coefs)
end
#=
CONSTRAINTS
=#
function KN_add_cons(m::Model, ncons::Integer)
indexes = zeros(Cint, ncons)
KN_add_cons(m, ncons, indexes)
return indexes
end
function KN_add_con(m::Model)
index = Cint[0]
KN_add_con(m, index)
return index[1]
end
@kn_get_values get_con_lobnd Cdouble
@kn_get_values get_con_upbnd Cdouble
@kn_get_values get_con_eqbnd Cdouble
@kn_get_values get_con_dual_value Cdouble
@kn_get_values get_con_value Cdouble
function KN_add_con_linear_struct(
m::Model,
index_cons::Vector{Cint},
index_vars::Vector{Cint},
coefs::Vector{Cdouble},
)
@assert length(index_cons) == length(index_vars) == length(coefs)
nnz = length(index_cons)
return KN_add_con_linear_struct(m, nnz, index_cons, index_vars, coefs)
end
function KN_add_con_linear_struct(
m::Model,
index_con::Integer,
index_vars::Vector{Cint},
coefs::Vector{Cdouble},
)
@assert length(index_vars) == length(coefs)
nnz = length(index_vars)
return KN_add_con_linear_struct_one(m, nnz, index_con, index_vars, coefs)
end
function KN_add_con_linear_struct(
m::Model,
index_con::Integer,
index_var::Integer,
coef::Cdouble,
)
nnz = 1
return KN_add_con_linear_struct_one(m, nnz, index_con, [index_var], [coef])
end
function KN_add_con_quadratic_struct(
m::Model,
index_cons::Vector{Cint},
index_vars1::Vector{Cint},
index_vars2::Vector{Cint},
coefs::Vector{Cdouble},
)
@assert length(index_cons) ==
length(index_vars1) ==
length(index_vars2) ==
length(coefs)
nnz = length(index_cons)
return KN_add_con_quadratic_struct(m, nnz, index_cons, index_vars1, index_vars2, coefs)
end
function KN_add_con_quadratic_struct(
m::Model,
index_con::Integer,
index_vars1::Vector{Cint},
index_vars2::Vector{Cint},
coefs::Vector{Cdouble},
)
@assert length(index_vars1) == length(index_vars2) == length(coefs)
nnz = length(index_vars1)
return KN_add_con_quadratic_struct_one(
m,
nnz,
index_con,
index_vars1,
index_vars2,
coefs,
)
end
function KN_add_con_quadratic_struct(
m::Model,
index_con::Integer,
index_var1::Integer,
index_var2::Integer,
coef::Cdouble,
)
nnz = 1
return KN_add_con_quadratic_struct_one(
m,
nnz,
index_con,
Cint[index_var1],
Cint[index_var2],
[coef],
)
end
function KN_set_compcons(
m::Model,
ccTypes::Vector{Cint},
indexComps1::Vector{Cint},
indexComps2::Vector{Cint},
)
# get number of constraints
nnc = length(ccTypes)
@assert nnc == length(indexComps1) == length(indexComps2)
return KN_set_compcons(m, nnc, ccTypes, indexComps1, indexComps2)
end
#=
RESIDUALS
=#
function KN_add_rsds(m::Model, ncons::Integer)
indexes = zeros(Cint, ncons)
KN_add_rsds(m, ncons, indexes)
return indexes
end
function KN_add_rsd(m::Model)
index = Cint[0]
KN_add_rsd(m, index)
return index
end
function KN_add_rsd_linear_struct(
m::Model,
indexRsds::Vector{Cint},
indexVars::Vector{Cint},
coefs::Vector{Cdouble},
)
nnz = length(indexRsds)
@assert nnz == length(indexVars) == length(coefs)
return KN_add_rsd_linear_struct(m, nnz, indexRsds, indexVars, coefs)
end
function KN_add_rsd_linear_struct(
m::Model,
indexRsd::Integer,
indexVar::Vector{Cint},
coefs::Vector{Cdouble},
)
nnz = length(indexVar)
@assert nnz == length(coefs)
return KN_add_rsd_linear_struct_one(m, nnz, indexRsd, indexVar, coefs)
end
#=
SOLVE
=#
function KN_solve(m::Model)
# Check sanity. If model has Julia callbacks, we need to ensure
# that Knitro is not multithreaded. Otherwise, the code will segfault
# as we have trouble calling Julia code from multithreaded C
# code. See issue #93 on https://github.com/jump-dev/KNITRO.jl.
if has_callbacks(m)
if KNITRO_VERSION >= v"13.0"
KN_set_param(m, KN_PARAM_MS_NUMTHREADS, 1)
KN_set_param(m, KN_PARAM_NUMTHREADS, 1)
KN_set_param(m, KN_PARAM_MIP_NUMTHREADS, 1)
else
KN_set_param(m, "par_numthreads", 1)
KN_set_param(m, "par_msnumthreads", 1)
end
end
# For KN_solve, we do not return an error if ret is different of 0.
m.status = KN_solve(m.env)
return m.status
end
#=
GETTERS
=#
function KN_get_solution(m::Model)
# we first check that the model is well defined to avoid segfault
@assert m.env != C_NULL
nx = KN_get_number_vars(m)
nc = KN_get_number_cons(m)
x = zeros(Cdouble, nx)
lambda = zeros(Cdouble, nx + nc)
status = Cint[0]
obj = Cdouble[0.0]
KN_get_solution(m, status, obj, x, lambda)
# Keep solution in cache.
m.status = status[1]
m.x = x
m.mult = lambda
m.obj_val = obj[1]
return status[1], obj[1], x, lambda
end
# some wrapper functions for MOI
function get_status(m::Model)
@assert m.env != C_NULL
if m.status != 1
return m.status
end
status = Cint[0]
obj = Cdouble[0.0]
KN_get_solution(m, status, obj, C_NULL, C_NULL)
# Keep status in cache.
m.status = status[1]
return status[1]
end
function get_objective(m::Model)
@assert m.env != C_NULL
if isfinite(m.obj_val)
return m.obj_val
end
status = Cint[0]
obj = Cdouble[0.0]
KN_get_solution(m, status, obj, C_NULL, C_NULL)
# Keep objective value in cache.
m.obj_val = obj[1]
return obj[1]
end
function get_solution(m::Model)
