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module Exceptions
using SearchLight
export NotPersistedException, MissingDatabaseConfigurationException, DatabaseAdapterException
export UnretrievedModelException, InvalidModelException
export TypeConversionException, MissingConversionMethodException
abstract type SearchLightException <: Exception end
Base.showerror(io::IO, ex::T) where {T<:SearchLightException} = print(io, ex.msg)
struct NotPersistedException{T} <: Exception
model::T
msg
end
NotPersistedException(model) = NotPersistedException(model, "NotPersistedException: Model is not persisted \n$model")
struct MissingDatabaseConfigurationException <: Exception
msg::String
end
MissingDatabaseConfigurationException() = MissingDatabaseConfigurationException("The database configuration can not be found")
struct NotConnectedException <: Exception
msg::String
end
NotConnectedException() = NotConnectedException("SearchLight is not connected to the database")
struct DatabaseAdapterException <: Exception
msg::String
end
DatabaseAdapterException() = DatabaseAdapterException("The SearchLight database adapter has thrown an unexpected exception")
struct UnretrievedModelException{T} <: Exception
model::T
id
msg
end
UnretrievedModelException(model, id) = UnretrievedModelException(model, id, "UnretrievedModelException: the $(typeof(model)) data could not be retrieved for id $id. \nModel: \n$model")
struct InvalidModelException{T} <: Exception
model::T
errors
msg
end
InvalidModelException(model, errors) = InvalidModelException("The $(typeof(model)) model has validation errors:\n$errors")
struct TypeConversionException{T,R,X<:Exception} <: Exception
model::T
field::Symbol
value::R
exception::X
end
struct MissingConversionMethodException{T,R} <: Exception
type::T
value::R
end
end | [
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] | 3.829787 | 470 |
using LCMCore
using CaesarLCMTypes
using Rotations, CoordinateTransformations
using MeshCat
using TransformUtils
using AprilTags
vis = Visualizer()
open(vis)
setobject!(vis["home"], Triad(1.0))
detector = AprilTagDetector()
# setobject!(vis["tag01"], Triad(0.2))
# settransform!(vis, Translation(-0.5, -0.5, 0))
function tagPicture(vis::Visualizer,msg::apriltag_t, drawdict)
# m=homography_to_pose(Array{Float64,2}(msg.homography),300,300,320,240)
m = msg.pose
tkey = "tag$(msg.id)"
if !haskey(drawdict, tkey)
setobject!(vis[tkey], Triad(0.2))
drawdict[tkey] = 1
end
wTc=LinearMap(RotX(pi/2))
cTt=Translation(m[1:3,4]...) ∘ LinearMap(Quat(m[1:3,1:3]))
settransform!(vis[tkey], wTc ∘ cTt)
#settransform!(vis[tkey], cTt)
nothing
end
function typed_callback(channel::String, msg::apriltag_t, drawdict)
tagPicture(vis,msg, drawdict)
end
drawdict = Dict{String, Int}()
lcm=LCM()
subscribe(lcm, "TEST_CHANNEL", (m,c)->typed_callback(m,c,drawdict), apriltag_t)
while true
handle(lcm)
end
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@inline function discrete_derivatives(x::AbstractArray{T} where T<:Real;
d::Int=1)
@assert d ≥ 0 "discrete derivatives parameter must be greater than 0 (now it is $d)"
res = zeros(eltype(x), length(x), d+1)
@inbounds @simd for i in 1:length(x)
res[i,1] = x[i]
end
@inbounds @simd for j in 2:(d+1)
@inbounds @simd for i in 1:(length(x)-j+1)
res[i,j] = res[i+1,j-1] - res[i,j-1]
end
end
return res
end
@inline function discrete_derivative(x::AbstractArray{T} where T<:Real; d::Int=1)
derivatives = discrete_derivatives(x, d=d)
return derivatives[1:length(x)-d,d+1]
end
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] | 2.130719 | 306 |
using DFControl: Band, Point3, Vec3, Point, Mat3
using DFControl.Utils: searchdir
using DFControl: Atom, Element, Projection, element, position
import DFControl: Length
# Cleanup Do we really need <:abstractfloat, check this!
struct OperatorBlock{T <: AbstractFloat}
L::Vector{Matrix{Complex{T}}}
S::Vector{Matrix{Complex{T}}}
J::Vector{Matrix{Complex{T}}}
end
getindex(A::Matrix, a1::T , a2::T) where {T<:Union{Atom, Projection}} =
getindex(A, range(a1), range(a2))
getindex(A::Matrix, a::Atom) =
getindex(A, a, a)
getindex(A::Vector, a::Atom) =
getindex(A, range(a))
view(A::Matrix, a1::T, a2::T) where {T<:Union{Atom, Projection}} =
view(A, range(a1), range(a2))
view(A::Matrix, a::Union{Atom, Projection}) =
view(A, range(a), range(a))
view(A::Vector, a::Union{Atom, Projection}) =
view(A, range(a))
struct RmnBlock{T<:AbstractFloat}
R_cart ::Vec3{T}
R_cryst ::Vec3{Int}
block ::Matrix{Point3{T}}
end
const TbRmn{T} = Vector{RmnBlock{T}}
struct SiteDiagonalD{T<:AbstractFloat}
values ::Vector{T}
T ::Matrix{Complex{T}}
end
# view(D::SiteDiagonalD, at::Atom) =
# ()
# mutable struct WanStructure{T<:AbstractFloat, LT<:Length{T}} <: AbstractStructure{T, LT}
# structure ::Structure{T, LT}
# tbhamis ::Vector{TbHami{T}}
# tbRmns ::Vector{TbRmn{T}}
# end
# WanStructure(structure::Structure, wan_atoms::Vector{<:WanAtom}, tbhamis, tbrmns) =
# WanStructure(Structure(structure, wan_atoms), tbhamis, tbrmns)
# WanStructure(structure::Structure{T}, tbhamis) where T =
# WanStructure(structure, tbhamis, TbRmn{T}[])
# WanStructure(structure_::WanStructure, args...) =
# WanStructure(structure(structure_), args...)
# structure(str::WanStructure) =
# str.structure
# #TODO does not handle the SOC case. Or the case where there is up and down
# #TODO handle so that the previous job doesn't get destroyed I mean it's not necessary
# # it also doesn't agree with the paradigm of julia
# function add_wan_data(structure::AbstractStructure{T}, job_dir::String, threaded=true) where T
# searchdir(str) =
# job_dir .* DFControl.searchdir(job_dir, str)
# xsf_files = searchdir(".xsf")
# hami_files = reverse(searchdir("_hr.dat")) #such that dn is last
# r_files = reverse(searchdir("_r.dat")) #such that dn is last
# centers = [c[:center] for c in read_wannier_output(searchdir(".wout")[1])[:final_state]]
# new_atoms = WanAtom{T}[]
# for at in atoms(structure)
# push!(new_atoms, WanAtom(at, WanAtData(Wfc3D{T}[])))
# end
# t_wfcs = Vector{Array{WfcPoint3{T},3}}(undef, length(xsf_files))
# if threaded
# Threads.@threads for i=1:length(xsf_files)
# t_wfcs[i] = read_xsf_file(T, xsf_files[i])
# end
# else
# for i=1:length(xsf_files)
# t_wfcs[i] = read_xsf_file(T, xsf_files[i])
# end
# end
# for (i, (wfc, c)) in enumerate(zip(t_wfcs, centers))
# t_at = new_atoms[1]
# for at in new_atoms[2:end]
# if norm(position(at) - c) < norm(position(t_at) - c)
# t_at = at
# end
# end
# push!(wfcs(t_at), wfc)
# end
# tbhamis = Vector{TbHami{T}}(undef, length(hami_files))
# tbrmns = Vector{TbRmn{T}}(undef, length(r_files))
# Threads.@threads for i=1:length(hami_files)
# tbhamis[i] = readhami(hami_files[i], structure)
# end
# Threads.@threads for i=1:length(r_files)
# tbrmns[i] = read_rmn_file(r_files[i], structure)
# end
# return WanStructure(structure, new_atoms, tbhamis, tbrmns)
# end
# function add_wan_data(job::Job)
# job.structure = add_wan_data(job.structure, job.local_dir)
# return job
# end
# """
# setsoc!(structure::Structure, socs...)
# Accepts a varargs list of atom symbols => soc,
# which will set the soc of the atoms in the structure to the specified values.
# """
# function setsoc!(structure::AbstractStructure{T}, socs...) where T
# for (at, soc) in socs
# for str_at in atoms(structure)
# if name(str_at) == at
# setlsoc!(str_at, T(soc))
# end
# end
# end
# return structure
# end
# """
# setsoc!(job::Job, socs...)
# Accepts a varargs list of atom symbols => soc,
# which will set the soc of the atoms in the job structure to the specified values.
# """
# function setsoc!(job::Job, socs...)
# setsoc!(job.structure, socs...)
# return job
# end
# """
# wfcs(structure::WanStructure)
# Returns the wavefunctions that are linked to the atoms inside the structure.
# """
# function wfcs(structure::WanStructure{T}) where T
# out = Array{WfcPoint3{T}, 3}[]
# for at in atoms(structure)
# for wfc in wfcs(at)
# push!(out, wfc)
# end
# end
# return out
# end
# function getwandim(structure::WanStructure{T}) where T
# dim = 0
# for at in atoms(structure)
# dim += length(wfcs(at))
# end
# return dim
# end
struct ThreadCache{T}
caches::Vector{T}
ThreadCache(orig::T) where {T} =
new{T}([deepcopy(orig) for i = 1:nthreads()])
end
@inline cache(t::ThreadCache) =
t.caches[threadid()]
for f in (:getindex, :setindex!, :copyto!, :size, :length, :iterate, :sum, :view, :fill!)
@eval Base.$f(t::ThreadCache{<:AbstractArray}, i...) = Base.$f(cache(t), i...)
end
for op in (:+, :-, :*, :/)
@eval $op(t::ThreadCache{T}, v::T) where {T} = $op(cache(t), v)
@eval $op(v::T, t::ThreadCache{T}) where {T} = $op(v, cache(t))
end
fillall!(t::ThreadCache{<:AbstractArray{T}}, v::T) where {T} =
fill!.(t.caches, (v,))
gather(t::ThreadCache) = sum(t.caches)
LinearAlgebra.mul!(t1::ThreadCache{T}, v::T, t2::ThreadCache{T}) where {T<:AbstractArray} =
mul!(cache(t1), v, cache(t2))
LinearAlgebra.mul!(t1::T, v::T, t2::ThreadCache{T}) where {T<:AbstractArray} =
mul!(t1, v, cache(t2))
LinearAlgebra.mul!(t1::ThreadCache{T}, t2::ThreadCache{T}, t3::ThreadCache{T}) where {T<:AbstractArray} =
mul!(cache(t1), cache(t2), cache(t3))
LinearAlgebra.adjoint!(t1::ThreadCache{T}, v::T) where {T} =
adjoint!(cache(t1), v)
####
#### Broadcasting
####
Base.ndims(::Type{ThreadCache{T}}) where {T<:AbstractArray} =
ndims(T)
# Base.broadcast(f, As::ThreadCache...) =
# broadcast(f, getindex.(getfield.(As, :caches), threadid()))
Base.Broadcast.broadcastable(tc::ThreadCache{<:AbstractArray}) =
cache(tc)
# Base.Broadcast.BroadcastStyle(::Type{ThreadCache{T}}) where {T<:AbstractArray} =
# Base.Broadcast.BroadcastStyle(T)
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] | 2.207362 | 2,961 |
import Pkg;
Pkg.add("MLJ")
## 通过 clone 的方式安装引入
Pkg.add("mocha")
Pkg.add("DataFrames")
Pkg.add("Plots")
Pkg.add("Flux")
Pkg.add("ECharts") | [
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] | 1.643678 | 87 |
# ------------------------------------------------------
_sep_freeids(freeid, freeids...) = (freeid, freeids)
function _fva_range(net, box, freeid)
lb, ub = fva(net, freeid)
ran = dim_range(box, freeid)
lbi = findapproxi(lb, ran)
ubi = findapproxi(ub, ran)
return ran[lbi:ubi]
end
# remove unfeasibles and trivial
_is_feasible(net) = any(fba(net) .!= 0.0)
function _fill_subspace!(containers::Vector,
net::MetNet, box, left_freeids, head;
filter::Function
)
curr_freeid, left_freeids = _sep_freeids(left_freeids...)
curr_free_range = _fva_range(net, box, curr_freeid)
if isempty(left_freeids)
# base case
for cur_freev in curr_free_range
freev = [head; cur_freev]
!filter(net, freev) && continue
# all(iszero.(freev)) && continue # remove trivial
for (vi, containeri) in zip(freev, containers)
push!(containeri, vi)
end
end
else
# recursive step
for cur_freev in curr_free_range
_head = [head; cur_freev]
fixxing(net, curr_freeid, cur_freev) do
_fill_subspace!(containers, net, box, left_freeids, _head; filter)
end
end
end
end
# one container per dim
_build_containers(freeids, jump_size = 10_000) = [Container{Float64}(jump_size) for free in freeids]
function _containers_pool_size(freeids, containerss_pool)
lens = zeros(Int, length(freeids))
for containers in containerss_pool
for i in eachindex(freeids)
lens[i] += length(containers[i])
end
end
return lens
end
function _reduce_containers_pool!(containerss_pool)
# one per thread
acc_containers = Dict()
for containers in containerss_pool
# one vector per dimension
for i in eachindex(containers)
!haskey(acc_containers, i) && (acc_containers[i] = containers[i]; continue)
push!(acc_containers[i], vec!(containers[i])...)
empty!(containers[i])
end
end
map(1:length(acc_containers)) do i
vec!(acc_containers[i])
end
end
function subspace(net::MetNet, box::BoxGrid, freeids::Vector{Symbol};
# stoitof = 0.01,
filter::Function = (net, v) -> true,
nthrs::Int = nthreads()
)
# Base
if length(freeids) == 1
_fill_subspace!(_build_containers(freeids), net, box, freeids, Float64[]; filter)
end
first_freeid, left_freeids = _sep_freeids(freeids...)
first_free_range = _fva_range(net, box, first_freeid)
# recursive step
containerss_pool = [_build_containers(freeids) for _ in 1:nthrs]
nets_pool = [deepcopy(net) for _ in 1:nthrs]
prog = Progress(length(first_free_range); dt = 0.5, desc = "Collecting... ")
function showvalues()
estimation = sum(_containers_pool_size(freeids, containerss_pool))
return [
("polV estimation", estimation),
("thid", threadid())
]
end
@threads for cur_freev in first_free_range
thid = threadid()
containers = containerss_pool[thid]
net_th = nets_pool[thid]
head = [cur_freev]
fixxing(net_th, first_freeid, cur_freev) do
_fill_subspace!(containers, net_th, box, left_freeids, head; filter)
end
next!(prog; showvalues)
end
finish!(prog)
_reduce_containers_pool!(containerss_pool)
end | [
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] | 2.168944 | 1,610 |
####################################################
# Implementing PRM and functions for plotting PRM
# Parameters incude num samples, connection distance, a list of obstacles, and start and goal states
# Graph search implemented is Astar. Edge cost is assumed to be equal to euclidean distance.
# To use, call ` include("PRM.jl") ` from any Julia file in the same folder.
# nouyang 2017
####################################################
using Plots
using DataStructures
using GeometryTypes
using Distributions
module algT
using GeometryTypes
# export GraphNode, Edge, Obstacle, Room
# struct PointAlg
# x::Int64
# y::Int64
struct GraphNode
id::Int64
state::Point{2, Float64}
end
struct Edge
startID::Int64
endID::Int64
#edge::LineSegment(Point{2, Float64}, Point{2, Float64})
#edge::LineSegment{}
end
struct Obstacle
id::Int64
rect::HyperRectangle{2, Vec}
#color
#fillalpha
end
struct Room
width::Int64
height::Int64
walls::Vector{LineSegment}
#walls::HyperRectangle
obstacles::Vector{HyperRectangle}
end
struct AlgParameters
#startGoal::Point{2, Float64}
#endGoal::Point{2, Float64}
numSamples::Int64
connectRadius::Int64
# goal region?? should be rectangle? aka error from goal allowed
end
struct queueTmp
#pt::Point{2, Float64}
node::algT.GraphNode
statesList::Vector{algT.GraphNode}
cost::Int64
end
struct roadmap
startstate::Point{2,Float64}
goalstate::Point{2,Float64}
nodeslist::Vector{GraphNode}
edgeslist::Vector{Edge}
end
#Base.show(io::IO, v::Vertex) =# #print(io, "V($(v.id), ($(v.state.x),$(v.state.y)))")
#Base.show(io::IO, p::Point) =# #print(io, "P($(p.x),$(p.y))")
#Base.show(io::IO, q::tempQueueType) =# #print(io, "Q($(q.v),$(q.statesList) $(q.cost))")
Base.isless(q1::queueTmp, q2::queueTmp) = q1.cost < q2.cost
#Base.isless(p1::Point, p2::Point) = q1.x< q2.x
#Base.isless(p1::Point, p2::Point) = q1[1] < q2[1]
end
module algfxn
using GeometryTypes
using algT
function ccw(A,B,C)
# determines direction of lines formed by three points
return (C[2]-A[2]) * (B[1]-A[1]) > (B[2]-A[2]) * (C[1]-A[1])
end
function intersects(line1, line2) #no ":" at the end!
A, B = line1[1], line1[2]
C, D = line2[1], line2[2]
return ( (ccw(A, C, D) != ccw(B, C, D)) && ccw(A, B, C) != ccw(A, B, D))
end
function decompRect(r::HyperRectangle) #GeometryTypes.HyperRectangle{2,Float64}
corners = decompose(Point{2, Float64}, r)
corners = [Point(pt) for pt in corners]
lineBottom = LineSegment(corners[1], corners[2])
lineTop = LineSegment(corners[3], corners[4])
lineLeft = LineSegment(corners[1], corners[3])
lineRight = LineSegment(corners[2], corners[4])
lines = Vector{LineSegment}()
push!(lines, lineTop, lineRight, lineBottom, lineLeft)
return lines
end
#function isCollidingNode(node::algT.GraphNode, obsList::Vector{algT.Obstacle}
function isCollidingNode(node::Point{2, Float64}, obsList::Vector{HyperRectangle})
for obs in obsList
if contains(obs, node)
return true
end
end
return false
end
function isCollidingObstacles(line::LineSegment, obsList::Vector{HyperRectangle})
for obs in obsList
rectLines = decompRect(obs)
for rectline in rectLines
if intersects(line, rectline)[1]
return true
end
end
end
return false
end
function isCollidingWalls(line::LineSegment, walls::Vector{LineSegment})
for wall in walls
if intersects(line, wall)
return true
end
end
return false
end
function findNearestNodes(nodestate, nodeslist, maxDist)
# given maxDist, return all nodes within that distance of node
nearestNodes = Vector{Tuple{algT.GraphNode, Float64}}()
for n in nodeslist
dist = min_euclidean(Vec(nodestate), Vec(n.state))
if dist < maxDist
push!(nearestNodes, (n, dist))
end
end
# return list sorted by distance?
# or just return list with distances included for now...
return nearestNodes
end
function costPath(solPath)
pathcost = 0
for i in 2:length(solPath)
curN = solPath[i].state
prevN = solPath[i-1].state
pathcost += min_euclidean(Vec(curN), Vec(prevN))
end
return pathcost
end
function findNode(nodeID, nodeslist)
#since we haven't removed any nodes, nodeslist should be sorted by index. but just in case, let's search through it
index = findfirst([node.id for node in nodeslist], nodeID)
return nodeslist[index]
end
end
module plotfxn
using GeometryTypes
using Plots
using algfxn
using algT
### Plots.jl recipes
@recipe function f(r::HyperRectangle)
points = decompose(Point{2,Float64}, r)
rectpoints = points[[1,2,4,3],:]
xs = [pt[1] for pt in rectpoints];
ys = [pt[2] for pt in rectpoints];
seriestype := :shape
color = :orange
#m = (:black, stroke(0))
s = Shape(xs[:], ys[:])
end
@recipe function f(pt::Point)
xs = [pt[1]]
ys = [pt[2]]
seriestype --> :scatter
color = :orange
markersize := 3
xs, ys
end
@recipe function f(l::LineSegment)
xs = [ l[1][1], l[2][1] ]
ys = [ l[1][2], l[2][2] ]
# seriestype = :line
color = :red
lw := 3
xs, ys
end
@recipe function f(rectList::Vector{<:HyperRectangle})
for r in rectList
@series begin
r
end
end
end
@recipe function f(ptList::Vector{<:Point})
for p in ptList
@series begin
p
end
end
end
@recipe function f(lineList::Vector{<:LineSegment})
for l in lineList
@series begin
l
end
end
end
####
function plotRoom(room)
aPlot = plot() #Todo! this assumes plotroom is first thing called()
roomWidth, roomHeight, walls, obstacles = room.width, room.height, room.walls, room.obstacles
# #print("\nPlotting Room\n")
plot!(aPlot, walls, color =:black)
plot!(aPlot, obstacles, fillalpha=0.5)
return aPlot
end
function plotPRM(roomPlot, roadmap, solPath, title)
startstate, goalstate, nodeslist, edgeslist = roadmap.startstate, roadmap.goalstate, roadmap.nodeslist, roadmap.edgeslist
x = [n.state[1] for n in nodeslist]
y = [n.state[2] for n in nodeslist]
scatter!(roomPlot, x,y, color=:black)
edgeXs, edgeYs = [], []
for e in edgeslist
startN = algfxn.findNode(e.startID, nodeslist)
endN = algfxn.findNode(e.endID, nodeslist)
x1,y1 = startN.state[1], startN.state[2]
x2,y2 = endN.state[1], endN.state[2]
push!(edgeXs, x1, x2, NaN) #the NaNs, keep spaces between edges correctly unplotted
push!(edgeYs, y1, y2, NaN)
end
plot!(roomPlot, edgeXs, edgeYs, color=:tan, linewidth=0.3)
# plot solution
prmPlot = plotSolPath(roomPlot, solPath)
#title!(prmPlot, title, titlefont = afont)
title!(prmPlot, title)
plot!(prmPlot, legend=false, size=(600,600), xaxis=((-5,25), 0:1:20 ),
yaxis=((-5,25), 0:1:20), foreground_color_grid= :black)
return prmPlot
end
function plotSolPath(aPlot, solPath)
print("\n ---Solution Path----- \n")
#@show solPath
print("\n -------- \n")
if solPath != Void
xPath = [n.state[1] for n in solPath]
yPath = [n.state[2] for n in solPath]
xstart, ystart = solPath[1].state
xend, yend = solPath[end].state
# plot start pt
scatter!(aPlot, [xstart], [ystart],
markercolor= :red, markershape = :circle, markersize = 6, markerstrokealpha = 0.5, markerstrokewidth=1)
# plot goal pt
scatter!(aPlot, [xend], [yend],
markerstrokecolor = :green, markershape = :star, markersize = 5, markerstrokealpha = 1, markerstrokewidth=5)
# plot path
plot!(aPlot, xPath, yPath, color = :orchid, linewidth=3, fillalpha = 0.3)
else
# #print("\n --- No solution path found ----- \n")
end
return aPlot
end
end
function preprocessPRM(room, parameters)
roomWidth, roomHeight, walls, obstacles = room.width, room.height, room.walls, room.obstacles
numPts, connectRadius = parameters.numSamples, parameters.connectRadius
nodeslist = Vector{algT.GraphNode}()
edgeslist = Vector{algT.Edge}()
currID = 1
# Sample points, create list of nodes
for i in 1:numPts
xrand, yrand = rand(Uniform(0, roomWidth), 2)
#xrand,yrand = rand(1.0:roomWidth-1,2)
n = Point(xrand, yrand) #new point in room
if !algfxn.isCollidingNode(n, obstacles) #should write fxn to check for sampling *on* a wall at some point TODO
newNode = algT.GraphNode(currID, n)
currID += 1
push!(nodeslist, newNode)
else
#print("\n NODE REMOVED: $n \n")
end
end
# Connect each node to its neighboring nodes within a ball or radius r, creating edges
for startnode in nodeslist
neighbors = [item[1] for item in algfxn.findNearestNodes(startnode.state, nodeslist, connectRadius)] # parent point
n = [algfxn.findNearestNodes(startnode.state, nodeslist, connectRadius)] # parent point
for endnode in neighbors
candidateEdge = LineSegment(startnode.state, endnode.state)
if !algfxn.isCollidingObstacles(candidateEdge, obstacles) & !algfxn.isCollidingWalls(candidateEdge, walls)
#line = LineSegment(startnode.state, endnode.state)
newEdge = algT.Edge(startnode.id, endnode.id) #by ID, or just store node? #wait no, i'd have multiple copies of same node for no real reason, mulitple edges per node
push!(edgeslist, newEdge)
else
#print("\n EDGE REMOVED: $startnode WITH $endnode \n")
end
end
end
#@show edgeslist
#@printf("\nPath found? %s Length of nodeslist: %d\n", isPathFound, length(nodeslist))
return nodeslist, edgeslist
end
function getSuccessors(curNode, edgeslist, nodeslist) #assuming bidirectional for now
#Find all edges that start or end at current node, and then make a list of the corresponding start or end nodes
nodeID = curNode.id
successorNodeIDs = Vector{Int}()
successors = Vector{algT.GraphNode}()
for e in edgeslist
if e.startID == nodeID
push!(successorNodeIDs, e.endID)
end
if e.endID == nodeID #should I include endID? it is bidirectional after all.
push!(successorNodeIDs, e.startID) #yes, because local planner connects in bidirectional way
end
end
for id in successorNodeIDs
node = algfxn.findNode(id, nodeslist)
push!(successors, node)
end
return successors
end
function queryPRM(startstate, goalstate, nodeslist, edgeslist, obstaclesList)
# to find nearest node, set connect radius to be infinity for now #todo
connectRadius = 99;
n_nearStart= algfxn.findNearestNodes(startstate, nodeslist, connectRadius) #Get distance from start, for all points in graph.
n_nearGoal= algfxn.findNearestNodes(goalstate, nodeslist, connectRadius)
## SECTION remove nodes that we cannot reach in a straight line without going through an obstacle
#todo: this is expensive, we should only check distances in order
# n_nearStart is tuple of node and distance
sort!(n_nearStart, by=n_nearStart->n_nearStart[2]) #Sort by distance and pick node with smallest distance from start
sort!(n_nearGoal, by=n_nearGoal->n_nearGoal[2])
#print("\n ----------------- \n")
#@show n_nearStart
#print("\n ----------------- \n")
#@show n_nearGoal
nodestart = algT.GraphNode(9999,Point(9999,9999))
nodegoal = algT.GraphNode(9999,Point(9999,9999))
for (n, dist) in n_nearStart
# bar = typeof(startstate)
# bar2 = typeof(n)
## #print("\n$bar, $bar2\n")
candidateline = LineSegment(Point(startstate), Point(n.state))
if !algfxn.isCollidingObstacles(candidateline, obstaclesList)
nodestart = n
break
end
end
for (n, dist) in n_nearGoal
candidateline = LineSegment(Point(goalstate), n.state)
if !algfxn.isCollidingObstacles(candidateline, obstaclesList)
nodegoal = n
break
end
end
if nodestart == algT.GraphNode(9999,Point(9999,9999))
nodestart = algT.GraphNode(0, Point(0,0))
#print("ahhhhhh didn't find a start node")
end
if nodegoal == algT.GraphNode(9999,Point(9999,9999))
nodegoal = algT.GraphNode(0, Point(0,0))
# #print("ahhhhhh didn't find a goal node")
end
## SECTION END
# #print("This is the beginState $(startstate) and the endState $(goalstate)\n")
# #print("This is the beginVertex--> $(nodestart) >>> and the endVertex--> $(nodegoal)\n")
pathNodes = Vector{algT.GraphNode}()
visited = Vector{algT.GraphNode}() #nodes we've searched through
frontier = PriorityQueue()
queue1 = algT.queueTmp(nodestart, pathNodes, 1)
enqueue!(frontier, queue1, 1) #root node has cost 0
pathcost = 99999
#################### A star search
while length(frontier) != 0
front = DataStructures.dequeue!(frontier)
pathVertices = []
curNode, pathNodes, totalEdgeCost = front.node, front.statesList, front.cost
if curNode == nodegoal
# #print("Hurrah! endState reached! \n")
unshift!(pathNodes, nodestart) #prepend our first path node back to pathVertices
unshift!(pathNodes, algT.GraphNode(0, startstate)) #prepend the start
push!(pathNodes, algT.GraphNode(0, goalstate)) #append the goal
finalPathCost = algfxn.costPath(pathNodes) #Assuming edge cost is Euclidean cost
isPathFound = true
return (finalPathCost, isPathFound, pathNodes) #list of nodes in solution path
else
if !(curNode in visited)
push!(visited, curNode) # Add all successors to the stack
for candidateNode in getSuccessors(curNode, edgeslist, nodeslist)
newEdgeCost = min_euclidean(Vec(curNode.state),
Vec(candidateNode.state))
#edgecost is euclidean dist(state,state). better to pass
# Node than to perform node lookup everytime (vs passing id)
f_x = totalEdgeCost + newEdgeCost + min_euclidean(Vec(candidateNode.state), Vec(nodegoal.state)) #heuristic = euclidean distance to end goal
p = deepcopy(pathNodes)
push!(p, candidateNode)
if !(candidateNode in keys(frontier))
newQ = algT.queueTmp(candidateNode, p, ceil(totalEdgeCost+ newEdgeCost))
enqueue!(frontier, newQ, ceil(f_x))
end
end
end
end
end
# Return None if no solution found
#@printf("No solution found! This is length of frontier, %d\n", length(frontier))
finalPathCost = Void
isPathFound = false
pathNodes = Void
return (finalPathCost, isPathFound, pathNodes)
end
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4781,
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] | 2.207201 | 7,360 |
module Reindex
export ShiftReindex,OddReindex,Reflect
struct ShiftReindex{T,N,O}
array :: Vector{T}
end
function ShiftReindex(v::Vector{T},o::Int) where T
N = length(v)+o
ShiftReindex{T,N,o}(v)
end
(vec::ShiftReindex{T,N,O})(i::Int) where {T,N,O} =
(i > N || i < 1+O) ? T(0) : vec.array[i-O]
(vec::ShiftReindex{T,N,O})(ir::AbstractArray{M}) where {T,N,O,M} =
(vec::ShiftReindex{T,N,O}).(collect(ir))
struct OddReindex{T,N}
array :: Vector{T}
end
function OddReindex(v::Vector{T}) where T
N = length(v)-1
OddReindex{T,N}(v)
end
(vec::OddReindex{T,N})(i::Int) where {T,N} =
abs(i) > N ? T(0) : i < 0 ? conj(vec.array[1-i]) : vec.array[1+i]
(vec::OddReindex{T,N})(ir::AbstractArray{M}) where {T,N,M} =
(vec::OddReindex{T,N}).(collect(ir))
ReindexedArray = Union{ShiftReindex,OddReindex}
struct Reflect{T}
array :: T
end
(vec::Reflect{<:ReindexedArray})(i) = vec.array(-i)
(vec::Reflect{<:AbstractArray})(i) = vec.array[-i]
end
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13,
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58,
12,
72,
60,
628,
198,
198,
437,
198
] | 1.984466 | 515 |
function largest_component_test()
A = load_matrix_network("dfs_example")
(Acc,p) = largest_component(A)
if size(Acc,1) != 5
error("largest_component failed")
end
(Acc,p) = largest_component(A,true)
if size(Acc,1) != 6
error("largest_component failed")
end
A = load_matrix_network("cores_example")
(Acc1,p1) = largest_component(A)
(Acc2,p2) = largest_component(A,true)
if !isequal(Acc1,Acc2)
error("largest_component failed")
end
return true
end | [
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886,
628,
220,
220,
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1441,
2081,
198,
437
] | 2.242678 | 239 |
import DSP.conv, RHEOS.convn, BenchmarkTools
# realistic test data sizes for RHEOS workflow
x = collect(0.0:0.1:1000.0)
y = exp.(-x/2.0)
z = [i<500.0 ? i : 500.0 - i for i in x]
# convolve them yeh
@btime conv($z, $y)
@btime convn($z, $y)
| [
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628
] | 2.169643 | 112 |
using EffectiveWaves, Test
using LinearAlgebra
@testset "Check material definitions" begin
include("../src/materials.jl")
@test true
end
@testset "Examples from: Reflection from multi-species.., Proc.R.Soc.(2018)" begin
include("../docs/src/examples/concrete/concrete_species.jl")
include("../docs/src/examples/concrete/concrete_species_volfrac.jl")
# include("../docs/src/examples/concrete/concrete_species_large-freq.jl") # takes longer
# Takes too long
include("../docs/src/examples/emulsion/fluid_species.jl")
include("../docs/src/examples/emulsion/fluid_species_volfrac.jl")
# include("../docs/src/examples/emulsion/fluid_species_large-freq.jl") # takes longer
@test true
end
include("specialfunctions.jl")
include("types_constructors.jl")
include("strong_low_freq_effective.jl")
include("high_frequency_effective.jl")
include("large_vol_low_freq_effective.jl")
# A heavy test..
include("weak_scatterers_effective.jl")
# test does not run on Julia version < 0.7 due to differences in Optim versions
include("path_mesh_wavenumbers.jl")
include("numerical_integration.jl")
include("integrated_reflection.jl")
include("average_integrand_kernel.jl")
include("match_wave.jl")
include("wiener-hopf-reflection.jl")
include("wiener-hopf-wave.jl")
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] | 2.886414 | 449 |
"""
add_variables_from_data!(gm::Union{JuMP.Model, GlobalModel},
X::DataFrame)
Adds/finds variables depending on the columns of X.
"""
function add_variables_from_data!(m::JuMP.Model,
X::DataFrame)
varkeys = names(X)
vars = []
data_vars = []
for key in varkeys
try
push!(vars, m[Symbol(key)])
catch KeyError
nv = @variable(m, base_name = string(key))
m[Symbol(key)] = nv
push!(vars, nv)
push!(data_vars, Symbol(key))
end
end
length(data_vars) != 0 && @info("Added $(length(data_vars)) variables from data: " * string(data_vars))
return vars
end
add_variables_from_data!(gm::GlobalModel, X::DataFrame) = add_variables_from_data!(gm.model, X)
"""
bound_to_data!(gm::Union{JuMP.Model, GlobalModel},
X::DataFrame)
Constrains the domain of relevant variables to the box interval defined by X.
"""
function bound_to_data!(m::JuMP.Model,
X::DataFrame)
vars = []
try
vars = [m[Symbol(key)] for key in names(X)]
catch UndefVarError
throw(OCTHaGOnException("Please make sure the keys of X match with variables in model $(gm)."))
end
ranges = Dict(m[Symbol(key)] => [minimum((X[!, Symbol(key)])), maximum((X[!, Symbol(key)]))] for key in names(X))
bound!(m, ranges);
return
end
bound_to_data!(gm::GlobalModel, X::DataFrame) = bound_to_data!(gm.model, X)
"""
add_datadriven_constraint(gm::GlobalModel,
X::DataFrame, Y::Array;
constraint::Union{Nothing, JuMP.ConstraintRef, Expr} = nothing,
vars::Union{Nothing, Array{JuMP.VariableRef, 1}} = nothing,
dependent_var::Union{Nothing, JuMP.VariableRef} = nothing,
name::String = "bbl" * string(length(gm.bbls) + 1),
equality::Bool = false)
Adds a data-driven constraint to GlobalModel. Data driven BBLs
do not allow for resampling.
"""
function add_datadriven_constraint(gm::GlobalModel,
X::DataFrame, Y::Array;
constraint::Union{Nothing, JuMP.ConstraintRef, Expr} = nothing,
vars::Union{Nothing, Array{JuMP.VariableRef, 1}} = nothing,
dependent_var::Union{Nothing, JuMP.VariableRef} = nothing,
name::String = "bbl" * string(length(gm.bbls) + 1),
equality::Bool = false)
if isnothing(vars)
vars = add_variables_from_data!(gm, X)
else
@assert size(X, 2) == length(vars)
end
expr_vars = vars
if isnothing(dependent_var)
new_bbl = BlackBoxClassifier(constraint = constraint, vars = vars, expr_vars = expr_vars,
equality = equality, name = name)
add_data!(new_bbl, X, Y)
push!(gm.bbls, new_bbl)
return
else
new_bbl = BlackBoxRegressor(constraint = constraint, vars = vars, expr_vars = expr_vars,
dependent_var = dependent_var, equality = equality, name = name)
add_data!(new_bbl, X, Y)
push!(gm.bbls, new_bbl)
return
end
end | [
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0,
7,
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65,
2436,
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1441,
198,
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886,
198,
437
] | 2.024074 | 1,620 |
import JSON
using Gadfly
adaptLog = JSON.parsefile("adapt3.log")
notAdaptLog = JSON.parsefile("notAdapt3.log")
p = plot(layer(x = map(x -> x["id"], adaptLog),
y = map(x -> mean(x["results"]), adaptLog),
xintercept = [10, 50],
Geom.point,
Geom.vline,
Theme(highlight_width = 0pt)),
layer(x = map(x -> x["id"], notAdaptLog),
y = map(x -> mean(x["results"]), notAdaptLog),
Geom.point,
Theme(default_color=colorant"red", highlight_width = 0pt)),
Scale.x_continuous(minvalue=0, maxvalue=60),
Scale.y_log10,
Guide.xlabel("Time (s)"),
Guide.ylabel("Response time (ms)"),
Guide.manual_color_key("Legend",
["Self-adaptive", "Non-self-adaptive"],
["deepskyblue", "red"]))
p2 = plot(
layer(x = map(x -> x["id"], adaptLog),
y = map(x->length(x["results"]), adaptLog),
Geom.bar,
Theme(bar_spacing=0.5mm)),
Guide.xlabel("Time (s)"),
Guide.ylabel("Number of requests"),
Scale.x_continuous(minvalue=0, maxvalue=70),
Scale.y_continuous(minvalue=0, maxvalue=30)
)
draw(PDF("myplot1.pdf", 10inch, 5inch), p)
draw(PDF("myplot2.pdf", 10inch, 5inch), p2)
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] | 1.903846 | 728 |
#!/usr/bin/env julia
#
# usage:
# script......... plaint.jl /path/to/system.yml
# interactively.. julia -qi plaint.jl
using YAML #read data file
using DelimitedFiles #output results
using LinearAlgebra
"""
ksnr(e, M, eps, max_iterations)
Solve Kepler's equation with NR method.
"""
function ksnr(e::Float64, M::Float64,
eps::Float64 = 1e-16, nm::Int64 = 16)
x = M + e * sin(M)
d = (x - e * sin(x) - M) / (1 - e * cos(x))
while abs(d) > eps && nm > 0
x -= d
d = (x - e * sin(x) - M) / (1 - e * cos(x))
nm -= 1
end
return x
end
"""
euler_rotd(Ω, i, ω)
Return Eulerian rotation.
"""
function euler_rotd(Ω::Float64, i::Float64, ω::Float64)
return [ cosd(Ω) -sind(Ω) 0; sind(Ω) cosd(Ω) 0; 0 0 1 ] *
[ 1 0 0; 0 cosd(i) -sind(i); 0 sind(i) cosd(i) ] *
[ cosd(ω) -sind(ω) 0; sind(ω) cosd(ω) 0; 0 0 1 ]
end
"""
oe_to_xv!(mu, j, orbital_elements, positions, velocities)
Convert orbital elements to cartesian vectors.
"""
function oe_to_xv!(mu::Float64, j::Int64,
oe::Array{Float64,2},
vr::Array{Float64,2}, vv::Array{Float64,2})
a, e, i, Ω, ω, M = oe[:, j]
E = ksnr(e, M * π / 180)
r = a * (1 - e * cos(E))
pof = [cos(E) - e, sqrt(1 - e^2) * sin(E), 0] * a
vof = [ - sin(E), sqrt(1 - e^2) * cos(E), 0] * (sqrt(mu * a) / r)
rot = euler_rotd(Ω, i, ω)
vr[:, j] = rot * pof
vv[:, j] = rot * vof
end
"""
xv_to_oe!(mu, j, positions, velocities, orbital_elements)
Convert cartesian vectors to orbital elements.
"""
function xv_to_oe!(mu::Float64, j::Int64,
rt::Array{Float64,2}, vt::Array{Float64,2},
oe::Array{Float64,2})
tiny = 1e-10
vr, vv = rt[:, j], vt[:, j]
r = norm(vr)
v = norm(vv)
vh = cross(vr, vv)
h = norm(vh)
vn = [-vh[2], vh[1], 0]
n = norm(vn)
ve = cross(vv, vh) / mu - vr / r
a = 1 / (2 / r - v^2 / mu)
e = norm(ve)
i = acos(vh[3] / h)
if e > tiny
if n > tiny
Ω = acos(vn[1] / n)
if vn[2] < 0
Ω = 2π - Ω
end
ω = acos(dot(ve, vn) / (e * n))
if ve[3] < 0
ω = 2π - ω
end
else
i = Ω = .0
ω = atan2(ve[2], ve[3])
if cross(ve, vr)[3] < 0
ω = 2π - ω
end
end
nu = acos(dot(ve, vr)/(e * r))
if dot(vr, vv) < 0
nu = 2π - nu
end
else
#not implemented
end
E = 2 * atan(sqrt((1 - e) / (1 + e)) * tan(nu / 2))
M = E - e * sin(E)
if M < 0
M += 2π
end
i, Ω, ω, M = [i, Ω, ω, M] * 180 / π
oe[:, j] = [a, e, i, Ω % 360, ω % 360, M % 360]
end
"""
grav_a!(no_of_bodies, masses, positions, acceleration)
Compute gravitational acceleration.
!!! note
The `a` should be multiplied with the appropriate gravitational constant.
"""
function grav_a!(n::Int64, m::Array{Float64,1},
x::Array{Float64,2}, a::Array{Float64,2})
fill!(a, 0)
@inbounds for i = 1:(n - 1), j = (i + 1):n
R = x[:, i] - x[:, j]
Rd3 = R / norm(R)^3
a[:, i] -= m[j] * Rd3
a[:, j] += m[i] * Rd3
end
end
"""
mksnr(dt, α, n, σ0, r0, eps, max_iterations)
Solve modified Kepler's equation with NR method.
!!! note
Argument `α` is the inverse of semimajor axis.
"""
function mksnr(dt::Float64, α::Float64, n::Float64, s::Float64, r0::Float64,
eps::Float64 = 1e-16, nm::Int64 = 16)
r0a, s0a, ndt = 1 - r0 * α, s * sqrt(α), n * dt
x = π
d = (x - r0a * sin(x) - s0a * (cos(x) - 1) - ndt) /
(1 - r0a * cos(x) + s0a * sin(x))
while abs(d) > eps && nm > 0
x -= d
d = (x - r0a * sin(x) - s0a * (cos(x) - 1) - ndt) /
(1 - r0a * cos(x) + s0a * sin(x))
nm -= 1
end
return x
end
"""
gauss_step!(dt, mu, no_of_bodies, positions, velocities)
Keplerian drift using Gauss' f, g functions.
"""
function gauss_step!(dt::Float64, mu::Float64, np::Int64,
rt::Array{Float64,2}, vt::Array{Float64,2})
@inbounds @simd for i = 1:np
vr0, vv0 = rt[:, i], vt[:, i]
r0, v0 = norm(vr0), norm(vv0)
α = 2 / r0 - v0^2 / mu
a, n = 1 / α, sqrt(mu * α^3)
s = dot(vr0, vv0) / sqrt(mu)
E = mksnr(dt, α, n, s, r0)
cose, sine = cos(E), sin(E)
r1 = a + (r0 - a) * cose + sqrt(a) * s * sine
a1ce = a * (cose - 1)
ivr0, ivr1 = 1 / r0, 1 / r1
ft = 1 + a1ce * ivr0
gt = dt + (sine - E) / n
fd = - a^2 * n * sine * ivr1 * ivr0
#gd = 1 + a1ce * ivr1
gd = (1 + gt * fd) / ft
rt[:, i] = ft * vr0 + gt * vv0
vt[:, i] = fd * vr0 + gd * vv0
end
end
"""
eam_dh(no_of_bodies, masses, positions, velocities)
Compute energy and angular momentum, with democratic heliocentric vectors.
"""
function eam_dh(np::Int64, m::Array{Float64,1},
x::Array{Float64,2}, v::Array{Float64,2})
et, am = norm(sum(m' .* v, dims = 2))^2 / 2, zeros(3)
@inbounds @simd for i = 1:np
et += m[i] * norm(v[:, i])^2 / 2
et -= m[i] / norm(x[:, i])
am += m[i] * cross(x[:, i], v[:, i])
end
@inbounds for i = 1:(np - 1), j = (i + 1):np
et -= m[i] * m[j] / norm(x[:, i] - x[:, j])
end
return et, norm(am)
end
"""
plaint_dhi(iostream, total_iterations, dt, step_iterations,
mu, masses, orbital_elements)
Perform planetary integration using democratic heliocentric integrator.
"""
function plaint_dhi(of::Array{IOStream, 1},
kmax::Float64, dt::Float64, kpri::Float64, mu::Float64,
mp::Array{Float64,1}, oe::Array{Float64,2})
mt = 1 / (sum(mp) + 1)
np = size(mp, 1)
an = Array{Float64}(undef, 3, np)
x = similar(an)
v = similar(an)
@inbounds @simd for j = 1:np
oe_to_xv!(mu, j, oe, x, v)
end
v .-= mt * sum(mp' .* v, dims = 2) #to_dh
el0 = eam_dh(np, mp, x, v)
println("Initial energy: ", el0[1], "\n",
"Initial angular momentum: ", el0[2], "\n")
d2 = dt / 2
@inbounds for k = 1:kmax
try
x .+= d2 * sum(mp' .* v, dims = 2)
gauss_step!(d2, mu, np, x, v)
grav_a!(np, mp, x, an)
v .+= dt * an
gauss_step!(d2, mu, np, x, v)
x .+= d2 * sum(mp' .* v, dims = 2)
if k % kpri == 0
eln = eam_dh(np, mp, x, v)
va = v .+ mt * sum(mp' .* v, dims = 2) #to_ac
writedlm(of[end], [collect(eln .- el0); collect(@. 1 - eln / el0)]')
@simd for j = 1:np
xv_to_oe!(mu, j, x, va, oe)
writedlm(of[j], oe[:, j]')
end
end
catch
println("Error: Exiting on ", k, "\n")
break
end
end
close.(of)
end
"""
data_get(file)
Get planetary system's data and integration options.
"""
function data_get(file::String)
data = YAML.load_file(file)
tmax::Float64 = data["options"]["tmax"]
step::Float64 = data["options"]["step"]
ever::Float64 = data["options"]["ever"]
unit = "solar"
try
unit = data["system"]["units"]
catch
end
ms = data["system"]["star_mass"]
datp = data["system"]["planets"]
sort!(datp, by = x -> x["orbital_elements"][1])
np = size(datp, 1)
name = Array{String}(undef, np)
mp = Array{Float64}(undef, np)
oe = Array{Float64}(undef, 6, np)
@inbounds @simd for i = 1:np
name[i] = data["system"]["star_name"] * " " * datp[i]["id"]
mp[i] = datp[i]["mass"]
oe[:, i] = datp[i]["orbital_elements"]
end
return tmax, step, ever, ms, name, mp, oe, unit
end
"""
data_prep(tmax, step, ever,
star_mass, file_names, masses, orbital_elements, units)
Prepare data and options to feed to the integrator.
"""
function data_prep(tmax::Float64, step::Float64, ever::Float64,
ms::Float64, name::Array{String, 1},
mp::Array{Float64, 1}, oe::Array{Float64,2},
unit::String = "solar")
if unit == "solar"
mp = mp * 9.547919e-4 / ms
else #natural
mp = mp / ms
end
oe[1, :] /= oe[1, 1]
tp = 2π
dt = step * tp
kmax = div(tmax * tp, dt)
kpri = div(ever * tp, dt)
of = map(f -> open("out/$f.txt", "w"), name)
push!(of, open("out/eamerr.txt", "w"))
println("Total simulation time is: ", tmax, " yr\n",
"Time of every step to be: ", step, " yr\n",
"Total number of steps is: ", kmax, " steps\n",
"Output will be every: ", ever, " yr\n",
"or differently every: ", kpri, " steps\n")
return of, kmax, dt, kpri, 1.0, mp, oe
end
"""
usage()
Return an exemplary session.