# We first check that the model is well defined to avoid segfault.
@assert m.env != C_NULL
if !isempty(m.x)
return m.x
end
nx = KN_get_number_vars(m)
x = zeros(Cdouble, nx)
status = Cint[0]
obj = Cdouble[0.0]
KN_get_solution(m, status, obj, x, C_NULL)
# Keep solution in cache.
m.x = x
return x
end
get_solution(m::Model, ix::Int) = isempty(m.x) ? get_solution(m)[ix] : m.x[ix]
function get_dual(m::Model)
# we first check that the model is well defined to avoid segfault
@assert m.env != C_NULL
if !isempty(m.mult)
return m.mult
end
nx = KN_get_number_vars(m)
nc = KN_get_number_cons(m)
lambda = zeros(Cdouble, nx + nc)
status = Cint[0]
obj = Cdouble[0.0]
KN_get_solution(m, status, obj, C_NULL, lambda)
# Keep multipliers in cache.
m.mult = lambda
return lambda
end
get_dual(m::Model, ix::Int) = isempty(m.mult) ? get_dual(m)[ix] : m.mult[ix]
function KN_get_objgrad_values(m::Model)
nnz = KN_get_objgrad_nnz(m)
indexVars = zeros(Cint, nnz)
objGrad = zeros(Cdouble, nnz)
KN_get_objgrad_values(m, indexVars, objGrad)
return indexVars, objGrad
end
function KN_get_jacobian_values(m::Model)
nnz = KN_get_jacobian_nnz(m)
jacvars = zeros(Cint, nnz)
jaccons = zeros(Cint, nnz)
jaccoef = zeros(Cdouble, nnz)
KN_get_jacobian_values(m, jacvars, jaccons, jaccoef)
return jacvars, jaccons, jaccoef
end
function KN_get_rsd_jacobian_values(m::Model)
nnz = KN_get_rsd_jacobian_nnz(m)
jacvars = zeros(Cint, nnz)
jaccons = zeros(Cint, nnz)
jaccoef = zeros(Cdouble, nnz)
KN_get_rsd_jacobian_values(m, jacvars, jaccons, jaccoef)
return jacvars, jaccons, jaccoef
end
function KN_get_hessian_values(m::Model)
nnz = KN_get_hessian_nnz(m)
indexVars1 = zeros(Cint, nnz)
indexVars2 = zeros(Cint, nnz)
hess = zeros(Cdouble, nnz)
KN_get_hessian_values(m, indexVars1, indexVars2, hess)
return indexVars1, indexVars2, hess
end
function KN_get_var_viols(kc::Model, index::Vector{Cint})
bndInfeas = zeros(Cint, length(index))
intInfeas = zeros(Cint, length(index))
viols = zeros(Cdouble, length(index))
KN_get_var_viols(kc, length(index), index, bndInfeas, intInfeas, viols)
return bndInfeas, intInfeas, viols
end
function KN_get_con_viols(kc::Model, index::Vector{Cint})
infeas = zeros(Cint, length(index))
viols = zeros(Cdouble, length(index))
KN_get_con_viols(kc, length(index), index, infeas, viols)
return infeas, viols
end
function KN_get_presolve_error(m::Model)
@assert m.env != C_NULL
component, index, error = Cint[0], Cint[0], Cint[0]
viol = Cdouble[0.0]
KN_get_presolve_error(m, component, index, error, viol)
return convert(Bool, component[1]),
convert(Int64, index[1]),
convert(Int64, error[1]),
convert(Float64, viol[1])
end
@kn_get_attribute get_number_vars Cint
@kn_get_attribute get_number_cons Cint
@kn_get_attribute get_obj_value Cdouble
@kn_get_attribute get_obj_type Cint
@kn_get_attribute get_number_iters Cint
@kn_get_attribute get_number_cg_iters Cint
@kn_get_attribute get_abs_feas_error Cdouble
@kn_get_attribute get_rel_feas_error Cdouble
@kn_get_attribute get_abs_opt_error Cdouble
@kn_get_attribute get_rel_opt_error Cdouble
@kn_get_attribute get_objgrad_nnz Cint
@kn_get_attribute get_jacobian_nnz KNLONG
@kn_get_attribute get_rsd_jacobian_nnz KNLONG
@kn_get_attribute get_hessian_nnz KNLONG
@kn_get_attribute get_solve_time_cpu Cdouble
@kn_get_attribute get_solve_time_real Cdouble
@kn_get_attribute get_mip_number_nodes Cint
@kn_get_attribute get_mip_number_solves Cint
@kn_get_attribute get_mip_abs_gap Cdouble
@kn_get_attribute get_mip_rel_gap Cdouble
@kn_get_attribute get_mip_incumbent_obj Cdouble
@kn_get_attribute get_mip_relaxation_bnd Cdouble
@kn_get_attribute get_mip_lastnode_obj Cdouble
#=
PARAMS
=#
function KN_set_param(m::Model, id::Integer, value::Integer)
return KN_set_int_param(m, id, value)
end
function KN_set_param(m::Model, param::AbstractString, value::Integer)
return KN_set_int_param_by_name(m, param, value)
end
function KN_set_param(m::Model, id::Integer, value::Cdouble)
return KN_set_double_param(m, id, value)
end
function KN_set_param(m::Model, param::AbstractString, value::Cdouble)
return KN_set_double_param_by_name(m, param, value)
end
function KN_set_param(m::Model, id::Integer, value::AbstractString)
return KN_set_char_param(m, id, value)
end
function KN_set_param(m::Model, param::AbstractString, value::AbstractString)
return KN_set_char_param_by_name(m, param, value)
end
function KN_get_int_param(m::Model, id::Integer)
res = Cint[0]
KN_get_int_param(m, id, res)
return res[1]
end
function KN_get_int_param(m::Model, param::AbstractString)
res = Cint[0]
KN_get_int_param_by_name(m, param, res)
return res[1]
end
function KN_get_double_param(m::Model, id::Integer)
res = Cdouble[0.0]
KN_get_double_param(m, id, res)
return res[1]
end
function KN_get_double_param(m::Model, param::AbstractString)
res = Cdouble[0.0]
KN_get_double_param_by_name(m, param, res)
return res[1]
end
function KN_get_param_name(m::Model, id::Integer)
output_size = 128
res = " "^output_size
KN_get_param_name(m, id, res, output_size)
return _format_output(res)
end
function KN_get_param_doc(m::Model, id::Integer)
output_size = 128
res = " "^output_size
KN_get_param_doc(m, id, res, output_size)