"""
function usage()
println("## Example session:\n",
"#set file containing data\n",
"file = \"data/TRAPPIST-1.yml\";\n",
"#get data from file\n",
"tmax, step, ever, ms, name, mp, oe = data_get(file);\n",
"#or enter manually\n",
"dgr = (tmax, step, ever, ms, name, mp, oe);\n",
"#prepare data\n",
"of, kmax, dt, kpri, mu, mp, oe = data_prep(dgr...);\n",
"#integrate, available: plaint_dhi, plaint_s6i\n",
"plaint_dhi(of, kmax, dt, kpri, mu, mp, oe);\n",
"#benchmark integration of a particular system\n",
"@time plaint_dhi(data_prep(data_get(file)...)...);")
end
## Session
# Run as script or, interactively in a Julia session.
if ! isinteractive()
@time plaint_dhi(data_prep(data_get(ARGS[1])...)...)
else
usage()
end
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15,
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304,
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62,
34985,
7,
3919,
62,
1659,
62,
65,
5042,
11,
14568,
11,
6116,
11,
11555,
420,
871,
8,
198,
198,
7293,
1133,
2568,
290,
32558,
12858,
11,
351,
10518,
932,
72,
420,
22317,
30104,
13,
198,
37811,
198,
8818,
304,
321,
62,
34985,
7,
37659,
3712,
5317,
2414,
11,
285,
3712,
19182,
90,
43879,
2414,
11,
16,
5512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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2124,
3712,
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90,
43879,
2414,
11,
17,
5512,
410,
3712,
19182,
90,
43879,
2414,
11,
17,
30072,
198,
220,
220,
220,
2123,
11,
716,
796,
2593,
7,
16345,
7,
76,
6,
764,
9,
410,
11,
5391,
82,
796,
362,
4008,
61,
17,
1220,
362,
11,
1976,
27498,
7,
18,
8,
198,
220,
220,
220,
2488,
259,
65,
3733,
2488,
14323,
67,
329,
1312,
796,
352,
25,
37659,
198,
220,
220,
220,
220,
220,
220,
220,
2123,
15853,
285,
58,
72,
60,
1635,
2593,
7,
85,
58,
45299,
1312,
12962,
61,
17,
1220,
362,
198,
220,
220,
220,
220,
220,
220,
220,
2123,
48185,
285,
58,
72,
60,
1220,
2593,
7,
87,
58,
45299,
1312,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
716,
15853,
285,
58,
72,
60,
1635,
3272,
7,
87,
58,
45299,
1312,
4357,
410,
58,
45299,
1312,
12962,
198,
220,
220,
220,
886,
198,
220,
220,
220,
2488,
259,
65,
3733,
329,
1312,
796,
352,
37498,
37659,
532,
352,
828,
474,
796,
357,
72,
1343,
352,
2599,
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198,
220,
220,
220,
220,
220,
220,
220,
2123,
48185,
285,
58,
72,
60,
1635,
285,
58,
73,
60,
1220,
2593,
7,
87,
58,
45299,
1312,
60,
532,
2124,
58,
45299,
474,
12962,
198,
220,
220,
220,
886,
198,
220,
220,
220,
1441,
2123,
11,
2593,
7,
321,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
13371,
62,
67,
5303,
7,
72,
455,
1476,
11,
2472,
62,
2676,
602,
11,
288,
83,
11,
2239,
62,
2676,
602,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
38779,
11,
14568,
11,
32362,
62,
68,
3639,
8,
198,
198,
5990,
687,
27047,
11812,
1262,
10518,
932,
72,
420,
22317,
4132,
12392,
13,
198,
37811,
198,
8818,
13371,
62,
67,
5303,
7,
1659,
3712,
19182,
90,
9399,
12124,
11,
352,
5512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
9806,
3712,
43879,
2414,
11,
288,
83,
3712,
43879,
2414,
11,
479,
3448,
3712,
43879,
2414,
11,
38779,
3712,
43879,
2414,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
29034,
3712,
19182,
90,
43879,
2414,
11,
16,
5512,
267,
68,
3712,
19182,
90,
43879,
2414,
11,
17,
30072,
198,
220,
220,
220,
45079,
796,
352,
1220,
357,
16345,
7,
3149,
8,
1343,
352,
8,
198,
220,
220,
220,
45941,
796,
2546,
7,
3149,
11,
352,
8,
198,
220,
220,
220,
281,
796,
15690,
90,
43879,
2414,
92,
7,
917,
891,
11,
513,
11,
45941,
8,
198,
220,
220,
220,
2124,
796,
2092,
7,
272,
8,
198,
220,
220,
220,
410,
796,
2092,
7,
272,
8,
198,
220,
220,
220,
2488,
259,
65,
3733,
2488,
14323,
67,
329,
474,
796,
352,
25,
37659,
198,
220,
220,
220,
220,
220,
220,
220,
267,
68,
62,
1462,
62,
87,
85,
0,
7,
30300,
11,
474,
11,
267,
68,
11,
2124,
11,
410,
8,
198,
220,
220,
220,
886,
198,
220,
220,
220,
410,
764,
12,
28,
45079,
1635,
2160,
7,
3149,
6,
764,
9,
410,
11,
5391,
82,
796,
362,
8,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1462,
62,
34985,
198,
220,
220,
220,
1288,
15,
796,
304,
321,
62,
34985,
7,
37659,
11,
29034,
11,
2124,
11,
410,
8,
198,
220,
220,
220,
44872,
7203,
24243,
2568,
25,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33172,
1288,
15,
58,
16,
4357,
37082,
77,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
24243,
32558,
12858,
25,
33172,
1288,
15,
58,
17,
4357,
37082,
77,
4943,
198,
220,
220,
220,
288,
17,
796,
288,
83,
1220,
362,
198,
220,
220,
220,
2488,
259,
65,
3733,
329,
479,
796,
352,
25,
74,
9806,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
764,
47932,
288,
17,
1635,
2160,
7,
3149,
6,
764,
9,
410,
11,
5391,
82,
796,
362,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
31986,
1046,
62,
9662,
0,
7,
67,
17,
11,
38779,
11,
45941,
11,
2124,
11,
410,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9067,
62,
64,
0,
7,
37659,
11,
29034,
11,
2124,
11,
281,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
764,
47932,
288,
83,
1635,
281,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
31986,
1046,
62,
9662,
0,
7,
67,
17,
11,
38779,
11,
45941,
11,
2124,
11,
410,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
764,
47932,
288,
17,
1635,
2160,
7,
3149,
6,
764,
9,
410,
11,
5391,
82,
796,
362,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
479,
4064,
479,
3448,
6624,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
77,
796,
304,
321,
62,
34985,
7,
37659,
11,
29034,
11,
2124,
11,
410,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
46935,
796,
410,
764,
10,
45079,
1635,
2160,
7,
3149,
6,
764,
9,
410,
11,
5391,
82,
796,
362,
8,
220,
1303,
1462,
62,
330,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1991,
276,
75,
76,
7,
1659,
58,
437,
4357,
685,
33327,
7,
45542,
764,
12,
1288,
15,
1776,
2824,
7,
31,
13,
352,
532,
1288,
77,
1220,
1288,
15,
15437,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
14323,
67,
329,
474,
796,
352,
25,
37659,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
85,
62,
1462,
62,
2577,
0,
7,
30300,
11,
474,
11,
2124,
11,
46935,
11,
267,
68,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1991,
276,
75,
76,
7,
1659,
58,
73,
4357,
267,
68,
58,
45299,
474,
60,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
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3419,
198,
437,
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] | 1.822432 | 5,412 |
module pkgExample
export plusone
greet() = print("Hello World!")
plusone(x::Int) = x + 1
include("sumRand.jl")
end # module
| [
21412,
279,
10025,
16281,
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505,
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70,
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7203,
16345,
38918,
13,
20362,
4943,
198,
198,
437,
1303,
8265,
198
] | 2.632653 | 49 |
using Test, CMDimData
function printsep(title)
println("\n", title, "\n", repeat("-", 80))
end
function show_testset_description()
@info Test.get_testset().description
end
@testset "CMDimData tests" begin
testfiles = ["EasyPlot.jl", "EasyData.jl"]
for testfile in testfiles
include(testfile)
end
end #testset
:Test_Complete
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198,
198,
25,
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] | 2.770492 | 122 |
#"""
#Return a vector of vectors indicating the indices of identical values in a
#`Column` or identical rows of a `Table` using a sorting algorithm (accepting
#keyword arguments of Julia's `sort` interface).
#"""
"""
Return a vector of vectors indicating the indices of identical values in a
`Column` or identical rows of a `Table` using a hashing algorithm.
"""
function groupinds(C::Union{Column,Table}; kwargs...) # TODO this method name might be added to Base in 0.5 (https://github.com/JuliaLang/julia/pull/15503/)
out = Dict{eltype(C),Vector{Int}}()
i = 1
for i = 1:length(C)
if !in(C.data[i], keys(out))
out[C.data[i]] = [i]
else
push!(out[C.data[i]],i)
end
end
collect(values(out))
end
"""
Return the indices of the first unique elements in a `Column` or a `Table`.
"""
function uniqueind(x::Column)
out = Vector{Int}()
seen = Set{eltype(x)}()
i = 1
for y in x
if !in(y, seen)
push!(seen, y)
push!(out, i)
end
i += 1
end
out
end
function uniqueind(x::Table)
out = Vector{Int}()
seen = Set{eltype(x)}()
i = 1
for y in x
if !in(y.data, seen)
push!(seen, y.data)
push!(out, i)
end
i += 1
end
out
end
function Base.unique(x::Union{Column,Table})
x[uniqueind(x)]
end
"""
Mutating form of `unique`.
"""
function unique!(x::Column)
idx = uniqueind(x)
x.data[1:length(idx)] == x.data[idx]
resize!(x.data, length(idx))
end
function unique!(x::Table)
idx = uniqueind(x)
for i = 1:ncol(x)
x.data[i][1:length(idx)] == x.data[i][idx]
resize!(x.data[i], length(idx))
end
end
Base.union(cell::Cell) = Column(cell)
Base.union(col::Column) = unique(col)
Base.union{Name}(col1::Union{Cell{Name},Column{Name}}, col2::Union{Cell{Name},Column{Name}}) =
Column{Name}(union(col1.data, col2.data))
Base.union{Name}(col1::Union{Cell{Name},Column{Name}}, col2::Union{Cell{Name},Column{Name}}, cols::Union{Cell,Column}...) =
union(union(col1, col2), cols...)
Base.intersect{Name}(col1::Union{Cell{Name},Column{Name}}, col2::Union{Cell{Name},Column{Name}}) =
Column{Name}(intersect(col1.data, col2.data))
Base.setdiff{Name}(col1::Union{Cell{Name},Column{Name}}, col2::Union{Cell{Name},Column{Name}}) =
Column{Name}(setdiff(col1.data, col2.data))
Base.union(row::Row) = vcat(row)
Base.union(table::Table) = unique(table)
function Base.union{Names}(table1::Union{Row{Names},Table{Names}}, table2::Union{Row{Names},Table{Names}})
seen = Set{eltypes(typeof(table1))}()
idx1 = Vector{Int}()
for i = 1:length(table1)
if !in(table1[i].data, seen)
push!(seen, table1[i].data)
push!(idx1, i)
end
end
idx2 = Vector{Int}()
for i = 1:length(table2)
if !in(table2[i].data, seen)
push!(seen, table2[i].data)
push!(idx2, i)
end
end
vcat(table1[idx1], table2[idx2])
end
function Base.union(table1::Union{Row,Table}, table2::Union{Row,Table}, tables::Union{Row,Table}...)
union(union(table1, table2), tables...) # TODO a bit inefficient to build the hash multiple times...
end
function Base.union!{Names}(table1::Table{Names}, table2::Union{Row{Names},Table{Names}})
seen = Set{eltypes(typeof(table1))}()
repeats = Vector{Int}()
for i = 1:length(table1)
if !in(table1[i].data, seen)
push!(seen, table1[i].data)
else
push!(repeats, i)
end
end
deleteat!(table1, repeats)
for i = 1:length(table2)
if !in(table2[i].data, seen)
push!(seen, table2[i].data)
push!(table1, table2[i])
end
end
end
function Base.union!(table1::Union{Row,Table}, table2::Union{Row,Table}, tables::Union{Row,Table}...)
union!(union!(table1, table2), tables...) # TODO a bit inefficient to build the hash multiple times...
end
function Base.intersect{Names}(table1::Union{Row{Names},Table{Names}}, table2::Union{Row{Names},Table{Names}})
seen1 = Set{eltypes(typeof(table1))}()
for i = 1:length(table1)
if !in(table1[i].data, seen1)
push!(seen1, table1[i].data)
end
end
seen2 = Set{eltypes(typeof(table2))}()
idx2 = Vector{Int}()
for i = 1:length(table2)
if in(table2[i].data, seen1)
if !in(table2[i].data, seen2)
push!(seen2, table2[i].data)
push!(idx2, i)
end
end
end
table2[idx2]
end
function Base.setdiff{Names}(table1::Union{Row{Names},Table{Names}}, table2::Union{Row{Names},Table{Names}})
idx1 = Dict{eltypes(typeof(table1)),Int}()
for i = 1:length(table1)
if !in(table1[i].data, keys(idx1))
idx1[table1[i].data] = i
end
end
for i = 1:length(table2)
if in(table2[i].data, keys(idx1))
delete!(idx1,table2[i].data)
end
end
table1[collect(values(idx1))]
end
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16,
58,
33327,
7,
27160,
7,
312,
87,
16,
4008,
60,
198,
437,
198
] | 2.143161 | 2,347 |
using Test
@testset "Van-Wijngaarden" begin include("test_van_wijngaarden.jl") end
@testset "Weibull Count" begin include("test_weibull_count.jl") end
@testset "Distribution" begin include("test_distribution.jl") end
| [
3500,
6208,
198,
198,
31,
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886,
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] | 2.868421 | 76 |
"""
change_sense(objective_sense)
Change the objective sense of optimization.
Possible arguments are `MOI.MAX_SENSE` and `MOI.MIN_SENSE`.
If you want to change the objective and sense at the same time, use
[`change_objective`](@ref) instead to do both at once.
"""
change_sense(objective_sense) =
(_, opt_model) -> set_objective_sense(opt_model, objective_sense)
"""
change_optimizer(optimizer)
Change the JuMP optimizer used to run the optimization.
This may be used to try different approaches for reaching the optimum, and in
problems that may require different optimizers for different parts, such as the
[`parsimonious_flux_balance_analysis`](@ref).
"""
change_optimizer(optimizer) = (_, opt_model) -> set_optimizer(opt_model, optimizer)
"""
change_optimizer_attribute(attribute_key, value)
Change a JuMP optimizer attribute. The attributes are optimizer-specific, refer
to the JuMP documentation and the documentation of the specific optimizer for
usable keys and values.
"""
change_optimizer_attribute(attribute_key, value) =
(_, opt_model) -> set_optimizer_attribute(opt_model, attribute_key, value)
"""
silence
Modification that disable all output from the JuMP optimizer (shortcut for
`set_silent` from JuMP).
"""
const silence = (_, opt_model) -> set_silent(opt_model)
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] | 3.285 | 400 |
#############################
## Numerical experiments associated with section 5.1.2
#############################
## While the paper does not explicitly treat third kind Volterra equations, an extension to these
## cases is relatively straightforward. Note that while in general there won't be exponential convergence
## unless the involved powers are favorably smooth, our method still achieves higher accuracy than many competitor methods.
##
using ApproxFun, MultivariateOrthogonalPolynomials, BandedMatrices, BlockBandedMatrices, SpecialFunctions, Plots
using SparseVolterraExamples
#############################
## Problem in Equation (17)
#############################
####
## The following block computes the coefficient vector of the approximation for a given polynomial order of approximation n.
## The accuracy obtained depends on both the polynomial order for the multiplication as well as the solution.
## Choosing the same order is fine in most cases but better accuracy can be obtained by adjusting for a given problem.
function solveSec512A(n,multin)
gf(x) = x^(2/3)*(10/3*x^(7/3)-3/16*x^(14/3));
Kfun(x,y) = y;
V = triVolterraFullKernelOpP01(Kfun,n,true);
V = reflectPabtoPba(n)*WLoweringP01P00(n)*V;
V = Conversion(Jacobi(0,0,0..1),Jacobi(1,2,0..1))[1:n,1:n]*V;
V = Multiplication(Fun(x->x^(2/3),Jacobi(1,2,0..1),multin),Jacobi(1,2,0..1))[1:n,1:n]*Derivative(Jacobi(0,1,0..1),1)[1:n,1:n]-V
coeff = [DirectEvalLHSP10at0(n);V[1:n-1,1:n]] \ [0;pad(Fun(x->gf(x),Jacobi(1,2, 0..1)).coefficients,n-1)]
return coeff
end
####
## Plot the analytic solution against the computed solution
coeff = solveSec512A(50,40)
plot(Fun(Jacobi(0,1,0..1),coeff),grid=false,xlabel="x",ylabel="u(x)",label="sparse method")
plot!(x->(x)^(10/3),0,1,grid=false,xlabel="x",ylabel="u(x)",label="analytic solution")
####
## Check the numerical error.
plot(x->(Fun(Jacobi(0,1,0..1),coeff)(x)-(x)^(10/3)),0,1,grid=false,xlabel="x",ylabel="error",label=false)
#############################
## Problem in Equation (18)
#############################
####
## The following block computes the coefficient vector of the approximation for a given polynomial order of approximation n.
## The accuracy obtained depends on both the polynomial order for the multiplication as well as the solution.
## Choosing the same order is fine in most cases but better accuracy can be obtained by adjusting for a given problem.
function solveSec512B(n,multin)
gf(x) = 9/2*x^4-1/20*x^(11/2)-1/6*x^6;
Kfun(x,y) = sqrt(y);
V = triVolterraFullKernelOpP01(Kfun,n,true,n);
V = reflectPabtoPba(n)*WLoweringP01P00(n)*V;
V = Conversion(Jacobi(0,0,0..1),Jacobi(1,2,0..1))[1:n,1:n]*V;
V = Multiplication(Fun(x->sqrt(x),Jacobi(1,2,0..1),multin),Jacobi(1,2,0..1))[1:n,1:n]*Derivative(Jacobi(0,1,0..1),1)[1:n,1:n]-(1/20)*Conversion(Jacobi(0,1,0..1),Jacobi(1,2,0..1))[1:n,1:n]*Multiplication(Fun(x->x,Jacobi(0,1,0..1),n),Jacobi(0,1,0..1))[1:n,1:n]-V
coeff = [DirectEvalLHSP10at0(n);V[1:n-1,1:n]] \ [0;pad(Fun(x->gf(x),Jacobi(1,2, 0..1)).coefficients,n-1)]
return coeff
end
####
## Plot the analytic solution against the computed solution
coeff = solveSec512B(80,30)
plot(Fun(Jacobi(0,1,0..1),coeff),grid=false,xlabel="x",ylabel="u(x)",label="sparse method")
plot!(x->(x)^(9/2),0,1,grid=false,xlabel="x",ylabel="u(x)",label="analytic solution")
####
## Check the numerical error.
plot(x->(Fun(Jacobi(0,1,0..1),coeff)(x)-(x)^(9/2)),0,1,grid=false,xlabel="x",ylabel="error",label=false)
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] | 2.529792 | 1,393 |
#
# COM API wrappers
#
function CoInitializeEx(concurrency_model)
res = ccall( (:CoInitializeEx, l_ole32), Uint32, (Csize_t, DWORD,), C_NULL, concurrency_model)
if (res == HRESULT.S_FALSE)
warn("COM interface already initialized")
elseif (res != HRESULT.S_OK)
error("Unable to initialize COM interface")
end
end
CoInitializeEx() = CoInitializeEx(COINIT.APARTMENTTHREADED)
CoInitialize() = CoInitializeEx()
function CLSIDFromProgID(id::String)
clsid = [GUID()]
res = ccall( (:CLSIDFromProgID, l_ole32), stdcall, Uint32, (LPCOLESTR, LPCLSID), utf16(id), pointer(clsid))
res == HRESULT.S_OK || error("Unable to locate program $id")
return CLSID(clsid[1])
end
# TODO
#function StringFromCLSID(clsid::CLSID)
# s = Ptr{Uint16}[C_NULL]
# res = ccall( (:StringFromCLSID, l_ole32), stdcall, Uint32,
# (REFCLSID, Ptr{Uint16}), clsid, s)
# return (res, s)
#end
function CoCreateInstance(clsid::CLSID; iid=None, clsctx=None)
if (clsctx == None)
clsctx = CLSCTX.SERVER
end
if (iid == None)
iid = IID[IUnknown]
end
ppv = [C_NULL]
res = ccall( (:CoCreateInstance, l_ole32), Uint32,
(LPCLSID, LPUNKNOWN, DWORD, REFIID, Ptr{Ptr{Void}}),
&clsid, C_NULL, clsctx, &iid, ppv)
if (res == REGDB.E_CLASSNOTREG)
error("Class not registered ($clsid)")
end
iid[ppv[1]]
end
################################################################################
#
# IUnknown interface
#
# QueryInterface
# AddRef
# Release
function QueryInterface{T <: IUnknown}(this::Ptr{T}, clsid::CLSID; err=false)
obj = [C_NULL]
println("calling")
res = @vcall(this, 1, HResult, &clsid::REFIID, pointer(obj)::Ptr{Ptr{Void}})
if (res != HRESULT.S_OK)
err && error("QueryInterface: $clsid not supported")
return C_NULL
end
println("res: ", res)
println("obj: ", obj)
return reinterpret(Ptr{typeof(clsid)},obj[1])
end
function AddRef{T<:IUnknown}(this::Ptr{T})
refcount = ccall( getvtptr(this,2), thiscall, Culong,
(Ptr{Void},), this)
end
function Release{T<:IUnknown}(this::Ptr{T})
refcount = ccall( getvtptr(this,3), thiscall, Culong,
(Ptr{Void},), this)
end
#
# IDispatch methods
#
# <: IUnknown
# GetTypeInfoCount
# GetTypeInfo
# GetIDsOfNames
# Invoke
function GetTypeInfoCount{T <: IDispatch}(this::Ptr{T})
pctinfo = [zero(Cuint)]
res = @vcall(this, 4, HResult, pctinfo::Ptr{Cuint})
show(res)
res != HRESULT.S_OK && error("failed GetTypeInfoCount")
return pctinfo[1]
end
# TODO
type ITypeInfo
end
function GetTypeInfo{T <: IDispatch}(this::Ptr{T}, tinfokind::Cuint)
lcid = C_NULL
itypeinfo = Ptr{Ptr{ITypeInfo}}[C_NULL]
res = ccall( getvtptr(this, 5), thiscall, Uint32,
(THISPTR,
Cuint, LCID, Ptr{Ptr{ITypeInfo}}),
o.ptr, tinfokind, lcid, itypeinfo)
res != HRESULT.S_OK && error("failed GetTypeInfo")
return itypeinfo[1]
end
function GetIDsOfNames{T <: IDispatch}(this::Ptr{T}, numNames)
lcid = DefaultLCID::LCID
rgszNames = Ptr{LPOLESTR}[C_NULL]
numNames = convert(Cuint, numNames)
refiid::REFIID
namesptr = Ptr{LPOLESTR}[C_NULL]
res = ccall(getvtptr(this, 6), thiscall, Uint32,
(THISPTR,
REFIID, Ptr{LPOLESTR}, Cuint, LCID, Ptr{DISPID}),
this,
BaseIID.IID_NULL, rgszNames, cNames, lcid, rgDispId)
res != HRESULT.S_OK && error("GetIDsOfNames error")
return rgszNames[1]
end
################################################################################
#
# High-level API
#
function getindex(c::COMGlobal, name::String)
# TODO: error message based on the HRESULT or REGDB value
clsid = CLSIDFromProgID(name)
CoCreateInstance(clsid)
end
# Intended interface:
# ie = COM["InternetExplorer.Application"]
# ie[:Navigate2]("www.julialang.org")
function getindex(c::COMGlobal, clsid::CLSID)
CoCreateInstance(clsid)
end
function getindex(c::IDispatch, x::Symbol)
end | [
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] | 2.184114 | 1,901 |
@testset "varstruct_call_parse" begin
expr, args_field, args_defaultvalue, args_type, args_param, T = VarStructs.varstruct_call_parse(
Main,
quote
Person(
name::String = "Amin",
number::Float64 = 20.0,
)
end
)
@test args_field == [:name, :number]
@test args_defaultvalue == ["Amin", 20.0]
@test args_type == [:String, :Float64]
@test args_param == [Expr(:kw, :name, "Amin"), Expr(:kw, :number, 20.0)]
@test T == :Person
end
@testset "varstruct_struct_parse" begin
argsexpr, args_field, args_defaultvalue, args_type, args_param, args_check, is_struct_mutable, T = VarStructs.varstruct_struct_parse(
Main,
quote
struct Person
name::String
number::Float64 = 20.0
end
end
)
@test args_field == [:name, :number]
@test any(args_defaultvalue .== [Unset(), 20.0])
@test args_type == [:String, :Float64]
@test args_param == [:name, Expr(:kw, :number, 20.0)]
@test T == :Person
end
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198,
220,
220,
220,
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62,
4906,
6624,
685,
25,
10100,
11,
1058,
43879,
2414,
60,
198,
220,
220,
220,
2488,
9288,
26498,
62,
17143,
6624,
685,
25,
3672,
11,
1475,
1050,
7,
25,
46265,
11,
1058,
17618,
11,
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2488,
9288,
309,
6624,
1058,
15439,
198,
437,
198
] | 2.086042 | 523 |
abstract type AbstractTokenType end
abstract type AbstractToken end
# Biological symbol is *any* biological symbol
struct BIOLOGICAL_SYMBOL <: AbstractTokenType end
# Biological type prefix is *any* type prefix
struct BIOLOGICAL_TYPE_PREFIX <: AbstractTokenType end
# Keywords used in the various sentences -
struct LPAREN <: AbstractTokenType end
struct RPAREN <: AbstractTokenType end
struct TRANSCRIPTION <: AbstractTokenType end
struct TRANSLATION <: AbstractTokenType end
struct CATALYZE <: AbstractTokenType end
struct BIND <: AbstractTokenType end
struct AND <: AbstractTokenType end
struct OR <: AbstractTokenType end
struct SPACE <: AbstractTokenType end
struct ARE <: AbstractTokenType end
struct IS <: AbstractTokenType end
struct A <: AbstractTokenType end
struct TO <: AbstractTokenType end
struct IN <: AbstractTokenType end
struct MODEL <: AbstractTokenType end
struct THE <: AbstractTokenType end
struct GENE_TYPE_SYMBOL <: AbstractTokenType end
struct mRNA_TYPE_SYMBOL <: AbstractTokenType end
struct tRNA_TYPE_SYMBOL <: AbstractTokenType end
struct regRNA_TYPE_SYMBOL <: AbstractTokenType end
struct PROTEIN_TYPE_SYMBOL <: AbstractTokenType end
struct METABOLITE_TYPE_SYMBOL <: AbstractTokenType end
struct RNA_POLYMERASE_II_SYMBOL <: AbstractTokenType end
struct RIBOSOME_SYMBOL <: AbstractTokenType end
struct TYPE <: AbstractTokenType end
struct OF <: AbstractTokenType end
struct PHOSPHORYLATE <: AbstractTokenType end
struct DEPHOSPHORYLATE <: AbstractTokenType end
struct COMPLEX <: AbstractTokenType end
struct FORM <: AbstractTokenType end
struct AT <: AbstractTokenType end
struct PROMOTER <: AbstractTokenType end
struct GENE <: AbstractTokenType end
struct POSITIVE <: AbstractTokenType end
struct NEGATIVE <: AbstractTokenType end
struct UNKNOWN <: AbstractTokenType end
# Bounds types -
struct SOURCE <: AbstractTokenType end
struct SINK <: AbstractTokenType end
struct BOUND <: AbstractTokenType end
struct UNBOUND <: AbstractTokenType end
# endpoint -
struct SEMICOLON <: AbstractTokenType end
# type that we will build -
struct MinervaToken <: AbstractToken
lexeme::String
type::AbstractTokenType
end
struct MinervaSentence
sentence::Array{MinervaToken,1}
end
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198,
437,
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] | 3.557166 | 621 |
module SphericalWrapper
using JavaCall
include("OnedWrapper/OnedWrapper.jl")
include("TwodWrapper/TwodWrapper.jl")
end
| [
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] | 2.711111 | 45 |
using Random
using WordTokenizers
"""
recursive_readdir(path::AbstractString="./")
recursive read all file from a dir. return a list of filenames.
"""
function recursive_readdir(path::AbstractString="./")
ret = String[]
for (root, dirs, files) in walkdir(path)
append!(ret, map(file->joinpath(root, file), files))
end
ret
end
"""
bert_pretrain_task(datachn::Channel, wordpiece::WordPiece;
buffer_size = 100,
channel_size = 100
wordpiece::WordPiece,
sentences_pool = sentences;
start_token = "[CLS]",
sep_token = "[SEP]",
mask_token = "[MASK]",
mask_ratio = 0.15,
real_token_ratio = 0.1,
random_token_ratio = 0.1,
whole_word_mask = false,
next_sentence_ratio = 0.5,
next_sentence = true,
tokenizer = tokenize,
istokenized = false,
return_real_sentence = false)
helper function to generate bert mask language modeling and next sentence prediction data. `datachn` is a `Channel` with input documents line by line.
"""
function bert_pretrain_task(datachn::Channel,
wordpiece::WordPiece;
buffer_size = 100,
channel_size = 100,
kwargs...
)
outchn = Channel(channel_size)
bert_pretrain_task(outchn, datachn, wordpiece; buffer_size = buffer_size, kwargs...)
outchn
end
function bert_pretrain_task(outchn::Channel,
datachn::Channel,
wordpiece::WordPiece;
buffer_size = 100,
kwargs...
)
task = @async begin
buffer = Vector(undef, buffer_size)
while isopen(datachn)
i = 1
eod = false
while i <= buffer_size
try
sentence = take!(datachn)
if isempty(sentence)
continue
else
buffer[i] = sentence
i+=1
end
catch e
if isa(e, InvalidStateException) && e.state==:closed
eod = true
break
else
rethrow()
end
end
end
i -= 1
if eod || i == buffer_size
bert_pretrain_task(outchn, @view(buffer[1:(eod ? i - 1 : i)]), wordpiece; kwargs...)
end
end
end
bind(outchn, task)
end
function bert_pretrain_task(sentences,
wordpiece::WordPiece,
sentences_pool = sentences;
channel_size = 100,
kwargs...
)
chn = Channel(channel_size)
task = @async bert_pretrain_task(chn, sentences, wordpiece, sentences_pool; kwargs...)
bind(chn, task)
chn
end
function bert_pretrain_task(chn::Channel,
sentences,
wordpiece::WordPiece,
sentences_pool = sentences;
start_token = "[CLS]",
sep_token = "[SEP]",
mask_token = "[MASK]",
mask_ratio = 0.15,
real_token_ratio = 0.1,
random_token_ratio = 0.1,
whole_word_mask = false,
next_sentence_ratio = 0.5,
next_sentence = true,
tokenizer = tokenize,
istokenized = false,
return_real_sentence = false)
foreach(enumerate(sentences)) do (i, sentence)
sentenceA = masksentence(
istokenized ? sentence : tokenizer(sentence),
wordpiece;
mask_token = mask_token,
mask_ratio = mask_ratio,
real_token_ratio = real_token_ratio,
random_token_ratio = random_token_ratio,
whole_word_mask = whole_word_mask
)
if next_sentence
if rand() <= next_sentence_ratio && i != length(sentences)
sentenceB = sentences[i+1]
isnext = true
else
sentenceB = rand(sentences_pool)
isnext = false
end
sentenceB = masksentence(
istokenized ? sentenceB : tokenizer(sentenceB),
wordpiece;
mask_token = mask_token,
mask_ratio = mask_ratio,
real_token_ratio = real_token_ratio,
random_token_ratio = random_token_ratio,
whole_word_mask = whole_word_mask
)
masked_sentence = _wrap_sentence(sentenceA[1],
sentenceB[1];
start_token = start_token,
sep_token = sep_token)
sentence = _wrap_sentence(sentenceA[2],
sentenceB[2];
start_token = start_token,
sep_token = sep_token)
mask_idx = _wrap_idx(sentenceA[3],
sentenceB[3],
length(sentenceA[1]))
else
masked_sentence = _wrap_sentence(sentenceA[1];
start_token = start_token,
sep_token = sep_token)
sentence = _wrap_sentence(sentenceA[2];
start_token = start_token,
sep_token = sep_token)
mask_idx = _wrap_idx(sentenceA[3])
end
masked_token = sentence[mask_idx]
if return_real_sentence
if next_sentence
put!(chn, (masked_sentence, mask_idx, masked_token, isnext, sentence))
else
put!(chn, (masked_sentence, mask_idx, masked_token, sentence))
end
else
if next_sentence
put!(chn, (masked_sentence, mask_idx, masked_token, isnext))
else
put!(chn, (masked_sentence, mask_idx, masked_token))
end
end
end
end
function _wrap_sentence(sentence1, sentence2...; start_token = "[CLS]", sep_token = "[SEP]")
pushfirst!(sentence1, start_token)
push!(sentence1, sep_token)
map(s->push!(s, sep_token), sentence2)
vcat(sentence1, sentence2...)
end
_wrap_idx(sentence1_idx, pre_len = 1) = sentence1_idx .+= pre_len
function _wrap_idx(sentence1_idx, sentence2_idx, len1)
_wrap_idx(sentence1_idx)
_wrap_idx(sentence2_idx, len1)
vcat(sentence1_idx, sentence2_idx)
end
masksentence(sentence::String,
tokenizer::Tf, wordpiece::WordPiece;
mask_token = "[MASK]",
mask_ratio = 0.15,
real_token_ratio = 0.1,
random_token_ratio = 0.1,
whole_word_mask = false) where Tf = masksentence(
tokenizer(sentence),
wordpiece;
mask_token = mask_token,
mask_ratio = mask_ratio,
real_token_ratio = real_token_ratio,
random_token_ratio = random_token_ratio,
whole_word_mask = whole_word_mask)
function masksentence(words,
wordpiece;
mask_token = "[MASK]",
mask_ratio = 0.15,
real_token_ratio = 0.1,
random_token_ratio = 0.1,
whole_word_mask = false)
if whole_word_mask
masked_word_idx = randsubseq(1:length(words), mask_ratio)
is_masked = fill(false, length(words))
map(masked_word_idx) do i
is_masked[i] = true
end
tokens = Vector{String}()
masked_idx = Vector{Int}()
sizehint!(tokens, length(words))
sizehint!(masked_idx, length(masked_word_idx))
len = 0
for (word, im) ∈ zip(words, is_masked)
wps = wordpiece(word)
wpl = length(wps)
append!(tokens, wps)
im && append!(masked_idx, map(i->i+len, 1:wpl))
len += wpl
end
else
tokens = wordpiece(words)
masked_idx = randsubseq(1:length(tokens), mask_ratio)
end
masked_tokens = copy(tokens)
for idx ∈ masked_idx
r = rand()
if r <= random_token_ratio
masked_tokens[idx] = rand(wordpiece.vocab)
elseif r > real_token_ratio + random_token_ratio
masked_tokens[idx] = mask_token
end
end
return masked_tokens, tokens, masked_idx
end
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] | 1.836148 | 4,559 |
function loadBrukerfolderautophase(full_path::String)
pdata_path = "$full_path/pdata/1"
PyCall.py"""
import numpy
import nmrglue as ng
s = $full_path
procdata_dir = $pdata_path
dic, data = ng.bruker.read(s)
# ---time correction before FT
precorr_time = ng.bruker.remove_digital_filter(dic, data)
precorr_frq0 = ng.proc_base.fft(precorr_time)
precorr_frq = ng.proc_autophase.autops(precorr_frq0, 'acme')
# precorr_frq = ng.proc_autophase.autops(precorr_frq0, 'peak_minima')
#---time correction after FT
postcorr_frq0 = ng.proc_base.fft(data)
postcorr_frq1 = ng.bruker.remove_digital_filter(dic, postcorr_frq0, post_proc=True)
postcorr_frq = ng.proc_autophase.autops(postcorr_frq1, 'acme')
#---data processed using TopSpin (3.5pl7)
bruker_frq = ng.bruker.read_pdata(procdata_dir)[1][::-1]
guess_udic = ng.fileio.bruker.guess_udic(dic, data)
"""
dic = PyCall.py"dic"
data = PyCall.py"data"
precorr_time = PyCall.py"precorr_time"
precorr_frq0 = PyCall.py"precorr_frq0"
precorr_frq = PyCall.py"precorr_frq"
postcorr_frq0 = PyCall.py"postcorr_frq0"
postcorr_frq1 = PyCall.py"postcorr_frq1"
postcorr_frq = PyCall.py"postcorr_frq"
bruker_frq = PyCall.py"bruker_frq"
guess_udic = PyCall.py"guess_udic"
return dic, data, precorr_time, precorr_frq0, precorr_frq,
postcorr_frq0, postcorr_frq1, postcorr_frq, bruker_frq,
guess_udic
end
"""
loadspectrum(full_path::String;
solvent_ppm = 4.8,
solvent_window_ppm = 0.3,
N_Ω::Int = 20000,
N_optim::Int = 1000,
n_particles::Int = 3,
max_iters::Int = 100,
α_lower = 1e-6,
α_upper_factor = 50.0,
λ_lower = 1e-10,
λ_upper = 20.0,
λ_initial = 3.0,
st_ind_default = 100,
verbose_flag::Bool = false)
Loads a Bruker 1D 1H NMR experiment. Uses the NMRGlue Python library. Estimates the 0 ppm and solvent resonance components. The solvent resonance component is estimated in the frequency interval `solvent_ppm` +/- `solvent_window_ppm`, in units ppm.
On a Linux terminal, run the following command if you don't have NMRGlue installed.
pip install nmrglue --user
Notes:
Either `st_ind_default` samples or `dead_time_st` number of samples are removed from the raw time-domain data to create the output time-domain data, s_t. This is to remove the silence at the beginning of the acquired data due to the dead-time property of practical NMR spectrometers. 'dead_time_st' is calculated automatically from the metadata of the experiment files.
Therefore, the returned sampling frequency `fs` from loadspectrum() and the sampling frequency in the returned metadata ditionary `dic` are slightly different.
...
# Outputs, in the order returned.
- `s_t::Vector{Complex{Float64}}`: time-domain experiment data.
- `S::Function`: Discrete-time Fourier transform of s_t.
- `hz2ppmfunc::Function`: conversion function from Hz to ppm for this experiment. The formula used is `hz2ppmfunc = uu->(uu - ν_0ppm)*SW/fs`.
- `ppm2hzfunc::Function`: conversion function from ppm to Hz for this experiment. The formula used is `ppm2hzfunc = pp->(ν_0ppm + pp*fs/SW)`.
- `ν_0ppm::Float64`: where the estimated frequency of the 0 ppm resonance component, in Hz.
- `fs::Float64`: the estimated sampling frequency of `s_t`.
- `SW::Float64`: the spectral window of the spectrometer, as provided by the metadata of the experiment. In units of ppm.
- `α_0ppm`: The amplitude parameter of the estimate resonance component for 0 ppm reference, in the free-induction decay model.
- `β_0ppm`: The phase parameter of the estimate resonance component for 0 ppm reference, in the free-induction decay model.
- `λ_0ppm`: The T2 parameter of the estimate resonance component for 0 ppm reference, in the free-induction decay model.
- `Ω_0ppm`: The radial frequency parameter of the estimate resonance component for 0 ppm reference, in the free-induction decay model. In units Hz/(2*π).
- `results_0ppm`: The NLopt.jl optimization results for the estimation of the 0 ppm reference. Inspect this to see if the estimated parameters for the 0 ppm reference are reliable.
- `dic`; The dictionary that contains all metadata from NMRGlue for the NMR experiment.
- `α_solvent`: The amplitude parameter of the estimate resonance component for 0 ppm reference, in the free-induction decay model.
- `β_solvent`: The phase parameter of the estimate resonance component for 0 ppm reference, in the free-induction decay model.
- `λ_solvent`: The T2 parameter of the estimate resonance component for 0 ppm reference, in the free-induction decay model.
- `Ω_solvent`: The radial frequency parameter of the estimate resonance component for 0 ppm reference, in the free-induction decay model. In units Hz/(2*π).
- `results_solvent`: The NLopt.jl optimization results for the estimation of the 0 ppm reference. Inspect this to see if the estimated parameters for the 0 ppm reference are reliable.
, , , , ,
# Examples
See load_experiment.jl in the /examples folder for an example.
"""
function loadspectrum(full_path::String;
solvent_ppm = 4.8,
solvent_window_ppm = 0.3,
N_Ω::Int = 20000,
N_optim::Int = 1000,
n_particles::Int = 3,
max_iters::Int = 100,
α_lower = 1e-6,
α_upper_factor = 50.0,
λ_lower = 1e-10,
λ_upper = 20.0,
λ_initial = 3.0,
st_ind_default = 100, # future: see if we can derive this from DE dead time later.
verbose_flag::Bool = false)
dic, data, precorr_time, precorr_frq0, precorr_frq, postcorr_frq0, postcorr_frq1,
postcorr_frq, Bruker_spectrums_nmrglue,
guess_udic = loadBrukerfolderautophase(full_path)
DE = dic["acqus"]["DE"] # in microseconds.
BF1 = dic["acqus"]["BF1"]
## see Bruker TopSpin acquistion commands and parameters v 003.
# TD - Time Domain; Number Of Raw Data Points. i.e., length(data)*2 - TD = 0.
# total samples from both time-series.
TD = dic["acqus"]["TD"]
# SW - Spectral Width in ppm
SW = dic["acqus"]["SW"]
# SFO1 - SFO8 - Irradiation (carrier) Frequencies For Channels f1 to f8
SFO1 = dic["acqus"]["SFO1"]
O1 = dic["acqus"]["O1"]
dic["acqus"]["NUC1"]
CAR = O1*1.0
fs_dic = dic["acqus"]["SW_h"]
N_diff = length(data)*2 - TD
t_data = gettimerange(length(data), fs_dic)
AQ = TD/(2*SW*SFO1)
dead_time = t_data[end] - AQ
dead_time_st = round(Int, dead_time/t_data[end] * length(data))
st_ind = max(st_ind_default, dead_time_st)
s_t = data[st_ind:end]
t = gettimerange(length(s_t), fs_dic)
fs = (length(s_t)-1)/t[end] # this is really fs_dic.
@assert abs(fs-fs_dic) < 1e-9
α_0ppm, β_0ppm, λ_0ppm, Ω_0ppm,
results_0ppm = estimatereferencecompoundfreq(CAR, fs, SW, s_t, t;
N_Ω = N_Ω,
N_optim = N_optim,
n_particles = n_particles,
max_iters = max_iters,
α_lower = α_lower,
α_upper_factor = α_upper_factor,
λ_lower = λ_lower,
λ_upper = λ_upper,
λ_initial = λ_initial,
verbose_flag = verbose_flag)
ν_0ppm = Ω_0ppm ./ (2*π)
hz2ppmfunc = uu->(uu - ν_0ppm)*SW/fs
ppm2hzfunc = pp->(ν_0ppm + pp*fs/SW)
# S is scaled DTFT of data.
S = vv->(computeDTFTch3eq29(s_t, vv, t)/fs)
# solvent.
u_solvent_lower = ppm2hzfunc(solvent_ppm-solvent_window_ppm)
u_solvent_upper = ppm2hzfunc(solvent_ppm+solvent_window_ppm)
α_solvent, β_solvent, λ_solvent, Ω_solvent,
results_solvent = estimatesinglet(fs, s_t, t,
u_solvent_lower, u_solvent_upper;
N_Ω = N_Ω,
N_optim = N_optim,
n_particles = n_particles,
max_iters = max_iters,
α_lower = α_lower,
α_upper_factor = α_upper_factor,
λ_lower = λ_lower,
λ_upper = λ_upper,
λ_initial = λ_initial,
verbose_flag = verbose_flag)
return s_t, S, hz2ppmfunc, ppm2hzfunc, ν_0ppm, fs, SW, α_0ppm, β_0ppm, λ_0ppm, Ω_0ppm,
results_0ppm, dic, α_solvent, β_solvent, λ_solvent, Ω_solvent, results_solvent
end
function estimatereferencecompoundfreq( CAR::T,
fs::T,
SW::T,
s_t::Vector{Complex{T}},
t;
N_Ω::Int = 20000,
N_optim::Int = 1000,
n_particles::Int = 3,
max_iters::Int = 100,
window_ppm::T = 0.3,
α_lower::T = 1e-6,
α_upper_factor::T = 50.0,
λ_lower::T = 1e-10,
λ_upper::T = 20.0,
λ_initial::T = 3.0,
verbose_flag::Bool = false) where T <: Real
# prepare frequency range.
ν0_initial, hz2ppmfunc0, ppm2hzfunc0 = getinitalguessreferencecompoundfreq(CAR, fs, SW)
bp_a = ppm2hzfunc0(-window_ppm)
bp_b = ppm2hzfunc0(window_ppm)
return estimatesinglet(fs, s_t, t, bp_a, bp_b;
N_Ω = N_Ω,
N_optim = N_optim,
n_particles = n_particles,
max_iters = max_iters,
α_lower = α_lower,
α_upper_factor = α_upper_factor,
λ_lower = λ_lower,
λ_upper = λ_upper,
λ_initial = λ_initial,
verbose_flag = verbose_flag)
end
# λ_initial: m a value of 3 seems decent for the λ of DSS under 600 MHz.
# no error checking on whether λ_initial ∈ [λ_lower, λ_upper].
function estimatesinglet( fs::T,
s_t::Vector{Complex{T}},
t,
u_lower,
u_upper;
N_Ω::Int = 20000,
N_optim::Int = 1000,
n_particles::Int = 3,
max_iters::Int = 100,
α_lower::T = 1e-6,
α_upper_factor::T = 100.0,
λ_lower::T = 1e-10,
λ_upper::T = 20.0,
λ_initial::T = 3.0,
verbose_flag::Bool = false,
xtol_rel::T = 1e-12,
nlopt_seed::Int = 25) where T <: Real
# prepare data.
DTFT_s_scaled = vv->(computeDTFTch3eq29(s_t, vv, t)/fs)
u_range_optim = LinRange(u_lower, u_upper, N_optim)
S_U = DTFT_s_scaled.(u_range_optim)
# set up optimization.
max_val, max_ind = findmax(abs.(S_U))
Ω = u_range_optim[max_ind] *2*π
#println("Ω = ", Ω)
costfunc = pp->evalcostreferencecompound(pp,
S_U, u_range_optim, Ω)
optim_lower_limit = [α_lower; -π; λ_lower]
optim_upper_limit = [max_val*α_upper_factor; π; λ_upper]
p0 = [max_val; 0.0; λ_initial]
# optimize.
op = Optim.Options( iterations = max_iters,
store_trace = false,
show_trace = verbose_flag)
results = Optim.optimize( costfunc,
p0,
Optim.ParticleSwarm(; lower = optim_lower_limit,
upper = optim_upper_limit,
n_particles = n_particles),
op)
#α, β, λ = results.minimizer
# println("α = ", α)
# Q = [1221.2329692741823;
# 1.5072497616902667;
# 4.266537181116996]
# println("cost Q = ", costfunc(Q))
# println("cost p = ", costfunc(results.minimizer))
# use NLopt.
grad_func = xx->FiniteDiff.finite_difference_gradient(costfunc, xx)
NLopt.srand(nlopt_seed)
p_initial = results.minimizer
#p_initial = p0
opt = NLopt.Opt(:GN_ESCH, length(p_initial))
#opt = NLopt.Opt(:LD_MMA, length(p_initial))
min_cost_p, min_p, ret_p, numevals_p = runNLopt!( opt,
p_initial,
costfunc,
grad_func,
optim_lower_limit,
optim_upper_limit;
max_iters = 100*max_iters,
xtol_rel = xtol_rel)
#
opt = NLopt.Opt(:LD_MMA, length(min_p))
min_cost_p, min_p, ret_p, numevals_p = runNLopt!( opt,
min_p,
costfunc,
grad_func,
optim_lower_limit,
optim_upper_limit;
max_iters = 100*max_iters,
xtol_rel = xtol_rel)
α, β, λ = min_p
# #
# println("p_initial = ", p_initial)
# println("α = ", α)
return α, β, λ, Ω, results
end
function evalcostreferencecompound( p::Vector{T},
S_U::Vector{Complex{T}},
u_range,
Ω) where T <: Real
# parse.
α, β, λ = p
cost = zero(T)
for i = 1:length(u_range)
q_u = evalcomplexLorentzian(u_range[i], α, β, λ, Ω)
cost += abs2(q_u - S_U[i])
#cost += (abs(q_u)-abs(S_U[i]))^2
end
return cost
end
function getinitalguessreferencecompoundfreq(CAR::T, fs::T, SW::T) where T <: Real
ν0_initial = fs - CAR
hz2ppmfunc0 = uu->(uu - ν0_initial)*SW/fs
ppm2hzfunc0 = pp->(ν0_initial + pp*fs/SW)
return ν0_initial, hz2ppmfunc0, ppm2hzfunc0
end
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] | 2.234994 | 5,481 |
# Contains all the data relative to quadrature in the sphere
struct SphereQuadraturervec
r1Vec::Array{Float64}
r2Vec::Array{Float64}
r3Vec::Array{Float64}
wVec::Array{Float64}
end
| [
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] | 2.419753 | 81 |
#=
run_tests
Copyright © 2019 Mark Wells <mwellsa@gmail.com>
Distributed under terms of the MIT license.