return _format_output(res)
end
function KN_get_param_type(m::Model, id::Integer)
res = Cint[0]
KN_get_param_type(m, id, res)
return res[1]
end
function KN_get_num_param_values(m::Model, id::Integer)
res = Cint[0]
KN_get_num_param_values(m, id, res)
return res[1]
end
function KN_get_param_value_doc(m::Model, id::Integer, value_id::Integer)
output_size = 128
res = " "^output_size
KN_get_param_value_doc(m, id, value_id, res, output_size)
return _format_output(res)
end
function KN_get_param_id(m::Model, name::AbstractString)
res = Cint[0]
KN_get_param_id(m, name, res)
return res[1]
end
#=
NAMES
=#
function KN_get_var_names(m::Model, max_length=1024)
return String[
KN_get_var_names(m, Cint(id - 1), max_length) for id in 1:KN_get_number_vars(m)
]
end
function KN_get_var_names(m::Model, index::Vector{Cint}, max_length=1024)
return String[KN_get_var_names(m, id, max_length) for id in index]
end
function KN_get_var_names(m::Model, index::Cint, max_length=1024)
rawname = zeros(Cchar, max_length)
ret = KN_get_var_name(m, index, max_length, rawname)
return String(strip(String(convert(Vector{UInt8}, rawname)), '\0'))
end
function KN_get_con_names(m::Model, max_length=1024)
return String[
KN_get_con_names(m, Cint(id - 1), max_length) for id in 1:KN_get_number_cons(m)
]
end
function KN_get_con_names(m::Model, index::Vector{Cint}, max_length=1024)
return String[KN_get_con_names(m, id, max_length) for id in index]
end
function KN_get_con_names(m::Model, index::Cint, max_length=1024)
rawname = zeros(Cchar, max_length)
ret = KN_get_con_name(m, index, max_length, rawname)
return String(strip(String(convert(Vector{UInt8}, rawname)), '\0'))
end
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] | 2.219527 | 9,935 |
using CartesianFDM
using Test
@testset "1D identities" begin
n = (5,)
top = (periodic(),)
ctx = cartesianfdmcontext(top, n)
# assumes 1D
((δ⁻,), (δ⁺,)) = getproperty(ctx, :δ)
((σ⁻,), (σ⁺,)) = getproperty(ctx, :σ)
((τ⁻,), (τ⁺,)) = getproperty(ctx, :τ)
Ψ, Φ = scalar(:Ψ, n), scalar(:Φ, n)
@testset "2x identity 53" begin
id = (σ⁺ * (δ⁻ * Φ)) .- (δ⁺ * (σ⁻ * Φ))
@test all(iszero, Symbolics.expand.(id))
id = (σ⁻ * (δ⁺ * Φ)) .- (δ⁻ * (σ⁺ * Φ))
@test all(iszero, Symbolics.expand.(id))
end
@testset "2x identity 56" begin
id = (σ⁺ * (Ψ .* (δ⁻ * Φ))) .+ (2Φ .* (δ⁺ * Ψ)) .- (δ⁺ * (Ψ .* (σ⁻ * Φ)))
@test all(iszero, expand.(id))
id = (σ⁻ * (Ψ .* (δ⁺ * Φ))) .+ (2Φ .* (δ⁻ * Ψ)) .- (δ⁻ * (Ψ .* (σ⁺ * Φ)))
@test all(iszero, expand.(id))
end
@testset "2x identity 57" begin
id = (Φ .* ((δ⁺ * (Ψ .* (σ⁻ * Φ))) .+ (σ⁺ * (Ψ .* (δ⁻ * Φ))))) .-
(δ⁺ * (Ψ .* ((Φ .* (τ⁻ * Φ)) .+ ((τ⁻ * Φ) .* Φ))))
@test all(iszero, expand.(id))
id = (Φ .* ((δ⁻ * (Ψ .* (σ⁺ * Φ))) .+ (σ⁻ * (Ψ .* (δ⁺ * Φ))))) .-
(δ⁻ * (Ψ .* ((Φ .* (τ⁺ * Φ)) .+ ((τ⁺ * Φ) .* Φ))))
@test all(iszero, expand.(id))
end
@testset "2x identity 59" begin
id = (((σ⁺ * Ψ) .* (δ⁺ * Φ)) .+ ((σ⁺ * Φ) .* (δ⁺ * Ψ))) - (2δ⁺ * (Ψ .* Φ))
@test all(iszero, expand.(id))
id = (((σ⁻ * Ψ) .* (δ⁻ * Φ)) .+ ((σ⁻ * Φ) .* (δ⁻ * Ψ))) - (2δ⁻ * (Ψ .* Φ))
@test all(iszero, expand.(id))
end
end
@testset "star stencil" begin
using SparseArrays
n = (3, 4, 5)
top = (periodic(), nonperiodic(), periodic())
ctx = cartesianfdmcontext(top, n)
(τ⁻, τ⁺) = getproperty(ctx, :τ)
X = scalar(:X, n)
###
F = rand(prod(n)) .* X
for i in eachindex(top)
F .+= τ⁻[i] * (rand(prod(n)) .* X)
end
for i in eachindex(top)
F .+= τ⁺[i] * (rand(prod(n)) .* X)
end
###
sym = linearize(star, ctx, F, X)
num = Dict{Int,Vector{Float64}}()
for (key, val) in sym
num[key] = eval(first(build_function(val)))()
end
A = spdiagm(num...)
###
@test all(iszero, F .- A * X)
end
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] | 1.681159 | 1,311 |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
module PolicyAssignmentsClient
using Random
using Dates
using Swagger
import Swagger: field_name, property_type, hasproperty, validate_property, SwaggerApi, SwaggerModel
import Base: convert, propertynames
include("modelincludes.jl")
include("api_PolicyAssignmentsApi.jl")
# export models
export convert, CloudError
export convert, ErrorAdditionalInfo
export convert, ErrorResponse
export convert, Identity
export convert, ParameterValues
export convert, ParameterValuesValue
export convert, PolicyAssignment
export convert, PolicyAssignmentListResult
export convert, PolicyAssignmentProperties
export convert, PolicySku
# export operations
export convert, PolicyAssignmentsApi
export check_required, field_name, property_type, hasproperty, propertynames, validate_property, convert
end
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] | 4.082969 | 229 |
###Cargar datos
using TopicModelsVB
import Distributions.sample
#Arreglar direccion
corp = readcorp(:mac)
corp.docs = vcat([sample(filter(doc -> round(doc.stamp / 100) == y, corp.docs), 400, replace=false) for y in 1984:2005]...)
fixcorp!(corp, abr=100, len=10) # Remove words which appear < 100 times and documents of length < 10.
basemodel = LDA(corp, 9)
train!(basemodel, iter=150, chkelbo=151)
# training...
model = DTM(corp, 9, 200, basemodel)
train!(model, iter=10) # This will likely take about an hour on a personal computer.
# Convergence for all other models is worst-case quadratic,
# while DTM convergence is linear or at best super-linear.
# training...
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] | 2.582734 | 278 |
using GNSSTools
using FFTW
using AllanDeviations
using Statistics
# using LsqFit
# using NNLS
using PyPlot
pygui(true)
# function psd_at_f(h_parms, f, v_0)
# h₋₂, h₋₁, h₀, h₁, h₂ = h_parms
# return v_0^2 * (h₋₂*f^(-4) + h₋₁*f^(-3) + h₀*f^(-2) + h₁*f^(-1) + h₂*f^0)
# end
# function fit_h_parms_to_signal_PSD(psd_dBc, f_c=10e6,
# freqs=[1,10,100,1e3,1e4,1e5])
# N = length(freqs)
# @assert N == length(psd_dBc)
# b = zeros(N-1)
# A = zeros(N-1)
# for i in 1:N-1
# j = N - i
# A[i] = (psd_dBc[j] + psd_dBc[j+1])/2 + 10*log10(freqs[j+1] - freqs[j])
# if i == 1
# b[i] = 2*10^(A[i]/10) / (freqs[j+1] - freqs[j]) # b₀
# elseif i == 2
# b[i] = 2*10^(A[i]/10) / log(freqs[j+1] - freqs[j]) # b₋₁
# else
# b[i] = 2*10^(A[i]/10) / ((2-i)*(freqs[j+1]^(2-i) - freqs[j]^(2-i)))
# end
# end
# # order is h2, h1, h0, h-1, h-2
# h = b ./ f_c.^2
# # reverse order to h-2, h-1, h₀, h₁, h₂
# return reverse(h)
# end
function calc_Sy(x, f_s)
N = length(x)
X = fft(x)[1:floor(Int, N/2)+1]
X = (1/(f_s*N)) .* abs2.(X)
X[2:end-1] .= 2 .* X[2:end-1]
X_log10 = 10 .* log10.(X)
frequencies = Array(range(0, f_s/2, length=length(X)))
return (X, X_log10, frequencies)
end
# f_s = 36e6
f_carrier1 = 16.3676e6
f_carrier2 = 10e6
f_s = 1*f_carrier1
t_length = 1
N = floor(Int, t_length*f_s)
N_over_2 = floor(Int, N/2)
t = Array(range(0, t_length, length=N))
frequencies = Array(range(0, f_s/2, length=N_over_2+1))
# carrier1 = cis.(2π.*f_carrier1.*t)
# carrier2 = cis.(2π.*f_carrier2.*t)
phase_noise = zeros(Complex{Float64}, N)
# signal = zeros(Complex{Float64}, N)
dBc_freq = [1., 1e1, 1e2, 1e3, 1e4]
X = zeros(Complex{Float64}, N_over_2+1)
x = zeros(Complex{Float64}, N)
rakon_it5300B = Dict("h_parms"=>[1.9e-6, 2e-6, 9.7e-8, 9.1e-11, 9.9e-15],
"dBc"=>[-57, -88, -112, -130, -140],
"dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4],
"name"=>"TCXO, Rakon IT5300B",
"f_0"=>f_carrier1)
agilent_e4424b = Dict("h_parms"=>[2.8e-9, 1.8e-11, 1.2e-10, 1e-11, 1.8e-15],
"dBc"=>[-88, -118, -128, -135, -147],
"dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4],
"name"=>"OCXO, Agilent E4424B",
"f_0"=>10e6)
isotemp_91_1 = Dict("h_parms"=>[2.2e-9, 1.6e-10, 1.6e-10, 7.1e-13, 7.2e-16],
"dBc"=>[-89, -120, -140, -151, -154],
"dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4],
"name"=>"OCXO, ISOTEMP 91-1",
"f_0"=>f_carrier2)
oscilloquartz_8607 = Dict("h_parms"=>[3.9e-14, 8.7e-13, 2.2e-14, 3.2e-13, 6.4e-15],
"dBc"=>[-122, -137, -143, -145, -145],
"dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4],
"name"=>"OCXO, Oscilloquartz 8607",
"f_0"=>f_carrier2)
srs_prs10 = Dict("h_parms"=>[5.3e-11, 4.7e-11, 1.6e-15, 9.6e-14, 9.9e-16],
"dBc"=>[-103, -135, -150, -152, -153],
"dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4],
"name"=>"Rubidum, SRS PRS10",
"f_0"=>f_carrier2)
cesium = Dict("h_parms"=>[1.5e-12, 6.2e-11, 7.6e-23, 1.4e-14, 6.2e-19],
"dBc"=>[-105, -135, -160, -170, -190],
"dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4],
"name"=>"Cesium, Typical Values",
"f_0"=>f_carrier2)
mti_250l = Dict("h_parms"=>[1.7e-9, 1e-10, 1.6e-10, 3.5e-13, 1.8e-16],
"dBc"=>[-90, -120, -140, -155, -160],
"dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4],
"name"=>"OCXO, MTI 250L",#, Low g-sensitivity",
"f_0"=>f_carrier2)
oscillators = [rakon_it5300B, agilent_e4424b, isotemp_91_1, oscilloquartz_8607,
srs_prs10, cesium, mti_250l]
# figsize = (6.5, 4)
colorz = ["k", "b", "g", "r", "y", "m", "c", "gray"]
fig = figure(figsize=(6.5,4))
ax1 = fig.add_subplot(1,1,1)
legend_markers = []
marker_names = []
for i in 1:length(oscillators)
# for i in 1:1
color = colorz[i]
oscillator = oscillators[i]
if oscillator["name"] == "TCXO, Rakon IT5300B"
# carrier = carrier1
f_carrier = f_carrier1
else
# carrier = carrier2
f_carrier = f_carrier2
end
h_parms = oscillator["h_parms"] ./ oscillator["f_0"]^2
generate_phase_noise!(phase_noise, t_length, f_carrier, h_parms)
# ϕ = real.(phase_noise)
# signal = carrier .* cis.(ϕ)
# signal .= carrier .* cis.(real.(phase_noise))
# x = signal
x .= real.(phase_noise) .+ 0im
# X, X_log, freqs = calc_Sy(x, f_s)
fft!(x);
X .= abs2.(x[1:N_over_2+1]) / (f_s*N);
X[2:end-1] .= 2 .* X[2:end-1];
X .= 10 .* log10.(X);
# X_max = maximum(real.(X));
# X = X .- X_max;
# ax1.plot(frequencies[2:end] .- f_carrier, X[2:end], color,
# label=string(oscillator["name"], " from h Parms"))
ax1.plot(frequencies[2:end], real.(X[2:end]), color)#),
# label=oscillator["name"])
# ax1.plot(frequencies[2:end], real.(X[2:end]), color,
# label=string(oscillator["name"], " from h Parms"))
ax1.scatter(dBc_freq, oscillator["dBc"].+(10*log10(2)),
c=color)#, label=string(oscillator["name"], " Truth"))
legend_marker = plt.Line2D((0,1),(0,0), color=color, marker="o", linestyle="-")
push!(legend_markers, legend_marker)
push!(marker_names, oscillator["name"])
end
xscale("log")
xlim([1, 1e6])
xlabel("Frequency (Hz)")
ylabel("dBc/Hz")
subplots_adjust(top=0.96, left=0.12, right=0.88, bottom=0.13)
ax1.legend(legend_markers, marker_names)
# savefig("figures/ch2_oscillator_psds.pdf", dpi=300) | [
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62,
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28,
15,
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28,
15,
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16,
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62,
862,
9310,
13,
12315,
1600,
288,
14415,
28,
6200,
8
] | 1.627456 | 3,562 |
"""
```
measurement(m::PoolModel{T}) where {T<:AbstractFloat}
```
Assign measurement equation for Dynamic Pools
```
p(y_t) = λ_t * p_1 + (1 - λ_t) * p_2
```
where
```
λ_t = weight assigned to model 1
p_1 = predictive density according to model 1
p_2 = predictive density according to model 2
```
Equal weights fixes λ = 1/2, and static weights computes the likelihood
as if λ is time-invariant. For Bayesian Model Averaging,
we compute the average according to weights computed according to BMA.
The outputs are a function that computes the average of
passed predictive densities and a degenerate measurement error Distribution.