=#
using InterpolatedRejectionSampling
using Test
import Random.seed!
seed!(1234)
@testset "irsample" begin
X = range(0,π,length=5)
Y = range(0,π/4,length=4)
knots = (X,Y)
prob = [sin(x)+tan(y) for x in X, y in Y]
n = 100
xy = irsample(knots, prob, n)
@test isa(xy, Matrix{Float64})
@test size(xy) == (2,n)
xy = Matrix{Union{Float64,Missing}}(missing,2,n)
irsample!(xy, knots, prob)
@test isa(xy, Matrix{Union{Missing,Float64}})
@test size(xy) == (2,n)
@test iszero(count(ismissing.(xy)))
x = irsample(X, sin.(X), n)
@test isa(x, Vector{Float64})
@test length(x) == n
end
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] | 2.188571 | 350 |
# Julia wrapper for header: IviDmm.h
# Automatically generated using Clang.jl
function IviDmm_init(logicalName, idQuery, resetDevice, vi)
ccall((:IviDmm_init, libnidmm), ViStatus, (ViRsrc, ViBoolean, ViBoolean, Ptr{ViSession}), logicalName, idQuery, resetDevice, vi)
end
function IviDmm_close(vi)
ccall((:IviDmm_close, libnidmm), ViStatus, (ViSession,), vi)
end
function IviDmm_reset(vi)
ccall((:IviDmm_reset, libnidmm), ViStatus, (ViSession,), vi)
end
function IviDmm_self_test(vi, selfTestResult, selfTestMessage)
ccall((:IviDmm_self_test, libnidmm), ViStatus, (ViSession, Ptr{ViInt16}, Ptr{ViChar}), vi, selfTestResult, selfTestMessage)
end
function IviDmm_error_query(vi, errorCode, errorMessage)
ccall((:IviDmm_error_query, libnidmm), ViStatus, (ViSession, Ptr{ViInt32}, Ptr{ViChar}), vi, errorCode, errorMessage)
end
function IviDmm_error_message(vi, statusCode, message)
ccall((:IviDmm_error_message, libnidmm), ViStatus, (ViSession, ViStatus, Ptr{ViChar}), vi, statusCode, message)
end
function IviDmm_revision_query(vi, driverRev, instrRev)
ccall((:IviDmm_revision_query, libnidmm), ViStatus, (ViSession, Ptr{ViChar}, Ptr{ViChar}), vi, driverRev, instrRev)
end
function IviDmm_InvalidateAllAttributes(vi)
ccall((:IviDmm_InvalidateAllAttributes, libnidmm), ViStatus, (ViSession,), vi)
end
function IviDmm_ResetWithDefaults(vi)
ccall((:IviDmm_ResetWithDefaults, libnidmm), ViStatus, (ViSession,), vi)
end
function IviDmm_Disable(vi)
ccall((:IviDmm_Disable, libnidmm), ViStatus, (ViSession,), vi)
end
function IviDmm_InitWithOptions(logicalName, IDQuery, resetDevice, optionString, vi)
ccall((:IviDmm_InitWithOptions, libnidmm), ViStatus, (ViRsrc, ViBoolean, ViBoolean, ViConstString, Ptr{ViSession}), logicalName, IDQuery, resetDevice, optionString, vi)
end
function IviDmm_GetAttributeViInt32(vi, channelName, attributeId, value)
ccall((:IviDmm_GetAttributeViInt32, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, Ptr{ViInt32}), vi, channelName, attributeId, value)
end
function IviDmm_SetAttributeViInt32(vi, channelName, attributeId, value)
ccall((:IviDmm_SetAttributeViInt32, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViInt32), vi, channelName, attributeId, value)
end
function IviDmm_CheckAttributeViInt32(vi, channelName, attributeId, value)
ccall((:IviDmm_CheckAttributeViInt32, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViInt32), vi, channelName, attributeId, value)
end
function IviDmm_GetAttributeViInt64(vi, channelName, attributeId, value)
ccall((:IviDmm_GetAttributeViInt64, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, Ptr{ViInt64}), vi, channelName, attributeId, value)
end
function IviDmm_SetAttributeViInt64(vi, channelName, attributeId, value)
ccall((:IviDmm_SetAttributeViInt64, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViInt64), vi, channelName, attributeId, value)
end
function IviDmm_CheckAttributeViInt64(vi, channelName, attributeId, value)
ccall((:IviDmm_CheckAttributeViInt64, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViInt64), vi, channelName, attributeId, value)
end
function IviDmm_GetAttributeViReal64(vi, channelName, attributeId, value)
ccall((:IviDmm_GetAttributeViReal64, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, Ptr{ViReal64}), vi, channelName, attributeId, value)
end
function IviDmm_SetAttributeViReal64(vi, channelName, attributeId, value)
ccall((:IviDmm_SetAttributeViReal64, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViReal64), vi, channelName, attributeId, value)
end
function IviDmm_CheckAttributeViReal64(vi, channelName, attributeId, value)
ccall((:IviDmm_CheckAttributeViReal64, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViReal64), vi, channelName, attributeId, value)
end
function IviDmm_GetAttributeViString(vi, channelName, attributeId, bufferSize, value)
ccall((:IviDmm_GetAttributeViString, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViInt32, Ptr{ViChar}), vi, channelName, attributeId, bufferSize, value)
end
function IviDmm_SetAttributeViString(vi, channelName, attributeId, value)
ccall((:IviDmm_SetAttributeViString, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViConstString), vi, channelName, attributeId, value)
end
function IviDmm_CheckAttributeViString(vi, channelName, attributeId, value)
ccall((:IviDmm_CheckAttributeViString, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViConstString), vi, channelName, attributeId, value)
end
function IviDmm_GetAttributeViBoolean(vi, channelName, attributeId, value)
ccall((:IviDmm_GetAttributeViBoolean, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, Ptr{ViBoolean}), vi, channelName, attributeId, value)
end
function IviDmm_SetAttributeViBoolean(vi, channelName, attributeId, value)
ccall((:IviDmm_SetAttributeViBoolean, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViBoolean), vi, channelName, attributeId, value)
end
function IviDmm_CheckAttributeViBoolean(vi, channelName, attributeId, value)
ccall((:IviDmm_CheckAttributeViBoolean, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViBoolean), vi, channelName, attributeId, value)
end
function IviDmm_GetAttributeViSession(vi, channelName, attributeId, value)
ccall((:IviDmm_GetAttributeViSession, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, Ptr{ViSession}), vi, channelName, attributeId, value)
end
function IviDmm_SetAttributeViSession(vi, channelName, attributeId, value)
ccall((:IviDmm_SetAttributeViSession, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViSession), vi, channelName, attributeId, value)
end
function IviDmm_CheckAttributeViSession(vi, channelName, attributeId, value)
ccall((:IviDmm_CheckAttributeViSession, libnidmm), ViStatus, (ViSession, ViConstString, ViAttr, ViSession), vi, channelName, attributeId, value)
end
function IviDmm_LockSession(vi, callerHasLock)
ccall((:IviDmm_LockSession, libnidmm), ViStatus, (ViSession, Ptr{ViBoolean}), vi, callerHasLock)
end
function IviDmm_UnlockSession(vi, callerHasLock)
ccall((:IviDmm_UnlockSession, libnidmm), ViStatus, (ViSession, Ptr{ViBoolean}), vi, callerHasLock)
end
function IviDmm_GetError(vi, errorCode, bufferSize, description)
ccall((:IviDmm_GetError, libnidmm), ViStatus, (ViSession, Ptr{ViStatus}, ViInt32, Ptr{ViChar}), vi, errorCode, bufferSize, description)
end
function IviDmm_ClearError(vi)
ccall((:IviDmm_ClearError, libnidmm), ViStatus, (ViSession,), vi)
end
function IviDmm_GetNextInterchangeWarning(vi, bufferSize, warning)
ccall((:IviDmm_GetNextInterchangeWarning, libnidmm), ViStatus, (ViSession, ViInt32, Ptr{ViChar}), vi, bufferSize, warning)
end
function IviDmm_ClearInterchangeWarnings(vi)
ccall((:IviDmm_ClearInterchangeWarnings, libnidmm), ViStatus, (ViSession,), vi)
end
function IviDmm_ResetInterchangeCheck(vi)
ccall((:IviDmm_ResetInterchangeCheck, libnidmm), ViStatus, (ViSession,), vi)
end
function IviDmm_GetNextCoercionRecord(vi, bufferSize, record)
ccall((:IviDmm_GetNextCoercionRecord, libnidmm), ViStatus, (ViSession, ViInt32, Ptr{ViChar}), vi, bufferSize, record)
end
function IviDmm_GetSpecificDriverCHandle(vi, specificDriverCHandle)
ccall((:IviDmm_GetSpecificDriverCHandle, libnidmm), ViStatus, (ViSession, Ptr{ViSession}), vi, specificDriverCHandle)
end
function IviDmm_GetSpecificDriverIUnknownPtr(vi, specificDriverIUnknownPtr)
ccall((:IviDmm_GetSpecificDriverIUnknownPtr, libnidmm), ViStatus, (ViSession, Ptr{Cvoid}), vi, specificDriverIUnknownPtr)
end
function IviDmm_ConfigureMeasurement(vi, _function, range, resolution)
ccall((:IviDmm_ConfigureMeasurement, libnidmm), ViStatus, (ViSession, ViInt32, ViReal64, ViReal64), vi, _function, range, resolution)
end
function IviDmm_ConfigureTrigger(vi, triggerSource, triggerDelay)
ccall((:IviDmm_ConfigureTrigger, libnidmm), ViStatus, (ViSession, ViInt32, ViReal64), vi, triggerSource, triggerDelay)
end
function IviDmm_Read(vi, maxTime, reading)
ccall((:IviDmm_Read, libnidmm), ViStatus, (ViSession, ViInt32, Ptr{ViReal64}), vi, maxTime, reading)
end
function IviDmm_Fetch(vi, maxTime, reading)
ccall((:IviDmm_Fetch, libnidmm), ViStatus, (ViSession, ViInt32, Ptr{ViReal64}), vi, maxTime, reading)
end
function IviDmm_Abort(vi)
ccall((:IviDmm_Abort, libnidmm), ViStatus, (ViSession,), vi)
end
function IviDmm_Initiate(vi)
ccall((:IviDmm_Initiate, libnidmm), ViStatus, (ViSession,), vi)
end
function IviDmm_IsOverRange(vi, measurementValue, isOverRange)
ccall((:IviDmm_IsOverRange, libnidmm), ViStatus, (ViSession, ViReal64, Ptr{ViBoolean}), vi, measurementValue, isOverRange)
end
function IviDmm_ConfigureACBandwidth(vi, minFreq, maxFreq)
ccall((:IviDmm_ConfigureACBandwidth, libnidmm), ViStatus, (ViSession, ViReal64, ViReal64), vi, minFreq, maxFreq)
end
function IviDmm_ConfigureFrequencyVoltageRange(vi, frequencyVoltageRange)
ccall((:IviDmm_ConfigureFrequencyVoltageRange, libnidmm), ViStatus, (ViSession, ViReal64), vi, frequencyVoltageRange)
end
function IviDmm_ConfigureTransducerType(vi, transducerType)
ccall((:IviDmm_ConfigureTransducerType, libnidmm), ViStatus, (ViSession, ViInt32), vi, transducerType)
end
function IviDmm_ConfigureFixedRefJunction(vi, fixedRefJunction)
ccall((:IviDmm_ConfigureFixedRefJunction, libnidmm), ViStatus, (ViSession, ViReal64), vi, fixedRefJunction)
end
function IviDmm_ConfigureThermocouple(vi, thermocoupleType, refJunctionType)
ccall((:IviDmm_ConfigureThermocouple, libnidmm), ViStatus, (ViSession, ViInt32, ViInt32), vi, thermocoupleType, refJunctionType)
end
function IviDmm_ConfigureRTD(vi, alpha, resistance)
ccall((:IviDmm_ConfigureRTD, libnidmm), ViStatus, (ViSession, ViReal64, ViReal64), vi, alpha, resistance)
end
function IviDmm_ConfigureThermistor(vi, resistance)
ccall((:IviDmm_ConfigureThermistor, libnidmm), ViStatus, (ViSession, ViReal64), vi, resistance)
end
function IviDmm_ConfigureMeasCompleteDest(vi, measCompleteDest)
ccall((:IviDmm_ConfigureMeasCompleteDest, libnidmm), ViStatus, (ViSession, ViInt32), vi, measCompleteDest)
end
function IviDmm_ConfigureMultiPoint(vi, triggerCount, sampleCount, sampleTrigger, sampleInterval)
ccall((:IviDmm_ConfigureMultiPoint, libnidmm), ViStatus, (ViSession, ViInt32, ViInt32, ViInt32, ViReal64), vi, triggerCount, sampleCount, sampleTrigger, sampleInterval)
end
function IviDmm_ReadMultiPoint(vi, maxTime, arraySize, readingArray, actualPts)
ccall((:IviDmm_ReadMultiPoint, libnidmm), ViStatus, (ViSession, ViInt32, ViInt32, Ptr{ViReal64}, Ptr{ViInt32}), vi, maxTime, arraySize, readingArray, actualPts)
end
function IviDmm_FetchMultiPoint(vi, maxTime, arraySize, readingArray, actualPts)
ccall((:IviDmm_FetchMultiPoint, libnidmm), ViStatus, (ViSession, ViInt32, ViInt32, Ptr{ViReal64}, Ptr{ViInt32}), vi, maxTime, arraySize, readingArray, actualPts)
end
function IviDmm_ConfigureTriggerSlope(vi, polarity)
ccall((:IviDmm_ConfigureTriggerSlope, libnidmm), ViStatus, (ViSession, ViInt32), vi, polarity)
end
function IviDmm_SendSoftwareTrigger(vi)
ccall((:IviDmm_SendSoftwareTrigger, libnidmm), ViStatus, (ViSession,), vi)
end
function IviDmm_GetApertureTimeInfo(vi, apertureTime, apertureTimeUnits)
ccall((:IviDmm_GetApertureTimeInfo, libnidmm), ViStatus, (ViSession, Ptr{ViReal64}, Ptr{ViInt32}), vi, apertureTime, apertureTimeUnits)
end
function IviDmm_GetAutoRangeValue(vi, autoRangeValue)
ccall((:IviDmm_GetAutoRangeValue, libnidmm), ViStatus, (ViSession, Ptr{ViReal64}), vi, autoRangeValue)
end
function IviDmm_ConfigureAutoZeroMode(vi, autoZeroMode)
ccall((:IviDmm_ConfigureAutoZeroMode, libnidmm), ViStatus, (ViSession, ViInt32), vi, autoZeroMode)
end
function IviDmm_ConfigurePowerLineFrequency(vi, powerLineFreq)
ccall((:IviDmm_ConfigurePowerLineFrequency, libnidmm), ViStatus, (ViSession, ViReal64), vi, powerLineFreq)
end | [
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] | 2.680322 | 4,467 |
module ConfigEnv
import Base: getindex, get, isempty, merge, merge!, haskey
export dotenv, dotenvx, isresolved, unresolved_keys
struct EnvProxyDict{T}
dict::Dict{String, String}
env::T
undefined::Vector{String}
circular::Vector{String}
end
EnvProxyDict(dict) = EnvProxyDict(dict, ENV, String[], String[])
EnvProxyDict(dict, env) = EnvProxyDict(dict, env, String[], String[])
getindex(ed::EnvProxyDict, key) = get(ed.dict, key, ed.env[key])
get(ed::EnvProxyDict, key, default) = get(ed.dict, key, get(ed.env, key, default))
isempty(ed::EnvProxyDict) = isempty(ed.dict)
Base.:*(epd1::EnvProxyDict, epd2::EnvProxyDict...) = merge(epd1, epd2...; overwrite = false)
function imprint(epd::EnvProxyDict, env)
for (k, v) in epd.dict
env[k] = v
end
end
function merge(epd1::EnvProxyDict, epd2::EnvProxyDict...; overwrite = true)
epd = EnvProxyDict(foldl((x, y) -> merge(x, y.dict), epd2; init = epd1.dict), epd1.env)
resolve!(epd, epd.env)
overwrite && imprint(epd, epd.env)
epd
end
haskey(ed::EnvProxyDict, key) = haskey(ed.dict, key) || haskey(ed.env, key)
function merge!(epd1::EnvProxyDict, epd2::EnvProxyDict...; overwrite = true)
foldl((x, y) -> merge!(x, y.dict), epd2; init = epd1.dict)
# You shouldn't use different envs during `merge!`. This is undefined behaviour.
# first dictionary environment can't be changed, cause it can be `ENV`
resolve!(epd1, epd1.env)
overwrite && imprint(epd1, epd1.env)
return epd1
end
function isdefin(s, i = 0, lev = 0)
i = nextind(s, i)
len = ncodeunits(s)
mode = 0
cnt_brackets = 0
while i <= len
c = s[i]
if c == '$'
mode = 1
elseif c == '{'
if mode == 1
return isdefin(s, i, 1)
else
cnt_brackets += 1
mode = 0
end
elseif c == '}'
mode = 0
cnt_brackets -= 1
lev == 1 && cnt_brackets < 0 && return false
else
mode = 0
end
i = nextind(s, i)
end
return true
end
mutable struct KVNode
key::String
value::String
children::Vector{KVNode}
parents_cnt::Int
isfinal::Bool
isresolved::Bool
end
KVNode(k, v) = KVNode(k, v, KVNode[], 0, true, false)
KVNode(v) = KVNode("", v, KVNode[], 0, false, false)
isresolved(kvnode::KVNode) = kvnode.isresolved
"""
isresolved(cfg::EnvProxyDict)
Returns whether templating procedure was successful or not. Templating can be unsuccessful if there are circular dependencies or templated variables do not exist in the environment.
"""
isresolved(epd::EnvProxyDict) = isempty(epd.undefined) && isempty(epd.circular)
"""
unresolved_keys(cfg::EnvProxyDict)
Returns tuple of `circular` and `undefined` keys, where `circular` are keys which depends on each other and `undefined` are keys, which use variables that do not exist in the environment.
"""
function unresolved_keys(edp::EnvProxyDict)
undefined = map(k -> k => edp.dict[k], edp.undefined)
circular = map(k -> k => edp.dict[k], edp.circular)
return (; circular = circular, undefined = undefined)
end
function destructure!(v::KVNode, stack, knodes, i = 0, env = ENV)
val = v.value
len = ncodeunits(val)
i0 = nextind(val, i)
i = i0
bracket_count = 0
mode = 0 # everything is normal
isvalid = v.isfinal ? true : false
while i <= len
c = val[i]
if c == '$'
mode = 1 # waiting for the opening {
elseif c == '{'
if mode == 1
node = KVNode(val)
push!(node.children, v)
i, valid = destructure!(node, stack, knodes, i, env) # should return index of the closing bracket }
if valid
v.parents_cnt += 1
isvalid = true
end
else
bracket_count += 1
end
mode = 0 # calm down
elseif c == '}' # we should produce some reasonable result if this is our closing bracket
bracket_count -= 1
mode = 0
if !v.isfinal
bracket_count >= 0 && continue # false alarm
if v.parents_cnt == 0 # we are key node
v.key = v.value[i0:i-1]
if haskey(knodes, v.key)
append!(knodes[v.key].children, v.children)
empty!(v.children)
elseif haskey(env, v.key)
v.value = env[v.key]
push!(stack, v)
else
# Unknown key
v.value = v.value[i0-2:i]
push!(stack, v)
end
else # we are intermidiate node
v.value = v.value[i0:i-1]
end
return i, true
end
else
mode = 0
end
if i <= len
i = nextind(val, i)
end
end
return i, isvalid
end
function resolve!(edp::EnvProxyDict, env = ENV)
knodes = Dict{String, KVNode}()
for (k, v) in edp.dict
knodes[k] = KVNode(k, v)
end
stack = KVNode[]
prepare_stack!(stack, knodes, env)
resolve!(stack, knodes, env)
empty!(edp.undefined)
empty!(edp.circular)
for (k, v) in knodes
edp.dict[k] = v.value
if !v.isresolved
push!(edp.circular, k)
elseif !isdefin(v.value)
push!(edp.undefined, k)
end
end
edp
end
function prepare_stack!(stack, knodes, env)
for v in values(knodes)
# we extract interpolated terms from the value and last tier should be put on
# the stack for further imprinting
destructure!(v, stack, knodes, 0, env)
end
for v in values(knodes)
# if we do not have anything to interpolate inside the value, we are good.
# If we still have some other value where node should be used for interpolation,
# we put node on the interpolation stack
if v.parents_cnt == 0
v.isresolved = true
if !isempty(v.children)
push!(stack, v)
end
end
end
end
function recursive_replace!(node, key, val)
occursin(key, node.value) || return
node.value = replace(node.value, key => val)
foreach(x -> recursive_replace!(x, key, val), node.children)
nothing
end
function resolve!(stack, knodes, env)
while !isempty(stack)
v = pop!(stack)
key = "\${" * v.key * "}"
while !isempty(v.children)
kid = pop!(v.children)
recursive_replace!(kid, key, v.value)
kid.parents_cnt -= 1
kid.parents_cnt == 0 || continue
if kid.isfinal
kid.isresolved = true
else
kid.key = kid.value
if haskey(knodes, kid.key)
append!(knodes[kid.key].children, kid.children)
empty!(kid.children)
if !isempty(knodes[kid.key].children) && knodes[kid.key].parents_cnt == 0
push!(stack, knodes[kid.key])
end
elseif haskey(env, kid.key)
kid.value = env[kid.key]
else
kid.value = "\${" * kid.key * "}"
end
end
if !isempty(kid.children)
push!(stack, kid)
end
end
end
end
"""
`ConfigEnv.parse` accepts a String or an IOBuffer (any value that
can be converted into String), and returns a Dict with
the parsed keys and values.
"""
function parse(src)
res = Dict{String,String}()
src = IOBuffer(src)
for line in eachline(src)
m = match(r"^\s*([\w.-]+)\s*=\s*(.*)?\s*$", line)
if m !== nothing
key = m.captures[1]
value = string(m.captures[2])
if (length(value) > 0 && value[1] === '"' && value[end] === '"')
value = replace(value, r"\\n"m => "\n")
end
value = replace(value, r"(^['\u0022]|['\u0022]$)" => "")
value = strip(value)
push!(res, key => value)
end
end
res
end
parse(src::IO) = parse(String(take!(src)))
########################################
# Main part
########################################
validatefile(s) = false
validatefile(s::AbstractString) = isfile(s)
"""
dotenv(path1, path2, ...; overwrite = true)
`dotenv` reads .env files from your `path`, parse their content, merge them together, stores result to `ENV`,
and finally return a `EnvProxyDict` with the content. If no `path` argument is given , then
`.env` is used as a default path. During merge procedure, if duplicate keys encountered
then value from the rightmost dictionary is used.
By default if key already exists in `ENV` it is overwritten with the values in .env file.
This behaviour can be changed by setting `overwrite` flag to `false` or using dual `dotenvx` function.
Examples
========
```
# .env
FOO = bar
USER = john_doe
# julia REPL
# load key-value pairs from ".env", `ENV` duplicate keys are overwritten
julia> ENV["USER"]
user1
julia> cfg = dotenv()
julia> ENV["FOO"]
bar
julia> ENV["USER"]
john_doe
julia> cfg["USER"]
john_doe
```
"""
function dotenv(path = ".env"; overwrite = true, env = ENV)
parsed = if (validatefile(path))
parse(read(path, String))
else
parse(path)
end
epd = EnvProxyDict(parsed, env)
resolve!(epd, env)
for (k, v) in epd.dict
if !haskey(env, k) || overwrite
env[k] = v
end
end
return epd
end
dotenv(paths...; overwrite = true, env = ENV) = merge!(dotenv.(paths; overwrite = overwrite, env = env)..., overwrite = overwrite)
"""
dotenvx(path1, path2, ...; overwrite = false)
`dotenvx` reads .env files from your `path`, parse their content, merge them together, stores result to `ENV`,
and finally return a `EnvProxyDict` with the content. If no `path` argument is given , then
`.env` is used as a default path. During merge procedure, if duplicate keys encountered
then value from the rightmost dictionary is used.
By default if key already exists in `ENV` it is overwritten with the values in .env file.
This behaviour can be changed by setting `overwrite` flag to `true` or using dual `dotenv` function.
Examples
========
```
# .env
FOO = bar
USER = john_doe
# julia REPL
# load key-value pairs from ".env", `ENV` duplicate keys are not overwritten
julia> ENV["USER"]
user1
julia> cfg = dotenvx()
julia> ENV["FOO"]
bar
julia> ENV["USER"]
user1
julia> cfg["USER"]
john_doe
```
"""
dotenvx(paths...; overwrite = false, env = ENV) = dotenv(paths...; overwrite = overwrite, env = env)
end
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287,
262,
2858,
13,
198,
37811,
198,
8818,
43264,
62,
13083,
7,
276,
79,
3712,
4834,
85,
44148,
35,
713,
8,
198,
220,
220,
220,
28721,
796,
3975,
7,
74,
4613,
479,
5218,
1225,
79,
13,
11600,
58,
74,
4357,
1225,
79,
13,
917,
18156,
8,
198,
220,
220,
220,
18620,
796,
3975,
7,
74,
4613,
479,
5218,
1225,
79,
13,
11600,
58,
74,
4357,
1225,
79,
13,
21170,
934,
8,
628,
220,
220,
220,
1441,
357,
26,
18620,
796,
18620,
11,
28721,
796,
28721,
8,
198,
437,
198,
198,
8818,
2244,
5620,
0,
7,
85,
3712,
42,
53,
19667,
11,
8931,
11,
638,
4147,
11,
1312,
796,
657,
11,
17365,
796,
12964,
53,
8,
198,
220,
220,
220,
1188,
796,
410,
13,
8367,
198,
220,
220,
220,
18896,
796,
299,
8189,
41667,
7,
2100,
8,
198,
220,
220,
220,
1312,
15,
796,
1306,
521,
7,
2100,
11,
1312,
8,
198,
220,
220,
220,
1312,
796,
1312,
15,
198,
220,
220,
220,
19096,
62,
9127,
796,
657,
198,
220,
220,
220,
4235,
796,
657,
1303,
2279,
318,
3487,
198,
220,
220,
220,
318,
12102,
796,
410,
13,
4468,
1292,
5633,
2081,
1058,
3991,
628,
220,
220,
220,
981,
1312,
19841,
18896,
198,
220,
220,
220,
220,
220,
220,
220,
269,
796,
1188,
58,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
611,
269,
6624,
705,
3,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4235,
796,
352,
1303,
4953,
329,
262,
4756,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
269,
6624,
705,
90,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4235,
6624,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10139,
796,
509,
53,
19667,
7,
2100,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
17440,
13,
17197,
11,
410,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1312,
11,
4938,
796,
2244,
5620,
0,
7,
17440,
11,
8931,
11,
638,
4147,
11,
1312,
11,
17365,
8,
1303,
815,
1441,
6376,
286,
262,
9605,
19096,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4938,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
13,
23743,
62,
66,
429,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
318,
12102,
796,
2081,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19096,
62,
9127,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4235,
796,
657,
1303,
9480,
866,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
269,
6624,
705,
92,
6,
1303,
356,
815,
4439,
617,
6397,
1255,
611,
428,
318,
674,
9605,
19096,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19096,
62,
9127,
48185,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4235,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
5145,
85,
13,
4468,
1292,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19096,
62,
9127,
18189,
657,
11405,
2555,
1303,
3991,
10436,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
410,
13,
23743,
62,
66,
429,
6624,
657,
1303,
356,
389,
1994,
10139,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
13,
2539,
796,
410,
13,
8367,
58,
72,
15,
25,
72,
12,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
468,
2539,
7,
15418,
4147,
11,
410,
13,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24443,
0,
7,
15418,
4147,
58,
85,
13,
2539,
4083,
17197,
11,
410,
13,
17197,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6565,
0,
7,
85,
13,
17197,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
468,
2539,
7,
24330,
11,
410,
13,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
13,
8367,
796,
17365,
58,
85,
13,
2539,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
25558,
11,
410,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
16185,
1994,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
13,
8367,
796,
410,
13,
8367,
58,
72,
15,
12,
17,
25,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
25558,
11,
410,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
1303,
356,
389,
987,
13602,
9386,
10139,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
13,
8367,
796,
410,
13,
8367,
58,
72,
15,
25,
72,
12,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1312,
11,
2081,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4235,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1312,
19841,
18896,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1312,
796,
1306,
521,
7,
2100,
11,
1312,
8,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
220,
220,
220,
1441,
1312,
11,
318,
12102,
198,
437,
198,
198,
8818,
10568,
0,
7,
276,
79,
3712,
4834,
85,
44148,
35,
713,
11,
17365,
796,
12964,
53,
8,
198,
220,
220,
220,
638,
4147,
796,
360,
713,
90,
10100,
11,
509,
53,
19667,
92,
3419,
198,
220,
220,
220,
329,
357,
74,
11,
410,
8,
287,
1225,
79,
13,
11600,
198,
220,
220,
220,
220,
220,
220,
220,
638,
4147,
58,
74,
60,
796,
509,
53,
19667,
7,
74,
11,
410,
8,
198,
220,
220,
220,
886,
198,
220,
220,
220,
8931,
796,
509,
53,
19667,
21737,
198,
220,
220,
220,
220,
198,
220,
220,
220,
8335,
62,
25558,
0,
7,
25558,
11,
638,
4147,
11,
17365,
8,
628,
220,
220,
220,
10568,
0,
7,
25558,
11,
638,
4147,
11,
17365,
8,
628,
220,
220,
220,
6565,
0,
7,
276,
79,
13,
917,
18156,
8,
198,
220,
220,
220,
6565,
0,
7,
276,
79,
13,
21170,
934,
8,
198,
220,
220,
220,
329,
357,
74,
11,
410,
8,
287,
638,
4147,
198,
220,
220,
220,
220,
220,
220,
220,
1225,
79,
13,
11600,
58,
74,
60,
796,
410,
13,
8367,
198,
220,
220,
220,
220,
220,
220,
220,
611,
5145,
85,
13,
271,
411,
5634,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
276,
79,
13,
21170,
934,
11,
479,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
5145,
271,
4299,
259,
7,
85,
13,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
276,
79,
13,
917,
18156,
11,
479,
8,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1225,
79,
198,
437,
198,
198,
8818,
8335,
62,
25558,
0,
7,
25558,
11,
638,
4147,
11,
17365,
8,
198,
220,
220,
220,
329,
410,
287,
3815,
7,
15418,
4147,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
356,
7925,
39555,
515,
2846,
422,
262,
1988,
290,
938,
14249,
815,
307,
1234,
319,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
262,
8931,
329,
2252,
29122,
278,
198,
220,
220,
220,
220,
220,
220,
220,
2244,
5620,
0,
7,
85,
11,
8931,
11,
638,
4147,
11,
657,
11,
17365,
8,
198,
220,
220,
220,
886,
628,
220,
220,
220,
329,
410,
287,
3815,
7,
15418,
4147,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
356,
466,
407,
423,
1997,
284,
39555,
378,
2641,
262,
1988,
11,
356,
389,
922,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1002,
356,
991,
423,
617,
584,
1988,
810,
10139,
815,
307,
973,
329,
39555,
341,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
356,
1234,
10139,
319,
262,
39555,
341,
8931,
198,
220,
220,
220,
220,
220,
220,
220,
611,
410,
13,
23743,
62,
66,
429,
6624,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
13,
271,
411,
5634,
796,
2081,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
5145,
271,
28920,
7,
85,
13,
17197,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
25558,
11,
410,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
437,
198,
198,
8818,
45115,
62,
33491,
0,
7,
17440,
11,
1994,
11,
1188,
8,
198,
220,
220,
220,
8833,
259,
7,
2539,
11,
10139,
13,
8367,
8,
8614,
1441,
198,
220,
220,
220,
10139,
13,
8367,
796,
6330,
7,
17440,
13,
8367,
11,
1994,
5218,
1188,
8,
198,
220,
220,
220,
1674,
620,
7,
87,
4613,
45115,
62,
33491,
0,
7,
87,
11,
1994,
11,
1188,
828,
10139,
13,
17197,
8,
198,
220,
220,
220,
2147,
198,
437,
198,
198,
8818,
10568,
0,
7,
25558,
11,
638,
4147,
11,
17365,
8,
198,
220,
220,
220,
981,
5145,
271,
28920,
7,
25558,
8,
198,
220,
220,
220,
220,
220,
220,
220,
410,
796,
1461,
0,
7,
25558,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
796,
37082,
3,
4895,
1635,
410,
13,
2539,
1635,
366,
36786,
198,
220,
220,
220,
220,
220,
220,
220,
981,
5145,
271,
28920,
7,
85,
13,
17197,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5141,
796,
1461,
0,
7,
85,
13,
17197,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
45115,
62,
33491,
0,
7,
38439,
11,
1994,
11,
410,
13,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5141,
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23743,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5141,
13,
271,
411,
5634,
796,
2081,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5141,
13,
2539,
796,
5141,
13,
8367,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
468,
2539,
7,
15418,
4147,
11,
5141,
13,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24443,
0,
7,
15418,
4147,
58,
38439,
13,
2539,
4083,
17197,
11,
5141,
13,
17197,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6565,
0,
7,
38439,
13,
17197,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
5145,
271,
28920,
7,
15418,
4147,
58,
38439,
13,
2539,
4083,
17197,
8,
11405,
638,
4147,
58,
38439,
13,
2539,
4083,
23743,
62,
66,
429,
6624,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
25558,
11,
638,
4147,
58,
38439,
13,
2539,
12962,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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2073,
361,
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24330,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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5141,
13,
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17365,
58,
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13,
2539,
60,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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5141,
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37082,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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611,
5145,
271,
28920,
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38439,
13,
17197,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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4574,
0,
7,
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220,
220,
220,
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220,
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220,
220,
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437,
198,
198,
37811,
198,
63,
16934,
4834,
85,
13,
29572,
63,
18178,
257,
10903,
393,
281,
314,
9864,
13712,
357,
1092,
1988,
326,
198,
460,
307,
11513,
656,
10903,
828,
290,
5860,
257,
360,
713,
351,
198,
262,
44267,
8251,
290,
3815,
13,
198,
37811,
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8818,
21136,
7,
10677,
8,
198,
220,
220,
220,
581,
796,
360,
713,
90,
10100,
11,
10100,
92,
3419,
198,
220,
220,
220,
12351,
796,
314,
9864,
13712,
7,
10677,
8,
198,
220,
220,
220,
329,
1627,
287,
1123,
1370,
7,
10677,
8,
198,
220,
220,
220,
220,
220,
220,
220,
285,
796,
2872,
7,
81,
1,
61,
59,
82,
9,
26933,
59,
86,
7874,
48688,
19415,
82,
9,
28,
59,
82,
9,
7,
15885,
19427,
59,
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3,
1600,
1627,
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220,
220,
220,
220,
220,
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611,
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5145,
855,
2147,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
796,
285,
13,
27144,
942,
58,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
4731,
7,
76,
13,
27144,
942,
58,
17,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
357,
13664,
7,
8367,
8,
1875,
657,
11405,
1988,
58,
16,
60,
24844,
705,
30543,
11405,
1988,
58,
437,
60,
24844,
705,
1,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
6330,
7,
8367,
11,
374,
1,
6852,
77,
1,
76,
5218,
37082,
77,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
6330,
7,
8367,
11,
374,
18109,
61,
17816,
59,
84,
405,
1828,
60,
91,
17816,
59,
84,
405,
1828,
60,
3,
16725,
5218,
366,
4943,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
10283,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
411,
11,
1994,
5218,
1988,
8,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
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220,
220,
581,
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198,
29572,
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10677,
3712,
9399,
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796,
21136,
7,
10100,
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20657,
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7,
10677,
22305,
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198,
29113,
7804,
198,
2,
8774,
636,
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29113,
7804,
198,
198,
12102,
378,
7753,
7,
82,
8,
796,
3991,
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82,
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23839,
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796,
318,
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82,
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37811,
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220,
16605,
24330,
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6978,
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3108,
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2644,
26,
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796,
2081,
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198,
198,
63,
26518,
24330,
63,
9743,
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24330,
3696,
422,
534,
4600,
6978,
47671,
21136,
511,
2695,
11,
20121,
606,
1978,
11,
7000,
1255,
284,
4600,
1677,
53,
47671,
198,
392,
3443,
1441,
257,
4600,
4834,
85,
44148,
35,
713,
63,
351,
262,
2695,
13,
1002,
645,
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6978,
63,
4578,
318,
1813,
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788,
220,
198,
44646,
24330,
63,
318,
973,
355,
257,
4277,
3108,
13,
5856,
20121,
8771,
11,
611,
23418,
8251,
12956,
198,
8524,
1988,
422,
262,
826,
1712,
22155,
318,
973,
13,
198,
198,
3886,
4277,
611,
1994,
1541,
7160,
287,
4600,
1677,
53,
63,
340,
318,
6993,
9108,
351,
262,
3815,
287,
764,
24330,
2393,
13,
220,
198,
1212,
9172,
460,
307,
3421,
416,
4634,
4600,
2502,
13564,
63,
6056,
284,
4600,
9562,
63,
393,
1262,
10668,
4600,
26518,
24330,
87,
63,
2163,
13,
198,
198,
27730,
198,
2559,
198,
15506,
63,
198,
2,
764,
24330,
198,
6080,
46,
796,
2318,
198,
29904,
796,
45610,
62,
67,
2577,
198,
198,
2,
474,
43640,
45285,
198,
2,
3440,
1994,
12,
8367,
14729,
422,
27071,
24330,
1600,
4600,
1677,
53,
63,
23418,
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389,
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21136,
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2073,
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220,
220,
220,
220,
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21136,
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79,
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220,
10568,
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7,
538,
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220,
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220,
329,
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74,
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67,
13,
11600,
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220,
220,
220,
220,
220,
220,
611,
5145,
10134,
2539,
7,
24330,
11,
479,
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8614,
49312,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17365,
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60,
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410,
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220,
220,
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17365,
26513,
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11,
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2644,
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3991,
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4277,
3108,
13,
5856,
20121,
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11,
611,
23418,
8251,
12956,
198,
8524,
1988,
422,
262,
826,
1712,
22155,
318,
973,
13,
198,
198,
3886,
4277,
611,
1994,
1541,
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287,
4600,
1677,
53,
63,
340,
318,
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3815,
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198,
437,
198
] | 2.14624 | 5,067 |
function test_thermalLoad_1()
T = 2
# With these values, the heater uses 0.2 power to keep a ΔT of 1
l = DR.DER.ThermalLoad(
index=1, T=2,
temp_min=[20.0, 20.0],
temp_max=[25.0, 25.0],
temp_ext=[0.0, 0.0],
temp_init=20.0,
pwr_min=[0.0, 0.0],
pwr_max=[10.0, 10.0],
C=1.0, η=1.0, μ=0.2,
binflag=false
)
# GLPK does not support -in-Interval constraints, so we bridge
model = MOI.Bridges.full_bridge_optimizer(GLPK.Optimizer(), Float64)
lmin = [0.0, 0.0]
lmax = [10.0, 10.0]
price = [2.0, 1.0]
# Instantiate initial model
h = DR.DER.House(
0, T,
lmin, lmax, price,
[l]
)
model, var2idx, con2idx = DR.DER.build_model!(h, model)
MOI.optimize!(model)
@test MOI.get(model, MOI.ObjectiveValue()) ≈ 12.0
# Temperature should be kept at minimum value, i.e., 20
T1 = var2idx[(:thermal, l.index, :temp, 1)]
T2 = var2idx[(:thermal, l.index, :temp, 2)]
@test MOI.get(model, MOI.VariablePrimal(), T1) ≈ 20.0
@test MOI.get(model, MOI.VariablePrimal(), T2) ≈ 20.0
# Check net load
pnet1 = var2idx[(:thermal, l.index, :pnet, 1)]
pnet2 = var2idx[(:thermal, l.index, :pnet, 2)]
@test MOI.get(model, MOI.VariablePrimal(), pnet1) ≈ 4.0
@test MOI.get(model, MOI.VariablePrimal(), pnet2) ≈ 4.0
return nothing
end
function test_thermalLoad_2()
T = 2
# With these values, the heater uses 0.2 power to keep a ΔT of 1
l = DR.DER.ThermalLoad(
index=1, T=2,
temp_min=[20.0, 20.0],
temp_max=[25.0, 25.0],
temp_ext=[0.0, 0.0],
temp_init=20.0,
pwr_min=[0.0, 0.0],
pwr_max=[10.0, 10.0],
C=1.0, η=1.0, μ=0.2,
binflag=false
)
# GLPK does not support -in-Interval constraints, so we bridge
model = MOI.Bridges.full_bridge_optimizer(GLPK.Optimizer(), Float64)
lmin = [0.0, 0.0]
lmax = [10.0, 10.0]
price = [1.0, 2.0] # Now it's worth heating more in the first period
# Instantiate initial model
h = DR.DER.House(
0, T,
lmin, lmax, price,
[l]
)
model, var2idx, con2idx = DR.DER.build_model!(h, model)
MOI.optimize!(model)
# T1 should be higher
# T2 should be at minimum, i.e., 20
T1 = var2idx[(:thermal, l.index, :temp, 1)]
T2 = var2idx[(:thermal, l.index, :temp, 2)]
@test MOI.get(model, MOI.VariablePrimal(), T1) ≈ 25.0
@test MOI.get(model, MOI.VariablePrimal(), T2) ≈ 20.0
# Check net load
pnet1 = var2idx[(:thermal, l.index, :pnet, 1)]
pnet2 = var2idx[(:thermal, l.index, :pnet, 2)]
@test MOI.get(model, MOI.VariablePrimal(), pnet1) ≈ 9.0
@test MOI.get(model, MOI.VariablePrimal(), pnet2) ≈ 0.0
# Check objective value
@test MOI.get(model, MOI.ObjectiveValue()) ≈ 9.0
return nothing
end
@testset "Thermal" begin
test_thermalLoad_1()
test_thermalLoad_2()
end | [
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] | 1.962475 | 1,519 |
function solve_diagTR(λ, Δ, g̃, δ, ϵ; M = [0.0])
# λ underestimates the required value λstar such that ||d̃(λstar)|| = δ
# where d̃(λ) = -(Δ + λ * M) .\ g̃
T = eltype(δ)
M = T.(M)
if M == [0.0]
# M = ones(Δ) ;
M = fill(T.(1.0), size(Δ)[1]);
end
@assert (minimum(M) > 0.0)
λin = T.(λ)
d̃ = -(Δ .+ λ * M) .\ g̃
d̃d̃ = d̃⋅d̃
normd̃ = sqrt(d̃d̃)
tol1 = sqrt(eps(T))
tolerance = tol1 * T(100.0)
# Newton iterations
iter_nwt = 0
while (normd̃ >= (δ + tolerance * normd̃)) && (iter_nwt < 40)
dotd̃ = (Δ .+ λ * M) .\ (M .* d̃)
Δλ = ((normd̃-δ)/δ) * (d̃d̃/(d̃ ⋅ dotd̃))
λeps = tol1 / T(100.0)
λ = max(λ + Δλ, λin + λeps)
d̃ = -(Δ .+ λ * M) .\ g̃
d̃d̃ = d̃⋅d̃
normd̃ = sqrt(d̃d̃)
@assert (λ >= 0.0)
iter_nwt += 1
end
return d̃, λ
end
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] | 1.516184 | 587 |
function ode_addsteps!{calcVal,calcVal2,calcVal3}(k,t,uprev,u,dt,f,cache::DiscreteCache,always_calc_begin::Type{Val{calcVal}} = Val{false},allow_calc_end::Type{Val{calcVal2}} = Val{true},force_calc_end::Type{Val{calcVal3}} = Val{false})
nothing
end
function ode_addsteps!{calcVal,calcVal2,calcVal3}(k,t,uprev,u,dt,f,cache::DiscreteConstantCache,always_calc_begin::Type{Val{calcVal}} = Val{false},allow_calc_end::Type{Val{calcVal2}} = Val{true},force_calc_end::Type{Val{calcVal3}} = Val{false})
nothing
end
function ode_addsteps!{calcVal,calcVal2,calcVal3}(k,t,uprev,u,dt,f,cache::DP5ConstantCache,always_calc_begin::Type{Val{calcVal}} = Val{false},allow_calc_end::Type{Val{calcVal2}} = Val{true},force_calc_end::Type{Val{calcVal3}} = Val{false})
if length(k)<4 || calcVal
@unpack a21,a31,a32,a41,a42,a43,a51,a52,a53,a54,a61,a62,a63,a64,a65,a71,a73,a74,a75,a76,b1,b3,b4,b5,b6,b7,c1,c2,c3,c4,c5,c6 = cache
@unpack d1,d3,d4,d5,d6,d7 = cache
k1 = f(t,uprev)
k2 = f(t+c1*dt,uprev+dt*(a21*k1))
k3 = f(t+c2*dt,uprev+dt*(a31*k1+a32*k2))
k4 = f(t+c3*dt,uprev+dt*(a41*k1+a42*k2+a43*k3))
k5 = f(t+c4*dt,uprev+dt*(a51*k1+a52*k2+a53*k3+a54*k4))
k6 = f(t+dt,uprev+dt*(a61*k1+a62*k2+a63*k3+a64*k4+a65*k5))
update = a71*k1+a73*k3+a74*k4+a75*k5+a76*k6
k7 = f(t+dt,uprev+dt*update)
copyat_or_push!(k,1,update)
bspl = k1 - update
copyat_or_push!(k,2,bspl)
copyat_or_push!(k,3,update - k7 - bspl)
copyat_or_push!(k,4,d1*k1+d3*k3+d4*k4+d5*k5+d6*k6+d7*k7)
end
nothing
end
function ode_addsteps!{calcVal,calcVal2,calcVal3}(k,t,uprev,u,dt,f,cache::DP5Cache,always_calc_begin::Type{Val{calcVal}} = Val{false},allow_calc_end::Type{Val{calcVal2}} = Val{true},force_calc_end::Type{Val{calcVal3}} = Val{false})
if length(k)<4 || calcVal
@unpack a21,a31,a32,a41,a42,a43,a51,a52,a53,a54,a61,a62,a63,a64,a65,a71,a73,a74,a75,a76,b1,b3,b4,b5,b6,b7,c1,c2,c3,c4,c5,c6 = cache.tab
@unpack d1,d3,d4,d5,d6,d7 = cache.tab
@unpack k1,k2,k3,k4,k5,k6,k7,dense_tmp3,dense_tmp4,update,bspl,utilde,tmp,atmp = cache
uidx = eachindex(uprev)
f(t,uprev,k1)
for i in uidx
tmp[i] = uprev[i]+dt*(a21*k1[i])
end
f(t+c1*dt,tmp,k2)
for i in uidx
tmp[i] = uprev[i]+dt*(a31*k1[i]+a32*k2[i])
end
f(t+c2*dt,tmp,k3)
for i in uidx
tmp[i] = uprev[i]+dt*(a41*k1[i]+a42*k2[i]+a43*k3[i])
end
f(t+c3*dt,tmp,k4)
for i in uidx
tmp[i] =uprev[i]+dt*(a51*k1[i]+a52*k2[i]+a53*k3[i]+a54*k4[i])
end
f(t+c4*dt,tmp,k5)
for i in uidx
tmp[i] = uprev[i]+dt*(a61*k1[i]+a62*k2[i]+a63*k3[i]+a64*k4[i]+a65*k5[i])
end
f(t+dt,tmp,k6)
for i in uidx
update[i] = a71*k1[i]+a73*k3[i]+a74*k4[i]+a75*k5[i]+a76*k6[i]
tmp[i] = uprev[i]+dt*update[i]
end
f(t+dt,tmp,k7)
copyat_or_push!(k,1,update)
for i in uidx
bspl[i] = k1[i] - update[i]
dense_tmp3[i] = update[i] - k7[i] - bspl[i]
dense_tmp4[i] = (d1*k1[i]+d3*k3[i]+d4*k4[i]+d5*k5[i]+d6*k6[i]+d7*k7[i])
end
copyat_or_push!(k,2,bspl)
copyat_or_push!(k,3,dense_tmp3)
copyat_or_push!(k,4,dense_tmp4)
end
nothing
end
function ode_addsteps!{calcVal,calcVal2,calcVal3}(k,t,uprev,u,dt,f,cache::DP5ThreadedCache,always_calc_begin::Type{Val{calcVal}} = Val{false},allow_calc_end::Type{Val{calcVal2}} = Val{true},force_calc_end::Type{Val{calcVal3}} = Val{false})
if length(k)<4 || calcVal
@unpack a21,a31,a32,a41,a42,a43,a51,a52,a53,a54,a61,a62,a63,a64,a65,a71,a73,a74,a75,a76,b1,b3,b4,b5,b6,b7,c1,c2,c3,c4,c5,c6 = cache.tab
@unpack d1,d3,d4,d5,d6,d7 = cache.tab
@unpack k1,k2,k3,k4,k5,k6,k7,dense_tmp3,dense_tmp4,update,bspl,utilde,tmp,atmp = cache
uidx = eachindex(uprev)
f(t,uprev,k1)
for i in uidx
tmp[i] = uprev[i]+dt*(a21*k1[i])
end
f(t+c1*dt,tmp,k2)
for i in uidx
tmp[i] = uprev[i]+dt*(a31*k1[i]+a32*k2[i])
end
f(t+c2*dt,tmp,k3)
for i in uidx
tmp[i] = uprev[i]+dt*(a41*k1[i]+a42*k2[i]+a43*k3[i])
end
f(t+c3*dt,tmp,k4)
for i in uidx
tmp[i] =uprev[i]+dt*(a51*k1[i]+a52*k2[i]+a53*k3[i]+a54*k4[i])
end
f(t+c4*dt,tmp,k5)
for i in uidx
tmp[i] = uprev[i]+dt*(a61*k1[i]+a62*k2[i]+a63*k3[i]+a64*k4[i]+a65*k5[i])
end
f(t+dt,tmp,k6)
for i in uidx
update[i] = a71*k1[i]+a73*k3[i]+a74*k4[i]+a75*k5[i]+a76*k6[i]
tmp[i] = uprev[i]+dt*update[i]
end
f(t+dt,tmp,k7)
copyat_or_push!(k,1,update)
for i in uidx
bspl[i] = k1[i] - update[i]
dense_tmp3[i] = update[i] - k7[i] - bspl[i]
dense_tmp4[i] = (d1*k1[i]+d3*k3[i]+d4*k4[i]+d5*k5[i]+d6*k6[i]+d7*k7[i])
end
copyat_or_push!(k,2,bspl)
copyat_or_push!(k,3,dense_tmp3)
copyat_or_push!(k,4,dense_tmp4)
end
nothing
end
function ode_addsteps!{calcVal,calcVal2,calcVal3}(k,t,uprev,u,dt,f,cache::Tsit5ConstantCache,always_calc_begin::Type{Val{calcVal}} = Val{false},allow_calc_end::Type{Val{calcVal2}} = Val{true},force_calc_end::Type{Val{calcVal3}} = Val{false})
if length(k)<7 || calcVal
@unpack c1,c2,c3,c4,c5,c6,a21,a31,a32,a41,a42,a43,a51,a52,a53,a54,a61,a62,a63,a64,a65,a71,a72,a73,a74,a75,a76,b1,b2,b3,b4,b5,b6,b7 = cache
copyat_or_push!(k,1,f(t,uprev))
copyat_or_push!(k,2,f(t+c1*dt,uprev+dt*(a21*k[1])))
copyat_or_push!(k,3,f(t+c2*dt,uprev+dt*(a31*k[1]+a32*k[2])))
copyat_or_push!(k,4,f(t+c3*dt,uprev+dt*(a41*k[1]+a42*k[2]+a43*k[3])))
copyat_or_push!(k,5,f(t+c4*dt,uprev+dt*(a51*k[1]+a52*k[2]+a53*k[3]+a54*k[4])))
copyat_or_push!(k,6,f(t+dt,uprev+dt*(a61*k[1]+a62*k[2]+a63*k[3]+a64*k[4]+a65*k[5])))
utmp = uprev+dt*(a71*k[1]+a72*k[2]+a73*k[3]+a74*k[4]+a75*k[5]+a76*k[6])
copyat_or_push!(k,7,f(t+dt,utmp))
end
nothing
end
function ode_addsteps!{calcVal,calcVal2,calcVal3}(k,t,uprev,u,dt,f,cache::Tsit5Cache,always_calc_begin::Type{Val{calcVal}} = Val{false},allow_calc_end::Type{Val{calcVal2}} = Val{true},force_calc_end::Type{Val{calcVal3}} = Val{false})
if length(k)<7 || calcVal
@unpack c1,c2,c3,c4,c5,c6,a21,a31,a32,a41,a42,a43,a51,a52,a53,a54,a61,a62,a63,a64,a65,a71,a72,a73,a74,a75,a76,b1,b2,b3,b4,b5,b6,b7 = cache.tab
@unpack k1,k2,k3,k4,k5,k6,k7,utilde,tmp,atmp = cache
uidx = eachindex(uprev)
for i in uidx
tmp[i] = uprev[i]+dt*(a21*k1[i])
end
f(t+c1*dt,tmp,k2)
for i in uidx
tmp[i] = uprev[i]+dt*(a31*k1[i]+a32*k2[i])
end
f(t+c2*dt,tmp,k3)
for i in uidx
tmp[i] = uprev[i]+dt*(a41*k1[i]+a42*k2[i]+a43*k3[i])
end
f(t+c3*dt,tmp,k4)
for i in uidx
tmp[i] = uprev[i]+dt*(a51*k1[i]+a52*k2[i]+a53*k3[i]+a54*k4[i])
end
f(t+c4*dt,tmp,k5)
for i in uidx
tmp[i] = uprev[i]+dt*(a61*k1[i]+a62*k2[i]+a63*k3[i]+a64*k4[i]+a65*k5[i])
end
f(t+dt,tmp,k6)
for i in uidx
tmp[i] = uprev[i]+dt*(a71*k1[i]+a72*k2[i]+a73*k3[i]+a74*k4[i]+a75*k5[i]+a76*k6[i])
end
f(t+dt,u,k7)
copyat_or_push!(k,1,k1)
copyat_or_push!(k,2,k2)
copyat_or_push!(k,3,k3)
copyat_or_push!(k,4,k4)
copyat_or_push!(k,5,k5)
copyat_or_push!(k,6,k6)
copyat_or_push!(k,7,k7)
end
nothing
end
"""
An Efficient Runge-Kutta (4,5) Pair by P.Bogacki and L.F.Shampine
Computers and Mathematics with Applications, Vol. 32, No. 6, 1996, pages 15 to 28
Called to add the extra k9, k10, k11 steps for the Order 5 interpolation when needed
"""
function ode_addsteps!{calcVal,calcVal2,calcVal3}(k,t,uprev,u,dt,f,cache::BS5ConstantCache,always_calc_begin::Type{Val{calcVal}} = Val{false},allow_calc_end::Type{Val{calcVal2}} = Val{true},force_calc_end::Type{Val{calcVal3}} = Val{false})
if length(k) < 8 || calcVal
@unpack c1,c2,c3,c4,c5,a21,a31,a32,a41,a42,a43,a51,a52,a53,a54,a61,a62,a63,a64,a65,a71,a72,a73,a74,a75,a76,a81,a83,a84,a85,a86,a87,bhat1,bhat3,bhat4,bhat5,bhat6,btilde1,btilde2,btilde3,btilde4,btilde5,btilde6,btilde7,btilde8 = cache
copyat_or_push!(k,1,f(t,uprev))
copyat_or_push!(k,2,f(t+c1*dt,uprev+dt*a21*k[1]))
copyat_or_push!(k,3,f(t+c2*dt,uprev+dt*(a31*k[1]+a32*k[2])))
copyat_or_push!(k,4,f(t+c3*dt,uprev+dt*(a41*k[1]+a42*k[2]+a43*k[3])))
copyat_or_push!(k,5,f(t+c4*dt,uprev+dt*(a51*k[1]+a52*k[2]+a53*k[3]+a54*k[4])))
copyat_or_push!(k,6,f(t+c5*dt,uprev+dt*(a61*k[1]+a62*k[2]+a63*k[3]+a64*k[4]+a65*k[5])))
copyat_or_push!(k,7,f(t+dt,uprev+dt*(a71*k[1]+a72*k[2]+a73*k[3]+a74*k[4]+a75*k[5]+a76*k[6])))
copyat_or_push!(k,8,f(t+dt,uprev+dt*(a81*k[1]+a83*k[3]+a84*k[4]+a85*k[5]+a86*k[6]+a87*k[7])))
end
if (calcVal2 && length(k)< 11) || calcVal3 # Have not added the extra stages yet
@unpack c6,c7,c8,a91,a92,a93,a94,a95,a96,a97,a98,a101,a102,a103,a104,a105,a106,a107,a108,a109,a111,a112,a113,a114,a115,a116,a117,a118,a119,a1110 = cache
copyat_or_push!(k,9,f(t+c6*dt,uprev+dt*(a91*k[1]+a92*k[2]+a93*k[3]+a94*k[4]+a95*k[5]+a96*k[6]+a97*k[7]+a98*k[8])))
copyat_or_push!(k,10,f(t+c7*dt,uprev+dt*(a101*k[1]+a102*k[2]+a103*k[3]+a104*k[4]+a105*k[5]+a106*k[6]+a107*k[7]+a108*k[8]+a109*k[9])))
copyat_or_push!(k,11,f(t+c8*dt,uprev+dt*(a111*k[1]+a112*k[2]+a113*k[3]+a114*k[4]+a115*k[5]+a116*k[6]+a117*k[7]+a118*k[8]+a119*k[9]+a1110*k[10])))
end
nothing
end
"""
An Efficient Runge-Kutta (4,5) Pair by P.Bogacki and L.F.Shampine
Computers and Mathematics with Applications, Vol. 32, No. 6, 1996, pages 15 to 28
Called to add the extra k9, k10, k11 steps for the Order 5 interpolation when needed
"""
function ode_addsteps!{calcVal,calcVal2,calcVal3}(k,t,uprev,u,dt,f,cache::BS5Cache,always_calc_begin::Type{Val{calcVal}} = Val{false},allow_calc_end::Type{Val{calcVal2}} = Val{true},force_calc_end::Type{Val{calcVal3}} = Val{false})
if length(k) < 8 || calcVal
uidx = eachindex(uprev)
@unpack k1,k2,k3,k4,k5,k6,k7,k8,utilde,uhat,tmp,atmp,atmptilde = cache
@unpack c1,c2,c3,c4,c5,a21,a31,a32,a41,a42,a43,a51,a52,a53,a54,a61,a62,a63,a64,a65,a71,a72,a73,a74,a75,a76,a81,a83,a84,a85,a86,a87,bhat1,bhat3,bhat4,bhat5,bhat6,btilde1,btilde2,btilde3,btilde4,btilde5,btilde6,btilde7,btilde8 = cache.tab
for i in uidx
tmp[i] = uprev[i]+dt*a21*k1[i]
end
f(t+c1*dt,tmp,k2)
for i in uidx
tmp[i] = uprev[i]+dt*(a31*k1[i]+a32*k2[i])
end
f(t+c2*dt,tmp,k3)
for i in uidx
tmp[i] = uprev[i]+dt*(a41*k1[i]+a42*k2[i]+a43*k3[i])
end
f(t+c3*dt,tmp,k4)
for i in uidx
tmp[i] = uprev[i]+dt*(a51*k1[i]+a52*k2[i]+a53*k3[i]+a54*k4[i])
end
f(t+c4*dt,tmp,k5)
for i in uidx
tmp[i] = uprev[i]+dt*(a61*k1[i]+a62*k2[i]+a63*k3[i]+a64*k4[i]+a65*k5[i])
end
f(t+c5*dt,tmp,k6)
for i in uidx
tmp[i] = uprev[i]+dt*(a71*k1[i]+a72*k2[i]+a73*k3[i]+a74*k4[i]+a75*k5[i]+a76*k6[i])
end
f(t+dt,tmp,k7)
for i in uidx
u[i] = uprev[i]+dt*(a81*k1[i]+a83*k3[i]+a84*k4[i]+a85*k5[i]+a86*k6[i]+a87*k7[i])
end
f(t+dt,u,k8)
copyat_or_push!(k,1,k1)
copyat_or_push!(k,2,k2)
copyat_or_push!(k,3,k3)
copyat_or_push!(k,4,k4)
copyat_or_push!(k,5,k5)
copyat_or_push!(k,6,k6)
copyat_or_push!(k,7,k7)
copyat_or_push!(k,8,k8)
end
if (calcVal2 && length(k)< 11) || calcVal3 # Have not added the extra stages yet
uidx = eachindex(uprev)
rtmp = similar(cache.k1)
@unpack tmp = cache
@unpack c6,c7,c8,a91,a92,a93,a94,a95,a96,a97,a98,a101,a102,a103,a104,a105,a106,a107,a108,a109,a111,a112,a113,a114,a115,a116,a117,a118,a119,a1110 = cache.tab
for i in uidx
tmp[i] = uprev[i]+dt*(a91*k[1][i]+a92*k[2][i]+a93*k[3][i]+a94*k[4][i]+a95*k[5][i]+a96*k[6][i]+a97*k[7][i]+a98*k[8][i])
end
f(t+c6*dt,tmp,rtmp); copyat_or_push!(k,9,rtmp)
for i in uidx
tmp[i] = uprev[i]+dt*(a101*k[1][i]+a102*k[2][i]+a103*k[3][i]+a104*k[4][i]+a105*k[5][i]+a106*k[6][i]+a107*k[7][i]+a108*k[8][i]+a109*k[9][i])
end
f(t+c7*dt,tmp,rtmp); copyat_or_push!(k,10,rtmp)
for i in uidx
tmp[i] = uprev[i]+dt*(a111*k[1][i]+a112*k[2][i]+a113*k[3][i]+a114*k[4][i]+a115*k[5][i]+a116*k[6][i]+a117*k[7][i]+a118*k[8][i]+a119*k[9][i]+a1110*k[10][i])
end
f(t+c8*dt,tmp,rtmp); copyat_or_push!(k,11,rtmp,Val{false})
end
nothing
end
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] | 1.614442 | 7,423 |
export compose_rotation
################################################################################
# Compose Rotations
################################################################################
"""
@inline function compose_rotation(R1, [, R2, R3, R4, R5, ...])