"""
function measurement(m::PoolModel{T}) where {T<:AbstractFloat}
obs = m.observables
weight_type = get_setting(m, :weight_type)
# Assumes λ to be weight on the first model
F_u = DiscreteUniform(0,0)
if weight_type == :dynamic
Ψ_dynamic_pm(x::Vector{Float64}, data::Vector{Float64}) = dot(data, x)
return Ψ_dynamic_pm, F_u
elseif weight_type == :equal
equal_wt = m[:λ].value
Ψ_equal_pm(x::Vector{Float64}, data::Vector{Float64}) = dot(data, equal_wt .* ones(2))
return Ψ_equal_pm, F_u
elseif weight_type == :static
Ψ_static_pm(x::Vector{Float64}, data::Vector{Float64}) = dot(data, x)
return Ψ_static_pm, F_u
elseif weight_type == :bma
Ψ_bma_pm(x::Vector{Float64}, data::Vector{Float64}) = dot(data, x)
return Ψ_bma_pm, F_u
end
end
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11,
376,
62,
84,
198,
220,
220,
220,
886,
198,
437,
198
] | 2.520478 | 586 |
using ExecutableSpecifications: @given, @when, @then, @expect
@given "some precondition which does nothing" begin
end
@when "we perform a no-op step" begin
end
@then "the scenario as a whole succeeds" begin
end
@when "a step has more than one matching step definition" begin
end
@when "a step has more than one matching step definition" begin
end
@when "foo is set to value 42" begin
context[:foo] = 42
end
@when "foo is set to value -17" begin
context[:foo] = -17
end
@then "foo is greater than zero" begin
@expect context[:foo] > 0
end
@then "foo is less than zero" begin
@expect context[:foo] < 0
end | [
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2488,
1069,
806,
4732,
58,
25,
21943,
60,
1279,
657,
198,
437
] | 2.930556 | 216 |
using MakieLayout
using Makie
begin
scene = Scene(resolution = (1000, 1000));
screen = display(scene)
campixel!(scene);
maingl = GridLayout(scene, 1, 3;
addedcolgaps = Fixed(0),
alignmode = Outside(30)
)
maingl[1, 2] = LAxis(scene)
maingl[1, 3] = LAxis(scene)
guigl = maingl[1, 1] = GridLayout(1, 3; height=Auto(false))
# guigl[1, 1] = LButton(scene, 100, 30, "button")
guigl[1, 1] = LText(scene, text="HelloWorld", halign=:left)
guigl[1, 2] = LText(scene, text="Blablo", halign=:left)
guigl[1, 3] = LButton(scene, width = 120, height = 30, label = "Bliblu")
guigl[2, 1] = LText(scene, text="Mamama", halign=:left)
guigl[2, 2] = LText(scene, text="Mimimi", halign=:left)
guigl[2, 3] = LButton(scene, width = 120, height = 30, label = "Momomo")
guigl[3, 1] = LText(scene, text="VeerrryLoong", halign=:left)
guigl[3, 2] = LText(scene, text="Blablo", halign=:left)
guigl[3, 3] = LButton(scene, width = 120, height = 30, label = "Short")
end
| [
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11,
6167,
796,
366,
16438,
4943,
198,
198,
437,
198
] | 2.238095 | 462 |
module day11
import ..aoc19: run_program!
using DelimitedFiles, UnicodePlots, REPL
readInput() = parse.(Int, split(readline(joinpath(@__DIR__,"input.txt")), ','))
const UP = (1, 0)
const RIGHT = (0, 1)
const DOWN = (-1, 0)
const LEFT = (0, -1)
turnLeft(d) = d == UP ? LEFT : (d == RIGHT ? UP : (d == DOWN ? RIGHT : DOWN))
turnRight(d) = d == UP ? RIGHT : (d == RIGHT ? DOWN : (d == DOWN ? LEFT : UP))
function part1(data)
pos = (0, 0)
dir = UP
painted = Dict{Tuple{Int, Int}, Int}()
input = Channel{Int}(Inf)
output = Channel{Int}(Inf)
task = @async run_program!(data, input, output)
while !istaskdone(task)
put!(input, get(painted, pos, 0))
color = take!(output)
turn = take!(output)
painted[pos] = color # paint
dir = turn == 0 ? turnLeft(dir) : turnRight(dir) # turn
pos = pos .+ dir # move forward
end
length(painted)
end
function part2(data)
pos = (0, 0)
dir = UP
painted = Dict((0, 0) => 1)
input = Channel{Int}(Inf)
output = Channel{Int}(Inf)
task = @async run_program!(data, input, output)
while !istaskdone(task)
put!(input, get(painted, pos, 0))
color = take!(output)
turn = take!(output)
painted[pos] = color # paint
dir = turn == 0 ? turnLeft(dir) : turnRight(dir) # turn
pos = pos .+ dir # move forward
end
white = filter(p -> p.second == 1, painted)
scatterplot(last.(keys(white)), first.(keys(white)), xlim=extrema(last.(keys(painted))),
ylim=extrema(first.(keys(painted))), height = 5, width = 70, color = :blue, canvas = BlockCanvas)
end
end | [
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198,
437,
198,
198,
437
] | 2.36 | 700 |
using Documenter, GMT
makedocs(
modules = [GMT],
format = :html,
sitename = "GMT",
pages = [
"Home" => "index.md",
"Some examples" => "examples.md",
"Draw rectangles examples" => "rectangles.md",
"Draw frames examples" => "frames.md",
"Manual" => [
"usage.md",
"monolitic.md",
"modules.md",
],
"The GMT types" => "types.md"
],
html_prettyurls = true,
)
deploydocs(
repo = "github.com/GenericMappingTools/GMT.jl.git",
target = "build",
julia = "0.7",
deps = nothing,
make = nothing
) | [
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220,
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220,
787,
220,
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] | 2.019355 | 310 |
using Plots
anim = @animate for i in 1:10
plot(t->sinpi(t+i/5), range(0, 2, length=100))
end
gif(anim, "test_animation.gif", fps=40) | [
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mutable struct TrackedReal{T<:Real}
data::T
grad::Union{Nothing,T}
children::Dict
# this field is only need for printing the graph. you can safely remove it.
name::String
end
track(x::Real,name="") = TrackedReal(x,nothing,Dict(),name)
function Base.show(io::IO, x::TrackedReal)
t = isempty(x.name) ? "(tracked)" : "(tracked $(x.name))"
print(io, "$(x.data) $t")
end
function accum!(x::TrackedReal)
if isnothing(x.grad)
x.grad = sum(accum!(v)*w for (v,w) in x.children)
end
x.grad
end
function gradient(f, args::Real...)
ts = track.(args)
y = f(ts...)
y.grad = 1.0
accum!.(ts)
end
########## RULES #############################################################
function Base.:*(a::TrackedReal, b::TrackedReal)
z = track(a.data * b.data, "*")
a.children[z] = b.data # dz/da=b
b.children[z] = a.data # dz/db=a
z
end
function Base.:+(a::TrackedReal{T}, b::TrackedReal{T}) where T
z = track(a.data + b.data, "+")
a.children[z] = one(T)
b.children[z] = one(T)
z
end
function Base.sin(x::TrackedReal)
z = track(sin(x.data), "sin")
x.children[z] = cos(x.data)
z
end
########## Optimizion 2D function ############################################
using Plots
g(x,y) = y*y + sin(x)
cscheme = cgrad(:RdYlBu_5, rev=true)
p1 = contour(-4:0.1:4, -2:0.1:2, g, fill=true, c=cscheme, xlabel="x", ylabel="y")
display(p1)
function descend(f::Function, λ::Real, args::Real...)
Δargs = gradient(f, args...)
args .- λ .* Δargs
end
function minimize(f::Function, args::T...; niters=20, λ=0.01) where T<:Real
paths = ntuple(_->Vector{T}(undef,niters), length(args))
for i in 1:niters
args = descend(f, λ, args...)
@info f(args...)
for j in 1:length(args)
paths[j][i] = args[j]
end
end
paths
end
xs1, ys1 = minimize(g, 1.5, -2.4, λ=0.2, niters=34)
xs2, ys2 = minimize(g, 1.8, -2.4, λ=0.2, niters=16)
scatter!(p1, [xs1[1]], [ys1[1]], markercolor=:black, marker=:star, ms=7, label="Minimum")
scatter!(p1, [xs2[1]], [ys2[1]], markercolor=:black, marker=:star, ms=7, label=false)
scatter!(p1, [-π/2], [0], markercolor=:red, marker=:star, ms=7, label="Initial Point")
scatter!(p1, xs1[1:1], ys1[1:1], markercolor=:black, label="GD Path", xlims=(-4,4), ylims=(-2,2))
anim = @animate for i in 1:max(length(xs1), length(xs2))
if i <= length(xs1)
scatter!(p1, xs1[1:i], ys1[1:i], mc=:black, lw=3, xlims=(-4,4), ylims=(-2,2), label=false)
end
if i <= length(xs2)
scatter!(p1, xs2[1:i], ys2[1:i], mc=:black, lw=3, label=false)
end
p1
end
gif(anim, "gd-path.gif", fps=15)
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] | 2.108215 | 1,266 |
struct ConfusionMatrix
tp::Int
fp::Int
fn::Int
tn::Int
end
function getConfusionMatrix(Δ::AbstractMatrix{T}, hΔ::AbstractMatrix{T}, thr=1e-4) where T
p = size(Δ, 1)
tp = fp = fn = tn = 0
for c=1:p
for r=c:p
if Δ[r, c] != zero(T)
if abs(hΔ[r, c]) > thr
# not zero estimated as not zero
tp += 1
else
# not zero estimated as zero
fn += 1
end
else
if abs(hΔ[r, c]) > thr
# zero estimated as not zero
fp += 1
else
# zero estimated as zero
tn += 1
end
end
end
end
ConfusionMatrix(tp, fp, fn, tn)
end
function getConfusionMatrix(Δ::AbstractMatrix{T}, hΔ::SparseIterate{T}, thr=1e-4) where T
p = size(Δ, 1)
tp = fp = fn = tn = 0
for c=1:p
for r=c:p
if Δ[r, c] != zero(T)
if abs(hΔ[sub2indLowerTriangular(p, r, c)]) > thr
# not zero estimated as not zero
tp += 1
else
# not zero estimated as zero
fn += 1
end
else
if abs(hΔ[sub2indLowerTriangular(p, r, c)]) > thr
# zero estimated as not zero
fp += 1
else
# zero estimated as zero
tn += 1
end
end
end
end
ConfusionMatrix(tp, fp, fn, tn)
end
function tpr(t::ConfusionMatrix)
dn = t.tp + t.fn
dn > 0 ? t.tp / dn : 0.
end
function fpr(t::ConfusionMatrix)
dn = t.fp + t.tn
dn > 0 ? t.fp / dn : 0.
end
function precision(t::ConfusionMatrix)
dn = t.tp + t.fp
dn > 0 ? t.tp / dn : 1.
end
recall(t::ConfusionMatrix) = tpr(t)
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7,
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18546,
4241,
46912,
8,
198,
220,
288,
77,
796,
256,
13,
34788,
1343,
256,
13,
46428,
198,
220,
288,
77,
1875,
657,
5633,
256,
13,
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1220,
288,
77,
1058,
352,
13,
198,
437,
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439,
7,
83,
3712,
18546,
4241,
46912,
8,
796,
256,
1050,
7,
83,
8,
198
] | 1.884211 | 855 |
using Clang
using Clang.Generators
cd(@__DIR__)
# current version checked in is v2.2.9
include_dir = normpath(joinpath(@__DIR__, "RadeonProRenderSDK", "RadeonProRender", "inc"))
# LIBCLANG_HEADERS are those headers to be wrapped.
headers = joinpath.(include_dir, [
"RadeonProRender_v2.h",
# "RadeonProRender_GL.h",
# "RadeonProRender_VK.h"
])
# wrapper generator options
options = load_options(joinpath(@__DIR__, "rpr.toml"))
# add compiler flags, e.g. "-DXXXXXXXXX"
args = get_default_args()
push!(args, "-I$include_dir")
ctx = create_context(headers, args, options)
# run generator
build!(ctx, BUILDSTAGE_NO_PRINTING)
function rewrite!(e::Expr)
if Meta.isexpr(e, :function)
func_args = e.args[1].args
last_arg = func_args[end]
body = e.args[2]
ccall_expr = body.args[end]
type_tuple = ccall_expr.args[4]
new_body = if startswith(string(last_arg), "out_")
last_type_ptr = type_tuple.args[end]
@assert Meta.isexpr(last_type_ptr, :curly)
@assert last_type_ptr.args[1] == :Ptr
last_type = last_type_ptr.args[2]
pop!(func_args) # remove from input args
:($(last_arg) = Ref{$(last_type)}(); check_error($(ccall_expr)); return $(last_arg)[])
elseif ccall_expr.args[3] == :rpr_status
:(check_error($(ccall_expr)))
else
ccall_expr
end
e.args[2] = new_body
end
return e
end
function rewrite!(dag::ExprDAG)
for node in get_nodes(dag)
for expr in get_exprs(node)
rewrite!(expr)
end
end
end
rewrite!(ctx.dag)
quote
function check_error(error_code)
error_code == RPR_SUCCESS && return
return error("Error code returned: $(error_code)")
end
end
cd(@__DIR__)
build!(ctx, BUILDSTAGE_PRINTING_ONLY)
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] | 2.139723 | 866 |
using MathProgBase
########################################################
## this code is based on ModelReader in NLPModels
## and KNITRO.jl
########################################################
export OnePhaseSolver #, OnePhaseMathProgModel, NonlinearModel
mutable struct OnePhaseSolver <: MathProgBase.AbstractMathProgSolver
options
end
OnePhaseSolver(;kwargs...) = OnePhaseSolver(kwargs)
type OnePhaseProblem
status::Symbol # Final status
# For MathProgBase
x::Vector{Float64} # Starting and final solution
lambda::Vector{Float64}
g::Vector{Float64} # Final constraint values
obj_val::Float64 # (length 1) Final objective
solve_time::Float64
# Custom attributes of the OnePhaseSolver
iter::Class_iterate
hist::Array{alg_history2,1}
pars::Class_parameters
function OnePhaseProblem()
return new()
end
end
########################################################
## BEGIN ModelReader CODE (with minor edits)
########################################################
mutable struct OnePhaseMathProgModel <: MathProgBase.AbstractMathProgModel
options
inner::OnePhaseProblem
eval :: Union{MathProgBase.AbstractNLPEvaluator, Void}
numVar :: Int
numConstr :: Int
x :: Vector{Float64}
y :: Vector{Float64}
lvar :: Vector{Float64}
uvar :: Vector{Float64}
lcon :: Vector{Float64}
ucon :: Vector{Float64}
sense :: Symbol
status :: Symbol
end
MathProgBase.NonlinearModel(solver :: OnePhaseSolver) = OnePhaseMathProgModel(solver.options,OnePhaseProblem(),nothing,
0,
0,
Float64[],
Float64[],
Float64[],
Float64[],
Float64[],
Float64[],
:Min,
:Uninitialized)
function MathProgBase.loadproblem!(m :: OnePhaseMathProgModel,
numVar, numConstr,
l, u, lb, ub,
sense,
eval :: MathProgBase.AbstractNLPEvaluator)