Compute a composed rotation using the rotations `R1`, `R2`, `R3`, `R4`, ..., in
the following order:
First rotation
|
|
R1 => R2 => R3 => R4 => ...
|
|
Second rotation
The rotations can be described by Direction Cosine Matrices or Quaternions.
Notice, however, that all rotations **must be** of the same type (DCM or
quaternion).
The output will have the same type as the inputs (DCM or quaternion).
# Example
```julia-repl
julia> D1 = angle2dcm(+pi/3,+pi/4,+pi/5,:ZYX);
julia> D2 = angle2dcm(-pi/5,-pi/4,-pi/3,:XYZ);
julia> compose_rotation(D1,D2)
3×3 StaticArrays.SArray{Tuple{3,3},Float64,2,9}:
1.0 0.0 5.55112e-17
0.0 1.0 5.55112e-17
5.55112e-17 5.55112e-17 1.0
julia> q1 = angle2quat(+pi/3,+pi/4,+pi/5,:ZYX);
julia> q2 = angle2quat(-pi/5,-pi/4,-pi/3,:XYZ);
julia> compose_rotation(q1,q2)
Quaternion{Float64}:
+ 1.0 + 0.0.i + 2.0816681711721685e-17.j + 5.551115123125783e-17.k
```
"""
@inline compose_rotation(D::DCM) = D
@inline compose_rotation(D::DCM, Ds::DCM...) = compose_rotation(Ds...)*D
@inline compose_rotation(q::Quaternion) = q
@inline compose_rotation(q::Quaternion, qs::Quaternion...) =
q*compose_rotation(qs...)
# This algorithm was proposed by @Per in
#
# https://discourse.julialang.org/t/improve-the-performance-of-multiplication-of-an-arbitrary-number-of-matrices/10835/24
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] | 2.296791 | 748 |
abstract type Analysis end
struct FlowAnalysis <: Analysis
reach::Dict
ancestors::Dict
sites::Set{Variable}
addrs::Set{Any}
map::Dict
inv::Dict
loops
ir::IR
end
@inline function get_successors(ra::FlowAnalysis, var)
haskey(ra.reach, var) || return []
ra.reach[var]
end
@inline function get_ancestors(ra::FlowAnalysis, var)
haskey(ra.ancestors, var) || return []
ra.ancestors[var]
end
@inline function get_variable_by_address(ra::FlowAnalysis, addr)
haskey(ra.inv, addr) || return (nothing, false)
return (getfield(ra, :inv)[addr], true)
end
function Base.display(ra::FlowAnalysis)
println(" __________________________________\n")
println(" IR Reference\n")
display(ra.ir)
println("\n ------------ Loops ------------\n")
display(ra.loops)
println("\n ------------ Addresses ------------\n")
println(ra.addrs)
println()
println(" ------------ Variable to address ------------\n")
display(ra.map)
println()
println(" ------------ Address to variable ------------\n")
display(ra.inv)
println("\n ------------ Reachability ------------\n")
for x in ra.sites
haskey(ra.reach, x) ? println(" $x => $(ra.reach[x])") : println(" $x")
end
println("\n ------------ Ancestors ------------\n")
for x in ra.sites
haskey(ra.ancestors, x) ? println(" $x => $(ra.ancestors[x])") : println(" $x")
end
println(" __________________________________\n")
end
mutable struct CallGraph <: Analysis
addresses::Set{Address}
dependencies::Dict{Address, Set{Address}}
CallGraph(addrs, map) = new(addrs, map)
CallGraph() = new(Dict{Address, Set{Address}}())
end
function Base.display(cg::CallGraph)
println(" __________________________________\n")
println(" Dependencies\n")
for x in cg.addresses
haskey(cg.dependencies, x) ? println(" $x => $(cg.dependencies[x])") : println(" $x")
end
println(" __________________________________\n")
end
# Reaching analysis.
function reaching!(reach::Vector{Variable}, p, var, ir)
for (v, st) in ir
st.expr isa Expr && begin
if var in st.expr.args
push!(reach, v)
reaching!(reach, p, v, ir)
end
end
end
end
function reaching(var::Variable, ir)
reach = Variable[]
for (v, st) in ir
st.expr isa Expr && begin
if var in st.expr.args
push!(reach, v)
reaching!(reach, var, v, ir)
end
end
end
return reach
end
function transitive_closure!(work, reach, s)
for (k, v) in reach
if s in v && k != s
push!(work, k)
transitive_closure!(work, reach, k)
end
end
end
function flow_analysis(ir)
sites = Set(Variable[])
addrs = Set(Any[])
var_addr_map = Dict{Variable, Any}()
reach = Dict{Variable, Any}()
for (v, st) in ir
MacroTools.postwalk(st) do e
@capture(e, call_(sym_, args__))
if call isa GlobalRef && call.name == :trace
push!(sites, v)
if sym isa QuoteNode
sym = sym.value
end
push!(addrs, sym)
var_addr_map[v] = sym
reach[v] = Set(reaching(v, ir))
elseif call == trace
push!(sites, v)
push!(addrs, unwrap(sym))
var_addr_map[v] = unwrap(sym)
reach[v] = Set(reaching(v, ir))
else
push!(sites, v)
reach[v] = Set(reaching(v, ir))
end
e
end
end
ancestors = Dict()
for s in sites
work = Set(Variable[])
for (k, v) in reach
s in v && begin
push!(work, k)
transitive_closure!(work, reach, k)
end
end
ancestors[s] = work
end
return FlowAnalysis(reach, ancestors, sites, addrs, var_addr_map, Dict( v => k for (k, v) in var_addr_map), detectloops(ir), ir)
end
function dependency(a::Analysis)
map = Dict{Any, Set{Any}}()
for (k, vars) in a.reach
depends = Any[]
for v in vars
haskey(a.map, v) && push!(depends, a.map[v].value)
end
map[a.map[k].value] = Set(depends)
end
addrs = [i.value for i in a.addrs]
return CallGraph(Set(addrs), map)
end
# ------------ Driver ------------ #
# Returns the dependency analysis in call graph (tree) form.
function construct_graph!(parent, addr, call, type)
ir = lower_to_ir(call, type)
if control_flow_check(ir)
graph = CallGraph()
else
analysis = flow_analysis(ir)
dependencies = dependency(analysis)
graph = CallGraph(dependencies)
end
parent[addr] = graph
end
function construct_graph(ir::IRTools.IR)
if !control_flow_check(ir)
graph = CallGraph()
else
analysis = flow_analysis(ir)
dependencies = dependency(analysis)
graph = dependencies
end
return graph
end
# Toplevel analysis driver.
function construct_graph(call::Function, type...)
ir = lower_to_ir(call, type...)
construct_graph(ir)
end
function construct_graph(call::Function)
ir = lower_to_ir(call)
construct_graph(ir)
end
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] | 2.205086 | 2,438 |
module RocketSyncActorTest
using Test
using Rocket
@testset "SyncActor" begin
println("Testing: actor SyncActor")
@testset begin
actor = KeepActor{Int}()
synced = SyncActor{Int, KeepActor{Int}}(actor)
source = interval(1) |> take(5)
subscribe!(source, synced)
wait(synced)
@test actor.values == [ 0, 1, 2, 3, 4 ]
end
@testset begin
@test sync(void(Int)) isa SyncActor{Int, VoidActor{Int}}
end
@testset begin
values = Int[]
factory = lambda(on_next = (d) -> push!(values, d))
synced = sync(factory)
subscribe!(interval(1) |> take(5), synced)
wait(synced)
@test values == [ 0, 1, 2, 3, 4 ]
end
@testset begin
completions = []
factory = lambda(on_complete = () -> push!(completions, 1))
synced = sync(factory)
subscribe!(completed(), synced)
wait(synced)
@test completions == [ 1 ]
end
@testset begin
errors = []
factory = lambda(on_error = (d) -> push!(errors, d))
synced = sync(factory)
subscribe!(faulted("e"), synced)
wait(synced)
@test errors == [ "e" ]
end
@testset begin
source = never(Int)
actor = sync(void(Int), timeout = 100)
subscribe!(source, actor)
@test_throws SyncActorTimedOutException wait(actor)
end
struct DummyActor end
@testset begin
@test_throws InvalidActorTraitUsageError sync(DummyActor())
end
end
end
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] | 2.208158 | 711 |
using LinearAlgebra
inner_product(V1::Vector,V2::Vector,η::Matrix) = (adjoint(V2)*η*V1)[1]
inner_product(V::Vector,η::Matrix) = (adjoint(V)*η*V)[1]
function normalization!(V::Matrix,i::Int,η::Matrix)
c = inner_product(V[:,i],η)
if abs(c) < 1E-8
nothing
else
V[:,i] .= V[:,i]./sqrt(c)
end
end
function gram_schmit(V::Matrix,s::Int,e::Int,η::Matrix)
# V_1' = V_1
normalization!(V,s,η)
for i = s+1:1:e
for j = s:1:i-1
V[:,i] .-= inner_product(V[:,i],V[:,j],η).*V[:,j]
end
normalization!(V,i,η)
end
return V
end
generate_metric(N::Int) = diagm(vcat([1.0 for i=1:N],[-1.0 for i=1:N]))
function process_complex!(tS::Vector,tV::Matrix)
tS .= real.(tS)
flag = 1
for i = 1:size(tV)[1]
if sum(abs.(imag.(tV[:,i])))!=0
if flag % 2 != 0
tV[:,i] .= 2.0 .*real.(tV[:,i])
else
tV[:,i] .= 2.0 .*imag.(tV[:,i])
end
flag = flag+1
end
end
tV .= real(tV)
end
function recanonicalize(h::Matrix,hamiltonian_type::String;tol=1E-10)
N = Int(size(h)[1]/2)
η = generate_metric(N)
ih = η*h
# S, V = eigen(h) # #TODO: EXPALIN IN MEETING S = [-3.454206332875306e-11, -3.4541994235883824e-11, 9.135733651675401e-8, 9.135733809775541e-8, 0.9999999999999786, 0.9999999999999795, 1.0000000000000218, 1.000000000000022]
# @show S
tS,tV = eigen(ih) # this step will give complex tS and tV.... let's deal with them....
process_complex!(tS,tV)
tS,tV = real(tS),real(tV)
S,V = copy(tS),copy(tV)
for i = 1:N
S[i] = tS[N+i]
S[N+i] = tS[i]
V[:,i] .= tV[:,N+i]
V[:,N+i] .= tV[:,i]
end #re sort eigen value from 0->large->0->small
start_flag = 1
end_flag = 1
while start_flag <= N
end_flag = start_flag
for i = start_flag+1:N
if real(S[start_flag]-S[i]) < tol
end_flag = i
else
break
end
end
if start_flag!= end_flag
V = gram_schmit(V,start_flag,end_flag,η)
else
normalization!(V,start_flag,η)
end
start_flag = end_flag+1
end
if hamiltonian_type == "Symmetry"
V[1:N,N+1:2N] .= conj.(V[N+1:2N,1:N])
V[N+1:2N,N+1:2N] .= conj.(V[1:N,1:N])
S[N+1:2N] .= S[1:N]
elseif hamiltonian_type == "AntiSymmetry"
V[1:N,N+1:2N] .= conj.(-V[N+1:2N,1:N])
V[N+1:2N,N+1:2N] .= conj.(V[1:N,1:N])
S[N+1:2N] .= S[1:N]
else
error("We do not support this type of hamiltonian~~~~~hahahah")
end
return S,V
end
# N = 2
# η = generate_metric(N)
# h = [1 1 1 1;1 1 1 1; 1 1 1 1; 1 1 1 1]*1.0
# S,V, = recanonicalize(h)
# display(S)
# display(V) | [
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] | 1.701637 | 1,649 |
#using DelimitedFiles
#using StaticArrays
ssCO = readdlm((@__DIR__)*"/CO_shielding_functions/shield.03.5.69-557-36.dat"); #CO linewidth = 0.3, T_ex = 5
const Nbin_nco = 46
const Nbin_nh2 = 41
const Nco = SVector{Nbin_nco,Float64}(ssCO[10:55])
const Nh2 = SVector{Nbin_nh2,Float64}(ssCO[58:98])
const istart = 100 #start line number for 12CO
function get_fco(is)
a=[]
for i in 0:4
a = vcat(a, ssCO[is+i,:])
end
a = a[1:Nbin_nco]
end
ssCOtable = zeros(Nbin_nco, Nbin_nh2)
for j in 1:Nbin_nh2
jj = istart+(j-1)*5
ssCOtable[:, j] .= get_fco(jj)
end
#=
fCOss = interpolate((Nco, Nh2), ssCOtable, Gridded(Linear()));
function fCOselfshield(Nco,Nh2)
Nh2 = Nh2 >= 1e23 ? 1e23 : Nh2
Nco = Nco >= 1e19 ? 1e19 : Nco
Nh2 = Nh2 <= 1e10 ? 1e10 : Nh2
Nco = Nco <= 1e10 ? 1e10 : Nco
return fCOss(Nco, Nh2)
end
=#
include("interpolation.jl")
const par_fCOss = InterpRange{Float64}(10., 19., 1/0.2, 15., 23., 1/0.2)
#const tblCO = SMatrix{Nbin_nco,Nbin_nh2}(ssCOtable) #size of SArray should not be larger than 100
const tblCO = Matrix{Float64}(ssCOtable)
function fCOselfshield(NCO,NH2)
x, y = log10(NCO), log10(NH2)
interp(x,y, tblCO, par_fCOss)
end
function fH2selfshield(Nh2)
x = Nh2 * 2e-15
b5 = 2.0
return 0.965 / (1.0 + x/b5)^2 + 0.035 / sqrt(1 + x) * exp(-8.5e-4 * sqrt(1 + x))
end
function fCselfshield(NC,NH2)
rH2 = 2.8e-22 * NH2
return exp( -1.6e-17 * NC ) * exp(-rH2) / (1.0 + rH2)
end
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] | 1.926509 | 762 |
#
# Adding separators to SCIP.Optimizer.
#
"""
include_sepa(
o::Optimizer,
sepa::SEPA;
name::String,
description::String,
priority::Int,
freq::Int,
maxbounddist::Float,
usesubscip::Bool,
delay::Bool
)
Include a user defined separator `sepa` to the SCIP optimizer instance `o`.
All parameters have default values, that can be set as keyword arguments.
"""
function include_sepa(o::Optimizer, sepa::SEPA;
name="", description="", priority=0, freq=1,
maxbounddist=0.0, usessubscip=false,
delay=false) where {SEPA <: AbstractSeparator}
include_sepa(o.inner.scip[], o.inner.sepas, sepa, name=name, description=description,
priority=priority, freq=freq, maxbounddist=maxbounddist,
usessubscip=usessubscip, delay=delay)
end
#
# Separator for cutcallbacks.
#
mutable struct CutCbSeparator <: AbstractSeparator
scipd::SCIPData
cutcallback::Function
end
# If no cut callback is given, the cut callback does nothing.
CutCbSeparator(scipd::SCIPData) = CutCbSeparator(scipd, cb_data -> nothing)
"""
Used for an argument to the cut callback, which in turn uses that argument to
obtain the LP-solution via `MOI.get` and to add cuts via `MOI.submit`.
"""
mutable struct CutCbData
sepa::CutCbSeparator
"Did the cut callback call submit?"
submit_called::Bool
end
function exec_lp(sepa::CutCbSeparator)
cb_data = CutCbData(sepa, false)
sepa.cutcallback(cb_data)
return cb_data.submit_called ? SCIP_SEPARATED : SCIP_DIDNOTFIND
end
#
# MOI Interface for cutcallbacks
#
function MOI.get(o::Optimizer, ::MOI.CallbackVariablePrimal{CutCbData}, vi)
return SCIPgetSolVal(o, C_NULL, var(o, vi))
end
function MOI.set(o::Optimizer, ::MOI.UserCutCallback, cb::Function)
if o.moi_separator === nothing
o.moi_separator = CutCbSeparator(o.inner, cb)
include_sepa(o, o.moi_separator)
else
o.moi_separator.cutcallback = cb;
end
end
MOI.supports(::Optimizer, ::MOI.UserCutCallback) = true
function MOI.submit(o::Optimizer, cb_data::MOI.UserCut{CutCbData},
func::SAF, set::S) where {S <: BOUNDS}
varrefs = [VarRef(t.variable.value) for t in func.terms]
coefs = [t.coefficient for t in func.terms]
lhs, rhs = bounds(set)
lhs = lhs === nothing ? -SCIPinfinity(o) : lhs
rhs = rhs === nothing ? SCIPinfinity(o) : rhs
add_cut_sepa(o.inner.scip[], o.inner.vars, o.inner.sepas, cb_data.callback_data.sepa, varrefs, coefs, lhs, rhs)
cb_data.callback_data.submit_called = true
end
MOI.supports(::Optimizer, ::MOI.UserCut{CutCbData}) = true
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] | 2.253942 | 1,205 |
using ExponentialAction
using Test
@testset "ExponentialAction.jl" begin
include("util.jl")
include("expv.jl")
include("expv_autodiff.jl")
end
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d = 2+rand(5)
dl = rand(4)
du = rand(4)
M = Tridiagonal(dl, d, du)
F = lufact(M)
src = rand(5,5,5)
for dim = 1:3
dest1 = mapslices(x->A_ldiv_B!(F, x), copy(src), dim)
dest2 = similar(src)
AxisAlgorithms.A_ldiv_B_md!(dest2, F, src, dim)
@test_approx_eq dest1 dest2
end
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"""
function random_mate(nsire, ndam, nsib)
---
Random mate `nsire` with `ndam`, each dam will have `nsib`.
Assuming the first `nsire` are sires.
The rest are dams.
This is for generation one.
And can be replaced by call `random_mate(shuffle(1:nid), nsire, ndam, nsib)`.
"""
function random_mate(nsire, ndam, nsib)
r = Int(ceil(ndam/nsire))
pa = repeat(repeat(shuffle(1:nsire), r), inner=nsib)
ma = repeat(1:ndam, inner = nsib) .+ nsire
[pa[1:length(ma)] ma]
end
"""
function random_mate(ID, nsire, ndam, nsib)
---
Random mate `ID`.
Its first `nsire` are sires, last `ndam` are dams.
**Note**: `nsire + ndam` must be of `length(ID)`.
"""
function random_mate(ID, nsire, ndam, nsib)
r = Int(ceil(ndam/nsire))
pa = repeat(repeat(shuffle(ID[1:nsire]), r), inner = nsib)
ma = repeat(ID[(nsire+1):(ndam+nsire)], inner = nsib)
[pa[1:length(ma)] ma]
end
"""
function haldane(pos; M = 1e10)
---
Return recommbination rate column given bp distances.
Haldane function:
- Recombination rate: r = .5(1 - exp(-2d))
- Distance in Morgan: d = -.5log(1 - 2r)
"""
function haldane(pos; M = 1e8)
chr = bp = 0
nl = size(pos)[1]
r = zeros(Float64, nl)
@inbounds for i in 1:nl
if pos[i, 1] == chr
d = (pos[i, 2] - bp) / M # distance in cM
r[i] = .5(1 - exp(-2d))
else
chr = pos[i, 1]
r[i] = 0.5
end
bp = pos[i, 2]
end
r
end
"""
function gdrop(base, ped)
---
Drop genotypes of a subset or all of `snp`, through `ped` into `osnp`.
The last will be returned.
## Note:
If to use `osnp = BitArray(undef, nsnp, 2noff)` to save memory,
the speed is greatly reduced.
## ToDo:
Needs optimization.
May be parallel into 10 tasks.
Least urgent.
"""
function gdrop(snp, ped, r)
noff = size(ped)[1]
nsnp = size(snp)[1]
osnp = zeros(Bool, nsnp, 2noff)
i = 1
sample = zeros(nsnp)
@inbounds for prt in eachrow(ped)
@inbounds for p in prt
hap = view(snp, :, 2p-1:2p)
off = view(osnp, :, i)
k = 1
rand!(sample)
@inbounds for j in 1:nsnp
sample[j] < r[j] && (k = 3 - k)
off[j] = hap[j, k]
end
i += 1
end
end
osnp
end
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] | 2.013937 | 1,148 |
# let's try to fetch variables with fullsweeps in them
allSweepVariables = filter(v -> :RADARSWEEP in getBigDataKeys(v), getVariables(fg));
saveDFG(fg, "$dfgDataFolder/fullsweep.tar.gz")
# Okay... see you lidar!
##############################################################################
########################### LASERS AND STUFF BELOW ###########################
##############################################################################
# Quick function to find the closest point as trivial example
# We're going to save this back into the data as 'processed data'
struct ClosestPoint
cp::Vector{Float32}
dist::Float32
end
function findClosestPoint(xyzData::Vector{Vector{Float32}})::ClosestPoint
# Judgement free zone please :) just a hacky example
dist = 1.0e6
cp = [0,0,0]
for p in xyzData
d = sqrt(p[1]^2 + p[2]^2 + p[3]^2)
if d < dist
dist = d
cp = p
end
end
return ClosestPoint(cp, dist)
end
cp = findClosestPoint(lidarData)
### Quick version of writing back - write this back to our data.
# Serialize it using JSON2...
using JSON2
newData = Vector{UInt8}(JSON2.write(cp))
# Make an element and entry.
element = GeneralBigDataEntry(fg, var, :LIDARCP, mimeType="application/json")
# Set it in the store
addBigData!(datastore, element, newData)
# Make it in the graph
addBigDataEntry!(var, element)
# Save our graph with the new entries.
saveDFG(fg, "$dfgDataFolder/dfg")
# You can now load this later and retrieve that data.
### Quick example of working with the data...
"""
Quick function to extract a frame of Float32's from raw data.
(this is used for pointcloud data)
"""
function extractDataFrame(rawData::Vector{UInt8}, floatStepSize::Int, extractIndexes::Vector{Int})
reformat = reinterpret(Float32, rawData)
#Set up a map to get the indexes out
dataset = map(i -> reformat[extractIndexes.+i], collect(0:(Int(length(reformat)/floatStepSize)-1))*floatStepSize)
return dataset
end
# Now extract and format to our dataframe
lidarData = extractDataFrame(rawData, 8, [1,2,3])
# Radar would be similar (need to confirm that function works though):
# radarData = extractDataFrame(msg.radar_data, 3, [1,2,3])
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] | 2.984064 | 753 |
module SetFloatParameterTrigger
using ..Ahorn, Maple
@mapdef Trigger "ShaderHelper/SetFloatParameterTrigger" SetFloatParameterTrigger(x::Integer, y::Integer, width::Integer=Maple.defaultTriggerWidth, height::Integer=Maple.defaultTriggerHeight, shader::String="shaderhelper/testshader", value::Float=0, key::String="key")
const placements = Ahorn.PlacementDict(
"Set Float Parameter Trigger (Shader Helper)" => Ahorn.EntityPlacement(
SetFloatParameterTrigger,
"rectangle"
)
)
end | [
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] | 3.123457 | 162 |
@testset "formula" begin
using StatsModels: hasresponse, hasintercept, omitsintercept
y, x1, x2, x3, a, b, c, onet = term.((:y, :x1, :x2, :x3, :a, :b, :c, 1))
## totally empty
t = @formula(0 ~ 0)
@test !hasresponse(t)
@test !hasintercept(t)
@test omitsintercept(t)
@test t.rhs == ConstantTerm(0)
@test issetequal(terms(t), [ConstantTerm(0)])
## empty lhs, intercept on rhs
t = @formula(0 ~ 1)
@test !hasresponse(t)
@test hasintercept(t)
@test !omitsintercept(t)
## empty RHS
t = @formula(y ~ 0)
@test hasintercept(t) == false
@test omitsintercept(t) == true
@test hasresponse(t)
@test t.rhs == ConstantTerm(0)
@test issetequal(terms(t), term.((:y, 0)))
t = @formula(y ~ -1)
@test hasintercept(t) == false
@test omitsintercept(t) == true
## intercept-only
t = @formula(y ~ 1)
@test hasresponse(t) == true
@test hasintercept(t) == true
@test t.rhs == onet
@test issetequal(terms(t), (onet, y))
## terms add
t = @formula(y ~ 1 + x1 + x2)
@test hasintercept(t) == true
@test t.rhs == (onet, x1, x2)
@test issetequal(terms(t), [y, onet, x1, x2])
## implicit intercept behavior: NO intercept after @formula
t = @formula(y ~ x1 + x2)
@test hasintercept(t) == false
@test omitsintercept(t) == false
@test t.rhs == (x1, x2)
@test issetequal(terms(t), [y, x1, x2])
## no intercept
t = @formula(y ~ 0 + x1 + x2)
@test hasintercept(t) == false
@test omitsintercept(t) == true
@test t.rhs == term.((0, :x1, :x2))
t = @formula(y ~ -1 + x1 + x2)
@test hasintercept(t) == false
@test omitsintercept(t) == true
@test t.rhs == term.((-1, :x1, :x2))
t = @formula(y ~ x1 & x2)
@test hasintercept(t) == false
@test omitsintercept(t) == false
@test t.rhs == x1&x2
@test issetequal(terms(t), [y, x1, x2])
## `*` expansion
t = @formula(y ~ x1 * x2)
@test hasintercept(t) == false
@test omitsintercept(t) == false
@test t.rhs == (x1, x2, x1&x2)
@test issetequal(terms(t), [y, x1, x2])
## associative rule:
## +
t = @formula(y ~ x1 + x2 + x3)
@test t.rhs == (x1, x2, x3)
## &
t = @formula(y ~ x1 & x2 & x3)
@test t.rhs == x1&x2&x3
@test issetequal(terms(t), [y, x1, x2, x3])
## distributive property of + and &
t = @formula(y ~ x1 & (x2 + x3))
@test t.rhs == (x1&x2, x1&x3)
@test issetequal(terms(t), [y, x1, x2, x3])
## ordering of interaction terms is preserved across distributive
t = @formula(y ~ (x2 + x3) & x1)
@test t.rhs == x2&x1 + x3&x1
## distributive with *
t = @formula(y ~ (a + b) * c)
@test t.rhs == (a, b, c, a&c, b&c)
## three-way *
t = @formula(y ~ a * b * c)
@test t.rhs == (a, b, c, a&b, a&c, b&c, a&b&c)
@test issetequal(terms(t), (y, a, b, c))
## Interactions with `1` reduce to main effect.
t = @formula(y ~ 1 & x1)
@test t.rhs == x1
t = @formula(y ~ (1 + x1) & x2)
@test t.rhs == (x2, x1&x2)
## PR #54 breaks formula-level equality because original (un-lowered)
## expression is kept on Formula struct. but functional (RHS) equality
## should be maintained
using StatsModels: drop_term
@test drop_term(@formula(foo ~ 1 + bar + baz), term(:bar)) ==
@formula(foo ~ 1 + baz)
@test drop_term(@formula(foo ~ 1 + bar + baz), term(1)) ==
@formula(foo ~ bar + baz)
# drop_term no longer checks for whether term is found...
@test_broken drop_term(@formula(foo ~ bar + baz), term(0))
@test_broken drop_term(@formula(foo ~ bar + baz), term(:boz))
form = @formula(foo ~ 1 + bar + baz)
@test form == @formula(foo ~ 1 + bar + baz)
@test drop_term(form, term(:bar)) == @formula(foo ~ 1 + baz)
# drop_term creates a new formula:
@test form != @formula(foo ~ 1 + baz)
# Incorrect formula separator
@test_throws LoadError @eval @formula(y => x + 1)
# copying formulas
f = @formula(foo ~ 1 + bar)
@test f == deepcopy(f)
f = @formula(foo ~ bar)
@test f == deepcopy(f)
end
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] | 2.133643 | 1,938 |
@inline function get_geom_weights_outerloop(
geom_weights::NTuple{3, <:AbstractArray{T, 2}}, i, ::Type{Cylindrical}
) where {T}
geom_weights[1][1, i],
geom_weights[1][2, i],
geom_weights[1][3, i],
geom_weights[1][4, i],
geom_weights[1][5, i],
geom_weights[1][6, i]
end
@inline function get_geom_weights_outerloop(
geom_weights::NTuple{3, <:AbstractArray{T, 2}}, i, ::Type{Cartesian}
) where {T}
geom_weights[1][1, i],
geom_weights[1][2, i],
geom_weights[1][3, i],
geom_weights[1][4, i + 1],
geom_weights[1][4, i]
end
@inline function prepare_weights_in_middleloop(
geom_weights::NTuple{3, <:AbstractArray{T, 2}}, ::Type{Cylindrical},
i2, in2,
pwwrr, pwwrl, r_inv_pwΔmpr, Δr_ext_inv_r_pwmprr, Δr_ext_inv_l_pwmprl, Δmpr_squared,
is_r0_t::Val{is_r0}
) where {T, is_r0}
pwwφr = geom_weights[2][1, in2]
pwwφl = geom_weights[2][2, in2]
pwΔmpφ = geom_weights[2][3, in2]
Δφ_ext_inv_r = geom_weights[2][4, i2]
Δφ_ext_inv_l = geom_weights[2][4, in2]
if is_r0
pwwφr = T(0.5)
pwwφl = T(0.5)
pwΔmpφ = T(2π)
Δφ_ext_inv_r = inv(pwΔmpφ)
Δφ_ext_inv_l = Δφ_ext_inv_r
end
pwwrr_pwwφr = pwwrr * pwwφr
pwwrl_pwwφr = pwwrl * pwwφr
pwwrr_pwwφl = pwwrr * pwwφl
pwwrl_pwwφl = pwwrl * pwwφl
pwΔmpφ_Δmpr_squared = pwΔmpφ * Δmpr_squared
Δr_ext_inv_r_pwmprr_pwΔmpφ = Δr_ext_inv_r_pwmprr * pwΔmpφ
Δr_ext_inv_l_pwmprl_pwΔmpφ = Δr_ext_inv_l_pwmprl * pwΔmpφ
r_inv_pwΔmpr_Δφ_ext_inv_r = r_inv_pwΔmpr * Δφ_ext_inv_r
r_inv_pwΔmpr_Δφ_ext_inv_l = r_inv_pwΔmpr * Δφ_ext_inv_l
return (
pwwrr, pwwrl, pwwφr, pwwφl,
pwwrr_pwwφr, pwwrl_pwwφr, pwwrr_pwwφl, pwwrl_pwwφl,
pwΔmpφ_Δmpr_squared,
Δr_ext_inv_r_pwmprr_pwΔmpφ, Δr_ext_inv_l_pwmprl_pwΔmpφ,
r_inv_pwΔmpr_Δφ_ext_inv_r, r_inv_pwΔmpr_Δφ_ext_inv_l
)
end
@inline function prepare_weights_in_middleloop(
geom_weights::NTuple{3, <:AbstractArray{T, 2}}, ::Type{Cartesian},
i2, in2,
pww3r, pww3l, pwΔmp3, Δ3_ext_inv_r, Δ3_ext_inv_l,
is_r0_t
) where {T}
pww2r = geom_weights[2][1, in2]
pww2l = geom_weights[2][2, in2]
pwΔmp2 = geom_weights[2][3, in2]
pwΔmp2_pwΔmp3 = pwΔmp2 * pwΔmp3
Δ2_ext_inv_r_pwΔmp3 = geom_weights[2][4, in2 + 1] * pwΔmp3
Δ2_ext_inv_l_pwΔmp3 = geom_weights[2][4, in2] * pwΔmp3
Δ3_ext_inv_r_pwΔmp2 = Δ3_ext_inv_r * pwΔmp2
Δ3_ext_inv_l_pwΔmp2 = Δ3_ext_inv_l * pwΔmp2
pww2r_pww3r = pww2r * pww3r
pww2r_pww3l = pww2r * pww3l
pww2l_pww3r = pww2l * pww3r
pww2l_pww3l = pww2l * pww3l
return (
pww3r, pww3l, pww2r, pww2l,
pww2r_pww3r, pww2r_pww3l, pww2l_pww3r, pww2l_pww3l,
pwΔmp2_pwΔmp3,
Δ3_ext_inv_r_pwΔmp2, Δ3_ext_inv_l_pwΔmp2,
Δ2_ext_inv_r_pwΔmp3, Δ2_ext_inv_l_pwΔmp3,
)
end
@fastmath function middleloop!(
i3::Int, rb_tar_idx::Int, rb_src_idx::Int,
pcs::PotentialCalculationSetup{T, S},
update_even_points::Val{even_points},
depletion_handling::Val{depletion_handling_enabled},
is_weighting_potential::Val{_is_weighting_potential},
only2d::Val{only_2d},
idx3iseven::Val{idx3_is_even},
is_r0_t::Val{is_r0}
)::Nothing where {T, S, even_points, depletion_handling_enabled, _is_weighting_potential, only_2d, idx3_is_even, is_r0}
@inbounds begin
in3 = i3 - 1
geom_weights_3 = get_geom_weights_outerloop(pcs.geom_weights, in3, S)
line_weights::Array{T, 2} = Array{T, 2}(undef, size(pcs.potential, 1) - 2, 6)
# Even though this causes some allocations it
# is faster than using a predefined array, e.g. stored in pcs
# Especially when using multiple threads
#=
Splitting this into two loops over even and uneven i2
seems to increase the performance quite a bit by hard-coding oscillating type
instead of one loop using:
rφi_is_even_t::Union{Val{true}, Val{false}} = Val(iseven(i3 + i2) ? true : false)
=#
for i2 in (only_2d ? (2,) : 2:2:(size(pcs.potential, 2) - 1))
in2 = i2 - 1
i23_is_even_t = Val(idx3_is_even ? true : false)
geom_weights_2 = prepare_weights_in_middleloop(
pcs.geom_weights, S, i2, in2,
geom_weights_3...,
is_r0_t
)
calculate_weights_for_innerloop!(line_weights, pcs, i2, in2, i3, in3,
update_even_points, i23_is_even_t,
geom_weights_2...
)
innerloop!(line_weights, pcs, i2, in2, i3, in3, rb_tar_idx, rb_src_idx,
update_even_points, i23_is_even_t,
depletion_handling, is_weighting_potential, only2d)
end
for i2 in 3:2:(size(pcs.potential, 2) - 1)
in2 = i2 - 1
i23_is_even_t = Val(idx3_is_even ? false : true)
geom_weights_2 = prepare_weights_in_middleloop(
pcs.geom_weights, S, i2, in2,
geom_weights_3...,
is_r0_t
)
calculate_weights_for_innerloop!(line_weights, pcs, i2, in2, i3, in3,
update_even_points, i23_is_even_t,
geom_weights_2...
)
innerloop!(line_weights, pcs, i2, in2, i3, in3, rb_tar_idx, rb_src_idx,
update_even_points, i23_is_even_t,
depletion_handling, is_weighting_potential, only2d)
end
end
end
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] | 1.634593 | 3,492 |
"""
Beta
* Parametrization
* Score
* Fisher Information
* `time_varying_params` map.
* Default link
"""
Beta
function score!(score_til::Matrix{T}, y::T, ::Type{Beta}, param::Matrix{T}, t::Int) where T
score_til[t, 1] = log(y) + digamma(param[t, 1] + param[t, 2]) - digamma(param[t, 1])
score_til[t, 2] = log(1 - y) + digamma(param[t, 1] + param[t, 2]) - digamma(param[t, 2])
return
end
function fisher_information!(aux::AuxiliaryLinAlg{T}, ::Type{Beta}, param::Matrix{T}, t::Int) where T
minus_trigamma_a_b = -trigamma(param[t, 1] + param[t, 2])
aux.fisher[1, 1] = trigamma(param[t, 1]) + minus_trigamma_a_b
aux.fisher[2, 2] = trigamma(param[t, 2]) + minus_trigamma_a_b
aux.fisher[2, 1] = minus_trigamma_a_b
aux.fisher[1, 2] = minus_trigamma_a_b
return
end
function log_likelihood(::Type{Beta}, y::Vector{T}, param::Matrix{T}, n::Int) where T
loglik = 0.0
for t in 1:n
loglik += (param[t, 1] - 1)*log(y[t]) + (param[t, 2] - 1)*log(1 - y[t]) - logbeta(param[t, 1], param[t, 2])
end
return -loglik
end
# Links
function link!(param_tilde::Matrix{T}, ::Type{Beta}, param::Matrix{T}, t::Int) where T
param_tilde[t, 1] = link(LogLink, param[t, 1], zero(T))
param_tilde[t, 2] = link(LogLink, param[t, 2], zero(T))
return
end
function unlink!(param::Matrix{T}, ::Type{Beta}, param_tilde::Matrix{T}, t::Int) where T
param[t, 1] = unlink(LogLink, param_tilde[t, 1], zero(T))
param[t, 2] = unlink(LogLink, param_tilde[t, 2], zero(T))
return
end
function jacobian_link!(aux::AuxiliaryLinAlg{T}, ::Type{Beta}, param::Matrix{T}, t::Int) where T
aux.jac[1] = jacobian_link(LogLink, param[t, 1], zero(T))
aux.jac[2] = jacobian_link(LogLink, param[t, 2], zero(T))
return
end
# utils
function update_dist(::Type{Beta}, param::Matrix{T}, t::Int) where T
small_threshold!(param, SMALL_NUM, t)
return Beta(param[t, 1], param[t, 2])
end
function params_sdm(d::Beta)
return Distributions.params(d)
end
function num_params(::Type{Beta})
return 2
end | [
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] | 2.223185 | 923 |
using Luxor, Colors
chord = [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0];
adjacency = hcat([circshift(chord, p) for p in 0:11] ...);
laplacian = sum(chord) * eye(12) - adjacency;
eigvs = eigvecs(laplacian);
radius = 20
Drawing(16*2*radius, 10*2*radius)
eigv = eigvs[:, 4]
bluered = diverging_palette(240, 10, 200)
origin()
background(bluered[101])
setopacity(1.0)
h = sqrt(3)
xs = [0, 3, 2, 2, 0, 4, 2, 1, 1, 3, 3, 1]
ys = [0, 1, 0, 2, 1, 2, 1, 0, 2, 0, 2, 1]
for p in 1:length(xs)
c = clamp(imag(eigv[p] * 2), -1, 1)
c = Int(round(c * 99)) + 101
setcolor(bluered[c])
for oct_x in -1:1
for oct_y in -1:1
x = xs[p]
y = ys[p]
ngon(
(x + mod(y, 2)/2 + 4oct_x + 1.5*oct_y - 2) * radius * h,
(y + 3oct_y - 1) * radius * 1.5,
radius, 6, pi/2, :fill)
text("C")
end
end
end
finish()
preview()
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] | 1.712727 | 550 |
using ProbNumODE
using ProbNumODE: remake_prob_with_jac
using DifferentialEquations
using LinearAlgebra
using Distributions
using StatsPlots
pyplot()
FILEDIR = dirname(@__FILE__)
# Problem and reference solution
prob = remake_prob_with_jac(lotka_volterra())
appxsol = solve(remake(prob, u0=big.(prob.u0)), abstol=1e-30, reltol=1e-30)
# Probabilistic solve and errors
sol = solve(prob, EKF1(), sigmarule=:fixedMLE, q=5,
abstol=1e-7, reltol=1e-4, smooth=true)
errors = sol.u .- appxsol.(sol.t)
# Sampling and plotting
sp = ProbNumODE.sample(sol, 10)
sp_errs = sp .- ProbNumODE.stack(sol.u)
p = plot(
sol.t, ProbNumODE.stack(errors), xlabel="\$t\$", legend=:bottomleft,
label=["\$(\\hat{y}(t)-y(t))_1\$" "\$(\\hat{y}(t)-y(t))_2\$"],
)
for i in 1:size(sp)[3]
plot!(p, sol.t, sp_errs[:, :, i], color=[1 2], label="", linewidth=0.5)
end
plot!(sol.t, ProbNumODE.stack(errors), color=:black, label="")
plot!(p, size=(400, 250))
savefig(joinpath(FILEDIR, "sampled_errors_ekf1.png"))
println("Plot saved to $FILEDIR")
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] | 2.271335 | 457 |
### A Pluto.jl notebook ###
# v0.14.7
using Markdown
using InteractiveUtils
# ╔═╡ 004d3056-5ee4-11ec-12ac-750b7ebee606
begin
using Plots
x=1:10
y=rand(10)
plot(x,y,title="first Plots demo")
end
# ╔═╡ 665707df-cbb3-4570-a4c7-c8bb98c1fdc9
begin
using Plots;
plot(rand(100, 4), layout = 4)
end
# ╔═╡ Cell order:
# ╠═004d3056-5ee4-11ec-12ac-750b7ebee606
# ╠═665707df-cbb3-4570-a4c7-c8bb98c1fdc9
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] | 1.782222 | 225 |
function xval_data(data::Data, K, strata=nothing)
N = length(data.T)
collect(kfolds(sample(1:N, N, replace=false), K))
end
merge_kfolds(x) = vcat(first.(x)...), vcat(last.(x)...)