# TODO: :JacVec is not yet available.
# [:Grad, :Jac, :JacVec, :Hess, :HessVec, :ExprGraph]
MathProgBase.initialize(eval, [:Grad, :Jac, :Hess, :HessVec, :ExprGraph])
m.numVar = numVar
m.numConstr = numConstr
m.x = zeros(numVar)
m.y = zeros(numConstr)
m.eval = eval
m.lvar = l
m.uvar = u
m.lcon = lb
m.ucon = ub
m.sense = sense
end
MathProgBase.setwarmstart!(m :: OnePhaseMathProgModel, x) = (m.x = x)
MathProgBase.status(m :: OnePhaseMathProgModel) = m.inner.status
MathProgBase.getsolution(m :: OnePhaseMathProgModel) = m.inner.x
MathProgBase.getobjval(m :: OnePhaseMathProgModel) = m.inner.iter.cache.fval
#MathProgBase.eval_f(m.eval, m.x)
mutable struct MathProgNLPModel <: AbstractNLPModel
meta :: NLPModelMeta
mpmodel :: OnePhaseMathProgModel
counters :: Counters # Evaluation counters.
jrows :: Vector{Int} # Jacobian sparsity pattern.
jcols :: Vector{Int}
jvals :: Vector{Float64} # Room for the constraints Jacobian.
hrows :: Vector{Int} # Hessian sparsity pattern.
hcols :: Vector{Int}
hvals :: Vector{Float64} # Room for the Lagrangian Hessian.
end
"Construct a `MathProgNLPModel` from a `OnePhaseMathProgModel`."
function MathProgNLPModel(mpmodel :: OnePhaseMathProgModel; name :: String="Generic")
nvar = mpmodel.numVar
lvar = mpmodel.lvar
uvar = mpmodel.uvar
nlin = length(mpmodel.eval.m.linconstr) # Number of linear constraints.
nquad = length(mpmodel.eval.m.quadconstr) # Number of quadratic constraints.
nnln = length(mpmodel.eval.m.nlpdata.nlconstr) # Number of nonlinear constraints.
ncon = mpmodel.numConstr # Total number of constraints.
lcon = mpmodel.lcon
ucon = mpmodel.ucon
jrows, jcols = MathProgBase.jac_structure(mpmodel.eval)
hrows, hcols = MathProgBase.hesslag_structure(mpmodel.eval)
nnzj = length(jrows)
nnzh = length(hrows)
meta = NLPModelMeta(nvar,
x0=mpmodel.x,
lvar=lvar,
uvar=uvar,
ncon=ncon,
y0=zeros(ncon),
lcon=lcon,
ucon=ucon,
nnzj=nnzj,
nnzh=nnzh,
lin=collect(1:nlin), # linear constraints appear first in MPB
nln=collect(nlin+1:ncon),
minimize=(mpmodel.sense == :Min),
islp=MathProgBase.isobjlinear(mpmodel.eval) & (nlin == ncon),
name=name,
)
return MathProgNLPModel(meta,
mpmodel,
Counters(),
jrows,
jcols,
zeros(nnzj), # jvals
hrows,
hcols,
zeros(nnzh), # hvals
)
end
##
import Base.show
show(nlp :: MathProgNLPModel) = show(nlp.mpmodel)
function obj(nlp :: MathProgNLPModel, x :: Array{Float64})
NLPModels.increment!(nlp, :neval_obj)
return MathProgBase.eval_f(nlp.mpmodel.eval, x)
end
function grad(nlp :: MathProgNLPModel, x :: Array{Float64})
g = zeros(nlp.meta.nvar)
return grad!(nlp, x, g)
end
function grad!(nlp :: MathProgNLPModel, x :: Array{Float64}, g :: Array{Float64})
NLPModels.increment!(nlp, :neval_grad)
MathProgBase.eval_grad_f(nlp.mpmodel.eval, g, x)
return g
end
function cons(nlp :: MathProgNLPModel, x :: Array{Float64})
c = zeros(nlp.meta.ncon)
return cons!(nlp, x, c)
end
function cons!(nlp :: MathProgNLPModel, x :: Array{Float64}, c :: Array{Float64})
NLPModels.increment!(nlp, :neval_cons)
MathProgBase.eval_g(nlp.mpmodel.eval, c, x)
return c
end
function jac_coord(nlp :: MathProgNLPModel, x :: Array{Float64})
NLPModels.increment!(nlp, :neval_jac)
MathProgBase.eval_jac_g(nlp.mpmodel.eval, nlp.jvals, x)
return (nlp.jrows, nlp.jcols, nlp.jvals)
end
function jac(nlp :: MathProgNLPModel, x :: Array{Float64})
return sparse(jac_coord(nlp, x)..., nlp.meta.ncon, nlp.meta.nvar)
end
function jprod(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64})
Jv = zeros(nlp.meta.ncon)
return jprod!(nlp, x, v, Jv)
end
function jprod!(nlp :: MathProgNLPModel,
x :: Array{Float64},
v :: Array{Float64},
Jv :: Array{Float64})
nlp.counters.neval_jac -= 1
NLPModels.increment!(nlp, :neval_jprod)
Jv[:] = jac(nlp, x) * v
return Jv
end
function jtprod(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64})
Jtv = zeros(nlp.meta.nvar)
return jtprod!(nlp, x, v, Jtv)
end
function jtprod!(nlp :: MathProgNLPModel,
x :: Array{Float64},
v :: Array{Float64},
Jtv :: Array{Float64})
nlp.counters.neval_jac -= 1
NLPModels.increment!(nlp, :neval_jtprod)