## get inidices of training and validation datasets stratified by strata
##function xval_data(data::Data, K, strata=ones(Int, length(data.T)))
##pool = 1:length(strata)
##kf = [kfolds(sample(pool[strata .== s], sum(strata .== s)), K) for s in unique(strata)]
##[merge_kfolds([s[k] for s in kf]) for k in 1:K]
##end
## estimate elpd for each subject in holdout set
function elpd_holdout(x::T, data::Data) where T <: AbstractFit
lp_h = dropdims(loglik_array(x, data), dims=(2))
map(logsumexp, eachcol(lp_h)) .- log(length(x.theta))
end
## cross-validation estimate of expected pointwise log predictive density
function xval(x::Vector{T}, data::Data, subsets) where T <: AbstractFit ##=xval_data(data, 10)) where T <: AbstractFit
##elpd = SharedArray{Float64,1}((length(data.T)))
##elpd = zeros(Float64, length(data.T))
##"$(length(subsets))-fold cross validation..."
elpd = @sync @distributed vcat for (train, test) in subsets
##init = [slice_param(y.init, train) for y in x]
y = merge(sampling(x[1].control, slice_data(data, train), typeof(x[1].init), x[1].prior; G=x[1].init.G)...)
##y = merge(sampling(x[1].control, slice_data(data, train), init, x[1].prior)...)
elpd_holdout(y, slice_data(data, test))
##elpd[test] =
end
sum(elpd), sqrt(length(elpd)*var(elpd)), elpd
end
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] | 2.369532 | 663 |
using Pkg
using GZip
using CSV
using FileIO
using DelimitedFiles
using Feather
using MatrixMarket
using CSVFiles
using SparseArrays
using NamedArrays
using Arpack
using LinearAlgebra: svd, lu!, qr!
using DataFrames
using BenchmarkTools
using StatsBase
using ThreadsX
using TSne
using UMAP
using Statistics: std, var
using Pipe
using Setfield
using ProgressMeter
using JuliaDB
using SortingLab
using Match
using ProgressBars
using Loess | [
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] | 3.425197 | 127 |
#=
VecE3: a 3d euclidean vector
=#
immutable VecE3 <: VecE
x :: Float64
y :: Float64
z :: Float64
VecE3() = new(0.0,0.0,0.0)
VecE3(x::Real, y::Real, z::Real) = new(x,y,z)
end
Base.length(::VecE3) = 3
Base.copy(a::VecE3) = VecE3(a.x, a.y, a.z)
Base.convert(::Type{Vector{Float64}}, a::VecE3) = [a.x, a.y, a.z]
function Base.convert{R<:Real}(::Type{VecE3}, a::AbstractArray{R})
@assert(length(a) == 3)
VecE3(a[1], a[2], a[3])
end
Base.show(io::IO, a::VecE3) = @printf(io, "VecE3(%.3f, %.3f, %.3f)", a.x, a.y, a.z)
@compat Base.:+(a::VecE3, b::Real) = VecE3(a.x+b, a.y+b, a.z+b)
@compat Base.:+(a::VecE3, b::VecE3) = VecE3(a.x+b.x, a.y+b.y, a.z+b.z)
@compat Base.:-(a::VecE3) = VecE3(-a.x, -a.y, -a.z)
@compat Base.:-(a::VecE3, b::Real) = VecE3(a.x-b, a.y-b, a.z-b)
@compat Base.:-(a::VecE3, b::VecE3) = VecE3(a.x-b.x, a.y-b.y, a.z-b.z)
@compat Base.:*(a::VecE3, b::Real) = VecE3(a.x*b, a.y*b, a.z*b)
@compat Base.:/(a::VecE3, b::Real) = VecE3(a.x/b, a.y/b, a.z/b)
@compat Base.:^(a::VecE3, b::Integer) = VecE3(a.x^b, a.y^b, a.z^b)
@compat Base.:^(a::VecE3, b::AbstractFloat) = VecE3(a.x^b, a.y^b, a.z^b)
# %(a::VecE3, b::Real) = VecE3(a.x%b, a.y%b, a.z%b)
@compat Base.:(==)(a::VecE3, b::VecE3) = isequal(a.x, b.x) && isequal(a.y, b.y) && isequal(a.z, b.z)
Base.isequal(a::VecE3, b::VecE3) = isequal(a.x, b.x) && isequal(a.y, b.y) && isequal(a.z, b.z)
Base.isfinite(a::VecE3) = isfinite(a.x) && isfinite(a.y) && isfinite(a.z)
Base.isinf(a::VecE3) = isinf(a.x) || isinf(a.y) || isinf(a.z)
Base.isnan(a::VecE3) = isnan(a.x) || isnan(a.y) || isnan(a.z)
Base.round(a::VecE3) = VecE3(round(a.x), round(a.y), round(a.z))
Base.floor(a::VecE3) = VecE3(floor(a.x), floor(a.y), floor(a.z))
Base.ceil(a::VecE3) = VecE3(ceil(a.x), ceil(a.y), ceil(a.z))
Base.trunc(a::VecE3) = VecE3(trunc(a.x), trunc(a.y), trunc(a.z))
Base.clamp(a::VecE3, lo::Real, hi::Real) = VecE3(clamp(a.x, lo, hi), clamp(a.y, lo, hi), clamp(a.z, lo, hi))
Base.abs(a::VecE3) = sqrt(a.x*a.x + a.y*a.y + a.z*a.z)
Base.abs2(a::VecE3) = a.x*a.x + a.y*a.y + a.z*a.z
function Base.norm(a::VecE3)
m = abs(a)
VecE3(a.x/m, a.y/m, a.z/m)
end
function dist(a::VecE3, b::VecE3)
Δx = a.x-b.x
Δy = a.y-b.y
Δz = a.z-b.z
sqrt(Δx*Δx + Δy*Δy + Δz*Δz)
end
function dist2(a::VecE3, b::VecE3)
Δx = a.x-b.x
Δy = a.y-b.y
Δz = a.z-b.z
Δx*Δx + Δy*Δy + Δz*Δz
end
Base.dot(a::VecE3, b::VecE3) = a.x*b.x + a.y*b.y + a.z*b.z
proj(a::VecE3, b::VecE3, ::Type{Float64}) = (a.x*b.x + a.y*b.y + a.z*b.z) / sqrt(b.x*b.x + b.y*b.y + b.z*b.z) # dot(a,b) / |b|
function proj(a::VecE3, b::VecE3, ::Type{VecE3})
# dot(a,b) / dot(b,b) ⋅ b
s = (a.x*b.x + a.y*b.y + a.z*b.z) / (b.x*b.x + b.y*b.y + b.z*b.z)
VecE3(s*b.x, s*b.y, s*b.z)
end
function Base.cross(a::VecE3, b::VecE3)
x = a.y*b.z - a.z*b.y
y = a.x*b.z - a.z*b.x
z = a.x*b.y - a.y*b.x
VecE3(x,y,z)
end
lerp(a::VecE3, b::VecE3, t::Real) = VecE3(a.x + (b.x-a.x)*t, a.y + (b.y-a.y)*t, a.z + (b.z-a.z)*t)
function rot(a::VecE3, axis::VecE3, θ::Real)
#=
rotate the point a: (x,y,z) about the axis: (u,v,w) by the angle θ following the
right hand rule
=#
x,y,z = a.x, a.y, a.z
u,v,w = axis.x, axis.y, axis.z
u² = u*u
v² = v*v
w² = w*w
m = u² + v² + w²
c = cos(θ)
s = sin(θ)
mc = 1.0 - c
rtms = sqrt(m)*s
magc = m*c
ux_vy_wz_mc = (u*x + v*y + w*z)*mc
new_x = (u*ux_vy_wz_mc + x*magc + rtms*(-w*y + v*z)) / m
new_y = (v*ux_vy_wz_mc + y*magc + rtms*( w*x - u*z)) / m
new_z = (w*ux_vy_wz_mc + z*magc + rtms*(-v*x + u*y)) / m
VecE3(new_x, new_y, new_z)
end
function rot_normalized(a::VecE3, axis::VecE3, θ::Real)
#=
rotate the point a: (x,y,z) about the axis: (u,v,w) by the angle θ following the
right hand rule
Here we assume axis is normalized
=#
x,y,z = a.x, a.y, a.z
u,v,w = axis.x, axis.y, axis.z
u² = u*u
v² = v*v
w² = w*w
m = u² + v² + w²
c = cos(θ)
s = sin(θ)
mc = 1.0 - c
ux_vy_wz_mc = (u*x + v*y + w*z)*mc
new_x = u*ux_vy_wz_mc + x*c + (-w*y + v*z)*s
new_y = v*ux_vy_wz_mc + y*c + ( w*x - u*z)*s
new_z = w*ux_vy_wz_mc + z*c + (-v*x + u*y)*s
VecE3(new_x, new_y, new_z)
end | [
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] | 1.590755 | 2,661 |
using HorizonSideRobots
function goToSide!(r::Robot, side::HorizonSide; save::Bool=false, mark::Bool = false)::Union{Tuple{Int, HorizonSide}, Nothing}
if save
shags = 0
while !isborder(r, side)
move!(r, side)
if mark putmarker!(r) end
shags+=1
end
return (shags, reverseSide(side))
else
while !isborder(r, side)
move!(r, side)
if mark putmarker!(r) end
end
end
end
function goToSideWithShag!(r::Robot, side::HorizonSide, shags::Int; save::Bool = false, checkBorder::Bool = false, mark::Bool = false)::Union{Tuple{Int, HorizonSide}, Nothing}
if save
shag = 0
for _ = 1:shags
if checkBorder && isborder(r, side) break end
move!(r, side)
if mark putmarker!(r) end
shag+=1
end
return (shag, reverseSide(side))
else
for _ = 1:shags
if checkBorder && isborder(r, side) break end
move!(r, side)
if mark putmarker!(r) end
end
end
end
function aroundBorder!(r::Robot, side::HorizonSide; save::Bool=false)
listOfShags = []
k = 0
while isborder(r, side)
push!(listOfShags, goToSideWithShag!(r, perSide(side), 1, save = true))
k+=1
end
push!(listOfShags, goToSideWithShag!(r, side, 1, save = true))
while isborder(r, reverseSide(perSide(side)))
push!(listOfShags, goToSideWithShag!(r, side, 1, save = true))
end
for _ = 1:k
push!(listOfShags, goToSideWithShag!(r, reverseSide(perSide(side)), 1, save = true))
end
if save
return listOfShags
end
end
function isRect(r::Robot, side::HorizonSide)::Bool
if (!isborder(r, side))
return false
end
result = false
k = 0
while !isborder(r, perSide(side))
move!(r, perSide(side))
k+=1
if !isborder(r, side)
result = true
break
end
end
for _ = 1:k
move!(r, reverseSide(perSide(side)))
end
return result
end
function reverseSide(side::HorizonSide)::HorizonSide
return HorizonSide(
(Integer(side)+2) % 4
)
end
function perSide(side::HorizonSide)::HorizonSide
return HorizonSide(
(Integer(side)+1) % 4
)
end
function goToStart!(r::Robot, listOfShags)::Nothing
reverse!(listOfShags)
for tup = listOfShags
for _ = 1:tup[1]
move!(r, tup[2])
end
end
end
function goToWestSud!(r::Robot)::Nothing
goToSide!(r, Sud)
goToSide!(r, West)
end | [
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] | 2.027301 | 1,282 |
using Turing
@model gaussdemo begin
@assume s ~ InverseGamma(2,3)
@assume m ~ Normal(0,sqrt(s))
@observe 1.5 ~ Normal(m, sqrt(s))
@observe 2.0 ~ Normal(m, sqrt(s))
@predict s m
end
# Sample and print.
res = sample(gaussdemo, SMC(10000))
println("Infered: m = $(mean(res[:m])), s = $(mean(res[:s]))")
# Compute analytical solution. Requires `ConjugatePriors` package.
exact = posterior(NormalInverseGamma(0,1,2,3), Normal, [1.5,2.0])
println("Exact: m = $(mean(exact)[1]), s = $(mean(exact)[2])")
| [
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] | 2.334862 | 218 |
using Test, StructTypes, Tables, Strapping
tbl = (a=[10], b=[3.14])
tbl2 = (id=[10, 10, 10], floats=[3.14, 3.15, 3.16])
struct AB
a::Int
b::Float64
end
StructTypes.StructType(::Type{AB}) = StructTypes.Struct()
ab = AB(10, 3.14)
@test Strapping.construct(AB, tbl) == ab
@test Strapping.construct(Vector{AB}, tbl) == [ab]
mutable struct ABM
a::Int
b::Float64
ABM() = new()
ABM(a::Int, b::Float64) = new(a, b)
end
Base.:(==)(a::ABM, b::ABM) = a.a == b.a && a.b == b.b
StructTypes.StructType(::Type{ABM}) = StructTypes.Mutable()
abm = ABM(10, 3.14)
@test Strapping.construct(ABM, tbl) == abm
@test Strapping.construct(Vector{ABM}, tbl) == [abm]
@test Strapping.construct(NamedTuple, tbl) == (a=10, b=3.14)
@test Strapping.construct(NamedTuple{(:a, :b), Tuple{Int, Float64}}, tbl) == (a=10, b=3.14)
@test Strapping.construct(NamedTuple{(:a, :b)}, tbl) == (a=10, b=3.14)
@test Strapping.construct(Dict, tbl) == Dict("a" => 10, "b" => 3.14)
@test Strapping.construct(Dict{String, Float64}, tbl) == Dict("a" => 10.0, "b" => 3.14)
@test Strapping.construct(Dict{Symbol, Any}, tbl) == Dict(:a => 10, :b => 3.14)
@test Strapping.construct(Array, tbl) == [10, 3.14]
@test Strapping.construct(Vector{Float64}, tbl) == [10.0] # because Float64 is scalar, only 1st field of result is used, other fields are ignored
@test Strapping.construct(Set, tbl) == Set([10, 3.14])
@test Strapping.construct(Tuple, tbl) == (10, 3.14)
@test Strapping.construct(Tuple{Int, Float64}, tbl) == (10, 3.14)
@test Strapping.construct(Vector{Any}, tbl) == [Dict("a" => 10, "b" => 3.14)]
struct AB2
id::Int
floats::Vector{Float64}
end
Base.:(==)(a::AB2, b::AB2) = a.id == b.id && a.floats == b.floats
StructTypes.StructType(::Type{AB2}) = StructTypes.Struct()
StructTypes.idproperty(::Type{AB2}) = :id
ab2 = AB2(10, [3.14, 3.15, 3.16])
@test Strapping.construct(AB2, tbl2) == ab2
@test Strapping.construct(Vector{AB2}, tbl2) == [ab2]
struct AB3
a::Int
ab::AB
end
StructTypes.StructType(::Type{AB3}) = StructTypes.Struct()
tbl3 = (a=[1], ab_a=[10], ab_b=[3.14])
ab3 = AB3(1, AB(10, 3.14))
@test Strapping.construct(AB3, tbl3) == ab3
@test Strapping.construct(Vector{AB3}, tbl3) == [ab3]
mutable struct AB4
a::Int
b::Float64
abs::Vector{AB}
AB4() = new()
AB4(a::Int, b::Float64, abs::Vector{AB}) = new(a, b, abs)
end
Base.:(==)(a::AB4, b::AB4) = a.a == b.a && a.b == b.b && a.abs == b.abs
StructTypes.StructType(::Type{AB4}) = StructTypes.Mutable()
StructTypes.idproperty(::Type{AB4}) = :a
tbl4 = (a=[10, 10, 10], b=[3.14, 3.14, 3.14], abs_a=[10, 10, 10], abs_b=[3.14, 3.15, 3.16])
ab4 = AB4(10, 3.14, AB[AB(10, 3.14), AB(10, 3.15), AB(10, 3.16)])
@test Strapping.construct(AB4, tbl4) == ab4
@test Strapping.construct(Vector{AB4}, tbl4) == [ab4]
# multiple levels of nesting
struct AB5
a::Int
ab::AB3
end
StructTypes.StructType(::Type{AB5}) = StructTypes.Struct()
tbl5 = (a=[10], ab_a=[10], ab_ab_a=[10], ab_ab_b=[3.14])
ab5 = AB5(10, AB3(10, AB(10, 3.14)))
@test Strapping.construct(AB5, tbl5) == ab5
@test Strapping.construct(Vector{AB5}, tbl5) == [ab5]
# deconstruction
@test columntable(Strapping.deconstruct(ab)) == tbl
@test columntable(Strapping.deconstruct(ab2)) == tbl2
@test columntable(Strapping.deconstruct(ab3)) == tbl3
@test columntable(Strapping.deconstruct(ab4)) == tbl4
@test columntable(Strapping.deconstruct(ab5)) == tbl5
struct AB6
a::Int
b::AB
c::Vector{AB}
end
Base.:(==)(a::AB6, b::AB6) = a.a == b.a && a.b == b.b && a.c == b.c
StructTypes.StructType(::Type{AB6}) = StructTypes.Struct()
StructTypes.idproperty(::Type{AB6}) = :a
tbl6 = (a=[1, 1, 1], b_a=[2, 2, 2], b_b=[0.01, 0.01, 0.01], c_a=[10, 11, 12], c_b=[1.1, 2.2, 3.3])
ab6 = AB6(1, AB(2, 0.01), [AB(10, 1.1), AB(11, 2.2), AB(12, 3.3)])
@test Strapping.construct(AB6, tbl6) == ab6
@test Strapping.construct(Vector{AB6}, tbl6) == [ab6]
@test columntable(Strapping.deconstruct(ab6)) == tbl6
# https://github.com/JuliaData/Strapping.jl/issues/12
struct AB7
id::Int
values::Vector{Float64}
end
Base.:(==)(a::AB7, b::AB7) = a.id == b.id && a.values == b.values
StructTypes.StructType(::Type{AB7}) = StructTypes.Struct()
StructTypes.idproperty(::Type{AB7}) = :id
ab7 = AB7(1, Float64[])
tbl = columntable(Strapping.deconstruct(ab7))
@test tbl.id[1] == 1
@test tbl.values[1] === missing
struct AB9
a::Int
b::String
c::Float64
d::String
e::Int
f::String
end
StructTypes.StructType(::Type{AB9}) = StructTypes.Struct()
struct AB10
id::Int
ab9::AB9
end
StructTypes.StructType(::Type{AB10}) = StructTypes.Struct()
struct AB11
id::Int
ab10::AB10
end
StructTypes.StructType(::Type{AB11}) = StructTypes.Struct()
ab11 = AB11(1, AB10(2, AB9(3, "4", 5.0, "6", 7, "8")))
tbl = columntable(Strapping.deconstruct(ab11))
@test length(tbl) == 8
@test tbl.id[1] == 1
@test tbl[end][1] == "8"
# https://github.com/JuliaData/Strapping.jl/issues/3
struct TestStruct
a::Float64
b::Float64
id::Int
end
StructTypes.StructType(::Type{TestStruct}) = StructTypes.Struct()
StructTypes.idproperty(::Type{TestStruct}) = :id
data = [ TestStruct(rand(2)..., n) for n = 1:5]
tbl = Strapping.deconstruct(data)
@test length(Tables.columntable(tbl)[1]) == 5
struct AA1
a::Int64
b::Dict{Symbol, Any}
end
StructTypes.StructType(::Type{AA1}) = StructTypes.Struct()
data = [ AA1(1, Dict{Symbol, Any}(:aa => 2, :bb => 3)) ]
tbl = Strapping.deconstruct(data)
tbl2 = Tables.columntable(tbl)
@test tbl2.a == [1]
@test tbl2.b_aa == [2]
@test tbl2.b_bb == [3]
#8
struct TestResult
id::Int
values::Vector{Float64}
end
StructTypes.StructType(::Type{TestResult}) = StructTypes.Struct()
StructTypes.idproperty(::Type{TestResult}) = :id
tbl = (id=[1, 1, 1, 2, 2, 2], values=[3.14, 3.15, 3.16, 40.1, 0.01, 2.34])
testresult = Strapping.construct(Vector{TestResult}, tbl)
struct Experiment
id::Int
name::String
testresults::TestResult
end
StructTypes.StructType(::Type{Experiment}) = StructTypes.Struct()
StructTypes.idproperty(::Type{Experiment}) = :id
StructTypes.fieldprefix(::Type{Experiment}, nm::Symbol) = nm == :testresults ? :testresults_ : :_
tbl2 = (id=[1, 1, 1], name=["exp1", "exp1", "exp1"], testresults_id=[1, 1, 1], testresults_values=[3.14, 3.15, 3.16])
experiment = Strapping.construct(Experiment, tbl2)
@test experiment.id == 1
@test experiment.name == "exp1"
@test experiment.testresults.values == [3.14, 3.15, 3.16]
struct Service
id::String
data::NamedTuple
end
StructTypes.StructType(::Type{Service}) = StructTypes.Struct()
StructTypes.idproperty(::Type{Service}) = :id
StructTypes.fieldprefix(::Type{Service}, nm::Symbol) = Symbol()
Tables.isrowtable(::Type{Service}) = true
function Tables.getcolumn(row::Service,i::Int)
if i == 1
return getfield(row,1)
else
return row.data[i-1]
end
end
function Tables.getcolumn(row::Service,nm::Symbol)
if nm == :id
return row.id
else
return getproperty(row.data,nm)
end
end
function Tables.columnnames(row::Service)
vcat(:id,keys(row.data)...)
end
x = Service("qux",(a=1,b=3))
xd = Strapping.deconstruct(x)
xc = Strapping.construct(Service,xd)
@test xc.id == "qux"
@test xc.data == (a=1, b=3)
x2 = Service("foo",(a=1,b=3))
X = [x,x2]
Xd = Strapping.deconstruct(X)
Xc = Strapping.construct(Vector{Service},Xd)
@test length(Xc) == 2
@test Xc[2].id == "foo"
@test Xc[2].data == (a=1, b=3)
struct Wrapper
x::NamedTuple{(:a, :b), Tuple{Int, String}}
end
StructTypes.StructType(::Type{Wrapper}) = StructTypes.CustomStruct()
StructTypes.lower(x::Wrapper) = x.x
StructTypes.lowertype(::Type{Wrapper}) = fieldtype(Wrapper, :x)
w = Wrapper((a=1, b="hey"))
tbl = Strapping.deconstruct(w) |> Tables.columntable
@test tbl == (a = [1], b = ["hey"])
w2 = Strapping.construct(Wrapper, tbl)
@test w == w2
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] | 2.282216 | 3,430 |
info("Testing ", MOD.translate!)
for T in FloatingPointTypes
A = T[1 2;
3 4;
5 6]
b = T[1;
2]
c = T[1;
2;
3]
@test_approx_eq MOD.translate!(copy(A), one(T)) (A .+ one(T))
@test_approx_eq MOD.translate!(one(T), copy(A)) (A .+ one(T))
@test_approx_eq MOD.translate!(copy(A), b) (A .+ b')
@test_approx_eq MOD.translate!(c, copy(A)) (A .+ c)
end
info("Testing ", MOD.perturb!)
for T in FloatingPointTypes
A = T[1 2 3;
4 5 6;
7 8 9]
B = T[1 2;
3 4;
5 6]
@test_approx_eq MOD.perturb!(copy(A), one(T)) (A + one(T)*I)
@test_throws DimensionMismatch MOD.perturb!(B, one(T))
end
info("Testing ", MOD.regularize!)
for T in FloatingPointTypes
A = T[1 2 3;
4 5 6;
7 8 9]
B = T[1 2;
3 4;
5 6]
a = trace(A)/3
@test_approx_eq MOD.regularize!(copy(A), zero(T), a) A
@test_approx_eq MOD.regularize!(copy(A), convert(T,0.5), a) convert(T,0.5)*(A + a*I)
@test_approx_eq MOD.regularize!(copy(A), one(T), a) a*eye(T,3)
@test_throws DimensionMismatch MOD.regularize!(B, one(T), one(T))
end
A = [1 2 3;
4 5 6;
7 8 9]
AL = [1 2 3;
2 5 6;
3 6 9]
AU = [1 4 7;
4 5 8;
7 8 9]
info("Testing ", MOD.syml)
for T in FloatingPointTypes
B = MOD.syml(convert(Array{T},A))
@test eltype(B) == T
@test_approx_eq B convert(Array{T}, AL)
end
info("Testing ", MOD.symu)
for T in FloatingPointTypes
B = MOD.symu(convert(Array{T},A))
@test eltype(B) == T
@test_approx_eq B convert(Array{T}, AU)
end
info("Testing ", MOD.components_svd!)
for T in FloatingPointTypes
X = rand(T,20,5)
V1, D1 = MOD.components_svd!(copy(X))
D2, V2 = svd(X)[2:3]
@test_approx_eq V1 V2
@test_approx_eq D1 (D2 .^ 2)
end
info("Testing ", MOD.components_eig!)
for T in FloatingPointTypes
X = rand(T,20,5)
S = X'X
V1, D1 = MOD.components_eig!(copy(S))
D2, V2 = eig(S)
@test_approx_eq abs(V1) abs(V2[:,end:-1:1]) # Signs may differ
@test_approx_eq abs(D1) abs(D2[end:-1:1])
end
info("Testing ", MOD.components_geig!)
for T in FloatingPointTypes
X1 = rand(T,20,5)
X2 = rand(T,20,5)
S1 = X1'X1
S2 = X2'X2
V1, D1 = MOD.components_geig!(copy(S1), copy(S2))
D2, V2 = eig(S1, S2)
@test_approx_eq abs(V1) abs(V2[:,end:-1:1])
@test_approx_eq abs(D1) abs(D2[end:-1:1])
end
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] | 1.879205 | 1,308 |
using Kinetic
using KitBase.OffsetArrays, KitBase.ProgressMeter
cd(@__DIR__)
D = read_dict("mixture_shock.txt")
for key in keys(D)
s = Symbol(key)
@eval $s = $(D[key])
end
begin
γ = heat_capacity_ratio(inK, 1)
set = Setup(matter, case, space, flux, collision, nSpecies, interpOrder, limiter, boundary, cfl, maxTime)
pSpace = PSpace1D(x0, x1, nx, nxg)
ue0 = umin * sqrt(mi / me)
ue1 = umax * sqrt(mi / me)
vSpace = MVSpace1D(umin, umax, ue0, ue1, nu, vMeshType, nug)
kne = knudsen * (me / mi)
gas = Mixture(
[knudsen, kne],
mach,
prandtl,
inK,
γ,
mi,
ni,
me,
ne,
)
wL, primL, hL, bL, bcL, wR, primR, hR, bR, bcR = ib_rh(gas.Ma, gas.γ, gas.K, mi, me, ni, ne, vSpace.u)
ib = IB2F(wL, primL, hL, bL, bcL, wR, primR, hR, bR, bcR)
ks = SolverSet(set, pSpace, vSpace, gas, ib, pwd())
end
begin
ctr = OffsetArray{ControlVolume1D2F}(undef, axes(ks.pSpace.x, 1))
face = Array{Interface1D2F}(undef, ks.pSpace.nx + 1)
idx0 = (eachindex(pSpace.x) |> collect)[1]
idx1 = (eachindex(pSpace.x) |> collect)[end]
for i in eachindex(ctr)
if i <= ks.pSpace.nx ÷ 2
ctr[i] = ControlVolume1D2F(ks.pSpace.x[i], ks.pSpace.dx[i], ks.ib.wL, ks.ib.primL,
ks.ib.hL, ks.ib.bL)
else
ctr[i] = ControlVolume1D2F(ks.pSpace.x[i], ks.pSpace.dx[i], ks.ib.wR, ks.ib.primR,
ks.ib.hR, ks.ib.bR)
end
end
face = Array{Interface1D2F}(undef, ks.pSpace.nx+1)
for i=1:ks.pSpace.nx+1
face[i] = Interface1D2F(ks.ib.wL, ks.ib.hL)
end
end
begin
iter = 0
res = zeros(3)
simTime = 0.0
dt = Kinetic.timestep(ks, ctr, simTime)
nt = Int(floor(ks.set.maxTime / dt))
end
res = zeros(3, 2)
begin
Kinetic.reconstruct!(ks, ctr)
Kinetic.evolve!(ks, ctr, face, dt)
Kinetic.update!(ks, ctr, face, dt, res; bc=:fix)
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] | 1.790485 | 1,093 |
using Revise
using PyPlot
using Infiltrator
using LinearAlgebra
using Bem2d
"""
gravityparticularfunctions()
From Pape and Bannerjee 1987
"""
function gravityparticularfunctions(x, y, g, rho, lambda, mu)
U = zeros(length(x), 2)
S = zeros(length(x), 3)
U[:, 1] = @. -lambda * rho * g / (4 * mu * (lambda + mu)) * x * y # Pape and Banerjee (1987) equation (6a)
U[:, 2] = @. (rho * g) / (8 * mu * (lambda + mu)) * (lambda * x^2 + (lambda + 2 * mu) * y^2) # Pape and Banerjee (1987) equation (6b)
S[:, 1] .= 0 # Pape and Banerjee (1987) equation (6c)
S[:, 2] = @. rho * g * y # Pape and Banerjee (1987) equation (6d)
S[:, 3] .= 0 # Pape and Banerjee (1987) equation (6e)
return U, S
end
"""
gravitysquareparticular()
Experiments with gravity body force.
"""
function gravitysquareparticular()
# TODO: Move particular solution to Bem2d.jl
# TODO: It's strange that the top of the model has to be at zero. Can we generalize this?
# TODO: Rule of thumb for choosing precondtioner value (alpha)?
# TODO: Make a version of gravityparticularfunctions() for BC generation
close("all")
alpha = 7e-8 # scalar preconditioner for traction terms
fontsize = 20
mu = 3e10
lambda = 3e10
nu = 0.25
rho = 2700
g = 9.81
nels = 20
npts = 100
L = 1e4
offset = 10
x, y = obsgrid(-L+offset, -2*L+offset, L-offset, 0-offset, npts)
# Define BEM geometry
els = Elements(Int(1e5))
x1, y1, x2, y2 = discretizedline(-L, -2*L, L, -2*L, nels) # Bottom
addelsez!(els, x1, y1, x2, y2, "B")
x1, y1, x2, y2 = discretizedline(L, -2*L, L, 0, nels) # Right hand side
addelsez!(els, x1, y1, x2, y2, "R")
x1, y1, x2, y2 = discretizedline(L, 0, -L, 0, nels) # Top
addelsez!(els, x1, y1, x2, y2, "T")
x1, y1, x2, y2 = discretizedline(-L, 0, -L, -2*L, nels) # Left hand side
addelsez!(els, x1, y1, x2, y2, "L")
# Common indexing
idx = getidxdict(els) # Should this return "all" - YES TODO
bcidxU = idx["B"] # Boundaries with *displacement* BCs
bcidxT = [idx["R"] ; idx["T"]; idx["L"]] # Boundaries with *traction* BCs
bcidxall = collect(1:1:els.endidx) # All boundaries
# Gravity square problem with quadratic elements
T_pU_qall, _ = PUTQ(slip2dispstress, els, bcidxU, bcidxall, mu, nu)
_, H_pT_qall = PUTQ(slip2dispstress, els, bcidxT, bcidxall, mu, nu)
TH = [T_pU_qall ; alpha .* H_pT_qall] # Assemble combined linear operator
# Particular solution and effective boundary conditions
xnodes = transpose(els.xnodes[idx["B"], :])[:]
ynodes = transpose(els.ynodes[idx["B"], :])[:]
UB, _ = gravityparticularfunctions(xnodes, ynodes, g, rho, lambda, mu)
bcs = zeros(6 * els.endidx)
bcs[1:2:6*nels] = UB[:, 1] # Bottom boundary (x-component)
bcs[2:2:6*nels] = UB[:, 2] # Bottom boundary (y-component)
bcs *= -1 # This is neccesary for the right answer and is consistent with derivation
# BEM solve to get particular solution
Ueffparticular = inv(TH) * bcs
# Evaluate and plot interior solution
Uinteriorcomplementary, Sinteriorcomplementary = quaddispstress(slip2dispstress, x, y, els, bcidxall, quadstack(Ueffparticular[1:2:end]), quadstack(Ueffparticular[2:2:end]), mu, nu)
Uinteriorparticular, Sinteriorparticular = gravityparticularfunctions(x, y, g, rho, lambda, mu)
U = @. Uinteriorcomplementary + Uinteriorparticular
S = @. Sinteriorcomplementary + Sinteriorparticular
plotfields(els, reshape(x, npts, npts), reshape(y, npts, npts), Uinteriorcomplementary, Sinteriorcomplementary, "Complementary solution")
plotfields(els, reshape(x, npts, npts), reshape(y, npts, npts), Uinteriorparticular, Sinteriorparticular, "Particular solution")
plotfields(els, reshape(x, npts, npts), reshape(y, npts, npts), U, S, "Complementary + Particular solutions")
# Interior point solution for nice vector plot
npts = 10
offset = 500
x, y = obsgrid(-L+offset, -2*L+offset, L-offset, 0-offset, npts)
Uinteriorcomplementary, Sinteriorcomplementary = quaddispstress(slip2dispstress, x, y, els, bcidxall, quadstack(Ueffparticular[1:2:end]), quadstack(Ueffparticular[2:2:end]), mu, nu)
Uinteriorparticular, Sinteriorparticular = gravityparticularfunctions(x, y, g, rho, lambda, mu)
U = @. Uinteriorcomplementary + Uinteriorparticular
S = @. Sinteriorcomplementary + Sinteriorparticular
# Plot of interior points
figure()
fill([-10000, 10000, 10000, -10000], [-20000, -20000, 0, 0] .+ 20000, color="lightgray", zorder=1)
quiver(x[:], y[:].+20000, U[:, 1], U[:, 2], units="width", color="cyan", edgecolor="k", linewidths=0.25, zorder=10)
xlabel("x (m)")
ylabel("y (m)")
gca().set_aspect("equal")
end
gravitysquareparticular()
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] | 2.360728 | 2,032 |
module UCIData
import CSV
using CategoricalArrays
using DataDeps
using DataFrames
using DelimitedFiles
using Dates
import LegacyStrings
const DATA_DIR = joinpath(@__DIR__, "data")
include("convert.jl")
function dataset(datasetname)
dataset_path = @datadep_str datasetname
df = CSV.read("$dataset_path/data.csv", DataFrame, header=true,
typemap=Dict(Date=>String, DateTime=>String))
for name in names(df)
if name[1] == 'C'
transform!(df, name => categorical, renamecols=false)
end
end
df
end
function list_dataset_types()
filter(x -> isdir(joinpath(DATA_DIR, x)), readdir(DATA_DIR))
end
function list_datasets(datasettype)
dir = joinpath(DATA_DIR, datasettype)
datasets = filter(x -> splitext(x)[end] == ".jl", readdir(dir))
map(x -> splitext(x)[1], datasets)
end
function __init__()
for datasettype in list_dataset_types()
for dataset in list_datasets(datasettype)
include(joinpath(DATA_DIR, datasettype, "$dataset.jl"))
end
end
end
end
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834,
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198,
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329,
27039,
4906,
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292,
316,
62,
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3419,
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220,
220,
220,
329,
27039,
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1351,
62,
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1039,
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628,
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198
] | 2.603581 | 391 |
"""
similar_rxns(model::MetNet, iders = eachindex(model.rxns); verbose = true)
Findall the reaction that transforms the same elements
if we define: r1 = 2A -> 2B, r2 = B <-> A, r3 = A <- B, r4 = A + B ->
the method will returns [[r1, r2, r3]]
This method only use the information in the binary S.
Bounds are not relevant either.
"""
function similar_rxns(model::MetNet,
iders = eachindex(model.rxns); verbose = true)
idxs = [rxnindex(model, ider) for ider in iders]
# collecting react and prods hashs
# (prods_hash, react_hash) => reaction idxs
# (react_hash, prods_hash) => reaction idxs
hash_table = Dict{Tuple{UInt64,UInt64}, Vector{Int}}()
for rxni in idxs
rhash = hash(Set(rxn_reacts(model, rxni)))
phash = hash(Set(rxn_prods(model, rxni)))
rxnis = get!(hash_table, (rhash, phash), Int[])
push!(rxnis, rxni)
end
# Form pairs
similars = Vector{Vector{Int}}()
for (fkey, frxnis) in hash_table
bkey = fkey |> reverse
brxnis = get(hash_table, bkey, Int[])
sims_ = union(frxnis, brxnis)
length(sims_) > 1 && push!(similars, sims_)
# deleting
delete!(hash_table, fkey)
delete!(hash_table, bkey)
end
if verbose
for rxnis in similars
println("Similars ", length(rxnis))
for rxni in rxnis
println(model.rxns[rxni], " [", rxni, "]: ", rxn_str(model, rxni))
end
println()
end
end
return similars
end
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220,
220,
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886,
198,
220,
220,
220,
1441,
985,
346,
945,
198,
437,
628
] | 2.146036 | 719 |
module AdjacentFloat
!isdefined(Float) && typealias Float AbstractFloat;
export prevFloat, nextFloat, nFloatsSeparate, nextNearerToZero, nextAwayFromZero
include("adjacentFloats.jl")
end # module
| [
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] | 3.174603 | 63 |
using DataFrames
using CSV
using SparseArrays
"""
admittance_matrices(nodes, lines, harmonics)
Build the nodal admittance matrices (admittance laplacian) for all harmonics.
Admittance scales with frequency: X_h = X_f * h. Return a dictionary of DataFrames.
"""
function admittance_matrices(net, harmonics)
LY = Dict()
for h in harmonics
LY[h] = spzeros(ComplexF64, net.n, net.n)
# non-diagonal elements
for line in eachrow(net.lines)
LY[h][line.fromID, line.toID] = -1/(line.R + 1im*line.X*h)
# nodal admittance matrix is assumed to be symmetric
LY[h][line.toID, line.fromID] = -1/(line.R + 1im*line.X*h)
end
# diagonal elements
for i in 1:net.n
# node shunt admittances only for harmonic frequencies
if net.nodes.X_sh[i] > 0 && h != 1
LY[h][i, i] = -sum(LY[h][i, :]) + 1/(1im*net.nodes.X_sh[i]*h)
else
LY[h][i, i] = -sum(LY[h][i, :])
end
# Adding shunt admittances for each pi-model line connected to node i
for line in eachrow(net.lines)
# check if line is connected to node i
if line.fromID == i || line.toID == i
LY[h][i, i] = LY[h][i, i] +
(line.G + 1im*h*line.B)/2
end
end
end
end
return LY
end
"""
init_voltages(nodes, settings)
Initialize voltages for all nodes and harmonics using guesses from on `Settings`.
"""
function init_voltages(nodes, settings)
u = Dict()
for h in settings.harmonics
if h == 1
u[h] = DataFrame(
v = ones(size(nodes, 1))*settings.v_f,
ϕ = ones(size(nodes, 1))*settings.ϕ_f
)
else
u[h] = DataFrame(
v = ones(size(nodes, 1))*settings.v_h,
ϕ = ones(size(nodes, 1))*settings.ϕ_h
)
end
end
return u
end
"""
fund_state_vec(net, u)
Take the voltage DataFrame `u` and return a vector of voltage phases and magnitudes.
"""
function fund_state_vec(net, u)
xϕ = u[1].ϕ[2:end]
xv = u[1].v[(net.c+1):end]
vcat(xϕ, xv)
end
"""
fund_mismatch(net, u, LY)
Evaluate active and reactive power mismatch at fundamental frequency.
"""
function fund_mismatch(net, u, LY)
LY_1 = LY[1]
u_1 = u[1].v .* exp.(1im*u[1].ϕ)
s = (net.nodes.P + 1im*net.nodes.Q)
mismatch = u_1 .* conj(LY_1*u_1) + s
f = vcat(real(mismatch[2:end]), imag(mismatch[(net.c+1):end]))
err = maximum(abs.(f))
return f, err
end
"""
fund_jacobian(net, u, LY)
Construct fundamental Jacobian, containing powers derived wrt voltages.
"""
function fund_jacobian(net, u, LY)
# helpful definitions for Jacobian construction
u_1 = u[1].v .* exp.(1im*u[1].ϕ)
i_diag = spdiagm(sparse(LY[1] * u_1))
u_1_diag = spdiagm(u_1)
u_1_diag_norm = spdiagm(u_1./abs.(u_1))
# complex power mismatch derivatives
dSdϕ = 1im*u_1_diag*conj(i_diag - LY[1]*u_1_diag)
dSdv = u_1_diag_norm*conj(i_diag) + u_1_diag*conj(LY[1]*u_1_diag_norm)
# divide sub-matrices into real and imag part, cut off slack
dPdϕ = real(dSdϕ[2:end, 2:end])
dPdv = real(dSdv[2:end, (net.c+1):end])
dQdϕ = imag(dSdϕ[(net.c+1):end, 2:end])
dQdv = imag(dSdv[(net.c+1):end, (net.c+1):end])
vcat(hcat(dPdϕ, dPdv),
hcat(dQdϕ, dQdv))
end
"""
update_state_vec!(J, x, f)
Perform Newton-Raphson iteration and update the fundamental state vector.
"""
function update_state_vec!(J, x, f)
x - J\f
end
"""
update_fund_voltages!(net, u, x)
Write new voltages from `x` to `u`.
"""
function update_fund_voltages!(net, u, x)
u[1].ϕ[2:end] = x[1:(net.n-1)]
u[1].v[(net.c+1):end] = x[net.n:end]
return u
end
"""
pf(net, settings, LY, plt_convergence = false)
Perform fundamental power flow, iterate until mismatch is smaller than threshold.
"""
function pf(net, settings, LY, plt_convergence = false)
# initialization
u = init_voltages(net.nodes, settings)
x_f = fund_state_vec(net, u)
f_f, err_f = fund_mismatch(net, u, LY)
n_iter_f = 0
err_f_t = Dict()
while err_f > settings.thresh_f && n_iter_f <= settings.max_iter_f
J_f = fund_jacobian(net, u, LY)
x_f = update_state_vec!(J_f, x_f, f_f)
u = update_fund_voltages!(net, u, x_f)
f_f, err_f = fund_mismatch(net, u, LY)
err_f_t[n_iter_f] = err_f # currently not returned
n_iter_f += 1
end
println(u[1])
if n_iter_f < settings.max_iter_f
println("Fundamental power flow converged after ", n_iter_f,
" iterations (err < ", settings.thresh_f, ").")
elseif n_iter_f == settings.max_iter_f
println("Maximum of ", n_iter_f, " iterations reached.")
end
return u
end
# Harmonic Power Flow functions
"""
import_Norton_Equivalents(nodes, settings, folder_path="devices\\")
Import NE parameters for all nonlinear devices in nodes from folder devices."""
function import_Norton_Equivalents(nodes, settings, folder_path="devices\\")
NE = Dict()
nl_components = unique(nodes[nodes.type .== "nonlinear", "component"])
for device in nl_components
NE_df = CSV.read(folder_path * device * "_NE.csv", DataFrame)
# transform to Complex type, enough to strip first paranthesis for successful parse
vals = mapcols!(col -> parse.(ComplexF64, strip.(col, ['('])), NE_df[:, 3:end])
NE_device = hcat(NE_df[:,1:2], vals)
# filter columns for considered harmonics
NE_device = NE_device[:, Between(begin, string(maximum(settings.harmonics)*settings.base_frequency))]
# change to pu system and choose if coupled
if settings.coupled
if maximum(settings.harmonics) != Int(parse(Int, names(NE_df)[end])/settings.base_frequency)
println("Warning! Inprecise results expected, as coupled method is being used but not the full spectrum taken into account.")
end
I_N = Array(NE_device[NE_device.Parameter .== "I_N_c", 3:end])/settings.base_current
LY_N_full = NE_device[NE_device.Parameter .== "Y_N_c", 2:end]
LY_N = Array(LY_N_full[LY_N_full.Frequency .<= maximum(settings.harmonics)*settings.base_frequency, 2:end])/settings.base_admittance
else
I_N = Array(NE_device[NE_device.Parameter .== "I_N_uc", 3:end])/settings.base_current
LY_N = Array(NE_device[NE_device.Parameter .== "Y_N_uc", 3:end])/settings.base_admittance
end
NE[device] = [I_N, LY_N]
end
return NE
end
"""
current_injections(nodes, nodeID, u, NE, harmonics)
Calculate the harmonic current injections at one node.
Uses coupled or uncoupled method based on dimensions of NE."""
function current_injections(nodes, nodeID, u, NE, harmonics)
component = nodes[nodes.ID .== nodeID, "component"][1]
I_N, LY_N = NE[component]
# u as dict of dfs makes building this vector a bit complicated
u_h = vcat([u[h][nodeID, "v"] .* exp.(1im*u[h][nodeID, "ϕ"]) for h in harmonics]...)
# coupled: Y_N is a matrix, uncoupled: vector
if size(LY_N)[1] > 1 # coupled case
i_inj = vec(I_N) - vec(LY_N*u_h)
else # uncoupled case
i_inj = vec(I_N) - spdiagm(vec(LY_N))*u_h
end
return i_inj
end
"""
current_balance(net, settings, u, LY, NE)
Evaluate current balance at all nodes and frequencies, except linear nodes at fundamental frequency.
"""
function current_balance(net, settings, u, LY, NE)
# fundamental admittance matrix for nonlinear nodes
LY_1_nl = LY[1][net.m:end,:]
u_1 = u[1].v .* exp.(1im*u[1].ϕ)
# fundamental line currents at nonlinear nodes
dI_1 = LY_1_nl * u_1
# harmonic admittance matrices as diagonal block matrix
LY_h = blockdiag([LY[h] for h in settings.harmonics[2:end]]...)
u_h = vcat([u[h][:, "v"] .* exp.(1im*u[h][:, "ϕ"]) for h in settings.harmonics[2:end]]...)
dI_h = LY_h * u_h
# subtract the injected currents at each nonlinear node i
for i in net.m:net.n
i_inj = current_injections(net.nodes, net.nodes.ID[i], u, NE, settings.harmonics)
dI_1[i-net.m+1] += i_inj[1] # add injections at fundamental frequency...
# ... and at all harmonic frequencies
for p in 0:(settings.K-1)
dI_h[p*net.n + i] += i_inj[p+2]
end
end
vcat(dI_1, dI_h)
end
"""
harmonic_mismatch(net, settings, u, LY, NE)
Combine current and power mismatch to construct harmonic mismatch vector `f`.
"""
function harmonic_mismatch(net, settings, u, LY, NE)
# fundamental power mismatch at linear nodes except slack
s = net.nodes.P[2:(net.m-1)] + 1im*net.nodes.Q[2:(net.m-1)]
u_i = u[1][2:(net.m-1), "v"].*exp.(1im*u[1][2:(net.m-1), "ϕ"])
u_j = u[1][:, "v"].*exp.(1im*u[1][:, "ϕ"])
LY_ij = LY[1][2:(net.m-1), :]
# power balance
sl = u_i.*conj(LY_ij*u_j)
ds = s + sl
di = current_balance(net, settings, u, LY, NE)
# harmonic mismatch vector
f_c = vcat(ds, di)
f = vcat(real(f_c), imag(f_c[(net.c):end])) # crop PV nodes
err_h = maximum(abs.(f))
return f, err_h
end
"""
harmonic_state_vec(net, u, harmonics)
Construct harmonic state vector `x` from fundamental and harmonic voltages.
"""
function harmonic_state_vec(net, u, harmonics)
# fundamental voltages, crop slack and magnitude for PV nodes
xϕ = u[1].ϕ[2:end]
xv = u[1].v[(net.c+1):end]
# all voltages and angles at all harmonic frequencies
for h in harmonics[2:end]
xϕ = vcat(xϕ, u[h].ϕ)
xv = vcat(xv, u[h].v)
end
vcat(xϕ, xv)
end
"""
build_harmonic_jacobian(net, settings, u, LY, NE)
Construct harmonic Jacobian `J` containing all derivatives of power and current mismatches wrt voltages from harmonic state vector.
"""
function build_harmonic_jacobian(net, settings, u, LY, NE)
m = net.m
n = net.n
c = net.c
K = settings.K
# helpful definitions for Jacobian construction
u_vec = vcat([u[h].v .* exp.(1im*u[h].ϕ) for h in settings.harmonics]...)
v_vec = vcat([u[h].v for h in settings.harmonics]...)
u_diag = spdiagm(u_vec)
u_norm = u_vec./v_vec
u_norm_diag = spdiagm(u_norm)
LY_diag = blockdiag([LY[h] for h in settings.harmonics]...)
# construct Jacobian sub-matrices
dIdv = LY_diag*u_norm_diag
dIdϕ = 1im*LY_diag*u_diag
# indices of first nonlinear node at each harmonic
nl_idx_start = m:n:n*(K+1)
nl_idx_all = vcat([nl:(nl+n-m) for nl in nl_idx_start]...)
u_nl = u_vec[nl_idx_all]
v_nl = v_vec[nl_idx_all]
u_nl_norm = u_nl./v_nl
if settings.coupled
# iterating through blocks vertically
for h in 0:K
# ... and horizontally
for p in 0:K
# iterating through nonlinear nodes
for i in m:n
# within NE "[2]" points to LY_N
LY_N = NE[net.nodes.component[i]][2]
# subtract derived current injections at respective idx
dIdv[h*n+i, p*n+i] -= LY_N[h+1, p+1]*u_nl_norm[(i-m+1)+p*(n-m+1)]
dIdϕ[h*n+i, p*n+i] -= 1im*LY_N[h+1, p+1]*u_nl[(i-m+1)+p*(n-m+1)]
end
end
end
else
# iterating through blocks diagonally (p=h)
for h in 0:K
for i in m:n
# LY_N is one-dimensional in the uncoupled case
LY_N = NE[net.nodes.component[i]][2]
dIdv[h*n+i, h*n+i] -= LY_N[h+1]*u_nl_norm[(i-m+1)+h*(n-m+1)]
dIdϕ[h*n+i, h*n+i] -= 1im*LY_N[h+1]*u_nl[(i-m+1)+h*(n-m+1)]
end
end
end
# crop slack and PV nodes
dIdϕ = dIdϕ[m:end, 2:end]
dIdv = dIdv[m:end, (c+1):end]
# helpful definitions for dS construction
LY_1 = LY[1]
u_1 = u[1].v .* exp.(1im*u[1].ϕ)
i_diag = spdiagm(LY_1*u_1)
u_diag = spdiagm(u_1)
u_diag_norm = spdiagm(u_1./abs.(u_1))
# fundamental powers derived wrt to voltage phase and magnitude
dS1dϕ1 = 1im*u_diag*(conj(i_diag - LY_1*u_diag))
dS1dv1 = u_diag_norm*conj(i_diag) + u_diag*conj(LY_1*u_diag_norm)
# only fundamental power derivatives are non-zero
dSdϕ = hcat(dS1dϕ1, zeros(n, n*K))
dSdv = hcat(dS1dv1, zeros(n, n*K))
# crop slack and PV nodes from power derivatives
dPdϕ = real(dSdϕ[2:(m-1), 2:end])
dPdv = real(dSdv[2:(m-1), (c+1):end])
dQdϕ = imag(dSdϕ[(c+1):(m-1), 2:end])
dQdv = imag(dSdv[(c+1):(m-1), (c+1):end])
# combine all sub-matrices and return complete Jacobian
vcat(hcat(dPdϕ, dPdv),
hcat(real(dIdϕ), real(dIdv)),
hcat(dQdϕ, dQdv),
hcat(imag(dIdϕ), imag(dIdv)))
end
"""
update_fund_voltages!(net, u, x, settings)
Write new voltages from `x` to `u` for all harmonics.
"""
function update_harmonic_voltages!(net, u, x, settings)
n = net.n
c = net.c
# slice x to separate voltage magnitude and phase
xϕ = x[1:(n*settings.K1-1)]
xv = x[n*settings.K1:end]
# xϕ = xϕ .% (2*π) # ensure phase smaller 2π, not really necessary
# update fundamental voltages
u[1].ϕ[2:end] = xϕ[1:(n-1)]
u[1].v[(c+1):end] = xv[1:(n-c)]
# crop slack and PV nodes
xϕ_h = xϕ[n:end]
xv_h = xv[(n-c+1):end]
for h in settings.harmonics[2:end]
# enumerate harmonics for easier indexing
i = findall(settings.harmonics[2:end] .== h)[1] - 1
u[h].ϕ = xϕ_h[(i*n+1):(i*n+n)]
u[h].v = xv_h[(i*n+1):(i*n+n)]
end
return u
end
"""
hpf(net, settings)
Solve the harmonic power flow problem for a given `PowerGrid` and `Settings`."""
function hpf(net, settings)
LY = admittance_matrices(net, settings.harmonics)
u = pf(net, settings, LY)
NE = import_Norton_Equivalents(net.nodes, settings)
f, err_h = harmonic_mismatch(net, settings, u, LY, NE)
x = harmonic_state_vec(net, u, settings.harmonics)
n_iter_h = 0
err_h_t = Dict()
while err_h > settings.thresh_h && n_iter_h < settings.max_iter_h
J = build_harmonic_jacobian(net, settings, u, LY, NE)
x = update_state_vec!(J, x, f)
u = update_harmonic_voltages!(net, u, x, settings)
f, err_h = harmonic_mismatch(net, settings, u, LY, NE)
err_h_t[n_iter_h] = err_h
n_iter_h += 1
end
# getting rid of negative voltage magnitudes:
for h in settings.harmonics
u[h].ϕ[u[h].v .< 0] .+= π
u[h].ϕ .= u[h].ϕ .% (2*π)
u[h].v[u[h].v .< 0] = -u[h].v[u[h].v .< 0]
end
if n_iter_h < settings.max_iter_h
println("Harmonic power flow converged after ", n_iter_h,
" iterations (err < ", settings.thresh_h, ").")
elseif n_iter_h == settings.max_iter_h
println("Maximum of ", n_iter_h, " iterations reached. Harmonic power flow did not converge.")
end
return u, err_h_t, n_iter_h
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11319,
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329,
289,
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6460,
13,
29155,
38530,
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11907,
23029,
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26933,
84,
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28629,
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1017,
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1102,
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11319,
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198,
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288,
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1459,
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277,
62,
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277,
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410,
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7,
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7,
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66,
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7,
69,
62,
66,
58,
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13,
66,
2599,
437,
60,
4008,
220,
1303,
13833,
31392,
13760,
198,
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220,
11454,
62,
71,
796,
5415,
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8937,
12195,
69,
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220,
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198,
220,
220,
220,
1441,
277,
11,
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198,
220,
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220,
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62,
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62,
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11,
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11,
25625,
873,
8,
198,
198,
42316,
49239,
1181,
15879,
4600,
87,
63,
422,
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290,
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1095,
13,
198,
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198,
8818,
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62,
5219,
62,
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7,
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11,
334,
11,
25625,
873,
8,
198,
220,
220,
220,
1303,
7531,
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1095,
11,
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290,
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329,
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198,
220,
220,
220,
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139,
243,
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334,
58,
16,
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139,
243,
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17,
25,
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60,
198,
220,
220,
220,
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85,
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334,
58,
16,
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85,
58,
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13,
66,
10,
16,
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60,
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220,
220,
220,
1303,
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1095,
290,
18333,
379,
477,
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19998,
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220,
220,
220,
329,
289,
287,
25625,
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60,
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220,
220,
220,
220,
220,
220,
2124,
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87,
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11,
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139,
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220,
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2124,
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886,
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628,
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198,
220,
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1382,
62,
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9229,
62,
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666,
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56,
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220,
285,
796,
2010,
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76,
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220,
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299,
796,
2010,
13,
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220,
220,
269,
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2010,
13,
66,
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220,
509,
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6460,
13,
42,
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2512,
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11319,
58,
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13,
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38530,
60,
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628,
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220,
1303,
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666,
850,
12,
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220,
220,
220,
288,
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85,
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56,
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363,
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62,
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220,
288,
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139,
243,
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320,
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62,
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363,
628,
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220,
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286,
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220,
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75,
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19571,
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611,
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66,
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10137,
198,
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220,
220,
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220,
1303,
11629,
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198,
220,
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289,
287,
657,
25,
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198,
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1303,
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198,
220,
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220,
220,
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220,
220,
220,
220,
220,
220,
329,
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] | 2.03494 | 7,384 |
## Packages
using Plots
## Files
include("VFI.jl")
include("LQ_Vaughan.jl")
################################### Running three methods ########################
md = Model();
kss,hss,lss,css = getSS(md);
## Construct capital grid
nk = 1000; # number of capital grid
kmin = 0.5*kss;
kmax = 1.5*kss;
kGrid = LinRange(kmin,kmax,nk);
V_VFI,kpol_VFI,hpol_VFI,cpol_VFI = VFI(md,nk,kGrid)
A_tld,B_tld,Q_tld,R,W = getM(md,kss,hss);
P_LQ,F_LQ = LQ(A_tld,B_tld,R,Q_tld);
Pv,Fv = Vaughan(A_tld,B_tld,Q_tld,R,W);
################################### Plotting ########################
## VFI
plot(kGrid,V_VFI[:,1])
plot!(kGrid,V_VFI[:,2])
plot(kGrid,kGrid,label = "", color = :black, linestyle = :dash)
plot!(kGrid,kpol_VFI[:,1],label = "low",legend = :topleft)
plot!(kGrid,kpol_VFI[:,3],label = "SS",legend = :topleft)
plot!(kGrid,kpol_VFI[:,5],label = "high",legend = :topleft)
plot(kGrid,hpol[:,1],label = "low",legend = :topleft)
plot!(kGrid,hpol[:,3],label = "high",legend = :topleft)
plot(kGrid,cpol[:,1],label = "low",legend = :topleft)
plot!(kGrid,cpol[:,3],label = "high",legend = :topleft)
## LQ and Vaughan
# Low shock
pol_L_LQ = zeros(2, 1, nk);
pol_L_v = zeros(2, 1, nk);
for i = 1:nk
pol_L_LQ[:,:,i] = -F_LQ*[kGrid[i]; -0.5; 1];
pol_L_v[:,:,i] = -Fv*[kGrid[i]; -0.5; 1];
end
kpol_L_LQ = zeros(1,nk);
hpol_L_LQ = zeros(1,nk);
kpol_L_v = zeros(1,nk);
hpol_L_v = zeros(1,nk);
for i = 1:nk
kpol_L_LQ[1,i] = pol_L_LQ[1,:,i][1];
hpol_L_LQ[1,i] = pol_L_LQ[2,:,i][1];
kpol_L_v[1,i] = pol_L_v[1,:,i][1];
hpol_L_v[1,i] = pol_L_v[2,:,i][1];
end
# High shock
pol_H_LQ = zeros(2, 1, nk);
pol_H_v = zeros(2, 1, nk);
for i = 1:nk
pol_H_LQ[:,:,i] = -F_LQ*[kGrid[i]; 0.5; 1];
pol_H_v[:,:,i] = -Fv*[kGrid[i]; 0.5; 1];
end
kpol_H_LQ = zeros(1,nk);
hpol_H_LQ = zeros(1,nk);
kpol_H_v = zeros(1,nk);
hpol_H_v = zeros(1,nk);
for i = 1:nk
kpol_H_LQ[1,i] = pol_H_LQ[1,:,i][1];
hpol_H_LQ[1,i] = pol_H_LQ[2,:,i][1];
kpol_H_v[1,i] = pol_H_v[1,:,i][1];
hpol_H_v[1,i] = pol_H_v[2,:,i][1];
end
# SS shock
pol_SS_LQ = zeros(2, 1, nk);
pol_SS_v = zeros(2, 1, nk);
for i = 1:nk
pol_SS_LQ[:,:,i] = -F_LQ*[kGrid[i]; 0.0; 1];
pol_SS_v[:,:,i] = -Fv*[kGrid[i]; 0.0; 1];
end
kpol_SS_LQ = zeros(1,nk);
hpol_SS_LQ = zeros(1,nk);
kpol_SS_v = zeros(1,nk);
hpol_SS_v = zeros(1,nk);
for i = 1:nk
kpol_SS_LQ[1,i] = pol_SS_LQ[1,:,i][1];
hpol_SS_LQ[1,i] = pol_SS_LQ[2,:,i][1];
kpol_SS_v[1,i] = pol_SS_v[1,:,i][1];
hpol_SS_v[1,i] = pol_SS_v[2,:,i][1];
end
################### Plots ##################################
# capital policy LQ
plot(kGrid,kGrid,label = "45 degree line", color = :black, linestyle = :dash,
title = "Optimal capital policy function (LQ)",xlabel = "k_{t}", ylabel = "k_{t+1}")
plot!(kGrid,vec(kpol_L_LQ),label = "Low_LQ",legend=:topleft)
plot!(kGrid,vec(kpol_H_LQ),label = "High_LQ")
plot!(kGrid,vec(kpol_SS_LQ),label = "SS_LQ")
savefig("figs/kpol_LQ.png")
# labor policy LQ
plot(kGrid,vec(hpol_L_LQ),label = "Low",legend=:topright,
title = "Optimal labor policy function (LQ)",xlabel = "k_{t}", ylabel = "h_{t}")
plot!(kGrid,vec(hpol_H_LQ),label = "High")
plot!(kGrid,vec(hpol_SS_LQ),label = "SS")
savefig("figs/hpol_LQ.png")
# capital policy Vaughan
plot(kGrid,kGrid,label = "45 degree line", color = :black, linestyle = :dash,
title = "Optimal capital policy function (Vaughan)",xlabel = "k_{t}", ylabel = "k_{t+1}")
plot!(kGrid,vec(kpol_L_v),label = "Low_Vaughan",legend=:topleft)
plot!(kGrid,vec(kpol_H_v),label = "High_Vaughan")
plot!(kGrid,vec(kpol_SS_v),label = "SS_Vaughan")
savefig("figs/kpol_Vaughan.png")
# labor policy Vaughan
plot(kGrid,vec(hpol_L_v),label = "Low",legend=:topright,
title = "Optimal labor policy function (Vaughan)",xlabel = "k_{t}", ylabel = "h_{t}")
plot!(kGrid,vec(hpol_H_v),label = "High")
plot!(kGrid,vec(hpol_SS_v),label = "SS")
savefig("figs/hpol_Vaughan.png")
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] | 1.893346 | 2,044 |
"""
get_sha(obj, directory::AbstractString = "data", file::AbstractString = "states_current.csv")::String
Navigate the entries in the tree to find the SHA1 of the file at path (directory/file)
"""
function get_sha(obj, directory::AbstractString = "data", file::AbstractString = "states_current.csv")::String
node = obj.node.tree.entries
node_ = node[findfirst(elem -> elem.name == directory, node)].object.entries
node_[findfirst(elem -> elem.name == file, node_)].oid
end
"""
find_shas(obj::GitHubPersonalAccessToken,
id::AbstractString = "MDEwOlJlcG9zaXRvcnkyNDY0MTE2MDc=",
directory::AbstractString = "data",
file::AbstractString = "states_current.csv",
just_last::Bool = false)
Return slug for the repository and SHA1 for the file at directory/file (all versions).
"""
function find_shas(obj::GitHubPersonalAccessToken,
id::AbstractString = "MDEwOlJlcG9zaXRvcnkyNDY0MTE2MDc=",
directory::AbstractString = "data",
file::AbstractString = "states_current.csv",
just_last::Bool = false)
# For testing
# obj = opt.pat
# id = "MDEwOlJlcG9zaXRvcnkyNDY0MTE2MDc="
# directory = "data"
# file = "states_current.csv"
# just_last = false
# The node ID is the repository with slug COVID19Tracking/covid-tracking-data
# The file that has the information we want is at: data/states_current.csv
# uva-bi-sdad/COVID_Tracking_Clean
# id = "MDEwOlJlcG9zaXRvcnkyNTIwNDUwOTQ="
# directory = "data"
# file = "daily.tsv"
# just_last = true
vars = Dict("id" => id,
"path" => "$directory/$file",
# When first started publishing the quality checks
"since" => "2020-03-20T23:00:07Z",
"cursor" => nothing,
"first" => just_last ? 1 : 100)
# The initial request finds out how many commits are for the file
# and the SHA1 for the last 100 commits affecting that file
response = graphql(obj, "Magic", vars, GITHUB_API_QUERY = GITHUB_API_QUERY)
json = JSON3.read(response.Data)
# Get the slug just in case
slug = json.data.node.nameWithOwner
# We store the total count of commits for a final check
total = json.data.node.defaultBranchRef.target.history.totalCount
# We get the SHA1 for each of the file/versions
shas = get_sha.(json.data.node.defaultBranchRef.target.history.edges, directory, file)
just_last && return shas[1]
# This strategy is valid while the total number of commits is below 1,000
@assert total ≤ 1_000 "Code needs to be updated for more than 1,000 commits!"
# If there are more than 100 commits we paginate
while json.data.node.defaultBranchRef.target.history.pageInfo.hasNextPage
response = graphql(obj,
"Magic",
merge(vars, Dict("cursor" => json.data.node.defaultBranchRef.target.history.pageInfo.endCursor)),
GITHUB_API_QUERY = GITHUB_API_QUERY)
json = JSON3.read(response.Data)
append!(shas, get_sha.(json.data.node.defaultBranchRef.target.history.edges, directory, file))
end
# Verify the total number of SHA1 matches the number of commits
@assert slug == "COVID19Tracking/covid-tracking-data" "Repository has been renamed or moved!"
@assert total == length(shas) "Some commit SHA1 were not collected!"
shas
end
"""
get_tbl(sha::AbstractString)
Uses the SHA1 to download from GitHub the version of [COVID19Tracking/covid-tracking-data/data/states_current.csv](https://github.com/COVID19Tracking/covid-tracking-data/blob/master/data/states_current.csv)
"""
function get_tbl(obj::GitHubPersonalAccessToken, sha::AbstractString)
response = restful(obj, "repos/COVID19Tracking/covid-tracking-data/git/blobs/$sha")
@assert response.status == 200
json = JSON3.read(response.body)
data = File(base64decode(json.content),
# select = [:state, :positiveScore, :negativeScore, :negativeRegularScore, :commercialScore, :checkTimeEt, :dataQualityGrade]) |>
) |>
DataFrame
names(data)
colnames = names(data)
if :dataQualityGrade in colnames
data = dropmissing!(data[!,[:state, :checkTimeEt, :dataQualityGrade]])
data[!,:checkTimeEt] = ZonedDateTime.(string.("2020/", data.checkTimeEt, " America/New_York"),
COVID_TRACKING_DT)
elseif all(elem -> elem ∈ colnames, (:positiveScore, :negativeScore, :negativeRegularScore, :commercialScore))
data = dropmissing!(data[!,[:state, :checkTimeEt, :positiveScore, :negativeScore, :negativeRegularScore, :commercialScore]])
data[!,:positiveScore] = isone.(data.positiveScore)
data[!,:negativeScore] = isone.(data.negativeScore)
data[!,:negativeRegularScore] = isone.(data.negativeRegularScore)
data[!,:commercialScore] = isone.(data.commercialScore)
data[!,:checkTimeEt] = ZonedDateTime.(string.("2020/", data.checkTimeEt, " America/New_York"),
COVID_TRACKING_DT)
data[!,:dataQualityGrade] = get.(Ref(Dict(1 => "D", 2 => "C", 3 => "B", 4 => "A")),
data.positiveScore + data.negativeScore + data.negativeRegularScore + data.commercialScore,
missing)
data = data[!,[:state, :checkTimeEt, :dataQualityGrade]]
else
data = DataFrame()
end
data
end
"""
states_daily()
Uses the Covid Tracking API to return the states/daily table.
"""
function states_daily()
response = request("GET", "https://covidtracking.com/api/states/daily.csv")
@assert response.status == 200
data = File(response.body) |>
(tbl -> select(tbl, :state, :dateChecked,
:positive, :negative, :pending,
:hospitalizedCurrently, :hospitalizedCumulative,
:inIcuCurrently, :inIcuCumulative,
:onVentilatorCurrently, :onVentilatorCumulative,
:recovered, :death)) |>
DataFrame
data[!,:dateChecked] = ZonedDateTime.(data.dateChecked, DateFormat("yyyy-mm-ddTHH:MM:SSz"))
rename!(data, :dateChecked => :checkts,
:hospitalizedCurrently => :hospitalized_currently,
:hospitalizedCumulative => :hospitalized_cumulative,
:inIcuCurrently => :icu_currently,
:inIcuCumulative => :icu_cumulative,
:onVentilatorCurrently => :ventilator_currently,
:onVentilatorCumulative => :ventilator_cumulative,
)
end
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] | 2.323721 | 2,913 |
const LICENSE_DIR = normpath(joinpath(@__DIR__, "..", "licenses"))
const LICENSES = Dict(
"MIT" => "MIT \"Expat\" License",
"BSD2" => "Simplified \"2-clause\" BSD License",
"BSD3" => "Modified \"3-clause\" BSD License",
"ISC" => "Internet Systems Consortium License",
"ASL" => "Apache License, Version 2.0",
"MPL" => "Mozilla Public License, Version 2.0",
"GPL-2.0+" => "GNU Public License, Version 2.0+",
"GPL-3.0+" => "GNU Public License, Version 3.0+",
"LGPL-2.1+" => "Lesser GNU Public License, Version 2.1+",
"LGPL-3.0+" => "Lesser GNU Public License, Version 3.0+",
"EUPL-1.2+" => "European Union Public Licence, Version 1.2+",
)
"""
available_licenses([io::IO]) -> Nothing
Print the names of all available licenses.
"""
available_licenses(io::IO) = print(io, join(("$k: $v" for (k, v) in LICENSES), "\n"))
available_licenses() = available_licenses(stdout)
"""
show_license([io::IO], license::AbstractString) -> Nothing
Print the text of `license`. Errors if the license is not found.
"""
show_license(io::IO, license::AbstractString) = print(io, read_license(license))
show_license(license::AbstractString) = show_license(stdout, license)
"""
read_license(license::AbstractString) -> String
Returns the contents of `license`. Errors if the license is not found. Use
[`available_licenses`](@ref) to view available licenses.
"""
function read_license(license::AbstractString)
path = joinpath(LICENSE_DIR, license)
if isfile(path)
return string(readchomp(path))
else
throw(ArgumentError("License '$license' is not available"))
end
end
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] | 2.730769 | 598 |
using GridapGmsh
using GridapGmsh: gmsh, GmshDiscreteModel
function add_pin!(gmsh, o, r, t, lc)
factory = gmsh.model.geo
# inner and outer circle
l1 = add_circle!(gmsh, o, r, lc)
l2 = add_circle!(gmsh, o, r + t, lc)
s1 = factory.addPlaneSurface([l1])
s2 = factory.addPlaneSurface([l2, l1]) # l1 is a hole
return s1, s2
end
function add_circle!(gmsh, o, r, lc)
factory = gmsh.model.geo
ox, oy = o
p1 = factory.addPoint(ox, oy, 0, lc) # center - origin
p2 = factory.addPoint(ox + r, oy, 0, lc) # right
p3 = factory.addPoint(ox, oy + r, 0, lc) # up
p4 = factory.addPoint(ox - r, oy, 0, lc) # left
p5 = factory.addPoint(ox, oy - r, 0, lc) # down
c1 = factory.addCircleArc(p2, p1, p3)
c2 = factory.addCircleArc(p3, p1, p4)
c3 = factory.addCircleArc(p4, p1, p5)
c4 = factory.addCircleArc(p5, p1, p2)
l1 = factory.addCurveLoop([c1, c2, c3, c4])
return l1
end
function add_square!(gmsh, s)
factory = gmsh.model.geo
p1 = factory.addPoint(0, 0, 0, lc)
p2 = factory.addPoint(s, 0, 0, lc)
p3 = factory.addPoint(s, s, 0, lc)
p4 = factory.addPoint(0, s, 0, lc)
l1 = factory.addLine(p1, p2)
l2 = factory.addLine(p2, p3)
l3 = factory.addLine(p3, p4)
l4 = factory.addLine(p4, p1)
cl1 = factory.addCurveLoop([l1, l2, l3, l4])
return cl1
end
N = 4
# in cm
p = 1.6 # pitch
ri = 0.5 # internal radius
t = 0.1 # wall thickness
ro = ri + t # external radius
lc = 0.1
gmsh.initialize()
gmsh.model.add("bwr")
# tags
pinTags = Int32[]
cladTags = Int32[]
gdPinTags = Int32[]
# gd pins positions
GD_pos = [(2, 3), (3, 2)]
for i in 1:N, j in 1:N
xo = p / 2 + (i - 1) * p
yo = p / 2 + (j - 1) * p
pinTag, cladTag = add_pin!(gmsh, (xo, yo), ri, t, lc)
push!(cladTags, cladTag)
if (i, j) in GD_pos
push!(gdPinTags, pinTag)
else
push!(pinTags, pinTag)
end
end
#! TODO: h2oTag = add_reflector!(gmsh, 4p)
s = N * p
factory = gmsh.model.geo
p1 = factory.addPoint(0, 0, 0, lc)
p2 = factory.addPoint(s, 0, 0, lc)
p3 = factory.addPoint(s, s, 0, lc)
p4 = factory.addPoint(0, s, 0, lc)
l1 = factory.addLine(p1, p2)
l2 = factory.addLine(p2, p3)
l3 = factory.addLine(p3, p4)
l4 = factory.addLine(p4, p1)
cl1 = factory.addCurveLoop([l1, l2, l3, l4])
h2oTag = factory.addPlaneSurface(vcat(cl1, pinTags, cladTags, gdPinTags))
pg1 = gmsh.model.geo.addPhysicalGroup(2, pinTags)
pg2 = gmsh.model.geo.addPhysicalGroup(2, cladTags)
pg3 = gmsh.model.geo.addPhysicalGroup(2, gdPinTags)
pg4 = gmsh.model.geo.addPhysicalGroup(2, [h2oTag])
pg5 = gmsh.model.geo.addPhysicalGroup(1, [l1])
pg6 = gmsh.model.geo.addPhysicalGroup(1, [l2])
pg7 = gmsh.model.geo.addPhysicalGroup(1, [l3])
pg8 = gmsh.model.geo.addPhysicalGroup(1, [l4])
gmsh.model.setPhysicalName(2, pg1, "pin")
gmsh.model.setPhysicalName(2, pg2, "cladding")
gmsh.model.setPhysicalName(2, pg3, "pin-gd")
gmsh.model.setPhysicalName(2, pg4, "water")
gmsh.model.setPhysicalName(1, pg5, "bottom")
gmsh.model.setPhysicalName(1, pg6, "right")
gmsh.model.setPhysicalName(1, pg7, "top")
gmsh.model.setPhysicalName(1, pg8, "left")
# removemos puntos de la geometria duplicados (origenes por ejemplo)
gmsh.model.geo.removeAllDuplicates()
gmsh.model.geo.synchronize()
gmsh.model.mesh.generate(2)
gmsh.write("bwr.msh")
if !("-nopopup" in ARGS)
gmsh.fltk.run()
end
gmsh.finalize()
using Gridap
mshfile = joinpath(@__DIR__,"../bwr.msh")
model = GmshDiscreteModel(mshfile; renumber=true)
Gridap.Io.to_json_file(model, "bwr.json")
jsonfile = joinpath(@__DIR__,"../bwr.json")
model = DiscreteModelFromFile(jsonfile)
# number of azimuthal angles
nφ = 16
# azimuthal spacing
δ = 0.002
# boundary conditions
bcs = BoundaryConditions(top=Reflective, bottom=Reflective, left=Reflective, right=Reflective)
# initialize track generator
tg = TrackGenerator(model, nφ, δ, bcs=bcs)
# perform ray tracing
trace!(tg)
# proceed to segmentation
segmentize!(tg) | [
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] | 2.077929 | 1,912 |
import SpecialFunctions: digamma, gamma
using Neighborhood: Theiler, KDTree, bulksearch
abstract type NearestNeighborEntropyEstimator <: EntropyEstimator end
function maximum_neighbor_distances(pts, est::EntropyEstimator)
theiler = Theiler(est.w)
est.w >= 0 || error("w, the number of neighbors to exclude, must be >= 0")
tree = KDTree(pts) # Euclidean metric forced
idxs, dists = bulksearch(tree, pts.data, NeighborNumber(est.k), theiler)
# Distance to k-th nearest neighbor for each of the points
return [d[end] for d in dists]
end
"Volume of a unit ball in R^d."
ball_volume(d::Int) = π^(d/2)/gamma((d/2)+1)
include("KozachenkoLeonenko.jl")
include("Kraskov.jl") | [
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] | 2.728346 | 254 |
import CUDA
using ModernGL
# 6 calculate posterior
# get likelihood, CPU version
function depth_ll(depth_predict::Matrix, depth_real::Matrix)
n_rows, n_cols = size(depth_predict)
ll = 0.0
vaild_count = 0
for c_idx = 1:n_cols
for r_idx = 1:n_rows
real_d_value = depth_real[r_idx, c_idx]
predict_d_value = depth_predict[r_idx, c_idx]
if (predict_d_value != 0.0) && (real_d_value != 0.0)
diff = predict_d_value - real_d_value
# @show diff
ll += diff^2
vaild_count += 1
end
end
end
return ll / max(1, vaild_count)
end
"""
for best performance, set tmp from a outside array
"""
function depth_ll_cuda(frame_buffer::Surfels.GLFrameBuffer,
depth_real_f32_cu::CUDA.CuArray{Float32, 2};
tmp::CUDA.CuArray{Float32, 2}=similar(depth_real_f32_cu))
w = frame_buffer.cam.width
h = frame_buffer.cam.height
Surfels.glCheckError()
glCopyImageSubData(frame_buffer.depth_render_buf, GL_TEXTURE_2D, 0, 0, 0, 0,
frame_buffer.depth_2nd_tex, GL_TEXTURE_2D, 0, 0, 0, 0,
w, h, 1);
Surfels.glCheckError()
# # ccall to cuda calculate
# using CUDA
# depth_real_f32 = convert(Matrix{Float32}, depth_real_uint) ./ 1000.0
# depth_real_f32_cu = cu(depth_real_f32)
# out_log_likelihood_cu = cu(depth_real_f32)
# a = proj_mat[3, 3]
# b = proj_mat[3, 4]
# c = proj_mat[4, 3]
# void cu_depth_log_likelihood(float* depth_real_ptr,
# void** depth_predict_tex_cuda_res_ptr,
# int width,
# int height,
# double a,
# double b,
# double c,
# float max_depth,
# float* out_log_likelihood);
out_log_likelihood_cu = tmp
ccall((:cu_depth_log_likelihood, :libsurfels),
Cvoid,
(CUDA.CuPtr{Cfloat}, Ptr{Ptr{Cvoid}}, Cint, Cint, Cdouble, Cdouble,
Cdouble, Cfloat, Cfloat, Cint, Cint, CUDA.CuPtr{Cfloat}),
depth_real_f32_cu.storage.buffer.ptr,
Base.unsafe_convert(Ptr{Ptr{Cvoid}}, frame_buffer.depth_cuda_res),
w,
h,
frame_buffer.a,
frame_buffer.b,
frame_buffer.c,
frame_buffer.min_depth,
frame_buffer.max_depth,
16, # thread block size: 16 x 16
Int32(0),
out_log_likelihood_cu.storage.buffer.ptr)
# out = collect(out_log_likelihood_cu)
# imshow(Gray.(out))
cu_ll = sum(out_log_likelihood_cu) #/ sum(out_log_likelihood_cu .!= 0.0)
cu_count = sum(out_log_likelihood_cu .!= 0.0)
return cu_ll / max(1, cu_count)
end
function DMP_RGBD(
rgbd_reader::T,
model_fpath::String,
id2Twc, # traj
cam::CameraModel.RgbdCamParams;
min_depth::Float64 = 0.3,
max_depth::Float64 = 15.0,
confidence_thres::Float64=0.1,
max_id::Int64=100000000000000,
tmp::CUDA.CuArray{Float32, 2}=CUDA.CuArray{Float32, 2}(
undef, Int64(cam.height), Int64(cam.width)
)
) where T <: SLAMData.RgbdReader
hidden_window = Surfels.create_hidden_gl_window(cam.width, cam.height)
program::ModernGL.GLuint = Surfels.create_gl_render_program()
frame_buffer::Surfels.GLFrameBuffer = Surfels.GLFrameBuffer(
cam.width, cam.height, cam, min_depth, max_depth, use_texture = true
)
# load GlModel
model::Surfels.GlSurfelsData = Surfels.read_surfels_file2gl(model_fpath)
ll = 0.0
count = 0
Surfels.glCheckError()
ids = sort(collect(keys(id2Twc)))
for frame_id = ids
if frame_id > max_id
continue
end
#render to get predicted observation
Surfels.render(
[model],
id2Twc[frame_id], # Twc
confidence_thres,
1,
200000,
program,
frame_buffer
)
Surfels.glCheckError()
# real observation
# depth_real_f32_cu = frames_d_cu[frame_id]
img, depth_real_uint = SLAMData.readRGBD(rgbd_reader, id=frame_id)
depth_real_f = convert(Matrix{Float32}, depth_real_uint) ./ 1000.0
depth_real_f32_cu = CUDA.cu(depth_real_f)
ll += depth_ll_cuda(frame_buffer, depth_real_f32_cu, tmp=tmp)
Surfels.glCheckError()
count += 1
end
# TODO destroy program
finalize(frame_buffer)
finalize(model)
finalize(hidden_window)
return ll #/ Float64(count)
end
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] | 1.928425 | 2,431 |
#
###
# Gather-Scatter Operators - enforce continuity/ periodicity
###
# TODO write GatherScatterOp that calls NNlib.gather, scatter
struct GatherScatter{D} <: AbstractGatherScatterOperator{Bool,D}
global_numbering
implementation
end
"""
Q*Q'*u where Q: local -> global operator
"""
function DSS(u,l2g,g2l)
Qu = NNlib.scatter(+,u,l2g)
QQtu = NNlib.gather(Qu,g2l)
return v
end
function Qmatrix(n::Integer, periodic::Bool)
Q = sparse(I,n, n-1)
if periodic
Q[end,1] = 1
end
Q
end
function GatherScatter(space::AbstractSpectralSpace{<:Number,D}) where{D}
domain = get_domain(space)
periodic = isperiodic(domain)
npoints = get_numpoints(space)
if !prod(periodic...)
return IdentityOp{D}()
end
Qmats = Qmatrix.(npoints, periodic)
Q = if D == 1
MatrixOp(Qmats...)
elseif D == 2
TensorProductOp2D(Qmats...)
elseif D == 3
TensorProductOp3D(Qmats...)
end
QQt = Q * Q' # replace with call to NNlib gather-scatter
end
###
# Boundary Condition application
#
# Apply this boundary condition to that boundary tag
###
struct DirichletBC end
struct BoundaryCondition{T,D} <: AbstractBoundaryCondition{T,D}
tag
type # dirichlet, neumann
dirichlet_func! # (ub, space) -> mul!(ub, I, false)
# neumann_func!
mask # implementation
end
function BoundaryCondition(tags, space::AbstractSpace<:Number,2;
dirichlet_func! =nothing, neumann_func! = nothing)
mask = generate_mask(tags, space)
BoundaryCondition()
end
"""
bc = (:Dirichlet,:Neumann,:Dirichlet,:Dirichlet) at (rmin, rmax, smin, smax)
:Dirichlet = Dirichlet = zeros ∂Ω data\n
:Neumann = Neumann = keeps ∂Ω data
A periodic mesh overwrites 'D' to 'N' in direction of periodicity.
To achieve inhomogeneous Dirichlet condition, apply the formulation
u = ub + uh, where uh is homogeneous part, and ub is an arbitrary
smooth function on Ω. Then, solve for uh
"""
function generate_mask(tags, space::AbstractSpace{<:Number,2})
(nr, ns,) = space.npoints
periodic = isperiodic(space.domain)
Ix = sparse(I,nr,nr)
Iy = sparse(I,ns,ns)
ix = collect(1:(nr))
iy = collect(1:(ns))
if(bc[1] == :Dirichlet) ix = ix[2:end] end
if(bc[2] == :Dirichlet) ix = ix[1:end-1] end
if(bc[3] == :Dirichlet) iy = iy[2:end] end
if(bc[4] == :Dirichlet) iy = iy[1:end-1] end
if(periodic[1]) ix = collect(1:(nr)); end
if(periodic[2]) iy = collect(1:(ns)); end
Rx = Ix[ix,:]
Ry = Iy[iy,:]
M = diag(Rx'*Rx) * diag(Ry'*Ry)'
M = Array(M) .== true
return M
end
function applyBC!(u::AbstractField{<:Number,D}, bc::BoundaryCondition{<:Number,D}) where{D}
return u
end
function applyBC!()
end
#
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] | 2.338371 | 1,191 |
# TODO Make work with multidimensional arrays
immutable EnumerableArray{T,S} <: Enumerable{T}
source::S
end
function query{TS,N}(source::Array{TS,N})
return EnumerableArray{TS,Array{TS,N}}(source)
end
function start{T,S}(iter::EnumerableArray{T,S})
return 1
end
function next{T,S}(iter::EnumerableArray{T,S}, state)
return iter.source[state], state+1
end
function done{T,S}(iter::EnumerableArray{T,S}, state)
return state>length(iter.source)
end
| [
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] | 2.596685 | 181 |
using Base.Test
using TALib
using TALib: basepath
using TALib: TA_MAType_SMA
using DataFrames
@testset "level 1" begin
epsilon = 1e-10
eps_price = 1e-6
@test Initialize()
for f in [GetVersionString, GetVersionMajor, GetVersionMinor, GetVersionBuild, GetVersionDate, GetVersionTime]
print(string(f) * ": ")
println(f())
end
@test length(GetVersionString()) > 3
@test GetVersionMajor() == "0"
s_xml = FunctionDescriptionXML()
s_xml_expected_header = "<?xml version=\"1.0\" encoding=\"utf-8\" ?>"
N = length(s_xml_expected_header)
@test s_xml[1:N] == s_xml_expected_header
angles = [0, pi/2, pi, 3pi/2, 0, pi/2, pi, 3pi/2]
outReal = COS(angles)
@test sum(outReal) ≈ 0 atol=epsilon
@test outReal == cos.(angles)
@test COS([float(pi)])[1] == -1
@test ACOS([0.0])[1] == pi / 2
outReal = ACOS(angles)
outReal = SIN(angles)
@test sum(outReal) ≈ 0 atol=epsilon
@test outReal == sin.(angles)
@test SIN([float(pi/2)])[1] == 1
@test ASIN([1.0])[1] == pi / 2
outReal = ASIN(angles)
outReal = TAN(angles)
outReal = ATAN(angles)
filename = joinpath(basepath(), "ford_2012.csv")
dfOHLCV = readtable(filename)
dfOHLCV[:Date] = Date(dfOHLCV[:Date])
dt = Array(dfOHLCV[:Date])
opn = Array(dfOHLCV[:Open])
hig = Array(dfOHLCV[:High])
low = Array(dfOHLCV[:Low])
cls = Array(dfOHLCV[:Close])
price = Array(dfOHLCV[:Close])
vol = Array(dfOHLCV[:Volume])
@test price[1] == 11.13
@test price[end] == 12.95
indic = MA(price)
@test indic[end] ≈ 11.546 atol=eps_price
time_period = 10
indic = MA(price, time_period=time_period, ma_type=TA_MAType_SMA)
@test indic[end] ≈ 12.219 atol=eps_price
@test sum(isnan.(indic)) == time_period - 1
indic = BBANDS(price)
time_period = 5
@test indic[end, 1] ≈ 13.13191 atol=eps_price # UpperBand
@test indic[end, 2] ≈ 12.75400 atol=eps_price # MiddleBand
@test indic[end, 3] ≈ 12.37609 atol=eps_price # LowerBand
@test sum(isnan.(indic)) == (time_period - 1) * 3
time_period = 10
indic = BBANDS(price, time_period=time_period, deviations_up=2.0, deviations_down=2.0, ma_type=TA_MAType_SMA)
@test indic[end, 1] ≈ 13.34468 atol=eps_price # UpperBand
@test indic[end, 2] ≈ 12.21900 atol=eps_price # MiddleBand
@test indic[end, 3] ≈ 11.09332 atol=eps_price # LowerBand
@test sum(isnan.(indic)) == (time_period - 1) * 3
@test Shutdown()
end
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2488,
9288,
40411,
3419,
198,
198,
437,
198
] | 2.23357 | 1,126 |
using ImageMagick
using Images, TestImages, StaticArrays, ImageTracking, ImageView, LinearAlgebra, CoordinateTransformations, Gtk.ShortNames
#=Image Credit: C. Liu. Beyond Pixels: Exploring New Representations and
#Applications for Motion Analysis. Doctoral Thesis. Massachusetts Institute of
#Technology. May 2009. =#
img1 = load("car2.jpg")
img2 = load("car1.jpg")
algorithm = Farneback(50, estimation_window = 11,
σ_estimation_window = 9.0,
expansion_window = 6,
σ_expansion_window = 5.0)
flow = optical_flow(Gray{Float32}.(img1), Gray{Float32}.(img2), algorithm)
# flow visualization
hsv = visualize_flow(flow, ColorBased(), RasterConvention())
imshow(RGB.(hsv))
save("./optical_flow_farneback.jpg", hsv)
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] | 2.581699 | 306 |
@testset "Genus" begin
Qx, x = FlintQQ["x"]
K, a = NumberField(x - 1, "a", cached = false)
OK = maximal_order(K)
rlp = real_places(K)
sig = Dict(rlp[1] => 2)
p2 = prime_decomposition(OK, 2)[1][1]
p3 = prime_decomposition(OK, 3)[1][1]
p5 = prime_decomposition(OK, 5)[1][1]
@test length(Hecke.local_genera_quadratic(K, p2, rank = 2, det_val = 0)) == 8
@test length(Hecke.local_genera_quadratic(K, p2, rank = 2, det_val = 1)) == 8
@test length(Hecke.local_genera_quadratic(K, p2, rank = 2, det_val = 2)) == 16
@test length(Hecke.local_genera_quadratic(K, p2, rank = 2, det_val = 3)) == 24
@test length(Hecke.local_genera_quadratic(K, p2, rank = 2, det_val = 4)) == 32
@test length(Hecke.local_genera_quadratic(K, p3, rank = 5, det_val = 0)) == 2
@test length(Hecke.local_genera_quadratic(K, p3, rank = 5, det_val = 1)) == 4
@test length(Hecke.local_genera_quadratic(K, p3, rank = 5, det_val = 2)) == 8
@test length(Hecke.local_genera_quadratic(K, p3, rank = 5, det_val = 3)) == 16
@test length(Hecke.local_genera_quadratic(K, p3, rank = 5, det_val = 4)) == 28
@test length(Hecke.local_genera_quadratic(K, p3, rank = 5, det_val = 5)) == 46
@test length(Hecke.local_genera_quadratic(K, p3, rank = 5, det_val = 6)) == 72
@test length(Hecke.local_genera_quadratic(K, p5, rank = 4, det_val = 2)) == 8
@test length(Hecke.local_genera_quadratic(K, p5, rank = 4, det_val = 1)) == 4
@test length(Hecke.local_genera_quadratic(K, p5, rank = 2, det_val = 0)) == 2
@test length(Hecke.local_genera_quadratic(K, p5, rank = 2, det_val = 1)) == 4
@test length(Hecke.genera_quadratic(K, rank = 2, signatures = sig, det = 5^2 * 8 * 9 * OK)) == 27
@test length(Hecke.genera_quadratic(K, rank = 2, signatures = sig, det = 5 * 7 * 8 * 9 * OK)) == 36
@test length(Hecke.genera_quadratic(K, rank = 2, signatures = sig, det = 2^5 * OK)) == 5
@test length(Hecke.genera_quadratic(K, rank = 2, signatures = sig, det = 11 * 13^2 * OK)) == 6
end
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] | 2.266055 | 872 |
const build_names = ["RealDouble","RealSingle","ComplexDouble","ComplexSingle"]
arches = Dict() # dict of dirname => (PETSC_DIR, PETSC_ARCH)
have_petsc = fill(false,4) # whether or not each version of Petsc is usable
libpetsc_name=Dict()
binary = Dict()
for (i, name) in enumerate(build_names)
julia_petsc_binary=string("JULIA_PETSC_", name, "_BINARY")
julia_petsc_dir=string("JULIA_PETSC_", name, "_DIR")
julia_petsc_arch=string("JULIA_PETSC_", name, "_ARCH")
julia_petsc_libname=string("JULIA_PETSC_", name, "_LIBNAME")
env_vars = [julia_petsc_binary,julia_petsc_dir,julia_petsc_arch,julia_petsc_libname]
if (name != "RealDouble")
for env_var in env_vars
if (haskey(ENV, env_var))
@warn "Ignoring $(env_var). PetscScalar $(name) not yet supported."
end
end
have_petsc[i]=false
else
if (haskey(ENV, julia_petsc_binary))
binary[name]=lowercase(ENV[julia_petsc_binary])
if !(binary[name]=="system" || binary[name]=="")
@error """Invalid value for $julia_petsc_binary env variable ($(binary[name]). If you want
to use a PETSc library installed into your system (e.g., HPC cluster), set this env variable to \"system\"
"""
end
else
binary[name]=nothing
end
if (binary[name]=="" || binary[name]==nothing)
@info "Using PETSc binary provided by PETSc_jll package for PetscScalar $(name)"
have_petsc[i]=true
end
if haskey(ENV, julia_petsc_dir) ||
haskey(ENV, julia_petsc_arch) ||
haskey(ENV, julia_petsc_arch)
if (binary[name]=="" || binary[name]==nothing)
@warn """
You have $(julia_petsc_dir) and/or $(julia_petsc_arch) and/or $(julia_petsc_libname) set, but $(julia_petsc_binary) is unset or set to the empty string. Thus, the values of these env variables will be ignored.
"""
elseif (binary[name]=="system")
if !(haskey(ENV, julia_petsc_dir) && haskey(ENV, string("JULIA_PETSC_", name, "_DIR")))
error("Must have either both or neither DIR and ARCH for JULIA_PETSC_$name")
else
have_petsc[i] = true
arches[name] = (ENV[julia_petsc_dir], ENV[julia_petsc_arch])
if (haskey(ENV, julia_petsc_libname))
libpetsc_name[name]=ENV[julia_petsc_libname]
else
libpetsc_name[name]="libpetsc"
end
end
end
end
end
end
# create deps.jl file with library locations
open("deps.jl", "w") do f
for (i, name) in enumerate(build_names)
if haskey(arches, name)
PETSC_DIR, PETSC_ARCH = arches[name]
libname=libpetsc_name[name]
path = abspath(PETSC_DIR, PETSC_ARCH, "lib", libname)
println(f, "const deps_file_petsc$name = \"", escape_string(path), "\"")
@info "Using a system installation of PETSc for PetscScalar $(name)"
@info "PETSc library will be search for on $(path)"
else
println(f, "const deps_file_petsc$name = \"\" ")
end
end
println(f, "const have_petsc = [", have_petsc[1], " ",
have_petsc[2], " ",
have_petsc[3], " ",
have_petsc[4],"]")
end
| [
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1039,
66,
58,
19,
17241,
60,
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198,
437,
198
] | 2.108003 | 1,537 |
## example for README
using AutoAligns
aa = AutoAlign(align = Dict(1 => left, :default => right));
for (i, r) in zip([1, 100, 10000], ["a", "bb", "ccc"])
print(aa, r)
for j in 1:5
print(aa, " ", i + j) # padding
end
println(aa)
end
print(STDOUT, aa)
| [
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] | 2.208 | 125 |
# This file was generated, do not modify it. # hide
mach = MLJ.machine(nnregressor, features, targets) | [
2,
770,
2393,
373,
7560,
11,
466,
407,
13096,
340,
13,
1303,
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796,
10373,
41,
13,
30243,
7,
20471,
2301,
44292,
11,
3033,
11,
6670,
8
] | 3.4 | 30 |
__precompile__(false)
import Base.print, Base.println
using TimeManager
using LoggerManager
using ThreadManager
using Distributed
######################################################
# Overwrite Base functions
PrintMutex = Threads.Mutex()
#ThreadManager.setPrintMutex(PrintMutex)
print(args...) = thread_call(() -> LoggerManager.print(args...);mutex=PrintMutex)
println(args...) = thread_call(() -> LoggerManager.println(args...);mutex=PrintMutex)
PushMutex = Threads.Mutex()
thread_push!(args...) = thread_call(() -> Base.push!(args...);mutex=PushMutex)
sleep(sec) = thread_sleep(sec)
######################################################
info(args...) = thread_call(() -> LoggerManager.info(args...);mutex=PrintMutex)
debug(args...) = thread_call(() -> LoggerManager.debug(args...);mutex=PrintMutex)
warn(args...) = thread_call(() -> LoggerManager.warn(args...);mutex=PrintMutex)
error(args...) = thread_call(() -> LoggerManager.error(args...);mutex=PrintMutex)
######################################################
Messages = String[] #shared object among threads
function message(this::Thread, args... ;mode="", title="", lineBreak=true)
global Messages
thread_push!(Messages, LoggerManager.msg(args... ;time=true, name=thread_id()*":"*this.name, mode=mode, title=title, lineBreak=lineBreak))
end
function showMessages()
global Messages
msgs = String[]
# copy & clear messages
thread_call(() -> begin msgs = deepcopy(Messages); Messages = Array{String,1}() end ;mutex=PushMutex)
thread_call(function()
if length(msgs) > 0
LoggerManager.println("---[Messages]---")
for msg in msgs LoggerManager.print(msg) end
LoggerManager.println("----------------")
end
end ;mutex=PrintMutex)
end
######################################################
print(this::Thread, args... ;title="") = message(this, args... ;title=title, lineBreak=false)
println(this::Thread, args... ;title="") = message(this, args... ;title=title)
info(this::Thread, args... ;title="") = message(this, args... ;mode=:Info, title=title)
debug(this::Thread, args... ;title="") = message(this, args... ;mode=:Debug, title=title)
warn(this::Thread, args... ;title="") = message(this, args... ;mode=:Warning, title=title)
error(this::Thread, args... ;title="") = message(this, args... ;mode=:Error, title=title)
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] | 3.166667 | 738 |
@testset "Utils" begin
De, Dx = 3, 2
g = Flux.batch([GNNGraph(erdos_renyi(10, 30),
ndata=rand(Dx, 10),
edata=rand(De, 30),
graph_type=GRAPH_T) for i=1:5])
x = g.ndata.x
e = g.edata.e
@testset "reduce_nodes" begin
r = reduce_nodes(mean, g, x)
@test size(r) == (Dx, g.num_graphs)
@test r[:,2] ≈ mean(getgraph(g, 2).ndata.x, dims=2)
end
@testset "reduce_edges" begin
r = reduce_edges(mean, g, e)
@test size(r) == (De, g.num_graphs)
@test r[:,2] ≈ mean(getgraph(g, 2).edata.e, dims=2)
end
@testset "softmax_nodes" begin
r = softmax_nodes(g, x)
@test size(r) == size(x)
@test r[:,1:10] ≈ softmax(getgraph(g, 1).ndata.x, dims=2)
end
@testset "softmax_edges" begin
r = softmax_edges(g, e)
@test size(r) == size(e)
@test r[:,1:60] ≈ softmax(getgraph(g, 1).edata.e, dims=2)
end
@testset "broadcast_nodes" begin
z = rand(4, g.num_graphs)
r = broadcast_nodes(g, z)
@test size(r) == (4, g.num_nodes)
@test r[:,1] ≈ z[:,1]
@test r[:,10] ≈ z[:,1]
@test r[:,11] ≈ z[:,2]
end
@testset "broadcast_edges" begin
z = rand(4, g.num_graphs)
r = broadcast_edges(g, z)
@test size(r) == (4, g.num_edges)
@test r[:,1] ≈ z[:,1]
@test r[:,60] ≈ z[:,1]
@test r[:,61] ≈ z[:,2]
end
@testset "softmax_edge_neighbors" begin
s = [1,2,3,4]
t = [5,5,6,6]
g2 = GNNGraph(s, t)
e2 = randn(Float32, 3, g2.num_edges)
z = softmax_edge_neighbors(g2, e2)
@test size(z) == size(e2)
@test z[:,1:2] ≈ NNlib.softmax(e2[:,1:2], dims=2)
@test z[:,3:4] ≈ NNlib.softmax(e2[:,3:4], dims=2)
end
end
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886,
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] | 1.654867 | 1,130 |
abstract type PipelineStep end
"""
run(step::PipelineStep, context, data)
Applies the operation `step` to `data`
"""
function run end
"""
run!(buf, step::PipelineStep, context, data)
Applies the operation `step` inplace to `buf`. `buf` is mutated.