Jtv[1:nlp.meta.nvar] = jac(nlp, x)' * v
return Jtv
end
# Uncomment if/when :JacVec becomes available in MPB.
# "Evaluate the Jacobian-vector product at `x`."
# function jprod(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64})
# jv = zeros(nlp.meta.ncon)
# return jprod!(nlp, x, v, jv)
# end
#
# "Evaluate the Jacobian-vector product at `x` in place."
# function jprod!(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}, jv :: Array{Float64})
# NLPModels.increment!(nlp, :neval_jprod)
# MathProgBase.eval_jac_prod(nlp.mpmodel.eval, jv, x, v)
# return jv
# end
#
# "Evaluate the transposed-Jacobian-vector product at `x`."
# function jtprod(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64})
# jtv = zeros(nlp.meta.nvar)
# return jtprod!(nlp, x, v, jtv)
# end
#
# "Evaluate the transposed-Jacobian-vector product at `x` in place."
# function jtprod!(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}, jtv :: Array{Float64})
# NLPModels.increment!(nlp, :neval_jtprod)
# MathProgBase.eval_jac_prod_t(nlp.mpmodel.eval, jtv, x, v)
# return jtv
# end
function hess_coord(nlp :: MathProgNLPModel, x :: Array{Float64};
obj_weight :: Float64=1.0, y :: Array{Float64}=zeros(nlp.meta.ncon))
NLPModels.increment!(nlp, :neval_hess)
MathProgBase.eval_hesslag(nlp.mpmodel.eval, nlp.hvals, x, obj_weight, y)
return (nlp.hrows, nlp.hcols, nlp.hvals)
end
function hess(nlp :: MathProgNLPModel, x :: Array{Float64};
obj_weight :: Float64=1.0, y :: Array{Float64}=zeros(nlp.meta.ncon))
return sparse(hess_coord(nlp, x, y=y, obj_weight=obj_weight)..., nlp.meta.nvar, nlp.meta.nvar)
end
function hprod(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64};
obj_weight :: Float64=1.0, y :: Array{Float64}=zeros(nlp.meta.ncon))
hv = zeros(nlp.meta.nvar)
return hprod!(nlp, x, v, hv, obj_weight=obj_weight, y=y)
end
function hprod!(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64},
hv :: Array{Float64};
obj_weight :: Float64=1.0, y :: Array{Float64}=zeros(nlp.meta.ncon))
NLPModels.increment!(nlp, :neval_hprod)
MathProgBase.eval_hesslag_prod(nlp.mpmodel.eval, hv, x, v, obj_weight, y)
return hv
end
############################
## END ModelReader CODE
############################
function status_One_Phase_To_JuMP(status::Symbol)
# since our status are not equal to JuMPs we need to do a conversion
if status == :Optimal
return :Optimal
elseif status == :primal_infeasible
return :Infeasible
elseif status == :dual_infeasible
return :Unbounded
elseif status == :MAX_IT || status === :MAX_TIME
return :UserLimit
else
return :Error
end
end
function create_pars_JuMP(options )
pars = Class_parameters()
for (param,value) in options
what = split(String(param),"!") # we represent a parameter such as init.mu_scale as init!mu_scale because we cannot pass init.mu_scale as a parameter
node = pars # root
for i in 1:length(what)
field = what[i]
if i < length(what)
node = getfield(node,Symbol(field))
else # at the leaf
setfield!(node,Symbol(field),value)
end
end
end
return pars
end
function MathProgBase.optimize!(m :: OnePhaseMathProgModel)
t = time()
nlp = MathProgNLPModel(m)
pars = create_pars_JuMP(m.options)
iter, status, hist, t, err, timer = one_phase_solve(nlp,pars)
m.inner.status = status_One_Phase_To_JuMP(status)
m.inner.x = get_original_x(iter)
m.inner.obj_val = iter.cache.fval
m.inner.lambda = get_y(iter)
m.inner.solve_time = time() - t
# custom one phase features
m.inner.pars = pars
m.inner.iter = iter
m.inner.hist = hist
end
MathProgBase.getconstrsolution(m::OnePhaseMathProgModel) = m.inner.g
MathProgBase.getrawsolver(m::OnePhaseMathProgModel) = m.inner
MathProgBase.getsolvetime(m::OnePhaseMathProgModel) = m.inner.solve_time
function MathProgBase.getreducedcosts(m::OnePhaseMathProgModel)
return get_reducedcosts(m.inner.iter)
end
function MathProgBase.getconstrduals(m::OnePhaseMathProgModel)
return get_constrduals(m.inner.iter)
end
#setvartype!(m::OnePhaseMathProgModel, typ::Vector{Symbol}) =
# (m.varType = map(t->rev_var_type_map[t], typ))
function MathProgBase.freemodel!(m::OnePhaseMathProgModel)
# TO DO
end
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220,
220,
285,
13,
5083,
13,
79,
945,
796,
13544,
198,
220,
220,
220,
285,
13,
5083,
13,
2676,
796,
11629,
198,
220,
220,
220,
285,
13,
5083,
13,
10034,
796,
1554,
198,
437,
198,
198,
37372,
2964,
70,
14881,
13,
1136,
1102,
2536,
82,
2122,
7,
76,
3712,
3198,
35645,
37372,
2964,
70,
17633,
8,
796,
285,
13,
5083,
13,
70,
198,
37372,
2964,
70,
14881,
13,
1136,
1831,
82,
14375,
7,
76,
3712,
3198,
35645,
37372,
2964,
70,
17633,
8,
796,
285,
13,
5083,
198,
37372,
2964,
70,
14881,
13,
11407,
6442,
2435,
7,
76,
3712,
3198,
35645,
37372,
2964,
70,
17633,
8,
796,
285,
13,
5083,
13,
82,
6442,
62,
2435,
198,
198,
8818,
16320,
2964,
70,
14881,
13,
1136,
445,
19513,
15805,
82,
7,
76,
3712,
3198,
35645,
37372,
2964,
70,
17633,
8,
198,
220,
220,
220,
1441,
651,
62,
445,
19513,
15805,
82,
7,
76,
13,
5083,
13,
2676,
8,
198,
437,
198,
198,
8818,
16320,
2964,
70,
14881,
13,
1136,
1102,
2536,
646,
874,
7,
76,
3712,
3198,
35645,
37372,
2964,
70,
17633,
8,
198,
220,
220,
220,
1441,
651,
62,
1102,
2536,
646,
874,
7,
76,
13,
5083,
13,
2676,
8,
198,
437,
198,
198,
2,
2617,
85,
433,
2981,
0,
7,
76,
3712,
3198,
35645,
37372,
2964,
70,
17633,
11,
2170,
3712,
38469,
90,
13940,
23650,
30072,
796,
198,
2,
220,
220,
220,
357,
76,
13,
7785,
6030,
796,
3975,
7,
83,
3784,
18218,
62,
7785,
62,
4906,
62,
8899,
58,
83,
4357,
2170,
4008,
198,
198,
8818,
16320,
2964,
70,
14881,
13,
19503,
368,
375,
417,
0,
7,
76,
3712,
3198,
35645,
37372,
2964,
70,
17633,
8,
198,
220,
220,
220,
1303,
5390,
8410,
198,
437,
198
] | 2.087689 | 5,702 |
using Test
using Provenance
| [
3500,
6208,
198,
3500,
1041,
574,
590,
198
] | 3.5 | 8 |
Subsets and Splits