"""
function run! end
"""
invert(step::PipelineStep, context, data)
Applies the inverse of the operation `step` to `data`
"""
function invert end
"""
invert!(buf, step::PipelineStep, context, data)
Applies the inverse of the operation `step` to `buf` inplace. `buf` is mutated,
"""
function invert! end
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] | 2.919192 | 198 |
#=doc
Utilities
=#
export crop_array_border
"""
throwaway the border region of an array (ndims > 3), currently only works for 3D cropsize.
"""
function crop_array_border(arr::Array, cropsize::Union{Vector,Tuple})
@assert ndims(arr) >= 3
sz = size(arr)
@assert sz[1]>cropsize[1]*2 &&
sz[2]>cropsize[2]*2 &&
sz[3]>cropsize[3]*2
return arr[ cropsize[1]+1:sz[1]-cropsize[1],
cropsize[2]+1:sz[2]-cropsize[2],
cropsize[3]+1:sz[3]-cropsize[3]]
end
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] | 1.965649 | 262 |
"""
me_solve_time_independent(rho_in::Array{T1,2},
H::Array{T2,2},
Gamma::Array{T3,3},
rates::Array{T4,1},
t0::AbstractFloat,
tf::AbstractFloat;
tstep::AbstractFloat=0.,
tols::Vector{Float64}=[1e-6,1e-3],
alg = Tsit5()) where {T1 <: Number, T2 <: Number, T3 <: Number, T4 <: AbstractFloat}
Time independent master equation solver using a non-vectorized algorithm.
## args
REQUIRED
* rho_in: d x d array, the density matrix of the initial state
* H: d x d array, system Hamiltonian
* Gamma: d x d x K array, all K Lindblad operators
* rates: K x 1 array, dissipative rate for each Lindblad operator
* t0: float, start time
* tf: float, end time
KEYWORD OPTIONAL
* tstep: float, time steps at which output data should be saved
* tols: 2 x 1 array, vector of solver tolernaces in the order [abstol, reltol]
* alg: function, algorithm from DifferentialEquations for the solver to use, default is Tsit5
* iter_num: number of iterations for an adpative solver
## returns
* tvec: vector of time points where the density matrix has been simulated
* rho_out: vector of simulated density matricies
"""
function me_solve_time_independent(rho_in::Array{T1,2},
H::Array{T2,2},
Gamma::Array{T3,3},
rates::Array{T4,1},
t0::AbstractFloat,
tf::AbstractFloat;
tstep::AbstractFloat=0.,
tols::Vector{Float64}=[1e-6,1e-3],
alg = Tsit5(),
iter_num=1e5) where {T1 <: Number, T2 <: Number, T3 <: Number, T4 <: AbstractFloat}
rho_in = convert(Array{ComplexF64,2},rho_in)
H = convert(Array{ComplexF64,2},H)
Gamma = convert(Array{ComplexF64,3},Gamma)
rates = convert(Array{Float64,1},rates)
dRho_L = Array{ComplexF64}(undef,size(rho_in,1),size(rho_in,2))
scratchA = similar(rho_in);
scratchB = similar(rho_in);
Gammas = [Gamma[:,:,ii].*sqrt(rates[ii]) for ii=1:size(Gamma,3)]
GammaTs = [collect(G') for G in Gammas]
GammaSqs = [0.5.*G'G for G in Gammas]
dif_f(du,u,p,t) = dRho!(du,u,H,Gammas,GammaTs,GammaSqs,dRho_L,scratchA,scratchB) # In place
tspan = (t0,tf)
prob = ODEProblem{true}(dif_f,rho_in,tspan)
if tstep < 1e-10
tstep = (tf-t0)./100
end
sol = solve(prob, alg, saveat = tstep, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2], maxiters=iter_num)
tvec = sol.t
rho_out = sol.u
return tvec, rho_out
end
"""
me_solve_H_time_dependent(rho_in::Array{T1,2},
H::Function,
Gamma::Array{T3,3},
rates::Array{T4,1},
t0::AbstractFloat,
tf::AbstractFloat;
tstep::AbstractFloat=0.,
tols::Vector{Float64}=[1e-6,1e-3],
alg = Tsit5(),
iter_num=1e5,
stop_points=[]
adapt::Bool = true,
δt::Float64 = tols[1],
override::Bool = false) where {T1 <: Number, T2 <: Number, T3 <: Number, T4 <: AbstractFloat}
Time dependent (Hamiltonian) master equation solver using a non-vectorized algorithm.
## args
REQUIRED
* rho_in: d x d array, the density matrix of the initial state
* H: function, system Hamiltonian, takes input of time and returns the
Hamiltonian at the given time in matrix form
* Gamma: d x d x K array, all K Lindblad operators
* rates: K x 1 array, dissipative rate for each Lindblad operator
* t0: float, start time
* tf: float, end time
KEYWORD OPTIONAL
* tstep: float, time steps at which output data should be saved
* tols: 2 x 1 array, vector of solver tolernaces in the order [abstol, reltol]
* alg: function, algorithm from DifferentialEquations for the solver to use, default is Tsit5
* iter_num: number of iterations for an adpative solver
* stop_points: vector of time-points the solver must step through (for adaptive solvers)
* adapt: Boolean. If false, the solver assumes it is using a fixed
timestep integration method.
* δt: Timestep for fixed timestep integration method.
* override: Booelan. If true, overrides the error checking that the data
save timestep (tstep) is not less than the fixed integration
timestep (δt).
## returns
* tvec: vector of time points where the density matrix has been simulated
* rho_out: vector of simulated density matricies
"""
function me_solve_H_time_dependent(rho_in::Array{T1,2},
H::Function,
Gamma::Array{T3,3},
rates::Array{T4,1},
t0::AbstractFloat,
tf::AbstractFloat;
tstep::AbstractFloat=0.,
tols::Vector{Float64}=[1e-6,1e-3],
alg = Tsit5(),
iter_num=1e5,
stop_points=[],
adapt::Bool = true,
δt::Float64 = tols[1],
override::Bool = false) where {T1 <: Number, T2 <: Number, T3 <: Number, T4 <: AbstractFloat}
rho_in = convert(Array{ComplexF64,2},rho_in)
Gamma = convert(Array{ComplexF64,3},Gamma)
rates = convert(Array{Float64,1},rates)
dRho_L = Array{ComplexF64}(undef,size(rho_in,1),size(rho_in,2))
H_temp = Array{ComplexF64}(undef,size(rho_in,1),size(rho_in,2))
scratchA = similar(rho_in);
scratchB = similar(rho_in);
Gammas = [Gamma[:,:,ii].*sqrt(rates[ii]) for ii=1:size(Gamma,3)]
GammaTs = [collect(G') for G in Gammas]
GammaSqs = [0.5.*G'G for G in Gammas]
dif_f(du,u,p,t) = dRho!(du,u,H,H_temp,Gammas,GammaTs,GammaSqs,dRho_L,scratchA,scratchB,t) # In place
tspan = (t0,tf)
prob = ODEProblem{true}(dif_f,rho_in,tspan)
if tstep < 1e-10
tstep = (tf-t0)./100
end
if adapt
sol = solve(prob, alg, saveat = tstep, tstops = stop_points, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2], maxiters=iter_num)
else
if tstep < δt && override == false
error("You are using a fixed-timestep solver and the save timestep is smaller than solver timestep. This is probably a bad idea. If you insist, set override = true in the input arguments to override this error checking.")
end
sol = solve(prob, alg, saveat = tstep, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2], adaptive = adapt, dt = δt)
end
tvec = sol.t
rho_out = sol.u
return tvec, rho_out
end
"""
me_solve_H_time_dependent(rho_in::Array{T1,2},
Hops::Array{T5,3},
Hfuncs::Function,
Gamma::Array{T3,3},
rates::Array{T4,1},
t0::AbstractFloat,
tf::AbstractFloat;
tstep::AbstractFloat=0.,
tols::Vector{Float64}=[1e-6,1e-3],
alg = Tsit5(),iter_num=1e5,
stop_points=[],
adapt::Bool = true,
δt::Float64 = tols[1],
override::Bool = false) where {T1 <: Number, T2 <: Number, T3 <: Number, T4 <: AbstractFloat, T5 <: Number}
Time dependent (Hamiltonian) master equation solver using a non-vectorized algorithm. Instead of inputing the Hamiltonian as a matrix function, it is input as a basis of matrices, and a function that describes the time evolution of the scalar prefactor of each basis element.
This solver also allows for fixed timestep integration.
Input arguments (different from above):
## args
REQUIRED
* Hops: Array of matrices that describe the Hamiltonian. In the most
general case is a full basis for operator space.
* Hfuncs: Function that takes as input a Float (the time) and returns
a Vector of Floats. Each element of the output vector is
the value of the scalar prefactor for the corresponding
basis element in the decomposition of the Hamiltonian.
KEYWORD OPTIONAL
* adapt: Boolean. If false, the solver assumes it is using a fixed
timestep integration method.
* δt: Timestep for fixed timestep integration method.
* override: Booelan. If true, overrides the error checking that the data
save timestep (tstep) is not less than the fixed integration
timestep (δt).
## returns
* tvec: vector of time points where the density matrix has been simulated
* rho_out: vector of simulated density matricies
"""
function me_solve_H_time_dependent(rho_in::Array{T1,2},
Hops::Array{T5,3},
Hfuncs::Function,
Gamma::Array{T3,3},
rates::Array{T4,1},
t0::AbstractFloat,
tf::AbstractFloat;
tstep::AbstractFloat=0.,
tols::Vector{Float64}=[1e-6,1e-3],
alg = Tsit5(),
iter_num=1e5,
stop_points=[],
adapt::Bool = true,
δt::Float64 = tols[1],
override::Bool = false) where {T1 <: Number, T2 <: Number, T3 <: Number, T4 <: AbstractFloat, T5 <: Number}
rho_in = convert(Array{ComplexF64,2},rho_in)
Gamma = convert(Array{ComplexF64,3},Gamma)
rates = convert(Array{Float64,1},rates)
Hops = convert(Array{ComplexF64,3},Hops)
dRho_L = Array{ComplexF64}(undef,size(rho_in,1),size(rho_in,2))
H_temp = Array{ComplexF64}(undef,size(rho_in,1),size(rho_in,2))
Hf_temp = zeros(ComplexF64,size(Hops,3))
scratchA = similar(rho_in);
scratchB = similar(rho_in);
Gammas = [Gamma[:,:,ii].*sqrt(rates[ii]) for ii=1:size(Gamma,3)]
GammaTs = [collect(G') for G in Gammas]
GammaSqs = [0.5.*G'G for G in Gammas]
dif_f(du,u,p,t) = dRho!(du,u,Hops,Hfuncs,H_temp,Hf_temp,Gammas,GammaTs,GammaSqs,dRho_L,scratchA,scratchB,t) # In place
tspan = (t0,tf)
prob = ODEProblem{true}(dif_f,rho_in,tspan)
if tstep < 1e-10
tstep = (tf-t0)./100
end
if adapt
sol = solve(prob, alg, saveat = tstep, tstops = stop_points, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2], maxiters=iter_num)
else
if tstep < δt && override == false
error("You are using a fixed-timestep solver and the save timestep is smaller than solver timestep. This is probably a bad idea. If you insist, set override = true in the input arguments to override this error checking.")
end
sol = solve(prob, alg, saveat = tstep, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2], adaptive = adapt, dt = δt)
end
tvec = sol.t
rho_out = sol.u
return tvec, rho_out
end
"""
me_solve_L_time_dependent(rho_in::Array{T1,2},
H::Array{T2,2},
Gamma::Array{T3,3},
rates::Function,
t0::AbstractFloat,
tf::AbstractFloat;
tstep::AbstractFloat=0.,
tols::Vector{Float64}=[1e-6,1e-3],
alg = Tsit5(),
iter_num=1e5,
stop_points=[],
adapt::Bool = true,
δt::Float64 = tols[1],
override::Bool = false) where {T1 <: Number, T2 <: Number, T3 <: Number}
Time dependent (dissipative rates) master equation solver using a non-vectorized algorithm.
## args
* rho_in: d x d array, the density matrix of the initial state
* H: d x d array, system Hamiltonian
* Gamma: d x d x K array, all K Lindblad operators
* rates: K x 1 array, dissipative rate for each Lindblad operator, takes input time and returns a vector of the dissipative rates at the given time
* t0: float, start time
* tf: float, end time
KEYWORD OPTIONAL
* tstep: float, time steps at which output data should be saved
* tols: 2 x 1 array, vector of solver tolernaces in the order [abstol, reltol]
* alg: function, algorithm from DifferentialEquations for the solver to use, default is Tsit5
* iter_num: number of iterations for an adpative solver
* stop_points: vector of time-points the solver must step through (for adaptive solvers)
* adapt: Boolean. If false, the solver assumes it is using a fixed
timestep integration method.
* δt: Timestep for fixed timestep integration method.
* override: Booelan. If true, overrides the error checking that the data
save timestep (tstep) is not less than the fixed integration
timestep (δt).
## returns
* tvec: vector of time points where the density matrix has been simulated
* rho_out: vector of simulated density matricies
"""
function me_solve_L_time_dependent(rho_in::Array{T1,2},
H::Array{T2,2},
Gamma::Array{T3,3},
rates::Function,
t0::AbstractFloat,
tf::AbstractFloat;
tstep::AbstractFloat=0.,
tols::Vector{Float64}=[1e-6,1e-3],
alg = Tsit5(),
iter_num=1e5,
stop_points=[],
adapt::Bool = true,
δt::Float64 = tols[1],
override::Bool = false) where {T1 <: Number, T2 <: Number, T3 <: Number}
rho_in = convert(Array{ComplexF64,2},rho_in)
H = convert(Array{ComplexF64,2},H)
Gamma = convert(Array{ComplexF64,3},Gamma)
scratchA = similar(rho_in);
scratchB = similar(rho_in);
Gammas = [Gamma[:,:,ii] for ii=1:size(Gamma,3)]
GammaTs = [collect(G') for G in Gammas]
GammaSqs = [0.5.*G'G for G in Gammas]
dRho_L = Array{ComplexF64}(undef,size(rho_in,1),size(rho_in,2))
rates_temp = Array{Float64}(undef,1)
dif_f(du,u,p,t) = dRho!(du,u,H,rates,rates_temp,Gammas,GammaTs,GammaSqs,dRho_L,scratchA,scratchB,t) # In place
tspan = (t0,tf)
prob = ODEProblem{true}(dif_f,rho_in,tspan)
if tstep < 1e-10
tstep = (tf-t0)./100
end
if adapt
sol = solve(prob, alg, saveat = tstep, tstops = stop_points, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2], maxiters=iter_num)
else
if tstep < δt && override == false
error("You are using a fixed-timestep solver and the save timestep is smaller than solver timestep. This is probably a bad idea. If you insist, set override = true in the input arguments to override this error checking.")
end
sol = solve(prob, alg, saveat = tstep, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2], adaptive = adapt, dt = δt)
end
tvec = sol.t
rho_out = sol.u
return tvec, rho_out
end
"""
me_solve_full_time_dependent(rho_in::Array{T1,2},
H::Function,
Gamma::Array{T3,3},
rates::Function,
t0::AbstractFloat,
tf::AbstractFloat;
tstep::AbstractFloat=0.,
tols::Vector{Float64}=[1e-6,1e-3],
alg = Tsit5(),
stop_points=[],
adapt::Bool = true,
δt::Float64 = tols[1],
override::Bool = false) where {T1 <: Number, T2 <: Number, T3 <: Number}
Time dependent (Hamiltonian and dissipative rates) master equation solver using a non-vectorized algorithm.
## args
REQUIRED
* rho_in: d x d array, the density matrix of the initial state
* H: function, system Hamiltonian, takes input of time and returns
the Hamiltonian at the given time in matrix form
* Gamma: d x d x K array, all K Lindblad operators
* rates: K x 1 array, dissipative rate for each Lindblad operator,
takes input time and returns a vector of the dissipative
rates at the given time
* t0: float, start time
* tf: float, end time
KEYWORD OPTIONAL
* tstep: float, time steps at which output data should be saved
* tols: 2 x 1 array, vector of solver tolernaces in the
order [abstol, reltol]
* alg: function, algorithm from DifferentialEquations for the solver
to use, default is Tsit5
* iter_num: number of iterations for an adpative solver
* stop_points: vector of time-points the solver must step through
(for adaptive solvers)
* adapt: Boolean. If false, the solver assumes it is using a fixed
timestep integration method.
* δt: Timestep for fixed timestep integration method.
* override: Booelan. If true, overrides the error checking that the data
save timestep (tstep) is not less than the fixed integration
timestep (δt).
## returns
* tvec: vector of time points where the density matrix has been simulated
* rho_out: vector of simulated density matricies
"""
function me_solve_full_time_dependent(rho_in::Array{T1,2},
H::Function,
Gamma::Array{T3,3},
rates::Function,
t0::AbstractFloat,
tf::AbstractFloat;
tstep::AbstractFloat=0.,
tols::Vector{Float64}=[1e-6,1e-3],
alg = Tsit5(),
iter_num=1e5,
stop_points=[],
adapt::Bool = true,
δt::Float64 = tols[1],
override::Bool = false) where {T1 <: Number, T2 <: Number, T3 <: Number}
rho_in = convert(Array{ComplexF64,2},rho_in)
Gamma = convert(Array{ComplexF64,3},Gamma)
dRho_L = Array{ComplexF64}(undef,size(rho_in,1),size(rho_in,2))
H_temp = Array{ComplexF64}(undef,size(rho_in,1),size(rho_in,2))
rates_temp = Array{Float64}(undef,1)
scratchA = similar(rho_in);
scratchB = similar(rho_in);
Gammas = [Gamma[:,:,ii] for ii=1:size(Gamma,3)]
GammaTs = [collect(G') for G in Gammas]
GammaSqs = [0.5.*G'G for G in Gammas]
dif_f(du,u,p,t) = dRho!(du,u,H,H_temp,rates,rates_temp,Gammas,GammaTs,GammaSqs,dRho_L,scratchA,scratchB,t) # In place
tspan = (t0,tf)
prob = ODEProblem{true}(dif_f,rho_in,tspan)
if tstep < 1e-10
tstep = (tf-t0)./100
end
if adapt
sol = solve(prob, alg, saveat = tstep, tstops = stop_points, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2], maxiters=iter_num)
else
if tstep < δt && override == false
error("You are using a fixed-timestep solver and the save timestep is smaller than solver timestep. This is probably a bad idea. If you insist, set override = true in the input arguments to override this error checking.")
end
sol = solve(prob, alg, saveat = tstep, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2], adaptive = adapt, dt = δt)
end
tvec = sol.t
rho_out = sol.u
return tvec, rho_out
end
"""
me_solve_time_independent_vec(rho_in::Array{T1,2},
H::Array{T2,2},
Gamma::Array{T3,3},
rates::Array{T4,1},
t0::AbstractFloat,
tf::AbstractFloat;
tstep::AbstractFloat=0.,
tols::Vector{Float64}=[1e-6,1e-3],
alg = Tsit5()) where {T1 <: Number, T2 <: Number, T3 <: Number, T4 <: AbstractFloat}
Time independent master equation solver using a non-vectorized algorithm but with vector ODE solvers
## args
REQUIRED
* rho_in: d x d array, the density matrix of the initial state
* H: d x d array, system Hamiltonian
* Gamma: d x d x K array, all K Lindblad operators
* rates: K x 1 array, dissipative rate for each Lindblad operator
* t0: float, start time
* tf: float, end time
KEYWORD OPTIONAL
* tstep: float, time steps at which output data should be saved
* tols: 2 x 1 array, vector of solver tolernaces in the
order [abstol, reltol]
* alg: function, algorithm from DifferentialEquations for the
solver to use, default is Tsit5
## returns
* tvec: vector of time points where the density matrix has been simulated
* rho_out: vector of simulated density matricies
"""
# LUKE: I think we can remove this functionality.
# function me_solve_time_independent_vec(rho_in::Array{T1,2},
# H::Array{T2,2},
# Gamma::Array{T3,3},
# rates::Array{T4,1},
# t0::AbstractFloat,
# tf::AbstractFloat;
# tstep::AbstractFloat=0.,
# tols::Vector{Float64}=[1e-6,1e-3],
# alg = Tsit5()) where {T1 <: Number, T2 <: Number, T3 <: Number, T4 <: AbstractFloat}
#
# rho_in = convert(Array{ComplexF64,2},rho_in)
# H = convert(Array{ComplexF64,2},H)
# Gamma = convert(Array{ComplexF64,3},Gamma)
# rates = convert(Array{Float64,1},rates)
#
# rho_in_vec = rho_in[:]
#
# dRho_L = Array{ComplexF64}(undef,size(rho_in,1),size(rho_in,2))
# rho_temp = Array{ComplexF64}(undef,size(rho_in,1),size(rho_in,2))
#
# dif_f(du,u,p,t) = dRho_vec(du,u,H,Gamma,rates,dRho_L,rho_temp) # In place
# tspan = (t0,tf)
#
# prob = ODEProblem{true}(dif_f,rho_in_vec,tspan)
#
# if tstep < 1e-10
# tstep = (tf-t0)./100
# end
#
# sol = solve(prob, alg, saveat = tstep, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2])
#
# tvec = sol.t
# rho_out_vec = sol.u
#
# rho_out = Array{Array{ComplexF64,2}}(undef,length(tvec))
# for jj = 1:1:length(tvec)
# rho_out[jj] = reshape(rho_out_vec[jj][:],size(H))
# end
#
# return tvec, rho_out
# end
# """
# Time dependent solver for Gaussian continuous variable systems using the covariance matrix approach. Defined in terms of the canonical X and P quadratures, the covariance matrix elements are defined by
# C_ab = <O_aO_b> - <O_a><O_b> where vec(O) = [X,P] with
# X = (a + a^†)/√2, P = -i(a - a^†)/√2 with a the lowering operator of a harmonic oscilaltor.
# Input Parameters:
# REQUIRED
# C_in: Array 2 x 2, initial condition for the covariance matrix
# a_in: vector 2 x 1, initial condition for the average value vector [<X>,<P>]
# h: Function that returnas as output a matrix 2 x 2, Hamiltonian evolution term for the covariance matrix. For H = ω a^†a + (λa^2 + conj(λ)a^†^2)/2, h = [Im(λ), (ω - Re(λ);-(ω + Re(λ)), -Im(λ)].
# Can use h = create_h_C(ω(t),λ(t),t) to generate the correct h.
# drv: Function that returns as output a vector 2 x 1, linear drive term. For H = αa + conj(α)a^†, drv = √2[Im(α), Re(α)]
# t0: float, start time
# tf: float, end time
# KEYWORD OPTIONAL
# tstep: float, time steps at which output data should be saved
# tols: 2 x 1 array, vector of solver tolernaces in the order [abstol, reltol]
# alg: function, algorithm from DifferentialEquations for the solver to use, default is Tsit5
# """
#
# function CV_solve_full_time_dependent(C_in::Array{T,2},a_in::Vector{Float64},h::Function,drv::Function,t0::AbstractFloat,tf::AbstractFloat; tstep::AbstractFloat=0.,tols::Vector{Float64}=[1e-6,1e-3],alg = Tsit5()) where {T <: Number}
#
# C_in = convert(Array{ComplexF64,2}, C_in)
# if tstep < 1e-10
# tstep = (tf-t0)./100
# end
# tspan = (t0,tf)
#
# # Covariance matrix
# h_tempL = Array{Float64}(undef,size(C_in,1),size(C_in,2))
# # h_tempR = Array{Float64}(undef,size(C_in,1),size(C_in,2))
# dif_C(du,u,p,t) = dCV(du,u,h,t,h_tempL) # In place
#
# prob_C = ODEProblem{true}(dif_C,C_in,tspan)
#
# sol_C = solve(prob_C, alg, saveat = tstep, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2])
#
# # Average
# h_temp = Array{Float64}(undef,size(C_in,1),size(C_in,2))
# drv_temp = Array{Float64}(undef,size(a_in,1))
# dif_a(du,u,p,t) = d_av(du,u,h,t,drv,h_temp,drv_temp) # In place
#
# prob_a = ODEProblem{true}(dif_a,a_in,tspan)
#
# sol_a = solve(prob_a, alg, saveat = tstep, dense=false, save_everystep=false, abstol = tols[1], reltol = tols[2])
#
# return sol_C.t, sol_C.u, sol_a.t, sol_a.u
# end
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] | 1.884319 | 14,419 |
module Direct
using jInv.Mesh
using jInv.Utils
using jInv.InverseSolve
using ShapeReconstructionPaLS.Utils
using ShapeReconstructionPaLS.ParamLevelSet
using MAT
using SparseArrays
using Distributed
import jInv.ForwardShare.getData
import jInv.ForwardShare.getSensTMatVec
import jInv.ForwardShare.getSensMatVec
import jInv.ForwardShare.ForwardProbType
## The rotation planes are:
# first rotation (theta) is for planes x1 and x2
# second rotation (phi) is for planes x1 and x3
# Sampling is done by summing planes of x1 and x2 for all values of x3.
export DirectParam, getDirectParam
mutable struct DirectParam <: ForwardProbType
Mesh :: RegularMesh
ndipsAll :: Int64
workerSubIdxs
#theta_phi_rad :: Array{Float64,2}
#b :: Array{Float64,2}
samplingBinning :: Int64
method :: String
Jacobian :: SparseMatrixCSC{Float32,Int32}
#S :: SparseMatrixCSC{Float32,Int32}
end
function getDirectParamInternal(Mesh::RegularMesh,ndipsAll::Int64,workerSubIdxs,samplingBinning::Int64,method)
return DirectParam(Mesh, ndipsAll, workerSubIdxs, samplingBinning, method,spzeros(Float32,Int32,0,0));
end
function getDirectParam(Mesh::RegularMesh,samplingBinning::Int64,numWorkers::Int64,method = MATBased)
## This function does use the parallel mechanism of jInv (i.e., returns a RemoteChannel), even if numWorkers=1.
if numWorkers > nworkers()
numWorkers = nworkers();
end
#SourcesSubInd = Array{Array{Int64,1}}(numWorkers);
SourcesSubInd = Array{Array{Int64}}(undef,numWorkers);
ActualWorkers = workers();
if numWorkers < nworkers()
ActualWorkers = ActualWorkers[1:numWorkers];
end
#pFor = Array{RemoteChannel}(numWorkers)
pFor = Array{RemoteChannel}(undef,numWorkers);
i = 1; nextidx() = (idx=i; i+=1; idx)
idx=i;
ndips = 1;
# send out jobs
@sync begin
for p = ActualWorkers
@async begin
while true
idx = nextidx();
if idx > numWorkers
break
end
I_p = getIndicesOfKthWorker(numWorkers,idx,ndips);
# println("Sending ",collect(I_p)," to worker ", p);
# find src and rec on mesh
#SEE IF CORRECT!!!!!!!!!!!!!!!!
tmp = initRemoteChannel(getDirectParamInternal,p, Mesh,ndips,I_p,samplingBinning,method);
pFor[1] = initRemoteChannel(getDirectParamInternal,p, Mesh,ndips,I_p,samplingBinning,method);
wait(pFor[1]);
end
end
end
end
return pFor # Array of Remote Refs
end
import jInv.Utils.clear!
function clear!(pFor::DirectParam)
pFor.SampleMat = spzeros(0);
clear!(pFor.Mesh);
return pFor;
end
#include("generateDipMatrix.jl");
#include("prepareSyntheticDipData.jl");
include("getData.jl");
include("sensitivityFuncs.jl");
end | [
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] | 2.558739 | 1,047 |
base_dir = dirname(dirname(pathof(PowerSystems)))
sys = PowerSystems.parse_standard_files(joinpath(MATPOWER_DIR, "case5_re.m"))
@testset "Check bus index" begin
@test sort([b.number for b in collect(get_components(Bus, sys))]) == [1, 2, 3, 4, 10]
@test sort(collect(Set([b.arc.from.number for
b in collect(get_components(Branch,sys))]))) == [1, 2, 3, 4]
@test sort(collect(Set([b.arc.to.number for
b in collect(get_components(Branch,sys))]))) == [2, 3, 4, 10]
# TODO: add test for loadzones testing MAPPING_BUSNUMBER2INDEX
end
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] | 2.310757 | 251 |
using kRPC
using CSV
using LinearAlgebra
using DataFrames
using Plots
using Rotations
pyplot()
conn = kRPCConnect("Aerodata")
using kRPC.Remote.SpaceCenter
totable = 1
outp_a = 1
outp_b = 1
vessel = ActiveVessel()
flight = Flight(vessel)
orbit = Orbit(vessel)
body = Body(orbit)
body_frame = ReferenceFrame(body)
function compute_reference_frame(body, lat, lon, alt::Float64)
landing_position = SurfacePosition(body, lat, lon, body_frame)
q_long = (
0.0,
sin(-lon * 0.5 * pi / 180),
0.0,
cos(-lon * 0.5 * pi / 180))
q_lat = (
0.0,
0.0,
sin(lat * 0.5 * pi / 180),
cos(lat * 0.5 * pi / 180))
cr1 = CreateRelative(body_frame, position=landing_position, rotation=q_long)
cr2 = CreateRelative(cr1,rotation=q_lat)
return CreateRelative(cr2,position=(alt,0.0,0.0))
end
rf = compute_reference_frame(body, -0.09720792138114814, -74.55767985150328, 0.0)
ref = ReferenceFrame(vessel)
gfl = Flight(vessel, referenceFrame=rf)
trq_vis = kRPC.Remote.Drawing.AddLine((0.0,0.0,0.0),(0.0,0.0,0.0),rf)
strq_vis = kRPC.Remote.Drawing.AddLine((0.0,0.0,0.0),(0.0,0.0,0.0),rf)
sfrc_vis = kRPC.Remote.Drawing.AddLine((0.0,0.0,0.0),(0.0,0.0,0.0),rf)
kRPC.Remote.Drawing.Color!(trq_vis, (0.0,1.0,0.0))
kRPC.Remote.Drawing.Color!(strq_vis, (0.0,0.0,1.0))
global lut = -10000.0
global lav = Float64[]
global iter = 0
add_multiple_streams([
kRPC.Remote.SpaceCenter.Delayed.AerodynamicTorque(gfl),
kRPC.Remote.SpaceCenter.Delayed.Velocity(gfl),
kRPC.Remote.SpaceCenter.Delayed.Position(vessel, rf)],
function (at, vel, pos)
if iter % 10 == 0
sat,saf = kRPC.SendMessage(conn, [
kRPC.Remote.SpaceCenter.Delayed.SimulateAerodynamicTorqueAt(gfl, body, pos, vel),
kRPC.Remote.SpaceCenter.Delayed.SimulateAerodynamicForceAt(gfl, body, pos, vel)])
kRPC.SendMessage(conn,
[kRPC.Remote.Drawing.Delayed.End!(trq_vis, pos.+at),
kRPC.Remote.Drawing.Delayed.End!(strq_vis, pos.+sat),
kRPC.Remote.Drawing.Delayed.End!(sfrc_vis, pos.+saf),
kRPC.Remote.Drawing.Delayed.Start!(trq_vis, pos),
kRPC.Remote.Drawing.Delayed.Start!(strq_vis, pos),
kRPC.Remote.Drawing.Delayed.Start!(sfrc_vis, pos)])
end
global iter = iter + 1
end)
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] | 2.132634 | 1,048 |
# This file includes the network
# --------------------------------------- ODENetwork construction and modificatio ------------------------------- #
"""
$TYPEDEF
# Fields
$TYPEDFIELDS
"""
mutable struct ODENetwork{T1<:AbstractNode,T2<:AbstractSimpleWeightedGraph,T3<:AbstractMatrix} <: AbstractNetwork
"Dynamical nodes"
nodes::Vector{T1}
"Coupling strength"
ϵ::Float64
"Graph explaining topology"
graph::T2
"Inner coupling matrix"
coupling::T3
end
ODENetwork(nodes = Lorenz[], ϵ = 1.0, graph = zeros(length(nodes), length(nodes)), coupling = diagm(ones(dimension(eltype(nodes))))) =
ODENetwork(nodes, ϵ, SimpleWeightedGraph(graph), coupling)
ODENetwork(::Type{T}, ϵ = 1.0, graph = fill(0.0, 0, 0), coupling = diagm(ones(dimension(T)))) where {T<:AbstractNode} =
ODENetwork([T() for i = 1:size(graph)[1]], ϵ, graph, coupling)
"""
$TYPEDEF
# Fields
$TYPEDFIELDS
"""
mutable struct SDENetwork{T1<:AbstractNode,T2<:AbstractSimpleWeightedGraph,T3<:AbstractMatrix,T4<:AbstractMatrix} <: AbstractNetwork
"Dynamical nodes"
nodes::Vector{T1}
"Coupling strength"
ϵ::Float64
"Graph explaining topology"
graph::T2
"Noise coupling matrix"
noisemat::T3
"Inner coupling matrix"
coupling::T4
end
function SDENetwork(nodes = Lorenz[], ϵ = 1.0, graph = zeros(length(nodes), length(nodes)), noisemat = nothing, coupling = fullstatecoupling(nodes))
weightedgraph = SimpleWeightedGraph(graph)
if noisemat === nothing
noisemat = getnoisemat(weightedgraph)
end
SDENetwork(nodes, ϵ, weightedgraph, noisemat, coupling)
end
SDENetwork(::Type{T}, ϵ = 1.0, graph = fill(0.0, 0, 0), noisemat = nothing, coupling = diagm(ones(dimension(T)))) where {T<:AbstractNode} =
SDENetwork([T() for i = 1:size(graph)[1]], ϵ, graph, noisemat, coupling)
"""
$TYPEDSIGNATURES
Returns full noise coupling matrix corresponding to graph.
"""
function getnoisemat(graph)
numnodes = nv(graph)
numedges = ne(graph)
mat = zeros(numnodes, numedges)
for (k, edge) in enumerate(edges(graph))
mat[edge.src, k] = 1
mat[edge.dst, k] = -1
end
mat
end
# Returns the innner coupling matrix corresponding to the fully-state coupled network.
fullstatecoupling(nodes) = diagm(ones(dimension(eltype(nodes))))
"""
$TYPEDSIGNATURES
Adds `node` to `net`
"""
function addnode!(net::AbstractNetwork, node::AbstractNode = eltype(net.nodes)())
add_vertex!(net.graph)
push!(net.nodes, node)
net
end
"""
$TYPEDSIGNATURES
Remoes the node with index `idx` from `net`.
"""
function remnode!(net::AbstractNetwork, idx::Int = lastindex(net.nodes))
deleteat!(net.nodes, idx)
rem_vertex!(net.graph, idx)
net
end
"""
$TYPEDSIGNATURES
Adds and edge in `net` between the nodes `src` and `dst` with weight `weight`
"""
function addedge!(net::AbstractNetwork, src::Int, dst::Int, weight::Real = 1.0, noisestrength::Real = 0.0)
add_edge!(net.graph, src, dst, weight)
is_directed(net.graph) && add_edge!(net.graph, dst, src, weight)
# Update the noise matrix of the network. Tehe
newcol = zeros(size(net.noisemat, 1), 1)
newcol[src] = noisestrength
newcol[dst] = -noisestrength
net.noisemat = [net.noisemat newcol]
net
end
"""
$TYPEDSIGNATURES
Removes the edge between `src` and `dst` from `net`
"""
function remedge!(net::AbstractNetwork, src::Int, dst::Int)
rem_edge!(net.graph, src, dst)
is_directed(net.graph) && rem_edge!(net.graph, dst, src)
# Find and delete the column of the noise matrix of the network.
rows = net.noisemat[[src, dst], :]
idx = [!iszero(col[1]) && !iszero(col[2]) for col in eachcol(rows)]
net.noisemat = net.noisemat[:, .!idx]
net
end
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] | 2.472477 | 1,526 |
InputFolder = './Images/VesselsSpots/';
OutputFolder = './Results/Images/VesselsSpots/Spots/';
Fill = -1;
@iA = '*.tif';
@fxm_mFilterObjSize [iA] > [M];
params.MinArea = 1;
params.MaxArea = 1;
/endf
/show iA > M;
/keep M > tif; | [
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] | 2.190476 | 105 |
### TODO: Rethink the CUDNN interface
using CUDNN: cudnnConvolutionDescriptor_t, cudnnCreateConvolutionDescriptor, cudnnSetConvolutionNdDescriptor_v3, cudnnDestroyConvolutionDescriptor, cudnnHandle, cptr, TD, FD, cudnnDataType
using CUDNN: CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM, CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0, CUDNN_CONVOLUTION_BWD_DATA_ALGO_0
using CUDNN: cudnnGetConvolutionNdForwardOutputDim, cudnnGetConvolutionForwardWorkspaceSize
using CUDNN: cudnnConvolutionBackwardFilter_v3, cudnnConvolutionBackwardData_v3
function cudnnConvolutionForward_v4(src, filter, dest;
handle=cudnnHandle, alpha=1.0, beta=0.0,
algorithm=CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM,
workSpace=C_NULL, workSpaceSizeInBytes=0,
padding=0, stride=1, upscale=1, mode=CUDNN_CONVOLUTION)
@assert eltype(filter) == eltype(src)
cd = cudnnConvolutionDescriptor_v4(ndims(src), eltype(src), padding, stride, upscale, mode)
osize = cudnnGetConvolutionNdForwardOutputDim(src,filter;convDesc=cd)
(dest == nothing) && (dest = CudaArray(eltype(src), osize))
@assert osize == size(dest)
@assert eltype(dest) == eltype(src)
wsize = cudnnGetConvolutionForwardWorkspaceSize(src, filter, dest; algorithm=algorithm)
if ((wsize > 0) && (workSpace == C_NULL || workSpaceSizeInBytes < wsize))
workSpaceSizeInBytes = wsize
ws = CudaArray(Int8, workSpaceSizeInBytes)
else
ws = workSpace
end
cudnnConvolutionForward(handle,
cptr(alpha,src),TD(src),src,
FD(filter),filter,
cd,algorithm,ws,workSpaceSizeInBytes,
cptr(beta,dest),TD(dest),dest)
free(cd)
ws === workSpace || free(ws)
return dest
end
# I am guessing if y=w*x+b going forward, the arguments below
# correspond to src=x, diff=dy, grad=dw.
function cudnnConvolutionBackwardFilter_v4(src, diff, grad;
handle=cudnnHandle, alpha=1.0, beta=0.0,
algo=CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0,
workSpace=C_NULL, workSpaceSizeInBytes=0,
padding=0, stride=1, upscale=1, mode=CUDNN_CONVOLUTION)
cd = cudnnConvolutionDescriptor_v4(ndims(src), eltype(src), padding, stride, upscale, mode)
cudnnConvolutionBackwardFilter_v3(handle,
cptr(alpha,src),TD(src),src,
TD(diff),diff,cd,
algo, workSpace, workSpaceSizeInBytes,
cptr(beta,grad),FD(grad),grad)
free(cd)
return grad
end
# I am guessing if y=w*x+b going forward, the arguments below
# correspond to filter=w, diff=dy, grad=dx.
function cudnnConvolutionBackwardData_v4(filter::AbstractCudaArray, diff::AbstractCudaArray, grad::AbstractCudaArray;
handle=cudnnHandle, alpha=1.0, beta=0.0,
algo=CUDNN_CONVOLUTION_BWD_DATA_ALGO_0,
workSpace=C_NULL, workSpaceSizeInBytes=0,
padding=0, stride=1, upscale=1, mode=CUDNN_CONVOLUTION)
cd = cudnnConvolutionDescriptor_v4(ndims(filter), eltype(filter), padding, stride, upscale, mode)
cudnnConvolutionBackwardData_v3(handle,cptr(alpha,diff),
FD(filter),filter,
TD(diff),diff,cd,
algo, workSpace, workSpaceSizeInBytes,
cptr(beta,grad),TD(grad),grad)
free(cd)
return grad
end
# Decided not to keep the cd in the Pool structure: not serializable.
function cudnnConvolutionDescriptor_v4(xdims, xtype, padding, stride, upscale, mode)
cd = cudnnConvolutionDescriptor_t[0]
cudnnCreateConvolutionDescriptor(cd)
nd = xdims-2
cudnnSetConvolutionNdDescriptor_v3(cd[1],nd,cdsize(padding,nd),cdsize(stride,nd),cdsize(upscale,nd),mode,cudnnDataType(xtype))
return cd[1]
end
function cdsize(w, nd)
isa(w,Integer) ? Cint[fill(w,nd)...] :
length(w)!=nd ? error("Dimension mismatch") :
Cint[reverse(w)...]
end
CUDArt.free(cd::cudnnConvolutionDescriptor_t)=cudnnDestroyConvolutionDescriptor(cd)
### TODO: THIS INTERFACE SHOULD GO INTO CUDNN:
using CUDNN: cudnnPoolingDescriptor_t, cudnnCreatePoolingDescriptor, cudnnSetPoolingNdDescriptor, cudnnDestroyPoolingDescriptor, cudnnHandle, cptr, TD, cudnnPoolingMode_t, cudnnGetPoolingNdDescriptor
function cudnnPoolingForward_v4(src, dest;
window=2, padding=0, stride=window, mode=CUDNN_POOLING_MAX,
handle=cudnnHandle, alpha=1.0, beta=0.0)
pd = cudnnPoolingDescriptor_v4(ndims(src)-2, window, padding, stride, mode)
cudnnPoolingForward(handle, pd,
cptr(alpha,src), TD(src), src,
cptr(beta,dest), TD(dest), dest)
free(pd)
return dest
end
function cudnnPoolingBackward_v4(src, srcDiff, dest, destDiff;
window=2, padding=0, stride=window, mode=CUDNN_POOLING_MAX,
handle=cudnnHandle, alpha=1.0, beta=0.0)
pd = cudnnPoolingDescriptor_v4(ndims(src)-2, window, padding, stride, mode)
cudnnPoolingBackward(handle, pd,
cptr(alpha,src), TD(src), src,
TD(srcDiff), srcDiff,
TD(dest), dest,
cptr(beta,destDiff), TD(destDiff), destDiff)
free(pd)
return destDiff
end
# Decided not to keep the pd in the Pool structure: not serializable.
function cudnnPoolingDescriptor_v4(nd, window, padding, stride, mode)
pd = cudnnPoolingDescriptor_t[0]
cudnnCreatePoolingDescriptor(pd)
cudnnSetPoolingNdDescriptor(pd[1],mode,nd,pdsize(window,nd),pdsize(padding,nd),pdsize(stride,nd))
return pd[1]
end
function cudnnGetPoolingNdDescriptor_v4(pd::cudnnPoolingDescriptor_t, nd)
m = cudnnPoolingMode_t[0]
n = Cint[0]
s = Array(Cint, nd)
p = Array(Cint, nd)
t = Array(Cint, nd)
cudnnGetPoolingNdDescriptor(pd, nd, m, n, s, p, t)
inttuple(x)=tuple(Int[x...]...)
(m[1], n[1], inttuple(s), inttuple(p), inttuple(t))
end
CUDArt.free(pd::cudnnPoolingDescriptor_t)=cudnnDestroyPoolingDescriptor(pd)
pdsize(w, nd)=Cint[reverse(psize(w,nd))...]
psize(w, nd)=(isa(w,Integer) ? fill(w,nd) : length(w) != nd ? error("Dimension mismatch") : w)
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] | 1.938307 | 3,485 |
"""
Bus
A power-system bus.
# Constructor
```julia
Bus(number, name, bustype, angle, voltage, voltagelimits, basevoltage)
```
# Arguments
* `number`::Int64 : number associated with the bus
* `name`::String : the name of the bus
* `bustype`::String : type of bus, [PV, PQ, SF]; may be `nothing`
* `angle`::Float64 : angle of the bus in degrees; may be `nothing`
* `voltage`::Float64 : voltage as a multiple of basevoltage; may be `nothing`
* `voltagelimits`::NamedTuple(min::Float64, max::Float64) : limits on the voltage variation as multiples of basevoltage; may be `nothing`
* `basevoltage`::Float64 : the base voltage in kV; may be `nothing`
"""
struct Bus <: Injection
# field docstrings work here! (they are not for System)
""" number associated with the bus """
number::Int64
""" the name of the bus """
name::String
""" bus type, [PV, PQ, SF] """
bustype::Union{String,Nothing} # [PV, PQ, SF]
""" angle of the bus in degrees """
angle::Union{Float64,Nothing} # [degrees]
""" voltage as a multiple of basevoltage """
voltage::Union{Float64,Nothing} # [pu]
""" limits on the voltage variation as multiples of basevoltage """
voltagelimits::Union{NamedTuple{(:min, :max),Tuple{Float64,Float64}},
Nothing} # [pu]
"""
the base voltage in kV
"""
basevoltage::Union{Float64,Nothing} # [kV]
end
# DOCTODO add this constructor type to docstring for Bus
Bus(; number = 0,
name = "init",
bustype = nothing,
angle = 0.0,
voltage = 0.0,
voltagelimits = (min = 0.0, max = 0.0),
basevoltage = nothing
) = Bus(number, name, bustype, angle, voltage,
orderedlimits(voltagelimits, "Voltage"), basevoltage)
# DOCTODO What are LoadZones? JJS 1/18/19
struct LoadZones <: Injection
number::Int
name::String
buses::Array{Bus,1}
maxactivepower::Float64
maxreactivepower::Float64
end
LoadZones(; number = 0,
name = "init",
buses = [Bus()],
maxactivepower = 0.0,
maxreactivepower = 0.0
) = LoadZones(number, name, buses, maxactivepower, maxreactivepower)
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] | 2.509849 | 863 |
""" plotvaf(VAF, [subclonefreqs=[]]; <keyword arguments>)
"""
# plotvaf
@userplot PlotVAF
@recipe function f(pv::PlotVAF; cumulative = false, fstep = 0.01, sampled=true)
VAFresult = pv.args[1]
VAF = sampled ? VAFresult.sampledVAF : VAFresult.trueVAF
df = gethist(VAF, fstep = fstep)
VAF = (df[!,:VAF] .*2 .- fstep) ./ 2 #set x values to middle of bins
freq = cumulative ? df[:,:cumfreq] : df[:,:freq]
if get(plotattributes, :yscale, nothing) == :log10
VAF, freq = VAF[freq .> 0], freq[freq .>0]
end
yguide = cumulative ? "Cumulative number of mutations" : "Number of mutations"
@series begin
seriestype --> :bar
linecolor --> :white
linewidth --> 0.2
fillcolor --> :darkslategrey
markerstrokecolor --> :white
VAF, freq
end
if length(VAFresult.subclonefreq) > 0
subclonefreq = VAFresult.subclonefreq ./VAFresult.input.ploidy * VAFresult.cellularity
@series begin
seriestype --> :vline
legend --> false
fillcolor --> :darkred
linewidth --> 2
legend --> false
grid --> false
subclonefreq
end
end
yguide --> yguide
xguide --> "VAF"
legend --> false
grid --> false
()
end
""" plotinversevaf(VAF; <keyword arguments>)
"""
plotvaf
@userplot PlotInverseVAF
@recipe function f(pv::PlotInverseVAF; fmin = 0.12, fmax = 0.24, fit_fmax=fmax, sampled=true,
fstep = 0.001, fitcoef = nothing, cumulative = true, dataseries = :line)
VAFresult = pv.args[1]
VAF = sampled ? VAFresult.sampledVAF : VAFresult.trueVAF
df = gethist(VAF, fmin = fmin, fmax = fmax, fstep = fstep)
VAF = df[!,:VAF]
freq = cumulative ? df[!,:cumfreq] : df[!,:freq]
yguide = cumulative ? "Cumulative numberof mutations" : "Number of mutations"
@series begin
yguide --> yguide
xguide --> "Inverse VAF"
seriestype --> dataseries
1 ./ VAF, freq
end
if fitcoef !== nothing
if length(fitcoef) == 1
m = fitcoef[1]
c = - m/fit_fmax
else
c, m = fitcoef
end
x, y = 1 ./ VAF, m ./ VAF .+ c
x, y = x[y .>= 0], y[y .>= 0]
@series begin
yguide --> yguide
xguide --> "Inverse VAF"
seriestype --> :line
linecolor --> :red
linestyle --> :dash
x, y
end
end
yguide --> yguide
xguide --> "Inverse VAF"
fvals = [df[end, :VAF], 2/(1/df[1, :VAF]+ 1/df[end, :VAF]), df[1, :VAF]]
xticks --> (1 ./ fvals, map(x -> "1/" * x, string.(round.(fvals, digits = 2))))
legend --> false
grid --> false
()
end
@recipe function f(output::ModuleTracker)
@series begin
yguide --> "Population size"
xguide --> "Time"
seriestype --> :line
output.tvec, output.Nvec
end
end
@recipe function f(multisim::MultiSimulation; plottype=:popsize, tstep=nothing)
if plottype == :modulesize
yguide --> "Module size"
xguide --> "Time"
for moduletracker in multisim
@series begin
seriestype --> :line
legend --> false
moduletracker.tvec, moduletracker.Nvec
end
end
elseif plottype == :popsize
yguide --> "Number of modules"
xguide --> "Time"
@series begin
seriestype --> :line
legend --> false
newmoduletimes(multisim), 1:length(multisim)
end
elseif plottype == :cellpopsize
tstep = isnothing(tstep) ? 1 / multisim.input.bdrate : tstep
yguide --> "Number of cells"
xguide --> "Time"
@series begin
seriestype --> :line
legend --> false
cellpopulationsize(multisim, tstep)
end
end
end
@userplot PairwisePlot
@recipe function f(pp::PairwisePlot; sampleids=nothing)
if length(pp.args) == 1
z = pp.args[1]
n = isnothing(sampleids) ? size(z)[1] : length(sampleids)
x, y = 1:n, 1:n
elseif length(pp.args) == 3
x, y, z = pp.args
end
if sampleids !== nothing
z = z[sampleids, sampleids]
end
yguide --> "Module"
xguide --> "Module"
title --> "Pairwise fixed differences"
@series begin
grid --> false
seriestype := :heatmap
seriescolor --> cgrad(:bone, rev=true)
xticks --> x
yticks --> y
aspectratio --> 1
size --> (500,510)
x, y, z
end
end
@userplot ModuleSharedPlot
@recipe function f(msp::ModuleSharedPlot)
@series begin
grid --> false
seriestype := :bar
xguide --> "Number of modules"
yguide --> "Number fixed mutations"
legend --> false
msp.args[1]
end
end
@userplot PairwiseDistributionPlot
@recipe function f(pdp::PairwiseDistributionPlot; sampleids=nothing, samplesize=nothing,
showclonal=false, xticks=:auto)
if typeof(pdp.args[1]) <: Dict
pfddata = reduce(vcat, [fill(key, val) for (key,val) in pdp.args[1]])
else
pfdmatrix = pdp.args[1]
n = size(pfdmatrix)[1]
if sampleids === nothing && samplesize !== nothing
sampleids = sample(1:n, samplesize, replace=false)
end
if sampleids !== nothing
pfdmatrix = pfdmatrix[sampleids, sampleids]
n = length(sampleids)
end
pfddata = convert_pfdmatrix_to_vector(pfdmatrix)
end
@series begin
grid --> false
seriestype := :hist
xguide --> "Number of pairwise fixed differences"
yguide --> "Frequency"
color --> :dimgrey
linecolor --> :dimgrey
legend --> false
titlefontsize -->10
titlelocation --> :left
pfddata
end
if showclonal && length(pdp.args) > 1
@series begin
legend --> false
grid --> false
xguide --> "Number of pairwise fixed differences"
yguide --> "Frequency"
titlefontsize -->10
titlelocation --> :left
seriestype --> :vline
fillcolor --> :red
linewidth --> 2
[mean(pdp.args[2])]
end
end
end
function convert_pfdmatrix_to_vector(pfdmatrix)
n = size(pfdmatrix)[1]
vals = Int64[]
for i in 1:n
for j in i+1:n
push!(vals, pfdmatrix[j,i])
end
end
return vals
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] | 2.062598 | 3,179 |
# Juliaを使った株価の取得と通知
using DataFrames, Dates, CSV
include("GetStockPrice.jl")
uniqueindex(x) = findfirst.(isequal.(unique(x)), [x]) # 重複しない行のindexを計算
# 株価データの初期化．新たに銘柄追加するときだけこれを実行する
function initdailystockprice(stock)
range = "7d" # 取得データの期間
interval = "1m" # 取得データの間隔
df = getstockprice(stock, range, interval)
# CSV書き込み前に時刻のフォーマットを変更しておく
dtfmt = "yyyy/mm/dd HH:MM:SS"
df."timestamp" = Dates.format.(df."timestamp", dtfmt)
# CSVへの書き込み
println("write stock price: " * stock * ".csv")
CSV.write("./data/" * stock * ".csv", df)
end
# 1日分の1分足データを取得し，CSVにマージする
function savedailystockprice(stock)
range = "1d" # 取得データの期間
interval = "1m" # 取得データの間隔
today = getstockprice(stock, range, interval)
dtfmt = "yyyy/mm/dd HH:MM:SS"
# CSV読み込み
past = DataFrame(CSV.File("./data/" * stock * ".csv", header = true))
past."timestamp" = DateTime.(past."timestamp", dtfmt)
# データを結合
all = vcat(past, today)
# 重複データを削除
all = all[uniqueindex(all.timestamp), :]
# 日付順でソート
all = sort(all)
# CSV書き込み前に時刻のフォーマットを変更しておく
all."timestamp" = Dates.format.(all."timestamp", dtfmt)
# CSVへの書き込み
println("write stock price: " * stock * ".csv")
CSV.write("./data/" * stock * ".csv", all)
end
function main()
# 取得対象の銘柄の指定．東証の株価は".T"をつける．
stocks = ["1557.T" "SPY" "SPXL" "SPXS" "TECL" "CURE" "SOXL" "DRN" "TNA" "EDC"]
try
savedailystockprice.(stocks)
catch err
println(err)
end
end
main()
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] | 1.659737 | 914 |
function value(obj::UnivariateFunction, arg0::jdouble)
return jcall(obj, "value", jdouble, (jdouble,), arg0)
end
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] | 2.809524 | 42 |
#=
---------------------------------------------------------------------------
Implements a few collections that the main mcmc sampler from the file
`mcmc.jl` uses to keep track of the current state and history of where it
was. The following structures are defined:
- AccptTracker : for tracking historical acceptance rate
- ParamHistory : for storing the parameter chain
- ActionTracker : keeps track of what to do on a given iteration
- Workspace : main workspace for `mcmc` function from `mcmc.jl`
- ParamUpdtDefn : defines a single parameter update step
- GibbsDefn : defines an entire gibbs sweep of parameter updates
---------------------------------------------------------------------------
=#
import Base: last, getindex, length, display, eltype
"""
AccptTracker
Tracks historical acceptance rate of the chain
"""
mutable struct AccptTracker
accpt_imp::Int64 # a number of accepted imputation samples
prop_imp::Int64 # a number of proposed imputation samples
accpt_updt::Vector{Int64} # a number of accepted parameter updates (one per parameter)
prop_updt::Vector{Int64} # a number of proposed parameter updates (one per parameter)
updt_len::Int64 # total number of parameter update steps
"""
AccptTracker(setup::MCMCSetup)
Initialise tracker of acceptance rate according to a `setup` of the mcmc
sampler
"""
function AccptTracker(setup::MCMCSetup)
updt_len = length(setup.updt_coord)
accpt_imp = 0
prop_imp = 0
accpt_updt = [0 for i in 1:updt_len]
prop_updt = [0 for i in 1:updt_len]
new(accpt_imp, prop_imp, accpt_updt, prop_updt, updt_len)
end
end
"""
update!(at::AccptTracker, ::ParamUpdate, i, accepted::Bool)
Update acceptance tracker: increment parameter update info on the `i`th
coordinate with 1 proposed and---if `accepted` is true---then also 1 accepted
sample
"""
function update!(at::AccptTracker, ::ParamUpdate, i, accepted::Bool)
at.prop_updt[i] += 1
at.accpt_updt[i] += 1*accepted
end
"""
update!(at::AccptTracker, ::Imputation, accepted::Bool)
Update acceptance tracker: increment imputation info with 1 proposed and---if
`accepted` is true---then also 1 accepted sample
"""
function update!(at::AccptTracker, ::Imputation, accepted::Bool)
at.prop_imp += 1
at.accpt_imp += 1*accepted
end
"""
accpt_rate(at::AccptTracker, ::ParamUpdate)
Return current acceptance rate for parameter updates
"""
accpt_rate(at::AccptTracker, ::ParamUpdate) = at.accpt_updt./at.prop_updt
"""
accpt_rate(at::AccptTracker, ::Imputation)
Return current acceptance rate for imputation
"""
accpt_rate(at::AccptTracker, ::Imputation) = at.accpt_imp/at.prop_imp
"""
display(at::AccptTracker)
Show the acceptance rates
"""
function display(at::AccptTracker)
print("Imputation acceptance rate: ", accpt_rate(at, Imputation()),
".\nParameter update acceptance rate: ",
accpt_rate(at, ParamUpdate()), ".\n")
end
"""
ParamHistory{T}
Stores information about parameter history
"""
mutable struct ParamHistory{T}
θ_chain::Vector{T} # parameter history
counter::Int64 # index of the most recently accepted parameter vector
"""
ParamHistory(setup::MCMCSetup)
Initialise tracker of parameter history according to a `setup` of the mcmc
sampler
"""
function ParamHistory(setup::MCMCSetup)
N, n = setup.num_mcmc_steps, setup.warm_up
updt_len = length(setup.updt_coord)
θ = params(setup.P˟)
T = typeof(θ)
θ_chain = Vector{T}(undef, (N-n)*updt_len+1)
θ_chain[1] = copy(θ)
new{T}(θ_chain, 1)
end
end
"""
update!(ph::ParamHistory, θ)
Update parameter history with a new accepted sample θ
"""
function update!(ph::ParamHistory, θ)
ph.counter += 1
ph.θ_chain[ph.counter] = copy(θ)
end
"""
last(ph::ParamHistory)
Return a copy of the most recently accepted paramter vector
"""
last(ph::ParamHistory) = copy(ph.θ_chain[ph.counter])
"""
ActionTracker
Keeps track of what to do on a given iteration
"""
struct ActionTracker{T,S,R,U}
save_iter::T # Save the path every ... iteration
verb_iter::S # Print progress message to console every ... iteration
warm_up::R # Number of steps of the chain in which no param update is made
readjust::U
param_updt::Bool # Flag for whether to update parameters at all
"""
ActionTracker(setup::MCMCSetup)
Initialise tracker of what to do on a given iteration according to a `setup`
of the mcmc sampler
"""
function ActionTracker(setup::MCMCSetup)
i1, i2, i3 = setup.save_iter, setup.verb_iter, setup.warm_up
i4 = setup.pCN_readjust_param.step
s1, s2, s3, s4 = typeof(i1), typeof(i2), typeof(i3), typeof(i4)
@assert (s1 <: Number) && (s2 <: Number) && (s3 <: Number) && (s4 <: Number)
new{s1,s2,s3,s4}(i1, i2, i3, i4, setup.param_updt)
end
end
"""
act(::SavePath, at::ActionTracker, i)
Determine whether to save path on a given iteration, indexed `i`
"""
function act(::SavePath, at::ActionTracker, i)
(i > at.warm_up) && (i % at.save_iter == 0)
end
"""
act(::Verbose, at::ActionTracker, i)
Determine whether to print out information to a console on a given iteration,
indexed `i`
"""
act(::Verbose, at::ActionTracker, i) = (i % at.verb_iter == 0)
"""
act(::ParamUpdate, at::ActionTracker, i)
Determine whether to update parameters on a given iteration, indexed `i`
"""
act(::ParamUpdate, at::ActionTracker, i) = at.param_updt && (i > at.warm_up)
act(::Readjust, at::ActionTracker, i) = (i % at.readjust == 0) && (i > at.warm_up)
mutable struct SingleElem{T} val::T end
set!(x::SingleElem{T}, y::T) where T = (x.val = y)
const RhoInfoType = NamedTuple{(:step, :scale, :minδ, :maxρ, :trgt, :offset),
Tuple{Int64, Float64, Float64, Float64, Float64, Int64}}
"""
Workspace{ObsScheme,S,TX,TW,R,ST}
The main container of the `mcmc` function from `mcmc.jl` in which most data
pertinent to sampling is stored
"""
struct Workspace{ObsScheme,B,ST,S,TX,TW,R,TP,TZ}# ,Q, where Q = eltype(result)
Wnr::Wiener{S} # Wiener, driving law
XXᵒ::Vector{TX} # Diffusion proposal paths
XX::Vector{TX} # Accepted diffusion paths
WWᵒ::Vector{TW} # Driving noise of proposal
WW::Vector{TW} # Driving noise of the accepted paths
Pᵒ::Vector{R} # Guided proposals parameterised by proposal param
P::Vector{R} # Guided proposals parameterised by accepted param
fpt::Vector # Additional information about first passage times
ρ::Vector{Vector{Float64}} # Memory parameter of the precond Crank-Nicolson scheme
recompute_ODEs::Vector{Bool} # Info on whether to recompute H,Hν,c after resp. param updt
accpt_tracker::AccptTracker # Object for tracking acceptance rate
θ_chain::ParamHistory # Object for tracking parameter history
action_tracker::ActionTracker # Object for tracking steps to perform on a given iteration
skip_for_save::Int64 # Thining parameter for saving path
paths::Vector # Storage with historical, accepted paths
time::Vector{Float64} # Storage with time axis
blocking::B
blidx::Int64
x0_prior::TP
z::SingleElem{TZ}
pCN_readjust_param::RhoInfoType
#result::Vector{Q} #TODO come back to later
#resultᵒ::Vector{Q} #TODO come back to later
"""
Workspace(setup::MCMCSetup{ObsScheme})
Initialise workspace of the mcmc sampler according to a `setup` variable
"""
function Workspace(setup::MCMCSetup{ObsScheme}) where ObsScheme
# just to make sure that nothing gets messed up if the user decides
# to later modify `setup` use deepcopies
x0_prior, Wnr = deepcopy(setup.x0_prior), deepcopy(setup.Wnr)
XX, WW = deepcopy(setup.XX), deepcopy(setup.WW)
P, fpt = deepcopy(setup.P), deepcopy(setup.fpt)
updt_coord = deepcopy(setup.updt_coord)
pCN_readjust = deepcopy(setup.pCN_readjust_param)
# forcedSolve defines type by the starting point, make sure it matches
x0_guess = eltype(eltype(XX))(setup.x0_guess)
TW, TX, S, R = eltype(WW), eltype(XX), valtype(Wnr), eltype(P)
ST, TP = typeof(setup.solver), typeof(x0_prior)
m = length(P)
y = copy(x0_guess)
for i in 1:m
WW[i] = Bridge.samplepath(P[i].tt, zero(S))
sample!(WW[i], Wnr)
WW[i], XX[i] = forcedSolve(Euler(), y, WW[i], P[i]) # this will enforce adherence to domain
while !checkFpt(ObsScheme(), XX[i], fpt[i])
sample!(WW[i], Wnr)
forcedSolve!(Euler(), XX[i], y, WW[i], P[i]) # this will enforce adherence to domain
end
y = XX[i].yy[end]
end
y = x0_guess
ll = logpdf(x0_prior, y)
ll += path_log_likhd(ObsScheme(), XX, P, 1:m, fpt, skipFPT=true)
ll += lobslikelihood(P[1], y)
XXᵒ = deepcopy(XX)
WWᵒ = deepcopy(WW)
Pᵒ = deepcopy(P)
# compute the white noise that generates x0_guess under the initial posterior
z = inv_start_pt(y, x0_prior, P[1])
TZ = typeof(z)
z = SingleElem{TZ}(z)
#TODO come back to gradient initialisation
skip = setup.skip_for_save
_time = collect(Iterators.flatten(p.tt[1:skip:end-1] for p in P))
θ_history = ParamHistory(setup)
blocking = set_blocking(setup.blocking, setup.blocking_params, P)
ρ = prepare_mem_param(setup.ρ, blocking)
display(blocking)
B = typeof(blocking)
(workspace = new{ObsScheme,B,ST,S,TX,TW,R,TP,TZ}(Wnr, XXᵒ, XX, WWᵒ, WW,
Pᵒ, P, fpt, ρ,
check_if_recompute_ODEs(setup),
AccptTracker(setup),
θ_history,
ActionTracker(setup),
skip, [], _time,
blocking, 1, x0_prior,
z, pCN_readjust),
ll = ll, θ = last(θ_history))
end
# NOTE this constructor is no longer in use, can be removed
"""
Workspace(ws::Workspace{ObsScheme,S,TX,TW,R}, new_ρ::Float64)
Copy constructor of `workspace`. Keeps everything the same as passed `ws`
with the exception of new memory parameter for the preconditioned
Crank-Nicolson scheme, which is changed to `new_ρ`.
"""
function Workspace(ws::Workspace{ObsScheme,B,ST,S,TX,TW,R,TP,TZ}, new_ρ::Vector{Vector{Float64}}
) where {ObsScheme,B,ST,S,TX,TW,R,TP,TZ}
new{ObsScheme,B,ST,S,TX,TW,R,TP,TZ}(ws.Wnr, ws.XXᵒ, ws.XX, ws.WWᵒ,
ws.WW, ws.Pᵒ, ws.P, ws.fpt, new_ρ,
ws.recompute_ODEs, ws.accpt_tracker,
ws.θ_chain, ws.action_tracker,
ws.skip_for_save, ws.paths, ws.time,
ws.blocking, ws.blidx, ws.x0_prior,
ws.z, ws.pCN_readjust_param)
end
function Workspace(ws::Workspace{ObsScheme,B,ST,S,TX,TW,R̃,TP,TZ},
P::Vector{R}, Pᵒ::Vector{R}, idx
) where {ObsScheme,B,ST,S,TX,TW,R̃,R,TP,TZ}
new{ObsScheme,B,ST,S,TX,TW,R,TP,TZ}(ws.Wnr, ws.XXᵒ, ws.XX, ws.WWᵒ,
ws.WW, Pᵒ, P, ws.fpt, ws.ρ,
ws.recompute_ODEs, ws.accpt_tracker,
ws.θ_chain, ws.action_tracker,
ws.skip_for_save, ws.paths, ws.time,
ws.blocking, idx, ws.x0_prior, ws.z,
ws.pCN_readjust_param)
end
end
eltype(::SamplePath{T}) where T = T
eltype(::Type{SamplePath{T}}) where T = T
solver_type(::Workspace{O,B,ST}) where {O,B,ST} = ST
next_set_of_blocks(ws::Workspace{O,NoBlocking}) where O = ws
"""
next(𝔅::ChequeredBlocking, XX, θ)
Switch the set of blocks that are being updated. `XX` is the most recently
sampled (accepted) path. `θ` can be used to change parametrisation.
"""
function next_set_of_blocks(ws::Workspace{O,<:ChequeredBlocking}) where O
XX, P, Pᵒ, 𝔅 = ws.XX, ws.P, ws.Pᵒ, ws.blocking
idx = (ws.blidx % 2) + 1
θ = params(P[1].Target)
vs = find_end_pts(𝔅, XX, idx)
Ls = 𝔅.Ls[idx]
Σs = 𝔅.Σs[idx]
ch_pts = 𝔅.change_pts[idx]
aux_flags = 𝔅.aux_flags[idx]
P_new = [GuidPropBridge(P[i], Ls[i], vs[i], Σs[i], ch_pts[i], θ, aux_flags[i])
for (i,_) in enumerate(P)]
Pᵒ_new = [GuidPropBridge(Pᵒ[i], Ls[i], vs[i], Σs[i], ch_pts[i], θ, aux_flags[i])
for (i,_) in enumerate(Pᵒ)]
Workspace(ws, P_new, Pᵒ_new, idx)
end
prepare_mem_param(ρ::Number, ::NoBlocking) = [[ρ]]
function prepare_mem_param(ρ::Number, blocking::ChequeredBlocking)
[[ρ for _ in block_seq] for block_seq in blocking.accpt_tracker.accpt]
end
"""
act(action, ws::Workspace, i)
Determine whether to perform `action` on a given iteration, indexed `i`
"""
act(action, ws::Workspace, i) = act(action, ws.action_tracker, i)
#NOTE temporary
act(action::Readjust, ws::Workspace, i) = (typeof(ws.blocking) <:ChequeredBlocking) && act(action, ws.action_tracker, i)
"""
savePath!(ws, wsXX, bXX)
Save the entire path spanning all segments in `XX`. Only 1 in every `ws.skip`
points is saved to reduce storage space. To-be-saved `XX` is set to `wsXX` or
`bXX` depending on whether blocking is used.
"""
function save_path!(ws)
skip = ws.skip_for_save
push!(ws.paths, collect(Iterators.flatten(ws.XX[i].yy[1:skip:end-1]
for i in 1:length(ws.XX))))
end
sigmoid(x, a=1.0) = 1.0 / (1.0 + exp(-a*x))
logit(x, a=1.0) = (log(x) - log(1-x))/a
function readjust_pCN!(ws, mcmc_iter)
at = ws.blocking.short_term_accpt_tracker
p = ws.pCN_readjust_param
δ = max(p.minδ, p.scale/sqrt(max(1.0, mcmc_iter/p.step-p.offset)))
accpt_rates = acceptance(at)
for (i, a_i) in enumerate(accpt_rates)
for (j, a_ij) in enumerate(a_i)
ws.ρ[i][j] = min(sigmoid(logit(ws.ρ[i][j]) - (2*(a_ij > p.trgt)-1)*δ), p.maxρ)
end
end
display_acceptance_rate(ws.blocking, true)
print_pCN(ws)
reset!(at)
end
#NOTE _print_info defined in `blocking_schedule`
function print_pCN(ws)
print("\nρ parameter:\n----------------------\n")
_print_info(ws.ρ[1])
_print_info(ws.ρ[2])
end
"""
ParamUpdtDefn
For a given, single parameter update step defines transition kernels, priors,
which coordinates are updated etc.
"""
struct ParamUpdtDefn{R,S,T,U}
updt_type::R # The type of update (Metropolis-Hastings/conjugate etc)
updt_coord::S # Which coordinates to update
t_kernel::T # Transition kernel for a given parameter update
priors::U # Prior over updated parameters
recompute_ODEs::Bool # Whether given param updt calls for recomputing H,Hν,c
"""
ParamUpdtDefn(updt_type::R, updt_coord::S, t_kernel::T, priors::U,
recompute_ODEs::Bool)
Initialisation of the complete definition of the parameter update step
"""
function ParamUpdtDefn(updt_type::R, updt_coord::S, t_kernel::T, priors::U,
recompute_ODEs::Bool
) where {R<:ParamUpdateType,S,T,U}
new{R,S,T,U}(updt_type, updt_coord, t_kernel, priors, recompute_ODEs)
end
end
"""
GibbsDefn
Definition of the entire Gibbs sweep.
"""
struct GibbsDefn{N}
updates::NTuple{N,ParamUpdtDefn}
"""
GibbsDefn(setup)
Initialises Gibbs sweep according to the `setup`
"""
function GibbsDefn(setup)
recompute_ODEs = check_if_recompute_ODEs(setup)
updates = [ParamUpdtDefn(ut, uc, tk, pr, ro) for (ut, uc, tk, pr, ro)
in zip(setup.updt_type, setup.updt_coord, setup.t_kernel,
setup.priors, recompute_ODEs)]
new{length(updates)}(Tuple(updates))
end
end
"""
getindex(g::GibbsDefn, i::Int)
Return `i`th definition of parameter update
"""
getindex(g::GibbsDefn, i::Int) = g.updates[i]
"""
length(g::GibbsDefn{N})
Return the total number of parameter updates in a single Gibbs sweep
"""
length(g::GibbsDefn{N}) where N = N
"""
init_adaptation!(adpt::Adaptation{Val{false}}, ws::Workspace)
Nothing to do when no adaptation needs to be done
"""
init_adaptation!(adpt::Adaptation{Val{false}}, ws::Workspace) = nothing
"""
init_adaptation!(adpt::Adaptation{Val{true}}, ws::Workspace)
Resize internal container with paths in `adpt` to match the length of imputed
paths
"""
function init_adaptation!(adpt::Adaptation{Val{true}}, ws::Workspace)
m = length(ws.XX)
resize!(adpt, m, [length(ws.XX[i]) for i in 1:m])
end
"""
update!(adpt::Adaptation{Val{false}}, ws::Workspace{ObsScheme}, yPr, i, ll,
solver::ODESolverType)
Nothing to be done for no adaptation
"""
function update!(adpt::Adaptation{Val{false}}, ws::Workspace{ObsScheme}, i,
ll) where ObsScheme
adpt, ll
end
"""
update!(adpt::Adaptation{Val{false}}, ws::Workspace{ObsScheme}, yPr, i, ll,
solver::ODESolverType)
Update the proposal law according to the adaptive scheme and the recently saved
history of the imputed paths
"""
function update!(adpt::Adaptation{Val{true}}, ws::Workspace{ObsScheme,B,ST},
i, ll) where {ObsScheme,B,ST}
if i % adpt.skip == 0
if adpt.N[2] == adpt.sizes[adpt.N[1]]
X_bar = mean_trajectory(adpt)
m = length(ws.P)
for j in 1:m
Pt = recentre(ws.P[j].Pt, ws.XX[j].tt, X_bar[j])
update_λ!(Pt, adpt.λs[adpt.N[1]])
ws.P[j] = GuidPropBridge(ws.P[j], Pt)
Ptᵒ = recentre(ws.Pᵒ[j].Pt, ws.XX[j].tt, X_bar[j])
update_λ!(Ptᵒ, adpt.λs[adpt.N[1]])
ws.Pᵒ[j] = GuidPropBridge(ws.Pᵒ[j], Ptᵒ)
end
solve_back_rec!(NoBlocking(), ws, ws.P)
#solveBackRec!(NoBlocking(), ws.Pᵒ, ST())
y = ws.XX[1].yy[1]
z = inv_start_pt(y, ws.x0_prior, ws.P[1])
set!(ws.z, z)
for j in 1:m
inv_solve!(Euler(), ws.XX[j], ws.WW[j], ws.P[j])
end
ll = logpdf(ws.x0_prior, y)
ll += path_log_likhd(ObsScheme(), ws.XX, ws.P, 1:m, ws.fpt)
ll += lobslikelihood(ws.P[1], y)
adpt.N[2] = 1
adpt.N[1] += 1
else
adpt.N[2] += 1
end
end
adpt, ll
end
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] | 2.092121 | 9,227 |
export TestCase, runtestcase
"""
TestCase(args[, kwargs[, tolerance]])
TestCase(args...; kwargs...[, _tolerance])
Construct a test case.
The `args` and `kwargs` are passed to the function that will be tested.
The `tolerance` is the allowable difference between the result of the testcase
and the desired result (_e.g._ the result of the instructor function).
Note that the `tolerance` is only used when the result of the test case is
something that has a natural real valued difference between two instances; for
example the difference between two real vectors is the infinity (max) norm.
See the `compare` function for more details.
# Examples
```julia-repl
julia> TestCase([1, 2, 3])
TestCase(Any[1, 2, 3], Dict{Symbol,Any}(), 1.0e-5)
julia> TestCase([1, 2, 3], Dict(:foo => "bar"))
TestCase(Any[1, 2, 3], Dict{Symbol,Any}(:foo=>"bar"), 1.0e-5)
julia> TestCase(1, 2, 3; foo="bar", _tolerance=1e-9)
TestCase(Any[1, 2, 3], Dict{Symbol,Any}(:foo=>"bar"), 1.0e-9)
```
"""
struct TestCase
args::Array{Any}
kwargs::Dict{Symbol, Any}
# Maximum numeric error tolerance.
# This is calculated according to calculate_error.
tolerance::Float64
# This needs to be an inner constructor to account for when the varargs
# constructor is used with three arguments.
TestCase(args::Array, kwargs::Dict=Dict(), tolerance::Float64=DEFAULT_TOLERANCE) = new(
convert(Array{Any}, args),
convert(Dict{Symbol, Any}, kwargs),
tolerance,
)
end
TestCase(args...; tolerance=DEFAULT_TOLERANCE, kwargs...) = TestCase(
Array{Any}([args...]),
convert(Dict{Symbol, Any}, kwargs),
tolerance,
)
struct TestCaseResult
result::Union{Any, Nothing}
# Note: exception is a string to avoid breaking (de)serialize if the
# exeption happens to reference context-specific data (eg. something in the
# _Submission module which wouldn't be present in the parent process)
exception::Union{String, Nothing}
backtrace::Union{String, Nothing}
time::Float64
stdout::String
stderr::String
end
"""
runtestcase(f::Function, testcase::TestCase)::TestCaseResult
Run the given test case on the specified function.
"""
function runtestcase(f::Function, testcase::TestCase)::TestCaseResult
@debug("Running testcase for function $(repr(f)).")
result, exception, backtrace = nothing, nothing, nothing
start_ns = Base.time_ns()
sub_stdout, sub_stderr = @capture_stdstreams begin
try
result = Base.invokelatest(f, testcase.args...; testcase.kwargs...)
catch e
exception = repr(e)
backtrace = sprint(showerror, e)
end
end
end_ns = Base.time_ns()
# Get the elapsed time in seconds (1e-9 converts from nanoseconds)
elapsedtime = (end_ns - start_ns) * 1e-9
@debug "Finished running testcase." result exception elapsedtime
return TestCaseResult(
result, exception, backtrace,
elapsedtime, sub_stdout, sub_stderr,
)
end
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198,
437,
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] | 2.748629 | 1,094 |
"""
MatAcoustFluidModule
Module for acoustic-fluid material.
"""
module MatAcoustFluidModule
using FinEtools.FTypesModule: FInt, FFlt, FCplxFlt, FFltVec, FIntVec, FFltMat, FIntMat, FMat, FVec, FDataDict
import FinEtools.MatModule: AbstractMat
"""
MatAcoustFluid <: AbstractMat
Type for acoustic fluid material.
"""
struct MatAcoustFluid <: AbstractMat
bulk_modulus::FFlt;# Bulk modulus
mass_density::FFlt;# Mass density
end
"""
bulkmodulus(self::MatAcoustFluid)
Return the bulk modulus.
"""
function bulkmodulus(self::MatAcoustFluid)
return self.bulk_modulus
end
end
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] | 2.610619 | 226 |
using ADCME
using PyCall
using LinearAlgebra
using PyPlot
using Random
using Test
# Random.seed!(233)
import ADCME: SparseAssembler
sparse_accumulator = load_op_and_grad("./build/libSparseAccumulator","sparse_accumulator", multiple=false)
sparse_accumulator_add = load_op_and_grad("./build/libSparseAccumulator","sparse_accumulator_add", multiple=false)
sparse_accumulator_copy = load_op_and_grad("./build/libSparseAccumulator","sparse_accumulator_copy", multiple=true)
"""
SparseAssembler(handle::Union{PyObject, <:Integer}, n::Union{PyObject, <:Integer}, tol::Union{PyObject, <:Real}=0.0)
Creates a SparseAssembler for accumulating `row`, `col`, `val` for sparse matrices.
- `handle`: an integer handle for creating a sparse matrix. If the handle already exists, `SparseAssembler` return the existing sparse matrix handle. If you are creating different sparse matrices, the handles should be different.
- `n`: Number of rows of the sparse matrix.
- `tol` (optional): Tolerance. `SparseAssembler` will treats any values less than `tol` as zero.
# Example
```julia
handle = SparseAssembler(100, 5, 1e-8)
op1 = accumulate(handle, 1, [1;2;3], [1.0;2.0;3.0])
op2 = accumulate(handle, 2, [1;2;3], [1.0;2.0;3.0])
J = assemble(5, 5, [op1;op2])
```
`J` will be a [`SparseTensor`](@ref) object.
"""
function SparseAssembler(handle::Union{PyObject, <:Integer}, n::Union{PyObject, <:Integer}, tol::Union{PyObject, <:Real}=0.0)
n = convert_to_tensor(n, dtype=Int32)
tol = convert_to_tensor(tol, dtype=Float64)
handle = convert_to_tensor(handle, dtype=Int32)
sparse_accumulator(tol, n, handle)
end
"""
accumulate(handle::PyObject, row::Union{PyObject, <:Integer}, cols::Union{PyObject, Array{<:Integer}}, vals::Union{PyObject, Array{<:Real}})
Accumulates `row`-th row. It adds the value to the sparse matrix
```julia
for k = 1:length(cols)
A[row, cols[k]] += vals[k]
end
```
`handle` is the handle created by [`SparseAssembler`](@ref).
See [`SparseAssembler`](@ref) for an example.
!!! Note
`accumulate` returns a `op::PyObject`. Only when `op` is executed, the nonzero values are populated into the sparse matrix.
"""
function accumulate(handle::PyObject, row::Union{PyObject, <:Integer}, cols::Union{PyObject, Array{<:Integer}},
vals::Union{PyObject, Array{<:Real}})
row = convert_to_tensor(row, dtype=Int32)
cols = convert_to_tensor(cols, dtype=Int32)
vals = convert_to_tensor(vals, dtype=Float64)
return sparse_accumulator_add(acc, row, cols, vals)
end
"""
assemble(m::Union{PyObject, <:Integer}, n::Union{PyObject, <:Integer}, ops::PyObject)
Assembles the sparse matrix from the `ops` created by [`accumulate`](@ref). `ops` is either a single output from `accumulate`, or concated from several `ops`
```julia
op1 = accumulate(handle, 1, [1;2;3], [1.0;2.0;3.0])
op2 = accumulate(handle, 2, [1;2;3], [1.0;2.0;3.0])
op = [op1;op2] # equivalent to `vcat([op1, op2]...)`
```
`m` and `n` are rows and columns of the sparse matrix.
See [`SparseAssembler`](@ref) for an example.
"""
function assemble(m::Union{PyObject, <:Integer}, n::Union{PyObject, <:Integer}, ops::PyObject)
if length(size(ops))==0
ops = reshape(ops, 1)
end
ii, jj, vv = sparse_accumulator_copy(ops)
return SparseTensor(ii, jj, vv, m, n)
end
handle = SparseAssembler(100, 5, 1e-8)
op1 = accumulate(handle, 1, [1;2;3], [1.0;2.0;3.0])
op2 = accumulate(handle, 2, [1;2;3], [1.0;2.0;3.0])
J = assemble(5, 5, [op1;op2])
# J = assemble(acc, 5, 5)
sess = Session(); init(sess)
run(sess, J)
m = 20
n = 100
handle = SparseAssembler(100, m, 0.0)
op = PyObject[]
A = zeros(m, n)
for i = 1:1
ncol = rand(1:n, 10)
row = rand(1:m)
v = rand(10)
for (k,val) in enumerate(v)
@show k
A[row, ncol[k]] += val
end
@show v
push!(op, accumulate(handle, row, ncol, v))
end
op = vcat(op...)
J = assemble(m, n, op)
B = run(sess, J)
@test norm(A-B)<1e-8
handle = SparseAssembler(100, 5, 1.0)
op1 = accumulate(handle, 1, [1;2;3], [2.0;0.5;0.5])
J = assemble(5, 5, op1)
B = run(sess, J)
@test norm(B-[2.0 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 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0])<1e-8
handle = SparseAssembler(100, 5, 0.0)
op1 = accumulate(handle, 1, [1;1], [1.0;1.0])
op2 = accumulate(handle, 1, [1;2], [1.0;1.0])
J = assemble(5, 5, [op1;op2])
B = run(sess, J)
@test norm(B-[3.0 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 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0])<1e-8
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] | 2.251603 | 2,027 |
import StateSpaceReconstruction:
cembed,
assign_bin_labels
import PerronFrobenius:
get_binvisits,
estimate_transferoperator_from_binvisits,
invariantmeasure
# Estimate transfer entropy from scratch from a random
# set of points `n_realizations` times.
ts_length = 100
# Estimate transfer entropy using the wrapper function,
# and manually (doing the steps performed inside the
# wrapper function) to make sure that they give the same
# result.
estimates_3D_wrapper = Vector{Float64}(undef, n_realizations)
estimates_3D_allsteps = Vector{Float64}(undef, n_realizations)
@testset "3D #$i" for i in 1:n_realizations
E = cembed([diff(rand(ts_length)) for i = 1:3])
ϵ = 3
# Test by doing all the dirty work and providing the raw input to the estimator
bins_visited_by_orbit = assign_bin_labels(E, ϵ)
bininfo = get_binvisits(bins_visited_by_orbit)
TO = estimate_transferoperator_from_binvisits(bininfo)
iv = invariantmeasure(TO)
v = TEVars([1], [2], [3], Int[])
estimates_3D_wrapper[i] = tetogrid(E, ϵ, v)
estimates_3D_allsteps[i] = tetogrid(bins_visited_by_orbit, iv, v)
@test estimates_3D_wrapper[i] >= 0
@test estimates_3D_allsteps[i] >= 0
end
ts_length = 200
estimates_4D_wrapper = Vector{Float64}(undef, n_realizations)
estimates_4D_allsteps = Vector{Float64}(undef, n_realizations)
@testset "4D #$i" for i in 1:n_realizations
E = cembed([diff(rand(ts_length)) for i = 1:4])
ϵ = 0.3
bins_visited_by_orbit = assign_bin_labels(E, ϵ)
bininfo = get_binvisits(bins_visited_by_orbit)
TO = estimate_transferoperator_from_binvisits(bininfo)
iv = invariantmeasure(TO)
v = TEVars([1], [2], [3, 4], Int[])
estimates_4D_wrapper[i] = tetogrid(E, ϵ, v)
estimates_4D_allsteps[i] = tetogrid(bins_visited_by_orbit, iv, v)
@test estimates_4D_wrapper[i] >= 0
@test estimates_4D_allsteps[i] >= 0
end
ts_length = 300
estimates_5D_wrapper = Vector{Float64}(undef, n_realizations)
estimates_5D_allsteps = Vector{Float64}(undef, n_realizations)
@testset "5D #$i" for i in 1:n_realizations
E = cembed([diff(rand(ts_length)) for i = 1:5])
ϵ = [0.2, 0.2, 0.1, 0.2, 0.3]
bins_visited_by_orbit = assign_bin_labels(E, ϵ)
bininfo = get_binvisits(bins_visited_by_orbit)
TO = estimate_transferoperator_from_binvisits(bininfo)
iv = invariantmeasure(TO)
v = TEVars([1], [2], [3, 4], [5])
estimates_5D_wrapper[i] = tetogrid(E, ϵ, v)
estimates_5D_allsteps[i] = tetogrid(bins_visited_by_orbit, iv, v)
@test estimates_5D_wrapper[i] >= 0
@test estimates_5D_allsteps[i] >= 0
end
# # If everything works as expected, there should be no negative
# # transfer entropy values.
#@show estimates_3D_wrapper
#@show estimates_4D_wrapper
#@show estimates_5D_wrapper
#show estimates_3D_allsteps
#@show estimates_4D_allsteps
#@show estimates_5D_allsteps
#@test all(estimates_3D_wrapper .>= 0)
#@test all(estimates_4D_wrapper .>= 0)
#@test all(estimates_5D_wrapper .>= 0)
#@test all(estimates_3D_allsteps .>= 0)
#@test all(estimates_4D_allsteps .>= 0)
#@test all(estimates_5D_allsteps .>= 0)
# The invariant distribution is estimated independently in the calls
# to the different transfer entropy estimators. The distribution is
# estimated by repeated application of the transfer operator on a
# randomly initialised distribution until convergence is achieved.
# Because a different initial distribution is used for each function
# call, the estimated transfer entropy will be slightly different.
# However, we do not expect the differences to be very large.
#@test all(abs.(estimates_3D_wrapper .- estimates_3D_allsteps) .< 1e-2)
#@test all(abs.(estimates_4D_wrapper .- estimates_4D_allsteps) .< 1e-2)
#@test all(abs.(estimates_5D_wrapper .- estimates_5D_allsteps) .< 1e-2)
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] | 2.600424 | 1,414 |
# OPTIMIZATION OPPORTUNITY
# should save u and du over the x_xgrid, it's an ODE option
# ℓᵧ is the Boltzmann hierarchy cutoff
function source_grid(par::AbstractCosmoParams{T}, bg, ih, k_grid,
integrator::PerturbationIntegrator; ℓᵧ=8, reltol=1e-11) where T
x_grid = bg.x_grid
grid = zeros(T, length(x_grid), length(k_grid))
@qthreads for (i_k, k) in enumerate(k_grid)
hierarchy = Hierarchy(BasicNewtonian(), par, bg, ih, k, ℓᵧ)
perturb = boltsolve(hierarchy; reltol=reltol)
for (i_x, x) in enumerate(x_grid)
u = perturb(x) # this can be optimized away, save timesteps at the grid!
du = similar(u)
Bolt.hierarchy!(du, u, hierarchy, x)
grid[i_x,i_k] = Bolt.source_function(du, u, hierarchy, x)
end
end
# return grid
itp = LinearInterpolation((x_grid, k_grid), grid, extrapolation_bc = Line())
return itp
end
# we make the assumption that shifting the coordinates upon which we integrate
# does not affect our result. that is, we choose coordinates where the integral converges
assume_nondual(x::ForwardDiff.Dual) = ForwardDiff.value(x)
assume_nondual(x::Real) = x
function bessel_interpolator(ℓ, kmax_η₀)
bessel_argmin = 0.0
bessel_argmax = assume_nondual(kmax_η₀)
Δg = bessel_argmax / 5000
bessel_xgrid = bessel_argmin:Δg:bessel_argmax
bessel_ygrid = [sphericalbesselj(ℓ, x) for x in bessel_xgrid]
bes = spline(bessel_xgrid, bessel_ygrid)
return bes
end
function quadratic_k(kmin::T, kmax::T, nk) where T
kmin, kmax, nk = assume_nondual(kmin), assume_nondual(kmax), assume_nondual(nk)
return T[kmin + (kmax - kmin) * (i/nk)^2 for i in 1:nk]
end
function Θl(x_i, k, s_itp, bes, par::AbstractCosmoParams{T}, bg) where {T}
s = zero(T)
xgrid = bg.x_grid
for i in x_i:length(xgrid)-1
x = xgrid[i]
sb = bes(k*(bg.η₀ - bg.η(x)))
source = s_itp(x, k)
s += sb * source * (xgrid[i+1] - xgrid[i])
end
return s
end
function cltt(ℓ, s_itp, kgrid, par::AbstractCosmoParams{T}, bg) where {T}
bes = Bolt.bessel_interpolator(ℓ, kgrid[end] * bg.η₀)
x_i = findfirst(bg.x_grid .> -8) # start integrating after recombination
s = zero(T)
for i in 1:length(kgrid)-1
k = kgrid[i]
dk = kgrid[i+1] - kgrid[i]
th = Θl(x_i, k, s_itp, bes, par, bg)
s += th^2 * dk / k
end
return s
end
function cltt(ℓ::Int, par::AbstractCosmoParams, bg, ih, sf)
dense_kgrid = quadratic_k(0.1bg.H₀, 1000bg.H₀, 5000)
cltt(ℓ, sf, dense_kgrid, par, bg)
end
function cltt(ℓ⃗, par::AbstractCosmoParams, bg, ih, sf)
dense_kgrid = quadratic_k(0.1bg.H₀, 1000bg.H₀, 5000)
return qmap(ℓ->cltt(ℓ, par, bg, ih, sf), ℓ⃗)
end
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] | 2.014641 | 1,366 |
"""
Converts and returns an array of string to an array of Symbol
"""
function strarr_to_symarr!(arr)
return arr = [ Symbol(str) for str in arr ]
end
"""
Converts and returns an equation string to an array of variables as string
"""
function equation_str_to_strarr!(equation)
if occursin("~", equation)
vars = split(replace(equation, r"\s+|\s+$/g" => " "), "~")
equation = [String(strip(var)) for var in vcat(vars[1], split(vars[2], "+"))]
else
equation = [String(strip(var)) for var in split(replace(equation, r"\s+|\s+$/g" => ","), ",")]
end
equation = filter(x->length(x) > 0, equation)
return equation
end
"""
Converts and returns a strarr equation with wildcards to a strarr with wildcards processed
"""
function equation_converts_wildcards!(equation, names)
new_equation = []
for e in equation
e = replace(e, "." => "*")
if e[end] == '*'
datanames_arr = vec([String(key)[1:length(e[1:end - 1])] == e[1:end - 1] ? String(key) : nothing for key in names])
append!(new_equation, filter!(x->x != nothing, datanames_arr))
else
append!(new_equation, [e])
end
end
equation = unique(new_equation)
return equation
end
"""
Gets datanames from data DataFrame or data Tuple
"""
function get_datanames_from_data(data, datanames)
if isa(data, DataFrames.DataFrame)
datanames = names(data)
elseif isa(data, Tuple)
datanames = data[2]
if !isa(datanames, Vector)
datanames = vec(datanames)
end
end
return strarr_to_symarr!(datanames)
end
"""
Gets datanames from data DataFrame or data Tuple
"""
function get_data_from_data(data)
if isa(data, DataFrames.DataFrame)
data = convert(Matrix{Union{Missing, Float64}}, data)
elseif isa(data, Tuple)
data = data[1]
end
return data
end
"""
Sorts data
"""
function sort_data(data, datanames; time=nothing, panel=nothing)
time_pos = ModelSelection.get_column_index(time, datanames)
panel_pos = ModelSelection.get_column_index(panel, datanames)
if time_pos != nothing && panel_pos != nothing
data = sortslices(data, by=x->(x[panel_pos], x[time_pos]), dims=1)
elseif panel_pos != nothing
data = sortslices(data, by=x->(x[panel_pos]), dims=1)
elseif time_pos != nothing
data = sortslices(data, by=x->(x[time_pos]), dims=1)
end
return data
end
"""
Filter data by selected columns
"""
function filter_data_by_selected_columns(data, equation, datanames)
columns = []
for i = 1:length(equation)
append!(columns, ModelSelection.get_column_index(equation[i], datanames))
end
data = data[:,columns]
datanames = datanames[columns]
return (data, datanames)
end
"""
Validates if there are panel gaps
"""
function validate_panel(data, datanames; panel=nothing)
if panel != nothing
return !any(ismissing, data[:, ModelSelection.get_column_index(panel, datanames)])
end
return true
end
"""
Validates if there are time gaps
"""
function validate_time(data, datanames; panel=nothing, time=nothing)
nobs = size(data, 1)
# TODO: Merge solutions
if panel == nothing
previous_value = data[1, ModelSelection.get_column_index(time, datanames)]
for value in data[2:end, ModelSelection.get_column_index(time, datanames)]
if previous_value + 1 != value
return false
end
previous_value = value
end
else
panel_index = ModelSelection.get_column_index(panel, datanames)
csis = unique(data[:, panel_index])
time_index = ModelSelection.get_column_index(time, datanames)
for csi in csis
rows = findall(x->x == csi, data[:,panel_index])
previous_value = data[rows[1], time_index]
for row in rows[2:end]
value = data[row, time_index]
if previous_value + 1 != value
return false
end
previous_value = value
end
end
end
return true
end
"""
Remove outliers from data
"""
function remove_outliers(data)
for column in 1:size(data, 2)
remove_outlier(data, column)
end
end
"""
Remove outliers from a data column
"""
function remove_outlier(data, column)
threshold = 3
col = @view(data[:,column])
aux_col = Array{Union{Int64, Float64, Missing}}(undef, size(col, 1), 2)
for i in keys(col)
aux_col[i, 1] = i
aux_col[i, 2] = col[i]
end
valid_data = deleteat!(aux_col[:, 2], findall(ismissing, aux_col[:, 2]))
mean_ = mean(valid_data)
std_ = std(valid_data)
for i in keys(col)
if !ismissing(col[i])
z_score = (col[i] - mean_) / std_
if abs(z_score) > threshold
col[i] = missing
end
end
end
return data
end
"""
Remove outliers from data
"""
function seasonal_adjustments(data, factor_dict, datanames)
for column in factor_dict
seasonal_adjustment(data, column[1], column[2], datanames)
end
end
"""
Seasonal adjustment from a data column
"""
function seasonal_adjustment(data, name, factor, datanames)
column = ModelSelection.get_column_index(name, datanames)
nobs = size(data, 2)
col = @view(data[:, column])
L = Int(round(nobs / 2 / factor)) * factor
yt, ys = analyze(col, L)
seasonal_component = sum(ys, dims=2)
col = col - seasonal_component
return data
end
"""
Add values to extras
"""
function addextras(data, method, seasonaladjustment, removeoutliers)
data.extras[ModelSelection.generate_extra_key(PREPROCESSING_EXTRAKEY, data.extras)] = Dict(
:datanames => vcat(data.depvar, data.expvars),
:depvar => data.depvar,
:expvars => data.expvars,
:data => DEFAULT_DATANAME,
:method => method,
:intercept => data.intercept,
:panel => data.panel,
:time => data.time,
:seasonaladjustment => seasonaladjustment,
:removeoutliers => removeoutliers,
:removemissings => data.removemissings
)
return data
end
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] | 2.324223 | 2,671 |
export iauEpv00
"""
Earth position and velocity, heliocentric and barycentric, with
respect to the Barycentric Celestial Reference System.
This function is part of the International Astronomical Union's
SOFA (Standards Of Fundamental Astronomy) software collection.
Status: support function.
Given:
date1,date2 double TDB date (Note 1)
Returned:
pvh double[2][3] heliocentric Earth position/velocity
pvb double[2][3] barycentric Earth position/velocity
Returned (function value):
int status: 0 = OK
+1 = warning: date outside
the range 1900-2100 AD
Notes:
1. The TDB date date1+date2 is a Julian Date, apportioned in any
convenient way between the two arguments. For example,
JD(TDB)=2450123.7 could be expressed in any of these ways, among
others:
date1 date2
2450123.7 0.0 (JD method)
2451545.0 -1421.3 (J2000 method)
2400000.5 50123.2 (MJD method)
2450123.5 0.2 (date & time method)
The JD method is the most natural and convenient to use in cases
where the loss of several decimal digits of resolution is
acceptable. The J2000 method is best matched to the way the
argument is handled internally and will deliver the optimum
resolution. The MJD method and the date & time methods are both
good compromises between resolution and convenience. However,
the accuracy of the result is more likely to be limited by the
algorithm itself than the way the date has been expressed.
n.b. TT can be used instead of TDB in most applications.
2. On return, the arrays pvh and pvb contain the following:
pvh[0][0] x }
pvh[0][1] y } heliocentric position, au
pvh[0][2] z }
pvh[1][0] xdot }
pvh[1][1] ydot } heliocentric velocity, au/d
pvh[1][2] zdot }
pvb[0][0] x }
pvb[0][1] y } barycentric position, au
pvb[0][2] z }
pvb[1][0] xdot }
pvb[1][1] ydot } barycentric velocity, au/d
pvb[1][2] zdot }
The vectors are with respect to the Barycentric Celestial
Reference System. The time unit is one day in TDB.
3. The function is a SIMPLIFIED SOLUTION from the planetary theory
VSOP2000 (X. Moisson, P. Bretagnon, 2001, Celes. Mechanics &
Dyn. Astron., 80, 3/4, 205-213) and is an adaptation of original
Fortran code supplied by P. Bretagnon (private comm., 2000).
4. Comparisons over the time span 1900-2100 with this simplified
solution and the JPL DE405 ephemeris give the following results:
RMS max
Heliocentric:
position error 3.7 11.2 km
velocity error 1.4 5.0 mm/s
Barycentric:
position error 4.6 13.4 km
velocity error 1.4 4.9 mm/s
Comparisons with the JPL DE406 ephemeris show that by 1800 and
2200 the position errors are approximately double their 1900-2100
size. By 1500 and 2500 the deterioration is a factor of 10 and
by 1000 and 3000 a factor of 60. The velocity accuracy falls off
at about half that rate.
5. It is permissible to use the same array for pvh and pvb, which
will receive the barycentric values.
This revision: 2017 March 16
SOFA release 2018-01-30
Copyright (C) 2018 IAU SOFA Board. See notes at end.
"""
# int iauEpv00(double date1, double date2,
# double pvh[2][3], double pvb[2][3])
function iauEpv00(date1::Real, date2::Real)
# Initialize function return variables
pvh = zeros(Float64, 3, 2)
pvb = zeros(Float64, 3, 2)
status = ccall((:iauEpv00, libsofa_c), Cint,
(Cdouble, Cdouble,
Ref{Cdouble}, Ref{Cdouble}),
convert(Float64, date1), convert(Float64, date2),
pvh, pvb)
return status, SMatrix{2,3}(pvh'), SMatrix{2,3}(pvb')
end | [
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